diff --git "a/datasets/\n NGA178\n _1.0.json" "b/datasets/\n NGA178\n _1.0.json" index bc0f88f54f..8f90a0027f 100644 --- "a/datasets/\n NGA178\n _1.0.json" +++ "b/datasets/\n NGA178\n _1.0.json" @@ -1,7 +1,7 @@ { "type": "Collection", "id": "\n NGA178\n _1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more.", "links": [ { diff --git "a/datasets/\n NGA183\n _1.0.json" "b/datasets/\n NGA183\n _1.0.json" index 762f7bc3c9..655b5c27f9 100644 --- "a/datasets/\n NGA183\n _1.0.json" +++ "b/datasets/\n NGA183\n _1.0.json" @@ -1,7 +1,7 @@ { "type": "Collection", "id": "\n NGA183\n _1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy\u2019s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy\u2019s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).", "links": [ { diff --git "a/datasets/\n NGA232\n _1.0.json" "b/datasets/\n NGA232\n _1.0.json" index 4538520344..041909f616 100644 --- "a/datasets/\n NGA232\n _1.0.json" +++ "b/datasets/\n NGA232\n _1.0.json" @@ -1,7 +1,7 @@ { "type": "Collection", "id": "\n NGA232\n _1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Remote sensing data collected from Brookhaven National Laboratory\u2019s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform \u2013 the Osprey \u2013 operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface \u201cskin\u201d temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy\u2019s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy\u2019s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).", "links": [ { diff --git a/datasets/ GEOS-CF Products_1.json b/datasets/ GEOS-CF Products_1.json index 3236673b9e..94a183d5a4 100644 --- a/datasets/ GEOS-CF Products_1.json +++ b/datasets/ GEOS-CF Products_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": " GEOS-CF Products_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Global Earth Observing System (GEOS) model has been expanded to provide global nearreal-\r\ntime forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (about 25\r\nkm). This GEOS Composition Forecast (GEOS-CF) system combines the GEOS weather analysis and\r\nforecasting system with the state-of-the-science GEOS-Chem chemistry module (Bey et al., 2001;\r\nKeller et al., 2014; Long et al., 2015) to provide detailed chemical analysis of a wide range of air\r\npollutants including ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).", "links": [ { diff --git a/datasets/001aab54-295d-4fb3-b269-748b8e0b9a04_NA.json b/datasets/001aab54-295d-4fb3-b269-748b8e0b9a04_NA.json index d0c92035fe..b01951765b 100644 --- a/datasets/001aab54-295d-4fb3-b269-748b8e0b9a04_NA.json +++ b/datasets/001aab54-295d-4fb3-b269-748b8e0b9a04_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "001aab54-295d-4fb3-b269-748b8e0b9a04_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/004fd44ff5124174ad3c03dd2c67d548_NA.json b/datasets/004fd44ff5124174ad3c03dd2c67d548_NA.json index 1a3afac90e..aa1912a292 100644 --- a/datasets/004fd44ff5124174ad3c03dd2c67d548_NA.json +++ b/datasets/004fd44ff5124174ad3c03dd2c67d548_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "004fd44ff5124174ad3c03dd2c67d548_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00c3\u00bcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.", "links": [ { diff --git a/datasets/024292dcda5d42ceb326850f89f8b40d_NA.json b/datasets/024292dcda5d42ceb326850f89f8b40d_NA.json index 5eb0e53bf2..2dff2698da 100644 --- a/datasets/024292dcda5d42ceb326850f89f8b40d_NA.json +++ b/datasets/024292dcda5d42ceb326850f89f8b40d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "024292dcda5d42ceb326850f89f8b40d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "links": [ { diff --git a/datasets/0470e96f2d8245549ef2ba81842cdfd8_NA.json b/datasets/0470e96f2d8245549ef2ba81842cdfd8_NA.json index 43f11daad0..ea7b7021a6 100644 --- a/datasets/0470e96f2d8245549ef2ba81842cdfd8_NA.json +++ b/datasets/0470e96f2d8245549ef2ba81842cdfd8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0470e96f2d8245549ef2ba81842cdfd8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from SARAL-AltiKa for 2013-2017. This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite. The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.The corresponding SEC grid from Cryosat-2 is included for comparison. The algorithm used to devive the product is described in the paper \u00e2\u0080\u009cImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry\u00e2\u0080\u009d by S.B. Simonsen and L.S. S\u00c3\u00b8rensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012. The approach used here corresponds to Least Squares Method (LSM) 5 described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 20 km.", "links": [ { diff --git a/datasets/04bc222136f7429eb04d3eb3543ef3e8_NA.json b/datasets/04bc222136f7429eb04d3eb3543ef3e8_NA.json index 677c54ded9..285f178350 100644 --- a/datasets/04bc222136f7429eb04d3eb3543ef3e8_NA.json +++ b/datasets/04bc222136f7429eb04d3eb3543ef3e8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "04bc222136f7429eb04d3eb3543ef3e8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ORAC algorithm, version 4.01. It covers the period from 1995-2003For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/057dd6c36f0741d3b97f9eee688b7835_NA.json b/datasets/057dd6c36f0741d3b97f9eee688b7835_NA.json index ccb59db405..663d965555 100644 --- a/datasets/057dd6c36f0741d3b97f9eee688b7835_NA.json +++ b/datasets/057dd6c36f0741d3b97f9eee688b7835_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "057dd6c36f0741d3b97f9eee688b7835_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/065f6040ef08485db989cbd89d536167_NA.json b/datasets/065f6040ef08485db989cbd89d536167_NA.json index 9eb7437b55..f697d0b5b5 100644 --- a/datasets/065f6040ef08485db989cbd89d536167_NA.json +++ b/datasets/065f6040ef08485db989cbd89d536167_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "065f6040ef08485db989cbd89d536167_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset.", "links": [ { diff --git a/datasets/0875b4675f1e46ebadb526e0b95505c5_NA.json b/datasets/0875b4675f1e46ebadb526e0b95505c5_NA.json index 05de36c5f4..889cd101eb 100644 --- a/datasets/0875b4675f1e46ebadb526e0b95505c5_NA.json +++ b/datasets/0875b4675f1e46ebadb526e0b95505c5_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0875b4675f1e46ebadb526e0b95505c5_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "links": [ { diff --git a/datasets/0944645.json b/datasets/0944645.json index 691c0433b1..d2234d9787 100644 --- a/datasets/0944645.json +++ b/datasets/0944645.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0944645", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis.\n\nPetrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the \u2018backside\u2019 of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids.", "links": [ { diff --git a/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json b/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json index 76ed432ccb..270f73a12a 100644 --- a/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json +++ b/datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0ac98747-eb94-4c9f-aef8-56f9d3a04740", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.", "links": [ { diff --git a/datasets/0b23b3c771db4fff8958196432d978cb_NA.json b/datasets/0b23b3c771db4fff8958196432d978cb_NA.json index da141f18b8..5629c3bd24 100644 --- a/datasets/0b23b3c771db4fff8958196432d978cb_NA.json +++ b/datasets/0b23b3c771db4fff8958196432d978cb_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0b23b3c771db4fff8958196432d978cb_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing. For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product.", "links": [ { diff --git a/datasets/0d2260ad4e2c42b6b14fe5b3308f5eaa_NA.json b/datasets/0d2260ad4e2c42b6b14fe5b3308f5eaa_NA.json index 2746cda4c6..f768d49d0f 100644 --- a/datasets/0d2260ad4e2c42b6b14fe5b3308f5eaa_NA.json +++ b/datasets/0d2260ad4e2c42b6b14fe5b3308f5eaa_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0d2260ad4e2c42b6b14fe5b3308f5eaa_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011.", "links": [ { diff --git a/datasets/0e289294f2c141bca545cd9d7fcb62d0_NA.json b/datasets/0e289294f2c141bca545cd9d7fcb62d0_NA.json index 668d01b70f..352ece3804 100644 --- a/datasets/0e289294f2c141bca545cd9d7fcb62d0_NA.json +++ b/datasets/0e289294f2c141bca545cd9d7fcb62d0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0e289294f2c141bca545cd9d7fcb62d0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA.json b/datasets/0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA.json index 30fcb4bc24..05370e7222 100644 --- a/datasets/0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA.json +++ b/datasets/0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/de/eoc/forschung-transfer/projekte-und-missionen/desis", "links": [ { diff --git a/datasets/10-16904-10_1.0.json b/datasets/10-16904-10_1.0.json index 2317600b3a..65863505f4 100644 --- a/datasets/10-16904-10_1.0.json +++ b/datasets/10-16904-10_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-10_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface. The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015. Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.", "links": [ { diff --git a/datasets/10-16904-19_1.0.json b/datasets/10-16904-19_1.0.json index 753c179deb..e24f6d12fc 100644 --- a/datasets/10-16904-19_1.0.json +++ b/datasets/10-16904-19_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-19_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented here corresponds to the publication \"A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation\" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains: * Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution. * ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability). * LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m). * meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015. * TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans. For more details about the simulation and observation data, see Gerber et al., 2017. __Publication__: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.", "links": [ { diff --git a/datasets/10-16904-1_7.json b/datasets/10-16904-1_7.json index 7cbca3ac10..72f4f890de 100644 --- a/datasets/10-16904-1_7.json +++ b/datasets/10-16904-1_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-1_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site. __Publication__ Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.", "links": [ { diff --git a/datasets/10-16904-21_1.0.json b/datasets/10-16904-21_1.0.json index 094d21d2f1..dc89d1f679 100644 --- a/datasets/10-16904-21_1.0.json +++ b/datasets/10-16904-21_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-21_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements. The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files. Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used. This dataset is the basis of the following publication: Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53", "links": [ { diff --git a/datasets/10-16904-22_1.0.json b/datasets/10-16904-22_1.0.json index 9d3126bb8c..62ec99901e 100644 --- a/datasets/10-16904-22_1.0.json +++ b/datasets/10-16904-22_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-22_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth. The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files. Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used. This dataset is the basis of the following publication: Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004", "links": [ { diff --git a/datasets/10-16904-23_1.0.json b/datasets/10-16904-23_1.0.json index ea24bbe12e..91e7d9b1a5 100644 --- a/datasets/10-16904-23_1.0.json +++ b/datasets/10-16904-23_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-23_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation. This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine. The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too. Please cite: _V\u00f6geli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._ Dataset is provided as a single zip file. The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input. The archive also contains the file: \"ReadMeMetadataDataSetPrecipitationScaling\" which explains the data structure.", "links": [ { diff --git a/datasets/10-16904-2_1.json b/datasets/10-16904-2_1.json index ab791fa03f..b38f86a086 100644 --- a/datasets/10-16904-2_1.json +++ b/datasets/10-16904-2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N\u00b083, IACS Contribution N\u00b01, UNESCO-IHP, Paris].", "links": [ { diff --git a/datasets/10-16904-3_1.json b/datasets/10-16904-3_1.json index bdd88cad00..10a2d7394f 100644 --- a/datasets/10-16904-3_1.json +++ b/datasets/10-16904-3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2013\u20132014, a survey was conducted in Switzerland to update the Forest Access Roads geo-dataset within the framework of the Swiss National Forest Inventory (NFI). The resulting nationwide dataset contains valuable information on truck-accessible forest roads that can be used to transport wood. The survey involved interviewing staff from the approximately 800 local forest services in Switzerland and recording the data first on paper maps and then in digitized form. The data in the NFI on the forest roads could thus be updated and additional information regarding their trafficability for specific categories of truck included. The information has now been attached to the geometries of the Roads and Tracks of the swissTLM3D (release 2012) of the Federal Office of Topography swisstopo. The resulting data are suitable for statistical analyses and modeling, but further (labour-intensive) validation work would be necessary if they are to be used as a basis for applications requiring more spatial accuracy, such as navigation systems. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available for third parties for non-commercial use provided they have purchased a TLM license. __Related Publication__: [doi: 10.3188/szf.2016.0136](http://dx.doi.org/10.3188/szf.2016.0136)", "links": [ { diff --git a/datasets/10-16904-4_1.json b/datasets/10-16904-4_1.json index 7719a739ed..93fc084f12 100644 --- a/datasets/10-16904-4_1.json +++ b/datasets/10-16904-4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38\u00b0 to 43\u00b0 with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2). __Publications__ 1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992\u20138004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560). 2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)", "links": [ { diff --git a/datasets/10-16904-5_1.json b/datasets/10-16904-5_1.json index 53602d43f2..421a9e970d 100644 --- a/datasets/10-16904-5_1.json +++ b/datasets/10-16904-5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rufiberg is a pre-alpine meadow site in Switzerland where shallow landslides have been observed after past intense rain storms. In order to assess the triggering mechanisms of these landslides, a comprehensive investigation was conducted within the project TRAMM from Nov 2009 to Oct 2012. It included meteorological observations, soil moisture measurements, bedrock groundwater measurements. The Rufiberg is located at the NW side of the Gnipen to the north of the village Arth-Goldau in the Canton of Schwyz. In the summer months, the site is used for pasturing. Usually, from December to March a snow cover is present at the Rufiberg. The site is at an altitude between 1080 \u2013 1180 m asl, is ENE oriented, and has an average slope of 30 -35\u00b0. The Subalpine Molasse in the region is inclined with 30 - 35\u00b0 to SE. In the area of the field site, beds of conglomerate with several m of thickness alter with beds of sandstone and marlstone. A ca. 2 \u2013 5 m thick eluvium/colluvium layer composed of silty and sandy clay covers the bedrock. This site has been chosen because on one hand, during heavy rainfall events, e.g. autumn 2005, numerous landslides occur in the region of the Gnipen and the Rufiberg. On the other hand, the Rufiberg is very appropriate for experiments due its location away from infrastructures and due to its accessibility. The goal of the investigation was to understand the hydrology and hydrogeology of the slope with regard to shallow landslides. More information: Br\u00f6nnimann, C., St\u00e4hli, M., Schneider, P., Seward, L. and Springman, S.M. 2013. Bedrock exfiltration as a triggering mechanism for shallow landslides. Water Resources Research, 49 (9): 5155\u20135167. DOI: 10.1002/wrcr.20386.", "links": [ { diff --git a/datasets/10-16904-6_1.json b/datasets/10-16904-6_1.json index 8dc1c64fdb..2f6c3eb703 100644 --- a/datasets/10-16904-6_1.json +++ b/datasets/10-16904-6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u\u2019) and the vertical (w\u2019) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below. __Publication__ http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract", "links": [ { diff --git a/datasets/10-16904-7_1.json b/datasets/10-16904-7_1.json index 59af8a975f..bf7591f866 100644 --- a/datasets/10-16904-7_1.json +++ b/datasets/10-16904-7_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-7_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land-use and climate change. Here the longest continuous runoff records (1903 - 2015) from small catchments (less than 1 km2) in Switzerland (and possibly worldwide) are provided as a data set. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized in St\u00e4hli et al., Environ Monit Assess (2011). The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty (also discussed in St\u00e4hli et al., 2011). This treasure trove of data can be used in various ways, eg. for analysis of the generalized extreme value distributions of the two catchments, of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic models.", "links": [ { diff --git a/datasets/10-16904-8_1.json b/datasets/10-16904-8_1.json index 54f05a41f7..8abf99efcc 100644 --- a/datasets/10-16904-8_1.json +++ b/datasets/10-16904-8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties. _Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._ __Related publication__: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101) Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.", "links": [ { diff --git a/datasets/10-16904-9_1.json b/datasets/10-16904-9_1.json index 79a7d0f580..ecae60a115 100644 --- a/datasets/10-16904-9_1.json +++ b/datasets/10-16904-9_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-9_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises of a post-processed set of terrestrial laser scans (TLS\u2019s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the \u2018xyz\u2019 coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software. ###How to cite: Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).", "links": [ { diff --git a/datasets/10-16904-envidat-24_1.0.json b/datasets/10-16904-envidat-24_1.0.json index ab9e21983b..d82bdab0c7 100644 --- a/datasets/10-16904-envidat-24_1.0.json +++ b/datasets/10-16904-envidat-24_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-24_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Abstract Snow and hydrological modeling in alpine environments remains a challenge because of the complexity of the processes complexity affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high-alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014-2015. Based on recent advances in Alpine3D, we examine how modeled snow distributions, and modeled liquid water transport within the snowpack influence runoff dynamics. By combining these results with multi-scale field data (snow lysimeter data, distributed snow depths and streamflow), we demonstrate the added value of a more realistic representation of snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the snowpack mass balance errors are corrected (R2 = 0.95 vs. R2 = 0.61). At the sub-basin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originated in the shallower areas while an extended melting period (by more than a month) is caused by slower snowmelt from deeper areas. This result is a marked improvement over results obtained using a less heterogeneous snow distribution (i.e., traditional precipitation interpolation method). Catchment hydrological response is also improved by the more realistic representation of snowpack heterogeneity (Nash coefficient of 0.85 vs. 0.74), even though the calibration process smoothens out the differences. The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at the sub-basin and basin scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well-instrumented area and present a model validation from multi-scale experimental datasets.", "links": [ { diff --git a/datasets/10-16904-envidat-25_1.json b/datasets/10-16904-envidat-25_1.json index 460f2e1f62..57073c04e4 100644 --- a/datasets/10-16904-envidat-25_1.json +++ b/datasets/10-16904-envidat-25_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-25_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007\u00b0. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.", "links": [ { diff --git a/datasets/10-16904-envidat-27_1.0.json b/datasets/10-16904-envidat-27_1.0.json index 012f2de8f2..0d8ed04e8e 100644 --- a/datasets/10-16904-envidat-27_1.0.json +++ b/datasets/10-16904-envidat-27_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-27_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate. ## Inventory data Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH \u2265 4 cm for Switzerland and \u2265 7 cm for Germany. ## Data selection We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH \u2265 7 cm). We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds.. ## Predictors of tree mortality We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare. ## Further information For further information, refer to H\u00fclsmann _et al_. (in press) How to kill a tree \u2013 Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.", "links": [ { diff --git a/datasets/10-16904-envidat-28_1.0.json b/datasets/10-16904-envidat-28_1.0.json index ba9d87eae6..db20927530 100644 --- a/datasets/10-16904-envidat-28_1.0.json +++ b/datasets/10-16904-envidat-28_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-28_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015. The data set contains for each site: * 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May) * 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October) Input files for SNOWPACK model: * .sno: snow profile at the end of winter * .smet: meteorological data measured by weather stations in the area For more details see Gr\u00fcnewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.", "links": [ { diff --git a/datasets/10-16904-envidat-29_1.0.json b/datasets/10-16904-envidat-29_1.0.json index a7616b6216..bc6d5b51a1 100644 --- a/datasets/10-16904-envidat-29_1.0.json +++ b/datasets/10-16904-envidat-29_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-29_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.", "links": [ { diff --git a/datasets/10-16904-envidat-30_1.0.json b/datasets/10-16904-envidat-30_1.0.json index 422fc8644c..58a1dfc6b4 100644 --- a/datasets/10-16904-envidat-30_1.0.json +++ b/datasets/10-16904-envidat-30_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10-16904-envidat-30_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data. Please refer to the README for more information about the data. This dataset is the basis of the following publication: Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.", "links": [ { diff --git a/datasets/10.25921_0haq-t221_Not Applicable.json b/datasets/10.25921_0haq-t221_Not Applicable.json index adbc55fa25..b0d400c6f3 100644 --- a/datasets/10.25921_0haq-t221_Not Applicable.json +++ b/datasets/10.25921_0haq-t221_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/0haq-t221_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, nutrients and other measurements. R/V Meteor Cruise No. 80/2 was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_16y6-9e29_Not Applicable.json b/datasets/10.25921_16y6-9e29_Not Applicable.json index 70e707bf98..b995a04aaf 100644 --- a/datasets/10.25921_16y6-9e29_Not Applicable.json +++ b/datasets/10.25921_16y6-9e29_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/16y6-9e29_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M135 (EXPOCODE 06MT20170302) in the South Pacific Ocean from 2017-03-02 to 2017-04-07. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Pacific Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_3bmf-xc16_Not Applicable.json b/datasets/10.25921_3bmf-xc16_Not Applicable.json index df1718762e..b2793be386 100644 --- a/datasets/10.25921_3bmf-xc16_Not Applicable.json +++ b/datasets/10.25921_3bmf-xc16_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/3bmf-xc16_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete bottle measurements of dissolved inorganic carbon (DIC), total alkalinity, pH on total scale, partial pressure of CO2, dissolved organic carbon (DOC), CFCs, temperature, salinity, oxygen, nutrients, and other variables measured during R/V N.B. Palmer cruise in the South Pacific Ocean on GO-SHIP/CLIVAR/SOCCOM Repeat Hydrography Sections P06W (EXPOCODE 320620170703) and P06E (EXPOCODE 320620170820) from 2017-07-03 to 2017-09-30. The Pacific Ocean P06 repeat hydrographic line was reoccupied for the US Global Ocean Carbon and Repeat Hydrography Program. Reoccupation of the P06E transect occurred on the RVIB Nathaniel B Palmer from August 20, 2017 to September 30, 2017. The survey of P06 2017 consisted of CTDO, rosette, LADCP, chipod, water samples and underway measurements. The ship departed from the port of Papeete on the island of Tahiti, French Polynesia and completed the cruise in the port of Valparaiso, Chile.", "links": [ { diff --git a/datasets/10.25921_3edp-9d76_Not Applicable.json b/datasets/10.25921_3edp-9d76_Not Applicable.json index a1be47fc81..720815c28a 100644 --- a/datasets/10.25921_3edp-9d76_Not Applicable.json +++ b/datasets/10.25921_3edp-9d76_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/3edp-9d76_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alabama Real-time Coastal Observing System (ARCOS) with support of the Dauphin Island Sea Lab is a network of continuously sampling observing stations that collect observations of meteorological and hydrographic data from fixed stations operating across coastal Alabama. Data were collected from 2003 through the present and include parameters such as air temperature, relative humidity, solar and quantum radiation, barometric pressure, wind speed, wind direction, precipitation amounts, water temperature, salinity, dissolved oxygen, water height, and other water quality data. Stations, when possible, are designed to collect the same data in the same way, though there are exceptions given unique location needs (see individual accession abstracts for details). Stations are strategically placed to sample across salinity gradients, from delta to offshore, and the width of the coast.", "links": [ { diff --git a/datasets/10.25921_43nw-j564_Not Applicable.json b/datasets/10.25921_43nw-j564_Not Applicable.json index 6bbc487513..12cc210491 100644 --- a/datasets/10.25921_43nw-j564_Not Applicable.json +++ b/datasets/10.25921_43nw-j564_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/43nw-j564_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity, dissolved oxygen, and sulfur hexafluoride (SF6) collected during the R/V Maria S. Merian cruise MSM28 (EXPOCODE 06M220130509) in the North Atlantic Ocean from 2013-05-09 to 2013-06-20.", "links": [ { diff --git a/datasets/10.25921_50xm-z231_Not Applicable.json b/datasets/10.25921_50xm-z231_Not Applicable.json index b80835d02d..716c01a093 100644 --- a/datasets/10.25921_50xm-z231_Not Applicable.json +++ b/datasets/10.25921_50xm-z231_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/50xm-z231_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Aurora Australis cruise along the Repeat Hydrography Section S04I (EXPOCODE 09AR19960119) in the Southern Ocean from 1996-01-19 to 1996-03-23. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons and other measurements.", "links": [ { diff --git a/datasets/10.25921_579p-6p65_Not Applicable.json b/datasets/10.25921_579p-6p65_Not Applicable.json index c8ceb067a4..cd3e757314 100644 --- a/datasets/10.25921_579p-6p65_Not Applicable.json +++ b/datasets/10.25921_579p-6p65_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/579p-6p65_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M130 (EXPOCODE 06MT20160828) in the Tropical Atlantic Ocean from 2016-08-28 to 2016-10-03. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_58yq-7g68_Not Applicable.json b/datasets/10.25921_58yq-7g68_Not Applicable.json index 788b2eafe4..b6309a6911 100644 --- a/datasets/10.25921_58yq-7g68_Not Applicable.json +++ b/datasets/10.25921_58yq-7g68_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/58yq-7g68_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Census data were collected from two penguin monitoring sites in the Antarctic peninsula region between 1977 and 2015 using traditional census methods. Seabirds observed in this study are Ad\u00c3\u00a9lie (Pygoscelis adeliae), chinstrap (P. antarctica), and gentoo (P. papua) penguins. The two study sites are the US AMLR Program sites at Cape Shirreff (Livingston Island) and Copacabana (King George Island) Antarctica.", "links": [ { diff --git a/datasets/10.25921_5p69-y471_Not Applicable.json b/datasets/10.25921_5p69-y471_Not Applicable.json index 126afdd272..d76daeb96c 100644 --- a/datasets/10.25921_5p69-y471_Not Applicable.json +++ b/datasets/10.25921_5p69-y471_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/5p69-y471_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broull\u00c3\u00b3n et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broull\u00c3\u00b3n et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1\u00c2\u00bax1\u00c2\u00ba spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m.", "links": [ { diff --git a/datasets/10.25921_66nr-kv23_Not Applicable.json b/datasets/10.25921_66nr-kv23_Not Applicable.json index fba05b35b1..b28cd6b7ed 100644 --- a/datasets/10.25921_66nr-kv23_Not Applicable.json +++ b/datasets/10.25921_66nr-kv23_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/66nr-kv23_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains cruise report including data on adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru in the North Pacific. The research report focuses on the reproductive biology of the Japanese eel (Anguilla japonica) and the larval feeding ecology. This is MSR RATS cruise U2013-005. These data are part of the World Data Services for Oceanography. Cruise report is in PDF.", "links": [ { diff --git a/datasets/10.25921_6k3e-3x27_Not Applicable.json b/datasets/10.25921_6k3e-3x27_Not Applicable.json index 4abbcb5d60..c4ea958519 100644 --- a/datasets/10.25921_6k3e-3x27_Not Applicable.json +++ b/datasets/10.25921_6k3e-3x27_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/6k3e-3x27_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT31/1 cruise (EXPOCODE 06MT19941230) in the Mediterranean Sea from 1994-12-30 to 1995-03-22. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_7c1m-rw73_2.61.json b/datasets/10.25921_7c1m-rw73_2.61.json index f6b462681f..f5eaf2aad1 100644 --- a/datasets/10.25921_7c1m-rw73_2.61.json +++ b/datasets/10.25921_7c1m-rw73_2.61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/7c1m-rw73_2.61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA-20 (hereafter, N20; also known as JPSS-1 or J1 prior to launch) is the second satellite in the US National Oceanic and Atmospheric Administration (NOAA) latest generation Joint Polar Satellite System (JPSS). N20 was launched on November 18, 2017. In conjunction with the first US satellite in JPSS series, Suomi National Polar-orbiting Partnership (S-NPP) satellite launched on October 28, 2011, N20 form the new NOAA polar constellation. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 500MB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/10.25921_7swn-9p71_Not Applicable.json b/datasets/10.25921_7swn-9p71_Not Applicable.json index cbb8850f34..872d7ae407 100644 --- a/datasets/10.25921_7swn-9p71_Not Applicable.json +++ b/datasets/10.25921_7swn-9p71_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/7swn-9p71_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity and dissolved oxygen collected during the R/V Maria S. Merian cruise MSM38 (EXPOCODE 06M220140507) in the North Atlantic Ocean from 2014-05-07 to 2014-06-05.", "links": [ { diff --git a/datasets/10.25921_8vaj-bk51_Not Applicable.json b/datasets/10.25921_8vaj-bk51_Not Applicable.json index 5f1e78743d..0d2a18ffca 100644 --- a/datasets/10.25921_8vaj-bk51_Not Applicable.json +++ b/datasets/10.25921_8vaj-bk51_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/8vaj-bk51_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains atmospheric measurements of carbon dioxide (CO2) and methane (CH4) from 12 sites sites located across the state of Utah. Data are in Comma Separated Value (CSV) ASCII text with one file for each station. QA/QC flags, measurements precision and accuracy statistics and calibrated observations are also provided.", "links": [ { diff --git a/datasets/10.25921_91sj-y926_Not Applicable.json b/datasets/10.25921_91sj-y926_Not Applicable.json index f1b0cf709d..f63e2d2193 100644 --- a/datasets/10.25921_91sj-y926_Not Applicable.json +++ b/datasets/10.25921_91sj-y926_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/91sj-y926_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M145 (EXPOCODE 06MT20180213) in the Tropical Atlantic Ocean from 2018-02-13 to 2018-03-14. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_9hsn-xq82_Not Applicable.json b/datasets/10.25921_9hsn-xq82_Not Applicable.json index 85356ab31b..5d74e22dc5 100644 --- a/datasets/10.25921_9hsn-xq82_Not Applicable.json +++ b/datasets/10.25921_9hsn-xq82_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/9hsn-xq82_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains a combined globally mapped estimate of the air-sea exchange of carbon dioxide (CO2) based on Surface Ocean CO2 Atlas Database (SOCAT) partial pressure of CO2 (pCO2) and calculated pCO2 from Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017. The pCO2 fields were created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT dataset (Bakker et al., 2016) starting in 1982 in various combinations with calculated pCO2 from biogeochemical ARGO floats starting in 2014 from the SOCCOM project (Johnson et al., 2017) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting these driving variables, i.e., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships. This results in monthly pCO2 fields at 1\u00c2\u00b0x1\u00c2\u00b0 resolution covering the entire globe with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 flux is then computed using a standard bulk formula.", "links": [ { diff --git a/datasets/10.25921_ayf6-c438_2.70.json b/datasets/10.25921_ayf6-c438_2.70.json index dcc5582a07..ba3705702a 100644 --- a/datasets/10.25921_ayf6-c438_2.70.json +++ b/datasets/10.25921_ayf6-c438_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/ayf6-c438_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST). Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available, where gridded L2P SSTs are reported, and BT layers omitted.", "links": [ { diff --git a/datasets/10.25921_b2g4-bs86_Not Applicable.json b/datasets/10.25921_b2g4-bs86_Not Applicable.json index 1b2b7e188b..060d2c7fdb 100644 --- a/datasets/10.25921_b2g4-bs86_Not Applicable.json +++ b/datasets/10.25921_b2g4-bs86_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/b2g4-bs86_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains benthic epifauna biomass and abundance data collected in the Chukchi Sea, U.S. Arctic during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise aboard the vessel Norseman II. The dataset contains two comma separated values (csv) files exported from Microsoft Excel. These data were generated from epifauna samples conducted using beam trawls during the research cruise. The data in the file named AMBON2015_epifauna_abundance_DWC.csv describes abundance per taxon of epibenthic invertebrates. The data in the file named AMBON2015_epifauna_biomass_DWC.csv describes biomass per taxon of epibenthic invertebrates. This dataset was transformed into a table structure using Darwin Core term names as column names.", "links": [ { diff --git a/datasets/10.25921_c1sn-9631_Not Applicable.json b/datasets/10.25921_c1sn-9631_Not Applicable.json index e271c494e2..ff1f4eb043 100644 --- a/datasets/10.25921_c1sn-9631_Not Applicable.json +++ b/datasets/10.25921_c1sn-9631_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/c1sn-9631_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession consists of global oceanic database of tritium and helium isotope measurements made by numerous researchers and laboratories over a period exceeding 60 years: from 1952-10-21 to 2016-01-22 in the Pacific Ocean, Atlantic Ocean, Indian Ocean, Southern Ocean, Arctic Ocean, Mediterranean Sea, Baltic Sea, Black Sea. Tritium and helium isotope data provide key information on ocean circulation, ventilation, and mixing, as well as the rates of biogeochemical processes, and deep-ocean hydrothermal processes. The dataset includes approximately 60,000 valid tritium measurements, 63,000 valid helium isotope determinations, 57,000 dissolved helium concentrations, and 34,000 dissolved neon concentrations. Some quality control has been applied in that questionable data have been flagged and clearly compromised data excluded entirely. Appropriate metadata has been included: geographic location, date, and sample depth. When available, water temperature, salinity, and dissolved oxygen were included. Data quality flags and data originator information (including methodology) are also included.", "links": [ { diff --git a/datasets/10.25921_c9h2-z342_Not Applicable.json b/datasets/10.25921_c9h2-z342_Not Applicable.json index b27295c60c..0e556416c0 100644 --- a/datasets/10.25921_c9h2-z342_Not Applicable.json +++ b/datasets/10.25921_c9h2-z342_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/c9h2-z342_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Knorr GEOTRACES 2011 cruise KN204A/B (EXPOCODE 316N20111106) in the North Atlantic Ocean from 2011-11-06 to 2011-12-11. These data include temperature, salinity, dissolved oxygen, nutrients, and chlorofluorocarbons (CFC-11, CFC-12, CFC113). A hydrographic survey consisting of rosette/CTD sections and Bio-Optical casts in the mid-latitude eastern Atlantic Ocean was carried out during November-December 2011. The R/V Knorr departed Woods Hole, MA on 6 November 2011. The cruise ended in Praia, Cabo Verde on 11 December 2011.", "links": [ { diff --git a/datasets/10.25921_cnwq-y130_Not Applicable.json b/datasets/10.25921_cnwq-y130_Not Applicable.json index b7fdcbca53..02ce14e003 100644 --- a/datasets/10.25921_cnwq-y130_Not Applicable.json +++ b/datasets/10.25921_cnwq-y130_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/cnwq-y130_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), dissolved oxygen, temperature and salinity collected during R/V Meteor cruise MT82.2 (EXPOCODE 06M320100804) in the North Atlantic Ocean from 2010-08-04 to 2010-09-01.", "links": [ { diff --git a/datasets/10.25921_cp7t-7118_Not Applicable.json b/datasets/10.25921_cp7t-7118_Not Applicable.json index 19b4e2a6c4..269247ff2a 100644 --- a/datasets/10.25921_cp7t-7118_Not Applicable.json +++ b/datasets/10.25921_cp7t-7118_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/cp7t-7118_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic Sea Ice Summer Melt Feature Classification product is derived from high-resolution Digital Mapping System (DMS) imagery acquired during low-altitude NASA Operation IceBridge airborne surveys over Arctic sea ice. DMS images were acquired in July, 2016 and 2017. For each image, meaningful geophysical parameters have been derived: melt pond fraction, sea ice concentration, and pond color fraction. Melt pond fraction is the percentage of the sea ice surface that is ponded. Sea ice concentration is the percentage of ocean covered by sea ice. Pond color fraction is the partitioning of dark, medium, and light color ponds as a percentage of total ponded area.", "links": [ { diff --git a/datasets/10.25921_f2z2-2437_Not Applicable.json b/datasets/10.25921_f2z2-2437_Not Applicable.json index 1101e42f68..84e1ffbc6c 100644 --- a/datasets/10.25921_f2z2-2437_Not Applicable.json +++ b/datasets/10.25921_f2z2-2437_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/f2z2-2437_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile data collected during the R/V Poseidon cruise (EXPOCODE 06PO19971023) in the Western Mediterranean Sea from 1997-10-23 to 1997-11-10. These data include temperature, salinity, chlorofluorocarbons (CFC-11.CFC-12), helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_ffd4-q868_Not Applicable.json b/datasets/10.25921_ffd4-q868_Not Applicable.json index d1ef5916ab..c5ebc0a1f0 100644 --- a/datasets/10.25921_ffd4-q868_Not Applicable.json +++ b/datasets/10.25921_ffd4-q868_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/ffd4-q868_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0144342 includes discrete sample and profile data collected from unknown platforms in the North Atlantic Ocean, North Sea and Norwegian Sea from 1874-01-01 to 2005-12-31. These data include DELTA CARBON-13, DELTA CARBON-14 and Percent modern carbon (PMC). The instruments used to collect these data include CTD and bottle.\n\nThese data were collected by Jan Heinemeier of Aarhus University; Department of Physics and Astronomy; AMS 14C Dating Centre, James D. Scourse of Bangor University; School of Ocean Sciences, Alan D. Wanamaker Jr. of Iowa State University; Department of Geological and Atmospheric Sciences, Paula J. Reimer of Queen's University Belfast; 14CHRONO Centre and Chris Weidman of Waquoit Bay National Estuarine Research Reserve as part of the Delta 14C Time Histories from Arctica Islandica Growth Increments data set. CDIAC associated the following cruise ID(s) with this data set: Marine Radiocarbon Bomb Pulse Across the Temperare North Atlantic", "links": [ { diff --git a/datasets/10.25921_fmr6-6z65_Not Applicable.json b/datasets/10.25921_fmr6-6z65_Not Applicable.json index 3a6b0fbc0d..eecba8b84c 100644 --- a/datasets/10.25921_fmr6-6z65_Not Applicable.json +++ b/datasets/10.25921_fmr6-6z65_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/fmr6-6z65_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons, oxygen, water temperature and salinity collected from R/V Maria S. Merian cruise MSM05_1 (EXPOCODE 06M220070414) in the North Atlantic Ocean from 2007-04-14 to 2007-05-03.", "links": [ { diff --git a/datasets/10.25921_fscp-wn85_Not Applicable.json b/datasets/10.25921_fscp-wn85_Not Applicable.json index 114ad2243e..04538f531d 100644 --- a/datasets/10.25921_fscp-wn85_Not Applicable.json +++ b/datasets/10.25921_fscp-wn85_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/fscp-wn85_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile data collected during the R/V Poseidon cruise (EXPOCODE 06PO19960522) in the North Atlantic Ocean, Strait of Gibraltar and Mediterranean Sea from 1996-05-22 to 1996-05-31. These data include temperature, salinity, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_ft9q-y196_Not Applicable.json b/datasets/10.25921_ft9q-y196_Not Applicable.json index 41fb0b6dee..40e2939aa6 100644 --- a/datasets/10.25921_ft9q-y196_Not Applicable.json +++ b/datasets/10.25921_ft9q-y196_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/ft9q-y196_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), dissolved oxygen, temperature, salinity and nutrients collected from R/V Pelagia cruise PE278 (EXPOCODE 64PE20071026) in the North Atlantic Ocean from 2007-10-26 to 2007-11-17.", "links": [ { diff --git a/datasets/10.25921_g4pn-7922_Not Applicable.json b/datasets/10.25921_g4pn-7922_Not Applicable.json index 256354a892..f7129e4c1b 100644 --- a/datasets/10.25921_g4pn-7922_Not Applicable.json +++ b/datasets/10.25921_g4pn-7922_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/g4pn-7922_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile data collected during the R/V Urania cruise MAI2 (EXPOCODE 48UR19970830) in the Mediterranean Sea from 1997-08-30 to 1997-09-08. These data include temperature, salinity, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_gh54-9h50_Not Applicable.json b/datasets/10.25921_gh54-9h50_Not Applicable.json index 6958261b95..7743637de4 100644 --- a/datasets/10.25921_gh54-9h50_Not Applicable.json +++ b/datasets/10.25921_gh54-9h50_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/gh54-9h50_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes estuarine water physical (salinity, temperature, water depth) and chemical parameters (total dissolved inorganic carbon, total alkalinity, pH on total scale observed at 25\u00cb\u009aC, dissolved oxygen concentration, ammonia, soluble reactive phosphate and silicate) in the semiarid Corpus Christi Bay, the northwestern Gulf of Mexico. The sample collections were done in June-August 2015 and June-September 2016. This dataset is described in the submitted article \"Characteristics of the Carbonate System in a Semi-Arid Estuary that Experiences Summertime Hypoxia\" by Melissa R. McCutcheon, Cory J. Staryk, Xinping Hu (https://doi.org/10.1007/s12237-019-00588-0) in the Journal Estuaries and Coasts.", "links": [ { diff --git a/datasets/10.25921_gtrd-mm40_Not Applicable.json b/datasets/10.25921_gtrd-mm40_Not Applicable.json index 42f99699a6..1750dd62d0 100644 --- a/datasets/10.25921_gtrd-mm40_Not Applicable.json +++ b/datasets/10.25921_gtrd-mm40_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/gtrd-mm40_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains description of measurements taken by acoustic echo-sounder and core sampler from the research vessel Alis in the South Pacific Ocean. The oceanographic campaign SAMOA-SPT (South Pacific Tsunami) on board R/V Alis (IRD research vessel of the IRD, Noum\u00c3\u00a9a, New Caledonia) has allowed the recognition of the acoustic (multibeam bathymetry and imagery), seismic (high resolution seismic) and sedimentary (interface and Kullenberg piston coring) characteristics of the backwash-related submarine tsunami and storm (tropical cyclone) deposits. This is US State Department Marine Scientific Research (MSR) Research Application Tracking System (RATS) U2015-021. Cruise report is in PDF.", "links": [ { diff --git a/datasets/10.25921_hn7s-ss77_Not Applicable.json b/datasets/10.25921_hn7s-ss77_Not Applicable.json index eb6cc7a609..3a693c0c85 100644 --- a/datasets/10.25921_hn7s-ss77_Not Applicable.json +++ b/datasets/10.25921_hn7s-ss77_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/hn7s-ss77_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT44/4 cruise (EXPOCODE 06MT19990410) in the Mediterranean Sea from 1999-04-10 to 1999-05-16. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_hvrw-wd52_Not Applicable.json b/datasets/10.25921_hvrw-wd52_Not Applicable.json index 65c9b0e09e..fa295b8d08 100644 --- a/datasets/10.25921_hvrw-wd52_Not Applicable.json +++ b/datasets/10.25921_hvrw-wd52_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/hvrw-wd52_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete bottle profile measurements of dissolved inorganic carbon (DIC), total alkalinity, pH on total scale, CFCs, temperature, salinity, oxygen, nutrients and other measurements obtained during the R/V Ryofu Maru cruises RF13-06 and RF13-07 in the Pacific Ocean on GO-SHIP Repeat Hydrography Section P03W (EXPOCODE 49UP20130619) from 2013-06-19 to 2013-09-18. The observation line along approximately 24\u00c2\u00b0N was observed by Scripps Institution of Oceanography (SIO), USA in 1985 and Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan in 2005\u00e2\u0080\u00932006. These cruises were carried out as \u00e2\u0080\u0098WHP-P03\u00e2\u0080\u0099, which is a part of WOCE (World Ocean Circulation Experiment) Hydrographic Programme, CLIVAR (Climate Variability and Predictability Project) and GO-SHIP (Global Ocean Ship-based Hydrographic Investigations Program).", "links": [ { diff --git a/datasets/10.25921_jafy-k651_Not Applicable.json b/datasets/10.25921_jafy-k651_Not Applicable.json index b7a4ac12ae..2d99810a3a 100644 --- a/datasets/10.25921_jafy-k651_Not Applicable.json +++ b/datasets/10.25921_jafy-k651_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/jafy-k651_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Cape-wide census of Antarctic fur seal (Arctocephalus gazella) pups (live and dead) occurs every year once pupping is over. The census occurs in the last days of December, on a day when conditions and visibility are favorable. Cape Shirreff is located on Livingston Island, in the South Shetlands off the Antarctic Peninsula.", "links": [ { diff --git a/datasets/10.25921_jag0-m328_Not Applicable.json b/datasets/10.25921_jag0-m328_Not Applicable.json index a916db2495..7f1b1dd8ff 100644 --- a/datasets/10.25921_jag0-m328_Not Applicable.json +++ b/datasets/10.25921_jag0-m328_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/jag0-m328_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete bottle profile measurements of dissolved inorganic carbon (DIC), total alkalinity, CFCs, temperature, salinity, oxygen and nutrients obtained during the R/V Maria S. Merian cruise MSM60 in the South Atlantic Ocean on GO-SHIP Repeat Hydrography Section A10.5 (EXPOCODE 06M220170104) from 2017-01-04 to 2017-02-01. The Maria S Merian MSM60 expedition was the first basin-wide section across the South Atlantic following the SAMBA/SAMOC line at 34\u00c2\u00b030\u00e2\u0080\u0099S. The scientific program consisted of full water depth sampling (up to 5300m) using the CTD/O2/lADCP rosette system.", "links": [ { diff --git a/datasets/10.25921_jxyb-c019_Not Applicable.json b/datasets/10.25921_jxyb-c019_Not Applicable.json index 9013f8121a..8c490f7c32 100644 --- a/datasets/10.25921_jxyb-c019_Not Applicable.json +++ b/datasets/10.25921_jxyb-c019_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/jxyb-c019_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains benthic macroinfaunal population level from sediment samples collected at each station for the Arctic Marine Biodiversity Observing Network (AMBON) cruise in 2015 on the Norseman II, identified by station number (#), Station name (Stn. Name), Date (YYYYMMDD), latitude (\u00c2\u00b0N), longitude (\u00c2\u00b0W), and station depth (m). The following macroinfaunal parameters were determined: abundance, wet weight biomass (gww/m2), dry weight biomass (gC/m2), and taxon type.\n\nThe Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature change.", "links": [ { diff --git a/datasets/10.25921_kwjz-1j67_Not Applicable.json b/datasets/10.25921_kwjz-1j67_Not Applicable.json index b41c9f764e..6b34cd0419 100644 --- a/datasets/10.25921_kwjz-1j67_Not Applicable.json +++ b/datasets/10.25921_kwjz-1j67_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/kwjz-1j67_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature and salinity collected during the R/V Maria S. Merian cruise MSM21/2 (EXPOCODE 06M220120625) in the North Atlantic Ocean from 2012-06-25 to 2012-07-24.", "links": [ { diff --git a/datasets/10.25921_mpfz-sv16_Not Applicable.json b/datasets/10.25921_mpfz-sv16_Not Applicable.json index c8a1cbd92d..1032ccc1e6 100644 --- a/datasets/10.25921_mpfz-sv16_Not Applicable.json +++ b/datasets/10.25921_mpfz-sv16_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/mpfz-sv16_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0100115 includes chemical, discrete bottle, physical and time series profile data collected from Kairei, MIRAI and NATSUSHIMA in the North Pacific Ocean and South Pacific Ocean from 1999-05-28 to 2008-10-26 and retrieved during cruise Time Series K2. These data include ALKALINITY - TOTAL, AMMONIUM, DISSOLVED INORGANIC CARBON, DISSOLVED OXYGEN, NITRATE, NITRITE, PHOSPHATE, SALINITY, SIGMA-THETA, SILICATE and TEMPERATURE. The instruments used to collect these data include bottle. These data were collected by Akihiko Murata of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and Shuichi Wantanabe, Makio Honda and Masahide Wakita of Japan Agency for Marine-Earth Science and Technology (JAMSTEC); Mutsu Institute for Oceanography; Ocean Observation and Research Department as part of the Time_Series_K2 data set.", "links": [ { diff --git a/datasets/10.25921_mzpp-pf74_Not Applicable.json b/datasets/10.25921_mzpp-pf74_Not Applicable.json index f01f937187..43c6499790 100644 --- a/datasets/10.25921_mzpp-pf74_Not Applicable.json +++ b/datasets/10.25921_mzpp-pf74_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/mzpp-pf74_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-12), temperature, salinity, oxygen, nutrients and sulfur hexaflouride (SF6) collected from R/V Maria S. Merian cruise MSM10_1 (EXPOCODE 06MM20081031) in the Tropical Atlantic Ocean from 2008-10-31 to 2008-12-05.", "links": [ { diff --git a/datasets/10.25921_n94z-zj83_Not Applicable.json b/datasets/10.25921_n94z-zj83_Not Applicable.json index b8a3054ebe..b64f90afca 100644 --- a/datasets/10.25921_n94z-zj83_Not Applicable.json +++ b/datasets/10.25921_n94z-zj83_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/n94z-zj83_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFCs), water temperature, salinity and sulfur hexaflouride (SF6) collected from R/V Maria S. Merian cruise MSM09_1 (EXPOCODE 06M220080723) in the North Atlantic Ocean from 2008-07-23 to 2008-08-18.", "links": [ { diff --git a/datasets/10.25921_ndgj-jp24_Not Applicable.json b/datasets/10.25921_ndgj-jp24_Not Applicable.json index 16dd81b893..8e13fb2eee 100644 --- a/datasets/10.25921_ndgj-jp24_Not Applicable.json +++ b/datasets/10.25921_ndgj-jp24_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/ndgj-jp24_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains global monthly climatology of oceanic total dissolved inorganic carbon (DIC). (DIC) monthly climatology was created from a neural network approach (Broull\u00c3\u00b3n et al., 2020). The neural network was trained with GLODAPv2.2019 (Olsen et al., 2019) and LDEOv2016 (Takahashi et al., 2017) data, using as predictor variables position (latitude, longitude and depth), year, temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. pCO2 from LDEOv2016 and AT from Broull\u00c3\u00b3n et al. (2019) were used to compute DIC surface values to increase the surface coverage in the training data. The relations extracted between the predictor variables and DIC were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broull\u00c3\u00b3n et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1\u00c2\u00bax1\u00c2\u00ba spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m.", "links": [ { diff --git a/datasets/10.25921_paw7-2n76_Not Applicable.json b/datasets/10.25921_paw7-2n76_Not Applicable.json index 9b2e112924..dfbd60bea7 100644 --- a/datasets/10.25921_paw7-2n76_Not Applicable.json +++ b/datasets/10.25921_paw7-2n76_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/paw7-2n76_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor GO-SHIP A06 cruise (EXPOCODE 06MT20140317) in the Tropical Atlantic Ocean from 2014-03-17 to 2014-04-14. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, nutrients and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_py0j-mz96_Not Applicable.json b/datasets/10.25921_py0j-mz96_Not Applicable.json index 4590f72712..ac5425c8f6 100644 --- a/datasets/10.25921_py0j-mz96_Not Applicable.json +++ b/datasets/10.25921_py0j-mz96_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/py0j-mz96_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine biodiversity is a key component of ocean health. Monitoring and understanding marine biodiversity is essential for our ability to forecast and respond to changes. The goal of the Arctic Marine Biodiversity Observing Network (AMBON) project is to demonstrate and build an operational marine biodiversity observing network from microbes to whales, integrating diversity levels from genetic to organismal. AMBON field region is located on the Chukchi Sea continental shelf in the US Arctic as a region exposed to climatic changes and anthropogenic influences.\n\nThis dataset contains biomass and abundance data collected in the Chukchi Sea during the August 2017 Arctic Marine Biodiversity Observing Network (AMBON) research cruise. Epifauna samples were collected using beam trawl during a research cruise during August 2017 in the Chukchi Sea, U.S. Arctic. The data consist of biomass per taxon of epibenthic invertebrates. The dataset is a comma separated values file exported from a Microsoft Excel spreadsheet.\n\nThis dataset was transformed from the native format into a table structure using Darwin Core term names as column names.", "links": [ { diff --git a/datasets/10.25921_qb25-f418_Not Applicable.json b/datasets/10.25921_qb25-f418_Not Applicable.json index 914e3b1625..f1636a1209 100644 --- a/datasets/10.25921_qb25-f418_Not Applicable.json +++ b/datasets/10.25921_qb25-f418_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/qb25-f418_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains the partial pressure of carbon dioxide (pCO2) climatology that was created by merging 2 published and publicly available pCO2 datasets covering the open ocean (Landsch\u00c3\u00bctzer et. al 2016) and the coastal ocean (Laruelle et. al 2017). Both fields were initially created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT open and coastal ocean datasets (Bakker et. al 2016) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting driving variables, e.g., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships (see Landsch\u00c3\u00bctzer et. al 2016 and Laruelle et. al 2017 for more detail). This results in monthly open ocean pCO2 fields at 1\u00c2\u00b0x1\u00c2\u00b0 resolution and coastal ocean pCO2 fields at 0.25\u00c2\u00b0x0.25\u00c2\u00b0 resolution. To merge the products, we divided each 1\u00c2\u00b0x1\u00c2\u00b0 open ocean bin into 16 equal 0.25\u00c2\u00b0x0.25\u00c2\u00b0 bins without any interpolation. The common overlap area of the products has been merged by scaling the respective products by their mismatch compared to observations from the SOCAT datasets (see Landsch\u00c3\u00bctzer et. al 2020)", "links": [ { diff --git a/datasets/10.25921_r8gb-5k98_Not Applicable.json b/datasets/10.25921_r8gb-5k98_Not Applicable.json index 3ae535e99f..793db54900 100644 --- a/datasets/10.25921_r8gb-5k98_Not Applicable.json +++ b/datasets/10.25921_r8gb-5k98_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/r8gb-5k98_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile data collected during the R/V Poseidon cruise (EXPOCODE 06PO19971023) in the Mediterranean Sea from 1997-10-23 to 1997-11-10. These data include temperature, salinity, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_rtf0-q898_2.70.json b/datasets/10.25921_rtf0-q898_2.70.json index 0aa178ec2c..7ce417ead1 100644 --- a/datasets/10.25921_rtf0-q898_2.70.json +++ b/datasets/10.25921_rtf0-q898_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/rtf0-q898_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules per 24hr interval, with a total data volume of 0.2GB/day. Fill values are reported at all invalid pixels, including pixels with 5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). All valid SSTs in L3C are recommended for users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/10.25921_s2zz-0453_Not Applicable.json b/datasets/10.25921_s2zz-0453_Not Applicable.json index 325d8d0af2..c6dbcbf513 100644 --- a/datasets/10.25921_s2zz-0453_Not Applicable.json +++ b/datasets/10.25921_s2zz-0453_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/s2zz-0453_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), sulfur hexafluoride (SF6), temperature, salinity, dissolved oxygen, helium, tritium and neon collected during the R/V Maria S. Merian cruise MSM43 (EXPOCODE 06M220150525) in the North Atlantic Ocean from 2015-05-25 to 2015-06-27.", "links": [ { diff --git a/datasets/10.25921_sfs7-9688_2.61.json b/datasets/10.25921_sfs7-9688_2.61.json index e95507f905..f99f0cf47d 100644 --- a/datasets/10.25921_sfs7-9688_2.61.json +++ b/datasets/10.25921_sfs7-9688_2.61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/sfs7-9688_2.61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS). N20 was launched on November 18, 2017. In conjunction with the first US satellite in JPSS series, Suomi National Polar-orbiting Partnership (S-NPP) satellite launched on October 28, 2011, N20 form the new NOAA polar constellation. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). VIIRS is a whiskbroom scanning radiometer, which takes measurements in the cross-track direction within a field of view of 112.56-deg using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3,060 km, providing global daily coverage for both day and night passes. VIIRS has 22 spectral bands, covering the spectrum from 0.4-12 um, including 16 moderate resolution bands (M-bands). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10 minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 27GB/day. In addition to pixel-level earth locations, Sun-sensor geometry, and ancillary data from the NCEP global weather forecast, ACSPO outputs include four brightness temperatures (BTs) in M12 (3.7um), M14 (8.6um), M15 (11um), and M16 (12um) bands, and two reflectances in M5 (0.67um) and M7 (0.87um) bands. The reflectances are used for cloud identification. Beginning with ACSPO v2.60, all BTs and reflectances are destriped (Bouali and Ignatov, 2014) and resampled (Gladkova et al., 2016), to minimize the effect of bow-tie distortions and deletions. SSTs are retrieved from destriped BTs. SSTs are derived from BTs using the Multi-Channel SST (MCSST; night) and Non-Linear SST (NLSST; day) algorithms (Petrenko et al., 2014). ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Fill values are reported in all pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), four BTs in M12/14/15/16 (included for those users interested in direct \"radiance assimilation\", e.g., NOAA NCEP, NASA GMAO, ECMWF) and two reflectances in M5/7 are reported, along with derived SST. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. Note that users of ACSPO data have the flexibility to ignore the ACSM and derive their own clear-sky mask, and apply it to BTs and SSTs. They may also ignore ACSPO SSTs, and derive their own SSTs from the original BTs. The L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), using another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). Corresponding clear-sky BTs are validated against RTM simulation in the Monitoring IR Clear-sky Radiances over Ocean for SST system (MICROS; Liang and Ignatov, 2011). A reduced size (1GB/day), equal-angle gridded (0.02-deg), ACSPO L3U product is also available, where gridded L2P SSTs with QL=5 only are reported, and BT layers omitted.", "links": [ { diff --git a/datasets/10.25921_ssc9-cp98_Not Applicable.json b/datasets/10.25921_ssc9-cp98_Not Applicable.json index f5adcf9499..02d4639212 100644 --- a/datasets/10.25921_ssc9-cp98_Not Applicable.json +++ b/datasets/10.25921_ssc9-cp98_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/ssc9-cp98_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity and dissolved oxygen collected during the R/V Maria S. Merian cruise MSM27 (EXPOCODE 06M220130419) in the North Atlantic Ocean from 2013-04-19 to 2013-05-06.", "links": [ { diff --git a/datasets/10.25921_stqn-xd35_Not Applicable.json b/datasets/10.25921_stqn-xd35_Not Applicable.json index 8b91fb0393..61ce76dd10 100644 --- a/datasets/10.25921_stqn-xd35_Not Applicable.json +++ b/datasets/10.25921_stqn-xd35_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/stqn-xd35_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity, dissolved oxygen, and sulfur hexafluoride (SF6) collected during the R/V Maria S. Merian cruise MSM42 (EXPOCODE 06M220150502) in the North Atlantic Ocean from 2015-05-02 to 2015-05-22.", "links": [ { diff --git a/datasets/10.25921_swbw-0w83_Not Applicable.json b/datasets/10.25921_swbw-0w83_Not Applicable.json index 4278b881d2..8dc436b6c9 100644 --- a/datasets/10.25921_swbw-0w83_Not Applicable.json +++ b/datasets/10.25921_swbw-0w83_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/swbw-0w83_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession contains measurements of the effects of elevated levels of CO2 on the early life stages of black sea bass (Centropristis striata) collected in Long Island Sound, Connecticut. In this study, we exposed fertilized black sea bass eggs to a range of CO2 levels (182.7 \u00ce\u00bcatm to 2252.6 \u00ce\u00bcatm) and measured survival and hatch rates, and skeletal abnormalities after 48 hours exposure. Adult male and female black sea bass were held in flowing seawater at ambient temperatures during the winters of 2012 to 2013, 2013 to 2014, and 2014 to 2015. Once fish came out of torpor, adults were fed squid during conditioning and spawning. Gamete development in fish occurred naturally and spawning took place in holding tanks in late July of all three years. Fertilized eggs were collected in screens placed at the seawater outflow and exposed to different levels of CO2.", "links": [ { diff --git a/datasets/10.25921_tgp1-w632_Not Applicable.json b/datasets/10.25921_tgp1-w632_Not Applicable.json index 4466127c09..01a1c8eef2 100644 --- a/datasets/10.25921_tgp1-w632_Not Applicable.json +++ b/datasets/10.25921_tgp1-w632_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/tgp1-w632_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains multibeam bathymetry, backscatter, and LiDAR bathymetry and reflectance. These GeoTiffs represent water depth and acoustic intensity of the seafloor from Phase II of the Long Island Sound (LIS) Benthic Habitat Priority Areas of Interest (AOI) project. The original Phase II datasets were surveyed by NOAA Ship Nancy Foster (R-352), NOAA Ship Thomas Jefferson, and the Navigation Response Team (NRT-5) using 400 khz Reson 7125 multibeam sonars from 2003 to 2014. In 2018, the LIS Cable Fund contracted the State University of New York (SUNY) at Stony Brook School of Marine and Atmospheric Sciences (SoMAS) to fill gaps and resurvey areas where multibeam data was not acceptable with the R/V Pritchard using 400 khz Kongsberg dual-swath EM2040c multibeam sonars in coordination with the NOAA National Centers for Coastal Ocean Science (NCCOS) Biogeography Branch and the NOAA Integrated Ocean and Coastal Mapping (IOCM) Program. The multibeam and LiDAR were corrected, calibrated, and integrated into a seamless 32-bit raster using CARIS and ArcGIS. Backscatter data was collected and mosaicked into a raster using Fledermaus Geocoder Toolbox, ArcGIS 10.4, and PCI Geomatica 2018 software.", "links": [ { diff --git a/datasets/10.25921_vwvq-5015_Not Applicable.json b/datasets/10.25921_vwvq-5015_Not Applicable.json index f667ee121c..077f7fb579 100644 --- a/datasets/10.25921_vwvq-5015_Not Applicable.json +++ b/datasets/10.25921_vwvq-5015_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/vwvq-5015_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion \"Hurricane Hunter\" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. These data include aircraft instrument microphysics data in netcdf file and video quick-looks showing size distributions and scalar summaries along with aircraft position in mp4 format.", "links": [ { diff --git a/datasets/10.25921_w90w-2032_Not Applicable.json b/datasets/10.25921_w90w-2032_Not Applicable.json index 5b44e821a4..0d792868e0 100644 --- a/datasets/10.25921_w90w-2032_Not Applicable.json +++ b/datasets/10.25921_w90w-2032_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/w90w-2032_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of Chlorofluorocarbons, oxygen, water temperature and salinity collected from R/V Thalassa cruise SPOL (EXPOCODE 35TH20050604) in the North Arctic Ocean from 2005-06-04 to 2005-07-12.", "links": [ { diff --git a/datasets/10.25921_wamc-d787_Not Applicable.json b/datasets/10.25921_wamc-d787_Not Applicable.json index c033b5380a..c4770e9091 100644 --- a/datasets/10.25921_wamc-d787_Not Applicable.json +++ b/datasets/10.25921_wamc-d787_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/wamc-d787_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-12), temperature, salinity, oxygen, nutrients, ammonium and sulfur hexaflouride (SF6) collected during the R/V Meteor cruise GUTRE_4 (EXPOCODE 06MT20101014) in the Tropical Atlantic Ocean from 2010-10-14 to 2010-12-13.", "links": [ { diff --git a/datasets/10.25921_x4sc-eb72_Not Applicable.json b/datasets/10.25921_x4sc-eb72_Not Applicable.json index 79e75b1e40..41d5c6b7ad 100644 --- a/datasets/10.25921_x4sc-eb72_Not Applicable.json +++ b/datasets/10.25921_x4sc-eb72_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/x4sc-eb72_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes bottle discrete data collected during the small boat Buzzards Baykeeper cruises and other platforms in the Buzzards Bay, Massachusetts from 2015-06-15 to 2017-09-01. These data include dissolved inorganic carbon (DIC), total alkalinity (TA), temperature, salinity, oxygen, nitrate plus nitrite, phosphate, total dissolved nitrogen, ammonium, total dissolved nitrogen, particulate organic carbon, particulate organic nitrogen and chlorophyll A. The field research was supported by John D. and Catherine T. MacArthur Foundation (14-106159-000-CFP).", "links": [ { diff --git a/datasets/10.25921_xry2-9078_Not Applicable.json b/datasets/10.25921_xry2-9078_Not Applicable.json index d9142b4ec7..21fed0006e 100644 --- a/datasets/10.25921_xry2-9078_Not Applicable.json +++ b/datasets/10.25921_xry2-9078_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/xry2-9078_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor GO-SHIP A06E cruise (EXPOCODE 06MT20130525) in the Tropical Atlantic Ocean from 2013-05-25 to 2013-06-23. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, nutrients and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.", "links": [ { diff --git a/datasets/10.25921_zfhg-8676_Not Applicable.json b/datasets/10.25921_zfhg-8676_Not Applicable.json index 8dde56cb0a..37f338e563 100644 --- a/datasets/10.25921_zfhg-8676_Not Applicable.json +++ b/datasets/10.25921_zfhg-8676_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/zfhg-8676_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains estimates of water mass contributions to the GLODAPv2 Atlantic data. The major water masses in the Atlantic Ocean were characteristics as Source Water Types (SWTs) from their formation areas and map out their distributions. The SWTs are described by six properties taken from the biased adjusted data product GLODAPv2, including both conservative (Temperature and Absolute Salinity) and non-conservative (oxygen, silicate, phosphate and nitrate) properties. The distributions of the water masses are estimated by using the Optimum Multi-parameter (OMP) model and the data are contained in the file that has the same length and order as the GLODAPv2 Atlantic data file. The following water masses were considered: Antarctic Intermediate Water (AAIW), Subarctic Intermediate Water (SAIW) and Mediterranean Water (MW), North Atlantic Deep Water (NADW, divided into its upper and lower components), Labrador Sea Water (LSW), Iceland-Scotland Overflow Water (ISOW), Denmark Strait Overflow Water (DSOW), Antarctic Bottom Water (AABW), North East Atlantic Bottom Water (NEABW), Circumpolar Deep Water (CDW), and Weddell Sea Bottom Water (WSBW).", "links": [ { diff --git a/datasets/10.25921_zft1-g981_Not Applicable.json b/datasets/10.25921_zft1-g981_Not Applicable.json index cbe4fad7de..d6bd6d79c5 100644 --- a/datasets/10.25921_zft1-g981_Not Applicable.json +++ b/datasets/10.25921_zft1-g981_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/zft1-g981_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete profile data collected during the R/V Urania cruise MAI2 (EXPOCODE 48UR19990211) in the Mediterranean Sea from 1999-02-11 to 1999-02-17. These data include temperature, salinity, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.25921_zgk5-ep63_Not Applicable.json b/datasets/10.25921_zgk5-ep63_Not Applicable.json index 817520bc14..2405567021 100644 --- a/datasets/10.25921_zgk5-ep63_Not Applicable.json +++ b/datasets/10.25921_zgk5-ep63_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/zgk5-ep63_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession contains the compiled data product of profile, discrete biogeochemical measurements from 35 individual cruise data sets collected from a variety of ships in the southern Salish Sea and northern California Current System (Washington state marine waters) from 2008-02-04 to 2018-10-19. Water-column time-series stations were occupied in the Salish Sea and adjoining northern California Current System coastal waters in Washington State. Each cruise was designed to obtain a synoptic or targeted snapshot of key carbon, physical, and other biogeochemical parameters as they relate to ocean acidification (OA) in Washington's estuarine and/or coastal environments. Two predominant subsets of sampling stations were occupied: 1) Puget Sound stations, wherein all basins within the sound and across the sill at its inlet are sampled, and have recurred regularly in April, July, and September since 2014; and 2) \"Sound-to-Sea\" cruises, associated with servicing the \u00c4\u0086h\u00c3\u00a1\u00ca\u0094ba\u00c2\u00b7 ocean acidification mooring off La Push, Washington, and including sampling at a suite of CTD stations located between Seattle and the mooring site off the coast, occurring most frequently in May and October. At all sampling stations, CTD casts were conducted to measure temperature, conductivity, pressure, and oxygen concentrations using CTD and oxygen sensors. Discrete water samples were collected throughout the water column at all stations in Niskin bottles. Laboratory analyses were run to measure dissolved inorganic carbon (DIC), oxygen, and nutrient concentrations and total alkalinity. More information, including a map of stations occupied during each cruise (and other Salish cruises), full-resolution CTD downcast data for all stations sampled, chlorophyll and phaeopigment concentrations, and other sensor data, can be found at nvs.nanoos.org/CruiseSalish by exploring the Map, Data, and Plots tabs. Maps of stations sampled during each cruise, along with the full discrete sample data set for each cruise, can be found by exploring the NOAA National Centers for Environmental Information landing page at https://www.nodc.noa.gov/ocads/oceans/SalishCruises_2008_2018.html for this compiled data product and pages linked therein. This effort was conducted in support of the estuarine and coastal monitoring and research objectives of the University of Washington Puget Sound Regional Synthesis Model (PRISM), the Washington Ocean Acidification Center (WOAC), the Northwest Association of Networked Ocean Observing Systems, the U.S. National Oceanic and Atmospheric Administration's Pacific Marine Environmental Laboratory's Carbon Group, and the U.S. National Oceanic and Atmospheric Administration's Ocean Acidification Program and conforms to climate-quality monitoring guidelines of the Global Ocean Acidification Observing Network (goa-on.org). For any questions about appropriate use or limitations of the data set, please contact Drs. Simone Alin and Jan Newton at email addresses above.", "links": [ { diff --git a/datasets/10.25921_zrw8-kn24_Not Applicable.json b/datasets/10.25921_zrw8-kn24_Not Applicable.json index 1f827c5903..4300968f9e 100644 --- a/datasets/10.25921_zrw8-kn24_Not Applicable.json +++ b/datasets/10.25921_zrw8-kn24_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.25921/zrw8-kn24_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession contains a compilation of inorganic carbon system and other hydrographic and chemical discrete profile measurements obtained during the fifty five Line P cruises in the Northeast Pacific Ocean over the period from 1990-05-10 to 2019-06-19. The data in the data set include dissolved inorganic carbon (DIC), total alkalinity (TA), water temperature, salinity, dissolved oxygen concentration and nutrients. The majority of the cruises from 1990 to 2015 have been reported elsewhere as individual files (e.g., GLODAP and PACIFICA databases). This data set is a combination of the available cruises into a single database, and extended the time series to June 2019. A secondary quality control was performed and the quality flags revised. Additionally, the suggested PACIFICA corrections for salinity, oxygen, dissolved inorganic carbon and nutrients were applied. Oxygen units were converted to \u00c2\u00b5mol/kg when reported in ml/L. Nutrient concentrations were converted to \u00c2\u00b5mol/kg from \u00c2\u00b5mol/L.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.carina_77dn20010717_Not Applicable.json b/datasets/10.3334_cdiac_otg.carina_77dn20010717_Not Applicable.json index ca7257cd88..32abfa914a 100644 --- a/datasets/10.3334_cdiac_otg.carina_77dn20010717_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.carina_77dn20010717_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.carina_77dn20010717_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0113589 includes chemical, discrete sample, physical and profile data collected from ODEN in the Arctic Ocean from 2001-07-17 to 2001-07-26 and retrieved during cruise CARINA/77DN20010717. These data include ALKALINITY, HYDROSTATIC PRESSURE, Potential temperature (theta), SALINITY and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by Leif Anderson of Gothenburg University; Department of Analytical and Marine Chemistry as part of the CARINA/77DN20010717 data set.\n\nThe CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.carina_omex2_Not Applicable.json b/datasets/10.3334_cdiac_otg.carina_omex2_Not Applicable.json index e9051d2f07..ffa9a5202a 100644 --- a/datasets/10.3334_cdiac_otg.carina_omex2_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.carina_omex2_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.carina_omex2_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0115763 includes chemical, discrete sample, physical and profile data collected from BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 and retrieved during cruise OMEX2. These data include ALKALINITY, AMMONIUM, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE, UREA and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by A. et al. Borges of University of Liege as part of the CARINA/OMEX2 data set.\n\nThe CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.clivar_mp_2003_Not Applicable.json b/datasets/10.3334_cdiac_otg.clivar_mp_2003_Not Applicable.json index 2c793fdc01..df196cd77e 100644 --- a/datasets/10.3334_cdiac_otg.clivar_mp_2003_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.clivar_mp_2003_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.clivar_mp_2003_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0108077 discrete profile chemical and physical data collected from R/V Ka'imikai-O-Kanaloa, R/V Kilo Moana and R/V Roger Revelle in the North Pacific Ocean from 2002-07-01 to 2003-08-21 during the MP-5, MP-6 and MP-9 cruises. These data include total alkalinity, dissolved inorganic carbon, salinity and temperature. The instruments used to collect these data include Alkalinity titrator, CTD, Coulometer for DIC measurement. These data were collected by Patricia L. Yager of University of Georgia; School of Marine Programs as part of the MP-5 cruise, MP-6 cruise and MP-9 cruise data set.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.clivar_s04p_2011_Not Applicable.json b/datasets/10.3334_cdiac_otg.clivar_s04p_2011_Not Applicable.json index eb0d85ec0d..b895b0f2e6 100644 --- a/datasets/10.3334_cdiac_otg.clivar_s04p_2011_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.clivar_s04p_2011_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.clivar_s04p_2011_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0109933 includes discrete sample data collected from NATHANIEL B. PALMER in the Southern Oceans from 2011-02-19 to 2011-04-23. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), DELTA CARBON-13, DELTA CARBON-14, DELTA HELIUM-3, DISSOLVED INORGANIC CARBON (DIC), DISSOLVED ORGANIC CARBON, DISSOLVED OXYGEN, HELIUM, HYDROSTATIC PRESSURE, NEON, NITRATE, NITRITE, Partial pressure (or fugacity) of carbon dioxide - water, Potential temperature (theta), SALINITY, SEA SURFACE TEMPERATURE, SULFUR HEXAFLUORIDE (SF6), TOTAL ALKALINITY (TA), Total Dissolved Nitrogen (TDN), Tritium (Hydrogen isotope), WATER TEMPERATURE, pH, phosphate and silicate. The instruments used to collect these data include Alkalinity titrator, CTD, Coulometer for DIC measurement, bottle and spectrophotometer.\n\nThese data were collected by Frank J. Millero and Dennis Hansell of Rosenstiel School of Marine and Atmospheric Science, Richard A. Feely and Christopher Sabine of US DOC; NOAA; OAR; Pacific Marine Environmental Laboratory and Andrew Dickson of University of California - San Diego; Scripps Institution of Oceanography as part of the CLIVAR_S04P_2011 data set.\n\nThe International CLIVAR Global Ocean Carbon and Repeat Hydrography Program carries out a systematic and global re-occupation of select WOCE/JGOFS hydrographic sections to quantify changes in storage and transport of heat, fresh water, carbon dioxide (CO2), and related parameters.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.nac13v1_Not Applicable.json b/datasets/10.3334_cdiac_otg.nac13v1_Not Applicable.json index a7877954fd..ce2e0f10f9 100644 --- a/datasets/10.3334_cdiac_otg.nac13v1_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.nac13v1_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.nac13v1_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI accession 0164569 presents a Del13C-DIC data set for the North Atlantic, which has undergone strict quality control. The data, all in all 6569 samples, originate from oceanographic research cruises that took place between 1981 and 2014. During a primary quality control step based on simple range tests obviously bad data has been flagged. In a second quality control step systematic biases between of all cruises were quantified through a crossover analysis. The data set consists of 32 cruises of which 24 could be compared quantitatively for systematic biases through an adequate crossover study. Additive adjustments were applied to 11 of the 24 cruises. Based on this analysis the internal consistency of this data set is estimated to be 0.017 o/oo.\n\nThe NAC13v1.csv file contains the 13C data, a simple quality flag ('Del13Cf', 2: good, 9: bad/not measured) and a 2nd QC-flag ('Del13Cqc', 1: quality controlled, 0: not quality controlled). The NAC13v1_expocode.csv-File contains the allocation of the cruise numbers used in NAC13v1 and their EXPOCODEs as well as the respective cruise numbers in GLODAPv2 and CARINA. For this analysis some cruises that belong together were condensed to one, e.g. the TTO-NA cruises.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.ndp094_Not Applicable.json b/datasets/10.3334_cdiac_otg.ndp094_Not Applicable.json index c1ce5b0f93..f94da2a212 100644 --- a/datasets/10.3334_cdiac_otg.ndp094_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.ndp094_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.ndp094_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Climatological mean monthly distributions of pH in the total H+ scale, total CO2 concentration (TCO2), and the degree of CaCO3 saturation for the global surface ocean waters (excluding coastal areas) are calculated using a data set for pCO2, alkalinity and nutrient concentrations in surface waters (depths less than 50 m), which is built upon the GLODAP, CARINA and LDEO database. The mutual consistency among these measured parameters is demonstrated using the inorganic carbon chemistry model with the dissociation constants for carbonic acid by Lueker et al. (2000) and for boric acid by Dickson (1990). The global ocean is divided into 24 regions, and the linear potential alkalinity (total alkalinity + nitrate) versus salinity relationships are established for each region. The mean monthly distributions of pH and carbon chemistry parameters for the reference year 2005 are computed using the climatological mean monthly pCO2 data adjusted to a reference year 2005 and the alkalinity estimated from the potential alkalinity versus salinity relationships. The climatological monthly mean values of pCO2 over the global ocean are compiled for a 4\u00c2\u00b0 x 5\u00c2\u00b0 grid for the reference year 2005, and the gridded data for each of 12 months are included in this database. This is updated version of Takahashi et al. (2009) for the reference year 2000 representing non-El Ni\u00c3\u00b1o years using a database of about 6.5 million pCO2 data (less coastal areas of North and South America) observed in 1957-2012 (Takahashi et al., 2013). The equatorial zone (4\u00c2\u00b0N-4\u00c2\u00b0S) of the Pacific is excluded from the analysis because of the large interannual changes associated with the El Ni\u00c3\u00b1o-Southern Oscillation events. The pH thus calculated ranges from 7.9 to 8.2. Lower values are located in the upwelling regions in the tropical Pacific and in the Arabian and Bering Seas; and higher values are found in the subpolar and polar waters during the spring-summer months of intense photosynthetic production. The vast areas of subtropical oceans have seasonally varying pH values ranging from 8.05 during warmer months to 8.15 during colder months. The warm tropical and subtropical waters are supersaturated by a factor of as much as 4.2 with respect to aragonite and 6.3 for calcite, whereas the cold subpolar and polar waters are less supersaturated only by 1.2 for aragonite and 2 for calcite because of the lower pH values resulting from greater TCO2 concentrations. In the western Arctic Ocean, aragonite undersaturation is observed.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.pacifica_49nz20040901_Not Applicable.json b/datasets/10.3334_cdiac_otg.pacifica_49nz20040901_Not Applicable.json index ba98cdba16..d2fb45b092 100644 --- a/datasets/10.3334_cdiac_otg.pacifica_49nz20040901_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.pacifica_49nz20040901_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0112357 includes biological, chemical, discrete sample, physical and profile data collected from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13. These data include AMMONIUM (NH4), CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), CHLOROPHYLL A, DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, Methane (CH4), NITRATE, NITRITE, SALINITY, TOTAL ALKALINITY (TA), WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD, Coulometer for DIC measurement and bottle.\n\nThese data were collected by Shigeto Nishino and Koji Shimada of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) as part of the PACIFICA_49NZ20040901 data set. CDIAC associated the following cruise ID(s) with this data set: MR04-05 and PACIFICA_49NZ20040901\n\nPACIFICA (PACIFic ocean Interior CArbon) was an international collaborative project for the data synthesis of ocean interior carbon and its related parameters in the Pacific Ocean. The North Pacific Marine Science Organization (PICES), Section of Carbon and Climate (S-CC) supported the project.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.tsm_estoc_Not Applicable.json b/datasets/10.3334_cdiac_otg.tsm_estoc_Not Applicable.json index 7c09831e45..74f61ebea4 100644 --- a/datasets/10.3334_cdiac_otg.tsm_estoc_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.tsm_estoc_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.tsm_estoc_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0100064 includes chemical, physical, time series and underway - surface data collected from METEOR, POSEIDON, TALIARTE and VICTOR HENSEN in the North Atlantic Ocean and South Atlantic Ocean from 1995-10-02 to 2009-11-25 and retrieved during cruise ESTOC cruises. These data include ALKALINITY - TOTAL, CARBON DIOXIDE - PARTIAL PRESSURE (pCO2), DISSOLVED INORGANIC CARBON, SALINITY, SEA SURFACE TEMPERATURE and pH. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer and Carbon dioxide (CO2) shower head chamber equilibrator. These data were collected by Melchor Gonz\u00c3\u00a1lez D\u00c3\u00a1vila of Universidad de Las Palmas de Gran Canaria as part of the ESTOC_Time_Series data set.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.tsm_tao170w_2s_Not Applicable.json b/datasets/10.3334_cdiac_otg.tsm_tao170w_2s_Not Applicable.json index d7dbbe2da5..ea5fb49f7f 100644 --- a/datasets/10.3334_cdiac_otg.tsm_tao170w_2s_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.tsm_tao170w_2s_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.tsm_tao170w_2s_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0100079 includes chemical, time series and underway - surface data collected from MOORINGS in the North Pacific Ocean and South Pacific Ocean from 1998-06-22 to 2004-11-23. These data include CARBON DIOXIDE - PARTIAL PRESSURE - DIFFERENCE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer and Carbon dioxide (CO2) laminar flow bubble equilibrator (for buoy measurement).\n\nThese data were collected by Francisco Chavez of MONTEREY BAY AQUARIUM RESEARCH INSTITUTE as part of the Mooring TAO170W2S data set. CDIAC assigned the following cruise ID(s) to this data set: TAO170W2S_1998_2004, TAO170W2S_2007_2008.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_alligatorhope_1999-2001_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_alligatorhope_1999-2001_Not Applicable.json index 47f26f3210..716a568697 100644 --- a/datasets/10.3334_cdiac_otg.vos_alligatorhope_1999-2001_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_alligatorhope_1999-2001_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_alligatorhope_1999-2001_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VOS Alligator Hope Line", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_ant20_2_2003_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_ant20_2_2003_Not Applicable.json index 0027f55c1d..05edf4a0f5 100644 --- a/datasets/10.3334_cdiac_otg.vos_ant20_2_2003_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_ant20_2_2003_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_ant20_2_2003_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway measurements from ANT-XX/2 cruise (South Atlantic Ocean)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_argau_line_2005_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_argau_line_2005_Not Applicable.json index f9b33bc3be..589cc18e00 100644 --- a/datasets/10.3334_cdiac_otg.vos_argau_line_2005_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_argau_line_2005_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_argau_line_2005_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface pCO2 measurements in the Argentinian Shelf during the ARGAU cruises.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_atlantic_companion_line_2007_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_atlantic_companion_line_2007_Not Applicable.json index 77ca14c352..e003e030f2 100644 --- a/datasets/10.3334_cdiac_otg.vos_atlantic_companion_line_2007_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_atlantic_companion_line_2007_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_atlantic_companion_line_2007_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M/V Atlantic Companion VOS Line", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_falstaff_2010_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_falstaff_2010_Not Applicable.json index 26d44b7144..b649316a49 100644 --- a/datasets/10.3334_cdiac_otg.vos_falstaff_2010_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_falstaff_2010_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_falstaff_2010_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M/V Falstaff VOS Line data", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_gef_patagonia_2006_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_gef_patagonia_2006_Not Applicable.json index 7abf2b7ec5..49eb751526 100644 --- a/datasets/10.3334_cdiac_otg.vos_gef_patagonia_2006_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_gef_patagonia_2006_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_gef_patagonia_2006_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea Surface pCO2 measurements in the Argentinian Shelf during the 2005-2006 GEF Patagonia cruises.", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_kofu_maru_1998-2002_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_kofu_maru_1998-2002_Not Applicable.json index f47d4d4d1e..bb7de63361 100644 --- a/datasets/10.3334_cdiac_otg.vos_kofu_maru_1998-2002_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_kofu_maru_1998-2002_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_kofu_maru_1998-2002_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "R/V Kofu Maru 1998-2002 data", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_10_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_10_Not Applicable.json index 357ba08a2e..1734694e1e 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_10_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_10_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_10_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-10 cruise (Southern Ocean).", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_11_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_11_Not Applicable.json index 1e99757686..35d423dbc6 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_11_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_11_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_11_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-11 cruise (Southern Ocean)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_12_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_12_Not Applicable.json index c5ecbfb5ee..ad10555b58 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_12_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_12_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_12_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-12 cruise (Southern Ocean)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_13_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_13_Not Applicable.json index e08d5d8cbb..0c47d016fe 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_13_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_13_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_13_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-13 cruise (Indian and Southern Oceans)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_14_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_14_Not Applicable.json index 251b441c51..801b8844a2 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_14_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_14_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_14_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-14 cruise (Indian and Southern Oceans)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_15_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_15_Not Applicable.json index 9a4f1ebb1c..8e0f48637c 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_15_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_15_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_15_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-15 cruise (South Indian, South Atlantic, and Southern Oceans)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_6_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_6_Not Applicable.json index eade870fe3..b5e21795c6 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_6_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_6_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_6_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-6 cruise (Indian and Southern Oceans)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_8_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_8_Not Applicable.json index 649c153a0a..2e018d4555 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_8_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_8_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_8_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-8 cruise (Indian and Southern Oceans)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_oiso_9_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_oiso_9_Not Applicable.json index fb0cab3b4b..2e4d4ca472 100644 --- a/datasets/10.3334_cdiac_otg.vos_oiso_9_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_oiso_9_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_oiso_9_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-9 cruise (Indian Ocean)", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_skaugran_1995-1999_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_skaugran_1995-1999_Not Applicable.json index 1a6137b92b..7287b8b4b8 100644 --- a/datasets/10.3334_cdiac_otg.vos_skaugran_1995-1999_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_skaugran_1995-1999_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_skaugran_1995-1999_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VOS Skaugran Line", "links": [ { diff --git a/datasets/10.3334_cdiac_otg.vos_tully_1989_Not Applicable.json b/datasets/10.3334_cdiac_otg.vos_tully_1989_Not Applicable.json index 7b04578d86..a3d209cd62 100644 --- a/datasets/10.3334_cdiac_otg.vos_tully_1989_Not Applicable.json +++ b/datasets/10.3334_cdiac_otg.vos_tully_1989_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.3334/cdiac/otg.vos_tully_1989_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway measurements from R/V John P. Tully 1989 cruise", "links": [ { diff --git a/datasets/10.7289_v50r9mn2_Not Applicable.json b/datasets/10.7289_v50r9mn2_Not Applicable.json index d220d73cd6..ea20e72db4 100644 --- a/datasets/10.7289_v50r9mn2_Not Applicable.json +++ b/datasets/10.7289_v50r9mn2_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v50r9mn2_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains atmospheric measurements of carbon dioxide (CO2) from the Salt Lake City CO2 measurement network from 2001-2015 as well as several supporting data sets used to interpret the mixing ratio data. The additional data sets include atmospheric footprints (i.e. the upstream influence region on the atmospheric measurement site), fluxes of CO2 from anthropogenic and biological sources, and gridded population in the state of Utah.", "links": [ { diff --git a/datasets/10.7289_v51v5bzm_Not Applicable.json b/datasets/10.7289_v51v5bzm_Not Applicable.json index 8332ef24fc..219d077a08 100644 --- a/datasets/10.7289_v51v5bzm_Not Applicable.json +++ b/datasets/10.7289_v51v5bzm_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v51v5bzm_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archival package contains aerial survey data from the surveys described below. The Bureau of Ocean Energy Management (BOEM), formerly the Minerals Management Service (MMS), and its precursor, the Bureau of Land Management, have funded aerial surveys in the Beaufort, Chukchi, and Bering seas since 1979. In 2008, through an Interagency Agreement between MMS and the Alaska Fisheries Science Center (AFSC, National Marine Fisheries Service, National Oceanic and Atmospheric Administration), the Marine Mammal Laboratory (MML, a division of AFSC), formerly the National Marine Mammal Laboratory assumed co-management responsibilities for these surveys. Throughout the history of the surveys, they have been referred to as the Bowhead Whale Aerial Survey Project (BWASP) and the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial surveys, both of which are described in more detail below. The surveys are currently conducted under the auspices of a single study, Aerial Surveys of Arctic Marine Mammals (ASAMM). Consistent survey protocol has been in effect on surveys conducted since 1982.\n\nWESTERN BEAUFORT SEA\nAerial surveys in the western Beaufort Sea (south of 72 degrees N, 140-157 degrees W) have been conducted each year since 1979. MMS personnel and contractors conducted the surveys from 1979-2007. From 2008-2019, the surveys were conducted by MML. The primary goal of the project, also known as BWASP, was to document bowhead whales (Balaena mysticetus) during their fall migration through the western Beaufort Sea, although data were also collected for all other marine mammals that were sighted during the surveys. The surveys were typically conducted during the open water (i.e., ice-free) months of September and October, when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Cross Island (village of Nuiqsut), and Utqia\u00c4\u00a1vik (formerly Barrow), Alaska. Additional surveys were conducted in the Beaufort Sea during spring and summer 1979-1986, and during summer 2011-2019.\n\nEASTERN CHUKCHI SEA\nAerial surveys in the eastern Chukchi Sea (68-73 degrees N, 157-169 degrees W) were conducted by MMS (now BOEM) contractors from 1982-1991. From 2008-2019, the surveys were conducted by MML using similar methodology to the surveys conducted in previous years. Beginning in 2014, surveys were expanded south to 67 degrees N. The goal of the surveys, also known as the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial survey project, was to investigate the distribution and relative abundance of marine mammals in the Chukchi Sea Planning Area (CSPA) during the open water (i.e., ice-free) months of June to October, when various species are undertaking seasonal migrations through the area. However, from 1979-1984, surveys were also conducted during spring.\n\nNORTHERN BERING AND SOUTHERN CHUKCHI SEAS\nAerial surveys in the northern Bering and southern Chukchi seas (63-68 degrees N, east of the International Date Line) were conducted by MMS (now BOEM) contractors from 1979-1985. The goal of these surveys was to investigate the distribution, abundance, migration timing, habitat relationships and behavior of endangered whales during the spring migration. Surveys were conducted from April-July.\n\nEASTERN BEAUFORT SEA AND AMUNDSEN GULF\nAerial surveys in the eastern Beaufort Sea and Amundsen Gulf (67-73 degrees N, 118-140 degrees W), were conducted by MML from 5 to 27 August 2019, in collaboration with BOEM, North Slope Borough, Department of Fisheries and Oceans Canada, Inuvialuit Game Council, and Fisheries Joint Management Committee. The goal of these surveys, known as the ASAMM Bowhead Abundance (ABA) project, was to collect aerial survey data specific to estimating the abundance of the Bering-Chukchi-Beaufort Seas bowhead whale population. The primary ABA study area in its entirety includes the Beaufort Sea shelf and Amundsen Gulf (118-158 degrees W).", "links": [ { diff --git a/datasets/10.7289_v52805n2_1.0.json b/datasets/10.7289_v52805n2_1.0.json index fca2f4d883..0889e67d66 100644 --- a/datasets/10.7289_v52805n2_1.0.json +++ b/datasets/10.7289_v52805n2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v52805n2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/10.7289_v52j68xx_Not Applicable.json b/datasets/10.7289_v52j68xx_Not Applicable.json index 25b7e248c9..7a2f3b6230 100644 --- a/datasets/10.7289_v52j68xx_Not Applicable.json +++ b/datasets/10.7289_v52j68xx_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v52j68xx_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AVHRR Pathfinder Version 5.3 (PFV53) L3C Sea Surface Temperature data set is a collection of global, twice-daily (Day and Night) 4km sea surface temperature (SST) data produced by the NOAA National Centers for Environmental Information (NCEI). L3C is generated with measurements combined from a single instrument into a space-time grid. In this process multiple passes/scenes of data are combined. PFV53 was computed with data from the AVHRR instruments on board NOAA's polar orbiting satellite series using an entirely modernized system based on SeaDAS (version 6.4). This system incorporates several key changes from its predecessors (mainly version 5.2: PFV52). The SSTs in PFV53 are now available for all quality levels, including quality '0' which was left out of PFV52 due to a memory issue in the version 5.2 code. The Sun glint regions are better represented in the data. Cloud tree tests for NOAA-7 and NOAA-19 are now consistent with the rest of the sensors in contrast to PFV52 where they were inconsistent. Similar to all previous versions of Pathfinder this version also includes L3C products. The sst_dtime variable is still not included in L3C (it was not included in PFV52 either). The global and variables attributes in netCDF files are revised, have better CF and ACDD compliance, and are consistent with the NCEI netCDF templates. Anomalous hot-spots at land-water boundaries are better identified and flagged in PFV53. The PFV53 land mask has been updated (based on Global Lakes and Wetlands Database: Lakes and Wetlands Grid Level 3, 2015). Sea ice data over the Antarctic ice shelves are marked as ice and flagged as 100% ice cover. The PFV53 output are netCDF version 4 in \"classic\" mode, whereas in PFV52 the netCDF-4 files were not explicitly identified as \"classic\". An extra bit (bit 6) is used under l2p_flags variable to flag out the daytime unrealistic SST values (>39.8\u00c2\u00b0C) that remain in pf_quality_level 4 to 7. Users are recommended to avoid these values.\n\nImportantly, PFV53 data provided in netCDF-4 (classic model, with internal compression and chunking) are nearly 100% compliant with the GHRSST Data Specification Version 2.0 (GDS2.0 revision 5) requirements. However, it must be noted that in L3C data the variables sses_bias, sses_standard_deviation, and sst_dtime are still empty. PFV53 data were collected through the operational periods of the NOAA-7 through NOAA-19 Polar Operational Environmental Satellites (POES), and are available from 1981 through Present. Data for all these years are available as multiple NCEI accessions. PFV5.3 production is running on operational mode and will be updated on quarterly basis.", "links": [ { diff --git a/datasets/10.7289_v53b5xcg_Not Applicable.json b/datasets/10.7289_v53b5xcg_Not Applicable.json index 91e3d57b6b..1890945603 100644 --- a/datasets/10.7289_v53b5xcg_Not Applicable.json +++ b/datasets/10.7289_v53b5xcg_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v53b5xcg_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0162618 includes discrete bottle measurements of Total Alkalinity, pH (on total scale), Oxygen, Nutrients, Temperature and Salinity from R/V Investigator SOCCOM cruise IN2016_v01 (EXPOCODE 096U20160108) in the Southern Ocean from 2016-01-08 to 2016-02-27. The R/V Investigator cruise IN2016_v01 is the part of the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project funded by National Science Foundation and the Heard Earth-Ocean-Biosphere Interactions (HEOBI) funded by multiple Australian agencies.", "links": [ { diff --git a/datasets/10.7289_v53j3b9v_Not Applicable.json b/datasets/10.7289_v53j3b9v_Not Applicable.json index e91b1b7c05..1eb11ebf53 100644 --- a/datasets/10.7289_v53j3b9v_Not Applicable.json +++ b/datasets/10.7289_v53j3b9v_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v53j3b9v_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20080901) in Davis Strait from 2008-09-01 to 2008-09-21.", "links": [ { diff --git a/datasets/10.7289_v54b2z78_Not Applicable.json b/datasets/10.7289_v54b2z78_Not Applicable.json index 878475436f..e71e60c055 100644 --- a/datasets/10.7289_v54b2z78_Not Applicable.json +++ b/datasets/10.7289_v54b2z78_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v54b2z78_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Atlas is a result of an international collaboration between the Arctic and Antarctic Research Institute (Russia), Geophysical Institute, University of Bergen (Norway), and the National Oceanographic Data Center (USA). The Atlas is based on more than 500,000 stations collected during 1900 - 2012 years. It contains decadal, periodic, annual and monthly climatological fields of water temperature, salinity, and density on 0.25-degree grid at different depths. In addition to the climatological maps, time-depth diagrams of all parameters, including oxygen, at twelve selected areas covered by long-term observational programs are available.", "links": [ { diff --git a/datasets/10.7289_v54b2zc2_Not Applicable.json b/datasets/10.7289_v54b2zc2_Not Applicable.json index 97776dac91..df082b2cdc 100644 --- a/datasets/10.7289_v54b2zc2_Not Applicable.json +++ b/datasets/10.7289_v54b2zc2_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v54b2zc2_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archival package contains chlorophyll a, temperature, salinity and other variables collected from surface underway observations during the East Coast Ocean Acidification (ECOA) Cruise. The East Coast Ocean Acidification (ECOA) Cruise on board the R/V Gordan Gunter from Newport, took place in the Gulf of Maine and then along the East US coast to Miami. The effort was in support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP). The cruise was designed to obtain a snapshot of key carbon, physical, and biogeochemical parameters as they relate to ocean acidification (OA) in the coastal realm. The cruise included a series of 11 transects approximately orthogonal to the Gulf of Maine and Atlantic coasts and a comprehensive set of underway measurements along the entire transect.", "links": [ { diff --git a/datasets/10.7289_v54f1p2c_Not Applicable.json b/datasets/10.7289_v54f1p2c_Not Applicable.json index 9892046142..a360cd9ea3 100644 --- a/datasets/10.7289_v54f1p2c_Not Applicable.json +++ b/datasets/10.7289_v54f1p2c_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v54f1p2c_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession includes the profile discrete measurements of dissolved inorganic carbon, total alkalinity, CTD salinity and temperature collected in the summer of 2012 aboard R/V Xuelong in the Chinese Arctic Research Expedition (CHINARE12) cruise. The CHINARE project is an international collaboration between U.S. and Chinese scientists to study the water column carbonate chemistry in the western Arctic Ocean and Bering Sea.", "links": [ { diff --git a/datasets/10.7289_v5794304_Not Applicable.json b/datasets/10.7289_v5794304_Not Applicable.json index 73a352369a..e79c4bad93 100644 --- a/datasets/10.7289_v5794304_Not Applicable.json +++ b/datasets/10.7289_v5794304_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5794304_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20091006) in Davis Strait from 2009-10-06 to 2009-10-28.", "links": [ { diff --git a/datasets/10.7289_v5862dr7_Not Applicable.json b/datasets/10.7289_v5862dr7_Not Applicable.json index c2abf07fa8..b3b7399e1d 100644 --- a/datasets/10.7289_v5862dr7_Not Applicable.json +++ b/datasets/10.7289_v5862dr7_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5862dr7_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession includes the profile discrete measurements of dissolved inorganic carbon, total alkalinity, CTD salinity and temperature collected in the summer of 2010 aboard R/V Xuelong in the Chinese Arctic Research Expedition (CHINARE10) cruise. The CHINARE project is an international collaboration between U.S. and Chinese scientists to study the water column carbonate chemistry in the western Arctic Ocean and Bering Sea.", "links": [ { diff --git a/datasets/10.7289_v58913vh_Not Applicable.json b/datasets/10.7289_v58913vh_Not Applicable.json index e36ee40909..fdf5e22d9e 100644 --- a/datasets/10.7289_v58913vh_Not Applicable.json +++ b/datasets/10.7289_v58913vh_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v58913vh_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were used for an analysis of Steller sea lion pup health and condition by Lander et al. (2013). Serum chemistry and hematological values were measured by analysis of blood samples taken from 1,231 Steller sea lion pups (<2 months old). Pups were captured by hand or with hoop nets at 37 rookeries across their Alaskan range during mid-June to early July. Blood samples were collected from the caudal gluteal vein into EDTA and serum separator tubes. For details on analytical methodology, see Lander et al. (2013). These data are also linked to NOAA/NMFS InPort ID 24630.", "links": [ { diff --git a/datasets/10.7289_v58p5xt9_Not Applicable.json b/datasets/10.7289_v58p5xt9_Not Applicable.json index 2fe179fe9f..cc2aa85251 100644 --- a/datasets/10.7289_v58p5xt9_Not Applicable.json +++ b/datasets/10.7289_v58p5xt9_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v58p5xt9_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20100804) in Davis Strait from 2010-08-04 to 2010-09-29.", "links": [ { diff --git a/datasets/10.7289_v58s4n8g_Not Applicable.json b/datasets/10.7289_v58s4n8g_Not Applicable.json index b79b0c3281..77bb58155e 100644 --- a/datasets/10.7289_v58s4n8g_Not Applicable.json +++ b/datasets/10.7289_v58s4n8g_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v58s4n8g_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MET5/6 cruise (EXPOCODE 06MT19870818) in the Mediterranean Sea from 1987-08-18 to 1987-09-24. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, helium, tritium and neon measurements.", "links": [ { diff --git a/datasets/10.7289_v5bz64cq_Not Applicable.json b/datasets/10.7289_v5bz64cq_Not Applicable.json index 1693e68a78..f557a4e73a 100644 --- a/datasets/10.7289_v5bz64cq_Not Applicable.json +++ b/datasets/10.7289_v5bz64cq_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5bz64cq_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20111002) in Davis Strait from 2011-10-02 to 2011-10-21.", "links": [ { diff --git a/datasets/10.7289_v5c8279z_Not Applicable.json b/datasets/10.7289_v5c8279z_Not Applicable.json index fdb3f1d3f2..944a0f526a 100644 --- a/datasets/10.7289_v5c8279z_Not Applicable.json +++ b/datasets/10.7289_v5c8279z_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5c8279z_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains physical, chemical, and biological data collected during research cruises for the Chukchi Sea Offshore Monitoring in Drilling Area (Chemical and Benthos) (COMIDA CAB) project. The study occurred at 65 stations in the Chukchi Sea in July and August of 2009 and 2010, and involved sensor measurements and sampling of water, sediment, and biota conducted by investigators from several universities and research organizations. The dataset includes more than 36,000 data values across 150+ variables, 500+ taxonomic names, and 100+ collection and analysis methods. Seawater samples include variables such as salinity, chlorophyll a, particulate organic carbon, total suspended solids, and temperature. Samples from the benthic zone include variables such as taxonomic counts, biomass, biomarkers, DNA damage, and stable isotopes. Sediment samples include variables such as hydrocarbons, metals, grain size distribution, and carbon to nitrogen molar ratio. Data are organized in folders for each investigator who collected the data. A sites.csv file provides station locations and dates for all samples.", "links": [ { diff --git a/datasets/10.7289_v5cv4g1w_Not Applicable.json b/datasets/10.7289_v5cv4g1w_Not Applicable.json index fb4847a0ca..ccf2a7c2b8 100644 --- a/datasets/10.7289_v5cv4g1w_Not Applicable.json +++ b/datasets/10.7289_v5cv4g1w_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5cv4g1w_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of temperature, salinity, oxygen, nutrients, chlorofluorocarbons (CFC-11, CFC-12), dissolved inorganic carbon (DIC), total alkalinity, pH on sea-water-scale, dissolved organic carbon (DOC, TDN), and other measurements obtained during NOAA Ship Ronald H. Brown cruise along the GO-SHIP Repeat Hydrography Section P18 (EXPOCODE 33RO20161119) in the Pacific Ocean from 2016-11-19 to 2017-02-03. This data are from the 2016/2017 occupation of the P18 hydrographic section aboard NOAA Ship Ronald H. Brown acting under the auspices of the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP).", "links": [ { diff --git a/datasets/10.7289_v5db8043_Not Applicable.json b/datasets/10.7289_v5db8043_Not Applicable.json index c79e683053..2db63ba061 100644 --- a/datasets/10.7289_v5db8043_Not Applicable.json +++ b/datasets/10.7289_v5db8043_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5db8043_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession consists of the data synthesis product files that include autonomous seawater pCO2, pH, sea surface temperature and salinity time series measurements from 40 surface buoys between 2004 and 2017. Ship-based time series, some now approaching over three decades long, are critical climate records that have dramatically improved our ability to characterize natural and anthropogenic drivers of ocean carbon dioxide (CO2) uptake and biogeochemical processes. Advancements in autonomous ocean carbon observing technology over the last two decades have led to the expansion of fixed time series stations with the added capability of characterizing sub-seasonal variability. Here we present a data product of 40 autonomous moored surface ocean pCO2 and pH time series established between 2004 and 2013. These time series characterize a wide range of seawater pCO2 and pH conditions in different oceanic (17 sites) and coastal (13 sites) regimes including coral reefs (10 sites). With well-constrained daily to interannual variability and an estimate of decadal variability, these data suggest the length of time series necessary to detect an anthropogenic trend in seawater pCO2 and pH varies from 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 time series, WHOTS in the subtropical North Pacific and Stratus in the South Pacific gyre, are longer than the estimated time of emergence, and deseasoned monthly means show anthropogenic trends of 1.9+/-0.3 \u00c2\u00b5atm yr-1 and 1.6+/-0.3 \u00c2\u00b5atm yr-1, respectively. In the future, it is possible that updates to this product will allow for estimating anthropogenic trends at more sites; however, the product currently provides a valuable tool in an accessible format for evaluating climatology and natural variability of surface ocean carbonate chemistry in a variety of regions.", "links": [ { diff --git a/datasets/10.7289_v5df6p8f_1.0.json b/datasets/10.7289_v5df6p8f_1.0.json index d00d29e4b4..465d9b3084 100644 --- a/datasets/10.7289_v5df6p8f_1.0.json +++ b/datasets/10.7289_v5df6p8f_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5df6p8f_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.", "links": [ { diff --git a/datasets/10.7289_v5df6pj1_Not Applicable.json b/datasets/10.7289_v5df6pj1_Not Applicable.json index dad62e1c36..4d9fd2792d 100644 --- a/datasets/10.7289_v5df6pj1_Not Applicable.json +++ b/datasets/10.7289_v5df6pj1_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5df6pj1_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Hudson cruise (EXPOCODE 18HU20050904) in Davis Strait from 2005-09-04 to 2005-09-22.", "links": [ { diff --git a/datasets/10.7289_v5dv1gxq_Not Applicable.json b/datasets/10.7289_v5dv1gxq_Not Applicable.json index 90dffcc53b..775f2b5648 100644 --- a/datasets/10.7289_v5dv1gxq_Not Applicable.json +++ b/datasets/10.7289_v5dv1gxq_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5dv1gxq_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data represent two outputs from the Northeast Fisheries Climate Vulnerability assessment. The first are the biological sensitivity and climate exposure scores for each of the 82 species. The second are the estimated effect of climate change on each of the 82 species. Climate change and decadal variability are impacting marine fish and invertebrate species worldwide and these impacts will continue for the foreseeable future. Quantitative approaches have been developed to examine climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species owing to the lack of mechanistic understanding sufficient for quantitative analyses, as well as the lack of scientific infrastructure to support these more detailed studies. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information. These methods combine the exposure of a species to a stressor (climate change and decadal variability) and the sensitivity of species to the stressor. These two components are then combined to estimate an overall vulnerability. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking.", "links": [ { diff --git a/datasets/10.7289_v5h41pcq_Not Applicable.json b/datasets/10.7289_v5h41pcq_Not Applicable.json index a7ee7a9737..45568693e2 100644 --- a/datasets/10.7289_v5h41pcq_Not Applicable.json +++ b/datasets/10.7289_v5h41pcq_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5h41pcq_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides counts of harbor seals from aerial surveys over Lake Iliamna, Alaska, USA. The data have been collated from three previously published sources (Mathisen and Kline 1992; Small 2001; ABR Inc. Environmental Research and Services 2011) and newly available data from the NOAA Alaska Fisheries Science Center and the Newhalen Tribal Council. The survey years range between 1984 and 2013. Counts are reported as summed totals across all identified waypoints in the lake for each survey date.\n\nThe NOAA National Marine Mammal Laboratory (NMML) (Alaska Fisheries Science Center, Seattle, Washington, USA) conducted aerial surveys of Iliamna Lake between 2008 and 2013. Surveys were conducted as part of annual harbor seal survey effort and in collaboration with local community participants and researchers at the University of Alaska. Surveys were flown using high wing, twin engine aircraft (Aero Commander 680, 690 or a de Havilland Twin Otter). Survey altitude was generally 330 m and at an aircraft speed of 120 kts. Surveys were performed seasonally for most years between 2008 and 2013. Surveys were timed so that one survey was conducted while the lake was mostly frozen (Late March/early April), one during pupping (mid July), and often several during the August molt, when the greatest number of seals typically haul out on shore. Surveys were flown, weather allowing, in the mid- to late-afternoon, when the number of seals hauled out was expected to be highest. Aircraft flight track was recorded by GPS and all seals sighted were digitally photographed using a high resolution digital SLR camera with a telephoto zoom lens (up to 400mm). Time, date, latitude, longitude, and altitude were automatically saved into the image metadata or georeferenced post survey using the GPS track and software.\n\nThe total number of seals hauled out were counted from the digital photographs and recorded for each identified site. Pups were determined by their smaller size, and close proximity (less than 1 body length; either nursing or laying right next) to a larger seal. Pups were no longer recorded beyond about mid-August when many have been weaned and cannot reliably be distinguished from other non-adult seals. In 2009, a collaborative effort between NMML and researchers from the Newhalen Tribal Council (Newhalen Tribal Council 2009) provided 10 additional surveys and similar techniques were used. The raw survey count data from these surveys was provided to NMML. Aerial surveys were authorized under a Marine Mammal Protection Act General Authorization (LOC No. 14590) issued to the NMML.\n\nBetween 2005 and 2007, ABR, Inc. Environmental Research and Services conducted a series of aerial surveys for harbor seals in Iliamna Lake (ABR Inc. Environmental Research and Services 2011). In addition, earlier counts from surveys conducted by ADFG (Small 2001) and a 1991 census by Mathisen and Kline (Mathisen and Kline 1992) were incorporated into the dataset to expand the historical reach. Geographic coordinates were provided (or, when not provided, determined based on descriptions or phyiscial maps) for each survey site and these sites were compared and merged with locations identified by NMML. In some cases, sites in very close geographic proximity were combined into a single site.\n\nThe iliamna_totalcounts file provides counts (n=96) and observed weather conditions for each survey date. Both total number of adult seals (adulttotal) and total number of identified pups (puptotal) are provided when available. puptotal is recorded as NA when adults and pups were not distinguished. In these cases, the adulttotal value is presumed to include pups. In addition to the seal count inforamtion, each record includes observed weather variables (airtemp (in ranges of degrees F), windspeed (in ranges of miles per hour), winddirection (cardinal), and descriptive categories for skycondition and precip). The datetime values correspond to local Alaska time.", "links": [ { diff --git a/datasets/10.7289_v5hq3wv3_Not Applicable.json b/datasets/10.7289_v5hq3wv3_Not Applicable.json index a983ce9ece..a4f0f34ec6 100644 --- a/datasets/10.7289_v5hq3wv3_Not Applicable.json +++ b/datasets/10.7289_v5hq3wv3_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5hq3wv3_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains nutrient concentrations, temperature, salinity, density and dissolved oxygen values measured by CTD profiles on the U.S. Northeast Continental Shelf in support of ocean acidification research. Nutrients were measured in the laboratory using water samples collected during the CTD profiles at discrete depths. Ocean acidification is associated with increased concentrations of carbon dioxide that forms carbonic acid when dissolved in water. Marine primary production plays an important part in the carbon cycle by converting inorganic forms of carbon into organic matter. Variations in the concentrations of nutrients can limit or enhance primary production rates. An understanding of nutrient dynamics is therefore important to understanding and predicting marine carbon cycling and possible future impacts of ocean acidification.", "links": [ { diff --git a/datasets/10.7289_v5j67dz9_1.0.json b/datasets/10.7289_v5j67dz9_1.0.json index 5d4aeac4e1..5aee55767d 100644 --- a/datasets/10.7289_v5j67dz9_1.0.json +++ b/datasets/10.7289_v5j67dz9_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5j67dz9_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteosat Second Generation (MSG-3) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 2.0.", "links": [ { diff --git a/datasets/10.7289_v5j67f79_Not Applicable.json b/datasets/10.7289_v5j67f79_Not Applicable.json index a24461c807..7dee6d38a7 100644 --- a/datasets/10.7289_v5j67f79_Not Applicable.json +++ b/datasets/10.7289_v5j67f79_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5j67f79_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20040922) in Davis Strait from 2004-09-22 to 2004-10-04.", "links": [ { diff --git a/datasets/10.7289_v5kk98s8_2.61.json b/datasets/10.7289_v5kk98s8_2.61.json index 722ea67c6d..a02e2f8814 100644 --- a/datasets/10.7289_v5kk98s8_2.61.json +++ b/datasets/10.7289_v5kk98s8_2.61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5kk98s8_2.61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The Suomi National Polar-orbiting Partnership (S-NPP) is a collaboration between NASA and NOAA. The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO SNPP/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 500MB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/10.7289_v5mc8x9q_Not Applicable.json b/datasets/10.7289_v5mc8x9q_Not Applicable.json index f95240edda..fe4eaa9874 100644 --- a/datasets/10.7289_v5mc8x9q_Not Applicable.json +++ b/datasets/10.7289_v5mc8x9q_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5mc8x9q_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon and other measurements obtained during the RSV Aurora Australis cruise 09AR9407_1 along the WOCE Repeat Hydrography Section SR03 (EXPOCODE 09AR19940101) in the Southern Ocean from 1994-01-01 to 1994-03-01. Oceanographic measurements were conducted in January 1994 along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica, and along a northward section lying between 82 and 86 deg E and crossing the Princess Elizabeth Trough.", "links": [ { diff --git a/datasets/10.7289_v5nz85pq_1.0.json b/datasets/10.7289_v5nz85pq_1.0.json index 59a5da92da..227c3e2119 100644 --- a/datasets/10.7289_v5nz85pq_1.0.json +++ b/datasets/10.7289_v5nz85pq_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5nz85pq_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/10.7289_v5pr7sx5_2.61.json b/datasets/10.7289_v5pr7sx5_2.61.json index 333ef9adb1..407d6597c5 100644 --- a/datasets/10.7289_v5pr7sx5_2.61.json +++ b/datasets/10.7289_v5pr7sx5_2.61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5pr7sx5_2.61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The Suomi National Polar-orbiting Partnership (S-NPP) is a collaboration between NASA and NOAA. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). VIIRS is a whiskbroom scanning radiometer, which takes measurements in the cross-track direction within a field of view of 112.56-deg using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3,060 km, providing global daily coverage for both day and night passes. VIIRS has 22 spectral bands covering the spectrum from 0.4-12 um, including 16 moderate resolution bands (M-bands). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 27GB/day. In addition to pixel-level earth locations, Sun-sensor geometry, and ancillary data from the NCEP global weather forecast, ACSPO outputs include four brightness temperatures (BTs) in M12 (3.7um), M14 (8.6um), M15 (11um), and M16 (12um) bands, and two reflectances in M5 (0.67um) and M7 (0.87um) bands. The reflectances are used for cloud identification. Beginning with ACSPO v2.60, all BTs and reflectances are destriped (Bouali and Ignatov, 2014) and resampled (Gladkova et al., 2016), to minimize the effect of bow-tie distortions and deletions. SSTs are retrieved from destriped BTs. SSTs are derived from BTs using the Multi-Channel SST (MCSST; night) and Non-Linear SST (NLSST; day) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Fill values are reported in all invalid pixels, including those with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), four BTs in M12/14/15/16 (included for those users interested in direct \"radiance assimilation\", e.g., NOAA NCEP, NASA GMAO, ECMWF) and two reflectances in M5/7 are reported, along with derived SST. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. Note that users of ACSPO data have the flexibility to ignore the ACSM and derive their own clear-sky mask, and apply it to BTs and SSTs. They may also ignore ACSPO SSTs, and derive their own SSTs from the original BTs. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014) using another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). Corresponding clear-sky BTs are validated against RTM simulations in the Monitoring IR Clear-sky Radiances over Ocean for SST system (MICROS; Liang and Ignatov, 2011). A reduced size (1GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available, where gridded L2P SSTs with QL=5 only are reported, and BT layers omitted.", "links": [ { diff --git a/datasets/10.7289_v5q23xgw_Not Applicable.json b/datasets/10.7289_v5q23xgw_Not Applicable.json index 8fd48088bd..cfe04d37a9 100644 --- a/datasets/10.7289_v5q23xgw_Not Applicable.json +++ b/datasets/10.7289_v5q23xgw_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5q23xgw_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0162317 includes discrete bottle measurements of Dissolved Inorganic Carbon (DIC), Total Alkalinity, pH (on total scale), Nutrients, Temperature and Salinity from R/V Professor Gagarinsky cruise PGB_201408 (EXPOCODE 90G220140827) in the Peter the Great Bay, Japan Sea from 2014-08-27 to 2014-09-05. The R/V Professor Gagarinsky cruise PGB_201408 is a part of the Long-tern Observation and Research in the Peter the Great Bay, Sea of Japan by the V.I. Il\u00e2\u0080\u0099ichev Pacific Oceanological Institute of Russian Academy of Science.", "links": [ { diff --git a/datasets/10.7289_v5q81b4p_Not Applicable.json b/datasets/10.7289_v5q81b4p_Not Applicable.json index 534c0f3c2d..57152aec84 100644 --- a/datasets/10.7289_v5q81b4p_Not Applicable.json +++ b/datasets/10.7289_v5q81b4p_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5q81b4p_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archival package contains gridded data of aragonite saturation state across the global oceans (spatial distributions with a resolution of 1x1 degree latitude and longitude) at depth levels of 0m, 50m, 100m, 200m, 500m, 1000m, 2000m, 3000m and 4000m. Ocean station data with at least dissolved inorganic carbon (DIC) and total alkalinity (TA) measurements were obtained from the Global Ocean Data Analysis Project (GLODAP), the Carbon Dioxide in the Atlantic Ocean (CARINA), the Pacific Ocean Interior Carbon (PACIFICA), and some recent cruise data sets. Aragonite saturation state was calculated using a Matlab version of CO2SYS from in-situ temperature, pressure, salinity, dissolved inorganic carbon (DIC), total alkalinity (TA), silicate and phosphate with the dissociation constants for carbonic acid of Lueker et al. [2000], potassium bisulfate (KHSO4-) of Dickson [1990a], boric acid of Dickson [1990b], and with the total borate concentration equations of Lee et al. [2010]. Aragonite saturation state was correct to January 1, 2000 before it was gridded to a world-wide grid with 1x1 degree latitude and longitude resolution. The Longitude values used in this data set are from 20 to 380 degrees. For more information about the data set, please read the below paper: Jiang, L.-Q., R. A. Feely, B. R. Carter, D. J. Greeley, D. K. Gledhill, and K. M. Arzayus (2015), Climatological distribution of aragonite saturation state in the global oceans, Global Biogeochem. Cycles, 29, 1656-1673, https://doi.org/10.1002/2015GB005198.", "links": [ { diff --git a/datasets/10.7289_v5q81bbc_Not Applicable.json b/datasets/10.7289_v5q81bbc_Not Applicable.json index 2703b1b867..c46bfb2f08 100644 --- a/datasets/10.7289_v5q81bbc_Not Applicable.json +++ b/datasets/10.7289_v5q81bbc_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5q81bbc_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data contained within this file covers about 40 days of surface meteorological data and turbulent fluxes at sea south of Tasmania from 14 March to 16 April 2016. This is part of the CAPRICORN (Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean) 2016 project. The data come from two sources, the NOAA ESRL PSD's flux system and the instruments permanently installed on the RV Investigator. NOAA's flux system is an instrument package that makes direct measurements of the exchange or flux of heat, water, and momentum between the atmosphere and the ocean. The system also measures meteorological variables such as sea surface temperature, wind speed, air temperature, humidity. Together, this information can be used to estimate how the ocean and atmosphere exchange heat in weather and climate models. The dataset contains both direct measurement and model outputs (COARE 3.5). The averaging period is 10 minutes. Data have been corrected for known measurement issues when possible and quality control flags are included to reject bad data due to ship contamination or maneuvering.", "links": [ { diff --git a/datasets/10.7289_v5qc01j0_Not Applicable.json b/datasets/10.7289_v5qc01j0_Not Applicable.json index 594f4a8572..10fe222f1c 100644 --- a/datasets/10.7289_v5qc01j0_Not Applicable.json +++ b/datasets/10.7289_v5qc01j0_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5qc01j0_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To provide an improved oceanographic foundation and reference for multi-disciplinary studies of the Arctic Ocean, NCEI developed a new set of high-resolution quality-controlled long-term annual, seasonal and monthly mean temperature and salinity fields on different depth levels. This new regional climatology is based on the World Ocean Database archive of temperature and salinity from observations spanning over more than a hundred years and incorporates a great deal of new data not previously available.", "links": [ { diff --git a/datasets/10.7289_v5qz27zg_Not Applicable.json b/datasets/10.7289_v5qz27zg_Not Applicable.json index 19e3a96340..f2935da92e 100644 --- a/datasets/10.7289_v5qz27zg_Not Applicable.json +++ b/datasets/10.7289_v5qz27zg_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5qz27zg_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of simulated hydrologic fluxes and states from the Variable Infiltration Capacity (VIC) model, gridded to a 1/16 degree (~6km) resolution that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53 degrees N for the period 1950-2013. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities making it suitable for estimating large-scale hydrologic phenomena.", "links": [ { diff --git a/datasets/10.7289_v5s180sx_Not Applicable.json b/datasets/10.7289_v5s180sx_Not Applicable.json index e64bd60c30..2c293fb462 100644 --- a/datasets/10.7289_v5s180sx_Not Applicable.json +++ b/datasets/10.7289_v5s180sx_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5s180sx_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes discrete sample and profile data collected during the R/V Aegaeo M4WF cruise (EXPOCODE 36AE19981014) in the Mediterranean Sea from 1998-10-14 to 1998-10-19. These data include chlorofluorocarbons, helium, tritium, temperature, salinity and oxygen measurements.", "links": [ { diff --git a/datasets/10.7289_v5sq8xfh_1.0.json b/datasets/10.7289_v5sq8xfh_1.0.json index c7df0c67f8..27aa8335e5 100644 --- a/datasets/10.7289_v5sq8xfh_1.0.json +++ b/datasets/10.7289_v5sq8xfh_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5sq8xfh_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis uses both daytime and nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/10.7289_v5v1233j_Not Applicable.json b/datasets/10.7289_v5v1233j_Not Applicable.json index 0bca34ae5a..5fe42d95da 100644 --- a/datasets/10.7289_v5v1233j_Not Applicable.json +++ b/datasets/10.7289_v5v1233j_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5v1233j_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise KN (EXPOCODE 316N20061001) in Davis Strait from 2006-10-01 to 2006-10-04.", "links": [ { diff --git a/datasets/10.7289_v5vq30xb_Not Applicable.json b/datasets/10.7289_v5vq30xb_Not Applicable.json index 6692fcecc9..026218b138 100644 --- a/datasets/10.7289_v5vq30xb_Not Applicable.json +++ b/datasets/10.7289_v5vq30xb_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5vq30xb_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0167410 includes discrete bottle measurements of dissolved inorganic carbon (DIC), total alkalinity, pH on sea water scale, partial pressure of CO2, dissolved organic carbon (DOC, TDN), CFCs (CFC-11, CFC-12), delta C14, delta C13, temperature, salinity, dissolved oxygen, nutrients, and other variables measured during NOAA Ship Ronald H. Brown GO-SHIP Section A16S_2013 cruise RB1307 (EXPOCODE 33RO20131223) in the South Atlantic Ocean, from 2013-12-23 to 2014-02-04.", "links": [ { diff --git a/datasets/10.7289_v5wd3xhb_Not Applicable.json b/datasets/10.7289_v5wd3xhb_Not Applicable.json index fe4adc2b93..cc2ca988bd 100644 --- a/datasets/10.7289_v5wd3xhb_Not Applicable.json +++ b/datasets/10.7289_v5wd3xhb_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5wd3xhb_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AVHRR Pathfinder Version 5.2 Sea Surface Temperature data set (PFV52) is a collection of global, twice-daily 4km sea surface temperature data produced in a partnership by the NOAA National Oceanographic Data Center and the University of Miami's Rosenstiel School of Marine and Atmospheric Science. PFV52 was computed from data from the AVHRR instruments on board NOAA's polar orbiting satellite series using an entirely modernized system based on SeaDAS. This system incorporates several key changes from Versions 5.0 and 5.1 of Pathfinder, including the use of an entirely new land mask, a modified grid, and the inclusion of sea ice, wind speed, and aerosol ancillary data to support the use of the SST data. Importantly, PFV52 data are provided in netCDF-4 (classic model, with internal compression and chunking) and are nearly 100% compliant with the GHRSST Data Specification Version 2.0 for L3C products. These data deviate from that standard only in that sses_bias, sses_standard_deviation, and sst_dtime variables are empty. PFV52 data were collected through the operational periods of the NOAA-7 through NOAA-19 Polar Operational Environmental Satellites (POES), and are available back to 1981. Data for all years are available as separate NODC accessions.", "links": [ { diff --git a/datasets/10.7289_v5x34vf6_Not Applicable.json b/datasets/10.7289_v5x34vf6_Not Applicable.json index fdd019e690..b5fb04aff3 100644 --- a/datasets/10.7289_v5x34vf6_Not Applicable.json +++ b/datasets/10.7289_v5x34vf6_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5x34vf6_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of observed daily and monthly averaged precipitation, maximum and minimum temperature, gridded to a 1/16\u00c2\u00b0 (~6km) resolution that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53\u00c2\u00b0 N for the period 1950-2013. The dataset improves previous products in spatial extent, orographic precipitation adjustment over Mexico and parts of Canada, and reduction of transboundary discontinuities. The precipitation is adjusted for orographic effects using an elevation-aware 1981-2010 precipitation climatology. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities making it suitable for estimating large-scale hydrometeorologic phenomena. Also included are daily wind data from the National Centers for Environmental Prediction - National Centers for Atmospheric Research (NCEP - NCAR) resampled to the same grid as temperature and precipitation.", "links": [ { diff --git a/datasets/10.7289_v5xg9p6p_1.0.json b/datasets/10.7289_v5xg9p6p_1.0.json index 7a57a26391..44cc406c15 100644 --- a/datasets/10.7289_v5xg9p6p_1.0.json +++ b/datasets/10.7289_v5xg9p6p_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5xg9p6p_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-15 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/10.7289_v5z899n6_Not Applicable.json b/datasets/10.7289_v5z899n6_Not Applicable.json index 81868c04f7..fd6a18a5e2 100644 --- a/datasets/10.7289_v5z899n6_Not Applicable.json +++ b/datasets/10.7289_v5z899n6_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5z899n6_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains observation-based pCO2 data and a derived monthly climatology. The observation-based pCO2 fields were created using a 2-step neural network method extensively described and validated in Landschu\u00cc\u0088tzer et al. 2013, 2014, 2016. The method first clusters the global ocean into biogeochemical provinces and in a second step reconstructs the non-liner relationship between CO2 driver variables and observations from the 4th release of the Surface Ocean CO2 Atlas (SOCAT, Bakker et al. 2016). This file contains the resulting monthly pCO2 fields at 1\u00c2\u00b0x1\u00c2\u00b0 resolution covering the global ocean with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 fluxes are computed from the air-sea CO2 partial pressure difference and a bulk gas transfer formulation following Landschu\u00cc\u0088tzer et al. 2013, 2014, 2016. Furthermore, the monthly climatology is created from the monthly average of the period 1985-present.", "links": [ { diff --git a/datasets/10.7289_v5zs2th4_Not Applicable.json b/datasets/10.7289_v5zs2th4_Not Applicable.json index b3a8900b26..c76f9d8e27 100644 --- a/datasets/10.7289_v5zs2th4_Not Applicable.json +++ b/datasets/10.7289_v5zs2th4_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5zs2th4_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains sightings of American horseshoe crab, Limulus polyphemus, during shoreline surveys conducted in late spring and summer in 2012 and 2013. The study area was in the northern Gulf of Mexico extending from Fort Morgan peninsula of the Alabama coast west to Horn Island off the Mississippi coast, which covers a total distance from east to west of about 60 km. Live crabs, dead crabs, and molts are included.", "links": [ { diff --git a/datasets/10.7289_v5zs2tt5_Not Applicable.json b/datasets/10.7289_v5zs2tt5_Not Applicable.json index a1f36b26de..a5a1498337 100644 --- a/datasets/10.7289_v5zs2tt5_Not Applicable.json +++ b/datasets/10.7289_v5zs2tt5_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10.7289/v5zs2tt5_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20071003) in Davis Strait from 2007-10-03 to 2007-10-21.", "links": [ { diff --git a/datasets/10Be-Law-Dome-10-year-composite_1.json b/datasets/10Be-Law-Dome-10-year-composite_1.json index 690a83e415..a4f4ff4280 100644 --- a/datasets/10Be-Law-Dome-10-year-composite_1.json +++ b/datasets/10Be-Law-Dome-10-year-composite_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "10Be-Law-Dome-10-year-composite_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record comprises composite 10Be concentrations from three Law Dome ice cores (DSS0506-core, DSS0809-core and DSS0910-core). Sample dating is revised from that presented in Pedro et al., clim. Past 7, 707-721, 2011 by accounting for sub-seasonal variability in snow accumulation. The accumulation record was derived from the European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim). See Appendix 1 of Pedro et al., J. Geophys. Res. 116, D23120, 2011 for details of method.", "links": [ { diff --git a/datasets/1115d8946ba74c7f8a9fc3bfee5513a0_NA.json b/datasets/1115d8946ba74c7f8a9fc3bfee5513a0_NA.json index 1f5810621c..5f3c1ef11b 100644 --- a/datasets/1115d8946ba74c7f8a9fc3bfee5513a0_NA.json +++ b/datasets/1115d8946ba74c7f8a9fc3bfee5513a0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1115d8946ba74c7f8a9fc3bfee5513a0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/1162_4_IPEV_FR.json b/datasets/1162_4_IPEV_FR.json index 4466066fc8..018b693876 100644 --- a/datasets/1162_4_IPEV_FR.json +++ b/datasets/1162_4_IPEV_FR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1162_4_IPEV_FR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "- Spectrograms or pictures of gape, tongue, eye-ring and bill of each adult that was caught on the tower in Middleton island. \n- Colour data obtained from those spectrograms and pictures.", "links": [ { diff --git a/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json b/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json index fe492fd08e..bf9f37cee1 100644 --- a/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json +++ b/datasets/118cc853-52a2-46e2-a5be-40e1f58ab46d_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "118cc853-52a2-46e2-a5be-40e1f58ab46d_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Russian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.\n\nThe database was prepared at UNEP/GRID-Geneva in collaboration with Denis Eckert (Centre National de la Recherche Scientifique / France) and the Centre Universitaire d'Ecologie Humaine/Universit\u00e9 de Gen\u00e8ve.\n\nBOUNDARY AND POPULATION DATA \n\n2486 third-level administrative units (1863 raions, 325 gorods, 298 gorsoviets) digitized by D. Eckert have been matched to the national boundaries of the widely used Digital Chart of the World. 649 cities have been digitized from various sources and adjusted to the administrative map. Population figures of the administrative units refer to the 1993 official figures whereas urban data was compiled from heterogeneous sources and projected to the year 1995.\n\nINTERPOLATION \n\nThe interpolation of the vectorial data to a population density raster grid at a resolution of 2.5 arc-minutes was carried out according to a model developed by Uwe Deichman for a previous work on Asia (UNEP/GRID-Geneva). The basic assumption upon which the model is based is that population densities are strongly correlated with accessibility. A complex transport network (more than 170'000 arcs) was used to distribute populations and densities to a raster grid.\n\nOUTPUTS\n\n- a vector dataset containing the 2846 administrative units fitted to the Digital Chart of the World used as template. Population figures of the 1993 official figures and subsequent projections to year 1995 are stored in the polygon attribute file. \n\n- a raster dataset of the interpolated and distributed population totals at a resolution of 2.5 arc-minutes \n\n- a raster dataset of the interpolated and distributed population densities at a resolution of 2.5 arc-minutes GIF images for display on the Web\n\n\n", "links": [ { diff --git a/datasets/11c5f6df1abc41968d0b28fe36393c9d_NA.json b/datasets/11c5f6df1abc41968d0b28fe36393c9d_NA.json index 6e8b2b992c..6d685c63c4 100644 --- a/datasets/11c5f6df1abc41968d0b28fe36393c9d_NA.json +++ b/datasets/11c5f6df1abc41968d0b28fe36393c9d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "11c5f6df1abc41968d0b28fe36393c9d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/12-hourly_interpolated_surface_position_from_buoys.json b/datasets/12-hourly_interpolated_surface_position_from_buoys.json index 9fe31c17a2..b13a3b2fbb 100644 --- a/datasets/12-hourly_interpolated_surface_position_from_buoys.json +++ b/datasets/12-hourly_interpolated_surface_position_from_buoys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "12-hourly_interpolated_surface_position_from_buoys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z.", "links": [ { diff --git a/datasets/12-hourly_interpolated_surface_velocity_from_buoys.json b/datasets/12-hourly_interpolated_surface_velocity_from_buoys.json index e659905a90..50c295025a 100644 --- a/datasets/12-hourly_interpolated_surface_velocity_from_buoys.json +++ b/datasets/12-hourly_interpolated_surface_velocity_from_buoys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "12-hourly_interpolated_surface_velocity_from_buoys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment.", "links": [ { diff --git a/datasets/12_hourly_interpolated_surface_air_pressure_from_buoys.json b/datasets/12_hourly_interpolated_surface_air_pressure_from_buoys.json index 7effb38c01..64a4a2815c 100644 --- a/datasets/12_hourly_interpolated_surface_air_pressure_from_buoys.json +++ b/datasets/12_hourly_interpolated_surface_air_pressure_from_buoys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "12_hourly_interpolated_surface_air_pressure_from_buoys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optimally interpolated atmospheric surface pressure over the Arctic Ocean Basin. Temporal format - twice daily (0Z and 12Z) Spatial format - 2 degree latitude x 10 degree longitude - latitude: 70 N - 90 N - longitude: 0 E - 350 E", "links": [ { diff --git a/datasets/142052b9dc754f6da47a631e35ec4609_NA.json b/datasets/142052b9dc754f6da47a631e35ec4609_NA.json index d2f8b411f3..42cfa2a087 100644 --- a/datasets/142052b9dc754f6da47a631e35ec4609_NA.json +++ b/datasets/142052b9dc754f6da47a631e35ec4609_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "142052b9dc754f6da47a631e35ec4609_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a multi-satellite merged time series of monthly gridded Sea Level Anomalies (SLA) has been produced from satellite altimeter measurements. The Sea Level Anomaly grids have been calculated after merging the altimetry mission measurements together into monthly grids, with a spatial resolution of 0.25 degrees. This version of the product is Version 2.0. The following DOI can be used to reference the monthly Sea Level Anomaly product: DOI: 10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00c3\u00a8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u00e2\u0080\u00932010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org", "links": [ { diff --git a/datasets/14c_of_soil_co2_from_ipy_itex_cross_site_comparison.json b/datasets/14c_of_soil_co2_from_ipy_itex_cross_site_comparison.json index 3a043461ad..5da12b981a 100644 --- a/datasets/14c_of_soil_co2_from_ipy_itex_cross_site_comparison.json +++ b/datasets/14c_of_soil_co2_from_ipy_itex_cross_site_comparison.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "14c_of_soil_co2_from_ipy_itex_cross_site_comparison", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Study sites: Toolik Lake Field Station Alaska, USA 68.63 N, 149.57 W; Atqasuk, Alaska USA 70.45 N, 157.40 W; Barrow, Alaska, USA 71.30 N, 156.67 W; Latnjajaure, Sweden 68.35 N, 18.50 E; Falls Creek, Australia: Site 2-unburned 36.90 S 147.29 E; Site 3-burned 36.89 S 147.28 E. Additional sites will be added summer 2008, but the exact sites are not finalized. Purpose: Collect soil CO2 for analysis of radiocarbon to evaluate the age of the carbon respired in controls and warmed plots from across the ITEX network. Treatments: control and ITEX OTC warming experiment (1994-2007). Design: 5 replicates of each treatment at dry site and moist site. Sampling frequency: Once per peak season.", "links": [ { diff --git a/datasets/159-96_03.json b/datasets/159-96_03.json index 2c82a9a718..d7e0fac4c7 100644 --- a/datasets/159-96_03.json +++ b/datasets/159-96_03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "159-96_03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In Southern Chile, plate configuration is characterized by ridge-ridge-trench\ncollision in correspondence of Taitao peninsula (Chile triple junction). The\ndifferent converging rates of Nazca and Antarctic plates favored the formation\nof a forearc sliver (Chiloe block) limited to west by a dextral transcurrent\nfault system, Known as Liquine-Ofqui fault system (LOFS). During the Quatenary\ntime, a series of monogenetic volcanic centers, as Puyuhuapi volcanic centers\n(PVC), formed along the LOFS. The PVC lavas have a primitive character; two\ngroups and can be distinguihed. Group-1 rocks show a K-AlKaline affinity and\nare nepheline normative with olivine and plagioclase as dominant phases.\nGroup-2 lavas have Na-affinity with olivine and hyperstene in the norm; olivine\nis the most abundant mineral phase. In contrast with overall alkaline affinity\nof PVC, the products from the neighboring central composite volcanoes are\ngenerally calcalkaline with the exception of the lavas from Maca Volcano, which\nshow tholeiitic affinity.", "links": [ { diff --git a/datasets/16920eb2-2eaf-4629-8337-3626e70e4770.json b/datasets/16920eb2-2eaf-4629-8337-3626e70e4770.json index 346fb74471..e4bace6bd0 100644 --- a/datasets/16920eb2-2eaf-4629-8337-3626e70e4770.json +++ b/datasets/16920eb2-2eaf-4629-8337-3626e70e4770.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "16920eb2-2eaf-4629-8337-3626e70e4770", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008.", "links": [ { diff --git a/datasets/16c633f003ef4d8481420f052356c11c_NA.json b/datasets/16c633f003ef4d8481420f052356c11c_NA.json index 2579b9729f..707fc6af04 100644 --- a/datasets/16c633f003ef4d8481420f052356c11c_NA.json +++ b/datasets/16c633f003ef4d8481420f052356c11c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "16c633f003ef4d8481420f052356c11c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u00e2\u0080\u0093 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/1747-ESDD.json b/datasets/1747-ESDD.json index 5470536522..7c2ae3bd92 100644 --- a/datasets/1747-ESDD.json +++ b/datasets/1747-ESDD.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1747-ESDD", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collection of Alaskan photography taken throughout the state by explorers and\n field geologists. Subjects include geology and geologic phenomenon, earthquake\n damage, landscapes and people. Collection contains both photographs and slides.\n Library indexed by subject, locality, and year.\n \n Written requests accepted. Give as much information as possible to ensure\n successful search. Lists of many of the photographs submitted by Alaskan\n geologists are held in the technical data unit of the USGS branch of Alaskan\n geology in Anchorage, Alaska.", "links": [ { diff --git a/datasets/1751a072-d00b-42e8-8c7d-dc078f2ee40a.json b/datasets/1751a072-d00b-42e8-8c7d-dc078f2ee40a.json index 007ed8cb8f..e9b5d2a6b7 100644 --- a/datasets/1751a072-d00b-42e8-8c7d-dc078f2ee40a.json +++ b/datasets/1751a072-d00b-42e8-8c7d-dc078f2ee40a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1751a072-d00b-42e8-8c7d-dc078f2ee40a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 250 years return period. It was generated by integration of the intensity values contained in the files \"Wind_Atlantic.AME\", \"Wind_EastPacific.AME\", \"Wind_NorthIndian.AME\", \"Wind_SudIndian.AME\", \"Wind_SudPacific.AME\" and \"Wind_WestPacific.AME\".", "links": [ { diff --git a/datasets/17767027aa484505b7b732aee6619c74_NA.json b/datasets/17767027aa484505b7b732aee6619c74_NA.json index 8ff92d7c12..3cf043ff9c 100644 --- a/datasets/17767027aa484505b7b732aee6619c74_NA.json +++ b/datasets/17767027aa484505b7b732aee6619c74_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "17767027aa484505b7b732aee6619c74_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Helheim glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 29/05/1996 and 26/2/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle of 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/198081050_1.json b/datasets/198081050_1.json index 18325b16a7..1fb8e36587 100644 --- a/datasets/198081050_1.json +++ b/datasets/198081050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "198081050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the MS Nella Dan Voyage V5 1980/81 (FIBEX).\n\nVoyage name : First International BIOMASS Experiment \nVoyage leader: Knowles Ronald Kerry \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/198283020_1.json b/datasets/198283020_1.json index 15fe3d14b3..3ce1662cb7 100644 --- a/datasets/198283020_1.json +++ b/datasets/198283020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "198283020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the MS Nella Dan Voyage V2 1982/83 (ADBEX1).\n\nVoyage name : Antarctic Division BIOMASS Experiment I \nVoyage leader: Knowles Ronald Kerry \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/198384050_1.json b/datasets/198384050_1.json index c54f017686..d7e28e21a3 100644 --- a/datasets/198384050_1.json +++ b/datasets/198384050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "198384050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the MS Nella Dan Voyage V5 1983/84 (ADBEX2).\n\nVoyage name : Antarctic Division BIOMASS Experiment II \nVoyage leader: Knowles Ronald Kerry \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/198485050_1.json b/datasets/198485050_1.json index 4df15f910b..35e8236ffa 100644 --- a/datasets/198485050_1.json +++ b/datasets/198485050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "198485050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the MS Nella Dan Voyage V5 1984/85 (SIBEX2).\n\nVoyage name : Second International BIOMASS Expedition Phase II \nVoyage leader: H J Marchant \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/198990072_1.json b/datasets/198990072_1.json index 19459431ef..951655c0dc 100644 --- a/datasets/198990072_1.json +++ b/datasets/198990072_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "198990072_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 7.2 1989-90 (HIMS) of the Aurora Australis. This was the maiden cruise of the AA and was a manned marine science voyage. DLS data types were logged at 60-second intervals. The observations were taken between May and July 1990 en route from Hobart to Heard Island and back to Hobart. The Programmer's and Data Quality Reports are available via the Related URL section.\n\nAlso available is a scan of a printed plot of a section of the Voyage 7.2 1989/90 (HIMS) track in the Heard Island area, 18 May to 14 June 1990.", "links": [ { diff --git a/datasets/199091020_1.json b/datasets/199091020_1.json index d9d475357f..e1d71b4c34 100644 --- a/datasets/199091020_1.json +++ b/datasets/199091020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199091020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 2 1990-91 (ICE) of the Aurora Australis. Unfortunately, these data are corrupt and may not be able to be recovered. They are therefore not available online. This was a resupply voyage, but marine science data were logged as part of the sea trial to test the DLS. DLS data types were logged at 20-second intervals. The observations were taken between October and November 1990 en route from Hobart to Casey to Mawson to Davis and back to Hobart.", "links": [ { diff --git a/datasets/199091040_1.json b/datasets/199091040_1.json index 469d00f511..e1e0622dab 100644 --- a/datasets/199091040_1.json +++ b/datasets/199091040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199091040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1990-91 of the Aurora Australis. This was a resupply voyage, with hydroacoustic gear being tested. DLS data were logged on raw tapes only and are not available online. The observations were taken in November 1990 en route from Hobart to Mawson to Davis and back to Hobart.", "links": [ { diff --git a/datasets/199091060_1.json b/datasets/199091060_1.json index dd62237f77..3b6753b3cd 100644 --- a/datasets/199091060_1.json +++ b/datasets/199091060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199091060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 6 1990-91 (AAMBER2) of the Aurora Australis. This was primarily a marine science voyage. DLS data types were logged at 60-second intervals. The observations were taken between January and March 1991 en route from Hobart to Prydz Bay to Mawson to Davis and back to Hobart. Marine Science Support Data Quality and Programmer's Reports are available via the Related URL section.\n\nTemperature and salinity data from the CTD were also obtained.\n\nThe fields in the CTD dataset are:\npressure\ntemperature\nsalinity\nvolume\ngeopotential", "links": [ { diff --git a/datasets/199192010_1.json b/datasets/199192010_1.json index f8252ee0e5..25c5f21eae 100644 --- a/datasets/199192010_1.json +++ b/datasets/199192010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199192010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 and 1.1 1991-92 (WOCE91) of the Aurora Australis. This was a marine science voyage, being the first WOCE SR3 transect from Tasmania to Dibble Ice Tongue and return. Voyage 1 went to Macquarie Island but returned with a broken winch. The program then resumed as Voyage 1.1. See the Marine Science Support Data Quality and Programmer's Reports via the Related URL section.", "links": [ { diff --git a/datasets/199192040_1.json b/datasets/199192040_1.json index 50c0836c49..b514ef68d0 100644 --- a/datasets/199192040_1.json +++ b/datasets/199192040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199192040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1991-92 of the Aurora Australis. This was a non-marine science voyage that visited Mawson and Davis, departing from and returning to Hobart. Underway data have not been quality checked.", "links": [ { diff --git a/datasets/199192040_Chlorophyll_1.json b/datasets/199192040_Chlorophyll_1.json index 6b4ea04d43..8c559f72a6 100644 --- a/datasets/199192040_Chlorophyll_1.json +++ b/datasets/199192040_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199192040_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chloropyll a data were collected on voyage 4 of the Aurora Australis, during the 1991-1992 season.\n\nThese data were collected as part of ASAC project 40 (ASAC_40) (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/199192060_1.json b/datasets/199192060_1.json index d9d5abd08e..a1e70034ff 100644 --- a/datasets/199192060_1.json +++ b/datasets/199192060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199192060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 6 1991-92 (FISHOG) of the Aurora Australis. Marine science was carried out in the vicinity of Heard Island. Mawson, Davis and Casey were also visited. Data and the Marine Science Support Data Quality Report can be obtained via the links at the Related URL section.\n\nAlso provided are scans of two printed plots of sections of the Voyage 6 1991/92 (FISHOG) track:\n(i) the section of the track in the Heard Island area, 22 January to 13 February 1992; and \n(ii) transects north of the Antarctic coastline between 66 degrees East and 82 degrees East, 18 February to 6 March 1992.", "links": [ { diff --git a/datasets/199293010_1.json b/datasets/199293010_1.json index 9c728f5bee..6ca71e85ae 100644 --- a/datasets/199293010_1.json +++ b/datasets/199293010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199293010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1992-93 (MONGREL) of the Aurora Australis. This voyage visited Mawson and Davis, leaving from and returning to Hobart. For further information, see the Marine Science Support via the Related URL section.", "links": [ { diff --git a/datasets/199293040_1.json b/datasets/199293040_1.json index 98ce3c401d..9b8beca142 100644 --- a/datasets/199293040_1.json +++ b/datasets/199293040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199293040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1992-93 of the Aurora Australis. This was a non-marine science voyage, but NoQalms data types were logged. Data were recorded at 60-second intervals from 26 November 1992 to 7 December 1992, then at irregular intervals of about 3 to 5 minutes for the remaining voyage. The observations were taken en route from Hobart to Mawson to Davis to Casey and back to Hobart. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/199293070_1.json b/datasets/199293070_1.json index 7f3c17cfd9..b608220289 100644 --- a/datasets/199293070_1.json +++ b/datasets/199293070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199293070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 7 1992-93 (KROCK) of the Aurora Australis. This was a manned marine science voyage. The observations were taken between January and March 1993 on route from Hobart to Davis to Mawson to Casey and back to Hobart. DLS and NoQalms data types were logged. See the Marine Science Support Data Quality and Programmer's Reports at the Related_URL section.\n\nAlso available is a scan of a printed plot of a section of the Voyage 7 1992/93 (KROCK) track: \ntransects north of the Antarctic coastline between 60 degrees East and 83 degrees East, 15 January to 7 February 1993.", "links": [ { diff --git a/datasets/199293090_1.json b/datasets/199293090_1.json index a8ec11120c..2ba5f4fcd0 100644 --- a/datasets/199293090_1.json +++ b/datasets/199293090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199293090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyages 9 and 9.1 1992-93 (WOES and WORSE) of the Aurora Australis. These were dedicated marine science voyages. Meteorological, bathymetric, fluorometer and thermosalinograph data are available via the AADC web page (see Related URL section). The Marine Science Support Data Quality Report can also be accessed via the Related URL section.\n\nCTD data were also obtained along the WOCE SR3 line.", "links": [ { diff --git a/datasets/199293091_1.json b/datasets/199293091_1.json index 7d43a4d8e5..47ea1b0890 100644 --- a/datasets/199293091_1.json +++ b/datasets/199293091_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199293091_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V9.1 1992/93 (WORSE).\n\nVoyage name : Wildlife Oceanography Retry Survey of Ecosystem Voyage Objectives : Marine Science \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/199394010_1.json b/datasets/199394010_1.json index 8ce956363e..62a038d642 100644 --- a/datasets/199394010_1.json +++ b/datasets/199394010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199394010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1993-94 (THIRST) of the Aurora Australis. This was a manned marine science voyage. DLS data types were logged at 10-second intervals. The observations were taken between August and October 1993 en route from Hobart to Macquarie Island to Heard Island and back to Hobart. See the Marine Science Support Programmer's and Data Quality Reports at the Related URL section.\n\nXBT data were obtained on the legs to and from Hobart. CTD data were collected around Heard Island.\n\nAlso available is a scan of a printed plot of a section of the Voyage 1 1993/94 (THIRST) track in the Heard Island area, 28 August to 28 September 1993.", "links": [ { diff --git a/datasets/199394020_1.json b/datasets/199394020_1.json index b70403c893..02f30d27fb 100644 --- a/datasets/199394020_1.json +++ b/datasets/199394020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199394020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 2 1993-94 of the Aurora Australis. This was a non-marine science voyage, but NoQalms data types were logged at 60-second intervals. The observations were taken between October and November 1993 en route from Hobart to Mawson to Davis and back to Hobart. See the Marine Science Support Report at the Related URL section.", "links": [ { diff --git a/datasets/199394040_1.json b/datasets/199394040_1.json index 5db508e18c..65d8214607 100644 --- a/datasets/199394040_1.json +++ b/datasets/199394040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199394040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1993-94 of the Aurora Australis. This was a non-marine science voyage, but NoQalms data types were logged at 20-second intervals. The observations were taken between November and December 1993 en route from Hobart to Davis to Mawson and back to Hobart. See the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199394070_1.json b/datasets/199394070_1.json index 6eff86ea6e..655707df52 100644 --- a/datasets/199394070_1.json +++ b/datasets/199394070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199394070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 7 1993-94 (SHAM) of the Aurora Australis. This was a manned marine science voyage. DLS and NoQalms data types were logged. The observations were taken between January and February 1994. The Programmer's Report is available via the Related URL section (includes a section on Data Quality).\n\nXBT and CTD data were also obtained.", "links": [ { diff --git a/datasets/1994-1997_S_GW_GG04_AN_ISOTOPE.json b/datasets/1994-1997_S_GW_GG04_AN_ISOTOPE.json index 89f91abe50..cb5de9877e 100644 --- a/datasets/1994-1997_S_GW_GG04_AN_ISOTOPE.json +++ b/datasets/1994-1997_S_GW_GG04_AN_ISOTOPE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1994-1997_S_GW_GG04_AN_ISOTOPE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice-cores of the Collins Ice Cap were all gained through the BZXJ-model\n ice-core drilling machine newly made by Lanzhou Institute of Glaciology and\n Geocryology, Chinese Academy of Sciences. During drilling and collecting\n ice-cores, strict protection measures against the pollution and melt were taken\n so that the sample as good as possible to satisfy the demands of physical and\n chemical analyses of ice-cores. Collected ice-cores were transported under\n frozen conditions from Antarctica to the low temperature laboratory of Polar\n Research Institute of China, partly to University of New Hampshire, USA, and\n were preserved under -25 degrees centigrade. Ice-cores were taken out before\n analyses, cut apart with a band saw on clean low-temperature working table. We\n scraped a few millimetres of surface ice to melt under normal air temperature.\n Oxygen isotope analyses of 0-13.96m depth ice-cores from Big Dome Summit of\n Collins Ice Cap were completed by the Glacier Research Group, Institute for the\n Study of Earth, Ocean and Space, University of New Hampshire, USA. Their\n sampling interval is 15-20cm, total is 87 samples. Oxygen isotope analyses of\n 13.96-20.02m depth and 27.78-30.52m depth ice-cores from Big Dome Summit of\n Collins Ice Cap and firn samples drawn from BDA, BDB, BDC and Small Dome Top\n (SDT) were completed in state key laboratory of mineralization in Nanjing\n University. Sampling interval (total of 10 samples) is between 30cm and 130cm,\n and the sampling interval of SDT (total of 20 samples) is 10-20cm.", "links": [ { diff --git a/datasets/199495010_1.json b/datasets/199495010_1.json index 4551013424..3fc2fb9591 100644 --- a/datasets/199495010_1.json +++ b/datasets/199495010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1994-95 of the Aurora Australis. This was a resupply cruise, with limited marine science being carried out. NoQalms data types were logged at 20-second intervals. The observations were taken between August and October 1995 en route from Hobart to Macquarie Island to Davis and back to Hobart. See the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199495020_1.json b/datasets/199495020_1.json index 4bcee82526..dbd2c114fd 100644 --- a/datasets/199495020_1.json +++ b/datasets/199495020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 2 1994-95 of the Aurora Australis. This was an resupply cruise, but NoQalms data types were logged at 20-second intervals. The observations were taken between October and December 1994 en route from Hobart to Casey to Davis and back to Hobart. See the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199495030_1.json b/datasets/199495030_1.json index 3d9a08b6ff..f292659f12 100644 --- a/datasets/199495030_1.json +++ b/datasets/199495030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 3 1994-95 (MIRTH) of the Aurora Australis. This was a resupply voyage, but was also used as a marine science training cruise. NoQalms data types were logged at 20-second intervals. The observations were taken in December 1994 en route from Hobart to Macquarie Island and back to Hobart. The Programmer's and Data Quality Reports are available via the Related URL section.", "links": [ { diff --git a/datasets/199495040_1.json b/datasets/199495040_1.json index c6de2fe720..693eaf2f39 100644 --- a/datasets/199495040_1.json +++ b/datasets/199495040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1994-95 (WOCET) of the Aurora Australis. This was a manned marine science cruise. DLS and NoQalms data types were logged at 10-second intervals. CTD and XBT data were also obtained on a run from Hobart to the ice edge along the WOCE SR3 line, then to Casey, and back to Hobart, between December 1994 and February 1995. The Programmer's and Data Quality Reports are available via the Related URL section.", "links": [ { diff --git a/datasets/199495060_1.json b/datasets/199495060_1.json index b6260c2dcb..43e9b9424d 100644 --- a/datasets/199495060_1.json +++ b/datasets/199495060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 6 1994-95 (BANGSS) of the Aurora Australis. This was a marine science voyage. The observations were taken between February and April 1995. DLS and NoQalms data types were logged at 10-second intervals. See the Marine Science Support Data Quality, Programmer's and Engineer's Reports via the Related URL section.\n\nXBT and CTD data were also obtained during this voyage.", "links": [ { diff --git a/datasets/199495070_1.json b/datasets/199495070_1.json index da5d7dd6f2..9c2824416a 100644 --- a/datasets/199495070_1.json +++ b/datasets/199495070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199495070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 7 1994-95 of the Aurora Australis. This was an un-manned resupply voyage, but NoQalms data types were logged at 20-second intervals. The observations were taken between April and May 1995 on route from Hobart to Macquarie Island to Casey and back to Hobart. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/199596010_1.json b/datasets/199596010_1.json index 602774826e..91ef03ed31 100644 --- a/datasets/199596010_1.json +++ b/datasets/199596010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199596010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1995-96 (ABSTAIN) of the Aurora Australis. This was a manned marine science voyage. DLS and NoQalms data types were logged at 10-second intervals. The observations were taken along the WOCE SR3 line and the FORMEX square, between July and September 1995. See Marine Science Support Data Quality and Programmer's Reports via the Related URL section.\n\nCTD and ADCP data were also obtained on a run from Hobart to the ice edge and back.", "links": [ { diff --git a/datasets/199596020_1.json b/datasets/199596020_1.json index 843a5e9d91..623ea376db 100644 --- a/datasets/199596020_1.json +++ b/datasets/199596020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199596020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 2 1995-96 of the Aurora Australis. This was an un-manned resupply voyage with a small marine science component. NoQalms data types were logged at 10-second intervals. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/199596030_1.json b/datasets/199596030_1.json index 718b3b2ac7..dba8f7daad 100644 --- a/datasets/199596030_1.json +++ b/datasets/199596030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199596030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 3 1995-96 of the Aurora Australis. This was a non-marine science voyage that departed Fremantle for Casey, Bunger Hills, Mawson, Davis and Law Base, and returned to Hobart. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/199596040_1.json b/datasets/199596040_1.json index db8a73bfb8..00c471a747 100644 --- a/datasets/199596040_1.json +++ b/datasets/199596040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199596040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 4 1995-96 (BROKE) of the Aurora Australis. This was a manned marine science cruise. The major projects were a hydro-acoustic/trawl krill population survey, and the MARGINEX oceanographic survey on bottom water formation. CTD data were also obtained. Marine Science Support Data Quality and Programmer's Reports are available via the Related URL section.", "links": [ { diff --git a/datasets/199596060_1.json b/datasets/199596060_1.json index 98f50b700e..6419f1a1ae 100644 --- a/datasets/199596060_1.json +++ b/datasets/199596060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199596060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 6 1995-96 of the Aurora Australis. This voyage visited Davis and Casey from Hobart and included a small marine science component. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y.json b/datasets/1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y.json index 18ffbc45ac..e875b49903 100644 --- a/datasets/1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y.json +++ b/datasets/1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1996/97 within and the north of Prydz Bay, the southern Indian Ocean.25 oceanographic stations were successfully completed and 3.77MB CTD data were obtained.", "links": [ { diff --git a/datasets/199697010_1.json b/datasets/199697010_1.json index f3d8ebe1cf..a9db9cb189 100644 --- a/datasets/199697010_1.json +++ b/datasets/199697010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1996-97 (WASTE) of the Aurora Australis. This was a manned marine science cruise. CTD data were also obtained from Hobart to the ice edge along the WOCE SR3 line. Oceanographic data from this voyage are held by the Principal Investigator Dr. Steve Rintoul at CSIRO. Marine Science Support Data Quality and Programmer's Reports are available via the Related URL section.", "links": [ { diff --git a/datasets/199697020_1.json b/datasets/199697020_1.json index d7526147aa..0b88c7db40 100644 --- a/datasets/199697020_1.json +++ b/datasets/199697020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 1996-97. This voyage departed Hobart for Casey and then travelled to Davis after completing some marine science research. Underway (meteorological, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199697030_1.json b/datasets/199697030_1.json index 5cbab07f70..b7c47073df 100644 --- a/datasets/199697030_1.json +++ b/datasets/199697030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 3 1996-97. This voyage visited Macquarie Island, leaving from and returning to Hobart. Underway (meteorological and water temperature) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199697040_1.json b/datasets/199697040_1.json index 8b31d3f30c..8f36c758bc 100644 --- a/datasets/199697040_1.json +++ b/datasets/199697040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains automatically logged underway data collected during the Aurora Australis Voyage 4 1996-97. This was a non-marine science voyage that visited Mawson, Davis, Casey and Macquarie Island, departing from and returning to Hobart. These data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL below.", "links": [ { diff --git a/datasets/199697050_1.json b/datasets/199697050_1.json index 32e46d76be..c976db5e27 100644 --- a/datasets/199697050_1.json +++ b/datasets/199697050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 5 1996-97 (BRAD) of the Aurora Australis. This was a manned marine science cruise. The main project was to conduct seismic, benthic and geological survey of Vincennes Bay, MacRobertson Shelf, Nilsen Basin, Iceberg Alley and Prydz Bay. Marine Science Support Data Quality and Engineer's Reports are available via the Related URL section.\n\nCTD data were also obtained. Bottom photos were taken at 10 of the CTD sites; digitized images of these bottom photos are stored on the marine science server: /BIGBIRD/marscisup/Data/Images/969705/.", "links": [ { diff --git a/datasets/199697060_1.json b/datasets/199697060_1.json index a1bc6d77ce..73a4ece968 100644 --- a/datasets/199697060_1.json +++ b/datasets/199697060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199697060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 6 1996-97. This voyage visited Casey and Macquarie Island ex-Hobart, as well as carrying out marine science activities. Underway data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/1997-1998_14-14_S_OC_OC05_LO_O011301_000_R0_Y.json b/datasets/1997-1998_14-14_S_OC_OC05_LO_O011301_000_R0_Y.json index 4fbc853d2b..fd8880956b 100644 --- a/datasets/1997-1998_14-14_S_OC_OC05_LO_O011301_000_R0_Y.json +++ b/datasets/1997-1998_14-14_S_OC_OC05_LO_O011301_000_R0_Y.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1997-1998_14-14_S_OC_OC05_LO_O011301_000_R0_Y", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1997/98 within and the north of Prydz Bay, the southern Indian Ocean.15 oceanographic stations were successfully completed.", "links": [ { diff --git a/datasets/199798010_1.json b/datasets/199798010_1.json index 54f7071f8e..e7867ce447 100644 --- a/datasets/199798010_1.json +++ b/datasets/199798010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from Voyage 1 1997-98 (WANDER) of the Aurora Australis. This was a manned marine science cruise. This is the first AA voyage to use the new Java-based NOODLES logging system. See the Marine Science Support Data Quality and Programmer's Reports at the Related URL section.\n\nCTD and XBT data were also obtained on this voyage.", "links": [ { diff --git a/datasets/199798020_1.json b/datasets/199798020_1.json index dcc0520f6f..c95146d182 100644 --- a/datasets/199798020_1.json +++ b/datasets/199798020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway (meteorology, thermosalinograph, fluorometer and bathymetry) data collected during the Aurora Australis Voyage 2 1997-98. This voyage visited Casey, Mawson and Davis as well as conducting a seal survey. Underway data are available online via the Australian Antarctic Division Data Centre (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199798030_1.json b/datasets/199798030_1.json index ae73e38abf..2ba7264aea 100644 --- a/datasets/199798030_1.json +++ b/datasets/199798030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 3 1997-98. This was a Macquarie Island changeover and resupply voyage. Underway (meteorology, fluorometer, thermosalinograph and bathymetry) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). See the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199798040_1.json b/datasets/199798040_1.json index aecae37463..c69d7e8ba6 100644 --- a/datasets/199798040_1.json +++ b/datasets/199798040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data from the Aurora Australis Voyage 4 (SEXY) 1997-98. This voyage was a part resupply/changeover, part seal survey cruise, which visited Casey, Davis, Mawson and Macquarie Island. Underway (meterological, fluorometer, thermosalinograph and bathymetry) data are available online via the Australian Antarctic Division Data Centre web page (see URL given below). Documentation on data quality and instrumentation are available via the Related URL section.", "links": [ { diff --git a/datasets/199798050_1.json b/datasets/199798050_1.json index 1a1b0cd4db..869dc76722 100644 --- a/datasets/199798050_1.json +++ b/datasets/199798050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data logged during the Aurora Australis Voyage 5 of the 1997-98 season. The purpose of this voyage was to resupply Mawson and retrieve expeditioners from Davis. There were no marine science personnel on board. Underway (meteorological, fluorometer, thermosalinograph and water depth) data are available online via the Australian Antarctic Division Data Centre web page. The Marine Science Support Data Quality Report is available via the Related URL section.", "links": [ { diff --git a/datasets/199798060_1.json b/datasets/199798060_1.json index 55de910455..f4b907a079 100644 --- a/datasets/199798060_1.json +++ b/datasets/199798060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 6 1997-98. This was a dedicated marine science cruise researching Subantarctic oceanography. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division web page. No Echolistener (depth) data were logged during this voyage. For further information, see the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199798070_1.json b/datasets/199798070_1.json index 9f83c88929..625c1061c4 100644 --- a/datasets/199798070_1.json +++ b/datasets/199798070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199798070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 7 1997-98. This was a marine science cruise, which also visited Davis, Casey and Macquarie Island. The marine science component included a Subantarctic fish survey, a pelagic ecosysytem survey and polynya mooring deployments along 145 degrees East. Underway (meteorological, fluorometer, thermosalinograph and bathymetry) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). See the Marine Science Support Data Quality and Programmer's Reports at the Related URL section.", "links": [ { diff --git a/datasets/1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y.json b/datasets/1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y.json index 330f695ce6..19898144cf 100644 --- a/datasets/1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y.json +++ b/datasets/1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1998/99 within and the north of Prydz Bay, the southern Indian Ocean.34 oceanographic stations were successfully completed and 3.77MB CTD data were obtained.", "links": [ { diff --git a/datasets/199899010_1.json b/datasets/199899010_1.json index 98564f87c3..b85caecd8f 100644 --- a/datasets/199899010_1.json +++ b/datasets/199899010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199899010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 1998-99. This was a dedicated marine science cruise aimed at researching winter-time oceanographic, glaciological, meteorological and biological processes within a polynya near the Mertz Glacier. However, the mission was aborted after a serious engine room fire occurred one week into the voyage. Underway data are available online via the Australian Antarctic Division Data Centre. For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199899040_1.json b/datasets/199899040_1.json index 395a21768b..1ac684eb49 100644 --- a/datasets/199899040_1.json +++ b/datasets/199899040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199899040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 1998-99 (SEXY II). This voyage departed Hobart to Casey, Davis and Samsom Island, returning to Fremantle after sustaining damage to the propeller system. Underway (meteorological, fluorometer, thermosalinograph and bathymetry) data are available online via the Australian Antarctic Division Data Centre web page (or via URL given below). For further information, see the Marine Science Support Data Quality Report via the Related URL section.", "links": [ { diff --git a/datasets/199899060_1.json b/datasets/199899060_1.json index 4f36ec4a19..60629049e9 100644 --- a/datasets/199899060_1.json +++ b/datasets/199899060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199899060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 6 (STAY) 1989-99. This voyage visited Mawson, Davis, Casey and Macquarie Island, departing from Fremantle and returning to Hobart. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199900010_1.json b/datasets/199900010_1.json index bd5e513697..e85431ea3c 100644 --- a/datasets/199900010_1.json +++ b/datasets/199900010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199900010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 1999-00 (IDIOTS). This was a dedicated marine science cruise researching winter-time oceanographic, glaciological, meteorological and biological processes within a polynya off the Mertz Glacier at about 145 degrees East. Underway (meteorological, fluorometer, thermosalinograph and bathymetry) data are available online via the Australian Antarctic Division Data Centre web page (or via URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199900020_1.json b/datasets/199900020_1.json index ba07e0afef..0fd48e633a 100644 --- a/datasets/199900020_1.json +++ b/datasets/199900020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199900020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis 1999-00 Voyage 2. This voyage visited Mawson, Davis and then Mawson again, prior to returning to Hobart. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (see Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199900040_1.json b/datasets/199900040_1.json index 0dadcf5f38..2174c5ba9c 100644 --- a/datasets/199900040_1.json +++ b/datasets/199900040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199900040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 1999-2000. This voyage visited Macquarie Island, Davis, Sansom Island and Mawson, as well as carrying out a ship and helicopter based seal survey. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199900050_1.json b/datasets/199900050_1.json index bf4c3bf095..9ce3cda9d1 100644 --- a/datasets/199900050_1.json +++ b/datasets/199900050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199900050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 5 1999-2000. This voyage visited Casey and Macquarie Island. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/199900060_1.json b/datasets/199900060_1.json index e4fcc56072..9647b983d7 100644 --- a/datasets/199900060_1.json +++ b/datasets/199900060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "199900060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 6 1999-2000. This voyage visited Mawson, Davis, Bunger Hills, Casey and Macquarie Island prior to returning to Hobart. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/1C_LIS3_STUC00GTD_1.0.json b/datasets/1C_LIS3_STUC00GTD_1.0.json index fefe366eda..060669a42a 100644 --- a/datasets/1C_LIS3_STUC00GTD_1.0.json +++ b/datasets/1C_LIS3_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1C_LIS3_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/1C_WIFS_STUC00GTD_1.0.json b/datasets/1C_WIFS_STUC00GTD_1.0.json index 950dd31ab5..9722fe0066 100644 --- a/datasets/1C_WIFS_STUC00GTD_1.0.json +++ b/datasets/1C_WIFS_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1C_WIFS_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data is acquired in four spectral bands, three in the visible and in NIR (VNIR B2, B3 and B4)and one in the short wave infrared (SWIR B5).The AWiFS camera is realized in two electro-optic modules viz. AWiFS-A and AWiFS-B, providing a combined swath of 740 Km with 56m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/1ab66621-5bb0-45b1-a0ad-a5caa263f366_NA.json b/datasets/1ab66621-5bb0-45b1-a0ad-a5caa263f366_NA.json index 29ad242513..5034f2e6a9 100644 --- a/datasets/1ab66621-5bb0-45b1-a0ad-a5caa263f366_NA.json +++ b/datasets/1ab66621-5bb0-45b1-a0ad-a5caa263f366_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1ab66621-5bb0-45b1-a0ad-a5caa263f366_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in ScanSAR mode. ScanSAR imaging allows for a spatial resolution of up to 18.5 m at a scene size of 100 km (across swath) x 150-1650 km (in orbit direction) in regular ScanSAR mode (4 beams) and up to 270 km (across swath) x 200-1500 km (in orbit direction) in Wide ScanSAR mode (6 beams). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.\t\t\tFor more information concerning the TerraSAR-X mission, the reader is referred to:https://www.dlr.de/content/de/missionen/terrasar-x.html", "links": [ { diff --git a/datasets/1ae74be6-8419-4f33-928f-b87b3441abac_NA.json b/datasets/1ae74be6-8419-4f33-928f-b87b3441abac_NA.json index 339110b2ee..2ef43587dd 100644 --- a/datasets/1ae74be6-8419-4f33-928f-b87b3441abac_NA.json +++ b/datasets/1ae74be6-8419-4f33-928f-b87b3441abac_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1ae74be6-8419-4f33-928f-b87b3441abac_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)\" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz.The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice.For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.", "links": [ { diff --git a/datasets/1d0ef791-a800-4be3-a121-347bbb89d56a_NA.json b/datasets/1d0ef791-a800-4be3-a121-347bbb89d56a_NA.json index 45285a5383..7b941bfbe7 100644 --- a/datasets/1d0ef791-a800-4be3-a121-347bbb89d56a_NA.json +++ b/datasets/1d0ef791-a800-4be3-a121-347bbb89d56a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1d0ef791-a800-4be3-a121-347bbb89d56a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)\" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz.The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice.For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps.", "links": [ { diff --git a/datasets/1da8dadcdfb642f4aad2384f02efe756_NA.json b/datasets/1da8dadcdfb642f4aad2384f02efe756_NA.json index eb179ae2b6..5ad8789847 100644 --- a/datasets/1da8dadcdfb642f4aad2384f02efe756_NA.json +++ b/datasets/1da8dadcdfb642f4aad2384f02efe756_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1da8dadcdfb642f4aad2384f02efe756_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.3 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/1dd4c30a78d84e628cd8097bae3148fd_NA.json b/datasets/1dd4c30a78d84e628cd8097bae3148fd_NA.json index 5c111cb15a..3d2359bb36 100644 --- a/datasets/1dd4c30a78d84e628cd8097bae3148fd_NA.json +++ b/datasets/1dd4c30a78d84e628cd8097bae3148fd_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1dd4c30a78d84e628cd8097bae3148fd_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 24/1/2015 and 22/03/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA.json b/datasets/1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA.json index c8f5691ec7..096e8d4f1f 100644 --- a/datasets/1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA.json +++ b/datasets/1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Helheim Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-08-29. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "links": [ { diff --git a/datasets/1f1940e4-ec31-4925-8fa8-942a59531888_NA.json b/datasets/1f1940e4-ec31-4925-8fa8-942a59531888_NA.json index c3c493d431..11e4a8d297 100644 --- a/datasets/1f1940e4-ec31-4925-8fa8-942a59531888_NA.json +++ b/datasets/1f1940e4-ec31-4925-8fa8-942a59531888_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "1f1940e4-ec31-4925-8fa8-942a59531888_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products.", "links": [ { diff --git a/datasets/200001010_1.json b/datasets/200001010_1.json index 46936518ba..aabaf7e2c7 100644 --- a/datasets/200001010_1.json +++ b/datasets/200001010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200001010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 2000-01. This voyage departed Hobart for Port Arthur to carry out calibrations prior to travelling on to Davis, Mawson, Heard Island, the McDonald Islands and then Fremantle. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL below.", "links": [ { diff --git a/datasets/200001040_1.json b/datasets/200001040_1.json index 441bc48fc9..183b8fc136 100644 --- a/datasets/200001040_1.json +++ b/datasets/200001040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200001040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 2000-01. This voyage departed Fremantle and visited Heard Island, Mawson, Davis and Sansom Island prior to returning to Hobart. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200001060_1.json b/datasets/200001060_1.json index 9271a585e4..9c83a2de00 100644 --- a/datasets/200001060_1.json +++ b/datasets/200001060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200001060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 6 2000-01. This was a marine science voyage that visited Mawson, Casey and Davis prior to returning to Hobart. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200001080_1.json b/datasets/200001080_1.json index 9113680225..5595b8746f 100644 --- a/datasets/200001080_1.json +++ b/datasets/200001080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200001080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 8 2000-01. This voyage went to Casey and Macquarie Island, leaving from and returning to Hobart. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL below.", "links": [ { diff --git a/datasets/2001-2002_18-18_S_ZS_GP02_LO_O019001_000_R0_Y.json b/datasets/2001-2002_18-18_S_ZS_GP02_LO_O019001_000_R0_Y.json index 0187cf66b7..d86d9b1f58 100644 --- a/datasets/2001-2002_18-18_S_ZS_GP02_LO_O019001_000_R0_Y.json +++ b/datasets/2001-2002_18-18_S_ZS_GP02_LO_O019001_000_R0_Y.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2001-2002_18-18_S_ZS_GP02_LO_O019001_000_R0_Y", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a 1:2000 Map of Antarctic Zhongshan Station in 2002 during CHINARE-18.", "links": [ { diff --git a/datasets/200102020_1.json b/datasets/200102020_1.json index 0ae2780cb6..e0c71f0cac 100644 --- a/datasets/200102020_1.json +++ b/datasets/200102020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200102020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 2001-02. This voyage went to Casey and Macquarie Island, leaving from and returning to Hobart. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL below.", "links": [ { diff --git a/datasets/200102030_1.json b/datasets/200102030_1.json index 21eb793787..e247b7fdf8 100644 --- a/datasets/200102030_1.json +++ b/datasets/200102030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200102030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 3 2001-02. This voyage undertook extensive marine science activities along the CLIVAR SR3 transect (140 degrees east) from southern Tasmania to the Antarctic coast. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200102050_1.json b/datasets/200102050_1.json index f249887cf3..c756b8ced6 100644 --- a/datasets/200102050_1.json +++ b/datasets/200102050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200102050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 5 2001-02. This voyage visited Casey, Prydz Bay and Mawson prior to returning to Hobart. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.\n\nDuring the course of the voyage, several illegal fishing vessels were encountered, as well as a Greenpeace vessel and ships of the Japanese whaling fleet. The Aurora Australis was also required to free the Polar Bird from sea ice in Prydz Bay.", "links": [ { diff --git a/datasets/200102070_1.json b/datasets/200102070_1.json index 7686b7a6a2..2afc5aad2a 100644 --- a/datasets/200102070_1.json +++ b/datasets/200102070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200102070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 7 2001-02. This voyage carried out marine science activities associated with the AMISOR project in the Prydz Bay region. It also visited Davis and Mawson prior to returning to Hobart. Underway data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200102080_1.json b/datasets/200102080_1.json index 98a71cb43c..b4427df6ef 100644 --- a/datasets/200102080_1.json +++ b/datasets/200102080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200102080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 8 2001-02. This voyage visited Macquarie Island, departing from and returning to Hobart. Underway data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200203010_1.json b/datasets/200203010_1.json index 342afbd36a..49eca595e2 100644 --- a/datasets/200203010_1.json +++ b/datasets/200203010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200203010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 2002-03. This voyage left and returned to Hobart, and visited Macquarie Island on the South bound leg. Underway (meteorological and sea surface parameters) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200203020_1.json b/datasets/200203020_1.json index c601eef3d2..09e76b91fd 100644 --- a/datasets/200203020_1.json +++ b/datasets/200203020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200203020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 2002-03. This voyage was a Davis resupply and restock of fuel at Sansom Island, and remove waste and equipment for return to Australia. Personnel and equipment were deployed to the Prince Charles Mountains (PCMEGA) Programme. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200203040_1.json b/datasets/200203040_1.json index 951e780224..1bce5e657a 100644 --- a/datasets/200203040_1.json +++ b/datasets/200203040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200203040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 2002-03. This voyage undertook extensive marine science activities North of Mawson. Mawson Harbour was visited twice during the cruise and Davis fly off position was reached once. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200203060_1.json b/datasets/200203060_1.json index dd154f78e7..623c4e386e 100644 --- a/datasets/200203060_1.json +++ b/datasets/200203060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200203060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V6 2002/03 ().\n\nVoyage Objectives : Macquarie Island Resupply \nVoyage leader: Don Hudspeth, Shane Hunniford \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200304010_1.json b/datasets/200304010_1.json index ae83e28e31..4859b18310 100644 --- a/datasets/200304010_1.json +++ b/datasets/200304010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 2003-04. This voyage went to the Casey area, leaving from and returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200304010_raw_2.json b/datasets/200304010_raw_2.json index bfb8d7c8e6..586b4a98db 100644 --- a/datasets/200304010_raw_2.json +++ b/datasets/200304010_raw_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304010_raw_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data represents the total collection of acoustic, underway and satellite data collected on voyage 1 of the Aurora Australis in the 2003-04 season.\n\nFor online access to the underway data for voyage 1 2003-04, see its specific metadata record, or the marine science database.\n\nThe Acoustics data (ADCP) are in SIMRAD EK64 format (binary), and the echoview software is required to read them. The Underway data are in ASCII format. The Satellite Images are in TERASCAN format, and TERASCAN software is required to read them.\n\nAn index sheet to the dataset is available as an excel download.", "links": [ { diff --git a/datasets/200304020_1.json b/datasets/200304020_1.json index f747489f69..2d2387df91 100644 --- a/datasets/200304020_1.json +++ b/datasets/200304020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 2003-04. This voyage went to Davis and Zhong Shan, leaving from Hobart and returning to Freemantle. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200304020_raw_1.json b/datasets/200304020_raw_1.json index 7ba860624b..38cad410c5 100644 --- a/datasets/200304020_raw_1.json +++ b/datasets/200304020_raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304020_raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data represents the total collection of acoustic, underway and satellite data collected on voyage 2 of the Aurora Australis in the 2003-04 season.\n\nFor online access to the underway data for voyage 2 2003-04, see its specific metadata record, or the marine science database.\n\nThe Acoustics data (ADCP) are in SIMRAD EK64 format (binary), and the echoview software is required to read them. The Underway data are in ASCII format. The Satellite Images are in TERASCAN format, and TERASCAN software is required to read them.", "links": [ { diff --git a/datasets/200304040_1.json b/datasets/200304040_1.json index 938fedccc1..41a96d6fab 100644 --- a/datasets/200304040_1.json +++ b/datasets/200304040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 4 2003-04. This voyage went to Heard Island, Zhong Shan and Davis, leaving from Freemantle and returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section.", "links": [ { diff --git a/datasets/200304040_raw_1.json b/datasets/200304040_raw_1.json index 50f1728921..6d5e6dc79a 100644 --- a/datasets/200304040_raw_1.json +++ b/datasets/200304040_raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304040_raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data represents the total collection of acoustic, underway and satellite data collected on voyage 4 of the Aurora Australis in the 2003-04 season.\n\nFor online access to the underway data for voyage 4 2003-04, see its specific metadata record, or the marine science database.\n\nThe Acoustics data (ADCP) are in SIMRAD EK64 format (binary), and the echoview software is required to read them. The Underway data are in ASCII format. The Satellite Images are in TERASCAN format, and TERASCAN software is required to read them.", "links": [ { diff --git a/datasets/200304070_1.json b/datasets/200304070_1.json index 6bddcf9749..c37fbbf185 100644 --- a/datasets/200304070_1.json +++ b/datasets/200304070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V7 2003/04 ().\n\nVoyage Objectives : MI resupply, Casey fly-off \nVoyage leader: Rob Easther \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200304070_raw_1.json b/datasets/200304070_raw_1.json index 5cb4143786..68e175ce07 100644 --- a/datasets/200304070_raw_1.json +++ b/datasets/200304070_raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200304070_raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data represents the total collection of acoustic, underway and satellite data collected on voyage 7 of the Aurora Australis in the 2003-04 season.\n\nFor online access to the underway data for voyage 7 2003-04, see its specific metadata record, or the marine science database.\n\nThe Acoustics data (ADCP) are in SIMRAD EK64 format (binary), and the echoview software is required to read them. The Underway data are in ASCII format. The Satellite Images are in TERASCAN format, and TERASCAN software is required to read them.", "links": [ { diff --git a/datasets/200405010_1.json b/datasets/200405010_1.json index bc37161131..0bc425ed58 100644 --- a/datasets/200405010_1.json +++ b/datasets/200405010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200405010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 2004-05. This voyage was a marine science voyage, but also went to Casey station before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200405010_raw_1.json b/datasets/200405010_raw_1.json index 2393fcfa5d..f4eca995e7 100644 --- a/datasets/200405010_raw_1.json +++ b/datasets/200405010_raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200405010_raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data represents the total collection of acoustic, underway and satellite data collected on voyage 1 of the Aurora Australis in the 2004-05 season.\n\nFor online access to the underway data for voyage 1 2004-05, see its specific metadata record, or the marine science database.\n\nThe Acoustics data (ADCP) are in SIMRAD EK64 format (binary), and the echoview software is required to read them. The Underway data are in ASCII format. The Satellite Images are in TERASCAN format, and TERASCAN software is required to read them.", "links": [ { diff --git a/datasets/200405020_1.json b/datasets/200405020_1.json index c4a81eacb6..545991d9ac 100644 --- a/datasets/200405020_1.json +++ b/datasets/200405020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200405020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 2004-05. This voyage began in Hobart, and went to Casey, Davis and Mawson before returning to Fremantle. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200405030_1.json b/datasets/200405030_1.json index f5283a7c40..60da554ba2 100644 --- a/datasets/200405030_1.json +++ b/datasets/200405030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200405030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 3 2004-05. This voyage was a marine science voyage, and went to Davis before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200405050_1.json b/datasets/200405050_1.json index b594ff9dd8..17e621714c 100644 --- a/datasets/200405050_1.json +++ b/datasets/200405050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200405050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 2004-05. This voyage began in Hobart, and went to Casey and Macquarie Island before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200506010_1.json b/datasets/200506010_1.json index 768567b936..a31f1d2e1f 100644 --- a/datasets/200506010_1.json +++ b/datasets/200506010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200506010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 1 2005-06. This voyage began in Hobart, and went to Casey and Davis before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200506020_1.json b/datasets/200506020_1.json index 66b44f0a16..5f943e8179 100644 --- a/datasets/200506020_1.json +++ b/datasets/200506020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200506020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 2 2005-06. This voyage began in Hobart, and went to Casey and conducted marine science before returning to Fremantle. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200506030_1.json b/datasets/200506030_1.json index d9c662d46a..9cb728d5f2 100644 --- a/datasets/200506030_1.json +++ b/datasets/200506030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200506030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 3 2005-06. This voyage began in Hobart, and went to Mawson and Davis and conducted marine science before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).\n\nThis voyage was also known as BROKE-West, and complements the BROKE-East voyage undertaken in the 1995/1996 season (voyage 4 - 199596040).", "links": [ { diff --git a/datasets/200506050_1.json b/datasets/200506050_1.json index 870f624c84..cf03ec818a 100644 --- a/datasets/200506050_1.json +++ b/datasets/200506050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200506050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage 5 2005-06. This voyage began in Hobart, and went to Casey and Macquarie Island before returning to Hobart. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200607010_1.json b/datasets/200607010_1.json index 80eb225a15..22d9f17c1e 100644 --- a/datasets/200607010_1.json +++ b/datasets/200607010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V1 2006/07 ().\n\nVoyage leader: Doug Thost \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200607011_1.json b/datasets/200607011_1.json index 22f71eee76..234342cfdb 100644 --- a/datasets/200607011_1.json +++ b/datasets/200607011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V1.1 2006/07 ().\n\nVoyage Objectives : Marine Science equipment trials. \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200607020_1.json b/datasets/200607020_1.json index 7dde52686a..ed8fac194c 100644 --- a/datasets/200607020_1.json +++ b/datasets/200607020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V2 2006/07 ().\n\nVoyage Objectives : Deploy CHINARE wintering and summer expeditioners and cargo. Davis changeover and retrieval. \nVoyage leader: Dave Tonna \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200607030_1.json b/datasets/200607030_1.json index 12b2a0f558..98ec864296 100644 --- a/datasets/200607030_1.json +++ b/datasets/200607030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V3 2006/07 (SAZ-SENSE).\n\nVoyage name : Sub-Antarctic Zone - Sensitivity to Environmental Change \nVoyage leader: Vicki Lytle \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).\n\nCalibrated data from this voyage are also available for download at the provided URL.\n\nTaken from the provided \"readme\" as part of the download file:\nThe underway data files contain data logged by the Aurora Australis\ndata logging system, including met data, bathymetry, GPS, and sea surface\nsalinity and temp. The data have been quality controlled.\nUnderway data are the property of the Australian Antarctic Division (except\nfor underway salinity).\n\nThe files are:\nsazsenseora.txt = column format ascii file\nsazsenseora.mat = matlab format", "links": [ { diff --git a/datasets/200607040_1.json b/datasets/200607040_1.json index 332a52d512..011c00c7cf 100644 --- a/datasets/200607040_1.json +++ b/datasets/200607040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V4 2006/07.\n\nVoyage leader: Robb Clifton \n\nRetrieve Davis and Mawson summer personnel. Delivery of remaining Mawson resupply cargo. Retrieve Casey summer personnel.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200607050_1.json b/datasets/200607050_1.json index 8d3a432307..c93adedbbb 100644 --- a/datasets/200607050_1.json +++ b/datasets/200607050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200607050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V5 2006/07.\n\nVoyage leader: Don Hudspeth \n\nMacquarie Island changeover and resupply.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200708010_1.json b/datasets/200708010_1.json index f1a02dd78c..016f450213 100644 --- a/datasets/200708010_1.json +++ b/datasets/200708010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V1 2007/08 (SIPEX).\nThis voyage began in Hobart, and travelled to the ice edge where a large number of scientific observations were collected.\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).\n\nSee also other SIPEX metadata records.", "links": [ { diff --git a/datasets/200708020_1.json b/datasets/200708020_1.json index 6edbff92f9..abef1dad60 100644 --- a/datasets/200708020_1.json +++ b/datasets/200708020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V2 2007/08.\n\nVoyage Objectives : DAVIS RESUPPLY Deploy and retrieve personnel \nVoyage leader: Mr. Don Hudspeth \n\nDeploy and retrieve personnel. Davis SAB (fuel) and full resupply. Deploy 2 x AS350BA helicopters.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200708030_1.json b/datasets/200708030_1.json index 129fe0b7df..2d6ae0a281 100644 --- a/datasets/200708030_1.json +++ b/datasets/200708030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V3 2007/08.\n\nVoyage Objectives : CEAMARC/CASO Marine Science \nLeader: Dr. Martin Riddle\nDeputy Leader: Miss. Sarah Robinson\n\nUndertake marine science as part of the CEAMARC-CASO program. CASO work was primarily undertaken on a later voyage, and this voyage mainly focussed on CEAMARC work.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200708040_1.json b/datasets/200708040_1.json index 3c55b5be9d..39e8c9b7e9 100644 --- a/datasets/200708040_1.json +++ b/datasets/200708040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V4 2007/08.\n\nVoyage Objectives : MAWSON and CASEY RESUPPLY Personnel retrieval \nVoyage leader: Ms. Nicki Chilcott \n\nDeploy and retrieve personnel - Casey, Mawson, Davis. The need to retrieve personnel by ship is subject to review on implementation of intercontinental air transport.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200708060_1.json b/datasets/200708060_1.json index 09d3eb20f0..0f40c97b50 100644 --- a/datasets/200708060_1.json +++ b/datasets/200708060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V6 2007/08.\n\nVoyage Objectives : CASO marine science \nLeader: Dr. Steve Rintoul\nDeputy Leader: Mr. Andrew Deep\n\nUndertake marine science as part of the CASO program.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1.json b/datasets/200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1.json index 66ffb51101..79e79dc716 100644 --- a/datasets/200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1.json +++ b/datasets/200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected surface seawater samples using trace clean 1L Nalgene bottles on the end of a long bamboo pole. We will analyse these samples for trace elements. Iron is the element of highest interest to our group. We will determine dissolved iron and total dissolvable iron concentrations. \n\nSamples collected from 7 sites: \nSites 1, 2, 3, 4 were a transect perpendicular to the edge of the iceberg to try and determine if there is a iron concentration gradient relative to the iceberg. \nSites 4, 5, 6 were along the edge of the iceberg to determine if there is any spatial variability along the iceberg edge. \nSite 7 was away from the iceberg to determine what the iron concentration is in the surrounding waters not influenced by the iceberg.", "links": [ { diff --git a/datasets/200712_imnavait_field.json b/datasets/200712_imnavait_field.json index 5f0dec726a..aa9c6b7c2d 100644 --- a/datasets/200712_imnavait_field.json +++ b/datasets/200712_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200712_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from December 2007.", "links": [ { diff --git a/datasets/200802_imnavait_field.json b/datasets/200802_imnavait_field.json index c13961c172..8ce3df1159 100644 --- a/datasets/200802_imnavait_field.json +++ b/datasets/200802_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200802_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from February 2008.", "links": [ { diff --git a/datasets/200809010_1.json b/datasets/200809010_1.json index 93818d4539..153a0b0fcd 100644 --- a/datasets/200809010_1.json +++ b/datasets/200809010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200809010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V1 2008/09.\n\nVoyage Objectives : Deploy and retrieve personnel - Davis Changeover and Resupply\nIce radar project \nVoyage leader: Tony Worby \n\nDeploy and retrieve personnel - subject to availability of intercontinental air transport capability.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200809020_1.json b/datasets/200809020_1.json index 19188fcb1f..7f7f0fa4bc 100644 --- a/datasets/200809020_1.json +++ b/datasets/200809020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200809020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V2 2008/09.\n\nVoyage Objectives : Casey Changeover and Davis Summer Personnel changeover \nVoyage leader: Robb Clifton \n\nDeploy and retrieve personnel from Casey and Davis.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200809030_1.json b/datasets/200809030_1.json index 623ab9f09c..eefd9693b1 100644 --- a/datasets/200809030_1.json +++ b/datasets/200809030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200809030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V3 2008/09.\n\nVoyage Objectives : Deploy and Retrieve Personnel - JARE. Conduct marine science en-route along 110E. Deploy and retrieve personnel and fully resupply Syowa Station via helicopter over 40 miles of fast ice. Load RTA cargo. Conduct marine science en-route along 150E. \nVoyage leader: Rob Bryson \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200809050_1.json b/datasets/200809050_1.json index 0de94630ee..7a05a0f7b2 100644 --- a/datasets/200809050_1.json +++ b/datasets/200809050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200809050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the underway data collected during the Aurora Australis Voyage V5 2008/09 ().\n\nVoyage Objectives : Davis Personnel Retrieval and Macquarie Island Resupply \nVoyage leader: Pete Perderson \n\nPersonnel retrieval from Davis. Full resupply of Macquarie Island.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200811_barrow_field_photos.json b/datasets/200811_barrow_field_photos.json index 1953855b46..6dc849a290 100644 --- a/datasets/200811_barrow_field_photos.json +++ b/datasets/200811_barrow_field_photos.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200811_barrow_field_photos", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Barrow field campaign photos from November 2008.", "links": [ { diff --git a/datasets/2008_carbon_water_and_energy_balance_unburned_site.json b/datasets/2008_carbon_water_and_energy_balance_unburned_site.json index b9484f95b4..02a73adc96 100644 --- a/datasets/2008_carbon_water_and_energy_balance_unburned_site.json +++ b/datasets/2008_carbon_water_and_energy_balance_unburned_site.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2008_carbon_water_and_energy_balance_unburned_site", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fluxes of C, water, and energy as measured at an eddy covariance met tower. Data are half-hourly averages collected June-August 2008", "links": [ { diff --git a/datasets/2008_carbon_water_energy_balance_moderately_burned_site.json b/datasets/2008_carbon_water_energy_balance_moderately_burned_site.json index 74f07eeed3..22f4e7e01c 100644 --- a/datasets/2008_carbon_water_energy_balance_moderately_burned_site.json +++ b/datasets/2008_carbon_water_energy_balance_moderately_burned_site.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2008_carbon_water_energy_balance_moderately_burned_site", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains eddy covariance met tower data from 2008 at moderately-burned site in the Anaktuvuk River Burn.", "links": [ { diff --git a/datasets/2008_carbon_water_energy_balance_severely_burned_site.json b/datasets/2008_carbon_water_energy_balance_severely_burned_site.json index 5c0bca7c48..4b712d0e0e 100644 --- a/datasets/2008_carbon_water_energy_balance_severely_burned_site.json +++ b/datasets/2008_carbon_water_energy_balance_severely_burned_site.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2008_carbon_water_energy_balance_severely_burned_site", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains eddy covariance met tower data from severely burned site in the Anaktuvuk River burn.", "links": [ { diff --git a/datasets/200904_imnavait_field.json b/datasets/200904_imnavait_field.json index 73647b8b60..4d14f932c2 100644 --- a/datasets/200904_imnavait_field.json +++ b/datasets/200904_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200904_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from April 2009.", "links": [ { diff --git a/datasets/200910000_1.json b/datasets/200910000_1.json index 2baaccfe74..69dfd0c05a 100644 --- a/datasets/200910000_1.json +++ b/datasets/200910000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during the Aurora Australis Voyage VTrials 2009/10.\n\nVoyage Objectives : Marine Science trials and Macquarie Island light resupply \nVoyage leader: Rob Bryson \n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910010_1.json b/datasets/200910010_1.json index 1aa94151c2..4e9a1fee79 100644 --- a/datasets/200910010_1.json +++ b/datasets/200910010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2009/10 season.\n\nVoyage Objectives : Davis resupply and refuel. Mawson winter/summer personnel in. \n\nVoyage Leader: Karin Beaumont\nDeputy Voyage Leader: Sharon Labudda\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910020_1.json b/datasets/200910020_1.json index b812442387..db3d4eb10d 100644 --- a/datasets/200910020_1.json +++ b/datasets/200910020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n\nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2009/10 season.\n\nCasey resupply. Davis summer personnel changeover. Marine Science - fishing and benthic studies. \n\nVoyage Leader: Dr. Doug Thost\nDeputy Voyage Leader: Aaron Spurr\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910030_1.json b/datasets/200910030_1.json index f197ce0f32..144bfa7a61 100644 --- a/datasets/200910030_1.json +++ b/datasets/200910030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2009/10 season.\n\nVoyage Objectives: Mawson resupply. Davis light essential.\n\nVoyage Leader: Rob Bryson\nDeputy Voyage Leader: Simon Langdon\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910040_1.json b/datasets/200910040_1.json index 3fcdb837f5..2a8e5cb50d 100644 --- a/datasets/200910040_1.json +++ b/datasets/200910040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2009/10 season.\n\nVoyage Objectives: Davis summer retrieval.\n\nVoyage Leader: Andy Cianchi\nDeputy Voyage Leader: Mick Stapleton\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910050_1.json b/datasets/200910050_1.json index 5fcbe4c0ff..9cbe72b0ab 100644 --- a/datasets/200910050_1.json +++ b/datasets/200910050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 5 of the Aurora Australis Voyage in the 2009/10 season.\n\nVoyage Objectives: Macquarie Island resupply and personnel change-over.\n\nVoyage Leader: Andy Cianchi\nDeputy Voyage Leader: Mick Stapleton\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/200910070_1.json b/datasets/200910070_1.json index 3601ff4285..a3316c8018 100644 --- a/datasets/200910070_1.json +++ b/datasets/200910070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "200910070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 7 of the Aurora Australis Voyage in the 2009/10 season - Voyage VE1 - the pest eradication voyage to Macquarie Island..\n\nVoyage Objectives: Macquarie Island resupply and personnel change-over.\n\nVoyage Leader: Andy Cianchi\nDeputy Voyage Leader: Graeme Beech\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2009oct_Chesapeake_0.json b/datasets/2009oct_Chesapeake_0.json index 8f9de76724..34ee03cfca 100644 --- a/datasets/2009oct_Chesapeake_0.json +++ b/datasets/2009oct_Chesapeake_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2009oct_Chesapeake_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Chesapeake Bay in October 2009.", "links": [ { diff --git a/datasets/201004_imnavait_field.json b/datasets/201004_imnavait_field.json index ad5c6dd345..a2e7f51bea 100644 --- a/datasets/201004_imnavait_field.json +++ b/datasets/201004_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201004_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from April 2010.", "links": [ { diff --git a/datasets/201011000_1.json b/datasets/201011000_1.json index 3b6997c206..fc576b31d0 100644 --- a/datasets/201011000_1.json +++ b/datasets/201011000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n \nThis dataset contains the underway data collected during the Trials Voyage of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Marine Science Trials.\n\nLeader: Mr. Rob Bryson\nDeputy Leader: Mr. Jono Reeve\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011002_1.json b/datasets/201011002_1.json index 5914b7e220..be6dde50cf 100644 --- a/datasets/201011002_1.json +++ b/datasets/201011002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n \nThis dataset contains the underway data collected during Voyage VE2 - Eradication 2 - of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Retrieve Pest Eradication Personnel.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011010_1.json b/datasets/201011010_1.json index df8065a8e4..d113082485 100644 --- a/datasets/201011010_1.json +++ b/datasets/201011010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Davis Resupply and Changeover.\n\nLeader: Dr. Karin Beaumont\nDeputy Leader: Miss. Sharon Labudda\nVM Trainee: Mr. Lance Bagster\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011020_1.json b/datasets/201011020_1.json index ad468eda72..d8fb11b3eb 100644 --- a/datasets/201011020_1.json +++ b/datasets/201011020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Casey Resupply.\n\nLeader: Miss. Sharon Labudda\nDeputy Leader: Dr. Fred Olivier\nVM Trainee: Ms. Kerry Steinberner\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011021_1.json b/datasets/201011021_1.json index 0ebda34f78..a163d17682 100644 --- a/datasets/201011021_1.json +++ b/datasets/201011021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage Marine Science (VMS) of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Marine Science SR3 Transect and Mertz Glacier.\n\nLeader: Dr. Steve Rintoul\nDeputy Leader: Dr. Fred Olivier\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011030_1.json b/datasets/201011030_1.json index 7749938852..39ca580b9d 100644 --- a/datasets/201011030_1.json +++ b/datasets/201011030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Mawson Resupply, Davis light essential Cargo deployment.\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Ms. Margaret Lindsay\nVM Trainee: Ms. Kate O'Malley\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011040_1.json b/datasets/201011040_1.json index 0804b17c36..1800c1fcc7 100644 --- a/datasets/201011040_1.json +++ b/datasets/201011040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Davis and Casey Summer Personnel Retrieval.\n\nLeader: Dr. Doug Thost\nDeputy Leader: Mr. George Osborne\nVM Trainee: Dr. Barbara Frankel\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201011050_1.json b/datasets/201011050_1.json index 2560768490..ca5b36a80e 100644 --- a/datasets/201011050_1.json +++ b/datasets/201011050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201011050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 5 of the Aurora Australis Voyage in the 2010/11 season.\n\nVoyage Objectives: Macquarie Island Resupply.\n\nLeader: Mr. Robb Clifton\nDeputy Leader: Ms. Leanne Millhouse\nVM Trainee: Mr. Martin Boyle\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof.json b/datasets/2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof.json index 4ce45921b4..3e82b98d55 100644 --- a/datasets/2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof.json +++ b/datasets/2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the Acrobat files from data underway along transects conducted near Barrow, AK from August 21 - September 8, 2010. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Date /time (UTC day, decimal time), time, position, bottom depth, and measured variables are listed as separate columns in each file. Each Acrobat file is named according to the transect line sampled, the year, and the year day of data collection (e.g., line_2_2010_233.dat). Because of a leaky motor can, data could not be collected from all transects sampled during the AON work using the Acrobat. Data were collected from the R/V Annika Marie using an Acrobat (Sea Sciences Inc.) towed undulating vehicle equipped with a SeaBird SBE49 conductivity-temperature-depth (CTD) sensor, a Wetlabs EcoTriplet with chlorophyll and CDOM fluorescence and optical backscatter sensors, and a Wetlabs data logger system. Data were acquired in real time from near-surface (1-m) to a few meters off of the bottom or to a maximum depth of 60 m. The inter-profile distance was usually ~150 m over the shelf and ~1 km seaward of the shelf break. The CTD was calibrated pre-cruise. No correction of chlorophyll fluorescence was done as comparison with the extracted chlorophyll from accompanying Niskin bottle samples indicated that the factory calibration was very good. The WetLabs software that calculates density from the observed temperature and conductivity cannot do so at temperatures below 0\u00b0C and a value of -999.999 is returned. Therefore users of these data should re-calculate density. Units of chlorophyll and CDOM are \u00b5g/L. For optical backscattering, the particulate volume scattering coefficient at 117 degrees and 660 nm with the scattering of water at 117 degrees subtracted out is shown.", "links": [ { diff --git a/datasets/2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton.json b/datasets/2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton.json index 0386f9aae6..18dc4eaa91 100644 --- a/datasets/2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton.json +++ b/datasets/2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arctic Observing Network (AON) Annual Observations of the Biological and Physical Marine Environment in the Chukchi and near-shore Beaufort Seas near Barrow, AK. Carin Ashjian, Woods Hole Oceanographic Institution Robert Campbell, University of Rhode Island Stephen Okkonen, University of Alaska Fairbanks NISKIN BOTTLE DATA This data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as \ufffdg C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2010. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise. Niskin bottles were deployed either just above the CTD (40 m) or by hand on a line over the side (0 m and 10 m samples) and tripped by messenger. Water was sampled immediately upon recovery of the Niskins. For chlorophyll a analysis, 100 ml of seawater was filtered onto GF-F glass fiber filters in triplicate for each bottle. Two hundred ml subsamples for determination of microzooplankton biomass and abundance were preserved with 5% final concentration acid Lugol solution for inverted microscopy. For flow cytometry samples, 3 ml aliquots were pipetted into 4 ml cryovials and preserved with 0.2% final concentration of freshly made paraformaldehyde. The samples were gently mixed and let sit in the dark at room temperature for 10 minutes before quick-freezing and storage -80 oC until flow cytometric analysis was performed. Analyses of nutrient, chlorophyll a, and flow cytometry samples followed methods described in Ashjian et al. (2010) that are reproduced below. Analysis of microzooplankton abundance followed methods described in Sherr et al. (in review) that are reproduced below. Nutrient and chlorophyll a samples were frozen in a -20\ufffdC freezer immediately after collection and transferred to a -80\ufffdC freezer within 6-8 hours. Water for the abundance of < 5 \ufffdm photosynthetic picoplankton by flow cytometry was drawn into 60 ml, brown bottles and kept cold for ~6-8 hours before being subsampled and frozen at -80\ufffdC. Chlorophyll a concentrations were analyzed within 2 months. “The filters were extracted in 6 ml of 90% acetone in 13 x 100 mm glass culture tubes at -20 oC for 18 to 24 hours. At the end of the extraction period, the filter was carefully removed from each tube, and the chlorophyll a concentration determined using a calibrated Turner Designs fluorometer. A solid chlorophyll a standard was used to check for fluorometer drift at the beginning of each reading of chlorophyll a samples. Extracted chlorophyll values were used to ground-truth the chlorophyll fluorescence sensors on the Acrobat and the CTD.” (Ashjian et al., 2010) “Nutrient analyses were performed using a hybrid Technicon AutoAnalyzer IITM and Alpkem RFA300TM system following protocols modified from Gordon et al. (1995). Standard curves with four different concentrations were run daily at the beginning and end of each run. Fresh standards were made prior to each run by diluting a primary standard with low-nutrient surface seawater. Triplicate deionized water blanks were analyzed at the beginning and end of each run to correct for any baseline shifts. In this protocol, the coefficients of variation for duplicates at low nutrient concentrations are typically < 1% (Fleischbein et al., 1999) while at high nutrient concentrations coefficients of variation are 2–3 % for nitrate and silicate (Corwith andWheeler, 2002). “ (Ashjian et al., 2010). Nutrient analyses were conducted by Joe Jennings at Oregon State University. “In the laboratory, samples for the abundance of < 5 \ufffdm photosynthetic microbes were thawed and kept on ice in a dark container until subsamples of 500 \ufffdl were enumerated on a Becton–Dickinson FACSCaliber flow cytometer with a 488-nm laser (Sherr et al. 2005). Populations of coccoid cyanobacteria and of photosynthetic eukaryotes were distinguished by differences in side light scatter (SSC) and by fluorescence in orange (cyanobacteria) and in red (eukaryotic phytoplankton) wavelengths. “ (Ashjian et al., 2010). Microzooplankton were enumerated from the Lugol-preserved samples. “From 15 to 50 ml were settled for a minimum of 24 hours and then the whole slide inspected by inverted light microscopy. A Nikon inverted microscope mated to a computer digitizing system via a drawing tube was used to identify and measure microzooplankton cells and to convert linear dimensions to cell volumes using equations appropriate for individual cell shapes (Roff and Hopcroft, 1986). All ciliate and dinoflagellate cells in each sample were counted and sized. From 60 to 400 protist cells were counted and sized in each sample inspected. Cell biomass for dinoflagellates was estimated using an algorithm of Menden-Deuer and Lessard (2000) and for ciliates was estimated using the 0.19 pgC μm-3 value of Putt and Stoecker (1989). Ratios of heterotrophic dinoflagellate biomass, and of > 40 μm sized microzooplankton biomass, as a fraction of total microzooplankton biomass were also calculated.” Microzooplankton were enumerated by Celia Ross, under the direction of Evelyn and Barry Sherr, at Oregon State University. Fluorescence values from the fluorometer on the CTD were ground-truthed using the extracted chlorophyll a data; the chlorophyll fluorescence values reported here for each bottle are derived from those corrected values from the CTD fluorometer. Ashjian, C.J., Braund, S.R., Campbell, R.G., George, J.C., Kruse, J. Maslowski, W., Moore, S.E., Nicolson, C.R., Okkonen, S.R., Sherr, B.F., Sherr, E.B., Spitz, Y. 2010. Climate variability, oceanography, bowhead whale distribution, and I\ufffdupiat subsistence whaling near Barrow, AK. Arctic 63: 179-194. Menden-Deuer, S., Lessard, E., 2000. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45, 569–579 Putt M., Stoecker D.K. 1989. An experimentally determined carbon: volume ratio for marine ‘‘oligotrichous’’ ciliates from estuarine and coastal waters. Limnology and Oceanography 34: 1097–1103. Roff J.C., Hopcroft R.R. 1986. High precision microcomputer based measuring system for ecological research. Canadian Journal of Fisheries and Aquatic Sciences 43: 2044–2048. Sherr, EB, Sherr, BF, Ross, C. Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions. In review, Deep-Sea Research II.", "links": [ { diff --git a/datasets/201104_imnavait_field.json b/datasets/201104_imnavait_field.json index 6af65062f1..7bb4d4c956 100644 --- a/datasets/201104_imnavait_field.json +++ b/datasets/201104_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201104_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from April 2011", "links": [ { diff --git a/datasets/201112000_1.json b/datasets/201112000_1.json index 122b519612..c4186df519 100644 --- a/datasets/201112000_1.json +++ b/datasets/201112000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during the Trials Voyage of the Aurora Australis Voyage in the 2011/12 season.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112010_1.json b/datasets/201112010_1.json index 091a9b52bb..5f6ae03791 100644 --- a/datasets/201112010_1.json +++ b/datasets/201112010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2011/12 season.\n\nLeader: Ms. Sharon Labudda\nDeputy Leader: Ms. Leanne Millhouse\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112020_1.json b/datasets/201112020_1.json index 7e667f1305..0786c29577 100644 --- a/datasets/201112020_1.json +++ b/datasets/201112020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2011/12 season.\n\nPurpose of voyage: Casey resupply\n\nLeader: Ms. Sharon Labudda\nDeputy Leader: Dr. Fred Olivier\nVM Trainee: Ms. Jill Hughes\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112030_1.json b/datasets/201112030_1.json index 387eade4c9..19e2e66bde 100644 --- a/datasets/201112030_1.json +++ b/datasets/201112030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2011/12 season.\n\nPurpose of voyage: Commonwealth Bay visit and Marine Science\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112040_1.json b/datasets/201112040_1.json index 608351fe13..f1aea2f383 100644 --- a/datasets/201112040_1.json +++ b/datasets/201112040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2011/12 season.\n\nPurpose of voyage: Mawson resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112050_1.json b/datasets/201112050_1.json index d367a61842..743c4274fa 100644 --- a/datasets/201112050_1.json +++ b/datasets/201112050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 5 of the Aurora Australis Voyage in the 2011/12 season.\n\nPurpose of voyage: Recover Davis and Casey summer personnel\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201112060_1.json b/datasets/201112060_1.json index 43beb986e8..47949c16e4 100644 --- a/datasets/201112060_1.json +++ b/datasets/201112060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201112060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 6 of the Aurora Australis Voyage in the 2011/12 season.\n\nPurpose of voyage: Macquarie Island resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2011_Toolik_Point_Counts.json b/datasets/2011_Toolik_Point_Counts.json index 9ab19f315a..3f12a49134 100644 --- a/datasets/2011_Toolik_Point_Counts.json +++ b/datasets/2011_Toolik_Point_Counts.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2011_Toolik_Point_Counts", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Weekly point count surveys were conducted at nineteen points along four routes near Toolik Field Station from late May to late July in 2011 using the methods described by the Alaska Landbird Monitoring Survey. At each point, an observer stood for ten minutes and recorded each individual bird detected, method of detection, and radial distance to the bird.", "links": [ { diff --git a/datasets/2011_niskin_bottlle_data_chlorophyll_nutrients.json b/datasets/2011_niskin_bottlle_data_chlorophyll_nutrients.json index 6b4cc8216b..d2ccd539b3 100644 --- a/datasets/2011_niskin_bottlle_data_chlorophyll_nutrients.json +++ b/datasets/2011_niskin_bottlle_data_chlorophyll_nutrients.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2011_niskin_bottlle_data_chlorophyll_nutrients", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as \ufffdg C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2011. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise.", "links": [ { diff --git a/datasets/201204_imnavait_field.json b/datasets/201204_imnavait_field.json index 398cf1f9e4..4e42c772e4 100644 --- a/datasets/201204_imnavait_field.json +++ b/datasets/201204_imnavait_field.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201204_imnavait_field", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imnavait field campaign data from April 2012. Between April 8th and 21st, 2012, sixteen participants worked in and around Toolik Lake, just north of the Brooks Range, measuring the snow pack using a variety of techniques, including ground and airborne LiDAR. Five dispatches were produced during that time and posted on the Scientific American website (http://blogs.scientificamerican.com/expeditions/tag/alaskan-north-slope/). They have been collected here as a report on the campaign. During the campaign four (4) types of data were taken: 1. Ground snow depths 2. Ground snow cores for SWE 3. Airborne LiDAR 4. Ground-based LiDAR These have been placed on ACADIS in the form of Excel spreadsheets for items 1 and 2, and raster files for 3 and 4. Snow depths were collected using GPS-enabled automatic depth probes which could not measure deeper than 120 cm. Values of 120 indict depths in excess of 120 cm. Additionally, depths <0 cm (resulting from slight calibration errors) should be assigned a zero-value. SWE measurements were made using Federal samplers, with cores weighed on digital balances accurate to 0.1 g. A narrative of the campaign appears in the readme documents.", "links": [ { diff --git a/datasets/201213001_1.json b/datasets/201213001_1.json index f0693ec030..2e097737be 100644 --- a/datasets/201213001_1.json +++ b/datasets/201213001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage VMS of the Aurora Australis Voyage in the 2012/13 season.\n\nPurpose of voyage: Marine Science - Sea-Ice Physics and Ecosystem Experiment (SIPEX)\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201213010_1.json b/datasets/201213010_1.json index e2e796e8f3..268d525fbb 100644 --- a/datasets/201213010_1.json +++ b/datasets/201213010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2012/13 season.\n\nPurpose of voyage: Davis Resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201213020_1.json b/datasets/201213020_1.json index b3ae93ecf4..b924cae929 100644 --- a/datasets/201213020_1.json +++ b/datasets/201213020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2012/13 season.\n\nPurpose of voyage: Casey Station resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201213030_1.json b/datasets/201213030_1.json index c1934b6d53..aea5708d4a 100644 --- a/datasets/201213030_1.json +++ b/datasets/201213030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2012/13 season.\n\nPurpose of voyage: Mawson Station resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201213040_1.json b/datasets/201213040_1.json index 32de7ccb7c..0c09cf4686 100644 --- a/datasets/201213040_1.json +++ b/datasets/201213040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2012/13 season.\n\nPurpose of voyage: Macquarie Island Station resupply\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201213_10_second_underway_1.json b/datasets/201213_10_second_underway_1.json index b026e17b3f..a189e2dc1d 100644 --- a/datasets/201213_10_second_underway_1.json +++ b/datasets/201213_10_second_underway_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201213_10_second_underway_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the track and underway data for all Australian Antarctic Division voyages carried out with the RSV Aurora Australia in the 2012-13 season, at 10 second resolution.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2012_niskin_bottle_data.json b/datasets/2012_niskin_bottle_data.json index 9fc9f2a58b..62a3b70b8a 100644 --- a/datasets/2012_niskin_bottle_data.json +++ b/datasets/2012_niskin_bottle_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2012_niskin_bottle_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "his data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as \u00b5g C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2012. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise.", "links": [ { diff --git a/datasets/201314010_1.json b/datasets/201314010_1.json index 01b22ac62d..da0c0fb43c 100644 --- a/datasets/201314010_1.json +++ b/datasets/201314010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201314010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2013/14 season.\n\nPurpose of voyage: On charter, load Davis resupply cargo, bunker vessel\n\nLeader: Mr. Tony Foy\nDeputy Leader: Mr. Mike Woolridge\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201314020_1.json b/datasets/201314020_1.json index 6575f7a04c..3acba6e3b5 100644 --- a/datasets/201314020_1.json +++ b/datasets/201314020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201314020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2/3 of the Aurora Australis Voyage in the 2013/14 season. Voyages 2 and 3 were combined in the 2013/2014 season due to the delay in voyage 1 resulting from the helicopter accident at Davis station. This voyage as also diverted to the Akademic Shokalsky tourist incident to render assistance.\n\nPurpose of voyage: Macquarie Island summer changeover and Casey resupply\n\nLeader: Ms. Leanne Millhouse\nDeputy Leader: Mr. Mark Skinner\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201314040_1.json b/datasets/201314040_1.json index 47865f40eb..e0958549e9 100644 --- a/datasets/201314040_1.json +++ b/datasets/201314040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201314040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2013/14 season.\n\nPurpose of voyage: Casey, Davis summer retrieval\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Mr. Brett Free\nVM Trainee: Mr. Dave Pinch\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201314060_1.json b/datasets/201314060_1.json index 2df6853041..7680c4b9ed 100644 --- a/datasets/201314060_1.json +++ b/datasets/201314060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201314060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 6 of the Aurora Australis Voyage in the 2013/14 season.\n\nPurpose of voyage: Mawson resupply and changeover/via helicopter\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201415010_1.json b/datasets/201415010_1.json index 5f67814481..35ae1466ac 100644 --- a/datasets/201415010_1.json +++ b/datasets/201415010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201415010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2014/15 season.\n\nPurpose of voyage: Davis resupply, refuel, summer deployment and changeover. Test bladder refuelling equipment.\n\nLeader: Mr. Doug Thost\nDeputy Leader: Mr. David Pryce\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201415020_1.json b/datasets/201415020_1.json index 32333194e8..de680069ea 100644 --- a/datasets/201415020_1.json +++ b/datasets/201415020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201415020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2014/15 season.\n\nPurpose of voyage: Casey Resupply, Totten US/AU Mooring Recovery and Marine Science\n\nLeader: Mr. Tony Foy\nDeputy Leader: Mr. Lloyd Symons\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201415030_1.json b/datasets/201415030_1.json index 2eeacfb22c..862d5b486d 100644 --- a/datasets/201415030_1.json +++ b/datasets/201415030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201415030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2014/15 season.\n\nPurpose of voyage: Mawson Resupply, Davis summer retrieval\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Mr. Simon Langdon\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201415040_1.json b/datasets/201415040_1.json index 5b86f2f9ac..1f484e7d1f 100644 --- a/datasets/201415040_1.json +++ b/datasets/201415040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201415040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2014/15 season.\n\nPurpose of voyage: Macquarie Island Resupply\n\nLeader: Ms. Nicki Wicks\nDeputy Leader: Mr. mike Woolridge\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201516010_1.json b/datasets/201516010_1.json index 0a0240d738..8be4ab514f 100644 --- a/datasets/201516010_1.json +++ b/datasets/201516010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201516010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2015/16 season.\n\nPurpose of voyage: Davis Resupply (Mawson pax by air via Davis)\n\nLeader: Ms. Leanne Millhouse\nDeputy Leader: Mr. Mick Stapleton\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201516020_1.json b/datasets/201516020_1.json index 4988dc590f..27a3490bb1 100644 --- a/datasets/201516020_1.json +++ b/datasets/201516020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201516020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2015/16 season.\n\nPurpose of voyage: Casey Resupply\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Mr. Vic Doust\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201516030_1.json b/datasets/201516030_1.json index 154790673c..2e7917e49a 100644 --- a/datasets/201516030_1.json +++ b/datasets/201516030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201516030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 (K-Axis) of the Aurora Australis Voyage in the 2015/16 season.\n\nPurpose of voyage: Marine Science, Mawson Resupply, Davis Summer Retrieval\n\nLeader: Mr. Lloyd Symons\nDeputy Leader: Mr. Brett Free\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).\n\nThis voyage carried out scientific work along the Kerguelen-Axis scientific area. This voyage also had the misfortune to run aground in Horshoe Harbour off Mawson Station during a blizzard. Consequently many passengers were flown back to Hobart, and the ship eventually sailed back under its own power.", "links": [ { diff --git a/datasets/201617010_1.json b/datasets/201617010_1.json index 992e02a442..19c9c51e8f 100644 --- a/datasets/201617010_1.json +++ b/datasets/201617010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201617010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2016/17 season.\n\nPurpose of voyage: Davis Resupply\n\nLeader: Mr. Lloyd Symons\nDeputy Leader: Mr. Dave Pryce\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201617020_1.json b/datasets/201617020_1.json index 39716f3cde..370ba510d9 100644 --- a/datasets/201617020_1.json +++ b/datasets/201617020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201617020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2016/17 season.\n\nPurpose of voyage: Casey Resupply, recover and deploy whale mooring, SOTS mooring, krill trawl and SR3 transect.\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Mr. Mike Woolridge\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201617030_1.json b/datasets/201617030_1.json index 7c1345d297..218f4ce4d0 100644 --- a/datasets/201617030_1.json +++ b/datasets/201617030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201617030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2016/17 season.\n\nPurpose of voyage: Mawson Resupply, Davis Summer Retrieval, recover and deploy whale mooring.\n\nLeader: Ms. Leanne Millhouse\nDeputy Leader: Mr. Simon Langdon\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201617040_1.json b/datasets/201617040_1.json index 6225a847db..b50dbeeb3b 100644 --- a/datasets/201617040_1.json +++ b/datasets/201617040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201617040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2016/17 season.\n\nPurpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2016gl071822_1.0.json b/datasets/2016gl071822_1.0.json index c6b4bbc284..d8adcf5c85 100644 --- a/datasets/2016gl071822_1.0.json +++ b/datasets/2016gl071822_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2016gl071822_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)). ## Manuscript Abstract: Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.", "links": [ { diff --git a/datasets/201718010_1.json b/datasets/201718010_1.json index b0c1aea474..be7269f593 100644 --- a/datasets/201718010_1.json +++ b/datasets/201718010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201718010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2017/18 season.\n\nPurpose of voyage: Davis Resupply - Davis over ice resupply, refuel and personnel deployment/retrieval. Deploy helicopters to Davis station.\n\nLeader: Dr. Doug Thost\nDeputy Leader: Mr. Andrew Cawthorn\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201718020_1.json b/datasets/201718020_1.json index 730182b0ad..cb88114905 100644 --- a/datasets/201718020_1.json +++ b/datasets/201718020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201718020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2017/18 season.\n\nPurpose of voyage: Casey Resupply, recover and deploy whale mooring, krill trawl.\n\nLeader: Mr. James Moloney\nDeputy Leader: Mr. Dave Pryce\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201718030_1.json b/datasets/201718030_1.json index c7c0a5e459..37529ca7e8 100644 --- a/datasets/201718030_1.json +++ b/datasets/201718030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201718030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2017/18 season.\n\nPurpose of voyage: Mawson over water resupply and refuel, deploy/retrieve personnel, recover and deploy whale mooring.\n\nLeader: Mr. Mark Skinner\nDeputy Leader: Dr. Fred Olivier\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201718040_1.json b/datasets/201718040_1.json index b3d9ed8ac6..da99ad9b51 100644 --- a/datasets/201718040_1.json +++ b/datasets/201718040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201718040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2017/18 season.\n\nPurpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.\n\nVoyage Leader: Mr Andy Cianchi\nDeputy Voyage Leader: Mr Justin Hallock\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201819010_1.json b/datasets/201819010_1.json index 2112d0656c..2b7611d4a2 100644 --- a/datasets/201819010_1.json +++ b/datasets/201819010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201819010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2018/19 season.\n\nPurpose of voyage: Davis Resupply - Davis over ice resupply, refuel and personnel deployment/retrieval. Deploy helicopters to Davis station. Krill trawling - 2 x 12hr night shifts of dedicated Krill time.\n\nLeader: Mr. Lloyd Symons\nDeputy Leader: Mr. Andrew Cawthorn\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201819020_1.json b/datasets/201819020_1.json index f42e91e479..8733075068 100644 --- a/datasets/201819020_1.json +++ b/datasets/201819020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201819020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2018/19 season.\n\nPurpose of voyage: Casey Resupply, recover and deploy whale mooring.\n\nLeader: Mr. James Moloney\nDeputy Leader: Mr. Brendan Hopkins\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201819030_1.json b/datasets/201819030_1.json index b94e3432d6..e58484ec53 100644 --- a/datasets/201819030_1.json +++ b/datasets/201819030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201819030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2018/19 season.\n\nPurpose of voyage: Mawson over water resupply and refuel, deploy/retrieve personnel, recover whale mooring.\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Miss Misty McCain\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201819040_1.json b/datasets/201819040_1.json index 437dda7921..1d930b9332 100644 --- a/datasets/201819040_1.json +++ b/datasets/201819040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201819040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2018/19 season.\n\nPurpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.\n\nVoyage Leader: Mr Anthony Hull\nDeputy Voyage Leader: Mr Justin Hallock\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/2019 Mali CropType Training Data_1.json b/datasets/2019 Mali CropType Training Data_1.json index b37306fc34..c2d1cd0ae7 100644 --- a/datasets/2019 Mali CropType Training Data_1.json +++ b/datasets/2019 Mali CropType Training Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2019 Mali CropType Training Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nThis dataset produced by the NASA Harvest team includes crop types labels from ground referencing matched with time-series of Sentinel-2 imagery during the growing season. Ground reference data are collected using an ODK app. Crop types include Maize, Millet, Rice and Sorghum. Labels are vectorized over the Sentinel-2 grid, and provided as raster files. Funding for this dataset is provided by Lutheran World Relief, Bill & Melinda Gates Foundation, and University of Maryland NASA Harvest program.", "links": [ { diff --git a/datasets/201920010_1.json b/datasets/201920010_1.json index 2d9d311129..b0c56741d1 100644 --- a/datasets/201920010_1.json +++ b/datasets/201920010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201920010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2019/20 season.\n\nPurpose of voyage: Davis Resupply - Davis over ice resupply, refuel and personnel deployment/retrieval. Deploy helicopters to Davis station.\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201920011_1.json b/datasets/201920011_1.json index 6c417d5dec..b6440a6225 100644 --- a/datasets/201920011_1.json +++ b/datasets/201920011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201920011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2019/20 season.\n\nPurpose of voyage: Deploy and retrieve IPEV personnel from Dumont D'Urville. Deploy cargo and helicopters, refuel station. Deploy and retrieve personnel to Macquarie Island via IRB. Vessel to depart MI as soon as personnel transfer is completed.\n\nVoyage Leader: Mr James Moloney\nDeputy Voyage Leader: Ms Leanne Millhouse\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201920020_1.json b/datasets/201920020_1.json index 12d3e3ebec..a7a4f738b1 100644 --- a/datasets/201920020_1.json +++ b/datasets/201920020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201920020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2019/20 season.\n\nPurpose of voyage: Casey Resupply, recover and deploy whale mooring.\n\nLeader: Mr. James Moloney\nDeputy Leader: Miss Anthea Fisher\nVM Trainee: Ms Gemma Dyke\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201920030_1.json b/datasets/201920030_1.json index fdfb37c82b..29c61d7861 100644 --- a/datasets/201920030_1.json +++ b/datasets/201920030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201920030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2019/20 season.\n\nPurpose of voyage: Mawson over water resupply and refuel, deploy/retrieve personnel to Mawson and Davis, retrieve and deploy whale acoustic mooring..\n\nLeader: Mr. Andy Cianchi\nDeputy Leader: Ms Amy Young\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/201920040_1.json b/datasets/201920040_1.json index 5072cf7f48..af90a9c08f 100644 --- a/datasets/201920040_1.json +++ b/datasets/201920040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "201920040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website.\n \nThese data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).\n\nThis dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2019/20 season.\n\nPurpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.\n\nVoyage Leader: Ms Nicki Wicksl\nDeputy Voyage Leader: Mr Chris Hill\n\nUnderway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "links": [ { diff --git a/datasets/20ec12f5d1f94e99aff2ed796264ee65_NA.json b/datasets/20ec12f5d1f94e99aff2ed796264ee65_NA.json index 9d9dd2861c..bedab3f1b6 100644 --- a/datasets/20ec12f5d1f94e99aff2ed796264ee65_NA.json +++ b/datasets/20ec12f5d1f94e99aff2ed796264ee65_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "20ec12f5d1f94e99aff2ed796264ee65_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/22254b5608ab430fa360d0ff7e71c34e_NA.json b/datasets/22254b5608ab430fa360d0ff7e71c34e_NA.json index 60edc04171..d7b060534c 100644 --- a/datasets/22254b5608ab430fa360d0ff7e71c34e_NA.json +++ b/datasets/22254b5608ab430fa360d0ff7e71c34e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "22254b5608ab430fa360d0ff7e71c34e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/2282b4aeb9f24bc3a1e0961e4d545427_NA.json b/datasets/2282b4aeb9f24bc3a1e0961e4d545427_NA.json index faaeebaa41..34452c27ae 100644 --- a/datasets/2282b4aeb9f24bc3a1e0961e4d545427_NA.json +++ b/datasets/2282b4aeb9f24bc3a1e0961e4d545427_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2282b4aeb9f24bc3a1e0961e4d545427_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. The L3U products provide these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 and the Long Term product v1.1. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/234Th_data_0.json b/datasets/234Th_data_0.json index 836de0d7ad..7c3bd4fec8 100644 --- a/datasets/234Th_data_0.json +++ b/datasets/234Th_data_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "234Th_data_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We had made time-series observations of 234Th and POC in the North Pacific. In this dataset, we present vertical profiles of 234Th, POC, PON, and Chlorophyll a in the North Pacific. These data will help further understanding of particle dynamics at the euphotic layer.", "links": [ { diff --git a/datasets/2457272c747f4d6ca33cb40833bd9cc2_NA.json b/datasets/2457272c747f4d6ca33cb40833bd9cc2_NA.json index 53f2b90b79..cc7059a430 100644 --- a/datasets/2457272c747f4d6ca33cb40833bd9cc2_NA.json +++ b/datasets/2457272c747f4d6ca33cb40833bd9cc2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2457272c747f4d6ca33cb40833bd9cc2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Zachariae and 79Fjord area in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 01/08/1991 and 07/02/2011. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/24dc5d5429434ccdb349db04a1a3233d_NA.json b/datasets/24dc5d5429434ccdb349db04a1a3233d_NA.json index ac59aa4e2a..79305a6ba0 100644 --- a/datasets/24dc5d5429434ccdb349db04a1a3233d_NA.json +++ b/datasets/24dc5d5429434ccdb349db04a1a3233d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "24dc5d5429434ccdb349db04a1a3233d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2016-2017, derived from Sentinel-1 SAR data acquired from 23/12/2016 to 27/02/2017, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. In total approximately 1800 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "links": [ { diff --git a/datasets/2785ee1ec6274be39d11e7e7ce51b381_NA.json b/datasets/2785ee1ec6274be39d11e7e7ce51b381_NA.json index db823e4782..1442bfbb70 100644 --- a/datasets/2785ee1ec6274be39d11e7e7ce51b381_NA.json +++ b/datasets/2785ee1ec6274be39d11e7e7ce51b381_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2785ee1ec6274be39d11e7e7ce51b381_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project, Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project. These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015. This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products. They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km. The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00c3\u00a8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u00e2\u0080\u00932010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org", "links": [ { diff --git a/datasets/27fc79c6e65f4302a18ec9788605c246_NA.json b/datasets/27fc79c6e65f4302a18ec9788605c246_NA.json index b7536ce7ad..90ecce0c10 100644 --- a/datasets/27fc79c6e65f4302a18ec9788605c246_NA.json +++ b/datasets/27fc79c6e65f4302a18ec9788605c246_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "27fc79c6e65f4302a18ec9788605c246_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Hagen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 26/08/1991 and 7/5/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/28458e44db959dd2b1e920457964665327a333f6.json b/datasets/28458e44db959dd2b1e920457964665327a333f6.json index 018fb3dd72..316cd96088 100644 --- a/datasets/28458e44db959dd2b1e920457964665327a333f6.json +++ b/datasets/28458e44db959dd2b1e920457964665327a333f6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "28458e44db959dd2b1e920457964665327a333f6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December.", "links": [ { diff --git a/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json b/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json index 21f932e34b..5cb0db2fa8 100644 --- a/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json +++ b/datasets/2940cda8-cf01-490a-a7ab-688bd54fb56a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2940cda8-cf01-490a-a7ab-688bd54fb56a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map (risk map) presents the results of earthquake probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.", "links": [ { diff --git a/datasets/294b4075ddbc4464bb06742816813bdc_NA.json b/datasets/294b4075ddbc4464bb06742816813bdc_NA.json index 37b02e1e31..fe9462a7d8 100644 --- a/datasets/294b4075ddbc4464bb06742816813bdc_NA.json +++ b/datasets/294b4075ddbc4464bb06742816813bdc_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "294b4075ddbc4464bb06742816813bdc_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "links": [ { diff --git a/datasets/296f4386-4af1-4a73-866c-d9192ec18685_NA.json b/datasets/296f4386-4af1-4a73-866c-d9192ec18685_NA.json index 2475288ea8..cae94d41cb 100644 --- a/datasets/296f4386-4af1-4a73-866c-d9192ec18685_NA.json +++ b/datasets/296f4386-4af1-4a73-866c-d9192ec18685_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "296f4386-4af1-4a73-866c-d9192ec18685_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps.", "links": [ { diff --git a/datasets/2ac9a3e7bdeb41b58b226a2fa612a4a3_NA.json b/datasets/2ac9a3e7bdeb41b58b226a2fa612a4a3_NA.json index c43ee8d949..a896465400 100644 --- a/datasets/2ac9a3e7bdeb41b58b226a2fa612a4a3_NA.json +++ b/datasets/2ac9a3e7bdeb41b58b226a2fa612a4a3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2ac9a3e7bdeb41b58b226a2fa612a4a3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json b/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json index 3726abd54d..b36a0f3075 100644 --- a/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json +++ b/datasets/2bb39206-6988-4127-89e5-85a0430e20cc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2bb39206-6988-4127-89e5-85a0430e20cc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes an estimate of earthquake frequency of MMI categories higher than 9 over the period 1973-2007.\n\nIt is based on Modified Mercalli Intensity map available in the Shakemap Atlas from USGS.\n\nUnit is expected average number of events per 1000 years.\n\nThis product was compiled by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR).\nIt was modeled using global data.\n\nCredit: GIS processing Shakemap Atlas from USGS, compilation and global hazard distribution UNEP/GRID-Europe.", "links": [ { diff --git a/datasets/2dimpacts_1.json b/datasets/2dimpacts_1.json index 17ec534951..50a4681ebc 100644 --- a/datasets/2dimpacts_1.json +++ b/datasets/2dimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2dimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Two-Dimensional Video Disdrometer (2DVD) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These data consist of the size, equivalent diameter, fall speed, oblateness, cross-sectional area of raindrops, particle concentration, total number of drops, total drop concentration, liquid water content, rain rate, reflectivity, and rain event characteristics. Data files are available from January 15, 2020 through February 28, 2020 in ASCII format.", "links": [ { diff --git a/datasets/2e54b40f184b44c797db36e192d2b679_NA.json b/datasets/2e54b40f184b44c797db36e192d2b679_NA.json index 21d3cbad7e..c9c21b9585 100644 --- a/datasets/2e54b40f184b44c797db36e192d2b679_NA.json +++ b/datasets/2e54b40f184b44c797db36e192d2b679_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2e54b40f184b44c797db36e192d2b679_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014. The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space. For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0.", "links": [ { diff --git a/datasets/2e656d34d016414c8d6bced18634772c_NA.json b/datasets/2e656d34d016414c8d6bced18634772c_NA.json index 1ffed7e024..324c9bbe0e 100644 --- a/datasets/2e656d34d016414c8d6bced18634772c_NA.json +++ b/datasets/2e656d34d016414c8d6bced18634772c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2e656d34d016414c8d6bced18634772c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7. L3 products are provided as daily and monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/2f423ac3eb244567a12b283894b869de_NA.json b/datasets/2f423ac3eb244567a12b283894b869de_NA.json index a5b332ed56..2d1cf3ca78 100644 --- a/datasets/2f423ac3eb244567a12b283894b869de_NA.json +++ b/datasets/2f423ac3eb244567a12b283894b869de_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "2f423ac3eb244567a12b283894b869de_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci MERIS+AATSR dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MERIS and AATSR (onboard ENVISAT) measurements and contains a variety of cloud properties which were derived employing the Freie Universit\u00c3\u00a4t Berlin AATSR MERIS Cloud (FAME-C) retrieval system. The core cloud properties contained in the Cloud_cci MERIS+AATSR dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "links": [ { diff --git a/datasets/3-hourly_interpolated_buoy_data.json b/datasets/3-hourly_interpolated_buoy_data.json index 3a9d7c1b7c..2a74410762 100644 --- a/datasets/3-hourly_interpolated_buoy_data.json +++ b/datasets/3-hourly_interpolated_buoy_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3-hourly_interpolated_buoy_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw observations position, sea level pressure and air temperature are interpolated to 3-hourly intervals.", "links": [ { diff --git a/datasets/3-hourly_interpolated_buoy_data_2004.json b/datasets/3-hourly_interpolated_buoy_data_2004.json index d42a0fc293..364143c22b 100644 --- a/datasets/3-hourly_interpolated_buoy_data_2004.json +++ b/datasets/3-hourly_interpolated_buoy_data_2004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3-hourly_interpolated_buoy_data_2004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw observations position, sea level pressure and air temperature data interpolated to 3-hourly intervals for 2004.", "links": [ { diff --git a/datasets/302939d341fa4013b6d96d231d6d4f40_NA.json b/datasets/302939d341fa4013b6d96d231d6d4f40_NA.json index 0b4212a18b..807a36894f 100644 --- a/datasets/302939d341fa4013b6d96d231d6d4f40_NA.json +++ b/datasets/302939d341fa4013b6d96d231d6d4f40_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "302939d341fa4013b6d96d231d6d4f40_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. It covers the period from 1995-2003.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/302f379334e84664bd3409d08eca6565_NA.json b/datasets/302f379334e84664bd3409d08eca6565_NA.json index 58555b8046..e6ba0ceb64 100644 --- a/datasets/302f379334e84664bd3409d08eca6565_NA.json +++ b/datasets/302f379334e84664bd3409d08eca6565_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "302f379334e84664bd3409d08eca6565_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data acquired from 01/10/2015 to 31/10/2016, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "links": [ { diff --git a/datasets/31137897d305407c9b83d49d124e4d1d_NA.json b/datasets/31137897d305407c9b83d49d124e4d1d_NA.json index 7f6255ed14..383cf20121 100644 --- a/datasets/31137897d305407c9b83d49d124e4d1d_NA.json +++ b/datasets/31137897d305407c9b83d49d124e4d1d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "31137897d305407c9b83d49d124e4d1d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.3 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/319691c5-0322-458c-80d3-c2d60cfbb86c_NA.json b/datasets/319691c5-0322-458c-80d3-c2d60cfbb86c_NA.json index 5ded6410eb..7c3506eee0 100644 --- a/datasets/319691c5-0322-458c-80d3-c2d60cfbb86c_NA.json +++ b/datasets/319691c5-0322-458c-80d3-c2d60cfbb86c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "319691c5-0322-458c-80d3-c2d60cfbb86c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps.", "links": [ { diff --git a/datasets/326bf808aedd41fd85594fc06678d20a_NA.json b/datasets/326bf808aedd41fd85594fc06678d20a_NA.json index bf7e364ac4..ad136038a5 100644 --- a/datasets/326bf808aedd41fd85594fc06678d20a_NA.json +++ b/datasets/326bf808aedd41fd85594fc06678d20a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "326bf808aedd41fd85594fc06678d20a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the ATSR2-AATSR L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003. To cite the full dataset, please use the following citation: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003", "links": [ { diff --git a/datasets/330b7c922a37420fabb3425671d7d7c6_NA.json b/datasets/330b7c922a37420fabb3425671d7d7c6_NA.json index 47afbb9868..c56be8bcbd 100644 --- a/datasets/330b7c922a37420fabb3425671d7d7c6_NA.json +++ b/datasets/330b7c922a37420fabb3425671d7d7c6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "330b7c922a37420fabb3425671d7d7c6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/35ea8189e75e4b6f95e7c86812080ecb_NA.json b/datasets/35ea8189e75e4b6f95e7c86812080ecb_NA.json index 7fd78955cc..9637a505ed 100644 --- a/datasets/35ea8189e75e4b6f95e7c86812080ecb_NA.json +++ b/datasets/35ea8189e75e4b6f95e7c86812080ecb_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "35ea8189e75e4b6f95e7c86812080ecb_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017. This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., S\u00c3\u00b8rensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.", "links": [ { diff --git a/datasets/3628cb2fdba443588155e15dee8e5352_NA.json b/datasets/3628cb2fdba443588155e15dee8e5352_NA.json index b1ee0e497f..8eddc3a0df 100644 --- a/datasets/3628cb2fdba443588155e15dee8e5352_NA.json +++ b/datasets/3628cb2fdba443588155e15dee8e5352_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3628cb2fdba443588155e15dee8e5352_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The MODIS Fire_cci v5.1 grid product described here contains gridded data on global burned area derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2019. This product supercedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has been periodically extended to include 2018 to 2020. This gridded dataset has been derived from the MODIS Fire_cci v5.1 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v2.0.7 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.", "links": [ { diff --git a/datasets/373638ed9c434e78b521cbe01ace5ef7_NA.json b/datasets/373638ed9c434e78b521cbe01ace5ef7_NA.json index 9455e20fc9..00615c29c0 100644 --- a/datasets/373638ed9c434e78b521cbe01ace5ef7_NA.json +++ b/datasets/373638ed9c434e78b521cbe01ace5ef7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "373638ed9c434e78b521cbe01ace5ef7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/376e342e-3fb8-4d98-bd1e-51a204e1268b_NA.json b/datasets/376e342e-3fb8-4d98-bd1e-51a204e1268b_NA.json index 37a364536f..3334040ac3 100644 --- a/datasets/376e342e-3fb8-4d98-bd1e-51a204e1268b_NA.json +++ b/datasets/376e342e-3fb8-4d98-bd1e-51a204e1268b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "376e342e-3fb8-4d98-bd1e-51a204e1268b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides seasonal maps.", "links": [ { diff --git a/datasets/38725_Not Applicable.json b/datasets/38725_Not Applicable.json index 78671998c7..bad657c709 100644 --- a/datasets/38725_Not Applicable.json +++ b/datasets/38725_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "38725_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACFHP database consist of three primary data tables, joined within SQL Server, a relational DBMS: 1. The Bibliographic table provides information on over 500 selected documents and data sources on Atlantic coastal fish species and habitats. 2. The Assessment table provides information on habitat condition indicators, threats, and conservation actions. 3. The Geospatial table provides location references for information recorded in the Bibliography and Assessment tables. In addition, a separate table enables the many-to-many relationship between bibliographic entries and locations.", "links": [ { diff --git a/datasets/38734_Not Applicable.json b/datasets/38734_Not Applicable.json index cf01af8b39..c75aa957db 100644 --- a/datasets/38734_Not Applicable.json +++ b/datasets/38734_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "38734_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of this project is to assess habitat conditions that influence biodiversity and distribution of benthic infaunal communities, contaminants, and chemical body burdens of resident organisms as measures of environmental health in Bristol Bay. Bristol Bay boasts one of the largest commercial and subsistence salmon fisheries in the world. Significant mining activities have been proposed within the bay's watershed that could impact Bristol Bay chemistry and biology, but baseline data are lacking. Baseline data will be essential for monitoring pollution control effectiveness in the watershed. The datasets generated from this study will be incorporated into the NOAA's National Status and Trend (NS&T) Program database which has been developing a dynamic quantitative database on contaminants, toxicity and benthic infaunal species distribution assessed in the coastal U.S. since 1991. Therefore, the value of this project stems not only from the importance of the locale, but also from the fact that it will continue to expand the Alaskan data set in a national online database readily accessible to Alaskan coastal managers, scientific and local communities, and which will support the Alaska Fish Monitoring Program. This is a collaborative effort between the NOAA National Centers for Coastal Ocean Science (NCCOS), the Univ. of Alaska Fairbanks (UAF), and the U.S. Fish and Wildlife Service (FWS). NPRB supplemental funding will allow the collaborators to conduct a comprehensive synoptic assessment of Nushagak and Kvichak Bays, which would not be otherwise possible.", "links": [ { diff --git a/datasets/38737_Not Applicable.json b/datasets/38737_Not Applicable.json index 633a13f584..8274299750 100644 --- a/datasets/38737_Not Applicable.json +++ b/datasets/38737_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "38737_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39071_Not Applicable.json b/datasets/39071_Not Applicable.json index 3de508f3b4..9289b651d1 100644 --- a/datasets/39071_Not Applicable.json +++ b/datasets/39071_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39071_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the United States Geological Survey; the National Park Service; and the National Geophysical Data Center to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the U.S. Coral Reef Task Force. Twenty-one distinct benthic habitat types within eight zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs. Benthic features were mapped that covered an area of 1600 km^2. In all, 49 km^2 of unconsolidated sediment, 721 km^2 of submerged vegetation, 73 km^2 of mangroves, and 756 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39083_Not Applicable.json b/datasets/39083_Not Applicable.json index 30006e0f1a..a163764541 100644 --- a/datasets/39083_Not Applicable.json +++ b/datasets/39083_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39083_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly turbidity imagery - Each image represents one calendar month.", "links": [ { diff --git a/datasets/39085_Not Applicable.json b/datasets/39085_Not Applicable.json index 8f4ba606af..5f8c2d391d 100644 --- a/datasets/39085_Not Applicable.json +++ b/datasets/39085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average seasonal turbidity imagery - Each image represents one three month Season", "links": [ { diff --git a/datasets/39092_Not Applicable.json b/datasets/39092_Not Applicable.json index dec49a9a3c..6adfbf0d79 100644 --- a/datasets/39092_Not Applicable.json +++ b/datasets/39092_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39092_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average Monthly Chlorophyll - Each image represents one calendar month", "links": [ { diff --git a/datasets/39094_Not Applicable.json b/datasets/39094_Not Applicable.json index 7391a038aa..60446301e9 100644 --- a/datasets/39094_Not Applicable.json +++ b/datasets/39094_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39094_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average seasonal Chlorophyll imagery - Each image represents one three month season", "links": [ { diff --git a/datasets/39206_Not Applicable.json b/datasets/39206_Not Applicable.json index 4dedf718a3..bc803eeb0d 100644 --- a/datasets/39206_Not Applicable.json +++ b/datasets/39206_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39206_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment;the United States Geological Survey; the National Park Service; and the National Geophysical Data Center to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the U.S. Coral Reef Task Force.Twenty-one distinct benthic habitat types within eight zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs. Benthic features were mapped that covered an area of 1600 km^2. In all, 49 km^2 of unconsolidated sediment, 721 km^2 of submerged vegetation, 73 km^2 of mangroves, and 756 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39207_Not Applicable.json b/datasets/39207_Not Applicable.json index 6815be337f..264618d8f9 100644 --- a/datasets/39207_Not Applicable.json +++ b/datasets/39207_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39207_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the United States Geological Survey; the National Park Service; and the National Geophysical Data Center, to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the U.S. Coral Reef Task Force.Twenty-one distinct benthic habitat types within eight zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs. Benthic features were mapped that covered an area of 1600 km^2. In all, 49 km^2 of unconsolidated sediment, 721 km^2 of submerged vegetation, 73 km^2 of mangroves, and 756 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39234_Not Applicable.json b/datasets/39234_Not Applicable.json index 50cba8ff73..3ea2f11ea0 100644 --- a/datasets/39234_Not Applicable.json +++ b/datasets/39234_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39234_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39235_Not Applicable.json b/datasets/39235_Not Applicable.json index 1b8bd8e27c..9a76f26a8b 100644 --- a/datasets/39235_Not Applicable.json +++ b/datasets/39235_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39235_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39236_Not Applicable.json b/datasets/39236_Not Applicable.json index 65ab2e8670..225f60c89c 100644 --- a/datasets/39236_Not Applicable.json +++ b/datasets/39236_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39236_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39238_Not Applicable.json b/datasets/39238_Not Applicable.json index 35268388de..fd48dc2614 100644 --- a/datasets/39238_Not Applicable.json +++ b/datasets/39238_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39238_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39244_Not Applicable.json b/datasets/39244_Not Applicable.json index f994e6ec20..d9350c6a92 100644 --- a/datasets/39244_Not Applicable.json +++ b/datasets/39244_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39244_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for American Samoa, Guam and the Commonwealth of the Northern Mariana Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 651 benthic habitat characterizations were completed for this work.", "links": [ { diff --git a/datasets/39245_Not Applicable.json b/datasets/39245_Not Applicable.json index 6226c3f96b..fc80a32587 100644 --- a/datasets/39245_Not Applicable.json +++ b/datasets/39245_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39245_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Common Wealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 71.5 square kilometers of which 10.56 were unconsolidated sediment and 60.94 were coral reef and hard bottom. Of the coral reef and hard bottom class, 62.8% is colonized by greater than 10% coral cover.", "links": [ { diff --git a/datasets/39246_Not Applicable.json b/datasets/39246_Not Applicable.json index 9ab7d9bd8b..d3c5b82af9 100644 --- a/datasets/39246_Not Applicable.json +++ b/datasets/39246_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39246_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Commonwealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 71.5 square kilometers of which 10.56 were unconsolidated sediment and 60.94 were coral reef and hard bottom. Of the coral reef and hard bottom class, 62.8% is colonized by greater than 10% coral cover.", "links": [ { diff --git a/datasets/39250_Not Applicable.json b/datasets/39250_Not Applicable.json index 2ef8844624..b6baa41e7b 100644 --- a/datasets/39250_Not Applicable.json +++ b/datasets/39250_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39250_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39251_Not Applicable.json b/datasets/39251_Not Applicable.json index 16b944abb5..5e684b04ab 100644 --- a/datasets/39251_Not Applicable.json +++ b/datasets/39251_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39251_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuaries (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39262_Not Applicable.json b/datasets/39262_Not Applicable.json index 5973b72dcf..fd456f6142 100644 --- a/datasets/39262_Not Applicable.json +++ b/datasets/39262_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39262_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the United States Geological Survey; the National Park Service; and the National Geophysical Data Center. The goal of this work was to develop coral reef mapping methods and compare the accuracy of benthic habitat maps generated from on-screen digitizing off of georeferenced color aerial photography, with maps digitized directly from hard copy photographs using a stereoplotter. Thematic accuracy of the Puerto Rico and U.S. Virgin Islands habitat maps was evaluated for the three most general habitat categories: unconsolidated sediment, submerged vegetation, and coral reef/hard bottom. Accuracy was estimated at two locations within the project area that included the full complement of habitat types, depth ranges, and water conditions representative of Puerto Rico and the U.S. Virgin Islands. For this reason, the accuracy of maps measured at these two locations is assumed to be representative of map accuracy elsewhere in the project area. This approach, which focused in two small areas, enabled a statistically robust evaluation of thematic accuracy to be conducted without the logistic difficulty of collecting data for accuracy assessment over the entire project area.Comparison with the accuracy assessment data revealed very similar levels of thematic accuracy between the two maps. Overall accuracy was 93.6 percent (Kappa 0.90) for on-screen digitizing and 87.8 percent (Kappa 0.82) for maps digitized directly from stereo pairs. Maps produced from on-screen digitizing were almost 100 percent accurate for the submerged vegetation and unconsolidated sediment categories but misclassified a small percentage of hardbottom sites as unconsolidated sediment. Similarly, the maps produced using the stereoplotter were 100 percent accurate at classifying submerged vegetation but misclassified a small percentage of hardbottom and unconsolidated sediment sites. These findings suggest that both of these mapping techniques result in acceptable levels of thematic accuracy for maps produced at this scale with this type of classification scheme.", "links": [ { diff --git a/datasets/39263_Not Applicable.json b/datasets/39263_Not Applicable.json index ad31d6151d..cf46d40494 100644 --- a/datasets/39263_Not Applicable.json +++ b/datasets/39263_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39263_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuary Program (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39264_Not Applicable.json b/datasets/39264_Not Applicable.json index b90d247a70..5a939d242a 100644 --- a/datasets/39264_Not Applicable.json +++ b/datasets/39264_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39264_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuaries (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39265_Not Applicable.json b/datasets/39265_Not Applicable.json index a0fa1ce4f4..6e4b2ab924 100644 --- a/datasets/39265_Not Applicable.json +++ b/datasets/39265_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39265_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuary (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39266_Not Applicable.json b/datasets/39266_Not Applicable.json index 72ec9576eb..7de378f967 100644 --- a/datasets/39266_Not Applicable.json +++ b/datasets/39266_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39266_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuary Program (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39267_Not Applicable.json b/datasets/39267_Not Applicable.json index 3de215a961..89b649e952 100644 --- a/datasets/39267_Not Applicable.json +++ b/datasets/39267_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39267_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuary Program (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39268_Not Applicable.json b/datasets/39268_Not Applicable.json index f06f3d6342..fe45312b56 100644 --- a/datasets/39268_Not Applicable.json +++ b/datasets/39268_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39268_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuary Program (ONMS) updates and revisees the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39269_Not Applicable.json b/datasets/39269_Not Applicable.json index 6ec3e69935..dcc3fa92ba 100644 --- a/datasets/39269_Not Applicable.json +++ b/datasets/39269_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39269_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office ofNational Marine Sanctuary Program (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39286_Not Applicable.json b/datasets/39286_Not Applicable.json index 493eb22731..64b227ef53 100644 --- a/datasets/39286_Not Applicable.json +++ b/datasets/39286_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39286_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39288_Not Applicable.json b/datasets/39288_Not Applicable.json index ae9e535636..bba9340a4f 100644 --- a/datasets/39288_Not Applicable.json +++ b/datasets/39288_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39288_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shallow-water, aggregated cover maps were produced by combining as many as four or more detailed habitat types into general cover categories. The original detailed habitat maps were produced by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the Northwestern Hawaiian Islands. This project is a cooperative effort among the National Oceanic and Atmospheric Administration, State of Hawaii Department of Land and Natural Resources, and the U.S. Fish and Wildlife Service to produce benthic habitat maps and georeferenced imagery for the Northwestern Hawaiian Islands. This project was conducted in support of the U.S. Coral Reef Task Force.", "links": [ { diff --git a/datasets/39307_Not Applicable.json b/datasets/39307_Not Applicable.json index 1f305ede33..2959644cbf 100644 --- a/datasets/39307_Not Applicable.json +++ b/datasets/39307_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39307_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overarching goal of this collaboration was to provide the Flower Garden Banks National Marine Sanctuary (FGBNMS) staff with information on biogeographic patterns within the Sanctuary. This specific project focused on the development of a plan to spatially and quantitatively characterize the fish communities in relatively shallow waters throughout the Sanctuary (less than 110 ft). This collaboration also included the initial implementation of that plan. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities.Monitoring of the biological communities has taken place at FGBNMS since the 1970s. This work has focused primarily on monitoring the benthos with video transects and photostations documenting transitions between coral, algae and sponge communities over time. Until relatively recently, little has been done to monitor or characterize the reef fish community. In 1994 the Reef Environmental Education Foundation (REEF) began surveys of the Sanctuary and utilized a combination of REEF personnel, volunteers, and Sanctuary staff to visually census reef fish populations via roving diver surveys. These surveys have been invaluable in terms of species list development and understanding the ranges of these species. Subsequently, a stationary point-count survey technique was utilized to begin to quantify community metrics such as species abundance and trophic structure at selected locations. These data provide an important starting point for characterizing the fish community; however, they are limited in scope of inference to small portions of the Sanctuary coral cap environment and are therefore difficult to utilize in developing population estimates at the scale of the Sanctuary.", "links": [ { diff --git a/datasets/39308_Not Applicable.json b/datasets/39308_Not Applicable.json index dc0bd630eb..0004102911 100644 --- a/datasets/39308_Not Applicable.json +++ b/datasets/39308_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39308_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed work develop baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys will employ diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39309_Not Applicable.json b/datasets/39309_Not Applicable.json index e64314da8b..a005a22a95 100644 --- a/datasets/39309_Not Applicable.json +++ b/datasets/39309_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39309_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed work develop baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys will employ diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39310_Not Applicable.json b/datasets/39310_Not Applicable.json index 456e137814..0b58c1e86c 100644 --- a/datasets/39310_Not Applicable.json +++ b/datasets/39310_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39310_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The work developed baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys employed diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project was to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39311_Not Applicable.json b/datasets/39311_Not Applicable.json index 6591d8592c..eb14442b3d 100644 --- a/datasets/39311_Not Applicable.json +++ b/datasets/39311_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39311_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed work develop baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys will employ diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39312_Not Applicable.json b/datasets/39312_Not Applicable.json index 99e3f1f407..92243844e3 100644 --- a/datasets/39312_Not Applicable.json +++ b/datasets/39312_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39312_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The work developed baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys employed diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39313_Not Applicable.json b/datasets/39313_Not Applicable.json index 474e825727..b19051d92f 100644 --- a/datasets/39313_Not Applicable.json +++ b/datasets/39313_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39313_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overarching goal of this collaboration was to provide the Flower Garden Banks National Marine Sanctuary (FGBNMS) staff with information on biogeographic patterns within the Sanctuary. This specific project focused on the development of a plan to spatially and quantitatively characterize the fish communities in relatively shallow waters throughout the Sanctuary (less than 110 ft). This collaboration also included the initial implementation of that plan. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. Monitoring of the biological communities has taken place at FGBNMS since the 1970s. This work has focused primarily on monitoring the benthos with video transects and photostations documenting transitions between coral, algae and sponge communities over time. Until relatively recently, little has been done to monitor or characterize the reef fish community. In 1994 the Reef Environmental Education Foundation (REEF) began surveys of the Sanctuary and utilized a combination of REEF personnel, volunteers, and Sanctuary staff to visually census reef fish populations via roving diver surveys. These surveys have been invaluable in terms of species list development and understanding the ranges of these species. Subsequently, a stationary point-count survey technique was utilized to begin to quantify community metrics such as species abundance and trophic structure at selected locations. These data provide an important starting point for characterizing the fish community; however, they are limited in scope of inference to small portions of the Sanctuary coral cap environment and are therefore difficult to utilize in developing population estimates at the scale of the Sanctuary.", "links": [ { diff --git a/datasets/39314_Not Applicable.json b/datasets/39314_Not Applicable.json index 7aea923129..ce2eb5870b 100644 --- a/datasets/39314_Not Applicable.json +++ b/datasets/39314_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39314_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed work develop baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys will employ diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39315_Not Applicable.json b/datasets/39315_Not Applicable.json index 9bf8063aff..c3f71c4543 100644 --- a/datasets/39315_Not Applicable.json +++ b/datasets/39315_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39315_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The work developed baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys employed diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39316_Not Applicable.json b/datasets/39316_Not Applicable.json index 3a35c4e559..f4e53dcd89 100644 --- a/datasets/39316_Not Applicable.json +++ b/datasets/39316_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39316_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed work develop baseline information on fish and benthic communities within the Flower Garden Banks National Marine Sanctuary (FGBNMS). Surveys will employ diving, technical diving, ROV, and hydroacoustics technologies for a comprehensive assessment of the fish and benthic habitat communities of the East and West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary's management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project will be to provide baseline data for all benthic habitats and communities.", "links": [ { diff --git a/datasets/39320_Not Applicable.json b/datasets/39320_Not Applicable.json index 498ae2df4c..cad15df5a8 100644 --- a/datasets/39320_Not Applicable.json +++ b/datasets/39320_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39320_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Commonwealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 104 square kilometers of which 32.9 were unconsolidated sediment and 71.6 were coral reef and hard bottom. Of the coral reef and hard bottom class, 35.6% is colonized by greater than 10% coral cover.", "links": [ { diff --git a/datasets/39324_Not Applicable.json b/datasets/39324_Not Applicable.json index 978f4fb419..2c43549269 100644 --- a/datasets/39324_Not Applicable.json +++ b/datasets/39324_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39324_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Office of National Marine Sanctuaries (ONMS) is updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39326_Not Applicable.json b/datasets/39326_Not Applicable.json index 99d05a0a6f..d036e57192 100644 --- a/datasets/39326_Not Applicable.json +++ b/datasets/39326_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39326_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39330_Not Applicable.json b/datasets/39330_Not Applicable.json index b6f68a29d9..96ca900a7b 100644 --- a/datasets/39330_Not Applicable.json +++ b/datasets/39330_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39330_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39332_Not Applicable.json b/datasets/39332_Not Applicable.json index 5cd960c6e8..7d9a83a13b 100644 --- a/datasets/39332_Not Applicable.json +++ b/datasets/39332_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39332_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography and hyperspectral imagery. Aerial photographs were acquired for the Main Eight Hawaiian Islands Benthic Mapping Project in 2000 by NOAA Aircraft Operation Centers aircraft and National Geodetic Survey cameras and personnel. Approximately 1,500, color, 9 by 9 inch photos were taken of the coastal waters of the Main Eight Hawaiian Islands at 1:24,000 scale. Specific sun angle and maximum percent cloud cover restrictions were adhered to when possible during photography missions to ensure collection of high quality imagery for the purpose of benthic mapping. In addition, consecutive photos were taken at 60 percent overlap on individual flight lines and 30 percent overlap on adjacent flight lines to allow for orthorectification and elimination of sun glint. The enhanced spectral resolution of hyperspectral and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The AURORA hyperspectral imaging system collected 72 ten nm bands in visible and near infrared spectral range with a 3 meter pixel resolution. The data was processed to select band widths, which optimized feature detection in shallow and deep water. The digital scans of aerial photos and hyperspectral imagery were orthorectified to eliminate sources of spatial distortion. With these orthorectified images photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.", "links": [ { diff --git a/datasets/39348_Not Applicable.json b/datasets/39348_Not Applicable.json index 3193a08e50..08d2eb89b1 100644 --- a/datasets/39348_Not Applicable.json +++ b/datasets/39348_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39348_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39351_Not Applicable.json b/datasets/39351_Not Applicable.json index 609cbe49ce..04ccb0ab1a 100644 --- a/datasets/39351_Not Applicable.json +++ b/datasets/39351_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39351_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39354_Not Applicable.json b/datasets/39354_Not Applicable.json index 6bc45d3d07..cacfec7ae9 100644 --- a/datasets/39354_Not Applicable.json +++ b/datasets/39354_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39354_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39361_Not Applicable.json b/datasets/39361_Not Applicable.json index 2cff495112..05fdb4e122 100644 --- a/datasets/39361_Not Applicable.json +++ b/datasets/39361_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39361_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39363_Not Applicable.json b/datasets/39363_Not Applicable.json index 18a2a6b782..f153ff475a 100644 --- a/datasets/39363_Not Applicable.json +++ b/datasets/39363_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39363_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39367_Not Applicable.json b/datasets/39367_Not Applicable.json index c33b731a52..94fb55e898 100644 --- a/datasets/39367_Not Applicable.json +++ b/datasets/39367_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39367_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39368_Not Applicable.json b/datasets/39368_Not Applicable.json index dadb91b943..2ab490ae9d 100644 --- a/datasets/39368_Not Applicable.json +++ b/datasets/39368_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39368_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for American Samoa, Guam and the Commonwealth of the Northern Mariana Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 1113 benthic habitat characterizations were completed for this work.", "links": [ { diff --git a/datasets/39375_Not Applicable.json b/datasets/39375_Not Applicable.json index 2e45c5fcee..fba94bd104 100644 --- a/datasets/39375_Not Applicable.json +++ b/datasets/39375_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39375_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39379_Not Applicable.json b/datasets/39379_Not Applicable.json index 0ddc2a6450..ec58ebbc13 100644 --- a/datasets/39379_Not Applicable.json +++ b/datasets/39379_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39379_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39383_Not Applicable.json b/datasets/39383_Not Applicable.json index 6e23a2d196..f24172bb55 100644 --- a/datasets/39383_Not Applicable.json +++ b/datasets/39383_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39383_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 638 benthic habitat characterizations were completed in UTM Zone 4 for this work.", "links": [ { diff --git a/datasets/39384_Not Applicable.json b/datasets/39384_Not Applicable.json index d2db736d09..72076a6604 100644 --- a/datasets/39384_Not Applicable.json +++ b/datasets/39384_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39384_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 39 benthic habitat characterizations were completed in UTM Zone 5 for this work.", "links": [ { diff --git a/datasets/39392_Not Applicable.json b/datasets/39392_Not Applicable.json index cd14c2a978..d7ac6e75cd 100644 --- a/datasets/39392_Not Applicable.json +++ b/datasets/39392_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39392_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39396_Not Applicable.json b/datasets/39396_Not Applicable.json index 8b1e38c58a..9e5a4cda39 100644 --- a/datasets/39396_Not Applicable.json +++ b/datasets/39396_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39396_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39401_Not Applicable.json b/datasets/39401_Not Applicable.json index 31d535d41e..a4cfd082f9 100644 --- a/datasets/39401_Not Applicable.json +++ b/datasets/39401_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39401_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Commonwealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 45.2 square kilometers of which 4.4 were unconsolidated sediment and 40.9 were coral reef and hard bottom. Of the coral reef and hard bottom class, 59.9% is colonized by greater than 10% coral cover.", "links": [ { diff --git a/datasets/39402_Not Applicable.json b/datasets/39402_Not Applicable.json index 4e87380ac4..2749d653e8 100644 --- a/datasets/39402_Not Applicable.json +++ b/datasets/39402_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39402_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39405_Not Applicable.json b/datasets/39405_Not Applicable.json index 57f3db7065..6f33e839da 100644 --- a/datasets/39405_Not Applicable.json +++ b/datasets/39405_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39405_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39411_Not Applicable.json b/datasets/39411_Not Applicable.json index 6c85d89fc1..1b2441fe5f 100644 --- a/datasets/39411_Not Applicable.json +++ b/datasets/39411_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39411_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones.", "links": [ { diff --git a/datasets/39413_Not Applicable.json b/datasets/39413_Not Applicable.json index 120e96c801..55442366c5 100644 --- a/datasets/39413_Not Applicable.json +++ b/datasets/39413_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39413_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39414_Not Applicable.json b/datasets/39414_Not Applicable.json index 29fc88b794..f176f86e3b 100644 --- a/datasets/39414_Not Applicable.json +++ b/datasets/39414_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39414_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped.", "links": [ { diff --git a/datasets/39423_Not Applicable.json b/datasets/39423_Not Applicable.json index 6ed7dd2480..11eda477e8 100644 --- a/datasets/39423_Not Applicable.json +++ b/datasets/39423_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39423_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; IMSG; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for The Republic of Palau. GPS field observations were used to establish the thematic accuracy of this thematic product. 623 benthic habitat characterizations were completed in UTM Zone 53N for this work.", "links": [ { diff --git a/datasets/39425_Not Applicable.json b/datasets/39425_Not Applicable.json index f1f0cc602b..cd08705f6d 100644 --- a/datasets/39425_Not Applicable.json +++ b/datasets/39425_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39425_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of Palau by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was used in this work. The scheme integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones.", "links": [ { diff --git a/datasets/39426_Not Applicable.json b/datasets/39426_Not Applicable.json index e5db1f72f5..05b33bdad8 100644 --- a/datasets/39426_Not Applicable.json +++ b/datasets/39426_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39426_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39427_Not Applicable.json b/datasets/39427_Not Applicable.json index 1d3f9df0dd..bc97c4f194 100644 --- a/datasets/39427_Not Applicable.json +++ b/datasets/39427_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39427_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39431_Not Applicable.json b/datasets/39431_Not Applicable.json index a1374a2470..35ffeae8f2 100644 --- a/datasets/39431_Not Applicable.json +++ b/datasets/39431_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39431_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39432_Not Applicable.json b/datasets/39432_Not Applicable.json index f195ff618c..1c33bc4604 100644 --- a/datasets/39432_Not Applicable.json +++ b/datasets/39432_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39432_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39433_Not Applicable.json b/datasets/39433_Not Applicable.json index c7eddbcf29..945f4f9404 100644 --- a/datasets/39433_Not Applicable.json +++ b/datasets/39433_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39433_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39436_Not Applicable.json b/datasets/39436_Not Applicable.json index e912c93f60..4587ef989b 100644 --- a/datasets/39436_Not Applicable.json +++ b/datasets/39436_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39436_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39438_Not Applicable.json b/datasets/39438_Not Applicable.json index 02e1a544a0..0ca73ad6e9 100644 --- a/datasets/39438_Not Applicable.json +++ b/datasets/39438_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39438_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39439_Not Applicable.json b/datasets/39439_Not Applicable.json index 22321437da..c00dd66584 100644 --- a/datasets/39439_Not Applicable.json +++ b/datasets/39439_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39439_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39440_Not Applicable.json b/datasets/39440_Not Applicable.json index 37c3419467..cd69dd2390 100644 --- a/datasets/39440_Not Applicable.json +++ b/datasets/39440_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39440_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39441_Not Applicable.json b/datasets/39441_Not Applicable.json index 2b01c05973..ffb9291ac1 100644 --- a/datasets/39441_Not Applicable.json +++ b/datasets/39441_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39441_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39442_Not Applicable.json b/datasets/39442_Not Applicable.json index 7e3b812f61..83268edbd6 100644 --- a/datasets/39442_Not Applicable.json +++ b/datasets/39442_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39442_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39443_Not Applicable.json b/datasets/39443_Not Applicable.json index 4ad667793a..1ea622613f 100644 --- a/datasets/39443_Not Applicable.json +++ b/datasets/39443_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39443_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39445_Not Applicable.json b/datasets/39445_Not Applicable.json index d4a970251b..c307df3735 100644 --- a/datasets/39445_Not Applicable.json +++ b/datasets/39445_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39445_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39446_Not Applicable.json b/datasets/39446_Not Applicable.json index 90f2043daa..a60e4ade23 100644 --- a/datasets/39446_Not Applicable.json +++ b/datasets/39446_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39446_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39448_Not Applicable.json b/datasets/39448_Not Applicable.json index b4fe00b833..457efa1d44 100644 --- a/datasets/39448_Not Applicable.json +++ b/datasets/39448_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39448_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39450_Not Applicable.json b/datasets/39450_Not Applicable.json index 40dc14fcda..76c09d1f59 100644 --- a/datasets/39450_Not Applicable.json +++ b/datasets/39450_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39450_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39456_Not Applicable.json b/datasets/39456_Not Applicable.json index b5bc7bca99..a65026e610 100644 --- a/datasets/39456_Not Applicable.json +++ b/datasets/39456_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39456_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39459_Not Applicable.json b/datasets/39459_Not Applicable.json index 8b84fd1445..2ee3a9fcef 100644 --- a/datasets/39459_Not Applicable.json +++ b/datasets/39459_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39459_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39460_Not Applicable.json b/datasets/39460_Not Applicable.json index 83be28c850..07675d12a8 100644 --- a/datasets/39460_Not Applicable.json +++ b/datasets/39460_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39460_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39461_Not Applicable.json b/datasets/39461_Not Applicable.json index 6b478c8688..4edf61cf6d 100644 --- a/datasets/39461_Not Applicable.json +++ b/datasets/39461_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39461_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39462_Not Applicable.json b/datasets/39462_Not Applicable.json index 9b4e69bfef..82b4840d05 100644 --- a/datasets/39462_Not Applicable.json +++ b/datasets/39462_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39462_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However, spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39480_Not Applicable.json b/datasets/39480_Not Applicable.json index b1b7533e0b..ab063a29a4 100644 --- a/datasets/39480_Not Applicable.json +++ b/datasets/39480_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39480_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 0.5 - 4.0 km.", "links": [ { diff --git a/datasets/39481_Not Applicable.json b/datasets/39481_Not Applicable.json index e654f38cc6..b8aecc36e0 100644 --- a/datasets/39481_Not Applicable.json +++ b/datasets/39481_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39481_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1988 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1988 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs.", "links": [ { diff --git a/datasets/39482_Not Applicable.json b/datasets/39482_Not Applicable.json index 6088cfb2e0..876ec446c0 100644 --- a/datasets/39482_Not Applicable.json +++ b/datasets/39482_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39482_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs taken by NOAA's National Geodetic Survey during 1992 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and in some areas extends beyond the park boundaries up to 2 km.", "links": [ { diff --git a/datasets/39483_Not Applicable.json b/datasets/39483_Not Applicable.json index 5b4b2206da..5b134a3498 100644 --- a/datasets/39483_Not Applicable.json +++ b/datasets/39483_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39483_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1992 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1992 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs.", "links": [ { diff --git a/datasets/39484_Not Applicable.json b/datasets/39484_Not Applicable.json index ee296b99e1..e320d9722e 100644 --- a/datasets/39484_Not Applicable.json +++ b/datasets/39484_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39484_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service). Aerial photographs were obtained for 2000 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. These habitats were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs.", "links": [ { diff --git a/datasets/39485_Not Applicable.json b/datasets/39485_Not Applicable.json index 4b050f0ba6..4c312db8da 100644 --- a/datasets/39485_Not Applicable.json +++ b/datasets/39485_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39485_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs taken by NOAA's National Geodetic Survey during 2000 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 3.3 km to the east and west, and between 0.5 - 1.2 km to the north and south.", "links": [ { diff --git a/datasets/39486_Not Applicable.json b/datasets/39486_Not Applicable.json index 9da4b9391c..1480e43c56 100644 --- a/datasets/39486_Not Applicable.json +++ b/datasets/39486_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39486_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service). Aerial photographs were obtained for 2000 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1992 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs.", "links": [ { diff --git a/datasets/39492_Not Applicable.json b/datasets/39492_Not Applicable.json index 9b23989533..7441bdde24 100644 --- a/datasets/39492_Not Applicable.json +++ b/datasets/39492_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39492_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Commonwealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 45.2 square kilometers of which 4.4 were unconsolidated sediment and 40.9 were coral reef and hard bottom. Of the coral reef and hard bottom class, 59.9% is colonized by greater than 10% coral cover.", "links": [ { diff --git a/datasets/39552_Not Applicable.json b/datasets/39552_Not Applicable.json index c0cecb27e4..10b463e876 100644 --- a/datasets/39552_Not Applicable.json +++ b/datasets/39552_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39552_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39555_Not Applicable.json b/datasets/39555_Not Applicable.json index 451f00d2db..2dbf77ddf1 100644 --- a/datasets/39555_Not Applicable.json +++ b/datasets/39555_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39555_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39556_Not Applicable.json b/datasets/39556_Not Applicable.json index 8bb3a38470..9f5e713923 100644 --- a/datasets/39556_Not Applicable.json +++ b/datasets/39556_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39556_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39557_Not Applicable.json b/datasets/39557_Not Applicable.json index f0344dc856..138d5b7089 100644 --- a/datasets/39557_Not Applicable.json +++ b/datasets/39557_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39557_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39558_Not Applicable.json b/datasets/39558_Not Applicable.json index 4324886fa3..b4c0d4fe62 100644 --- a/datasets/39558_Not Applicable.json +++ b/datasets/39558_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39558_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39559_Not Applicable.json b/datasets/39559_Not Applicable.json index 209102d940..7a795f6e50 100644 --- a/datasets/39559_Not Applicable.json +++ b/datasets/39559_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39559_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39560_Not Applicable.json b/datasets/39560_Not Applicable.json index a59df7b1b8..7b43cf8358 100644 --- a/datasets/39560_Not Applicable.json +++ b/datasets/39560_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39560_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39561_Not Applicable.json b/datasets/39561_Not Applicable.json index 068551b6e7..2c8ff43f9b 100644 --- a/datasets/39561_Not Applicable.json +++ b/datasets/39561_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39561_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39562_Not Applicable.json b/datasets/39562_Not Applicable.json index e29e738f69..de6ace450d 100644 --- a/datasets/39562_Not Applicable.json +++ b/datasets/39562_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39562_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39563_Not Applicable.json b/datasets/39563_Not Applicable.json index de92780318..c82cac8c6b 100644 --- a/datasets/39563_Not Applicable.json +++ b/datasets/39563_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39563_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39564_Not Applicable.json b/datasets/39564_Not Applicable.json index 7dfa86744e..09d1cf295a 100644 --- a/datasets/39564_Not Applicable.json +++ b/datasets/39564_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39564_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39565_Not Applicable.json b/datasets/39565_Not Applicable.json index cdaa061478..9ed76036ed 100644 --- a/datasets/39565_Not Applicable.json +++ b/datasets/39565_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39565_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39566_Not Applicable.json b/datasets/39566_Not Applicable.json index 85b02f7884..0b34ab7be3 100644 --- a/datasets/39566_Not Applicable.json +++ b/datasets/39566_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39566_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month.", "links": [ { diff --git a/datasets/39570_Not Applicable.json b/datasets/39570_Not Applicable.json index e2610852e1..d7046574c8 100644 --- a/datasets/39570_Not Applicable.json +++ b/datasets/39570_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39570_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reef fish populations are a conspicuous and essential component of USVI coral reef ecosystems. Yet despite their importance, striking population and community level changes have occurred in the recent past due to fishing pressure and habitat degradation. The monitoring methodologies described in this document are necessary for understanding how natural and anthropogenic stressors are changing reef fish populations and communities and will be critical for their sustainable management. A collaborative research effort between the NOAA's National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) and the National Park Service (NPS) has been used to inventory and assess reef fish populations in reef and reef-associated habitats in the northeast region of St. Croix from 2001-2011. The survey method previously used has been refined to enable broader region-wide coverage at the scale of the USVI yet maintains high precision at the Marine Protected Area (MPA) spatial level. Region-wide population metric estimates are required to effectively manage reef fisheries but are also imperative for spatial management and understanding ecosystem-level processes. For example, the ability to place protected fish resources in the context of the greater region not only allows for the evaluation of management actions but it also provides the ability to determine the ecological role of an MPA in the greater ecosystem. The monitoring method previously used by the Biogeography Branch and other partners in St. Croix and other regions within the USVI and Puerto Rico will be used to characterize and establish baseline data for future monitoring. St. Croix was chosen to serve as the first area to implement the protocol and to evaluate the logistics necessary to implement a long term monitoring program in the USVI as part of the National Coral Reef Monitoring Program (NCRMP). Characterization and monitoring of fish communities requires a quantitative measure of the spatial distribution and variation of those communities. These measures will enable managers to make targeted management decisions (e.g. where to allow mooring or where to allow recreational activities such as snorkeling and SCUBA diving). Additionally, the spatial setting, both within and outside protected regions allows managers to assess the impact, if any, of a change in regulation such as the prohibition of fishing. It also enables analysis of any differential effect (i.e. the effect may be the same throughout the region or it may be more effective toward an edge or center of a management area). To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. The sampling domain includes all hardbottom habitats around St. Croix at depths less than 30m. The benthic habitat map and a habitat classification scheme were used to create a sample frame constructed with 50 x 50 m grids. Grids were stratified based on three variables: Hardbottom habitat type, depth zone, and region/management area. Habitat within these grids was stratified into 5 habitat categories (scattered coral/ rock, pavement, bedrock, patch reef and linear reef) each with two depth classifications (shallow (0-11.9 m) and deep (12- 30m)). Further stratification was assigned based on management zones and region of the island. There are three managed areas in St. Croix. Two federal marine protected areas are managed by the Department of Interior's National Park Service: Buck Island Reef National Monument and Salt River Bay National Historical Park and Ecological Reserve. The St. Croix East End Marine Park is a territorial marine protected area managed by the USVI Department of Planning and Natural Resources. Other strata include specific regions of St. Croix: North, East, West, and South shores. Overall there were 70 possible strata: 5 habitat types, 2 depth zones and 8 management areas/regions. The monitoring objectives of this protocol are to determine status, trends, and variability in exploited reef fish species and communities within the USVI region and inside vs. outside different management zones, using measures such as relative abundance (density), spatial distribution, size structure and diversity. The survey design is optimized for nine economically and ecologically important species in the USVI: blue tang (Acanthurus coeruleus). queen triggerfish (Balistes vetula), coney (Cephalopholis fulva), red hind (Epinephelus guttatus), foureye butterflyfish (Chaetodon capistratus), French grunt (Haemulon flavolineatum), yellowtail snapper (Ocyurus chrysurus), stoplight parrotfish (Sparisoma viride) and threespot damselfish (Stegastes planifrons). These species were chosen to include a broad range of life history traits as well as a variety of habitat utilization patterns. The sample design is optimized with the respect to these species, but because all fish species are recorded, monitoring efforts also obtain important information about many non-targeted species, the overall trophic structure, and form the scientific basis for effective management actions. As such, the sample allocation for this mission is based upon the existing community metrics and the above species specific distribution from the northeast region of St. Croix. It was determined that 250 samples among the various strata would be sufficient to characterize hard bottom habitats around the island and have comparable coefficient of variation (CV) to values observed in the northeast region of St. Croix. The goal was to survey as many of the 250 sites as possible in a two week time period. We organized a strong science field team and completed 286 fish and benthic surveys around the island.", "links": [ { diff --git a/datasets/39572_Not Applicable.json b/datasets/39572_Not Applicable.json index 3acd53b0c9..3329e749b9 100644 --- a/datasets/39572_Not Applicable.json +++ b/datasets/39572_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39572_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCCOS' Center for Coastal Monitoring and Assessment (CCMA) is working closely with a number of divisions in the USVI DPNR (e.g., Divisions of Fish and Wildlife and Coastal Zone Management), the University of the Virgin Islands (UVI), and The Nature Conservancy (TNC) to develop the baseline characterization of chemical contamination, toxicity, and the marine resources in the St. Thomas East End Reserve (STEER) in St. Thomas, USVI. The STEER contains extensive mangroves, seagrass beds and coral reefs. Within the watershed, however, are a large active landfill, numerous marinas, various commercial/industrial activities, an EPA Superfund Site, resorts, and several residential areas served by individual septic systems. This baseline assessment will provide managers with critical information needed to help preserve and restore habitats, including a number of nursery areas within the STEER that are important to commercial and recreational fisheries. As part of the characterization, a field survey was conducted in June 2012 to conduct a biological assessment of fish communities and benthic habitats within the STEER and at select hardbottom locations adjacent to STEER. The basis for this work was the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps were stratified to select sampling stations. Sites were randomly selected within strata to ensure coverage of the entire study region. The habitat stratification was divided into three major habitat types: hardbottom which includes reef, pavement, etc. inside STEER; softbottom which consists of sand and seagrass, and mangrove. In addition, two harbottom areas outside STEER of interest to STEER's Core Team were included as a separate stratum. Using standardized protocols of NOAA's Coral Reef Ecosystem Monitoring Project, the fish and benthic habitat survey was conducted by two scientific divers. During each dive one diver quantified the species and size of fish within a 25 x 4 m transect while a second diver characterized the habitat and invertebrate community.", "links": [ { diff --git a/datasets/39573_Not Applicable.json b/datasets/39573_Not Applicable.json index 586e1e8aa1..69bbe1e614 100644 --- a/datasets/39573_Not Applicable.json +++ b/datasets/39573_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39573_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCCOS' Center for Coastal Monitoring and Assessment (CCMA) is working closely with a number of divisions in the USVI DPNR (e.g., Divisions of Fish and Wildlife and Coastal Zone Management), the University of the Virgin Islands (UVI), and The Nature Conservancy (TNC) to develop the baseline characterization of chemical contamination, toxicity, and the marine resources in the St. Thomas East End Reserve (STEER) in St. Thomas, USVI. The STEER contains extensive mangroves, seagrass beds and coral reefs. Within the watershed, however, are a large active landfill, numerous marinas, various commercial/industrial activities, an EPA Superfund Site, resorts, and several residential areas served by individual septic systems. This baseline assessment will provide managers with critical information needed to help preserve and restore habitats, including a number of nursery areas within the STEER that are important to commercial and recreational fisheries. As part of the characterization, a field survey was conducted in June 2012 to conduct a biological assessment of fish communities and benthic habitats within the STEER and at select hardbottom locations adjacent to STEER. The basis for this work was the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps were stratified to select sampling stations. Sites were randomly selected within strata to ensure coverage of the entire study region. The habitat stratification was divided into three major habitat types: hardbottom which includes reef, pavement, etc. inside STEER; softbottom which consists of sand and seagrass, and mangrove. In addition, two harbottom areas outside STEER of interest to STEER's Core Team were included as a separate stratum. Using standardized protocols of NOAA's Coral Reef Ecosystem Monitoring Project, the fish and benthic habitat survey was conducted by two scientific divers. During each dive one diver quantified the species and size of fish within a 25 x 4 m transect while a second diver characterized the habitat and invertebrate community.", "links": [ { diff --git a/datasets/39575_Not Applicable.json b/datasets/39575_Not Applicable.json index 14bc8d7f38..962be26783 100644 --- a/datasets/39575_Not Applicable.json +++ b/datasets/39575_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39575_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic Tracking of Reef Fishes to Elucidate Habitat Utilization Patterns and Residence Times Inside and Outside Marine Protected Areas Around the Island of St. John, USVI NOAA's Biogeography Branch, National Park Service (NPS), US Geological Survey, and the University of Hawaii used acoustic telemetry to quantify spatial patterns and habitat affinities of reef fishes around the island of St. John, US Virgin Islands. The objective of the study was to define the movements of reef fishes among habitats within and between the Virgin Islands Coral Reef National Monument (VICRNM), the Virgin Islands National Park (VIIS), and Territorial waters surrounding St. John. In order to better understand species' habitat utilization patterns among management regimes, we deployed an array of hydroacoustic receivers and acoustically tagged reef fishes. Thirty six receivers were deployed in shallow nearshore bays and across the shelf to depths of approximately 30 m. We tagged 184 individual fishes representing 19 species from 10 different families with VEMCO V9-2L-R64K transmitters.", "links": [ { diff --git a/datasets/39578_Not Applicable.json b/datasets/39578_Not Applicable.json index 46bc639a07..4dda955004 100644 --- a/datasets/39578_Not Applicable.json +++ b/datasets/39578_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39578_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39584_Not Applicable.json b/datasets/39584_Not Applicable.json index 57ddd62022..899589d30e 100644 --- a/datasets/39584_Not Applicable.json +++ b/datasets/39584_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39584_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAAs National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).", "links": [ { diff --git a/datasets/39589_Not Applicable.json b/datasets/39589_Not Applicable.json index e5e0648911..23b11c7bb2 100644 --- a/datasets/39589_Not Applicable.json +++ b/datasets/39589_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39589_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary.", "links": [ { diff --git a/datasets/39590_Not Applicable.json b/datasets/39590_Not Applicable.json index 3f836a28b8..23ca3b7ce1 100644 --- a/datasets/39590_Not Applicable.json +++ b/datasets/39590_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39590_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary.", "links": [ { diff --git a/datasets/39604_Not Applicable.json b/datasets/39604_Not Applicable.json index 9f460d535f..14f3bdb2f9 100644 --- a/datasets/39604_Not Applicable.json +++ b/datasets/39604_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39604_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39605_Not Applicable.json b/datasets/39605_Not Applicable.json index 24b8d108d7..7e8e139dbc 100644 --- a/datasets/39605_Not Applicable.json +++ b/datasets/39605_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39605_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39606_Not Applicable.json b/datasets/39606_Not Applicable.json index 0265cff2bd..ced2ba8135 100644 --- a/datasets/39606_Not Applicable.json +++ b/datasets/39606_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39606_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39607_Not Applicable.json b/datasets/39607_Not Applicable.json index 45ca1f1e14..c3678b3233 100644 --- a/datasets/39607_Not Applicable.json +++ b/datasets/39607_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39607_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor.", "links": [ { diff --git a/datasets/39623_Not Applicable.json b/datasets/39623_Not Applicable.json index 2fc59565f9..a240cb8b59 100644 --- a/datasets/39623_Not Applicable.json +++ b/datasets/39623_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39623_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989).", "links": [ { diff --git a/datasets/39624_Not Applicable.json b/datasets/39624_Not Applicable.json index 5fd290033c..841ee10ae2 100644 --- a/datasets/39624_Not Applicable.json +++ b/datasets/39624_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39624_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989).", "links": [ { diff --git a/datasets/39909dc233b34118a80dd6fa8a7af553_NA.json b/datasets/39909dc233b34118a80dd6fa8a7af553_NA.json index 9f0ee1a7d5..b09d34b95a 100644 --- a/datasets/39909dc233b34118a80dd6fa8a7af553_NA.json +++ b/datasets/39909dc233b34118a80dd6fa8a7af553_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39909dc233b34118a80dd6fa8a7af553_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 daily and monthly aerosol products from the ATSR-2 instrument on the ERS-2 satellite, using the Swansea University (SU) algorithm, version 4.3. Data cover the period 1995 - 2003.For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA.json b/datasets/39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA.json index de70e90090..5f3f42a099 100644 --- a/datasets/39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA.json +++ b/datasets/39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.", "links": [ { diff --git a/datasets/3DIMG_L1B_STD.json b/datasets/3DIMG_L1B_STD.json index 0c8b2142ad..af3c3912c6 100644 --- a/datasets/3DIMG_L1B_STD.json +++ b/datasets/3DIMG_L1B_STD.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L1B_STD", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-1B Standard Product containing 6 channels data in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L1C_SGP.json b/datasets/3DIMG_L1C_SGP.json index 0a958b7f67..9e18e3d03b 100644 --- a/datasets/3DIMG_L1C_SGP.json +++ b/datasets/3DIMG_L1C_SGP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L1C_SGP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-1C Sector Product (Geocoded, all pixels at same resolution) contains 6 channels data in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2B_CMK.json b/datasets/3DIMG_L2B_CMK.json index 058246517f..5ba61ad354 100644 --- a/datasets/3DIMG_L2B_CMK.json +++ b/datasets/3DIMG_L2B_CMK.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2B_CMK", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2B Cloud Map Product in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2B_HEM.json b/datasets/3DIMG_L2B_HEM.json index 3c7493abcd..e30624df39 100644 --- a/datasets/3DIMG_L2B_HEM.json +++ b/datasets/3DIMG_L2B_HEM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2B_HEM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2B Precipitation using Hydroestimator Technique in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2B_OLR.json b/datasets/3DIMG_L2B_OLR.json index f5a973f5d1..33235c8f5d 100644 --- a/datasets/3DIMG_L2B_OLR.json +++ b/datasets/3DIMG_L2B_OLR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2B_OLR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2B Outgoing Longwave Radation (OLR) in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2B_SST.json b/datasets/3DIMG_L2B_SST.json index f449d07d54..8e90e9027a 100644 --- a/datasets/3DIMG_L2B_SST.json +++ b/datasets/3DIMG_L2B_SST.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2B_SST", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2B Sea Surface Temperature in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2B_UTH.json b/datasets/3DIMG_L2B_UTH.json index acbbafefaf..823d2104bd 100644 --- a/datasets/3DIMG_L2B_UTH.json +++ b/datasets/3DIMG_L2B_UTH.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2B_UTH", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2B Upper Tropospheric Humidity (UTH) in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2C_FOG.json b/datasets/3DIMG_L2C_FOG.json index a94f833eef..158a674458 100644 --- a/datasets/3DIMG_L2C_FOG.json +++ b/datasets/3DIMG_L2C_FOG.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2C_FOG", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2C FOG Map in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2C_SNW.json b/datasets/3DIMG_L2C_SNW.json index 96d3b9ac07..94fa373522 100644 --- a/datasets/3DIMG_L2C_SNW.json +++ b/datasets/3DIMG_L2C_SNW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2C_SNW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INSAT-3D Imager Level-2C SNOW Map in HDF-5 Format", "links": [ { diff --git a/datasets/3DIMG_L2P_IRW.json b/datasets/3DIMG_L2P_IRW.json index 5d7bbbba4a..00f73e9225 100644 --- a/datasets/3DIMG_L2P_IRW.json +++ b/datasets/3DIMG_L2P_IRW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2P_IRW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Suitable tracers are identified in TIR1 band imagery and tracked in subsequent half-hourly imageries to determine cloud motion vector", "links": [ { diff --git a/datasets/3DIMG_L2P_MRW.json b/datasets/3DIMG_L2P_MRW.json index f7eab4c90c..86bc2703f8 100644 --- a/datasets/3DIMG_L2P_MRW.json +++ b/datasets/3DIMG_L2P_MRW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2P_MRW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Suitable tracers are identified in MIRband imagery and tracked in subsequent half-hourly imageries to determine cloud motion vector", "links": [ { diff --git a/datasets/3DIMG_L2P_SMK.json b/datasets/3DIMG_L2P_SMK.json index 6e1b25a3cd..e12e9b2da6 100644 --- a/datasets/3DIMG_L2P_SMK.json +++ b/datasets/3DIMG_L2P_SMK.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2P_SMK", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an Active Smoke product, which identifies pixels having smoke using Visible albedo and Brightness Temperature of MIR, TIR-1 and TIR-2 channels", "links": [ { diff --git a/datasets/3DIMG_L2P_VSW.json b/datasets/3DIMG_L2P_VSW.json index 544d318b4d..62e14eecf5 100644 --- a/datasets/3DIMG_L2P_VSW.json +++ b/datasets/3DIMG_L2P_VSW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2P_VSW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Suitable tracers are identified in VISIBLE band imagery and tracked in subsequent half-hourly imageries to determine cloud motion vector", "links": [ { diff --git a/datasets/3DIMG_L2P_WVW.json b/datasets/3DIMG_L2P_WVW.json index 1c017a2316..e577e538d0 100644 --- a/datasets/3DIMG_L2P_WVW.json +++ b/datasets/3DIMG_L2P_WVW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3DIMG_L2P_WVW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Suitable tracers are identified in WV(Water Vapour) band imagery and tracked in subsequent half-hourly imageries to determine cloud motion vector", "links": [ { diff --git a/datasets/3He_Exposure_dates_Mt_Waesche.json b/datasets/3He_Exposure_dates_Mt_Waesche.json index 669ad4cec2..123d24a0da 100644 --- a/datasets/3He_Exposure_dates_Mt_Waesche.json +++ b/datasets/3He_Exposure_dates_Mt_Waesche.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3He_Exposure_dates_Mt_Waesche", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are 3He exposure ages from lateral moraine bands on Mount Waesche, a\n volcanic nunatak in Marie Byrd Land, West Antarctica. The proximal part of the\n moraine is up to 45 meters above the present ice level was deposited\n approximately 10,000 years ago, well after the glacial maximum in the Ross\n Embayment. The upper distal part of the moraine may record multiple earlier\n ice advances. The data are all generated by crushing and melting mineral\n separates (mostly olivine) in vacuo, and measurements with a noble gas mass\n spectrometer at Woods Hole Oceanographic Institution. Full details can be\n found in Ackert et al. (Science, 1999, vol. 286, p.276-280).", "links": [ { diff --git a/datasets/3ac333b828b54e3495c7749f5bce2fe3_NA.json b/datasets/3ac333b828b54e3495c7749f5bce2fe3_NA.json index 3bdc1de3de..140fb9984c 100644 --- a/datasets/3ac333b828b54e3495c7749f5bce2fe3_NA.json +++ b/datasets/3ac333b828b54e3495c7749f5bce2fe3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3ac333b828b54e3495c7749f5bce2fe3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a number of oceanic indicators of mean sea level changes have been produced from merging satellite altimetry measurements of sea level anomalies. The oceanic indicators dataset consists of static files covering the whole altimeter period, describing the evolution of the project's monthly sea level anomaly gridded product (see separate dataset record).The oceanic indicators that are provided are: 1) the temporal evolution of the global Mean Sea Level (MSL) DOI: 10.5270/esa-sea_level_cci-IND_MSL_MERGED-1993_2015-v_2.0-201612 ;2) the geographic distribution of Mean Sea Level changes (MSLTR) DOI: 10.5270/esa-sea_level_cci-IND_MSLTR_MERGED-1993_2015-v_2.0-201612 ;3) Maps of the amplitude and phase of the annual cycle (MSLAMPH) DOI: 10.5270/esa-sea_level_cci-IND_MSLAMPH_MERGED-1993_2015-v_2.0-201612.The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612.When using or referring to the SL_cci products, please mention the associated DOIs and also use the following citation where a detailed description of the SL_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00c3\u00a8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u00e2\u0080\u00932010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these products please email: info-sealevel@esa-sealevel-cci.org", "links": [ { diff --git a/datasets/3bdb21a4cd004e5f8cc148fea5f1d4e3_NA.json b/datasets/3bdb21a4cd004e5f8cc148fea5f1d4e3_NA.json index 95bf7466e2..f10fd7f630 100644 --- a/datasets/3bdb21a4cd004e5f8cc148fea5f1d4e3_NA.json +++ b/datasets/3bdb21a4cd004e5f8cc148fea5f1d4e3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3bdb21a4cd004e5f8cc148fea5f1d4e3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "links": [ { diff --git a/datasets/3bfe0c2d51544f72837a99306a74e359_NA.json b/datasets/3bfe0c2d51544f72837a99306a74e359_NA.json index 15f73b67de..eb985a30f5 100644 --- a/datasets/3bfe0c2d51544f72837a99306a74e359_NA.json +++ b/datasets/3bfe0c2d51544f72837a99306a74e359_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3bfe0c2d51544f72837a99306a74e359_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "links": [ { diff --git a/datasets/3c324bb4ee394d0d876fe2e1db217378_NA.json b/datasets/3c324bb4ee394d0d876fe2e1db217378_NA.json index c76b48ca8e..7d3086b586 100644 --- a/datasets/3c324bb4ee394d0d876fe2e1db217378_NA.json +++ b/datasets/3c324bb4ee394d0d876fe2e1db217378_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3c324bb4ee394d0d876fe2e1db217378_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\u00e2\u0080\u00a2\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\u00e2\u0080\u00a2\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\u00e2\u0080\u00a2\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \u00e2\u0080\u00a2\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \u00e2\u0080\u00a2\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.", "links": [ { diff --git a/datasets/3d_snow_models_4.0.json b/datasets/3d_snow_models_4.0.json index c98128c6b0..44c95dadc7 100644 --- a/datasets/3d_snow_models_4.0.json +++ b/datasets/3d_snow_models_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3d_snow_models_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).", "links": [ { diff --git a/datasets/3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA.json b/datasets/3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA.json index 25c4cae96e..65b59dfdf3 100644 --- a/datasets/3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA.json +++ b/datasets/3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational HCHO total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/3fe263d2-99ed-4751-b937-d26a31ab0606_NA.json b/datasets/3fe263d2-99ed-4751-b937-d26a31ab0606_NA.json index 227ca05b74..6d42889be8 100644 --- a/datasets/3fe263d2-99ed-4751-b937-d26a31ab0606_NA.json +++ b/datasets/3fe263d2-99ed-4751-b937-d26a31ab0606_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "3fe263d2-99ed-4751-b937-d26a31ab0606_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the \"Normalized Difference Vegetation Index\" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the \"greenness\" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/", "links": [ { diff --git a/datasets/4003949a-cb4b-41b7-9710-915269990bcd_NA.json b/datasets/4003949a-cb4b-41b7-9710-915269990bcd_NA.json index 6978f26ecc..2d2a10b0e0 100644 --- a/datasets/4003949a-cb4b-41b7-9710-915269990bcd_NA.json +++ b/datasets/4003949a-cb4b-41b7-9710-915269990bcd_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "4003949a-cb4b-41b7-9710-915269990bcd_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/41e2300068b44fa190f24272dc08dcd0_NA.json b/datasets/41e2300068b44fa190f24272dc08dcd0_NA.json index fe0ad4086f..b48c436997 100644 --- a/datasets/41e2300068b44fa190f24272dc08dcd0_NA.json +++ b/datasets/41e2300068b44fa190f24272dc08dcd0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "41e2300068b44fa190f24272dc08dcd0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/41e9783d4caa447b99f653c065805579_NA.json b/datasets/41e9783d4caa447b99f653c065805579_NA.json index 487ed262e7..56e7a11634 100644 --- a/datasets/41e9783d4caa447b99f653c065805579_NA.json +++ b/datasets/41e9783d4caa447b99f653c065805579_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "41e9783d4caa447b99f653c065805579_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017. The surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.The algorithm used to devive the product is described in the paper \u00e2\u0080\u009cImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry\u00e2\u0080\u009d by S.B. Simonsen and L.S. S\u00c3\u00b8rensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012", "links": [ { diff --git a/datasets/42ad984d-a92e-41c2-af23-f28ecd22018d_1.json b/datasets/42ad984d-a92e-41c2-af23-f28ecd22018d_1.json index c565815c2d..e9950ba672 100644 --- a/datasets/42ad984d-a92e-41c2-af23-f28ecd22018d_1.json +++ b/datasets/42ad984d-a92e-41c2-af23-f28ecd22018d_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "42ad984d-a92e-41c2-af23-f28ecd22018d_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The African Cities Population Database (ACPD) has been produced by the\nBirkbeck College of the University of London in 1990 at the request of\nthe United Nations Environment Programme (UNEP) in Nairobi, Kenya.\nThe database contains head counts for 479 cities in Africa which either\nhave a population of over 20,000 or are capitals of their nation state.\nListed are the geographical location of the cities and their population\nsizes. The material is primarily derived from a 1988 report of the\nEconomic Commission for Africa (ECA) and several issues of the United\nNations Demographic Yearbook (1973-81). Severe problems were found with\nseveral countries such as Togo, Ghana and South Africa. For South Africa,\nthe data were derived from the United Nations Demographic Yearbook 1987.\n \nWCPD is an Arc/Info point coverage. It has no projection, as the cities\nare located on the basis of their latitude and longitude. Coordinates\nwere assigned on the basis of gazetteers or African maps. Each record\nin the data base contains details of the city name, country name,\nlatitude and longitude of the city, and its population at a defined\ntime. The Arc/Info attribute table contains the following fields:\n \nAREA Arc/Info item\nPERIMETER Arc/Info item\nACPD# Arc/Info item\nACPD-ID Arc/Info item\nID-NUM Unique number for each city\nCITY City name\nCOUNTRY Country name\nCITY-POP Population of city proper\nYEAR Latest available year of collection\n \nACPD comes as an Arc/Info EXPORT file originally called \"ACPD.E00\" and \ncontains 67 Kb of data. The file has a record length of 80 and a block\nsize of 8000 (blocking factor = 100). The file can be read from tape\nusing Arc/Info's TAPEREAD command or any other generic copy utility. \nIf distributed on a diskette it can be read using the ordinary DOS 'COPY'\ncommand. The file has to be converted to Arc/Info internal format using\nits IMPORT command.\n \nReferences to the WCPD data set can be found in:\n \n - SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.\n - D. Rhind. \"Cartographically-related research at Birkbeck College\n 1987-91\" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.\n \nThe source of the WCPD data set as held by GRID is Birkbeck College,\nUniversity of London, Department of Geography, London, UK.", "links": [ { diff --git a/datasets/42f7230ab55641cdac1bba84eabd446a_NA.json b/datasets/42f7230ab55641cdac1bba84eabd446a_NA.json index 4cdd5f86b5..796b8dfbf7 100644 --- a/datasets/42f7230ab55641cdac1bba84eabd446a_NA.json +++ b/datasets/42f7230ab55641cdac1bba84eabd446a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "42f7230ab55641cdac1bba84eabd446a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/43d73291472444e6b9c2d2420dbad7d6_NA.json b/datasets/43d73291472444e6b9c2d2420dbad7d6_NA.json index 3a4f66e665..b76b569708 100644 --- a/datasets/43d73291472444e6b9c2d2420dbad7d6_NA.json +++ b/datasets/43d73291472444e6b9c2d2420dbad7d6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "43d73291472444e6b9c2d2420dbad7d6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "links": [ { diff --git a/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json b/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json index 84621ee386..70fa819bc3 100644 --- a/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json +++ b/datasets/43f81a9f-f903-43d4-8333-dcda52b2bc63.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "43f81a9f-f903-43d4-8333-dcda52b2bc63", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes an estimate of the global risk induced by flood hazard.\n\nUnit is estimated risk index from 1 (low) to 5 (extreme).\n\nThis product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data.\n\nCredit: UNEP/GRID-Europe.", "links": [ { diff --git a/datasets/466b48b8-78c4-4009-97a2-c8d70f9075bf_NA.json b/datasets/466b48b8-78c4-4009-97a2-c8d70f9075bf_NA.json index b4cb77d729..eb393305f9 100644 --- a/datasets/466b48b8-78c4-4009-97a2-c8d70f9075bf_NA.json +++ b/datasets/466b48b8-78c4-4009-97a2-c8d70f9075bf_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "466b48b8-78c4-4009-97a2-c8d70f9075bf_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps.", "links": [ { diff --git a/datasets/46d136149d0a4f1cb8de7efbe8abf4b2_NA.json b/datasets/46d136149d0a4f1cb8de7efbe8abf4b2_NA.json index ae10acafb8..1d36f8a38d 100644 --- a/datasets/46d136149d0a4f1cb8de7efbe8abf4b2_NA.json +++ b/datasets/46d136149d0a4f1cb8de7efbe8abf4b2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "46d136149d0a4f1cb8de7efbe8abf4b2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document.", "links": [ { diff --git a/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json b/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json index 6883004d37..3ef2de8d72 100644 --- a/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json +++ b/datasets/47211801-72f3-4064-8c01-715cd2b7dc71_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "47211801-72f3-4064-8c01-715cd2b7dc71_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes.\n \nMatthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice.\n \nThe proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\"\n \nThe Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes.", "links": [ { diff --git a/datasets/474ac06235e54e6cb0ec6eed635e1213_NA.json b/datasets/474ac06235e54e6cb0ec6eed635e1213_NA.json index 7bfa4233de..ebaef7b90a 100644 --- a/datasets/474ac06235e54e6cb0ec6eed635e1213_NA.json +++ b/datasets/474ac06235e54e6cb0ec6eed635e1213_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "474ac06235e54e6cb0ec6eed635e1213_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "links": [ { diff --git a/datasets/48fc3d1e8ada405c8486ada522dae9e8_NA.json b/datasets/48fc3d1e8ada405c8486ada522dae9e8_NA.json index c3914212bd..169387fccd 100644 --- a/datasets/48fc3d1e8ada405c8486ada522dae9e8_NA.json +++ b/datasets/48fc3d1e8ada405c8486ada522dae9e8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "48fc3d1e8ada405c8486ada522dae9e8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "links": [ { diff --git a/datasets/4afb736dc395442aa9b327c11f0d704b_NA.json b/datasets/4afb736dc395442aa9b327c11f0d704b_NA.json index 7d33ab0344..e189938438 100644 --- a/datasets/4afb736dc395442aa9b327c11f0d704b_NA.json +++ b/datasets/4afb736dc395442aa9b327c11f0d704b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "4afb736dc395442aa9b327c11f0d704b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/4b0773a84e8142c688a628c9ce62d4ec_NA.json b/datasets/4b0773a84e8142c688a628c9ce62d4ec_NA.json index 439ee87673..53373df050 100644 --- a/datasets/4b0773a84e8142c688a628c9ce62d4ec_NA.json +++ b/datasets/4b0773a84e8142c688a628c9ce62d4ec_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "4b0773a84e8142c688a628c9ce62d4ec_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v1.1 (also available), which covers Sub-Saharan Africa for the year 2016, by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution.", "links": [ { diff --git a/datasets/4e106bb70a6b42d8a5a86c4635c855b9_NA.json b/datasets/4e106bb70a6b42d8a5a86c4635c855b9_NA.json index 97f3c30e59..5e7c3a9c08 100644 --- a/datasets/4e106bb70a6b42d8a5a86c4635c855b9_NA.json +++ b/datasets/4e106bb70a6b42d8a5a86c4635c855b9_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "4e106bb70a6b42d8a5a86c4635c855b9_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the MIPAS instrument on the ENVISAT satellite. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \"ESACCI-OZONE-L3-LP-MIPAS_ENVISAT-MZM-2008-fv0001.nc\u00e2\u0080\u009c contains monthly zonal mean data for MIPAS in 2008.", "links": [ { diff --git a/datasets/4eb4e801424a47f7b77434291921f889_NA.json b/datasets/4eb4e801424a47f7b77434291921f889_NA.json index ab1136938f..1082fbeed9 100644 --- a/datasets/4eb4e801424a47f7b77434291921f889_NA.json +++ b/datasets/4eb4e801424a47f7b77434291921f889_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "4eb4e801424a47f7b77434291921f889_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 3 nadir profile ozone data from the ESA Ozone Climate Change Initiative (CCI) project. The Level 3 data are monthly averages on a regular 3D grid derived from level 2 ozone profiles. In this version 2 of the dataset, data are available for 1997 and 2007 and 2008 only, and use data from the GOME instrument on ERS (1997) and the GOME-2 instrument on METOP-A (2007, 2008).", "links": [ { diff --git a/datasets/512c252f-34ac-41fd-a156-f2e96a608f79_NA.json b/datasets/512c252f-34ac-41fd-a156-f2e96a608f79_NA.json index 9bc80a22e7..3209d48531 100644 --- a/datasets/512c252f-34ac-41fd-a156-f2e96a608f79_NA.json +++ b/datasets/512c252f-34ac-41fd-a156-f2e96a608f79_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "512c252f-34ac-41fd-a156-f2e96a608f79_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring", "links": [ { diff --git a/datasets/53554204-282b-457e-b36d-a168679a0c1f_NA.json b/datasets/53554204-282b-457e-b36d-a168679a0c1f_NA.json index 08725faac2..c5499a28d8 100644 --- a/datasets/53554204-282b-457e-b36d-a168679a0c1f_NA.json +++ b/datasets/53554204-282b-457e-b36d-a168679a0c1f_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "53554204-282b-457e-b36d-a168679a0c1f_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in StripMap mode. StripMap imaging allows for a spatial resolution of up to 3 m at a scene size of 30 km (across swath) x 50-1650 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.\t\t\tFor more information concerning the TerraSAR-X mission, the reader is referred to:\t\t\thttps://www.dlr.de/content/de/missionen/terrasar-x.html", "links": [ { diff --git a/datasets/54e2ee0803764b4e84c906da3f16d81b_NA.json b/datasets/54e2ee0803764b4e84c906da3f16d81b_NA.json index 4df5d9a8da..c900f4bda5 100644 --- a/datasets/54e2ee0803764b4e84c906da3f16d81b_NA.json +++ b/datasets/54e2ee0803764b4e84c906da3f16d81b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "54e2ee0803764b4e84c906da3f16d81b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.", "links": [ { diff --git a/datasets/550d938da3184d0ca44a06a4c0c14ffa_NA.json b/datasets/550d938da3184d0ca44a06a4c0c14ffa_NA.json index d98aed762c..605a41c267 100644 --- a/datasets/550d938da3184d0ca44a06a4c0c14ffa_NA.json +++ b/datasets/550d938da3184d0ca44a06a4c0c14ffa_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "550d938da3184d0ca44a06a4c0c14ffa_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "links": [ { diff --git a/datasets/56f81895cb094bd8a1638aa12d6c7499_NA.json b/datasets/56f81895cb094bd8a1638aa12d6c7499_NA.json index 684db4a3ea..42c4406397 100644 --- a/datasets/56f81895cb094bd8a1638aa12d6c7499_NA.json +++ b/datasets/56f81895cb094bd8a1638aa12d6c7499_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "56f81895cb094bd8a1638aa12d6c7499_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4.The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).", "links": [ { diff --git a/datasets/57485ff5-8523-4251-9d01-2f497a31cc48_NA.json b/datasets/57485ff5-8523-4251-9d01-2f497a31cc48_NA.json index 8a5553a582..29e4d48c73 100644 --- a/datasets/57485ff5-8523-4251-9d01-2f497a31cc48_NA.json +++ b/datasets/57485ff5-8523-4251-9d01-2f497a31cc48_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "57485ff5-8523-4251-9d01-2f497a31cc48_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring", "links": [ { diff --git a/datasets/57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA.json b/datasets/57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA.json index 1acacb2b04..01cf9223d0 100644 --- a/datasets/57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA.json +++ b/datasets/57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2", "links": [ { diff --git a/datasets/58f00d8814064b79a0c49662ad3af537_NA.json b/datasets/58f00d8814064b79a0c49662ad3af537_NA.json index 833573a558..4f74bafba5 100644 --- a/datasets/58f00d8814064b79a0c49662ad3af537_NA.json +++ b/datasets/58f00d8814064b79a0c49662ad3af537_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "58f00d8814064b79a0c49662ad3af537_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2020. The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carr\u00c3\u00a9e projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation.", "links": [ { diff --git a/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json b/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json index 95e743057d..c91a1fe205 100644 --- a/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json +++ b/datasets/5940d3fb-860d-4f3e-bc3a-4022639c272a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5940d3fb-860d-4f3e-bc3a-4022639c272a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes.\n \nMatthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice.\n \nThe proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\"\n \nThe Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes.\n", "links": [ { diff --git a/datasets/5970b33c92ef444793fb6d7e54d1230e_NA.json b/datasets/5970b33c92ef444793fb6d7e54d1230e_NA.json index e577aedb21..8e8c318257 100644 --- a/datasets/5970b33c92ef444793fb6d7e54d1230e_NA.json +++ b/datasets/5970b33c92ef444793fb6d7e54d1230e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5970b33c92ef444793fb6d7e54d1230e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065", "links": [ { diff --git a/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json b/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json index 5f83b48fee..f7297345a8 100644 --- a/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json +++ b/datasets/598f86dc-01da-49e3-824e-c8b8f1089a0e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "598f86dc-01da-49e3-824e-c8b8f1089a0e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes.\n \nMatthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice.\n \nThe proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\"\n \nThe Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes.\n", "links": [ { diff --git a/datasets/5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA.json b/datasets/5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA.json index 8d3d4df763..ee1b392ddd 100644 --- a/datasets/5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA.json +++ b/datasets/5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations.", "links": [ { diff --git a/datasets/5a168a35-8cd2-4960-a134-2f319bb06760_NA.json b/datasets/5a168a35-8cd2-4960-a134-2f319bb06760_NA.json index 423bda7d19..9f0ca7d19f 100644 --- a/datasets/5a168a35-8cd2-4960-a134-2f319bb06760_NA.json +++ b/datasets/5a168a35-8cd2-4960-a134-2f319bb06760_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5a168a35-8cd2-4960-a134-2f319bb06760_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/5b6033bfb7f241e89132a83fdc3d5364_NA.json b/datasets/5b6033bfb7f241e89132a83fdc3d5364_NA.json index 6be9c78464..4dddbd438b 100644 --- a/datasets/5b6033bfb7f241e89132a83fdc3d5364_NA.json +++ b/datasets/5b6033bfb7f241e89132a83fdc3d5364_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5b6033bfb7f241e89132a83fdc3d5364_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.", "links": [ { diff --git a/datasets/5c9935b8b8854baeb7a256446293c03b_NA.json b/datasets/5c9935b8b8854baeb7a256446293c03b_NA.json index 4df121d9fd..35c6106a76 100644 --- a/datasets/5c9935b8b8854baeb7a256446293c03b_NA.json +++ b/datasets/5c9935b8b8854baeb7a256446293c03b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5c9935b8b8854baeb7a256446293c03b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Hagen glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/5db2099606b94e63879d841c87e654ae_NA.json b/datasets/5db2099606b94e63879d841c87e654ae_NA.json index 8bd9325dfc..23e01285b7 100644 --- a/datasets/5db2099606b94e63879d841c87e654ae_NA.json +++ b/datasets/5db2099606b94e63879d841c87e654ae_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5db2099606b94e63879d841c87e654ae_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/5e52c881-6209-438a-a3e3-309fb4d303f6_NA.json b/datasets/5e52c881-6209-438a-a3e3-309fb4d303f6_NA.json index e207bb9f41..cad8092a7f 100644 --- a/datasets/5e52c881-6209-438a-a3e3-309fb4d303f6_NA.json +++ b/datasets/5e52c881-6209-438a-a3e3-309fb4d303f6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5e52c881-6209-438a-a3e3-309fb4d303f6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks.", "links": [ { diff --git a/datasets/5e557c5e-a31d-41fb-a60b-98714d0aff86_NA.json b/datasets/5e557c5e-a31d-41fb-a60b-98714d0aff86_NA.json index 3f742bade1..e638285277 100644 --- a/datasets/5e557c5e-a31d-41fb-a60b-98714d0aff86_NA.json +++ b/datasets/5e557c5e-a31d-41fb-a60b-98714d0aff86_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5e557c5e-a31d-41fb-a60b-98714d0aff86_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks.", "links": [ { diff --git a/datasets/5f331c418e9f4935b8eb1b836f8a91b8_NA.json b/datasets/5f331c418e9f4935b8eb1b836f8a91b8_NA.json index a5b0209fe4..5261d43f15 100644 --- a/datasets/5f331c418e9f4935b8eb1b836f8a91b8_NA.json +++ b/datasets/5f331c418e9f4935b8eb1b836f8a91b8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5f331c418e9f4935b8eb1b836f8a91b8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR instrument and JAXA\u00e2\u0080\u0099s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", "links": [ { diff --git a/datasets/5f66a881adf846bfaad58b0e6068f0ea_NA.json b/datasets/5f66a881adf846bfaad58b0e6068f0ea_NA.json index 334e0d1c68..1a4240e9a4 100644 --- a/datasets/5f66a881adf846bfaad58b0e6068f0ea_NA.json +++ b/datasets/5f66a881adf846bfaad58b0e6068f0ea_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5f66a881adf846bfaad58b0e6068f0ea_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/5f75fcb0c58740d99b07953797bc041e_NA.json b/datasets/5f75fcb0c58740d99b07953797bc041e_NA.json index c49977e193..b0b9b9388c 100644 --- a/datasets/5f75fcb0c58740d99b07953797bc041e_NA.json +++ b/datasets/5f75fcb0c58740d99b07953797bc041e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "5f75fcb0c58740d99b07953797bc041e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 25km grid spacing is also available.", "links": [ { diff --git a/datasets/62866635ab074e07b93f17fbf87a2c1a_NA.json b/datasets/62866635ab074e07b93f17fbf87a2c1a_NA.json index 74a4083380..a93c4ef178 100644 --- a/datasets/62866635ab074e07b93f17fbf87a2c1a_NA.json +++ b/datasets/62866635ab074e07b93f17fbf87a2c1a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "62866635ab074e07b93f17fbf87a2c1a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide.", "links": [ { diff --git a/datasets/62c0f97b1eac4e0197a674870afe1ee6_NA.json b/datasets/62c0f97b1eac4e0197a674870afe1ee6_NA.json index 70634ccdbd..8169c7f9e3 100644 --- a/datasets/62c0f97b1eac4e0197a674870afe1ee6_NA.json +++ b/datasets/62c0f97b1eac4e0197a674870afe1ee6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "62c0f97b1eac4e0197a674870afe1ee6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.The CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/63a8f458-da8b-461a-afa4-7166d1cdc817_1.json b/datasets/63a8f458-da8b-461a-afa4-7166d1cdc817_1.json index a482c7398b..0d125acc64 100644 --- a/datasets/63a8f458-da8b-461a-afa4-7166d1cdc817_1.json +++ b/datasets/63a8f458-da8b-461a-afa4-7166d1cdc817_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "63a8f458-da8b-461a-afa4-7166d1cdc817_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IIASA Climate Database was created at the International Institute for Applied System Analyses (IIASA; Laxenburg, Austria) by Rik Leemans and Wolfgang P. Cramer to represent current global climate. There are three variables included in the Database: average monthly cloudiness, precipitation and temperature; and 12 monthly values per variable. These values were calculated from existing historical weather records of a diverse nature, but with the common feature that most cover at least five years during the period between 1930 and 1960. The weather records from up to eight different sources were standardized, ranked in quality, selected, interpolated and smoothed to fit a one-half degree (.5 ) latitude/longitude terrestrial grid surface (there are no values for non-land areas). The three variables have been treated in somewhat different fashion in their processing at GRID, as explained below.\n \nThe areas with the best data coverage are Europe, the USA, southern Canada, East Asia and Japan, while Africa and Australia have less complete coverage. High latitude, arid and mountainous zones exhibit the least coverage, especially Siberia, northern Canada, South America, China, Mongolia and the Tibetan Plateau. Despite certain data gaps and inconsistencies, the IIASA Climate Database is considered appropriate for use at least at regional scales and above, in various applications relating to agriculture, biogeography, ecology, geography and especially vegetation models. The full and proper reference to the Database is: Leemans, Rik and Wolfgang P. Cramer, 1991. The IIASA Database for Mean Monthly Values of Temperature, Preicipitation and Cloudiness of a Global Terrestrial Grid. IIASA, Laxenburg, Austria, RR-91-18, 62 pages. The original IIASA Climate Database is distributed by Leemans and Cramer in tabular form (a series of ascii files, with binary conversion program) on diskettes. There are three tables (one for cloudiness, precipitation and temperature variables) each having a long series of data records with 14 values as follows: longitude, latitude, 12 monthly values (January to December). GRID-Geneva has converted these tables into separate monthly data files with a standard image format. That is, for each of the three variables/12 months there exists a 360-row (line, record) by 720-column (element, pixel, sample) array of values which can be manipulated as an image. The original data values have been preserved by storing them in four-byte real (floating point) or two-byte integer arrays, where the geographic location (center point) of each pixel is known. GRID has also produced simplified one-byte image arrays for all three variables' data files, which are generalized versions for portrayal on most image display systems, rather than being suitable for analysis.\n \n----------------------------------------------------------------------\n \nCloudiness Data Set\n \nThe IIASA mean monthly cloudiness data set is based on fewer stations, and thus contains only about one-quarter the number of data records (approximately 1600) compared with the other two variables. It is often derived from estimated rather than computed data. Cloudiness is defined as the actual number of bright sunshine hours over the potential number, and is thus expressed as a percentage figure. The data set shows slight distortions which probably resulted from the interpolation routine. These are more pronounced with odd patterns in high-latitude zones, where fewer stations were available and more extrapolation was done.\n \nThe GRID version of this data set includes 12 monthly average cloudiness data files, each in one-byte (eight-bit) image format. The data arrays are all 360 rows (lines, records) by 720 columns (elements, pixels, or samples), and cover the entire globe from 90 degrees North latitude and 180 degrees West longitude, to 90 degrees South latitude and 180 degrees East longitude. The data values are within a range from 0 to 100 (per cent), except for the oceans where values equal 255. The data files are in the Plate Carree (Simple Cylindrical) projection, which is a particular form of the Equirectangular. This projection is described in a book entitled \"Map projections used by the U. S. Geological Survey, Geological Survey Bulletin 1532 (second ed.), U. S. Government Printing Office, Washington D.C., 1982\" p. 89, or by request directly from GRID.\n", "links": [ { diff --git a/datasets/65abcfdc-306a-47f6-9696-f1f6c6171def.json b/datasets/65abcfdc-306a-47f6-9696-f1f6c6171def.json index 7ef3b1a226..5e91ca3206 100644 --- a/datasets/65abcfdc-306a-47f6-9696-f1f6c6171def.json +++ b/datasets/65abcfdc-306a-47f6-9696-f1f6c6171def.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "65abcfdc-306a-47f6-9696-f1f6c6171def", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map (risk map) presents the results of cyclonic wind probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.\n", "links": [ { diff --git a/datasets/67a3f8c8dc914ef99f7f08eb0d997e23_NA.json b/datasets/67a3f8c8dc914ef99f7f08eb0d997e23_NA.json index 6c9d862774..463b339227 100644 --- a/datasets/67a3f8c8dc914ef99f7f08eb0d997e23_NA.json +++ b/datasets/67a3f8c8dc914ef99f7f08eb0d997e23_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "67a3f8c8dc914ef99f7f08eb0d997e23_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.Case A: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/690fdf8f229c4d04a2aa68de67beb733_NA.json b/datasets/690fdf8f229c4d04a2aa68de67beb733_NA.json index 2f03e8c358..9ecc9b81ca 100644 --- a/datasets/690fdf8f229c4d04a2aa68de67beb733_NA.json +++ b/datasets/690fdf8f229c4d04a2aa68de67beb733_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "690fdf8f229c4d04a2aa68de67beb733_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.", "links": [ { diff --git a/datasets/6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA.json b/datasets/6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA.json index 0ab5e25069..454f541799 100644 --- a/datasets/6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA.json +++ b/datasets/6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/6e2091cb0c8b4106921b63cd5357c97c_NA.json b/datasets/6e2091cb0c8b4106921b63cd5357c97c_NA.json index ae8cca85f0..cdf167c7b1 100644 --- a/datasets/6e2091cb0c8b4106921b63cd5357c97c_NA.json +++ b/datasets/6e2091cb0c8b4106921b63cd5357c97c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "6e2091cb0c8b4106921b63cd5357c97c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).Case A: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/723067f77b8b43609079d721e3b4a3c7_NA.json b/datasets/723067f77b8b43609079d721e3b4a3c7_NA.json index 729853e0e9..19d5363b87 100644 --- a/datasets/723067f77b8b43609079d721e3b4a3c7_NA.json +++ b/datasets/723067f77b8b43609079d721e3b4a3c7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "723067f77b8b43609079d721e3b4a3c7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Kangerlussuaq glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data aquired between 02/01/1992 and 17/12/2008. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 3 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/7813eb75a131474a8d908f69c716b031_NA.json b/datasets/7813eb75a131474a8d908f69c716b031_NA.json index 4fca3905e4..27aa2a3f0d 100644 --- a/datasets/7813eb75a131474a8d908f69c716b031_NA.json +++ b/datasets/7813eb75a131474a8d908f69c716b031_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7813eb75a131474a8d908f69c716b031_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "links": [ { diff --git a/datasets/7ae5a791-b667-4838-9733-a44e4cf2d715_NA.json b/datasets/7ae5a791-b667-4838-9733-a44e4cf2d715_NA.json index 3c09b26b62..5c66498623 100644 --- a/datasets/7ae5a791-b667-4838-9733-a44e4cf2d715_NA.json +++ b/datasets/7ae5a791-b667-4838-9733-a44e4cf2d715_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7ae5a791-b667-4838-9733-a44e4cf2d715_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing.", "links": [ { diff --git a/datasets/7bcddca7-f59e-4298-978a-37dd39ac6dba_NA.json b/datasets/7bcddca7-f59e-4298-978a-37dd39ac6dba_NA.json index a3e8683735..5d3a97baba 100644 --- a/datasets/7bcddca7-f59e-4298-978a-37dd39ac6dba_NA.json +++ b/datasets/7bcddca7-f59e-4298-978a-37dd39ac6dba_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7bcddca7-f59e-4298-978a-37dd39ac6dba_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the product coverage size of 800 km x 800 km make WiFS products a valuable source for application fields such as flood and snow melt monitoring.", "links": [ { diff --git a/datasets/7db4459605da4665b6ab9a7102fb4875_NA.json b/datasets/7db4459605da4665b6ab9a7102fb4875_NA.json index f50f6654ae..e5357cc25e 100644 --- a/datasets/7db4459605da4665b6ab9a7102fb4875_NA.json +++ b/datasets/7db4459605da4665b6ab9a7102fb4875_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7db4459605da4665b6ab9a7102fb4875_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/7dd46ee62153409f8e1b2b7b251177c1_NA.json b/datasets/7dd46ee62153409f8e1b2b7b251177c1_NA.json index c781ac1fe0..3bbe150d85 100644 --- a/datasets/7dd46ee62153409f8e1b2b7b251177c1_NA.json +++ b/datasets/7dd46ee62153409f8e1b2b7b251177c1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7dd46ee62153409f8e1b2b7b251177c1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci MODIS-Aqua dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Aqua) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Aqua dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "links": [ { diff --git a/datasets/7e139108035142a9a1ddd96abcdfff36_NA.json b/datasets/7e139108035142a9a1ddd96abcdfff36_NA.json index 83c290ad5a..e992a63bf8 100644 --- a/datasets/7e139108035142a9a1ddd96abcdfff36_NA.json +++ b/datasets/7e139108035142a9a1ddd96abcdfff36_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7e139108035142a9a1ddd96abcdfff36_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced. The CCI WB v4.0 is composed of two layers:1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.This product is delivered at 150 m as a stand-alone product but it is consistent with class \"Water Bodies\" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water2. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D\u00e2\u0080\u0099Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036", "links": [ { diff --git a/datasets/7f60b26b50c98fab019e9351b45ba946c7d04047.json b/datasets/7f60b26b50c98fab019e9351b45ba946c7d04047.json index e8fb252cbd..8d856baad8 100644 --- a/datasets/7f60b26b50c98fab019e9351b45ba946c7d04047.json +++ b/datasets/7f60b26b50c98fab019e9351b45ba946c7d04047.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7f60b26b50c98fab019e9351b45ba946c7d04047", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for June.", "links": [ { diff --git a/datasets/7fb8fd2761484b1eae4f7df4a3e65f75_NA.json b/datasets/7fb8fd2761484b1eae4f7df4a3e65f75_NA.json index 6710166374..3cf6d55e90 100644 --- a/datasets/7fb8fd2761484b1eae4f7df4a3e65f75_NA.json +++ b/datasets/7fb8fd2761484b1eae4f7df4a3e65f75_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7fb8fd2761484b1eae4f7df4a3e65f75_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 gridded stratospheric aerosol properties from the GOMOS instrument on the ENVISAT satellite. This version of the data is version 3.00, and has been derived using the AERGOM algorithm by BIRA (Belgian Institute for Space Aeronomy). For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/7fc9df8070d34cacab8092e45ef276f1_NA.json b/datasets/7fc9df8070d34cacab8092e45ef276f1_NA.json index fbe81d1a6d..5dc0874d63 100644 --- a/datasets/7fc9df8070d34cacab8092e45ef276f1_NA.json +++ b/datasets/7fc9df8070d34cacab8092e45ef276f1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "7fc9df8070d34cacab8092e45ef276f1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.1.0 of the dataset.The six thematic climate variables included in this dataset are:\u00e2\u0080\u00a2 Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\u00e2\u0080\u00a2 Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\u00e2\u0080\u00a2 Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\u00e2\u0080\u00a2 Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\u00e2\u0080\u00a2 Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\u00e2\u0080\u00a2 Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.Satellite sensors associated with the thematic climate variables are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Cr\u00c3\u00a9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z", "links": [ { diff --git a/datasets/802569b8-fb56-4d78-a2e8-3e4549ff475b_NA.json b/datasets/802569b8-fb56-4d78-a2e8-3e4549ff475b_NA.json index 37da2eb730..b877f08f04 100644 --- a/datasets/802569b8-fb56-4d78-a2e8-3e4549ff475b_NA.json +++ b/datasets/802569b8-fb56-4d78-a2e8-3e4549ff475b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "802569b8-fb56-4d78-a2e8-3e4549ff475b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the \u201cbar coded\u201d images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/", "links": [ { diff --git a/datasets/810631f4-c311-44f2-9ced-c2260df2bc06_NA.json b/datasets/810631f4-c311-44f2-9ced-c2260df2bc06_NA.json index 064827077c..ebbfddf0d1 100644 --- a/datasets/810631f4-c311-44f2-9ced-c2260df2bc06_NA.json +++ b/datasets/810631f4-c311-44f2-9ced-c2260df2bc06_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "810631f4-c311-44f2-9ced-c2260df2bc06_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\tOCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/81332b9a10f14bda8a1a83b6463bb6de_NA.json b/datasets/81332b9a10f14bda8a1a83b6463bb6de_NA.json index aab1c6e31d..6c74bb4497 100644 --- a/datasets/81332b9a10f14bda8a1a83b6463bb6de_NA.json +++ b/datasets/81332b9a10f14bda8a1a83b6463bb6de_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "81332b9a10f14bda8a1a83b6463bb6de_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 22/1/2015-19/3/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/8175ede3a1d642deba8f4cce49d7bda8_NA.json b/datasets/8175ede3a1d642deba8f4cce49d7bda8_NA.json index 7cccf1f5e8..90a4d67925 100644 --- a/datasets/8175ede3a1d642deba8f4cce49d7bda8_NA.json +++ b/datasets/8175ede3a1d642deba8f4cce49d7bda8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8175ede3a1d642deba8f4cce49d7bda8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \u00c2\u00b5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669\u00e2\u0080\u0093694, https://doi.org/10.5194/amt-16-669-2023, 2023.", "links": [ { diff --git a/datasets/82e4ede59fe746ba810009d9a30e0153_NA.json b/datasets/82e4ede59fe746ba810009d9a30e0153_NA.json index debb67ce3f..d648636e00 100644 --- a/datasets/82e4ede59fe746ba810009d9a30e0153_NA.json +++ b/datasets/82e4ede59fe746ba810009d9a30e0153_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "82e4ede59fe746ba810009d9a30e0153_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2014-2015, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.", "links": [ { diff --git a/datasets/8381d3f3998143fd9b53c7086b7061e3_NA.json b/datasets/8381d3f3998143fd9b53c7086b7061e3_NA.json index cc83d370e0..6ba04322ec 100644 --- a/datasets/8381d3f3998143fd9b53c7086b7061e3_NA.json +++ b/datasets/8381d3f3998143fd9b53c7086b7061e3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8381d3f3998143fd9b53c7086b7061e3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Storstrommen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 06/10/1991 and 20/03/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/84403d09cef3485883158f4df2989b0c_NA.json b/datasets/84403d09cef3485883158f4df2989b0c_NA.json index 3ffe4488a1..ec8544952e 100644 --- a/datasets/84403d09cef3485883158f4df2989b0c_NA.json +++ b/datasets/84403d09cef3485883158f4df2989b0c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "84403d09cef3485883158f4df2989b0c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR instrument and JAXA\u00e2\u0080\u0099s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes.", "links": [ { diff --git a/datasets/84b5cf8380894d719b61deac5abf3bae_NA.json b/datasets/84b5cf8380894d719b61deac5abf3bae_NA.json index 85817d32b8..d52adbf5a1 100644 --- a/datasets/84b5cf8380894d719b61deac5abf3bae_NA.json +++ b/datasets/84b5cf8380894d719b61deac5abf3bae_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "84b5cf8380894d719b61deac5abf3bae_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Greenland margin from the PALSAR instrument on the ALOS satellite. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. This dataset consists of a time series of ice velocity with yearly sampling, derived from intensity tracking of PALSAR data acquired between 20-12-2016 and 17-03-2011. It provides components of the ice velocity and the magnitude of the velocity. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by GEUS. For further details, please consult the Product User Guide (v2.0)Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product.", "links": [ { diff --git a/datasets/84faf575c8e841a3a16476b05cbd657d_NA.json b/datasets/84faf575c8e841a3a16476b05cbd657d_NA.json index ae67a9e3dd..b59c34b18b 100644 --- a/datasets/84faf575c8e841a3a16476b05cbd657d_NA.json +++ b/datasets/84faf575c8e841a3a16476b05cbd657d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "84faf575c8e841a3a16476b05cbd657d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Upernavik Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-15 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The product was generated by S[&]T Norway.", "links": [ { diff --git a/datasets/86d360431f3b4184b89cdd1cd707bb33_NA.json b/datasets/86d360431f3b4184b89cdd1cd707bb33_NA.json index b543471174..16b3bdee3f 100644 --- a/datasets/86d360431f3b4184b89cdd1cd707bb33_NA.json +++ b/datasets/86d360431f3b4184b89cdd1cd707bb33_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "86d360431f3b4184b89cdd1cd707bb33_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "links": [ { diff --git a/datasets/8889dfe3de45406e815bce13ae8a0c92_NA.json b/datasets/8889dfe3de45406e815bce13ae8a0c92_NA.json index 24508c6231..273a994942 100644 --- a/datasets/8889dfe3de45406e815bce13ae8a0c92_NA.json +++ b/datasets/8889dfe3de45406e815bce13ae8a0c92_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8889dfe3de45406e815bce13ae8a0c92_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet, produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. The calving front location has been derived by manual delineation using SAR (Synthetic Aperture Radar) data from the ERS-1/2, Envisat and Sentinel-1 satellites and satellite imagery from LANDSAT 5,7,8. The digitized calving fronts are stored in ESRI vector shape-file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The product was generated by ENVEO (Environmental Earth Observation Information Technology GmbH)", "links": [ { diff --git a/datasets/88d02eb5a6c14952aa88028894d8a69c_NA.json b/datasets/88d02eb5a6c14952aa88028894d8a69c_NA.json index 723a5a08b6..1c15c671e9 100644 --- a/datasets/88d02eb5a6c14952aa88028894d8a69c_NA.json +++ b/datasets/88d02eb5a6c14952aa88028894d8a69c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "88d02eb5a6c14952aa88028894d8a69c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains an optical ice velocity time series for the D\u00c3\u00b8cker Smith Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2016-05-08 and 2016-05-18. It is part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway.", "links": [ { diff --git a/datasets/8a587870-2ad7-4626-9228-4caad2fc9246_NA.json b/datasets/8a587870-2ad7-4626-9228-4caad2fc9246_NA.json index 3a55be7838..f871208400 100644 --- a/datasets/8a587870-2ad7-4626-9228-4caad2fc9246_NA.json +++ b/datasets/8a587870-2ad7-4626-9228-4caad2fc9246_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8a587870-2ad7-4626-9228-4caad2fc9246_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/8b63d36f6f1e4efa8aea302b924bc46b_NA.json b/datasets/8b63d36f6f1e4efa8aea302b924bc46b_NA.json index 30aaae7997..58627895eb 100644 --- a/datasets/8b63d36f6f1e4efa8aea302b924bc46b_NA.json +++ b/datasets/8b63d36f6f1e4efa8aea302b924bc46b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8b63d36f6f1e4efa8aea302b924bc46b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/8b9d461f245b4efd8ea9fa080366e3b1_NA.json b/datasets/8b9d461f245b4efd8ea9fa080366e3b1_NA.json index 021b113240..2e0f52f3be 100644 --- a/datasets/8b9d461f245b4efd8ea9fa080366e3b1_NA.json +++ b/datasets/8b9d461f245b4efd8ea9fa080366e3b1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8b9d461f245b4efd8ea9fa080366e3b1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "links": [ { diff --git a/datasets/8d475d7d92894765ad1ddda16de0e610_NA.json b/datasets/8d475d7d92894765ad1ddda16de0e610_NA.json index 8d3861751e..d992967e92 100644 --- a/datasets/8d475d7d92894765ad1ddda16de0e610_NA.json +++ b/datasets/8d475d7d92894765ad1ddda16de0e610_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8d475d7d92894765ad1ddda16de0e610_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "links": [ { diff --git a/datasets/8ecae26f390b4938b67a97cbce3ecd8b_NA.json b/datasets/8ecae26f390b4938b67a97cbce3ecd8b_NA.json index 3999e83375..27afaa5a33 100644 --- a/datasets/8ecae26f390b4938b67a97cbce3ecd8b_NA.json +++ b/datasets/8ecae26f390b4938b67a97cbce3ecd8b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8ecae26f390b4938b67a97cbce3ecd8b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", "links": [ { diff --git a/datasets/8f5623a85d2e4b9b8ab5313f65a7c994_NA.json b/datasets/8f5623a85d2e4b9b8ab5313f65a7c994_NA.json index 900b4f1d75..6e5a0924cc 100644 --- a/datasets/8f5623a85d2e4b9b8ab5313f65a7c994_NA.json +++ b/datasets/8f5623a85d2e4b9b8ab5313f65a7c994_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "8f5623a85d2e4b9b8ab5313f65a7c994_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4.The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.", "links": [ { diff --git a/datasets/90049a6555d1480bb5ce9637051dede8_NA.json b/datasets/90049a6555d1480bb5ce9637051dede8_NA.json index 40ce76b622..dbd6fccf21 100644 --- a/datasets/90049a6555d1480bb5ce9637051dede8_NA.json +++ b/datasets/90049a6555d1480bb5ce9637051dede8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "90049a6555d1480bb5ce9637051dede8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called \u00e2\u0080\u0098retracking\u00e2\u0080\u0099) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856). The dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Leg\u00c3\u00a9r Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Ni\u00c3\u00b1o Fernando, Legeais Jean Fran\u00c3\u00a7ois, Oelsmann Julius, Benveniste J\u00c3\u00a9r\u00c3\u00b4me (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean Fran\u00c3\u00a7ois, Oelsmann Julius, Restano Marco, Benveniste J\u00c3\u00a9r\u00c3\u00b4me (2022). Sea level along the world\u00e2\u0080\u0099s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z - Benveniste J\u00c3\u00a9r\u00c3\u00b4me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean Fran\u00c3\u00a7ois, L\u00c3\u00a9ger Fabien, Ni\u00c3\u00b1o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002\u00e2\u0080\u00932018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w", "links": [ { diff --git a/datasets/90682bac7d0e4e418085f30eba43dfba_NA.json b/datasets/90682bac7d0e4e418085f30eba43dfba_NA.json index 7eb71e2292..3f49c30f52 100644 --- a/datasets/90682bac7d0e4e418085f30eba43dfba_NA.json +++ b/datasets/90682bac7d0e4e418085f30eba43dfba_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "90682bac7d0e4e418085f30eba43dfba_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "links": [ { diff --git a/datasets/916b93aaf1474ce793171a33ca4c5026_NA.json b/datasets/916b93aaf1474ce793171a33ca4c5026_NA.json index 428b0ec425..1f2817ee54 100644 --- a/datasets/916b93aaf1474ce793171a33ca4c5026_NA.json +++ b/datasets/916b93aaf1474ce793171a33ca4c5026_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "916b93aaf1474ce793171a33ca4c5026_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SST's to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "links": [ { diff --git a/datasets/9255faeb392f41debf5402caa40dada8_NA.json b/datasets/9255faeb392f41debf5402caa40dada8_NA.json index fc4a657dd0..1fb8974719 100644 --- a/datasets/9255faeb392f41debf5402caa40dada8_NA.json +++ b/datasets/9255faeb392f41debf5402caa40dada8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9255faeb392f41debf5402caa40dada8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).", "links": [ { diff --git a/datasets/925e3f0e807243e2936cc492f5207af6_NA.json b/datasets/925e3f0e807243e2936cc492f5207af6_NA.json index 14e23af5ab..6c33571c9a 100644 --- a/datasets/925e3f0e807243e2936cc492f5207af6_NA.json +++ b/datasets/925e3f0e807243e2936cc492f5207af6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "925e3f0e807243e2936cc492f5207af6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/93444bc1c4364a59869e004bf9bfd94a_NA.json b/datasets/93444bc1c4364a59869e004bf9bfd94a_NA.json index fa53f5f559..7df042db57 100644 --- a/datasets/93444bc1c4364a59869e004bf9bfd94a_NA.json +++ b/datasets/93444bc1c4364a59869e004bf9bfd94a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "93444bc1c4364a59869e004bf9bfd94a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/93587051-2f12-4d37-a97b-520af56144ce_NA.json b/datasets/93587051-2f12-4d37-a97b-520af56144ce_NA.json index bb4fe5a06e..ac36cfae82 100644 --- a/datasets/93587051-2f12-4d37-a97b-520af56144ce_NA.json +++ b/datasets/93587051-2f12-4d37-a97b-520af56144ce_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "93587051-2f12-4d37-a97b-520af56144ce_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)\" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz.The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice.For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-days maps.", "links": [ { diff --git a/datasets/936b319d-5253-425d-bd29-4b6ebce067ff_NA.json b/datasets/936b319d-5253-425d-bd29-4b6ebce067ff_NA.json index cbad748ed6..058d88ba87 100644 --- a/datasets/936b319d-5253-425d-bd29-4b6ebce067ff_NA.json +++ b/datasets/936b319d-5253-425d-bd29-4b6ebce067ff_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "936b319d-5253-425d-bd29-4b6ebce067ff_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)\" is a fixed grid map (in stereographic projection) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between \u201339.5\u00b0C and +87\u00b0C with a radiometric resolution of 0.5\u00b0C. A value of \u201340\u00b0C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/", "links": [ { diff --git a/datasets/94421633457375.json b/datasets/94421633457375.json index efaaf0338c..fe25cd5eb0 100644 --- a/datasets/94421633457375.json +++ b/datasets/94421633457375.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "94421633457375", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The British Antarctic Survey (BAS) began regional aeromagnetic surveys\nover the Antarctic Peninsula in 1973. The first four seasons up to\n1980, together with supplementary data from subsequent seasons,\nprovided 36 000 line km of data \" north of 72 degrees S. The survey\nwas extended southwards over southern Palmer Land and Ellsworth Land\nduring 1986.\n\nSince 1980, activity has been concentrated farther south. In 1983,\ndata were collected over the Ronne Ice Shelf as part of the BAS\nWeddell Province Project to investigate the relationship between East\nand West Antarctica. Two seasons have been completed with US\nlogistical support during the joint BAS-United States Antarctic\nResearch Programme (USARP) project investigating the structure and\ntectonic history of the area. As part of this work, data were\ncollected from the area of the Ellsworth and Thiel mountains during\n1984. Ellsworth Land, the Ellsworth Mountains and Bryan coast were\ncovered during the final survey in 1987. Metadata records for each\nsurvey are available by following the Related_URL link to the BAS data\ncatalogue.", "links": [ { diff --git a/datasets/94447955166780.json b/datasets/94447955166780.json index b270cd3a0a..6b41d68159 100644 --- a/datasets/94447955166780.json +++ b/datasets/94447955166780.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "94447955166780", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The acquistion in 1973 of an aeromagnetic system enabled the British\nAntarctic Survey (BAS) to initiate a systematic geophysical survey.\nIn addition to a regional survey, areas of specific local geological\ninterest were surveyed in greater detail. The first local datasets\nwere collected during the 1970s and 1980s from four locations:\nHorseshoe Island, Graham Land; Neny Fjord, Graham Land; Staccato\nPeaks, Alexander Island; Beethoven Peninsula, Alexander Island.\n\nSubsequent surveys have expanded on this and metadata records for each\nsurvey are available by following the Related_URL link to the BAS data\ncatalogue. These data have all been incorporated into the Antarctic\nDigital Magnetic Anomaly Project (ADMAP).", "links": [ { diff --git a/datasets/94f3670150de4bac90773806e26646f2_NA.json b/datasets/94f3670150de4bac90773806e26646f2_NA.json index 6e152d11d1..b54d814e15 100644 --- a/datasets/94f3670150de4bac90773806e26646f2_NA.json +++ b/datasets/94f3670150de4bac90773806e26646f2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "94f3670150de4bac90773806e26646f2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Petermann Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-09-14. It has been produced as part of the ESA Greenland Ice sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "links": [ { diff --git a/datasets/96159374900008.json b/datasets/96159374900008.json index 8e083f553f..ce78ae46ec 100644 --- a/datasets/96159374900008.json +++ b/datasets/96159374900008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "96159374900008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The British Antarctic Survey has deployed data loggers at a number of\nlocations on Alexander Island, to collect microclimate\n(micrometerological) data.\n\nVarious types of logger are used, recording a number of parameters,\nincluding, temperature, relative humidity and wind speed. Sensors\ntend to be deployed at or near ground level and in and around\nparticular types of vegetation, or other experimental sites, such a\ncloches.", "links": [ { diff --git a/datasets/96159393396972.json b/datasets/96159393396972.json index e81f153f78..92cc2139a6 100644 --- a/datasets/96159393396972.json +++ b/datasets/96159393396972.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "96159393396972", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The British Antarctic Survey has deployed data loggers at a number of\nlocations on Adelaide Island, to collect microclimate\n(micrometerological) data.\n\nVarious types of logger are used, recording a number of parameters,\nincluding, temperature, relative humidity and wind speed. Sensors\ntend to be deployed at or near ground level and in and around\nparticular types of vegetation, or other experimental sites, such a\ncloches.", "links": [ { diff --git a/datasets/96d5b75ea29946c5aab8214ddbab252b_NA.json b/datasets/96d5b75ea29946c5aab8214ddbab252b_NA.json index 95fb1c6956..18fb57e8e7 100644 --- a/datasets/96d5b75ea29946c5aab8214ddbab252b_NA.json +++ b/datasets/96d5b75ea29946c5aab8214ddbab252b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "96d5b75ea29946c5aab8214ddbab252b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "links": [ { diff --git a/datasets/971dc69b-a7a8-406e-a5bc-fad76b51156f.json b/datasets/971dc69b-a7a8-406e-a5bc-fad76b51156f.json index bc0e83852d..b04c813714 100644 --- a/datasets/971dc69b-a7a8-406e-a5bc-fad76b51156f.json +++ b/datasets/971dc69b-a7a8-406e-a5bc-fad76b51156f.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "971dc69b-a7a8-406e-a5bc-fad76b51156f", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 500 years return period. It was generated by integration of the intensity values contained in the files \"Wind_Atlantic.AME\", \"Wind_EastPacific.AME\", \"Wind_NorthIndian.AME\", \"Wind_SudIndian.AME\", \"Wind_SudPacific.AME\" and \"Wind_WestPacific.AME\".\n", "links": [ { diff --git a/datasets/9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA.json b/datasets/9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA.json index c86a694c55..e151a09a62 100644 --- a/datasets/9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA.json +++ b/datasets/9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/97ea298b-c382-46ef-9e36-926dead6a19d_NA.json b/datasets/97ea298b-c382-46ef-9e36-926dead6a19d_NA.json index 1675ba942d..e0e70fb962 100644 --- a/datasets/97ea298b-c382-46ef-9e36-926dead6a19d_NA.json +++ b/datasets/97ea298b-c382-46ef-9e36-926dead6a19d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "97ea298b-c382-46ef-9e36-926dead6a19d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational NO2 tropospheric column products are generated using the algorithm GDP (GOME Data Processor) version 4.x for NO2 [Valks et al. (2011)] integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region using the DOAS method. An additional algorithm is applied to derive the tropospheric NO2 column: after subtracting the estimated stratospheric component from the total column, the tropospheric NO2 column is determined using an air mass factor based on monthly climatological NO2 profiles from the MOZART-2 model. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/99b2c1d2-f2fd-4133-848a-8de849b958f7.json b/datasets/99b2c1d2-f2fd-4133-848a-8de849b958f7.json index d56a9af5cd..1b5998365e 100644 --- a/datasets/99b2c1d2-f2fd-4133-848a-8de849b958f7.json +++ b/datasets/99b2c1d2-f2fd-4133-848a-8de849b958f7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "99b2c1d2-f2fd-4133-848a-8de849b958f7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 1000 years return period. It was generated by integration of the intensity values contained in the files \"Wind_Atlantic.AME\", \"Wind_EastPacific.AME\", \"Wind_NorthIndian.AME\", \"Wind_SudIndian.AME\", \"Wind_SudPacific.AME\" and \"Wind_WestPacific.AME\".", "links": [ { diff --git a/datasets/9bdeb99d91a743fe84623264587ad043_NA.json b/datasets/9bdeb99d91a743fe84623264587ad043_NA.json index 3b0c2986b3..fe035c99e1 100644 --- a/datasets/9bdeb99d91a743fe84623264587ad043_NA.json +++ b/datasets/9bdeb99d91a743fe84623264587ad043_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9bdeb99d91a743fe84623264587ad043_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2013-2014, derived from RADARSAT-2 data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The ice velocity data were derived from intensity-tracking of RADARSAT-2 data aquired between 21/1/2014 and 02/04/2014. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the Eastings and Northings direction of the grid; the vertical displacement, derived from a digital elevation model, is also provided. Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. This product was generated by DTU Space - Microwaves and Remote Sensing.", "links": [ { diff --git a/datasets/9ed2813d2eda4d958e92ab3ce1ab1fe6_NA.json b/datasets/9ed2813d2eda4d958e92ab3ce1ab1fe6_NA.json index 40bf027e69..4f30dc7c57 100644 --- a/datasets/9ed2813d2eda4d958e92ab3ce1ab1fe6_NA.json +++ b/datasets/9ed2813d2eda4d958e92ab3ce1ab1fe6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9ed2813d2eda4d958e92ab3ce1ab1fe6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).", "links": [ { diff --git a/datasets/9f002827ba7d48f59019fcfd3577a57e_NA.json b/datasets/9f002827ba7d48f59019fcfd3577a57e_NA.json index 4bb00f5e68..4889b88ef4 100644 --- a/datasets/9f002827ba7d48f59019fcfd3577a57e_NA.json +++ b/datasets/9f002827ba7d48f59019fcfd3577a57e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9f002827ba7d48f59019fcfd3577a57e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4.The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).", "links": [ { diff --git a/datasets/9f6324ebe92940b989ebf273d5f8bf33_NA.json b/datasets/9f6324ebe92940b989ebf273d5f8bf33_NA.json index c994df5c5a..fbb40cc174 100644 --- a/datasets/9f6324ebe92940b989ebf273d5f8bf33_NA.json +++ b/datasets/9f6324ebe92940b989ebf273d5f8bf33_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "9f6324ebe92940b989ebf273d5f8bf33_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on ENVISAT, derived using the ADV algorithm, version 2.31. Data is available for the period 2002-2012.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/A Fusion Dataset for Crop Type Classification in Germany_1.json b/datasets/A Fusion Dataset for Crop Type Classification in Germany_1.json index 32759a6db9..c5e6352f94 100644 --- a/datasets/A Fusion Dataset for Crop Type Classification in Germany_1.json +++ b/datasets/A Fusion Dataset for Crop Type Classification in Germany_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "A Fusion Dataset for Crop Type Classification in Germany_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nThis dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. \n\nInput imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. \n\nThe Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. \n", "links": [ { diff --git a/datasets/A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1.json b/datasets/A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1.json index 3c4a464bab..3609db3c55 100644 --- a/datasets/A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1.json +++ b/datasets/A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nThis dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition. \n\nInput imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. \n\nThe Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. \n\nThe Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data.\n", "links": [ { diff --git a/datasets/A crop type dataset for consistent land cover classification in Central Asia_1.json b/datasets/A crop type dataset for consistent land cover classification in Central Asia_1.json index e6eeae6497..4b2f0f360c 100644 --- a/datasets/A crop type dataset for consistent land cover classification in Central Asia_1.json +++ b/datasets/A crop type dataset for consistent land cover classification in Central Asia_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "A crop type dataset for consistent land cover classification in Central Asia_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data is missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compiles samples for 40 crop types and is dominated by \u201ccotton\u201d (40%) and \u201cwheat\u201d, (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns.", "links": [ { diff --git a/datasets/A6_Survey_1.json b/datasets/A6_Survey_1.json index f5c4574dd6..f7703fae2d 100644 --- a/datasets/A6_Survey_1.json +++ b/datasets/A6_Survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "A6_Survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Construction of a suitable runway near Casey was a major objective of the Air Link from Hobart to the Australian Antarctic Territory and the main focus of the 2003/03 season's fieldwork. The 2001/2002 summer season's Air Transport Study identified several 'Blue Ice' runway sites and undertook a detailed investigation at a site named R3 in the upper Peterson Glacier (UPG) region approximately 50 km south east of Casey Station.\n\nThis season a six-member team was assembled to undertake additional investigation work and initial site and runway surface construction capable of supporting wheeled aircraft conducting inter-continental flights from Hobart. The Air Transport Project team members included:\n\nGeorge Blaisdell - Civil Engineer \nAaron Read - Surveyor\nSeane Hall - Mechanic \nSimon Larkman - Communications Technician \nLeigh Maclagan - Plant Operator \nRob Sheers - Mechanical Engineer\n\nThe objective for this season\u2019s survey was to undertake further work on R3 and if it was not suited for constructing a runway surface, then locate and map a site that met the criteria for a blue ice runway. From Landsat 7 satellite imagery it was evident that larger and higher elevated patches of blue ice were visible south of the R3 and within these areas a potential runway site might be located. \n\nA suitable runway site was located about 65 km south east of Casey and this report documents the work undertaken in locating and mapping this site. \n\nAdditional to the runway survey a number of other tasks were also carried out in support of science programs being undertaken at Casey.\n\nThe report covers the survey field work undertaken by Air Transport Project (ATP) during the 2002/2003 ANARE Summer Field Season. Data collected in support of other scientific programs has been included in this report primarily as a record of work undertaken by the mapping program. These data have been supplied to the various scientists for inclusion in their studies.", "links": [ { diff --git a/datasets/AADC-00009_1.json b/datasets/AADC-00009_1.json index 5d5409ad66..7efb42050c 100644 --- a/datasets/AADC-00009_1.json +++ b/datasets/AADC-00009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract from ANARE Research Notes 72 The Antarctic fur seal Arctocephalus gazella has increased in numbers at Heard Island since the Australian National Antarctic Research Expeditions (ANARE) station was established in 1947. Increases have also been recorded at other breeding sites in the South Atlantic and South Indian Oceans this century, particularly at South Georgia.\n\nIn the 1987-88 summer, fur seals at Heard Island were counted in several age and sex categories. The aims of the project were to determine the location of pupping sites, the extent of the pupping season and the size of the population, and to record the changes in numbers of animals ashore during the summer. Maps of the colonies and main haul-out areas, together with descriptions of census areas and tabulations of counts, provide a basis for future comparison.\n\nThis dataset contains the results from surveys of Antarctic Fur Seals (Arctocephalus gazella) on Heard Island during the summer of 1987-1988. As well as habitat descriptions, age, sex, count of adults and pups were determined. The three major aims of the study include: to determine accurately the location of pupping sites; to determine the extent of the pupping season, the median date of birth and the number of pups born; and to census fur seals on as much of the island as possible in order to determine the number of animals ashore and to document changes in numbers during the summer. The results are listed in the document, which includes detailed tabulations of counts made at colonies and major haul-out sites on Macquarie Island during summer 1987-88, and descriptions and maps of these locations. Tagging, mainly of pups, was also undertaken, and a total of 234 pups, 8 under-yearlings, 9 yearlings, 2 juveniles and 1 sub-adult male were tagged. Counts at 3-day intervals (pups) were made between 25 November and 19 December 1987, and major censuses were made between 19 December 1987 and 25 February 1988.\n\nThe fields in this dataset are:\nLocality\nAge Class\nDate\nColony\nBulls\nCows\nPups", "links": [ { diff --git a/datasets/AADC-00016_1.json b/datasets/AADC-00016_1.json index c45bb1dfa7..9984b3188b 100644 --- a/datasets/AADC-00016_1.json +++ b/datasets/AADC-00016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes a bibliography compiled by Dr Donald S. Horning of the Tumblegum Research Laboratory about Macquarie Island. The download file contains three word documents, all of which contain separate bibliographies (there is no duplication of entries between the word documents). One of the word documents also contains either abstracts, extracts from the referenced paper, or personal summaries by Dr Horning about each reference in the bibliography.", "links": [ { diff --git a/datasets/AADC-00017_1.json b/datasets/AADC-00017_1.json index 63f28ca71a..79f458ac05 100644 --- a/datasets/AADC-00017_1.json +++ b/datasets/AADC-00017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a guide to the Euphausiacea of the Southern Ocean, in particular Euphausia superba Dana (Antarctic krill). It lists all the known species and with illustrated diagrams provides a guide to their taxonomic identification.\n\nThe document is available for download as a pdf from the URL given below.", "links": [ { diff --git a/datasets/AADC-00018_1.json b/datasets/AADC-00018_1.json index eac9a2751d..c7491e0afb 100644 --- a/datasets/AADC-00018_1.json +++ b/datasets/AADC-00018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a document describing the Decapoda of the Southern Ocean. It lists all the known species and with illustrated diagrams provides a guide to their taxonomic identification.\n\nThe document is available for download as a pdf from the provided URL.", "links": [ { diff --git a/datasets/AADC-00019_1.json b/datasets/AADC-00019_1.json index bec080adc6..4612730212 100644 --- a/datasets/AADC-00019_1.json +++ b/datasets/AADC-00019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a document describing the Chaetognaths of the Southern Ocean. The synonymy, diagnostic characters, geographical and bathymetric distribution of each species is given together with an illustration of body, head and a seminal vesicle, and a distribution map.\n\nThe document is available for download as a pdf from the provided URL.", "links": [ { diff --git a/datasets/AADC-00022_1.json b/datasets/AADC-00022_1.json index 965ea7a674..4cb5d193e3 100644 --- a/datasets/AADC-00022_1.json +++ b/datasets/AADC-00022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a document describing the 66 species of Hydromedusae found in the Southern Ocean. It lists all the known species, and with illustrated diagrams provides a guide to their taxonomic identification. It also includes general information about Hydromedusae and collection and preservation.\n\nThe document is available for download as a pdf from the provided URL.", "links": [ { diff --git a/datasets/AADC-00025_1.json b/datasets/AADC-00025_1.json index 08fd44d2fb..6c832a0af8 100644 --- a/datasets/AADC-00025_1.json +++ b/datasets/AADC-00025_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00025_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a document describing the Ctenophores of the Southern Ocean. It lists all the known species and with illustrated diagrams provides a guide to their taxonomic identification.\n\nThe document is available for download as a pdf from the provided URL.", "links": [ { diff --git a/datasets/AADC-00031_1.json b/datasets/AADC-00031_1.json index 7a5c1fb496..f386e95b4f 100644 --- a/datasets/AADC-00031_1.json +++ b/datasets/AADC-00031_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00031_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "(Abstract from 'The ducks of Macquarie Island')\nEarly reference to waterfowl on Macquarie Island and observations made by ANARE expeditioners between 1949 and 1985 are reviewed and discussed. Apart from a unique (perhaps erroneous) record of a mute swan Cygnus olor, information is restricted to the Pacific black duck Anas superciliosa, the grey teal A. gibberifrons and the alien mallard A. platyrhynchos and its hybrids.\n\nBlack duck and grey teal were seen by early visitors to the Island, but despite the infrequent potential for escapes of domestic ducks, mallards were not recorded until 1949. Occasional teal and mallards were seen in the years following the establishment of the permanent scientific station (1948) but mallards (and hybrids) have become more numerous in recent years. Though grey teal may disperse to Macquarie Island in times of drought on the Australian mainland, the source of mallards may be New Zealand or the less distant Campbell and Auckland Islands.\n\nThe few available records of breeding (eggs, ducklings and nests) for black duck suggest that laying begins in September and extends at least into January. Zooplankton is most abundant in spring and summer, but ducks may obtain high protein foods from the littoral and sublittoral areas and may also take seeds of terrestrial plants.\n\nAvailable information does not allow separation of habitats used by black duck or mallards. However, most observations are around coastal areas. There is some indication that records have increased along the south-western and eastern sides of the Island, but generally there are few observations of either species on the higher, central plateau.\n\nThe intrusion of mallards onto the Island and the resultant hybridisation with black duck poses a threat for the future integrity of the latter native species.\n\nThis dataset contains a review of the data available for Pacific black duck (Anas superciliosa), mallard (Anas platyrhynchos) and grey teal (Anas gibberifrons) and hybrids on Macquarie Island, collected from mid-1949 to January 1985. There is a period of relatively continuous and extensive data between February 1963 and January 1985. Discussion and references about habitat, breeding, feeding and hybrids is provided in the dataset (see the reference), as well as distribution maps.\n\nThe fields in this dataset are:\nYear\nMonth\nPresence/Absence", "links": [ { diff --git a/datasets/AADC-00038_1.json b/datasets/AADC-00038_1.json index 005fe3d41a..7f7712a364 100644 --- a/datasets/AADC-00038_1.json +++ b/datasets/AADC-00038_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00038_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thirteen species of fish have so far been caught in the inshore waters around the Vestfold Hills, including the Rauer Islands, in depths down to approximately 100 m. Species caught depend markedly on the type of fishing gear used, but three species are clearly dominant numerically. Pagonthenia bernacchii is most abundant in the shallower (less than 20 m deep) weedy and rocky habitats, while Chionodraco hamatus is dominant in the deeper (greater than 20 m deep) nearshore troughs and further offshore. Pagonthenia borchgrevinki occupies the specialised habitat associated with sea ice and close-inshore areas, including fjords and Burton Lake. The species list from the Vestfold Hills area is similar to lists from comparable locations in East Antarctica except for the major difference that C. hamatus has not yet been recorded from such shallow waters at the other locations, while P. bernacchi and P. hansoni are much more abundant in water deeper than 20 m at those sites than at Davis. This work was completed as part of ASAC project 239.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the URL given below. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068\nAADC-00073\nAADC-00075 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00039_1.json b/datasets/AADC-00039_1.json index 2d5a9c701f..1c3867c72e 100644 --- a/datasets/AADC-00039_1.json +++ b/datasets/AADC-00039_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00039_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a record of sightings and strandings of cetaceans (mainly whales) at Macquarie Island. Results have been reported from 1968 to 1990 on an irregular basis. The related publication (ANARE Research Notes 91) discusses a number of species including southern right (Balaena glacialis), minke (Balaenoptera acutorostrata), strap-toothed (Mesoplodon layardii), sperm (Physter macrocephalus), longfin pilot (Globicephala melaene) and killer (Orcinus orca) whales. Quantitative data relating to orcas (from tables 1 and 2) are provided here, along with data related to other species extracted from the text.\n\nThe fields in this dataset are:\n\n(Table 1)\nnumber_of_individuals_in_pod\nnumber_of_sightings\n\n(Table 2)\nmonth\nnumber_of_sightings\nminimum_number_of_individuals\nmaximum_number_of_individuals\nnumber_of_males\nnumber_of_females\nnumber_of_adults\nnumber_of_juveniles\nnumber_of_calves\n\n(From the text)\nobservation_date\nlocation_name\nlongitude\nlatitude\ncommon_name\nscientific_name_original\nscientific_name\nabundance\nnotes", "links": [ { diff --git a/datasets/AADC-00040_1.json b/datasets/AADC-00040_1.json index 18a1ffc10f..ecff31e22d 100644 --- a/datasets/AADC-00040_1.json +++ b/datasets/AADC-00040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a document describing the Metazoan Zooplankton of the Southern Ocean. It lists all the known species and with illustrated diagrams provides a guide to their taxonomic identification.\n\nThe document is available for download as a pdf from the provided URL.", "links": [ { diff --git a/datasets/AADC-00043_1.json b/datasets/AADC-00043_1.json index 85f9caeb14..32f22327bd 100644 --- a/datasets/AADC-00043_1.json +++ b/datasets/AADC-00043_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00043_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Otoliths from 76 fish species are illustrated and described as an aid to the identification of stomach contents of Antarctic birds and mammals. Material was obtained from waters off Australian Antarctic Territory and from around Macquarie and Heard Islands. Information is also given on distribution, habits and known predators of the fish species.\n\nThese data were published as an ANARE Research Note (75 - A guide to the fish otoliths from waters off the Australian Antarctic Territory, Heard and Macquarie Islands). See the reference for more details.", "links": [ { diff --git a/datasets/AADC-00064_1.json b/datasets/AADC-00064_1.json index 4c03f51ea7..c0c3076bc1 100644 --- a/datasets/AADC-00064_1.json +++ b/datasets/AADC-00064_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00064_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is summarised using the abstract from ANARE Research Notes 64, Ice sheet topography and surface characteristics in eastern Wilkes Land, East Antarctica.\n\nA comprehensive survey of the ice-sheet topography and surface physical characteristics was conducted by ANARE glaciological teams in eastern Wilkes Land during the period 1980-86. Oversnow operations were between 112E and 132E extending from the coast to 69S and covered the elevation range 800-2300 m asl. This report presents the data set collected on the surface topography, and the spatial distribution of snow accumulation rates, snow surface microrelief and surface wind fields, snow surface physical properties and firn temperatures.\n\nThe fields in this dataset include:\n\nSurface Topography:\nMark\nDistance (km)\nElevation (m)\nSlope (%)\nDate\nAccumulation: (m), (m/a) and (kg/m2/a)\nSurface Microrelief Type, Size and Orientation\nSite\nErosion\nReshuffled\nDeposition\nGlaze\nErosion\nMean Surface Microrelief Balance\nDeposition microrelief (m)\nErosional microrelief (m)\nBalance (m)\nDensity (0-5cm)\nHardness (0-5cm)\nType\nDepth Firn Temperatures\nDepth (m)\nAltitude (m)\nGeodetic Position Control\nLatitude (deg)\nLongitude (deg)", "links": [ { diff --git a/datasets/AADC-00065_1.json b/datasets/AADC-00065_1.json index e34216acfb..c787e0bdae 100644 --- a/datasets/AADC-00065_1.json +++ b/datasets/AADC-00065_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00065_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A shallow firn core drilling program was conducted in eastern Wilkes Land in 1985 by an ANARE glaciological team. In conjunction with surveys carried out on ice sheet topography and snow surface characteristics, 250 m of firn cores were retrieved from 15 shallow boreholes to investigate the firn-pack structure and firn stratigraphy. Firn layer density, grain size and visible stratigraphy were measured on all cores. The measured firn core data are presented.\n\nA major objective of the International Antarctic Glaciological Project (IAGP) during the 1980's has been to measure and define the mass balance distribution over the Wilkes Land region of the East Antarctic ice sheet. Australia has participated by conducting Australian National Antarctic Research Expeditions (ANARE) glaciological traverses in Wilkes Land between 1971 and 1986. These traverses operated from Casey station and achieved the greatest areal coverage of Wilkes Land from 1978-86 by establishing an eastern, southern and western route.\nThe eastern and western routes approximately traverse the 2000m contour between 95 degrees East and 131 degrees East, whilst the southern route extends from the coast to 74 degrees South inland approximately along the 112 degrees East longitude.\n\nDuring 1985, in conjunction with the resurvey of the eastern route, shallow firn core drilling and stratigraphic investigations were carried out at 50 km intervals between 112 degrees East and 132 degrees East. This route is located wholly within the katabatic slope region of East Antarctica. The dominating feature of the region is the persistent katabatic wind which drains cold air down from the ice sheet's interior to the coast. This katabatic wind is fundamental in controlling the firnification processes operating at the snow surface and within the snow/firn-pack.\n\nPreliminary snow stratigraphic investigations were carried out in 2-3 m deep pits along the same route route in 1982, and were reported by Jones (1983). These observations showed that the region was dominated by annual net accumulation which was marked by thin crusts in the snow-pack. These ice crusts were attributed to surface sintering of saltating snow grains following kinetic energy loss under constantly strong katabatic wind flow. Jones (1983) also reported the formation of thin radiation glazes during the summer season. From his observations it was recognised that a detailed investigation of the physical characteristics and stratigraphy of the firn-pack could produce extended records of annual net accumulation and define the firn-pack structure for later correlation with remotely sensed data, in particular satellite passive microwave (ESMR) data.\n\nThis report lists the firn core data obtained during the 1985 drilling and stratigraphic investigations and describes the drilling operations. Detailed density, grain size and visible stratigraphic profiles were measured in the field on a total of 250 m of firn cores drilled from 15 boreholes ranging from 10-35 m in depth. Temporal accumulation records have been interpreted from both the firn core data listed in this report and additional oxygen-isotope measurements made on the cores in Australia. A major supporting data bank on snow surface characteristics, accumulation rates and topography obtained in 1985 along the eastern route reported in Goodwin (1988).\n\nMETHODS\nA total 250m of firn cores were drilled and retrieved using the PICO (Polar Ice Coring Office) lightweight hand-operated coring auger described by Koci and Kuivinen (1984).\n\nShallow boreholes were drilled at 5 m, 10 m and 30-35 m depths along the traverse route. The drilling sites were located at each of the 15 Doppler satellite positioning survey stations spaced at 50 km intervals along the route. The 10 m boreholes were drilled to obtain firn core representing accumulation over the previous decade (1975-85). These holes were extended to 30-35 m depth at 150 km intervals along the route (GD03, GD06, GD09, GD12 and GD15) to represent accumulation over the past five decades (1935-85). These depths were estimated from accumulation rates measured on marker canes at the surface between 1982 and 1985, prior to the drilling operations. The 5m depth holes were adjacent (within 1 m) to the deeper 30-35 m depth poles to provide duplicate cores for additional measurements.\n\nThe 10 m holes were drilled without lifting tackle and the total drilling time including setting up and logging the core totalled 1.5 hours. For holes drilled deeper than 10 m a 'tripod' system was used to lift the drill string. The 'tripod' consisted of a bipod constructed from scaffold pipe erected on the raised (1.5 m high) blade of a Caterpillar D5 tractor. The bipod arrangement was supported by rope and chain to the rear of the cabin. The tripod was 6 m above the drilling platform. This enabled 5 m lengths of the drill string to be assembled or disassembled in the hole, each time the string was raised or lowered. The lifting tackle comprised 15 mm thick manila rope, one double sheaf block and one single sheaf block. To break the core before raising the drill string, two loops of rope were attached around the T handle, to take the full weight of the drill and to apply a constant force, whilst two people lifted the T handle with a jerking motion. This method of lifting and breaking proved to be simple and effective. The total drilling time for 0-25 m, 0-30 m and 0-35 m depths was 5.5 hours, 8.5 hours and 13 hours respectively. All cores were logged on site.\n\nExcellent core retrieval and quality was achieved using 45 degree angled, drill head cutters and a 2 m long core barrel. Generally, each retrieved core section was between 0.85 and 1.1 m long.\n\nFIRN CORE MEASUREMENTS AND DATA\nFollowing the completion of the drilling and core retrieval phase at each site, the cores were measured and sampled in a field laboratory. The cores, except GD06 and GD09 which were archived for detailed analysis in Australia, were measured for visible stratigraphy, grain size and density.\n\nCore sections were measured and logged for visible stratigraphy and grain size on a transmission light box in the outer cold room of the laboratory. The temperature of the work area was maintained by the outside ambient air temperature, which was generally -15 to -35 degrees C. The position of every optically different firn layer was measured by tape and the corresponding grain size of each layer was measured using a hand held 7 x optical magnifier with a 0.1 mm resolving graticule. Thus, the grain size measurements were made on bulk longitudinal sections. Ice crusts, including transparent radiation crusts/glazes and opaque wind/crusts/glazes were described and their thickness measured.\n\nDensity measurements were made on every layer identified in the visible stratigraphy. Each firn layer was cut from the remaining core section and its diameter and length measured using vernier calipers and weighed on a 2.5 kg beam balance. The densities were then calculated from the core dimensions and core mass.\n\nFirn core data comprising layer density, grain size and ice crust thickness are listed for the borehole sizes (except GD06 and GD09) in the file held at the url below. The site characteristics are also held in a spreadsheet at the url given below. These include geographic position, surface elevation, accumulation rate and mean annual surface temperature. Detailed firn temperature-depth profiles for each borehole are reported in Goodwin (1988).\n\nBoth the firn layer density and grain size profiles display cyclicity, which results from the development of depth hoar and within the annual firn layer. The depth hoar corresponds to the lower density values in the profile and develops beneath the strong ice crusts identified by the visible stratigraphy, as a result of upward water vapour transport under strong temperature gradients. The ice crusts represent the successive seasonal surface layers which were observed throughout the ANARE traverses in the region. The thickest crusts in the range 0.7-4.0 mm represent the autumn or early winter wind crust which forms under persistent strong winds (30-50 knots) during a major hiatus in snow supply and consequently marks the end of the balance year. It is spatially continuous and well developed which results in its preservation in the firn-pack. The thinner crusts in the range 0.3-0.5 mm represent the late spring and summer radiation crusts. Both types of crusts are impermeable.\n\nThe fields in this dataset are:\n\nSite\nLatitude\nLongitude\nElevation\nAccumulation Rate\nTemperature\nDepth\nDensity\nGrain Size\nIce Crust Thickness", "links": [ { diff --git a/datasets/AADC-00066_1.json b/datasets/AADC-00066_1.json index 45f2ab5fe1..42c7a1e457 100644 --- a/datasets/AADC-00066_1.json +++ b/datasets/AADC-00066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aspects of the health of two isolated Australian communities at Mawson, Antarctica and at subantarctic Macquarie Island are examined. The social, occupational and physical characteristics of these communities are described.\n\nAll medical records are analysed. Trauma represented 45% of cases at Mawson and 38% at Macquarie Island. Occupational influence on trauma is assessed, and risk factors for Antarctic expeditioners described.\n\nMonthly physiological parameters for expeditioners in each group were analysed. These revealed significant seasonal effect on body weight, skinfold thickness, blood pressure and pulse rate. Physical fitness, based on the Harvard Step Test, increased with time. Thyroid and sex hormones were studied monthly for one year at Macquarie Island. Significant seasonal effects were found.\n\nIsolated living with limited fresh food precipitated a case of nutmeg toxicity and a case of anorexia nervosa in a male. Case details, with a full anthropometric and hormonal profile of the anorexic expeditioner during weight loss, are given.\n\nHormonal parameters of stress and depression were measured monthly on a Mawson group. Few results outside the normal range were obtained. No significant changes with time were found in the diurnal variation of salivary cortisol concentration, the Dexamethasone Suppression Test of the urinary cortisol measurements. The concentration of salivary cortisol tended to increase over the period of the experiment, and there was a significant relationship between the change in subjects' salivary cortisol levels, and the change in their tension-stress psychological questionnaire scores over time.\n\nMelatonin, a hormone produced in response to light/dark variations, was investigated in Mawson subjects. No endogenous circannual rhythm for human melatonin excretion was found in spite of extreme seasonal shifts in ambient light/dark in Antarctica. However, a significantly lower level for melatonin was found in December, which correlated with elevated depression scores on the psychological questionnaire for this month.\n\nCell mediated immune responses using CMI Multitest revealed diminished scores and decreased total numbers of positive responses to seven antigens in Mawson subjects compared to other healthy populations in temperate regions. Mawson subjects had significantly elevated levels of anergy and hypoergy.\n\nMonthly questionnaires for anxiety, depression and tension/stress were given to Mawson expeditioners. No significant mid-winter depression effect was found. However the end of the year period of summer rebuilding and increased population was associated with a significant increase in depression. The onset of an anxiety state in an expeditioner during this period is described.\n\nAlcohol use at Macquarie Island was studied and an annual consumption of absolute alcohol of 16.29L per head calculated. Blood alcohol levels during social functions ranged from 0 to 0.22mg/dL, and on these nights 44-61% of members were intoxicated. Screening tests for alcoholism were evaluated at Mawson, and alcohol use correlated with fitness and tobacco use.\n\nSome of the difficulties associated with research in Antarctica are described.\n\nThe fields in this dataset are:\nNo. of cases of Illness and Poisoning\nDate\nSubject\nCortisol Change per month\nStress / Tension\nMonth\nNo. of Subjects\nMelatonin Levels\nDaily Sunshine Hours\nAntigen\nPattern of Responses\nNumber of Wintering Years\nOccupation\nNumber of Winters\nAnxiety\nDepression\nTension/Stress\nAlcohol Consumption\nAge\nDeath Rate\nPopulation", "links": [ { diff --git a/datasets/AADC-00068_1.json b/datasets/AADC-00068_1.json index 48815eba18..a1eeb0397a 100644 --- a/datasets/AADC-00068_1.json +++ b/datasets/AADC-00068_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00068_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results from an investigation of the fishery off Macquarie Island. The data were obtained from the Austral Leader, an 85m long stern trawler, between December 1994 and February 1995. The aim of the fishing voyage was to determine whether sufficient catches could be made to enable dedicated voyages to the area to be profitable, and if time allowed to search for other fishable areas. The Austral Leader had been granted a licence by the Australian Fisheries Management Authority (AFMA) to fish in Commonwealth controlled waters within the Australian EEZ around Macquarie Island. A previous voyage in November-December 1994 had made a preliminary survey of the grounds surrounding the island. 59 tows were made in the vicinity of the island, and all but 10 were made in an area west of the island. No worthwhile catches were made in any other areas. The main species caught were the Patagonian Toothfish (Dissostichus eleginoides) and Rats Tails (Macrourus carinatus). Basic biological data were collected on the catches where possible including: standard and total length, sex, gonad maturity, and stomach contents.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the URL given below. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068\nAADC-00073\nAADC-00075 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00071_1.json b/datasets/AADC-00071_1.json index 8cbfb3374a..12ff02bc18 100644 --- a/datasets/AADC-00071_1.json +++ b/datasets/AADC-00071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains results from the Aurora Australis Voyage 7 (KROCK) 1992-93, related to mesoscale distribution of krill and zooplankton communities in Prydz Bay in relation to physical and biological oceanographic parameters. There were five objectives of this project: to define the distribution patterns and abundance of krill in the krill dominated continental shelf area of the Prydz Bay region; to define the krill population structure within this area and the distribution pattern of developmental stages, especially spawning females; to define the distribution patterns and composition of the other two principal communities, neritic and oceanic, which border the krill dominated community; to specifically determine the zooplankton composition within the main feeding area of Adelie Penguins from Bechervaise Island monitoring site, Mawson; to record and analyse various physical and biological processes, eg. salinity, temperature, ice and phytoplankton, to determine how these parameters affect the observed distribution patterns. Surveys of krill and other zooplankton were taken in Prydz Bay, Antarctica between January and February 1993. At each station, rectangular midwater trawls and CTDs/bottle casts were made. During the program, echosounders and echointegrators were operating to provide krill abundance and distribution data, in addition to that from the RMT trawls. Initial analysis has shown that Euphausia crystallorophias dominates the neritic community on the shelf, while Euphausia superba was found not to occur in high abundance in the central Prydz Bay area between 70 and 78 degrees East. This dataset is a subset of the full cruise.", "links": [ { diff --git a/datasets/AADC-00073_1.json b/datasets/AADC-00073_1.json index 7f7eac4a63..3f824fb52b 100644 --- a/datasets/AADC-00073_1.json +++ b/datasets/AADC-00073_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00073_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 1 1993-94 of the Aurora Australis. The observations were taken from around Heard Island between August and September 1993. The data contains temperature, salinity, dissolved oxygen and nutrient data from a CTD survey and the results from various pelagic fish trawl surveys. The major species were Champsocephalus gunnari, Champsocephalus rhinoceratus, Lepidonotoften squamifrons and Dissostichus eleginoides. Numbers, species identity, guts and gonad data were obtained. This is a subset of the data for the whole voyage.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068\nAADC-00073\nAADC-00075 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00075_1.json b/datasets/AADC-00075_1.json index 973e576f09..fdb1c3cbd9 100644 --- a/datasets/AADC-00075_1.json +++ b/datasets/AADC-00075_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00075_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 7.2 (HIMS) 1989-90 of the Aurora Australis. There were three objectives of the fish program: To assess the distribution and abundance of demersal fish on the shelf in the AFZ around Heard Island; to investigate the biology of the more important species; to take samples to determine by mitochondrial DNA (mtDNA) analysis whether the Heard Island Champsocephalus gunnari are a genetically distinct stock from those around Kerguelen Islands. The observations were taken from around Heard Island between May and June 1990. The data contains temperature and salinity data from a CTD survey and the results from various trawl surveys. The major species were Dissostichus eleginoides, macrourus holotrachys, Champsocephalus gunnari and Notothenia squamifrons. Numbers, species identity, guts and gonad data were obtained. This is a subset of the data for the whole voyage.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068 \nAADC-00073 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00080_1.json b/datasets/AADC-00080_1.json index 663b159bcc..0e978ed52a 100644 --- a/datasets/AADC-00080_1.json +++ b/datasets/AADC-00080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 6 (FISHOG) 1991-92 of the Aurora Australis. The observations were taken from the Heard Island area in January and February 1992. The data contains temperature and salinity data from a CTD survey and the results from demersal fish trawl surveys. The major species were Dissostichjus eleginoides, Channichthys rhinoceratus, Channichthys gunnari, Lepidonotothen squamifrons, Gymnoscopelus nicholsi and Paradiplospinus gracilis. This is a subset of the data for the whole voyage. The objectives of the fish program were: to assess the distribution and abundance of demersal fish on the shelf in the AFZ around Heard Island; to investigate the biology of the more important species.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068\nAADC-00073\nAADC-00075 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00082_1.json b/datasets/AADC-00082_1.json index 348d946e2d..b1e7d5d565 100644 --- a/datasets/AADC-00082_1.json +++ b/datasets/AADC-00082_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00082_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 6 1990-91 of the Aurora Australis. The observations were taken from the Prydz Bay area, Antarctica in January and February 1991. The data contains the results from pelagic fish trawl surveys. The major species were Pleuragramma antarcticum, Channich thyid, Dacodraco hunteri and Neopagetopsis ionah. This is a subset of the data for the whole voyage. The objectives of the fish program were: to assess the distribution and abundance of pelagic fish in the Prydz Bay area; to re-sample bottom fish at sites on the continental shelf previously sampled with a small beam trawl, but using a large atter trawl to check the validity of the beam trawl's samples; to investigate the biology of the more important species. 177 midwater trawls were successfully completed at 59 stations.\n\nA Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068\nAADC-00073\nAADC-00075 \nAADC-00080 \nAADC-00082 \nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/AADC-00083_1.json b/datasets/AADC-00083_1.json index 4d5a264564..7bd222bb26 100644 --- a/datasets/AADC-00083_1.json +++ b/datasets/AADC-00083_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00083_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains results from the Aurora Australis Voyage 6 1990-91. Surveys of krill and other zooplankton were taken in Prydz Bay, Antarctica between January and February 1991. Species identity and abundance data, length, age, growth rate and mortality rate data were obtained. The major species investigated were Euphausia superba, Euphausia frigidia, Euphausia crystallorophias and Thysanoessa macrura. Other pteropods and cephalopods were also studied. This dataset is a subset of the full cruise.", "links": [ { diff --git a/datasets/AADC-00084_1.json b/datasets/AADC-00084_1.json index f398927ae9..837bb1a906 100644 --- a/datasets/AADC-00084_1.json +++ b/datasets/AADC-00084_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00084_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ANARE Research Note examines the modification of major mutable cardiovascular risk factors in members of the Australian National Antarctic Research Expedition (ANARE), in isolation for a year at Davis, Antarctica.\n\nThe use of an ambulatory blood pressure monitor allowed for a detailed analysis of changes in blood pressure (BP) and heart rate during normal daily activities and sleep, without the problems of observer error or potentially stressful situation of clinic measurements.\n\nA major part study examined the effects of BP and heart rate of varying levels of activity. The same ten subjects were studied over a year for three different seasons. Weights were kept constant throughout the study. During the active summer period, further increases in activity had no effect on BP. There was a decrease in heart rate, which correlated with the increase in fitness observed.\n\nDuring quieter and less active winter months, a significant rise in BP was noted following a period of minimal activity. This was returned to normal (summer) values following a period of increased activity.\n\nAlthough the level of fitness increased activity during both seasons, there was little correlation between changes in fitness and BP levels. That is, while fitness increased over the summer season, there was no change in systolic BP. However, following the winter period of minimal activity, the BP was greater than at any stage in the summer, although the fitness level was significantly greater than the summer.\n\nThe third part of the exercise study was done in the spring when there was a high level of stress and disruption on the station due to a demanding field program. Although maximal levels of fitness were noted, BP and heart rate were elevated and not affected by increased activity during the program. It was concluded that moderate exercise three to seven times weekly provides optimal benefits for BP, and further increased in activity levels may only reduce heart rate and increase fitness, with no evidence of further reductions in coronary heart disease (CHD) risk factors. In addition, any changes in BP due to exercise may be counteracted by the presence of psychological stress. In the absence of true exposure, there was no evidence that the cold climate can have any significant effect on the cardiovascular status.\n\nTotal high-density lipoprotein (HDL) cholesterol, triglyceride and glucose tolerance were studied concurrently. No significant changes were seen in triglyceride levels; increased activity during summer program led to raised concentrations of HDL cholesterol; increased activity did not alter cholesterol levels although, from summer to winter, there was an increase in total cholesterol levels; as the varying levels of activity had no effect, the changes were attributed to an increase in dietary fat intake in the winter. All results were within normal limits and it was concluded that no stage of this study allowed the subjects to be sufficiently sedentary to demonstrate the effects of small increases in activity. That is, only true sedentary population would benefit from an increase in exercise in regard to their lipid profile and glucose tolerance.\n\nA study was made of acute and chronic effects of alcohol consumption on the diurnal pattern of BP and heart rate, in comparison to a control period.\n\nTen subjects participated in a study of the effects of regular alcohol consumption on glucose tolerance and lipid profile. The addition of dietary fish oil, omega-3 eicosapentaenoic acid, was also studied. The study confirmed the finding that alcohol caused an elevation of triglyceride on plasma, however, this adverse effect was successfully reversed by the addition of fish oil to the diet.\n\nThe increase in total plasma cholesterol following alcohol consumption was partly due to a rise in HDL cholesterol. There was zero effect of fish oil on total cholesterol, due to a concomitant increase in HDL- and decrease in low-density lipoprotein (LDL)- cholesterol. Therefore, the dietary supplementation of fish oil seems to have a net beneficial effect on plasma lipid levels.\n\nHowever, this must be weighed against the finding that the addition of fish oil to the diet caused a significant elevation of plasma glucose, accompanied by increased plasma insulin levels.\n\nThe fields in this dataset are:\nHDL Cholesterol\nActivity (ie sleep, lie awake, sit stand, light work, moderate and heavy exercise.\nSubjects\nNon-participants\nAge\nHeight\nTotal Weight\nQuetelet body - Mass index\nSystolic Pressure\nDiastolic Pressure\nHeart Rate\nSummer\nWinter\nPulse Rate\nPeriod\nCODE\nAGE\nWEIGHT\n% FAT", "links": [ { diff --git a/datasets/AADC-00086_1.json b/datasets/AADC-00086_1.json index bcc60d3e4e..cf8175c1f2 100644 --- a/datasets/AADC-00086_1.json +++ b/datasets/AADC-00086_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00086_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a record of a nutrition project held on Davis station during the year 1989. The abstract of the report is as follows.\n\nMany countries have drawn up dietary guidelines for their populations in an attempt to decrease the premature mortality and morbidity associated with coronary heart disease (CHD). One of the consistent recommendations has been the partial substitution of saturated fatty acids (SFA) by polyunsaturated fatty acids (PUFA). More recently, as a shadow has been cast over the long-term safety of high PUFA intakes, epidemiological and intervention studies have suggested that monosaturated fatty acids (MUFA), especially oleic acid, enrichment of the diet might not only improve plasma lipid and lipoprotein profiles as effectively as the PUFA, but may have other advantages including anti-thrombotic activity and anti-oxidation properties.\n\nThe principle aims of this nutrition project were to examine the effectiveness of dietary goals in practice and to compare MUFA and PUFA in terms of improvement of lipid and lipoprotein profiles and dietary acceptance. This project was undertaken at Davis station, Princess Elizabeth Land, Antarctica. Thirty expeditioners, all members of the Australian National Antarctic Research Expedition's (ANARE) 1989 wintering expedition, acted as subjects in the study. The subjects were all fit and healthy men and women on no medication.\n\nThe fields in this dataset include:\nCholesterol\nEnergy\nFat Protein\nCarbohydrate\nAlcohol\nSFA\nMUFA\nPUFA\nChange in TC level\nChange in LDL-C level\nDate\nDietary Period\nNutrient\nPost Dietary Period Analysis\nControl Stations\nMean station Levels + SEM (mmol/L)\nHDL-C+SEM\nLDL-C+SEM\nLDL-C / HDLC\nMean Triglyceride Levels\nMean Gamma-Glutamyl Transferase\nFatty Acid Class (Mean % of total)\nWeek\nWeight\nSkinfold\nWaist/hip Ratio\nBlood Pressure", "links": [ { diff --git a/datasets/AADC-00094_1.json b/datasets/AADC-00094_1.json index 2417f7bcda..130a03b505 100644 --- a/datasets/AADC-00094_1.json +++ b/datasets/AADC-00094_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00094_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the chlorophyll a data from Voyage 6 (FISHOG) 1991-92 of the Aurora Australis. The observations were taken from the Heard Island area in January and February 1992.\n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/AADC-00095_1.json b/datasets/AADC-00095_1.json index 119f1368bb..7f37348d59 100644 --- a/datasets/AADC-00095_1.json +++ b/datasets/AADC-00095_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00095_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 6 1990-91 of the Aurora Australis. The observations were taken from the Prydz Bay area, Antarctica in January and February 1991. Taxonomic identity and abundance data were obtained, together with an extensive range of pigment analysis. Over 60 pigments are analysed (only the major ones are listed here). The major phytoplankton investigated were diatoms, dinoflagellates and flagellates. This dataset is a subset of the full cruise.", "links": [ { diff --git a/datasets/AADC-00096_1.json b/datasets/AADC-00096_1.json index 48afbc9fa2..2e36c77166 100644 --- a/datasets/AADC-00096_1.json +++ b/datasets/AADC-00096_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00096_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 7 1992-93, taken in the Prydz Bay region between January and February 1993.\n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/AADC-00098_1.json b/datasets/AADC-00098_1.json index 5aab0eef2e..75b0d79a31 100644 --- a/datasets/AADC-00098_1.json +++ b/datasets/AADC-00098_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00098_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data from Voyage 7.2 1989-90 of the Aurora Australis. The observations were taken from around Heard Island between May and June 1990. The objective of the zooplankton program was to determine the composition, distribution and abundance of zooplankton with the Heard Island-Kerguelen area, thus providing information of food availability to planktivorous fish. Surveys of krill and other zooplankton were made to obtain species identity and abundance data, length and age. Euphausia valentini and Themisto gaudichaudi were found to be the dominant species in the region. Other major species included the euphausiid Thysanoessa, the copepod Rhincalanus gigas and chaetognaths of the genus Sagitta. This dataset is a subset of the full cruise.", "links": [ { diff --git a/datasets/AADC-00101_1.json b/datasets/AADC-00101_1.json index 634ce35cc5..4f53a9c190 100644 --- a/datasets/AADC-00101_1.json +++ b/datasets/AADC-00101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results from studies of the Elephant Seal (Mirounga leonina) at Macquarie Island. Results from branding surveys and photographs between 1950 and 1965 are reported. Numbers, life stage, sex, moult stage and migration patterns have been reported.", "links": [ { diff --git a/datasets/AADC-00102_1.json b/datasets/AADC-00102_1.json index 3ab3c81b86..c6e3b3fd19 100644 --- a/datasets/AADC-00102_1.json +++ b/datasets/AADC-00102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results from studies of the Elephant Seal (Mirounga leonina) at Macquarie Island. Results from branding surveys and photographs from 1985 onwards are reported. Numbers, life stage, sex, moult stage and migration patterns have been reported. Currently some 2000 pups a year are branded and the dataset includes birth dates, weights at birth and weaning and at 6, 12 and 18 months.\n\nThis work was completed as part of ASAC (AAS) project 2265 (ASAC_2265).\n\nObjectives:\n\n1. To prepare research papers, from the extensive southern elephant seal dataset, that deal with key demographic parameters of the population such as size, age specific survivorship, fecundity, recruitment into the breeding population, age specific growth rates, and intrinsic rate of change of the population. In addition, later papers will investigate interannual variability in these parameters, how these relate to changing environmental conditions, and the effects of this on long term population fluctuations.\n\n2. To analyse and compare stable isotope ratios in the facial vibrissae of the seals and the hard parts of their prey to determine the geographical positions of the major foraging grounds of the seals. The isotope values will also allow the food webs, that support the seals, to be better defined.\n\n3. To measure the growth rates of elephant seal vibrissae so that changing isotope values, related to the prey and foraging areas, can be referred to particular foraging periods. Elephant seals characteristically have two separate periods of foraging: one in summer and one in winter. The positions of these episodes on a vibrissa can be identified once the growth rates of vibrissae are known.\n\nTaken from the progress report for the 2009-2010 season:\n\nProgress against objectives:\n1. One paper published from the elephant seal dataset. Two papers also published during 2009/10 using data collected opportunistically during the life of this project.\n\n2. PhD student Andrea Walters continues to analyse the results of the whisker analyses. She has presented some of her results at the AMSA 2009 marine connectivity conference in Adelaide.\n\nAn honours student has been engaged (start date March 2010) to analyse the squid component of the seals' diet.\n\n3. John van den Hoff spent the early summer at Macquarie Island finalising the collection of the demographic data. 2154 tag/brand resights were recorded. Collection of the data has continued on the island by Chris Oosthuizen, Ben Arthur and Iain Field since John returned to Australia. When those field workers return data collection will cease.", "links": [ { diff --git a/datasets/AADC-00103_1.json b/datasets/AADC-00103_1.json index 18c338afa8..7573e24275 100644 --- a/datasets/AADC-00103_1.json +++ b/datasets/AADC-00103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data from drifting buoys that are along the ice edge or frozen into the ice. The data are observed around the Australian sector of Antarctica and have been recorded since February 1985 and are ongoing. Observations exist for around 20 buoys over this area and are not continuous over this area for this time period. Data from the period 1995-1998 only have been archived.\n\nThis dataset also forms part of the International Program for Antarctic Buoys (IPAB - see the IPAB metadata record).\n\nThe data are available below via several urls below. Further information and the data can be obtained from the IPAB home page url. The data and documentation are also available directly from the NSIDC website. Finally, a copy of the data are also held locally on the Australian Antarctic Data Centre's servers.\n\nThe documentation held at the NSIDC website provides important information on interpreting the dataset. A static copy of this document is included with the local copy of the dataset held on the Australian Antarctic Data Centre's servers.\n\nThis work was also completed as part of ASAC projects 742 and 2678.\n\nThe fields in this dataset are:\nBuoy Number\nYear\nTime\nLongitude\nLatitude\nARGOS Positional Accuracy\nSea Ice Flag\nAir Pressure\nAir Temperature\nWater Temperature\nVelocity", "links": [ { diff --git a/datasets/AADC-00106_1.json b/datasets/AADC-00106_1.json index 800e885926..485e7f6292 100644 --- a/datasets/AADC-00106_1.json +++ b/datasets/AADC-00106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains records of ice thickness and snow thickness from Mawson, Antarctica. Measurements were attempted on a weekly basis and have been recorded since 1954 and are ongoing, although this record only contains data up until the end of 1989. The observations are not continuous however.\n\nThe dataset is available via the provided URL.\n\nThese data were also collected as part of ASAC projects 189 and 741.\n\nLogbooks(s):\nGlaciology Sea Ice Log, Mawson 1969\nGlaciology Mawson Sea Ice Logs, 1995-2000", "links": [ { diff --git a/datasets/AADC-00107_1.json b/datasets/AADC-00107_1.json index c400e61acc..c2633622f1 100644 --- a/datasets/AADC-00107_1.json +++ b/datasets/AADC-00107_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00107_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains records of ice thickness and snow thickness from Casey, Antarctica. Measurements were attempted on a weekly basis and were recorded between 1979 and 1992. The observations are not continuous however.\n\nThe dataset is available via the provided URL.\n\nThis data were also collected as part of ASAC projects 189 and 741.\n\nThe Casey fast ice thickness data are no longer being collected.", "links": [ { diff --git a/datasets/AADC-00108_1.json b/datasets/AADC-00108_1.json index 74e24458a5..6655b497c3 100644 --- a/datasets/AADC-00108_1.json +++ b/datasets/AADC-00108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains records of ice thickness and snow thickness from Davis Antarctica. Measurements were attempted on a weekly basis and have been recorded since 1957 and are ongoing, although data have only been archived here until 2002. The observations are not continuous however.\n\nThe dataset is available via the provided URL.\n\nThis data were also collected as part of ASAC projects 189 and 741.\n\nLogbook(s): \nGlaciology Davis Sea Ice Logs 1992-1999", "links": [ { diff --git a/datasets/AADC-00109_1.json b/datasets/AADC-00109_1.json index a909f295aa..b65a928768 100644 --- a/datasets/AADC-00109_1.json +++ b/datasets/AADC-00109_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00109_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains temperature and salinity data from CTD observations at Mawson, Antarctica. Profiles to 370m were attempted on an approximately monthly basis between October 1980 and October 1982. A representative value for each month of the year has been obtained during this 2 year period.\n\nThe fields in this dataset are:\n\nobservation_date (the date of observation, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ. This information is also separated into the year, month, day, etc components) \nobservation_date_year (the year of the observation date) \nobservation_date_month (the month of the observation date) \nobservation_date_day (the day of the observation date) \ndepth (the depth at which measurements were made in m) \ntemperature (the measured water temperature in degrees C) \nsalinity (the measured salinity in ppt) \nsigma_t (kgm^-3)", "links": [ { diff --git a/datasets/AADC-00859_2.json b/datasets/AADC-00859_2.json index d32edfb0b7..5402fddf84 100644 --- a/datasets/AADC-00859_2.json +++ b/datasets/AADC-00859_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-00859_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset includes data on all fur seals tagged at Macquarie Island since 1989. The dataset includes information on the sex and species of individuals, information on their reproductive histories, resight data and tagging history.\n\nThe program began in 1986, but no data are available pre-1989.\n\nThe download file consists of a wide-range of files: an access database, a large number of excel spreadsheets, word documents, pdf files and text files. Data are contained in the access database (1994-1997) and excel spreadsheets and text files (all other years). The word documents and pdf files contain a lot of further information about the data collected in each season.\n\nA readme document containing some general information about the datset is also part of the download file - in the top level directory.\n\nThe fields in this dataset are:\ndate\ntype\nID number\ntag\ntagged\nprevious tag\nweight\ndigit\nsole\nwidth\nheadgaurd\nmuzzle\ncoat\nbelly\nbiopsy\nblood\nmilk\ngirth\nlength\nsex\nbirth date\nweaning date\nbirth mass\nmass at weaning\ndate of weaning\ndeath date\ncomments\nmother tag\nbreeding\nfather\nlast seen\nyear\nstatus\nterritory\n\n2007/2008 Season update\nA successful field season was undertaken at Macquarie Island during the 07/08 summer. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation were produced, and the preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island has been completed and will be submitted shortly. \n\nProgress has been made of three main fronts:\n1. Completed field season at Macquarie Island and maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses.\n2. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation,\n3. The preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island.\nWe plan to make significant developments in demographic database management and analyses over the 08/09.\n\nTaken from the 2008-2009 Progress Report:\nProject objectives:\nBackground 1986-2008\nThe 'conservation and management of fur seals in the Antarctic marine ecosystem' research program (hereafter referred to as \"the fur seal program\") aims to provide key performance measures for recovering fur seal populations, and key Antarctic State of the Environment indicators, to monitor how biological and physical oceanographic change in Southern Ocean ecosystems, effects the reproductive performance of high trophic-level predators such as fur seals. Fur seals were the most heavily exploited of all of the Antarctic marine biota, and populations on both of Australia's subantarctic islands (Macquarie and the Heard and MacDonald Islands, HIMI), have yet to recover to pre-sealing numbers.\n\nOver the last twenty years (1986-2007), research undertaken on this and former programs (managed by Dr Peter Shaughnessy) have aimed to provide information on:\n- the population status and ecology of recovering fur seal populations on Macquarie and Heard Islands,\n- species identification and composition,\n- the extent, trends, processes and implications of hybridisation among fur seals at Macquarie Island,\n- the impact of commercial sealing on the genetic variation and population structure of southern ocean fur seal populations,\n- the foraging ecology and lactation strategies of fur seals at Heard and Macquarie Islands,\n- the trophic linkages between fur seals and commercial fisheries at Macquarie and Heard Islands, and\n- how physical and biological oceanographic changes affect the reproductive performance of fur seals.\n\nThe fur seal program has successfully achieved these aims, and in doing so made significant contributions to implementing the many milestones of Australia's Antarctic Science Strategy (both past and present). In addition, the program has provided important advice on the conservation and management of Southern Ocean fur seal populations and marine systems, including:\n- providing information to Australian Fisheries Management Authority (AFMA) to assist ecological sustainable development (ESD) of the Patagonian toothfish fisheries around Macquarie and Heard Islands.\n- proving information to Environment Australia (now DEWR) on the distribution of fur seal foraging effort to assist planning and development of the Macquarie Island Marine Park.\n- providing specific data on the status of the subantarctic fur seal at Macquarie Island to DEWR, and assisting as a member of the subantarctic fur seal Recovery Team.\n- providing regular updates on the status of fur seal populations at Macquarie and Heard Islands to the SCAR Expert Group on Seals.\n- reporting to the Antarctic State of the Environment (Indicator 32).\n\nThe fur seal program is now one of the longest standing ongoing biological studies supported by the Australian Antarctic Division, providing an important time-series of population recovery following human exploitation, and most recently (after identification of sensitive demographic responses to small changes in sea surface temperatures), important ecological performance indicators and reference points that provide some of the best examples of how climate change may impact high trophic-level predator populations in the Southern Ocean.\n\nThe next five years (2008-2012)\nOver the next five years, the fur seal program aims to build on the above successes and continue core aspects population monitoring and demography. There will be a continued focus on undertaking research with clear applied management applications and a strong strategic focus targeting specific priorities of Australia's Antarctic Science Program Science Strategy. Applied applications include ESD of fisheries, MPA management and planning, acting on research and management priorities set out in the Department of the Environment and Heritage \"The Action Plan for Australian Seals\", the Recovery Plans for the Subantarctic fur seal and Antarctic State of the Environment reporting (SEO Indicator No. 32). All of these are in accord with and will help implement Australia's Oceans Policy.\n\nThe last five years of the fur seal program have seen considerable advancement in our understanding of the extent, trends and processes that facilitate and limit hybridisation among the three fur seal species at Macquarie Island. We have also identified highly significant relationships between fur seal reproductive success (fecundity and pup growth rates) and small changes to local sea surface temperature (STT) north of Macquarie Island associated with the subantarctic front. We also have a considerable data base on the survival and reproductive success of known-aged animals extending back to the early 1990s, and because of significant progress in developing molecular methods for identification of species and hybrids over the last five years, we now also have detailed genotype data for a large proportion of these seals (approx. 1,300).\n\nWith these data sets and knowledge, the focus of the fur seal program over the next five years will be to integrate molecular, demographic and oceanographic data to provide further insights into the how climatic and oceanographic changes in the Southern Ocean affect fur seal population on both annual and lifetime scales. The specific aims of the fur seal program will be to:\n\n1. Maintain the population monitoring programs at Macquarie and Heard Islands\n2. Maintain the long-term demographic program at Macquarie Island and analysis of data to determine age-specific survival and fecundity rates for each species and determine the reproductive costs of hybridisation.\n3. Calculate annual changes in foraging ecology, survival, recruitment, reproductive rates and pup growth, and relate these to annual changes in regional oceanography.\n\nThe scientific relevance of these objectives are detailed below.\n\nProgress against objectives:\nProgress has been made of three main fronts:\n1. Field season at Macquarie Island during the 2008/09 summer has been completed. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses.\n2. A publication titled: \"Fur seals at Macquarie Island: post-sealing colonisation, trends in abundance and hybridisation of three fur seals species\" has been accepted for publication in Polar Biology.\n3. Some database maintenance has been undertaken on the demographic database.\n\nTaken from the 2010-2011 Progress Report:\nPublic summary of the season progress:\nA successful field season was undertaken at Macquarie Island during the 10/11 season. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. A total of 255 pups were recorded this season, about an 8% increases since the 2009/10 season and more than any previous year since recolonisation. A new PhD program has commenced this year the focus will be analyses of the 25 year demographic dataset, and the impacts of climate change on population recovery.", "links": [ { diff --git a/datasets/AADC-02128_1.json b/datasets/AADC-02128_1.json index 5e8c71186c..c109286c8f 100644 --- a/datasets/AADC-02128_1.json +++ b/datasets/AADC-02128_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC-02128_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Public summary for project 2128:\n\nThe aim of this study is to relate the foraging behaviour of Antarctic fur seals breeding on the Kerguelen Plateau at Iles Kerguelen and Heard Island, to the distribution of prey species at sea. Specifically this project seeks to examine the relationship between predators and prey, and how their locations at sea vary according to the position of major productive zones, such as the Antarctic Polar Frontal Zone. This project will provide important data on the relationship between predators and their prey and the developing commercial fisheries in the region. These data are central to improved conservation and management of marine resources on the Kerguelen Plateau.\n\nVariations made to the work plan\nThe original comparative aspects of the program planned for the 1999/00 season, where fur seals from Iles Kerguelen and Heard Island were to be satellite tracked simultaneously could not be undertaken because of original 1999/00 field season to Heard Island was re-scheduled to 2000/01. Fortunately the project collaborator Dr Christophe Guinet (French CEBC-CNRS) agreed to extend the work program at Iles Kerguelen another season, and the comparative and integrated fur seal-prey-fisheries study over the Kerguelen Plateau was undertaken the following season (2000/01). Details of this study are presented in ASAC project 1251 (CI - Goldsworthy)and 1085 (CI-Robertson).\n\nSignificant findings:\n\nThe distribution of the foraging activity of Antarctic fur seal females was investigated at Cap Noir (49 degrees 07 S, 70 degrees 45E), Kerguelen Island in February 1998. Eleven females were fitted with a satellite transmitter and Time Depth recorder. The two sets of data were combined to locate spatially the diving activity of the seals. The fish component of the fur seal diet was determined by the occurrence of otolotihs found in 55 scats collected during the study period at the breeding colony. Oceanographic parameters were obtained simultaneously through direct sampling and satellite imagery. The mesopelagic fish community was sampled on 20 stations along four transects where epipelagic trawls were conducted at night at 50 meters of depth. We then investigated, using geographic information systems, the relationship between the spatial distribution of the diving activity of the fur seals and oceanographic factors that included sea surface temperature, surface chlorophyll concentration, prey distribution and bathymetry obtained at the same spatio-temporal scale as the spatial distribution of the diving activity of our study animals. An inverse relationship was found between the main fish species preyed by fur seal and those sampled in trawl nets. However, the diving activity of Antarctic fur seal females was found to be significantly related to oceanographic conditions, fish-prey distribution and to the distance from the colony but these relationships changed with the spatial scale investigated. A probabilistic model of the Kerguelen Plateau was developed that predicted where females should concentrate their foraging activity according to the oceanographic conditions of the year, and the locations of their breeding colonies.\n\nMaternal allocation in growth of the pup was measured in Antarctic fur seals (Arctocephalus gazella) at Iles Kerguelen during the 1997 austral summer. Absolute mass gain of pups following a maternal foraging trip was independent of the sex of the pup but was positively related to the foraging trip duration and to maternal length. However, daily mass gain, i.e. the absolute mass gain of the pup divided by the foraging trip duration, decreased with increasing foraging trip duration but increased with maternal length. While fasting, the daily mass loss of the pup was related to the sex of the pup and initial body mass, with both heavier pups and female pups losing more mass per day than lighter pups and male pups. The mass specific rate of mass loss was significantly higher in female pups than in male pups. Over the study period, the mean growth rate was zero with no difference between female and male pups. The growth rate in mass of the pup was positively related to maternal length but not maternal condition, negatively related to the foraging trip duration of the mother and the initial mass of the pup. This indicated that during the study period heavier pups grew more slowly due to their higher rate of daily mass loss during periods of fasting . Interestingly, for a given maternal length, the mean mass of the pup during the study period was higher for male than for female pups, despite the same rate of daily mass gain. Such differences are likely to result from sex differences in the mass specific rate of mass loss. As female pups lose a greater proportion of their mass per day, a zero growth rate i.e. mass gain only compensates for mass loss, is reached at a lower mass in female pups compared to male pups. Our results indicate that there are no differences in maternal allocation according to the sex of the pup but suggest that both sexes follow a different growth strategy.\n\nResults are in line with the objectives of the project.\n\nanimal_id (identifier of the individual animal) \nlocation_class (the Argos location class quality, 0-3) \nlatitude (decimal degrees) \nlongitude (decimal degrees) \nobservation_date (the date of observation, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ. This information is also separated into the year, month, day, etc components) \nobservation_date_year (the year of the observation date) \nobservation_date_month (the month of the observation date) \nobservation_date_day (the day of the observation date) \nobservation_date_hour (the hour of the observation date) \nobservation_date_minute (the minute of the observation date) \nobservation_date_time_zone (the time zone of the observation date) \ndeployment_longitude (location that the tracker was deployed, decimal longitude) \ndeployment_latitude (location that the tracker was deployed, decimal latitude) \ntrip (the identifier of the trip made by this animal) \nat_sea (whether this point was at sea (1) or on land (0)) \ncomplete (was this trip complete - i.e. did the animal return to the colony) \nscientific_name (scientific name of the tracked animal) ", "links": [ { diff --git a/datasets/AADC_Heard_bathy_grid_1.json b/datasets/AADC_Heard_bathy_grid_1.json index 6d0f463c45..eefddf78c4 100644 --- a/datasets/AADC_Heard_bathy_grid_1.json +++ b/datasets/AADC_Heard_bathy_grid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC_Heard_bathy_grid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record is a modified child record of an original parent record originating from custodians of data associated with Geoscience Australia (The identifier of the parent record is ANZCW0703009248, and can be found on the Australian Spatial Data Directory website - see the URL given below).\n\nTaken from the report:\nA bathymetric grid of the Heard Island-Kerguelen Plateau Region (Longitudes 68 degrees E - 80 degrees E, Latitudes 48 degrees S - 56 degrees S) is produced. In doing so, the individual datasets used have been closely examined and any deficiencies noted for further follow up or have been rectified immediately and the changes documented. These datasets include modern multibeam data, coastline data obtained from the World Vector Shoreline, echosounder data from research, fishing and Customs vessels and satellite derived bathymetric data.\n\nA hierarchical system was employed whereby the best and most extensive datasets were gridded first and applied as a mask to the next best dataset. A new masking grid would be formed from these datasets to pass non-overlapping data in the next best dataset. This procedure was employed until finally the satellite data were masked. All the various levels of masked data were then brought together by the gridding algorithm (Intrepid - Desmond Fitzgerald Associates) and an ERMapper format grid produced.\n\nA grid cell size of 0.005 degrees (nominal 500m) was used with many iterations of minimum curvature gridding and several passes of smoothing.\n\nThe final grid is available in ERMapper, ArcInfo and ASCII xyz formats.", "links": [ { diff --git a/datasets/AADC_Macquarie_bathy_grid_1.json b/datasets/AADC_Macquarie_bathy_grid_1.json index 8f2ed87ede..0acc833c07 100644 --- a/datasets/AADC_Macquarie_bathy_grid_1.json +++ b/datasets/AADC_Macquarie_bathy_grid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADC_Macquarie_bathy_grid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record is a modified child record of an original parent record originating from custodians of data associated with Geoscience Australia (The identifier of the parent record is ANZCW0703006701, and can be found on the Australian Spatial Data Directory website - see the URL given below).\n\nA bathymetric grid of the Macquarie Island Region (Longitudes 151 E and 167 E, Latitudes 48 S and 62 S) was produced. In doing so, the individual datasets used were closely examined and any deficiencies noted for further follow up or were rectified immediately and the changes documented. These datasets include modern multibeam data, coastline data obtained from georeferenced SPOT imagery, hydrographic quality data, echosounder data from research and fishing vessels and satellite derived bathymetric data.\n\nA hierarchical system was employed whereby the best and most extensive datasets were gridded first and applied as a mask to the next best dataset. A new masking grid would be formed from these datasets to pass non-overlapping data in the next best dataset. This procedure was employed until finally the satellite data were masked. All the various levels of masked data were then brought together by the gridding algorithm (Intrepid and Desmond Fitzgerald Associates) and an ERMapper format grid produced.\n\nA grid cell size of 0.00225 (nominal 250m) was used with many iterations of minimum curvature gridding and several passes of smoothing.\n\nThe final grid is available in geotiff, ArcInfo ascii and xyz text formats.\n\nA detailed report of the work completed is also available.", "links": [ { diff --git a/datasets/AADFishDatabase_5.json b/datasets/AADFishDatabase_5.json index 5e79ea2da6..ef6b6681bc 100644 --- a/datasets/AADFishDatabase_5.json +++ b/datasets/AADFishDatabase_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AADFishDatabase_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset is primarily data collected by Fisheries Observers on Australian commercial fishing vessels targeting Patagonian Toothfish and Mackerel Icefish in the HIMI (Heard Island and McDonald Islands) and Macquarie Island fisheries. Further Data were collected on Aurora Australis research voyages in the Southern Ocean and during new and exploratory fisheries carried out in CCAMLR area 58.4.2. Data include shot position information, biological data on target and prominent bycatch species, length at age estimates for toothfish, catch size, catch composition, bird and marine mammal observations.\n\nThe data are held in an secure SQL database.", "links": [ { diff --git a/datasets/AAD_Ant_AIS_vel_series_1.json b/datasets/AAD_Ant_AIS_vel_series_1.json index a80ea5a63e..8c444b4cad 100644 --- a/datasets/AAD_Ant_AIS_vel_series_1.json +++ b/datasets/AAD_Ant_AIS_vel_series_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_AIS_vel_series_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A time series of ice velocity values have been derived by analysis of LandSat-7 ETM+ images for the Lambert Glacier and Amery Ice Shelf system. \n\nThe analysis technique uses feature tracking in pairs of Landsat-7 ETM+ images. This process uses surface features that persist with time and move with the ice as tracers of the ice motion. The displacement of these features over the time interval between acquisition of the two images in a pair is determined by image correlation. The analysis is made at regular increments across and along the images, to produce a regular grid of values. The derived values are filtered and validated according to set of a prior constraints for the flow in a local region and the statistics of a set of velocity values within a window. The images have been projected onto a common reference system, and spliced together in order to produce a seamless set of velocity values.\n\nHorizontal components of strain rate are derived from the velocity data using a set of derivative operators in a least-squares solution of an over-constrained set of equations, which uses all velocity values within a computation window. This procedure effectively produces a set of average velocity and strain rate values and accounts for much of the noise in the individual velocity observations. Values of the local longitudinal, transverse and shear strain rate components are derived by rotation of the cartesian values to the local flow direction. The procedure is described in Young and Hyland (2002).\n\nThis metadata record has been derived from work performed under the auspices of ASAC project 3067 (ASAC_3067).", "links": [ { diff --git a/datasets/AAD_Ant_LG-AIS_vel_strain_1.json b/datasets/AAD_Ant_LG-AIS_vel_strain_1.json index 75bcea146e..4e647c83fa 100644 --- a/datasets/AAD_Ant_LG-AIS_vel_strain_1.json +++ b/datasets/AAD_Ant_LG-AIS_vel_strain_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_LG-AIS_vel_strain_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of ice velocity and strain rate have been derived by analysis of satellite images for the Lambert Glacier and Amery Ice Shelf system. Two techniques have been applied in the production of the two main sets of velocity values.\n\nOne technique uses 'feature tracking' in pairs of Landsat TM images. This process uses surface features that persist with time and move with the ice as tracers of the ice motion. The displacement of these features over the time interval between acquisition of the two images in a pair is determined by image correlation. A reference sub-image is extracted from one image and the best correlation is searched for in the other image. The pair of images were registered by comparing fixed features such as rock outcrops or areas of known ice velocity. The analysis is carried at regular increments across and along the images, to produce a regular grid of values. The derived values are edited and accepted according to whether they satisfy certain a priori constraints for the flow in a local region and the statistics of a set of velocity values within a window. The TM images have been pre-processed to project them onto a common reference and projection system, and spliced together, in order to produce a seamless set of velocity values. Many tens of thousands of observations have been extracted along the entire length of the system (about 600+ km).\n\nThe other technique has been applied to analysis of Synthetic Aperture Radar images. It uses a procedure applied during SAR interferometry [InSAR] to register small sections of the SAR complex image for generation of the phase difference or fringe image. The process we have applied uses maximum coherence as a test for best match or correlation of two image chips extracted from a pair of coherent complex SAR images. This procedure uses the phase information inherent in the SAR data in place of features as used for the TM analysis. From this analysis a set of displacements is derived comparable to the results for feature tracking. The displacements are derived in the range coordinate system of the complex SAR images. The displacements are converted to velocity values in the ground coordinate system. Corrections are also applied at this stage to allow for errors in the satellite orbits for the two sets of SAR acquisitions. One velocity data set derived from analysis of SAR data from the Canadian Space Agency's Radarsat covers an 800 km length of the system. Further data are being extracted by InSAR analysis of SAR data from the European Space Agency's ERS tandem mission.\n\nHorizontal components of strain rate are derived from the velocity data using a set of derivative operators in a least-squares solution of an over-constrained set of equations, which uses all velocity values within a computation window. This procedure effectively produces a set of average velocity and strain rate values and accounts for much of the 'noise' in the individual velocity observations. Values of the local longitudinal, transverse and shear strain rate components are derived by rotation of the cartesian values to the local flow direction.\n\nThis metadata record has been derived from work performed under the auspices of ASAC project 2224 (ASAC_2224).", "links": [ { diff --git a/datasets/AAD_Ant_WLD_firn_temp_1.json b/datasets/AAD_Ant_WLD_firn_temp_1.json index ab5d474640..426fc17087 100644 --- a/datasets/AAD_Ant_WLD_firn_temp_1.json +++ b/datasets/AAD_Ant_WLD_firn_temp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_WLD_firn_temp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of temperature within the snow / firn cover in Wilkes Land East Antarctica were made along the routes of oversnow traverses operating out of Casey (66.6 S 110 E). In general, measurements were made at a nominal depth of ten metres as a first estimate of mean annual surface temperature of the ice sheet, and for many sites, at some additional depths, typically around twenty metres. Actual depths of measurements are included with the temperature data as well as date of observation. No attempt has been made to apply any correction for seasonal or interannual variation.\n\nTwo different methods were used to drill the access holes: an ice coring auger (usually PICO drill, originally SIPRE drill); a steam drill. In some areas depth of drilling was limited by problems associated with the texture of the firn and difficulties in recovery of material from the hole.\n\nWhere relevant, date of drilling is included with the data, or time elapsed between drilling and temperature measurement. Temperature measurements were made using 100 ohm platinum resistance elements with resistance measured using a 'Leeds and Northrup' resistance bridge. The bridge uses a zero/nul measurement technique, thus avoiding heating effects in the sensor Refer quality entry for further details).\n\nThese data form part of the data collected for ASAC projects 456 (Properties and Structure of Antarctic Snow and Ice ), 458 (Characteristics, Dynamics and Mass Budget of the Ice Sheet in Wilkes Land ) and 2224 (Glacier dynamics and mass discharge from the Antarctic ice sheet, 45 degrees -160 degrees E) - ASAC_456, ASAC_458, ASAC_2224).\n\nThe fields in this dataset are:\n\nMarker\nDate\nTemperature (degrees C)\nDepth of Observation (m)\nLatitude\nLongitude\nDrill\nTime since drilling\nComments", "links": [ { diff --git a/datasets/AAD_Ant_WLD_ice_vel_1.json b/datasets/AAD_Ant_WLD_ice_vel_1.json index 9223190d85..2db837b089 100644 --- a/datasets/AAD_Ant_WLD_ice_vel_1.json +++ b/datasets/AAD_Ant_WLD_ice_vel_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_WLD_ice_vel_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of ice velocity on the surface of the ice sheet in Wilkes Land East Antarctica were made along the routes of oversnow traverses operating out of Casey (66.6 S 110 E). In general, measurements were made at a nominal spacing of 50 km. Additional measurements were made at closer spacing in some areas in order to resolve finer scale variation in the flow pattern. Three main routes were covered: two routes (east at latitude 69 S and west at latitude 68.5 S) approximately followed the line of the 2000 m elevation contour, and a third route headed south from summit of Law Dome ice cap along the line to Vostok station.\n\nAll velocity data were derived from positions determined using Doppler Satellite Positioning techniques with data collected from the (US) NAVSAT systems using JMR receivers, with the doppler data processed in combination with Precise Ephemeris data supplied by the (US) DMAHTC and the DOPPLR software packages. Processing of the data were carried out by the Australian Division of National Mapping. \n\nThe time interval between first and last position observations was three years for the western route, six years for the southern route, and three to five years for the eastern route.\n\nThese data form part of the data collected for ASAC projects 458 (Characteristics, Dynamics and Mass Budget of the Ice Sheet in Wilkes Land) and 2224 (Glacier dynamics and mass discharge from the Antarctic ice sheet, 45 degrees -160 degrees E) - (ASAC_458, ASAC_2224).\n\nThe fields in this dataset are:\n\nStation\nLatitude\nLongitude\nVelocity\nAzimuth\n\nThe printouts from the recording and reduction of the doppler observations have been archived at the Australian Antarctic Division.\n\nAll logbooks have been archived at the Australian Antarctic Division.\n\nCopies of the document details forms for the logbooks are available for download from the provided URL.", "links": [ { diff --git a/datasets/AAD_Ant_iceberg_SAR_1.json b/datasets/AAD_Ant_iceberg_SAR_1.json index 11ca88d69f..e281f57155 100644 --- a/datasets/AAD_Ant_iceberg_SAR_1.json +++ b/datasets/AAD_Ant_iceberg_SAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_iceberg_SAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map shows the distribution of the iceberg data extracted from ERS SAR images.\n\nIcebergs are identified in Synthetic Aperture Radar [SAR] images by image analysis using the texture and intensity of the microwave backscatter observations. The images are segmented using an edge detecting algorithm, and segments identified as iceberg or background, which may be sea ice, open water, or a mixture of both. Dimensions of the icebergs are derived by spatial analysis of the corresponding image segments. Location of the iceberg is derived from its position within the image and the navigation data that gives the location and orientation of the image.\n\nMore than 20,000 individual observations have been extracted from SAR images acquired by the European Space Agency's ERS-1 and 2 satellites and the Canadian Space Agency's Radarsat satellite. Because images can overlap, some proportion of the observations represent multiple observations of the same set of icebergs.\n\nMost observations relate to the sector between longitudes 70E and 135E. The data set includes observations from several other discrete areas around the Antarctic coast. In general observations are within 200 km of the coast but in limited areas extend to about 500 km from the coast.\n\nThis metadata record has been derived from work performed under the auspices of ASAC project 2187 (ASAC_2187).\n\nThe map in the pdf file shows the extent of the coverage of individual SAR scenes used in the analysis and the abundance and size characteristics (by a limited colour palette) of the identified icebergs.", "links": [ { diff --git a/datasets/AAD_Ant_surf_snow_grain_size_1.json b/datasets/AAD_Ant_surf_snow_grain_size_1.json index 417be6e510..1ca4771ece 100644 --- a/datasets/AAD_Ant_surf_snow_grain_size_1.json +++ b/datasets/AAD_Ant_surf_snow_grain_size_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Ant_surf_snow_grain_size_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The spatial distribution of surface snow grain size over the Antarctic snow cover has been determined from analysis of near-thermal-infra-red data acquired with the ATSR-2 instrument on the European Space Agency's ERS-2 satellite. Scattering from a snow surface in the short wave infra-red part of the spectrum (0.9 to 3.5 micron) is strongly dependent on grain size, and to a lesser extent on shape. A relation between snow surface reflectance, illumination incidence-angle, and grain size, has been established using a model of BRDF [Bi-Directional Reflectance Distribution Function] for snow and laboratory measurements of the BRDF behaviour of snow. This relation is used to invert the derived values of reflectance at Top-Of-Atmosphere to grain size. TOA reflectance values are derived from satellite observations of radiance in two channels with wave-band centred at 0.87 and 1.6 micron. A ratio of the two channels is used to correct for the effect of local surface slope variation on the apparent reflectance. Grain size is determined at a regular spacing in a grid of cells, each 16 km x 16 km. The ATSR-2 instrument provides observations on a 1 km pixel, so that 256 observations are accumulated to improve signal/ratio response. Calculations are made on a per orbit basis. Cloud affected cells are detected by two methods: a variance test within the 16 km cell; and a minimum grain size criterion. Very small grain size (less than 25 micron) occurs almost exclusively in clouds. A maximum solar incidence angle of 75 degrees is imposed and relatively small viewing incidence angles are used in order to minimise possible errors that could be introduced by the BRDF effect of the snow surface and its roughness.\n\nApproximately 5400 scenes of ATSR-2 data (512 km x 512 km) were analysed. These data span the time interval from 16 November 1999 to 26 January 2000. The results are presented as a spatial distribution of grain size values on the regular array of 16 km cells. They include mean values accumulated over all orbits for the 1999-2000 summer season, together with standard deviation, and density of observations contributing to the mean. Time series of grain size values for each cell can also be extracted from the data set for the individual orbits. For the 1999-2000 austral summer season, incidence of cloud cover was very high in West Antarctica, and to a lesser extent around the near-coastal margin. Cloud cover over much of East Antarctica was low.\n\nThis metadata record has been derived from work performed under the auspices of ASAC project 2200 (ASAC_2200).", "links": [ { diff --git a/datasets/AAD_Bathy_Acoustic_1985-2012_1.json b/datasets/AAD_Bathy_Acoustic_1985-2012_1.json index 391871840b..ae8eafbe45 100644 --- a/datasets/AAD_Bathy_Acoustic_1985-2012_1.json +++ b/datasets/AAD_Bathy_Acoustic_1985-2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Bathy_Acoustic_1985-2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of underway data, including bathymetric data, collected aboard Australian Antarctic Division research vessels between 1985 and 2012. The data are available in csv format and the raw SIMRAD format. In the csv files bathymetric data is in the WTR_DEPTH_M column. Some voyages will not have bathymetric data associated with them.\n\nThe csv data may have been quality checked. Most of the underway data was quality checked ('dot zapped') up to and including voyage 4 2003/04. Data quality reports are available by searching at \nhttp://data.aad.gov.au/aadc/voyages/ \n \nOther than on Marine Science voyages, the Aurora Australis bathymetric data gathering procedures prior to about 2000 were not checked during the voyage. The echo sounder was turned on in Hobart and if it stopped working during the voyage, then there was no one to get it going again.\n\nBathymetric data from these voyages that has been processed by the Royal Australian Navy is available via other metadata records linked to the parent record with ID AAD_voyage_soundings.", "links": [ { diff --git a/datasets/AAD_Hydroacoustics_data_1.json b/datasets/AAD_Hydroacoustics_data_1.json index e11e6f7769..0c190d0b74 100644 --- a/datasets/AAD_Hydroacoustics_data_1.json +++ b/datasets/AAD_Hydroacoustics_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Hydroacoustics_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydroacoustics data obtained from Australian Antarctic Division voyages from 1993 to 2004. Voyages were made to various locations within the Southern Ocean. Data are stored on 14 hard disks, 1 CD-R and 1 DVD-R for archiving in a secure storage area.\n\nA catalogue describing what data are held on each media is available for download from the provided URL.\n\nThe hard disks in the archive box are labelled as 'Status 1'.\n\nThese data were collected under several ASAC projects - ASAC 357 (Hydroacoustic Determination of the Abundance and Distribution of Krill in the Region of Prydz Bay, Antarctica) and ASAC 1250 (Krill flux, acoustic methodology and penguin foraging - an integrated study) - ASAC_357 and ASAC_1250.\n\n2008-11-07\nNote - all Australian Antarctic Division hydroacoustic data have now been collated on the AAD Storage Area Network (SAN). This digital collection supersedes the collection of hard disks, and comprises (as of now) the sum total of all AAD hydroacoustic data. Ideally as more hydroacoustic data are collected by AAD vessels, they will be added to the SAN. See the metadata record entitled \"Hydroacoustic data collected from Southern Ocean Cruises by the Australian Antarctic Division\" for more information.", "links": [ { diff --git a/datasets/AAD_Hydroacoustics_data_All_1.json b/datasets/AAD_Hydroacoustics_data_All_1.json index c299b96241..46c17c6a8c 100644 --- a/datasets/AAD_Hydroacoustics_data_All_1.json +++ b/datasets/AAD_Hydroacoustics_data_All_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_Hydroacoustics_data_All_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division (AAD) has been collecting hydroacoustic data from its ocean going vessels for a number of years. This collection represents all hydroacoustic data gathered since 1990.\n\nThe data are stored on the AAD Storage Area Network (SAN), and as such are only directly accessible by AAD personnel. Currently a very large volume of data are stored (greater than 2 TB), hence distribution of these data are logistically feasible really only for people with access to the SAN.\n\nAs well as data, a large amount of documentation is provided - including methods used to collect these data, as well as any products resulting from these data (e.g. papers, reports, etc).\n\nIn the past, these data have been collected under several ASAC projects, ASAC 357 (Hydroacoustic Determination of the Abundance and Distribution of Krill in the Region of Prydz Bay, Antarctica) and ASAC 1250 (Krill flux, acoustic methodology and penguin foraging - an integrated study) - ASAC_357 and ASAC_1250.\n\nAs of 2019-12-19 the folders present in the acoustics data directory are:\n\n1990-05_Aurora-Australis_HIMS\n1991-01_Aurora-Australis_AAMBER2\n1991-10_Aurora-Australis_WOCE91\n1992-01_Aurora-Australis_Calibration_Great-Taylors-Bay\n1993-01_Aurora-Australis_Calibration_Port-Arthur\n1993-01_Aurora-Australis_KROCK\n1993-02_Aurora-Australis_Calibration_Mawson\n1993-03_Aurora-Australis_WOES-WORSE\n1993-08_Aurora-Australis_Calibration_Port-Arthur\n1993-08_Aurora-Australis_THIRST\n1994-01_Aurora-Australis_SHAM\n1994-12_Aurora-Australis_WOCET\n1995-02_Aurora-Australis_Calibration_Casey\n1995-07_Aurora-Australis_HI-HO_HI-HO\n1996-01_Aurora-Australis_BROKE\n1996-01_Aurora-Australis_Calibration_Port-Arthur\n1996-02_Aurora-Australis_Calibration_Casey\n1996-08_Aurora-Australis_WASTE\n1997-01_Aurora-Australis_BRAD\n1997-09_Aurora-Australis_ON-ICE\n1997-09_Aurora-Australis_WANDER\n1997-11_Aurora-Australis_SEXY\n1997-11_Aurora-Australis_V3\n1997-98-050_V5\n1998-02_Aurora-Australis_SNARK\n1998-04_Aurora-Australis_PICCIES\n1998-07_Aurora-Australis_FIRE-and-ICE\n1998-09_Aurora-Australis_V2\n1998-10_Aurora-Australis_SEXYII\n1999-01_Aurora-Australis_V5\n1999-03_Aurora-Australis_STAY\n1999-07_Aurora-Australis_Calibration_Port-Arthur\n1999-07_Aurora-Australis_IDIOTS\n1999-10_Aurora-Australis_V2\n1999-11_Aurora-Australis_V4\n2000-01_Aurora-Australis_V5\n2000-02_Aurora-Australis_V6\n2000-10_Aurora-Australis_Calibration_Port-Arthur\n2000-11_Aurora-Australis_V1\n2000-12_Aurora-Australis_KACTAS\n2001-01_Aurora-Australis_Calibration_Mawson\n2001-02_Aurora-Australis_Calibration_Davis\n2001-10_Aurora-Australis_CLIVAR\n2002-01_Aurora-Australis_LOSS\n2002-09_Aurora-Australis_V1\n2002-10_Aurora-Australis_Calibration_Port-Arthur\n2003-01_Aurora-Australis_KAOS\n2003-02_Aurora-Australis_Calibration_Mawson\n2003-03_Aurora-Australis_Off-charter\n2003-09_Aurora-Australis_ARISE\n2003-09_Aurora-Australis_Calibration_NW-Bay\n2003-11_Aurora-Australis_V2\n2003-12_Aurora-Australis_HIPPIES\n2004-02_Aurora-Australis_V7\n2004-05_AAD_Lab-testing\n2004-06_Aurora-Australis_Off-charter\n2004-10\n2004-10_Aurora-Australis_Calibration_NW-Bay\n2004-10_Aurora-Australis_V1\n2004-11_Aurora-Australis_V2\n2004-11_Howard-Burton_NW-Bay-testing\n2004-12_Aurora-Australis_ORCKA\n2004-12_Howard-Burton_NW-Bay-testing\n2005-02_Aurora-Australis_V5\n2005-04_Howard-Burton_Bruny-Island-testing\n2005-11_Aurora-Australis_Calibration_Port-Arthur\n2005-11_Aurora-Australis_V2\n2006-01_Aurora-Australis_BROKE-West\n2006-02_Aurora-Australis_Calibration_Mawson\n2006-03_Aurora-Australis_V5\n2006-09_Aurora-Australis_V1\n2006-12_Aurora-Australis_V2\n2007-01_Aurora-Australis_SAZ-SENSE\n2007-04_Aurora-Australis_V5\n2007-08_Aurora-Australis_SIPEX\n2011_10_20_Aurora_Calibration\n200910_Aurora-Australis_BathymetryProcessing\n201803_tankExperiments\n20150102_Tangaroa\n200708030_Aurora-Australis_V3_CEAMARC\n200708040_Aurora-Australis_V4\n200708060_Aurora-Australis_V6_CASO\n200809000_Aurora-Australis_VTrials\n200809010_Aurora-Australis_V1\n200809020_Aurora-Australis_V2\n200809030_Aurora-Australis_V3\n200809050_Aurora-Australis_V5\n200910000_Aurora-Australis_VTrials\n200910010_Aurora-Australis_V1\n200910020_Aurora-Australis_V2\n200910030_Aurora-Australis_V3\n200910040_Aurora-Australis_V4\n200910050_Aurora-Australis_V5\n200910070_Aurora-Australis_VE1\n201011000_Aurora-Australis_VTrials\n201011002_Aurora-Australis_VE2\n201011010_Aurora-Australis_V1\n201011020_Aurora-Australis_V2\n201011021_Aurora-Australis_VMS\n201011030_Aurora-Australis_V3\n201011040_Aurora-Australis_V4\n201011050_Aurora-Australis_V5\n201112000_Aurora-Australis_VTrials\n201112001_Aurora-Australis_VE1\n201112010_Aurora-Australis_V1\n201112020_Aurora-Australis_V2\n201112030_Aurora-Australis_V3\n201112040_Aurora-Australis_V4\n201112050_Aurora-Australis_V5\n201112060_Aurora-Australis_V6\n201213000_Aurora-Australis_VTrials\n201213001_Aurora-Australis_VMS_SIPEX\n201213010_Aurora-Australis_V1\n201213020_Aurora-Australis_V2\n201213020_Aurora-Australis_V3\n201213040_Aurora-Australis_V4\n201314010_Aurora-Australis_V1\n201314020_Aurora-Australis_V2\n201314040_Aurora-Australis_V4\n201314060_Aurora-Australis_V6\n201415000_AuroraAustralis-Trials\n201415010-AuroraAustralis_V1\n201415020_AuroraAustralis_V2\n201415030_AuroraAustralis_V3\n201415040_AuroraAustralis_V4\n201516000-AuroraAustralis_VTrials\n201516010_AuroraAustralis_V1\n201516020_AuroraAustralis_V2\n201516030-AuroraAustralis_V3\n201617010-AuroraAustralis_V1\n201617020-AuroraAustralis_V2\n201617030-AuroraAustralis_V3\n201617040-AuroraAustralis_V4\n201718010-AuroraAustralis_V1\n201718020-AuroraAustralis_V2\n201718030-AuroraAustralis_V3\n201718040-AuroraAustralis_V4\n201819010-AuroraAustralis_V1\n201819020-AuroraAustralis_V2\n201819030-AuroraAustralis_V3\n201819040-AuroraAustralis_V4\n201920000-AuroraAustralis_VTrials\n201920010-AuroraAustralis_V1\n201920011-AuroraAustralis_VMI", "links": [ { diff --git a/datasets/AAD_voyage_soundings_1.json b/datasets/AAD_voyage_soundings_1.json index 8c2401983a..a7d8b983ba 100644 --- a/datasets/AAD_voyage_soundings_1.json +++ b/datasets/AAD_voyage_soundings_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_voyage_soundings_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic depth soundings are routinely collected on Australian Antarctic Division voyages.\nThis metadata record links to child records which describe processed soundings datasets from voyages since 1985. \nDocumentation is included with the datasets.", "links": [ { diff --git a/datasets/AAD_voyage_soundings_HI513_1.json b/datasets/AAD_voyage_soundings_HI513_1.json index 3ed892aeae..21dd75eb18 100644 --- a/datasets/AAD_voyage_soundings_HI513_1.json +++ b/datasets/AAD_voyage_soundings_HI513_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_voyage_soundings_HI513_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office.\nThe dataset is titled HI513.\n\nThe data was processed was collected on the following voyages:\n1997/98 V2, V4, V6\n1998/99 V1, V4, V5\n2003/04 V1, V3, V7, V9\n2004/05 V4, V5\n2005/06 V2, V5\n2006/07 V1, V2\n2007/08 V1, V2, V3, V5, V6\n2010/11 V3, V4, V5\n2011/12 V1, V2, V3, VE1\n\nThe data has not been through the verification process for use in charts.", "links": [ { diff --git a/datasets/AAD_voyage_soundings_HI534_1.json b/datasets/AAD_voyage_soundings_HI534_1.json index 3d07c5e541..b93ef804d3 100644 --- a/datasets/AAD_voyage_soundings_HI534_1.json +++ b/datasets/AAD_voyage_soundings_HI534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAD_voyage_soundings_HI534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office.\nThe dataset is titled HI534.\n\nThe data processed was collected on the following voyages:\n2012/13 voyages MS, 1, 2 and 3\n\nThe data has not been through the verification process for use in charts.", "links": [ { diff --git a/datasets/AAMBER_II_Chlorophyll_1.json b/datasets/AAMBER_II_Chlorophyll_1.json index f6330f1308..dce7bf9edd 100644 --- a/datasets/AAMBER_II_Chlorophyll_1.json +++ b/datasets/AAMBER_II_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAMBER_II_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll a data collected on the AAMBER II cruise of the Aurora Australis from January to March of 1991. The voyage traveled to the Prydz Bay region, and data were collected en route and in the area.", "links": [ { diff --git a/datasets/AAOT_0.json b/datasets/AAOT_0.json index bec11c5d99..8f3f3bdcc6 100644 --- a/datasets/AAOT_0.json +++ b/datasets/AAOT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAOT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the Acqua Alta Oceanographic Tower (AAOT), an Italian installation off the coast of Venice in the Adriatic Sea from 1999 to 2002.", "links": [ { diff --git a/datasets/AAS1300_PW_BATCHIX_1.json b/datasets/AAS1300_PW_BATCHIX_1.json index 4ef9b03523..26d781fcfb 100644 --- a/datasets/AAS1300_PW_BATCHIX_1.json +++ b/datasets/AAS1300_PW_BATCHIX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS1300_PW_BATCHIX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of ion-exchange batch equilibrium experiments were undertaken with laboratory solutions spiked with heavy metals to investigate the removal of these metals by Amberlite IRC748 at variable salinity and temperature. Spreadsheet 1 contains sample ID descriptions for experiments at 20 degrees celsius and spreadsheet 2 contains sample ID descriptions for experiments at 4 degrees celsius. Spreadsheets 3,4,5 contain the raw data for the batch experiments.\n\n#########\n\nSome terms and abbreviations used in these datasets are:\n\nLR - low range\nMR - mid range\nCorr - corrected for drift\nQC - in-house spiked standard (multicomponent metals)\nRinse - de-ionised water rinse through ICP-MS to check for contamination or carryover NIST - NIST standard 1640: trace metal water standard (NIST stands for National Institue of Standards and Technology) Ref Values - expected values from analysis of the NIST standard DilBlk - dilution blank\nConcentration AVG - average concentration (of three readings)\n\n#########\n\nThis work was carried out as part of ASAC project 1300 (ASAC_1300) - Development and application of technologies for the removal of heavy-metal contaminants from run-off associated with abandoned waste disposal sites.\n\nSome of the fields in this dataset are:\n\nIsotope\nDilution\nRinse\nAnalysis\nSample\nSeawater", "links": [ { diff --git a/datasets/AAS1300_PW_COLUMNSIX_1.json b/datasets/AAS1300_PW_COLUMNSIX_1.json index d7ce8c8096..ff0fee6ef7 100644 --- a/datasets/AAS1300_PW_COLUMNSIX_1.json +++ b/datasets/AAS1300_PW_COLUMNSIX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS1300_PW_COLUMNSIX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of ion-exchange column breakthrough experiments were undertaken with laboratory solutions spiked with heavy metals to investigate the removal of these metals by Amberlite IRC748 at variable salinity and temperature. Spreadsheets 1,2 contains sample ID descriptions for experiments at 20 degrees celsius, spreadsheet 3 contains sample ID descriptions for experiments at 20 degrees celsius (with a buffer), spreadsheet 4 contains sample ID descriptions for experiments at 4 degrees celsius, and spreadsheet 5 contains sample ID descriptions for the trial experiment. Spreadsheets 6,7,8,9,10,11,12 contain the raw data for the column experiments.\n\n#########\n\nSome terms and abbreviations used in these datasets are:\n\nLR - low range\nMR - mid range\nCorr - corrected for drift\nQC - in-house spiked standard (multicomponent metals)\nRinse - de-ionised water rinse through ICP-MS to check for contamination or carryover NIST - NIST standard 1640: trace metal water standard (NIST stands for National Institute of Standards and Technology) Ref Values - expected values from analysis of the NIST standard DilBlk - dilution blank\nConcentration AVG - average concentration (of three readings)\n\n#########\n\nThis work was carried out as part of ASAC project 1300 (ASAC_1300) - Development and application of technologies for the removal of heavy-metal contaminants from run-off associated with abandoned waste disposal sites.\n\nSome of the fields in this dataset are:\n\nIsotope\nDilution\nRinse\nAnalysis\nSample\nSeawater", "links": [ { diff --git a/datasets/AAS1300_PW_FIELDTRIALIX_1.json b/datasets/AAS1300_PW_FIELDTRIALIX_1.json index a6b8613192..494b6584e7 100644 --- a/datasets/AAS1300_PW_FIELDTRIALIX_1.json +++ b/datasets/AAS1300_PW_FIELDTRIALIX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS1300_PW_FIELDTRIALIX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ion-exchange columns were used in the WTP (water treatment plant) for water treatment during the clean-up of the Thala Valley tip - data collected during the operation were used as a field trial to investigate the effectiveness of Amberlite IRC748 for the application. Spreadsheets 1,2 contain sample ID descriptions for samples collected from the WTP, spreadsheet 3 contains sample ID descriptions for samples of meltwater collected from the tip site, and spreadsheet 4 contains sample ID descriptions for on-site operational monitoring samples. Spreadsheets 5,6,7,8,9 contain the raw data for the field trial.\n\n#########\n\nSome terms and abbreviations used in these datasets are:\n\nLR - low range\nMR - mid range\nCorr - corrected for drift\nQC - in-house spiked standard (multicomponent metals)\nRinse - de-ionised water rinse through ICP-MS to check for contamination or carryover NIST - NIST standard 1640: trace metal water standard (NIST stands for National Institue of Standards and Technology) Ref Values - expected values from analysis of the NIST standard DilBlk - dilution blank\nConcentration AVG - average concentration (of three readings)\n\n#########\n\nThis work was carried out as part of ASAC project 1300 (ASAC_1300) - Development and application of technologies for the removal of heavy-metal contaminants from run-off associated with abandoned waste disposal sites.\n\nSome of the fields in this dataset are:\n\nIsotope\nDilution\nRinse\nAnalysis\nSample\nSeawater", "links": [ { diff --git a/datasets/AAS380_1.json b/datasets/AAS380_1.json index 325870af53..d8e4d71b67 100644 --- a/datasets/AAS380_1.json +++ b/datasets/AAS380_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS380_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report and images taken as part of ASAC (AAS) project 380 - Archaeological Investigation of Sealing Sites at Heard Island.\n\nTaken from the report:\n\nFrom November 1986 to January 1987 the authors participated in the ANARE expedition to Heard Island. Our objectives were:\n\n1) To undertake a survey of historic archaeological sites of Heard Island and to compile an inventory of sites.\n\n2) At the request of the Antarctic Division to salvage certain sealing-era artefacts identified in the 1985/86 ANARE report as being at risk.\n\n3) At the request of the Antarctic Division, to record the site of the historic ANARE station at Atlas Cove, to assess the significance of surviving site features and make recommendations for the conservation of significant elements.\n\n4) To provide a report to the Australian Heritage Commission documenting the location, description and assessment of the various sealing and other historic places located during the 1986-87 expedition.\n\nSealing sites on Heard Island can be categorised into five types based on structural and functional differences. These five types are:\n\na) stone platforms\nb) hut footings or ruins\nc) occupied caves\nd) barrels\ne) grave\n\nAll types of sites are found on the various beaches around the island, except that stone platforms appear to be confined to the southern beaches, and only one grave is known.", "links": [ { diff --git a/datasets/AAS4180_BenthicBiodiversityDatabase_1.json b/datasets/AAS4180_BenthicBiodiversityDatabase_1.json index f053e0d4cd..6056da2a19 100644 --- a/datasets/AAS4180_BenthicBiodiversityDatabase_1.json +++ b/datasets/AAS4180_BenthicBiodiversityDatabase_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS4180_BenthicBiodiversityDatabase_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access database containing biological and environmental data collected by the Australian Antarctic Division, Human Impacts Benthic Biodiversity group.", "links": [ { diff --git a/datasets/AAS_1236_MillIs_IceCore_TraceIon_Chem_1.json b/datasets/AAS_1236_MillIs_IceCore_TraceIon_Chem_1.json index 163323def2..15ba98b349 100644 --- a/datasets/AAS_1236_MillIs_IceCore_TraceIon_Chem_1.json +++ b/datasets/AAS_1236_MillIs_IceCore_TraceIon_Chem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_1236_MillIs_IceCore_TraceIon_Chem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a trace ion chemical record from Mill Island (65o33'10\"S, 100o47'06\"E) 120m ice core. The ice core was drilled in January 2010 using the intermediate-depth ice core drill (ECLIPSE ice coring drill, Icefield Instruments, Inc.). The ice core was then processed in a clean freezer laboratory to retrieve samples for trace ion chemistry measurements. \nTrace ion chemical measurements were carried out using a suppressed ion chromatograph (IC). Samples were analysed using a Dionex (TM) AS18 ICS- 55 3000 (2 mm) microbore ion chromatograph. The major ion species measured in this data were methanesulfonic acid (CH3SO-3 [MSA]), chloride (Cl-), nitrate (NO-3 ), sulphate (SO2-4 ), sodium (Na+), magnesium (Mg2+), and calcium (Ca2+). Anions (i.e. MSA, Cl-, SO2-4 , and NO-3 ) were analysed using an IonPac (R) AS18 separation column and AG18 guard column. Cation (i.e. Na+, Mg2+, and Ca2+) analysis was performed using CS12A separation columns. The system performed anion and cation analysis simultaneously using dual isocratic pumps.", "links": [ { diff --git a/datasets/AAS_1313_JaneWasley-FieldNotebook-Casey-2002-3_1.json b/datasets/AAS_1313_JaneWasley-FieldNotebook-Casey-2002-3_1.json index 2dd20f6733..a98608b0b5 100644 --- a/datasets/AAS_1313_JaneWasley-FieldNotebook-Casey-2002-3_1.json +++ b/datasets/AAS_1313_JaneWasley-FieldNotebook-Casey-2002-3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_1313_JaneWasley-FieldNotebook-Casey-2002-3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scanned copy of field notebook and laboratory notebook\n\nBook owner: Jane Wasley\nASAC: 1313\nSeason: 2002/03\nLocation: Casey\n\nThe original hard copy of this notebook was, in January 2016, located in Jane Wasley's office at AAD. \n\nThe notebook is scanned as two PDF files: \n1. Field notebook\n2. Lab notebook\n\nThe content of the books are field and lab notes made in relation to: \n - 2002/03 baseline vegetation transects (Robinson Ridge and ASPA 135)\n - Field trasplant experiments\n - Water and nutrient experiment; Chlorophyll fluoresence, IR surface temperature measurements and photos\n - ibutton temperature sensors", "links": [ { diff --git a/datasets/AAS_2201_Casey_Monitoring_Meiofauna_1.json b/datasets/AAS_2201_Casey_Monitoring_Meiofauna_1.json index 377623df0f..6782df5fbb 100644 --- a/datasets/AAS_2201_Casey_Monitoring_Meiofauna_1.json +++ b/datasets/AAS_2201_Casey_Monitoring_Meiofauna_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2201_Casey_Monitoring_Meiofauna_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine sediment meiofauna community composition and sediment environmental data collected in 2005 and published in\nStark, J. S., M. Mohammad, A. McMinn, and J. Ingels. 2020. Diversity, abundance, spatial variation and human impacts in marine meiobenthic nematode and copepod communities at Casey station, East Antarctica. Frontiers in Marine Science 7:480.\n\nFrom the abstract:\nThe composition, spatial structure, diversity and abundance of Antarctic nematode and copepod meiobenthic communities was examined in shallow (5 \u2013 25 m) marine coastal sediments at Casey Station, East Antarctica. The sampling design incorporated spatial scales ranging from 10 meters to kilometres and included testing for human impacts by comparing disturbed (metal and hydrocarbon contaminated sediments adjacent to old waste disposal sites) and control areas. A total of 38 nematode genera and 20 copepod families were recorded with nematodes being dominant, comprising up to 95% of the total abundance. Variation was greatest at the largest scale (km\u2019s) but each location had distinct assemblages. At smaller scales there were different patterns of variation for nematodes and copepods. There were significant differences between communities at control and disturbed locations. Community patterns had strong correlations with concentrations of anthropogenic metals in sediments as well as sediment grain size and total organic content. Given the strong association with environmental patterns, particularly anthropogenic disturbance, meiofauna may be seen as very useful indicators of natural and anthropogenic environmental changes in Antarctica.\n\nMethods derived from:\nStark, J. S., M. Mohammad, A. McMinn, and J. Ingels. 2020. Diversity, abundance, spatial variation and human impacts in marine meiobenthic nematode and copepod communities at Casey station, East Antarctica. Frontiers in Marine Science 7:480.\n\nSampling design\nSampling was undertaken using a hierarchical, nested design with three spatial scales, Locations (separated by kms); within each location there were two sites (~ 100 m apart) and at each site there were two plots (~10m apart). Within each plot (1m diameter), two replicate cores were taken for meiofauna and two for environmental analysis, making a total of 8 meiofauna and 8 environmental cores per location, except at O\u2019Brien Bay-5 where one meiofauna core was lost during sampling. Six locations were sampled around Casey Station. There were three control locations, two of which were within O\u2019Brien Bay to the south of Casey (O\u2019Brien Bay-1 (OB-1) and O\u2019Brien Bay-5 (OB-5)); and one within Newcomb Bay, in McGrady Cove (Fig. 1). There were three locations adjacent to waste disposal sites: two locations were situated along a gradient of pollution within Brown Bay (Inner and Middle)(Stark et al. 2004, Stark 2008); and a third location was at Wilkes, adjacent to the abandoned waste disposal site at the derelict Wilkes station (Stark et al. 2003a), all within Newcomb Bay (Fig. 1). These waste disposal sites were used historically to dispose of all waste and rubbish generated on station and included used oil, building materials, electronics and batteries, food, clothing and chemicals (Snape et al. 2001, Stark et al. 2006). Both waste disposal sites are contaminated with metals and hydrocarbons above background levels (Stark et al. 2008, Stark et al. 2014b, Fryirs et al. 2015).\n\nSample collection, meiofauna preparation and identification\nSediment samples were collected by divers using modified 60 ml syringes with their intake end cut off to form a small core tube (28mm internal diameter). Cores were pushed into the sediment to a depth of 10 cm, extracted, and the bottom end was capped. In a few cases samples were only taken down to 5-7 cm, where sediments were less than 10 cm deep due to underlying rock. No sediments less than 5 cm deep were sampled. \nCores were transported to Casey Station laboratories where they were emptied into sample jars and 4% formalin was added to each sample. Prior to processing, each sample was washed through a 500 \u03bcm sieve to remove the macrofauna and the coarser sediment fraction. A 32 \u03bcm sieve was used to retain the meiofauna size fraction. Meiofauna were extracted through a modified gravity gradient centrifugation technique (Heip et al. 1985, Pfannkuche et al. 1988) using a % solution of Ludox HS40 and Ludox AS in distilled water (Witthoft-Muhlmann et al. 2005). Ludox is a silicasol (a colloidal solution of Si02) which causes no plasmolysis. Samples were rinsed thoroughly over a sieve of 32 \u00b5m with tap water to prevent flocculation of Ludox. The samples were then transferred from the sieve to a large centrifuge tube. Ludox was diluted with water to specific gravity 1.18 g/ml (60% Ludox and 40% water; density = 1.18) and added to each tube until the level of the mixture was balanced for centrifuging. The sample was then centrifuged at 2800 rpm for 10 min. The supernatant was decanted and collected, and the remaining sediment pellet was resuspended. This process was repeated three times. \nAll supernatants were filtered on a 32 \u00b5m sieve, which was rinsed with tap water to avoid a reaction between the Ludox and formalin. After the extraction, 4% formalin and 1% of Rose Bengal (to facilitate counting) was added to preserve meiofauna before identification.\n\nNematodes and copepods retained on the 32 \u00b5m sieve were counted and sorted using a dissecting microscope at 25X magnification (Zeiss Stemi 2000; Zeiss Inc., Germany). Two hundred nematodes per sample were picked out at random and mounted on slides in glycerine after a slow evaporation procedure (modified after Riemann, 1988) and identified to genus level using Platt and Warwick (1983, 1988) and Warwick et al. (1998) and NeMys online identification (Steyaert et al. 2005). All copepods were picked out and mounted on slides in glycerine without evaporation for identification to family level using THAO: the Taxonomische Harpacticoida Archiv Oldenburg 2005 and Bodin (1997). The identification of nematodes and copepods was conducted on a compound microscope (1000 x magnification).\n\nEnvironmental variables\nSediment samples were taken for analysis of grain size, metals and total organic matter (TOM) using a 5 cm diameter core pushed 10 cm into the sediment. Cores were frozen at -20\u00b0C until analysis. Each core was subsampled from the top 5 cm of the frozen core, which was then homogenized by stirring and then subsampled further for separate analysis of grain size, metals and TOM. Full details of analytical methods can be found in Stark et al. (2014a) and are briefly summarised below.\nTotal organic matter was calculated by mass-loss on ignition at 550 \u00b0 for four hours to determine ash free dry weight following Heiri et al. (2001), on a on a 2 g homogenised wet sub-sample, from 2 replicate cores in each plot for a total of 4 cores per location. \nGrain size analysis: The outer 5 mm edge of the top 5 cm of the core was removed with a scalpel blade and dried at 45 \u00b0C, then sieved through a 2mm sieve. The less than 2 mm fraction and the greater than 2 mm fraction were weighed separately. A 5 g sample of the less than 2mm fraction was analysed using a Mastersizer 2000 Particle Size Analyser with Hydro 2000MU accessory at the Department of Physical Geography, Macquarie University, Sydney.\nAnalysis of metals in sediments were done on a 3 g sub-sample of homogenised wet sediment. A 1:10 w/v 1 M HCl digest was used as recommended by (Scouller et al. 2006), which gives an estimate of bioavailable elements and those more likely to have an anthropogenic source. Samples were analysed by ICP-MS at the Central Science Laboratories (CSL), University of Tasmania for a suite of ions which included: Sr, Mo, Ag, Cd, Sn, Sb, Pb, Mg, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Al, Ba.", "links": [ { diff --git a/datasets/AAS_2265_Elephant_seal_wallows_Vestfold_Hills_1.json b/datasets/AAS_2265_Elephant_seal_wallows_Vestfold_Hills_1.json index fc73c58cc1..54be96f67c 100644 --- a/datasets/AAS_2265_Elephant_seal_wallows_Vestfold_Hills_1.json +++ b/datasets/AAS_2265_Elephant_seal_wallows_Vestfold_Hills_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2265_Elephant_seal_wallows_Vestfold_Hills_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Results from a February 2007 survey of the Vestfold Hills coastline and offshore islands for used and disused southern elephant seal wallows. The data here are point locations of the wallows, not the extents or boundaries of the wallows.\n\nThe table below gives the coordinates (decimal degrees) for the elephant seal wallows found, their unofficial names and the wallow status as used or disused at the time of survey. \n\nData were used in the 2018 Vestfold Hills/Davis Station Helicopter map:\n\nWallow name \tLatitude\tLongitude\tStatus\nHawker Island\t-68.637360\t77.840040\tUsed\nHawker Island\t-68.634950\t77.841310\tUsed\nHawker Island\t-68.632180\t77.841560\tUsed\nMule Island\t-68.647860\t77.825900\tUnused\nMule Island\t-68.646650\t77.823920\tUnused\nZappert Point\t-68.505100\t78.081020\tUnused\nOld Wallow\t-68.598345\t77.937185\tUsed\nDavis beach\t-68.577926\t77.967032\tUsed\nHeidemann Bay\t-68.592067\t77.945325\tUsed\nNorth of station\t-68.571916\t77.971011\tUsed\n\n", "links": [ { diff --git a/datasets/AAS_2691_Clark_et_al_2016_1.json b/datasets/AAS_2691_Clark_et_al_2016_1.json index e03c33a57a..18ab7deea1 100644 --- a/datasets/AAS_2691_Clark_et_al_2016_1.json +++ b/datasets/AAS_2691_Clark_et_al_2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2691_Clark_et_al_2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data repository for the paper:\n\n\"The roles of sea-ice, light and sedimentation in structuring shallow Antarctic benthic communities\"\nGraeme F. Clark, Jonathan S. Stark, Anne S. Palmer, Martin J. Riddle, Emma L. Johnston.\nPLoS ONE\n\nData are boulder communities (epifauna), annual light budgets, and sediment traps. See the paper for more details.\n\nABSTRACT\nOn polar coasts, seasonal sea-ice duration strongly influences shallow marine environments by affecting environmental conditions, such as light, sedimentation, and physical disturbance. Sea-ice dynamics are changing in response to climate, but there is limited understanding of how this might affect shallow marine environments and benthos. Here we present a unique set of physical and biological data from a single region of Antarctic coast, and use it to gain insights into factors shaping polar benthic communities. At sites encompassing a gradient of sea-ice duration, we measured temporal and spatial variation in light and sedimentation and hard-substrate communities at different depths and substrate orientations. Biological trends were highly correlated with sea-ice duration, and appear to be driven by opposing gradients in light and sedimentation. As sea-ice duration decreased, there was increased light and reduced sedimentation, and concurrent shifts in community structure from invertebrate to algal dominance. Trends were strongest on shallower, horizontal surfaces, which are most exposed to light and sedimentation. Depth and substrate orientation appear to mediate exposure of benthos to these factors, thereby tempering effects of sea-ice and increasing biological heterogeneity. However, while light and sedimentation both varied spatially with sea-ice, their dynamics differed temporally. Light was sensitive to the site-specific date of sea-ice breakout, whereas sedimentation fluctuated at a regional scale coincident with the summer phytoplankton bloom. Sea-ice duration is clearly the overarching force structuring these shallow Antarctic benthic communities, but direct effects are imposed via light and sedimentation, and mediated by habitat characteristics.\n\n\nData files:\n\nBoulder_community_data.csv\n- Percentage cover data for sessile organisms (invertebrates and algae) growing on boulder surfaces.\n- Columns 1 to 5 are sample attributes, columns 6 to 57 are measured variables (species or bare space).\n\nLight_budget_data.csv\n- Annual light budgets at each site, recorded by light metres.\n- Columns are site name and annual light budget (mol photons m-2 year-1)\n\nSediment_trap_data.csv\n- Total sediment collected in sediment traps\n- Columns are site label, position in bay, replicate, dates deployed and retrieved, and the calculated sediment flux (g m-2 d-1)", "links": [ { diff --git a/datasets/AAS_2780_1.json b/datasets/AAS_2780_1.json index 206817aecc..68bf106916 100644 --- a/datasets/AAS_2780_1.json +++ b/datasets/AAS_2780_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2780_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 2780.\n\nPublic Summary\nThe distribution of plants in Antarctica is chiefly limited by the availability of water and sufficiently high temperatures. This project assesses and simulates variation in these factors as experienced by Antarctic moss species, measures how mosses physiologically respond to temperature and moisture changes, and how they will fare in possible future climate scenarios.\n\nProject objectives:\n\nThe objectives of this project are:\n\n1) To assess and monitor the seasonal and inter-seasonal variation in temperature and moisture regimes of moss vegetation in continental Antarctica\n\n2) To assess the response of Antarctic moss species to the interaction of moisture and temperature, and different cycles of freezing/thawing and drying/wetting\n\n3) To assess the physiological response of Antarctic moss species to simulated climate change by experimental warming in the field\n\n4) To provide baseline data for modelling the productivity of moss vegetation in response to moisture/temperature interactions, and the possible response of vegetation to short- and long-term changes in climatic patterns in continental Antarctica\n\nBackground\n\nThe distribution of plants in Antarctica is chiefly limited by the availability of water, nutrients, and temperatures that are sufficiently high to allow the plant to physiologically operate, as well as to provide water in liquid form. Water availability and temperature are tightly linked. Where plants have access to liquid water, a 'window' is created where the plant can acquire carbon and grow.\n\nIn the arid climate of eastern continental Antarctica, mosses can occur in areas where mild temperatures during part of the year allow snow or ice to melt and provide the necessary water for carbon acquisition and growth. When water becomes scarce, mosses desiccate and usually survive dry periods until the next 'window of opportunity' opens.\n\nMoss growth is limited by the number and duration of such 'window' periods. There are, however, trade-offs; adjusting to repeated freezing and thawing or drying and re-wetting often reduces the photosynthetic performance of mosses (Kennedy 1993, Lovelock et al. 1995a,b, Robinson et al. 2000). It has been suggested for mosses from other xeric environments that carbon balance limits the distribution of desiccation-tolerant mosses where repeated drought alternates with short wetting periods (Alpert and Oechel 1985). Studies of photosynthetic performance during dehydration (Robinson et al. 2000) or after re-wetting (Schlensog et al. 2004, Wasley 2004, Wasley et al submitted, Schortemeyer, Siebke, Medek and Ball, unpublished data from AAD project 2544) show considerable differences between moss species in the timing of the decline or increase in photosynthesis during drying or after re-wetting, respectively. Some species recover their photosynthetic competence after re-wetting more rapidly than others, and some species lose their photosynthetic competence faster during drying. In addition, cushion size will affect drying and wetting patterns and has been shown to influence the response of photosynthesis to drying and wetting (Zotz et al. 2000). Differential responses of moss species to freezing and drying cycles will influence the comparative performance of species and ultimately species distribution and vegetation composition.\n\nThere are good temperature records that extend for more than half a century for a number of sites in continental Antarctica. However, temperature data gathered by weather stations often do not reflect the temperatures of soil or ice surfaces, and importantly, of moss cushions or turfs, which can be substantially warmer than the ambient air temperature (Melick and Seppelt 1997).\n\nWhile maritime Antarctica shows clear warming trends over the last 50 years (Turner et al. 2005), the patterns for continental Antarctica are less clear. Melick and Seppelt (1997) have suggested a long-term drying pattern for the Windmill Islands region, consistent with a decrease in moss and an increase in lichen vegetation. Inter-annual variation in temperature and moisture can be highly variable and often obscure long-term trends. Whichever way temperature, precipitation and wind patterns develop, they will greatly affect vegetation that is at the edge of its distribution, in a tightly balanced system in the world's most marginal sites for terrestrial plant life.\n\nMosses are (together with lichens) the principal component of continental Antarctic vegetation. To assess the response of mosses to changes in temperature and moisture during a season, these factors must be monitored at the moss level. This project proposes to monitor moisture and temperature in several moss species along moisture gradients near Casey Station (Wilkes Land). We will measure the physiological response of the different species to different regimes of freezing, thawing, drying, and wetting, in field-based free-air heating experiments as well as in controlled laboratory environments.\n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\n\nAll preparations for the planned experiments were made, including construction, purchase and testing of equipment. Unfortunately, the research program had to be postponed because unusually warm temperatures caused the flights to be cancelled and consequently we were not able to travel to Casey.\n\nTaken from the 2010-2011 Progress Report:\n\nProgress against objectives:\nAll preparations for the planned experiments were made, including testing of equipment. Unfortunately, the research program had to be postponed because unusually warm temperatures caused the flights to be cancelled and consequently we were not able to travel to Casey.", "links": [ { diff --git a/datasets/AAS_2780_dryingresponse_1.json b/datasets/AAS_2780_dryingresponse_1.json index 3ce3464896..892b48c2ff 100644 --- a/datasets/AAS_2780_dryingresponse_1.json +++ b/datasets/AAS_2780_dryingresponse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2780_dryingresponse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We compared the drying responses of clumps of moss from Casey Station, Antarctica, evaluating the changes in mass, water content, photosynthesis (measured by chlorophyll fluorescence), cellular solute concentration (measured by differential scanning calorimetry) and cellular dimensions. \n \nDrying was performed at different temperatures and VPDs to evaluate the relative impacts of temperature and humidity on moss physiology.\n\nThis dataset is comprised of several spreadsheets (all in comma separated values format). The primary data sheet (Moss-desiccation tolerance Master Datasheet.csv) compiles the overall experimental data, including means derived from several other data sheets. \n\nMoss-fluorescence Datasheet.csv reports data from Walz MaxiPAM chlorophyll fluorescence measurements of moss clumps at each timepoint. \n\nMoss-fluorescence micro Datasheet.csv reports data from Walz Microscope PAM chlorophyll fluorescence measurements of individual leaves and shoots sampled at each timepoint and kept under oil to retain hydration. \n\nMoss-meas Datasheet.csv reports the dimensions (area, perimeter, length and width) of cells from individual leaves sampled at each timepoint and kept under oil to retain hydration. \nSolute concentration of the shoots was determined by performing differential scanning calorimetry (DSC) on shoots sampled at each timepoint. \n\nTo convert the melting/freezing points measured to concentrations requires calibration, the data for which are provided in Moss-DSC calib.csv.\n\nThe data are laid out with columns for each variable considered, and rows for each sample. The columns for each spreadsheet are as follows:\n\nMoss-desiccation tolerance Master Datasheet.csv:\nDate-date of experiment\nRun-experimental run (1 to 18)\nClumpSp-dominant species of moss clump: C-Ceratodon purpureus; S- Schistidium antarctici; BC- Bryum pseudotriquetrum and Ceratodon purpureus codominant Species-species of shoot examined in calorimetry/microscopy: B- Bryum pseudotriquetrum; C-Ceratodon purpureus; S- Schistidium antarctici Replicate-replicate (here equal to Run) Pseudoreplicates-replicates of the same species during the same run Temp-Treatment temperature: 8 degrees C, 16 degrees C or 24 degrees C VPD-Vapour pressure differential of treatment: 0.2kPa, 0.5kPa or 1.2kPa Timepoint-Sampling timepoint (0 = initial conditions) Time-Time in hours since start of experimental run Treatment-Experimental phase: Drying, Recovery (rehydration following drying process), ReRun (second round of drying following recovery) Basket mass-Mass in grams of basket containing moss clump Basket + moss mass-Mass in grams of basket and moss sample Fresh mass-Fresh mass in grams of moss clump (calculated) Dry mass-Mass in grams of oven-dried moss clump RWC-Relative water content of moss clump in g water per g dry mass Percentage of initial RWC-Relative water content as a percentage of initial RWC Mean Fo-Mean Fo of clump (from Moss-fluorescence Datasheet.csv) Sd Fo-Standard deviation of the Fo of the clump (from Moss-fluorescence Datasheet.csv) Mean Fm-Mean Fo of clump (from Moss-fluorescence Datasheet.csv) Sd Fm-Standard deviation of the Fo of the clump (from Moss-fluorescence Datasheet.csv) Mean Fv.Fm-Mean Fo of clump (from Moss-fluorescence Datasheet.csv) Sd Fv.Fm-Standard deviation of the Fo of the clump (from Moss-fluorescence Datasheet.csv) Mean Fo'-Mean Fo of clump (from Moss-fluorescence Datasheet.csv) Sd Fo'-Standard deviation of the Fo of the clump (from Moss-fluorescence Datasheet.csv) Mean Fm'-Mean Fo of clump (from Moss-fluorescence Datasheet.csv) Sd Fm'-Standard deviation of the Fo of the clump (from Moss-fluorescence Datasheet.csv) Tin mass-Mass in grams of the empty calorimetry tin Tin + moss wet mass-Mass in grams of moss shoot and calorimetry tin Tin + moss dry mass-Mass in grams of oven-dried moss shoot and calorimetry tin RWC stems DSC-Relative water content of shoot used for calorimetry in g water per g dry mass Tm-1-Melting point in uncorrected degrees C of shoot water (first replicate) PeakT1-Melting peak in uncorrected degrees C of shoot water (first replicate) Peak1Area-Area of melting peak in DSC units (first replicate) Tm-2-Melting point in uncorrected degrees C of shoot water (second replicate) PeakT2-Melting peak in uncorrected degrees C of shoot water (second replicate) Peak2Area-Area of melting peak in DSC units (first replicate) Tm-3-Melting point in uncorrected degrees C of shoot water (third replicate) PeakT3-Melting peak in uncorrected degrees C of shoot water (third replicate) Peak3Area-Area of melting peak in DSC units (first replicate) Tm-mean-Mean melting point in uncorrected degrees C of shoot water -see Calibs.csv for conversion to solute concentration PeakT-mean-Mean melting peak in uncorrected degrees C of shoot water -see Calibs.csv for conversion to solute concentration PeakArea-mean-Mean area of melting peak in DSC units -see Calibs.csv for conversion to water mass Tm-corr-Melting point of shoot water, corrected for calorimetry ramp rate offset PeakT-corr-Melting peak of shoot water, corrected for calorimetry ramp rate offset Dsc-notes-Notes on appearance of differential scanning calorimetry peaks Stem mass-Mass in g of shoot used for microscopy Mean area-Mean area in mm2 of laminar cells (from Moss-meas Datasheet.csv) Sd area-Standard deviation of cell area (from Moss-meas Datasheet.csv) Mean perimeter-Mean perimeter in mm of laminar cells (from Moss-meas Datasheet.csv) Sd perimeter-Standard deviation of cell perimeter (from Moss-meas Datasheet.csv) Mean width-Mean width in mm of laminar cells (from Moss-meas Datasheet.csv) \nSd width-Standard deviation of cell width (from Moss-meas Datasheet.csv) Mean length-Mean length in mm of laminar cells (from Moss-meas Datasheet.csv) Sd length-Standard deviation of cell length (from Moss-meas Datasheet.csv) Micro Mean Fo-Mean Fo of leaves (from Moss-fluorescence micro Datasheet.csv) Micro Sd Fo-Standard deviation Fo of leaves (from Moss-fluorescence micro Datasheet.csv ) Micro Mean Fm-Mean Fm of leaves (from Moss-fluorescence micro Datasheet.csv) Micro Sd Fm- Standard deviation Fm of leaves (from Moss-fluorescence micro Datasheet.csv) Micro Mean Fv.Fm-Mean FvFm of leaves (from Moss-fluorescence micro Datasheet.csv) Micro Sd Fv.Fm- Standard deviation FvFm of leaves (from Moss-fluorescence micro Datasheet.csv)\n\nMoss-fluorescence Datasheet.csv\nDate-date of experiment\nRun-experimental run (1 to 18)\nClumpSp-dominant species of moss clump: C-Ceratodon purpureus; S- Schistidium antarctici; BC- Bryum pseudotriquetrum and Ceratodon purpureus codominant Replicate-replicate (here equal to Run) Pseudoreplicates-replicates of the same species during the same run Temp-Treatment temperature: 8 degrees C, 16 degrees C or 24 degrees C VPD-Vapour pressure differential of treatment: 0.2kPa, 0.5kPa or 1.2kPa Timepoint-Sampling timepoint (0 = initial conditions) Time-Time in hours since start of experimental run Treatment-Experimental phase: Drying, Recovery (rehydration following drying process), ReRun (second round of drying following recovery) Problem-Identifies if there were any problems with the measurements Fo-Fluorescence in absence of light Fm-Dark adapted maximum fluorescence Fv/Fm-Maximum quantum yield of PSII Fo'1-Minimal light fluorescence (first replicate) Fo'2-Minimal light fluorescence (second replicate) Fo'3-Minimal light fluorescence (third replicate) Fm'1-Maximal light fluorescence (first replicate) Fm'2-Maximal light fluorescence (second replicate) Fm'3-Maximal light fluorescence (third replicate) Mean Fo-Mean Fo of clump Sd Fo-Standard deviation of the Fo of the clump Mean Fm-Mean Fo of clump Sd Fm-Standard deviation of the Fo of the clump Mean Fv.Fm-Mean Fo of clump Sd Fv.Fm-Standard deviation of the Fo of the clump Mean Fo'-Mean Fo of clump Sd Fo'-Standard deviation of the Fo of the clump Mean Fm'-Mean Fo of clump Sd Fm'-Standard deviation of the Fo of the clump\n\nMoss-fluorescence Datasheet.csv\nDate-date of experiment\nRun-experimental run (1 to 18)\nClumpSp-dominant species of moss clump: C-Ceratodon purpureus; S- Schistidium antarctici; BC- Bryum pseudotriquetrum and Ceratodon purpureus codominant Replicate-replicate (here equal to Run) Temp-Treatment temperature: 8 degrees C, 16 degrees C or 24 degrees C VPD-Vapour pressure differential of treatment: 0.2kPa, 0.5kPa or 1.2kPa Timepoint-Sampling timepoint (0 = initial conditions) Time-Time in hours since start of experimental run Treatment-Experimental phase: Drying, Recovery (rehydration following drying process), ReRun (second round of drying following recovery) RWC stems-Relative water content of shoot in g water per g dry mass (from calorimetry tins) Fo-Fluorescence in absence of light Fm-Dark adapted maximum fluorescence Fv/Fm-Maximum quantum yield of PSII Mean Fo-Mean Fo of clump Sd Fo-Standard deviation of the Fo of the clump Mean Fm-Mean Fo of clump Sd Fm-Standard deviation of the Fo of the clump Mean Fv.Fm-Mean Fo of clump Sd Fv.Fm-Standard deviation of the Fo of the clump\n\nMoss-meas Datasheet.csv\nDate-date of experiment\nRun-experimental run (1 to 18)\nClumpSp-dominant species of moss clump: C-Ceratodon purpureus; S- Schistidium antarctici; BC- Bryum pseudotriquetrum and Ceratodon purpureus codominant Replicate-replicate (here equal to Run) Temp-Treatment temperature: 8 degrees C, 16 degrees C or 24 degrees C VPD-Vapour pressure differential of treatment: 0.2kPa, 0.5kPa or 1.2kPa Timepoint-Sampling timepoint (0 = initial conditions) Time-Time in hours since start of experimental run Treatment-Experimental phase: Drying, Recovery (rehydration following drying process), ReRun (second round of drying following recovery) RWC-Relative water content of moss clump in g water per g dry mass RWC stems-Relative water content of shoot in g water per g dry mass (from calorimetry tins) Area-Area in mm2 of leaf lamina cell Perimeter-Perimeter in mm of leaf lamina cell Width-Width in mm of leaf lamina cell Length-Length in mm of leaf lamina cell Mean area-Mean area in mm2 of laminar cells Sd area-Standard deviation of cell area Mean perimeter-Mean perimeter in mm of laminar cells Sd perimeter-Standard deviation of cell perimeter Mean width-Mean width in mm of laminar cells Sd width-Standard deviation of cell width Mean length-Mean length in mm of laminar cells Sd length-Standard deviation of cell length\n\nCalib.csv\nSolution-Water or sucrose\nBymass-Concentration of sucrose solution in g per g water Concentration-Molar concentration of sucrose solution Volume-Volume in microliters of sample Mass tin-Mass in g of empty calorimetry tin Mass wet-Mass in g of calorimetry tin + sample Mass sample-Mass in g of sample Tm- Melting point in uncorrected degrees C of solution\nPeak- Melting peak in uncorrected degrees C of solution\nArea- Area of melting peak of solution in DSC units Tf-Freezing point in uncorrected degrees C of solution\nAreaf- Area of freezing peak of solution in DSC units\n\n\nThis work was conducted under the auspices of AAS projects 2780 and 2061.", "links": [ { diff --git a/datasets/AAS_2899_haloarchaea_1.json b/datasets/AAS_2899_haloarchaea_1.json index a7029deac4..d496980c4d 100644 --- a/datasets/AAS_2899_haloarchaea_1.json +++ b/datasets/AAS_2899_haloarchaea_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2899_haloarchaea_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "See the referenced paper for additional details.\n\nSample collection and processing for metagenomics from Deep Lake. Water samples were collected from DL (68o33\u201936.8S, 78o11\u201948.7E), Vestfold Hills, Antarctica between November 30 and December 5, 2008 (Fig. S1). Water was collected by dinghy from above the deepest point in the lake by pumping water directly from 5, 13, 24 and 36 m depths into 25L drums and immediately processing samples on-shore by sequential size fractionation through a 20 \u00b5m prefilter directly onto filters 3.0, 0.8 and 0.1 micron pore sized, 293 mm polyethersulfone membrane filters (12,13,17-22). A volume of 50 L was filtered for 5, 13 and 24 m depths, and 25 L for 36 m depth. Samples were preserved in buffer and cryogenically stored, and DNA extracted as previously described (12,13,18-22).\n\nIsolation, growth and genomic DNA extraction of DL haloarchaea. tADL (NCBI taxon ID 758602), DL31 (NCBI taxon ID 756883) and DL1 (NCBI taxon ID 751944) were isolated from DL surface water collected December 2006 (tADL) and November 2008 (DL31, DL1) (Fig. S1). Pure cultures were recovered from water samples using an extinction dilution method (23) and DBCM2 medium (24,25). All cultures were incubated at 30 degrees C. Repeated rounds of limiting dilution titrations produced pure cultures, as assessed by microscopy and 16S rRNA gene sequencing (16). For large-scale cultivation, cells were inoculated into 200 ml of DBCM2 medium in 500 ml capacity, cotton-wool stoppered flasks. Cultures were shaken (100 rpm) at 30 degrees C until late exponential phase and cells harvested by centrifugation (5,000 rpm = 4066 x g, 15 min, 4 degrees C, Sorvall GSA rotor). The cell pellet was resuspended gently in 2 ml of a solution containing 20% (v/v) glycerol and 2 M NaCl. Cell lysis and DNA purification was performed using Qiagen genomic tips (500/G), and the manufacturer\u2019s protocol for the extraction of DNA from bacteria (Qiagen genome DNA handbook). The resulting DNA was checked for quantity and quality by spectroscopy (A260/A280 greater than or equal to 1.95) and by agarose gel electrophoresis. PCR and sequencing of the 16S rRNA genes was used to confirm identity and purity of the DNA preparations.\n\nDNA sequencing. Metagenome libraries for pyrosequencing were constructed using DNA from 0.1 \u00b5m filters (at 5 m, 13 m and 24 m depth), 0.8 um and 3.0 um filters (at 24 m depth) or pooled DNA from all three filter sizes (at 36 m depth) using the RAPID protocol (Roche) and sequenced using 454 technology (26) on a 454-FLX machine using Titanium chemistry. Illumina sequencing (27) was performed from libraries made using DNA from 0.8 um and 3.0 micron filters from the 24 m sample only, using recommended protocols (Illumina), and were sequenced on the Illumina GAIIx using paired-end 76 cycle reads. Pyrosequencing of PCR amplified V8 region of small subunit (SSU) rRNA genes was used to generate microbial community profiles. The 454 adaptor-added SSU rRNA gene primer set, 926Fw (5\u2019-3\u2019AAACTYAAAKGAATTGRCGG) and 1392R (5\u2019-3\u2019ACGGGCGGTGTGTRC) was used in PCR to amplify the V6-V8 region, with 5-bp barcodes incorporated into the reverse primer to multiplex samples. PCR amplicons were sequenced by the DOE Joint Genome Institute, using the Roche 454 GS Titanium technology as previously described (28). Sequences were analyzed through the Pyrotagger computational pipeline (http://pyrotagger.jgi-psf.org) for quality trimming, clustering to operational taxonomic units (OTUs) based on 97% sequence identity, and taxonomic assignment by blastn against the Greengenes database (29). Singletons and potential chimeras were removed to minimize PCR artifacts. Draft genomes of the DL haloarchaea were generated at the DOE Joint Genome Institute (JGI) using a combination of Illumina (27) and 454 technologies (26). Briefly, for each genome we constructed and sequenced an Illumina GAii shotgun library, a 454 Titanium standard library and a paired end 454 library with an average insert size of 8-10 kb. All general aspects of library construction and sequencing performed at the JGI can be found at http://www.jgi.doe.gov/. The initial draft assemblies were generated using a Newbler/VELVET (30) hybrid approach. Manual finishing of all genomes was then performed at Los Alamos National Laboratory using a combination of computational tools, as well as PCR fragment subcloning and PCR-based primer walks.", "links": [ { diff --git a/datasets/AAS_2926_1.json b/datasets/AAS_2926_1.json index 10bfba1af2..05131464fc 100644 --- a/datasets/AAS_2926_1.json +++ b/datasets/AAS_2926_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2926_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 2926.\n\nPublic Summary\nDNA based approaches will be used to study key features of the ecology of whales, penguins and krill. Standard methods cannot accurately estimate what prey species these predators consume, how old they are, or how they are related to the rest of their species. This project will apply novel DNA based methods to biopsy or scat samples as a non-invasive means of improving our understanding of the diet, age and population structure of these important predators.\n\nProject objectives:\n\nThe overall objective of this project is to use molecular biology to study aspects of the ecology of key Southern Ocean predators that cannot be addressed with other methodologies. The organisms that the project would focus upon have been chosen because they are large biomass components of the Southern Ocean food web and because they are important to the Australian Governments commitments to the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and the International Whaling Commission (IWC). This project is integral to the work of the Australian Centre for Applied Marine Mammal Science (ACAMMS) that has recently been formed within the Science Branch of the AAD. The focus predators are baleen whales (primarily Minke whales, Balaenoptera edeni and Humpback whales, Megaptera novaengliae), Antarctic krill (Euphausia superba) and Adelie penguins (Pygoscelis adeliae). Within this overall goal, there are three major objectives:\n\n1. To characterise and monitor predation by key Southern Ocean organisms with dietary DNA analysis. \n2. To use population genetics to study the stock structure and population size of baleen whales and Antarctic krill.\n3. To develop and validate DNA-based age estimation methods for whales.\n\n1. DNA Based Dietary Research\nA major objective of this project is to apply DNA based methods for dietary analysis to large sample sets taken to address specific ecological questions. My group at the Australian Antarctic Division has been at the forefront of developing DNA based methods to study animal diet. We have been especially active in researching DNA as a non-invasive means of studying the diet of large mammals and birds by reconstructing diet with prey DNA that we can identify in scats from predators. Our development of new DNA-based methodologies (Jarman et al., 2002; Jarman et al., 2004; Deagle et al., 2005; Jarman et al., 2006a) and accompanying software tools (Jarman 2004; Jarman 2006) have led to more efficient dietary analysis methods and has produced a substantial volume of good quality published research and stimulated international interest in these methodologies, which are now being pursued by several overseas laboratories. We have completed short descriptive studies of the diet of Antarctic krill (Passmore et al., 2006), whales (Jarman et al., 2002; Jarman et al., 2004; Jarman et al., 2006b), fur seals (Casper et al., in prep) and macaroni penguins (Deagle et al., in prep) with these methods, but have not had comprehensive sets of samples with which we can address broader ecological questions. The ecological questions that the dietary component of this project will address are:\n\n1a. What is the diversity and identity of prey species consumed by populations of the key predators?\n1b. What are the relative biomass proportions of prey species consumed by key predator populations?\n1c. What temporal variation is there in diversity, identity and abundance of prey consumed by each key predator population?\n1d. What spatial variation is there in diversity, identity and abundance of prey consumed by each key predator population?\n\nThe focus species cover three trophic levels of the Southern Ocean food web. Krill are thought to feed predominately on primary producers with some heterotrophic prey taken as well. Adelie penguins feed on krill and other small nekton and plankton, as well as being prey of leopard seals and killer whales, making them a mid-to-high level predator. Baleen whales feed on diverse planktonic and nektonic organisms, preferring crustaceans and small fish that tend to form high-concentration swarms and are top predators. By studying krill and their most abundant predators (Adelie penguins) and their largest predators (baleen whales) we get an assessment of trophic flow from primary production to both a mid-level predator and a top-level predator. It is clearly not possible to study all components of the Southern Ocean food web, so by targeting these three key groups it is hoped that we will not only gather information that is most directly relevant to the objectives of the science program, but that this information will also be an efficient means of assaying some of the most important trophic interactions in the Southern Ocean food web as a whole.\n\nKrill are highly abundant and quite easy to sample. They are generalist feeders, which makes them a good organism for monitoring changes in populations of primary producers and small heterotrophs. Furthermore, they are the target organism of the world's largest crustacean fishery (Nicol and Endo, 1997). This makes them a species of major interest to CCAMLR. Our scientific objective in studying krill diet with DNA based methods is to improve our understanding of this critically important organism. This research should contribute to Australia's role in CCAMLR and consequent influence within the Antarctic treaty system. \n\nAdelie penguins are the only land-based predators in this study. They are the most abundant penguin and can be found in high concentrations at breeding colonies at many points along the Antarctic coastline. This makes their population size and condition relatively easy to estimate when compared to completely marine organisms. These features make them an excellent animal to survey for ecosystem monitoring purposes and they have been selected by CCAMLR as their main organism for the CEMP (CCAMLR Ecosystem Monitoring Program). The objective of the Adelie penguin DNA based diet research is to develop non-invasive diet analysis methods that can rapidly and cheaply analyse large numbers of scat samples for prey DNA. This technology would allow us to monitor penguin diet without stomach flushing and would also enable the generation of much finer-scale temporal and spatial information on Adelie penguin diet. It is hoped that the development of these methods to the point where they become practical and cheap to apply on a large scale may eventually allow them to be recommended to CEMP as a replacement for stomach flushing as a dietary analysis method.\n\nBaleen whales are highly visible components of the Southern Ocean ecosystem and despite their relative scarcity, they are very well studied because of their charisma and being the focus of a prominent international fishery and conservation organisation, the IWC. The diet of baleen whales is difficult to study with any methodology, so our previous development of DNA based methods to analyse prey DNA found in whale scats as part of AAS project 2301 was scientifically quite a useful advance. It was also a useful political advance for Australia as we can now argue that lethal whaling for 'scientific' studies is less necessary than previously claimed. The objective of the baleen whale diet work is to continue our previous research in this area to maintain our position as the only country within the IWC that is capable of doing truly non-invasive dietary research on whales.\n\n2. Population Genetics Research\nThis project would also include studies of the population genetics of humpback whales, minke whales and Antarctic krill. These studies have two goals. The first is to study genetic differentiation within each of these species. For humpback whales this work would focus on attempts to link whales found in Australian Antarctic waters during the summer feeding season with the whales that migrate past the west and east coasts of Australia and which breed near south Pacific islands. For Antarctic krill, the genetic differentiation work aims to identify genetic 'stocks' of krill to assist in policy decisions for managing the krill fishery, as well as potentially providing a tool for measuring flux of krill between different regions of the Southern Ocean.\n\nThe second goal of the population genetics work is to use genetic data to estimate population size. Simple methods for estimating the size of an animal breeding population (the 'effective population') have been available for some time. We would apply these methods and also work on newer genetic 'mark and recapture' type methods that estimate overall population size, rather than just the size of the proportion of the population that reproduces. Another aspect of this goal is the estimation of past population sizes, which would give us a better idea of pre-exploitation stocks of whales and their relative recovery from exploitation to date.\n\n3. DNA-Based Age Estimation\nAnother major goal of the project is to develop genetic methods for estimating the age of whales. This would be a major advance for cetacean science as the methods could be performed on DNA collected through biopsy samples, or potentially even from the 'sloughed' skin that a whale leaves behind when diving. There are currently no validated, non-lethal methods for estimating cetacean age in adults. The only alternative methods for age estimation involve lethal sampling for collection of ear bones in which growth rings can be counted. One of the main claims promulgated by the Japanese scientific whaling program is that lethal sampling of whales is necessary for aging them. The political objective of this research would be to neutralise this claim in the same way that our DNA based dietary research has previously neutralised the claim that lethal sampling is necessary for dietary analysis. Alongside this political objective is the scientific objective that the development of a widely applicable, non-lethal aging method for whales would provide a wealth of information on the age structure of whale populations. This is an especially important feature of their ecology as most of the great whales are still recovering from human exploitation, which should have led to skewed age distributions in these populations when compared to the natural age distribution. Better knowledge of their population age structure will greatly improve our understanding of the recovery process and the current status of whale populations.\n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\n\n1. DNA based diet work. We converted our DNA based diet analysis work to next-generation sequencing based methodologies and refined blocking primer approaches for eliminating predator DNA in the libraries that we sequence. This approach was published as Deagle et al (2009) as listed in the papers below.\n2. Population genetics research. A microsatellite and mitochondrial sequence dataset for humpback whale population samples in eastern Australian waters, West Australian waters and Antarctic waters in the Ross Sea has been generated, analysed and a paper written.\n3. DNA based age estimation. Libraries of cDNA from juvenile, sub Adult and Adult humpback whales have been analysed. ~1.2 gb data was produced for each library. We are currently analysing these to identify genes that are differentially expressed among the three age classes.", "links": [ { diff --git a/datasets/AAS_2941_4101_SRW_tracks_1.json b/datasets/AAS_2941_4101_SRW_tracks_1.json index f696110548..6eb0869557 100644 --- a/datasets/AAS_2941_4101_SRW_tracks_1.json +++ b/datasets/AAS_2941_4101_SRW_tracks_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2941_4101_SRW_tracks_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sixteen satellite tags were deployed on adult southern right whales. Six of these tags were deployed on adult southern right whales at the Auckland Islands (AI), New Zealand (50.5\u02daS 166.3\u02daE) between 24 July and 2 August 2009 and one tag was deployed on a sub-adult at Pirates Bay (PB), Tasmania (43.2\u02daS 147.9\u02daE) in October 2010. Nine tags were deployed on adult southern right whales at Head of the Bight (HOB), South Australia (31.5\u02daS 131.1\u02daE) on 6 and 7 September 2015. However, tag performance was highly variable. Three of the six tags deployed at the AI ceased transmitting before the individuals moved out of the winter aggregation area. No transmissions were received from a fourth tag until 39 days after deployment at which point the whale was south of Western Australia and although the tag transmitted for 22 days, there was insufficient data to interpolate a track suitable to be included in analyses. Of the nine tags deployed at HOB, three tags failed to transmit, and three tags ceased transmitting within six days. \n\nMigratory movements from coastal calving grounds were successfully obtained for six individuals (AI=2, PB =1, HOB = 3) and detailed in the publication: Migratory movements of Southern right whales (Eubalaena australis) from Australia and New Zealand.\n\nThis file includes the following data fields -\nPTT: the unique Argos identifier assigned to each satellite tag\nDatetime: the date and time in gmt with the format 'yyyy-mm-dd hh:mm:ss'\nLongitude\nLatitude\nQuality: the Argos assigned location class (see paper for details)\nLocation: deployment location", "links": [ { diff --git a/datasets/AAS_2941_blue_whale_Argos_sda_filter_tracks_1.json b/datasets/AAS_2941_blue_whale_Argos_sda_filter_tracks_1.json index 1369a3b628..806eef42e4 100644 --- a/datasets/AAS_2941_blue_whale_Argos_sda_filter_tracks_1.json +++ b/datasets/AAS_2941_blue_whale_Argos_sda_filter_tracks_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_2941_blue_whale_Argos_sda_filter_tracks_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This csv details the raw Argos locations generated from satellite tags attached to pygmy blue whales in order to describe their migratory movements through Australian waters as described in:\nDouble MC, Andrews-Goff V, Jenner KCS, Jenner M-N, Laverick SM, et al. (2014) Migratory Movements of Pygmy Blue Whales (Balaenoptera musculus\nbrevicauda) between Australia and Indonesia as Revealed by Satellite Telemetry. PLoS ONE 9(4): e93578. doi:10.1371/journal.pone.0093578\n\nThis csv includes the following data fields -\nptt: the unique Argos identifier assigned to each satellite tag\ngmt: the date and time in gmt with the format 'yyyy-mm-dd hh:mm:ss'\nclass: the Argos assigned location class (see paper for details)\nlatitude\nlongitude\ndeploydate: deployment date and time in gmt for each tag with the format 'yyyy-mm-dd hh:mm:ss'\nfilt: the outcome of the sdafilter (see paper for details) - either \"removed\" (location removed by the filter), \"not\" (location not removed) or \"end_location\" (location at the end of the track where the algorithm could not be applied)", "links": [ { diff --git a/datasets/AAS_3010_Sea_Spiders_Genetics_1.json b/datasets/AAS_3010_Sea_Spiders_Genetics_1.json index 63bb302c80..a3ebeea337 100644 --- a/datasets/AAS_3010_Sea_Spiders_Genetics_1.json +++ b/datasets/AAS_3010_Sea_Spiders_Genetics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3010_Sea_Spiders_Genetics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data collected as part of Australian Antarctic Science project 3010 in the Australian Antarctic program.\n\nFrom the abstract of the referenced paper:\n\nThe evolutionary history of Antarctic organisms is becoming increasingly important to understand and manage population trajectories under rapid environmental change. The Antarctic sea spider Nymphon australe, with an apparently large population size compared with other sea spider species, is an ideal target to look for molecular signatures of past climatic events. We analysed mitochondrial DNA of specimens collected from the Antarctic continent and two Antarctic islands (AI) to infer past population processes and understand current genetic structure. Demographic history analyses suggest populations survived in refugia during the Last Glacial Maximum. The high genetic diversity found in the Antarctic Peninsula and East Antarctic (EA) seems related to multiple demographic contraction-expansion events associated with deep-sea refugia, while the low genetic diversity in the Weddell Sea points to a more recent expansion from a shelf refugium. We suggest the genetic structure of N. australe from AI reflects recent colonization from the continent. At a local level, EA populations reveal generally low genetic differentiation, geographically and bathymetrically, suggesting limited restrictions to dispersal. Results highlight regional differences in demographic histories and how these relate to the variation in intensity of glaciation-deglaciation events around Antarctica, critical for the study of local evolutionary processes. These are valuable data for understanding the remarkable success of Antarctic pycnogonids, and how environmental changes have shaped the evolution and diversification of Southern Ocean benthic biodiversity.", "links": [ { diff --git a/datasets/AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1.json b/datasets/AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1.json index fc43165634..c9a1f1200c 100644 --- a/datasets/AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1.json +++ b/datasets/AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRES is a high-resolution (100m) synthetic bed elevation terrain for the whole Antarctic continent. The synthetic bed surface preserves topographic roughness characteristics of airborne and ground-based ice-penetrating radar data from the Bedmap1 compilation and the ICECAP consortium. Broad-scale features of the Antarctic landscape are incorporated from a lowpass filter of the Bedmap2 bed elevation data. The data are available in NetCDF classic format on a 100m resolution grid in a Polar Stereographic Projection (Central Meridian 0 degrees, Standard Parallel 71 degrees S) with respect to the WGS84 geoid. The 100m grid is 66661 rows by 66661 columns, where the corner of the lower left cell is located at a polar stereographic easting and northing of -3333000 m and -3333000 m, respectively. The value for missing data is -9999.", "links": [ { diff --git a/datasets/AAS_3016_Macquarie_Island_flora_models_1.json b/datasets/AAS_3016_Macquarie_Island_flora_models_1.json index 674c1e74e6..82d8773159 100644 --- a/datasets/AAS_3016_Macquarie_Island_flora_models_1.json +++ b/datasets/AAS_3016_Macquarie_Island_flora_models_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3016_Macquarie_Island_flora_models_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Species distribution models (SDMs) are developed for nine major vascular plant taxa native to Macquarie Island, based on field data (point locations with three categories for each taxon: presence/ less than 25% foliage cover / greater than 25% cover). Spatial models of total range and core range (where the taxon is a dominant feature of the vegetation) for each taxon were used to predict the distribution of vegetation communities.", "links": [ { diff --git a/datasets/AAS_3016_Macquarie_Island_lapserates_1.json b/datasets/AAS_3016_Macquarie_Island_lapserates_1.json index 8753796716..cf820c5ad7 100644 --- a/datasets/AAS_3016_Macquarie_Island_lapserates_1.json +++ b/datasets/AAS_3016_Macquarie_Island_lapserates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3016_Macquarie_Island_lapserates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air temperature lapse rates vary geographically and temporally. Sub-Antarctic Macquarie Island provides an opportunity to compare lapse rates between windward and leeward slopes in a hyper-oceanic climate. Development of orographic cloud is expected to modify lapse rates, given the theoretical shift between dry and saturated adiabatic lapse rates that occurs with condensation of water vapour.\nThis dataset is part of a PhD project examining vegetation patterns and drivers on Macquarie Island. Data loggers were placed along an east-west altitudinal transect across the narrow axis of Macquarie Island to record air temperature from August 2014 to March 2016.A random sample of digital photographs from the AAD webcam at Macquarie Island Station was used to classify cloud base level as observed from the Station.\nThis dataset includes air temperature data from LogTag loggers, analysis of near surface atmospheric lapse rates, observations of cloud cover from webcam images and relevant data supplied by Bureau of Meteorology used in analysis.\n\nReference: Fitzgerald, N. B., and Kirkpatrick, J. B. (2020). Air temperature lapse rates and cloud cover in a hyper-oceanic climate. Antarctic Science, 14. https://doi.org/10.1017/S0954102020000309", "links": [ { diff --git a/datasets/AAS_3051_AbatusMicrosatellites_2.json b/datasets/AAS_3051_AbatusMicrosatellites_2.json index d4528c9d26..a04a78fe2e 100644 --- a/datasets/AAS_3051_AbatusMicrosatellites_2.json +++ b/datasets/AAS_3051_AbatusMicrosatellites_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3051_AbatusMicrosatellites_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present data set corresponds to the genotypes for seven microsatellite markers for three Antarctic sea urchin species of the genus Abatus. Sea urchin individuals were collected in five sites separated by up to 5 km in the near-shore area surrounding Davis Station in the Vestfold Hills Region, East Antarctica. For each microsatellite loci, the size of each allele was scored (in base pairs) using the CEQ 8000 Genetic Analysis System software v.8.0. Fragments were separated on an automated sequencer (CEQ 8000, Beckman Coulter) in the Central Science Laboratory at University of Tasmania.", "links": [ { diff --git a/datasets/AAS_3054_09_10_ecotox_hydrocarbon_1.json b/datasets/AAS_3054_09_10_ecotox_hydrocarbon_1.json index f6836a9d35..bcd46c824e 100644 --- a/datasets/AAS_3054_09_10_ecotox_hydrocarbon_1.json +++ b/datasets/AAS_3054_09_10_ecotox_hydrocarbon_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3054_09_10_ecotox_hydrocarbon_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results of replicate experiments which measured the total hydrocarbon content (THC) in water accommodated fractions (WAFs) of three fuels; Special Antarctic Blend diesel, Marine Gas oil and intermediate fuel oil IFO 180.", "links": [ { diff --git a/datasets/AAS_3054_THC_WAF_integrated_conc_10_11_1.json b/datasets/AAS_3054_THC_WAF_integrated_conc_10_11_1.json index b17eb928c5..2abb46a1ab 100644 --- a/datasets/AAS_3054_THC_WAF_integrated_conc_10_11_1.json +++ b/datasets/AAS_3054_THC_WAF_integrated_conc_10_11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3054_THC_WAF_integrated_conc_10_11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Experiments were done to quantify the Total Hydrocarbon Content (THC) in water accommodated fractions (WAF) of three fuels; Special Antarctic Blend diesel (SAB), Marine Gas Oil diesel (MGO) and an intermediate grade of marine bunker Fuel Oil (IFO 180).These tests measured the hydrocarbon content in freshly decanted WAFs and the resulting loss of hydrocarbons over time when WAFs were exposed in temperature controlled cabinets at 0\u00b0C. These tests are detailed in Dataset AAS_3054_THC_WAF. \nThe results of hydrocarbon WAF tests were used to calculate integrated concentration from measured hydrocarbon concentrations weighted to time to be used as the exposure concentrations for toxicity tests with Antarctic invertebrates. \nExposure concentrations used to model sensitivity estimates were derived by calculating the time weighted mean THC between pairs of successive measurements in the 100% WAFs and dilutions to give overall exposure concentrations for each time point.These modelled concentrations integrated the loss of hydrocarbons over time, and renewal of test solutions at 4 d intervals \nExposure concentrations of THC in \u00b5g/L are shown for endpoints from 24 h to 21 d", "links": [ { diff --git a/datasets/AAS_3121_1.json b/datasets/AAS_3121_1.json index f142b75b8a..4c008e7b16 100644 --- a/datasets/AAS_3121_1.json +++ b/datasets/AAS_3121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Linked to this record are a report providing further details about the project, as well as the data from the project.\n\nPublic Summary\nRegions of Antarctica are undergoing significant change in response to the Earth's changing climate. This project will provide a state of the art contemporary insight into the changing behaviour of the Totten drainage basin in East Antarctica - an area of vital importance in understanding ice/ocean/atmosphere and climate interactions in the Australian region of Antarctica. We will estimate the contribution of the Totten Glacier drainage basin to present-day sea level rise and simultaneously provide a critical validation of the European Space Agency (ESA) CryoSat-2 satellite mission over this region.\n\nProject #3121 investigated the mass balance of the Totten basin and provided an Australian contribution to the validation of CryoSat-2 data over Law Dome and the Totten Glacier. With field seasons in 2010/11 and 2011/12, the project gathered a range of in situ data using field and airborne data collection techniques. These data include geodetic quality GPS observations from up to 6 quasi-permanent GPS sites from which ice velocity, tropospheric water vapour and in some cases, tidal motion are derived. These sites were equipped with temperature and atmospheric pressure sensors, and in some cases, acoustic snow accumulation sensors. GPS equipped skidoo surveys were undertaken over the survey region on Law Dome to facilitate the generation of a validation surface to compare against airborne LiDAR and ASIRAS based DEMs. In the 2011/12 season, AWI collaborators achieved 4 days of survey flights in Polar-6, obtaining LiDAR and ASIRAS data over specific flight lines spanning Law Dome and the Totten Glacier.\n\nProject objectives:\nThis project will provide a state-of-the-art contemporary insight into the most recent changes in the surface elevation of the Totten drainage basin in East Antarctica, whilst simultaneously providing a critical and unique contribution to the calibration and validation of the new European Space Agency (ESA) CryoSat-2 satellite mission and the Australian Antarctic Division (AAD) LiDAR/RADAR system. The present-day mass balance change of Antarctica plays a key role in understanding the effects of global warming on the Earth system, in particular the contribution of melting Antarctic ice to present-day sea level rise. The Totten Glacier is known to be undergoing significant surface lowering and is perhaps the most significant basin in the East Antarctic (e.g., Shepherd and Wingham, 2007). The basin itself drains approximately 1/8th of the East Antarctic Ice Sheet (EAIS) and, as a marine-based system, is analogous to the West Antarctic Ice Sheet (WAIS) whose changing mass balance dominates the Antarctic contribution to global sea level rise(Lemke et al., 2007). The TOT-Cal project will independently lead Australian research in understanding the contribution of Antarctic ice to changing sea-levels by focusing new data on this key drainage basin of international scientific interest. Importantly, this region can be reached with relative ease by AAD logistics - it is located literally at the doorstep of the Australian Casey station, in close proximity to the Wilkins intercontinental airstrip. With international interest focused on this region, this project provides a showcase of AAD short-stay logistics in support of vital time-critical research and a major new ESA satellite mission that will undoubtedly play a major role in cryospheric science into the future.\n\nThe TOT-Cal project will draw upon key resources and personnel within the University of Tasmania (UTAS), Australian National University (ANU), Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales (LEGOS, France), Scripps Institution of Oceanography (SIO, USA) and the AAD, requiring the collection and analysis of field based, airborne and satellite data over a multi-season campaign. It builds upon and extends related past, existing and planned Australian Antarctic Science (AAS), Australian Research Council (ARC) and International Polar Year (IPY) projects, addressing three specific questions:\n\n1) What is the present-day mass balance of the Totten drainage basin and what is its contribution to global sea level change? This will be assessed through a combination of airborne LiDAR/RADAR observations, satellite altimetry observations including Seasat (1978), Geosat (1985-1989), ERS-1 (1992-1996), ERS-2 (1995-2005), Envisat-RA2 (2002 to present), ICESat (2003-present) and CryoSat-2 (expected launch 2009), space gravity observations (GRACE), along with ground-based validation experiments.\n\n2) What are the accuracies and uncertainty characteristics of the altimetry measurement systems? (In other words, what is the expected accuracy of the altimetry-derived mass balance estimates?) With an emphasis on the new CryoSat-2 and AAD LiDAR/RADAR systems, this will be assessed through repeated ground and airborne experiments, providing direct contribution to the CryoSat-2 international Calibration, Validation and Retrieval Team (CVRT), whilst also providing an important cross-calibration of synchronous ICESat, Envisat and CryoSat-2 data. Of particular focus will be the understanding of the different surface interactions between the incident radar and laser waveforms (both satellite and airborne) with the surface snow/ice characteristics (topography, firn, seasonal changes, etc).\n\n3) What is the magnitude of the present-day Glacial Isostatic Adjustment (GIA) in the region that needs to be removed from the space-based geodetic observations in order to estimate mass balance using a space geodetic approach? Present uncertainty in the magnitude of GIA is a dominant error source in the mass balance error budget and requires an analysis of recent models and in-situ geodetic evidence in order to fully understand and minimise this error contribution.\n\nEach of the objectives set out above will be assessed with data acquired over the coming three summer seasons, leading into participating in the larger period of logistics support around the Totten Glacier in 2011/12. This also enables this project to provide state-of-the-art estimates of surface lowering to the Australian AAD/ACECRC modelling team (R.Warner et al) for integration into dynamic ice models in the subsequent years of this project. These estimates will be fundamental in improving conventional forward ice models which to date, are not able to predict the observed changes in the Totten Glacier (van der Veen et al. 2008). The timing of the work outlined in this proposal is critical given the CryoSat-2 launch (expected late 2009) and the impending conclusion of the GRACE mission, this research needs to be undertaken now for the field seasons indicated in order to maximise the scientific impact and provide the necessary complement to other planned AAS projects that will operate over the same future field seasons. \n\nPublic summary of the season progress:\n2010/11 was the first field season for this project. Valuable GPS field data were acquired in the Law Dome and Totten Glacier regions to assist with providing an Australian contribution to the validation of the CryoSat-2 ice monitoring satellite mission, and to further understand ice shelf/ocean interactions and climate change in this region. Planned airborne surveys by the German AWI Polar-5 aircraft were unable to be completed due to poor weather. Collaboration with the 'Investigating the Cryospheric Evolution of the Central Antarctic Plate' project (ICECAP - UTexas) yielded important airborne scanning laser altimeter elevation data over the Law Dome site.", "links": [ { diff --git a/datasets/AAS_3129_1.json b/datasets/AAS_3129_1.json index 95f40ccaf5..4714b40183 100644 --- a/datasets/AAS_3129_1.json +++ b/datasets/AAS_3129_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3129_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3219.\n\nPublic Summary\nWe will use mosses to investigate the changing climate in Antarctica and the implications this has for terrestrial biodiversity. Mosses grow incrementally from the tip, thus shoot sections contain a record of atmospheric carbon corresponding to each growing season, in a similar fashion to tree rings. This method has been used to age East Antarctic mosses and indicates that some individuals are more than 60 years old. Analysing stable isotopes of carbon and oxygen in cell walls tells us how climate has changed around these mosses over time and allows us to determine which sites are drying and becoming inhospitable.\n\nProject Objectives\nOur hypothesis is that the carbon and oxygen isotope composition of bryophytes can be used as a proxy for desertification, inundation and precipitation regimes in Antarctica.\n\nWe will determine whether stable isotopes of carbon and oxygen in plant tissues can be used as a surrogate for changes in effective growing season by determining whether they provide an accurate record of water availability to moss beds through time. To do this we will:\n\n1) determine if long term water availability is accurately recorded in cell wall delta13C and delta18O signatures of moss, and if\n2) short term, within/between season changes in moss submergence are reflected in the delta13C of sugars. In addition we will\n3) measure instantaneous fractionation of carbon isotopes during photosynthesis in moss under different water availabilities.\n\nTaken from the 2010-2011 Progress Report\nProgress against objectives:\nMoss, snow and water samples for Objectives 1 and 3 were collected in February 2011. These were from ASPA136 (Stevenson's Cove and a ridge site near Whitney Pt), on Bailey Peninsula (ASPA135, Science and Red Shed locations) and from Robinson Ridge\nThese samples have all been identified.\n\nObjective 2 requires a longer season and was not possible in the time available at Casey.\n\nLaboratory activity/analysis:\nThese samples have all been identified at Wollongong\nStable isotope analysis is planned for September-October 2011 in Vienna (Bramley-Alves and Robinson).\nAn experiment to investigate the fractionation of mosses under different water availabilities is planned with some of the samples that were collected and transferred to ANU (Bramley-Alves, Robinson and Ball).\n\nJess Bramley-Alves has applied for a 2011 AINSE Postgraduate Research Award, which would provide research funding and access to radiocarbon dating facilities at ANSTO. This will allow us to date the samples and track stable isotope changes over time.\n\nTransplant experiment will be conducted and additional samples will be collected in 2011/12\nThese will be analysed in 2012.\n\nProgress to date is excellent given the short season at Casey.", "links": [ { diff --git a/datasets/AAS_3129_JBA_GCB_1.json b/datasets/AAS_3129_JBA_GCB_1.json index af4a48d6b2..b9270e8f5c 100644 --- a/datasets/AAS_3129_JBA_GCB_1.json +++ b/datasets/AAS_3129_JBA_GCB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3129_JBA_GCB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increased aridity is of global concern. Polar regions provide an opportunity to monitor changes in bioavailable water free of local anthropogenic influences. However, sophisticated proxy measures are needed. We explored the possibility of using stable carbon isotopes in segments of moss as a fine-scale proxy for past bioavailable water. Variation in delta 13C with water availability was measured in three species across three peninsulas in the Windmill Islands, East Antarctica and verified using controlled chamber experiments. The delta 13C from Antarctic mosses accurately recorded long-term variations in water availability in the field, regardless of location, but significant disparities in delta 13C between species indicated some make more sensitive proxies. delta 13CSUGAR derived from living tissues can change significantly within the span of an Antarctic season (5 weeks) in chambers, but under field conditions, slow growth means that this technique likely represents multiple seasons. delta 13CCELLULOSE provides a precise and direct proxy for bioavailable water, allowing reconstructions for coastal Antarctica potentially over past centuries. \n\nStable isotopes as a proxy for water:\nAnalysis of stable carbon isotopes (delta 13C) in moss tissues, where delta 13C values indicate water bioavailability in the environment during the growth season the tissue was produced. Elevated (less negative) delta 13C signatures indicate moss tissue is covered by water (causing diffusional limitations), while more negative delta 13C signatures are indicative of a drier growth environment. Long shoots of moss may be analysed to reconstruct past water availability over previous centuries. Methods as per Bramley-Alves et al. 2015.\n\nData sheets:\n1.\tInformation\n2.\tAntarctic delta 13CBULK field measurements: Moss plugs (~ 2 cm2) of each species were collected from three peninsulas in the Windmill Islands (Robinsons Ridge, Baily and Clark) from hydric areas where moss is known to remain submerged throughout the season ('wet'), xeric areas where moss relies on ephemeral water sources such as snowfall ('dry' and mesic areas ('intermediate') in a transitional water environment. Each sample was identified at a cellular level by assessing between 5 to 6 leaves per plug under both a dissecting microscope (Leica, MS5, Australia) and at 10x and 40x magnification (Olympus, BHA, Japan).\n3.\tAntarctic TWC and moss cellulose comparison: To allow spot measurements of moss turf water content (TWC) to be compared with the visual estimates of long-term water environments and delta 13C, moss plugs were also sampled from established wet, intermediate and dry study sites across two permanent, long-term monitoring sites: Antarctic Specially Protected Area (ASPA) 135 on Bailey Peninsula and Robinson Ridge.\n4.\tAntarctic moss pilot chamber manipulations: A five-week pilot study was conducted (January - February 2012) to evaluate if delta 13CCELLULOSE and delta 13CSUGAR in Antarctic moss varied in response to changing water environments within a single Antarctic growth season. Five weeks was deemed ample time to generate sufficient new growth for analysis based on similar chamber studies that demonstrated growth rates of 9.87 plus or minus 0.83 mm for B. pseudotriquetrum and 5.17 plus or minus 0.39 mm for C. purpureus. Plugs (depth ~1 cm) of C. purpureus, S. antarctici and B. pseudotriquetrum) were collected from a range of water environments on Bailey peninsula. Samples were placed in microplates (24 well, Corning, Australia) and randomly allocated to one of three water treatments within growth chambers in the science laboratory at Casey station. The treatments represented three different environmental conditions: wet; where samples were kept submerged under more than 3 mm of water, intermediate; where samples were not submerged but were provided with an ample water supply, or dry; where samples were given the minimum level of water to allow growth (greater than 2 g H2O g-1 Dry Weight) and were never inundated. To avoid formation of a water film, dry samples were watered at the base of the moss core via a Pasteur pipette. Chambers were kept at a constant 15 degrees C with a natural summer photoperiod (22 hours ~700 umols m-2 s-1 PAR) to mirror moss turf conditions in the field during the summer growth period, where turf temperatures can reach greater than 20 degrees C. The reported photosynthetic optimum is 15 degrees C for both B. pseudotriquetrum and C. purpureus and greater than 15 degrees C for S. antarctici. Therefore, it was assumed that 15 degrees C was likely to produce sufficient new growth to capture the optimum kinetics of changes in delta 13C. Chamber relative humidity was greater than 60% (Kestrel 3500, Delta T, USA).\n5.\tAntarctic moss chamber manipulations: An extended 22-week study was conducted to evaluate if delta 13CCELLULOSE and delta 13CSUGAR in Antarctic moss varied in response to changing water environments over multiple Antarctic growth seasons. Sample collection and chamber conditions are as described above, however treatments were reduced to jus 'wet' and 'dry'. \n6.\tAntarctic leaf morphology: Cell wall thickness of all three species were recorded across both wet and dry field environments to examine if this could account for diffusional limitations and therefore affect delta 13C signatures. Samples were placed under a dissecting microscope (Leica, MS5, Australia) and five leaves removed and transferred to a glass slide. Five images of each species from each field environment were then captured using a digital camera (DCM510, 5M pixels) attached to a Microscope (Olympus, BHA, Japan) at 40x magnification and downloaded into Photoshop (Ver. CS6, Adobe) for analysis.\n7.\tAntarctic etiolation experiment: B. pseudotriquetrum samples (~ 2 cm2) were collected from similar growth environments (intermediate access to water on an East-facing slope) at Casey Station. Samples were grown for five weeks in chamber conditions (described above) under different levels (0% (control), 25%, 50%, 75% and 100%) of artificial moss cover in the form of polystyrene pieces. Seven single gametophytes were randomly selected from each sample, with dead, juvenile and/or abnormal gametophytes excluded from the selection. Measurements were conducted using the microscope described directly above. Photosynthetic tissue length, leaf area and stem etiolation were recorded. The number of leaves was counted in the top 3.5 mm from the gametophyte tip. \n8.\tAntarctic transplant experiment: A full reciprocal transplant study was carried out (December - January 2013) across a water gradient at Casey station to test if intra-season changes in delta 13CSUGAR could be detected under field conditions. Plugs of each species (n = 24) collected from wet and dry environments were transferred to wet, intermediate and dry locations. Samples were randomly assigned to one of three metal trays and inserted into a foam mat that operated as a surrogate for moss turf in the treatment location. Initial delta 13CSUGAR was measured for nine samples per species to act as a control and to monitor d13CSUGAR changes within the season.", "links": [ { diff --git a/datasets/AAS_3129_JBA_PPP_1.json b/datasets/AAS_3129_JBA_PPP_1.json index d5e905792d..7078a8ee66 100644 --- a/datasets/AAS_3129_JBA_PPP_1.json +++ b/datasets/AAS_3129_JBA_PPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3129_JBA_PPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The success of mosses in East Antarctica to accurately record long-term variations in water availability through d13C encourages the use of this technique as a promising proxy solution for subantarctic locations, where cold climate conditions restrict the growth rates of intact moss shoots enough to generate meaningful data over a long period, unlike similar species in more temperate regions. \n\nWith this data we explored the possible expansion of the use of d13C signatures in moss as a proxy of growth water environment by examining carbon isotope fractionation in a range of subantarctic moss species collected from wet, intermediate and dry locations across Macquarie Island. Specifically we examined: (1) the relationship between d13CCELLULOSE and d13CBULK plant material in subantarctic species; (2) the influence of growth water environment on d13CBULK in subantarctic moss under field conditions; (3) inter-species variability, including the effect of cell wall thickness on d13CBULK; and (4) differences and similarities in d13CBULK in mosses between Antarctic and subantarctic locations in comparison to the Bramley-Alves, J.E., Robinson, S. (2016) metadata entry. \n\nStable isotopes as a proxy for water:\nAnalysis of stable carbon isotopes (d13C) in moss tissues, where d13C values indicate water bioavailability in the environment during the growth season the tissue was produced. Elevated (less negative) d13C signatures indicate moss tissue is covered by water (causing diffusional limitations), while more negative d13C signatures are indicative of a drier growth environment. Long shoots of moss may be analysed to reconstruct past water availability over previous centuries. For further information on methods please see Bramley-Alves et al. 2016.\n\nData sheets:\n1.\tInformation\n2.\tSubantarctic moss d13CBULK and d13CCELLULOSE field measurements: For each of the three study species (Breutelia pendula, Brachythecium austro-salebrosum and Sanionia uncinata), moss plugs (~ 2 cm2 by 4 cm deep) were collected from areas in which moss was observed to grow in different water environments along a gradient. Moss growing in or directly beside a steam was classified as wet, mid-way up the bank was classified as intermediate and those growing higher up the bank, with no access to stream water were classified as dry. All sites were selected based on two main features: firstly, the presence of all three species and secondly, a substantial water availability gradient with elevation/topographic distance from a running stream. Dual samples of bulk and cellulose were extracted as described in Bramley-Alves et al. (2016).\n3.\tSubantarctic moss morphology: The cell wall thickness of all three subantarctic species were examined in samples collected from an intermediate water environment. Samples were placed under a dissecting microscope (Leica, MS5, Australia) and five leaves removed and transferred to a glass slide. Images were then captured using a digital camera (DCM510, 5M pixels) attached to a Microscope (Olympus, BHA, Japan) at 40x magnification and downloaded into Photoshop (Ver. CS6, Adobe) for analysis of cell wall thickness using the ruler tool. 10 measurements were made per leaf.\n4.\tSubantarctic moss surface temperature: Subantarctic moss surface temperatures were measured using 12 iButtons over the months of April and May 2015 at the base of Pyramid Peak, next to the track. ibuttons were pinned flat onto the moss turf.", "links": [ { diff --git a/datasets/AAS_3130_moss_beds_2010_1.json b/datasets/AAS_3130_moss_beds_2010_1.json index daee3c3b2d..a27def9ff3 100644 --- a/datasets/AAS_3130_moss_beds_2010_1.json +++ b/datasets/AAS_3130_moss_beds_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3130_moss_beds_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photography was acquired in January 2010 from a remote controlled helicopter at three locations - Antarctic Specially Protected Area (ASPA) 135, Robinson Ridge and near the Red Shed at Casey. In addition, highly accurate terrain data (points) was collected at each area using differential GPS. A detailed Digital Elevation Model (DEM) was generated for each area using the terrain data. \nThe point data included the locations of small metal tags glued to rocks used for locating nearby quadrats. The quadrats have been used for long term monitoring of vegetation at ASPA 135 and Robinson Ridge. The quadrats were established in 2002/03 under AAS Project 1313 and have been monitored at approximately five yearly intervals under this project and the subsequent AAS Project 4036 (Chief Investigator: Professor Sharon Robinson, University of Wollongong).\nNear the Red Shed, an outline of the moss beds, streamlines and the general edge of the melt lake were captured using differential GPS.\nAt Robinson Ridge, streamlines and the snowline were captured using differential GPS.\nThe GPS data has an estimated horizontal accuracy of 2 cm.\nA georeferenced mosaic was created from the aerial photography for each area.\n\nASPA 135 dataset: \naspa135dem - DEM in ESRI grid format\naspa135shade - hillshade in ESRI grid format, derived from aspa135dem ASPA135_quadrat_markers - shapefile with the locations of the quadrat markers ASPA135_roversall - shapefile with all the differentially corrected GPS data\nASPA135_UAVmosaic2010 - georeferenced aerial photograph mosaic\n\nCasey Red Shed dataset: \nredsheddem - DEM in ESRI grid format\nredshedshade - hillshade in ESRI grid format, derived from aspa135dem\nUAVphoto_2010 - georeferenced aerial photograph mosaic RedShed_lake - shapefile with the general edge of the melt lake RedShed_moss_outline - shapefile with the 'outline' of the moss beds (quite a conservative outside edge) RedShed_streamline - shapefile with the streamlines RedshedWetness - Topographic wetness index in PCRaster format\n\nRobinson Ridge dataset:\nRobbos_quadrat_markers - shapefile with the locations of the quadrat markers Robbos_rover_all - shapefile with all the differentially corrected GPS data\nRobbos_mosaic2010 - georeferenced aerial photograph mosaic Robbos_snowline - shapefile with the snowline Robbos_streams - shapefile with the streamlines\n\nThe above three datasets also include an ArcMap document displaying the data.\n\nFor further information see the paper\nLucieer, A., Robinson, S., and Turner, D. (2010). Using an unmanned aerial vehicle (UAV) for ultra-high resolution mapping of Antarctic moss beds. In: Proceedings of the Australasian Remote Sensing and Photogrammetry Conference (15th ARSPC), Alice Springs, Australia, September 2010 which is available from a Related URL.", "links": [ { diff --git a/datasets/AAS_3132_1.json b/datasets/AAS_3132_1.json index 2a09a2f49b..3d506ec7c3 100644 --- a/datasets/AAS_3132_1.json +++ b/datasets/AAS_3132_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3132_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3132.\n\nPublic \nThis research will determine variability in the influx and mineralogy of cosmic dust to the Southern Ocean during the Holocene from peat bog cores. Cosmic dust contains significant quantities of soluble iron, a micronutrient required for photosynthesis. Therefore, variations in the deposition of cosmic dust could significantly affect primary production in the Southern Ocean. This may also play an important role in global climate due to its influence on carbon dioxide draw-down from, and emission of volatile sulphur compounds to, the atmosphere.\n\nThe download file contain a csv spreadsheet of carbon dating from geochemical peat cores collected from Green Gorge on Macquarie Island.\n\nProject objectives:\nThis project will sample peat bogs on Macquarie Island to:\n1. Quantify and develop a high-temporal resolution record of the variability in cosmic dust deposition during the Holocene;\n2. Determine the mineralogy and quantify the solubility of iron contained in the cosmic dust;\n\nIron is a micronutrient required for photosynthetic reactions within chloroplasts. Martin [1990] proposed that many oceanic phytoplankton, especially those in the high nutrient - low chlorophyll (HNLC) regions of the world's oceans (such as the Southern Ocean) were limited by the availability of iron. Martin et al. [1991] demonstrated that nanomolar increases in dissolved iron stimulated phytoplankton blooms in the North and Equatorial Pacific and Southern Oceans. Several large-scale field experiments (see de Baar et al [2005] for a summary) demonstrated that the addition of iron stimulated phytoplankton productivity significantly. Eleven further experiments have confirmed these results in many other regions [Boyd, et al., 2007] and models of the cellular processes by which iron fertilisation stimulates phytoplankton blooms are now available [Fasham, et al., 2006]. The response of phytoplankton to iron fertilisation has attracted much research effort because phytoplankton blooms increase the draw-down of carbon from the atmosphere and ultimately export a fraction to the deep ocean where it is stored as particulate organic carbon [Watson, et al., 2000] and hence may play an important role in climate.\n\nCosmic and terrestrial dust can both contain significant quantities of soluble, bio-available iron [Fung, et al., 2000; Plane, 2003]. The potential for iron contained in aeolian terrestrial dust to affect climate was recently assessed by Kohfeld et al. [2005], who concluded that dust-induced iron-fertilisation of ocean ecosystems might account for 30 - 50 ppm of atmospheric CO2 draw-down during the last glacial period. Satellite data provide support for these hypotheses at the regional scales at which terrestrial dust deposition events occur [Cropp, et al., 2003; Gabric, et al., 2002]. The influx of cosmic dust to the oceans could be significantly different to terrestrial dust inputs as it is likely to be uniformly distributed around the globe [Johnson, 2001], vary on longer time scales (although this is not well understood [Winckler and Fischer, 2006]), and is expected to be of finer particle-size and contrasting mineralogy [Plane, 2003].\n\nIce cores provide excellent long-term records of terrestrial and cosmic dust deposition, however, cores from ombrotrophic peat bogs, that receive their inputs exclusively from the atmosphere, can provide high temporal resolution records of cosmic and terrestrial dust during the Holocene [Cortizas and Gayoso, 2002]. Data from ice cores in Greenland and ocean sediment cores in the tropical Pacific have revealed variations in cosmic dust influx between glacial and inter-glacial periods, with increases in cosmic dust influx associated with cooler temperatures [Dalai, et al., 2006; Gabrielli, et al., 2004; Karner, et al., 2003]. Johnson [2001] calculated that the current background cosmic dust deposition of about 40,000 tonnes per annum delivered 30-300% of the aeolian iron flux due to terrestrial dust and about 20% of the upwelled iron flux in the Southern Ocean. Ombrotrophic peatlands, such as those found on Macquarie Island, which receive inputs of material solely from the atmosphere, provide especially useful records of cosmic dust deposition over the Holocene.\n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nPeat core samples were collected on Macquarie Island in April 2010. These samples will be analysed over the coming year.", "links": [ { diff --git a/datasets/AAS_3134_urchins_climate_change_2.json b/datasets/AAS_3134_urchins_climate_change_2.json index 714f5e17a1..e5a9bfc4e3 100644 --- a/datasets/AAS_3134_urchins_climate_change_2.json +++ b/datasets/AAS_3134_urchins_climate_change_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3134_urchins_climate_change_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The effect of pH, temperature and sperm concentration on the fertilisation of Sterechinus neumayeri was investigated. Adult Sterechinus neumayeri were collected from Ellis Fjord Narrows between December and January 2011-12 and held in the Ecotox Field Aquarium Module until used. Between 3-4 male and female individuals were spawned using 0.5M KCl and gametes were collected separately before being fertilised in treatment. \n\nThe data set shows the percentage of fertilised and non-fertilised eggs of Sterechinus neumayeri scored at 20h post-fertilisation. Eggs were fertilised in various combinations of pH, temperature and sperm concentration treatments (pH: 8.0 (Control), 7.8 and 7.6; Temperature: 1 degrees C (Control), 3 degrees C and 5 degrees C; Sperm concentration (sperm:egg ratio): 1000:1 (Control), 750:1, 250: 1, 50:1 and 5:1). At 20h post fertilisation, 5 ml aliquot was removed from fertilisation vials and eggs were counted and determined if they were fertilised or not. Seawater parameters of treatments were measured at the start and end of the experiment.\n\nDetailed information of the spreadsheets are as follows:\nSeawater Parameters column headings:\nTemperature - measured in degrees C , shows the temperature treatments used\npH - shows the pH levels used\nSubheading pH - pH level measured for the day using NIST certified buffers\nSubheading MV - pH level measured for the day in millivolts\nSubheading Total pH - total pH level in seawater obtained from MV measurements\nSubheading Temp - temperature of seawater measured for the day\n\n1 deg C column headings:\nExperiment - number of experiments \npH - shows the pH for each treatment\nSperm Concentration - shows the sperm concentration used for each treatment in a egg:sperm ratio\nRep - shows the number of replicates per experiment\nUnfertilised eggs - eggs without visible fertilisation envelope and no cleavage after 20h\nFertilised eggs - eggs with visible fertilisation envelope and/or cleavage after 20h\nFertilised deformed eggs - eggs with visible fertilisation envelope but deformed\nTotal eggs - total eggs scored (whether fertilised or unfertilised)\n% Fertilised - fertilised eggs (deformed and non-deformed)/Total eggs\n\n3 deg C and 5 deg C have the same column headings as 1 deg C.\n\nAAS3134 Abatus sp Growth Experiment Davis 2011-12:\nThe effect of pH and temperature on the growth rate of juvenile Abatus ingens and Abatus shackletoni were investigated. Adult Abatus were collected off Airport Beach in waters 4-5m depth. \n\nData set shows the growth rate of juveniles of Abatus ingens and Abatus shackletoni after a 4-week exposure to various combinations of pH and temperature. Juveniles of each species was removed from maternal pouches and photographed on the oral side before being exposed to combinations of pH (8.0 (Control), 7.8 and 7.6) and temperature (-1 degrees C (Control) and 1 degrees C) levels. They were incubated in treatments for 4 weeks before being removed and rephotographed. The lengths of 10 spines per juvenile were measured in the pre- and post-experiment photographs using ImageJ and the difference calculated to get a growth rate per juvenile. Seawater parameters of treatments were measured at the beginning of the experiment and subsequently once a day until the end of the experiment.\n\nDetailed information of the spreadsheets are as follows:\n\nA ingens (pre-exp) i.e. juvenile Abatus ingens spine lengths measured before exposure to experimental treatments. Column headings are:\nSpine number and length (mm): Length of each spine (1 - 10) measured per juvenile in mm.\nR1 - R12: Number of juveniles \n\nA ingens (post-exp) i.e. juvenile Abatus ingens spine lengths measured after 4-week exposure to experimental treatments. Column headings are identical to the above.\n\nA shackletoni (pre-exp) i.e. juvenile Abatus shackletoni spine lengths measured before exposure to experimental treatments. Column headings are identical to the above.\n\nA shackletoni (post-exp) i.e. juvenile Abatus shackletoni spine lengths measured after 4-week exposure to experimental treatments. Column headings are identical to the above.\n\n2011-12 Aquarium pH and temp main headings show different treatment parameters. Column sub-headings are:\nDate - Date of measured seawater parameters\nSalinity - salinity of seawater measured\nPpm - Amount of CO2 gas pumped into water recorded in parts per million\npH - measured pH of seawater using NIST-certified buffers\nMV - pH of seawater recorded in millivolts\nTotal pH - total pH of seawater derived from MV\nTemp - Temperature of seawater measured in degrees C.", "links": [ { diff --git a/datasets/AAS_3140_1.json b/datasets/AAS_3140_1.json index 081fb2f2b8..2099ccc13e 100644 --- a/datasets/AAS_3140_1.json +++ b/datasets/AAS_3140_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3140_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) Project 3140\nSee the link below for public details on this project.\n\nPublic Summary\nA thorough understanding of the coupling and dynamics of the Antarctic lower atmosphere is critical for understanding how it will respond to climate change. However, this region of the atmosphere has not been studied in sufficient detail. Energy and momentum are redistributed in the atmosphere by large scale planetary waves and small scale gravity (buoyancy) waves. By combining the high-resolution instruments from Davis with global satellite observations, these waves and their effect on the atmosphere will be understood. Results from this project will be of value to modellers for improving global climate models. \n\nProject objectives:\nThis project will study the variability, dynamics and coupling of the Antarctic lower atmosphere. The objective is to determine some of the most important and urgently needed information for global climate models by examining high-resolution observational datasets. Areas where understanding is limited and need to be improved include the effects of atmospheric gravity (buoyancy) waves on the lower atmosphere and their relation to the cold biases observed in the polar stratospheres of models (Sato and Yoshiki, 2008), determining critical wave parameter information (Alexander et al., 2008a), and studying troposphere - stratosphere coupling, particularly in relation to the polar night jet (e.g. Baumgaertner and McDonald 2007, Hei et al 2008).\n\nIn order to achieve this, data which are collected at Davis as part of the current ASAC projects: a) the lidar - project 737 (Klekociuk et al. 2003) and b) the VHF MST radar - project 2325 (Morris et al. 2006) will be analysed. These results will be combined with data collected by the Bureau of Meteorology (radiosondes and ozonesondes launched at Davis) and various satellites including the CHAMP (Challenging Minisatellite Payload) and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation experiments (Alexander et al. 2008c).\n\nThe multi-year ground-based observational records at Davis collected by the lidar and radar will be used to study the spatial and temporal variability of gravity waves in the troposphere and stratosphere over a wide range of scales. Waves and their sources will be identified and quantified. Such sources include the stratospheric polar night jet, orographic waves, tropospheric weather frontal systems and storms. The lidar and radar data will be combined with ozonesonde and radiosonde data from routine Bureau of Meteorology flights made at Davis for studies of stratosphere-troposphere interactions, dynamics, mixing, folding and mass transport across the tropopause.\n\nSatellite-based data, including those made by GPS radio occultation, will be used to set the Davis results into a regional and global scale context. The energy and momentum of small-scale gravity waves and large scale planetary waves will be examined. In particular, the stratospheric polar night jet will be studied to investigate wave generation and upward and downward propagation and understand how the downward propagating waves affect the troposphere.\n\nThis project will establish a world-wide reputation for AAD as providing leading-edge studies, analysis and interpretation of the dynamic variability of the Antarctic lower atmosphere.\n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nGravity wave activity associated with both the Antarctic and Arctic polar stratospheric vortices has been quantified using COSMIC GPS satellite data (Alexander et al. 2009). The high resolution nature of these data allowed information on regional scales and short duration wave processes to be identified and quantified. In particular, large intermittent bursts of orographic wave activity were identified above the Antarctic Peninsula. This has led to a continuing investigation of the effect of these waves on Polar Stratospheric Clouds (PSCs) by incorporation of CALIPSO satellite lidar data and MLS trace gas observations, both from the lower stratosphere. Foundations for this PSC / wave interaction were laid with work completed during the first year of project 3140, i.e. both the gravity wave analysis of Alexander et al (2009) and the planetary wave results of Alexander and Shepherd (2010).\n\nLidar temperature data obtained in the upper troposphere - lower stratosphere (UTLS) region have been analysed and in particular one case study of a stratospheric intrusion during May 2008 has been identified and studied in detail. With the addition of satellite and radiosonde data, the lidar results are allowing quantification of small scale gravity wave parameters as the passage of a large scale planetary wave results in irreversible mixing of stratospheric air into the troposphere. Further UTLS experiments were run during winter 2009 by the chief investigator, thus allowing a statistical analysis of these events to be conducted in the future. A comparison between MST radar tropospheric winds and radiosonde winds revealed issues in the MST data which are still being addressed before these data become ready to use.", "links": [ { diff --git a/datasets/AAS_3145_Advection_1.json b/datasets/AAS_3145_Advection_1.json index 327f94dc2e..2f5e371b1e 100644 --- a/datasets/AAS_3145_Advection_1.json +++ b/datasets/AAS_3145_Advection_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3145_Advection_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "See the referenced paper for additional details.\n\nSampling. Sampling was conducted on board the RSV Aurora Australis during cruise V3 from 20 January to 7 February 2012. This cruise occupied a latitudinal transect from waters north of Cape Poinsett, Antarctica (65_ S) to south of Cape Leeuwin, Australia (37_ S) within a longitudinal range of 113-115_ E. Sampling was performed as described in ref. 29, with sites and depths selected to provide coverage of all major SO water masses. At each surface station, E250-560 l of seawater was pumped from E1.5 to 2.5m depth. At some surface stations, an additional sample was taken from the Deep Chlorophyll Maximum (DCM), as determined by chlorophyll fluorescence measurements taken from a conductivity, temperature and depth probe (CTD) cast at each sampling station. Samples of mesopelagic and deeper waters (E120-240 l) were also collected at some stations using Niskin bottles attached to the CTD. Sampling depths were selected based on temperature, salinity and dissolved oxygen profiles to capture water from the targeted water masses. Profiles were generated on the CTD descent, and samples were collected on the ascent at the selected depths. Deep water masses were identified by the following criteria: CDW 1/4 oxygen minimum (Upper Circumpolar Deep) or salinity maximum (Lower Circumpolar Deep); AABW 1/4 deep potential temperature minimum; AAIW 1/4 salinity minimum 18. The major fronts of the SO, which coincide with strong horizontal gradients in temperature and salinity 19,30, separate regions with similar surface water properties. The AZ lies south of the Polar Front (which was at 51_ S during sampling), whereas the PFZ lies between the Polar Front and the Subantarctic Front. In total, 25 samples from the AZ, PFZ, SAMW, AAIW, CDW and AABW were collected for this study (Fig. 1, Supplementary Data 1). Seawater samples were prefiltered through a 20-mm plankton net, biomass captured on sequential 3.0-, 0.8- and 0.1-mm 293-mm polyethersulphone membrane filters and filters immediately stored at _80 _C31,32.\n\nDNA extraction and sequencing. DNA was extracted with a modified version of the phenol-chloroform method 31. Tag pyrosequencing was performed by Research and Testing Laboratory (Lubbock, USA) on a GS FLXb platform (Roche, Branford, USA) using a modification of the standard 926F/1392R primers targeting the V6-V8 hypervariable regions of bacterial and archaeal 16S rRNA genes (926wF: 50-AAA-CTY-AAA-KGA-ATT-GRC-GG-30 , 1,392 R: 50-ACG-GGCGGT-GTG-TRC-30). Denoising, chimera removal and trimming of poor quality read ends were performed by the sequencing facility.", "links": [ { diff --git a/datasets/AAS_3214_1.json b/datasets/AAS_3214_1.json index 2ec9055977..c86247ac0e 100644 --- a/datasets/AAS_3214_1.json +++ b/datasets/AAS_3214_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3214_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was collected as part of an honours project by Jessica Wilks at Macquarie University (submitted May 2012). The samples analysed were taken from an expedition conducted by Dr Leanne Armand in 2011 as part of the KEOPS2 mission (KErguelen: compared study of the Ocean and the Plateau in Surface water). During this mission 7 locations (A3-1, A3-2, E1-3, E14W2, NPF-L, R2 and TEW) around the Kerguelen Plateau were sampled for seafloor sediment. Each attached spreadsheet represents the data from one of these locations. Three tubes of sediment were taken for each location. The data within each spreadsheet is separate for the three tubes. \n\nAfter the tubes of seafloor sediment were processed to remove organic material and carbonates (leaving nothing but siliceous material, primarily diatoms) slides were made with a small amount of material, three slides per tube of sediment. Diatoms were identified using a light microscope at 40x magnification. Approximately 400 frustules were counter per tube (ie per set of 3 slides) in order to represent the diversity of the species present. The number of each species or subspecies of diatom are tallied in the spreadsheets attached. Species identifications follow Armand et al 2008. \n\nOther information in the attached spreadsheets includes the seafloor depth at the point of sampling, the distance from the Kerguelen shoreline at the point of sampling, the amount of suspended material used on each slide, the number of field of view (at 40X) viewed to count the quota of 400 diatom frustules, and the calculated number of frustules/ gram of dry sediment weight.\n\nCounting protocol: centric frustules were counted only when 1) more than half of the frustule was intact; and 2) the frustule was clearly identifiable. If 1) but not 2) then the frustule was counted as \"unidentified centric\". For Rhizosolenia spp, frustules were couned if the apex was present and identifiable, otherwise it was counted as \"R. unknown\". Thalassiothrix and Tricotoxon were only counted if one end was present and identifiable. The number was later divided by 2, to give the number of complete frustules. \n\nAbbreviations:\nA. spp= Actinocyclus\nAs. spp= Asteromphalus\nAz. spp= Azpeita\nCh. spp= Chaetoceros\nCo. spp= Coscinodiscus\nC. spp= Cocconeis\nD. spp= Dactyliosen\nE. spp= Eucampia\nF. spp= Fragilariopsis\nO. spp= Odontella\nP. spp= Paralia\nPo. spp= Porosira\nR. spp= Rhizosolenia\nTh. spp= Thalassionema\nT. spp= Thalassiosira\n\n\nLocations\nA3-1, Kerguelen Plateau: -50.65333 S, 72.04 E\nA3-2, Kerguelen Plateau: -50.64722 S, 72.07 E\nE1-3, Kerguelen Plateau: -48.11667 S, 71.96667 E\nE14W2, Kerguelen Plateau: -48.7775 S, 71.43833 E\nNPF-L, Kerguelen Plateau: -48.62417 S, 74.81222 E\nR2, Kerguelen Plateau: -50.39389 S, 66.69944 E\nTEW, Kerguelen Plateau: -49.16083 S, 69.83389 E", "links": [ { diff --git a/datasets/AAS_3214_Photos_1.json b/datasets/AAS_3214_Photos_1.json index 2fcb4e3dc4..cb56a62b33 100644 --- a/datasets/AAS_3214_Photos_1.json +++ b/datasets/AAS_3214_Photos_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3214_Photos_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was collected as part of an honours project by Jessica Wilks at Macquarie University (submitted May 2012). The samples analysed were taken from an expedition conducted by Dr Leanne Armand in 2011 as part of the KEOPS2 mission (KErguelen: compared study of the Ocean and the Plateau in Surface water). During this mission 7 locations (A3-1, A3-2, E1-3, E14W2, NPF-L, R2 and TEW) around the Kerguelen Plateau were sampled for seafloor sediment. \n\nThis study involved identification of over 50 species of diatoms as part of a species assemblage/ distribution study. A photograph of each diatom encountered in this study is included in the attached plates.", "links": [ { diff --git a/datasets/AAS_3217_Davis_CurrentMetersDispersalModelling_1.json b/datasets/AAS_3217_Davis_CurrentMetersDispersalModelling_1.json index 079ad90352..b0a4140820 100644 --- a/datasets/AAS_3217_Davis_CurrentMetersDispersalModelling_1.json +++ b/datasets/AAS_3217_Davis_CurrentMetersDispersalModelling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3217_Davis_CurrentMetersDispersalModelling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record contains an Excel workbook of current meter data and a report derived from this data detailing an analysis of the mean and variability of the longshore component of the current using observations from four current meters, and, simple modelling of the effluent outfall using a model originally developed for shoreline discharges from the oil industry.\n\nThe Excel workbook contains data from 4 of the 6 analogue Anderra current that meters were deployed in the area in front of Davis Station in early 2010. Data was not retrievable from meters CM4 and CM6. The meters were deployed at approximately 5 m below the surface.\n\nRefer to the Davis STP reports lodged under metadata record Davis_STP for current meter locations and deployment and retrieval details. \n\nBackground of the Davis STP project - Refer to the Davis STP reports lodged under metadata record Davis_STP.", "links": [ { diff --git a/datasets/AAS_3227_predicted_habitat_1.json b/datasets/AAS_3227_predicted_habitat_1.json index 71167be0dd..77d889475d 100644 --- a/datasets/AAS_3227_predicted_habitat_1.json +++ b/datasets/AAS_3227_predicted_habitat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3227_predicted_habitat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract of the referenced paper:\n\nSatellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adelie, Pygoscelis adeliae and emperor, Aptenodytes forsteri penguins, light-mantled albatross, Phoebetria palpebrata, Antarctic fur seals, Arctocephalus gazella, southern elephant seals, Mirounga leonina, and Weddell seals, Leptonychotes weddellii. The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. The results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts.\n\nThe data files provided here comprise the model predictions of the preferred habitat for each of the six species listed above, as well as the overlap results obtained by combining these six sets of results. See the paper for methods used to generate the model predictions and to combine the individual species results.\n\nFile names for individual species are of the form results_SPP_TYPE.asc, where SPP is one of \"afs\" (Antarctic fur seal), \"ap\" (Adelie penguin), \"ep\" (emperor penguin), \"lma\" (light-mantled albatross), \"ses\" (southern elephant seal), or \"ws\" (Weddell seal. TYPE is either \"mean\" (mean estimate of habitat preference) or \"iqr\" (inter-quartile range of uncertainty in the estimate; see paper for details). Data values for individual species results are percentiles of the study area, so that values of 90% or higher are pixels corresponding to the most important 10% of habitat for that species, values of 80% or greater are the top 20% of habitat, and so on.\n\nThe overlap results files are named overlay_results_mean.asc and overlay_results_iqr.asc. Values in these files represent the average of the top four individual species results in a given pixel (see paper for details).", "links": [ { diff --git a/datasets/AAS_3229_1.json b/datasets/AAS_3229_1.json index bdfea6805d..e779bcaabd 100644 --- a/datasets/AAS_3229_1.json +++ b/datasets/AAS_3229_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3229_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3229.\n\nPublic Summary:\nWe investigate the impact of Black Saturday Australian bushfire in 2009 on the atmosphere above Australia and in the southern hemisphere in general, including Antarctica. Using high quality measurements collected by modern satellite and ground-based instruments, we study vertical and horizontal motion of the smoke plume, chemical composition of this plume, and chemical reactions between various molecules in the plume and other atmospheric gases. We want to answer an important question on how the bushfire plume may interact with the ozone molecules and whether it adds to the depletion of the protective ozone layer above Australia and above Antarctica.\n\nProject Objectives:\n- Using satellite and ground-based measurements, investigate the horizontal and vertical transport of the plume that resulted from Black Saturday Australian bushfire in February 2009. This includes analysis of the short-term (within one month) and long-term (up to several years) transport of plume material. Perform this analysis for other significant bushfire events that may occur in the southern hemisphere throughout the duration of this project and result in the injection of plume material into the stratosphere.\n- Study the evolution in chemical composition of stratospheric aerosols associated with Black Saturday bushfire and other significant pyrocarbon events.\n- Analyse the short- and long-term effects of Black Saturday bushfire and other significant pyrocarbon events in the southern hemisphere on the stratospheric ozone concentration at various locations and in particular on the Antarctic ozone hole.\n- Analyse the climate impact of bushfire plume material injected into the stratosphere.\n\nTaken from the 2010-2011 Progress Report:\n- We used the Odin/OSIRIS and CALIPSO satellite data and investigated the horizontal and vertical transport of the Australian-2009 Black Saturday bushfire smoke plume in the stratosphere in February-June 2009.\n- We identified the enhanced water absorption bands in the OSIRIS spectra of smoke plume. We are currently studying this smoke hydration in the stratosphere using multiple satellite instruments. A paper for Geophysical Research letters is currently in preparation.\n- We are currently investigating the horizontal spread of bushfire smoke material to all locations in the Northern and Southern hemispheres, up to the polar regions.\n\n2012-11-12 Update\nThe data are from the OSIRIS (Optical Spectrograph and Infrared Imager System) instrument on the Odin satellite.\n\nThe exact data used in project 3229 are: Level 1 spectral solar irradiances measured by OSIRIS in February - June 2009. The detailed description of the wavelengths used and the approach to data analysis are given in the paper:\n\nSiddaway, J. M. and S. V. Petelina (2011), Transport and evolution of the 2009 Australian Black Saturday bushfire smoke in the lower stratosphere observed by OSIRIS on Odin, J. Geophys. Res., 116, D06203, doi:10.1029/2010JD015162.", "links": [ { diff --git a/datasets/AAS_3289_1.json b/datasets/AAS_3289_1.json index 529658d530..bdcad5c697 100644 --- a/datasets/AAS_3289_1.json +++ b/datasets/AAS_3289_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3289_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two samples were collected at Cape Denison (Permit no. ATEP 10-11-3289), both by Dr David Tingay (2010 - 2011 Mawson's Huts Foundation Expedition). Both samples were cleaned with water prior to RTA and cleared in Hobart by AQIS (permit attached)\n\nThe first was a sample of the Cape Denison Orthogneiss (GA sample_no 2122491; Lat 67.008 S; long 142.655E). The sample was taken from loose material on the surface on the west side of Memorial Hill out of sight of Mawson's Hut. The location of the sample site is shown in the attached image. \n\nThe second was a sample of the Cape Denison Amphibolite (GA sample_no 2122492; Lat 67.008 S; long 142.657E). The sample was taken from loose material adjacent to Granholm Hut (see attached image). \n\nThe results appears in a brief format in AusGeo News (a GA publication) for the 100th AAE celebrations (AusGeo News 104 'Dec 2011' can downloaded at http://www.ga.gov.au/ausgeonews/download.jsp ). Also SHRIMP results are discussed in the Cape Denison Map (available at https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=72710 ). \n\nBoth rock types are approved Geoscience Australia lithological names\n\nThe sample sites are shown in the attached images\n\nRock samples are stored at Geoscience Australia.", "links": [ { diff --git a/datasets/AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1.json b/datasets/AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1.json index 1bbbbf43d2..45205abd79 100644 --- a/datasets/AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1.json +++ b/datasets/AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSV Aurora Australis V2 \u2013 Casey Resupply and Marine Science Voyage took place from 5 December 2014 to 25 January 2015. The voyage code is v2_201415020. The principal objective of the voyage was to undertake the Casey Resupply and then conduct marine science in the Dalton Polynya and near the Mertz Glacier. \n\nA downwards looking video camera system was fitted to the CTD and operated during most casts. The system was remotely controlled and typically operated only while the CTD was near the bottom although some videos show the complete descent through the water column. \n\nThe video footage for each deployment was labelled as follows:\nVOYAGE_DATE_TIME_SITE.MTS\nWhere:\nVOYAGE = v2_201415020\nDATE = YYYY-MM-DD\nTIME = HHMMUTC (in 24 hr time)\nSITE = the CTD site name (e.g. SiteA5)\n\nDetails on each site, including geographic coordinates and depth, are available in the Marine Data Voyage Report. The underway data from the voyage is available here: https://data.aad.gov.au/metadata/records/201415020", "links": [ { diff --git a/datasets/AAS_3326_bathymetric_grid_casey_2013-2015_1.json b/datasets/AAS_3326_bathymetric_grid_casey_2013-2015_1.json index 2184e161f1..3dceccfdea 100644 --- a/datasets/AAS_3326_bathymetric_grid_casey_2013-2015_1.json +++ b/datasets/AAS_3326_bathymetric_grid_casey_2013-2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3326_bathymetric_grid_casey_2013-2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica was produced by Geoscience Australia by combining data from two multibeam hydrographic surveys:\n \n1) A survey conducted by the Royal Australian Navy in 2013/14. \nRefer to the metadata record 'Hydrographic survey HI545 by the RAN Australian Hydrographic Service at Casey, December 2013 to January 2014' with ID HI545_hydrographic_survey.\n\n2) A survey conducted by Geoscience Australia and the Royal Australian Navy in 2014/15. \n\nRefer to the metadata record 'Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015' with ID HI560_hydrographic_survey and the metadata record 'Seafloor Mapping Survey, Windmill Islands and Casey region, Antarctica, December 2014 - February 2015' with ID AAS_3326_seafloor_mapping_casey_2014_15.\n\nThe grid has a cell size of one metre and is stored in a UTM Zone 49S projection, based on WGS84.\n\nFurther information is available from the Geoscience Australia website (see a Related URL).", "links": [ { diff --git a/datasets/AAS_3338_Davis_Gravel_Runway_2.json b/datasets/AAS_3338_Davis_Gravel_Runway_2.json index af9983835b..dc864fca36 100644 --- a/datasets/AAS_3338_Davis_Gravel_Runway_2.json +++ b/datasets/AAS_3338_Davis_Gravel_Runway_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_3338_Davis_Gravel_Runway_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Project 3338 (2012-13), 3372 (2013-14), 5007 (2014-17)\n\nDuring the 2012/13 field season geotechnical and environmental investigations were undertaken at Davis Station in order to investigate the viability of the 'Coastal Site' as a potential gravel or hard surface runway through standard site investigation and environmental sampling techniques (Project 3338). The study area was referred to as 'Adams' Flat' for project purposes. Ongoing data acquisition was managed through Project 3372. For Project 5007 the sites of interest include Heidemann Valley. Both areas are in close proximity to Davis on Broad Peninsula.", "links": [ { diff --git a/datasets/AAS_339_geomor_1.json b/datasets/AAS_339_geomor_1.json index a14e5c4c57..fe701cbd7a 100644 --- a/datasets/AAS_339_geomor_1.json +++ b/datasets/AAS_339_geomor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_339_geomor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains geomorphological data relating to the Windmill Islands, Wilkes Land, Antarctica. The dataset was captured at 1:10,000 and comprises of\n- glacial sediments, collected and identified from documented field observations (point data)\n- the locations of raised beach sequences (polygon data)\n- the Holocene Marine Incursion limits. The marine incursion limits are represented by a height in meters (line data). The attribute table contains an attribute hlmi_height_m field.\n- glacial sediments, collected and identified from documented field observations (point data)\n- of a Jokulhlaup event near Casey Station winter 1985 (ie outburst of water from beneath a cold ice-cap terminus on Law Dome) (point data). This event was observed and documented by Dr Ian D Goodwin. The attribute tables contains additional information. From the results of oxygen-isotope and solute analysis, the water was found to have originated as basal melt water. It contained a high total solute load with a dominant enrichment in alkalis, indicating that it has been squeezed through subglacial sediments for an extensive time period. This event represents one of the first known recordings in Antarctica and provides further insight into determining the subglacial hydrological regime beneath the Law Dome ice cap.\n- contour boundaries defining the marine incursion limits (line data). Interpretation of these boundaries can be carried out using sediment samples collected predominantly along the shoreline of these lakes. They represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands.\n- the locations of Windmill Islands lichenometric observations. Attribute data contains the maximum thallus size recorded at each location. It includes observations of lichens growing on nunataks for which the time of deglaciation is known and observations of lichens growing on supraglacial moraine ridges for which the time of formation is not known. This dataset thus provides the basis for a relative chronology of moraine development based on the assumption that the growth of the lichen thallus has been constant since the time of deglaciation.\n- lakes and ponds identified from documented field observations (polygon and point data). Each lake/pond is represented as a polygon with a central point label which is linked to a separate an attribute table containing additional information. Sediment samples collected predominantly along the shoreline of these lakes represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands.\n- Point data assigned to topographic profiles and transects and to the respective samples represented along these profiles. The point and line data contains attribute tables profile.aat and profile.pat assigned with the following items respectively :\nprofile_name, descript, descript1, descript2, descript3 and profile.pat :\nprofile_name, site, s_elev, br_elev, s_elev_source, br_elev_source, s_elev_qual, br_elev_qual. \n\nData does not conform to Geoscience Australia's Data Dictionary as the data is too detailed.\n\nGlacial sediments were collected and identified from documented field observations compiled by Dr Ian D Goodwin from his own field notes and from the records of other workers, as well as topographic and surface features identified and interpreted on aerial photographs taken by then AUSLIG in the 1993-94 field season. Sediment samples collected predominantly along the shoreline of these lakes represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands.", "links": [ { diff --git a/datasets/AAS_4011_PLATO_CSTAR_1.json b/datasets/AAS_4011_PLATO_CSTAR_1.json index cc1d3ac158..ee2afa771a 100644 --- a/datasets/AAS_4011_PLATO_CSTAR_1.json +++ b/datasets/AAS_4011_PLATO_CSTAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4011_PLATO_CSTAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The original CSTAR (Chinese Small Telescope ARray) consisted of four identical f/1.2 Schmidt telescopes on a common mount, located at Kunlun Station at Dome A, Antarctica.\n\nThe CSTAR mount was fixed to look at the South Celestial Pole, with no tracking, in order to simplify the instrument. Each telescope had an entrance aperture of 145 mm and a 4.5 x 4.5 degree field-of-view. The telescopes used Andor 1k x 1k CCDs with a pixel size of 13 microns, giving 15 arcseconds per pixel. Each telescope observed through a different filter: either g, r, i or open. \n\nCSTAR was developed by Purple Mountain Observatory, the Nanjing Institute of Astronomical Optics and Technology, and the National Astronomical Observatories of China. CSTAR was supported by UNSW's PLATO observatory.\n\nThe original CSTAR operated from 2008 to 2011 inclusive, producing about 3 TB of image data. The star catalog and photometry from 2008 is available here:\n\nhttp://casdc.china-vo.org/archive/cstar/\n\nand duplicated in the AAD data archive. The data are freely available. You are invited to cite the relevant papers in the \"papers/\" directory.\n\nThe data are described in the following two papers:\n\nWang, L., Macri, L. M., Krisciunas, K., Wang, L., Ashley, M. C. B., Cui, X., Feng, L.-L., Gong, X., Lawrence, J. S., Liu, Q., Luong-Van, D., Pennypacker, C. R., Shang, Z., Storey, J. W. V., Yang, H., Yang, J., Yuan, X., York, D. G., Zhou, X., Zhu, Z., 2011, Photometry of Variable Stars from Dome A, Antarctica, The Astronomical Journal, 142, 155.\n\nZhou, X., Fan, Z., Jiang, Z., Ashley, M. C. B., Cui, X., Feng, L., Gong, X., Hu, J., Kulesa, C. A., Lawrence, J. S., Liu, G., Luong-Van, D. M., Ma, J., Moore, A. M., Qin, W., Shang, Z., Storey, J. W. V., Sun, B., Travouillon, T., Walker, C. K., Wang, J., Wang, L., Wu, J., Wu, Z., Xia, L., Yan, J., Yang, J., Yang, H., Yuan, X., York, D., Zhang, Z., Zhu, Z., 2010, The First Release of the CSTAR Point Source Catalogue from Dome A, Antarctica, Publications of the Astronomical Society of the Pacific, 122, 347\u2013353.\n\nThe files/directories here are:\n\nREADME.txt A description of the data.\npapers/ Published papers from CSTAR (listed below).\ncatalog.dat The coordinates of the 21845 stars in this study.\ncatalog.fits The catalogue in FITS format.\npng/ Light curves for each of the 21845 stars.\nfits/ The photometric data in FITS format.\n\nIn January 2015 a new CSTAR instrument was installed. This consists of two of the original CSTAR telescopes, placed on a tracking mount.\n\nFor more information visit the website of the Chinese Center for Antarctic Astronomy (CCAA).", "links": [ { diff --git a/datasets/AAS_4011_PLATO_HEAT_DR1_1.json b/datasets/AAS_4011_PLATO_HEAT_DR1_1.json index 01c1de1e56..f990fb3e48 100644 --- a/datasets/AAS_4011_PLATO_HEAT_DR1_1.json +++ b/datasets/AAS_4011_PLATO_HEAT_DR1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4011_PLATO_HEAT_DR1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HEAT (the High Elevation Antarctic Terahertz telescope) is a 60cm aperture telescope designed to obtain wide-field spectroscopic maps of the Milky Way in frequency bands from 0.5 to 2 THz. The telescope is described here: http://soral.as.arizona.edu/HEAT/. HEAT is part of the PLATO-R experiment, described here: https://mcba1.phys.unsw.edu.au/~plato-r/. The data set shows the strength of various atomic and molecular emission lines across wide (tens of square degrees) fields-of-view in the Galactic Plane. The spectroscopic information (i.e., doppler shift of the line frequencies) allows an estimate to be made of the distance to the emission.", "links": [ { diff --git a/datasets/AAS_4011_PLATO_HRCAM_1.json b/datasets/AAS_4011_PLATO_HRCAM_1.json index b2a878d8fb..8f37491cfe 100644 --- a/datasets/AAS_4011_PLATO_HRCAM_1.json +++ b/datasets/AAS_4011_PLATO_HRCAM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4011_PLATO_HRCAM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains images of the sky taken with the HRCAM (High Resolution CAMera) instrument. HRCAM is a digital SLR camera (Canon EOS 50D; 15 megapixels) equipped with a fish-eye lens (Sigma 4.5-mm f/2.8) for all-sky coverage on a 1.6 crop sensor.\n\nHRCAM was primarily designed by Daniel Luong-Van. Nick Tothill helped with construction. Michael Ashley helped with design, construction, software, and operation.\n\nThe directories here are:\n\n/documentation PDFs of papers and a PhD thesis describing the instrument and results\n/raw Canon RAW image files - these are the original images \n/jpg JPG images extracted from some of the raw files\n/thumbs Thumbnail images extracted from some of the raw files\n\nSeveral versions of HRCAM have been built. You can tell which one is used from the serial numbers of the Canon EOS 50D camera, as stored in the RAW image files. Camera serial number 0330104673 was sent to Dome A, 2110703496 to Ridge A, and 2210700089 to Dome Fuji.\n\nThe image filenames in the various directories contain the UNIX epoch in seconds at which the exposure started. To convert the UNIX epoch to a date/time, you can use the Linux date command, as per the following example:\n\ndata -u -d @1282890600\n\nImages from Dome A:\n\nThe original HRCAM was first installed in PLATO at Dome A during January 2010. The camera was refurbished and reinstalled in PLATO-A for 2015.\n\nThere are 12703 images, with cadences of 240 and 660 seconds (with different exposure times), taken from Dome A. The images are in raw/h1*.raw, and start with h1282890600.raw at Fri 27 Aug 2010 06:30:00 UTC, and end with h1295604000.raw at Fri 21 Jan 2011 10:00:00 UTC. In addition, there are 709 images, with cadences of 900 seconds in jpg/h12*.jpg, starting with h1282915324.jpg at Fri 27 Aug 2010 13:22:04 UTC, and ending with h1284925919.jpg at Sun 19 Sep 2010 19:51:59 UTC. In addition, there are 10339 images, with cadences from 120 to 1800 seconds, taken from Dome A. The images are in raw/h1-*.raw and jpg/h1-*.jpg, with thumbnails in thumbs/*jpg, and start with h1-1421930703.jpg at Thu 22 Jan 2015 12:45:03 UTC, and end with h1-1434252625.jpg at Sun 14 Jun 2015 03:30:25 UTC.\n\nImages from Dome Fuji:\n\nHRCAM2 was taken to Dome Fuji and installed in PLATO-F by the Japanese 52nd JARE expedition during the 2010/2011 season. In the images, green Engine Module is at the top of the image, two small iridium aerials are at left and bottom, the Iridium Openport antenna is in the insulated white box at lower left, the meteorological tower is also visible. The \"egg of vision\" is on the right, just above the Iridium aerial. The image reaches to the horizon in all directions.\n\nThere are 21336 images, with cadences of 240 and 660 seconds (with different exposure times), taken from Dome Fuji. The images are in raw/h1*.raw, and start with h1296361840.raw at Sun 30 Jan 2011 04:30:40 UTC, and end with h1310294940.raw at Sun 10 Jul 2011 10:49:00 UTC. In addition, there are 8487 images, with cadences of 900 seconds in jpg/h1*.jpg, starting with h1299704642.jpg at Wed 09 Mar 2011 21:04:02 UTC, and ending with h1310295070.jpg at Sun 10 Jul 2011 10:51:10 UTC.\n\nImages from Ridge A:\n\nHRCAM3 was taken to Ridge A and installed in PLATO-R during the 2011/2012 season by a team led by Craig Kuleas of the University of Arizona. In the images, the solar panels are at the top of the image, the Ubob cameras are at 40 degrees CW, the HEAT telescope is at 60 degrees, the SCAR flag is at 110 degrees, and the meteorological tower at 200 degrees. North is at approximately 20 degrees. The image reaches very close to the horizon in all directions.\n\nThere are 12730 images, with cadences of 300 seconds, taken from Ridge A. The images are in raw/h3-*.raw, and start with h3-1331181001.raw at Thu 08 Mar 2012 04:30:01 UTC, and end with h3-1335099900.raw at Sun 22 Apr 2012 13:05:00 UTC.\n\nProcessing the RAW images:\n\nexiftool is a convenient piece of free software for processing Canon RAW images. dcraw can be used to extract images.\n\nOn a Linux computer you can use commands such as these:\n\nTo list all the metadata in a RAW file:\n\nexiftool -all h1282890600.raw\n\nTo extract the PreviewImage and ThumbnailImage from a RAW file:\n\nexiftool -b -PreviewImage h1282890600.raw - h1282890600.jpg\nexiftool -b -ThumbnailImage h1282890600.raw - h1282890600-thumb.jpg\n\nTo copy metadata to a JPG file:\n\nexiftool -tagsFromFile h1282890600.raw -exif:all -overwrite_original h1282890600.jpg", "links": [ { diff --git a/datasets/AAS_4011_PLATO_SNODAR_1.json b/datasets/AAS_4011_PLATO_SNODAR_1.json index b1181b1c0a..7dd2eeb47b 100644 --- a/datasets/AAS_4011_PLATO_SNODAR_1.json +++ b/datasets/AAS_4011_PLATO_SNODAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4011_PLATO_SNODAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This directory contains raw and reduced data from two sonic radars (the instrument is called \"Snodar\") that were installed at the Chinese station at Dome A.\n\nSnodar was built and designed by Colin Bonner, a PhD student at UNSW supervised by Michael Ashley. Contributions were made by the authors on the scientific papers in the \"papers\" directory.\n\nThe data are freely available for use. If you publish a paper using them, please cite our Snodar papers (see below). \n\nThe purpose of Snodar was to determine the turbulence in the atmosphere above the ice level, to a height of 180m, with a resolution of 1m. The minimum observable height was about 8m. Snodar sent out an acoustic pulse at about 5 kHz, and listened for the echo.\n\nThe directories here are:\n\n/papers\t\t\tPDFs of papers and a PhD thesis describing the instrument and results\n/2009_Data_analysis Reduced data\n/snodar Raw data\n/snodar09\t\t\tRaw data\n/extracted\t\tRaw data\n\nThe raw data contain echo strengths, and are in files with names similar to the following:\n\noutput/SNODAR_DATA_1203077424_265709\noutput/RAW_SNODAR_DATA_1255074792_749298\n\nThe filenames end with the UNIX time epoch (in seconds, followed by microseconds after the \"_\") of the start of the observation.\n\nThere are also some WAV files such as:\n\nsnodar-1205534620.wav.bz2\n\nwhich contain the raw acoustic echoes from the Snodar.\n\nSnodar was first deployed to Dome A in early 2008. It measured a stable diurnal boundary layer with a minimum height of 9 m and a maximum height of 89 m between the 10th and 14th of February 2008. A minor electrical failure crippled the instrument after 12000 samples. The instrument could not be repaired until 2009 as Dome A is an unmanned site. A second-generation of Snodar was deployed to Dome A in January 2009 and included several modifications to expand its capabilities and increase its robustness. The modifications included a new antenna design to hinder frost formation. The 2008 Snodar was also upgraded and as such there were two Snodars operating simultaneously at Dome A during 2009, with a separation of approximately 20 m. The agreement between the two instruments was extremely good. The data files are a \"1\" or \"2\" in their filenames to distinguish them. The pre-2009 Snodar is number 1.\n\nThe reduced data are in the form of CSV files showing boundary layer height and UNIX epoch, with postscript plots. See the matlab file \"2009_Data_analysis/monthly_anal.m for\" the data analysis program. Sub-folders within \"2009_Data_analysis\" show results with alternative definitions for the boundary layer height.\n\nFor help, email Michael Ashley and/or Colin Bonner .", "links": [ { diff --git a/datasets/AAS_4014_McInnes_et_al_2017_MolecularEcology_2.json b/datasets/AAS_4014_McInnes_et_al_2017_MolecularEcology_2.json index 7ce4afa050..647b9d3047 100644 --- a/datasets/AAS_4014_McInnes_et_al_2017_MolecularEcology_2.json +++ b/datasets/AAS_4014_McInnes_et_al_2017_MolecularEcology_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4014_McInnes_et_al_2017_MolecularEcology_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This spreadsheet provides the sequences counts for the DNA groups found in the scats of black-browed albatross at New Island and Steeple Jason Island, Falkland Islands; Diego Ram\u00edrez and Albatross Islet, Chile; Bird Island, South Georgia; Canyon des Sourcils Noirs, Kerguelen Archipelago, France; Macquarie Island, Australia; and Campbell albatross at Campbell Island, NZ.\n\nScat samples were collected in 2013/14 and 2014/15 at New Island, Steeple Jason Island, Macquarie Island, Campbell Island and Bird Island; in 2013/14 and 2015/16 at Kerguelen; in 2014/15 and 2015/16 at Albatross Islet and in 2013/14 at Diego Ram\u00edrez. Samples were collected during Incubation (Oct-Nov), early chick-rearing (Dec-Jan) or late-chick rearing (Feb-Mar). Due to the availability of birds at the colony, samples were predominantly collected from adults during incubation and early chick-rearing and chicks during late chick rearing. Samples sizes were too low during this study to directly compare dietary differences between chicks and adults; however, dietary comparisons between breeding stages were examined for sites where samples were collected during multiple breeding stages.\n\nSamples were PCR amplified with a universal metazoan primer set that is highly conserved and amplifies a region of the nuclear small subunit ribosomal DNA gene (18S rDNA). Details of the molecular methods and synthesis of this data can be found in: McInnes, J.C., Alderman, R., Raymond, B., Lea, M-A., Deagle, B., Catry, P., Gras, M., Phillip, R.A., Stanworth, A., Suazo, C., Thompson, D., Weimerskirch, H., Gras. M., and Jarman, S.N. High occurrence of jellyfish predation by black-browed and Campbell albatross identified by DNA metabarcoding. Molecular Ecology.", "links": [ { diff --git a/datasets/AAS_4014_whale_age_1.json b/datasets/AAS_4014_whale_age_1.json index 32846c8731..b0c822bafc 100644 --- a/datasets/AAS_4014_whale_age_1.json +++ b/datasets/AAS_4014_whale_age_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4014_whale_age_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a local copy of a metadata record and dataset stored at Dryad. This local copy is maintained in order to provide a link to the originating Australian Antarctic program project. See the link to the Dryad site at the provided URL for full details on this data set.\n\nAge is a fundamental aspect of animal ecology, but is difficult to determine in many species. Humpback whales exemplify this as they have a lifespan comparable to humans, mature sexually as early as four years and have no reliable visual age indicators after their first year. Current methods for estimating humpback age cannot be applied to all individuals and populations. Assays for human age have recently been developed recently based on age-induced changes in DNA methylation of specific genes. We used information on age-associated DNA methylation in human and mouse genes to identify homologous gene regions in humpbacks. Humpback skin samples were obtained from individuals with a known year of birth and employed to calibrate relationships between cytosine methylation and age. Seven of 37 cytosines assayed for methylation level in humpback skin had significant age-related profiles. The three most age-informative cytosine markers were selected for a humpback epigenetic age assay. The assay has an R2 of 0.787 (p = 3.04e-16) and predicts age from skin samples with a standard deviation of 2.991 years. The epigenetic method correctly determined which of parent-offspring pairs is the parent in more than 93% of cases. To demonstrate the potential of this technique, we constructed the first modern age profile of humpback whales off eastern Australia and compared the results to population structure five decades earlier. This is the first epigenetic age estimation method for a wild animal species and the approach we took for developing it can be applied to many other non model organisms.", "links": [ { diff --git a/datasets/AAS_4015_Krill_Gonad_Transcriptome_1.json b/datasets/AAS_4015_Krill_Gonad_Transcriptome_1.json index 61ce4299bc..688961ff5c 100644 --- a/datasets/AAS_4015_Krill_Gonad_Transcriptome_1.json +++ b/datasets/AAS_4015_Krill_Gonad_Transcriptome_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4015_Krill_Gonad_Transcriptome_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "RNA was extracted from pooled gonad tissues and tails of five sexually mature males and females, respectively, originating from the krill aquarium at the AAD in Tasmania, Australia. For RNA extractions, RNeasy mini kits (QIAGEN) were used and total RNA (8 micrograms each) was sent to Geneworks, South Australia (www.geneworks.com.au), for Illumina TruSeq 75 bp paired-end sequencing in two technical replica.\n\n Reads Yield Total Yield\nKrill_Male_sex_a_read1_sequence.txt 8,120,993 609,074,475 bases 1,218,148,950 bases\nKrill_Male_sex_a_read2_sequence.txt 8,120,993 609,074,475 bases\nKrill_Male_sex_b_read1_sequence.txt 10,465,586 784,918,950 bases 1,569,837,900 bases\nKrill_Male_sex_b_read2_sequence.txt 10,465,586 784,918,950 bases\n\nKrill_Male_tissue_a_read1_sequence.txt 7,867,804 590,085,300 bases 1,180,170,600 bases\nKrill_Male_tissue_a_read2_sequence.txt 7,867,804 590,085,300 bases\nKrill_Male_tissue_b_read1_sequence.txt 10,956,251 821,718,825 bases 1,793,118,450 bases\nKrill_Male_tissue_b_read2_sequence.txt 10,956,251 821,718,825 bases\n\nKrill_Female_sex_read1a_sequence.txt 29,447,654 2,208,574,050 bases 4,417,148,100 bases\nKrill_Female_sex_read2a_sequence.txt 29,447,654 2,208,574,050 bases\nKrill_Female_sex_read1b_sequence.txt 18,223,515 1,366,763,625 bases 2,733,527,250 bases\nKrill_Female_sex_read2b_sequence.txt 18,223,515 1,366,763,625 bases\n\nThe insert size for these libraries is approx 160bp.", "links": [ { diff --git a/datasets/AAS_4024_Macquarie_Island_Base_Stations_1.json b/datasets/AAS_4024_Macquarie_Island_Base_Stations_1.json index 7df0bd2572..5121b82f44 100644 --- a/datasets/AAS_4024_Macquarie_Island_Base_Stations_1.json +++ b/datasets/AAS_4024_Macquarie_Island_Base_Stations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4024_Macquarie_Island_Base_Stations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Using a Leica differential GPS system, together with existing survey marks at the main station, highly accurate (plus/minus 5cm) base stations were installed at 3-6 km intervals from the Macquarie Island station to just south of Green Gorge. The location of each base station was calculated using existing survey marks or the previously set up base station. Each location has been marked with a survey stake and covered with a rock cairn.", "links": [ { diff --git a/datasets/AAS_4024_Non-native_plant_survey_1.json b/datasets/AAS_4024_Non-native_plant_survey_1.json index f5606f7bd5..3b42bad009 100644 --- a/datasets/AAS_4024_Non-native_plant_survey_1.json +++ b/datasets/AAS_4024_Non-native_plant_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4024_Non-native_plant_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2010-11 a whole island survey or Macquarie Island was undertaken by Justine Shaw and Aleks Terauds. Quadrats (1m * 1m and 10m *10m) formed the basis of these surveys. At a minimum, quadrats were surveyed at the centroid of each 1 km * 1 km grid square. Other quadrats were surveyed along the survey track depending on the presence of non-native plants. Native plant coverage was also recorded in most quadrats.", "links": [ { diff --git a/datasets/AAS_4024_heard_springtails_2003-04_1.json b/datasets/AAS_4024_heard_springtails_2003-04_1.json index 4e986822cb..89a75f87a9 100644 --- a/datasets/AAS_4024_heard_springtails_2003-04_1.json +++ b/datasets/AAS_4024_heard_springtails_2003-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4024_heard_springtails_2003-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2003-04 70mm soil cores were collected from locations with a range of vegetation types at Heard Island. The cores were collected at 112 sites with 3 to 4 samples per site and 431 samples in total.\nThis file contains two spreadsheets: site descriptions and abundance of springtail species at the sites.\nProcessing of the samples was carried out as part of Australian Antarctic Science (AAS) Project 4024 and this work is currently being written up.", "links": [ { diff --git a/datasets/AAS_4024_macquarie_springtails_2010-11_1.json b/datasets/AAS_4024_macquarie_springtails_2010-11_1.json index a3eda9b575..88cdde791f 100644 --- a/datasets/AAS_4024_macquarie_springtails_2010-11_1.json +++ b/datasets/AAS_4024_macquarie_springtails_2010-11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4024_macquarie_springtails_2010-11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2010-11 70mm soil cores were collected from locations at Macquarie Island rich in the invasive plant Poa annua. The cores were collected at 22 sites, with 10 samples per site. Processing and identification of species was completed in 2016.\nThis file contains three spreadsheets: site descriptions, complete sample descriptions and abundance of springtail species at the sites.\nThe work was carried as part of Australian Antarctic Science (AAS) Project 4024 and is currently being written up into several papers.\nThe 'Quad veg' column gives the percentage vegetation cover in the one metre square quadrat. The 'sample veg' column gives the percentage vegetation cover in the 70mm soil core. The numbers in these columns are percentages and the letters are abbreviations for vegetation types:\npa = Poa annua, cal = Callitriche sp., ttg = tall tussock grassland (Poa foliosa), sg = short grassland (range of species), colo = Colobanthus spp.\nIn the 'rabbit presence' column 1 means there was evidence of rabbit presence in the quadrat and 0 means otherwise.", "links": [ { diff --git a/datasets/AAS_4024_non-native_plant_shapefiles_1.json b/datasets/AAS_4024_non-native_plant_shapefiles_1.json index 3337c1f527..d943286e70 100644 --- a/datasets/AAS_4024_non-native_plant_shapefiles_1.json +++ b/datasets/AAS_4024_non-native_plant_shapefiles_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4024_non-native_plant_shapefiles_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from the Macquarie Island non-native plant survey was consolidated into 1km grid squares. Data are provided on the presence and abundance of Stellaria media, Cerastium fontanum, Poa annua on both the contemporary 1km grid, and the historical Tasmanian Parks and Wildlife Service grid.", "links": [ { diff --git a/datasets/AAS_4026_Flow_Cytometry_1.json b/datasets/AAS_4026_Flow_Cytometry_1.json index 8a25f24cae..bb9c9ee106 100644 --- a/datasets/AAS_4026_Flow_Cytometry_1.json +++ b/datasets/AAS_4026_Flow_Cytometry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Flow_Cytometry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected from a ocean acidification minicosm experiment performed at Davis Station, Antarctica during the 2014/15 summer season. It includes:\n- description of methods for all data collection and analyses.\n- flow cytometry counts; autotrophic cells, heterotrophic nanoflagellates, and prokaryotes", "links": [ { diff --git a/datasets/AAS_4026_Meta-analysis_Data_1.json b/datasets/AAS_4026_Meta-analysis_Data_1.json index deadbae996..dcdf9fb705 100644 --- a/datasets/AAS_4026_Meta-analysis_Data_1.json +++ b/datasets/AAS_4026_Meta-analysis_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Meta-analysis_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A meta-analysis was undertaken to examine the vulnerability of Antarctic marine biota occupying waters south of 60 degrees S to ocean acidification. Comprehensive database searches were conducted to compile all English language, peer-reviewed journals articles and literature reviews that investigated the effect of altered seawater carbonate chemistry on Southern Ocean and/or Antarctic marine organisms.\n \nA document detailing the methods used to collect these data is included in the download file.", "links": [ { diff --git a/datasets/AAS_4026_Metadata_Molecular_Data_1.json b/datasets/AAS_4026_Metadata_Molecular_Data_1.json index 9d748ab361..6c8d04188c 100644 --- a/datasets/AAS_4026_Metadata_Molecular_Data_1.json +++ b/datasets/AAS_4026_Metadata_Molecular_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Metadata_Molecular_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Experimental Design \nA six-level, dose-response ocean acidification experiment was run on a natural microbial community from nearshore Antarctica, between 19th November and 7th December 2014. Seawater was collected from approximately 1 km offshore of Davis Station, Antarctica (68\u25e6 35\u2019 S, 77\u25e6 58\u2019 E), pre-filtered (200 \u03bcm), and transferred into six 650 L tanks (minicosms) located in a temperature-controlled shipping container. Six CO2 levels were achieved by altering the fugacity of carbon dioxide (\u0192CO2) within each minicosms. The \u0192CO2 was adjusted stepwise to the target concentrations for each minicosm (343, 506, 634, 953, 1140, 1641 \u03bcatm) over a five-day period using 0.2 \u03bcm filtered seawater enriched with CO2. This acclimation to CO2 was conducted at low light (0.9 \u00b1 0.2 \u03bcmol m\u22122 s\u22121) so there was low growth of the phytoplankton. Light levels were then increased over a further two days to 90.52 \u00b1 21.45 \u03bcmol m\u22122 on a 19:5 light/dark non-limiting light cycle. After this acclimation period, the microbial community was allowed to grow for 10 days (days 8-18), during which the \u0192CO2 levels within each minicosm was adjusted daily to maintain the target \u0192CO2 level for each minicosm, and light levels were kept constant. No nutrients were added during the experiment. \n\nFor a more detailed description of minicosm set-up, lighting and carbonate chemistry see;\nDavidson, A. T., McKinlay, J., Westwood, K., Thomson, P. G., van den Enden, R., de Salas, M., Wright, S., Johnson, R., and Berry, K.:Enhanced CO2 concentrations change the structure of Antarctic marine microbial communities, Mar. Ecol. Prog. Ser., 552, 93-113, 2016.\nDeppeler, S. L., Petrou, K., Westwood, K., Pearce, I., Pascoe, P., Schulz, K. G., and Davidson, A. T. Ocean acidification effects on productivity in a coastal Antarctic marine microbial community, Biogeosciences, 15(1), 2018.\n\nSample Collection \nSamples of 40-400 L were collected and sequentially size-fractionated filtered onto 293 mm biomass filters with 3.0 and 0.1 \u03bcm pore-sized polyethersulfone membrane filters (Pall XE20206 Disc 3.0 \u03bcm Versapor 293 mm and 656552 Disc 0.1 \u03bcm Supor 293 mm) using the design of the Global Ocean Sampling expedition (Rusch et al., 2007). Samples were collected on days 0 (immediately after seawater collection), 12 (mid-exponential growth) and 18 (end of experiment). On day 0, 400 L of seawater was collected from the reservoir tank (pre-filtered 200 \u03bcm), from which all the minicosms were filled, to allow characterisation of the initial community. This sample was collected from the reservoir, and not the minicosms, due to the large volume needed to collect sufficient microbial biomass on the filters. On day 12 and 18, 40 L was collected from each minicosm for filtration. The later samples were of a smaller volume due to the increase in biomass in the minicosms during the experiment, meaning less volume of water was required to gain sufficient material on the filters to perform molecular analysis. The filter membranes containing the concentrated microbial biomass were stored in 15 mL of storage buffer, flash frozen in liquid nitrogen and stored at - 80\u25e6C. The storage buffer was freshly prepared on each sampling day with a mixture of 2.5 mM EGTA, 2.5 mM EDTA, 0.1 mM Tris-EDTA, RNA Later (0.5x house prepared), 1 mM PMSF and Protease Inhibitor Cocktail VI (Ng et al., 2010). Between samples the filtration apparatus was sequentially washed with 2 x 25 L 0.1 M NaOH, 2 x 25 L 0.07% Ca(OCl)2 and 2 x 25 L fresh water. \nAll samples were stored and transported at -80\u25e6C to the Australian Antarctic Division, Hobart, Australia for DNA extraction. \n\nDNA Extraction and Sequencing \nThe DNA was extracted from half of each filter (3.0 and 0.1 \u03bcatm per sample) via the method described in Rusch et al. (2007). In short, the filters were cut into small pieces and agitated in a lysozyme and sucrose buffer for 60 minutes and underwent three freeze/thaw cycles in a Proteinase K solution. This was followed by a gentler agitation at 55\u25e6C for 2 hrs to remove all contents from the filter membranes. DNA was then separated using buffer saturated phenol, pelleted and washed in alcohol. The final DNA pellet was dissolved and stored in a 3 M sodium acetate (pH 8.0) and 100% ethanol solution and stored at - 80\u25e6C. The DNA was transported and stored at 4\u25e6C to the University of Queensland, St Lucia, Australia for sequencing within two months of extraction. \nEukaryotic 18S rRNA genes (V8-V9 regions) were amplified using polymerase chain reaction (PCR) with the primers V8f (5\u2019 - AT AAC AGG TCT GTG ATG CCC T - \u20193) and 1510r (5\u2019 - CCT TCY GCA GGT TCA CCT AC - \u20193) (Bradley, 2016). The 16S rRNA genes V8 region were amplified using PCR and primers 926F (5\u2019-AAA CTY AAA KGA ATT GAC GG-3\u2019) and 1392wR (5\u2019-ACG GGC GGT GTG RC-3\u2019) (Engelbrektson et al., 2010). PCR was performed using 1 or 1.5 \u03bcL of sample DNA, 2.5 \u03bcL 1x PCR buffer minus Mg+2 (Invitrogen), 0.75 \u03bcL MgCl2, 0.5 \u03bcL deoxynucleoside triphosphate (dNTPs, Invitrogen), 0.125 \u03bcL U Taq DNA Polymerase (Invitrogen), 0.625 \u03bcL of forward/reverse primer and made up to the final volume of 25 \u03bcL using molecular biology grade water. Forward and reverse primers were modified at the 5\u2019-end to contain an Illumina overhang adaptor with P5 and i7 Nextera XT indices, respectively. The PCR thermocycling conditions were as follows: 94\u25e6C for 3 min, 35 cycles of 94\u25e6C for 45 sec, 55\u25e6C for 30 sec, 7\u25e6C for 10 min and a final extension of 72\u25e6C for 10 min. Amplifications were performed using a Vertiti\u00ae96-well Thermocycler (Applied Biosystems) and success, amplicon size and quality was determined by gel electrophoresis. \nThe resultant amplicons were purified using Agencourt AMPure magnetic beads (Axygen Biosciences), dual indexed using Nextera XT Index Kit (Illumina). The indexed amplicons were purified using Agencourt AMPure XP beads and quantified using PicoGreen dsDNA Quantification Kit (Invitrogen). Equal concentrations of each sample were pooled and sequenced on an Illumina MiSeq at the University of Queensland\u2019s School for Earth and Environmental Science using 30% PhiX Control v3 (Illumina) and a MiSeq Reagent Kit v3 (600 cycle; Illumina). \n\nBioinformatics \nSequencing data and runs were merged to produced single FASTQ file for 16S and 18S rDNA per sample and imported in QIIME2 (v2019.9) (Caporaso et al., 2010). A modified version of the UPARSE analysis pipeline was used to analyse the data. Specifically, the primer sequences were removed from forward reads of the 16S rDNA and reverse complement of the 18S rDNA Illumina read pairs, and chimeras removed using UCHIME2 (Edgar, 2016). These were then trimmed to a length of 200 bp and high-quality sequences identified using USEARCH (v10.0.240) (Edgar, 2010). Duplicate sequences were removed and a set of unique operational taxonomic units (OTUs) were generated using USEARCH employing a 97% OTU similarity radius. Mitochondrial and chloroplast OTUs were classified and removed from the 16S rDNA sequence data using the BIOM tool suite (McDonald et al., 2012). Representative OTU sequences were assigned taxonomy using SILVA132 (Quast et al., 2012) and PR2 (Guillou et al., 2012) for the eukaryotic group Bacillariophyceae (diatoms). Taxonomic assignments were validated against microscopy identifications conducted on the same samples (Chapter 3, Hancock et al. 2018) as well as phylogenetic trees built in iTOL (Letunic and Bork, 2006). Residual eukaryotic chloroplast and mitochondrial sequences were removed from the 16S rDNA data. Other obvious contaminants were removed manually including: Escherichia-Shigella (16S rDNA OTU75) and Saccharomycetales (18S rDNA OTU7, 146 and 160). Escherichia-shigella was removed as this group likely represents external contamination, similarly Saccharomycetales are yeast and are obvious skin-driven contaminants. A total of 9448 OTUs were identified from the 16S rDNA reads and 232 OTUs from the 18S rDNA read data. The number of reads were rarefied to 1300 and 1200 reads per sample for the 18S and 16S rDNA datasets respectively. \nThe following samples were removed due to lack of extracted, amplified and/or sequenced DNA, or due to low quality reads and/or low read numbers:\n18S, 3.0 \u03bcm, day 18, 634 \u03bcatm \u0192CO2 treatment\n18S, 0.1 \u03bcm, day 12, 343 \u03bcatm or control \u0192CO2 treatment \n18S, 0.1 \u03bcm, day 18, 343 \u03bcatm or control \u0192CO2 treatment \n16S, 0.1 \u03bcm, day 18, 506 \u03bcatm \u0192CO2 treatment \n\nStatistical Analysis \nThe minicosm experiment was based on a repeated measure design, therefore due to being a dose-response experiment with no replication, no formal statistics could be undertaken on the interactions between time and \u0192CO2. The richness (number of taxa) and evenness (equivalent to abundances within a sample) of the eukaryotic and prokaryotic microbial communities within each minicosm over time was estimated using three different alpha diversity indexes: observed number of OTUs (Sobs) (DeSantis et al., 2006), the Chao1 estimator of richness (Colwell et al., 2004), and Simpson\u2019s diversity index and Berger-Parker index which account for both richness and evenness (Simpson, 1949; Berger and Parker, 1970) using QIIME2. \nClustering and ordinations were performed on Bray-Curtis resemblance matrices of the rarefied, square-root transformed OTU data as per Chapter 3 (Hancock et al., 2018). In brief, hierarchical agglomerative cluster analyses were performed using group-average linkage, and significantly different clusters were determined using similarity profile permutations method (SIMPROF) (Clarke et al., 2008). Both unconstrained (non-metric multidimensional scaling, nMDS) and constrained (canonical analysis of principal coordinates, CAP) ordinations were performed using the Bray-Curtis resemblance matrixes (Kruskal, 1964a,b; Oksanen et al., 2017). The constraining variables in the CAP analysis were \u0192CO2, Si, P and NOx. All cluster and ordination analyses were performed using R v.1.1.453 (R Core Team, 2016) and the add-on package Vegan v.2.5-3 (Oksanen et al., 2017). \n\nA full description of the statistical methods used for this paper is described in;\nHancock, A. M., Davidson, A. T., McKinlay, J., McMinn, A., Schulz, K. G., and van den Enden, R. L. Ocean acidification changes the structure of an Antarctic coastal protistan community, Biogeosciences, 15(1), 2018.", "links": [ { diff --git a/datasets/AAS_4026_Minicosm_Environmental_Data_1.json b/datasets/AAS_4026_Minicosm_Environmental_Data_1.json index 7dd67c62e9..9ee5bd7962 100644 --- a/datasets/AAS_4026_Minicosm_Environmental_Data_1.json +++ b/datasets/AAS_4026_Minicosm_Environmental_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Minicosm_Environmental_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected from a ocean acidification minicosm experiment performed at Davis Station, Antarctica during the 2014/15 summer season. It includes:\n- description of methods for all data collection and analyses.\n- environmental data logged throughout the experiment; nutrients, temperature, light climate.\n- carbonate chemistry data; pH (on Total scale), fugacity of CO2, dissolved inorganic carbon concentration, practical alkalinity, Omega calculations for both araganite and calcite.\n- product datasheet (including transmission spectra) of Osram 150W HQI-TS/NDL metal halide lamps.", "links": [ { diff --git a/datasets/AAS_4026_Ocean_Acidification_Marine_Microbes_Parent_1.json b/datasets/AAS_4026_Ocean_Acidification_Marine_Microbes_Parent_1.json index 9316896567..4b1e7cb48f 100644 --- a/datasets/AAS_4026_Ocean_Acidification_Marine_Microbes_Parent_1.json +++ b/datasets/AAS_4026_Ocean_Acidification_Marine_Microbes_Parent_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Ocean_Acidification_Marine_Microbes_Parent_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record is the parent umbrella under which data from the 2008/09, 2013/14 and 2014/15 summer will be housed. See the child records for access to the data.\n\nManmade CO2 has increased ocean acidity by 30% and it is projected to rise 300% by 2100. Antarctic waters will be amongst the earliest and most severely affected by this increase. Microbes are the base of the marine food chain and primary drivers of the biological pump. This project will incubate natural communities of Antarctic marine microbes in minicosms at a range of CO2 concentrations to quantify changes in their structure and function, the physiological responses that drive these changes, and provide input to models that predict effects on biogeochemical cycles and Antarctic food webs", "links": [ { diff --git a/datasets/AAS_4026_Pigments_CHEMTAX_1.json b/datasets/AAS_4026_Pigments_CHEMTAX_1.json index 6b54b7dc45..68d2eebb11 100644 --- a/datasets/AAS_4026_Pigments_CHEMTAX_1.json +++ b/datasets/AAS_4026_Pigments_CHEMTAX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Pigments_CHEMTAX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data reports the pigment concentrations and results of CHEMTAX analysis for 2 summer seasons in Antarctic. In 2008/09 three experiments in which 6 x 650 l minicosms (polythene tanks) were used to incubate natural microbial communities (less than 200 um diameter) at a range of CO2 concentrations while maintained at constant light, temperature and mixing. The communities were pumped from ice-free water ~60 m offshore on 30/12/08, 20/01/09 and 09/02/09. These experiments received no acclimation to CO2 treatment. A further experiment was performed in 2014/15 using water helicoptered from ~ 1 km offshore amongst decomposing fast ice on 19/11/14. This experiment included a 5 day period during which the community was exposed top low light and the CO2 was gradually raised to the target value for each tank, followed by a two day period when the light was raised to an irradiance that was saturating but not inhibitory for photosynthesis. \nA range of coincident measurements were performed to quantify the structure and function of the microbial community (see Davidson et al. 2016 Mar Ecol Prog Ser 552: 93\u2013113, doi: 10.3354/meps11742 and Thomson et al 2016 Mar Ecol Prog Ser 554: 51\u201369, 2016, doi: 10.3354/meps11803).\nThe data provides a matrix of samples against component pigment concentration and the output from CHEMTAX that best explained the phytoplankton composition of the community based on the ratios of the component pigments. \nFor the 2008/09 experiments, samples were obtained every 2 days for 10, 12 and 10 days in experiments 1, 2 and 3 respectively. In 2014/15 samples were obtained from each incubation tank on days 1,3, 5, and 8 during th acclimation period and every 2 days until day 18 thereafter. For each sample a measured volume was filtered through 13 mm Whatman GF/F filters for 20 mins. Filters were folded in half, blotted dry, and immediately frozen in liquid nitrogen for analysis in Australia. Pigments were extracted, analysed by HPLC, and quantified following the methods of Wright et al. (2010). Pigments (including Chl a) were extracted from filters with 300 micro l dimethylformamide plus 50 micro l methanol, containing 140 ng apo-8'-carotenal (Fluka) internal standard, followed by bead beating and centrifugation to separate the extract from particulate matter. Extracts (125 micro l) were diluted to 80% with water and analysed on a Waters HPLC using a Waters Symmetry C8 column and a Waters 996 photodiode array detector. Pigments were identified by comparing retention times and spectra to a mixed standard sample from known cultures (Jeffrey and Wright, 1997), run daily before samples. Peak integrations were performed using Waters Empower software, checked manually for corrections, and quantified using the internal standard method (Mantoura and Repeta, 1997).", "links": [ { diff --git a/datasets/AAS_4026_Productivity_PAM_Phytoplankton_Bacteria_1.json b/datasets/AAS_4026_Productivity_PAM_Phytoplankton_Bacteria_1.json index c30de834e9..e7cbc6e791 100644 --- a/datasets/AAS_4026_Productivity_PAM_Phytoplankton_Bacteria_1.json +++ b/datasets/AAS_4026_Productivity_PAM_Phytoplankton_Bacteria_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Productivity_PAM_Phytoplankton_Bacteria_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected from a ocean acidification minicosm experiment performed at Davis Station, Antarctica during the 2014/15 summer season. It includes:\n- description of methods for all data collection and analyses.\n- marine microbial community data; Chlorophyll a concentration, particulate organic matter concentration (carbon and nitrogen), bacterial cell abundance.\n- phytoplankton primary productivity data; 14C-sodium bicarbonate incorporation raw data (decays per minute: DPM) and modelled productivity from photosynthesis versus irradiance (PE) curves, O2-evolution derived net community productivity, respiration, and gross primary productivity.\n- phytoplankton photophysiology data; community photosynthetic efficiency from PAM measurements (maximum quantum yield of PSII: Fv/Fm), PAM steady state light curve data and derived non-photochemical quenching of Chl a fluorescence (NPQ), relative electron transport rates (rETR), and effective quantum yield of PSII (delta F/Fm').\n- phytoplankton carbon concentrating mechanism (CCM) data; maximum quantum yield of PSII (Fv/Fm) and effective quantum yield of PSII (\u2206F/Fm') from PAM measurements on size-fractionated phytoplankton samples (less than 10 microns and greater than 10 microns cells) exposed to; ethoxzolamide (EZA) which inhibits both intracellular carbonic anhydrase (iCA) and extracellular carbonic anhydrase (eCA), acetazolamide (AZA), which blocks eCA only, and a control (no inhibitor) sample. \n- bacterial productivity data; 14C-Leucine incorporation raw data (decays per minute: DPM) and calculated productivity.", "links": [ { diff --git a/datasets/AAS_4026_Silicification_CO2_1.json b/datasets/AAS_4026_Silicification_CO2_1.json index d662b73658..0d1d7f6d9c 100644 --- a/datasets/AAS_4026_Silicification_CO2_1.json +++ b/datasets/AAS_4026_Silicification_CO2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Silicification_CO2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected during an ocean acidification mesocosm experiment performed at Davis Station, Antarctica during the 2014/15 summer season. It includes:\n- description of methods for all data collection and analyses.\n- diatom cell volume\n- bulk silicification\n- species specific silicification via fluorescence microscopy\n- bulk community Fv/Fm on day 12\n- single-cell PAM fluorometry data (maximum quantum yield of PSII: Fv/Fm)\n\nA natural community of Antarctic marine microbes from Prydz Bay, East Antarctica were exposed to a range of CO2 concentrations in 650 L minicosms to simulate possible future ocean conditions up to the year ~2200. Diatom silica precipitation rates were examined at CO2 concentrations between 343 to 1641 micro atm, measuring both the total diatom community response and that of individual species, to determine whether ocean acidification may influence future diatom ballast and therefore alter carbon and silica fluxes in the Southern Ocean.\n\nDescribed and analysed in:\nAntarctic diatom silicification diminishes under ocean acidification (submitted for review)\t\n\t\nMethods described in:\t\nAntarctic diatom silicification diminishes under ocean acidification (submitted for review)\t\n\t\nLocation: Prydz bay, Davis Station, Antarctica (68 degrees 35'S, 77 degrees 58' E) \nDate: Summer 2014/2015\n\nWorksheet descriptions:\t\n\t\nBulk silicification - raw data\t\nMeasured total and incorporated biogenic silica using spectrophotometer for all tanks on day 12 after 24 h incubation with PDMPO - raw data\t\n\t\nBulk Fv/Fm - dark-adapted maximum quantum efficiency of PSII (Fv/Fm) on whole community - raw data\t\nMeasured Fv/Fm of individual cells from 3 mesocosm tanks.\t\n\t\nSingle-cell silicificiation, Fluorescence microscopy - raw data\t\nMeasured autofluorescence and PDMPO fluorescence of individual diatoms from 6 mesocosm tanks \t\n\t\nSingle-cell PAM, dark-adapted maximum quantum efficiency of PSII (Fv/Fm) - raw data\t\nMeasured Fv/Fm of individual cells from 3 mesocosm tanks.\t\n\t\nCell volume\t\nCalculated cell volume (um3) of 7 species from minicosm tanks 1 and 6 - raw data\t\n\t\n\t\nAbbreviations:\t\nFv/Fm\tMaximum quantum yield of PSII\nPDMPO\t2-(4-pyridyl)-5-((4-(2-dimethylaminoethylaminocarbamoyl)methoxy)phenyl)oxazole \nTant\tThalassiosira antarctica\nDiscLg\tLarge Discoid centric diatoms\nStella\tStellarima microtrias\nChaeto\tChaetoceros spp.\nProb\tProboscia truncata\nPseu\tPseudonitzschia turgiduloides\nFragLg\tFragilariopsis cylindrus / curta\nCentric \tLarge Discoid centric diatoms\nLargeThalassiosira\tLarge Discoid centric diatoms", "links": [ { diff --git a/datasets/AAS_4026_Variance_Experiment_1.json b/datasets/AAS_4026_Variance_Experiment_1.json index 28414c13d3..e360d9e0f4 100644 --- a/datasets/AAS_4026_Variance_Experiment_1.json +++ b/datasets/AAS_4026_Variance_Experiment_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4026_Variance_Experiment_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected from two minicosm experiments conducted at Davis Station, Antarctica.\n1. Variance experiment - 2013/14 summer season\n2. Ocean acidification experiment - 2014/15 summer season\n\nIt includes:\n- description of methods for all data collection and analyses.\n- environmental data logged throughout the experiment; nutrients, temperature, light climate.\n- flow cytometry counts; autotrophic cells, heterotrophic nanoflagellates, and prokaryotes.\n- FlowCam counts; individual phytoplankton species data.\n- microscopy counts; individual phytoplankton species data.", "links": [ { diff --git a/datasets/AAS_4029_Field_EC_1.json b/datasets/AAS_4029_Field_EC_1.json index d6d80fe92d..936dad386e 100644 --- a/datasets/AAS_4029_Field_EC_1.json +++ b/datasets/AAS_4029_Field_EC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4029_Field_EC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "(See the metadata file in the downloadable dataset for more information, including graphs and formulas which cannot be reproduced here)\n\nThe permeable reactive barrier under assessment was originally constructed in the 2005/06 summer for details refer to (Mumford et al., 2013). The reactive gate consists of five modified Australian Antarctic Division cage pallets with external dimensions 1.8 x L 1.1 W x 0.75 H m.\n\nDuring the 2012/13 field season, media in the fifth cage was replaced with a new trial media sequence. Refilling occurred on the 16th of January. In this process mixed media sequences were homogenised by a cement mixer in 40 L batches and then placed in the PRB cage. \n\nTo evaluate the retention time and hydraulic conductivity of the new sequence relative to the pre-existing material, non-reactive tracer tests were conducted over both monitoring seasons.\n\nFrom the conductivity distribution of salt measured downstream from a pulse as shown in Figure 1, the mean fluid age at each point of measurement can be calculated. The further away from the source, the longer the retention time and the more dispersed the response.\n\nMethod\nTracer tests were conducted on the 22nd of January 2013 and 16th of January 2014. For both occasions the tracer source was a pulse of sodium chloride with a conductivity of approximately 50 mS cm-1 pumped at a rate of 0.83 L s-1 for five minutes. At the end of the salt pulse the flow of melt water recommenced at the original rate of 0.91 L s-1. \n\nAt set time intervals 30 mL of water was drawn for EC measurement from multiports at a depth of 700 mm. For the cages in which media was not replaced four multiports were sampled, in the fifth cage an additional two sample points were included. The EC of all samples was determined over a two day period at room temperature using a TPS WP-81 meter and associated probe. \n\nThis method and sampling location were consistent for both assessments. However, there was a minor modification in 2014 as multiports 6 (frozen) and 11 (no suction, dry or broken tube) were sampled at 600 mm instead of the standard value of 700 mm. \n\nThe dataset consists of two excel files:\n1. 0-year 2013 tracer test results and calculation\n2. 1-year 2014 tracer test results and calculation", "links": [ { diff --git a/datasets/AAS_4029_Field_ICP_1.json b/datasets/AAS_4029_Field_ICP_1.json index 67ba200f1b..1f531c067a 100644 --- a/datasets/AAS_4029_Field_ICP_1.json +++ b/datasets/AAS_4029_Field_ICP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4029_Field_ICP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of field solution metal concentrations for a new sequence of material installed within an existing permeable reactive barrier at the Casey Station Main Powerhouse. Three Excel files from ICP analysis are included.\n1. The limit of quantification and certified reference material results from 2013/14\n2. Field sample results from 2012/13\n3. Field sample results from 2013/14\n\nThe barrier was originally constructed in the 2005/06 summer for details refer to (Mumford et al., 2013). The reactive gate consists of five modified Australian Antarctic Division cage pallets with external dimensions 1.8 x L 1.1 W x 0.75 H m.\n\nDuring the 2012/13 field season, media in the fifth cage was replaced with a new trial media sequence (Statham draft publication). Refilling occurred on the 16th of January. In this process mixed media sequences were homogenised by a cement mixer in 40 L batches and then placed in the PRB cage.\n\nSamples for metal concentration analysis collected six times over each of the 2012/13 and 2013/14 summer seasons. Samples were drawn from the lowest unfrozen depth of each multiport in cage 5. To minimise collection interference the procedure started at the most downstream multiport and continued upgradient. From each port two 30 mL samples were drawn, the first was filtered to 0.45 microns, the second was used to measure pH, Eh and EC readings. All samples were acidified (pH less than 2 using nitric acid) and frozen for storage and transport to Australia.\n\nElement concentrations and analysed by inductively couple plasma-optical emission spectroscopy (ICP-OES) using a Varian 720-ES spectrometer at the Australian Antarctic Division. The emission intensity of standards and sample solutions was measured following 1:1 in-line mixing with a solution of 0.75% (w/v) CsCl matrix modifier in 10% (v/v) HNO3 containing 5 mg L-1 yttrium as an internal standard. It was deemed unnecessary to use a matrix modifier for the 2014 analysis.", "links": [ { diff --git a/datasets/AAS_4029_Lab_ICP_1.json b/datasets/AAS_4029_Lab_ICP_1.json index 2a3c55a95a..3d7db0d7a1 100644 --- a/datasets/AAS_4029_Lab_ICP_1.json +++ b/datasets/AAS_4029_Lab_ICP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4029_Lab_ICP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of two types of assessments of permeable reactive barrier (PRB) media: aqua regia digestions and 1 M HCl acid extractions. All analytical analysis was completed using inductively couple plasma-optical emission spectroscopy (ICP-OES).\n\nAqua regia digestions were conducted on to assess the purity of three iron sources (Peerless, Connelly and Chem-Supply). 15 mL of aqua regia (3:1 concentrated hydrochloric acid: nitric acid) was added to 0.5 g iron samples in triplicated Teflon containers. The solution was heated at 80 degrees C for three hours and left at 40 degrees C for a further three days. The mixture was diluted to 100 mL and filtered at 0.45 microns before analysis. \n\nThe Lower PRB at the Casey Main Power House was originally constructed in the 2005/06 summer for details refer to (Mumford et al., 2013). The reactive gate consists of five modified Australian Antarctic Division cage pallets with external dimensions 1.8 x L 1.1 W x 0.75 H m. During the 2012/13 field season, media in the fifth cage was replaced with a new trial media sequence (Statham draft publication). Refilling occurred on the 16th of January. In this process mixed media sequences were homogenised by a cement mixer in 40 L batches and then placed in the PRB cage.\n\nMaterial samples were cored from within the new media sequence of the PRB, three in GAC/ZeoPro, three in ZVI/sand and two in the zeolite section (Statham unpublished manuscript). All samples were collected from a depth of 45-60 cm then stored at -18 degrees C for transport to Australia. The concentration of bioavailable metals within the field and raw material samples were using a method recommended by Snape et al. (2004). All extractions used 2 g of oven dried medium and 40 mL of HCl solution and were conducted in new duplicated polypropylene containers. The mixtures were revolved on a suspension mixer for four hours, promptly filtered at 0.45 microns before analysis \n\nElement concentrations were determined by ICP-OES using a Varian 720-ES spectrometer at the Australian Antarctic Division. The emission intensity of standards and sample solutions was measured following 1:1 in-line mixing with a solution of 0.75% (w/v) CsCl matrix modifier in 10% (v/v) HNO3 containing 5 mg L-1 yttrium as an internal standard. Aqua regia iron concentrations were determined from a 1 in 50 dilution of the filtered solution. The matrix modifier was deemed unnecessary for the HCl extractions. A blank samples for both methods indicated that the presence of the analysed elements was due to dissolution from PRB media and not other sources.\n\nThe limit of quantification (LOQ) was determined by the NATA extrapolation method (NATA, 2009) using concentrations of 20, 50 and 100 microns L-1 for phosphorus and potassium and 4, 10 and 20 microns L-1 for all other measured elements. \n\nThe data consists of four excel files of ICP analysis results:\n1. The limit of quantification and certified reference material results from aqua regia digestion\n2. The limit of quantification and certified reference material results from 1 M HCl extractions\n3. Aqua regia digestion results and calculations\n4. 1 M HCl extractions results and calculations", "links": [ { diff --git a/datasets/AAS_4029_Magnetometry_1.json b/datasets/AAS_4029_Magnetometry_1.json index b279131f14..bd5d53eed7 100644 --- a/datasets/AAS_4029_Magnetometry_1.json +++ b/datasets/AAS_4029_Magnetometry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4029_Magnetometry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The abandoned Wilkes Station remains a significant waste problem in Antarctica with a large proportion of this waste buried in the ice for much of the year. A large proportion of this waste is locked up beneath the ice at the northern boundary of Newcomb Bay on the Clark Peninsula, approximately 3 km north of Casey Station. The magnetometer survey was conducted to delineate the spatial extent of the landfill for the purpose of guiding potential clean-up operations in the near future.\n\nThe magnetic survey was performed using a GeoMetrics Inc G-856 Proton Precession Magnetometer and covered an area of 350 x 150m survey grid, guided by previous GPR surveying. All magnetic readings were recorded in nanotesla (nT). Magnetic data were uploaded to GeoMetrics MagMap2000, a post-acquisition processing and analytical tool. Using MagMap2000, magnetic data were spatially adjusted from dGPS site measurements and diurnal corrections were made using base station flux-gate magnetometer readings. Magnetic and spatial information data were then exported to Golden Software's Surfer 9 mapping program for display. Where landfill material was detected, modelling of magnetic anomalies and susceptibilities was conducted using Model Vision Software V.12.00.07 (Tensor Research).\n\nThe data showed that the Wilkes landfill possesses sufficient magnetic properties to be distinguishable as an anomalous feature within the survey area. From the anomalies generated, it is estimated that the main waste deposit covers 6250m2 and comprises 17 000m3. The short wavelength and depression between anomalies suggests that steel drums have been piled near the surface resulting in regular exposure during the summer months. However, this exposure and measured wavelengths are subject to annual variation, regulated by the intensity and path of summer melt flows. The unevenly configured fall-off rate and asymmetry of the main peak also reveals voids between material and suggests that drums were empty when dumped or have since discharged fuel. Recognising that many of the drums may still contain fuel is critical if 'dig and haul' clean-up techniques are implemented at the site.\n\nThe clean up and removal of landfill material at Wilkes is required under the Protocol on Environmental Protection to the Antarctic Treaty (1991). As clean up of the landfill will occur over multiple field seasons, ground magnetometry can be applied to prioritize locations for material removal. The bedrock ridgeline occurs at a higher elevation than the landfill, providing a trapping mechanism for contaminants migrating in the soil profile from the north of the landfill. Furthermore, with material 1-2m below the ice surface in the north, disturbance could enhance and concentrate melt stream flow capacity and flush contaminants in the lower sections of the landfill. As this zone is located about 150m from Newcomb Bay, it may not pose an immediate threat to the marine environment. \n \nIn contrast, subsurface waste material detected south of the bedrock ridgeline probably poses a more urgent threat to the marine ecosystem. Subsurface imaging shows no topographical barriers to contaminant migration with large melt streams recorded across this region. Clean up of landfill material downhill of the ridgeline should be a priority in order to prevent greater adverse environmental impacts in accordance with Article 1(5) of Annex III (SCAR1993). Subsequent clean up in the north of the landfill may require the installation of a permeable reactive barrier in the south to ensure contaminants migrating off-site are captured and treated effectively.\n\nGround magnetometry was well suited to the spatial characterization of the landfill at Wilkes. Buried material and the boundaries of the landfill were clearly delineated from anomalies produced. Buried drums, which can contain dangerous pollutants, were identified with high amplitude anomalies of ~1000 nT in the north of the landfill. Varying amplitudes were used to infer the presence of smaller metallic material amongst steel drums. These data provide a platform for developing and implementing future clean-up strategies at the Wilkes Station landfill.\n\nFor further reference to figure, tables and maps, follow this link to the published manuscript \nhttp://journals.cambridge.org/repo_A91h0PoJ", "links": [ { diff --git a/datasets/AAS_4029_Particle_Sizing_1.json b/datasets/AAS_4029_Particle_Sizing_1.json index 7e8624adb7..f2a3b26428 100644 --- a/datasets/AAS_4029_Particle_Sizing_1.json +++ b/datasets/AAS_4029_Particle_Sizing_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4029_Particle_Sizing_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The impact of freeze-thaw cycling on a ZVI and inert medium was assessed using duplicated Darcy boxes subjected to 42 freeze-thaw cycles. This dataset consists of particle sizing during the decommissioning process of the experiment. \n\nTwo custom built Perspex Darcy boxes of bed dimensions: length 362 mm, width 60 mm and height 194 mm were filled with a mixture of 5 wt% Peerless iron (Peerless Metal Powders and Abrasive, cast iron aggregate 8-50 US sieve) and 95 wt% glass ballotini ground glass (Potters Industries Inc. 25-40 US sieve). This ratio of media was selected to ensure that most aqueous contaminant measurements were above the analytical limit of quantification (LOQ) for feed solutions at a realistic maximum Antarctic metal contaminant concentration at a realistic field water flow rate. All solutions were pumped into and out of the Darcy boxes using peristaltic pumps and acid washed Masterflex FDA vitron tubing.\n\nDry media was weighed in 1 kg batches and homogenised by shaking and turning end over end in a ziplock bag for 1 minute. To ensure that the media was always saturated, known amounts of Milli-Q water followed by the homogenised media were added to each box in approximately 1 cm layers. 20 mm of space was left at the top of the boxes to allow for frost heave and other particle rearrangement processes.\n\nOn completion of freeze-thaw cycling and solution flow (refer to Statham 2014), an additional series of assessments was conducted. The media from between the entry weir and the first sample port was removed in five approximately 400 g samples of increasing depth. This procedure was repeated between the last sample port and the exit weir. These samples were left to dry in a fume cabinet before duplicated particle sizing using a Endcotts minor sieve shaker.", "links": [ { diff --git a/datasets/AAS_4030_CTD_Toothfish_Fishery_2.json b/datasets/AAS_4030_CTD_Toothfish_Fishery_2.json index 656d1f1947..faa226d9bc 100644 --- a/datasets/AAS_4030_CTD_Toothfish_Fishery_2.json +++ b/datasets/AAS_4030_CTD_Toothfish_Fishery_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4030_CTD_Toothfish_Fishery_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Australian fishing vessels involved in exploratory fishing for Antarctic toothfish in East Antarctica under the auspices of the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) collected data required under their exploratory fishing permit. Conductivity, temperature and depth (CTD) loggers were attached to bottom longlines sets to collect data while fishing for Antarctic toothfish in Antarctic waters.\nThe data relates to Objective 2 of the research work required: Collect and utilise environmental data to inform spatial management approaches for the conservation of toothfish, bycatch species and representative areas of benthic biodiversity (CCAMLR 2016).\nData were collected on two fishing vessels during the austral summers (December to February) of 2015/16, 2016/17 and 2017/18 in CCAMLR Divisions 58.4.1 and 58.4.2.\nThe data were collected with DST CTD (Conductivity, Temperature and Depth Recorder) from Star-Oddi (Conductivity: 13-50 mS/cm, maximum depth: 2400 m). Files were then downloaded with SeaStar and are available in the original data format. Recordings were made at 5 or 10 second intervals for the duration of up to around 24h, recording data throughout the water column while setting the longline and then while stationary on the sea floor. Each deployment has data on time, temperature (degrees C), salinity (psu), conductivity (mS/cm) and depth (m), and is linked to geographical coordinates. \n\nNumber of deployments: \n2015/16: 34\n2016/17: 31\n2017/18: 75\n\nCCAMLR (2016) Joint research proposal for the Dissostichus spp. exploratory fishery in East Antarctica (Divisions 58.4.1 and 58.4.2) by Australia, France, Japan, Republic of Korea and Spain. Delegations of Australia, France, Japan, Republic of Korea and Spain. Report to Fish Stock Assessment Working Group, WG-FSA-16/29, CCAMLR, Hobart, Australia.\n\nDates and times in the data files are recorded in UTC.\n\nFurther information is provided in a pdf document in the download file.", "links": [ { diff --git a/datasets/AAS_4030_ToothfishAssessment_1.json b/datasets/AAS_4030_ToothfishAssessment_1.json index acf0aef266..1261cfb3b0 100644 --- a/datasets/AAS_4030_ToothfishAssessment_1.json +++ b/datasets/AAS_4030_ToothfishAssessment_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4030_ToothfishAssessment_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This integrated stock assessment for the Patagonian toothfish (Dissostichus eleginoides) fishery at the Heard Island and the McDonald Islands in CCAMLR Division 58.5.2, with data until end of July 2015, is based on the best available estimates of model parameters, the use of abundance estimates from a random stratified trawl survey (RSTS), longline tag-release data from 2012-2014 and longline tag-recapture data from 2013-2015, and auxiliary commercial composition data to aid with the estimation of year class strength and selectivity functions of the trawl, longline and trap sub-fisheries.All model runs were conducted with CASAL version 2.30-2012-03-21 (Bull et al. 2012). \n \nThe assessment model leads to an MCMC estimate of the virgin spawning stock biomass B0 = 87 077 tonnes (95% CI: 78 500-97 547 tonnes). Estimated SSB status in 2015 was 0.64 (95% CI: 0.59-0.69). Using this model, a catch limit of 3405 tonnes satisfies the CCAMLR decision rules. Similarly to the 2014 assessment, the projected stock remains above the target level for the entire projection period.", "links": [ { diff --git a/datasets/AAS_4036_Casey_Assessments_1.json b/datasets/AAS_4036_Casey_Assessments_1.json index a3fc7f5ad7..cd350f64d8 100644 --- a/datasets/AAS_4036_Casey_Assessments_1.json +++ b/datasets/AAS_4036_Casey_Assessments_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4036_Casey_Assessments_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the documents in the download file:\n\nCasey West Wing Soil Reuse Risk Assessment Final\nIt is the Australian Antarctic Division\u2019s stated goal as a responsible environmental steward to reduce the environmental impact and waste generated by its program. This includes cleaning up legacy waste and minimising the transport and introduction of unnecessary materials, both in and out of Antarctica.\n\nThis document addresses the proposed reuse of formerly diesel contaminated soil as fill material underneath the Casey Red Shed West Wing. This soil was excavated from the environment surrounding the (since decommissioned) Emergency Power House Settling Tank in the summer of 2012/13. It was placed in a contained biopile, and biodegradation was stimulated through the addition of inorganic fertiliser and regular turning. Annual monitoring has determined that hydrocarbon concentrations in the soil have now been reduced to a level where it is suitable for an appropriate reuse.\n\nReusing the once contaminated soil as \u201cfill\u201d avoids the quarrying, sterilisation, transport and introduction of a bulk quantity of material to Antarctica for this explicit purpose.\n\nThe proposed reuse of remediated hydrocarbon impacted soil as fill material underneath the Casey Red Shed West Wing is assessed as posing negligible human health risk from dermal contact and vapour inhalation. Leachable concentrations of hydrocarbons in the soil are low enough that they do not pose an ecological or human health (drinking water) risk to the adjacent melt-lake, even under the unlikely scenario where the soil does not freeze back and groundwater migrates through the soil and into the melt-lake.\n\nThe concentration and type of residual hydrocarbons left in the soil poses some ecological risk for organisms that live within the soil, but that must be considered in the context that the soil is ecologically compromised by being placed underneath a building.\n\nIt is emphasised that the soil has not yet been restored to its natural biological function, and that residual nutrient concentrations are higher than the background levels naturally occurring at the site, but within the range observed from nearby sites. Once emplaced, some in situ ammonia volatilisation will occur, and nitrification is expected to occur at very low rates.\n\nIn a future scenario where the Casey West Wing building is moved or demolished, there is a medium risk of localised groundwater and melt-water eutrophication as soluble ammonia, nitrate and phosphate are washed from the soil. This localised eutrophication is not abnormal for the Antarctic environment, as it is typically associated with penguin colonies and other animal congregation areas, of which many active and relict sites are located in the Casey area.\n\nFuture localised eutrophication following building demolition can be reduced to low risk via appropriate management. The recommended management steps are to add the spatial location and chemical metadata of the soil to a Data Centre record (either under the contaminated sites database or as another layer within the infrastructure geodatabase), and to consult with Antarctic scientists about the appropriate placement of the soil within the landscape as the site is rehabilitated.\n\nThis risk assessment deems that the identified soil may be used as backfill under the Casey Red Shed West Wing with low risk to human health and the local environment.\n\n\nEnvironmental and Human Health Risk Assessment Casey CUB_Final\nAs a result of the Casey EPH Flange fuel spill (IHIS 3466), fuel contamination migrated into the building footprint of the Casey Utility Building (CUB). Construction of the CUB is scheduled to continue through the summer of 2015/16. Following confirmation of high levels of contamination in the vicinity of the CUB, an up-gradient barrier was installed to divert melt water and fuel away from the CUB building towards a Permeable Reactive Barrier (PRB) and to minimize further migration of fuel into the CUB footprint. The presence of heavily contaminated soil within the footprint of the CUB has been identified as potentially a high risk to human health, environment and engineering and construction if left in place.\n\nThe scope of this document is to assess the environmental and human health risk associated with the construction of the CUB and the proposed reuse of the specific biopile soil as part of the foundation. This is in addition to the assessment provided by external consultants (GHD), who were engaged to provide advice on the Human Health and Engineering/Construction risk based on conventional vapour intrusion modelling (for Human Health). Results presented herein should be considered collectively along with those presented in the GHD report(s).\n\nA conservative approach using multiple lines of scientific evidence, consistent with national and international risk assessment method, was used to assess risks. The approach included:\n\n 1) A physical and chemical assessment of the soil with a comparison against existing risk-based guidelines from Australia and other International jurisdictions;\n 2) Consideration of a toxicological and/or ecological assessment; and\n 3) A human health risk assessment for the specific use of the biopile soil in the foundations of the CUB.\n \nTPH concentrations in the area of the CUB contaminated by SAB from the EPH Flange spill range from less than 5 to 1,600 mg/kg in the C6-C9 range, and 610 to 75,000 mg/kg in the C10-C14 range1. Large quantities of soil remains saturated with fuel, and free phase SAB was observed on a number of occasions from November (initial investigations) to January (soil excavation). The bulk of the contaminated soil has now been removed from the CUB foundation, and there is a need to backfill the area with uncontaminated fill or an appropriate substitute. Although a low permeability barrier has been installed along the east and south sides of the CUB foundation to minimise migration of contamination back into the excavated CUB footprint, backfilling the excavation with clean fill will inevitably result in the contamination of that material with residual fuel (i.e. migration of contamination from soil underneath the concrete perimeter beam which can\u2019t be removed, as well as migration through bedrock fractures). As such, it is prudent to consider alternative sources of backfill material which may be suitable for reuse. One potential source is the partially remediated soil from the MPH biopile remediation work.\n\nTPH concentrations measured in the biopile soil in early 2015 range from 9 \u2013 20 mg/kg in the F1 range, and from 390 to 720 mg/kg in the F2 range. The site specific modelling results presented here demonstrate that there is minimal residual risk to environmental receptors or people inside the building from backfilling the CUB excavation with biopile soil. We recommend that the construction of the CUB proceed with the agreed engineering controls and installed as part of the building foundation (appropriate installation of an approved vapour barrier as specified in this report). Installation of this specific vapour barrier is recommended to protect against possible further ingress of fuel, and potential future spills, given that future spill responses will be impossible once the building is completed. The vapour barrier modelling presented here includes the possibility of a future spill event where contaminated soil and free phase fuel from below the footprint cannot be removed.\n\nUtilising biopile soil as backfill under the CUB foundation will not increase the risk to sensitive environmental or health receptors, but will increase the soil nutrient concentrations under the building. In order to combat any risk from nutrient migration from under the building slab, and from any free-phase fuel not removed by excavation works, we recommend a further engineering control, involving the installation of a drain on the southern side to minimise fuel ingress and on the northern side of the CUB to direct any groundwater flow to the lower EPH PRB. The lower EPH PRB will need to be modified to adequately cope with an increased catchment area.\n\nThis document only addresses the reuse of partially remediated soil for foundation backfill in the Casey CUB. A broader soil reuse policy is currently being developed but not presented here. The scope of this document does not include an assessment of the state of remediation of Casey contaminated sites, but includes considerations relevant to construction of the CUB.", "links": [ { diff --git a/datasets/AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4.json b/datasets/AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4.json index 1f15f0a2bf..71894ccff1 100644 --- a/datasets/AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4.json +++ b/datasets/AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nAn ecotoxicological risk assessment of groundwater from two Macquarie Island fuel spill sites was conducted to assess the level of risk posed by the sites to the adjacent marine receiving environment. Experiments were conducted on Macquarie Island during the summer season of 2017/18. \n\nThe two fuel spill sites (known as: Fuel Farm and Power House, see file: Map-macquarie_building_and_structures_14676.pdf) within the vicinity of the Macquarie Island research station had undergone intensive in situ remediation by the Australian Antarctic Division over the previous decade. Despite remediation efforts, groundwater leaching from the sites continued to contain some residual fuel contamination, with sheen observed at several shoreline seeps and chemical analysis of groundwater samples confirmed some hydrocarbon contamination remained. This study aimed to assess the level of residual risk posed by groundwater from these sites as it enters the adjacent marine environment.\n\nWe ran a series of toxicity tests using composited samples of salinity-adjusted groundwater discharge, as an exposure medium to test the sensitivity of 11 locally collected marine invertebrate species to the groundwater. \n\nGroundwater sampling was conducted over two periods: 23-29/11/17 and 18-20/12/17, for use in two rounds of toxicity testing (referred to as test round 1 (A and B) and test round 2). Groundwater samples were collected from 22 groundwater monitoring points; 12 surface seeps and 7 previously installed piezometers. These monitoring points were located along the coastal margin of the of the fuel spill sites, at their boundary with the adjacent marine environment (see: Locations-Fuel Farm-groundwater monitoring.pdf and Locations-Powerhouse-groundwater monitoring.pdf). The 22 groundwater samples were used to prepare seven salinity-adjusted composite test solutions (TS), each composed of equal volumes of up to nine groundwater samples. Salinity adjustment was to approximately that of ambient seawater (34 ppt), using hypersaline brine (prepared from locally collected clean seawater, which was frozen, then partially defrosted to collect concentrated brine). A total of approximately 6 L of was prepared for each of the seven TSs. See file: MI Ecotox-2017-18_TestSolutions_v03.xlsx for TS details (including: collection, preparation and physicochemical analysis results).\n\nEleven locally collected marine invertebrate species were used in the tests. Biota were collected from two sites on Macquarie Island, both within the vicinity of the research station but away from areas of known fuel contamination: 1). Garden Bay on the East Coast (54\u00b0 29' 56.9\" S, 158\u00b0 56' 28.8\" E) and 2). Hasselborough Bay on the West Coast (54\u00b0 29' 45.6\" S, 158\u00b0 55' 55.8\" E). See: Map-macquarie_building_and_structures_14676.pdf. Dates of collection of test biota were 1/12/2017 (for test round 1A), 6/12/2017 (for test round 1B) and 20 and 22/12/17 (for test round 2). The 11 test taxa were from six broad taxonomic groups: 2 amphipods (Paramoera sp., Parawaldeckia kidderi), 2 flatworms (Obrimoposthia wandeli, Obrimoposthia ohlini), 2 copepods (Tigriopus angulatus, Harpacticus sp.), 2 gastropods (Laevilitorina caliginosa, Macquariella hamiltoni), 2 bivalves (Gaimardia trapesina, Lasaea hinemoa) and 1 isopod (Exosphaeroma gigas).\n\nTest biota were observed for 14 or 21 days and survival observed periodically. Full details of toxicity test conditions are provided in the file: MI Ecotox-2017-18_RawTestObs v02.xlsx (worksheets: TestSummary, Species and Endpoints). This file also contains, on subsequent worksheets, the raw toxicity test observations for each text taxa. These raw result data are compiled in the file: MI Ecotox-2017-18_Test-DATA.xlsx, worksheet: Survival-ALL contains survival data for all tests and taxa. Subsequent worksheets provide data for each test taxa separately and also include any sublethal observations that were made. All data associated with test solution collection, composition and chemistry are provided in the file: MI Ecotox-2017-18_TestSolutions.xlsx.\n\nThe following (A. \u2013 I.) provides a description for the files provided with this record: \nA.\tMI Ecotox-2017-18_A-Map-Groundwater monitoring sites.png\nImages of study sites. A.) Overall Macquarie Island station environment, with Fuel Farm (red) and Power House (blue) indicated and showing the close proximity of the two land based sites to the adjacent high energy marine receiving environment. B.) Line map indicating relative location sites; Power House (blue) and Fuel Farm (red) sites, within the Macquarie Island station area. C.) and D.) Aerial images of the two sites, showing groundwater monitoring point locations (piezometers and seeps) used to prepare the seven test solutions (TS) as per key; Power House (TS4 and TS5) and Fuel Farm (TS1, TS2, TS3, TS6 and TS7), respectively. Monitoring point labels correspond with those provided in the file: MI Ecotox-2017-18_D-TestSolutions.xlsx / TS-Collection. \n\nB.\tMI Ecotox-2017-18_B-Map-macquarie_building_and_structures_14676.pdf\nMap of overall Macquarie Island station area, showing locations referred to in this study relative to other station infrastructure; Fuel Farm and Power House (land based fuel contaminated sites) and Hasselborough Bay and Garden Bay (clean marine areas for collection of test biota). Produced by the Australian Antarctic Data Centre, July 2018. Map available at: https://data.aad.gov.au/aadc/mapcat/. Map Catalogue No. 14676. \u00a9 Commonwealth of Australia 2018. \n\nC.\tMI Ecotox-2017-18_C-RawTestObs.xlsx\nToxicity test condition details (in worksheets named: TestSummary, Species, Endpoints) and raw toxicity test observations for each text taxa (in subsequent worksheets).\n\nD.\tMI Ecotox-2017-18_D-TestSolutions.xlsx\nDetails of test solutions, including collection, composition and chemistry.\n\nE.\tMI Ecotox-2017-18_E-Test-DATA.xlsx\nCompiled raw toxicity test results in long format. Worksheet: Survival-ALL contains survival data for all tests and taxa. Subsequent worksheets provide data for each test taxa separately and includes sublethal observations if made). \n\nF.\tMI Ecotox-2017-18_F-ScanLabBook.pdf\nScanned copy of the laboratory notebook associated with these tests. Notes were recorded by Cath King and Jessica Holan during the 17/18 Macquarie Island field season. \n\nG.\tMI Ecotox-2017-18_G-ScanObservationSheets.pdf\nScanned copy of the handwritten raw observation sheets used to record test observations (observations scored by: Cath King and Jessica Holan). \n\nH.\tMI Ecotox-2017-18_H-ChemicalAnalysis-ALS-COA.pdf\nCertificate of Analysis for chemistry results for samples analysed by Australian Laboratory Services (ALS) Environmental, Melbourne. Includes Total Recoverable Hydrocarbons (TRH; with and without silica gel clean up), nutrients (nitrogen) and a standard toxicity test (Microtox). Client sample ID with \u201cEcotox TS\u201d prefix are those relevant to this study (other samples are associated with broader site remediation monitoring for the 17/18 season).\n\nI.\tMI Ecotox-2017-18_I-ChemicalAnalysis-ALS-QAQC.pdf\nQuality Assurance (QA) and Quality Control (QC) report provided by ALS, in association with the Certificate of Analysis. As previous, Client sample ID with \u201cEcotox TS\u201d prefix are relevant to this study. \n\nJ.\tMI Ecotox-2017-18_J-size measurements.zip\nMeasures of specimen body lengths (mm). The .zip file contains a text file named: SizeMeasurements-README.txt, providing a description of the content associated with these data. \n", "links": [ { diff --git a/datasets/AAS_4036_GPS_Survey_1.json b/datasets/AAS_4036_GPS_Survey_1.json index 17bca80159..77edf2fa92 100644 --- a/datasets/AAS_4036_GPS_Survey_1.json +++ b/datasets/AAS_4036_GPS_Survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4036_GPS_Survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data was collected by Lisa Meyer with a Leica1200 RTK dGPS unit loaned from the Australian Antarctic Divisions Science Branch. Point data was collected within the Macquarie Island station limits to enable accurate mapping of the existing and newly installed infrastructure, as well as sampling sites, associated with the Remediation Program.\n\nData included:\n1. Infrastructure - the water sampling sites (mini/piezometers and seeps), soil sampling sites for the 2014-15 season (Annual pits, Environment Protection Authority sampling sites), aeration manifolds, the Permeable Reactive Barriers and drainage channels installed at the Main Power House (MPH) remediation site;\n2. Building boundaries, footpaths, fence lines around the Fuel Farm and MPH, and any other permanent features close to, or closely associated with the Remediation infrastructure.\n3. External position of the newly built Machinery Shed (a.k.a. the helicopter shed during station resupply) for mapping by the AADC.\n4. Height data for the isthmus area relating to the remediation work. These are referred to as 'spot heights' and are not useful to generate contour maps, but rather give some general idea as to the terrain around sampling sites and potential transport pathways from the isthmus area to the adjacent ocean.\n5. State Permanent Markers (10708, 10709, AUS211-RM3).\n6. Base station data collected at permanent survey marker NMX1.\n7. Surveying points used by Parks and Wildlife Service Tasmania, to assess the location of the shoreline around the isthmus. \n\nData available include:\n1. GPS raw data downloaded from the Leica GPS unit. There are three field survey files (one for each day's surveying). There are also three separate base station data files, associated with each field survey. This data requires the program Leica Geo Office to visualise the data and export it (LGO has a free download that can be used). Note: see comments in Q.7.\n2. Updated raw data files - Three CSV files of the updated raw data, created by Scott Strong (DPIPWE) using the full version of Leica Geo Office. The datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected - Universal Transverse Mercator Zone 57.\n3. A PDF file showing duplicate Point ID's that were changed in the three LGO projects.\n4. Scott Strong shapefiles - Three ArcGIS shapefiles of the field data. These files were used to create individual shapefiles for separate features e.g. 'PRB infrastructure', because each of the field data files contains data from the entire days surveying.\n5. Updated shapefiles - \n(i) The Scott Strong shapefiles copied and renamed with \"_ITRF2000\" in the name. \n(ii) The Scott Strong shapefiles copied and the data shifted to match the station WGS84 datum and shapefiles renamed with \"_WGS84\" in the name. \nSee further details below.\n\nScott Strong generated three shapefiles of the original field data, which were named:\nMacca02122014FieldSurvey\nMacca05122014FieldData\nMacca06122014FieldData\nThese three files are in 'AAD Macquarie Island Dec 2014.zip'.\nThe datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected UTM Zone 57. \n\nThese shapefiles were renamed to:\nMacca02122014FieldData_ITRF2000\nMacca05122014FieldData_ITRF2000\nMacca06122014FieldData_ITRF2000\n\nNote: The AADC uses the WGS84 datum from the mid-1990s for previously surveyed data of Macquarie Island. To transform data surveyed on ITRF2000@2000 to WGS84 apply \"The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N.\" given on page 3 of the survey report \"Macquarie Island OSG Survey Campaign, Voyage 8 Round Trip, March 2002\" by John VanderNiet and Nick Bowden. i.e. the eastings of the WGS84 data will be 1.40 metres greater than the ITRF2000@2000 data and the northings of the WGS84 data will be 0.20 metres greater than the ITRF2000@2000 data.\n\nA copy of the ITRF2000 shapefiles was created and edited in ArcMap to shift the data points to match the station WGS84 reference frame (i.e. by subtracting -1.4 m from the eastings and -0.2 m from the northings). The projection data is also changed to WGS84, rather than ITRF2000.\n\nThese shapefiles are named: \nMacca02122014FieldData_WGS84station\nMacca05122014FieldData_WGS84station\nMacca06122014FieldData_WGS84station\n\nThe data point names are not changed in the WGS84 shapefiles - the points that were averaged in the raw data, which Scott changed to his codes. e.g. NW1SS1 (point is called NW1 Scott Strong1). NW1 (i.e. north west 1) is a corner on the FF bund and also a point up in the Power House area somewhere (PRB mini cage). \n\nA Point Description (Point_Desc) field was added to the attribute tables of the WGS84 shapefiles to explain what the point codes are for each of the data points.\n\nThe Macca06122014FieldData_WGS84station shapefile includes points collected in a survey of the isthmus shoreline at the request of Chris Howard of the Tasmanian Parks and Wildlife Service. These include points on the beach and up on the road along several transects. They have been labelled with codes as specified by Chris and have 'Parks and Wildlife isthmus survey mark' in the Point_desc field in the attribute table.\n\nThe Australian Antarctic Data Centre used data from the WGS84 shapefiles to create data in its GIS database representing the Machinery Shed and some fences, gates and foot paths at the station.", "links": [ { diff --git a/datasets/AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1.json b/datasets/AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1.json index 668a99b6c4..361748635a 100644 --- a/datasets/AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1.json +++ b/datasets/AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data is all contained within an Access database. This is the historical data for the Remediation Project from 2009 up until the end of 2016.The database contains some unprocessed data from the 2016-17 field season so this will likely be updated in future revisions/submissions of this database. \nIn terms of locality, this covers Macquarie Island, Casey Station (biopiles, fuel spill sites), Davis and Lake Dingle.\n\nTables/Data included: \nSampling metadata (barcodes, collector, season, location, etc.) \nLaboratory batch information (details of analysis events in lab) \nHydrocarbon (TPH) in soils \nHydrocarbon (TPH) in water \nNutrients in soils \nNutrients in water \nVolatile compounds (VOC) in soils \nVolatile compounds (VOC) in water \nSample weights and volumes \nSample moisture levels \nSoil grain size \nQAQC analysis of the above data \nMinor analysis on invertebrates\n\nThe database includes some data collected for another project in addition to AAS 4036. For example, AAS 4029. \nThe database includes barcodes which were allocated to the data. Each barcode is linked to an AAS project. However, only one barcode could be allocated to any given data even if it was collected for more than one project.", "links": [ { diff --git a/datasets/AAS_4036_aerial_mosaic_macquarie_jan2015_1.json b/datasets/AAS_4036_aerial_mosaic_macquarie_jan2015_1.json index 4b7781d58c..5b4b3a5595 100644 --- a/datasets/AAS_4036_aerial_mosaic_macquarie_jan2015_1.json +++ b/datasets/AAS_4036_aerial_mosaic_macquarie_jan2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4036_aerial_mosaic_macquarie_jan2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A custom built flying wing (FX79 airframe) Unmanned Aerial Vehicle (UAV) was flown at approximately 120 metres above surface level over the isthmus at Macquarie Island on 31 January 2015. The isthmus is where the station is located. Attached to the UAV were a Canon EOS M camera and a GPS. \nHigh resolution aerial photographs were taken and a georeferenced mosiac of the photographs was later created using Pix4D software. The mosaic was not orthorectified.\nThe data includes the original georeferenced mosaic as a geotiff and a version that has been further georeferenced to the station WGS84 horizontal datum.\nThe pixel size is 1.3 centimetres.", "links": [ { diff --git a/datasets/AAS_4037_4050_Krill_Microscopy_1.json b/datasets/AAS_4037_4050_Krill_Microscopy_1.json index 0db4c0700b..493c54db17 100644 --- a/datasets/AAS_4037_4050_Krill_Microscopy_1.json +++ b/datasets/AAS_4037_4050_Krill_Microscopy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4037_4050_Krill_Microscopy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microscopy imaging of live Antarctic krill using a Leica M205C dissecting stereo-microscope with a Leica DFC 450 camera and Leica LAS V4.0 software. Krill were held in a custom made 'krill trap', details provided in manuscript in section eight of this form.\n\nThe data are available as a single video file.\n\nThese data are part of Australian Antarctic Science (AAS) projects 4037 and 4050.\n\nProject 4037 - Experimental krill biology: Response of krill to environmental change\nThe experimental krill research project is designed to focus on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the project will concentrate on studies into impacts of climate change on key aspects of krill biology and ecology.\n\nProject 4050 - Assessing change in krill distribution and abundance in Eastern Antarctica\nAntarctic krill is the key species of the Southern Ocean ecosystem. Its fishery is rapidly expanding and it is vulnerable to changes in climate. Australia has over a decade of krill abundance and distribution data collected off Eastern Antarctica. This project will analyse these datasets and investigate if krill abundance and distribution has altered over time. The results are important for the future management of the fishery, as well as understanding broader ecological consequences of change in this important species.", "links": [ { diff --git a/datasets/AAS_4037_Krill_Modelling_1.json b/datasets/AAS_4037_Krill_Modelling_1.json index c572fcb74d..971b969fe9 100644 --- a/datasets/AAS_4037_Krill_Modelling_1.json +++ b/datasets/AAS_4037_Krill_Modelling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4037_Krill_Modelling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model was produced as part of Australian Antarctic Science project 4037 - Experimental krill biology: Response of krill to environmental change - The experimental krill research project is designed to focus on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the project will concentrate on studies into impacts of climate change on key aspects of krill biology and ecology.\n\nThis metadata record is to reference the paper that describes the model. There is no archived data output from this data product.\n\nTaken from the abstract of the referenced paper:\n\nEstimates of productivity of Antarctic krill, Euphausia superba, are dependent on accurate models of growth and reproduction. Incorrect growth models, specifically those giving unrealistically high production, could lead to over-exploitation of the krill population if those models are used in setting catch limits. Here we review available approaches to modelling productivity and note that existing models do not account for the interactions between growth and reproduction and variable environmental conditions. We develop a new energetics moult-cycle (EMC) model which combines energetics and the constraints on growth of the moult-cycle. This model flexibly accounts for regional, inter- and intra-annual variation in temperature, food supply, and day length. The EMC model provides results consistent with the general expectations for krill growth in length and mass, including having thin krill, as well as providing insights into the effects that increasing temperature may have on growth and reproduction. We recommend that this new model be incorporated into assessments of catch limits for Antarctic krill.", "links": [ { diff --git a/datasets/AAS_4037_Long-term_Krill-CO2_1.json b/datasets/AAS_4037_Long-term_Krill-CO2_1.json index a062faefd1..ff8f047d7b 100644 --- a/datasets/AAS_4037_Long-term_Krill-CO2_1.json +++ b/datasets/AAS_4037_Long-term_Krill-CO2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4037_Long-term_Krill-CO2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long-term experiment on increased CO2 level on krill physiology.\nKrill were exposed to a range of CO2 conditions 400-4000ppm over a year, and their growth, mortality, and physiology were monitored.\n\n-List of files-\nEricson Krill Ocean Acidification Study Raw Data_for data centre.xlsx: This file contains data on krill growth, mortality, physiology, and biochemistry, as well as information on water chemistry throughout 1 year period of the experiment.\nEricson et al. Adult krill OA MS final submission.pdf: Unpublished manuscript of the experiment including all methods of the experiment.", "links": [ { diff --git a/datasets/AAS_4046_Estimate_Abundance_Model_1.json b/datasets/AAS_4046_Estimate_Abundance_Model_1.json index 8f077bd318..bdb51d1b61 100644 --- a/datasets/AAS_4046_Estimate_Abundance_Model_1.json +++ b/datasets/AAS_4046_Estimate_Abundance_Model_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4046_Estimate_Abundance_Model_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains R code for a model that estimates the relative abundance of species using only presence-absence data. It is demonstrated using two examples with Antarctic mosses: One that explains spatial patterns along a water availability gradient, and one that explains temporal changes between 2000 and 2013. It could be modified to work with other species or datasets.", "links": [ { diff --git a/datasets/AAS_4046_Spectroscopy_Moss_Vigour_1.json b/datasets/AAS_4046_Spectroscopy_Moss_Vigour_1.json index c49e4c9080..7a043452f6 100644 --- a/datasets/AAS_4046_Spectroscopy_Moss_Vigour_1.json +++ b/datasets/AAS_4046_Spectroscopy_Moss_Vigour_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4046_Spectroscopy_Moss_Vigour_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "(Two supporting figures are contained within the metadata document in the download file)\n\nThe ground-based imaging spectroscopy data were acquired with the Headwall Photonics Micro-Hyperspec VNIR scanner (Headwall Inc., USA) attached to a computer-controlled rotating/tilting platform. The sensor unit was placed approximately 2.5 m above the ground on a single pole mounted to a geodetic tripod. The Micro-Hyperspec is a push-broom scanner, which collects light passing through a lens objective with an aperture of f/2.8 (FOV of 49.8 degrees) and through a slit entrance of 25 microns. The spectral wavelengths are split by an aberration-corrected convex holographic diffraction grating and projected onto a charge-coupled device (CCD) matrix with a digital dynamic range of 12-bits and size of 1004 by 1004 pixel units. The CCD registers the captured light split into 324 (full spectral extent, FWHM of 4.12-4.67 nm) or 162 spectral bands (binning of two neighbouring spectral pixels as a single recording unit, FWHM of 4.75-5.25 nm). To ensure a high signal-to-noise ratio and to prevent oversaturation of the CCD dynamic range, the spectral binning (162 bands) combined with an integration time of 40 milliseconds (ms) was applied and oblique hyperspectral images (azimuth viewing angles of 44 degrees and 60 degrees) were collected at two test sites.\n\nThe two research plots of c. 10-15 m2 at ASPA 135, colonised dominantly by Schistidium antarctici, were scanned with the Micro-Hyperspec at solar noon on the 10th and 30th of January 2013. The first plot (Fig. 1a - see download file), evaluated as a DRY (exposed, water limited, and considerably stressed) moss-bed of lower vigour, was located at the top of a hill above the ASPA 135 fresh water lake. The second plot (Fig. 1b - see download file), representing a WET (lengthily snow covered, well watered, and less stressed) moss-bed of higher vigour, was positioned in a local terrain depression with water supply originating from snowmelt and possibly from infiltration of melt lake water located above. The image of the DRY site was acquired under full overcast conditions, while the WET site image was taken under a clear sky. A distance of about 3.5 m between the sensor and objects resulted in images of 3260 by 1004 pixels with varying across-track spatial resolution of less than 10 mm. The 12-bit spectral images were radiometrically calibrated into radiance (mW cm-2 sr-1?m-1) and transformed into relative hemispherical reflectance by applying an empirical line atmospheric correction (Lucieer et al., 2014).\n\nThe epsilon Support Vector Regression (SVR) learning machine based on the nonlinear Gaussian radial basis function (RBF) kernel was applied on both reflectance hyperspectral images to estimate the total chlorophyll a and b content (Cab) and the effective leaf density (LD) of observed moss turfs. The SVM algorithms were trained and validated using the laboratory spectral measurements of moss samples collected and measured at the Australian Antarctic polar station Casey in 2013 and 1999 (Lovelock and Robinson, 2002). SVMs were then applied on hemispherical-directional reflectance of each pixel in hyperspectral images of both research sites to retrieve Cab and LD maps. To provide a single moss health indicator, the Cab and LD maps were merged into a synthetic map of a relative vigour indicator (RVI), which was computed as the arithmetic mean of Cab and inverted LD, both scaled between zero and the largest value measured in laboratory (i.e. Cab = 1500 nmol gdw-1 and LD = 15 leaves mm-1). The RVI map represents relative vigour, where 100% indicates optimally growing healthy moss, and 0% indicates moss highly stressed by unfavourable environmental conditions. Details regarding the design, training, validation and application of the SVR algorithms, as well as the moss vigour assessment are provided in Malenovsky et al. (2015). \nAll scientific articles refereed in this document are available in the folder named 'References'.\n\nAll image datasets are provided in three file formats:\n- *.bsq - band sequential image file\n- *.hdr - header ASCII file containing all essential metadata about the complementary *.bsq file\n- *_copy.tif - copy of the *.bsq file with the same name in the Tagged Image File Format (TIFF)\nDatasets provided for both study sites under 'DRYsite_30Jan2013' and 'WETsite_30Jan2013' folders:\n- ASPA135_\"DRY or WET\" site__QuickView_FalseColours.png - QuickView of the hyperspectral image of given site in Portable Network Graphic file as a false coloured near-infrared composite.\n- ASPA135_\"DRY or WET\" site_Chlorophyll_classes - chlorophyll content of photosynthetically active moss turf sorted in 6 classes between 0 and 1500 nmol gdw-1 (see the *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_Chlorophyll_data - chlorophyll content of photosynthetically active moss turf in nmol gdw-1 retrieved with the SVR algorithm from the ground-based hyperspectral imagery (for more information see the complementary *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_LeafDensity_classes - effective leaf density of photosynthetically active moss turf sorted in 7 classes between 0 and 15 leaves mm-1 (see the *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_LeafDensity_data - effective leaf density of photosynthetically active moss turf in nmol gdw-1 retrieved with the SVR algorithm from the ground-based hyperspectral imagery (for more information see the complementary *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_Reflectance_data - image of relative hemispherical-directional reflectance acquired for study sites at ASPA 135 with the Micro-Hyperspec spectroradiometer (for more information see the complementary *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_RelativeVigour_classes - relative vigour of photosynthetically active moss turf sorted in 7 classes between 0 and 100% (see the *.hdr ASCII file).\n- ASPA135_\"DRY or WET\" site_RelativeVigour_data - relative vigour of photosynthetically active moss turf in % generated and mean of chlorophyll content and inverted leaf density scaled between 0 and the maximal measured values (for more information see the *.hdr ASCII file).", "links": [ { diff --git a/datasets/AAS_4046_Temperature_Optima_Model_1.json b/datasets/AAS_4046_Temperature_Optima_Model_1.json index d28e036217..6a108609e4 100644 --- a/datasets/AAS_4046_Temperature_Optima_Model_1.json +++ b/datasets/AAS_4046_Temperature_Optima_Model_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4046_Temperature_Optima_Model_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains the R code for an R2OpenBugs Bayesian model that fits penalised splines to a species response curve and then estimates the means and 95% credible intervals for the optimum, peak, upper and lower limits, and niche breadth. Two response curves can then be compared and the probability that one curve has an optima greater than the other can then be calculated. Six files are included: Two R2OpenBugs models (one logit transformed to deal with presence-absence data, and one untransformed), the R code for running the models on example data, and 3 files containing the relevant example data (Antarctic mosses). More information can be found in Ashcroft et al. (2016) Ecological Informatics 34: 35\u201343, which should be cited in any publications using this model.", "links": [ { diff --git a/datasets/AAS_4046_quadrat_locations_1.json b/datasets/AAS_4046_quadrat_locations_1.json index b0fe1453cf..7931f06aaa 100644 --- a/datasets/AAS_4046_quadrat_locations_1.json +++ b/datasets/AAS_4046_quadrat_locations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4046_quadrat_locations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are comprised of a spreadsheet with locations (latitude and longitude) of the centres of moss bed quadrats and labels for the quadrats. The quadrats are located at two sites: Antarctic Specially Protected Area 135 near Casey and on Robinson Ridge south of Casey.\nThe letter in the quadrat label indicates the vegetation community type: B for Bryophyte, T for Transitional and L for Lichen.\nThe latitudes and longitudes were obtained by Diana King, PhD candidate, School of Biological Sciences, University of Wollongong on 29 October 2014. See the Quality section of this record for the procedure used.\nThe locations are shown in maps 14450 and 14451 in the SCAR Map Catalogue (see Related URLs).\nThese locations supersede the quadrat locations which are a subset of the data described by the metadata record 'Moss beds at Casey: detailed map of experimental sites', Entry ID: ASAC_1313_Casey_Moss_Map_2003.", "links": [ { diff --git a/datasets/AAS_4046_spectroscopy_chlorophyll_1.json b/datasets/AAS_4046_spectroscopy_chlorophyll_1.json index 30bc92ec39..75aa78f698 100644 --- a/datasets/AAS_4046_spectroscopy_chlorophyll_1.json +++ b/datasets/AAS_4046_spectroscopy_chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4046_spectroscopy_chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the complete description, including images and original formatting, see the metadata file in the downloadable dataset.\n\nResearch sites\nAll remote sensing data sets were collected at two pilot research sites, Antarctic Specially Protected Area 135 (ASPA) and Robinson Ridge (Robbos), that host significant populations of Antarctic moss species, particularly: Schistidium antarctici (Cardot) L.I. Savicz and Smirnova, Bryum pseudotriquetrum (Hedw.) Gaertn., Meyer and Scherb., and Ceratodon purpureus (Hedw.) Brid. Verification of remote sensing products was performed with data from a long-term monitoring project of Windmill Islands' plant communities using observations of 13 permanent quadrats, which were established at ASPA and Robbos in 2003 (Wasley et al., 2012). Laboratory spectral and biochemical measurements for training of predictive machine leaning algorithms were performed on moss samples collected in the vicinity of the Casey polar station in 2013 and previously in 1999 (Lovelock and Robinson, 2002).\n\nAirborne UAS hyperspectral image data\nUAS imaging spectroscopy data were acquired with a Headwall Photonics Micro-Hyperspec VNIR scanner (Headwall Inc., USA) mounted on an Aeronavics Skyjib multirotor (oktokopter) heavy-lift airframe. The Micro-Hyperspec push-broom scanner, equipped with an objective of 8 mm focal length, a field of view (FOV) of 49.8 degrees, a slit entrance of 25 microns and a 12- bit charge-coupled device (CCD) of 1004 pixels, was flown in a binned mode with the frame period and integration time of 20 milliseconds (maximum rate of 50 frames s-1) 11 m above ground level at a speed of 2.5 m s-1. The acquired imagery of 162 spectral bands between 361 and 961 nm had a bandwidth from 4.75 to 5.25 nm and a spatial resolution of 5.0 cm. The raw hyperspectral data was radiometrically standardized and corrected for atmospheric interferences. Digital counts of recorded light were converted to physical units of at-sensor radiance (mW cm2 sr-1 microns-1) and to relative reflectance by applying sensor-specific radiometric calibration coefficients and an empirical line atmospheric correction as described in Lucieer et al. (2014). The accuracy of the resulting UAS reflectance was assessed as acceptable using spectral signatures of several spatially homogeneous natural targets (6 large rocks and 9 green moss patches) measured on ground with an ASD HandHeld-2 spectroradiometer (ASD, Inc. and PANalytical, Boulder, Colorado, USA). To provide georeferenced images and derived maps, the hyperspectral images were orthorectified and mosaicked using detailed (1 cm resolution) three-dimensional digital surface models and orthophotos of research plots into the map coordinate system of WGS84 UTM zone 49 South, with a rubber sheeting triangulation based on 50 evenly distributed artificial ground control points. Final hyperspectral mosaic for ASPA is depicted in Figure 1 and light lines over Robbos in Figure 2 (see the metadata file in the downloadable dataset for the figures).\n\nFig. 1. Hyperspectral mosaic in false colours (acquired on 2nd and 8th February 2013) superimposed over orthophoto of the Antarctic Specially Protected Area 135 (ASPA 135) research site acquired in 2013 (red colour = moss canopy).\n\nThe epsilon Support Vector Regression (SVR) learning machine, using the nonlinear Gaussian radial basis function (RBF) kernel, was applied on reflectance hyperspectral data to estimate the total chlorophyll a and b content (Cab) and the effective leaf density (ELD) of investigated moss turfs. To produce a single moss health evaluator, the Cab and ELD maps were merged into a synthetic map of a relative vigour indicator (RVI), which was computed as the arithmetic mean of Cab and inverted LD, both scaled between zero and the largest value measured in laboratory (i.e. Cab = 1500 nmol.gdw-1 and LD = 15 leaves.mm-1). The RVI maps represent relative vigour, where 100% indicates optimally growing healthy moss, and 0% indicates moss highly stressed by unfavourable environmental conditions. Details regarding the method, i.e. design, training, validation and application of the SVR algorithms, are provided in Malenovsky et al. (2015).\n\nFig. 2. Two hyperspectral flight lines in false colours (acquired on 5th and 6th February 2013) superimposed over ortho-photomap of the Robinson Ridge (Robbos) study site from 2011 (red colour = moss canopy).\nAll UAS airborne data are located in the directory Airborne_UAS. All image datasets are stored in two file formats:\n- *.bsq - band sequential image file and\n- *.hdr - header ASCII file containing all essential metadata about the complementary *.bsq file.\nThe following UAS image datasets are provided for both study sites:\n- '0208 or 05/06'FEB2013_'ASPA135 or ROBBOS'_'geomosaic or flightline#'_living_moss_Cab -' chlorophyll content of living moss turf in nmol.gdw-1 retrieved with the SVR algorithm from the hyperspectral imagery (for more information see complementary *.hdr ASCII file).\n- '0208 or 05/06' FEB2013_'ASPA135 or ROBBOS'_'geomosaic or flightline#'_living_moss\n_ELD -' effective leaf density of living moss turf in leaves.mm-1 retrieved with the SVR algorithm from hyperspectral imagery (for more information see complementary *.hdr ASCII file).\n- '0208 or 05/06' FEB2013_'ASPA135 or ROBBOS'_'geomosaic or flightline#'_living_moss\n_RVI -' relative vigour index of living moss turf in % generated as mean of chlorophyll content and inverted leaf density scaled between 0 and the maximal measured values (for more information see *.hdr ASCII file).\n- '0208 or 05/06' FEB2013_'ASPA135 or ROBBOS'_'geomosaic or flightline#'_moribund_moss\n_MASK -' classification of moribund moss (value = 1) derived from the MTVI2 optical index (MTVI2 greater than or equal to 0.25) computed from hyperspectral images (more information in *.hdr ASCII file).\n- '0208 or 05/06' FEB2013_'ASPA135 or ROBBOS'_'geomosaic or flightline#'_reflectance - 'image of relative hemispherical-directional reflectance acquired with the Micro-Hyperspec spectroradiometer mounted to Skyjib multirotor UAS (more information in *.hdr ASCII file).\nThe Microsoft Excel file Hyperspec_SVR_inputs.xlsx contains 4 spreadsheets with datasets used for training and testing of leaf chlorophyll content (Cab in in nmol.gdw-1) and effective leaf density (ELD in number of leaves. mm-1) estimating SVR machines applicable to Micro-Hyperspec VNIR bands (CR ~ continuum removed reflectance and R ~ reflectance at given wavelength in nm).\n\nSatellite spectral image data\nThe multispectral WorldView-2 (WV2) space-borne images (DigitalGlobe, Inc., Westminster, Colorado, USA) of the Windmill Islands, containing 8 spectral bands at spatial resolution of 2.2 m, were acquired on 30th January 2011 for Robbos and 7th February 2011 for ASPA. Radiometric calibration, converting the 11-bit image into physically meaningful radiance, was performed with the WV2 calibration coefficients available in the ENVI/IDL image processing software (Harris Geospatial Solutions/Exelis Visual Information Solutions, Inc., Boulder, Colorado, USA) and atmospheric correction was carried out with the fast line-of-sight atmospheric analysis of hypercubes (FLAASH) module. The reflectance images were projected into the Universal Transverse Mercator coordinate system (UTM Zone 49 South, datum WGS84). Only image pixels with greater than 50% abundance of vigorous moss were used in the health assessment analyses. These pixels were selected by applying the threshold of the normalized difference vegetation index (NDVI greater than 0.6) in combination with the spectral mixture tuned matched filtering (MTMF). The same type of the SVR machines were trained and applied to estimate the total chlorophyll a and b content (Cab) and the effective leaf density (ELD) of investigated moss turfs. Subsequently, the relative moss vigour (RVI) was computed as in Malenovsky et al. (2015).\nThe satellite datasets are located in the directory Satellite_WV2. All image data is stored in two file formats:\n- *.bsq - band sequential image file and\n- *.hdr - header ASCII file containing all essential metadata about the complementary *.bsq file.\nThe following WV2 image datasets are provided for both study sites:\n- WV2_'07FEB or 30JAN'2011_'ASPA135 or ROBBOS'_moss_Cab -' chlorophyll content in nmol.gdw-1 for pixels with more than 50% moss abundance retrieved with the SVR algorithm from the WV2 multispectral imagery (for more information see complementary *.hdr ASCII file).\n- WV2_'07FEB or 30JAN'2011_'ASPA135 or ROBBOS'_moss_ELD -' effective leaf density in leaves.mm-1 for pixels with more than 50% moss abundance retrieved with the SVR algorithm from the WV2 multispectral imagery (for more information see complementary *.hdr ASCII file).\n- WV2_'07FEB or 30JAN'2011_'ASPA135 or ROBBOS'_moss_RVI -' relative vigour index in % for pixels with more than 50% moss abundance generated as mean of chlorophyll content and inverted leaf density scaled between 0 and the maximal measured values (for more information see *.hdr ASCII file).\n- WV2_'07FEB or 30JAN'2011_'ASPA135 or ROBBOS'_moss_reflectance -' image of relative hemispherical-directional reflectance for pixels with more than 50% moss abundance acquired by the WorldView-2 satellite spectroradiometer (for more information see *.hdr ASCII file).\nThe Microsoft Excel file WV2_SVR_inputs.xlsx contains 4 spreadsheets with datasets used for training and testing of leaf chlorophyll content (Cab in in nmol.gdw-1) and effective leaf density (ELD in number of leaves. mm-1) estimating SVR machines applicable to WorldView-2 multispectral bands (CR ~ continuum removed reflectance and R ~ reflectance at given wavelength in nm).\n\nGround validation measurements\nApplicability of the remote sensing moss health indicators was validated by direct one-to-one comparison with the relative abundance of healthy, stressed and moribund moss in 13 monitoring quadrats of 25x25 cm in size. The ground-collected data are stored in the directory Ground_validation. Ground validation data per quadrat and complementary remote sensing products obtained by interpretation of the red-green-blue (RGB) colour composite photographs and the hyperspectral UAS data, respectively, are listed in the Microsoft Excel file spreadsheet Validation_input_data quadrats2013.xlsx. Geo-locations of the validation quadrats in UTM Zone 49 South (datum WGS84) are available in the ESRI vector shape file Validation_quadrats_FEB2013.shp (with the ancillary files *.shx, *.dbf, *.prj and *.qpj). \n\nReferences\nLovelock, C. E. and Robinson S. A. (2002), Surface reflectance properties of Antarctic moss and their relationship to plant species, pigment composition and photosynthetic function. Plant Cell and Environment, 25, 1239-1250.\nLucieer, A., Malenovsky, Z., Veness, T. and Wallace, L. (2014a), HyperUAS - Imaging spectroscopy from a multi-rotor unmanned aircraft system. Journal of Field Robotics, 31, 571-590.\nMalenovsky, Z., Turnbull, J. D., Lucieer, A. and Robinson, S. A. (2015), Antarctic moss stress assessment based on chlorophyll, water content, and leaf density retrieved from imaging spectroscopy data. New Phytologist, 208, 608-624.\nWasley, J., Robinson, S. A., Turnbull, J. D., King, D. H., Wanek, W. and Popp, M. (2012), Bryophyte species composition over moisture gradients in the Windmill Islands, East Antarctica: Development of a baseline for monitoring climate change impacts. Biodiversity, 13, 257-264.", "links": [ { diff --git a/datasets/AAS_4050_EK60_1.json b/datasets/AAS_4050_EK60_1.json index 3db61c6a17..6d8acf377d 100644 --- a/datasets/AAS_4050_EK60_1.json +++ b/datasets/AAS_4050_EK60_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4050_EK60_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The attached file details the workflow for the processing and analysis of active acoustic data (Simrad EK60; 12, 38, 120 and 200 kHz) collected from RSV Aurora Australis during the 2006 BROKE-West voyage. The attached file is in Echoview(R) (https://www.echoview.com/) version 8 format.\n \nThe Echoview file is suitable for working with fisheries acoustics, i.e. water column backscatter, data collected using a Simrad EK60 and the file is set-up to read 38, 120 and 200 kHz split-beam data. The file has operators to remove acoustic noise, e.g. spikes and dropped pings, and operators for removing surface noise and seabed echoes. Echoes arising from krill are isolated using the \u2018dB-difference\u2019 method recommended by CCAMLR. The Echoview file is set-up to export the results of krill echo integration as both intervals and swarms. Full details of the method are available in Jarvis et al. (2010) and the krill swarms methods are described in Bestley et al. (2017).", "links": [ { diff --git a/datasets/AAS_4050_SWARM_1.json b/datasets/AAS_4050_SWARM_1.json index 02067add16..8ddcfc62ec 100644 --- a/datasets/AAS_4050_SWARM_1.json +++ b/datasets/AAS_4050_SWARM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4050_SWARM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is data describing acoustically observed krill swarms that was used in the Bestley et al. (2017) paper 'Predicting krill swarm characteristics important for marine predators foraging off East Antarctica' (http://onlinelibrary.wiley.com/doi/10.1111/ecog.03080/full).\n \nAbstract of the paper presented here:\n\nOpen ocean predator-prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large-scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional-scale spatial predictions using a 10-yr remotely-sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid-summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill-dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.\n\n\nThis comma separated variable (CSV) file contains the krill swarm data used in:\n\nBestley, S., Raymond, B., Gales, N.J., Harcourt, R.G., Hindell, M.A., Jonsen, I.D., Nicol, S., Peron, C., Sumner, M.D., Weimerskirch, H. and Wotherspoon, S.J., Cox, M.J. (2017). Predicting krill swarm characteristics important for marine predators foraging off East Antarctica. Ecography.\n\nThe column descriptions are:\n\nDepth_mean_m = (units m) mean depth of a krill swarm\nDate = (YYYYMMDD) observation date (UTC)\nTime = (HH:mm:ss.ss) observation time (UTC)\nLat = (dd.ddddd) latitude\nLon = (ddd.ddddd) longitude\ntransect = BROKE West transect number 7 to 11 (see Fig. 1, Bestley et al. 2017)\ndenVolgm3 = (units g wet mass m-3) internal krill swarm density in gram wet mass per cubic metre.", "links": [ { diff --git a/datasets/AAS_4061_DSS_2000-year_annual_snow_accumulation_1.json b/datasets/AAS_4061_DSS_2000-year_annual_snow_accumulation_1.json index 8d137773e2..3fcee46a97 100644 --- a/datasets/AAS_4061_DSS_2000-year_annual_snow_accumulation_1.json +++ b/datasets/AAS_4061_DSS_2000-year_annual_snow_accumulation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_DSS_2000-year_annual_snow_accumulation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS_2000-year annual snow accumulation record is the annual snow accumulation record for the \"DSS\" Law Dome ice core with extensions (e.g. As described in Roberts et al., 2015) from overlapping ice cores which are dated by comparing multiple chemical species.", "links": [ { diff --git a/datasets/AAS_4061_DSS_ECM_DMP_1.json b/datasets/AAS_4061_DSS_ECM_DMP_1.json index 55163b9910..7767ddc1da 100644 --- a/datasets/AAS_4061_DSS_ECM_DMP_1.json +++ b/datasets/AAS_4061_DSS_ECM_DMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_DSS_ECM_DMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS_ECM_DMP_submit.xlsx is the measured electrical conductivity for 'DSS' (Dome Summit South) Law Dome ice core.", "links": [ { diff --git a/datasets/AAS_4061_DSS_Hydrogen_peroxide_DMP_1.json b/datasets/AAS_4061_DSS_Hydrogen_peroxide_DMP_1.json index c9ba6651db..387176cb6d 100644 --- a/datasets/AAS_4061_DSS_Hydrogen_peroxide_DMP_1.json +++ b/datasets/AAS_4061_DSS_Hydrogen_peroxide_DMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_DSS_Hydrogen_peroxide_DMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS_Hydrogen_peroxide_DMP_submit.xlsx is the measured hydrogen peroxide data for the 'DSS' (Dome Summit South) Law Dome ice core.", "links": [ { diff --git a/datasets/AAS_4061_DSS_Particles_DMP_1.json b/datasets/AAS_4061_DSS_Particles_DMP_1.json index 9dddef4c68..5d8e275bc1 100644 --- a/datasets/AAS_4061_DSS_Particles_DMP_1.json +++ b/datasets/AAS_4061_DSS_Particles_DMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_DSS_Particles_DMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS_Particles_DMP_submit.xlsx is the measured particle count and particle size for 'DSS' (Dome Summit South) Law Dome ice core.", "links": [ { diff --git a/datasets/AAS_4061_DSSmain_chemistry_20190705_1.json b/datasets/AAS_4061_DSSmain_chemistry_20190705_1.json index 9becc9f8d0..90f8d20dc0 100644 --- a/datasets/AAS_4061_DSSmain_chemistry_20190705_1.json +++ b/datasets/AAS_4061_DSSmain_chemistry_20190705_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_DSSmain_chemistry_20190705_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSSmain_chemistry_data is the measured chemical concentrations of trace ions for the \u2018DSS\u2019 (Dome Summit South) ice core. Trace chemistry samples are prepared using clean techniques (Curran et al., 1998) and analysed based on methods described by Curran and Palmer (2001) using Dionex ICS 3000 ion chromatograph, measuring anions (Cl, NO3 and SO4) and cations (Na, NH4, K, Mg and Ca) to ppb levels.", "links": [ { diff --git a/datasets/AAS_4061_Law_Dome_Holocene_trace_chemistry_1.json b/datasets/AAS_4061_Law_Dome_Holocene_trace_chemistry_1.json index 75a8ed1fdc..600ca517b0 100644 --- a/datasets/AAS_4061_Law_Dome_Holocene_trace_chemistry_1.json +++ b/datasets/AAS_4061_Law_Dome_Holocene_trace_chemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_Law_Dome_Holocene_trace_chemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Holocene chemical concentrations of trace ions from the Law Dome - Dome Summit South (DSS) ice core. DSSmain_Holocene_trace_chemistry_data is the measured Holocene chemical concentrations of trace ions for the 'DSS' (Dome Summit South) ice core. Trace chemistry samples are prepared using clean techniques (Curran et al., 1998) and analysed based on methods described by Curran and Palmer (2001) using Dionex ICS 3000 ion chromatograph, measuring anions (Cl, NO3 and SO4) and cations (Na, NH4, K, Mg and Ca) to ppb levels.", "links": [ { diff --git a/datasets/AAS_4061_Law_Dome_sulphate_1.json b/datasets/AAS_4061_Law_Dome_sulphate_1.json index 86ddde45d5..c786449133 100644 --- a/datasets/AAS_4061_Law_Dome_sulphate_1.json +++ b/datasets/AAS_4061_Law_Dome_sulphate_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4061_Law_Dome_sulphate_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Law_Dome_sulphate_data record is the volcanic sulphate (non sea-salt sulphate) record for the \"DSS\" Law Dome ice core with extensions (e.g. As described in Plummer et al., 2012) from overlapping ice cores which are dated by comparing multiple chemical species.", "links": [ { diff --git a/datasets/AAS_4062_DSS1314_icecore_analysis_ionic_composition_1.json b/datasets/AAS_4062_DSS1314_icecore_analysis_ionic_composition_1.json index 016a8b51dc..ab52878d8a 100644 --- a/datasets/AAS_4062_DSS1314_icecore_analysis_ionic_composition_1.json +++ b/datasets/AAS_4062_DSS1314_icecore_analysis_ionic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1314_icecore_analysis_ionic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1314_Glacial ice trace ionic composition_DMP_submit.xlsx is the measured trace ionic composition data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1314) collected during the Antarctic 13/14 season.\n\nColumn descriptions:\t\nSample Name: Sample identifier\nSample Top Depth (m): Measured top depth (m) of sample\nSample Bottom Depth (m): Measured bottom depth (m) of sample\nMid sample depth (m): Calculated mid depth (m) of sample\nMSA: Methansulphonic acid (MSA) concentration data\nCl: Cl- concentration data\nNO3: NO3- concentration data\nSO4: SO42- concentration data\nNa: Na+ concentration data\nK: K+ concentration data\nMg: Mg2+ concentration data\nCa: Ca2+ concentration data\n\t\nLocation: Lat -66 degrees 46' 18.0\", Long 112 degrees 48' 32.6\"\nDate drilled: 02/02/2014, using Kovac shallow ice corer\n\t\nLab work:Trace ionic composition was determined using suppressed ion chromatography (IC) with conductivity detection and ultra-clean sample preparation", "links": [ { diff --git a/datasets/AAS_4062_DSS1314_icecore_analysis_isotopic_composition_1.json b/datasets/AAS_4062_DSS1314_icecore_analysis_isotopic_composition_1.json index 38d6787c30..eed0d4e9bd 100644 --- a/datasets/AAS_4062_DSS1314_icecore_analysis_isotopic_composition_1.json +++ b/datasets/AAS_4062_DSS1314_icecore_analysis_isotopic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1314_icecore_analysis_isotopic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1314_Glacial isotopic composition_DMP_submit.xlsx is the measured oxygen and deuterium isotopic data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1314) collected during the Antarctic 13/14 season.\n\nColumn descriptions:\t\nCore: Ice core identifier\nSample Name: Sample identifier\nSample Top Depth (m): Measured top depth (m) of sample\nSample Bottom Depth (m): Measured bottom depth (m) of sample\nMid sample depth (m): Calculated mid depth (m) of sample\ndD (ppt): Deuterium (dD) content in ppt SMOW (Standard Mean Ocean Sea Water)\nd18O (ppt): Oxygen isotope (d18O) content in ppt SMOW (Standard Mean Ocean Sea Water)\n\t\nLocation: Lat -66 degrees 46' 18.0\", Long 112 degrees 48' 32.6\"\nDate drilled: 02/02/2014, using Kovac shallow ice corer\n\t\nLab work: dD and d18O analysis was performed on the Picarro L2130-I water isotope analyser at the AAD Glaciology labs at ACE CRC.", "links": [ { diff --git a/datasets/AAS_4062_DSS1415_icecore_analysis_ionic_composition_1.json b/datasets/AAS_4062_DSS1415_icecore_analysis_ionic_composition_1.json index 4102501838..2261a8ff7a 100644 --- a/datasets/AAS_4062_DSS1415_icecore_analysis_ionic_composition_1.json +++ b/datasets/AAS_4062_DSS1415_icecore_analysis_ionic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1415_icecore_analysis_ionic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1415_Glacial ice trace ionic composition_DMP_submit.xlsx is the measured trace ionic composition data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1415) collected during the Antarctic 14/15 season.\n\nColumn descriptions:\t\nSample Name: Sample identifier\nSample Top Depth (m): Measured top depth (m) of sample\nSample Bottom Depth (m): Measured bottom depth (m) of sample\nMid sample depth (m): Calculated mid depth (m) of sample\nMSA: Methansulphonic acid (MSA) concentration data\nCl: Cl- concentration data\nNO3: NO3- concentration data\nSO4: SO42- concentration data\nNa: Na+ concentration data\nK: K+ concentration data\nMg: Mg2+ concentration data\nCa: Ca2+ concentration data\n\t\nLocation:Lat -66 degrees 46'17.1\", Long 112 degrees 48' 31.3\"\nDate drilled: 07/02/2015, using Kovac shallow ice corer\n\t\nLab work: Trace ionic composition was determined using suppressed ion chromatography (IC) with conductivity detection and ultra-clean sample preparation", "links": [ { diff --git a/datasets/AAS_4062_DSS1415_icecore_analysis_isotopic_composition_1.json b/datasets/AAS_4062_DSS1415_icecore_analysis_isotopic_composition_1.json index e8fecb6d0f..2563bf1033 100644 --- a/datasets/AAS_4062_DSS1415_icecore_analysis_isotopic_composition_1.json +++ b/datasets/AAS_4062_DSS1415_icecore_analysis_isotopic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1415_icecore_analysis_isotopic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1415_Glacial isotopic composition_DMP_submit.xlsx is the measured oxygen and deuterium isotopic data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1415) collected during the Antarctic 14/15 season.\n\nColumn descriptions:\t\nCore: Ice core identifier\nSample Name: Sample identifier\nSample Top Depth (m): Measured top depth (m) of sample\nSample Bottom Depth (m): Measured bottom depth (m) of sample\nMid sample depth (m): Calculated mid depth (m) of sample\ndD (ppt): Deuterium (dD) content in ppt SMOW (Standard Mean Ocean Sea Water)\nd18O (ppt): Oxygen isotope (d18O) content in ppt SMOW (Standard Mean Ocean Sea Water)\n\t\nLocation: Lat -66 degrees 46'17.1\", \tLong 112 degrees 48' 31.3\"\nDate drilled: 07/02/2015, using Kovac shallow ice corer\n\t\nLab work: dD and d18O analysis was performed on the Picarro L2130-I water isotope analyser at the AAD Glaciology labs at ACE CRC.", "links": [ { diff --git a/datasets/AAS_4062_DSS1516_icecore_analysis_ionic_composition_1.json b/datasets/AAS_4062_DSS1516_icecore_analysis_ionic_composition_1.json index 7b02d0a472..f841a65258 100644 --- a/datasets/AAS_4062_DSS1516_icecore_analysis_ionic_composition_1.json +++ b/datasets/AAS_4062_DSS1516_icecore_analysis_ionic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1516_icecore_analysis_ionic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1516_Glacial ice trace ionic composition_DMP_submit.xlsx is the measured trace ionic composition data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1516) collected during the Antarctic 15/16 season.\n\nColumn descriptions:\nSample Name: - Sample identifier\nSample Top Depth (m): - Measured top depth (m) of sample\nSample Bottom Depth (m): - Measured bottom depth (m) of sample\nMid sample depth (m): - Calculated mid depth (m) of sample\nMSA - Methansulphonic acid (MSA) concentration data\nCl - Cl- concentration data\nNO3 - NO3- concentration data\nSO4 - SO42- concentration data\nNa - Na+ concentration data\nK - K+ concentration data\nMg - Mg2+ concentration data\nCa - Ca2+ concentration data\n\t\nLocation: - Lat -66 degreers 46'23\", Long 112 degrees 48' 41\"\nDate drilled: 11/02/2016, using Kovac shallow ice corer\n\t\nLab work: - Trace ionic composition was determined using suppressed ion chromatography (IC) with conductivity detection and ultra-clean sample preparation", "links": [ { diff --git a/datasets/AAS_4062_DSS1516_icecore_analysis_isotopic_composition_1.json b/datasets/AAS_4062_DSS1516_icecore_analysis_isotopic_composition_1.json index 641af8eeb0..cd953d4601 100644 --- a/datasets/AAS_4062_DSS1516_icecore_analysis_isotopic_composition_1.json +++ b/datasets/AAS_4062_DSS1516_icecore_analysis_isotopic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1516_icecore_analysis_isotopic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1516_Glacial isotopic composition_DMP_submit.xlsx is the measured oxygen and deuterium isotopic data for the \"DSS\" (Dome Summit South) Law Dome ice core (DSS1516) collected during the Antarctic 15/16 season.\n\nColumn descriptions:\t\nCore: Ice core identifier\nSample Name: Sample identifier\nSample Top Depth (m): Measured top depth (m) of sample\nSample Bottom Depth (m): Measured bottom depth (m) of sample\nMid sample depth (m): Calculated mid depth (m) of sample\ndD (ppt):Deuterium (dD) content in ppt SMOW (Standard Mean Ocean Sea Water)\nd18O (ppt): Oxygen isotope (d18O) content in ppt SMOW (Standard Mean Ocean Sea Water)\n\t\nLocation: Lat -66 degrees 46'23\", Long 112 degrees 48' 41\"\nDate drilled: 11/02/2016, using Kovac shallow ice corer\n\t\nLab work: dD and d18O analysis was performed on the Picarro L2130-I water isotope analyser at the AAD Glaciology labs at ACE CRC.", "links": [ { diff --git a/datasets/AAS_4062_DSS1617A_Glacial_isotopic_composition_1.json b/datasets/AAS_4062_DSS1617A_Glacial_isotopic_composition_1.json index aa29e2223f..2aeefa7bb5 100644 --- a/datasets/AAS_4062_DSS1617A_Glacial_isotopic_composition_1.json +++ b/datasets/AAS_4062_DSS1617A_Glacial_isotopic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1617A_Glacial_isotopic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1617_Glacial_isotopic_composition is the measured oxygen and deuterium isotopic data for the \u2018DSS\u2019 (Dome Summit South) ice core (DSS1617) collected during the Antarctic 16/17 season. The glacial isotopic composition was measured on melted ice samples using a Picarro water isotope analyser (L2130-i). A key output for this project will be the time series of water isotopes (d18O and dD), which provides a temperature proxy record. In addition, deuterium excess can be computed, to explore moisture source conditions, including its applicability as a source temperature. In addition, the time series of water isotopes (d18O and dD) will also contribute to the multi proxy approach for the chronological control of the recently collected DSS ice cores.", "links": [ { diff --git a/datasets/AAS_4062_DSS1617_Glacial_isotopic_composition_1.json b/datasets/AAS_4062_DSS1617_Glacial_isotopic_composition_1.json index 2d43916e1f..bc29a2354b 100644 --- a/datasets/AAS_4062_DSS1617_Glacial_isotopic_composition_1.json +++ b/datasets/AAS_4062_DSS1617_Glacial_isotopic_composition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4062_DSS1617_Glacial_isotopic_composition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSS1617_Glacial_isotopic_composition is the measured oxygen and deuterium isotopic data for the \u2018DSS\u2019 (Dome Summit South) ice core (DSS1617) collected during the Antarctic 16/17 season. The glacial isotopic composition was measured on melted ice samples using a Picarro water isotope analyser (L2130-i). A key output for this project will be the time series of water isotopes (d18O and dD), which provides a temperature proxy record. In addition, deuterium excess can be computed, to explore moisture source conditions, including its applicability as a source temperature. In addition, the time series of water isotopes (d18O and dD) will also contribute to the multi proxy approach for the chronological control of the recently collected DSS ice cores.", "links": [ { diff --git a/datasets/AAS_4075_ABN1314_BoreholeTemperature_1.json b/datasets/AAS_4075_ABN1314_BoreholeTemperature_1.json index 4a518cd2c0..bdbec690bd 100644 --- a/datasets/AAS_4075_ABN1314_BoreholeTemperature_1.json +++ b/datasets/AAS_4075_ABN1314_BoreholeTemperature_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4075_ABN1314_BoreholeTemperature_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABN1314 borehole temperature profile was completed on the 13/1/2014 using the CIC borehole temperature logger. Measurements were completed by Simon Sheldon.", "links": [ { diff --git a/datasets/AAS_4075_ABN1314_Glacial_isotopic_composition_2.json b/datasets/AAS_4075_ABN1314_Glacial_isotopic_composition_2.json index 1522a3d038..05e6b40b20 100644 --- a/datasets/AAS_4075_ABN1314_Glacial_isotopic_composition_2.json +++ b/datasets/AAS_4075_ABN1314_Glacial_isotopic_composition_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4075_ABN1314_Glacial_isotopic_composition_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABN1314_Glacial_isotopic_composition is the measured oxygen and deuterium isotopic data for the 'ABN' (Aurora Basin North) ice core (ABN1314) collected during the Antarctic 13/14 season. The glacial isotopic composition was measured on melted ice samples using a Picarro water isotope analyser (L2130-i). A key output for the ABN project will be the time series of water isotopes (d18O and dD), which provides a temperature proxy record. In addition, deuterium excess can be computed, to explore moisture source conditions, including its applicability as a source temperature. In addition, the time series of water isotopes (d18O and dD) will also contribute to the multi proxy approach for the chronological control of the ABN ice core.\n\nAn extra isotope spreadsheet was added to the dataset in June, 2020.", "links": [ { diff --git a/datasets/AAS_4075_ABN1314_chem_data_1.json b/datasets/AAS_4075_ABN1314_chem_data_1.json index 2a00a0eb3c..c5fdb39d72 100644 --- a/datasets/AAS_4075_ABN1314_chem_data_1.json +++ b/datasets/AAS_4075_ABN1314_chem_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4075_ABN1314_chem_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical concentrations of trace ions from the Aurora Basin North ice core (ABN1314 main) drilled as part of AAS#4075. The ABN1314 ice core extends from 3.9m to 303m. Chemical concentrations of trace ions are given in micro equivalents per litre (uEq/L). Ions measured are MSA, Nitrate, Sulphate, Sodium, Potassium, Magnesium and Calcium.", "links": [ { diff --git a/datasets/AAS_4075_ABN_continuousGas-CFA_1.json b/datasets/AAS_4075_ABN_continuousGas-CFA_1.json index 8d7bd76f3f..39a5291346 100644 --- a/datasets/AAS_4075_ABN_continuousGas-CFA_1.json +++ b/datasets/AAS_4075_ABN_continuousGas-CFA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4075_ABN_continuousGas-CFA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABN_continuousGas-CFA is the measured methane (CH4) and carbon monoxide (CO) from the ABN (Aurora Basin North) ice core\n(ABN1314) collected during the Antarctic 13/14 season.", "links": [ { diff --git a/datasets/AAS_4077_ELEV_1.json b/datasets/AAS_4077_ELEV_1.json index 3119d84170..c15e1478fd 100644 --- a/datasets/AAS_4077_ELEV_1.json +++ b/datasets/AAS_4077_ELEV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4077_ELEV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IceBridge Riegl Laser Altimeter L2 Geolocated Surface Elevation Triplets (ILUTP2) data set contains surface range values for Antarctica and Greenland derived from measurements captured using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project,", "links": [ { diff --git a/datasets/AAS_4077_GRAV_1.json b/datasets/AAS_4077_GRAV_1.json index 1644b4b2ab..d6ab59b1a6 100644 --- a/datasets/AAS_4077_GRAV_1.json +++ b/datasets/AAS_4077_GRAV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4077_GRAV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project", "links": [ { diff --git a/datasets/AAS_4077_ICE_THICKNESS_1.json b/datasets/AAS_4077_ICE_THICKNESS_1.json index 9bf69ea874..b6ee0d6c14 100644 --- a/datasets/AAS_4077_ICE_THICKNESS_1.json +++ b/datasets/AAS_4077_ICE_THICKNESS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4077_ICE_THICKNESS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project", "links": [ { diff --git a/datasets/AAS_4077_MAG_1.json b/datasets/AAS_4077_MAG_1.json index 02c93f49e0..42cc8660a5 100644 --- a/datasets/AAS_4077_MAG_1.json +++ b/datasets/AAS_4077_MAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4077_MAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geometrics 823A Cesium Magnetometer L2 Geolocated Magnetic Anomalies (IMGEO2) data set contains magnetic anomaly measurements taken over Antarctica using the Geometrics 823A Cesium Magnetometer. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project", "links": [ { diff --git a/datasets/AAS_4078_Diatom_Images_1.json b/datasets/AAS_4078_Diatom_Images_1.json index b72e561cc7..47f76e796f 100644 --- a/datasets/AAS_4078_Diatom_Images_1.json +++ b/datasets/AAS_4078_Diatom_Images_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4078_Diatom_Images_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The collection aims to showcase the range of Southern Ocean diatom species found in the major hydrological provinces of the Australian Sector of the Southern Ocean along the 140 degrees E. The collection includes specimens collected in the Sub-Antarctic Zone (SAZ), Polar Frontal Zone (PFZ) and Antarctic Zone (AZ). \n\nSamples were collected with McLane Parflux time series sediment traps placed at several depths in the SAZ (47 degrees S site), PFZ (54 degrees S site) and AZ and (61 degrees S site) during the decade 1997-2007. The shortest sampling intervals were eight days and corresponded with the austral summer and autumn, whereas the longest interval was 60 days and corresponded with austral winter. Split aliquots were obtained for taxonomic analysis via scanning electron microscopy (SEM). For improved taxonomic imaging, samples were treated with hydrochloric acid and hydrogen peroxide to remove carbonates and organic matter, respectively. A micropipette was used to transfer the suspension of selected samples to a round-glass cover slip following standard decantation method outlined by Barcena and Abrantes (1998). Samples were air-dried and coated with gold for SEM analysis. SEM analysis was carried out using a JEOL 6480LV scanning electron microscope.\n\nTaxonomy \n\nDiatoms include all algae from the Class Bacillariophyceae and follow the standardised taxonomy of World Register of Marine Species (WoRMS).\n\nOrder Asterolamprales\n\nFamily Asterolampraceae\nAsteromphalus hookeri Ehrenberg \nAsteromphalus hyalinus Karsten \n\nOrder Achnanthales\n\nFamily Cocconeidaceae\nCocconeis sp. \n\nOrder Bacillariales\n\nFamily Bacillariaceae\nFragilariopsis curta (Van Heurck) Hustedt \nFragilariopsis cylindrus (Grunow) Krieger \nFragilariopsis kerguelensis (O'Meara) Hustedt \nFragilariopsis pseudonana (Hasle) Hasle \nFragilariopsis rhombica (O'Meara) Hustedt \nFragilariopsis separanda Hustedt \nNitzschia bicapitata Cleve\nNitzschia kolaczeckii Grunow\nNitzschia sicula (Castracane) Husted var. bicuneata (Grunow) Hasle \nNitzschia sicula (Castracane) Husted var. rostrata Hustedt \nPseudo-nitzschia heimii Manguin \nPseudo-nitzschia lineola (Cleve) Hasle \nPseudo-nitzschia turgiduloides Hasle \n\nOrder Chaetocerotanae incertae sedis\n\nFamily Chaetoceraceae\nChaetoceros aequatorialis var. antarcticus Cleve \nChaetoceros atlanticus Cleve \nChaetoceros dichaeta Ehrenberg \nChaetoceros peruvianus Brightwell \nChaetoceros sp.\n\nOrder Corethrales\n\nFamily Corethraceae\nCorethron spp. \n\nOrder Coscinodiscales\n\nFamily Coscinodiscaceae\nStellarima stellaris (Roper) Hasle et Sims\n\nFamily Hemidiscaceae\nActinocyclus sp.\nAzpeitia tabularis (Grunow) Fryxell et Sims \nHemidiscus cuneiformis Wallich\nRoperia tesselata (Roper) Grunow \n\nOrder Hemiaulales\n\nFamily Hemiaulaceae\nEucampia antarctica (Castracane) Mangin\n\nOrder Naviculales\n\nFamily Plagiotropidaceae\nTropidoneis group \n\nFamily Naviculaceae\nNavicula directa (Smith) Ralfs \n\nFamily Pleurosigmataceae\nPleurosigma sp. \n\nOrder Rhizosoleniales\n\nFamily Rhizosoleniaceae\nDactyliosolen antarcticus Castracane\nRhizosolenia antennata f. semispina Sundstrom \nRhizosolenia antennata (Ehrenberg) Brown f. antennata\nRhizosolenia cf. costata Gersonde\nRhizosolenia polydactyla Castracane f. polydactyla\nRhizosolenia simplex Karsten\nProboscia alata (Brightwell) Sundstrom\nProboscia inermis (Castracane) Jordan Ligowski\n\nOrder Thalassiosirales\n\nFamily Thalassiosiraceae\nPorosira pseudodenticulata (Hustedt) Jouse \nThalassiosira ferelineata Hasle et Fryxell\nThalassiosira gracilis (Karsten) Hustedt \nThalassiosira lentiginosa (Janisch) Fryxell \nThalassiosira oestrupii (Ostenfeld) Hasle var. oestrupii Fryxell et Hasle\nThalassiosira oliveriana (O'Meara) Makarova et Nikolaev \nThalassiosira tumida (Janisch) Hasle\n\t\nOrder Thalassionematales\n\nFamily Thalassionemataceae\nThalassionema nitzschioides var. lanceolatum Grunow\nThalassiothrix antarctica Schimper ex Karsten \n\n\nData available: 73 SEM images of the most abundant diatom species found at the three sampling sites. \n\nSamples were collected by several sediment traps placed at different depths in the Subantarctic Zone (47 degrees S site), Polar Frontal Zone (54 degrees S site) and Antarctic Zone (61 degrees S site) during the decade 1997-2007. The collection site and date for each species image can be found in Table 1 (see the word document in the download file).", "links": [ { diff --git a/datasets/AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1.json b/datasets/AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1.json index 5ba0147aaa..f4ffed1fe8 100644 --- a/datasets/AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1.json +++ b/datasets/AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is derived from sediment trap records collected by Thomas Trull as part of the multidisciplinary SAZ Project initiated in 1997 by the Antarctic Cooperative Research Centre (ACE CRC) (Trull et al 2001b). The current submission provides data not included in Wilks et al. (submitted) 'Biogeochemical flux and phytoplankton assemblage variability: A unique year-long sediment trap record in the Australian Sector of the Subantarctic Zone.' \n\nThis dataset contains three parts: Supplementary Table 1 describes sediment trap deployment information and current speed measured during deployment. Supplementary tables 2a and 2b are raw diatom counts of every species encountered at the site, at every sampling cup. Table 2a contains the 500 m trap depth record, while table 2b is for the 2000 m trap depth record. Supplementary table 3 contains environmental data (chlorophyll-a, photosynthetically active radiation, and sea surface temperature) for each cup record.", "links": [ { diff --git a/datasets/AAS_4078_diatoms_biogenic_flux_1.json b/datasets/AAS_4078_diatoms_biogenic_flux_1.json index e1bc9d1a20..ce63bb52e8 100644 --- a/datasets/AAS_4078_diatoms_biogenic_flux_1.json +++ b/datasets/AAS_4078_diatoms_biogenic_flux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4078_diatoms_biogenic_flux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diatom and biogenic particle fluxes were investigated over a one-year period (2001-02) at the southern Antarctic Zone in the Australian Sector of the Southern Ocean. Two vertically moored sediment traps were deployed at 60 degrees 44.43'S 139 degrees 53.97' E at 2000 and 3800 m below sea-level. In these data sets we present the results on the temporal and vertical variability of total diatom flux, species composition and biogenic particle fluxes during a year. A detailed description of the field experiment, sample processing and counting methods can be found in Rigual-Hernandez et al. (2015).\n\nTotal fluxes of particulates at both traps were highly seasonal, with maxima registered during the austral summer (up to 1151 mg m-2 d-1 at 2000 m and 1157 mg m-2 d-1 at 3700 m) and almost negligible fluxes during winter (up to 42 mg m-2 d-1 at 2000 m and below detection limits at 3700 m). Particulate fluxes were slightly higher at 2000 m than at 3700 m (deployment average = 261 and 216 mg m-2 d-1, respectively). Biogenic silica (SiO2) was the dominant bulk component, regardless of the sampling period or depth (deployment average = 76% at 2000 and 78% at 3700 m). Highest relative contribution of opal was registered from the end of summer through early-autumn at both depths. Secondary contributors were carbonate (CaCO3) (7% at 2000 m and 9% at 3700 m) and particulate organic carbon (POC) (1.4% at 2000 m and 1.2% at 3700 m). The relative concentration of carbonate and POC was at its highest in austral spring and summer.\n\nDiatom frustules from 61 taxa were identified over the entire experiment. The dominant species of the diatom assemblage was Fragilariopsis kerguelensis with a mean flux between 53 x 106 and 60 x 106 valves m-2 day-1 at 2000 m (annualized mean and deployment average, respectively). Secondary contributors to the diatom assemblage at 2000 and 3700 m were Thalassiosira lentiginosa, Thalassiosira gracilis var. gracilis, Fragilariopsis separanda, Fragilariopsis pseudonana, Fragilariopsis rhombica, Fragilariopsis curta and Azpeitia tabularis. \n\nData available: two excel files containing sampling dates and depths, raw counts, relative abundance and fluxes (valves m-2 d-1) of the diatom species, and biogenic particle fluxes found at 2000 m and 3700 m depth. Each file contains four spreadsheets: raw diatom valve counts, relative abundance of diatom species and valve flux of diatom species and biogenic particle composition and fluxes. Detailed information of the column headings is provided below.\n\nCup - Cup (=sample) number\nDepth - vertical location of the sediment trap in meters below the surface \nMid-point date - Mid date of the sampling interval\nLength (days) - number of days the cup was open\n\nGirdle bands instead of valves were counted for Dactyliosolen antarcticus Castracane. Therefore, D. antarcticus girdles counts were not included in relative abundance calculations", "links": [ { diff --git a/datasets/AAS_4078_diatoms_biogenic_flux_subantarctic_1.json b/datasets/AAS_4078_diatoms_biogenic_flux_subantarctic_1.json index 884354a779..425d58560c 100644 --- a/datasets/AAS_4078_diatoms_biogenic_flux_subantarctic_1.json +++ b/datasets/AAS_4078_diatoms_biogenic_flux_subantarctic_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4078_diatoms_biogenic_flux_subantarctic_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diatom and biogenic particle fluxes were investigated over a two-year and six-year periods at the Subantarctic and Polar Frontal Zones, respectively, in the Australian Sector of the Southern Ocean. Both sites were located along ~ 140 degrees E: station 47 degrees S was set on the abyssal plain of the central SAZ whereas station 54 degrees S was placed on a bathymetric high of the Southeast Indian Ridge in the PFZ. The data sets contain diatom species and biogeochemical flux data measured at 1000 m at the 47 degrees S site between 1999-2001 and at 800 m at the 54 degrees S site during six selected years between 1997-2007. All traps were MacLane Parflux sediment traps: conical in shape with a 0.5 m2 opening area and equipped with a carousel of 13 or 21 sampling cups. Shortest intervals corresponded with the austral summer and autumn ranging typically between 4.25 and 10 days, whereas the longest intervals were 60 days and corresponded with winter.\nTotal fluxes of particulates at both traps were highly seasonal, with maxima registered during the austral spring and summer and very low fluxes during winter. Seasonality was more pronounced in the 54 degrees S site. Biogenic silica (SiO2) was the dominant bulk component in the PFZ while carbonate (CaCO3) dominated the particle fluxes at the SAZ. POC export was relatively similar between sites despite significant differences in the total diatom flux. \nDiatom frustules from 94 taxa were identified over the entire experiment. The dominant species of the diatom assemblage was Fragilariopsis kerguelensis at both sites, representing 43% and 59% of the integrated diatom assemblage at the 47 degrees S and 54 degrees S sites, respectively. Secondary contributors to the diatom assemblage at the 47 degrees S were Azpeitia tabularis, Thalassiosira sp. 1, Nitzschia bicapitata, resting spores of Chaetoceros spp., Thalassiosira oestrupii var. oestrupii, Hemidiscus cuneiformis and Roperia tesselata. Subordinate contributions to the diatom assemblage correspond to Pseudo-nitzschia lineola cf. lineola, Pseudo-nitzschia heimii, Thalassiosira gracilis group and Fragilariopsis pseudonana, Fragilariopsis rhombica and Thalassiosira lentiginosa. \n\nData available: two excel files containing sampling dates and depths, raw counts, relative abundance and fluxes (valves m-2 d-1) of the diatom species, and biogenic particle fluxes measured at 1000 m and 800 m depth at the 47 degrees S and 54 degrees S sites, respectively. Each file contains four spreadsheets: raw diatom valve counts, relative abundance of diatom species and valve flux of diatom species and biogenic particle composition and fluxes. Detailed information of the column headings is provided below.\n\nCup - Cup (=sample) number\nDepth - vertical location of the sediment trap in meters below the surface \nMid-point date - Mid date of the sampling interval\nLength (days) - number of days the cup was open\n\nGirdle bands instead of valves were counted for Dactyliosolen antarcticus Castracane. Therefore, D. antarcticus girdles counts were not included in relative abundance calculations.\n\nDates of data collection:\n47 degrees S site: July 1999 - October 2001 (two-year record)\n54 degrees S site: September 1997 - February 1998, July 1999 - August 2000, November 2002 - October 2004 and December 2005 - October 2007 (six-year record).", "links": [ { diff --git a/datasets/AAS_4086_Weighbridge_1.json b/datasets/AAS_4086_Weighbridge_1.json index bccadb7d8d..291ea13377 100644 --- a/datasets/AAS_4086_Weighbridge_1.json +++ b/datasets/AAS_4086_Weighbridge_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4086_Weighbridge_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises records of crossings by Adelie penguins of a weighbridge and gateway established on Bechervaise Island. The weighbridge and gateway are positioned so that most or all of the penguins breeding in a set of sub-colonies on the island cross the weighbridge when they leave the colony to forage and when they return from foraging. The gateway records the time of each crossing, the dynamic weight of the penguin as it crosses, and the identity of penguins that have been sub-cutaneously tagged. The weighbridge and gateway operate continuously throughout the austral breeding season. The data are currently in an unprocessed form.", "links": [ { diff --git a/datasets/AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1.json b/datasets/AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1.json index 70ed61c37b..f89bbe2358 100644 --- a/datasets/AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1.json +++ b/datasets/AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The foraging ecology of three fulmarine petrels including Cape petrels, Southern fulmars and Antarctic petrels were investigated at Hop Island during the 2015/16 austral summer. Two datasets were generated: 1) tracking data from Fulmarine petrels, and 2) stable isotope analysis of blood, feathers and egg shells. Tracking data were collected using Ecotone GPS trackers attached to the birds back feathers with tape. Location data has been interpolated using great circle distance to a time step of 15 minutes and include a record of whether the bird dived during that time period or not. Each location point was assigned a breeding stage (incubation or chick rearing) based on individual nest activities. Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14N) were determined by analysing 1 mg aliquots through continuous flow - elemental analysis - isotope ratio mass spectrometry (CF-EA-IRMS). Isotopic values of blood reflect approximately the last 52 days before sampling and thus the incubation period of all three species. Egg membranes and feathers remain metabolically inert after formation, and hence reflect the trophic niche during the pre-laying and moult period, respectively. We collected moult feathers during the chick-rearing period and therefore assumed that these were formed one year prior to the collection date and thus represent the trophic niche of the chick-rearing period one year earlier (austral summer 2014-15).", "links": [ { diff --git a/datasets/AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1.json b/datasets/AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1.json index 5a6372772c..337782f821 100644 --- a/datasets/AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1.json +++ b/datasets/AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At Hop Island in the Rauer Group during the 2012/13 field season combinations of data loggers were deployed on different adelie penguins. The data loggers were GPS (two types), time-depth recorders and accelerometers. The accelerometer records head movement to identify when the bird captures prey. The units were later retrieved and the data downloaded. A document included with the data has further information about the data. \nThe data were collected following protocols approved by the Australian Antarctic Animal Ethics Committee and supported through the Australian Antarctic program through Australian Antarctic Science project 4087. \nData from GPS units deployed at Hop Island in 2011/12 is described by the metadata record with ID AAS_4087_adelie_penguin_tracking_hop_island_2011_12.", "links": [ { diff --git a/datasets/AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1.json b/datasets/AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1.json index b2d741d90b..8354072fa5 100644 --- a/datasets/AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1.json +++ b/datasets/AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS units were deployed on Adelie penguins at Hop Island in the Rauer Group during the 2011/12 field season. Deployments were made during the incubation, guard and creche periods. The units were later retrieved and the data downloaded.\nThe data were collected following protocols approved by the Australian Antarctic Animal Ethics Committee and supported through the Australian Antarctic program through Australian Antarctic Science project 4087.\nThe GPS units were supplied by Louise Emmerson of the Australian Antarctic Division through the AAS project 4087 budget and deployed and retrieved by Nobuo Kokubun of the National Institute of Polar Research, Japan with field assistance from Barbara Wienecke of the Australian Antarctic Division.\nFurther information is available with the data.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_Counts_Cameras_1.json b/datasets/AAS_4088_Adelie_Counts_Cameras_1.json index a6e1ddcec4..17424ac0cb 100644 --- a/datasets/AAS_4088_Adelie_Counts_Cameras_1.json +++ b/datasets/AAS_4088_Adelie_Counts_Cameras_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_Counts_Cameras_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises counts of Adelie penguins attending breeding sites from images obtained with 20 remotely operating cameras across East Antarctica. Counts were made of adults, occupied nests and chicks every few days throughout the breeding season from October through to February. Locations of cameras are given in an associated dataset (Photographic images of seabird nesting sites in the Antarctic, collected by remote camera) which also provides the images obtained from the cameras.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_Diet_2.json b/datasets/AAS_4088_Adelie_Diet_2.json index aae9d5385a..b8f245da6d 100644 --- a/datasets/AAS_4088_Adelie_Diet_2.json +++ b/datasets/AAS_4088_Adelie_Diet_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_Diet_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These spreadsheets provide the proportions of prey DNA sequences in the scats of Adelie penguins at Bechervaise Island and Whitney Point in East Antarctica. \n\nSamples were collected during two stages of the breeding season: mid brood guard (Bechervaise Island-January 4-6th 2013, Whitney Point 23- 28th December 2012) and mid creche (23-26th January 2013). Scat samples were collected from breeding birds, chicks and non-breeders at Bechervaise Island and breeding birds and chicks at Whitney Point. 'Breeders' were identified as individuals brooding or provisioning a chick, whereas 'non-breeders' were usually pairs that had reoccupied the colony and were building new practice nests with no chick present. Non-breeders in the colony include immature birds that have not yet bred and mature birds of breeding age that did not breed in a particular season (e.g. no partner or insufficient body condition)\n\nDNA from each sample was extracted and sequenced as per the protocols in the following paper:\nJarman, S.N., McInnes, J.C., Faux, C., Polanowski, A.M., Marthick, J., Deagle, B.E., Southwell, C. and Emmerson, L. 2013 Adelie penguin population diet monitoring by analysis of food DNA in scats. PLoS One 8, e82227. (doi:10.1371/journal.pone.0082227).\n\nThe Raw Data spreadsheet contains the proportion of each prey group of each individual sample, plus the total sequence count of prey items. Only samples with greater than 100 prey sequences are included in the dataset. The summary datasheet contains only prey taxa which contained greater than 2% of the proportion of sequences.\n\nAnalysis of these data have been published in: McInnes JC, Emmerson L, Southwell C, Faux C, Jarman SN. (2016) Simultaneous DNA-based diet analysis of breeding, non-breeding and chick Adelie Penguins http://dx.doi.org/10.1098/rsos.150443", "links": [ { diff --git a/datasets/AAS_4088_Adelie_breeding_colony_boundaries_1.json b/datasets/AAS_4088_Adelie_breeding_colony_boundaries_1.json index b261e24450..80ee1cc7d3 100644 --- a/datasets/AAS_4088_Adelie_breeding_colony_boundaries_1.json +++ b/datasets/AAS_4088_Adelie_breeding_colony_boundaries_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_breeding_colony_boundaries_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains boundaries of Adelie penguin breeding colonies at numerous breeding sites across east Antarctica. The boundary data were obtained using a range of methods which are detailed in separate spatial group-season accounts. \n\nThe database of potential Adelie penguin breeding habitat in Southwell et al. (2016a) was used to associate colony boundaries to a particular breeding site and structure how the boundaries are stored. The breeding site database has a unique identifying code of every site of potential breeding habitat in East Antarctica, and the sites are aggregated into spatial sub-groups and then spatial groups. The file structure in which the boundaries are stored has a combination of 'group' and 'split-year breeding season' at the top level (eg VES 2015-16 contains all boundaries in spatial group VES (Vestfold Hills and islands) taken in the 2015-16 breeding season). Within each group-year folder are sub-folders for each breeding site where photos were taken (eg IS_72276 is Gardner Island in the VES group). ", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Balaena_1.json b/datasets/AAS_4088_Adelie_occupancy_Balaena_1.json index ba19c487a6..ae39705ddd 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Balaena_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Balaena_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Balaena_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey on 26 January 2012 found 1 island (70166) along the coast between 111 degrees 00'E - 111 degrees 10'E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of the occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E158) and the boundaries of penguin colonies were digitised from the geo-referenced photos with not intentional buffer. Note the quality of the aerial photos was poor and so the resultant boundary mapping will not be very accurate. Also in the Balaena Islands there is a historic record from the 50s of penguins nesting on Thompson Islet (70166). When aerial photos were taken of this island penguins could not be detected.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2013_1.json b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2013_1.json index 912ea9e97f..5e9c04f94e 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2013_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Bechervaise_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All subcolonies on Bechervaise Island were mapped with a hand held GPS (Garmin Legend) on the 9th of January 2013). The mapping was undertaken by Julie McInnes and Helen Achurch. The colonies were mapped at a constant 2m buffer. If subcolonies were less than 2m apart they were mapped in the same outline, colonies greater than 2m apart were mapped separately. The final layer has a 2m buffer around the colony included in the layer.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2016_1.json b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2016_1.json index bb4b38c6ae..aec707b304 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2016_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Bechervaise_2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie colony boundaries at Bechervaise Island were mapped by Matthew Pauza on the 21 Dec 2016. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. \nThis buffer distance was reduced by .5m to between 2m in the final shapefiles. ", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1.json b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1.json index 2301402e7b..e58d0ac84a 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Six colonies with breeding Adelie colonies were mapped this season on Kista Island. On Bechervaise Island subcolonies C and R were not mapped and so are missing from the final layer, but birds were present in these subcolonies. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Biscoe_1.json b/datasets/AAS_4088_Adelie_occupancy_Biscoe_1.json index 243d6eaf26..4fefeeeeb8 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Biscoe_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Biscoe_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Biscoe_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial and ground photos taken during a visit to Mount Biscoe in 1985 were used to map the extent of old guano and unoccupied pebble nests found in the area. The guano extended from the beach up the northern slope of the massif to an altitude of approximately 200m. Very few birds were present when the site was visited. The map was hand drawn and put into the paper documented below. \n\nWith the aid of satellite imagery, the diagram was converted into a shapefile for the purposes of mapping the potential colony extent in this location.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Bolingen_1.json b/datasets/AAS_4088_Adelie_occupancy_Bolingen_1.json index d3010b807a..212da7cb60 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Bolingen_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Bolingen_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Bolingen_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2009 and December 2010 (Southwell and Emmerson 2013) found a total of 2 Adelie penguin breeding sites in the Bolingen Island group between longitudes 75.333oE-75.912oE. The boundaries of breeding sub-colonies at 1 of these sites (Lichen Island, 73030) were subsequently mapped from vertical aerial photographs taken for abundance surveys on 20 November 2010 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. The other breeding site (73156) was photographed obliquely from a helicopter using a hand-held camera on 6 December 2010. Colony boundaries for this site were drawn and digitised by eye.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Chick_Henry_2012_1.json b/datasets/AAS_4088_Adelie_occupancy_Chick_Henry_2012_1.json index 81d6014978..20b31e214d 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Chick_Henry_2012_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Chick_Henry_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Chick_Henry_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in 26 January 2012 found a total of 2 islands along the coast between 120o30\u2019E - 121o02\u2019E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E159) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are:\n\nChick: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159\nHenry 1: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Kista_2015_1.json b/datasets/AAS_4088_Adelie_occupancy_Kista_2015_1.json index 8c6dc50086..d92767c9bf 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Kista_2015_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Kista_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Kista_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seven colonies with breeding Adelie colonies were mapped this season in the Kista Island group between the 17th and 27th of November 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track.\n\nWhen mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Klung Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. \n\nNote when mapping was undertaken at Peterson Island (74507) two subcolonies were not mapped when compared to mapping in the 13/14 season. The larger of these colonies was missed but the smaller colony did not exist in the 15/16 season. ", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Knox_2009-2010_1.json b/datasets/AAS_4088_Adelie_occupancy_Knox_2009-2010_1.json index 345bf24151..54a3d93c1a 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Knox_2009-2010_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Knox_2009-2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Knox_2009-2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are:\n\nMerrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157\nCape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011\nIvanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Knox_2011_1.json b/datasets/AAS_4088_Adelie_occupancy_Knox_2011_1.json index 08a07b8da7..5d27fb4c80 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Knox_2011_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Knox_2011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Knox_2011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are:\n\nMerrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157\nCape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011\nIvanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Lewis_2012_1.json b/datasets/AAS_4088_Adelie_occupancy_Lewis_2012_1.json index 881bfa94e7..059b1ec204 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Lewis_2012_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Lewis_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Lewis_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are:\n\nMerrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157\nCape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011\nIvanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Low_Tongue_2015_1.json b/datasets/AAS_4088_Adelie_occupancy_Low_Tongue_2015_1.json index b33b948a92..40dc18bd71 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Low_Tongue_2015_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Low_Tongue_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Low_Tongue_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Mawson_Taylor_1.json b/datasets/AAS_4088_Adelie_occupancy_Mawson_Taylor_1.json index 96376408a5..7bc6efd9a5 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Mawson_Taylor_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Mawson_Taylor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Mawson_Taylor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs taken towards the end of the 2014/15 summer between Mawson and Taylor Glacier. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Murray_2010_1.json b/datasets/AAS_4088_Adelie_occupancy_Murray_2010_1.json index 4ddc2c8ae6..cf468a356b 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Murray_2010_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Murray_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Murray_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Murray Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. Some sections of the digitised Murray Monolith colonies near the crescent shaped moraine were moved so they were contained within the shapefile \u2018rock_exposed_for_modelling_Scullin_Murray\u2019) \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Rauer_2009_1.json b/datasets/AAS_4088_Adelie_occupancy_Rauer_2009_1.json index 9955ee121a..947dbb26ae 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Rauer_2009_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Rauer_2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Rauer_2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Rauer_2010_1.json b/datasets/AAS_4088_Adelie_occupancy_Rauer_2010_1.json index e79b997057..553fa17dd1 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Rauer_2010_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Rauer_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Rauer_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009. The remaining breeding site (IS_72922) was photographed obliquely from a helicopter using a hand-held camera on 20 December 2010. Colony boundaries for this site were drawn and digitised by eye. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Robinson_2006_1.json b/datasets/AAS_4088_Adelie_occupancy_Robinson_2006_1.json index 44a387dd59..acd1671ee3 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Robinson_2006_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Robinson_2006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Robinson_2006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these islands with larger populations were subsequently mapped for abundance surveys by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx or Vista C) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of 2-5m between themselves and the penguins at the sub-colony boundary. This buffer distance was reduced to between 1 and 4m in the final shapefiles. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Robinson_2013_1.json b/datasets/AAS_4088_Adelie_occupancy_Robinson_2013_1.json index aa8a8a91f9..f76969e468 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Robinson_2013_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Robinson_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Robinson_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these were mapped in Nov 2006 for abundance surveys. Nine of these breeding sites were remapped on the 29th of November 2013 in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Rookery_2013_1.json b/datasets/AAS_4088_Adelie_occupancy_Rookery_2013_1.json index 4d14c683c4..a0c1c9181b 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Rookery_2013_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Rookery_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Rookery_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Six colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Islands 74814 and the main Rookery Island 74721 were not mapped this season. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Rookery_2014_1.json b/datasets/AAS_4088_Adelie_occupancy_Rookery_2014_1.json index 2ee6fc51cb..353cf1ba46 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Rookery_2014_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Rookery_2014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Rookery_2014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Rookery_2015_1.json b/datasets/AAS_4088_Adelie_occupancy_Rookery_2015_1.json index 8050528830..a88132a525 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Rookery_2015_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Rookery_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Rookery_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fourteen colonies with breeding Adelie colonies were mapped this season in the Rookery Island group between the 29th November and 14th of December 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. \n\nWhen mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Gibbney and Rookery Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. \n\nNote on Gibbney and Giganteus there were at least two subcolonies on both islands that were mapped but the density of breeding birds in these mapped sections was much less than that in the surrounding colonies. Subcolonies were tagged with L at the end of their name in the track files. This will not be shown in the final layer and if information on this is needed then the subcolonies can be identified from the original track data or created shapefiles for the individual subcolonies on the island. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Scullin_2010_1.json b/datasets/AAS_4088_Adelie_occupancy_Scullin_2010_1.json index 48db07d027..6b608b37ad 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Scullin_2010_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Scullin_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Scullin_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Scullin Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Stanton_2015_1.json b/datasets/AAS_4088_Adelie_occupancy_Stanton_2015_1.json index cc8d9dfabf..af67d911ee 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Stanton_2015_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Stanton_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Stanton_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises Adelie penguin colony boundaries at three sites in the vicinity of Stanton Island. Boundaries were derived from oblique aerial photographs taken in the Stanton Island group. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Stillwell_2015_1.json b/datasets/AAS_4088_Adelie_occupancy_Stillwell_2015_1.json index 782fec0a17..dc83cbf1cd 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Stillwell_2015_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Stillwell_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Stillwell_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises Adelie penguin colony boundaries on one island in the Stillwell Island group. Boundaries were derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Svenner_2010_1.json b/datasets/AAS_4088_Adelie_occupancy_Svenner_2010_1.json index 3f7ecdeeaf..fe29805de8 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Svenner_2010_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Svenner_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Svenner_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2009 and December 2010 (Southwell and Emmerson 2013) found a total of 15 Adelie penguin breeding sites in the Svenner Islands between longitudes 76.50oE to 77.50oE. The boundaries of breeding sub-colonies were subsequently mapped from vertical aerial photographs taken for abundance surveys on 20 November 2010 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. When photos of Island 73036 were viewed there was no colony to map so only 14 islands were mapped. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2009_1.json b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2009_1.json index fd4bbca840..80e73c4508 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2009_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Vestfold_2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009 (for details of aerial photography see Southwell et al. 2013). These boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2011_1.json b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2011_1.json index 5e2d857683..90f03d3086 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2011_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Vestfold_2011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs A further two breeding sites (IS_72295 and McCallie Rocks_72205) were photographed obliquely from a helicopter using a hand-held camera on 10 January. Colony boundaries for 72295 were drawn and digitised by eye. Colony boundaries for 72295 were sketched onto a rough island polygon from the oblique photo without being rectified. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2012_1.json b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2012_1.json index 4ca7973440..4ef63d17ba 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Vestfold_2012_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Vestfold_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Vestfold_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009. Two breeding sites were photographed obliquely from a helicopter using a hand-held camera on the 13 December 2012. Colony boundaries for these 2 sites were drawn and digitised by eye. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Welch_2014_1.json b/datasets/AAS_4088_Adelie_occupancy_Welch_2014_1.json index a5ba32c00b..eb8b3d4c34 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Welch_2014_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Welch_2014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Welch_2014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie colony boundaries at Welch Island were mapped on the 30 Nov 2014 to provide a boundary for the pole camera survey. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Legend and Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. \nThis buffer distance was reduced by .5m to between 2m in the final shapefiles.", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Wilkes_2011_1.json b/datasets/AAS_4088_Adelie_occupancy_Wilkes_2011_1.json index f0f7cc52bf..99645e6302 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Wilkes_2011_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Wilkes_2011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Wilkes_2011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey on 21 January 2011 found a total of 7 islands along the Wilkes Land coastline had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site except Haswell Island. The aerial photographs were geo-referenced to a satellite image and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are:\n\nAdams: Photographs taken on 21 January 2011 and geo-referenced to a Quickbird satellite image taken on 30 January 2009\nFulmar: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011\nZykov: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011\nBuromskiy: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011\nStroitley: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011\nTokarev: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011\nHaswell: No photographs taken, no penguin colonies were digitised\n\n\nNote there are two colony boundary layers in each folder except Adams. One is the original layer mapped as above. The second is an adjusted layer that was created so that the mapped boundaries would land on the exposed rock layer. Mapping of some of the islands contained within the coast layer had been coarsely done using imagery available at the time. Now with more accurate satellite imagery the island mapping could potentially be updated which would more accurately locate these islands. If this occurred, the original colony boundary mapping may be a more appropriate fit. \n", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Windmill_1.json b/datasets/AAS_4088_Adelie_occupancy_Windmill_1.json index a783849e30..35aca61f4a 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Windmill_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Windmill_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Windmill_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in January 2011 found a total of 14 islands/sites in Windmill group had populations of breeding Adelie penguins. The boundaries of breeding colonies at 11 of the 14 islands were subsequently mapped for abundance surveys. Four of the islands, Nelly Island, Hollin Island, Midgley Island and Beall Island were mapped from aerial photos taken in January 2011. Images were taken on the 2 January 2011 [Hollin, Midgley, Beall] and 23 January 2011[Nelly]. Mapping involved digitising polygons around sub-colonies from vertical aerial photographs. The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. ", "links": [ { diff --git a/datasets/AAS_4088_Adelie_occupancy_Windmill_2012-2013_1.json b/datasets/AAS_4088_Adelie_occupancy_Windmill_2012-2013_1.json index 92c810e669..19af38017d 100644 --- a/datasets/AAS_4088_Adelie_occupancy_Windmill_2012-2013_1.json +++ b/datasets/AAS_4088_Adelie_occupancy_Windmill_2012-2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Adelie_occupancy_Windmill_2012-2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An occupancy survey in January 2011 found a total of 14 islands/sites in Windmill group had populations of breeding Adelie penguins. The boundaries of breeding colonies at 11 of the 14 islands were subsequently mapped for abundance surveys. Seven islands were mapped on the ground with GPS: Whitney Point, Blakeney Point, Shirley Island, Odbert Island, Berkley Island, Cameron Island and O'Connor Island between 10 December 2012 to 9 January 2013 ). The buffer distance was reduced to 1-2 m in the shapefiles created from the ground maps. Ground mapping involved circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx or Vista C) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of 2-3 m between themselves and the penguins at the sub-colony boundary to minimise disturbance. ", "links": [ { diff --git a/datasets/AAS_4088_Cape_Petrel_Vestfold_2017_1.json b/datasets/AAS_4088_Cape_Petrel_Vestfold_2017_1.json index e8092e5304..1a0bf4b33a 100644 --- a/datasets/AAS_4088_Cape_Petrel_Vestfold_2017_1.json +++ b/datasets/AAS_4088_Cape_Petrel_Vestfold_2017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Cape_Petrel_Vestfold_2017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains boundaries of Cape petrel nesting areas at numerous breeding sites on islands off the Vestfold Hills, Antarctica. Boundaries of nesting sites were obtained from aligning ground observations and photographs from land or the sea-ice adjacent to the breeding sites onto maps of islands in the region. The observations were made and the photographs taken between 18 and 30 November 2017. Marcus Salton and Kim Kliska made the ground observations, took the photographs and delineated the GIS boundaries representing the nesting areas.\nThe data is a polygon shapefile with each polygon designated Type A or Type B. Type A indicates nests present. Type B indicates this area was searched and no nests were present.\nAlso included are three images showing the Type A polygons and the associated nest counts.", "links": [ { diff --git a/datasets/AAS_4088_SGP_2011-2015_1.json b/datasets/AAS_4088_SGP_2011-2015_1.json index 1c637bbf88..48e61e6359 100644 --- a/datasets/AAS_4088_SGP_2011-2015_1.json +++ b/datasets/AAS_4088_SGP_2011-2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_SGP_2011-2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The spreadsheet contains detailed information (by nest) of the timing of certain events during the breeding season of southern giant petrels (SGPs). The information was taken from images obtained from automated cameras monitoring the colonies. The numbers are 'Days since 1 June', the date chosen to indicate the start of the breeding cycle. Nest numbers were kept constant between years. Only highly visible nests were chosen. For methods and definitions see: Otovic et al. (2018) Marine Ornithology 46: 129-138.", "links": [ { diff --git a/datasets/AAS_4088_Snow_Storm_Nests_1.json b/datasets/AAS_4088_Snow_Storm_Nests_1.json index 6c10ad24f4..0a989e230c 100644 --- a/datasets/AAS_4088_Snow_Storm_Nests_1.json +++ b/datasets/AAS_4088_Snow_Storm_Nests_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Snow_Storm_Nests_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the locations of plots where cavity nesting species (snow petrels or Wilsons storm petrels) were found to be present in surveys of cavity nesting species in the Mawson region in 2010-11 and the Davis region in 2015-16, 2016-17 and 2017-18, plus records of the locations of cavity nesting birds seen outside plots. These data will be used by the AADC to show seabird presence on maps. \n\nFor most locations the nests were found via a structured survey, using a GPS to find a random 50x50m plot, then manual searching of the plot by a ground observer. In addition, some locations were also found while walking between plots. Plots were not revisited in subsequent survey years.", "links": [ { diff --git a/datasets/AAS_4088_Spatial_reference_system_coastal_east_Antarctica_1.1.json b/datasets/AAS_4088_Spatial_reference_system_coastal_east_Antarctica_1.1.json index aa0a4ab96c..9ede7800c3 100644 --- a/datasets/AAS_4088_Spatial_reference_system_coastal_east_Antarctica_1.1.json +++ b/datasets/AAS_4088_Spatial_reference_system_coastal_east_Antarctica_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_Spatial_reference_system_coastal_east_Antarctica_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises a table and set of maps of all geographic sites of ice-free land along the East Antarctica coastline between longitudes 37\u00b0E and 160\u00b0E. Each geographic site comprises a discrete area of ice-free land and includes islands within 100 km of the coast and outcrops of ice free continental rock within 1 km of the coast. The geographic sites were identified in a geographic information system using polygons sourced from the AAT Coastline 2003 dataset produced by Geoscience Australia and the Australian Antarctic Division, and exposed rock polygons sourced from the Antarctic Digital Database version 4.0 produced for the Scientific Committee on Antarctic Research. The maps are grouped into sub-regions and regions, with multiple maps in most sub-regions. The maps were designed to be of a scale that could be used in the field to identify sites by their shape and location. This dataset has previously been used in the specific context of potential breeding habitat for Adelie penguins (doi:10.4225/15/5758F4EC91665) but has potential for broader use in a wide range of ecological and environmental studies.\n\n2021-06-30 - an updated copy of the spatial reference system spreadsheet was uploaded. The update was only minor.", "links": [ { diff --git a/datasets/AAS_4088_flying_seabirds_Rauer_Svenner_Vestfold_2017_1.json b/datasets/AAS_4088_flying_seabirds_Rauer_Svenner_Vestfold_2017_1.json index e84bc998a9..5a25fd3893 100644 --- a/datasets/AAS_4088_flying_seabirds_Rauer_Svenner_Vestfold_2017_1.json +++ b/datasets/AAS_4088_flying_seabirds_Rauer_Svenner_Vestfold_2017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_flying_seabirds_Rauer_Svenner_Vestfold_2017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains boundaries of nest areas of surface nesting flying seabirds at numerous breeding sites across Prydz Bay, Antarctica. The sites are at islands in the Rauer Group, the Svenner Islands and two islands (Bluff Island and Gardner Island) off the Vestfold Hills. The boundary data were obtained from aerial photos of slopes where flying seabirds had been previously observed. The aerial photos were taken on 1 December 2017. Marcus Salton and Kim Kliska conducted the aerial photography and delineated the GIS boundaries representing the nesting areas.\n\nThe database of potential Adelie penguin breeding habitat as described by the metadata record 'Sites of potential habitat for breeding Adelie penguins in East Antarctica' (http://data.aad.gov.au/metadata/records/AAS_4088_Adelie_Potential_Habitats) was used to associate flying seabird nest areas to a particular island and to structure how the boundaries are stored. The Adelie penguin breeding site database has a unique identifying code for every island in East Antarctica, and the islands are aggregated into spatial sub-groups and then spatial groups. The file structure in which the boundaries are stored has a combination of \u2018island\u2019, \u2018sub-group\u2019 and \u2018spatial group\u2019 (or region) at the top level (eg VES_SG_10 contains all boundaries in spatial group VES (Vestfold Hills and islands) and sub-group 10). Within each sub-group folder are folders for each island where photos were taken (eg IS_72276 is Gardner Island in the VES_SG_10 group). \n\nThe data is comprised of:\n(i) a polygon shapefile for each island on which flying bird nest areas were observed; and\n(ii) a single polygon shapefile for each of Rauer Group, Svenner Islands and Vestfold Hills in which the polygons in (i) are combined.\n \nThe polygons in the shapefiles have a Type attribute with values ranging from A to E.\nA = Nests present\nB = Searched and no nests present\nC = Nests or salt stains (the investigators were unable to decide whether what they were seeing was nests or salt stains)\nD = Snow cover\nE = Not searched", "links": [ { diff --git a/datasets/AAS_4088_historical_adelie_estimates_1.json b/datasets/AAS_4088_historical_adelie_estimates_1.json index 122e6d713c..36b1b40f6a 100644 --- a/datasets/AAS_4088_historical_adelie_estimates_1.json +++ b/datasets/AAS_4088_historical_adelie_estimates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4088_historical_adelie_estimates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecologists are increasingly turning to historical abundance data to understand past changes in animal abundance and more broadly the ecosystems in which animals occur. However, developing reliable ecological or management interpretations from temporal abundance data can be difficult because most population counts are subject to measurement or estimation error.\n\nThere is now widespread recognition that counts of animal populations are often subject to detection bias. This recognition has led to the development of a general framework for abundance estimation that explicitly accounts for detection bias and its uncertainty, new methods for estimating detection bias, and calls for ecologists to estimate and account for bias and uncertainty when estimating animal abundance. While these methodological developments are now being increasingly accepted and used, there is a wealth of historical population count data in the literature that were collected before these developments. These historical abundance data may, in their original published form, have inherent unrecognised and therefore unaccounted biases and uncertainties that could confound reliable interpretation. Developing approaches to improve interpretation of historical data may therefore allow a more reliable assessment of extremely valuable long-term abundance data.\n\nThis dataset contains details of over 200 historical estimates of Adelie penguin breeding populations across the Australian Antarctic Territory (AAT) that have been published in the scientific literature. The details include attributes of the population count (date and year of count, count value, count object, count precision) and the published estimate of the breeding population derived from those attributes, expressed as the number of breeding pairs. In addition, the dataset contains revised population estimates that have been re-constructed using new estimation methods to account for detection bias as described in the associated publication. All population data used in this study were sourced from existing publications.", "links": [ { diff --git a/datasets/AAS_4091_MSLP_1.json b/datasets/AAS_4091_MSLP_1.json index d6bfc75034..2404b30e16 100644 --- a/datasets/AAS_4091_MSLP_1.json +++ b/datasets/AAS_4091_MSLP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4091_MSLP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data are the MSLP (Mean Sea Level Pressure) field of the Antarctic Mesoscale Prediction System (AMPS) (http://www2.mmm.ucar.edu/rt/amps/) available to download via www.earthsystemgrid.org. Data are 45km resolution for the domain d001 (lower left lat/lon = -24.72209 N, 38.30463 E, upper right lat/lon = -21.82868 N, -144.07805 E). Data are 3-hourly forecasts (t=0 to t=120) made every 12 hours using the Polar Weather Research and Forecasting (WRF) model. Data has been converted from grib to nc, 45km resolution polar stereographic to a 0.5 degree resolution latlon grid and concatenated into a single continuous dataset using the first 4 forecasts from each 12-hours. Where data was missing forecasts from the previous 12-hours are used.\n\nData available: 28/10/2008 to 31/12/2012.\n\nData were processed in this manner to be usable by the Melbourne University cyclone tracking scheme (Murray, R. J., and I. Simmonds (1991) A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme, Australian Meteorological Magazine, 39, 155-166.) to investigate Antarctic polar lows.\n\nData are 3-hourly forecasts (from t=0 to t=120) made every 12 hours, which have been processed into a continuous 3-hourly dataset using the first 4 forecasts of every 12 hours. Missing data are filled by previous forecasts.", "links": [ { diff --git a/datasets/AAS_4092_1159_1.json b/datasets/AAS_4092_1159_1.json index 5191070742..e02eaa10a3 100644 --- a/datasets/AAS_4092_1159_1.json +++ b/datasets/AAS_4092_1159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4092_1159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoscience Australia operates an integrated geophysical observing system in Australia, its neighbouring region and Antarctica. This program represents the Antarctic and sub-Antarctic components of this system. It consists of four key elements: a geodetic element that maintains and develops a precise geodetic infrastructure that supports research and global geospatial activity; a geomagnetic element that monitors geomagnetic-field changes in the polar region; a seismic element that monitors global earthquake activity and nuclear tests; and an infrasound element that monitors nuclear tests.\n\nProject Objectives:\n1. Establish and maintain permanent observatories for improved geodetic (Casey, Davis, Mawson, Macquarie Island, Grove Mountains, Prince Charles Mountains and Bunger Hills), geomagnetic (Casey, Mawson, Macquarie Island), seismological (Casey, Mawson, Macquarie Island) and infrasound (to be completed at Davis 2014/15) monitoring in Antarctica.\n\n2. Acquire geodetic data throughout the Australian Antarctic Territory (AAT) to support improving and extending the International Terrestrial Reference Frame (ITRF); monitoring deformations of the solid Earth, variations in sea level and in Earth rotation; determining orbits of scientific satellites; and monitoring the troposphere and ionosphere\n\n3. Provide a common geospatial reference for all Antarctic scientists and operators\n\n4. Acquire precise GNSS data to contribute to an International Terrestrial Reference Frame solution\n\n5. Develop the geodetic infrastructure of the Antarctic continent through international collaboration (GIANT - expert group of SCAR)\n\n6. Maintain and further develop a precise geodetic infrastructure to support Antarctic research and geospatial activity\n\n7. Collect data on geomagnetic field changes in the AAT and sub-Antarctic as part of Australian and global observatory networks\n\n8. Provide geomagnetic data to Australian and international data repositories for geomagnetic reference field modelling, space weather forecasting, airborne geophysical surveys and research\n\n9. Support compass-based maritime and aviation navigation as required under international treaties\n\n10. Collect seismological data as part of Australian and global observatory networks\n\n11. Provide seismological data for earthquake monitoring, tsunami warning and nuclear test monitoring\n\n12. Provide infrasound data for nuclear test monitoring\n\n\nThis is a parent record for other metadata records listed under AAS (ASAC) projects 1159 (Antarctic Geodesy Program [observational]) and 4092 (Geoscience Australia geodetic and geophysical monitoring program).", "links": [ { diff --git a/datasets/AAS_4092_Geomagnetic_Field_Model_1.json b/datasets/AAS_4092_Geomagnetic_Field_Model_1.json index 0506fabd0a..a5d59aa3b9 100644 --- a/datasets/AAS_4092_Geomagnetic_Field_Model_1.json +++ b/datasets/AAS_4092_Geomagnetic_Field_Model_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4092_Geomagnetic_Field_Model_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IGRF-12 and WMM15 are global magnetic field models which describe the geomagnetic main field using a set of spherical harmonic coefficients. The models are derived from geomagnetic data collected via satellite, geomagnetic observatories, aircraft and marine vessels.\nThe models are the product of a collaborative effort between magnetic field modellers and the institutes involved in collecting and disseminating magnetic field data from satellites, geomagnetic observatories and surveys around the world. A description of the models, the model coefficients and software to evaluate the models are available from:\n\nhttp://www.ngdc.noaa.gov/geomag/WMM/DoDWMM.shtml\n\nand\n\nhttp://www.ngdc.noaa.gov/IAGA/vmod/igrf.html\n\nThese models are crucial to the progress of AAS project 4092.\n\nA detailed metadata record for the World Magnetic Model is available from NOAA (National Oceanic and Atmospheric Administration) at the provided URL.", "links": [ { diff --git a/datasets/AAS_4096_AM01_Brancker_1.json b/datasets/AAS_4096_AM01_Brancker_1.json index 88a3dc351e..cbc824674e 100644 --- a/datasets/AAS_4096_AM01_Brancker_1.json +++ b/datasets/AAS_4096_AM01_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM01_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\n\nAM01b borehole drilled mid-December 2003.\nNo new thermistor strings deployed.\n\nConsult Readme file for detail of data files and formats.\n\n2011-2012 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM02_Brancker_1.json b/datasets/AAS_4096_AM02_Brancker_1.json index bdd0a3adb6..f56feed2ba 100644 --- a/datasets/AAS_4096_AM02_Brancker_1.json +++ b/datasets/AAS_4096_AM02_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM02_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\n\nConsult Readme file for detail of data files and formats.\n\n2011-2012 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM03_Brancker_1.json b/datasets/AAS_4096_AM03_Brancker_1.json index d990533183..4bc13f8510 100644 --- a/datasets/AAS_4096_AM03_Brancker_1.json +++ b/datasets/AAS_4096_AM03_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM03_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\n\nConsult Readme file for detail of data files and formats.\n\n2011-2012 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM03_MicroCAT_1.json b/datasets/AAS_4096_AM03_MicroCAT_1.json index 163c0306b1..0005b9f130 100644 --- a/datasets/AAS_4096_AM03_MicroCAT_1.json +++ b/datasets/AAS_4096_AM03_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM03_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\n2011-2012 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM04_Brancker_1.json b/datasets/AAS_4096_AM04_Brancker_1.json index 4cd7cf1a8a..97227a9d94 100644 --- a/datasets/AAS_4096_AM04_Brancker_1.json +++ b/datasets/AAS_4096_AM04_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM04_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\n\nConsult Readme file for detail of data files and formats.\n\n2011-2012 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM05_MicroCAT_1.json b/datasets/AAS_4096_AM05_MicroCAT_1.json index 576cd9d253..5d2f72c3bc 100644 --- a/datasets/AAS_4096_AM05_MicroCAT_1.json +++ b/datasets/AAS_4096_AM05_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM05_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\n2012-2013 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4096_AM06_ApRES_1.json b/datasets/AAS_4096_AM06_ApRES_1.json index 92604dbd14..50a10a1c95 100644 --- a/datasets/AAS_4096_AM06_ApRES_1.json +++ b/datasets/AAS_4096_AM06_ApRES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM06_ApRES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw data from two autonomous phase sensitive radar (ApRES) installations on Amery Ice Shelf, East Antarctica.\n\nSite, Lat, Lon, Installation, Retrieval\nAM06_borehole, -70.228432, 71.391693, 17-Jan-2015, 03-Feb-2018\nAM06_downstream, -70.225635, 71.395988, 09-Mar-2015, 03-Feb-2018\n\nApRES phase-sensitive radar is a low-power, light-weight instrument developed in a collaboration between BAS and University College London. It is a 200-400 MHz FMCW radar, with a 1-second chirp, run by controller. Each radar was set to produce a burst of 50 chirps every 4 hrs, and a config file with radar settings is provided with each dataset. \n\nFiles:\n*.dat - binary files containing raw data\nconfig.ini - config file containing all radar settings used for each site\n\nSoftware for processing the raw data can be obtained from Dr. Keith Nicholls, British Antarctic Survey. Limited Matlab scripts are provided here to open the raw data.\n\nCommand: f=fmcw_load('filename.DAT')\n\nData structure:\nVariable name \tUnit \tDescription\nNattenuators \t- \tNumber of attenuation settings used (1 or 2)\nAttenuator_1 \tdB \tRF Attenuator value 1\nAttenuator_2 \tdB \tRF Attenuator value 2\nChirpsInBurst \t- \tNumber of chirps in burst\nTimeStamp \tday \tTime of first chirp (Matlab date format)\nTemperature_1 C \tInstrument temperature 1\nTemperature_2 C \tInstrument temperature 2\nBatteryVoltage \tV \tBattery voltage\nBurst \t\t- \tNumber of burst in file\nFileFormat\t- \tIdentifies file format from different equipment versions\nvif \t\tV \tVoltage\nchirpTime \tday \tTime of chirp (Matlab date format)\nfilename \t- \tFilename\nSamplesPerChirp - \tNumber of samples per chirp\nfs \t\tHz \tSampling frequency\nf0 \t\tHz \tStart frequency\nK \t\trad/s/s \tChirp gradient (200MHz/s)\nf1 \t\tHz \tStop frequency\nB \t\tHz \tBandwidth\nfc \t\tHz \tCentre frequency\ner \t\t- \tMaterial permittivity\nci \t\tm/s \tVelocity in material\nlambdac \tm \tCentre wavelength\nt \t\ts \tSampling time (relative to first sample)\nf \t\tHz \tFrequency stamp for sample\n", "links": [ { diff --git a/datasets/AAS_4096_AM06_MicroCAT_1.json b/datasets/AAS_4096_AM06_MicroCAT_1.json index 2a5659c8ee..03cea44138 100644 --- a/datasets/AAS_4096_AM06_MicroCAT_1.json +++ b/datasets/AAS_4096_AM06_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4096_AM06_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\n2012-2013 data may be final data from the unit owing to battery failure.\n\nThe original project for this dataset was ASAC 1164, but recent data fall under the auspices of project AAS 4096.", "links": [ { diff --git a/datasets/AAS_4100_Biopile-microbial-communities_1.json b/datasets/AAS_4100_Biopile-microbial-communities_1.json index 78bae74628..1348569401 100644 --- a/datasets/AAS_4100_Biopile-microbial-communities_1.json +++ b/datasets/AAS_4100_Biopile-microbial-communities_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4100_Biopile-microbial-communities_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil was collected between November and December 2012 at Casey.\nExperimental incubations were conducted between November 2012 and February 2013.\nLaboratory analyses were carried out between February and April 2013.\n\nTwo workbooks are included in this dataset. Each workbook has a sheet called information with vital experimental and sample details. The datasheets are named for the gene that was measured: alkB (alkane monooxygenase), cat23 (catechol 2,3 dioxygenase) nosZ (nitrous oxide reductase), nifH (nitrogenase) and rpoB (ribosomal polymerase). The first columns in each of these worksheets describes the samples, then the average (of 4 measurements) number of copies of the gene per g dry weight of soil is given, followed by the standard deviation. The final sheet in each book is the hydrocarbon chemistry for the relevant samples (the measured TPH values in each worksheet were derived from this data).\n\nEffect of fresh diesel\nData in workbook freshdiesel.xlsx\nA sample of uncontaminated soil was obtained from the uncontaminated control biopile from the bioremediation site at Old Casey Station, in December 2012. 1.54g of Castrol BP Antarctic Diesel was added to 30.63g and stirred to mix thoroughly to create a starting concentration of approximately 50000mg/kg. A 1 in 2 serial dilution was performed by mixing uncontaminated soil with the diesel spiked soil from the previous mix to create 11 samples and one blank. The soil was incubated at 4 degrees for 5 weeks and was then frozen at -18 degrees until analysis.\n\nDilution of soil contaminated with weathered diesel.\nData in workbook weathereddiesel.xlsx\nThis trial was designed to mimic a range of weathered fuel concentrations by performing a serial dilution of contaminated biopile soil with uncontaminated control pile soil. Soils from biopiles at the Casey Station remediation site were collected during November 2012. Five composite samples were collected in total: four from active biopiles (two each from two different biopiles) and one from a control biopile with no hydrocarbon contamination. Two vertical profiles were collected from each biopile and each composite sample was formed by combining samples from the vertical profile.\n\nMeasurement of functional gene numbers.\nThe concentration of microbial genes ribosomal polymerase (rpoB), alkane monooxygenase (alkB), catechol dioxygenase (cat23), nitrous oxide reductase (nosZ) and nitrogenase (nifH) were measured by quantitative PCR (qPCR). The optimisation and validation of the qPCR methods is described in detail in Richardson (2013).\n\nDNA was extracted from the soil samples using the MoBio PowerSoil kit according the manufacturer's directions. qPCR assays were carried out using SensiFASt No-ROX mix (Bioline) on a Rotorgene 3000 (Corbett). Details of primers can be found in Table 1. Cycling conditions were 95 degrees for 3 minutes, then 40 cycles of 95 degrees for 5 seconds, annealing for 10 seconds and extension at 72 degrees for 15 seconds then acquisition for 15 seconds. PCR reaction mix conditions and individual cycling details are in Table 2. Raw data into LinRegPCR 2012.x (Ruijter, Ilgun and Gunst 2009) for regression analysis. The one point calibration (OPC) method described in Brankatschk et al. (2012) was used to calculate the copy numbers in each sample.\n\nMeasurement of total petroleum hydrocarbons.\nAll extractions were performed as described by (Schafer, Snape and Siciliano 2007). Samples were analysed using an Agilent 6890 GC (Agilent, Palo Alto, CA, USA) with flame ionization detection. The column used was a Capillary Column (not installed) BP-1(length, 25m; inner diameter, 0.22 mm; film thickness, 0.25 microns; SGC International, Melbourne, VIC, Australia). Samples wre introduced with a 15:1 split ratio into a focus liner (SGE International) at 310 degrees. GC oven program was 50 degrees for 3 minutes, then a ramp of 18 degrees /min to 320 degrees, and a final hold of 10 minutes. Helium was used as a carrier gas, with an initial flow rate of 1.3mL/min for 17 minutes, then a ramp increase of 0.25mL/min up to 3.0mL/min, with a final hold time of 7.00mins. The temperature of the flame ionization detector was 330 degrees. Total signal in the C9-C28 range were measured.", "links": [ { diff --git a/datasets/AAS_4100_MI_marine_Cu_multiple_stressor_1.json b/datasets/AAS_4100_MI_marine_Cu_multiple_stressor_1.json index 5c01f7bef8..589fe5b39f 100644 --- a/datasets/AAS_4100_MI_marine_Cu_multiple_stressor_1.json +++ b/datasets/AAS_4100_MI_marine_Cu_multiple_stressor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4100_MI_marine_Cu_multiple_stressor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Study location and test species\nSubantarctic Macquarie Island lies in the Southern Ocean, just north of the Antarctic Convergence at 54 degrees 30' S, 158 degrees 57' E. Its climate is driven by oceanic processes, resulting in highly stable daily and inter-seasonal air and sea temperatures (Pendlebury and Barnes-Keoghan, 2007). Temperatures in intertidal rock pools (0.5 to 2 m deep) were logged with Thermochron ibuttons over two consecutive summers and averaged 6.5 (plus or minus 0.5) degrees C. The island is relatively pristine and in many areas there has been no past exposure to contamination. To confirm sites used for invertebrate collections were free from metal contamination, seawater samples were taken and analysed by inductively coupled plasma optical emission spectrometry (ICP-OES; Varian 720-ES; S1)\n\nThe four invertebrate species used in this study were drawn from a range of taxa and ecological niches (Figure 1). The isopod Limnoria stephenseni was collected from floating fronds of the kelp Macrosystis pyrifera, which occurs several hundred meters offshore. The copepod Harpacticus sp. and bivalve Gaimardia trapesina were collected from algal species in the high energy shallow, subtidal zone. Finally, the flatworm Obrimoposthia ohlini was collected from the undersides of boulders throughout the intertidal zone. We hypothesised L. stephenseni would be particularly sensitive to changes in salinity and temperature due to its distribution in the deeper and relatively stable subtidal areas, while O. ohlini would be less sensitive due to its distribution high in the intertidal zone and exposure to naturally variable conditions. We reasoned that the remaining two species, G. trapesina, and Harpacticus sp. were intermediate in the conditions to which they are naturally exposed and hence would likely be intermediate in their response. \n\nTest procedure\nThe combined effect of salinity, temperature and copper on biota was determined using a multi-factorial design. A range of copper concentrations were tested with each combination of temperatures and salinities, so that there were up to 9 copper toxicity tests simultaneously conducted per species (Table 1). Experiments on L. stephenseni and Harpacticus sp. were done on Macquarie Island within 2 to 3 days of collection, during which they were acclimated to laboratory conditions. While, G. trapesina and O. ohlini were transported by ship to Australia in a recirculating aquarium system and maintained in a recirculating aquarium at the Australian Antarctic Division in Hobart, both at 6 degreesC. These two taxa were used in experiments within 3 months of their collection. A limited number of G. trapesina and O. ohlini were available, resulting in fewer combinations of stressors tested. \n\nControls for the temperature and salinity treatments were set at ambient levels of 35 plus or minus 0.1 ppt and 5.5 to 6 degreesC for all species. The lowered control temperature for the bivalve reflected the cooler seasonal temperatures at time of testing and lower position within the intertidal. Previous tests conducted under these ambient conditions provided information on the ranges of relevant copper concentrations, appropriate test durations, and water change regimes for each taxon (Holan et al., 2017, Holan et al., 2016b). From these previous studies, we determined that a test duration of 14 d was sometimes required with 7 d often being the best outcome for most species due to high control survival and sufficient response across concentrations. The bivalve G. trapesina was an exception to this due to unfavourable water quality after 3 days in previous work (Holan et al., 2016). For the other three species, this longer duration for acute tests, compared to tests with tropical and temperature species (24 to 96 h) was consistent with previous Antarctic studies that have required longer durations in order to elicit an acute response in biota (King and Riddle, 2001, Marcus Zamora et al., 2015, Sfiligoj et al., 2015). Experimental variables (volume of water, density of test organisms, copper concentrations, temperatures and salinities) differed for each experiment due to differences between each species (Table 1). The temperature increases that were tested (2 to 4 degreesC) reflected the increased sea and air temperatures predicted for the region tested by 2100 (Collins et al., 2013). \n\nTreatments were prepared 24 h prior to the addition of animals. Seawater was filtered to 0.45 microns and water quality was measured using a TPS 90-FL multimeter at the start and end of tests. Dissolved oxygen was greater than 80% saturation and pH was 8.1 to 8.3 at the start of tests. All experimental vials and glassware were washed with 10% nitric acid and rinsed with MilliQ water three times before use. Salinity of test solutions was prepared by dilution through the addition of MilliQ water. Copper treatments using the filtered seawater at altered salinities were prepared using 500mg/L CuSO4 (Analytical grade, Univar) in MilliQ water stock solution. Samples of test solutions for metal analysis by ICP-OES were taken at the start and end of tests (on days 0 and 14). Details of ICP-OES procedures are described in the Supplemental material (S4). Samples were taken using a 0.45 \u00b5m syringe filter that had been acid and Milli-Q rinsed. Samples were then acidified with 1% diluted ultra-pure nitric acid (65% Merck Suprapur). Measured concentrations at the start of tests were within 96% of nominal concentrations. In order to determine approximate exposure concentrations for each treatment, we averaged the 0 d and 14 d measured concentrations (Table 1). Tests were conducted in temperature controlled cabinets at a light intensity of 2360 lux. At the Macquarie Island station a light-dark regime of 16:8 h was used to mimic summer conditions. In the laboratories in Kingston, Australia, a 12:12 h regime was used to simulate Autum light conditions (as appropriate for the time of testing). Test individuals were slowly acclimated to treatment temperatures over 1 to 2 h before being added to treatments. Temperatures were monitored using Thermochron ibutton data loggers within the cabinets for the duration of the tests. \n\nDetermination of mortality of individuals differed for each taxon. Mortality was recorded for Gaimardia trapesina when shells were open due to dysfunctional adductor muscles; for Obrimoposthia ohlini when individuals were inactive and covered in mucous; for Limnoria stephenseni when individuals were inactive after gentle stimulation with a stream of water from a pipette; and for Harpacticus sp. when urosomes were perpendicular to prosomes (as used in other studies with copepods; see Kwok and Leung, 2005). All dead individuals were removed from test vials.", "links": [ { diff --git a/datasets/AAS_4100_Statistical-Modelling-Methods_2019_A.Proctor_1.json b/datasets/AAS_4100_Statistical-Modelling-Methods_2019_A.Proctor_1.json index 50fbf9d442..6ecd04aec3 100644 --- a/datasets/AAS_4100_Statistical-Modelling-Methods_2019_A.Proctor_1.json +++ b/datasets/AAS_4100_Statistical-Modelling-Methods_2019_A.Proctor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4100_Statistical-Modelling-Methods_2019_A.Proctor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract from submitted PhD thesis: \nIn the field of ecotoxicology, which studies the fate and effects of contaminants on biota, concentration-response experiments (toxicity tests) are conducted to determine the sensitivity of a single species to a toxicant. Critical Effect Concentrations (CECs) are estimated from the results of toxicity tests, to provide a measure of the tolerance threshold for that species. Once CECs have been generated for a sufficient number of taxa, the values are then used to establish a distribution of sensitivity estimates for the ecosystem, known as a species sensitivity distribution (SSD). It is from a SSD that environmental guidelines values (GVs) are frequently derived by estimating the Protective Concentration for x% of the community (PCx). \nSuccess in GV derivation requires the development and application of statistical approaches that improve the interpretation and application of ecotoxicological research. The methods we use to analyze ecotoxicological data to obtain CECs, together with the methods used to derive SSDs, impact the quality of the derived GVs. As such, reliable, user-friendly, and accurate statistical methods are critical to ensuring derived GVs are effective for environmental protection. \nIn this thesis, I focus on three different areas to improve the analysis and modeling of ecotoxicological data. First, I investigate how additional stressors, such as differing environmental conditions, can be incorporated into traditional dose-response modeling. Second, I investigate the use of alternate methods to calculate CECs to improve the analysis of data from tests with extended exposure durations. Lastly, I present three new approaches to constructing SSDs, the first approach integrates variation around each CEC estimate via the direct integration of raw toxicity test data. The second and third approaches are an extension of the presented integrated model with the use of a heavy-tailed distribution and the use of a truncated distribution. \nToxicity tests typically investigate the response of a single species to a single contaminant under standardized and optimized environmental conditions in the laboratory. However, organisms are rarely exposed to chemical or environmental stressors in isolation. Multiple stressor experiments provide a method to study how environment variability (i.e. temperature, pH, and salinity) can alter an organism's response to a contaminant. Yet, there is no standardized statistical method that allows you to easily incorporate these additional stressors into doseresponse regression, the most commonly used toxicity analysis method. \nIn Chapter 2, I present an extended dose-response regression method that simultaneously calculates Lethal Concentration estimate for x% of the population (LCx), with integrated handling of control mortality, for each stressor combination studied. The outcome of this model is a consistent framework to provide interpretable results that meaningfully deal with environmental variables and their possible impacts on the LCx estimates. To provide easy access to this model, it was incorporated into an R-package. \nWe illustrate this method with data for a subantarctic marine invertebrate, to investigate its response to copper under levels of increasing temperature and decreasing salinity. These environmental conditions, intended to reflect future climate change scenarios, have the potential to impact the survival of individuals exposed to copper. The use of our model reveals that, while the additional stressors were not found to interact, a punctuated increase in temperature contributed to a significant decrease in the LCx estimate (indicating increased sensitivity). \nWhile dose-response regression is the main methodology to analyze ecotoxicological data, its resulting metric of sensitivity, the EC/LCx, is criticized for its dependency on exposure duration. The No Effect Concentration (NEC) is widely suggested as an improvement to the EC/LCx, as it represents a concentration threshold below which no effect occurs, irrespective of the exposure duration. \nThere are two currently proposed dose response analysis methods to calculate NECs. One method uses segmented regression to estimate an NEC in an empirical model, the other uses a mechanistic, toxicokinetic-toxicodynamic (TKTD) model to parameterize the time course of survival. To date, the use of either of these NEC models has been limited, due the increase in computational complexity and lack of user friendly software packages or code. \nIn Chapter 3, I compare NEC estimates from the two model types to LCx estimates from traditional dose-response regression. To do this, I use survival data through time for four Antarctic marine invertebrates in response to copper. For Antarctic biota, toxicity tests are conducted at low temperatures and typically require an extended exposure to illicit an acute response, with tested durations regularly extending up to 42 days. Without knowledge of the life history of Antarctic biota and the likely duration and nature of exposure they would experience in situ, EC/LCx values are limited in their ecological relevance. \nThe use of NEC models with Antarctic data shows that TKTD models provide an NEC and have the potential to provide information about the biological response of individuals. However, they are computationally difficult. Segmented regression provides an adequate approximation, assuming the NEC estimated from the mechanistic model is a true threshold. I also find that LCx values estimated from the later observation times, are generally similar to NECs. This is likely due to LCx values decreasing (indicating an increasing sensitivity) with time until an asymptotic, incipient value is reached. \nThis work highlights the time dependency of CEC values in the derivation of guidelines, especially for Polar Regions where the response of organisms is slow. In all regions, without the use of extended toxicity tests, the use of dose-response regression may over-estimate CECs, unless the likely in situ exposure duration is known. However, the use of dose-response regression may be reasonable if toxicity tests are extended until an incipient LCx can be estimated or extrapolated. \nTypically, inclusion of sensitivity estimates (CECs) into a SSD is currently limited in that only the mean point estimate for a species is used. Any variation around the data point is not included. The effect of incorporating this variation into SSDs has been little studied, despite being a possible improvement in the derivation of GVs. \nIn Chapters 4 and 5, I present three new approaches to constructing SSDs to include estimates of variation. In Chapter 4, I look at the integration of the analysis of raw dose-response data into the construction of SSDs. The addition of CEC variation into the SSD, using simulated data, did not greatly change the resulting distribution nor the PC values estimated from them. The lack of difference in results is likely due to the simulation of data that meets the assumptions of the distribution. Chapter 5 presents an extension to the integrated Bayesian SSD, which uses a truncated distribution to fit data below the mean CEC estimate. Often the upper tail of the distribution on the right, where the most tolerant species lie, affects the fit of the distribution at the lower tail on the left. A truncated SSD, estimated with a heavy tailed t-distribution, proved to be a reliable estimator of PCx values when fit to data simulated to represent a range of scenarios intended to reflect commonly encountered characteristics of SSD data sets. The truncated distribution allows better focus on the distributions below the median where high PC values for 90, 95, or 99% of species (PC90, PC95, or PC99) are estimated. \nBy improving the tools used to analyze toxicity data we not only improve our understanding of the fate and effects of contaminants but provide more reliable information for the derivation of environmental GVs. The work presented in this thesis describes important improvements in statistical modeling tools in ecotoxicology, which incorporate ecological relevancy into LCx estimates, show reduced time-dependency in CECs, and add flexibility and robustness into the construction of SSDs. This work contributes to improving methods in risk assessments by providing more accurate CECs and improved methodologies for guideline derivation for environmental protection.", "links": [ { diff --git a/datasets/AAS_4102_2012_Blue_Whale_Voyages_1.json b/datasets/AAS_4102_2012_Blue_Whale_Voyages_1.json index 9a9529b899..333ea77f1b 100644 --- a/datasets/AAS_4102_2012_Blue_Whale_Voyages_1.json +++ b/datasets/AAS_4102_2012_Blue_Whale_Voyages_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_2012_Blue_Whale_Voyages_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record is a parent for all data collected during the 2012 Blue whale voyages. Description of specific data sets can be found within child datasets.", "links": [ { diff --git a/datasets/AAS_4102_2013_Antarctic_Blue_Whale_Voyage_1.json b/datasets/AAS_4102_2013_Antarctic_Blue_Whale_Voyage_1.json index 8c58f118d6..aca5942924 100644 --- a/datasets/AAS_4102_2013_Antarctic_Blue_Whale_Voyage_1.json +++ b/datasets/AAS_4102_2013_Antarctic_Blue_Whale_Voyage_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_2013_Antarctic_Blue_Whale_Voyage_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record is a parent for all data collected during the 2013 Antarctic Blue Whale Voyage. Description of specific data sets can be found in the Voyage Science Plan and within child datasets.", "links": [ { diff --git a/datasets/AAS_4102_4104_4050_EchoviewR_1.json b/datasets/AAS_4102_4104_4050_EchoviewR_1.json index 359bd9c43b..65d22d6586 100644 --- a/datasets/AAS_4102_4104_4050_EchoviewR_1.json +++ b/datasets/AAS_4102_4104_4050_EchoviewR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_4104_4050_EchoviewR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a supplement to the R package, EchoviewR. EchoviewR is a free software package that acts as an interface between R and Echoview. It uses Component Object Model scripting to enable automated processing of active acoustic data. This data set contains the data necessary to run the vignette tutorials and package examples. \n\nThe .raw files are acoustic data collected using an EK60 echosounder. They are a subset of the full acoustic data collected on the Krill Acoustic and Oceanographic Survey (KAOS) off Antarctica in the summer of 2003. The .EV template file was created using Echoview v6.1. The .ecs calibration file, .evl line object file and .evr region files are for use with this template. The region files designate off transect regions. The three pdf vignettes contain examples of reading data using EchoviewR, conducting school detection and running biomass estimation of Antarctic Krill. These data are intended only as a supplement to demonstrate the use of EchoviewR.\n\nThis data is a subset of the KAOS data and as such, must NOT be used to formally estimate krill biomass.\n\nThese data are a subset of data described in the metadata record at the provided URL.", "links": [ { diff --git a/datasets/AAS_4102_AcousticEventLog2013_1.json b/datasets/AAS_4102_AcousticEventLog2013_1.json index 6407ca617a..5c3f2a46b3 100644 --- a/datasets/AAS_4102_AcousticEventLog2013_1.json +++ b/datasets/AAS_4102_AcousticEventLog2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_AcousticEventLog2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. The acoustic tracking software, difarBSM, stored processed bearings from acoustic events and cross bearings in tab delimited text files. Each event was assigned a classification by the acoustician, and events for each classification were stored in separate text files. The first row in each file contains the column headers, and the content of each column is as follows:\n\nbuoyID:\tBuoy ID number is the number of the sonobuoy on which this event was detected. This can be used as a foreign key to link to the sonobuoy deployment log. \n\ntimeStamp_matlabDatenum:\tDate and time (UTC) at the start of the event represented as a Matlab datenum (i.e. number of days since Jan 0 0000). \n\nLatitude:\tLatitude of the sonobuoy deployment in decimal degrees. Southern hemisphere latitudes should be negative.\n\nLongitude:\tLongitude of sonobuoy deployment in decimal degrees. Western hemisphere longitudes should be negative.\n\nAltitude:\tDepth of the sonobuoy deployment in metres. For DIFAR sonobuoys either 30, 120 or 300.\n\nmagneticVariation_degrees:\tThe estimated magnetic variation of the sonobuoy in degrees at the time of the event. Positive declination is East, negative is West. At the start of a recording this will be entered from a chart. As the recording progresses, this should be updated by measuring the bearing to the vessel.\n\nbearing_degreesMagnetic:\tMagnetic bearing in degrees from the sonobuoy to the acoustic event. Magnetic bearings were selected by the acoustician by choosing a single point on the bearing-frequency surface (AKA DIFARGram) produced by the analysis software difarBSM. \n\nfrequency_Hz:\tThe frequency in Hz of the magnetic bearing that the acoustician selected from the bearing-frequency surface (DIFARGram).\n\nlogDifarPower:\tThe base 10 logarithm of the height of the point on the DIFARGram\n\nreceiveLevel_dB:\tThis column contains an estimate of the The RMS receive level (dB SPL re 1 micro Pa) of the event. Received levels were estimated by applying a correction for the shaped sonobuoy frequency response, the receiver\u2019s frequency response, and were calculated over only the frequency band specified in each classification (see below).\n\nsoundType:\tsoundType is the classification assigned to the event by the acoustician.\n\nAnalysis parameters for each classification are included in the csv file classificationParameters.txt. The columns of this file are as follows:\n\noutFile:\tThe name of the tab-separated text file that contains events for this classification.\nanalysisType:\tA super-class describing the broad category of analysis parameters \nsoundType:\tThe name of the classification\nsampleRate:\tWhen events are processed, they are downsampled to this sample rate (in Hz) in order to make directional processing more efficient and precise\nFFTLength:\tThe duration (in seconds) used for determining the size of the FFT during difar beamforming (i.e. creation of the DIFARGram). \nnumFreqs:\tNot used during this voyage\ntargetFreq:\tThe midpoint of the frequency axis (in Hz) displayed in the DIFARGram\nBandwidth:\tThis describes the half-bandwidth (Hz) of the frequency axis of the DIFARGram. The frequency axis of the DIFARGram starts at targetFreq-bandwidth and ends at targetFreq + bandwidth\nfrequencyBands_1:\tThe lower frequency (Hz) used for determining RMS received level.\nfrequencyBands_2:\tThe upper frequency (Hz) used for determining RMS received level.\npreDetect:\tDuration of audio (in seconds) that will be loaded before the start of the event. The processed audio includes the time-bounds of the event marked by the acoustician as well as preDetect seconds before the start of the event.\npostDetect:\tDuration of audio (in seconds) that will be loaded after the end of the event. The processed audio includes the time-bounds of the event marked by the acoustician + postDetect seconds.", "links": [ { diff --git a/datasets/AAS_4102_AcousticGPSData2013_1.json b/datasets/AAS_4102_AcousticGPSData2013_1.json index a954fe2559..a11f6d8e01 100644 --- a/datasets/AAS_4102_AcousticGPSData2013_1.json +++ b/datasets/AAS_4102_AcousticGPSData2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_AcousticGPSData2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS data were recorded on the Sonobuoy Workstation as daily text files containing the raw NMEA 0183 sentences from an independent Garmin GPS receiver located at the acoustic workstation.", "links": [ { diff --git a/datasets/AAS_4102_AcousticTrackingLog2013_1.json b/datasets/AAS_4102_AcousticTrackingLog2013_1.json index cfb07c249a..41fd7073f2 100644 --- a/datasets/AAS_4102_AcousticTrackingLog2013_1.json +++ b/datasets/AAS_4102_AcousticTrackingLog2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_AcousticTrackingLog2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. A written summary of the event log was recorded during data collection at approximately 15 minute intervals, and this summary comprises the Whale Tracking Log. \n\n- The acoustician on-duty recorded the average bearings or locations of each calling whale/group every 15 minutes in the written Whale Tracking Log. \n- Entries in the written Sonobuoy Tracking Log (on the bench in the acoustics workstation) also include total number of different whales heard in that 15 minute interval.\n- If multiple whales/groups were detected, then the acoustician on-duty, in consultation with the lead acoustician and/or voyage management designateded one of the whales the 'target' whale, and attempted to encounter this target first. \n- When targeting a whale/group, the acoustician on-duty continued to track all other whales/groups in the area as these tracked whales/groups may become the next target after obtaining concluding with the current target. \n\nDate: (UTC) written only at top of datasheet\n\nTime: (UTC) on the hour, 15 past, half past, and 15 to.\n\nTrack: Unique identifier for each whale/group tracked in the past 15 minutes. \nEach track will have:\n\nLocation:\tEither an average bearing from a sonobuoy (eg 220 degrees from SB18) or a Lat/Lon from the most recent triangulation\n\nNotes:\tWhat is the vessel action with respect to this tracked whale/group? (eg. Is this the current or previous 'target'? Are we presently photographing this whale? Did we finish photographing the whale?) Has the whale gone silent? Has this track crossed paths with another?", "links": [ { diff --git a/datasets/AAS_4102_KrillAcoustics_2015_1.json b/datasets/AAS_4102_KrillAcoustics_2015_1.json index 18d46a9e81..29b9eeee26 100644 --- a/datasets/AAS_4102_KrillAcoustics_2015_1.json +++ b/datasets/AAS_4102_KrillAcoustics_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_KrillAcoustics_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Echosounder data were collected on a multidisciplinary research voyage conducted from the RV Tangaroa, operated by New Zealand\u2019s National Institute of Water and Atmospheric Research Limited (NIWA). The voyage lasted 42 days, departing from Wellington, New Zealand on January 29th , 2015 and returning to the same port on 11th March 2015.\nActive acoustic data were obtained continuously using a calibrated scientific echosounder (Simrad EK60, Horten, Norway). The echosounder operated at 38 and 120 kHz for the duration of the voyage with a pulse duration of 1.024 ms, a pulse repetition rate of one ping per second and a 7\u00b0 beam width. The echosounder data here are a subset of that collected throughout the voyage and include only data from south of 65\u00b0S. This subset of data focuses on research questions pertaining to Antarctic blue whales and krill.", "links": [ { diff --git a/datasets/AAS_4102_all_photo_ID_images_2012_1.json b/datasets/AAS_4102_all_photo_ID_images_2012_1.json index dc0e44d17e..2a0fb66366 100644 --- a/datasets/AAS_4102_all_photo_ID_images_2012_1.json +++ b/datasets/AAS_4102_all_photo_ID_images_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_all_photo_ID_images_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All photos taken during the two Blue whale voyages undertaken in January and March 2012 in an attempt to get a best photo identification image of pygmy blue whales. \n\nWhales from the January voyage are numbered sequentially beginning with 1; whales from the March voyage are numbered sequentially beginning with 101. The folder contains a best left side and a best right side photo of each whale (if available). Identification photos of whales where a dorsal fin was not visible are included only if there was a dorsal fin visible in a good identification photo of the other side of the whale.\n\nPhoto filenames include the photographer\u2019s initials:\nCJ = Catriona Johnson\nDD = Dave Donnelly\nMD = Mike Double\nJS = Josh Smith\nNS = Nat Schmitt\nPE = Paul Ensor\nPO = Paula Olson\nRS = Rob Slade\nVAG = Virginia Andrews-Goff", "links": [ { diff --git a/datasets/AAS_4102_all_photo_ID_images_2013_1.json b/datasets/AAS_4102_all_photo_ID_images_2013_1.json index 2dd49880f1..abad9b4fb5 100644 --- a/datasets/AAS_4102_all_photo_ID_images_2013_1.json +++ b/datasets/AAS_4102_all_photo_ID_images_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_all_photo_ID_images_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All photos taken during the Antarctic blue whale voyage in an attempt to get a best photo identification image of Antarctic blue whales, pygmy blue whales, killer whales, right whales and humpback whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet.", "links": [ { diff --git a/datasets/AAS_4102_all_photo_ID_images_2015_1.json b/datasets/AAS_4102_all_photo_ID_images_2015_1.json index d0646e64ad..96a8476eef 100644 --- a/datasets/AAS_4102_all_photo_ID_images_2015_1.json +++ b/datasets/AAS_4102_all_photo_ID_images_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_all_photo_ID_images_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All photos taken during the NZ-Australia Antarctic Ecosystems Voyage to the Ross Sea 2015 in an attempt to get a best photo identification image of blue whales, killer whales, humpback whales and minke whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet.", "links": [ { diff --git a/datasets/AAS_4102_longTermAcousticRecordings_3.json b/datasets/AAS_4102_longTermAcousticRecordings_3.json index c7158fe557..bb54089cf6 100644 --- a/datasets/AAS_4102_longTermAcousticRecordings_3.json +++ b/datasets/AAS_4102_longTermAcousticRecordings_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_longTermAcousticRecordings_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains long-term underwater acoustic recordings made under Australian Antarctic Science Projects 4101 and 4102, and the International Whaling Commission\u2019s Southern Ocean Research Partnership (IWC-SORP) Southern Ocean Hydrophone Network (SOHN). \nCalibrated measurements of sound pressure were made at several sites across several years using custom moored acoustic recorders (MARs) designed and manufactured by the Science Technical Support group of the Australian Antarctic Division. These moored acoustic recorders were designed to operate for year-long, deep-water, Antarctic deployments. Each moored acoustic recorder included a factory calibrated HTI 90-U hydrophone and workshop-calibrated frontend electronics (hydrophone preamplifier, bandpass filter, and analog-digital converter), and used solid state digital storage (SDHC) to reduce power consumption and mechanical self-noise (e.g. from hard-drives with motors and rotating disks). Electronics were placed in a glass instrumentation sphere rated to a depth of 6000 m, and the sphere was attached to a short mooring with nylon straps to decouple recorder and hydrophone from sea-bed. The hydrophone was mounted above the glass sphere with elastic connections to the mooring frame to reduce mechanical self-noise from movement of the hydrophone. The target noise floor of each recorder was below that expected for a quiet ocean at sea state zero. The analog-digital converter, based on an AD7683B chip, provides 100 dB of spurious free dynamic range, but a total signal-to-noise and distortion of 86 dB which yields 14 effective bits of dynamic range at a 1 kHz input frequency. \n The data for each recording site comprise a folder of 16-bit WAV audio files recorded at a nominal sample rate of 12 kHz. The names of each WAV file correspond to a deployment code followed by the start time (in UTC) of the file as determined by the microprocessor\u2019s real-time clock e.g. 201_2013-12-25_13-00-00.wav would correspond to a wav file with deployment code 201 that starts at 1 pm on December 25th 2013 (UTC). \nRecording locations were chosen to correspond to sites used during AAS Project 2683. These sites were along the resupply routes for Australia\u2019s Antarctic stations, and typically there was only one opportunity to recover and redeploy MARs each year.", "links": [ { diff --git a/datasets/AAS_4102_sat_tag_1.json b/datasets/AAS_4102_sat_tag_1.json index 6a7bd9c35e..a9f70e0243 100644 --- a/datasets/AAS_4102_sat_tag_1.json +++ b/datasets/AAS_4102_sat_tag_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4102_sat_tag_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the deployment metadata for satellite tag deployments during the Antarctic blue whale voyage 2013. Specifically, this file contains:\nArgos Number \u2013 the platform transmitting terminal identification number assigned by Argos\t\nDate (UTC)\t\nTime (UTC)\t\nLocation (at deployment)\t\nField trip (field trip identifier)\t\nDeployment Lat\titude\nDeployment Longitude\t\nSpecies\t\nSex (as determined via biopsy sample analysis)\t\nBody condition\t\nMaturity\t\nGroup Size\t\nInitial Activity\t\nDeployment Method (used to deploy satellite tag)\t\nAirgun Pressure (bar)\t\nShot distance (m)\t\n%age Implanted (percentage of tag implanted \u2013 100% = full implantation)\t\nReaction (to tagging)\t\nBoat driver\nTag Shooter\t\nBiopsy Shooter\t\nFilmed?\t\nPhoto Id taken?\t\nFrame number\t(of photo ID image)\nBiopsy taken?\t\nBiopsy ID (sample identification number)", "links": [ { diff --git a/datasets/AAS_4116_Coastal_Complexity_1.json b/datasets/AAS_4116_Coastal_Complexity_1.json index 11934a57b9..702efede04 100644 --- a/datasets/AAS_4116_Coastal_Complexity_1.json +++ b/datasets/AAS_4116_Coastal_Complexity_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Coastal_Complexity_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic outer coastal margin (i.e., the coastline itself, or the terminus/front of ice shelves, whichever is adjacent to the ocean) is the key interface between the marine and terrestrial environments. Its physical configuration (including both length scale of variation and orientation/aspect) has direct bearing on several closely associated cryospheric, biological, oceanographical and ecological processes, yet no study has quantified the coastal complexity or orientation of Antarctica\u2019s coastal margin. This first-of-a-kind characterisation of Antarctic coastal complexity aims to address this knowledge gap. We quantify and investigate the physical configuration and complexity of Antarctica\u2019s circumpolar outer coastal margin using a novel, technique based on ~40,000 random points selected along a vector coastline derived from the MODIS Mosaic of Antarctica dataset. At each point, a complexity metric is calculated at length scales from 1 to 256 km, giving a multiscale estimate of the magnitude and direction of undulation or complexity at each point location along the entire coastline.\n \nGeneral description of the data\n--------------------------------------------\nA shapefile of ~40,000 random points selected along a vector coastline derived from the MODIS Mosaic of Antarctica dataset. At each point coastal complexity is calculated including magnitude and orientation at multiple scales and features such as bays and peninsulas identified. The structure of the dataset is as follows:\n\nFields Definitions\n--------------------------------------------------------\nSTATION\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Station number\nEASTING\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Easting Polar Stereographic\nNORTHING\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Northing Polar Stereographic\nX_COORD\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.X geographic coordinate \nY_COORD\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Y geographic coordinate\nCOAST_EDGE\u2026\u2026\u2026\u2026\u2026\u2026.Type of coast \u2018Ice shelf/Ground\u2019\n*FEAT_01KM \u2013 256KM\u2026\u2026...Described feature \u2018Bay/Peninsula\u2019\n*AMT_01KM \u2013 256KM\u2026\u2026\u2026.Measure of complexity, Angled Measurement Technique 0-180 degrees\n*MAG_01KM \u2013 256KM\u2026\u2026\u2026Measure of complexity - Magnitude on dimensionless scale 0-100 \n*ANG_01KM \u2013 256KM\u2026\u2026\u2026Angle (absolute angle of station points from reference 0, 0) \n*ANGR_01KM \u2013 256KM\u2026.\u2026Angle of \u2018Magnitude\u2019 (relative to coastline - directly offshore being 0/360\u00b0)\n\n*Repeated for length scales 1, 2, 4, 8, 16, 32, 64, 128 and 256 kms at each point", "links": [ { diff --git a/datasets/AAS_4116_Coastal_Exposure_1.json b/datasets/AAS_4116_Coastal_Exposure_1.json index 4d534e0668..c352940c39 100644 --- a/datasets/AAS_4116_Coastal_Exposure_1.json +++ b/datasets/AAS_4116_Coastal_Exposure_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Coastal_Exposure_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a simple index which looks at the 360x1-degree longitudinal wedges around the Antarctic continent to see if there is any sea ice (where sea ice concentration is greater than 15%) to the north of the continent in each of these wedges. \nThe index goes from 0 (sea ice to the north off the continent in every longitude wedge) to 360 (no sea ice around the continent at all.\n\nNotes about the spreadsheet:\n\"-\" means no data. Satellite data was not available for those years.\n\nOtherwise the index goes from 0 through to 360. \n- Zero means that there is no longitude around the continent where there is coastal exposure.\n- 18 (for example) means that there are 18 longitudinal wedges around the continent with coastal exposure.\n\nThis project used the following NASA data to develop the coastal exposure index:\n\nCavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. [1979-2015]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/8GQ8LZQVL0VL. [2016-05-30]", "links": [ { diff --git a/datasets/AAS_4116_Fraser_fastice_circumantarctic_2.2.json b/datasets/AAS_4116_Fraser_fastice_circumantarctic_2.2.json index dd4f35f109..d70d471b5b 100644 --- a/datasets/AAS_4116_Fraser_fastice_circumantarctic_2.2.json +++ b/datasets/AAS_4116_Fraser_fastice_circumantarctic_2.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Fraser_fastice_circumantarctic_2.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (provided as a series of CF-compatible netcdf file) consists of 432 consecutive maps of Antarctic landfast sea ice, derived from NASA MODIS imagery. There are 24 maps per year, spanning the 18 year period from March 2000 to Feb 2018.\nThe data are provided in a polar stereographic projection with a latitude of true scale at 70 S (i.e., to maintain compatibility with the NSIDC polar stereographic projection).", "links": [ { diff --git a/datasets/AAS_4116_Fraser_fastice_mawson_capedarnley_1.json b/datasets/AAS_4116_Fraser_fastice_mawson_capedarnley_1.json index c614399efc..89384bece9 100644 --- a/datasets/AAS_4116_Fraser_fastice_mawson_capedarnley_1.json +++ b/datasets/AAS_4116_Fraser_fastice_mawson_capedarnley_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Fraser_fastice_mawson_capedarnley_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landfast sea ice (also known as fast ice) is sea ice which is mechanically fastened against the coast and/or grounded icebergs. Its extent is highly variable on a range of time-scales, but it tends to recur in certain locations based upon the location of grounded icebergs. In Antarctica, it tends to form between the coast and the ~400 m isobath (which approximately defines the depth in which icebergs can ground).\nThis dataset (in NetCDF format) contains a time series of 336 consecutive 15-day 'composite' images of landfast sea ice extent along the Mawson Coast to Cape Darnley, East Antarctica. The data period is from March 2000 (when Terra MODIS was commissioned) to March 2014, i.e., a span of 14 years. Fast ice data were retrieved from MODIS satellite imagery, based on a semi-automated fast ice edge retrieval algorithm.\nAlso included in the NetCDF file are projection parameters, latitude/longitude arrays and timestamps.", "links": [ { diff --git a/datasets/AAS_4116_IceShelfStudy_1.json b/datasets/AAS_4116_IceShelfStudy_1.json index 76eb51000c..00a096b49f 100644 --- a/datasets/AAS_4116_IceShelfStudy_1.json +++ b/datasets/AAS_4116_IceShelfStudy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_IceShelfStudy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are from our Nature Article from June 2018: \"Antarctic ice shelf disintegration triggered by sea ice loss and ocean swell\".\n\nThe abstract is:\n\"Understanding the causes of recent catastrophic ice shelf disintegrations is a crucial step towards improving coupled models of the Antarctic Ice Sheet and predicting its future state and contribution to sea-level rise. An overlooked climate-related causal factor is regional sea ice loss. Here we show that for the disintegration events observed (the collapse of the Larsen A and B and Wilkins ice shelves), the increased seasonal absence of a protective sea ice buffer enabled increased flexure of vulnerable outer ice shelf margins by ocean swells that probably weakened them to the point of calving. This outer-margin calving triggered wider-scale disintegration of ice shelves compromised by multiple factors in preceding years, with key prerequisites being extensive flooding and outer-margin fracturing. Wave-induced flexure is particularly effective in outermost ice shelf regions thinned by bottom crevassing. Our analysis of satellite and ocean-wave data and modelling of combined ice shelf, sea ice and wave properties highlights the need for ice sheet models to account for sea ice and ocean waves.\"\n\nDetails of the analyses and data used, and the data generated by this study, are given in the paper: https://www.nature.com/articles/s41586-018-0212-1.\n\nCode availability: Analytical scripts used in this study are freely available from the authors\nvia the corresponding author upon reasonable request.\n\nData availability: The datasets and products generated during the current study are available from the corresponding author on reasonable request.\n\nThe datasets forming the basis of the study are available as follows:\n\n(1) Sea ice: Daily estimates of satellite-derived sea ice concentration (gridded at a spatial\nresolution of 25 x 25 km) derived by the NASA Bootstrap algorithm for the period 1979-2010 were obtained from the US National Snow and Ice Data Center (NSIDC) dataset at:\nhttp://nsidc.org/data/NSIDC-0079. Accessed August 2015.\n\n(2) Waves: Ocean wave-field data were obtained from the CAWCR (Collaboration for\nAustralian Weather and Climate Research) Wave Hindcast 1979\u20132010 dataset run on a 0.4 x 0.4\u00b0 global grid: https://doi.org/10.4225/08/523168703DCC5. Accessed September 2017.\n\n(3) Satellite visible and thermal infrared imagery of ice shelves and disintegration events: The NOAA AVHRR image of the Larsen1995 disintegration used in Figure 2 was obtained from the British Antarctic Survey: http://www.nerc-bas.ac.uk/icd/bas_publ.html. Accessed June 2015.\nMODIS visible and 839 thermal infrared imagery from the US NSIDC archive at:\nhttp://nsidc.org/data/iceshelves_images/. Accessed June 2012.\n\nThe study involved 2 model components, and model output is described below. The 2 models are: (i) a model of ocean swell attenuation by sea ice; and (ii) an ice shelf-ocean wave interaction model. Descriptions of both are given in the Nature paper (Methods section).\n\nDESCRIPTIONS OF THE 13 INDIVIDUAL DATA FILES PROVIDED (NB DESCRIPTIONS OF DATASETS GENERATED RELATIVE TO THE FIGURES) ARE GIVEN IN THE FILES:\n(1) Source data for Figures 4 (parts a-d), 5 and 6a are given in Excel spreadsheet files \"Source-Data_2017-07-09041A_Figure.....xlsx\".\n\n(2) Source data for Extended Data Figures 1 (parts a-b), 3 (parts b,d and parts a,c), 4 (parts b,d and a,c) and 6 are given in Excel spreadsheet files \"Source-Data_2017-07-09041A_EDFig.....xlsx\".", "links": [ { diff --git a/datasets/AAS_4116_Iceberg_Distribution_1.json b/datasets/AAS_4116_Iceberg_Distribution_1.json index d6ea52e5ec..8da8001511 100644 --- a/datasets/AAS_4116_Iceberg_Distribution_1.json +++ b/datasets/AAS_4116_Iceberg_Distribution_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Iceberg_Distribution_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a collection of points that describe the location and size of icebergs surrounding the Antarctic continent. The points locations are in Polar Stereographic -71 latitude (with corresponding x, y geographic coordinates)\nThe dataset was extracted and compiled using a novel technique from the 'RAMP AMM-1 SAR Image Mosaic of Antarctica (Version 2)' available from the National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/NSIDC-0103/versions/2.\n\nThe data are available in NetCDF and csv formats.", "links": [ { diff --git a/datasets/AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1.json b/datasets/AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1.json index df19b2f4b0..2f0c5e1199 100644 --- a/datasets/AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1.json +++ b/datasets/AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset relates to long-term change and variability in annual timings of sea ice advance, retreat and resultant ice season duration in East Antarctica derived from the satellite passive-microwave time series dating back to Nimbus 7. These were calculated from satellite-derived ice concentration data for the period 1979/80 to 2009/10. The dataset includes more detailed analysis of change and variability in sea ice conditions along meridional transects i.e., 110 degrees E and 140 degrees E relating to sea ice concentration and extent, and along 90 deg E, 100 deg E, 110 deg E and 140 deg E for trends in sea ice concentration for the period 1979-2010. Also included are monthly sea-surface temperature (SST) trends mapped north of the East Antarctic sea-ice zone for the period 1982-2010. The SST data are from the Reynolds and Smith OLv2 dataset.\n\nThese data form the basis of the publication: Massom, R.A., P. Reid, S. Stammerjohn, B. Raymond, A. Fraser and S. Ushio. 2013. Change and variability in East Antarctic sea ice seasonality, 1979/80-2009/10. PloS ONE, 8(5), e64756, doi:10.1371/journal.pone.0064756", "links": [ { diff --git a/datasets/AAS_4121_Ecosystem_Model_Parameters_1.json b/datasets/AAS_4121_Ecosystem_Model_Parameters_1.json index 7346bf7cbb..8779e704b5 100644 --- a/datasets/AAS_4121_Ecosystem_Model_Parameters_1.json +++ b/datasets/AAS_4121_Ecosystem_Model_Parameters_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4121_Ecosystem_Model_Parameters_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This parameter set was developed to provide a plausible implementation for the ecological model described in Bates, M., S Bengtson Nash, D.W. Hawker, J. Norbury, J.S. Stark and R. A. Cropp. 2015. Construction of a trophically complex near-shore Antarctic food web model using the Conservative Normal framework with structural coexistence. Journal of Marine Systems. 145: 1-14. The ecosystem model used in this paper was designed to have the property of structural coexistence. This means that the functional forms used to describe population interactions in the equations were chosen to ensure that the boundary eigenvalues of every population were all always positive, ensuring that no population in the model can ever become extinct. This property is appropriate for models such as this that are implemented to model typical seasonal variations rather than changes over time. The actual parameter values were determined by searching a parameter space for parameter sets that resulted in a plausible distribution of biomass among the trophic levels. The search was implemented using the Boundary Eigenvalue Nudging - Genetic Algorithm (BENGA) method and was constrained by measured values where these were available.\n\nThis parameter set is provided as an indicative set that is appropriate for studying the partitioning of Persistent Organic Pollutants in coastal Antarctic ecosystems. It should not be used for predictive population modelling without independent calibration and validation.", "links": [ { diff --git a/datasets/AAS_4123_model_comparisons_1.json b/datasets/AAS_4123_model_comparisons_1.json index 6c17dbebfe..50bbc7627e 100644 --- a/datasets/AAS_4123_model_comparisons_1.json +++ b/datasets/AAS_4123_model_comparisons_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4123_model_comparisons_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although the floating sea ice surrounding the Antarctic damps ocean waves, they may still be detected hundreds of kilometres from the ice edge. Over this distance the waves leave an imprint of broken ice, which is susceptible to winds, currents, and lateral melting. The important omission of wave-ice interactions in ice/ocean models is now being addressed, which has prompted campaigns for experimental data. These exciting developments must be matched by innovative modelling techniques to create a true representation of the phenomenon that will enhance forecasting capabilities.\n\nThis metadata record details laboratory wave basin experiments that were conducted to determine:\n\n(i) the wave induced motion of an isolated wooden floe;\n(ii) the proportion of wave energy transmitted by an array of 40 floes; and\n(iii) the proportion of wave energy transmitted by an array of 80 floes.\n\nMonochromatic incident waves were used, with different wave periods and wave amplitudes.\n\nThe dataset provides:\n\n(i) response amplitude operators for the rigid-body motions of the isolated floe; and\n(ii) transmission coefficients for the multiple-floe arrays,\n\nextracted from raw experimental data using spectral methods.\n\nThe dataset also contains codes required to produce theoretical predictions for comparison with the experimental data. The models are based on linear potential flow theory.\n\nThese data models were developed to be applicable to Southern Ocean conditions.", "links": [ { diff --git a/datasets/AAS_4124_CEAMARC200708_BenthicStills_1.json b/datasets/AAS_4124_CEAMARC200708_BenthicStills_1.json index c1063fab93..48f77b654d 100644 --- a/datasets/AAS_4124_CEAMARC200708_BenthicStills_1.json +++ b/datasets/AAS_4124_CEAMARC200708_BenthicStills_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_CEAMARC200708_BenthicStills_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Derived dataset from the forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible, and operational taxonomic units (OTUs) were aggregated by feeding type afterwards. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. \nThis dataset contains: \n- the mean longitude of all images aggregated per site. \n- the mean latitude of all images aggregated per site. \n- the number of images scored per site - the aggregated abundances (given in %-cover) for three main benthic groups (SF=Suspension Feeder, DF=Deposit Feeder, PR=Predator). \n- number of OTUs observed per benthic group per site.\n- the total number of OTUs observed per site.", "links": [ { diff --git a/datasets/AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2.json b/datasets/AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2.json index c61152f6d4..cb0cce2291 100644 --- a/datasets/AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2.json +++ b/datasets/AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Percent-cover estimates from forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. \nThis dataset contains: \n(1)\n- species/ morphotypes identified to the highest taxonomic resolution possible\n- broader taxonomic classification (phylum/class)\n- each species mobility, feeding-type and body-shape if possible\n- average abundances in percent-cover at each site\n(2)\n- the mean longitude of all images aggregated per site\n- the mean latitude of all images aggregated per site\n- the number of images scored per site", "links": [ { diff --git a/datasets/AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1.json b/datasets/AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1.json index 57d6bdee09..b197670da1 100644 --- a/datasets/AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1.json +++ b/datasets/AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains raster files (.grd) for food-availability and predicted distribution of suspension feeder abundances averaged across a five year time-period before (2005-2009) and after (2011-2016) the calving of the Mertz Glacier Tongue in 2010. \nThe following data are included:\n- sinking, settling and horizontal flux of food-particles along the seafloor\n- suspension feeder abundances and standard deviation of the predicted distribution\nAll data has been generated as part of the paper: \nJansen et al. (2018) Mapping Antarctic suspension feeder abundances and seafloor-food availability, and modelling their change after a major glacier calving. Frontiers in Ecology and Evolution", "links": [ { diff --git a/datasets/AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1.json b/datasets/AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1.json index b6ab942c04..b3a2465df6 100644 --- a/datasets/AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1.json +++ b/datasets/AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains environmental layers used to model the predicted distribution of demersal fish bioregions for the paper: Hill et al. (2020) Determining Marine Bioregions: A comparison of quantitative approaches, Methods in Ecology and Evolution.\nIt contains climatological variables from satellite and modelled data that represent sea floor and sea surface conditions likely to affect the distribution of demersal fish including: depth, slope, seafloor temperatures, seafloor current, seafloor nitrate, sea surface temperature, chlorophyll-a standard deviation and sea surface height standard deviation. Layers are presented at 0.1 degree resolution.\n\"prediction_space\" is a Rda file for R that consists of two objects:\nenv_raster: a raster stack of the environmental layers\npred_sp: a data.frame version of the env_raster where some variables have been transformed for statistical analysis and bioregion prediction.\n\"Env_data_sources.xlsx\" contains a description of each environmental variable and it's source.", "links": [ { diff --git a/datasets/AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1.json b/datasets/AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1.json index 2ff6e358d0..3d620798ca 100644 --- a/datasets/AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1.json +++ b/datasets/AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Demersal fish form an important component of sub-Antarctic ecosystems. While understanding the distribution of key commercial species is the subject of much current research, patterns in the distribution of benthic fish assemblages as a whole and associated diversity has received less attention. Here we combine Australian (source: AAD Random Stratified Trawl Surveys) and French (source: POKER 2006, 2010, 2013) demersal fish datasets with synoptic environmental data to quantify and predict the distribution of fish assemblages across the Kerguelen Plateau. We achieve this by applying a recently developed method, called Regions of Common Profile (RCP), which quantifies distinct environmental regions containing a similar profile of species. The RCP method directly models species simultaneously (rather than dissimilarities or single species at a time) and offers advantages over previous methods in the areas of model diagnostics, the interpretability of model outputs, and providing estimates of uncertainty. We define the contents, environmental correlates and spatial extent of several assemblages across the plateau. The files provided here are the outputs of the RCP analyses.\n\nFiles\nKP_RCP_Predictions.csv: Region of Common Profile (RCP) spatial predictions for entire Kerguelen Plateau. The resolution of the grid is 0.1 x 0.1 degrees (Long, Lat, WGS84) and predictions were restricted to depths shallower than 1200 m. The probability of each grid cell belonging to each RCP is reported (RCP_1 - RCP_7) as well as the most likely RCP (HClass) and the most likely RCP's probability of occurrence (HClass_prob)\n\nRCP_Species_Composition_Average.xls: Average (standard deviation) of probability of occurrence for each species in each RCP. Statistics calculated by taking 500 bootstrap samples of model parameters, generating expected probability of occurrences for each species in each RCP for each level of the sampling factor Year/Season/Gear and summarising over the 3500 (7 levels of sampling effect x 500 bootstraps) values.\nRCP_Species_Composition_SampEff.xls: Average (Standard deviation) probability of occurrence of species for each RCP for each level of the sampling factor (Year/Season/Gear).\n\nMarginal_env_plots (Folder): Marginal plots of the response of each RCP to depth (m), chl-a yearly mean (mg/m3) and surface temperature yearly mean (degrees Celsius). Plots were generated by predicting RCP membership for each trawl site based on its environmental covariates only and plotting. \n\nInteractive maps showing the predicted spatial distribution of the RCP groups, as well as the species profile and environmental conditions characterising each group, and the coverage of the HIMI Marine reserve can be found at doi: 10.4225/15/58169d06ee8fc. Contains the above results in an interactive map with the following layers:\n1) Assemblage maps: Species Profile: \nMap of the most likely RCP group. The pop up graphic shows pictures of the four most likely species to occur in this assemblage as well as the expected occurrence of all species (the species profile).\n2) Assemblage maps: Environment Characteristics:\nMap of the most likely RCP group. The pop up graphic shows the response of each assemblage to depth, surface temperature yearly mean and chl-a yearly mean. These inform us of the environmental characteristics of each RCP group. Plots were generated by predicting RCP group membership for each trawl site based only on its environmental covariates.\n3) Group Membership:\nMap of the most likely RCP group and the uncertainty associated with this group.\n4) HIMI Reserve Coverage: Location of Heard and McDonald Islands Marine Reserve with pop-up table of the proportion of each RCP group contained within the reserve. Proportion calculated within the Australian EEZ only.", "links": [ { diff --git a/datasets/AAS_4124_cephalopod_habitat_suitability_1.json b/datasets/AAS_4124_cephalopod_habitat_suitability_1.json index b252a07f42..7f7c52c58f 100644 --- a/datasets/AAS_4124_cephalopod_habitat_suitability_1.json +++ b/datasets/AAS_4124_cephalopod_habitat_suitability_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_cephalopod_habitat_suitability_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean Biogeographic Information System, biodiversity.aq, the Australian Antarctic Data Centre, and the National Institute of Water and Atmospheric Research. The habitat suitability modelling was conducted using the Maxent software package (v3.3.3k, Phillips et al., 2006). Maxent allows for nonlinear model terms by formulating a series of features from the predictor variables. Due to relatively limited sample sizes, we constrained the complexity of most models by considering only linear, quadratic, and product features. A multiplier of 3.0 was used on automatic regularization parameters to discourage overfitting; otherwise, default Maxent settings were used. Predictor variables were chosen from a collection of Southern Ocean layers. These variables were selected as indicators of ecosystem structure and processes including water mass properties, sea ice dynamics, and productivity. A 10-fold cross-validation procedure was used to assess model performance (using the area under the receiver-operating curve) and variable permutation importance, with values averaged over the 10 fitted models. The final predicted distribution for each species was based on a single model fitted using all data: these are the predictions included in this data set. \n\t\t\t\t \nThe individual habitat suitability models were overlaid to generate a 'hotspot' index of species richness. The predicted habitat suitability for each species was converted to a binary presence/absence layer by applying a threshold, such that habitat suitability values above the threshold were converted to presences. The threshold used for each species was the average of the thresholds (for each of the 10 training models) chosen to maximize the test area under the receiver-operating curve. The binary layers were then summed to give the number of species estimated to be present in each pixel in the study region.", "links": [ { diff --git a/datasets/AAS_4124_pelagic_regionalisation_1.json b/datasets/AAS_4124_pelagic_regionalisation_1.json index 45f7312ce3..e9c19c5ae3 100644 --- a/datasets/AAS_4124_pelagic_regionalisation_1.json +++ b/datasets/AAS_4124_pelagic_regionalisation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4124_pelagic_regionalisation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40 degrees S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice.\n\nThe processing methods follow those of Grant et al. (2006) and the CCAMLR Bioregionalisation Workshop (SC-CAMLR-XXVI 2007). Briefly, a non-hierarchical clustering algorithm was used to reduce the full set of grid cells to 250 clusters. These 250 clusters were then further refined using a hierarchical (UPGMA) clustering algorithm. The first, non-hierarchical, clustering step is an efficient way of reducing the large number of grid cells, so that the subsequent hierarchical clustering step is tractable. The hierarchical clustering algorithm produces a dendrogram, which can be used to guide the clustering process (e.g. choices of data layers and number of clusters) but is difficult to use with large data sets. Analyses were conducted in Matlab (Mathworks, Natick MA, 2011) and R (R Foundation for Statistical Computing, Vienna 2009).\nThree variables were used for the pelagic regionalisation: sea surface temperature (SST), depth, and sea ice cover. Sea surface temperature was used as a general indicator of water masses and of Southern Ocean fronts (Moore et al. 1999, Kostianoy et al. 2004). Sea surface height (SSH) from satellite altimetry is also commonly used for this purpose (e.g. Sokolov and Rintoul 2009), and may give front positions that better match those from subsurface hydrography than does SST. However, SSH data has incomplete coverage in some near-coastal areas (particularly in the Weddell and Ross seas) and so in the interests of completeness, SST was used here. During the hierarchical clustering step, singleton clusters (clusters comprised of only one datum) were merged back into their parent cluster (5 instances, in cluster groups 2, 3, 8, and 13). Additionally, two branches of the dendrogram relating to temperate shelf areas (around South America, New Zealand, and Tasmania) were merged to reduce detail in these areas (since such detail is largely irrelevant in the broader Southern Ocean context).", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_AmbientSeawaterTemperature_1.json b/datasets/AAS_4127_antFOCE_AmbientSeawaterTemperature_1.json index 9ba64bbf78..694706f77c 100644 --- a/datasets/AAS_4127_antFOCE_AmbientSeawaterTemperature_1.json +++ b/datasets/AAS_4127_antFOCE_AmbientSeawaterTemperature_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_AmbientSeawaterTemperature_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Refer to antFOCE report section 2.3 for deployment, sampling and analysis details.\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nThe download file contains an Excel workbook with a series of data spreadsheets - one for each of the Onset Hoboware Tidbit v2 (UTBI-001) temperature loggers that were attached to the outside of various pieces of the underwater experimental infrastructure across the antFOCE site. A Notes spreadsheet is also included with information relevant to the data. \n\nBackground \n\nThe antFOCE experimental system was deployed in O'Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. \n\nDetails of the antFOCE experiment can be found in the report \u2013 \"antFOCE 2014/15 \u2013 Experimental System, Deployment, Sampling and Analysis\". This report and a diagram indicating how the various antFOCE data sets relate to each other are available at:\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_ArtificialSubstrateUnits_1.json b/datasets/AAS_4127_antFOCE_ArtificialSubstrateUnits_1.json index efbe22711b..7731053e13 100644 --- a/datasets/AAS_4127_antFOCE_ArtificialSubstrateUnits_1.json +++ b/datasets/AAS_4127_antFOCE_ArtificialSubstrateUnits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_ArtificialSubstrateUnits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Refer to antFOCE report section 4.5.2 for deployment, sampling and analysis details.\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nThe download file contains an Excel workbook with one data spreadsheet and one of notes relevant to the data. The data are the total number of each organism collected from artificial substrate units (plastic pot scourers) deployed in chambers or open plots during the antFOCE experiment (Data = Number of Individuals). Analysis methods are detailed in the Notes spreadsheet. \n\nBackground \n\nThe antFOCE experimental system was deployed in O\u2019Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. \n\nDetails of the antFOCE experiment can be found in the report \u2013 \u201cantFOCE 2014/15 \u2013 Experimental System, Deployment, Sampling and Analysis\u201d. This report and a diagram indicating how the various antFOCE data sets relate to each other are available at:\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_Biofilms_Eukaryotes_2.json b/datasets/AAS_4127_antFOCE_Biofilms_Eukaryotes_2.json index 4a809b86af..e31744878d 100644 --- a/datasets/AAS_4127_antFOCE_Biofilms_Eukaryotes_2.json +++ b/datasets/AAS_4127_antFOCE_Biofilms_Eukaryotes_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_Biofilms_Eukaryotes_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record contains an Excel spreadsheet with Operational Taxonomic Units (OTUs) gained from Eukaryotic 18S rDNA PCR amplification and high-throughput sequencing of samples from Biofilm slides deployed as part of the antFOCE experiment in the austral summer of 2014/15 at Casey station, East Antarctica. \n\nRefer to antFOCE report section 4.5.3 for deployment, sampling and analysis details.\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nSampling design\n2 trays of 8 horizontal standard glass microscope slides (72 x 25 mm) per chamber. Four of the glass slides were scored with a diamond pencil approximately 18 mm from the right hand end of the slide and deployed scored side up. The remaining four slides were unmodified. Slides were sampled at:\n- Tmid - one tray per chamber / open plot. The sampled try was repopulated with fresh slides and redeployed\n- Tend \u2013 2 slides trays per chamber / open plot.\n\nSampling procedure\nAfter 31 days deployment, 1 slide tray per chamber / open plot was sampled. At Tend both trays in each chamber / open plot were sampled. To minimize disturbance while being raised to the surface, each tray was removed from the tray holder by divers and placed in a seawater filled container with a lid. On the surface, slides were removed from the tray using ethanol sterilized forceps. The four unscoured slides per chamber / open plot were placed in a plastic microscope slide holder with a sealable lid. The scoured slides were placed individually in 70 ml plastic sample jars. \n\nLab procedure - Casey\nThe slide holder (4 unscoured slides) from each chamber / open plot was frozen at -20C immediately upon return to the lab. The scoured slides were preserved in sea water containing 1% final concentration glutaraldehyde in separate jars.\n\nPreservation Issue: Scoured slides were not refrigerated, either at Casey, during RTA or in Kingston before the 26th Nov 2015, when they were transferred to the 4C Cold Store. \n\n\nantFOCE Background \n\nThe antFOCE experimental system was deployed in O\u2019Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. \n\nDetails of the antFOCE experiment can be found in the report \u2013 \"antFOCE 2014/15 \u2013 Experimental System, Deployment, Sampling and Analysis\". This report and a diagram indicating how the various antFOCE data sets relate to each other are available at:\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nAntFOCE biofilm DNA methods\nLaurence Clarke, Shane Powell, Bruce Deagle\nDNA extraction\nThe biofilm was removed from the top of each slide with a cotton swab and DNA extracted directly from the swab using the MoBio PowerBiofilm DNA isolation kit following the manufacturer\u2019s protocol. Extraction blanks were extracted in parallel to detect contamination. \nEukaryotic 18S rDNA PCR amplification and high-throughput sequencing\nDNA extracts were PCR-amplified in triplicate with primers designed to amplify 140-170 bp of eukaryotic 18S ribosomal DNA (Jarman et al. 2013). The forward primer was modified to improve amplification of protists.\nTable 1. First and second round primers, including MID tags (Xs).\nILF_ProSSU3'F_X \tTCGTCGGCAGCGTCAGATGTGTATAAGAGACAG XXXXXX CACCGCCCGTCGCWMCTACCG\nILR_SSU3'R_Y\tGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG XXXXXX GGTTCACCTACGGAAACCTTGTTACG\nmsqFX\tAATGATACGGCGACCACCGAGATCTACAC XXXXXXXXXX TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG\nmsqRY\tCAAGCAGAAGACGGCATACGAGAT XXXXXXXXXX GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG\n\nPCR amplifications were performed in two rounds, the first to amplify the 18S region and add sample-specific multiplex-identifier (MID) tags and Illumina sequencing primers, the second to add the P5 and P7 sequencing adapters and additional MIDs.\nEach reaction mix for the first PCR contained 0.1 \u00b5M each of forward and reverse primer, 0.2 \u00b5g/\u00b5L BSA, 0.2 U Phusion DNA polymerase in 1 x Phusion Master Mix (New England Biolabs, Ipswich, MA, USA) and 1 micro L DNA extract in a total reaction volume of 10 micro L. PCR thermal cycling conditions were initial denaturation at 98 degrees C for 30 secs, followed by 25 cycles of 98 degrees C for 5 secs, 67 degrees C for 20 secs and 72 degrees C for 20 secs, with a final extension at 72 degrees C for 5 min. Replicate PCR products were pooled then diluted 1:10 and Illumina sequencing adapters added in a second round of PCR using the same reaction mix and thermal cycling conditions as the first round, except the concentration of BSA was halved (0.1 micro g/micro L), and the number of cycles was reduced to 10 with an annealing temperature of 55 degrees C.\nProducts from each round of PCR were visualized on 2% agarose gels. Second round PCR products were pooled in equimolar ratios based on band intensity. The pooled products were purified using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) and the concentration of the library measured using the Qubit dsDNA HS assay on a QUBIT 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). The pool was diluted to 2 nM and paired-end reads generated on a MiSeq (Illumina, San Diego, CA, USA) with MiSeq Reagent Nano kit vs (300-cycles). \n\nBacterial 16S rDNA PCR amplification and high-throughput sequencing\n\nBioinformatics\nReads were sorted by sample-specific MIDs added in the second round PCR using the MiSeq Reporter software. Fastq reads were merged using the -fastq_mergepairs command in USEARCH v8.0.1623 (Edgar 2010). Merged reads were sorted by \"internal\" 6 bp MID tags, and locus-specific primers trimmed with custom R scripts using the ShortRead package (Morgan et al. 2009), with only reads containing perfect matches to the expected MIDs and primers retained. Reads for all samples were dereplicated and global singletons discarded (-derep_fulllength -minuniquesize 2), and clustered into OTUs with the UPARSE algorithm (Edgar 2013) using the '-cluster_otus' command. Potentially chimeric reads were also discarded during this step. Reads for each sample were then assigned to OTUs (-usearch_global -id .97), and an OTU table generated using a custom R script.\nTaxonomy was assigned to each OTU using MEGAN version 5.10.5 (Huson et al. 2011) based on 50 hits per OTU generated by BLASTN searches against the NCBI 'nt' database (downloaded August 2015). Default LCA parameters were used, except Min support = 1, Min score = 100, Top percent = 10. Alpha and beta-diversity analyses were performed based on a rarefied OTU table with QIIME v1.8.0 (alpha_rarefaction.py, beta_diversity_through_plots.py, Caporaso et al. 2010). \n\nReferences\nCaporaso JG, Kuczynski J, Stombaugh J, et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335-336.\nHuson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC (2011) Integrative analysis of environmental sequences using MEGAN4. Genome Research 21, 1552-1560.\nJarman SN, McInnes JC, Faux C, et al. (2013) Adelie penguin population diet monitoring by analysis of food DNA in scats. PLoS One 8, e82227.", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_CarbonateChemistry_1.json b/datasets/AAS_4127_antFOCE_CarbonateChemistry_1.json index 81af1ccc8e..7a9fa707c8 100644 --- a/datasets/AAS_4127_antFOCE_CarbonateChemistry_1.json +++ b/datasets/AAS_4127_antFOCE_CarbonateChemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_CarbonateChemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbonate chemistry data sets for the Antarctic Free Ocean Carbon Dioxide Enrichment experiment, Casey Station, East Antarctica, 2014/15.\n\nProject Summary:\nCurrently, a quarter of the CO2 we emit is absorbed by the ocean. CO2 absorption in seawater changes its chemistry \u2013 reducing ocean pH (raising its acidity) \u2013 which has significant impacts on biological processes and serious implications for the resilience of marine ecosystems. As CO2 is more soluble in cold water we expect polar ecosystems to bear the heaviest burden of this 'ocean acidification'. We will perform the first in situ polar CO2 enrichment experiment to determine the likely impacts of ocean acidification on Southern Ocean sea-floor communities under increasing CO2 emissions.", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_EnvironmentalData_1.json b/datasets/AAS_4127_antFOCE_EnvironmentalData_1.json index d165cab02c..e58306717e 100644 --- a/datasets/AAS_4127_antFOCE_EnvironmentalData_1.json +++ b/datasets/AAS_4127_antFOCE_EnvironmentalData_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_EnvironmentalData_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record AAS_4127_antFOCE_EnvironmentalData contains seafloor Ambient Light and ambient Seawater Temperature data sets collected at the antFOCE site during the experiment. Ambient Light data was collected using Photosynthetically Active Radiation sensors (Odyssey Dataflow 392 photo diode light meters) distributed around the antFOCE site as well as several inside the experimental chambers and open plots. Seawater Temperature data were collected using Onset Hoboware Tidbit v2 (UTBI-001) temperature loggers attached to the outside of various pieces of the underwater experimental infrastructure across the antFOCE site. \n\nRefer to antFOCE report section 2.3 for deployment, sampling and on-station analysis details. \n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nBackground \n\nThe antFOCE experimental system was deployed in O'Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. \n\nDetails of the antFOCE experiment can be found in the report \u2013 \"antFOCE 2014/15 \u2013 Experimental System, Deployment, Sampling and Analysis\". This report and a diagram indicating how the various antFOCE data sets relate to each other are available at:\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_HardSubstrateFauna_1.json b/datasets/AAS_4127_antFOCE_HardSubstrateFauna_1.json index b3a70e3bf3..08b8c45a4c 100644 --- a/datasets/AAS_4127_antFOCE_HardSubstrateFauna_1.json +++ b/datasets/AAS_4127_antFOCE_HardSubstrateFauna_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_HardSubstrateFauna_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record AAS_4127_antFOCE_HardSubstrateFauna contains all data sets relating to the fauna sampled from hard substrates during the antFOCE experiment, including recruitment tiles, artificial substrate units and biofilm slides. \n\nRefer to antFOCE report section 4.5 for deployment, sampling and on-station analysis details. \n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127\n\nBackground \n\nThe antFOCE experimental system was deployed in O\u2019Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. \n\nDetails of the antFOCE experiment can be found in the report \u2013 \u201cantFOCE 2014/15 \u2013 Experimental System, Deployment, Sampling and Analysis\u201d. This report and a diagram indicating how the various antFOCE data sets relate to each other are available at:\n\nhttps://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_Project4127_1.json b/datasets/AAS_4127_antFOCE_Project4127_1.json index a8a7800a4d..92df258ea0 100644 --- a/datasets/AAS_4127_antFOCE_Project4127_1.json +++ b/datasets/AAS_4127_antFOCE_Project4127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_Project4127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Parent node for data sets for the Antarctic Free Ocean Carbon Dioxide Enrichment experiment (antFOCE), AAS project 4127.\n \nProject Summary:\nCurrently, a quarter of the CO2 we emit is absorbed by the ocean. CO2 absorption in seawater changes its chemistry \u2013 reducing ocean pH (raising its acidity) \u2013 which has significant impacts on biological processes and serious implications for the resilience of marine ecosystems. As CO2 is more soluble in cold water we expect polar ecosystems to bear the heaviest burden of this 'ocean acidification'. We will perform the first in situ polar CO2 enrichment experiment to determine the likely impacts of ocean acidification on Southern Ocean sea-floor communities under increasing CO2 emissions.", "links": [ { diff --git a/datasets/AAS_4127_antFOCE_SeawaterCarbonateChemistry_1.json b/datasets/AAS_4127_antFOCE_SeawaterCarbonateChemistry_1.json index 3dae694766..258e1387ee 100644 --- a/datasets/AAS_4127_antFOCE_SeawaterCarbonateChemistry_1.json +++ b/datasets/AAS_4127_antFOCE_SeawaterCarbonateChemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4127_antFOCE_SeawaterCarbonateChemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbonate chemistry data for the antFOCE seawater samples.\n \nThe download file contains an Excel spreadsheet with a number of worksheets detailing the samples collected from O'Brien Bay, Casey Station. The dataset includes information on oxygen levels, pH levels, temperature and salinity levels, as well as the concentrations of various elements (dissolved inorganic carbon, phosphate, nitrate, nitrite, silicate).\n\nFree-ocean CO2 enrichment (FOCE) experiments have been deployed in marine ecosystems to manipulate carbonate system conditions to those predicted in future oceans. We investigated whether the pH/carbonate chemistry of extremely cold polar waters can be manipulated in an ecologically relevant way, to represent conditions under future atmospheric CO2 levels, in an in-situ FOCE experiment in Antarctica. We examined spatial and temporal variation in local ambient carbonate chemistry at hourly intervals at two sites between December and February and compared these with experimental conditions. We successfully maintained a mean pH offset in acidified benthic chambers of -0.38 (plus or minus 0.07) from ambient for approximately 8 weeks. Local diel and seasonal fluctuations in ambient pH were duplicated in the FOCE system. Large temporal variability in acidified chambers resulted from system stoppages. The mean pH, \u2126arag and fCO2 values in the acidified chambers were 7.688 plus or minus 0.079, 0.62 plus or minus 0.13 and 912 plus or minus 150 micro-atm respectively. Variation in ambient pH appeared to be mainly driven by salinity and biological production and ranged from 8.019 to 8.192 with significant spatio-temporal variation. This experiment demonstrates the utility of FOCE systems to create conditions expected in future oceans that represent ecologically relevant variation, even under polar conditions.", "links": [ { diff --git a/datasets/AAS_4130_2.json b/datasets/AAS_4130_2.json index 389c363049..8ddfce3309 100644 --- a/datasets/AAS_4130_2.json +++ b/datasets/AAS_4130_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4130_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A long-standing problem is why the mid-latitude ionosphere (eg over Australia) is sometimes enhanced during space weather storms, and sometimes depleted. While storms occur mainly at high latitudes, their effects propagate equatorward via upper atmosphere's winds, waves, electric fields, and chemical composition but we do not understand why the relative importance of these varies from storm to storm. By measuring these various drivers over Antarctica and their subsequent impacts at mid-latitude during many storms over a 5-year period we will determine the statistical importance of each driver.\n\nThe spectrometers are currently based at Mawson and Davis. This record details thermospheric and mesospheric winds, temperatures, and emission intensities from the Fabry Perot Spectrometer.\n\nThis project has replaced ASAC project 2699 - the data from which are held in the metadata record \"Fabry-Perot_Spectrometer\".", "links": [ { diff --git a/datasets/AAS_4131_au1602_2.json b/datasets/AAS_4131_au1602_2.json index 96e2686c7e..649d7408a9 100644 --- a/datasets/AAS_4131_au1602_2.json +++ b/datasets/AAS_4131_au1602_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4131_au1602_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard Aurora Australis cruise au1602, voyage 2 2016/2017, from 8th December 2016 to 21st January 2017. The cruise commenced with a Casey resupply, followed by work around the Dalton Polynya/Moscow University Iceshelf, then the Mertz Glacier region, and then around the Ninnis Polynya. 14 stations at the southern end of the SR3 transect were also completed. Ice conditions prevented access to the front of the Totten Glacier. A total of 73 CTD vertical profile stations were taken on the cruise, most to within 12 metres of the bottom (Table 1). Over 800 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate, ammonia and nitrite), dissolved inorganic carbon (i.e. TCO2), alkalinity, Th-234, POC, Chla, PAM, HPLC, Nd, Po-210/Pb-210, bacteria, O-18, caesium, and Teflon pollutants, using a 24 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package. Upper water column current profile data were collected by a ship mounted ADCP. Meteorological and water property data were collected by the array of ship's underway sensors. 8 Argo floats were also deployed (Table 13) on the transit from Hobart to Casey.\n\nThe data set contains CTD dbar data and Niskin bottle data (i.e. core hydrochemistry only - salinity, dissolved oxygen and nutrients). A detailed data report is included, with a description of the data and important data quality information.", "links": [ { diff --git a/datasets/AAS_4134_Building_Temperatures_1.json b/datasets/AAS_4134_Building_Temperatures_1.json index e22a97d048..b79fc64499 100644 --- a/datasets/AAS_4134_Building_Temperatures_1.json +++ b/datasets/AAS_4134_Building_Temperatures_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4134_Building_Temperatures_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These images are infrared thermal images of points in and around Casey station. Thermal images were taken using a Testo 881 Thermal Imaging Camera. The thermal imaging camera records infrared radiation of a particular area. These radiation values are then translated on the camera into apparent temperature by calculating the temperature using the heat transfer equation q=sigma epsilon T4, where q is the heat transfer per unit area (W/m2,) sigma is the Stefan-Boltzmann constant (5.6703x10-8 (W/m2K4)), T is the apparent temperature (K) and epsilon is the emissivity. The inferred temperature relies on an assumed emissivity. Because actual emissivity is not fixed, the apparent temperature does not necessarily translate into actual temperature. As such, care should be taken when interpreting the thermal image files. The emissivity value can be adjusted later using IRSoft software (see below).\n\nFile name Description\nIV_00903.BMT Bar fridge\nIV_00904.BMT Bar fridge\nIV_00908.BMT TV next to pool table\nIV_00909.BMT TV next to pool table\nIV_00910.BMT Library window\nIV_00911.BMT Library window\nIV_00912.BMT Main cold porch\nIV_00913.BMT Main cold porch\nIV_00914.BMT Main cold porch\nIV_00915.BMT Main cold porch\nIV_00916.BMT Main cold porch\nIV_00917.BMT Mess hall, main three door fridge\nIV_00918.BMT Mess hall, main three door fridge\nIV_00919.BMT Coffee machine\nIV_00920.BMT Catch and kill fridge and upright freezer\nIV_00921.BMT Door to chef's fridge\nIV_00922.BMT Catch and kill fridge and upright freezer\nIV_00923.BMT Refrigertion units in kitchen\nIV_00924.BMT Wallow\nIV_00925.BMT Wallow\nIV_00926.BMT Main cold porch\nIV_00927.BMT West side of redshed, ice build-up from blizz tail\nIV_00928.BMT West side of redshed, ice build-up from blizz tail\nIV_00929.BMT West side of redshed, ice build-up from blizz tail\nIV_00930.BMT South side of redshed\nIV_00931.BMT South side of redshed\nIV_00932.BMT South side of redshed\nIV_00934.BMT Ops building\nIV_00935.BMT Ops building, west side, ice build-up from blizz-tail\nIV_00936.BMT Ops building, main door\nIV_00937.BMT Ops building, main door\nIV_00939.BMT Ops building, west side, ice build-up from blizz-tail\nIV_00950.BMT Central Wallow Orignal triple glaze, ground floor, immediately west of point 3\nIV_00951.BMT Central Wallow Orignal triple glaze, ground floor, immediately west of point 3\nIV_00952.BMT Central Wallow NE facing pane on bay window\nIV_00953.BMT Central Wallow NE facing pane on bay window\nIV_00954.BMT West wing skylight, immediately above landing in second floor. Eastern most skylight\nIV_00960.BMT West wing skylight, immediately above landing in second floor. Eastern most skylight\nIV_00961.BMT Central skylight, east side, LHS\nIV_00963.BMT Central skylight, east side, LHS\nIV_00965.BMT Central skylight, east side, RHS\nIV_00967.BMT Central skylight, east side, RHS\nIV_00972.BMT Central Wallow NE facing pane on bay window\nIV_00973.BMT Central Wallow Original triple glaze, ground floor, immediately west of point 3\nIV_00974.BMT Central cold porch, near west wing extension\nIV_00975.BMT Central cold porch, near west wing extension\nIV_00976.BMT Central cold porch, near west wing extension\nIV_00977.BMT Central cold porch, near west wing extension\nIV_00978.BMT Central Wallow Original triple glaze, ground floor, immediately west of point 3\nIV_00979.BMT Central Wallow Original triple glaze, ground floor, immediately west of point 3\nIV_00980.BMT Emergency door, west wing, south side\nIV_00981.BMT Emergency door, west wing, south side\nIV_00982.BMT Window, west wing, north side, west window\nIV_00983.BMT Window, west wing, north side, east window\nIV_00984.BMT West wing - West side wall\nIV_00985.BMT West wing - West side wall\nIV_00986.BMT Emergency door, west wing, north side\nIV_00987.BMT Emergency door, west wing, north side\nIV_00988.BMT Emergency door, west wing, north side\nIV_00989.BMT Emergency door, west wing, north side\nIV_00990.BMT Window, west wing, north side, west window\nIV_00991.BMT Window, west wing, north side, east window\nIV_00992.BMT Emergency door, west wing, south side\nIV_00993.BMT Emergency door, west wing, south side\nIV_01032.BMT Window on bedroom R33, south side of west wing, ground floor\nIV_01033.BMT Ops building, internal door, south facing\nIV_01034.BMT Ops building, internal door, south facing\nIV_01035.BMT Workshop roller door\nIV_01036.BMT Workshop door\nIV_01037.BMT Workshop door\nIV_01038.BMT Workshop door\nIV_01039.BMT Workshop door\nIV_01040.BMT Workshop door\nIV_01041.BMT Workshop door\nIV_01042.BMT Workshop door\nIV_01043.BMT Workshop door\n\nThey can be used to infer surface temperatures. The files can be opened with TESTO IRSoft software. TESTO IRSoft is available for download for free from http://www.testolimited.com/download-centre\n\nPlain text or infrared datapoints from this data cannot be extracted from the datafiles; BMT is the raw format.", "links": [ { diff --git a/datasets/AAS_4134_Casey_Food_1.json b/datasets/AAS_4134_Casey_Food_1.json index f71e8fbbc7..5cb3b5cff8 100644 --- a/datasets/AAS_4134_Casey_Food_1.json +++ b/datasets/AAS_4134_Casey_Food_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4134_Casey_Food_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data will be used to determine the amount (mass) and types of food sent to Casey station.\n\nThese data have been provided by Noel Tennant of the Australian Antarctic Division, and were not collected as part of the project.\n\nBelow is information provided by Noel:\n\"Attached is a copy of our full food stock list. This one shows the stock on hand figures from the previous year at Casey, along with a par level for each item and an order quantity.\nIn the par level column you will see that some figures are in orange while the rest are in blue. Those in orange indicate the number of kg's per person/year, while the blue figures are the total number of units of each product required for a year. We estimate from the stock-take figures how much will be remaining when the ship arrives for resupply. This estimated quantity remaining (EQR) is subtracted from the par level amount giving the quantity to order.\nMost frozen products are written off at the end of the resupply year so a full supply of each of those items is supplied every year.\"", "links": [ { diff --git a/datasets/AAS_4135_Hydrocarbon_Toxicity_1.json b/datasets/AAS_4135_Hydrocarbon_Toxicity_1.json index 31f6897db3..0148c8c4f6 100644 --- a/datasets/AAS_4135_Hydrocarbon_Toxicity_1.json +++ b/datasets/AAS_4135_Hydrocarbon_Toxicity_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_Hydrocarbon_Toxicity_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the product of three high-throughput qPCR dynamic array Fluidigm chips. Data is semi-processed, as Fluidigm software does not allow the export of raw fluorescence data. \n\nIn-situ soil mesocosms (n=20) were set up on Macquarie Island in February 2013. Following a year's equilibration, mesocosms were spiked in triplicate with a fuel mixture mimicking the composition of aged fuel spills on Macquarie Island, in addition to five solvent-only controls. Spiking concentrations range from 50mg/kg to 10000 mg/kg, all in triplicate, in addition to 5 solvent - only controls. Soils have been sampled from initial set up until April 2015, with a total of 2 pre-spike and 4 post-spike sample sets. \n\nDNA was extracted from soil in triplicate. Automated Ribosomal Intergenic Spacer analysis (ARISA) was conducted for both Fungal and Bacterial soil communities across all extractions (n = 270). ARISA results confirmed uniformity of replicate extractions. One replicate for each sample was selected for further work (n=90).\n\nMicrofluidic qPCR was conducted with the 90 samples and assays for 16 different genes, 14 of which worked. See Thesis in publications section for more details. \n\nFirst dataset is the semi-processed data output from Fluidigm program (it does not allow the export of un-processed data). \n\nConsult Fluidigm Real-Time PCR Analysis User Guide (PN 68000088 J1) for more information on columns A-N (ID, Type, relative Conc, Value, Calibrated rConc, Quality, Threshold, In Range, Out Range, Peak Ratio).\n Assay = gene assayed, see Thesis in publication for more details. Name = sample name and individual extraction replicate; A,B,or C. Plate = individual Fluidigm chip; three were run, 90, 91 and 92. Time refers to sample set, see Question 2. Treatment = nominal spiking concentration of mesocosm. Dilution factor = dilution factor of sample prior to submission for microfluidic qPCR. Copies/ ul = gene copy number of DNA sample. Initial volume = extraction volume. G of soil = weight of soil extracted. Comb_cond is purely for ease of processing with factor qPCR. Factor qPCR corrected copies/g = output once interpolate differences were corrected with factor qPCR. \n\n\nProcessed gene copy number data (copies/g) is provided in second spreadsheet in 'Biological'. 'Environmental' tab provides additional data on samples, including TPH data and vegetation observations at one time point (expressed as percentage of coverage). Contact Sarah Houlahan for more information on chemical data.", "links": [ { diff --git a/datasets/AAS_4135_Hydrocarbon_Toxicity_Earthworms_1.json b/datasets/AAS_4135_Hydrocarbon_Toxicity_Earthworms_1.json index fde11e04df..5a7f574426 100644 --- a/datasets/AAS_4135_Hydrocarbon_Toxicity_Earthworms_1.json +++ b/datasets/AAS_4135_Hydrocarbon_Toxicity_Earthworms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_Hydrocarbon_Toxicity_Earthworms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the results of a series of laboratory-based toxicity tests in which earthworms were exposed soils that were spiked with a mixture of hydrocarbon compounds to mimic the existing contamination on Macquarie Island. Two series of experiments were done. The first used the earthworm Microscolex macquariensis which is native to Macquarie Island and soils from Macquarie Island. The second used the model test species Eisenia fetida and an artificial spiked soil. Eisenia fetida was further exposed to individual hydrocarbon compounds.\n\nThe effects of the hydrocarbons on worm survival, body mass, cocoon production, cocoon hatching success, aggregation behaviour and preference were recorded.", "links": [ { diff --git a/datasets/AAS_4135_Hydrocarbon_Toxicity_amplicon_2.json b/datasets/AAS_4135_Hydrocarbon_Toxicity_amplicon_2.json index cc881e9ad3..e3f9bdafc9 100644 --- a/datasets/AAS_4135_Hydrocarbon_Toxicity_amplicon_2.json +++ b/datasets/AAS_4135_Hydrocarbon_Toxicity_amplicon_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_Hydrocarbon_Toxicity_amplicon_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is Illumina 16s (bacterial) amplicon sequencing data for the Macquarie Island mesocosm ecotoxicology study.\nIn-situ soil mesocosms (n=20) were set up on Macquarie Island in February 2013. Following a year\u2019s equilibration, mesocosms were spiked in triplicate with a fuel mixture mimicking the composition of aged fuel spills on Macquarie Island, in addition to five solvent-only controls. Spiking concentrations range from 50mg/kg to 10000 mg/kg, all in triplicate, in addition to 5 solvent \u2013 only controls. Soils have been sampled from initial set up until April 2015, with a total of 2 pre-spike and 4 post-spike sample sets. \n\nDNA was extracted from soil in triplicate. Automated Ribosomal Intergenic Spacer analysis (ARISA) was conducted for both Fungal and Bacterial soil communities across all extractions (n = 270). ARISA results confirmed uniformity of replicate extractions. One replicate for each sample was selected for further work (n=90).\n\nIllumina sequencing was conducted with the 90 samples and these samples correspond with those also selected for microfluidic qPCR. See Thesis in publications section for more details. \n\nThis data was processed in Mothur, using only the forward read. Missing samples are due to quality control, and duplicate samples are due to re-run in a subsequent Illumina submission. Three spreadsheets are included; \n- OTU abundance by sample, which is the key output document following processing in Mothur\n- OTU taxonomy classification, which identifies each OUT\n- Illumina with Enviro for Primer, which is a processed copy of the first document, together with key environmental data, for use in multivariate analysis and cross-referencing with microfluidic qPCR data . 'Environmental' tab provides additional data on samples, including TPH data and vegetation observations at one time point (expressed as percentage of coverage). Contact Sarah Houlahan for more information on chemical data.\n\nOTU = operational taxonomic unit.\n\nSample Set/Action ### Date ### Weeks after spiking\n\nEstablishment ### 17/01/2013 ### -45 weeks\nP1 ### 01/02/2013 ### -43 weeks\nP2 ### 30/10/2013 ### -3 weeks\nSpiking ### 19-25/11/2013 ### 0 weeks\nT1 ### 10/12/2013 ### 2 weeks\nT2 ### 30/03/2014 ### 18 weeks\nT3 ### 07/01/2015 ### 59 weeks\nT4 ### 24/03/2015 ### 69 weeks", "links": [ { diff --git a/datasets/AAS_4135_MI_Soil_Chemistry_1.json b/datasets/AAS_4135_MI_Soil_Chemistry_1.json index e8fc10c9f9..9dadcc408e 100644 --- a/datasets/AAS_4135_MI_Soil_Chemistry_1.json +++ b/datasets/AAS_4135_MI_Soil_Chemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_MI_Soil_Chemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil cores were collected from numerous locations across the Macquarie Island Isthmus. Soil samples were collected in clean glass jars with Teflon septa in the lid and stored at 4 deg C until analysed. Samples were solvent extracted twice with n-hexane using an Accelerated Solvent Extractor [ASE300] (preheat 5 minutes, heat 5 minutes to 70\u00b0C, static 5 minutes, flush 70% volume, purge 300 seconds, 1500 Psi, 3 cycles). Extracts were using gas chromatography-mass spectrometry (GC-MS) using an Agilent 7890A GC-MS operating in one dimension, coupled to a Pegasus time-of-flight-mass spectrometer (GCxGC-ToFMS).\n\nColumn labels\nSite name\tLocation identifier code\nSite description\tDescription of sampling site location\nSeason collected\tSummer season during which samples were collected\nLatitude\tDecimal degrees\nLongitude\tDecimal degrees\nDepth (m)\tSampling Depth \u2013 depth below ground from which sample was collected (m)\nC10-37 n-Alkanes (ug/g)\tConcentration of Alkanes (ug) with carbon chain lengths 10-37 per gram (g) of soil. nd = not detected\nC10-C22 n-alkanes (ug/g)\tConcentration of Alkanes (ug) with carbon chain lengths 10-22 per gram (g) of soil. nd = not detected\nC23-C37 n-alkanes (ug/g)\tConcentration of Alkanes (ug) with carbon chain lengths 23-37 per gram (g) of soil. nd = not detected\nC12-C22 n-alkanes (ug/g)\tConcentration of Alkanes (ug) with carbon chain lengths 12-22 per gram (g) of soil. nd = not detected\nC23-C34 n-alkanes (ug/g)\tConcentration of Alkanes (ug) with carbon chain lengths 23-34 per gram (g) of soil. nd = not detected", "links": [ { diff --git a/datasets/AAS_4135_soil_invertebrates_1.json b/datasets/AAS_4135_soil_invertebrates_1.json index 907fa7beb8..e3d8f1094b 100644 --- a/datasets/AAS_4135_soil_invertebrates_1.json +++ b/datasets/AAS_4135_soil_invertebrates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_soil_invertebrates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil cores were collected from numerous locations across the Macquarie Island Isthmus. Replicate samples were collected from each location and a single sample was collected from 50cm depth below the surface. Soil cores were cylindrical, diameter 70 mm, depth 70 mm and included surface vegetation where present. Invertebrates were extracted from the soil core using a heat gradient process. Specimens were preserved in ethanol following extraction.\nInvertebrates were identified under microscope to the lowest practicable level, which was species level for most taxa. Mites (Acarina) were identified to morphotype.\n\n\nColumn labels\nSample - Unique numerical sample identifier\nDepth - Depth of collection: surface = 0 cm, depth = 50 cm below surface\nLocation - Location identifier code\nEasting - easting (m)\nNorthing - northing (m)\nElevation (m) - Height above sea level (m)\nParisotoma insularis - Abundance of Parisotoma insularis in core sample\nFolsotoma punctata - Abundance of Folsotoma punctata in core sample\nTullbergia bisetosa - Abundance of Tullbergia bisetosa in core sample\nCeratophysella denticulata - Abundance of Ceratophysella denticulata in core sample\nHypogastrura viatica - Abundance of Hypogastrura viatica in core sample\nHypogastrura purpurescens - Abundance of Hypogastrura purpurescens in core sample\nCryptopygus antarcticus - Abundance of Cryptopygus antarcticus in core sample\nCryptopygus caecus - Abundance of Cryptopygus caecus in core sample\nCryptopygus lawrencii - Abundance of Cryptopygus lawrencii in core sample\nCryptopygus tricuspus - Abundance of Cryptopygus tricuspus in core sample\nPolykatianna davidii - Abundance of Polykatianna davidii in core sample\nLepidocyrtus sp. - Abundance of Lepidocyrtus sp. in core sample\nMegalothorax sp. - Abundance of Megalothorax sp. in core sample\nAcarina 1 - Abundance of Acarina 1 in core sample\nAcarina 2 - Abundance of Acarina 2 in core sample\nAcarina 3 - Abundance of Acarina 3 in core sample\nAcarina 4 - Abundance of Acarina 4 in core sample\nAcarina 5 - Abundance of Acarina 5 in core sample\nAcarina 6 - Abundance of Acarina 6 in core sample\nAcarina 7 - Abundance of Acarina 7 in core sample\nAcarina 8 - Abundance of Acarina 8 in core sample\nBig dark tick - Abundance of Big dark tick in core sample\nSpider - Abundance of Spider in core sample\nMicroscolex macquariensis - Abundance of Microscolex macquariensis in core sample\nEnchytraeus albidus - Abundance of Enchytraeus albidus in core sample\nNematode species - Abundance of Nematode species in core sample\nStyloniscus otakensis - Abundance of Styloniscus otakensis in core sample\nHarpacticoid - Abundance of Harpacticoid in core sample\nPuhuruhuru patersoni - Abundance of Puhuruhuru patersoni in core sample\nStenomalium sp. - Abundance of Stenomalium sp. in core sample\nThinophilus (fly) - Abundance of Thinophilus (fly) in core sample\nAustralimyza - Abundance of Australimyza in core sample\nGrasshopper? - Abundance of Grasshopper? in core sample\nSlug sp. - Abundance of Slug sp. in core sample\n\nThe easting, northing and elevation for each sample site was collected by Lauren Wise of the AAD and Josie van Dorst of the University of NSW using a Trimble differential GPS and the post processing was done by Dan Wilkins of the AAD. The elevations were derived using the global geoid model EGM96. \nTo convert the eastings and northings of the sample sites to eastings and northings on the WGS84 datum of the Australian Antarctic Data Centre's GIS data representing the Macquarie Island station buildings and structures, add 1.40 metres to the eastings and 0.2 metres to the northings as given on page 3 of the survey report \"Macquarie Island OSG Survey Campaign, Voyage 8 Round Trip, March 2002\" by John VanderNiet and Nick Bowden of the Office of the Surveyor General, Tasmania.", "links": [ { diff --git a/datasets/AAS_4135_soil_properties_1.json b/datasets/AAS_4135_soil_properties_1.json index eb2b48c434..cf86810347 100644 --- a/datasets/AAS_4135_soil_properties_1.json +++ b/datasets/AAS_4135_soil_properties_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4135_soil_properties_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil cores were collected from numerous locations across the Macquarie Island Isthmus to test the residual toxicity of petroleum hydrocarbons in sub-Antarctic soils.. Replicate samples were collected from each location and a single sample was collected from 50cm depth below the surface. Soil samples were collected in plastic sample jars.\n\nColumn labels\nSample - Unique numerical sample identifier\nLocation - Location identifier code\nEasting - easting (m)\nNorthing - northing (m)\nDepth - Depth of collection (cm) below surface\n% Organic - Organic matter determined by loss on ignition at 550 degrees centigrade\n% 2mm - Proportion of sample by mass with particle size greater than 2mm determined by dry sieving\n% 63 um - Proportion of sample by mass with particle size greater than 63 micron, less than 2mm determined by dry sieving\n% less than 63 um - Proportion of sample by mass with particle size less than 63 micron determined by dry sieving\n% Water - Proportion of water in field collected sample, determined by loss of mass after drying at 80deg C until constant weight\nCond uS/cm - Electrical conductivity of soil determined using field conductivity probe after addition of milli-Q water and shaking for 2 h\npH - pH of soil determined using field pH probe after addition of milli-Q water and shaking for 2 h", "links": [ { diff --git a/datasets/AAS_4140_Environmentaldata_1.json b/datasets/AAS_4140_Environmentaldata_1.json index 5980ba1203..5224310f86 100644 --- a/datasets/AAS_4140_Environmentaldata_1.json +++ b/datasets/AAS_4140_Environmentaldata_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4140_Environmentaldata_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton were collected during the winter-spring transition during two cruises of the Aurora Australis: SIPEX in 2007 and SIPEX II in 2012. As part of the collections sea ice cores were collected to describe the ice habitat during the period of zooplankton collections. Ice cores were taken with a 20 cm diameter SIPRE corer and sectioned in the field with an ice core. Temperature was measured in the section using a spike thermometer and slivers of each section were melted without filtered water to record salinity. The remainders of each section were melted at 4oC in filtered seawater and the melted water was used to measure chlorophyll a concentration, and meiofauna species and abundance.\n", "links": [ { diff --git a/datasets/AAS_4140_IceMeiofauna_2.json b/datasets/AAS_4140_IceMeiofauna_2.json index 644549f630..f7d4896e0b 100644 --- a/datasets/AAS_4140_IceMeiofauna_2.json +++ b/datasets/AAS_4140_IceMeiofauna_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4140_IceMeiofauna_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton were collected during the winter-spring transition during two cruises of the Aurora Australis: SIPEX in 2007 and SIPEX II in 2012. As part of the collections sea ice cores were collected to describe the ice habitat during the period of zooplankton collections. Ice cores were taken with a 20 cm diameter SIPRE corer and sectioned in the field with an ice core. Temperature was measured in the section using a spike thermometer and slivers of each section were melted without filtered water to record salinity. The remainders of each section were melted at 4oC in filtered seawater and the melted water was used to measure chlorophyll a concentration, and meiofauna species and abundance. Meiofauna were counted and identified using a Leica M12 microscope: to species in most cases and down to stage during 2012.", "links": [ { diff --git a/datasets/AAS_4140_Zooplankton_lengths_1.json b/datasets/AAS_4140_Zooplankton_lengths_1.json index 8192ae2e28..19b79222e1 100644 --- a/datasets/AAS_4140_Zooplankton_lengths_1.json +++ b/datasets/AAS_4140_Zooplankton_lengths_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4140_Zooplankton_lengths_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton were collected during the winter-spring transition during two cruises of the Aurora Australis: SIPEX in 2007 and SIPEX II in 2012. To determine size and biomass, key species were measured. Measurements of Prosome, Urosome and Total length are provided. The zooplankton were taken from samples collected with umbrella nets, RMT1 net and sea ice cores. They were measured under a Leica M165C steromicroscope using an ocular micrometer. The ocular micrometer was calibrated against a stage micrometer (+/- 0.01 um).", "links": [ { diff --git a/datasets/AAS_4156_Campbell_Diatoms_1.json b/datasets/AAS_4156_Campbell_Diatoms_1.json index 8f9ce409b7..fe649cc6fd 100644 --- a/datasets/AAS_4156_Campbell_Diatoms_1.json +++ b/datasets/AAS_4156_Campbell_Diatoms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4156_Campbell_Diatoms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Public Summary of AAS project 4156 - High resolution reconstructions of climate and ecosystem variability in the sub-Antarctic during the last two millennia\n\nOur understanding of global climate and ability to predict future changes is limited by a lack of long-term (palaeoclimate) data from the Southern Hemisphere (SH). Sub-Antarctic islands are the only landmasses between Antarctica and the mid latitudes where terrestrial palaeoclimate records exist, making them crucial locations for linking data from the mid and high latitudes. Using lake sediments from sub-Antarctic islands, we will examine how the climate and ecosystems have changed over the last 2000 years. This will contribute vital information to understand SH climate and ecosystem variability\n\nTaken from the abstract of the referenced paper:\n\nSub-Antarctic islands are ideally placed to reconstruct past changes in Southern Hemisphere westerly wind behaviour. They lie within their core belt (50-60 degrees South) and the strong winds deliver sea salt ions to the islands resulting in a west to east conductivity gradient in their water bodies. This means that the stronger (or weaker) the winds, the higher (or lower) the conductivity values measured in the water bodies. A survey of the water chemistry and diatom assemblages of lakes and ponds on sub-Antarctic Campbell Island (52 degrees 32 minutes S, 169 degrees 8 minutes E) revealed that, similar to other sub-Antarctic islands, conductivity was the most important, statistically significant ecological variable explaining turnover in diatom community structure. Based on this, a diatom-conductivity transfer function was developed (simple weighted averaging with inverse deshrinking). This transfer function will be applied to lake sediment cores from the western edge of the Campbell Island plateau to reconstruct past conductivity/sea spray and therefore directly reconstruct changes in Southern Hemisphere westerly wind strength within their core belt.", "links": [ { diff --git a/datasets/AAS_4156_Macquarie_Island_Emerald_Lake_1.json b/datasets/AAS_4156_Macquarie_Island_Emerald_Lake_1.json index 9550a7ad39..31309fc40e 100644 --- a/datasets/AAS_4156_Macquarie_Island_Emerald_Lake_1.json +++ b/datasets/AAS_4156_Macquarie_Island_Emerald_Lake_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4156_Macquarie_Island_Emerald_Lake_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstructed sea spray and minerogenic data for a 12,000 year lake sediment record from Emerald Lake, Macquarie Island. Proxies are based on biological (diatoms) and geochemical (micro x-ray fluorescence and hyperspectral imaging) indicators.\nData correspond to the figures in: Saunders et al. 2018 Holocene dynamics of the Southern Hemisphere westerly winds and possible links to CO2 outgassing. Nature Geoscience 11:650-655. doi.org/10.1038/s41561-018-0186-5.\nDetailed supplementary information: https://static-content.springer.com/esm/art%3A10.1038%2Fs41561-018-0186-5/MediaObjects/41561_2018_186_MOESM1_ESM.pdf\n\nAbstract: The Southern Hemisphere westerly winds (SHW) play an important role in regulating the capacity of the Southern Ocean carbon sink. They modulate upwelling of carbon-rich deep water and, with sea ice, determine the ocean surface area available for air\u2013sea gas exchange. Some models indicate that the current strengthening and poleward shift of these winds will weaken the carbon sink. If correct, centennial- to millennial-scale reconstructions of the SHW intensity should be linked with past changes in atmospheric CO2, temperature and sea ice. Here we present a 12,300-year reconstruction of wind strength based on three independent proxies that track inputs of sea-salt aerosols and minerogenic particles accumulating in lake sediments on sub-Antarctic Macquarie Island. Between about 12.1 thousand years ago (ka) and 11.2 ka, and since about 7 ka, the wind intensities were above their long-term mean and corresponded with increasing atmospheric CO2. Conversely, from about 11.2 to 7.2 ka, the wind intensities were below their long-term mean and corresponded with decreasing atmospheric CO2. These observations are consistent with model inferences of enhanced SHW contributing to the long-term outgassing of CO2 from the Southern Ocean.", "links": [ { diff --git a/datasets/AAS_4156_Macquarie_Island_unnamed_lake_1.json b/datasets/AAS_4156_Macquarie_Island_unnamed_lake_1.json index 863b26f71e..c1c48bd635 100644 --- a/datasets/AAS_4156_Macquarie_Island_unnamed_lake_1.json +++ b/datasets/AAS_4156_Macquarie_Island_unnamed_lake_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4156_Macquarie_Island_unnamed_lake_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Age-depth and geochemical data for a 2000 year lake sediment record from an unnamed lake on Macquarie Island. The lake is the small lake to the west of Major Lake, on the edge of the Macquarie Island plateau. The chronology is based on lead-210 (last ca. 100 years) and radiocarbon (extending to ca. 2000 years). Geochemistry is based on micro x-ray fluroescence, and carbon, nitrogen and sulphur contents. Grain size and water content were also measured. \nData correspond to the publication: Saunders et al. in prep.Southern Hemisphere westerly wind variability in the sub-Antarctic and relationships to mid-latitude precipitation for the last 2000 years", "links": [ { diff --git a/datasets/AAS_4157_Clouds_1.json b/datasets/AAS_4157_Clouds_1.json index 3659ffdfba..b11a8a476b 100644 --- a/datasets/AAS_4157_Clouds_1.json +++ b/datasets/AAS_4157_Clouds_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4157_Clouds_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "It had been shown that remote cloud detection can be performed with the use of new generation Thermopile detectors. The detection method is based on the fact that a cloudy sky will be warmer than a clear sky. An ideal cloud detection system would also need to account for the effects of relative humidity and barometric pressure, however good performance can still be obtained by ignoring these effects. \n\nAAD Thermopile Detector\n=====================\nA Thermopile detector is used to remotely measure the temperature of the sky. The TPS 534 Thermopile detector chosen is fitted with a 5.5um Longpass (standard) IR filter, which allows precise remote temperature measurement of an ideal black body source. \n\nThe TPS 534 Thermopile detector produces an output voltage that is positive when the temperature of the scene it is viewing is higher than the temperature of itself, and a negative output voltage when the temperature of the scene it is viewing is lower than the temperature of itself. For this reason it is necessary to compensate for the temperature of the detector. The TPS 534 Thermopile detector has an internal NTC Thermistor which can be used for temperature compensation. \n\nThis Cloud Detector design implements a very simple analogue form of temperature compensation. The main drawback of an analogue temperature compensation system is that the NTC Thermistor has a very non-linear response with temperature which can only be partially corrected using a linearization resistance network. The other main drawback of an analogue temperature compensation system is that the system gains and voltage levels must be precisely adjusted by trial and error to guarantee correct operation over the desired operational temperature range.\n\nThe Cloud Detector is designed for an operational temperature range of -30 degrees to +25 degrees Celsius. Operation outside of this range may cause internal signal saturation, and incorrect temperature compensation performance. The Cloud Detector optical field of view has been constrained to a 30 degrees full angle with the use of a cylindrical baffle assembly fitted directly to the Thermopile detector. The dimensions of the cylindrical baffle assembly could in theory be defined such that any field of view up to 80 degrees could be achieved.\n\nThe Cloud Detector provides three plus or minus 10V output voltage signals to the data logging hardware :\n\n- Uncompensated Sensor Output Signal :\n\nThermopile detector output signal without any analogue temperature compensation.\n\nThe output voltage is proportional to the amount of cloud detected within the field of view of the \tinstrument.\n\n- Compensated Sensor Output Signal :\n\nThermopile detector output signal with analogue temperature compensation.\n\nThe output voltage is proportional to the amount of cloud detected within the field of view of the \tinstrument.\n\n- Temperature Output Signal :\n\nLinearised NTC Thermistor output signal used to apply analogue temperature compensation to the Thermopile detector output signal.\n\nThe output voltage is proportional to the temperature of the Thermopile detector. The output voltage is uncalibrated, however the temperature verses output voltage could easily be measured.\n\nBoltwood Cloud Sensor\n===================\n\nThis is a commercial cloud sensor unit manufactured by diffraction limited.", "links": [ { diff --git a/datasets/AAS_4158_POA_ANNUA_Herbicide_1.json b/datasets/AAS_4158_POA_ANNUA_Herbicide_1.json index fd15ec3a1c..57cbdfa100 100644 --- a/datasets/AAS_4158_POA_ANNUA_Herbicide_1.json +++ b/datasets/AAS_4158_POA_ANNUA_Herbicide_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4158_POA_ANNUA_Herbicide_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes the persistence and movement of herbicides in 2 soil types from Macquarie Island. The soil characterization spreadsheet provides physical and chemical analyses of several Macquarie Island soils. A column leaching experiment was then used to assess the leaching and persistence in two Macquarie Island soils. Details of this experimental set up, and collection of leachate samples is provided in the Core leaching data spreadsheet. These samples were then analysed using LCMS to determine the concentration of glyphosate and AMPA in the leachate (Leachate samples_analysis)", "links": [ { diff --git a/datasets/AAS_4158_POA_ANNUA_Management_1.json b/datasets/AAS_4158_POA_ANNUA_Management_1.json index 7b768bb8e3..0b965f21f4 100644 --- a/datasets/AAS_4158_POA_ANNUA_Management_1.json +++ b/datasets/AAS_4158_POA_ANNUA_Management_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4158_POA_ANNUA_Management_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes several experiments undertaken to determine the efficacy of various control methods on Poa annua on Macquarie Island.\n\nThe Management Trials spreadsheet quantifies the efficacy of several physical control methods on Poa annua in situ on Macquarie Island, and their impact on species richness.\n\nThe herbicide efficacy_1 rate spreadsheet quantifies the efficacy and selectivity of 12 herbicide treatments on Poa annua grown ex situ under sub-Antarctic temperatures.\n\nThe herbicide efficacy_several rates spreadsheet quantifies the efficacy and selectivity of the 3 herbicides deemed to be most effective and selective on Poa annua in the above dataset, at different rates and using different application methods ex situ at sub-Antarctic temperatures\n\nThe sites datasheet describes the study sites used in the Management Trials spreadsheet.", "links": [ { diff --git a/datasets/AAS_4159_GPS_Radio_Occultation_1.json b/datasets/AAS_4159_GPS_Radio_Occultation_1.json index d7f092f361..516221d80d 100644 --- a/datasets/AAS_4159_GPS_Radio_Occultation_1.json +++ b/datasets/AAS_4159_GPS_Radio_Occultation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4159_GPS_Radio_Occultation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPS RO technique has a number of advantages over the traditional RS such as its global coverage, high vertical resolution, 24 hour availability, high accuracy, all weather capability and lack of bias effects. The RO data contains high resolution height, temperature, pressure, refractivity, bending angle and water vapour content at the tangent point locations. There are a number of satellite constellations that are capable of deriving RO measurements. In this project we utilised data from the FORMOSAT-3 (Taiwan's Formosa Satellite Missions # 3)/ COSMIC (Constellation Observing System for Meteorology Ionosphere and Climate) and CHAMP (CHAllenging Minisatellite Payload) satellites. These constellations are capable of producing many measurements daily, the Cosmic Data Analysis and Archive Centre (CDAAC) provides around 1800 neutral atmospheric profiles per day. \n\nRS measurements have been the dominant method for the acquisition of upper air atmospheric information for the last 70 years. The RS monitoring technique measures atmospheric profiles of pressure, temperature and humidity using sensors attached to balloons. The data collected by the sensors is transmitted to the ground based weather station. The usual operational frequency is two times per day (0000 and 1200 UT). A global RS network of approximately 1500 stations is currently in operation. The RS monitoring method has a limited coverage, low spatial and temporal resolution and is normally restricted to land masses. \n\nIn the Antarctic region there are only 16 weather stations mainly distributed along the coastal fringe due to the environmental harshness and costs involved. As such this RS network is far from ideal for studying the atmosphere, meteorology and climatology in the Antarctic region. It does however provide excellent reference stations to test and validate the RO technique as a suitable meteorological data type in the Antarctic region.\n\nThese data were downloaded from the CDAAC: COSMIC Data Analysis and Archive Centre website.\nhttp://cdaac-www.cosmic.ucar.edu/cdaac/index.html\n\nThese data are freely available. \n\nWe downloaded and used data from the CHAMP and COSMIC wetPrf, atmPrf and sonPrf data files.\n\nThe GPS RO data was tested against co-located radiosonde measurements from 16 radiosonde weather stations located in Antarctica. We investigated the spatial and temporal buffer required for a large and accurate data set. We found that a spatial and temporal buffer set of 300km and 3 hours to be appropriate to test the RO data sets. The RO data sets were found to match well with the radiosonde measurements in the Antarctic region.\n\nWe then used these data sets to investigate annual, bimonthly temperature trends at various heights (pressure levels) and at various locations.\n\nThese data were collected by the CDAAC: COSMIC Data Analysis and Archive Centre.\nWe used COSMIC data collected from 1st January 2007 to 31st December 2014.\nWe used CHAMP data collected from 2003 to 2008.", "links": [ { diff --git a/datasets/AAS_4167_GASLAB_FLASK_3.json b/datasets/AAS_4167_GASLAB_FLASK_3.json index 428d7444d9..24c9014eb3 100644 --- a/datasets/AAS_4167_GASLAB_FLASK_3.json +++ b/datasets/AAS_4167_GASLAB_FLASK_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4167_GASLAB_FLASK_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CSIRO Oceans and Atmosphere - GASLAB discrete flask data from Antarctic/Sub-Antarctic sampling sites, which is part of a global sampling network. Air samples were collected in glass flasks at Casey (CYA), Mawson (MAA), and Macquarie Island (MQA) and analyzed at CSIRO Aspendale Laboratories for methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), hydrogen (H2), carbon monoxide (CO) and carbon 13 of CO2 (CO2C13).\n\nSampling site details:\nCYA: Current Sampling Altitude(m): 55.0\t\tStation Altitude:47.0\t\tIntake Height:8.0\nMQA: Current Sampling Altitude(m): 13.0\t\tStation Altitude:6.0\t\tIntake Height:7.0\nMAA: Current Sampling Altitude(m): 42.0\t\tStation Altitude:32.0\t\tIntake Height:10.0\n\nTrace gas species:\n\nSpecies: CO2; Scale: WMOX2007 CO2 mole fraction scale; Units: parts per million (ppm) or micromoles per mole of dry air.\nSpecies: CH4; Calibration scale: WMO X2004A CH4 scale; Units: parts per billion (ppb) or nanomoles per mole of dry air.\nSpecies: CO; Calibration scale: CSIRO94 CO scale (derived from the scale maintained by NOAA/CMDL in 1991); Units: parts per billion (ppb) or nanomoles per mole of dry air\nSpecies: co2c13; Calibration scale: VPDB-CO2 scale (CSIRO2005) - units of per mill\nSpecies: co2o18; Calibration scale: VPDB-CO2 scale (CSIRO2005) - units of per mill\nSpecies: H2; Calibration scale: MPI 2009 H2 scale (Max Planck Institute, Germany); Units: parts per billion (ppb) or nanomoles per mole of dry air\nSpecies: N2O; Calibration Scale: NOAA 2006A N2O scale (National Oceanic and Atmospheric Administration, USA); Units: parts per billion (ppb) or nanomoles per mole of dry air\n\nContacts:\n david.etheridge@csiro.au\n\t\tpaul.steele@csiro.au\n\t\tpaul.krummel@csiro.au\n\t\tray.langenfelds@csiro.au\n\t\tmarcel.vanderschoot@csiro.au\n\nFor further information and before using these data, please refer to \nthe relevant README file accessible at :\nftp://gaspublic:gaspublic@ftp.dar.csiro.au/pub/data/gaslab\n\nThis data is part of an integrated global greenhouse gas dataset maintained by the World Data Centre for Greenhouse Gases (http://ds.data.jma.go.jp/gmd/wdcgg/).\n\nThe latest data update was provided on July 6, 2021.", "links": [ { diff --git a/datasets/AAS_4177_Abatus_Morphology_Davis_2012_13_2.json b/datasets/AAS_4177_Abatus_Morphology_Davis_2012_13_2.json index 498124b7fe..98c69851c4 100644 --- a/datasets/AAS_4177_Abatus_Morphology_Davis_2012_13_2.json +++ b/datasets/AAS_4177_Abatus_Morphology_Davis_2012_13_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_Abatus_Morphology_Davis_2012_13_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on the morphological and reproductive responses of 4 species of wild caught Abatus heart urchins (A. nimrodi, A. shackletoni, A. ingens, and A. philippii) to sewage effluent from the Davis station sewage outfall. Between 19 and 21 individuals of each species were collected from three sites close to the station. The Sewage outfall site, which acted as the impacted site for the study, and two reference sites, one at Airport Beach, and a second and Heidemann Bay.\n\nMorphological measurements taken from each individual were length, width, height, anterior length, and posterior length. A qualitative assessment of the calcareous test of each individual was conducted to determine the presence of any abnormalities (as per Land 2005, PhD thesis) in the individuals morphology. \n\nReproductive data collected were a gonadosotic index (calculated by dividing the gonal mass of a individual by the total mass of that individual). And for females morphological measurements (length and width) of each brood pouch were taken, and the type and number of juveniles in each pouch was counted.\n\nData available: In the spreadsheet provided a description of measurements is given in the first tab. \n\nAll morphological and reproductive data is presented in the second tab. In full these are;\nParent Barcode (for tracking purposes) \nIndividual Barcode (for tracking purposes),\ndate collected (date the animal was collected)\ndate processed (date data were collected)\nsite (site the animal came from)\nspecies (nimrodi, shackletoni, ingens, or philippii)\nsex (male or female)\nsamples taken for other projects (morphology, genetics, histology)\nMorphological measurements (length, width, height, posterior length, anterior length, all recorded in millimetres)\nAny of a possible 6 abnormalities observed.\nBrood pouch morphometrics (length and width in millimeters of each of the 4 brood pouches for a female) \nReproductive fitness, being the number of young at any of 3 stages in each of the 4 brood pouches and the total number of juveniles produced by the adult female. \nTotal Wet Mass (mass of the entire animal recorded in grams)\nGonad Wet Mass (mass of the gonad of an individual)\nGonadosmotic Index (measure of reproductive fitness, and is the Gonad Wet Mass divided by the Total Wet Mass of each individual)\n\nA blank datasheet used to record the data is contained within the third tab. \n\nThe two final tabs are appendices used to aid the qualitative assessments. The first (Appendix 1) gives photo descriptions of each of the known abnormalities in Abatus sp (Adapted from Lane (2005) PhD thesis). The second (Appendix 2) gives photo descriptions of each of the developmental stages of juveniles in Abatus sp.", "links": [ { diff --git a/datasets/AAS_4177_CaseyStationIsotopesShellfish_1.json b/datasets/AAS_4177_CaseyStationIsotopesShellfish_1.json index a89f564835..9b70e69751 100644 --- a/datasets/AAS_4177_CaseyStationIsotopesShellfish_1.json +++ b/datasets/AAS_4177_CaseyStationIsotopesShellfish_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_CaseyStationIsotopesShellfish_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data shows carbon and nitrogen stable isotope concentration in siphon tissue of laternula elliptica from three sites adjacent to Casey Station. McGrady Cove, Brown Bay Inner and Shannon Bay. All shellfish were collected by divers during the 2014/15 summer season. Samples were sent to Cornell University Stable Isotope laboratory for analysis.", "links": [ { diff --git a/datasets/AAS_4177_CaseyStationIsotopes_1.json b/datasets/AAS_4177_CaseyStationIsotopes_1.json index 98353b83dd..e5e25256f0 100644 --- a/datasets/AAS_4177_CaseyStationIsotopes_1.json +++ b/datasets/AAS_4177_CaseyStationIsotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_CaseyStationIsotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data show results of carbon and nitrogen stable isotopes in muscle tissue of Trematomus bernacchii collected at 5 sites adjacent to Casey Station. Sites are contaminated Brown Bay, near Wilkes Station, Shannon Bay and reference O'Brien Bay and Sparkes Bay. Approximately 1cm3 of muscle tissue from the left side of each fish was taken for stable isotopes analysis.", "links": [ { diff --git a/datasets/AAS_4177_DavisStationTrematomusGrossBodyMeasurements_2.json b/datasets/AAS_4177_DavisStationTrematomusGrossBodyMeasurements_2.json index d4524e7095..5611703a0b 100644 --- a/datasets/AAS_4177_DavisStationTrematomusGrossBodyMeasurements_2.json +++ b/datasets/AAS_4177_DavisStationTrematomusGrossBodyMeasurements_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_DavisStationTrematomusGrossBodyMeasurements_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data show length (cm) and weight (g) of Trematomus bernacchii from four sites along a gradient south in the direction of the current from the Davis Station wastewater outfall (Outfall (0km); Torkler Rocks (1km); Warriner Island (4km) and Kazak Island (9km)) and two reference sites north of the outfall (Long Fjord (9kmN) and Bandits Hut (16km N). Fish were collected in the summer of 2012/13 using line and box traps. Fish were transported immediately back to the lab for analysis.", "links": [ { diff --git a/datasets/AAS_4177_LaternulaGrossBodyMeasurementsCaseyStation_2.json b/datasets/AAS_4177_LaternulaGrossBodyMeasurementsCaseyStation_2.json index 544b92ab50..31233170e3 100644 --- a/datasets/AAS_4177_LaternulaGrossBodyMeasurementsCaseyStation_2.json +++ b/datasets/AAS_4177_LaternulaGrossBodyMeasurementsCaseyStation_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_LaternulaGrossBodyMeasurementsCaseyStation_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data shows gross body measurements of lantern shellfish (Laternula elliptica) collected by divers at McGrady Cove; Brown Bay Inner, and Shannon Bay. Measurements include length, width, and height of shell and weight with shell on and shell off.", "links": [ { diff --git a/datasets/AAS_4177_Rock_Cod_Wastewater_1.json b/datasets/AAS_4177_Rock_Cod_Wastewater_1.json index 1a5acd576e..88bca8dca8 100644 --- a/datasets/AAS_4177_Rock_Cod_Wastewater_1.json +++ b/datasets/AAS_4177_Rock_Cod_Wastewater_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_Rock_Cod_Wastewater_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record will contain the results of analyses of tissue samples from Antarctic Rock-cod (Trematomus bernacchii) collected at sites around Davis station to determine wastewater exposure and sub-lethal impact. AAS Project 4177. The results of metal analysis, stable isotope analysis and images of histological analysis of fish from Davis Station are in this dataset. \n\n\nSample sites and fish collection\n\nAntarctic Rock-cod were collected at 6 sites from Prydz Bay near Davis Station East Antarctica, during the 2012/13 summer. Approximately twenty fish were collected from each site by line and in box traps from four sites along a (9 km) spatial gradient starting from the Davis Station wastewater outfall, southward 0km (within 250m of the point of discharge), 1km, 4km and 9km, in the direction of the predominant current. Additionally, two reference sites were sampled 9 km and 16 km north of the discharge point. Once collected, fish were immediately returned to the Davis Station laboratories and sacrificed individually by immersion in an Aqui-s solution (~15ml/L). Once no signs of life were present (approximately 5 min), fish length and weight were measured. Tissues were preserved in various ways for a number of analyses to be conducted at a later date. \n\nStable Isotope analysis. \n\nDavis Station Laboratory\nDorsolateral muscle tissue from the left side of each individual was removed, placed in aluminium foil and frozen at -20 degrees C for later analysis. \n\nTissue processing\nA section of frozen tissue was removed (approximately 1 x 1 cm cubed), placed into a clean, acid washed glass crucible and cut into small pieces. This was then dried at 80 degrees C for 48 h. Tissue from each fish was carefully removed and placed into separate 2 ml Eppendorf tubes, each containing an washed, dried stainless steel ball bearing and the lids closed tightly to ensure no moisture could enter. Tissue was crushed into a fine powder by shaking in a Tissue II Lyser. Ball bearings were removed from vials and crushed tissue samples were sent to Cornell University Stable Isotope laboratory for d13C (carbon stable isotope) and d15N (nitrogen stable isotope) analysis. Stable isotope ratios are expressed in parts per thousand units using the standard delta (d) notation d13C and d15N. \n\nData Set \nThis data set consists of an Excel spreadsheet containing raw data of Nitrogen and Carbon Stable Isotope analysis from 6 sites in the Prydz Bay area of East Antarctica. It includes site distance and direction from wastewater discharge point.\n\nThe file name code stable isotope analysis is;\nProject number_Season_Taxa_analysis type\nAAS_4177_12_13_Trematomus_Isotopes\nProject number : AAS_4177\nSeason : 2012/13 season\nTaxa: Trematomus\nAnalysis type: Stable Isotope\n\nMetal analysis. \n\nDavis Station Laboratory\nDorsolateral muscle tissue from the right side of each individual was removed, placed in a plastic zip lock bag and frozen at -20 degrees C for later analysis. \n\nTissue processing\n10g of frozen muscle tissue was sent to Advanced Analytical Australia for metal analysis of a suite of metals (Cd, Cr, Cu, Hg, Mn, Zn, Al, Ni, Pb). \n\nData Set \nThis data set consists of an Excel spreadsheet containing raw data of metal analysis (mg/kg) from 6 sites in the Prydz Bay area of East Antarctica. It includes site distance and direction from wastewater discharge point.\n\nThe file name code stable isotope analysis is;\nProject number_Season_Taxa_analysis type\nAAS_4177_12_13_Trematomus_Metals\nProject number : AAS_4177\nSeason : 2012/13 season\nTaxa: Trematomus\nAnalysis type: Metal analysis\n\nHistological analysis\n\nDavis Station Laboratory\nA small piece of a number of fish tissues (gill, liver, spleen, head kidney, gonad), were collected immediately after death of the fish to ensure no degradation of tissue and preserved in 10% seawater buffered formalin for later analysis\n\nTissue processing\nEach piece of tissue was dehydrated in ascending grades of ethanol (30-100%), cleared in Histolene and embedded in paraffin wax. Tissue was sectioned using a HM 32 Micron microtome at 4 microns. Standard haematoxylin and eosin (H and E) stain was used to stain all tissue sections. Each section was examined blind (i.e. the examiner did not know the field location of the tissue samples) using a Zeiss AxioPlan microscope at 100-400 x magnification. Histological analysis is ongoing. \n\nData Set \nThis data set consists of a pdf file with images of normal and potential sub-lethal histological alterations. \n\nThe file name code stable isotope analysis is;\nProject number_Season_Taxa_analysis type\n AAS_4177_12_13_Trematomus_Histology\nProject number : AAS_4177\nSeason : 2012/13 season\nTaxa: Trematomus\nAnalysis type: Histopathology", "links": [ { diff --git a/datasets/AAS_4177_TrematomusGrossBodyMeasurmentsCaseyStation_2.json b/datasets/AAS_4177_TrematomusGrossBodyMeasurmentsCaseyStation_2.json index 2d37d2cb8b..e060374b79 100644 --- a/datasets/AAS_4177_TrematomusGrossBodyMeasurmentsCaseyStation_2.json +++ b/datasets/AAS_4177_TrematomusGrossBodyMeasurmentsCaseyStation_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4177_TrematomusGrossBodyMeasurmentsCaseyStation_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gross body measurements of fish length (cm), weight (g), and sex (M/F). Fish were collected on line and in box traps at Brown Bay, Shannon Bay, near Wilkes Station, O'Brien Bay and Sparkes Bay. Sex was determined after dissection for other analyses.", "links": [ { diff --git a/datasets/AAS_4180_TV-LTM1415_SedimentChemistry_1.json b/datasets/AAS_4180_TV-LTM1415_SedimentChemistry_1.json index 884d6b914a..21c7bbf5d4 100644 --- a/datasets/AAS_4180_TV-LTM1415_SedimentChemistry_1.json +++ b/datasets/AAS_4180_TV-LTM1415_SedimentChemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4180_TV-LTM1415_SedimentChemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains chemical parameters determined for marine sediment samples collected in the 2014-15 summer field season as part of the Thala Valley Long term Monitoring (TV-LTM) project. The aim of this project is to examine changes in the marine benthic ecosystem in the vicinity of Casey station following clean-up of the abandoned Thala Valley waste disposal ('tip') site in 2003-04.\n\nThe chemical parameters are:\n(1) 1 M hydrochloric acid-extractable elements (mainly metals) by ICP-AES (inductively coupled plasma - atomic emission spectrometry)\n(2) water-extractable nutrients by FIA (flow injection analysis)\n(3) petroleum hydrocarbon fractions (TPH: total petroleum hydrocarbons) and persistent organic pollutants (POPs) - polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) - by GC-FID, GC-ECD and GC-MS (gas chromatography - flame ionization detector / electron capture detector / mass spectrometry), respectively\n(4) Loss on Ignition at 550 degrees Celsius (LOI; as a proxy for Total Organic Matter) and Dry Matter Fraction (DMF) by gravimetric analysis.\n\nData sets 1, 3 and 4 were obtained for composite samples prepared from the 0-5 cm section of 51 marine sediment cores collected by SCUBA divers from impacted (contaminated) and control (pristine) locations around Casey. Data set 2 was obtained for a subsample of the surface section (0-1 cm) of each of 74 marine sediment cores collected during the same sampling campaign.\n\nSample locations:\n* Brown Bay (BB) - inner, mid and outer sites\n* Casey Wharf\n* McGrady Cove\n* O'Brien Bay (OB) - OB1, OB2, OB3 sites\n* Shannon Bay\n* Wilkes (adjacent to abandoned station)\n\nAnalytical labs involved:\n* Wild Lab, AAD, Kingston, Tasmania (data sets 1 and 4; sample preparation for data set 2)\n* Analytical Services Tasmania (AST), New Town, Tasmania (data set 2)\n* Analytical Services Unit (ASU), Queen's University, Kingston, Ontario, Canada (data set 3)\n\nInformation concerning analytical data quality (method reporting limits, accuracy and precision), are included with each data set. Complete analytical method details are available in a separate summary document.", "links": [ { diff --git a/datasets/AAS_4184_Spsequences_1.json b/datasets/AAS_4184_Spsequences_1.json index 36c40b83f9..6587b600d6 100644 --- a/datasets/AAS_4184_Spsequences_1.json +++ b/datasets/AAS_4184_Spsequences_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4184_Spsequences_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of eight files: seven files with the nucleotide sequence data for seven genetic markers (2 mitochondrial and 5 nuclear) and a 'Read me' file containing the information for each marker as well as information regarding sampling locations and individual labels. The sequences were obtained by the Sanger sequencing method in the Australian Genome Research Facility (AGRF) and correspond to Snow petrel (Pagodroma nivea) samples collected at three regions in East Antarctica: Mac. Robertson (area nearby Mawson station and Prince Charles Mountains), Princess Elizabeth Land region (area nearby Davis station) and Wilkes Land (area nearby Casey station).", "links": [ { diff --git a/datasets/AAS_4184_seabirds_seals_genetics_1.json b/datasets/AAS_4184_seabirds_seals_genetics_1.json index 3a9f4bf186..ea876ea2b3 100644 --- a/datasets/AAS_4184_seabirds_seals_genetics_1.json +++ b/datasets/AAS_4184_seabirds_seals_genetics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4184_seabirds_seals_genetics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS project 4184.\n\nPublic summary\n\nThe Weddell seal, Adelie penguin and emperor penguin are key Antarctic predators that are particularly vulnerable to the effects of climate change, as they live within the sea-ice zone and are adapted to thrive in the coldest environment on Earth. Given this threat, an understanding of their resilience to long-term environmental change is of immediate concern. Our research aims to better understand the resilience of Antarctic seabirds and seals to climate change using genetic approaches. We are investigating how these predators were affected by historical climate change by determining changes in their population sizes over the past 80,000 years. We are also assessing patterns of interbreeding among colonies to determine the extent of species dispersal, an important component in understanding the likely responses of populations to climate change. \n\nProject objectives\n\n1. Identify the number of genetic lineages, examine evidence of past population bottlenecks and likely distribution patterns for selected seabirds and seals, particularly during past glacial and inter glacial periods when the breeding habitats for those species were probably very different to present conditions.\n\n2. Determine from genetic studies the phylogeographic relationships among present day populations, the present day migration patterns and population connectivity over a range of spatial scales including the nature, magnitude and direction of movement for populations of selected species of seabirds and seals.\n\nData: Genetic datasets - Sanger sequencing, Illumina short reads, SNP datasets", "links": [ { diff --git a/datasets/AAS_4191_metamorphic_events_1.json b/datasets/AAS_4191_metamorphic_events_1.json index cec6dfd5c8..4be86667ee 100644 --- a/datasets/AAS_4191_metamorphic_events_1.json +++ b/datasets/AAS_4191_metamorphic_events_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4191_metamorphic_events_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The age and conditions of metamorphism of the Windmill Islands were investigated using samples collected in the 2013/2014 field season from locations throughout the Windmill Islands region. The Windmill Islands vary in metamorphic grade, from amphibolite-facies in the north, to high-grade granulite-facies in the south. Samples were selected from peninsulas and islands from north to south to explore the changing conditions in detail throughout the region.\n\nThese are interim results and further work will be undertaken in 2015, particularly to further constrain the P-T conditions.\n\nA list and short summary of all files is provided below. Detailed methodology for data collection is provided in summaries 1 and 2.\n\nSampling locations\nSummary 1 (table 1) provides a table summarising sampling locations for selected samples (.docx file)\n\nAppendix 1- gives all GPS coordinates for sampling locations (.xls file)\n\nAppendix 2- Field photographs from selected locations (.pdf file)\n\n\n\nData relating to age\nSummary 1- provides information on sample locations for geochronology, methodology for LAICPMS and SHRIMP geochronology and a summary of results (.docx file)\n\nFigure 1- U-Pb zircon concordia plots from four structurally constrained magmatic rocks, collected by Post (2000), has been added to further constrain the timing of deformation (.pdf file)\n\nFigure 2- U-Pb monazite concordia plots from metapelites to constrain timing of metamorphism (.pdf file)\n\nTable 2- All SHRIMP U-Pb analyses from structurally controlled magmatic rocks (.xls file)\n\nTable 3- All U-Pb LAICPMS monazite analyses from metapelites (.xls file)\n\nData relating to constraining metamorphic conditions\nSummary 2- information on methodology for constraining PT conditions, petrographic descriptions of selected samples and an explanation of the calculated metamorphic phase diagram (.docx file)\n\nFigure 3- Photomicrographs from selected samples (referred to petrography section of summary 2) (.pdf file)\n\nFigure 4- Calculated metamorphic phase diagram from sample WI-1B (.pdf file)\n\nTable 4- whole rock geochemistry for selected samples, with major elements used for the calculation of metamorphic phase equilibria (.xls file)\n\nTable 5- electron microprobe analyses for selected samples (interim- more to be collected) (.xls file)", "links": [ { diff --git a/datasets/AAS_4192_2015-16_Macquarie_Is_field_data_1.json b/datasets/AAS_4192_2015-16_Macquarie_Is_field_data_1.json index 26dbc7b330..6d157a1905 100644 --- a/datasets/AAS_4192_2015-16_Macquarie_Is_field_data_1.json +++ b/datasets/AAS_4192_2015-16_Macquarie_Is_field_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4192_2015-16_Macquarie_Is_field_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main purpose of the field campaign of Nov-Dec 2015 was to glean real-world data to facilitate the use of a model-data fusion process to infer the exchange of sensible heat and moisture fluxes from healthy and unhealthy A. macquariensis cushions in an effort to quantify differences in the exchange of heat and moisture between stressed and unstressed plants. Recent observations (April 2015) and previous research into the decline of A. macquariensis on the island suggest that exposure may also be linked to cushion health which raises a concern that changing microclimates as a result of changing synoptic weather may provide a key stressor for the cushions. Thus, the field campaign of Nov-Dec 2015 concentrated on an area where there was a good selection of healthy and unhealthy cushions across the landscape and where we could find facing sheltered and exposed slopes of similar steepness and cushion density. The research site chosen was located near Pyramid Peak where a selection of healthy and unhealthy cushions was available at both sites, unlike the northern part of the island where most cushions appear to be damaged and southern Macquarie Island where most cushions show little signs of damage.\nData were collected using a standard automatic weather station (AWS) that provided general weather data for the region from 27 Nov, 2015 to 23 Feb, 2016. Intensive sites were located on each slope at similar heights from 28 Nov to 18 Dec, 2015. Meteorological measurements at these sites included net radiation, PAR, wind speed, temperature, relative humidity, soil moisture and soil and cushion temperatures. In addition, three intensive 24-hour sampling campaigns occurred to determine the daily evolution of wind and temperature profiles together with cushion surface temperatures (using IR thermography) under different meteorological conditions; cloudy, partly cloudy and clear (8-9 Dec; 12-13 Dec; 15 Dec, 2015). Cushion temperatures were also measured from 19 Dec, 2015 to the end of February, 2016.\n\n\nTaken from the abstract of the \"Meta Data Report\" in the download file:\n\nThis report provides metadata and explanations to support the datasets collected during the field excursion to Macquarie Island 2015-2016 as part of Project 4192, Ball.\nThe main purpose of the field campaign of Nov-Dec 2015 was to glean real-world data to facilitate the use of a model-data fusion process to infer the exchange of sensible heat and moisture fluxes from healthy and unhealthy A. macquariensis cushions in an effort to quantify differences in the exchange of heat and moisture between stressed and unstressed plants. Recent observations (April 2015) and previous research into the decline of A. macquariensis on the island suggest that exposure may also be linked to cushion health which raises a concern that changing microclimates as a result of changing synoptic weather may provide a key stressor for the cushions. Thus, the field campaign of Nov-Dec 2015 concentrated on an area where there was a good selection of healthy and unhealthy cushions across the landscape and where we could find facing sheltered and exposed slopes of similar steepness and cushion density. The research site chosen was located near Pyramid Peak where a selection of healthy and unhealthy cushions was available at both sites, unlike the northern part of the island where most cushions appear to be damaged and southern Macquarie Island where most cushions show little signs of damage.\nData were collected using a standard automatic weather station (AWS) that provided general weather data for the region from 27 Nov, 2015 to 23 Feb, 2016. Intensive sites were located on each slope at similar heights from 28 Nov to 18 Dec, 2015. Meteorological measurements at these sites included net radiation, PAR, wind speed, temperature, relative humidity, soil moisture and soil and cushion temperatures. In addition, three intensive 24-hour sampling campaigns occurred to determine the daily evolution of wind and temperature profiles together with cushion surface temperatures (using IR thermography) under different meteorological conditions; cloudy, partly cloudy and clear (8-9 Dec; 12-13 Dec; 15 Dec, 2015). Cushion temperatures were also measured from 19 Dec, 2015 to the end of February, 2016.\nSection 1 presents detailed information about instruments, variables and sampling periods for each dataset. Section 2 provides some useful information on the experimental design.", "links": [ { diff --git a/datasets/AAS_4192_BRYOPHYTES_1.json b/datasets/AAS_4192_BRYOPHYTES_1.json index 5347d30e9f..0025a11577 100644 --- a/datasets/AAS_4192_BRYOPHYTES_1.json +++ b/datasets/AAS_4192_BRYOPHYTES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4192_BRYOPHYTES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Paradoxically, climate warming increases plant injury and death from freezing and heat-induced water stress. Survival depends on a functional water transport system. However, features that enhance freeze or drought resistance also constrain maximum water transport, limiting carbon gain under warmer temperatures, restricting each species' range of optimal environments. We will determine the diversity in stress tolerance and their bases in structure and physiology across subantarctic plant species. Our data will inform prediction of community composition changes under climate warming scenarios.\n\nSpecimens of vascular plants and bryophytes were collected from several sites across Macquarie island between March 5th 2013 and March 10th 2013. The detailed collection location locations for each of the species and the sampling protocol are described in further detail in the file \"Sampling report.doc\".\n\nBryophyte samples were dissected and photographs taken of the overall specimen (clump, turf or mat according to the species in question), individual shoots, fully developed individual leaves and cross-sections of the stems at the base of the photosynthetically active zone. The file names for each of the photographs contain the species name, the site, the replicate number (1, 2 or 3) and the magnification. Often multiple photographs were taken to ensure all features were clearly in focus, the specific images used for measurements of each specimen are included in the results datasheet (described below).\n\nSubsamples of vascular plants and bryophytes were separated and dried for greater than 72 hours at 60 degrees C. These samples were ground and analysed for d18O, d13C, d15N, %N and %C of dry matter. Analyses were made using a for Oxygen measurements and a coupled EA-MS system (EA 1110 Carlo Erba, Milan, Italy; Micromass Isochrom, Middlewhich, UK) for Carbon and Nitrogen measurements.\n\nAll data from bryophyte samples (including some calculated variables) has been compiled into a single spreadsheet in comma-separated format, \"Macquarie_Bryo_Rawdata.csv\". A description of the values of each column follows, and is also contained in the file \"Moss Metadata.txt\".\n\nThe following are descriptions of the contents of each of the columns in the spreadsheet \"Macquarie_Bryo_Rawdata.csv\". Samples were collected between March 5th 2013 and March 10th 2013 during the annual Australian Antarctic Division resupply voyage (V4) and associated with AAS project no. 4192. Samples were collected by the following people: Kate Kiefer (Australian Antarctic Division), Dr. Marilyn Ball (the Australian National University) and Dr. Daniel Stanton (the Australian National University). Subsequent processing (dissection and photography) was conducted by Dr. Vivien Rolland (the Australian National University) and D.S. \n##Specimen identifiers\nCode: 6 letter abbreviation of binomial\nSite: Sampling site; BB-Bauer Bay, STH-Green Gorge/Pyramid Peak, NTH-Brother's Point Elevation:U-upland, L-lowland\nNumber: Plant number within site (3 replicates for most species) Genus:Genus of sample Species:species epithet of sample\nType: Growth form (cushion, tuft, mat or aquatic)\nFuncGroup: Alternative classification of growth form (Cush-cushion, Aqua-aquatic)\nClade: Bry-for Bryophyta or Hep- for Hepatica\nFamily: taxonomic family of species\n##Shoot characters\nNleaves: Number of green photosynthetic leaves/shoot\nNleaves2: Number of seemingly living leaves (some chlorophyll, but not always dense), includes Nleaves + sometimes more\nNBranches: Number of side branches (for Sphagnum) LPhoto1:Depth of photosynthetically active zone if Nleaves is less than Nleaves2 (mm)\nWPhoto1: Width of photosynthetically active zone if Nleaves is less than Nleaves2 (mm)\nAPhoto1: Area of photosynthetically active zone if Nleaves is less than Nleaves2 (side view) (mm2) LPhoto2:Depth of photosynthetically active zone for subreplicate A (mm)\nWPhoto2: Width of photosynthetically active zone for subreplicate A (mm)\nAPhoto2: Area of photosynthetically active zone for subreplicate A (side view) (mm2)\nAV: surface area of shoot viewed from above (mm2)\nLMass: Total dry mass of leaves from a single shoot (g)\nShootMass: Dry mass of shoot after leaves are removed (g)\nBase: If \"y\", then a portion of the non-photosynthetic base of the cushion was included in the shoot measured for dry mass\nLeavesMass: Number of leaves in measurement of dry matter mass, usually all leaves on a representative shoot. In the case of thalloid liverworts this is categorised as NA ##Leaf characters\nPhotoLeaf: Reference photo for leaf measurements (if composite of multiple photos used, then \"Comp\")\nLeafLength: Leaf length (base to tip) (micrometers)\nLeafWidthMax: Maximum leaf width (micrometers)\nLeafWidthMid: Leaf width halfway along leaf (micrometers)\nLeafArea: Total leaf area (micrometers^2)\nAlarArea: Area of alar regions, if present\nCostaLength: Length of costa (micrometers)\nCostaWidth: Width of costa near base (micrometers)\nCostaArea: Total area of costa (micrometers^2)\nLeafNotes: Miscellaneous notes on leaf data ##Stem characters\nPhotoStem: Reference photo for stem cross-section measurements\nDiamStem1: Widest diameter of stem cross-section (micrometers)\nDiamStem2: Narrowest diameter of stem cross-section (micrometers)\nAreaStem: Cross-sectional area of stem (micrometers^2)\nNLayers: Number of distinct cell type layers (1.5 indicates gradient from midstem to epidermis without clear break)*\nCortex: Thickness of outer cuticle if present* (micrometers)\nL1W: Lumen diameter of outer layer cells* (micrometers)\nL1Cw: Cell wall thickness of outer layer cells (doubled, measured lumen edge to lumen edge)* (micrometers)\nL1A: Area of outer layer (and all enclosed layers)* (micrometers^2)\nL1D: Diameter of outer layer (and all enclosed layers)* (micrometers)\nL2W: Lumen diameter of second layer cells* (micrometers)\nL2Cw: Cell wall thickness of second layer cells (doubled, measured lumen edge to lumen edge)* (micrometers)\nL2A: Area of second layer (and all enclosed layers)* (micrometers^2)\nL2D: Diameter of second layer (and all enclosed layers)* (micrometers)\nL3W: Lumen diameter of third layer cells* (micrometers)\nL3Cw: Cell wall thickness of third layer cells (doubled, measured lumen edge to lumen edge)* (micrometers)\nL3A: Area of third layer (and all enclosed layers)* (micrometers^2)\nL3D: Diameter of third layer (and all enclosed layers)* (micrometers)\nL4W: Lumen diameter of fourth layer cells (micrometers)\nL4Cw: Cell wall thickness of fourth layer cells (doubled, measured lumen edge to lumen edge) (micrometers)\nL4A: Area of fourth layer (and all enclosed layers) (micrometers^2)\nL4D: Diameter of fourth layer (and all enclosed layers) (micrometers)\nNotesStem: Miscellaneous notes on stem data ##Dry matter and isotopic characters\nd18O: Oxygen stable isotope ratio (delta 18O relative to SMOW) of dried shoots\nd15N: Nitrogen stable isotope ratio (delta 15N relative to air) of dried shoots\nd13C: Carbon stable isotope ratio (delta 13C relative to VPDB) of dried shoots\nXN: Percent nitrogen content (by mass) of dried shoots\nXC: Percent carbon content (by mass) of dried shoots ##Water content characters: \nVial_wet: Mass of vial + field-wet bryophyte shoot (mg)\nVial_dry: Mass of vial + oven-dry bryophyte shoot (mg)\nVial_empty: Mass of empty vial (mg)\nRWC: (calculated) relative water content (H2O mass / dry shoot mass) \n\n####Calculated characters\nVarea: (Calculated) shoot surface area to vertical radiation (assuming a cylindrical shoot) (mm2)\nVarea_SI: (Calculated) shoot surface area to vertical radiation (assuming a cylindrical shoot) (m2) For the two thallose liverwort species this has been set as equal to APhoto2\nVPhoto: (Calculated) Photosynthetic shoot volume (assuming a cylindrical shoot) (mm3)\nVPhoto_SI: (Calculated) Photosynthetic shoot volume (assuming a cylindrical shoot with just green leaves) (m3)\nVPhoto2_SI: (Calculated) Photosynthetic shoot volume (assuming a cylindrical shoot with all leaves) (m3)\nAPhoto_SI: (Calculated) Photosynthetic shoot surface area (assuming a cylindrical shoot with just green leaves) (m3)\nAPhoto2_SI: (Calculated) Photosynthetic shoot surface area (assuming a cylindrical shoot with all leaves) (m3)\nL1A.adj: (Calculated) Area of stem layer 1 (L1A) after substracting enclosed layers (micrometers^2)\nL1D.adj: (Calculated) Diameter of stem layer 1 (L1D) after substracting enclosed layers (micrometers)\nL2A.adj: (Calculated) Area of stem layer 2 (L2A) after substracting enclosed layers (micrometers^2)\nL2D.adj: (Calculated) Diameter of stem layer 2 (L2D) after substracting enclosed layers (micrometers)\nL3A.adj: (Calculated) Area of stem layer 3 (L3A) after substracting enclosed layers (micrometers^2)\nL3D.adj: (Calculated) Diameter of stem layer 3 (L3D) after substracting enclosed layers (micrometers)\nLeafArea_SI: (Calculated) Individual leaf area (m2)\nStemArea_SI: (Calculated) Cross-sectional area of stem (m^2)\nLeafMass: (Calculated) Dry mass of of an individual leaf (g)\nLMA: (Calculated) leaf mass per area (LeafMass/LeafArea) (g/micrometers^2)\nLMA_SI: (Calculated) leaf mass per area (LeafMass/LeafArea) (g/m^2)\nSMA: (Calculated) leaf mass per area (LeafMass/LeafArea) (g/micrometers^2)\nSMA: (Calculated) leaf mass per area (LeafMass/LeafArea) (g/m^2)\nDens: (Calculated) potential density (1/Varea) (mm^-2)\nDens_SI: (Calculated) potential density (1/Varea_SI) (m^-2)\nNarea: (Calculated) Nitrogen content on a leaf area basis (g/m^2)\nNarea_SMA: (Calculated) Nitrogen content on a shoot area basis (g/m^2)\nPhotoArea: (Calculated) Total photosynthetic leaf area per shoot (Leaf Area x Number of Leaves) (m^2)\nPhotoArea2: (Calculated) Total leaf area per shoot (Leaf Area x Number of Leaves 2) (m^2) \n\n*Note, for thallose liverworts these layers are measured from the upper epidermis downwards. No area is available and LnD indicates the thickness of a given layer.", "links": [ { diff --git a/datasets/AAS_4192_images_1.json b/datasets/AAS_4192_images_1.json index a0549597d4..1569308b7a 100644 --- a/datasets/AAS_4192_images_1.json +++ b/datasets/AAS_4192_images_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4192_images_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set deals with embolism repair in two species of cushion plants (Colobanthus muscoides and Azorella macquariensis) which grow on Macquarie island and rely on protoxylem for water transport. Detailed description of each file can also be found in the readme.doc file.\n\nIndex:\nMovies 1 and 2: Movies of in vitro embolism resorption in Colobanthus muscoides. 1 frame per 0.5s. Both movies have the same scale bar, shown on Movie 2.\nImages 1-13: Bright field time series of in vivo protoxylem embolism resorption in Colobanthus muscoides. Scale identical for all images and visible on Image_6.\nImages 14-17: Confocal images of protoxylem of Azorella macquariensis (Image_14 and Image_15) and Colobanthus muscoides (Image_16 and Image_17). Scale identical for all images and visible on Image_16 and Image_17.\nImages 18-23, 26: Cryo-SEM images of protoxylem of Azorella macquariensis (Image_18, Image_19 and Image_26) and Colobanthus muscoides (Image_20 to Image_23).\nImages 24-25: Bright field images of in vitro protoxylem embolisms in Colobanthus muscoides.\nMeasurements.xls: Measurements on individual bubbles obtain from in vitro embolism resorption in Colobanthus muscoides.\n\n\nSee the download file for a detailed description of the methods used in this project.", "links": [ { diff --git a/datasets/AAS_4200_Be_data_1.json b/datasets/AAS_4200_Be_data_1.json index 9a91f48670..e017c1ef09 100644 --- a/datasets/AAS_4200_Be_data_1.json +++ b/datasets/AAS_4200_Be_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4200_Be_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains measurements of cosmogenic beryllium-10 concentration in firn at Law Dome, Antarctica. The record spans from the end of 2004 until the end of 2014 and is comprised of individual sample sets, generally collected in February of each year. Together, they comprise a quasi-monthly record over this time span and in conjunction with Pedro data set (https://data.aad.gov.au/metadata/records/10Be-Law-Dome-10-year-composite), a continuous quasi-monthly record from 2000-2016. Additionally, this record contains ~ monthly cosmogenic 7Be concentration from mid 2007 until end 2014; these measurements typically span July - January. Dating of the samples is by water isotopes, tied to the AAD DSS master chronology record. All 7Be and 10Be measurements were made at the Australian Nuclear Science and Technology Organisation (ANSTO).", "links": [ { diff --git a/datasets/AAS_4289_temp_change_1.json b/datasets/AAS_4289_temp_change_1.json index 4bb9b37890..6a6bd5aaf1 100644 --- a/datasets/AAS_4289_temp_change_1.json +++ b/datasets/AAS_4289_temp_change_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4289_temp_change_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data collected during and after a series of ice deformation experiments. Seven of the experiments are controls, run at a constant temperature of either -2, -7 or -10 degrees celsius, and four involve a change in temperature partway through the experiment. Vertical displacement and temperature data were collected during the experiments, and microstructural data (fabric analyser thin sections) were collected at the conclusion of each experiment.\n \nThe experimental methods and our interpretations are described thoroughly in Craw, et al. (in prep).\n\nIn folder mechanical_data:\n- One .csv file for each experiment containing a header with information on experimental conditions, and columns of data corresponding to time (hours), vertical displacement (mm), and temperature (degrees celsius) throughout the experiment. This is raw data, there will be points recorded from before weights were added at the beginning of the experiment, and after the temperature was lowered at the end.\n\nin folder microstructural_data:\n- One .mat file for each experiment, containing microstructural data (spatially indexed Euler orientations) formatted to be read by the MTEX toolbox (https://mtex-toolbox.github.io/). There is also a file for an example of the starting material, \"standard\" laboratory ice. These data are converted from the .cis files which are generated by the G50 fabric analyser.\n- One .m script (plot_microstructural_data.m) containing commands for plotting spatial maps, histograms of grain size distribution and pole figures of c-axis orientation from the .mat files in this directory.", "links": [ { diff --git a/datasets/AAS_4291_2016_seaice_1.json b/datasets/AAS_4291_2016_seaice_1.json index 4e16db655a..fd4f3628c1 100644 --- a/datasets/AAS_4291_2016_seaice_1.json +++ b/datasets/AAS_4291_2016_seaice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4291_2016_seaice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data describe a set of sea-ice and seawater physical and biochemical parameters obtained from seawater samples and ice cores drilled from land fast sea ice in the vicinity of Davis Station, East Antarctica at six different dates (stations 1-6) during late Spring 2016.\nStations 1: 16 Nov. 2016\nStations 2: 21 Nov. 2016\nStations 3: 23 Nov. 2016\nStations 4: 26 Nov. 2016\nStations 5: 29 Nov. 2016\nStations 6: 02 Dec. 2016\nParameters measured:\n- Temperature, salinity;\n- Iron: Dissolved (less than 0.2um), soluble (less than 0.02um) colloidal (between 0.02 and 0.2um) and Particulate fractions (greater than 0.2um);\n- Macronutrients: Nitrate (NO3), nitrite (NO2), silicate (Si), phosfate (PO4) and ammonium (NH4);\n- Chlorophyll-a (Chla);\n- Particulate Organic Matter: Particulate Organic Carbon (POC) and Particulate Organic Nitrogen (PON)\n\nSW0: seawater collected at the surface\nSW3: seawater collected at 3m depth\nSW10: seawater collected at 10m depth", "links": [ { diff --git a/datasets/AAS_4291_seaicebgc_1.json b/datasets/AAS_4291_seaicebgc_1.json index abdeea6d07..2249f85d14 100644 --- a/datasets/AAS_4291_seaicebgc_1.json +++ b/datasets/AAS_4291_seaicebgc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4291_seaicebgc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A times series of data was collected from coastal (land-fast) sea ice at Davis Station, Eastern Antarctica (68 degrees 34' 36\" S, 77 degrees 58' 03\" E; Figure 1) from November 16 to December 2, 2015. Sea ice temperature and salinity, as well as macro-nutrients (nitrate NO3-, nitrite NO2-, ammonium NH4+, phosphate PO43- and DSi), particulate organic carbon (POC) and chlorophyll a (Chla) in the sea ice were measured six times in 16 days of austral spring and early summer (Nov. 16, Nov. 20, Nov. 23, Nov. 26, Nov. 29, and Dec. 2; in days of the year, 320, 325, 327, 330, 333, and 336). Depths were measured from the top of the ice cores. Seawater below the ice was also sampled for comparison.\n\nSamples of snow, sea ice, brine and under-ice seawater were collected under trace metal clean conditions near Davis station during the transition of sea ice from winter to spring conditions (October 2015), on a regular basis (every 4 days) for 3 weeks. 6 sampling events were successfully achieved. The list of parameters collected during the fast ice study include in situ temperature, ice texture, pH, oxygen, iron and Chla, Br/I, carbonate, nutrients and POC, incubations with stable N and C isotopes. Samples are currently returning on V3 and will be analysed in the US, Belgium and Australia in the coming months. The biogeochemical observations will allow us to determine the roles of light versus iron in the initiation of the spring bloom in this region, and the role of the melting fast ice in fertilising the spring time primary production.", "links": [ { diff --git a/datasets/AAS_4292_Aurora_Australis_Cloud_Camera_1.json b/datasets/AAS_4292_Aurora_Australis_Cloud_Camera_1.json index ac52fe54b8..ff0a30e005 100644 --- a/datasets/AAS_4292_Aurora_Australis_Cloud_Camera_1.json +++ b/datasets/AAS_4292_Aurora_Australis_Cloud_Camera_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Aurora_Australis_Cloud_Camera_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains 1-minute resolution all-sky (hemispheric fisheye) images obtained on Aurora Australis voyages 1,2 and 3 over the 2015-16 season. \nImages were obtained with a Moonglow Technologies All-Sky-Cam and associated software ( http://www.moonglowtech.com/products/AllSkyCam/ ) \nImages are 720x576 pixel resolution.", "links": [ { diff --git a/datasets/AAS_4292_Aurora_Australis_Radiometers_1.json b/datasets/AAS_4292_Aurora_Australis_Radiometers_1.json index a44ab98a68..d2034f50a4 100644 --- a/datasets/AAS_4292_Aurora_Australis_Radiometers_1.json +++ b/datasets/AAS_4292_Aurora_Australis_Radiometers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Aurora_Australis_Radiometers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily files of 1-minute resolution samples of \nincoming short-wave (285-2800nm) solar radiation measured with a Kipp and Zonan CMP21 Pyranometer and \noutgoing long-wave (4500-42000nm) far-IR thermal radiation measured with a Kipp and Zonan CGR4 Pyrgeometer. \n\nPyranometer \n\n- Raw sample Ave, Stdev, Max, Min (uV) \n- Logger temperature (Lab. degrees C) \n- Raw Sensor Temperature (mV/mV) \n- Scaled Irradiances Ave, Stdev, Max, Min (W/m2) \n- Sensor Temperature (degrees C) \n\nPyrgeometer \n\n- Raw sample Ave, Stdev, Max, Min (uV) \n- Logger temperature (Lab. degrees C) \n- Raw Sensor Temperature (mV/mV) \n- Scaled Irradiances Ave, Stdev, Max, Min (W/m2) \n- Sensor Temperature (degrees C) \n\nAurora Australis Voyages 1,2,and 3 over the 2015-2016 season \nShip position information obtained from netCDF underway data\nTimestamp in UT", "links": [ { diff --git a/datasets/AAS_4292_Cloud_Camera_K-Axis_1.json b/datasets/AAS_4292_Cloud_Camera_K-Axis_1.json index a865173a66..d21d29d09b 100644 --- a/datasets/AAS_4292_Cloud_Camera_K-Axis_1.json +++ b/datasets/AAS_4292_Cloud_Camera_K-Axis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Cloud_Camera_K-Axis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-sky images taken from the Cloud-Cam instrument on the RSV Aurora Australis during the K-Axis campaign between 22-Jan-2016 and 16-Feb-2016 [days 22 to 47]. Images were acquired at 1-minute cadence, and are presented as timelapse movies analysed for cloud fraction, and keograms (obtained by horizontally accumulating a vertical slice through the zenith pixel of each image).", "links": [ { diff --git a/datasets/AAS_4292_Davis_All_Sky_Camera_1.json b/datasets/AAS_4292_Davis_All_Sky_Camera_1.json index 92162f3e95..d68b86d160 100644 --- a/datasets/AAS_4292_Davis_All_Sky_Camera_1.json +++ b/datasets/AAS_4292_Davis_All_Sky_Camera_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Davis_All_Sky_Camera_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains 1-minute resolution all-sky (hemispheric fisheye) images obtained at Davis Station between 10-Jun-2015 and 30-Nov-2019.\nImages were obtained with a Moonglow Technologies All-Sky-Cam and associated software ( http://www.moonglowtech.com/products/AllSkyCam/ )\nImages are 720x576 pixel resolution\n\nImages are captured at a rate of one per minute.", "links": [ { diff --git a/datasets/AAS_4292_Davis_BASTA_Radar_1.json b/datasets/AAS_4292_Davis_BASTA_Radar_1.json index 0f7e07bf6c..e7ecade0b7 100644 --- a/datasets/AAS_4292_Davis_BASTA_Radar_1.json +++ b/datasets/AAS_4292_Davis_BASTA_Radar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Davis_BASTA_Radar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw, unprocessed return signal from the Bureau of Meteorology's W-band cloud radar located at Davis, Antarctica. W-band (95GHz) cloud radar reflectivity collected by the Bureau of Meteorology\u2019s BASTA cloud radar, located adjacent to the CPC Building.\n\nOne netCDF file is saved every minute at four different vertical resolutions: 12.5m, 25m, 100m and 200m. One vertical profile is recorded every 10 seconds.\n\nThe files contain the raw reflectivity and raw velocity, thus require calibration prior to use.", "links": [ { diff --git a/datasets/AAS_4292_Davis_Cirrus_Cloud_1.json b/datasets/AAS_4292_Davis_Cirrus_Cloud_1.json index 8c5f5bd366..ded9c15afe 100644 --- a/datasets/AAS_4292_Davis_Cirrus_Cloud_1.json +++ b/datasets/AAS_4292_Davis_Cirrus_Cloud_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Davis_Cirrus_Cloud_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains all the data used in the analysis of a cirrus cloud case study from 14 - 15 June 2011 at Davis.\n\nData in separate directories are as follows:\n\n1) The lidar data are contained in a single netCDF and include the scattering ratio, aerosol extinction, aerosol backscatter, molecular backscatter, and uncorrected scattering ratio.\n\n2) MODIS satellite data used in the radiative transfer calculations \n\n3) A file containing the radar-derived tropopause altitudes (km) in two-hourly time-steps\n\n4) A netCDF file for each of the radiosondes during this case study, including full documentation within each file\n\n5) The radiative transfer code used", "links": [ { diff --git a/datasets/AAS_4292_Dumont_d'Urville_Radiometers_1.json b/datasets/AAS_4292_Dumont_d'Urville_Radiometers_1.json index 7b10542acc..032b9f7be2 100644 --- a/datasets/AAS_4292_Dumont_d'Urville_Radiometers_1.json +++ b/datasets/AAS_4292_Dumont_d'Urville_Radiometers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Dumont_d'Urville_Radiometers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nThis dataset contains daily files of 1-minute resolution samples of incoming short-wave (285-2800nm) solar radiation measured with a Kipp and Zonan CMP21 Pyranometer and outgoing long-wave (4500-42000nm) far-IR thermal radiation measured with a Kipp and Zonan CGR4 Pyrgeometer. Pyranometer - Raw sample Ave, Stdev, Max, Min (uV) - Logger temperature (Lab. degrees C) - Raw Sensor Temperature (mV/mV) - Scaled Irradiances Ave, Stdev, Max, Min (W/m2) - Sensor Temperature (degrees C) Pyrgeometer - Raw sample Ave, Stdev, Max, Min (uV) - Logger temperature (Lab. degrees C) - Raw Sensor Temperature (mV/mV) - Scaled Irradiances Ave, Stdev, Max, Min (W/m2) - Sensor Temperature (degrees C) Located at Dumont d'Urville (Lat -66.662778 degrees S, Long 140.001944 degrees E) Timestamp in UT\n", "links": [ { diff --git a/datasets/AAS_4292_ExhaustID_201718_AA_MARCUS_1.json b/datasets/AAS_4292_ExhaustID_201718_AA_MARCUS_1.json index fdb1f1d015..b3c89d5a9b 100644 --- a/datasets/AAS_4292_ExhaustID_201718_AA_MARCUS_1.json +++ b/datasets/AAS_4292_ExhaustID_201718_AA_MARCUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_ExhaustID_201718_AA_MARCUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Exhaust identification product calculated using CN and CO data from the ARM AOS facility aboard the Aurora Australis during the 2017/18 resupply season (Austral summer) as part of the Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) project. \n\nThe product is a binary timeseries (UTC) indicating the presence (True) or absence (False) of exhaust on the atmospheric measurements. \n\nThe algorithm is described in https://doi.org/10.5194/amt-12-3019-2019 and is modified for this dataset. Included here are a number of permutations which vary the number of median absolute deviations (MAD) and percentage threshold for the window filter (see section 3.3 of paper). It is left to the end-user to assess the suitability to their dataset and choose the appropriate permutation for their needs or expand the window filtering to make it more stringent. The authors recommend the exhaust_4mad02thresh as the best output. \n\nFor use in publications, please contact Ruhi Humphries (Ruhi.Humphries@csiro.au) to discuss appropriate acknowledgement. \n\nThis dataset is associated with AAD projects 4292 and 4431.", "links": [ { diff --git a/datasets/AAS_4292_MARCUS_Case_Studies_1.json b/datasets/AAS_4292_MARCUS_Case_Studies_1.json index 55ae56d3e4..90847b21c4 100644 --- a/datasets/AAS_4292_MARCUS_Case_Studies_1.json +++ b/datasets/AAS_4292_MARCUS_Case_Studies_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_MARCUS_Case_Studies_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all the data, not available on other data servers, used in the analysis of three cyclonic case studies as part of the MARCUS campaign, aboard Aurora Australis during summer 2017/18. Specifically, one event offshore of Davis (25-26 Jan) and two cyclone events when the ship was at Mawson (11-13 and 14-16 Feb). \n\nCloud_and_Precipitation...*nc.gz: Source of the Microwave Radiometer Liquid Water Path, from Roj Marchand, University of Washington. \n\n2018*.nc: netCDF files containing calibrated MPL backscatter and cloud phase \n\n201718*.csv: Underway ship's data containing surface pressure\n\nMARCUS refers to the research campaign, \"Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS)\"", "links": [ { diff --git a/datasets/AAS_4292_Macquarie_Cloud_Radar_1.json b/datasets/AAS_4292_Macquarie_Cloud_Radar_1.json index 193356b135..bf49fcc807 100644 --- a/datasets/AAS_4292_Macquarie_Cloud_Radar_1.json +++ b/datasets/AAS_4292_Macquarie_Cloud_Radar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Macquarie_Cloud_Radar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw, unprocessed return signal from the Bureau of Meteorology's W-band cloud radar at Macquarie Island. W-band (95GHz) cloud radar reflectivity collected by the Bureau of Meteorology\u2019s BASTA cloud radar, located at the Clean Air Laboratory on Macquarie Island.\n\nOne netCDF file is saved every minute at four different vertical resolutions: 12.5m, 25m, 100m and 200m. One vertical profile is recorded every 10 seconds.\n\nThe files contain the raw reflectivity and raw velocity, thus require calibration prior to use.", "links": [ { diff --git a/datasets/AAS_4292_Macquarie_Island_Cloud_Camera_1.json b/datasets/AAS_4292_Macquarie_Island_Cloud_Camera_1.json index 13d7c0d643..70afdd953b 100644 --- a/datasets/AAS_4292_Macquarie_Island_Cloud_Camera_1.json +++ b/datasets/AAS_4292_Macquarie_Island_Cloud_Camera_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Macquarie_Island_Cloud_Camera_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains 1-minute resolution all-sky (hemispheric fisheye) images obtained on Macquarie Island between 4-April-2016 and 14-Mar-2018\nImages were obtained with a Moonglow Technologies All-Sky-Cam and associated software ( http://www.moonglowtech.com/products/AllSkyCam/ )\nImages are 720x576 pixel resolution", "links": [ { diff --git a/datasets/AAS_4292_Macquarie_Island_Radiometers_1.json b/datasets/AAS_4292_Macquarie_Island_Radiometers_1.json index ea993d51dd..48a8ebd476 100644 --- a/datasets/AAS_4292_Macquarie_Island_Radiometers_1.json +++ b/datasets/AAS_4292_Macquarie_Island_Radiometers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Macquarie_Island_Radiometers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily files of 1-minute resolution samples of \nincoming short-wave (285-2800nm) solar radiation measured with a Kipp and Zonan CMP21 Pyranometer and \noutgoing long-wave (4500-42000nm) far-IR thermal radiation measured with a Kipp and Zonan CGR4 Pyrgeometer.\n\nPyranometer \n\n- Raw sample Ave, Stdev, Max, Min (uV) \n- Logger temperature (Lab. degrees C) \n- Raw Sensor Temperature (mV/mV) \n- Scaled Irradiances Ave, Stdev, Max, Min (W/m2) \n- Sensor Temperature (degrees C) \n\nPyrgeometer\n\n- Raw sample Ave, Stdev, Max, Min (uV) \n- Logger temperature (Lab. degrees C)\n- Raw Sensor Temperature (mV/mV)\n- Scaled Irradiances Ave, Stdev, Max, Min (W/m2)\n- Sensor Temperature (degrees C)\n\nLocated at Macquarie Island Clean Air Lab -54.499586 degrees S 158.934626 degrees E\nTimestamp in UT (local = UT+10)", "links": [ { diff --git a/datasets/AAS_4292_Macquarie_Lidar_1.json b/datasets/AAS_4292_Macquarie_Lidar_1.json index 73fdd0257e..ef0471f978 100644 --- a/datasets/AAS_4292_Macquarie_Lidar_1.json +++ b/datasets/AAS_4292_Macquarie_Lidar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4292_Macquarie_Lidar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset consists of the raw signal return from the AAD POLAR (Polarisation) lidar for both the co-polarisation and cross-polarisation channels.\n\nResolution: 30m vertically x 10 seconds\n\nTime period: April 2016 - March 2018.\n\nNote that the data have not been range-corrected, overlap-corrected, calibrated nor background-corrected. These need doing prior to using these data for further analyses.", "links": [ { diff --git a/datasets/AAS_4293_Ozonesonde_1.json b/datasets/AAS_4293_Ozonesonde_1.json index 63937ff072..93d4a77659 100644 --- a/datasets/AAS_4293_Ozonesonde_1.json +++ b/datasets/AAS_4293_Ozonesonde_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4293_Ozonesonde_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division (AAD) in collaboration with the Chinese Academy of Meteorological Science (CAMS) and the Australian Bureau of Meteorology (BoM) operates a program of regular ozonesonde measurements at Davis, Antarctica. An ozonesonde is a balloon-borne instrument which uses a chemical cell as a transducer to measure ozone concentration a function of height. The measurement technique provides a convenient, accurate and cost-effective means profiling atmospheric ozone. At Davis, the measurements are ongoing since early 2003, and are made using Science Pump Corporation type ECC-6A ozonesondes that are flown on 1200 gram meteorological balloons. The desired measurement frequency is weekly throughout the year, although limitations in materials has at times meant that only one flight per month has been possible. \n\nDuring each flight, ozone concentration and standard parameters from a Vaisala RS-92 radiosonde (including pressure, temperature and humidity as a function of location) are obtained. The cathode solution (3.0cc) consists of 1% potassium iodide (KI) w/vol with potassium bromide (KBr) and buffers. The anode solution (1.5cc) is saturated KI solution. The solutions are made using 10g of KI, 25g of KBr, and added buffers, per litre of water.\n\nData holdings in the AADC consist of ASCII text files produced by BoM the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). The WOUDC can also be accessed externally at http://woudc.org/data/explore.php by selecting 'OzoneSonde' under 'Dataset' and 'Davis (450)' under 'Station'. The data are obtained with approximately 10 metre vertical resolution (approximately 2 second sampling). The data held at the AADC (including the WOUDC data) are provided in approximately 50 metre vertical resolution (10 second sampling). The 2 second data can be provided on request, although in practice, the time constant of the electrochemical cell (1/e time constant of approximately 20 seconds) means that the higher resolution does not necessarily provide more information.\nThe WOUDC files contain the most detailed information, including information on applied calibrations and corrections, and integrated ozone amounts. The main data fields presented are: air pressure (hPa), ozone partial pressure (mPa), temperature (K), wind speed (m/s), wind direction (degrees), elapsed time (sec), geopotential height (m), relative humidity (%), and electrochemical cell temperature (K).\n\nThe BoM data contains a subset of this information, as well as accumulated ozone to each height (DU), and ozone remaining above this height to the top of the profile (DU). The estimated total ozone column (from the ground to the top of the atmosphere) is also provided based on methods by the World Meteorological Organisation (see code 2 on page 27 of http://woudc.org/archive/Documentation/GuideBooks/O3_guide.pdf), and McPeters and Labow, 2011 (McPeters, R. D. and Labow, G. J.: Climatology 2011: An MLS and sonde derived ozone climatology for satellite retrieval algorithms, J. Geophys. Res.-Atmos., 117, D10303, doi:10.1029/2011JD017006, 2012).", "links": [ { diff --git a/datasets/AAS_4296_ASPA_Biodiversity_Data_1.json b/datasets/AAS_4296_ASPA_Biodiversity_Data_1.json index 27ebca9e15..10c37353e4 100644 --- a/datasets/AAS_4296_ASPA_Biodiversity_Data_1.json +++ b/datasets/AAS_4296_ASPA_Biodiversity_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_ASPA_Biodiversity_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biodiversity data from Antarctic Specially Protected Area Management Plans. We extracted and digitized all spatially explicit biodiversity data reported in the 72 existing ASPA management plans", "links": [ { diff --git a/datasets/AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2_1.json b/datasets/AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2_1.json index 03a4e4cdf1..185f1a2197 100644 --- a/datasets/AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2_1.json +++ b/datasets/AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Here we provide a revised version of the ACBRs, reflecting updates in underlying spatial layers, together with the results of new analysis justifying the inclusion of a 16th bioregion. This updated version now covers all ice-free areas of Antarctica.", "links": [ { diff --git a/datasets/AAS_4296_Antarctic_Specially_Protected_Areas_v2_1.json b/datasets/AAS_4296_Antarctic_Specially_Protected_Areas_v2_1.json index 6e6e30be19..11585d66e9 100644 --- a/datasets/AAS_4296_Antarctic_Specially_Protected_Areas_v2_1.json +++ b/datasets/AAS_4296_Antarctic_Specially_Protected_Areas_v2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_Antarctic_Specially_Protected_Areas_v2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two shapefiles are contained in this update on the location and extent of Antarctic Specially Protected Areas (ASPAs). The first is a point file (centroids of ASPA locations) and the second i sa polygon file showing the spatial extent and boundary of each ASPA. This update builds on the point and polygon files originally provided by Environmental Research and Assessment (2011). The update includes the removal of ASPAs that have been de-designated and new ASPAs that have been designated since 2011. New ASPA boundaries were created from coordinates provided in the management plans.", "links": [ { diff --git a/datasets/AAS_4296_Antarctic_spatial_layers_1km_1.json b/datasets/AAS_4296_Antarctic_spatial_layers_1km_1.json index a2cc4af7b4..1fe39c62a4 100644 --- a/datasets/AAS_4296_Antarctic_spatial_layers_1km_1.json +++ b/datasets/AAS_4296_Antarctic_spatial_layers_1km_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_Antarctic_spatial_layers_1km_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spatial layers used to inform the delineation of 'habitats' in terrestrial Antarctica. These layers include climate data from the Antarctic Mesoscale Prediction System (e.g. temperature, precipitation, cloud and wind), derived layers from these data (degree days), derived data from the RAMP DEM (rugosity, solar radiation, slope, elevation), and an index of snow melt.", "links": [ { diff --git a/datasets/AAS_4296_Antarctic_terrestrial_biodiversity_DB_2.json b/datasets/AAS_4296_Antarctic_terrestrial_biodiversity_DB_2.json index 95a8f7a94c..9d46a200bc 100644 --- a/datasets/AAS_4296_Antarctic_terrestrial_biodiversity_DB_2.json +++ b/datasets/AAS_4296_Antarctic_terrestrial_biodiversity_DB_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_Antarctic_terrestrial_biodiversity_DB_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A spatially explicit database of Antarctic biota. Taxa range from microbes to vertebrates and cover the entire ice-free area of the Antarctic continent. Records in this database were originally sourced from the SCAR Biodiversity Database and under AAS project 4296. Thousands of records have been added over the life of the project and all records have been checked for spatial accuracy and taxonomic consistency. Spatial duplicates have also been removed.", "links": [ { diff --git a/datasets/AAS_4296_Updated_ASPAs_2018_1.json b/datasets/AAS_4296_Updated_ASPAs_2018_1.json index 44c58fb397..3382bb1d5e 100644 --- a/datasets/AAS_4296_Updated_ASPAs_2018_1.json +++ b/datasets/AAS_4296_Updated_ASPAs_2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4296_Updated_ASPAs_2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting with the most recently updated polygon shapefile of ASPAs (Terauds and Lee 2016), which contained some minor improvement on the original ASPA spatial layer first made publicly available in 2011, we first cross-checked the location of ASPA polygons with the spatially explicit locations provided in the ASPAs Management Plans. Once polygons were aligned with the Management Plans, we then georeferenced the maps provided in the management plans to check the ASPA boundaries in relation to known landscape features, In some cases, there was a lack of concurrence between co-ordinates, PDF map, coastline, rock layer or Google Earth. In these cases the following protocol was followed: snap to coordinates (unless clearly wrong), otherwise align to rock outcrop layer based on the PDF map, otherwise align to coastline. Full details of the updates made to each ASPA can be found in the README file accompanying the updated layer.", "links": [ { diff --git a/datasets/AAS_4297_PTM_Data_1.json b/datasets/AAS_4297_PTM_Data_1.json index cafafa8980..7f05985e80 100644 --- a/datasets/AAS_4297_PTM_Data_1.json +++ b/datasets/AAS_4297_PTM_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4297_PTM_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Here we provide two datasets that underpin the Antarctic Priority Threat Management analysis. These data were generated by biodiversity experts during a two-day workshop in Belgium, July 2017.\n\nExperts provided assessments of the benefits of applying various conservation strategies to different taxonomic groups in Antarctica. They provided future baseline intactness values (where no strategy is applied) for each taxonomic group and subsequent intactness values where each conservation strategy had been applied. The intactness values represent the status of the taxonomic group in 2100, and could be visualised as population numbers, extinction risk, range extent, cover, density or other relevant metrics.\n\nThe expert intactness values were averaged across experts to provide one set of intactness values per taxonomic group. \n\nBenefits were subsequently calculated as the strategy intactness values minus the baseline intactness values. ", "links": [ { diff --git a/datasets/AAS_4298_Chlorophyll_a_1970_2015_2.json b/datasets/AAS_4298_Chlorophyll_a_1970_2015_2.json index e2b2f8ded8..c6544a9f97 100644 --- a/datasets/AAS_4298_Chlorophyll_a_1970_2015_2.json +++ b/datasets/AAS_4298_Chlorophyll_a_1970_2015_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4298_Chlorophyll_a_1970_2015_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic Fast Ice Algae Chlorophyll-a (AFIAC) dataset is a compilation of currently available sea ice chlorophyll-a data from land-fast sea ice (i.e., excluding pack ice (see ASPeCt-Bio, Meiners et al. 2012)) cores collected at circum-Antarctic locations during the period 1970 to 2015. Data come from peer-reviewed publications, field-reports, data repositories and direct contributions by field-research teams. During all campaigns the chlorophyll-a concentration (in micrograms per litre) was measured from melted ice-core sections, using standard procedures, e.g., by melting the ice at less than 5 degrees C in the dark; filtering samples onto glassfibre filters; and fluorometric analysis according to standard protocols [Holm-Hansen et al., 1965; Evans et al., 1987]. Ice samples were melted either directly or in filtered sea water, which does not yield significant differences in chlorophyll-a concentration [Dieckmann et al., 1998]. The dataset consists of 888 geo-referenced ice cores, consisting of 5718 individual ice core sections, and including 404 full vertical profiles with a minimum of three sections. Samples/sections from the remaining cores represent: i) bottom 0.05 m only (n= 32), ii) bottom 0.1 m only (n = 301), complete cores (n = 66), as well as intermittent profiles (n = 85) with at least 3 sections but gaps in-between them. \nFor questions about this dataset please contact: Klaus Meiners and Martin Vancoppenolle\nThis data compilation was carried out under the auspices of the Scientific Committee on Antarctic Research - ASPeCt program and the Scientific Committee on Ocean Research (SCOR) working group on Biogeochemical Exchange Processes at the Sea-Ice Interfaces (WG-140). It also contributes to SCOR WG-152 on Measuring Essential Climate Variables in Sea Ice (ECV-Ice).\n\nAn update to this dataset was submitted in September, 2018.", "links": [ { diff --git a/datasets/AAS_4298_Davis_Ice_Transects_1.json b/datasets/AAS_4298_Davis_Ice_Transects_1.json index 162a6e899d..37017945d4 100644 --- a/datasets/AAS_4298_Davis_Ice_Transects_1.json +++ b/datasets/AAS_4298_Davis_Ice_Transects_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4298_Davis_Ice_Transects_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In situ measurements of ice and snow thickness, and freeboard along an irregular transect on the fast, complementing the repeat ROV (Remotely Operated Vehicle) transects. During our deployment at Davis in 2015 logistics and environmental conditions permitted measurements along 4 transects. The location of the reference grid (ROV box) had its origin (x=0, y=0) at (-68.568904 degrees N,+77.945439 degrees E). Transects 1 \u2013 4 started at x=60, x=70, x=80 and x=90 m and were sampled at y-positions of 0m, 0.5m, 1m, 2m, 4m, 8m, 16m, 32m, 64m, 128m, (256m, and 512m), respectively. Depending on working conditions the overall transect lengths varied from 128 \u2013 512 m.\n\nSampling dates for in situ ice physcis: \nTransect ID\tDate of sampling\tZice and FB measured at\tIce core taken at\tSnowpit measured at\nT1\t19/11/2015\t0, 0.5, 1, 2, 4, 8, \u2026 64m.\t0m, 128m, 512m\t0m, 128m, 512m\nT2\t23/11/2015\t0, 0.5, 1, 2, 4, 8, \u2026 64m.\t0m, 128m, 512m\t0m, 128m\nT3\t29/11/2015\t0, 0.5, 1, 2, 4, 8, \u2026 64m.\t0m, 128m\t0m, 128m\nT4\t02/12/2015\t0, 0.5, 1, 2, 4, 8, \u2026 64m.\t0m, 128m\t0m, 128m\n\nIce cores and snow pits were collected at the 0m, 50m and 100m mark along the transect, where possible. Additionally, ice cores for density analysis were taken at a few of the ice-core sites for independent verification of ice density.", "links": [ { diff --git a/datasets/AAS_4298_Under_Ice_ROV_Observations_1.json b/datasets/AAS_4298_Under_Ice_ROV_Observations_1.json index 92d958c899..cef5efafaf 100644 --- a/datasets/AAS_4298_Under_Ice_ROV_Observations_1.json +++ b/datasets/AAS_4298_Under_Ice_ROV_Observations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4298_Under_Ice_ROV_Observations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data were collected during deployments of an instrumented Remotely Operated Vehicle on 5 sampling days to determine sea ice physical properties and measure transmitted under-ice radiance spectra (combined with surface irradiance measurements) to estimate the spatial distribution and temporal development of ice algal biomass in land-fast sea ice. The ROV was instrumented with a navigation/positioning system (linked to surface GPS), upward-looking sonar and accurate depth sensor (Valeport 500 (to determine sea-ice draft)), and a upward-looking TriOS Ramses radiance sensor as well as several video-cameras collecting under-ice footage. Parallel measurements included surface irradiance measurements.\n\nA readme file in the download explains the folder structure of the dataset.", "links": [ { diff --git a/datasets/AAS_4301_ASCAT_backscatter_parameters_1.json b/datasets/AAS_4301_ASCAT_backscatter_parameters_1.json index c76d1a4001..c661bd4065 100644 --- a/datasets/AAS_4301_ASCAT_backscatter_parameters_1.json +++ b/datasets/AAS_4301_ASCAT_backscatter_parameters_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4301_ASCAT_backscatter_parameters_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A primary determinant of microwave backscatter is the Rayleigh-scale roughness. For instruments observing at C-band and at oblique angles, this corresponds to a roughness scale of ~ 1 cm.\nUsing ASCAT, a C-band scatterometer in polar orbit, we can determine the backscatter (and hence cm-scale roughness) of the sea ice cover on the ocean. This dataset uses a complex parameterisation of multi-day (5-day) backscatter measurements to parameterise the backscatter into several parameters. For more details on the backscatter parameterisation, see Fraser et al., 2014 (https://ieeexplore.ieee.org/document/6527913).\nThis dataset consists of 5-daily maps of all backscatter parameters spanning the time period from March 2007 to December 2017. Each map is presented as unformatted binary, and is provided in the NSIDC polar stereographic projection (12.5 km resolution, southern hemisphere, 632*664 px, 32-bit floating point - see https://nsidc.org/data/polar-stereo/ps_grids.html).", "links": [ { diff --git a/datasets/AAS_4301_Scatterometer-derived_ice_thickness_proxy_1.json b/datasets/AAS_4301_Scatterometer-derived_ice_thickness_proxy_1.json index 0809cfc4fd..90a773bf31 100644 --- a/datasets/AAS_4301_Scatterometer-derived_ice_thickness_proxy_1.json +++ b/datasets/AAS_4301_Scatterometer-derived_ice_thickness_proxy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4301_Scatterometer-derived_ice_thickness_proxy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a time series of sea ice freeboard proxy estimation based on ASCAT C-band backscatter measurements. \nFile format is unformatted binary, with each file 632*664 pixels, and 32 bits per pixel (floating point).\nTwo datasets are presented here, as detailed in University of Tasmania Honours thesis by Nicola Ramm: \"unmasked\", i.e., no attempt to mask multiyear and marginal sea ice, and \"masked\", where these are masked based on backscatter.\n\nThe grid used by this dataset is described here:\nhttps://nsidc.org/data/polar-stereo/ps_grids.html\n\nThe methods are described in an honours thesis by Nicola Ramm, University of Tasmania.", "links": [ { diff --git a/datasets/AAS_4305_BrownSkua_breeding_diet_1.json b/datasets/AAS_4305_BrownSkua_breeding_diet_1.json index a45b23740f..948f2a0856 100644 --- a/datasets/AAS_4305_BrownSkua_breeding_diet_1.json +++ b/datasets/AAS_4305_BrownSkua_breeding_diet_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4305_BrownSkua_breeding_diet_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains:\n1) count data from a series of surveys of brown skua nest numbers and breeding success conducted from 2008/09 to 2017/18;\n2) historic skua nest count data and concurrent estimates of rabbit abundance (1974/75 to 2009/10);\n3) stable isotope data from feather samples taken from skua chicks during the 2017/18 season to compare stable isotope ratios between different feather types;\n4) stable isotope data from feather samples taken from skua chicks from 2008/09 to 2017/18;\n5) a small dataset of stable isotope data from skua prey tissue samples;\n6) the nest locations of skuas surveyed in the seasons 2009/10, 2012/13 and 2017/18.", "links": [ { diff --git a/datasets/AAS_4305_Macquarie_Is_Invertebrate_Surveys_2015-2018_2.json b/datasets/AAS_4305_Macquarie_Is_Invertebrate_Surveys_2015-2018_2.json index 134e99dbc1..6c933e4dee 100644 --- a/datasets/AAS_4305_Macquarie_Is_Invertebrate_Surveys_2015-2018_2.json +++ b/datasets/AAS_4305_Macquarie_Is_Invertebrate_Surveys_2015-2018_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4305_Macquarie_Is_Invertebrate_Surveys_2015-2018_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contemporary survey data from 20 sites on Macquarie Island collected over the 2015/16, 2016/17. 2017/18 field seasons in different habitat types (short grass, tall grass (tussock), Stilbocarpa polaris (Cabbage), herbfield, feldmark). Survey methods include pitfall traps, beating, sweep netting, sticky traps and direct collection from litter samples.\n \nInvertebrate community assemblages. These were community assessments undertaken at historic sites (i.e. previously trapped in 1980s, 1990s and 2000. Sites were relocated to the best of our ability and resurveyed. New sites were established. Data comprise species presence and abundance per site, potentially with additional site information (e.g. environmental conditions).\n\n2017-2018 Update:\nThese data were collected in 2017/18 to assess vegetation on Macquarie Island following eradication of rabbits and rodents\n\nSites were random in the landscape or associated with nesting skuas, invertebrate monitoring sites or burrowing petrel colonies.\n\nSurveys were either taken as 10 x 1m2 quadrats at each locations OR 1 x 10m2 circle at a location\n\nAll vascular plant species present were recorded, and cover classes assigned\nCover class: 0 (not present), 1 (<5%), 2 (5-25%), 3 (25-50%), 4 (50-75%), 5 (75-100%)\nVegetation height was recorded\nPeat depth was recorded to greater than 1.5m", "links": [ { diff --git a/datasets/AAS_4305_Macquarie_Is_historic_invert_surveys_1.json b/datasets/AAS_4305_Macquarie_Is_historic_invert_surveys_1.json index 5a40a71b68..8d79e371c1 100644 --- a/datasets/AAS_4305_Macquarie_Is_historic_invert_surveys_1.json +++ b/datasets/AAS_4305_Macquarie_Is_historic_invert_surveys_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4305_Macquarie_Is_historic_invert_surveys_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A compilation for historical data on Macquarie Island invertebrate data from the following surveys\n- 1986/87 (Penny Greenslade)\n- 2009/10 (Mark Stevens)\n- 1993/94 (Disney)\n- 1991-1993 (Davies and Melbourne)\n\nInvertebrate community assemblages. These were community assessments undertaken at historic sites (i.e. previously trapped in 1980s, 1990s and 2000. Sites were relocated to the best of our ability and resurveyed. New sites were established. Data comprise species presence and abundance per site, potentially with additional site information (e.g. environmental conditions).\n\nListed below are some of the data sources for this data compilation:\n\nGreenslade, P. (1987). Report on invertebrate fieldwork Macquarie Island, December 1986 \u2013 January 1987. Unpublished report. Department of Primary Industry, Parks, Water and the Environment. Tasmania, Australia. \nEstablished 8 invertebrate monitoring sites in four vegetation communities in the northernmost part of the island. Pitfalls and Litter, hand collecting over a ~ 5 week spring/summer period.\n\nDisney (91) - Reported in Stevens et al. 2010. Resampled four of Greenslade's sites. Pitfalls large and small over a year. \n\nDavies, K.F. and Melbourne, B.A. (1999). 'Statistical models of invertebrate distribution on Macquarie Island: a tool to assess climate change and local human impacts'. Polar Biology 21: 240\u2013250. Sampled 69 sites across the island in different vegetation communities over a 6 week summer period. Litter, pitfalls, yellow pans and handcollecting. \n\nStevens, M., Hudson, P., Greenslade, P. and Potter, M. (2010). Report on invertebrate monitoring of long term field sites on Macquarie Island 2009/2010. Unpublished report. South Australia: South Australia Museum. Resampled seven of Greenslade's sites, plus a further eight over a 5 week spring/summer. Litter, yellow pans, pitfalls.", "links": [ { diff --git a/datasets/AAS_4305_Macquarie_Island_burrowing_petrels_1.json b/datasets/AAS_4305_Macquarie_Island_burrowing_petrels_1.json index eb2bea367a..fa55fe5101 100644 --- a/datasets/AAS_4305_Macquarie_Island_burrowing_petrels_1.json +++ b/datasets/AAS_4305_Macquarie_Island_burrowing_petrels_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4305_Macquarie_Island_burrowing_petrels_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the Supplementary Material for a review of uncertainty in petrel population estimates. It contains raw data from the literature review, source code for the full analysis, and additional text accompanying the manuscript.\n\nRaw data were extracted from a literature review of petrel population estimates on islands. References were sourced from the Web of Science bibliographic index searched on 20 January 2020 using the search terms \"burrowing seabird\" OR \"burrow-nesting seabird\" OR \"burrow-nesting petrel\" OR \"burrowing petrel\" OR \u201cscientific name\u201d OR \u201ccommon name\u201d (taxonomy followed HBW and BirdLife International, 2018) for all species in the families Procellariidae, Hydrobatidae and Oceanitidae, AND \u201cabundance\u201d OR \u201cpopulation\u201d in the title, abstract or keywords. \n\nThe data contain the original reference with metadata on year, journal, species studied, island studied, motivations for the study. We extracted published population estimates reported in each paper. Most represented a mean, but where only minima or maxima were reported we used this as the estimate, and where only minima and maxima were reported we used their average as the estimate. To allow comparison between studies we extracted basic dispersion statistics and manipulated them to approximate confidence intervals (see paper for methods).\n\nThe full dataset includes:\n1. data.csv - the raw data from the literature review including information for 60 variables.\n\n2. supplementary_code.rmd - full code for the analysis.\n\n3. Supplementary material.docx - supporting text including methods, results and references.", "links": [ { diff --git a/datasets/AAS_4307_Casey_01_1.json b/datasets/AAS_4307_Casey_01_1.json index 03be1ea446..4a37b74f17 100644 --- a/datasets/AAS_4307_Casey_01_1.json +++ b/datasets/AAS_4307_Casey_01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4307_Casey_01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of abundances of Nanorchestes antarcticus Strandtmann, 1963 (Acari) generated while searching for any presence of springtails at Casey Station and surroundings. Laura Phillips and Jessica Hoskins of Monash University generated the data.\n\nA spatially explicit database of sites where live springtails were collected, including altitude, vegetation type and collection method. The idea was to search for a Cryptopygus species reported from Casey station. See Nielsen, U. N., and King, C. K. (2015). Abundance and diversity of soil invertebrates in the Windmill Islands region, East Antarctica. Polar Biology, 38(9), 1391-1400. doi:doi:10.1007/s00300-015-1703-2 and also Greenslade, P. (2015) Synonymy of two monobasic Anurophorinae genera (Collembola: Isotomidae) from the Antarctic Continent. New Zealand Entomologist 38, 134-141. doi: 10.1080/00779962.2015.1033810. No springtails were detected in the field.\n\nSpatial data in degrees, minutes, seconds, temperature in degrees Celsius, elevation in metres, date as day/ month/year, cover in percentage, animal abundance in numbers of individuals.\n\nAnimals were collected by searching and aspiration or by extraction of small moss samples using a modified Tullgren extraction method.", "links": [ { diff --git a/datasets/AAS_4307_Macquarie_01_1.json b/datasets/AAS_4307_Macquarie_01_1.json index a6f00f887a..c8847a03ea 100644 --- a/datasets/AAS_4307_Macquarie_01_1.json +++ b/datasets/AAS_4307_Macquarie_01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4307_Macquarie_01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data were collected as part of the Chown 4307 project on physiology of springtails. The collections are a record of species presence and absence (real zeros) for the island based on our collections on this expedition. The data are just provided as ones for presence and zeros for absence.\n \nAnimals were collected by aspiration or by beating of vegetation. Moss turves (of 5-15 cm on each side) were returned to the laboratory and animals extracted from them by modified Tullgren extraction.", "links": [ { diff --git a/datasets/AAS_4312_Azorella_condition_site_topo_1.json b/datasets/AAS_4312_Azorella_condition_site_topo_1.json index 4f8cf3d6bf..36de155222 100644 --- a/datasets/AAS_4312_Azorella_condition_site_topo_1.json +++ b/datasets/AAS_4312_Azorella_condition_site_topo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_Azorella_condition_site_topo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The endemic, keystone species Azorella macquariensis (Macquarie cushion) has undergone rapid widespread decline across Macquarie Island in 2008/2009, resulting in its listing as critically endangered in 2010. Initial research suggests that Azorella dieback is likely driven by a decadal reduction in plant available water, as a result of a significantly changed regional climate, which may have facilitated a secondary putative pathogenic infection of weakened plants (Bergstrom et al. 2015, Whinam et al. 2014). This data was collected as part of Catherine Dickson's PhD thesis 'Impact of climate change on a sub-Antarctic keystone species Azorella macquariensis (Apiaceae)'. \n\nAzorella macquariensis Orchard (Apiaceae, Macquarie cushion) cover and condition (dieback) records were taken from 90 sites across Macquarie Island between January and March 2017, including eight null sites with no Azorella cover. Seventy sites (706.86m2, 15m radius) were randomly stratified using a terrain class model (TCM) to ensure that all potential microclimates that A. macquariensis might be exposed to were surveyed. An additional 20 sites were established to increase coverage with core Azorella habitat, most were collocated with historical sites (Bergstrom et al. 2015, Whinam et al. 2014, Bricher et al. 2013). Methods for the TCM are in Dickson et al. in prep and Baker et al. in prep. The centre of each site was recorded (UTM, GDA94, +/-5m), but not permanently marked.\n\nAzorella macquariensis cover and dieback was recorded for each of the four quadrants within the large site (divided by cardinal points, i.e. NE, SE, SW, NW), to the nearest 1% for values under 10% and 5% for values over 10%. Site values were subsequently calculated in both percent and m2. Bare ground was also assessed using this method. For this data set no differentiation between the \u2018types\u2019 of A. macquariensis dieback was made, i.e. very low levels of wind scouring dieback and extensive death from pathogens. No attempt was made to count the number of A. macquariensis individuals, as cushions and mats can be made up of multiple individuals. Form and size classes were made for this study and presence/absence was recorded on site, including whether the form had dieback within it. The vegetation community was described and a presence/absence of all vascular flora was recorded on site. \n\nThe proportion of gravel size classes were recorded for visible surface bare ground (Sur) across the site and in one representative soil pit which was dug to 250mm deep, within the primary root zone of A. macquariensis. Site values of nine topographic variables (derived from the Macquarie Island DEM) thought to affect evapotranspiration rates are provided, following the methods of Bricher et al. 2013. Topographic values are a point value, taken at the centre of the site.\n\nAn excel file containing the location and biotic and abiotic sites values for 90 sites is available. Headings include:\nSite name, Data, Location (UTM, GDA94), terrain class model (TCM) cluster code, Azorella cover and dieback (%), Azorella form and size classes (x10), presence of Azorella dieback in size and form classes (x10), site topographic values (elevation (m), distance to coast (m), curvature, distance to west coast (m), topographic wetness index, topographically derived wind speed (km/hr), annual solar radiation (W/m^2), aspect (deg), slope(deg) ), site bare ground, surface gravel classes (% x6 classes), soil pit gravel classes (% x4 classes), parent geology, vegetation community, presence/absence of all vascular flora.", "links": [ { diff --git a/datasets/AAS_4312_Azorella_leaftraitDB_CRD_1.json b/datasets/AAS_4312_Azorella_leaftraitDB_CRD_1.json index b00f32f3a1..90f7da5c07 100644 --- a/datasets/AAS_4312_Azorella_leaftraitDB_CRD_1.json +++ b/datasets/AAS_4312_Azorella_leaftraitDB_CRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_Azorella_leaftraitDB_CRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Azorella macquariensis leaf traits were assessed across Macquarie Island (lat, longs: N - 54.50534, S: -54.76911, E: 158.9263, W, 158.8009). Leaves were collected between 2 January 2017 to 28 February 2017 and fixed in 70% ethanol on site. Leaves were measured at Monash University 1 June 2018 to 29 June 2018.\n\nThe endemic, keystone species Azorella macquariensis (Macquarie cushion) has undergone rapid widespread decline across Macquarie Island in 2008/2009, resulting in its listing as critically endangered in 2010. Initial research suggests that Azorella dieback is likely driven by a decadal reduction in plant available water, as a result of a significantly changed regional climate, which may have facilitated a secondary putative pathogenic infection of weakened plants (Bergstrom et al. 2015, Whinam et al. 2014). \n\nAzorella macquariensis Orchard (Apiaceae, Macquarie cushion) leaf samples were taken from 62 sites across Macquarie Island between January and March 2017. Sites (706.86m2, i.e. 15m radius) were randomly stratified using a terrain class model (TCM) to ensure that all potential microclimates that A. macquariensis might be exposed to were surveyed. Methods for the TCM are described in Dickson et al. in prep. Six samples were taken at each site, one from within each of the six 2mx2m plots, which were located on Azorella with representative condition (dieback) variation. Samples (3-5 5cm rosette lengths) were taken from the healthiest cushion in the plot and fixed immediately in 70% ethanol. Department of Primary Industries, Parks, Water and Environment permit number TFL1676. Samples were returned to Monash University and stored at room temperature.\n\nLeaf samples were measured at Monash University in June 2018. One rosette branchlete was used from each sample (barcode) and the new (green) growth of the 2016/2017 season measured, indicated by the colour variation on the sample. For each sample (representing one individual, six per site) the following was measured, new stem growth (green), number of new leaves (green). Four leaves were measured per sample, some only had 3 new leaves, so only 3 would be measured. L1 was closest at the growing tip and L4 furthest from the tip. L1:L4 were flattened between slides and scanned (tif files), for measurements and then the number of spines per leaf were counted under a dissecting microcscope. Green leaf area (mm2), maximum green leaf width (mm), green leaf length (mm) and sheath width (mm) were measured using standard methods in FIJI (Image J 1.52f) from the scanned images.\n\nData available:\nExcel files containing the location and barcode of samples (372) and individual leaf measurements (1488) for each A. macquariensis sample. Headings include:\n\nSite:\nEasting and Northing \u2013 location of site, UTM, Zone 57S, GDA94, +/-5m\nDate_collected\nBarcode\nStem_length - This season's new (green) stem growth (mm) - one measurement per barcode\nLeaves_new - Number of new leaves in this season - one measurement per barcode\nSample \u2013 Spiny/glabrous\nLeaf_sample - Leaf sample number (L1, L2, L3, L4) - taken from the new growth stem. Only new season's leaves measured.\nSpines \u2013 Number of spines per leaf\nLeaflets \u2013 number of leaflets per leaf\nArea_mm^2 \u2013 green area, measured in Image J\nLength_mm \u2013 maximum green leaf length, measured in Image J\nSheath_width_mm \u2013 leaf sheath width, measured in Image J\nLeaf_width_mm \u2013 maximum green leaf width, measured in Image J\nSpine_density_sp/mm^2 \u2013 number of spines divided by green leaf area", "links": [ { diff --git a/datasets/AAS_4312_MI_Azorella_plot_condition_microclimate_1.json b/datasets/AAS_4312_MI_Azorella_plot_condition_microclimate_1.json index f389d595bc..ed8469389d 100644 --- a/datasets/AAS_4312_MI_Azorella_plot_condition_microclimate_1.json +++ b/datasets/AAS_4312_MI_Azorella_plot_condition_microclimate_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_MI_Azorella_plot_condition_microclimate_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The endemic, keystone species Azorella macquariensis (Macquarie cushion) has undergone rapid widespread decline across Macquarie Island in 2008/2009, resulting in its listing as critically endangered in 2010. Initial research suggests that Azorella dieback is likely driven by a decadal reduction in plant available water, as a result of a significantly changed regional climate, which may have facilitated a secondary putative pathogenic infection of weakened plants (Bergstrom et al. 2015, Whinam et al. 2014). This data was collected as part of the PhD thesis of Catherine Dickson. Coarse-scale (site level) condition and topographic data found a latitudinal relationship between Azorella condition (decreasing to the north) and cover (increasing to the south), however, there was no relationship between topographic variables that may have influenced evapotranspiration rates (Dickson et al. 2019). To further clarify this relationship, finescale A. macquariensis condition classes (types of dieback) were recorded and microclimate (temperature and relative humidity) data used to examine the relationship between cushion condition/dieback and microclimate.\n\nAzorella macquariensis Orchard (Apiaceae, Macquarie cushion) condition (dieback) records were taken from 62 sites across Macquarie Island between January and March 2017. Photographs were taken from six randomly stratified plots (2m2) at each of the 62 sites (Site = 706.86m2, 15m radius). Sites were randomly stratified across Macquarie Island using a terrain class model (TCM) to ensure that all potential microclimates that A. macquariensis might be exposed to were surveyed (Dickson et al. 2019). The cluster code is provided in the data set (clust).\n\nAzorella macquariensis condition classes were defined, including three healthy, wind-scour, five dieback and recovery classes. Dieback progression classes were defined (active, thinning and advanced) from the five dieback classes. Polygons of each condition class, vascular flora, bryophytes and bare ground were delineated on Images of each plot (six per site) by the same person and measured in ImageJ 1.52i (Rueden et al. 2017) to determine the area within each plot. No attempt was made to count the number of A. macquariensis individuals, as cushions and mats can be made up of multiple individuals. A detailed description is provided in Dickson (2020) and Dickson et al. (in prep).\n\nTerrain variables were derived at the site level (15m radius) using SAGA GIS (Conrad et al. 2015) in RSAGA (Brenning et al. 2018) from the Macquarie Island digital elevation model (Brolsma 2008). Variables included aspect, distance to coast, distance to freshwater, total incoming radiation, slope, topographically derived wetness index, southwest and northwest windshelter. Detailed methodology is available in Dickson (2020) and Dickson et al. (in prep). Terrain values are a point value, taken at the centre of the site.\n\nA network of in situ microclimate data loggers (one per site) were used to take microclimate observations (4 hourly, temperature and relative humidity). Microclimate variables calculated including temperature and humidity extremes, vapour pressure deficit and number of freezing days. Detailed methodology is available in Dickson (2020) and Dickson et al. (in prep). \n\nThe proportion of gravel size classes were recorded for visible surface bare ground (Sur) across the site and in one representative soil pit (Soil) which was dug to 250mm deep, within the primary root zone of A. macquariensis. Data and methods from Dickson et al. 2019. The RCODE, MI parent rock type is provided for each site.\n\nRelevant references:\nDickson, C.R., Baker, D.J., Bergstrom, D.M., Bricher, P.K., Brookes, R.H., Raymond, B., Selkirk, P.M., Shaw, J., Terauds, A., Whinam, J., McGeoch, M.A., 2019. Spatial variation in the ongoing and widespread decline of keystone plant species. Austral Ecology 44, 891-905.\n\nDickson, CR, 2020, Impact of climate change on a sub-Antarctic keystone cushion plant, Azorella macquariensis (Apiaceae). Unpublished PhD Thesis, Monash Univeristy, Clayton, Victoria.\n\nBaker, D.J., Dickson, C.R., Bergstrom, D.M., Whinam, J., McGeoch, M.A., unpublished. Are microrefugia likely to exist as conservation features for cold-adapted species across the sub-Antarctic islands?\n\nDickson, C.R., Baker, D.J., Bergstrom, D.M., Brookes, R.H., Whinam, J. and McGeoch, M.A. (2020), Widespread dieback in a foundation species on a sub-Antarctic World Heritage Island: Fine\u2010scale patterns and likely drivers. Austral Ecology. https://doi.org/10.1111/aec.12958", "links": [ { diff --git a/datasets/AAS_4312_MI_Cold_Refugia_Model_1.json b/datasets/AAS_4312_MI_Cold_Refugia_Model_1.json index 7c755a169e..8a245118f8 100644 --- a/datasets/AAS_4312_MI_Cold_Refugia_Model_1.json +++ b/datasets/AAS_4312_MI_Cold_Refugia_Model_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_MI_Cold_Refugia_Model_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a spatially explicit Cold Refugia Model (CRM) across the extent of Macquarie Island (extent: N-54.50534, S-54.76911, E158.9263, W158.8009) in October 2016. This dataset was created as part of AASP4312: Nowhere to hide? Conservation options for a sub-Antarctic keystone species.\n\nA spatially explicit Cold Refugia Model (CRM) was produced for the full extent of the Macquarie Island plateau to identify areas on Macquarie Island climatically buffered from changing climate conditions through the effects on terrain on local climate conditions (i.e. microrefugia).\n\n\nA network of in situ microclimate data loggers were used to take microclimate observations (4 hourly, temperature and relative humidity). We used these observations to model the relative importance of terrain variables (coast distance, wind shelter index, wetness index, solar radiation) in explaining variation in microclimate conditions (Tmax; Tmin; Vapour pressure deficit [VPD]) over and above the influence of macroscale drivers of local climate conditions (latitude, elevation). We identified the distribution of areas where predicted microclimate conditions were significantly lower for the Macro+terrain model than the Macro-only model, as indicated by a two-sample t-test of the bootstrapped predictions (\uf061 \u2264 0.01 was considered statistically significant). These locations were mapped across the island. Areas where there was evidence for terrain buffering from high values of Tmax, Tmin, and VPD were identified as those where the terrain effects resulted in a lower and statistically significant microclimate prediction for all three variables.\n\nResults. The importance of terrain variables in explaining variation in microclimate conditions differed between climate variables and season. Terrain effects could produce similar relative prediction strengths to that of elevation (e.g. wind shelter effect on Tmax and VPD in the summer). In the growing season (early summer), effect sizes of > 1 \u00b0C reduction in Tmax due to terrain effects were predicted in some areas, with the median effect size across buffered sites estimated as 0.35 \u00b0C. In this same season, effects on Tmin are smaller and mainly restricted to wind exposed slopes near the coast. As a contrast, the largest effect of buffering from high VPD occurred in the centre of the island. In the winter seasons the size of the buffering effects on microclimate conditions was typically smaller than in summer and the distribution of these sites were more fragmented. Despite variation in the location of terrain effects on microclimate between climate variables, up to 15% of the fellfield is simultaneously buffered from thermal and hydrological stress conditions in the critical growing period. Spatial overlap declined to just 3.5% in late summer but increased in both winter seasons (Early Winter = 10.1%; Late Winter = 11.9%). These areas showed some degree of spatial aggregation, but due to the seasonally variable effect of terrain on microclimate conditions, the location of areas of consensus were not necessarily consistent through time. \n\nMain conclusions. These results show that despite its narrow topographic range, terrain variation creates microclimate variation across Macquarie Island, and suggests a plausible basis for the existence of microrefugia that could support cold-adapted species on across the sub-Antarctic region under climate change.\n\nArcGIS shapefiles (.dbf, .prj,.shp, .shx) WGS_1984_UTM_Zone_57S, with a grid cell resolution of approximately 27 m x 27 m (size of AASP4312 Azorella biological monitoring sites), in zipped files:\nConsensus_microrefugia_ES .zip (ES = early summer)\nConsensus_microrefugia_EW.zip (EW = early winter)\nConsensus_microrefugia_LS.zip (LS = late summer)\nConsensus_microrefugia_LW.zip (LW = late winter)\n\nCells are described as 0, 1 or 2.\n0 = no buffering\n1 = significant buffering of one variable (Tmax, Tmin, or VPD)\n2 = significant buffering of all three variables (Tmax, Tmin, and VPD)\n\nThis dataset was created as part of the following manuscript and is embargoed until published.\nBaker, D.J., Dickson, C.R., Bergstrom, D.M., Whinam, J., McGeoch, M.A., unpublished. Are microrefugia likely to exist as conservation features for cold-adapted species across the sub-Antarctic islands?", "links": [ { diff --git a/datasets/AAS_4312_MI_Terrain_Class_Model_1.json b/datasets/AAS_4312_MI_Terrain_Class_Model_1.json index 53e767fc5f..991ed0ae9c 100644 --- a/datasets/AAS_4312_MI_Terrain_Class_Model_1.json +++ b/datasets/AAS_4312_MI_Terrain_Class_Model_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_MI_Terrain_Class_Model_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A spatially explicit terrain class model (TCM) was produced of the full extent of Macquarie Island to determine areas on Macquarie Island with broadly the same microclimate and ensure that potential microrefugia could be identified. Non-correlated terrain variables likely to affect evapotranspiration rates were included in the model (elevation, surface curvature, topographic wetness index, solar radiation and topographically deflected wind speed). Terrain variables were derived from the Macquarie Island digital elevation model (DEM, Brolsma 2008), see Bricher et al. 2013 for methods. A fuzzy c-means clustering was applied to identify broadly similar climatic conditions and three different metrics (Xie-Beni Index, cluster stability, PCA) determined the optimum number of terrain clusters to be five. See Dickson et al. 2019 and Baker et al. in prep for full methodology. A \u20182nd Membership\u2019 was also produced that only contains cells with at least double that of the second highest cluster centre membership association, which acknowledges that the highest membership \u2018hard clustering\u2019 contains some noise from the DEM and cells that may exist on the boundary of two memberships.\n\nArcGIS shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .shx) in a zipped file:\nMI_TCM_2nd_Membership.zip\nMI_TCM_Highest_Membership.zip\n\nMore details in the referenced publication.", "links": [ { diff --git a/datasets/AAS_4312_MI_microclimate_data_Dec16Dec17_62sites_1.json b/datasets/AAS_4312_MI_microclimate_data_Dec16Dec17_62sites_1.json index 869c2b2dae..600a5643e1 100644 --- a/datasets/AAS_4312_MI_microclimate_data_Dec16Dec17_62sites_1.json +++ b/datasets/AAS_4312_MI_microclimate_data_Dec16Dec17_62sites_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4312_MI_microclimate_data_Dec16Dec17_62sites_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the raw temperature and humidity data recorded 4 hourly at 62 sites across the extent of Macquarie Island between 15/12/2016 and 03/01/2018. This dataset was created as part of AASP4312: Nowhere to hide? Conservation options for a sub-Antarctic keystone species.\n\nThe endemic, keystone species Azorella macquariensis (Macquarie cushion) has undergone rapid widespread decline in condition (dieback) across Macquarie Island in 2008/2009, resulting in its listing as critically endangered in 2010. Initial research suggests that Azorella dieback is likely driven by a decadal reduction in plant available water, as a result of a significantly changed regional climate, which may have facilitated a secondary putative pathogenic infection of weakened plants (Bergstrom et al. 2015, Whinam et al. 2014). \n\nSixty-two microclimate and Azorella condition sites were randomly stratified across Macquarie Island, using the terrain class model (AAS_4312_MI_Terrain_Class_Model) and spatial blocks to ensure the full breadth of microclimates were sampled on the plateau. The datalogger deployment elevation ranged from 125m above sea level (asl) to 373m asl. Methods are available in Dickson et al. in prep and Baker et al. in prep. To ensure a full set of microclimate data (temp and humidity) each site had a minimum of two DS1923 Hygrochon Temperature and Humidity iButtons (Maxim) and most had an additional DS1925 Thermochron iButton (Maxim) deployed (total 180). One Hygrochron was located at the central site point (A) and the second hygrochron 5-12 m away (B) collocated with a Thermochron on a separate patch of ground. iButtons were housed in light grey PVC containers in a free hanging fob. Three slits were cut in each side of the housing to increase airflow, while sheltering from direct solar radiation and precipitation. The housings were fixed to a wooden stake 4cm above the ground to assess the microclimate of the study\u2019s focal species, Azorella macquariensis. Where possible iButtons were located away from vegetation. iButtons were set to record at high resolution, every 4 hours (starting at 03:00am), to get the most amount of data between seasons and to coincide with the 15:00 Bureau of Meteorology (BoM) readings. One Hygrochron was located at the BoM site, adjacent to the weather station and thermometers. iButtons were programed to start while on-site and had run out before retrieval, however occasionally dataloggers ran either side of deployment. The dates that the datalogger have been recorded within 4312_MI_temp_hum_62sites.xlxs for all iButtons and data trimmed to the dates of site deployment.\n\nBasic iButtons site variables within a 1x1m quadrat (inc. slope, aspect, vegetation cover) were recorded and are available in 4312_MI_iButton_location_site_dets.xls. Related biotic and abiotic (inc, DEM derived topographic values) site variables for the associated biological monitoring site (706.86m^2 ) can be found in the data for AAS_4312_Azorella_condition_site_topo ( doi:10.26179/5bf382d899d95). A generalised site \u2018map\u2019 is included in 4312_MI_iButton_location_site_dets_interp_library.xlsx\n\nDataloggers were deployed on site between 15/12/2016 and 03/01/2018, where the median deployment was 338 days (min 293d and max 350d). \n\nData provided includes the following headings:\n4312_MI_temp_hum_62sites.scv - SITE_CODE, DateTime (AEST; UTC+10:00), Temperature (deg), Humidity (%), Year, Month, Day, Hour, Site, iButton, type, deployment_start, deployment_end.\n\n4312_MI_iButton_location_site_dets.xls: site name, location (UTM, GDA94), iButton number, date of establishment, distance and bearing to other iButtons on site, 1x1m quadrat data around iButtons (slope, aspect, vegetation cover, description), comments.\n\n4312_MI_iButton_QA_notes.xlxs: site, ibutton, sitecode, deployment start and finish dates, serial numbers, type, data quality comments.\n\nInterpretation libraries are provided for each file.\n\nThis dataset was created as part of Catherine Dickson's PhD thesis 'Impact of climate change on a sub-Antarctic keystone species Azorella macquariensis (Apiaceae)'.", "links": [ { diff --git a/datasets/AAS_4313_Genetic_CPR_1.json b/datasets/AAS_4313_Genetic_CPR_1.json index 6e1783807f..58a3cad3fc 100644 --- a/datasets/AAS_4313_Genetic_CPR_1.json +++ b/datasets/AAS_4313_Genetic_CPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4313_Genetic_CPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data stored in a Dryad package (doi:10.5061/dryad.c75sj) associated with the publication:\n\nGenetic monitoring of open ocean biodiversity: an evaluation of DNA metabarcoding for processing continuous plankton recorder samples Authors: Bruce Deagle , Laurence Clarke , John Kitchener, Andrea Polanowski, Andrew Davidson. Molecular Ecology Resources.\n\nThe Continuous Plankton Recorder (CPR) has been used to characterise zooplankton biodiversity along transects covering hundreds of thousands of kilometres in the Southern Ocean CPR survey. Plankton collected by the CPR is currently identified using is classical taxonomy (i.e. using a microscope and morphological features). We investigated the potential to use DNA metabarcoding (species identification from DNA mixtures using high-throughput DNA sequencing) as a tool for rapid collection of taxonomic data from CPR samples.\n\nIn our study, zooplankton were collected on CPR silks along two transects between Tasmania and Macquarie Island. Plankton were identified using standard microscopic methods and by sequencing a mitochondrial COI marker. Data provided in the Dryad Data entry include the DNA sequences (Illumina MiSeq) recovered, the morphological identifications and the R-code used to analyse these data.\n\nThe results from our study show that a DNA-based approach increased the number of metazoan species identified and provided high resolution taxonomy of groups problematic in conventional surveys (e.g. larval echinoderms and hydrozoans). Metabarcoding also generally produced more detections than microscopy, but this sensitivity may make cross-contamination during sampling a problem. In some samples, the prevalence of DNA from larger plankton (such as krill) masked the presence of smaller species. Overall, the genetic data represents a substantial shift in perspective, making direct integration into current long-term time-series challenging. We discuss a number of hurdles that exist for progressing this powerful DNA metabarcoding approach from the current snapshot studies to the requirements of a long-term monitoring program.", "links": [ { diff --git a/datasets/AAS_4313_KAXIS_EUK_OTUS_1.json b/datasets/AAS_4313_KAXIS_EUK_OTUS_1.json index 459852fdc6..924cd09e6c 100644 --- a/datasets/AAS_4313_KAXIS_EUK_OTUS_1.json +++ b/datasets/AAS_4313_KAXIS_EUK_OTUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4313_KAXIS_EUK_OTUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling\nSamples were collected on board the RSV Aurora Australis between 22 January and 17 February 2016. The cruise surveyed the region south of the Kerguelen Plateau including the Princess Elizabeth Trough and BANZARE Bank in a series of eight transects covering 8165 km. Plankton communities were collected at 45 conductivity temperature depth (CTD) stations and seven additional underway stations, with biological replicates collected at two stations (52 independent sites). Surface water was sampled from 4 plus or minus 2 m depth using the uncontaminated seawater line. Deep Chlorophyll Maximum (DCM, 10-74 m) water samples were obtained using 10 L Niskin bottles mounted on a Seabird 911+ CTD. Plankton communities were size-fractionated by sequentially filtering 10 L seawater through 25 mm 20 micron (nylon) and 5 micron filters (PVDF), and 0.45 micron Sterivex filters (PVDF). Filters were stored frozen at -80 \u00b0C.\n\nDNA extraction and high-throughput sequencing\nDNA was extracted from half of each filter using the MoBio PowerSoil DNA Isolation kit at the Australian Genome Research Facility (AGRF, Adelaide, Australia; http://www.agrf.org.au). The V4 region of the 18S rDNA (approximately 380 bp excluding primers) was PCR-amplified using universal eukaryotic primers from all extracts and sequenced on an Illumina MiSeq v2 (2 x 250 bp paired-end) following the Ocean Sampling Day protocol (Piredda et al. 2017). Amplicon library preparation and high-throughput sequencing were carried out at the Ramaciotti Centre for Genomics (Sydney, Australia).\n\nSequence analysis, OTU picking and assignment followed the Biomes of Australian Soil Environments (BASE) workflow (Bissett et al. 2016). Taxonomy was assigned to OTUs based on the PR2 database using the \u2018classify.seqs\u2019 command in mothur version 1.31.2 with default settings and a bootstrap cut-off of 60%. OTUs representing any terrestrial contaminants (e.g. human) and samples with low sequencing coverage (less than 7000 reads) were removed from the dataset.\n\nThe date of sea ice melt for each station was estimated from daily SSM/I-derived sea-ice spatial concentration from the National Snow and Ice Data Centre (NSIDC) at 25 x 25 km resolution. Days since melt was considered to be the number of days between the date on which sea ice concentration first fell below 15% and the date of sampling.\n\nOther environmental variables included are in situ chlorophyll a, as an indicator of biological production, and near-surface salinity (mean over the upper 10 m) as an indicator for recent sea ice melt. Both environmental measurements were taken from the associated CTD seawater samples. The surface chlorophyll a in seawater (1-2 L) collected in Niskin bottles was analysed by high performance liquid chromatography (HPLC, provided by Karen Westwood and Imojen Pearce, Australian Antarctic Division, doi:10.4225/15/5a94c701b98a8).\n\nSampling times are given in UTC.", "links": [ { diff --git a/datasets/AAS_4313_KAXIS_FISH_DIET_1.json b/datasets/AAS_4313_KAXIS_FISH_DIET_1.json index c0cc248a3c..65bde6971f 100644 --- a/datasets/AAS_4313_KAXIS_FISH_DIET_1.json +++ b/datasets/AAS_4313_KAXIS_FISH_DIET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4313_KAXIS_FISH_DIET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-throughput DNA-sequencing data for mesopelagic fish stomach contents sampled during the Kerguelen Axis voyage (January-Februay 2016). \n\nMesopelagic fish form an important link between zooplankton and higher trophic levels in Southern Ocean food webs, however their diets are poorly known. Most of the dietary information available comes from morphological analysis of stomach contents and to a lesser extent fatty acid and stable isotopes. DNA sequencing could substantially improve our knowledge of mesopelagic fish diets, but has not previously been applied. We used high-throughput DNA sequencing (HTS) of the 18S ribosomal DNA and mitochondrial cytochrome oxidase I (COI) to characterise stomach contents of four myctophid and one bathylagid species collected at the southern extension of the Kerguelen Plateau (southern Kerguelen Axis), one of the most productive regions in the Indian sector of the Southern Ocean. Diets of the four myctophid species were dominated by amphipods, euphausiids and copepods, whereas radiolarians and siphonophores contributed a much greater proportion of HTS reads for Bathylagus sp. Analysis of mitochondrial COI showed that all species preyed on Thysanoessa macrura, but Euphausia superba was only detected in the stomach contents of myctophids. Size-based shifts in diet were apparent, with larger individuals of both bathylagid and myctophid species more likely to consume euphausiids, but we found little evidence for regional differences in diet composition for each species over the survey area. The presence of DNA from coelenterates and other gelatinous prey in the stomach contents of all five species suggests the importance of these taxa in the diet of Southern Ocean mesopelagics has been underestimated to date. Our study demonstrates the use of DNA-based diet assessment to determine the role of mesopelagic fish and their trophic position in the Southern Ocean and inform the development of ecosystem models.\n \nFor more detail, see Clarke LJ, Trebilco R, Walters A, Polanowski AM, Deagle BE (2018). DNA-based diet analysis of mesopelagic fish from the southern Kerguelen Axis. Deep Sea Research Part II: Topical Studies in Oceanography. DOI: 10.1016/j.dsr2.2018.09.001.", "links": [ { diff --git a/datasets/AAS_4313_KRILL_MICROBIOME_1.json b/datasets/AAS_4313_KRILL_MICROBIOME_1.json index 7eb46915c4..bb3d7388e8 100644 --- a/datasets/AAS_4313_KRILL_MICROBIOME_1.json +++ b/datasets/AAS_4313_KRILL_MICROBIOME_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4313_KRILL_MICROBIOME_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Krill-associated bacterial communities characterised by high-throughput DNA sequencing of the 16S ribosomal RNA gene. \nThe data is decribed in 'Clarke LJ, Suter L, King R, Bissett A and Deagle BE (2019) Antarctic Krill Are Reservoirs for Distinct Southern Ocean Microbial Communities. Front. Microbiol. 9:3226. doi: 10.3389/fmicb.2018.03226' available here: https://www.frontiersin.org/articles/10.3389/fmicb.2018.03226/full", "links": [ { diff --git a/datasets/AAS_4313_Larval_Fish_Bycatch_ID_1.json b/datasets/AAS_4313_Larval_Fish_Bycatch_ID_1.json index ccdafdaea1..34206272e5 100644 --- a/datasets/AAS_4313_Larval_Fish_Bycatch_ID_1.json +++ b/datasets/AAS_4313_Larval_Fish_Bycatch_ID_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4313_Larval_Fish_Bycatch_ID_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1st Experiment 24/11/16\n************************************************************************************************\nSee 2016_11_24_Miseq_Sheet\n1. Sanger Sequencing\nPlate #4 - 25mg of Tissue was extracted by AGRF. DNA was diluted to 5ng/ul.\nSamples were sanger sequenced with 16SAR (Palumbi) primer. If they failed, I used COI3 cocktail (Ivanova). FASTA sequences from Plate 4 are in the folder named Sanger Sequence FASTA Plate #4.\nNaming - Plate position, primer, sample ID. ie reater than A1-16S-AR_1952.\n\n2. DNA and Tissue Pools of Plate 4\nWe wanted to explore the possibility of using a metabarcoding approach.\nFor metabarcoding we re-examined specimens already identified from sanger sequences. We mixed DNA from many samples (n=16 or n=96) and did a single amplification (i.e. up to 96 DNA extractions processed in a\nsingle-tube marker amplification). We also took it a step further and tried blending a set\namount of tissue from many fish specimens (n=16 or n=96) and did a single DNA extraction\non the tissue mixes (i.e. a single DNA extraction and single tube amplification for up to 96\nsamples). See 2016_11_24_Miseq_Sheet for DNA and Tissue Pool mixes.\n\n3. Miseq Run\n16 samples were ran on a 250bp pe read. Each sample was amplified with 3 primer sets - COI (please note one dual labelled set was used), 12s and 16s (Primers listed on 2016_11_24_Miseq_Sheet). They were diluted 1:10 and illumina sequencing adaptors were added (please note I used same I7 and I5 per sample, so they had to be sorted on amplicon).\n2016_11_24_fastq_files has the data from miseq. and 2016_11_24_merged_fastq_files has the merged files.\nFor some unknown reason 16s tissue produced no data.\n\n2nd Experiment 04/07/17\n*************************************************************************************************\n1. DNA Extractions\nPlate #1, 2 and 3 - 25mg of Tisse was extracted by AGRF. DNA was diluted to 5ng/ul. We also used Plate #4 from experiment above. See Plate Layout for sample allocation.\n\n2. Tissue and DNA Pools\nDNA pools were from Plate 1, 2, 3 and 4.\nTissue Mixes were from Plate 2 and 4 only.\nWe wanted to explore the possibility of using a metabarcoding approach.\nWe mixed DNA from many samples (n=16 or n=96) and did a single amplification (i.e. up to 96 DNA extractions processed in a single-tube marker amplification). We also took it a step further and tried blending a set\namount of tissue from many fish specimens (n=16 or n=96) and did a single DNA extraction\non the tissue mixes (i.e. a single DNA extraction and single tube amplification for up to 96\nsamples). See plate layout for DNA and Tissue Pool mixes.\n\n3. Miseq Run\n577 samples were sequenced in a 250bp pe read. See 2017_07_04_Miseq Sheet.\nPlate 1, 2 3 and 4 were all sequenced with Leray Primers.(Please note I accidentally amplified the first half of plate one with one pair of dual labelled COI primers, index on miseq sheet).\nI also made a plate of tissue and DNA pools (see plate layout for DNA and Tissue Pool mixes) and amplified those with 4 primers (primer sequences on miseq sheet)\nCOI (individual dual labelled primers, 1st round index are on miseq sheet)\n12s Fish\n16s Chordate\nNADH\nThe last 4 samples with 12s were to add to database as there are no 12S sequences for those species on genbank.\nSee PCR recipes for annealing temp and cycling etc \n\nI accidentally put the marker under sample name so the original sample ID was lost and miseq gave it a new name (name from miseq output) and then another new name from merged file. Finally I gave them a unique sample ID. See name file if you need more information.\n2017_07_04 has the data from miseq. and 2017_07_04_merged_fastq_files has the merged files.\nSamples were clustered using zero radius OTU's.\n\n4.Results\nSee Results database. \nThe spreadsheet has all of the possible name combinations from the run. It also contains the Haul ID and date, time, lat, long etc. There is a morph taxa ID which refers to what the observer has identified the fish and then there is Seq_Taxa_ID which is the sequencing result. There is also a list of primers that were used to identify the fish. 0 indicated that the primer wasnt used, 1 indicates it was. The second tab has all of the info for the samples that failed. \n*************************************************************************************************", "links": [ { diff --git a/datasets/AAS_4315_DMSP-phytobacto_2.json b/datasets/AAS_4315_DMSP-phytobacto_2.json index e7097e00d5..849c3bea2e 100644 --- a/datasets/AAS_4315_DMSP-phytobacto_2.json +++ b/datasets/AAS_4315_DMSP-phytobacto_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4315_DMSP-phytobacto_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data come from a set of experiments conducted on the coastal waters near Davis Station in January 2017. The first set of data are from a transect near the Sorsdal glacier and out to sea, to characterise DMSP-mediated phytoplankton bacteria interactions along a salinity gradient. The second data set are from a series of incubation experiments to gain deeper insight into the role of various infochemicals in Antarctic phytoplankton-bacteria relationships. Specifically, DMSP, VitB12, Tryptophan and Methionine. The last data set is derived from two incubation experiments: a short term DMSP addition experiment to look at its uptake and utilisation by the microbial community; and a longer-term (5 day) stable isotope probing experiment to track DMSP through the lower trophic food web.", "links": [ { diff --git a/datasets/AAS_4316_40Ar_39Ar_1.json b/datasets/AAS_4316_40Ar_39Ar_1.json index f35e162050..86c214a4aa 100644 --- a/datasets/AAS_4316_40Ar_39Ar_1.json +++ b/datasets/AAS_4316_40Ar_39Ar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4316_40Ar_39Ar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Processed 40Ar/39Ar age data from Heard Island rock samples.\n\n40Ar/39Ar geochronology spreadsheet for samples analysed from around Heard Island, Kerguelen Plateau. Two analytical laboraties were used for these analyses as listed in the spreadsheet. \n\nSample Preparation: Both groundmass concentrations of basalts and one glass separate were prepared for this study. High-purity basalt groundmass concentrates (>99% purity); were obtained using standard separation techniques. All groundmass and glass separates were rigorously put through a series of acid leaching procedures. Each sample was treated with 1N and 6N HCl, 1N and 3N HNO3. Glass separates were treated in a dilute bath of HF (~5%) for approximately 5-10 minutes. Final mineral separates were hand picked under a binocular microscope to a purity of >99% with particular attention to excluding grains with abundant inclusions, adhering material, carbonate, or alteration. All groundmass concentrates range in size between 60-100 mesh (250-150 \uf06dm). Visible phenocrysts were removed using a magnetic separator and detailed hand picking. Both glass and groundmass concentrates were washed in triple distilled water (3X) to dissolve any remaining fine particles and possible acid. \n\nBetween 40 and 20 mg of high purity groundmass and glass were hand picked using a binocular microscope. They were then encapsulated in aluminum and loaded with a standard of known age (FCT-NM-Fish Canyon Tuff sanidine standard produced from the New Mexico Geochronology Research Laboratory in Socorro, New Mexico) and vacuum sealed in quartz vials. The samples geometries (sample heights) were determined using a vernier caliper. After irradiation, the samples were separated from the flux monitors. Prior to analyzing the basalt samples, the flux monitors (FCT-NM sanidines) were analyzed in order to create a J-curve for the age calculation.\nThe new 40Ar/39Ar ages were obtained by incremental heating using the ARGUS-VI mass spectrometer. 4 groundmass splits and one glass sample were irradiated for 6 hours at 1 Megawatt power (Irradiation 16-OSU-05) in the TRIGA (CLICIT-position) nuclear reactor at Oregon State University, along with the FCT sanidine (28.201 \u00b1 0.023 Ma, 1\u03c3) flux monitor (Kuiper et al. 2008). Individual J-values for each sample were calculated by parabolic extrapolation of the measured flux gradient against irradiation height and typically give 0.2-0.3% uncertainties (1\u03c3). The term \u201cplateau\u201d refers to two or more contiguous temperature steps with apparent dates that are indistinguishable at the 95% confidence interval and represent \uf0b3 50% of the total 39ArK released (Fleck et al., 1977). Isochron analysis (York, 1969) of all samples was used to assess if non-atmospheric argon components were trapped in any samples, and in some cases, confirm the Plateau ages for each sample. A total gas age (Total Fusion Age), analogous to conventional K-Ar age, is calculated for each sample by weight averaging all ages of all gas fractions for the sample. \nThe 40Ar/39Ar incremental heating age determinations were performed on a multi-collector ARGUS-VI mass spectrometer at Oregon State University that has 5 Faraday collectors (all fitted with 1012 Ohm resistors) and 1 ion-counting CuBe electron multiplier (located in a position next to the lowest mass Faraday collector). This allows us to measure simultaneously all argon isotopes, with mass 36 on the multiplier and masses 37 through 40 on the four adjacent Faradays. This configuration provides the advantages of running in a full multi-collector mode while measuring the lowest peak (on mass 36) on the highly sensitive electron multiplier (which has an extremely low dark-noise and a very high peak/noise ratio). Irradiated samples were loaded into Cu-planchettes in an ultra-high vacuum sample chamber and incrementally heated by scanning a defocused 25 W CO2 laser beam in preset patterns across the sample, in order to release the argon evenly. After heating, reactive gases were cleaned up using an SAES Zr-Al ST101 getter operated at 400\u00b0C for ~10 minutes and two SAES Fe-V-Zr ST172 getters operated at 200\u00b0C and room temperature, respectively. All ages were calculated using the corrected Steiger and J\u00e4ger (1977) decay constant of 5.530 \u00b1 0.097 x 10-10 1/yr (2\u03c3) as reported by Min et al. (2000). For all other constants used in the age calculations we refer to Table 2 in Koppers et al. (2003). Incremental heating plateau ages and isochron ages were calculated as weighted means with 1/\u03c32 as weighting factor (Taylor 1997) and as YORK2 least-square fits with correlated errors (York 1969) using the ArArCALC v2.6.2 software from Koppers (2002) available from the http://earthref.org/ArArCALC/ website.", "links": [ { diff --git a/datasets/AAS_4318_GIA_model_output_1.json b/datasets/AAS_4318_GIA_model_output_1.json index dceddd50c9..d4132eda19 100644 --- a/datasets/AAS_4318_GIA_model_output_1.json +++ b/datasets/AAS_4318_GIA_model_output_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4318_GIA_model_output_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes predictions of bedrock motion due to glacial isostatic adjustment from a range of published forward and inverse models. The predictions are only for the geodetic GPS sites considered within AAS4318:\nSite(4-character ID), longitude (decimal degrees), latitude (decimal degrees) \nCAS1 110.519729 -66.283391\nDAV1 77.972630 -68.577332\nBHIL 100.599007 -66.251025\nCAD2 86.100628 -68.566511\nCAD3 96.363668 -66.521104\nCAD4 99.143786 -67.419656\nCAD5 107.764024 -66.552476\nCAD6 120.990964 -66.789283\n\nThe file is ascii text with each row the GIA prediction for one site. The columns are given as:\nSite(4-character ID) Latitude(decimal degrees) Lon(decimal degrees) G14(mm/yr) C18(mm/yr) REGINA(mm/yr) RATES(mm/yr) ICE6G_D IJ05R2_115kmLT(mm/yr) W12_best(mm/yr)\n\nValues are interpolated from the resolution of the grids as published using a bicubic interpolator using GMT5 grdtrack with default settings.\n\nThe reference frame of the different GIA model predictions vary, with the forward models in centre-of-mass of the solid Earth (CE) and the inverse solutions likely in centre-of-mass of the whole Earth system (CM). The original publications should be checked to confirm this.\n\nReferences\nG14: Gunter, B. C., Didova, O., Riva, R. E. M., Ligtenberg, S. R. M., Lenaerts, J. T. M., King, M. A., van den Broeke, M. R., and Urban, T.: Empirical estimation of present-day Antarctic glacial isostatic adjustment and ice mass change, The Cryosphere, 8, 743\u2013760, https://doi.org/10.5194/tc-8-743-2014, 2014\n\nC18: Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., and Blewitt, G. (2018). GIA model statistics for GRACE hydrology, cryosphere, and ocean science. Geophysical Research Letters, 45, 2203\u2013 2212. https://doi.org/10.1002/2017GL076644\n\nREGINA: Ingo Sasgen, Alba Mart\u00edn-Espa\u00f1ol, Alexander Horvath, Volker Klemann, Elizabeth J Petrie, Bert Wouters, Martin Horwath, Roland Pail, Jonathan L Bamber, Peter J Clarke, Hannes Konrad, Mark R Drinkwater, Joint inversion estimate of regional glacial isostatic adjustment in Antarctica considering a lateral varying Earth structure (ESA STSE Project REGINA), Geophysical Journal International, Volume 211, Issue 3, December 2017, Pages 1534\u20131553, https://doi.org/10.1093/gji/ggx368\n\nRATES: Mart\u00edn-Espa\u00f1ol, A. , Zammit-Mangion, A. , Clarke, P. J. , Flament, T. , Helm, V. , King, M. A. , Luthcke, S. B. , Petrie, E. , R\u00e9my, F. , Sch\u00f6n, N. , Wouters, B. and Bamber, J. L. (2016): Spatial and temporal Antarctic Ice Sheet mass trends, glacio-isostatic adjustment, and surface processes from a joint inversion of satellite altimeter, gravity, and GPS data , Journal of Geophysical Research: Earth Surface, 121 (2), pp. 182-200 . doi: 10.1002/2015JF003550\n\nICE6G_D: Peltier, W.R., Argus, D.F. and Drummond, R. (2018) Comment on \"An Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model\" by Purcell et al. J. Geophys. Res. Solid Earth, 123, 2019-2018, doi:10.1002/2016JB013844.\n\nIJ05R2_115kmLT: Ivins, E. R., James, T. S., Wahr, J., O. Schrama, E. J., Landerer, F. W., and Simon, K. M. (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118, 3126\u2013 3141, doi:10.1002/jgrb.50208.\n\nW12_best: Whitehouse, P.L., Bentley, M.J., Milne, G.A., King, M.A. and Thomas, I.D. (2012), A new glacial isostatic adjustment model for Antarctica: calibrated and tested using observations of relative sea\u2010level change and present\u2010day uplift rates. Geophysical Journal International, 190: 1464-1482. https://doi.org/10.1111/j.1365-246X.2012.05557.x", "links": [ { diff --git a/datasets/AAS_4318_GNSS_bedrock_coordinate_timeseries_1.json b/datasets/AAS_4318_GNSS_bedrock_coordinate_timeseries_1.json index 4adeeba3de..ba59b79cc3 100644 --- a/datasets/AAS_4318_GNSS_bedrock_coordinate_timeseries_1.json +++ b/datasets/AAS_4318_GNSS_bedrock_coordinate_timeseries_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4318_GNSS_bedrock_coordinate_timeseries_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coordinate time series, in local topocentric east, north, up reference frame. Underlying coordinates were derived in IGS14 reference frame. Coordinate time series generated using GIPSY6.3 using NASA JPL clocks and orbits from repro2 as operational circa 2020.\n\n# The files available are geodetic GPS coordinate time series for bedrock displacements \n# for a range of sites in East Antarctica. They were collected under AAS4318. Data should only be used when appropriately cited.\n# The Coordinates were estimated using GIPSYv6.3 using JPL repro2 clocks and orbits\n#\n# Data are in TSVIEW format, a simple ascii file format\n# TSVIEW is an open Matlab code for visualising and analysing GPS time series but the series can be easily read by other software\n# TSVIEW is available at http://www-gpsg.mit.edu/~tah/GGMatlab/\n# and described in Herring, T. MATLAB Tools for viewing GPS velocities and time series. GPS Solutions 7, 194\u2013199 (2003). https://doi.org/10.1007/s10291-003-0068-0\n#\n# For each site, there are three files, ones for each of North, East and Up.\n# For example, for site bhil\n# mb_bhil_NEh.dat1 is for the North component\n# mb_bhil_NEh.dat2 is for the East component\n# mb_bhil_NEh.dat3 is for the Up component\n#\n# each file contains three header lines followed by the data with one row per epoch, such as\n# 1995.1020 0.03150 0.00400\n# with column 1 in decimal years, column 2 is the estimate (north, east or height) in metres, and column 3 is the uncertainty (1 sigma) of the measurement in metres\n#\n# North, East and Up are relative displacements and are not absolute coordinates\n#\n# Data for eight GPS sites are included in the archive, with approximate site coordinates:\n# SiteID, lat (decimal degrees), lon (decimal degrees), site geographical location\nCAS1 -66.283391207 110.519728963 Casey Station International GNSS Service site\nDAV1 -68.577332044 77.972630424 Davis Station International GNSS Service site\nBHIL -66.251024752 100.599006649 Bunger Hills site, originally established by Geoscience Australia\nCAD2 -68.566510985 86.100627506 Mt Brown, new site established under this project\nCAD3 -66.521103808 96.363667501 Gillies Island, new site established under this project\nCAD4 -67.419656438 99.143785987 Mt Strathcona, new site established under this project\nCAD5 -66.552475869 107.764024273 Snyder Rocks, new site established under this project\nCAD6 -66.789282506 120.990964280 Chick Island, new site established under this project", "links": [ { diff --git a/datasets/AAS_4318_GNSS_bedrock_raw_data_1.json b/datasets/AAS_4318_GNSS_bedrock_raw_data_1.json index 7072c6a6f2..148c9701c2 100644 --- a/datasets/AAS_4318_GNSS_bedrock_raw_data_1.json +++ b/datasets/AAS_4318_GNSS_bedrock_raw_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4318_GNSS_bedrock_raw_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS daily files in RINEX v2 format with International GNSS Service format log files in a single tar file. Files are compressed using gzip after 'Hatanaka' compression. \n\nCompression information: Yuki Hatanaka (hatagsi.go.jp) (GSI) wrote and maintains rnx2crx and crx2rnx, which allows the user to compress/decompress, respectively, a RINEX observation file into a smaller ASCII format. The Hatanaka-compressed ASCII format version of a RINEX observation file is frequently used in conjunction with the UNIX compress, zip, gzip or other generalized compression utilities to create a very small file for Internet transfer.\n\nCompression information: open utlities rnx2crx and crx2rnx allow the user to compress/decompress, respectively, a RINEX observation file into a smaller ASCII format. The Hatanaka-compressed ASCII format version of a RINEX observation file is compressed here with gzip.\n\nRINEX files are stored in the following structure within the tar file.\n./YYYY/DDD/YYt/mmmmDDD#.YYt.gz\n\nYYYY=4-digit year\nDDD=3-digit day of year\nmmmm=4-char site name\n#='0'\nYY=2-digit year\nt='o'\n\nIGS log files are in \n./logs\n\n\nApproximate site locations are given below in units of decimal degrees (WGS84) as (latitude longitude) pairs:\nCAD1 approx coordinates only - Carey Ntk - as no data yet downloaded\nCAD2 -68.566510985 86.100627506 Mt Brown, new site established under this project\nCAD3 -66.521103808 96.363667501 Gillies Island, new site established under this project\nCAD4 -67.419656438 99.143785987 Mt Strathcona, new site established under this project\nCAD5 -66.552475869 107.764024273 Snyder Rocks, new site established under this project\nCAD6 -66.789282506 120.990964280 Chick Island, new site established under this project\nBHIL -66.251024752 100.599006649 Bunger Hills site, originally established by Geoscience Australia\n\nData spans December 2015 to Jan 2019 but exact durations depend on the site.", "links": [ { diff --git a/datasets/AAS_4318_GPS_bedrock_data_1.json b/datasets/AAS_4318_GPS_bedrock_data_1.json index a7beab1d9c..7c080e3775 100644 --- a/datasets/AAS_4318_GPS_bedrock_data_1.json +++ b/datasets/AAS_4318_GPS_bedrock_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4318_GPS_bedrock_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geodetic GPS data collected with antennas connected to rock outcrops as part of AAS 4318. Six new GPS sites were established (sites CAD1, CAD2, CAD3, CAD4, CAD5 and CAD6) and measurements at a previously established site (BHIL) was recommenced. The sites were deployed continuously, although measurements were intermittent based on power restrictions and hardware performance issues.\n\nApproximate site locations are given below in units of decimal degrees (WGS84) as (latitude longitude) pairs:\nCAD1 approx coordinates only - Carey Ntk - as no data yet downloaded\nCAD2 -68.566510985 86.100627506 Mt Brown, new site established under this project\nCAD3 -66.521103808 96.363667501 Gillies Island, new site established under this project\nCAD4 -67.419656438 99.143785987 Mt Strathcona, new site established under this project\nCAD5 -66.552475869 107.764024273 Snyder Rocks, new site established under this project\nCAD6 -66.789282506 120.990964280 Chick Island, new site established under this project\nBHIL -66.251024752 100.599006649 Bunger Hills site, originally established by Geoscience Australia\n\nWe also analysed the International GNSS Service data from Casey (CAS1) and Davis (DAV1). We provide time series for all sites. We provide raw carrier phase and pseudorange data in RINEX format for sites other than DAV1 and CAS1 as these data are freely available within many public archives.\n\nSite metadata for DAV1 and CAS1 is available at the International GNSS Service (igs.org) in the form of site logs. Site logs for BHIL and CAD1-6 are provided as part of the data archive in International GNSS format. \n\nAntennas for CAD1-6 were connected to bedrock via a POLENET antenna monument drilled and epoxied into bedrock. Existing monuments were used for BHIL, CAS1 and DAV1. Choke ring drainage holes were plugged after deployment of sites. \n\nSite changes\n============\nInstruments were deployed continuously but did not operate continuously due to solar powering of the instruments with a small battery array.\n\nThere were no hardware changes at the sites up to the time of writing (Feb 2021). The exception was a receiver swap at site CAD3 on 2019-10-18. The metadata within the RINEX files is correct and reflects any changes.\n\nData sets:\n=========\n1. bedrock coordinate time series \n# Data are in TSVIEW format, a simple ascii file format\n# TSVIEW is an open Matlab code for visualising and analysing GPS time series but the series can be easily read by other software\n# TSVIEW is available at http://www-gpsg.mit.edu/~tah/GGMatlab/\n# and described in Herring, T. MATLAB Tools for viewing GPS velocities and time series. GPS Solutions 7, 194\u2013199 (2003). https://doi.org/10.1007/s10291-003-0068-0\n#\n# For each site, there are three files, ones for each of North, East and Up.\n# For example, for site bhil\n# mb_bhil_NEh.dat1 is for the North component\n# mb_bhil_NEh.dat2 is for the East component\n# mb_bhil_NEh.dat3 is for the Up component\n#\n# each file contains three header lines followed by the data with one row per epoch, such as\n# 1995.1020 0.03150 0.00400\n# with column 1 in decimal years, column 2 is the estimate (north, east or height) in metres, and column 3 is the uncertainty (1 sigma) of the measurement in metres\n\nCoordinate time series are one file per coordinate component per site.\n\nCoordinates are in IGS14 reference frame and derived using GIPSYv6.3 using JPL repro2 clocks and orbits.\n\n2. raw RINEX v2 data. RINEX format definitions are provided at https://www.igs.org/wg/rinex/\n\n# RINEX files are provided one per day per site, covering 24hr UT.", "links": [ { diff --git a/datasets/AAS_4320_East_Antarctic_sediment_carbonate_mineralogy_1.json b/datasets/AAS_4320_East_Antarctic_sediment_carbonate_mineralogy_1.json index a3df6ab640..5bd92ee087 100644 --- a/datasets/AAS_4320_East_Antarctic_sediment_carbonate_mineralogy_1.json +++ b/datasets/AAS_4320_East_Antarctic_sediment_carbonate_mineralogy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4320_East_Antarctic_sediment_carbonate_mineralogy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine sediments often represent an important reservoir of carbonate minerals that will react rapidly to changing seawater chemistry as a result of ocean acidification. Ocean acidification (the reaction of CO2 with seawater) lowers the saturation state with respect to carbonate minerals and may lead to dissolution of these minerals if undersaturation occurs.\nThere are three main carbonate minerals found in marine sediments:\n1. aragonite\n2. calcite (also referred to as low-magnesium calcite, containing less than 4mol% MgCO3)\n3. high-magnesium calcite (greater than 4 mol% MgCO3)\nDue to the different structure of these minerals, they have different solubilities with high-Mg calcite the most soluble, followed by aragonite and then calcite. As seawater CO2 increases and the saturation state with respect to carbonate minerals decreases, high-Mg calcite will be the first mineral subject to undersaturation and dissolution.\nBy measuring the carbonate mineral composition of sediments, we can determine which areas are most at risk from dissolution. This information forms an important baseline with which we can assess future climate change. The effect of ocean acidification on carbonates in marine sediments will occur around the world, but due to the lower seawater temperatures in Antarctica, solubility is much lower so the impacts will occur here first.\n \nThis dataset is a compilation of carbonate mineralogy data from surface sediments collected from the East Antarctic margin. The dataset includes sample metadata, bulk carbonate content, %calcite, % aragonite and mol% MgCO3 (i.e. the magnesium content of high-Mg calcite). This dataset was compiled from new (up to 2020) and archived sediment samples that contacted sufficient carbonates (typically greater than 3% CaCO3)/", "links": [ { diff --git a/datasets/AAS_4320_NBP14-02_seafloor_imagery_data_1.json b/datasets/AAS_4320_NBP14-02_seafloor_imagery_data_1.json index d53cab9c8d..c242a0d56e 100644 --- a/datasets/AAS_4320_NBP14-02_seafloor_imagery_data_1.json +++ b/datasets/AAS_4320_NBP14-02_seafloor_imagery_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4320_NBP14-02_seafloor_imagery_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 701 still images were analysed from 10 transects on the Sabrina Coast continental shelf. Imagery was collected from the RVIB Nathaniel B Palmer (NBP 14-02, 29 January - 16 March 2014) across a greater than 3000 km2 area. A 'yoyo' camera, with downward facing digital still and video cameras mounted within a tubular steel frame, was deployed on a coaxial cable to image the seafloor. The Ocean Imaging Systems DSC 10000 digital still camera (10.2 megapixel, 20 mm, Nikon D-80 camera) was contained within titanium housing. Camera settings were: F-8, focus 1.9 m, ASA-400. An Ocean Imaging Systems 3831 Strobe (200 W-S) was positioned 1m from the camera at an angle of 26 degrees from vertical. A Model 494 bottom contact switch triggered the camera and strobe at 2.5m above the sea floor, imaging ~ 4.8m2 of sea floor. Parallel laser beams (10 cm separation) provided a reference scale for the images. Transects were conducted at a ship's speed of ~1 knot. \nStill images were characterised for main taxonomic groups and sediment properties based on the CATAMI scheme of Althaus et al. 2015.", "links": [ { diff --git a/datasets/AAS_4321_K-Axis_1.json b/datasets/AAS_4321_K-Axis_1.json index cc9006ec9a..b3d7b36371 100644 --- a/datasets/AAS_4321_K-Axis_1.json +++ b/datasets/AAS_4321_K-Axis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4321_K-Axis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution and abundance of zooplankton, krill and fish were observed on the K-axis transect using deployments of RMT1+8 net. Towing speed of the RMT1+8 were approximately 2 knots. All krill, fish and squid in the catch were sorted, identified to species and counted. The density at each station were determined from the counts per calibrated flow-meter readings attached to the net. Morphometric measures were taken and, for larger taxa.\n\nList of files\nK-Axis Morph combined_for data centre.xlsx: Morphological data for all krill and zooplankton captured in RMT-8 net haul.\nRMT data entry_v1_for data centre.xlsx: Trawl data.\nRMT8 filtered volume_for data centre.xlsx: Filtered volume for each haul.\nMap_all.tif: Map showing all trawl stations.\nMap_RMTR.tif: Map showing only regular trawl stations.\nMap_RMTT.tif: Mapn showing only target trawl stations.\n\n\nK-Axis description\nThis dataset includes biological data from \u201cK-Axis voyage, 2016 and \u201cVoyage 3, 2015\u201d.\n\n[Data from K-Axis voyage, 2016]\nDistribution and abundance of zooplankton, krill and fish were observed on the K-axis transect using deployments of RMT1+8 net. Towing speed of the RMT1+8 were approximately 2 knots. All krill, fish and squid in the catch were sorted, identified to species and counted. The density at each station were determined from the counts per calibrated flow-meter readings attached to the net. Morphometric measures were taken and, for larger taxa.\n-List of files-\nK-Axis Morph combined_for data centre.xlsx: Morphological data for all krill and zooplankton captured in RMT-8 net haul.\nMap_all.tif\nMap_RMTR.tif\nMap_RMTT.tif\nRMT data entry_v1_for data centre.xlsx: Trawl data.\nRMT8 filtered volume_for data centre.xlsx: Filtered volume for each haul.\n\n[Data from Voyage 3, 2015]\nThe Australian Antarctic research and resupply vessel, RV Aurora Australis, was directed to undertake an opportunistic marine science survey for 17 days during 21 February to 10 March 2015 using ship time that became available due to unexpectedly favourable ice conditions for Mawson station resupply.\nThe purpose of this opportunistic Marine Science work was to assess:\n1. The spatial variability, particularly along the shelf break, of the prey field for penguins, flying seabirds and marine mammals in East Antarctica.\n2. The small scale variability of prey in key foraging locations near to land-based colonies of penguins and flying seabirds in East Antarctica.\n3. Feasibility and potential of utilising annual station resupply voyages as a cost effective means to undertake monitoring and research to better understand the ecosystem in the region.\nThe survey completed 5 acoustic box surveys including a total of 53 RMT target and routine trawls, 6 demersal trawls, 131 phytoplankton samples from underway sampling, and 214 hourly observations of predators. These activities were successfully supervised remotely.\n-List of files-\nemm-15-22.pdf: Prelminary report of the voyage to CCAMLR WG-EMM\nFigure_V3_all_euphausiids.pdf: Map of Euphausiid abundance distribution.\nFigure_V3_Clione_antarctica.pdf: Map of Clione antarctica abundance distribution.\nFigure_V3_crystal_krill.pdf: Map of Euphausia crystallorophias abundance distribution.\nFigure_V3_frigida.pdf: Map of Euphausia frigida abundance distribution. \nFigure_V3_larval_fish_abundances.pdf: Map of fish larvae abundance distribution. \nFigure_V3_superba.pdf: Map of Antarctic krill abundance distribution. \nFigure_V3_tmacrura.pdf: Map of Thysanoessa macrura abundance distribution. \nV3_final_for data centre.xlsx: Trawl station data and density data of each taxa caught. \nVoyage 3 Marine Science Program Final.docx: Voyage report.", "links": [ { diff --git a/datasets/AAS_4326_DGT_Soil_Deployments_2.json b/datasets/AAS_4326_DGT_Soil_Deployments_2.json index 424e105500..879f2caa1f 100644 --- a/datasets/AAS_4326_DGT_Soil_Deployments_2.json +++ b/datasets/AAS_4326_DGT_Soil_Deployments_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4326_DGT_Soil_Deployments_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data describe the physicochemistry and metal content of soils collected from 11 sites around Casey and Wilkes stations in the Windmill Islands, East Antarctica during the 2017-2018 summer season for project AAS4326. Four metal fractions are reported in these data: (1) diffusive gradient in thin-film, DGT, labile fraction; (2) water-soluble fraction; (3) dilute-acid extractable fraction, and; (4) total recoverable metal fraction.\n\nThe excel spreadsheet contains a number of worksheets which explain acronyms and units used as well as several sheets of data.", "links": [ { diff --git a/datasets/AAS_4326_DGT_marine_deployments_1.json b/datasets/AAS_4326_DGT_marine_deployments_1.json index 881ad3576f..fa6a8d85d3 100644 --- a/datasets/AAS_4326_DGT_marine_deployments_1.json +++ b/datasets/AAS_4326_DGT_marine_deployments_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4326_DGT_marine_deployments_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data describe the field deployments of the trace-metal passive sampling tools, diffusive gradients in thin-films (DGT). Deployments occurred over the summer 2017/2018 season in the coastal region adjacent to Casey and Wilkes stations. Deployments of DGT to the nearshore marine environment was achieved with small watercraft and shallow (less than 5m deep) moorings, which were left in situ for 21-37 days, depending on the site.", "links": [ { diff --git a/datasets/AAS_4326_DGTvalidation_1.json b/datasets/AAS_4326_DGTvalidation_1.json index 948ae34080..4f6bf9e37a 100644 --- a/datasets/AAS_4326_DGTvalidation_1.json +++ b/datasets/AAS_4326_DGTvalidation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4326_DGTvalidation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study assessed the performance of diffusive gradients in thin-films (DGT) with a binding resin that used Chelex-100 (iminodiacetic acid functional groups) to measure cadmium, copper, nickel, lead, and zinc contaminants in Antarctic marine conditions. To do this, three sets of experiments were done: (I) the uptake of metals to DGT samplers was assessed over time when deployed to three metal mixtures of known concentrations (DGT performance page). This allowed for the determination of metal diffusion coefficients in Antarctic marine conditions and demonstrated when metal competition for binding sites were likely to occur. (II) the DGT were deployed in the presence of the microalga Phaeocystis antarctica at a concentration of 1000-3000 cells/mL to investigate how environmentally realistic concentrations of an Antarctic marine microalgae affect the uptake of metals (DGT uptake with algae page). Finally, the DGT-labile concentrations from part (II) were used in reference toxicity mixture models to predict toxicity to the microalgae so they could be compared to a previous study that investigated the toxicity of metal mixtures to Phaeocystis antarctica and Cryothecomonas armigera (DGT toxicity modelling page).", "links": [ { diff --git a/datasets/AAS_4326_bioaccumulation_2.json b/datasets/AAS_4326_bioaccumulation_2.json index 7b7c80830a..5c73b3c38d 100644 --- a/datasets/AAS_4326_bioaccumulation_2.json +++ b/datasets/AAS_4326_bioaccumulation_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4326_bioaccumulation_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data describes the cellular metal concentrations of Phaeocystis antarctica and Cryothecomonas armigera following exposure to metals singly and in mixtures in laboratory studies. Microalgae were cultured in 80 mL of filtered (less than 0.45 um) seawater and low concentrations of nutrients supplemented with metal stocks to give a range of single and mixture exposures to the metals cadmium, copper, nickel, lead, and zinc. The cellular accumulation and partitioning are used to explain the metal's toxicity (cellular metal fractions are compared to the toxicity data provided in 10.4225/15/5ae93ff723ff8) and assess the risk bioaccumulation of metals to Antarctic marine microalgae may pose in the Southern Ocean food web.", "links": [ { diff --git a/datasets/AAS_4326_limnoterrestrial_metal_risk_1.json b/datasets/AAS_4326_limnoterrestrial_metal_risk_1.json index f90d2af25c..99816ceabf 100644 --- a/datasets/AAS_4326_limnoterrestrial_metal_risk_1.json +++ b/datasets/AAS_4326_limnoterrestrial_metal_risk_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4326_limnoterrestrial_metal_risk_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data reflect the metal content of some Casey and Wilkes melt lakes and streams. In melt lakes, the dissolved metal concentration and diffusive gradient in thin-film technique (DGT) with a Chelex-100 binding resin to measure DGT-labile metal concentrations. In melt streams, dried sediments were analysed using a strong acid, weak acid, and water extractions, as well as using DGT probes. Physical chemistry of melt streams and lake waters were also collected and included the temperature, conductivity, pH, dissolved oxygen, and dissolved organic carbon concentration of waters and the texture, organic matter content, and inorganic matter content in sediments. \n\nThe diatom community within the collected melt stream sediments were taxonomically identified. \n\nAn ecotoxicological bioassay was conducted that exposed field-collected samples of Ceratadon purpeus to concentrations of cadmium, copper, nickel, lead, and zinc and their mixtures. The start and end dissolved metal concentrations are reported as well as the measured photosynthetic efficiency. \n\nFor each data set provided in this entry, the corresponding methods and details are provided in the first tab.", "links": [ { diff --git a/datasets/AAS_4331_Zooplankton_Gut_Contents_1.json b/datasets/AAS_4331_Zooplankton_Gut_Contents_1.json index 7dd8dddce2..ea94769f79 100644 --- a/datasets/AAS_4331_Zooplankton_Gut_Contents_1.json +++ b/datasets/AAS_4331_Zooplankton_Gut_Contents_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4331_Zooplankton_Gut_Contents_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We studied the gut contents of four dominant copepod species (Calanoides acutus, Calanus propinquus, Calanus simillimus and Rhincalanus gigas) during the summer (2014-2015) along a latitudinal gradient (sampled every 5\u00b0 between 40\u00b0S and 65\u00b0S) in the Indian sector of the SO. Diatoms were the most abundant food item found in the guts, comprising 24 of the 25 species found, and 15 were common to the four species of copepod studied. Diatoms accounted for the lowest proportion of the diet in the warmer, northern waters while all the large diatoms (e.g. Chaetoceros atlanticus, C. criophilus, C. dichaeta, Corethron spp.) were only found at 65oS. The most frequent species in the guts were the centric diatoms Thalassiosira spp. (4 to 57%) and the pennate diatoms Fragilariopsis kerguelensis (27 to 80%) and Trichoctoxon reinboldii (2 to 50%); proportions varied within a species across locations. These species were found at all sites examined, whereas some diatoms were specific to one copepod species: Asteromphalus spp. (in R. gigas), C. criophilus and C. dichaeta (in C. acutus), Nitzschia lecointei and N. sicula (in C. propinquus).", "links": [ { diff --git a/datasets/AAS_4331_Zooplankton_isotopes_1.json b/datasets/AAS_4331_Zooplankton_isotopes_1.json index fdce359bef..a4d4b18278 100644 --- a/datasets/AAS_4331_Zooplankton_isotopes_1.json +++ b/datasets/AAS_4331_Zooplankton_isotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4331_Zooplankton_isotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Krill, salps and pteropods were collected with an RMT8 net during the K-Axis cruise. Specimens were removed from the samples, measured and frozen at -20C until ready for analysis in Hobart. Individuals of known species were dried at -60C, ground to a fine powder, encapsulated into tin cups and analysed with an ICP-MS in the Central Science Laboratories, University of Tasmania. Samples were analysed for delta15N and delta13C. The salp was the common Southern Ocean species Salpa thompsoni and the krill were Euphausia superba, E. triacantha, E. frigida and Thysanoessa macrura. A small number (2) of the siphonphore Diphyes antarctica were also analysed. Pteropods analysed included both shelled (thecosomes) and naked (gymnosomes) pteropods.\n \nColumns E-O in the Pteropods worksheet in the spreadsheet are expressed as ratios.", "links": [ { diff --git a/datasets/AAS_4331_salp_Tmac_abundance_1.json b/datasets/AAS_4331_salp_Tmac_abundance_1.json index d53543be21..189f439398 100644 --- a/datasets/AAS_4331_salp_Tmac_abundance_1.json +++ b/datasets/AAS_4331_salp_Tmac_abundance_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4331_salp_Tmac_abundance_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton were collected with a Rectangular Midwater Trawl (RMT 8+1 net) from 37 sampling sites on and near the Southern Kerguelen Plateau. The contents of each net were preserved in 5% buffered formaldehyde. This dataset covers the counts of the contents of the RMT8 net and includes the abundances for the euphausiid Thysanoessa macrura and the salp Salpa thompsoni. The contents were identified and counted under a Leica M165C stereo-microscope. A flow meter attached to the mouth of the RMT 8 was used to record the volume of seawater passing through the net. The count for Thysanoessa macrura includes the total of all developmental stages. For the salps abundances are shown for the 2 developmental phases - solitary individuals and aggregates.", "links": [ { diff --git a/datasets/AAS_4338_Bio_Optics_1.json b/datasets/AAS_4338_Bio_Optics_1.json index 0c0d6a8a6d..d4b85995e3 100644 --- a/datasets/AAS_4338_Bio_Optics_1.json +++ b/datasets/AAS_4338_Bio_Optics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4338_Bio_Optics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bio-optical measurements (radiometry, spectral backscatter, attenuation, absorption) for particle and phytoplankton characterisation acquired during Australian Marine National Facility RV Investigator voyage IN2016_V01.\n\nThe biooptical package consisted of SeaBird 19plus CTD, Satlantic HyperOCR upwelling radiance and downwelling irradiance sensors, WetLabs ac-9, HobiLabs Hydroscat-6. At selected stations the bio-optical package was lowered to the depth of 240 m (or 20 m above the sea bottom if the depth was lower than 260 m) at 20 m/minute. The radiometric measurements were taken only during the day.\nParameters measured: \n\nSeaBird CTD (4 Hz frequency):\n- Temperature\n- Salinity\n- Pressure\n- PAR\n- Fluorescence \n- Oxygen\n\nSatlantic HyperOCR:\n- Upwelling radiance (Lu) - spectral\n- Downwelling irradiance (Ed) \u2013 spectral\n- Pressure\n\nHobiLabs Hydroscat:\n- Backscattering coefficient at 6 wavelengths (442, 488, 550, 589, 676, 850 nm)\n- Fluorescence (550, 676 nm)\n- Pressure\n\nWetLabs ac-9 (2 Hz frequency)\n- Light absorption coefficient at 9 wavelengths (412, 440, 488, 510, 532, 555, 650, 676, 715 nm)\n- Light attenuation coefficient at 9 wavelengths (412, 440, 488, 510, 532, 555, 650, 676, 715 nm)\n\nAt some stations transmissometer data at 650 nm using the Wetlabs c-Star were collected.\nData type product(s) created: raw and calibrated data files were created on board, processed and quality controlled files (.dat and/or .csv) will be available by the end of 2016.\nOwner of instrument: CSIRO\n\nUnits:\nCTD data: units given in the header\nHydroscat data: bbp_HEOBI_all: all bbp in m^-1, slope unitless\nCalibrated: depth in m, all bb in m^-1,all betabb sr^-1 m^-1\nRadiometers: all Ed uW/cm^2/nm \nAll Lu uW/cm^2/nm/sr\nDepth is always given in meters.\n\nSee the metadata file in the download for more information.", "links": [ { diff --git a/datasets/AAS_4340_macquarie_flux_10Hz_2016-2018_1.json b/datasets/AAS_4340_macquarie_flux_10Hz_2016-2018_1.json index d9431a2e59..c45a96c4f7 100644 --- a/datasets/AAS_4340_macquarie_flux_10Hz_2016-2018_1.json +++ b/datasets/AAS_4340_macquarie_flux_10Hz_2016-2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4340_macquarie_flux_10Hz_2016-2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the project summary:\n\nBoth satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds, large waves and heavy sea spray. These conditions lead to the greatest fractional cloud cover over any place on the globe. Much of these biases are a direct consequence of a poor understanding of the structure and dynamics of the SO atmospheric boundary layer, which in turn is a consequence of the sparse observations being available due to the harsh conditions. This proposals call for employing unmanned aerial vehicles/systems from Macquarie Island to make unprecedented observations of the boundary layer processes over the SO. These observations will be used to both model the boundary layer dynamics and clouds and evaluate satellite products and numerical simulations of surface fluxes, cloud properties and sea spray.\n \nThe data was recorded at lat: -54.5, lon:158.935. The observations include measurements of Absolute Humidity, Carbon Concentration, 3D wind, pressure, and ambient temperature.\nThe data is in netcdf4 format with medium compression, and have all available information in the attributes of each variable. The data can be easily previewed with an application like Panoply (https://www.giss.nasa.gov/tools/panoply/).\nThe variable names are:\nAh_7500 \nCc_7500 \nDiag_7500\nDiag_CSAT\nTa_HMP \nTv_CSAT \nUx_CSAT \nUy_CSAT \nUz_CSAT \ne_HMP \nps_7500 \ntime", "links": [ { diff --git a/datasets/AAS_4340_macquarie_flux_30min_2016-2018_1.json b/datasets/AAS_4340_macquarie_flux_30min_2016-2018_1.json index ff035ab9ff..38cdc81547 100644 --- a/datasets/AAS_4340_macquarie_flux_30min_2016-2018_1.json +++ b/datasets/AAS_4340_macquarie_flux_30min_2016-2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4340_macquarie_flux_30min_2016-2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the project summary:\n\nBoth satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds, large waves and heavy sea spray. These conditions lead to the greatest fractional cloud cover over any place on the globe. Much of these biases are a direct consequence of a poor understanding of the structure and dynamics of the SO atmospheric boundary layer, which in turn is a consequence of the sparse observations being available due to the harsh conditions. This proposals call for employing unmanned aerial vehicles/systems from Macquarie Island to make unprecedented observations of the boundary layer processes over the SO. These observations will be used to both model the boundary layer dynamics and clouds and evaluate satellite products and numerical simulations of surface fluxes, cloud properties and sea spray.\n\nThe data was recorded at lat: -54.5, lon:158.935. The observations include fluxes for Absolute Humidity, Heat, and Carbon.\nThe data is in netcdf4 format with medium compression, and have all available information in the attributes of each variable. The data can be easily previewed with an application like Panoply (https://www.giss.nasa.gov/tools/panoply/).\nThe variable names are:\n7500_Warn \nAGC_7500_Avg \nAmph_CSAT_Tot \nAmpl_CSAT_Tot \nCSAT_Warn \nChopper_7500_Tot \nDelT_CSAT_Tot \nDetector_7500_Tot \nFc_Avg \nFc_raw_Avg \nFe_Avg \nFe_raw_Avg \nFh_Avg \nFm_Avg \nPll_7500_Tot \nSync_7500_Tot \nTrack_CSAT_Tot \ncovAhAh \ncovAhTv \ncovCcAh \ncovCcCc \ncovCcTv \ncovTvTv \ncovUxAh \ncovUxCc \ncovUxTv \ncovUxUx \ncovUxUy \ncovUyAh \ncovUyCc \ncovUyTv \ncovUyUy \ncovUzAh \ncovUzCc \ncovUzTv \ncovUzUx \ncovUzUy \ncovUzUz \nn_Tot \ntime \ntime_YYYYmmDDHHMMSS", "links": [ { diff --git a/datasets/AAS_4340_macquarie_met_30min_2016-2018_1.json b/datasets/AAS_4340_macquarie_met_30min_2016-2018_1.json index b42693f019..5adaaee12f 100644 --- a/datasets/AAS_4340_macquarie_met_30min_2016-2018_1.json +++ b/datasets/AAS_4340_macquarie_met_30min_2016-2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4340_macquarie_met_30min_2016-2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the project summary:\n\nBoth satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds, large waves and heavy sea spray. These conditions lead to the greatest fractional cloud cover over any place on the globe. Much of these biases are a direct consequence of a poor understanding of the structure and dynamics of the SO atmospheric boundary layer, which in turn is a consequence of the sparse observations being available due to the harsh conditions. This proposals call for employing unmanned aerial vehicles/systems from Macquarie Island to make unprecedented observations of the boundary layer processes over the SO. These observations will be used to both model the boundary layer dynamics and clouds and evaluate satellite products and numerical simulations of surface fluxes, cloud properties and sea spray.\n\nThe data was recorded at lat: -54.5, lon:158.935. The observations include Absolute Humidity, Relative Humidity, Ambient Temperature, Potential Temperature, 3D wind speed, and Carbon concentration.\nThe data is in netcdf4 format with medium compression, and have all available information in the attributes of each variable. The data can be easily previewed with an application like Panoply (https://www.giss.nasa.gov/tools/panoply/).\nThe variable names are:\nAh_7500_Avg \nCc_7500_Avg \nRH_HMP_Avg \nTa_HMP_Avg \nTpanel_Avg \nTv_CSAT_Avg \nUx_CSAT_Avg \nUy_CSAT_Avg \nUz_CSAT_Avg \nVbat_Avg \nWD_CSAT_Avg \nWD_CSAT_Compass_Avg \nWD_CSAT_Sd \nWS_CSAT_Avg \nps_7500_Avg \nrho_a_Avg \ntime \ntime_YYYYmmDDHHMMSS", "links": [ { diff --git a/datasets/AAS_4341_DISTRIBUTION_1.json b/datasets/AAS_4341_DISTRIBUTION_1.json index 3742a13caa..08e42ccfc3 100644 --- a/datasets/AAS_4341_DISTRIBUTION_1.json +++ b/datasets/AAS_4341_DISTRIBUTION_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4341_DISTRIBUTION_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution data for Stellaria media on Macquarie Island.\n\nThere are 8 files on distribution data for Stellaria media on Macquarie Island. The first 3 files provide the way points of all located Stellaria media plants on Macquarie Island as well as the sizes of a subsample of these plants. The density and distribution of these plants is mapped in file 4. In file 5 more detailed measurements are given from certain of those subsampled sites with regards to plant width, depth, height, age, dominant phenology, and dominance with regards to other vegetation. The final 2 files are summary tables of environmental and vegetation (including Stellaria) characteristics from each of the 8 major population sites at which Stellaria is found on the island. More detailed methods are given in each of the files.\n\nDistribution, % cover and size of S. media will be mapped across MI along with environmental and dispersal factors. Detailed vegetation surveys conducted at 6 infested sites over 3 seasons to determine directional changes in vegetation community structure.\n\nThe AA_NVA excel file records the locations from which samples of seeds and plants were collected from a variety of introduced and native species for use in other trials back in Australia.", "links": [ { diff --git a/datasets/AAS_4341_HERBICIDE_CONTROL_1.json b/datasets/AAS_4341_HERBICIDE_CONTROL_1.json index ed14e6c817..ecd0acdebb 100644 --- a/datasets/AAS_4341_HERBICIDE_CONTROL_1.json +++ b/datasets/AAS_4341_HERBICIDE_CONTROL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4341_HERBICIDE_CONTROL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Injury data for Stellaria media and native plant species collected from Macquarie Island when treated with herbicides at different times and with different rates grown in a plant growth cool room.\n \nThere are 4 files on injury data for Stellaria media and native plant species collected from Macquarie Island when treated with herbicides at different times and with different rates grown in pots in a plant growth cool room at the University of New England. The first file provides injury data for the first experiment recorded on a scale from 1-10 (from least to most injury) for 7 herbicides and a water control for post-emergence application on Stellaria media alone when herbicides were applied at their recommended rates. The second file provides seedling emergence, leaf number and dry weight of Stellaria media plants in pots for herbicides applied pre-emergence in a second experiment at either the recommended rate or half the recommended rate. The third file provides injury data for three weeds and several native species in a third experiment when herbicides were applied post-emergence at 0.25, 0.5 and 1 x the recommended rate to determine selectivity of herbicides between Stellaria media and the other weeds on the island and several of the more common and co-occurring native species. The last file contains graphs and statistical analyses of the results for the above 3 experiments. More detailed methods are provided in the Master of Science in Agriculture thesis of Waqas Zahid (University of New England, 2018).\n\nInitial screening of herbicides on S. media, the closely related alien, C. fontanum, and several native species in pots under sub-Antarctic conditions in the dedicated UNE facility using live plants collected from MI. While herbicides may be used in various situations in the Arctic and sub-Antarctic, MI managers have preferred us to trial herbicides off island, and use the research outcomes to direct herbicide application decisions. The data will be comprised of plant growth measurements for the weed and a variety of native species and herbicide symptom assessments.", "links": [ { diff --git a/datasets/AAS_4341_HERBICIDE_DYNAMICS_1.json b/datasets/AAS_4341_HERBICIDE_DYNAMICS_1.json index 6ed7dff7f2..01f59b7505 100644 --- a/datasets/AAS_4341_HERBICIDE_DYNAMICS_1.json +++ b/datasets/AAS_4341_HERBICIDE_DYNAMICS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4341_HERBICIDE_DYNAMICS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on glyphosate and aminomethylphosphonic acid (AMPA) concentrations over time at different depths in a sandy soil from Macquarie Island.\n \nThere are 2 files providing data on residual glyphosate and its derivative aminomethylphosphonic acid (AMPA) concentrations remaining in soil over time at different depths in a common sandy soil from Macquarie Island. These data result from a leaching experiment using 20 cm deep soil columns collected from MI and returned to the University of New England for testing. Glyphosate was applied to the surface of the columns at the recommended rate of 1.5 kg/ha and then the columns were leached with water simulating MI rainfall, and over a period of 48 weeks individual columns were subsampled on several occasions at 4 depths (0-5, 5-10, 10-15 and 15-20 cm) for glyphosate and AMPA to determine potential for herbicide residues of glyphosate on MI. The first file provides summary statistics of the results while the second file contains the raw data for all replicates and the controls. More detailed methods are provided in the PhD thesis of Laura Williams (University of New England, 2016). Laura analysed the leachates from these columns in a previous project (4158) while residue analysis was undertaken in this project by PhD student Kirsten Drew.\n\nSoils (100 intact cores (60 mm diameter x 200 mm) from a range of field sites infested with S. media \u2013 GPS referenced) collected from MI and returned to UNE. A replicated, multi-factorial incubation trial conducted in the sub-Antarctic growth chamber to investigate the interactive effects of soil type, herbicide type and incubation period on herbicide fate.", "links": [ { diff --git a/datasets/AAS_4341_LONGEVITY_1.json b/datasets/AAS_4341_LONGEVITY_1.json index efea7a0dfb..2c821ea8bc 100644 --- a/datasets/AAS_4341_LONGEVITY_1.json +++ b/datasets/AAS_4341_LONGEVITY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4341_LONGEVITY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Longevity, development and flowering data for Stellaria media plants on Macquarie Island.\n \nThere are 5 spreadsheets here on the longevity, development and flowering of Stellaria media plants on Macquarie Island. In building a picture of infestations of Stellaria media the first spreadsheet provides density and percent cover of Stellaria media in the densest areas of the weed at 4 sites. The second spreadsheet provides data on the survival of tagged plants over 2 years. The third spreadsheet shows how many seeds are produced by Stellaria media plants of different sizes and whether those plants were growing above or below (ie their dominance towards) other vegetation. The next spreadsheet shows data on 20 plants that were temporarily tagged at several sites and monitored for several months to assess changes in reproductive capacity (buds, flowers, seed pods) over time. In the final spreadsheet we provide data on the development over a one week period of individual flowers. More detailed methods are given in each of the files.\n\n20 plants tagged at each of 3 sites (GPS referenced) for long term monitoring, with seedlings also monitored during the summer for their development, size (height and diameter) and time to flowering.", "links": [ { diff --git a/datasets/AAS_4341_PHYSICAL_CONTROL_1.json b/datasets/AAS_4341_PHYSICAL_CONTROL_1.json index d3a541a1b0..6f0b1e40fe 100644 --- a/datasets/AAS_4341_PHYSICAL_CONTROL_1.json +++ b/datasets/AAS_4341_PHYSICAL_CONTROL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4341_PHYSICAL_CONTROL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on the regrowth and emergence of Stellaria media following the application of several physical weed control methods on Macquarie Island.\n \nThere is one spreadsheet of data on the regrowth and emergence of Stellaria media in small plots following the application of several physical weed control methods for the weed on Macquarie Island including hand weeding, trimming, and scalping as well as an undisturbed control. The experiment was undertaken at 4 sites, Bauer Bay, Brother\u2019s Point, Island Lake and Tractor Rock and included a second hand weeding for some treatments. Percentage cover of the weed and other species were recorded in each of the plots. More detailed methods are given in the file.\n\n\nIn-situ experiments involving treatments (1x1m plots) such as hand weeding, digging, cutting plants at ground level and scalping established at 6 infested sites to assess remediation effectiveness in subsequent seasons.", "links": [ { diff --git a/datasets/AAS_4342_ActiveSeis_2015-2016_1.json b/datasets/AAS_4342_ActiveSeis_2015-2016_1.json index 8ca4d77dd5..9bea3d6917 100644 --- a/datasets/AAS_4342_ActiveSeis_2015-2016_1.json +++ b/datasets/AAS_4342_ActiveSeis_2015-2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_ActiveSeis_2015-2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The active seismics are all part of a survey of the Sorsdal Glacier. They are designed to measure ice thickness, and the thickness of the water column underneath the ice shelf where the ice is floating. This was part of an investigation to determine the location of the grounding line on Sorsdal Glacier.\n\nSorsdal Glacier site S04 - 14/12/2015\nTiming: 2000 - 2150\nCoordinates: S68 42.006, E78 09.439\nTeam: Sue Cook, James Hamilton, Marty Benavente\nWeather: Some high cloud, low wind, good visibility\nSite notes: Crevasses greater than 2 m wide running in along flow direction (parallel to geophone cables). Surface very thin layer of icy/hard packed snow with blue ice underneath.\nEquipment: Geometrics Geode Seismograph communicating with a laptop via ethernet cable using Geometrics Seismodule Controller Software Version 11.1.69.0 (Geometrics Inc., 2014)\nSampling: 4-second records at 4000 samples/second\nGeophones: 40 Hz Blue/yellow geophones used.\nSeismic survey layout: 2 x 36 m cables (3 m spacing)\nGeophones all set into surface by drilling small hole with handheld drill. Bearing of seismic line: 338 degrees (on compass, no magnetic declination applied)\nGeophones 1-12 to East, 13-24 to West\nHammer blows began from East end of line (geophone 1), moving every 6 m towards West end of line. 10 blows at each location.\n\nSources:\nGeophone,File,Hammer swinger\n1, 2.dat, JH\n3, 3.dat, JH\n5, 4.dat, JH\n7, 5.dat, JH\n9, 6.dat, JH\n11, 7.dat, JH\n24, 8.dat, MB\n22, 9.dat, MB\n20, 10.dat, MB\n18, 11.dat, MB\n16, 12.dat, MB\n14, 13.dat, MB\ncentre, 14.dat, MB", "links": [ { diff --git a/datasets/AAS_4342_ActiveSeis_2016-2017_1.1.json b/datasets/AAS_4342_ActiveSeis_2016-2017_1.1.json index 76e3c7de3b..3b002f12fc 100644 --- a/datasets/AAS_4342_ActiveSeis_2016-2017_1.1.json +++ b/datasets/AAS_4342_ActiveSeis_2016-2017_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_ActiveSeis_2016-2017_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The active seismics are all part of a survey of the Sorsdal Glacier. They are designed to measure ice thickness, and the thickness of the water column underneath the ice shelf where the ice is floating. This was part of an investigation to determine the location of the grounding line on Sorsdal Glacier.\n\nActive seismics on Sorsdal Glacier 2016-2017 using hammer and plate source method\nSites visited: S02, S04, S06, S08, S11, Channel Lake\nFiles included:\n*.dat - raw data files each containing seismic record from single shot\n*.xlsx - record of acquisition layout and settings, including notes for each shot\nSchaap_Thesis.pdf - Honours thesis from Tom Schaap, UTAS containing more informtaion on data\ne.g. Figure 3.1 - map of locations, Figure 3.2 - Diagram of geophone layout and shot locations for each site\ncsv file with measured ice thickness and water column thickness is attached here. Data included: Site, Latitude, Longitude, Ice thickness (m), Ice thickness uncertainty (m), Water column thickness (m), Water column thickness uncertainty (m),Notes\n\n2021-07-12 - an update to the dataset was made to correct a latitude/longitude figure for site S06 in the file \"AAS4342_ActiveSeis_2016-2017_Thickness.csv\".", "links": [ { diff --git a/datasets/AAS_4342_ApRES_2017-2019_1.json b/datasets/AAS_4342_ApRES_2017-2019_1.json index 8025d39429..890bcb867e 100644 --- a/datasets/AAS_4342_ApRES_2017-2019_1.json +++ b/datasets/AAS_4342_ApRES_2017-2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_ApRES_2017-2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Autonomous phase sensitive radar (ApRES) installation at sites S02 and S04 on Sorsdal Glacier\nLocations: S02 (68.70774 S, 78.101115 E) and S04 (68.70995 S, 78.214617 E)\nInstallation dates: S02 (17th Jan 2017) and S04 (17th Feb 2017)\nData retrieval: 24th December 2018\nMeasurement interval: 2 hr\n\nApRES phase-sensitive radar is a low-power, light-weight instrument developed in a collaboration between BAS and University College London. It is a 200-400 MHz FMCW radar, with a 1-second chirp, run by controller. The radar\u2019s transmit aerial and receive aerial are spaced 1.5 meters each side of the electronics box and mounted at the ice surface in a plywood box. The radar was set to record thickness every 2 hrs and this has been converted to a timeseries of thickness change.\n\nFiles:\n*.dat - binary files containing raw data\nS0*_config.ini - config file containing all radar settings used for each site\nAAS4342_S0*_rate_thickness_change.csv - timeseries of rate of change of ice thickness\n\nData file format: Date, Time (UTC), rate of thickness change (m/day), uncertainty in rate of thickness change (m/day)\n\nSoftware for processing the raw data can be obtained from Dr. Keith Nicholls, British Antarctic Survey.", "links": [ { diff --git a/datasets/AAS_4342_ApRES_S02_2015-16_1.json b/datasets/AAS_4342_ApRES_S02_2015-16_1.json index 46ccd4ac9f..8343d92863 100644 --- a/datasets/AAS_4342_ApRES_S02_2015-16_1.json +++ b/datasets/AAS_4342_ApRES_S02_2015-16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_ApRES_S02_2015-16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ApRES installation at site S02 Sorsdal Glacier\nInstallation date: 8th December 2015\nData retrieval: 24th December 2016\nCoordinates: 68 degrees 42.424 S, 78 degrees 06.575 E, Elevation: 75 m\nMeasurement interval: 1 hr\nThis instrument was installed on the floating section of the Sorsdal Glacier to monitor changes in ice thickness.\n\nApRES phase-sensitive radar is a low-power, light-weight instrument developed in a collaboration between BAS and University College London. It is a 200-400 MHz FMCW radar, with a 1-second chirp, run by controller. The radar\u2019s transmit aerial and receive aerial are spaced 1.5 meters each side of the electronics box and are all buried. The radar antenna boxes were quickly infiltrated by water making summer data extremely noisy. The equipment eventually failed when the refreezing water snapped a cable connector. Thicknesses were retrieved for the period 2nd March 2016 - 19th June 2016 only.\n\nFiles:\n*.dat - binary files containing raw data\nconfig.ini - config file containing all radar settings used\nAAS4342_ApRES_S02_Thickness.csv - derived timeseries of ice thickness\n\nUncertainty in the measured ice thickness derives from three main sources:\nInherent resolution of instrument (0.03 m)\nPotential mis-identification of basal return (2 m)\nUncertainty in the speed of light in solid ice (1.2% of ice thickness, McNabb et al. 2012)\n\nSoftware for processing the raw data can be obtained from Dr. Keith Nicholls, British Antarctic Survey.\n\nReference: Mcnabb, R., Hock, R., O\u2019Neel, S., Rasmussen, L., Ahn, Y., Braun, M., . . . Truffer, M. (2012). Using surface velocities to calculate ice thickness and bed topography: A case study at Columbia Glacier, Alaska, USA. Journal of Glaciology, 58(212), 1151-1164. doi:10.3189/2012JoG11J249", "links": [ { diff --git a/datasets/AAS_4342_GPR_2016-2017_1.json b/datasets/AAS_4342_GPR_2016-2017_1.json index b7920973d7..e3c7d554dd 100644 --- a/datasets/AAS_4342_GPR_2016-2017_1.json +++ b/datasets/AAS_4342_GPR_2016-2017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_GPR_2016-2017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground penetrating radar (GPR) survey of Channel Lake feature on Sorsdal Glacier \nInstrument: MALA X3Mc control system\nAntennas: MALA Ramac 250 and 800 MHz\nFiles: \n*.zip - Raw radar return\n*.xlsx - Notes for each survey line\nSchaap_Thesis.pdf - Honours thesis of Tom Schaap containing further details of survey.\n\nNumerical models of outlet glacier dynamics provide indicators for the state of the ice sheets from which they originate. Basement characteristics and englacial meltwater behaviour are important considerations in these models. Seismic, airborne radio-echo sounding, ground-penetrating radar, and gamma-ray spectrometry surveys have been analysed for information which may improve dynamics modelling of Sorsdal Glacier, East Antarctica.\nSeismic reflection data indicate that Sorsdal Glacier sits on a retrograde bed, with measured ice thickness above water ranging from 611 plus or minus 28 m towards the calving front to 1045 plus or minus 48 near the grounding line. The maximum measured grounded ice thickness was 1647 plus or minus 77 m. The maximum measured water column thickness was 500 plus or minus 13 m. The grounding line position was constrained to within 4 km between seismic soundings. Refraction and surface wave analyses indicate that there is no near-surface low-velocity firn layer in the lower portion of Sorsdal Glacier.\nTwo airborne radio-echo sounding profiles have revealed internal stratigraphy and basement topography in the ice sheet adjacent to Sorsdal Glacier, but do not show the base of the glacier likely due to the effects of scattering of radio waves in highly deformed ice.\nGround-penetrating radar surveys in the Channel Lake area delineate subsurface reflective features at depths between 5 and 10 m. There features are interpreted as former englacial drainage conduits beneath the basin and may indicate the presence of an interconnected network of channels.\nHeat production values between 1.1 plus or minus 0.4 micro W/m3 and 1.6 plus or minus 0.5 micro W/m3 were estimated using gamma-ray spectrometry for lithologies in the Vestfold Hills adjacent to Sorsdal Glacier. These values are low compared to estimates from other East Antarctic rocks, and global averages.\n\nGPR data are colelcted in a standard GPR format, and can be viewed with the GPRSoft software at the provided URL.", "links": [ { diff --git a/datasets/AAS_4342_HeloGPR_2017-2018_1.json b/datasets/AAS_4342_HeloGPR_2017-2018_1.json index 37c08dbca8..0b9477a085 100644 --- a/datasets/AAS_4342_HeloGPR_2017-2018_1.json +++ b/datasets/AAS_4342_HeloGPR_2017-2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4342_HeloGPR_2017-2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground penetrating radar (GPR) is an active geophysical technique that uses high frequency radio waves to map the subsurface. The basic principle of GPR measurements is the transition of electromagnetic pulses of suitable frequency down into the ground through a transmitting antenna, and to detect the reflected energy as a function of time, amplitude and phase from any subsurface targets through a receiver antenna. If the electromagnetic travel velocity of the subsurface is known, time can be converted to depth. Wave reflections are generated from the boundaries of materials of different electromagnetic properties. The large contrast between the electromagnetic properties of rock, ice, water, and some sediments makes GPR a particularly effective method for mapping in frozen environments.\n\nA M\u00e5la ProEx radar system with a shielded antenna, centre frequency 100 MHz, connected to a handheld Garmin GPS. The antenna was housed in wooden box, slung on a longline, 15 metres long, from the helicopter cargo hook. Two surveys were carried out on the S\u00f8rsdal Glacier with the aim of mapping englacial meltwater channels. \n\nOuter survey bounds\n-68.66, 78.302\n-68.65, 78.532\n-68.72, 78.544\n-68.73, 78.313\n\nSurvey 1 - 21/12/2017 - 11 parallel survey lines 8 km long and 2 cross profiles 3.2 km long\nSurvey 2 - 22/01/2018 - 12 survey lines 1.3 km long", "links": [ { diff --git a/datasets/AAS_4344_Calibrated_Fluorescence_Profiles_K-Axis_1.json b/datasets/AAS_4344_Calibrated_Fluorescence_Profiles_K-Axis_1.json index caa30d6116..f0b4708bff 100644 --- a/datasets/AAS_4344_Calibrated_Fluorescence_Profiles_K-Axis_1.json +++ b/datasets/AAS_4344_Calibrated_Fluorescence_Profiles_K-Axis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_Calibrated_Fluorescence_Profiles_K-Axis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Processed CTD instrument data - Corrected fluorescence profiles at the Southern Kerguelen Plateau, Indian Sector of the Southern Ocean. The fluorometer was calibrated through the regression of burst measurements against in situ chlorophyll a measured at the same depths and sites using high performance liquid chromatography (Wright et al. 2010). Zero chlorophyll a reference points were included in the regression and were obtained through averaging fluorometry data over 200-300 m bins. The resulting linear equation used to convert flourometry data was: chlorophyll = 0.262*fluorescence + 0.101. Column measurements (\u00b5g L-1) and integrated data (0-150 m, mg m-2) for each CTD station are provided.", "links": [ { diff --git a/datasets/AAS_4344_Gross_Primary_Production_1.json b/datasets/AAS_4344_Gross_Primary_Production_1.json index 0724c6da62..1c7aacfca6 100644 --- a/datasets/AAS_4344_Gross_Primary_Production_1.json +++ b/datasets/AAS_4344_Gross_Primary_Production_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_Gross_Primary_Production_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gross Primary Production\nSix depths were sampled per CTD station ranging from near-surface to 125 m. Sample depths were based on downward fluorescence profiles and two of six samples always included both near-surface (approximately 5-10 m) and the depth of the chlorophyll maximum where applicable.\nPhotosynthetic rates were determined using radioactive NaH14CO3. Incubations were conducted according to the method of Westwood et al. (2011). Cells were incubated for 1 hour at 21 light intensities ranging from 0 to 1200 \u00b5mol m-2 s-1 (CT Blue filter centred on 435 nm). Carbon uptake rates were corrected for in situ chlorophyll a (chl a) concentrations (\u00b5g L-1) measured using high performance liquid chromatography (HPLC, Wright et al. 2010), and for total dissolved inorganic carbon availability, analysed according to Dickson et al. (2007). Photosynthesis-irradiance (P-I) relationships were then plotted in R and the equation of Platt et al. (1980) used to fit curves to data using robust least squares non-linear regression. Photosynthetic parameters determined included light-saturated photosynthetic rate [Pmax, mg C (mg chl a)-1 h-1], initial slope of the light-limited section of the P-I curve [\u03b1, mg C (mg chl a)-1 h-1 (\u00b5mol m-2 s-1)-1], light intensity at which carbon-uptake became maximal (calculated as Pmax/ \u03b1 = Ek, \u00b5mol m-2 s-1), intercept of the P-I curve with the carbon uptake axis [c, mg C (mg chl a)-1 h-1] , and the rate of photoinhibition where applicable [\u03b2, mg C (mg chl a)-1 h-1 (\u00b5mol m-2 s-1)-1]. \nGross primary production rates were modelled using R. Depth interval profiles (1 m) of chl a from the surface to 200 m were constructed through the conversion of up-cast fluorometry data measured at each CTD station. For conversions, pooled fluorometry burst data from all sites and depths was linearly regressed against in situ chl a determined using HPLC. Gross daily depth-integrated water-column production was then calculated using chl a depth profiles, photosynthetic parameters (Pmax, \u03b1 , \u03b2, see above), incoming climatological PAR, vertical light attenuation (Kd), and mixed layer depth. Climatological PAR was based on spatially averaged (49 pixels, approx. 2 degrees) 8 day composite Aqua MODIS data (level 3, 2004-2017) obtained for Julian day 34. Summed incoming light intensities throughout the day equated to mean total PAR provided by Aqua MODIS. Kd for each station was calculated through robust linear regression of natural logarithm-transformed PAR data with depth. In cases where CTD stations were conducted at night, Kd was calculated from a linear relationship established between pooled chlorophyll a concentrations and Kd\u2019s determined at CTD stations conducted during the day (Kd = -0.0421 chl a * -0.0476). Mixed layer depths were calculated as the depth where density (sigma) changed by 0.05 from a 10 m reference point. Gross primary production was calculated at 0.1 time steps throughout the day (10 points per hour) and summed.", "links": [ { diff --git a/datasets/AAS_4344_K-AXIS_Science_Event_Log_Scanned_1.json b/datasets/AAS_4344_K-AXIS_Science_Event_Log_Scanned_1.json index e4ea3009e8..8f723d0d0c 100644 --- a/datasets/AAS_4344_K-AXIS_Science_Event_Log_Scanned_1.json +++ b/datasets/AAS_4344_K-AXIS_Science_Event_Log_Scanned_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_K-AXIS_Science_Event_Log_Scanned_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the K-Axis marine voyage from mid Jan-late Feb 2016, a diverse range of sampling techniques were employed to collect specimens and data. Each sampling event was recorded by scientists and technical support staff in a logbook that was kept in the operations room on board the Aurora Australis. This is a PDF of the scanned original document, compiled on paper during the voyage.\n \nevent_number: A unique event identifier in the log, in the order that the events were written down (usually but not always chronologically)\nevent_type: The code defined and used by each research project to identify the types of equipment deployed or samples collected for an event. \nevent_type_prefix: A non-mandatory prefix field used by some research projects to identify the type of an event\nevent_type_number: A sequential number or alphanumeric-number combination defined and used by each research project to identify unique equipment deployment or sample collection events\nstation_number: A universal (voyage-wide) station number used across all projects to identify a nominal lat/lon position defined during voyage planning\nleg: A nominally straight-line section of the voyage track defined during voyage planning. The voyage track was planned as a series of roughly N-S and E-W transects that intersected in some locations. Legs start at a station and continue through more stations to a vertex-station which is the start of the next leg. Legs are numbered consecutively.\nwaypoint: A GPS waypoint used by Aurora Australis crew, AAD science technical support and researchers to identify target lat/lon positions in the voyage. Some waypoints correspond with station numbers.\nstart_date_utc: The start date of the event in UTC\nstart_time_utc: The start time of the event in UTC\nstart_lat_deg: The latitude (whole degrees) of the vessel at the beginning of the event\nstart_lat_min: The latitude (minutes) of the vessel at the beginning of the event\nstart_lat_dec_deg: The latitude (decimal degrees) of the vessel at the beginning of the event\nstart_lon_deg: The longitude (whole degrees) of the vessel at the beginning of the event\nstart_lon_min: The longitude (minutes) of the vessel at the beginning of the event\nstart_lon_dec_deg: The longitude (decimal degrees) of the vessel at the beginning of the event\nend_date_utc: The end date of the event in UTC\nend_time_utc: The end time of the event in UTC\nend_lat_deg: The latitude (whole degrees) of the vessel at the end of the event\nend_lat_min: The latitude (minutes) of the vessel at the end of the event\nend_lat_dec_deg: The latitude (decimal degrees) of the vessel at the end of the event\nend_lon_deg: The longitude (whole degrees) of the vessel at the end of the event\nend_lon_min: The longitude (minutes) of the vessel at the end of the event\nend_lon_dec_deg: The longitude (decimal degrees) of the vessel at the end of the event\nremarks: Comments/remarks written by researchers when completing the paper log", "links": [ { diff --git a/datasets/AAS_4344_K-AXIS_Science_Event_Log_UW_LatLon_1.json b/datasets/AAS_4344_K-AXIS_Science_Event_Log_UW_LatLon_1.json index 4b6682da64..941af76d5a 100644 --- a/datasets/AAS_4344_K-AXIS_Science_Event_Log_UW_LatLon_1.json +++ b/datasets/AAS_4344_K-AXIS_Science_Event_Log_UW_LatLon_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_K-AXIS_Science_Event_Log_UW_LatLon_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the K-Axis marine voyage from mid Jan-late Feb 2016, a diverse range of sampling techniques were employed to collect specimens and data. Each sampling event was recorded by scientists and technical support staff in a logbook that was kept in the operations room on board the Aurora Australis. This is a direct digital transcription of the paper logbook with interpolated lat/lon from underway data to supplement start times as recorded in the log. The method used to obtain the supplementary position is described in the associated eventlog_matchup.html file.\n \nevent_number: A unique event identifier in the log, in the order that the events were written down (usually but not always chronologically)\nevent_type: The code defined and used by each research project to identify the types of equipment deployed or samples collected for an event. \nevent_type_prefix: A non-mandatory prefix field used by some research projects to identify the type of an event\nevent_type_number: A sequential number or alphanumeric-number combination defined and used by each research project to identify unique equipment deployment or sample collection events\nstation_number: A universal (voyage-wide) station number used across all projects to identify a nominal lat/lon position defined during voyage planning\nleg: A nominally straight-line section of the voyage track defined during voyage planning. The voyage track was planned as a series of roughly N-S and E-W transects that intersected in some locations. Legs start at a station and continue through more stations to a vertex-station which is the start of the next leg. Legs are numbered consecutively.\nwaypoint: A GPS waypoint used by Aurora Australis crew, AAD science technical support and researchers to identify target lat/lon positions in the voyage. Some waypoints correspond with station numbers.\nstart_date_utc: The start date of the event in UTC\nstart_time_utc: The start time of the event in UTC\nstart_lat_deg: The latitude (whole degrees) of the vessel at the beginning of the event\nstart_lat_min: The latitude (minutes) of the vessel at the beginning of the event\nstart_lat_dec_deg: The latitude (decimal degrees) of the vessel at the beginning of the event\nstart_lon_deg: The longitude (whole degrees) of the vessel at the beginning of the event\nstart_lon_min: The longitude (minutes) of the vessel at the beginning of the event\nstart_lon_dec_deg: The longitude (decimal degrees) of the vessel at the beginning of the event\nend_date_utc: The end date of the event in UTC\nend_time_utc: The end time of the event in UTC\nend_lat_deg: The latitude (whole degrees) of the vessel at the end of the event\nend_lat_min: The latitude (minutes) of the vessel at the end of the event\nend_lat_dec_deg: The latitude (decimal degrees) of the vessel at the end of the event\nend_lon_deg: The longitude (whole degrees) of the vessel at the end of the event\nend_lon_min: The longitude (minutes) of the vessel at the end of the event\nend_lon_dec_deg: The longitude (decimal degrees) of the vessel at the end of the event\nremarks: Comments/remarks written by researchers when completing the paper log\ntranscribe_comments: Comments/remarks made by the transcriber when the log was digitised\nutc: The start date and time of the event in UTC \nstart_lon_dec_deg_interp: The latitude (decimal degrees) of the vessel at the beginning of the event interpolated from the vessel underway data\nstart_lat_dec_deg_interp: The longitude (decimal degrees) of the vessel at the beginning of the event interpolated from the vessel underway data", "links": [ { diff --git a/datasets/AAS_4344_K-AXIS_Science_Event_Log_nonQC_1.json b/datasets/AAS_4344_K-AXIS_Science_Event_Log_nonQC_1.json index 2ee4476dd1..2c0a882416 100644 --- a/datasets/AAS_4344_K-AXIS_Science_Event_Log_nonQC_1.json +++ b/datasets/AAS_4344_K-AXIS_Science_Event_Log_nonQC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_K-AXIS_Science_Event_Log_nonQC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the K-Axis marine voyage from mid Jan-late Feb 2016, a diverse range of sampling techniques were employed to collect specimens and data. Each sampling event was recorded by scientists and technical support staff in a logbook that was kept in the operations room on board the Aurora Australis. This is a direct digital copy/transcription of the paper logbook.\n \nevent_number: A unique event identifier in the log, in the order that the events were written down (usually but not always chronologically)\nevent_type: The code defined and used by each research project to identify the types of equipment deployed or samples collected for an event. \nevent_type_prefix: A non-mandatory prefix field used by some research projects to identify the type of an event\nevent_type_number: A sequential number or alphanumeric-number combination defined and used by each research project to identify unique equipment deployment or sample collection events\nstation_number: A universal (voyage-wide) station number used across all projects to identify a nominal lat/lon position defined during voyage planning\nleg: A nominally straight-line section of the voyage track defined during voyage planning. The voyage track was planned as a series of roughly N-S and E-W transects that intersected in some locations. Legs start at a station and continue through more stations to a vertex-station which is the start of the next leg. Legs are numbered consecutively.\nwaypoint: A GPS waypoint used by Aurora Australis crew, AAD science technical support and researchers to identify target lat/lon positions in the voyage. Some waypoints correspond with station numbers.\nstart_date_utc: The start date of the event in UTC\nstart_time_utc: The start time of the event in UTC\nstart_lat_deg: The latitude (whole degrees) of the vessel at the beginning of the event\nstart_lat_min: The latitude (minutes) of the vessel at the beginning of the event\nstart_lat_dec_deg: The latitude (decimal degrees) of the vessel at the beginning of the event\nstart_lon_deg: The longitude (whole degrees) of the vessel at the beginning of the event\nstart_lon_min: The longitude (minutes) of the vessel at the beginning of the event\nstart_lon_dec_deg: The longitude (decimal degrees) of the vessel at the beginning of the event\nend_date_utc: The end date of the event in UTC\nend_time_utc: The end time of the event in UTC\nend_lat_deg: The latitude (whole degrees) of the vessel at the end of the event\nend_lat_min: The latitude (minutes) of the vessel at the end of the event\nend_lat_dec_deg: The latitude (decimal degrees) of the vessel at the end of the event\nend_lon_deg: The longitude (whole degrees) of the vessel at the end of the event\nend_lon_min: The longitude (minutes) of the vessel at the end of the event\nend_lon_dec_deg: The longitude (decimal degrees) of the vessel at the end of the event\nremarks: Comments/remarks written by researchers when completing the paper log\ntranscribe_comments: Comments/remarks made by the transcriber when the log was digitised", "links": [ { diff --git a/datasets/AAS_4344_K-AXIS_Voyage_1.json b/datasets/AAS_4344_K-AXIS_Voyage_1.json index 5bb90e3c18..19e65222e2 100644 --- a/datasets/AAS_4344_K-AXIS_Voyage_1.json +++ b/datasets/AAS_4344_K-AXIS_Voyage_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_K-AXIS_Voyage_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Kerguelen Axis voyage was planned to collect data to enhance the realism of end-to-end ecosystem models being developed in the Antarctic Climate and Ecosystems Cooperative Research Centre, to investigate the effects of climate change and ocean acidification on Southern Ocean ecosystems in the Indian Sector (particularly in relation to factors affecting the northern distribution of Antarctic krill) and to contribute to assessment of the spatial relationship of mesopelagic mid-trophic level species, in particular zooplanktivores, to foraging strategies by marine mammals and birds on the Kerguelen Plateau.\n\nNine projects were undertaken aboard the Aurora Australis. Each project had individual objectives and outputs, and there are metadata records for each data set collected. They were designed to be complementary in order that the whole data set and project analyses could be used to address the objectives of the Kerguelen Axis program. Observations will be contributed to the Southern Ocean Observing System (SOOS) and will facilitate the design of future ecosystem observing in the region.", "links": [ { diff --git a/datasets/AAS_4344_K-Axis_Chlorophyll_2.json b/datasets/AAS_4344_K-Axis_Chlorophyll_2.json index 34b67a6ad5..a106340d7b 100644 --- a/datasets/AAS_4344_K-Axis_Chlorophyll_2.json +++ b/datasets/AAS_4344_K-Axis_Chlorophyll_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_K-Axis_Chlorophyll_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Size fractionated chlorophyll a data (total and less than 20 \u00b5m) analysed using high performance liquid chromatography (HPLC). Underway samples were taken using a seawater line in the oceanographic lab on RSV Aurora Australis (approx. depth 4 m). CTD samples were taken using Niskin bottles attached to a CTD rosette. Six depths were sampled per station, based on fluorescence profiles from the CTD. Two of the two of six samples always included both near-surface (approximately 10 m) and the depth of the chlorophyll maximum where applicable. HPLC analyses were conducted according to the method of Wright et al. (2010). Column chlorophylls (\u00b5g L-1) and integrated chlorophylls (mg m-2) are shown in two separate tabs within the Excel spreadsheet.", "links": [ { diff --git a/datasets/AAS_4344_KAXIS_Microscopy_1.json b/datasets/AAS_4344_KAXIS_Microscopy_1.json index c4f421d0e1..cedb91e7bf 100644 --- a/datasets/AAS_4344_KAXIS_Microscopy_1.json +++ b/datasets/AAS_4344_KAXIS_Microscopy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_KAXIS_Microscopy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected using a prototype basket sampler that concentrated phytoplankton from the underway water supply in the OG lab onboard Aurora Australis. The sampler filtered water during transit, and the distance travelled and the approximate volume of water sampled was recorded. A phytoplankton net tow was collected at each station. The majority of imaging was undertaken using a Leica DMLB2 microscope with phase contrastand Leica ICC50 digital in body camera. Samples were preserved with either glutaraldhyde or Lugols iodine for later examination as well. Details of sample collected are included in the Voyage sample log.", "links": [ { diff --git a/datasets/AAS_4344_au1603_CTD_version27sep2017_3.json b/datasets/AAS_4344_au1603_CTD_version27sep2017_3.json index 5a8326972b..4a227b175e 100644 --- a/datasets/AAS_4344_au1603_CTD_version27sep2017_3.json +++ b/datasets/AAS_4344_au1603_CTD_version27sep2017_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_au1603_CTD_version27sep2017_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard Aurora Australis cruise au1603, voyage 3 2015/2016, from 11th January to ~24th February 2016. The cruise commenced with the K-AXIS project, the major marine science component of the cruise. This was the Australian component (P.I.\u2019s Andrew Constable, Steve Rintoul and others) of a combined biological and oceanographic study in the vicinity of the Kerguelen Axis. After conclusion of marine science work the ship went to Mawson for a resupply. During a storm on 24th February the ship broke free of its mooring lines and ran aground on the rocks at West Arm in Horseshoe Harbour, thus ending the cruise. Expeditioners were eventually taken to Casey on the Shirase, then flown home. Meanwhile the Aurora Australis was refloated and sailed to Fremantle, then on to Singapore for repairs.\nThis report discusses the oceanographic data from CTD operations on the cruise. A total of 47 CTD vertical profile stations were taken on the cruise (Table 1). Over 850 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite and silicate), dissolved inorganic carbon (i.e. TCO2), alkalinity, POC and PN, and biological parameters, using a 24 bottle rosette sampler. A UVP particle counter/camera system was attached to the CTD package (P.I. Emmanuel Laurenceau). A separate trace metal rosette system was deployed from the trawl deck (P.I. Andrew Bowie). Upper water column current profile data were collected by a ship mounted ADCP, and meteorological and water property data were collected by the array of ship's underway sensors. Eight drifting floats were deployed over the course of the cruise.\nProcessing/calibration and data quality for the main CTD data are described in this report. Underway sea surface temperature and salinity data are compared to near surface CTD data. CTD station positions are shown in Figure 1, while CTD station information is summarised in Table 1. Float deployments (5 x Argo/Apex, 2 x SOCCOM and 1 x Provor) are summarised in Table 10. Further cruise itinerary/summary details can be found in the voyage leader report (Australian Antarctic Division unpublished report: Voyage 3 2015-2016, RSV Aurora Australis, Voyage Leader\u2019s report - see the metadata record \"Aurora Australis Voyage 3 2015/16 Track and Underway Data\" for access to the Voyage Report).", "links": [ { diff --git a/datasets/AAS_4344_dFe_1.json b/datasets/AAS_4344_dFe_1.json index 975f5ad5c4..21a3f8c21c 100644 --- a/datasets/AAS_4344_dFe_1.json +++ b/datasets/AAS_4344_dFe_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4344_dFe_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling was conducted according to GEOTRACES protocols. Samples for trace element analyses, including dissolved iron (dFe), were filtered through acid-cleaned 0.2 um cartridge filters (Pall Acropak) under constant airflow from several ISO class 5 HEPA units. All plastic ware was acid-cleaned prior to use, following GEOTRACES protocols. Samples were collected into low-density polyethylene (LDPE) bottles, acidified immediately to pH 1.7 with Seastar Baseline hydrochloric acid (HCl), double-bagged and stored at room temperature until analysis on shore. \n\nSamples for dFe analysis were pre-concentrated offline (factor 40) on a SeaFAST S2 pico (ESI, Elemental Scientific, USA) flow injection system with a Nobias Chelate-PA1 column. Samples were eluted from the column in 10% distilled nitric acid (HNO3), with calibration based on the method of standard additions in seawater (made using multi-element standards in a 10% HNO3 matrix, rather than an HCl matrix). Pre-concentrated samples were analysed using Sector Field Inductively Coupled Plasma Mass Spectrometry (SF-ICP-MS, Thermo Fisher Scientific, Inc.). Data were blank-corrected by subtracting an average acidified milli-Q blank that was treated similarly to the samples. \n\nThe dFe detection limit for a given analysis run on the SeaFAST/SF-ICP-MS was calculated as 3 x standard deviation of the milli-Q blank on that run. Detection limits ranged from 0.016 to 0.067 nmol kg-1, with a median of 0.026 nmol kg-1 (n=12). GEOTRACES reference materials were analyzed along with samples and results were in good agreement with consensus values: SAFe D1 was measured at 0.69 +/- 0.05 nmol kg-1 (n=7; consensus value = 0.67 +/- 0.04 nmol kg-1) and GD was measured at 1.02 +/- 0.01 nmol kg-1 (n=6; consensus value = 1.00 +/- 0.1 nmol kg-1). \n\nComments regarding the data spreadsheet:\nNaN = no sample\ndFe QC flags: \t 1 = high confidence in data quality\n 2 = detection limit\n 3 = low confidence in data quality\ndetection limits: dFe data that were below the daily detection limit were replaced with the respective detection limit. They are flagged with the number 2 in the dFe QC flag column.", "links": [ { diff --git a/datasets/AAS_4346_Airborne_Ocean_Sensors_2.json b/datasets/AAS_4346_Airborne_Ocean_Sensors_2.json index 845abebd8e..ad99d23c6a 100644 --- a/datasets/AAS_4346_Airborne_Ocean_Sensors_2.json +++ b/datasets/AAS_4346_Airborne_Ocean_Sensors_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_Airborne_Ocean_Sensors_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extracted Level 0 data are provided as audio files recorded in flight with a Sony PX470 voice recorder. These files were processed to generate the associated Level 2 products.\n\nProject 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective.\n\nAlthough several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG.", "links": [ { diff --git a/datasets/AAS_4346_Airborne_Ocean_Sensors_Level_2_1.json b/datasets/AAS_4346_Airborne_Ocean_Sensors_Level_2_1.json index f2186ae6bf..55d4c193a9 100644 --- a/datasets/AAS_4346_Airborne_Ocean_Sensors_Level_2_1.json +++ b/datasets/AAS_4346_Airborne_Ocean_Sensors_Level_2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_Airborne_Ocean_Sensors_Level_2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extracted Level 2 data include three data types: \n1)\tPosition data are included in .GPX files organized by campaign where \u201cICP8\u201d refers to the 2016-2017 ICECAP2 field season and \u201cICP9\u201d refers to the 2017-2018 field season. We recommend opening these files in QGIS or on similar platform. Metadata for each sonobuoy deployment include the unique identifier for each profile as well as the date, time, and aircraft longitude, latitude, elevation, and speed (in East, North, Up coordinates) at the time of deployment. Season identifier, flight number, and unique profile identifier are also displayed. In QGIS, for example, clicking on the drop locations using the \u201cIdentify Features\u201d tool is a convenient way of investigating the metadata.\n2)\tProfile data are released as Exportable Data Files (EDF), an ASCII format with a metadata header followed by the profile data.\n3)\tProfile data are also released as Hierarchical Data Format (HDF) files using a .h5 extension. This format is provided so users can take advantage of numerous and freely available Python and MATLAB resources simplifying importing and investigating the profiles.\n\n\nProject 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective.\n\nAlthough several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG.", "links": [ { diff --git a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1.json b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1.json index a27150913b..ee7bf308f6 100644 --- a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1.json +++ b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerogeophysical data were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346.\n\nThis data collection represents geolocated, time registered geophysical observations (L2 data). These data are derived from L0 and L1B data published as separate datasets. The data format are space delimited ASCII files, following the formats used for UTIG/AAD/NASA's predecessor ICECAP/OIB project at NASA's NSIDC DAAC. Fields are described in the # delimited detailed header for each granule.", "links": [ { diff --git a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_AEROGEOPHYSICS_1.json b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_AEROGEOPHYSICS_1.json index 2771763502..de1f240354 100644 --- a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_AEROGEOPHYSICS_1.json +++ b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_AEROGEOPHYSICS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_EAGLE_ICECAP_LEVEL2_AEROGEOPHYSICS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerogeophysical data were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346.\n\nThis data collection represents geolocated, time registered geophysical observations (L2 data). These data are derived from L0 and L1B data published as separate datasets. The data format are space delimited ASCII files, following the formats used for UTIG/AAD/NASA's predecessor ICECAP/OIB project at NASA's NSIDC DAAC. Fields are described in the # delimited detailed header for each granule. \n\nMAGNETICS\nMagnetics provides constraints on the depth to crystalline rock, and hence indicates the density of bathymetry\nEMGEO2 \tgeolocated magnetic anomaly profiles, 10 Hz ASCII\n\nGRAVITY\nGravity data is used to infer bathymetry, by looking at the density contrast between water and bathymetry\nEGCMG2\tgeolocated free air gravity disturbance profiles; 10 Hz ASCII\n\nALTIMETRY\nRepeat track laser altimetry provides a history of thinning of outlet glaciers\nELUTP2\tgeolocated ice surface elevation profiles; 3.5 Hz ASCII\n\nRADAR \nIce penetrating radar provides the geometry of the ice shelves and outlet glaciers, and provides constraints on properties at the base of the ice (e.g. subglacial waters, sub ice shelf melting)\nER2HI2\t\tgeolocated ice thickness, bed and surface elevation and echo amplitude, and surface range; 4 Hz ASCII; incoherent, unmigrated processing (pik1).", "links": [ { diff --git a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_RADAR_DATA_1.json b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_RADAR_DATA_1.json index 67f3f39f81..61a0e6b483 100644 --- a/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_RADAR_DATA_1.json +++ b/datasets/AAS_4346_EAGLE_ICECAP_LEVEL2_RADAR_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_EAGLE_ICECAP_LEVEL2_RADAR_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These radargrams were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346.\n\nThese data collection represents georeferenced, time registered instrument measurements (L1B data) converted to SI units, and is of most interest to users who wish to reprocess the data. Users interested in geophysical observables should used the derived Level 2 dataset. The data format are netCDF3 files, following the formats used for NASA/AAD/UTIG's ICECAP/OIB project at NASA's NSIDC DAAC. Metadata fields can be accessed using the open source ncdump tool, or c, python or matlab modules. See https://www.loc.gov/preservation/digital/formats/fdd/fdd000330.shtml for resources on NetCDF-3, and https://nsidc.org/data/IR2HI1B/versions/1 for a description of the similar OIB dataset.\n\nRADAR \nIce penetrating radar provides the geometry of the ice shelves and outlet glaciers, and provides constraints on properties at the base of the ice (e.g. subglacial waters, sub ice shelf melting)\nER2HI1B\t\tgeoreferenced radar echo data; 4 Hz NetCDF \n\nData Acquisition Parameters\nA 1-\u00ce\u00bcsec transmitted chirp was used for both surface and bed. Two 14-bit digitizer channels with offset receiver gain were used to record returned echoes over 64 \u00ce\u00bcsec, accommodating 120 dB of dynamic range, including accurate representations of power of the surface and bed echoes.\n\nBandwidth: 52.5-67.5 MHz\nTx power: 5700 W\nWaveform: 1 \u00ce\u00bcsec FM chirp generation, analog down-conversion to 10 MHz center\nSampling: 12-bit ADC at 50 MHz sampling\nRecord window: 64.74 \u00ce\u00bcsec\nAcquisition: two gain channels separated by 47 dB\nDynamic Range: 120 dB\nMonostatic Rx/Tx\nData rate: 2.2 MB/sec\nMaximum Doppler frequency: 36 Hz\nPulse Repetition Frequency: 6250 Hz\nOnboard stacking: 32x\n\nProcessing Approach\nUnfocused Synthetic aperture radar (SAR) processing was done (internally referred to as pik1). This is a quick form of processing with no dependencies on other instruments. The first 10 recorded stacks are coherently summed resulting in a 20 Hz sample rate. Then, a narrow band notch filter is applied at 10 MHz to remove local oscillator (LO) leakage. The pulse is compressed using frequency domain convolution of over-scaled synthetic chirp waveform. This results in gains of 83 dB from overscaled chirp, 11.7 dB from range compression, and -3 dB from Hanning window. These are converted to magnitude and five of these stacks are incoherently summed resulting in the final 4 Hz sample rate.\n\nError Sources\nFor this Level 1B product, errors in power may be due to transmitter or receiver malfunctions. Elevated background noise may occur with areas of strong surface scattering (for example crevasses) or Radio Frequency (RF) noise from anthropogenic sources (for example radio calls from the aircraft or other radar systems).", "links": [ { diff --git a/datasets/AAS_4346_EAGLE_ICECAP_Level1B_AEROGEOPHYSICS_1.json b/datasets/AAS_4346_EAGLE_ICECAP_Level1B_AEROGEOPHYSICS_1.json index 605fbece5d..9e63431ae0 100644 --- a/datasets/AAS_4346_EAGLE_ICECAP_Level1B_AEROGEOPHYSICS_1.json +++ b/datasets/AAS_4346_EAGLE_ICECAP_Level1B_AEROGEOPHYSICS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_EAGLE_ICECAP_Level1B_AEROGEOPHYSICS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerogeophysical data were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346.\n\nThis data collection represents georeferenced, time registered instrument measurements (L1B data) converted to SI units, and is of most interest to users who wish to reprocess the data. Users interested in geophysical observables should used the derived Level 2 dataset. The data format are space delimited ASCII files, following the formats used for UTIG/AAD/NASA's predecessor ICECAP/OIB project at NASA's NSIDC DAAC. Fields are described in the # delimited detailed header for each granule. \n\nPOSITIONING\nGPS provides the positioning (and timing) for all other data streams\nEPUTG1B\t\tpost processed 50 Hz positions; ASCII; used as source for interpolated position data in all Level 2 data streams\n\nMAGNETICS\nMagnetics provides constraints on the depth to crystalline rock, and hence indicates the density of bathymetry\nEMGEO1B \tgeoreferenced total magnetic field data; 10 Hz ASCII\n\nALTIMETRY\nRepeat track laser altimetry provides a history of thinning of outlet glaciers\nELUTP1B\t\tgeoreferenced laser range data, no orientation corrections; 3.5 Hz ASCII", "links": [ { diff --git a/datasets/AAS_4346_ICECAP_OIA_RADARGRAMS_1.json b/datasets/AAS_4346_ICECAP_OIA_RADARGRAMS_1.json index 598537a7d3..896331a19d 100644 --- a/datasets/AAS_4346_ICECAP_OIA_RADARGRAMS_1.json +++ b/datasets/AAS_4346_ICECAP_OIA_RADARGRAMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4346_ICECAP_OIA_RADARGRAMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These radargrams were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7). These data were in part funded by the US National Science Foundation (SPICECAP; grant PLR-1443690 to UTIG), ACE-CRC and the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346. \n\nThese data collection represents georeferenced, time registered instrument measurements (L1B data) converted to SI units, and is of most interest to users who wish to reprocess the data. Users interested in geophysical observables should used the derived Level 2 dataset. The data format are netCDF3 files, following the formats used for NASA/AAD/UTIG's ICECAP/OIB project at NASA's NSIDC DAAC. Metadata fields can be accessed using the open source ncdump tool, or c, python or matlab modules. See https://www.loc.gov/preservation/digital/formats/fdd/fdd000330.shtml for resources on NetCDF-3, and https://nsidc.org/data/IR2HI1B/versions/1 for a description of the similar OIB dataset. \n\nRADAR \nIce penetrating radar provides the geometry of the englacial layers, the bedrock topography, and the basal conditions.\nSR2HI1B georeferenced radar echo data; 4 Hz NetCDF \n\nData Acquisition Parameters \nA 1-microsec transmitted chirp was used for both surface and bed. Two 14-bit digitizer channels with offset receiver gain were used to record returned echoes over 64 \u00ce\u00bcsec, accommodating 120 dB of dynamic range, including accurate representations of power of the surface and bed echoes. \n\nBandwidth: 52.5-67.5 MHz \nTx power: 5700 W \nWaveform: 1 microsec FM chirp generation, analog down-conversion to 10 MHz center \nSampling: 12-bit ADC at 50 MHz sampling \nRecord window: 64.74 \u00ce\u00bcsec \nAcquisition: two gain channels separated by 47 dB \nDynamic Range: 120 dB \nMonostatic Rx/Tx \nData rate: 2.2 MB/sec \nMaximum Doppler frequency: 36 Hz \nPulse Repetition Frequency: 6250 Hz \nOnboard stacking: 32x \n\nProcessing Approach \nWhere possible focused data (Peters et al., 2007) was included, as this was preferred for englacial reflector tracing. Where not available, unfocused SAR was substituted. These are converted to magnitude and five of these stacks are incoherently summed resulting in the final 4 Hz sample rate. \n\nError Sources \nFor this Level 1B product, errors in power may be due to transmitter or receiver malfunctions. Elevated background noise may occur with areas of strong surface scattering (for example crevasses) or Radio Frequency (RF) noise from anthropogenic sources (for example radio calls from the aircraft or other radar systems).", "links": [ { diff --git a/datasets/AAS_4347_Sea_Ice_Model_Configurations_1.json b/datasets/AAS_4347_Sea_Ice_Model_Configurations_1.json index 0c2ba92fe8..ac7a3b2776 100644 --- a/datasets/AAS_4347_Sea_Ice_Model_Configurations_1.json +++ b/datasets/AAS_4347_Sea_Ice_Model_Configurations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4347_Sea_Ice_Model_Configurations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the \"Supporting Information\" for the main paper. See the referenced papers for more information.\n\nOur results are based on numerical simulation of Southern Ocean sea ice, conducted using the Los Alamos numerical sea-ice model CICE version 4.0 [CICE4; Bailey et al., 2010] configured in stand-alone mode on a 0.25 degree x 0.25 degree grid, extending to 45 degrees S, with 3-hourly output [Stevens, 2013]. The atmospheric forcing for CICE4 came from the hemispheric forecasting model Polar Limited Area Prediction Systems [Polar- LAPS; Adams, 2006] and ocean forcing from the global ocean general circulation model Australian Climate Ocean Model [AusCOM; Bi and Marsland, 2010]. The model is well-constrained in its representation of processes of sea ice formation and melt, and comparison with observed areal ice extent shows minimal deviations over the 1998-2003 period, particularly during winter [Stevens 2013]. Stevens [2013] evaluates the sensitivity of the model to the number of ice thickness categories. Sea ice thickness sensitivities in the CICE model are considered in detail in Hunke [2010, 2014].\n\nFor the warm climate scenario, changes were implemented that are consistent with the A1B scenario from the Fourth Assessment from the IPCC [Meehl et al., 2007]. This is a mid-range scenario that assumes rapid economic growth before introduction of new and more efficient technologies mid century. Specifically, the following changes were applied uniformly to the current climate forcing field for a single year: a 2 degrees C increase in air temperature, a 0.2 mm/day increase in rain, a 1.5% increase in cloud fraction, a -2.3 hPa change in surface air pressure, a 25% increase in wind, a 12 Wm-2 increase in long wave downward radiation and a 20% increase in humidity.\n\nOutputs and forcings from CICE4 that are relevant for consideration of under-ice habitats for larval krill include: snow depth, ice thickness, ice concentration, movement, ridging rate, day length (dependent on day-of-year and latitude), radiation above the ice (influenced by cloud cover), and radiation below the ice (influenced by ice and snow depth). Table 1 in the main text describes how these were used in the following two filters and one overlay for evaluating the location and suitability of potential larval krill habitat during winter.\n\nTaken from the abstract of the main paper:\n\nOver-wintering of larvae underneath Antarctic pack ice is a critical stage in the life cycle of Antarctic krill. However, there are no circumpolar assessments of available habitat for larval krill, making it difficult to evaluate how climate change may impact this life stage. We use outputs from a circumpolar sea-ice model, together with a set of simple assumptions regarding key habitat features, to identify possible regions of larval krill habitat around Antarctica during winter. In particular we assume that the location and suitability of habitat is determined by both food availability and three dimensional complexity of the sea ice. We then compare the combined area of these regions under current conditions to that under a warm climate scenario. Results indicate that, while total areal sea-ice extent decreases, there is a consistently larger area of potential larval krill habitat under warm conditions. These findings highlight that decreases in sea-ice extent may not necessarily be detrimental for krill populations and underline the complexity of predicting future trajectories for this key species in the Antarctic ecosystem.", "links": [ { diff --git a/datasets/AAS_4355_Daczko_etal_2018_1.json b/datasets/AAS_4355_Daczko_etal_2018_1.json index 0c8e29f043..7dbb68a2df 100644 --- a/datasets/AAS_4355_Daczko_etal_2018_1.json +++ b/datasets/AAS_4355_Daczko_etal_2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4355_Daczko_etal_2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These dataset files (3 figures, 3 tables) are supplementary material to: \nDaczko, N.R., Halpin, J.A., Fitzsimons, I.C.W., Whittaker, J.M., 2018. A cryptic Gondwana-forming orogen located in Antarctica. Scientific Reports 8, 8371.\nhttps://www.nature.com/articles/s41598-018-26530-1#Sec13\nhttps://doi.org/10.1038/s41598-018-26530-1\n\nThey include:\nSupplementary Fig. 1. Zircon CL images and spot analyses for sample (8628)5807\nSupplementary Fig. 2. Zircon CL images and spot analyses for sample (8628)6006\nSupplementary Fig. 3. Monazite BSE images and spot analyses for samples (8628)5606, (8628)5638, (8628)5628 and (8628)6001 \nSupplementary Table 1. Compilation of offshore zircon data\nSupplementary Table 2. New zircon SHRIMP data\nSupplementary Table 3. New monazite LA-ICPMS data\n\nDetails of analytical methods from Daczko et al. (2018): \nZircon sample preparation and SHRIMP U-Pb analyses\nZircon grains from 8628\u20135807 and 8628\u20136006 were hand-picked and mounted into a 25-mm diameter epoxy resin disc along with grains of reference zircons BR266 (559\u2009Ma, 909\u2009ppm U; Stern and Amelin, 2003) and OGC-1 (3465\u2009Ma; Stern et al., 2009) and a fragment of NBS610 glass (used to center the 204Pb peak). The mount was polished to expose the zircon grains and reference materials, then carbon-coated for cathodoluminescence imaging on a TESCAN Mira 3 scanning electron microscope in the John de Laeter Centre, Curtin University. The carbon coat was removed and the mount gold-coated prior to U-Pb isotope analysis on the SHRIMP II sensitive high resolution ion microprobe at the John de Laeter Centre, Curtin University.\n\nAnalytical procedures for the Curtin SHRIMP II facility were described by Kennedy and De Laeter (1994) and De Laeter and Kennedy (1998) and are similar to those described by Compston et al. (1984) and Williams (1998). A mass-filtered primary beam of O2\u2013 ions at 10 keV with 25\u201330 \u03bcm diameter was used to sputter secondary ions from the target material. The primary beam current measured at the mount surface was ~2.0 nA, and the beam was rastered over each analysis site for 3\u20134 minutes to remove surface contamination before secondary ions were collected in 6 scans through the following masses: 196 (90Zr216O+, 2 seconds), 204 (204Pb+, 10 seconds), 205.5 (background, 10 seconds), 206 (206Pb+, 20 seconds), 207 (207Pb+, 30 seconds), 238 (238U+, 3 seconds), 248 (232Th16O+, 2 seconds) and 254 (238U16O+, 3 seconds). Values of 206Pb/238U in zircons from 8628\u20135807 and 8628\u20136006 were calibrated using analyses of reference zircon BR266, assuming a power law relationship between 206Pb+/238U+ and 238U16O+/238U+ and a fixed exponent of 2 (Claou\u00e9-Long et al., 1995). External spot-to-spot uncertainty (1\u03c3) in 238U/206Pb values in BR266 over the analytical session was 1.03%. Values of 207Pb/206Pb were monitored using the OGC-1 reference zircon which yielded an error-weighted mean 207Pb/206Pb date (95% confidence) of 3466.3 \u00b1 4.8 Ma for the analytical session, within uncertainty of the reference value (3465.4 Ma).\n\nData were processed and displayed using the Excel add-ins SQUID 2.50.09.08.06 (Ludwig, 2009) and Isoplot 3.76.12.02.24 (Ludwig, 2012). All analyses were corrected for common Pb based on measured 204Pb (Compston et al., 1984) and common Pb isotope ratios appropriate for the approximate age of zircon crystallization according to the Stacey and Kramers (1975) model of Pb isotope evolution. This assumes that any common Pb is inherent to the zircon crystal, which appears to be the case here given that common Pb contents vary consistently between different zircon domains. In particular, the highest common Pb contents in 8628\u20136006 are typically associated with high Th/U cores whereas low Th/U cores and rims mostly have lower levels of common Pb. Uncertainties for individual spot analyses of unknown zircons include errors from counting statistics, errors from the common Pb correction and the U-Pb calibration errors based on reproducibility of U-Pb measurements of the standard, and are quoted at the 1\u03c3 level in the Supplementary data tables and figures, but error ellipses in concordia diagrams are plotted at the 2\u03c3 level. Uncertainties on discordia upper and lower intercepts are quoted with 95% confidence limits.\n\nMonazite sample preparation and LA-ICPMS analyses\nMonazite grains from samples 8628\u20135606, 8628\u20135638, 8628\u20135628 and 8628\u20136001were analysed in situ in polished blocks mounted in 2-inch round mounts. Monazite grains were identified using a FEI Quanta 600 SEM controlled by an automated software package (Mineral Liberation Analyser), and high resolution, high contrast BSE images (Supplementary Fig. 3) were obtained for individual monazite grains using a Hitachi SU-70 field emission (FE)-SEM at the Central Science Laboratory, University of Tasmania. Further details on sample preparation and in situ monazite identification can be found in Halpin et al. (2014). U\u2013Pb monazite analyses were performed on an Agilent 7500cs quadrupole ICPMS with a 193\u2009nm Coherent Ar\u2013F gas laser and the Resonetics S155 ablation cell at the University of Tasmania. LA-ICPMS setup and conditions, and monazite data reduction and reproducibility, are described in detail in Halpin et al. (2014) and summarised below. Tera-Wasserburg diagrams and weighted mean age calculations (Fig. 4) were made using Isoplot v4.11 (Ludwig, 2012). Error ellipses on Tera-Wasserburg plots are calculated at the two-sigma level and weighted mean and intercept ages are reported at 95% confidence limits. Full tabulation of monazite isotopic data is presented in Supplementary Table 3.\n\nEach analysis was pre-ablated with 5 laser pulses to remove the surface contamination then the blank gas was analysed for 30 s followed by 30 s of monazite ablation at 5 Hz and ~2 J/cm2 using a spot size of 9 \u03bcm; keeping U and Th in the pulse counting mode of detection on the electron multiplier. Elements measured included 31P, 56Fe, 89Y, 202Hg, 204Pb, 206Pb, 207Pb, 208Pb, 232Th and 238U with each element being measured sequentially every 0.16 s with longer counting time on the Pb isotopes compared to the other elements. The down hole fractionation, instrument drift and mass bias correction factors for Pb/U and Pb/Th ratios on monazites were calculated using analyses on the in-house primary standard (14971 Monazite) and secondary standard monazite grains (RGL4B and Banaeira) analysed at the beginning of the session and every 15\u201320 unknowns, using the same spot size and conditions as used on the samples to provide an independent control to assess accuracy and precision. The correction factor for the 207Pb/206Pb ratio was calculated using 8 analyses of the international glass standard NIST610 analysed throughout analytical session and corrected using the values recommended by Baker et al. (2004). All data reduction calculations and error propagations were done within Microsoft Excel\u00ae via macros designed at the University of Tasmania and summarised in Halpin et al. (2014). 207Pb corrected 206Pb/238U weighted mean age for the secondary monazite standard Banaeira is 507 \u00b1 4 Ma (n = 5, MSWD = 0.50), within error of the reference ages of 507.7 \u00b1 1.3 Ma (Gon\u00e7alves et al., 2016). 207Pb corrected 206Pb/238U weighted mean age for the secondary monazite standard RGL4b is 1560 \u00b1 13 Ma (n = 5, MSWD = 0.30), within error of the reference age of 1566 \u00b1 3 Ma (Rubatto et al., 2001).", "links": [ { diff --git a/datasets/AAS_4361_ASPA135_2011_REF_1.json b/datasets/AAS_4361_ASPA135_2011_REF_1.json index 31f70d62d5..f784c9a497 100644 --- a/datasets/AAS_4361_ASPA135_2011_REF_1.json +++ b/datasets/AAS_4361_ASPA135_2011_REF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4361_ASPA135_2011_REF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains an orthomosaic of multispectral imagery collected by a 6 band camera (Tetracam Mini-MCA - http://www.tetracam.com/). Data was collected in February 2011 over the ASPA135 moss bed. The camera was carried aboard a small Unmanned Aircraft System (UAS) that flew a grid pattern over the area at 50m above ground level. \nOrthomosaic was georeferenced via ground control points placed in ASPA135 moss bed area and with their location then measured with an RTK GPS that was running corrections from a local base station. \nThe file provided in this dataset is a 6 band binary file with an associated text header file such that software, such as ENVI or ArcGIS, can easily open the file for display. Pixel values are in reflectance percentage, i.e. 0.0-1.0, all information as to the dimensions and georeferencing of the data can be found in the header file.", "links": [ { diff --git a/datasets/AAS_4361_ASPA135_2014_Ref_1.json b/datasets/AAS_4361_ASPA135_2014_Ref_1.json index 6f8073764e..6d64fa7331 100644 --- a/datasets/AAS_4361_ASPA135_2014_Ref_1.json +++ b/datasets/AAS_4361_ASPA135_2014_Ref_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4361_ASPA135_2014_Ref_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains an orthomosaic of multispectral imagery collected by a 6 band camera (Tetracam Mini-MCA - http://www.tetracam.com/). Data was collected in February 2014 over the ASPA135 moss bed. The camera was carried aboard a small Unmanned Aircraft System (UAS) that flew a grid pattern over the area at 50m above ground level. \nOrthomosaic was georeferenced via ground control points placed in ASPA135 moss bed area and with their location then measured with an RTK GPS that was running corrections from a local base station. \nThe file provided in this dataset is a 6 band binary file with an associated text header file such that software, such as ENVI or ArcGIS, can easily open the file for display. Pixel values are in reflectance percentage, i.e. 0.0-1.0, all information as to the dimensions and georeferencing of the data can be found in the header file.", "links": [ { diff --git a/datasets/AAS_4361_ASPA_2014_Health_1.json b/datasets/AAS_4361_ASPA_2014_Health_1.json index f8228b51ef..360e847a34 100644 --- a/datasets/AAS_4361_ASPA_2014_Health_1.json +++ b/datasets/AAS_4361_ASPA_2014_Health_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4361_ASPA_2014_Health_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record contains a map of modeled moss health as a percentage based on multi sensor data. Data was collected in February 2014 over ASPA135 moss bed. The sensors were carried aboard a small Unmanned Aircraft System (UAS) that flew a grid pattern over the area at 50m above ground level. \nInput data for the model were georeferenced via ground control points placed in ASPA135 moss bed area and with their location then measured with an RTK GPS that was running corrections from a local base station. \nThe file provided in this dataset is a 1 band floating pointy file with an associated text header file such that software, such as ENVI or ArcGIS, can easily open the file for display. Pixel values are in modeled health percentage, i.e. 0.0-100.0%, all information as to the dimensions and georeferencing of the data can be found in the header file", "links": [ { diff --git a/datasets/AAS_4387_Aurora_Australis_MRRPRO_1.json b/datasets/AAS_4387_Aurora_Australis_MRRPRO_1.json index 41ec9fd362..3c856494bd 100644 --- a/datasets/AAS_4387_Aurora_Australis_MRRPRO_1.json +++ b/datasets/AAS_4387_Aurora_Australis_MRRPRO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4387_Aurora_Australis_MRRPRO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro Rain Radar data collected aboard Aurora Australis on Voyage 1 and Voyage 4, season 2017-18. NetCDF files containing one minute of observations made by the Micro Rain Radar (MRR) PRO aboard Aurora Australis on Voyage 1 and Voyage 4 during season 2017-18. Note that these data are uncalibrated. \n\nThe original Doppler spectra are also saved in these files. Parameters within the file include:\nZa \u2013 Attenuated Reflectivity (dbZ)\nZ \u2013 Reflectivity (dbZ)\nZe - Equivalent Reflectivity (dbZ)\nZea - Attenuated Equivalent Reflectivity (dbZ)\nRR \u2013 Rain Rate (mm/hr)\nLWC \u2013 Liquid Water Content (g/m^3)\nW \u2013 Fall Velocity (m/s)\n\nDataset resolution: 35m x 10 s\n\nHeight coverage: 30m \u2013 4475m above instrument level.", "links": [ { diff --git a/datasets/AAS_4387_Davis_MRRPRO_1.json b/datasets/AAS_4387_Davis_MRRPRO_1.json index 2562770a4e..d5198a0094 100644 --- a/datasets/AAS_4387_Davis_MRRPRO_1.json +++ b/datasets/AAS_4387_Davis_MRRPRO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4387_Davis_MRRPRO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises Micro Rain Radar data collected adjacent to the Bureau of Meteorology building at Davis Station, Antarctica between November 2018 and November 2019.\n\nThe original Doppler spectra are also saved in these files. Parameters within the file include:\nZa \u2013 Attenuated Reflectivity (dbZ)\nZ \u2013 Reflectivity (dbZ)\nZe - Equivalent Reflectivity (dbZ)\nZea - Attenuated Equivalent Reflectivity (dbZ)\nRR \u2013 Rain Rate (mm/hr)\nLWC \u2013 Liquid Water Content (g/m^3)\nW \u2013 Fall Velocity (m/s)\n\nDataset resolution: 35m x 10 s\n\nHeight coverage: 30m \u2013 4475m above instrument level.", "links": [ { diff --git a/datasets/AAS_4387_Davis_Parsivel_1.json b/datasets/AAS_4387_Davis_Parsivel_1.json index 944989a828..de03db7416 100644 --- a/datasets/AAS_4387_Davis_Parsivel_1.json +++ b/datasets/AAS_4387_Davis_Parsivel_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4387_Davis_Parsivel_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Parsivel disdrometer (OceanRAIN Eigenbrodt disdrometer) records precipitation. Specifically, the particle size velocity and drop size distribution data for snowfall events collected at Davis station were recorded for the period November 2018 - November 2019.\n\nData are stored in MIS files (openable by text editor), and contain data on:\n\nDate,\nTime,\nIntensity of precipitation (mm/h),\nPrecipitation since start (mm),\nWeather code SYNOP WaWa,\nWeather code METAR/SPECI,\nWeather code NWS,\nRadar reflectivity (dBz),\nMOR Visibility (m),\nSignal amplitude of Laserband,\nNumber of detected particles,\nTemperature in sensor (\u00b0C),\nHeating current (A),\nSensor voltage (V),\nKinetic Energy,\nSnow intensity (mm/h)\n\nThere are 749 files covering the collection period.", "links": [ { diff --git a/datasets/AAS_4405_cap_IC_1.json b/datasets/AAS_4405_cap_IC_1.json index 987e1700b2..e22b6b06ea 100644 --- a/datasets/AAS_4405_cap_IC_1.json +++ b/datasets/AAS_4405_cap_IC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4405_cap_IC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Development of a new method to measure trace ions in ice cores using low volume capillary ion chromatography.\n\nFrom the project description:\nThe high costs associated with collection of ice-cores from Antarctica demand scientists extract the absolute maximum data from these precious commodities. Traditional analytical techniques are neither optimised or indeed able to meet the demands of delivering high value multi-species data from sub-mL sample volumes, to provide higher temporal resolution in subsequent paleoclimatic records. To extract the most information from analytical data derived from these valuable ice cores, and/or low accumulation sites, the amount of sample required for each analysis must be drastically reduced by between 10 and 100 fold. Capillary ion chromatography (cap-IC) presents a new analytical capability to provide quantification of inorganic and organic ions based upon such sample volumes, and improve temporal resolution in ice-core records by 10-20 times. This new technology, using methods developed in a recent pilot study, will be applied to existing ice cores from Law Dome and other Antarctic sites.", "links": [ { diff --git a/datasets/AAS_4406_BP_MP_2005_1.json b/datasets/AAS_4406_BP_MP_2005_1.json index 8fff042d2f..97a34798bb 100644 --- a/datasets/AAS_4406_BP_MP_2005_1.json +++ b/datasets/AAS_4406_BP_MP_2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4406_BP_MP_2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample collection:\nAll soils were collected in 2005 and stored at -80\u02daC prior to gDNA extraction using the FastDNA SPIN kit for soil (MP Biomedicals, NSW, Australia). Three soil samples were obtained from Mitchell Peninsula (66\u00b031'S, 110\u00b059'E) and three were obtained from Browning Peninsula (66\u00b027'S, 110\u00b032'E). Samples from both sites were selected along a transect, with specific information provided below. \n\nMetagenome sequencing and assembly:\nMetagenomic shotgun libraries were prepared from community gDNA extractions using the Nextera DNA Flex Library Prep Kit (Illumina Inc.). Sequencing was performed on an Illumina NextSeq500 platform with 2\u00d7150bp base pair high output run chemistry and 5GB coverage per sample.\n\nDescription of ACE Sample IDs (including in filenames) and the relevant soil samples: \n*\tSB9748\n-\tSoil barcode= 91062\n-\tOriginal barcode= 91604\n-\tSample Site= Browning Peninsula\n-\tTransect= 3\n-\tDistance samples= 2m\n-\tConcentration of DNA sent= 1.2ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 80\u00b5L\n*\tSB9749\n-\tSoil barcode= 91604\n-\tOriginal barcode= 26587\n-\tSample Site= Browning Peninsula\n-\tTransect= 3\n-\tDistance samples= 102m\n-\tConcentration of DNA sent= 1ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 80\u00b5L\n*\tSB9750\n-\tSoil barcode= 91606\n-\tOriginal barcode= 26617\n-\tSample Site= Browning Peninsula\n-\tTransect= 3\n-\tDistance samples= 202m\n-\tConcentration of DNA sent= 0.3ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 180\u00b5L\n*\tSB9751\n-\tSoil barcode= 91447\n-\tOriginal barcode= 36260\n-\tSample Site= Mitchell Peninsula\n-\tTransect= 2\n-\tDistance samples= 2m\n-\tConcentration of DNA sent= 0.86ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 80\u00b5L\n*\tSB9752\n-\tSoil barcode= 91477\n-\tOriginal barcode= 36290\n-\tSample Site= Mitchell Peninsula\n-\tTransect= 2\n-\tDistance samples= 102m\n-\tConcentration of DNA sent= 0.9ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 80\u00b5L\n*\tSB9753\n-\tSoil barcode= 91507\n-\tOriginal barcode= 36320\n-\tSample Site= Mitchell Peninsula\n-\tTransect= 2\n-\tDistance samples= 202m\n-\tConcentration of DNA sent= 1.9ng/\u00b5L (required concentration)\n-\tVolume DNA sent= 80\u00b5L", "links": [ { diff --git a/datasets/AAS_4414_MountBrownSouth_LawDome_icecores_seasalt_accumulation_2020_1.json b/datasets/AAS_4414_MountBrownSouth_LawDome_icecores_seasalt_accumulation_2020_1.json index 5c62e89597..c272e14054 100644 --- a/datasets/AAS_4414_MountBrownSouth_LawDome_icecores_seasalt_accumulation_2020_1.json +++ b/datasets/AAS_4414_MountBrownSouth_LawDome_icecores_seasalt_accumulation_2020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4414_MountBrownSouth_LawDome_icecores_seasalt_accumulation_2020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data used in Crockart et al. (submitted 2020, 'El Ni\u00f1o Southern Oscillation signal in a new East Antarctic ice core, Mount Brown South') for three ice cores collected from Mount Brown South (MBS, 69.111\u00b0 S, 86.312\u00b0 E) in East Antarctica (MBS1718-Main, MBS1718-Alpha, MBS1718-Charlie) in summer 2017/2018, and two successive ice cores collected from Law Dome (LD, Dome Summit South site, 66.461\u00b0 S, 112.841\u00b0 E) in East Antarctica (DSS1617 drilled in 2016/2017, and DSS97 presented in Vance et al. 2013) over the period 1975-2015/6. The data includes the annual log-transformed sea salt concentrations (chloride and sodium), and the annual ice equivalent (IE) snowfall accumulation (in m yr-1 IE) for the MBS and LD ice cores. \n\nIsotope and ion chemistry analyses\nThe oxygen isotopes and ion chemistry in the MBS and LD ice cores were analysed according to established methods (see Curran and Palmer 2001; Curran et al. 2003; Palmer et al. 2001; Plummer et al. 2012). Discrete samples for water stable isotope (1.5 cm) and trace chemistry (3 cm) samples were cut under trace clean conditions. Isotopic values are expressed as per mil (\u2030) relative to the Vienna Standard Mean Oceanic Water (VSMOW) standard. The standard deviations of \u03b418O for repeated measurements of laboratory reference water samples were less than 0.07 \u2030 (for the LD ice core) and 0.5 \u2030 (for the MBS ice cores). The Thermo-Fisher/Dionex ICS3000 ion chromatograph was used to determine the sea salt concentrations (chloride (Cl-) and sodium (Na+)) and sulphate (SO42\u2212). Non sea salt sulphate (nssSO42\u2212) was calculated according to the methods in Plummer et al. (2012)\n\nDating\nAnnual depth layers were assigned to the MBS and LD ice cores using seasonally varying species, principally \u03b418O, nssSO42-,Na+ and the ratio of SO42-/Cl- (see Plummer et al. 2012 for more details). Volcanic ash layers (indicated by nssSO42\u2212 peaks) linked to the Pinatubo volcanic eruption in the Philippines in mid-1991 were used as a reference depth horizon to cross-check the annual depth layer chronology. Each ice core was dated individually and independently, without reference to other site records to ensure independence of the method.\n\nAccumulation\nThe annual depth layers were used in combination with an empirical density model (see equation 1) to determine the annual ice equivalent snowfall accumulation rates (see Robert et al. 2015 for more details). \nEmpirical Density = [\u03c1] \u2013 [883.5356 * exp(-0.011078644) * d] + [436.8285] \u2013 [1.887488 200 * d] Eq. (1)\n\nSea salts\nSea salt concentrations were log-transformed to create a normally distributed record, as the raw concentrations are skewed toward infrequent high concentration events. The sea salt concentration for the year 1987 in the MBS Charlie ice core was excluded.\n\nCurran, M. A. J. and Palmer, A. S.: Suppressed ion chromatography methods for the routine determination of ultra low level anions and cations in ice cores, J. Chromatogr., 919, 107\u2013113, doi:10.1016/S0021-9673(01)00790-7, 2001.\n\nCurran, M. A. J., van Ommen, T. D., Morgan, V. I., Phillips, K. L., Palmer, A. S.: Ice core evidence for Antarctic sea ice\ndecline since the 1950s, Science, 302, 1203\u20131206, doi:10.1126/science.1087888, 2003.\n\nPalmer, A. S., van Ommen T. D., Curran, M. A. J., Morgan, V. , Souney, J. M. and Mayewski, P.A.: High precision dating of volcanic events (AD 1301-1995) using ice cores from Law Dome, Antarctica, J. Geophys. Res., 106, 28089-28095,\ndoi:10.1029/2001JD000330, 2001.\n\nPlummer, C. T., Curran, M. A. J., van Ommen, T. D., Rasmussen, S. O., Moy, A. D., Vance, T. R., Clausen, H. B., Vinther, B. M., Mayewski, P. A.: An independently dated 200- yr volcanic record from Law Dome, East Antarctica, including a new perspective on the dating of the c. 1450s eruption of Kuwae, Vanuatu, Climate Past 795 Discuss., 8, 1567\u20131590, doi:10.5194/cpd-8-1567-2012, 2012.\n\nRoberts, J., Plummer, C., Vance, T., van Ommen, T., Moy, A., Poynter, S., Treverrow, A., Curran, M., George, S.: A 2000-year annual record of snow accumulation rates for Law Dome, East Antarctica, Clim. Past, 11, 697\u2013707, doi:10.5194/cp-11-697-2015, 2015.\n\nVance, T. R., van Ommen, T. D., Curran, M. A. J., Plummer, C. T., Moy, A. D.: A Millennial Proxy Record of ENSO and Eastern Australian Rainfall from the Law Dome Ice Core, East Antarctica, J. Clim., 26, 710\u2013725, doi:10.1175/JCLI-D-12-00003.1, 2013.", "links": [ { diff --git a/datasets/AAS_4419_IN2017-V01-C012-PC05-TraceMetals_1.json b/datasets/AAS_4419_IN2017-V01-C012-PC05-TraceMetals_1.json index b0ef6cadc0..5af8fe11e4 100644 --- a/datasets/AAS_4419_IN2017-V01-C012-PC05-TraceMetals_1.json +++ b/datasets/AAS_4419_IN2017-V01-C012-PC05-TraceMetals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4419_IN2017-V01-C012-PC05-TraceMetals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace metal concentrations are reported in micrograms per gram of sediment in core C012-PC05 (64\u2070 40.517\u2019 S, 119\u2070 18.072\u2019 E, water depth 3104 m). Each sediment sample (100-200mg) was ground using a pestle and mortar and digested following an initial oxidation step (1:1 mixture of H2O2 and HNO3 acid) and open vessel acid on a 150 degree C hotplate using 2:5:1 mixture of concentrated distilled HCl, HNO3 and Baseline Seastar HF acid. After converting the digested sample to nitric acid, an additional oxidation step was performed with 1:1 mixture of concentrated distilled HNO3 and Baseline Seastar HClO4 acid. A 10% aliquot of the final digestion was sub-sampled for trace metal analyses.\n\nTrace metal concentrations were determined by external calibration using an ELEMENT 2 sector field ICP-MS from Thermo Fisher Scientific (Bremen, Germany) at Central Science Laboratory (University of Tasmania). The following elements were analysed in either low (LR) or medium resolution (MR): Sr88(LR), Y89(LR), Mo95(LR), Ag107(LR), Cd111(LR), Cs133(LR), Ba137(LR), Nd146(LR), Tm169(LR), Yb171(LR), Tl205(LR), Pb208(LR), Th232(LR), U238(LR), Na23(MR), Mg24(MR), Al27(MR), P31(MR), S32(MR), Ca42(MR), Sc45(MR), Ti47(MR), V51(MR), Cr52(MR), Mn55(MR), Fe56(MR), Co59(MR), Ni60(MR), Cu63(MR), Zn66(MR).", "links": [ { diff --git a/datasets/AAS_4419_KC14_IBRDFlux_1.json b/datasets/AAS_4419_KC14_IBRDFlux_1.json index 97ecf1c8e0..598d909dbf 100644 --- a/datasets/AAS_4419_KC14_IBRDFlux_1.json +++ b/datasets/AAS_4419_KC14_IBRDFlux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4419_KC14_IBRDFlux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice-rafted debris is characterised by coarse material with typically angular grains, transported within icebergs and deposted in the ocean as the icebergs melt. This iceberg rafted debris (IBRD) flux data submitted here, was calculated by quantifying the coarse sand fraction (CSF) as a percentage of the bulk sample (weight of grains in the 250 micron to 2 mm size fraction), the dry bulk density (DBD) and the linear sedimentation rate (LSR) (following Krissek et al., 1995, Patterson et al., 2014). \n\nA method for quantifying the IBRD flux uses the coarse sand fraction (CSF) as a percentage of the bulk sample, dry bulk density (DBD) and the linear sedimentation rate (LSR) (Krissek et al., 1995, Patterson et al., 2014):\n\nThe CSF (250\u03bcm-2mm) was acquired from samples at 10cm intervals along KC14 by wet-sieving approximately 20g of sediment per sample. Authigenic grains and microfossils were removed from the samples under a microscope. The remaining material was weighed on a microbalance and calculated as a percentage of the bulk sample. The DBD was calculated by subsampling approximately 8cm3 of sediment from the same depth intervals and dividing the dry weight of the sediment by the volume of the subsampler. The LSR was approximated by dividing the distance (cm) between the calibrated bulk carbon ages by the difference in time (kyr). The IBRD flux was then quantified using the above equation for each depth interval.", "links": [ { diff --git a/datasets/AAS_4422_Slope_Meltrate_1.json b/datasets/AAS_4422_Slope_Meltrate_1.json index 33d14a889a..65b483e9a4 100644 --- a/datasets/AAS_4422_Slope_Meltrate_1.json +++ b/datasets/AAS_4422_Slope_Meltrate_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4422_Slope_Meltrate_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Direct Numerical Simulation (DNS) was used to study the effect of sloping the ice-shelves on the dissolution/melt rate at the ice-ocean interface. The simulations were done on the HPC Raijin at NCI, Canberra over March 2015 to June 2017. \n\nNumerical experiments were carried out over a range of slope angle (5 degrees \u2013 90 degrees) of the ice-shelves measured from the horizon. Turbulent flow field is simulated over the domain length of 1.8 m, (for slope angle greater than or equal to 50 degrees) and 20 m (for slope angle less than or equal to 20 degrees) respectively; the flow-field is laminar otherwise. A constant ambient temperature 2.3 degrees C and salinity 35 psu is maintained throughout the simulations.\n\nThe DNS successfully resolved all possible turbulence length scales and relative contributions of diffusive and turbulent heat transfer into the ice wall is measured.\n\nData available:\n\nExcel file Meltrate_vs_slopeangle_lam_turb.xlsx contains both simulated laminar and turbulent dissolution/melt rate as a function of slope angle along with their analytical values based on laminar and turbulent scaling theory respectively.", "links": [ { diff --git a/datasets/AAS_4422_Thermal_Salinity_Profiles_1.json b/datasets/AAS_4422_Thermal_Salinity_Profiles_1.json index 70327fe0a5..d6e618d1c2 100644 --- a/datasets/AAS_4422_Thermal_Salinity_Profiles_1.json +++ b/datasets/AAS_4422_Thermal_Salinity_Profiles_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4422_Thermal_Salinity_Profiles_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Direct Numerical Simulation (DNS) was used to study the effect of sloping the ice-shelves on the dissolution/melt rate at the ice-ocean interface. The simulations were done on the HPC Raijin at NCI, Canberra over March 2015 to June 2017. \n\nNumerical experiments were carried out over a range of slope angle (5 degrees \u2013 90 degrees) of the ice-shelves measured from the horizon. Turbulent flow field is simulated over the domain length of 1.8 m, (for slope angle greater than or equal to 50 degrees) and 20 m (for slope angle less than or equal to 20 degrees) respectively; the flow-field is laminar otherwise. A constant ambient temperature 2.3 degrees C and salinity 35 psu is maintained throughout the simulations.\n\nThe DNS successfully resolved all possible turbulence length scales and relative contributions of diffusive and turbulent heat transfer into the ice wall is measured.\n\nData available:\nExcel file Profile_salinity_temperature_velocity.xlsx contains along-slope velocity, temperature and salinity as a function of wall normal distance for slope angle 50 degrees, 65 degrees and 90 degrees respectively for the domain length 1.8 m.", "links": [ { diff --git a/datasets/AAS_4431_CAMMPCAN_GEOS_Chem_Model_AA_2017-18_1.json b/datasets/AAS_4431_CAMMPCAN_GEOS_Chem_Model_AA_2017-18_1.json index 59e6d408de..bba635b2c5 100644 --- a/datasets/AAS_4431_CAMMPCAN_GEOS_Chem_Model_AA_2017-18_1.json +++ b/datasets/AAS_4431_CAMMPCAN_GEOS_Chem_Model_AA_2017-18_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4431_CAMMPCAN_GEOS_Chem_Model_AA_2017-18_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset provides model output from the GEOS-Chem chemical transport model to support the CAMMPCAN and MARCUS 2017-2018 voyages.\n\nThe model version used was GEOS-Chem v12.8.1, with DOI: 10.5281/zenodo.3837666. The DOI should be cited when using this dataset. Modifications were made to the standard v12.8.1 to include abiotic ocean emissions of volatile organic compounds as implemented in Travis et al. (2020).\n\nThe model was running using MERRA-2 meteorology at 2\u00b0x2.5\u00b0 (latitude x longitude) horizontal resolution with 47 vertical levels throughout the entire period of the voyages. These model runs were preceded by a 6-month spin-up at 4\u00b0x5\u00b0 beginning 1 May 2017.\n\nThe following output types are included (for variable names, see below):\n\n1.\tOutput along the shiptrack\nFilenames: mrg20m_SS_ss_YYYYMMDD.txt\nThe model has been sampled to directly match the location of the ship at every minute during the voyages. The output is then averaged to ensure there is only one data point for each unique model gridbox-timestep combination. The resulting dataset is 1-dimensional, with values for latitude, longitude, and time along with the data variables. These are text files (with space as separator character).\n2.\tGlobal monthly means\nFilenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_monmean.nc4\n{DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below).\n{YYYYMM} is the year and month for the data included in the file.\nThe model output has been averaged on a monthly timescale and is provided for all model gridboxes globally. The resulting dataset is 3-dimensional (longitude, latitude, level). These are netcdf files.\n3.\tRegional daily means\nFilenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_regional.nc4\n{DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below).\n{YYYYMM} is the year and month for the data included in the file.\nThe model output has been averaged on a daily timescale and is provided for all gridboxes in the region bounded by 30-90\u00b0S (inclusive). The resulting dataset is 4-dimensional (longitude, latitude, level, time). These are netcdf files.\n4.\tEmissions\nFilenames: HEMCO_diagnostics.{YYYYMM}01.nc4\n{YYYYMM} is the year and month for the data included in the file.\nThe model emissions (and related variables) have been averaged on a monthly timescale and are provided for all gridboxes globally. The resulting dataset includes both 2-dimensional (longitude, latitude) and 3-dimensional (longitude, latitude, level) variables. These are netcdf files.\n\nOutput data types (correspond to filenames given above):\n1.\tSpeciesConc: Concentrations of advected model species.\n2.\tStateMet: Meteorological fields and other derived quantities. \n3.\tAerosols: Diagnostics for aerosol optical depth and related quantities from full-chemistry simulations.\n4.\tAerosolMass: Diagnostics for aerosol mass and particulate matter\n\n\nVariable names for each output data type are provided in the file GEOSChem_Diagnostics.xlsx (one tab for each output data type). For species names (used in the SpeciesConc files and along-shiptrack files) and properties, see file GEOS-Chem_Species_Database.json. For emission diagnostic names (used in the HEMCO_diagnostics files) see file HEMCO_Diagn.rc.\n\n\nReferences:\nThe International GEOS-Chem User Community. (2020, May 21). geoschem/geos-chem: GEOS-Chem 12.8.1 (Version 12.8.1). Zenodo. http://doi.org/10.5281/zenodo.3837666\n\nTravis, K. R., Heald, C. L., Allen, H. M., Apel, E. C., Arnold, S. R., Blake, D. R., Brune, W. H., Chen, X., Commane, R., Crounse, J. D., Daube, B. C., Diskin, G. S., Elkins, J. W., Evans, M. J., Hall, S. R., Hintsa, E. J., Hornbrook, R. S., Kasibhatla, P. S., Kim, M. J., Luo, G., McKain, K., Millet, D. B., Moore, F. L., Peischl, J., Ryerson, T. B., Sherwen, T., Thames, A. B., Ullmann, K., Wang, X., Wennberg, P. O., Wolfe, G. M., and Yu, F.: Constraining remote oxidation capacity with ATom observations, Atmos. Chem. Phys., 20, 7753\u20137781, https://doi.org/10.5194/acp-20-7753-2020, 2020.", "links": [ { diff --git a/datasets/AAS_4431_MAXDOAS_1.json b/datasets/AAS_4431_MAXDOAS_1.json index 75135a69df..c933fdd068 100644 --- a/datasets/AAS_4431_MAXDOAS_1.json +++ b/datasets/AAS_4431_MAXDOAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4431_MAXDOAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectra: one binary file per spectrum. Spectra can be processed using DOASIS or QDOAS software. Spectrum files are saved in folders numbered by date.\nDaily log files: for spectra (extra geometric information as well as latitude, longitude, solar zenith angle) and temperature (instrument, internal and external temperature measurements).\nAccelerometer: One ascii file per day with pitch, roll and yaw euler angles as the columns\nImages: taken by a small camera, co-directional with the MAX-DOAS, for context of broad light conditions (i.e. checking sunny/cloudy weather)\nCalibration files: Binary and text files for dark current, offset, slit function shape and wavelength calibrations", "links": [ { diff --git a/datasets/AAS_4434_ACE_GPS_1.json b/datasets/AAS_4434_ACE_GPS_1.json index dfaaee6beb..2958e1abdf 100644 --- a/datasets/AAS_4434_ACE_GPS_1.json +++ b/datasets/AAS_4434_ACE_GPS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4434_ACE_GPS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw GPS and ship motion data collected during the Antarctic Circumnavigation Expedition 2016/2017.\n \nWaves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft. \n \nThere is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment. \n \nBy installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Dara were collected during the Antarctic Circmumnavigaion Expedition, which took place from Dec. 2016 to Mar. 2017. The instrumentation operated in any weather and visibility conditions, and at night, monitoring the ocean continuously over the entire Circumnavigation.\n \nRecords can support \n \n1. the assessment of metocean conditions in the Southern Oceans; and \n \n2. calibration and validation of wave and global circulation models. \n \nData - AAS_4434_ACE_GPS contains basic metereological conditions acquired form the ship\u2019s meteo-station, gepgraphical coordinates (latitude, longitude and altitude) from the ship\u2019s GPS and ship motion data from the ship\u2019s Inertial Measurement Unit (IMU). These data are stored as time series with a sampling frequency of 1Hz.", "links": [ { diff --git a/datasets/AAS_4434_ACE_HOSM_1.json b/datasets/AAS_4434_ACE_HOSM_1.json index 84fb4852e0..17bc125ef6 100644 --- a/datasets/AAS_4434_ACE_HOSM_1.json +++ b/datasets/AAS_4434_ACE_HOSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4434_ACE_HOSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstructed nonlinear surface from WAMOS (marine radar) data collected during the 3rd leg of Antarctic Circumnavigation Expedition, from the end of January to the end of March 2017.\n\nWAMOS data (AAS_4434_ACE_WAMOS) are processed with the Higher Order Spectral Method (HOSM) to provide the nonlinear surface elevation and the corresponding spectrum of waves during ACE. A Montecarlo approach is adopted to reproduce the natural variability of the sea state and gain reliable statistics of the underlying nonlinear surface elevation. Details on the method can be found on Toffoli, Alessandro, et al. \"Evolution of weakly nonlinear random directional waves: laboratory experiments and numerical simulations.\" Journal of Fluid Mechanics 664 (2010): 313-336.\n\nFile structure: Folder name corresponds to the time stamp of the input spectrum (yyyyMMddhhmmss) from AAS_4434_ACE_WAMOS. \nEach folder contains:\n1. The surface elevation for 250 random realisations at 10 instant in times from initialisation saved every 5 dominant wave periods apart (0,5,10,15,\u2026,50 Tp). The ten digits name is structured as 0000NRRttt where NRR is the number of the random realisation (from 1 to 250) and ttt denotes the time index (from 0 to 10).\n2. NEW_SPECTRUM.DAT the 2D spectrum (64x64) as a columnar vector of the initial spectrum read from the AAS_4434_ACE_WAMOS.\n3. INPUT_SPECTRUM.DAT the 2D spectrum (256x256) as a columnar vector of the initial spectrum for the HOSM.\n4. WAVENUMBERSX.DAT and WAVENUMBERSY.DAT the wavenumber in x and y respectively\n5. PP_INFO.DAT contains the peak period (Tp) in seconds\n6. RUN_INFO.DAT contains the resolution in x of the WAMOS spectrum (64), the resolution in y of the WAMOS spectrum (64), the delta x for the surface elevation in m, the delta y for the surface elevation in m. Subsequent parameters are flags for the HOSM method.\n\nWaves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft. \n\nThere is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.", "links": [ { diff --git a/datasets/AAS_4434_ACE_WAMOS_3.json b/datasets/AAS_4434_ACE_WAMOS_3.json index bd99494952..93cfee2902 100644 --- a/datasets/AAS_4434_ACE_WAMOS_3.json +++ b/datasets/AAS_4434_ACE_WAMOS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4434_ACE_WAMOS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WAMOS (marine radar) data collected during the Antarctic Circumnavigation Expedition (ACE, https://spi-ace-expedition.ch/), from December 2016 to March 2017.\n\nWaves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft.\n\nThere is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.\n\nBy installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Dara were collected during the Antarctic Circmumnavigaion Expedition, which took place from Dec. 2016 to Mar. 2017. The instrumentation operated in any weather and visibility conditions, and at night, monitoring the ocean continuously over the entire Circumnavigation.\n\nRecords can support\n\n1. the assessment of metocean conditions in the Southern Oceans; and\n\n2. calibration and validation of wave and global circulation models.\n\nData - AAS_4434_ACE_WAMOS contains sea state conditions monitored continuously with a Wave and Surface Current Monitoring System (WaMoS II), a wave devise based on the marine X-Band radar (see Hessner, K. G., Nieto-Borge, J. C., and Bell, P. S., 2007, Nautical Radar Measurements in Europe: Applications of WaMoS II as a Sensor for Sea State, Current and Bathymetry. In V. Barale, and M. Gade, Sensing of the European Seas, pp. 435-446, Springer). Sea state consists of the directional wave energy spectrum, angular frequency and direction of propagation. Basic parameters such as the significant wave height (a representative measure of the average wave height), the dominant period, wavelength, mean wave direction, etc\u2026 were inferred from the wave spectrum. Surface current speed and the concurrent direction were also detected. Post processed data are available anytime the X-Band radar was operated in a range of 1.5NM; a full spectrum was generally obtained evert 20 minutes.\n\nData are subdivided in:\n- WaMoS II frequency spectrum (1-D spectra)\n- WaMoS II wave number spectrum (2-D spectra)\n- WaMoS II frequency direction spectrum (2-D spectra)\n\nData are quality controlled.\n\n**************************************************************************************************************\nFile informations\n\nPath to the spectra: \\RESULTS\\YYYY\\MM\\DD\\HH\\ : Year, month, day, hour.\nspace\\ : spatial mean results.\nsingle\\ : raw spectra.\nmean\\ : time averaged files.\n\nHeader of the spectra:\nAdditional information that might be needed for data analysis is stored in the headers.\nThe output results generated using different WaMoS II software modules are separated by comment lines starting with \u2018CC\u2019. All headers are subdivided into:\n1) Polar Header: including data acquisition parameters.\n2) Car Header: including Cartesian transformation parameters.\n3) Wave-Current Analysis Header: including wave and current analysis related parameters.\nThere is a keyword of maximum 5 characters in each line of the header followed by some values and a comment, after the comment marker \u2018CC\u2019, describing the keyword.\nValues of missing parameters are set to -9, -9.0, -99.0, etc. depending on the data type.\nThe 'end of header' keyword 'EOH', indicated the last line of the header section.\n\n*******************************************************************\nWaMoS II frequency spectrum (1-D spectra):\n\nFile Name:\nYYYY : Year.\nMM : Month.\nDD : Day.\nHH : Hour.\nMM : Minute.\nSS : Second.\nrigID : WaMoS II platform\u2019s ID code (3 letters)\n\nSuffix:\n\u2019*.D1S\u2019 : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view.\n\u2018*.D1M\u2019 : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.\n\nTime reference: CPU clock.\n\nData Content:\nFrequency (f - Hz).\nSpectral energy (S(f) - m*m/Hz).\nMean Wave Direction (MDIR(f) - deg), \ufffd\ufffd\ufffdcoming from\u2019.\nDirectional Spreading (SPR(f) - deg/Hz).\n\n*******************************************************************\nWaMoS II wave number spectrum (2-D spectra):\n\nFile Name:\nYYYY : Year.\nMM : Month.\nDD : Day.\nHH : Hour.\nMM : Minute.\nSS : Second.\nrigID : WaMoS II platform\u2019s ID code (3 letters)\n\nSuffix:\n\u2019*.D2S\u2019 : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view.\n\u2018*.D2M\u2019 : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.\n\nTime reference: CPU clock.\n\nData Content:\nSpectral energy (S(kx,ky) - m*m/(Hz*rad)) as a function of wave number (kx and ky - rad/m).\n\nData related header information\nMATRIX: Size of Matrix.\nDKX: Spectral resolution in Kx direction (2*Pi/m).\nDKY: Spectral resolution in Ky direction (2*Pi/m).\n\n*******************************************************************\nWaMoS II frequency direction spectrum (2-D spectra): \n\nFile Name:\nYYYY : Year.\nMM : Month.\nDD : Day.\nHH : Hour.\nMM : Minute.\nSS : Second.\nrigID : WaMoS II platform\u2019s ID code (3 letters)\n\nSuffix:\n\u2018*.FTH\u2019 : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view.\n\u2019*.FTM\u2019 : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.\n\nTime reference: CPU clock.\n\nData Content:\nSpectral energy (S(f,\u03b8) - m*m/(Hz*rad)) as a function of frequency (f \u2013 Hz) and direction (\u03b8 - deg).\n\nData information\nMf : number of frequency sampling points.\nMth : number of direction sampling points.\nData Matrix: Row 1 frequency sampling points, Column 1 direction sampling points.\n\nThe dataset download also includes a file, \"Available_Measurements\", which is a general calendar that provides the list (day and time) of available measurements.", "links": [ { diff --git a/datasets/AAS_4434_ACE_WAMOS_timeseries_1.json b/datasets/AAS_4434_ACE_WAMOS_timeseries_1.json index eb115dcdfc..870c357f88 100644 --- a/datasets/AAS_4434_ACE_WAMOS_timeseries_1.json +++ b/datasets/AAS_4434_ACE_WAMOS_timeseries_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AAS_4434_ACE_WAMOS_timeseries_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time series of metocean variables derived form WAMOS (marine radar) data collected during the Antarctic Circumnavigation Expedition (ACE, https://spi-ace-expedition.ch/), from December 2016 to March 2017.\n\nWaves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.\n\nBy installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Data were collected during the Antarctic Circumnavigation Expedition, which took place from Dec. 2016 to Mar. 2017.\n\nThe dataset contains timeseries of relevant metocean variables divided in\n- Sea state and current parameters (PARA, MPAR)\n- Sea state and current parameters (PEAK, MPEK)\n- Ship course, position and speed (COURSE)\n- Wind speed and direction file (WIND)\n\n**********************************************************\nSea state and current parameters files (PARA, MPAR)\n\nFile Name: -Prefix-_-rigID-_YYYYMM.txt\n- Prefix:\n1) \u2018PARA\u2019 : spatial mean of the parameters (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view.\n2) \u2018MPAR\u2019 : temporal average parameters calculated using all data collected during the past dt=20 minutes of the time specified in the file.\n- YYYY : Year.\n- MM : Month.\n- rigID : WaMoS II platform\u2019s ID code (3 letters)\n\nTime reference: CPU clock.\n\nValues of missing parameters are set to -9, -9.0.\n\nList of parameters:\n- date : Date and TIME of acquisition (YYYYMMDDHHMMSS).\n- Hs : Significant wave height (m).\n- Tp : Peak wave period (s).\n- Tm2 : Mean wave period (s).\n- Lp : Peak wave length (m).\n- MDir : Mean wave direction (deg).\n- PDir : Peak wave direction (deg).\n- TpS : First swell system - wave period (s).\n- PDS : First swell system - peak wave direction (deg).\n- lpS : First swell system - peak wave length (m).\n- TpW : Wind sea peak wave period (s).\n- PDW : Wind sea wave direction (deg).\n- lpW : Wind sea wave length (m).\n- Usp : Surface current speed (m/s).\n- Udir : Surface current direction (deg).\n- IQ : Quality index, ranging from 0 ('no problems detected') to 999 ('images cannot be analysed').\n- NSPEC : Number of averaged spectra.\n- INDEX : Quality index threshold (OK: IQ49 degrees north) at 1-km spatial resolution. The data were produced through simulations of the Arctic Terrestrial Carbon Flux Model (TCFM-Arctic) and are provided at the daily time step for the years 2003-2015. TCFM-Arctic uses a light-use efficiency approach driven by satellite estimates of FPAR (fraction of absorbed photosynthetically active radiation) to estimate GPP, and autotrophic respiration (Rauto) is estimated as a fraction of GPP. Heterotrophic respiration (Rhetero) is estimated using decomposition rates with environmental constraints applied to three near-surface soil organic carbon (SOC) pools, and Reco is determined as the sum of Ra and Rh. Methane production is estimated using optimal CH4 production rates with environmental constraints applied to the labile carbon pool, and transfer of CH4 from the soil to the atmosphere is modeled through vegetation, soil diffusion, and water ebullition pathways. The model estimates were calibrated and evaluated using >60 tower eddy covariance (EC) sites. Baseline carbon pools were initialized by continuously cycling (spinning-up) the model for 1,000 model years using recent climatology from 1985 to 2002 to reach a dynamic steady-state between estimated net primary productivity (NPP = GPP - Rauto) and near-surface SOC pools. The TCFM-Arctic simulations were extended to the full Arctic-boreal domain at a 1-km spatial resolution using land cover maps representing high latitude vegetation communities. The data are provided in NetCDF and comma-separated values (CSV) formats.", "links": [ { diff --git a/datasets/ABoVE_Concise_Experiment_Plan_1617_1.1.json b/datasets/ABoVE_Concise_Experiment_Plan_1617_1.1.json index 33d0347fe4..1f42ea492c 100644 --- a/datasets/ABoVE_Concise_Experiment_Plan_1617_1.1.json +++ b/datasets/ABoVE_Concise_Experiment_Plan_1617_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Concise_Experiment_Plan_1617_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them.", "links": [ { diff --git a/datasets/ABoVE_Domain_Projected_LULC_2353_1.json b/datasets/ABoVE_Domain_Projected_LULC_2353_1.json index 91228cd78e..866b79e215 100644 --- a/datasets/ABoVE_Domain_Projected_LULC_2353_1.json +++ b/datasets/ABoVE_Domain_Projected_LULC_2353_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Domain_Projected_LULC_2353_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.", "links": [ { diff --git a/datasets/ABoVE_Fire_Severity_dNBR_1564_1.json b/datasets/ABoVE_Fire_Severity_dNBR_1564_1.json index 5a405e9663..e6d872a35b 100644 --- a/datasets/ABoVE_Fire_Severity_dNBR_1564_1.json +++ b/datasets/ABoVE_Fire_Severity_dNBR_1564_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Fire_Severity_dNBR_1564_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs.", "links": [ { diff --git a/datasets/ABoVE_Footprints_WRF_AK_NWCa_1896_1.json b/datasets/ABoVE_Footprints_WRF_AK_NWCa_1896_1.json index fef1137aee..afa3ad1077 100644 --- a/datasets/ABoVE_Footprints_WRF_AK_NWCa_1896_1.json +++ b/datasets/ABoVE_Footprints_WRF_AK_NWCa_1896_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Footprints_WRF_AK_NWCa_1896_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/ABoVE_Forage_Lichen_Maps_1867_1.json b/datasets/ABoVE_Forage_Lichen_Maps_1867_1.json index 99da0e16dc..a9b21c366e 100644 --- a/datasets/ABoVE_Forage_Lichen_Maps_1867_1.json +++ b/datasets/ABoVE_Forage_Lichen_Maps_1867_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Forage_Lichen_Maps_1867_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation.", "links": [ { diff --git a/datasets/ABoVE_ForestDisturbance_Agents_1924_1.json b/datasets/ABoVE_ForestDisturbance_Agents_1924_1.json index c855893f28..575325e60f 100644 --- a/datasets/ABoVE_ForestDisturbance_Agents_1924_1.json +++ b/datasets/ABoVE_ForestDisturbance_Agents_1924_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_ForestDisturbance_Agents_1924_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness. Disturbances were detected by a Continuous Change Detection and Classification (CCDC) harmonic regression model applied to the time series. The dTC indices and disturbance results are provided.", "links": [ { diff --git a/datasets/ABoVE_Frac_Open_Water_1362_1.json b/datasets/ABoVE_Frac_Open_Water_1362_1.json index cfe4255bd3..bf8785d79d 100644 --- a/datasets/ABoVE_Frac_Open_Water_1362_1.json +++ b/datasets/ABoVE_Frac_Open_Water_1362_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Frac_Open_Water_1362_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change.", "links": [ { diff --git a/datasets/ABoVE_GrowingSeason_Lake_Color_1866_1.json b/datasets/ABoVE_GrowingSeason_Lake_Color_1866_1.json index b0d55c49ca..192cc68b13 100644 --- a/datasets/ABoVE_GrowingSeason_Lake_Color_1866_1.json +++ b/datasets/ABoVE_GrowingSeason_Lake_Color_1866_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_GrowingSeason_Lake_Color_1866_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world’s highest concentrations of lakes where climate change may have significant impacts.", "links": [ { diff --git a/datasets/ABoVE_Izaviknek_Field_Data_1772_1.json b/datasets/ABoVE_Izaviknek_Field_Data_1772_1.json index 774aac018e..97860874b7 100644 --- a/datasets/ABoVE_Izaviknek_Field_Data_1772_1.json +++ b/datasets/ABoVE_Izaviknek_Field_Data_1772_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Izaviknek_Field_Data_1772_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls. Vegetation species were sampled along transects using the vegetation point-intercept (VPI) sampling approach and summarized by three metrics of vegetation cover: (1) top-hit cover, (2) any-hit cover, and (3) multi-hit cover. Each metric is the total number of hits for a species divided by the total number of sample points. The VPI any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); bryophytes, herbs, lichen, and shrubs for the upland tundra area. Photographs of vegetation transects and soil pits are included as companion files.", "links": [ { diff --git a/datasets/ABoVE_L1_P_SAR_1800_1.json b/datasets/ABoVE_L1_P_SAR_1800_1.json index 3cb5b51e86..6a91cd8953 100644 --- a/datasets/ABoVE_L1_P_SAR_1800_1.json +++ b/datasets/ABoVE_L1_P_SAR_1800_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_L1_P_SAR_1800_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 1 (L1) polarimetric radar backscattering coefficient (Sigma-0 or S-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the UAVSAR P-band SAR radar instrument collected over 74 study sites across Alaska, USA, and western Canada. The radar instrument is a fully polarimetric P-band (ultra-high frequency) SAR operating in the 420-440 MHz band. The flight campaigns took place periodically in May-August 2017 onboard a NASA Gulfstream-III aircraft. Each set of products was produced from a data take (i.e., acquisition) of the UAVSAR P-band SAR radar instrument, where one data take is equivalent to one flight line over a site. Two to four data takes were sought for each site, although for some sites as few as one or as many as six are provided. There were a total of 139 data takes over the 74 sites.", "links": [ { diff --git a/datasets/ABoVE_LVIS_VegetationStructure_1923_1.json b/datasets/ABoVE_LVIS_VegetationStructure_1923_1.json index 7c4761e8eb..0a8b011d2b 100644 --- a/datasets/ABoVE_LVIS_VegetationStructure_1923_1.json +++ b/datasets/ABoVE_LVIS_VegetationStructure_1923_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_LVIS_VegetationStructure_1923_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 3 (L3) footprint-level gridded metrics and attributes collected from NASA's Land, Vegetation, and Ice Sensor (LVIS)-Facility instrument for each flightline from 2017 and 2019. In 2017, the LVIS-Facility instrument was flown at a nominal flight altitude of 28,000 ft onboard a Dynamic Aviation Super King Air B200T. In 2019, the LVIS-Facility and LVIS-Classic instruments were flown at a nominal flight altitude of 41,000 feet onboard the NASA Gulfstream V. LVIS data are collected as waveforms over footprints of ~10-m diameter. The L3 data include grids of canopy relative height (RH), complexity, canopy cover (CC), ground elevation, and the number of LVIS footprints available to produce a pixel's estimate.. These 30-m resolution grids describe the vertical column of the vegetation canopy in detail with relative canopy height metrics and are enriched with an additional set of canopy cover estimates at a variety of height thresholds. The LVIS-Facility instrument 2017 and 2019 acquisitions span Arctic, boreal, temperate, and sub-tropical landscapes in support of a variety of Arctic-Boreal Vulnerability Experiment (ABoVE)- and Global Ecosystem Dynamics Investigation (GEDI)-related science. In the ABoVE study domain of arctic and boreal Alaska and Western Canada, some of these acquisitions coincide spatially with legacy small-footprint airborne lidar. Data are included for the ABoVE domain and also for the continental U.S. and central America in support of GEDI calibration and validation. Data files are provided in GeoTIFF format and one geopackage file shows flightlines.", "links": [ { diff --git a/datasets/ABoVE_MODIS_MAIAC_Reflectance_1858_1.json b/datasets/ABoVE_MODIS_MAIAC_Reflectance_1858_1.json index c5419db750..2edfde3539 100644 --- a/datasets/ABoVE_MODIS_MAIAC_Reflectance_1858_1.json +++ b/datasets/ABoVE_MODIS_MAIAC_Reflectance_1858_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_MODIS_MAIAC_Reflectance_1858_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects. The original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12) were preserved. The resulting surface reflectance data are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. The data cover 11 different Terra and Aqua satellite MODIS MAIAC tiles.", "links": [ { diff --git a/datasets/ABoVE_NWT_2017_Field_Data_1771_1.json b/datasets/ABoVE_NWT_2017_Field_Data_1771_1.json index e5166a6c9a..33dca07d30 100644 --- a/datasets/ABoVE_NWT_2017_Field_Data_1771_1.json +++ b/datasets/ABoVE_NWT_2017_Field_Data_1771_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_NWT_2017_Field_Data_1771_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites.", "links": [ { diff --git a/datasets/ABoVE_Open_Water_Map_1643_1.json b/datasets/ABoVE_Open_Water_Map_1643_1.json index b901dae1c1..22b5e03195 100644 --- a/datasets/ABoVE_Open_Water_Map_1643_1.json +++ b/datasets/ABoVE_Open_Water_Map_1643_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Open_Water_Map_1643_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region.", "links": [ { diff --git a/datasets/ABoVE_PBand_SAR_1657_1.json b/datasets/ABoVE_PBand_SAR_1657_1.json index bbf151cdaf..03f237e4d0 100644 --- a/datasets/ABoVE_PBand_SAR_1657_1.json +++ b/datasets/ABoVE_PBand_SAR_1657_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_PBand_SAR_1657_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter.", "links": [ { diff --git a/datasets/ABoVE_Particles_WRF_AK_NWCa_1895_1.json b/datasets/ABoVE_Particles_WRF_AK_NWCa_1895_1.json index bf48c2263e..5d71aaff2d 100644 --- a/datasets/ABoVE_Particles_WRF_AK_NWCa_1895_1.json +++ b/datasets/ABoVE_Particles_WRF_AK_NWCa_1895_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Particles_WRF_AK_NWCa_1895_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset.", "links": [ { diff --git a/datasets/ABoVE_Planning_Field_Sites_1582_1.json b/datasets/ABoVE_Planning_Field_Sites_1582_1.json index 216d41756e..b5f4c7721f 100644 --- a/datasets/ABoVE_Planning_Field_Sites_1582_1.json +++ b/datasets/ABoVE_Planning_Field_Sites_1582_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Planning_Field_Sites_1582_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions.", "links": [ { diff --git a/datasets/ABoVE_Plot_Data_Burned_Sites_1744_1.json b/datasets/ABoVE_Plot_Data_Burned_Sites_1744_1.json index 7a175f7da2..5af9b06387 100644 --- a/datasets/ABoVE_Plot_Data_Burned_Sites_1744_1.json +++ b/datasets/ABoVE_Plot_Data_Burned_Sites_1744_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Plot_Data_Burned_Sites_1744_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file.", "links": [ { diff --git a/datasets/ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3.json b/datasets/ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3.json index 8e888e7891..e89fce01bd 100644 --- a/datasets/ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3.json +++ b/datasets/ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data.", "links": [ { diff --git a/datasets/ABoVE_SAR_Surveys_2150_1.json b/datasets/ABoVE_SAR_Surveys_2150_1.json index d644b7f740..475ed3467e 100644 --- a/datasets/ABoVE_SAR_Surveys_2150_1.json +++ b/datasets/ABoVE_SAR_Surveys_2150_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_SAR_Surveys_2150_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. Legacy lines acquired between 2012 and 2015 by other projects are included for completeness and to enable longer times series creation. The data files and companion file contain L-band and P-band airborne SAR metadata acquired during the ABoVE airborne campaigns. Included are detailed descriptions of ~80 SAR flight lines and how each fits into the ABoVE experimental design. Extensive maps, tables, and hyperlinks give direct access to every flight plan as well as individual flight lines. This entry is a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.", "links": [ { diff --git a/datasets/ABoVE_SnowModel_Data_2105_1.json b/datasets/ABoVE_SnowModel_Data_2105_1.json index 353cafd08b..2fa02b315e 100644 --- a/datasets/ABoVE_SnowModel_Data_2105_1.json +++ b/datasets/ABoVE_SnowModel_Data_2105_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_SnowModel_Data_2105_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily SnowModel simulation outputs on a 3-km grid for the period 1 September 1980 through 31 August 2020, covering the Core ABoVE Domain. The daily outputs include: air temperature (deg C), relative humidity (%), wind speed (m/s), wind direction (deg from True North), total precipitation (rain+snow) (m), rainfall (m), snowfall (m), snow melt (m), snow sublimation (m), runoff (m), surface temperature (deg C), bulk snowpack thermal resistance (K/W), snow depth (m), snow density (kg/m3), and snow-water-equivalent (SWE) depth (m). Model data inputs included land cover and topography, ground-based observations of snow, remote sensing observations of snow from satellites and aircraft, and meteorological forcing data from weather stations and reanalysis data. The SnowModel includes the processing modules MicroMet, Enbal, SnowDunes, SnowAssin, SnowPack, and SnowTran-3D. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/ABoVE_Soil_Radiocarbon_NWT_1664_1.json b/datasets/ABoVE_Soil_Radiocarbon_NWT_1664_1.json index 0b7e7c453b..0115158a8b 100644 --- a/datasets/ABoVE_Soil_Radiocarbon_NWT_1664_1.json +++ b/datasets/ABoVE_Soil_Radiocarbon_NWT_1664_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Soil_Radiocarbon_NWT_1664_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of \"young\" stands (less than 60 years old at time of fire) was estimated.", "links": [ { diff --git a/datasets/ABoVE_Soil_Respiration_Maps_1935_1.json b/datasets/ABoVE_Soil_Respiration_Maps_1935_1.json index 5246c79add..bc8d28f522 100644 --- a/datasets/ABoVE_Soil_Respiration_Maps_1935_1.json +++ b/datasets/ABoVE_Soil_Respiration_Maps_1935_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Soil_Respiration_Maps_1935_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.", "links": [ { diff --git a/datasets/ABoVE_Soil_ThawDepth_Moisture_1903_1.json b/datasets/ABoVE_Soil_ThawDepth_Moisture_1903_1.json index a0044a5fde..77cf3fe059 100644 --- a/datasets/ABoVE_Soil_ThawDepth_Moisture_1903_1.json +++ b/datasets/ABoVE_Soil_ThawDepth_Moisture_1903_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Soil_ThawDepth_Moisture_1903_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15.", "links": [ { diff --git a/datasets/ABoVE_Thaw_Depth_1579_1.0.json b/datasets/ABoVE_Thaw_Depth_1579_1.0.json index 139a60ee00..b0089ecb9a 100644 --- a/datasets/ABoVE_Thaw_Depth_1579_1.0.json +++ b/datasets/ABoVE_Thaw_Depth_1579_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Thaw_Depth_1579_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites. At each site, three 30-meter transects were established and thaw depth was measured at 1-meter increments along each transect using a 1.15 m T-shaped thaw depth probe. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/ABoVE_Uncertainty_Maps_1652_1.json b/datasets/ABoVE_Uncertainty_Maps_1652_1.json index 4d0f1ef42c..7722482302 100644 --- a/datasets/ABoVE_Uncertainty_Maps_1652_1.json +++ b/datasets/ABoVE_Uncertainty_Maps_1652_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_Uncertainty_Maps_1652_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e. standard deviation, from the Trends in Net Land Atmosphere Carbon Exchanges program (TRENDY) and the North American Carbon Program (NACP) regional synthesis model outputs averaged to annual means. This total uncertainty integrates both structural uncertainty of land-surface physics among models as well as inherent parametric uncertainty introduced within models, and uncertainty from forcing data.", "links": [ { diff --git a/datasets/ABoVE_reference_grid_v2_1527_2.1.json b/datasets/ABoVE_reference_grid_v2_1527_2.1.json index a766bcba95..122f5ca78b 100644 --- a/datasets/ABoVE_reference_grid_v2_1527_2.1.json +++ b/datasets/ABoVE_reference_grid_v2_1527_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ABoVE_reference_grid_v2_1527_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection.", "links": [ { diff --git a/datasets/ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1.json b/datasets/ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1.json index ac8bcd5979..57e9105ade 100644 --- a/datasets/ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1.json +++ b/datasets/ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data is the cloud and aerosol remote sensing data from the Roscoe lidar collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1.json b/datasets/ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1.json index b2adc63451..75a144d81f 100644 --- a/datasets/ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1.json +++ b/datasets/ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_Aerosol_AircraftInSitu_WB57_Data is the in-situ aerosol data collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Particle Analysis by Laser Mass Spectrometry - Next Generation (PALMS-NG), Single Particle Soot Photometer (SP2), Nucleation-Mode Aerosol Size Spectrometer (N-MASS), Printed Optical Particle Spectrometer (POPS), and the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) is featured in this collection. Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_AircraftInSitu_WB57_Water_Data_1.json b/datasets/ACCLIP_AircraftInSitu_WB57_Water_Data_1.json index 88e69c4657..a4f581453e 100644 --- a/datasets/ACCLIP_AircraftInSitu_WB57_Water_Data_1.json +++ b/datasets/ACCLIP_AircraftInSitu_WB57_Water_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_AircraftInSitu_WB57_Water_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_AircraftInSitu_WB57_Water_Data is the in-situ water data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Chicago Water Isotope Spectrometer\r\n(ChiWIS) is featured in this collection. Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_Cloud_AircraftInSitu_WB57_Data_1.json b/datasets/ACCLIP_Cloud_AircraftInSitu_WB57_Data_1.json index 2304cec29d..fbf3997e0e 100644 --- a/datasets/ACCLIP_Cloud_AircraftInSitu_WB57_Data_1.json +++ b/datasets/ACCLIP_Cloud_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_Cloud_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_Cloud_AircraftInSitu_WB57_Data is the in-situ cloud data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Cloud, Aerosol, and Precipitation Spectrometer (CAPS) is featured in this collection. Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_Merge_WB57-Aircraft_Data_1.json b/datasets/ACCLIP_Merge_WB57-Aircraft_Data_1.json index 199d84dbab..cdd2978e50 100644 --- a/datasets/ACCLIP_Merge_WB57-Aircraft_Data_1.json +++ b/datasets/ACCLIP_Merge_WB57-Aircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_Merge_WB57-Aircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_Merge_WB57-Aircraft_Data is the pre-generated merge files created from a variety of in-situ instrumentation collecting measurements onboard the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_MetNav_AircraftInSitu_WB57_Data_1.json b/datasets/ACCLIP_MetNav_AircraftInSitu_WB57_Data_1.json index 0a07d99ec0..a8f5790aca 100644 --- a/datasets/ACCLIP_MetNav_AircraftInSitu_WB57_Data_1.json +++ b/datasets/ACCLIP_MetNav_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_MetNav_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_MetNav_AircraftInSitu_WB57_Data is the in-situ meteorology and navigational data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Meteorological Measurement System (MMS) and Diode Laser Hygrometer (DLH) is featured in this collection. Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_Model_WB57_Data_1.json b/datasets/ACCLIP_Model_WB57_Data_1.json index a126af76d8..228d8bc3d7 100644 --- a/datasets/ACCLIP_Model_WB57_Data_1.json +++ b/datasets/ACCLIP_Model_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_Model_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_Model_WB57_Data contains modeled meteorological, chemical, and aerosol data along the flight tracks of the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1.json b/datasets/ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1.json index 6d9657b66e..641eb62bde 100644 --- a/datasets/ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1.json +++ b/datasets/ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACCLIP_TraceGas_AircraftInSitu_WB57_Data is the in-situ trace gas data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Airborne Carbon Oxide Sulfide Spectrometer (ACOS), Carbon monOxide Measurement from Ames (COMA), Laser Induced Fluorescence - Nitrogen Oxide (LIF-NO), In Situ Airborne Formaldehyde (ISAF), Carbon Oxide Laser Detector 2 (COLD 2), and the NOAA UAS O3 Photometer (UASO3) is featured in this collection. Data collection for this product is complete.\r\n\r\nACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.\r\n\r\nThe ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.", "links": [ { diff --git a/datasets/ACE-ASIA_0.json b/datasets/ACE-ASIA_0.json index 257a6f11e7..8a5a3172a5 100644 --- a/datasets/ACE-ASIA_0.json +++ b/datasets/ACE-ASIA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE-ASIA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Asia in the East China Sea, Sea of Japan, and Pacific Ocean.", "links": [ { diff --git a/datasets/ACE-INC_0.json b/datasets/ACE-INC_0.json index 95632434d0..7b7c7e1dce 100644 --- a/datasets/ACE-INC_0.json +++ b/datasets/ACE-INC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE-INC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made as a part of the Aerosol Characterization Experiment (ACE) for the in-land Chesapeake Bay region between 2002 and 2003.", "links": [ { diff --git a/datasets/ACEPOL_AircraftRemoteSensing_AirHARP_Data_1.json b/datasets/ACEPOL_AircraftRemoteSensing_AirHARP_Data_1.json index 1214c87f2b..37803066ae 100644 --- a/datasets/ACEPOL_AircraftRemoteSensing_AirHARP_Data_1.json +++ b/datasets/ACEPOL_AircraftRemoteSensing_AirHARP_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_AircraftRemoteSensing_AirHARP_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL Airborne Hyper Angular Rainbow Polarimeter (AirHARP) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_AirHARP_Data) are remotely sensed measurements collected by the Airborne Hyper Angular Rainbow Polarimeter (AirHARP) onboard the ER-2 during ACEPOL. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which is a valuable resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACEPOL_AircraftRemoteSensing_AirSPEX_Data_1.json b/datasets/ACEPOL_AircraftRemoteSensing_AirSPEX_Data_1.json index 0aebfaa6d6..7993ebcb87 100644 --- a/datasets/ACEPOL_AircraftRemoteSensing_AirSPEX_Data_1.json +++ b/datasets/ACEPOL_AircraftRemoteSensing_AirSPEX_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_AircraftRemoteSensing_AirSPEX_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL_AircraftRemoteSensing_AirSPEX_Data are remotely sensed measurements collected by the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACEPOL_AircraftRemoteSensing_CPL_Data_1.json b/datasets/ACEPOL_AircraftRemoteSensing_CPL_Data_1.json index 8bd805670e..1c75aa4830 100644 --- a/datasets/ACEPOL_AircraftRemoteSensing_CPL_Data_1.json +++ b/datasets/ACEPOL_AircraftRemoteSensing_CPL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_AircraftRemoteSensing_CPL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL Cloud Physics Lidar (CPL) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_CPL_Data) are remotely sensed measurements collected by the Cloud Physics Lidar (CPL) onboard the ER-2 during ACEPOL. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACEPOL_AircraftRemoteSensing_HSRL2_Data_1.json b/datasets/ACEPOL_AircraftRemoteSensing_HSRL2_Data_1.json index cee3cdd97b..a766500fb6 100644 --- a/datasets/ACEPOL_AircraftRemoteSensing_HSRL2_Data_1.json +++ b/datasets/ACEPOL_AircraftRemoteSensing_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_AircraftRemoteSensing_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL High Spectral Resolution Lidar 2 (HSRL-2) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_HSRL2_Data) are remotely sensed measurements collected by the High-Spectral Resolution Lidar (HSRL-2) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACEPOL_AircraftRemoteSensing_RSP_Data_1.json b/datasets/ACEPOL_AircraftRemoteSensing_RSP_Data_1.json index 32228de9d8..f07e46e3e6 100644 --- a/datasets/ACEPOL_AircraftRemoteSensing_RSP_Data_1.json +++ b/datasets/ACEPOL_AircraftRemoteSensing_RSP_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_AircraftRemoteSensing_RSP_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL Research Scanning Polarimeter (RSP) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_RSP_Data) are remotely sensed measurements collected by the Research Scanning Polarimeter (RSP) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which is a valuable resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACEPOL_MetNav_AircraftInSitu_Data_1.json b/datasets/ACEPOL_MetNav_AircraftInSitu_Data_1.json index 0ea99053cf..5fa4a0f964 100644 --- a/datasets/ACEPOL_MetNav_AircraftInSitu_Data_1.json +++ b/datasets/ACEPOL_MetNav_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACEPOL_MetNav_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACEPOL_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA\u2019s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.", "links": [ { diff --git a/datasets/ACE_0.json b/datasets/ACE_0.json index fcbd4262f1..c807c3a118 100644 --- a/datasets/ACE_0.json +++ b/datasets/ACE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Spain, Portugal, and Northern Africa in the Atlantic Ocean.", "links": [ { diff --git a/datasets/ACE_AME_Bibliography_1.json b/datasets/ACE_AME_Bibliography_1.json index 269127a985..f23a5f4a90 100644 --- a/datasets/ACE_AME_Bibliography_1.json +++ b/datasets/ACE_AME_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_AME_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This bibliography is a selected list of scientific papers collected by scientists in the ACE-CRC's Antarctic Marine Ecosystem research programme.", "links": [ { diff --git a/datasets/ACE_EPAM_LEVEL2.json b/datasets/ACE_EPAM_LEVEL2.json index 26c569fa2a..b06a82bdd9 100644 --- a/datasets/ACE_EPAM_LEVEL2.json +++ b/datasets/ACE_EPAM_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_EPAM_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Electron, Proton, and Alpha Monitor (EPAM) is composed of five\n telescope apertures of three different types. Two Low Energy Foil\n Spectrometers (LEFS) measure the flux and direction of electrons above\n 30 keV (geometry factor = 0.397 cm2*sr), two Low Energy Magnetic\n Spectrometers (LEMS) measure the flux and direction of ions greater\n than 50 keV (geometry factor = 0.48 cm2*sr), and the Composition\n Aperture (CA) measures the elemental composition of the ions (geometry\n factor = 0.24 cm2*sr). The telescopes use the spin of the spacecraft\n to sweep the full sky. Solid-state detectors are used to measure the\n energy and composition of the incoming particles.\n \n EPAM Level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the Level 2 data contains time averages of energetic charged\n particle fluxes over the following time periods:\n \n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n \n The DE30 detector, (Deflected Electrons), measures electrons at 30\n degrees from the spacecraft spin axis. Electrons entering the LEMS30\n detector are swept out by a rare-earth magnet and are deflected into\n the B detector. The 4 DE channels are pure electron channels. The\n geometrical factor for the DE30 channels is 0.14 (cm2*sr). \n \n The CA60 telescope, (Composition Aperture) measures ion\n composition. It's look-direction is oriented 60 degrees from the\n spacecraft spin-axis.\n \n The CA telescope is capable of determining ion composition using a dE\n X E detection scheme. Although the principal responsibility of EPAM is\n to monitor electrons, protons, and alphas, the CA provides an\n unambiguous determination of ion composition, unlike the LEMS\n detectors. The CA60 telescope is comprised of three solid state\n detectors, a thin, ~5 micron epitaxial silicon detector referred to as\n the D detector, and two thick (200 micron) totally depleted surface\n barrier silicon detectors known as C and B. The B detector, as\n measures deflected electrons from the LEMS30 head, but also acts as\n the anti-coincidence detector for the CA.\n \n The CA system uses log amplifiers to extend the dynamic range of the\n detector. These amplifiers are extremely temperature sensitive, and\n therefore are thermally regulated with heaters to maintain\n calibration. The logic used in the CA depends on slanted\n discriminators to define each species group. The eight Ca rate\n channels, denoted by the symbols W1 - W8, count all particles in a\n given energy/nucleon range. Multiple species may therefore be\n associated with a single Ca rate channel. As a result, a species group\n is identified by the dominant species in that group.\n \n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/epam_l2desc.html", "links": [ { diff --git a/datasets/ACE_LEVEL2.json b/datasets/ACE_LEVEL2.json index 150fcadc86..c852872c8f 100644 --- a/datasets/ACE_LEVEL2.json +++ b/datasets/ACE_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cosmic Ray Isotope Spectrometer (CRIS) on the Advanced Composition Explorer\n (ACE) spacecraft is intended to be a major step in ascertaining the isotopic\n composition of the Galactic Cosmic Rays and hence a major step in determining\n their origin. The GCRs (Galactic Cosmic Rays) consist, by number, primarily of\n hydrogen nuclei (~92%) and He nuclei (~7%). The heavier nuclei (1%) provide\n most of the information about cosmic-ray origin through their elemental and\n isotopic composition. The intensities of these heavy cosmic rays are very low\n and progress in the past has been impeded by limited particle collection power,\n particularly regarding individual isotopes. CRIS is designed to have far\n greater collection power (~250 cm2*sr) than previous satellite instruments (<\n 10 cm2*sr) while still maintaining excellent isotopic resolution through Z=30\n (Zinc) and beyond.\n \n CRIS level 2 data is organized into 27-day time periods (Bartels Rotations -\n roughly one solar rotation period). For each Bartels Rotation, the level 2 data\n contains time averages of energetic charged particle fluxes over the following\n time periods:\n \n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n Currently, flux data are available for 24 elements, in units of\n particles/(cm2*sr*sec*Mev/nucleon), in seven energy ranges. The energy ranges\n are different for each element. The elements for which data are available are:\n \n - B, C, N, O, F, Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti, V, Cr,\n Mn, Fe, Co, Ni\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/cris_l2desc.html", "links": [ { diff --git a/datasets/ACE_MAG_LEVEL2.json b/datasets/ACE_MAG_LEVEL2.json index ec81d1f7ce..4d2381a5a3 100644 --- a/datasets/ACE_MAG_LEVEL2.json +++ b/datasets/ACE_MAG_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_MAG_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Magnetic Field Experiment (MAG) consists of twin vector fluxgate\n magnetometers controlled by a common CPU. The sensors are mounted on\n booms extending 4.19 meters from the center of the spacecraft at\n opposite sides along the +/-Y axes of the spacecraft. The instrument\n returns 6 magnetic field vector measurements each second, divided\n between the two sensors, with onboard snapshot and FFT buffers to\n enhance the high-frequency resolution.\n \n MAG level 2 data is organized into 27 day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of the magnetic\n field data over the following time periods:\n \n - 16 seconds\n - 4 minutes\n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/mag_l2desc.html", "links": [ { diff --git a/datasets/ACE_PARTCLE_FLUXES.json b/datasets/ACE_PARTCLE_FLUXES.json index a1a4bdc082..7fc7d061e0 100644 --- a/datasets/ACE_PARTCLE_FLUXES.json +++ b/datasets/ACE_PARTCLE_FLUXES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_PARTCLE_FLUXES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Composition Explorer (ACE) is an Explorer mission that is\n being managed by the Office of Space Science Mission and Payload\n Development Division of the National Aeronautics and Space\n Administration (NASA). The primary purpose of ACE is to determine and\n compare the isotopic and elemental composition of several distinct\n samples of matter, including the solar corona, the interplanetary\n medium, the local interstellar medium, and Galactic matter.\n \n The ACE spacecraft measures the flux of charged particles from solar\n wind energies (300 km/sec) up through galactic cosmic rays (500\n MeV/nucleon) and the interplanetary magnetic field upstream of\n earth. The ACE data includes energetic particles from solar wind\n cosmic ray energies. In addition, this data set covers both atomic\n and isotopic composition data for most energy ranges. This pace data\n is at L1 (approx. 1.5 million km upstream along earth-sun line).\n \n ACE browse data are designed for monitoring large scale particle and field\n behavior and for selecting interesting time periods. The data are automatically\n generated from the spacecraft data stream using simple algorithms provided by\n the instrument investigators. They are not routinely checked for accuracy and\n are subject to revision. Use these data at your own risk, and consult with the\n appropriate instrument investigators about citing them.\n \n Browse parameters are a subset of measurements by the ACE instruments which are\n created at the Science Center during level one processing. They are delivered\n to the public domain as soon as possible. Their purpose is to allow monitoring\n of the solar wind and large-scale particle and magnetic field behavior, and\n selection of interesting time periods for more intensive study. Interesting\n time periods might include solar energetic particle events, or the passage of\n an interplanetary shock. An additional use of the browse parameters is to\n investigate relationships between the data from the various ACE instruments,\n and between ACE data and data from other sources.\n \n The browse parameters include unsectored fluxes of ions at many different\n energies and electrons at a few energies. They also include the interplanetary\n magnetic field, and solar wind parameters such as proton speed and temperature.\n They therefore furnish a very abbreviated description of what is being observed\n by the ACE instruments, without the relatively high cost of storing and\n analyzing all the level one data. Eventually they may be supplemented with\n event data from the particle detectors, but experience with the flight data is\n a prerequisite for delivering useful products of that type.\n \n See: http://www.srl.caltech.edu/ACE/ASC/browse/browse_info.html\n for more information.", "links": [ { diff --git a/datasets/ACE_SEPICA_LEVEL2.json b/datasets/ACE_SEPICA_LEVEL2.json index ba8af8d181..ef196aaaac 100644 --- a/datasets/ACE_SEPICA_LEVEL2.json +++ b/datasets/ACE_SEPICA_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_SEPICA_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Energetic Particle Charge Analyser (SEPICA) is used to\n determine the charge state distribution of energetic particle\n distributions. SEPICA is designed to measure the ionic charge state,\n Q, the kinetic energy, E, and the nuclear charge, Z, of energetic ions\n above 0.2 MeV/Nuc. This includes ions accelerated in solar flares as\n well as in interplanetary space during energetic storm particle (ESP)\n and co-rotating interaction region (CIR) events. For low mass numbers\n SEPICA will also separate isotopes -- for example, 3He and 4He. During\n solar quiet times, SEPICA should also be able to directly measure the\n charge states of anomalous cosmic ray nuclei, including H, N, O, and\n Ne, which are presumed to be singly-charged. With the capability to\n differentiate the charge states of ions, the instrument will also be\n able to separate neutral atoms (Q = 0) from ions. Thus it may be able\n to identify energetic neutrals created through charge exchange.\n \n SEPICA level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of solar energetic\n particle fluxes over the following time periods:\n \n - 120-second (H and He only)\n - hourly\n - (all elements) daily\n - (all elements) 27 days (1 Bartels rotation) (all elements)\n \n Currently, spin-averaged flux data are available for 8 elements, in\n units of particles/(cm2*Sr*sec*MeV/nucleon), in a number of energy\n ranges. The energy ranges are different for each element. The elements\n for which data are available are:\n \n - H, He, C, O, Ne, Mg, Si and Fe.\n \n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/sepica_l2desc.html", "links": [ { diff --git a/datasets/ACE_SIS_LEVEL2.json b/datasets/ACE_SIS_LEVEL2.json index f02efe3cea..f30e31d573 100644 --- a/datasets/ACE_SIS_LEVEL2.json +++ b/datasets/ACE_SIS_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_SIS_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Isotope Spectrometer (SIS) is designed to provide high\n resolution measurements of the isotopic composition of energetic\n nuclei from He to Ni (Z=2 to 28) over the energy range from ~10 to\n ~100 MeV/nucleon. During large solar events, when particle fluxes can\n increase over quiet-time values by factors of up to 10000, SIS\n measures the isotopic composition of the solar corona, while during\n solar quiet times SIS measures the isotopes of low-energy Galactic\n cosmic rays and the composition of the anomalous cosmic rays which are\n thought to originate in the nearby interstellar medium. The solar\n energetic particle measurements are useful to further our\n understanding of the Sun, while also providing a baseline for\n comparison with the Galactic cosmic ray measurements carried out by\n CRIS.\n \n SIS has a geometry factor of ~40 cm2--sr, which is significantly\n larger than previous satellite solar particle isotope\n spectrometers. It is also designed to provide excellent mass\n resolution during the extremely high particle flux conditions which\n occur during large solar particle events. \n \n SIS level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of energetic charged\n particle fluxes over the following time periods:\n \n - 256 seconds\n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n Currently, flux data are available for 8 elements, in units of\n particles/(cm2 Sr sec MeV/nucleon), in eight energy ranges. The energy\n ranges are different for each element. The elements for which data are\n available are:\n \n - He, C, N, O, Ne, Mg, Si, S, and Fe.\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/sis_l2desc.html", "links": [ { diff --git a/datasets/ACE_SWEPAM_LEVEL2.json b/datasets/ACE_SWEPAM_LEVEL2.json index 0d89052876..e299bbe644 100644 --- a/datasets/ACE_SWEPAM_LEVEL2.json +++ b/datasets/ACE_SWEPAM_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_SWEPAM_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) measures\n the solar wind plasma electron and ion fluxes (rates of particle flow)\n as functions of direction and energy. These data provide detailed\n knowledge of the solar wind conditions and internal state every\n minute. SWEPAM also provides real-time solar wind observations which\n are continuously telemetered to the ground for space weather purposes.\n \n Electron and ion measurements are made with separate sensors. The ion\n sensor measures particle energies between about 0.26 and 36 KeV, and\n the electron sensor's energy range is between 1 and 1350 eV. Both\n sensors use electrostatic analyzers with fan-shaped\n fields-of-view. The electrostatic analyzers measure the energy per\n charge of each particle by bending their flight paths through the\n system. The fields-of-view are swept across all solar wind directions\n by the rotation of the spacecraft.\n \n WEPAM level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of solar wind\n parameters over the following time periods:\n \n - 64 seconds (ion data only)\n - 128 seconds (electron data only)\n - hourly\n - (all data) daily\n - (all data) 27 days (1 Bartels rotation) (all data)\n \n SWEPAM level 2 data consists of the following data items:\n \n - Ion data\n o Proton Density (np in cm -3)\n o Radial Component of the Proton Temperature (TP,rr in o Kelvin)\n o Ratio of Alpha Density to proton Density (nHe/nP)\n o Proton Speed (VP in km/s)\n o Proton Velocity Vector in GSE coordinates (in km/s)\n o Proton Velocity Vector in RTN coordinates (in km/s)\n o Proton Velocity Vector in GSM coordinates (in km/s)\n \n - Electron data\n o Electron Temperature (in o Kelvin) (not yet available)\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/swepam_l2desc.html", "links": [ { diff --git a/datasets/ACE_SWICS_SWIMS_LEVEL2.json b/datasets/ACE_SWICS_SWIMS_LEVEL2.json index d1a6ef6b48..b7f60d6993 100644 --- a/datasets/ACE_SWICS_SWIMS_LEVEL2.json +++ b/datasets/ACE_SWICS_SWIMS_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_SWICS_SWIMS_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Wind Ion Composition Spectrometer (SWICS) and the Solar Wind\n Ion Mass Spectrometer (SWIMS) on ACE are instruments optimized for\n measurements of the chemical and isotopic composition of solar and\n interstellar matter. SWICS determines uniquely the chemical and\n ionic-charge composition of the solar wind, the temperatures and mean\n speeds of all major solar-wind ions, from H through Fe, at all solar\n wind speeds above 300 km/s (protons) and 170 km/s (Fe+16), and\n resolves H and He isotopes of both solar and interstellar\n sources. SWICS will measure the distribution functions of both the\n interstellar cloud and dust cloud pickup ions up to energies of 100\n keV/e. SWIMS will measure the chemical and isotopic and charge state\n composition of the solar wind for every element between He and\n Ni. Each of the two instruments uses electrostatic analysis followed\n by a time-of-flight and, as required, an energy measurement. The\n observations made with SWICS and SWIMS will make valuable\n contributions to the ISTP objectives by providing information\n regarding the composition and energy distribution of matter entering\n the magnetosphere.\n \n SWICS level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of solar wind\n parameters over the following time periods:\n \n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n SWICS level 2 data consists of the following solar wind data items:\n \n - Bulk and Thermal ion Speeds (km/s) => H+, He+2, O+6, Mg+10, and Fe+11\n \n - Ratio of Elements => 4He+2/O, 20Ne+8/O, 24Mg+10/O, and 56Fe+(7 to 12)/O\n \n - Ratio of Charge States of the Same Element => C+5/C+6, O+7/O+6, and Fe+11/Fe+9\n \n - Isotope ratios => 3He/4He, 22Ne/20Ne, 24Mg/26Mg\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/swics_swims_l2desc.html", "links": [ { diff --git a/datasets/ACE_ULEIS_LEVEL2.json b/datasets/ACE_ULEIS_LEVEL2.json index f6f3e5bc91..14ca5fbc05 100644 --- a/datasets/ACE_ULEIS_LEVEL2.json +++ b/datasets/ACE_ULEIS_LEVEL2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACE_ULEIS_LEVEL2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ultra Low Energy Isotope Spectrometer (ULEIS) measures ion fluxes\n over the charge range from H through Ni from about 20 keV/nucleon to\n 10 MeV/nucleon, thus covering both suprathermal and energetic particle\n energy ranges. Exploratory measurements of ultra-heavy species (mass\n range above Ni) will also be performed in a more limited energy range\n near 0.5 MeV/nucleon. ULEIS will be studying the elemental and\n isotopic composition of solar energetic particles, and the mechanisms\n by which these particles are energized in the solar corona. ULEIS will\n also investigate mechanisms by which supersonic interplanetary shock\n waves energize ions.\n \n ULEIS level 2 data is organized into 27-day time periods (Bartels\n Rotations - roughly one solar rotation period). For each Bartels\n Rotation, the level 2 data contains time averages of energetic charged\n particle fluxes over the following time periods:\n \n - hourly\n - daily\n - 27 days (1 Bartels rotation)\n \n Currently, flux data are available for 7 species, in several energy\n intervals for each species. Flux data are in units of particles/(cm2\n Sr sec MeV/nucleon).\n \n The species for which data are available are:\n \n - H, 3He, 4He, C, O, Ne-S and Fe.\n \n See:\n http://www.srl.caltech.edu/ACE/ASC/level2/uleis_l2desc.html", "links": [ { diff --git a/datasets/ACIDD_0.json b/datasets/ACIDD_0.json index 138da21d87..c67152ecbe 100644 --- a/datasets/ACIDD_0.json +++ b/datasets/ACIDD_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACIDD_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACIDD (Across the Channel Investigating Diel Dynamics) project, in the Santa Barbara Channel, was initially designed to characterize daily variations in phytoplankton populations, but with the Thomas Fire in the Santa Barbara Hills December 2017, this project evolved into a study to characterize the effects of smoke and ash on the mixed layer in the Santa Barbara Channel.", "links": [ { diff --git a/datasets/ACIDRAINSENDAI.json b/datasets/ACIDRAINSENDAI.json index 33b074a6f6..52e2ae7c4b 100644 --- a/datasets/ACIDRAINSENDAI.json +++ b/datasets/ACIDRAINSENDAI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACIDRAINSENDAI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The pH, EC and 8 chemical compositions (e.g. NO3, SO4, NH4, Ca etc..)\nin acid rain were surveyed from 1975 through 1991 in Sendai, Japan.\n\nThe input data capacity of latitude and longitude values are limited\nonly to degrees.\n\nThe exact values are as follows:\n Min. Latitude: 38deg.15min. N\n Max. Latitude: 38deg.15min. N\n Min. Longitude: 140deg.52min. E\n Max. Longitude: 140deg.52min. E", "links": [ { diff --git a/datasets/ACOSMonthlyGriddedXCO2_3.json b/datasets/ACOSMonthlyGriddedXCO2_3.json index 0027081ba3..4278058bdf 100644 --- a/datasets/ACOSMonthlyGriddedXCO2_3.json +++ b/datasets/ACOSMonthlyGriddedXCO2_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACOSMonthlyGriddedXCO2_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Greenhouse Gases Observing Satellite (GOSAT) bias corrected data.\n\nThis is the latest version of this collection.", "links": [ { diff --git a/datasets/ACOS_L2S_7.3.json b/datasets/ACOS_L2S_7.3.json index 43bd44ffaf..c646f43677 100644 --- a/datasets/ACOS_L2S_7.3.json +++ b/datasets/ACOS_L2S_7.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACOS_L2S_7.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7.3 is the current version of the data set. Version 3.5 is no longer available and has been superseded by Version 7.3.\n\nThis data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, \"ACOS\" data are still produced and improved, using approaches applied to the OCO-2 spectra.\n\nThe \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3.\n\nThe GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.\n\nEven though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. \n\nElimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. \n\nAn important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag.\n\n\nLastly, users should continue to carefully observe and weigh information from three important flags:\n \n \"warn_level\" - Provides a value that summarizes each sounding's acceptability to a larger set of quality filters. A high warn level predicts that the sounding would fail most data filters applied to it. A low warn level suggests that the sounding would pass most quality filters that might be applied. \n\n \"sounding_qual_flag\" - quality of input data provided to the retrieval processing \n\n \"outcome_flag\" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) \n\n \"master_quality_flag\" - four possible values: \"Good\", \"Caution\" and \"Bad\", and \"Failed\", as determined from other flags in the L2 productThe short name for this data type is ACOS_L2S.", "links": [ { diff --git a/datasets/ACOS_L2S_9r.json b/datasets/ACOS_L2S_9r.json index 9a37678f02..790a5b1f5f 100644 --- a/datasets/ACOS_L2S_9r.json +++ b/datasets/ACOS_L2S_9r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACOS_L2S_9r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r.\n\nThis data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, \"ACOS\" data are still produced and improved, using approaches applied to the OCO-2 spectra.\n\nThe \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3.\n\nThe GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.\n\nEven though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. \n\nElimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. \n\nAn important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag.\n\n\nLastly, users should continue to carefully observe and weigh information from three important flags:\n \n\n \"sounding_qual_flag\" - quality of input data provided to the retrieval processing \n\n \"outcome_flag\" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) \n\n ", "links": [ { diff --git a/datasets/ACOS_L2_Lite_FP_7.3.json b/datasets/ACOS_L2_Lite_FP_7.3.json index 776759a44e..f60c390d10 100644 --- a/datasets/ACOS_L2_Lite_FP_7.3.json +++ b/datasets/ACOS_L2_Lite_FP_7.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACOS_L2_Lite_FP_7.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. \n\nThe \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances.\n\nThe GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.", "links": [ { diff --git a/datasets/ACOS_L2_Lite_FP_9r.json b/datasets/ACOS_L2_Lite_FP_9r.json index d36e5ed91f..eccf2216fd 100644 --- a/datasets/ACOS_L2_Lite_FP_9r.json +++ b/datasets/ACOS_L2_Lite_FP_9r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACOS_L2_Lite_FP_9r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r.\nThe ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. \n\nThe \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances.\n\nThe GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.", "links": [ { diff --git a/datasets/ACR3L2DM_1.json b/datasets/ACR3L2DM_1.json index c9cd563717..fa00d1bc6e 100644 --- a/datasets/ACR3L2DM_1.json +++ b/datasets/ACR3L2DM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACR3L2DM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day.", "links": [ { diff --git a/datasets/ACR3L2SC_1.json b/datasets/ACR3L2SC_1.json index da73f65cc0..a5abb3f40a 100644 --- a/datasets/ACR3L2SC_1.json +++ b/datasets/ACR3L2SC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACR3L2SC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite.", "links": [ { diff --git a/datasets/ACRIMII_TSI_UARS_NAT_1.json b/datasets/ACRIMII_TSI_UARS_NAT_1.json index 920c22f654..9bd3373948 100644 --- a/datasets/ACRIMII_TSI_UARS_NAT_1.json +++ b/datasets/ACRIMII_TSI_UARS_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACRIMII_TSI_UARS_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACRIMII_TSI_UARS_NAT data are Active Cavity Radiometer Irradiance Monitor II (ACRIM II) Total Solar Irradiance (TSI) aboard the Upper Atmosphere Research Satellite (UARS) Data in Native (NAT) format. The ACRIMII_TSI_UARS_NAT data product consists of the Level 2 total solar irradiance in the form of daily means gathered by the ACRIM II instrument on the UARS satellite. The daily means are constructed from the shutter cycle results for each day. This data set is considered Version 2.", "links": [ { diff --git a/datasets/ACTAMERICA-PICARRO_Ground_1568_1.1.json b/datasets/ACTAMERICA-PICARRO_Ground_1568_1.1.json index dbb503a29e..8fb905388b 100644 --- a/datasets/ACTAMERICA-PICARRO_Ground_1568_1.1.json +++ b/datasets/ACTAMERICA-PICARRO_Ground_1568_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA-PICARRO_Ground_1568_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided.", "links": [ { diff --git a/datasets/ACTAMERICA_Hskping_1574_1.1.json b/datasets/ACTAMERICA_Hskping_1574_1.1.json index 172c5875fc..ecf107eece 100644 --- a/datasets/ACTAMERICA_Hskping_1574_1.1.json +++ b/datasets/ACTAMERICA_Hskping_1574_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_Hskping_1574_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides aircraft navigational parameters and related meteorological data (often referred to as \"housekeeping\" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling.", "links": [ { diff --git a/datasets/ACTAMERICA_MFFLL_1649_1.1.json b/datasets/ACTAMERICA_MFFLL_1649_1.1.json index 3002ca08d4..546522fd4a 100644 --- a/datasets/ACTAMERICA_MFFLL_1649_1.1.json +++ b/datasets/ACTAMERICA_MFFLL_1649_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_MFFLL_1649_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred.", "links": [ { diff --git a/datasets/ACTAMERICA_MFLL_L1_1817_1.json b/datasets/ACTAMERICA_MFLL_L1_1817_1.json index ac1e8b2714..23271a7822 100644 --- a/datasets/ACTAMERICA_MFLL_L1_1817_1.json +++ b/datasets/ACTAMERICA_MFLL_L1_1817_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_MFLL_L1_1817_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases.", "links": [ { diff --git a/datasets/ACTAMERICA_Merge_1593_1.2.json b/datasets/ACTAMERICA_Merge_1593_1.2.json index f44cc197fd..3cc98bc3aa 100644 --- a/datasets/ACTAMERICA_Merge_1593_1.2.json +++ b/datasets/ACTAMERICA_Merge_1593_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_Merge_1593_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America.", "links": [ { diff --git a/datasets/ACTAMERICA_PFP_1575_1.2.json b/datasets/ACTAMERICA_PFP_1575_1.2.json index 0f74848e7f..51a0c06e90 100644 --- a/datasets/ACTAMERICA_PFP_1575_1.2.json +++ b/datasets/ACTAMERICA_PFP_1575_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_PFP_1575_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019.", "links": [ { diff --git a/datasets/ACTAMERICA_PICARRO_1556_1.2.json b/datasets/ACTAMERICA_PICARRO_1556_1.2.json index 48fe869167..232679f0e1 100644 --- a/datasets/ACTAMERICA_PICARRO_1556_1.2.json +++ b/datasets/ACTAMERICA_PICARRO_1556_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_PICARRO_1556_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided.", "links": [ { diff --git a/datasets/ACTAMERICA_WRF_Chem_Output_1884_1.json b/datasets/ACTAMERICA_WRF_Chem_Output_1884_1.json index d41f9eee32..a924c060c9 100644 --- a/datasets/ACTAMERICA_WRF_Chem_Output_1884_1.json +++ b/datasets/ACTAMERICA_WRF_Chem_Output_1884_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTAMERICA_WRF_Chem_Output_1884_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6).", "links": [ { diff --git a/datasets/ACTIVATE-FLEXPART_1.json b/datasets/ACTIVATE-FLEXPART_1.json index 9da9e2afc5..730dfbe76a 100644 --- a/datasets/ACTIVATE-FLEXPART_1.json +++ b/datasets/ACTIVATE-FLEXPART_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE-FLEXPART_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE-FLEXPART is the FLEXible PARTicle dispersion model back-trajectories ending at the HU-25 Falcon locations. \r\nACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE-MODIS-MERRA2_1.json b/datasets/ACTIVATE-MODIS-MERRA2_1.json index 0fc4bfdf5d..55737f58ce 100644 --- a/datasets/ACTIVATE-MODIS-MERRA2_1.json +++ b/datasets/ACTIVATE-MODIS-MERRA2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE-MODIS-MERRA2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE-MODIS-MERRA2 is the merged CERES MODIS and MERRA-2 dataset (pixel-level geostationary cloud products) produced by SatCORPS group at NASA Langley Research Center in support of ACTIVATE. \r\nACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE-Satellite_1.json b/datasets/ACTIVATE-Satellite_1.json index 010ba095b7..8927284609 100644 --- a/datasets/ACTIVATE-Satellite_1.json +++ b/datasets/ACTIVATE-Satellite_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE-Satellite_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Satellite_Data_1 is the GOES-16 satellite data supporting the ACTIVATE suborbital campaign.\r\nACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1.json b/datasets/ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1.json index 1e7e273a92..f7f03852da 100644 --- a/datasets/ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1.json +++ b/datasets/ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data is the aerosol and cloud data collected onboard the B-200 King Air aircraft via remote sensing instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.\r\n\r\nMarine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1.json b/datasets/ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1.json index 7ea0c2d734..5218f45468 100644 --- a/datasets/ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1.json +++ b/datasets/ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data is the aerosol data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1.json b/datasets/ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1.json index ba077a6ec0..e1c67139e5 100644 --- a/datasets/ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1.json +++ b/datasets/ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Cloud_AircraftInSitu_Falcon_Data is the cloud data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.\r\n\r\n", "links": [ { diff --git a/datasets/ACTIVATE_Merge_Data_1.json b/datasets/ACTIVATE_Merge_Data_1.json index 2272ee38ff..7defbd4938 100644 --- a/datasets/ACTIVATE_Merge_Data_1.json +++ b/datasets/ACTIVATE_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.\r\n\r\nMarine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1.json b/datasets/ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1.json index 892cd1ec1f..1827615e27 100644 --- a/datasets/ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1.json +++ b/datasets/ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_MetNav_AircraftInSitu_Falcon_Data is the meteorological and navigational data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.\r\n\r\n", "links": [ { diff --git a/datasets/ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1.json b/datasets/ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1.json index 6d43276aa9..7d0eb084d2 100644 --- a/datasets/ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1.json +++ b/datasets/ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_MetNav_AircraftInSitu_KingAir_Data is the meteorological and navigational data collected onboard the B-200 King Air aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.\r\n\r\nMarine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_Miscellaneous_Data_1.json b/datasets/ACTIVATE_Miscellaneous_Data_1.json index 25e4dc2a45..7e3048370f 100644 --- a/datasets/ACTIVATE_Miscellaneous_Data_1.json +++ b/datasets/ACTIVATE_Miscellaneous_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_Miscellaneous_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Miscellaneous_Data is the supplementary miscellaneous data collected and utilized during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_Model_Data_1.json b/datasets/ACTIVATE_Model_Data_1.json index 196f5d68cc..6d0ced8fea 100644 --- a/datasets/ACTIVATE_Model_Data_1.json +++ b/datasets/ACTIVATE_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_Model_Data is the MERRA-2 variables sampled along the HU-25 flight tracks during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1.json b/datasets/ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1.json index ac4dd0943e..a8a23c6a68 100644 --- a/datasets/ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1.json +++ b/datasets/ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data is the trace gas data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth\u2019s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.", "links": [ { diff --git a/datasets/ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1.json b/datasets/ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1.json index 74fca73c45..da22166744 100644 --- a/datasets/ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1.json +++ b/datasets/ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.", "links": [ { diff --git a/datasets/ADAM.Surface.Reflectance.Database_3.0.json b/datasets/ADAM.Surface.Reflectance.Database_3.0.json index 4973857ef9..368fa2e57e 100644 --- a/datasets/ADAM.Surface.Reflectance.Database_3.0.json +++ b/datasets/ADAM.Surface.Reflectance.Database_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADAM.Surface.Reflectance.Database_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1\u00b0 spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005.", "links": [ { diff --git a/datasets/ADBEX_III_density_1.json b/datasets/ADBEX_III_density_1.json index d367e111e8..f984284a4b 100644 --- a/datasets/ADBEX_III_density_1.json +++ b/datasets/ADBEX_III_density_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADBEX_III_density_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the ADBEX III voyage, many samples were taken of the sea ice and snow. These samples were analysed to determine water density, with the results recorded in a physical note book that is archived at the Australian Antarctic Division.\n\nLogbook(s):\n- Glaciology ADBEX III Water Density Results\n- Glaciology ADBEX III Oxygen Isotope Sample Record", "links": [ { diff --git a/datasets/ADBEX_III_ice_floe_1.json b/datasets/ADBEX_III_ice_floe_1.json index df7eb01d04..bbcef7d325 100644 --- a/datasets/ADBEX_III_ice_floe_1.json +++ b/datasets/ADBEX_III_ice_floe_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADBEX_III_ice_floe_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the ADBEX III voyage, a number of core samples, observations and measurements were taken on the ice surrounding the ship. Records of the snow/ice conditions around the \"station\" where each set of observations were made, notes on the cores taken, and several ice temperature readings, were all recorded in log books. Logbooks are archived at the Australian Antarctic Division.\n\nLogbook(s):\nGlaciology ADBEX III Ice Floe Field Notes", "links": [ { diff --git a/datasets/ADBEX_III_oxygen_isotope_1.json b/datasets/ADBEX_III_oxygen_isotope_1.json index 572687b4e2..92c9f37afa 100644 --- a/datasets/ADBEX_III_oxygen_isotope_1.json +++ b/datasets/ADBEX_III_oxygen_isotope_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADBEX_III_oxygen_isotope_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the ADBEX III voyage, 254 samples of sea ice and snow drift on sea ice was collected. Careful notes on the date and location of the samples was kept. The samples were then analysed to determine the level of oxygen isotopes present. The results were noted in log books, archived at the Australian Antarctic Division.\n\nLogbook(s):\n- Glaciology ADBEX III Oxygen Isotope Sample Record\n- Glaciology ADBEX III Oxygen Isotope Results", "links": [ { diff --git a/datasets/ADBEX_III_strain_grid_1.json b/datasets/ADBEX_III_strain_grid_1.json index b23984b9de..11571df03c 100644 --- a/datasets/ADBEX_III_strain_grid_1.json +++ b/datasets/ADBEX_III_strain_grid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADBEX_III_strain_grid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Details of the setup and (re)measurements taken of the strain grid laid out on the sea ice during the ADBEX III voyage of the Nella Dan. The grid was made up of six canes (plus the bridge, used as one of the measurement points).\n\nPhysical log book is archived at the Australian Antarctic Division.\n\nLogbook(s):\nGlaciology ADBEX III Sea Ice Strain Grid Measurements", "links": [ { diff --git a/datasets/ADBEX_I_nutrient_1.json b/datasets/ADBEX_I_nutrient_1.json index a35ad8eecc..8ef76f45c7 100644 --- a/datasets/ADBEX_I_nutrient_1.json +++ b/datasets/ADBEX_I_nutrient_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADBEX_I_nutrient_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract and introduction of ANARE Research Notes 44 - ADBEX I cruise to the Prydz Bay region, 1982: nutrient data.\n\nNitrate, phosphate and silicate concentrations obtained during the ADBEX I cruise to the Prydz Bay region in November and December 1982 are plotted with depth and the raw data are tabulated. Location of the sampling stations and the average concentration of each nutrient in the top 100 m of the water column is mapped.\n\nThe ADBEX I (Antarctic Division BIOMASS Experiment) cruise is part of a long-term, national program of field surveys aimed at fulfilling the objectives of the BIOMASS (Biological Investigation of Marine Antarctic Systems and Stocks) program. The ADBEX I cruise on MV Nella Dan to the Prydz Bay region between 19 November and 17 December 1982, is the second Antarctic Division cruise to contribute to BIOMASS, the first being FIBEX (First International Biomass Experiment) in 1981.\n\nNutrient data were collected at twenty-eight of the seventy-nine hydrographic stations to provide information for the interpretation of phytoplankton distribution and abundance. The sampling locations and depths were not selected, therefore, on the basis of nutrient-related considerations.\n\nThe concentration of nitrate, phosphate and silicate is plotted to 600 m for each station and where casts were much deeper or much shallower, a second plot is shown. To show water column structure at the time of sampling, sigma-t values were also plotted, unless data for a cast were unavailable. In addition to the depth profiles, the average concentration to 100 m of each nutrient species is mapped to give a first-order approximation of the horizontal pattern of nutrient distribution in the upper layers.", "links": [ { diff --git a/datasets/ADCP_5MINUTE_SO.json b/datasets/ADCP_5MINUTE_SO.json index bd1ddfae49..84f716fc4e 100644 --- a/datasets/ADCP_5MINUTE_SO.json +++ b/datasets/ADCP_5MINUTE_SO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADCP_5MINUTE_SO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are\n reported from 7 ship cruises to the Antarctic, March - September 2001\n and 2002. The survey area includes the continental margin off the\n Western Antarctic Peninsula and the adjacent inshore water bodies of\n Marguerite Bay and Crystal Sound. Ancillary north/south sections\n across the Drake Passage are reported for transects from Punta Arenas,\n Chile to the study area and return.\n \n Data reported: five minute ensemble averaged values of the U\n (east-west) and V (north-south) components of ocean currents, for 8\n meter depth bins between 26 and ~350 meters, along the ships track.\n \n Ships/cruises/dates:\n \n AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001\n AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001\n AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001\n RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001\n RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001\n RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002\n RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002\n \n Related data set:\n \n file: ADCP_hourly. Hourly averaged data derived from the 5 minute\n ensemble values are available for each cruise at the above referenced\n web site.", "links": [ { diff --git a/datasets/ADCP_HOURLY_SO.json b/datasets/ADCP_HOURLY_SO.json index 1efdba7fe1..6ad8f5d363 100644 --- a/datasets/ADCP_HOURLY_SO.json +++ b/datasets/ADCP_HOURLY_SO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADCP_HOURLY_SO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are\n reported from 7 cruises to the Antarctic, March - September 2001\n and 2002. The survey area includes the continental margin off the\n Western Antarctic Peninsula and the adjacent inshore water of\n Marguerite Bay and Crystal Sound. Ancillary north/south sections\n across the Drake Passage are reported for transects from Punta Arenas,\n Chile to the study area and return.\n \n Data reported: hourly averaged values of the U (east-west) and V\n (north-south) components of ocean currents, for 8 meter depth bins\n between 26 and ~350 meters, along the ships' tracks.\n \n Ships/cruises/dates:\n \n AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001\n AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001\n AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001\n RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001\n RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001\n RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002\n RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002\n \n Related data set:\n \n file: ADCP_5minute. The original ADCP 5 minute averaged ensemble data\n set for each cruise is found at the above referenced web site.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L1A_NA.json b/datasets/ADEOS-II_AMSR_L1A_NA.json index d8f1151b2c..325c3b00c4 100644 --- a/datasets/ADEOS-II_AMSR_L1A_NA.json +++ b/datasets/ADEOS-II_AMSR_L1A_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L1A_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L1A dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.The Level 1A product is extracted data in range of a half orbit between the South Pole and North Pole from level 0 data and stores the value of observed microwave radiation from the earth surface.This dataset includes digital count value (raw data) with the missing values filled with dummy data. Quality information and Land/Ocean flag are appended. For AMSR/AMSR-E, they correspond to digital numbers (DN) converted from instrument output voltages. Other necessary information for higher-level processing, including satellite attitudes and the instrument condition, is also included. Data are not map-projected, but stored in the swath format. (Not open to public)The provided format is HDF4. The current version of the product is \"Version 3\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L1B_NA.json b/datasets/ADEOS-II_AMSR_L1B_NA.json index b504988344..2f5d98e121 100644 --- a/datasets/ADEOS-II_AMSR_L1B_NA.json +++ b/datasets/ADEOS-II_AMSR_L1B_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L1B_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L1B dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.This dataset includes the brightness temperature converted by the radiometric correction coefficients from observed sensor data of level 1A. It also contains the ancillary data stored in level 1A product. The physical quantity unit is Kelvin.For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format.The provided format is HDF4. The current version of the product is \"Version 3\". The generation unit is scene(defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_AP_NA.json b/datasets/ADEOS-II_AMSR_L2_AP_NA.json index ffc0b65fd6..010afbc851 100644 --- a/datasets/ADEOS-II_AMSR_L2_AP_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_AP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_AP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Amount of Precipitation dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Amount of Precipitation. Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/h.The provided format is HDF4. The current version of the product is \"Version 3\". The generation unit is scene(defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_CLW_NA.json b/datasets/ADEOS-II_AMSR_L2_CLW_NA.json index 39f85977f3..fb6071ea39 100644 --- a/datasets/ADEOS-II_AMSR_L2_CLW_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_CLW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_CLW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Cloud Liquid Water dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_IC_NA.json b/datasets/ADEOS-II_AMSR_L2_IC_NA.json index ad547701c3..9982f624d1 100644 --- a/datasets/ADEOS-II_AMSR_L2_IC_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_IC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_IC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Ice Concentration dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Ice Concentration (IC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene(defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_SM_NA.json b/datasets/ADEOS-II_AMSR_L2_SM_NA.json index 12e64ba48a..44efb4b5da 100644 --- a/datasets/ADEOS-II_AMSR_L2_SM_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_SM_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_SM_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Soil Moisture dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture (SM). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_SST_NA.json b/datasets/ADEOS-II_AMSR_L2_SST_NA.json index 110518100f..84c8b64e53 100644 --- a/datasets/ADEOS-II_AMSR_L2_SST_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Sea Surface Temperature dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene(defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_SSW_NA.json b/datasets/ADEOS-II_AMSR_L2_SSW_NA.json index a5eecdcc26..9e0ebf5f7c 100644 --- a/datasets/ADEOS-II_AMSR_L2_SSW_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_SSW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_SSW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Sea Surface Wind dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_SWE_NA.json b/datasets/ADEOS-II_AMSR_L2_SWE_NA.json index 1cdb371ee8..d6023595a6 100644 --- a/datasets/ADEOS-II_AMSR_L2_SWE_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_SWE_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_SWE_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Snow Water Equivalent dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Snow Water Equivalent (SWE). Compared with non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25 GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwelling radiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L2_WV_NA.json b/datasets/ADEOS-II_AMSR_L2_WV_NA.json index 4797aa615c..5b5aaf0528 100644 --- a/datasets/ADEOS-II_AMSR_L2_WV_NA.json +++ b/datasets/ADEOS-II_AMSR_L2_WV_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L2_WV_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L2 Water Vapor dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. If PWI is out of range of look-up table, the flag 'low accuracy' is added. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA.json index 7dc45b32b2..c03adefa37 100644 --- a/datasets/ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Amount of Precipitation (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is \"Version 3\". The statistical period is 1 day.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA.json index d5a6a44900..4bbd8a7668 100644 --- a/datasets/ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is \"Version 3\". The statistical period is 1 month.The projection method is EQR. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA.json index ed656e96fa..868e224b5a 100644 --- a/datasets/ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA.json index edc32d7e17..0c19b131fc 100644 --- a/datasets/ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA.json index 5146cb94ea..7c7554917f 100644 --- a/datasets/ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA.json index 18c8240d62..35f2b860b8 100644 --- a/datasets/ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Ice Concentration (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA.json index 4c15cec110..7363d72d4e 100644 --- a/datasets/ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Soil Moisture (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographi (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA.json index 45777c635f..61feb7cf29 100644 --- a/datasets/ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Soil Moisture (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular(EQR) or Polar Stereographic(PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA.json index 217931023a..f6446da4a7 100644 --- a/datasets/ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA.json index 109f0124de..1bd0fd4e46 100644 --- a/datasets/ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Sea Surface Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA.json index 61f82536ba..052dc3c8c0 100644 --- a/datasets/ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA.json index e249496d4b..7625709dbc 100644 --- a/datasets/ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA.json index 5b310dbbb6..01cf988f50 100644 --- a/datasets/ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA.json index 0735ba4a86..6dea3faf88 100644 --- a/datasets/ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA.json index a4bfc97001..5f9d2e6891 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA.json index 7ecf69ec91..326e96684d 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA.json index 8b3afcabf4..52ec318c72 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA.json index 1e5570f802..75f6d10c53 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA.json index d794b0d9a1..a98e23c8cf 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA.json index 92e1a27bcf..4b0f618a8d 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA.json index 8975b6bbe8..9b1defb702 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA.json index f9d45a1cf4..fb3ba3deae 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA.json index 60371f5683..e3fddff0d5 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA.json index 29ec14c3c8..07eef9c407 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA.json index 00d3b30523..4befaacbfb 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA.json index 574b62586b..3ea933f0e5 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA.json index 15f7199d3a..daa5877c3d 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA.json index 6830db42e6..8b57bb4df0 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA.json index 91ab7c61ac..a96cf02f91 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA.json index 3aa2a1d3cb..bb94061089 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA.json index 668a000a04..5f5c767f94 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA.json index 3e49037b19..63f6f84221 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA.json index 4b300afd60..31643a91de 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA.json index 1fcd0702f8..40474d3482 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA.json index 691c124d34..c868eb8e0e 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA.json index 2ef966d660..f6373b4ac9 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA.json index 6427bccf8f..70ffb12dd6 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA.json index 53b658357a..7d931b1dfb 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA.json index a8a6acab31..6d09198c10 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA.json index ef19835b40..4a1ddf8e8b 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA.json index be9a348a5b..5b7c7ae05a 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA.json index 7236151cb6..df4efb7f3d 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA.json index e2b702e214..eae7a6bb55 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA.json index 314e4f420b..cefbe1d953 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA.json index bee1f56478..7a1279a23b 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA.json index 1a1df2afb2..31b026f7c2 100644 --- a/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0Hz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA.json index cbb9e8b881..25ea5e06d1 100644 --- a/datasets/ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Water Vapor (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 day.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA.json b/datasets/ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA.json index b1ce63b1ad..d629f38498 100644 --- a/datasets/ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA.json +++ b/datasets/ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/AMSR L3 Water Vapor (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is \"Version 7\". The statistical period is 1 month.The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1A_250m_NA.json b/datasets/ADEOS-II_GLI_L1A_250m_NA.json index 0581099be7..13464af901 100644 --- a/datasets/ADEOS-II_GLI_L1A_250m_NA.json +++ b/datasets/ADEOS-II_GLI_L1A_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1A_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The dataset resolution is 1 km. GLI-250m's uncorrected data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and so forth are attached. The dataset resolution is 250 m. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1A_MTIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1A_MTIR_1km_NA.json index fe1618bbfc..5c7e51b508 100644 --- a/datasets/ADEOS-II_GLI_L1A_MTIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1A_MTIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1A_MTIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1A Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. MTIR data is always acquired. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1A_SWIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1A_SWIR_1km_NA.json index 921378ad64..a2d78d2fa1 100644 --- a/datasets/ADEOS-II_GLI_L1A_SWIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1A_SWIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1A_SWIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1A Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1A_VNIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1A_VNIR_1km_NA.json index aa7ee98ffd..2e4395622f 100644 --- a/datasets/ADEOS-II_GLI_L1A_VNIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1A_VNIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1A_VNIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1A Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1B_250m_NA.json b/datasets/ADEOS-II_GLI_L1B_250m_NA.json index 056ab9320d..2c74cc56e5 100644 --- a/datasets/ADEOS-II_GLI_L1B_250m_NA.json +++ b/datasets/ADEOS-II_GLI_L1B_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1B_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. The dataset resolution is 1 km. GLI-250m's data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 250 m. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1B_MTIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1B_MTIR_1km_NA.json index befcba981b..e2b02374a3 100644 --- a/datasets/ADEOS-II_GLI_L1B_MTIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1B_MTIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1B_MTIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1B Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1B_SLPT_1km_NA.json b/datasets/ADEOS-II_GLI_L1B_SLPT_1km_NA.json index 6de6149d70..de3c324f9e 100644 --- a/datasets/ADEOS-II_GLI_L1B_SLPT_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1B_SLPT_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1B_SLPT_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1B Satellite Position is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product satellite position product includes space craft information needed to calculate satellite position for each channel. The provided format if HDF. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1B_SWIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1B_SWIR_1km_NA.json index fddb4bd876..f0dc8c97e1 100644 --- a/datasets/ADEOS-II_GLI_L1B_SWIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1B_SWIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1B_SWIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1B Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km'SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L1B_VNIR_1km_NA.json b/datasets/ADEOS-II_GLI_L1B_VNIR_1km_NA.json index 2481063cc9..5f8a5e73ab 100644 --- a/datasets/ADEOS-II_GLI_L1B_VNIR_1km_NA.json +++ b/datasets/ADEOS-II_GLI_L1B_VNIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L1B_VNIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L1B Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data radiometric and geometric correction applied. Projection coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2A_LC_NA.json b/datasets/ADEOS-II_GLI_L2A_LC_NA.json index 017dccc21b..084f0604e5 100644 --- a/datasets/ADEOS-II_GLI_L2A_LC_NA.json +++ b/datasets/ADEOS-II_GLI_L2A_LC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2A_LC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Land and Cryosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is ADEOS-II/GLI L2 Land and Cryosphere data is map-projected global full resolution product. This data is generated each 16days and most cloud-free pixel is selected, mosaicking is performed. This product consists of 56 areas. Northern and southern 4 area is polar-stereographic projected. Middle latitude region is equi-rectangular grid and separated 48 areas (30deg. x 30deg.). The resolution is about 1km. The provided format is HDF. The channels (band: 1,5,8,13,15,17,19,24,26,27,28,29,30,31,34,35,36) necessary for land and cryosphere algorithms are included. Cloud flag and land-water flag are attached. The resolution is 1km. Each pixel has solar and satellite zenith/azimuth angle, observation date. In 16 days, there are 4 opportunities in one ground point at least because ADEOS-II recurrent period is 4 days. The time difference of adjacent pixels are 16 days in maximum. The observation condition of each pixel is different. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2A_OA_NA.json b/datasets/ADEOS-II_GLI_L2A_OA_NA.json index edce9169a2..43fe53e084 100644 --- a/datasets/ADEOS-II_GLI_L2A_OA_NA.json +++ b/datasets/ADEOS-II_GLI_L2A_OA_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2A_OA_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2A Ocean and Atmosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km.This product is a basic product for atmosphere and ocean level 2 products. Level 2A_OA consists of 4 pixel/4 line sampled all 1km GLI ch. Data, auxiliary data for atmosphere and ocean, cloud flag data and deviation table for removed data. The scene separated level 1B images are connected to tilt segment and eliminated overlapped scan lines.Map projection is not performed. MTIR ch. data are filled in path, but VNIR and SWIR data are filled in only half path because they are worked only in daytime. All GLI channels except 250m resolution ch. are included. (ch.1-19, 24-36: although ch.28, 29 are 250m resolution, 2km sampled data are also acquired) The provided format is HDF. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_ACLC_NA.json b/datasets/ADEOS-II_GLI_L2_ACLC_NA.json index 7e54f521db..dfe8105473 100644 --- a/datasets/ADEOS-II_GLI_L2_ACLC_NA.json +++ b/datasets/ADEOS-II_GLI_L2_ACLC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_ACLC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Atmospheric correction data for land and cryosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is atmospheric correction data which is atmospherically correct the composited, normalized radiances for \"Rayleigh scattering and ozone absorption\". Rayleigh scattering and ozone absorption are corrected with the assistance of ancillary data, such as the TOMS data set and ETOPO 5. This product includes radiance data for channel 1, 5, 8, 13, 15, 17, 19, 24, 26, 27, 28, 29, 30, 31, 34, 35, 36. The physical quantity unit is W/m^2/micro-m/sr.This product also includes Satellite Zenith Angle, Solar Zenith Angle, Relative Azimuth Angle and Quality Control Flag. The provided format is HDF. Map projection is EQR and PS. Generation unit is area. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_ARAE_NA.json b/datasets/ADEOS-II_GLI_L2_ARAE_NA.json index cdce845728..e4707e90b7 100644 --- a/datasets/ADEOS-II_GLI_L2_ARAE_NA.json +++ b/datasets/ADEOS-II_GLI_L2_ARAE_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_ARAE_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Aerosol Angstrom Exponent is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_AROP_NA.json b/datasets/ADEOS-II_GLI_L2_AROP_NA.json index 6ffaab6e92..83413fe380 100644 --- a/datasets/ADEOS-II_GLI_L2_AROP_NA.json +++ b/datasets/ADEOS-II_GLI_L2_AROP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_AROP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Aerosol Optical Thickness is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLER_i_e_NA.json b/datasets/ADEOS-II_GLI_L2_CLER_i_e_NA.json index 4e55823bea..6bb1a727f4 100644 --- a/datasets/ADEOS-II_GLI_L2_CLER_i_e_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLER_i_e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLER_i_e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLER_w_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLER_w_r_NA.json index ae9e110571..781300c525 100644 --- a/datasets/ADEOS-II_GLI_L2_CLER_w_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLER_w_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLER_w_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLFLG_p_NA.json b/datasets/ADEOS-II_GLI_L2_CLFLG_p_NA.json index 914498ba3c..7261ee8dd0 100644 --- a/datasets/ADEOS-II_GLI_L2_CLFLG_p_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLFLG_p_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLFLG_p_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud flag is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud mask data which indicate whether a given view of the earth surface is unobstructed by clouds or optically thick aerosol, and whether that clear scene is contaminated by a shadow, and L1B data is used as input data. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is not done. The spatial resolution is 0.25 degree and the statistical period is 4 days. This product also includes Day/Night Flag, Sunlit Flag, Snow / Ice background Flag and Land/Water Flag. Note that this product has an error for \"L1B_bound\" data. L1B_bound is a parameter that decides whether or not the granule scene of L1B data crosses the boundary of latitude and/or longitude. As an alternative, CLFLG_P has attribute information that includes the latitude and longitude of four corners of the granule scene that can be used for the same decision. Hence, we do not plan to reprocess CLFLG_P to correct this error. Please use CLFLG_P to resolve this issue. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLFR_NA.json b/datasets/ADEOS-II_GLI_L2_CLFR_NA.json index 9d943996b9..15eb92c5ce 100644 --- a/datasets/ADEOS-II_GLI_L2_CLFR_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLFR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLFR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud fraction is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 0.25 degree\u00ef\u00bd\u0098 0.25 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLHT_w_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLHT_w_r_NA.json index 5a5f68a2be..30ab5b325a 100644 --- a/datasets/ADEOS-II_GLI_L2_CLHT_w_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLHT_w_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLHT_w_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Top Height of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is km. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLOP_i_e_NA.json b/datasets/ADEOS-II_GLI_L2_CLOP_i_e_NA.json index 310f0f60a7..886d6427a4 100644 --- a/datasets/ADEOS-II_GLI_L2_CLOP_i_e_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLOP_i_e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLOP_i_e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLOP_i_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLOP_i_r_NA.json index 62198b3218..e647f54f0a 100644 --- a/datasets/ADEOS-II_GLI_L2_CLOP_i_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLOP_i_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLOP_i_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 \u00ce\u00bcm) and 35 (10.8 \u00ce\u00bcm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLOP_w_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLOP_w_r_NA.json index 39a55f795a..a8ae3db15f 100644 --- a/datasets/ADEOS-II_GLI_L2_CLOP_w_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLOP_w_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLOP_w_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Optical Thickness of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLTT_i_e_NA.json b/datasets/ADEOS-II_GLI_L2_CLTT_i_e_NA.json index 137df500e4..220997dfbf 100644 --- a/datasets/ADEOS-II_GLI_L2_CLTT_i_e_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLTT_i_e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLTT_i_e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Top Temperature of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLTT_w_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLTT_w_r_NA.json index 3603503a7e..5606038ce2 100644 --- a/datasets/ADEOS-II_GLI_L2_CLTT_w_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLTT_w_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLTT_w_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CLWP_w_r_NA.json b/datasets/ADEOS-II_GLI_L2_CLWP_w_r_NA.json index 185a336a2f..c091c6405d 100644 --- a/datasets/ADEOS-II_GLI_L2_CLWP_w_r_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CLWP_w_r_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CLWP_w_r_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is g/m^2. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_CS_LR_NA.json b/datasets/ADEOS-II_GLI_L2_CS_LR_NA.json index 69f1954156..f16cfebceb 100644 --- a/datasets/ADEOS-II_GLI_L2_CS_LR_NA.json +++ b/datasets/ADEOS-II_GLI_L2_CS_LR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_CS_LR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Ocean color is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. They are derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_NW_NA.json b/datasets/ADEOS-II_GLI_L2_NW_NA.json index 6a7f06f867..d0fb7ca628 100644 --- a/datasets/ADEOS-II_GLI_L2_NW_NA.json +++ b/datasets/ADEOS-II_GLI_L2_NW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_NW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Normalized water leaving radiance is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680 nm, Normalized water-leaving radiance at 678, 865 nm by in-water model, Aerosol radiance at 865, 380 nm, Angstrom exponent derived from 520 and 865 nm, Aerosol optical thickness at 865 nm, Photosynthetically available radiation.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. The unit of Normalized water-leaving radiance, Aerosol radiance and Aerosol albedo is mW cm^-2 um^-1 sr^-1. Photosynthetically available radiation is Ein m^-2 D^-1. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_PGCP_NA.json b/datasets/ADEOS-II_GLI_L2_PGCP_NA.json index f328535d75..166a68a1e8 100644 --- a/datasets/ADEOS-II_GLI_L2_PGCP_NA.json +++ b/datasets/ADEOS-II_GLI_L2_PGCP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_PGCP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Precise Geometric Correction Parameter is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Precise geometric correction parameter. This parameter is a parameter that combines with L1B, and obtains precise geometry correction image. The provided format is HDF. The physical quantity unit is none. Map projection is None and generation unit is scene. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_SNGI_NA.json b/datasets/ADEOS-II_GLI_L2_SNGI_NA.json index 1c0c65c11f..e6b1ddcb20 100644 --- a/datasets/ADEOS-II_GLI_L2_SNGI_NA.json +++ b/datasets/ADEOS-II_GLI_L2_SNGI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_SNGI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Snow Grain and Impurities is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm and 1640nm band, Snow impurities as soot, Snow surface temperature, Surface classification flag.Snow grain size retrieved with 865nm is using GLI channels 5 (0.46 \u00ce\u00bcm) and 19 (0.865 \u00ce\u00bcm), is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter.Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 \u00ce\u00bcm) independently to retrieve snow grain size at very top surface. The physical quantity is micro meter. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code.The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. The physical quantity is ppmw. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Surface classification flag uses L2A_LC data in channels 8,13, 17, 19, 24, 27, 30, 31, 34, 35 and 36 is used as input to this product. The output of the cloudy/clear and snow/sea-ice discriminator algorithm will be an 8-bit word for each field of view. It includes information about whether a view of the surface is obstructed by cloud and the surface type for each pixel. There are four levels of confidence to indicate whether a pixel is judged to be cloudy or clear. The physical quantity is dimensionless. The provided format is HDF. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_ST_LR_NA.json b/datasets/ADEOS-II_GLI_L2_ST_LR_NA.json index c5d093dad2..77647f60bb 100644 --- a/datasets/ADEOS-II_GLI_L2_ST_LR_NA.json +++ b/datasets/ADEOS-II_GLI_L2_ST_LR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_ST_LR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Sea surface temperature is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature, which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. The Multi-Channel SST (MCSST) technique is used. This product is generated from Level-2A_OA product. The physical quantity is Kelvin. This product also includes Quality flag or mask, Satellite Zenith Angle, Satellite Azimuth Angle, Solar Zenith Angle, Solar Azimuth Angle, Tilt Angle Flag as supplement data. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L2_VGI_NA.json b/datasets/ADEOS-II_GLI_L2_VGI_NA.json index 429d2bf867..963707c4f7 100644 --- a/datasets/ADEOS-II_GLI_L2_VGI_NA.json +++ b/datasets/ADEOS-II_GLI_L2_VGI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L2_VGI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L2 Vegetation Indices is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. They use L2_ACLC data as input. The GLI VI products will be spatially and temporally re-sampled, and designed to provide cloud free vegetation index maps at nominal resolutions of 1 km. The composited surface reflectance data from each pixel is used to compute both the NDVI and the EVI gridded products. The bands used to compute the VI are as follows: Red band: Band 13, BIR band: band 19, Blue band: band 5. The gridded VIs is produced at 16-day (half- month) also Monthly gridded VI products based on temporal averaging of the 16 days products is available. The provided format is HDF. The physical quantity unit is dimensionless. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 0.25 degree and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA.json index b6cb037b2c..d469b8f27d 100644 --- a/datasets/ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA.json index c53ce513e5..44b43c2045 100644 --- a/datasets/ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA.json index 39c3928c6e..8385a072c8 100644 --- a/datasets/ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA.json index 475c264867..8459b6acfc 100644 --- a/datasets/ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA.json index 630e7a925a..aea53f43e6 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA.json index 40bef56cc2..74633a7d80 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA.json index 158341896e..25aa684353 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA.json index 7548ee2451..5b8e6300b6 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA.json index 4d55bb865a..ba02fabfac 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA.json index 80436d9f8d..3ceee06070 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and time resolution are 1month. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA.json index 6efd77dc05..02e4c2af82 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA.json index c889d7683f..dc16165007 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA.json index 52d9985e4b..c25d998fa3 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA.json index 1f242e059c..ebb44cf82f 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA.json index 93d3115c6b..006ddce3df 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 \u00ce\u00bcm) and 35 (10.8 \u00ce\u00bcm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA.json index 239432c5f5..cc65c946ed 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 \u00ce\u00bcm) and 35 (10.8 \u00ce\u00bcm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA.json index f9405d032d..1ab61329dd 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA.json index 1b216ed1d8..8f3f329266 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA.json index ef4957f085..731242dd9e 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA.json index 22be140c41..edbc7bd315 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA.json index 972c159210..62185208f9 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA.json index 5651152de8..c2d51245be 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA.json index 275d5ec6b5..2db356708f 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA.json index ea6bbd516a..29b4a13bb8 100644 --- a/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CS_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_CS_1day_9km_NA.json index 87399d627b..4af7db92a0 100644 --- a/datasets/ADEOS-II_GLI_L3B_CS_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CS_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CS_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Ocean Color (1day, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CS_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_CS_1month_9km_NA.json index b52d3f356e..ecb73d94a5 100644 --- a/datasets/ADEOS-II_GLI_L3B_CS_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CS_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CS_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Ocean Color (1month,9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_CS_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_CS_8days_9km_NA.json index 4a863a09a9..da973cdeb2 100644 --- a/datasets/ADEOS-II_GLI_L3B_CS_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_CS_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_CS_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Ocean Color (8days, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_LA_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_LA_1day_9km_NA.json index f1f7ebd7d1..3219374692 100644 --- a/datasets/ADEOS-II_GLI_L3B_LA_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_LA_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_LA_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol radiance (1day, 9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_LA_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_LA_1month_9km_NA.json index 8ac6ca1d72..7f168bf820 100644 --- a/datasets/ADEOS-II_GLI_L3B_LA_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_LA_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_LA_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol radiance (1month, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_LA_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_LA_8days_9km_NA.json index c08b3cd3c5..262f174d8a 100644 --- a/datasets/ADEOS-II_GLI_L3B_LA_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_LA_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_LA_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_NW_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_NW_1day_9km_NA.json index 2f9500c396..3b182d7d0d 100644 --- a/datasets/ADEOS-II_GLI_L3B_NW_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_NW_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_NW_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9km and the statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_NW_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_NW_1month_9km_NA.json index 715c837dca..953aa67a98 100644 --- a/datasets/ADEOS-II_GLI_L3B_NW_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_NW_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_NW_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_NW_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_NW_8days_9km_NA.json index d770e17ed2..07640876b4 100644 --- a/datasets/ADEOS-II_GLI_L3B_NW_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_NW_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_NW_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA.json index 2722a271b4..db35a10579 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 \u00ce\u00bcm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA.json index 2bc162b5d6..2b6d8c248e 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 \u00ce\u00bcm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA.json index 17cf1e1579..09f2ff9453 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 \u00ce\u00bcm) and 19 (0.865 \u00ce\u00bcm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA.json index 8bce2891de..c873debe14 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 \u00ce\u00bcm) and 19 (0.865 \u00ce\u00bcm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA.json index 02d2c0b8ad..8889206491 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA.json index b5565181bc..1a845d5295 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain sizeand mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA.json index cf92cccca8..62cbdda2f2 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA.json index 67daed8bf0..27d94189bb 100644 --- a/datasets/ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_ST_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_ST_1day_9km_NA.json index db107a3f7e..f30cb0136a 100644 --- a/datasets/ADEOS-II_GLI_L3B_ST_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_ST_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_ST_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and night time are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_ST_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_ST_1month_9km_NA.json index 6e8c73b9fa..226f7f73f4 100644 --- a/datasets/ADEOS-II_GLI_L3B_ST_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_ST_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_ST_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3B_ST_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3B_ST_8days_9km_NA.json index e12e232f23..2260ef699a 100644 --- a/datasets/ADEOS-II_GLI_L3B_ST_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3B_ST_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3B_ST_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA.json index 2a067ce3fc..44a27392ce 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA.json index eba06c74d1..d2303b557e 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA.json index 467abbb91b..8fda305557 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA.json index 4a0ba9c762..a4c885920c 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA.json index 1dfd14ec89..1962d487c4 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA.json index be91ec2c12..7eed23594e 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA.json index 27f4165f48..67d12670c0 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA.json index 6a9b2a5206..bcb166ce99 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA.json index 340f162c05..2cfa434e26 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA.json index 45e3c0b6db..08ef88f1ca 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Chlorophyll-a (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA.json index bb908204c4..25a892f448 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA.json index 73f8f4ff12..d1bf5eac0f 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA.json index e1fcbe08cf..3a68f594a5 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA.json index becd54e877..0d7d0b4918 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA.json index 59d07317bd..bc7baa82fe 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree\u00ef\u00bd\u0098 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA.json index ced65ef586..93e109fe3c 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree\u00ef\u00bd\u0098 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA.json index 64a73f3e2b..dc9fa4fbe9 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA.json index eb75eb6f66..4d1ae82c7c 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA.json index 8870318ba6..ee43a691e8 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 25 km and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA.json index 7f8c854970..a0b7d31c2d 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA.json index 8327d7f2b8..a29c8c07d1 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 \u00ce\u00bcm) and 35 (10.8 \u00ce\u00bcm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 deg and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA.json index 00bcdd52c1..b284ce2881 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 \u00ce\u00bcm) and 35 (10.8 \u00ce\u00bcm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA.json index 8febe09297..9d6e14dbc7 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA.json index 728e90f6f1..323ac82b14 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA.json index b40f0bb209..e0ed1c15b2 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA.json index 5176d04dab..a58de50bfa 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA.json index 2952532a5f..b0e2d17b0d 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA.json index 723e285ede..bfcd489c6d 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA.json index 7efe2f38c6..2cb4e4cadf 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA.json index b1621d6d9d..7e2b1d54e9 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA.json index eee7c126b7..f7c2decd81 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA.json index 5f644d6285..8bf43646d3 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA.json index c4f895b9dc..0ffc836068 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA.json index e85d9f19e1..7e6d09b07b 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA.json index e14af91503..1d64fb24f6 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA.json index a09c43230b..f33f717f8d 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA.json index 52d93ff592..11da7849a8 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA.json index 4f3899f895..b5fd2d9405 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA.json index 910f52d338..bd29bf352c 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,1/12deg) and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA.json index 8a0bcc2218..5c81fac933 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 \u00ce\u00bcm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA.json index 5f4adc6cba..25d27ceebf 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 \u00ce\u00bcm) independently to retrieve snow grain size at very top surface. Level 2 snowimpurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA.json index c5de6e591a..5ab2b63ffd 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 \u00ce\u00bcm) and 19 (0.865 \u00ce\u00bcm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA.json index 2da194ab51..1d9a9ea119 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 \u00ce\u00bcm) and 19 (0.865 \u00ce\u00bcm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA.json index 0d6b2cc1d8..7075b42ddf 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA.json index 73bb794df6..fbd3f355e1 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA.json index b278547e57..ae729b8d17 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA.json index c80c6a19ce..cd88611364 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA.json index 5bab9e96a9..9483162058 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Suspended solid weight (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA.json index 078ab8ad22..9f2341b6d3 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA.json index 6bdf41957b..64c13b1b1e 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Suspended solid weight (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA.json index 8d208ac1cf..80be19a113 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA.json index 4c6f16e4e0..dc80eb80ab 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA.json index ec8a3f1ea3..5a7d2f7728 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA.json index 53b25dc5aa..16577f8ef5 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA.json index ef7271960a..d19caf8e90 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA.json index 0535b55896..cca39a77df 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA.json b/datasets/ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA.json index 9b43731e66..22d3b2399e 100644 --- a/datasets/ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA.json +++ b/datasets/ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS-II/GLI L3 STA Map Vegetation Index (16days,1/12deg)is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed \"Midori II\" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. This product is generated from Level-2 product. All zone of Level-2 product is connected and northern and southern polar stereographic region is projected to equi-rectangular. Level 3 STA Map product of land is the representative values, which are estimated from level 2 binned product and projected onto map. As for the estimation arithmetic mean method is applied.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days. Map projection is EQR. The generation unit is global. The current version of the product is \"Version 2\".", "links": [ { diff --git a/datasets/ADEOS_AVNIR_L1A_MU_NA.json b/datasets/ADEOS_AVNIR_L1A_MU_NA.json index 8e9ad92851..fa4ffab53a 100644 --- a/datasets/ADEOS_AVNIR_L1A_MU_NA.json +++ b/datasets/ADEOS_AVNIR_L1A_MU_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_AVNIR_L1A_MU_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS AVNIR L1A Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 \u00c2\u00b5m (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 \u00c2\u00b5m, panchromatic band (visible): 1 band 0.52-0.69 \u00c2\u00b5m) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 5403x5017 array tile. The spatial resolution is 16 m. Supplemental data include such as radiometric correction information and geometric correction information.", "links": [ { diff --git a/datasets/ADEOS_AVNIR_L1A_PAN_NA.json b/datasets/ADEOS_AVNIR_L1A_PAN_NA.json index 91dbb7a6aa..388441f212 100644 --- a/datasets/ADEOS_AVNIR_L1A_PAN_NA.json +++ b/datasets/ADEOS_AVNIR_L1A_PAN_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_AVNIR_L1A_PAN_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS AVNIR L1A Panchromatic band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Panchromatic band data. AVNIR has 5 bands from 0.42 - 0.89 \u00c2\u00b5m (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 \u00c2\u00b5m, panchromatic band (visible): 1 band 0.52-0.69 \u00c2\u00b5m) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660\u00c3\u009710028 array tile. The spatial resolution is 8 m. Supplemental data include such as radiometric correction information and geometric correction information.", "links": [ { diff --git a/datasets/ADEOS_AVNIR_L1B2_MU_NA.json b/datasets/ADEOS_AVNIR_L1B2_MU_NA.json index 4b9b11da94..9d8f75daea 100644 --- a/datasets/ADEOS_AVNIR_L1B2_MU_NA.json +++ b/datasets/ADEOS_AVNIR_L1B2_MU_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_AVNIR_L1B2_MU_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone.The Level L1B2 product is radiometically and geometrically corrected image from Level1B data, geometically corrected, projected on the map. This product is ADEOS AVNIR L1B2 Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 \u00c2\u00b5m (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 \u00c2\u00b5m, panchromatic band (visible): 1 band 0.52-0.69 \u00c2\u00b5m) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution.The provided format is CEOS, 5403\u00c3\u00975017 array tile. The spatial resolution is 16 m.", "links": [ { diff --git a/datasets/ADEOS_AVNIR_L1B2_PAN_NA.json b/datasets/ADEOS_AVNIR_L1B2_PAN_NA.json index 174796ae7f..697dcf0036 100644 --- a/datasets/ADEOS_AVNIR_L1B2_PAN_NA.json +++ b/datasets/ADEOS_AVNIR_L1B2_PAN_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_AVNIR_L1B2_PAN_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level L1B2 product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1B2 Panchromatic band data. AVNIR has 4 bands from 0.42 - 0.89 \u00c2\u00b5m (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 \u00c2\u00b5m, panchromatic band (visible): 1 band 0.52-0.69 \u00c2\u00b5m) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660\u00c3\u009710028 array tile. The spatial resolution is 8 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L1A_GAC_TI_NA.json b/datasets/ADEOS_OCTS_L1A_GAC_TI_NA.json index 0fe6ea46e2..a73432e213 100644 --- a/datasets/ADEOS_OCTS_L1A_GAC_TI_NA.json +++ b/datasets/ADEOS_OCTS_L1A_GAC_TI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L1A_GAC_TI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L1A GAC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) thermal infrared band (VI) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L1A_GAC_VNR_NA.json b/datasets/ADEOS_OCTS_L1A_GAC_VNR_NA.json index e635a5b16e..ec17415af0 100644 --- a/datasets/ADEOS_OCTS_L1A_GAC_VNR_NA.json +++ b/datasets/ADEOS_OCTS_L1A_GAC_VNR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L1A_GAC_VNR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L1A GAC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L1A_RTC_TI_NA.json b/datasets/ADEOS_OCTS_L1A_RTC_TI_NA.json index 3cbfcff298..5b5d29e2d0 100644 --- a/datasets/ADEOS_OCTS_L1A_RTC_TI_NA.json +++ b/datasets/ADEOS_OCTS_L1A_RTC_TI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L1A_RTC_TI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L1A RTC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.\u00c2\u00a0This product is RTC (Real Time Coverage) thermal infrared band (TI) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L1A_RTC_VNR_NA.json b/datasets/ADEOS_OCTS_L1A_RTC_VNR_NA.json index 0ec593d327..090b31ca5b 100644 --- a/datasets/ADEOS_OCTS_L1A_RTC_VNR_NA.json +++ b/datasets/ADEOS_OCTS_L1A_RTC_VNR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L1A_RTC_VNR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L1A RTC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_GAC_OC1_NA.json b/datasets/ADEOS_OCTS_L2_GAC_OC1_NA.json index e8f6f2bfab..f8220e94f3 100644 --- a/datasets/ADEOS_OCTS_L2_GAC_OC1_NA.json +++ b/datasets/ADEOS_OCTS_L2_GAC_OC1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_GAC_OC1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 GAC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.\u00c2\u00a0This product is GAC (Global Area Coverage) ocean color1 (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_GAC_OC2_NA.json b/datasets/ADEOS_OCTS_L2_GAC_OC2_NA.json index 6dc0d6a1b2..c956f4e406 100644 --- a/datasets/ADEOS_OCTS_L2_GAC_OC2_NA.json +++ b/datasets/ADEOS_OCTS_L2_GAC_OC2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_GAC_OC2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 GAC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) ocean color2 (OC2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration Chlorophyll a concentration Diffusion attenuation coefficient at 490 nm ,and Level 2 Quality flags. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_GAC_SST_NA.json b/datasets/ADEOS_OCTS_L2_GAC_SST_NA.json index 34a48ed6cd..7fd0e2e5db 100644 --- a/datasets/ADEOS_OCTS_L2_GAC_SST_NA.json +++ b/datasets/ADEOS_OCTS_L2_GAC_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_GAC_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 GAC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.\u00c2\u00a0This product is GAC (Global Area Coverage) sea surface temperature (SST) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_GAC_VI_NA.json b/datasets/ADEOS_OCTS_L2_GAC_VI_NA.json index 349b738487..e5ebaaf6e1 100644 --- a/datasets/ADEOS_OCTS_L2_GAC_VI_NA.json +++ b/datasets/ADEOS_OCTS_L2_GAC_VI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_GAC_VI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. OCTS has 12 bands and 1400 km swath.This product is vegetation indices GAC (Global Area Coverage) product (VI), transformed to vegetation index from level 1B data. The provided format is HDF4 format, with 700m ground resolution. Supplemental data files include such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others. ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.\u00c2\u00a0This product is GAC (Global Area Coverage) vegetation indices (VI) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_RTC_OC1_NA.json b/datasets/ADEOS_OCTS_L2_RTC_OC1_NA.json index e03fdd5a1f..eaa216222a 100644 --- a/datasets/ADEOS_OCTS_L2_RTC_OC1_NA.json +++ b/datasets/ADEOS_OCTS_L2_RTC_OC1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_RTC_OC1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 RTC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time coverage) ocean color (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_RTC_OC2_NA.json b/datasets/ADEOS_OCTS_L2_RTC_OC2_NA.json index 322130efeb..bf342e1069 100644 --- a/datasets/ADEOS_OCTS_L2_RTC_OC2_NA.json +++ b/datasets/ADEOS_OCTS_L2_RTC_OC2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_RTC_OC2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 RTC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.\u00c2\u00a0This product is RTC (Real Time Coverage) OC2 (ocean color2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration, Chlorophyll-a concentration, Diffuse attenuation coefficient, and quality information. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_RTC_SST_NA.json b/datasets/ADEOS_OCTS_L2_RTC_SST_NA.json index 11bb9cbec6..fb61066ea7 100644 --- a/datasets/ADEOS_OCTS_L2_RTC_SST_NA.json +++ b/datasets/ADEOS_OCTS_L2_RTC_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_RTC_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (real time coverage) sea surface temperature product (SST)product, transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L2_RTC_VI_NA.json b/datasets/ADEOS_OCTS_L2_RTC_VI_NA.json index 51d892ecb0..073c42e1c4 100644 --- a/datasets/ADEOS_OCTS_L2_RTC_VI_NA.json +++ b/datasets/ADEOS_OCTS_L2_RTC_VI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L2_RTC_VI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L2 RTC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) Vegetation Indices product (VI), transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1day_NA.json index 748fcb1657..da2d8807aa 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCC_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1month_NA.json index d8cc6117d7..911dd50f41 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCC_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1week_NA.json index d315dc130a..0756e9c6f3 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCC_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1year_NA.json index 4c47470dac..53bfb747f6 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCC_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCC_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1day_NA.json index d737391c28..a4759048e2 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCK_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCK 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is \"m-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1month_NA.json index 68637fcd4e..f09c0e8f59 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCK_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCK 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is \"m-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1week_NA.json index ff155e5496..71195c8d3c 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCK_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCK 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is \"m-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1year_NA.json index 19a4d99f74..e9f3f0cefe 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCK_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCK_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCK 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is \"m-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1day_NA.json index b16802c15b..8fc2a8b6cb 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCL_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCL 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is \"mW/cm-2/mm-1/sr-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1month_NA.json index 4a8b35170e..4826b11678 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCL_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCL 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is \"mW/cm-2/mm-1/sr-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1week_NA.json index b81bba9a67..2aad658224 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCL_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCL 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is \"mW/cm-2/mm-1/sr-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1year_NA.json index a346546079..3d0b5189b3 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCL_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCL_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCL 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) Ocean Color (OC) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is \"mW/cm-2/mm-1/sr-1\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1day_NA.json index 0bd3676e72..ffb7da147e 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCP_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCP 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1month_NA.json index e55bf37b73..88c0bf3794 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCP_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCP 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1week_NA.json index fcccf037a0..78dfefbfbd 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCP_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCP 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1year_NA.json index 07842d7d3d..82ea2655df 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_OCP_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_OCP_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC OCP 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is \"mg/m-3\". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1day_NA.json index 321e24ca55..3f9681e4bf 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_SST_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"Kelvin\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1month_NA.json index 0732d5e956..3490b32de3 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_SST_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"Kelvin\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1week_NA.json index a4d205c48e..e4af0bd1bf 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_SST_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"Kelvin\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1year_NA.json index 7e6b1a14cb..c048067f71 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_SST_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_SST_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_SST_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"Kelvin\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1day_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1day_NA.json index fdc1d77f2f..9881194ad8 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_VI_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC VI 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"dimensionless\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1month_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1month_NA.json index d779e07ce2..8586801bf1 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_VI_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC VI 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"dimensionless\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1week_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1week_NA.json index 4cef04c429..6de5e03a84 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_VI_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC VI 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"dimensionless\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1year_NA.json b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1year_NA.json index b2776f2d60..ae3ac492d0 100644 --- a/datasets/ADEOS_OCTS_L3BM_GAC_VI_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3BM_GAC_VI_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3BM_GAC_VI_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3BM GAC VI 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is \"dimensionless\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_OC_1day_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_OC_1day_NA.json index 426459a344..170d1461a2 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_OC_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_OC_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_OC_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC OC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is \"mW/cm-2/mm-1/sr-1\". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is \"mg/m-3\". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is \"mg/m-3\". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is \"m-1\". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is \"mg/m-2\". The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_OC_1month_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_OC_1month_NA.json index 28009adf9e..9f650cbd3c 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_OC_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_OC_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_OC_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC OC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm. CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is \u00e2\u0080\u009dmW/cm-2/mm-1/sr-1\u00e2\u0080\u009d. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is \"mg/m-3\". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is \"mg/m-3\". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is \"m-1\". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is \"mg/m-2\".The provided format is HDF4 format. The unit of geophysical quantity in this product is \"mg/m^3\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_OC_1week_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_OC_1week_NA.json index 8c4fd87e32..834ebdbdcb 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_OC_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_OC_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_OC_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC OC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weely L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is \u00e2\u0080\u009dmW/cm-2/mm-1/sr-1\u00e2\u0080\u009d. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is \"mg/m-3\". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is \"mg/m-3\". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is \"m-1\". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is \"mg/m-2\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_OC_1year_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_OC_1year_NA.json index ca246a5944..9f3fbbd494 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_OC_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_OC_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_OC_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC OC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC)product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is \"mW/cm-2/mm-1/sr-1\". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is \"mg/m-3\". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is \"mg/m-3\". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is \"m-1\". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is \"mg/m-2\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_SST_1day_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_SST_1day_NA.json index d957af0dfe..b5d5af6a1c 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_SST_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_SST_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_SST_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is \"Kelvin\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_SST_1month_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_SST_1month_NA.json index c0f0c53df9..04c8f267d8 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_SST_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_SST_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_SST_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is \"Kelvin\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_SST_1week_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_SST_1week_NA.json index c2301979df..26a4055c2c 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_SST_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_SST_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_SST_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is \"Kelvin\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_SST_1year_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_SST_1year_NA.json index b27aca8270..6ae6a5fb38 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_SST_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_SST_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_SST_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is \"Kelvin\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_VI_1day_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_VI_1day_NA.json index 4696ea9dc7..a06e3ecc13 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_VI_1day_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_VI_1day_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_VI_1day_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC VI 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is \"dimensionless\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_VI_1month_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_VI_1month_NA.json index 79eaddbbdf..676f394e4b 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_VI_1month_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_VI_1month_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_VI_1month_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC VI 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is \"dimensionless\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_VI_1week_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_VI_1week_NA.json index cc714d707d..abc65cf52e 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_VI_1week_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_VI_1week_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_VI_1week_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC VI 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is \"dimensionless\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3B_GAC_VI_1year_NA.json b/datasets/ADEOS_OCTS_L3B_GAC_VI_1year_NA.json index 402216712a..6ff69dd25b 100644 --- a/datasets/ADEOS_OCTS_L3B_GAC_VI_1year_NA.json +++ b/datasets/ADEOS_OCTS_L3B_GAC_VI_1year_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3B_GAC_VI_1year_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3B GAC VI 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is \"dimensionless\".The provided format is HDF4 format.", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3M_RTC_OCC_NA.json b/datasets/ADEOS_OCTS_L3M_RTC_OCC_NA.json index d3808a79ee..eae543edca 100644 --- a/datasets/ADEOS_OCTS_L3M_RTC_OCC_NA.json +++ b/datasets/ADEOS_OCTS_L3M_RTC_OCC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3M_RTC_OCC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3M RTC OCC dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean Color-Chlorophyll-a concentration (OCC) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of Ocean Color-Chlorophyll-a concentration.The provided format if HDF4 format. The unit of geophysical quantity in this product is \"mg/m^3\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3M_RTC_OCK_NA.json b/datasets/ADEOS_OCTS_L3M_RTC_OCK_NA.json index 37d26fe997..68e8c471df 100644 --- a/datasets/ADEOS_OCTS_L3M_RTC_OCK_NA.json +++ b/datasets/ADEOS_OCTS_L3M_RTC_OCK_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3M_RTC_OCK_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3M RTC OCK dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean color (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of diffuse attenuation coefficient at 490nm.The provided format if HDF4 format. The unit of geophysical quantity in this product is \"m-1\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3M_RTC_OCP_NA.json b/datasets/ADEOS_OCTS_L3M_RTC_OCP_NA.json index 380a764e59..685f4fdf74 100644 --- a/datasets/ADEOS_OCTS_L3M_RTC_OCP_NA.json +++ b/datasets/ADEOS_OCTS_L3M_RTC_OCP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3M_RTC_OCP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3M RTC OCK dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean Color-CZCS like pigment concentration (OCP) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of CZCS-like Pigment Concentration.The provided format if HDF4 format. The unit of geophysical quantity in this product is \"mg m-3\".", "links": [ { diff --git a/datasets/ADEOS_OCTS_L3M_RTC_SST_NA.json b/datasets/ADEOS_OCTS_L3M_RTC_SST_NA.json index 56b9ff64f7..a6f3c1ae48 100644 --- a/datasets/ADEOS_OCTS_L3M_RTC_SST_NA.json +++ b/datasets/ADEOS_OCTS_L3M_RTC_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADEOS_OCTS_L3M_RTC_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ADEOS OCTS L3M RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Sea Surface Temperature (SST) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of sea surface temperature.The provided format if HDF4 format. The unit of geophysical quantity in this product is \"Kelvin\".", "links": [ { diff --git a/datasets/ADS_WRI.json b/datasets/ADS_WRI.json index 9d5db58827..2244d4d3bd 100644 --- a/datasets/ADS_WRI.json +++ b/datasets/ADS_WRI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ADS_WRI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following information was abstracted from a WRI Publications\n announcement:\n \n Africa Data Sampler (ADS)\n \n The ADS is an internationally comparable set of digital maps at a scale of\n 1:1 million for every country in Africa. The ADS is an integration of map\n data from several GIS databases. Roads, rivers, settlements,\n topography, and other essential base map features were extracted from\n the Arc/Info version of the Digital Chart of the World (ESRI, Redlands,\n CA). Data representing forests, wetlands, and protected areas from the\n Biodiversity Map Library (World Conservation Monitoring Center,\n Cambridge, UK), and sub-national boundaries and population estimates\n from the National Center for Geographic Information and Analysis (Santa\n Barbara, CA) were integrated with the DCW data sets. Over twenty\n layers of data are available for most countries.\n \n The ADS comprises a CD-ROM and User's Guide. The CD-ROM contains\n digital maps in PC ARC/INFO format for 53 countries in Robinson\n projection, five sample views in ArcView 1 format for each country, and\n ARC/INFO Export files for all countries in geographic projection.\n The 150-page User's Guide is available in both English and French and\n gives detailed information on the ADS data sources, data quality, and\n applications.\n \n The Africa Data Sampler is available on CD-ROM usable in UNIX,\n MS-DOS, or Macintosh environments. \n For more information on WRI publicatons on Africa, please see:\n http://www.wri.org/", "links": [ { diff --git a/datasets/AERDB_D3_ABI_G16_1.json b/datasets/AERDB_D3_ABI_G16_1.json index 1f9d6755c3..eb7f0ed640 100644 --- a/datasets/AERDB_D3_ABI_G16_1.json +++ b/datasets/AERDB_D3_ABI_G16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_ABI_G16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G16\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_D3_ABI_G17_1.json b/datasets/AERDB_D3_ABI_G17_1.json index 4c033897e0..1376b76383 100644 --- a/datasets/AERDB_D3_ABI_G17_1.json +++ b/datasets/AERDB_D3_ABI_G17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_ABI_G17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_D3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G17\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_D3_AHI_H08_1.json b/datasets/AERDB_D3_AHI_H08_1.json index 0a2bcafff3..f3c8d8abb3 100644 --- a/datasets/AERDB_D3_AHI_H08_1.json +++ b/datasets/AERDB_D3_AHI_H08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_AHI_H08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid product, short-name AERDB_D3_AHI_H08, derived from the L2 (AERDB_L2_AHI_H08) input data, each D3 AHI/Himawari-8 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_D3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_AHI_H08\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_D3_GEOLEO_Merged_1.json b/datasets/AERDB_D3_GEOLEO_Merged_1.json index 19d7954f75..7da32cbc30 100644 --- a/datasets/AERDB_D3_GEOLEO_Merged_1.json +++ b/datasets/AERDB_D3_GEOLEO_Merged_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_GEOLEO_Merged_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEO-LEO Merged Deep Blue Aerosol Daily 1 x 1 degree Gridded L3 product, short-name AERDB_D3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L2G product (AERDB_L2G_GEOLEO_Merged) using best-estimate AOT values. Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach. The final retrievals used in the aggregation process are QA-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. Each L3 daily aggregated datafile is spatially comprised of a 1\u02da x 1\u02da horizontal grid that exists for every 30 minutes. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol Daily 1 x 1-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_D3_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDSs. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_GEOLEO_Merged\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_D3_VIIRS_NOAA20_2.json b/datasets/AERDB_D3_VIIRS_NOAA20_2.json index 822feb83bb..2ec5c82267 100644 --- a/datasets/AERDB_D3_VIIRS_NOAA20_2.json +++ b/datasets/AERDB_D3_VIIRS_NOAA20_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_VIIRS_NOAA20_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_NOAA20), and is provided in a 1x1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element\u2019s bounding limits. Other measures like standard deviation are also provided. This aggregated product is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three retrieved measurements to render a given grid as valid on any given day. This daily product record starts from January 5th, 2018. This L3 daily product, in netCDF, contains 45 Science Data Set (SDS) layers.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_NOAA20\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_D3_VIIRS_SNPP_1.1.json b/datasets/AERDB_D3_VIIRS_SNPP_1.1.json index e263bf08dd..98825cfcc0 100644 --- a/datasets/AERDB_D3_VIIRS_SNPP_1.1.json +++ b/datasets/AERDB_D3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_SNPP product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean as gridded aggregates, on a daily basis, globally. This aggregated daily product is derived from the Collection-1.1 (C1.1) L2 6-minute swath-based products (AERDB_L2_VIIRS_SNPP), and is provided in a 1degree x 1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element\u2019s bounding limits. Other measures like standard deviation are also provided. The AERDB_D3_VIIRS_SNPP is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three such day-of-interest retrieved measurements to render a given cell as valid on any given day.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_SNPP\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_D3_VIIRS_SNPP_2.json b/datasets/AERDB_D3_VIIRS_SNPP_2.json index 1b24e86d22..ef90fece61 100644 --- a/datasets/AERDB_D3_VIIRS_SNPP_2.json +++ b/datasets/AERDB_D3_VIIRS_SNPP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_D3_VIIRS_SNPP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element\u2019s bounding limits. Other measures like standard deviation are also provided. This aggregated product is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three retrieved measurements to render a given grid as valid on any given day. This daily product record starts from March 1st, 2012 . This L3 daily product, in netCDF, contains 45 Science Data Set (SDS) layers.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_SNPP\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_L2G_GEOLEO_Merged_1.json b/datasets/AERDB_L2G_GEOLEO_Merged_1.json index 4b69ac830a..bb77611456 100644 --- a/datasets/AERDB_L2G_GEOLEO_Merged_1.json +++ b/datasets/AERDB_L2G_GEOLEO_Merged_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2G_GEOLEO_Merged_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEO-LEO Merged Deep Blue Aerosol 0.25x0.25 degree Gridded L2 product, short-name AERDB_L2G_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength, derived from seven merged GEO-LEO AOT layers (G16-ABI, G17-ABI, H08-AHI, SNPP-VIIRS, NOAA20-VIIRS, Terra MODIS and Aqua MODIS) and from each of the individual (three GEO and four LEO) instrument sources. Each L2G aggregated datafile is spatially comprised of a 0.25\u02da x 0.25\u02da horizontal grid that exists for every 30 minutes. This represents a 30-minute Deep Blue best-estimate AOT from each of the seven sources besides an error-weighted merged AOT layer. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-2G (L2G) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol 0.25 x 0.25-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_L2G_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDSs. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2G_GEOLEO_Merged\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_L2_ABI_G16_1.json b/datasets/AERDB_L2_ABI_G16_1.json index 3ca2a75cbf..aa3e8d2f91 100644 --- a/datasets/AERDB_L2_ABI_G16_1.json +++ b/datasets/AERDB_L2_ABI_G16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_ABI_G16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G16 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G16 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products\u2019 image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor\u2019s scanning geometry and Earth\u2019s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-2 (L2) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_L2_ABI_G16 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_ABI_G16\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_L2_ABI_G17_1.json b/datasets/AERDB_L2_ABI_G17_1.json index 3f7b87082c..6a82cbc9ff 100644 --- a/datasets/AERDB_L2_ABI_G17_1.json +++ b/datasets/AERDB_L2_ABI_G17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_ABI_G17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G17 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G17 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products\u2019 image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor\u2019s scanning geometry and Earth\u2019s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-2 (L2) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_L2_ABI_G17 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_ABI_G17\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_L2_AHI_H08_1.json b/datasets/AERDB_L2_AHI_H08_1.json index d22b00b0ea..19f619aed7 100644 --- a/datasets/AERDB_L2_AHI_H08_1.json +++ b/datasets/AERDB_L2_AHI_H08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_AHI_H08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Himawari-08 AHI Deep Blue Aerosol L2 Full Disk product, short-name AERDB_L2_AHI_H08 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products\u2019 image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor\u2019s scanning geometry and Earth\u2019s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-2 (L2) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_L2_AHI_H08 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_AHI_H08\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_NOAA20_2.json b/datasets/AERDB_L2_VIIRS_NOAA20_2.json index 18a238a428..2b85fd1f03 100644 --- a/datasets/AERDB_L2_VIIRS_NOAA20_2.json +++ b/datasets/AERDB_L2_VIIRS_NOAA20_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_NOAA20_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_ NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nm reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.\r\n\r\nThe Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) over both land and ocean and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This L2 description pertains to the VIIRS Deep Blue Aerosol Version 2.0 (V2.0) product, whose record starts from February 17, 2018.\r\n\r\nThe L2 netCDF product, acquired every 6 minutes, contains 55 Science Data Set (SDS) layers. The V2.0 VIIRS deep blue aerosol products are available from both NOAA20 and SNPP platforms. Significant changes have been made to the V2.0 Deep Blue/SOAR algorithms to further improve the data quality. A number of other improvements and changes have been added that can be found from Product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_NOAA20\r\n\r\nConsult the VIIR Deep Blue product user guide for additional information regarding the global attributes, data field attributes, quality flags, software to handle and use these data products, etc., at: \r\nhttps://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Deep_Blue_Aerosol_User_Guide_v2.pdf", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_NOAA20_NRT_2.json b/datasets/AERDB_L2_VIIRS_NOAA20_NRT_2.json index e849ad298a..907cde4a6c 100644 --- a/datasets/AERDB_L2_VIIRS_NOAA20_NRT_2.json +++ b/datasets/AERDB_L2_VIIRS_NOAA20_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_NOAA20_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land.\r\n\r\nThis orbit-level product (Short-name: AERDB_L2_VIIRS_NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_SNPP_1.1.json b/datasets/AERDB_L2_VIIRS_SNPP_1.1.json index 1186bcb5e8..75f9553914 100644 --- a/datasets/AERDB_L2_VIIRS_SNPP_1.1.json +++ b/datasets/AERDB_L2_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.\r\n\r\nThis L2 description pertains to the SNPP VIIRS Deep Blue Aerosol collection-1.1 (C1.1) product, whose record starts from March 1st 2012. The primary reason for generating this new C1.1 version is that it no longer contains out-of-valid-range pixels, except in the case of the solar zenith angle. A number of other improvements and changes have been added that can be found from Product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_SNPP\r\n\r\nFor more information consult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_SNPP_2.json b/datasets/AERDB_L2_VIIRS_SNPP_2.json index 0f1fafed95..5ffd0c983e 100644 --- a/datasets/AERDB_L2_VIIRS_SNPP_2.json +++ b/datasets/AERDB_L2_VIIRS_SNPP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_SNPP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.\r\n\r\nThis L2 description pertains to the VIIRS Deep Blue Aerosol collection version 2.0 (C2) product. Significant changes have been made to the V2.0 Deep Blue/SOAR algorithms to further improve the data quality. For C2.0, the aerosol products are available for NOAA20 VIIRS in addition to SNPP. Some of changes in the retrieval algorithms and data products include, new SDS suite for prognostic uncertainties of 550 nm AOT over both land and ocean is added, surface pressure is better accounted for for both over-land and over-ocean retrievals by adding surface pressure nodes in the aerosol lookup table, and a number of other improvements which can be found from Product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_SNPP\r\n\r\nFor more information consult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_SNPP_NRT_1.1.json b/datasets/AERDB_L2_VIIRS_SNPP_NRT_1.1.json index dbe0ab98b6..7f5aef4803 100644 --- a/datasets/AERDB_L2_VIIRS_SNPP_NRT_1.1.json +++ b/datasets/AERDB_L2_VIIRS_SNPP_NRT_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_SNPP_NRT_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land.\r\n\r\nThis orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.", "links": [ { diff --git a/datasets/AERDB_L2_VIIRS_SNPP_NRT_2.json b/datasets/AERDB_L2_VIIRS_SNPP_NRT_2.json index 596a21176f..cd2e40b243 100644 --- a/datasets/AERDB_L2_VIIRS_SNPP_NRT_2.json +++ b/datasets/AERDB_L2_VIIRS_SNPP_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_L2_VIIRS_SNPP_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land.\r\n\r\nThis orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor\u2019s scanning geometry and Earth\u2019s curvature. Viewed differently, this product\u2019s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well.", "links": [ { diff --git a/datasets/AERDB_M3_ABI_G16_1.json b/datasets/AERDB_M3_ABI_G16_1.json index 2def279408..c92fd9feb2 100644 --- a/datasets/AERDB_M3_ABI_G16_1.json +++ b/datasets/AERDB_M3_ABI_G16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_ABI_G16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product\u2019s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments.\n\nThe AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_M3_ABI_G17_1.json b/datasets/AERDB_M3_ABI_G17_1.json index aeaf056614..33610ebf71 100644 --- a/datasets/AERDB_M3_ABI_G17_1.json +++ b/datasets/AERDB_M3_ABI_G17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_ABI_G17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G17, derived by aggregating the L3 daily (AERDB_D3_ABI_G17) input data, each M3 ABI/GOES-17 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product\u2019s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\n The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_M3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G17\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_M3_AHI_H08_1.json b/datasets/AERDB_M3_AHI_H08_1.json index a26f4817db..886170a115 100644 --- a/datasets/AERDB_M3_AHI_H08_1.json +++ b/datasets/AERDB_M3_AHI_H08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_AHI_H08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The H08 Deep Blue Level 3 Monthly aerosol data, 1x1 degree grid product, short-name AERDB_M3_AHI_H08, derived by aggregating the L3 daily (AERDB_D3_AHI_H08) input data, each M3 AHI/ Himawari-8 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product\u2019s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future.\n\nThe Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_M3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_AHI_H08\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_M3_GEOLEO_Merged_1.json b/datasets/AERDB_M3_GEOLEO_Merged_1.json index 9e6027d47a..7b5e68eb68 100644 --- a/datasets/AERDB_M3_GEOLEO_Merged_1.json +++ b/datasets/AERDB_M3_GEOLEO_Merged_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_GEOLEO_Merged_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEO-LEO Merged Deep Blue Aerosol Monthly 1 x 1 degree Gridded L3 product, short-name AERDB_M3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L3 daily product (AERDB_D3_GEOLEO_Merged). Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach. The final retrievals used in the aggregation process are QA-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. Each L3 daily aggregated datafile is spatially comprised of a 1\u02da x 1\u02da horizontal grid that exists for every 30 minutes. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. \n\nThe Level-3 (L3) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol Daily 1 x 1-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.\n\nThe AERDB_M3_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDS layers. \n\nFor more information consult LAADS product description page at:\n\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_GEOLEO_Merged\n\nOr, Deep Blue aerosol project webpage at: \nhttps://earth.gsfc.nasa.gov/climate/data/deep-blue", "links": [ { diff --git a/datasets/AERDB_M3_VIIRS_NOAA20_2.json b/datasets/AERDB_M3_VIIRS_NOAA20_2.json index 0eb8d3aefb..25a07e551e 100644 --- a/datasets/AERDB_M3_VIIRS_NOAA20_2.json +++ b/datasets/AERDB_M3_VIIRS_NOAA20_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_VIIRS_NOAA20_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_NOAA20), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products. To exclude poorly sampled grid elements, the algorithm requires at least 3 valid days\u2019 worth of data to render a given monthly grid element as valid. This monthly product record starts from January 5th, 2018. This L3 monthly product, in netCDF format, contains 45 Science Data Set (SDS) layers that are named identical to the SDSs in the daily L3 product.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_VIIRS_NOAA20\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_M3_VIIRS_SNPP_1.1.json b/datasets/AERDB_M3_VIIRS_SNPP_1.1.json index a853203299..d41bfe8b55 100644 --- a/datasets/AERDB_M3_VIIRS_SNPP_1.1.json +++ b/datasets/AERDB_M3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_SNPP product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean as gridded aggregates, on a monthly basis, globally. This monthly aggregated product is derived from the Collection-1.1 (C1.1) daily L3 gridded products (AERDB_D3_VIIRS_SNPP), and is provided in a 1degree x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products. To exclude poorly sampled grid elements, the algorithm requires at least 3 valid days\u2019 worth of monthly data to populate the monthly grid element. This monthly product collection\u2019s record starts from March 1st 2012.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_VIIRS_SNPP\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDB_M3_VIIRS_SNPP_2.json b/datasets/AERDB_M3_VIIRS_SNPP_2.json index 45ecdf8072..1cab317740 100644 --- a/datasets/AERDB_M3_VIIRS_SNPP_2.json +++ b/datasets/AERDB_M3_VIIRS_SNPP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDB_M3_VIIRS_SNPP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products. To exclude poorly sampled grid elements, the algorithm requires at least 3 valid days\u2019 worth of data to render a given monthly grid element as valid. This monthly product record starts from March1st, 2012. This L3 monthly product, in netCDF format, contains 45 Science Data Set (SDS) layers that are named identical to the SDSs in the daily L3 product.\r\n\r\nFor more information about the product and Science Data Set (SDS) layers, consult product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_VIIRS_SNPP\r\n\r\nOr\r\n\r\nConsult Deep Blue aerosol team Page at: \r\nhttps://deepblue.gsfc.nasa.gov", "links": [ { diff --git a/datasets/AERDT_L2_VIIRS_NOAA20_2.json b/datasets/AERDT_L2_VIIRS_NOAA20_2.json index 07819a1fd3..c451f414ff 100644 --- a/datasets/AERDT_L2_VIIRS_NOAA20_2.json +++ b/datasets/AERDT_L2_VIIRS_NOAA20_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDT_L2_VIIRS_NOAA20_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Dark Target Aerosol L2 6-Min Swath 6 km product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) - Visible Infrared Imaging Radiometer Suite (VIIRS) incarnation of the dark target (DT) aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission's MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).\r\n\r\nThis orbit-level product (Short-name: AERDT_L2_VIIRS_NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. This Version-2 set of products is the first collection of the Level-2 Dark Target Aerosol derived from the NOAA-20 VIIRS source. Hence, it bears outlining the differences between the products derived from NOAA-20 VIIRS as against the Suomi National Polar-orbiting Partnership (SNPP) VIIRS.\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_NOAA20\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/AERDT_L2_VIIRS_NOAA20_NRT_2.json b/datasets/AERDT_L2_VIIRS_NOAA20_NRT_2.json index 15b00920b0..db90b7007b 100644 --- a/datasets/AERDT_L2_VIIRS_NOAA20_NRT_2.json +++ b/datasets/AERDT_L2_VIIRS_NOAA20_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDT_L2_VIIRS_NOAA20_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) - Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions' Moderate Imaging Spectroradiometer (MODIS) instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible wavelengths).\r\n\r\nThis orbit-level product (Short-name: AERDT_L2_VIIRS_NOAA20_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. This Version-2 set of products is the first collection of the Level-2 Dark Target Aerosol derived from the NOAA-20 VIIRS source. Hence, it bears outlining the differences between the products derived from NOAA-20 VIIRS as against the Suomi National Polar-orbiting Partnership (NOAA20) VIIRS.", "links": [ { diff --git a/datasets/AERDT_L2_VIIRS_SNPP_2.json b/datasets/AERDT_L2_VIIRS_SNPP_2.json index b378406ef4..969ad9ea41 100644 --- a/datasets/AERDT_L2_VIIRS_SNPP_2.json +++ b/datasets/AERDT_L2_VIIRS_SNPP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDT_L2_VIIRS_SNPP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) incarnation of the dark target (DT) aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).\r\n\r\nThis orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1).\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_SNPP\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/AERDT_L2_VIIRS_SNPP_NRT_1.1.json b/datasets/AERDT_L2_VIIRS_SNPP_NRT_1.1.json index 7cca43090a..c5938c182c 100644 --- a/datasets/AERDT_L2_VIIRS_SNPP_NRT_1.1.json +++ b/datasets/AERDT_L2_VIIRS_SNPP_NRT_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDT_L2_VIIRS_SNPP_NRT_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission\u2019s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).", "links": [ { diff --git a/datasets/AERDT_L2_VIIRS_SNPP_NRT_2.json b/datasets/AERDT_L2_VIIRS_SNPP_NRT_2.json index 13bb7c5bf4..df8b3fa8ce 100644 --- a/datasets/AERDT_L2_VIIRS_SNPP_NRT_2.json +++ b/datasets/AERDT_L2_VIIRS_SNPP_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERDT_L2_VIIRS_SNPP_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission\u2019s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).\r\n\r\nThis orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1).", "links": [ { diff --git a/datasets/AERIALDIGI.json b/datasets/AERIALDIGI.json index a1bf6cb4a6..647e8b9f70 100644 --- a/datasets/AERIALDIGI.json +++ b/datasets/AERIALDIGI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERIALDIGI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aeronautics and Space Administration (NASA) Aircraft \n Scanners data set contains digital imagery acquired from several \n multispectral scanners, including Daedalus thematic mapper simulator\n scanners and the thermal infrared multispectral scanner. Data are\n collected from selected areas over the conterminous United States,\n Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating\n from the NASA Ames Research Center in Moffett Field, California, and by\n NASA Learjet aircraft, operating from Stennis Space Center in Bay St.\n Louis, Mississippi. Limited international acquisitions also are\n available.\n \n In cooperation with the Jet Propulsion Laboratory and Daedalus \n Enterprises,Inc., NASA developed several multispectral sensors. The\n data acquired from these sensors supports NASA's Airborne Science and\n Applications Program and have been identified as precursors to the\n instruments scheduled to fly on Earth Observing System platforms.\n \n THEMATIC MAPPER SIMULATOR\n \n The Thematic Mapper Simulator (TMS) sensor is a line scanning device \n designed for a variety of Earth science applications. Flown aboard \n NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of \n View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) \n at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second \n with 716 pixels per scan line. Swath width is 8.3 nautical miles \n (15.4 kilometers) at 65,000 feet while the scanner's Field of View is \n 42.5 degrees. \n \n NS-001 MULTISPECTRAL SCANNER \n \n The NS-001multispectral scanner is a line scanning device designed to \n simulate Landsat thematic mapper (TM) sensor performance, including a \n near infrared/short-wave infrared band used in applications similar to those \n of the TM sensor (e.g., Earth resources mapping, vegetation/land cover \n mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001\n sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a\n ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a\n variable scan rate (10 to 100 scans per second) with 699 pixels per scan line,\n but the available motor drive supply restricts the maximum stable scan speed\n to approximately 85 revolutions per second. A scan rate of 100 revolutions per\n second is possible, but not probable, for short scan lines; therefore, a\n combination of factors, including aircraft flight requirements and maximum\n scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9\n nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or\n field of regard for the sensor is 100 degrees, plus or minus 15 degrees for\n roll compensation. \n \n THERMAL INFRARED MULTISPECTRAL SCANNER \n \n The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning \n device originally designed for geologic applications. Flown aboard NASA\n C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a \n nominal Instantaneous Field of View of 2.5 milliradians with a ground \n resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a \n selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels \n per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at \n 10,000 feet while the scanner's Field of View is 76.56 degrees.", "links": [ { diff --git a/datasets/AERONET_aerosol_706_1.json b/datasets/AERONET_aerosol_706_1.json index 517038361a..a5b3738d8f 100644 --- a/datasets/AERONET_aerosol_706_1.json +++ b/datasets/AERONET_aerosol_706_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AERONET_aerosol_706_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign. AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes.", "links": [ { diff --git a/datasets/AEROSE_0.json b/datasets/AEROSE_0.json index 6b7de96907..ccab9f77e9 100644 --- a/datasets/AEROSE_0.json +++ b/datasets/AEROSE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AEROSE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AEROSE is an internationally recognized series of trans-Atlantic field campaigns conducted onboard the NOAA Ship Ronald H. Brown designed to explore African air mass outflows and their impacts on climate, weather, and environmental health.", "links": [ { diff --git a/datasets/AE_5DSno_2.json b/datasets/AE_5DSno_2.json index 05d78a947e..eeb678f42f 100644 --- a/datasets/AE_5DSno_2.json +++ b/datasets/AE_5DSno_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_5DSno_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).", "links": [ { diff --git a/datasets/AE_DyOcn_2.json b/datasets/AE_DyOcn_2.json index 0ee6f48a0e..6c3e4253d1 100644 --- a/datasets/AE_DyOcn_2.json +++ b/datasets/AE_DyOcn_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_DyOcn_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-3 daily product (AE_DyOcn), weekly product (AE_WkOcn), and monthly product (AE_MoOcn) include SST, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over oceans in a 0.25 degree by 0.25 degree grid, generated from AE_Ocean.", "links": [ { diff --git a/datasets/AE_DySno_2.json b/datasets/AE_DySno_2.json index 5b721fb57a..d0a7bfd210 100644 --- a/datasets/AE_DySno_2.json +++ b/datasets/AE_DySno_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_DySno_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).", "links": [ { diff --git a/datasets/AE_L2A_4.json b/datasets/AE_L2A_4.json index db6416f92b..d26e8ad229 100644 --- a/datasets/AE_L2A_4.json +++ b/datasets/AE_L2A_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_L2A_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E Level-2A product (AE_L2A) contains daily 50 minute half-orbit swath brightness temperatures for six channels ranging from 6.9 GHz through 89 GHz. Data are resampled to spatial resolutions ranging from 5.4 km to 56 km. Each file is packaged with geolocation and quality information as well as ancillary data.", "links": [ { diff --git a/datasets/AE_Land3_2.json b/datasets/AE_Land3_2.json index f7602f3e4a..41b9fe758a 100644 --- a/datasets/AE_Land3_2.json +++ b/datasets/AE_Land3_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Land3_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This gridded Level-3 land surface product (AE_Land3) includes daily measurements of surface soil moisture and vegetation/roughness water content interpretive information, as well as brightness temperatures and quality control variables. Ancillary data include time, geolocation, and quality assessment.", "links": [ { diff --git a/datasets/AE_Land_2.json b/datasets/AE_Land_2.json index 76f9f22ac2..6f180f280e 100644 --- a/datasets/AE_Land_2.json +++ b/datasets/AE_Land_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Land_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2B land surface product (AE_Land) includes daily measurements of surface soil moisture, vegetation/roughness water content interpretive information, and quality control variables. Ancillary data include time, geolocation, and quality assessment.", "links": [ { diff --git a/datasets/AE_Land_3.json b/datasets/AE_Land_3.json index 4159c5a1ad..ee16637548 100644 --- a/datasets/AE_Land_3.json +++ b/datasets/AE_Land_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Land_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains gridded estimates of soil moisture in the top 1 cm of soil, averaged over the AMSR-E retrieval footprint. Soil moisture is estimated from AMSR-E/Aqua L2A brightness temperature (Tb) measurements using two different approaches: the Normalized Polarization Difference algorithm (NPD) and the Single Channel Algorithm (SCA).\n\nAncillary data are also provided to help interpret the soil moisture observations, including vegetation roughness, observation counts for various surface conditions, and QA flags.", "links": [ { diff --git a/datasets/AE_MoOcn_2.json b/datasets/AE_MoOcn_2.json index 4068140f37..2715e73cf5 100644 --- a/datasets/AE_MoOcn_2.json +++ b/datasets/AE_MoOcn_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_MoOcn_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-3 daily product (AE_DyOcn), weekly product (AE_WkOcn), and monthly product (AE_MoOcn) include SST, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over oceans in a 0.25 degree by 0.25 degree grid, generated from AE_Ocean.", "links": [ { diff --git a/datasets/AE_MoSno_2.json b/datasets/AE_MoSno_2.json index 473f5f367e..b0b813d491 100644 --- a/datasets/AE_MoSno_2.json +++ b/datasets/AE_MoSno_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_MoSno_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).", "links": [ { diff --git a/datasets/AE_Ocean_2.json b/datasets/AE_Ocean_2.json index e4e5ea6912..523b04cd92 100644 --- a/datasets/AE_Ocean_2.json +++ b/datasets/AE_Ocean_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Ocean_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This daily Level-2B swath data set includes Sea Surface Temperature (SST), Near-Surface Wind Speed, Columnar Water Vapor, and Cloud liquid Water data arrays, and was used as input to generate the following daily, weekly, and monthly Level-3 gridded ocean products; AE_DyOcn, AE_WkOcn, and AE_MoOcn.", "links": [ { diff --git a/datasets/AE_Rain_3.json b/datasets/AE_Rain_3.json index fe1bdff61f..4342203985 100644 --- a/datasets/AE_Rain_3.json +++ b/datasets/AE_Rain_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Rain_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2010 Version 2 algorithm using Version 3 of the AMSR-E Level-2A Brightness Temperatures.", "links": [ { diff --git a/datasets/AE_Rain_4.json b/datasets/AE_Rain_4.json index 1bb2d99463..8c8b9ab906 100644 --- a/datasets/AE_Rain_4.json +++ b/datasets/AE_Rain_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_Rain_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2017 algorithm using Version 4 of the AMSR-E Level-2A Brightness Temperatures.", "links": [ { diff --git a/datasets/AE_RnGd_2.json b/datasets/AE_RnGd_2.json index a871b17dd6..b5f3f7260d 100644 --- a/datasets/AE_RnGd_2.json +++ b/datasets/AE_RnGd_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_RnGd_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 rainfall accumulation product (AE_RnGd) consists of two grids of 28 rows by 72 columns of monthly averaged rainfall accumulation over ocean and land. Both grids are 5 degree by 5 degree resolution.", "links": [ { diff --git a/datasets/AE_SI12_3.json b/datasets/AE_SI12_3.json index 48f7c7d0e1..4791d8a9ab 100644 --- a/datasets/AE_SI12_3.json +++ b/datasets/AE_SI12_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_SI12_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 gridded product (AE_SI12) includes brightness temperatures at 18.7 through 89.0 GHz, sea ice concentration, and snow depth over sea ice.", "links": [ { diff --git a/datasets/AE_SI25_3.json b/datasets/AE_SI25_3.json index b5411c931f..5942d294d3 100644 --- a/datasets/AE_SI25_3.json +++ b/datasets/AE_SI25_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_SI25_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 gridded product (AE_SI25) includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations.", "links": [ { diff --git a/datasets/AE_SI6_3.json b/datasets/AE_SI6_3.json index 08311e74a1..ebaeafba5d 100644 --- a/datasets/AE_SI6_3.json +++ b/datasets/AE_SI6_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_SI6_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 gridded product (AE_SI6) includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 6.25 km spatial resolution. This product is an intermediate product during processing of AMSR-E Level-3 sea ice products at 12.5 km and 25 km resolution.", "links": [ { diff --git a/datasets/AE_SID_1.json b/datasets/AE_SID_1.json index b8ef2d3792..8717a66567 100644 --- a/datasets/AE_SID_1.json +++ b/datasets/AE_SID_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_SID_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides 6.25 km sea ice drift grids for the Northern and Southern Hemispheres.", "links": [ { diff --git a/datasets/AE_WkOcn_2.json b/datasets/AE_WkOcn_2.json index 0b5804eac8..00c7dc7a71 100644 --- a/datasets/AE_WkOcn_2.json +++ b/datasets/AE_WkOcn_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AE_WkOcn_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-3 daily product (AE_DyOcn), weekly product (AE_WkOcn), and monthly product (AE_MoOcn) include SST, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over oceans in a 0.25 degree by 0.25 degree grid, generated from AE_Ocean.", "links": [ { diff --git a/datasets/AFLVIS1B_1.json b/datasets/AFLVIS1B_1.json index d20d2e8bdf..f83014d52f 100644 --- a/datasets/AFLVIS1B_1.json +++ b/datasets/AFLVIS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AFLVIS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains return energy waveform data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.", "links": [ { diff --git a/datasets/AFLVIS2_1.json b/datasets/AFLVIS2_1.json index 273e86c246..81a84e503f 100644 --- a/datasets/AFLVIS2_1.json +++ b/datasets/AFLVIS2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AFLVIS2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.", "links": [ { diff --git a/datasets/AFOLVIS1A_1.json b/datasets/AFOLVIS1A_1.json index 018478dfab..fee026b25f 100644 --- a/datasets/AFOLVIS1A_1.json +++ b/datasets/AFOLVIS1A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AFOLVIS1A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.", "links": [ { diff --git a/datasets/AG100_003.json b/datasets/AG100_003.json index fdef0861df..a03af46463 100644 --- a/datasets/AG100_003.json +++ b/datasets/AG100_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AG100_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters.\r\n\r\nThe National Aeronautics and Space Administration\u2019s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. ", "links": [ { diff --git a/datasets/AG1km_003.json b/datasets/AG1km_003.json index d4f488c0ea..5f0b6e3b39 100644 --- a/datasets/AG1km_003.json +++ b/datasets/AG1km_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AG1km_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG1KM data are available globally at spatial resolution of 1 kilometer.\r\n\r\nThe National Aeronautics and Space Administration\u2019s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.", "links": [ { diff --git a/datasets/AG5KMMOH_041.json b/datasets/AG5KMMOH_041.json index 81296eee6a..eb9eb3d718 100644 --- a/datasets/AG5KMMOH_041.json +++ b/datasets/AG5KMMOH_041.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AG5KMMOH_041", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product (MOD10CM) (https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded NDVI product (MOD13C2) (https://doi.org/10.5067/MODIS/MOD13C2.006). ASTER GED Monthly V041 data products are offered in Hierarchical Data Format 5 (HDF5).\r\n\r\nThe National Aeronautics and Space Administration\u2019s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.", "links": [ { diff --git a/datasets/AGB_CanopyHt_Cover_NewEngland_1854_1.json b/datasets/AGB_CanopyHt_Cover_NewEngland_1854_1.json index 452887871f..e12804cfb7 100644 --- a/datasets/AGB_CanopyHt_Cover_NewEngland_1854_1.json +++ b/datasets/AGB_CanopyHt_Cover_NewEngland_1854_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AGB_CanopyHt_Cover_NewEngland_1854_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.", "links": [ { diff --git a/datasets/AGB_Carbon_Sequestration_RGGI_1922_1.json b/datasets/AGB_Carbon_Sequestration_RGGI_1922_1.json index f14bdfb945..eaad835e6d 100644 --- a/datasets/AGB_Carbon_Sequestration_RGGI_1922_1.json +++ b/datasets/AGB_Carbon_Sequestration_RGGI_1922_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AGB_Carbon_Sequestration_RGGI_1922_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.", "links": [ { diff --git a/datasets/AGB_Great_Slave_Lake_NWT_2365_1.json b/datasets/AGB_Great_Slave_Lake_NWT_2365_1.json index 448115c7e9..b372bc9ecc 100644 --- a/datasets/AGB_Great_Slave_Lake_NWT_2365_1.json +++ b/datasets/AGB_Great_Slave_Lake_NWT_2365_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AGB_Great_Slave_Lake_NWT_2365_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds aboveground biomass (ABG) estimates for areas in the Great Slave Lake Region in the Northwest Territories of Canada for 2019. ABG was estimated from L-band synthetic aperture radar (SAR) data obtained from JAXA's Advanced Land Observing Satellite-2 (ALOS-2/PALSAR-2) and supplemented with data from NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument. SAR data were collected from 2017 to 2021. In situ AGB measurements at 14 plots sampled in 2019 were used to calibrate a logarithmic regression to relate the radar datasets to in situ AGB data. Then, AGB was mapped over available ALOS-2 tiles. The estimates are provided in 20-Mg ha-1 bins at 100-m resolution in cloud optimized GeoTIFF format.", "links": [ { diff --git a/datasets/AGB_NEP_Disturbance_US_Forests_1829_2.json b/datasets/AGB_NEP_Disturbance_US_Forests_1829_2.json index 6db08a0de1..5a2bd9e95a 100644 --- a/datasets/AGB_NEP_Disturbance_US_Forests_1829_2.json +++ b/datasets/AGB_NEP_Disturbance_US_Forests_1829_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AGB_NEP_Disturbance_US_Forests_1829_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset, derived from the National Forest Carbon Monitoring System (NFCMS), provides estimates of forest carbon stocks and fluxes in the form of aboveground woody biomass (AGB), total live biomass, total ecosystem carbon, aboveground coarse woody debris (CWD), and net ecosystem productivity (NEP) as a function of the number of years since the most recent disturbance (i.e., stand age) for forests of the conterminous U.S. at a 30 m resolution for the benchmark years 1990, 2000, and 2010. The data were derived from an inventory-constrained version of the Carnegie-Ames-Stanford Approach (CASA) carbon cycle process model that accounts for disturbance processes for each combination of forest type, site productivity, and pre-disturbance biomass. Also provided are the core model data inputs including the year of the most recent disturbance according to the North American Forest Dynamics (NAFD) and the Monitoring Trends in Burn Severity (MTBS) data products; the type of disturbance; biomass estimates from the year 2000 according to the National Biomass and Carbon Dataset (NBCD); forest-type group; a site productivity classification; and the number of years since stand-replacing disturbance. The data are useful for a wide range of applications including monitoring and reporting recent dynamics of forest carbon across the conterminous U.S., assessment of recent trends with attribution to disturbance and regrowth drivers, conservation planning, and assessment of climate change mitigation opportunities within the forest sector.", "links": [ { diff --git a/datasets/AGB_Pantropics_Amazon_Mexico_1824_1.json b/datasets/AGB_Pantropics_Amazon_Mexico_1824_1.json index aa3365260e..743bb0faf8 100644 --- a/datasets/AGB_Pantropics_Amazon_Mexico_1824_1.json +++ b/datasets/AGB_Pantropics_Amazon_Mexico_1824_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AGB_Pantropics_Amazon_Mexico_1824_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.", "links": [ { diff --git a/datasets/AHI_H08-STAR-L2P-v2.70_2.70.json b/datasets/AHI_H08-STAR-L2P-v2.70_2.70.json index c1a5f60fc8..c773f9b1d0 100644 --- a/datasets/AHI_H08-STAR-L2P-v2.70_2.70.json +++ b/datasets/AHI_H08-STAR-L2P-v2.70_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHI_H08-STAR-L2P-v2.70_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Himawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST. Accurate sensor calibration, image navigation and (co)registration, high spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping) offer vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P and MTSAT-2 Imagers. From altitude 35,800km, H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km at view zenith angle 67-deg, with a 10-min temporal sampling. The AHI L2P (swath) SST product is derived at the native sensor resolution using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO processes every 10-min FD data, identifies good quality ocean pixels (Petrenko et al., 2010) and derives SST using the four-band (8.4, 10.3, 11.2 and 12.3um) Non-Linear SST (NLSST) regression algorithm (Petrenko et al., 2014), trained against in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise. The collated L2P reports SSTs and brightness temperatures (BTs) in clear-sky water pixels (defined as ocean, sea, lake or river), and fill values elsewhere. All pixels with valid SSTs are recommended for use. ACSPO files also include sun-sensor geometry, l2p_flags (day/night, land, ice, twilight, and glint flags), and NCEP wind speed. The L2P is reported in NetCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume 0.6GB/day. Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). The H08 AHI SSTs and BTs are continuously validated against in situ data in SQUAM (Dash et al, 2010), and RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), 0.02-deg equal-angle gridded ACSPO L3C product is available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L3C-v2.70.", "links": [ { diff --git a/datasets/AHI_H08-STAR-L3C-v2.70_2.70.json b/datasets/AHI_H08-STAR-L3C-v2.70_2.70.json index 33a2bc5e9b..0ea0496c76 100644 --- a/datasets/AHI_H08-STAR-L3C-v2.70_2.70.json +++ b/datasets/AHI_H08-STAR-L3C-v2.70_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHI_H08-STAR-L3C-v2.70_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L2P-v2.70. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules available per 24hr interval, with a total data volume of 0.2GB/day. Valid SSTs are found over clear-sky oceans, sea, lakes or rivers, with fill values reported elsewhere. The following layers are reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST (Petrenko et al., 2016). The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/AHS_Surveys_Casey_ITRF2000_1.json b/datasets/AHS_Surveys_Casey_ITRF2000_1.json index 466427dc40..27ad5f642b 100644 --- a/datasets/AHS_Surveys_Casey_ITRF2000_1.json +++ b/datasets/AHS_Surveys_Casey_ITRF2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHS_Surveys_Casey_ITRF2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This consolidated dataset consists of Australian Hydrographic Service (AHS) surveys HI621A and HI545 converted to International Terrestrial Reference Frame 2000 (ITRF2000) horizontal datum with Z conversion values for multiple height datums. The data was provided to the Australian Antarctic Division by Paul Digney of Jacobs consulting in February 2021.\n\nIncluded survey datasets:\n-\tHI621A.shp (Validated folder)\n-\t1812_5093-HI621A_CASEY_Terrestrial.shp\n-\tQC_HI545_12pt5_appraised\n\nAll data are in horizontal datum ITRF2000 and have been combined into a single ESRI geodatabase feature class titled AHS_Surveys_Casey_ITRF2000.\nAttribute data shows quality information, conversion factors (shift in metres) for multiple datums and the MSL orthometric height for Casey:\n\nColumn Name,\tAlias,\tMeaning\nEasting,\tEasting,\tEasting ITRF2000\nNorthing,\tNorthing,\tNorthing ITRF2000\nCD_To_GRS8,\tCD_To_GRS80,\tLAT (Chart Datum) to the Ellipsoid\nCD_TO_MSL_Casey,\tCD_To_MSL_Casey,\tEllipsoid to Casey MSL\nZ_To_GRS80,\tZ_To_GRS80,\tHeight to the Ellipsoid\nZ_To_MSL_Casey,\tZ_To_MSL_Casey,\tLocal MSL orthometric height\nVert_Uncer,\tVertical_Uncertainty,\tHow good is the Vertical Position\nHoriz_Unce,\tHorizontal Uncertainty,\tHow good is the Horizontal Position\nUncertaint,\tUncertainty Comments,\t\nDepth_Comm,\tDepth_Comments,\t\n\nVertical uncertainty ranges from 0.05 to 0.64 m and horizontal uncertainty ranges from 0.05 to 1.0 m\n\nSee the attached document \u2018Metadata Record Casey Final.xlsx\u2019 for further details.", "links": [ { diff --git a/datasets/AHS_Surveys_Davis_ITRF2000_1.json b/datasets/AHS_Surveys_Davis_ITRF2000_1.json index 38d03ac776..8dc58865b4 100644 --- a/datasets/AHS_Surveys_Davis_ITRF2000_1.json +++ b/datasets/AHS_Surveys_Davis_ITRF2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHS_Surveys_Davis_ITRF2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This consolidated dataset consisting of Australian Hydrographic Service (AHS) surveys HI468, HI590, HI621 and HI634 converted to International Terrestrial Reference Frame 2000 (ITRF2000) horizontal datum with Z conversion values for multiple height datums. The data was provided to the AAD by Paul Digney of Jacobs consulting in February 2021.\nIncluded survey datasets:\n-\tHI468_Davis_Z43_Appraised_Part_ITRF2000\n-\tHI468_Davis_Z44_Appraised_ITRF2000\n-\tHI590_Davis_Part_ITRF2000\n-\tHI621B_Davis_Merged_ITRF2000\n-\tHI634_Davis_AreaA_ITRF2000\n-\tHI634_Davis_AreaD_ITRF2000\n-\tHI634_Davis_AreaF_ITRF2000\n-\tHI634_Davis_AreaI_ITRF2000\n-\tHI634_Davis_AreaJ3_ITRF2000\n-\tHI634_Davis_AreaJ4_ITRF2000\n-\tHI634_Davis_Rocks_ITRF2000\n\nAll data are in horizontal datum ITRF2000 and have been combined into a single ESRI geodatabase feature class titled AHS_Surveys_Davis_ITRF2000.\nAttribute data shows quality information, conversion factors (shift in metres) for multiple datums and the MSL orthometric height in the Davis 83 datum:\n\nColumn Name\t Alias\t Meaning\nEasting,\t Easting,\t Easting ITRF2000\nNorthing,\t Northing,\t Northing ITRF2000\nCD_To_GRS8,\t CD_To_GRS80,\t LAT (Chart Datum) to the Ellipsoid\nGRS80_To_D,\t GRS80_To_DAVIS83_MSL,\t Ellipsoid to DAVIS Height Datum 83\nZ_To_GRS80,\t Z_To_GRS80,\t Height to the Ellipsoid\nZ_To_DAVIS,\t Z_To_DAVIS83_MSL,\t Local MSL orthometric height (DAVIS Height Datum 83)\nVertical_U,\t Vertical_Uncertainty,\t How good is the Vertical Position\nHorizontal,\t Horizontal Uncertainty,\t How good is the Horizontal Position\nUncertaint,\t Uncertainty Comments,\t\nDepth_Comm,\t Depth_Comments,\t\n\nVertical uncertainty is 0.24 to 0.5 m for hydrographic values and 0.25 to 0.5 m for terrestrial values.\nSee the attached document \u2018Metadata_Record_Davis_Final (002).xlsx\u2019 for further details.", "links": [ { diff --git a/datasets/AHS_Surveys_Macca_ITRF2000_2.json b/datasets/AHS_Surveys_Macca_ITRF2000_2.json index c5053399cc..54b21f389f 100644 --- a/datasets/AHS_Surveys_Macca_ITRF2000_2.json +++ b/datasets/AHS_Surveys_Macca_ITRF2000_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHS_Surveys_Macca_ITRF2000_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AADC (Australian Antarctic Data Centre) is in the process of converting all internally held spatial datasets to the ITRF2000 horizontal datum. This consolidated dataset consists of surveys HI623_alatB_gg, HI625_alatB_GG, HI632_alat_B_gg, HI632_alat_C_gg, LADSII_MMI20756_HSDB_T0001_SD_100029052_op, LADSII_MMI20756_HSDB_T0001_SD_100029053_op, LADSII_MMI20756_HSDB_T0001_SD_100029054_op converted to ITRF2000 horizontal datum with Z conversion values for multiple height datums. The data was provided to the AAD by Paul Digney of Jacobs consulting in March 2021.\n\nIncluded survey datasets:\n\u2022\tHI623_alatB_gg\n\u2022\tHI625_alatB_GG\n\u2022\tHI632_alat_B_gg\n\u2022\tHI632_alat_C_gg\n\u2022\tLADSII_MMI20756_HSDB_T0001_SD_100029052_op\n\u2022\tLADSII_MMI20756_HSDB_T0001_SD_100029053_op\n\u2022\tLADSII_MMI20756_HSDB_T0001_SD_100029054_op\n\nAll data are in horizontal datum ITRF2000 and have been combined into a single ESRI geodatabase feature class titled AHS_Surveys_Macca_ITRF2000.\n\nAttribute data shows quality information, conversion factors (shift in metres) for multiple datums and the MSL orthometric height:\n\nColumn Name\t Alias\tMeaning\nEasting\tEasting\tEasting ITRF2000\nNorthing\tNorthing\tNorthing ITRF2000\nLAT_to_GRS\tLAT_to_GRS\tLAT (Chart Datum) to GSR80\nLAT_to_Mac\tLAT_to_Mac\tLAT to Macca MSL\nZ_To_GRS80\tZ_To_GRS80\tHeight to the Ellipsoid\nZ_To_Macca\tZ_To_Macca\tLocal MSL orthometric height\nVertical_U\tVertical_U\tHow good is the Vertical Position\nHorizontal\tHorizontal\tHow good is the Horizontal Position\nUncertaint\tUncertaint\tUncertainty Comments\nDepth_Comm\t Depth_Comments\t\n\t\t\nVertical uncertainty ranges from 0.5 to 1.2 m and horizontal uncertainty ranges from 2 to 5.5 m. Null values indicate unknown uncertainty.\n\nSee the attached document \u2018Metadata_Record_Macqaurie Island Final.xlsx\u2019 for further details.", "links": [ { diff --git a/datasets/AHS_Surveys_Mawson_ITRF2000_1.json b/datasets/AHS_Surveys_Mawson_ITRF2000_1.json index f4076f7984..aa689d7bd4 100644 --- a/datasets/AHS_Surveys_Mawson_ITRF2000_1.json +++ b/datasets/AHS_Surveys_Mawson_ITRF2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHS_Surveys_Mawson_ITRF2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This consolidated dataset consists of Australian Hydrographic Service (AHS) surveys HI621C, 5135 (Terrestrial), HI364, HI514, and HI607 converted to International Terrestrial Reference Frame 2000 (ITRF2000) horizontal datum with Z conversion values for multiple height datums. The data was provided to the AAD by Paul Digney of Jacobs consulting in February 2021.\n\nIncluded survey datasets:\n\u2022\tHI621C_MAWSON_merged.shp\n\u2022\tHI621C_MAWSON_merged.shp\n\u2022\tTerrestrial_Data_5135\n\u2022\tHI364_HSDB_T0001_SD_100035029_op_soundings\n\u2022\tQC_HI 514 HDCS_FDD_appraised (Mawson Approches)\n\u2022\tHI607.Shp\n\nAll data are in horizontal datum ITRF2000 and have been combined into a single ESRI geodatabase feature class titled AHS_Surveys_Mawson_ITRF2000.\n\nAttribute data shows quality information, conversion factors (shift in metres) for multiple datums and the MSL orthometric height:\n\nColumn Name,\t Alias,\tMeaning\nEasting,\tEasting,\tEasting ITRF2000\nNorthing,\tNorthing,\tNorthing ITRF2000\nCD_To_GRS8,\tCD_To_GRS80,\t LAT (Chart Datum) to the Ellipsoid\nLAT_to_GRS80,\tLAT_to_GRS80,\tLAT (Chart Datum) to GSR80\nLAT_to_MSL_Mawson,\tLAT_to_MSL_Mawson,\tLAT to Mawson MSL\nZ_To_GRS80,\tZ_To_GRS80,\tHeight to the Ellipsoid\nZ_To_MSL_Mawson,\tZ_To_MSL_Mawson,\tLocal MSL orthometric height\nVertical_U,\tVertical_Uncertainty,\tHow good is the Vertical Position\nHorizontal,\tHorizontal Uncertainty,\tHow good is the Horizontal Position\nUncertaint,\tUncertainty Comments,\t\nDepth_Comm,\t Depth_Comments,\t\n\t\t\nVertical uncertainty ranges from 0.05 to 0.64 m and horizontal uncertainty ranges from 0.05 to 1.0 m\nSee the attached document \u2018Metadata_Record_Mawson Final REV2.xlsx\u2019 for further details.", "links": [ { diff --git a/datasets/AHS_hydrographic_surveys_antarctica_1.json b/datasets/AHS_hydrographic_surveys_antarctica_1.json index d22a24cea0..c2a0ec877c 100644 --- a/datasets/AHS_hydrographic_surveys_antarctica_1.json +++ b/datasets/AHS_hydrographic_surveys_antarctica_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AHS_hydrographic_surveys_antarctica_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service have conducted hydrographic surveys near the coasts of the Australian Antarctic Territory and Heard Island and Macquarie Island.\nData and metadata for hydrographic surveys by the RAN Australian Hydrographic Service in Antarctica and at Heard Island and Macquarie Island has been provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The survey locations in Antarctica were Mawson, Davis, Casey and Commonwealth Bay. The surveys were conducted since December 1993. Generally the surveys are denoted by a Hydrographic Instruction (HI) eg HI176. There is a metadata record for each survey from which the survey data and metadata can be obtained. The metadata records for the individual surveys are linked to this metadata record.\n\nThe Australian Antarctic Data Centre has also had the soundings digitised from the fair sheets produced from earlier hydrographic surveys by the RAN Australian Hydrographic Service at Casey, Davis and Mawson. Metadata records describe the digitised soundings are also linked to this metadata record.", "links": [ { diff --git a/datasets/AIMS_REEF_LTM.json b/datasets/AIMS_REEF_LTM.json index f177e3c34c..1cd1bf67b4 100644 --- a/datasets/AIMS_REEF_LTM.json +++ b/datasets/AIMS_REEF_LTM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIMS_REEF_LTM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surveys of coral species richness were carried out at nearshore reefs of the Great Barrier Reef, Australia in conjunction with surveys of size structure and percentage cover of hard and soft coral communities. Species lists (Presence / Absence) were compiled at 2m and 5m below datum at two sites on 33 reefs between Mackay and Cooktown (latitude 16-23 degrees South) in 2004. The aim of the study was to document the status of near-shore coral communities in this region to serve both as a baseline against which future change could be compared and also identify communities potentially at risk from anthropogenic activities. Hard corals were identified to species level where possible though on occasion identification was limited to genus, soft corals were identified to genus.\n\nTotal Distribution Records : 8,906\n\nTotal Number of Taxa : 97 genera, 310 species", "links": [ { diff --git a/datasets/AIRABRAD_005.json b/datasets/AIRABRAD_005.json index f07b9146ea..efbeddd8a6 100644 --- a/datasets/AIRABRAD_005.json +++ b/datasets/AIRABRAD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRABRAD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.", "links": [ { diff --git a/datasets/AIRABRAD_NRT_005.json b/datasets/AIRABRAD_NRT_005.json index 1f4cb9c869..f2d171ca6e 100644 --- a/datasets/AIRABRAD_NRT_005.json +++ b/datasets/AIRABRAD_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRABRAD_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_NRT_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.", "links": [ { diff --git a/datasets/AIRG2SSD_006.json b/datasets/AIRG2SSD_006.json index e8a8fd369e..6aeba48154 100644 --- a/datasets/AIRG2SSD_006.json +++ b/datasets/AIRG2SSD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRG2SSD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This precipitation estimate from AIRS is using TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily \"Level 2G\" global grid with dimensions (24x1440x720). Thus every hour is a \"layer\", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are \"empty\". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file.", "links": [ { diff --git a/datasets/AIRG2SSD_IRonly_006.json b/datasets/AIRG2SSD_IRonly_006.json index 12df1d7ebd..b2e1318625 100644 --- a/datasets/AIRG2SSD_IRonly_006.json +++ b/datasets/AIRG2SSD_IRonly_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRG2SSD_IRonly_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily \"Level 2G\" global grid with dimensions (24x1440x720). Thus every hour is a \"layer\", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are \"empty\". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file.", "links": [ { diff --git a/datasets/AIRG2SSD_IRonly_7.0.json b/datasets/AIRG2SSD_IRonly_7.0.json index 4758ed4c98..1a164a6504 100644 --- a/datasets/AIRG2SSD_IRonly_7.0.json +++ b/datasets/AIRG2SSD_IRonly_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRG2SSD_IRonly_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily \"Level 2G\" global grid with dimensions (24x1440x720). Thus every hour is a \"layer\", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are \"empty\". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file.", "links": [ { diff --git a/datasets/AIRH2CCF_006.json b/datasets/AIRH2CCF_006.json index 62c6a7eacd..d0bacf98c3 100644 --- a/datasets/AIRH2CCF_006.json +++ b/datasets/AIRH2CCF_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2CCF_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH2CCF_7.0.json b/datasets/AIRH2CCF_7.0.json index dc4dad0aa6..789a702264 100644 --- a/datasets/AIRH2CCF_7.0.json +++ b/datasets/AIRH2CCF_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2CCF_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH2RET_006.json b/datasets/AIRH2RET_006.json index ffcadac5cf..b2ae9417a6 100644 --- a/datasets/AIRH2RET_006.json +++ b/datasets/AIRH2RET_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2RET_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240\ngranules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH2RET_7.0.json b/datasets/AIRH2RET_7.0.json index 7d63942d45..2376fde3ed 100644 --- a/datasets/AIRH2RET_7.0.json +++ b/datasets/AIRH2RET_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2RET_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH2SUP_006.json b/datasets/AIRH2SUP_006.json index f1e21fc56d..b74f4c07f3 100644 --- a/datasets/AIRH2SUP_006.json +++ b/datasets/AIRH2SUP_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2SUP_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data with 30 footprints cross track by 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH2SUP_7.0.json b/datasets/AIRH2SUP_7.0.json index 587f01c140..21ffba65d4 100644 --- a/datasets/AIRH2SUP_7.0.json +++ b/datasets/AIRH2SUP_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH2SUP_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. \n\nAn AIRS granule has been set as 6 minutes of data with 30 footprints cross track by 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRH3QP5_006.json b/datasets/AIRH3QP5_006.json index f082adeb0a..096dc8c89f 100644 --- a/datasets/AIRH3QP5_006.json +++ b/datasets/AIRH3QP5_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3QP5_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).", "links": [ { diff --git a/datasets/AIRH3QPM_006.json b/datasets/AIRH3QPM_006.json index 626701c000..3526c9bfcc 100644 --- a/datasets/AIRH3QPM_006.json +++ b/datasets/AIRH3QPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3QPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).", "links": [ { diff --git a/datasets/AIRH3SP8_006.json b/datasets/AIRH3SP8_006.json index 764ce8aabf..61a971dc9e 100644 --- a/datasets/AIRH3SP8_006.json +++ b/datasets/AIRH3SP8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3SP8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRH3SPD_006.json b/datasets/AIRH3SPD_006.json index 95fb4ae6a7..631594c93e 100644 --- a/datasets/AIRH3SPD_006.json +++ b/datasets/AIRH3SPD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3SPD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRH3SPD_7.0.json b/datasets/AIRH3SPD_7.0.json index 69c98d3915..e7a798c283 100644 --- a/datasets/AIRH3SPD_7.0.json +++ b/datasets/AIRH3SPD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3SPD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. \n\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRH3SPM_006.json b/datasets/AIRH3SPM_006.json index e53becc0f5..4ffef7bf56 100644 --- a/datasets/AIRH3SPM_006.json +++ b/datasets/AIRH3SPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3SPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRH3SPM_7.0.json b/datasets/AIRH3SPM_7.0.json index 98a7091749..5627f43e4d 100644 --- a/datasets/AIRH3SPM_7.0.json +++ b/datasets/AIRH3SPM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3SPM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRH3ST8_006.json b/datasets/AIRH3ST8_006.json index ac9bcb8cf0..d781da1cda 100644 --- a/datasets/AIRH3ST8_006.json +++ b/datasets/AIRH3ST8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3ST8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3ST8. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRH3STD_006.json b/datasets/AIRH3STD_006.json index c8be59274d..7019b3d8f0 100644 --- a/datasets/AIRH3STD_006.json +++ b/datasets/AIRH3STD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3STD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STD. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRH3STD_7.0.json b/datasets/AIRH3STD_7.0.json index 8e519d74a6..1617d08f7b 100644 --- a/datasets/AIRH3STD_7.0.json +++ b/datasets/AIRH3STD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3STD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STD. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.\n\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRH3STM_006.json b/datasets/AIRH3STM_006.json index 57391194b3..c4235c7904 100644 --- a/datasets/AIRH3STM_006.json +++ b/datasets/AIRH3STM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3STM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRH3STM_7.0.json b/datasets/AIRH3STM_7.0.json index 5739f9385f..8f00fbca72 100644 --- a/datasets/AIRH3STM_7.0.json +++ b/datasets/AIRH3STM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRH3STM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRHBRAD_005.json b/datasets/AIRHBRAD_005.json index b9b8e7d955..a15a655cf2 100644 --- a/datasets/AIRHBRAD_005.json +++ b/datasets/AIRHBRAD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRHBRAD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The HSB level 1B data set contains HSB calibrated and geolocated brightness temperatures in degrees Kelvin. This data set is generated from HSB Level 1A digital numbers (DN), including 4 microwave channels in the 150 - 190 GHz region of the spectrum. A day's worth of data is divided into 240 scenes each of 6 minute duration. For the HSB measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per HSB scene, which coincide very closely with the AIRS infrared footprints. HSB is primarily a humidity sounder that provides information on snow/ice cover and precipitation using the 150 GHz window channel, and the coarse distribution of moisture in the troposphere using the 183 GHz channels. Combined with simultaneous measurements from the AIRS and AMSU-A instruments, the calibrated HSB brightness temperatures will be used to initialize the atmospheric moisture profile required for the retrieval of the final AIRS geophysical products. An HSB level 1B daily summary browse product is also available to provide users with a global quick look capability when searching for data of interest. Summary Browse Products are high-level pictorial representations of AIRS Instrument (AIRS Infrared, AMSU-A and HSB) data designed as an aid to ordering data from the GSFC DISC or EDG. the HSB instrument failed in November of 2003.", "links": [ { diff --git a/datasets/AIRI2CCF_006.json b/datasets/AIRI2CCF_006.json index 09f36b14d1..0a3143deb0 100644 --- a/datasets/AIRI2CCF_006.json +++ b/datasets/AIRI2CCF_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRI2CCF_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRI2CCF_7.0.json b/datasets/AIRI2CCF_7.0.json index 297b5e8e6d..e4cad9000d 100644 --- a/datasets/AIRI2CCF_7.0.json +++ b/datasets/AIRI2CCF_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRI2CCF_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRIBQAP_005.json b/datasets/AIRIBQAP_005.json index 844f9d4fa6..c15b068119 100644 --- a/datasets/AIRIBQAP_005.json +++ b/datasets/AIRIBQAP_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRIBQAP_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination.", "links": [ { diff --git a/datasets/AIRIBQAP_NRT_005.json b/datasets/AIRIBQAP_NRT_005.json index 5d4d57b177..b0bc3dedc5 100644 --- a/datasets/AIRIBQAP_NRT_005.json +++ b/datasets/AIRIBQAP_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRIBQAP_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRS Level 1B Near Real Time (NRT) product (AIRIBQAP_NRT_005) differs from the routine product (AIRIBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination.", "links": [ { diff --git a/datasets/AIRIBRAD_005.json b/datasets/AIRIBRAD_005.json index 6745bb3ad3..8951578a26 100644 --- a/datasets/AIRIBRAD_005.json +++ b/datasets/AIRIBRAD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRIBRAD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nWARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.\n\nThe thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the \u201cspectral_freq\u201d parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K.\n\nUsers of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING.\n\n\n\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.", "links": [ { diff --git a/datasets/AIRIBRAD_NRT_005.json b/datasets/AIRIBRAD_NRT_005.json index c929b6753b..63723131ab 100644 --- a/datasets/AIRIBRAD_NRT_005.json +++ b/datasets/AIRIBRAD_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRIBRAD_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nWARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.\n\nThe thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the \u201cspectral_freq\u201d parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K.\n\nUsers of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING.\n\n\n\nThe AIRS Level 1B Near Real Time (NRT) product (AIRIBRAD_NRT_005) differs from the routine product (AIRIBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_NRT_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.", "links": [ { diff --git a/datasets/AIRIBRAD_NRT_BUFR_005.json b/datasets/AIRIBRAD_NRT_BUFR_005.json index 4ca1180262..2db6194c4e 100644 --- a/datasets/AIRIBRAD_NRT_BUFR_005.json +++ b/datasets/AIRIBRAD_NRT_BUFR_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRIBRAD_NRT_BUFR_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.\n\nThe thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the \u201cspectral_freq\u201d parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K.\n\nUsers of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING.\n\n\n\n\nThis product is a 324-channel subset of the AIRIBRAD_NRT_005 product in which the AMSU footprints from AIRABRAD_NRT_005 product are also included and converted to binary Universal Form for the Representation of meteorological data (BUFR). The AIRS and AMSU Level 1B products differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.", "links": [ { diff --git a/datasets/AIRICRAD_6.7.json b/datasets/AIRICRAD_6.7.json index 517285347b..3e14da414b 100644 --- a/datasets/AIRICRAD_6.7.json +++ b/datasets/AIRICRAD_6.7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRICRAD_6.7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct\nto this page. For this collection the switchover occurred on June 1, 2020.", "links": [ { diff --git a/datasets/AIRICRAD_NRT_6.7.json b/datasets/AIRICRAD_NRT_6.7.json index 015d0f6d1a..86dc9b58f0 100644 --- a/datasets/AIRICRAD_NRT_6.7.json +++ b/datasets/AIRICRAD_NRT_6.7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRICRAD_NRT_6.7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research.\n\nAs a Near Real Time (NRT) product this differs from AIRICRAD.6.7 AIRS differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude.", "links": [ { diff --git a/datasets/AIRMISR_BARC_2001_1.json b/datasets/AIRMISR_BARC_2001_1.json index e89de53d00..5ffade1ee6 100644 --- a/datasets/AIRMISR_BARC_2001_1.json +++ b/datasets/AIRMISR_BARC_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_BARC_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AirMISR BARC 2001 data were acquired during a flight over the Beltsville Agricultural Research Center (BARC) on July 21, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_BARTLETT_2003_1.json b/datasets/AIRMISR_BARTLETT_2003_1.json index c4494e30f5..b7339dcd71 100644 --- a/datasets/AIRMISR_BARTLETT_2003_1.json +++ b/datasets/AIRMISR_BARTLETT_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_BARTLETT_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_BARTLETT_2003 data were acquired during a flight over the Bartlett Experimental Forest, New Hampshire, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction.", "links": [ { diff --git a/datasets/AIRMISR_CLAMS_2001_1.json b/datasets/AIRMISR_CLAMS_2001_1.json index 1263d89601..2f7220d7a0 100644 --- a/datasets/AIRMISR_CLAMS_2001_1.json +++ b/datasets/AIRMISR_CLAMS_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_CLAMS_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_CLAMS_2001 data were acquired during the CLAMS campaign on July 12, July 17, August 1, and August 2 of 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign was held in the summer of 2001 at the CERES Ocean Validation Experiment (COVE) site in the Chesapeake Bay, 20 km east of Virginia Beach. CLAMS is a clear-sky, shortwave closure campaign in conjunction with MISR, CERES, MODIS-Atmospheres and the Global Aerosol Climatology Project (GACP). Its goals were to obtain more accurate broadband fluxes at sea surface and within the atmosphere, space-time variability of spectral BRDF of the sea surface, and aerosol retrievals. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_HARVARD_2003_1.json b/datasets/AIRMISR_HARVARD_2003_1.json index 6b090688a2..9de6afaac5 100644 --- a/datasets/AIRMISR_HARVARD_2003_1.json +++ b/datasets/AIRMISR_HARVARD_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_HARVARD_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_HARVARD_2003 data set was acquired during a flight over the Harvard Forest, Massachusetts, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction.", "links": [ { diff --git a/datasets/AIRMISR_HOWLAND_2003_1.json b/datasets/AIRMISR_HOWLAND_2003_1.json index 34f0c5577e..7e5e75af2b 100644 --- a/datasets/AIRMISR_HOWLAND_2003_1.json +++ b/datasets/AIRMISR_HOWLAND_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_HOWLAND_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_HOWLAND_2003 data were acquired during a field mission which overflew Howland Forest, Maine on August 28, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_KONVEX_1.json b/datasets/AIRMISR_KONVEX_1.json index bd2714ec48..14f4fc022d 100644 --- a/datasets/AIRMISR_KONVEX_1.json +++ b/datasets/AIRMISR_KONVEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_KONVEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_KONVEX data were acquired during the KONza Validation EXperiment (KONVEX) which occurred 11 - 18 July 1999. The AIRMISR_KONVEX data were obtained on 13 July 1999, flight #36 only. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_LUNAR_LAKE_2000_1.json b/datasets/AIRMISR_LUNAR_LAKE_2000_1.json index 4cfdf88bc2..87639cfd14 100644 --- a/datasets/AIRMISR_LUNAR_LAKE_2000_1.json +++ b/datasets/AIRMISR_LUNAR_LAKE_2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_LUNAR_LAKE_2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_LUNAR_LAKE_2000 data were acquired during a flight over Lunar Lake, Nevada on June 11, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_LUNAR_LAKE_2001_1.json b/datasets/AIRMISR_LUNAR_LAKE_2001_1.json index 95788c9989..805026ae1c 100644 --- a/datasets/AIRMISR_LUNAR_LAKE_2001_1.json +++ b/datasets/AIRMISR_LUNAR_LAKE_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_LUNAR_LAKE_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_LUNAR_LAKE_2001 data were acquired during a flight over Lunar Lake, Nevada on June 30, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_MONTEREY_1999_1.json b/datasets/AIRMISR_MONTEREY_1999_1.json index f94a86d834..6f2bf6300e 100644 --- a/datasets/AIRMISR_MONTEREY_1999_1.json +++ b/datasets/AIRMISR_MONTEREY_1999_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_MONTEREY_1999_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_MONTEREY_1999 data were acquired on June 29, 1999 during a field mission which focused on Monterey, California. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_MORGAN_MONROE_2003_1.json b/datasets/AIRMISR_MORGAN_MONROE_2003_1.json index 24629f155d..5b1936c033 100644 --- a/datasets/AIRMISR_MORGAN_MONROE_2003_1.json +++ b/datasets/AIRMISR_MORGAN_MONROE_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_MORGAN_MONROE_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_MORGAN_MONROE_2003 data were acquired during a flight over the Morgan Monroe State Forest, Indiana, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 19, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction.", "links": [ { diff --git a/datasets/AIRMISR_ROGERS_LAKE_2001_1.json b/datasets/AIRMISR_ROGERS_LAKE_2001_1.json index 4740f23a40..0d0b615538 100644 --- a/datasets/AIRMISR_ROGERS_LAKE_2001_1.json +++ b/datasets/AIRMISR_ROGERS_LAKE_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_ROGERS_LAKE_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_ROGERS_LAKE_2001 data were acquired during a flight over Roger's Lake, California on June 6, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_SAFARI_1.json b/datasets/AIRMISR_SAFARI_1.json index c97abcb2d2..a0393f71de 100644 --- a/datasets/AIRMISR_SAFARI_1.json +++ b/datasets/AIRMISR_SAFARI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_SAFARI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_SAFARI data were acquired on September 6, 7, 13 and 14, 2000 during the SAFARI 2000 campaign. The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological and man-made sources such as biomass burning. The area of study included Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_SERC_2003_1.json b/datasets/AIRMISR_SERC_2003_1.json index c4a7a12cd6..320ac384d2 100644 --- a/datasets/AIRMISR_SERC_2003_1.json +++ b/datasets/AIRMISR_SERC_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_SERC_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_SERC_2003 data were acquired during a flight over the Smithsonian Environmental Research Center, Maryland, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 20, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There was a total of one run during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction.", "links": [ { diff --git a/datasets/AIRMISR_SNOW_ICE_2001_1.json b/datasets/AIRMISR_SNOW_ICE_2001_1.json index a0470d422c..a7f9e7f128 100644 --- a/datasets/AIRMISR_SNOW_ICE_2001_1.json +++ b/datasets/AIRMISR_SNOW_ICE_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_SNOW_ICE_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_SNOW_ICE_2001 data were acquired during the Colorado snow albedo field experiment in the Yampa Valley of Colorado during February and March, 2001. This experiment focused on snow albedo and atmospheric characterization as part of a validation effort for estimating snow albedo from the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The validation site is located at 40.4N, 106.8W, just south of Steamboat Springs, Colorado. AirMISR and MODIS Airborne Simulator (MAS) data were collected on March 8, 2001. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRMISR_WISCONSIN_2000_1.json b/datasets/AIRMISR_WISCONSIN_2000_1.json index 2ec142aa00..5a11351f20 100644 --- a/datasets/AIRMISR_WISCONSIN_2000_1.json +++ b/datasets/AIRMISR_WISCONSIN_2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRMISR_WISCONSIN_2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRMISR_WISCONSIN_2000 data were acquired during a field mission which overflew Wisconsin and the Atmospheric Radiation Measurement/Program Cloud And Radiation Testbed (ARM/CART) site in Oklahoma on March 3, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2).", "links": [ { diff --git a/datasets/AIRS2CCF_006.json b/datasets/AIRS2CCF_006.json index 8ccc25a210..b1ac8ef653 100644 --- a/datasets/AIRS2CCF_006.json +++ b/datasets/AIRS2CCF_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2CCF_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products, and many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2CCF_7.0.json b/datasets/AIRS2CCF_7.0.json index 46e5ad9c5a..a0bc7ca422 100644 --- a/datasets/AIRS2CCF_7.0.json +++ b/datasets/AIRS2CCF_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2CCF_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products, and many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2CCF_NRT_006.json b/datasets/AIRS2CCF_NRT_006.json index b30ee9b8a6..e91ad47178 100644 --- a/datasets/AIRS2CCF_NRT_006.json +++ b/datasets/AIRS2CCF_NRT_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2CCF_NRT_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.", "links": [ { diff --git a/datasets/AIRS2CCF_NRT_7.0.json b/datasets/AIRS2CCF_NRT_7.0.json index 77e02f6fa7..d0fe03fca6 100644 --- a/datasets/AIRS2CCF_NRT_7.0.json +++ b/datasets/AIRS2CCF_NRT_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2CCF_NRT_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_7.0) differs from the routine product (AIRS2CCF_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. \n\nThe AIRS2CCF_NRT_7.0 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.", "links": [ { diff --git a/datasets/AIRS2RET_006.json b/datasets/AIRS2RET_006.json index b6a674f0aa..9c0a1b8f5e 100644 --- a/datasets/AIRS2RET_006.json +++ b/datasets/AIRS2RET_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2RET_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240\ngranules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2RET_7.0.json b/datasets/AIRS2RET_7.0.json index 38b277f09f..941b6c4a3c 100644 --- a/datasets/AIRS2RET_7.0.json +++ b/datasets/AIRS2RET_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2RET_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It produced using AIRS IR only because the radiometric noise in several AMSU channels began increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. \n\nThe horizontal resolution is 50 km for AMSU, and 13.5 for an IR footprint. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2RET_NRT_006.json b/datasets/AIRS2RET_NRT_006.json index 016499c830..23f5628316 100644 --- a/datasets/AIRS2RET_NRT_006.json +++ b/datasets/AIRS2RET_NRT_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2RET_NRT_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240\ngranules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2RET_NRT_7.0.json b/datasets/AIRS2RET_NRT_7.0.json index 15c6263dbb..10d17cf7ff 100644 --- a/datasets/AIRS2RET_NRT_7.0.json +++ b/datasets/AIRS2RET_NRT_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2RET_NRT_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_7.0) differs from the routine product (AIRS2RET_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2SPC_005.json b/datasets/AIRS2SPC_005.json index 99c84fc98e..f451784ae4 100644 --- a/datasets/AIRS2SPC_005.json +++ b/datasets/AIRS2SPC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2SPC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. The Level 2 retrieval products and climatology CO2 are assumed as the initial state for the Vanishing Partial Derivative (VPD) retrieval algorithm that separately determines the tropospheric CO2 mixing ratio. The AIRS Level 2 tropospheric CO2 product is the average of the solutions for a 2 x 2 array of adjacent AIRS Level 2 retrieval spots, covering a 90 km x 90 km area at nadir. Retrievals for which the solutions for the 2 x 2 arrays satisfy a spatial coherence QA that requires agreement of the separate retrievals to be within 2 ppm in an RMS sense are included in the standard product. Retrievals that fail this QA test are included in the support product.", "links": [ { diff --git a/datasets/AIRS2STC_005.json b/datasets/AIRS2STC_005.json index cf1fc953c9..42c0bf9938 100644 --- a/datasets/AIRS2STC_005.json +++ b/datasets/AIRS2STC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2STC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track.", "links": [ { diff --git a/datasets/AIRS2SUP_006.json b/datasets/AIRS2SUP_006.json index 239ecddcb5..7649c56d10 100644 --- a/datasets/AIRS2SUP_006.json +++ b/datasets/AIRS2SUP_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2SUP_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2SUP_7.0.json b/datasets/AIRS2SUP_7.0.json index 7ec8720ea1..e84dfdbf1b 100644 --- a/datasets/AIRS2SUP_7.0.json +++ b/datasets/AIRS2SUP_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2SUP_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2SUP_NRT_006.json b/datasets/AIRS2SUP_NRT_006.json index 2d391cacca..6478b581f7 100644 --- a/datasets/AIRS2SUP_NRT_006.json +++ b/datasets/AIRS2SUP_NRT_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2SUP_NRT_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS2SUP_NRT_7.0.json b/datasets/AIRS2SUP_NRT_7.0.json index bf38ce9512..32b0de7bb5 100644 --- a/datasets/AIRS2SUP_NRT_7.0.json +++ b/datasets/AIRS2SUP_NRT_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS2SUP_NRT_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRS3C28_005.json b/datasets/AIRS3C28_005.json index 03f783ec76..6743f68f70 100644 --- a/datasets/AIRS3C28_005.json +++ b/datasets/AIRS3C28_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3C28_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRS3C2D_005.json b/datasets/AIRS3C2D_005.json index a54f1c3ef3..e61e1bbdc0 100644 --- a/datasets/AIRS3C2D_005.json +++ b/datasets/AIRS3C2D_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3C2D_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is daily gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRS3C2M_005.json b/datasets/AIRS3C2M_005.json index df4d01061f..4754871b32 100644 --- a/datasets/AIRS3C2M_005.json +++ b/datasets/AIRS3C2M_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3C2M_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is a monthly gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 degree longitude x 2 degree latitude grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRS3QP5_006.json b/datasets/AIRS3QP5_006.json index f85ebffd96..fb22e02225 100644 --- a/datasets/AIRS3QP5_006.json +++ b/datasets/AIRS3QP5_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3QP5_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 pentad quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).", "links": [ { diff --git a/datasets/AIRS3QPM_006.json b/datasets/AIRS3QPM_006.json index ba835c0618..a7a78b3cfb 100644 --- a/datasets/AIRS3QPM_006.json +++ b/datasets/AIRS3QPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3QPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 monthly quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).", "links": [ { diff --git a/datasets/AIRS3SP8_006.json b/datasets/AIRS3SP8_006.json index 21f74268da..2f10319744 100644 --- a/datasets/AIRS3SP8_006.json +++ b/datasets/AIRS3SP8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3SP8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRS3SPD_006.json b/datasets/AIRS3SPD_006.json index 8907ea4dc2..d7ecbc958b 100644 --- a/datasets/AIRS3SPD_006.json +++ b/datasets/AIRS3SPD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3SPD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRS3SPD_7.0.json b/datasets/AIRS3SPD_7.0.json index f945f4c34a..58e8278be9 100644 --- a/datasets/AIRS3SPD_7.0.json +++ b/datasets/AIRS3SPD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3SPD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRS3SPM_006.json b/datasets/AIRS3SPM_006.json index b06f84b170..e1cce77f1f 100644 --- a/datasets/AIRS3SPM_006.json +++ b/datasets/AIRS3SPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3SPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRS3SPM_7.0.json b/datasets/AIRS3SPM_7.0.json index 8bec44b22e..75efdcb011 100644 --- a/datasets/AIRS3SPM_7.0.json +++ b/datasets/AIRS3SPM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3SPM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRS3ST8_006.json b/datasets/AIRS3ST8_006.json index c19850cb0e..938755eada 100644 --- a/datasets/AIRS3ST8_006.json +++ b/datasets/AIRS3ST8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3ST8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRS3STD_006.json b/datasets/AIRS3STD_006.json index baaf3f163a..e1ffd0cff8 100644 --- a/datasets/AIRS3STD_006.json +++ b/datasets/AIRS3STD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3STD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRS3STD_7.0.json b/datasets/AIRS3STD_7.0.json index 32bcf19230..574321ff61 100644 --- a/datasets/AIRS3STD_7.0.json +++ b/datasets/AIRS3STD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3STD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.\n\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRS3STM_006.json b/datasets/AIRS3STM_006.json index 341275cbd2..8941259492 100644 --- a/datasets/AIRS3STM_006.json +++ b/datasets/AIRS3STM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3STM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRS3STM_7.0.json b/datasets/AIRS3STM_7.0.json index 7ebf80c525..1efd8f949f 100644 --- a/datasets/AIRS3STM_7.0.json +++ b/datasets/AIRS3STM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS3STM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRSAC3MNH3_3.json b/datasets/AIRSAC3MNH3_3.json index 1395d114a9..c9e3f5e40a 100644 --- a/datasets/AIRSAC3MNH3_3.json +++ b/datasets/AIRSAC3MNH3_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAC3MNH3_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The mass concentration ammonia in the atmosphere, consists of products generated for the study of atmospheric ammonia. Atmospheric ammonia is an important component of the global nitrogen cycle. In the troposphere, ammonia reacts rapidly with acids such as sulfuric and nitric to form fine particulate matter. These ammonium containing aerosols affect Earth's radiative balance, both directly by scattering incoming radiation and indirectly as cloud condensation nuclei. Major sources of atmospheric ammonia involve agricultural activities including animal husbandry, especially concentrated animal feeding operations and fertilizer use. Major sinks of atmospheric ammonia involve dry deposition and wet removal by precipitation, as well as conversion to particulate ammonium by reaction with acids. Measurements of ambient NH3 are sparse, but satellites provide a means to monitor atmospheric composition globally. Using the AIRS/AMSU satellite this algorithm provides monthly measurements of derived atmospheric NH3 for September 2002 through August 2016.", "links": [ { diff --git a/datasets/AIRSAQIRL1B_8.0.json b/datasets/AIRSAQIRL1B_8.0.json index 3ae9821e3b..e699e2418d 100644 --- a/datasets/AIRSAQIRL1B_8.0.json +++ b/datasets/AIRSAQIRL1B_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAQIRL1B_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of the spectrum. The AIRS Level 1B product consists of calibrated radiances, geolocation coordinates, quality control parameters, and calibration engineering support information. This product converts the AIRS raw data in digital counts to radiances referenced to SI (Syst\u00e8me international) traceable standards established at NIST for each of the 2378 AIRS channels, and contains ancillary information pertaining to the instrument calibration and performance. In general, the differences between the previous version, V5, and V8 are extremely small and not significant for most applications, however the improvements may have relevance for certain climate applications. The differences also make the product more versatile and contains more information about the calibration that will be useful for future modifications. More information can be found in the AIRS Version 8 Level 1B ATBD and Test Report. \n\nThe AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRSAQIRL1B_8 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.\n", "links": [ { diff --git a/datasets/AIRSAR_INT_JPG_1.json b/datasets/AIRSAR_INT_JPG_1.json index b6e5f77944..44784f156b 100644 --- a/datasets/AIRSAR_INT_JPG_1.json +++ b/datasets/AIRSAR_INT_JPG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_INT_JPG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR along-track interferometric browse product JPG", "links": [ { diff --git a/datasets/AIRSAR_NASA_JPL.json b/datasets/AIRSAR_NASA_JPL.json index cdc8fe0356..a28166791c 100644 --- a/datasets/AIRSAR_NASA_JPL.json +++ b/datasets/AIRSAR_NASA_JPL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_NASA_JPL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirSAR is an airborne Synthetic Aperature Radar imaging radar\n instrument. AirSAR has been flown on many flights and is involved in\n many experiments. The AirSAR data and image database at NASA JPL\n contains survey and precision data as well as complex radar data. SAR\n radar imagery is also available from the AirSAR web site for a number\n of locations and time periods. The Survey, precision, and complex data\n sets consists of data in TOPSAR and POLSAR data modes from C-, L-, and\n P-band polarizations.\n \n See: \n & http://southport.jpl.nasa.gov/desc/AIRSdesc.html & for information on AirSAR and access to\n data and images.", "links": [ { diff --git a/datasets/AIRSAR_POL_3FP_1.json b/datasets/AIRSAR_POL_3FP_1.json index 32d2e5e637..020b5d29da 100644 --- a/datasets/AIRSAR_POL_3FP_1.json +++ b/datasets/AIRSAR_POL_3FP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_POL_3FP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR three-frequency polarimetric frame product", "links": [ { diff --git a/datasets/AIRSAR_POL_SYN_3FP_1.json b/datasets/AIRSAR_POL_SYN_3FP_1.json index b579060f52..9b22d1a770 100644 --- a/datasets/AIRSAR_POL_SYN_3FP_1.json +++ b/datasets/AIRSAR_POL_SYN_3FP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_POL_SYN_3FP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR three-frequency polarimetric synoptic product", "links": [ { diff --git a/datasets/AIRSAR_TOP_C-DEM_STOKES_1.json b/datasets/AIRSAR_TOP_C-DEM_STOKES_1.json index 79c493034b..286e0d7dca 100644 --- a/datasets/AIRSAR_TOP_C-DEM_STOKES_1.json +++ b/datasets/AIRSAR_TOP_C-DEM_STOKES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_C-DEM_STOKES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model C_Stokes product", "links": [ { diff --git a/datasets/AIRSAR_TOP_DEM_1.json b/datasets/AIRSAR_TOP_DEM_1.json index 52b5e8ebed..d249f18a66 100644 --- a/datasets/AIRSAR_TOP_DEM_1.json +++ b/datasets/AIRSAR_TOP_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model product", "links": [ { diff --git a/datasets/AIRSAR_TOP_DEM_C_1.json b/datasets/AIRSAR_TOP_DEM_C_1.json index 62d863169f..d4bd1d855f 100644 --- a/datasets/AIRSAR_TOP_DEM_C_1.json +++ b/datasets/AIRSAR_TOP_DEM_C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_DEM_C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model CTIF product", "links": [ { diff --git a/datasets/AIRSAR_TOP_DEM_L_1.json b/datasets/AIRSAR_TOP_DEM_L_1.json index 4a98e1e8ec..93a0457907 100644 --- a/datasets/AIRSAR_TOP_DEM_L_1.json +++ b/datasets/AIRSAR_TOP_DEM_L_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_DEM_L_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model LTIF product", "links": [ { diff --git a/datasets/AIRSAR_TOP_DEM_P_1.json b/datasets/AIRSAR_TOP_DEM_P_1.json index a3a4c264b3..e7e74bd8bd 100644 --- a/datasets/AIRSAR_TOP_DEM_P_1.json +++ b/datasets/AIRSAR_TOP_DEM_P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_DEM_P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model PTIF product", "links": [ { diff --git a/datasets/AIRSAR_TOP_L-STOKES_1.json b/datasets/AIRSAR_TOP_L-STOKES_1.json index d3d86b0ca5..1dd6dcf94d 100644 --- a/datasets/AIRSAR_TOP_L-STOKES_1.json +++ b/datasets/AIRSAR_TOP_L-STOKES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_L-STOKES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model L_Stokes product", "links": [ { diff --git a/datasets/AIRSAR_TOP_P-STOKES_1.json b/datasets/AIRSAR_TOP_P-STOKES_1.json index 9c26964d31..cb8c7e3c4b 100644 --- a/datasets/AIRSAR_TOP_P-STOKES_1.json +++ b/datasets/AIRSAR_TOP_P-STOKES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSAR_TOP_P-STOKES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AIRSAR topographic SAR digital elevation model P_Stokes product", "links": [ { diff --git a/datasets/AIRSIL3MSOLR_6.1.json b/datasets/AIRSIL3MSOLR_6.1.json index e6595e3694..86efdd3b2d 100644 --- a/datasets/AIRSIL3MSOLR_6.1.json +++ b/datasets/AIRSIL3MSOLR_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSIL3MSOLR_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3 Spectral Outgoing Longwave Radiation (OLR) is derived using the AIRS radiances to compute spectral fluxes based on an algorithm developed by Xianglei Huang at the University of Michigan. It uses data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft.\n\nThe Aqua AIRS Huang Level-3 Spectral OLR product contains OLR parameters derived from the AIRS version 6 data: all-sky and clear-sky OLR both spectrally resolved at 10 cm-1 bandwidth and integrated to a single value per grid square. This is monthly product on a 2x2 degree latitude/longitude grid.", "links": [ { diff --git a/datasets/AIRSM_CPR_MAT_3.2.json b/datasets/AIRSM_CPR_MAT_3.2.json index aa031de39d..ea2923b293 100644 --- a/datasets/AIRSM_CPR_MAT_3.2.json +++ b/datasets/AIRSM_CPR_MAT_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRSM_CPR_MAT_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.\n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information,\nand develop a merged, multi-sensor climatology of atmospheric water vapor as a\nfunction of altitude, stratified by the cloud classes. This is a large science\nanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find\nspace/time \"matchups\" between pairs of instruments, and process years of\nsatellite data to produce the climate data records.\n\nThe short name for this collection is AIRSM_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CH4_total_column|Retrieved total column CH4| (molecules/cm2)\n CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None)\n CloudLayers| Number of hydrometeor layers| (count)\n clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2)\n CO_total_column|Retrieved total column CO| (molecules/cm2)\n CPR_Cloud_mask| CPR Cloud Mask |(None)\n Data_quality| Data Quality |(None)\n H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg)\n H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air)\n MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None)\n MODIS_scene_var |MODIS scene variability| (None)\n nSurfStd|1-based index of the first valid level|(None)\n O3VMRStd|Ozone Volume Mixing Ratio|(vmr)\n olr|All-sky Outgoing Longwave Radiation|(Watts/m**2)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n Sigma-Zero| Sigma-Zero| (dB*100)\n TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K)\n TCldTopStd|Cloud top temperature|(K)\n totH2OStd|Total precipitable water vapor| (kg/m**2)\n totO3Std|Total ozone burden| (Dobson)\n TSurfAir|Atmospheric Temperature at Surface|(K)\n TSurfStd|Surface skin temperature|(K)\nEnd of parameter information", "links": [ { diff --git a/datasets/AIRS_CPR_IND_4.0.json b/datasets/AIRS_CPR_IND_4.0.json index 453d7e7725..fae6eb62eb 100644 --- a/datasets/AIRS_CPR_IND_4.0.json +++ b/datasets/AIRS_CPR_IND_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS_CPR_IND_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1.\n\nThis is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project.\n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each \"scene\" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time \"matchups\" between pairs of instruments, and process years of satellite data to produce the climate data records.\n\nThe short name for this collection is AIRS_CPR_IND", "links": [ { diff --git a/datasets/AIRS_CPR_MAT_3.2.json b/datasets/AIRS_CPR_MAT_3.2.json index 27cf0c6474..84e5aa3eef 100644 --- a/datasets/AIRS_CPR_MAT_3.2.json +++ b/datasets/AIRS_CPR_MAT_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS_CPR_MAT_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. \n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time \"matchups\" between pairs of instruments, and process years of satellite data to produce the climate data records.\n\nThe short name for this collection is AIRS_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CldFrcStdErr|Cloud Fraction|(None)\n CloudLayers| Number of hydrometeor layers| (count)\n CPR_Cloud_mask| CPR Cloud Mask| (None)\n DEM_elevation| Digital Elevation Map| (m)\n dust_flag|Dust Flag|(None)\n latAIRS|AIRS IR latitude|(deg)\n Latitude|CloudSat Latitude |(degrees)\n LayerBase| Height of Layer Base| (m)\n LayerTop| Height of layer top| (m)\n lonAIRS|AIRS IR longitude|(deg)\n Longitude|CloudSat Longitude| (degrees)\n MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n radiances|Radiances|(milliWatts/m**2/cm**-1/steradian)\n Sigma-Zero| Sigma-Zero| (dB*100)\n spectral_clear_indicator|Spectral Clear Indicator|(None)\n Vertical_binsize|CloudSat vertical binsize| (m)\nEnd of parameter information", "links": [ { diff --git a/datasets/AIRS_MDS_IND_1.0.json b/datasets/AIRS_MDS_IND_1.0.json index ed4a1abdbc..6354fbc0ef 100644 --- a/datasets/AIRS_MDS_IND_1.0.json +++ b/datasets/AIRS_MDS_IND_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS_MDS_IND_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Aqua AIRS-MODIS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MODIS.\n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each \"scene\" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time \"matchups\" between pairs of instruments, and process years of satellite data to produce the climate data records.\n\nThe short name for this collections is AIRS_MDS_IND\n\n", "links": [ { diff --git a/datasets/AIRS_MLS_IND_1.0.json b/datasets/AIRS_MLS_IND_1.0.json index 4d6a91aaf2..ea81b794f7 100644 --- a/datasets/AIRS_MLS_IND_1.0.json +++ b/datasets/AIRS_MLS_IND_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRS_MLS_IND_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of MEaSUREs 2012 Program, and represent Aqua/AIRS-Aura/MLS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MLS.\n\nThe A-Train provides water vapor (H2O) retrievals from both the Atmospheric Infrared Sounder (AIRS) and Microwave Limb Sounder (MLS). While AIRS loses sensitivity to H2O at the elevated portions of the upper troposphere (UT), MLS cannot detect H2O below 316 hPa. Therefore, to obtain a full profile of H2O in the whole column of air, this dataset manages to join the two products together by utilizing their own averaging kernels (AK). In doing so, the dataset builds a solid H2O of the whole column of air, which will help understand the H2O budget and many processes governing the humidity around the upper troposphere and lower stratosphere (UTLS). \n\nThe short name for this collections is AIRS_MLS_IND\n\n", "links": [ { diff --git a/datasets/AIRVBQAP_005.json b/datasets/AIRVBQAP_005.json index cf290928f3..2ff5093689 100644 --- a/datasets/AIRVBQAP_005.json +++ b/datasets/AIRVBQAP_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRVBQAP_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes \"state\" that user should check before using any VIS/NIR Level 1B data radiance and \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD.", "links": [ { diff --git a/datasets/AIRVBQAP_NRT_005.json b/datasets/AIRVBQAP_NRT_005.json index bdfdbd7d7e..0f309baaa4 100644 --- a/datasets/AIRVBQAP_NRT_005.json +++ b/datasets/AIRVBQAP_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRVBQAP_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) Visible/Near Infrared (VIS/NIR) instrument in combination with the AIRS Infrared Spectrometer, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) constitute an innovative atmospheric sounding group aboard the Earth Observing System (EOS) Aqua platform in a near-polar Sun-synchronous orbit with a 1:30 AM/PM equator crossing time and an ~705 km altitude. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes \"state\" that user should check before using any VIS/NIR Level 1B data radiance and \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD.", "links": [ { diff --git a/datasets/AIRVBRAD_005.json b/datasets/AIRVBRAD_005.json index a9a4cdf1be..0eb94f81da 100644 --- a/datasets/AIRVBRAD_005.json +++ b/datasets/AIRVBRAD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRVBRAD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set includes 4 channels in the 0.4 to 1.0 um region of the spectrum. Each day of AIRS data are divided into 240 granules each of 6 minute duration. However, the VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules. The primary purpose of the VIS/NIR channels is the detection and flagging of significant inhomogeneities in the infrared field-of-view,which may adversely impact the quality of the temperature and moisture soundings. Therefore the VIS/NIR radiance product has a higher spatial resolution than the Infrared radiance product. Each VIS/NIR scan has 9 alongtrack footprints and 720 across track footprints. For ease in comparing with the infrared product which has 135 along track footprints and 90 across track footprints, the VIS/NIR product has additional dimensions to account for the 9 additional alongtrack and 8 additional across track footprints.", "links": [ { diff --git a/datasets/AIRVBRAD_NRT_005.json b/datasets/AIRVBRAD_NRT_005.json index e1cf5c6b23..f707e0288a 100644 --- a/datasets/AIRVBRAD_NRT_005.json +++ b/datasets/AIRVBRAD_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRVBRAD_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules.", "links": [ { diff --git a/datasets/AIRX2RET_006.json b/datasets/AIRX2RET_006.json index d03c81af3b..dab1c7cfd7 100644 --- a/datasets/AIRX2RET_006.json +++ b/datasets/AIRX2RET_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2RET_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRX2RET_7.0.json b/datasets/AIRX2RET_7.0.json index 6942b951e5..10e9f3602b 100644 --- a/datasets/AIRX2RET_7.0.json +++ b/datasets/AIRX2RET_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2RET_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS combination with the Advanced Microwave Sounding Unit (AMSU) constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRX2SPC_005.json b/datasets/AIRX2SPC_005.json index 3e738f7c55..c6c8c559ae 100644 --- a/datasets/AIRX2SPC_005.json +++ b/datasets/AIRX2SPC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2SPC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. In particular, this support product focuses on the tropospheric CO2 retrieval. In general, AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data.", "links": [ { diff --git a/datasets/AIRX2STC_005.json b/datasets/AIRX2STC_005.json index 6d5ecc40c0..380d2705b1 100644 --- a/datasets/AIRX2STC_005.json +++ b/datasets/AIRX2STC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2STC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track.", "links": [ { diff --git a/datasets/AIRX2SUP_006.json b/datasets/AIRX2SUP_006.json index 611f0d9d87..f0bc6aae08 100644 --- a/datasets/AIRX2SUP_006.json +++ b/datasets/AIRX2SUP_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2SUP_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRX2SUP_7.0.json b/datasets/AIRX2SUP_7.0.json index 5e58911913..68a0b5ef62 100644 --- a/datasets/AIRX2SUP_7.0.json +++ b/datasets/AIRX2SUP_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX2SUP_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU), AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. \n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/AIRX3C28_005.json b/datasets/AIRX3C28_005.json index 9302b52f54..72006b80e0 100644 --- a/datasets/AIRX3C28_005.json +++ b/datasets/AIRX3C28_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3C28_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRX3C2D_005.json b/datasets/AIRX3C2D_005.json index 3aad92034f..d216502fb8 100644 --- a/datasets/AIRX3C2D_005.json +++ b/datasets/AIRX3C2D_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3C2D_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is daily gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRX3C2M_005.json b/datasets/AIRX3C2M_005.json index 9c81cf1330..18f77858a8 100644 --- a/datasets/AIRX3C2M_005.json +++ b/datasets/AIRX3C2M_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3C2M_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.", "links": [ { diff --git a/datasets/AIRX3QP5_006.json b/datasets/AIRX3QP5_006.json index 6071b4e201..8a58928989 100644 --- a/datasets/AIRX3QP5_006.json +++ b/datasets/AIRX3QP5_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3QP5_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. \nThe quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval \nproducts (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. \nThe QP products combine the Level 2 standard data parameters over grid \ncells of 5 x 5 deg spatial extent for temporal periods of five days from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. \nFor each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRX3QPM_006.json b/datasets/AIRX3QPM_006.json index ed1bd5800c..a0a2aa90b5 100644 --- a/datasets/AIRX3QPM_006.json +++ b/datasets/AIRX3QPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3QPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).", "links": [ { diff --git a/datasets/AIRX3SP8_006.json b/datasets/AIRX3SP8_006.json index 9283dcf9a5..c236e51434 100644 --- a/datasets/AIRX3SP8_006.json +++ b/datasets/AIRX3SP8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3SP8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRX3SPD_006.json b/datasets/AIRX3SPD_006.json index 0c3de7e5c7..3ff8b39d3d 100644 --- a/datasets/AIRX3SPD_006.json +++ b/datasets/AIRX3SPD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3SPD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRX3SPD_7.0.json b/datasets/AIRX3SPD_7.0.json index e9d64e8911..924fac3dff 100644 --- a/datasets/AIRX3SPD_7.0.json +++ b/datasets/AIRX3SPD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3SPD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. \n\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRX3SPM_006.json b/datasets/AIRX3SPM_006.json index a240378bae..3fa0bc60aa 100644 --- a/datasets/AIRX3SPM_006.json +++ b/datasets/AIRX3SPM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3SPM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.", "links": [ { diff --git a/datasets/AIRX3SPM_7.0.json b/datasets/AIRX3SPM_7.0.json index c7d9296352..6715bd5b01 100644 --- a/datasets/AIRX3SPM_7.0.json +++ b/datasets/AIRX3SPM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3SPM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.\n\n\nThe value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the\nbox.", "links": [ { diff --git a/datasets/AIRX3ST8_006.json b/datasets/AIRX3ST8_006.json index f5945c4efc..2a5620a29f 100644 --- a/datasets/AIRX3ST8_006.json +++ b/datasets/AIRX3ST8_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3ST8_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRX3STD_006.json b/datasets/AIRX3STD_006.json index ea43bcbe46..fcc7e41d74 100644 --- a/datasets/AIRX3STD_006.json +++ b/datasets/AIRX3STD_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3STD_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRX3STD_7.0.json b/datasets/AIRX3STD_7.0.json index cc19a40905..e23e1ee758 100644 --- a/datasets/AIRX3STD_7.0.json +++ b/datasets/AIRX3STD_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3STD_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRX3STM_006.json b/datasets/AIRX3STM_006.json index 7ad4cd7620..99b0c08459 100644 --- a/datasets/AIRX3STM_006.json +++ b/datasets/AIRX3STM_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3STM_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRX3STM_7.0.json b/datasets/AIRX3STM_7.0.json index e1b2e9546d..b0fc9a6880 100644 --- a/datasets/AIRX3STM_7.0.json +++ b/datasets/AIRX3STM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRX3STM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.", "links": [ { diff --git a/datasets/AIRXAMAP_005.json b/datasets/AIRXAMAP_005.json index 79a4b75001..24a4d3e167 100644 --- a/datasets/AIRXAMAP_005.json +++ b/datasets/AIRXAMAP_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRXAMAP_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The AIRS Granule Map Product consists of images of granule coverage in PDF and JPG format. The images are daily ones but updated every 6 minutes to capture any new available granule. Granules are assembled by ascending, descending, in north and south hemisphere, and the maps are in global cylindrical projection and satellite projection for better view.", "links": [ { diff --git a/datasets/AIRXBCAL_005.json b/datasets/AIRXBCAL_005.json index d8e29e5753..bcbc0a6534 100644 --- a/datasets/AIRXBCAL_005.json +++ b/datasets/AIRXBCAL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIRXBCAL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level-1B calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. The AIRS Visible/Near Infrared (VIS/NIR) level 1B data set contains AIRS visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 4 channels in the 0.4 to 1.0 um region of the spectrum.", "links": [ { diff --git a/datasets/AIS_1968_borehole_1.json b/datasets/AIS_1968_borehole_1.json index e18673ed43..72c79bca53 100644 --- a/datasets/AIS_1968_borehole_1.json +++ b/datasets/AIS_1968_borehole_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_1968_borehole_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1968 the Australian Antarctic Division had a group of four men spend over a year on the Amery Ice Shelf. As part of their program of work, they drilled a number of boreholes on the shelf and took temperature readings at various depths down the hole. Stratigraphy notes and density measurements were also made on the holes drilled.\n\nThe records of the recorded temperatures, notes on the temperature probes used, stratigraphy and density measurements, and a few notes on the work carried out, have been archived at the Australian Antarctic Division. \n\nLogbook(s):\nGlaciology Borehole Measurements, Amery 1968 - Borehole tempurature readings Glaciology Borehole Logs, Amery 1968 - Stratigraphy and density measurements\n\nAMERY ICE SHELF PARTY - February 1968 to February 1969 all IN on V2(67-68) all OUT on V2(68-69)\nOfficer-in-Charge: Maxwell John Corry\nMedical Officer: Julian R Sansom\nEngineer(Electronics): Alan H F Nickols Senior Diesel Mechanic: Neville Joseph Collins \n\nConducted a glaciological program including ice drilling and surveying the Lambert Glacier", "links": [ { diff --git a/datasets/AIS_1968_met_obs_1.json b/datasets/AIS_1968_met_obs_1.json index d1fffb6fd4..810b94a7ee 100644 --- a/datasets/AIS_1968_met_obs_1.json +++ b/datasets/AIS_1968_met_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_1968_met_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1968 the Australian Antarctic Division had a group of four men spend over a year on the Amery Ice Shelf. As part of their program of work, they took regular (usually daily) records of the weather where they were located. Observations included wind speed, wind direction, temperature, air pressure, cloud cover, and general notes about the weather condition.\n\nAll log books have been archived at the Australian Antarctic Division.\n\nCopies of the document details forms for the logbooks are available for download from the provided URL.\n\nAMERY ICE SHELF PARTY - February 1968 to February 1969\nall IN on V2(67-68) all OUT on V2(68-69)\nOfficer-in-Charge: Maxwell John Corry\nMedical Officer: Julian R Sansom\nEngineer(Electronics): Alan H F Nickols\nSenior Diesel Mechanic: Neville Joseph Collins\n\nConducted a glaciological programme including ice drilling and surveying the Lambert Glacier", "links": [ { diff --git a/datasets/AIS_1968_traverse_1.json b/datasets/AIS_1968_traverse_1.json index 4c034dbf3e..97c936a33b 100644 --- a/datasets/AIS_1968_traverse_1.json +++ b/datasets/AIS_1968_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_1968_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1968 the Australian Antarctic Division had a team of four people spend just over a year on the Amery Ice Shelf, undertaking a range of studies including ice velocity determination (horizontal and vertical), ice thickness and surface profile measurements, snow accumulation studies, investigation of temperature, density and crystal structure of the ice, continuous measurements of wind velocities, air and snow temperatures, air pressure, humidity, and radiation.\n\nMany journals were kept during the traverse, detailing general daily activities carried out, but also including assorted measurements and observations that weren't specifically recorded in stand-alone logs.\n\nAll logbooks have been archived at the Australian Antarctic Division.\n\nCopies of the document details forms for the logbooks is available for download from the provided URL.\n\nAMERY ICE SHELF PARTY - February 1968 to February 1969\nall IN on V2(67-68) all OUT on V2(68-69)\nOfficer-in-Charge: Maxwell John Corry\nMedical Officer: Julian R Sansom\nEngineer(Electronics): Alan H F Nickols\nSenior Diesel Mechanic: Neville Joseph Collins\n\nConducted a glaciological program including ice drilling and surveying the \nLambert Glacier", "links": [ { diff --git a/datasets/AIS_1970_iceradar_1.json b/datasets/AIS_1970_iceradar_1.json index 9fe993e5d7..7275d56495 100644 --- a/datasets/AIS_1970_iceradar_1.json +++ b/datasets/AIS_1970_iceradar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_1970_iceradar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1970 the Australian Antarctic Division carried out ice depth work using radar, mounted within a van towed on the traverse.\n\nRecords for this work have been archived at the Australian Antarctic Division. \n\nLogbook(s):\nAmery Ice Depth Radar Log 1970 - Record of settings used on equipment", "links": [ { diff --git a/datasets/AIS_GEOM_1.json b/datasets/AIS_GEOM_1.json index ab632c975e..ebde624e3c 100644 --- a/datasets/AIS_GEOM_1.json +++ b/datasets/AIS_GEOM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_GEOM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean circulation beneath ice shelves and associated rates of melting and freezing are influenced strongly by water column thickness and depth. The shape of the cavity beneath the Amery Ice Shelf is important for our understanding of ice shelf stability and freshwater input to the ocean and their dependence on climate. New seismic surveys of the centre region of the Amery Ice Shelf and ice-draft data taken at the grounding line has provided a considerable amount of new water-column thickness and bathymetry data. The data is adjusted in the unknown region south of 71 degrees 35 minutes S by comparing the complex error between simulated tides against in situ GPS observations. A finite element, hydrodynamic ocean tide model is used to simulate the 4 major constituents (S2, M2, K1 and O1). The new data differs from a previous bathymetry map in a number of places. Significantly, there is channel that leads from the Prydz bay depression into the deepest part of the AIS cavity in the south through a series of depressions. This technique has particular application when the water column beneath ice shelves is inaccessible and in situ GPS data is available.", "links": [ { diff --git a/datasets/AIS_thickness_bottom_1.json b/datasets/AIS_thickness_bottom_1.json index a3601c36f3..ce3d0575f7 100644 --- a/datasets/AIS_thickness_bottom_1.json +++ b/datasets/AIS_thickness_bottom_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AIS_thickness_bottom_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Point datasets showing ice thickness over the Amery Ice Shelf and Lambert Glacier Basin were collected by Rachael Hurd under contract with the Glaciology program of the Australian Antarctic Division in 2007. All available metadata for these datasets (how the data were originally collected, resolution, accuracy, etc) were also collected, and recorded in the one document. \nThe surveys collated were:\n\n1.ANARE_5759 - ANARE seismic and gravity survey during the period of the IGY (1957-59)\n2.AMERY_6871 - ANARE Amery Ice Shelf Expedition 1968 and 1970/71.\n3.PCMTS_7274 - ANARE Aerial RES of the Southern Prince Charles Mountains 1972/73 and 1973/74. 17 sorties. \n4.ENDERBY_7980 - ANARE Aerial RES of Enderby and Kemp Lands, 1979/80\n5.AMERY89 - ANARE Aerial RES of Amery Ice Shelf 1989, December 1989, First Phase.\n6.AMERY_89B - ANARE Aerial RES of Amery Ice Shelf, December 1989, Second Phase.\n7.LAMBERT_8995 - ANARE Lambert Glacier Basin Traverse 1989/90 to 1994/95.\n8.WILHELM_9798 - ANARE Airborne RES data Jan-Feb 1998. \n9.Airborne RES data collected by ANARE - Jan-Feb 2000.\n10.Airborne RES data (icards) collected on the AIS by ANARE (2002/2003) as part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP).\n11.Airborne RES data collected on the southern AIS and over the PCMs (2002/2003) as part of the PCMEGA project.\n12.Airborne RES data collected on the AIS (2003/2004) in collaboration with Italian expedition.\n13.SAE_7174_1 - Soviet Antarctic Expedition (SAE17-19) airborne RES survey in Enderby Land east to West Ice shelf (1971-74). \n14.SAE_7174_2 - Soviet Antarctic Expedition seismic surveys - East Antarctica (1970/71 - 1983/84).\n15.SAE_8586_1 - Soviet Antarctic Expedition (SAE31) airborne RES survey - Prince Charles Mountains (1985-86).\n16.SAE_8687 - Soviet Antarctic Expedition (SAE32) airborne RES survey - Amery Ice shelf and Ingrid Christensen Coast (1986-87).\n17.SAE_8788_1 - Soviet Antarctic Expedition (SAE33) airborne RES survey - Prince Charles Mountains, Mac Robertson Land (1987-88). \n18.SAE_8788_2 - Soviet Antarctic Expedition (SAE33) airborne RES survey - Enderby LandMac Robertson Land (1987-88). \n19.SAE_8889_2 - Soviet Antarctic Expedition (SAE34) airborne RES survey - Dronning Maud Land (1988-89). \n20.SAE_8990 - Soviet Antarctic Expedition (SAE35) airborne RES survey - Lambert Glacier, Mac Robertson Land (1989-90). \n21.SAE_8990_2 - Soviet Antarctic Expedition (SAE35) airborne RES survey - Enderby Land (Oct - Nov 1989).\n22.SAE_9091 - Soviet Antarctic Expedition (SAE36) airborne RES survey - Princess Elizabeth Land (1990-91). \n23.RAE_9495 - Russian Antarctic Expedition (RAE39) airborne RES survey - Amery Ice Shelf and Mac Robertson Land (1994-95). \n24.RAE_9596 - Russian Antarctic Expedition (RAE40) airborne RES survey - Mac Robertson Land (1995-96). \n\nUsing the metadata as a guide on data quality, the individual datasets were then merged by Rob Driessen (Datavision, now known as RIA Mobile GIS) to create a merged ice thickness dataset for the Amery Ice Shelf. The order of priority and rules of merging for each of the datasets was determined by Ian Allison, with several combinations tried until a set of results that was determined to be reasonable was obtained.\n\nThe intention was to interpolate an ice thickness grid from the merged point dataset. This grid could then be subtracted from a surface DEM to get a get an elevation grid of the bottom of the ice shelf. In the longer term the whole process could be repeated for the whole Lambert Glacier Basin.", "links": [ { diff --git a/datasets/AJAX_CH2O_1.json b/datasets/AJAX_CH2O_1.json index 80772d22ac..fd1550303b 100644 --- a/datasets/AJAX_CH2O_1.json +++ b/datasets/AJAX_CH2O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AJAX_CH2O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alpha Jet Atmospheric eXperiment (AJAX) is a partnership between NASA's Ames Research Center and H211, L.L.C., facilitating routine in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. The standard payload complement includes rigorously-calibrated ozone (O3), formaldehyde (HCHO), carbon dioxide (CO2), and methane (CH4) mixing ratios, as well as meteorological data including 3-D winds. Multiple vertical profiles (to ~8.5 km) can be accomplished in each 2-hr flight. The AJAX project has been collecting trace gas data on a regular basis in all seasons for over a decade, helping to assess satellite sensors' health and calibration over significant portions of their lifetimes, and complementing surface and tower-based observations collected elsewhere in the region.\r\n\r\nAJAX supports NASA's Orbiting Carbon Observatory (OCO-2/3) and Japan's Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2, and collaborates with many other research organizations (e.g. California Air Resources Board (CARB), NOAA, United States Forest Service (USFS), Environmental Protection Agency (EPA)). AJAX celebrated its 200th science flight in 2016, and previous studies have investigated topics as varied as stratospheric-to-tropospheric transport, forest fire plumes, atmospheric river events, long-range transport of pollution from Asia to the western US, urban outflow, and emissions from gas leaks, oil fields, and dairies.", "links": [ { diff --git a/datasets/AJAX_CO2_CH4_1.json b/datasets/AJAX_CO2_CH4_1.json index 0f61715060..f715f35e8d 100644 --- a/datasets/AJAX_CO2_CH4_1.json +++ b/datasets/AJAX_CO2_CH4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AJAX_CO2_CH4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alpha Jet Atmospheric eXperiment (AJAX) is a partnership between NASA's Ames Research Center and H211, L.L.C., facilitating routine in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. The standard payload complement includes rigorously-calibrated ozone (O3), formaldehyde (HCHO), carbon dioxide (CO2), and methane (CH4) mixing ratios, as well as meteorological data including 3-D winds. Multiple vertical profiles (to ~8.5 km) can be accomplished in each 2-hr flight. The AJAX project has been collecting trace gas data on a regular basis in all seasons for over a decade, helping to assess satellite sensors' health and calibration over significant portions of their lifetimes, and complementing surface and tower-based observations collected elsewhere in the region.\r\n\r\nAJAX supports NASA's Orbiting Carbon Observatory (OCO-2/3) and Japan's Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2, and collaborates with many other research organizations (e.g. California Air Resources Board (CARB), NOAA, United States Forest Service (USFS), Environmental Protection Agency (EPA)). AJAX celebrated its 200th science flight in 2016, and previous studies have investigated topics as varied as stratospheric-to-tropospheric transport, forest fire plumes, atmospheric river events, long-range transport of pollution from Asia to the western US, urban outflow, and emissions from gas leaks, oil fields, and dairies.", "links": [ { diff --git a/datasets/AJAX_MMS_1.json b/datasets/AJAX_MMS_1.json index 8fea06647c..96838ee565 100644 --- a/datasets/AJAX_MMS_1.json +++ b/datasets/AJAX_MMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AJAX_MMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alpha Jet Atmospheric eXperiment (AJAX) is a partnership between NASA's Ames Research Center and H211, L.L.C., facilitating routine in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. The standard payload complement includes rigorously-calibrated ozone (O3), formaldehyde (HCHO), carbon dioxide (CO2), and methane (CH4) mixing ratios, as well as meteorological data including 3-D winds. Multiple vertical profiles (to ~8.5 km) can be accomplished in each 2-hr flight. The AJAX project has been collecting trace gas data on a regular basis in all seasons for over a decade, helping to assess satellite sensors' health and calibration over significant portions of their lifetimes, and complementing surface and tower-based observations collected elsewhere in the region.\r\n\r\nAJAX supports NASA's Orbiting Carbon Observatory (OCO-2/3) and Japan's Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2, and collaborates with many other research organizations (e.g. California Air Resources Board (CARB), NOAA, United States Forest Service (USFS), Environmental Protection Agency (EPA)). AJAX celebrated its 200th science flight in 2016, and previous studies have investigated topics as varied as stratospheric-to-tropospheric transport, forest fire plumes, atmospheric river events, long-range transport of pollution from Asia to the western US, urban outflow, and emissions from gas leaks, oil fields, and dairies.", "links": [ { diff --git a/datasets/AJAX_O3_1.json b/datasets/AJAX_O3_1.json index 5db7aaa737..3d6912961d 100644 --- a/datasets/AJAX_O3_1.json +++ b/datasets/AJAX_O3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AJAX_O3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alpha Jet Atmospheric eXperiment (AJAX) is a partnership between NASA's Ames Research Center and H211, L.L.C., facilitating routine in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. The standard payload complement includes rigorously-calibrated ozone (O3), formaldehyde (HCHO), carbon dioxide (CO2), and methane (CH4) mixing ratios, as well as meteorological data including 3-D winds. Multiple vertical profiles (to ~8.5 km) can be accomplished in each 2-hr flight. The AJAX project has been collecting trace gas data on a regular basis in all seasons for over a decade, helping to assess satellite sensors' health and calibration over significant portions of their lifetimes, and complementing surface and tower-based observations collected elsewhere in the region.\r\n\r\nAJAX supports NASA's Orbiting Carbon Observatory (OCO-2/3) and Japan's Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2, and collaborates with many other research organizations (e.g. California Air Resources Board (CARB), NOAA, United States Forest Service (USFS), Environmental Protection Agency (EPA)). AJAX celebrated its 200th science flight in 2016, and previous studies have investigated topics as varied as stratospheric-to-tropospheric transport, forest fire plumes, atmospheric river events, long-range transport of pollution from Asia to the western US, urban outflow, and emissions from gas leaks, oil fields, and dairies.", "links": [ { diff --git a/datasets/AKFED_V1_1282_1.json b/datasets/AKFED_V1_1282_1.json index fe7283fe52..b9d3562a1d 100644 --- a/datasets/AKFED_V1_1282_1.json +++ b/datasets/AKFED_V1_1282_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AKFED_V1_1282_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of annual carbon emissions (kg carbon per square meter) from boreal fires at 450-m resolution for the state of Alaska between 2001 and 2013. To produce these data, daily burned area for 2001 to 2013 was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with perimeters from the Alaska Large Fire Database. Carbon consumption was calibrated using available field measurements from black spruce forests in Alaska. Above- and below-ground carbon consumption were modeled based on environmental variables including elevation, day of burning within the fire season, pre-fire tree cover and the differenced normalized burn ratio (dNBR). Modeled uncertainties in carbon consumption are included in the data set. The derived burn area and carbon emissions product, referred to as the Alaskan Fire Emissions Database (AKFED), provides a resource for study of the environmental controls on daily fire dynamics, boreal fire emissions in biogeochemical models, and potential feedbacks from changing fire regimes.", "links": [ { diff --git a/datasets/AKWANAVT_0.json b/datasets/AKWANAVT_0.json index 9bab57e41d..c3574d7192 100644 --- a/datasets/AKWANAVT_0.json +++ b/datasets/AKWANAVT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AKWANAVT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Aegean and Black seas during 1997 onboard the R/V Akwanavt.", "links": [ { diff --git a/datasets/AK_AVHRR.json b/datasets/AK_AVHRR.json index c3b2d22707..130ad74e2e 100644 --- a/datasets/AK_AVHRR.json +++ b/datasets/AK_AVHRR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AK_AVHRR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of the Alaska Advanced Very High Resolution Radiometer (AVHRR) project is to compile a time series data set of calibrated, georegistered daily observations and twice-monthly maximum normalized difference vegetation index (NDVI) composites for Alaska's annual growing season (April-October). This data set has applications for environmental monitoring and for assessing impacts of global climate change. An Alaska AVHRR data set is comprised of twice-monthly maximum NDVI composites of daily satellite observations. The NDVI composites contain 10 bands of information, including AVHRR channels 1-5, maximum NDVI, satellite zenith, solar zenith, and relative azimuth. The daily observations, bands 1-9, have been calibrated to reflectance, scaled to byte data, and geometrically registered to the Albers Equal-Area Conic map projection. The 10th band is a pointer to identify the date and scene ID of the source daily observation (scene) for each pixel.\n\nThe compositing process required each daily overpass to be registered to a common map projection to ensure that from day to day each 1-km pixel represented the exact same ground location. The Albers Equal-Area Conic map projection provides for equal area representation, which enables easy measurement of area throughout the data. Each daily observation for the growing season was registered to a base image using image-to-image correlation.\n\nThe NDVI data are calculated from the calibrated, geometrically registered daily observations. The NDVI value is the difference between near-infrared (AVHRR Channel 2) and visible (AVHRR Channel 1) reflectance values divided by total measured reflectance. A maximum NDVI compositing process was used on the daily observations. The NDVI is examined pixel by pixel for each observation during the compositing period to determine and retain the maximum value. Often when displaying data covering large areas, such as AVHRR data, it is beneficial to include an overlay of either familiar linework for reflectance or polygon data sets to derive statistical summaries of regions. All of the linework images represent lines in raster format as 1-km cells and the strata are represented as polygons registered to the AVHRR data. The linework and polygon data sets include international boundaries, Alaskan roads with the Trans-Alaska Pipeline, and a raster polygon mask of the State.\n", "links": [ { diff --git a/datasets/AK_North_Slope_NEE_CH4_Flux_1562_1.json b/datasets/AK_North_Slope_NEE_CH4_Flux_1562_1.json index fad4b1fdde..32c975008e 100644 --- a/datasets/AK_North_Slope_NEE_CH4_Flux_1562_1.json +++ b/datasets/AK_North_Slope_NEE_CH4_Flux_1562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AK_North_Slope_NEE_CH4_Flux_1562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09.", "links": [ { diff --git a/datasets/AK_Regional_CO2_Flux_1389_1.json b/datasets/AK_Regional_CO2_Flux_1389_1.json index bbcc2b3c55..bd972397aa 100644 --- a/datasets/AK_Regional_CO2_Flux_1389_1.json +++ b/datasets/AK_Regional_CO2_Flux_1389_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AK_Regional_CO2_Flux_1389_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of 3-hourly net ecosystem CO2 exchange (NEE) at 0.5-degree resolution over the state of Alaska for 2012-2014. The NEE estimates are the output are from Geostatistical Inverse Modeling of a subset of CARVE aircraft CO2 data, WRF-STILT footprints, and PVPRM-SIF data from flux towers (CRV: located in Fox, AK and BRW: located just outside Barrow, AK). Daily mean NEE is also provided as calculated for all of Alaska and for four sub-regions (0.5-degree resolution) that were defined across Alaska, based on general landcover type: North Slope Tundra, South and West Tundra, Boreal Forests, and Mixed (all other). Also provided are derived annual carbon budgets for (1) all of Alaska with defined contributions from biogenic, fossil fuel, and biomass burning sources and (2) annual biogenic carbon budgets for the four landcover-type regions of Alaska. Provided for completeness are the CARVE aircraft atmospheric measurement data used in estimating NEE.", "links": [ { diff --git a/datasets/AK_Tundra_PFT_FractionalCover_1830_1.json b/datasets/AK_Tundra_PFT_FractionalCover_1830_1.json index 567ca79ce2..9cbeb57cab 100644 --- a/datasets/AK_Tundra_PFT_FractionalCover_1830_1.json +++ b/datasets/AK_Tundra_PFT_FractionalCover_1830_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AK_Tundra_PFT_FractionalCover_1830_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included.", "links": [ { diff --git a/datasets/AK_Yukon_PFT_TopCover_2032_1.1.json b/datasets/AK_Yukon_PFT_TopCover_2032_1.1.json index 41202448e2..efdaec0d72 100644 --- a/datasets/AK_Yukon_PFT_TopCover_2032_1.1.json +++ b/datasets/AK_Yukon_PFT_TopCover_2032_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AK_Yukon_PFT_TopCover_2032_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics.", "links": [ { diff --git a/datasets/ALAN_VIIRS_CONUS_1.json b/datasets/ALAN_VIIRS_CONUS_1.json index 9ebb766d62..9bdaeb1a58 100644 --- a/datasets/ALAN_VIIRS_CONUS_1.json +++ b/datasets/ALAN_VIIRS_CONUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALAN_VIIRS_CONUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides detailed information about the satellite-based data on artificial light at night (ALAN). The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime lights (NTL) product (VNP46A4, DOI: 10.5067/VIIRS/VNP46A4.001 ) in NASA\u2019s Black Marble suite is used to derive annual summary of ALAN levels throughout the CONUS at both county and tract level for the period of 2012-2020. The PI Dr. Qian Xiao is a member of NASA Heath and Air Quality Applied Sciences Team (HAQAST). ", "links": [ { diff --git a/datasets/ALERA.json b/datasets/ALERA.json index a201c3fced..824946acce 100644 --- a/datasets/ALERA.json +++ b/datasets/ALERA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALERA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALERA is an experimental atmospheric reanalysis dataset for about one and a half years from 1 May 2005 produced on the Earth Simulator. It provides not only the ensemble mean but also spread of the ensemble members. The spread could be used as a measure of the analysis error.\n \nThis datatset is produced under the collaboration among the Japan Meteorological Agency (JMA), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Chiba Institute of Science (CIS). ALERA may be used for research purposes for free under the terms and conditions .\n \nAFES (AGCM for the Earth Simulator) is run at a resolution of T159/L48 (about 80-km in the horizontal and 48 layers in the vertical). The ensemble size is chosen to be 40. Observational data excluding satellite radiances are assimillated using the LETKF (local ensemble transform Kalman filter).", "links": [ { diff --git a/datasets/ALERA2.json b/datasets/ALERA2.json index 1b22a2e9b7..cd3ddc3828 100644 --- a/datasets/ALERA2.json +++ b/datasets/ALERA2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALERA2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALERA2 is an experimental atmospheric reanalysis dataset from 1 Jan 2008 to 5 Jan 2013 produced on the Earth Simulator. This dataset is the second generation of ALERA. In ALERA2, the ensemble size is increased from 40 to 63 and the data assimilation system is updated from the previous one (see Enomoto et al. 2013).\n\nThis dataset is produced by Japan Agency for Marine-Earth Science and Technology (JAMSTEC). ALERA2 may be used for research purposes for free under the terms and conditions.\n\nAFES (AGCM for the Earth Simulator) is run at a resolution of T119L48 (about 100 km in the horizontal and 48 layers in the vertical). The PREPBUFR complied by the National Centers for Environmental Prediction (NCEP) and archived at the University Corporation for Atmospheric Research (UCAR) is used for the observational data and assimilated using the LETKF (local ensemble transform Kalman filter).", "links": [ { diff --git a/datasets/ALOS-2_CIRC_L1_RAD_NA.json b/datasets/ALOS-2_CIRC_L1_RAD_NA.json index b2dc09d4a9..8f6b2945af 100644 --- a/datasets/ALOS-2_CIRC_L1_RAD_NA.json +++ b/datasets/ALOS-2_CIRC_L1_RAD_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS-2_CIRC_L1_RAD_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS-2/CIRC L1 Radiance is obtained by Compact Infrared Camera (CIRC) onboard ALOS-2 and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Land Observing Satellite-2 (ALOS-2, \"DAICHI-2\") is Sun-synchronous sub-recurrent Orbit satellite launched on May 24, which is a follow-on mission from the ALOS \"Daichi\". CIRC is an infrared sensor primarily intended for detecting forest fires, which present a serious problem for the various countries of Southeast Asia, particularly considering the effects of global warming and climate change. The spatial resolution and field of view are 210 m and 128 km \u00c3\u0097 96 km from an altitude of 628 km in the case of ALOS-2. Main characteristic of the CIRC is also an athermal optics. The athermal optics compensates the defocus due to the temperature change by using Germanium and Chalcogenide glass which have different coefficient of thermal expansion and temperature dependence of refractive index.This dataset includes radiance data derived from Level 0 data and the radiometric correction applied. The physical quantity is W/um/sr/m^2.The provided format is GeoTIFF. The spatial resolution is about 210 m. The projection method is UTM. The current version is 11.0.", "links": [ { diff --git a/datasets/ALOS.AVNIR-2.L1C_7.0.json b/datasets/ALOS.AVNIR-2.L1C_7.0.json index a1d1b7886e..18e78756c8 100644 --- a/datasets/ALOS.AVNIR-2.L1C_7.0.json +++ b/datasets/ALOS.AVNIR-2.L1C_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS.AVNIR-2.L1C_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is providing access to the ALOS-1 AVNIR-2 (Advanced Visible and Near Infrared Radiometer type 2) L1C data acquired by ESA stations in the ADEN zone plus some worldwide data requested by European scientists. The ADEN zone (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf) was the area belonging to the European Data node and covered both the European and the African continents, large part of the Greenland and the Middle East.\rThe full mission is covered, obviously with gaps outside to the ADEN zone:\r\u2022 Time windows: from 2006-04-28 to 2011-04-20\r\u2022 Orbits: from 1375 to 27898\r\u2022 Path (corresponds to JAXA track number): from 1 to 670\r\u2022 Row (corresponds to JAXA scene centre frame number): from 370 to 5230\rOne single Level 1C product types is offered for the OBS instrument mode: AV2_OBS_1C.\rThe Level 1C product is a multispectral image (three bands in VIS and one in NIR) in GEOTIFF format with 10 m resolution.", "links": [ { diff --git a/datasets/ALOS.PALSAR.FBS.FBD.PLR.products_NA.json b/datasets/ALOS.PALSAR.FBS.FBD.PLR.products_NA.json index 3a2deb70a1..5b1ff903e0 100644 --- a/datasets/ALOS.PALSAR.FBS.FBD.PLR.products_NA.json +++ b/datasets/ALOS.PALSAR.FBS.FBD.PLR.products_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS.PALSAR.FBS.FBD.PLR.products_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains all ESA acquisitions over the ADEN zone (Europe, Africa and the Middle East) plus some products received from JAXA over areas of interest around the world. Further information on ADEN zones can be found in this technical note (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf). ALOS PALSAR products are available in following modes:\u2022 Fine Beam Single polarisation(FBS): single polarisation (HH or VV), swath 40-70km, resolution 10m, temporal coverage from 02/05/2006 to 30/03/2011 \u2022 Fine Beam Double polarisation (FBD): double polarisation (HH/HV or VV/VH) ), swath 40-70km, resolution 10m, temporal coverage from 02/05/2006 to 30/03/2011 \u2022 Polarimetry mode (PLR), with four polarisations simultaneously: swath 30km, resolution 30m, temporal coverage from 26/08/2006 to 14/04/2011 \u2022 ScanSAR Burst mode 1 (WB1), single polarization: swath 250-350km, resolution 100m, temporal coverage from 12/06/2006 to 21/04/2011 Following processing levels are available: \u2022 RAW( level 1.0): Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene. \u2022 GDH (level 1.5):Ground range Detected, Normal resolution product \u2022 GEC (level 1.5): Geocoded product", "links": [ { diff --git a/datasets/ALOSIPY_9.0.json b/datasets/ALOSIPY_9.0.json index 1226721606..42c776b145 100644 --- a/datasets/ALOSIPY_9.0.json +++ b/datasets/ALOSIPY_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOSIPY_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "International Polar Year (IPY), focusing on the north and south polar regions, aimed to investigate the impact of how changes to the ice sheets affect ocean and climate change to the habitats in these regions. IPY was a collaborative project involving over sixty countries for two years from March 2007 to March 2009. To meet the project goal, world space agencies observed these regions intensively using their own Earth observation satellites. One of these satellites, ALOS - with the PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor - observed these regions independently from day-night conditions or weather conditions. Carrying on this initiative, ESA is providing the ALOS PALSAR IPY Antarctica dataset, which consists of full resolution ALOS PALSAR ScanSAR WB1 products (100m spatial resolution) over Antarctica from July 2008 (cycle 21) to December 2008 (Cycle 24) and from May 2009 (cycle 27) to March 2010 (cycle 31). Missing products between the two periods above is due to L0 data over Antarctica not being available in ADEN archives and not processed to L1. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://alos-ds.eo.esa.int/smcat/ALOSIPY/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/ALOS_AVNIR_OBS_ORI_2.json b/datasets/ALOS_AVNIR_OBS_ORI_2.json index c767303802..1a22802957 100644 --- a/datasets/ALOS_AVNIR_OBS_ORI_2.json +++ b/datasets/ALOS_AVNIR_OBS_ORI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_AVNIR_OBS_ORI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS AVNIR-2 OBS ORI", "links": [ { diff --git a/datasets/ALOS_NA.json b/datasets/ALOS_NA.json index f83aff90f1..dabe828571 100644 --- a/datasets/ALOS_NA.json +++ b/datasets/ALOS_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " This collection provides access to images archived at ROSCOSMOS for ALOS mission.", "links": [ { diff --git a/datasets/ALOS_PRISM_L1B_7.0.json b/datasets/ALOS_PRISM_L1B_7.0.json index 08ed81fa73..553a7eeec2 100644 --- a/datasets/ALOS_PRISM_L1B_7.0.json +++ b/datasets/ALOS_PRISM_L1B_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PRISM_L1B_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) L1B data acquired by ESA stations in the ADEN zone plus some data requested by European scientists over their areas of interest around the world. The ADEN zone (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf) was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East.\r\rThe full mission is covered, though with gaps outside of the ADEN zone:\r\rTime window: from 2006-07-09 to 2011-03-31\rOrbits: from 2425 to 24189\rPath (corresponds to JAXA track number): from 1 to 668\rRow (corresponds to JAXA scene centre frame number): from 55 to 7185.\rTwo different Level 1B product types (Panchromatic images in VIS-NIR bands, 2.5 m resolution at nadir) are offered, one for each available sensor mode:\r\rPSM_OB1_11 -> composed of up to three views; Nadir, Forward and Backward at 35 km swath\rPSM_OB2_11 -> composed of up to two views; Nadir view at 70 km width and Backward view at 35 km width.\rAll ALOS PRISM EO-SIP products have, at least, the Nadir view which is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the view ID according to the JAXA naming convention.", "links": [ { diff --git a/datasets/ALOS_PSR_KMZ_1.json b/datasets/ALOS_PSR_KMZ_1.json index 88e8055173..007916643c 100644 --- a/datasets/ALOS_PSR_KMZ_1.json +++ b/datasets/ALOS_PSR_KMZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_KMZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS PALSAR KMZ", "links": [ { diff --git a/datasets/ALOS_PSR_L1.0_1.json b/datasets/ALOS_PSR_L1.0_1.json index 3f3fc1a6a9..5a97a8d476 100644 --- a/datasets/ALOS_PSR_L1.0_1.json +++ b/datasets/ALOS_PSR_L1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_L1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS PALSAR Level 1.0", "links": [ { diff --git a/datasets/ALOS_PSR_L1.1_1.json b/datasets/ALOS_PSR_L1.1_1.json index 4714771e06..c128fafc5f 100644 --- a/datasets/ALOS_PSR_L1.1_1.json +++ b/datasets/ALOS_PSR_L1.1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_L1.1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS PALSAR Level 1.1", "links": [ { diff --git a/datasets/ALOS_PSR_L1.5_1.json b/datasets/ALOS_PSR_L1.5_1.json index c73b7ca578..9f706df2bf 100644 --- a/datasets/ALOS_PSR_L1.5_1.json +++ b/datasets/ALOS_PSR_L1.5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_L1.5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS PALSAR Level 1.5", "links": [ { diff --git a/datasets/ALOS_PSR_L2.2_1.json b/datasets/ALOS_PSR_L2.2_1.json index 376c09d196..a6c1dd6809 100644 --- a/datasets/ALOS_PSR_L2.2_1.json +++ b/datasets/ALOS_PSR_L2.2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_L2.2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALOS PALSAR Level 2.2", "links": [ { diff --git a/datasets/ALOS_PSR_RTC_HIGH_1.json b/datasets/ALOS_PSR_RTC_HIGH_1.json index 332e8bbf9d..001910b82c 100644 --- a/datasets/ALOS_PSR_RTC_HIGH_1.json +++ b/datasets/ALOS_PSR_RTC_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_RTC_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PALSAR_Radiometric_Terrain_Corrected_high_res", "links": [ { diff --git a/datasets/ALOS_PSR_RTC_LOW_1.json b/datasets/ALOS_PSR_RTC_LOW_1.json index bff626b50d..3b21be12d5 100644 --- a/datasets/ALOS_PSR_RTC_LOW_1.json +++ b/datasets/ALOS_PSR_RTC_LOW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALOS_PSR_RTC_LOW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PALSAR_Radiometric_Terrain_Corrected_low_res", "links": [ { diff --git a/datasets/ALTIKA_SARAL_L2_OST_XOGDR_f.json b/datasets/ALTIKA_SARAL_L2_OST_XOGDR_f.json index f73fa6fc38..073635c474 100644 --- a/datasets/ALTIKA_SARAL_L2_OST_XOGDR_f.json +++ b/datasets/ALTIKA_SARAL_L2_OST_XOGDR_f.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALTIKA_SARAL_L2_OST_XOGDR_f", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL). SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013. The major difference between these data and the Operational Geophysical Data Record (OGDR) data produced by the project is that the orbit from SARAL has been adjusted using SSHA differences with those from the OSTM/Jason-2 GPS-OGDR-SSHA product at inter-satellite crossover locations. This produces a more accurate NRT orbit altitude for SARAL with accuracy of 1.5 cm (RMS), taking advantage of the 1 cm (radial RMS) accuracy of the GPS-based orbit used for the OSTM/Jason-2 GPS-OGDR-SSHA product. This dataset also contains all data from the project (reduced) OGDR, and improved altimeter wind speeds and sea state bias correction. More information on the SARAL mission can be found at: http://www.aviso.oceanobs.com/en/missions/current-missions/saral.html", "links": [ { diff --git a/datasets/ALT_GPR_Barrow_1355_1.json b/datasets/ALT_GPR_Barrow_1355_1.json index 7a721e36e0..31369dd97f 100644 --- a/datasets/ALT_GPR_Barrow_1355_1.json +++ b/datasets/ALT_GPR_Barrow_1355_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALT_GPR_Barrow_1355_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of Active Layer Thickness (ALT) determined with ground-based measurements, and calculated soil volumetric water content (VWC) at four selected sites around Barrow, Alaska in August 2013. ALT was determined using a ground-penetrating radar (GPR) system and traditional mechanical probing. Calculated uncertainties are also included. GPR measurements were taken along four transects of varying length (approx. 1 to 7 km). Mechanical probing included several high-density surveys (every 1 m within 100-m survey line) along each GPR transect. VWC of the active layer soil was calculated at 3-8 calibration points per site where the probe measurement was exactly co-located with a GPR trace.", "links": [ { diff --git a/datasets/ALT_Maps_AK_CA_2332_1.json b/datasets/ALT_Maps_AK_CA_2332_1.json index 3c7c16df45..c037d0b7d1 100644 --- a/datasets/ALT_Maps_AK_CA_2332_1.json +++ b/datasets/ALT_Maps_AK_CA_2332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALT_Maps_AK_CA_2332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format.", "links": [ { diff --git a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json index 76912740be..a0d5ed1a14 100644 --- a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json +++ b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through November 4, 2009. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.", "links": [ { diff --git a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json index 7fa6935922..3ad57861ff 100644 --- a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json +++ b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through January 19, 2011. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.", "links": [ { diff --git a/datasets/AM1EPHNE_6.1NRT.json b/datasets/AM1EPHNE_6.1NRT.json index 1ba0f4bd1b..3528d51335 100644 --- a/datasets/AM1EPHNE_6.1NRT.json +++ b/datasets/AM1EPHNE_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AM1EPHNE_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss\nwhere from left to right: \nE = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information: http://modis.gsfc.nasa.gov/sci_team/", "links": [ { diff --git a/datasets/AMAZE-08_1308_1.json b/datasets/AMAZE-08_1308_1.json index 16d4ecf443..55fa5889b0 100644 --- a/datasets/AMAZE-08_1308_1.json +++ b/datasets/AMAZE-08_1308_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMAZE-08_1308_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements from the Amazonian Aerosol Characterization Experiment (AMAZE-08) carried out during the wet season from February 4 to March 21, 2008 in the central Amazon Basin. Aerosol and atmospheric samples and measurements were collected at Tower TT34 located 60 km NNW of downtown Manaus, and at Tower K34, located 1.6 km from the TT34 site. Physical characterization of aerosols included size, mass, and number distributions and light scattering properties. Chemical characterization included mass concentrations of organics, major anions and cations, and trace metals. Aerosol sources were estimated with measurements of black carbon and biogenic particles. Meteorological and atmospheric conditions including relative humidity, temperature, wind speed and direction, rain, photosynthetically active radiation (PAR), downward and upward solar irradiance, and condensation nuclei were measured. Atmospheric trace gases and volatile organic compounds (VOCs) were sampled and analyzed.", "links": [ { diff --git a/datasets/AMDBLWV_1.json b/datasets/AMDBLWV_1.json index 1c3bfc44d2..036a3da846 100644 --- a/datasets/AMDBLWV_1.json +++ b/datasets/AMDBLWV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMDBLWV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate the marine boundary layer water vapor beneath uniform cloud fields. Microwave radiometry from AMSR-E and AMSR-2 provides the total column water vapor, while the near-infrared imagery from MODIS provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor.", "links": [ { diff --git a/datasets/AMDBLWV_2.json b/datasets/AMDBLWV_2.json index bbd0783955..498e03c247 100644 --- a/datasets/AMDBLWV_2.json +++ b/datasets/AMDBLWV_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMDBLWV_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2 is the current version of this dataset. Version 2 uses an improved methodology to screen out high clouds.\nThis data set provides an estimate the marine boundary layer water vapor beneath uniform cloud fields. Microwave radiometry from AMSR-E and AMSR-2 provides the total column water vapor, while the near-infrared imagery from MODIS provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor.", "links": [ { diff --git a/datasets/AMISOR_ship_1.json b/datasets/AMISOR_ship_1.json index 4d1efd9ac6..c864099fc2 100644 --- a/datasets/AMISOR_ship_1.json +++ b/datasets/AMISOR_ship_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMISOR_ship_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the vicinity of the Amery Ice Shelf on two cruises, during the southern summers of 2000/2001 and 2001/2002. A CTD transect parallel to the front of the Amery Ice Shelf was occupied on both cruises, including repeat occupations on each cruise. A total of 100 CTD vertical profile stations were taken near the ice shelf, most to within 20 m of the bottom, and over 1150 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, helium, tritium, oxygen 18 and biological parameters, using a 12 bottle rosette sampler mounted on either a 24 or 12 bottle frame. On the first cruise, an additional 39 CTD stations were occupied around an experimental krill survey area in the vicinity of Mawson. Additional CTD stations were taken at the end of each cruise for calibration of CTD instrumentation from borehole sites on the Amery Ice Shelf. Near surface current data were collected on both cruises using a ship mounted ADCP. An array of 9 moorings comprising current meters, thermosalinographs and upward looking sonars were deployed along the ice shelf front in February 2001 during the first cruise, and retrieved on the second cruise in February 2002. A summary of all data and data quality is presented in the data report.", "links": [ { diff --git a/datasets/AMLR_0.json b/datasets/AMLR_0.json index a73f084558..032c8fc446 100644 --- a/datasets/AMLR_0.json +++ b/datasets/AMLR_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMLR_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken under the U.S. Antarctic Marine Living Resources (AMLR) program spanning 1997 to 2008.", "links": [ { diff --git a/datasets/AMLR_honours_krill_01_1.json b/datasets/AMLR_honours_krill_01_1.json index 9616d8e810..9f43a57391 100644 --- a/datasets/AMLR_honours_krill_01_1.json +++ b/datasets/AMLR_honours_krill_01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMLR_honours_krill_01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Instantaneous growth rates (IGR) of Antarctic krill kept under experimental conditions were measured. The measured appendages included the uropods, telson (both standard length measurements with the IGR technique) and the pleopod endopodite and pleopod exopodite were investigated as an alternate length measurement. IGR measurements were recorded on 90 experimental animals.\n\nThe total carbon content of 45 krill of various size ranges (collected directly from the field) was determined. The relationship between the change in length in carbon as a function of growth was investigated. The parameters measured were total length, mean uropod length, telson length, wet weight, dry weight and total carbon content.\n\nThis dataset was collected as part of ASAC project 141. See metadata record ASAC_141 - Collection of live Antarctic krill 'Euphausia superba'.\n\nThe fields in this dataset are:\nKrill\nTotal length (mm)\nTelson length (mm)\nMean uropod length (mm)\nWet weight (g)\nDry weight (g)\nDry Weight (mg)\nCarbon content as a % of dry weight\nTotal carbon content (g)\nMoult\nSex", "links": [ { diff --git a/datasets/AMMA_0.json b/datasets/AMMA_0.json index bb7aefae41..c091b0ab3d 100644 --- a/datasets/AMMA_0.json +++ b/datasets/AMMA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMMA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the west coast of Africa in 2006 as part of the AMMA (African Monsoon Multidisciplinary Analyses) program.", "links": [ { diff --git a/datasets/AMMBLWV_1.json b/datasets/AMMBLWV_1.json index d13a197e0f..9812852fd0 100644 --- a/datasets/AMMBLWV_1.json +++ b/datasets/AMMBLWV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMMBLWV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate the marine boundary layer water vapor beneath uniform cloud fields. Microwave radiometry from AMSR-E and AMSR-2 provides the total column water vapor, while the near-infrared imagery from MODIS provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor.", "links": [ { diff --git a/datasets/AMMBLWV_2.json b/datasets/AMMBLWV_2.json index 2fc6d89e52..d17cf9ad84 100644 --- a/datasets/AMMBLWV_2.json +++ b/datasets/AMMBLWV_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMMBLWV_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2 is the current version of this dataset. Version 2 uses an improved methodology to screen out high clouds.\nThis data set provides an estimate the marine boundary layer water vapor beneath uniform cloud fields. Microwave radiometry from AMSR-E and AMSR-2 provides the total column water vapor, while the near-infrared imagery from MODIS provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor.", "links": [ { diff --git a/datasets/AMMC_11-15_GBR_humpback_aerial_survey_1.json b/datasets/AMMC_11-15_GBR_humpback_aerial_survey_1.json index 8d604d0c30..124a86aca5 100644 --- a/datasets/AMMC_11-15_GBR_humpback_aerial_survey_1.json +++ b/datasets/AMMC_11-15_GBR_humpback_aerial_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMMC_11-15_GBR_humpback_aerial_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a collection of dedicated humpback whale sightings and effort from a double platform line transect aerial survey program conducted in the Great Barrier Reef. The survey was undertaken 3-10 August 2012 using a Partenavia Observer P-68B six-seater, twin engine, high-wing aircraft at a ground speed of 100 knots in passing mode at an altitude of 1000 ft. The survey was undertaken to coincide with peak humpback whale abundance within the breeding season, when it is assumed whales are utilising habitat important to their breeding behaviour and not engaging in migratory behaviour. For more details of the survey see:\n\nSmith, J. N., N. Kelly, and I. W. Renner. 2020. Validation of presence-only models for management applications: humpback whale breeding grounds in the Great Barrier Reef World Heritage Area. Ecological Applications. Accepted.\n\nSmith, J. N., Kelly, N., Childerhouse, S., Redfern, J. V., Moore, T. J. and Peel, D. (2020) Quantifying ship strike risk to breeding whales in a multiple-use marine park: the Great Barrier Reef. Frontiers in Marine Science 7:1-15. doi: 10.3389/fmars.2020.00067\n\nData were collected under the Australian Marine Mammal Grant Program for project 11/15 \u2018Identification of humpback whale breeding grounds in the Great Barrier Reef: validation of a spatial habitat model\u2019.", "links": [ { diff --git a/datasets/AMSR-L1A_3.json b/datasets/AMSR-L1A_3.json index 63bc527797..28c39c952c 100644 --- a/datasets/AMSR-L1A_3.json +++ b/datasets/AMSR-L1A_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR-L1A_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR/ADEOS-II L1A Raw Observing Counts (AMSR-L1A) data set was processed from Level 0 science packet data by the JAXA Earth Observation Center (EOC) in Japan.", "links": [ { diff --git a/datasets/AMSR2-REMSS-L2P-v8.2_8.2.json b/datasets/AMSR2-REMSS-L2P-v8.2_8.2.json index 226d924757..2f2accb36e 100644 --- a/datasets/AMSR2-REMSS-L2P-v8.2_8.2.json +++ b/datasets/AMSR2-REMSS-L2P-v8.2_8.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L2P-v8.2_8.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides a \u201cFinal\u201d (Refined) Level-2 Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The \u201cFinal\u201d SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-2PR8A. ", "links": [ { diff --git a/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json b/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json index 7967ea721e..7479bb3b8e 100644 --- a/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json +++ b/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L2P_RT-v8.2_8.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides a near-real-time (NRT) Level-2 Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The NRT SST is made as available as soon as possible, generally within 3 hours latency. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-2TR8A. ", "links": [ { diff --git a/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json b/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json index d65415548a..84e8b9133d 100644 --- a/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json +++ b/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L3U-v8.2_8.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a \u201cFinal\u201d (Refined) Level-3U Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The \u201cFinal\u201d SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8.2\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days. The v8.2 L3U SST supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-3UR8A.", "links": [ { diff --git a/datasets/AMSR2-REMSS-L3U-v8a_8a.json b/datasets/AMSR2-REMSS-L3U-v8a_8a.json index 4bf2325553..625c240e46 100644 --- a/datasets/AMSR2-REMSS-L3U-v8a_8a.json +++ b/datasets/AMSR2-REMSS-L3U-v8a_8a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L3U-v8a_8a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GDS2 Version -The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Golbal Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. From about 700 km above the Earth, AMSR2 will provide us highly accurate measurements of the intensity of microwave emission and scattering. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Remote Sensing Systems (RSS, or REMSS), providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v8\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days.", "links": [ { diff --git a/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json b/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json index 51fb46ecc7..50bf66d4d0 100644 --- a/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json +++ b/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L3U_RT-v8.2_8.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a near-real-time (NRT) Level-3U Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The NRT SST is made as available as soon as possible, generally within 3 hours latency. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-3TR8A.", "links": [ { diff --git a/datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json b/datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json index b36ad76d91..6ef21b5e54 100644 --- a/datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json +++ b/datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSR2-REMSS-L3U_RT-v8a_8a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GDS2 Version -The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Golbal Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. From about 700 km above the Earth, AMSR2 will provide us highly accurate measurements of the intensity of microwave emission and scattering. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Remote Sensing Systems (RSS, or REMSS), providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v8\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days.", "links": [ { diff --git a/datasets/AMSRE-REMSS-L2P-v7a_7a.json b/datasets/AMSRE-REMSS-L2P-v7a_7a.json index a104b47c25..f721583d13 100644 --- a/datasets/AMSRE-REMSS-L2P-v7a_7a.json +++ b/datasets/AMSRE-REMSS-L2P-v7a_7a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSRE-REMSS-L2P-v7a_7a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/AMSRE-REMSS-L3U-v7a_7a.json b/datasets/AMSRE-REMSS-L3U-v7a_7a.json index 70ca5e53e3..d31181eb3f 100644 --- a/datasets/AMSRE-REMSS-L3U-v7a_7a.json +++ b/datasets/AMSRE-REMSS-L3U-v7a_7a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSRE-REMSS-L3U-v7a_7a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/AMSREL1A_3.json b/datasets/AMSREL1A_3.json index c6034527a2..937fc06c87 100644 --- a/datasets/AMSREL1A_3.json +++ b/datasets/AMSREL1A_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSREL1A_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR-E Level-1A observation counts are processed from Level-0 science packet data by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Center (EOC) in Japan.", "links": [ { diff --git a/datasets/AMSRERR_CPR_002.json b/datasets/AMSRERR_CPR_002.json index 05f54c91be..6e32bd6b7d 100644 --- a/datasets/AMSRERR_CPR_002.json +++ b/datasets/AMSRERR_CPR_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSRERR_CPR_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a subset of AMSR-E rain rate product along CloudSat field of view track. The goal of the subset is to select and return AMSR-E data that are within -100 km across the CloudSat track. Thus resultant subset swath is 45 pixels cross-track. Apart from that, all efforts are made to preserve the original HDF-EOS formatting of the source full-sized data.\n \n The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles.\n \n The original, full-sized, product is Level-2B swath product (AE_Rain), and it contains instantaneous measurements of rain rate and rain type (convective vs. stratiform), generated from Level-2A brightness temperatures (AE_L2A). The Goddard Space Flight Center (GSFC) Profiling algorithm determines rain rate and type over ocean areas, and a Modified GSFC Profiling algorithm over land. Data are stored in HDF-EOS (HDF4) format, and are available from 18 June 2002 until the AMSR-E instrument was turned off due to antenna problems in October 2011.", "links": [ { diff --git a/datasets/AMSRE_AVRMO_005.json b/datasets/AMSRE_AVRMO_005.json index 71e10eb0cd..2f3e4d0ed4 100644 --- a/datasets/AMSRE_AVRMO_005.json +++ b/datasets/AMSRE_AVRMO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSRE_AVRMO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly-mean soil moisture statistics (average values) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids. Version 2 ). The dataset covers the time period from 2002-10-01 to 2011-09-30.", "links": [ { diff --git a/datasets/AMSRE_STDMO_005.json b/datasets/AMSRE_STDMO_005.json index 02d2860e51..2cf41b0ba3 100644 --- a/datasets/AMSRE_STDMO_005.json +++ b/datasets/AMSRE_STDMO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMSRE_STDMO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly soil moisture statistics (standard deviation ) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids. Version 2 ). The dataset covers the time period from 2002-10-01 to 2011-09-30.", "links": [ { diff --git a/datasets/AMT_0.json b/datasets/AMT_0.json index 59a4917eab..15c02bbe92 100644 --- a/datasets/AMT_0.json +++ b/datasets/AMT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken during Atlantic Meridional Transect (AMT) cruises.", "links": [ { diff --git a/datasets/AMZ1-WFI-L4-SR-1_NA.json b/datasets/AMZ1-WFI-L4-SR-1_NA.json index 68c16a5d33..2ee98a71c2 100644 --- a/datasets/AMZ1-WFI-L4-SR-1_NA.json +++ b/datasets/AMZ1-WFI-L4-SR-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AMZ1-WFI-L4-SR-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMAZONIA-1/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", "links": [ { diff --git a/datasets/ANACONDAS_0.json b/datasets/ANACONDAS_0.json index eecf8c0a6d..391af95b8f 100644 --- a/datasets/ANACONDAS_0.json +++ b/datasets/ANACONDAS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANACONDAS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This research project sutided the effects of the Amazon River plume on the carbon and nitrogen cycling of the western tropical North Atlantic Ocean. Phytoplankton blooms triggered by the river plume are thought to be responsible for significant cabon dioxide drawdown from the atmosphere. Our team came together to try to understand the factors affecting the phytoplankton bloom and also the fate of its production, including the amount of carbon dioxide taken up by the plume. Fieldwork in the western tropical North Atlantic onboard the RV Knorr took place along the salinity gradient of the river plume (16 ppt to 36 ppt) at a series of stations within and adjacent to the pluem.", "links": [ { diff --git a/datasets/ANARE-26_1.json b/datasets/ANARE-26_1.json index 98105acf32..55f68a82da 100644 --- a/datasets/ANARE-26_1.json +++ b/datasets/ANARE-26_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE-26_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A comparative study made on the amount of sea salt (dominantly NaCl) deposited on Macquarie Island due to atmospheric precipitation. It is found that the scavenging of solid salt particles alone cannot account for all the salt budget over certain areas of the Island. It is considered that sea spray droplets carried aloft by winds and scavenged by precipitation in the immediate vicinity of the shoreline is responsible for this deficit.\n\nThe fields in this dataset are:\nSite details: Altitude, Distance from west coast and Mean annual precipitation.\nChemical component\nBubble size diameter\nMass of salt particle\nDry salt particle radius\nNumber of equivalent days of continuous precipitation\nSite: Plateau, Wireless Hill, Isthmus\nDry salt particles\nSea spray droplets\nTotal fallout", "links": [ { diff --git a/datasets/ANARE-71_1.json b/datasets/ANARE-71_1.json index 1256a1effc..17270c3c52 100644 --- a/datasets/ANARE-71_1.json +++ b/datasets/ANARE-71_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE-71_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Adelie penguin colonies and coastline digitised from Eric J. Woehler, G.W. Johnstone and Harry R. Burton, 'ANARE Research Notes 71, The distribution and abundance of Adelie penguins, Pygoscelis adeliae, in the Mawson area and at the Rookery Islands (Specially Protected Area 2), 1981 and 1988'.", "links": [ { diff --git a/datasets/ANARE-74_1.json b/datasets/ANARE-74_1.json index 90a0d6641c..2f4d8b37c3 100644 --- a/datasets/ANARE-74_1.json +++ b/datasets/ANARE-74_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE-74_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract of the ANARE Research Note:\n\nThe Larsemann Hills are a series of granite and gneiss peninsulas extending into Prydz Bay, between the Amery Ice Shelf and the Sorsdal Glacier. They are dissected by steep-sided valleys produced by at least two glacial stages in the Holocene. There are over 150 freshwater lakes in the hills, ranging from small ponds less than 1 m deep, to glacial lakes up to 10 ha and 38 m deep. The lakes are young, with the oldest basins being about 9000 years old. Variations in the characteristics of the lakes reflect deglaciation history, proximity to the continental ice margin and exposure to the ocean. The main source of the water is snow melt, augmented by sea spray into the more exposed lakes. The waters are well mixed by katabatic winds. Most lakes thaw for up to 2 months in summer, but some are permanently frozen.\n\nThe waters have mainly low conductivity and exceptionally low turbidity, and have near-neutral pH values. The ionic order is Na+ greater than Mg2+ greater than Ca2+ greater than K+. This reflects a strong marine influence, with calcium dominating in a very few catchments.\n\nThe Larsemann Hills were discovered in 1935 by Captain Klarius Mikkelsen in the Thorshavn. Australian, Chinese and Russian stations were established in the area in the mid-late 1980's. Law (Australia) was commenced in 1986 when an Apple Hut was unloaded from MV Nella Dan. A subsequent visit was made during the 1986 winter. The first Australian scientific expedition visited the area during the 1986-87 austral summer. Progress Station (Russia) was occupied at the time. Building of Zhong Shan commenced in January 1989.\n\n*******************\n\nSeveral files are associated with this metadata record:\n1) A PDF copy of the original ANARE Research Note\n2) A CSV file containing the data presented in the ANARE Research Note\n3) A shapefile of the lakes presented in the ANARE Research Note\n\nThe fields in this dataset are:\n\nlake_id\nlake_name (text)\nlocation (text description)\nlongitude (decimal degrees)\nlatitude (decimal degrees)\naltitude (m)\nlake_area (ha)\ncatchment_area (ha)\nmaximum_depth (m)\ndimensions (m)\ndistance_from_polar_plateau (m)\ndescription (text)\ngeology (text)\nwater_temperature (C)\npH\nwater_conductivity (micro mho/cm)\nEh (reduction potential, mV)\nca_concentration (Ca++, ppm)\nmg_concentration (Mg++, ppm)\nna_concentration (NA+, ppm)\nk_concentration (K+, ppm)\nionic_ratios_na_ca_mg_k (ionic ratio of na:(ca+mg+mk))\nionic_ratios_ca_na_k_mg (ionic ratio of ca:(na+k+mg))\nbottom_sediment_grab_sample (text description of results)", "links": [ { diff --git a/datasets/ANARE-98_1.json b/datasets/ANARE-98_1.json index c90bd18192..6db114da4b 100644 --- a/datasets/ANARE-98_1.json +++ b/datasets/ANARE-98_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE-98_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract of the referenced publication:\n\nFive possible runway sites have been proposed within 4 km of Davis on the northwestern part of Broad Peninsula, Vestfold Hills. Most are on on thin, young sedimentary sequences on low level flat areas, although two are dominantly on Precambrian basement, one of which is at low elevation.\n\nThis report reviews the geology of each site as a background summary for use by engineers in the event of a decision to build.\n\nPermafrost level in the area is normally within 100+/- 20-30 cm of the surface and appears to vary depending on location and proximity to water masses.\n\nThe report uses as much information as can be assembled from earlier dispersed reports and adds detailed grain size data from eight sites cored during the 1993-94 summer.\n\nStratigraphy of the sediment sections is not well understood and is best documented in the Heidemann Valley. Maximum sediment thickness known is about 4 m. All sediments appear to be younger than one million years and probably are much younger.\n\nSpeculation is given about the origin and significance of some of the features of the area.\n\n\nAvailable for download:\n\n1 The ANARE Research Notes 98 publication as a pdf.\n\n2 AutoCAD drawing file data digitised in November 1996 from original survey plans of the possible runway sites survey undertaken in February 1984 by the Australian Survey Office. \nThe original survey plan drawing number is 3276/001 in 9 sheets.\nThe coordinates system for the drawing file is WGS84 UTM grid Zone 44.\n\n3 Eleven maps of the possible runway sites in Adobe Illustrator (.ai) format.\nThe maps were produced by AUSLIG (Australian Survey and Land Information Group) using the digitised survey data and are included in the publication.\nThe maps are also available from the Antarctic Map Catalogue as pdf.\nThe map catalogue numbers are 13363 to 13373.\nSee a Related URL for a link to the Antarctic Map Catalogue.", "links": [ { diff --git a/datasets/ANARE_Expeditions_1947-1966_1.json b/datasets/ANARE_Expeditions_1947-1966_1.json index 2f9d5e67d8..94fd3f6f2a 100644 --- a/datasets/ANARE_Expeditions_1947-1966_1.json +++ b/datasets/ANARE_Expeditions_1947-1966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE_Expeditions_1947-1966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A double sided map titled Australian National Antarctic Research Expeditions 1947-1966 was published in 1989. It included details on Phillip Law and the history of Australians in Antarctica and all ANARE expeditions during this time. This zip file contains two text documents containing this text.\n\nAustralia's long history of involvement in Antarctica has its foundations in the 19th century. In its early years Australia depended on the sea for its trade and communications and was conscious of the vast unknown region that lay close to the south. Because of this proximity it was inevitable that Australia became closely involved in Antarctic exploration. \n\nThe sailing vessels upon which the colonies depended for their supplies and trade with Europe followed the Great Circle routes south of the Cape of Good Hope and sought the favourable westerly winds found well to the south. These voyages brought familiarity with the high latitudes, but were not without risk -in the second year of settlement HMS Guardian was almost lost after striking an iceberg. \n\nFrom the first days of colonisation in 1788, Australia was closely associated with sealing and whaling industries. These industries rapidly assumed commercial importance but, as Australian waters became exhausted, the attention of sealers and whalers turned inevitably to the subantarctic islands. By 1820, just ten years after the discovery of Macquarie Island, the fur seal had been virtually exterminated and elephant seals were being slaughtered for their oil. \n\nOver-exploitation around Australia also forced whalers to explore the southern waters. The Hobart barque Venus reached 72 degrees S in search of whales in 1831. Its return to Australia with a cargo of sperm whale oil stimulated others to explore the far south. Elsewhere around Antarctica other voyages by English, American and Russian vessels were making significant discoveries. The geographic and scientific exploration of Antarctica was thus encouraged by the early commercial ventures.\n\nMany explorers bound for the Antarctic, including John Biscoe, Charles Wilkes, Dumont d'Urville and James Clark Ross, visited Australia for supplies for their southern journeys. The use of Hobart as a port of call for most of these expeditions and its support for the southern sealing and whaling industries fostered Australian interest in Antarctica.", "links": [ { diff --git a/datasets/ANARE_History_Timelines_1.json b/datasets/ANARE_History_Timelines_1.json index 40d833d8aa..d439bec476 100644 --- a/datasets/ANARE_History_Timelines_1.json +++ b/datasets/ANARE_History_Timelines_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANARE_History_Timelines_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four documents - Winter expedition list (1948 - 1994), Index of ANARE News items (1988 - 1995), Aviation timeline (1911- 2000) and a master timeline (1929 -1994)\n\nWinter expedition list - produced by Max Corry from ANARE News and ANARE Club documents.\n\nIndex of ANARE News - authored by Evlyn Barrett, AAD Librarian (deceased) as part of ANARE Jubilee (1996)\n\nAviation timeline - by Gordon Bain and Annie Rushton. There are comments on missing or suspect information.\n\nMaster timeline - authored by Evlyn Barrett, AAD Librarian (deceased) as part of ANARE Jubilee (1996). Lists significant Australian Antarctic events.", "links": [ { diff --git a/datasets/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json b/datasets/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json index 980f810347..bc067480b0 100644 --- a/datasets/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json +++ b/datasets/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL063Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability are provided as an ASCII table.", "links": [ { diff --git a/datasets/ANTARES_Baja_California_Station_0.json b/datasets/ANTARES_Baja_California_Station_0.json index 7febd07c2c..807f750839 100644 --- a/datasets/ANTARES_Baja_California_Station_0.json +++ b/datasets/ANTARES_Baja_California_Station_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANTARES_Baja_California_Station_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ANTARES regional network is composed of coastal time-series stations located around Latin America (www.antares.ws). The main goal is to study long-term changes due to both climate and anthropogenic effects, as well as for ocean color purposes of satellite match-ups and algorithm development.", "links": [ { diff --git a/datasets/ANTARES_Ubatuba_Station_0.json b/datasets/ANTARES_Ubatuba_Station_0.json index b9b7a05d0b..a6051d8a2c 100644 --- a/datasets/ANTARES_Ubatuba_Station_0.json +++ b/datasets/ANTARES_Ubatuba_Station_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANTARES_Ubatuba_Station_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ANTARES regional network is composed of coastal time-series stations located around Latin America. The main goal is to study long-term changes due to both climate and anthropogenic effects, as well as for ocean color purposes of satellite match-ups and algorithm development. The Ubatuba-ANTARES station is located in the Southeast Brazilian Bight, 12 nautical miles from the coastline, at approximately 40 m depth. Ubatuba inner shelf is influenced by mesoscale cyclonic meandering of the Brazil Current system at a region with a crosscurrent transfer of slope waters into the shelf. This ecosystem is mainly oligo-mesotrophic, but also strongly influenced by the South Atlantic Central Water upwelled locally or remotely forced from northeastern upwelling cores mainly during austral summer. In the winter, colder, less saline and relatively richer waters from southern latitudes advect northwards along the shelf.", "links": [ { diff --git a/datasets/ANT_0.json b/datasets/ANT_0.json index 07dc8f251a..38562c49d5 100644 --- a/datasets/ANT_0.json +++ b/datasets/ANT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ANT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the African Coast by a North Atlantic Transect (ANT) program under the US-funded GEOTRACES program.", "links": [ { diff --git a/datasets/AOL_0.json b/datasets/AOL_0.json index cea7fb3dbc..9e64e57a6b 100644 --- a/datasets/AOL_0.json +++ b/datasets/AOL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AOL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken off the New England Coast in 1997.", "links": [ { diff --git a/datasets/AOPEX_0.json b/datasets/AOPEX_0.json index 6994db26af..9c97b1929c 100644 --- a/datasets/AOPEX_0.json +++ b/datasets/AOPEX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AOPEX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Spain and Portugal under the AOPEX program.", "links": [ { diff --git a/datasets/AOSNII_0.json b/datasets/AOSNII_0.json index 38039f6174..6f442fdf57 100644 --- a/datasets/AOSNII_0.json +++ b/datasets/AOSNII_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AOSNII_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Autonomous Ocean Sampling Networks (AOSN) second deployment in the Monterey Bay area in 2003.", "links": [ { diff --git a/datasets/APG_ATLAS_1.0.json b/datasets/APG_ATLAS_1.0.json index 8f45c7f0a6..4a7942c4d0 100644 --- a/datasets/APG_ATLAS_1.0.json +++ b/datasets/APG_ATLAS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "APG_ATLAS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three decades after the last Alaska-wide compilations of glacial\n geology (Karlstrom et al., 1964; Coulter et al., 1965), we have\n coordinated a broadly collaborative effort to create a digital map of\n reconstructed Pleistocene glaciers. The Alaska PaleoGlacier Atlas is a\n geospatial summary of Pleistocene glaciation across Alaska. The layers\n in the atlas depict: 1) the extent of glaciers during the late\n Wisconsin glaciation (i.e. Last Glacial Maximum, about 20,000 years\n ago), and 2) the maximum extent reached during the last ca. 3 million\n years by the northwestern Cordilleran Ice Sheet, ice caps, and valley\n glaciers. The atlas is targeted for a scale of 1 to 1,000,000 --\n suitable for visualization and regional analyses. Former glacier\n extents are based on decades of field-based mapping, air-photo\n interpretation, and a variety of dating methods. In all, the first\n version combines glacial-geologic information from 26 publications and\n 42 source maps.\n \n Revisions will be made and released as time and resources allow. A\n companion paper (Kaufman and Manley, subm.; part of an INQUA effort\n for a global atlas with regional reviews) summarizes the\n glacial-geologic evidence and highlights recent revisions, remaining\n uncertainties, and implications for paleoclimate forcing.\n \n See:\n \"http://instaar.Colorado.EDU/QGISL/ak_paleoglacier_atlas/apg_overview.html\"", "links": [ { diff --git a/datasets/APIS_1.json b/datasets/APIS_1.json index 46e8ad3674..1e5253f4d7 100644 --- a/datasets/APIS_1.json +++ b/datasets/APIS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "APIS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "APIS data were collected between 1994 and 1999. This dataset also includes some historical data collected between 1985 and 1987. Both aerial and ship-board surveys were conducted. Studies on the behaviour of Pack-ice or Crabeater Seal (Lobodon carcinophagus) in the Southern Ocean and in the Australian Sector of Antarctica were also conducted as part of this study. Satellite tracking was used to determine their movement, durations on land and at sea, dive depths and dive duration etc. The four species of Antarctic pack ice seals (crabeater, leopard, Weddell, and Ross seals) are thought to comprise up to 50% or more of the world's total biomass of seals. As long-lived, top level predators in Southern Ocean ecosystems, pack ice seals are scientifically interesting because they can assist in monitoring shifts in ecosystem structure and function, especially changes that occur in sensitive polar areas in response to global climate changes. \n \nThe APIS Program focuses on the ecological importance of pack ice seals and their interactions with physical and biotic features of their environment. This program is a collaborative, multi-disciplinary research initiative whose planning and implementation has involved scientists from more than a dozen countries. It is being developed and coordinated by the Group of Specialists on Seals of the Scientific Committee on Antarctic Research (SCAR), and represents an important contribution to SCAR's Antarctic Global Change Program. Australian researchers have undertaken an ambitious science program studying the distribution and abundance of pack ice seals in support of the APIS Program. An excellent overview of this work is provided at the Australian Antarctic Division's web site. The following paragraphs provide a brief progress report of some of that work through 1998. ------------------------------------------------------------------------------- \n \nFour years of developmental work have now been completed in preparation for the Australian contribution to the circumpolar survey that will take place in December 1998. Until recently the main effort has been directed towards designing and building a system for automatic data logging of line transect data by double observers. Two systems identical in concept have been designed for aerial survey and shipboard survey. The systems consist of a number of sighting guns and keypads linked to a central computer. The sightings guns are used to measure the exact time and angle of declination from the horizon of seals passing abeam of the survey platform. Also logged regularly (10 second intervals) are GPS position and altitude (aerial survey only). The aerial survey system also has an audio backup. The aerial survey system has been trialled over three seasons and the shipboard system over one season. Preliminary analysis of aerial data indicates that the essential assumption of the line transect method is badly violated, reinforcing the need for double observers. Assumption violation is likely to be less in shipboard survey, but assessment of the assumption of perfect sightability on the line is still important. User manuals have been written for both the aerial and shipboard systems. An aerial survey system is being constructed for use by BAS in the coming season. A backup manual system for aerial and shipboard survey has also been developed in the event of the automatic system failing. The aerial backup system uses the perspex sighting frame developed by the US. A database has been designed for storage and analysis of aerial and shipboard data. Importing of data is fast and easy, allowing post-survey analysis and review immediately after each day's survey effort. Aides for training observers have been developed. A video on species identification has been produced. A Powerpoint slide show has been designed to simulate aerial survey conditions and use of the automatic data logging system. Currently effort has been directed toward developing an optimal survey design. While a general survey plan is necessary, it must be flexible to deal with unpredictable ice and weather conditions. It is planned to use both the ship and two Sikorsky 76 helicopters as survey platforms. The ship will be used to survey into and out from stations, and inwards from the ice edge for approximately 60 miles. The helicopters will be used to survey southwards from the ship for distances up to 140 miles in favourable weather. Helicopters will fly in tandem, with transects 10 miles apart. Studies of crabeater seal haul-out behaviour have been conducted over the past four seasons. Twenty SLTDRs have been deployed in the breeding season (September-October). The length of deployments varies from a few days to 3 months. No transmissions have been received after mid-January, probably due to loss of instruments during the moult. Most instruments have transmitted data through the survey period of November-December. Haul-out behaviour is consistent between animals and years. However, five more instruments will be deployed in the survey season to ensure there is haul-out data concurrent with the survey effort. Some observations of penguins and whales were also made. \n \nThe accompanying dataset includes three Microsoft Access databases (stored in both Access 97 and Access 2002 formats), as well as two Microsoft Word documents, which provide additional information about these data. The fields in this dataset are: Date Time Time since previous sighting Side (of aircraft/ship) Seen by (observer) Latitude Longitude Number of adults Number of pups Species (LPD - Leopard Seal, WED - Weddell Seal, SES - Southern Elephant Seal, CBE - Crabeater Seal, UNS - Unknown Seal, ADE - Adelie Penguin, ROS - Ross Seal, EMP - Emperor Penguin, MKE - Minke Whale, ORC - Orca Whale, UNP - Unknown Penguin, UNW - Unknown Whale) SpCert - How certain the observer was of correct identification - a tick indicates certainty Distance from Observer (metres) Movement Categories - N: no data, S: stationary, MB: moved body, MBP: moved body and position, movement distance: -99 no data, negative values moved towards flight line, positive distance moved away from flight line \nDistance dart gun fired from animal (in metres) Approach method (S = ship, H = helicopter, Z = unknown) Approach distance (metres) Group (S = single, P = pair, F = family (male, female and pup)) Sex Guessed Weight (kg) Drugs used Maximum Sedation Level (CS = Colin Southwell, MT = Mark Tahmidjis) Time to maximum sedation level Time to return to normal Heart rate (maximum, minimum) Respiration rate (maximum, minimum, resting) Arousal Level (1 = calm, 2 = slight, 3 = strong) Arousal Level Cat1 (1 = calm, 2 = 2+3 from above) Apnoea (maximum length of apnoea in minutes) Comments Time at depth - reading taken every 10 seconds, and whichever depth incremented upwards by 1. Time period (NT - 21:00-03:00, MN - 03:00-09:00, MD - 09:00-15:00, AF - 15:00-21:00) Seal Age - (A = Adult, SA = sub-Adult) WCId - Wildlife Computers Identification Number for SLTDR Length, width, girth (body, head, flippers) (cm) Blood, blubber, skin, hair, tooth, scat, nasal swab - sample taken, yes or no. In general, Y = Yes, N = No, ND = No Data\n\nThis work was also completed as part of ASAC projects 775 and 2263.", "links": [ { diff --git a/datasets/APPSS_0.json b/datasets/APPSS_0.json index 114ca02f24..17df5741b8 100644 --- a/datasets/APPSS_0.json +++ b/datasets/APPSS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "APPSS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observations from the Autonomous Polar Productivity Sampling System.", "links": [ { diff --git a/datasets/APSF.json b/datasets/APSF.json index 2fa619a25f..542ca82d47 100644 --- a/datasets/APSF.json +++ b/datasets/APSF.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "APSF", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aerial Photography Single Frame Records collection is a large and diverse group of imagery acquired by Federal organizations from 1937 to the present. Over 6.4 million frames of photographic images are available for download as medium and high resolution digital products. The high resolution data provide access to photogrammetric quality scans of aerial photographs with sufficient resolution to reveal landscape detail and to facilitate the interpretability of landscape features. Coverage is predominantly over the United States and includes portions of Central America and Puerto Rico. Individual photographs vary in scale, size, film type, quality, and coverage.", "links": [ { diff --git a/datasets/AP_Bibliography_1.json b/datasets/AP_Bibliography_1.json index ed52b2b7a7..a458f88604 100644 --- a/datasets/AP_Bibliography_1.json +++ b/datasets/AP_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AP_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic Petrels Bibliography compiled by Jan Van Franeker of the SCAR Bird Biology Subgroup contains 176 records.\n\nThe fields in this dataset are:\nyear\nauthor\ntitle\njournal\npetrel", "links": [ { diff --git a/datasets/AQ2_SM_5.json b/datasets/AQ2_SM_5.json index b01c36360e..9c1b467ffd 100644 --- a/datasets/AQ2_SM_5.json +++ b/datasets/AQ2_SM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ2_SM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-2 global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_ANSM_5.json b/datasets/AQ3_ANSM_5.json index 43bd56b68f..620088f705 100644 --- a/datasets/AQ3_ANSM_5.json +++ b/datasets/AQ3_ANSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_ANSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded annual global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_DYSM_5.json b/datasets/AQ3_DYSM_5.json index 60b7dba11f..cbe8c04e26 100644 --- a/datasets/AQ3_DYSM_5.json +++ b/datasets/AQ3_DYSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_DYSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded daily global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_FT_5.json b/datasets/AQ3_FT_5.json index 84e51623d7..d07a93bdf8 100644 --- a/datasets/AQ3_FT_5.json +++ b/datasets/AQ3_FT_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_FT_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) data set includes weekly Northern Hemisphere landscape freeze/thaw (FT) data derived from L-band radiometer brightness temperature observations, acquired by the Aquarius sensor on board Argentina's Sat\u00e9lite de Aplicaciones Cient\u00edficas (SAC-D) satellite. The data are distributed on the Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0), with a spatial resolution of 36 km. The SAC-D satellite was developed collaboratively between NASA and Argentina's space agency, Comisi\u00f3n Nacional de Actividades Espaciales (CONAE).", "links": [ { diff --git a/datasets/AQ3_MCSM_5.json b/datasets/AQ3_MCSM_5.json index e20588c7c9..9f6ec37439 100644 --- a/datasets/AQ3_MCSM_5.json +++ b/datasets/AQ3_MCSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_MCSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded monthly global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_MOSM_5.json b/datasets/AQ3_MOSM_5.json index 4e0bc8eb37..43a9fb72c6 100644 --- a/datasets/AQ3_MOSM_5.json +++ b/datasets/AQ3_MOSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_MOSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded monthly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_NRCS_5.json b/datasets/AQ3_NRCS_5.json index c7bb05d20c..ad8f8fd861 100644 --- a/datasets/AQ3_NRCS_5.json +++ b/datasets/AQ3_NRCS_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_NRCS_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of weekly polar-gridded Level-3 products of Aquarius L-band Normalized Radar Cross Section (NRCS) retrievals from the Aquarius/Sat\u00e9lite de Aplicaciones Cient\u00edficas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisi\u00f3n Nacional de Actividades Espaciales (CONAE).", "links": [ { diff --git a/datasets/AQ3_SCSM_5.json b/datasets/AQ3_SCSM_5.json index 21b7927398..c46738abc7 100644 --- a/datasets/AQ3_SCSM_5.json +++ b/datasets/AQ3_SCSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_SCSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded seasonal global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_SNSM_5.json b/datasets/AQ3_SNSM_5.json index a7e34b963b..61aa7addc5 100644 --- a/datasets/AQ3_SNSM_5.json +++ b/datasets/AQ3_SNSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_SNSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded seasonal global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQ3_SSS_5.json b/datasets/AQ3_SSS_5.json index 98e0a7a014..724bdff1d8 100644 --- a/datasets/AQ3_SSS_5.json +++ b/datasets/AQ3_SSS_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_SSS_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer Sea Surface Salinity (SSS) retrievals from the Aquarius/Sat\u00e9lite de Aplicaciones Cient\u00edficas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisi\u00f3n Nacional de Actividades Espaciales (CONAE).", "links": [ { diff --git a/datasets/AQ3_TB_5.json b/datasets/AQ3_TB_5.json index ddeb9ac282..f91bd2d2f3 100644 --- a/datasets/AQ3_TB_5.json +++ b/datasets/AQ3_TB_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_TB_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer brightness temperature (TB) observations and Sea Surface Salinity (SSS) retrievals from the Aquarius/Sat\u00e9lite de Aplicaciones Cient\u00edficas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisi\u00f3n Nacional de Actividades Espaciales (CONAE).", "links": [ { diff --git a/datasets/AQ3_WKSM_5.json b/datasets/AQ3_WKSM_5.json index 27a5c0a907..461f6e5886 100644 --- a/datasets/AQ3_WKSM_5.json +++ b/datasets/AQ3_WKSM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQ3_WKSM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 gridded weekly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Satélite de Aplicaciones Científicas (SAC-D).", "links": [ { diff --git a/datasets/AQUAECO.json b/datasets/AQUAECO.json index ceac9c2cd1..f9b19405d8 100644 --- a/datasets/AQUAECO.json +++ b/datasets/AQUAECO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUAECO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecoregions are based on perceived patterns of a combination of causal and\nintegrative factors including land use, land surface form, potential natural\nvegetation, and soils (Omernik, 1987). This is a copy of the ecoregion coverage\nof Omernik (1987) with some item names modified.\n\nThis coverage is intended for national-level studies of water resources.\n\nReviews_Applied_to_Data. The Ecoregions were plotted on a terminal to ensure\nthe EXPORT file was imported properly. No claims are made regarding the\naccuracy of the original data or linework.\n\n[Summary provided by the EPA]", "links": [ { diff --git a/datasets/AQUALOOKS_0.json b/datasets/AQUALOOKS_0.json index 6152481279..91131e0719 100644 --- a/datasets/AQUALOOKS_0.json +++ b/datasets/AQUALOOKS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUALOOKS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AQUALOOKS project aimed to improve remote sensing observations of coastal and inland waters thanks to multi-look observations. Today, correcting remote sensing observations for air-water interface BRDF (i.e. skyglint and sunglint) or water BRDF is still challenging in turbid waters. This results in an increased uncertainty in final products. To improve BRDF knowledge and BRDF correction algorithms, AQUALOOKS project focused on multi-look observations. Multi-look observations were achieved in water, above water surface and at top of the atmosphere. Multi-look in water measurements were performed during a 3-weeks fieldwork experiment in Belgium. Multi-look above water measurements were performed with the PANTHYR-2 autonomous hyperspectral system at RT 1 station and multi-look top of atmosphere measurements were achieved by satellite sensors CHRIS-PROBA and Pleiades A/B, after acquisition requests. Multi-views observation data were used in addition with a radiative transfer model to better characterize BRDF and to develop correction algorithms.", "links": [ { diff --git a/datasets/AQUARIUS_ANCILLARY_CELESTIALSKY_V1_1.json b/datasets/AQUARIUS_ANCILLARY_CELESTIALSKY_V1_1.json index 16e130b6c1..91dbf3cfef 100644 --- a/datasets/AQUARIUS_ANCILLARY_CELESTIALSKY_V1_1.json +++ b/datasets/AQUARIUS_ANCILLARY_CELESTIALSKY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_ANCILLARY_CELESTIALSKY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky (\"Galaxy\") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000. They are sampled over 721 Declinations between -90 degrees and +90 degrees and 1441 Right Ascensions between 0 degrees and 360 degrees, all evenly spaced at 0.25 degrees intervals. The brightness temperatures are assumed temporally invariant and polarization has been neglected. They include microwave continuum and atomic hydrogen line (HI) emissions. The maps differ only in how the strong radio source Cassiopeia A has been included into the whole sky background surveys: 1/ TB_no_Cas_A does not include Cassiopeia A and reports only the whole Sky surveys. 2/ TB_Cas_A_1cell spread Cas A total flux homogeneously over 1 map grid cell (i.e. 9.8572E-6 sr). 3/ TB_Cas_A_beam spreads Cas A over surrounding grid cells using a convolution by a Gaussian beam with HPBW of 35 arcmin (equivalent to the instrument used for the Sky surveys). Cassiopeia A is a supernova remnant (SNR) in the constellation Cassiopeia and the brightest extra-solar radio source in the sky at frequencies above 1.", "links": [ { diff --git a/datasets/AQUARIUS_ANCILLARY_RFI_V1_1.json b/datasets/AQUARIUS_ANCILLARY_RFI_V1_1.json index 21c534024c..ab8b2f2379 100644 --- a/datasets/AQUARIUS_ANCILLARY_RFI_V1_1.json +++ b/datasets/AQUARIUS_ANCILLARY_RFI_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_ANCILLARY_RFI_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius ancillary Radio Frequency Interference (RFI) product used in ADPS mission processing contains monthly-averaged Radio Frequency Interference (RFI) data for ascending/descending passes as detected by the Aquarius radiometers and scatterometer. The data is available for ascending (northward) and descending (southward) passes of the satellite only and ascending/descending passes combined.\n\nThe values stored in this product are the percentage of radiometer and scatterometer measurements identified as corrupted by interference by the RFI detection algorithms [1,2] within each data point, averaged over one month. An additional RFI flag [3] is used to identify locations where the measured brightness temperature over land exceeds the expected limits of surface emissivity. This flag is not used to remove samples from further processing, but, in generating the radiometer RFI data, 100% RFI is assigned to points where this flag is raised. \n\nThis product can be used to reproduce the RFI maps available on the Aquarius website at University of Maine (https://aquarius.umaine.edu/cgi/gal_radiometer.htm for the radiometer, and https://aquarius.umaine.edu/cgi/gal_scatterometer.htm for the scatterometer), by plotting the variables Rad_RFI_percent_AscDes_AllBeams and Scat_RFI_percent_AscDes_AllBeams on the latitude/longitude grid. Additionally, the user can generate maps by using only a particular beam or only ascending passes, for example. All combinations of beams and ascending/descending passes are possible.\n\nThis product contains information about RFI, but it is also relevant for the retrieved Sea Surface Salinity (SSS). Over the ocean, the RFI percentage in this product corresponds to the amount of raw measurements discarded due to RFI contamination before SSS retrieval. Therefore, maps of the RFI percentage can give the user an indication of where RFI is more likely to have affected the quality of SSS retrievals, for a particular month, or for a series of months.", "links": [ { diff --git a/datasets/AQUARIUS_L2_SSS_CAP_V5_5.0.json b/datasets/AQUARIUS_L2_SSS_CAP_V5_5.0.json index 01cd9669b5..70eaa845f1 100644 --- a/datasets/AQUARIUS_L2_SSS_CAP_V5_5.0.json +++ b/datasets/AQUARIUS_L2_SSS_CAP_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L2_SSS_CAP_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L2_SSS_V5_5.0.json b/datasets/AQUARIUS_L2_SSS_V5_5.0.json index af46719aea..c1de91ba95 100644 --- a/datasets/AQUARIUS_L2_SSS_V5_5.0.json +++ b/datasets/AQUARIUS_L2_SSS_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L2_SSS_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 5.0 Aquarius Level 2 product is the official third release of the orbital/swath data from AQUARIUS/SAC-D mission. The Aquarius Level 2 data set contains sea surface salinity (SSS) and wind speed data derived from 3 different radiometers and the onboard scatterometer. Included also in the Level 2 data are the horizontal and vertical brightness temperatures (TH and TV) for each radiometer, ancillary data, flags, converted telemetry and navigation data.\nEach data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. Enhancements to the version 5.0 Level 2 data relative to v4.0 include: improvement of the salinity retrieval geophysical model for SST bias, estimates of SSS uncertainties (systematic and random components), and inclusion of a new spiciness variable.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index bfc165bb53..93a9a8b0e3 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 28-Day running mean, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5_5.0.json index 1742881d97..4132f6f205 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Seasonal, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5_5.0.json index 72f05b9bc1..45411d62e7 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5_5.0.json index 49ed00672b..3823f80d3d 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Annual, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5_5.0.json index 1257233259..728242e08d 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the mission series mean or cumulative, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5_5.0.json index 203c82af19..6d4c5ee62e 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Daily, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index 3acb3e849f..977712f72a 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the monthly climatology ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5_5.0.json index 1289e4eb56..41a970d1c5 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Monthly, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index d6d293ebf2..554efaaac2 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the seasonal climatology, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index b9f0e5819e..f164ff1503 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 28-Day running mean, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5_5.0.json index be59678d62..7ccac4b29e 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Seasonal, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5_5.0.json index 5f26b79c31..297b314879 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5_5.0.json index 748b306ceb..5aed2830f5 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Annual, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5_5.0.json index 79ef8c32e1..034fd7cd48 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the mission series mean or cumulative, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5_5.0.json index 6f32109cad..64e3be5ab9 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Daily, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index 488d428d76..986401a389 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the monthly climatology, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5_5.0.json index f93512b39f..b3fbc0c5e9 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Monthly, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index 3a5445b070..263367f34b 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the seasonal climatology, descending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index a5ec234b9a..b2561391ff 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 28-Day running mean, ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5_5.0.json index 0fd95516af..1c29c5ef2c 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the seasonal ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index f945260854..e75fb86ae5 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day running mean ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5_5.0.json index c505e2960b..cf70c9a808 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5_5.0.json index ebf83efece..3404009364 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Annual ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5_5.0.json index 212aaa8c69..3e7225bb27 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the mission series mean or cumulative ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5_5.0.json index aeeaa9a92a..ddfbff5086 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Daily ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index c28fe1fb76..1476caf3eb 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the monthly climatology ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5_5.0.json index 8d133032d9..939591fabf 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the monthly ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index a9232b6b92..8f4da6e7e4 100644 --- a/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_ANCILLARY_SST_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the seasonal climatology, ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index 380ce222df..09a35ce7b4 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5_5.0.json index 630d0d60e2..b2e0b582fe 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_7DAY_V5_5.0.json index 2a71740014..8f042780cb 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface density product\nfor version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5_5.0.json index 1f2aeac148..f37b19f24a 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5_5.0.json index 04b21b6ece..b80600a6bd 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_DAILY_V5_5.0.json index 22f9463402..4d7632d182 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface density product\nfor version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index 73b0eca7c1..1c50e27f9c 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology,\nAscending sea surface density product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5_5.0.json index 9f9f950512..bb58c60275 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index ce42e18508..bf1fdefbe8 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index a122cd485c..2d8606b196 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_3MONTH_V5_5.0.json index 8844314dbd..4ea5c22274 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_7DAY_V5_5.0.json index c747d39585..82916d0752 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface density product\nfor version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5_5.0.json index 20dcbfb1e7..fe12e725ef 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5_5.0.json index 440276c548..c04537ccd0 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_DAILY_V5_5.0.json index 0802305264..7b6f20a2ae 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nDaily, Descending sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index abcdc93a7c..81d5d142cf 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5_5.0.json index 16ab4765a5..0321c7a552 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface density\nproduct for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index a16b932155..7cc4c495c4 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea\nsurface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index 5eceddcd31..95d4b37da3 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface density\nproduct for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_3MONTH_V5_5.0.json index d8d4895086..1d02f21124 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, sea surface density product for\nversion 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index e336b7d224..3e97fc127e 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean, sea surface density\nproduct for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY_V5_5.0.json index a15d612d8b..ea76a4d007 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, sea surface density product for version\n5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5_5.0.json index a9950b6422..bdeeb36d27 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, sea surface density product for version 5.0\nof the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5_5.0.json index e8359e4866..8ca3890bbe 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission\nseries mean or cumulative, sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_DAILY_V5_5.0.json index a18d2ccd2c..1542ec31e1 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, sea surface density product for version\n5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index 4ac7e25ce2..8ee29fe1d5 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, sea surface density\nproduct for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5_5.0.json index 5b55cad6c0..4beb6f2d55 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, sea surface density product for\nversion 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index d67d30a616..9e25ffc059 100644 --- a/datasets/AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, sea surface density\nproduct for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index 77d57cca4a..de3ef61346 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and\nseasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5_5.0.json index c50e03c943..b975d5b345 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Seasonal, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5_5.0.json index d171f95073..d2545d4a2f 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5_5.0.json index 49c64c3e9b..f50cb75e2d 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Annual, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5_5.0.json index ed0e439477..2b07735699 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the mission series mean or cumulative, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5_5.0.json index 83e6bc911a..c306e082d4 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Daily, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index c0c3bce6b2..a5dad2a064 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly,\nand seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface spiciness product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5_5.0.json index ceadecc319..535e35755b 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Monthly, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index e539864c2f..861ab5476a 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the seasonal climatology, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index d96aaa66ca..ceba46655e 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the 28-Day running mean, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5_5.0.json index 3bd7dbdea9..5359787776 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Seasonal, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_7DAY_V5_5.0.json index 1133087278..92007099c8 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5_5.0.json index 7cff3fc5b3..4b6dde8543 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Annual, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5_5.0.json index 6e27765f4f..d0e4ae8a1e 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the mission series mean or cumulative, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_DAILY_V5_5.0.json index 038149c8ca..e9faecdb90 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index 49cc893a42..a627a257e0 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the monthly climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5_5.0.json index 9c9416c118..408dad0979 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Monthly, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index 977d3fc17c..24c25a0056 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the seasonal climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index b8cf9b2507..11ca022a93 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness\nstandard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface spiciness product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5_5.0.json index 99f8e077ba..af64333dc0 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index bbdf9a0241..8441a26fd3 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY_V5_5.0.json index 0f5f88185f..2c8520dbce 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5_5.0.json index 9b366ea2be..4cddcbf375 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5_5.0.json index e869b8fc67..575912d572 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7\nday, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_DAILY_V5_5.0.json index a9f286bc59..98e7144370 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index 51111cb731..eae7ad757c 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5_5.0.json index ae099eedb1..124fb16cd6 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface spiciness product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index 456fa3b033..8673865768 100644 --- a/datasets/AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index 88f4ccec44..662d466dd6 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the 28-Day running mean, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5_5.0.json index 2e7d8f4661..17fc07e19b 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Seasonal, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5_5.0.json index 406f6fb092..716e27c28c 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5_5.0.json index 4ce04333bc..32c7d180d2 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Annual, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5_5.0.json index 71419817d9..d9b087e75c 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the mission cummulative, Ascending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5_5.0.json index ff4f969c7b..7695b82cf7 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Daily, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index 70362dd4e5..7750f03677 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly,\nand seasonal time scales. This particular data set is the monthly climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5_5.0.json index fe5c9f1ea7..e9e238fd28 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Monthly, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index f196a55009..7eeaab553e 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the seasonal climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index b46192afee..12300c9af9 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the 28-Day running mean, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5_5.0.json index dab02a5804..de109cfe85 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Seasonal, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5_5.0.json index b13965c26b..d45de4ed51 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5_5.0.json index fcfe9c4124..918f655889 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the Annual, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5_5.0.json index 6abe18b62a..d4e80b32a3 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the mission cummulative, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5_5.0.json index 34da18d444..c7fb40666b 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index 278cddbcf0..e28a089880 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and\nseasonal time scales. This particular data set is the monthly climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5_5.0.json index 94a234eb0d..36be6b538a 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and\nseasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index 5e6e56081b..58f5c5ce3d 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and\nseasonal time scales. This particular data set is the seasonal climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index 096e664c8f..47a833b976 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS)\nrain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5_5.0.json index 9ac4ab3907..89d26ed63a 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index 90d93106b1..fe8e319016 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5_5.0.json index 73adbd02d5..da8dc828b0 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5_5.0.json index 12e5c3b209..b01bc1fd5b 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5_5.0.json index 6f2592e5fd..b52a887ade 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5_5.0.json index 321aed8fac..6fb9ce3ba5 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over\ndaily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index 428a6d99f0..600a0b4465 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5_5.0.json index aef5fdb635..f670cd3921 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index c4ec9d6758..8ed4526d4d 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged\nover daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5_5.0.json index bdfbf68235..5274108535 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Monthly, Ascending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5_5.0.json index 6432c7553b..bc11a8bc02 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Monthly, Descending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5_5.0.json index 7f72beac8a..46155512d8 100644 --- a/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily,\n7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_CAP_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_CAP_7DAY_V5_5.0.json index 51d73f5fdb..abb1e6e885 100644 --- a/datasets/AQUARIUS_L3_SSS_CAP_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_CAP_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_CAP_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_CAP_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_CAP_MONTHLY_V5_5.0.json index 144fe7f10c..0e0372f459 100644 --- a/datasets/AQUARIUS_L3_SSS_CAP_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_CAP_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_CAP_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5_5.0.json index ebc0ed1c1b..9abec8a220 100644 --- a/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5_5.0.json index 2bb5f6dbe1..6473124cda 100644 --- a/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_RAINCORRECTED_CAP_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index 399af6b7c3..64280a222e 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day\nrunning mean, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_3MONTH_V5_5.0.json index 16b14c9fc1..2c34149e2a 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal,\nAscending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_7DAY_V5_5.0.json index 15caa202f9..f03b71f2a7 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day,\nAscending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_ANNUAL_V5_5.0.json index cced547abf..3b4e5a08df 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual,\nAscending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5_5.0.json index 0674f19606..aee8621457 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This\nparticular data set is the mission series mean or cumulative, Ascending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_DAILY_V5_5.0.json index ddce0fca61..2cce65305e 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily,\nAscending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index 5b6b34608a..dbf151dbb6 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nmonthly climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY_V5_5.0.json index b4f9b3c461..c95382d22d 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly,\nAscending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index 89ab9d28a4..a0e7dc5452 100644 --- a/datasets/AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nseasonal climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index 2bcb7c0b4d..c526e48a34 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day\nrunning mean, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_3MONTH_V5_5.0.json index 3a855dbecd..b5c95e7b1c 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal,\nDescending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_7DAY_V5_5.0.json index 5058386e9b..6c02bf56ea 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day,\nDescending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_ANNUAL_V5_5.0.json index 557b94608e..693d933eb7 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual,\nDescending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5_5.0.json index 912cd524fd..61da2ba7a2 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This\nparticular data set is the mission series mean or cumulative, Descending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_DAILY_V5_5.0.json index 8106ff2d61..94206c40d5 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily,\nDescending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index 502944065d..36d06ce598 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nmonthly climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY_V5_5.0.json index 9bc54ed2ae..966a1e5cc3 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly,\nDescending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index b82ad833ef..8ce97fea6d 100644 --- a/datasets/AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nseasonal climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index 1027ce4ff1..14c0996dc1 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains\ngridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_3MONTH_V5_5.0.json index edc3105567..093eab4ae1 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Seasonal sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index 32b802470c..bea64753dd 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day running mean sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_7DAY_V5_5.0.json index 25d57edd12..a65a682d5e 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the 7-Day sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_ANNUAL_V5_5.0.json index 5010ddfae4..8cc0be3c30 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Annual sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5_5.0.json index e1bfd69978..cb366c9c13 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day,\nmonthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_DAILY_V5_5.0.json index e49c7a0ebe..dd5ee689b7 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Daily sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index 4731f2fa1e..d84d20833d 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the monthly climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY_V5_5.0.json index ced1ebca2a..af7f644669 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the Monthly sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index 7443f8c23b..9777b68e74 100644 --- a/datasets/AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the seasonal climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5_5.0.json index f69d986809..bed8e7a01b 100644 --- a/datasets/AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface\nsalinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5_5.0.json index a95237bcb1..d87a2451ff 100644 --- a/datasets/AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface\nsalinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5_5.0.json index 4903adcbca..c474f07064 100644 --- a/datasets/AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set\nis the Monthly sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5_5.0.json index 76247f4058..b88f8b2d65 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_CAP_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5_5.0.json index 7a8b13ab66..699df2bde1 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_CAP_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json index c78a1d4018..2e07d10818 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the 28-Day running mean, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5_5.0.json index 6b01239366..a9a1108fb4 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nSeasonal, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5_5.0.json index cbf1036057..8fc0906a6b 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5_5.0.json index fbfc3b8786..e3bd3e7db3 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nAnnual, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5_5.0.json index b34bc35cdb..f83b6c7859 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales.\nThis particular data set is the mission series mean or cumulative, Ascending wind speed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5_5.0.json index 683efbd560..a5745086bd 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nDaily, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json index fff5672181..8b7ea8bded 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is\nthe monthly climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5_5.0.json index 4aca13e5e5..7ba4c5c35b 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nMonthly, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json index 65cfa2c966..060a0b677c 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is\nthe seasonal climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5_5.0.json index b8fa2cc487..e45d635dee 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\n28-Day running mean, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5_5.0.json index c5e7d10b80..fcf19d160d 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nSeasonal, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5_5.0.json index efef14df7b..fe6621760f 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day,\nDescending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5_5.0.json index a48944c1f4..a428ba9063 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nAnnual, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5_5.0.json index bd2735c411..af20950cc0 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily,\n7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5_5.0.json index f4097aeafc..0aaee5e85b 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nDaily, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json index 4b16f89cf4..edec9f29b4 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nmonthly climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5_5.0.json index 9c167877cd..493272a779 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the\nMonthly, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json index 06313ca33c..e3550ee8b3 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is\nthe seasonal climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5_5.0.json index 3f54e045c3..b09fadcb15 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the 28-Day running mean, wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5_5.0.json index 8d263f4fbe..54a219c25e 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Seasonal wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5_5.0.json index f378cf68b0..db204e7d9c 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonaltime scales. This particular data set is the 7-Day running mean wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5_5.0.json index 187e5e2c9d..0c42e0d1f5 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the 7-Day wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5_5.0.json index 12a6cc5c4f..6f1d754635 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Annual wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5_5.0.json index 9fa4777455..415ec8bb34 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7\nday, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5_5.0.json index abb81a9997..b7485c0cd1 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Daily wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json index eaf203b76a..aa9460766c 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the monthly climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0.json index 8dda49c603..f29488a0b6 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time\nscales. This particular data set is the Monthly wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json index c6b8060bf8..f9537f0b65 100644 --- a/datasets/AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json +++ b/datasets/AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal\ntime scales. This particular data set is the seasonal climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.", "links": [ { diff --git a/datasets/AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0.json b/datasets/AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0.json index 1bb5a6bf00..95691a9489 100644 --- a/datasets/AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0.json +++ b/datasets/AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.", "links": [ { diff --git a/datasets/ARB_48_IN_LIDAR_1.json b/datasets/ARB_48_IN_LIDAR_1.json index c4e0074f25..874741075f 100644 --- a/datasets/ARB_48_IN_LIDAR_1.json +++ b/datasets/ARB_48_IN_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARB_48_IN_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ARB_48_IN_LIDAR data set contains data collected from a 48-inch lidar system located at NASA Langley Research Center. Each granule consists of one year of data. The days of data are different in each granule. Each measurement consists of four parameters: stratospheric integrated backscatter over altitude, altitude levels, scattering ratio at each altitude level, and aerosol backscattering coefficient at each altitude level. An image was produced to represent the data collected for each granule.The Aerosol Research Branch (ARB) Light Detection and Ranging (LIDAR) project has been taking ground based LIDAR measurements from Langley Research Center in Hampton, Virginia since May 1974. These LIDAR measurements provide high resolution vertical profiles of the upper tropospheric and stratospheric aerosols. The LIDAR system has evolved over the years and provides a valuable long-term history of the middle-latitude stratospheric aerosol.The measurements for ARB were made using a LIDAR system. This system uses a ruby laser that emits one joule per pulse with a repeat rate of 0.15 hertz (Hz) at a wavelength of 0.6943 micrometers. This system also uses a 48-inch cassegrainian configured telescope mounted on a movable platform. The transmitter laser beam has a divergence of about 1.0 mrad, and the maximum receiver field of view is 4.0 mrad. The LIDAR has a signal bandwidth of 1 MHz, and this is equal to a 150 meter vertical resolution. Three photomultiplier tubes are used to enhance the dynamic range. These tubes are electronically switched on at specific times after the laser has been fired. The photomultiplier tube output signals are processed by 12-bit Computer Automated Measurement and Control (CAMAC) based digitizers and acquired by a personal computer. The data are archived on optical discs.", "links": [ { diff --git a/datasets/ARB_California_Air_Quality_Data.json b/datasets/ARB_California_Air_Quality_Data.json index 9c8b34f62a..05f1acafef 100644 --- a/datasets/ARB_California_Air_Quality_Data.json +++ b/datasets/ARB_California_Air_Quality_Data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARB_California_Air_Quality_Data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The California Air Resources Board has available two CD-ROMs (CDs) with 20\n years of air quality data. Both CDs contain essentially the same air quality\n data, but provide these data in different formats. The CDs contain 20 years\n of Criteria Pollutant air quality data (1980-1999), 10 years of Toxics air\n quality data (1990-1999), 12 years of dichotomous sampler (Dichot) data\n (1988-1999), and 6 years of non-methane organic compound (NMOC) data\n (1994-1999). These CDs are updates to the air quality data CDs released\n before 2001. One of the many new additions to the new CDs is a hyperlinked\n version of supporting documents.\n \n The first CD contains data that are displayed graphically using Voyager (a\n program contained on the CD, which displays data on maps and as time series\n graphs). This CD also includes annual data summaries in table format, which\n can be viewed using selection buttons and pull-down menus. Graphing templates\n are available for plotting the annual data trends. The CD runs under\n Windows 3.1 and higher. Request CD Number: PTSD-00-013-CD\n \n The second CD contains the same data content as the first CD, but stores the\n data in other forms (ASCII, DBF, etc.) used by analysts who process their own\n data. This CD also includes annual and daily summaries in table format, which\n are accessible through selection buttons and pull-down menus. Graphing\n templates are available for plotting the annual data trends. Request CD\n Number:\n PTSD-00-014-CD\n \n There was not enough space to carry complete hourly data for all the years.\n Consequently, the hourly data for the earliest years have been made available\n for downloading from the internet:\n \n Voyager hourly files 1980-1989\n ASCII hourly files 1980-1989\n \"http://www.arb.ca.gov/aqd/aqdcd/aqdcddld.htm\"", "links": [ { diff --git a/datasets/ARC02_0.json b/datasets/ARC02_0.json index 535d2ff014..120f1f15cd 100644 --- a/datasets/ARC02_0.json +++ b/datasets/ARC02_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARC02_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Chukchi and Beaufort sea in the Arctic region north of Alaska in 2002.", "links": [ { diff --git a/datasets/ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data_1.json index f3e0791a02..fa79223e70 100644 --- a/datasets/ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_AerosolTraceGas_AircraftInSitu_DC8_Data is the in-situ aerosol trace gas data collected by the DC-8 aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data was collected by ion chromatographs, gamma ray spectrometers, and alpha-spectrometers. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_Aerosol_AircraftInSitu_DC8_Data_1.json index c1517f31ab..f08a18f3ca 100644 --- a/datasets/ARCTAS_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data for the DC-8 aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data from the APS, SMPS, CPC, Nephelometer, UHSAS, AMS, SP2, CCN Counter, PILS/IC and PILS/WSOC are featured in this product. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/ARCTAS_Aerosol_AircraftInSitu_P3B_Data_1.json index 858d235dd7..d31c061111 100644 --- a/datasets/ARCTAS_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/ARCTAS_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected by the P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data was collected by the Particle Soot Absorption Photometer (PSAP), Aerodynamic Particle Sizer (APS), Condensation Particle Counter (CPC), Single Particle Soot Photometer (SP2), Differential Mobility Analyzer (DMA), Long Differential Mobility Analyzer (LDMA), Tandem Differential Mobility Analyzer (TDMA), Optical Particle Counter (OPC), and the Aerosol Mass Spectrometer (AMS). Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data_1.json b/datasets/ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data_1.json index 41790b9543..084b173c5d 100644 --- a/datasets/ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data_1.json +++ b/datasets/ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_AircraftRemoteSensing_BE200_HSRL_Data contains data collected by the High Spectral Resolution Lidar (HSRL) onboard the BE-200 aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data_1.json b/datasets/ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data_1.json index a6e69751a4..5801a40177 100644 --- a/datasets/ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data_1.json +++ b/datasets/ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_AircraftRemoteSensing_DC8_DIAL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data_1.json b/datasets/ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data_1.json index 040d3720d3..e8abda14d9 100644 --- a/datasets/ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data_1.json +++ b/datasets/ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_AircraftRemoteSensing_P3B_AATS14_Data contains remotely sensed data collected via the Ames 14-Channel Airborne Tracking Sunphotometer (AATS14) onboard the P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_AircraftRemoteSensing_P3B_CAR_Data_1.json b/datasets/ARCTAS_AircraftRemoteSensing_P3B_CAR_Data_1.json index f3398c29a0..3610a32c22 100644 --- a/datasets/ARCTAS_AircraftRemoteSensing_P3B_CAR_Data_1.json +++ b/datasets/ARCTAS_AircraftRemoteSensing_P3B_CAR_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_AircraftRemoteSensing_P3B_CAR_Data contains remotely sensed data collected via the Cloud Absorption Radiometer (CAR) onboard the P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_Cloud_AircraftInSitu_DC8_Data_1.json index 1cc40abc87..bb61b65da7 100644 --- a/datasets/ARCTAS_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data for the DC-8 aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data from the CAPS instrument is featured in this data product and data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Ground_Data_1.json b/datasets/ARCTAS_Ground_Data_1.json index 6479000483..66344f2d9b 100644 --- a/datasets/ARCTAS_Ground_Data_1.json +++ b/datasets/ARCTAS_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Ground_Data is the ground site data collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. The ground site was located at Barrow, Alaska. This data product features BrO measured by the MAX-DOAS method and data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_JValue_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_JValue_AircraftInSitu_DC8_Data_1.json index b846bbe57b..4a19502022 100644 --- a/datasets/ARCTAS_JValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_JValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_JValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_JValue_AircraftInSitu_DC8_Data is the in-situ photolysis rate data collected by the DC-8 aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data was collected by the CCD-based Actinic Flux Spectroradiometer (CAFS). Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Merge_DC8-Aircraft_Data_1.json b/datasets/ARCTAS_Merge_DC8-Aircraft_Data_1.json index d201d3eef6..3682f75e45 100644 --- a/datasets/ARCTAS_Merge_DC8-Aircraft_Data_1.json +++ b/datasets/ARCTAS_Merge_DC8-Aircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Merge_DC8-Aircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Merge_DC8_Aircraft_Data is the pre-generated merge files created from a variety of in-situ instrumentation collecting measurements onboard the DC-8 aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Merge_P3B-Aircraft_Data_1.json b/datasets/ARCTAS_Merge_P3B-Aircraft_Data_1.json index 8500bd85c1..0e0bba7ddf 100644 --- a/datasets/ARCTAS_Merge_P3B-Aircraft_Data_1.json +++ b/datasets/ARCTAS_Merge_P3B-Aircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Merge_P3B-Aircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Merge_P3B-Aircraft_Data contains pre-generated merge data files for the P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_MetNav_AircraftInSitu_DC8_Data_1.json index 865d25df4f..b57e29457c 100644 --- a/datasets/ARCTAS_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorological and navigational data for the DC-8 aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Also featured in this product is water vapor data from the DLH. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1.json b/datasets/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1.json index 046d5ef24a..d468087be8 100644 --- a/datasets/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1.json +++ b/datasets/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_MetNav_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorological and navigational data for the P-3B aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Model_Data_1.json b/datasets/ARCTAS_Model_Data_1.json index afdc5c2874..387d20ebcb 100644 --- a/datasets/ARCTAS_Model_Data_1.json +++ b/datasets/ARCTAS_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Model_Data contains modeled chemical and aerosol data along the flight tracks of the DC-8 and P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Models used include the GEOS-5, GEOS-Chem, STEM Model Forecasts, MOZART-4, and CMAQ models. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Ozonesondes_Data_1.json b/datasets/ARCTAS_Ozonesondes_Data_1.json index 9cb5700242..741a230d28 100644 --- a/datasets/ARCTAS_Ozonesondes_Data_1.json +++ b/datasets/ARCTAS_Ozonesondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Ozonesondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Ozonesondes_Data contains data collected via ozonesonde launches during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Radiation_AircraftInSitu_P3B_Data_1.json b/datasets/ARCTAS_Radiation_AircraftInSitu_P3B_Data_1.json index 90e993e5c4..6c164598a3 100644 --- a/datasets/ARCTAS_Radiation_AircraftInSitu_P3B_Data_1.json +++ b/datasets/ARCTAS_Radiation_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Radiation_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_AircraftInSitu_Radiation_P3B_Data is the in-situ radiation data collected onboard the P-3B aircraft as part of the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) sub-orbital campaign. Data in this product were collected via the Broadband Radiometer (BBR) and Solar Spectral Flux Radiometer (SSFR). Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Satellite_Data_1.json b/datasets/ARCTAS_Satellite_Data_1.json index fc38641b6e..4e5c5cd7db 100644 --- a/datasets/ARCTAS_Satellite_Data_1.json +++ b/datasets/ARCTAS_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Satellite_Data is the supplementary satellite data for the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data from TES, MOPITT and OMI are featured in this data product and data collection is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/ARCTAS_TraceGas_AircraftInSitu_DC8_Data_1.json index 716dedbc20..45b61ba4fe 100644 --- a/datasets/ARCTAS_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/ARCTAS_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected by the DC-8 aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data was collected by the Trace Organic Gas Analyzer (TOGA), Airborne Tropospheric Hydroxides Sensor (ATHOS), HOx Chemical Ionization Mass Spectrometer (HOxCIMS), Thermal Dissociation - Laser Induced Fluorescence (TD-LIF), Differential Absorption of CO, CH4, N2) Measurements (DACOM), Differential Absorption Lider (DIAL), Chemical Ionization Mass Spectrometer (CIMS), Non-dispersive Infrared Gas Analyzer (NDIR Gas Analyzer), NCAR NOxyO3, and the Proton Transfer Reaction Mass Spectrometer (PTR-MS). Data was also collected by gas chromatography and fluorescence spectroscopy. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_TraceGas_AircraftInSitu_P3B_Data_1.json b/datasets/ARCTAS_TraceGas_AircraftInSitu_P3B_Data_1.json index b52f5404c0..a10ffb21ea 100644 --- a/datasets/ARCTAS_TraceGas_AircraftInSitu_P3B_Data_1.json +++ b/datasets/ARCTAS_TraceGas_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_TraceGas_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data for the P-3B aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. This product features data from the Carbon monOxide by Attenuated Laser Transmission (COBALT) instrument. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTAS_Trajectory_Data_1.json b/datasets/ARCTAS_Trajectory_Data_1.json index 4dbd14b190..d1201fca52 100644 --- a/datasets/ARCTAS_Trajectory_Data_1.json +++ b/datasets/ARCTAS_Trajectory_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTAS_Trajectory_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS_Trajectory_Data is the Kinematic Backward and Forward Trajectories derived for the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. The kinematic trajectories are driven by hourly FSU-WRF winds and initialized at a variety of pressure levels (flight level, 850 HPa, 700 HPa, 500 HPa, and 300 HPa). Data collection for this product is complete. \r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.", "links": [ { diff --git a/datasets/ARCTICCC_0.json b/datasets/ARCTICCC_0.json index 7f8f4833bd..0bb7905874 100644 --- a/datasets/ARCTICCC_0.json +++ b/datasets/ARCTICCC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARCTICCC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project conducted field sampling in the Yukon River, delta and plume waters for two transects in spring/summer of 2022 and 2023 and acquisition of additional transect samples during similar flow regimes through our collaborators. Field measurements included a number of water quality parameters relevant to Arctic biogeochemical function and NASA products, including dissolved organic matter (DOM), particulate organic matter (POM), suspended particulate matter (SPM), chlorophyll-a, radiometry, in situ inherent optical properties, discrete dissolved and particle absorption, fluorescent DOM (FDOM), lignin phenols, HPLC pigments, bioavailability of dissolved organic carbon (DOC).", "links": [ { diff --git a/datasets/ARESE_ER2_MAS_1.json b/datasets/ARESE_ER2_MAS_1.json index 09a208d114..e9322b2782 100644 --- a/datasets/ARESE_ER2_MAS_1.json +++ b/datasets/ARESE_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARESE_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ARM Enhanced Shortwave Experiment (ARESE) was conducted at the Department of Energy's ARM Southern Great Plains (SGP) Central Facility between September 22, 1995 and November 1, 1995. ARESE used a combination of satellite, aircraft, and ground observations to make highly accurate solar flux measurements at different altitudes throughout the atmospheric column. At the heart of this was a carefully stacked Twin Otter and Egrett cloud sandwich with the Otter at 1500 - 5000 feet and the Egrett at 43,000 feet overflown by an ER-2 flying at 65,000 feet.", "links": [ { diff --git a/datasets/ARIA_S1_GUNW_1.json b/datasets/ARIA_S1_GUNW_1.json index 2262806a92..52147e5f18 100644 --- a/datasets/ARIA_S1_GUNW_1.json +++ b/datasets/ARIA_S1_GUNW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARIA_S1_GUNW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-2 interferometric products generated by the Jet Propulsion Lab (JPL) ARIA project. The creation, discovery, and distribution of these products support InSAR science around tectonically active regions, volcanoes, or areas of subsidence/uplift. The generation of the ARIA-S1-GUNW products was in part funded through collaborations with the AWS Open Data Program and NASA ROSES.", "links": [ { diff --git a/datasets/ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data_1.json b/datasets/ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data_1.json index 393680d3af..f48ca2fb17 100644 --- a/datasets/ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data_1.json +++ b/datasets/ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARISE_Aerosol-TraceGas_AircraftRemoteSensing_C130_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARISE_Cloud_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ cloud data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via the Spectrometers for Sky-scanning, Sun-Tracking Atmospheric Research (4STAR) instrument. Data collection is complete.\r\n\r\nARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth\u2019s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA\u2019s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.", "links": [ { diff --git a/datasets/ARISE_Cloud_AircraftInSitu_C130_Data_1.json b/datasets/ARISE_Cloud_AircraftInSitu_C130_Data_1.json index c85d420077..54612acc54 100644 --- a/datasets/ARISE_Cloud_AircraftInSitu_C130_Data_1.json +++ b/datasets/ARISE_Cloud_AircraftInSitu_C130_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARISE_Cloud_AircraftInSitu_C130_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARISE_Cloud_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ cloud data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via two cloud probes, the cloud droplet probe (CDP) and WCM-200 Multi-Element Water Content System. Data collection is complete.\r\n\r\nARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth\u2019s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA\u2019s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.", "links": [ { diff --git a/datasets/ARISE_Merge_Data_1.json b/datasets/ARISE_Merge_Data_1.json index eb1d34dc08..68355cf448 100644 --- a/datasets/ARISE_Merge_Data_1.json +++ b/datasets/ARISE_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARISE_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARISE_Merge_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 pre-generated aircraft (C-130) merge data files. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data collection is complete.\r\n\r\nARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth\u2019s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA\u2019s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.", "links": [ { diff --git a/datasets/ARISE_MetNav_AircraftInSitu_C130_Data_1.json b/datasets/ARISE_MetNav_AircraftInSitu_C130_Data_1.json index 10f719ea36..353a137d23 100644 --- a/datasets/ARISE_MetNav_AircraftInSitu_C130_Data_1.json +++ b/datasets/ARISE_MetNav_AircraftInSitu_C130_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARISE_MetNav_AircraftInSitu_C130_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARISE_MetNav_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ meteorological and navigational data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via GPS, temperature sensors, pitot-static systems, pressure transducers, and hygrometers. Data collection is complete.\r\n\r\nARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth\u2019s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA\u2019s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.", "links": [ { diff --git a/datasets/ARISE_Radiation_AircraftInSitu_C130_Data_1.json b/datasets/ARISE_Radiation_AircraftInSitu_C130_Data_1.json index d45c280721..65593d2608 100644 --- a/datasets/ARISE_Radiation_AircraftInSitu_C130_Data_1.json +++ b/datasets/ARISE_Radiation_AircraftInSitu_C130_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARISE_Radiation_AircraftInSitu_C130_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARISE_Radiation_AircraftInSitu_C130_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 in-situ cloud data product. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data were collected via the Solar Spectral Flux Radiometer (SSFR), BroadBand Radiometer (BBR), and Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR). Data collection is complete.\r\n\r\nARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth\u2019s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA\u2019s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.", "links": [ { diff --git a/datasets/ARK_0.json b/datasets/ARK_0.json index 9ae4e01613..a9d6929a62 100644 --- a/datasets/ARK_0.json +++ b/datasets/ARK_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARK_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Arctic Ocean, east of Greenland and north of Scandinavia in 2002 and 2003.", "links": [ { diff --git a/datasets/ARME_898_1.json b/datasets/ARME_898_1.json index 3c3e6556d7..bc588d484e 100644 --- a/datasets/ARME_898_1.json +++ b/datasets/ARME_898_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARME_898_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Amazonian Region Micrometeorological Experiment (ARME) data contain micrometeorological data (climate, interception of precipitation, mircometeorology and soil moisture) on the elements of the energy balance and evapotranspiration for the Amazonian forest. ASCII text data files for each of the four data types have been zipped toghether. One of the many scientific findings of this experiment was that tropical forest does not experience water stress due to the lack of precipitation, during periods when evapotranspiration is at the potential rate (Shuttleworth, 1988). ARME data types include climate (meteorological), interception of precipitation, micrometeorology, and soil moisture. These data are described in the Data Description section below. ", "links": [ { diff --git a/datasets/ARNd0001_103.json b/datasets/ARNd0001_103.json index ce073118c2..c746123dbd 100644 --- a/datasets/ARNd0001_103.json +++ b/datasets/ARNd0001_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0001_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Based on fossil fuel statistics from OECD, UNEP and the World bank.\n(SAF, Centre for Applied Res., Norw. School of Economy & Buisiness\nAdministration, Oslo.)SAF Working Paper no. 59/89 included\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: CO2 Emission database - 2 floppy disks\nVector \nCO2 Emission database on 2 floppy disks in Lotus 2.01 format.\nSAF Working Paper no. 59/89.", "links": [ { diff --git a/datasets/ARNd0002_103.json b/datasets/ARNd0002_103.json index d8499461dd..c858a37acd 100644 --- a/datasets/ARNd0002_103.json +++ b/datasets/ARNd0002_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0002_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global vegetation change due to climate change modelled by using\nclimate change models from Global Fluid Dynamics Laboratory (GFD,\nPrinceton), Goddard Inst. for Space Studies (GISS, NASA) and Oregon\nState University combined with the by the Biomes classification system\n(Cramer & Prentice 1990). Vegetation regions are defined by\nparameters. University of Trondheim, Dep. of Geography, Norway\nProgramme toidrisi to convert from tabular data. Programme found in\n/global/themes/cl_clima/inform\n\nAttached Raster(s):\n Member_ID: 1\nRaster Name: GFDL Coldest month in degrees Celsius\nRaster \n\nGlobal 30 min lat/lon raster model; digital terrain model drived from\nETOPO5. Cell value origo is in lower left/SW corner: Cell value is in\nSW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 2\nRaster Name: GIS Coldest month in degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n\nMember_ID: 3\nRaster Name: GIS Warmest moth in degrees celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 4\nRaster Name: GFDL Warmest month in degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 5\nRaster Name: GFD Actual evotranspiration/potenial evotranspiration\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 6\nRaster Name: GISS Actual evotranspiration/potenial evotranspiration\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 7\nRaster Name: GIS Growing degree days baseline 0 degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 8\nRaster Name: GIS Growing degree days baseline 5 degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 9\nRaster Name: GFDL Growing degree days baseline 5 degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 10\nRaster Name: GFDL Growing degree days baseline 0 degrees Celsius\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 11\nRaster Name: Global DTM from Cramer 30 mm lat/long grid from ETOPO5\n\nRaster Global 30 min lat/lon raster model; digital terrain\nmodel drived from ETOPO5. Cell value origo is in lower left/SW\ncorner: Cell value is in SW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 12\nRaster Name: Global 3\" lat/long grid, % sunshine hours of possible\n\nRaster 12 images from January through to December. Global 30\nmin lat/lon raster model; digital terrain model derived from\nETOPO5. Cell value origo is in lower left/SW corner: Cell value is in\nSW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 13\nRaster Name: Global 30\" lat/long grid, modelled precipitation in mm\n\nRaster 12 images from January through to December. Global 30\nmin lat/lon raster model; digital terrain model derived from\nETOPO5. Cell value origo is in lower left/SW corner: Cell value is in\nSW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 14\nRaster Name: Global 3\" lat/long grid, modelled normal monthly temperature\n\nRaster 12 images from January through to December. Global 30\nmin lat/lon raster model; digital terrain model derived from\nETOPO5. Cell value origo is in lower left/SW corner: Cell value is in\nSW lat/lon corner, not in centre.\n\nAttached Raster(s):\n Member_ID: 15\nRaster Name: Biome 1.1 - normal climate (reclassified)\n\nRaster Categories include: ice, tundra, boreal forest,\ntemp. deciduous, temp. evergreen, steppe, savannah, semi-desert,\ndesert, trop. seasonal, trop. evergreen, cool desert.\n\nAttached Raster(s):\n Member_ID: 16\nRaster Name: Biome 1.1 - OSU climate (reclassified)\n\nRaster Categories include: ice, tundra, boreal forest,\ntemp. deciduous, temp. evergreen, steppe, savannah, semi-desert,\ndesert, trop. seasonal, trop. evergreen, cool desert.\n\nAttached Raster(s):\n Member_ID: 17\nRaster Name: Shift of the Boreal Zone - OSU climate\n\nRaster Categories include: decreasing, stable, available.\n\nAttached Raster(s):\n Member_ID: 18\nRaster Name: Shift of the Boreal Zone - OSU climate - larger grid\n\nRaster Categories include: decreasing, stable, available.\n\nAttached Raster(s):\n Member_ID: 19\nRaster Name: Shift of the Boreal Zone in Europe - OSU climate\n\nRaster Categories include: decreasing, stable, available.\n\nAttached Raster(s):\n Member_ID: 20\nRaster Name: Percent change in temperature sum > 5 degrees Celsius (OSU)\n\nRaster Catergories include: 0-10, 10-20, 20-30, 30-40, 40-50,\n50-60, 60-70, 70-80, 80-90, 90-100, 100-150, 150-200, >200.\n\nAttached Raster(s):\n Member_ID: 21\nRaster Name: Absolute change of the vegetative period >5 degrees Celsius (OSU)\n\nRaster Categories include: no change, <7 days, 7-14, 14-21,\n21-28, 28-35, 35-42, 42-49, 49-56, 56-63, 63-70, >70.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 22\nVector Name: Original data files\n\nVector Original data files for global vegetation regions and\nclimate change effects. Files include: bic30gfd.dta, bic30gis.dta,\nbic30nor.dta, bic30osu.dta, bic30ukm.dta, clo30.gdd, prc30.gdd,\ntmp30.gdd.", "links": [ { diff --git a/datasets/ARNd0003_103.json b/datasets/ARNd0003_103.json index 143de62eed..c9849d49f6 100644 --- a/datasets/ARNd0003_103.json +++ b/datasets/ARNd0003_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0003_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Climate model based on climate stations and parameters from national\nweather monitoring stations, and digital terrain model. Interpolation\nmethod used: Partial thin-plate spline smoothing (Huthcinson).\nAttached Raster(s):\n\nMember_ID: 1\n\nRaster Name: Modeled percent sunshine hours of possible .Global 30 min\nlatitude/longitude Raster Global 30 min lat/lon raster model;\ndigital terrain model drived from ETOPO5. Cell value origo is in\nlower left/SW corner: Cell value is in SW lat/lon corner, not in\ncentre.\n\nAttached Raster(s):\n Member_ID: 2\n\nRaster Name: Modeled precipitation in mm. Global 30 min\nlatitude/longitude Raster Global 30 min lat/lon raster model;\ndigital terrain model drived from ETOPO5. Cell value origo is in\nlower left/SW corner: Cell value is in SW lat/lon corner, not in\ncentre.\n\nAttached Raster(s):\n Member_ID: 3\n\nRaster Name: Modeled monthly temperature in degrrees Celsius Global 30\nmin latitude/longitude Raster Global 30 min lat/lon raster\nmodel; digital terrain model drived from ETOPO5. Cell value origo is\nin lower left/SW corner: Cell value is in SW lat/lon corner, not in\ncentre.", "links": [ { diff --git a/datasets/ARNd0012_103.json b/datasets/ARNd0012_103.json index 62fd51b9f1..738a4e2af9 100644 --- a/datasets/ARNd0012_103.json +++ b/datasets/ARNd0012_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0012_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contourlines with ekv. 300 meters in the scale of 1:1mill. Gridded\ndata extracted from the same source are also available. Generated on\nbasis of data from Norwegian Mapping Authorities, H?nefoss, Norway", "links": [ { diff --git a/datasets/ARNd0013_103.json b/datasets/ARNd0013_103.json index 69079d9ebd..9e3af2f5a3 100644 --- a/datasets/ARNd0013_103.json +++ b/datasets/ARNd0013_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0013_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extract from the ETOPO5 for the continental shelf of Norway.\nRaster 0.5 * 0.5 degree**2. Depthlines from (NSKV) Mapping\nauthority of Norway are also available.\n\nDigital terrain model continental shelf Norway", "links": [ { diff --git a/datasets/ARNd0015_103.json b/datasets/ARNd0015_103.json index dd26554bfd..dc5d650e8c 100644 --- a/datasets/ARNd0015_103.json +++ b/datasets/ARNd0015_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0015_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastline, islands and iceshelf in the Arctic area.\nMembers informations:\nAttached Vector(s):\nMemberID: 1\nVector Name: ArcInfo export file of the Arctic coastlines\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\n\nVector \nArcInfo export file of the Arctic coastlines derived from\ndata received from the Norwegian Polar Institute.", "links": [ { diff --git a/datasets/ARNd0016_103.json b/datasets/ARNd0016_103.json index fb0b43e943..0e659fac4d 100644 --- a/datasets/ARNd0016_103.json +++ b/datasets/ARNd0016_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0016_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arctic areas under Environmental protection\n \n Protected areas in the Arctic region.\n Members informations:\n Attached Vector(s):\n MemberID: 1\n Vector Name: ArcInfo polygon coverage of protected areas in Norway\n Feature_type: poly\n Vector \n ArcInfo polygon coverage of protected areas in Norway\n \n Members informations:\n Attached Vector(s):\n MemberID: 2\n Vector Name: ArcInfo point coverage of protected areas in Norway\n Feature_type: point\n Vector \n ArcInfo point coverage of protected areas in Norway\n \n Members informations:\n Attached Vector(s):\n MemberID: 3\n Vector Name: ArcInfo polygon coverage of protected areas in Svalbard\n Feature_type: polygon\n Vector \n ArcInfo polygon coverage of protected areas in Svalbard\n \n Members informations:\n Attached Vector(s):\n MemberID: 4\n Vector Name: ArcInfo point coverage of bird sanctuaries on Svalbard\n Feature_type: points\n Vector \n ArcInfo point coverage of bird sanctuaries on Svalbard.\n \n Members informations:\n Attached Vector(s):\n MemberID: 5\n Vector Name: Six ArcInfo coverage of protected areas on Svalbard\n Projection: geographic/lambert\n Projection_meas: decimal degrees/metres\n Feature_type: polyarcpt\n Vector \n Six ArcInfo coverage used for the study of protected areas on\n Svalbard. Coverage includes: svalco, svalco1, svalco2, sval_eco,\n sval_veg and svern1.", "links": [ { diff --git a/datasets/ARNd0033_103.json b/datasets/ARNd0033_103.json index 36fa634754..41b44d9a31 100644 --- a/datasets/ARNd0033_103.json +++ b/datasets/ARNd0033_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0033_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Image file of Baltic sea, Kattegat and Skagerat\nA time dependent budget model for nutrients in the Baltic Sea.\n\nAttached Raster(s):\n Member_ID: 1\nRaster Name: Bathymtry of the Baltic sea original raster data file\nRaster \nERDAS files containing information about the bathymetry of the\nBaltic sea, Kattegat and Skagerak Files include .gis and .lan files\nplus ERDAS image files and a .pcx files", "links": [ { diff --git a/datasets/ARNd0040_103.json b/datasets/ARNd0040_103.json index 24e61f266a..b6501086f6 100644 --- a/datasets/ARNd0040_103.json +++ b/datasets/ARNd0040_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0040_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Various coverages representing bedrock geology in Norway.\nOne aml for producing eps files.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: Coverage showing bedrock geology in Norway\nVector \n\nDescription to be added\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: Line coverage displaying geological faults in Norway\nVector \nDescription to be added\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: Coverage displaying trust/reversed faults\nVector \nDescription to be added", "links": [ { diff --git a/datasets/ARNd0071_103.json b/datasets/ARNd0071_103.json index 43773e4817..dbb86764d8 100644 --- a/datasets/ARNd0071_103.json +++ b/datasets/ARNd0071_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0071_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digitised from paper maps by Lativan Environment Data Centre.\nConverted from DXF files to ARC/INFO coverage (GRID-Arendal).\nDetails start-end date ask Sindre\nTo get precise coordinates use ARC/INFO command \"describe\" on member 1\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: Rivers of Latvia\nProjection: UTM\nProjection_desc: Zone 35\nProjection_meas: Metres\nFeature_type: lline\nVector \nSource map is Russian topographic map, scale supposed to be 1:500 000.", "links": [ { diff --git a/datasets/ARNd0073_103.json b/datasets/ARNd0073_103.json index a93ff70096..c4ff01aff8 100644 --- a/datasets/ARNd0073_103.json +++ b/datasets/ARNd0073_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0073_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The polar regions hold large masses of water in the form of\nice, and this ice has a modifying effect on temperature variations.\n\nAn increase in the mean temperature of the Arctic, as has been\npredicted, may result in an intensified melting of the sea ice. While\na compact ice cover absorbs 15-50% of the incoming solar radiation, an\nice free ocean absorbs about 90%. The absorbed radiation causes\nwarming and evaporation. A change in the ice cover may therefore\ndrastically effect the heat budget of the sea surface. This\nillustrates an important self-magnifying effect of the increased heat\nabsorption causing the acceleration of ice melting.\n\nAn increased greenhouse effect due to changes in the gas composition\nof the atmosphere could therefore be monitored by studying the changes\nin the total mass of sea ice in the Arctic.\n\nIce charts of the Barents Sea , with the ice separated in ten ice\nclasses, weekly, from 1966 to 1989. The classes are from open water to\ndense pack ice.\n\nAttached Raster(s):\n Member_ID: 1\nRaster Name: *.BPIX files - internal format of Norwegian Polar Institute\nRaster \nRaster files received from the Norwegian Polar Institute in their own\ninternal format.\n\nAttached Raster(s):\n Member_ID: 2\nRaster Name: ArcInfo grids representing sea ice in the barents region\nRaster \nArcInfo grids representing the barents sea ice cover from 1966 to 1989.\n\nAttached Raster(s):\n Member_ID: 3\nRaster Name: ArcInfo grids for january 1989.\nRaster \nFive ArcInfo grids representing ice coverage in January 1989.\n\nAttached Raster(s):\n Member_ID: 4\nRaster Name: Arcinfo grid of sea ice for February 1989.\nRaster \nArcInfo grid representing sea ice cover for February 1989.\n\nAttached Raster(s):\n Member_ID: 5\nRaster Name: ArcInfo grid of sea ice for January - year unknown.\nRaster \nArcInfo grid representing sea ice cover in January - year unknown.\n\nAttached Raster(s):\n Member_ID: 6\nRaster Name: Arcinfo grid of sea ice\nRaster \nArcInfo grid representing sea ice - date unknown.\nNeed better documentation for this grid - need to know the coverage date.\n\nAttached Raster(s):\n Member_ID: 7\nRaster Name: ArcInfo grid of sea ice for July and August\nRaster \nArcInfo grid representing sea ice cover for July and August, year unknown.\n\nAttached Raster(s):\n Member_ID: 8\nRaster Name: ArcInfo grid of minimum sea ice extent for 1989.\nRaster \nArcinfo grid representing minimum sea ice extent for 1989.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 9\nVector Name: Vector coverages of ice extent\nFeature_type: polygons\nVector \nVector coverages representing ice extent for the years 1966 through to 1989.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 10\nVector Name: Vector ArcInfo coverages of sea ice extent\nFeature_type: polygons\nVector \nVector coverages representing sea ice extent for the years 1966\nthrough to 1989.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 11\nVector Name: Clipping area for the barents sea region\nFeature_type: polygon\nVector \nArcInfo clipping vector coverage of the Barents sea region.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 12\nVector Name: ArcInfo coverage of July-August ice extent\nFeature_type: polygon\nVector \nArcInfo vector coverage representing the ice extent for July and August.\nYear unknown", "links": [ { diff --git a/datasets/ARNd0075_103.json b/datasets/ARNd0075_103.json index 342ce3adec..70cc24b9d2 100644 --- a/datasets/ARNd0075_103.json +++ b/datasets/ARNd0075_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0075_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The antarctic coastline. Map with geographical coordinates. Coastline,\nislands and iceshelf in the Antarctic area.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\n\nVector Name: Arcinfo coverage of The Antarctic incl South America &\nSouthern most island\n\nProjection: geographic\nProjection_desc: Lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \nArcinfo coverage representing the Antarctic coastline. The surrounding\nareas are also included in this coverage such as the southern coast of\nSouth America and the southern most islands.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: ArcInfo export file of The Antarctic coastline.\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \n\nArcinfo export coverage of The Antarctic coastline not including\nsurrounding areas.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: Arcinfo coverage of southern coastline of South America\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \n\nArcInfo coverage representing the southern coast of South America to\nbe used together with The Antarctic coastline.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 4\n\nVector Name: ArcInfo coverage of southern most islands surrounding The\nAntarctic.\n\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \n\nArcinfo coverage representing the southern most islands found around\nThe Antarctic. To be used together with The Antarctic coastline.", "links": [ { diff --git a/datasets/ARNd0076_103.json b/datasets/ARNd0076_103.json index 001bdbdd8c..99948f4147 100644 --- a/datasets/ARNd0076_103.json +++ b/datasets/ARNd0076_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0076_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A circumpolar basemap of The Arctic.\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: Circumpolar ArcInfo coverage of The Arctic\nSource Map Name: ArcWorld on CD-ROM\nSource Map Scale: 25000000\nProjection: Lambert Azimuthal\nProjection_desc: Lat of centre: 90 0 0\nProjection_meas: metres\nFeature_type: arcs/polys\nVector \n\nCircumpolar ArcInfo coverage of The Arctic derived from ESRI's\nArcWorld 1:25M basemap of the world.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: ArcInfo coverage of latitude line 50 degrees\nProjection: Azimuthal\nProjection_desc: lat of centre: 90 0 0\nProjection_meas: metres\nFeature_type: arcs/polys\nVector \nCircumpolar ArcInfo coverage of the latitude line 50 degrees.", "links": [ { diff --git a/datasets/ARNd0078_103.json b/datasets/ARNd0078_103.json index ef071fa23a..90806a5cbd 100644 --- a/datasets/ARNd0078_103.json +++ b/datasets/ARNd0078_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0078_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastline of Bear Island.\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo coverage of the coastline of Bear Island\n\nVector ArcInfo coverage of Bear Island as dervied from data\nprovided by the Norwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\n\nVector Name: ArcInfo coverage of the coastline of Bear Island in UTM\ncoordinates\n\nVector ArcInfo coverage of the coastline of Bear Island as\nderived from data received from the Norwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: ArcInfo generate file of the coastline of Bear Island\n\nVector ArcInfo generate file representing the coastline of\nBear Island derived from data received from the Norwegian Polar\nInstitute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 4\nVector Name: Norwegian Polar Institute internal format file of the\ncoastline of Bear Island\n\nVector Norwegian Polar Institute internal format file\nrepresenting the coastline of Bear Island as received from the\nNorwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 5\nVector Name: Line-koordinate file of the coastline of Bear Island\n\nVector Line-koordinate file representing the coastline of\nBear Island derived from data received from the Norwegian Polar\nInstitute.", "links": [ { diff --git a/datasets/ARNd0079_103.json b/datasets/ARNd0079_103.json index e8064e46a2..6a7bf336d5 100644 --- a/datasets/ARNd0079_103.json +++ b/datasets/ARNd0079_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0079_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Franz Josef Land coastline information.\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: Six ArcInfo coverages of the coastline of Franz Josef Land\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \nSix ArcInfo coverages representing the coastline of Franz Josef\nland. FRAJO1.....FRAJO5 represent only parts of the whole coverage\nFRAJO. These coverages are derived from data received from the\nNorwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: ArcInfo coverage of the coastline fo Franz Josef Land in\nUTM coordinates\nProjection: UTM\nProjection_desc: zone 33\nProjection_meas: metres\nFeature_type: arcs/polys\nVector ArcInfo coverage of the coastline of Franz Josef Land\nas derived from data received from the Norwegian Polar Institute.\n\nAttached Raster(s):\n Member_ID: 3\nRaster Name: Projection file, geographic to utm and utm to geographic.\n\nRaster Projection files used to project coverages in\ngeographic coordinates to UTM coordinates, zone 33 and vise versa.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 4\nVector Name: ArcInfo generate files of the coastline of Franz Josef Land\nVector Five ArcInfo generate files representing the coastline\nof Franz Josef Land derived from data received from the Norwegian\nPolar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 5\nVector Name: Norwegian Polar Institute internal format files of the\ncoast of Franz Josef Land\nVector Five Norwegian Polar Institute internal format files\nof the coastline of Franz Josef Land as received from the Norwegian\nPolar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 6\nVector Name: Line-koordinate files (.lik) of the coastline of Franz\nJosef Land\nVector Five line-koordinate files (.lik) representing the\ncoastline of Franz Josef Land derived from data received from the\nNorwegian Polar Institute.", "links": [ { diff --git a/datasets/ARNd0082_103.json b/datasets/ARNd0082_103.json index ac70e16edb..7b80e91564 100644 --- a/datasets/ARNd0082_103.json +++ b/datasets/ARNd0082_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0082_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Jan Mayen Island coastline.\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo generate file of the coastline of Jan Mayen Island\nFeature_type: arcs\nVector ArcInfo generate file representing the coastline of\nJan Mayen Island derived from data received from the Norwegian Polar\nInstitute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: Norwegian Polar Institute internal format file of the Jan\nMayen coastline\n\nVector Norwegian Polar institute file representing the\ncoastline of Jan Mayen island as received from the Norwegian Polar\nInstitute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: Several ArcInfo coverages representing basemap\ninformation for Jan Mayen Island\n\nSource Map Name: DCW\nSource Map Scale: 1000000\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: metres\nFeature_type: polyarcpt\n\nVector Several ArcInfo coverages representing basemap\ninformation for Jan Mayen Island extract from the Digital Chart fo the\nWorld CD-ROM. Layers include cultural point features (clpoint),\ndrainage network (dnnet), data quality layer (dqnet), supplemental\ndrainage points (dspoint), hyposography supplemental lines (hsline),\nhypsography supplemental points (hspoint), hypsography network\n(hynet), hypsography points (hypoint), political and oceanic\nboundaries (ponet).\n\nMembers informations:\nAttached Vector(s):\n MemberID: 4\nVector Name: Several ArcInfo coverages representing basemap info for\nJan Mayen Island in UTM\n\nSource Map Name: DCW\nSource Map Scale: 1000000\nProjection: UTM\nProjection_desc: zone 29\nProjection_meas: metres\nFeature_type: polyarcpt\n\nVector Several ArcInfo coverages representing basemap\ninformation for Jan Mayen Island extract from the Digital Chart fo the\nWorld CD-ROM. Layers include drainage network (dnnet), hyposography\nsupplemental lines (hsline), hypsography network (hynet), political\nand oceanic boundaries (ponet). Associated projection file used is\nincluded (geo-utm).", "links": [ { diff --git a/datasets/ARNd0083_103.json b/datasets/ARNd0083_103.json index 5623c4b8f7..ea0b762a8b 100644 --- a/datasets/ARNd0083_103.json +++ b/datasets/ARNd0083_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0083_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Basemap of Iceland\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo generate file of Iceland's coastline\nVector \nArcInfo generate file representing the coastline of Iceland derived\nfrom data received from the Norwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\n\nVector Name: Norwegian Polar Institute internal fromat files of the\nIcelandic coastline Vector Norwegian Polar Institute internal\nformat files of the Icelandic coastline as received from the Norwegian\nPolar institute.", "links": [ { diff --git a/datasets/ARNd0084_103.json b/datasets/ARNd0084_103.json index b7afce33ab..182e8b1ad5 100644 --- a/datasets/ARNd0084_103.json +++ b/datasets/ARNd0084_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0084_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Basemap information of Greenland\nIncorrect bounding box\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo generate files of Greenlands coastline\nVector \nThirteen ArcInfo generate files representing the coastline of\nGreenland derived from data received from the Norwegian Polar\nInsitute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: Norwegian Polar Institute internal format files of the\nGreenland coastline\nVector Norwegian Polar Institute internal format files\nrepresenting the coastline of Greenland as received from the norwegian\nPolar Institute.", "links": [ { diff --git a/datasets/ARNd0086_103.json b/datasets/ARNd0086_103.json index 3945e770b8..24e94ed000 100644 --- a/datasets/ARNd0086_103.json +++ b/datasets/ARNd0086_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0086_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Basemap of Alaska.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo generate file of Alaskan coastline\nVector ArcInfo generate file representing the coastline of\nAlaska derived from data received from the Norwegian Polar Institute.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\n\nVector Name: Norwegian Polar Institute internal format file of Alaskan\ncoastline\nVector Norwegian Polar Institute internal format file\nrepresenting the coastline of Alaska as received from the Norwegian\nPolar Institute.", "links": [ { diff --git a/datasets/ARNd0098_103.json b/datasets/ARNd0098_103.json index ca761b21c1..96ec4cca94 100644 --- a/datasets/ARNd0098_103.json +++ b/datasets/ARNd0098_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0098_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nordisk Kartdatabas/Nordic Cartographic Database. Details of the\ninternal boundaries of the Nordic countries co-ordinated by the\nNational Land Survey, Sweden. Area not strictly Scandinavia but\nNordic.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: Export file of the fylke/lan/district boundaries of the\nNordic countries\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \nExport file of the fylke/lan/district boundaries of the Nordic countries.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: Export file of the kommune/county boundaries of the\nNordic countries\nProjection: geographic\nProjection_desc: lat/long\nProjection_meas: decimal degrees\nFeature_type: arcs\nVector \nExport file of the kommune/county boundaries of the Nordic countries.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: ArcInfo coverage of the coastline/borders of Norway,\nSweden and Finland\nFeature_type: polyarcpt\nVector \nArcInfo coverage of the coastline/borders of Norway, Sweden and Finland.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 4\nVector Name: ArcInfo coverage of the coastline of Norway and border\nwith Sweden Feature_type: polyarcpt\nVector \nArcInfo coverage of the coastline of Norway and border with Sweden.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 5\nVector Name: Clipped ver of ArcInfo coverage of coastline/border of\nNorway, Sweden & Finland Feature_type: polyarcpt\n\nVector Clipped version of ArcInfo coverage of\ncoastline/border of Norway, Sweden and Finland.", "links": [ { diff --git a/datasets/ARNd0105_103.json b/datasets/ARNd0105_103.json index 4be57384d6..e823f39bc8 100644 --- a/datasets/ARNd0105_103.json +++ b/datasets/ARNd0105_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0105_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Climatic zones in Norway at different times of the year.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo coverages of climatic zones of\nNorway at different times of the year\n\nFeature_type: polys/arcs\nVector \n\nArcInfo coverages of climatic zones of Norway at different times of\nthe year. Coverages include: kl623, klima613, klima622, klima623,\nklima626, klima626b, klima632, klima632b, klima653, klima653b,\nklima656, klima656b.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: ArcInfo coverage of temperature isolines for January.\nFeature_type: polyarcpt\nVector \nArcInfo coverage of temperature isolines for January, including Svalbard.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 3\nVector Name: ArcInfo coverage of temperature isolines for July.\nFeature_type: polyarcpt\nVector \nArcInfo coverage of temperature isolines for July, including Svalbard.", "links": [ { diff --git a/datasets/ARNd0117_103.json b/datasets/ARNd0117_103.json index 55f180e225..85a18c0451 100644 --- a/datasets/ARNd0117_103.json +++ b/datasets/ARNd0117_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0117_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Economic boundaries within Europe. This shows which countries belong\nto the E?S, the agreement between countries belonging to EFTA\n(European Trade Agreement) and the EU.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 1\nVector Name: ArcInfo coverage showing countries belonging to the E?S\nSource Map Name: WDBII\nProjection: geographic\nProjection_meas: decimal degrees\nFeature_type: polyarcpt\nVector \n\nArcInfo coverage showing countries belonging to the E?S, the agreement\nbetween countries belonging to EFTA (European Trade Agreement) and the\nEU. This coverage was taken from the World Databank II data set.\n\nMembers informations:\nAttached Vector(s):\n MemberID: 2\nVector Name: ArcInfo coverage showing countries\nbelonging to the E?S in a polar projection\n\nProjection: polar\nProjection_desc: long 10 0 0/lat 60 0 0\nProjection_meas: metres\nFeature_type: polyarcpt\nVector \n\nArcInfo coverage showing countries belonging to the E?S (in a polar\nprojection), the agreement between countries belonging to EFTA\n(European Trade Agreement) and the EU. This coverage was taken from\nthe World Databank II data set.", "links": [ { diff --git a/datasets/ARNd0132_103.json b/datasets/ARNd0132_103.json index 550db691e6..6c8771543c 100644 --- a/datasets/ARNd0132_103.json +++ b/datasets/ARNd0132_103.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARNd0132_103", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "When the Ministers of the Arctic countries adapted the Arctic Environmental\nProtection Strategy (AEPS) in 1991, they signalled out habitat conservation as\nan area for special attention. Consequently when the International Working\nGroup for the Conservation of Arctic Flora and Fauna (CAFF) met at its\ninaugural meeting in 1992, it included Arctic habitat conservation as a\npriority in its Work Plan. Norway, on behalf of the eight Arctic countries,\naccepted the task of examining the current status of habitat protection within\nthe Arctic countries as the first phase of CAFF's long-term strategy on\nhabitat. This datasets holds data concerning existing protected areas in the Arctic.", "links": [ { diff --git a/datasets/ARPANSA_BIO_12.json b/datasets/ARPANSA_BIO_12.json index a48ab339d1..f9c88b4dfd 100644 --- a/datasets/ARPANSA_BIO_12.json +++ b/datasets/ARPANSA_BIO_12.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ARPANSA_BIO_12", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset also forms part of the set of State of the Environment (SOE) indicators.\n\nINDICATOR DEFINITION\nDaily measurements of solar Ultra-Violet radiation at Casey and Davis stations, reported in units of standard erythemal dose (SED).\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION and PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nStratospheric ozone depletion began in the mid-1970's and is likely to persist until mid this century or beyond. Ozone depletion allows more short wavelength, biologically damaging, UVB radiation (280-320 nm) to reach the Earth's surface. Thus, organisms living beneath depleted ozone are likely to be impacted by enhanced UVB irradiances. Enhanced UVB irradiances can increase the incidence of skin cancer, cataract eye disease and even immune system suppression in humans. It can also reduce the growth, productivity and survival of marine organisms and can cause changes in the structure and function of Antarctic marine communities. This indicator provides a direct measure of the extent and magnitude to which UV irradiances are enhanced and provides vital data against which biological responses to UV exposure can be normalised.\n\nLiving organisms are sensitive to UV radiation because vital biological molecules such as DNA, lipids and proteins absorb strongly in these wavelengths. DNA, with a peak absorption at 260 nm, is particularly sensitive, and is liable to mutation. DNA damage has been extensively studied in microbial and mammalian systems where UV-induced damage produces two distinct effects, mutagenesis and toxicity. In humans the impact of DNA damage manifests mainly as skin cancer. DNA damage in plants has been the subject of relatively few studies (Britt, 1999; Taylor et al, 1996; Vornarx et al, 1998) with most research examining impacts of UV-B on growth or photosynthesis, predominantly using crop plants. Terrestrial plants are potentially very vulnerable to UV-B induced DNA damage. Firstly the levels of UV-B are higher on land than in water. In addition plants rely on light for photosynthesis and are therefore adapted to absorb high levels of solar radiation (and the associated, harmful UV-B). Defence mechanisms to protect against damaging high energy UV radiation are also found in plants. Compounds such as flavonoids, and carotenoids absorb UV radiation and act as sun-screens, reducing the levels of UV-B at the molecular level. Research has been limited in Antarctic plants but there are clear differences in protective pigment levels in 3 Antarctic mosses with Grimmia antarctici (an endemic species) showing low levels of these pigments compared to other cosmopolitan species (Robinson et al 2001). This suggests that the endemic species may be more vulnerable to UV-B damage. Studies have recently commenced to investigate DNA-damage in these plants. Work by Skotnicki and coworkers (Skotnicki et al 2000) which shows high levels of somatic mutation could also be a result of UV-B exposure.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: The Australian Radiation Protection and Nuclear Safety Agency take broadband in situ observations at Antarctic mainland stations (Casey, Davis and Mawson) and at Macquarie Island.\n\nFrequency: Continuous measurements\n\nMeasurement Technique: Broad band UV radiometry (use of biometer or biologically effective UVR detector). Total UVR measurements are also made using an Eppley TUV radiometer (responds across 290 to 400 nm wavelength range). Spectral measurements have also been made at Davis station. Readings are taken every ten minutes and the total SED's calculated for the day.\n\nRESEARCH ISSUES\nA need exists for a comprehensive monitoring network of broadband measurements, complemented by a small baseline network of precision spectral measurements across the nation. Such a network is being planned by the Bureau of Meteorology to link directly with the basic national meteorological observations. Validation of satellite data with surface based measurements (ARPANSA) over Australia for the period 1979-1992 has been carried out (Udelhofen et al 1999) and a follow up is planned for 1992-2000. Validation of satellite data and surface UVR measurements over the Antarctic and sub-Antarctic is planned between the Antarctic Division, ARPANSA and Dan Lubin at UCLA.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 9 - Daily records of total column ozone at Macquarie Island\n\nDATA DESCRIPTION\n10 minute averages of weighted UVR (CIE 1987 spectral effectiveness).\n\nThe data in the files is :\n\nDate, time, total solar radiation (counts), gain 1, Total UVR (counts), gain 2, UVB(counts), gain 3, biometer , temperature.\n\nMain Detector is Solar Light UVBiometers (SL501)\n\nDetector 1 - Eppley total solar radiation pyranometer.\nDetector 2 - Eppley total UVR (TUV) radiometer - covers wavelength range 290 to 400 nm.\nDetector 3 - International Light UVB radiometer - covers wavelength range 290 to 315 nm.\nDetector 4 - Solar Light UVBiometer (SL501) - approximates CIE erythemal spectral effectiveness.\n\nThe 2nd last column is the biometer in MEDs/hr (1 MED is 200 J/m2 effective weighted with the CIE (1987) erythemal response) and the last column is temperature inside the detector.\n\nThe 3 other detectors, with outputs in counts, are the total solar, Total UVR (TUV) and the UVB.\n\nData are stored as zipped up .dat files, and in excel spreadsheets. Last data were added in 2020.\n\nThe fields in this dataset are:\nDate\nTime\nTotal Solar Radiation (counts)\nGain 1\nTotal UVR (counts)\nGain 2\nUVB(counts)\nGain 3\nBiometer\nTemperature", "links": [ { diff --git a/datasets/ASAC2100_1.json b/datasets/ASAC2100_1.json index 58d5863a92..beca345189 100644 --- a/datasets/ASAC2100_1.json +++ b/datasets/ASAC2100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC2100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From 1991 to 2000 14 voyages have been completed in the Southern Ocean. Measurements of DMS (Dimethylsulfide) and DMSP (Dimethylsulfoniopropionate) have been carried out on surface and subsurface waters together with physical and biological measurements, with a view to understanding the main processes that affect DMS in the Southern Ocean. The first flux measurements have been carried out for DMS (see Curran and Jones 2000) in the last 3 years a concerted study has been carried out in the seasonal ice zone this study aims to identify the major phytoplankton assemblages responsible for DMS and DMSP production in the sea ice zone. It is thought that the sea ice zone also contributes to DMS in the atmosphere. This is being quantified.\n\nThe fields in this dataset are:\n\nSite\nDate\nTime (local)\nLatitude\nLongitude\nSnow Cover (metres)\nCore\nLength (metres)\nDMSPt (nano Mols)\nChlorophyl a (micrograms per litre)\nSea Ice depth (metres)\nPigments\nFucoxanthin (micrograms per litre)\nPeridinin (micrograms per litre)\n19' hexanoyloxyfucoxanthin (micrograms per litre)\nSalinity (ppt)\nNitrate (micro Mols)\nNitrite (micro Mols)\nSilicate (micro Mols)\nPhosphate (micro Mols)", "links": [ { diff --git a/datasets/ASAC_1.json b/datasets/ASAC_1.json index 53c72922b5..a563426cbd 100644 --- a/datasets/ASAC_1.json +++ b/datasets/ASAC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstracts of some of the referenced papers:\n\nMegafloral remains recovered from the Jetty Member and the upper part of the Flagstone Bench Formation, Amery Group include Dicroidium and Pagiophyllum. Dicrodium zuberi and D.crassinervis forma stelznerianum occur with Pteruchus dubius and support a Mid to Late Triassic age. A new species of conifer, Pagiophyllum papillatus, is recognised along with an undetermined conifer pollen cone.\n\nAn Australian National Antarctic Research Expedition in the summer of 1989-1990 made possible a reconnaissance of the avifauna of the Prince Charles Mountains, Mac.Robertson Land, Antarctica. Sixteen scientists, scattered widely throughout the range, were moved periodically by helicopter to new sites. A staff of nine people was located at Dovers Field Base near the base of Farley Massif. These people made opportunistic observations of birds from 26 December, 1989 to 18 February, 1990. The present report summarises their collective findings.\n\nThe East Antarctic Craton contains only one substantial outcrop of Palaeozoic-Mesozoic strata between 0 and 150 degrees East; this lies in Mac.Robertson Land, on the eastern margin of the northern Prince Charles Mountains. These rocks are known as the Amery Group (Mond 1972, McKelvey and Stephenson 1990) and comprise dominantly fluviatile sandstones, with subordinate shales, coals and conglomerates. The lower formations of the Amery Group, the Radok Conglomerate and Bainmedart Coal Measures, contain a diverse Stage 5 palynomorph assemblage indicating a Baigendzhinian-Tatarian age (late Early-Late Permian), hereafter abbreviated as mid-Late Permian. The uppermost formation within the Amery Group, the Flagstone Bench Formation, was studied in detail by Webb and Fielding (1993), who revised the stratigraphy and defined a new member, the Jetty Member. They described for the first time a Triassic megaflora from this unit, considerably extending the time range for the Amery Group, which was previously regarded as entirely mid to Late Permian in age.", "links": [ { diff --git a/datasets/ASAC_1001_1.json b/datasets/ASAC_1001_1.json index 2f08abeae9..918cf685ea 100644 --- a/datasets/ASAC_1001_1.json +++ b/datasets/ASAC_1001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The factors that control the number of animals in a population are often difficult to understand. However, this basic understanding is central to managing those populations and assessing how they might respond to human induced pressures. For animals living in the Antarctic, like penguins, the marine environment that they depend on for food can vary due to natural events such as El Nino, and potentially due to human induced changes such as global warming. This study uses modern computer technology to track Royal penguins at sea and to monitor their time on land. By relating where the birds go to feed, what they feed on, and how successfully they catch their food to the survival rates of their chicks, this study will describe how fluctuations in a major Antarctic oceanographic feature (the Antarctic Polar Front) can influence the size of the Royal penguin population at Macquarie Island.\n\nInformation on breeding success, diet and foraging success were collected each year between 1997-2001. Diving behaviour and at-sea movements were also quantified between 1997 and 1999.\n\nThese data will also be available in the ARGOS satellite tracking database.\n\nAttached to this metadata record are ARGOS tracking data collected by Cindy Hull between 1994 and 2000. The tracking data have been collected from 19 different royal penguins. The download file contains a csv file with tracking data.", "links": [ { diff --git a/datasets/ASAC_1002_1.json b/datasets/ASAC_1002_1.json index ae41cce3c7..466606cb32 100644 --- a/datasets/ASAC_1002_1.json +++ b/datasets/ASAC_1002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1002 See the link below for public details on this project.\n\nTaken from the abstracts of the referenced papers:\n\nA morphological and physiological characterization of yeast strains CBS 8908, CBS 8915, CBS 8920, CBS 8925(T) and CBS 8926, isolated from Antarctic soils, was performed. Phylogenetic analyses of the sequences of the D1/D2 regions and the adjacent internal transcribed spacer (ITS) regions of the large-subunit rDNA of these strains placed them into the Tremellales clade of the Hymenomycetes. The sequence data identified strains CBS 8908, CBS 8915 and CBS 8920 as belonging to the species Cryptococcus victoriae. Strains CBS 8925(T) and CBS 8926 were found to represent an unique clade within the Hymenomycetes, with Dioszegia crocea CBS 6714(T) being their closest phylogenetic relative. Fatty acid composition and proteome fingerprint data for these novel strains were also obtained. No sexual state was observed. A novel basidiomycetous species, Cryptococcus statzelliae, is proposed for strains CBS 8925(T) and CBS 8926.\n\n#######\n\nSoil, snow and organic material, collected in November 1997 from the Vestfold Hills, Davis Base, Antarctica, were screened for yeasts. Two isolates, which were shown to be indistinguishable by rDNA sequencing and protein analysis by SDS-PAGE, are described in this communication as a novel species, Cryptococcus watticus sp. nov. (type culture, CBS 9496T=NRRL Y-27556T). Sequence analyses of the 26S rDNA D1/D2 region placed C. watticus in the hymenomycetous yeasts in a cluster with Holtermannia corniformis and Cryptococcus nyarrowii. This species has been allocated to the genus Cryptococcus on the basis of physiological and morphological characteristics.\n\n#######\n\nIn December 1997, 196 soil and snow samples were collected from Vestfold Hills, Davis Base, Antarctica. Two isolates, CBS 8804T (pink colonies) and CBS 8805 (yellow colonies), were shown by proteome analysis and DNA sequencing to represent the same species. Results from the sequencing of the D1/D2 region of the large rDNA subunit placed this species in the hymenomycetous tree in a unique sister clade to the Trichosporonales and the Tremellales. The clade consists of Holtermannia corniformis CBS 6979 and CBS strains 8804T, 8805, 8016, 7712, 7713 and 7743. Morphological and physiological characteristics placed this species in the genus Cryptococcus, with characteristics including the assimilation of D-glucuronate and myo-inositol, no fermentation, positive Diazonium blue B and urease reactions, absence of sexual reproduction and production of starch-like compounds. Fatty acid analysis identified large proportions of polyunsaturated lipids, mainly linoleic (C18:2) and, to a lesser extent, linolenic (C18:3) acids. On the basis of the physiological and phylogenetic data, isolates CBS 8804T and CBS 8805 are described as Cryptococcus nyarrowii sp. nov. \n\n#######\n\nWorldwide glaciers are annually retreating due to global overheating and this phenomenon determines the potential lost of microbial diversity represented by psychrophilic microbial population sharing these peculiar habitats. In this context, yeast strains, all unable to grow above 20 degrees C, consisting of 42 strains from Antarctic soil and 14 strains isolated from Alpine Glacier, were isolated and grouped together based on similar morphological and physiological characteristics. Sequences of the D1/D2 and ITS regions of the ribosomal DNA confirmed the previous analyses and demonstrated that the strains belong to unknown species. Three new species are proposed: Mrakia robertii sp. nov. (type strain CBS 8912), Mrakia blollopis sp. nov. (type strain CBS 8921) and a related anamorphic species Mrakiella niccombsii sp. nov. (type strain CBS 8917). Phylogenetic analysis of the ITS region revealed that the new proposed species were closely related to each other within the Mrakia clade in the order Cystofilobasidiales, class Tremellomycetes. The Mrakia clade now contains 8 sub-clades. Teliospores were observed in all strains except CBS 8918 and for the Mrakiella niccombsii strains.", "links": [ { diff --git a/datasets/ASAC_1003_2.json b/datasets/ASAC_1003_2.json index 57ef7594b6..bce2894bf0 100644 --- a/datasets/ASAC_1003_2.json +++ b/datasets/ASAC_1003_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1003_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 1003\nFurther investigations of the effects of the Nella Dan oil spill on intertidal benthic communities at Macquarie Island: continued recovery of kelp holdfast communities.\nSee the link below for public details on this project.\n\nThe project investigated spatial variation in kelp holdfast macrofaunal communities 7 years after the initial oil spill. The project was expanded to cover more sites than were sampled in projects 250 (ASAC_250) and 672 (ASAC_672). Results indicated that an impact was still detectable at one of the 3 oiled sites.\n\nThis dataset contains the 1988 and 1994 data. Holdfast data from the 1994/1995 season is also included (comparing east versus west).\n\nThe numbers are total individuals of each species that were found in each holdfast sample. This is a basic, though standard, species-abundance matrix.\n\nThe site codes used in this project are:\n\nSB = Sandy Bay\nSEC = Secluded Bay\nBB = Buckles Bay\nGC = Garden Cove\nGG = Green Gorge\nGB = Goat Bay\nHMB = Half Moon Bay\nBAUER = Bauer Bay\nOther codes as for oil spill data\n\nThe first number given after the site code is the site number at that sampling location. The second number is the replicate at that site. Thus sb(1)3 is Sandy Bay site 1, replicate 3.\n\nThe fields in this dataset are:\n\nSpecies\nYear\nSite", "links": [ { diff --git a/datasets/ASAC_1004_1.json b/datasets/ASAC_1004_1.json index 6187527bc2..0427ac6921 100644 --- a/datasets/ASAC_1004_1.json +++ b/datasets/ASAC_1004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air from the ice and firn (compressed snow) of the Antarctic ice sheet will be extracted and measured for atmospheric composition in the past. Gases of interest are greenhouse gases (carbon dioxide, methane, nitrous oxide) and ozone depleting gases (CFCs, halons). The aim is to understand the budgets of these important atmospheric constituents.\n\nThe ice cores drilled for the gas measurements will also be measured for isotopic ratios and chemical impurities, which provides information about past climate.\n\nA download of 'Halocarbon data from Law Dome firn air and from Cape Grim' is available at the url given below.\n\nThe fields in this dataset are:\n\nCFC\nHCFC\nHFC\nHalon\nCarbon tetrachloride\nmethyl chloroform\nAge\nConcentration\nUncertainty\nMethane\nCH4\nAir age\nC13 CO2\nDepth\nIce age\nMethyl bromide\nMethyl chloride\nChloroform\nDichloromethane", "links": [ { diff --git a/datasets/ASAC_1005_1.json b/datasets/ASAC_1005_1.json index a6e2f5f213..bdb69dd211 100644 --- a/datasets/ASAC_1005_1.json +++ b/datasets/ASAC_1005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1005 Metal and organic contaminants in marine invertebrates from Antarctica, field study of their concentrations, laboratory study of their toxicities. See the link below for public details on this project.\n\nData from this project are now unrecoverable. Several publications arising from the work are attached to this metadata record, and are available to AAD staff only.\n\nTaken from the referenced publications:\n\nBioaccumulation of Cd, Pb, Cu and Zn in the Antarctic gammaridean amphipod Paramoera walkeri was investigated at Casey station. The main goals were to provide information on accumulation strategies of the organisms tested and to verify toxicokinetic models as a predictive tool. The organisms accumulated metals upon exposure and it was possible to estimate significant model parameters of two compartment and hyperbolic models. These models were successfully verified in a second toxicokinetic study. However, the application of hyperbolic models appears to be more promising as a predictive tool for metals in amphipods compared to compartment models, which have failed to adequately predict metal accumulation in experiments with increasing external exposures in previous studies. The following kinetic bioconcentration factors (BCFs) for the theoretical equilibrium were determined: 150-630 (Cd), 1600-7000 (Pb), 1700-3800 (Cu) and 670-2400 (Zn). We find decreasing BCFs with increasing external metal dosing but similar results for treatments with and without natural UV radiation and for the combined effect of different exposure regimes (single versus multiple metal exposure) and/or the amphipod collective involved (Beall versus Denison Island). A tentative estimation showed the following sequence if sensitivity of P. walkeri to an increase of soluble metal exposure: 0.2-3.0 micrograms Cd per litre, 0.12-0.25 micrograms Pb per litre, 0.9-3.0 micrograms Cu per litre and 9-26 micrograms Zn per litre. Thus, the amphipod investigated proved to be more sensitive as biomonitor compared to gammarids from German coastal waters (with the exception of Cd) and to copepods from the Weddell Sea inferred from literature data.\n\n#######\n\nThis study provides information on LC50 toxicity tests and bioaccumulation of heavy metals in the nearshore Antarctic gammarid, Paramoera walkeri. The 4 day LC50 values were 970 micrograms per litre for copper and 670 micrograms per litre for cadmium. Net uptake rates and bioconcentration factors of these elements were determined under laboratory conditions. After 12 days of exposure to 30 micrograms per litre, the net uptake rates were 5.2 and 0.78 micrograms per gram per day and the bioconcentration factors were 2080 and 311 for copper and cadmium respectively. The body concentrations of copper were significantly correlated with the concentrations of this element in the water. Accumulation of copper and cadmium continued for the entire exposure suggesting that heavy metals concentrations were not regulated to constant concentrations in the body. Using literature data about two compartments (water-animal) first-order kinetic models, a very good agreement was found between body concentrations observed after exposure and model predicted. Exposure of P. walkeri to mixtures of copper and cadmium showed that accumulation of these elements can be assessed by addition of results obtained from single exposure, with only a small degree of uncertainty. The study provides information on the sensitivity of one Antarctic species towards contaminants, and the results were compared with data of similar species from lower latitudes. An important finding is that sensitivity to toxic chemicals and toxicokinetic parameters in the species investigated are comparable with those of non-polar species. The characteristics of bioaccumulation demonstrate that P. walkeri is a circumpolar species with the potential to be a standard biological indicator for use in monitoring programmes of Antarctic nearshore ecosystems. the use of model prediction provide further support to utilise these organisms for biomonitoring.\n\n#######\n\nHeavy-metal concentrations were determined in tissues of different species of benthic invertebrates collected in the Casey region where an old waste-disposal tip site is a source of contamination. the species studied included the bivalve Laternula elliptica, starfish Notasterias armata, heart urchins Abatus nimrodi and A. ingens and gammaridean amphipod Paramoera walkeri. The specimens were collected at both reference and contaminated locations where lead was the priority element and copper was the next most important in terms of increased concentrations. The strong association between a gradient of contamination and concentrations in all species tested indicated that they are reflecting well the environmental changes, and that they appear as appropriate biological indicators of heavy-metal contamination. Aspects of the biology of species with different functional roles in the marine ecosystem are discussed in relation to their suitability for wider use in Antarctic monitoring programmes. For example, in terms of heavy-metal bioaccumulation, the bivalve appears as the most sensitive species to detect contamination; the starfish provides information on the transfer of metals through the food web while the heart urchin and gammarid gave indications of the spatial and temporal patterns of the environmental contamination. The information gathered about processes of contaminant uptake and partitioning among different tissues and species could be used in later studies to investigate the behaviour and the source of contaminants.", "links": [ { diff --git a/datasets/ASAC_100_1.json b/datasets/ASAC_100_1.json index b75c12d3ed..28ef7821b1 100644 --- a/datasets/ASAC_100_1.json +++ b/datasets/ASAC_100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 100\nSee the link below for public details on this project.\n\nFrom the abstract of one of the referenced papers:\n\nBetween November 1988 and March 1989, scats were collected from three species of fur seals (Arctocephalus forsteri, A. gazella and A. tropicalis) at the northern end of Macquarie Island and from A. forsteri between January and March 1989 at the southern end. All fed mainly on fish. For A. gazella/A. tropicalis an average of 99.2% of scats in monthly collections contained fish remains, while for A. forsteri the figure for North Head was 100% and for Hurd Point was 94.9%. Arctocephalus forsteri at Hurd Point took less fish and more penguins than at North Head and there were significant differences in the composiiton of the fish diet in two of three months. At North Head, the fish diet of A. gazella/A. tropicalis differed significantly from that of A. forsteri in three of the five months studied. Food resources for fur seals around Macquarie Island are considered to be less available than they are around Heard Island.", "links": [ { diff --git a/datasets/ASAC_1012_1.json b/datasets/ASAC_1012_1.json index e9bac98860..7c1e4f077a 100644 --- a/datasets/ASAC_1012_1.json +++ b/datasets/ASAC_1012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set includes information relevant for the study and description of sea-ice bacteria contains the following dataset subgroups and is organised by REFERENCE number.\n\n1) Isolation data: strain designations (e.g. culture collection names are indicated for type cultures); media used for isolation and routine cultivation; temperature used for incubation; any special conditions (e.g. enrichment conditions) used for isolation; isolation site and type (e.g. sea-ice); availability of the indicated strain from the chief investigator (J. Bowman)\n\n2) Phenotypic data: Includes morphological, physiological and biochemical tests performed. Details on how these were performed are indicated in the relevant reference.\n\n3) Growth/temperature data: data for temperature related growth curves are given where available. Methods are indicated in the associated reference.\n\n4) Fatty acid/chemotaxonomy data: fatty acid and other related data are given where available. Methods are indicated in the associated reference.\n\n5) Genotypic data: data for DNA-guanosine/cytosine-content and genomic DNA:DNA hybridization are shown where available. Methods are indicated in the associated reference.\n\n6) Phylogenetic data: data for sequences are cross-referenced to the GenBank database. In some cases, aligned sequence datasets are available in FASTA format and can be viewed in the programs BIOEDIT (www.mbio.ncsu.edu/BioEdit/bioedit.html) or CLUSTAL W (www.ebi.ac.uk/clustalw).\n\n7) Other related published references which are useful or relevant to the dataset e.g. related sequences published subsequent to the ASAC study", "links": [ { diff --git a/datasets/ASAC_1015_HIGPS03_04_1.json b/datasets/ASAC_1015_HIGPS03_04_1.json index 846af9c6a4..68d1603674 100644 --- a/datasets/ASAC_1015_HIGPS03_04_1.json +++ b/datasets/ASAC_1015_HIGPS03_04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_HIGPS03_04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata notes for RiSCC Heard Island 2003_04 season (ASAC 1015) - DGPS data and Base Station data DGPS data are described below, and associated data files listed.\n\n1. Three Island study - Phenology and Morphology of Heard Island vascular plants. This study uses a combination of latitudinal and altitudinal investigations to separate the effects of temperature per se and seasonality on the phenology and morphology of plants. Most latitudinal studies are confounded by covariation of seasonality and temperature, whereas with altitudinal variation at different latitudes one can disentangle these effects. The presence of the Polar Frontal Zone (APFZ), which has a major effect on seasonality, would be a key feature that would enable us to investigate this. Three islands (Marion, Kerguelen, Heard) were used in the study, each of which lies in a different place relative to the APFZ and each of which is inhabited by a similar suite of species, thus removing confounding effects of species identity in understanding responses.\n\n1a Phenology data\nThe collection of positional data for Heard Island Scarlet Hill Phenology was collected at each site; 4 m, 50 m, 100 m, 200 m and 250 m (ASAC 1015). At each site plants of Pringlea antiscorbutica, Acaena magellanica, Poa cookii and Azorella selago were chosen (NB at 100m and 200 m no Acaena magellanica was present, and at 250 m only Pringlea antiscorbutica was sampled) within a 50 x 50 m area, where possible, and were deemed typical of the site. Only healthy mature plants at each site were chosen. At the 4 m, 100 m and 200 m altitude levels, sites were established around AWSs (Automatic Weather Stations). Each plant was flagged and numbered. Numbered flags were removed from around/beside plants at the end of the study. The numbers of plants are represented in the GPS data. Positional data are in the form of points, lines and areas. The positional data are found in the following files.\n\n4 m phenology\nPT021412A.SSF4 m phenology data, N15 Poa cookii data, Poa annua record, 50 m phenology data JDS011811A.SSFcoastal study area, AWS site and phenology site\n\n50 m phenology\nJDS012314A.SSF50 m phenology (Scarlet Hill) and Stephenson camp location\nPT020910A.SSF50 m Azorella phenology, water meadow and Poa cookii N15 sites\n\n100 m phenology\nJDS020714A.SSF200 m Phenology and 100 m AWS and phenology data\nJDS021313A.SSF100 m Phenology (Pringlea)\nPT021614A.SSF250 m and 200 m phenology data, 100m phenology\n\n200 m phenology\nJDS020712A.SSF200 m Phenology site and AWS\nJDS020714A.SSF200 m Phenology and 100 m AWS and phenology data\nPT021614A.SSF250 m and 200 m phenology data, 100m phenology\n\n250 m phenology\nPT021614A.SSF250 m and 200 m phenology data, 100m phenology\n\n1b Morphology data\nDGPS points were only taken by JDS from Fairchild Beach morphology collection sites.\n\nJDS0104.SSFAcaena magellanica, Fairchild Beach morphology JDS010511ATR3.SSFFairchild Beach morphology\n\n2. Positional data for the mapping of the distribution of Ranunculus crassipes The distribution of Ranunculus crassipes at Heard Island was mapped between the 14-1-2004 and 15-2-2004. This mapping was undertaken by JDS, PT and JJS. Data were collected from the Skua Beach bluffs to Sooty Valley. Positional data are in the form of points, lines and areas. Data include areas of rock water meadow. The positional data are found in the following files.\n\nPTRAN021513A.SSFRanunculus crassipes transect, points and rock water meadow JDS020816A.SSFRanunculus crassipes mapping on Skua bluffs JJS011417B.SSFRanunculus crassipes mapping on Skua bluffs\nPT020910A.SSF50 m Azorella phenology, water meadow and Poa cookii N15 sites\n\nThis mapping of the distribution of Ranunculus crassipes together with mapping of Carex trifida and Poa litorosa on Macquarie Island described by the metadata record with ID ASAC_1015_MIGPS03 contributed to the paper:\nBergstrom, D.M., Turner, P.A.M., Scott, J., Copson, G. and Shaw, J. (2006) Restricted plant species on sub-Antarctic Macquarie and Heard Islands. Polar Biology 29 532-539.\n\n3. High altitude vascular plant points and transect data Records of high altitude plants were taken by JDS and RC. Some data from the files JDS012510A.SSF and JDS012510A_CPscarlet.ssf have not been corrected, as stated above.\nJDS012510A.SSFScarlet Hill high altitude transect JDS012510A_CPscarlet.ssfScarlet Hill high altitude transect - control points only for JJS JDS013111A.SSFLong Beach high altitude data and Apple location JDS020112A.SSFLong Beach high altitude data RC0302.SSFRobb Clifton control points for JJS 'and high altitude'\nRC0402.SSFRobb Clifton control points for JJS 'and high altitude'\nRC0502.SSFRobb Clifton control points for JJS 'and high altitude'\n\nOther datafiles recorded by PT, JDS and RC under ASAC 1015 include PT022012A.SSFFuel drum retaining wall, Spit Camp JDS012113A.SSFWinston Lagoon JDS012415A.SSFAcaena - 100 m south edge of Scarlet Hill JDS012914A.SSFLambeth 1 JJS Control point JDS123112A.SSFPoa annua - Dovers moraine JDSPHOTO020118A.SSFPhoto points - Dana Bergstrom data\nPT010621A.SSFSK25 - not sure what this data are RC0202.SSFRobb Clifton control points for JJS", "links": [ { diff --git a/datasets/ASAC_1015_MET0_3HRLY_DATA_1.json b/datasets/ASAC_1015_MET0_3HRLY_DATA_1.json index 28eaa2dcaa..02f000b36c 100644 --- a/datasets/ASAC_1015_MET0_3HRLY_DATA_1.json +++ b/datasets/ASAC_1015_MET0_3HRLY_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_MET0_3HRLY_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was collected under the auspices of ASAC project 1015 (ASAC_1015). It forms part of the RiSCC project (regional sensitivity to climate change).\n\nThree hourly meteorological records for mean sea level pressure, surface air temperature, dew point temperature and relative humidity collected at the meteorological observatory on Macquarie Island. Data were acquired from the Bureau of Meteorology (BOM), Australia and corrected from records held on the island.", "links": [ { diff --git a/datasets/ASAC_1015_MIGPS03_1.json b/datasets/ASAC_1015_MIGPS03_1.json index c30b5b19e8..dfbc7fb3ba 100644 --- a/datasets/ASAC_1015_MIGPS03_1.json +++ b/datasets/ASAC_1015_MIGPS03_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_MIGPS03_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current positional data for Poa litorosa and Carex trifida at Handspike Point area (Macquarie Island) were collected using Global Positioning System (GPS) equipment and standard observational techniques. Different techniques were employed, depending on available expertise and equipment. These techniques are described in detail below. Areas were free from overhead obstructions and therefore ideally suited to GPS data collection.\n\nThis mapping of the distribution of Poa litorosa and Carex trifida on Macquarie Island together with the mapping of Ranunculus crassipes described by the metadata record with ID ASAC_1015_HIGPS03_04 contributed to the paper: \nBergstrom, D.M., Turner, P.A.M., Scott, J., Copson, G. and Shaw, J. (2006) Restricted plant species on sub-Antarctic Macquarie and Heard Islands. Polar Biology 29 532-539.", "links": [ { diff --git a/datasets/ASAC_1015_MI_Inverts_1.json b/datasets/ASAC_1015_MI_Inverts_1.json index 1b22415058..3ec79452c7 100644 --- a/datasets/ASAC_1015_MI_Inverts_1.json +++ b/datasets/ASAC_1015_MI_Inverts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_MI_Inverts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MICROINVERTEBRATE SAMPLING PROTOCOL\nMacquarie Island\n01 October 2001 - 28 February 2002\n\n\nA.HABITATS SAMPLED\n8 habitats representative of the following vegetation types were chosen:\n\n1.Azorella macquariensis - Open cushion areas\n2.Acaena (magellanica and minor) herbfield\n3.Colobanthus muscoides (coastal cushion plants)\n4.Mires - Upland\n5.Pleurophyllum hookerii dominated areas\n6.Poa foliosa Tall tussock\n7.Short grassland (incl. Agrostis magellanica/ Festuca contracta/ Luzula)\n8.Stilbocarpa polaris dominated coastal herbfield\n\nB.HABITAT LOCALITIES\n1.Range within which quadrats for a chosen habitat were located :\na) Altitudinal limits- Lowland (coast to +/- 300 - 350m)\nb) Area- Spread over whole island\nc) Distance- i) 500m min. distance from the perimeter of the Base/logistic zone Viz. none in the logistic zone.\n- ii) 100m min. distance from an established hut\n- iii) 50m min. distance from an established path\nd) Aspect- East and west coasts\n\n2.Types\na) Homogeneous areas\nb) Least impacted areas (viz. Avoided heavily grazed Rabbit areas)\n (viz. Avoided Alien dominated areas)\n (viz. Avoided previously sampled or long term study sites)\n\nC.GENERAL SAMPLING STRATEGY FOR EACH HABITAT\n1.For each habitat Five 2m x 2m quadrats were located (similar in vegetation structure) and marked 1-5.\n2.From each quadrat two random samples were taken with the O'Connor split corer (as per sampling protocol D below). Viz: 10 cores from each habitat.\n3.Each sample was retained separately (in it's core-tube placed in a plastic bag) and marked accordingly. Viz: A and B from 1 through to 5 (e.g.: Poa1A-B, Poa2A-B, etc to Poa5A-B).\n4.On return from the field samples were immediately stored the in a cool, safe (rodent free) place (lab refrigerator) for processing.\n5.Invertebrate extraction followed as per protocol E below. Sample numbers were retained throughout the sampling period, together with sampling date.\n6.Each habitat was sampled on an average of once every five - six weeks.\n\nD.SAMPLING METHOD\n1.Random numbers were obtained using a table of random numbers.\n2.Numbers 1-100 are in top left quarter, progressing clockwise in the remaining three quarters for 101-200, 201-300 and 301-400.\n3.If the position chosen for the first core had already been cored, the next random number and so on was used.\n4.The core sample comprised a 70mm depth from ground level (viz. not including above ground vegetation growth and flowering parts).\n5.Care was taken to disturb as little as possible of the vegetation in and around quadrat, as well as approach to site.\n6.Sampling in or directly after heavy rain was avoided to prevent poor results (although it never rained hard or long enough for this situation to have occurred).\n7.Samples were processed within 4 days (max) after return or safe / cool storage.\n8.Before re-using any equipment (corer, cores, plastic bags, collecting jars and mesh cover etc), it was cleaned thoroughly to avoid contamination.\n\nE.EXTRACTION AND SORTING\nMESO-INVERTEBRATES : (These include all collembola and mites and enchytraeid earthworms).\n1.In the collecting bottle of each sample placed in the HG extractor, an amount (+/- 2 cms high) of propylene * glycol was poured (*propylene glycol; CH3 CH(OH) CH2 OH = 76.10).\n2.Core-samples were separated into litter-like top and about 5- 7 cm of soil.\n3.Samples were retained in their respective core-rings, and where above ground vegetation biomass was more than could fit the depth of a ring, this was placed into additional rings. The veg (top)-side was covered with mesh or mutton cloth (approx. 1.5-2mm diam.) and secured with elastic bands (shock cord 3mm diam.).\n4.The mesh covered side was placed facing down over the collection bottle in the HG extractor. The HG was left running for the first 2 days at 25 degrees C, and for the following two days (3rd and 4th days) at 30 degrees C. 5.Samples were transferred to 99% or 100% alcohol by draining off the propylene glycol through a 60 micron mesh, picking all the colembola and mites off it with a very fine paint-brush through the view of a good microscope, and placing these into labeled vials.\n6.The filtered propylene glycol was re-used a couple of times.\n7.Where time allowed, mites and colembola were separated for certain samples.\n8.Sample details were noted in pencil on labels provided on the outside of each vial, and printed labels were inserted into each sample vial (see Macca Colembola and Mite labels 2001-02.doc).\n\nF.DATA ACQUISITION AND ARCHIVAL\n1.Field data were captured in pencil using one A6 hard-cover note-book.\n2.Data was transferred to spreadsheet and document and stored on CD-R discs with a back-up copy.\n\nThis work was completed as part of the RiSCC project (Regional Sensitivity to Climate Change).\n\nThe fields in this dataset are:\nSite name\nHabitat\nLocation\nLatitude\nLongitude", "links": [ { diff --git a/datasets/ASAC_1015_macca_veg_change_1.json b/datasets/ASAC_1015_macca_veg_change_1.json index 4d8c2ee223..e25b073575 100644 --- a/datasets/ASAC_1015_macca_veg_change_1.json +++ b/datasets/ASAC_1015_macca_veg_change_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_macca_veg_change_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the report in the download file:\n\nPlot data and satellite imagery were used to examine changes in vegetation between 2000 and 2007. These data were examined in light of changes in rabbit numbers (data owned by and provided to us from Parks and Wildlife Tasmania).\n\nMethods and data\n\nVegetation Change. Kate Kiefer established 18 relatively homogenous plots of 25 m2 in a range of vegetation types between November and March 2001. Individual plant species cover was visually calculated within five random 1m2 quadrats within each site and mean values determined. Dana Bergstrom, Kate Kiefer, Jane Wasley and Arko Lucieer re-sampled the same sites in April 2007. The data matrix consisted of 18 sites, 34 taxa and temporally separated sampling intervals: 2001 and 2007. Species also included collective categories for leafy bryophytes, lichens, bare ground and dead vegetation. At each site altitude, slope, aspect, a subjective estimate of the wind exposure and the degree of waterlogging were also recorded. The data matrix of sites and mean cover for the site (mean of cover from 5 x 1 m2 quadrats) is provided. Also in the data matrix is GPS location of the site which is recorded for each star picket that marks each site on the island. Site code consist of site number (first two numerals) - year 01 or 07 (2001 or 2007). The data matrix also includes some site information: a subjective exponential soil-water scale (1- 5: dry - wet); a subjective exponential site exposure scale (1-5: sheltered to exposed); slope, altitude, aspect and mean substrate depth (mean of three random probes across the site) (Data Table 1). Changes at the site and rabbit activity are summarised in Data Table 2.\n\nRemote Sensing Imagery. Information on changes in vegetation communities were scaled up to whole-island level using satellite imagery. We used Landsat ETM+ imagery acquired on 12 December 2000 and Quickbird imagery acquired on 15 March 2007 to detect changes in vegetation cover on Macquarie Island. The Quickbird image with its 2.4 m pixel size was resampled (by pixel averaging) to the 25 m Landsat pixel size to compare the images at the same resolution. The images were orthorectified to correct terrain and geometric distortions. Radiometric, illumination, and atmospheric differences were also corrected. These corrections are crucial for change detection algorithms as false changes are often introduced by geometric offsets and shadowing effects. Multispectral bands 1 (blue), 2 (green), 3 (red), and 4 (near-infrared (NIR)) of both images were used for change detection. \n\nThe fields in this dataset are:\n\nSite\nSpecies\nLatitude\nLongitude\nWater\nExposure\nSlope\nAspect\nAltitude\nRabbits\nCover\nTemperature", "links": [ { diff --git a/datasets/ASAC_1015_spiders_1.json b/datasets/ASAC_1015_spiders_1.json index 3bdcee60aa..b5be5c534c 100644 --- a/datasets/ASAC_1015_spiders_1.json +++ b/datasets/ASAC_1015_spiders_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1015_spiders_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are mainly based on a paper by Phil Pugh (Pugh 2004, Biogeography of spiders on the islands of the Southern Ocean, Journal of Natural History, 38:1461-1487), but has been updated for Subantarctic and Antarctic regions. The names of people who have contributed to this update are listed in the dataset.\n\nThe data are presented in a series of worksheets in an excel file.\n\nThe introduction worksheet provides some basic information about the dataset.\n\nThe references worksheet is a list of references from Pugh's paper that he cited as well as more recent references. It also has some notes on the dataset.\n\nThe initial table worksheet is table 1 from Pugh (2004)\n\nThe antarctic-subantarctic worksheet are data retrieved from Pugh's (2004) table 1 specifically for subantarctic and Antarctic regions. These data have been checked and updated for the region.\n\nThe transposed antarc-subantarctic- worksheet are selected data from Table 1 transposed.\n\nFrom the abstract of the Pugh paper:\n\nThe araneofauna of the extreme Southern Hemisphere is highly impoverished and disharmonic. Four dead anthropogenic immigrant spiders have been collected from Antarctica while only 115 verified species from 26 families are reported on islands of the Southern Ocean. Cluster analysis of the verified Southern Ocean species distribution data identifies a weak, but distinct, Neotropical/South Atlantic association together with robust South Indian and South Pacific biogeographic clusters. These groupings, largely attributed to vicariance and/or endemism, contain little evidence of post-Pleistocene dispersal. Indeed the 14 records of anthropogenic origin suggest that the pace of recent human-mediated introduction has been at least 30 times more rapid than that of Holocene natural dispersal.", "links": [ { diff --git a/datasets/ASAC_101_1.json b/datasets/ASAC_101_1.json index 50c6a4af21..55b6ba1ffa 100644 --- a/datasets/ASAC_101_1.json +++ b/datasets/ASAC_101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 101\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nThe diets of Mus musculus and Rattus rattus on Macquarie Island were investigated by analysis of stomach contents collected monthly for 12 months. The diet of the house mouse was found to be mainly invertebrate matter but that of the ship rat was mainly plant material. Seasonal variations were found in both diets but were greater in that of the ship rat than that of the house mouse.\n\nObservations of ducks on Macquarie Island in December 1985 and 1986 are summarised. Although the island has many wetlands, previous records suggest that ducks mainly use those within wet tussock grasslands in the lowland, coastal areas: recent observations confirm this. Reduced primary productivity on plateau wetlands may result in minimal secondary production of foods in a relatively harsh environment, one where nesting cover has been degraded by introduced rabbits and where predatory skuas are prevalent. Ducks, including hybrids between Grey Duck and the alien Mallard, used Square Lake and Duck Lagoon for feeding and resting, although their rate of feeding was higher at Square Lake. Broods were recorded only at Duck Lagoon, where Poa foliosa provides extensive cover. Introgression on Macquarie Island has occurred unsupported by local liberations, distant from human activity, and has implications for the gene pool of Grey Duck elsewhere.\n\nThe total number of Royal Penguins (Eudyptes schlegeli) breeding on subantarctic Macquarie Island is estimated at 848 719 pairs (plus or minus 10.5%) based on two methods of estimation. The sizes and locations of all 57 colonies are given as a baseline for future changes in the species' abundance. Current estimates of the sizes of two colonies are compared with historical estimates made by the Australasian Antarctic Expedition in 1912-13.", "links": [ { diff --git a/datasets/ASAC_102_1.json b/datasets/ASAC_102_1.json index c560aca2cf..09f6665b3c 100644 --- a/datasets/ASAC_102_1.json +++ b/datasets/ASAC_102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 102\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nSix species of marine microalgae, namely Phaeodactylum tricornutum Bohlin, Dunaliella tertiolecta Butcher, Isochrysis galbana Parke, Porphyridium purpureum (Bory) Ross, Chroomonas sp., and Oscillatoria woronichinii Anis., have been examined with respect to their gas exchange characteristics and the inorganic carbon species taken up by the cells from the bulk medium. All species showed a high affinity, in photosynthesis, for inorganic carbon and low CO2 compensation concentrations. Such data are suggestive of operation of a 'CO2-concentrating mechanism' in these microalgae. Direct measurements of internal organic carbon pools in four of the species studied confirm this (O. woronichinii and Chroomonas were not tested). By comparison of achieved photosynthetic rates with calculated rates of CO2 supply from the dehydration of bicarbonate, it was shown that Phaeodactylum, Porphyridium and Dunaliella could utilise the bicarbonate present in the medium. Data for the other species were inconclusive although the pH dependence of K 1/2CO2 for photosynthesis by Oscillatoria indicated that this species too could utilise bicarbonate. Such observations could, however, not be used as evidence that, at least in the eucaryotic algae examined, bicarbonate was the inorganic carbon species crossing the plasmalemma as Phaeodactylum, Porphyridium and Dunaliella, and Isochrysis all showed the presence of carbonic anhydrase activity in intact cells as well as in crude extracts. 'External' carbonic anhydrase activity represented from 1/4 to 1/2 of the total activity in the cells of these algae. It is concluded that, as a consequence of a CO2-concentrating mechanism, photorespiration was suppressed in the marine microalgae examined although the data obtained did not allow any firm conclusions to be drawn regarding the species of inorganic carbon transported into the cell.\n\nAnalysis of the age composition of a given species within a community is fundamental to any study of population dynamics and to the subsequent analyses of community interactions such as competition, succession and productivity. A problem exists in that calendar age often provides little information on the role played by any given individual plant within a population. For many populations the most useful definition of population structure is obtained from an analysis of both the functional age and the vitality of the component plants. Data from such studies on populations of marine macroalgae are lacking mainly because of the lack of suitable methods. This paper provides a review of the methods which have ben applied to such analyses in both terrestrial and marine communities, discusses these methods in the context of marine algae and presents the results of a case study on the analysis of population structure in the large brown alga Durvillaea potatorum.\n\nEvidence is presented for the occurrence of sexual reproduction including plasmogamy and meiosis, events previously undescribed in the life history of Ascoseira mirabilis. Ascoseira is monoecious. Gametangia are formed in chains within conceptacles. Synaptonemal complexes, structures concerned with chromosome pairing in meiosis, have been observed in the nucleus of gametangial initials. Mature male and female gametes have the same size and appearance, and resemble typical brown algal zoids. Sexual interaction begins after the female gamete settles down, and both zygotes and unfused gametes develop into sporophytes. It is concluded that Ascoseira has the same basic pattern of life history that characterises the order Fucales, and it is argued that this is probably the result of convergent evolution rather than being indicative of close phylogenetic relationship.\n\nLife histories are of central importance in understanding evolution and phylogeny of brown algae. Like other hereditary traits, life history characteristics evolve by processes of natural selection, but because they are important determinants of biological fitness they have special evolutionary significance. Concepts of life history, as traditionally applied to brown algae, do not adequately reflect this, and they need to be broadened to include consideration of additional characteristics such as longevity and reproductive span. Life histories can be interpreted as adaptive strategies. Experimental evidence indicates that heteromorphic life histories probably evolved in response to seasonal change. Isomorphic life histories are possible adapted to stale environments, although some may also possess certain features which are adaptations to seasonal change. Life histories that lack an independent gametophyte generation may have evolved through reduction of heteromorphic life histories. It is argued that a significant increase in the longevity of sporophytes may have ben critical for the evolution of life histories lacking a free-living gametophyte, and also for the evolution of oogamy, phenomena which have occurred in several brown algal evolutionary lines. The common absence of asexual reproduction in advanced taxa probably indicates that its accessory ecological role in maintaining population size has become redundant, as well as reflecting the advantage of sexual over asexual reproduction. However, there is good evidence that sexual reproduction has been lost in a few species of brown algae, and the possible mechanisms and adaptive significance of this are discussed.\n\nStudies on Durvillaea antarctica on Macquarie Island, in the subantarctic, were conducted throughout the 1984 and in the summers of 1983 and 1985. Thereafter the annual sequence of conceptacle initiation, development, maturation and senescence was examined, using light and electron microscopy. Durvillaea antarctica on Macquarie Island releases mature ova and spermatozooids from February to Ausgust, with early stages of conceptacle development being observed during November, December and January, and senescent conceptacles from September to December. Both intertidal and subtidal forms of Durvillaea antarctica are found on Macquarie Island, the subtidal form lacking air cavities. In the light of mating experiments which resulted in successful cross-fertilisation, the two forms are considered to be conspecific.", "links": [ { diff --git a/datasets/ASAC_1043_1.json b/datasets/ASAC_1043_1.json index 29fe5e0cee..e5e34e0551 100644 --- a/datasets/ASAC_1043_1.json +++ b/datasets/ASAC_1043_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1043_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract for the referenced paper.\n\nAlien invertebrates pose considerable threats to subantarctic island ecosystems and with warming climates, because the likelihood of immigrants establishing breeding populations on these islands, is increasing. These species can have profound effects on ecosystem structure and function and are capable of influencing landscape values. An assessment protocol has been designed to allow prioritisation of the risk of alien invasion. The protocol is tested for Heard Island using Collembola. Twenty species already present on other subantarctic islands were chosen as candidate taxa. They were scored from 1 to 5 according to five criteria, distribution, life history, habitat, ecosystem synchrony and dispersal ability. They can be considered to represent:\n\n1) proximity potential\n2) population potential\n3) establishment potential\n4) persistence potential\n5) spread potential\n\nThe scores are summed to give a total invasion risk potential, so that species can be ranked in order of probability of introduction to Heard Island. The highest ranked species include members of the family Hypogastruridae, already recorded from South Georgia and the Antarctic Peninsula, and certain soil-dwelling, parthenogenic Isotomidae. Appropriate management strategies are proposed to reduce the risk of the high priority species being introduced to Heard Island. \n\nA further breakdown of the five criteria is listed below:\n\nEach criterion is divided into 4 parts (or 5 in the case of criteria 2), and each part consists of a question for which only a yes/no answer is possible. A positive answer to each question gives a score of 1, whereas a negative answer gives a score of zero.\n\n1) Maximum score 4. a) Proximity: does it originate in the Northern Hemisphere?; b) extent: has it dispersed from its origin?; c) dispersal ability: is the area it has so far invaded large probably as a result of multiple invasions?; d) area invaded: does it occur in adjacent regions with similar climates (in this case other subantarctic islands? If so, how many of the six; Crozet, Heard, Kerguelen, Macquarie, Marion, South Georgia)?\n\n2) Maximum score 5. a) Reproduction: Is it parthenogenetic?; b) population size: does it have a rapid intrinsic rate of increase ie is it r selected?; c) length of life cycle: is the life cycle short at the ambient temperatures to be encountered?; d) feeding type: does it have generalist feeding habits?; e) seasonality: will it be able to survive from season to season ie does it have a resting or resistant stage? \n\n3) Maximum score 4. a) General habitat: are there any suitable habitats available?; b) microhabitat preference: is the preferred microhabitat present?; c) macrohabitat preference: is the preferred macrohabitat present?; d) predator vulnerability: will it be relatively free of heavy predation?\n\n4) Maximum score 4. a) Climatic limitations, temperature: are the normal climatic temperatures to be encountered suitable for at least some of the time?; b) climatic limitations, humidity: are the normal humidities to be encountered suitable?; c) Tolerance of climatic variations to be encountered: can the normal climatic variations to be encountered tolerable?; d) tolerance of climatic extremes to be encountered: can the extreme climatic variations to be encountered be tolerated?\n\n5) Maximum score 4. a) Wind: can the species be dispersed by wind?; b) water: can the species be dispersed by water (fresh or saline)?; c) human intervention: is the species dispersed in mechanised transport systems in packing materials, plants, soil or food?; d) animals/birds: is the species dispersed naturally by other organisms eg birds?\n\nThe fields in this dataset are:\n\nFamily\nSpecies\nAuthority\nExotic Species", "links": [ { diff --git a/datasets/ASAC_1049_1.json b/datasets/ASAC_1049_1.json index 2bc422ebf4..01b5985cdb 100644 --- a/datasets/ASAC_1049_1.json +++ b/datasets/ASAC_1049_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1049_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data for Crooked Lake and Lake Druzhby (CL and LD), Vestfold Hills.Programme Dec 98 - Feb 00\nPI: Prof J Laybourn-Parry\nWinterer: Tracey Henshaw (any questions regarding the data sheets:\nplxtlh@nottingham.ac.uk)\n\nFour sites were sampled in the two lakes\nCl - 68 36 30 S 78 21 50 E\nLD1 - 68 35 47 S 78 14 56 E\nLD2 - 68 35 15 S 78 18 00 E\nLD3 - 68 35 40 S 78 19 20 E\n\nOriginally the sites were known by names;\nLD1 - Watts site\nLD2 - LDD or LD deep site\nLD3 - LDS or LD Shallow or LD Upper site\n\nFolders\nThere are four folders (Physical, Chemical, Biological and Production Data) each containing the relevant workbooks ie: ammonia, heterotrophic bacteria etc Within each workbook, each sites' data set is on a separate sheet (with any related graphs) with any special notes regarding that data set.\n\nNotes\nChemical Folder - all units are micrograms per litre\nPhysical folder - all units specified on the sheets.\nBiological Folder - Chl a is in micrograms per litre.\nSome microscopy samples were counted by Johanna Laybourn-Parry and so there are no data available for sizing or for heterotropic bacteria rods v cocci. These are mainly in Nov 99 and indicated on the sheet as 'JLP' and nd. CL does not contain cyanobacteria, so there are no entries for CL in the cyano workbook.\nPNAN and HNAN, ciliates and rotifers were sized once in Nov 99 to give a carbon pool snapshot. PNAN and HNAN sizing data are in the PNAN and HNAN workbook, ciliate and rotifer abndance and sizing is in the carbon pool workbook.\nTotal biomass data are also in the carbon pool workbook.\n\nProduction Folder - Bacterial production (ng C l-1h-1) raw data are given, calculations are to the left of the raw data and graphs at the bottom of the data.\nFractionation and Nutrient addition workbooks relate to work done separating the bacterial fraction into free and aggregate associated bacterial fractions and to spiking with nutrients and measuring production.\n\nGraphs\nMany of the graphs do not have labelled X axes (as decimal dating was used throughout) but dates are given with the data.\n\nSampling\nSites LD1 and LD3 were shallow sites, so samples were originally taken from 3 depths but this was reduced in June/July to two depths. Sites CL and LD2 are deep sites and samples were originally taken from 0, 2, 4, 6, 8, 10, 15, 20, 30, 40 m but this was reduced in Mar/May to 0, 2, 5, 8, 10, 15, 20 and 40m.\nSeveral sampling sites for LD1 and LD3 were tried at the start of the programme (denoted as 'old' or 1x 1a sites on the spreadsheets).\nIn January 00 only one sample from LD1 and LD3 was collected.\n\nSampling Dates\nSampling dates vary between the sites, but are given with each data set. There may appear to be discrepancies for example, LDS ammonia sampled on 21 Jan but DOC on 25 Jan - but sampling was broken down at the start while I learnt the technique\n\nThe fields in this dataset are:\nAmmonia concentration\nHeterotrophic bacteria\nCyanobacteria abundance\nCiliates\nRotifers\nnitrite\nnitrate\ntemperature\noxygen (O)\ncysts\nbiomass\nvolume\nPH\nconductivity\nlight data\nphosphorus concentrations\nsaturation\nconditions\nair temperature\nice thickness\nfactionation\nchlorophyll a isoclines\nstandard deviation\nmean\nconversion factor\ncarbon produced", "links": [ { diff --git a/datasets/ASAC_1049_Micromat_1.json b/datasets/ASAC_1049_Micromat_1.json index 9f33ae5268..4f9389adfb 100644 --- a/datasets/ASAC_1049_Micromat_1.json +++ b/datasets/ASAC_1049_Micromat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1049_Micromat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the Micromat home page:\n\nResearch on microbial biodiversity in Antarctica is still in a starting phase though it is a very promising area of research. Antarctica is characterised by its geographical and climatic isolation. The extreme climate has led to the evolution of novel biochemical adaptations to severe low temperatures and hypersalinity (in lakes), and possibly also of indigenous species. In addition, most of the continent has experienced little or no anthropogenic influence. This offers a unique opportunity to gather data on diversity of pristine biotopes. Diverse ice-covered lakes which include both freshwater and saline systems will be sampled during this project. Their bottom areas which receive sufficient solar radiation are covered by microbial mats dominated by cyanobacteria.\n\nFossil layers of tens of thousands of years can be found in several lakes. The information on the mats is relatively spare. As we know now that only a small fraction of the true microbial diversity in natural environments has been observed and even less has been cultivated, this project will also assess the use of cultivation versus molecular methods to describe the biodiversity of these microbial mats for the different types of microorganisms present (Bacteria, Archaea, cyanobacteria, fungi, photosynthetic and heterotrophic protists). \n\nThis part of the project:\n\nSampling in the Vestfold Hills was carried out by partners from the University of Nottingham and BAS with the logistical support of the Australian Antarctic Division. The field sampling program was carried out from the Australian Davis station.\nSampling in water bodies comprised both the collection of samples for water chemistry as well as the collection of surface sediment samples and long cores from selected water bodies. The sites were chosen to cover the entire salinity gradient of the lakes. Physical and chemical analysis of the water were carried out at the time of sampling.\n\nField sampling in the Vestfold Hills was carried out by Johanna Laybourn-Parry, Gareth Murtagh, Paul Dyer, Ingmar Janse, Tracey Henshaw and Wendy Quayle with assistance from Davis personnel (Mark Clear, Tony Morland and others).\n\nFrom the Excel Spreadsheet:\n\nWe isolated 59 strains of cyanobacteria from the benthic microbial mats of 23 Antarctic lakes, from 5 locations in 2 regions, in order to characterise their morphological and genotypic diversity and screen them for bioactive activities. On the basis of their morphology, the cyanobacteria were assigned to 12 species that included 4 Antarctic endemic taxa. Sequences of the ribosomal RNA gene were determined for 56 strains. In general, the strains closely related at the 16S rRNA gene level belonged to the same morphospecies. Nevertheless, divergences were found concerning the diversity in terms of species richness, novelty and geographical distribution. For 56 strains, 21 OTUs (Operational Taxonomic Unit, defined as groups of partial 16S rRNA gene sequences with more than 97.5% similarity) were found, including 9 novel and 3 exclusively Antarctic OTUs. Two sequences of Petalonema cf involvens and Chondrocystis sp. were the first to be determined for these genera. The Internally Transcribed Spacer (ITS) between the 16S and the 23S rRNA genes was sequenced for 33 strains and similar groupings were found with the 16S rRNA gene and the ITS, even when the strains were derived from different lakes and regions. After determination of the best cultivation conditions, 48 strains were grown in mass at 20 degrees C and then screened for antimicrobial and cytotoxic activities. Most strains exhibited low productivities and growth rates, similar to those reported in the literature, but were photosensitive. Seventeen strains were bioactive. The frequency of antibacterial activity against the Gram-positive Staphylococcus aureus was 29%. No activities were detected vs. the Gram-negative Escherichia coli and the yeast Candida albicans, whereas 4% and 20% of the strains inhibited the growth of Aspergillus fumigatus and Cryptococcus neoformans, respectively. Half of the strains were cytotoxic to the mammalian cell line. Given the biotechnological potential of these cyanobacterial strains, further work is in progress on the chemical characterisation of their constituent metabolites.\n\nThe fields in this dataset are:\n\nRegion\nLake\nLocation\nLatitude\nLongitude\nStrain\nIsolation media\nNumber of trichomes\nFalse branching\nCross-wall constriction\nCross-wall granulation\nNecridic cell\nCell shape\nCell width\nCell length\nSpecies\nMorphospecies\nOTU\nOperational Taxonomic Unit\nITS\nInternally Transcribed Spacer\nCultivation method\nProductivity\nPhotosensitivity\nCytotoxicity", "links": [ { diff --git a/datasets/ASAC_104_1.json b/datasets/ASAC_104_1.json index 2eb44c48f0..ba5f805298 100644 --- a/datasets/ASAC_104_1.json +++ b/datasets/ASAC_104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project were:\n\nTo monitor wind transported insects to Macquarie Island to establish which species can disperse long distances on wind in the Subantarctic and from where they originate in order to predict what successful new introductions could occur and increase understanding of long distance dispersal by insects. (2) Synchronously with the wind trapping to trap ground invertebrates on long term monitoring sites set up in 1990-91 in an attempt to document any changes that might be taking place.\n\nA range of work was completed as part of this project. Some of the aspects were:\n\nData on isopods - exotic species\nData on amphipods\nWind trap data\nMonitoring of tourist areas for exotic invertebrates\nInvertebrate Modelling\nA mouse exclusion experiment to examine the effects on spider numbers.\n\nOther metadata records which are associated with this project are:\n\nMacquarie Island Baseline Invertebrate Survey 1994\nThe Invertebrates of Subantarctic Bishop Island\n\nThe fields in this dataset are:\n\nDate\nSeason\nSample\nSite\nWeight\nSoil Moisture\nIsopod\nSpecies\nWind trap\nIndividuals", "links": [ { diff --git a/datasets/ASAC_104_mice_1.json b/datasets/ASAC_104_mice_1.json index 12fffbf22a..7070cd15a0 100644 --- a/datasets/ASAC_104_mice_1.json +++ b/datasets/ASAC_104_mice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_104_mice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Macquarie Island is a Nature Reserve under the Tasmanian National Parks and Wildlife Act 1970 and also a World Heritage Area but it has been modified significantly as the result of the introduction and establishment of exotic species including the house mouse, Mus musculus (Brothers and Copson 1988). Current attitudes favour the reversal of changes caused by such introductions, however, to date, efforts on the island have concentrated on the control of rabbits (Oryctolagus cuniculus) and cats (Felis catus). Although cats were extirpated some few years ago, this was followed by a considerable increase in the rabbit population. Control of both rabbits and rodents is currently being addressed (Anon 2007).\n\nInvertebrates are rarely considered in conservation decisions even though invertebrate interactions have been established as playing an integral role in maintaining ecosystem function emphasizing their ecological importance (Majer 1987; Wilson 1987; Kremen et al. 1993). Examples of their various roles are their importance in soil aeration and drainage, litter decomposition and nutrient cycling, pollination, seed distribution and survival and herbivory (Majer 1987). Comparative studies of secondary production by insects and vertebrates invariably show that insects are greater producers of biomass and conduits of energy through communities than vertebrates (Price 1984). In the subantarctic environment, where many of these processes occur at a low rate much of the time (Hnatiuk 1993), altering the composition of invertebrate communities could have a significant impact on ecosystem processes (Hanel and Chown 1998). Moreover, macroinvertebrates have been shown to be responsible for most litter decomposition on subantarctic Marion Island (Chown and Smith 1993; Smith 1993; Hanel and Chown, 1998). \n\nIntroduced rodents have the potential to indirectly alter ecosystems of subantarctic islands through their impact on the invertebrate fauna (Crafford 1990). On Macquarie Island, Copson (1986) found that spiders made up a significant proportion of the diet of the house mouse. Of 108 stomach contents examined, spiders were recorded in 84% of stomachs and were common or abundant in 49% of those. The three spider species that occur on Macquarie Island (Greenslade 2006) are probably the major predators of small invertebrates. It is possible therefore that alteration of spider density has significant flow-on effects in both the invertebrate community and the systems of which they are a part. It is not clear however if mouse predation is important in the regulation of spider densities.\n\nThe aim of this study was to test the hypothesis that predation by M. musculus affects the densities of the three spider species, Myro kerguelensis O. P. Cambridge, Parafroneta marrineri (Hogg) and Haplinis mundenia (Urquhart) present on Macquarie Island. An exclusion experimental design was used. \n\nThis work was completed as part of ASAC project 104 (ASAC_104).", "links": [ { diff --git a/datasets/ASAC_105_1.json b/datasets/ASAC_105_1.json index 5a45f0b40f..0380a5fd1f 100644 --- a/datasets/ASAC_105_1.json +++ b/datasets/ASAC_105_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_105_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 105\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nCalc-silicate granulites from the Bolingen Islands, Prydz Bay, East Antarctica, exhibit a sequence of reaction textures that have been used to elucidate their retrograde P-T path. The highest temperature recorded in the calc-silicates is represented by the wollastonite- and scapolite-bearing assemblages which yield at least 760 degrees C at 6 kbar based on experimental results. The calc-silicates have partially re-equilibrated at lower temperatures (down to 450 degrees C) as evidenced by the successive reactions: (1) wollastonite + scapolite + calcite = garnet + CO2, (2) wollastonite + CO2 = calcite + quartz, (3) wollastonite + plagioclase = garnet + quartz, (4) scapolite + plagioclase + clacite + quartz, (5) garnet + CO2 + H2O = epidote + calcite + quartz, and (6) clinopyroxene + CO2 + H2O = tremolite + calcite + quartz. The reaction sequence observed indicates that alpha CO2 was relatively low in the wollastonite-bearing rocks during peak metamorphic conditions, and may have been further lowered by local infiltration of H2O from the surrounding migmatitic gneisses on cooling. Fluid activities in the Bolingen calc-silicates were probably locally variable during the granulite facies metamorphism, and large-scale CO2 advesction did not occur. A retrograde P-T path, from the sillimanite stability field (c. 760 degrees C at 6 kbar) into the andalusite stability field (c. 450 degrees C at ~3 kbar), is suggested by the occurrence of secondary andalusite in an adjacent cordierite-sillimanite gneiss in which sillimanite occurs as inclusions in cordierite.\n\nHigh-grade gneiss in the northern Prince Charles Mountains, East Antarctica, has a complex intrusive and deformational history. Outcrop is dominated by homogenous felsic orthogneiss, which encloses boudinaged mafic and ultramafic units. These boudins preserve structures not seen in the host gneiss, and are interpreted as transposed and boudinaged dykes. A sedimentary protolith is inferred for less homogenous felsic gneiss interlayered with semi-pelite, calc-silicate and rare pelite. These basement lithologies were deformed into a series of flat-lying structures consistent with progressive horizontal shear, and then into a series of upright structures culminating in the development of regional synforms, antiforms and monoclines separated by zones of intense upright fabric. The D3 to D6 time interval was associated with several episodes of partial melting which produced discordant leucogneissbodies, and with the emplacement of mafic dykes and charnockite plutons correlated with 950 to 1000 Ma charnockite elsewhere in East Antarctica. The stability of granulite assemblages throughout the D3 to D6 interval is attributed to a widespread 1000 Ma metamorphic event. Thermobarometry of garnet-orthopyroxene-plagioclase-quartz gneiss and pelite yield peak conditions of 700-800 degrees C and 0.6-0.7 GPa for this proterozoic metamorphism. Petrogenetic grid constraints on calc-silicate assemblages indicate peak temperatures of 830 degrees C, suggesting that the lower temperatures derived by thermometry have been reset. Mineral assemblages in interlayered felsic and calc-silicate units imply H2O-rich conditions during prograde metamorphism, but indicate that peak metamorphism was fluid absent, or associated with volatile fluid buffering on a local scale. Calc-silicate reaction textures reflect a retrograde evolution dominated by cooling, which is supported by mineral zonation trends in the garnet-orthopyroxene- plagioclase-quartz gneiss. Post-D6 intrusive and deformational events reflect a decrease in grade to greenschist facies and a transition from ductile to brittle deformation between 950 and 500 Ma.\n\nMafic garnet-bearing assemblages from Sostrene Island, 150 km southwest of Davis Station on the coast of Prydz Bay, East Antarctica, exhibit two-stage symplectic coronas on garnet, formed after peak metamorphic conditions. An outer corona of Opx + Pl + minor Hbl mantles a finer-grained inner corona of Opx + Pl + Spl. Both symplectites contain minor ilemenite-magnetite intergrowths. The finer-grained symplectite also occurs along a fracture cleavage in the garnet. The outer corona originated during a second metamorphic event via the reaction Grt + Cpx + SiO2 = Opx + Pl, whereas the inner corona formed later in response to decompression and minor deformation, resulting in the fracture cleavage in the garnet, according to the reaction required for the stoichiometric breakdown by reaction. The mafic rocks are silica undersaturated, and the SiO2 for reaction was most probably derived externally from the surrounding felsic gneisses. Preferred P-T estimated for M1 based on garnet core matrix Opx-Cpx-Hbl pairs are c. 10 kbar at 980 degrees C. The fine-grained symplectite formed post-peak M2 at c. 7 kbar and 850 degrees C. The enclosing felsic gneisses yield pressure estimates of between 5 and 7 kbar, which compare with conditions of c. 6 kbar and 775 degrees C in the nearby Bolingen Islands. These lower P-T estimates are considered to be representative of the widespread 1100-Ma metamorphic event recognised in outcrops along the Prydz Bay coast. The high-P, high-T estimates derived from the garnet relics provide evidence for an earlier, possibly Archaean, high-grade metamorphic event.", "links": [ { diff --git a/datasets/ASAC_1060_1.json b/datasets/ASAC_1060_1.json index 0f97a64f9f..984e0f7d03 100644 --- a/datasets/ASAC_1060_1.json +++ b/datasets/ASAC_1060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1060\nSee the link below for public details on this project.\n\nTaken from the referenced publications:\n\nSea ice exhibits a marked transition in its fluid transport properties at a critical brine volume fraction Pc of about 5 percent, or temperature Tc of about -5 degrees Celsius for salinity of 5 parts per thousand. For temperatures warmer than Tc brine carrying heat and nutrients can move through the ice, whereas for colder temperatures the ice is impermeable. This transition plays a key role in the geophysics, biology, and remote sensing of sea ice. Percolation theory can be used to understand this critical behaviour or transport in sea ice. The similarity of sea ice microstructure to compressed powders is used to theoretically predict Pc of about 5 percent. \n\nThe snow cover on Antarctic sea ice often depresses the ice below sea level, allowing brine or seawater to infiltrate, or flood the snowpack. This significantly reduces the thermal insulation properties of the snow cover, and increases the ocean/atmosphere heat flux. The subsequent refreezing of this saturated snow or slush layer, to form snow-ice, can account for a significant percentage of the total ice mass in some regions. The extent of saturated snow cannot presently be estimated from satellite remote-sensing data and, because it is often hidden by a layer of dry snow, cannot be estimated from visual observations. Here, we use non-parametric statistics to combine sea-ice and snow thickness data from drillhole measurements with routine visual observations of snow and ice characteristics to estimate the extent of brine-infiltrated snow.\n\nDuring a field experiment in July 1994, while the R.V. Nathaniel B. Palmer was moored to a drifting ice floe in the Weddell Sea, Antarctica, data were collected on the sea-ice and snow characteristics. We report on the evolution of ice which grew in a newly opened lead. As expected with the cold atmospheric conditions, congelation ice initially formed in the lead. Subsequent snow accumulation and large ocean heat fluxes resulted in melt at the base of the ice, and enhanced flooding of the snow on ice surface. This flooded snow subsequently froze, and, five days after the lead opened, all the congelation ice had melted and twenty-six centimetres of snow ice had formed. We use measured sea-ice and snow salinities, thickness and oxygen isotope values of the newly formed lead ice to calculate the salt flux to the ocean. Although there was a salt flux to the ocean as the ice initially grew, we calculate a small net fresh-water input to the upper ocean by the end of the 5 day period. Similar processes of basal melt and surface snow-ice formation also occurred on the surrounding, thicker sea ice. Oceanographic studies in this region of the Weddell Sea have shown that salt rejection by sea-ice formation may enhance the ocean vertical thermohaline circulation and release heat from the deeper ocean to melt the ice cover. This type of deep convection is thought to initiate the Weddell polynya, which was observed only during the 1970s. Our results, which show than an ice cover can form with no salt input to the ocean, provide a mechanism which may help explain the more recent absence of the Weddell polynya.", "links": [ { diff --git a/datasets/ASAC_1066_1.json b/datasets/ASAC_1066_1.json index 0f284485f7..ddb9df78a4 100644 --- a/datasets/ASAC_1066_1.json +++ b/datasets/ASAC_1066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASAC project 1066 conducted Samarium-Neodymium isochron dating of recrystallised mafic dykes located in the SW perimeter of the Vestfold Hills. These dykes have experienced amphibolite-facies metamorphism, but the timing of this event is uncertain though many workers in the region postulate an age of ~1000 Ma or ~500Ma. Project 1066 provided preliminary data that suggests that this amphibolite-facies event in the southwest Vestfold Hills occurred at ~730 Ma.\n\nFurther work is underway (project 1248) to confirm and expand on the initial data.\n\nThe data are available from the URL given below.\n\nThe data are mineral isotopic data for Samarium (Sm) and Neodymium (Nd). The samples are from mafic dykes from the southwest region of the Vestfolds Hills which were metamorphosed at upper amphibolite facies. the columns in order from left to right are: \n\n1) Sample number (this refers to the individual mineral separate for each rock sample ie sample 28, 35, 36, 38, 39 are from rock sample 9B), sample 30, 31, 32, 34, 40 are from rock sample 8A)\n2) the measured ratio of 147Sm to 144Nd\n3) the error on that measurement at 95% confidence limits\n4) the measured ratio of 143Nd to 144 Nd\n5) the error on that measurement\n\nThe comment boxes contain the results of the analysis for each rock sample - ie the age of upper amphibolite metamorphism of the southwest Vestfold hills is 735.8 +/- 9.7 Ma (sample 9B) and 714 +/- 38Ma (sample 8a).\n\nThe work was conducted by Dr Jon Woodhead at the School of Earth Science, University of Melbourne.", "links": [ { diff --git a/datasets/ASAC_1071_Geomorphic_Map_1.json b/datasets/ASAC_1071_Geomorphic_Map_1.json index 068faa2173..a895ce07af 100644 --- a/datasets/ASAC_1071_Geomorphic_Map_1.json +++ b/datasets/ASAC_1071_Geomorphic_Map_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1071_Geomorphic_Map_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data include a 1:10,000 scale map of the surfical glacial and periglacial features of the Amery Oasis, East Antarctica. Features currently include:\n\nareas covered by exposed bedrock, fluvial sediments and moraine\nmoraine ridges\nstreams (flowing during the 2003/04 season)\nwatercourses\nlakes\ncosmogenic exposure ages\ndegree of weathering\n\nand includes areas of glaciers, glacial bedforms, scree and patterned ground.\n\nRock samples are currently held by Duanne White (as at 2015-09-23), but will eventually be archived at Geoscience Australia.", "links": [ { diff --git a/datasets/ASAC_1071_Loewe_1.json b/datasets/ASAC_1071_Loewe_1.json index 9edc8ca623..3ecebd5a74 100644 --- a/datasets/ASAC_1071_Loewe_1.json +++ b/datasets/ASAC_1071_Loewe_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1071_Loewe_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At Loewe Massif and Amery Oasis, samples were taken;\n- for sediment analysis (XRF geochemistry and grain size)\n- for geochronology (cosmogenic isotope analysis).\n\nThe custodian for these samples is Dr Damian Gore, Macquarie University.\n\nLake sediment samples were taken from Lake Terrasovoje, Radok Lake and Beaver Lake. The custodian for these lacustrine samples is Dr Martin Melles, Leipzig University.\n\nThe dataset also includes weather/meteorological observations.\n\nFurther work in project 1071 was also completed as part of PCMEGA.\n\nThe fields in this dataset are:\n\nDate\nSite\nLatitude\nLongitude\nTime\nAltitude\nTemperature\nPressure\nWind direction\nWind Speed\nCloud\nRelative Humidity", "links": [ { diff --git a/datasets/ASAC_1080_1.json b/datasets/ASAC_1080_1.json index b881decc1f..d10f3202a5 100644 --- a/datasets/ASAC_1080_1.json +++ b/datasets/ASAC_1080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sea ice data are the SMMR/SMMI data for the period 1978-96. These are in the form of daily (or bi-diurnal) concentration amounts on a regular grid. The data on the extratropical cyclones has been obtained using the automatic algorithm of Simmonds and Keay (2000, Journal of Climate, 873-885). This algorithm was applied to the NCEP reanalysis product for the period 1978-96.\n\nIn this project, sea ice data were sourced from the National Snow and Ice Data Center (CIRES, University of Colorado, Boulder, CO 80309-0449, USA). The NCEP reanalysis data set was sourced from: NOAA/ National Weather Service, National Centers for Environmental Prediction (5200 Auth Road, Camp Springs, Maryland, 20746 USA).\n\nThe sea ice concentration data used were for the Antarctic only (the entire Antarctic sea ice domain). Data started in 1978. All data were collected by satellite. A link to a metadata record for these data are available from the URL given below.\n\nTwo NCEP reanalysis data sets were used in this study. The first was NCEP/NCAR, with 6-hourly data available from 1958 (see the URL provided below for further information). The second was the NCEP/DOE set, with 6-hourly data available from 1979 (see the URL provided below for further information).\n\nIn this project the following model/analysis was applied:\n\nApplication of The University of Melbourne cyclone tracking scheme (Simmonds et al., 2003, Monthly Weather Review, 131, 272-288) and a broad range of statistical tests. Brief details are provided in the Summary. See the link for the pdf document for more detailed information.\n\nThese complex statistical analyses were run over the entire length of the project (1998/99 - 2000/01). They were run on the Sun Workstation cluster in the School of Earth Sciences, The University of Melbourne.", "links": [ { diff --git a/datasets/ASAC_1087_Desiccation_1.json b/datasets/ASAC_1087_Desiccation_1.json index 02230a15ee..21a76eb4b2 100644 --- a/datasets/ASAC_1087_Desiccation_1.json +++ b/datasets/ASAC_1087_Desiccation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1087_Desiccation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This series of experiments were conducted in the Casey station laboratories, using field collected moss samples, during the 1999/2000 summer field season. The work is fully described in Wasley et al. 2006 and Chapter 5 of Wasley 2004 (pp. 118-152), full citation details are:\n- Wasley J., Robinson S.A., Lovelock C.E., Popp M. (2006) Some like it wet \u2014 biological characteristics underpinning tolerance of extreme water stress events in Antarctic bryophytes, Functional Plant Biology 33. 443-455. \n- Wasley J. (2004) The Effect of Climate Change on Antarctic Terrestrial Flora, Doctor of Philosophy, University of Wollongong 191pp. \nIn summary, three byrophyte species were investigated: Bryum pseudotriquetrum, Ceratodon purpureus and Grimmia antarctici (later taxonomically revised as Schistidium antarctici). \nSamples of the three moss species were collected early, mid and late season (2/12/99, 24/1/00 and 27/2/00) from ASPA 135 on Bailey Peninsula. Additional samples of G. antarctici were also collected from the edge of the melt lake behind the Casey station accommodation building. \nSelected samples were used to determine a range of biological traits for the three species, including: \n- morphology (gametophyte density and width) \n- physiological response to desiccation and subsequent recovery\n- a range of plant biochemical characteristics (soluble carbohydrates, fatty acids, nitrogen and carbon contents and N and C stable isotope signatures)\nThese traits were used to assess the biological characteristics underpinning relative tolerance of desiccation in the three Antarctic bryophytes species. This work improves our understanding of how these three species survive extreme water stress events in the Antarctic environment.\n\nThe raw data associated with this work, in the form of laboratory notebook scans are available in Metadata record name: JWasley-LabBook-Casey-1999-2000 (http://data.aad.gov.au/metadata/records/JWasley-LabBook-Casey-1999-2000). Following is a description of these scanned data \u2013 which are arranged in three sections: 1. early-season, 2. mid-season and 3. late-season experiments. \n1.\tThe early-season experiment, using samples of moss collected from the field on 2 December 1999, is recorded on pages 5-22 of the laboratory notebook and uses filename \"desiccation 991203\". This batch of work, includes: \n-\tSub-sample weights for analysis of soluble sugars prior to desiccation; T0 sugars (p5)\n-\tKey to randomised sample locations in 24 well tray (p6)\n-\tMethod description and notes (p7)\n-\tDesiccation experiment data; Fv/Fm (photosynthetic efficiency) and sample mass (for calculation of relative water content). Starts T0 (5:30PM, 3/12/99) on p9 and continues to T12 (4PM, 6/12/99) on p12.\n-\tEstimation of gametophyte densities; desiccated on 7/12/99 and rehydrated on 13/12/99. Includes methods notes (p14)\n-\tRecovery from desiccation: planning notes and methods (p15-17); recovery data up to T11 at 24 hours (1440 min), with time since hydration recorded (min:sec) and corresponding Fv/Fm measured. Sample weight recorded at T0 when still desiccated and at 24 h since hydration. (p18-21)\n-\tSub-sample weights for analysis of soluble sugars after desiccation (T1 sugars) and after recovery from desiccation (T2 sugars) (p22)\n\n2.\tThe mid-season experiment, using samples of moss collected from the field on 21 January 2000, is recorded on pages 47-79 of the laboratory notebook and uses filename \"desiccation 000125\". This batch of work, includes:\n-\tSample collection and methods notes (p47)\n-\tKey to randomised sample locations in 24 well tray (p48)\n-\tSub-sample weights for analysis of soluble sugars prior to desiccation; T0 sugars (p49)\n-\tDesiccation experiment data; Fv/Fm (photosynthetic efficiency) and sample mass (for calculation of relative water content). Starts T0 12:30AM, 26/01/00) on p50 and continues to T22 (12PM, 03/02/00; 203.5 hours) on p59.\n-\tRecovery from desiccation: up to T13 at ~24 hours, with time since hydration recorded (min:sec) and corresponding Fv/Fm measured. Sample weight recorded at T0 when still desiccated and at T9, T12 and T13 ~1,4 and 24 h since hydration (p62-65)\n-\tNote: p66-69 are blank\n-\tSub-sample weights for analysis of organic content and method notes (p70)\n-\tSub-sample weights for analysis of soluble sugars prior to desiccation; T0 sugars (p71), after desiccation (T1 sugars) and after recovery from desiccation (T2 sugars) (p73-75), sugar sub-sample label details and notes (p79).\n-\tEstimation of gametophyte densities; desiccated and rehydrated (p76-77) and methods notes (p78). \n\n3.\tThe late-season experiment, using samples of moss collected from the field on 27 February 2000, is recorded on pages 87-89 and 94-121 of the laboratory notebook and uses filename \"desiccation 000228\". This batch of work, includes:\n-\tSample collection and methods notes (p87)\n-\tKey to randomised sample locations in 24 well tray (p88)\n-\tEstimation of gametophyte densities; desiccated and rehydrated and methods notes (p89)\n-\tNote: p90-93 are associated with a different experiment.\n-\tDesiccation experiment data; Fv/Fm (photosynthetic efficiency) and sample mass (for calculation of relative water content). Starts T0 12:00AM (midnight), 28/02/00) on p94 and continues to T30 (11:30AM, 08/03/00) on p117. Note: p96-99 are associated with a different part of this same experiment (see below).\n-\tSub-sample weights for analysis of soluble sugars prior to desiccation; T0 sugars (p96-97), after desiccation (T1 sugars) and after recovery from desiccation (T2 sugars) (p98-99), sugar sub-sample label details and notes (p79).\n-\tRecovery from desiccation: up to T19 at 20 hours, with time since hydration recorded (min:sec) and corresponding Fv/Fm measured. Sample weight recorded at T0 when still desiccated and at T9, T12 and T13 ~1,4 and 24 h since hydration (p62-65)\n\nData files, additional to laboratory notebook \nFilename \u2013 description\n-\tDesiccation_991203_updated2018.xlsx - early-season desiccation experiment, measurements primarily photosynthetic efficiency and water content\n\n-\tDesiccation_000125_updated2018.xlsx \u2013 mid-season desiccation experiment, measurements primarily photosynthetic efficiency and water content\n\n-\tDesiccation_000228_updated2018.xlsx \u2013 late-season desiccation experiment, measurements primarily photosynthetic efficiency and water content\n\n-\tDesiccation_Suagars.xlsx \u2013 soluble carbohydrate (sugar) contents of moss samples collected in association with the three desiccation experiments\n\n-\tDesiccation_Gametophyte Densities.xlsx \u2013 moss gametophyte densities measured for turf samples in association with the three desiccation experiments\n", "links": [ { diff --git a/datasets/ASAC_1087_WaterNutrientExpt_1.json b/datasets/ASAC_1087_WaterNutrientExpt_1.json index ff581762b3..e312d031eb 100644 --- a/datasets/ASAC_1087_WaterNutrientExpt_1.json +++ b/datasets/ASAC_1087_WaterNutrientExpt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1087_WaterNutrientExpt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Description of the work is provided in the file named: ASAC 1087_WN-descriptions.doc\n\nThe work consists of a multiseason manipulative field experiment investigating the effect of increased water and nutrient availability on Antarctic terrestrial communities. For full details refer to Chapter 4 in Wasley (2004). \n\nThe experiment was conducted in Antarctic Specially Protected Area (ASPA) 135, on Bailey Peninsula, approximately 1 km east of Casey Station. The site was located on the western edge of a meltlake, at 66 degrees 16.03' S, 110 degrees 32.53' E. \n\nWithin the site, four community types, along a community gradient, were identified based on the percentage cover of four key community components: healthy bryophytes, moribund bryophytes, crustose lichens and macrolichens of the genus Usnea (herein referred to as: Bryophyte, Moribund, Crustose and Usnea, respectively). The four communities occurred along a gentle slope, with an easterly aspect, between the meltlake edge and the side of a small ridge. The Bryophyte community occurred closest to the meltlake, and the other three communities were positioned with increasing distance from the meltlake edge, in the order of: Moribund community, Crustose community and, furthest from the meltlake, the Usnea community, which was closest to the ridge.\n\nWithin each community, 32 quadrats (25 x 25 cm) were randomly assigned one of the following four treatments: (1) no-treatment (NT-), (2) water only (W-), (3) nutrient only (N-), or (4) water and nutrient (WN-). The two water addition treatments (W- and WN-) had 500 ml of meltlake water applied approximately every two days during the 1998/99 and 1999/00 summer seasons (December - February). Quadrats receiving nutrient additions (N- and WN-) had 10 g of slow release fertiliser beads (Osmocote, Scotts Australia Pty. Ltd., Castle Hill, NSW, Australia) applied at the start of the treatment period (15/12/99). A low phosphorous Osmocote variety was used, composed of 18% nitrogen, 4.8% phosphorous and 9.1% potassium.\n\nThe effect of increased water and nutrient availability was assessed via a suite of ecological, physiological and biochemical endpoints (as described in ASAC 1087_WN-descriptions.doc), including plant pigment and nutrient contents, field photosynthetic rates, composition of soluble sugars and species/community compositions.\n\nInformation regarding sample ID: \nThis experiment used a three letter code to denote Sample ID. Letters correspond with colours that were used for markers placed at quadrat locations in the field. The markers were made of three coloured plastic beads on a wire peg. The letters in the sample ID code correspond with the three colours used to mark the quadrat (e.g. GPR = Green, Purple, Red), using the following key:\n\n1.\tFirst bead / first letter: indicated REPLICATES, where:\nRep1 = B (Blue) \nRep 2 = P (Purple)\nRep 3 = R (Red)\nRep 4 = O (Orange)\nRep 5 = Y (Yellow)\nRep 6 = L (Lime)\nRep 7 = G (Green)\nRep 8 = W (White)\n\n2.\tSecond bead / second letter: indicates TREATMENT, where: \nControl = W (White)\nWater = B (Blue)\nNutrient = L (Lime) \nWater and Nutirent = P (Purple)\n\n3.\tThird bead / third letter: indicates COMMUNITY, where: \nBryophyte = G (Green)\nMoribud = O (Orange)\nCrustose = Y (Yellow)\nUsnea = R (Red)\n\n\nNote for data file: ASAC 1087_WN-NPC.xls\nThis file provides treatment and community level codes, but is missing replicate ID. \nTo source replicate ID, match up Column A: No. (97 to 208) with that provided in the laboratory notebook scan at: https://data.aad.gov.au/metadata/records/JWasley-LabBook-Casey-1999-2000; pages 188 to 194). \n\nSample ID (three letter) codes are listed: \nFrom: Page 188, line marked #97 = LPR = Lime, Purple, Red = Rep6, W&N Treatment, Usnea Community\nTo: Page 194, line marked #208 = OWG = Orange, White, Green = Rep4, Control Treatment, Bryophyte Community\nUse sample ID code information above in description to identify other Sample ID codes in this file.", "links": [ { diff --git a/datasets/ASAC_1090_1.json b/datasets/ASAC_1090_1.json index b76baf3899..d47e33011e 100644 --- a/datasets/ASAC_1090_1.json +++ b/datasets/ASAC_1090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although the most abundant of all mammalian predators in the Antarctic marine ecosystem, crabeater seals are also one of the least understood. The most fundamental question of all - how many are there? - is the focus of an extensive international collaborative program (the Antarctic Pack-ice Seal Program, or APIS). This study supplements APIS by providing additional data on the diving behaviour and food requirements of crabeater seals, that can be used in conjunction with census data to provide information on the role of crabeater seals in the antarctic ecosystem.\n\nWinter densities and distributions of Crabeater seals were collected during 1999. Crabeater seals were most often encountered on the shelf break. The data collected include numbers of seals sighted per hour in relation to the amount of time the ship spent in each 0.5 degree grid square.\n\nThis study is the first to describe the winter distribution of crabeater seals (Lobodon carcinophagus) in East Antarctica. The study was conducted in the Mertz Glacier Polynya region from July to August 1999. In total 89 crabeater seals were seen in 26 groups which ranged in size from 1 to 35 animals (mean = 3.2). The mean observed haulout density along a 200m wide strip transect was 0.108 seals per square kilometre, or 0.042 groups per square kilometre. Crabeater seals were not uniformly distributed in the polynya but selected areas of stable ice over shallow (less than 1000m) waters. We used a generalised linear model to assess the relationship of seal distribution to the physical attributes of sea ice concentration, thickness, and ocean depth. We found that ice thickness and ocean depth were the most important determinants of seal distribution. Crabeater seals occurred in areas where the ice affords them a stable haulout platform while allowing them access to Antarctic krill that live directly beneath the ice.", "links": [ { diff --git a/datasets/ASAC_1092_1.json b/datasets/ASAC_1092_1.json index 7c32d9c7ea..30ec1b6231 100644 --- a/datasets/ASAC_1092_1.json +++ b/datasets/ASAC_1092_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1092_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic ice provides an archive of Earths atmospheric composition over time. It therefore records evidence of human impact on the atmosphere. Lead is a toxic element, whose isotopic fingerprint can also be used as a tracer. This project will investigate a 400 year Pb pollution record in ice from Law Dome.\n\nFrom the abstracts of the referenced papers:\n\nTechniques for Pb measurements have reached the stage where Antarctic ice with sub-picogram per gram concentrations can be reliably analysed for isotopic composition. Here, particular attention has been given to measuring the quantity of Pb added during the decontamination and sample storage stages of the sample preparation process because of their impact on accuracy at low concentrations. These stages, including the use of a stainless steel chisel for the decontamination, contributed ~5.2pg to the total sample analysed, amounting to a concentration increase of ~13fg per gram, which is significantly less than expected. Consequently the corrections to the isotopic ratios and concentration were also smaller. Other contributions to the blank, such as Pb fallout onto critical working areas in the HEPA-filtered laboratories, were also relatively small as was the amount of Pb leached from preconditioned perfluoroalkoxy (PFA) beakers during sample processing. The ion source contributed typically 89plus or minus 19 fg to the blank. Although this was relatively large, its influence depended upon the amount of Pb available for analysis and it had the greatest impact when small volumes of samples with a very low concentration were analysed. A 15 months investigation of the leaching characteristics of Pb from a low-density polyethylene (LDPE) sample storage bottle showed 11 fg per cm per cm per day was released immediately following the initial 2 months cleaning process, but this decreased to immeasurable values after a further 3 months of cleaning.\n\nLead isotopic compositions and Pb and Ba concentrations have been measured in ice cores from Law Dome, East Antarctica, covering the past 6500 years. 'Natural' background concentrations of Pb (~0.4 pg/g) and Ba (~1.3 pg/g) are observed until 1884 AD, after which increased Pb concentrations and lowered 206Pb/207Pb ratios indicate the influence of anthropogenic Pb. The isotopic composition of 'natural' Pb varies within the range 206Pb/207Pb=1.20-1.25 and 208Pb/207Pb=2.46-2.50, with an average rock and soil dust Pb contribution of 8-12%. A major pollution event is observed at Law Dome between 1884 and 1908 AD, elevating the Pb concentration four-fold and changing 206Pb/207Pb ratios in the ice to ~1.12. Based on Pb isotopic systematics and Pb emission statistics, this is attributed to Pb mined at Broken Hill and smelted at Broken Hill and Port Pirie, Australia. Anthropogenic Pb inputs are at their greatest from ~1900 to ~1910 and from ~1960 to ~1980. During the 20th Century, Ba concentrations are consistently higher than 'natural' levels and are attributed to increased dust production, suggesting the influence of climate change and/or changes in land coverage with vegetation.\n\nThe fields in this dataset are:\n\nSample\nDate\n206Pb/207Pb\n208Pb/207Pb\n206Pb/204Pb\nPb concentration\nBa concentration", "links": [ { diff --git a/datasets/ASAC_1093_1.json b/datasets/ASAC_1093_1.json index 569afe969f..f9cad7cad4 100644 --- a/datasets/ASAC_1093_1.json +++ b/datasets/ASAC_1093_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1093_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project was a continuation of ASAC project 870 (ASAC_870).\n\nThe aim of the project is to assess the possible routes for the carriage of microorganisms by humans to Antarctic soil habitats. Such pathways include footwear, vehicle treads and washings of root vegetables. These have been examined and measures have already been implemented to minimise further risk from these sources. A further aim was to determine the likelihood of foreign microorganisms successfully competing with indigenous terrestrial antarctic microorganisms; this has proved more difficult to ascertain under simulated antarctic conditions. Work is continuing on this aspect.\n\nThe danger posed by microbial contaminants on expeditioners' boots to antarctic wildlife habitats has been assessed, with the finding that bacterial spores would survive current walk-through disinfection procedures using 2% Virkon or 3%-chlorine bleach, although coliforms would not. The problem of spore-survival in sanitizing solutions is exacerbated by residual clay in boot treads. It is recommended that boots be scrubbed clean of residual soil or clay before entering antarctic waters, and again as necessary between geographically isolated areas within Antarctica. Soaking for four hours in 2.0% Virkon or for 11 minutes in 3% chlorine bleach is needed for a 3-log reduction in Bacillus spore numbers.\n\nResults:\nThe following Decimal Reduction Times (DRTs) were obtained:\nB. subtilis, 80 and 84 minutes in 2% Virkon , 3.6 min in 3%-chlorine bleach.\nB. polymyxa, 32 minutes in 2% Virkon , 3.0 min in 3%-chlorine bleach.\nVegetative cells of E. coli died very rapidly in both Virkon and 3%-chlorine bleach, with DRTs being less than 18 seconds, the limit of detection using the described method.", "links": [ { diff --git a/datasets/ASAC_1100_field_lab_books_1.json b/datasets/ASAC_1100_field_lab_books_1.json index 367d8ca238..06bf047a1e 100644 --- a/datasets/ASAC_1100_field_lab_books_1.json +++ b/datasets/ASAC_1100_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1100_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 1998 and 2003 as part of ASAC (AAS) project 1100 - Contaminants in the Antarctic environment.", "links": [ { diff --git a/datasets/ASAC_1101_1.json b/datasets/ASAC_1101_1.json index 56426c171a..ade0fcf3ab 100644 --- a/datasets/ASAC_1101_1.json +++ b/datasets/ASAC_1101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1101\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nMost of our knowledge of the Antarctic marine ecosystems comes from summer surveys. There are very few observations of this ecosystem in winter and there is a fundamental lack of knowledge of understanding of even basic questions such as 'what is there?' and 'what's it doing?'. The proposed visit to the sea ice zone in winter is a rare opportunity to conduct observations on phytoplankton, krill, birds, seals and whales, so that we can begin to understand the biological processes that go on in winter.\n\nData for this project were intended to be collected on a 1998 winter voyage of the Aurora Australis, but a fire on board meant that the voyage had to return to port before work could be carried out.\n\nData were then collected the following year during a 1999 winter voyage of the Aurora Australis (IDIOTS), which ran from July to September.\n\nData attached to this metadata record, include zooplankton and CTD data collected from the Mertz Glacier region.\n\nThe data have been compiled by Angela McGaffin, and can be found in the \"processed\" folder of the download file. Original datasets are also available in the \"Original Datasets\" folder.", "links": [ { diff --git a/datasets/ASAC_1101_Protists_1.json b/datasets/ASAC_1101_Protists_1.json index 25f70b38de..d9f6457d4a 100644 --- a/datasets/ASAC_1101_Protists_1.json +++ b/datasets/ASAC_1101_Protists_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1101_Protists_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1101\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nMost of our knowledge of the Antarctic marine ecosystems comes from summer surveys. There are very few observations of this ecosystem in winter and there is a fundamental lack of knowledge of understanding of even basic questions such as 'what is there?' and 'what's it doing?'. The proposed visit to the sea ice zone in winter is a rare opportunity to conduct observations on phytoplankton, krill, birds, seals and whales, so that we can begin to understand the biological processes that go on in winter.\n\nData for this project were intended to be collected on a 1998 winter voyage of the Aurora Australis, but a fire on board meant that the voyage had to return to port before work could be carried out.\n\nData were then collected the following year during a 1999 winter voyage of the Aurora Australis (IDIOTS), which ran from July to September.\n\nData attached to this metadata record, include protist, bacteria and virus data collected from the Mertz Glacier region.\n\nThe purpose of this research was to quantify and identify the different assemblages of phytoplankton, change in virus populations and the number of live/dead bacteria between Tasmania and the polynya, within the polynya and sea ice. A variety of methods were used including HPLC, light and fluorescent microscopy and the collection of samples for subsequent analysis upon return to the Antarctic Division. \n\nMore information is available in the download file.\n\nThe samples were collected by Rick van den Enden.", "links": [ { diff --git a/datasets/ASAC_1104_1.json b/datasets/ASAC_1104_1.json index 3a98fd9485..243e83b483 100644 --- a/datasets/ASAC_1104_1.json +++ b/datasets/ASAC_1104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1104\nSee the link below for public details on this project.\n\n---- Public Summary from Project----\nMosses are dominant plants in the vegetation of continental Antarctica. This projects measurements of moss growth rates in several habitats will allow estimates of the ages of stands of moss, predictions of the rate of recovery from disturbance, and predictions of moss growth rates under changed climatic conditions.\n\nFrom the abstract of the referenced paper:\n\nUsing steel pins inserted into growing moss colonies near Casey Station, Wilkes Land, continental Antarctica, we have measured the growth rate of three moss species: Bryum pseudotriquetrum and Schistidium antarctici over 20 years and Ceratodon purpureus over 10 years. This has provided the first long term growth measurements for plants in Antarctica, confirming that moss shoots grow extremely slowly in Antarctica, elongating between 1 and 5 mm per year. Moss growth rates are dependent on availability of water. Antheridia were observed on some stems of B. pseudotriquetrum; no archegonia or sporophytes were observed. Stems bearing antheridia elongated much more slowly than vegetative stems in the same habitat. Two other methods of growth rate measurement were tested, and gave similar rates of elongation over shorter periods of time. However, for long-term measurements, the steel pin measurements proved remarkably reproducible and reliable.", "links": [ { diff --git a/datasets/ASAC_1118_1.json b/datasets/ASAC_1118_1.json index 6fd7fc41b0..bc23ff49c0 100644 --- a/datasets/ASAC_1118_1.json +++ b/datasets/ASAC_1118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1. PHOTOGRAPHIC RECORDS\n(sets to be made available to AAD multimedia centre for permanent record)\n\n1a Glacier margin positions\nAerial oblique and some ground-based photographs\n\n1b Summit crater of Big Ben\nAerial oblique photographs\n\n1c Environmental impacts of research\nGround-based photographs of research excavation sites (before excavation, during excavation, after rehabilitation, GPS locations) \n\n2. TOPOGRAPHIC PROFILES\n\n2a Intended long term coastal monitoring sites\nSimple profiles from permanent markers (with GPS) compiled from compas and clinometer traverses (amply detailed to capture changes at the scales being observed and simple to repeat)\n\n2b Beach profiles\nSimple profiles compiled from clinometer and tape traverses to characterise selected beaches as part of geomorphological survey; GPS locations\n\n2c. Dovers Moraine profiles\nSimple profiles compiled from clinometer, compass and tape traverses to characterise ridge sequence and establish approximate altitude of palaeolake shorelines; some GPS locations.\n\nDetails are given in the spreadsheet at the url below.\n\n3. GLACIAL LANDFORMS AND RELATIVE DATING\n\n3a Reconnaissance-level landform survey\nMorphostratigraphic relationships between landforms recorded in note form, photographically, or directly onto field maps; by estimation with some GPS control.\n\n3b Criteria for relative dating\nSemiquantitative data on moraine morphology; descriptive data on subsurface weathering status; GPS locations\n\n3c Sampling\nSamples obtained for laboratory analysis (mechanical and chemical analysis and some radiometric dating - processing will be dependent upon obtaining adequate financial resources; GPS locations; samples, sampling sites and desired use as indicated in the spreadsheet at the url below.\n\nThe downloadable dataset contains:\n\n1. Photographic record of glacier margins and summit of Big Ben as at December 2000\n2. Simple topographic profiles of eroding coastlines for long term monitoring processes, selected beach profiles and profiles over Dovers Moraine\n3. Data on glacial geomorphology and glacial geology including postdepositional modification of moraines and glacial sediments and samples for weathering studies and possible radiometric dating.\n\nThe fields in this dataset are:\nnorthing\neasting\nheight (ASL)\nlocation\nsample number\nsample type\nsample date\nlongitude\nlatitude\nlandform\ngeneral profile\nweathering and sediment stratigraphy\nsampling\nphotographs", "links": [ { diff --git a/datasets/ASAC_1119_1.json b/datasets/ASAC_1119_1.json index c3b130f3fa..86d7f3b3eb 100644 --- a/datasets/ASAC_1119_1.json +++ b/datasets/ASAC_1119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1119\nSee the link below for public details on this project.\n\nA marked bend in the Hawaiian-Emperor seamount chain supposedly resulted from a recent major reorganization of the plate-mantle system there 50 million years ago. Although alternative mantle-driven and plate-shifting hypotheses have been proposed, no contemporaneous circum-Pacific plate events have been identified. We report reconstructions for Australia and Antarctica that reveal a major plate reorganization between 50 and 53 million years ago. Revised Pacific Ocean sea-floor reconstructions suggest that subduction of the Pacific-Izanagi spreading ridge and subsequent Marianas/Tonga-Kermadec subduction initiation may have been the ultimate causes of these events. Thus, these plate reconstructions solve long-standing continental fit problems and improve constraints on the motion between East and West Antarctica and global plate circuit closure.", "links": [ { diff --git a/datasets/ASAC_1120_1.json b/datasets/ASAC_1120_1.json index 9b0196feaa..e5d68bbe45 100644 --- a/datasets/ASAC_1120_1.json +++ b/datasets/ASAC_1120_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1120_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets consist of static GPS data collected on the Amery Ice Shelf using Leica CRS1000 receivers. Additional data at Landing Bluff, Dalton Corner and Beaver Lake were collected by ANU (see ASAC project 1112). All data are provided in UNIX Z compressed RINEX (Receiver INdependent EXchange) format, as described in the IGS standards - see http://www.igs.org/products\n\nThe standard RINEX file naming convention is used, i.e., an eight digit file name as bbbbddds.yyt, where bbbb refers to a four digit station name, ddd refers to the day number of the year, s refers to a session number and yyt is the file extension number where yy refers to the year and t defines the file type (o for observation file and n for navigation file). All files are compressed using the UNIX Z compression scheme, as shown by the extension .Z. For example, base0010.00o.Z and base0010.00n.Z. \n\nThe files are set out in the following directories on the ftp site:\nseason1999_2000\n\\amery\n\\land\n\\raw\n\nData are also available for download from the Australian Antarctic Data Centre at the provided URL.\n\nRaw data, where available, is stored in the aw directory in standard Leica LB2 Binary format. Conversion routines are available: \nhttp://www.unavco.org/software/software.html\n\nGPS data collected at the permanent stations at Casey, Davis and Mawson are available from Geoscience Australia (previously AUSLIG) - see http://www.ga.gov.au/geodesy/antarc/antgps.jsp\n\nThe fields in this dataset are:\nGPS\nmarker number\nmarker name\nobserver/agency\napproximate position\nantenna\nwavelength\ninterval", "links": [ { diff --git a/datasets/ASAC_1121_1.json b/datasets/ASAC_1121_1.json index db6dc523ab..8c8ac1bf0c 100644 --- a/datasets/ASAC_1121_1.json +++ b/datasets/ASAC_1121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1121\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nImproved knowledge on the state of the ionosphere in the higher latitudes is important for communications and navigation purposes. Ionospheric scintillations, the rapid variations in amplitude and phase of radio signals resulting from irregularities in the ionosphere, can have significant effects on signals from satellites around sunspot maximum which is expected to peak in the year 2000. This project aims to improve our understanding of both the ionisation content variability in the higher latitude upper atmosphere and the scintillation occurrences and their effect on signals from satellites such as GPS and the mobile satellite phones.\n\nData were collected from Casey and Davis stations in the Australian Antarctic Territory, and Macquarie Island in the Southern Ocean.\n\nRaw and processed datasets are included.\n\nData explanations:\n\nThis is a fragment from a program that was used to convert the raw files into a more usable form;\n\nscin_temp.fieldnames=['week','time','prn','warm','azi','elev','cno','s4tot', 's4cor','s1','s3','s10','s30','s60','cdiv','sdiv','spect','phaset','k0','k1' ,'k2','k3']\n\nColumn 1; week - is GPS week number. This is the number of weeks that have elapsed since the GPS week rollover which was week beginning 22/8/99.\n\nColumn 2; time - time in seconds since the start of the week\n\nColumn 3; prn - GPS satellite PRN number - each satellite has a unique code\n\nColumn 4; warm - Satellite Lock time (seconds) (not sure about this one - this may be something else)\n\nColumn 5; azi - Satellite's current azimuth angle (degrees)\n\nColumn 6; elev - Satellite's current elevation angle (degrees)\n\nColumn 7; cno - C / No (dB/Hz) (don't know what this is used for)\n\nColumn 8; s4tot - S4 index (a standard scaled measure of amplitude scintillation activity) (dimensionless)\n\nColumn 9; s4cor - S4 index corrected to take into account the elevation angle of the satellite. (dimensionless)\n\nColumn10; s1 - phase sigma index 1 second averaged (radians)\n\nColumn11; s3 - phase sigma index 3 second averaged (radians)\n\nColumn12; s10 - phase sigma index 10 second averaged (radians)\n\nColumn13; s30 - phase sigma index 30 second averaged (radians)\n\nColumn14; s60 - phase sigma index 60 second averaged (radians)\n\nColumn15; cdiv - Average of Code/Carrier divergence (meters)\n\nColumn16; sdiv - Sigma of Code/Carrier divergence (meters)\n\nColumn17; spect - Phase spectral strength T (dB)\n\nColumn18; phaset - Phase spectral slope P (dB)\n\nColumn19; k0 - Amplitude Spectrum K0 (dB)\n\nColumn20; k1 - Amplitude Spectrum K1 (dB)\n\nColumn21; k2 - Amplitude Spectrum K2 (dB)\n\nColumn22; k3 - Amplitude Spectrum K3 (dB)", "links": [ { diff --git a/datasets/ASAC_1126_1.json b/datasets/ASAC_1126_1.json index 06e945cf43..f2b36723ae 100644 --- a/datasets/ASAC_1126_1.json +++ b/datasets/ASAC_1126_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1126_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preliminary Metadata record for data expected from ASAC Project 1126\nSee the link below for public details on this project.\n---- Public Summary from Project ----\nPrevious work on anti-freeze proteins (AFPs) in bacteria isolated from saline lakes in the Vestfold Hills, has shown that only around 10% of isolates possessed AFP activity. This suggests that the majority of bacteria may be using other mechanisms to avoid freezing or possibly are non-functional at sub-zero temperatures. We propose building on our previous work to ascertain if AFP occurrence is characteristic of particular taxonomic groups, or whether its evolution is random among different species.\n\nThe fields in this dataset are:\n\nLake\nDate\nAir Temperature\nIce Thickness\nSample Type\nDepth\nHeight of ice core sample from ice/water interface\nThickness of Ice core sample\nSalinity\nWater Temperature\nNitrate\nNitrite\nAmmonia\nPhosphate\nBacteria\nFlagellates\nChlorophyll\nDOC - Dissolved Organic Carbon\nCOV of DOC - Coefficient of Variance", "links": [ { diff --git a/datasets/ASAC_1131_1.json b/datasets/ASAC_1131_1.json index 258c611dbd..234b58ba0b 100644 --- a/datasets/ASAC_1131_1.json +++ b/datasets/ASAC_1131_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1131_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beaver Lake, a large epishelf lake in Eastern Antarctica was sampled on two occasions during the austral summer of 2000. Two sites, one 1km offshore and another 6km offshore were sampled at intervals to depths of 40m and 110m respectively. The lake is an end member of ultra-oligotrophic lake systems with a very low carbon pool. Dissolved organic carbon concentrations ranged between 95-652 micro grams per litre. Nutrient levels were generally low with soluble reactive phosphorus ranging from undetectable to 8.4 micro grams per litre, ammonium ranged between 1.8-5.0 micro grams per litre, nitrate from undetectable to 161 micro grams per litre and nitrite 1.1-5.3 micro grams per litre. Chlorophyll a concentrations ( 0.39 - 4.38 micro grams per litre) showed an unusual distribution with the highest levels close to the lake bottom at the offshore site (110m) where the phototrophic nanoflagellates displayed strong autofluorescence. Bacterial concentrations were low, with a maximum of 7.60 x 107 per litre, as were the concentrations of heterotrophic nanoflagellates that exploit them. Primary production ranged between 19.7 - 25.49 micro grams C per litre day-1 and bacterial production from 0.32 - 1.15 micro grams C per litre day-1. In common with other continental Antarctic lakes, the system was dominated by a microbial plankton. However, a dwarf variety of the calanoid copepod, Boeckella poppei, occurred below 25m at concentrations of 3-5 per litre. The data suggest that primary production and bacterial production were not limited by nutrient availability, but by other factors e.g. in the case of bacterial production by organic carbon concentrations and primary production by low temperatures.\n\nThe fields in this dataset are:\nEvolution\nBiological\nlake\nsalinity\ndepth m\nciliates per litre\ncysts\nboeckella\nbacteria\nglucose\nglycine\nparticulate organic carbon (POC)\ntotal organic carbon (TOC)\nDiAskenasia 15/2/00\nsloved organic carbon (DOC)\nMonodinium\nAskenasia\nStrombidium\nHeliozoa\nscuticociliates\nHolophyra\nPNAN = Phototrophic nanoflagellates HNAN= heterotrophic nanoflagellates", "links": [ { diff --git a/datasets/ASAC_1132-2_1.json b/datasets/ASAC_1132-2_1.json index c706181e3a..feb8927708 100644 --- a/datasets/ASAC_1132-2_1.json +++ b/datasets/ASAC_1132-2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1132-2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adult Weddell seals (Leptonychotes weddellii) exhibit site fidelity to where they first breed but juveniles, and perhaps transient adult males, may disperse from their natal location. If there is mixing between adjacent breeding groups, we would expect that common vocalisations would exhibit clinal patterns. Underwater Trill vocalisations of male Weddell seals at Mawson, Davis, Casey, McMurdo Sound, Neumayer and Drescher Inlet separated by ca. 500 to greater than 9,000 km, were examined for evidence of clinal variation. Trills are only emitted by males and have a known territorial defence function. Trills from Davis and Mawson, ca. 630 km apart, were distinct from each other and exhibited the greatest number of unique frequency contour patterns.\n\nThe acoustic features (duration, waveform, frequency contour) of Trills from Neumayer and Drescher Inlet, ca. 500 km apart, were more distinct from each other than they were from the other four locations. General Discriminant Analysis and Classification Tree Analysis correctly classified 65.8 and 76.9% of the Trills to the correct location. The classification errors assigned more locations to sites greater than 630 km away than to nearest neighbours. Weddell seal Trills exhibit geographic variation but there is no evidence of a clinal pattern. This suggests that males remain close to single breeding areas throughout their lifetime.\n\nThis work was completed as part of ASAC project 1132 and 2122 (ASAC_1132, ASAC_2122).", "links": [ { diff --git a/datasets/ASAC_1144_2.json b/datasets/ASAC_1144_2.json index 3493f5e31e..bb49f72dd8 100644 --- a/datasets/ASAC_1144_2.json +++ b/datasets/ASAC_1144_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1144_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2000/2001 season\n31 quad based surveys were conducted along the pack-ice edge to identify where leopard seals could be accessed. 31 one hour aerial surveys were also conducted to identify the position and number of seals in the region. 36 boat based surveys were conducted to identify the size and sex of leopard seals, whether they were a resight and the possibility of sedating seals. There were a total of 23 leopard seal captures. Resights from the 1999/2000 season were made of 5 known seals. Samples were collected from a total of 19 known and 20 unknown leopards seals. Samples were also collected from 14 known weddell seals. All blood, fur, whisker, scat, and morphmetric measurements were collected. Three satellite tracking units were deployed following the moult on adult leopard seals, and one crittercam unit. 14 blood samples were taken from leopard seals, 13 blood samples from weddell seals. 6 blubber samples from leopard seals, 17 fur samples from leopard seals and 7 whiskers from leopard seals and 2 from weddell seals 32 scats from leopard seals, 50 urine and 30 scat samples from weddell seals. Voucher samples for stable isotope analysis from 2 weddell seals, 26 penguins and 64 fish were collected.\n\nSpatial movements and haul out data from 11 leopard seals has been analysed. The blood, skin muscle, whisker, fat and fur has been prepared for later analysis. 42 separate scats have been analysed to determine diet composition. The captive feeding trials have been performed using two captive leopard seals. For each seal the following tests have been conducted, biochemical analysis of fresh serum, manual packed cell volme and white cell counts and differential white cell counts from blood smears and all haematological analysis. The refinement of the anaesthetic protocol of Zolazepam/ Tiletamine in leopard seals has been continued and this combination appears to provide a deeper and more reliable level of immobilisation compared with other anaesthetic combinations to date.\n\n2001/2002 season\nIn the Prydz Bay area, 28 one-hour aerial surveys were conducted by Squirrel helicopter, 23 quad based surveys and 12 boat based surveys were conducted between latitudes 68 degrees 20'S and 68 degrees 40'S along the fast ice edge to identify the position and number of leopard seals in the region. 110 leopard seals were sighted overall and of those 5 were positively identified as resight animals, tagged during previous seasons. Five leopard seal capture procedures were performed and postmortem samples, blood fur, blubber, skin, whiskers, scats, urine and morphometric measurements were collected from two leopard seals. 6 urine and 15 scat samples collected from known and unknown leopard seals and 7 fur samples including 2 from resight animals tagged during the previous two seasons. Three Weddell seal capture procedures were performed and blood samples were collected from each seal. 125 weddell seal urine and 112 weddell seal scat samples were also collected. For stable isotope and signature fatty acid analysis, the following samples were collected as voucher samples; 1 weddell seal muscle sample, 3 adelie penguin muscle samples, 1 elephant seal whisker, muscle and skin sample, 73 Antarctic cod muscle samples, 23 ice fish and 20 krill.\n\nForaging Information\nScats collected from 20 seals and will be analysed for diet information.\n\nStable isotope analysis involved fur, blood and whiskers collected from 35 animals. A key to the stable isotopes is provided in the download file. Fatty acid analysis involved collection of blubber from 35 animals.\n\nThe fields in this dataset are:\nSpatial Data\nSeal Id: adult female Ptt tag number\nDate: date data collected\nTime: time data collected\nLocation Class: ARGOS location classes 3 (0-150m), 2 (150-350m) and 1 (350-1000m).\nSouth: latitude decimal degrees\nEast: longitude decimal degrees\n\nAmphipods\nID = ID of seal from which scat sample collected\nLength = length of amphipod\nWt = weight of amphipod\nSpecies = species of amphipod\nbroken specimens = not whole specimens.\n\nOtolith data;\nNo = number collected\nSpecies = species of fish identified from otolith\nLength/breadth/width = measurements of otolith in mm\nEqn = calculation used to determine Standard length of fish from otolith size\nMass = mass calculation of fish from otolith measurements\nAge and Length classes = size of mass of fish classified into groups\n\nFatty acids\nRet Time = retention time of individual fatty acid\nArea counts = TBA\nArea % = TBA\n\nLS Scat\nID refers to the Identification number we gave to each seal.\nU refers to a unknown seal\nDate = date sample collected\nSex = sex of seal\nAge = juvenile, sub adult or adult\nSeal = seal fur found in scat\npenguin = penguin remains found in scat and so on for each other column including fish, otolith, krill rocks, amphipod and seaweed.\nSt weight refers to stomach weight.", "links": [ { diff --git a/datasets/ASAC_1148_Weddells_1.json b/datasets/ASAC_1148_Weddells_1.json index 962fc8002b..410bf48e1a 100644 --- a/datasets/ASAC_1148_Weddells_1.json +++ b/datasets/ASAC_1148_Weddells_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1148_Weddells_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The number of people travelling to Antarctica is growing, with much of the recent increase in visitor numbers attributable to an expansion in commercial tourism (Enzenbacher 1992; 1994). Most visitors to the region seek direct interactions with the wildlife and so visit breeding groups of seals and seabirds (Stonehouse 1965; Muller-Schwarze 1984). Invariably, this involves travelling to breeding sites by helicopter, inflatable motorised boat (e.g. zodiac) or over-snow vehicle, then making relatively close approaches on foot to photograph and observe the animals. At present, there is information to suggest that visitation can have a negative effect on some Antarctic wildlife, causing changes to behaviour, physiology and breeding success (Culik et al. 1989; Woehler et al. 1994, Giese 1996; Giese 1998, Giese and Riddle 1999). However, the responses of Weddell seals (Leptonychotes weddellii) to human activity have never been systematically examined. As a result, any guidelines to control human activity around these animals are based either on opportunistic observations of seal response, and/or assumptions as to the level of disturbance seals are experiencing.\n\nTherefore, the primary objective of the research is to measure the responses of Weddell seals to various human disturbance stimuli. In so doing, the research aims to make quality information available for the development of a comprehensive and scientifically based set of guidelines for managing interactions between people and Antarctic seals.\n\nThe research will adopt an experimental approach, whereby seals are experimentally exposed to particular types and intensities of human activity while their responses are objectively quantified. As far as possible, experiments are designed to replicate actual disturbances that Weddell seals are presently exposed to in Antarctica. As such, the responses of cow/pup pairs to approaches by pedestrians, over-snow vehicles and helicopters will be examined. In particular, experiments will investigate how approach distance (or altitude), approach speed, time of day, weather conditions and the time of the breeding season, influence the responses of Weddell Seals to these disturbance stimuli. Disturbance responses will be quantified by measuring the behaviour and heart rate of individual seals and the haul-out behaviour of entire groups of animals. Experiments will also be conducted to quantify the sound generated by vehicle operations in Antarctica to help determine whether anthropogenic noise effects vocal communication among Weddell seal, as indicated by changes in their calling rates. \n\nAlso see the metadata record entitled: Behavioural responses of Weddell seals to human activity.\n\nAt this stage most of the analysis is in progress and therefore it is not possible to provide complete data sets. These will be submitted upon the completion of the work. The attached word document summarises the experiments that have been completed during the three field seasons to date (up to the end of the 2002/2003 season), which included, the experiment type, location and sample size.\n\nThe two excel data sheets 'Experimental recording details' provide information on the video recordings that were made during the 2001/2002 and the 2002/2003 summers. These details state the experimental procedure, the details of the experimental, the time, date etc. They include Hi8 video camera recordings of Weddell seal behaviour and DAT recordings of vocalisations.\n\nBiological data collected during the 2002/2003 summer include:\nCollected 10 sample of blood (up to 50 ml each)\nCollected 6 samples of urine\nCollected 11 samples of fur\nCollected 9 samples of blubber\nCollected 6 samples of faecal swabs (from the ice or thermometer)\nConducted a post mortem on a recently deceased seal and collected organ and tissue samples.\n\nThese samples are being analysed by investigators in ASAC 1144. When results are available they will documented in either ASAC 1148 or 1144.\n\nThe fields in this dataset are:\n\nDate\nTime\nTape Number\nCounter Number\nCamera Number\nCow ID\nNew ID\nEvent\nRespiration Rate\nHeart Rate\nWhere Approached\nPosition of Pup\nDistance of Closest Pair\nDistance of Tide Crack\nLocation\nWind Direction\nCloud Cover\nTemperature\nWind Speed\nConductivity\nSalinity\npH\n \nFurther data has been added to the archive for up to the end of the 2006. These include data files, plus scanned field notes taken during the project. Finally, video tapes relating to the project have also been stored in the Australian Antarctic Division's multimedia library.", "links": [ { diff --git a/datasets/ASAC_1158_1.json b/datasets/ASAC_1158_1.json index 754553b245..39056b20ae 100644 --- a/datasets/ASAC_1158_1.json +++ b/datasets/ASAC_1158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report describes and presents all the data gathered on Brown Glacier during the 2000 field season. The goal of the study (ASAC project 1158) was to characterize one of the Heard Island glaciers that has a relatively simple geometry and is easy to work on. A land terminating glacier was chosen because of the larger expected response to climate change. The plan was to collect data on the dynamic characteristics, mass balance, sub-glacial topography and recent fluctuations of a Heard Island glacier. The data will ultimately be used to construct and validate a numerical model of the glacier. The model will be used to simulate the possible cause of the glacier fluctuations, and help to derive a climate-proxy record for this remote and sensitive area.\n\nThe study of the morphology, dynamics and mass balance of the Brown Glacier on the northeastern side of Heard Island was undertaken between mid-October and late November 2000. An ANARE field party, operating from a small field camp near the foot of the glacier, comprised three glaciologists (M.T., A.R. and D.T.) until early November, with two staying for the full period (M.T. and D.T.). This group was independent of two other ANARE field camps located at Atlas Cove and Spit Bay.\n\nThe following field measurements were successfully completed:\n\nA differential GPS survey of the surface elevation along the centre-line of the glacier (from 1 100 m elevation to the snout below 200 m) and across five transverse sections,\n\nA differential GPS survey of the location of the present snout of the glacier, and the 1947 lateral moraines,\n\nBathymetric measurements of the lagoon formed by retreat of the glacier since the 1950's,\n\nDetailed ice thickness measurements (with a portable radio echo-sounder) across a transverse profile of the glacier at 500 m elevation, and spot thickness measurements across three other profiles,\n\nSurface ice velocity measurements along the centre-line of the glacier (10 locations) and along two transverse sections at 500 m and 400 m elevation (five and four locations respectively). Most of these sites were measured over two epochs, and time varying velocity measurements were made for nearly four weeks at one site,\n\nSurface mass balance measurements during November at the location of all velocity measurements. Other members of the ANARE party made additional measurements in mid-January,\n\nTemperature and density measurements, and snow sampling from a pit and a crevasse,\n\nDetailed meteorological measurements from a temporary automatic weather station (AWS) on the glacier surface (at 500 m elevation) during November, and ongoing satellite-relayed meteorological measurements from a larger AWS on rock adjoining the Brown Glacier (~550 m elevation). The glacier surface measurements included half-hourly surface height measurements with an acoustic ranger to record short time scale changes in surface mass budget.\n\nData Files available:\n\nDEM Folder contents\ndem.ascHeard DEM in ASCII format, obtained from the AAD\nallprof.datall kinematic GPS profiles\nbrownarea.datHeard DEM cropped to the Brown Glacier area\nbrownpoints.datPoints surveyed with static GPS methods\ndem.datHeard DEM without header\nnewdem.datBrown Glacier area DEM corrected with GPS profiles\noldglacier.dat1947 Glacier outline\noutline.datGlacier outline, digitized from newdem.dat\n*.mvarious Matlab programs to adjust the DEM\ncreatedem.mMatlab program used for final DEM adjustment\ndem.mMatlab program to display and crop the Heard DEM\ndrawdem.mMatlab program to display a map (with various options)\nicelost.mMatlab program to calculate volume change\ndem.prjProjection parameters for Heard Island DEM\n*.profkinematic GPS profiles in ASCII format\nGlacier velocities folder contents\ndaily.*ASCII and Excel file of daily velocities measured at BG35\nGPSpoints.*ASCII and Excel file of index points surveyed with static GPS\nindex.mMatlab program to display velocities\nindexpoints.txtFile with velocity data used in index.m\nIsotope Analysis folder contents\nDel018.xlsExcel file containing Oxygen isotope data from BG35 and crevasse\nkinematic profiles folder contents\n*.txtall the kinematic GPS profiles in ASCII format\nLagoon bathymetry folder contents\nlagoon.mMatlab program to reduce bathymetry data in bathy.txt\nlagoon.figMatlab figure showing the bathymetry data\nbathy.txtASCII data file for use in lagoon.m\nshore.txtASCII data file for the surveyed shore line\nbathymetry.xlsExcel file with bathymetry data\nMatlab folder contents\nareasize.mprogram to calculate the area enclosed in a polygon\nchkload.mprogram to load a file with additional options\ncircle.mfunction to draw a circle (used in index.m)\nRES folder contents\nbg**.datRES raw data (format is specified in RES.m help)\nbg**bottom.datDigitized bed from RES returns\nbg**.resprocessed RES files (format specified in RES.m help)\nRES.mMatlab program to reduce RES data\ndrwelip.m\nfitline.mMatlab programs used by RES.m\nWeather folder contents\nrock_aws.xlsoriginal weather data files from the rock AWS\nglacier_aws.xlsoriginal weather data files from the glacier AWS\nsnow_surface.xlssnow surface height measurements from the bamboo poles at the\nindex sites (longitudinal and transverse).", "links": [ { diff --git a/datasets/ASAC_115_1.json b/datasets/ASAC_115_1.json index 399bf7c095..27e352c43e 100644 --- a/datasets/ASAC_115_1.json +++ b/datasets/ASAC_115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 115 See the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nUmbilicaria decussata, Usnea sphacelata, Ceratodon purpureus and surface soil samples were collected at 10 m intervals for 90 m downwind of a concrete batching site at Casey Station, East Antarctica. Comparable samples were collected from a similar uncontaminated remote site (SSSI 16 - now known as ASPA 135). Surface soil was alkaline in the immediate vicinity of the batching site (max pH 8.8) and tended to decrease with distance. In the SSSI control site, surface soil was acidic (pH 4.7). Lichens growing downwind of the batching site were more susceptible to damage from airborne alkaline pollution than the mosses and were moderately to severely bleached. This chapter describes the relation between mean total chlorophyll concentration, chlorophyll a/b ratio, distance from the batching site and soil pH. Low temperature (77 K) fluorescence of healthy plants from the SSSI and polluted plants 40 m downwind of the batching plant were compared. Variable fluorescence, indicative of photosynthetic electron transport was observed in all cases, from which we deduce that even severely bleached lichens contain live algal cells. The results presented provide quantitative baseline data against which further change (recovery or further deterioration) can be measured.", "links": [ { diff --git a/datasets/ASAC_1163_WilkesGIS_2010_1.json b/datasets/ASAC_1163_WilkesGIS_2010_1.json index ee7c3236cc..a0a02ebc8e 100644 --- a/datasets/ASAC_1163_WilkesGIS_2010_1.json +++ b/datasets/ASAC_1163_WilkesGIS_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1163_WilkesGIS_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2010 we undertook sampling of soil and waters around Wilkes station and the main tip site. We repeated the work of Snape et al. 1999 and took a more dense set of samples across the area to determine the extent and types of contamination. These data are complementary to the 1999 GIS dataset (see the metadata record with ID - ASAC_1163_WilkesGis_1999).\n\nDescription: Additional spatial data were collected and added to the existing AAD Wilkes GIS database (Nadia Babicka map). Locations of sediment, water, potentially hydrocarbon contaminated sediment, potentially hydrocarbon contaminated water and background samples were collected by Macquarie University and AAD staff, in January 2010, including hyperlinked sample photos; locations of Wilkes station buildings with numbers from Snape et al (1998)\n\nCreated by: Macquarie University, Oct 2010\n\nFiles:\nThe data are held as an ArcGIS 9.3 document with associated shapefiles.\n\nWilkes_Bab_MU2010: ArcGIS map file\nWilkesMac2010: excel spreadsheet of sample data collected by Macquarie University staff, Jan 2010\nBab_gis_data: underlying data for Babicka GIS database\nSample_Pics: Photos of sites of sample collected by Macquarie University Staff, Jan 2010\nLayer files: layer files are all listed as MU_Sed2010; MU_Water2010; MU_HCSed2010; MU_HCWater2010; MU_Bkgnd2010; Buildings\n\n\nOperation:\nHyperlinked photos activated by selecting 'lightning bolt' icon from tool bar - features with hyperlinks are highlighted in blue - wave mouse pointer over desired feature, when lightning bolt turns black, left click and hyperlinked doc will pop up.", "links": [ { diff --git a/datasets/ASAC_1163_WilkesGis_1999_1.json b/datasets/ASAC_1163_WilkesGis_1999_1.json index 5ba18e253d..18ae32b4c7 100644 --- a/datasets/ASAC_1163_WilkesGis_1999_1.json +++ b/datasets/ASAC_1163_WilkesGis_1999_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1163_WilkesGis_1999_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset resulted from a GPS survey of contaminants at the abandoned Wilkes station, Windmill Islands, Antarctica in January, February 1999. The survey was carried out by Nadia Babicka of Macquarie University in collaboration with Dr Ian Snape of the Australian Antarctic Division.\nAssistance was provided by David Smith of the Australian Antarctic Data Centre in converting Nadia's GPS data to GIS format and creating an interactive map to display the data.\n\nThe zip file available for download from this metadata record includes:\n1 A readme file outlining its contents;\n2 An ArcMap 9.2 document displaying Nadia's data against a background of topographic data;\n3 A folder of photos taken by Nadia. These photos are linked in the ArcMap document to the point, line and polygon features collected by Nadia.\n\nThe topographic data displayed in the ArcMap document is from the Australian Antarctic Data Centre's GIS database. The AADC also has an orthophoto of Clark Peninsula which could be used as background in the ArcMap document. Submit a request at https://data.aad.gov.au/aadc/requests/ if you would like a copy of the orthophoto. \n\nA more recent survey was carried out by Kirstie Fryirs of Macquarie University in 2010. Refer to the metadata record with ID 'ASAC_1163_WilkesGIS_2010'.", "links": [ { diff --git a/datasets/ASAC_1163_field_lab_books_1.json b/datasets/ASAC_1163_field_lab_books_1.json index b5e7775bf8..7d4f95f083 100644 --- a/datasets/ASAC_1163_field_lab_books_1.json +++ b/datasets/ASAC_1163_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1163_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the electronic scanned copies of the lab and field books used for ASAC (AAS) project 1163 (Remediation of petroleum contaminants in the Antarctic and subantarctic) between 1999 and 2012. These field/lab books were used at Casey, Davis and Macquarie Island Stations and in the Kingston Laboratories.\n\nPersonnel involved in recording these books were:\n\nAdam Brotchie, Alexis Schafer, Andrew Pond, Anne Palmer, Belinda Hill, Brendan Pitt, Chris Rigby, Dan Wilkins, Elijah Marshall, Greg Hince, Ian Snape, Jane Wasley, Jim Walworth, John Rayner, Penny Woodberry, Kate Mumford, Lauren Wise, Lisa Meyer, Meredith Nation, Michael Brown, Tim Spedding, Paul Harvey, Susan Ferguson, Teresa O'Leary", "links": [ { diff --git a/datasets/ASAC_1164_1.json b/datasets/ASAC_1164_1.json index 60d7965cd3..2e8ab5b0e6 100644 --- a/datasets/ASAC_1164_1.json +++ b/datasets/ASAC_1164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nMost of the snow falling on inland Antarctica drains via large ice streams and floating ice shelves to the sea where it lost by iceberg calving or as melt beneath the shelves. Ocean interaction beneath the shelves is complicated, and regions of basal refreezing as well as melt occur. These processes are important not only because they are a major component of the Antarctic mass budget, but because they also modify the characteristics of the ocean, influencing the formation of Antarctic Bottom Water which plays a major role in the global ocean circulation. The processes are sensitive to climate change, and shifts in ocean temperature or circulation near Antarctica could lead to the disappearance of all Antarctic ice shelves.\n\nThe Amery\nIce Shelf is the major embayed shelf in East Antarctica, and the subject of considerable previous ANARE investigation. Ocean interaction processes occurring beneath the shelf are only poorly understood, and this project will directly measure water characteristics and circulation in the cavity underneath the ice shelf, and the rates of melt and freezing on the bottom of the shelf. These measurements will be made through a number of access holes melted through the shelf. The project is closely linked with other projects investigating the circulation and interactions in the open ocean to the north of the shelf, and studies of the ice shelf flow and mass budget.\n\nThere will be child records for each of the following data sets:\nAM01 and AM01 b boreholes\n* CTD profiles through water column\n* CTD annual records at selected depths\n* Ocean current profiles through water column\n* Temperature measurements through ice shelf and across ice-water interface\n* Small ice core samples\n* 0.5 m sea floor sediment core\n* Video footage of borehole walls (including marine ice) and sea floor benthos\n* GPS records of surface tidal motion\n* Video\n\nAM02 borehole\n* CTD profiles through water column\n* CTD annual records at selected depths\n* Borehole diameter caliper profiles\n* Temperature measurements through ice shelf and across ice-water interface\n* 1.5 m sea floor sediment core\n* GPS records (surface elevation, ice motion)\n \nAM03 borehole\n* Aquadopp current meter data\n* Brancker thermistor data\n* Caliper data\n* FSI-CTD profile data\n* Drilling parameters data\n* Seabird MicroCAT CTD moorings at three depths in ocean cavity beneath the shelf\n* Video\n\nAM04 borehole\n* Aquadopp current meter data\n* Brancker thermistor data\n* Caliper data\n* FSI-CTD profile data\n* Drilling parameters data\n* Seabird MicroCAT CTD moorings at three depths in ocean cavity beneath the shelf\n* Video\n\nAM05 borehole\n* Aquadopp current meter data\n* Caliper data\n* FSI-CTD profile data\n* Drilling parameters data\n* Seabird MicroCAT CTD moorings in ocean cavity beneath the shelf\n\nAM06 borehole\n* Aquadopp current meter data\n* Caliper data\n* FSI-CTD profile data\n* Drilling parameters data\n* Seabird MicroCAT CTD moorings in ocean cavity beneath the shelf\n\n\nTaken from the 2008-2009 Progress Report:\nProgress Against Objectives:\nThe work undertaken in the past 12 months has continued to relate chiefly to the first of our objectives - \"quantify the characteristics and circulation of ocean water in the cavity beneath the Amery Ice Shelf\". Data from the AMISOR project have provided the first record of a seasonal cycle of ice shelf-ocean interaction. After recovering the 2008 data we now have near-continuous oceanographic data from beneath the Amery at 3 different depths for 6, 6, 3, and 3 years from 4 different sites. Note that the instruments at AM01 and AM02 (6 annual cycles of data each) are no longer recording due to expiration of the onboard batteries (3-5 years expected life cycle). This allows us to investigate the \"real\" 3-D, seasonally varying, circulation and melt/freezing cycle beneath an ice shelf - rather than the steady state, simplified \"2-D ice pump circulation\" that has mostly been assumed previously.\n\nAs much as 80% of the continental ice that flows into the Amery Ice Shelf from the Lambert Glacier basin is lost as basal melt melt beneath the southern part of the shelf, but a considerable amount of ice is also frozen onto the base in the north-western part of the shelf. These processes of melt and refreezing are due to a pattern of water circulation beneath the ice shelf which is driven by sea ice formation outside the front of the shelf. Our multi-year data from 4 sites beneath the Amery ice shelf show that there is a very strong seasonal cycle in the characteristics of the ocean water beneath the shelf, and strong interseasonal variability in this. The seasonal cycle is driven mostly by the seasonal cycle of sea ice formation and decay in Prydz Bay, and interseasonal variations are due to differences in the general ocean circulation, and in particular the upwelling of Circumpolar Deep Water onto the continental shelf in Prydz Bay. The melt and freeze processes beneath the ice shelf, also themselves modify the water characteristics.\n\nTaken from the 2009-2010 Progress Report:\nThe AMISOR project drilled two new 600 m deep boreholes on the Amery Ice Shelf in 2009-10: the first on the marine ice flowline to enhance understanding of the re-freezing process beneath the shelf; and the second in a region of known interest with respect to circulation patterns in the ocean cavity below the shelf. Instrument deployments at both sites should provide valuable annual cycle data over the next 4-5 years.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_1.json b/datasets/ASAC_1164_AM01_1.json index 2054def6e7..6bc4a6d953 100644 --- a/datasets/ASAC_1164_AM01_1.json +++ b/datasets/ASAC_1164_AM01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled mid-January 2002.\nProfiling measurements conducted over a period of one week.\nLong term monitoring instruments installed 2002-01-16.\n\nAM01b borehole drilled mid-December 2003.\nVideo recording of borehole walls and sea floor benthos.\nSediment sample collected from sea floor.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_Aquadopp_1.json b/datasets/ASAC_1164_AM01_Aquadopp_1.json index 81ea26a40c..869aa2477e 100644 --- a/datasets/ASAC_1164_AM01_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM01_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nCurrent meter data dips collected during routine CTD operations over a period of 4 days upon completion of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_Brancker_1.json b/datasets/ASAC_1164_AM01_Brancker_1.json index e5c74e3bf4..9520c39560 100644 --- a/datasets/ASAC_1164_AM01_Brancker_1.json +++ b/datasets/ASAC_1164_AM01_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nPartial annual data retrieved for 2002, and 2003.\n\nAM01b borehole drilled mid-December 2003.\nNo new thermistor strings deployed.\n\nConsult Readme file for detail of data files and formats.\n\nNew data and readme added July 2006.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_CTD_1.json b/datasets/ASAC_1164_AM01_CTD_1.json index ef03fe1030..449ba380dc 100644 --- a/datasets/ASAC_1164_AM01_CTD_1.json +++ b/datasets/ASAC_1164_AM01_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nData collected in series of casts over a period of 5 days following completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_Drilling_1.json b/datasets/ASAC_1164_AM01_Drilling_1.json index 4839fb9e22..dbb9574c86 100644 --- a/datasets/ASAC_1164_AM01_Drilling_1.json +++ b/datasets/ASAC_1164_AM01_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nData collected in series of files over a period of 2 days during production of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_GPR_1.json b/datasets/ASAC_1164_AM01_GPR_1.json index 481f782a5d..d8b64b966c 100644 --- a/datasets/ASAC_1164_AM01_GPR_1.json +++ b/datasets/ASAC_1164_AM01_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nData collected in series of files over a period of 2 days after completion of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_GPS_1.json b/datasets/ASAC_1164_AM01_GPS_1.json index 481cec89d0..a4f3af4313 100644 --- a/datasets/ASAC_1164_AM01_GPS_1.json +++ b/datasets/ASAC_1164_AM01_GPS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_GPS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\n\nGPS data collected in two segments: over 3 days 'static' around 07-Jan-2002, and a short kinematic sequence on 23-Jan-2002.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_MicroCAT_1.json b/datasets/ASAC_1164_AM01_MicroCAT_1.json index 0e856fb6a7..3aeb7f7fdd 100644 --- a/datasets/ASAC_1164_AM01_MicroCAT_1.json +++ b/datasets/ASAC_1164_AM01_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\nNew information added:\n\nJuly 2006,\nSeptember 2009.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_Other_1.json b/datasets/ASAC_1164_AM01_Other_1.json index 68d5bb898c..b54f6ee597 100644 --- a/datasets/ASAC_1164_AM01_Other_1.json +++ b/datasets/ASAC_1164_AM01_Other_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_Other_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\n\nSamples collected during drilling and scientific sampling phases of work.\n\nAWS continuing to operate.", "links": [ { diff --git a/datasets/ASAC_1164_AM01_caliper_1.json b/datasets/ASAC_1164_AM01_caliper_1.json index 6893167246..80dbaf6f73 100644 --- a/datasets/ASAC_1164_AM01_caliper_1.json +++ b/datasets/ASAC_1164_AM01_caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01_caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01 borehole drilled January 2002.\nProfiling measurements conducted to test borehole diameter integrity.\n\nAM01b borehole drilled mid-December 2003.\nNo new caliper data collected due to faulty wiring on instrument winch slip ring.", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_1.json b/datasets/ASAC_1164_AM01b_1.json index e5b332c7d7..f333f1d869 100644 --- a/datasets/ASAC_1164_AM01b_1.json +++ b/datasets/ASAC_1164_AM01b_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole drilled mid-December 2003.\nProfiling measurements conducted over a period of a few days.\nVideo recording of borehole walls and sea floor benthos.\nSediment sample collected from sea floor.\nNo long term monitoring instruments installed.\n\nAM01b borehole was drilled within a few hundred metres of where the ice shelf had carried the original AM01 borehole to, in the intervening 2 years. As the AM01 borehole had a mooring suite of instruments, none were emplaced in the AM01b borehole.", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_Aquadopp_1.json b/datasets/ASAC_1164_AM01b_Aquadopp_1.json index 741290c4f5..60daeec63b 100644 --- a/datasets/ASAC_1164_AM01b_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM01b_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole drilled December 2003.\nCurrent meter data dip collected during routine CTD profiling over a period of 1 day upon completion of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_CTD_1.json b/datasets/ASAC_1164_AM01b_CTD_1.json index 12c2b16344..9b0eab0700 100644 --- a/datasets/ASAC_1164_AM01b_CTD_1.json +++ b/datasets/ASAC_1164_AM01b_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole drilled December 2003.\nCTD profiling collected over a period of 1 day upon completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_GPS_1.json b/datasets/ASAC_1164_AM01b_GPS_1.json index 1a7933f158..bd584622ea 100644 --- a/datasets/ASAC_1164_AM01b_GPS_1.json +++ b/datasets/ASAC_1164_AM01b_GPS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_GPS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole site\nSmall amount of static GPS data at each of four sites in a 500 m x 500 m square strain grid.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_Other_1.json b/datasets/ASAC_1164_AM01b_Other_1.json index 4f6d83ca25..8c4f178b44 100644 --- a/datasets/ASAC_1164_AM01b_Other_1.json +++ b/datasets/ASAC_1164_AM01b_Other_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_Other_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole site\nSamples collected during drilling and scientific sampling phases of work.\nAWS continuing to operate (not a new station, but ongoing AM01 station).", "links": [ { diff --git a/datasets/ASAC_1164_AM01b_video_1.json b/datasets/ASAC_1164_AM01b_video_1.json index 8f67ee264f..194dc6a72a 100644 --- a/datasets/ASAC_1164_AM01b_video_1.json +++ b/datasets/ASAC_1164_AM01b_video_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM01b_video_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM01b borehole drilled mid-December 2003.\nProfiling measurements conducted over a period of a few days.\nVideo recording of borehole walls and sea floor benthos.\nSediment sample collected from sea floor.\nNo long term monitoring instruments installed.\n\nAM01b borehole was drilled within a few hundred metres of where the ice shelf had carried the original AM01 borehole to, in the intervening 2 years. As the AM01 borehole had a mooring suite of instruments, none were emplaced in the AM01b borehole.\n\nThere are several video files attached to this metadata record, and further details about them are provided in the accompanying readme document. The data file contains downcam video, sidecam video and miscellaneous video.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_1.json b/datasets/ASAC_1164_AM02_1.json index 1832da54d6..35b503d464 100644 --- a/datasets/ASAC_1164_AM02_1.json +++ b/datasets/ASAC_1164_AM02_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\nProfiling measurements conducted over a period of one week.\nLong term monitoring instruments installed 2001-01-06.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_Brancker_1.json b/datasets/ASAC_1164_AM02_Brancker_1.json index c442bd0a1b..c00fad5848 100644 --- a/datasets/ASAC_1164_AM02_Brancker_1.json +++ b/datasets/ASAC_1164_AM02_Brancker_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_Brancker_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\nPartial annual data retrieved for 2001.\nComplete annual data retrieved for 2002, and 2003.\n\nConsult Readme file for detail of data files and formats.\n\nNew data and readme added July 2006.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_CTD_1.json b/datasets/ASAC_1164_AM02_CTD_1.json index b2bec70d7f..127c33603a 100644 --- a/datasets/ASAC_1164_AM02_CTD_1.json +++ b/datasets/ASAC_1164_AM02_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\n\nSeveral CTD profiles obtained in 360 m wide ocean cavity beneath 373 m thick ice shelf.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_Caliper_1.json b/datasets/ASAC_1164_AM02_Caliper_1.json index d0b33625e9..e972d69be3 100644 --- a/datasets/ASAC_1164_AM02_Caliper_1.json +++ b/datasets/ASAC_1164_AM02_Caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_Caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\nSeveral caliper profiles obtained as a 'first look' at borehole closure rates.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_Drilling_1.json b/datasets/ASAC_1164_AM02_Drilling_1.json index 5daec305d4..e20ab9a395 100644 --- a/datasets/ASAC_1164_AM02_Drilling_1.json +++ b/datasets/ASAC_1164_AM02_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\n\nLogging files collected during drilling operations including water pressure, temperature and flow rate, as well as drill speed and depth.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_MicroCAT_2.json b/datasets/ASAC_1164_AM02_MicroCAT_2.json index bd0755d72f..10626378d5 100644 --- a/datasets/ASAC_1164_AM02_MicroCAT_2.json +++ b/datasets/ASAC_1164_AM02_MicroCAT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_MicroCAT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\n\n3 x Seabird 37IM CTD units moored long term in ocean cavity beneath the shelf.\n\nData for 2001, and 2003 in 0.5 hr sampling.\n\nData for 2002 not recorded.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\nNew information added:\n\nJuly 2006,\nSeptember 2009.", "links": [ { diff --git a/datasets/ASAC_1164_AM02_Other_1.json b/datasets/ASAC_1164_AM02_Other_1.json index b996a7af70..5dd75f2ae8 100644 --- a/datasets/ASAC_1164_AM02_Other_1.json +++ b/datasets/ASAC_1164_AM02_Other_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM02_Other_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM02 borehole drilled December 2000.\nSeveral Niskin water bottle samples collected in ocean cavity.\n1.44 m sediment core collected from seafloor at 780 m below sea level.\nOngoing Automatic Weather Station data available on: http://aws.acecrc.org.au/ Consult Readme file.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_1.json b/datasets/ASAC_1164_AM03_1.json index 8c0cb1b80e..abe4807415 100644 --- a/datasets/ASAC_1164_AM03_1.json +++ b/datasets/ASAC_1164_AM03_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM03 borehole drilled mid-December 2005. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity).\n\nThis is a parent record - see the child records for further information.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_Aquadopp_1.json b/datasets/ASAC_1164_AM03_Aquadopp_1.json index 80414f590a..b27dab598e 100644 --- a/datasets/ASAC_1164_AM03_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM03_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\nCurrent meter data dips collected during routine CTD operations over a period of 4 days upon completion of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_Brancker_2.json b/datasets/ASAC_1164_AM03_Brancker_2.json index 53af3e40ac..d9955cb572 100644 --- a/datasets/ASAC_1164_AM03_Brancker_2.json +++ b/datasets/ASAC_1164_AM03_Brancker_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_Brancker_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\nPartial annual data retrieved for 2006, and 2007.\n\nConsult Readme file for detail of data files and formats.\n\nDataset was updated on 2011-12-01 to include 2011 data.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_CTD_1.json b/datasets/ASAC_1164_AM03_CTD_1.json index cc3654a190..1264c45a12 100644 --- a/datasets/ASAC_1164_AM03_CTD_1.json +++ b/datasets/ASAC_1164_AM03_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\nData collected in series of casts over a period of 5 days following completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_Drilling_1.json b/datasets/ASAC_1164_AM03_Drilling_1.json index 8c03e9e5bb..a99238ae8a 100644 --- a/datasets/ASAC_1164_AM03_Drilling_1.json +++ b/datasets/ASAC_1164_AM03_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\nData collected in series of files over a period of 2 days during production of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_MicroCAT_4.json b/datasets/ASAC_1164_AM03_MicroCAT_4.json index 44b13c4243..181eac0c51 100644 --- a/datasets/ASAC_1164_AM03_MicroCAT_4.json +++ b/datasets/ASAC_1164_AM03_MicroCAT_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_MicroCAT_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\nNew Data added:\n\nSeptember 2009\nNovember 2011", "links": [ { diff --git a/datasets/ASAC_1164_AM03_caliper_1.json b/datasets/ASAC_1164_AM03_caliper_1.json index 2e67c5fa76..b0dab9b540 100644 --- a/datasets/ASAC_1164_AM03_caliper_1.json +++ b/datasets/ASAC_1164_AM03_caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM03 borehole drilled December 2005.\nProfiling measurements conducted to test borehole diameter integrity.", "links": [ { diff --git a/datasets/ASAC_1164_AM03_video_1.json b/datasets/ASAC_1164_AM03_video_1.json index c7176f3a01..b8efa9a9d2 100644 --- a/datasets/ASAC_1164_AM03_video_1.json +++ b/datasets/ASAC_1164_AM03_video_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM03_video_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM03 borehole drilled mid-December 2005. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity).\n\nThere are several video files attached to this metadata record, and further details about them are provided in the accompanying readme document.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_1.json b/datasets/ASAC_1164_AM04_1.json index 61c47ff4ea..16ccdb276c 100644 --- a/datasets/ASAC_1164_AM04_1.json +++ b/datasets/ASAC_1164_AM04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM04 borehole drilled mid-January 2006. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. Collection of targeted ice core samples. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity). \n\nThis is a parent record - see the child records for further information.\n\nThis device stopped working by the 2011/2012 season, and all sensors were declared non-functional.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_Aquadopp_1.json b/datasets/ASAC_1164_AM04_Aquadopp_1.json index aef4a998bf..9e64a77748 100644 --- a/datasets/ASAC_1164_AM04_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM04_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006. A single current meter data dip was collected during routine CTD operations over a period of 4 days upon completion of borehole. \n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_Brancker_2.json b/datasets/ASAC_1164_AM04_Brancker_2.json index 3be402e94d..9f73934f84 100644 --- a/datasets/ASAC_1164_AM04_Brancker_2.json +++ b/datasets/ASAC_1164_AM04_Brancker_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_Brancker_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\nAnnual data retrieved for 2006, and 2007.\n\nConsult Readme file for detail of data files and formats.\n\nNew data for 2011 was added in November of 2011.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_CTD_1.json b/datasets/ASAC_1164_AM04_CTD_1.json index a7bd52a925..774c9218bf 100644 --- a/datasets/ASAC_1164_AM04_CTD_1.json +++ b/datasets/ASAC_1164_AM04_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\nData collected in series of casts over a period of 4 days following completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_Drilling_1.json b/datasets/ASAC_1164_AM04_Drilling_1.json index 397426e7ff..490d094eb7 100644 --- a/datasets/ASAC_1164_AM04_Drilling_1.json +++ b/datasets/ASAC_1164_AM04_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\nData collected in series of files over a period of 4 days during production of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_MicroCAT_3.json b/datasets/ASAC_1164_AM04_MicroCAT_3.json index 29f1b82a44..eb1479dc28 100644 --- a/datasets/ASAC_1164_AM04_MicroCAT_3.json +++ b/datasets/ASAC_1164_AM04_MicroCAT_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_MicroCAT_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.\n\nNew Data added:\n\nSeptember 2009\nNovember 2011", "links": [ { diff --git a/datasets/ASAC_1164_AM04_caliper_1.json b/datasets/ASAC_1164_AM04_caliper_1.json index 2d60929903..8016b729b7 100644 --- a/datasets/ASAC_1164_AM04_caliper_1.json +++ b/datasets/ASAC_1164_AM04_caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM04 borehole drilled January 2006.\nProfiling measurements conducted to test borehole diameter integrity.", "links": [ { diff --git a/datasets/ASAC_1164_AM04_video_1.json b/datasets/ASAC_1164_AM04_video_1.json index da850e6e63..044fc2d1f2 100644 --- a/datasets/ASAC_1164_AM04_video_1.json +++ b/datasets/ASAC_1164_AM04_video_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM04_video_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM04 borehole drilled mid-January 2006. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. \nCollection of targetted ice core samples. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity). \n\nThere are several video files attached to this metadata record, and further details about them are provided in the accompanying readme document.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_1.json b/datasets/ASAC_1164_AM05_1.json index 63bfc60d19..4d68d1296a 100644 --- a/datasets/ASAC_1164_AM05_1.json +++ b/datasets/ASAC_1164_AM05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM05 borehole drilled mid-December 2009. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. \nCollection of targeted ice core samples. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity). \n\nThis is a parent record - see the child records for further information.\n\nSome general readme documents are available for download from the provided URL.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_Aquadopp_1.json b/datasets/ASAC_1164_AM05_Aquadopp_1.json index 0618351701..67ea3584cc 100644 --- a/datasets/ASAC_1164_AM05_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM05_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\n\nSee the pdf file as part of the download for more information on the work carried out as part of this borehole.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_CTD_1.json b/datasets/ASAC_1164_AM05_CTD_1.json index cf0242a9b4..a503f98d38 100644 --- a/datasets/ASAC_1164_AM05_CTD_1.json +++ b/datasets/ASAC_1164_AM05_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\nData collected in series of casts following completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_Drilling_1.json b/datasets/ASAC_1164_AM05_Drilling_1.json index 5563619aab..8228dcc4fb 100644 --- a/datasets/ASAC_1164_AM05_Drilling_1.json +++ b/datasets/ASAC_1164_AM05_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\nData collected in series of files following production of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_MicroCAT_1.json b/datasets/ASAC_1164_AM05_MicroCAT_1.json index ef3e1d8f56..860f2bb5cd 100644 --- a/datasets/ASAC_1164_AM05_MicroCAT_1.json +++ b/datasets/ASAC_1164_AM05_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM05_caliper_1.json b/datasets/ASAC_1164_AM05_caliper_1.json index 2ba8c77872..460aba0386 100644 --- a/datasets/ASAC_1164_AM05_caliper_1.json +++ b/datasets/ASAC_1164_AM05_caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM05_caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM05 borehole drilled December 2009.\n \nProfiling measurements conducted to test borehole diameter integrity.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_1.json b/datasets/ASAC_1164_AM06_1.json index 1d5f462709..2d65a0271f 100644 --- a/datasets/ASAC_1164_AM06_1.json +++ b/datasets/ASAC_1164_AM06_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amery Ice Shelf AM06 borehole drilled early January 2010. Sub-shelf water profiling measurements conducted over a period of a few days. Partial video recording of borehole walls and sea floor benthos. \nCollection of targeted ice core samples. Sediment sample collected from sea floor. Long term monitoring instruments installed (thermistors in ice, 3 x CTD in ocean cavity). \n\nThis is a parent record - see the child records for further information, and access to the data.\n\nSome general readme documents are available for download from the provided URL.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_Aquadopp_1.json b/datasets/ASAC_1164_AM06_Aquadopp_1.json index 76820f5699..2ded6580e9 100644 --- a/datasets/ASAC_1164_AM06_Aquadopp_1.json +++ b/datasets/ASAC_1164_AM06_Aquadopp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_Aquadopp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\n\nSee the pdf file as part of the download for more information on the work carried out as part of this borehole.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_CTD_1.json b/datasets/ASAC_1164_AM06_CTD_1.json index 41da41b183..2ce5fd1269 100644 --- a/datasets/ASAC_1164_AM06_CTD_1.json +++ b/datasets/ASAC_1164_AM06_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\nData collected in series of casts following completion of borehole.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_Drilling_1.json b/datasets/ASAC_1164_AM06_Drilling_1.json index e504367742..4a3d63dfda 100644 --- a/datasets/ASAC_1164_AM06_Drilling_1.json +++ b/datasets/ASAC_1164_AM06_Drilling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_Drilling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\nData collected in series of files following production of borehole.\n\nConsult Readme file for detail of data files and formats.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_MicroCAT_1.json b/datasets/ASAC_1164_AM06_MicroCAT_1.json index 1dcfd35b0d..64f9c73416 100644 --- a/datasets/ASAC_1164_AM06_MicroCAT_1.json +++ b/datasets/ASAC_1164_AM06_MicroCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_MicroCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\n\nData being collected at annual re-visits to site.\n\nConsult Readme file for detail of data files and formats. A word document providing further information is also available as part of the download.\n\nAll of the .dat files of data can be viewed with a text editor such as Wordpad.", "links": [ { diff --git a/datasets/ASAC_1164_AM06_caliper_1.json b/datasets/ASAC_1164_AM06_caliper_1.json index 01957af414..9413a07a4f 100644 --- a/datasets/ASAC_1164_AM06_caliper_1.json +++ b/datasets/ASAC_1164_AM06_caliper_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1164_AM06_caliper_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AM06 borehole drilled January 2010.\n \nProfiling measurements conducted to test borehole diameter integrity.", "links": [ { diff --git a/datasets/ASAC_1165_1.json b/datasets/ASAC_1165_1.json index eb6ec3afd8..e1311796e1 100644 --- a/datasets/ASAC_1165_1.json +++ b/datasets/ASAC_1165_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1165_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic sediments and sea-ice are important regulators in global biogeochemical and atmospheric cycles. These ecosystems contain a diverse range of bacteria whose biogeochemical roles remains largely unknown and which inhabit what are continually low temperature habitats. An integrated molecular and chemical approach will be used to investigate the coupling of microbial biogeochemical processes with community structure and cold adaptation within coastal Antarctic marine sediments and within sea-ice. Overall the project expects to make an important contribution to our understanding of biological processes within low temperature habitats.\n\nDATA SET ORGANISATION:\n\nThe dataset is organised on the basis of publication and is organised on the basis of the following sections:\n\n1. SEDIMENT SAMPLES and ISOLATES\n\nSamples collected are described in terms of location, type and where data were obtained chemical features. The designation, source, media used for cultivation and isolation and availability of sediment and other related isolates are provided. Samples included are from the following locations: Clear Lake, Pendant Lake, Scale Lake, Ace Lake, Burton Lake, Ekho Lake, Organic Lake, Deep lake and Taynaya Bay (Burke Basin), Vestfold Hills region; and the Mertz Glacier Polynya region.\n\n2. BIOMASS and ENZYME ACTIVITY DATA\n\nBiomass, numbers and extracellular enzyme activity data are provided for Bacteria and Archaea populations from Mertz Glacier Polynya shelf sediments.\n\n3. FATTY ACID and TETRAETHER LIPID DATA\n\nPhospholipid and tetraether lipid data are provided for Mertz Glacier Polynya shelf sediments. Whole cell fatty acid data are provided for various bacterial isolates described officially as new genera or species.\n\n4. RNA HYBRIDISATION DATA\n\nRNA hybridisation data for Mertz Glacier Polynya sediment samples is provided, including data for oligonucleotide probes specifc for total Bacteria, Archaea, the Desulfosarcina group (class Deltaproteobacteria, sulfate reducing bacterial clade), phylum Planctomycetes, phylum Bacteroidetes (Cytophaga-Flavobacterium-Bacteroides), class Gammaproteobacteria, sulfur-oxidizing and related bacteria (a subset of class Gammaproteobacteria) and Eukaryota.\n\n5. PHYLOGENETIC DATA\n\n16S rRNA gene sequence data are indicated including aligned datasets for three clone libraries derived from the Mertz Glacier Polynya including GenBank accession numbers. Sequence accession numbers are provided for Vestfold Hills lake sediment samples. In addition GenBank numbers are provided for denaturing gradient gel electrophoresis band sequence data from Mertz Glacier Polynya shelf sediment. Other forms of this DGGE data (banding profile analysis) are available in reference Bowman et al. 2003 (AAD ref 10971).", "links": [ { diff --git a/datasets/ASAC_1166_1.json b/datasets/ASAC_1166_1.json index 5af9c7d355..6a6a75ff28 100644 --- a/datasets/ASAC_1166_1.json +++ b/datasets/ASAC_1166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nThe lakes and fjords of the Vestfold Hills region of Antarctica provide unique ecosystems for studying environmental changes in Antarctica over the past 8000 years. Studies of the changes in organic matter composition in sediment cores provide information how the microbial and plankton communities have changed over time in response to varying chemical and physical conditions. Our study will provide new information about how the cycles of the biologically-important elements carbon and sulfur are linked and why some sediments can preserve large amounts of organic carbon. This information will be useful for studies of palaeoclimate and will also provide valuable insights into the processes that produce petroleum source rocks.\n\nFrom the abstracts of the referenced papers:\n\nPreserved ribosomal DNA of planktonic phototrophic algae was recovered from Holocene anoxic sediments of Ace Lake (Antarctica), and the ancient community members were identified based on comparative sequence analysis. The similar concentration profiles of DNA of haptophytes and their traditional lipid biomarkers (alkenones and alkenoates) revealed that fossil rDNA also served as quantitative biomarkers in this environment. The DNA data clearly revealed the presence of six novel phylotypes related to known alkenone and alkenoate-biosynthesising haptophytes with Isochrysis galbana UIO 102 as their closest relative. The relative abundance of these phylotypes changed as the lake chemistry, particularly salinity, evolved over time. Changes in the alkenone distributions reflect these population changes rather than a physiological response to salinity by a single halophyte. Using this novel palaeo-ecological approach of combining data from lipid biomarkers and preserved DNA, we showed that the post-glacial development of Ace Lake from freshwater basin to marine inlet and the present-day lacustrine saline system caused major qualitative and quantitative changes in the biodiversity of the planktonic populations over time. \n\nPost-glacial Ace Lake (Vestfold Hills, Antarctica), which was initially a freshwater lake and then an open marine system, is currently a meromictic basin with anoxic, sulfidic and methane-saturated bottom waters. Lipid and 16S ribosomal RNA gene stratigraphy of up to 10,400-year-old sediment core samples from the lake revealed that these environmentally induced chemical and physical changes caused clear shifts in the species composition of archaea and aerobic methanotrophic bacteria. The combined presence of lipids specific for methanogenic archaea and molecular remains of aerobic methanotrophic bacteria (13C-depleted delta8(14)-sterols and 16S rRNA genes) revealed that an active methane cycle occurred in Ace Lake during the last 3000 calendar years and that the extant methanotrophs were most likely introduced when it became a marine inlet (9400 y BP); rDNA sequences showed 100% sequence similarity with Methanosarcinales species from freshwater environments and were the source of sn-2- and sn3-hydroxyarchaeols. Archaeal phylotypes related to uncultivated Archaea associated with various marine environments were recovered from the present-day anoxic water column and sediments deposited during the meromictic and marine period.", "links": [ { diff --git a/datasets/ASAC_1171_1.json b/datasets/ASAC_1171_1.json index e5e5c617c0..e282920c40 100644 --- a/datasets/ASAC_1171_1.json +++ b/datasets/ASAC_1171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The demographic performance of high level antarctic predators is ultimately determined by the oceanic processes that influence the spatial and temporal distribution of primary productivity. This study will quantify the links between the foraging performance of southern elephant seals and a range of oceanographic parameters, including sea surface temperature, productivity and bathymetry. These data are a crucial component in understanding how antarctic predators will respond to changes in the distribution of marine and will be an important contribution to our understanding of the on-going decline in elephant seal numbers.\n\nData were originally collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the URL given below). However, these data have been replaced by an Access Database version, and have also been loaded into the Australian Antarctic Data Centre's ARGOS tracking database. The database can be accessed at the provided URLs.", "links": [ { diff --git a/datasets/ASAC_1174_1.json b/datasets/ASAC_1174_1.json index 19ec8268b6..17ec1a87d7 100644 --- a/datasets/ASAC_1174_1.json +++ b/datasets/ASAC_1174_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1174_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1174\nSee the link below for public details on this project.\n---- Public Summary from Project ----\nAustralia's World Heritage listed subantarctic Heard and McDonald Islands are experiencing rapid climate change. Their terrestrial and coastal ecosystems are poorly known. This multidisciplinary project will gather baseline data on ecosystem biodiversity and species distribution, and make predictions about responses to future environmental change, providing vital information for conservation protocols.\n\nThe download file contains two excel spreadsheets and a pdf copy of the referenced paper.\n\nFrom the abstract of the referenced paper:\n\nDuring an early summer visit in 2000, mosses were collected from sites around Heard Island. Three species, Bryoerythrophyllum recurvirostrum, Philonotis cf. tenuis and Syntrichia filaris have been added to the list of moss species known from the island, bringing the total to 40 moss taxa. Syntrichia anderssonii was found with sporophytes, whereas previously its sporophytes were known in the subantarctic only from Macquarie Island. Extensions of range on Heard Island have been recorded for several species. There are few geographical differences in species composition between locations around the island, provided appropriate habitats exist.", "links": [ { diff --git a/datasets/ASAC_1180_1.json b/datasets/ASAC_1180_1.json index 8a2035397a..f35ec12967 100644 --- a/datasets/ASAC_1180_1.json +++ b/datasets/ASAC_1180_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1180_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1180\nGlobal change, biodiversity and conservation in terrestrial and coastal ecosystems on Heard and McDonald Islands: statistical models for monitoring and predicting effects of climate change and local human impacts on invertebrates.\n\nWe identified the major environmental variables as altitude and vegetation type. We selected sites for sampling by designing a stratified survey that sampled along gradients of altitude and vegetation type. We included 60 sites in the survey design, distributed across Heard Island in five areas or blocks: Round Hill, Scarlet Hill, Cape Lockyer, Long Beach and Mt Drygowski. This level of sampling was achievable within the five-month period and was sufficient to produce reliable statistical models of invertebrate distribution in the subantarctic (Davies and Melbourne, 1999).\n\nWe included altitude as a continuous variable (i.e. altitude was measured to +/- 5 meters). For the purpose of getting a reasonably balanced design and for selecting sites we defined altitude classes as: 0-100 m, 100-200 m, 200 - 300 m, 300 - 400 m, 400-500 m, 500-600 m, 600 m +. We included five vegetation categories, which were not meant to cover all kinds of vegetation but instead span the range of vegetation types. Our vegetation classes were Poa cookii dominated (greater than 75%), Pringlea dominated (greater than 75%), Azorella dominated (greater than 75%), feldmark (less than 50% vegetation but with the vegetation that is present consisting of at least 50% moss), and 'patchy Azorella' (50-75% Azorella but less than 75% total vegetation). Not all vegetation classes occurred at all altitudes so we have crossed vegetation with altitude to give roughly 13-16 altitude-vegetation combinations. At most we managed to find between 10-13 sites in each of five areas giving us a total of 60 sites.\n\nAt all of our sites, we took pitfall samples, and at a selected subset of sites (20 sites) we did hand searches to get density estimates. The trapping took place between January and March 2001. The species that we trapped were: Anatalanta aptera, Myro kerguelenensis, Canonopsis sericeus, Ectemnorhinus viridis, Bothrometopus brevis, Bothrometopus gracilipes, Notodiscus hookeri, Embryonopsis halticella, Calycopteryx mosleyi, Amalopteryx maritima. We produced statistical models that describe the distribution of these species over Heard Island, based on altitude and vegetation type.\n\nWe took standardised photographs of a 10 m transect at each of our 60 sample sites. These will allow us to look for changes in vegetation composition at these sites in the future. The photographs are in a digital format.\n\nThe fields in this dataset are:\n\nRegion\nAltitude\nSite Code\nSlope\nAspect\nLatitude\nLongitude\nDirt\nSpecies\nDate\nTime\nVegetation Type\nSample Number", "links": [ { diff --git a/datasets/ASAC_1181_1.json b/datasets/ASAC_1181_1.json index cdf1725e42..876fe2463a 100644 --- a/datasets/ASAC_1181_1.json +++ b/datasets/ASAC_1181_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1181_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2000-01 fieldwork undertaken\n\nHeard Island 53 deg. 05 min. S., 73 deg. 30 min. E. Field area covered: southeast end of island from Long Beach to Gilchrist Beach. 150 GPS control points collected on 1986-1987 airphotos, using handheld GPS unit. To be used for a vegetation mapping project in progress for mapping of Heard Island vegetation communities, along with vegetation quadrat data covering major plant communities of the island. Vegetation quadrat data collected on monitoring sites set up in 1987 to record changes in vegetation, especially the grass Poa annua. Fixed photo-points re-photographed to record aspects of vegetation and glacier-front changes over 12 years. Heard Island environments are changing steadily as global climate change occurs. This project gathered information that will enable completion of a vegetation map for Heard Island; revisited sites studied and marked in 1987 to document changes in vegetation, including colonisation of recently deglaciated land; and provided advice for visitor management protocols to minimise vegetation disturbance. The parts of the island visited were surveyed for new species that may be recently arrived or previously unrecorded, and changes in species which had limited distribution in 1987 were documented.\n\n2003-04 fieldwork undertaken\n\nThe fieldwork provides strong evidence of vegetation and habitat changes over the past 16 years, and a sound basis for future monitoring in a dynamic environment sensitive to global change. Work included documentation of 200 photopoints, mapping of limited-distribution plant species, and ground-checking of vegetation mapping from 1987. A solid baseline of 1987 data (photopoints, fixed transects) was remeasured. A new vascular plant species was recorded, Leptinella plumosa, increasing the vascular species list to 12. Differential GPS allowed precise documentation of 200 mapping control points for AADC and accurate locations of all photopoints, species mapping and fixed transects.\n\nDescriptions of data collected for each JJS Data dictionary feature, and additional attributes, are included in metadata notes (word docs) accompanying the DGPS data.\n\nThe data associated with this metadata record are as follows:\n\na) Differential GPS data. The DGPS data in (b) and (c) below originate from these data.\n\ni) Corrected data (Trimble)\nii) Uncorrected data (Trimble)\niii) Shapefiles of the corrected data with original data dictionary IDs\niv) Metadata notes (word doc) for Trimble files\n\nb) Control point data\n\ni) Shapefile of DGPS control points\nii) Metadata notes (word doc) for DGPS control points\niii) Excel file of hand-held control points 2000\niv) Metadata notes (word doc) for hand-held control points 2000\nv) Excel file of hand-held control points 2003\nvi) Metadata notes (word doc) for hand-held control points 2003\n\nc) Vegetation and miscellaneous data\n\ni) Five shapefiles of DGPS locations for vegetation data and miscellaneous features\nii) One shapefile of hand-held GPS locations for vegetation data.\niii) Metadata notes (word doc) for veg. and misc. data", "links": [ { diff --git a/datasets/ASAC_1184_1.json b/datasets/ASAC_1184_1.json index 5d202fd85d..a5df921d44 100644 --- a/datasets/ASAC_1184_1.json +++ b/datasets/ASAC_1184_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1184_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project exploited the unique exposures of the uppermost oceanic crust found on Macquarie Island as a window into the internal structure of the oceanic crust. The form of rock units and the way in which they are arranged on the Island provided a means of understanding how they were assembled. This assembly occurred beneath a mid-ocean ridge spreading center, an area that can probably never be directly investigated. The general process by which this crust has formed is responsible for the creation of about 60% of the bedrock geology of the Earth.\n\nThe Macquarie Island ophiolite is an uplifted block of oceanic crust formed at the Australia-Pacific spreading centre between 12 and 9 Ma. The sense of motion and geological processes across this plate boundary reflect an evolution from orthogonal spreading through progressively more oblique spreading to the present-day transpressional regime. The crust that makes up the island was formed during an interval of oblique spreading along east-trending spreading segments punctuated by a series of northwest-trending discontinuities. The discontinuities are accommodation zones marked by oblique-slip dextral-normal faults, localised dikes and lava flows, and extensive hydrothermal alteration, indicating that these zones were active near the spreading axis. These features provide a window into the internal structure of oceanic crust generated by oblique spreading.\n\nThe download file contains:\n\nI. Publication folder (PDF files):\n\n1. Alt, J.C., G. Davidson, D.A.H. Teagle and J.A. Karson, The isotopic composition of gypsum in the Macquarie Island Ophiolite: Implications for sulfur cycle and the subsurface biosphere in oceanic crust, Geology, 31, 549-552, 2003.\n\n2. Rivizzigno, P.A. and J.A. Karson, Mid-ocean ridge fault zones preserved on Macquarie Island: Faulting, hydrothermal processes and magmatism in an oblique-spreading environment, Geology 32, 125-128, 2004.\n\n3. Rivizzigno, P. A., The Major Lake Fault Zone: An Oblique Spreading Structure Exposed in the Macquarie Island Ophiolite, Southern Ocean, MS Thesis, Duke University, Durham, NC USA, 2002, 59 pp.\n\nII. Macca Maps folder (TIFF files):\n\n1. Helicopter Video: Macca map showing the path and view direction from a video made during a helicopter trip over the island in 2000 during an unusually clear day. Copies of the video were left with ANARE and with various people at UTas (R. Varne, G. Davidson and others).\n\n2. JAK2000Samples: Macca map with locations of samples collected by J.A. Karson during the 2000 field season. Samples are numbered MAC00-XX. Samples are under study at Duke University.\n\n3. JAKMK2000Samples: Macca map with locations of samples of dike rocks collected for geochemicial studies by J.A. Karson during the 2000 dield season. Samples are numbered MK-XX. They were left with Dr. R. Varne (UTas) in 2000.\n\n4. PAR2000Samples: Macca map with locations of samples collected by P.A. Rivizzigno during the 2000 field season. Samples are under study at Duke University and reported in Rivizzigno (2002) and Rivizzigno and Karson (2004).\n\n5. PARMK2000: Macca map with locations of samples of dike rocks collected for geochemicial studies by J.A. Karson during the 2000 dield season. Samples are numbered MK-XX. They were sent to Dr. R. Varne (UTas) in 2000.\n\n6. Geological map from Rivizzigno (2002) in vector art (Canvas 8.0) and bitmap (jpeg) formats. New data are plotted on a base map by Goscombe and Everard (1998).\n\nIII. Other Information folder (WORD files):\n\n1. References: citations of journal articles, theses, abstracts from this project.\n\n2. JAK Sample Log: List of samples, locations, etc. for Karson samples from 2000.", "links": [ { diff --git a/datasets/ASAC_1189_200910020_1.json b/datasets/ASAC_1189_200910020_1.json index 371b48bd22..d94f4a6e37 100644 --- a/datasets/ASAC_1189_200910020_1.json +++ b/datasets/ASAC_1189_200910020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1189_200910020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project aims to assess the vulnerability of and risks to habitats in Australian fisheries in the Australian Exclusive Economic Zone (EEZ)/Australian Fishing Zone (AFZ) of the Southern Ocean to impacts by different demersal gears - trawl, longline and traps. The project which is a collaborative initiative between the Australian Antarctic Division (AAD), the Australian Fisheries Management Authority (AFMA), industry and research partners, and substantially funded by the Fisheries Research and Development Corporation, was developed in order to resolve outstanding questions relating to the potential impacts and sustainability of demersal fishing practices in the AFZ at Heard Island and the McDonald Islands (HIMI). It will also help resolve similar outstanding questions for other fisheries in the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) in which Australian industry participates and provide technology for use in other fisheries to address similar questions.\n\nThe proposed project will assess the degree to which demersal gears interact with and possibly damage benthic habitats. It will also assess the degree to which these habitats might be damaged within the AFZ in the HIMI region. The project is not intended to estimate rates of recovery of benthic habitats following damage by demersal gears. However, information from the literature on rates of recovery of different benthic species and habitats will be used to assess the risks of long-term sustainability of these habitats.\n\nObjectives\nTo develop deep sea camera technologies that can be easily deployed during fishing operations, to facilitate widespread observations of demersal fishing activities (trawl, longline and trap) and their interactions with benthic environments.\n\nTo assess the vulnerability of benthic communities in Sub-Antarctic (Australian AFZ) and high latitude areas of the Southern Ocean (Australian EEZ) to demersal fishing using trawls, long-lines or traps, using video and still camera technologies.\n\nTo assess the risk of demersal fishing to long-term sustainability of benthic communities in these areas, based on the assessment of vulnerability and information from the literature on potential recovery of benthic species and habitats.\n\nTo recommend mitigation strategies by avoidance or gear modification, where identified to be needed, and practical guidelines to minimise fishing impacts on benthic communities.\n\nField work:\nField work for this project is well advanced. Sampling of benthic habitats was conducted off East Antarctica from the AA in the summer season of 2009/10. Sampling yielded biological samples and camera footage over a number of sites spread across a large section of the East Antarctic coast and across a range of benthic habitats, however sampling was limited by the extent of ice and number of ship days (10) allocated (the project was originally planned for 16 ship days and later in the summer, when ice was predicted to be less extensive). The camera units are currently deployed on commercial vessels fishing the sub-Antarctic. The close of the 2010 commercial fishing season in September 2010 will mark the conclusion of field activities for this project.", "links": [ { diff --git a/datasets/ASAC_1189_Report_1.json b/datasets/ASAC_1189_Report_1.json index 77314a52f9..0da1c1f31d 100644 --- a/datasets/ASAC_1189_Report_1.json +++ b/datasets/ASAC_1189_Report_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1189_Report_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Objectives of the project:\n1. To develop deep-sea camera technologies that can be easily deployed during fishing operations, to facilitate widespread observations of demersal fishing activities (trawl, longline and trap) and their interactions with benthic environments.\n\n2. To assess the vulnerability of benthic communities in Subantarctic (Australian AFZ) and high latitude areas of the Southern Ocean (Australian EEZ) to demersal fishing using trawls, longlines or traps, using video and still camera technologies.\n\n3. To assess the risk of demersal fishing to long-term sustainability of benthic communities in these areas, based on the assessment of vulnerability and information from the literature on potential recovery of benthic species and habitats.\n\n4. To recommend mitigation strategies by avoidance or gear modification, where identified to be needed, and practical guidelines to minimise fishing impacts on benthic communities.\n\nNon-Technical Summary\nAustralia's domestic legislation and obligations under international agreements such as the Convention for the Conservation of Antarctic Marine living Resources (CCAMLR) requires that Australia's fishing activities in the Subantarctic and Antarctic Southern Ocean avoids unsustainable impacts to the ecosystem and biodiversity. As Australia uses bottom fishing methods, including demersal trawls and longlines to target Patagonian toothfish and mackerel icefish in this region there is the potential to impact upon benthic habitats. However, understanding the scale of disturbance caused by Australia's bottom fishing activities in the deep Southern Ocean is hampered by a paucity of data, theory and procedures. This project set out to address these issues by developing tools to allow such an assessment, with a focus on the fishery that has operating since 1997 targeting Patagonian toothfish and mackerel icefish in the EEZ around Heard Island and the McDonald Islands (HIMI).\n\nA significant output of this project was the development of a versatile camera system which was successfully deployed on trawls and longlines during commercial and research fishing activities in the EEZ at HIMI, BANZARE Bank and East Antarctica. It revealed for the first time the in situ nature and extent of demersal longline interactions in the deep ocean, as well as revealing the types of habitats and organisms on the seafloor where fishing takes place. This information, combined with comprehensive effort data from the fishery and scientific sampling of the types and abundance of organisms living on the seafloor across a range of depths and seafloor features, enabled the development of an assessment model to estimate the amount of disturbance caused by the fishery.\n\nThis assessment indicates that the great majority of vulnerable organisms live on the seafloor in depths less than 1200 m. This range overlaps with the depths targeted by the trawl fishery, and to a lesser extent by the longline fishery. However due to the fact that the majority of trawling has focussed on a few relatively small fishing grounds, less than 1.5% of all the biomass in waters less than 1200 m are estimated to have been damaged or destroyed. Furthermore, the HIMI Marine Reserve, established in 2003, is estimated to contain over 40% of the biomass of the groups of benthic organisms considered as most vulnerable to bottom fishing at HIMI. Overall, an estimated 0.7% of the seafloor area within the EEZ at HIMI has had some level of interaction with bottom fishing gear between 1997 and 2013.\n\nThe results of this project provide a process for assessing the levels of disturbance by bottom fishing which complements the existing processes that have been developed recently to conduct the Ecological Risk Assessment for the Effects of Fishing (ERA-EF) in other Commonwealth fisheries, as well as measures being developed by CCAMLR to avoid significant adverse impacts to vulnerable marine ecosystems. \n\nData\nThese data are commercial in confidence and therefore embargoed. They are located at aad\\files\\ERM and access is controlled due to the commercial in confidence nature of the raw data. There are approximately 1 TB of data and metadata including videos (.avi and .mov formats), spreadsheets (.xls), databases (.mdb), R scripts and data files (.r and .rdata) and documents (.txt. and .doc). They are organised into folders which broadly map onto the chapters in the final report, with subfolders for data, analyses and text development.", "links": [ { diff --git a/datasets/ASAC_1189_benthic_database_1.json b/datasets/ASAC_1189_benthic_database_1.json index b8ee4f1efb..4e44871b04 100644 --- a/datasets/ASAC_1189_benthic_database_1.json +++ b/datasets/ASAC_1189_benthic_database_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1189_benthic_database_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project aims to assess the vulnerability of and risks to habitats in Australian fisheries in the Australian Exclusive Economic Zone (EEZ)/Australian Fishing Zone (AFZ) of the Southern Ocean to impacts by different demersal gears - trawl, longline and traps. The project which is a collaborative initiative between the Australian Antarctic Division (AAD), the Australian Fisheries Management Authority (AFMA), industry and research partners, and substantially funded by the Fisheries Research and Development Corporation, was developed in order to resolve outstanding questions relating to the potential impacts and sustainability of demersal fishing practices in the AFZ at Heard Island and the McDonald Islands (HIMI). It will also help resolve similar outstanding questions for other fisheries in the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) in which Australian industry participates and provide technology for use in other fisheries to address similar questions.\n\nThe proposed project will assess the degree to which demersal gears interact with and possibly damage benthic habitats. It will also assess the degree to which these habitats might be damaged within the AFZ in the HIMI region. The project is not intended to estimate rates of recovery of benthic habitats following damage by demersal gears. However, information from the literature on rates of recovery of different benthic species and habitats will be used to assess the risks of long-term sustainability of these habitats.\n\nObjectives\nTo develop deep sea camera technologies that can be easily deployed during fishing operations, to facilitate widespread observations of demersal fishing activities (trawl, longline and trap) and their interactions with benthic environments.\n\nTo assess the vulnerability of benthic communities in Sub-Antarctic (Australian AFZ) and high latitude areas of the Southern Ocean (Australian EEZ) to demersal fishing using trawls, long-lines or traps, using video and still camera technologies.\n\nTo assess the risk of demersal fishing to long-term sustainability of benthic communities in these areas, based on the assessment of vulnerability and information from the literature on potential recovery of benthic species and habitats.\n\nTo recommend mitigation strategies by avoidance or gear modification, where identified to be needed, and practical guidelines to minimise fishing impacts on benthic communities.\n\nTarget Outcomes\n1. Assessment of the vulnerability of benthic habitats and species to damage by demersal fishing practices, based on field observations and experiments.\n\n2. Assessment of risks from demersal fishing to the sustainability of benthic habitats based on field work and knowledge from the literature on recovery of different types of benthic species and habitats.\n\n3. Modifications, as needed, to either fishery management or fishery practices in the HIMI and/or other Southern Ocean fisheries resulting in long-term sustainability of benthic habitats.\n\n4. Improved knowledge of the distribution and species composition of marine benthic ecosystems in the Australian EEZ.\n\n5. Video and still camera technologies that can be easily used by AFMA Observers and marine research institutions (both domestic and international) investigating the interactions of demersal gears (trawls, longlines and traps) with benthic environments.\n\nNotes from the Word document written by Kirrily Moore:\n\nThe original core of the database (ie the taxa tree) was copied from a similar taxonomic database at CSIRO Marine Research in late 2005. At the time I was just starting to sort the benthic samples obtained in the cruise Southern Champion 26 (SC26) which formed the main part of the assessment of the conservation values of the HIMI Conservation Zones. There wasn't a database immediately available and applicable to the species or taxa I was likely to encounter so we (Tim Lamb and I) sourced the taxa tree and all the taxonomic hierarchy from CSIRO as a starting point. Tim then designed the forms and tables for the cruise, haul and sample details based on the existing FishLog database. There are many species in the taxa tree which are not Antarctic or sub-Antarctic, they were simply already in the taxa tree when we obtained the sanctioned copy. The database is a work in progress which has developed as Tim has responded to my requests for changes. The demands of the database have changed in the last few months as we've been working through the backlog of invertebrate taxa in the freezer. It has extended from the original cruise (SC26) to many cruises and thus now includes pelagic invertebrates more commonly associated with fishing gear (rather than purely benthic taxa collected in beam trawls and benthic sleds).\n\nThe download file includes an access database and a word document detailing some information about the database. A folder containing photos that needs to be associated with the database is also available, but as it is over 3 GB in size, it is not available as a download, but will be available on request to the AADC (once this dataset is publicly available).\n\nTaken from the 2009-2010 Progress Report:\nProject objectives:\n\n1/ To develop deep sea camera technologies that can be easily deployed during fishing operations, to facilitate widespread observations of demersal fishing activities (trawl, longline and trap) and their interactions with benthic environments.\n\n2/ To assess the vulnerability of benthic communities in Sub-Antarctic (Australian AFZ) and high latitude areas of the Southern Ocean (Australian EEZ) to demersal fishing using trawls, long-lines or traps, using video and still camera technologies.\n\n3/ To assess the risk of demersal fishing to long-term sustainability of benthic communities in these areas, based on the assessment of vulnerability and information from the literature on potential recovery of benthic species and habitats.\n\n4/ To recommend mitigation strategies by avoidance or gear modification, where identified to be needed, and practical guidelines to minimise fishing impacts on benthic communities.\n\nProgress against objectives:\n1/ Progress against objective 1 is well advanced. Underwater camera system units have been developed, refined and are currently deployed on commercial vessels fishing in the subantarctic.\n\n2/ Progress against objective 2 is well advanced. Underwater camera system units, beam trawls and benthic sleds have been used to assess the types and distribution of benthic habitats in the sub-Antarctic and in high latitude areas of the Southern Ocean. Theoretical and empirical analyses of the resistance of key habitat-forming benthic invertebrates to impact from demersal fishing gear is ongoing. This will form the basis of an assessment of the vulnerability of the various habitat types to demersal fishing operations.\n\n3/ Progress against objective 3 is ongoing. Theoretical analysis of the resilience of key habitat-forming benthic invertebrates to impact from varying levels of demersal fishing pressure is ongoing. Analysis of current fishing effort and future fishing scenarios is ongoing. The risk of fishing to the sustainability of benthic communities in these areas will be assessed from their vulnerability to impact, their resilience or ability to recovery from impact, and from current and potential future patterns of demersal fishing.\n\n3/ Progress against objective 4 is ongoing. Analysis of in-situ video footage of commercial and simulated demersal fishing operations captured with the underwater camera systems, with reference to factors such as depth, habitat type, wind, sea-state, current and gear configuration is revealing strategies for mitigating and minimising the impact of demersal fishing.", "links": [ { diff --git a/datasets/ASAC_1200_1.json b/datasets/ASAC_1200_1.json index 84c573e5b5..a78f9547cd 100644 --- a/datasets/ASAC_1200_1.json +++ b/datasets/ASAC_1200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim of the study was to characterise the genetic biodiversity of populations of the copepod Paralabidocera antarctica and the cladoceran Daphniopsis studeri in the Australian Antarctic Territory. Sampling was finalised during November and December 2000.\n\nDaphniopsis studeri were sampled from freshwater lakes in the Vestfold and Larsemann Hills, and from small ponds on Heard Island. Paralabidocera antarctica were collected from saline lakes, fjords and embayments around the Vestfold Hills.\n\nEach population was analysed at 16 allozyme loci using cellulose acetate electrophoresis. Allozyme data were recorded as multilocus genotypes for each individual. The observed number of multi-locus genotypes were tested against expected values to determine whether populations of Daphniopsis studeri reproduce by obligate or cyclic parthenogenesis. Geographic genetic structure of the crustacean populations was assessed using genetic distance measures and cluster analysis. Local and regional gene flow was estimated using Fst and multivariate statistics.\n\nBy using genetic tools to measure indirectly dispersal and gene flow among populations with each species, we hope to reconstruct the history of these species in Antarctica and to determine the relative significance of historical versus contemporary ecological conditions.", "links": [ { diff --git a/datasets/ASAC_1207_1.json b/datasets/ASAC_1207_1.json index 6711ad9f4b..450a2630eb 100644 --- a/datasets/ASAC_1207_1.json +++ b/datasets/ASAC_1207_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1207_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected ASAC Project 1207\nSee the link below for public details on this project.\n---- Public Summary from Project ----\nProject title: 'Effects of variability in ocean surface forcing on the properties of SAMW and AAIW in the South Indian Ocean'\n\nThis project will study the formation and subduction processes and the properties of Antarctic Intermediate Water and Sub-Antarctic Mode Water as simulated by an Ocean General Circulation model, with particular reference to the South Indian Ocean. The study will attempt to determine how its formation and properties are affected by interannual variations in SST and wind forcing and by differing prescriptions of mixing and convection processes occurring in mid-to high latitude oceanic frontal regions of the Southern Ocean. The investigation of the ocean response in the Indian Ocean will profit from the use of a model employing general orthogonal coordinates and efficient variable resolution grids which are global but concentrated in the Indian sector.\n\nFrom the abstracts of the referenced papers:\n\nThis article considers how some of the measures used to overcome numerical problems near the North Pole affect the ocean solution and computational time step limits. The distortion of the flow and tracer contours produced by a polar island is obviated by implementing a prognostic calculation for a composite polar grid cell, as has been done at NCAR. The severe limitation on time steps caused by small zonal grid spacing near the pole is usually overcome by Fourier filtering, sometimes supplemented by the downward tapering of mixing coefficients as the pole is approached; however, filtering can be expensive, and both measures adversely affect the solution. Fourier filtering produces noise, which manifests itself in such effects as spurious static instabilities and vertical motions; this noise can be due to the separate and different filtering of internal and external momentum modes and tracers, differences in the truncation at different latitudes, and differences in the lengths of filtering rows, horizontally and vertically. Tapering has the effect of concentrating tracer gradients and velocities near the pole, resulting in some deformation of fields. In equilibrium ocean models, these effects are static and localised in the polar region, but with time-varying forcings or coupling to atmosphere and sea ice it is possible that they may seriously affect the global solution. The marginal stability curve in momentum and tracer time-step space should have asymptotes defined by diffusive, viscous, and internal gravity wave stability criteria; at large tracer time steps, tracer advection stability may become limiting. Tests with various time-step combinations and a flat-bottomed Arctic Ocean have confirmed the applicability of these limits and the predicted effects of filtering and tapering on them. They have also shown that the need for tapering is obviated by substituting a truncation which maintains a constant time step limit rather than a constant minimum wave number over the filtering range.\n\nContinuous and finite difference forms of the governing equations are derived for a version of the Bryan-Cox-Semtner ocean general circulation model which has been recast in orthogonal, transversely curvilinear coordinates. The coding closely follows the style of the Geophysical Fluid Dynamics Laboratory modular ocean model No. 1. Curvilinear forms are given for the tracer, internal momentum, and stream function calculations, with the options of horizontal and isopycnal diffusion, eddy-induced transport, nonlinear viscosity, and semiimplicit treatment of the Coriolis force. The model is designed to operate on a rectangular three-dimensional array of points and can accomodate reentrant boundary conditions at both 'northern' and 'east-west' boundaries. Horizontal grid locations are taken as input and need to be supplied by a separate grid generation program. The advantages of using a better behaved and more economical grid in the north polar region are investigated by comparing simulations performed on two curvilinear grids with one performed on a latitude-longitude grid and by comparing filtered and unfiltered latitude-longitude simulations. Resolution of horizontally separated currents in Fram Strait emerges as a key challenge for representing exchanges with the Arctic in global models.\n\nIt is shown that a global curvilinear grid with variable resolution is an efficient way of providing a high density of grid points in a particular region. In equilibrium experiments using asynchronous time steps, this type of grid has been found to allow a better representation of smaller-scale features in the high-resolution region while maintaining contact with the rest of the World Ocean, provided that lateral mixing coefficients be scaled with grid size so as to maintain marginal numerical stability. In this study, the region of interest is the southern Indian Ocean and, in particular, that of the South Indian Ocean Current. In all experiments, decreased viscosities and diffusivities were found to control tracer gradients on isopycnals but not isopycnal slopes, while thickness diffusivities controlled isopycnal slopes but only to a small degree tracer gradients. Changes to mixing coefficients in the coarse part of the grid had hardly any influence on the frontal properties examined, although they did affect currents in the Indian Ocean to some extent via their control on size of the Antarctic Circumpolar Current and the Pacific-Indian Throughflow.", "links": [ { diff --git a/datasets/ASAC_1208_1.json b/datasets/ASAC_1208_1.json index 462eadb8b5..c17105b127 100644 --- a/datasets/ASAC_1208_1.json +++ b/datasets/ASAC_1208_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1208_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Because of the inaccessibility of the deep-ocean floor, our knowledge about the composition and structure of the oceanic crust is very limited. Macquarie Island is the only fragment of ocean crust exposed above sea-level in the world, providing a unique opportunity to study the ocean crust directly in unprecedented detail.\n\nFrom the abstract of the referenced paper:\n\nMacquarie Island preserves largely in-situ Miocene oceanic crust and mantle formed at a slow-spreading ridge. The crustal section on the island does not conform to a simple 'layer cake pseudo-stratigraphy', but is the result of multiple magmatic episodes. Macquarie Island crust did not grow by top-down cooling, but rather from the base up. Peridotites cooled first and formed the basement into which gabbro plutons were intruded. This was followed by cooling and deformation, and by intrusion of dykes that fed a sheeted dyke-basalt complex. Finally, lava filled grabens were formed. These relative age relations rule out simple co-genetic relations between rock units.", "links": [ { diff --git a/datasets/ASAC_1212_1.json b/datasets/ASAC_1212_1.json index 8030ed95b8..f8b31610e0 100644 --- a/datasets/ASAC_1212_1.json +++ b/datasets/ASAC_1212_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1212_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1212.\nSee the link below for public details on this project.\n---- Public Summary from Project ----\nThis project aims to improve ship-based sea-ice thickness measurements made using an electromagnetic induction device by performing a theoretical analysis of the sensitivity of the electromagnetic instrument to factors such as instrument height and orientation, ice conductivity and thickness, and seawater conductivity. The results of the theoretical study will be used to assist the interpretation of an existing sea-ice thickness data set from the Mertz Glacier polynya cruise (V1, 1999/2000). \n\nThe data set consists of the results of numerical modelling of the response of the EM31 electromagnetic instrument to typical one- and three-dimensional sea ice structures. One-dimensional model calculations were performed using software written specifically for the project. Three-dimensional model calculations were performed using Marco_air version 2.3, written by Z. Xiong and A. Raiche, CSIRO Mathematical Geophysics Group. Technical descriptions of this program are given in the preceding References section.\n\nThe download file below contains some numerical output from the models, as well as a detailed description of the models used.", "links": [ { diff --git a/datasets/ASAC_1214_NISM_2001_1.json b/datasets/ASAC_1214_NISM_2001_1.json index 5382ab9526..22841f3f3f 100644 --- a/datasets/ASAC_1214_NISM_2001_1.json +++ b/datasets/ASAC_1214_NISM_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1214_NISM_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Infrared sky brightness data for 2.4 microns above the South Pole during the Austral winter of 2001.\n\nProvided, as a function of time, are:\n\n(i) the best estimate, plus the lower and upper bounds, on the 2.4 micron sky flux (depending on assumptions made),\n\n(ii) the optical depth calculated at 2.4 microns,\n\n(iii) the effective atmospheric temperature determined from the sky flux and optical depth,\n\n(iv) the ground temperature, as measured by the South Pole weather station\n\nThe Antarctic plateau provides the best terrestrial location for infrared and submillimetre astronomy. The best sites lie within the Australian Antarctic Territory. From them the most sensitive observations of the faint light from distant stars and galaxies could be made. This program aims to determine where the best site is, and quantify the gains that would be achievable compared to temperate latitude observatories. Our method is to deploy an autonomous observatory, the AASTO, at various sites on the plateau and gather data remotely over the Antarctic winter on the atmospheric conditions that affect the conduct of astronomy.\n\nFile gives data for the University of New South Wales Near Infrared Sky Monitor (NISM) operated at the South Pole during winter 2001. Prepared by Jon Lawrence (jl@phys.unsw.edu.au) June 2002\n\nThe fields in this dataset are:\nDate: yyyyMMDDhh\nFlux: Sky Spectral Brightness (in micro Jy/ arsec^2) at 2.4 microns\nFluxM: Minimum value of the Sky Spectral Brightness\nFluxP: Maximum value of Sky Spectral Brightness\ntau: atmospheric optical depth\ntemp: atmospheric temperature calculated from flux and tau (deg C)\ngtemp: ground temperature from South Pole meteorological records", "links": [ { diff --git a/datasets/ASAC_1215_Mawson_Stinear_1.json b/datasets/ASAC_1215_Mawson_Stinear_1.json index 6fa89a0da8..f9f4faea24 100644 --- a/datasets/ASAC_1215_Mawson_Stinear_1.json +++ b/datasets/ASAC_1215_Mawson_Stinear_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1215_Mawson_Stinear_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Crustal Cross-Sections along the Mawson Escarpment and Mount Stinear, Southern Prince Charles Mountains (East Antarctica): Correlating the Ruker Complex across the Lambert Glacier.\n \nFrom the attached paper:\n \nPrevious workers in the southern Prince Charles Mountains have described geologic similarities between Mount Stinear and the southern Mawson Escarpment. At each location, quartzite, metconglomerate and garnet-staurolite-kyanite metapelite are intercalated with various bodies of felsic orthogneiss of probable Archaean age. In this contribution, we present detailed geologic cross-sections of the Mawson Escarpment and Mount Stinear arising from fieldwork completed during the 2002-03 Prince Charles Mountains Expedition of Germany and Australia (PCMEGA). A correlation of the Ruker Complex across the Lambert Glacier appears tenable using the mapping results together with the available geochronological and aeromagnetic data from both areas.", "links": [ { diff --git a/datasets/ASAC_1216_1.json b/datasets/ASAC_1216_1.json index 1745ff5fcf..9ced002fa2 100644 --- a/datasets/ASAC_1216_1.json +++ b/datasets/ASAC_1216_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1216_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preliminary data set contains details of cores processed (eg. sample name/interval, dry weights, reactions, notes) and the methodology used. The future data set will document diatoms observed and counted for each sample. This project also has links to ASAC project 1044 (ASAC_1044), the Wilkes Land Glacial History (WEGA) project.\n\nWEGA cores from both the continental shelf (PC7,11,12) and slope (PC19, 20, 21) region have been silica-selectively processed for their diatom content (see methodology file for details). The slides are mounted for quantitative assessment. Details of the cores that have been prepared are listed in the file WEGA_cores.csv in the downloadable dataset.\n\nAdditional silica-selective slides of the surface sediments on the continental shelf were processed for quantitative assessment. Details of the samples and their locations are listed in the file sediment_samples.csv in the downloadable dataset.\n\nThere have been no publications from the prepared slides as of the 15/04/02. An unpublished Honours Thesis uses limited diatom counts from slides prepared from PC 12. (Ms J. Erbs 2001). Title to be forthcoming.\n\nAny further enquiries referring to sample availability, curation, additional samples or publications arising from this material should be directed to Dr L. Armand.\n\nThe fields in this dataset are:\ndry weight (gm)\nSol A* reaction\nHCL reaction\nfinal dilution\nnotes\ncm depth\nbottom depth\nlatitude\nlongitude\ncore depth", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_CD_02_1.json b/datasets/ASAC_1219_AAT_APen_CD_02_1.json index f8e17c2a6d..9d9d565982 100644 --- a/datasets/ASAC_1219_AAT_APen_CD_02_1.json +++ b/datasets/ASAC_1219_AAT_APen_CD_02_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_CD_02_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diana Patterson carried out a census of Adelie Penguins and flying birds at Cape Denison in November, December 2002 at the request of Dr Eric Woehler.\nDiana was at Cape Denison as part of a Mawson's Huts Foundation expedition.\n\nThe data include:\n1 - Sketches of colony boundaries and nest locations and annotations on a map by Diana.\nThe map was provided to Diana by Eric and showed bird colonies resulting from an earlier survey by Jim and Yvonne Claypole: refer to the metadata record 'Cape Denison Adelie Penguin census, November - December 1999'.\nGIS data with the polygon (colony) and point (nest) data has been created.\n\n2 - Documents with notes and counts.\nThese data and the data from other bird surveys at Cape Denison are being analysed by Eric and he and Diana intend to publish a paper.\nThe data will then be released.\n\nThese data have been incorporated into ASAC project 1219 (ASAC_1219).", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_CD_97_1.json b/datasets/ASAC_1219_AAT_APen_CD_97_1.json index 1bb741e220..2f9f851771 100644 --- a/datasets/ASAC_1219_AAT_APen_CD_97_1.json +++ b/datasets/ASAC_1219_AAT_APen_CD_97_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_CD_97_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Penguin counts conducted between 25 November and 2 December 1997. The census covered the following areas and rookeries in the Cape Dension area: rookeries north of Gadget Hut, outside Greenholm Hut and both sides of harbour, Penguin Knob, Azimuth Hill, Memorial Hill, Lands End Ridge, below Sorensen's Hut, east of Sorensen's Hut. A total of 24542 penguins were censused for the Cape Denison area, excluding McKellar Island Rookeries.\n\nThe fields in this dataset are:\n\nArea\nRookery locations\nNumber", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_CD_99_1.json b/datasets/ASAC_1219_AAT_APen_CD_99_1.json index 7c731ed724..03fd8c9715 100644 --- a/datasets/ASAC_1219_AAT_APen_CD_99_1.json +++ b/datasets/ASAC_1219_AAT_APen_CD_99_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_CD_99_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguin census November - December 1999 by Jim and Yvonne Claypole following their winter at Cape Denison. \nA shapefile with the colony boundaries is available but counts are not available.\n\nOn 2 March 2016 David Smith of the Australian Antarctic Data Centre contacted Jim Claypole to see if he and Yvonne still had a copy of the counts as the Australian Antarctic Data Centre does not have a copy of the counts. Jim and Yvonne recall emailing the results of their survey to the Australian Antarctic Division soon after returning to Australia after wintering at Cape Denison in 1999. On 11 April 2016 Jim Claypole advised David that unfortunately they had not been able to find any record of their survey and they didn't have emails from that time.", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_D_73_1.json b/datasets/ASAC_1219_AAT_APen_D_73_1.json index 74f789cba1..ece19ba473 100644 --- a/datasets/ASAC_1219_AAT_APen_D_73_1.json +++ b/datasets/ASAC_1219_AAT_APen_D_73_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_D_73_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) along the Vestfold Hills coast (including colonies on the mainland and offshore islands) during November 1973. The data are obtained from counts at the colonies and black and white photographs. Some aerial photographs were taken at Davis in 1981-82 and 1987-88, and will be compared to the results of this survey. The results are listed in the documentation. A total of 174178 26127 breeding pairs were counted. An increase in Adelie penguin population was found at most locations in East Antarctica.\n\nData from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219).", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_M_1.json b/datasets/ASAC_1219_AAT_APen_M_1.json index 938693b8f0..275cfc75a9 100644 --- a/datasets/ASAC_1219_AAT_APen_M_1.json +++ b/datasets/ASAC_1219_AAT_APen_M_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_M_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) in the Mawson area, Antarctica during 1981 and 1988. The data are obtained from aerial photographs obtained at various times, during the 1981-82 and 1988-89 seasons. The results are listed in the documentation. Comparisons are made with census data collected in the 1971-72 summer.\n\nData from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219).", "links": [ { diff --git a/datasets/ASAC_1219_AAT_APen_M_Area_1.json b/datasets/ASAC_1219_AAT_APen_M_Area_1.json index 628bc8cad8..17bc22a09e 100644 --- a/datasets/ASAC_1219_AAT_APen_M_Area_1.json +++ b/datasets/ASAC_1219_AAT_APen_M_Area_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_APen_M_Area_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The relationship between colony area and population density of Adelie Penguins Pygoscelis adeliae was examined to determine whether colony area, measured from aerial or satellite imagery, could be used to estimate population density, and hence detect changes in populations over time. Using maps drawn from vertical aerial photographs of Adelie Penguin colonies in the Mawson region, pair density ranged between 0.1 and 3.1 pairs/m2, with a mean of 0.63 - 0.3 pairs/m2. Colony area explained 96.4% of the variance in colony populations (range 90.4 - 99.6%) for 979 colonies at Mawson. Mean densities were not significantly different among the 19 islands in the region, but significant differences in mean pair density were observed among colonies in Mawson, Whitney Point (Casey, East Antarctica) and Cape Crozier (Ross Sea) populations. \n\nThis work was completed as part of ASAC project 1219 (ASAC_1219).\n\nThe fields in this dataset are:\n\nIsland\nLatitude\nLongitude\nDate\nColony area\nBreeding Pairs\nBreeding Pairs per square metre\nArea per nest\nNumber of nests\nNumber of adults", "links": [ { diff --git a/datasets/ASAC_1219_AAT_Img_C_90_1.json b/datasets/ASAC_1219_AAT_Img_C_90_1.json index a047448494..7fa5e74b64 100644 --- a/datasets/ASAC_1219_AAT_Img_C_90_1.json +++ b/datasets/ASAC_1219_AAT_Img_C_90_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_AAT_Img_C_90_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Population data derived from counts of penguins on aerial photographs taken at 500m altitude. Odbert Island colonies were not photographed as the island is within ASPA 103. 23 flight runs were made. Census data for Whitney Point and Shirley Island were included in Woehler et al 1994. Census data for the remaining islands will be incorporated into a regional synthesis following aerial photography planned for 2004/05.\n\nPenguins were counted from the photographs by eye.\n\nThis project was instigated under a one off ASAC project (ASAC_234), but has now been subsumed under ASAC_1219.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_Img87_2.json b/datasets/ASAC_1219_HIMI_Img87_2.json index 8cc92888fb..07556abfcd 100644 --- a/datasets/ASAC_1219_HIMI_Img87_2.json +++ b/datasets/ASAC_1219_HIMI_Img87_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_Img87_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs of Heard Island taken by 1987/88 ANARE.\n\nThe information below was taken from the ANARE report prepared after the Heard Island visit.\n\nPHOTOGRAPHIC ACTIVITIES, 1987/88.\n\nEric J. Woehler\n\nMovie and aerial photography were undertaken during the 87/88 visit.\n\nAERIAL PHOTOGRAPHY\n\nThe aerial photography was limited to two days, concurrent with the resupply visit by MV Nella Dan during Voyage 2, 18 and 19 October 1987.\n\nA Linhoff camera was mounted in one (HRK) of the two Hughes 500D helicopters on board and two 100 metre rolls of 70mm film were exposed. The first roll was exposed at Spit Island and North and South Spit on 18 October. The second roll was exposed around the Four Bays region, Saddle Point, Hoseason Beach and the archaeological site at Sealers Corner, Corinthian Bay on 19 October. Details of each roll are given in Table 1.\n\nTable 1. Details of aerial photography (M = magnetic)\n\nExposures Height Bearing Subject\n\nRoll 1 18 October 1987\n\n001-050 300' 130 degrees M Spit Island (harems)\n051-102 300' 310 degrees Spit Island (harems)\n103-139 300' 120 degrees Spit Island (harems)\n140-175 300' 310 degrees Spit Island (harems)\n176-252 500' 310 degrees Spit Point towards Dovers Moraine (South Spit)\n253-303 400'-500' 300 degrees Spit Point towards Spit Camp (North Spit)\n\nRoll 2 19 October 1987\n\n020-117 040'-700' 330 degrees M Archaeological site, Sealers Corner Corinthian Bay rising from 040' to 700' (Exp 070) then down to 500-550'\n118-124 200' Seal harems on Walrus Beach, Atlas Cove.\n125-132 250' Flight path following coastline. Atlas Cove.\n133-136 350' Flight path following coastline. Atlas Cove.\n137-143 300' Seal harems on West Bay beach\n144-165 280'-300' 000 degrees Seal harems on South West Bay beach\n166-174 280'-300' 000 degrees Seal harems on South West Bay beach\n175-231 300'-250' Seal harems on Corinthian Bay beach flight path following coastline.\n232-247 300' 300 degrees Seal harems on Saddle Point saddle\n248-254 300' 100 degrees Seal harems on Saddle Point saddle\n255-257 300' 300 degrees Seal harems b/w Saddle Point and Challenger Glacier\n258-271 250' 300 degrees Seal harems on Hoseason Beach\n272-276 200' 300 degrees Seal harems on Hoseason Beach\n277-285 350' Atlas Cove Station area\n\nThe aerial photography at the eastern Heard Island allowed for the harems on Spit Island to be censused as the island is otherwise inaccessible. Photography of the harems on North and South Spit and the Four Bays region provided the opportunity of verifying ground counts made on the same day. All exposures were made at approximately 60 knots ground-speed. Flying heights were generally confined to below 350' at Atlas Cove by a low cloud cover and to 500' at Spit by strong winds at higher altitudes.\n\nFlight lines and photo centres representing the Roll 1 aerial photography (Film ANTC1082) are included in the aerial photography data available for download (see provided URL) and have Qinfo = 760. The flight lines and photo centres are provided as shapefiles and Qinfo is an attribute of the shapefiles. \nSee the Quality field for comments about the flight lines and photo centres.\n\nThe Australian Antarctic Division (AAD) has prints of some Roll 1 frames. They were stitched together, and glued to a presentation board but have since been separated from the board and stored in an archival box. Print = Y in the attribute table of the photo centres shapefile indicates there is a print for that frame. The prints are not available for loan outside the AAD. \nContact the Australian Antarctic Data Centre for access to the prints:\nhttps://data.aad.gov.au/aadc/requests/", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_Pen_1.json b/datasets/ASAC_1219_HIMI_Pen_1.json index 5d44a120e6..18aa55bb0b 100644 --- a/datasets/ASAC_1219_HIMI_Pen_1.json +++ b/datasets/ASAC_1219_HIMI_Pen_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_Pen_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains information on the distribution of Penguins and their breeding colonies on Heard Island, as of 1983. It forms Australia's contribution to the International Survey of Antarctic Seabirds (ISAS). The results are listed in the documentation. These include counts of chicks, adults and nests, as well as colony distribution maps. The Heard Island survey includes King Penguins, Gentoo Penguins, Macaroni Penguins, Rockhopper Penguins and Chinstrap Penguins. This dataset is a subsection of the whole dataset, which surveys the Australian Antarctic Territory, Heard, McDonald and Macquarie Islands.\n\nOriginal data were taken from ANARE Research Notes 9.\n\nOnly data from the Heard and McDonald Islands are described in this metadata record.\n\nImages of rough maps detailing the locations of each of the colonies are available for download from the provided URL. Observation and count data have been incorporated into the Australian Antarctic Data Centre's Biodiversity Database.\n\nThe data are presented in the format of Croxall and Kirkwood (1979) as recommended by the Report of the Subcommittee on Bird Biology held in Pretoria. In the tables all counts are estimates of the number of breeding pairs except where otherwise indicated. The numerical estimates and counts are of three kinds, indicated by the coded N, C or A:\n\nNESTS (N = count of NESTS or breeding/incubating pairs) The most accurate count of breeding pairs is that derived from a count of nests. This is usually carried out during incubation, but may also be made while chicks are still in the nest, before creches are formed. Such counts are only underestimates of breeding pairs by the number of breeding failures sustained between egg laying and the date of the count.\n\nCHICKS (C = count of CHICKS)\nLate in the breeding season the only counts possible are those of chicks. In general most pygosceild penguins raise one chick per pair per season, so a count of chicks gives a reasonable approximation of the original number of breeding pairs. However, season to season variation in breeding success can often be considerable. For example Yeates (1968) reports breeding success in Adelie Penguins at Cape Royds of twenty-six per cent, forty-seven per cent and sixty-eight per cent ever three seasons. Also, Macaroni Penguins only raise approximately 0.5 chicks per pair per season, so that chick counts of this species may be a considerable underestimate of the true breeding population.\n\nADULTS (A = count of ADULTS)\nMany colony counts and estimates were expressed as total number of birds or adults. These figures are difficult to interpret as they depend on the time during the breeding season at which they were made. For some days prior to and until laying is finished, both birds of a pair will be present at the nest site while during incubation it is more likely that only one bird will be present. A further problem with counts of 'birds' is that they may include individuals who are not breeding and this gives an overestimate of the true breeding population. The counts of 'birds' or 'adults' which appear unqualified in log books have been divided by two to give an estimate of the number of breeding pairs. It must be stressed therefore that these counts are the least accurate.\n\nThe degree of accuracy of these counts is inevitably highly variable and it is often difficult to ascertain on what basis a figure was arrived at. For the present survey counts have been allocated to one of five degrees of accuracy. \n\n1. Pairs/nests essentially individually counted. The count is probably accurate to better than + 5 per cent.\n\n2. Numbers of pairs in a known area counted individually and knowing the total area of the colony, the overall total calculated. This technique is useful for very large colonies.\n\n3. Accurate estimates; + 10-15 per cent accuracy.\n\n4. Rough estimate; accurate to 25-50 per cent.\n\n5. Guesstimate; to nearest order of magnitude.\n\nMany references are in the form ANARE (Johnstone) or simply ANARE. These refer to unpublished reports extracted from ANARE station biology logs. Those in the form Budd (1961) refer to published records and are listed in the references at the end of this publication.\n\nThe locations of some colonies are indicated on maps. Place names that (as of 1983) have not yet been approved are shown in the tables and on the maps in parentheses, for example: (ROCKERY ISLAND).", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_Pet_1.json b/datasets/ASAC_1219_HIMI_Pet_1.json index abe4a2ecd7..793d7b85e0 100644 --- a/datasets/ASAC_1219_HIMI_Pet_1.json +++ b/datasets/ASAC_1219_HIMI_Pet_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_Pet_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surveys were conducted at the eastern and western ends of Heard Island during the 1987/1988 season. Burrow densities in different habitat types (vegetated and unvegetated) were determined from fixed width transects. Extensive areas at both ends of the island were surveyed and detailed information was obtained on distribution and abundance on 4 species of burrowing petrels.\n\nThis work was completed as part of ASAC project 451 (ASAC_451).\n\nThis work also falls under the umbrella project, ASAC 1219 (ASAC_1219).", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_archaeology_1.json b/datasets/ASAC_1219_HIMI_archaeology_1.json index 2598568028..a0108c6553 100644 --- a/datasets/ASAC_1219_HIMI_archaeology_1.json +++ b/datasets/ASAC_1219_HIMI_archaeology_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_archaeology_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two excel spreadsheets of archaeological data/sites from Heard Island. Compiled by Eric Woehler from his, and others, work in February of 2004.\n\nThe spreadsheets contain:\n\nLocations\nSite Names\nDescriptions\nOrigin\nLatitude\nLongitude\nComments", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_artefacts00-01_1.json b/datasets/ASAC_1219_HIMI_artefacts00-01_1.json index c2ef37e18d..f6515f6a38 100644 --- a/datasets/ASAC_1219_HIMI_artefacts00-01_1.json +++ b/datasets/ASAC_1219_HIMI_artefacts00-01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_artefacts00-01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Artefacts and miscellaneous items located on Heard Island by Eric Woehler (AAD) during the 2000/01 field season.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_artefacts03-04_1.json b/datasets/ASAC_1219_HIMI_artefacts03-04_1.json index 41207cd72a..022070ef95 100644 --- a/datasets/ASAC_1219_HIMI_artefacts03-04_1.json +++ b/datasets/ASAC_1219_HIMI_artefacts03-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_artefacts03-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data were collected by GPS survey by Eric Woehler (AAD) using a Garmin 12XL receiver (not differentially corrected) and represent artefacts and miscellaneous items located during the 2003/2004 field season.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_seabirds00-01_1.json b/datasets/ASAC_1219_HIMI_seabirds00-01_1.json index 0c3d1d7784..e88560711e 100644 --- a/datasets/ASAC_1219_HIMI_seabirds00-01_1.json +++ b/datasets/ASAC_1219_HIMI_seabirds00-01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_seabirds00-01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A GPS survey of seabirds on Heard Island during the Australian Antarctic Program's 2000/01 expedition.\nThis layer is stored as two datasets (polygon and point) in the Geographical Information System (GIS).\nPolygon data represent flying bird and penguin colony extents.\nPoint data represent nest locations and the location of the observation point for flying birds and penguins.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_seabirds03-04_1.json b/datasets/ASAC_1219_HIMI_seabirds03-04_1.json index 6765916ace..5b253695dd 100644 --- a/datasets/ASAC_1219_HIMI_seabirds03-04_1.json +++ b/datasets/ASAC_1219_HIMI_seabirds03-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_seabirds03-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A GPS survey of seabirds on Heard Island during the Australian Antarctic Program's 2003/04 expedition.\nThis layer is stored as two datasets (point and polygon) in the Geographical Information System (GIS). Data represent flying bird and penguin colony extents and nesting sites.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_topo00-01_1.json b/datasets/ASAC_1219_HIMI_topo00-01_1.json index fcc672586e..7a0c5f1eb9 100644 --- a/datasets/ASAC_1219_HIMI_topo00-01_1.json +++ b/datasets/ASAC_1219_HIMI_topo00-01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_topo00-01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Topographic features surveyed by GPS during the Australian Antarctic Program's 2000/01 expedition on Heard Island.\nPoint data includes the location of volcanic cones, graves, landing areas, automatic weather stations, refuges, summits, boulders, named features and trig points.\nLine data include lava edges and a walking track.", "links": [ { diff --git a/datasets/ASAC_1219_HIMI_topo03-04_1.json b/datasets/ASAC_1219_HIMI_topo03-04_1.json index 2c2d9d34d1..de39f10212 100644 --- a/datasets/ASAC_1219_HIMI_topo03-04_1.json +++ b/datasets/ASAC_1219_HIMI_topo03-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1219_HIMI_topo03-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Topographical data collected as part of a GPS Survey during the Australian Antarctic Program's 2003/04 expedition on Heard Island.\nThe data are available for downloading as a spreadsheet. The locations include 'Clay Pipe Creek', an unofficial locality name used since the 1980s. The creek was mapped to provide spatial reference for this name. A line representing this creek is available as a shapefile.", "links": [ { diff --git a/datasets/ASAC_1220_1.json b/datasets/ASAC_1220_1.json index 38e90f805c..d534565f74 100644 --- a/datasets/ASAC_1220_1.json +++ b/datasets/ASAC_1220_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1220_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detailed sedimentary information and palaeontological samples were collected from Battye Glacier Formation, of the Pagodroma Group in the Prince Charles Mountains, an area where little information is presently available. The mid to Upper Cenozoic Pagodroma Group provides direct evidence for past changes in climate and glacial environments from deep within the Antarctic continent. Evidence from several geological formations in the Pagodroma Group, many of them fossil-bearing, will help to determine the history of fluctuations in climate and the size of the East Antarctic Ice Sheet (EAIS). This will provide baseline data to help validate the predictive numerical models of ice sheet dynamics. There is a clear need to study the response of the EAIS to past times of global warming. Periods of significance include times when atmospheric CO2 levels were similar to today (Poore and Sloan 1996). Another key time interval is during the late Neogene, prior to the development of Northern Hemisphere glaciation, which has largely governed Antarctic Ice Sheet volume changes during the Quaternary (Clapperton and Sugden 1990; Mabin 1990; Huybrechts 1990, 1992).\n\nAn important aspect of the research is to build onto the geological data-set collected by ODP Leg 119, 120 and 188 in Prydz Bay. These operations have concentrated on the periphery of Antarctica and, therefore, record ice sheet retreat and advance at its outer-limits. The Pagodroma Group provides significant information about ice sheet variation at its the inner reaches. Together, these data-sets will shape our understanding of major fluctuations of the ice sheet through the Cenozoic, and will assist and test the models developed to predict ice sheet behavior in the future. Direct geological evidence for climatic conditions and the extent of the ice sheet during times of glacial retreat can be obtained only from onshore geological records, such as the Pagodroma Group. This is important given the current warming trends, expected ice sheet retreat and global sea-level rise, and general lack of geological data from onshore Antarctica for predicting the effects of this on the EAIS.\n\nFieldwork was conducted during November - December (2000). A number of significant findings were made from the Amery Oasis:\n1) New outcrops of the glacio-marine Battye Glacier Formation were located and mapped. Up to 800 m of geological section was logged and sampled. Similar Antarctic records have only been made available through expensive international drilling efforts around the Antarctic shelf. This project highlights that there are extensive records exposed on land, that can be studied for a fraction of the cost of off-shore marine geoscience.\n2) Unique diatomaceous marine mudstone deposits were discovered (~9 m thick). This is the most diatomaceous (up to 12% biogenic silica), in situ marine deposit that has have been found from inland Antarctica. Diatom biostratigraphy indicates that the formation is middle - late Miocene in age.\n3) In situ and articulate marine mollusc fossil horizons were discovered. These occur over a lateral distance of ~ 1km and provide undisputable evidence for a major ice sheet retreat in the past.\n4) Three erratics containing marine mollusc fossils were discovered. These erratic are potentially Cretaceous in age (Stilwell, pers. comm.). This is the first marine sediment of this age found in the Lambert Graben catchment.\n\nEleven pdf figures are available for download from the provided URL. Also included is a text file which explains what each of the figures are. Furthermore, two excel spreadsheets of data are also available.\n\nThe two excel spreadsheets in the download directly relate to the paper Whitehead, et al (2003).\n\nSome explanatory notes for the excel files are:\n\nQualitative assessment of fossil preservation\nvf = very fragmented with a few intact specimens seen per traverse of a microscope slide.\nmf = moderately fragmented with an intact specimen seen every few fields of view (at 600x magnification).\n\nSee Whitehead et al (2003) for more information.\n\nQualitative fossil abundance, where\nX = (present) one valve (Diatom valves)/fossil seen during entire examination.\nR = (rare) greater than 3 valves/fossils seen during all microscope traverses on slide.\nF = (few) greater than 1 valve/fossil per 10 microscope fields of view (at 600x magnification).\nC = (common) valves/fossils in each microscope field of view (at 600x magnification).\n\nThe fields in this dataset are:\n\nStratigraphic Intervals\nSamples\nOpal%\nMcLeod Beds\nBed A\nclasts\nFossil Preservation\nBenthic Diatom Abundance\nSpecies\nBardin Bluffs Formation\nFisher Bench Formation\nDiatoms", "links": [ { diff --git a/datasets/ASAC_1223_1.json b/datasets/ASAC_1223_1.json index 4191ebc5db..a36f3e7365 100644 --- a/datasets/ASAC_1223_1.json +++ b/datasets/ASAC_1223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic lake cores record a history of precipitation in the preservation of climate sensitive microbial communities. Comprehensive integration of our precipitation records with other climate proxies such as ice core temperature records and historical climate data are dependent upon accurate dating of this lake sediment.\n\nFourteen lakes and ponds of the Windmill Islands were sampled in 1998 for diatoms and in 1999 for water chemistry. The waterbodies included in this study fall into one of 3 broad categories: saline lake (greater than 5m deep; greater than, or equal to, 3 parts per thousand - salinity), saline pond (less than 5m deep; greater than, or equal to, 3 parts per thousand - salinity) or freshwater pond (less than 5m deep; less than 3 parts per thousand - salinity). \n\nSaline Lakes\nBeall Lake, the largest lake on Beall Island, is situated in a rocky catchment with evidence of breeding penguin pairs nearby. Outflow into the small lake on the northwestern point of Beall Lake occurs at elevated lake levels. \n\nHoll Lake, the largest lake on Holl Island, is contained by ridges to the NE and SW with an obvious outflow to the SE. At the time of sampling (20 Dec 1998), penguin feathers were observed in the sediment. In 2001 large numbers of penguins were observed behind the NE ridge in addition to the numerous skuas nesting on most nearby peaks.\n\nLake A is the westernmost lake on Browning Peninsula. This large closed saline lake has a very thick ice cover (~2.5 m) and very little evidence of birdlife.\n\nLake M is the easternmost lake sampled on Browning Peninsula. This large closed saline lake had a very thick ice cover (3.0 m) at the time of sampling.\n\nSaline Ponds\nLake Warrington is the largest waterbody on Warrington Island. Found in the centre of Warrington Island, this small shallow (1.9 m) saline pond was almost completely frozen (ice cover of 1.6 m), with ca. 0.3 m of water below the ice at the time of sampling. The lake catchment is muddy with runoff towards Robertson Channel (to the NE) and the ice cover showed signs of sediment entrapment giving a gritty texture.\n\nLake G is located on northeastern Peterson Island. This very saline (greater than 60 ppt) shallow (1.0 m) pond was almost completely frozen (ice cover of 0.8 m), with ca. 0.1 m of water below the ice at the time of sampling. Lake G is close to breeding penguin sites and there was a noticeable discolouration of the surface water at the time of sampling.\n\nLake I is the easternmost of the three sites visited on southern Peterson Island. This shallow (0.3 m) saline pond is very close to breeding penguin sites and was sampled by hand as the ice cover (0.1 m) was almost as thick as the lake depth.\n\nLake K is approx. 400 m to the west of Lake I on central southern Peterson Island. This completely frozen saline pond is also very close to breeding penguin sites.\n\nLake L is the southernmost pond sampled on Peterson Island. This almost completely frozen shallow (~0.8 m/0.8 m ice cover) saline pond is very close to breeding penguin sites with noticeable discolouration of the top ca. 0.2 - 0.3 m of water at the time of sampling.\n\nFreshwater Ponds\nLake B, a shallow (0.9 m) freshwater pond, is located on the western side of Browning Peninsula, approx. 500 m to the south of Lake A.\n\nLake C is a shallow (1.0 m) freshwater pond in the central valley of Browning Peninsula.\n\nLake D is a shallow (0.5 m) freshwater pond in the central valley of Browning Peninsula approx. 500 m to the north of Lake C. This lake was sampled by hand as the ice cover (~0.5 m) was almost as thick as the lake depth.\n\nLake E is a shallow (3.1 m) freshwater pond in the central valley of Browning Peninsula approx. 250 m to the north of Lake D.\n\nLake F is the northernmost pond sampled from the central valley of Browning Peninsula. This freshwater pond is approx. 500 m to the north-west of Lake E.\n\nThe sediment/species samples were collected in November and December 1998, the water samples were collected in December 1999.\n\nThe fields in this dataset are:\n\nLake Name\nCode\nLocation\nLatitude\nLongitude\nLake Depth\nIce Depth\nWater Sample\nSalinity\nLake Area\nCatchment\nElevation\nNitrite\nNitrate\nSilicon\nPhosphate\npH\nSpecies\n\nThe numbers given in the species spreadsheet are for percentage abundance, ie the relative abundance of each species in the community.", "links": [ { diff --git a/datasets/ASAC_1224_1.json b/datasets/ASAC_1224_1.json index b2a7e72df6..b87fe6d54e 100644 --- a/datasets/ASAC_1224_1.json +++ b/datasets/ASAC_1224_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1224_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1224\nSee the link below for public details on this project.\n---- Public Summary from Project ----\nIce core drilling is used to extract information from glacial ice, which aids in the reconstruction of past climatic conditions, and produces proxy records of environmental parameters. The main aim of this project is to produce a record of methanesulphonic acid (MSA) over the last 1-2 decades, and investigate links with biological activity, and with sea-ice extent, in the Prydz Bay region. To achieve this, ice core records will be extracted from the Amery Ice Shelf. This work will also compliment the important Law Dome results by improving spatial coverage of high-resolution climate records and will contribute to the International Trans Antarctic Scientific Expedition (ITASE).\n\nObjectives:\n\nTo obtain ice cores for high-resolution environmental and climatic signals from the Prydz Bay region.\n\nTo produce a record of Methanesulphonic acid (MSA) over the 1-2 last decades, and to investigate teleconnections with indicators of biological activity from the Prydz Bay region, and links with sea-ice extent.\n\nOther parameters will also be determined (including major trace ions, oxygen isotopes and hydrogen peroxide) to define the dating and assist in interpretation of the MSA record.\n\nSix shallow cores (~5m) have been collected between 1999 and 2002 (G2, A506, AM1, AM2, AM3, and AM4). Data for two of these cores are currently archived in the Australian Antarctic Data Centre (G2, A506).", "links": [ { diff --git a/datasets/ASAC_1227_1.json b/datasets/ASAC_1227_1.json index e591a9bcfc..7e3e41b6da 100644 --- a/datasets/ASAC_1227_1.json +++ b/datasets/ASAC_1227_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1227_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project used computer-based modelling and existing field data to analyse the production and cycling of dimethylsulphide (DMS) and predicted its role in climate regulation in the Antarctic Southern Ocean.\n\nFrom the Final Report:\n\nAims\n(i) To calibrate an existing dimethylsulphide (DMS) production model in a section of the Antarctic Southern Ocean.\n\n(ii) To use the calibrated model to investigate the effect of GCM-predicted climate change on the production and sea-to-air flux of DMS under current and enhanced greenhouse climatic conditions.\n\n(iii) To provide regional assessments of the sign and strength of the DMS-climate feedback in the Southern Ocean. Characteristics of Study Region:\n\nOur study region extends from 60-65 degrees S, 123-145 degrees E in the Antarctic Southern Ocean, and was the site of a major biological study in the austral summer of 1996 (Wright and van den Enden, 2000). Field observations show that a short-lived spring-summer bloom event is typical of these waters (El-Sayed, 1988, Skerratt et al. 1995); however there can be high interannual variability in the timing and magnitude of the bloom (Marchant and Murphy, 1994). The phytoplankton community structure has been described by Wright and van den Enden (2000), who report maximum chlorophyll (Chl) concentrations during January-March in the range (1.0-3.4) microgL-1. During this survey, macronutrients did not limit phytoplankton growth. Thermal stratification of the mixed layer was strongly correlated with high algal densities, with strong subsurface Chl maxima (at the pycnocline) observed. The mixed layer depth determined both phytoplankton community composition and maximum algal biomass. Coccolithophorids (noted DMS producers) were favoured by deep mixed layers, with diatoms dominating the more strongly stratified waters. Pycnocline depth varied from 20-50 m in open water. Algal abundance appeared to be controlled by salp and krill grazing.\n\nField data support the existence of seasonal DMS production in the Antarctic region. However, a large range in DMS concentrations has been reported in the open ocean , reflecting both seasonal and spatial variability (Gibson et al., 1990, Berresheim, 1987; Fogelqvist, 1991). Blooms of the coccolithophores, and prymnesiophytes such as Phaeocystis, form a significant fraction (~23%) of the algal biomass (Waters et al 2000). Concentrations of DMS in sea ice are reported to be very high (Turner et al. 1995) and may be responsible for elevated water concentrations during release from melt water (Inomata et al. 1997). Field measurements of dissolved DMS made in the study region have been summarised by Curran et al. (1998). DMS concentrations were variable in the open ocean during spring and summer (range: 0-22 nM), with the higher values recorded in the seasonal ice zone and close to the Antarctic continent. Zonal average monthly mean DMS in the study region have been estimated by Kettle et al. (1999).\n\n(See downloadable full report for reference list).\n\nA copy of the referenced publication is also available for download by AAD staff. It contains the modelling information.", "links": [ { diff --git a/datasets/ASAC_1228_Terrestrial_1.json b/datasets/ASAC_1228_Terrestrial_1.json index 6196e1b8a3..fca92b3c73 100644 --- a/datasets/ASAC_1228_Terrestrial_1.json +++ b/datasets/ASAC_1228_Terrestrial_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1228_Terrestrial_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria were isolated at 4 degrees C, with oil as their sole carbon and energy source, from the soils collected at the locations below.\nMany of the bacteria will be identified using 16S rRNA analysis, as well as by tradational taxonomic methods.\nThe degradation pathways and the genes involved in the bioremediation of oil will be determined for these bacteria.\nThis can potentially be used to speed up the rate of bioremediation at low temperatures.\nThe differences in the community structures between oil-contaminated and pristine sites around the Windmill Islands will also be determined.\n\n2005-05-30\nUnfortunately all samples were lost when an incubator malfunctioned. No data were collected from this project.", "links": [ { diff --git a/datasets/ASAC_1233_1.json b/datasets/ASAC_1233_1.json index 432d466f25..a17e68b363 100644 --- a/datasets/ASAC_1233_1.json +++ b/datasets/ASAC_1233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increased ultraviolet radiation (UV-A and/or UV-B) may impact on the establishment and structure of a shallow water benthic invertebrate assemblages. A global experiment in more than 8 countries, using an identical methodology (transparent UV filters) will add significantly to our understanding of the impacts of anthropogenically induced global change on natural systems. \n\nTo appraise the effects of increased UVR on shallow marine benthic assemblages, five experimental rafts were deployed in protected bays west of Shirley Island near Casey Station, Antarctica (66.16oS 110.30oE). Each raft consisted of eight experimental units, each of which contained a colonization panel (ceramic tile) positioned horizontally and submerged 4-6 cm underwater. Irradiation treatments were randomly assigned to each unit with the use of UV cut-off filters. The treatments were as follows: No UVR (transmits photosynthetically active radiation or PAR, 400-700nm), No UVB (transmits PAR + UVA, 320-700nm), Perspex (full spectrum, 280-700nm, procedural control), or No filter (full spectrum, treatment control). In addition there were three levels of consumer treatments: With consumption (container sides removed), without consumption (container sides perforated with 4 mm holes), and a control (3 sides perforated, 1 side removed). After seven weeks tiles were removed to the laboratory for examination. All tiles were dominated by diatoms and no sessile invertebrates were apparent. \n\nThe first trial has been completed, but several other panels are still in place. A conference will be held in early 2002 between participating countries to discuss results and findings.\n\nThe 2001\\2002 summer season consisted of experimental designs divided up into three separate projects.\nThe aims were all to provide a corrollary to the previous seasons data.\nProject 1 consisted of the extraction and redeployment of settlement depth arrays situated in Geoffrey's Bay and Hollin Island Channel. Due to prevailing weather conditions resulting in limited boating hours and diving program, only one array was retrieved. On inspection of the array it was decided to deploy further replicates to gain a better temporal understanding of the communities.\nProjects 2 and 3 consisted of a similar experimental design, however monitoring the shallower depths of settlement (depths of 1m and 2m below sea level) for a period of one month. Project 2 consisted of arrays with two depths and 2 panels per depth, triple replicated, under the icesheet in O'Brien Bay and Shirley Channel, with a substrate depth of 20m. Diatom samples are to be analyzed in Australia. Project 3 was of a similar design to project 2 though it was measuring recruitment in shallow open water. The two sites consisted of Noonan Cove and Geoffrey's Bay at substrate depths of 5m. These tiles are also to be analyzed on return to Australia.\n\nThere were 5 rafts used in this study - they are listed as R1 to R5 there were two factors in the design -(i) predator access: Caged (C) Half caged (H) and Open (O) and ii) UV exposure: Perspex (P), Macrolon (M), No filter (N) and Film + perspex (F).\n\nA list of the diatoms found on the settlement panels is provided at the URL below.\n\nThe fields in this dataset are:\nSpecies\nSample", "links": [ { diff --git a/datasets/ASAC_1236_1.json b/datasets/ASAC_1236_1.json index a82f6e13e1..ae17d4bae4 100644 --- a/datasets/ASAC_1236_1.json +++ b/datasets/ASAC_1236_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1236_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1236\nThe dataset will include snow accumulation time series 1980-2000, with major ion chemistry, stable oxygen isotopes.\n\n ---- Public Summary from Project ----\nThis is a US-Australian collaborative project to investigate climate variability over the last few decades in George V Land, East Antarctica. The project will produce data on the recent history of atmospheric circulation associated with the Antarctic Circumpolar Wave (ACW) and the El Nino Southern Oscillation (ENSO) in the Southern Ocean sector of East Antarctica. The proxy climate data on frequency of precipitation events, snow accumulation rates, and moisture source, will be combined with instrumental meteorological records to understand the mechanisms controlling the interaction between atmospheric circulation between south-east Australia and Antarctica. The project will form a component of the International Trans Antarctic Scientific Expedition (ITASE).", "links": [ { diff --git a/datasets/ASAC_1242_2.json b/datasets/ASAC_1242_2.json index a1ab7e3e92..925482accb 100644 --- a/datasets/ASAC_1242_2.json +++ b/datasets/ASAC_1242_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1242_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1242\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThis project will undertake preliminary assessment of Southern Ocean squid stocks. Squids will be collected by jigging and light trapping off research vessels in the region of Macquarie Island and other selected locations where the opportunity arises. Little is known about squid biology in the Pacific and Indian sectors of the Southern Ocean. This project will help to provide initial basic biological data on the squid species present.\n\n18 squid we caught on-board the Aurora Australis in November, 2001.\n\nAll were caught 200-300 kms south of Tasmania, by a hand-held squid jig, at latitude 47 South at a depth of 1m.\n\nAll samples caught on the 5/11/01 have the code QA/AA/80/01. There was no code written for others caught on 3/11/01.\n\nThe fields in this dataset are:\n\nSpecies\nDate\nMantle length (mm)\nWeight (g)\nSex\nMaturity\nGonad weight (g)\n\nSee also the metadata record for ASAC project 1340 (ASAC_1340), Squid in the antarctic and subantarctic, their biology and ecology.", "links": [ { diff --git a/datasets/ASAC_1243_1.json b/datasets/ASAC_1243_1.json index 60963eb0fb..d7b07b5631 100644 --- a/datasets/ASAC_1243_1.json +++ b/datasets/ASAC_1243_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1243_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is using ultramafic xenoliths - samples of the mantle beneath Heard Island to study the formation and evolution of the Kerguelen-Heard Plateau. Such plateaus are thought to represent an important stage in the formation of continents over the earths history. Additionally, new evidence suggests the Kerguelen Plateau may contain ancient continental fragments, which are possible remnants of Gondwana.\nUltramafic xenoliths were collected from Heard Island during field season 2000 from 6 locations, 5 of which were previously unreported. Petrological and geochemical information is being used to characterise the mantle beneath Heard Island, and Re-Os isotopic dating is being used to constrain the formation age of the plateau. Initial results indicate the mantle beneath Heard Island is highly depleted in basaltic components. The presence of reaction textures containing small amounts of carbonate in some xenoliths is an indication of possible carbonatitic metasomatism, which has been observed for similar samples from Kerguelen.\n\nThe fields in this dataset are:\nMU Number (Macquarie University Catalogue Number)\nSample Number\nLocation\nLatitude\nLongitude\nDescription", "links": [ { diff --git a/datasets/ASAC_1246_1.json b/datasets/ASAC_1246_1.json index cda85ed638..ddde296974 100644 --- a/datasets/ASAC_1246_1.json +++ b/datasets/ASAC_1246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tasman International Geospace Environment Radar (TIGER) is an over-the-horizon radar that locates ionospheric structures in the region between Tasmania and Antarctica, measuring their velocities. Compared with other similar (SuperDARN) radars, TIGER is uniquely sited to detect phenomena occurring in the region equatorward of the normal auroral oval. This project exploits this advantage to study the physical processes generating phenomena that are poorly understood, such as sub-auroral convection flows and the regeneration of the plasmapause after magnetic storms. Results are important for developing improved space weather predictions necessary, for example, for communications and satellite navigation systems.\n\nThe data comes in two forms DAT files and FIT files. The FIT files are reasonably versatile, and smaller, and are stored online, but have not yet made them available outside IPS.\n\nData are available on request to IPS (Kehe Wang), and will be made available via FTP.\n\nThe radar has been operating since November 1999.\n \nProject objectives: The project objectives, as stated in the project application round 2008/09, appear below:\nThe fundamental objective of the TIGER project is to study and understand, crucial outstanding problems in the physics of the outermost part of our environmental envelope (Geospace) - problems that are of scientific importance and fundamental to improving space weather predictions. The TIGER radar will be used to investigate:\n\n1. the dynamics of the auroral oval;\n2. sub-auroral convection flows;\n3. the dynamics of the plasmapause during sub-storm activity;\n4. coupling between the dayside and nightside of the magnetosphere;\n5. generation of travelling ionospheric disturbances (TIDs) and their propagation to, and energy deposition at, mid-latitudes over Australia;\n6. the development of reliable prediction/nowcasting techniques of real benefit to Australian HF users;\n7. mesospheric winds;\n8. the sea state.\n\nTIGER will provide real-time data for:\n\n1. mesospheric winds above the Southern Ocean region;\n2. real-time maps of ionospheric convection in the southern hemisphere;\n3. surface wind direction across the Southern Ocean.\n\nAn important new aspect of this proposal is the addition of the second TIGER radar, TIGER/Unwin, that is located near Invercargill, New Zealand, and which will begin operations late 2004. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nThe TIGER radar at Bruny Island has operated continuously for over 95% of the time during the last year. Special campaigns are run for approximately one week each month during SuperDARN Discretionary Time periods, the remainder of the time TIGER operates in synchronism with all other SuperDARN radars during SuperDARN Common Time periods.\n\nData is now transferred via satellite from Bruny to the La Trobe TIGER data server. Data is still backed up on hard disk at the radar site until storage and backup on the TIGER server is confirmed.\n\nDuring Discretionary time special campaign modes have been run to study PMSE, sea-state, micropulsations, and the scattering processes that generate the ionospheric echoes detected by the TIGER radars. Many of these campaigns have been coordinated with similar operations of the TIGER Unwin radar located in NZ and some used the phasing box that swings the TIGER Bruny radar footprint over Macquarie Island to give coincidence measurements with the ionosonde and magnetometer instruments at Macquarie Island.\n\nFor the 2008-09 summer, arrangements were again made for the all the southern hemisphere SuperDARN radars to operate a Common Time mode most suited to the detection of PMSE. This is particularly important for collaborative PMSE studies with the VHF radar and other instruments at Davis.\n\nSeveral major research projects have been completed and the work published and presented at national and international scientific meetings. This includes projects conducted by other members of the TIGER consortium who are not investigators on this specific project.\n\nIn the past year results have been published or reported at conferences on the dynamics of the auroral oval; coupling from the nightside magnetosphere to aurora in the Harang discontinuity region, sub-auroral convection flows. Other science topics listed in the objectives have been studied and reported in previous years. \n\nTaken from the 2009-2010 Progress Report:\nPublic summary of the season progress\nThe Tasman International Geospace Environment Radar (TIGER) is a dual, over-the-horizon radar system that locates ionospheric structures, meteors and sea echoes in the region between Tasmania-NZ and Antarctica. In the last year observations of motions in the auroral and sub-auroral ionosphere have been used to study the evolution of space weather systems and to identify the causes of phenomena not previously understood. Results contribute to improving space weather predictions necessary, for example, to support communications, satellite navigation systems such as GPS and the Jindalee Over-The-Horizon Radar system that provides surveillance of Australia's coastline.", "links": [ { diff --git a/datasets/ASAC_1250_1.json b/datasets/ASAC_1250_1.json index 2cca9a4064..15ee101c11 100644 --- a/datasets/ASAC_1250_1.json +++ b/datasets/ASAC_1250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nThis project is designed to provide an understanding of the interactions between krill, other zooplankton, the physical environment and the predators dependent on krill. This will directly address a number of pressing problems facing CCAMLR (the Commission for the Conservation of Antarctic Marine Living Resources) in its attempts to manage the krill fishery using an 'ecosystem approach'.\n\nExpected outcomes:\nAs a result of logistic operations (i.e. diversion to Casey) the 29 days on site allocated to this work was reduced to 10 days. Hence only a fraction of the intended program of work was conducted.\n\nAcoustics:\nAcoustics data (for 38, 120, 200kHz) was collected for the top 250m of the water column for nine and a half of the planned 13 transects in our 60 x 60 nautical mile survey region.", "links": [ { diff --git a/datasets/ASAC_1251_1.json b/datasets/ASAC_1251_1.json index a5439187f0..56eb6e0e9c 100644 --- a/datasets/ASAC_1251_1.json +++ b/datasets/ASAC_1251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1251 See the link below for public details on this project.\n\n---- Public Summary from Project ----\nThe aim of this study is to develop spatial GIS models of fur seal foraging density over the Kerguelen Plateau that will enable a rapid assessment method for identifying areas of high conservation value for Marine Protected Area planning and management. These models will be based on data on fur seal foraging densities in the HIMI region, and oceanographic data on bathymetry, sea-surface temperature and ocean colour (primary productivity).\n\nFrom the abstract of the referenced paper:\n\nWe investigated the spatial and temporal distribution of foraging effort by lactating Antarctic fur seals Arctocephalus gazella at Heard Island using satellite telemetry and time-depth recorders. Two principal diving types were identified: 'deep' dives averaging 48.6 m, and 'shallow' dives averaging 8.6 m. Discriminant function analyses were used to assign dives based on their depth and duration. Generalised linear mixed-effects models of night dives (greater than 80% of all dives) indicated both spatial and temporal effects on the distribution of deep and shallow dives. Deep dives were more common in the deeper shelf waters of the Kerguelen Plateau, and these dives predominantly occurred after sunset and before sunrise. In contrast, shallow dives were more common in slope waters on the southeastern margin of the Kerguelen Plateau in the hours either side of local midnight. We suggest that these 2 distinct diving types reflect the targeting of channichthyid (deep dives) and myctophid (shallow dives ) fish, and are indicative of spatial and temporal differences in the availability of these 2 important prey groups. We also identified 3 distinct behavioural dive groups (based on multidimensional scaling of 19 diving and foraging trip parameters) that also differed in their spatial distribution and in their relative importance of deep and shallow dives. The present study provides some of the first evidence that diving strategies are not only influenced by where foraging takes place, but also when.\n\nThe fields in the campaign_41_tracks.csv file are:\n\ncampaign_id (the campaign identifier: aadc_campaign_41)\nanimal_id (the identifier of the individual animal)\nscientific_name (scientific name: Arctocephalus gazella)\nptt_id (the identifier of the PTT device on the animal. Note that individual PTT devices were deployed multiple times on different animals)\ndeployment_location (the location of deployment: Spit Bay, Heard Island))\ndeployment_longitude (longitude of deployment location)\ndeployment_latitude (latitude of deployment location)\nobservation_date (the date of observation, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ. This information is also separated into the year, month, day, etc components)\nobservation_date_year (the year of the observation date)\nobservation_date_month (the month of the observation date)\nobservation_date_day (the day of the observation date)\nobservation_date_hour (the hour of the observation date)\nobservation_date_minute (the minute of the observation date)\nobservation_date_second (the second of the observation date)\nobservation_date_time_zone (the time zone of the observation date)\nlatitude (the latitude of the observed position, in decimal degrees)\nlongitude (the longitude of the observed position, in decimal degrees)\nlocation_class (the Argos location class of the observed position: one of (in increasing order of accuracy) B,A,0,1,2,3)\ntrip (the trip number of the animal)\nat_sea (whether the observed position occurred at sea)\ncomplete (whether the complete trip was recorded)\n\n\nThe fields in the campaign_41_supplementary.csv file are:\n\nanimal_id (the identifier of the individual animal)\nbehavioural_dive_group (1 = deep; 2 = shallow-active; 3 = shallow)\ndeparture_date (date of departure of the animal on the trip)\ndeparture_mass (mass of the animal on departure, in kg)\nstandard_length (standard length of the animal, in cm)\ntrip_duration (duration of the trip, in days)\ndive_rate (dives per hour)\nnight_dive_rate (dives per hour)\nmean_dive_duration (in seconds)\nproportion_time_submerged\nproportion_night_time_submerged\nproportion_dives_in_bouts\nmean_number_dives_per_bout\nproportion_dives_at_night\nvertical_depth_travelled_per_hr_of_night (in m)\nproportion_vertical_depth_dived_at_night\nvertical_depth_travelled_per_day (in m)\nmean_dive_depth (in m)\nmean_depth_deep_dives (in m)\nmean_depth_shallow_dives (in m)\nproportion_night_shallow_dive_duration\nmaximum_distance (in km)\nheading (in degrees) \n\n", "links": [ { diff --git a/datasets/ASAC_1252_1.json b/datasets/ASAC_1252_1.json index 8d324b3c1c..e511167cd2 100644 --- a/datasets/ASAC_1252_1.json +++ b/datasets/ASAC_1252_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1252_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1252 See the link below for public details on this project.\n\nCurrently three datasets are attached to this metadata record. Dive data collected in 1988, track data from adult birds collected in 1994 and track data from fledglings collected in 1995.\n\nDive data are available in Microsoft Word format, while the track data are available in Microsoft Excel format.\n\nA readme file (txt) is included in each download file to explain column headings, etc.\n\n---- Public Summary from Project ----\nTo breed successfully the winter-breeding emperor penguins must fatten on two occasions: once before the onset of moult in January, and again prior to the commencement of the new breeding season in March. Interference with the capacity of the penguins to fatten in summer might be detrimental to the their breeding performance and survival later on in winter. This study seeks to determine the likely impact of commercial fishing operations on emperor penguin colonies at the Mawson Coast. More specifically, the data pertains to the locations of emperor penguins when fattening prior to the moult, and prior to the new breeding season.\n \nProject objectives:\n1. To determine the extent and location of foraging areas of post-breeding adult Emperor penguins in summer.\n3. To determine the extent and locations of foraging areas of fledgling Emperor penguins on their first trip to sea.\n4. To identify interseasonal and interannual variations in foraging areas in conjunction with changes in seaice conditions and compare these with results from different colonies.\n5. To survey the coastline of the AAT to verify the existence (or non-existence) of Emperor penguin colonies.\n\nEmperor penguins are icons of Antarctic wildlife and their conservation is of paramount interest to the wider community. They are also key consumers of marine resources in several areas and consequently there is great potential for interactions between feeding penguins and harvesting of fish and krill. Emperor penguins are one of the few species to breed on the fast ice (although there are three known land-based colonies, one of which has all but ceased to exist in recent years). Thus, the breeding habitat of Emperor penguins is subject to direct alteration as a result of climate change. Colonies of Emperors are found across a wide latitudinal range, from deep in the Ross Sea to the tip of the Antarctic Peninsula. This range includes breeding areas where significant changes in seaice are not (yet?) thought to be occurring to areas where seaice is changing rapidly. Accordingly, studies at multiple locations will provide valuable clues on how this species will be affected by a warming Antarctic. Additionally, Emperor penguins are large animals that live in a relatively small number of discrete locations. It is therefore more than feasible, using an international effort, to study an entire species and to make some predictions about their response to a warming world and to current and future fishing practices. This project aims to make the first steps towards an overall conservation assessment of Emperor penguins through studies in several locations around the Antarctic continent. Should these attempts be successful, then a more ambitious international project will be launched to take a species-wide perspective.", "links": [ { diff --git a/datasets/ASAC_1257_2.json b/datasets/ASAC_1257_2.json index cbc0bc750d..93db5f531b 100644 --- a/datasets/ASAC_1257_2.json +++ b/datasets/ASAC_1257_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1257_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "With a population of about 2 million pairs macaroni penguins are the most abundant penguin in the HIMI region. These birds feed on mesopelagic fish and, to a lesser extent, mackerel icefish. Despite their great abundance and comparatively proximate links in the food chain to the toothfish fishery, virtually nothing is known about the foraging ecology of macaroni penguins at HIMI. This will identify which regions of the ocean Macaroni penguins use as foraging areas, and in combination with diet studies quantify the potential for competition with fisheries operations in the HIMI region.\n\nThe data are stored in a csv excel file.\n\nThe fields in this dataset are:\n\nLatitude\nLongitude\nDate\nDirection\nRange\nSpeed\nBearing", "links": [ { diff --git a/datasets/ASAC_1261_1.json b/datasets/ASAC_1261_1.json index 319610129a..e838cb510a 100644 --- a/datasets/ASAC_1261_1.json +++ b/datasets/ASAC_1261_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1261_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Computer models have been developed to investigate the present and future balance of ice accumulation and loss for the Antarctic ice sheet. New information about the flow properties of ice has been included to improve models. Comparisons with field observations and projections for sea-level and climate change will be investigated.\n\nThis metadata record is a parent metadata record for several child metadata records. The child records contain information on:\n\n1) Balance ice fluxes for the Antarctic Ice sheet\n2) Balance ice velocities for the Antarctic Ice sheet\n\nFor details on the model used, etc, see the child metadata records.\n\nThis work is now part of AAS (ASAC) project 2698.\n\nProject objectives:\nThis multi-strand project aims to improve our understanding of the dynamical system of ice sheet and ice shelves and their place within the global climate system, with the major objectives of quantifying (a) the contribution of Antarctica to sea-level change, past, present and future, and (b) variations in the discharge of fresh-water into the Southern Ocean from ice shelf ocean interactions.\nSpecifically the project will\n- assess the state of mass balance of individual drainage basins of East Antarctica, to improve understanding of the present contribution to global sea level rise\n- improve understanding of the flow properties of ice through modelling, laboratory studies and analysis of remote sensing data, and incorporate this into dynamical models of the Antarctic ice sheet and ice shelves\n- refine our quantitative understanding of the interaction between ice shelves and the underlying ocean and determine the influence of ice shelves on dynamics of the grounded ice sheet and hence on mass budgets and sea level change\n- determine whether, and on what time scale, global warming might lead to irreversible change in the ice sheet-ice shelf system\n- develop models of the ice sheet and ice shelves that can be linked to coupled atmosphere/ocean models (in an Earth Systems Model)\n\nThe project is constructed around the Science Strategy, season workplan, and the goals of the Antarctic Climate and Ecosystems CRC, as discussed under 3.1.2 below.\nThese are long term objectives, and this proposal seeks approval for a five year period culminating in the final round of ACE CRC research milestone/outputs in 2010 - and accordingly a workplan briefly indicating research for future years is included. \n\nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nPatterns of present day ice sheet flows have been contrasted with marine geological evidence of palaeo ice stream flow in the Amundsen Sea region of West Antarctica. Models predicting Antarctic ice sheet thickness have assisted aerogeophysical field programs. Several theoretical models of ice flow have been tested using ice deformation experiments and crystal microstructure measurements. Interpretation of our measured ice velocities for Mertz Glacier tongue is revealing dynamic interactions with surrounding fast ice, with implications for mutual stability. Development of an ice sheet system model continues, aimed at improving predictions of ice sheet evolution and sea level rise. \n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\n- Antarctic Mass Balance\n\nRoberts has improved the algorithm in his Lagrangian ice sheet balance flux computer code, vastly reducing the computational running time. \n\nNew Antarctic balance fluxes have been calculated with the Lagrangian code using the latest available Antarctic ice sheet digital elevation models, including that of Bamber, Gomez-Dans and Griggs (2009) and for ice accumulation fields from a regional atmospheric modelling study (van de Berg, W., M. van den Broeke, C. Reijmer, and E. van Meijgaard, 2006. 'Reassessment of the Antarctic surface mass balance using calibrated output of a regional atmospheric climate model.' J. Geophys. Res. 111, D11104.), as well as earlier accumulation data compilations. \n\nNew computed ice fluxes for the Amundsen Sea sector of West Antarctica advanced our collaboration with Frank Nitsche (Columbia University's Lamont-Doherty Earth Observatory) , exploring the contrasts between present day ice sheet drainage, and paleo-ice sheet ice streams deduced from exploration of submarine troughs across the continental shelf.\n\nWork comparing the new computed balance fluxes to the observed flows in the East Antarctic Ice Sheet to explore patterns of ice sheet mass imbalance at a regional scale is nearing completion.\n\nWarner has also continued collaboration with Jaehyung Yu (Texas A and M University), Hongxing Liu (University of Cincinnati), Kenneth Jezek (Byrd Polar Research Center) and Jiahong Wen (Shanghai Normal University) on the mass balance of the drainage basins that feed the Amery ice shelf. The detailed analyses suggests the ice sheet catchment is in overall positive budget, partially offsetting losses elsewhere in Antarctica, but they also highlight a crucial need for ice flow estimates at the southernmost grounding zone to resolve conflicts with other published estimates.\n\n- Ice Flow Properties\n\nWarner continued to be involved with Prof W. F. Budd in finalizing revision of a major paper connecting ice deformation studies under combined compressive and shear stresses with a simple model for enhanced ice flow proposed for use in ice sheet modeling.\n\nThe investigation of crystal fabrics in glacial and marine ice samples from Amery Ice Shelf bore-holes (led by Adam Treverrow - UTAS) was prepared for publication (joint activity with AMISOR project- AAS 1164).\n\n- Ice shelf - ocean interaction\n\nWarner's mass balance collaboration with Yu, Liu, Jezek and Wen (above) also arrived at new broad-scale estimates of the rates of basal melting and freezing beneath the Amery ice shelf.\n\nBen Galton-Fenzi (UTAS) completed his Ph D thesis on 'Modelling ice shelf ocean interaction'. Warner advised regarding calculation of the accretion of marine ice beneath the Amery ice shelf from the ocean model basal melt/freeze pattern. Galton-Fenzi's results for the Amery ice shelf basal melt/freeze show good general agreement with estimates from glaciological observations when realistic present day climate forcing is applied in the model, and this ocean model development brings capacity to make projections of how ice shelves will respond to climate change much closer. His work also indicates the importance of treating frazil ice processes in marine ice accretion.\n\nWarner and Galton-Fenzi commenced collaboration on coupling the current ACE CRC ice shelf dynamics model with the ROMS subglacial circulation ocean model.\n\n- Ice shelf dynamics\n\nResearch on the dynamics of the floating Mertz Glacier Tongue (MGT) led by Robert Massom continued, particularly regarding the possible stabilising influence on the MGT of a large slab of thick and consolidated landfast multi-year sea ice (\"fast ice\") attached to its eastern edge. To date this has mainly involved interpretation of remote sensing work (also associated with AAS 3024 Remote Sensing of Near-Coastal Antarctic Sea Ice and Its Impacts on Ice Shelves and Ecosystems), but also provides material for future ice shelf modelling work. Ironically, after our study the main, more northerly, section of the MGT calved in February 2010. \n\n- Ice Sheet System model development\n\nProgress continued on developing a next generation \"full stress solution\" model for treating the dynamics of ice sheet, ice stream and ice shelf flow. Roberts has developed a numerical method that allows for the efficient calculation of derivatives for arbitrarily distributed points. This method will be used in the ice-sheet model, allowing the selection of the discretisation grids for numerical solutions to be based on accurate implementation of boundary conditions rather than dictated by requirements for evaluating gradients. In the vertical, the grid will be of terrain following type - but with minimum grid spacing and automatic clustering in areas of high gradients.\n\nIn a separate modelling activity, Roberts has developed a novel scheme for interpolating between ice sheet thickness measurements, typically from Radio Echo Sounding (RES), drawing on ice flow trajectories, ice balance fluxes and earlier thickness inference modelling (Warner, R.C. and W.F. Budd (2000) Derivation of ice thickness and bedrock topography in data-gap regions over Antarctica. Annals of Glaciology, 31. 191-197). The skill of this interpolation scheme has been evaluated using the denser coverage from the first season of RES data gathered by the ICECAP international collaboration (see AAS 3103) over the region south of Casey station, encompassing the Aurora Subglacial Basin and Totten and Denman glacier streams. Warner and Roberts have recently applied this interpolation scheme to the generally sparse publicly available ice thickness data for the entire Antarctic continent, to produce a new view of the broad-scale subglacial landscape. We hope this scheme will be of value in the international effort (BEDMAP 2) to assemble a new ice thickness and bedrock dataset from existing and new IPY-era RES data.", "links": [ { diff --git a/datasets/ASAC_1261_Balance_Code_1.json b/datasets/ASAC_1261_Balance_Code_1.json index 6d315898f0..5aaba0e4ce 100644 --- a/datasets/ASAC_1261_Balance_Code_1.json +++ b/datasets/ASAC_1261_Balance_Code_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1261_Balance_Code_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice Sheet Balance Flux computer code BalanceV2.f\n\nFortran computer source code.\n\nThis program computes the ice fluxes and optionally the ice velocities that would maintain an ice sheet of specified shape (surface elevation) in equilibrium with a prescribed ice accumulation distribution. Inputs: regular orthogonal gridded datasets of ice sheet surface elevation, ice accumulation, and (for velocities) ice sheet thickness.\n\nThe model provides a variety of output fields - including component ice fluxes on a staggered grid, and the magnitude and direction of the ice flux vector field.\n\nThis program (Balance Version 2) was written by Roland Warner at the Cooperative Research Centre for Antarctic and Southern Ocean Environment (Antarctic CRC) University of Tasmania, Hobart AUSTRALIA.\n\nIt implements a variant of the balance flux schemes described in 'A computer scheme for rapid calculations of balance-flux distributions' by W. F. Budd and R. C. Warner, Annals of Glaciology, 23, 21-27. 1996. The specific algorithm is described in more detail in FRICKER, H.A., WARNER R., and ALLISON I. - Mass balance of the Lambert Glacier - Amery Ice Shelf system, East Antarctica: a comparison of computed balance fluxes and measured fluxes, Journal of Glaciology, 46(155), 561-570, 2000.\n\nPlease contact the investigator before use of this code. Further details are provided in 'Use_Constraints'.", "links": [ { diff --git a/datasets/ASAC_1261_Balance_Flux_1.json b/datasets/ASAC_1261_Balance_Flux_1.json index 4a33371ef5..700638bd04 100644 --- a/datasets/ASAC_1261_Balance_Flux_1.json +++ b/datasets/ASAC_1261_Balance_Flux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1261_Balance_Flux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Balance Ice Fluxes for the Antarctic ice sheet.\n\nThese ice fluxes (in km^2/yr)represent the (hypothetical) distribution of ice flux that would keep the Antarctic ice sheet in its present shape (i.e. surface topography), under the influence of a prescribed accumulation distribution.\n\nThe present fluxes were computed using computer code BalanceV2 (by Warner) (outlined in Budd and Warner 1996, and detailed in Fricker, Warner and Allison 2000), using the surface accumulation dataset of Vaughan et al (1999), and the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999).\n\nThis ice flux dataset represents the (hypothetical) distribution of ice flux that would keep the ice sheet topography in its present shape, under the influence of the given accumulation distribution.", "links": [ { diff --git a/datasets/ASAC_1261_Balance_Velocities_1.json b/datasets/ASAC_1261_Balance_Velocities_1.json index d43f404665..fa01993876 100644 --- a/datasets/ASAC_1261_Balance_Velocities_1.json +++ b/datasets/ASAC_1261_Balance_Velocities_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1261_Balance_Velocities_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Balance Ice Velocities for the Antarctic ice sheet.\n\nThese ice velocities (in m/yr) represent the (hypothetical) distribution of depth-averaged ice velocities that would keep the Antarctic ice sheet in its present shape (i.e. surface topography and thickness), under the influence of a prescribed accumulation distribution.\n\nThe present fluxes were computed using computer code BalanceV2 (by Warner) (outlined in Budd and Warner 1996, and detailed in Fricker, Warner and Allison 2000), using the surface accumulation dataset of Vaughan et al (1999), the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999), and the ice sheet thickness compilation distributed by the BEDMAP consortium (Lythe et al 2001).", "links": [ { diff --git a/datasets/ASAC_1263_1.json b/datasets/ASAC_1263_1.json index c6eaae3e45..3cbda85c5a 100644 --- a/datasets/ASAC_1263_1.json +++ b/datasets/ASAC_1263_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1263_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1263 See the link below for public details on this project.\n\n---- Public Summary from Project ----\nThe project will involve making a series of measurements of the ice-sheet topography using GPS static and kinematic procedures so that they can be used to calibrate/validate measurements made from the new generation of satellite geoscience laser altimeter systems (GLAS). The measurements of the ice sheet topography will be made near-simultaneously (within 8-16 days) from both GPS and laser systems (and possibly also from an aircraft laser altimeter) and used to assess the error budgets of the GLAS satellite. The overall goal of the project is to determine the seasonal and interannual variation in surface elevation of the Antarctic ice sheet. This information is essential for predicting future changes in ice volume and sea-level.\n \nSee the documentation provided in the dataset for more information.", "links": [ { diff --git a/datasets/ASAC_1264_1.json b/datasets/ASAC_1264_1.json index ebf8ec7ca4..9989777222 100644 --- a/datasets/ASAC_1264_1.json +++ b/datasets/ASAC_1264_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1264_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected ASAC Project 1264 See the\nlink below for public details on this project.\n\n---- Public Summary from Project---- \nAustralia's subantarctic islands are both precious wilderness areas and a biological resource. This project is establishing a world class Subantarctic House at the Royal Tasmanian Botanical Gardens in Hobart. This centre will be a major tourist and educational attraction informing the community about these rare island ecosystems, and a valuable research facility. This project also aims to develop to the commercial stage, new horticultural and food crops from material collected from the islands. Without damage to our subantarctic island environments and hence maintaining their conservation value, we will develop new industries with these novel plants.\n\nPlants were collected by Dana Bergstrom, Kate Kiefer, Craig Tweedie, Justine Shaw, Tore Pedersen, Tony Orchard and Jim Cane. The plants were mainly collected from Macquarie Island, but some were collected from Heard Island. On Macquarie Island, many plants were collected within the vicinity of the station at the Isthmus - in an arc between Handspike Pt (for Poa littorosa and Carex trifida), up Gadget Gully, across to North Mountain, down Mt Elder and back along the coast. As the plants were only being collected for cultivation back in Tasmania, and for storage in the Royal Tasmanian Botanical Gardens Herbarium, little information about each plant was recorded at the time of collection. Plants were generally collected in a non-scientific manner and often the day before the ship was due to leave.\n\nAn excel spreadsheet detailing which species were collected, an approximate location, the collectors name, whether the plant is still alive in the Royal Tasmanian Botanical Gardens, and their accession numbers at the RTBG is available for download at the URL given below. A word document with some propagation information is also available for download at the same URL.", "links": [ { diff --git a/datasets/ASAC_1265_1.json b/datasets/ASAC_1265_1.json index 2838f9bc7c..a6afa182ec 100644 --- a/datasets/ASAC_1265_1.json +++ b/datasets/ASAC_1265_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1265_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Mapping Division (Geoscience Australia) has produced a Technical Report on the Heard Island Geodesy project (ASAC 1265).\n\nThe main objectives of the 2000 geodetic survey of Heard Island were to upgrade and extend the existing geodetic survey network to give a better coverage of the island and to establish accurate, globally compatible coordinates for all spatial data applications on the Island.\n\nTaken from the Technical Report:\nHeard Island, with an area of 368 km, is the principal island of the Territory of Heard and McDonald Islands. Its major physical feature is Big Ben whose summit, Mawson Peak, is 2745 metres above sea level. Big Ben is an intermittently active volcano with a roughly circular base, some 20 km in diameter, which dominates the shape of the island. Over 80 percent of the island is covered by glacial ice. Coastal cliffs and exposed high rocky beaches around the Island make access from the sea difficult and hazardous.\n\nThe Island is relatively rich in flora and fauna with six major plant communities (tussock grassland, meadow herbfield, pool complex, cushion carpet and fellfield). The indigenous mammals of the Island include seven species of seals. The southern elephant seal is by far the most abundant seal on the island and others include the southern elephant seal, the Antarctic fur seal and the sub Antarctic fur seal. Thirty-four bird species have been recorded at Heard Island, the most abundant of which are the penguins which return annually to the island to breed and moult.\n\nAustralian research interests were first established on Heard Island in 1929 when Sir Douglas Mawson and nine members of the British Australian and New Zealand Antarctic Research Expedition (BANZARE) stayed for eight days, undertaking surveying, photography, biology and exploration. An Australian National Antarctic Research Expedition (ANARE) station was established in 1947 at Atlas Cove and closed in March 1955. A temporary station was later established at Spit Bay (summer 1991-92) to accommodate five people in the first wintering party since 1954. This 2000-2001 summer season expedition was the first major expedition to the island since then.\n\nBob Dovers initiated the Heard Island Geodetic Network in the early 1950s when he established a number of geodetic stations in a triangulation network. These stations were last occupied by National Mapping in 1980. The 1980 terrestrial observations (directions and distances) were combined with TRANSIT satellite Doppler fixes at a number of existing and new geodetic control stations. Due to the dominate gale-force westerly winds, access to the top of the steep cliffs on the western and southern sides of the island was virtually impossible by helicopter, so the survey network concentrated mainly on the northern side of the island.\n\nWith the advent of the Global Positioning System (GPS), the International GPS Service (IGS) and sophisticated GPS processing software, it is possible to obtain accurate coordinates in terms of the International Reference Framework (ITRF) anywhere in the world. The expedition to Heard Island in 2000 provided the opportunity to establish these coordinates on this remote Island. This report describes the work undertaken to achieve these results.", "links": [ { diff --git a/datasets/ASAC_127_1.json b/datasets/ASAC_127_1.json index d4ae547dcc..295b8a4f10 100644 --- a/datasets/ASAC_127_1.json +++ b/datasets/ASAC_127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 127\nSee the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nThe effect of nutrient and water enhancement on the biodegradation of petroleum was tested in Antarctic mineral soils. Nitrogen, phosphorus and potassium were applied in solution, with or without gum xanthan or plastic covers, to sites artificially contaminated with distillate. The effectiveness of these procedures was assessed by measuring changes in total petroleum hydrocarbons; heptadecane/pristane and octadecane/phytane ratios; in concentrations of major hydrocarbon components and in microbial numbers and activity.\n\nSignificantly lower hydrocarbon concentrations were recorded after one year in soils treated with fertiliser solutions, but only in the surface 3 cm. These soils also showed lowered heptadecane/pristane and octadecane/phytane ratios and had the highest levels of microbial activity relative to other plots. Soils treated with gum xanthan or covered with plastic had the highest residual hydrocarbon levels. Both treatments inhibted evaporative loss of hydrocarbon, and there were indications that gum xanthan was utilised by the microbiota as an alternative carbon source to distillate. Higher temperatures were recorded under the plastic but no stimulation of biodegradation was detected.\n\nEstimated numbers of metabolically active bacteria were in the range of 10^7 to 10^8 per gram dry weight of soil, with an estimated biomass of 0.03 to 0.26 milligrams per gram of soil. Estimated numbers of amoebae were in the range 10^6 to 10^7 per gram soil (biomass of 2 to 4 milligrams per gram). The highest populations were recorded in fertilised, contaminated soils, the only soils where petroleum degradation was demonstrated.", "links": [ { diff --git a/datasets/ASAC_12_1.json b/datasets/ASAC_12_1.json index c5a232196e..acfb83d591 100644 --- a/datasets/ASAC_12_1.json +++ b/datasets/ASAC_12_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_12_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ANARE Health Register, which has been in operation since 1987, is designed to gather, store, analyse and report on all health related events occurring in the ANARE population. The principal aims of the project are to:\n\n- quantify the occurrence of ill health in Antarctic personnel.\n- compare the incidence rates with those in the domestic population.\n- assess any trends in health events.\n- identify high risk groups, in order to modify conditions accordingly.\n- assess the role of pre-existing health conditions.\n- examine the causes of injury.\n- quantify the procedures performed and drugs administered.\n\nThe results of all medical consultations are coded according to the International Classification of Diseases and analysed on both a monthly and an annual basis in order to assess any emerging trends. In addition to serving as a long-term data base for epidemiological studies, the Health Register is proving to be a useful tool in the day-to-day operations of the Polar Medicine Branch of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/ASAC_1300_PRB_1.json b/datasets/ASAC_1300_PRB_1.json index 7238ad0e31..2e8ccbae7a 100644 --- a/datasets/ASAC_1300_PRB_1.json +++ b/datasets/ASAC_1300_PRB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1300_PRB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The contamination of soils by heavy metals is a problem that faces communities the world over. Contamination is of particular concern when it is mobilised into ground and surface waters, where it can migrate into rivers, lakes and the marine environment. Clean up of contaminated sites can exacerbate water pollution by enabling previously immobilised heavy metals to be exposed to water sources. The development of adsorption technologies and permeable reactive barriers to remove mobilised pollutants from ground and surface waters, will prevent the spread of contamination from contaminated sites. \n\nThe objectives of this research are to develop technology for the containment and treatment of metal contaminated waters at contaminated sites in Antarctica. In this proposal we intend to develop the use of permeable reactive barrier (PRB) technologies for the in situ containment of metal contaminants dispersed from abandoned waste disposal sites, such as at Wilkes and Davis. The scientific findings of this proposal in the years 2003 -2008 indicate that the general principle of cold regions PRB's are sound, but the performance of off the shelf granular media, such as zeolites, are adversely affected by low temperature and freezing. These factors reduce the capacity and kinetics of metal uptake. Zeolites are also limited to the adsorption of metal cations and for example do not adsorb oxy metal anions such as arsenic, chromium etc, which also occur in these areas. Therefore before this technology is applied in Antarctica, modification or development of new material that is robust and predictable in its performance and able to stabilise a broader range of materials in freezing environments is required.\n\nThus the scientific aims of this proposal are\n1. To develop PRB materials applicable for deployment in the Antarctic environment\n2. To develop predictive models for predicting the performance of the PRBs\n\nA permeable reactive barrier (PRB) was built and installed in way of a fuel spill located at the Casey Station Main Power House (MPH). This PRB consisted of five cages and was filled with different materials designed to bind and degrade fuel. Each cage had eight multi-ports (MP) throughout its length from which water samples were extracted at different depths, and alongside material samples collected i.e. cage 1 - MP1 to MP 8, Cage 2 - MP9 to MP16. This spreadsheet contains analytical results from 2005 to 2009.\n\nFollowing are some explanations of the spreadsheets available as part of the download file:\n\n1) PRB Samples - a summary of information i.e. Sample Tracking Database Barcode, label given by sampler, sample type.\n2) Nutrients - Water extractable and Potassium Chloride extractable ion concentrations in the PRB material samples given on a dry matter basis (mg/kg).\n3) Samples soil only - a summary of samples of material taken from the PRB\n4) TPH and total P - Total Petroleum Hydrocarbons (from C9 to C28) and Total Phosphorous concentrations in the PRB material samples given in a dry matter basis (mg/kg)\n5) Water Samples - ion concentrations in water samples taken from the barrier from water samples (barcodes link to the summary information)\n6) Total P - Total Phosphorous\n7) Waters\n\nTPH - Total Petroleum Hydrocarbons\nTotal P - Total Phosphorous\nDMB - Dry Matter Basis\nDMF - Dry Matter Fraction\n\nCo-ordinates for the four corners of the lower PRB in lat long:\n110 31' 31.672\" E \n66 16' 54.151\" S\n110 31' 31.816\" E \n66 16' 54.129\" S\n110 31' 32.044\" E \n66 16' 54.266\" S\n110 31' 31.903\" E \n66 16' 54.303\" S\n\nAll samples were collected within one square metre of these locations.\n\nThis work was also associated with ASAC project 2570 (ASAC_2570) - Constraints on hydrocarbon adsorption and nutrient release from zeolites at low temperatures for hydrocarbon remediation in Antarctica. Project 2570 had the following objectives:\n\nProject objectives:\nThe objectives of this research are to further develop low temperature technologies for the containment and remediation of hydrocarbon contaminated waters at contaminated sites in Antarctica. This process requires the examination and development of a number of materials for use in permeable (bio-) reactive barriers and landfarming trials. This process includes; (i) quantification of the nutrient holding capacities of a number of controlled release nutrient products (CRNs); (ii) determination of binary and multi-component exchange equilibria of exchangeable cations with nutrients loaded onto exchange material at low temperatures; (iii) modelling of exchange equilibria using a novel two parameter temperature dependent semi-empirical thermodynamic model; (iv) undertaking dynamic studies and modelling involving exchange material; (v) quantification of adsorption characteristics of Special Antarctic Blend diesel (SAB) emulsions onto barrier media at low temperatures; (vi) the development and understanding of superior hydrocarbon adsorption materials including covalently bonded surfactant modified zeolites and chitson coated sand (vii) determining the effect of petroleum hydrocarbon presence on ion exchange characteristics and the performance of the barrier; (viii) determination of the effect of biofilm growth on ion exchange characteristics and the performance of the barrier (ix) to what extent and how to manipulate ionic strength and composition of ions in solution to achieve optimal release of nutrients for the metabolism of petroleum hydrocarbons by indigenous Antarctic microorganisms; and (x) test and validate the performance of the barrier media under field conditions.", "links": [ { diff --git a/datasets/ASAC_1300_UMELB_SC_1.json b/datasets/ASAC_1300_UMELB_SC_1.json index b14b8a9ecd..c890626804 100644 --- a/datasets/ASAC_1300_UMELB_SC_1.json +++ b/datasets/ASAC_1300_UMELB_SC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1300_UMELB_SC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division (AAD) has identified the Thala Valley Tip near Casey Station as a high priority site for remediation. However there are difficulties with regards to contaminant dispersal by melt-water during extraction of wastes and contaminated sediments. Characterisation of contaminants and other site-specific conditions is crucial when designing appropriate water treatment technologies. Analysis of contaminants in Tip waters has found that heavy metals are predominantly associated with particles, although there are substantial concentrations of dissolved metals as well. The combined chemical and physical analyses indicate the main heavy metals transport mechanism as adsorption to the surface of particles, which are then carried by surface and sub-surface runoff.\n\nThe data set contains geochemical and morphological data of sediment samples taken from the Thala Valley tip site in the 1997-98 and 2000-01 summer seasons. The data set includes particle size distribution, heavy metals analysis, scanning electron microscopy and x-ray diffraction results.\n\nAbstract from referenced paper:\n\nAntarctica is commonly regarded a pristine environment, but more than a century of human activity has left an extensive legacy of abandoned waste. The Australian Antarctic Division (AAD) has identified the Thala Valley Tip near Casey Station as a high priority site for remediation. However there are difficulties with regards to contaminant dispersal by melt-water during extraction of wastes and contaminated sediments. Characterisation of contaminants and other site-specific conditions is crucial when designing appropriate water treatment technologies. Analysis of contaminants in Tip waters has found that heavy metals are predominantly associated with particles, although there are substantial concentrations of dissolved metals as well. The combined chemical and physical analyses indicate the main heavy metals transport mechanism as adsorption to the surface of particles, which are then carried by surface and sub-surface runoff.\n\nTo remove heavy metals from contaminated water during the proposed clean-up of Thala Valley a multistage treatment process will be required. The first stage is one of particle removal. A plant has been designed that uses coagulation and flocculation chemicals to produce a fast settling, flocculated suspension. The flocculated particles will settle out in an inclined settler, producing a clarified effluent and a concentrated sludge. The clarified water will then be passed through a 1 mm filter to remove any residual particles prior to dissolved heavy metals removal either by ion exchange or distillation. Future research will focus on optimisation of the water treatment system, especially coagulation and flocculation processes and the impact of pH, turbidity, low temperature and water chemistry on flocculation efficiency.\n\nThe download file contains 6 excel spreadsheets of data.\n\nThe fields in these datasets are:\n\nDate\npH\nTurbidity\nPressure\nSuspended Solids\nFlow\nArsenic\nCadmium\nChromium\nCopper\nIron\nManganese\nLead\nNickel\nZinc\nSilver\nSettling velocity\nSediment", "links": [ { diff --git a/datasets/ASAC_1300_field_lab_books_1.json b/datasets/ASAC_1300_field_lab_books_1.json index 9b1601d8c9..3e6ab9e985 100644 --- a/datasets/ASAC_1300_field_lab_books_1.json +++ b/datasets/ASAC_1300_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1300_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 1998 and 2003 as part of ASAC (AAS) project 1300 - Development and application of technologies for the removal of heavy-metal contaminants from run-off associated with abandoned waste disposal sites.", "links": [ { diff --git a/datasets/ASAC_1301_1.json b/datasets/ASAC_1301_1.json index e995bf9d04..33976e5a31 100644 --- a/datasets/ASAC_1301_1.json +++ b/datasets/ASAC_1301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Peter Sedwick collected water column samples (6 depths, less than 350m) and measured dissolved iron in these samples, using specialised trace-metal clean techniques, at 9 stations along the SR3 transect between 47 deg S and 66 deg S. These are the first such data for this oceanographic sector during spring. The dissolved iron levels were generally very low (less than 0.2 nM nM) in the upper water column, particularly south of the Subantarctic Front, and surprisingly there was no evidence of significant iron inputs from melting sea ice in our study region. Ongoing work quantified various size fractions of dissolved iron as well as total acid soluble iron.\n\nIn addition, Jack DiTullio collected water samples for measurements of five biogenic sulfur pools at most shallow water CTD casts. The sulfur pools measured include: dimethylsulfide (DMS), particulate and dissolved dimethylsulfoniopropionate (DMSP) and particulate and dissolved pools of dimethylsulfoxide (DMSO).\n\nTaken from the referenced paper:\n\nA shipboard-deployable, flow-injection (FI) based instrument for monitoring iron(II) in surface marine waters is described. It incorporates a miniature, low-power photoncounting head for measuring the light emitted from the iron-(II)-catalyzed chemiluminescence (CL) luminol reaction. System control, signal acquisition, and data processing are performed in a graphical programming environment. The limit of detection for iron(II) is in the range 8-12 pmol L-1(based on 3s of the blank), and the precision over the range 8-1000 pmol L-1 varies between 0.9 and 7.6% (n )4). Results from a day-night deployment during a north to-south transect of the Atlantic Ocean and a daytime transect in the Sub-Antarctic Front are presented together with ancillary temperature, salinity, and irradiance data. The generic nature of the components used to assemble the instrument make the technology readily transferable to other laboratories and the modular construction makes it easy to adapt the system for use with other CL chemistries.", "links": [ { diff --git a/datasets/ASAC_1310_1.json b/datasets/ASAC_1310_1.json index 36e96044db..9c10aa55c2 100644 --- a/datasets/ASAC_1310_1.json +++ b/datasets/ASAC_1310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1310\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nIncreasing UV-radiation over Antarctica each spring may damage DNA in plants. This research determined the susceptibility of Antarctic plants to such damage and investigated the effectiveness of protective and repair mechanisms. This helps predict how plants globally will cope with future climate change.\n\nSamples have been collected from Heard Island, and near Casey Station, Antarctica. Three excel files constitute this dataset.\n\nHeard Island 2003/4 Samples collected for project 1310\n\nVascular plant UVB site\n\nGPS coordinates of vascular plant UV site 53 06 57 S, 73 43 30E A total of 4 g of each of 5 species (Pringlea antiscorbutica, Poa cookii. Deschampsia antarctica. Azorella selago, Acaena magellanica) were collected on 5 days over the season. These were analysed for DNA damage, UVB absorbing pigments, and photosynthetic and photoprotective pigments. Chlorophyll fluorescence and leaf temperature were measured on the sampled plants.\n\nNutrient gradient\n\nGPS co-ordinates: high nutrient site: 53 06 0.419S, 73 43 0.105E 4g of P. antiscorbutica and 0.8g of P. cookii were taken at the high nutrient site, along with chlorophyll fluorescence measurements.\n\nGPS co-ordinates: low nutrient site: 53 06 29.09S, 73 43 00.36E 7.2g of P. antiscorbutica and 3.2g of P. cookii were taken at the low nutrient site, along with chlorophyll fluorescence measurements.\n\nScarlett Hill\n\nGPS co-ordinates: 53 05 0.645S, 073 40 0.339E\n3.2 g of P. antiscorbutica plants was sampled and chlorophyll fluorescence measurements taken.\n\nSpit Camp\n\nGPS co-ordinates:\n3.2g of P. antiscorbutica plants was sampled and chlorophyll fluorescence measurements taken.\n\nCeratodon Paddick Valley\n\nGPS co-ordinates: 53 08 43.44S, 73 40 35.42E 3 g of Ceratodon purpureus was collected.\n\nThe fields in this dataset are:\n\nSpecies\nDate\nWeight\ngrams dry weight (gdw)\nWater Content\nAspect\nCommunity type\nTime (Local Time)\nFluorescence\nLeaf temperature (1, 2, etc)\nF - Chlorophyll Fluorescence\nFm' - Fluorescence maximum measured in the light\nYield - Yield of fluorescence\nNo. and Mark are stamps put on the data during download", "links": [ { diff --git a/datasets/ASAC_1313_1.json b/datasets/ASAC_1313_1.json index b141a52f70..9530c98051 100644 --- a/datasets/ASAC_1313_1.json +++ b/datasets/ASAC_1313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1313\nSee the link below for public details on this project.\n \nThis research will monitor the effect of climate change on bryophyte communities in the Windmill Islands region of Antarctica through repeated surveys of permanent vegetation transects. This will help us to predict future changes in bryophyte communities with changing climate, and to anticipate how biodiversity will be affected by global climate change.\n \nSee the child and related metadata records for access to the data.", "links": [ { diff --git a/datasets/ASAC_1313_Baseline_1.json b/datasets/ASAC_1313_Baseline_1.json index 5c6006409b..d924d271d5 100644 --- a/datasets/ASAC_1313_Baseline_1.json +++ b/datasets/ASAC_1313_Baseline_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1313_Baseline_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes supplementary material to accompany the listed publication, and the data described in the paper and the supplementary material relates to AAS (ASAC) project 1313, as well as the State of the Environment indicator (SOE 72) relating to Windmill Islands vegetation.\n\nThe vegetation surveys described in this publication are the 1999/2000 baseline data associated with SOE 72 and AAS 1313.\n\nTaken from the publication:\nExtreme environmental conditions prevail on the Antarctic continent and limit plant diversity to cryptogamic communities, dominated by bryophytes and lichens. Even small abiotic shifts, associated with climate change, are likely to have pronounced impacts on these communities that currently exist at their physiological limit of survival. Changes to moisture availability, due to precipitation shifts or alterations to permanent snow reserves will most likely cause greatest impact. In order to establish a baseline for determining the effect of climate change on continental Antarctic terrestrial communities and to better understand bryophyte species distributions in relation to moisture in a floristically important Antarctic region, this study surveyed finescale bryophyte patterns and turf water and nutrient contents along community gradients in the Windmill Islands, East Antarctica. The survey found that the Antarctic endemic, Schistidium antarctici, dominated the wettest habitats, Bryum pseudotriquetrum distribution spanned the gradient, whilst Ceratodon purpureus and Cephaloziella varians were restricted to driest habitats. These patterns, along with knowledge of these species relative physiology, suggest the endemic Schistidium antarctici will be negatively impacted under a drying trend. This study provides a model for quantitative finescale analysis of bryophyte distributions in cryptogamic communities and forms an important reference site for monitoring impacts of climate change in Antarctica.", "links": [ { diff --git a/datasets/ASAC_1313_Casey_Moss_Quadrats_2007_1.json b/datasets/ASAC_1313_Casey_Moss_Quadrats_2007_1.json index a161aa3df3..955938ea4d 100644 --- a/datasets/ASAC_1313_Casey_Moss_Quadrats_2007_1.json +++ b/datasets/ASAC_1313_Casey_Moss_Quadrats_2007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1313_Casey_Moss_Quadrats_2007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are comprised of a spreadsheet with locations (latitude and longitude) and labels for moss bed quadrats. The quadrats are located in two sites: Antarctic Specially Protected Area 135 (formerly Site of Special Scientific Interest 16) near Casey Station; and on Robertson Ridge south of Casey. The letter in the quadrant label indicates the vegetation community type: before bryophyte, M for transitional and A for lichen. The original 60 quadrats are now described in metadata record \"AAS_4046_quadrat_locations\". This metadata record describes nine additional locations for associated work conducted at the sites in 2007-08 by Ellen Ryan-Colton. The latitude and longitude were collected using a handheld GPS. The quadrats are identified by markers in the field comprised of small metal discs glued to rocks.\n\nMoss bed and Antarctic vegetation are generally rare and fragile in Antarctica. Mapping the exact location of moss bed is very useful for management of protected areas and the follow up of biological research work.\n\nThis work was completed as part of ASAC Project 1313 (ASAC_1313), Impact of global climate change on Antarctic flora: long-term monitoring and ecophysiological studies of bryophyte community dynamics in the Windmill Islands.", "links": [ { diff --git a/datasets/ASAC_1313_Moss_Field_Measurements_1.json b/datasets/ASAC_1313_Moss_Field_Measurements_1.json index fb9d6c81a8..3354eafe7d 100644 --- a/datasets/ASAC_1313_Moss_Field_Measurements_1.json +++ b/datasets/ASAC_1313_Moss_Field_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1313_Moss_Field_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected at ASPA 135, at the melt lake, northeast side.\n\nThis metadata record provides data collected during 2002/3 at Casey. Temperature sensors (i-buttons) were inserted into moss beds and a range of associated measurements were recorded (e.g. moisture and photosynthetic activity). The work was conducted by Jane Wasley and Johanna Turnbull over a two week period, from 15 to 28 January 2003.\n\nField site location\nThe field site was located in ASPA 135, on the northeast side of the melt lake. Approximate coordinates of the field site are: \nNW 110 32 32.5E, 66 16 56.0S\nNE 110 32 34.9E, 66 16 56.0S\nSW 110 32 37.6E, 66 16 58.5S\nSE 110 32 40.6E, 66 16 58.5S \n\nThese coordinates for the site were estimated visually by Jane Wasley 15 April 2015. The area bound by theses coordinates is therefore approximate only. The shape of the site bounded by these coordinates is a parallelogram (see red polygon: ASPA 135_PML field site 2002-3.docx) and is positioned in the north-east corner of the ASPA135 meltlake, running along the eastern side. \n\nRelated files - \nJTurnbull labbook Casey2002-3.pdf\nThis file is a scanned copy of selected pages of Johanna Turnbull's laboratory notebook from Casey 2002/3 that provides mud map sketches of the field sites she sampled from during 2002/3 at Casey. This includes, on the page numbered 53, a mud map of \"PML\". (the ibutton field notes 2002-3.pdf file notes the work related to this data set was conducted \"all in PML\"). \n\nASPA 135_PML field site 2002-3.docx\nThis file provides an image of the approximate location of the field site \"PML\", marked as a red polygon. This polygon was estimated visually by Jane Wasley 15/4/15. \n\nSite name abbreviations used in field notebook (ibutton field notes 2002-3.pdf): PML = Plateau Melt Lake. Note that this site is also referred to, in other documentation for the season (not provided), as MLE = Melt Lake East, where PML and MLE are the same site (initially referred to as PLM, but later changed name to MLE). \n\nMethods:\nOn 15 January 2003, 18 ibutton temperature sensors were inserted into moss beds. The sensors were pushed into the moss turf, so they were positioned just below the moss turf surface (measuring moss turf temperate close to turf surface, but away from surface effects of wind etc). Three species of moss were included (Ceratodon purpureus; Bryum pseudotriquetrum; Grimmia antarctici (since taxonomically revised as: Schistidium antarctici) and two micro-positions (ridge or valley) were used (moss grows in an undulating ridge/valley formation). A moisture reading (in volts) was recorded for each of the 18 sampling points (and corresponding reading obtained with probe submerged in water). Sponge cores were inserted into the sampling points to estimate moss turf moisture contents (to be removed for measurement at week 1, then a new set inserted for removal at week 2). \n\nAt 1 week, on 23/1/03, the following measurements were made at each of the 18 sampling points:\n1. Chlorophyll fluorescence using a mini-PAM (Waltz, Germany). See \"SET\" letters A to R, MEM # and YIELD in field notebook. Corresponding PAM data file not available to date (will be added to this metadata record later if can be found). If found, data in file will correspond to notebook via \"Mark\", No. and Yield, respectively. \n2. Moss turf moisture content via sponge cores measured gravimetrically. See \"changed vial moisture probe #\" in field notebook (ibutton field notes 2002-3.pdf) and 'Tube #' in lab notebook (ibutton lab notes 2002-3.pdf) for wet weight and dry weight of sponge cores, to estimate turf water content. \n3. Moss surface temperature (degrees C) using a hand held infrared thermometer (Scotchtrack T Heat tracer IR1600L; 3M, Austin TX, USA), see field notebook (ibutton field notes 2002-3.pdf) for data. \n4. Moss turf moisture content (in volts) using a \"PJ\" moisture probe. No further information available about make/model. The moisture probe intermittently measured also in a salt solution (\"Salty O\"), using 2 x rounded teaspoons of table salt in approximately 200 mL of tap water. \n\nAt week 2, on 28/1/03, measurements were repeated as per 23/1/03. The chlorophyll fluorescence data is provided in PAM_030129.xls. \n\nList of abbreviations (used in data files and/or field notebook): \nC = Ceratodon purpureus; B = Bryum pseudotriquetrum; G = Grimmia antarctici\nR = Ridge; V = Valley\nletters A to R indicate \"SET\" in field notebook (23 and 28/1/03) and \"Mark\" in file PAM_030129.xls\nI.R = infrared\nYield = photosynthetic yield, measured via chlorophyll fluorescence\n\nRelated files - \nScanned copies of relevant pages from field (ibutton field notes 2002-3.pdf) and laboratory (ibutton lab notes 2002-3.pdf) notebooks. \n\nData files (ibutton field expt_030129.xls and PAM_030129_working.xls).", "links": [ { diff --git a/datasets/ASAC_131_1.json b/datasets/ASAC_131_1.json index 94266c6a24..3001ac627d 100644 --- a/datasets/ASAC_131_1.json +++ b/datasets/ASAC_131_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_131_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC project 131 (ASAC_131).\n\nTaken from the referenced publication:\n\nThe diet of Heard Island cormorants was investigated by examination of casts over three summer seasons. The diet was composed of mainly benthic organisms, with polychaetes being the most common prey for the greater part of the population. Fish were taken commonly only by the small breeding population at the western end of the island, whereas elsewhere only 22% of casts contained any fish remains at all. The diet is therefore different from that reported for Phalacrocorax atriceps at other localities.", "links": [ { diff --git a/datasets/ASAC_1324_1.json b/datasets/ASAC_1324_1.json index 59b2d25e8f..38dd88fcba 100644 --- a/datasets/ASAC_1324_1.json +++ b/datasets/ASAC_1324_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1324_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1324\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nHuge amounts of energy from the solar wind (the Suns expanding atmosphere), are stored in the magnetosphere (Earth's outer atmosphere), and released in geomagnetic storms that affect space and ground engineering systems (power systems, communications satellites, etc.). VLF radiowave observations at Casey will help us understand a fundamental sub-process: the substorm.\n\nFrom the project webpage:\n\nThis project records very low frequency and extremely low frequency (300 Hz to 10 kHz) radio waves produced naturally in 'geospace' and received on the ground at high latitudes; the characteristics of these waves are used to study physical processes which occur in the magnetosphere, and in particular the substorm process. A VELOX (VLF/ELF Logger Experiment) instrument recording these fascinating waves has been operating at Halley Research Station, Antarctica, for several years. We are now in the process of setting up a global network of such systems --- VELOXnet. \n\nThe substorm project is part of the British Antarctic Survey's research programme on Magnetic Reconnection, Substorms and their Consequence\n \nA local copy of this dataset is maintained at the Australian Antarctic Data Centre. The dataset contains all VELOX data collected at Casey (in the period 2001-2005) in binary format, plus a batch file and exe file for converting the data to ascii format. A copy of the data manual describing the data is also included.\n\nTaken from the 2008-2009 Progress Report:\nThe objectives of the research are:\n\nThe purpose of the project is to observe high energy radiation belt particles at low altitudes, using the VLF system operating at Casey. The software in this system will be enhanced to allow the detection of energetic particle precipitation events occurring in the region between Australia and Antarctica. This work will (1) form part of the WARP project undertaken by BAS during 2005-2010, (2) provide input into the Australian Antarctic Science Program - through the Space and Atmospheric Sciences program: solar variability and weather, (3) provide input into the Particle Precipitation Effects on the Middle Atmosphere project proposed by the University of Otago to the New Zealand Marsden Fund.\n\nWith the VLF system in this configuration it will become part of the world-wide group of receivers looking at energetic precipitation events into the atmosphere. The network of receivers is known as Antarctic/Arctic Radiation-belt Dynamic Deposition VLF Atmospheric Research Kartell (AARDDVARK).\n\nThis research continues the work of the project started in 2001, but includes some small, easily achieved adjustments of the system software (done via the internet link) to extend the scientific capability of the instrument. The research will include several new members of the scientific team: Prof Fred Menk, Dr Craig Rodger, and Dr Mark Cliverd.\n\nProgress against objectives:\nIn the last 12 months (April 2008 to March 2009) the project has formed part of the WARP project undertaken by BAS during 2005-2010, and generated two publications for the AAD publications database. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nIn the last 12 months the AARDVARK receiver at Casey has continued to operate and provide good quality data. No significant system issues have occurred. Work has been undertaken to compare energetic electron precipitation events observed by the AARDVARK network in 2009. The focus is on simultaneous events observed at Casey (AAD) and Scott Base (Antarctica New Zealand and Otago University, Dunedin, NZ). Additional supporting event data has come from the riometer located at Macquarie Island.\n\nTaken from the 2010-2011 Progress Report:\nProgress against objectives:\nThe VLF system at Casey has run continuously throughout 2010/11. There have been no hardware, software, or data transfer issues. Despite the very low levels of solar variability and geomagnetic storms some examples of energetic electron precipitation events have been observed. In contrast to previous publications we have been able to analyse 2009 and 2010 events using radio wave phase measurements, following the successful upgrade of the system in Feb 2009. The event data are being combined with, and compared against, similar observations made from the New Zealand Scott Base, riometer absorption measurements from Macquarie Island, and THEMIS satellite observations. Detailed comparisons between ground-based and space-based instrumentation will provide new insight into the way energetic electrons are precipitated into the upper atmosphere. Publication of the results of this study are expected in 2011.", "links": [ { diff --git a/datasets/ASAC_1327_1.json b/datasets/ASAC_1327_1.json index 63bbff7605..97c82d5552 100644 --- a/datasets/ASAC_1327_1.json +++ b/datasets/ASAC_1327_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1327_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1327\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nAntarctic lake cores record a history of evaporation and precipitation in the preservation of climate sensitive microbial communities. Integration of high resolution lake records with other climate proxies, such as ice core temperature records, will allow a comprehensive assessment of recent climate change in the Windmill Islands, East Antarctica.\n\nA series of high resolution short (~30cm) sediment cores were collected from 5 Windmill Island lakes by mini gravity corer in the 2001/02 Casey summer field season (Holl Lake, Beall Lake, Lake 'E', lake 'F' and Lake 'M'). At each lake site, 3 cores were collected: 1 for palaeoclimate analysis via diatom assemblages, 1 for Lead-210 dating, and 1 for palaeopigment analysis. Diatom analysis is underway on the Holl Lake and Beall Lake cores, Lead-210 dating of Holl, Beall, F and M is in progress and palaeopigments of Holl and Beall will be started early in 2003.\n\nFurther notes and information are contained in the dataset.\n\nThe fields in this dataset are:\nLake\nLocation\nLatitude\nLongitude\nLake Depth\nIce Depth\nWater Sample Depth\nSalinity\nLake Area\nCatchment Area\nElevation\nPb210 Date\nWater Temperature\nConductivity\nDissolved Oxygen\npH", "links": [ { diff --git a/datasets/ASAC_132_1.json b/datasets/ASAC_132_1.json index c85a38a93b..5f64e892d3 100644 --- a/datasets/ASAC_132_1.json +++ b/datasets/ASAC_132_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_132_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the abstract of the referenced papers:\n\nMaternal attendance behaviour was studies in Antarctic (Arctocephalus gazella) and subantarctic fur seals (Arctocephalus tropicalis) which breed sympatrically at subantarctic Macquarie Island. Data on attendance were obtained using telemetric methods. Both species undertook two types of foraging trips: overnight foraging tips which were of less than 1 day duration and occurred exclusively overnight, and extended foraging trips which lasted longer than 1 day. The mean duration of overnight foraging trips was 0.43 and 0.39 days, while the duration of extended foraging trips was 3.6 and 3.8 days in A. gazella and A. tropicalis, respectively. The duration of overnight and extended foraging trips did not differ significantly between species. Two types of shore attendance bouts that differed in duration were also observed in these species. Short attendance bouts lasted less than 0.9 days, while long attendance bouts lasted longer than 0.9 days. Short attendance bouts lasted 0.4 and 0.5 days, while long attendance bouts lasted 1.6 and 1.7 days in A. gazella and A. tropicalis, respectively, and did not differ significantly between species. The most significant differences between the attendance behaviour of both species was in the percentage of foraging time allocated to overnight foraging trips (15% and 25% in A. gazella and A. tropicalis, respectively), and the percentage of time spent ashore (30% and 38% in A. gazella and A. tropicalis, respectively). The nearness of pelagic waters to Macquarie Island is considered to be the main reason that lactating females are able to undertake overnight foraging trips. These trips may be used by females as a means of optimising the costs of fasting and nursing ashore. Females may be able to save energy by only nursing pups when milk transfer efficiencies are high, and reduce the time and energy costs of fasting ashore when milk transfer efficiency is low. Of the female A. gazella that still carried transmitters at the end of lactation, 83% continued regular attendance for between 21 and 150 days post-lactation (when data collection ceased). Overwintering of A. gazella females at breeding sites has not been previously reported in other populations.\n\nBreeding colonies of the Antarctic fur seal Arctocephalus gazella on Heard Island (53.18S, 73.5E) are situated on the sheltered northern and eastern coasts on flat vegetated terrain near streams and pools. Pupping in the 1987/88 summer began on 21 November, with 90% of births in 26 d. The median birth date was 11 December. Pup counts at Heard Island made in seven breeding seasons from 1962/63 to 1987/88 show an exponential rate of increase of 21%, which may be inflated due to undercounting in early years. The total of 248 births in 1987/88 represents an exponential increase of 37% since the previous year, but pups may have been undercounted then. Based on the number of pups born, the breeding population is estimated at 870-1,120. During the breeding season, the largest number of animals ashore was 835. Many non-breeding fur seals began hauling out from early January and 15,000 animals were estimated to be ashore by late February, a far larger number than expected from the size of the breeding population. Both the breeding and non-breeding components of the population may be augmented by immigration. The source of immigrants may be undiscovered breeding colonies of this species in the northwestern sector of the Kerguelen Archipelago or the concentration at South Georgia. Further censuses are required at Heard Island to monitor the population growth.", "links": [ { diff --git a/datasets/ASAC_1332_1.json b/datasets/ASAC_1332_1.json index 68e4d8378d..aa6c2b14c7 100644 --- a/datasets/ASAC_1332_1.json +++ b/datasets/ASAC_1332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geological evidence from the Framnes Mountain, East Antarctica, will reveal changes in ice thickness from the Last Glacial Maximum 20,000 years ago to the present. A computer simulation of changes in ice thickness will show how the ice sheet interacts with climate and sea level, which is important for predicting future changes. \n\nCosmogenic isotope samples were taken from 29 locations including Welch Island (1 sample), Mawson area (1 sample), Mt Henderson area (7 samples), and the northern (13 samples), central (3 samples) and southern (2 samples) Masson ranges. No samples were taken from the Casey Range or the David Range (with the exception of the Mt Hordern area (2 samples). Mapping of the glacial geology was undertaken - few trimlines were evident and moraines where present consisted dominantly of local lithologies. The glacial clasts sampled for cosmogenic isotope analysis were felsic erratics perched on or near to stable hilltop surfaces, with clear sky exposure (conditions ideal for cosmogenic isotope dating).\n\nSediment analyses to support interpretations of the glacial history are being undertaken in 2004-2005.\n\nThe fields in this dataset are:\n\nSite\nLocation\nAltitude\nErratic\nRock\nGravel\nSand\nSalt\nSchmidt Hammer\nWeathering", "links": [ { diff --git a/datasets/ASAC_1342_1.json b/datasets/ASAC_1342_1.json index 3401c4c474..4a10b4d27d 100644 --- a/datasets/ASAC_1342_1.json +++ b/datasets/ASAC_1342_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1342_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 1342\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThis project involves field trialling of software written as part of ASAC project 1212 (2000-2001) to determine sea-ice thickness in real-time from ship-borne electromagnetic induction measurements. Computer simulation of ship- and helicopter-borne electromagnetic induction measurements over realistic sea-ice structures will also be performed in order to assess the suitability and cost-effectiveness of helicopter-mounted systems for future Antarctic sea-ice thickness measurements.\n\nEquipment used in this study were the IBEO PS100 infrared laser altimeter and the Geonics EM31 geophysical electromagnetic induction device.\n\nThe fields in this dataset are:\n\nDAY is Julian day\nTIME is in seconds after midnight (UTC).\nLASER is the laser altitude above the snow/ice (metres). A zero reading\nindicates no return (open water).\nPITCH is pitch of the system in degrees.\nROLL is roll of the system in degrees.\nCOND-A is analogue conductivity from the EM31 (not used).\nPHASE-A is analogue in-phase response from the EM31 (not used).\nCOND is the estimated depth to seawater (metres) from the EM31-ICE processing module.\nPHASE is the EM31 in-phase response (expressed as parts per thousand of the primary field). A value of 9.99 indicates the magnetic field was too large to be recorded.\nSITE\nLATITUDE\nLONGITUDE\nSNOW THICKNESS\nICE THICKNESS\nFREEBOARD\na is the electrode spacing.\nR is the measured resistance.\nRho is the apparent conductivity (not true conductivity) = 2 aR.\nCONDUCTIVITY = 1/Rho.", "links": [ { diff --git a/datasets/ASAC_1343_1.json b/datasets/ASAC_1343_1.json index 14f37828ce..fd3e363437 100644 --- a/datasets/ASAC_1343_1.json +++ b/datasets/ASAC_1343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_1343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preliminary Metadata record for data expected from ASAC Project 1343\nSee the link below for public details on this project.\nComparative study of the processes controlling carbon export in Southern Ocean environments characterised by a different hydrodynamical and ecological functioning.\n\nWork on this project was carried out on Voyage 3 of the Aurora Australis (CLIVAR) of the 2001 and 2002 season.\n\nWork at sea target sampling sites were the 8 'particle stations' along the CLIVAR SR3 repeat transect: the SAZ at 47 degrees and 49 degrees S; the SAF at 51 degrees S; the PFZ at 54 degrees S; the IPFZ at 57 degrees S; the SPZ at 59 degrees and 61 degrees S; the SACCF at 63 degrees S and the SSIZ at 64 degrees S. Some of these (64 degrees, 61 degrees and 51 degrees S) were sampled again on the way back to assess temporal evolution. All proxy studies (new production; Ba; delta30Si; 234Th-deficit) were done at each particle station but not necessarily on the same CTD casts.\n\nNew production assessment Surface water (at 5, 25, 50 and 70m) was sampled with the CTD rosette at all particle stations. Different aliquots of 1L seawater were spiked with 15N-nitrate, 15N-ammonium or 15N-urea. All samples were spiked with 13C-bicarbonate; the latter in order to assess net primary production rates. Incubations (12 H) were done in a thermo stated algal cabinet, using appropriate neutral density screens for samples from depths below 5m. The samples were submitted to a constant light flux of 0.7x10power16 quanta/cm2/sec. Furthermore, samples from 5m depth were amended with increasing doses of ammonium (+0.1 micro M; +0.25 micro M; +0.5 micro M and +1 micro M) having natural 15N/14N abundance to assess susceptibility of N-uptake (ammonium, nitrate, urea) to ammonium. Similar experiments were run for three iron amended and control cultures in collaboration with Pete Sedwick, Dave Hutchins and Phil Boyd. Analysis of ammonium related to the incubation work was done on board by colorimetry. As a side product we obtained ammonium profiles at all particle stations and also six shallow CTD's in the southern part of the transect (greater than 61 degrees S).\n\nSuspended particle sampling for trace element analysis and isotopic composition of Si For biogenic-Ba was also carried out. Typically 14 depths were sampled between the surface and 1000m. On board filtration was performed on Nuclepore membranes. These were dried (60 degrees C) and stored for analysis in the shore-based lab. Occasionally, we also sampled large particles - size fractions (greater than 70 micro m and 20 less than 70 micro m) - from the upper 150m for Ba, using the bow pump system of Tom Trull. Ba and Sr incubations on large settling particles sampled with the Snatcher were also performed at 5 particle stations. For delta30Si, all 24 depths of the deep CTD casts at the particle stations 1 to 8 were sampled. Filtered seawater and suspended matter filtered on Nuclepore membranes (dried at 60 degrees C) were saved for later analysis in the home based laboratory.\n\n234Th work - we refer to the report by Ken Buesseler for the major part of this work. In addition we performed some work using the 'Snatcher' Large Volume sampler and sedimentation column. Total 234Th deficit and 234Th activity on particles and solution was assessed at T0 and T4 H after return of the sampling device on board, in an attempt to construct the 234Th mass balance and eventually get at the settling speed (and flux) of 234Th carrying particles. These analyses went together with flow cytometry analyses (collaboration with Clive Crossley) to check for sedimentation by (fluorescent) particles and also with POC and biogenic silica in order to determine the elemental ratios of suspended and sinking particles. Flow cytometer results did not indicate there was significant sedimentation of life cells going on at this time of the year.\n\nDissolved Ba Seawater samples were taken at all depths sampled by deep CTD's during the southward transect. Samples were acidified and kept for later analysis of dissolved barium by isotope dilution ICP-MS. Comparison of the dissolved Ba distribution along the transect with the one reconstructed through a multiple end-member mixing model will help understanding of the relative contribution of in-situ processes (uptake, dissolution) versus conservative mixing, thus improving our understanding of the oceanic Ba biogeochemistry.\n\nAnalysis\nNew production. Isotope ratio analysis of the 15N and 13C spiked natural plankton samples will be conducted in the home lab., using emission spectrometry and mass spectrometry. Mass balance calculations will allow assessing relative importance of new production as well as the fraction of new production that is in the particulate form and represents the potential for export.\n\nBa and trace elements. Suspended matter samples will be acid digested (HNO3, HCl, HF) and analysed per ICP-MS and ICP-AES for contents of Ba, Ca, Sr, Al, Fe, Mn, Th, U, REE, Ti. The vertical concentration profiles will inform on the latitudinal and temporal variability of the biogeochemical control processes between SAZ, PFZ, ACC and SSIZ subsystems. For the sites with sediment trap deployments, particulate trace element distributions in the water column will be compared with trace element composition of fast settling particles intercepted by the traps.\n\nBa-uptake / barite formation. Isotope ratio analysis (135Ba/138Ba; 86Sr/87Sr) of suspended matter incubated after spiking with 135Ba and 86Sr will be analysed by ICP-MS to investigate on the barite formation process. Abundance and type of barite crystals will be studied by SEM-EMP (mapping + photographs). delta30Si, In the home based lab. particle samples will be extracted using base (NaOH). Silicates in filtered seawater will be precipitated and analysed using a multi collector ICP-sectorial Mass Spectrometer (MC-ICP-MS) once this new method is set up.\n\n234Th. Total, particulate and dissolved 234Th measurements were performed on board using low beta counters. Background (after 6 months decay) and chemical yields will be measured at Ken Buesseler's lab (WHOI, USA), using beta counters and ICP-MS respectively.\n\nThe worksheets contained in the excel spreadsheet are:\nPhyo biomass\nNew production and cell counts\nParticulate barium\nDissolved barium\nd29Si isotope signature of dissolved silicic acid\n\nThe fields in this dataset are:\n\nCarbon\nSeawater\nCLIVAR\ntemperature\npressure\nsalinity\ndepth\nbarium\nlatitude\nlongitude\noxygen\nsilicate\nphophate\nnitrate\nflagellates\ndiatoms\npicoplankton\nplankton\nurea\nammonia\ncoccolithophores", "links": [ { diff --git a/datasets/ASAC_135_1.json b/datasets/ASAC_135_1.json index 79b08e1ace..f8fefa644e 100644 --- a/datasets/ASAC_135_1.json +++ b/datasets/ASAC_135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 135\n\nTaken from the abstracts of some of the referenced papers:\n\nVestfold Hills, Antarctica exhibits marked contrasts in the weathering surface and glacial sediments between its eastern and western parts. The boundary between these zones coincides with a regional chemical boundary termed the 'salt line'. The area west of the salt line is saturated with marine-derived halite and thenardite that are particularly aggressive agents of rock weathering. In contrast, the area east of the salt line exhibits significantly fewer deposits of these salts. Rock surfaces west of the salt line are characterised by well-developed weathering forms, while glacial polish and striae are largely absent. In contrast, rock surfaces to the east commonly retain glacial polish and striae. In places, differential weathering has caused thin basaltic dykes and felsic veins to stand above the surrounding gneiss. The rate of lowering of the gneiss and dykes to the west of the salt line has been estimated at 0.024 mm and 0.015 mm per year respectively. These measurements suggest that the weathering surface in parts of the Vestfold Hills may record more than 70ka of subaerial exposure.\nGlacial sediments are much more abundant, coarser and better sorted northwest of the salt line than to the southeast. The abundant grus produced by physical weathering is coarser grained and better sorted that that produced by subglacial erosion. Such sediment lying on the land surface would be transported and redeposited during glacial advances. The change in nature of the sediments to either side of the salt line, together with the weathering forms found on clasts in the moraines, indicates that the weathering surface prior to the last glacial advance was similar to that of today and must also have developed during long periods of subaerial exposure.\n\n#####\n\nRadiocarbon dating of marine, lacustrine or terrestrial biogenic deposits is the main technique used to determine when deglaciation of the oases of East Antarctica occurred. However, at many of the oases of East Antarctica, including the Schirmacher Oasis, Stillwell Hills, Amery Oasis, Larsemann Hills, Taylor Islands and Grearson Oasis, snow and ice presently forms extensive blankets that fills valleys and some lake basins, covers perennial lake ice and in places overwhelms local topography to form ice domes up to hundreds of square kilometres in area. Field observations from Larsemann Hills and Taylor Islands suggest that under these conditions, terrestrial and lacustrine biogenic sedimentation is neither widespread nor abundant. If similar conditions prevailed in and around the oases immediately following retreat of the ice sheet, then a lengthy hiatus might exist between deglaciation and the onset of widespread or abundant biogenic sedimentation. As a result, radiocarbon dating might be a clumsy tool with which to reconstruct deglaciation history, and independent dating methods that record emergence of the hilltops from the continental ice must be employed as well.", "links": [ { diff --git a/datasets/ASAC_13_1.json b/datasets/ASAC_13_1.json index fb5dce5b1d..106b3d3fd2 100644 --- a/datasets/ASAC_13_1.json +++ b/datasets/ASAC_13_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_13_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 13\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nPersonnel wintering on the Australian National Antarctic Research Expeditions (ANARE) live in total physical isolation in one of the harshest environments on Earth, for periods of up to nine months of the year. The research hopes to gain an understanding of the effects of the Antarctic environment on humans, with particular emphasis on studies that facilitate living and working in Antarctica. Collaboration between the Antarctic Divisions Polar Medicine Branch and national and international universities and agencies includes NASA and the use of Antarctica as an analogue for long-duration space travel.\n\nTaken from the 2007-2008 Progress Report:\nThe 2007-8 season focus has been Photobiology -the impact of the duality of the polar Solar(UV) Radiation environment on individuals temporarily residing in Antarctica. The Solar UV radiation environment has been continuously monitored collaboratively with ARPANSA (AAS2276) at all stations and on the RSV Aurora Australis. Preparation and analysis of peer reviewed papers as a result of studies undertaken during the summers 2005-6 and 2006-7 will elucidate further the personal Solar (UV) exposure levels of Antarctic expeditioners during resupply and at air transport plateau sites. Studies of Ultraviolet radiation exposure will inform Vitamin D, Immune and long term health studies and are of increasing relevance in determining actual personal exposures relevant to Commonwealth occupational ultraviolet radiation exposure guidelines and the increasingly issues around vitamin D deficiency.\nContinued data analysis and reduction on NASA psychology studies/Immune studies and neuropeptides is anticipated in conjunction with Professor Des Lugg who continues to work with NASA.\nFunding support for USARIEM studies completion has not been possible..\nIt is anticipated that further manuscripts will be forthcoming from Dr K Donovan's immunology studies completed in the winters of 1975, 1986, 1996. Dr Donovan has furthered his data analysis during 2006-8 in conjunction with UTAS/Menzies Research Institute A/Professor Greg Woods. It is hoped the outputs will be useful adjuncts to the above work and that of Tingate (MD Thesis 2001).\nSecretory Immune System changes in Antarctic expeditions 1992-1997 studied by Gleeson, Lugg, Ayton, Francis et al remain of interest with further peer reviewed outputs anticipated.\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nProgress on Photobiology studies has informed human occupational medicine and health aspects of Australia's Antarctic program improving efficiency and safety of participants. In particular the personal dosimetry and long term UV radiation studies have highlighted the higher than expected UV radiation environment in East Antarctica during the Austral Summer. Analysis of UV ambient and exposure data highlighted the potential impacts in sudden changes and variation in ozone layers predominantly during Austral Spring and also unexpected events during Austral Summer. Assessment of occupational risk of new expedition roles and activities including that of Airlink workers and Resupply workers has been undertaken.\nLinkages with UV radiation deficiency highlights the duality of UV radiation in polar regions and has allowed key linkages to inform the debate on baseline human Vitamin D requirements juxtaposed against relatively high UV radiation environment of the Austral summer and concomitant occupational risk including occupational skin cancer risk. This is of direct application to the health and safety of Antarctic expeditioners and forms part of the requirements under Commonwealth Occupational Health and Safety legislation.\nProgress has been made through this project in informing AAD occupational medicine advice and ensuring monitoring, assessment and improvement of health and well-being of Australia's Antarctic employees and participants.\nImmunology and related psychology studies have further potential if completed to inform the health and well-being of future expeditioners across all of Antarctica and other extreme environments and potentially apply to Australian population in general.\nTheses studies are being conducted in the extreme environment of the Antarctic and have direct implications for other Antarctic national operators and given Antarctica is a proven space analogue, for those planning long term space missions to Moon, Mars and beyond.", "links": [ { diff --git a/datasets/ASAC_140_1.json b/datasets/ASAC_140_1.json index 20a382b838..875008f922 100644 --- a/datasets/ASAC_140_1.json +++ b/datasets/ASAC_140_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_140_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 140\nSee the link below for public details on this project.\n\nA published document includes reports of marine mammals from the Sir Douglas Mawson's Australasian Antarctic Expedition of 1911-14 (AAE) and the British, Australian and New Zealand Antarctic Research Expedition of 1929-31 (BANZARE). ). Five typescript reports and three manuscripts from archives of The Mawson Institute for Antarctic Research, University of Adelaide are published. Five deal with marine mammals of the AAE and three are from the BANZARE.\n\nA copy of the published ANARE Report is available for download from the provided URL.\n\nAustralasian Antarctic Expedition of 1911-14.\nBritish, Australian and New Zealand Antarctic Research Expedition of 1929-31.", "links": [ { diff --git a/datasets/ASAC_156_1.json b/datasets/ASAC_156_1.json index 9950ba18ba..01b4718f8a 100644 --- a/datasets/ASAC_156_1.json +++ b/datasets/ASAC_156_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_156_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project were:\n\nTo gather data from a small scale experimental freeze-drying unit, using natural local conditions, with a view to extending the experiment to a larger scale installation sufficient to deal with timbers from archaeological shipwrecks and other timber constructions. The climate of the Vestfold Hills at Davis Base is exceptionally dry and, apart from a short summer period, the temperature is below freezing. The dryness and the low temperature of the area makes it a theoretically ideal location to naturally freeze-dry large water-logged wooden items. A small 2m3 container housing a selection of waterlogged wood has been installed below ground level. Water vapour from the frozen samples is extracted by a wind driven venturi system. Changes in temperature, sample weight, and air pressure are logged and the data are transferred regularly to Canberra.\n\nThe fields in this dataset are:\n\nDate\nTime\nTemperature\nAtmospheric Pressure\nIce Weight\nWood Weight\nWindspeed", "links": [ { diff --git a/datasets/ASAC_15_1.json b/datasets/ASAC_15_1.json index 3dcf8f736f..5e95cd4ad0 100644 --- a/datasets/ASAC_15_1.json +++ b/datasets/ASAC_15_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_15_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes records from ANARE Research Notes 76; The scientific plan for the deep ice drilling on Law Dome. Per the abstract of the ANARE 76 report:\n\nInformation on the past climatic and environmental conditions which existed on the surface of the earth and in its oceans, atmosphere and cryosphere can be gained by analysis of the solids, gases, water and dissolved matter contained in the Antarctic ice sheet. The Australian Antarctic Division will undertake a deep drilling program in the summer seasons 1989-90, 1990-91 and 1991-92 near the summit of Law Dome, Antarctica, to extract a 1240m ice core using an electromechanical drill in a fluid-filled borehole. The drill site has been selected to give optimum conditions for a detailed study of climatic and other changes. The snow accumulation rate at the site is 530 kg m^-2 a^-1, the surface temperature is -22 degrees C; and there is no evidence of the occurrence of surface melting in the summer months. It will be possible to determine an accurate age-depth scale for the core by counting annual layers, which are expected to be detectable to a depth of about 800m, equivalent to an age of 10,000 years. The age of the basal ice is expected to be of the order of 50,000 years.\n\nThe report outlines the types of records it is intended to obtain from analysis of the core and surveys of the borehole, their potential applications and scientific justification. The recommended ice core analysis plan suggests the type and frequency of sampling required for the different parameters and describes the types of measurements and observations that will be made; e.g. visible features, oxygen and hydrogen isotope ratios, solid DC-conductivity, density, total gas content, gas composition, trace chemical and particulate content, radio-isotopes, crystal structure, etc. The high accumulation rate and low surface temperature at the site give excellent conditions for gas composition studies with an age resolution to as good as 20 years for the contained gases. It should also be possible to study the changes that have occurred in many parameters since the last ice age, at any temporal resolution from long term trends down to seasonal variations. Surveys of the borehole will be made to determine the vertical temperature profile and deformation rates inside the ice cap.\n\nThe interrelation and interdependence of the various measurements is discussed. Experience gained from previous drilling and core analysis programs has been drawn upon to design a core processing and analysis plan. The schedule of activities has been arranged to optimise core conservation and the efficiency of the scheme. Available analysis facilities are reviewed and opportunities for collaboration with institutes in Australia and from other nations are highlighted.\n\nAn outline of the logistic support required for the efficient running of the field program is included. A full environmental impact evaluation has been carried out elsewhere. A summary of the points addressed in the evaluation is included for information. They are specified in accordance with the Australian Antarctic Division guidelines.\n\nThe fields in this dataset are:\nYear drilled\nLocation\nDrilling method\nDepth of drilling\nAge at hole bottom\nTotal thickness\nMean annual surface temperature\nAnnual accumulation\n \nThis project was rolled into ASAC project 757 (ASAC_757).", "links": [ { diff --git a/datasets/ASAC_194_1.json b/datasets/ASAC_194_1.json index 66f9e5f16c..ec2a8a71a4 100644 --- a/datasets/ASAC_194_1.json +++ b/datasets/ASAC_194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nitrogen fixing biota of Macquarie Island are dominated by cyanobacteria growing epiphytically or symbiotically with plants or lichens. Highest rates of acetylene reduction (N-fixation) were found in the leafy lichen Peltigera sp. Colonising herbfields and short grasslands, and in the coastal angiosperm Colobanthus muscoides. Significant rates of N-fixation were also associated with the liverwort Jamesoniella colorata commonly occurring in coastal and plateau mires, in a moss-bed of Dicranella cardotii colonising a land-slip face on the grassland slopes at 100m altitude, and within polsters of the mosses Ditrichum strictum and Andreaea sp. found in exposed localities on the plateau at 200-300m altitude. It was concluded that the common feature of plants supporting active N-fixation in dry habitats was the dense packing of stems and leaves, enabling water translocation to the cyanobacterial zone by wick action. Epiphytic cyanobacterial fixation in wet habitats was widespread and not restricted to plant species.\n\nThis work was published in Polar Biology, 11: 601-606.", "links": [ { diff --git a/datasets/ASAC_2050_1.json b/datasets/ASAC_2050_1.json index 3821dcd38d..0aad73c5a5 100644 --- a/datasets/ASAC_2050_1.json +++ b/datasets/ASAC_2050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report completed as part of this project is available for download from the URL given below. Extracts of the report are presented in the metadata record.\n\nSee the report for full details.\n\nSeveral species of Antarctic fish were collected from the shallow waters off Davis Station during the 2000-01 season as part of a study examining the properties of 'antifreeze' proteins contained within the blood of these animals. Fish were sampled at regular intervals from a range of depths and various sites near the station.\n\nThe main objectives of the study were to collect serum and selected tissues from Nototheniid (cod) and Channichthyid (ice fish) species. Over 170 fish were collected throughout the calendar year. Samples were taken as required, processed and the fish preserved for further analysis on return to Australia. In Australia the serum will be tested for special antifreeze molecules that allow these animals to live in water that is colder than the usual freezing point of their body fluids. Such molecules, once identified, may be synthesised in a laboratory, and have numerous potential practical applications, from the preservation of frozen foods, to preservation of blood plasma and organs for human transplant. Analyses of this nature will be undertaken at the University of Sydney.", "links": [ { diff --git a/datasets/ASAC_2085_1.json b/datasets/ASAC_2085_1.json index ac45593f06..7c962300f8 100644 --- a/datasets/ASAC_2085_1.json +++ b/datasets/ASAC_2085_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2085_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2085\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nOver the past 20 years, Australian glaciologists have measured the ice thickness, snow accumulation rate and ice surface movement rate around the Antarctic continent, approximately following the 2,000 m elevation contour. They have completed this survey for the entire Australian Antarctic sector, except for one section between Davis and the Russian station, Mirny. This project will carry out the measurements in this last section.\n\nIt will also carry out detailed measurements of ice thickness and ice movement rate on the Lambert Glacier and some of its tributaries. This glacier is the largest in the world and it drains about one eighth of the Antarctic ice mass into the sea.\n\nFrom these measurements, calculations of the mass flux (i.e. the amount of ice flowing through the section) are made. Changes over time in the mass flux indicate whether the ice sheet is getting larger or smaller, and this in turn is related to climate and sea level change.\n\nThis project aims to determine the ice thickness, surface ice velocity and mass discharge of the region between Mirny and the Larsemann Hills. This is the remaining gap in the otherwise comprehensive ANARE measurements of mass discharge across the 2000 m elevation contour between 40E and 130E. Observations were conducted over three summer field seasons from Jan. 1998 to Jan. 2000, with the use two Sikorsky S76 long range helicopters based at Davis. Ice thickness was obtained with the ANARE 100 MHz ice radar mounted in one of the helicopters. The transmitter and receiver configurations are essentially the same as that used on the Lambert Glacier tractor traverse (see Higham et al.,1995). To accommodate speeds of up to 180 km/hr in airborne operations the slower digital oscilloscope system has been replaced by a high speed digital signal processor and a high speed analogue to digital converter. The airborne antenna used by the helicopter is smaller than that used by tractor traverses and the signal processing power of the DSP has been improved to compensate for reduced antenna gain. Ice velocity and surface elevation were measured at selected locations with dual frequency GPS instruments. Accumulation and gravity observations were also made at these sites. An automatic weather station (AWS) was installed at one of the survey sites 50 km south of Mt Brown. In addition to filling a major gap in the synoptic network, the AWS will be used to assist in the interpretation of a shallow snow core.", "links": [ { diff --git a/datasets/ASAC_2122_2.json b/datasets/ASAC_2122_2.json index 61566cc51e..f4321f6304 100644 --- a/datasets/ASAC_2122_2.json +++ b/datasets/ASAC_2122_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2122_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underwater vocalisations of Weddell seals were recorded at Casey (1997) and Davis (1992 and 1997) Antarctica. The goal of the study was to determine if it would be possible to identify geographic variations between the Casey and Davis seals using easily measured, narrow bandwidth calls (and not broadband or very short duration calls). Two observers measured the starting and ending frequency (Hz), duration (msec) and number of elements (discrete sounds) of four categories of calls; long duration trills, shorter descending frequency whistles, ascending frequency whistles and constant frequency mews. The statistical analyses considered all calls per base, single and multiple element calls, and individual call types. Except for trills, discriminant function analysis indicated less variation between the call attributes from Davis in 1992 and 1997 than between either of the Davis data sets and Casey 1997. The data set contains measures from 2966 calls; approximately 1000 calls per base and year. Up to 100 consecutive calls were measured from each recording location per day of recording so the data set indicates the relative occurrence of each of the call types per base and year. There were very few ascending whistles at Casey. All of the trills and mews contained a single element. This data set was published in Bioacoustics 11: 211-222.\n\nThe fields in this dataset are:\nObserver\nStation\nLocation\nTime\nCall Number\nCall Type\nFrequency\nDuration\nElements\nOverlap\n\nIn 2011, another download file was added to this record, providing recording locations made during the project in 2010.\n\nFurthermore:\nIn 1997 Daniela Simon made some opportunistic recordings for the project near Casey. The recording locations were:\n\nBerkley Island 110 38'E, 66 12' 40\"S\nHerring Island 110 40'E, 66 25'S\nO'Brien Bay 110 31'E, 66 18' 30\"S\nEyres Bay 110 32'E, 66 29\" 20\"S\n\nThe Davis sites:\nIN 1990 THERE WAS ONLY ONE RECORDING SITE - 78 12.5' E, 68 31.6' S\n\nIN 1997 RECORDINGS WERE MADE AT THE FOLLOWING SITES\n\nEAST SIDE OF WEDDELL ARM - 78 07.55' E 68 32.17' S PARTIZAN ISLAND - 78 13.66' E 68 29.57' S LONG FJORD - 78 18.95' E 68 30.24' S TOPOGRAV ISLAND - 78 12.40' E 68 29.33'S OFFSHORE - 77 58.73'E 68 26.35'S TRYNE BAY - 78 26.25'E 68 24.87'S LUCAS ISLAND - 77 57.00'E 68 30.36'S WYATT EARP ISLANDS - 78 31.51'E 68 21.31'S \n================================================================================\n\nThe attached document is \"a listing of the Weddell seal breeding locations near Mawson where Patrick Abgrall in 2000 and Phil Rouget in 2002 made underwater recordings\". The sound recording effort in 2000 was not as high as it was in 2002, hence fewer locations are listed.\n\nThe Abgrall sites are referred to in the paper 'Variation of Weddell seal underwater vocalizations over mesogeographic ranges' that Abgrall, Terhune Burton co-authored, published in Aquatic mammals in 2003.\nThis paper also refers to the Casey and Davis sites above.\n\nThe Rouget sites relate to the metadata record 'Weddell Seal underwater calling rates during the winter and spring near Mawson Station, Antarctica'\nEntry ID: ASAC_1132-1\n\nIn general the seals can create breathing holes in areas where tide cracks form, namely close to grounded icebergs, the shoreline and islands. I doubt that they could/would create breathing holes through solid 2 m ice.", "links": [ { diff --git a/datasets/ASAC_2152_1.json b/datasets/ASAC_2152_1.json index 5322ef5083..d90caf5ae8 100644 --- a/datasets/ASAC_2152_1.json +++ b/datasets/ASAC_2152_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2152_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples from Macquarie Island were collected between 1998 and 1999. Samples from Heard Island were collected during 2000. Terrestrial vegetation in Antarctica is restricted to isolated populations of mosses, lichens and algae, with a few higher plants on subantarctic islands. Little is known of the biodiversity, origins or dispersal of these plants, and current conservation measures are based mainly on their local abundance.\n\nFurther data for Macquarie Island and Casey station between 2000 and 2007 were added in 2007.\n\nAccession numbers for samples collected in the Prince Charles Mountains were added in 2012.\n\nThis project will investigate:\n\n(1) the extent of genetic diversity in Antarctic and subantarctic plant populations, especially mosses,\n(2) the probable origins and colonisation history of these plants,\n(3) the effect of increased UV-B irradiation on genetic variability in mosses, and\n(4) the utility of molecular taxonomy techniques for identification of Antarctic organisms.\n\nThis will provide a sound basis for development of practical conservation strategies, and baseline information from which to monitor effects of human impacts and climate change on the fragile ecosystems.\n\nThe downloadable data contain a list of locations from which the specimens were collected as well as Genbank sequence and accession numbers. Note that not all of the samples may be publicly accessible in Genbank as yet (as of May 2007).\n\nThe samples were kept frozen at -20 degrees Celsius.\n\nThis project is also related to ASAC projects 1041 and 2153 (ASAC_1041 and ASAC_2153).\n\nSamples collected for this project were also used in ASAC project 2545 (ASAC_2545).\n\nThe fields in this dataset are:\n\nSpecies\nCollection\nSite \nGenbank Number\nCollection Number\nComments \n \nProject objectives: The project objectives, as stated in the project application round 2008/09, appear below:\nThe main aim of this project is to apply techniques of molecular genetics to investigations of plant biodiversity and responses to environmental change in Antarctica. The project has been very successful so far, with well over 20 publications, and will continue to concentrate on four main areas:\n\ni) moss diversity, dispersal, and evolution in Antarctica and subantarctic islands,\nii) higher plant diversity, origins and dispersal on subantarctic islands,\niii) plant adaptation and genetic responses to climate change in Antarctica, and\niv) molecular taxonomy to assist rapid identification of Antarctic species, especially mosses, as there is now a significant database of gene sequences available for comparative analysis.\n\nThis project has been funded previously, and has been very productive so far. Therefore we are applying for a renewal of the project for a further three years, to complete the research already under way, and to finalise and submit additional manuscripts for publication, in order to obtain maximum benefit from the research already initiated and results already obtained.\n\nThe overall objectives have not been changed, except to include utilisation of our excellent database of DNA sequence information for further research. Now that the initial stages of this project are complete, with over twenty papers published in recognised international journals on the genetic diversity of Antarctic plants, we have started to build on this sound framework by investigating DNA sequences of individual genes and by analysis of plant adaptation to environmental change.\n\nThis project will provide information on several species of moss in different habitats and at different latitudes that will be important to international programs investigating adaptation to environmental change in the Antarctic. Similar techniques will also be used, as appropriate, to investigate other Antarctic and subantarctic organisms (especially, but not exclusively, plants) in collaboration with other Antarctic research scientists.\n\nSpecifically, the objectives for the next few years are:\n\n1. To continue to assess the extent of genetic diversity within and between moss\npopulations in different regions of continental Antarctica. This will provide information on the level of biodiversity in these terrestrial Antarctic plants, which is required for (a) development of effective conservation and management strategies, and (b) minimisation of human impacts and (c) understanding responses and adaptation to climate change.\n\n2. To continue to investigate the origins and mechanisms of dispersal of Antarctic and subantarctic mosses. This will assist in determining (a) the moss colonisation history of Antarctica, (b) which populations are most in need of protection, and (c) likely sources of colonisation on newly exposed ground e.g. with glacial retreat.\n\n3. To provide systematic baseline data from which to assess and predict changes in Antarctic vegetation dynamics due to human impacts and climate change. This objective will include assessment of the natural rates of somatic mutation in Antarctic mosses, both in the past, by analysis of long shoots 50 years old (or more), and in the present by analysis of current growth which is already exposed to increased UV-B irradiation. The fascinating and exciting possibility of analysing mutational history within living plants (without killing the plant) is probably an opportunity uniquely available in Antarctic mosses, due to their unusual combination of genetic, physiological and environmental characteristics.\n\n4. To build on research already done in this project, to test the hypothesis (based on results obtained) that moss genetic variation is greater in the Australian Antarctic Territory and subantarctic islands than in the Ross Sea region, and to investigate whether this is correlated with latitude and climate change (especially increased UV-B irradiation).\n\n5. To use similar genetic techniques to (a) assess levels of genetic variability, (b) analyse genetic responses to climate change and (c) assist in resolving taxonomic uncertainties in a range of Antarctic and subantarctic plants, including algae and higher plants. Some of these have already been collected, some have been sequenced for individual genes, and/or subjected to RAPD population analysis, and others will be collected if necessary to supplement existing collections during future expeditions.\n\nTaken from the 2008-2009 Progress Report:\n\nProgress against objectives:\nGood progress has been made with this project. Analysis of DNA sequencing results for both mosses and higher plants has continued this year. Several papers are nearing completion on these results. These results and publications are in line with the objectives of the project. \n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nGood progress has again been made with the project this season, with the main focus continuing towards completion of publications.\nWe have continued to analyse moss DNA sequence data for many different Antarctic and subantarctic moss samples, and are using this information for taxonomic and phylogenetic comparisons.\nWe have published new records of moss species on Heard Island, and new mosses recorded in the Vestfold Hills and Prince Charles Mountains are being prepared for publication.\nWe are continuing to analyse the ITS gene sequences from subantarctic higher plants such as several grass species, to determine their dispersal patterns.\nWe are also using DNA sequence information to analyse the extent of genetic diversity within moss species, and to compare populations from different Antarctic locations.", "links": [ { diff --git a/datasets/ASAC_2153_1.json b/datasets/ASAC_2153_1.json index a84e8c2371..bfd542d8a7 100644 --- a/datasets/ASAC_2153_1.json +++ b/datasets/ASAC_2153_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2153_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples from Macquarie Island were collected between 1998 and 2004. Samples from Heard Island were collected during 2000. Continental samples were collected between 2004 and 2006. This project aims to confirm that viruses are the cause of disease symptoms observed in several plant species from Macquarie Island, and to characterise the viruses. These would be the first examples of terrestrial plant viruses found in Antarctica, and the southernmost plant viruses found. The results would be of fundamental biological significance, and will enable investigation of how plant viruses evolve in such an isolated location. The possibility of terrestrial plant viruses on Heard Island will also be investigated.\n\nA species from this project that has been entered into the Genbank database, a partial sequence of Stilbocarpa virus from Macquarie island - AF478691 (Genbank number).\n\nSee also ASAC project 2152 (ASAC_2152).\n\nThe fields in this dataset are:\n\nSpecies\nDate\nLatitude\nLongitude\nCollection Site\nGenbank Number\nCollection Number\nInternal Transcribed Spacers\nComments\n \nProject objectives: The project objectives, as stated in the project application round 2008/09, appear below:\nThis project has already enabled identification and characterisation of a new virus in Stilbocarpa polaris on Macquarie Island. This is the first example of a terrestrial plant virus found in Antarctica, and is of of fundamental biological significance. It is the southernmost plant virus known, and occurs on one of the most isolated and geologically recent islands. We have determined the complete genomic sequence of this virus, and have started to analyse the dispersal and origins of this virus.\n\nThe main objectives of the next phase of this project are:\n1. to further investigate the genetic variability, origins and evolution of the Stilbocarpa virus SMBV, and compare it with other badnaviruses to assess whether it has an extra gene compared with other viruses in the group\n\n2. to analyse its means of transmission between Stilbocarpa plants and its dispersal around the island, and the extent of its effect on the host plants (such as significantly reduced seed set).\n\n3. to analyse the effect of climate change, already happening on Macquarie Island, on SMBV and its host plants.\n\n4. to analyse disease symptoms observed in several other subantarctic plant species, especially Cardamine corymbosa, to test whether these species are also virus-infected. Totally different virus-like particles have also been observed by electron microscopy in one sample of diseased leaves of Stilbocarpa polaris from Macquarie Island. These will be further characterised.\n\n5. to investigate the biodiversity and dispersal of other plant pathogens such as fungi, and their consequences on plant health. A fungal pathogen of the moss Bryum argenteum from continental Antarctica has been identified, and two others will be characterised from mosses on Heard and Macquarie Islands.\n\nThe further extension of this project will make use of specimens already collected on Heard and Macquarie Islands, to obtain as much information as possible about plant diseases in these remote locations, and their environmental adaptation to climate change.\n\nTaken from the 2008-2009 Progress Report:\n\nProgress against objectives:\nGood progress has been made with this project, in objectives where rabbit damage on Macquarie Island has not prevented progress.\nAnalysis of DNA sequencing results for variants of the Stilbocarpa mosaic bacilliform virus has continued this year. Two papers are nearing completion on these results.\nIt has proven difficult to analyse the means of transmission of the virus in Stilbocarpa at present, mainly due to rabbits completely eating plants at sites which were being monitored. However, this season we were able to sample some very young Stilbocarpa seedlings under plants difficult for rabbits to access, and this gives the possibility of testing for seed transmission of the virus.\nThe potential new plant virus previously observed in Cardamine could not be followed up, as the area has been completely denuded of Cardamine plants by rabbits. An exclosure has been erected to attempt to germinate potentially infected seedlings in the area where diseased plants had been observed.\nFungal infection of mosses colonising dead Poa foliosa tussocks was observed on Macquarie Island this year, and these colonies will be further examined.\nThe results and publications are in line with the objectives of the project. \n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nGood progress has been made with this project, in objectives where rabbit damage on Macquarie Island has not prevented progress.\nAnalysis of DNA sequencing results for variants of the Stilbocarpa mosaic bacilliform virus has continued this year. Two papers are nearing completion on these results.\nIt has proven difficult to analyse the means of transmission of the virus in Stilbocarpa at present, mainly due to rabbits completely eating plants at sites which were being monitored.\nThe potential new plant virus previously observed in Cardamine could not be followed up, as the area has been completely denuded of Cardamine plants by rabbits. An exclosure had been erected to attempt to germinate potentially infected seedlings in the area where diseased plants had been observed, but was removed this year as it had unfortunately been erected some 100m from the required site,and no infected Cardamine was growing inside the fencing. However, extensive searching in nearby locations this season has possibly revealed a new site for this potential virus, and samples will be analysed on their return to Australia in April.\nFungal infection of mosses colonising dead Poa foliosa tussocks was again observed on Macquarie Island this year, and these colonies will be further examined.\nThe results and publications are in line with the objectives of the project.", "links": [ { diff --git a/datasets/ASAC_2154_1.json b/datasets/ASAC_2154_1.json index bc948a53c8..8d12858983 100644 --- a/datasets/ASAC_2154_1.json +++ b/datasets/ASAC_2154_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2154_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nBacteria are an important part of the planktonic community of lakes and other aquatic environments. They use dissolved organic carbon in the water as a source of energy. This project aims to characterise the chemical nature of the pool of dissolved organic carbon, and manner in which bacteria use different fractions of it during the course of the year. Such information is crucial to constructing models of carbon cycling in lake communities. Models which characterise energy flow are important in understanding how these extreme, fragile lake ecosystems function.\n\nMethods used in the research (from the paper available in the download):\n\n(i) Sampling and sites - Crooked Lake and Lake Druzhby in the Vestfold Hills, Eastern Antarctica (68 degrees S, 78 degrees E) were studied between January 1999 and February 2000 (Figure 1 - see download). Crooked Lake has an area of 9 km2 and a maximum depth of 160m and was sampled at one site at 60m. Lake Druzhby has an area of 7 km2. It is a complex of three basins, two of which are shallow (sites 1 and 3) with maximum depths of 7m and 5m respectively, and one deep basin (site 2) with a maximum depth of 40m. Each of the basins was sampled at one site indicated on Figure 1. Sampling and production measurements were conducted monthly when logistics allowed access to the lakes. Access in summer was by helicopter and in winter, when the sea ice was sufficiently thick, by caterpillar track vehicle (Hagglunds). Vehicle access over land is not permitted for environmental reasons. The lakes were sampled by drilling a hole in the ice with a Jiffy drill and depth samples taken with a Kemmerer sampler from 0m (immediately under the ice), 2, 5, 8,10, 15 and 20m in Crooked Lake and site 2 of Lake Drzuhby, and 0m and 5m in the shallow basins. During a short phase of open water in Lake Druzhby during summer the lake was sampled from a boat. Water temperatures were measured with a digital thermometer. Aliquots of water from each depth were collected as follows: 1L in acid washed, deionsed water rinsed bottles for inorganic nutrients, dissolved organic carbon (DOC), dissolved amino acids (DAA) and dissolved carbohydrates (DCHO) analyses; 50 mL was fixed in buffered glutaradehyde (final concentration 2%) for counts of bacterial abundances.\n\nii) Analysis of samples - Samples for inorganic nutrient analysis (soluble reactive phosphorus PO4-P, ammonium NH4-N, nitrate NO3-N) were filtered through GF/F glass fibre filters and concentrations assayed colorimetrically according to the methods of Mackereth et al. (1989) and Eisenreich et al. (1975). DOC concentrations were determined on GF/F filtered samples in a Shimadzo TOC 5000 carbon analyser. Concentrations of bulk DCHO were determined using MBTH according to Pakulski and Benner (1992) and bulk DAA using the o-phlaldialdehide/b-mercaptoethanol fluorescence procedure of Jones et al. (2002) with a LS-5B Fluorimeter (Perkin Elmer Corp, Boston,MA) with the emission wavelength set to 340 nm (slit width = 15 nm) and the emission wavelength set to 450 nm (slit width 20 nm). Total organic nitrogen (TON) was determined using a Shimadzu TC/TN analyser equipped with chemo-luminescence detection. Dissolved organic nitrogen was calculated by subtracting the inorganic N present in samples from the TON vlaues.\n\nBacteria concentrations were determined on 10 mL glutaradehyde fixed aliquots. Each was sonicated for 2 minutes to disperse bacteria attached to particles of organic carbon that were noted in previous studies (Laybourn-Parry et al., 1994). Aliquots were stained with DAPI ( 4',6-diamidino-2-phenylindole, Sigma) then filtered through a black 0.2 micro m polycarbonate filter and viewed under epifluorescence micrcoscopy with UV excitation at x 1600. Bacterial biomass was calculated by measuring 50 cells on each preparation with a Patterson graticule, calculating cell volume using a sphere or ellipsoid as appropriate and converting volumes to carbon equivalents using a conversion factor of 0.20 pg C micro m3 (Bratbak and Dundas, 1984). \n\n(iii) Determination of bacterial production - Experiments were conducted in situ on water collected from 0, 5 and 10 m in Crooked Lake and site 2 of Lake Druzhby. In the shallow basins of Lake Druzhby (sites 1 and 3) experiments were conducted at 0m and 5m. The experiments were suspended from a frame through a hole in the ice, at the depths from which the water was collected. During the summer phase of open water in Lake Druzhby, incubations were undertaken in the laboratory at Davis under field light and temperature conditions. Bacterial production was determined using the dual labelling procedure for assessing the incorporation of thymidine into DNA and leucine into protein (Chin-Leo and Kirchman, 1988) with some modification (Zohary and Robarts, 1993). Saturation experiments on Crooked Lake and Lake Druzhby indicated that a minimum of 40 nM of [3H] thymidine and 20 nM of [14C] leucine was appropriate. To each incubation [3H] thymidine (specific activity 49 Ci mmol-1; Amerhsam) was added to a final concentration of 40nM and 14C-labelled leucine (specific activity 315 mCi mmol-1) was added to a final concentration of 20nM. At each depth four 20mL experimental and two control incubations were run in Whirlpaks. After incubation for 90 minutes the reaction was terminated by the addition of 0.6ml of formalin to give a final concentration of 4% and ice-cold trichloroacetic acid (TCA) to give a final concentration of 10%. Samples were filtered through 0.22 micro m cellulose acetate filters and washed with two volumes (5ml) of 5% ice cold TCA. The filters were dissolved with 1mL ethyl acetate, 10mL of scintillation fluid added and counts conducted in Beckman LS6500 scintillation counter. A conversion factor of 2x1018 cell mol-1 was applied to the incorporation rates of thymidine into DNA. Freshwater studies have demonstrated that where generation time exceeds 20 h a conversion factor in the region of 2.5 x 1018 cells mol-1 is appropriate, whereas where generation times are less than 20 h a conversion factor of 11.8 x 1018 cell mol-1 occurs (Smits and Riemann, 1988). We assumed similar low generation times in the cold waters of the saline lakes in this study. A conversion factor of 1.42 x 1017 cells mol-1 for the incorporation of leucine was applied (Chin-Leo and Kirchman, 1988). The determination of bacterial cell sizes and conversion to carbon is described under (ii) above.\n\n(iv) Nutrient addition effects on bacterial production - In these experiments the DOC from 10-12 litres of water were separated into 2 molecular weight fractions: less than 1000 Da and greater than 1000 Da, using a Pellicon-2 tangential ultra-filtration system (Millipore, USA). The water samples used were integrated from 2 m, 5 m, 10 m and 20 m. To prepare the water samples for enrichment incubations, a 0.2 micro M membrane filter plate was installed in the Pellicon-2 system. This membrane provided a sterilized DOC sample (less than 1000 Da fraction). The 0.2 micro M membrane plate was then replaced with 2 x 1000 Da membrane plates thereby providing efficient sample throughput. The samples were run until 2 litres of greater than 1000 Da fraction remained. Five hundred mL of raw lake water was then added to each of the water fractions (less than 1000 Da and greater than 1000 Da) in a ratio of 1:1 (v/v). A series of flasks each containing 1 L of water were set up, of which three acted as controls: lake water, less than 1000 Da 1:1 with lake water and greater than 1000 Da 1:1 with lake water. To a further three identical flasks 1 mL of a composite standard of inorganic nutrients were added made up of 100 micro g mL-1 PO4-P, NO3-N and NH4-N using KH2PO4, KN03 and NH4Cl respectively. The experiment was incubated (shaken) for three days in the dark at 4oC. Each flask was sub-sampled at 0, 24, 48 and 72 hours. Thirty-two mL aliquots were taken for DOC and inorganic nutrient analysis bacterial enumeration as outlined above. Fifty mL was removed for bacterial production determinations as described above.\n\n(v) Aggregate versus 'free' bacterial production - small particles of particulate organic matter have been shown to have high concentrations of bacteria and different rates of production relative to free floating bacteria (refs). To test any differences integrated water samples were collected from Crooked Lake and site 2 of Lake Druzhby. Two hundred mL samples where reverse gravity filtered through 18 micro m bolting silk sieves to produce 180 mL of filtrate and a residue of 20 mL concentrated particles, which was then made up to 200 mL with 0.2 micro m filtered lake water. Aliquots (20 mL) were fixed in buffered glutaradehyde, as in (ii) above, for determinations of bacterial abundance and biomass. Bacterial production determinations were conducted on each fraction and whole water controls as outlined in (iii) above.\n\nFor further information, see the attached paper.\n\nThe fields in this dataset are:\n\nDate\nLake\nDepth\nDissolved Organic Carbon\nDissolved carbohydrates and amino acids\nInorganic Nutrient Concentrations\nPrimary Production\nChlorophyll a", "links": [ { diff --git a/datasets/ASAC_2201_1.json b/datasets/ASAC_2201_1.json index 431793901b..be77d7acbf 100644 --- a/datasets/ASAC_2201_1.json +++ b/datasets/ASAC_2201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The natural world is a mosaic of different habitats and biological communities; the tiles of this mosaic may be small but the patterns formed can be measured at many scales from metres to thousands of kilometres. Understanding these patterns is important to protecting biodiversity. We will identify major scales of variability in Antarctic coastal habitats, biological communities and processes that create them. We will also document scales of impacts caused by humans in Antarctica and potential impacts of future climate change driven by key processes (changes in sea-ice). This information will contribute to environmental management to protect Antarctic coastal ecosystems.\n\nThis record is the parent record for all metadata records relating to ASAC project 2201. See the child metadata records for access to the data arising from this project.\n\nSee the project link for a full listing of personnel involved in this project.", "links": [ { diff --git a/datasets/ASAC_2201_Bacterial_Mat_Infauna_1.json b/datasets/ASAC_2201_Bacterial_Mat_Infauna_1.json index 748e10b63a..d198bd5c89 100644 --- a/datasets/ASAC_2201_Bacterial_Mat_Infauna_1.json +++ b/datasets/ASAC_2201_Bacterial_Mat_Infauna_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Bacterial_Mat_Infauna_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Infaunal marine invertebrates were collected from inside and outside of patches of white bacterial mats from several sites in the Windmill Islands, Antarctica, around Casey station during the 2006-07 summer. Samples were collected from McGrady Cove inner and outer, the tide gauge near the Casey wharf, Stevenson's Cove and Brown Bay inner. Sediment cores of 10cm depth and 5cm diameter were collected by divers using a PVC corer from inside (4 cores) and outside (4 cores) each bacterial patch. The size of each patch varied from site to site. Cores were sieved at 500 microns and the extracted fauna preserved in 4 percent neutral buffered formalin. All fauna were counted and identified to species where possible or assigned to morphospecies based on previous infaunal sampling around Casey.\n\nAn excel spreadsheet is available for download at the URL given below. The spreadsheet does not represent the complete dataset, and is only the bacterial mat infauna data.\n\nRegarding the infauna dataset:\n\n- in - in the mat or patch of bacteria and out is in the \"normal\" sediment surrounding the patch without evidence of any bacterial mat presence.\n- Patch numbers were allocated to ensure there was no confusion between patches in the same area.\n- Fauna names are our identification codes for each species. Some we have confirmed identifications for, some not. Species names, where we have them and as we get them, are listed against these codes in the Casey marine soft-sediment fauna identification guide.\n\nThis work was completed as part of ASAC 2201 (ASAC_2201).", "links": [ { diff --git a/datasets/ASAC_2201_Casey_SRE1_1.json b/datasets/ASAC_2201_Casey_SRE1_1.json index 6f3c93e656..0dcfdfcb99 100644 --- a/datasets/ASAC_2201_Casey_SRE1_1.json +++ b/datasets/ASAC_2201_Casey_SRE1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Casey_SRE1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The effect of location, depth and sediment contamination on recruitment of soft-sediment assemblages were examined in a pilot experiment at Casey Station, East Antarctica. Two locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and an undisturbed control (O'Brien Bay). At each location two types of defaunated sediment (polluted and control) were placed at 2 depths, 15 m and 25 m. Sediments were left in place over the Austral winter, from March - November. There were large differences in recruitment between the two locations and depths and some differences between the two sediment types. Brown Bay had greater recruitment than O'Brien Bay. Shallow sites had generally greater recruitment than deep, but deep sites had greater diversity (H'), richness (d) and evenness (J'). Control sediment recruited greater numbers of arthropod, gammarid and isopod taxa. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control and deep locations more variable, and recruitment in the control sediment more variable than the polluted sediment. Recruitment was influenced by a combination of location, depth and sediment type. There is some evidence of an environmental impact at the polluted site. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods.\n\nA total of 56 recruitment samples were collected. Samples were sieved at 500 micro metres and sorted mainly to species. Metal concentrations and total organic carbon concentrations are also included.\n\nAlso links to ASAC 1100.\n\nThe fields in this dataset are:\n\nSpecies\nLocation\nSite\nTreatment (tmt)\nSite and replicate\nToxicity\nArsenic\nCadmium\nCopper\nLead\nSilver\nZinc", "links": [ { diff --git a/datasets/ASAC_2201_Casey_SRE2_1.json b/datasets/ASAC_2201_Casey_SRE2_1.json index ff27de7afa..6cb3e24f1f 100644 --- a/datasets/ASAC_2201_Casey_SRE2_1.json +++ b/datasets/ASAC_2201_Casey_SRE2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Casey_SRE2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The effect of location and sediment contamination on recruitment of soft-sediment assemblages were examined in field experiment at Casey Station, East Antarctica. Four locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay), a bay adjacent to the Casey Station sewage outfall, and two undisturbed control locations in O'Brien Bay. At each location two types of defaunated sediment (polluted and control) were placed 12 - 18 m, in experimental trays. Half of the experimental sediments were left in place over the Austral winter, from March - November, and the remaining sediments were collected after a total of one year, in February 1999.\n\nThere were large differences in recruitment between the two locations and significant differences between the polluted and control sediment. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control locations more variable than polluted locations, and recruitment in the control sediment more variable than the polluted sediment. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods.\n\nA total of 64 recruitment samples were collected after 9 months and 52 samples after one year. Samples were sieved at 500 micro m and sorted mainly to species.\n\nSamples are rows in data sheet. Site codes include place name (e.g. BB2) and experimental treatment (e.g. C1 - control 1). See accompanying sheet for full details of codes, including species names. Sediment chemistry data are means (and standard errors) for each treatment (averaged over 2 trays).\n\nAlso links to ASAC 1100.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nSample\nAbundance\nToxicity\nArsenic\nCadmium\nCopper\nLead\nSilver\nZinc", "links": [ { diff --git a/datasets/ASAC_2201_Casey_SRE3_1.json b/datasets/ASAC_2201_Casey_SRE3_1.json index 0837c7b81a..59e98b16bd 100644 --- a/datasets/ASAC_2201_Casey_SRE3_1.json +++ b/datasets/ASAC_2201_Casey_SRE3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Casey_SRE3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The effects of hyrdocarbon and heavy metal contamination of marine sediments on recruitment of soft-sediment assemblages were examined in a field experiment at Casey Station, East Antarctica. Three locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and two control locations (O'Brien Bay and Sparkes Bay). At each location three types of defaunated sediment (hydrocarbon treated, heavy metal treated and control) were placed at approximately 15 m depth and left in place for 3 months, from December to February. Sediments were artificially contaminated with hydrocarbons and metals at concentrations which were representative of levels found in sediments at contaminated sites around Casey Station.\n\nThere were large differences in recruitment between the three locations and significant differences between the control and contaminated sediment. Sediments in the experiment were also examined for evidence of degradation and attenuation of hydrocarbons and heavy metals. A total of 104 recruitment samples were collected. Samples were sieved at 500 micro m and sorted mainly to species. Other work to arise from this experiment includes examination of the effects on diatom communities and microbial communities.\n\nData includes fauna, metals and hydrocarbon concentrations in experiment. Pre-deployment concentrations (before experiment was deployed in water) are indicated as 'pre-deployment'. Concentrations of contaminants in sediments surrounding the experiment (within several metres) are indicated as 'surrounding'.\n\nThis project also links to ASAC 1100.\n\nThe fields in this dataset are:\n\nLocation\nSite\nTreatment (tmt)\nSite and replicate\nSpecies\nToxicity\nArsenic\nCadmium\nCopper\nLead\nSilver\nZinc\nSpecial Antarctic Blend Fuel (SAB)\nLube\nTPH", "links": [ { diff --git a/datasets/ASAC_2201_Casey_tiles_1_mobile_1.json b/datasets/ASAC_2201_Casey_tiles_1_mobile_1.json index 75a99bc457..c9e4a34932 100644 --- a/datasets/ASAC_2201_Casey_tiles_1_mobile_1.json +++ b/datasets/ASAC_2201_Casey_tiles_1_mobile_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Casey_tiles_1_mobile_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The recruitment of mobile epifauna on hard-substratum was examined in a field experiment using tiles. A total of 160 tiles were deployed at five locations, with 32 tiles at each location, arranged in a spatially nested design. There were three potentially impacted locations locations (two in Brown Bay and one in Shannon Bay) and two control locations (in O'Brien Bay).\n\nThis metadata record describes data from the first sampling time only. Eight tiles were collected from each location 15 months after the initial deployment. The experiment was setup so that the combined recruitment of mobile epifauna to the upper and lower sides of the tiles could be examined. The sessile epifauna on the tiles were also collected and are described in a separate metadata record.\n\nA total of 40 samples are included in this data.\n\nAlso links to ASAC 1100.", "links": [ { diff --git a/datasets/ASAC_2201_Casey_tiles_1_sessile_1.json b/datasets/ASAC_2201_Casey_tiles_1_sessile_1.json index c10ec9885d..591bd2fc56 100644 --- a/datasets/ASAC_2201_Casey_tiles_1_sessile_1.json +++ b/datasets/ASAC_2201_Casey_tiles_1_sessile_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Casey_tiles_1_sessile_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The recruitment of epifauna (sessile and mobile) on hard-substratum was examined in a field experiment using tiles. A total of 160 tiles were deployed at five locations, with 32 tiles at each location, arranged in a spatially nested design. There were three potentially impacted locations locations (two in Brown Bay and one in Shannon Bay) and two control locations (in O'Brien Bay).\n\nThis metadata record describes data from the first sampling time only. Eight tiles were collected from each location 15 months after the initial deployment. The experiment was setup so that recruitment of sessile epifauna to both the upper and lower sides of the tiles could be examined. The mobile epifauna on the tiles were also collected and are described in a separate metadata record.\n\nHeavy recruitment was observed on the underside of the tile and only light recruitment was observed on the upper surface.\n\nAlso links to ASAC 1100.", "links": [ { diff --git a/datasets/ASAC_2201_Depth_Experiment_1.json b/datasets/ASAC_2201_Depth_Experiment_1.json index fbe4a566ba..9c9ae8cf83 100644 --- a/datasets/ASAC_2201_Depth_Experiment_1.json +++ b/datasets/ASAC_2201_Depth_Experiment_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Depth_Experiment_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Depth related changes in the composition of infaunal invertebrate communities were investigated at two sites in the Windmill Islands around Casey station, East Antarctica, during the 2006/07 summer. Sediment cores (10cm deep x 10cm diameter) were collected from 4 depths (7m, 11m, 17, and 22m) from each of three transects at two sites (McGrady Cove and O'Brien Bay 1). Cores were sieved through a 500 micron mesh and extracted fauna were preserved in 8% formalin and were later counted and identified to species or to morphospecies established through previous infaunal research at Casey. This work was conducted as part of ASAC 2201 (ASAC_2201).", "links": [ { diff --git a/datasets/ASAC_2201_Depth_Sediment_1.json b/datasets/ASAC_2201_Depth_Sediment_1.json index e4b23b72ae..d90060ab93 100644 --- a/datasets/ASAC_2201_Depth_Sediment_1.json +++ b/datasets/ASAC_2201_Depth_Sediment_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Depth_Sediment_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Depth related changes in sediment characteristics and the composition of infaunal invertebrate communities were investigated at two sites in the Windmill Islands around Casey station, East Antarctica, during the 2006/07 summer. Sediment characteristics were investigated via sediment cores (5cm deep x 5cm diameter) collected from 4 depths (7m, 11m, 17, and 22m) from each of three transects at two sites (McGrady Cove and O'Brien Bay 1). Measured sediment characteristics included grain size distribution, total organic carbon and the concentration of a range of heavy metals. This work was conducted as part of ASAC 2201 (ASAC_2201).", "links": [ { diff --git a/datasets/ASAC_2201_HCL_0.5_1.json b/datasets/ASAC_2201_HCL_0.5_1.json index c6a098c239..39632a1a9a 100644 --- a/datasets/ASAC_2201_HCL_0.5_1.json +++ b/datasets/ASAC_2201_HCL_0.5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_HCL_0.5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These results are for the 0.5 hour extraction of HCl.\n\nSee also the metadata records for the 4 hour extraction of HCl, and the time trial data for 1 M HCl extractions.\n\nA regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica.\n\nThe fields in this dataset are\n\nLocation\nSite\nReplicate\nAntimony\nArsenic\nCadmium\nChromium\nCopper\nIron\nLead\nManganese\nNickel\nSilver\nTin\nZinc", "links": [ { diff --git a/datasets/ASAC_2201_HCL_4_1.json b/datasets/ASAC_2201_HCL_4_1.json index 1984d266d2..eb990bb609 100644 --- a/datasets/ASAC_2201_HCL_4_1.json +++ b/datasets/ASAC_2201_HCL_4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_HCL_4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These results are for the 4 hour extraction of HCl.\n\nSee also the metadata records for the 0.5 hour extraction of HCl, and the time trial data for 1 M HCl extractions.\n\nA regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica.\n\nThe fields in this dataset are\n\nLocation\nSite\nReplicate\nAntimony\nArsenic\nCadmium\nChromium\nCopper\nIron\nLead\nManganese\nNickel\nSilver\nTin\nZinc", "links": [ { diff --git a/datasets/ASAC_2201_Long-term_Sediment_Metals_1.json b/datasets/ASAC_2201_Long-term_Sediment_Metals_1.json index 8225bdf8aa..6c127af269 100644 --- a/datasets/ASAC_2201_Long-term_Sediment_Metals_1.json +++ b/datasets/ASAC_2201_Long-term_Sediment_Metals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Long-term_Sediment_Metals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment cores (5cm diameter x 10cm deep), collected as part of the long-term monitoring of the Thala Valley waste disposal site clean-up (Casey station), were sectioned and a portion of each core analysed for a range of heavy metals. Metals were extracted from the sediment via a 4 hour 1M HCl acid extraction. Concentrations were gained from ICP-MS analysis of the resulting extracts (ICP-MS conducted at the School of Chemistry, University of Tasmania). Cores were collected from various control and potentially impacted sites in the Windmill Islands around Casey station.\n\nThis work was conducted as part of ASAC 2201 (ASAC_2201).", "links": [ { diff --git a/datasets/ASAC_2201_Runcie_1.json b/datasets/ASAC_2201_Runcie_1.json index 2de00983ae..759a838fdc 100644 --- a/datasets/ASAC_2201_Runcie_1.json +++ b/datasets/ASAC_2201_Runcie_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_Runcie_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of macroalga Desmarestia menziesii, assessing adaptation to high light exposure after sea ice breakout, and impact of Thala Valley tip wastes.\n\n2. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of sediment diatom material assessing adaptation to high light exposure after sea ice breakout, and impact of Thala Valley tip wastes.\n\n3. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of sponge Latrunculia decipiens assessing adaptation to high light exposure after sea ice breakout.\n\n4. Ecotoxicological experiments where Desmarestia menziesii was exposed to copper in indoor aquaria, aim to determine EC50, NOEC, LOEC for copper.\n\n5. Field collections of various macroalgae for stable isotope analysis: for determination of physiological mechanisms.\n\n6. Field collections of sponge and diatom material for pigment analysis.", "links": [ { diff --git a/datasets/ASAC_2201_field_lab_books_1.json b/datasets/ASAC_2201_field_lab_books_1.json index b4715c1ae2..da8b8d0bfd 100644 --- a/datasets/ASAC_2201_field_lab_books_1.json +++ b/datasets/ASAC_2201_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2201_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station and Davis Station between 1997 and 2012 as part of ASAC (AAS) project 2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities.", "links": [ { diff --git a/datasets/ASAC_2208_seabirds_1.json b/datasets/ASAC_2208_seabirds_1.json index 82a52557f8..b8ca62c0cd 100644 --- a/datasets/ASAC_2208_seabirds_1.json +++ b/datasets/ASAC_2208_seabirds_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2208_seabirds_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises data on the distribution and abundance of seabirds in the Southern Indian Ocean.\n\nA database of seal, whale and bird observations made from ships since the 1978/1979 austral summer shipping season. Observations are typically made from the bridge out one side of the ship. Data held in this database greater than two years old are publicly available. This work forms part of ASAC project 1219 (ASAC_1219).\n\nInitial datasets were from the BIOMASS cruises (ADBEX1,ADBEX2,ADBEX3, SIBEX, FIBEX).\n\nThis work was completed as part of ASAC project 2208 (ASAC 2208).\n\nThis project also incorporates several other ASAC projects. These are:\n\nASAC_533 - The Distribution and Abundance of Seabirds at Sea in Prydz Bay in Relation to Physical and Biological Parameters\nASAC_657 - Prey Consumption by Seabirds around Heard Island in Relation to Physical and Biological Oceanographic Parameters\nASAC_725 - The role of the marginal ice zone in the consumption of marine resources by Antarctic seabirds in the Southern Indian Ocean\nASAC_1219 - Monitoring for long-term or cumulative impacts in Southern Ocean seabirds\n\nData from these projects were fed directly into project 2208.\n\nThe fields in the csv file are:\n\nwov_data_id (the internal identifier of the record)\nobservation_date (the date of observation, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ. This information is also separated into the year, month, day, etc components)\nobservation_date_year (the year of the observation date)\nobservation_date_month (the month of the observation date)\nobservation_date_day (the day of the observation date)\nobservation_date_hour (the hour of the observation date)\nobservation_date_minute (the minute of the observation date)\nobservation_date_second (the second of the observation date)\nobservation_date_time_zone (the time zone of the observation date)\nlatitude (the latitude of the observation, in decimal degrees)\nlongitude (the longitude of the observation, in decimal degrees)\nship_speed (knots)\nship_course (degrees)\nseastate (scale of wave conditions, from \"calm-glassy\" to \"phenomenal (over 14 m)\")\nsea_temperature (sea surface temperature from the ship's instrumentation, in degrees C)\nseaice (tenths of sea ice cover)\nvisibility (the limit of effective visibility, from \"less than 300 m\" to \"Unrestricted visibility\")\nsalinity (sea surface salinity from the ship's instrumentation, in practical salinity units PSU)\ndepth (water depth in metres)\ncloud_cover (oktas)\nprecipitation (description of precipitation conditions, from \"No precipitation/ clear\" to \"Thunderstorms\")\nwindforce (scale of wind conditions with speed range in knots, from \"Calm (0-0.9)\" to \"Hurricane (109-112)\")\nwind_direction (the direction the wind is blowing from, in degrees [north=0, east=90])\nair_temperature (degrees C)\nair_pressure (hectopascals)\nship_activity (short description of the ship activity at the time of the observation)\nspecies_type (Bird, Seal, Whale)\ntaxon_id (internal identification code for the species observed)\nbird_age (juvenile, subadult, or adult)\ndistance (distance of observed animal from the ship: \"Near (0-300m)\", \"Middle 300-1000 m\", or \"Far + 1000 m\" )\nspecies_count (the number of individuals observed in total)\nfeeding_count (the number of individuals observed that were feeding)\nsitting_on_water_count (the number of individuals observed that were sitting on water)\nsitting_on_ice_count (the number of individuals observed that were sitting on ice)\nsitting_on_ship_count (the number of individuals observed that were sitting on the ship)\nin_hand_count (the number of individuals observed that were held in the hand)\nflying_past_count (the number of individuals observed that were flying past)\naccompanying_count (the number of individuals observed that were accompanying the vessel)\nfollowing_wake_count (the number of individuals observed that were following the wake of the vessel)\nbird_direction (direction of travel of observed individuals, in degrees [northwards travel=0, eastwards travel=90])\nobservation_code (the type of observation activity being conducted, e.g. \"Stern count\", \"Forward Quadrant\", or \"null observation - no birds seen\")\nswimming_past_count (the number of individuals observed that were swimming past the vessel)\nfloat_in_water_count (the number of individuals observed that were floating in the water)\nporpoising_count (the number of individuals observed that were porpoising)\nfollowing_count (the number of individuals observed that were following the vessel)\nsurfacing_count (the number of individuals observed that were surfacing)\nbreaching_count (the number of individuals observed that were breaching)\nblowing_count (the number of individuals observed that were blowing)\nmove_thru_ice_count (the number of individuals observed that were moving through ice)\nfrolicking_count (the number of individuals observed that were frolicking)\nvoyage_id (the internal identifier of the voyage)\nnotes (other notes recorded during the observation)\ndate_created (the date the observation record was created, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ)\ndate_revised (the date the observation record was last revised, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ)\nhusky_code (the species code used in the husky data logging system)\nidentification_status (status of the species identification: \"Confirmed\" or \"Unconfirmed\")\nwind_speed (wind speed, in knots)\ndata_notes (notes about the data associated with the observation)\ngenus (genus name of the observed species)\nspecies (specific epithet of the observed species)\ncommon_name (common name of the observed species)\nsubspecies (subspecies name of the observed species)\naphia_id (the taxonomic identifier of the observed species, from the Register of Antarctic Marine Species and the World Register of Marine Species, http://www.marinespecies.org/)", "links": [ { diff --git a/datasets/ASAC_2233_1.json b/datasets/ASAC_2233_1.json index 1ef28d6908..5a8dec0c52 100644 --- a/datasets/ASAC_2233_1.json +++ b/datasets/ASAC_2233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data consists of records of two species of earthworms from 70 sites on Macquarie Island collected and identified by Dr R. Blakemore. The species are:\n\nFamily ACANTHODRILIDAE .\n Genus MICROSCOLEX ROSS, 1887\n\nSpecies Microscolex macquariensis (Beddard, 1896)\n\nFamily LUMBRICIDAE\n Genus DENDRODRILUS Omodeo, 1956\n\nSpecies Dendrodrilus rubidus (Savigny, 1826) F. subrubicundus Eisen, 1874\n\nThe material is deposited in the Queen Victoria Museum.\n\nThere are no publications from this project at this date.\n\nFurthermore, two flat worm were collected for the first time on this project.\n\nCollection details: summer 97/98. Lusitania Creek, collected Dr R. Blakemore. Identified L. Winsor.\n\nSpecies:\n\n1. Arthurdenyus n. sp.\n2. New genus new species.\n\nMaterial of these two species is deposited with Leigh Winsor, School of Tropical Biology, c/- Central Services Office, James University, Townsville, Qld 4810.\n\nAn excel spreadsheet of sampling locations is available for download at the provided URL.\n\nThe fields in this dataset are:\n\nDate\nCollector\nLocation\nNotes", "links": [ { diff --git a/datasets/ASAC_2237_1.json b/datasets/ASAC_2237_1.json index 67d5e9507e..4e3d154cf8 100644 --- a/datasets/ASAC_2237_1.json +++ b/datasets/ASAC_2237_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2237_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2237\nSee the link below for public details on this project.\n\nTwo excel spreadsheets are available for download from the provided URL.\n\nTaken from the 1997-1998 Progress Report for this project:\n\nINAA (instrumental neutron activation analysis) analyses have been made of subsamples of each OSL (Optically stimulated luminescence) sample, for dosimetry calculation. The samples were then dated at Royal Holloway, University of London (RHUL) which is the worlds leading lab for this work.\n\nTwo very significant findings were made: (i) That the OSL technique works, and is reliable in Antarctica. These are the first OSL dates from Antarctica; (ii) The overriding hypothesis of Colhoun et al. (ASAC 926) has been vindicated: that Bunger Hills was not fully glaciated at the last glacial maximum.", "links": [ { diff --git a/datasets/ASAC_2239_1.json b/datasets/ASAC_2239_1.json index cf3a0f9aba..3acd86220f 100644 --- a/datasets/ASAC_2239_1.json +++ b/datasets/ASAC_2239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The United States Department of Energy - Environmental Measurements Laboratory located in New York City has been monitoring the naturally occurring and man-made radionuclides for the past 40 years throughout the world. We have been using simple and very rugged air sampler which collect air from the surrounding environment. With this method and diverse location of sampling stations we have been able to detect with gamma counting method Beryllium 7, lead 210 as natural radionuclides and also some anthropogenic or man-made radionuclides such as Zirconium 95, Cesium 137, Cerium 144 which half-lives are fairly long. Come to visit us at: http://www.wipp.energy.gov/NAMP/EMLLegacy/index.htm and search for databases especially Surface Air Sampling Program. \n\nThe Surface Air Sampling Program (SASP) database provides information on EML's archived air filter samples and sample measurements. The program was established in 1957 to track the global dispersion of radioactive debris resulting from atmospheric testing of nuclear bombs. Air filter samples were collected at locations throughout the world and analyzed for nuclear debris. In the 1980's, the program focused on the global distributions of the naturally occurring radionuclides, beryllium-7 and lead-210. The resulting database is the most comprehensive and extensive record of its kind in the world.", "links": [ { diff --git a/datasets/ASAC_2275_1.json b/datasets/ASAC_2275_1.json index 0e25ca11c6..b685c6ef53 100644 --- a/datasets/ASAC_2275_1.json +++ b/datasets/ASAC_2275_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2275_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 2275.\nSee the link below for public details on this project.\n\nThe papers record microscope evidence and molecular evidence for the occurrence of a fungal symbiosis in an Antarctic leafy liverwort. Fungi isolated from the leafy liverwort Cephaloziella exiliflora collected in Australia and continental Antarctica were compared with Hymenoscyphus ericae using internal transcribed spacer (ITS) region DNA sequences. The isloates displayed less than 2.1% sequence divergence within the ITS region, indicating that the endophytes from C. exiliflora are probably H. ericae. The data significantly extend the known host range and geographical distribution of H. ericae and indicate that the fungus has a global distribution. The dataset also describes infections by hyaline, septate fungal hyphae in rhizoids and adjacent axial cells of the foliose liverwort Cephaloziella exiliflora collected from two locations in continental Antarctica. Evidence is presented that the fungus in the rhizoids is an ascomycete and that the endophytic infections are mycorrhiza-like or mycothalli, refuting an earlier proposal that mycorrhizas might be absent from the Antarctic.", "links": [ { diff --git a/datasets/ASAC_2277_1.json b/datasets/ASAC_2277_1.json index 67c2f3de50..eb08893aa3 100644 --- a/datasets/ASAC_2277_1.json +++ b/datasets/ASAC_2277_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2277_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2277 See the link below for public details on this project.\n\nA product of this project was a report \"Macquarie Island Track Monitoring and Assessment\" prepared for the Tasmanian Parks and Wildlife Service by Judith Urquhart, September 1996. A copy of the report is held in the Australian Antarctic Division library.\n\nExecutive Summary from the report:\n\"Certainly in comparison to many of Tasmania's walking tracks in similar environments, those on Macquarie Island are generally in good condition. The key purpose of this study however is to gather data over the long and short term which will inform management practices in order to prevent the environmental damage evident elsewhere.\n\nGradient and vegetation cover would appear to be the most significant factors in determining the condition of tracks, although the added factor of usage levels influences rates of deterioration to a lesser degree. Climatic conditions result in extremely slow rates of recovery of vegetation on steep coastal slopes has been observed but this period is too short to re-establish vegetation and so long-term recovery is precluded. In those locations where wooden stairs, boardwalks and viewing platforms have been built to accommodate tourists, vegetation and soils have been protected and recovery of previously damaged vegetation is evident.\n\nThe most vulnerable walking environment is the Tall Herbfield. Damage is principally to the peat soils and to the foliage of the Stilbocarpa, particularly on very steep slopes. On the plateau top, Azorella macquariensis is extremely vulnerable and damaged in several, but localised sites. Bootprints easily damage the surface and recovery is extremely slow. Poorly drained locations in all environments are subject to churning of the surface and the creation of bare muddy swathes which, given the weather tend to stay in that condition. Environments dominated by native grasses and sedges are the most resistant to trampling and provided boots do not break through the surface they are durable even when the watertable is at or near the surface. Mosses, Poa annua and Acaena species which tend to colonise damaged surfaces are resistant to trampling and so create sound track surfaces.\n\nNumbers of visitors (other than tourists) to the island tend to be determined by logistic limitations and the capacity of the ANARE station to support expeditioners, rather than environmental considerations off the station. It is not unreasonable to speculate that tourism activity may increase on the island, including overnight stays and plateau walks. Further observation in vulnerable locations and on-going monitoring at established sites will be necessary to determine whether sustainable thresholds have been reached over much tracked area. Certain critical coastal slopes need immediate attention as damage is already severe and any additional pressure would be unacceptable. Opportunities exist now, with changes to field huts and to scientific research sites, to rationalise routes, closely monitor change and consider limits to numbers walking in the more vulnerable environments.\"\n\nA track monitoring survey on Macquarie Island carried out by Natasha Adams, surveyor, between September and November 1997 is described by the metadata record 'Surveys on Macquarie Island for the Australian Antarctic Division, September to November 1997', Entry ID: macca_survey97_gis.", "links": [ { diff --git a/datasets/ASAC_2283_1.json b/datasets/ASAC_2283_1.json index 708230d0c0..310cbc921b 100644 --- a/datasets/ASAC_2283_1.json +++ b/datasets/ASAC_2283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record is concerned with the measurement of deterioration processes affecting historic sites. Temperature and relative humidity data were collected from sensors inside Mawson's Huts. The data are in the form of Excel spreadsheets that enable plotting of temperature and relative humidity variation at various locations.\n\nThe Heard Island document available in pdf form is reproduced with the permission of the Papers and Proceedings of the Royal Society of Tasmania.\n\nThe paper was published in the Heard Island volume by the Royal Society of Tasmania (GPO Box 1166M, Hobart 7001, Tasmania, Australia) from whom the entire volume is available for A$22; plus postage (A$2.45) for orders from within Australia and A$20; plus postage (A$6; in Asia and the Pacific and A$9; elsewhere; payment in Australian currency) for orders from beyond Australia.\n\nThe fields in this dataset are:\nsite\nlatitude\ndistance from sea\ndays exposed\ncorrosivity\nmass loss (g)\nblank loss (g)\n% blank loss*\nDate\nTime\nTemperature\nRelative Humidity\nThermocouple Profile", "links": [ { diff --git a/datasets/ASAC_2286_1.json b/datasets/ASAC_2286_1.json index e85eb74c41..d34df87f1a 100644 --- a/datasets/ASAC_2286_1.json +++ b/datasets/ASAC_2286_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2286_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2286\nSee the link below for public details on this project.\n\nThere are two global networks associated with these data, Old and New.\n\nThe old network is the CPMN (Circum Pacific Magntometer Network). See the abstract provided below, and the full paper for complete details.\n\nThe new network is called MAGDAS (MAGnetic Data Acquisition System). Deployment began in 2005, and will be completed in 2006. 51 units will be deployed. MAGDAS will be operated for ten years by the Space Environment Research Centre (SERC).\n\n######################################################\n\nFrom the abstract of the paper 'Globally Coordinated Magnetic Observations Along 210 degree Magnetic Meridian during STEP Period' (1992)\n\nThe Solar-Terrestrial Environment Laboratory (STEL), Nagoya University, is carrying out multinationally coordinated magnetic observations along the 210 degree magnetic meridian from high latitudes, through middle and low latitudes, to the equatorialregion, spanning L = 9.05-1.03, in cooperation with 14 institutes in Japan, Australia, USA, and russia during the STEP period of 1990-1997. In this paper, we introduce in detail the project of globalmagnetic observations along the 210 degree magnetic meridian, and illustrate preliminary results of power spectrum and cross correlation analysis of low-latitude pulsations at the 6 chain stations installed in 1990.\n\nThe results can be summarised as follows:\n1) There are two spectral peaks in the Pc 3 range. A shorter-period component in the 10-20 sec range exhibits standing field-line resonance behaviour around L = 1.58, while the longer-period component in the 20-50 sec range indicates three different characters, a standing field-line oscillation at L greater than 2.1, a second-harmonic cavity resonance oscillation in the plasmasphere, and propagating-mode waves with phase delays from lower to higher latitudes.\n2) An ssc with deltaH ~ 215 nT magnitude at L = 1.22 on March 24, 1991, was found to simulate cavity-mode Pc 3 pulsations with duration less than 20 min and identical 15.5 and 25.3 mHz frequencies over the L = 1.14-2.13 low-latitude region.\n\n######################################################\n\nEach file contained in the dataset is compressed by gzip. Please extract these files by using a suitable archiver (e.g., gunzip command on UNIX shell).\n\nData Format:\n\nEach file has a file name, which is composed of 7 characters with a 3 character extension. The first byte is a character 'a'. It denotes that this file is ASCII-text. The following 6 bytes denote the 'YYMMDD'. The 3 character extension should be a 3 character abbreviation of a station name (MCQ).\n\nEach file consists of 86400 lines. Each line consist of 9 values, each value is separated by a space (YYYY MM DD hh mm ss H-comp D-comp Z-comp). YYYY is the year, MM is the month of the year, DD is the day of the month, hh is the hour of the day, mm is the minute of the hour, ss is the second of the minute. H-comp is the magnetic data of the North-South direction, D-comp is the magnetic data of the East-West direction, Z-comp is the magnetic data of the vertical direction, the unit of each component is the nanotesla (nT).\n\nThere is a time lag associated with this dataset:\n\n1. MCQ data have a time lag up to 12 hours from 23:30 UT April 3, 2001 to 18:15 UT April 19, 2001.\n\n2. MCQ data have a time lag up to 24 hours from 18:15 UT April 19, 2001 to July 4, 2002.\n\n3. MCQ data have a time lag up to 1 minute from July 5, 2002 until now.", "links": [ { diff --git a/datasets/ASAC_2290_1.json b/datasets/ASAC_2290_1.json index 7f4f2b3672..cea4838272 100644 --- a/datasets/ASAC_2290_1.json +++ b/datasets/ASAC_2290_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2290_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Small-scale cultures of a phenotyped Antarctic bacterium, Shewanella gelidimarina (ACAM 456T; Accession number U85907 (16S rDNA)), were grown aerobically with shaking at 4 degrees C in Difco Marine broth supplemented with potassium nitrate (5 mM). Cells were centrifuged and the periplasmic fraction harvested and assayed for nitrite production (using the Greiss reaction) as a measure of the potential expression of the enzyme, periplasmic nitrate reductase. Subsequent protein purification experiments identified a protein aggregate which gave a positive response in the Greiss assay with properties (denaturing PAGE: 42 kDa) that were inconsistent with periplasmic nitrate reductase enzymes characterized from alternate bacteria. N-Terminal sequencing (20 residues: A D P L T V Y G K L N V T A Q S N D V N) showed a high sequence homology to a putative outer membrane porin from Shewanella oneidensis MR-1 (Accession number: gi:24347323). The expression of periplasmic nitrate reductase has since been unambiguously established from cultures of S. gelidimarina grown under iron-limited conditions (i.e.; where the Fe(III) dissimilatory respiratory pathway of this genus is downregulated) in nitrate supplemented media. This work is ongoing and is aimed towards the chemical (spectroscopy) and biochemical (enzyme kinetics) characterisation of cold-adapted redox active metalloproteins.\n\nThis work is based upon phenotyped Antarctic bacteria (S. gelidimarina; S.frigidimarina) that was collected at another time (Refer: Psychrophilic Bacteria from Antarctic Sea-ice and Phospholipids of Antarctic sea ice algal communities new sources of PUFA [ASAC_708] and Biodiversity and ecophysiology of Antarctic sea-ice bacteria [ASAC_1012]).\n\n---- Public Summary from Project ----\nCold-adapted bacteria resident in the Antarctic express proteins that have unusual properties. To date, only one metal containing protein (metalloprotein) expressed by cold-loving bacteria has been preliminarily characterised. The characterisation of cold-adapted metalloproteins will provide an innovative Australian-based research program that may lead to novel biotechnology and/or bioremediation applications.", "links": [ { diff --git a/datasets/ASAC_2295_IWL_2002-2003_1.json b/datasets/ASAC_2295_IWL_2002-2003_1.json index 28fdea4141..d0d4a76a93 100644 --- a/datasets/ASAC_2295_IWL_2002-2003_1.json +++ b/datasets/ASAC_2295_IWL_2002-2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2295_IWL_2002-2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This research was a manipulative experiment on autoline ling vessels in the New Zealand ling fishery. The vessels were the Janas and the Avro Chieftain. The experiment examined both seabird bycatch data and fish catch data, as well as operational aspects of fishing with integrated weight longline. The data is a little bit complicated and it is essential that any users be familiar with the methodologies in the scientific paper that was published from the work. That will provide a lot of necessary guidance as well as a context for the research. The data covers 2002 and 2003, as indicated on the files.\n\nThe data submitted includes relevant information of i) seabird by-catch; ii) catch rates of target fish; iii) catch rates on non-target fish. There is replication in some of the data sheets provided. There are headers in each data file that are explanatory.", "links": [ { diff --git a/datasets/ASAC_2297_1.json b/datasets/ASAC_2297_1.json index 4a85ff3a10..66e9551031 100644 --- a/datasets/ASAC_2297_1.json +++ b/datasets/ASAC_2297_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2297_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2297: Iron content of Southern Ocean phytoplankton: implications for carbon transfer to the deep sea.\n\nData on size-fractionated distribution of suspended trace elements (including iron) in marine particles taken from the surface Southern Ocean south of Australia. Data for 4 size-fractions at 4 stations along ~142 degrees E are included.\n\nExplanation of codes used in the dataset:\n\nThe isotope of the element of interest is listed down column A. LR refers to low resolution and MR medium resolution (that is the resolution of the ICPMS analytical instrument). So Mn55(LR) is the manganese isotope 55 ran in low resolution.\n\nSFP1_2um_B2_1:\nSFP=size-fractionated particles\n1=station 1\n2um=2micron filter size\nB2=blank 2\n1=replicate 1\n\nRSD%=relative standard deviation in %\n\nBlank=field blank\nBlk/sample%=blank-to-sample ratio in %\n\nCRMs_261102=certified reference materials (ran on 26 Nov 2002)\nScaled up=previous column times multiplication factor (a serial dilution was used)\nBlk=blank subtracted\ncertified=the value certified by the manufacturer of the reference material\nSLRS_1in25_1= the CRM 'SLRS', ran with a 1 in 25 dilution factor, replicate 1\n\nDigBlk1_1=digestion blank 1, replicate 1\n\nSee the link below for public details on this project.", "links": [ { diff --git a/datasets/ASAC_229_1.json b/datasets/ASAC_229_1.json index f46b1e0f4e..486cf78893 100644 --- a/datasets/ASAC_229_1.json +++ b/datasets/ASAC_229_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_229_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 229 See the link below for public details on this project.\nFrom the abstracts of some of the referenced papers:\n \nIn January 1985 a net sampling survey was carried out on the distribution and abundance of euphausiid larvae in the Prydz Bay region. Euphausia superba occurred in low abundance, probably due to sampling preceding the main spawning period. Thysanoessa macrura occurred throughout the study area in consistently high abundance. Euphausia crystallorophias as marginally more abundant within its restricted range. Distinct north-south variations in larval age and development stages of T. macrura were observed indicating regional differences in spawning. Euphausia frigida was mainly confined to the upper 200 m of the Antarctic Circumpolar Current. Larvae originating on the shelf moved rapidly west in the East Wind drift. E. crystallorophias had the same westward dispersion, but some larvae appeared to return eastward via the Prydz Bay Gyre and remain in the region. The data indicate that most E. superba larvae, providing they survive injurious cold temperature and food deprivation, will leave the area, suggests that Prydz Bay krill may not be a self sustaining stock.\n \n#####\n\nThis paper presents results of net sampling carried out in four marine science cruises between 1981 and 1985, in the Prydz Bay region of Antarctica by the Australian Antarctic Division. Krill exhibited a patchy distribution and overall low abundance. The majority of sampling sites in January 1985 returned no post-larval krill or densities of less than 1 individual per 1000 cubic metres. The estimated mean abundance of E. superba in January 1985 was 6 indivduals or 2 g (wet wt.) per 1000 cubic metres integrated for the upper 200m of the water column which represented 3.4% of the total zooplankton biomass. No more than five years-groups, including the larvae, were observed in Prydz Bay, with mean lengths of groups 1+, 2+, 3+ and 4+ being 24, 38, 46 and 53 mm (standard 1), respectively in the middle of January. A high proportion of naupliar stages observed in January 1985 indicated that spawning in Prydz Bay begins in January and examination of adult maturation showed that the spawning continues at least to March.\n\n#####\n\nSixty female Antarctic krill (Euphausia superba Dana) spawned in shipboard experiments and the interval between egg-laying and ecdysis was noted. The number of eggs laid per female ranged from 263-3662, most females produced only one batch of eggs before moulting, and the post spawn ovaries of all females contained few, if any, mature oocytes. As reported in other studies, the total number of eggs produced per female was not well correlated with body size. Females appeared to spawn at all times during the moulting cycle and although no diurnal rhythm in spawning was observed, moulting occurred mainly at night-time despite the animals being kept in near-constant darkness. No evidence of synchronous moulting was detected.\n\n#####\n\nData from this project were collected on five Antarctic voyages:\n\nHIMS - Heard Island Marine Science - 1990-05-04 - 1990-07-01 AAMBER II - Australian Antarctic Marine Biological Ecosystem Research II - 1991-01-3 - 1991-03-19 FISHOG - Fish and Oceanography - 1992-01-09 - 1992-03-27 KROCK - Krill and Rocks - 1993-01-05 - 1993-03-09 BROKE - Baseline Research on Oceanography, Krill and the Environment - 1996-01-02 - 1996-03-31\n\nAll data are available in the download file.", "links": [ { diff --git a/datasets/ASAC_2300_1.json b/datasets/ASAC_2300_1.json index 8fe97b6273..37b0e6e938 100644 --- a/datasets/ASAC_2300_1.json +++ b/datasets/ASAC_2300_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2300_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2300\nSee the link below for public details on this project.\n\n---- Public Summary from Project----\n\nAntarctic reefs, like their tropical counterparts, harbour a high diversity of animal life. For the first time we will determine how global warming will affect food availability to the animals which comprise the structural components of the reefs. Ultimately, we wish to predict the cascading effect through the community as one component changes.\n\nWith the confirmation that sponges in Antarctic waters graze on ultraplankton there is now a global overview that sponges are the primary benthic organism that is responsible for linking the pelagic microbial food web to the benthos. Like other shallow water demosponges, sponges in Antarctica are omnivorous sponges that graze nonselectively, consuming both heterotrophic and phototrophic organisms. Retention efficiencies of ultraplankton are similar to other sponges measured using similar techniques from shallow water to the deep sea, the tropics to boreal waters. \n\nThe large amounts of water processed by these benthic suspension feeders and their diet places these sponges squarely within the functional group of organisms that link the pelagic microbial food web to the benthos. The number of macroinvertebrates that have been shown to side- step the microbial loop and directly utilize the base of the microbial food web as a primary food source is ever growing and currently includes demosponges, ascidians, soft corals, and bivalves. Dense macroinvertebrate communities dominated by demosponges and corals in shallow water have been shown to remove as much as 90% of the ultraplankton from the water that passes over them. The daily fluxes of ultraplankton to these communities ranges from 9 to 1970 mg C day-1 m-2. We conservatively estimate that this single species of sponge, which comprises only a portion of the benthos, mediates a flux of 444 mg mg C day-1 m-2 from the water column, which places it in the range of shallow-water temperate and boreal systems.\n\nFurthermore, we found that physical disturbance results in changes in community structure. The subtidal rocky coasts near Casey are similar to many of the exposed rocky coasts of the world that support extensive stands of macroalgae that form a strong positive association with understorey encrusting coralline algae. Loss of canopies of algae on temperate coasts often triggers large and predictable changes to the assemblage of understorey taxa. We observed large negative effects of removing canopies of H. grandifolius on encrusting corallines growing beneath, with such effects consistent with predictions of previous research on tropical and temperate coasts. However, elevating concentrations of nutrients did not greatly reduce the magnitude of the negative effects of canopy removal. Nevertheless, our results suggest that disturbance (removal) to canopies of H. grandifolius has large consequences for those organisms associated with this widely distributed (circumpolar) species of canopy-forming algae. \n\nSee the full copy of the final report (available for download from the URL given below) for more information.\n\nAlso included in the download file, are five Excel spreadsheets. The spreadsheets contain the data collected from the transects, quadrats, etc (see the final report for more information). Where possible the spreadsheets have been converted to csv files.\n\nThe fields in this dataset are:\n\nLocation\ndepth\nSpecies\nTransect\nQuadrat\nIrradiance\nPAR", "links": [ { diff --git a/datasets/ASAC_2301_1.json b/datasets/ASAC_2301_1.json index 6dc3b3ca71..b0e11712a5 100644 --- a/datasets/ASAC_2301_1.json +++ b/datasets/ASAC_2301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2301 See the link below for public details on this project.\n\n---- Public Summary from Project ----\nThis study develops and combines the latest molecular and electronics technology into a comprehensive investigation of diet and food-web relationships of Southern Ocean predators (whales, seals, penguins) and commercial marine resources (krill, fish, squid). This type of information is essential for ecosystem models that set sustainable catch limits for fisheries.\n\nFrom the abstract of the referenced paper:\n\nWe describe seven group-specific primer pairs that amplify small sections of ribosomal RNA genes suitable for identification of animal groups of major importance as prey items in marine ecosystems. These primer sets allow the isolation of DNA from the target animal groups from mixed pools of DNA, where DNA-based identification using universal primers is unlikely to succeed. The primers are designed for identifying prey and animal diets, but could be used in any situation where these animal groups are to be identified by their DNA.\n\nProgress report from the 2006/2007 Season:\nOverall objective\n\nThis new multi-year initiative project within the AMLR program aims to develop and combine the latest molecular and electronics technology to facilitate a comprehensive investigation of appropriately scaled and strategically located trophodynamics of Southern Ocean higher marine predators and commercial marine living resources. The objectives and early experimental design are largely responsive to needs determined by the Australian Antarctic Division's core-function obligations to CCAMLR, as well as other international organisations, the most relevant of which are the International Whaling Commission (IWC) and Southern Ocean Global Ocean Ecology Dynamics (SO-GLOBEC).\n\nTraditionally studies of diet of higher predators have often relied upon the use of a single, uncalibrated, methodology, and samples are usually collected in a manner that precludes stratification by age and sex class. Such studies are often subordinate experiments to a larger overall project. In contrast, the power of this new initiative project will be its focus on calibration across a suite of established and novel molecular and macroscopic techniques, feeding trials in controlled situations, direct linkage of samples to age and sex classes, and a detailed knowledge of the foraging behaviour of a sub-set of sampled animals. The parallel development and incorporation of electronic tools to measure predator foraging ecology further strengthens this work.\n\nIn order to achieve the aims of this study a multi-disciplinary, widely collaborative and multi-streamed program has been developed. Methodological development underpins the potential power of this project to delivery its objectives. The detailed design-phase of incorporating these new approaches into an experimental framework will follow this developmental phase. In order to best represent the sub-objectives of each phase of this study, the work has been divided into the following core components:\n\n* Experimental Design (phase 1: methodological development)\n* Development of DNA-based molecular techniques to measure prey harvesting\n* Validation trials of molecular techniques\n* Modelling/analysis to develop a matrix of methodologies to best predict prey composition in predator diet\n* Development of electronic equipment to measure prey harvesting\n* Validation trials of electronic equipment\n* Experimental Design (phase 2: ecological experiments)\n* Integrated, question driven, field experiments\n\nSome components of this work will run contemporaneously (eg. development of molecular and electronic tools).\n\nThis project has now been completed. The novel DNA based methods for studying animal diet have been researched thoroughly in controlled conditions and demonstrated to be useful and an advance on existing methods. The DNA based dietary methods have also been successfully applied to studying the diet of Blue whales, Fin whales, Antarctic fur seals, Macaroni penguins, Antarctic krill and bottlenose dolphins.", "links": [ { diff --git a/datasets/ASAC_2307_1.json b/datasets/ASAC_2307_1.json index ae81856c80..912f92b353 100644 --- a/datasets/ASAC_2307_1.json +++ b/datasets/ASAC_2307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2307\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThe project investigates microbial life in the Southern Ocean. The studies will investigate two areas - the role of bacteria in the regeneration of the important nutrient silica via decomposition of planktonic biomass and to assess the importance of prokaryotic polyunsaturated fatty acid (PUFA) entering the marine food web from natural communities in Antarctic sea ice and the Southern Ocean.\n\nProject objectives:\n1. Investigate the role of bacteria in the colonisation and decomposition of phytoplankton and concomitant redispersal of silica from phytoplankton in seawater of the Southern Ocean at various different latitudes.\n\n2. Validate real-time PCR (5-prime nuclease PCR assay) for rapid quantification of key bacterial found in seawater to determine their association with phytoplankton decomposition and silica redispersal.\n\nSignificance:\n\nRecent studies (Bidle and Azam, 1999) demonstrate that much silica regeneration in seawater is due to bacterial enzymatic activity and that diatom decomposition and silica release is highly accelerated in the presence of an active colonising bacterial population. The formation of bacterial biofilms and production of extracellular enzymes on phytoplanktic detritus and aggregates appears to lead to the direct breakdown of proteins and polysaccharides which hold together the diatom frustules. In the Southern Ocean this process could be significant as the foodweb there is sustained by phytoplanktonic (mostly diatom) primary productivity (Bunt 1963) whether it be in sea-ice or in the pelagic zone. If silica redispersal does not occur diatoms would instead eventually become buried in sediment with silica supplies becoming limited, except that supplied by aeolian and terrigenous input. In the marine environment half of primary-produced organic matter is degraded by bacteria (Cole et al., 1988). Thus the bacterial decomposition of diatom biomass and subsequent release of dissolved silica should be an important and relatively rapid process in Southern Ocean waters. At this stage there is still limited data on the role of bacteria in regeneration of silica in the overall marine environment. The study of Bidle and Azam (1999) examined seawater off of California and mostly examined the process itself. Currently, the role of specific bacteria is being examined by Kay Bidle (personal communication) and John Bowman is supplying various marine bacteria to assess this. In the proposed study we wish to examine the role of bacteria in the Southern Ocean in the decomposition of diatom biomass, rate of release of dissolved silica and bacterial groups involved in the process. This research should reveal some fundamental knowledge on a integral role of bacteria in Southern Ocean ecosystems. In order to assess the bacterial role in silica redispersal we wish to use three molecular ecological techniques: fluorescent in situ hybridisation (FISH), denaturing gradient gel electrophoresis (DGGE) and real-time PCR. FISH and DGGE analysis are well established in John Bowmans laboratory and are being used routinely for analysis of Antarctic and Tasmanian natural samples (seawater and sediment). The real-time PCR analysis which can be used as a sensitive quantitative assay for bacterial populations in natural samples is currently in development using a recently purchased Rotorgene (Corbett Research) instrument. The method has been used to great effect in measuring rapidly bacterial populations in seawater (eg., Suzuki et al. 2000). Using these methods will allow us to accurately measure changes in bacterial populations during colonisation and decomposition of the diatom biomass during the silica redispersal experiments.\n\nThere are two data files associated with this project.\n\nPart 1:\nTotal of 9 files:\nFile 1. Seawater sample data - information from two cruises in 2000 and 2001 - includes position of sample, types of sample, temperature and analyses performed subsequently.\nFile 2. 16S rRNA gene sequences derived from Southern ocean seawater bacterial isolates. Sequences are all deposited in the GenBank nucleotide database and are in FASTA format.\nFile 3. 16S rRNA gene sequences derived from denaturing gradient gel electrophoretic gel slices via extraction, PCR and cloning. Sequences are all deposited in the GenBank nucleotide database and are in FASTA format.\nFile 4. Flavobacteria abundance in Southern Ocean samples on the basis of depth. Abundance determined using fluorescent insitu hybridisation using universal bacterial probe EUB338 and flavobacteria specific probe. Details of sites analysed are included in the seawater sample file.\nFile 5. Flavobacteria abundance in Southern Ocean samples on the basis of latitude (transect from 47 S to 63 S). Abundance determined using fluorescent insitu hybridisation using universal bacterial probe EUB338, alphaproteobacteria, gammaproteobacteria and flavobacteria specific probe. Total count of bacteria was determined by epifluorescence using DAPI. Details of sites analysed are included in the seawater sample file.\nFile 6. Nutrient and chlorophyll a data for samples studied (see seawater sample file) including nitrate, phosphate and silica.\nFile 7. Bacterial isolate information including strain designations, site location, and identification to genus level.\nFile 8. . Bacterial isolate fatty acid data for strains designated as novel in bacterial isolate information file. Fatty acids determined using GC-MS analytical methods.\nFile 9. Bacterial isolate phenotypic data for strains designated as novel in bacterial isolate information file. Includes morphological, physicochemical, biochemical and nutritional profile data. \n\nPart 2:\nTotal of 4 files:\nFile 1. 16S rRNA gene sequences derived from denaturing gradient gel electrophoretic (DGGE) gel slices via extraction, PCR and cloning. DGGE analysis performed on samples analysed over 30 days from 20 litre microcosms derived from southern seawater to which was added 10 mg sterile diatom detritus derived from axenic Nitszchia closterium. Sequences are all deposited in the GenBank nucleotide database and are in FASTA format.\nFile 2. Flavobacteria abundance in Southern Ocean seawater microcosms over 30 days. Abundance determined using real-time PCR using universal bacterial and flavobacteria specific PCR primers.\nFile 3. Bacterial mediated silica release data from Southern Ocean seawater microcosms over 30 days. Includes non-detritus amended controls that indicate the natural level of of seawater silica. Silica analysis performed by a chemical procedure.\nFile. 4. Seawater sample data obtained during 2001 indicating the sites for seawater used for creating 20 l microcosms and used to assess silica release by bacteria from diatom detritus.", "links": [ { diff --git a/datasets/ASAC_2313_1.json b/datasets/ASAC_2313_1.json index c382b98839..04bdc218a0 100644 --- a/datasets/ASAC_2313_1.json +++ b/datasets/ASAC_2313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic lake cores contain sensitive microalgal recorders of climate change. To date, very recent climate information from lakes has been limited by core sampling resolution. A new, high resolution corer will be manufactured for use in Antarctica to enable accurate finescale sampling (~1-2 mm) of these climatically sensitive environments.\n\nData was collected by Lou Trenerry and Fi Spruzen for Rachael Parkinson.\n\nFrom top of core to 10 cm depth = 5 mm section\nFrom 10 cm until bottom of core = 1 cm sections\n\n1)Weddell - sunny weather, blowing 20 knots; several deployments of corer; core was very gravely and lost some when bringing corer to surface.\n\n2)Williams - sampled on 30-10-02; clear sunny weather, blowing 20 knots; blue ice with snow up to 5 cm thickness covering about 1/10th of lake surface; thick, black sediment collected with stones throughout core, especially in 15-25 mm section no sample was collected on 16-11-02 despite the drilling of several holes around the middle of the lake; bottom of lake was hard and only muddy water was in tube.\n\n3)Collerson - clear sunny day, blowing about 10 knots; blue ice with very little snow cover\n-McMinn core: top of the sediment was up into the core barrel; approx 1 cm of sediment from inside the core barrel was collected in a 'pre-core' bag and the top layer of sediment from the tube was put into 0-5 mm bag and onwards as normal\n-Gibson core: as above, with top layers of sediment collected in a 'pre-core' bag.\n\n4)Pendant - overcast, blowing ~15 knots, blue ice with no snow cover; 2 cores collected (1 x McMinn, 1 x Gibson).\n\n5)Ace - clear sunny day, no wind; blue ice with snow up to 5 cm thickness covering about 1/10th of lake surface.\n\n6)LP1 - due to lack of notation on map, the most westerly (smaller) of the two lakes was labelled LP1; clear sunny day, no wind; blue ice; only able to collect about 5 cm core after several attempts - hit the bottom with jiffy on 2 occasions and deployed corer onto very hard surface on 3 occasions (rock?) where no sediment was collected; 45-50 mm section includes remainder of core as pump was only pumping air into tube and not water and ended up scraping the rest of sediment out of tube).\n\n7)LP2 - the most easterly (larger) of the two lakes was labelled LP2; clear sunny day, no wind; blue ice.\n\n8)Grace - overcast, blowing 20 knots, very clear blue ice with no snow cover; had problems with corer freezing and lost small screw from one lever arm of cocking mechanism - this was replaced with a piece of binding from spiral notebook; top section of core was up inside the barrel so collected what we could in 0 - 15 mm bags; cored remainder as normal and collected 30 cm+ in one bag (~8 cm). \n\n9)Bisernoye - no safe, accessible route to the lake was found due to icy snow banks on western ridge.\n\n10)940980 - very hard blue ice with about 5 cm snow cover over whole of lake, edges of lake starting to melt; melt stream runoff into small valley at one end of lake; collected 30 cm+ in one bag.\n\n11)Depot - overcast, blowing ~10 knots, very clear, blue ice; corer hit rock on first deployment so was re-deployed; second attempt successful - top of core had thick algal mat which was difficult to section accurately so bagged altogether; some sections down the core also difficult to section due to strands of algae.\n\n12)Watts - fine weather; blue ice, rough surface; drilled twice - first time the weight did not release cock and when pulled up, the rope and the top of the core barrel were covered with algae/slime which had prevented the weight from reaching the corer\n-second time, the core was successfully collected although still algae/slime on rope and top of corer\n-sectioned core as accurately as possible, however did encounter strands of algae throughout the sample\n\n13)Highway - very overcast, blowing 30+ knots, blowing snow and poor visibility, several attempts as rope kept freezing and weight wouldn't release mechanism; eventually collected core closer to shore than middle of lake.\n\n14)Waterfall - not accessible as no helicopters on station between V1.1 and V2\n\nA summary of the locations data were sampled from is available for download from the url given below.\n\nHoles were drilled in the ice with a Jiffy Drill. Sediment was sampled with a Mini-Gravity Corer. Water samples were collected with a Kemmerer bottle.\n\nThe fields in this dataset are:\n\nLake\nDate\nIce Depth\nWater Depth\nCore Length", "links": [ { diff --git a/datasets/ASAC_2315_1.json b/datasets/ASAC_2315_1.json index 8c4af06db8..92c1d21513 100644 --- a/datasets/ASAC_2315_1.json +++ b/datasets/ASAC_2315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2315\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nProject title:\n\nEFFECTS OF THE MODULATION OF THE SURFACE SHEAR STRESS BY THE WAVE FIELD IN A MODEL OF THE SOUTHERN OCEAN\n\nThis project will investigate the sensitivity of currents and tracer properties in a non-eddy-resolving ocean general circulation model to a formulation of the surface shear stress which takes account of surface air and water velocities induced by the ocean wave field. These velocities will be computed accurately from archived model wave fields and also parameterised from wind and current velocities.\n\nFrom the abstract of the reference paper:\n\nWe present a basic analysis of the propagation of deep-water waves on curved trajectories. The key feature is that the amplitude of the wave varies transversely, and may in the generation of a short-crested of high amplitude. The properties of there waves are explored, and it is suggested that they are a model for extreme waves, which may violate the conditions under which the classical distribution of wave heights has been derived. In their full development, they are manifested a generic rouge waves.\n\nFrom the 2002/2003 season:\nThe aim of this project was to investigate mode water formation south of Australia in an ocean general circulation model (OGCM). The grant monies were used to employ a numerical modeller (Dr Harun Rashid) who became familiar with the curvilinear grid version of the modular ocean model No. 1 (MOM1) model developed by Ross Murray, and then applied the model with high resolution (0.6 x 0.4 degree) in the region south-west of Tasmania, where recent observations obtained on Franklin cruise (Fr9801) to the west of the SR3 section, indicated that mode water was being formed.\n\nThe model was found to be inadequate to the task of simulating the formation region, as also were the OCCAM simulations, which have been downloaded and compared with the MOM1 simulations. The reason for this negative conclusion was sought during the course of the project, and it was determined that in the OGCMs: (a) the westward advection south of Tasmania was too strong, and (b) the coefficients of lateral diffusion at deeper levels in the water column were too large.\n\nThe cruise data, which were investigated by Paul Barker as part of his Ph.D. thesis, indicated that the region of water mass formation south-west of Tasmania, occurs over the depth range of the mode water and the intermediate water and through to the upper circumpolar deep water (300 - 1500 m). It was deduced that the formation mechanism involves the mixing of two source waters, one from the Tasman Sea, the other from the Southern Ocean, which combine to form Tasmanian Subantarctic Mode Water (TSAMW), Tasmanian Intermediate Water (TIW), and probably Tasmanian Upper Circumpolar Deep Water (TUCDW). The dynamical reason for the location of the water mass formation appears to be the existence of a saddlepoint in the streamflow (at which the mean horizontal velocity is zero) over the depth range (300 - 1500m), due to the gyral circulation of the South Australian Basin to the west and the retroflection of the Tasman Outflow to the east. In order to represent this physics, it is very important to simulate correctly the advection at each level in the water column This is not done by the OGCMs, but in the course of the project, the importance of advection on the position of the saddlepoint was demonstrated in a series of simulations using the transports obtained from a simple Sverdrup transport model. The modelled fields were then used to advect temperature and salinity at each level with lateral diffusion coefficients adjusted for the best match with the observed property fields. These 'best fit' lateral diffusion coefficients in the deeper levels were found to be much smaller than those used in the OGCMs.\n\nThe mechanism outlined above is distinct from that in earlier work in which mode water formation was interpreted using Ekman rather then gyral dynamics, without attention being given to the deeper levels. A simple balance shows that the gyral current is of similar magnitude to the Ekman current in the surface layer, and below the surface layer the Ekman current is absent.\n\nRecently (December 2003) Ross Murray has indicated that the problem addressed in this 2002-2003 grant can be revisited, using a 20 year simulation he is obtaining with TPAC NCEP II forcing on a resolution of 1/8 degree. It is our intention to work with Ross in February 2004 to see if the problems detailed above can be overcome, so that the ocean physics in this important water mass formation region can be simulated.", "links": [ { diff --git a/datasets/ASAC_2317_1.json b/datasets/ASAC_2317_1.json index f89e7d4d50..92cd47f98f 100644 --- a/datasets/ASAC_2317_1.json +++ b/datasets/ASAC_2317_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2317_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project is a longterm monitoring program which commenced in 1980. The aim was to monitor vegetation and erosion changes on Macquarie Island (tall tussock vegetation on the steep coastal slopes, erosion on the coastal slopes (landslips) and mid-altitude plateau) and document changes as a result of changing impacts from feral rabbits and, if possible, as a result of climate change.\n\nData sets consist of the following: 1. 27 vegetation monitoring sites set up in 1980 on steep coastal slopes around Macquarie Island, disturbed by landslipping and/or rabbit grazing, containing 200 permanent vegetation plots including 30 undisturbed control plots. 2. 48 erosion monitoring sites with varied origins and erosion histories set up in 1980 on mid-altitude plateau sites around Macquarie Island. 3. Two 5 x 5m rabbit exclusion plots set up in 1990 to monitor vegetation changes. 4. Series of photopoints along two sections of west coast set up to monitor large scale vegetation change resulting from changes in rabbit populations since 1980 (Mawson Point to Aurora Point, 7 km) and since 1990 (Davis Point to Cape Toutcher, 4 km). 5. Over 100 photopoints documenting changes in vegetation, erosion and landscape around Macquarie Island since 1980.\n\nThe download file consists of 12 excel spreadsheets and 2 word documents detailing the contents of the excel files. These files are associated with part 1, detailed above '27 vegetation monitoring sites...'.\n\nThis project is an updated version of ASAC_76, 'Effects of Feral Rabbits, Native Fauna and Humans on Vegetation and Soil Stability, Macquarie Island'. The project was continued in 2002-2003 under a new project number - 2317 (ASAC_2317) and new title - 'Longterm Changes in Vegetation and Soil Stability, Macquarie Island'. \n\nTaken from the 2008-2009 Progress Report:\nProject objectives:\nThis project aims to resurvey long-term monitoring sites on Macquarie Island and ultimately document the recovery of ecosystems post-eradication of rabbits and rodents. The specific objectives of the project are:\n\n(1) Re-survey of steep coastal slopes. Re-measure sites established on Macquarie Island in 1980-81 to monitor changes in vegetation in 2008-09 (pre-eradication) and then in 2013-14 and 2018-19 (post-eradication).\n(2) Re-photograph two stretches of steep coastal slopes on the west coast, between Mawson Point and Aurora Point (7 km) and between Davis Point and Cape Toutcher (4 km) in 2008-09 (pre-eradication) and then in 2013-14 and 2018-19 (post-eradication).\n(3) Resurvey the known populations of the rare plant Poa litorosa at the south end of the island in 2008-09 (pre-eradication) and then in 2013-14 and 2018-19 (post-eradication). This objective is linked with the Poa litorosa monitoring objective of Project 1015.\n(4) Re-photograph over 100 fixed photo-points documenting the extent of vegetation, erosion and landscape change since 1980 in 2008-09 (pre-eradication) and then in 2013-14 and 2018-19 (post-eradication).\n\nProgress against objectives:\n(1) Fieldwork undertaken as proposed.\n(2) Fieldwork undertaken as proposed.\n(3) Fieldwork not undertaken.\n(4) Fieldwork undertaken as proposed.", "links": [ { diff --git a/datasets/ASAC_2318_1.json b/datasets/ASAC_2318_1.json index 5c14d88fb0..abb2e095d4 100644 --- a/datasets/ASAC_2318_1.json +++ b/datasets/ASAC_2318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2318\n See the link below for public details on this project.\n ---- Public Summary from Project ----\n This project is a cooperative venture between Australian and Italian institutes in a study of the mass budget of a large sector of the East Antarctic ice sheet. The mass budget represents the difference between the net input of moisture from the ocean as snow accumulating on the surface and the discharge of ice from the grounded part of the ice sheet. Any mismatch between these input and out put terms produces a direct contribution to change in sea level. Measurements of ice thickness along the coastal margin of the grounded ice sheet, combined with velocity data derived from satellite remote sensing methods, will provide the first direct measurements of the total mass output for the part of the ice sheet draining through this sector of the coastline. The observations will also a baseline against which future change in the mass flux can be assessed.\n\nFrom the abstract of the referenced paper:\n\nAn air-borne radio echo sounding survey was conducted over the Amery Ice Shelf, East Antarctica, in December 2003. Nine transverse profiles where obtained approximately normal to the flow of direction, together with one longitude profile for a total length of of flight lines of about 1000km. We determined the spatial distribution of the characteristics of the RES signal returned from the ice-water interface at the base of the shelf. The key characteristics are described by the amplitude of the echo and the slope of the leading edge of the returned pulse.\n\nFor the mass flux we ice thickness values extracted from our RES data, and velocities derived form InSAR maximum coherence tracking in pairs of Radarsat SAR images, with control provided by surface measurements. We calculate the mass budget for a set of discrete areas bounded by our RES survey lines across the shelf, and flow-lines derived from the velocity field (modified in the across shelf components of the velocity) and from linear flow-features identified in the satellite images. using available estimates of the snow accumulation rate at the upper surface, and assuming the ice thickness and velocities are stationary in time, we attribute the mass imbalance within a sub-area to be the result of melt or freeze at the base of the shelf.\n\nBoth, the distribution of values of melt-freeze rates, and of the characteristics basal echoes form distinct patterns that appear to be influenced by variations in the ice draft and conditions in the sub-shelf ocean cavity. We find a net melt regime associated with the deeper draft to the South and under the central part of the shelf, and net freeze conditions in the North and sides, particularly towards the western margin. the strength of the electromagnetic signals reflected from the ice water-interface shows a similar pattern of variation along all nine transverse profiles. We find a strong association with basal mass balance\n\nSummary of progress 2004/2005\nThe field phase of the project was completed in 03/04 season. Current work involves analysis and interpretation of the Radio Echo Sounding measurements of ice thickness. An initial appraisal was completed of all RES data. A coarsely-spaced set of preliminary estimates of ice thickness were extracted and merged with GPS position data for generation of a first-cut distribution of ice thickness round a large sector of the ice sheet margin. New analysis systems have been developed and to-date applied to 25% of the data for extraction of detail ice thickness information and characteristics of the radar echoes.\n\nAn initial assessment of all the RES data collected during the field work has been completed. Preliminary ice thickness estimates have been extracted at coarse spacing along the flight lines to generate a first-cut assessment of the ice thickness distribution for planning next phases of the project and for assessment of the need for additional field work. Those data have been merged with GPS position data collected at a ten-second time-interval along the flight track. This work has been carried out primarily by the Italian investigators.\n\nIn Australia, software systems have been developed to carry out the full-resolution analysis of the digital RES data. This analysis system is based on developments undertaken in AAS project # 2224. To-date, data analysis using this system has been completed for all flights over the Amery Ice Shelf, and analysis of flights over the Law Dome, Totten Glacier, Moscow University Ice Shelf is nearing completion.\n\nAs part of the depth extraction procedure, algorithms have been developed and implemented that allow extraction of the echo characteristics for an horizon identified in the digital record. For the echoes forming that horizon, the leading-edge slope, peak and base power, echo-width, and height are extracted. These characteristics vary with the characteristics of the reflector in the ice and can be related to roughness, reflectivity coefficient (dielectric contrast), etc.\n\nFor the flight lines over the Amery Ice Shelf we have found a spatially coherent pattern in the variation in the echo-height and leading-edge-slope of the basal echo that appears to correspond to variation between individual flow-bands of reflection properties of the ice-water interface. This is a recent preliminary finding and work is continuing with these aspects.\n\nThis detail analysis phase of the project is being carried out jointly by N Young and A Forieri during a 5-month visit by A Forieri to the ACE CRC funded by PNRA (Italy) and ACE CRC.", "links": [ { diff --git a/datasets/ASAC_2319_1.json b/datasets/ASAC_2319_1.json index 2477c68769..00049fa8da 100644 --- a/datasets/ASAC_2319_1.json +++ b/datasets/ASAC_2319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nUnderstanding the strength of possible biological feedbacks is crucial to the science of climate change. This project aims to improve our understanding of one such feedback, the biogenic production of dimethylsulphide (DMS) and its impact on atmospheric aerosols. The Antarctic ocean is potentially a major source of DMS-derived aerosols. The project will investigate the coupling between satellite-derived aerosol optical depth, phytoplankton biomass and DMS production in the Antarctic Southern Ocean.\n\nFrom the abstract of the attached paper:\n\nWe analysed the correlation between zonal mean satellite data on surface chlorophyll (CHL) and aerosol optical depth (AOD), in the Southern Ocean (in 5-degree bands between 50-70 degrees south) for the period 1997-2004), and in sectors of the Eastern Antarctic, Ross and Weddell Seas. Seasonality is moderate to strong in both CHL and AOD signatures throughout the study region. Coherence in the CHL and AOD time series is strong between 50-60 degrees south, however this synchrony is absent south of 60 degrees south. Marked interannual variability in CHL occurs south of 60 degrees south. We find a clear latitudinal difference in the cross-correlation between CHL and AOD, with the AOD peak preceding the CHL bloom by up to six weeks in the sea ice zone (SIZ). This is consistent with the ventilation of dimethysulphide (DMS) from sea-ice during melting, and supports field data that records high levels of sulfur species in sea-ice and surface seawater during ice-melt.\n\nThe fields in this dataset are:\n\nTimeseries Worksheet:\n\nDate\nMean Chlorophyll (mg CHL/cubic metre)\nMean Aerosol Optical Depth (no units)\n5 Day mean chlorophyll averages\n5 day mean aerosol optical depth averages\n\nCorrelation Worksheet:\n\nn - number\nlag\nr - correlation coefficient\nt - student t statistic\n\nGlobal Worksheet\n\nColumn A = SeaWiFS filename\nCounter+1 is a counter to indicate the image number in series\nDate\nMean Chlorophyll (mg CHL/cubic metre)\nMean Aerosol Optical Depth (no units)\nChlorophyll Standard Deviation\nMean Aerosol Optical Depth Standard Deviation\nChlorophyll Standard Error\nMean Aerosol Optical Depth Standard Error\nChlorophyll Count (the number of data 'pixels' in the image - the basic pixel size is 9x9km2)\nMean Aerosol Optical Depth (the number of data 'pixels' in the image - the basic pixel size is 9x9km2)", "links": [ { diff --git a/datasets/ASAC_2323_Crooked_1.json b/datasets/ASAC_2323_Crooked_1.json index d3bdb820e3..8fe12a15a7 100644 --- a/datasets/ASAC_2323_Crooked_1.json +++ b/datasets/ASAC_2323_Crooked_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2323_Crooked_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2323\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nE-science is an exciting new branch of inter-disciplinary science which effectively uses powerful computers, high speed networks and sophisticated visualisation techniques to address important questions. It allows scientists to access large metadata bases via the internet to produce complex models or visualisations. This proposal will assess the potential of Antarctic lakes as indicators of climate change. For example we could produce a three dimensional picture of the light climate in the water column of a lake and show how this will change as ice-cover thins, becomes opaque and breaks out. In turn this could be used to produce a detailed picture of how photosynthesis will respond to changing light climate, which in turn impacts on carbon cycling. This proposal involves five senior scientists from a range of disciplines including a polar limnologist, an environmental modeller, a remote sensing engineer and computer/IT scientists who are the forefront of the E-Science initiative.\n\nThe Fields in this dataset (arranged by column) are described in a text document in the download file.", "links": [ { diff --git a/datasets/ASAC_2323_OGorman_1.json b/datasets/ASAC_2323_OGorman_1.json index ad343990ca..8164b07788 100644 --- a/datasets/ASAC_2323_OGorman_1.json +++ b/datasets/ASAC_2323_OGorman_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2323_OGorman_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2323\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nE-science is an exciting new branch of inter-disciplinary science which effectively uses powerful computers, high speed networks and sophisticated visualisation techniques to address important questions. It allows scientists to access large metadata bases via the internet to produce complex models or visualisations. This proposal will assess the potential of Antarctic lakes as indicators of climate change. For example we could produce a three dimensional picture of the light climate in the water column of a lake and show how this will change as ice-cover thins, becomes opaque and breaks out. In turn this could be used to produce a detailed picture of how photosynthesis will respond to changing light climate, which in turn impacts on carbon cycling. This proposal involves five senior scientists from a range of disciplines including a polar limnologist, an environmental modeller, a remote sensing engineer and computer/IT scientists who are the forefront of the E-Science initiative.\n\nThe Fields in this dataset (arranged by column) are described in a text document in the download file.", "links": [ { diff --git a/datasets/ASAC_2337_1.json b/datasets/ASAC_2337_1.json index 3f6e8d00a9..355f36d140 100644 --- a/datasets/ASAC_2337_1.json +++ b/datasets/ASAC_2337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) Project 2337.\n\nAn excel spreadsheet is available for download from the URL given below. The spreadsheet contains three worksheets:\n\n - a summary of the data\n - validated data\n - all data\n\nPublic\nThe experimental krill research program is focussed on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the program will concentrate on studies into schooling, growth, ageing, behaviour and reproduction of krill as well as into the operation, behaviour and trends of the krill fishery.\n\nProject objectives:\nTo investigate key aspects of the biology of Antarctic krill and its management utilising the facilities at the Australian Antarctic Division. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nWe succeeded to take krill larvae reproduced in-house last year up to adult stage (external maturity) for the first time in our research laboratory (the second facility ever outside Antarctica), which means that last year's larvae reached maturity within a year in our aquarium, compared to 2-3 years in the wild, however spawning from this population is yet to be recorded. We also had successful reproduction this year and currently these animals are at late larval stage. We have now succeeded in reproduction two years in a row and have established the technique. This is a major step forward in closing Antarctic krill's life cycle in our aquarium. This achievement makes us the only research facility outside Antarctica to be able to conduct live krill experiments for the entire life stage and contribute information on biological parameters important for krill management.\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nThis year for the first time we have succeeded in closing the entire krill life cycle in our research laboratory (the second facility ever outside Antarctica. We also achieved successful reproduction this year and currently these animals are at the late larval stage. We have now succeeded in krill reproduction for three years in a row and have firmly established the technique. This achievement makes us the only research facility outside Antarctica to be able to conduct live krill experiments for the entire life stage and contribute information on biological parameters important for krill management.\n\nTaken from the 2010-2011 Progress Report:\nPublic summary of the season progress:\nUnderstanding how krill may respond to various environments is the fundamental information to predict the future of krill centric ecosystem under the climate change. The project's current focus is to study impacts of ocean acidification on krill. The AAD aquarium facility for ocean acidification study is continuing to be upgraded to increase its capacity and stability to undertake its experiments at larger scale. Negative impacts of ocean acidification on krill has been investigated and the range of CO2 level fatal to krill embryonic development was broadly identified, and was published for the first time.", "links": [ { diff --git a/datasets/ASAC_2341_1.json b/datasets/ASAC_2341_1.json index cd5735e6f3..e08bc7dbae 100644 --- a/datasets/ASAC_2341_1.json +++ b/datasets/ASAC_2341_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2341_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2341. See the link below for\npublic details on this project.\n\n---- Public Summary from Project ----\nThis project aims to determine bacterial diversity in Antarctica, including non-culturable strains, by the use of various techniques. Soil, sediment and water samples will be collected from different sites e.g. relatively pristine sites, sites impacted by human activities, penguin rookeries. Isolation of bacteria from these samples will be carried out and isolates will be screened for special traits such as biodegradation of persistent organic pollutants (POP) or synthesis of biopolymers. Positive isolates will be compared with tropical bacteria isolates having similar abilities.\n\nSamples were collected from two Sites of Special Scientific Interest in the Casey area. These were SSSI 16 and SSSI 17. Samples were also collected from around Casey Station. SSSI 16 and SSSI 17 are now known as Antarctic Specially Protected Areas. The new designation for SSSI 16 and 17 respectively is ASPA 135 and ASPA 136.\n\nThe following work was completed as part of this project:\n\n1) Obtained some bacteria isolates from antarctic soil and sediment, using common and selective microbiological media.\n2) From the micropore membranes, we extracted genomic DNA, PCR the 16S rRNA gene, cloned and sequence the gene to determine bacterial species.\n\nWater samples were filtered through micropore membranes to concentrate bacteria.\n\nThe download file contains data on bacterial diversity in meltlake water and sediment from two sites around Casey station, ASPA 135 and ASPA 136.\n\nASPA 135 meltlake (66.28251 degrees S, 110.54192 degrees E)\nTemperature 5.8 degrees C, pH 6.06, salinity 0\nSample collection date 27 December 2002\n\nASPA 136 meltlake (66.25131 degrees S, 110.53978 degrees E)\npH 7.35, salinity 2.8\nSample collection date 28 December 2002\n\nThe download files contain a series of accession numbers for the Genbank Database. The actual data can be obtained from Genbank.", "links": [ { diff --git a/datasets/ASAC_2341_DGGE_1.json b/datasets/ASAC_2341_DGGE_1.json index 68cb99c532..c08e976e7a 100644 --- a/datasets/ASAC_2341_DGGE_1.json +++ b/datasets/ASAC_2341_DGGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2341_DGGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacterial community structures in soils collected from eight sites around Casey Station, Antarctica, were investigated using denaturing gradient gel electrophoresis (DGGE) of amplified 16S rRNA gene fragments. Higher bacterial diversity was found in soils from protected or relatively low human-impacted sites in comparison to highly impacted sites. However, the highest diversity was detected in samples from Wilkes Tip, a former waste disposal site that has been undisturbed for the last 50 years. Comparison of community structure based on non-metric multidimensional scaling plots revealed that all sites, except the hydrocarbon-contaminated (oil spill) site, were clustered with a 45% similarity. A total of 23 partial 16S rRNA gene sequences were obtained from the excised DGGE bands, with the majority of the sequences closely related to those of the Cytophaga-Flexibacter-Bacteroides group. No significant correlation was established between environmental variables, including soil pH, electrical conductivity, carbon, nitrogen, water content and heavy metals, with bacterial diversity across the eight study sites.\n\nIrene K. P. Tan - Project leader\nC. W. Chong - sample collection, molecular and statistical analyses\nG. Y. Annie Tan - provided some advice in molecular analysis\nRichard C. S. Wong - provided advice in chemical analyses\nMartin J. Riddle - assisted in manuscript writing and provided advice in metal analyses\n\nLocations used in this experiment were:\n\n- Casey Red Shed\n- Oil Spill site\n- Thala Valley\n- Wilkes Tip\n- Browning Peninsula\n- Mitchell Peninsula\n- ASPA 135\n- ASPA 136", "links": [ { diff --git a/datasets/ASAC_2341_PHA_1.json b/datasets/ASAC_2341_PHA_1.json index 08e4863620..07d3f82ba0 100644 --- a/datasets/ASAC_2341_PHA_1.json +++ b/datasets/ASAC_2341_PHA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2341_PHA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacterial isolates were obtained from soil samples collected from various locations around Casey station. Soil samples were serial diluted in sterile water and plated on low-strength nutrient agar containing cycloheximide. The agar plates were incubated at 5 degrees C until colonies were observed. Bacterial colonies were picked and purified on fresh agar plates. \n\nThe bacterial isolates were screened for the production of polyhydroxyalkanoates (PHA) by growing them in nitrogen-limiting medium. PHA in the cells was detected by Nile Blue A staining and by Nile Red staining of the colonies. PHA-producing isolates were tested with individual carbon sources i.e. glucose, sodium octanoate and saponified palm kernel oil. The intracellular PHA was extracted by chloroform, precipitated by methanol, and analysed by gas-chromatography (GC) to determine the monomer compositions. Selected PHA-producing isolates were identified by their 16S rRNA gene sequences.\n\nTwo main groups of PHA-producing bacteria were identified from among the isolates: Janthinobacterium spp. which produce polyhydroxybutyrate (PHB) from glucose, and Pseudomonas spp. which produce medium-chain-length PHA (mcl-PHA) from octanoate. Some but not all of the Pseudomonas isolates were able to produce mcl-PHA from glucose. The monomer composition of the mcl-PHA produced by these Pseudomonas strains was found to be related to the carbon substrates fed to the bacteria. \n\nIn selected isolates, the PHA biosynthesis genes (phaC) and associated PHA depolymerase gene (phaZ) will be analysed and compared with those from non-antarctic bacteria.\n\nThe attached excel file lists the PHA-producing bacterial isolates obtained from various soil samples, and their closest homology to GenBank entry and type culture based on the 16S rRNA gene sequence. \n\n\nFeb 2004 (soil samples collected by member from another research group) and \nDec 2005 (soil samples collected by C.W.Chong)\n\nSoil samples were collected from the following locations:\n- Around Casey station - (Red Shed, Lab building, etc)\n- Peterson Island\n- Around Wilkes Land (Wilkes Land, Whitney Point, Thala Valley)\n- Browning Peninsula\n- Mitchell Peninsula\n\nIrene K. P. TAN - Project leader\nYuh Shan GOH - isolated and screened bacteria for polyhydroxyalkanoates (PHA); identified the PHA-producing bacterial isolates; characterised the PHA\nChun Wie CHONG - collected the soil samples from around Casey station in December 2005", "links": [ { diff --git a/datasets/ASAC_2348_1.json b/datasets/ASAC_2348_1.json index 039fe68f12..818e1c5d02 100644 --- a/datasets/ASAC_2348_1.json +++ b/datasets/ASAC_2348_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2348_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Exopolysaccharide (EPS) is complex sugar made by many microbes in the Antarctic marine environment. This project seeks to understand the ecological role of microbial EPS in the Southern Ocean, where it is known to strongly influence primary production. We will investigate the chemical composition and structure of EPS obtained from Antarctic microbes, which will improve our knowledge of its ecological significance and biotechnological potential.\n\nDataset includes the following:\n\n1) Information on Exopolysaccharide-producing bacterial isolates, isolation sites, media used and growth conditions.\n\n2) 16S rRNA gene sequence and fatty acid data of isolates for strain identification.\n\n3) Exopolysaccharide chemistry data including EPS carbohydrate composition, organic acid composition, sulfate content, molecular weight.\n\n4) Physiology of exopolysaccharide synthesis. Effects of temperature and other factors on EPS yield and glucose conversion efficiency.\n\n5) Iron binding properties.\n\nThe download file includes:\n11 files\nFile 1. Bacterial isolate 16S rRNA gene sequences obtained from Southern Ocean seawater or ice samples. The sequences are all deposited on the GenBank nucleotide (NCBI) database. Sequences are in FASTA format.\nFile 2. Seawater and sea-ice sample information including sites samples, sample type.\nFile 3. Data for exopolysaccharide (EPS)-producing bacteria isolated and subsequently selected for further studied. Information indicates special treatments used to obtain strains including plankton towing, filtration method, and enrichment. Identification to species level was determined by 16S rRNA gene sequence analysis.\nFile 4. EPS-producing bacterial isolate fatty acid content determined using GC/MS procedures.\nFile 5. Basic chemical data for EPS from Antarctic isolates including protein, sulfate, and sugar type relative content (determined by chemical procedures), molecular weight in kilodaltons and polydispersity (determined by gel-based molecular seiving).\nFile 6 Monosaccharide unit composition determined by GC/MS of EPS from Antarctic isolates.\nFile 7. Effect of temperature on culture viscosity and growth of EPS-producing bacterium Pseudoalteromonas sp. CAM025 as affected by temperature.\nFile 8. Effect of temperature on EPS and cell yields and EPS synthesis efficiency (as indicated by glucose consumption) of EPS-producing bacterium Pseudoalteromonas sp. CAM025 as affected by temperature.\nFile 9. Efficiency of copper and cadmium metal ion adsorption onto EPS from EPS-producing bacterium Pseudoalteromonas sp. CAM025.\nFile 10. Phenotypic characteristics data for novel EPS-producing Antarctic strain CAM030. Represents type strain of Olleya marilimosa.\nFile 11. Effect of temperature on chemical make up of EPS from EPS-producing bacterium Pseudoalteromonas sp. CAM025.", "links": [ { diff --git a/datasets/ASAC_2350_1.json b/datasets/ASAC_2350_1.json index 0101a92d8c..dee9800bda 100644 --- a/datasets/ASAC_2350_1.json +++ b/datasets/ASAC_2350_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2350_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes data collected as part of ASAC project 2350 - Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust?\n\nAs a direct result of the field mapping during this project (and previous fieldwork by myself and others) 'we' have produced a 1:25000 map of the geology of the Larsemann Hills. This was collaboration between the AAD and Geoscience Australia (with considerable assistance by Phil O'Brien and Henk Brolsma) and published by GA earlier in 2007. The map is referenced below.\n\nAdditionally, several papers are linked to this record, plus copies of the field report and two documents which details the photos taken, and the locations of the field sites.\n\nExtended abstract\nThe Larsemann Hills region is dominated by two major lithological associations, a Palaeoproterozoic felsic/mafic orthogneiss complex (Sostrene Orthogneiss) which occurs as basement to a sequence of pelitic, psammitic and felsic paragneiss (supergroup = Brattstrand Paragneiss) and felsic intrusives. The depositional age of the Brattstrand Paragneiss sequences are controversial but isotopic data suggest derivation from the basement Sostrene Orthogneiss. Current geochronology indicates that the region experienced medium to low pressure granulite-facies metamorphism during the Early Palaeozoic (~500 Ma). Although the paragneiss sequences record no evidence of earlier metamorphism, relicts of a previous metamorphic event at ~1000 Ma are preserved in the Sostrene Orthogneiss. Within the Larsemann Hills region, the Early Palaeozoic event is characterised by peak metamorphism of ~7 kbar at ~800-850 degrees C, with the post-peak evolution characterised by decompression, with some cooling, to 4 kbars at 750 degrees C, then to 2-3 kbar at 600-650 degrees C during final stages of orogenesis, with exhumation largely driven by crustal extension. Tectonic models generally argue for a continental-continental collisional scenario, with thermal input derived from a thinned mantle lithosphere.\n\nStructural evolution\nThe various high-grade structural frameworks proposed by different workers have been distilled by Fitzsimons (1997) into three major events Da, Db and Dc which broadly correlates D1, D2 and D3 proposed by Stuwe et al. (1989), Thost et al. (1994), Carson et al. (1995b) and D1, D2 and D3-D6 of Dirks and Hand (1995) and D3, D4 and D5 of Fitzsimons and Harley (1991). Within the Larsemann Hills, the dominant outcrop structures are attributed to Db (using the nomenclature of Fitzsimons, 1997). Db can be sub-divided into low and high strain zones, low strain zones preserve complex multiple fold generations that fold lithological layering (Da) and high-strain zones which transpose Da into a new planar gneissosity, Db. Similarly, Dc high-strain zones overprint and locally transposes Db structures, which are completely replaced by a new gneissic layering, Sc, and mineral lineation, Lc, in the northern and southern regions of the Larsemann Hills. Much of the Larsemann Hills is, therefore, a window of Dc low-strain in which Db structures are preserved, although these are reorientated by large, relatively open, upright Dc low-strain folds. Fold hinges and mineral extension lineations preserved on gneissic surfaces within both domains are co-linear and have a characteristic orientation; easterly to southerly plunging for Db and consistently south-west plunging for Dc.\nThe major difference of the structural scheme of Carson et al. (1995) and Dirks and Hand (1995) from other schemes is they present kinematic indicators and argue that Db is characterised by crustal compression along an easterly transport vector (D2 in their scheme), and a extensional domain, Dc, developed along a southwesterly transport vector (D3). They also argue on the basis of the co-linear nature of structures in both low- and high-strain zones within each domain that both low-strain and high-strain zones evolved synchronously and represent components within one structural episode rather than indicating overprinting relationships (e.g. as both Sa and Sb have parallel linear structural elements, then Sa and Sb developed synchronously). The description of two structural domains, characterised by parallel linear elements and, particularly, kinematics is a structural interpretation that is critical to the structural model proposed by Carson et al. (1995) and Dirks and Hand (1995). Post high-grade deformation is confined to the development of up to 20 cm wide, amphibolite-grade mylonite zones that formed along and within planar north-south trending garnet-sillimanite-spinel bearing pegmatites (Dirks et al., 1993; Carson et al., 1995). Movement sense is typically dextral, east-down along a moderately south-pitching sillimanite lineation and offsets are less than 20 metres.\n\nThe Map\nA draft geological map (scale 1:25 000) of the Larsemann Hills was generated by Rupert Summerson (National Resource Information Centre) and Dr Doug E Thost (AGSO, = Geoscience Australia, GA) for the Australian Antarctic Division (AAD) on the 27 Jan 1997. Geological information depicted on that map is derived from a number of sources, primarily from unpublished field data of Carson (1991/92, 1992/93 and 1993/94), Carson et al. (1995b) and Stuwe et al. (1989), with additional geological interpretation by Doug Thost. That map was not published.\nWith a view to upgrading that draft map to publication, Dr Chris Carson added new unpublished field data from Stornes Peninsula (Carson and Grew 2003/04, ASAC 2350) and appended and corrected known errors that existed on the original draft map. The current map therefore combines elements of the original draft geological map and the new geological information acquired by Carson and Grew (2003/04). The map is primarily a lithological map, illustrating the distribution of primary rock types present in the Larsemann Hills region.\nThis work was conducted at SKM Consulting, 214 Northbourne Ave Canberra, between 29 March and 30 April 2004. Carson was assisted by Bruce Donaldson (MapInfo) and Gordon Sue (ArcView).\n\nThe current map is overlaid on topographic information provided by Henk Brolsma of the AAD (coastline, rock boundaries, lakes, snowfields etc) that have been previously digitised from aerial photography flown on Jan/Feb 1998, at an elevation of 3000m. The mapping conducted by Carson and Grew during ASAC 2350 used two air photos covering the bulk of northern Stornes Peninsula (ANTC1063, Run 3 frame 96) and the outcrops between the southern end of Thala Fjord and the eastern end of Wilcock Bay (ANTC1063 run 5, frame 16). These photos were projected onto the WGS 1984 using UTM (zone 43) geographical co-ordinates and were then ortho-rectified using contour information based on the 1998 aerial photography to accurately match the provided topographic data.\n\nThe new geological map was drafted in MapInfo v_7, lithological contacts were digitised and are either self enclosed or terminate at snow, lake, ice or coastline arcs, or another lithological boundary. The MapInfo layers containing the new geology polylines or arcs and the coastline, rock_bdy and snow polylines (supplied by AAD as *.shx autoCAD files) were then transferred to ArcView.\n\nRock boundaries\nMany of the lithological boundaries defined on this map are approximate. This is a function of the diffuse, subtle and gradational nature of many of the rock boundaries in this complex high-grade geological terrain. Many of the lithological boundaries on Broknes Peninsula are approximate for this reason. Furthermore, many workers have acquired the geological information contained in this map over some 20 years. Many of the original notes, primary information, air photo overlays and detailed site data have been misplaced (or otherwise unavailable) during intervening years, preventing detailed reference to the primary source of geological information and some lithological boundaries may be derived from geological maps from published manuscripts.\nLithological boundaries on Stornes Peninsula are generally accurate, largely due to the rather distinctive nature of the rock types found there, but also the cleaner nature of the rock surface, i.e. the lack of a deeply weathered surface, and access to superior recent colour air photo set which allows a better determination of the lithological boundaries.\n\nRenamed rock units\nRock units originally represented in this current map have been provisionally reassessed and renamed according to naming systematics according to GA requirements. Many of the names listed in Carson et al. (1995b) and Stuwe et al. (1989) and CHINARE publications have been superseded. Fitzsimons (1997) subdivided all rocks types in southern Prydz Bay into two broad divisions; the Sostrene Orthogneiss and the Brattstrand Paragneiss. All of the metasedimentary units described here are formations within the Brattstrand paragneiss.\n\nThe Brattstrand paragneiss is tentatively listed as the supergroup in stratigraphic terms within which all the listed formations or rock units occur.\n\nAll Grid References (GR easting, northing) listed below are taken from the 1:25 000 topographic map published by the AAD in March 1991.\n\n- Psammite1 and psammite2 (from Carson et al. 1995b) have been unified on the basis that they are essentially and practically indistinguishable in the field. Renamed Gentner psammite based on the name of the peak on western Broknes Peninsula where outcrops of Gentner psammite are present, although the unit is widespread through out Larsemann Hills. This unit is described as a quartzo-feldspathic psammite, with variable amounts of garnet and biotite. May contain small pods of sillimanite-spinel and/or magnetite and hosts lenses of hornblende-plagioclase (*biotite, *opx) metabasite. Contacts with other units are gradational and diffuse, and as such it is difficult to place lithological boundaries with any certainty.\n- White Hill leucogneiss, named after distinctive unit on White Hill, central Stornes Peninsula (* White gneiss of Stuwe et al. 1989, * felsic cordierite gneiss of Carson et al. 1995b). Light grey leucocratic gneiss, variable biotite, quartz and plagioclase, locally contains 1-5 cm dia. course grained cordierite+quartz symplectites, with tightly folded K-feldspar bearing veins or leucosomes. Unit may locally be rarely garnet bearing. Possibly of volcanic derivation. Forms topographic highs as ridges and domes. Unit is best-observed at the type locality at White Hill (GR 543005 2299450) though many examples exist on Central eastern Stornes Peninsula.\n- Stuwe pelite (Stuwe et al. 1989, blue gneiss; Carson et al. 1995b composite pelite2). Characteristic dark coloured, sillimanite dominated pelite, variable amounts of cordierite usually greater than 25%, minor magnetite and/or spinel, and contains isoclinally folded leucosomes dominated by orange microcline. May contain large pods of sillimanite. Good example of this unit is on Gneiss Peak, western Stornes Peninsula.\n- Lake Ferris pelite. Garnet magnetite and/or spinel pelite with variable amounts of accessory sillimanite and cordierite (* pelite3 of Carson et al. 1995b). Typical example on ridges immediately south of Lake Ferris (grid reference 542800 2296750), and is relatively common on Stornes Peninsula.\n- Stornes gneiss. Grey biotite plagioclase gneiss, with characteristic layers and pods of course grained prismatine (= B-kornerupine) typically with fresh cordierite and biotite. Prismatine+cordierite+biotite pods may contain accessory grandidierite as mm scale needle-like crystals. Unit contains narrow dismembered K-feldspar leucosomes. Possible volcanic protolith (contain abundant prismatine layers and apatite pods, the unit is thus highly enriched in boron and phosphorus). The Mg-phosphate, wagnerite (Ren et al., 2003), is also found in the Stornes gneiss (central to western Stornes Peninsula) along discrete conformable layers. Individual orange subhedral crystals may reach 3 cm in diameter!\n- Thala tourmaline meta-quartzites. A package of tightly folded black granular (sugary) tourmaline quartzites interlayered with yellow quartzo-feldspathic psammites. Thala metaquartzite typically contain abundant borosilicates e.g. grandidierite and prismatine and phosphate minerals (possibly apatite or wagnerite). Good examples at grid reference 543400 2295600 on outcrops to SE of ice dome. Named after nearby Thala Fjord (to the east). Thala tourmaline quartzites may also appear as discontinous lenses or pods within the prismatine-bearing Stornes gneiss. May be a genetic relationship between Thala meta-quartzites and the borosilicate-rich Stornes gneiss. These units are also described in Carson et al (1995b).\n- Broknes paragneiss. Yellow-pale coloured garnet- and biotite-bearing felsic paragneiss, with rare to minor sillimanite, spinel, and cordierite. Renamed unit - the semi-pelite of Carson et al. (1995) and the yellow gneiss of Stuwe et al (1989). Named after Broknes Peninsula where this unit is widespread.\n- Tumbledown Hill meta-quartzites. Generally thin (1-10m wide) rusty coloured package of biotite psammite and dark glassy quartzite (with rare garnet) layers. Commonly with malachite staining on crusty surface. Typically deeply weathered. Clearly sulphide bearing based on weathering colourations with rare pyrrhotite observed. Named after Tumbledown Hill (GR 542200 229805, spot height 114m) where they outcrop as continuous ENE trending bands withinin the Blundell granitic orthogneiss.\n- Wilcock Bay pelite. Very distinctive unit, though of very limited occurrence. Pale-yellowish leucocratic rock unit with abundant borosilicate mineralogy, principally grandidierite and prismatine with lesser amounts of tourmaline and rare dumortierite. Sillimanite is common and in association with grandidierite where both minerals defined the local mineral lineation. Closely associated with Thala tourmaline meta-quartzites. Type locality at GR 543400 2295600. Also present at GR 544200 2295600.\n- Tassie Tarn metaquartzites. Narrow bands (~25m) of dark grey-blue biotite * magnetite quartzites and biotite psammites, that are intermittently exposed along central E-W axis of Stornes Peninsula. Can contain layers with large euhedral crystals (1-3cm) of orthopyroxene. Good examples at near Tassie Tarn at GR 541900 2298800 and on eastern Stornes Peninsula, GR 544150 2299600.\n- Easther Island porphyroblastic gneiss. Distinctive grey biotite-plagioclase gneiss with large (1-3cm) porphyroblasts of garnet and/or cordierite, typically with biotite absent halo. First described in a general sense by Stuwe and Powell (1989) but Carson et al. (1995b) described the rock type as granular-porphyroblastic gneiss. Excellent examples at Easther Island and also on southern Stornes Peninsula around GR 544100 2295450. Named after the island after which Kurt Stuwe described the occurrence of this rock type in Stuwe and Powell (1989) on Upsoy Island, however this island was renamed by AAD to Easther Island on the 1:25 000 topographic map.\n- Wilcock Bay quartzite*. (not named on new map, attributed on new map as biotite-garnet quartzite*). Comprised of biotite and garnet bearing quartzites, interleaved and infolded with narrow bands of Easther Island porphyroblastic gneiss. Contains quartz veins (+/- K-feldspar) and large 100-200 mm diameter. Masses of unknown brown phosphate, probably apatite or wagnerite. Of minor aerial extent and limited to outcrops to the southeast of Allison ice dome.\n- Thala Fjord paragneiss**. (not named on new map, attributed on new map as biotite quartzo-feldspathic paragneiss**) located at southern Stornes Peninsula. Biotite quartzo-feldspathic gneiss, with minor garnet and rare cordierite present as coronas on garnet. Garnet may reach 3 cm in dia. Good example at spot height 141 at GR 544300 2295150. Minor aerial extent, no specific geographical name assigned for this minor unit.\n- Allison quartzo-feldspathic gneiss. Similar to above but variable, but minor, sillimanite and cordierite, sillimanite aligned. Also hosts large pods of brown phosphate (apatite or wagnerite?) in quartzose (+/- K-feldspar) veins. Named after Allison ice dome, which appears on map in Stuwe et al. (1989) and is unnamed on current 1:25 000 topographic map. Good examples at on southern Stornes Peninsula, at GR 543450 2295400.\n- Donovan prismatine leucocratic gneiss. (Not named on new map, attributed on new map as leucocratic prismatine tourmaline paragneiss***). Very minor aerial extent as narrow discontinuous lenses. Pale yellow quartzose +/- feldspathic unit, with aggregates of course prismatine and cordierite that also contains both fine grained sugary and coarse (1-2cm dia.) euhedral tourmaline and patches of coarsely granular rounded quartz with interstitial anhedral tourmaline. Sparsely biotite-bearing and contains small crystals of a metallic opaque mineral, possibly rutile. Two known exposures at GR 541750 2298800, and 544470 2299700. Second location associated with margin of a large body of White Hill leucogneiss, to which this unit may be genetically related on the basis of lithological similarity.\n\nIntrusives (or possible intrusives)\n- Composite orthogneiss (undifferentiated) was renamed by Fitzsimons (1995) to Sostrene orthogneiss and that name will be used here. Typically inferred to represent basement to the meta-sedimentary sequences of the Larsemann Hills and is present on the northern offshore islands, (e.g. McLeod and Manning Islands) and on outcrops to the south west of McCarthy Point. Felsic component typically quartz-plagioclase*K-feldspar, biotite and locally garnet. Dismembered mafic layers contain variable amounts of hornblende, plagioclase, orthopyroxene, rarely clinopyroxene. Described in many publications for example Carson et al. (1995b), Stuwe et al. (1989), Fitzsimons (1995) to name a few.\nSeveral workers have also suggested a tentative correlation between the Sostrene Orthogneiss, the Archaean orthogneiss complex of the Vestfold Hills and tectonically reworked Archaean orthogneiss in the Rauer Islands, however, isotopic evidence suggest that these crustal fragments are temporally unrelated - Nella mafic granulite. Named after Nella Fjord. Unit is best exposed immediately to the north of Zhong Shan station and described by several CHINARE papers, Stuwe et al. (1989) and Carson et al. (1995b) under several names. The unit is a mafic granulite dominated by variable hornblende, orthopyroxene, clinopyroxene, plagioclase, *biotite.\n- Blundell granitic orthogneiss. Much of Stornes Peninsula was thought to be psammite1 (Carson et al. 1995b) but remapping during 2003/04 and more detailed examination suggest the unit that makes up much of southern Stornes Peninsula is a composite orthogneiss complex. The two orthogneiss units described by Carson et al (1995) from Tonagh Promontory as augen k-spar orthogneiss1 and (variably) porphyroblastic k-spar orthogneiss2 and these units probably represent the bulk of the subtypes that make up the Blundell granitic orthogneiss. Blundell granitic orthogneiss on Stornes Penisula is composed of these two components, typically a cream to yellowish coloured garnet-bearing felsic orthogneiss, locally with large K-feldspar megacrysts with minor biotite and is locally intermingled with a greyish variety with large (1-2cm) recrystallised K-feldspar augens. Where clear relationships are observed, the augen variety intrudes (variably) porphyroblastic variety, e.g. Carson et al (1995b, figure 5 page 157). \n- Johnston granitic orthogneiss. Leucocratic light grey felsic garnet-cordierite-biotite orthogneiss, with generally homogenous appearance. Best observed at NW tip of Johnston Fjord at GR 542000 2300100. Minor unit and probably gradational with, or is part of, the Blundell granitic orthogneiss.\n- Tassie Tarn pegmatite. Microcline bearing medium to course grained, variably foliated pegmatite, typically assoc. with Tassie Tarn quartzite unit. Common pegmatite but rarely of mappable size. May contain rare tourmaline+quartz symplectites. Possibly related to Progress Granite and in which case is early Cambrian in age. Good examples at GR544150 2299600 and 541850 2298750 on Stornes Peninsula.\n\nGeological mapping confidence\nThe mapping represented on this map is subject to differing degrees of geological confidence, based on extent and detail of mapping in various areas.\nOn Stornes Peninsula the geological confidence level is high, This is based on geological mapping during 2003/04 as part of ASAC project 2350. The geological boundaries are accurate and rock types have been repeatedly subject to ground truthing via numerous foot traverses during a long duration visit to the area of up to several weeks. Recently acquired aerial photography (1998) greatly assisted the on-ground geological interpretation. Broknes Peninsula the geological confidence level is also high, based on numerous geological observations and publications by CHINARE geologists, and mapping by Stuwe et al. (1989) and Carson et al. (1995b). Minor peninsulas (Grovnes and Brattnevet, located between Stornes and Broknes, as named in Stuwe et al. 1989), Tonagh Promontory, Fisher Is., Manning Is. and Lovering Is. geological confidence is medium (geological information is based on limited ground truthing during shorter duration visits, typically one or two days).\nMany the small offshore islands to the N and NW of Stornes Peninsula and numerous small outcrops inland have low geological confidence. These regions have either had very limited ground truthing, (i.e. one visit for several hours) or no ground truthing and geological interpretation might be based on aerial photos.\nMany very small outcrops have never been visited. These are small inland rocky exposures and very small offshore islands. They are unmapped and the rock types are uncertain and difficult to assess from aerial photography.\n\nThe fields in the photolist are:\n\nCarson Site Number\nGrew Site Number\nLatitude\nLongitude\nPhotos (roll, frame number) and comments\nGeological structure", "links": [ { diff --git a/datasets/ASAC_2355_1.json b/datasets/ASAC_2355_1.json index 2badd8c916..655630eb9b 100644 --- a/datasets/ASAC_2355_1.json +++ b/datasets/ASAC_2355_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2355_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2355 See the link below for public details on this project.\n\n---- Public Summary from Project ----\nWe will measure biodiversity of ecologically-important invertebrates (Collembola) in Antarctica, the sub-antarctic islands, and in Australia and New Zealand. Using molecular and morphological techniques we will contribute to understanding of species distributions, and provide molecular data that will lead to automated species identifications.\n\nThis work is also related to ASAC project 2397, \"Introduced invasive terrestrial invertebrates on Macquarie Island: studies on ecology, origins and control\".\n\nField work was completed by David Gee on projects 2397 and 2355 on Macquarie Island from 4 to 13th March 2008.\n\nSee the word document in the download file for further information on the field work completed and the data collected. Four excel spreadsheets are also available in the download file - spreadsheets for amphipods, collembola, flatworms and isopods.\n\nSome general comments about the spreadsheets:\n\n* FID and Id columns are those that are automatically generated when creating a layer in ArcGis.\n\n* Counts of all organisms in samples has not yet been completed. Penny Greenslade will be attending to this in the future. However, some count numbers are present on the isopod and amphipod sheets - this refers to isopods and amphipods observed during field work.\n\n* The tag column is a standard column for naming any points in the NSW government system.\n\nTaken from the 2009-2010 Progress Report:\n\nPublic summary of the season progress:\nProgress on this project has been excellent in its first year. The timely appointment of a new PhD student in Oct 2009 (funded by The University of Adelaide) was fortunate and he went south with one assistant in Dec 2009. We collected all the soil samples that had been planned for the 2009/2010 season. Collections were focused on the Vestfold Hills, Larsemann Hills, Hop Island, Mather Peninsula, Sansom Island where the majority of time was spent, with opportunistic sampling at Casey (ASMA) and Mawson (Framnes) whilst in transit on the Aurora Australis. Once we obtain the samples in Adelaide (arrived on V4) which were returned to Hobart frozen then the processing will commence to retrieve the invertebrates.\n\nThe 2009-2010 data are accompanied by a spreadsheet detailing all column headings.", "links": [ { diff --git a/datasets/ASAC_2355_phylogeographic_1.json b/datasets/ASAC_2355_phylogeographic_1.json index 7536f728f8..ea5d506c29 100644 --- a/datasets/ASAC_2355_phylogeographic_1.json +++ b/datasets/ASAC_2355_phylogeographic_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2355_phylogeographic_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Some, or all, of the raw data for this project have been sourced from the Australian Antarctic Division Biodiversity Database.\n\nTaken from the abstract of the referenced paper:\n\nWe review current phylogeographic knowledge from across the Antarctic terrestrial landscape with a focus on springtail taxa. We describe consistent patterns of high genetic diversity and structure among populations which have persisted in glacial refugia across Antarctica over both short (less than 2 Mya) and long (greater than 10 Mya) timescales. Despite a general concordance of results among species, we explain why location is important in determining population genetic patterns within bioregions. We complete our review by drawing attention to the main limitations in the field of Antarctic phylogeography, namely that the scope of geographic focus is often lacking within studies, and that large gaps remain in our phylogeographic knowledge for most terrestrial groups.", "links": [ { diff --git a/datasets/ASAC_2357_2.json b/datasets/ASAC_2357_2.json index 795b9773b9..72ccc010b8 100644 --- a/datasets/ASAC_2357_2.json +++ b/datasets/ASAC_2357_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2357_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2357 See the link below for public details on this project.\n\n---- Public Summary from Project ----\nContaminants like PCBs and DDE have hardly been used Antarctica. Hence, this is an excellent place to monitor global background levels of these organochlorines. In this project concentrations in penguins and petrels will be compared to 10 years ago, which will show time trends of global background contamination levels.\n\nData set description\nFrom several birds from Hop Island, Rauer Islands near Davis, samples were collected from preenoil (oil that birds excrete to preen their feathers. This preenoil was then analysed for organochlorine pollutants like polychlorinated biphenyls, (PCBs), hexachlorobenzene (HCB), DDE and dieldrin. The species under investigation were the Adelie penguin (Pygoscelis adeliae) and the Southern Fulmar (Fulmarus glacialoides). The samples were collected from adult breeding birds, and stored in -20 degrees C as soon as possible. The analysis was done with relatively standard but very optimised methods, using a gas-chromatograph and mass-selective detection. \n\nData sheets:\nThe data are available in excel-sheets, located at Alterra, The Netherlands (the affiliation of the PI Nico van den Brink.). Data are available on PCB153 (polychlorinated biphenyl congener numbered 153), hexachlorobenzene (HCB), DDE (a metabolite of the pesticide DDT), and dieldrin (an insecticide).\n\nThe metadata are in 4 sheets (in meta data 2357.xls):\n1. 'Concentrations fulmars'\n2. 'Morphometric data fulmars'\n3. 'Concentrations Adelies'\n4. 'Morphometric data Adelies'\n\nThe column headings are: \n\n1. 'Concentrations fulmars'\n\n- Fulmar: bird number, corresponds with sheet 'morphometric data fulmars'.\n- PCB153: concentration of PCB-congener 153 (ng/g lipids)\n- HCB: concentration of hexachlorobenzene (ng/g lipids)\n- DDE: concentration of DDE (ng/g lipids)\n- Dieldrin: concentration of dieldrin (ng/g lipids)\n- Sample size weight of collected amount of preenoil\n\n2. Morphometric data fulmars\n\n- Fulmar: bird number, corresponds with sheet 'Concentrations fulmars'. \n- Bill Length (mm): length of bill (tip to base)\n- Head Length (mm): length of head (tip of bill to back of head)\n- Tarsus (mm): length of tarsus\n- Wing Length (cm): length of right wing\n- Weight (kg): weight of bird (without bag)\n\n3. 'Concentrations Adelies'\n\nAdelie: bird number, corresponds with sheet 'morphometric data Adelies'.\n- PCB153: concentration of PCB-congener 153 (ng/g lipids)\n- HCB: concentration of hexachlorobenzene (ng/g lipids)\n- DDE: concentration of DDE (ng/g lipids)\n- Dieldrin: concentration of dieldrin (ng/g lipids)\n- Sample size weight of collected amount of preenoil\n\n4. 'Morphometric data Adelies'\n\n- Adelie: bird number, corresponds with sheet 'Concentrations Adelies'. \n- Bill (mm): length of bill (tip to base)\n- Head Length (mm): length of head (tip of bill to back of head)\n- Tarsus (mm): length of tarsus\n- Flipper Length (cm): length of right flipper (wing)\n- Weight (kg): weight of bird (without bag)\n\nIn sheets on concentrations: less than d.l.: concentrations below detection limits.", "links": [ { diff --git a/datasets/ASAC_2363_1.json b/datasets/ASAC_2363_1.json index d7a78285e2..ec33b9a366 100644 --- a/datasets/ASAC_2363_1.json +++ b/datasets/ASAC_2363_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2363_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report describes the data collected for ASAC project 2363 (a continuation of ASAC 1158). A full report of the data collected and the work completed is available for download with the dataset.\n\nThe download file contains data in the following areas:\n\nAblation\nChemistry\nDEM - Digital Elevation Model\nLagoon Bathymetry\nMeltwater\nPhotos\nReport\nRES - Radio Echo Sounder\nSurveys\nWeather Observations\n\nThis CD contains data collected by the Heard Island glaciology team during the 2003-04 Australian Antarctic Division expedition, as well as some data from the previous expedition in November 2000. The data report associated with these files is provided as a PDF in the Report folder.\n\nDescription of data files available on CD\n\nAblation folder\nsurvey_canes_ablation.xls\nExcel file with the measured height of each survey wand above snow/ice surface for the field season.\nBG35_pinger.xls\nExcel file with sonic ranger data for BG35.\nBG50_pinger.xls\nExcel file with sonic ranger data for BG50.\n\nChemistry folder\nIon_Chemistry.xls\nExcel file with analyses of chloride, sulphate, nitrate, Mg, Ca, Na of samples collected from crevasses and cores.\nOxygen_isotopes.xls\nExcel file with dO18 analyses of samples collected from crevasses and cores.\n\nDEM folder\ndem_2003.grd\nASCII file with the 2003 DEM grid, generated using Golden Software Surfer v7.0. Header file format is:\n\nid The identification string DSAA that identifies the file as an ASCII grid file.\n\nnx nynx is the integer number of grid lines along the X axis (columns)\nny is the integer number of grid lines along the Y axis (rows)\n\nxlo xhi xlo is the minimum X value of the grid\nxhi is the maximum X value of the grid\n\nylo yhi ylo is the minimum Y value of the grid\nyhi is the maximum Y value of the grid\n\nzlo zhi zlo is the minimum Z value of the grid\nzhi is the maximum Z value of the grid\n\ngrid row 1\ngrid row 2\ngrid row 3 -these are the rows of Z values of the grid, organized in row order. Each row has a constant Y coordinate. Grid row 1 corresponds to ylo and the last grid row corresponds to yhi. Within each row, the Z values are arranged from xlo to xhi. Blanked grid nodes are given a Z value of 1.070141E+038. Rows are 39.855 m apart, Columns are 40 m apart.\n\ndem_11.xls\nExcel file with all points used to calculate the dem_2003.xls grid (refer to A2).\nThe folder also contains high resolution jpeg images of Fig. 16 and the data distribution figure (A2).\n\nLagoon bathymetry folder\nFolder containing Excel files with Easting, Northing (acquired using Garmin GPS V; WGS84, UTM zone 43) and depth (acquired using Garmin 'Fishfinder' depth sounder) for each lagoon surveyed. Also high resolution jpeg images of bathymetric maps reproduced in appendix A3.\n\nMeltwater folder\nContains an excel file with stream profiles and flux calculations, and repeat measurements of Brown Lagoon outflow stream. Also contains jpeg photos of three of the inflow streams, and an image showing their location using the Quickbird satellite image for reference.\n\nPhotos folder\nContains jpeg digital photos used in this report.\n\nReport folder\nHI_data_report_screen.pdf\nHI_data_report_print.pdf\nThis data report is reproduced as both a low and high resolution Adobe Acrobat PDF file, for on-screen viewing and printing respectively.\n\nRES folder\nBG_35_2000.xls\nExcel file with RES data for the BG35 profile, 2000 field season.\nRES.xls\nExcel file with RES data for the BG25 and BG20 profiles, 2003-04 field season.\n\nSurveys folder\nall_survey_points.xls\nExcel file with the position of the survey markers and additional points.\ndaily_position_BG50.xls\nExcel file with daily (occasionally more frequent) DGPS position near BG50\nkinematic_2000.xls\nExcel file with all DGPS kinematic surveys conducted during the 2000 field season.\nkinematic_surveys.xls\nExcel file with all DGPS kinematic surveys conducted during the 2003-04 field season.\nsurface_site_surveys.xls\nExcel file with the DGPS repeat survey positions of each survey site, for the 2000 and 2003-04 field seasons, and velocity calculations for each epoch.\nterminus_surveys.xls\nExcel file with the DGPS surveys of the position of the glacier terminus.\n\nWeather observations folder\nAANDERAA_data.xls\nExcel file with data recorded by the automatic weather station at 550 m a.s.l.\nall_data_comparison.xls\nExcel file with compilation and graphs of all data from each of the Brown Glacier AWS.\nMAWS1_data.xls\nExcel file with data recorded by the automatic weather station at 115 m a.s.l.\nMAWS2_data.xls\nExcel file with data recorded by the automatic weather station at 920 m a.s.l.\nprecipitation_record.xls\nExcel file with rain gauge records from Jacka Valley, Brown Hut, Spit Bay and Capsize Beach.\nttec_T_RH_data.xls\nExcel file containing temperature and relative humidity data from the three T-TEC loggers, deployed at Jacka Valley, Capsize Beach, and Doppler Hill.\nwx station photos folder\nFolder containing jpeg photos of each of the weather stations, as well as the field camp observing sites. Missing is Spit Bay.", "links": [ { diff --git a/datasets/ASAC_2377_1.json b/datasets/ASAC_2377_1.json index 911da23e98..e119aa9182 100644 --- a/datasets/ASAC_2377_1.json +++ b/datasets/ASAC_2377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2377\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThe scientifically and historically important collection of marine hydroids from Sir Douglas Mawson's Antarctic BANZARE Expeditions 1929-1931 has never been studied. In view of the increasing scientific and economic interest in Antarctic seas study of these organisms could provide a valuable tool in assessment and management of marine protected areas.\n\nTaken from the referenced publication:\n\nThe BANZARE Expeditions (British, Australian, New Zealand, Antarctic Research Expeditions) 1929-1931 sampled the marine benthos in the Southern Ocean, at the Kerguelen Islands, Heard Island, Macquarie Island, and south-west of Tasmania and along the coast of the Australian Antarctic Territory. Forty six stations at depths of 2 - 640 m were occupied along the Australian Antarctic Territory coast. Eight species of Halecium including five new and Hydrodendron arboreum were found and recorded from eight stations.\n\nForty six stations were occupied along the Australian Antarctic Territory coast and samples collected using various trawls to depths of 640 m; some coastal collections were also made in shallow water 2 m deep. The hydroid collection was originally deposited in the British Museum, Natural History (BMNH), London. There, preserved material was sorted during the 1960s and microslide mounts prepared. A small amount of material left over from the earlier AAE (Australian Antarctic Expedition) 1911-1914 was also incorporated into the BANZARE collection as Station No. 1785. The entire BANZARE hydroid collection was sent to the National Museum of Victoria in Melbourne for identification.", "links": [ { diff --git a/datasets/ASAC_2381_1.json b/datasets/ASAC_2381_1.json index 4cd57f91e0..66d0f09d28 100644 --- a/datasets/ASAC_2381_1.json +++ b/datasets/ASAC_2381_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2381_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2381\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThis project will compare sea ice electrical conductivities measured in-situ over lateral scales of tens of metres with those determined by small-scale laboratory measurements on core samples. These measurements will be used to determine appropriate bulk sea ice conductivities for interpretation of sea ice thickness from electromagnetic sounding data.\n\nData collection for this project took place on voyage 1 of the Aurora Australis in the 2003/2004 season.\n\nA word document thoroughly detailing the project and the data are included in the download file.\n\nThe fields in this dataset are:\n\nLatitude\nLongitude\nTime\nDate\nConductivity\nTemperature", "links": [ { diff --git a/datasets/ASAC_2382_1.json b/datasets/ASAC_2382_1.json index 0c6a1d8eee..d62fb200dc 100644 --- a/datasets/ASAC_2382_1.json +++ b/datasets/ASAC_2382_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2382_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected ASAC Project 2382 See the link below for public details on this project.\n\nThis entry contains: Locations for sampling sites for ASAC project 2382 on voyage 3 of the Aurora Australis in the 2004/5 season, collected between December and February of 2004/5; CTD bottle-derived seawater viscosity data and CTD bottle-derived in vivo fluorescence data.\n\nThere are four spreadsheet files in this download file. Each spreadsheet file contains several worksheets.\n\n1) I9_Stations.xls: Transect 1 (CLIVAR I9 = 'I9') station and sampling details: CTD stations, CTD profiles, Surface samples.\n2) PET_Stations.xls: Transect 2 (Kerguelen Plateau and Princess Elizabeth Trough = 'PET') station and sampling details: CTD stations, CTD profiles.\n3) Viscosity.xls: Viscosity data.\n4) Fluorescence.xls: In vivo fluorescence data.\n\nFor all files -999 = missing data\n\nA word document details the sampling protocols for viscosity and in vivo fluorescence.\n\nNote: ASAC project 2382 operates in direct collaboration with ASAC project 2596 (Three-dimensional microscale distribution and production of plankton populations).", "links": [ { diff --git a/datasets/ASAC_2385_1.json b/datasets/ASAC_2385_1.json index 08e6fb59be..64ca4d0c3f 100644 --- a/datasets/ASAC_2385_1.json +++ b/datasets/ASAC_2385_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2385_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2385\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nFacilities for chemical analysis of environmental samples in Antarctica are limited, with samples frequently shipped at great expense to Australia for analysis. Development of a technique to concentrate metals from environmental samples into a thin film which can be easily transported to a laboratory for analysis is currently underway.\n\nDGT stands for Diffusive gradients in thin films, they are a passive sampling technique for trace metals based on Fick's First Law of diffusion. Basically the theory being the method: Zhang, H. and Davison, W., Anal Chem, 1995, 67, 3391-400 and Davison, W. and Zhang, H., Nature (London), 1994, 367, 546-8.\n\nDescription of spreadsheets:\n\nAll data were collected using DGT sediment probes or water samplers prepared from polyacrylamide diffusion layer (0.8 mm thickness, covered with a 0.13 mm thick membrane filter) and Chelex 100 binding layer (0.4 mm thick).\n\nMetadata 0304 sediment -\nDGT sediment probes were deployed during the 0304 summer. Samples were deployed in a 3 x 2 back-to-back array at the inner and outer sites in Brown and O'Brien Bay. ie 1.1 and 1.2 are back to back pair. All samplers were deployed for 34 days. More accurate date are on the attached s'sheet. Results shown are nanograms of metals per square centimetre accumulated in the samplers at a resolution of 2 cm. The detection limits of the metals for the samplers are based on 3 x stdev of the field blank probes. Where value = &nd& the value was less than the method detection limit.\n\nMetadata 0304 sediment Characterisation -\nCores were sampled in Dec 2003 - Jan 2004 from Casey Station region. All characterisation was performed on the same 1 cm slices of core. Cores were sampled and analysed in anoxic conditions.\n\nLatitudes and Longitudess\nBrown Bay inner66.2803 S, 110.5414 E\n\nBrown Bay outer66.2802 S, 110.5451 E\n\nO'Brien Bay inner66.3122 S, 110.5147 E\n\nO'Brien Bay outer66.3113 S, 110.5162 E\n\nMetadata 0203 sediment -\nResults shown are sediment profile in nanograms of metals per square centimetre accumulated in the samplers at a resolution of 1 m. Samples 1.x were deployed for 5 days before the summer melt, 2.x were deployed for 10 days before the melt, 3.x were deployed for 15 days before the melt, 4.x were deployed for 21 days before the melt, 5.x were deployed for 28 days before the melt, 6.x were deployed for 5 days during the melt and 7.x were deployed for 20 days during the melt. The detection limits of the metals for the samplers are based on 3 x stdev of the field blank probes. Where value = 'nd' the value was less than the method detection limit.\n\nMetadata 0304 water -\nResults show metals in DGT water samplers deployed for 28 days. Actual times are on spreadsheet attached. Samplers were deployed in triplicate at three depths in the water column, with the depth from the sed bed meaning metres above the sea bed in the water column. Values in the original spreadsheet is nanograms of metals accumulated in sampler of 3.14cm2 area. The detection limits of the metals for the samplers are based on 3 x stdev of the field blank. Where value = 'nd' the value was less than the method detection limit.\n\nMetadata 0203 water -\nResults show metals in DGT water samplers deployed for 8 days. Samplers were deployed in triplicate at three depths in the water column. Depth from seabed is a measure of distance from the sea bed to the deployment depth in the water column. Values in the original spreadsheet is nanograms of metals accumulated in sampler of 3.14cm2 area. The detection limits of the metals for the samplers are based on 3 x stdev of the field blank. Where value = 'nd' the value was less than the method detection limit.\n\n----\nOne thing to note, although the metal isotopes are listed, ie Cd111(LR), this is still a measure of the elemental Cd (ie all isotopes), it is just how the ICP-MS analyst presents the data when I get the raw data back. I probably should have corrected this by remove the number to remove any ambiguity involved.\n\nA pdf file of supplementary figures created from the raw data are also included as a download file. Explanations of the figures are presented below.\n\nSupplementary Data Figure Captions\n\nFigure S1. 2002 - 03 DGT water sampling results for Cd, Fe and Ni, before the melt (upper) and during the melt (lower). BB Brown Bay, OBB O'Brien Bay, top top depth, mid middle depth, bot bottom depth. Error bars represent minimum and maximum values based on three replicates and horizontal line is the detection limit based on 3s \n\nFigure S2. 2002 - 03 DGT uptake results for Mn, Fe and As in Brown Bay (upper) and O'Brien Bay (lower) for various deployment times\n\nFigure S3. 2003 - 04 DGT sediment probes results for Brown Bay outer. Upper axis represents maximum porewater concentration assuming no resupply; symbols are for 6 replicate DGT probes. Detection limit, based on 3s is represented by vertical line\n\nFigure S4. 2003 - 04 DGT sediment probes results for O'Brien Bay inner. Upper axis represents maximum porewater concentration assuming no resupply; symbols are for 6 replicate DGT probes. Detection limit, based on 3s is represented by vertical line\n\nFigure S5. 2003 - 04 DGT sediment probes results for O'Brien Bay outer. Upper axis represents maximum porewater concentration assuming no resupply; symbols are for 6 replicate DGT probes. Detection limit, based on 3s is represented by vertical line\n\nFigure S6. Sediment porewater concentrations from replicate Brown Bay outer cores\n\nFigure S7. Sediment porewater concentrations for O'Brien Bay inner (open circles) and outer (closed circles)", "links": [ { diff --git a/datasets/ASAC_2385_field_lab_books_1.json b/datasets/ASAC_2385_field_lab_books_1.json index c0907549c6..5cf129d2a1 100644 --- a/datasets/ASAC_2385_field_lab_books_1.json +++ b/datasets/ASAC_2385_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2385_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 1997 and 2012 as part of ASAC (AAS) project 2385 - Development and application of DGT devices for passive sampling of contaminated waters in the Antarctic environment.", "links": [ { diff --git a/datasets/ASAC_2386_1.json b/datasets/ASAC_2386_1.json index 2e1be3bc99..7cbd6ff3a2 100644 --- a/datasets/ASAC_2386_1.json +++ b/datasets/ASAC_2386_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2386_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2386\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\n'Frozen dunes: An indicator of climate variability, McMurdo Dry Valleys, Antarctic'. This is a two year study involving scientists from Australia and New Zealand, which aims to use the internal structure of frozen sand dunes to identify climate change in the unique hyper-arid region of the McMurdo Dry Valleys.\n\nThis metadata record describes data collected from an automatic weather station (AWS) situated in the McMurdo Dry Valleys of Antarctica.\n\nThe download file contains an excel spreadsheet, which provides some general site information about the location of the AWS, as well as ten minute observations from the end of November 2004 to early December 2004.\n\nThe fields in this dataset are:\n\nDate\nTime\nPressure (mbar)\nWind Speed (m/s)\nGust Speed (m/s)\nTemperature (degrees C)\nWind Direction (degrees)\nSolar Radiation (W/m^2)\nDew Point (degrees C)\nRelative Humidity (%)", "links": [ { diff --git a/datasets/ASAC_2388_1.json b/datasets/ASAC_2388_1.json index 85abf9f283..a26c3a2110 100644 --- a/datasets/ASAC_2388_1.json +++ b/datasets/ASAC_2388_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2388_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2388\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nThe HIMI Ecosystem Project is aimed at examining key predators based on Heard Island, including albatross, penguins and seals, and their interactions with prey, the ocean and benthic environment and commercial fisheries. Work will include examining the diet of the animals, tracking them to determine where they feed, and examining prey available in the feeding areas. Scientists from a variety of backgrounds including bird and mammal biologists, marine biologists, oceanographers and marine geologists will be involved.\n\nThese data were collected on Aurora Australis voyage 4 2004 ('HIPPIES'). The data are stored in a Microsoft Access 97 file. The data includes sampling information for trawls using an IYGPT (International Young Gadoid Pelagic Trawl) Net and RMT 8 (8 square meter Rectangular Midwater Trawl) Nets. Sampling data, including location and time, are stored in the 'Hauls' table. Data from the analysis of the contents of each trawl, including species identifications, mass and counts of each taxon of mesopelagic fish and zooplankton are stored in the 'Bycatch' table. Weights, lengths and other biological data collected from individual mesopelagic fish are stored in the 'Fish Data' table. On opening the file the Haul form launches automatically giving access to the data.\n\nThe fields in this dataset are:\n\nLatitude\nLongitude\nSpecies\nDate\nHaul Number\nTime\nDepth (m)\nTow distance\nWire out\nTow Speed (knots)\nHeadline height (m)\nHeadline width (m)\nGear\nComments\nFishing Ground\nFishery\nTraps\nLine\nHooks\nMagazines\nFish length\nOtoliths\nScale sample\nFish Weight\nStomach\nGonads\nStomach contents", "links": [ { diff --git a/datasets/ASAC_2392_1.json b/datasets/ASAC_2392_1.json index 757f8667c7..b1c594b55f 100644 --- a/datasets/ASAC_2392_1.json +++ b/datasets/ASAC_2392_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2392_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of Hyperion satellite imagery, as well as GPS ground truthing of vegetation quadrats.\n\nThe aims of this project were:\n1. to produce a spectral library of the major subantarctic terrestrial plant species and community types from ground spectroradiometery measurements .\n2. to use the spectral library to assist in classification of vegetation communities.\n\nFile: 2392HI2003_04 Vegetation Survey Data.xls\n\nTable of vegetation data collected from Heard Island in the summer of 2003-2004 by Johanna Turnbull. Areas surveyed were Paddick Valley, Fairchild Beach, Dovers Moraine and Skua Beach. Ten 1x1 m quadrats were sampled with each 30x30 m site surveyed. Quadrats were selected haphazardly. Numbers are given as percentage cover of each species, averaged out over the ten sampled quadrats, unless otherwise stated.\n\nThe Codes used for species/ground cover types and vegetation communities/associations can be found in sheet 2 of the excel file, called 'vegetation codes'. They are also listed below:\n\nVegetation - Species/Ground Cover Types\nCode - Species/ground cover types\nAM - Acaena magellanica\nAS - Azorella selago\nCA - Callitriche\nCA w/ H2O - Callitriche in water\nCO - Colobanthus sp.\nDE - Deschampsia\nG - Gravel\nL - Lichen\nLI - Liverwort\nM - Moss/Bryophytes\nMO - Montia fontana\nPA - Pringlea antiscorbutica\nPAN - Poa annua\nPC - Poa cookii\nPK - Poa kuerguelensis\nPK/PC - P. kerguelensis / P. cookii Hybrid\nR - rock\nS - sand / soil\nW - Water\n\n\nVegetation - Communities / Associations\nCode - Community\nDCC - Closed Cushionfield\nDCC w/ Aceana - Closed Cushionfield with Aceana\nDCC/H - Closed Cushionfield/Herbfield\nDCC/H/T - Cushionfield/Herbfield/Tussock\nFF - Fellfield\nH - Herbfield\nMF - Mossfield\nPC/M - Pool Complex/Meadow\nPCC - Open Cushionfield\nPCC/MF - Open Cushionfield/Mossfield\nSM - Mire/Flush/Meadow\nSM/PCC - Mire/Flush/Open Cushionfield\nTHD - Tussock with Cushionfield/Herbfield", "links": [ { diff --git a/datasets/ASAC_2396_1.json b/datasets/ASAC_2396_1.json index 42c2b797b1..cb50693988 100644 --- a/datasets/ASAC_2396_1.json +++ b/datasets/ASAC_2396_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2396_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "---- Public Summary from Project ----\nHeard Island offers scientists a unique subantarctic laboratory for investigating climate change. We will establish a reference set of microalgal floras from lakes and lagoons and ultimately use the microalgal floras of today to investigate changes in fossil microalgal communities of Heard Island lake and lagoonal ecosystems to better understand regional subantarctic climate changes.\n\nSediments were sampled with hand corers.\nWater samples were collected with a Niskin bottle.\n\nThe dataset contains a summary of the locations data were sampled from, as well as average isotope concentrations from each sampling location.\n\nThe fields in this dataset are:\n\nDate\nLocation\nSalinity\npH\nGPS\nIsotopes\nConcentration (ppb)", "links": [ { diff --git a/datasets/ASAC_2397_1.json b/datasets/ASAC_2397_1.json index d1e48ceecf..8d2acf0602 100644 --- a/datasets/ASAC_2397_1.json +++ b/datasets/ASAC_2397_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2397_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2397\nSee the link below for public details on this project.\n---- Public Summary from Project---- \n\nThree soil animals, a land shrimp, a slater and a flat worm, were all introduced to Macquarie Island early last century. The three species were probably imported accidentally with sealers and their supplies from New Zealand. This study will investigate the origins and methods of dispersal of these animals and what factors limit their spread in order to advise on possible removal and improved quarantine risk management for the island.\n\nFrom the abstract of the referenced paper:\n\nInvasive species are a serious threat to biodiversity worldwide. The relatively simple ecological systems of the subantarctic have the potential to be significantly damaged by predatory species that invade. Two species of exotic, predatory, terrestrial flatworms were first collected in 1997 from two localitles only 2km apart, in the southeast of subantarctic Macquarie Island. \nThe species were later identified as Kontikia andersoni and Arthurdendyus vegrandis. We report here the results of fieldwork in 2004 that established that both species now occupy about a seventh of the southeast of the island which has a total area of only 170 square kilometres and that there seem to be no barriers to further expansion. The island was first discovered in 1810 and so it is likely the species were introduced by means of human intervention within the last 200 years. We provide evidence to show that both species originated in New Zealand and have probably been on the island for approximately 100 years giving an average rate of spread of about 10 metres per year. Other species of Arthurdendyus have been introduced from New Zealand to the United Kingdom where they prey on earthworms. The quarantine significance of A. vegrandis for Australia is discussed and recommendations made to reduce the probability of it entering Tasmania where it has the potential to become an agriculturla pest.\n\nThe fields in this dataset are:\n\nSite\nEasting\nNorthing\nSpecies", "links": [ { diff --git a/datasets/ASAC_2409_2.json b/datasets/ASAC_2409_2.json index 486248ab28..429becc32f 100644 --- a/datasets/ASAC_2409_2.json +++ b/datasets/ASAC_2409_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2409_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although oceanic crust covers about 60% of the Earth, relatively little is known of its geology and the processes that have created it. Macquarie Island represents a unique subaerial exposure of the seafloor, and an exceptional environment for active study and research into the ocean crust. We plan to utilise geological and geophysical techniques to help us better understand the lithological complexity and evolution of the oceanic crust.\n\nProject objectives:\n\nOur primary objective is to conduct coordinated ground- and air-based magnetic and electromagnetic surveys of the oceanic crust that comprises Macquarie Island and the surrounding seafloor for ~ 5 km from the island. We will integrate these geophysical data with the results of our recent studies of the Island and additional follow-up geological investigations. Together these data will improve our understanding of the tectonic and hydrothermal evolution of Macquarie Island ocean crust and through it, the evolution of oceanic crust in a more general sense. We believe the acquisition of these data will allow us to: (1) better resolve the complex geologic structure of the island; (2) determine the three-dimensional extent of the hydrothermal alteration of this example of oceanic crust; (3) map active fault zones across the island; and (4) correlate the geology of the Island with the offshore geology, linking it to regional data sets and the nearby active plate boundary. \n\nThe dataset has two forms. The main dataset is magnetic field data recorded in the Bauer Bay to Boot Hill area of Macquarie Island, on 200 m line spacings (csv file).\nThe subsidiary dataset are sample locations for the same area for a small set of rock samples obtained to check on magnetic character (word file).\n\nData were collected using a GEM Systems GSM-19 Overhauser Magnetometer.\n\nThe fields in this dataset are:\n\nEasting\nNorthing\nSample\nRock Type\nMagnetic Intensity (nT)\n \nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nThis project was in abeyance for the 2007-8 season due to our scientific field program being postponed as a necessity of the rabbit eradication program on Macquarie Island. A detailed study of the formation of specific magnetic lows from our regional ground magnetic survey, with the aim of determining their cause, and gaining insight into interpretation of magnetic lows in ocean crust in general. Hydrothermal alteration in ocean crust typically results in magnetic lows because it involves magnetite destruction. However, it is apparent that on Macquarie Island this is not the only cause of magnetic lows. There are 5 principal study sites:\n\n(1) Prion Lake to Brothers Point, and including the Mt Tulloch summit and slopes;\n(2) Waterfall Lake and surrounds;\n(3) Hurd Point to the coast immediately east of Mt Jefferies;\n(4) East Ainsworth area, east of the Caroline Cove protection zone;\n(5) Whisky Creek area, cutting through the eastern escarpment ~ 5 km north of Hurd Point.\n\nThe 2008-9 season has involved (1) compiling of geological mapping from each site and rectification with the available topographic base and most recent satellite imagery; (2) processing of magnetic data from each of the detailed surveys; (3) extraction of field observations into a digital database that can be accessed within his GIS platform; (4) petrographic description of ~100 polished thin sections to evaluate magnetite behaviour; and (5) a brief return to Macquarie Island to attempt to infill areas of geological data/sample deficiency.\n\nIn terms of the objective of correlating the geology of the island with the offshore geology, this has been in process within the USGS under the supervision of Dr Carol Finn. This part of the project is employing heli-magnetics obtained with the cooperation of AAD during resupply, using a USGS instrument The data was partly processed at Utas by Dr Michael Roach, and then transferred on for more detailed processing at the USGS.", "links": [ { diff --git a/datasets/ASAC_246_1.json b/datasets/ASAC_246_1.json index c5014de214..101e56c716 100644 --- a/datasets/ASAC_246_1.json +++ b/datasets/ASAC_246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 246\nSee the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nAnalyses of data collected during the summer of 1979-80 from pollen traps and a flag tatter experiment along a transect across Macquarie Island, between Bauer Bay and Sandy Bay, suggest that the most sheltered locations are in the lee of prominent ridges, especially below the eastern coastal cliff tops, but also, paradoxically, on the lower slopes of the windward (west) coast. The presence of feldmark community pollen grains from 'downwind' and higher altitudes in the west coast pollen traps indicates that, during the period here documented, the island formed a topographic barrier such that when there were winds from westerly quarters of sufficient velocity, not only would the predictable lee side rotor circulations have been produced, but also a trapped windward (west coast) rotor that, at low altitudes, would have run counter to the prevailing winds. Flag tatter data from the most exposed sites indicate local wind climates of greater severity than any measured in the same way at comparable maritime latitudes across similar topographic barriers elsewhere. It can be inferred that windward rotors are not uncommon on Macquarie Island.\n\nThe effectiveness of Macquarie Island as a barrier to local ocean and atmospheric circulation is a function of balances between tectonic and denudational (including marine) processes and glacial eustasy, and would have varied over time. This, and the resulting effect of local wind circulation on fallout patterns, need to be considered when interpreting pollen diagrams.", "links": [ { diff --git a/datasets/ASAC_2504_1.json b/datasets/ASAC_2504_1.json index ad6a94d9af..536863625a 100644 --- a/datasets/ASAC_2504_1.json +++ b/datasets/ASAC_2504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2504\nSee the link below for public details on this project.\n\nIn this project a sea-ice model for application in Southern Ocean climate and forecasting studies will be developed to amend identified deficiencies in numerical models (i.e. unaccounted short-term dynamics; or non-suitable ice rheology). In-situ deformation and ice-stress data will be used to derive parameterisations suitable for the Southern Ocean pack. \n\nAntarctic sea ice is an important component of the Southern Hemisphere climate. It provides a habitat for algae, plankton and for larger species such as mammals or penguins. It is a transport medium for freshwater and biological matter. On the other hand it acts like a barrier between ocean and atmosphere in regard to the exchange of thermal energy, water vapour and gases. Sea ice affects the polar climate in many ways: E.g., by effectively insulating the ocean from the colder atmosphere the sea ice enables an advection of relatively warm water onto the shallow Antarctic continental shelf. This warmer water is then available to interact with other components of the climate system, such as by basal melting of the continental ice shelves [Jenkins and Holland, 2002]. Also, due to its high albedo, the sea ice has a large-scale effect on the net incoming solar radiation [Ebert et al., 1995] and reduces the absorption of solar energy into the upper ocean. The thermodynamic growth of seaice and the consequent desalination of the ice gives rise to a transport of salt from the ice into the ocean, which increases the water density over the shelf, thereby driving the deep vertical overturning cell in the global ocean circulation. High ice-growth rates (e.g., in regions of polynyas) are generally concentrated in small areas in shallow waters. These regions are often insufficiently resolved or even unresolved in coupled climate models, which are generally configured to run at a spatial resolution of 2 degree longitude by 1 degree latitude or coarser [Zhang and Hunke, 2001].\n\nThe specific objectives of this project are to:\n\n* identify the variabilities in the sea-ice characteristics and the underlying physical processes;\n\n* identify the time scales, at which the sea ice interacts with the ocean and atmosphere;\n\n* assess the contribution of sub-daily ice motion and deformation due to tidal forcing and inertial response to changes within the Antarctic ocean-ice-atmosphere system;\n\n* derive the impact of sub-daily ice dynamics on the sea-ice area, extent and mass on interannual and decadal time scales;\n\n* determine the scale effect of dynamic processes on the accuracy of modelled sea-ice parameters using a global high-resolution model;\n\n* identify model uncertainties through comprehensive validation studies.\n\nHowever, logistical problems prevented the project from collecting any data in the field.\n\nTo overcome the paucity of planned buoy data we used the following data sets to address some of the aspects of the original proposal:\n\n1) Sea-ice buoy data:\n ISPOL 2004: See AAS #2500 for metadata.\n\n2) Numerical investigations:\n\nWe have investigated the failure of sea ice using an isotropic model [Hibler, 1979], where ice strength is modelled as a random variable in the model space. In situ weakening was prescribed by a fracture-based Coulombic rheology [Hibler and Schulson, 2000]. We realised this by parameterising weakening with an ice-strength parameter of 1000 and initialising the ice strength across the model grid by random. The simulations were run over a 2000 km by 2000 km region and forced, from rest, with an idealised wind field. We analysed the sensitivity of failure to ice strength and wind stress as well as the intersection angle of the wind stress, and conducted idealised 2D failure experiments.", "links": [ { diff --git a/datasets/ASAC_2505_1.json b/datasets/ASAC_2505_1.json index 14b4e1b38b..5b26256bc1 100644 --- a/datasets/ASAC_2505_1.json +++ b/datasets/ASAC_2505_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2505_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vitamin D deficiency is detrimental to the skeleton resulting in accelerated bone loss, and reductions in bone mineral density (BMD). The main source of vitamin D in healthy people is that which they produce in the skin when it is exposed to ultraviolet (UV) radiation eg. sunlight. During winter when there is less sunlight, less vitamin D is produced and bone loss increases. This seasonal change to bone is reversed once the skin is exposed to sunlight again. The longer a person is not exposed to sunlight, the greater the detrimental effect on bones. It is not known if prolonged periods of sun deprivation will permanently effect bone. Studying healthy adults during their time in Antarctica, when UV exposure is negligible provides valuable information about the effect of prolonged sun deprivation on bone. Following the expeditioners up on their return to a temperate climate will reveal if the detriment to bone is transient or permanent. Findings from this study may have applications beyond expeditioners to include elderly in aged care and space travel.\n\nBaseline blood samples (as at 2004-06-30):\nLocationNumber\nMacquarie Island14\nCasey14\nDavis20\nMawson16\n\n3 month blood samples:\nMawson15\n\n6 month blood samples and diet records:\nMawson17", "links": [ { diff --git a/datasets/ASAC_2509_1.json b/datasets/ASAC_2509_1.json index 8b1c5232fe..f231ee8f55 100644 --- a/datasets/ASAC_2509_1.json +++ b/datasets/ASAC_2509_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2509_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2509\nSee the link below for public details on this project.\n\nThis project studied the limnology of lakes in the Framnes Mountains and Stillwell Hills near Mawson Station. Present-day chemical, biological and physical characteristics of the lakes were measured, and sediment cores were collected to determine how the lake environments have developed since the lakes were formed.\n\nData were collected with a Hydrolab 4 profiler.\nGiven for each lake is the date of sampling, the location of the sampling site, and the thickness of the ice.\nProfiles of temperature (degrees C), pH, conductivity (microS/cm - microsiemens per centimetre), concentration of dissolved oxygen (DO, unit mg/L) and the percentage saturation of dissolved oxygen are given below.\nThe zero point for all the profile was the water level in the hole through the ice.\n\nThe fields in this dataset are:\n\nLake\nDate\nLatitude\nLongitude\nIce Thickness (m)\nDepth (m)\nWater Temperature (degrees C)\npH\nConductivity\nDissolved Oxygen concentration\nDissolved Oxygen saturation", "links": [ { diff --git a/datasets/ASAC_250_1.json b/datasets/ASAC_250_1.json index 91d48dbeee..a440ed0f56 100644 --- a/datasets/ASAC_250_1.json +++ b/datasets/ASAC_250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 250\nSee the link below for public details on this project.\n\nThe study investigated the impacts of oiling on the biota of rocky shores. Five shore zones were evaluated and kelp holdfasts were collected (but not evaluated as part of this project). Data were collected using quadrat and line transect methods using counts and percentage cover as variables.\n\nData for this work was also used in ASAC projects 672 and 1003 (ASAC_672, ASAC_1003).\n\nThis dataset contains the 1988 data only.\n\nThe site codes used in this project are:\n\nSB = Sandy Bay\nSEC = Secluded Bay\nBB = Buckles Bay\nGC = Garden Cove\nGG = Green Gorge\nGB = Goat Bay\n\nThe first number given after the site code is the site number at that sampling location. The second number is the replicate at that site. Thus sb(1)3 is Sandy Bay site 1, replicate 3.\n\nThe numbers are total individuals of each species that were found in each holdfast sample. This is a basic, though standard, species-abundance matrix.\n\nThe fields in this dataset are:\n\nSpecies\nYear\nSite", "links": [ { diff --git a/datasets/ASAC_2518_1.json b/datasets/ASAC_2518_1.json index c7ef097463..9a69bb3aad 100644 --- a/datasets/ASAC_2518_1.json +++ b/datasets/ASAC_2518_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2518_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2518\nSee the link below for public details on this project.\n\nGlobal climate change will lead to a reduction in the duration and thickness of sea ice in coastal areas. We will determine whether this will lead to a decrease in primary production and food value to higher predators.\n \nProject objectives:\nOur primary objective is to determine what effect will declining sea ice cover have on Antarctic coastal primary production?\n\nHypotheses to be tested\n\n- A decrease in sea ice algal production will lead to a net reduction in total primary production.\n\n- A decrease in sea ice will result in less water column stratification which will reduce the significance of phytoplankton blooms.\n\n- Less sea ice will lead to a change in phytoplankton bloom composition away from diatoms towards un-nutritious nuisance blooms such as Phaeocystis\n\n- Benthic microalgal production will increase\n\n- Seaweed production will increase slightly\n\n- A decrease in sea ice thickness will increase ice algal production (as they are generally light limited)\n\n- Ice algae, benthic microalgae, and phytoplankton will acclimate to an elevated light climates by changing their photosynthetic efficiency and capacity\n\n- Ice algae, benthic microalgae, and phytoplankton will acclimate to an altered light quality.\n\nTo answer these questions we will also need to determine:\n\n- What is the total annual primary production at coastal Antarctic sites; this consists of the contributions from the sea ice algal mats, benthic microalgal, seaweed and phytoplankton?\n\n- What is the effect of major environmental variables, such as UV, salinity, currents oxygen toxicity, cloud cover, nutrient availability and temperature on production.\n\n- What is the inter-annual variability in primary production?\n\nA broader scale issue that our data will contribute to providing answers to is the question\n\n- What effect will changing primary production have on higher trophic levels?\n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nThe 2009/10 field and laboratory season focused on the second of our primary questions, i.e. 'What is the effect of major environmental variables, such as UV, salinity, currents oxygen toxicity, cloud cover, nutrient availability and temperature on production'. In particular we focused on light and light transmission though the sea ice.\n\nThe science program AAS2518 was executed at Casey station from 11 Nov to 5 Dec 2009. The project was split into a field and a lab-based component. In situ spectral light transmission data were collected on first year sea ice within the vicinity of Jack's Hut. Ice cores were collected and transported to the laboratory at Casey station for spectral attenuation profiles within sea ice, and for measurements of spectral absorption by particulate and dissolved organic matter.\n\nOverall, the program was successful: in situ sea-ice spectral transmission data was collected in combination with vertical profiles of absorption coefficients of particulate (algae and detritus) and dissolved organic matter. Samples for analysis of photosynthetic pigments were collected and shipped to Sydney. Their analysis is underway. Due to logistical issues associated with the collection and transport of sea ice cores, the protocol for vertical profiling of spectral attenuation was modified (see below) and analysis of the data is currently underway.\nThe field component of the program was successful as spectral transmission data was collected for first year sea-ice, and the chosen site contained a thriving sea ice algal community for bio-optical measurements. It was initially planned to sample multiple sites offering a range of varying sea-ice thickness, but this was not possible during this campaign. Many sites in the vicinity of Casey station had already started to melt and break up, so that for logistical and safety reasons the area around Jack's hut was the only workable option. The field period instead spanned ~ 20 days during the melt period at Jack's, during which the porosity of sea ice increased but thickness remained constant.\n\nIce cores destined for spectral transmission profiles were to be collected whole and intact, but due to the presence of fractures in the sea ice, drilling (manual as well as motor powered) resulted in fractured core samples. The protocol was therefore modified: cores were sectioned in 20 cm sections and spectral transmission measured for each section. Spectral transmission profiles across the entire thickness of sea ice are to be re-constructed from the discrete data points. The accuracy of the approach will be assessed against the in situ spectral transmission data.\n\nThe download file contains three spreadsheets (two of them are csv files), and a readme document which provides detailed information about the three spreadsheets.", "links": [ { diff --git a/datasets/ASAC_251_1.json b/datasets/ASAC_251_1.json index 8cd7b81de7..e0f6737ffb 100644 --- a/datasets/ASAC_251_1.json +++ b/datasets/ASAC_251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 251 See the\nlink below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nThe fungal floras of plant communities and mineral soils were determined at locations both close to and away from sites of human activity. Petroleum contaminated soils and discarded wood which occur near Stations were also studied, the former or bacterial as well as fungal colonisation. The fungal floras of uncontaminated natural communities comprised relatively few species, Geomyces pannorum, Phoma herbarum and Thelebolus microsporus being the most common, together with Epicoccum nigrum at Mawson. P. herbarum dominated the fungal floras of mosses at Mossell Lake but E. nigrum was common in Mawson mossbeds. G. pannorum was widespread and colonised a range of different habitats, particularly in the Vestfold Hills. T. microsporus was also widespread particularly at sites frequented by birds and seals. Phialophora fastigiata was common around the stations, especially Davis Station, in soils including those contaminated with oil and in wood, and is thought to have been introduced with softwood packing crates. A greater range of taxa including Mortierella, Mucor, Penicillium and Cladosporium spp. was recorded from Mawson Station than from other sites, and this was attributed to the effects of human activity, Few fungi but a range of bacteria were isolated from the petroleum contaminated soils. A high percentage of these soils contained bacteria which could utilise hydrocarbons as a sole carbon source. Some of these bacteria showed a strong degradative potential, namely Flavobacterium spp., Corynebacterium spp., Bacterillus spp., and an isolate from the family Enterobacteriaceae. One isolate of Corynebacterium and the Enterobacteriaceae isolate were active hydrocarbon degraders at 1 degree C. Hormoconis resinae, the imperfect state of Amorphotheca resinae was only isolated from oil spill soils and then only from sites of recent spills. Geomyces pannorum and Thelebolus microsporus were less common in oil contaminated soils than in uncontaminated soils.", "links": [ { diff --git a/datasets/ASAC_2529_1.json b/datasets/ASAC_2529_1.json index 7049d36190..659af2b65a 100644 --- a/datasets/ASAC_2529_1.json +++ b/datasets/ASAC_2529_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2529_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2529\nSee the link below for public details on this project.\n\nPublic \nA meteor radar will be installed at Davis Station to measure temperatures and wind velocities in the 80 to 100 km region of the atmosphere. It will do this by tracking the trail of ionised gas produced by meteors as they pass through this region. These trails are blown along by the winds after they are formed, and so act as tracers of the wind, before being dispersed. Understanding the region is important, because it is believed to be providing indications of climate change.\n\nProject objectives:\nThe upgraded meteor radar will complement the MF radar, the MST radar, the lidar, and the photometer operating at Davis Station. The increased power will provide a higher meteor count rate that will allow the vertical temperature structure of the tidal motions to be investigated [Hocking and Hocking, 2002]. There will be no routine summer measurements of MLT temperature at high southern latitudes apart from those that will be provided by the meteor radar. This is a key parameter in understanding the PMSE. In addition, the increased count rate and the new transmit configuration will allow an investigation of the utility of using high powered meteor radars to measure gravity wave momentum fluxes. In the winter months, the meteor radar will provide estimates of the mean and fluctuating temperatures in the 80 to 100 km height region that complement the T-OH measurements that when combined will allow the density and pressure of the region to be inferred. The meteor radar will also provide wind measurements that complement those derived from the MF radar. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nThe Davis system has operated in interleaved meteor mode during the entire season. Winds and mesospheric temperatures are available. The new high power transmit antenna developed and trialled on the sister system to the Davis VHF Radar at the University of Adelaide's Buckland Park field station has delivered excellent results. This antenna is capable of operation with the full power of the Davis ST VHF radar. The sister transmitter at BP has been optimised and a procedure developed to apply this on the Davis system. The new combiner unit has been operated routinely at Buckland Park and a catastrophic failure mode identified and rectified.\n\nThe Davis 55 MHz atmospheric radar can be run in a meteor detection mode by selecting an alternate set of transmitting and receiving antennas. These consist of a single circularly polarized transmitting antenna and five linear polarized receiving antennas arranged in a 'Mills Cross' configuration. Approximately 10 percent of radar observing time is committed to these observations although that figure has been larger at times through the life of the project. \nIn meteor mode, circularly polarized pulses are transmitted at a high repetition rate and the received signal is sampled at ranges sensitive to returns from the altitude range of 80-110 km approximately. If a meteor trail is present in the antenna field of view, increases of power of duration less than are second can be detected. The range is calculated from the pulse transit time and the direction of arrival is inferred from the relative phases of the signals at each receive antenna. Data files with a '.met' extension contain the analysed data products from these detections and these include:\nEvent start time - The time of the detection\nRange - The distance from the radar to the meteor trail\nSNR - The signal to noise ratio of the detection\nAngle of arrival - The azimuth and zenith angles of the direction from the radar to the meteor trail\nDecay time - The exponential decay time of the detected signal (and its error)\nDiffusion coefficient - An inferred trail diffusion coefficient (and its error)\nRadial velocity - The speed with which the trail was moving toward or away (positive) from the radar (and its error)\nPhase differences - The mean phase differences for each pair combination of the five antennas.\n(See attached description of the 'met' analysed data record for more information.)\n\nIf enough meteors are detected, it is possible to infer a horizontal wind field at the height of the detections. This is done my assuming the wind flows without divergence or convergence in the vicinity of the radar over a selected averaging interval. Horizontal and vertical components of the wind are derived in this way and stored with their heights. These data are stored in files with a '.vel' extension. (See attached description of 'vel' postanalysed data records for more information.)", "links": [ { diff --git a/datasets/ASAC_2534_1.json b/datasets/ASAC_2534_1.json index efb486b04b..418d1a6c4e 100644 --- a/datasets/ASAC_2534_1.json +++ b/datasets/ASAC_2534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2534\nSee the link below for public details on this project.\n\nThe Holocene sea-ice project brings together for the first time, records from the Antarctic continent and deep sea sediments that will allow us to calibrate three sea-ice extent surrogates, validate their use in contrast to satellite observations and explore climatic influence on the physio-ecological environment over the last 10,000 years.\n\nTaken from the 2004-2005 Progress Report:\n\nProgress Objectives:\nOur objective is to instigate synthesis between deep sea and continental ice core records of Antarctic sea ice variability over the Holocene (last 10,000 yrs BP).\n\nThe relevance of this novel evaluation is three-fold:\n\n- To appraise for the first time the relationships between proxy sea ice predictions beyond the instrumental record from the land and sea.\n\n- To assess variability differences and similarities from the various records that can then be used to probe the dynamics of the climate/environmental system in the East Antarctic sector.\n\n- To provide insights on the ecological response sea ice plays through the Holocene.\n\nPublic summary of the season progress:\nBasic analysis of samples from Core E27-23 have been complete except for seven new samples from near the top of the core. This includes counts of diatoms, foraminifera, ice-rafted debris, volcanic glass. A greater variety of parameters is available than expected.\nDramatic downhole changes represent oceanographic changes over last 25 000 years at the site including in evidence for carbonate dissolution and water temperature.\nNow needs statistical analysis of diatom data, extra radiocarbon dates and integration with data from Law Dome ice-core.", "links": [ { diff --git a/datasets/ASAC_2545_1.json b/datasets/ASAC_2545_1.json index 567c6c50b2..c13b21c718 100644 --- a/datasets/ASAC_2545_1.json +++ b/datasets/ASAC_2545_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2545_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 2545\nSee the link below for public details on this project.\n\nThe orchid Nematoceras (Corybas) dienema has been found in several distinct locations on Macquarie Island. Using molecular genetics, we will investigate the orchid biodiversity and whether these populations have a common origin, the fungal association with the roots which is necessary for orchid dispersal and colonisation, and the need for conservation of particular populations. We will also identify the myccorhizal fungus and search for new orchid species on Macquarie Island.\n\nA list of Genbank accession numbers are provided in one of the attached excel spreadsheets.\n\nFrom the abstract of one of the referenced papers:\n\nSubantarctic Macquarie Island is an isolated, treeless windswept island approximately 34 km long, situated at 55 degrees S in the Southern Ocean about half way between Australia and Antarctica. It is the only island in the world to be formed from uplifted oceanic crust. New Zealand and its Southern Ocean islands lie to the north east. Climatically Macquarie Island is cool, moist and windy with only a 3-4 degrees C temperature variation between seasons. The flora is restricted to bryophytes, lichens and low-growing vascular plants and has been established after long-distance transoceanic dispersal of seeds and spores carried by ocean currents, winds or seabirds. It has affinities with other southern ocean islands. In 1978 an orchid was identified on the island as Corybas macranthus, and this was later described as the endemic Corybas dienemus (now Nematoceras dienema). The genus Nematoceras now includes most of the Corybas from New Zealand and its islands. We now report a second endemic orchid species on Macquarie Island and its confirmation by rDNA sequence analysis of the internal transcribed spacer (ITS) region of the 18-26S nuclear ribosomal repeat unit.\n\nThe fields in this spreadsheet are:\n\nTaxon/Species\nProvenance\nCollector\nGenbank Accession Number\n \nProject Objectives:\nThrough field and laboratory-based studies, we plan to study Australia's southernmost orchids and:\n\n1) determine the range, population size and distribution of the two endemic and endangered orchids Nematoceras dienemum and Nematoceras sulcatum on Macquarie Island, and to confirm the identity of orchid species in all known populations\n\n2) investigate the reproductive biology of these orchids including the identity of associated mycorrhizal fungi, and whether these species require pollination by insects or can set seed by cleistogamy;\n\n3) add to our understanding of the biology of these two orchid species on Macquarie Island, and assess their status for conservation and likely response to rabbit damage in the short term, and to climate change in the longer term.\n\n4) search for new orchid species and populations in likely habitat areas already identified on Macquarie Island from vegetation maps and field experience, as well as investigating the potential for hybridisation between the two known species.\n\nIn the first year of this proposal, we hope to visit the island in early summer, if possible, to maximise the possibility of finding orchids in flower, and in the second year we will complete laboratory identification of orchids, their diversity and their mycorrhizal fungi.\n\nThis study will provide crucial baseline data from which to provide useful conservation and management information for these unique orchids, and will then facilitate further research into their origins, dispersal and evolution using genetic techniques.\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nExcellent progress was made this year with fieldwork for this project, due to the possibility of an extended field season on Macquarie Island made possible by provision of berths on tourist ships (thank you). Known populations of each orchid species have been checked in midsummer for population size, location, and species identity. A new population of one species was found near Sawyer Creek waterfall, after extensive searches of a range of likely habitats over much of the island between Waterfall Bay and Handspike Corner. No new orchid species were found, but leaf specimens were collected from several populations to confirm the species identity.", "links": [ { diff --git a/datasets/ASAC_2547_1.json b/datasets/ASAC_2547_1.json index 2802396a2c..195fb50136 100644 --- a/datasets/ASAC_2547_1.json +++ b/datasets/ASAC_2547_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2547_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2547\nSee the link below for public details on this project.\n\nPue (greater than 90% as determined by SDS-PAGE) samples of nitrate reductase have been isolated from the Antarctic bacterium, Shewanella gelidimarina (ACAM 456T; Accession number U85907 (16S rDNA)). The protein is ~90 kDa (similar to nitrate reductase enzymes characterised from alternate bacteria) and stains positive in an in-situ nitrate reduction (native) assay technique. The protein may be N-terminal blocked, although further sequencing experiments are required to confirm this. \n\nThis work is based upon phenotyped Antarctic bacteria (S. gelidimarina; S.frigidimarina) that was collected during other ASAC projects. (Refer: Psychrophilic Bacteria from Antarctic Sea-ice and Phospholipids of Antarctic sea ice algal communities new sources of PUFA [ASAC_708] and Biodiversity and ecophysiology of Antarctic sea-ice bacteria [ASAC_1012]).\n\nThe download file contains 4 scientific papers produced from this work - one of these papers also contains a large set of accession numbers for data stored at GenBank.", "links": [ { diff --git a/datasets/ASAC_2561_1.json b/datasets/ASAC_2561_1.json index 1183bb7b42..f4b7e56318 100644 --- a/datasets/ASAC_2561_1.json +++ b/datasets/ASAC_2561_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2561_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2561\nSee the link below for public details on this project.\n\nThe project aims to investigate the multidimensional sub-surface structures in the southern Prince Charles Mountains using the airborne geophysical data acquired during Prince Charles Mountains Expedition of Germany-Australia (PCMEGA). This will be of national significance as it will identify continental geological processes occurring in Australia-Antarctica, prior to and during their separation ~120 million years ago, and relate them to present day observations.\n\nTaken from the abstracts of the referenced papers:\n\nGeological exposures in the Lambert Rift region of East Antarctica comprise scattered coastal outcrops and inland nunataks sporadically protruding through the Antarctic ice sheet from Prydz Bay to the southernmost end of the Prince Charles Mountains. This study utilised airborne magnetic, gravity, and ice radar data to interpret the distribution and architecture of tectonic terranes that are largely buried beneath the thick ice sheet. Free-air and Bouger gravity data are highly influenced by the subice and mantle topography, respectively. Gravity stripping facilitated the removal of the effect of ice and Moho, and the residual gravity data set thus obtained for the intermediate crustal level allowed a direct comparison with magnetic data. Interpretation of geophysical data also provided insight into the distribution and geometry of four tectonic blocks: namely, the Vestfold, Beaver, Mawson, and Gamburtsev domains. These tectonic domains are supported by surface observations such as rock descriptions, isotopic data sets, and structural mapping.\n\n*********\n\nThree dimensional modelling of airborne magnetic data acquired in the Prince Charles Mountains, East Antarctica, provides an insight into the sub-ice distribution, and three-dimensional geometry of a Neoproterozoic sedimentary basin. A three-dimensional starting model was created from two two-dimensional GM-SYS modelling and our current geological understanding of the study area. Three-dimensional VPmg inversion modelling was performed on the aeromagnetic data to obtain an acceptable fit between the observed response collected in the field, and the calculated response of the three dimensional model. Modelling suggests that the base of the basin undulates and is relatively unstructured, however the margins of the basin thicken from north (~4 km) to south (~11 km). The volume of the Sodruzhestvo Group that in-fills the sedimentary basin has been calculated at approximately 35,000 cubic kilometres. Modelling of Banded Iron Formations at the southern margin of the basin and a sharp magnetic contrast in the north, reveal that both contacts dip toward the south. We interpret this asymmetric geometry of the sedimentary basin as having began as a set of half grabens, bounded by an underlying listric fault that flattened at depth below the sedimentary basin. Subsequent Early Palaeozoic inversion of this structure resulted in reactivation along the low-angle basal detachment, but rather than taking its original course underneath the sedimentary basin, the fault ramped up along the southern margin. This process caused exhumation of the underlying Banded Iron Formations, which are now juxtaposed at similar crustal levels to the Neoproterozoic cover rocks.", "links": [ { diff --git a/datasets/ASAC_2562_1.json b/datasets/ASAC_2562_1.json index 49b20a0877..b540a9727f 100644 --- a/datasets/ASAC_2562_1.json +++ b/datasets/ASAC_2562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2562\nSee the link below for public details on this project.\n\nThis project will:\n\n1. Identify factors that promote psychological adaptation and resilience in Antarctic expeditioners and describe their relationship to positive and negative change arising from the expedition experience,\n\n2. Identify factors that promote psychological adaptation and resilience in Antarctic expeditioners families, and describe their relationship to positive and negative change arising from the separation experience,\n\n3. Describe the quality and nature of the reintegration experience by comparing the processes and outcomes of each of the above and their implications for the process of reintegration over a 12 month period, and\n\n4. Use these data to develop a reintegration program based on the identification of changes and their reconciliation across both groups.\n\nPrevious Antarctic research tends to reflect a pathogenic approach, focusing on the negative aspects of living and working in an extreme and unusual environment, while largely ignoring the positive aspects. Consistent with recent work on the psychological consequences of working in adverse environments, ongoing polar research has revealed a more balanced view of life in Antarctica. Many researchers have concluded that Antarctic expedition members are generally well adjusted, competent and able to cope with the stress of living in an isolated and confined environment. Indeed, the majority of Antarctic personnel complete their assignments successfully, smoothly, and harmoniously (Suedfeld and Weiss, 2000, p. 11). Other studies have found that Antarctic personnel report more positive than negative experiences during the austral winter (Wood, Hysong, Lugg and Harm, 2000).\n\nAttention in this regard has focused on personal and group resources that facilitates the ability of some people to readily adapt to challenging situations (resilience) and regain prior levels of personal, family and work functioning on return. This work has also indicated a potential for such experiences to contribute to their experiencing an enduring sense of growth (Paton, in press; Paton, Violanti and Smith, 2003; Shakespeare-Finch, Paton and Violanti, 2003). However, more work is needed to examine the operation of these theoretical processes within complex social systems. For example, as the number of interacting groups increases, the possibility of incompatible outcomes increases. In the context of the present research, for example, positive changes in the family could be represent adverse events for expeditioners and vice versa. Consequently, the comprehensive analysis of adaptation must include both perspectives.\n\nDespite the growing recognition do positive outcomes in expeditioners, relatively little attention has been paid to the psychological issues that surround individuals following their return to home life after an extended expedition, or the effects of separation on the family. Previous research has indicated that separation from family and friends can pose several challenges for polar expedition members, military personnel, and other individuals whose work takes them away from home for extended periods of time (Dunn and Flemming, 2001; Godwin, 1988; Palmai, 1963; Taylor, 1973). Some of the potential reintegration issues identified include disrupted communication patterns, missing developmental milestones in children's lives, losing authority and place within the family, as well as challenges associated with maintaining a strong parent-child attachment (Kelley, Hock, Bonney, Jarvis, Smith and Gaffney, 2001). A common issue for couples is the redefinition of roles and the renegotiation of the relationship (Norwood, Fullerton, and Hagen, 1996). Nevertheless, some researchers have reported positive events experienced by personnel, including the development of a greater level of self-discipline, tolerance, patience and self-understanding (Natani and Shurley, 1974; Taylor, 1987) and many residents consider their time in Antarctica to be one of the best years of their lives (West, 1984). While other studies have investigated the effects of separation on spouses and children (Amen, Jellen, Merves, and Lee, 1988; Bell, Bartone, Bartone, Schumm, and Gade, 1997), as yet, no studies have conducted a parallel analyses of personnel and their families over a time frame long enough to examine change and adaptation processes and outcomes and that affords opportunity to systematically examine precursors of positive adaptation. This will be included in the present study, and this process has several implications for the manner in which the reintegration is managed and for the shift in transportation arrangements likely to take effect in 2005/06.\n\nAnother novel aspect of the present study concerns examining changes and adaptation processes in expeditioners and families in parallel. Because change is taking place in both parties during the course of the separation experience, it is important to understand these changes and how they interact to influences perceptions of family life in both groups at the same time. Thus, the task of reintegration is not just about how the expeditioner to return to normal routines and activities. While this remains a key task, the fact that the effectiveness of this process is a function of what has and is happening for other family members, and vice versa, must also be recognised. Adaptation is thus conceptualised as a process of readjustment in both parties as they work to reconcile changes in family experiences in ways that contribute to the well being of the family.\n\nTo systematically understand how both expeditioners and families adapt during reintegration, information on two factors is required. Firstly, it is necessary to identify what it is that people are required to adapt to (the context of adaptation). It is argued here that this context represents the collective changes experienced by expeditioners and family members over the full course of the separation and their expression within the family system on return. Collectively, these experiences can be captured with the mental maps used by both to represent the family system. Secondly, it is necessary to identify the personal, group and environmental factors that that facilitate an ability to adapt (resilience factors) on reintegration and those that hinder it (vulnerability factors). We need to understand the relative contributions of expeditioner and family experiences and changes to the context and we need to examine how resilience and vulnerability resources contribute to effective adaptation.\n\nIt recognises the fact that expeditioners and family members alike are required to adapt to their own unique set of circumstances. Consequently, the relative contributions of expeditioner and family experiences to the model of family life, how they are integrated and how they are reconciled to determine adaptation define the context of adaptation.\n\nResilience is a composite of interdependent variables at the individual, cognitive, social and environmental levels (Paton et al., 2003). To examine personal-level resilience factors, the personal resilience scale (Reivich and Shatte, 2002) will be used. This scale comprises six sub-scales and each covers resilience and vulnerability factors in relation to dispositional factors (self-awareness, self focus, optimism, causal analysis, empathy, self-efficacy, and curiosity). A measure of social support and perceived support effectiveness will be included (Frone, 2003). Coping and family environment factors will be examined using the Family Functioning Style Scale (measuring family commitment, coping strategies, flexibility and communication) (Trivette et al., 1990) and the Circumplex Model (measuring family coherence) (Olson, 1992). It is also important to consider how the degree of support afforded within the work-family context on return influences outcomes. The latter will be assessed using the Work-family conflict and Family-work conflict scales (Netemeyer, Boles, and McMurrian, 1996). The interactive role of these factors as predictors of adaptation will be examined in this study.\n\nIf salient predictors of resilience can be identified and the mechanisms linking them to adaptive and growth outcomes articulated, we will be in a better position to intervene to enhance this capacity prior to reintegration, to identify potential problems in early and so assist with the effective management of reintegration. This project will investigate these processes simultaneously in expeditioners and families.\n\nIf the key predictors within the adaptational process can be identified, we will be in a better position to identify potential problems and intervene in ways that sustain the beneficial or growth aspects of separation experiences. This process also has implications for the debriefing of personnel, which currently occurs during the return voyage. In the absence of information on family adaptation during the period of deployment, the debriefing has rightly focused on the Antarctic experience. However, if the quality of integration is influenced by the degree of synchrony between these parallel adaptational processes, the debriefing process may not be addressing all the issues likely to affect reintegration. Nor may it be including the positive changes that have occurred amongst both parties. Furthermore, inconsistencies between issues dealt with during debriefing and the reintegration experience could be compounded by the duration of the voyage home. This issue, and those concerned with differences between the 'adverse' environment and the reintegration environment, has been implicated as a problem in work on recovery and debriefing effectiveness in disaster and emergency personnel (Paton, 1996, 1997a,b; Shakespeare-Finch et al., 2003). In addition to exploring this issue per se, the shift from ship-based personnel movement to air-based personnel movement will allow an additional test of the hypothesised role of time in this regard.\n\nThis work will contribute to the development of programs that facilitate, as far as possible, their capability to adapt to separation experience. It will also complement existing processes that aim to facilitate well-being in expeditioners and contribute to enriching their personal, family and professional lives. By articulating the mental maps held by both parties and the processes that influence their reconciliation, we will be in a better position to understand and facilitate the reintegration process. Furthermore, by focusing on resilience, the capability for managing reintegration will be placed more in the hands of expeditioners and their families and workplaces. The role of mental health resources will function to empower this process. It also provides additional resources that could be incorporated into the selection, training and reintegration planning for those who work in Antarctica and their families.\n\nFrom the 2007/2008 Season:\n\nPre-departure (05/06, 06/07, 07/08)\n\nExpeditioner response profiles\nExpeditioner response profiles during the pre-departure period are relatively homogeneous. There were no significant differences in coping or relationship dynamics reported by expeditioners as a function of the demographic variables measured (age, sex, experience, anticipated length of absence, or relationship status).\n\nLength of romantic relationship significantly influenced optimism, personal growth initiative, and perceptions of the work-family interface reported by expeditioners. For both optimism and perceptions of the work-family interface, expeditioners in the 3.1-4 year relationship length category consistently reported significantly lower scores than other relationship length categories. Furthermore, the pattern of results followed a 'u' shape curve. In contrast, personal growth initiative response patterns demonstrated an inverted 'u' shape curve, and those expeditioners in the 3.1-4 year relationship category reported relatively higher scores.\n\nAnother interesting finding is that relationship status influenced expeditioner response profiles when assessing quality of life and personal growth initiative. Specifically, female expeditioners not in a relationship reported higher scores than those in a relationship. Conversely, male expeditioners not in a relationship reported lower scores than those in a relationship.\n\nExpeditioner age and experience influenced psychological health. Expeditioners within the 40-49 year age category who had previous Antarctic experience reported significantly greater psychological difficulties than those without experience, as well as those in other age categories.\n\nPartner response profiles\nPartner profiles during the pre-departure period are very heterogeneous with large differences occurring as a function of age, sex, anticipated length of expeditioner absence, experience, and relationship length.\n\nThe most consistent finding across analyses was that female partners within the 40-49 year age category consistently reported higher levels of psychological distress than all other age categories. This negatively affected the use of adaptive coping strategies, optimism, relationship dynamics, and quality of life.\n\nSimilarly, there was a consistent finding that anticipated length of expeditioner absence affected response patterns. Specifically, those partners anticipating an absence of 7-14 months reported significantly more difficulties than those anticipating an absence of 3-6 or 15+ months.\n\nPrior experience of an expeditioners Antarctic employment also influenced partner response patterns. However, the nature of these influences was not uniform. In some circumstances previous experience imbued positive effects (e.g. relationship dynamics) however in others it resulted in higher levels of psychological difficulties (e.g. quality of life).\n\nIn general, male partners reported better psychological functioning than female partners.\n\nComparing expeditioners in relationships with partners\nThere were a number of significant differences in the profiles of expeditioners with partners when compared to partners. Partners endorsed the use of active coping, restraint, and emotional social support to a greater degree than expeditioners, however reported significantly lower levels of personal growth initiative.\n\nThe nature of relationship dynamics indicates that whilst partners are more committed to the relationship and engage more avoidant strategies during the pre-departure period, expeditioners are more globally satisfied with the nature of their relationships at this time. Similarly, expeditioners report significantly greater health, and quality of life compared to partners during this time.\n\nAbsence (05/06, 06/07)\n\nExpeditioner response profiles\nNo significant differences were found. No third quarter phenomenon identified. Regardless of experience, those who immediately re-engaged in the work force reported greater family interference with work and lowered quality of life.\n\nPartner response profiles\nResults indicate that the absence experience appeared to be related to the life stage of the family rather than possessing previous experience. Women without child raising responsibilities appeared to engage in a more positive experience than those women with such responsibilities as they were able to more readily pursue individual goals such as hobbies, skills, and holidays.\n\nComparing expeditioners with partners\nOverall, partners reported greater levels of distress than expeditioners throughout the absence. Results indicate that partners experience of the expeditioners absence was more related to their overall perception of the Antarctic employment and the need to concurrently deal with routine stressors (such as familial obligations) rather than the absence itself. Both expeditioners and partners appeared to romanticise their relationship so as to enhance the others positive attributes and diminish interpersonal difficulties during the absence.\n\nExpeditioners with partners reported frustration and feelings of powerlessness that they could not assist with challenges faced by the family in their absence.\n\nQualitative themes\nWhilst responses varied over time, there were salient themes that occurred throughout the Antarctic absence for both expeditioners and partners. In particular, expeditioner responses (both positive and negative) were predominantly related to work issues, with evidence of externalisation of negative issues and internalisation of positive issues (reported by 98% of expeditioners). Secondary to this, positive themes were associated with experience of the environment (reported by 82% of expeditioners) and negative themes with absence from their partner (reported by 80% of expeditioners). Overall, positive themes were reported more frequently than negative themes. In contrast, partners reported fewer themes overall and clearer differentiation in the relative frequencies of each theme. The most positive themes for partners related to self-development and increased contact with extended family (reported by 76% of partners). The most negative theme (reported by 88% of partners) related to challenges associated with the absence of the expeditioner, particularly being overwhelmed with concerns for self and concerns for partner. Within this participant category positive and negative themes were reported with equal frequency.\n\nReunion and Reintegration (05/06, 06/07)\n\nExpeditioner response profiles\nExperienced expeditioners appear to 'tread more carefully' during reunion than non-experienced counterparts. Overall, there is a decline in functioning upon return. However, at reintegration (12 months post-return) functioning patterns have returned to pre-departure levels and most identify positive benefits associated with their experience.\n\nPartner response profiles\nExperienced partners are more aware of the expeditioners needs at this time - they are less likely to immediately re-engage the expeditioner with large groups of friends, instead gradually undertaking this process. Overall there is an increase in psychological health and functioning upon the expeditioners return. As with expeditioners, functioning patterns have returned to pre-departure levels when assessed at reintegration (12 months post-return), however perceptions of relationship functioning have improved.\n\nComparing expeditioners in relationships with partners\nHigh levels of flexibility in the family appear to facilitate reunion as the family is more able to 'make room' for the expeditioner to re-enter the family. Conversely, high levels of cohesion appear to negatively impact the reunion experience.\n\nThemes identified through interviews\n\nPartners have expressed a desire to engage in greater communication with other partners who have, or are undergoing, the Antarctic employment experience. Many feel that they are alone in their experience as they do not live close to other individuals undergoing the same experience.\n\nConcurrent positive and negative experiences are reported by both expeditioners and partners throughout the experience of Antarctic employment.\n\nReintegration for expeditioners appears to be easier when there has been a greater time interval between the current and previous Antarctic employment. This appears to be due to both personal and familial factors.\n\nA strong bond develops between Antarctic expeditioners who winter together. This bond often endures after Antarctic employment ceases and people have returned to their homes. Those stationed together for a winter often think of themselves as a family, and this appears to provide avenues for increased social support thereby alleviating some feelings of isolation from home. Following reunion, members of the 'Antarctic family' continue to seek and provide social support to one another. Those who have wintered before are looked to for advice, and sharing of experiences appear to assist those having some difficulty reintegrating (again, this appears to be through a process of normalising of experiences). Many winterers indicate that they feel more comfortable, and perceive themselves to gain more benefit from, this informal peer support than formalised debriefing procedures. Additionally, many expeditioners (both summer and winter) indicate that they find the sea voyage home to be a time to think about their experience and psychologically prepare for reunion. Some expressed a concern that air-based personnel movement would not allow for this 'alone time' to occur.\n\nExpeditioners appear to have a 'time limit' for the optimum Antarctic experience, after this point they are not as satisfied by their employment. This was more marked for summer versus winter personnel.\n\nExpeditioners indicated that they did not share all information regarding their impending or resultant Antarctic experience with their partners. The primary reason for this was to avoid creating distress or conflict within the relationship. As a result, many partners knew little of the experience beyond the information provided in the pre-departure packages provided by the Australian Antarctic Division.\n\n\nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nAntarctic employment involves prolonged separation from social support networks. Previous research demonstrated variations in expeditioner mood whilst in Antarctica and the subsequent impacts on both physical and psychological functioning. However, the concurrent experience of partners and their influence on expeditioner health is not well understood. This study investigates the experience of Antarctic absences in expeditioners and their partners. It highlights psychological health effects characteristic of each stage of deployment. This research provides an holistic understanding of Antarctic employment, and identifies implications for individual and family adjustment at all stages of the Antarctic employment experience.", "links": [ { diff --git a/datasets/ASAC_2569_751_4.json b/datasets/ASAC_2569_751_4.json index 47ef9e94c4..3205aba156 100644 --- a/datasets/ASAC_2569_751_4.json +++ b/datasets/ASAC_2569_751_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2569_751_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Databases (Wandering, Black-browed, Light-mantled Sooty and Grey-headed albatrosses, Northern Giant Petrels and Southern Giant Petrels):\n\nThese databases summarise all banding and resight information that has been collected from these species during the years that the albatross project has been run on Macquarie Island. These databases also include historical banding and resight information collated by albatross project staff from historical biological logbooks on Macquarie Island.\n\nThe download file contains several access databases:\nMI_Albatross and MI_Albatross_2k are different versions of the same database - they were merely designed for different systems. All the data are held in MI_Albatross_Data. MI_Albatross and MI_Albatross_2k must be referenced to MI_Albatross_Data if you wish to use the front ends available in these versions.\n\nThe download file also contains detailed field reports written after the 2004/2005 season and the 2006/2007 season, and all satellite tracking data obtained on Macquarie Island between 1999 and 2003. Finally, the download file also contains a number of excel spreadsheets, which are observations for specific years.\n\nThese data were originally collected as part of ASAC project 751 (ASAC_751) - Status and conservation of albatrosses on Macquarie Island. The project has now been continued as ASAC project 2569 (ASAC_2569) - Conservation and population status of albatrosses and giant petrels on Macquarie Island.\n\n2007/2008 Season - Brief Report\n\nThe objectives of this program were substantially advanced during the 2007-08 season. Indeed, additional objectives were met that are fundamental to the seabird monitoring required for the rabbit and rodent eradication program being implemented on Macquarie Island.\n\nAll breeding pairs of Wandering albatross (n=5 pairs), Black-browed albatross (n=41 pairs) and Grey-headed albatrosses (n=60 pairs) were identified in order to continue to assess the survival parameters for both adults and juveniles. The study colonies of breeding adult Light-mantled albatrosses were also monitored for survival estimates. Entire island censuses of breeding pairs of both Northern giant petrels (n=1840 pairs) and Southern giant petrels (n=2573 pairs) were undertaken in order to track their population trends.\n\nBreeding success rates were documented for all the six species of threatened albatross and giant petrels. Comparing these results to long-term data acquired during this program, hatching and fledging success rates were within the typical levels of variation for most species. However, for Wandering albatrosses this season, only five eggs were laid and two hatched, representing a breeding success of only 40%. The breeding effort was the lowest recorded since the inception of the current program in 1994 (previous program number 751) and the lowest on Macquarie Island since 1984. Additionally it was the third consecutive year of low hatching success and low chick productivity. The likelihood of the survival of the Wandering albatross population on Macquarie Island requires urgent consideration.\n\nThe whole island surveys of giant petrels has indicated that Southern giant petrels are remaining stable, contrasting to the increases in numbers of breeding Northern giant petrels. Continued documentation of the conservation status of these two threatened populations is especially significant as both species are likely to be impacted by the rabbit and rodent eradication program as a result of secondary poisoning (via consumption of poisoned rabbit carcasses) and also as a result of the changes in predator/prey interactions. Continued monitoring of the trends of both species of giant petrels will be required to measure the impacts of the eradication program on non-target species (as required under EPBC Act).\n\nFurther linkages with the eradication program with this threatened seabird monitoring program were achieved though assessments of the extent of rabbit damage at the albatross breeding sites (listed as Critical Habitat).\n\nProgress in determining the foraging distribution of these species was achieved by the retrieval of all four geologgers that had been deployed on Light-mantled albatrosses in 2005. These units have been returned to British Antarctic Survey for analyses of location data. The satellite tracking data acquired from both Northern and Southern giant petrels has been submitted for publication and is in review. Additionally a synthesis of all at-sea data that has been acquired during this program (satellite tracking and geologger data) is being undertaken in order to assess the overlap of these six seabird species with the different Regional Fisheries Management Organisations (RFMOs).\n\nIn summary, the Objective of this program - to assess and monitor the conservation and population status of the four species of albatross and two species of giant petrel on Macquarie Island - continues to be achieved at a high level. Importantly the results of this program continue to be contributed to global efforts and initiatives to better protect these highly threatened seabird species. Included among the forums to which the results of this program have been contributed in 2007/08 are CCAMLR, ACAP, SCAR, longline fishing TAP team, SAFAG and the draft EIS for the eradication program. The results of this program have also been widely documented in the draft Issues Paper that serves as the Appendix to the National Recovery Plan for Albatrosses and Giant Petrels (2008).\n\n2008-2009 Report:\nIn summary, the Objective of this program - to assess and monitor the conservation and population status of the four species of albatross and two species of giant petrel on Macquarie Island - continued to be achieved at a high level during the 2008-09 season. Results from this season provide a 15 year continuous time series of rigorous population and demographic data, representing one of few such comprehensive studies in the southern ocean and as such, one of global importance.\n\nThe results of this program continue to be contributed to global efforts and initiatives to better protect these highly threatened seabird species. Included among the forums to which the results of this program will be contributed in 2008/09 are CCAMLR, ACAP, longline fishing TAP team and SARAG, and the BirdLife International Global tracking database. Importantly, the results from this season have also been incorporated into the EIS for the rabbit and rodent eradication program, as required by the EPBC Act. In August 2008, the CI of this program, Rosemary Gales delivered a presentation at the Fourth International Albatross and Petrel Conference in South Africa, a presentation which addressed the status of albatrosses and petrels at Macquarie Island and the conservation strategies that have been implemented to assist with their long-term survival.\n\nPopulation dynamics and demographic parameters - All species of albatrosses and giant petrels on Macquarie Island are listed as threatened species. Island wide surveys of breeding pairs of wandering (13 eggs), black-browed (66 eggs) and grey-headed albatrosses (115 eggs) were conducted as they have done so annually since the inception of the program. The number of breeding pairs of light-mantled albatross (367 eggs) in the study areas was also counted this season. These time series data are consistent and robust and so allow us to detect and quantify real trends in the population trajectories. At present these populations appear stable, albeit at critically low numbers for some species. A collaborative global review of the population trends and trajectories of the endangered wandering albatrosses has significantly progressed during 2008-09 with data being contributed from Macquarie Island (this program), Marion Island, South Georgia and Kerguelen. The trends of these populations, which vary among ocean sectors, are currently being prepared for publication (Ryan et al. in prep).\n\nThis year, as has occurred in some previous seasons, an island wide census of the Northern and Southern giant petrels was undertaken. A total of 2049 Southern giant petrel eggs, and 1683 Northern giant petrel eggs were counted which indicates that these populations have remained relatively stable in recent years. These species are amongst the most likely to be impacted by the planned rabbit and rodent eradication program - through secondary poisoning - and so spatial and temporal information on the location and numbers of breeding pairs is important. Continued monitoring of the trends of both species of giant petrels will be required to measure the impacts of the eradication program on non-target species (as required under EPBC Act).\n\nFurther linkages with the eradication program with this threatened seabird monitoring program were achieved though assessments of the extent of rabbit damage at the albatross breeding sites (listed as Critical Habitat). Increased rabbit grazing of tussock continues to destabilise much of the nesting slopes in many areas and nests must be considered 'inaccessible' if personnel safety is potentially compromised or if there is a risk of researchers damaging fragile slopes to the detriment of nesting birds.\nIn addition to the numbers of breeding pairs, parameters such as hatching success and breeding success were determined for these species this year, adding to long-term time series. As with survivorship data, long-term time series are fundamental in detecting changes in the breeding parameters of long-lived seabirds with low reproductive output. Changes to breeding parameters of seabirds are predicted under climate change scenarios. Sustained changes, even slight ones, will ultimately change the trajectory of a population. The critically small populations on Macquarie Island are therefore potentially threatened by this process.\n\nIndividual band numbers have been obtained from all breeding wandering, black-browed, grey-headed and light-mantled albatross that could safely be accessed. This banding re-sight information is fundamental to assessing the survival parameters for both adults and juveniles and is in the process of being incorporated into appropriate databases and re-analysed. Individual survivorship analyses including all seasons up to 2008 are near completion. This data will be contributed to ACAP to update species assessments and will be published in a peer-reviewed journal during the next 12 months.\n\nForaging ecology and oceanic distribution - One geolocation logger was retrieved from a wandering albatross adult. This unit was deployed in 2005 and has been collecting at-sea data for more than three years. When analyses of all retrieved loggers are complete (in collaboration with BAS), these data will provide critically important information on foraging distribution, and spatial and temporal overlap with fisheries that will enhance our ability to manage and mitigate the risk of fisheries related mortality.\n\nDuring 2008-09 significant advances have been achieved in analysing foraging distribution data from other species on Macquarie Island. A manuscript summarising the satellite tracking of Northern and Southern giant petrels and an assessment of their overlap with regional fisheries management organisations has recently been published (Trebilco et al. 2008). A global synthesis of the spatial usage of black-browed albatrosses is nearing completion, this review combining data from populations in all oceanic sectors including Macquarie Island (this program), Chile, the Falkland Islands, South Georgia and Kerguelen Islands. This collaborative review (Wakefield et al. in prep) is scheduled for publication in 2009.\n\n-------------------------------------\n\n---- Public Summary from Project ----\nAcross the Southern Ocean populations of albatrosses and giant petrels have declined as a result of interactions with fishing operations. The current status of these birds on Macquarie island is unknown. This program aims to allow confident and accurate assessments of the population status and trends of the albatrosses and giant petrels on Macquarie Island. The long-term monitoring study is required to obtain information regarding population size and productivity, adult and juvenile survival rates and age- and sex-related effects on reproductive performance and survival. The oceanic movements of the birds are being investigated so that questions regarding temporal and spatial overlap with fishing operations can be addressed. With this knowledge we will be well placed to make realistic conservation assessments for the populations and be able to provide appropriate input into management protocols.\n\nThe fields in this dataset are:\n\nBird ID\nBand Number\nDate\nLocation\nBird Status (e.g. Adult)\nNest Number\nComments\nSex\nResight\nMate\nEgg\nFledged\nChick\nSpecies\n\nThis project has been superseded by AAS project 4112 (Status and trends of Macquarie Island Albatrosses and Giant Petrels: management and conservation of threatened seabirds). See that project for all updated datasets.", "links": [ { diff --git a/datasets/ASAC_2570_field_lab_books_1.json b/datasets/ASAC_2570_field_lab_books_1.json index f203995387..aa8e07df59 100644 --- a/datasets/ASAC_2570_field_lab_books_1.json +++ b/datasets/ASAC_2570_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2570_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 2004 and 2012 as part of ASAC (AAS) project 2570 - Constraints on hydrocarbon adsorption and nutrient release from zeolites at low temperatures for hydrocarbon remediation in Antarctica.", "links": [ { diff --git a/datasets/ASAC_2576_field_lab_books_1.json b/datasets/ASAC_2576_field_lab_books_1.json index f0c4d3359e..03c5983178 100644 --- a/datasets/ASAC_2576_field_lab_books_1.json +++ b/datasets/ASAC_2576_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2576_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 2004 and 2007 as part of ASAC (AAS) project 2576 - The hydraulic behaviour of Permeable Reactive Barrier materials under freeze-thaw conditions.", "links": [ { diff --git a/datasets/ASAC_257_1.json b/datasets/ASAC_257_1.json index 3c0f48a504..a0fefdbdc7 100644 --- a/datasets/ASAC_257_1.json +++ b/datasets/ASAC_257_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_257_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 257 See the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nAnatomical and physiological studies of southern elephant seals (Mirounga leonina), particularly in the post-natal period, raise questions of relative musculature growth, control of metabolism, circulation and temperature regulation, which could be important in our understanding of these processes in mammals and of their contribution to adaptation to environmental extremes.\n\nThe diving behaviour of 14 adult southern elephant seals was investigated using time depth recorders. Each of the seals performed some dives that were longer than its theoretical aerobic dive limit. Forty-four percent of all dives made by post-moult females exceeded the calculated limit compared with 7% of those made by postbreeding females and less than 1% of those made by adult males. The extended dives displayed characteristics that suggested they were predominantly foraging dives, although some were apparently rest dives. Dives longer than the calculated aerobic limits often occurred in bouts; the longest consisted of 63 consecutive dives and lasted 2 days. Postmoult females performed longer bouts of extended dives than postbreeding females. Extended surface periods (longer than 30 min) were not related to the occurrence of extended dives or bouts of extended dives. The possible physiological mechanisms that permit such prolonged continuous dives are discussed. Southern elephant seals may increase the aerobic capacity of dives by lowering their metabolism to approximately 40% of the resting metabolic rate on long dives. There is substantial interseal variability in the methods used to cope with long dives. Some animals appear to use phsyiological strategies that allow them to prolong the time available to them at the bottom of a dive, while others use alternative strategies that may limit the time available at the bottom of their dives.\n\nFourteen time-depth-temperature recorders were recovered from adult southern elephant seals (Mirounga leonina) returning to Macqaurie Island to breed or moult. The resulting temperature/depth profiles indicated that all four males spent most of their time in waters lying over the Antarctic Continental Shelf, whereas only one of the ten females spent any time there. Five of the females foraged just off the Antarctic Continental Shelf, and the other five remained near the Antarctic Polar Front.\n\n1) Mark-resight data were analysed for thirteen cohorts from a declining population of southern elephant seals branded at Macquarie Island between 1951 and 1965.\n2) First year survival was essential stable during the 1950s at about 46% for females and 42% for males. There was a dramatic fall in first year survival during the 1960s, declinging to less than 2% for both sexes in 1965. Post-year-1 survival did not change between the 1950s and the 1960s.\n3) Comparisons with a stable population of southern elephant seals at South Georgia indicated that both first year and adult survival were lower in the Macquarie Island population. There were no changes in the age at first breeding of the Macquarie Island seals during the study, but this was on average 1 year later than at South Georgia.\n4) It is hypothesised that the current decline in elephant seal numbers at several of their major breeding islands is due to the populations returning to pre-sealing levels after they had risen to abnormally high levels with the end of commercial exploitation early this century.\n5) Possible tests of the hypothesis include studying the diet and foraging behaviour of southern elephant seals to gain an understanding of the predator-prey relationships, continuing to census the Macquarie Island population to determine if the population levels out at around the estimated pre-sealing levels, and monitoring northern elephant seal populations which were also severely exploited but are currently increasing rapidly.", "links": [ { diff --git a/datasets/ASAC_2581_1.json b/datasets/ASAC_2581_1.json index d1526c9f52..38e0c983d8 100644 --- a/datasets/ASAC_2581_1.json +++ b/datasets/ASAC_2581_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2581_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2581\nSee the link below for public details on this project.\n\nThe break-up of Antarctic ice shelves has highlighted the need for a better understanding of the dominant fracture processes occurring within the ice shelves and whether there is any link to climate variability. Using a combination of in-situ (GPS, seismic) and satellite (optical and radar imagery, synthetic aperture radar (SAR)) measurements and airborne ice radar measurements, we will quantify the deformation and fracture processes in different regions on the Amery Ice Shelf, leading to improved fracture mechanics models.\n\nGPS measurements were taken across large crevasses in the shear margins on the eastern side of the Amery Ice Shelf, north of Gillock Is. These measurements will give us an opportunity to measure the three dimensional deformation across active fracture zones, leading to a better understanding of fracture processes on ice shelves.\n\nThree GPS networks, each network consisting of 4 GPS units in a quadrilateral shape, were measured over the period 17-28 Jan, 2007. These data will be processed during 2007 to compute the deformation and strain across and within the crevassed areas.", "links": [ { diff --git a/datasets/ASAC_2584_1.json b/datasets/ASAC_2584_1.json index 1ee1781f6a..a666a1143a 100644 --- a/datasets/ASAC_2584_1.json +++ b/datasets/ASAC_2584_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2584_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2584\nSee the link below for public details on this project.\n\nThe Southern Ocean plays a significant role in the biogeochemical cycling of sulphur due to high spring-summer fluxes of dimethylsulfide (DMS), particularly south of 60 degrees S. Recent DMS flux perturbation simulations have recently highlighted the key role of the SO between 50-70 degrees S in the DMS-climate feedback hypothesis [Gabric et al., 2003; Gabric et al., 2004]. This project examines the interactions and feedback between marine polar plankton and global climate through the use of biogeochemical and global climate models, and explores the sensitivity of climate to the current and future biogenic production of dimethylsulphide at polar latitudes.\n\nThis was a modelling project, and as such did not collect any data of its own.\n\nTaken from the abstracts of the referenced papers:\n\nThe global climate is intimately connected to changes in the polar oceans. The variability of sea ice coverage affects deep-water formations and large-scale thermohaline circulation patterns. The polar radiative budget is sensitive to sea-ice loss and consequent surface albedo changes. Aerosols and polar cloud microphysics are crucial players in the radioactive energy balance of the Arctic Ocean. The main biogenic source of sulfate aerosols to the atmosphere above remote seas is dimethylsulfide (DMS).\nRecent research suggests the flux of DMS to the Arctic atmosphere may change markedly under global warming. This paper describes climate data and DMS production (based on the five years from 1998 to 2002) in the region of the Barents Sea (30-35 degrees E and 70-80 degrees N). A DMS model is introduced together with an updated calibration method. A genetic algorithm is used to calibrate the chlorophyll-a (CHL) measurements (based on satellite SeaWiFS data) and DMS content (determined from cruise data collected in the Arctic). Significant interannual variation of the CHL amount leads to significant interannual variability in the observed and modelled production of DMS in the study region. Strong DMS production in 1998 could have been caused by a large amount of ice algae being released in the southern region.\nForcings from a general circulation model (CSIRO Mk3) were applied to the calibrated DMS model to predict the zonal mean sea-to-air flux of DMS for contemporary and enhanced greenhouse conditions at 70-80 degrees N. It was found that significantly decreasing ice coverage, increasing sea surface temperature and decreasing mixed-layer depth could lead to annual DMS flux increases of more than 100% by the time of equivalent CO2 tripling (the year 2080). This significant perturbation in the aerosol climate could have a\nlarge impact on the regional Arctic heat budget and consequences for global warming.\n\n###############\n\nThe response of oceanic phytoplankton to climate forcing in the Arctic Ocean has attracted increasing attention due to its special geographical position and potential susceptibility to global warming. Here, we examine the relationship between satellite derived (sea-viewing wide field-of-view sensor, SeaWiFS) surface chlorophyll-a (CHL) distribution and climatic conditions in the Barents Sea (30-35 degrees E, 70-80 degrees N) for the period 1998-2002. We separately examined the regions north and south of the Polar Front (~76 degrees N). Although field data are rather limited, the satellite CHL distribution was generally consistent with cruise observations. The temporal and spatial distribution of CHL was strongly influenced by the light regime, mixed layer depth, wind speed and ice cover. Maximum CHL values were found in the marginal sea-ice zone (72-73 degrees N) and not in the ice-free region further south (70-71 degrees N). This indicates that melt-water is an important contributor to higher CHL production. The vernal phytoplankton bloom generally started in late March, reaching its peak in late April.\nA second, smaller CHL peak occurred regularly in late summer (September). Of the 5 years, 2002 had the highest CHL production in the southern region, likely\ndue to earlier ice melting and stronger solar irradiance in spring and summer.\n\n###############\n\nArctic ecosystems and global climate are closely related. This paper studies the distributions and the coupling relationship between Chlorophyll a (Chl a) and aerosol optical thickness (AOD) in Greenland Sea (10 degrees W - 10 degrees E, 70 degrees N - 85 degrees N) during 2003-2009 using satellite ocean colour data from MODIS Aqua. The regression analysis of EViews shows that Chl a and AOD are correlated with a time lag. Based on the lag of Chl a and AOD, co-integration inquiry finds that there is co-integration between them, which means that they will have a long-term equilibrium relationship. In general, Chl a starts from March, and gradually increases to a peak in July. The peak of AOD is usually in May, 11 weeks before Chl a. After shifting the time lag, the correlation between Chl a and AOD is 0.98 in the spring in 80 degrees N - 85 degrees N. Apart from the year of 2005, when Chl a and AOD had no time lag, the other years' intervals increased about 6 weeks within the 7 years. The peaks of AOD shifted one and a half months ahead, while Chl a also shifted about two months ahead. In northern part (75 degrees N - 85 degrees N), Chl a and AOD were much higher in the summer and autumn of 2009 than those in other years. The reason could be the much larger ice melting and higher AOD. The results indicate that the global warming has significant impact on the ecosystem in the Arctic Ocean.", "links": [ { diff --git a/datasets/ASAC_2590_2.json b/datasets/ASAC_2590_2.json index 3e2bc692f5..5f64fb1b4e 100644 --- a/datasets/ASAC_2590_2.json +++ b/datasets/ASAC_2590_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2590_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of about 20 isolates of Antarctic microalgae from the Windmill Islands region, around Casey Station has been established in the University of Malaya Algae Culture Collection (UMACC). The Antarctic microalgae in the collection includes Chlamydomonas, Chlorella, Stichococcus, Navicula. Ulothrix and Chlorosarcina. Comparative studies on the effect of global warming and UVR stress on these Antarctic microalgae and the tropical collection are being conducted.\n\nFrom the abstract of one of the referenced papers:\n\nThe growth, biochemical composition and fatty acid profiles of six Antarctic microalgae cultured at different temperatures, ranging from 4, 6, 9, 14, 20 to 30 degrees C, were compared. The algae were isolated from seawater, freshwater, soil and snow samples collected during our recent expeditions to Casey, Antarctica, and are currently deposited in the University of Malaya Algae Culture Collection (UMACC). The algae chosen for the study were Chlamydomonas UMACC 229, Chlorella UMACC 234, Chlorella UMACC 237, Klebsormidium UMACC 227, Navicula UMAC 231 and Stichococcus UMACC 238. All the isolates could grow at temperatures up to 20 degrees C; three isolates, namely Navicula UMACC 231 and the two Chlorella isolates (UMACC 234 and UMACC 237) grew even at 30 degrees C. Both Chlorella UMACC 234 and Stichococcus UMAC 238 had broad optimal temperatures for growth, ranging from 6 to 20 degrees C (growth rate = 0.19 - 0.22 per day) and 4 to 14 degrees C (growth rate = 0.13 - 0.16 per day), respectively. In constrast, optimal growth temperatures for Navicula UMACC 231 and Chlamydomonas UMACC 229 were 4 degrees C (growth rate = 0.34 per day) and 6 to 9 degrees C (growth rate = 0.39 - 0.40 per day), respectively. The protein content of the Antarctic algae was markedly affected by culture temperature. All except Navicula UMACC 231 and Stichococcus UMACC contained higher amount of proteins when grown at low temperatures (6-9 degrees C). The percentage of PUFA, especially 20:5 in Navicula UMACC 231 decreased with increasing culture temperature. However, the percentages of unsaturated fatty acids did not show consistent trend with culture temperature for the other algae studied.\n\nThere are three spreadsheets available in the download file.\n\nASAC_2590 - provides detail about where each species of algae was collected from.\nASAC_2590a - provides data from Teoh Ming-Li et al (2004)\nASAC_2590b - provides data from Wong Chiew-Yen et al (2004)\n\nThe fields in this dataset are:\n\nIsolate\nCulture Collection number\nOrigin (Location)\nFatty acids\nsaturated fatty acids\npolyunsaturated fatty acids\nmonounsaturated fatty acids\nTemperature\ngrowth rate\nPAR\nUVB", "links": [ { diff --git a/datasets/ASAC_2647_lake_bathymetry_1.json b/datasets/ASAC_2647_lake_bathymetry_1.json index 28f73dc12f..19b5fe44c9 100644 --- a/datasets/ASAC_2647_lake_bathymetry_1.json +++ b/datasets/ASAC_2647_lake_bathymetry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2647_lake_bathymetry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 2647\nSee the link below for public details on this project.\n \nThis project required that water sampling be conducted at the deep point in Ace, Pendant and Ekho Lakes. Detailed surveys were conducted over the deeper sections of each lake and the deep point located.\n\nSurveys were completed between May and July 2006. In each case a grid was laid out on each lake marked with 2m bamboo poles. The grid was based on the standard metric map grid and established with a handheld GPS. It therefore contains the typical x-y error (+ or - 15 m) inherent in all GPS data where a differential correction has not been applied. \n\nNo contouring software was available, so a demo version of SURFER was downloaded. The demo version does not allow saving files or printing so the 'maps' are screen dumps saved as .jpg files. The raw data are also available in the attached Excel spreadsheet.\n\nFinally, during August 2006 the lake water surface (again piezometric height of the water in a fully penetrating drill hole) was tied to bench marks and the water surface elevation reduced relative to the Davis MSL.\n\n(1983) datum as follows:\n\nAce Lake - 9.094 m (tied to NMV/S/75)\nPendant Lake - 3.029 m (tied to NMV/S/43)\nEkho Lake - -1.649 m (tied to NMV/s/24)", "links": [ { diff --git a/datasets/ASAC_2663_1.json b/datasets/ASAC_2663_1.json index 2760974e1f..aca0732847 100644 --- a/datasets/ASAC_2663_1.json +++ b/datasets/ASAC_2663_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2663_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2663\nSee the link below for public details on this project.\n\nPublic Summary\nMacquarie Island offers scientists a unique laboratory for investigating subantarctic climate change. We will establish the biodiversity of microalgal flora (specifically diatoms) within the lakes and lagoons of Macquarie Island and ultimately use the flora of today to investigate changes in fossil microalgal communities of Macquarie Island lake and lagoon ecosystems to better understand past, present and future climate change in the Australian Subantarctic.\n\nTaken from the abstract of the referenced paper:\nThis study is the first established survey of diatom-environment relationships on sub-Antarctic Macquarie Island. Fifty-eight sites in 50 coastal and inland lakes were sampled for benthic diatoms and water chemistry. 208 diatom species from 34 genera were identified. Multivariate analyses indicated that the lakes were distributed along nutrient and conductivity gradients. Conductivity, pH, phosphate (SRP), functions provide a quantitative basis for palaeolimnological studies of past climate change and human impacts, and can be used to establish baseline conditions for assessing the impacts of recent climate change and the introduction of non-native plants and animals. Statistically robust diatom transfer functions for conductivity, phosphate and silicate were developed, while pH and temperature transfer functions performed less well. The lower predictive abilities of the pH and temperature transfer functions probably reflect the broad pH tolerance range of diatoms on Macquarie Island and uneven distribution of lakes along the temperature gradient. This study contributes to understanding the current ecological distribution of Macquarie Island diatoms and provides transfer functions that will be applied in studies of diatoms in lake sediment cores to quantitatively reconstruct past environmental changes.", "links": [ { diff --git a/datasets/ASAC_2665_1.json b/datasets/ASAC_2665_1.json index ec52223f1c..08af199394 100644 --- a/datasets/ASAC_2665_1.json +++ b/datasets/ASAC_2665_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2665_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2665\n\nSee the link below for public details on this project.\n\nThe Antarctic environment with its harsh climatic conditions, minimal human activity and its unique ecosystems is unlike any of the World's other environments. As such, it is important that an understanding of the Antarctic environment is developed in order to gain a full appreciation of the impacts of human activities in Antarctica and to determine the most effective means to remediate and protect the Antarctic environment. To achieve these goals, new sensitive and selective techniques for sampling metal contaminant levels in marine sediments are being developed.\n\nThe project is not an environmental study of the Antarctic environment (ie no metal concentrations in water or sediments), but rather the development of an analytical technique for use in Antarctica. We are still in the process of developing this technique and much of the development phase has involved qualitative assessment rather than generating quantitative data. We are currently trialling the technique in the lab and will conduct field trials in the Derwent Estuary.\n\nTaken from the abstract of the referenced paper:\n\nA novel binding phase was developed for use in diffusive gradients in thin-film (DGT) sampling for Cu(II) by employing methylthymol blue as a chelating and chromogenic agent. Methylthymol blue was adsorbed onto beads of Dowex 1x8 resin (200-400 mesh) and the resin beads were then immobilised onto an adhesive disc. Analysis of exposed binding discs by either UV-vis spectrophotometry or computer imaging densitometry provided robust quantification of adsorbed Cu(II) in the 0.2-1 micro gcm-2 range, allowing detection at micro gL-1 concentrations in the test solution (ca. 17 micro gL-1 for a 24 h deployment), and in good\nagreement with established DGT theory. The method was shown to be a potential replacement for binding phases based on Chelex 100 where a colorimetric response to a specific metal is desired.", "links": [ { diff --git a/datasets/ASAC_2668_1.json b/datasets/ASAC_2668_1.json index 1621befa7d..71133aeb12 100644 --- a/datasets/ASAC_2668_1.json +++ b/datasets/ASAC_2668_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2668_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2668\nSee the link below for public details on this project.\n\nThe dataset contains data in the following formats:\n\nThe *.met files contain the height, time, direction and range of a meteor detection. \n\nThe *.vel file contains meteor determined wind velocities: the horizontal and vertical velocities.\n\nThere are other ancillary parameters in each file but these are the main ones.\n\nThe parameters are described in the pdf document included in the dataset. We have been able to get IDL based reading routines from the radar company (ATRAD) but in general, one is expected to write ones own software for reading the datasets.\n\n\nPublic \nThe gap in our knowledge of the mesosphere and lower thermosphere (MLT) has stemmed from a difficulty in probing this remote region of our atmosphere. Spanning the height range between 50 and 110 km, the MLT is sometimes jokingly termed the 'ignorosphere'. However, observations from sites in Antarctica can now be combined with satellite data to overcome the limitations of our observing techniques. This project seeks to learn more about the many processes that contribute to the character of this region, with the goal of enhancing our understanding of the earth's atmosphere and identifying the effects of global climate change.\n\nProject objectives:\nThis project aims to provide a point of focus within the Australian Antarctic Program for investigations of the polar mesosphere and lower thermosphere (MLT) using satellite observations. Ground-based measurements typically have excellent vertical and temporal resolution, but are limited in their horizontal coverage. Satellite observations, on the other hand, provide a global perspective that cannot be achieved with ground-based instruments. Our knowledge of the polar MLT and its role in the global climate system can be significantly enhanced through studies that combine ground-based and satellite based measurements.\n\nThe importance of ground-based measurements of the structure and dynamics of the polar MLT is underlined by the Australian Antarctic Program's support of the unique combination of experiments operated at Davis station. An MF (medium frequency) radar measures horizontal wind speeds in this region every few minutes. A VHF (very high frequency) radar, LIDAR (laser radar) and a spectrometer provide other wind and temperature measurements when conditions allow. And all of these instruments yield data with a temporal and altitude resolution that cannot be achieved using a satellite.\n\nSatellite observations of the MLT have, until recently, neglected the polar regions. The Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) mission, whose primary goal is to investigate and understand the basic structure, variation, and energy balance of the MLT region and the Ionosphere [Yee, 2003], sought to redress this neglect. Since its launch in December 2001, the TIMED satellite has made observations that extend well into the polar regions and include the latitude of Davis\n\nSignificantly, the instigators of TIMED recognised the contribution that ground-based experiments will make to its scientific yield by explicitly including them in the mission. A group of Ground Based Investigators (GBIs) have been funded to facilitate the incorporation of ground-based data sets into TIMED activities. The Davis MF radar is one of the instruments to be included in the TIMED mission through this mechanism.\n\nIt is therefore timely to focus some of our research activity on the opportunities provided by satellites such as TIMED. The availability of polar satellite data extends the reach of our existing ground-based experiments and adds value to our scientific endeavours. As a result, the common goals of the TIMED mission and the Australian Antarctic Science Program are achieved, our understanding of the role of Antarctica in the global climate system is enhanced and our international scientific profile is increased.\n\nA document providing further details about the history of the project is available for download at the provided URL.\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\n-Adding value to satellite data and ground-based data:\nAs a result of the Fulbright sponsored visit of co-investigator Palo in late 2008, it is now clear that, due to differences in the characteristics of space- and ground-based data, the design of techniques for combining data sets should be specific to the wave class being considered (principally planetary waves and tides).\n\nSignificant contributions to the Aeronomy of Ice in the Mesosphere (AIM) satellite mission have been made using the tidal observations and analysis that form part of project 674. In the context of the current project, progress has been made in the following areas.\n\nThe 2007/2008 season of southern hemisphere observations has become a focus because both the AIM satellite instruments and the Antarctic MF radars operated well for much of that time. The Cloud Imaging and Particle Size (CIPS) instrument on AIM has now been used extensively to image and map the occurrence of Polar Mesospheric Clouds (PMC) and to identify gravity wave signatures within these clouds The position and time of the centre pixel of each usable CIPS image in the 2007/2008 season forms the basis of a number of our studies. These locations and times are combined with a representation of the tidal wind field that can be calculated for the mesosphere and lower thermosphere south of about 60 degrees. Values of the tides at the time of the CIPS samples provide a measure of the wind variations due to the tides (but not the mean winds of planetary waves) throughout the season.\n\nThis extensive tidal data base is being used to consider the temporal and seasonal variability of PMC occurrence. Satellite up-leg and down-leg observations show systematic differences that are yet to be explained. A proxy for the temperature history of air parcels sampled by the satellite that considers the tidal perturbations due to the zonally symmetric tides (diurnal and semidiurnal) has been proposed. Knowledge of the spatial and temporal variation of the wind field obtained from the tides is then used to trace the air parcel position back in time by 3 or 6 hours (estimates of the time taken to form a PMC) and to assess the extent of the upwelling and thus temperature influence on the observed air parcel. Similarities to the PMC occurrence are apparent and are being further investigated.\n\nTides are a possible modulator of gravity wave activity in the polar mesosphere so the role they might play in distorting the observed distribution of gravity waves is being explored. The distribution of the winds in the tidal wind field sampled by the CIPS instrument (whose sampling scheme is determined by the orbit period and satellite precession rate) has been compared to the actual distribution (derivable from the tidal winds by applying a regular sampling regime). Although the potential for bias is present, the range of heights below the cloud layer in which the tides have had significant amplitude is only a few kilometres so it is currently thought the bias will not be great. Comparisons of the distributions of the zonally and meridionally propagating gravity waves are to be made by our colleagues to consider this question further.\n\nThe potential for the AIM sampling scheme to 'alias' tidal variations into the planet-scale maps of ice occurrence has been considered. Regularly sampled tidal winds and those sampled by a CIPS sampling scheme have been analysed for their spatial and temporal variations and comparisons made to see if aliasing is occurring. However, this study is yet to be extended to the entire season. At this stage, only wind effects have been included. Improvements to a model that calculates the tidal temperature response is required and a strategy for making those improvements has been identified but has not been programmed into software.\n\nIn addition to the AIM satellite studies, some more general areas of investigation have been pursued (albeit at a low level of activity).\n\nA technique whereby the theoretical structure of atmospheric tides (described using Hough modes) is extended to include the characteristics of a real atmosphere (Hough mode extensions or HMEs) has been proposed for combining data sets and is being explored. Discussions with our colleagues from NCAR (USA) and Clemson University (USA) (who generate the HMEs) have identified some concerns about the quality of the representation of tidal dissipation and the effect this has on the HMEs. We await further advice on this.\n\nA technique whereby planetary-wave heat fluxes can be calculated using space-based temperatures and ground-based winds has been designed and is to be tested using the results of a previous ground-based only study. The long period (multiple days) and large scale of these waves, along with the ability to remove the mean temperature by decomposition, (and therefore any instrumental biases) make this study possible given the practical difficulties noted elsewhere. The software required for the extraction of the necessary data from the TIMED/SABER instrument data base at the University of Colorado is being developed in conjunction with colleagues there.\n\nAn explanation for a climatological dip in ground-based measurements of temperature at 87 km above Davis was proposed after TIMED/SABER satellite observations of large scale structures showed the presence of slowly moving wavenumber one features at the time of the dip. The outline of a manuscript on this subject has been drafted but the software required for some of the diagrams of the paper using University of Colorado computers is still being developed (see above). On completion, the proposed explanation will be tested against a more extensive data base.\n\nThese data represent 33MHz data. For 55MHz data from the meteor radar, see the related metadata record at the provided URL.", "links": [ { diff --git a/datasets/ASAC_2677_2.json b/datasets/ASAC_2677_2.json index ea68722b45..9c28498b31 100644 --- a/datasets/ASAC_2677_2.json +++ b/datasets/ASAC_2677_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2677_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2677 \n\nData on the sensitivity of Antarctic marine organisms to contaminants is limited, and is essential to understanding the risks contaminants pose to the Antarctic environment. The use of traditional toxicity assessment approaches, to collect high quality sensitivity data for a range of species, is a time consuming and difficult process, especially in remote and hostile environments like Antarctica. In this project, we used a rapid toxicity test approach (described by Kefford et al. 2005) to determine the approximate sensitivity of a large and representative sample of Antarctic marine invertebrates to three common metals (cadmium, copper, zinc). Sensitivity estimates generated via this method are likely to be less precise than those derived from traditional toxicity test methods (due to lower replication and fewer exposure concentrations), but a much larger number of estimates for a wider and more representative range of taxa are able to be produced (under equivalent resourcing). This is advantageous for subsequent Species Sensitivity Distribution (SSD) models, which will include more species and will be more robust, producing protective concentration values that represent a greater proportion of the biodiversity of the region. In this study, a total of 88 different taxa were tested during the 2005/06 Austral summer at Casey station; specimens were collected from a wide range of intertidal and shallow sub-tidal marine sites, providing good representation of the nearshore marine invertebrate community as a whole for this region. Tests were of 10 day duration, with a water change at 4 days. Sensitivity estimates were modelled (LCx; concentrations lethal to x% of the test populations) at 4 and 10 days of exposure, calculated using measured metal concentrations. A series of SSDs were constructed using LC50 values, each one including sensitivity estimates for up to 87 taxa. SSDs were constructed using the Kaplan-Meier function (results provided here) and a log-likelihood based method (available via Kefford et al submitted 2018), both of which allowed inclusion of right- and interval-censored sensitivity data. The results of this work provides a basis for estimating the risk of exposure to three common metal contaminants to Antarctic marine invertebrates. \n\nFiles: \nFour files are attached to this record: \n1.\tASAC_2677-1-Supplementary-Tables.xlsx\nExcel file containing: 1) LC50 values for all taxa tested, for 4 and 10 d exposure durations. Both modelled and non-modelled estimates are provided. 2) Taxonomic details for all taxa tested. 3) Hazardous concentrations (HCy) to 1%, 5%, 10%, 20% and 50% of the taxa tested (HC1, HC5, HC10, HC20 and HC50, respectively) in \u03bcg/L measured on various subgroups calculated from log-normal distributions.\n\n2.\tAAS_2677-2-CaseyRapidTests_Modelled LCx.xlsx\nExcel file containing sensitivity estimate values. See \u2018FileInfo\u2019 worksheet for description of fields. \n\n3.\tAAS_2677-3-CaseyRapidTests_Figs-Kaplan-Meier.docx\nWord document containing Species Sensitivity Distribution model plots, generated using the Kaplan-Meier function. Data are provided for cadmium, copper and zinc based on 4 day and 10 day LC50 values for Antarctic marine invertebrates (subgroup comparisons by phyla, Arthropoda order, abundance category), generating using a rapid testing approach. LC50 values used to generate these plots are provided in the Supplementary Information of Kefford et al (submitted 2018). \n\n4.\tAAS_2677-4-CaseyRapidTests_Tables-Kaplan-Meier.xlsx\nExcel file containing results modelled using the Kaplan-Meier function. Includes two worksheets: \n-\tTable 1: Summary statistics of 4 and 10d LC50 values (\u00b5g/L measured) estimated from Kaplan-Meier functions for the taxa tested and various sub-groups. Values in brackets are 95% confidence intervals (CI). Values and CI omitted were not calculable with the data available. See Supplementary Figures S10-S22 for plots of the Kaplan-Meier functions.\n-\tTable 2: Hypothesis testing for differences in the Kaplan-Meier functions between SSD models (constructed using LC50 sensitivity estimates) for 3 metal and 2 exposure durations (4 and 10d) on various sub-groups using Log Rank (Mantel-Cox) test. NC = not calculable with the number of species tested.", "links": [ { diff --git a/datasets/ASAC_2683_PAR_Casey2004_1.json b/datasets/ASAC_2683_PAR_Casey2004_1.json index af5b80175c..bc65d794a7 100644 --- a/datasets/ASAC_2683_PAR_Casey2004_1.json +++ b/datasets/ASAC_2683_PAR_Casey2004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2683_PAR_Casey2004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains digitized passive acoustic recordings from a hydrophone connected to an autonomous recording device both moored near the sea-floor in the Southern Ocean. Recordings were digitised at a sample rate of 500 Hz and were continuous over the period of operation. The intended purpose of these recordings was to collect baseline data on the acoustic environment (i.e. underwater sound fields). Underwater sounds that were recorded include sounds generated by Antarctic sea ice, marine mammals, and man-made sounds from ships and geo-acoustic surveys. Marine mammal sounds include calls from blue, fin, humpback, and minke whales.", "links": [ { diff --git a/datasets/ASAC_2683_PAR_Kerguelen2005_1.json b/datasets/ASAC_2683_PAR_Kerguelen2005_1.json index c4451bd3a5..5388240db2 100644 --- a/datasets/ASAC_2683_PAR_Kerguelen2005_1.json +++ b/datasets/ASAC_2683_PAR_Kerguelen2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2683_PAR_Kerguelen2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains digitized passive acoustic recordings from a hydrophone connected to an autonomous recording device both moored near the sea-floor in the Southern Ocean. Recordings were digitised at a sample rate of 500 Hz and were continuous over the period of operation. The intended purpose of these recordings was to collect baseline data on the acoustic environment (i.e. underwater sound fields). Underwater sounds that were recorded include sounds generated by Antarctic sea ice, marine mammals, and man-made sounds from ships and geo-acoustic surveys. Marine mammal sounds include calls from blue, fin, humpback, and minke whales.\n\nThe hydrophone was deployed on a mooring on the Kerguelen Plateau.", "links": [ { diff --git a/datasets/ASAC_2683_PAR_Kerguelen2006_1.json b/datasets/ASAC_2683_PAR_Kerguelen2006_1.json index f470b94fac..b401020394 100644 --- a/datasets/ASAC_2683_PAR_Kerguelen2006_1.json +++ b/datasets/ASAC_2683_PAR_Kerguelen2006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2683_PAR_Kerguelen2006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains digitized passive acoustic recordings from a hydrophone connected to an autonomous recording device both moored near the sea-floor in the Southern Ocean. Recordings were digitised at a sample rate of 500 Hz and were continuous over the period of operation. The intended purpose of these recordings was to collect baseline data on the acoustic environment (i.e. underwater sound fields). Underwater sounds that were recorded include sounds generated by Antarctic sea ice, marine mammals, and man-made sounds from ships and geo-acoustic surveys. Marine mammal sounds include calls from blue, fin, humpback, and minke whales.\n\nThe hydrophone was deployed on a mooring on the Kerguelen Plateau in 2006.", "links": [ { diff --git a/datasets/ASAC_2683_PAR_Prydz2005_1.json b/datasets/ASAC_2683_PAR_Prydz2005_1.json index 8504e99926..aa98c3b974 100644 --- a/datasets/ASAC_2683_PAR_Prydz2005_1.json +++ b/datasets/ASAC_2683_PAR_Prydz2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2683_PAR_Prydz2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains digitized passive acoustic recordings from a hydrophone connected to an autonomous recording device both moored near the sea-floor in the Southern Ocean. Recordings were digitised at a sample rate of 500 Hz and were continuous over the period of operation. The intended purpose of these recordings was to collect baseline data on the acoustic environment (i.e. underwater sound fields). Underwater sounds that were recorded include sounds generated by Antarctic sea ice, marine mammals, and man-made sounds from ships and geo-acoustic surveys. Marine mammal sounds include calls from blue, fin, humpback, and minke whales.\n\nThe data were collected in 2005 from a hydrophone deployed on a mooring in the Prydz Bay area.", "links": [ { diff --git a/datasets/ASAC_2683_PAR_Prydz2006_1.json b/datasets/ASAC_2683_PAR_Prydz2006_1.json index 07b464bffd..6dacf263b5 100644 --- a/datasets/ASAC_2683_PAR_Prydz2006_1.json +++ b/datasets/ASAC_2683_PAR_Prydz2006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2683_PAR_Prydz2006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains digitized passive acoustic recordings from a hydrophone connected to an autonomous recording device both moored near the sea-floor in the Southern Ocean. Recordings were digitised at a sample rate of 500 Hz and were continuous over the period of operation. The intended purpose of these recordings was to collect baseline data on the acoustic environment (i.e. underwater sound fields). Underwater sounds that were recorded include sounds generated by Antarctic sea ice, marine mammals, and man-made sounds from ships and geo-acoustic surveys. Marine mammal sounds include calls from blue, fin, humpback, and minke whales.\n\nThe data were collected in 2006 from a hydrophone deployed on a mooring in the Prydz Bay area.", "links": [ { diff --git a/datasets/ASAC_2688_1.json b/datasets/ASAC_2688_1.json index 21a64e48bd..e80027fba2 100644 --- a/datasets/ASAC_2688_1.json +++ b/datasets/ASAC_2688_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2688_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preliminary Metadata record for data expected from ASAC Project 2688 See the link below for public details on this project.\n\n'Cold outbreaks' are severe meteorological events which have significant impacts on many aspects economic and social life in mid-latitude communities. This project will lead to a better scientific understanding of these events, and particularly will quantify the role played by the Antarctic topography and sea ice. The research will reveal how their frequency and intensity have changed over recent decades, and how these might be expected to change under global warming.\n\nThis project used data from various sources for it's analysis, and as such did not produce any data of it's own. The findings are presented in various publications, some of which are available for download to AAD staff only at the provided URL.\n\nData sources included:\nStation data of daily maximum temperature and rainfall for Melbourne and Perth Australian Bureau of Meteorology\n\nNational Centers for Environmental Prediction (NCEP) (Washington USA) reanalysis\n\nEuropean Centre for Medium-Range Weather Forecasts (Reading, UK) ERA-40 Re-Analysis dataset\n\nSea ice data from National Snow and Ice Data Center (NSIDC) (Boulder, USA)", "links": [ { diff --git a/datasets/ASAC_2690_1.json b/datasets/ASAC_2690_1.json index f8fdebb691..7db6785a1f 100644 --- a/datasets/ASAC_2690_1.json +++ b/datasets/ASAC_2690_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2690_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected ASAC Project 2690\n\nSee the link below for public details on this project.\n\nRelating ages, determined using the decay of radioactive elements in minerals, to geological events is central to understanding mountain building and continental evolution. This research will use carefully sampled rocks from Antarctica to improve current estimates of the distribution of elements that occur at only trace (parts per million) concentrations between the key mineral used in dating, zircon, and the common mineral garnet. This information will then be used to link zircon ages to the major events and processes that occurred during the assembly of ancient pieces of crust to form what is now East Antarctica, and other continents.\n\nAccessory mineral behaviour during partial melting in the crust - improving the geochronology of granulite terrains (ASAC_2690)\n\nThe aim of this project was to collect geological material appropriate for evaluating the chemical signatures of the minerals zircon, monazite and garnet formed during various stages of partial melting, melt accumulation and melt extraction in rocks undergoing deformation and metamorphism at high grades. A second objective was to collect gneisses in which the mineral record of early metamorphic events might be clarified in the complex Prydz Bay region.\n\nGeologists SL Harley, NM Kelly and T Hokada undertook field work over the period 21 December 2006 to 2 March 2007. Samples were collected by the usual techniques, using hammer and chisel, and documented in the field using sketches, photographs and GPS. \n\nSampling was complemented by detailed outcrop- and larger-scale mapping centred on defining the precise relationships between the rocks and mineral sites sampled.\n \nLocalites / areas visited, mapped and sampled included the Larsemann Hills (Broknes Peninsula, Stornes Peninsula, McLeod Island, Manning Island), Steinnes, the Brattstrand Bluffs coast, the Rauer Group (Torckler, Varyag, Tango, Pchelka, Lunnyy, Sapozhok Islands; Mather and Macey Peninsulas), and two subareas in the Vestfold Hills (Taynaya Bay, Pioneer Crossing).\n\nThe data set consists of an excel workbook containing three spreadsheets. The three spreadsheets provide listings of the geological rock samples collected during the course of fieldwork by SL Harley, NM Kelly and T Hokada respectively. Each sheet provides sample numbers/codes, locations by name (and by code name if used in the collectors field notebook) and by latitude and longitude given in terms of degrees and decimalised minutes. Each sample is also described in terms of its mineralogy, some aspects of its structural or relational setting, and purpose for collection.\n\nSample numbers are described in the following way: The letters are the initials of the collector (sh, TH, NK) and the sample number is usually of the form year/num e.g. 06/45.\n\nThe abbreviations used on the worksheets in the Excel spreadsheet are:\n\nHarley Sheet:\n\nCrd, Li-B: Li and B analysis of cordierite\nU-Pb, chem: geochemistry and U-Pb dating\nZrc-Mon: zircon and monazite phase and chemical relations\nP-T: pressure and temperature calculations\nP-T-Ky: pressure and temperature calculations and evaluation of relict kyanite \ncsil P-T-fluid: pressure, temperature and fluid composition \ncalculations on calcsilicate mineral assemblage\n\nKelly sheet:\n\nAcc / Gx: accessory mineral behaviour and geochronology\nAcc / Grt comp: accessory mineral / garnet relations and REE distributions \nAcc / Min: mineralogy of complex accessory phases Grt REE comp: composition of garnet in terms of trace elements\nGx: geochronology\nAcc / Pet: petrology, pressure-temperature calculations and accessory mineral stability\nPet: petrology and pressure-temperature calculations\n\nKelly Mineral Assemblage abbreviations\n\nBt biotite\nCc calcite\nCpx clinopyroxene\nCrd cordierite\nDiop diopside\nFsp felsspar\nGrs grossular garnet\nGrt garnet\nHbl hornblende\nIlm ilmenite\nKfs K-feldspar\nKrn kornerupine / prismatine\nKy kyanite\nMnz monazite\nOpq opaque\nOpx orthopyroxene\nPl plagioclase\nQtz quartz\nScap scapolite\nSil sillimanite\nSpl spinel\nSpr sapphirine\nWoll wollastonite\nZrc zircon\n\nHokada sheet:\n\nU-Pb zrn: zircon U-Pb geochronology\n\nThe fields in this dataset are:\n\nSample Number\nLocation Name\nDate\nLocation Code\nLatitude\nLongitude\nField Description\nCollected For\nAdditional Notes", "links": [ { diff --git a/datasets/ASAC_2691_1.json b/datasets/ASAC_2691_1.json index 15750207d3..0d5cf2a2e7 100644 --- a/datasets/ASAC_2691_1.json +++ b/datasets/ASAC_2691_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2691_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2691\nSee the link below for public details on this project.\n\nContaminants may persist in marine sediments and be re-suspended during storms or by the activity of animals. This project will assess the impact of contaminated sediments on plants and animals that live directly above the sediment. Rocky-reef organisms form a large component of Antarctica's biodiversity and include algae as well as filter feeding animals such as sponges, lace corals, and fanworms. Many of these plants and animals live on boulders embedded within sediments. Information on the response of individuals, populations and communities to contamination will be used to develop sediment quality guidelines appropriate for the protection of the Antarctic environment.\n\nThe toxicity of aqueous metals and metal-contaminated resuspended sediment to the spirorbid polychaete Spirorbis nordenskjoldi Ehlers, 1900 was assessed in assays conducted during the 2005/6 and 2006/7 field seasons. A more detailed description of the design of experiments and the methods used can be found in Hill et al, 2009. Spirorbids were exposed to aqueous solutions of copper, lead and zinc singularly, and in mixtures. Spirorbids were also exposed to resuspended metal-spiked sediments.\n\nSpirorbids attached to the brown alga Desmarestia sp were collected from Beall Island, Windmill Islands, East Antarctica, a clean site located approximately 2 km from Casey Station. Algae and animals were kept in the aquarium facility on station, in seawater maintained at 1 C and a 12-h light:dark photoperiod. Seawater was constantly aerated and changed every 5\nto 6 d. Spirorbids were used within two weeks of their collection and fed once per week with plankton. Spirorbids were removed from the surface of algal blades 24 h before the start of a test, and allowed to recover in a constant-temperature chamber (CTC) at 0.5 C. Immediately before the start of tests, spirorbids were examined, and only healthy individuals were selected for tests. Spirorbids were determined to be healthy if their tentacular crown (fan) was extended and retracted quickly in response to stimuli.\n\nThe download file contains further information on the data.", "links": [ { diff --git a/datasets/ASAC_2720_ADCP_1.json b/datasets/ASAC_2720_ADCP_1.json index 9dfe89155c..9acefcf49b 100644 --- a/datasets/ASAC_2720_ADCP_1.json +++ b/datasets/ASAC_2720_ADCP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2720_ADCP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2720\nSee the link below for public details on this project.\n\nThe overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition.\n\nThese samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). \n\nSAZ-SENSE VOYAGE AU0703 ADCP DATA\n\n* The complete ADCP data for cruise au0703 are in the files:\nau070301.cny (ascii format)\na0703dop.mat (matlab format)\n\n* The \"on station\" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files:\nau0703_slow35.cny (ascii format)\na0703dop_slow35.mat (matlab format)\n\n* The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin.\n\n* The data are 30 minute averages. Each 30 minute averaging period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from\n120000 to 123000).\n\n* ADCP currents are absolute - i.e. ship's motion has been subtracted out.\n\n* Note that the top few bins can have bad data from water dragged along by the ship. \n\n* Beware of data when the ship is underway - it's often suspect.\n\n* Important data quality information can be found in the data report referenced above.\n\n* The figure a0703difship30.eps shows the speed difference between vertical bin 2 and all other bins, where the data have been divided up into different speed classes for ship speed. The apparent vertical shear for bins ~1-10, and below bin ~40, is an error, possibly due to acoustic ringing from an air/water interface in the seachest. Data where ship speed is 0 to 1 m/s does not show this error.", "links": [ { diff --git a/datasets/ASAC_2720_CTD_1.json b/datasets/ASAC_2720_CTD_1.json index 7d7460e4fc..7ec87df7a0 100644 --- a/datasets/ASAC_2720_CTD_1.json +++ b/datasets/ASAC_2720_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2720_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2720\nSee the link below for public details on this project.\n\nThe overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition.\n\nThese samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). \n\nSAZ-SENSE VOYAGE AU0703 CTD DATA\n\nOceanographic measurements were collected aboard Aurora Australis cruise au0703 (voyage 3 2006/2007, 17th January to 20th February 2007) as part of the \"SAZ-SENSE\" experiment south of Tasmania, between 43 degrees and 55 degrees south. A total of 109 CTD vertical profile stations were taken to various depths, focussing chiefly on the upper water column. Over 1300 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate, ammonia and nitrite), dissolved inorganic carbon, alkalinity, particulate organic carbon/nitrogen/silicate, dissolved and particulate barium, thorium, dissolved organic carbon, ammonium, pigments, phytoplankton, bacteria, viruses, diatoms, amino acids, and other biological parameters (list incomplete), using a 24 bottle rosette sampler. Near surface current profile data were collected by a ship mounted ADCP. Data from the array of ship's underway sensors are included in the data set.\n\nThis report describes the processing/calibration of the CTD and ADCP data, and details the data quality. An offset correction is derived for the underway sea surface temperature and salinity data, by comparison with near surface CTD data.", "links": [ { diff --git a/datasets/ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1.json b/datasets/ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1.json index 97cdee298b..221005b50f 100644 --- a/datasets/ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1.json +++ b/datasets/ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of two shapefiles created by Darren Southwell of the Australian Antarctic Division (AAD) by digitising the boundaries of adelie penguin colonies at the Rauer Group and the Vestfold Hills. The digitising was done from images resulting from the scanning and georeferencing of aerial photographs taken on 24 November 1993. \nThe aerial photographs were taken for the AAD with a Linhof camera. Records of the photographs are included in the Australian Antarctic Data Centre's Aerial Photograph Catalogue.", "links": [ { diff --git a/datasets/ASAC_2722_SP_GLS_1.json b/datasets/ASAC_2722_SP_GLS_1.json index 5a76b2f857..4bc23fad64 100644 --- a/datasets/ASAC_2722_SP_GLS_1.json +++ b/datasets/ASAC_2722_SP_GLS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2722_SP_GLS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS tag deployments on Snow petrels (Pagodroma nivea) in 2011 from Bechervaise Island, Mawson Coast and Filla Island, Rauer Group, as part of AAS project 2722.\n\nIdentifying potential threats from a changing environment on snow petrel populations requires understanding key ecological processes and their driving factors. This project focuses on determining driving factors for the species' at-sea distribution and foraging habitat. The data will be linked to spatio-temporally coincident data of biological and physical characteristics of the ecosystem to develop explanatory models and, where possible, predictive models to explore the outcomes of plausible scenarios of future environmental change on snow petrel populations.\n\nTags were deployed on Snow Petrels in the Mawson and Davis areas for tracking purposes. The types of tags used were BAS (British Antarctic Survey) geolocators (Mk18)\n\nThe GLS data are in hexadecimal format, and will need appropriate software to interpret them.", "links": [ { diff --git a/datasets/ASAC_2750_1.json b/datasets/ASAC_2750_1.json index adf8848f5a..3ac563e53b 100644 --- a/datasets/ASAC_2750_1.json +++ b/datasets/ASAC_2750_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2750_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2750\nSee the link below for public details on this project.\n\nGlaciers are not frozen rivers, but another aquatic ecosystem in the cryosphere. Most life on glaciers occurs in numerous shallow holes called cryoconites as simple microbial communities. We will study the functioning of these communities and link it to the important processes of carbon and nitrogen cycling. Biological processes change the nature of the glacier surface and may increase melting, which in turn may contribute to more rapid glacier retreat.\n\nAccession Numbers for three samples held in the Genbank library are as follows:\n\nVestfold bacteria: GU298843 - GU298966\n\nVestfold eukaryotes: GU298125 - GU298216\n\nVestfold archaea: GU298283 - GU298285\n\nThis will include the sequences of every clone that was used in the \nVestfold analysis.\n\nTaken from the 2008-2009 Progress Report:\nProject objectives:\nBACKGROUND\n\nContrary to what is generally supposed glaciers are not lifeless, frozen rivers. One of the key factors for sustaining life is a source of liquid water. During summer there are significant quantities of liquid water on a glacier surface. Much of this water is contained in abundant, small, straight-sided holes that develop throughout summer on the glacier surface. These are known as cryoconites. They may be up to half a metre deep and half a metre wide and usually contain a layer of inorganic and organic material on their bottom. Qualitative observations of the contents of cryoconites have revealed biological elements including cyanobacteria, various algae including diatoms, snow algae and desmids, rotifers and fungi (Steinbeck, 1935; Charlesworth, 1957; Gerdel and Druet, 1960; Wharton et al., 1981; Takeuchi et al., 2001a). We conducted a quantitative study of cryoconites on a Svalbard glacier (Midre Lovenbreen) in 2000 (Sawstrom et al. 2002) which revealed concentrations of bacteria between 2.8 to 7.0 x 104 cell mL-1 in the sediment and water column and heterotrophic and autotrophic flagellates up to 4 x 102 mL-1. Effectively the cryoconites resembled Antarctic lakes in their community structure (Laybourn-Parry, 1997). Photosynthesis in cryoconites was high, reaching rates of 156.9 plus or minus 4.0 C L-1 h-1 in the bottom sediment and 1.2 plus or minus 0.27 C L-1 h-1 in the water column (Sawstrom et al. 2002). These rates are higher than those recorded in Arctic lakes (O'Brien, 1992; Markager et al., 1999). Given the density of cryoconites on the glacier surface in summer, the levels of carbon fixation on the whole glacier are likely to be significant. During biological processes nitrogen and phosphorus was recycled, and it is this biogeochemical cycling which explains anomalies seen by glaciologists in glacier nutrient budgets.\n\nInvestigations of the glacier snow pack of Midre Lovenbreen in the high Arctic by two of the applicants has shown that it contains significant concentrations of organic carbon which sustains a community of bacteria, flagellates and viruses. That snow supports actively metabolising bacteria has been demonstrated in snow at the South Pole, where low rates of DNA and protein synthesis were measurable in a bacterial community that reached concentrations of 5000 cells mL-1 (Carpenter et al., 2000). Within the ice there may be liquid veins that provide microhabitats for bacteria. Ice cores from a Greenland glacier have revealed bacterial concentrations of 6 x 107 cell mL-1, and molecular analysis of cultures of viable bacteria from ancient ice cores showed considerable phylogenetic diversity, including new species (Sheridan et al., 2003). Photosynthetic processes occur in snow, mediated by phytoflagellates known as snow algae. They accumulate in clear annual patterns that can be used as a tool in dating snow accumulations (Yoshimura et al., 2000).\n\nAlthough nutrient cycling in snow-covered catchments has received significant attention over the last decade (see Jones et al, 2002), there have been few studies of the ecology of catchments characterised by permanent glacier ice. As indicated in (i) above there is compelling evidence that glaciers are biologically active entities. Recent work by one of the applicants has shown that nutrient cycling in Arctic glaciers involves transformation, loss and acquisition of important inorganic nutrients (N and P) on a sufficiently large scale to support the hypothesis that glaciers are important ecosystems (Hodson et al., In Press). On the Midre Lovenbreen and neighbouring Austre Broggerbreen glaciers, a significant sink of ammonium (NH4) exists accounting for 50% to 70% of inputs via bulk deposition, which ranged between 10 - 37 kg km-2 yr-1. Moreover, run-off of nitrate (NO3) exceeded depositional inputs. These glaciers also receive significant deposition of dissolved organic and particulate nitrogen as well as organic carbon (Hodson et al, In Press; Unpublished Data).\n\nAll of this material supports a food web. Inorganic nutrients are required for photosynthesis by snow algae and the photosynthetic elements of cryoconite communities along with water, CO2, trace elements and light energy. Heterotrophic bacteria require a source of organic carbon as a food substrate. This can be supplied through deposition of organic carbon from the atmosphere, or by the photosynthetic communities that exude some of the organic carbon they manufacture during photosynthesis and through decomposition of dead organic matter. Bacteria also require sources of P and N, which can be of inorganic or organic origin. The grazers of bacteria, the flagellated, ciliated and sarcodine protozoa recycle N and P through metabolism and excretion. In addition some of the cyanobacteria of cryoconites are likely to be fixers of atmospheric nitrogen, and within the bacteria community there are likely to be nitrifying bacteria and other functional groups that play a role in the nitrogen cycle. All of these biological processes can be used to explain why the nutrient budgets of glaciers do not balance. Clearly nutrient cycling in glacier basins is dynamic, and is not solely related to deposition, elution and transport of solutes from the winter snow pack during melt.\n\nGlaciers are not homogeneous environments and undergo very considerable changes when summer melting occurs. A very important, but as yet unquantified source of surface heterogeneity is due to the capacity for biological elements to reduce albedo, and through differential melt rates beneath darker organic matter, cause the surface roughness to increase. Thus the biota influence the two key terms of glacier surface energy balance by enhancing radiative warming and turbulent heat transfer. The former is particularly significant because it probably helps sustain the cryoconite hole environment, and secondly because incident radiation is responsible for circa 80% of summer ablation (Hodson et al, In Press). For example a reduction in surface albedo from values typical of clean bare glacier ice (circa 0.4) to those typical of cryoconite punctuated glacier (ca 0.1) would therefore cause a 30% more incident radiation to be available for melting, having clear implications for glacier mass balance. In more extreme Antarctic environments, the impact of the dark organic material on the bottom of cryoconite holes is more significant, because solar heating of organic matter (typically entombed by a clear ice lid) is responsible for the only melting that takes place on or near the glacier surface (Fountain et al., in press).\n\nOne of the aims of this proposal is to produce a wider picture of cryoconite formation and distribution. There is debate as to how they are formed. In summer they are filled to their surface by water that is usually less than 0.2oC, while in winter they refreeze. A direct positive relationship between elevation and cryoconite depth has been found (Gribbon, 1979), suggesting that the decrease in sensible and latent heat inputs to the glacier surface with altitude may encourage the formation of deeper holes. However, the formation of cryoconites is related to other terms in the surface energy balance of glacier ice, because dark wind blown organic and inorganic material is first deposited on the surface, and warms in the sun to melt a small depression in the ice. Once formed the depression grows into a cryoconite through a series of physical and biological processes (Gribbon, 1979; Wharton et al., 1985; Gerdel and Drouet, 1960). There is debate as to the exact contribution of biological and physical processes. Our own observations on Midre Lovenbreen suggest that cryoconites may persist from year to year, freezing and re-opening, and that new holes may be formed by different processes. It is quite evident that many of the cryoconites develop through the coalescence of very small holes developed from mm sized debris. However, the evolution of smaller (ca. 0.001 m2) holes in to the 1 m2 holes observed in the Antarctic is poorly understood. For example, in more extreme Antarctic glaciers of the Dry Valleys, these larger cryoconites typically have ice covers and are effectively entombed. Lack of contact with the atmosphere has very significant impacts on the water within the hole giving pH values as high as 11 and log10 p (CO2) values as low as -7 (Tranter et al. 2004). Surprisingly microbial life has adapted to these difficult environments. In the Arctic the holes are open to the atmosphere for most of the summer, and despite low temperatures there is significant productivity. Our preliminary observations in the Vestfold Hills indicate that cryoconites are common and that in summer they are open and not entombed.\n\nWe will develop a glacier-wide, temporal picture of cryoconite development using imagery from a small uninhabited aerial vehicle (UAV), which together with on the ground measurements of physical, chemical and biological parameters, will enable us to gain an understanding of their formation, distribution and overall contribution to productivity and nutrient cycling.\n\nOBJECTIVES\nWe aim to develop a picture of the linkages between biological and geochemical processes on the Sorsdal Glacier. In addition we aim to understand how cryoconite holes develop on the glacier and the extent of their coverage and relationship to biological processes. This proposal forms part of an International Polar Year project MERGE (led by Takeshi naganuma), that also includes studies of cryoconites in the American Dry Valleys and in the Arctic (Svalbard). This current proposal involves Laybourn-Parry (Nottingham - from October Keele University), Prof Martyn Tranter (Bristol University) and Dr A.J. Hodson (University of Sheffield).\n\nSPECIFIC AIMS\n1. To produce carbon and nitrogen budgets for the Sorsdal Glacier.\n2. To study the formation and distribution of cryoconite holes on a glacier wide scale and produce a model of their role in nitrogen and carbon cycling.\n3. To produce a detailed picture of biological processes in cryoconites and to link this to carbon and nitrogen budgets (geochemistry).\n\nProgress against objectives: Please describe the progress you have made against each objective in the last twelve (12) months.\nThe data collection for the listed objectives has been undertaken. Material is being returned for analysis at Sheffield University, UK and the University of Tasmania. However, time constraints of a short fieldwork season (5 weeks) will limit the outputs. We anticipate producing two papers.", "links": [ { diff --git a/datasets/ASAC_2763_1.json b/datasets/ASAC_2763_1.json index d6282ee850..40b2c7514c 100644 --- a/datasets/ASAC_2763_1.json +++ b/datasets/ASAC_2763_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2763_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2763\nSee the link below for public details on this project.\n\nAncient Antarctic glacial ice is a potential resource of trapped microorganisms dating back several hundreds of thousand years that give a snapshot of the past. Nucleic acid, such as DNA, has been identified in samples as old as these from Bacteria, Archaea and Viruses, and this will be the focus of this study. Outcomes of this research will determine the type of organisms that become trapped in these ancient samples, and whether they are able to survive such an extreme condition, and may even lead to novel species being discovered, or even new genes and products.\n\nProject objectives:\nDetermine the type of microorganisms that become trapped in ancient Antarctic glacial ice and ascertain whether glacial ice in Antarctica harbours biota that are of evolutionary or biotechnological interest.\n\nPublic summary of the season progress:\nAncient glacial ice samples were collected from ice cliff areas located near Casey Station during the just recent Summer expedition. Ice samples were transported back to Australia and will be subject to 454 sequencing analysis in the next few months in collaboration with Prof. Alan Cooper, Centre for Ancient DNA, University of Adelaide. Other work being performed was completed included an initial molecular-based microbial survey of unusual alkaline, permanently ice-covered, continental lakes located in the Framnes Mountains, inland from Mawson \n\nThe first download file contains:\n10 files\nFile 1. Chemical and oxygen isotope data for Law Dome ice cores that were used in microbiological studies (isolation and DNA analyses).\nFile 2. Chemical and oxygen isotope data for an Amery Ice Shelf ice core that was used in microbiological studies (isolation and DNA analyses).\nFile 3. 16S rRNA gene sequence data obtained from Law Dome ice core samples. All sequences are clones derived after direct PCR amplification of DNA extracts, no isolates were obtained.\nFile 4. 16S rRNA gene sequence data obtained from Amery Ice Shelf ice core sample. All sequences are clones derived after direct PCR amplification of DNA extracts, no isolates were definitively obtained.\nFile 5. Soil and similar samples obtained from either the Vestfold Hills, Eastern Antarctica or from Macquarie Harbour. These soils are the source of actinobacteria screened in the project for antimicrobial activity.\nFile 6. Actinobacteria/actinomycete isolates information detailing isolation procedure, colonial/cellular characteristics, and tentative identification. Barcode system was used to track isolates. Represents the \"(University of Tasmania Antarctic Actinobacteria) UTAA\" collection.\nFile 7. Preliminary antimicrobial screening trial data for 1267 Antarctic actinobacterial isolates against 5 strains of Listeria monocytogenes. A secondary screen was performed to identify those with reliable bioactivity.\nFile 8. Broader analysis of antimicrobial activities of Antarctic isolates against a panel of bacterial pathogenic bacteria. All strains were identified to species level within the genus Streptomyces.\nFile 9. Hydrocarbon profiles of selected Antarctic actinobacterial strains against a range of aliphatic and polycyclic hydrocarbons.\nFile 10. Phenotypic, chemotaxonomic and identification (by 16S rRNA gene sequencing) data of Antarctic hydrocarbon degrading strains.\n\nThe second download file contains:\n3 files:\nFile 1. Framnes Mountain epiglacial lake sample data. Indicates lakes sampled, location, chemical data (pH, temperature).\nFile 2. Cloned 16S rRNA gene sequences obtained from Patterned Lake water DNA extracts. All sequnces are in FASTA format.\nFile 3. Cloned 16S rRNA gene sequences obtained from Sonic Lake water DNA extracts. All sequences are in FASTA format.", "links": [ { diff --git a/datasets/ASAC_2767_1.json b/datasets/ASAC_2767_1.json index d44ef2c3ea..b8e045a191 100644 --- a/datasets/ASAC_2767_1.json +++ b/datasets/ASAC_2767_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2767_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 2767 See the link below for public details on this project.\n\nA multidisciplinary survey of the processes linking sea ice with biological elements of Antarctic marine ecosystems was conducted in winter 2007. The survey provided large-scale information on sea ice biological and physical parameters in the 100-130 degree East sector off East Antarctica. The distribution of sea ice algae and krill were measured using various methods including ice coring surveys and trawls. These measurements were complemented by shipborne measurements and an intensive sea ice sampling program. Use of an ROV was attempted but did not result in quantitative/geo-referenced data. Under-ice video files are available from the Chief-Investigator. \n \nIndividual word documents are available from this metadata record for each ice station. These contain information on the ice station number, date and time of record and the parameters/ samples.", "links": [ { diff --git a/datasets/ASAC_276_1.json b/datasets/ASAC_276_1.json index 33233d1062..de85bae766 100644 --- a/datasets/ASAC_276_1.json +++ b/datasets/ASAC_276_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_276_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstracts of some of the referenced papers:\n\nThe raised marine terraces of the icebound Bunger Oasis are described. The Holocene marine transgression entered the oasis before 7.7ka BP and raised beaches with marine limits 7.5 plus or minus 1 metres above the modern limit were formed throughout most of the oasis by 5.6-5ka BP. All raised beaches recorded are of middle to late Holocene age and indicate an average isostatic uplift rate of 1.4m/ka during this time. The raised beaches occur at similar altitudes to those at Vestfold Hills (up to 10m) but are lower than the beaches on the Windmill Islands (23-30m). Morphological evidence suggests that at Bunger Hills open water wave action may have been more important during middle than late Holocene times when strong sea ice pushing occurred on most beaches. The last glaciation ice cover over the inner continental shelf at Bunger Hills appears to have been relatively thin, probably between about 154 and 400m in thickness. The similarity in maximum altitudes of raised beaches at Vestfold Hills suggests similar ice thicknesses while the higher beaches at Windmill Islands suggests the ice may have been about 450m thick. The evidence from the raised beaches in East Antarctica suggests that the expansion of continental ice was about 50% that envisaged by the Hughes et al (1981) model derived from the Ross Sea. The ice sheet may not have extended to the edge of the continental shelf though more evidence is required from the shelf to determine its extent at maximum glaciation.\n\nThe Bunger Hills in East Antarctica occupy a land area of approximately 400 square kilometres. They have been exposed by Holocene retreat of the Antarctic ice sheet and its outlet glaciers. The accompanying sea level rise flooded the marine inlets that now separate the northern islands and peninsulas from the major part of the hills. During deglaciation the continental ice sheet margin retreated south-eastwards with several temporary halts, during which ice-dammed lakes were formed in some valleys. These lakes were maintained long enough to permit formation of beaches of sand and gravel, and for the erosion of shore platforms and low cliffs in bedrock. Around the western end of Fish Tail Bay impressive shoreline features 20m above sea level define a former ice-dammed lake that was 5.5km long. A similar 7km long former ice-dammed lake was formed at Lake Dolgoe. The more extensive and deeper glacial lake is revealed by well-developed and preserved shoreline features cut at 29m which is 16m above present lake level. In addition, several small ice-dammed lakes existed temporarily near Lake Shchel and in the valley to the west. Lake Fish Tail existed more than 6,900 14C years ago and Lake Shchel probably more than 6,680 14C years ago. It is inferred that the shore platforms and beaches were formed by lake ice and wave action over considerable periods when the lakes were impounded by steep cold ice margins. There appears to have been a balance between meltwater input and evaporative loss from the lakes in the cold dry continental climate. There is no evidence for rapid lake level fluctuations, and there was very little input of clastic sediment. This resulted in poor development of deltaic and rhythmically laminated lake floor deposits. It is suggested that such deposits are more characteristic of ice-dammed lakes formed in association with wet-based temperate ice than those associated with dry-based polar ice.\n\nSubglacial curved and winding meltwater winding channels, and Sichelwannen are recorded from Cape Jones in teh Obruchev Hills. Such channels, sometimes referred to as P-forms, amy have a variety of origins. After briefly considering the origins, it is deduced that those at Cape Jones were formed subglacially while the ice surface of the Denman Glacier became lower and bedrock was exposed. The presence of water-laid glacial deposits on the summit of Cape Jones, and lakes on and adjacent to the eastern margin of the Denman Glacier suggests that the sudden release of impounded meltwater was important.", "links": [ { diff --git a/datasets/ASAC_2784_1.json b/datasets/ASAC_2784_1.json index d94b399707..3c8c070d73 100644 --- a/datasets/ASAC_2784_1.json +++ b/datasets/ASAC_2784_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2784_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2784\nSee the link below for public details on this project.\n\nThis project utilised an existing 55 year model reanalysis (SODA) - so no new models were developed. The methodologies/data used are described in the referenced publications.\n\nModelling investigations of the shoaling of iron-rich upper circumpolar deep water (UCDW) and its role in the regulation of primary production at 60-65S.\n\nTaken from the project application:\nWe intend to utilise a number of existing data sources to study the factors leading to spatiotemporal variability in the upwelling of iron-rich UCDW in the 60-65S zone, which, as discussed above, seems critical to regional ecosystem function, and the carbon and sulphur budgets of the SO. As sea-ice extent appears to have declined in the Southern Ocean since the 1950s (Curran et al., 2003) it will also be extremely interesting to examine whether this has had any affect on the upwelling of the UCDW.\n\nGiven the restricted spatial domain of in situ field data in the Southern Ocean, satellite products provide us with one of the few means to investigate coherent variability over large spatial and temporal scales. This study takes advantage of our previous AAS funded work (Projects: 2584, 2319), where we have gained considerable experience in the coupling of biogeochemical and climate models and where we have already assembled satellite data sets on wind speed, sea-ice, SST, aerosols and chlorophyll-a concentration. This previous experience will allow us to examine the relationship between the physical forcings, the dynamics of the UCDW and the biological response on seasonal and interannual timescales over the period 1950-2000.\n\nThe key scientific questions we seek to answer include:\n\n- What is the range of interannual and interdecadal variability in upwelling of the UCDW and how does this relate to variability in primary production?\n\n- Is there a connection between interannual/decadal variability in sea-ice extent and the strength or location of upwelling of UCDW and hence the character of regional primary production?\n\n- Is there a relation between the seasonal production of DMS and associated S-aerosols and the dynamics of UCDW?\n\nDetails from previous years are available for download from the provided URL.\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nThis three-year project has been investigating the nexus between the large-scale meridional circulation patterns in the SO, in particular UCDW upwelling, and concomitant iron delivery to surface waters and the phytoplankton.\n\nKey Scientific Questions to be considered by the project\n\nWhat is the range of interannual and inter-decadal variability in upwelling of the UCDW and how does this relate to variability in primary production?\n\nThis study initially focussed on the Australian region of the Southern Ocean (110-160 degrees S, 40-70 degrees E) and the physical oceanographic data for the project came from monthly Simple Ocean Data Assimilation (SODA) reanalysis data, which covers the period 1958-2007 over the global ocean. Decadal-scale trends in upper ocean structure and meridional circulation were analysed, including the upwelling of nutrient-rich UCDW, and these results were initially documented in presentation (3) below and will shortly be published in publication (1) listed below.\n\nThe project identified UCDW in SODA using temperature and density criteria and, using this, a number of variables were developed to characterise UCDW and its upwelling: UCDW vertical velocity, temperature, density and salinity, UCDW top depth (the shallowest depth at which UCDW is found) and UCDW southern-most position. Climatological values were found for each of the 5-degree strips in the sector and, in addition, trends were found over the period 1958-2005. Later work involved comparing these results with those of two more Southern Ocean sectors - one in the Pacific (130-80 degrees W) and one in the Indian Ocean (20-60 degrees E). These results were presented at the AMOS conference in January 2010 (see Presentation (1) below) and are also the subject of a paper in the Proceedings of that conference (see Publication (2) below).\n\nIt was found that during 1958-2005:\n(1) UCDW top depth varies seasonally, peaking in March, and displays considerable interannual variability;\n(2) Climatological properties for UCDW variables such as temperature, vertical velocity and upwelling depth vary between the three ocean sectors, as do trends (1958-2005) in the UCDW variables;\n(3) UCDW vertical velocity (ie. upwelling) appears to be increasing with time in most intermediate and deep waters of the three ocean sectors;\n(4) UCDW temperature is increasing in intermediate waters in the Pacific sector, at all depths in the Indian sector and at shallow depths in the Australian sector, but is decreasing in intermediate and deep waters in the Australian sector;\n(5) UCDW southern-most position is moving south in the Australian and Pacific sectors;\n(6) UCDW is upwelling closer to the surface in the Australian and Indian sectors and, in the case of the Australian sector, this translates into an increase in the number of times that UCDW can be detected in the mixed layer (a finding of possible importance for primary production);\n(7) UCDW trends in the Australian sector do not appear to be affected by trends in the winds, but by forcings acting on longer than decadal time-scales. This is not the case, however, for the other two sectors, leading to the speculation that these variables may be affected by the re-entry into UCDW of recirculated waters from the Indian and Pacific Oceans, which may themselves be affected by winds.\n(8) The Australian sector of the SO has been shown to have its own unique characteristics, distinct from either the Pacific or Indian sectors.\n\nMore recent work has involved looking at the initial Australian sector considered above, over the period of the high resolution satellite data capture era (1997-2007), with the aim of using satellite data on chlorophyll a (chl a), sea-ice concentration and photosynthetically active radiation (PAR), as well as modelled data for primary production (PP), in addition to the reanalysis data, to look at factors that influence chl a and PP over that time period. Initial work was presented at the AMOS conference in January 2009 (see Presentation (2) below) and final work is reported in Publication (3) listed below, which is almost ready for submission.\n\nIt was found that in the Australian sector during 1997-2007:\n(1) The most important controls on chl a in spring are sea-ice concentration and PAR in the southern-most zones (and mixed layer depth, SST, stratification and PAR in zones further north);\n(2) The situation is more complex in summer, especially in the southern-most zones (the areas of highest production, excluding the most northerly zone near Tasmania). In particular, in the 60-65 degrees S zone in summer, a variety of inter-acting controls affect chl a (and PP), including SST, stratification and UCDW top depth;\n(3) The number of times that UCDW is detected in the mixed layer is decreasing in summer during 1997-2007;\n(4) It is difficult to identify trends that are statistically significant over such a short time period and trends that are found are often opposite in sign to those for 1958-2005 and up to an order of magnitude larger. Thus care needs to be taken with trends found for chl a, PP and hydrodynamic variables over the short period of the satellite era, since there is a large range of such ten-year trends in the period 1958-2005.\n\n\nIs there a connection between interannual/decadal variability in sea-ice extent and the strength or location of upwelling of UCDW and hence the character of regional primary production?\n\nGiven that UCDW upwells south of the Polar Front and no further south than the Southern Boundary of the ACC (approximately 65 degrees S in this sector), then UCDW, as identified here in its pure form, is not able to affect the 65-70 degrees S zone (although this is possible in its modified form, which is not studied here).\n\nIt was found that, for the period 1997-2007 in the Australian sector of the SO, the southern-most position of UCDW is not correlated with sea-ice concentration, but that there are weak (90% level) correlations in 60-65 degrees S between UCDW top depth and sea-ice concentration in autumn (positive), the temperature of UCDW and sea-ice concentration in summer (positive) and northward Ekman transport and sea-ice concentration in summer (negative).\n\nIt was found that, for 1997-2007 in the Australian sector of the SO, sea-ice concentration has a significant (inverse) relationship with chl a and PP in 60-70 degrees S in spring and 65-70 degrees S in summer. In addition, UCDW top depth and northward Ekman transport (ie. how quickly the UCDW nutrients are transported northwards and away from the zone) have a minor effect on chl a in 60-65 degrees S in summer.", "links": [ { diff --git a/datasets/ASAC_2788_1.json b/datasets/ASAC_2788_1.json index 8e7c85814a..9a3ad6cd4a 100644 --- a/datasets/ASAC_2788_1.json +++ b/datasets/ASAC_2788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2788\n\nSee the link below for public details on this project.\n \nPublic \nThis project will reassess the mass balance of Law Dome and a large sector of the Antarctic ice sheet south of Casey by exploiting the time history of change that can be obtained by resurveying historical surface elevation and gravity networks, started in the 1960s. The changes in position and gravity from these terrestrial traverses act as important constraints and, in some cases, calibration for recent satellite altimeter missions. Such a reassessment and model improvement should see a significant drop in the current uncertainty of the contribution of the Antarctic ice sheet to global sea level rise.\n \n2007-2008 Season:\nHistorical and recent data over Law Dome has been successfully re-reduced and integrated with recent satellite data over a period spanning 1962 to 2005. These data show that the surface elevation of Law Dome decreased from 1962 to about 1971 and then generally increased till 2005 indicating that ice volumes have recently varied over decadal time spans. The variations that are present within our gravity data set are in agreement with the deep core stratigraphy results from the Law Dome summit. Since our data cover the full extent of Law Dome, we are able to state with good confidence that the changes we see are consistent with climate-related signals where increased precipitation is due to increased atmospheric water vapour. This phenomenon has been reported in the Norwegian Arctic. The detection of these localised complex changes, over decadal time scales , highlights problems associated with interpreting relatively short time scale altimetric measurements.", "links": [ { diff --git a/datasets/ASAC_2792_1.json b/datasets/ASAC_2792_1.json index 728f73dc72..56c31ae85d 100644 --- a/datasets/ASAC_2792_1.json +++ b/datasets/ASAC_2792_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2792_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2792\nSee the link below for public details on this project.\n\nAustralia's Census of Antarctic Marine Life project.\n\nThis project is a part of the international \"Census of Antarctic Marine Life\" (CAML) which is to be conducted during the International Polar Year. It is a collaborative contribution by Australia and France to understand the biodiversity of the oceans surrounding Antarctica, with particular emphasis on the fishes of the eastern part of the Australian Antarctic Territory. The biodiversity data, when added to that obtained by all other nations participating in the CAML, will serve as a robust reference for future examinations of the health of the Southern Ocean, and assist in the conservation and management of the region.\n \n2007/2008 Season\n\nA. Plankton\n1. The impact of climate change on the plankton. The pelagic ecosystem in the Southern Ocean has taken the brunt of human impact in the region and there is evidence that it is already responding to the effects of global climate change. Plankton is particularly sensitive to climate change and change in their biodiversity is expected to have serious ramifications through the rest of the ecosystem including the survival of higher predators. Some species are adapted to cold waters of Antarctic where some are supposedly cosmopolitan. Which will survive global warming? For how long will there be an Antarctic marine ecosystem?\n2. Consequences of environmental change driven by past and current exploitation of living resources in the region, e.g. current scale fish and krill fisheries, fishery by-catch species, recovery of whales and seals.\n3. \"Ecosystem services\" - The role of Southern Ocean plankton as source of human food (krill fishery or other) carbon draw down/mediation, bio-climate feedback though dimethyl sulphide production, bioproducts, sensitive indicators of ocean health, and foundation of the Antarctic marine ecosystem - no plankton, no ecosystem.\nB. Fish\n1. What is the composition of the epipelagic, mesopelagic and benthic ichthyofaunas between the Antarctic Divergence and the coast at Dumont d'Urville?\n2. How does the physical and biological structure of the water column, conditions of ice-cover and bottom topography influence the composition and distribution of these ichthyofaunas?\n3. What changes in the community structure of the benthic ichthyofauna as a result from the passage of large icebergs?\nC. Benthos\n1. What are the ecological and historical factors affecting benthic diversity?\n2. How will benthic communities respond to change? We do not know how sensitive the Antarctic benthic communities are to global climate change, or to localised environmental change as seen in the Antarctic peninsula area, or to the impacts of increased trawling. We have no benchmark to compare the effects of change, although the effects of iceberg scouring and rate of recovery/re-colonisation will serve as a useful analogy for trawling perturbation.\n3. What are the links between Antarctic and other faunas? This includes benthic-pelagic coupling, the benthos as a foraging zone for higher predators, and through the Antarctic Circumpolar Current - connections with other southern continents.\nField sampling for this project was undertaken in the 2007/08 season, commencing in December and finishing in February 2008. Consequently, sample processing has only been underway for one or two months for plankton and pelagic fish samples. The demersal fish and benthic samples have only recently arrived at the National Natural History Museum (MNHN) in Paris ready for distribution to taxonomists and analysts. However, key CEAMARC collaborators who attended the recent post-field season CEAMARC workshop, Calvi April 2008, agreed that the use of three vessels for the field programme, instead of one ship as originally proposed, more than met expectations should sufficiently address all the objectives. Specifically, we have collected a substantial number of samples with sufficient sampling intensity and resolution to set the required benchmark of biodiversity in the survey for the pelagic, mesobathypelagic and benthic environments. This biodiversity benchmark will allow us to:\n- Compare changes in biodiversity with future CAML surveys and also with past surveys\n- Define legacy sites in the survey area for future CAML surveys and interim annual or biennial monitoring programmes to continuing the effects of climate change\n- Which species are most likely to be affected by climate change and those most likely to survive\n- Contribute to models looking at long term changes in species composition, ecosystem structure and function, survivorship of key species, effects of global warming, ocean acidification, and impacts on ecosystem service\n- Studies of the impact of trawling and iceberg scouring on the benthic and demersal communities\n- Compare pelagic, demersal and benthic communities in the survey area with those in the other CAML survey areas around Antarctica\nSufficient samples of plankton, fish and benthos were also collected for genetic and molecular analyses to improve our taxonomic knowledge and address the CAML objective on understanding species radiation.\n\nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nThis project is a part of the international \"Census of Antarctic Marine Life\" (CAML) conducted during International Polar Year. It is a collaborative contribution by Australia, France, Japan and Belgium to understand the biodiversity of Antarctic waters, with particular emphasis on plankton, fish and benthos of eastern Antarctica. In 2007/08, three ships surveyed this area with a range of traditional and modern sampling gear. The biodiversity data from this survey will be added to other CAML projects to serve as a robust reference for future examinations of the health of the Southern Ocean, and assist in its conservation and management.", "links": [ { diff --git a/datasets/ASAC_288_1.json b/datasets/ASAC_288_1.json index b402da136d..da6dfd631e 100644 --- a/datasets/ASAC_288_1.json +++ b/datasets/ASAC_288_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_288_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 288 See the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n \nIn January-February 1991, in Prydz Bay, phytoplankton bloom was evident in the inner shelf area with the dominant diatoms being represented mainly by pennate species of the Nitzschia-Fragilariopsis group. Dinoflagellates and naked flagellates were most abundant in the centre of the bay; however, larger heterotrophic species prevailed at the southern stations. Cell carbon values (average 317 micro grams per litre; range 92-1048 micrograms per litre) found in the bloom in the south were chiefly due to pennate diatoms and larger heterotrophic dinoflagellates. Much lower carbon values (average 51 micro grams per litre; range 7-147 micro grams per litre) in the outer shelf region were mainliy contributed by large centric diatoms (70-110 micro metres) and small dinoflagellates (5-25 micro metres). Wide ranges of algal cell sizes were observed in both southern and northern communities; the overlapping of sizes of diatoms and flagellates, the latter containing heterotrophs, suggested complex trophic relationships within the plankton and an enhanced heterotrophic activity in the south. North-to-south variations in surface delta 13 C of suspended particulate organic matter (SPOM), (range -31.85 to -20.12 parts per thousand) were directly related to the concentration of particulate matter: this suggested the effect of biomass, and thus of dissolved CO2 limitation on carbon fractionation. Three types of species assemblages were distinguished, corresponding to different narrow ranges of delta 13 C values (-20.12 to -22.37 parts per thousand; -24.50 to -26.65 parts per thousand; -29.73 to -31.85 parts per thousand); dominant species within each assemblage are the likely major determinants of the carbon isotopic composition and variation of SPOM. Pennate diatoms, such as Nitzschia curta and N. subcurvata appear to have made the major imprint on the highest delta 13 C values. Phaeocystis, naked flagellates, autotrophic dinoflagellates and centric diatoms are likely to have caused the lower delta 13 C values of SPOM. It appears that variations in both biomass concentration and in phytoplankton species composition have contributed to the carbon isotopic values of SPOM in Prydz Bay.", "links": [ { diff --git a/datasets/ASAC_2899_1.json b/datasets/ASAC_2899_1.json index 05dfd9fa7d..4f985cc83e 100644 --- a/datasets/ASAC_2899_1.json +++ b/datasets/ASAC_2899_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2899_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2899\nSee the link below for public details on this project.\n\nWe conducted a genomic analysis of Archaea and Bacteria collected from lakes in the Vestfold Hills, Antarctica. This provided a new level of understanding about the life forms inhabiting these cold lakes. Linked to knowledge of meteorological, geological, chemical and physical data that has been collected over years of previous research, the new genomic data will generate a complete understanding of how the microorganisms have evolved and how they have transformed and presently interact with the Antarctic environment. Deriving an integrated understanding of microbial ecology is essential for determining ways of preserving the health of the World's ecosystems.\n\nThe data are available for download as an excel spreadsheet and a word document from the URL given below.\n\nThe GPS coordinates where samples were collected from are as follows:\n\n(Note these are UTM (Universal Transverse Mercator) coordinates, from zone 44D)\n\nAce Lake: 44D 0384881 (easting), 2401821 (northing)\nDeep Lake: 44D 0385351, 2391772\nOrganic Lake: 44D 0384928, 2403550\n\nThe fields in this dataset are:\n\nWater temperature - degrees Celsius\n\nSpecific conductivity - micro Seimens per centimetre\n\nConductivity - micro Seimens per centimetre\n\nSalinity - parts per trillion\n\nDissolved oxygen % - %\n\nDissolved oxygen concentration - milligrams per litre\n\nDissolved oxygen charge - This is an engineering value. The value is unit less, the recommended reading is 50 plus or minus 25. If you have a low reading it generally means you need to replace the membrane and if you have a high reading you need to recondition the probe. \n\nPressureA (This a depth reading of the Sonde) - (pounds-force per square inch absolute)\n\nWater depth - metres\n\npH\n\npHmV (This is the pH millivolt reading that the probe is outputting the Sonde) - millivolts\n\nTurbidity - (nephelometric turbidity unit)\n\nBP (Barometric Air Pressure) - psi (pounds per square inch)\n \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nNew lake and ocean samples, including additional opportunistic samples from Heard Island, were obtained Oct-Dec 2008. All samples from 2006 forward are being processed. This includes DNA (metagenomics) and protein (proteomics). A great deal of bioinformatic analyses have been performed on metagenome data. Metaproteomics has also proceeded well. Details of some of the progress are as follows:\n\nIn the reporting period 1,064,488 Sanger sequencing reads were produced with 967,410 passing quality control, which at an average of 700bp provided 677Mb of sequence data. The reads were produced in batches for each sample. We generated assembly statistics and phylogenetic profiles after the completion of each batch. Sample diversity then guided the sequence allocation for each sample. A number of pragmatic software tools have been created to perform the analyses. As an example, for one sample the whole sample assembly was characterised by read depth, GC content, di-nucleotide frequency (Tetra) and tri-nucleotide frequency (Tetra) on a per scaffold basis. The intrinsic properties then formed vectors in a feature space on which a self-organising map clustering analysis was performed. The cluster which comprised the most abundant species was isolated and the genes annotated. This represented 9 contigs with a total of 1.7Mb and 1683 predicted genes. For this sample, proteins were extracted and metaproteomics performed resulting in a total of 3970 confident peptides matched providing identities for 504 proteins (at least 2 peptide matches per protein) representing about 30% coverage. In comparison, a total of 170 proteins were identified against the non-redundant database.\n\nIn other metaproteomic analyses, samples from 4 lake depths provided a total of 7,925 peptides providing the identification of 1015 proteins against the NCBI non-redundant protein database (matches not yet performed to annotated metagenome data). For testing detection limits and accuracy of identifications using a metaprotomics approach, a simulated mixed community study was performed using S. alaskensis and E. coli. This has shown that cell numbers, protein abundance and cell volumes all impact the ability to detect proteins of individual microorganisms within a population. The type and size of the database the metaproteomic dataset is searched against (non-redundant versus S. alaskensis + E. coli protein database) also resulted in differences in protein detection. The work has been useful for optimising parameters used for metaproteomics of the Antarctic samples.\n\nAn interesting eukaryotic virus that dominates the biomass of one of the samples is being analysed with the present work focusing on classifying and characterising. Transmission electron microscopy of the water sample revealed virus-like particles of approximately 150nm but it was unclear from morphology if they represented a single virus type or several. Two complementary metagenomic assembly approaches are being used to produce the most complete assembly possible of the large viral sequences. The first assembly strategy follows a conventional metagenomic workflow consisting of assembly of the whole metagenomic dataset followed by taxonomic binning of the constructs. An initial assembly has been constructed after determining the optimum acceptable degree of error. A high degree of assembly was evident with the largest scaffold spanning 108kb with 6 X coverage. A BLASTx search of the five largest contigs (greater than 10kb) produced two alignments to Major Capsid Protein (MCP) genes; one to the short MCP gene of Chyrsochromulina ericina virus (28% identity) and the other to the full MCP gene of Phaeocytis pouchetii virus (76% identity). Sequence flanking the full MCP gene corresponds to conserved hypothetical protein sequences from Ostreococcus virus 5 (45% identity) and Paramecium sp. Chlorella virus AR158 (39% identity). These large deeply assembling contigs will be used to 'tune' the parameters to improve assembly of the entire metagenome. A preliminary attempt to bin the scaffolds using tetra nucleotide frequencies from the initial assembly has not completely resolved into clear taxonomic clusters. A multi-dimensional binning approach including sequence coverage, GC content, nucleotide frequencies along with identification of marker genes is being developed and will be applied once an optimum whole metagenomic assembly has been completed. Although the presence of conserved genes is a promising sign of accurate assembly, validation of the scaffolds by comparison to sequenced virus genomes is uninformative as viruses are poorly represented in the public databases and extremely diverse. Instead, a second assembly strategy is underway that will conservatively extract and compile the viral sequence. The reads assigned in an initial MEGAN analysis to the large dsDNA viral clade were used in a preliminary round of assembly. This first assembly will be used as a reference to recruit more overlapping fragments and combined in another round assembly extending the construct from the high confidence 'seeds'. Cycles of recruitment and assembly will continue until the assembly reaches an end point. This is a new method of assembly that potentially can be used to extract and produce confident assemblies of other species with no sequenced representatives. Comparison between this virus specific assembly and the conventional metagenomic assembly will allow evaluation of the fidelity of both processes.", "links": [ { diff --git a/datasets/ASAC_2899_Ace_1.json b/datasets/ASAC_2899_Ace_1.json index a645e8af29..e46c6bf550 100644 --- a/datasets/ASAC_2899_Ace_1.json +++ b/datasets/ASAC_2899_Ace_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2899_Ace_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2899\nSee the link below for public details on this project.\n\nWe conducted a genomic analysis of Archaea and Bacteria collected from lakes in the Vestfold Hills, Antarctica. This provided a new level of understanding about the life forms inhabiting these cold lakes. Linked to knowledge of meteorological, geological, chemical and physical data that has been collected over years of previous research, the new genomic data will generate a complete understanding of how the microorganisms have evolved and how they have transformed and presently interact with the Antarctic environment. Deriving an integrated understanding of microbial ecology is essential for determining ways of preserving the health of the World's ecosystems.\n\nThis metadata record covers a specific dataset collected at Ace Lake in the Vestfold Hills. Comprehensive documentation describing this dataset is not available. For further information on the project, see other metadata records related to project 2899.", "links": [ { diff --git a/datasets/ASAC_2901_RAASTI_1.json b/datasets/ASAC_2901_RAASTI_1.json index 7ad1be7cd7..c2d64135ec 100644 --- a/datasets/ASAC_2901_RAASTI_1.json +++ b/datasets/ASAC_2901_RAASTI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2901_RAASTI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Public Summary for project 2901\nThis research will contribute to a large multi-disciplinary study of the physics and biology of the Antarctic sea ice zone in early Spring 2007. The physical characteristics of the sea ice will be directly measured using satellite-tracked drifting buoys, ice core analysis and drilled measurements, with detailed measurements of snow cover thickness and properties. Aircraft-based instrumentation will be used to expand our survey area beyond the ship's track and for remote sampling. The data collected will provide valuable ground-truthing for existing and future satellite missions and improve our understanding of the role of sea ice in the climate system.\n\nProject objectives:\n(i) to quantify the spatial variability in sea ice and snow cover properties over scales of metres to hundreds of kilometres in the region of 110 - 130 degrees E, in order to improve the accuracy of sea ice thickness estimates from satellite altimetry and polarimetric synthetic aperture radar (SAR) data.\n\n(ii) To determine the drift characteristics, and internal stress, of sea ice in the region 110 - 130 degrees E.\n\n(iii) To investigate the relationships between the physical sea ice environment and the structure of Southern Ocean ecosystems (joint with AAS Proposal 2767). \n\nTaken from the abstract of the PhD thesis accompanying the dataset:\nAntarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data from an airborne platform is currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels. These are predicted to increase with effects of climate change, and are difficult to measure as snow fall is frequently lost to wind-blown redistribution, sublimation and snow-ice formation. Additionally, accurate regional scale snow thickness data will increase the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates.\n\nAirborne snow-depth investigation techniques are one method for providing regional estimation of these parameters. The airborne datasets are better suited to validating satellite algorithms, and are themselves easier to validate with in-situ measurement. The development and practicality of measuring snow thickness over sea ice in Antarctica using a helicopter-borne radar forms the subject of this thesis. The radar design, a 2-8 GHz Frequency Modulated Continuous Wave Radar, is a product of collaboration and the expertise at the Centre for Remote Sensing of Ice Sheets, Kansas University.\n\nThis thesis presents a review of the theoretical basis of the interactions of electromagnetic waves with the snow and sea ice. The dominant general physical parameters pertinent to electromagnetic sensing are presented, and the necessary conditions for unambiguous identification of the air/snow and snow/ice interfaces by the radar are derived. It is found that the roughness's of the snow and ice surfaces are dominant determinants in the effectiveness of layer identification in this radar. Motivated by these results, the minimum sensitivity requirements for the radar are presented.\n\nExperiments with the radar mounted on a sled confirm that the radar is capable of unambiguously detecting snow thickness. Helicopter-borne experiments conducted during two voyages into the East Antarctic sea-ice zone show however, that the airborne data are highly affected by sweep frequency non-linearities, making identification of snow thickness difficult. A model for the source of these non-linearities in the radar is developed and verified, motivating the derivation of an error correcting algorithm. Application of the algorithm to the airborne data demonstrates that the radar is indeed receiving reflections from the air/snow and snow/ice interfaces.\n\nConsequently, this thesis presents the first in-situ validated snow thickness estimates over sea ice in Antarctica derived from a Frequency Modulated Continuous Wave radar on a helicopter-borne platform. Additionally, the ability of the radar to independently identify the air/snow and snow/ice interfaces allows for a relative estimate of roughness of the sea ice to be derived. This parameter is a critical component necessary for assessing the integrity of satellite snow-depth retrieval algorithms such as those using the data product provided by the Advanced Microwave Scanning Radiometer - Earth Observing System sensor on board NASA's Aqua satellite.\n\nThis thesis provides a description, solution or mitigation of the many difficulties of operating a radar from a helicopter-borne platform, as well as tackling the difficulties presented in the study of heterogeneous media such as sea ice and its snow cover. In the future the accuracy of the snow-depth retrieval results can be increased as technical difficulties are overcome, and at the same time the radar architecture simplified. However, further validation studies are suggested to better understand the effect of heterogeneous nature of sea ice and its snow cover on the radar signature.\n\nRAASTI = Radar For Antarctic Snow Thickness Investigation", "links": [ { diff --git a/datasets/ASAC_2904_1.json b/datasets/ASAC_2904_1.json index 82548f994d..6a13608566 100644 --- a/datasets/ASAC_2904_1.json +++ b/datasets/ASAC_2904_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2904_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 2904\n\nSee the link below for public details on this project.\n\nInternational Polar Year (IPY) Aliens in Antarctica will assess the threat of humans carrying non-native seeds and spores into Antarctica. We will identify routes of transport and attempt to calculate how many seeds and spores are transported each year. Our data will be used to develop techniques to mitigate this threat and hence protect Antarctica.\n\nThe impact of non-native (alien) species on ecosystems is one of the big issues of the 21st Century. Antarctica is not immune to this problem with some alien species having established on the Antarctic continent and on most sub-Antarctic islands. The impacts of alien species can include substantial loss of biodiversity and damage to ecosystem processes. Such impacts will be exacerbated by the rapid climate change, now being experienced in parts of Antarctica.\n\nSurrounded by the vast Southern Ocean, Antarctica's protective isolation is being chipped away by the movement of people and cargo to the region by national programs and the now booming tourist industry. Over 40,000 people travel to the Antarctic each year. This international project will assess the pathways of propagule (seeds, eggs, spores etc) transfer, the extent to which people from many nations, unintentionally carry propagules of alien species into the Antarctic region and the size of the threat. It will lead to the creation of appropriate mitigation methods by the Antarctic Treaty to protect the fragile Antarctic ecosystem. Furthermore, the project will provide valuable insight into the movement of alien propagules worldwide. It has been estimated that by 2010, the number of tourists crossing international boarders globally each year, will be around 1 billion people.\n\nThe travel histories of some 15,000 Antarctic tourists and researchers will be complied, assisted by the cooperation of four tourist operators, 15 supply vessels of national Antarctic programmes, and six air operators. One thousand items of cargo from 7 National Antarctic programmes will be inspected for propagules of alien species. The study has the full support from the Council of Managers of National Antarctic Programs, the International Association of Antarctic Tour Operators, and researchers from seven nations.\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nConsiderable progress has been made on all objectives. All samples of propagules (greater than 1000 samples from over 50 voyages and examination of cargo/ food/ building material from 5 nations) have been sorted and propagules extracted. The majority of these propagules have been photographed and where possible identified. Analysis of the data is currently underway. \n\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nThe International Polar Year project is examining the type and amount of 'propagules' (seed, spores and eggs) that are unintentionally imported into the region on clothes, shoes or hand luggage, as well as how many propagules are likely to be deposited and whether they will germinate and grow. Cargo, fresh food and travellers' gear destined for Antarctica were inspected during the first season of IPY and are now currently being analysed. Considerable progress on the quantifiaction of the threat of alien species to Antarctic ecosystems has been made. Results of our analysies will be presented at ATCM 33.", "links": [ { diff --git a/datasets/ASAC_2904_Food_1.json b/datasets/ASAC_2904_Food_1.json index 2d24287aeb..fd40f119fc 100644 --- a/datasets/ASAC_2904_Food_1.json +++ b/datasets/ASAC_2904_Food_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2904_Food_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "International Polar Year (IPY) Aliens in Antarctica project aims to identify human-mediated pathways for alien propagules into the Antarctic ecosystem (www.aliensinantarctica.aq). As part of this international project, AAD staff examined fresh food and cargo for evidence of propagules prior to shipping south by the Australian Antarctic Program. This report summarises the findings of our food inspections.\n\nA total of 2094 items of fresh fruit and/or vegetables were inspected over the season. Of these 89% (1865 items) were deemed 'clean' (ie no evidence of propagules or infections), 191 (9%0 had evidence of fungal infections, and 54 items (2%) had invertebrates, soil or other propagules such as seeds. Apples, cantaloupes, carrots, grapefruit, limes, oranges, potatoes and tomatoes were recorded as consistently having clean rates of 90% or greater over the 07/08 shipping season.\n\nWith regard to the food items found with propagules, a number of significant observations were made. The most notable of these was that of the 56 pears examined at the beginning of the season (Voyage 2) only one was deemed 'clean': the remainder (99%) were rotting with blue moulds. Similarly only 11% of onions destined for Voyage 2 and 49% of bananas were 'clean'; the remainder were observed with fungal infections or other propagules. Other notable observations were that some cabbages and iceberg lettuces were contaminated with soil, and live thrips and white flies (Bemisia sp?) were found in two boxes.", "links": [ { diff --git a/datasets/ASAC_2914_2.json b/datasets/ASAC_2914_2.json index 0fcf6e0b04..1b1777fe16 100644 --- a/datasets/ASAC_2914_2.json +++ b/datasets/ASAC_2914_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2914_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2914\nSee the link below for public details on this project.\n\nCan animals raft between countries on floating seaweed? We aim to answer that question using powerful genetic tools. We can tell whether gene flow is strong between populations of animals by comparing their mitochondrial DNA; this could show us whether animals from one species in New Zealand are isolated from individuals of the same species in Chile. If they are not isolated, how are they managing to maintain gene flow? We know there are many millions of clumps of floating seaweed in the Southern Ocean, and these might provide a means of intercontinental travel for a range of small invertebrates.\n\nProject objectives:\nThe primary objective of the project is to determine the effectiveness of rafting as a dispersal mechanism for sessile and semi-sessile organisms around the Southern Ocean using genetic tools.\n\nThe secondary objectives, by which the primary objective will be addressed, are:\n\n- to examine the biogeography of bull kelp (Durvillaea antarctica) and its holdfast fauna around the Southern Ocean\n\n- to undertake genetic analysis of a wide range of macroalgal (seaweed) species throughout the Southern Ocean to assess 1) whether sea ice indeed extended further north than previously believed, and 2) the ecological and evolutionary impacts of historic ice scour on Southern Ocean islands.\n\n- to determine which holdfast invertebrates are the most common and ubiquitous in holdfasts of Durvillaea antarctica around the Southern Ocean\n\n- to compare the genetic structure of populations of both the kelp itself, and select invertebrate taxa* from its holdfasts, on a number of spatial scales:\n--- genetic variation at HOLDFAST level: are members of a single species, e.g., the isopod Limnoria stephenseni, closely related within a single holdfast?\n--- genetic variation at SITE level: are members of a single species, e.g., Durvillaea antarctica itself, closely related at one site? In this case, a 'site' means a single intertidal rock platform.\n--- genetic variation at NATIONAL level: are there distinct biogeographic separations of species, or does a single species show distinct genetic disjunction, along the Chilean coastline and around the south island of New Zealand?\n--- genetic variation at OCEAN level: are species clearly connected (by gene flow) between Southern Ocean landmasses? The landmasses of interest are: Chile, New Zealand, and the subantarctic islands on which Durvillaea antarctica grows.\n\n* The proposed taxa that this project will focus on are: the isopod genus Limnoria; the amphipod Parawaldeckia kidderi; the chiton genus Onithochiton; the polychaete worm families Terebellidae and Syllidae; a topshell; a bivalve; barnacles.\n\nProgress against objectives:\n\nConsiderable progress has been made against the primary objective since the start of the project in 2006. We have collected (/ been sent) and analysed samples of bull-kelp (Durvillaea antarctica) and its associated invertebrate holdfast fauna from numerous sites around the Southern Ocean (subantarctic islands including Macquarie, Gough, Marion, Kerguelen, Crozet, Auckland, Antipodes, Campbell, Falkland Islands; along the coasts of New Zealand and Chile). Our results thus far have allowed us to determine not only that rafting facilitates long-distance dispersal of these otherwise sedentary taxa, but also that sea ice during the last ice ice likely had significant impacts on subantarctic intertidal ecosystems. Our conclusions have been published in several papers in high-impact journals.\n\nThe secondary objectives, by which the primary objective will be addressed, are:\n\n- to examine the biogeography of bull kelp (Durvillaea antarctica) and its holdfast fauna - these objectives have now largely been achieved, and results published.\n\n- to undertake genetic analysis of a wide range of macroalgal (seaweed) species throughout the Southern Ocean - this part of the project is ongoing, and will make use of samples collected over the austral summer from Macquarie Island (and other locations around the southern hemisphere). all samples have now been collected and are being processed in the laboratory.\n\n- to determine which holdfast invertebrates are the most common and ubiquitous - this objective has been partially achieved (see Nikula et al. 2010), but research is ongoing.\n\n- to compare the genetic structure of populations of both the kelp itself, and select invertebrate taxa from its holdfasts, on a number of spatial scales - this objective has been partially achieved (see Nikula et al. 2010 for results of Limnoria and Parawaldeckia genetic research) but additional research on these and other taxa continues.\n\n\nThe download file contains an excel spreadsheet detailing collection locations and accession numbers for the samples collected on Macquarie Island. A text document providing accession numbers for non-Antarctic related samples used in this project is also part of the download file.", "links": [ { diff --git a/datasets/ASAC_2918_1.json b/datasets/ASAC_2918_1.json index 6ff4c94076..547fd7d9df 100644 --- a/datasets/ASAC_2918_1.json +++ b/datasets/ASAC_2918_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2918_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2918\n\nSee the link below for public details on this project.\n\nThis project will assess the extent of changes to the freshwater stream invertebrate communities of Macquarie Island since they were last sampled 15 years ago. It will also assess whether spatial variation in these stream communities is related to changes in water temperature, it will experimentally examine the temperature tolerance of these freshwater taxa and will provide a long-term dataset to assess future changes, including those resulting from climate change. The use of stream macroinvertebrates as biomonitoring tools to detect impacts from human activities on Macquarie Island and other sub-Antarctic Islands will be examined.\n\nThe download file contains a pdf document with several side-by-side comparison images taken in 1992 by Richard Marchant during his studies for ASAC project 555 (ASAC_555), \"A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island\", and in 2010 by James Doube.\n\nAlso see the metadata record \"Stream invertebrate communities of Macquarie Island\" (AAS_3261) for more information.", "links": [ { diff --git a/datasets/ASAC_2933_1.json b/datasets/ASAC_2933_1.json index a5d1ee5c10..b4cdfeabbd 100644 --- a/datasets/ASAC_2933_1.json +++ b/datasets/ASAC_2933_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2933_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) Project 2933.\n\nSee the child records for access to the datasets.\n\nPublic \nWhile it is generally thought that Antarctic organisms are highly sensitive to pollution, there is little data to support or disprove this. Such data is essential if realistic environmental guidelines, which take into account unique physical, biological and chemical characteristics of the Antarctic environment, are to be developed. Factors that modify bioavailability, and the effects of common contaminants on a range of Antarctic organisms from micro-algae to macro-invertebrates will be examined. Risk assessment techniques developed will provide the scientific basis for prioritising contaminated site remediation activities in marine environments, and will contribute to the development of guidelines specific to Antarctica.\n\nProject objectives:\n1. Develop and use toxicity tests to characterise the responses of a range of Antarctic marine invertebrates, micro- and macro-algae to common inorganic and organic contaminants.\n\n2. To examine factors controlling bioavailability and the influence of physical, chemical and biological properties unique to the Antarctic environment on the bioavailability and toxicity of contaminants to biota.\n\n3. To compare the response of Antarctic biota to analogous species in Arctic, temperate and tropical environments in order to determine the applicability of using toxicity data and environmental guidelines developed in other regions of the world for use in the Antarctic.\n\n4. Develop a suite of standard bioassay techniques using Antarctic species to assess the toxicity of mixtures of contaminants (aqueous and sediment-bound) including tip leachates, sewage effluents and contaminated sediments.\n\n5. To establish risk assessment models to predict the potential hazards associated with contaminated sites in Antarctica to marine biota, and to develop Water and Sediment Quality Guidelines for Antarctica to set as targets for the remediation of contaminated marine environments. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nDue to logistical constraints, only a short field season (5 weeks) was conducted at Casey in 2008/09 and no berths were allocated solely to this project. A team of 6 scientists worked together on an intensive marine sampling program under TRENZ (AAS project 2948, CI Stark) in support of 5 different AAS projects, including this one. The lack of adequate personnel dedicated to this project and the limited time that we were allocated on station hindered progress and meant that no experiments on Antarctic organisms were able to be conducted in situ. The airlink was however successfully used to transport marine invertebrates collected at Casey and held in seawater at 0degC back to Hobart on 3 separate flights. These invertebrates are currently being maintained in the cold water ecotoxicology aquarium facilities at Kingston. Once they are sorted and where possible established in cultures, they will be used in toxicity tests.\n\nProgress against specific objects are:\n1) Much effort and time has been put towards the husbandry and culture of the collected Antarctic marine invertebrates. Some species are now successfully breeding in the laboratory providing new generations and sensitive juvenile stages of invertebrates to work with in toxicity tests. This culturing capability, if able to be developed, will hugely extend opportunities for carrying out research for this project, by giving us access to live material over the winter months and during summer when berths to or space on station in Antarctica is limited. Toxicity tests using some of the common amphipods and gastropods collected in the 0809 season at Casey will commence shortly at Kingston.\n2) Toxicity tests to commence shortly using invertebrates collected in the 0809 season now being maintained in the Ecotoxicology aquarium will focus on interactions and potentially synergistic effects of contaminants along with other environmental stressors including increases in temperature and decreases in salinity associated with predicted environmental changes in response to climate change.\n3) A phD candidate has recently started on this project and is currently reviewing all available literature on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes.\n4) Invertebrates collected in the 0809 season that are being maintained in the Ecotoxicology aquarium will be screened in toxicity tests to commence shortly. Methods will then be developed using the most suitable and sensitive species to form the basis of standard bioassay procedures that can be used to test mixtures such as sewage effluents and tip leachates in the upcoming season.\n5) The establishment of risk assessment models and Environmental Quality Guidelines for Antarctica is a long term goal of this project when data from the first 4 objectives can be synthesised and hence has not yet been addressed. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nObjectives 1 and 2: Metal effects on the behaviour and survival of three marine invertebrate species were investigated during the field season. Two replicate toxicity tests were conducted on the larvae of sea urchin Sterechinus neumayeri where combined effects of metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) were to be investigated on developmental success. However, due to lower than optimal fertilisation success, both tests were terminated before any meaningful results could be derived.\n\nFour tests were conducted on the adult amphipod, Paramorea walkeri. Two replicate tests investigated combined metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) effects, and two tests investigated the effects of copper, cadmium, lead, zinc and nickel exposure at ambient sea water temperature of -1 degrees Celsius.\n\nOne test was conducted with the micro-gastropod Skenella paludionoides being exposed to copper, cadmium, lead, zinc and nickel at ambient sea water temperature.\n\nThe larvae of bivalve Laternula sp. were also investigated as a potential test organism for metal toxicity. Strip spawning was conducted a number of times, however, this technique did not provide adequate levels of fertilisation success and as such, the toxicity tests on larval development were not completed.\n\nObjective 3: A phD candidate working on this project is in the process of compiling a review of all available date on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. This literature review will form a major component of her thesis' first chapter\n\nObjective 4: Methods for Standard bioassay procedures were developed using the most suitable and sensitive species, the amphipod Paramoera walkeri and the microgastropod Skenella paludionoides. These standard tests were then used to assess the toxicity of sewage effluent at Davis Station (in conjunction with project 3217).\n\nObjective 5: Toxicity tests on sewage effluent were conducted as part of a risk assessment to determine hazards associated with the current discharge. The determined toxicity of the sewage effluent will provide a basis for guideline recommendations on the required level of treatment and on what constitutes an adequate or 'safe' dilution factor for dispersal of the effluent discharge to the near shore marine environment.", "links": [ { diff --git a/datasets/ASAC_2933_field_lab_books_1.json b/datasets/ASAC_2933_field_lab_books_1.json index 9bdff59492..89bc1ee972 100644 --- a/datasets/ASAC_2933_field_lab_books_1.json +++ b/datasets/ASAC_2933_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2933_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station, Davis Station, Macquarie Island and Kingston between 2007 and 2012 as part of ASAC (AAS) project 2933 - Developing water and sediment quality guidelines for Antarctica: Responses of Antarctic marine biota to contaminants.", "links": [ { diff --git a/datasets/ASAC_2937_field_lab_books_1.json b/datasets/ASAC_2937_field_lab_books_1.json index 1bf24f3ea8..3b8aa299d0 100644 --- a/datasets/ASAC_2937_field_lab_books_1.json +++ b/datasets/ASAC_2937_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2937_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station in 2009, and 2010/11 summer seasons, for ASAC (AAS) project 2937 - In situ chemical stabilisation of contaminants in freezing ground. \n\nThe focus of these books is on the field and lab work done in relation to PRB, Thala Valley stockpile and metal stabilization work at Casey Station.\n\nPersonnel involved in recording these books were:\n\nTim Spedding, Kate Mumford, Tom Statham", "links": [ { diff --git a/datasets/ASAC_2940_Bird_Island_1.json b/datasets/ASAC_2940_Bird_Island_1.json index c1d5c486f4..777c1dbb6a 100644 --- a/datasets/ASAC_2940_Bird_Island_1.json +++ b/datasets/ASAC_2940_Bird_Island_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Bird_Island_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the post-breeding movements of adult female Antarctic females (Arctocephalus gazella) we tracked females using Biotrack GLS (geolocation) data loggers. Females were captured towards the end of the lactation period (March/April) and the GLS tag, affixed to a Dalton flipper tag, was deployed in the trailing edge of the left or right foreflipper. Tags were generally retrieved just prior to or after giving birth the following season. Data files were extracted from the tags using BASTrak software. \n\n.lig - light data\n.tem - temperature data\n.act - activity data\n\nMetadata for each individual include:\nSite, year, GLS ID, sex, age, deployment site, lat and long of deployment site, flipper tag number, deployment and retrieval times (GMT).", "links": [ { diff --git a/datasets/ASAC_2940_Bird_Island_Isotopes_1.json b/datasets/ASAC_2940_Bird_Island_Isotopes_1.json index 8a5e10da6a..128d16ff0e 100644 --- a/datasets/ASAC_2940_Bird_Island_Isotopes_1.json +++ b/datasets/ASAC_2940_Bird_Island_Isotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Bird_Island_Isotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the dietary preferences and trophic level consumption of post-breeding adult female Antarctic fur seals (Arctocephalus gazella), we analysed the carbon:nitrogen composition of whiskers and blood samples from the females. Females were captured towards the end of the lactation period (March/April) and whiskers and a blood sample were collected at this time. Females were generally recaptured just prior to or after giving birth the following season and a further whisker and blood sample were collected at this time. \n\nMetadata for each individual include:\nSite, GLS ID, year, flipper tag number, season, sampling date, tissue type, whisker segment number, cumulative length along whisker of the segment, d15N, d13C, percentage N, percentage C and CN ratio.", "links": [ { diff --git a/datasets/ASAC_2940_Cape_Shirreff_1.json b/datasets/ASAC_2940_Cape_Shirreff_1.json index 41996d5c62..62712a5b86 100644 --- a/datasets/ASAC_2940_Cape_Shirreff_1.json +++ b/datasets/ASAC_2940_Cape_Shirreff_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Cape_Shirreff_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the post-breeding movements of adult female Antarctic females (Arctocephalus gazella) we tracked females using Biotrack GLS (geolocation) data loggers. Females were captured towards the end of the lactation period (March/April) and the GLS tag, affixed to a Dalton flipper tag, was deployed in the trailing edge of the left or right foreflipper. Tags were generally retrieved just prior to or after giving birth the following season. Data files were extracted from the tags using BASTrak software. \n\n.lig - light data\n.tem - temperature data\n.act - activity data\n\nMetadata for each individual include:\nSite, year, GLS ID, sex, age, deployment site, lat and long of deployment site, flipper tag number, deployment and retrieval times (GMT).", "links": [ { diff --git a/datasets/ASAC_2940_Cape_Shirreff_Isotopes_1.json b/datasets/ASAC_2940_Cape_Shirreff_Isotopes_1.json index 862e1b2061..0240613a98 100644 --- a/datasets/ASAC_2940_Cape_Shirreff_Isotopes_1.json +++ b/datasets/ASAC_2940_Cape_Shirreff_Isotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Cape_Shirreff_Isotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the dietary preferences and trophic level consumption of post-breeding adult female Antarctic fur seals (Arctocephalus gazella), we analysed the carbon:nitrogen composition of whiskers and blood samples from the females. Females were captured towards the end of the lactation period (March/April) and whiskers and a blood sample were collected at this time. Females were generally recaptured just prior to or after giving birth the following season and a further whisker and blood sample were collected at this time. \n\nMetadata for each individual include:\nSite, GLS ID, year, flipper tag number, season, sampling date, tissue type, whisker segment number, cumulative length along whisker of the segment, d15N, d13C, percentage N, percentage C and CN ratio.", "links": [ { diff --git a/datasets/ASAC_2940_Marion_Island_1.json b/datasets/ASAC_2940_Marion_Island_1.json index 3cacf39dd3..a4d57e9c15 100644 --- a/datasets/ASAC_2940_Marion_Island_1.json +++ b/datasets/ASAC_2940_Marion_Island_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Marion_Island_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the post-breeding movements of adult female Antarctic females (Arctocephalus gazella) we tracked females using Biotrack GLS (geolocation) data loggers. Females were captured towards the end of the lactation period (March/April) and the GLS tag, affixed to a Dalton flipper tag, was deployed in the trailing edge of the left or right foreflipper. Tags were generally retrieved just prior to or after giving birth the following season. Data files were extracted from the tags using BASTrak software. \n\n.lig - light data\n.tem - temperature data\n.act - activity data\n\nMetadata for each individual include:\nSite, year, GLS ID, sex, age, deployment site, lat and long of deployment site, flipper tag number, deployment and retrieval times (GMT).", "links": [ { diff --git a/datasets/ASAC_2940_Marion_Island_Isotopes_1.json b/datasets/ASAC_2940_Marion_Island_Isotopes_1.json index 2b69c706d7..5c8cc18870 100644 --- a/datasets/ASAC_2940_Marion_Island_Isotopes_1.json +++ b/datasets/ASAC_2940_Marion_Island_Isotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2940_Marion_Island_Isotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the dietary preferences and trophic level consumption of post-breeding adult female Antarctic fur seals (Arctocephalus gazella), we analysed the carbon:nitrogen composition of whiskers and blood samples from the females. Females were captured towards the end of the lactation period (March/April) and whiskers and a blood sample were collected at this time. Females were generally recaptured just prior to or after giving birth the following season and a further whisker and blood sample were collected at this time. \n\nMetadata for each individual include:\nSite, GLS ID, year, flipper tag number, season, sampling date, tissue type, whisker segment number, cumulative length along whisker of the segment, d15N, d13C, percentage N, percentage C and CN ratio.", "links": [ { diff --git a/datasets/ASAC_2941_1.json b/datasets/ASAC_2941_1.json index 04240df567..f34618aeb4 100644 --- a/datasets/ASAC_2941_1.json +++ b/datasets/ASAC_2941_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2941_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 2941.\n\nThis project replaced project 2301 after 2006-2007 (ASAC_2301).\n\nPublic\nThis work addresses Australian Government marine mammal conservation, management and policy needs with an emphasis on priorities from the International Whaling Commission and the Commission for the Conservation of Antarctic Marine Living Resources. The science outcomes will directly address knowledge gaps in our understanding of population structure, abundance, trend and distribution of the great whales and other predators, their ecological linkages and the role these animals play in the Southern Ocean ecosystem. Such science forms a powerful unpinning of Australia's important and high profile policy and management objectives nationally and in international conventions.\n\nProject objectives:\nThis project has been specifically designed to deliver science outcomes against the Australian Government's marine mammal conservation, management and policy needs. It forms a central component of the work to be conducted by the staff of the newly established Australian Centre for Applied Marine Mammal Science (ACAMMS) in the AAD's Science Branch. The ACAMMS has been established as a central marine mammal science hub to build on existing, but disparate research and provide an integrated, strategic, cross-jurisdictional research effort to redress key knowledge gaps and underpin and support Australia's marine mammal conservation, management and policy priorities. The staff at the AAD hub will continue a research focus around IWC and Southern Ocean priorities.\n\nThe objectives of this project will build upon the work on trophic linkages of marine mammals and their prey conducted since 2002 (AAS 2301) and will be fully integrated with other marine mammal focused research projects (AAS 2683 and 2926 in particular).\n\nThe research emphasis will primarily focus upon the relevant scientific priorities from the International Whaling Commission (IWC) and the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), but will also be responsive to needs identified in national recovery plans (for threatened species) and broader management and policy needs of the Department primarily of Environment and Heritage. The research in the Southern Ocean will focus on the great whales, but will necessarily include key elements of the remaining marine mammal fauna (e.g. fine to meso-scale trophic interactions of krill predators in the pack-ice) The major research objectives will be to:\n- further develop marine ecosystem modelling to investigate issues of:\no spatial and dynamic aspects of top-down trophic linkages\no spatially and temporally structured predator movement and foraging dynamics. This will assist in the determination of trophic niches and potential for ecological competition between krill predators. Further, it will establish linkages between predator dynamics at meso(regional) and large scales;\n- quantify the dynamics of marine mammal population structure, distribution, abundance and trend through methodological improvements and implementation of;\no genetic techniques (AAS 2926)\no passive acoustic techniques (AAS 2683)\no line transect survey techniques\no biologging (telemetry transmitters and data-loggers)\n- continued development and implementation of powerful, non-lethal research techniques to improve the understanding of marine mammals\n- develop and 'mine' the cetacean sightings and strandings databases that have been recently passed to the AAD Data Centre by the DEH in order to provide strategic and relevant outputs for marine mammal management and conservation needs (work to be done in conjunction with the AADC). \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nAnimal tracking work: This work specifically defines the predator movement data needed as inputs into ecosystem models (Objective 1), as well as defines whale movements relevant to Objective 2. These data form an important component under Objective 3 of data needed to define management and conservation needs.\n\nGenetics: This work determines sex and stock structure and is also relevant to all Objectives, and in most particularly to 2 and 3.\n\nAerial Survey: This major work contributes to understanding whale distribution in sea-ice and is highly relevant to all objectives.\n\nDatabase work: a great deal of progress has been made in the development of the structure and functionality of the data base and data input procedures for cetacean sightings, particularly in relation to data collected by the oil and gas industry. This work is a major contribution to objective 4. \n\nTaken from the 2009-2010 Progress Report:\n\nProgress against objectives:\nThe AMMC has made substantial progress against the objectives, having conducted a number of large-scale field campaigns in the last twelve months:\n\nThe second whale aerial survey was conducted successfully. This major work contributes to understanding whale distribution in sea-ice and is highly relevant to objectives 1, 2 and 3 as above.The previous survey in the 2008/09 summer was considered a 'pilot' and focussed on the Vincennes Bay polynya in December 2008. Survey effort this year (the austral summer of 2009/10) started in December 2009 and largely repeated the survey design from the first year, but also targeted areas around the Shackleton Ice Shelf and the Davis Sea, and finished with more effort over the Vincennes Bay polynya in late January and early February 2010. The aim of the aerial survey was to collaborate with a concurrent IWC-SOWER voyage surveying north of the ice edge, and to collect environmental information to study the distribution of minke whales within pack-ice environments. In total, 4,923 nm of effort was achieved, covering around 55,559 nm2 of survey area. Across the entire survey period there were 24 on-effort sightings (34 individuals) of minke whales; 5 sightings (5 individuals) of 'like' minke whales; and 5 sightings (5 individuals) of minke whales observed off-effort. Other species sighted were killer whales, southern right whales, sperm whales, southern bottlenose whales and a number of sightings of unknown species. Two papers were presented to the International Whaling Commission Scientific Committee in 2010 (please see section 1.6). The AMMC has produced basic estimates of relative densities in order to begin exploring abundance and distribution of minke whales within pack-ice both between and within the 2008/09 and 2009/10 austral summers in east Antarctica. These are, however, preliminary results and we intend to undertake a full analysis in the coming year.\n\nThe AMMC continued its work on biologging and genetic research, conducting two satellite tag deployments on whales in Australian waters, one in New Zealand waters and one in Antarctica in early 2010 (see below). The satellite telemetry work specifically defines the predator movement data needed as inputs into ecosystem models (Objective 1), as well as defines whale movements relevant to Objective 2. These data form an important component under Objective 3 of data needed to define management and conservation needs. The genetic work determines sex and stock structure and is also relevant to all Objectives, and in most particularly to 2 and 3. Please see attached table of usage for numbers of tags deployed and biopsies collected on each trip. These datasets will be used to define the spatial and temporal migratory behaviour of these whales in Australian waters and beyond. The AMMC has also continued to develop and refine the design of the satellite tags used, moving to a tag that now has two AA batteries, substantially increasing the potential tracking duration.\n\nThe joint Australian-New Zealand Antarctic Whale Expedition (AWE) completed its six week, non-lethal whale research voyage to Antarctic waters onboard the New Zealand Research Vessel Tangaroa on March 15th 2010 in Wellington, New Zealand. The research voyage was the first major activity of the Australian-led International Whaling Commission initiative in support of the multi-national Southern Ocean Research Partnership (SORP). The voyage objectives were to contribute directly to the research projects that are currently being developed for SORP. Major accomplishments of the AWE research voyage include:\n- Completion of the first successful non-lethal whale research voyage which directly contributes towards the core research projects of the Southern Ocean Research Partnership.\n- Demonstration of a successful model of using small boats, working around a capable ship, for non-lethal whale research in high latitude high seas.\n- The collection of over 60 biopsy skin samples, and over 60 individually identifiable tail fluke photographs from humpback whales on their Southern Ocean feeding grounds.\n- The satellite tagging of 30 humpback whales on their Southern Ocean feeding grounds.\n- The demonstration of the use of passive acoustics to track and locate vocalising Antarctic blue whales beginning at a distance of over 100 nautical miles.\n- The recording of humpback whale 'songs' on the feeding grounds. Prior to this, such songs have only been shown to occur on lower latitude breeding grounds and nearby migratory routes.\n- The detection of sounds most likely associated with Antarctic minke whales; a species that has been historically difficult to define acoustically.\n- The collection of hydro-acoustics data of whale prey in regions of high and low whale densities which can be used to better define the correlations between krill and whales in the Southern Ocean.\n\nWith regards the databases, a great deal of progress has been made in the development of the structure and functionality of the data base and data input procedures for cetacean sightings, particularly in relation to data collected by the oil and gas industry. This work is a major contribution to objective 4.\n\nTaken from the 2010-2011 Progress Report:\nPublic summary of the season progress:\nGood progress has been made in this marine mammal conservation and management project. New and important information on whale migratory behaviour (humpback, southern right, and blue whales) has been acquired, along with improved understandings of the way populations of right whales and humpback whales are divided. We have also continued the development of new and powerful techniques in measuring whale abundance, distribution in ice, and their age. Progress has also been made on design and function of the cetacean sightings database which enables this valuable tool to more effectively collect data and make it available to managers for conservation decisions.", "links": [ { diff --git a/datasets/ASAC_2941_Aerial_Data_1.json b/datasets/ASAC_2941_Aerial_Data_1.json index f434f8428d..a41e7e267a 100644 --- a/datasets/ASAC_2941_Aerial_Data_1.json +++ b/datasets/ASAC_2941_Aerial_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2941_Aerial_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "With the aim of estimating the proportion of Antarctic minke whales (Balaenoptera bonaerensis) in pack ice over summer, an Australian fixed-wing aerial survey programme, based in east Antarctica, was conducted in the austral summers of 2007/2008, 2008/09 and 2009/10 (See Kelly et al. 2010; SC/62/IA8). The first season (2007/08) comprised of three 'test' flights. As such, there were no real 'survey' data collected during these three flights, but video and digital stills data have been included in the dataset supplied. \n\nThe surveys (2008/09 and 2009/10) covered two general regions: Vincennes Bay (66 degrees 24'S 110 degrees 18'E) which was surveyed multiple times across both seasons and within the 2009/10 season, and north and east of the Shackleton Ice Shelf and into the eastern section of the Davis Sea, which was surveyed once (2009/10). The primary focus was on Antarctic minke whales, however sightings of other species were also collected (killer whale, Southern right whale, penguins and seals).\n\nThe survey was conducted in a CASA 212:400 aircraft at an altitude was 228m (750ft) and survey speed was 204 km/hr (110 knots). The survey was conducted as independent double-platform: the front and back observers were isolated visually and audibly. The aircraft was also fitted with a number of digital still, video and infrared cameras.\n \nData Available\n1. Sighting data set\n\nA .csv file of animal sightings. Two files, one for each survey season, has been supplied. The observers field of view was between 30 degrees and 60 degrees declination (approximately) from the horizon, corresponding to an on the ground area width of 264 metres each side of the aircraft. Protocol was followed as for traditional line transect surveys for marine mammals, with observers searching ahead of the aircraft in a 'D' pattern. \n\nThe recorded observations consisted of cue counting (where possible) and the angle of declination when the animals were abeam to the observer (using a Suunto inclinometer). Cues were not recorded after the animals had moved past abeam. The angle of declination of groups was measured at the centre of the group. Perpendicular distance out to animals was calculated using angle of declination and flying height (but no correction for curvature of the earth or aircraft drift angle was applied).\n\nOther information recorded included species, group size (minimum, maximum and best estimate), cue type, number of animals at surface when perpendicular, direction of travel and any behavioural features of the animal(s). \n\nPlease note that no formal sighting data was collected for the January 2008 test flights. \n\n2. Effort data set\n\nA .csv file of survey effort and environmental conditions. Two files, one for each survey season, has been supplied. The flight leader recorded environmental covariates (ice coverage (to the nearest 10%), glare, Beaufort sea state, and cloud cover, etc) at regular intervals, or when conditions changed.\n\n3. Still images\n\nThe data includes jpeg files of images. A still camera was mounted vertically in the base of the aircraft to cover the trackline (10 megapixel Nikon D200 with 35mm lens); camera was situated behind a Perspex window. In addition in the final survey year (2009/10) two Nikon D300 cameras (12 megapixel with 50mm lens) were mounted at the side windows obliquely at an angle of 45 degrees (please note side-camera was used only during final season of survey, Dec 2009-Feb 2010). Focus set to infinity, and image settings given to account for high-light, high-contrast environments. GPS/altitude data was embedded in each images EXIF information. Still image coverage underneath the aircraft was uninterrupted along the trackline with a shutter-release of around 1 photograph per second and a swath width of around 157 m. Similarly the oblique mounted cameras had a coverage over 450 m each side of the trackline (i.e., configured to be approximately the same as the human observers).\n\n4. Video cameras\n\nA number of streampix video files. Two high definition video cameras (Prosilica GC1350C GigE with 5mm F1.4 lens) were also fitted to the aircraft. Streampix is propriety software.\n\n5. Infrared \nA number of .mov files recorded from an Infra-red camera (FLIR Photon 320 with 9mm lens) mounted in the base of the aircraft. Infrared camera was situated behind an infrared window. \n\n6. Telemetry\nA number of text files (.txt) containing aircraft telemetry (yaw/roll etc) and gps. The telemetry is not that reliable, nor does it go anywhere close to covering all flights conducted (see below), but included for completeness.\n\n7. Flight data\n'dat' files dumped from the aircraft flight recorder containing flight data, including geographical position, velocity and altitude. These are ascii files. \n\n8. GPS data\nIn addition to flight and telemetry data, we've also included two post-processed GPS data files (two .csv files, one for each survey season). These files contain GPS data from a number of sources; this was to help buffer against GPS drop-outs. Therefore, this data is much more complete than the telemetry and flight data, and has been corrected for any time syncing issues. \n\n9. \"Season_overview_2010.xls\" \nThis Excel spreadsheet file contains details on each transect, effort and other sighting information. It accompanies the .csv files for the 2009/10 season as an overview. (A similar summary does not exist for 2008/09 season.)", "links": [ { diff --git a/datasets/ASAC_2942_1.json b/datasets/ASAC_2942_1.json index fd36712f8d..35535073dc 100644 --- a/datasets/ASAC_2942_1.json +++ b/datasets/ASAC_2942_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2942_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2942\nSee the link below for public details on this project. \n\nPublic \nEcological sustainability of fisheries is a primary goal for managing human activities in the marine environment. Management decisions must be based on clear, operational objectives and reliable assessment methods that are robust against uncertainties in our understanding of how the ecosystem functions, measurement error and natural variation. This project aims to provide the tools for developing management procedures for Antarctic and Sub-Antarctic fisheries. These tools will include a flexible set of ecosystem and food web models of the Antarctic and Sub-Antarctic regions for testing management procedures before they are put into practice.\n\nProject objectives:\nPROJECT THEMES AND KEY QUESTIONS\n\nThe themes of this work aim to provide a framework for determining how to manage Antarctic fisheries, such as Antarctic krill, Patagonian toothfish and mackerel icefish:\na. Models of the Antarctic Marine Ecosystem\nb. Biology and Ecology of Fish in the Southern Indian Ocean\nc. Management Procedures for Antarctic Fisheries\n\na/ Models of the Antarctic Marine Ecosystem\n\nKey Questions\n- What is the status of knowledge on Antarctic and Sub-Antarctic vertebrates and squid, including population status and trends, demography, and diet?\n- What plausible operating models can be developed for Antarctic and Sub-Antarctic systems that take account of spatial heterogeneity and temporal variability?\n\nb/ Biology and Ecology of Fish in the Southern Indian Ocean\n\nKey Questions\n- Fisheries-related research at Heard Island and McDonald Islands\n- What are the population dynamics of toothfish and icefish stocks in the region of Heard Island and McDonald Islands?\n- How does primary and secondary productivity vary spatially and temporally in the region? What are the primary causes of variability in the region?\n- What is the strength of interactions between land-based predators and commercially fished species in the region?\n- Ecology of fish in the southern Indian Ocean Fish of the Kerguelen Plateau /Crozet Region\n- What is the composition of fish fauna around and to the west of Kerguelen Plateau /Crozet ? What factors influence the distribution and abundance of these fauna?\n- What is the relationship between the distribution and abundance of fish fauna and the habitat features of the continental shelf in the Kerguelen region?\n\nc/ Management Procedures for Antarctic Fisheries\n\nKey Questions\n- What operational objectives could be used as a guide to managing fisheries in an ecologically sustainable way?\n- What are performance measures for target species and food webs?\n- What are the key parameters to monitor the status of the system to signal change before it becomes irreversible?\n- What quantitative/statistical methods are best used for assessing the status of populations and ecosystems?\n- How might an integrated modelling framework be designed to best facilitate the development of management procedures for Antarctic fisheries?\n- Could a spatially-structured management system, such as a mosaic of open and closed areas, enhance the approaches to managing fisheries in the Antarctic? \n\nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nThe Ecological and Resource Modelling and Fish and Fisheries Ecology group has made significant contributions to understanding the commercial fish stocks and ecosystem in the vicinity of Heard Island, has coordinated the fisheries observer program in CCAMLR waters, and has contributed significantly to the scientific work of CCAMLR, particularly in relation to ecosystem modelling, stock assessment and conservation of the Southern Ocean. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\na) Models of the Antarctic Marine Ecosystem\nFurther progress has been made on quantifying the spatial variation in the productivity of krill through the development of a krill productivity model in the Ecosystem Productivity Ocean Climate modelling framework.\nFurther estimation of population parameters for toothfish and icefish.\nFurther development of an ecosystem model for Heard Island.\nFurther work to develop and evaluate management strategies for Antarctic fisheries through the CCAMLR Working Group on Statistics, Assessments and Modelling (WG-SAM) of which Andrew Constable is the Convenor.\n\nb) Biology and Ecology of Fish in the Southern Indian Ocean\nTwelve months of intensive processing of otolith samples has resulted in the preparation and ageing of over 5,000 otoliths of Patagonian toothfish (Dissostichus eleginoides), collected over the past 10 years from the Heard Island - McDonald Islands (HIMI) fishery and the Macquarie Island fishery. Patagonian toothfish otolith ageing and microchemistry work will continue over a number of years.\n\nFurther processing, maintenance and cataloguing of the collection of preserved fish specimens has been undertaken.\n\nWork has also continued on the processing and cataloguing of benthic samples collected by observers or field work previously undertaken in the HIMI region. This work continues to increase the known diversity of benthic fauna of the HIMI region.\n\nc) Management Procedures for Antarctic Fisheries\nContribution of advice to the Subantarctic Resource Assessment Group of AFMA.\n\nContribution of advice to the CCAMLR Working Groups, WG-FSA and WG-EMM on the evaluation of management strategies and assessments of stocks for fisheries in which Australia is involved, including Heard Island as well as in new and exploratory fisheries in higher latitudes.\n\nContribution in WG-FSA and SC-CAMLR on issues pertaining to the CCAMLR observer scheme and at sea implementation of conservation measures. Dirk Welsford is the Co-Convenor for the CCAMLR Technical Group on At-Sea Operations (TASO) which held its first meeting in July 2008 and reports directly to SC-CAMLR and its Working Groups.\n\nContribution from project 2942 to Australian Delegation to CCAMLR resulted in the following outcomes being achieved:\n1) Catch limit on BANZARE Bank was reduced to zero pending results of research fishing in 2009/10 (CONSERVATION MEASURE 41-07, 2009)\n2) Reduction of the catch limit and enforcement of the need for a recovery plan from a proposal by Japan to undertake fishing for scientific research in a closed area on Ob and Lena Banks. Australia endorsed the need for ongoing review of 'scientific fishing' programs such as those proposed by Japan.\n3) The need for systematic observation on all krill fishing vessels to achieve CCAMLR objectives and development of new conservation measure 51-06 for scientific observer coverage on krill fishery.\n4) Endorsement of the need for robust otolith sampling strategies and comprehensive age-length datasets as key inputs into Dissostichus assessments and the need for quality control processes in ageing programs.\n5) Endorsement of the Scientific Committee to use PATCH model to evaluate impacts of fishing on VMEs.\n6) Adoption of toothfish assessment strategy and results , and yield recommendations for next 2 seasons, (CONSERVATION MEASURE 41-08)\n7) Adoption for icefish survey assessment strategy and results and recommended yield for next season. (CONSERVATION MEASURE 42-02).\n8) Endorsements of current by-catch limits and rates of by-catches in 58.5.2 as being appropriate at mitigating significant impacts on skates.\n\nThe assessment for TOP (Candy and Constable, 2008) was revised using catch-at-age data as a key outcome of the FRDC funded work involving ageing of TOP catch and construction of age length keys at HIMI (Welsford et al, 2009; Candy and Welsford, 2009).", "links": [ { diff --git a/datasets/ASAC_2946_1.json b/datasets/ASAC_2946_1.json index 576202ba2d..2a48604b96 100644 --- a/datasets/ASAC_2946_1.json +++ b/datasets/ASAC_2946_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2946_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2946.\n \nPublic \nShallow nearshore marine habitats are rare in the Antarctic but human activities have led to their contamination. Preliminary studies suggest the characteristics of Antarctica nearshore sediments are different to elsewhere and that contaminant partitioning and absorption, and hence bioavailability, will also be very different. Predictive exposure-dose-response (effects) models need to be established to provide the theoretical basis for the development of sediment quality guidelines to guide remediation activities. Such a model will be possible through the development of an artificial 'living' sediment, which can be used to understand physical and chemical properties that control partitioning and absorption of contaminants.\n \nTaken from the 2009-2010 Progress Report:\nProject objectives:\n1. Collate and review existing knowledge on sediment properties in nearshore marine sediments in Antarctica to determine their physical, chemical and microbiological properties and identify gaps in our knowledge of sediment characteristics\n \n2. Construct a range of artificial sterile sediments taking into account characteristics of naturally occurring nearshore sediments in the Antarctic. Examine physical and chemical properties of these sediments and understand the properties that control partitioning of contaminants by manipulation of bulk sediment composition and measuring the adsorption isotherms of important metal contaminants (Cu, Cd, Pb, As, Sn, Sb) in these artificial sediments\n \n3. Produce 'living' sediments by inoculation of sterile sediments with Antarctic bacteria and diatoms that will support natural microbial communities. Examine physical and chemical properties of these sediments and understand the properties that control the partitioning and absorption of contaminants by manipulation of the bulk sediment composition and spiking metal contaminants into these artificial sediments.\n \nProgress against objectives:\nUsing published literature the approximate composition of Antarctic sediments was determined. Representative sediment phases were collected form a uncontaminated environment, the chemical composition measured and absorption capacities of Cd and Pb established.\n\nThe download file contains several excel spreadsheets. Some information about them is provided below:\n\nMy =ref is reference in thesis\nEN =is endnote reference\n\nNearby station = is closest known reference point to where samples collected\nTOC = total organic carbon\nTOM = Total organic matter\nBPC =biogenic particulate carbon\nTN = total nitrogen\nTP = Total phosphorus\nBSi = biogenic silica\nCi = initial aqueous phase concentration\nqe = solid phase equilibrium concentration", "links": [ { diff --git a/datasets/ASAC_2952_field_lab_books_1.json b/datasets/ASAC_2952_field_lab_books_1.json index 43f4a158e7..d990e8a474 100644 --- a/datasets/ASAC_2952_field_lab_books_1.json +++ b/datasets/ASAC_2952_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2952_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station, Macquarie Island and Kingston between 2007 and 2012 as part of ASAC (AAS) project 2952 - Spatial variability in polar soil ecosystems: An integrated study of genes, microbial biodiversity and landform evolution as a baseline for monitoring climate change.", "links": [ { diff --git a/datasets/ASAC_2960_1.json b/datasets/ASAC_2960_1.json index ba6096137d..bd9057ecb1 100644 --- a/datasets/ASAC_2960_1.json +++ b/datasets/ASAC_2960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_2960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2960\n\nSee the link below for public details on this project\n\nPublic \nThe ocean's thermohaline circulation (THC) plays a fundamental role in global climate, transporting heat poleward and regulating the uptake of anthropogenic CO2. Multiple steady-states in the THC have been identified in the North Atlantic, including an \"off\" state where no deep water is formed, yet little is known regarding the possibility for multiple equilibria of the Southern Ocean THC. This study aims to (1) examine hysteresis behaviour and possible multiple equilibria of the Southern Ocean THC, and (2) quantify the role of the Southern Ocean THC by examining the difference between \"on\" and \"off\" states in various water-masses.\n\nProject objectives:\nThe overarching goal of the proposed study is to explore the possibility of multiple steady-states of the Southern Ocean (SO) thermohaline circulation (THC) and to explore their role in the global climate system. Multiple steady-states in the ocean's THC have been identified in the Northern Hemisphere [e.g., Marotzke, 2000; Rahmstorf, 2002]. While substantial climate variability and change can be inferred from palaeoclimate data for the Southern Hemisphere, our understanding of the underlying physics of SO THC variability and the associated climate dynamics remains limited. It is also unclear how the Southern Ocean THC will change in the future. This study aims to:\n\n1. Examine the hysteresis behaviour of the Southern Ocean thermohaline circulation in relation to surface freshwater forcing, both for AABW and AAIW,\n\n2. Explore the possibility for multiple steady-states in the Southern Ocean THC,\n\n3. Estimate how the present-day Southern Ocean THC may be changing in relation to this hysteresis diagram, and how this relates to global climate, and\n\n4. Quantify the role of the present-day Southern Ocean THC by examining the difference between \"on\" and \"off\" states. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nProgress on this Antarctic Sciences project during 2008/2009 can be summarised as below. Each of the four main aims have been touched upon during the past 12 months, although the most significant progress has been against items 1, 3, and 4 as listed in Section 1.1 above.\n\nThe existence of teleconnections of Southern Ocean freshwater anomalies to the North Atlantic THC was investigated, primarily in the context of past climates (Trevena, Sijp and England, 2008a). We found that a Southern Ocean freshwater pulse of comparable magnitude to meltwater pulse 1A, shuts down, instead of strengthens, NADW in a glacial climate simulation. Unlike a modern-day simulation, the glacial experiment is associated with a more fragile North Atlantic thermohaline circulation, whereby freshwater anomalies that propagate into the North Atlantic are able to dominate the bipolar density see-saw.\n\nThe possibility for large-scale collapse and/or multiple steady-states in the Southern Ocean THC was also investigated using a coupled climate model of intermediate complexity. Also investigated was the impact of a slowdown of Antarctic Bottom Water (AABW) on regional Southern Hemisphere climate. This involved the gradual addition of meltwater anomalies to strategic locations of the Southern Ocean, then removal of these anomalies to explore whether the regional thermohaline circulation (THC) exhibits saddle-node instabilities (bifurcation points) as have been commonly found for the North Atlantic. We found that no stable AABW \"off\" state could persist, regardless of the freshwater anomaly imposed. We did, however, identify a significant impact on regional climate during the transient slow down of AABW (Trevena, Sijp and England, 2008b). In particular, during peak FW forcing, Antarctic surface sea and air temperatures decrease by a maximum of 2.5 degs C and 2.2 degs-C respectively. This is of a similar magnitude to the corresponding response in the North Atlantic. \n\n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nProgress on this Antarctic Sciences project during 2009/2010 can be summarised as below. Each of the four main aims have been touched upon during the past 12 months, although the most significant progress has been against items 2 and 4 as listed in Section 1.1 above.\n\nA large set of experiments were configured and analysed to examine Southern Ocean THC states in the global climate system. Specifically we conducted experiments using the Canadian University of Victoria Earth System Climate Model (the 'UVic' model) wherein the model is perturbed in some way to explore the possibility for multiple steady-states in the Southern Ocean THC. Where multiple steady states were obtained, the difference between \"on\" and \"off\" states was examined to quantify the role of the Southern Ocean THC in global climate.\n\nThree papers were published in the 2009/2010 period that were produced using support from this Antarctic Research project:-\n\nSijp, W. P., M. H. England, and J.R. Toggweiler, 2009: Effect of ocean gateway changes under greenhouse warmth, J. Climate, 22, 6639-6652.\n\nIn this study Southern Ocean gateway changes and the THC were examined under a suite of atmospheric CO2 levels, spanning pre-industrial (280 ppm) up to values relevant to the Eocene (1500 ppm). A markedly stronger gateway response is found under elevated CO2 levels, suggesting past work has underestimated the effects of gateway changes at the Oligocene-Eocene boundary.\n\nSen Gupta, A., A. Santoso, A.S. Taschetto, C.C. Ummenhofer, J. Trevena and M.H. England, 2009: Projected changes to the Southern Hemisphere ocean and sea-ice in the IPCC AR4 climate models, J. Climate, 22, 3047-3078.\n\nIn this study simulations of the Southern Ocean THC, water-masses, and mixed layer depth were examined and compared across a series of IPCC-class global climate models, under both present-day and climate change scenarios.\n\nSijp, W. P. and M. H. England, 2009: The control of polar haloclines by along-isopycnal diffusion in climate models, J. Climate, 22, 486-498.\n\nIn this study the ocean THC was shown to be sensitive to along-isopycnal diffusion rates in global climate models. This potentially impacts on past studies wherein multiple equilibria were obtained at unrealistic values of this mixing parameter.", "links": [ { diff --git a/datasets/ASAC_3010_2.json b/datasets/ASAC_3010_2.json index 982897f13a..d5649923f7 100644 --- a/datasets/ASAC_3010_2.json +++ b/datasets/ASAC_3010_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3010_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3010.\n\nPublic \n\nPycnogonids are primitive, bizarre arthropods. Found worldwide, Antarctic pycnogonids are the most diverse, abundant, and include some of the most spectacular forms. Near 250 species from the region are known, many in need for taxonomic revision, and more species new to science likely to be found. This project will document diversity of pycnogonids and target widely distributed species to obtain morphological, genetic and ecological information on distribution patterns and evolutionary history. This combined approach should provide a better insight of the roles of sea spiders in Antarctic biodiversity and the evolution and radiation of Antarctic marine benthic fauna.\n\nProject objectives:\n1. To document the diversity of Australian Antarctic pycnogonids at species level and to target species with potential to investigate ecological interactions, zoogeographical patterns and genetic variability.\n\n2. To examine connectivity patterns and genetic differentiation in populations of target species of pycnogonids across large spatial scales inferring diversification processes and possibly speciation rates.\n\n3. To investigate the distribution patterns and possible mechanisms of dispersal of species with apparent wide distributions (e.g. circumpolar distribution, Antarctic -Pacific distribution and Antarctic-Arctic), based on molecular tools.\n\n4. To explore how sea spiders fit evolutionary models testing the origin of deep sea fauna and proposing hypothesis for colonisation mechanisms and radiation processes, as many pycnogonid taxa from the deep sea are also represented on the continental shelf.\n\n5. To resolve phylogenetic questions regarding the affinities among Antarctic species and lower latitude species to understand the evolutionary history of a highly diverse and cosmopolitan lineage (Callipallenidae-Nymphonidae).\n\nDetails from previous years are available for download from the provided URL.\n\nTaken from the 2009-2010 Progress Report:\n\nObjective 1\n- During this second year of the project more than 500 lots of unsorted samples of pycnogonids are being sorted and identified, many to species level.\n-In July 2009, 130 lots from the Ross Sea and Subantarctic areas deposited at NIWA in NZ, were sorted, identified and many of them barcoded. Some material has been requested on loan to continue taxonomic studies probably leading to description of new species.\n\n-In November 2009, more than 330 lots of CEAMARC samples of sea spiders were received on loan from the Natural History Museum in Paris, where they were deposited in 2008. This material is extremely relevant not only for its diversity but also numbers of individuals per sample. CEAMARC samples (including additional 136 samples from AAD) have provided a unique opportunity to obtain appropriate numbers of individuals of target species such Nymphon australe, with more than 1000 individuals collected. This material is currently being used in analyses about genetic differentiation and diversity at different spatial scales.\n\n-Current work in progress on the species level identification of the CEAMARC material would lead to a proper characterisation of the pycnogonid fauna from an extremely important area of the Australian Antarctic territory. We have identified Nymphon australe, Colossendeis megalonyx, Nymphon spp., Austropallene spp. and Pallenopsis spp, as the most frequent and abundant Australian Antarctic pycnogonids and it is expected to correlate abundance and occurrence patterns to other biotic and abiotic parameters that could explain the numbers and diversity of these taxa in the area.\n\n- I co-authored a pioneering paper with H. Griffiths (senior author) from BAS and others, on the diversity and biogeography of Antarctic pycnogonids, which was submitted last month to journal Ecography.\n\n- At least two new species to science are to be described based on CEAMARC material currently studied.\n\nObjective 2\n\n-There is a publication in press (Arango et al.) in the journal Deep-Sea Research II presenting a genetic analysis of the most abundant Antarctic sea spider species Nymphon australe. The study includes 131 individuals of N. australe collected from Antarctic Peninsula, Weddell Sea and East Antarctica.\n\n-Additional material of N. australe from CEAMARC made available by MNHN in Paris is currently being analysed to expand the published study and focus on the possible explanations for such wide distribution of a species with apparent limited dispersal capabilities.\n\n- Just recently, I established research collaboration with Dr. F. Leese at Ruhr University Bochum, Germany, who is currently interested in the population genetics and genetic connectivity of Antarctic sea spiders. This collaborative effort should prove to be very successful in terms of geographic cover of samples, molecular markers used and analyses implemented.\n\nObjective 3\n\n-The paper in press mentioned above addresses the question of circumpolarity of N. australe and finds it might be one of the few 'true' circumpolar species given that the dataset does not reflect cryptic speciation. Preliminary data for other species are showing contrasting results and might reflect 'unknown' species considered cryptic or perhaps just reflects necessity of fine detail taxonomy--. This work on Colossendeis megalonyx is partly in collaboration with Leese's team in Germany.\n\n-Material from New Zealand, Tasmania and NSW are currently used for analysis on phylogenetic affinities between Antarctic and non-Antarctic taxa, and also to compare patterns of genetic differentiation among different habitats and taxa. Achelia species distributed from Antarctica to tropical areas will be looked at in a future project depending on funding.\n\nObjective 4\n\n-Objective 4 part of a proposal submitted to Australian Biological Resources Study (ABRS) to study deep phylogeny and divergence times of Pycnogonida to understand evolutionary links between Antarctic, deep-sea and Australian shallow waters species, in collaboration with J. Strugnell. During the first and second year of the project advances have been made in terms of literature review, discussion with specialists and most importantly acquisition of material for molecular work that will complement the dataset published in 2007 (Arango and Wheeler 2007).\n\nObjective 5\n\n- Since September 2009 I have been actively working on constructing datasets for phylogenetic analyses of Nymphon, the most diverse and abundant taxon of sea spiders in the world, and their closest relatives, the callipallenids, with centre of diversity in Australasia. I am working on including morphological and molecular characters for as many representative species as possible. So far, 30 species are included, and at least 50 morphological are being scored. More species are desired, so I am permanently seeking donation of material, collaborations, etc. the genes COI and 16S are sequenced for at least 50% of the samples included so far, I am currently investigating other molecular markers that might be suitable to resolve a phylogeny at this level.\n\n- Given the availability of material from many different species of Colossendeidae, and the relevance and impact of this group --being the family of the giant sea spiders, I am currently collecting material (i.e. tissue samples, DNA sequences, morphological descriptions) to work on the phylogeny of this cosmopolitan family with more than 40 species in the Southern Ocean. At least 15 species have been sequenced so far. The same techniques and methodology as for the Nymphon phylogeny are being applied.", "links": [ { diff --git a/datasets/ASAC_3022_1.json b/datasets/ASAC_3022_1.json index 30355c95c5..f0fe95ee2f 100644 --- a/datasets/ASAC_3022_1.json +++ b/datasets/ASAC_3022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3022.\n\nPublic \nThe Vestfold Hills contains a suite of marine derived brackish to saline lakes that have simple food webs dominated by microorganisms, including dinoflagellates that are members of the phytoplankton. The lakes possess differing salinities that impact on other physical and chemical characteristics so that the original marine creatures have been subject to differing evolutionary pressures that have resulted in the evolution of distinct strains of dinoflagellate in each lake. We will look at the degree of speciation in dinoflagellates and their ability to colonise different lake environments.\n\nTaken from the 2008-2009 Progress Report:\nProject objectives:\nSPECIFIC OBJECTIVES\nThe evolution and biogeography of macroorganisms has been investigated for more than two centuries. While for micro-organisms these issues have only recently received attention. Currently there is a heated debate as to whether free-living microbes are present in all environments that they can exploit (everything is everywhere - but the environment selects) or whether they exhibit biogeographic patterns due to geographical isolation, natural selection, or invasion sequence. We propose approaching this controversy by studying two of the fundamental mechanisms that are known to generate biogeographic patterns in macroorganisms: a) colonization and b) subsequent genetic divergence due to new environmental conditions (selection) and/or genetic isolation. As model organisms, we will use dinoflagellates, an ecologically and economically important group of phytoplankton. With their short generation time and ability to switch between asexual and sexual reproduction they are ideal for experimental evolution studies. We will work with strains of dinoflagellates from the suite of marine derived brackish to saline lakes in the Vestfold Hills. These lakes have simple microbially dominated food webs and offer us a unique natural laboratory in which to test a series of hypotheses outlined below:\n\n- Lake dinoflagellates have diverged rapidly among themselves and from their marine ancestors since the formation of the lakes in the last 10,000 years. Local adaptation to different lake conditions has driven the genetic and phenotypic divergence between populations, and between lake populations and their marine ancestors.\n\n- Lake populations out-compete marine strains, thereby preventing the re-colonisation of lakes by marine immigrants.\n\n- The populations from the different lakes are reproductively isolated among themselves and from their marine ancestors.\n\nBiogeography is the study of biodiversity over space and time and attempts to elucidate processes such as speciation, extinction, dispersal, and species interactions (Hughes Martiny et al. 2006). Although there is a consensus on the existence of biogeography in macroorganisms, the biogeography of microorganisms remains debated. Proponents of the 'everything is everywhere - but the environment selects' (Baas Becking 1934) argue that aquatic microorganisms are cosmopolitan, i.e., have no dispersal limitation and low global species diversity (Finlay 2002). They claim that due to the small size and huge abundance of unicellular organisms, there are no barriers for their dispersal and gene flow, and consequently no allopatric speciation (Fenchel 2005). However, recent studies dispute the idea that 'everything is everywhere'. Several reports using molecular techniques show unexpectedly high microorganism biodiversity (Fawley et al. 2004; Venter et al. 2004) and that they may exhibit biogeographic patterns (Pommier et al. 2005; Whitaker et al. 2003). Evidence from our research suggests that natural selection can give rise to speciation in phytoplankton in a very short time period (less than 10,000 years) (Logares et al. 2007). Within this proposal we will focus on some processes that shape biogeography in aquatic eukaryotic organisms.\n\nDINOFLAGELLATES AS MODEL ORGANISMS\nDinoflagellates occur both in freshwater and marine ecosystems and can form intense blooms. They are important components of the planktonic food web, and are considered high food quality to predators. Toxic dinoflagellate blooms in marine habitats are a major environmental and economic problem worldwide e.g. (Hallegraeff 1993), and hence of major scientific interest. Dinoflagellates have a reproductive system of alternating asexual and sexual reproduction, and many species have a resistant and long-lived resting propagule (cyst) (Pfiester and Anderson 1987). Most important for this proposal, however, is that dinoflagellates are ideal for experimental evolution studies. They can be cultured, they have a short generation time (\n\n\nBIOGEOGRAPHY AND THE SPECIES CONCEPT\nA central problem when debating microbial biodiversity is the lack of a definite and operational species concept and taxonomic unit. In unicellular organisms, the widely used biological species concept (BSC) is rarely applied, since many species reproduce asexually. Instead the morphological species concept, the 'morphospecies', prevails. The problem with the morphospecies concept is that similarity in appearance does not necessarily mean that they are evolutionarily closely related. Microorganisms (such as phytoplankton) simply have few morphological characteristics that are useful for species characterisation. For example, many phytoplankton are spherical and green, and are simply referred to as 'small round greens'. As a result, phytoplankton taxonomists and ecologists have lumped together things that look alike within one species. Thus, the relationship of lower species richness with decreasing size may or may not therefore be an artifact of taxonomic lumping.\n\nThere is growing evidence that variation within a single algal morphospecies can be relatively large. Modern phylogenetic molecular studies on phytoplankton show that many morphospecies are in fact composed of several genetic lineages, also known as cryptic species (Montresor et al. 2003b). For instance, Coleman (2001) showed that there are at least 30 sexually isolated groups of the Pandorina/Volvulina species complex. Fawley et al. (2004) analogously did not detect so called cosmopolitan species in a big survey on green algae, but found several hundred new isolates with restricted distributions. Moreover, Kim et al. (2004) found that two dinoflagellate populations belonging to the same species, but with different physiological requirements were genetically distinct comparable to species level differences, despite being separated by only 400 m.\n\nAlthough the use of molecular markers has revolutionised the view on microbial diversity and phylogeny, the choice of markers must be done with caution. For instance, while no differences may be found in the small subunit (SSU) of the ribosomal DNA, large differences can be found within the less conserved ITS region (Cho and Tiedje 2000; Kim et al. 2004) For example two distinct morphospecies (Peridinium aciculiferum and Scrippsiella hangoei) present in different habitats (freshwater and Baltic Sea) were found to have identical ribosomal rRNA sequences (Logares et al. 2007). However, the two species could be separated based on cytochrome b mitochondrial DNA sequences and Amplified Fragment Length Polymorphism (AFLP) (which operates on the entire genome). This indicates a case of rapid adaptive evolution, but also emphasizes the need to use a combination of molecular markers. Drettman et al. (2003a) showed that a multilocus genealogical approach in the fungal genus Neurospora allowed to identify traditional biological species. Another important finding was that they could show that phylogenetic divergence could precede reproductive isolation (Drettman et al. 2003b).\n\nCAN PHYTOPLANKTON DISPERSE AND EASILY COLONISE NEW WATER BODIES?\nAn assumption of the cosmopolitan view is that all microorganisms disperse easily and have a high environmental tolerance. The basis for this view is studies that show a huge number of microorganisms being transported in the air (Griffin et al. 2002) or water (e.g. Lindstrom et al. 2006). However, Hughes Martiny et al. (2006) found no clear correlation between body size and dispersal capacity and concluded that while some microbes disperse widely, others may have limited dispersal.\nDispersal of planktonic protists and algae can occur through three major mechanisms; by water, air, or organisms (Kristiansen 1996). Coleman (1996), for instance, showed distinct genetic groups of a green alga, which showed patterns correlating to bird migratory patterns. Although many microorganisms undisputedly disperse far by birds, to date, there is no evidence on how many and which kinds of species actually survive dispersal by air or organisms. In a recent experiment, we were able to show that dinoflagellate vegetative cells were not able to survive the passage through a bird gut, while their resting cysts survived and germinated (Weissbach and Rengefors, unpubl).\nAnother key concern with the cosmopolitan view is that it is assumed that dispersal leads to colonisation of new habitats. However, the findings of Maguire (1963) suggest that only a limited number of the small aquatic species being dispersed actually colonise new habitats. De Meester (2002) argues that despite high ability to disperse and rapid colonisation of some limnic zooplankton, it is very unlikely that it will also colonise the new habitat, since the endemic populations likely will have an adaptive advantage over the coloniser. De Meester refers to this as the Monopolisation Hypothesis. Further, the presence of a large resting propagule bank provides a buffer against newly invading genotypes enhancing the priority effect. Many phytoplankton species, including dinoflagellates, produce long-lived resting propagules, indicating that the Monopolisation Hypothesis may apply to phytoplankton as well.\n\nSPECIATION IN MICROORGANISMS\nThe adherers of the cosmopolitanism view argue that because there are no geographic barriers to dispersal of microorganism, allopatric speciation is rare in unicellular organisms due to the homogenising action of gene flow. However, allopatric speciation is only one of the recognised speciation modes. We argue that genetic divergence and ultimately speciation in unicellular organisms, such as freshwater phytoplankton is more frequent and rapid than claimed. First of all, due to their shorter generation time, speciation can be quicker in microbes. Moreover, the large population sizes of microbes can harbour a very high genetic variability upon which natural selection can act, leading to a rapid adaptive divergence. Hairston et al. (1999) established that rapid evolution may occur within certain systems or species due to strong selection pressure. Likewise, Whitaker et al. (2003) showed recent divergence in microorganisms in geothermal spring.\nSecondly, many eukaryotic phytoplankton species, such as the dinoflagellates, have a life cycles promoting rapid genetic differentiation. These life cycles consist of alternating asexual and sexual reproduction. Speciation due to strong local adaptation is hypothesised to be more common in species with alternating sexual and asexual reproduction (De Meester et al. 2002). Due to the combination of sexual recombination generating genetic diversity, and clone formation propagating the entire genome, certain traits are more likely to become permanent in these species.\nThirdly, limnic phytoplankton are especially interesting to study as lakes may function as ecological islands, i.e. isolated entities to which colonisation is restricted, at least if they have a long turnover time. Even when lakes are in close vicinity of each other, dispersal and colonisation can be effective barriers as argued above. Thus populations may become reproductively isolated, as reproductive isolation is considered (by some researchers) to be a prerequisite for maintaining species integrity in sexually reproducing species.\n\nPRELIMINARY RESULTS\nWe have conducted preliminary work in the Vestfold Hills of Antarctica. This coastal ice-free area contains suites of freshwater and saline lakes. This suite of saline lakes has several characteristics that make them ideal for speciation studies: 1) The lakes formed as a result of isostatic rebound about 10, 000 years ago and consequently the dinoflagellate assemblage derives from relic marine populations. 2) The planktonic food web in these lakes is severely truncated, with few competitors and predators. 3) The lakes vary in salinity from brackish to hypersaline (10x seawater), and are ice-covered most of the year. 4) The area is remote from other limnic habitats (limited dispersal sources).\nIn 2004/5 we collected and isolated dinoflagellate cells from Lake Abraxas and Ehko Lake. Several clonal cultures were established for two different species in each of the lakes sampled. Microscopic and preliminary sequence analyses of the SSU rDNA have allowed us to identify them as Polarella glacialis and a Scrippsiella sp. The former is a species with bi-polar distribution found both in the sea-ice and the water (Montresor et al. 2003a). The other is yet unidentified to the species level. Our first AFLP analyses of the different strains showed a promising pattern. Lake Polarellas were very different in their band pattern from strains isolated from the sea. Moreover, strains differed more among lakes than within lakes.\n\nWe propose sampling and establishing cultures from a range of other lakes across a salinity spectrum (Highway, Pendant, Williams, Watts, Lebed, Ace) and establishing clonal cultures for return to our laboratories for further analysis. This will involve both molecular analysis at Lund and physiological investigations at the University of Tasmania.\n\nThe dinoflagellates present in the Vestfold Hill lakes have undergone rapid divergence after the lakes became isolated from the sea. The selection pressures are very different in these lakes compared to the sea; i.e. a large relatively homogeneous habitat (the sea) in contrast to smaller habitats (lakes) with strong natural selection in different directions. Instead of thousands of dinoflagellate species competing as in the sea, these lakes contain only a handful of species. Predation pressure is likely much lower, with only one metazoan zooplankton and a few unicellular potential predators. Finally, the chemical composition (nutrients and salinity) and the light climate differ from the sea, being both more oligotrophic and ice covered to a higher extent. Nevertheless, cysts do disperse by wind from the sea and could potentially colonise these lakes continuously at times when they are ice-free (Downs, 2004 unpublished Ph.D.thesis).\n\nProgress against objectives: Please describe the progress you have made against each objective in the last twelve (12) months.\nThe postdoctoral scientist at Davis has established a significant clonal collection of dinoflagellate cultures from a range of lakes and the sea. These will be returned to Lund University (Sweden) for molecular analysis.\n\n\nThe download files contain an excel spreadsheet of data, a word document containing a table of data, as well as details on the methods used to collect the data, and a copy of the referenced publications with a manuscript (the latter is available to AAD staff only).", "links": [ { diff --git a/datasets/ASAC_3025_1.json b/datasets/ASAC_3025_1.json index 9e15188f58..1ca31e57ee 100644 --- a/datasets/ASAC_3025_1.json +++ b/datasets/ASAC_3025_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3025_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 3025.\n\nPublic\nAn ice core drilling expedition is proposed for Aurora Basin, between Law Dome and Dome C. This will provide a climate record in excess of 2000 years and will be used to compare coastal and inland Antarctic records. This will improve interpretation of ice core climate records and increase our knowledge of the role of Antarctica in the global climate system.\n\nProject objectives:\nThe overall goal for this project is to recover a 2000 year plus climate record from a site ('GC41') in Aurora Basin, inland East Antarctica.\n\nThe project aims to achieve a number of objectives:\n1 To provide a new, high resolution accurately dated, ice core climate record (greater than 2000 years) from the sparsely explored Aurora Basin region in the East Antarctic sector;\n\n2 To gain an improved synthesis of the regional climate signals through better connection between the Law Dome (coastal) and EDC (inland) climate records in the pre-industrial late Holocene and into the period of anthropogenic climate change;\n\n3 To provide better interpretation of ice core records through comparison of deposition and preservation mechanisms from the high accumulation coastal zone through to the low accumulation interior;\n\n4 To contribute towards locating a site for drilling a very old record, in excess of 1 million years;\n\n5 Finally, although not an objective with immediate scientific return, this project is designed to demonstrate and develop remote ice coring logistical capabilities using Australia's new combined inter-/intra-continental air transport system.\n\nFigure 1: Map of Antarctic ice sheet thickness showing selected Australian traverse lines and the location of GC41 (71o36'10\"S 111o15'46\"E 2791m elevation) which is ~600km inland of Casey Station.\n\nAcronyms/Notation used throughout Section 3:\n\n[Objective 1] - indicates the accompanying text specifically serves project objective 1.\nAAD - Australian Antarctic Division\nACE - Antarctic Climate and Ecosystem\nAGCS - Antarctica and the Global Climate System AINSE - Australian Institute of Nuclear Science and Engineering AME - Antarctic Marine Ecosystems ANSTO - Australian Nuclear Science and Technology Organisation AWS - Automated Weather Station\nCO2 - Ocean control of Carbon Dioxide (Program of ACE-CRC) CRC - Cooperative Research Centre CVC - Climate Variability and Change (Program of ACE-CRC) DRI - Desert Research Institute EDML - EPICA Dronning Maud Land EDC - EPICA Dome C EPICA - European Project for Ice Coring in Antarctica\nIGBP- International Geosphere-Biosphere Programme\nIOAC - Ice, Ocean, Atmosphere and Climate (Program of AAD) IPCC - Intergovernmental Panel on Climate Change IPICS - International Partnerships in Ice Core Sciences IPY - International Polar Year ITASE - International Trans-Antarctic Scientific Expeditions MSA - Methanesulphonic acid PAGES - Past Global Changes SCAR - Scientific Committee for Antarctic Research SOE - Southern Ocean Ecosystem (Program of AAD)\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nIce core drilling at Aurora Basin (site GC41) was not achieved, however ice cores were recovered from Law Dome (site W10k, 127m; site DSS 10m), Mill Island (site MI 17m) and Totten Glacier (site TOT1 17m; TOT2 15m).\n\n(a) Planning changes prior to getting into the field The shortened flying season due to a medical incident at Davis initially led to a clash for limited C212 resources on Casey station between the proposed ABN project and other C212 operational requirements (mainly the whale counting project). The whale counting project was given preference and it was suggested to modify the ABN project to reduce C212 requirements. A modified ABN project was proposed and accepted with the following changes:\n- No mid-season changeover of personnel (this essentially ended the participation of the international field personnel)\n- Lighter camp requiring less C212 deployment flights (we removed all field-based ice core processing tasks and consolidated our living arrangements, reducing power requirements and therefore fuel, generators and tents)\n- Shortened our field season requiring less fuel/food (again removing ice core processing allowed for a faster drilling rate)\n\nThis proposed modified project would achieve the primary goal of retrieving an ice core, however it lost two very important components:\n- International field participation (4 persons, Denmark and USA)\n- Field based ice core processing\n\nThe medical incident at Davis led to delays in the A319 season and field personnel travelled to Casey by ship on V2 (instead of the planned A319 transport).\n\n(b) Planning changes on getting into the field All 8 ABN field personnel arrived at Casey station on 1st December 2008. At Casey a number of reconnaissance flights and a skidrag were undertaken to Aurora Basin, however, we could not access our original GC41 drilling site. The topography at the site was considered too rough to land an aircraft. The surface was very different than that from reconnaissance in 2006/2007 and may be due to un-seasonal storm activity in the area? Further reconnaissance in the area failed to find a suitable landing site. These operational reasons led to withdrawing of the ABN project for 2008/2009.\n\nThis was not an issue exclusive to the C212 aircraft - it was the opinion of many that even the best available aircraft could not have landed at this site. The C212 aircraft proved very suitable for landing at other green-sites (Totten Glacier and Mill Island) and were a very easy platform to work from. We fully support C212 aircraft for this type of work.\n\nThis raises the issue of how to access Aurora Basin? Possibilities of how to access a site to groom a skiway need to be explored, including using traverse (either our own or possibly the French or a combination, and consideration of a lightweight traverse) or other aircraft (e.g. helicopters, twin otter etc).\n\nTaken from the 2009-2010 Progress Report:\nThis year was spent processing and analysing the ice cores collected in the 2008/2009 field season. As stated in last years progress report (AAS 3025), this analysis was reported on in AAS 757, and will also be reported on here. This will result in duplication between this report and progress reported on in AAS 757.\n\nApproximately 80% of the core processing and analysis has been achieved, including the handling of just over 19,000 samples (see table 2). It has been a busy year in the laboratory and a student (Chris Plummer) processed 2 of the PICO cores as part of an honours study at the University of Tasmania. Since this project Chris has come on board as a PhD student. This thesis was entitled 'The effect of snow accumulation rate on trace ion chemistry records on Law Dome'. Chris found that the chemistry species were predominantly wet deposited across Law Dome indicating that despite the accumulation differences, ice cores across the dome are sampling the same air mass.\nTessa Vance was employed to work on processing and analysing the ice cores for this project and has done a large amount of work on these cores. Tessa will finish up soon and begin a post-doc with us at the ACE CRC. We will hire a technical replacement for Tessa to assist Barbara Frankel to complete the remaining laboratory work.", "links": [ { diff --git a/datasets/ASAC_3030_1.json b/datasets/ASAC_3030_1.json index 36deb9cbd4..8b31762605 100644 --- a/datasets/ASAC_3030_1.json +++ b/datasets/ASAC_3030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from ASAC project 3030.\n\nPublic summary for the project:\nThis project will measure the sea ice thickness off East Antarctica, over spatial scales up to hundreds of kilometers. Sea ice is a likely sensitive indicator of climate variations and change. No large scale sea ice thickness measurements exist in the Antarctic. An estimation of trends of change in Antarctic sea ice thickness and volume is therefore not currently possible. To address this deficiency and to provide an independent data set for the validation of models and the calibration of remote-sensing data, we will conduct high accuracy air borne laser scanner measurements in the sea ice zone off East Antarctica.\n\nMore information about the project can be found in lidar.pdf (which is available with the data).", "links": [ { diff --git a/datasets/ASAC_3064_1.json b/datasets/ASAC_3064_1.json index 868618f13b..421eaba87a 100644 --- a/datasets/ASAC_3064_1.json +++ b/datasets/ASAC_3064_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3064_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) Project 3064.\n\nPublic \n10Be and 7Be are naturally-occurring radioactive isotopes produced in the Earth's atmosphere and surface by cosmic rays, at a rate controlled by the activity of the Sun (and other factors). 10Be is long-lived while 7Be 'decays' much more quickly. Using the Air-link we now have an opportunity to return ice core samples containing the short-lived 7Be isotope to Australia for measurement. This project proposes to use 10Be and 7Be measurements in Antarctic ice to improve our interpretation of past solar activity and better understand its linkage to global climate change.\n\nProject objectives:\nCosmogenic 10Be (t1/2 = 1.5 x 10^6 years) from polar ice cores is the most reliable proxy for investigating past solar variability on decadal to millennial time scales [Muschler et al., 2007; Beer 2000]. However, interpretation of 10Be records is currently hindered by a poor understanding of the processes which deliver 10Be to the polar ice core archives [Muscheler et al., 2007; Bard and Frank, 2006]. The processes responsible for the transport and deposition of 10Be to the polar ice sheets must be understood in order to reconstruct an accurate history of solar activity.\n\nThis project uses a new technique, based on the rapid sample transport capability of the Air-link, to examine transport and deposition of 10Be. The Air-link opens the opportunity to retrieve and return to Australia in a reasonable time frame, samples of the shorter-lived cosmogenic Be isotope, 7Be (t1/2 = 53 days), alongside the longer-lived 10Be. The 10Be/7Be ratio can then be interpreted as a 'lock' for air mass age providing valuable information on atmospheric pathways and residence times [Dibb et al., 1994].\n\nThe project will collect six shallow (5 m) PICO cores from Dome Summit South (DSS) Law Dome. The samples will be delivered by plane at first opportunity to Hobart and then transferred directly to Australian Nuclear Technology Organisation (ANSTO) in Sydney for measurement of 7Be by gamma ray spectroscopy and 10Be by accelerator mass spectroscopy. There will be sufficient material for a high resolution record of 7Be spanning eight months and 10Be spanning four years.\n\nScientific objectives of the project are as follows:\n1. Use the 10Be/7Be ratio to inform on atmospheric pathways, atmospheric residence times and stratosphere / troposphere exchange patterns responsible for 10Be transport and deposition to Law Dome.\n2. Constrain the seasonal cycle in 10Be and the timing, pervasiveness and origin of the summer maximum in 10Be concentrations.\n3. Work toward quantifying climate modulation of 10Be concentrations at Law Dome.\n4. Provide guidelines for improving the interpretation of solar activity from ice core records of 10Be.\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\n7 samples from a snow pit (S0-S6) along with 2 'blank' samples have been measured by gamma spectroscopy for 7Be and then by accelerator mass spectrometry (AMS) for 10Be. Additionally, between these two measurements aliquots were taken from each sample and measured by Inductively Coupled Plasma (ICP) Atomic Emission Spectroscopy (AES) at two separate laboratories for 9Be (carrier) concentration. As at 16th April 2009 this data is still under interpretation. The data appears to be of good quality and we hope that it will advance the scientific objectives. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\n1. 6 samples from a 0.6m snow pit plus 3 associated 'blanks' were prepared and measured for 7Be by gamma spectroscopy. Aliquots were taken and measured for 9Be concentration by ICP-AES. These samples were then further processed and have been measured for 10Be/9Be by AMS on the ANTARES accelerator. A preliminary plot of the 7Be and 10Be concentrations and 7Be/10Be ratio is available on request. Interpretation of this record, and the record from the 08/09 season, is underway and will be advanced by modeling output from EACHAM5-HAM; Joel Pedro is arranging this with Ulla Heikkila (further discussions in Europe in June 2010). Furthermore, the AMS cathodes have been re-measured twice for 7Be/9Be by AMS on the ANTARES accelerator, following successful development of this new technique in April 2010.\n2. 19 samples from 3x 2.66m PICO plus 2 associated 'blanks' were processed and have been measured for 10Be/9Be by AMS on the ANTARES accelerator. AMS data analysis is essentially complete however full interpretation of this record requires age/depth chronology from del18O analysis. I believe that this will be finalised by the end of April. The 10Be/9Be data is of high quality and apparently confirms the expected seasonal signal.\n3. The two data sets mentioned above will further advance this objective.\n4. Comparison of the measured 10Be concentration in Law Dome ice and the neutron monitor record from McMurdo, along with recognition of seasonal signals at Law Dome, are providing guidelines for interpreting the recent solar activity record at this site. Further comparison with modelled data should permit extension to the palaeo-record.\n5. This objective has not yet been achieved.\n\nWe have developed a unique, high resolution (~ monthly) long-term (since 2000) record of 10Be concentration in Antarctic ice. In recent years we have been able to add 7Be concentration data to this record.\n\nContinuation of this unique record is scientifically important, as is evident from the scientific objectives stated above. It was particularly important that our record included in 7Be and 10Be measurements from this unprecedented (at least during the instrumental period) epoch of low solar activity. This offers special (perhaps once in a lifetime) opportunity to better understand production, transport and deposition of cosmogenic beryllium into the Antarctic ice sheet.\n\nTaken from the 2010-2011 Progress Report:\n\nProgress against the stated Scientific objectives of the project:\n\n1. To study the production and deposition of 7Be and 10Be during the deepest solar minimum in nearly a century (and possibly as the Sun enters a new cycle), at high temporal resolution.\n\nSamples collected, 7Be and 10Be measurements completed, chronology and interpretation pending.\n\n2. Use the 10Be/7Be ratio to inform on atmospheric pathways, atmospheric residence times and stratosphere / troposphere exchange patterns responsible for 10Be transport and deposition to Law Dome.\n\nThis is ongoing work, building particularly on the good data collected last season (09/10). Progress against this objective will intensify when Post Doctoral student Dr Ulla Heikkila commences work at ANSTO on 04/08/11. Dr Heikkila will be modelling the production, transport and deposition of 7Be and 10Be to Law Dome using the EACHAM5-HAM General Circulation Model. Work has already commenced on installing and running this model at ANSTO.\n\n3. Identify short-term spikes in 10Be and 7Be caused by solar proton events (SPE).\n\nThe new data has not yet been scrutinised for such events.\n\n4. Further constrain the observed seasonal cycle in 10Be and the timing, pervasiveness and origin of the summer maximum in 10Be concentrations.\n\nOnce the chronology becomes available this objective will be achieved.\n\n5. Work toward quantifying climate modulation of 10Be concentrations at Law Dome.\n\nOngoing.\n\n6. Provide guidelines for improving the interpretation of palaeo-solar activity from ice core records of 10Be.\n\nOngoing \n\nSee the child record(s) for the data.", "links": [ { diff --git a/datasets/ASAC_3095-Macca-soil-0910-Jared_1.json b/datasets/ASAC_3095-Macca-soil-0910-Jared_1.json index 724bf9f7f8..b8a3323bcb 100644 --- a/datasets/ASAC_3095-Macca-soil-0910-Jared_1.json +++ b/datasets/ASAC_3095-Macca-soil-0910-Jared_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3095-Macca-soil-0910-Jared_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains the spatial information associated with the sites from which the soil samples were collected. It also contains the initial weights for each soil sample.\n\nSoil samples were collected as part of the PhD thesis of Jared Abdul-Rahman, Spatiotemporal characteristics and causes of damage to Azorella macquariensis cushions.\n\nAzorella macquariensis is a perennial cushion-forming herb that is endemic to Macquarie Island. During the 2008/09 austral summer, widespread dieback in A. macquariensis was observed, and is regarded by many to be a new phenomenon. The dieback is perceived to have spread across the entire island, affecting up to ~0 % of cushions in some areas. As a result of this perception the species has been listed as critically endangered under the Environment Protection and Biodiversity Conservation (EPBC) Act 1999.\n\nAlthough there have been speculations about potential contributing factors, a definite cause has not been determined. The spatiotemporal characteristics of three dominant damage types affecting the cushion plant were investigated to help determine the cause/s of the putative increasing damage. In the austral summer of 2009/10, data were collected in the course of four studies: a cushion profile study; mapping of the spatial variation of cushion health; monitoring of temporal variation in cushion health; and a study relating soils to health. There was no significant relationship between overall cushion health and environmental variables apart from health decreasing with increasing exposure to strong winds. Type 1 damage was found to be more concentrated on the windward sectors of cushions, was significantly related to cushion exposure, substrate and vegetation community and did not display any major temporal variability. Type 2 damage was found to be more concentrated on the leeward sectors of cushions, was significantly related to cushion exposure, substrate and vegetation community and also did not display any major temporal variability. Type 3 damage was not significantly related to any particular sectors of cushions, nor to the environmental variables with the exception of cushion substrate, and expanded rapidly during the warm season. This suggested that the spatially restricted Type 3 damage might be responsible for the perception of increased dieback. Its cause is uncertain, although many of the symptoms are similar to those of a pathogen. Future monitoring should concentrate on this type of damage.", "links": [ { diff --git a/datasets/ASAC_3095_Antarctic_Biogeography_shapefiles_1.json b/datasets/ASAC_3095_Antarctic_Biogeography_shapefiles_1.json index 0d4682a0c3..7f58072ba1 100644 --- a/datasets/ASAC_3095_Antarctic_Biogeography_shapefiles_1.json +++ b/datasets/ASAC_3095_Antarctic_Biogeography_shapefiles_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3095_Antarctic_Biogeography_shapefiles_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOTE - the shapefile representing Antarctic Conservation Biogeographic Regions (ACBRs) available from this record \nhas been superseded by the shapefile representing ACBRs available from \"AAS_4296_Antarctic_Conservation_Biogeographic_Regions_v2\" (see the provided URL) (2018-02-08).\n \nIn association with the Scientific Committee for Antarctic Research, Aleks Terauds (Australian Antarctic Division) and Steven Chown (Stellenbosch University, Monash University from July 2012) have been leading research into biogeographical classification of terrestrial Antarctica. This research culminated in the manuscript 'Conservation biogeography of the Antarctic', full citation:\n\nTerauds, A., Chown, S.L., Convey, P., Peat, H., Watts, D., Morgan, F., Keys, H. and Bergstrom, D.M. (2012) Conservation biogeography of the Antarctic. Diversity and Distributions. In press.\n\nThe primary finding of this paper was that terrestrial Antarctica could be classified into 15 biologically distinct regions or Antarctic Conservation Biogeographic Regions. A shapefile of the polygons representing these ice free regions is available for download. The layer is based on the most up to date map of ice-free areas of Antarctica (from the Antarctic Digital Database v5 copyright SCAR 1993-2006). Therefore use of this shapefile in analyses is subject to the copyright restrictions of SCAR and interested parties can also contact Aleks.Terauds@aad.gov.au with any questions. Other shapefiles provided here are the expert-defined bioregions (one of the spatial frameworks used in the analyses of Terauds et al. 2012) and the ASPA/ASMA point and polygon shapefiles. A separate PDF is included in the folder containing the ASPA/ASMA shapefiles with terms of use.", "links": [ { diff --git a/datasets/ASAC_3095_Vegetation_Change_1.json b/datasets/ASAC_3095_Vegetation_Change_1.json index ffa722ee44..5bc588c9ff 100644 --- a/datasets/ASAC_3095_Vegetation_Change_1.json +++ b/datasets/ASAC_3095_Vegetation_Change_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3095_Vegetation_Change_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plot data and satellite imagery were used to examine changes in vegetation between 2000 and 2007. These data were examined in light of changes in rabbit numbers (data owned by and provided to the Australian Antarctic Division from Parks and Wildlife Tasmania).\n\nVegetation Change. Kate Kiefer established 18 relatively homogenous plots of 25 m2 in a range of vegetation types between November and March 2001. Individual plant species cover was visually calculated within five random 1m2 quadrats within each site and mean values determined. Dana Bergstrom, Kate Kiefer, Jane Wasley and Arko Lucieer re-sampled the same sites in April 2007. The data matrix consisted of 18 sites, 34 taxa and temporally separated sampling intervals: 2001 and 2007. Species also included collective categories for leafy bryophytes, lichens, bare ground and dead vegetation. At each site, altitude, slope, aspect, a subjective estimate of the wind exposure and the degree of waterlogging were also recorded. The data matrix of sites and mean cover for the site (mean of cover from 5 x 1 m2 quadrats) is provided. Also in the data matrix is a GPS location of the site which is recorded for each star picket that marks each site on the island. Site code consists of site number (first two numerals) - year 01 or 07 (2001 or 2007). The data matrix also includes some site information: a subjective exponential soil-water scale (1- 5: dry - wet); a subjective exponential site exposure scale (1-5: sheltered to exposed); slope, altitude, aspect and mean substrate depth (mean of three random probes across the site).\n \nRemote Sensing Imagery. Information on changes in vegetation communities were scaled up to whole-island level using satellite imagery. We used Landsat ETM+ imagery acquired on 12 December 2000 and Quickbird imagery acquired on 15 March 2007 to detect changes in vegetation cover on Macquarie Island. The Quickbird image with its 2.4 m pixel size was resampled (by pixel averaging) to the 25 m Landsat pixel size to compare the images at the same resolution. The images were orthorectified to correct terrain and geometric distortions. Radiometric, illumination, and atmospheric differences were also corrected. These corrections are crucial for change detection algorithms as false changes are often introduced by geometric offsets and shadowing effects. Multispectral bands 1 (blue), 2 (green), 3 (red), and 4 (near-infrared (NIR)) of both images were used for change detection.", "links": [ { diff --git a/datasets/ASAC_3103_1.json b/datasets/ASAC_3103_1.json index 8526a906e3..39f0342e80 100644 --- a/datasets/ASAC_3103_1.json +++ b/datasets/ASAC_3103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) project 2936.\n\nPublic\nThe ICECAP Project will conduct a major airborne survey in East Antarctica. The survey will employ multi-frequency ice penetrating radar, laser altimeter, magnetometers and a gravity meter to study a large and relatively unexplored part of the continent. The survey extends from Casey, inland across the Aurora Subglacial Basin, which holds some of the deepest and possibly oldest ice on the continent. This will improve understanding of the ice sheet itself, its past, and potential future impact on sea-level, and of the underlying geology. It will also guide the search for suitable icecore sites for recovering the oldest possible record.\n\nProject objectives:\nThe objectives of the ICECAP project arise from a major collaborative airborne geophysical survey in East Antarctica. ICECAP (\"Investigating the Cryospheric Evolution of the Central Antarctic Plate\") is supported by the Australian Antarctic Division, the U.S. National Science foundation, and the U.K.'s National Environmental Research Council, and is scheduled to survey both the Aurora and Wilkes Subglacial Basins over two seasons (2008/09, 2009/10), with the possibility of a third season concentrating only on the Wilkes Subglacial Basin (2010/2011).\n\nSpecific Australian involvement is in the Aurora Subglacial Basin (ASB), and includes the Totten Glacier and Law Dome. This work will take place out of Casey skiway. Australia's involvement seeks to facilitate this major exploration within the AAT by providing operational support. This is a major general objective of the project.\n\nFollowing on this logistical involvement, the Australian Program is engaged in the science objectives which may be grouped in four themes:\n1) The configuration and basal conditions of the Aurora Subglacial Basin\n2 The potential of Totten Glacier to contribute to sea-level\n3) The ice-sheet history - modelling and potential for very old ice core climate records\n4) The Law Dome ice cap - dynamics, history and relationship to Law Dome ice cores The relevance of each of these is discussed in the next section.\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nProgress to report in this first season is primarily based on field activities, although data reduction has commenced. It is too early in the life of the project to report against detailed analysis or synthesis objectives. The ICECAP Project Casey season proceeded fully to plan, with acquisition of more than 30 thousand line km of survey over the Aurora Subglacial Basin. Data reduction already commenced in the field and is continuing in the post-field season. Initial flight position data are released at the ICECAP project website, and primary bedrock and surface elevation data will be available by October 2009.", "links": [ { diff --git a/datasets/ASAC_310_1.json b/datasets/ASAC_310_1.json index 2234bd03d0..1d028638f8 100644 --- a/datasets/ASAC_310_1.json +++ b/datasets/ASAC_310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sedimentological, chemical and isotopic characteristics of sediment cores from three slightly saline to hypersaline lakes (Highway, Ace and Organic Lakes) and two marine inlets (Ellis Fjord and Taynaya Bay) in the Vestfold Hills, Antarctica have been examined.\nSections of the cores deposited in marine environments are characterised by uniform, regularly laminated, fine grained, organic-rich sediments, with uniform organic delta 13C values (-18.0 to 19.4 ppt vs. PDB) and sulfur contents. In contrast, sediments deposited in lacustrine environments are extremely heterogeneous, varying from finely laminated mat-like sequences to poorly sorted clastic-rich sediments. Authigenic monohydrocalcite and aragonite occur in some lake sediments. \nThe delta 13C values of organic matter in the lacustrine sediments exhibit an extremely wide range (-10.5 to -25.3 ppt) that can be related to variations in physico-chemical conditions in the lake waters. Strongly negative organic-delta 13C values coupledwith high sulfur contents are indicative of an anoxic zone in the overlying lake waters, whereas less negative organic-delta 13C values coupled with low sulfur contents are indicative of well-mixed oxic conditions. Particularly high organic-delta 13C values result during high levels of microbial activity in the lakes, due to high rates of photosynthetic CO2 fixation. The large shifts in organic-delta 13C are not necessarily accompanied by any change in macroscopic sedimentological characteristics, illustrating the utility if isotopic investigations in these environments. The delta 13C composition of authigenic carbonate in hypersaline Organic Lake sediments provides a record of changes in palaeoproductivity, while the delta 18O of the carbonate provides information on rates of meltwater input and evaporation in the lake. 14C-dating suggests that Highway Lake was isolated from the sea by isostatic uplift at least 4600 years before present (BP) whereas Organic Lake was isolated at approximately 2700 years BP. Apparent emergence rates calculated from the 14C ages range from 1.0 to 2.1 mm per year.\n\nThe 'reservoir effect' in the lacustrine and marine environments is variable, but probably does not exceed ~ 1000 years in any of the lakes examined.", "links": [ { diff --git a/datasets/ASAC_3115_1.json b/datasets/ASAC_3115_1.json index fde9f1430c..05c465d391 100644 --- a/datasets/ASAC_3115_1.json +++ b/datasets/ASAC_3115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary input of Persistent Organic Pollutant (POP) contamination to the Antarctic is expected to be via Long Range Atmospheric Transport (LRAT) from emissions in neighbouring Southern hemisphere nations. In addition to LRAT, system input of POPs must increasingly consider alternate pathways. Human activity in the Antarctic represents a potential direct source of both legacy and current-use chemicals.\n\nIt has been two decades since the organic chemical composition of air masses arriving in the Australian Antarctic Territory (AAT), which spans the majority of the eastern Antarctic sector, was last investigated. The results presented here are the first atmospheric measurements made as part of a new continuous monitoring effort at Casey station (66 degrees 17' S 110 degrees 31' E), one of Australia's all-year research stations. These results are evaluated alongside POP contamination data of soil samples collected around the Casey station perimeter. Here we assess contaminant profiles for clues as to local and distant contamination sources.", "links": [ { diff --git a/datasets/ASAC_3134_field_lab_books_1.json b/datasets/ASAC_3134_field_lab_books_1.json index 14e95bb6d5..932fe806e0 100644 --- a/datasets/ASAC_3134_field_lab_books_1.json +++ b/datasets/ASAC_3134_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3134_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Davis Station and Kingston between 2009 and 2012 as part of ASAC (AAS) project 3134 - Vulnerability of Antarctic marine benthos to increased temperatures and ocean acidification associated with climate change.", "links": [ { diff --git a/datasets/ASAC_3137_SUMMIT_DOMEA_PLATO_1.json b/datasets/ASAC_3137_SUMMIT_DOMEA_PLATO_1.json index 6ffa286193..143fd96bc2 100644 --- a/datasets/ASAC_3137_SUMMIT_DOMEA_PLATO_1.json +++ b/datasets/ASAC_3137_SUMMIT_DOMEA_PLATO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3137_SUMMIT_DOMEA_PLATO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 3137\nSee the link below for public details on this project.\n\nRobotic Science from the High Plateau\n\nAustralia's astronomers are exceptionally well placed to lead and to partner major international programs in Antarctic astronomy. These bring Australian industry increased access to cutting edge technology, and create business opportunities in the infrastructure and support of Antarctic research. This project aims to capture the lead for Australia in Antarctic astronomy, allowing us to fully capture the benefits of future international investment. Australia's participation in these programs also ensures continued technology exchange, and builds our knowledge base in robotics, harsh-environment engineering and computational fluid dynamics, while creating important new astronomical opportunities. It serves to demonstrate robotic science from the high plateau.\n\nData from the first year of the project is available for download from the provided URL.\n\nProject objectives:\nWithin the next decade, the first major optical/infrared telescopes will be built on the Antarctic Plateau, taking advantage of the remarkable conditions known to exist at established sites such as Dome C. In January 2008 our autonomous observatory, PLATO, was deployed by a Chinese team to Dome A, the highest point on the Antarctic Plateau and potentially the best observing site on earth. With Dome A now accessible for the first time, we will lead a detailed multi-year study to compare Dome A and Dome C, creating an improved understanding of the Antarctic atmosphere and providing the essential data needed by designers of Antarctic telescopes, interferometers and adaptive optics systems.\n\nThis project makes use of robotic technologies in order to gather the data needed for its science. It is a prime example of the way to conduct science from remote locations, such as the Antarctic plateau, where human presence is limited. It can serve as a model for the way other such investigations could be carried out in the future - robotic science from the high plateau. \n\nPublic summary of the season progress:\nDome A is the highest point on the Antarctic plateau, and lies within the Australian Antarctic Territory. It is likely the coldest and driest location on the surface of the Earth, and possibly the finest site to make sensitive observations of the faint light from the distant Cosmos. A Chinese scientific station is now under construction there, Kunlun Station. An Australian autonomous observatory, PLATO (PLATeau Observatory), built at the University of New South Wales, was installed at Dome A in 2008. It has now completed two seasons of operations, completely unattended following the departure of the Chinese commissioning expedition. A suite of instruments operated by PLATO are now returning data on the atmospheric conditions at Dome A, in particular relating to the sensitivity that future telescopes could have. These are remarkable achievements and demonstrate Australian leadership and ingenuity in the development of the Antarctic plateau for frontier scientific investigations.", "links": [ { diff --git a/datasets/ASAC_314_1.json b/datasets/ASAC_314_1.json index 2b5703dfdc..01753a456c 100644 --- a/datasets/ASAC_314_1.json +++ b/datasets/ASAC_314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the variation of the magnetic elements (North, East and Vertical) for 7 sites on Kerguelen at one minute interval. The data were recorded between 14/1/1988 and 18/2/1988.\n\nThe header file of each data file contains the locality coordinates and some sensor information. Each subsequent block contains one hour data values. The block header line gives the date, time temperature and battery voltages, followed by the full field value for the North, East and Vertical element. Subsequent lines contain the last 3 digits of the values. 3 lines for North, East and Vertical. The values are in NanoTesla.\n\nThe data were recorded with a three-component self recording fluxgate magnetometer. (Chamalaun and Walker, J. Geomag. Geoelectr. 34, 491-507, 1982).\n\nThe experiment was conducted in cooperation with Bureau central de Magnetisme Terrestre (France), and attempted to determine the magnetic coast effect for Kerguelen island.\n\nThe fields for this dataset are:\nClock start\nclock correct(sec/month)\ntemperature coefficent\ngeography correction (deg/count)", "links": [ { diff --git a/datasets/ASAC_31_1.json b/datasets/ASAC_31_1.json index a70988c7b3..8af95864a7 100644 --- a/datasets/ASAC_31_1.json +++ b/datasets/ASAC_31_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_31_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 31\nSee the link below for public details on this project.\n\nAmmoelphidiella antarctica Conato and Segre occurs in thin unconsolidated muddy sediments at 55m above sea level in the Larsemann Hills, East Antarctica. The specimens appear to be in situ and this implies an early Pliocene age for the sediments and their contained faunas, similar to that of recently identified early to mid-Pliocene age sediments in the Vestfold Hills about 100 km distant. This is the first evidence from foraminifera of Pliocene sediments around the margin of the East Antarctic craton and augurs well for future discoveries of coeval sediments. Dominant elements of the entirely calcareous benthic fauna are Globocassidulina crassa (d'Orbigny), and Cassidulinoides cf. parkerianus (Brady).", "links": [ { diff --git a/datasets/ASAC_3217_field_lab_books_1.json b/datasets/ASAC_3217_field_lab_books_1.json index 90a11e6b38..92ee103c3e 100644 --- a/datasets/ASAC_3217_field_lab_books_1.json +++ b/datasets/ASAC_3217_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_3217_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Davis Station, and Kingston between 2009 and 2011 as part of ASAC (AAS) project 3217 - Environmental assessment of Davis sewage treatment plant up-grade.", "links": [ { diff --git a/datasets/ASAC_32_1.json b/datasets/ASAC_32_1.json index 12760c84ed..9f3e16c762 100644 --- a/datasets/ASAC_32_1.json +++ b/datasets/ASAC_32_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_32_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 32\nSee the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nFoods of the South Polar Skua in the eastern Larsemann Hills:\n\nRegurgitated pellets and food remains were collected near nest sites, and from a club site, of south polar skuas Catharacta maccormicki in the eastern Larsemann Hills, Princess Elizabeth Land, East Antarctica, during the skuas' presence in the area. The samples indicated that the snow petrel Pagodroma nivea, the most abundant seabird species breeding locally, formed the major dietary component, comprising some 66% of food items identified in pellets and 80% of the food remains obtained. Adelie penguins Pygoscelis adeliae (which do not breed in the Larsemann Hills), other seabirds, fish and marine foods were rarely found as remains or in pellets. However, refuse (meat, fish and vegetable remains) taken as food by skuas from nearby stations occurred in pellets at all sites and formed about 12% of the food remains collected and identified. In this study, foods taken by skuas were related both to the local breeding distribution of snow petrels, and to the possession of a feeding territory. Birds without feeding territories took somewhat fewer snow petrels and included more refuse from local stations in their diet, as did those at the club site. Future monitoring of the influence of anthropogenic foods (and indelible waste materials) on the species' ecology is considered important.", "links": [ { diff --git a/datasets/ASAC_354_1.json b/datasets/ASAC_354_1.json index d83cd7bd4c..5d2ffab36a 100644 --- a/datasets/ASAC_354_1.json +++ b/datasets/ASAC_354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An analysis of bedrock and associated soils was conducted at a series of coastal localities in East Antarctica as well as further inland in the Prince Charles Mountains. Protozoans and micrometazoans were extracted from soil samples and an assessment made of their ecological relations with each other and with soil characteristics.\n\nMineral soils, regardless of topographic elevation and proximity to the coast, were characterised by large gravel fractions (fragments of underlying bedrock) and minimal clay fractions, implying that these soils were predominantly the products of physical weathering, with little chemical alteration. Only where humans, dogs or birds contributed organic matter were there elevated concentrations of nitrogen, phosphorus or organic matter.\n\nGenerally, mineral nitrogen did not seem to have resulted from microbial mineralisation, but at some sites there was evidence that atmospheric nitrate had been concentrated by sublimination of snow. Water-soluble and dilute acid-soluble phosphorous concentrations were surprisingly high for such organically poor soils. There was a sufficiently large labile pool of common macronutrients to sustain the autotrophic activity likely to occur within the bounds of prevailing temperatures and moisture; thus nutrients are not likely to be limiting for these soil communities.\n\nThere was a limited fauna. Flagellates were rare and ciliates occurred only in the coastal areas sampled, whereas amoebae were found over a greater geographic and elevational span. Micrometazoans such as rotifers, tardigrades and nematodes were more common in coastal soils than in those further inland, but occurred in soils over most of the naturally occurring range of soil moistures, acidities, nutrient levels, electrolyte levels and organic contents. Exceptions were the exclusion of rotifers from alkaline soils with high nutrient levels, and the tendency of nematodes to be absent from soils with low pH. Tardigrades were found at almost all levels of soil characterisitcs.\n\nThe occurrence of these metazoan phyla under such a range of environments probably resulted from their known capacity to alternate between endurance of inclement conditions in a state of deep dormancy (anhydrobiosis), and taking advantage of ephemeral favourable conditions by temporarily resuming metabolic activity. The conditions measured in soils containing micrometazoans may merely indicate thise conditions these animals can survive while dormant, not those under which active animals can carry out vital processes. In some localities there were positive associations between various taxon-pairs of metazoans and protozoans, whereas at others their occurrences seemed to be random with respect to each other.\n \nThe fields for this dataset are:\nTARDIGRADES\nsite\nAMOEBAE\nbedrock type\nquartz\ngarnet\npyroxene\nhomblende\nbiotite\nK-feldspar\nplagioclase\nOthers\nrock type\nfield sample number\ncatalogue Number\nmaterial\nUSDA texture class\nclay mineralology\nwater content\npH KCl\npH H2O\nmicro siemens per centimetre\nwater solution P\nwater solution K\ncolour (Munsell)\nTotal N\nMinimum N (KCl)\nNH3-N\nDilute acid solution P\nloss on ignition (%)\nCiliates\nnematodes\nassociations", "links": [ { diff --git a/datasets/ASAC_36_1.json b/datasets/ASAC_36_1.json index b6eee55e69..96242a0ffa 100644 --- a/datasets/ASAC_36_1.json +++ b/datasets/ASAC_36_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_36_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 36\nSee the link below for public details on this project.\n\nFrom the abstract of one of the referenced papers - &Environmental conditions and microbiological properties from soils and lichens from Antarctica (Casey Station, Wilkes Land)&:\n\nMicrobial habitats of an Antarctic coastal terrestrial environment are described. Emphasis was laid on soil with different contents of organic matter and some representative lichen species. Parameters for this description were microclimatic conditions, organic and inorganic matter, epifluorescence microscopy, and parameters describing microbial and enzymatic activity. The data of direct measurements of bacterial standing stock (number and biomass) and of exoenzymatic activity via FDA-hydrolysis were found to be the most valuable parameters for microbiological properties. The data show significant differences with regard to nutrients and microbial activity. Scales of patches are only a few square decimetres with wide ranges of environmental and microbiological properties within short distances.", "links": [ { diff --git a/datasets/ASAC_378_1.json b/datasets/ASAC_378_1.json index 31bdc3818b..44ee9c08f2 100644 --- a/datasets/ASAC_378_1.json +++ b/datasets/ASAC_378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 378\nSee the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nObservations on five groups of crabeater seals were conducted between 29 October and 17 November 1985 in the Antarctic pack ice near 66 degrees S 50 degrees E, off Enderby Land. The pups studied were born in the last half of October. Two of them increased in weight at a rate of approximately 4.2kg per day. The lactation period was 2-3 weeks and thus is one of the breifest among pinnipeds. Pups decreased in weight after weaning. The only pup visited after it left the natal floe must have been feeding, as it had only lost 2kg in a 10-day period during which it moved 13 km. Molt of lanugo appeared to be influenced by more a pups weight than by whether or not it was weaned.", "links": [ { diff --git a/datasets/ASAC_38_1.json b/datasets/ASAC_38_1.json index 9742e942ed..7a467a8b67 100644 --- a/datasets/ASAC_38_1.json +++ b/datasets/ASAC_38_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_38_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 38\nSee the link below for public details on this project.\n\nFrom the abstracts of the referenced papers:\n\nThe origin of echinoderms from Macquarie Island in the Southern Ocean is analysed through a novel application of multivariate statistics. Ordinations are produced from a combination of species distribution, bathymetric, habitat and life history data in order to assess patterns of migration. The analyses distinguish groups of species derived from the Kerguelen Plateau, New Zealand and eastern Antarctica. These groups correlate with attributes expected for epiplanktonic dispersal and range expansion along the North and South Macquarie Ridges respectively. There is no convincing evidence for long-distance pelagic dispersal, migration from the abyssal plain or for human translocation of species. The results indicate that taxonomic groups differ in their ability to disperse, and emphasise the importance of depth in biogeographical analyses. Dispersal by range expansion appears to have been more significant than epiplanktonic dispersal and vicariant rather than long-distance dispersal mechanisms are the preferred explanation for some disjunct distribution patterns.\n \n Fifty two echinoderm species are recorded from off Macquarie Island and the Macquarie Ridge in the Southern Ocean. One new asteroid Odontohenricia anarea sp. nov. and one new holothurian Trachythyone nelladana sp. nov. are described. The asteroid genus Calvasterias is synonymised with Anasterias. The asteroids Cycethra macquariensis and Asterina hamiltoni are synonymised with Asterina frigida and placed in the same genus Cycethra. The asteroid Ceramaster lennoxkingi is synonymised with C. patagonicus, Solaster dianae with S. notophrynus, and Anasterias sphoerulatus with A. mawsoni. The asteroids Psilaster charcoti, Odontaster penicillatus, Ceramaster patagonicus, Crossaster multispinus, Solaster notophrynus, Pteraster affinis, Henricia studeri, the ophiuroid Ophioplocus incipiens, and the holothurians Paelopatides ovalis, Synallactes challengeri, Laetmogone sp, Taeniogyrus sp are recorded from the island for the first time. The following species previously recorded from Macquarie Island have been re-identified: the asteroids Odontaster auklandensis (=O. penicillatus), Henricia aucklandiae (=H. studeri), Henricia lukinsi (=H. obesa), Smilasterias irregularis (=S. clarkailsa), Anasterias antarctica (=A. directa), and the ophiuroid Ophiacantha pentagona (=O. vilis). The existence at Macquarie Island of the species Hymenaster sp, Goniocidaris umbraculum and Ocnus calcareus require confirmation. The asteroids Anasterias mawsoni, Pteraster affinis, Porania antarctica and Odonaster meridionalis are reported from the shore around Heard Island. The ecology and relationships of echinoderms from Macquarie Island are discussed.", "links": [ { diff --git a/datasets/ASAC_400_1.json b/datasets/ASAC_400_1.json index da051acff9..7a94a01fb0 100644 --- a/datasets/ASAC_400_1.json +++ b/datasets/ASAC_400_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_400_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 400\nSee the link below for public details on this project.\n\nTaken from one of the referenced papers:\n\nOceanic basalts are produced by melting of the Earth's mantle and are widely used to probe its composition. The observation of systematic, coupled variations in the neodymium, strontium and lead isotopic compositions of these basalts has been widely interpreted as reflecting the mixing of identifiable mantle components. White and Zindler and Hart have suggested that the isotopic compositions of mid-ocean-ridge and ocean island basalts (MORB and OIB) can be considered as mixtures of a depleted MORB-type mantle component with a few other components reflecting long-term enrichment of Rb/Sr, Nd/Sm and/or U/Pb ratios. It remains unclear whether these components should be considered as hypothetical mixing endmembers, or whether they exist physically as mantle reservoirs. This question may be addressed if the mixing endmembers of individual mantle plumes can be identified. To this end, we report linear Pb-Pb and curvilinear Pb-Nd and Pb-Sr isotopic covariations in Recent lavas of Heard Island, southern Indian Ocean, indicating binary mixing. The hyperbolic relationships are unique among oceanic basalts and tightly constrain the isotopic compositions of the plume source components, which do not coincide with the mantle endmember components of ref. 3. If our results are generally applicable to other plumes, they call into question the existence of large mantle reservoirs corresponding to these components, and indicate that actual oceanic basalt source reservoirs have intermediate isotopic compositions.", "links": [ { diff --git a/datasets/ASAC_40_Phytoplankton_Samples_LOSS_1.json b/datasets/ASAC_40_Phytoplankton_Samples_LOSS_1.json index 1b08acbd42..682c324b60 100644 --- a/datasets/ASAC_40_Phytoplankton_Samples_LOSS_1.json +++ b/datasets/ASAC_40_Phytoplankton_Samples_LOSS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_40_Phytoplankton_Samples_LOSS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Locations of sampling sites for ASAC project 40 on voyage 7 of the Aurora Australis in the 2001/2002 season. The dataset also contains information on chlorophyll, carotenoids, coccolithophorids and species identification and counts. The voyage acronym was LOSS. There are 203 observations in the collection.\n\nThese data are available via the biodiversity database.\n\nThe taxa represented in this collection are (species names at time of data collection, 2001-2002):\n\nAcanthoica quattrospina\nCalcidiscus leptoporus\nCoronosphaera mediterranea\nEmiliania huxleyi\nGephyrocapsa oceanica\nPentalamina corona\nSyracosphaera pulchra\nTetraparma pelagica\nTriparma columacea subsp. alata\nTriparma laevis subsp. ramispina\nTriparma strigata\nUmbellosphaera tenuis", "links": [ { diff --git a/datasets/ASAC_419_1.json b/datasets/ASAC_419_1.json index 6777bc935b..eb835b06ad 100644 --- a/datasets/ASAC_419_1.json +++ b/datasets/ASAC_419_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_419_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 419\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nThe population size and breeding success of Emperor Penguins (Aptenodytes forsteri) at the Auster and Taylor Glacier colonies were estimated during the 1988 breeding season. At Auster a total of 10963 pairs produced about 6350 fledglings for a breeding success of 58%. At Taylor Glacier about 2900 pairs raised 1774 fledglings for a breeding success of 61%. Fledglings left Taylor Glacier over a period of 33 days at a mean mass of 10.56kg.\n\nThe accuracy of the tritiated water (HTO) and sodium-22 (22Na) turnover methods as estimators of dietary water and sodium intake was evaluated in emperor penguins fed separate diets of squid and fish. Emperor penguins assimilated 76.2% and 81.8% of available energy in the squid and fish diets, respectively. Both isotopes had equilibrated with body water and exchangeable sodium pools by 2h after intramuscular injection. The tritium method yielded reliable results after blood isotope levels had declined by 35%. On average the tritium method underestimated water intake by 2.9%, with a range of -10.3% to +11.1%. The 22Na method underestimated Na intake on average by 15.9% with the errors among individuals ranging from -37.2% to -1.8%. Discrepancies with 22Na turnover were significantly greater with the squid diet than the fish diet. The results confirm the reliability of the tritium method as an estimator of food consumption by free-living emperor penguins (provided seawater and freshwater ingestion is known) and support the adoption of the 22Na method to derive an approximation of seawater of seawater intake by tritiated emperor penguin chicks and by tritiated adults on foraging trips of short duration.\n\nThe diet composition of Emperor Penguin Aptenodytes forsteri chicks was examined at Auster and Taylor Glacier colonies, near Australia's Mawson station, Antarctica, between hatching in mid-winter and fledging in mid-summer by 'water-offloading' adults. Chicks at both colonies were fed a similar suite of prey species. Crustaceans occurred in 82% of stomach samples at Auster and 87% of stomachs at Taylor Glacier and were heavily digested; their contribution to food mass could not be quantified. Fish, primarily bentho-pelagic species, accounted for 52% by number and 55% by mass of chick diet at Auster, and squid formed the remainder. At Taylor Glacier the corresponding values were 27% by number and 31% by mass of fish and 73% by number and 69% by mass of squid. of the 33 species or taxa identified, the fish Trematous eulepidotus and the squid Psychroteuthis glacialis and Alluroteuthis antarcticus accounted for 64% and 74% of the diets by mass at Auster and Taylor Glacier, res pectively. The sizes of fish varied temporally but not in a linear manner from winter to summer. Adult penguins captured fish ranging in length from 60 mm (Pleuragramma antarcticum) to 250 mm (T. eulepidotus) and squid (P. glacialis) from 19 to 280 mm in mantle length. The length-frequency distribution of P. glacialis showed seasonal variation, with the size of squid increasing from winter to summer. The energy density of chick diet mix increased significantly prior to 'fledging'.", "links": [ { diff --git a/datasets/ASAC_41_587_1.json b/datasets/ASAC_41_587_1.json index d9137b4d1e..124e293463 100644 --- a/datasets/ASAC_41_587_1.json +++ b/datasets/ASAC_41_587_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_41_587_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 587\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nThe concentration of fluoride in the body parts of a range of Antarctic crustaceans from a variety of habits was examined with the aim of determining whether fluoride concentration is related to lifestyle or phylogenetic grouping. Euphausiids had the highest overall fluoride concentrations of a range of Antarctic marine crustaceans examined; levels of up to 5477 micro grams per gram were found in the exoskeleton of Euphausia crystallorophias. Copepods had the lowest fluoride levels (0.87 micrograms per gram) whole-body); some amphipods and mysids also exhibited relatively high fluoride levels. There was no apparent relationship between the lifestyle of the crustaceans and their fluoride level; benthic and pelagic species exhibited both high and low fluoride levels. Fluoride was concentrated in the exoskeleton, but not evenly distributed through it; the exoskeleton of the head carapace and abdomen contained the highest concentrations of fluoride, followed by the feeding basket and pleopods, and the eyes. The mouthparts of E. superba contained almost 13,000 microgams F per gram dry weight. Antarctic krill tail muscle had low levels of fluoride. After long-term (1 to 5 year) storage in formalin, fluoride was almost completely lost from whole euphausiids.\n\nA series of experiments was carried out to determine the relationship between feeding, moulting, and fluoride content in Antarctic krill (Euphausia superba). Starvation increased the intermoult period in krill, but had no effect on the fluoride concentrations of the moults produced. Addition of excess fluoride to the sea water had no direct effect on the intermoult period, the moult weight, or moult size. Additions of 6 micrograms per litre and 10 micrograms per litre fluoride raised the fluoride concentrations of the molts produced and of the whole animals. The whole body fluoride content varied cyclically during the moult cycle, reaching a peak 6 days following ecdysis. Fluoride loss at ecydsis could largely be explained by the amount of this ion shed in the moult.\n\nThis work was completed as part of ASAC projects 41 and 587 (ASAC_41, ASAC_587).", "links": [ { diff --git a/datasets/ASAC_423_1.json b/datasets/ASAC_423_1.json index 011d862f8f..4e2af492df 100644 --- a/datasets/ASAC_423_1.json +++ b/datasets/ASAC_423_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_423_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected ASAC Project 423\nSee the link below for public details on this project.\n\nFrom the abstracts of the referenced papers:\n\nEight species of vascular plants were previously known from subantarctic Heard Island. three additional species, Montia fontana, Ranunculus biternatus and Poa annua, were discovered during the 1986 austral summer. Details of their habitat and known distribution on the island, and their possible means of arrival, are discussed.\n\nThe Heard and McDonald Islands are the only subantarctic group which appears to be free of human-introduced animals and plants. Vegetation changes in its species-poor flora are therefore likely to be due to natural factors. Significant glacial recession has exposed new areas for colonisation over the past 40 years. Analysis of vegetation transect data from seven glacier retreat zones and adjacent areas indicates four main patterns of primary colonisation, with moisture availability and effects of animal disturbance being major differentiating environmental factors. Vegetation colonisation can be rapid under the most favourable environmental conditions, for example abundant surface drainage from springs or snow melt with or without effects of nutrient enrichment by animals. It can be expected that with continuing climate amelioration and glacial recession, the size of vegetated areas will expand. Changes in distribution of some vascular plant species around the island have been noted and tentatively linked with climatic warming, and additional changes are predicted. Future effects of changing trends in population numbers of animals utilising and interacting with terrestrial vegetation communities are uncertain. Further changes can now be monitored from recently established reference points.", "links": [ { diff --git a/datasets/ASAC_465_1.json b/datasets/ASAC_465_1.json index 8a1e756fb7..4f165aa684 100644 --- a/datasets/ASAC_465_1.json +++ b/datasets/ASAC_465_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_465_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 465\nSee the link below for public details on this project.\n\nFrom the abstracts of the referenced papers:\n\n#############\n\nThe diet composition of King penguins Aptenodytes patagonicus at Heard Island (53deg 05S; 73 deg 30E) was determined from stomach contents of 98 adults captured as they returned to the island throughout 1992. During the two growth seasons, the diet was dominated by the myctophid fish Krefftichthys anderssoni (94 % by number, 48 % by mass). The paralepidid fish Magnisudis prionosa contributed less than 1 % by numbers but 17 % by mass. Mackerel icefish Champsocephalus gunnari accounted for 17 % by mass of chick diet in late winter, when chicks were malnourished and prone to starvation, although its annual contribution to the penguins diet was only 3 %. Squid was consumed only between April and August; Martialia hyadesi was the commonest squid taken, comprising 40 to 48 % of the winter diet. The remainder of the diet consisted of the squid Moroteuthis ingens and fish other than K. anderssoni. The energy content of the diet mix fed to the chicks varied seasonally being highest during the growth seasons (7.83 plus or minus 0.25 kJ.g-1) and lowest in winter (6.58 plus or minus 0.19 kJ.g-1). From energetic experiments we estimated that an adult penguin consumed 300 kg of food each of which its chick received 55 kg during the 1992 season. The chicks received large meals at the beginning of winter (1.2 plus or minus 0.3 kg) and during the middle of the second growth season (1.2 plus or minus 0.3 kg), and their smallest meals in late winter (0.4 plus or minus 0.1 kg). The gross energy required to rear a King penguin chick was estimated to be 724 MJ. The potential impact of commercial fisheries on the breeding activities of King penguins is discussed.\n\n#############\n\n23 king penguins (Aptenodytes patagonicus) from Macquarie Island were tracked by satellite during the late incubation period in 1998-1999 to determine the overlap in the foraging zone of king penguins with an area to be declared a marine protected area (MPA) near the island. While all penguins left the colony in an easterly direction and travelled clockwise back to the island, three penguins foraged in the northern parts of the general foraging area and stayed north of 56 south. The remaining 20 penguins ventured south and most crossed 59 south before returning to the island. The total foraging area was estimated to be 156,000 square kilometres with 36,500 square kilometres being most important (where penguins spend greater than 150 hours in total). North-foraging penguins reached on average 331 plus or minus 24 kilometres from the colony compared to 530 plus or minus 76 kilometres for the south-foraging penguins. The latter travelled an average total distance of 1313 p lus or minus 176 kilometres, while the northern foragers averaged 963 plus or minus 166 kilometres. Not only did the penguins spend the majority of their foraging time within the boundaries of the proposed MPA, they also foraged chiefly within the boundaries of a highly protected zone. Thus, the MPA is likely to encompass the foraging zone of king penguins, at least during incubation.\n\n#############\n\nThe foraging strategies of king penguins from Heard and Macquarie islands were compared using satellite telemetry, time-depth recorders and diet samples. Trip durations were 16.8 plus or minus 3.6 days and 14.8 plus or minus 4.1 days at Macquarie and Heard islands, respectively. At Macquarie Island, total distances travelled were 1281 plus or minus 203 km compared to 1425 plus or minus 516 km at Heard Island. The total time the penguins spent at sea was 393 plus or minus 66 h at Macquarie Island and 369 plus or minus 108 h at Heard Island. The penguins from Macquarie Island performed more deep dives than those from Heard Island. King penguins from Macquarie Island travelled 1.5 plus or minus 0.2 km h-1 day-1 compared to 1.3 plus or minus 0.1 km h-1 day-1. At Macquarie Island, 19% of dives were up to 70-90 m depth compared to 35% at Heard Island. The main dietary prey species were the fish Krefftychthis anderssoni and the squid Moroteuthis ingens in both groups. The differences in the at-sea distribution and the foraging behaviour of the two groups of penguins were possibly related to differences in oceanography and bathymetric conditions around the two islands. Dietary differences may be due to interannual variability in prey availability since the two colonies were studied during incubation but in different years.\n\n#############\n\nNearly 36,000 vertical temperature profiles collected by 15 king penguins are used to map oceanographic fronts south of New Zealand. There is good correspondence between Antarctic Circumpolar Current (ACC) front locations derived from temperatures sampled in the upper 150m along the penguin tracks and front positions inferred using maps of sea surface height (SSH). Mesoscale features detected in the SSH maps from this eddy-rich region are also reproduced in the individual temperature sections based on dive data. The foraging strategy of Macquarie Island king penguins appears to be influenced strongly by oceanographic structure: almost all the penguin dives are confined to the region close to and between the northern and southern branches of the Polar Front. Surface chlorophyll distributions also reflect the influence of the ACC fronts, with the northern branch of the Polar Front marking a boundary between low surface chlorophyll to the north and elevated values to the south.\n\n#############", "links": [ { diff --git a/datasets/ASAC_466_1.json b/datasets/ASAC_466_1.json index 3f0738dfca..a1cc87dd1e 100644 --- a/datasets/ASAC_466_1.json +++ b/datasets/ASAC_466_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_466_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic Fur Seals from Heard Island fed mainly on fish, but the prey species changed both seasonally and inter-annually. The majority of prey were pelagic myctophids characteristic of deep oceanic water, and were generally taken in autumn and winter. The only other fish taken in significant numbers was Champsocephalus gunnari which was mostly taken from late winter through early autumn when it was co-dominant in the diet with the Krefftichthys anderssoni. Males and females foraged in different localities and in different parts of the water column. Males foraged mainly to the south of Heard Island in winter usually diving deep by day, feeding on scattering layers. In summer males also fed on the shelf, presumably to the north and east of Heard Island on K. anderssoni at shallow depths primarily at night. Although diet studies provided little evidence of feeding on crustaceans, diving data indicate that some males may travel to Antarctic waters in winter to feed on krill.\n\nThe fields in this dataset are:\n\nMonths\nSpecies\nScats\nTime foraging\nNumber of Dives\nTime Submerged (minutes)\nMean Dive Duration (minutes)\nMaximum Depth (metres)", "links": [ { diff --git a/datasets/ASAC_504_1.json b/datasets/ASAC_504_1.json index bf1b9b45af..9799f148bb 100644 --- a/datasets/ASAC_504_1.json +++ b/datasets/ASAC_504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An obligately anaerobic bacterium that lacked a cell wall was isolated from the hypolimnion of Ace Lake, Antarctica. Cells were very pleomorphic, forming cocci, filaments up to 25 micrometres in length, and annular shapes. The organism was morphologically very similar to some members of the class Mollicutes which contains two genera of obligately anaerobic bacteria, Anaeroplasma and Asteroleplasma. Like members of the class, the isolate was resistant to high concentrations of penicillin (1000 Units per millilitre). Similar to Anaeroplasma, the organism had a low DNA G+C content (29.3 +/- 0.4) and produced hydrogen, carbon dioxide, acetic acid, lactic acid and succinic acid from the fermentation of glucose. However, the taxonomic status of the strain remained unclear as, unlike members of the class Mollicutes, the isolate had a relatively large genome size (2.26 +/- 0.11 x 10 to the 9 daltons), did not pass through 0.45 micrometre pore size filters, and did not form typical mycoplasma-like colonies. The organism was psychrophilic with an optimum temperature for growth between 12 degrees C and 13 degrees C. A phenotypic description of the organism is given and the ecological role of the organism is inferred from its phenotype and the characteristics of its Antarctic habitat.", "links": [ { diff --git a/datasets/ASAC_507_1.json b/datasets/ASAC_507_1.json index fdd2028dcc..6fa2b2911e 100644 --- a/datasets/ASAC_507_1.json +++ b/datasets/ASAC_507_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_507_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The interaction between the solar wind and the Earth's magnetic field creates a vast magnetic cavity within the solar wind flow, known as the magnetosphere. Through various solar wind-magnetosphere interactions, about one million megawatts of energy enters the magnetosphere to drive electrical currents, energize plasma, and produce complex and variable patterns of plasma convection. Since the ionosphere and magnetosphere are electrically coupled by the anisotropic behaviour of the plasma in the magnetospheric field, ground magnetic observations of ionospheric phenomena made at high latitudes have become a focus for a variety of investigations. On Earth's surface, the 'electromagnetic weather' which results as energy and momentum are transferred between the solar wind, magnetosphere, and ionosphere can be monitored using ground magnetometers.\n\nIn a joint effort, IZMIRAN (Moscow, Russia, http://www.izmiran.rssi.ru/ ) and the Australian Antarctic Division (with the follow-on collaboration with the University of Michigan, http://mist.nianet.org/ ) deployed the digital quartz magnetometer and VLF data logging system at Davis in 1992 in the framework of the project 'Studies of the Southern polar cap boundary from magnetometer and very-low-frequency observations in Antarctica', sponsored by the Australian Antarctic Foundation. The collected data have been analysed and the results published in ANARE Research Notes 95 (1996).", "links": [ { diff --git a/datasets/ASAC_517_1.json b/datasets/ASAC_517_1.json index 73e2eda075..a47b2cea43 100644 --- a/datasets/ASAC_517_1.json +++ b/datasets/ASAC_517_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_517_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This set is taken from a long simulation with a General Circulation Model. The extract used here is the pressure and temperature analyses of the 3d simulated atmosphere on days upon which strong surface winds are simulated at Casey.\n\nTaken from the abstract of the referenced paper:\n\nStrong wind events occurring near Casey (Antarctica) in a long July GCM simulation have been studied to determine the relative roles played by the synoptic situation and the katabatic flow in producing these episodes. It has been found that the events are associated with strong katabatic and strong gradient flow operating together. Both components are found to increase threefold on average for these strong winds, and although the geostrophic flow is the stronger, it rarely produces strong winds without katabatic flow becoming stronger than it is in the mean. The two wind components do not flow in the same direction; indeed there is some cancellation between them, since katabatic flow acts in a predominant downslope direction, while the geostrophic wind acts across slope.\n\nThe stronger geostrophic flow is associated with higher-than-average pressures over the continent and the approach of a strong cyclonic system toward the coast and a blocking system downstream. The anomalous synoptic patterns leading up to the occasions display a strong wavenumber 4 structure. The very strong katabatic flow appears to be related to the production of a supply of cold air inland from Casey by the stronger-than-average surface temperature inversions inland a few days before the strong winds occur. The acceleration of this negatively buoyant air mass down the steep, ice-sheet escarpment results in strong katabatic flow near the coast.", "links": [ { diff --git a/datasets/ASAC_519_1.json b/datasets/ASAC_519_1.json index ccc8068337..a6ac81b451 100644 --- a/datasets/ASAC_519_1.json +++ b/datasets/ASAC_519_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_519_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstracts of some of the referenced papers:\n\nThe East Antarctic mobile belt as exposed in Prydz Bay presents an excellent example of a poly-metamorphic terrain where complicated high-grade structures can be grouped on the basis of lineation direction, sense of shear and metamorphic grade. In this way local intricacies resulting from truncating and interfering foliations and folds can be simplified by ordering structures with respect to their kinematic connotation. The resulting deformation scheme is simple and facilitates regional correlations, and simultaneously places metamorphic textures in a kinematic-structural background.\n\nThe mobile belt in Prydz Bay is exposed along 200km of coastline, and consists of granulite-facies gneiss in which low- to mid-crustal, compressional D1-2 structures, and mid- to upper-crustal, extensional D3-6 structures and related decompression cooling textures are best explained in an exhumation model involving extensional collapse of upper crust in an overall compressional tectonic setting. This implies that compression and extension structures are genetically related. However, D3-6 structures in the Larsemann Hills appear to have formed between 550 and 500 Ma, whilst D2 structures in the Rauer Group have been dated at 1100-1000 Ma. Reinterpretation of the latter dates is possible, implying that most high-grade deformation in Prydz Bay is early Palaeozoic.\n\n##################\n\nMeta-sediments in the Larsemann Hills that preserve a coherent stratigraphy, form a cover sequence deposited upon basement of mafic-felsic granulite. Their outcrop pattern defines a 10 kilometre wide east-west trending synclinal trough structure in which basement-cover contacts differ in the north and the south, suggesting tectonic interleaving during a prograde, D1, thickening event. Subsequent conditions reached low-medium pressure granulite grade, and structures can be divided into two groups, D2 and D3, each defined by a unique lineation direction and shear sense. D2 structures which are associated with the dominant gneissic foliation in much of the Larsemann Hills, contain a moderately east-plunging lineation indicative of west-directed thrusting. D2 comprises a co-linear fold sequence that evolved from early intrafolial folds to late upright folds. D3 structures are associated with a high-strain zone, to the south of the Larsemann Hills, where S3 is the dominant gneissic layering and folds sequences resemble D2 folding. Outside the D3 high-strain zone occurs a low-strain D3 window, preserving low-strain D3 structures (minor shear bands and upright folds) that partly re-orient D2 structures. All structures are truncated by a series of planar pegmatites and parallel D4 mylonite zones, recording extensional dextral displacements.\n\nD2 assemblages include coexisting garnet-orthopyroxene pairs recording peak conditions of ~ 7 kbar and ~ 780 degrees C. Subsequent retrograde decompression textures partly evolved during both D2 and D3 when conditions of ~ 4-5 kbar and ~750 degrees C were attained. This is followed by D4 shear zones which formed around 3 kbar and ~550 degrees C.\n\nIt is tempting to combine D2-4 structures in one tectonic cycle involving prograde thrusting and thickening followed by retrograde extension and uplift. The available geochronological data, however, present a number of interpretations. For example, D2 was possibly associated with a clockwise P-T path at medium pressures around ~1000Ma, by correlation with similar structures developed in the Rauer Group, whilst D3 and D4 events occurred in response to extension and heating at low pressures at ~550 Ma, associated with the emplacement of numerous granitoid bodies. Thus, decompression textures typical for the Larsemann Hills granulites maybe the combined effect of two separate events.\n\n##################\n\nThe Larsemann Hills represent a low-pressure granulite terrain with a complex structural-metamorphic history that comprises two parts: 1) granulite facies D1 structures transposed within an early form surface that probably formed at 1000 Ma, and 2) a sequence of progressive, upper amphibolite to lower granulite facies D2-D6 structures that formed during the Pan-African at 500 Ma and were associated with the emplacement of granites and pegmatites with high-grade alteration zones. D2-D6 events comprise an early form surface that has been tightly folded and sheared twice after which it was warped and transected by discrete mylonites. D2-D6 assembalges are associated with decompression textures on D1 peak-assemblages, such as cordierite coronas on garnet + sillimanite in metapelite and plagioclase coronas on garnet in metabasite. This suggests that D2-D6 formed at slightly lower pressures than D1 structures. However, the spatial correlation between the coronas and alteration zones around pegmatitc intrusives indicates that the apparent decompression textures may have partly resulted from transient fluxes in water pressure following melt crystallisation. Throughout East Antarctica tectonic provinces have been recognised in which the 1000 Ma tectonothermal events are identified as the main stage in the evolution, and Pan-African events are dismissed as a minor thermal overprint. Although the Larsemann Hills are small in area, they are representative of a great many granulite terrains in East Antarctica, and suggest that great care is needed in the structural-metamorphic analysis of such terrains to ensure the separation of tectonic stages before an interpretation of the tectonic path is attempted.", "links": [ { diff --git a/datasets/ASAC_51_1.json b/datasets/ASAC_51_1.json index 72b0709317..923a90734a 100644 --- a/datasets/ASAC_51_1.json +++ b/datasets/ASAC_51_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_51_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The structural and metamorphic history of the Proterozoic of the northern Prince Charles Mountains will be determined and compared with the geological history of the Prydz Bay region. The nature of the 1000 Ma granulite facies metamorphism, and its regional variability will be assessed. The role of fluids in this metamorphism will also be evaluated.\n\nFor more details see the full report at the provided URL.\nTemporal coverage is only approximate.", "links": [ { diff --git a/datasets/ASAC_520_1.json b/datasets/ASAC_520_1.json index 1245861b3a..e887ce7005 100644 --- a/datasets/ASAC_520_1.json +++ b/datasets/ASAC_520_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_520_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A variety of different chemical restraints, or anaesthetics, were trialed on Southern Elephant Seals at Heard Island and Macquarie Island. The trials were performed on female and juvenile seals, in their pre-moult stages.\n\nFurther information can be found in the papers listed in the reference section below.\n\nThe chemicals used in this study include:\n\ndoxapram\nketamine\nxylazine\nmidazolam\npethidine\nthiopentone\ncyclohexamine based compounds\ndiazepam\ntiletamine\nzolazepam\n4-aminopyridine\nSarmazenil\nYohimbine\n\nThe fields in this dataset are:\n\nAnaesthetic\nAntagonist\nDose\nMass\nTime\nHeart Rate\nRespiratory Rate\nSide Effects\npH\nPvO2\nPvCO2\nHCO3\nTotal CO2", "links": [ { diff --git a/datasets/ASAC_537_1.json b/datasets/ASAC_537_1.json index 734166b6d7..3feabc8515 100644 --- a/datasets/ASAC_537_1.json +++ b/datasets/ASAC_537_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_537_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctica is the world's coldest, driest, highest and least polluted continent. Accepted wisdom is that atmospheric corrosion rates in Antarctica should be low because of the extreme dry cold. Russian research suggested that temperatures below 0 degrees C alone are insufficient to eliminate corrosion although temperatures consistently below -25 degrees C will markedly decrease corrosivity. The severe and unfamiliar Antarctic conditions challenge assumptions about the behaviour of materials. In the 1960's, snow and ice was removed from Captain Scott's hut at Cape Evens revealing buried artefacts in excellent condition. The excavation changed the microclimate radically and significant deterioration of several materials, especially metals, has since occurred. The need to objectively measure corrosivity arose from the unexpectedly severe corrosion problems at several historic sites and the need to develop treatment and preventative conservation strategies. Significant corrosion problems also affect old sealing and whaling stations and artefacts on subantarctic islands. International cooperation has been sought to enable the exposure of standard steel coupons and measurement of atmospheric corrosivity rates in different climate zones in Antarctica. Ten locations on the continent and various sites on four subantarctic islands have been monitored, chosen because of the potential to access the site and availability of meteorological data from research bases and automatic weather stations. Observations are that the method is sufficiently sensitive to measure low rates of corrosion. The results are consistent with the Russian hyopothesis that temperatures below 0 degrees C alone will not significantly reduce corrosion. Steel corrosion rates range by a factor of more than 500 in Antarctica from the coast to far inland. Temperatures at coastal sites rarely exceed freezing and never at inland sites. A highly significant factor is atmospheric salt deposition since rain is rare. This project has determined that the lowest corrosivity rate ever measured is at Vostok, the coldest place on earth, which is 1200 km from the sea.\n\nThe Heard Island document available in pdf form at the provided URL is reproduced with the permission of the Papers and Proceedings of the Royal Society of Tasmania.\nThe paper was published in the Heard Island volume by the Royal Society of Tasmania (GPO Box 1166M, Hobart 7001, Tasmania, Australia) from whom the entire volume is available for A$22; plus postage (A$2;.45) for orders from within Australia and A$20; plus postage (A$6; in Asia and the Pacific and A$9; elsewhere; payment in Australian currency) for orders from beyond Australia.\n\nThe fields for this dataset are:\ndistance from sea (km)\ndays exposed\ncorrosivity\nmass loss (g)\nBlank loss (g)\n% blank loss", "links": [ { diff --git a/datasets/ASAC_555.json b/datasets/ASAC_555.json index 0665b029ab..349e1c1b80 100644 --- a/datasets/ASAC_555.json +++ b/datasets/ASAC_555.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_555", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes.\n\nData are presence absence data.\n\nSee the publication for further details.\n\nThe fields in this dataset are:\n\nSite\nName\nLatitude\nLongitude\nAltitude (m)\nWater Temperature (C)\npH\nWater Conductivity (micro siemens/cm)\nStream width (cm)\nStream Depth (cm)\nStream Velocity (cm/s)\nSpecies", "links": [ { diff --git a/datasets/ASAC_555_1.json b/datasets/ASAC_555_1.json index 7d3b773f01..3781f4e592 100644 --- a/datasets/ASAC_555_1.json +++ b/datasets/ASAC_555_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_555_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes.\n\nData are presence absence data.\n\nSee the publication for further details.\n\nThe fields in this dataset are:\n\nSite\nName\nLatitude\nLongitude\nAltitude (m)\nWater Temperature (C)\npH\nWater Conductivity (micro siemens/cm)\nStream width (cm)\nStream Depth (cm)\nStream Velocity (cm/s)\nSpecies", "links": [ { diff --git a/datasets/ASAC_556_1.json b/datasets/ASAC_556_1.json index 816724e7cc..7efcc1179e 100644 --- a/datasets/ASAC_556_1.json +++ b/datasets/ASAC_556_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_556_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underwater recordings of vocalisations of Weddell seals were obtained at 8 locations within the Vestfold Hills (7) and Larsemann Hills (1). The recordings were made near groups of seals on the ice during the mid to late part of the breeding season. Recordings were obtained using a variety of hydrophones and both Sony Digital Audio Tape (130 during 1992 season) and standard analogue cassette (60 during 1991 season) formats. Over 11,000 vocalizations were analyzed. The calls were classified into 12 major call types (Pahl et al. 1997 Australian Journal of Zoology 45:171-187). The underwater repertoire is different than that of the seals at McMurdo Sound or the Palmer Penninsula (Thomas et al. 1988 Hydrobiologica 165:279-284). The Weddell seals at the Vestfold Hills do not exhibit the between-fjord vocal differences reported by Morrice et al. (1994 Polar Biology 14:441-446). The relative usage of each call type did not vary between the earlier and later recordings (Pahl et al. 1996 Australian Journal of Zoology 44:75-79). The recordings are currently being used to support other studies on Weddell seal vocalizations.\n\nLegend for ASAC_556.csv - csv text format.\n\nThe following legend describes the 39 variables in this file. The codes for some of the variables are presented in the 1997 publication:\n\nPahl, B.C., Terhune, J.M., and Burton, H.R. 1997. Repertoire and geographic variation in underwater vocalisations of Weddell seals (Leptonychotes weddellii, Pinnipedia: Phocidae) at the Vestfold Hills, Antarctica. Australian Journal of Zoology 45: 171-187.\n\nThe fields in this dataset are:\n\nVariableSubject or code\n\n1LOCATION; recording location; see AJZ article, Figure 1\n2DATE; reference day, (date of day 1 has been lost)\n3YEAR; 1 = 1991, 2 = 1992\n4CASSETTE; cassette number, identifies individual recordings\n5CALNO; call number, case numbers of each call, sequential\n6CTYPE; call type, provisional call type, subjective initial classification (see below)\n7NOELM; number of elements (discrete sounds) in the call\n8EL_NO; element within that call relating to next 12 variables, for variable 8, only data from the first element is used\n9WVFRM; waveform of element, see AJZ article for codes\n10CLSHP; call shape, see AJZ article, Figure 2 for codes\n11E_D; duration of the first element (seconds)\n12IND1; duration of the interval between the end of the first element and the start of the second element (seconds)\n13CALLD; total duration of the call (all elements; seconds)\n14INCD; duration between sequential calls (seconds)\n15O_LAP; overlap, is call overlapped by another call? 0 = no, 1 = yes\n16S2STM; unknown measure\n17SFREQ; frequency at start of first element (Hz)\n18EFREQ; frequency at end of first element (Hz)\n19HFREQ; highest frequency of first element (Hz)\n20LFREQ; lowest frequency of first element (Hz)\n21E_NO; element number, half way through the call. Data for the next 9 variables relate to this element, applies only to multiple element calls\n22CLSHP; call shape of the middle element, same code as variable 10\n23WVFRM: waveform of the middle element, same code as variable 9\n24E_D; duration of the middle element (seconds)\n25IND1; duration of the inter-element interval before the middle element\n26IND2; duration of the inter-element interval after the middle element\n27SFREQ; frequency at start of the middle element (Hz)\n28EFREQ; frequency at end of middle element (Hz)\n29HFREQ; highest frequency of middle element (Hz)\n30LFREQ; lowest frequency of middle element (Hz)\n31E_NO; element number of the last element of the call. Data for the next 8 variables relate to this element, applies only to multiple element calls\n32CLSHP; call shape of the last element, same code as variable 10\n33WVFRM: waveform of the last element, same code as variable 9\n34E_D; duration of the last element (seconds)\n35IND2; duration of the inter-element interval before the last element\n36SFREQ; frequency at start of the last element (Hz)\n37EFREQ; frequency at end of last element (Hz)\n38HFREQ; highest frequency of last element (Hz)\n39LFREQ; lowest frequency of last element (Hz)\n\nCodes for call types (variable 6).\n\nThe provisional call types were amalgamated into 50 call types that were arbitrarily numbered from 201 to 250. These were subsequently classified into 13 broad categories (Pahl et al. 1997). The amalgamation of the provisional call types of variable 6 into the 50 call types presented in Pahl et al. (1997) is as follows:\n\nCall TypeProvisional Call Types (variable 6)\n\n2011 7, 24, 36, 72, 31, 40, 73, 77, 107, 110, 31, 136\n2023, 46, 54, 128, 33, 13, 140, 10, 25, 9, 139, 88, 46, 27, 126, 67, 91, 27,\n126, 135\n20359\n204113\n20514, 48, 69, 64, 49, 19, 92, 43, 75, 127, 99\n206122, 124\n2072, 41, 58, 93\n20847, 138\n20962, 132\n210102\n211115\n21221, 23, 45, 35\n21368, 80, 84\n214114\n2154\n216118\n21752, 78\n2185, 6, 11\n219104\n22017, 22, 65, 97, 32, 26\n22128\n22283, 100, 101, 111, 105\n22329, 30, 42, 51, 44, 94, 95\n22487\n22512\n22682\n2278\n22818, 20, 57, 108\n229109, 119\n23034, 70, 130, 53, 121\n23163\n23298, 120\n23389\n23490\n23556, 117\n23671, 106\n23785\n238103\n23974\n24096\n24176, 123, 133\n24281, 86\n24315\n244112\n24538\n24679\n24739, 127, 129, 55, 60\n24816, 37, 50\n249116\n25066\n\nFor additional information or clarification, please contact Dr. J. Terhune, Dept. of Biology, University of New Brunswick, P.O. Box 5050, Saint John, NB, Canada E2L 4L5, terhune@unbsj.ca or +1 506 648 5633.\n\nSee the link below for public details on this project.", "links": [ { diff --git a/datasets/ASAC_562_1.json b/datasets/ASAC_562_1.json index 144292db63..bb133a9648 100644 --- a/datasets/ASAC_562_1.json +++ b/datasets/ASAC_562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 562\nSee the link below for public details on this project.\n\nFrom the abstracts of the referenced papers:\n\nA regional chemical boundary termed the 'salt line',in the Vestfold Hills of East Antarctica, was investigated using X-ray diffraction and electron probe analyses of surficial salts, and conductivity of surficial sediments. West of the salt line, halite and thenardite are abundant. These salts are derived from dispersal of marine aerosols,saturation of sediment by seawater during postglacial marine transgression,and glacial dispersal of salt-saturated fjord bottom sediments. East of the salt line,subglacial calcium carbonates and salts formed by chemical weathering of their substrates may be found. The weathering products are formed from chemically and morphologically diverse minerals,which include two minerals not found previously in Antarctica, dypingite and hydromagnesite, and the first confirmed occurrence of brushite.\n\n########################\n\nThree ice dams in southeastern Vestfold Hills, East Antarctica, dam a system of five lakes periodically, impounding more than ~1.5 x 106 m3 of water. Dam #a impounds 1.1 x 106 m3 of water, while dams #b and #c prevent the free drainage of the lake below Dam #a, and impound the remaining 0.4 x 106 m3. The mode of failure of these dams and the rate of impoundment release were not known until January 1993, when dams #a and #b failed, allowing a flood to travel along a channel incised in sediment, and into Crooked Lake at greater than 8 m3s-1; four times the peak midsummer discharge of the largest stream in Vestfold Hills. The flowpath from Lake #10 is determined by which of two dams fails first; the northwestern dam (#b) allows the impoundment to travel into Crooked Lake via Grimmia Gorge (observed during January 1993), and the northern dam (#c) into Crooked Lake via Sickle Lake, Lake Verkhneye and Foot Lake (observed during 1979 and 1990). Formation and failure of these Vestfold Hi lls ice dams is similar to snow dams described from the Canadian Arctic. Floods released from the failure of the Vestfold dams provides an alternative explanation for a sudden increase in discharge at Ellis Rapids in January, 1976. This evidence of abundant meltwater is at odds with sublimation till previously described from Vestfold Hills.\n\n############################\n\nVestfold Hills, East Antarctica exhibits marked contrasts in the weathering surface, glacial sediments and terrain between its eastern and western parts. The boundary between these zones coincides with a regional chemical boundary termed the salt line. The area west of the salt line is saturated with marine-derived halite and thenardite that are particularly aggressive agents of rock weathering. In contrast, the area east of the salt line exhibits significantly fewer deposits of these salts. Rock surfaces west of the salt line are characterised by well-developed weathering forms, while glacial polish and striae are largely absent. In contrast, rock surfaces to the east commonly retain glacial polish and striae. In places, differential weathering has caused thin basaltic dykes and felsic veins to stand above the surrounding gneiss. The rate of lowering of the gneiss and dykes to the west of the salt line has been estimated at 0.024 mm and 0.015 mm per year respectively (Spate et al. 1995). These measurements suggest that the weathering surface in parts of Vestfold Hills may record more than 70 ka of subaerial exposure. Glacial sediments are much more abundant, coarser and better sorted northwest of the salt line than to the southeast. The abundant grus produced by physical weathering is coarser grained and better sorted than that produced by subglacial erosion. Such sediment lying on the land surface would be transported and redeposited during glacial advances. The change in nature of the sediments to either side of the salt line, together with the weathering forms found on clasts in the moraines, indicates that the weathering surface prior to the last glacial advance was similar to that of today and must also have developed during long periods of subaerial exposure.\n\n###########################", "links": [ { diff --git a/datasets/ASAC_565_1.json b/datasets/ASAC_565_1.json index ea52771fcd..35febaab4b 100644 --- a/datasets/ASAC_565_1.json +++ b/datasets/ASAC_565_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_565_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Project 565:\nThe database provides a list of species of ciliates and testate amoebae (Protozoa: Ciliophora; Testacea) recorded in various edaphic habitats, e.g., mineral soils (fellfield), ornithogenic soils, terrestrial mosses, from ice-free coastal areas and inshore islands in the area of Casey Station, Wilkes Land, coastal continental Antarctica. 26 ciliate (9 first records for continental Antarctica, 1 undescribed) and 5 testacean species (3 new records) were found. \n\nSea ice study (Weddell Sea):\nThe ciliate biodivesity was studied in several types of sea ice (mainly young pancake ice) from the Weddell Sea, Antarctica, in the austral autumn 1992 (March-May) during the cruise ANT X/3 of RV Polarstern. 49 ciliate species were predominantly found in sea ice and 6 spp. in the pelagial; 20 of these were new to science.\n\nA word document containing a list of species that were recorded as part of the project is available for download from the provided URL. These data have also been incorporated into the biodiversity database.", "links": [ { diff --git a/datasets/ASAC_56_1.json b/datasets/ASAC_56_1.json index ca9e8e5a65..0e3d2e33b4 100644 --- a/datasets/ASAC_56_1.json +++ b/datasets/ASAC_56_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_56_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 56\nSee the link below for public details on this project.\n\nFrom the abstract of the referenced paper:\n\nThin mafic dykes emplaced in ca. 100 Ma granulite-facies basement and Permian Amery Group strata around Radok Lake, Northern Prince Charles Mountains, are high K/Na alkaline andesites or basalts. They include three petrographic groups, namely (1) plagioclase plus or minus olivine-phyric, (2) clinopyroxene plus or minus olivine-phyric and (3) aphyric dykes. Clionpyrozene and phenocrysts in group (2) dykes comprise low-P phenocrysts, xenocrysts of possible mantle derivation, and probably high-P cognate phenocrysts. Incompatible element rations separate the dykes into three or four groups (broadly though not exactly coinciding with the petrographic subdivision) probably representing separate magmas derived either from chemically dissimilar mantle sources, or by different degrees of melting of a source in which one or more phases rich in incompatible elements (eg phlogopite) persisted in the residua. The dykes have 10 Mg/ (Mg+Fe2+) ratios and Ni and Cr abundan ces too low for primary magmas derived from commonly envisaged uppedr mantle peridotite sources [with 100 Mg/(Mg+Fe)-89]. Instead, they could represent either primary magmas derived from relatively Fe-rich mantle, or be the products of pre-emplacement fractional crystallisation of olivine and clinopyroxene from more Mg-rich parental magmas. The high incompatible-element and REE abundances of the dykes suggest that their mantle sources were either undepleted or re-enriched, and that the degree of melting was probably small. It seems likely that this alkaline magmatic activity was related to the formation of the nearby Lambert Graben.", "links": [ { diff --git a/datasets/ASAC_570_1.json b/datasets/ASAC_570_1.json index 6060cd4cf1..27b03642b2 100644 --- a/datasets/ASAC_570_1.json +++ b/datasets/ASAC_570_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_570_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 570\nSee the link below for public details on this project.\n\nFrom the abstracts of the referenced papers:\n\nThe Larsemann Hills, Princess Elizabeth Land, Antarctica, has been the site for large winter bases built by the People's Republic of China and the USSR and a small summer base built by Australia. The three bases are located within 3 km of one another. Station development has occurred since 1986 and consequently environmental changes have been very sudden. Associated with station development has been the establishment of a road network on one peninsula. Visitors to the area have risen in number from 71 in 1987 to a conservative estimate of 445 in 1991. The increased number of human visitors has resulted in rubbish and other evidence of visitation being observed over almost 10 square kilometres. This paper attempts to outline environmental impacts observed by the authors since 1986.", "links": [ { diff --git a/datasets/ASAC_589_1.json b/datasets/ASAC_589_1.json index b9e0d9c3d0..0c881bf3c1 100644 --- a/datasets/ASAC_589_1.json +++ b/datasets/ASAC_589_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_589_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Southern elephant seals are among the deepest diving of all marine mammals. This study examined physiological and behavioural mechanisms used by the seals to conserve energy while diving and estimated metabolic rate.\n\nData were collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the provided URL).", "links": [ { diff --git a/datasets/ASAC_58_1.json b/datasets/ASAC_58_1.json index 0a9b5d5015..7ac91bc34e 100644 --- a/datasets/ASAC_58_1.json +++ b/datasets/ASAC_58_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_58_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An analysis of bedrock and associated soils was conducted at a series of coastal localities in East Antarctica as well as further inland in the Prince Charles Mountains. Protozoans and micrometazoans were extracted from soil samples and an assessment made of their ecological relations with each other and with soil characteristics.\nMineral soils, regardless of topographic elevation and proximity to the coast, were characterised by large gravel fractions (fragments of underlying bedrock) and minimal clay fractions, implying that these soils were predominantly the products of physical weathering, with little chemical alteration. Only where humans, dogs or birds contributed organic matter were there elevated concentrations of nitrogen, phosphorus or organic matter.\nGenerally, mineral nitrogen did not seem to have resulted from microbial mineralisation, but at some sites there was evidence that atmospheric nitrate had been concentrated by sublimination of snow. Water-soluble and dilute acid-soluble phosphorous concentrations were surprisingly high for such organically poor soils. There was a sufficiently large labile pool of common macronutrients to sustain the autotrophic activity likely to occur within the bounds of prevailing temperatures and moisture; thus nutrients are not likely to be limiting for these soil communities.\nThere was a limited fauna. Flagellates were rare and ciliates occurred only in the coastal areas sampled, whereas amoebae were found over a greater geographic and elevational span. Micrometazoans such as rotifers, tardigrades and nematodes were more common in coastal soils than in those further inland, but occurred in soils over most of the naturally occurring range of soil moistures, acidities, nutrient levels, electrolyte levels and organic contents. Exceptions were the exclusion of rotifers from alkaline soils with high nutrient levels, and the tendency of nematodes to be absent from soils with low pH. Tardigrades were found at almost all levels of soil characterisitcs.\nThe occurrence of these metazoan phyla under such a range of environments probably resulted from their known capacity to alternate between endurance of inclement conditions in a state of deep dormancy (anhydrobiosis), and taking advantage of ephemeral favourable conditions by temporarily resuming metabolic activity. The conditions measured in soils containing micrometazoans may merely indicate thise conditions these animals can survive while dormant, not those under which active animals can carry out vital processes. In some localities there were positive associations between various taxon-pairs of metazoans and protozoans, whereas at others their occurrences seemed to be random with respect to each other.\n\nThe fields in this dataset are:\nAMOEBAE\nANHYDROBIOSIS\nMICROMETAZOAN\nSOIL\nTARDIGRADES\nlocation\nsite\nsample\nSAMPLE CODE\nPERCENT DRY WEIGHT\nPERCENT VOLUME\nbedrock type\nquartz\nplagioclase\nK-feldspar\ngarnet\nbiotite\npyroxene\nhomblende\nothers\nrock type(field sample number)catalogue NO\nmaterial\nGranitic Gneiss (BI )\nAmphibolite (B4)\nQuartz Sandstone (J1)\nGarnet Pegmatite (W3)\npH KCI(1:5) \npH H20(1:5)\nmicro siemens cm-1(1:5)\nWater-Sol. P(1:5)(micro grams mL-1)\nWater-Sol. K(1:5)(micro grams mL-1) Water-Sol. K(1:5)(micro grams g-1)Water+ Sol.K(cmol.kg-1)\nColour (Munsell)\nTotal N(micro grams g-1)\nMin. (KCI)NH4-N(micro grams g-1)\nMin. N(KCI)NH3-N(micro grams g-1)\nDilute Acid-Sol. P(micro grams g-1)\nLoss on ignition(%)\nnumber of samples from which extractions were made\nAMOEBAE\nCILIATES\nNUMBER (%) OF SAMPLES CONTAINING:ROTIFERS\nNUMBER (%) OF SAMPLES CONTAINING:TARDIGRADES\nNUMBER (%) OF SAMPLES CONTAINING:NEMATODES\nVARIABLES\nRHO\nP", "links": [ { diff --git a/datasets/ASAC_590_1.json b/datasets/ASAC_590_1.json index 30d6f18f1e..77d9489d4c 100644 --- a/datasets/ASAC_590_1.json +++ b/datasets/ASAC_590_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_590_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As top predators seabirds have the potential to accumulate marine pollutants. This study quantified heavy metal loads in 3 species of albatross.\n\nFrom the abstract of the referenced paper:\n\nCadmium and mercury concentrations were measured in the tissues of 64 individual albatrosses (23 wandering albatrosses - (Diomedea exulans), 9 royal albatrosses (Diomedea epomophora) and 32 shy albatrosses (Thalassarche cauta)) which were killed as by-catch in longline fishing activities between 1991 and 1994. Mercury concentrations were also determined for 33 shy albatross eggs (excluding shells). The birds were all sexed and assigned to one of two age classes (immature and adult). The three species exhibited differences both in overall concentrations of cadmium and mercury, and also in the pattern of accumulation of metals with age and sex. Wandering albatrosses exhibited the highest mercury concentrations with a mean concentration in adult liver samples of 920.0 plus or minus 794.1 micrograms per gram dry weight. Shy albatrosses had the lowest mercury concentrations with mean concentrations in adult livers of 36.3 plus or minus 21.4 micrograms per gram dry weight. The highest mercury concentration was 1800 micrograms per gram for an adult female wandering albatross. Cadmium concentrations were less variable, with adult royal albatrosses having the highest average concentrations (180.0 plus or minus 165.0 in adult kidneys) and adult shy albatrosses the lowest (40.1 plus or minus 20.0 in adult kidney). The highest individual cadmium concentration was 287 micrograms per gram for a juvenile wandering albatross. There was no evidence of increased accumulation of cadmium with age in any of the species, but wandering albatrosses showed higher mercury concentrations in adults than juveniles. Female wandering albatrosses also had significantly higher mercury concentrations than males. The mercury contents of the shy albatross eggs were very low, with a maximum concentration of 5.4 micrograms per gram. The results of this study are consistent with the findings of previous work on albatrosses and support the notion that the life-history strategy of these species (i.e. long-lived with low reproductive output) may be an important determinant in the concentrations of some metals found in their tissues.\n\nIt should be noted that there is a significant typographical error in the abstract of the published paper, where shy albatross mercury concentrations are expressed in milligrams instead of micrograms.", "links": [ { diff --git a/datasets/ASAC_668_1.json b/datasets/ASAC_668_1.json index 784ed3036e..e3f58bcd11 100644 --- a/datasets/ASAC_668_1.json +++ b/datasets/ASAC_668_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_668_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 668\nSee the link below for public details on this project.\n\nFrom the abstracts of some of the referenced papers:\n\nBody shrinkage may be one of the strategies that Antarctic krill use to cope with food scarcity, particularly during winter. Despite their demonstrated ability to shrink, there are only very limited data to determine how commonly shrinkage occurs in the wild. It has been previously shown that laboratory-shrunk krill tend to conserve the shape of the eye. This study examined whether the relationship between the eye diameter and body length could be used to detect whether krill had been shrinking. By tracking individuals over time and examining specimens sampled as groups, it was demonstrated that fed and starved krill are distinguishable by the relationship between the eye diameter and body length. The eye diameter of well-fed krill continued to increase as overall length increased. This created a distinction between fed and starved krill, while no separation was detected in terms of the body length to weight relationship. Eye growth of krill re-commenced with re-growth of krill following shrinkage although there was some time lag. It would take approximately 2 moult cycles of shrinkage at modest rates to significantly change the eye diameter to body length relationship between normal and shrunk krill. If krill starve for a prolonged period in the wild, and hence shrink, the eye diameter to body length relationship should be able to indicate this. This would be particularly noticeable at the end of winter.\n\nA series of experiments was carried out to examine the relationship between feeding, moulting, and fluoride content in Antarctic krill (Euphausia superba). Starvation increased the intermolt period in krill, but had no effect on the fluoride concentration of the moults produced. Addition of excess fluoride to the sea water had no direct effect on the intermoult period, the moult weight, or moult size. Additions of 6 micrograms per litre and 10 micrograms per litre fluoride raised the fluoride concentrations of the moults produced and the whole animals. The whole body fluoride content varied cyclically during the moult cycle, reaching a peak 6 days following ecdysis. Fluoride loss at ecdysis could largely be explained by the amount of this ion shed in the moult.", "links": [ { diff --git a/datasets/ASAC_67_1.json b/datasets/ASAC_67_1.json index ce7c444528..a95f13199c 100644 --- a/datasets/ASAC_67_1.json +++ b/datasets/ASAC_67_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_67_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The body surface, mouth, gills, internal organs and tissues of 368 teleost fish of 26 species from Prydz Bay, Heard Island, Macquarie Island, Davis Station and Casey Station in Antarctica were examined for parasites. At least eight species of Monogenea, seven species of Copepoda, and five or six species of Acanthocephala were recorded. Overall, the fauna of Monogenea and Copepoda of Antarctic fish is much poorer than that of lower latitudes, and there are fewer species of Gyrodactylidae relative to other Monogenea than at higher northern latitudes. Abundance and species richness of Acanthocephala are relatively high.", "links": [ { diff --git a/datasets/ASAC_687_1.json b/datasets/ASAC_687_1.json index 1765be32d4..cd3e0e2491 100644 --- a/datasets/ASAC_687_1.json +++ b/datasets/ASAC_687_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_687_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract of some of the referenced papers: \n\nAn expert system is being developed which will apply knowledge-based techniques to the automated interpretation of remotely sensed sea-ice images taken over East Antarctica by the NOAA series of meteorological satellites. It is capable of accepting satellite images, deriving characteristic features from them and then performing knowledge-based reasoning to identify regions of cloud, land, open water and various categories of sea-ice. \n\nXXXXXXXXXXXXX\n\nThis paper describes the system design of SPARTEX, a system developed to use information from remote sensing and geographic information systems linked to expert systems. It aims to automate the process of classifying information about the actual or potential use of part of the earth's surface. \n\nSee the link below for public details on this project.", "links": [ { diff --git a/datasets/ASAC_706_1.json b/datasets/ASAC_706_1.json index 3f1432483a..2668cd7029 100644 --- a/datasets/ASAC_706_1.json +++ b/datasets/ASAC_706_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_706_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data set contains sediment cores from three saline lakes in the Vestfold Hills. The three lakes are Ace Lake, Lake Pendant and Lake Abraxas. Short cores from each lake are sectioned into 1 cm intervals. One 1.8 m core from Ace Lake is sectioned into 1 cm intervals. All sediments maintained at 4 degrees C.\n\nDetailed analyses of sections for remains of invertebrates - including tintinnids, forams, copepod eggs, copepod spermatophores, rotifer loricae, rotifer eggs, copepod exoskeletons, ciliates and tardigrades.\n\nData for this project are unfortunately not available, as they have been lost. All that remains are copies of the theses produced from this work (stored at the University of Tasmania), as well as three publications, which are attached to this metadata record, and are available for download to Australian Antarctic Division staff only.\n\nTaken from the abstracts of the referenced papers:\n\nThe sediment record of the fauna of Ace Lake, a saline meromictic lake in the Vestfold Hills, Antarctica, consists of copepod eggs, spermatophores and exoskeletal fragments, rotifer and tintinnid loricae, and foraminiferal and folliculinid tests. The relative abundance of these remains, along with other characteristics of the core, allows the development of a coherent picture of the progress of Ace Lake from a species-poor, freshwater lake early in the Holocene to a biodiverse marine basin following a marine transgression. Subsequent sea level fall reformed Ace Lake as a saline lake and productivity initially increased after isolation. After a major event, possibly associated with overturn of the meromictic lake, biodiversity and productivity decreased, and have continued to do until the present.\n\nEvidence is provided from a sediment core from saline Abraxas Lake, Vestfold Hills, that indicates that the lake existed through the Last Glacial Maximum, or at most was covered by a thin, non-erosive cold-based ice sheet. The evidence for the continued existence of Abraxas Lake includes a 14C date that significantly predates the Last Glacial Maximum (though this cannot be considered direct proof of the existence of the lake prior to the Last Glacial Maximum); the presence of saline porewater throughout the core, including in compacted sediments deposited during the glacial period, which implies that the lake obtained its salt prior to any Holocene marine highstand; and the occurrence of marine-derived fauna from the onset of significant biological activity late in the Pleistocene. The occurrence of ice-free land in the Vestfold Hills and similar oases suggests that the margin of the polar ice cap did not reach far beyond its current position at the Last Glacial Maximum, at least in regions now occupied by these oases.\n\nParatrochammina minutissima n. sp. is described from Abraxas and Ace Lakes in the Vestfold Hills, East Antarctica. The species is characterised by very small size (120 microns in diameter), 4.5 chambers in the final whorl, weak adherence of particles to the tectin chamber lining and a relatively prominent proloculus. Similar species occur in the fully marine environment and often in the abyssal ocean. Subfossil tests were observed in sediment cores from Abraxas Lake, possibly indicating a lifestyle partly attached to zooplankton or floating debris, or floating unattached on density surfaces within the meromictic lake. The distribution of subfossil Paratrochammina minutissima in the sediments of Ace Lake was consistent with a marine origin for the species, while the distribution in the Abraxas Lake sediment indicated that the lake might predate the last glacial maximum.", "links": [ { diff --git a/datasets/ASAC_709_1.json b/datasets/ASAC_709_1.json index 6a459787ea..889e8529e0 100644 --- a/datasets/ASAC_709_1.json +++ b/datasets/ASAC_709_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_709_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gentoo penguins are the least numerous of the penguins breeding at Macquarie Island, and the only species to rear two chicks. This project examined the interactions between diving behaviour, diet and reproductive strategy.\n\nData were collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the url given below).", "links": [ { diff --git a/datasets/ASAC_70_1.json b/datasets/ASAC_70_1.json index cbb8541776..a94b022fa9 100644 --- a/datasets/ASAC_70_1.json +++ b/datasets/ASAC_70_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_70_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Major ion data for 41 samples from 10 lakes in the Vestfold Hills.\n\nLakes: Cemetery, Club, Deep, Dingle, Jabs, Laternula, Lebed', Oblong, Organic and Stinear\n\nParameters (measured and calculated):\n- Major anions and cations\nTotal halides (mol/kg) by potentio titrn\nCl (chlorinity (unitless, g/kg) calculated by Jacobsen and Knudsen formula\nCl (mol/kg and g/kg) calculated by difference\nBr (mmol/kg and g/kg) from titration\nSO4 (mmol/kg and g/kg) from gravimetry\ntotal alkalinity (mmol or meq/kg and g/kg as HCO3)\nCa (mmol/kg and g/kg) by photo titrn\nMg+Sr (mmol/kg) by photo titrn\nM(II) (mmol/kg) calculated by summation of photo titrn data\n* Note: M(II) = total alkaline earths (Mg+Ca+Sr)\nMg (mmol/kg and g/kg) calculated by difference\nSr (mmol/kg and mg/kg) by flame-AAS\nNa (mol/kg and g/kg) by flame-AES\nK (mol/kg and g/kg) by flame-AES\nM(II) (mmol/kg) by potentio titrn (more accurate than photo titrn data)\nMg (mmol/kg, g/kg) by difference (more accurate than data derived from photo titrn)\n\n- density of brine at 20 degrees C (g/cm3)\n* calculated values of relative density (relative to water) at 20 degrees C, and specific gravity\n\n- ion balance error (%)\n* calculated from selected mol/kg data for anions and cations\n\n- absolute salinity (g/kg)\n* calculated from selected g/kg data for anions and cations\n\n- empirical composition-chlorinity relations for VH brines\n\n- empirical and theoretical density-chlorinity or density-salinity relations\n* density expressed as relative density; vs chlorinity or absolute salinity; linear, quadratic, Root, Redlich\n* density expressed as specific gravity; vs chlorinity or absolute salinity; linear, quadratic\n\n- residual sulphate concentrations form composition analysis of brines\n\n- composition functions for VH brines\n* 'freezing' and 'gypsum' functions", "links": [ { diff --git a/datasets/ASAC_724_1.json b/datasets/ASAC_724_1.json index 53ae07950f..3b7a05fcac 100644 --- a/datasets/ASAC_724_1.json +++ b/datasets/ASAC_724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A gravity survey was undertaken in the 93/94 ANARE season between Voyages 5 and 6 (early Dec - mid Jan). The survey area was the Windmill Islands, Antarctica based at Casey Station. 53 gravity stations were obtained, including BMR 6842.0004 which is part of the ISGN 71 system. The stations have been located by an AUSLIG (now Geoscience Australia) surveying team and the data will be forwarded when it is processed. Transport for the survey was provided on station and mainly utilised helicopters. The gravity data will be processed and used to produce a Bouguer Gravity map. This map will then be interpreted in association with available geological data.\n\nThe download file contains a word document outlining the work completed, as well as several text documents of data.\n\nWINDIS is a copy of the projects report\nwindg00 is a file of principal facts for the gravity stations Suzanne acquired\nWINDISGR.txt is a file of the field data, for the gravity reduction program\nWINDISGR_gmred.txt is the output file from that program\n\n\nThe folder HYSLOP contains a letter from John Hyslop of Geoscience Australia, together with the files referred to in that letter. The coordinates in those files, if they differ from the coordinates in windg00, will be updated versions.\n\nThe fields in this dataset are:\n\nlatitude\nlongitude\neasting\nnorthing\nelevation\ngobs\nfree\nboug\nterr\n\nDescriptions of some of those fields are below:\ngobs - 'Observed Gravity' - 'Observed' gravity acceleration at the station (the result of standard manipulations of the raw gravimeter observations.\n\nfree - 'Free Air Gravity' - gravity acceleration adjusted for the height of the station above the datum (usually the geoid), and also usually with 'normal' gravity (the value of gravity at the same lat and long on the geoid) subtracted. Then known as 'Free Air Gravity Anomaly'. Function of elevation only.\n\nboug - 'Bouguer Gravity' - Free Air anomaly further adjusted, approximately, for the mass of material between the station and the datum plane. This is the Bouguer Adjustment (often Bouguer Correction, but it is not a correction as there is nothing wrong with the data), and the value is known as the Bouguer Anomaly. The header should state the density of material assumed in the calculation, but if it is not stated it would be assumed to be 2.67 t.m3 . (Given elevation, free, and boug, density can be reverse-engineered trivially anyway.)\n\nterr - 'Terrain Anomaly' - Bouguer Anomaly corrected for difference between the Bouguer Plate assumption and actual topography. The Bouguer Adjustment assumes that mass between station and datum is represented by a parallel-sided, horizontal slab; the Terrain Correction attempts to take care of the difference between that model and the real terrain.", "links": [ { diff --git a/datasets/ASAC_737_lidar_cloudcam_1.json b/datasets/ASAC_737_lidar_cloudcam_1.json index d67138e3dd..50fdfd3d0a 100644 --- a/datasets/ASAC_737_lidar_cloudcam_1.json +++ b/datasets/ASAC_737_lidar_cloudcam_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_737_lidar_cloudcam_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project wound up at the end of the 2011/2012 Antarctic season. Data have continued to be collected beyond 2012-06-30 under a new project - 4292, The Antarctic Clouds and Radiation Experiment (ACRE).\n\nThe CloudCam system captures timelapse images of the sky above Davis Station, Antarctica. So far it has been used to detect both noctilucent clouds (NLC) during summer and polar stratospheric clouds (PSC) during winter but it is also capable of imaging aurorae and tropopsheric clouds. The CloudCam system sits in a dome in the SAS building at Davis and consists of a camera, a motorised pod, a standard camera tripod and a standard PC which drives the image capture process and receives the imagery. Image capture process is driven by automated scripts on the PC which are executed as scheduled jobs using MacroScheduler software. All images are directly transferred to the PC where they are stored and processed by the scripts (no images are stored in the camera flash memory). The camera is powered from a mains outlet and connects to the PC using a standard USB 2.0 cable.\n\nMacroScheduler sripts perform the following tasks:\n- capture an image at user specified frequency,\n- transfer image to the PC,\n- time stamp the image with a user specified stamp,\n- rename the image file to YYYYMMDD_HHMM.JPG,\n- move the image file to the appropriate directory (YYYYMMDD),\n- every 24 hours make a timelapse movie of the images for a complete UTC day, and\n- create a new directory as required.\n\nThe scripts also periodically close and restart the Canon Remote Capture software. This is a workaround for a bug which periodically freezes the Remote Capture software unless it is restarted. The PC time is synchronised to the Davis SAS NTP server and all image timestamps derive from the NTP time. Currently the camera azimuth is fixed but the TrackerCam software can drive the motorised pod for azimuth tracking (eg. when the sunrise azimuth changes rapidly after the polar night and during the spring when PSCs are are still present).", "links": [ { diff --git a/datasets/ASAC_755_1.json b/datasets/ASAC_755_1.json index 15ecfa9561..873e12a37f 100644 --- a/datasets/ASAC_755_1.json +++ b/datasets/ASAC_755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstracts of some of the referenced papers:\n\nDuring November and December 1985, we examined outcrops of the Sirius Formation in the Miller, Queen Alexandra (The Cloudmaker), and Dominion ranges and on Mount Sirius. Outcrops in the Dominion Range were visited earlier. Deposits in the Miller Range and at The Cloudmaker were not previously known. In this report we provide briefly a review of preliminary data on Sirius Formation stratigraphy, sedimentation, and glacial history in the central Transantarctic Mountains. Other aspects of our research on the Sirius Formation presented in the 1986 review issue of the Antarctic Journal are: glacial history and tectonic relations; Dominion Erosion Surface topography; siliceous microfossil biostratigraphy and Pliocene marine environments; palynomorphs; modern southern hemisphere botanical analogs; and Pliocene terrestrial environments, flora, and biogeography. Microfossil analyses will be reported in the December 1987 issue of the Antarctic Journal.\n\nASAC project 52 (ASAC_52) was incorporated into this project.", "links": [ { diff --git a/datasets/ASAC_756_1.json b/datasets/ASAC_756_1.json index 88d1057f4b..01edebf797 100644 --- a/datasets/ASAC_756_1.json +++ b/datasets/ASAC_756_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_756_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 756 See the link below for public details on this project.\n\nFrom the abstract of one of the referenced papers:\n\nThe shore environments of most sub-Antarctic islands have been described in a number of previous studies. However there have been few attempts to quantify the population and community patterns over different spatial scales. The objectives of this study were to provide an analysis of the differences in the community structure of the biota of three exposed shore zones and of the macrofauna inhabiting holdfasts of the kelp Durvillaea antarctica across spatial scales of hundreds of metres, kilometers, and between a sheltered and exposed coast. Data were collected using a combination of quadrat, transect and direct sampling methods over the 1994-95 summer season. The results indicated that there were significant differences between coast for some of the biotic variables in most of the habitats examined but that differences at the smaller spatial scales were more often significant. Thus, although wave exposure exerts an obvious effect on the shore biota of Macquarie Island, these effects are modified by other factors operating at smaller spatial scales. For the holdfast macrofauna, the overall patterns of community structure are likely to be due to the differential response of the component taxa to variation in holdfast volume and holdfast sediment content as well as other, currently undetermined factors.", "links": [ { diff --git a/datasets/ASAC_757_1.json b/datasets/ASAC_757_1.json index 9297584a5c..65beb34ced 100644 --- a/datasets/ASAC_757_1.json +++ b/datasets/ASAC_757_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_757_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Prediction of future climate change requires knowledge of past changes. Polar snow forms an archive of environmental conditions that is accessible by drilling and analysing ice cores. This project uses ice core data to reconstruct records, including past temperature and atmospheric composition, to improve understanding of the climate system.\n\nReport from the 2007/2008 season\nThis proposal encompasses the laboratory-based component of ice core research at the Australian Antarctic Division. The project is principally focused on analysis of currently archived ice core material but will include analysis of new cores (to be collected in future field activities that will be the subject of separate research proposals through the duration of the project). This work is conducted as part of the ACE-CRC (Antarctic Climate and Ecosystems Cooperative Research Centre).\n\nThe overall general aim for this AAS project is to understand past climate variability and change, through the study of Antarctic ice cores. More specifically, this research explores the role of Antarctica in hemispheric and global climate, with particular emphasis on climate variability and change in the Southern Ocean, mid-latitudes, and the Australian sector. To effectively achieve this aim, we have defined four research questions, broadly based on a separation at different temporal and spatial scales:\n\n1. What do high resolution comparisons of instrumental climate data and ice cores reveal about calibration of ice core signals and underlying mechanisms?\n2. What is the spatial and temporal variability in climate across the wider East Antarctic region in the last few centuries, particularly spanning the onset of anthropogenic influence? How is this connected with overall variability in the Antarctic, and the Southern Ocean, particularly the Australian sector?\n3. What changes and modes of variability are seen in Holocene Antarctic and Southern Ocean climate from high resolution ice cores?\n4. What climate changes were seen in coastal Antarctica through the last glacial and deglaciation, and how does the timing compare with other records, especially the Northern Hemisphere records?\n\nFeeding into these research questions are a number of specific scientific objectives (listed below, with clearly identified methodology to achieve outcomes). These objectives address issues essential to a number of research fields across the Australian Antarctic program (see 3.1.3), and have been identified through knowledge gained from the earlier AAS project 757 and the scientific literature (discussed in more detail in section 3.1.2). Research will use high-resolution ice core studies as a tool to probe climate variability on timescales from seasonal through to millennial. This ability to access very high resolution climate records through ice cores is of major importance because it is the only means of calibrating the ice core recorder against observed meteorology. Also, the seasonal- to interannual-timescales capture climate variability that is not readily probed in other records. The high snow accumulation on Law Dome, combined with a 1.2km thick ice sheet, provides a unique high resolution record of the Holocene and access to the last Glacial-interglacial cycle.\n\nThe main objectives are listed below, with a brief explanation of the methodology employed to achieve these objectives:\n- Extend the time-series of ice core chemical and physical measurements\n- Focussed on East Antarctic sites, (particularly Law Dome). The length and resolution of records so far obtained will be increased and the range of measured parameters increased. This includes from the DSS core: completion of a full 90 thousand year record of trace ion data to accompany the completed d18O isotope series; high-resolution (subannual) series for trace ions and d18O over the last 2000 years; new measurements including d13CH4 (and potentially dCH3D) in collaboration with University of Colorado, NIWA and CSIRO and deuterium excess measurements using new mass-spectrometry facilities.\n\n- Calibrate ice core measurements against instrumental records\n- Calibrate ice core measurements against meteorological, and other proxy series, in order to better understand the climate signals in ice cores and to provide new proxies. This work will use ultra-high resolution data, especially through the period of instrumental overlap (for Antarctic records, this period covers the nearly 50 years since the first IGY). The study is expected to draw data from a field activity in 2008/09 summer in conjunction with IPY, which has a 'special observing period' for tracking airmasses arriving at ice core sites.\n\n\n- Investigate modes of climate variability\n- Investigate the strength, variability and alteration in modes of variability for specific climate processes, especially to examine any recent changes in these from Holocene background. In particular, processes or indices that will be explored include ENSO, the Southern Annular Mode, sea-ice extent, decadal variability in coupled ocean-atmosphere modes such as the Antarctic Circumpolar Wave (White and Peterson, 1996), atmospheric circulation indices (e.g. stratospheric markers such as nitrate or beryllium-10 and dust or trace-metal variations).\n\n- Examine response and sensitivity to forcing variations and explore mechanisms\n- This includes studies of: insolation links to climate variability, the timing and magnitude of major volcanic events, and variations associated with atmospheric composition changes (the '8200 BP' event, deglacial interhemispheric climate variations and abrupt changes in the last glacial).\n\n- Improve the understanding of the Antarctic climate system using multiple records\n- Explore relationships between the high resolution ice core records and other ice cores including the Antarctic interior to better understand both the spatial structure of the Antarctic climate system (including teleconnections), and the interpretation of the ice cores themselves.\n\n- Contribute to Antarctic mass-balance and sea-level rise\n- Derive records of accumulation input and variability for the last 100-200 years at sites in eastern Wilkes Land and for the last 90 thousand years at Law Dome. These records contribute to understanding Antarctic mass-balance and sea-level impacts.\n\n- Develop and maintain facilities and expertise for analysis of ice cores\n- Continue to develop and maintain facilities and expertise for analysis of ice cores and related climate studies. The facilities supported by this project provide a core capacity for downstream analysis and interpretation of Australian field studies, by the AAD, and also by collaborative partners in CSIRO, University of Newcastle, Curtin University of Technology as well as several important international partnerships.\n\nIn the last 12 months, the project has predominantly been in a laboratory/measurement phase and so progress is predominantly against the first and last objectives at 1.1 (Extend time series, Develop facilities). The isotope and trace chemistry records for the Law Dome cores are being extended and in-filled where gaps occur. The time series have been extended. Most measurements have been undertaken using recently drilled new core material (DSS0506 from AAS2384), as this is providing an opportunity to derive new series (deuterium) and check existing data for inter-core fidelity. New core material which brings records up to January 2008 has been analysed and the data are being combined with other cores to provide continuous series.\n\nFor the interpretive objectives, progress consists predominantly of results that have so far been presented at various meetings. We now have new data that strongly mitigate against the \"EPICA hypothesis\" that posits that sea-salts in ice cores (particularly inland cores) are specifically connected with sea-ice extent. We are able to quantify the degree of influence of sea-ice surface as a source of salt and demonstrate that it decreases with distance from the coast. We have further investigated the snowfall accumulation at Law Dome and are probing links seen to rainfall in Southwest Western Australia. We have also investigated subannual variations in snowfall accumulation and find that winter accumulation variability dominates the annual signal. We have new results from very high resolution studies of beryllium-10 which demonstrate a shorter atmospheric residence time for this cosmogenically produced species than has been accepted. This work has potential to improve the use of beryllium-10 as a proxy for solar variability and has implications for understanding of atmospheric transport.\n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nThis year's activities have been focused upon data generation and also with associated fieldwork for AAS 3025 (Aurora Basin North Ice Core Drilling). A deliberate slowing of progress on AAS 757 this year was planned because of a large investment of personnel time toward AAS 3025, however good progress has nevertheless been made.\nWhile the intention of AAS 3025 was to generate data within an independent project, field constraints forced a change to theatre of operations - providing core material that now fits within the scope of this project. This fieldwork produced ~130m of core from a new site on Law Dome (DSSW10k), extensions of the record at Law Dome Summit South (10m), new cores on the lower Totten Glacier (~17m) and Totten-Law Dome Trench (~15m) and Mill Is (~17m). Analysis of these cores within AAS 757 has already commenced.\nThe DSSW10k core provides a new ~250 year record from Law Dome that will be useful in its own right, but will provide an opportunity to test both deposition processes and ice core proxy fidelity. The core comes from a location only 10km from the main coring site, but it has only half the snow accumulation rate. Comparison of the records will allow testing of the influence of snowfall rate on preservation of ice core signals. The shallow cores at Totten Glacier and Mill Is are the first records from these locations and will permit assessment of the suitability of these sites for deeper drilling. The Totten cores may also shed light on recent accumulation changes in a location where substantial surface lowering is occurring.\nMost of the non-field activities are directed at the first and last objectives at 1.1 (Extend time series, develop facilities), although some significant work has also been conducted towards the second and third objectives (calibration of ice core records against instrumental records, and investigating modes of variability). This has been through further investigation of the modes of variability the linking Law Dome snow accumulation with rainfall in southwest Western Australia. Calibration with both ERA-40 and NCEP reanalysis data sets and investigation of links with meridional circulation have brought this work to the point where a manuscript has been submitted on the topic.\nEmerging work from a recently commenced PhD student is expanding the record of water isotopes from Law Dome cores, in particular providing a time series of deuterium excess. Early results are suggestive of a new finding in which major volcanic eruptions leave a differential signal in isotopes of hydrogen and oxygen, possibly due to stratospheric oxidation processes. Work is underway to test this.\nOther new work toward synthesis objectives includes an interdisciplinary study tying the ice core record of methanesulphonic acid into a larger consideration of seasonal phytoplankton stress and solar irradiance. A manuscript reporting this has also been submitted.\n\nThis project wound up in 2012, and was replaced by other ice-coring projects.", "links": [ { diff --git a/datasets/ASAC_757_LD2000_1.json b/datasets/ASAC_757_LD2000_1.json index 56f2ce357c..c053ec3ccd 100644 --- a/datasets/ASAC_757_LD2000_1.json +++ b/datasets/ASAC_757_LD2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_757_LD2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\"LD2000\" Isotope record: 1800AD-1999AD\" \nThis data set is that used in Schneider et al., 2006 and should be cited as such: \n\nSchneider, D. P., Steig, E. J., van Ommen, T. D., Dixon, D. A., Mayewski, P. A., Jones, J. M. and Bitz, C. M. 2006. Antarctic temperatures over the past two centuries from ice cores. Geophysical Research Letters.\"\n\nThis record, referred to as LD2000, takes the record to end 1999AD. It is based on fine annual isotope data (~10-20 samples/year), here averaged to annual values.\n\nThe dating of the record is based on absolute counting of years in isotope and trace ion data. \n\nThis record is a stack in upper portions of five different cores taken near the main DSS drill site on Law Dome, Wilkes Land. \n\nDating of DSS/DSS97/DSS99 \"Anne S. Palmer, Tas D. van Ommen, A. J. Curran, Mark, Vin Morgan, Joe M. Souney, and Paul A. Mayewski. High precision dating of volcanic events (A.D. 1301-1995) using ice cores from Law Dome, Antarctica. J. Geophys. Res., 106(D22):28,089-28,096, 2001\"\n\nCalibration of the isotopes: \nOther \"Tas van Ommen and Vin Morgan. The peroxide record from the DSS ice core, Law Dome, Antarctica: Preliminary results. In E.W. Wolff and R.C. Bales, editors, Chemical Exchange Between the Atmosphere and Polar Snow, volume 43 of NATO Advanced Sciences Institutes Series I, pages 623-627. Springer-Verlag, 1996.\"\n\n\"Tas D. van Ommen and Vin Morgan. Calibrating the ice core paleothermometer using seasonality. J. Geophys. Res., 102(D8):9351-9357, 1997\"\n\nOther information \n\"The core is dated so that the 'year' runs from one isotope maximum to the next, with the use of trace chemical and peroxide data to help resolve\" true mid-summer isotopic maxima from occasional maxima that are not mid-summer. Studies show that the mean timing of the isotope maximum \"at Law Dome is around January 10: eg. See van Ommen and Morgan, 1996 and van Ommen and Morgan, 1997\"\n\nFrom the abstract of the referenced paper:\n\nWe present a reconstruction of Antarctic mean surface temperatures over the past two centuries based on water stable isotope records from high-resolution, precisely dated ice cores. Both instrumental and reconstructed temperatures indicate large interannual to decadal scale variability, with the dominant pattern being anti-phase anomalies between the main Antarctic continent and the Antarctic Peninsula region. Comparative analysis of the instrumental Southern Hemisphere (SH) mean temperature record and the reconstruction suggests that at longer timescales, temperatures over the Antarctic continent vary in phase with the SH mean. Our reconstruction suggests that at longer timescales, temperatures over the Antarctic continent vary in phase with the SH mean. Our reconstruction suggests that Antarctic temperatures have increased by about 0.2 degrees C since the late nineteenth century. The variability and the long-term trends are strongly modulated by the SH Annular Mode in the atmospheric circulation.\n\nThis work was completed as part of ASAC project 757 (ASAC_757).", "links": [ { diff --git a/datasets/ASAC_757_LD_d18O_1.json b/datasets/ASAC_757_LD_d18O_1.json index 590208d2e6..584a547f3d 100644 --- a/datasets/ASAC_757_LD_d18O_1.json +++ b/datasets/ASAC_757_LD_d18O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_757_LD_d18O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LD2.1ky-grid4y - Law Dome d18O data 2100BP (Present=2000AD) to 1996 as 4-year averages.\n\nData set used in:\t\nJones, P.D. and M.E. Mann, Climate Over Past Millennia, Rev. Geo. 42(2), RG2002, doi:10.1029/2003RG000143, \n\t\nMann, M.E. and Jones, P.D. Global Surface Temperatures over the Past Two Millennia, GRL 30(15), 1820, doi: 10.1029/2003GL017814\t\n\t\nRecords for this site are in active development: check with tas.van.ommen@aad.gov.au for information on updated or improved Law Dome isotope data sets.\t\n\nNotes:\t\nAges older than 700BP estimated from flow-model constrained by layer-thickness data (similar to Morgan et al., J. Glaciol, 43(143), 3-10, 1997).\t\nEstimated error at 2ky BP on the order of a few decades.\t\nAges younger than 700BP from layer counting. Estimated error +/-1 year at 700BP scaling to absolute (zero error) from 193-0BP.\t\n\nd18O values in per mille. Approximate accuracy of individual measurements 0.1 per mille, but each datum presented here is an integration of between 4 and 40 individual measurements, dependent upon\tage and sampling scheme.\t\n\nFor conversion to temperature, values of ~0.2-0.3 per mille/degree C are deduced from short-term comparisons. Longer term comparison of seasonal amplitude (van Ommen and Morgan, JGR, 102(D8), 1997) indicates conversion coefficient of 0.44 per mille/degree C. Spatially derived conversion coefficients are on the order of 0.6-0.7 pe r mille/degree C, but are unlikely to be appropriate.\t\n\t\nNote on annual phasing:\t\nA year for the purposes of averaging of the resolved-annual cycles, is by convention marked by the isotope maximum. Thus, calendar year 1994, for example, starts with the isotope peak in the Austral summer of 1993/1994 and concludes at the 1994/1995 peak. Studies show that on the average, the isotope peak occurs around January 10 (van Ommen and Morgan, JGR, 102(D8), 9351-9357, 1997).\n\nThis work was completed as part of ASAC/AAS project 757.", "links": [ { diff --git a/datasets/ASAC_760_1.json b/datasets/ASAC_760_1.json index 3b4ca7c109..97999b9dd8 100644 --- a/datasets/ASAC_760_1.json +++ b/datasets/ASAC_760_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_760_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoscience Australia Geophysical Observatories\n\nNote, there are separate meta-data records for Geomagnetism and for seismology/nuclear monitoring.\n\nBrief summary: AGSO (now Geoscience Australia) operates Geomagnetic and Seismological Observatories at Mawson, Macquarie Island and Casey.\n\nThere have been geomagnetic and seismological observatories at Heard Island and Wilkes in the past, closed to open up new observatories at Mawson and at Casey.\n\nThe geophysical observatories are part of a global network. Contact Geoscience Australia for data.\n\nFurther information in the other metadata records related to this project.\n\nThe data can be accessed from Geoscience Australia's website via the provided URLs.\n\nThe geomagnetic elements X, Y and Z are the components of the vector field in the Geographic North, Geographic East and Vertical directions. They are in a cartesian coordinate system. The magnetic field is completely defined by three independent components such as X,Y and Z. It can also be expressed in polar coordinates as D,F,I where D is the declination, I is the inclination and F is the magnitude of the vector field. There is one other component used: H. This is the horizontal component. H,D and Z or D,H and F are also commonly used to define the magnetic field.\n\nThe fields in this dataset are:\n\nDate\nX (nT)\nY (nT)\nZ (nT)", "links": [ { diff --git a/datasets/ASAC_765_1.json b/datasets/ASAC_765_1.json index e099644b2d..cadf1dbf88 100644 --- a/datasets/ASAC_765_1.json +++ b/datasets/ASAC_765_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_765_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstract of some of the papers:\n\nIt has been suggested that increased springtime UVB radiation caused by stratospheric ozone depletion is likely to reduce primary production and induce changes in the species composition of Antarctic marine phytoplankton. Experiments conducted at Arthur Harbour in the Antarctic Peninsula revealed a reduction in primary productivity at both ambient and increased levels of UVB. Laboratory studies have shown that most species in culture are sensitive to high UVB levels, although the level at which either growth or photosynthesis is inhibited is variable. Stratospheric ozone depletion, with resultant increased springtime UVB irradiance, has been occurring with increasing severity since the late 1970's. Thus the phytoplankton community has already experienced about 20 years' exposure to increasing levels of UVB radiation. Here we present analyses of diatom assemblages from high-resolution stratigraphic sequences from anoxic basins in fjords of the Vestfold HIlls, Antarctica. We find that compositional changes in the diatom component of the phytoplankton community over the past 20 years cannot be distinguished from long-term natural variability, although there is some indication of a decline in the production of some sea-ice diatoms. We anticipate that our results are applicable to other Antarctic coastal regions, where thick ice cover and the timing of the phytoplankton bloom protect the phytoplankton from the effects of increased UVB radiation.\n\nGrowth rate, survival, and stimulation of the production of UV-B (280 to 320 nm) absorbing compounds were investigated in cultures of five commonly occurring Antarctic marine diatoms exposed to a range of UV-B irradiances. Experimental UV-B exposures ranged from 20 to 650% of the measured peak surface irradiance at an Antarctic coastal site (0.533 J per square metre per second). The five diatom species (Nitzschia lecointei, Proboscia alata, P. inermis, Thalassiosira tumida and Stellarima microtrias) appear capable of surviving two to four times this irradiance. In contrast to Phaeocystis cf. pouchetti, another major component of the Antarctic phytoplankton, the concentrations of pigments with discrete UV absorption peaks in diatoms were low and did not change significantly under increasing UV-B irradiance. Absorbance of UV-B by cells from which pigments had been extracted commonly exceeded that of the pigments themselves. Most of this absorbance was due to oxidisable cell contents, with the frustule providing the remainder. Survival of diatoms did not correlate with absorption by either pigments, frustules or oxidisable cell contents, indicating that their survival under elevated UV-B irradiances results from processes other than screening mechanisms.\n\nSpringtime UV-B levels have been increasing in Antarctic marine ecosystems since the 1970's. Effects on natural phytoplankton and sea-ice algal communities, however, remain unresolved. At the Marginal Ice Edge Zone, enhanced springtime UV-B levels coincide with a shallow, stratified water column and a major phytoplankton bloom. In these areas it is possible that phytoplankton growth and survival is adversely impacted by enhanced UV-B. In coastal areas, however, the sea ice, which attenuates most of the UV-B before it reaches the water column, remains until December/January, by which time UV-B levels have returned to long-term seasonal averages. Phytoplankton from these areas are unlikely to show long-term changes resulting from the hole in the ozone layer. Fjords of the Vestfold Hills, eastern Antarctica, have anoxic basins which contain high-resolution, unbioturbated sedimentary sequences. Diatom assemblages from these sequences reflect the diatom component of the phytoplankton and sea-ice algal assemblages at the time of deposition. Twenty-year records from these sequences show no consistent record of change in species composition, diversity or species richness. Six-hundred-year records from the same area also show changes in species abundance greater than those seen in the last 20 years. From these records it can be seen that recent changes in diatom abundances generally fall within the limits of natural variability and there is little evidence of recent changes that might be associated with UV-B-induced change.", "links": [ { diff --git a/datasets/ASAC_769_1.json b/datasets/ASAC_769_1.json index bcb341c82f..accdce264c 100644 --- a/datasets/ASAC_769_1.json +++ b/datasets/ASAC_769_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_769_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Elephant seals use a suite of physiological and behavioural mechanisms to maximise the time they can be submerged. Of these hypo-metabolism is one of the most important, so this study quantified maximum O2 consumptions relative to dove depth and swim speed.\n\nFrom the abstract of the referenced paper:\n\nThe ability of air-breathing marine predators to forage successfully depends on their ability to remain submerged. This is in turn related to their total O2 stores and the rate at which these stores are used up while submerged. Body size was positively related to dive duration in a sample of 34 adult female southern elephant seals from Macquarie Island. However, there was no relationship between body size and dive depth. This indicates that smaller seals, with smaller total O2 stores, make shorter dives than larger individuals but operate at similar depths, resulting in less time being spent at depth. Nine adult female elephant seals were also equipped with velocity time depth recorders. In eight of these seals, a plot of swimming speed against dive duration revealed a cloud of points with a clear upper boundary. This boundary could be described using regression analysis and gave a significant negative relationship in most cases. These results indicate that metabolic rate varies with activity levels, as indicated by swimming speed, and that there are quantifiable limits to the distance that a seal can travel on a dive of a given swimming speed. However, the seals rarely dive to these physiological limits, and the majority of their dives are well within their aerobic capacity. Elephant seals therefore appear to dive in a way that ensures that they have a reserve of O2 available.\n\nData were collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the url given below).\n\nData for this project is the same data that was collected for ASAC projects 857 and 589 (ASAC_857 and ASAC_589).", "links": [ { diff --git a/datasets/ASAC_789_1.json b/datasets/ASAC_789_1.json index 6f85dfdd45..c18b52cab7 100644 --- a/datasets/ASAC_789_1.json +++ b/datasets/ASAC_789_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_789_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The original aims of this project were:\n\nTo obtain publication standard line drawings of all stages of the thirty three species of Pterygote insect and spider which occur on Macquarie Island to illustrate the text of a comprehensive publication on the fauna which includes keys and biological information on each species.\n\nThe research that was carried out was:\n\nSpecimens of invertebrates which had been collected in previous years from Macquarie Island were selected from the collection in the Australian National Insect Collection for illustration. The artist, Karina McInnes spent two months at the University of Queensland's Department of Entomology drawing both whole animals and key characters for all the species under the supervision of R. van Klinken. Sixty four drawings of a very high quality were completed. All species of insect, terrestrial Crustacea and spider on the island were illustrated to a highly professional publishable standard. Because the artist worked faster than anticipated, more drawings were able to be completed than planned.\n\nFrom the Summary of the referenced book:\n\nSubantarctic Macquarie Island, lying nearly 1500km south-south-east of Tasmania, is uniformly cool, wet and windy. Its isolation means both the flora and fauna are depauperate in species and disjunct in composition. However, the invertebrate fauna is relatively well studied compared to other areas of Australia of similar size. In this book, I summarise the biological information available on the terrestrial and fresh water invertebrates that reside there, and provide illustrated keys to identify many of the species.\n\nAltogether there are over 350 terrestrial, fresh and brackish water species recorded from the island but a few are not residents. Of the permanent residents, approximately 12% are considered endemic and 25% are cosmopolitan in distribution. A number of transient, synanthropic and species of unknown status are known. Insects are in a minority with only 20 native resident species while the most diverse taxon is the Acarina (mites) with nearly 120 species. An approximately equal number of species have affinities with faunas of New Zealand and its cool temperate islands (10%) to the northeast or with subantarctic islands to the west (11%) but the highest number of species are of unknown distribution as they have not yet been described (30%). The remainder are varied in origin.\n\nCopies of the illustrations and the book are available for download at the provided URLs.", "links": [ { diff --git a/datasets/ASAC_825_1.json b/datasets/ASAC_825_1.json index 7fef8256a8..bd838ca887 100644 --- a/datasets/ASAC_825_1.json +++ b/datasets/ASAC_825_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_825_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of geological maps of Macquarie Island.\nTwo maps at scale of 1:25,000 and seven maps at a scale of 1:10,000", "links": [ { diff --git a/datasets/ASAC_829_1.json b/datasets/ASAC_829_1.json index 0e18273ab8..a96d4cda22 100644 --- a/datasets/ASAC_829_1.json +++ b/datasets/ASAC_829_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_829_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 829\nSee the link below for public details on this project.\n\nFrom the abstract of one of the referenced papers:\n\nDuring the intensive field operations period (November 15 to December 14, 1995) of the First Aerosol Characterisation Experiment (ACE 1) cold front activity was generally above average, resulting in below average temperatures, pressures, and rainfall. The principal cause was the presence for much of the experiment of a long wave trough. This trough was mobile, traversing the ACE area during the project, with some warm anomalies evident in teh areas under the influence of the long wave ridges. There is evidence of greater convective activity than normal, possibly leading to a slightly deeper than average mixing layer. A greater west to northwesterly component to the air flow than average during November appears to have led to higher than average concentrations of radon and particles in the clean, marine or 'baseline'; sector at Cape Grim (190 degrees to 280 degrees). This is likely to have resulted from inclusion of continental air from western parts of the Australian mainland in the baseline sector winds. Although aerosol-bound sulfur species were generally near their normal concentrations across the ACE 1 area, the overall pattern including atmospheric dimethylsulfide suggest slightly higher than usual sulfur species levels in the southern part of the region and lower concentrations in the northern part during November. This could be related to changes in marine biogenic productivity, air-sea exchange, or atmospheric removal. In December, the changing long wave pattern brought an increase in south and southwesterly flow over the entire region. The baseline sector became less affected by continental species, but it appears that the colder conditions brought by this pattern have led to lower than usual atmospheric concentrations of biogenic species, as the region went into one of the coldest summers on record.", "links": [ { diff --git a/datasets/ASAC_857_1.json b/datasets/ASAC_857_1.json index 9fd4d6946d..92e802de2b 100644 --- a/datasets/ASAC_857_1.json +++ b/datasets/ASAC_857_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_857_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Elephant seals use a suite of physiological and behavioural mechanisms to maximise the time they can be submerged. Of these hypo-metabolism is one of the most important, so this study quantified maximum O2 consumptions relative to dove depth and swim speed.\n\nFrom the abstract of the referenced paper:\n\nHeart rate, swimming speed, and diving behaviour were recorded simultaneously for an adult female southern elephant seal during her postbreeding period at sea with a Wildlife Computers heart-rate time depth recorder and a velocity time depth recorder. The errors associated with data storage versus real-time data collection of these data were analysed and indicated that for events of short duration (i.e., less than 10 min or 20 sampling intervals) serious biases occur. A simple model for estimating oxygen consumption based on the estimated oxygen stores of the seal and the assumption that most, if not all, dives were aerobic produced a mean diving metabolic rate of 3.64 mL O2 kg-1, which is only 47% of the field metabolic rate estimated from allometric models. Mechanisms for reducing oxygen consumption while diving include cardiac adjustments, indicated by reductions in heart rate on all dives, and the maintenance of swimming speed at near the minimum cost of transport for most of the submerged time. Heart rate during diving was below the resting heart rate while ashore in all dives, and there was a negative relationship between the duration of a dive and the mean heart rate during that dive for dives longer than 13 min. Mean heart rates declined from 40 beats min-1 for dives of 13 min to 14 beats min-1 for dives of 37 min. Mean swimming speed per dive was 2.1 m s-1, but this also varied with dive duration. There were slight but significant increases in mean swimming speeds with increasing dive depth and duration. Both ascent and descent speeds were also higher on longer dives.\n\nData were collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the provided URL).\n\nData for this project is the same data that was collected for ASAC projects 769 and 589 (ASAC_769 and ASAC_589).", "links": [ { diff --git a/datasets/ASAC_867_1.json b/datasets/ASAC_867_1.json index b26dbcd21a..c4c17a70a1 100644 --- a/datasets/ASAC_867_1.json +++ b/datasets/ASAC_867_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_867_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 867\nSee the link below for public details on this project.\n\nDataset\n\nSea-ice bacteria data are associated with ASAC_1012 and included there\n\nData for bacteria from ornithogenic soil samples collected from the Vestfold Hills Region is included (associated with ref 9899):\n1) Isolate designations, availability, media used and growth conditions.\n2) Phenotypic data - morphology, nutritional and biochemical traits\n3) Chemical data - fatty acids, wax esters\n4) Genotypic data - DNA base composition, DNA:DNA hybridisation analysis\n5) Phylogenetic data - 16S rRNA gene sequences\n\n\nThe download file contains:\nSample data obtained. Includes sea-ice sampling sites, location, information on ice cores including presence or absence of algal assemblage band communities and whether under-ice seawater was collected or not. Samples were melted and/or melted then filtered (0.2 micron size) for cultivation and DNA-related analyses carried out primarily in AAS project 1012.", "links": [ { diff --git a/datasets/ASAC_869_1.json b/datasets/ASAC_869_1.json index 716cf2772f..3c3e795df6 100644 --- a/datasets/ASAC_869_1.json +++ b/datasets/ASAC_869_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_869_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 869\nSee the link below for public details on this project.\n\nDataset included are for the characterisation of methanotrophs from saline lakes of the Vestfold Hills:\n \n1. Strain designations, availability, media and growth conditions employed\n2. Population data: most probable number counting and epifluorescent direct counts\n3. Phenotypic data: morphology, nutritional and biochemical characteristics\n4. Chemical characteristics: fatty acid data\n5. Genotypic data: DNA base composition and DNA:DNA hybridisation data\n6. Phylogenetic data:\n\n16S rRNA gene sequences (data stored in Genbank).\n\nThe download file contains:\n\nFile 1: Lakes sampled in project including sample type, sample depth.\nFile 2: Lake water sample chemical data (methane, oxygen, temperature and salinity).\nFile 3. Methanotroph abundance data - includes total direct cell count, methanotroph abundance estimated by most probable number counting.\nFile 4. Methanotroph phenotypic data (morphology, physicochemical and biochemical profiles).\nFile 5. Methanotroph fatty acid data determined by GC/MS analysis that could be definitively identified.\nFile 6. Methanotroph genetic comparison data \nFile 7. Text file. 16S rRNA gene sequence of representative strain from study (Methylosphaera hansonii type strain).\n\nSee the referenced publications for more information.", "links": [ { diff --git a/datasets/ASAC_874_1.json b/datasets/ASAC_874_1.json index 2b8a2a6f6a..21b947d03d 100644 --- a/datasets/ASAC_874_1.json +++ b/datasets/ASAC_874_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_874_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Using the ECMWF analyses for the three FROST periods, a data set has been extracted to show the anomalous mean sea level pressure over these periods. In addition a comprehensive analysis of all cyclones in the sub Antarctic region during the special observing periods is part of the set.\n\nFrom the abstracts of some of the referenced papers:\n\nThe data collected during the three special observing periods (SOPs) of the Antarctic First Regional Observing Study of the Troposphere project provide an excellent base upon which to study the behaviour of cyclonic systems in winter, spring, and summer in the Southern Hemisphere. This paper provides a report on the behaviour of these cyclonic systems during the three SOPs as revealed in the twice-daily ECMWF operational analyses.\nThe study has been undertaken with an objective cyclone tracking algorithm applied to the digital analyses. The results revealed cyclone behaviour generally in accord with long-term climatologies developed with this scheme. In the SOPs the authors observed many systems to be generated in the western part of the ocean basins and then to move east and, to a lesser extent, south. In the three periods they found a concentration of tracks just to the north of the Antarctic continent, being particularly noticeable in the Indian Ocean. At the same time, they found significant differences in cyclone behaviour between the climatology and the SOPs in specific regions. The monthly mean sea level pressure (MSLP) anomalies during the SOPs were quite large (and exceeded 10 hPa in places), particularly in the Pacific and in the region to the south of Australia. It appears that the anomalous cyclone structure during the SOPs could be related to the anomalies of the MSLP. The authors suggest that the three SOPs cannot be regarded as typical of their time of year, but it could be argued that no specific period could be so regarded.\nThe results obtained with these high quality analyses during the SOPs have confirmed the Antarctic coast as a region of high cyclone density and of very active cyclogenesis. The identification of these high levels of coastal cyclogenesis appears to differ from early studies that suggested the greatest (winter) cyclogenetic activity to be much farther north in the 40-50S region, The results presented here, however, concur with recent studies undertaken with high-resolution satellite data and four-dimensional data analyses, and the theoretical consequences of the baroclinic structure of the Antarctic coastal region.\n\nThe Antarctic First Regional Observing Study of the Troposphere (FROST) project had three one-month Special Observing Periods (SOPs) during which the commitment was made to ensure that all additional data collected were passed on via the Global Telecommunication System (GTS) to operational centres for use in the construction of the analyses. These analyses can be regarded as the best available for these times of year, given the special effort to include additional data south of 50S during these periods.\nThe availability of these high-quality analyses has stimulated us to refine the Melbourne University numerical cyclone tracking algorithm, with additional synoptic guidance gained from a manual analysis of southern hemisphere cyclones in the winter SOP (July 1994). Using the refined scheme we have compiled and compared statistics of cyclone tracks obtained objectively from the Australian GASP (Global Assimilation and Prediction) system analyses and manually from semi-independent analyses. Our results show that the cyclones found by the numerical and manual approaches bear considerable similarity to each other, even for complex systems for which such unanimity might not have been expected. In general, the automatic algorithm tended to 'find' more systems than did the manual analyst, with these extra systems being predominantly those identified as weak and/or open. The results emphasise the difference in perception of what constitutes a low.\nThe overall behaviour of cyclones revealed by the objective scheme in July 1994 was consistent with that identified in various climatologies in that many systems were generated in the western part of the ocean basins and moved to the east and, to a lesser extent, to the south. A concentration of tracks was found just to the north of the Antarctic continent. On the other hand, this specific month was anomalous in a number of respects; this was reflected in the nature and distribution of cyclone activity. The consistency of the findings with those of an experienced, practicing synoptician means that the state-of-the-art numerical algorithm can be applied to numerical analyses and model output with confidence.\n\nIt is argued that mathematical and numerical models can be of immense value to the climatologist and palaeoclimatologist as these tools can provide the 'glue' and framework which can tie together various pieces of climatic information. The power of these models lies in the fact that they are based on the basic physics governing the complex processes which determine climate and its variability and changes.\nThe discussion presents some specific examples of where the modelling philosophy is able to contribute significantly to the task of interpreting palaeoclimatic information, ensuring the internal consistency of proxy data, and gaining new perspectives on the climate matrix.", "links": [ { diff --git a/datasets/ASAC_887_1.json b/datasets/ASAC_887_1.json index e737ff002f..4b63166a6c 100644 --- a/datasets/ASAC_887_1.json +++ b/datasets/ASAC_887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets consist of static and kinematic GPS data collected on the Amery Ice Shelf using Leica 299 receivers. Additional GPS data were collected at Beaver Lake. All data are provided in UNIX Z compressed RINEX (Receiver INdependent EXchange) format, as described in the IGS standards - see http://www.igs.org/products\n\nThe standard RINEX file naming convention is used, i.e., an eight digit file name as bbbbddds.yyt, where bbbb refers to a four digit station name, ddd refers to the day number of the year, s refers to a session number and yyt is the file extension number where yy refers to the year and t defines the file type (o for observation file and n for navigation file). All files are compressed using the UNIX Z compression scheme, as shown by the extension .Z. For example, base0010.95o.Z and base0010.95n.Z.\n\nThe files are set out in the following directories on the ftp site:\nseason1995_1996\n\\beaver\n\\camp\n\nGPS data collected at the permanent stations at Casey, Davis and Mawson are available from Geoscience Australia - see http://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy\n\n\nThe fields in this dataset are:\nGPS\nmarker number\nmarker name\nobserver/agency\napproximate position\nantenna\nwavelength\ninterval", "links": [ { diff --git a/datasets/ASAC_915_2253_1.json b/datasets/ASAC_915_2253_1.json index d6beb8b85d..4ec7fc397d 100644 --- a/datasets/ASAC_915_2253_1.json +++ b/datasets/ASAC_915_2253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_915_2253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 915 and 2253\nSee the link below for public details on these projects.\n\nOur cetacean research is conducted on multidisciplinary cruises aimed at investigating environmental change and ecosystem effects. Our research approach now integrates broad scale acoustic monitoring with fine scale ecology experiments during annual surveys with AMLR. These data will allow us to connect fine scale variability with regional and circum-Antarctic processes, and eventually to understand how the dynamics of the Antarctic ecosystem and environmental change might affect the recovery of whale populations. The BROKE WEST multidisciplinary survey to be held in the 2005/2006 season will provide a large-scale simultaneously collected dataset within which to analyse the cetacean distribution, ecological and acoustic data.\n\nThese sightings were made on Australian Antarctic Division voyages. For further information about these voyages, see the URL given below. Codes provided in the download file for voyage come in two formats:\n\nV70102 - Voyage 7 of the 2001/2002 season\nKK0102 - Use of the Kapitan Khlebnikov by the Australian Antarctic Division in the 2001/2002 season.\n\nThe download file will include an excel spreadsheet of sightings, resightings and incidental sightings, as well as an explanatory word document. For further details on methods used, and an explanation of the types of data collected, see the above mentioned word document.\n\nThese data were collected as part of ASAC projects 915 and 2253 (ASAC_915 and ASAC_2253).\n\nThe fields in this dataset are:\n\nVoyage\nData Logger (Logger/Wincruz)\nDate\nTime\nObserver\nMethod\nBearing\nDistance (nautical miles)\nSwim Direction\nNear Ice\nSpecies\nReaction\nGroup Size\nLatitude\nLongitude", "links": [ { diff --git a/datasets/ASAC_919_1.json b/datasets/ASAC_919_1.json index cc433f89e1..db23cc72de 100644 --- a/datasets/ASAC_919_1.json +++ b/datasets/ASAC_919_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_919_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 919.\nSee the link below for public details on this project.\n\nThe plankton dynamics of Ace Lake, a saline, meromictic basin in the Vestfold Hills, eastern Antarctica was studied between December 1995 and February 1997. The lake supported two distinct plankton communities; an aerobic microbial community in the upper oxygenated mixolimnion and an anaerobic microbial community in the lower anoxic monimolimnion. Phytoplankton development was limited by nitrogen availability. Soluble reactive phosphorus was never limiting. Chlorophyll a concentrations in the mixolimnion ranged between 0.3 and 4.4 micrograms per litre during the study period and a deep chlorophyll maximum persisted throughout the year below the chemo/oxycline. Bacterioplankton abundance showed considerable seasonal variation related to light and substrate availability. Autotrophic bacterial abundance ranged between 0.02 and 8.94 x 10 to the 8 per litre and heterotrophic bacterial abundance between 1.26 and 72.8 x 10 to the 8 per litre throughout the water column. the mixolimnion phtyoplankton was dominated by phytoflagellates, in particular Pyramimonas gledicola. P. geldicola remained active for most of the year by virtue of its mixotrophic behaviour. Photosynthetic dinoflagellates occurred during the austral summer, but the entire population encysted for the winter. Two communities of heterotrophic flagellates were apparent; a community living in the upper monimolimnion and a community living in the aerobic mixolimnion. Both exhibited different seasonal dynamics. The cliliate community was dominated by the autotroph Mesodinium rubrum. The abundance of M. rubrum peaked in summer. A proportion of the population encysted during winter. Only one other ciliate, Euplotes sp., occurred regularly. Two species of Metazoa occurred in the mixolimnion; a calanoid copepod (Paralabidocera antarctica) and a rotifer (Notholca sp.). However, there was no evidence of grazing pressure on the microbial community. In common with most other Antarctic lakes, Ace Lake appears to be driven by 'bottom-up' forces.\n\nThe fields in this dataset are:\nAce Lake\nAerobic monimolimnion\nAmmonia\nAmmonium\nAsh free dry weight\nAutotrophic Bacteria\nBacterial Production Leucine\nBacterial Production Thymidine\nBiomass\nCarbon\nCell\nChlorophyll a\nConcentration\nCopepods\nDate\nDate Code\nDepth\nDiatoms\nDinoflagellates\nDissolved Organic Carbon\nDissolved Oxygen\nDoubling\nGeneration Time\nHeterotrophic Bacteria\nHeterotrophic Nanoflagellates\nIce Thickness\nIntrinsic Growth Rate\nJulian Day\nJulian Month\nMesodinium rubrum\nMesodinium rubrum cysts\nMixolimnion\nMonimolimnion\nNauplii\nNitrate\nNitrite\nNotholca sp.\nOther Ciliates\nOxygenated strata\nParalabidocera antarctica copepodid\nParalabidocera antarctica naupliar\nParticulate Organic Carbon\nPhosphate\nPhototrophic Nanoflagellates\nSalinity\nSeason\nSoluble Reactive Phosphorus\nTotal Ciliates\nWater Temperature", "links": [ { diff --git a/datasets/ASAC_933_1.json b/datasets/ASAC_933_1.json index 0a1624e45b..e57c96a922 100644 --- a/datasets/ASAC_933_1.json +++ b/datasets/ASAC_933_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_933_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 933\nSee the link below for public details on this project.\n\nAustralian Antarctic and Southern Ocean Profiling Project (AASOPP) was the outcome of a government decision in 1999 that it would carry out the necessary work to place Australia in a position to be able to prepare a submission defining the outer limit the 'extended Continental Shelf' (ECS) off the Australian Antarctic Territory (AAT). The ECS is the area of seabed/subsoil jurisdiction extending beyond the 200 nautical mile Exclusive Economic Zone, and is defined by Article 76 of the United Nations Convention on the Law of the Sea. \nAASOPP was set up in 2000, under the management of the Department of Finance and Administration and in consultation with the Australian Antarctic Division, to undertake the acquisition and interpretation of the data that would underpin a UN submission. Technical aspects of the work were largely the responsibility of the Australian Geological Survey Organisation and the Australian Surveying and Land Information Group (later Geoscience Australia).\nMarine geophysical surveys were conducted in 2001/2 and 2002/3 by the primary contractors, FUGRO Geoteam supervised by AGSO (Geoscience Australia) using the vessels Geoarctic and Polar Duke (survey numbers GA227, GA228 and GA229). Data collected were seismic reflection, sonobuoy seismic refraction, magnetic and gravity profiles. Data processing was supervised by Geoscience Australia where they are archived. Seismic data were lodged with the SCAR Seismic Data Library.\nLaw of the Sea interpretations were lodged as part of the Australian submission to the United Nations by November, 2004 with a request not to examine the Antarctic case until requested.", "links": [ { diff --git a/datasets/ASAC_941_1.json b/datasets/ASAC_941_1.json index 0021436f33..9e9c104893 100644 --- a/datasets/ASAC_941_1.json +++ b/datasets/ASAC_941_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_941_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We investigated how surface reflectance properties and pigment concentrations of Antarctic moss varied over species, sites, icrotopography, and with water content. We found that species had significantly different surface reflectance properties, particularly in the region of the red edge (approximately 700 nm), but this did not correlate strongly with pigment concentrations. Surface reflectance of moss also varied in the visible region and in the characteristics of the red edge over different sites. Reflectance parameters, such as the Photochemical Reflectance Index (PRI) and Cold Hard Band (CBH) were useful discriminators of site, microtopographic position and water content. The PRI was correlated both with the concentrations of active xanthophyll-cycle pigments and the photosynthetic light use efficiency, Fv/Fm, measured using chlorophyll fluorescence. Water content of moss strongly influenced the amplitude and position of the red-edge as well as the PRI, and may be responsible for observed differences in reflectance properties for different species and sites. All moss showed sustained high levels of photoprotective xanthophyll pigments, especially at exposed sites, indicating moss is experiencing continual high levels of photochemical stress. \n\nThe fields in this dataset are:\n\nSample\nRidge/Valley\nSite\nSpecies\nPigments\n\nThe site codes used in this dataset are:\nROB = Robinson Ridge (Windmill Islands)\nRS = Red Shed (Casey Living Quarters - inside station limits)\nSC = Science Building (Casey - inside station limits)\n\nThe species codes used in this dataset are:\nB = Bryum pseudotriquetrum\nC = Ceratodon purpureus\nG= Grimmia antarctici", "links": [ { diff --git a/datasets/ASAC_949_1.json b/datasets/ASAC_949_1.json index 008cf180f7..70ed6c0f42 100644 --- a/datasets/ASAC_949_1.json +++ b/datasets/ASAC_949_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_949_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim is to measure structure and drift in polar-cap ionospheric layers in order to test current theories and models, identify unexplained behaviour and develop theoretical explanations. The polar cap ionosphere is affected significantly by conditions in the solar wind, so it is important to understand this outer region of our environment, not just to extend our knowledge, but to improve space weather predictions. Specific outcomes will include advances in understanding and predicting the behaviour of the polar cap ionosphere, development of new ionospheric radar techniques, improved models of the polar cap electric field and training of postgraduate students.\n\nThis project is also related to ASAC project 128 (ASAC_128).", "links": [ { diff --git a/datasets/ASAC_953_1.json b/datasets/ASAC_953_1.json index 112a41a56e..aa03788c2e 100644 --- a/datasets/ASAC_953_1.json +++ b/datasets/ASAC_953_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_953_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data revealing the incidence of bacterial, viral and parasitic disease causing agents in Antarctic bird populations. Samples for disease analysis have been collected from various species of Antarctic birds during the course of ASAC project 953 and are stored at the Department of Microbiology, University of Western Australia and CSIRO Australian Animal Health Laboratories (AAHL). All analysis is being performed at the Department of Microbiology, University of Western Australia. A summary of samples collected and stored for each species is listed below.\n\nAdelie penguin (Pygoscelis adeliae): Serum (blood) and faecal (cloacal) swabs were collected from chicks and adults in the Mawson station area, the Vestfold Hills and Terra Nova Bay. Samples from approximately 1200 birds have been stored. Tissue samples have been collected from chick carcasses found in the Vestfold Hills area. Carcasses were collected on an opportunistic basis.\n\nEmperor penguin (Aptenodytes forsteri): Serum (blood) and faecal (cloacal) swabs were collected from chicks at Amanda Bay, Auster and Cape Washington. Tissue samples have been obtained from 20 chick carcasses collected from Auster Rookery.\n\nSouth polar skua (Catharacta maccormicki): Serum (blood) and faecal (cloacal) swabs were collected from 125 adult birds in the Vestfold Hills area.\n\nThis project has close ties with ASAC project 1336 (ASAC_1336 - South polar skuas as vectors of disease). See that metadata record for related datasets.\n\nThe fields in this dataset are:\n\nInfectious bursal disease virus\nAvian influenzae\nAvian adenovirus\nSample\nSpecies\nAge\nYear\nRegion\nColony Location\nStage of Breeding Season\nBlood Sample\nCloacal Swab\nSerum", "links": [ { diff --git a/datasets/ASAC_966_1.json b/datasets/ASAC_966_1.json index 1d25699b0c..897b64ec76 100644 --- a/datasets/ASAC_966_1.json +++ b/datasets/ASAC_966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the abstracts of the referenced papers:\n\nTechniques for Pb measurements have reached the stage where Antarctic ice with sub-picogram per gram concentrations can be reliably analysed for isotopic composition. Here, particular attention has been given to measuring the quantity of Pb added during the decontamination and sample storage stages of the sample preparation process because of their impact on accuracy at low concentrations. These stages, including the use of a stainless steel chisel for the decontamination, contributed ~5.2pg to the total sample analysed, amounting to a concentration increase of ~13fg per gram, which is significantly less than expected. Consequently the corrections to the isotopic ratios and concentration were also smaller. Other contributions to the blank, such as Pb fallout onto critical working areas in the HEPA-filtered laboratories, were also relatively small as was the amount of Pb leached from preconditioned perfluoroalkoxy (PFA) beakers during sample processing. The ion source contributed typically 89 plus or minus 19 fg to the blank. Although this was relatively large, its influence depended upon the amount of Pb available for analysis and it had the greatest impact when small volumes of samples with a very low concentration were analysed. A 15 months investigation of the leaching characteristics of Pb from a low-density polyethylene (LDPE) sample storage bottle showed 11 fg per cm per cm per day was released immediately following the initial 2 months cleaning process, but this decreased to immeasurable values after a further 3 months of cleaning. \n\nLead isotopic compositions and Pb and Ba concentrations have been measured in ice cores from Law Dome, East Antarctica, covering the past 6500 years. 'Natural' background concentrations of Pb (~0.4 pg/g) and Ba (~1.3 pg/g) are observed until 1884 AD, after which increased Pb concentrations and lowered 206Pb/207Pb ratios indicate the influence of anthropogenic Pb. The isotopic composition of 'natural' Pb varies within the range 206Pb/207Pb=1.20-1.25 and 208Pb/207Pb=2.46-2.50, with an average rock and soil dust Pb contribution of 8-12%. A major pollution event is observed at Law Dome between 1884 and 1908 AD, elevating the Pb concentration four-fold and changing 206Pb/207Pb ratios in the ice to ~1.12. Based on Pb isotopic systematics and Pb emission statistics, this is attributed to Pb mined at Broken Hill and smelted at Broken Hill and Port Pirie, Australia. Anthropogenic Pb inputs are at their greatest from ~1900 to ~1910 and from ~1960 to ~1980. During the 20th Century, Ba concentrations are consistently higher than 'natural' levels and are attributed to increased dust production, suggesting the influence of climate change and/or changes in land coverage with vegetation.\n\nThe fields in this dataset are:\n\nVeneer\nMass\nPb\nConcentration\n206Pb/207Pb", "links": [ { diff --git a/datasets/ASAC_973_1.json b/datasets/ASAC_973_1.json index b22098e80f..8f2abb18fa 100644 --- a/datasets/ASAC_973_1.json +++ b/datasets/ASAC_973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated field, geochemical, isotopic and geochronological study of Gaussberg: Constraints on timing, character and petrogenesis of holocene lamproitic volcanics in the eastern Antarctica Shield and the nature of the underlying lithosphere.\n\nSee the link below for public details on this project.\n\nAvailable for download are three tables of data in spreadsheet form, as well as two papers arising from the work (in pdf format).\n\nFrom the abstracts of the attached papers:\nPetrogenetic models for the origin of lamproites are evaluated using new major element, trace element, and Sr, Nd, and Pb isotope data for Holocene lamproites from the Gaussberg volcano in the East Antarctic shield. Gaussberg lamproites exhibit very unusual Pb isotope compositions (206Pb/204Pb = 17.44-17.55 and 207Pb/204Pb = 15.56-15.63), which in common Pb isotope space plot above mantle evolution lines and to the left of the meteorite isochron. Combined with very unradiogenic Nd, such compositions are shown to be inconsistent with an origin by melting of sub-continental lithospheric mantle. Instead, a model is proposed in which late Archaean continent-derived sediment is subducted as K-hollandite and other ultra-high pressure phases and sequestered in the Transition Zone (or lower mantle) where it is effectively isolated for 2-3 Gyr. The high 207Pb/204Pb ratio is thus inherited from ancient continent derived sediment, and the relatively low 206Pb/204Pb ratio is the result of a single stage of U/Pb fractionation by subduction-realted U loss during slab dehydration. Sr and Nd isotope ratios, and trace element characteristics (eg Nb/Ta rations) are consistent with sediment subduction and dehydration-related fractionation. Similar models that use variable time of isolation of subducted sediment can be derived for all lamproites. Our interpretation of lamproite sources has important implications for ocean island basalt petrogenesis as well as the preservation of geochemically anomalous reservoirs in the mantle.\n\nThe first terrestrial Pb-isotope paradox refers to the fact that on average, rocks from the Earth's surface (ie the accessible Earth) plot significantly to the right of the meteorite isochron in a common Pb-isotope diagram. The Earth as a whole, however, should plot close to the meteorite isochron, implying the existence of at least one terrestrial reservoir that plots to the left of the meteorite isochron. The core and the lower continental crust are the two candidates that have been widely discussed in the past. Here we propose that subducted oceanic crust and associated continental sediment stored as garnetite slabs in the mantle Transition Zone or mid-lower mantle are an additional potential reservoir that requires consideration. We present evidence from the literature that indicates that neither the core nor the lowest crust contains sufficient unradiogenic Pb to balance the accessible Earth. Of all mantle magmas, only rare alkaline melts plot significantly to the left of the meteorite isochron. We interpret these melts to be derived from the missing mantle reservoir that plots to the left of the meteorite isochron but significantly, above the mid-ocean ridge basalt (MORB)-source mantle evolution line. Our solution to the paradox predicts the bulk silicate Earth to be more radiogenic in 207Pb/204Pb than present-day MORB-source mantle, which opens the possibility that undegassed primitive mantle might be the source of certain ocean island basalts (OIB). Further implications for mantle dynamics and oceanic magmatism are discussed based on a previously justified proposal that lamproites and associated rocks could derive from the Transition Zone.\n\nSee the papers for full details of the data tables.\n\nThe fields in this dataset are:\n\nSite\nSilicon dioxide\nTitanium dioxide\nAluminium oxide\nT-Iron three oxide\nManganese oxide\nMagnesium oxide\nCalcium oxide\nSodium oxide\nPotassium oxide\nPhosphorous pentoxide\nLithium\nBeryllium\nScandium\nVandium\nChromium\nCobalt\nNickel\nCopper\nZinc\nGallium\nRubidium\nStrontium\nYttrium\nZirconium\nNiobium\nTin\nCesium\nBarium\nLanthanum\nCerium\nPraseodymium\nNeodymium\nSamarium\nEuropium\nTerbium\nGadolinium\nDysprosium\nHolmium\nErbium\nThulium\nYtterbium\nLutetium\nHafnium\nTantalum\nLead\nThorium\nUranium", "links": [ { diff --git a/datasets/ASAC_974_1.json b/datasets/ASAC_974_1.json index f96cb361e3..e7800298ff 100644 --- a/datasets/ASAC_974_1.json +++ b/datasets/ASAC_974_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_974_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected under a collaborative arrangement between the Australian Antarctic Division (Principal Investigator: Gary Burns) and the Russian Antarctic Expeditions (Chief Russian Investigator: Oleg Troshichev, Institute of Arctic and Antarctic Studies, St Petersburg).\n\nVertical electric field data were collected with an electric field mill (EFM) at Vostok intermittently over the interval 1998-2004. [A different electric field mill was installed at Vostok in late December 2006, and metadata from this instrument is found at ASAC_974_2]. The data were initially collected with 10 second resolution. The EFM is calibrated monthly by placing a Faraday box containing parallel plates over the rotating dipole. A range of voltages are applied to the plates and the instrument is calibrated in volts per metre relative to the calibration box. Absolute values are not possible to determine, as the instrument compression is unknown. The values should be treated as relative.\n\nAn additional factor to be noted is the height of the EFM. When it was initially installed, it was on a metal pole ~1.5 m above the snow surface. When the site was visited in December 2005, the height of the pole indicated the instrument would have been 1.05m above the snow surface. A change in height will alter the instrument compression (higher height implies larger instrument compression). It would be extremely difficult to accurately interpret these data to determine a long term trend.\n\nThe 10 second resolution data have been averaged to yield minute resolution values. Calibrations are linearly interpolated and have been applied to the data.\n\n1 minute averages are provided in this download for 1998-2004. 10 second averages are provided for 2005-2006.\n\nFiles of the form \"VEFCalibrationInfo_1998.txt\" indicate the calibration data that has been applied to each year.\n\nAny data for any month is in files of the form: \"VosEF_1998_01.txt\", where \"1998\" indicates the year and \"01\" indicates the month.\n\nThe monthly data files list the data as \"Year,DoY,UT Hour,UT Min,EF-all(V/m),EF-fair(V/m)\". This is the also the first line of each monthly file.\n\"Year\" is a 4 digit representation of the year.\n\"DoY\" is the day-of-year number from 1 to 365/366.\n\"UT Hour\" is the Universal Time hour for the observation.\n\"UT Min\" is the Universal Time minute for the observation.\n\"EF-all(V/m)\" is the electric field value determined for that minute, with the calibration applied, independent of whether the data are selected as 'fair-weather'.\n\"EF-fair(V/m)\" is the electric field value determined for that minute, with the calibration applied, IF that data has been selected as 'fair-weather'.\n\nAn outline of how 'fair-weather' has been determined is given in the publication: Burns, G.B., Frank-Kamenetsky, A.V., Troshichev, O.A., Bering, E.A., Reddell, B.D. (2005) Interannual consistency of bi-monthly differences in diurnal variations of the ground-level, vertical electric field. Journal of Geophysical Research 110, D10106. doi:10.1029/2004JD005469.\n\nThe data are comma delimited in the monthly processed data files.\nThus the line\n\n1998,60,0,25,179.14,179.14\nmeans a fair-weather value of 179.14 volts per metre (relative to the calibration box) is determined for the 25th minute, of the zeroth UT hour, of the 60th day of the year 1998.\n\n1998,60,3,7,244.75,\nmeans a value of 244.75 volts per metre (relative to the calibration box) is determined for the 7th minute, of the 3rd UT hour, of the 60th day of the year 1998; and that this value is not determined to be 'fair-weather'.\n\nThe monthly processed data files list every minute for the month, even if there is no value for that minute. A lack of data is indicated by an absence of values, but a retention of the separating comma.\n\nThus the line\n1998,62,16,8,,\nindicates that no values were recorded for the 8th minute, of the 16th UT hour, of the 62nd day of the year 1998.\n\n'Fair-weather' data were recorded at Vostok ~55% of the time the instrument was operational, but there are seasonal and summer diurnal variations. [See previous reference]. Please note: Vostok is a difficult operational environment and the electric field mill suffered considerable intervals of instrument failure. If there is no monthly file, then no data were recorded for that month.\n\nVostok was not occupied during 2003, so no data exists for that time.\n\nThe electric field mill used to collect these data incorporated carbon brush commutators to rectify the signal from the rotating dipole. Over the years of operation, wear on these led to greater variability in the monthly calibrations. As discussed in the paper previously referenced, the monthly calibration data for 2002 were twice as variable as any of the earlier years and thus only data from the years 1998 through 2001 were combined to yield seasonal-diurnal averages. The 2004 data (not available at the time of the referenced analysis) have a large monthly variability, but not as great as 2002. When utilising the data, these difficulties need to be considered. The yearly calibration data files can be used to quantify the level of uncertainty in the measurements.", "links": [ { diff --git a/datasets/ASAC_987_1.json b/datasets/ASAC_987_1.json index c272628aaa..055fe9ddfc 100644 --- a/datasets/ASAC_987_1.json +++ b/datasets/ASAC_987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project has no actual data to archive, but several pdf copies of publications produced from this work are available for download to AAD staff only from the provided URL.\n\nTaken from the abstracts of some of the referenced papers:\n\nWater isotopes are commonly used as indicators of climate state even though many biases and variations in processes affecting the polar signal have not been quantified. Results from the Melbourne University General Circulation Model suggest the annual cycle explains half of the monthly d18O variance, and a semi-annual variation contributes more than 15 in places. Eddy moisture convergence drives gross accumulation, while stationary flux allows sublimation of 25-30% of the precipitation. Part of the monthly anomaly variance is associated with a dominant annular disturbance in the circulation. This oscillatory mode alters the character of the transport processes through changes to the preferred location and strength of baroclinic cyclones. A Rayleigh model indicates that a third of the continental d18O anomaly can be explained by temperature-dependent fractionation, while changes to the condensation give 3 times too much depletion. The residual is explained by the migration of the zone from which mid-latitude air is entrained into the polar environment by cyclonic storms. The positive phase of the annular mode is associated with an increased contribution from the near-coastal region, which enriches the continental precipitation. Such vacillation introduces bias in reconstruction using modern analogues because the spatial temperature-isotope slope is modified.\n\n############\n\nThe Melbourne University spectral atmospheric general circulation model is adapted to include prediction of stable water isotopes. The new scheme performs well when the modeled d 18O of precipitation is compared to both monthly observations from a global network and high-frequency measurements from two neighbouring southern Australian sites. The associations between the modelled isotopic signal, temperature, and precipitation are examined on a variety of timescales by exploring the spatial distribution of temporal partial correlations. In contrast to the view commonly taken in palaeoclimate studies, typically less than 20% of d 18O variance can be explained by temperature changes. The association with temperature is strongest when daily data are considered while the precipitation is more important on longer (interannual) timescales. This shows that as information about individual events is lost through the averaging process, simple distillation models, which have a strong theoretical temperature dependence, become less applicable. It is suggested that reconstruction of precipitation is more reliable on timescales longer than those considered, and the temperature dependence of precipitation facilitates an association between temperature and d 18O in proxy records. The small magnitudes of the correlation coefficients suggest that direct interpretation of proxy records such as temperature, or precipitation, should proceed under utmost scrutiny because reconstruction is far more complex than the simple problem of local regression. Specifically, should strong associations with temperature or precipitation exist, it is only partially due to the henomenological covariance at the deposition site. As such, relationships used for palaeoclimate reconstruction that incorporate information about the origin and condensation history of the moisture should be encouraged in place of overly simplistic relationships that involve just local conditions.", "links": [ { diff --git a/datasets/ASAC_988_1.json b/datasets/ASAC_988_1.json index e5eca01e4e..6d39ac40ba 100644 --- a/datasets/ASAC_988_1.json +++ b/datasets/ASAC_988_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_988_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Some scanning electron microscope images were taken of dinoflagellates sampled as part of this project. A catalogue of the images taken is provided as part of the download file at the provided URL. The images are currently held by the Electron Microscope Unit of the Australian Antarctic Division, but have not yet been entered into their electron microscope database (as at the 30th of April, 2004).\n\nFrom the abstracts of the referenced paper:\n\nThe abundance and biomass of ciliates, dinoflagellates and heterotrophic and phototrophic nanoflagellates were determined at three sites along an ice-covered Antarctic fjord between January and November 1993. The water column showed little in the way of temperature and salinity gradients during the study period. In general, the protozooplankton exhibited a seasonal variation which closely mirrored that of chlorophyll a and bacterioplankton. The fjord mouth, which was affected by the greatest marine influences, consistently had the highest densities of ciliates and the most diverse community, with up to 18 species during the sampling period. Small aloricate ciliates were present throughout the year with Strobilidium spp. being dominant during the winter. Larger loricate and aloricate ciliates became more prominent during January and November, along with the autotrophic ciliate Mesodimium rubrun and two mixotrophic species (Strombidium wulffi and a type resembling Tontonia) suggesting evidence of species successions. Data on dinoflagellates were less extensive, but these protists showed greatest species diversity in the middle reaches of the fjord. A total of 13 species of dinoflagellate were recorded.\n\nCiliates made a significant contribution to the biomass of the microbial community in summer, particularly in the middle and at the seaward end of the fjord. In winter, heterotrophic flagellates (HNAN) and phototrophic nanoflagellates (PNAN) were the dominant component of protistan biomass. In terms of percentage contribution to the microbial carbon pool, bacteria dominated during winter and spring. To the authors' knowledge, this is the first seasonal study of an Antarctic fjord. The Ellis Fjord is very unproductive compared to lower latitude systems, and supports low biomass of phytoplankton and microbial plankton during most of the year. This relates to severe climatic and seasonal conditions, and the lack of allochthonous carbon inputs to the system. Thus, high latitude estuaries may differ significantly from lower latitude systems, which generally rank among the most productive aquatic systems in the world.\n\nThe fields in this dataset are:\nEMU Image Number\nFiona Scott Image Number\nSpecies\nSEM Stub Number\nLocation\nCollector", "links": [ { diff --git a/datasets/ASAC_992_1.json b/datasets/ASAC_992_1.json index 20846fa444..1e1516655f 100644 --- a/datasets/ASAC_992_1.json +++ b/datasets/ASAC_992_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_992_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quartz stone sublithic microbial communities and underlying soil, retrieved from the Vestfold Hills were investigated using a variety of traditional and molecular methods. Although direct epifluorescent counts of the sublithic biota averaged 1.1 x 109 cells g-1 dry weight and underlying soil, 0.5 x 109 cells g-1 dry weight, viable counts were on average 3-orders of magnitude higher in sublithic samples cf. underlying soil. Enrichment and molecular analyses revealed the predominant cyanobacteria were non-halophilic, able to grow optimally at 15-20 degrees C, and were related to the Phormidium subgroup with several distinct morphotypess and phylotypes present. Sublithic heterotrophic bacterial populations and those of underlying soils included mostly psychrotolerant taxa typical of Antarctic soil. However, psychrophilic and halophilic bacteria, mostly members of the alpha subdivision of the Proteobacteria and the order Cytophagales, were abundant in the sublithic growth (20-40% of the viable count and about 50% of isolated individual taxa) but absent from underlying soils. It is suggested that quartz stone subliths might constitute a 'refuge' for psychrophilic bacteria.\n\nThe download file contains:\n8 files:\nFile 1. - Sample location data\". Lists quartz stones collected for analysis, indicating site location, date of sampling, description of surrounding area and indicates whether or not soil immediately adjacent to the stone was collected.\nFile 2. - Gross colony diversity data quartz stone sublith\". Indicates colony diversity (indicated by number of grossly different colony types on agar medium used).\nFile 3. - Strain information quartz stone sublith isolates\". Lists strains isolates, including strain designation, stone sample origin, and basic cellular and colonial morphological characteristics.\nFile 4. - Temperature/salinity response data\". \". Lists responses of quartz stone sublith isolates to temperature and tolerance to salinity.\nFile 5. - Phenotypic data\". Lists detailed phenotypic data for select quartz stone sublith isolates.\nFile 6. - Cell population data quartz stone subliths and soil\". Indicates plate count estimates obtained after use of different media (salty, non-salty, dilute, non-dilute) as well as epifluoresecent direct count data for both quartz stone subliths and for immediately adjacent soil not covered by the stone.\nFile 7. - Bacterial isolate 16S rRNA gene sequence data\". \" Sequence data for quartz stone sublith derived isolates. All sequences deposited on the GenBank (NCBI) nucleotide database. All sequences are shown in the FASTA format.\nFile 8. - Cloned 16S rRNA gene sequence data\". Sequence data for cloned sequences obtained by direct PCR from quartz stone sublith biofilm . All sequences deposited on the GenBank (NCBI) nucleotide database. All sequences are shown in the FASTA format.", "links": [ { diff --git a/datasets/ASAC_996_1.json b/datasets/ASAC_996_1.json index 091e70d5dd..5fcc0133b1 100644 --- a/datasets/ASAC_996_1.json +++ b/datasets/ASAC_996_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_996_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data expected from ASAC Project 996\nSee the link below for public details on this project.\n\nThe study investigated the effects of the small sewage outfall on algal epifauna in the isthmus area. No impacts were detected and patterns of community structure were tentatively explained by local differences in wave exposure gradients.\n\nFrom the abstract to the referenced paper:\n\nAs part of a wider programme investigating the effects of human presence on Antarctic and sub-Antarctic ecosystems, this study evaluated the impact of the small sewage outfall at Macquarie Island on the epifauna living within turfs of the intertidal red alga Chaetangium fastigiatum. Sampling was conducted during early December (austral summer) in both 1996 and 1997 at six sites, two sites within each of three adjacent bays. The site closest to the outfall was 3m from the point of discharge. Data analyses at the population and community levels failed to demonstrate a significant effect of the outfall. Small scale spatial patterns, probably related to wave exposure, and inter-annual variation in recruitment, are suggested as the main causes of variation in patterns of epifaunal dominance during the study.\n\nThe site codes used in this dataset are:\n\nGCS - Garden Cove South\nGCN - Garden Cove North\nGBS - Bay 1 South\nGBN - Bay 1 North\nCS - Bay 2 North\nCN - Bay 2 South\n\nAt each site 5 replicates were taken.\n\nThe numbers are total individuals of each species that were found in each Chaetangium sample. This is a basic, though standard, species-abundance matrix.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nYear", "links": [ { diff --git a/datasets/ASAC_999_1.json b/datasets/ASAC_999_1.json index 8692e01100..d8654ffd0b 100644 --- a/datasets/ASAC_999_1.json +++ b/datasets/ASAC_999_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_999_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preliminary Metadata record for data expected from ASAC Project 999\nSee the link below for public details on this project.\n\n---- Public Summary from Project ----\nLarge numbers of insects, mites, spiders and other biological material are transported southwards from source areas across southern Australia on warm prefrontal airflows moving at 100 km/h or more which develop ahead of eastward moving cold fronts centred over the Southern Oceans. Migrating invertebrates need to remain airborne for only 18-24 hours to be transported from Australia to Macquarie Island. This project involves the use of invertebrate traps (mainly wind traps and light traps) to monitor transfers of biological material between continental land masses and sub-Antarctic Macquarie and Heard Islands, and theoretical consideration and modelling of meteorological parameters governing these transfers. The project extends to monitoring of dispersal on, and colonisation of, sub-Antarctic Macquarie and Heard islands by invertebrate animals, including those introduced by human activities.\n\nSome data are available for this project. Such data are attached to this metadata record via the related URL section. The data that is available was compiled for archival by Penny Greenslade. Some of the collected invertebrate samples from the island are available in the Queen Victoria Museum in Launceston, Tasmania.\n\nThe following datasets (and their fields) are currently available.\n\nLocation of nematode sampling sites\n\nLocation\nSampled in 1951\nSample Number\nEast\nWest\n\nNote - this dataset also refers to work completed by Bunt in 1951 (see metadata record 'The Soil Inhabiting Nematodes of Macquarie Island'). Furthermore, the nematode dataset has become 'confused' with time, and the meaning of some of the columns is not clear.\n\nLocation of oligochaete sampling sites\n\nDate\nTime\nSite\nLocation\nLatitude\nLongitude\nVegetation\nSample\nComments", "links": [ { diff --git a/datasets/ASAC_Harley_1.json b/datasets/ASAC_Harley_1.json index 7f584b532b..50d840f4cf 100644 --- a/datasets/ASAC_Harley_1.json +++ b/datasets/ASAC_Harley_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASAC_Harley_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the collected work arising from ASAC projects 263, 351, 497 and 716 (ASAC_263, ASAC_351, ASAC_497, ASAC_716). The data are pooled together into a single excel file, and presented by year. Descriptions/explanations of acronyms used are given at the bottom of each spreadsheet. One worksheet also details all publications arising from (and related to) the four ASAC projects.\n\nThe full titles of the four ASAC projects are:\n\nASAC 263: Metamorphic Evolution and Tectonic Setting of Granulites from Eastern Prydz Bay\n\nASAC 351: The Role of Partial Melting in the Genesis of Mafic Migmatites and Orthogenesis within the Rauer islands\n\nASAC 497: Structural and Chemical Processes in Granulite Metamorphism: the Rauer Group and Brattstrand Bluffs Region, Prydz Bay\n\nASAC 716: Archaean Crustal Accretion Histories and Significance for Geological Correlations Between the Vestfold Block and Rauer Group\n\nThe fields in this dataset are:\n\nArchive\nCollector\nSample Number\nLocation\nLocation Code\nLatitude\nLongitude\nField description\nCollected for\nReported in\nComments\nType\nGrid reference\nWorker", "links": [ { diff --git a/datasets/ASA_AP__0P_Scenes_9.0.json b/datasets/ASA_AP__0P_Scenes_9.0.json index 0a18913c17..939c3ccd13 100644 --- a/datasets/ASA_AP__0P_Scenes_9.0.json +++ b/datasets/ASA_AP__0P_Scenes_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASA_AP__0P_Scenes_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASAR Alternating Polarization Mode Level 0 (Co-polar and Cross-polar H and V) products contain time-ordered Annotated Instrument Source Packets (AISPs) corresponding to one of the three possible polarisation combinations: HH & HV, VV & VH and HH & VV, respectively. The echo samples in the AISPs have been compressed to 4 bits/sample using FBAQ. This is a high-rate, narrow swath mode, so data is only acquired for partial orbit segments. There are two co-registered images per acquisition and may be from one of seven different image swaths. The Level 0 product was produced systematically for all data acquired within this mode. Data Size: 56-100 km across track x 100 km along track There are three AP Mode Level 0 products: - ASA_APH_0P: The Cross-polar H Level 0 product corresponds to the polarisation combination HH/HV. - ASA_APV_0P: The Cross-polar V Level 0 product corresponds to the polarisation combination VV/VH. - ASA_APC_0P: The Co-polar Level 0 product corresponds to the polarisation combination HH/VV= H and H received/V transmit and V received.", "links": [ { diff --git a/datasets/ASC.json b/datasets/ASC.json index fe736b70ce..cc92a2954f 100644 --- a/datasets/ASC.json +++ b/datasets/ASC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASC data set is archived at the World Data Center-B Research\nInstitute of Hydrometeorological Information (RIHMI), Kaluga, Russia.\nThe parameters include upper-air temperature, humidity, pressure, and\ncloud data such as amount, zero isotherm height, turbulence,\ninversion, icing, and isotherms for Africa, Asia, Europe, and\nAustralia since 1983.", "links": [ { diff --git a/datasets/ASCATA-L2-25km_Operational_Near-Real-Time.json b/datasets/ASCATA-L2-25km_Operational_Near-Real-Time.json index 7d21af860d..0c33bd3afd 100644 --- a/datasets/ASCATA-L2-25km_Operational_Near-Real-Time.json +++ b/datasets/ASCATA-L2-25km_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA-L2-25km_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCATA-L2-Coastal_Operational_Near-Real-Time.json b/datasets/ASCATA-L2-Coastal_Operational_Near-Real-Time.json index e28aa98b5c..0bfd764396 100644 --- a/datasets/ASCATA-L2-Coastal_Operational_Near-Real-Time.json +++ b/datasets/ASCATA-L2-Coastal_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA-L2-Coastal_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json b/datasets/ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json index 9ce2aba4e5..b1d464529f 100644 --- a/datasets/ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json +++ b/datasets/ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains model output interpolated in space and time to the ESDR product from the MetOp-A ASCAT (ASCAT-A) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-A Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.\r\n

\r\nThe dataset represents the first science quality release of these data with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json b/datasets/ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json index 5776a91025..1a75009bcf 100644 --- a/datasets/ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json +++ b/datasets/ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.\r\n

\r\nThe dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/ASCATA_L2_25KM_CDR_1.0.json b/datasets/ASCATA_L2_25KM_CDR_1.0.json index b7a6d219c8..b4427aca1d 100644 --- a/datasets/ASCATA_L2_25KM_CDR_1.0.json +++ b/datasets/ASCATA_L2_25KM_CDR_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA_L2_25KM_CDR_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the first historically reprocessed Level 2 ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 25 km grid. Products at 25-km sampling are less noisy than 12.5-km products, but also contain less geophysical information on small scales and near the coasts. The wind vector retrievals are currently processed using the CMOD7 geophysical model function using a Hamming filter to spatially average the Level 1 Sigma-0 data over 25 km swath grid cells. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 25-km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-25km. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.", "links": [ { diff --git a/datasets/ASCATA_L2_COASTAL_CDR_1.0.json b/datasets/ASCATA_L2_COASTAL_CDR_1.0.json index 389783ce02..058d22250d 100644 --- a/datasets/ASCATA_L2_COASTAL_CDR_1.0.json +++ b/datasets/ASCATA_L2_COASTAL_CDR_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATA_L2_COASTAL_CDR_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the vector cell wind using the same CMOD7 geophysical model function as in the operational OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds are computed as close to ~15 km from the coast. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 12.5km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-Coastal. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.", "links": [ { diff --git a/datasets/ASCATB-L2-25km_Operational_Near-Real-Time.json b/datasets/ASCATB-L2-25km_Operational_Near-Real-Time.json index 7cb01cdb3f..0b96e0ff29 100644 --- a/datasets/ASCATB-L2-25km_Operational_Near-Real-Time.json +++ b/datasets/ASCATB-L2-25km_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATB-L2-25km_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCATB-L2-Coastal_Operational_Near-Real-Time.json b/datasets/ASCATB-L2-Coastal_Operational_Near-Real-Time.json index ffbb40a8c5..7bc332a0f0 100644 --- a/datasets/ASCATB-L2-Coastal_Operational_Near-Real-Time.json +++ b/datasets/ASCATB-L2-Coastal_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATB-L2-Coastal_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD5.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1.json b/datasets/ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1.json index 7065dda1e6..9fa9da58cc 100644 --- a/datasets/ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1.json +++ b/datasets/ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains model output interpolated in space and time to observations from the MetOp-B ASCAT (ASCAT-B) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-B Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.\r\n

\r\nThe dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1.json b/datasets/ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1.json index 39d48dfa2d..af1c560785 100644 --- a/datasets/ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1.json +++ b/datasets/ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.\r\n

\r\nThe dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/ASCATC-L2-25km_Operational_Near-Real-Time.json b/datasets/ASCATC-L2-25km_Operational_Near-Real-Time.json index 36e1df8b95..ad2ccf2921 100644 --- a/datasets/ASCATC-L2-25km_Operational_Near-Real-Time.json +++ b/datasets/ASCATC-L2-25km_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATC-L2-25km_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-C platform. For more information about the MetOp-C platform and mission, please refer to: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCATC-L2-Coastal_Operational_Near-Real-Time.json b/datasets/ASCATC-L2-Coastal_Operational_Near-Real-Time.json index 9c1ec43f17..0cf46579a9 100644 --- a/datasets/ASCATC-L2-Coastal_Operational_Near-Real-Time.json +++ b/datasets/ASCATC-L2-Coastal_Operational_Near-Real-Time.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCATC-L2-Coastal_Operational/Near-Real-Time", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-C platform. For more information on the MetOp-C mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.", "links": [ { diff --git a/datasets/ASCENDS_AVOCET_CA_NV_Feb_2016_2115_1.json b/datasets/ASCENDS_AVOCET_CA_NV_Feb_2016_2115_1.json index 83ce99c37b..27899cacdd 100644 --- a/datasets/ASCENDS_AVOCET_CA_NV_Feb_2016_2115_1.json +++ b/datasets/ASCENDS_AVOCET_CA_NV_Feb_2016_2115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCENDS_AVOCET_CA_NV_Feb_2016_2115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over California and Nevada on February 10-11, 2016. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. CO2 was measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument while over California and Nevada. The objective of this deployment was to assess the performance of the 2016 version of the CO2 Sounder LiDAR. The two flights were flown to compare results from an experimental LiDAR sensor with the AVOCET instrument. Aircraft navigation and flight meteorological data are also provided. The data are provided in ICARTT and comma-separated values (CSV) formats.", "links": [ { diff --git a/datasets/ASCENDS_LAS_IN_Sept_2014_2116_1.json b/datasets/ASCENDS_LAS_IN_Sept_2014_2116_1.json index cd71898224..772e0927e2 100644 --- a/datasets/ASCENDS_LAS_IN_Sept_2014_2116_1.json +++ b/datasets/ASCENDS_LAS_IN_Sept_2014_2116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASCENDS_LAS_IN_Sept_2014_2116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over Indianapolis, Indiana (IN) on September 3, 2014 during the morning commuter period with heavy traffic emissions. Stationary source emissions are also included. The observed CO2 plume downwind of the urban area, along with the prevailing wind speed and direction, enabled estimations of emission rates. CO2 was measured with an airborne CO2 Laser Absorption Spectrometer (JPL CO2LAS) developed at NASA's Jet Propulsion Laboratory (JPL) to demonstrate the airborne Integrated Path Differential-Absorption (IPDA) lidar technique as a stepping stone to a capability for global measurements of CO2 concentrations from space. The CO2LAS measures the weighted, column averaged carbon dioxide between the aircraft and the ground using a continuous-wave heterodyne technique. The instrument operates at a 2.05 micron wavelength optimized for enhancing sensitivity to boundary layer carbon dioxide. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. The data are provided in HDF-5 format.", "links": [ { diff --git a/datasets/ASIRI_0.json b/datasets/ASIRI_0.json index 0c29df8ba1..ec8e4215ae 100644 --- a/datasets/ASIRI_0.json +++ b/datasets/ASIRI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASIRI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air-Sea Interaction Research Initiative (ASIRI) is an ONR research initiative involving multiple institutions and scientists, which, in partnership with India and Sri Lanka, aims to improve our understanding of the upper ocean and its atmospheric interactions", "links": [ { diff --git a/datasets/ASO_3M_PCDTM_1.json b/datasets/ASO_3M_PCDTM_1.json index e00a6e9b5a..1761f87984 100644 --- a/datasets/ASO_3M_PCDTM_1.json +++ b/datasets/ASO_3M_PCDTM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASO_3M_PCDTM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides 3 m gridded, bare-earth elevations (excluding trees) that are used as the baseline for the Airborne Snow Observatory (ASO) snow-on products. The data were collected during snow-free conditions as part of the NASA/JPL ASO aircraft survey campaigns.", "links": [ { diff --git a/datasets/ASO_3M_SD_1.json b/datasets/ASO_3M_SD_1.json index 6737e9dd90..d676d6c6f9 100644 --- a/datasets/ASO_3M_SD_1.json +++ b/datasets/ASO_3M_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASO_3M_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 3 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.", "links": [ { diff --git a/datasets/ASO_50M_SD_1.json b/datasets/ASO_50M_SD_1.json index 86ea88979e..336c7257a0 100644 --- a/datasets/ASO_50M_SD_1.json +++ b/datasets/ASO_50M_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASO_50M_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 50 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.", "links": [ { diff --git a/datasets/ASO_50M_SWE_1.json b/datasets/ASO_50M_SWE_1.json index b686d1cfcc..194edf6fab 100644 --- a/datasets/ASO_50M_SWE_1.json +++ b/datasets/ASO_50M_SWE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASO_50M_SWE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 50 m gridded snow water equivalent (SWE) values collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns. The data were derived from the ASO L4 Lidar Snow Depth 50m UTM Grid data product and from modeled snow density.", "links": [ { diff --git a/datasets/ASPECT_1.json b/datasets/ASPECT_1.json index 8a57ee5406..2bf6a840bb 100644 --- a/datasets/ASPECT_1.json +++ b/datasets/ASPECT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASPECT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ship observations, collected over the period 1980 - 2004, will be used to determine the regional and seasonal variability of the Antarctic sea ice thickness distribution. The thickness of Antarctic sea ice is not well understood, and cannot be determined from remote sensing, yet it plays an integral role in the climate system and is climatically sensitive. This project aims to establish a baseline of sea ice thickness using data compiled from many different countries, which is required by scientists across many disciplines.\n\nThis is a parent record for the project. For details on individual cruises, see the child records.\n\nThis work was completed as part of ASAC project 2669 - ASAC_2699.", "links": [ { diff --git a/datasets/ASPECT_2007_2013_1.json b/datasets/ASPECT_2007_2013_1.json index f481e8534e..49131b43e5 100644 --- a/datasets/ASPECT_2007_2013_1.json +++ b/datasets/ASPECT_2007_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASPECT_2007_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic Sea ice Processes and Climate [ASPeCt] data sets submitted here have been collected systematically from the bridge of an icebreaker, while it transited through the pack ice. Quantifiable observations of sea ice thickness and related characteristics of the sea ice, snow, ocean and surface atmosphere are recorded hourly while the vessel moves through the sea ice. If the vessel is stopped or has not moved at least 6nm since the previous observation, no observation will be conducted. The observation protocol has been endorsed by the Scientific Commission for Antarctic Research (under their ASPeCt programme) as the preferred method for conducting ship-based observations of sea-ice characteristics. Details can be found in Worby and Allison [1999]\n\nThe spreadsheet information below is also included in the word document in the download file.\n\nThe relevant spreadsheets (xls files) contain the following information: \nHeader name\tPhysical parameter\tUnit\nYear\tYear\t\nDate\tDay/Month/Year\t\nJulian Day\tDay of year\t\nTime (UT)\tTime of day in Universal time: Hours/Minutes/Seconds\t\nLat (oN)\tLatitude\toN\nLon(oE)\t\toE\nConc\tTotal ice concentration\tTenth\nOW\tOpen-water classification\tSee Worby and Allison [1999]\nc1\tIce concentration of primary ice category\tTenth\nty1\tIce type of primary ice category\tSee Worby and Allison [1999]\niz1\tThickness of primary ice category\tcm\nf1\tFloe size of primary ice category\tSee Worby and Allison [1999]\nt1\tTopography of primary ice category\tSee Worby and Allison [1999]\ns1\tSnow type on primary ice category\tSee Worby and Allison [1999]\nsz1\tSnow thickness on primary ice category\tcm\nc2\tIce concentration of secondary ice category\tTenth\nty2\tIce type of secondary ice category\tSee Worby and Allison [1999]\niz2\tThickness of secondary ice category\tcm\nf2\tFloe size of secondary ice category\tSee Worby and Allison [1999]\nt2\tTopography of secondary ice category\tSee Worby and Allison [1999]\ns2\tSnow type on secondary ice category\tSee Worby and Allison [1999]\nsz2\tSnow thickness on secondary ice category\tcm\nc3\tIce concentration of tertiary ice category\tTenth\nty3\tIce type of tertiary ice category\tSee Worby and Allison [1999]\niz3\tThickness of tertiary ice category\tcm\nf3\tFloe size of tertiary ice category\tSee Worby and Allison [1999]\nt3\tTopography of tertiary ice category\tSee Worby and Allison [1999]\ns3\tSnow type on tertiary ice category\tSee Worby and Allison [1999]\nsz3\tSnow thickness on tertiary ice category\tcm\nSea\tSea-surface temperature\toC\nAir\tSurface-air temperature\toC", "links": [ { diff --git a/datasets/ASPECT_AF050692_1.json b/datasets/ASPECT_AF050692_1.json index ec722d6e9a..48f8cf2ea5 100644 --- a/datasets/ASPECT_AF050692_1.json +++ b/datasets/ASPECT_AF050692_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASPECT_AF050692_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data describe pack ice characteristics in the Antarctic sea ice zone.\nThese data are in the ASPeCt format.\n\nNational program: Russia\nVessel: Akademic Fedorov\nDates in ice: May 1992 to June 1992\nObservers: Unknown\n\nThe fields in this dataset are:\nSEA ICE CONCENTRATION\nSEA ICE FLOE SIZE\nSEA ICE SNOW COVER\nSEA ICE THICKNESS\nSEA ICE TOPOGRAPHY\nSEA ICE TYPE\nRECORD\nDATE\nTIME\nLATITUDE\nLONGITUDE\nOPEN WATER\nTRACK\nSNOW THICKNESS\nSNOW TYPE\nSEA TEMPERATURE\nAIR TEMPERATURE\nWIND VELOCITY\nWIND DIRECTION\nFILM COUNTER\nFRAME COUNTER FOR FILM\nVIDEO RECORDER COUNTER\nVISIBILITY CODE\nCLOUD\nWEATHER CODE\nCOMMENTS", "links": [ { diff --git a/datasets/ASPeCt-Bio_1.json b/datasets/ASPeCt-Bio_1.json index 4a7e195b92..1b3915a8ab 100644 --- a/datasets/ASPeCt-Bio_1.json +++ b/datasets/ASPeCt-Bio_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASPeCt-Bio_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASPeCt - Bio dataset is a compilation of currently available sea ice chlorophyll a (chl-a) data from pack ice (i.e., excluding fast ice) cores collected during 32 cruises to the Southern Ocean sea ice zone from 1983 to 2008 (Table S1). Data come from peer-reviewed publications, cruise reports, data repositories and direct contributions by field-research teams. During all cruises the chl-a concentration (in micrograms per litre) was measured from melted ice core sections, using standard procedures, e.g., by melting the ice at less than 5 degrees C in the dark; filtering samples onto glassfibre filters; and fluorometric analysis according to standard protocols [Holm-Hansen et al., 1965; Evans et al., 1987]. Ice samples were melted either directly or in filtered sea water, which does not yield significant differences in chl-a concentration [Dieckmann et al., 1998]. The dataset consists of 1300 geo-referenced ice cores, consisting of 8247 individual ice core sections, and including 990 vertical profiles with a minimum of three sections.\n\nAn updated dataset was provided in 2017-12-15, which included a compilation Net CDF file.", "links": [ { diff --git a/datasets/AST14DEM_003.json b/datasets/AST14DEM_003.json index ba9aee1b0e..85800f6406 100644 --- a/datasets/AST14DEM_003.json +++ b/datasets/AST14DEM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST14DEM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Digital Elevation Model (AST14DEM) product is generated (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) using bands 3N (nadir-viewing) and 3B (backward-viewing) of an (ASTER Level 1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The band 3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a base-to-height ratio of 0.6 and an intersection angle of 27.7 degrees. There is a time lag of approximately one minute between the acquisition of the nadir and backward images. For a better understanding, refer to this (diagram) (https://lpdaac.usgs.gov/documents/301/ASTER_Along_Track_Imaging_Geometry.png) depicting the along-track imaging geometry of the ASTER VNIR nadir and backward-viewing sensors.\r\n\r\nThe accuracy of the new LP DAAC produced DEMs will meet or exceed accuracy specifications set for the ASTER relative DEMs by the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/81/AST14_ATBD.pdf). Users likely will find that the DEMs produced by the new LP DAAC system have accuracies approaching those specified in the ATBD for absolute DEMs. Validation testing has shown that DEMs produced by the new system frequently are more accurate than 25 meters root mean square error (RMSE) in xyz dimensions.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\nAs of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1B input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website (http://www.silc.co.jp/en/products.html).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\n\r\n", "links": [ { diff --git a/datasets/AST14DMO_003.json b/datasets/AST14DMO_003.json index 28ac81eb27..e676c1c620 100644 --- a/datasets/AST14DMO_003.json +++ b/datasets/AST14DMO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST14DMO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Digital Elevation Model and Orthorectified Registered Radiance at the Sensor (AST14DMO) product (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) form a multi-file product. The product contains both a Digital Elevation Model (DEM) and up to 15 orthorectified images representing Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) data layers for each available ASTER scene, if acquired. The spatial resolution is 15 m (VNIR), 30 m (SWIR), and 90 m (TIR) with a temporal coverage of 2000 to present.\r\n\r\nFor more information, see the links below:\r\n\r\n(AST14DEM) (https://doi.org/10.5067/ASTER/AST14DEM.003)\r\n(AST14OTH) (https://doi.org/10.5067/ASTER/AST14OTH.003)\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\nAs of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1B input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website (http://www.silc.co.jp/en/products.html).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n", "links": [ { diff --git a/datasets/AST14OTH_003.json b/datasets/AST14OTH_003.json index 1b4f5f08ff..cf2d60e13e 100644 --- a/datasets/AST14OTH_003.json +++ b/datasets/AST14OTH_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST14OTH_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Orthorectified Registered Radiance at the Sensor (AST14OTH) product (https://lpdaac.usgs.gov/documents/618/ASTER_Earthdata_Search_Order_Instructions.pdf) contains imagery transformed from a perspective projection to an orthogonal one. An orthorectified image possesses the geometric characteristics of a map with near-vertical views for every location. These products are terrain corrected, provide radiometrically calibrated radiance, and are mapped to the Universal Transverse Mercator (UTM) coordinate system. The spatial resolution is 15 m (VNIR), 30 m (SWIR), and 90 m (TIR) with a temporal coverage of 2000 to present. \r\n\r\nThe inputs include the following: an ASTER Level 1A Reconstructed Unprocessed Instrument dataset; georeferencing information from the ASTER instrument's and Terra platform's ephemeris and attitude data; and an ASTER-derived digital elevation model (DEM). The output product includes fifteen orthorectified ASTER Level 1B calibrated radiance images, one per band, as listed below.\r\n\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\nAs of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1B input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website (http://www.silc.co.jp/en/products.html).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\n\r\n", "links": [ { diff --git a/datasets/ASTGTM_003.json b/datasets/ASTGTM_003.json index bb87fa23eb..bd56dee887 100644 --- a/datasets/ASTGTM_003.json +++ b/datasets/ASTGTM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTGTM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator).\r\n\r\nThe development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan\u2019s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. \r\n\r\nThe ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1 degree latitude by 1 degree longitude tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) (https://doi.org/10.5067/ASTER/ASTWBD.001) Version 1 data product was also generated. \r\n\r\nThe geographic coverage of the ASTER GDEM extends from 83\u00b0 North to 83\u00b0 South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. \r\n\r\nProvided in the ASTER GDEM product are layers for DEM and number of scenes (NUM). The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data.\r\n\r\nWhile the ASTER GDEM Version 3 data products offer substantial improvements over Version 2, users are advised that the products still may contain anomalies and artifacts that will reduce its usability for certain applications. \r\n\r\nImprovements/Changes from Previous Versions \r\n\u2022 Expansion of acquisition coverage to increase the amount of cloud-free input scenes from about 1.5 million in Version 2 to about 1.88 million scenes in Version 3.\r\n\u2022 Separation of rivers from lakes in the water body processing. \r\n\u2022 Minimum water body detection size decreased from 1 km2 to 0.2 km2. ", "links": [ { diff --git a/datasets/ASTGTM_NC_003.json b/datasets/ASTGTM_NC_003.json index b19b67d896..0505ef2a31 100644 --- a/datasets/ASTGTM_NC_003.json +++ b/datasets/ASTGTM_NC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTGTM_NC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator).\n\nThe development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan\u2019s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. \n\nThe ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1 degree latitude by 1 degree longitude tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) (https://doi.org/10.5067/ASTER/ASTWBD.001) Version 1 data product was also generated. \n\nThe geographic coverage of the ASTER GDEM extends from 83\u00b0 North to 83\u00b0 South. Each tile is distributed in NetCDF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. \n\nEach ASTGTM_NC data product contains a DEM file, which provides elevation information. The corresponding ASTGTM_NUMNC file indicates the number of scenes that were processed for each pixel and the source of the data.\n\nWhile the ASTER GDEM Version 3 data products offer substantial improvements over Version 2, users are advised that the products still may contain anomalies and artifacts that will reduce its usability for certain applications. \n\nImprovements/Changes from Previous Versions \n\u2022 Expansion of acquisition coverage to increase the amount of cloud-free input scenes from about 1.5 million in Version 2 to about 1.88 million scenes in Version 3.\n\u2022 Separation of rivers from lakes in the water body processing. \n\u2022 Minimum water body detection size decreased from 1 km2 to 0.2 km2. \n", "links": [ { diff --git a/datasets/ASTGTM_NUMNC_003.json b/datasets/ASTGTM_NUMNC_003.json index 2c630a8ae6..be4a980167 100644 --- a/datasets/ASTGTM_NUMNC_003.json +++ b/datasets/ASTGTM_NUMNC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTGTM_NUMNC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator).\r\n\r\nThe development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan\u2019s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. \r\n\r\nThe ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1 degree latitude by 1 degree longitude tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) (https://doi.org/10.5067/ASTER/ASTWBD.001) Version 1 data product was also generated. \r\n\r\nThe geographic coverage of the ASTER GDEM extends from 83\u00b0 North to 83\u00b0 South. Each tile is distributed in NetCDF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. \r\n\r\nEach ASTGTM_NUMNC file indicates the number of scenes that were processed for each pixel and the source of the data.. The corresponding ASTGTM_NC data product contains a DEM file, which provides elevation information. \r\n\r\nWhile the ASTER GDEM Version 3 data products offer substantial improvements over Version 2, users are advised that the products still may contain anomalies and artifacts that will reduce its usability for certain applications. \r\n\r\nImprovements/Changes from Previous Versions \r\n\u2022 Expansion of acquisition coverage to increase the amount of cloud-free input scenes from about 1.5 million in Version 2 to about 1.88 million scenes in Version 3.\r\n\u2022 Separation of rivers from lakes in the water body processing. \r\n\u2022 Minimum water body detection size decreased from 1 km2 to 0.2 km2. ", "links": [ { diff --git a/datasets/ASTI.json b/datasets/ASTI.json index 77c9e52094..72434d92da 100644 --- a/datasets/ASTI.json +++ b/datasets/ASTI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Agricultural Science and Technology Indicators (ASTI) initiative\n compiles, processes, and makes available internationally comparable\n data on institutional developments and investments in agricultural R&D\n worldwide, and analyzes and reports on these trends in the form of\n occasional policy digests for research policy formulation and priority\n setting purposes. The project involves a large amount of original and\n ongoing survey work focused on developing countries, but also\n maintains access to relevant data for developed countries produced by\n the OECD Science and Technology Indicators unit, the U.S. National\n Science Foundation, and other similar agencies. The activities are led\n jointly by the International Food Policy Research Institute (IFPRI)\n and the International Service for National Agricultural Research\n (ISNAR), and involve collaborative alliances with a large number of\n national and regional R&D agencies, as well as international\n institutions.\n \n The ASTI database collects, screens and summarizes agricultural R&D\n expenditure and related R&D personnel data for both developed and\n developing countries. Data are mainly collected at institute level and\n summarized in four institutional categories of implementing agencies:\n (1) Government; (2) Nonprofit; (3) University; and (4) Business. The\n first three categories together constitute a \"public sub-total\". R&D\n activities undertaken by international organizations are explicitly\n excluded and will be reported separately.\n \n The statistical coverage of the four institutional categories varies\n quite a bit. Government and nonprofit research agencies are usually\n well covered both in terms of research expenditures and research\n personnel. The university category is rather problematic as\n estimations of time spent on research by faculty staff are rather\n sketchy. Usually a fixed percentage ranging between 10-50% is applied\n across all faculty staff, for all years. The contribution of PhD\n students is usually not covered. Research expenditure data by\n universities are seldom directly obtained and usually estimated\n indirectly. However, collecting data on research by public and private\n businesses constitutes the biggest challenge. In most developing\n countries business R&D surveys are not in place yet, but also in the\n developed countries ^?agriculture, forestry and fisheries^? businesses\n tend to be covered quite loosely by business R&D\n \n The ASTI database can be queried by country or region, Implementing\n Agencies, Agricultural R&D Indicators and Years.\n \n Data link: http://www.asti.cgiar.org/\n This information was obtained from the ASTI web site:\n http://www.asti.cgiar.org", "links": [ { diff --git a/datasets/ASTWBD_001.json b/datasets/ASTWBD_001.json index 2e78bb0091..bdc66f4bf7 100644 --- a/datasets/ASTWBD_001.json +++ b/datasets/ASTWBD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTWBD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. \r\n\r\nThe ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.\r\n\r\nTo generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake waterbodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. \r\n\r\nThe geographic coverage of the ASTWBD extends from 83\u00b0N to 83\u00b0S. Each tile is distributed in GeoTIFF format and referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each data product is provided as a zipped file that contains an attribute file with the water body classification information and a DEM file, which provides elevation information in meters. ", "links": [ { diff --git a/datasets/ASTWBD_ATTNC_001.json b/datasets/ASTWBD_ATTNC_001.json index bc8bd891ed..10470b7fcd 100644 --- a/datasets/ASTWBD_ATTNC_001.json +++ b/datasets/ASTWBD_ATTNC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTWBD_ATTNC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. \r\n\r\nThe ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.\r\n\r\nTo generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake waterbodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. \r\n\r\nThe geographic coverage of the ASTWBD extends from 83\u00b0N to 83\u00b0S. Each tile is distributed in NetCDF format and referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each ASTWBD_ATTNC file contains an attribute file with the water body classification information. The corresponding ASTWBD_NC data product DEM file, which provides elevation information in meters.", "links": [ { diff --git a/datasets/ASTWBD_NC_001.json b/datasets/ASTWBD_NC_001.json index 5c8bf7a995..9e030f8458 100644 --- a/datasets/ASTWBD_NC_001.json +++ b/datasets/ASTWBD_NC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ASTWBD_NC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. \r\n\r\nThe ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A (https://doi.org/10.5067/ASTER/AST_L1A.003) scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.\r\n\r\nTo generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake waterbodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. \r\n\r\nThe geographic coverage of the ASTWBD extends from 83\u00b0N to 83\u00b0S. Each tile is distributed in NetCDF format and referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each ASTWBD_NC data product DEM file, which provides elevation information in meters. The corresponding ASTWBD_ATTNC file contains an attribute file with the water body classification information.", "links": [ { diff --git a/datasets/AST_05_003.json b/datasets/AST_05_003.json index d09c688e00..326d4e72fc 100644 --- a/datasets/AST_05_003.json +++ b/datasets/AST_05_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_05_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER L2 Surface Emissivity is an on-demand product ((https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf)) generated using the five thermal infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 \u00b5m spectral range. It contains surface emissivity over the land at 90 meters spatial resolution. Estimates of surface emissivity were thus far only derived using surrogates such as land-cover type or vegetation index. \r\n\r\nThe Temperature/Emissivity Separation (TES) algorithm is used to derive both E (emissivity) and T (surface temperature). The main goals of the TES algorithm include: recovering accurate and precise emissivities for mineral substrates, and estimating accurate and precise surface temperatures especially over vegetation, water and snow.The TES algorithm is executed in the ASTER processing chain following generation of ASTER Level-2 Surface Radiance (TIR). The land-leaving radiance and down-welling irradiance vectors for each pixel are taken in account. Emissivity is estimated using the Normalized Emissivity Method (NEM), and is iteratively compensated for reflected sunlight. The emissivity spectrum is normalized using the average emissivity of each pixel. The minimum-maximum difference (MMD) of the normalized spectrum is calculated and estimates of the minimum emissivity derived through regression analysis. These estimates are used to scale the normalized emissivity and compensate for reflected skylight with the derived refinement of emissivity.\r\n\r\n ASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nV003 data set release date: 2002-05-03\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).\r\n", "links": [ { diff --git a/datasets/AST_07XT_003.json b/datasets/AST_07XT_003.json index 166b99e0cc..7ae2f161cd 100644 --- a/datasets/AST_07XT_003.json +++ b/datasets/AST_07XT_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_07XT_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Reflectance VNIR and Crosstalk Corrected SWIR (AST_07XT) dataset (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) contains measures of the fraction of incoming solar radiation reflected from the Earth\u2019s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding (ASTER L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003) image.\r\n\r\nAST_07XT is a multi-file product that contains atmospherically corrected data for both the VNIR and SWIR sensors. \r\n\r\nThe crosstalk corrected product no longer displays blurred images initiated by stray light that caused multiple reflections with the SWIR bands.\r\n\r\n ASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).", "links": [ { diff --git a/datasets/AST_07_003.json b/datasets/AST_07_003.json index f0fb5375a3..1cdd3ad1ac 100644 --- a/datasets/AST_07_003.json +++ b/datasets/AST_07_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_07_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Reflectance VNIR and SWIR (AST_07) data product (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) contains measures of the fraction of incoming solar radiation reflected from the Earth\u2019s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. \r\n\r\n ASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).\r\n\r\n", "links": [ { diff --git a/datasets/AST_08_003.json b/datasets/AST_08_003.json index 63405951dc..a0b222fa5c 100644 --- a/datasets/AST_08_003.json +++ b/datasets/AST_08_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_08_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Kinetic Temperature (AST_08) is generated (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 \u00b5m spectral range. It contains surface temperatures at 90 m spatial resolution for the land areas only. Surface kinetic temperature provides a vital input to studies of volcanism, thermal inertia, surface energy, and high-resolution mapping of fires. This product is derived using the same algorithm as the ASTER Surface Emissivity (AST_05) (https://doi.org/10.5067/ASTER/AST_05.003) Product. \r\n\r\nSurface kinetic temperature is determined by applying Planck's Law using the emissivity values from the Temperature/Emissivity Separation (TES) algorithm, which uses atmospherically corrected ASTER surface radiance (TIR) data. The TES algorithm first estimates emissivity in the TIR channels using the Normalized Emissivity Method (NEM). These estimates are used along with Kirchoff's Law to account for the land-leaving TIR radiance that is due to sky irradiance. That figure is subtracted from TIR radiance iteratively to estimate the emitted radiance from which temperature is calculated using the NEM module.\r\n\r\n ASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).", "links": [ { diff --git a/datasets/AST_09T_003.json b/datasets/AST_09T_003.json index fb4fac97e0..9810d247f7 100644 --- a/datasets/AST_09T_003.json +++ b/datasets/AST_09T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_09T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Radiance TIR (AST_09T) is generated (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 \u00b5m spectral range. It provides surface-leaving radiance for the TIR bands at a spatial resolution of 90 meters, which includes both surface-emitted and surface-reflected components. It also provides the downwelling sky irradiance values (in W/m2/\u00b5m) for each of the TIR bands. This product is atmospherically corrected, and the surface-leaving radiance is of known accuracy and valid only for clear-sky scenes (cloud-free pixels). This atmospherically corrected product provides the input for generating two other higher-level products: surface spectral emissivity and surface kinetic temperature.\r\n\r\nThe algorithm to correct atmospheric effects involves two elements: 1) it uses a radiative transfer model which is capable of estimating the magnitude of atmospheric emission, absorption, and scattering. It uses the Moderate Resolution Transmittance Code (MODTRAN) radiative transfer model, which calculates atmospheric transmittance and radiance for frequencies from 0 to 50,000 cm\u02c9\u00b9 at moderate spectral resolution. 2) It identifies and incorporates all the necessary atmospheric parameters applicable to the location and time for which the measurements require correction. These include temperature, water vapor, elevation, ozone, and aerosols.\r\n\r\n ASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\n", "links": [ { diff --git a/datasets/AST_09XT_003.json b/datasets/AST_09XT_003.json index a4221d0573..1ba622987d 100644 --- a/datasets/AST_09XT_003.json +++ b/datasets/AST_09XT_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_09XT_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Radiance VNIR and Crosstalk Corrected SWIR (AST_09XT) is a multi-file product (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) that contains atmospherically corrected data for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The crosstalk phenomenon was discovered during the nascent stage of the Terra Mission. It is whereby the incident light with band 4 caused multiple reflections for the SWIR bands, which resulted in blurred images. This has been corrected with the ASTER L2 Surface Radiance VNIR and Crosstalk Corrected SWIR data product. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. Both the VNIR and the SWIR data are atmospherically corrected using the corresponding bands from an (ASTER Level 1B) (https://doi.org/10.5067/ASTER/AST_L1B.003) image.\r\n\r\nASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).", "links": [ { diff --git a/datasets/AST_09_003.json b/datasets/AST_09_003.json index 6b715ebd2b..37c34f3b63 100644 --- a/datasets/AST_09_003.json +++ b/datasets/AST_09_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_09_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASTER Surface Radiance VNIR and SWIR (AST_09) is a multi-file product (https://lpdaac.usgs.gov/documents/996/ASTER_Earthdata_Search_Order_Instructions.pdf) that contains atmospherically corrected data for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. The more obvious distinguishing feature is the file size; the VNIR file is always the larger of the two. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding (ASTER Level 1B) (https://doi.org/10.5067/ASTER/AST_L1B.003) image.\r\n\r\nASTER Level 2 data requests for observations that occurred after May 27, 2020 will resort back to using the climatology ozone input. Additional information can be found in the ASTER L2 Processing Options Update (https://lpdaac.usgs.gov/news/aster-l2-processing-options-update/).\r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n\r\nAs of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include:\r\n\u2022\tAura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same.\r\n\u2022\tToolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.\r\n\r\nAdditionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\r\n\r\nAura OMI data are no longer available as an input for ASTER Level 2 data acquisitions after October 6, 2023. For data acquired after this date, ozone inputs will automatically fall back to climatology ozone inputs when Aura OMI is selected as an input. For more details, please refer to the Discontinuation of Aura OMI as an Input news announcement (https://lpdaac.usgs.gov/news/discontinuation-of-aura-omi-as-an-ancillary-ozone-input-for-aster-products/).", "links": [ { diff --git a/datasets/AST_L1AE_003.json b/datasets/AST_L1AE_003.json index e26dc99d1a..31c42f1c58 100644 --- a/datasets/AST_L1AE_003.json +++ b/datasets/AST_L1AE_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1AE_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Expedited Level 1A Reconstructed Unprocessed Instrument Data (AST_L1AE) global product contains reconstructed, unprocessed instrument digital data derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). This data product is similar to the (AST_L1A) (http://doi.org/10.5067/ASTER/AST_L1A.003) with a few notable exceptions. These include:\r\n* The AST_L1AE is available for download within 48 hours of acquisition in support of field calibration and validation efforts, in addition to emergency response for natural disasters where the quick turn-around time from acquisition to availability would prove beneficial in initial damage or impact assessments.\r\n* The registration quality of the AST_L1AE is likely to be lower than the AST_L1A, and may vary from scene to scene.\r\n* The AST_L1AE data product does not contain the VNIR 3B (aft-viewing) Band.\r\n* This dataset does not have short-term calibration for the Thermal Infrared (TIR) sensor.\r\n* The AST_L1AE data product is only available for download 30 days after acquisition. It is then removed and reprocessed into an AST_L1A product.", "links": [ { diff --git a/datasets/AST_L1A_003.json b/datasets/AST_L1A_003.json index 9c50c10ce4..f372a4ae68 100644 --- a/datasets/AST_L1A_003.json +++ b/datasets/AST_L1A_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1A_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1A (AST_L1A) contains reconstructed, instrument digital numbers (DNs) derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). Additionally, geometric correction coefficients and radiometric calibration coefficients are calculated and appended to the metadata, but not applied. The spatial resolution is 15 m (VNIR), 30 m (SWIR), and 90 m (TIR) with a temporal coverage of 2000 to present. \r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n", "links": [ { diff --git a/datasets/AST_L1BE_003.json b/datasets/AST_L1BE_003.json index 9474464184..6d90cb6c69 100644 --- a/datasets/AST_L1BE_003.json +++ b/datasets/AST_L1BE_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1BE_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Expedited Level 1B Registered Radiance at the Sensor global data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11. The Expedited Level 1B data product is similar to the (AST_L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003) with a few notable exceptions. These include:\r\n* The AST_L1BE is available for download within 48 hours of acquisition in support of field calibration and validation efforts, in addition to emergency response for natural disasters where the quick turn-around time from acquisition to availability would prove beneficial in initial damage or impact assessments.\r\n* The registration quality of the AST_L1BE is likely to be lower than the AST_L1B, and may vary from scene to scene.\r\n* The AST_L1BE dataset does not contain the VNIR 3B (aft-viewing) Band.\r\n* This dataset does not have short-term calibration for the Thermal Infrared (TIR) sensor.", "links": [ { diff --git a/datasets/AST_L1B_003.json b/datasets/AST_L1B_003.json index dfc94c18b2..f9ce297297 100644 --- a/datasets/AST_L1B_003.json +++ b/datasets/AST_L1B_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1B_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level-1B (AST_L1B) Registered Radiance at the Sensor data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11. The spatial resolution is 15 m (VNIR), 30 m (SWIR), and 90 m (TIR) with a temporal coverage of 2000 to present. \r\n\r\nStarting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article.\r\n\r\nTsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\r\n", "links": [ { diff --git a/datasets/AST_L1T_003.json b/datasets/AST_L1T_003.json index d59bf54296..a613a641ac 100644 --- a/datasets/AST_L1T_003.json +++ b/datasets/AST_L1T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3.\r\n\r\nThe precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).\r\n\r\nFor daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both as a text file and a single band browse images with the valid GCPs overlaid.\r\n\r\nThis multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) dataset. ", "links": [ { diff --git a/datasets/AST_L1T_031.json b/datasets/AST_L1T_031.json index 61e33593bf..5eb153ed15 100644 --- a/datasets/AST_L1T_031.json +++ b/datasets/AST_L1T_031.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AST_L1T_031", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B AST_L1B (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T V3.1 is created from a single resampling of the corresponding ASTER L1A AST_L1A (https://doi.org/10.5067/ASTER/AST_L1A.003) product. Radiometric calibration coefficients Version 5 (RCC V5) are applied to this product to improve the degradation curve derived from vicarious and lunar calibrations. The bands available in the AST_L1T V3.1 depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T V3.1 dataset does not include the aft-looking VNIR band 3.\r\n\r\nThe 3.1 version uses a precision terrain correction process that incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).\r\n\r\nFor daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both a text file and a single band browse image with the valid GCPs overlaid.\r\n\r\nThis multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depend on the bands available in the corresponding AST_L1A dataset.\r\n\r\nThe AST_L1T V3.1 data product is only available through NASA\u2019s Earthdata Search. The ASTER L1T V3.1 Order Instructions provide step-by-step directions for ordering this product.\r\n", "links": [ { diff --git a/datasets/ATCS_0.json b/datasets/ATCS_0.json index fc445eb705..d1472521dc 100644 --- a/datasets/ATCS_0.json +++ b/datasets/ATCS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATCS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat.", "links": [ { diff --git a/datasets/ATL02_006.json b/datasets/ATL02_006.json index a1bca0223c..df3d5b8fe1 100644 --- a/datasets/ATL02_006.json +++ b/datasets/ATL02_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL02_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.", "links": [ { diff --git a/datasets/ATL03_006.json b/datasets/ATL03_006.json index 022f298dbb..4af4a60d81 100644 --- a/datasets/ATL03_006.json +++ b/datasets/ATL03_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL03_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.", "links": [ { diff --git a/datasets/ATL03_ANC_MASKS_1.json b/datasets/ATL03_ANC_MASKS_1.json index c24e71ffd7..1c3021b92a 100644 --- a/datasets/ATL03_ANC_MASKS_1.json +++ b/datasets/ATL03_ANC_MASKS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL03_ANC_MASKS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively.\nA detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references.", "links": [ { diff --git a/datasets/ATL04_006.json b/datasets/ATL04_006.json index a43ab43450..54ee4cd7b7 100644 --- a/datasets/ATL04_006.json +++ b/datasets/ATL04_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL04_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL06_006.json b/datasets/ATL06_006.json index bdf631053d..5a451b63f5 100644 --- a/datasets/ATL06_006.json +++ b/datasets/ATL06_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL06_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL07QL_006.json b/datasets/ATL07QL_006.json index 456d923c23..c3f39b137d 100644 --- a/datasets/ATL07QL_006.json +++ b/datasets/ATL07QL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL07QL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL07_006.json b/datasets/ATL07_006.json index 8a6ec18534..681451ed25 100644 --- a/datasets/ATL07_006.json +++ b/datasets/ATL07_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL07_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL08QL_006.json b/datasets/ATL08QL_006.json index 8a8e7c985c..41794b69c0 100644 --- a/datasets/ATL08QL_006.json +++ b/datasets/ATL08QL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL08QL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. \nATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL08_006.json b/datasets/ATL08_006.json index 01b96a3529..866efc199a 100644 --- a/datasets/ATL08_006.json +++ b/datasets/ATL08_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL08_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL09QL_006.json b/datasets/ATL09QL_006.json index bbdb758c69..0aa25a5b5f 100644 --- a/datasets/ATL09QL_006.json +++ b/datasets/ATL09QL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL09QL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. \nATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL09_006.json b/datasets/ATL09_006.json index 95213b1cef..acbdbf2b85 100644 --- a/datasets/ATL09_006.json +++ b/datasets/ATL09_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL09_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL10QL_006.json b/datasets/ATL10QL_006.json index cfcad72ff3..8494068d1b 100644 --- a/datasets/ATL10QL_006.json +++ b/datasets/ATL10QL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL10QL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed.\nATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL10_006.json b/datasets/ATL10_006.json index 60990c2c94..715a71ac0b 100644 --- a/datasets/ATL10_006.json +++ b/datasets/ATL10_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL10_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL11_006.json b/datasets/ATL11_006.json index 476797b01e..70ce96663c 100644 --- a/datasets/ATL11_006.json +++ b/datasets/ATL11_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL11_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.", "links": [ { diff --git a/datasets/ATL12_006.json b/datasets/ATL12_006.json index 1c1eae752a..0107c91a94 100644 --- a/datasets/ATL12_006.json +++ b/datasets/ATL12_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL12_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "links": [ { diff --git a/datasets/ATL13QL_006.json b/datasets/ATL13QL_006.json index d21313b3c4..05d9d54572 100644 --- a/datasets/ATL13QL_006.json +++ b/datasets/ATL13QL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL13QL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).", "links": [ { diff --git a/datasets/ATL13_006.json b/datasets/ATL13_006.json index 46eedce636..8b963bfc59 100644 --- a/datasets/ATL13_006.json +++ b/datasets/ATL13_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL13_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).", "links": [ { diff --git a/datasets/ATL14_003.json b/datasets/ATL14_003.json index b26450560e..2e17b9a41a 100644 --- a/datasets/ATL14_003.json +++ b/datasets/ATL14_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL14_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). \n\nATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "links": [ { diff --git a/datasets/ATL14_004.json b/datasets/ATL14_004.json index f458edafb0..7b65778cff 100644 --- a/datasets/ATL14_004.json +++ b/datasets/ATL14_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL14_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).", "links": [ { diff --git a/datasets/ATL15_003.json b/datasets/ATL15_003.json index 5e573646a3..f536645ce2 100644 --- a/datasets/ATL15_003.json +++ b/datasets/ATL15_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL15_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). \n\nATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "links": [ { diff --git a/datasets/ATL15_004.json b/datasets/ATL15_004.json index 1a439b76a2..0dde9980e4 100644 --- a/datasets/ATL15_004.json +++ b/datasets/ATL15_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL15_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).", "links": [ { diff --git a/datasets/ATL16_005.json b/datasets/ATL16_005.json index 845cdd5c7e..85ae39f966 100644 --- a/datasets/ATL16_005.json +++ b/datasets/ATL16_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL16_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "links": [ { diff --git a/datasets/ATL17_005.json b/datasets/ATL17_005.json index d25fb8e598..eb08d2dc55 100644 --- a/datasets/ATL17_005.json +++ b/datasets/ATL17_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL17_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.", "links": [ { diff --git a/datasets/ATL19_003.json b/datasets/ATL19_003.json index 2ee867a06d..34d5cbb749 100644 --- a/datasets/ATL19_003.json +++ b/datasets/ATL19_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL19_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.", "links": [ { diff --git a/datasets/ATL20_004.json b/datasets/ATL20_004.json index 0048dbfa00..5d075e2669 100644 --- a/datasets/ATL20_004.json +++ b/datasets/ATL20_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL20_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.", "links": [ { diff --git a/datasets/ATL21_003.json b/datasets/ATL21_003.json index f00f4513d9..02dc163811 100644 --- a/datasets/ATL21_003.json +++ b/datasets/ATL21_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL21_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.", "links": [ { diff --git a/datasets/ATL22_003.json b/datasets/ATL22_003.json index a7782d5c5a..a0cbfd145d 100644 --- a/datasets/ATL22_003.json +++ b/datasets/ATL22_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL22_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.", "links": [ { diff --git a/datasets/ATL23_001.json b/datasets/ATL23_001.json index 677f1e5396..d66aaf17fc 100644 --- a/datasets/ATL23_001.json +++ b/datasets/ATL23_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATL23_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.", "links": [ { diff --git a/datasets/ATLAS_DEALIASED_SASS_L2_1.json b/datasets/ATLAS_DEALIASED_SASS_L2_1.json index f5c7383b48..128e889d85 100644 --- a/datasets/ATLAS_DEALIASED_SASS_L2_1.json +++ b/datasets/ATLAS_DEALIASED_SASS_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATLAS_DEALIASED_SASS_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz.", "links": [ { diff --git a/datasets/ATLAS_Veg_Plots_1541_1.json b/datasets/ATLAS_Veg_Plots_1541_1.json index f1c4b7809a..fb1094a683 100644 --- a/datasets/ATLAS_Veg_Plots_1541_1.json +++ b/datasets/ATLAS_Veg_Plots_1541_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATLAS_Veg_Plots_1541_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected from study sites on the North Slope and Seward Peninsula of Alaska during the Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-1 sites on the North Slope, located in Barrow, Atqasuk, Oumalik, and Ivotuk, were sampled in 1998-1999. ATLAS-2 sites located at Council and Quartz Creek on the Seward Peninsula were sampled in 2000. Specific attributes include dominant vegetation species and cover, biomass, soil chemistry and moisture, leaf area index (LAI), normalized difference vegetation index (NDVI), topography and elevation, and plant cover abundance.", "links": [ { diff --git a/datasets/ATMOSL1_3.json b/datasets/ATMOSL1_3.json index 8370db3236..a0b9fc4e08 100644 --- a/datasets/ATMOSL1_3.json +++ b/datasets/ATMOSL1_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL1_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 1 product containing spectra and runlog (i.e. ) information in a netCDF format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. The transmission spectra are ratioed from ATMOS high sun observations, on a scale of 0 to 1. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.", "links": [ { diff --git a/datasets/ATMOSL2AF_3.json b/datasets/ATMOSL2AF_3.json index fa6fba262f..59626879a3 100644 --- a/datasets/ATMOSL2AF_3.json +++ b/datasets/ATMOSL2AF_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2AF_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 100 levels from 0.5 to 99.5 km. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2AT) exists that contains these same data in a spreadsheet friendly tab delimited format.", "links": [ { diff --git a/datasets/ATMOSL2AT_3.json b/datasets/ATMOSL2AT_3.json index 9f7d2e158f..9d7acdd4f0 100644 --- a/datasets/ATMOSL2AT_3.json +++ b/datasets/ATMOSL2AT_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2AT_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 100 levels from 0.5 to 99.5 km. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2AF) exists that contains these same data in a FORTRAN friendly fixed field format.", "links": [ { diff --git a/datasets/ATMOSL2PF_3.json b/datasets/ATMOSL2PF_3.json index e78c594fd5..895613d929 100644 --- a/datasets/ATMOSL2PF_3.json +++ b/datasets/ATMOSL2PF_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2PF_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 85 levels from 1 to 10-7 atm. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab-3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2PT) exists that contains these same data in a spreadsheet friendly tab delimited format.", "links": [ { diff --git a/datasets/ATMOSL2PT_3.json b/datasets/ATMOSL2PT_3.json index 4b35a668f9..4c76d8e4d4 100644 --- a/datasets/ATMOSL2PT_3.json +++ b/datasets/ATMOSL2PT_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2PT_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 85 levels from 1 to 10-7 atm. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2PF) exists that contains these same data in a FORTRAN friendly fixed field format.", "links": [ { diff --git a/datasets/ATMOSL2TF_3.json b/datasets/ATMOSL2TF_3.json index db8066766a..90ea811991 100644 --- a/datasets/ATMOSL2TF_3.json +++ b/datasets/ATMOSL2TF_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2TF_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 53 levels from 280 to 3950 K. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab-3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2TT) exists that contains these same data in a spreadsheet friendly tab delimited format.", "links": [ { diff --git a/datasets/ATMOSL2TT_3.json b/datasets/ATMOSL2TT_3.json index f94c3fa8b5..4c3b71cb02 100644 --- a/datasets/ATMOSL2TT_3.json +++ b/datasets/ATMOSL2TT_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATMOSL2TT_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 53 levels from 280 to 3950 K. Data files also include time, geolocation and other information.\nThe data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\nA similar product (ATMOSL2TF) exists that contains these same data in a FORTRAN friendly fixed field format.", "links": [ { diff --git a/datasets/ATSM2AEF_001.json b/datasets/ATSM2AEF_001.json index d9a01966d0..60a3c04d2c 100644 --- a/datasets/ATSM2AEF_001.json +++ b/datasets/ATSM2AEF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSM2AEF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 FIRSTLOOK Aerosol Product subset for the ARCTAS region. It contains Aerosol optical depth and particle type, with associated atmospheric data, produced using ancillary inputs from the previous time period.", "links": [ { diff --git a/datasets/ATSM2LSF_001.json b/datasets/ATSM2LSF_001.json index bc1f7dd1c0..e1d5b68ac3 100644 --- a/datasets/ATSM2LSF_001.json +++ b/datasets/ATSM2LSF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSM2LSF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 2 FIRSTLOOK Land Surface product subset for the ARCTAS region contains directional reflectance properties,albedo(spectral & PAR integrated),FPAR,radiation parameters & terrain-referenced geometric parameters, produced using ancillary input from the previous time period.", "links": [ { diff --git a/datasets/ATSM2STF_001.json b/datasets/ATSM2STF_001.json index b0d6251313..09186adf91 100644 --- a/datasets/ATSM2STF_001.json +++ b/datasets/ATSM2STF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSM2STF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 FIRSTLOOK TOA/Cloud Stereo Product subset for the ARCTAS region. It contains the Stereoscopically Derived winds, heights and cloud mask along with associated data, produced using ancillary inputs (TASC) from the previous time period.", "links": [ { diff --git a/datasets/ATSMIB2E_003.json b/datasets/ATSMIB2E_003.json index 6cc2b65bdd..6a1f80116f 100644 --- a/datasets/ATSMIB2E_003.json +++ b/datasets/ATSMIB2E_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSMIB2E_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Ellipsoid-projected TOA Radiance subset for the ARCTAS region,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22", "links": [ { diff --git a/datasets/ATSMIB2T_003.json b/datasets/ATSMIB2T_003.json index d7caeee91c..56c9d12ac5 100644 --- a/datasets/ATSMIB2T_003.json +++ b/datasets/ATSMIB2T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSMIB2T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Terrain-projected TOA Radiance subset for the ARCTAS region,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22", "links": [ { diff --git a/datasets/ATSMIGEO_002.json b/datasets/ATSMIGEO_002.json index f36c0ddfa9..0b3beb01e7 100644 --- a/datasets/ATSMIGEO_002.json +++ b/datasets/ATSMIGEO_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATSMIGEO_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the Geometric Parameters subset for the ARCTAS region which measures the sun and view angles at the reference ellipsoid", "links": [ { diff --git a/datasets/ATTREX-Aircraft_Radiation_Measurements_1.json b/datasets/ATTREX-Aircraft_Radiation_Measurements_1.json index 13036beb0b..ed1f454965 100644 --- a/datasets/ATTREX-Aircraft_Radiation_Measurements_1.json +++ b/datasets/ATTREX-Aircraft_Radiation_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATTREX-Aircraft_Radiation_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATTREX-Aircraft_Radiation_Measurements are in-situ radiation measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ radiation properties collected by the Solar Spectral Flux Radiometer (SSFR) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\r\n\r\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.", "links": [ { diff --git a/datasets/ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1.json b/datasets/ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1.json index 72b4b7ceff..03c78053a7 100644 --- a/datasets/ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1.json +++ b/datasets/ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\r\n\r\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.", "links": [ { diff --git a/datasets/ATTREX-Aircraft_insitu_Cloud_property_Measurements_1.json b/datasets/ATTREX-Aircraft_insitu_Cloud_property_Measurements_1.json index e609372db6..457ba76d13 100644 --- a/datasets/ATTREX-Aircraft_insitu_Cloud_property_Measurements_1.json +++ b/datasets/ATTREX-Aircraft_insitu_Cloud_property_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATTREX-Aircraft_insitu_Cloud_property_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATTREX-Aircraft_insitu_Cloud_property_Measurements are in-situ cloud measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ cloud properties collected by the Hawkeye-FCDP (Hawkeye-Fast Cloud Droplet Probe) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\r\n\r\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.", "links": [ { diff --git a/datasets/ATTREX-Aircraft_insitu_TraceGas_Measurements_1.json b/datasets/ATTREX-Aircraft_insitu_TraceGas_Measurements_1.json index 8876d862f4..60d44f359f 100644 --- a/datasets/ATTREX-Aircraft_insitu_TraceGas_Measurements_1.json +++ b/datasets/ATTREX-Aircraft_insitu_TraceGas_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATTREX-Aircraft_insitu_TraceGas_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATTREX-Aircraft_insitu_TraceGas_Measurements are in-situ trace gas measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ trace gas measurements collected by the Diode Laser Hygrometer (DLH), UCATS Gas Chromatograph, Advanced Whole Air Sampler (AWAS), Harvard University Picarro Cavity Ringdown Spectrometer, 2 channel internal path Tunable-Diode Laser (TDL) absorption spectrometer, and Dual-channel Ultraviolet (UV) absorption spectrometer for O3 measurements during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\r\n\r\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.", "links": [ { diff --git a/datasets/ATTREX-Aircraft_navigational_and_meteorological_Measurements_1.json b/datasets/ATTREX-Aircraft_navigational_and_meteorological_Measurements_1.json index 0418b9ff5a..190d47fdb4 100644 --- a/datasets/ATTREX-Aircraft_navigational_and_meteorological_Measurements_1.json +++ b/datasets/ATTREX-Aircraft_navigational_and_meteorological_Measurements_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATTREX-Aircraft_navigational_and_meteorological_Measurements_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ATTREX-Aircraft_navigational_meteorological_Measurements are in-situ navigational and meteorological measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ meteorological and navigational properties collected by the Meteorological Measurement System (MMS) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\r\n\r\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.", "links": [ { diff --git a/datasets/ATom_AMP_Instrument_Data_1671_1.json b/datasets/ATom_AMP_Instrument_Data_1671_1.json index 6852f2ba10..c868a35f23 100644 --- a/datasets/ATom_AMP_Instrument_Data_1671_1.json +++ b/datasets/ATom_AMP_Instrument_Data_1671_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_AMP_Instrument_Data_1671_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the number, surface area, and volume concentrations and size distributions of dry aerosol particles measured by the Aerosol Microphysical Properties (AMP) instrument package during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. Five instruments--two nucleation-mode aerosol size spectrometers (NMASS), two ultra-high sensitivity aerosol spectrometers (UHSAS), and a laser aerosol spectrometer (LAS)--comprise the AMP package. The AMP payload provides size distributions with up to one-second time resolution for dry aerosol particles between 0.003 and 4.8 microns in diameter.", "links": [ { diff --git a/datasets/ATom_AO2_Instrument_Data_V2_1880_2.json b/datasets/ATom_AO2_Instrument_Data_V2_1880_2.json index 5793f15f7c..df5358fe69 100644 --- a/datasets/ATom_AO2_Instrument_Data_V2_1880_2.json +++ b/datasets/ATom_AO2_Instrument_Data_V2_1880_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_AO2_Instrument_Data_V2_1880_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ atmospheric oxygen and carbon dioxide concentrations measured by the NCAR Airborne Oxygen Instrument (AO2) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The AO2 Instrument measures O2 concentration using a vacuum-ultraviolet absorption technique. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.", "links": [ { diff --git a/datasets/ATom_ATHOS_Instrument_Data_V2_1930_2.json b/datasets/ATom_ATHOS_Instrument_Data_V2_1930_2.json index 97169ee16d..4a44db699d 100644 --- a/datasets/ATom_ATHOS_Instrument_Data_V2_1930_2.json +++ b/datasets/ATom_ATHOS_Instrument_Data_V2_1930_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_ATHOS_Instrument_Data_V2_1930_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the mixing ratios of hydrogen oxides measured by the Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during the ATom 1-4 campaigns. ATHOS uses laser-induced fluorescence (LIF) to measure hydroxide (OH) and hydroperoxyl (HO2) simultaneously. The measurements include OH and HO2 mixing ratios and the OH interference determined by chemical removal of OH. The reactivity of OH is measured by the OH Reactivity (OHR) instrument using the discharge flow method and is integrated into the ATHOS electronics. These data provide insights into the oxidative state of the global atmosphere. These data are useful for testing the oxidation chemistry in models and other analytical methods being developed to deduce the atmosphere's oxidative state.", "links": [ { diff --git a/datasets/ATom_Aerosol_Properties_V2_2111_2.1.json b/datasets/ATom_Aerosol_Properties_V2_2111_2.1.json index aebaf99726..43d78e0a47 100644 --- a/datasets/ATom_Aerosol_Properties_V2_2111_2.1.json +++ b/datasets/ATom_Aerosol_Properties_V2_2111_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Aerosol_Properties_V2_2111_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains comprehensive measurements of aerosol microphysical, chemical, and optical properties derived for both dry and ambient conditions from in situ measurements made during the four ATom campaigns. The dataset includes composition-resolved size distributions the integrated mass of sulfate, organics, nitrate, sea salt, dust, black carbon, and other compounds in coarse and fine fractions; extinction and absorption coefficients from each species at both dry and ambient conditions; asymmetry parameters; Angstrom exponents; and fitted lognormal functions to describe the size distribution. Optical parameters are calculated for 10 wavelengths from the near UV to the near IR, and size distributions range from 3 nm to 50 um in diameter. One file contains these data at 1-minute time intervals. Another file contains a subset of these data averaged into 1-km vertical bins for each vertical profile the aircraft made, as well as composition-resolved integrated aerosol optical depth derived from each profile. The concentration of cloud condensation nuclei is calculated for 5 supersaturations.", "links": [ { diff --git a/datasets/ATom_Aerosols_Meteorology_1684_1.json b/datasets/ATom_Aerosols_Meteorology_1684_1.json index ad2d33c149..47eed369df 100644 --- a/datasets/ATom_Aerosols_Meteorology_1684_1.json +++ b/datasets/ATom_Aerosols_Meteorology_1684_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Aerosols_Meteorology_1684_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides (1) the results of in situ aerosol particle property measurements collected over remote tropical areas of both Pacific and Atlantic Oceans during the NASA airborne Atmospheric Tomography (ATom) campaigns for ATom-1 and ATom-2 and (2) modeled outputs of comparable aerosol properties, atmospheric chemistry and meteorology at 70 m resolution from four chemical-transport models matched to the location and time of the aircraft measurements.", "links": [ { diff --git a/datasets/ATom_CAFS_Instrument_Data_V2_1933_2.json b/datasets/ATom_CAFS_Instrument_Data_V2_1933_2.json index 6ef14e0efd..7179d7e40c 100644 --- a/datasets/ATom_CAFS_Instrument_Data_V2_1933_2.json +++ b/datasets/ATom_CAFS_Instrument_Data_V2_1933_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_CAFS_Instrument_Data_V2_1933_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains actinic flux and photolysis frequencies for photodissociation reactions for a variety of chemical species during the four ATom campaigns. Spectrally resolved actinic flux was measured by the down- and up-welling Charged-coupled device Actinic Flux Spectroradiometers (CAFS) from approximately 280-650 nm. Photolysis frequencies were calculated from the actinic flux and published cross sections and quantum yield values for atmospherically relevant molecules. Solar radiation drives the chemistry of the atmosphere, including the evolution of ozone, greenhouse gases, biomass burning, and other anthropogenic and natural trace constituents.", "links": [ { diff --git a/datasets/ATom_CAPSVienna_Data_1981_1.json b/datasets/ATom_CAPSVienna_Data_1981_1.json index 00c88e8eb0..6982045143 100644 --- a/datasets/ATom_CAPSVienna_Data_1981_1.json +++ b/datasets/ATom_CAPSVienna_Data_1981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_CAPSVienna_Data_1981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains cloud type and coarse aerosol contents measured by the University of Vienna's second-generation Cloud Aerosol and Precipitation Spectrometer (CAPS) instrument mounted to the NASA DC-8 aircraft during the four ATom campaigns that occurred from 2016 to 2018. CAPS measures particle size distributions in a size range between nominally 0.5 micrometers and 960 micrometers. The sizes range between approximately 0.5 and 50 micrometers is covered by the optical particle counter component of CAPS-the Cloud and Aerosol Spectrometer with Depolarization Detection (CAS-DPOL). The sizes range from 15 to 930 micrometers is measured with the optical array probe called Cloud imaging Probe (CIP). Cloud types are determined using an algorithm developed to detect and classify clouds using measurements of CAPS. Relative humidity and temperature are considered by the algorithm. The cloud indicator provides a classification on a 1 Hz basis and separates data in cloud-free, aerosol-cloud transition regime (ACTR), liquid clouds, clouds in the mixed-phase temperature regime (MPTR), and cirrus clouds. The coarse aerosol product provides cloud and aerosol particle number concentrations at standard pressure (1013.25 hPa) and standard temperature (273.15 K) in selected size ranges. Particle sizes refer to ammonium sulfate optical equivalent diameter (m=1.52 + 0.0i).", "links": [ { diff --git a/datasets/ATom_CESM2_1878_1.json b/datasets/ATom_CESM2_1878_1.json index 1ed15855f2..ef2e141008 100644 --- a/datasets/ATom_CESM2_1878_1.json +++ b/datasets/ATom_CESM2_1878_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_CESM2_1878_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains CAM-chem (Community Atmosphere Model with Chemistry) model outputs along ATom flight tracks. CAM-chem is a component of the Community Earth System Model Version 2 (CESM2) and is used for simulations of global tropospheric and stratospheric atmospheric composition and for studies of chemistry-climate interactions. In general, CAM-chem uses the MOZART chemical mechanism, with various choices of complexity for tropospheric and stratospheric chemistry. For this dataset, CAM-chem used the MOZART-TS1 chemical mechanism, and the model was nudged to reanalysis meteorology from MERRA2.", "links": [ { diff --git a/datasets/ATom_CIT_Instrument_Data_V2_1927_2.json b/datasets/ATom_CIT_Instrument_Data_V2_1927_2.json index 1a059fe551..15721c3d55 100644 --- a/datasets/ATom_CIT_Instrument_Data_V2_1927_2.json +++ b/datasets/ATom_CIT_Instrument_Data_V2_1927_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_CIT_Instrument_Data_V2_1927_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the concentrations of gas-phase organic and inorganic analytes measured by the California Institute of Technology (CIT) Chemical Ionization Mass Spectrometer (CIMS), or CIT-CIMS, flown on the NASA DC-8 aircraft during the four ATom campaigns. The CIT-CIMS employs CF3O-ion chemistry with two independent mass spectrometers (compact time-of-flight and triple quadrupole) to enable sensitive and specific measurements of atmospheric trace gases. The measurements include hydrogen peroxide (H2O2), hydrogen cyanide (HCN), nitric acid (HNO3), methyl hydrogen peroxide (CH3OOH), peroxyacetic acid (C2O3H4), peroxynitric acid (HO2NO2), and sulfur dioxide (SO2), in units of parts-per-trillion-by-volume.", "links": [ { diff --git a/datasets/ATom_CO_GEOS_1604_1.json b/datasets/ATom_CO_GEOS_1604_1.json index 64c7a97a07..f4ab1a735c 100644 --- a/datasets/ATom_CO_GEOS_1604_1.json +++ b/datasets/ATom_CO_GEOS_1604_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_CO_GEOS_1604_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains carbon monoxide (CO) observations at 10-second intervals from flights during the ATom-1 campaign in 2016 and simulated CO concentrations from the Goddard Earth Observing System version 5 (GEOS-5) model for the corresponding locations along the ATom flight tracks. The Atmospheric Tomography Mission (ATom) is a NASA Earth Venture Suborbital-2 mission studying the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. The airborne observations were collected using the Quantum Cascade Laser System (QCLS) instrument, a high-frequency laser spectroscopy instrument for in situ atmospheric gas sampling. This dataset provides a direct comparison of observational and simulated CO that will be used to inform future atmospheric modeling experiments. The dataset also contains simulated tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. This dataset contributes to one of the ATom mission objectives to create an observation-based chemical climatology of important atmospheric constituents and their reactivity in the remote troposphere.", "links": [ { diff --git a/datasets/ATom_Carbon_Aerosol_Loadings_1618_1.json b/datasets/ATom_Carbon_Aerosol_Loadings_1618_1.json index 557cdf640f..05d06e025d 100644 --- a/datasets/ATom_Carbon_Aerosol_Loadings_1618_1.json +++ b/datasets/ATom_Carbon_Aerosol_Loadings_1618_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Carbon_Aerosol_Loadings_1618_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides black carbon (BC) mass mixing ratios (in units of ng BC / kg air) measured during NASA's Atmospheric Tomography (ATom)-1 flight campaign during July and August 2016. The BC-core masses of BC-containing aerosol particles were measured using a Single Particle Soot Photometer (SP2). Conversion to mass mixing ratio (MMR) is achieved by monitoring sample flow. Influences in air mass composition were determined using the Particle Analysis by Laser Mass Spectrometry (PALMS) instruments. Also included here are data from the Cloud, Aerosol and Precipitation Spectrometer (CAPS) instrument which are used to identify measurements taken while in clouds. Finally, the associated latitude, longitude, altitude, and the timestamp of each measurement are included. All data are at ten seconds resolution. ATom-1 flights originated from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America.", "links": [ { diff --git a/datasets/ATom_Clouds_Aerosols_2250_1.json b/datasets/ATom_Clouds_Aerosols_2250_1.json index 74dc188d73..a60bfac0b0 100644 --- a/datasets/ATom_Clouds_Aerosols_2250_1.json +++ b/datasets/ATom_Clouds_Aerosols_2250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Clouds_Aerosols_2250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the basis for the development of the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The included data were used in the analysis and development of figures for the related publication. The Cloud Indicator algorithm was developed based on particle size distribution measurements from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) combined with measurements of relative humidity and temperature from other sensors, to automatically detect flight sequences in clouds and classify the cloud type. Measurements were collected on 2016-08-20 as part of the Atmospheric Tomography Mission (ATom-1) Campaign and on 2017-04-20 as part of the Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics (A-LIFE) project. As an additional criterion for the Cloud Indicator, a cloud-aerosol volume factor was established to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning to reduce misclassifications. Data are provided in netCDF (*.nc) format.", "links": [ { diff --git a/datasets/ATom_DLH_Instrument_Data_V2_1937_2.json b/datasets/ATom_DLH_Instrument_Data_V2_1937_2.json index aa9f365955..de1ac3c7d8 100644 --- a/datasets/ATom_DLH_Instrument_Data_V2_1937_2.json +++ b/datasets/ATom_DLH_Instrument_Data_V2_1937_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_DLH_Instrument_Data_V2_1937_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the concentrations of water measured by the Diode Laser Hygrometer (DLH) flown on the NASA DC-8 during the ATom 1-4 campaigns from 2016 - 2018. The DLH measures the water vapor in the atmosphere by wavelength modulated differential absorption spectroscopy of an isolated rovibrational line. The measurements include water vapor mixing ratio in parts-per-million-by-volume (ppmv) and relative humidity in percent. Relative humidity, both with respect to liquid water and with respect to ice, are quantities derived from measurements of water vapor mixing ratio as well as ambient temperature and pressure.", "links": [ { diff --git a/datasets/ATom_FlightTrack_Influences_1889_1.json b/datasets/ATom_FlightTrack_Influences_1889_1.json index 63f988a662..2fc63cc380 100644 --- a/datasets/ATom_FlightTrack_Influences_1889_1.json +++ b/datasets/ATom_FlightTrack_Influences_1889_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_FlightTrack_Influences_1889_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology. Back trajectory analysis determines the origin of air masses by modeling the path of an air parcel backward in time. It can be used to better understand the sources of atmospheric compounds. Boundary layer Influences were determined based on 30 Day Back Trajectories. The atmospheric boundary layer is the lowest part of the troposphere that is directly influenced by earth's surface. The boundary layer influences wind patterns and thus the dispersal of pollutants and other atmospheric compounds of interest. Convective influences were based on 10 Day Back Trajectories and NASA Langley cloud products. Convective influences model the effects of convection on the movement of water vapor through the atmosphere, which influences cloud behavior.", "links": [ { diff --git a/datasets/ATom_Forward_Flight_Videos_1938_1.json b/datasets/ATom_Forward_Flight_Videos_1938_1.json index 5780472412..1138d046a9 100644 --- a/datasets/ATom_Forward_Flight_Videos_1938_1.json +++ b/datasets/ATom_Forward_Flight_Videos_1938_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Forward_Flight_Videos_1938_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains images taken from the front of the NASA DC-8 aircraft during the first three ATom campaigns from 2016-2017. Images were taken with an Axis P1357 High Definition camera with a Theia TH138A wide-angle lens. These images were then stitched together at a 10-second frequency into an MP4 (*.mp4) video for each flight. The forward camera shows the visible atmosphere that DC-8 flew through, allowing the in situ measurements to be placed in the context of cloud fields, smoke and haze layers, and boundary layers.", "links": [ { diff --git a/datasets/ATom_FullModel_DataStream_1877_1.json b/datasets/ATom_FullModel_DataStream_1877_1.json index 94b7237600..dda03ae8bf 100644 --- a/datasets/ATom_FullModel_DataStream_1877_1.json +++ b/datasets/ATom_FullModel_DataStream_1877_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_FullModel_DataStream_1877_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.", "links": [ { diff --git a/datasets/ATom_GT_CIMS_Instrument_Data_1715_1.json b/datasets/ATom_GT_CIMS_Instrument_Data_1715_1.json index f2f7ecdb11..70a826c59e 100644 --- a/datasets/ATom_GT_CIMS_Instrument_Data_1715_1.json +++ b/datasets/ATom_GT_CIMS_Instrument_Data_1715_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_GT_CIMS_Instrument_Data_1715_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of two important components of photochemical smog - peroxyacetyl nitrate (PAN) and peroxyl propionyl nitrate (PPN)- measured by the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The GT-CIMS measures reactive nitrogen species in the lower atmosphere. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.", "links": [ { diff --git a/datasets/ATom_GlobalModelInitiative_CTM_1897_1.json b/datasets/ATom_GlobalModelInitiative_CTM_1897_1.json index a671f8bb80..7842cea415 100644 --- a/datasets/ATom_GlobalModelInitiative_CTM_1897_1.json +++ b/datasets/ATom_GlobalModelInitiative_CTM_1897_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_GlobalModelInitiative_CTM_1897_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Global Modeling Initiative (GMI) Chemical Transport Model (CTM) outputs from the four Atom campaigns. GMI simulations of the ATom flight periods have a horizontal resolution of 1.0 x 1.25 degrees, with output every 15 minutes. The ICARTT files are generated by spatially and temporally interpolating the output to the ATom flight track. Vertical interpolation is linear in log-pressure. The netCDF files provide three-dimensional (3D) GMI simulation output for the region surrounding the flight track every 15 minutes at the original model resolution. GMI is a 3-D CTM that includes full chemistry for both the troposphere and stratosphere. GMI simulates the concentrations of many of the species measured during ATom.", "links": [ { diff --git a/datasets/ATom_HIPPO_ORCAS_1788_1.json b/datasets/ATom_HIPPO_ORCAS_1788_1.json index 0a89492789..6d187fc983 100644 --- a/datasets/ATom_HIPPO_ORCAS_1788_1.json +++ b/datasets/ATom_HIPPO_ORCAS_1788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_HIPPO_ORCAS_1788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm. The data span a wide range of latitudes poleward of 40 degrees in both the Northern and Southern Hemispheres and cover the period 2009-01-10 to 2018-05-21.", "links": [ { diff --git a/datasets/ATom_HR-AMS_Instrument_Data_1716_1.1.json b/datasets/ATom_HR-AMS_Instrument_Data_1716_1.1.json index c6fa90993f..efdf4f95d0 100644 --- a/datasets/ATom_HR-AMS_Instrument_Data_1716_1.1.json +++ b/datasets/ATom_HR-AMS_Instrument_Data_1716_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_HR-AMS_Instrument_Data_1716_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the atmospheric concentrations of separated ions from inorganic and organic species measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) collected during flights of the NASA ATom Mission. Data are available from all four ATom Campaigns. The HR-AMS detects non-refractory submicron aerosol composition by impaction on a vaporizer at 600 degrees C, followed by electron ionization and time-of-flight mass spectral analysis. The measurements include chemically speciated submicron non-refractory particulate mass at a one second and 60 second resolution, and the size distribution of chemically speciated submicron non-refractory particulate mass at 60 second resolution.", "links": [ { diff --git a/datasets/ATom_ISAF_Instrument_Data_1730_1.json b/datasets/ATom_ISAF_Instrument_Data_1730_1.json index b9c5dc326c..9ea82585cf 100644 --- a/datasets/ATom_ISAF_Instrument_Data_1730_1.json +++ b/datasets/ATom_ISAF_Instrument_Data_1730_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_ISAF_Instrument_Data_1730_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the atmospheric volume mixing ratio of formaldehyde measured during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. The NASA In Situ Airborne Formaldehyde (ISAF) instrument, based at the Goddard Space Flight Center, measures formaldehyde on high-altitude NASA aircraft. The instrument uses laser-induced fluorescence (LIF) to obtain the high detection sensitivity needed to detect formaldehyde in the upper troposphere and lower stratosphere where abundances are 10 parts per trillion. LIF also enables a fast time response needed to measure the abundance of formaldehyde in the finely structured outflow of convective storms. These measurements of formaldehyde will be used elucidate mechanisms of convective transport and quantify the effects of boundary layer pollutants on the ozone photochemistry and cloud microphysics of the upper atmosphere.", "links": [ { diff --git a/datasets/ATom_MMS_Instrument_Data_1731_1.json b/datasets/ATom_MMS_Instrument_Data_1731_1.json index 48f109b3a5..ca99fcdc9c 100644 --- a/datasets/ATom_MMS_Instrument_Data_1731_1.json +++ b/datasets/ATom_MMS_Instrument_Data_1731_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_MMS_Instrument_Data_1731_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains measurements from the Meteorological Measurement System (MMS) instrument from the four ATom campaigns. MMS is a state-of-the-art instrument for measuring accurate, high resolution in situ airborne state parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector). These key measurements enable our understanding of atmospheric dynamics, chemistry, and microphysical processes. The MMS is used to investigate atmospheric mesoscale (gravity and mountain lee waves) and microscale (turbulence) phenomena. An accurate characterization of the turbulence phenomenon is important for the understanding of dynamic processes in the atmosphere, such as the behavior of buoyant plumes within cirrus clouds, diffusions of chemical species within wake vortices generated by jet aircraft, and microphysical processes in breaking gravity waves. Accurate temperature and pressure data are needed to evaluate chemical reaction rates as well as to determine accurate mixing ratios. Accurate wind field data establish a detailed relationship with the various constituents and the measured wind also verifies numerical models used to evaluate air mass origin.", "links": [ { diff --git a/datasets/ATom_Mapping_OH_Troposphere_1669_1.json b/datasets/ATom_Mapping_OH_Troposphere_1669_1.json index 7e3bb4b952..a389a309ed 100644 --- a/datasets/ATom_Mapping_OH_Troposphere_1669_1.json +++ b/datasets/ATom_Mapping_OH_Troposphere_1669_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Mapping_OH_Troposphere_1669_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides profile-integrated column densities of formaldehyde (HCHO), hydroxyl (OH), and OH production rates, diel tropospheric mean OH concentrations, and uncertainties that were derived from direct observation data from selected profiles of NASA Atmospheric Tomography (ATom) mission 1 and 2 flights for the period July 29, 2016 to February 21, 2017. These calculated products were combined with coincident HCHO column retrievals from the Ozone Monitoring Instrument (OMI) to scale and extend the profile results to a global gridded (0.5 deg latitude x 0.625 deg longitude) product. In addition to OMI formaldehyde column data, model output products from the Global Modeling Initiative (GMI) including average tropopause height, scaling factor, column air mass, and column-average formaldehyde photolysis frequency are provided. The GMI model output products were used in calculations and are included for user convenience.", "links": [ { diff --git a/datasets/ATom_Medusa_Instrument_Data_V2_1881_2.json b/datasets/ATom_Medusa_Instrument_Data_V2_1881_2.json index c73400bff0..7ffa2bcd43 100644 --- a/datasets/ATom_Medusa_Instrument_Data_V2_1881_2.json +++ b/datasets/ATom_Medusa_Instrument_Data_V2_1881_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Medusa_Instrument_Data_V2_1881_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides O2/N2, CO2, Ar/N2, and stable isotope ratios of CO2 measured in flasks collected by the Medusa Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Medusa collected 32 cryogenically dried, flow, and pressure-controlled samples per flight. The samples are collected by an automated sampler into 1.5 L glass flasks that integrate over 25 seconds. Medusa provides discretely-sampled comparisons for onboard in situ O2/N2 ratio and CO2 measurements and unique measurements of Ar/N2 and 13C, 14C, and 18O isotopologues of CO2. Medusa flasks are analyzed on a sector-magnet mass spectrometer and a LiCor non-dispersive infrared CO2 analyzer by the Scripps O2 Program at Scripps Institution of Oceanography.", "links": [ { diff --git a/datasets/ATom_Mineral_Dust_Cirrus_Cloud_2006_1.json b/datasets/ATom_Mineral_Dust_Cirrus_Cloud_2006_1.json index 81f4f25d7e..c99ed927e0 100644 --- a/datasets/ATom_Mineral_Dust_Cirrus_Cloud_2006_1.json +++ b/datasets/ATom_Mineral_Dust_Cirrus_Cloud_2006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Mineral_Dust_Cirrus_Cloud_2006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides: (1) In situ dust aerosol concentration measurements over remote tropical Pacific and Atlantic Oceans by NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) airborne single-particle mass spectrometer combined with Aerosol Microphysical Properties (AMP) aerosol size spectrometers. Measurements were made aboard the NASA DC8 aircraft during the four ATom campaigns that occurred from 2016 to 2018 (2) Model output of dust and meteorology from the CESM global transport model extracted at the time and location of the aircraft; (3) Model output of dust, other aerosol, and meteorology from the GEOS global transport model extracted at the time and location of the aircraft; (4) CESM model global output of dust and meteorology for dust emitted by specific source regions; (5) NCEP Global Forecast System forward trajectories of air parcels initiated at the time and location of the aircraft; and (6) The location and properties of cirrus clouds formed along the forward trajectories simulated using a parcel model. These data have been applied to better understand the role of mineral dust in cirrus cloud formation.", "links": [ { diff --git a/datasets/ATom_Modeled_Observed_Data_1857_1.json b/datasets/ATom_Modeled_Observed_Data_1857_1.json index ebcd101045..f669ed6db8 100644 --- a/datasets/ATom_Modeled_Observed_Data_1857_1.json +++ b/datasets/ATom_Modeled_Observed_Data_1857_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Modeled_Observed_Data_1857_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides observations collected during eleven airborne campaigns from 2006–2017 and associated input and output from nine widely used chemical transport models (CTMs). The airborne campaigns include ARCTAS-A, ARCTAS-B, ATom-1 and ATom-2, CalNex, DC3, INTEX-B, KORUS-AQ, MILAGRO, SEAC4RS, and WINTER, and they sampled mainly tropospheric air over the conterminous U.S. and the state of Alaska, Mexico, Canada, Greenland, and South Korea and remote areas over the Arctic, Pacific, Southern, and Atlantic Oceans. The CTMs are the AM4.1, CCSM4, GEOS-5, GEOS-Chem TOMAS, GEOS-Chem v10, GEOS-Chem v12, GISS-MATRIX, GISS-ModelE, and TM4-ECPL-F, and the output includes sulfate, nitrate, temperature, specific humidity, mixing ratio of ammonium, the volume mixing ratio of nitric acid, surface pressure, gas-phase ammonia, gas-phase nitric acid, pressure, total ammonium, etc. The observations were collected in-situ from a variety of instruments, including the Aerosol Microphysical Properties (AMP), HR Aerodyne Aerosol Mass Spectrometer (AMS), CIT Chemical Ionization Mass Spectrometer (CIMS), diode laser hygrometer (DLH), a mist chamber/ion chromatography system (MC/IC), Particle Analysis by Laser Mass Spectrometer (PALMS), Single Particle Soot Photometer (SP2), and UCI Whole Air Sampler (WAS). In-situ data also include latitude, longitude, and pressure. These observations were used to investigate how aerosol pH and ammonium balance change from polluted to remote regions, such as over oceans, and were compared to predictions from the CTMs.", "links": [ { diff --git a/datasets/ATom_NMASS_Data_1607_1.json b/datasets/ATom_NMASS_Data_1607_1.json index bd17356ded..791fc898db 100644 --- a/datasets/ATom_NMASS_Data_1607_1.json +++ b/datasets/ATom_NMASS_Data_1607_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_NMASS_Data_1607_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides extensive calibration and in-flight performance data for two nucleation mode aerosol size spectrometer (NMASS) instruments utilized in the NASA Atmospheric Tomography Mission (ATom). Each NMASS has five condensation particle counters (CPCs) that detect particles above a different minimum size, determined by the maximum vapor supersaturation encountered by the particles. Operated in parallel, the CPCs provide continuous concentrations of particles in different cumulative size classes between 3 and 60 nm. Knowing the response function of each CPC, numerical inversion techniques were applied to recover size distributions from the continuous concentrations. Data provided include: NMASS counting efficiencies and diameters of calibration aerosols, inverted particle size distributions; comparisons of NMASS and Scanning Mobility Particle Sizer (SMPS) results; and performance at flows, temperatures, and pressures measured by both NMASSs and comparison with Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) concentrations collected on board the NASA DC-8 aircraft during an ATom flight in February 2017.", "links": [ { diff --git a/datasets/ATom_NOyO3_Instrument_Data_1734_1.json b/datasets/ATom_NOyO3_Instrument_Data_1734_1.json index b68f478e94..a1a8281aa2 100644 --- a/datasets/ATom_NOyO3_Instrument_Data_1734_1.json +++ b/datasets/ATom_NOyO3_Instrument_Data_1734_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_NOyO3_Instrument_Data_1734_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ concentrations of nitric oxide (NO), nitrogen dioxide (NO2), total reactive nitrogen oxides (NOy), and ozone (O3) measured by the NOAA Nitrogen Oxides and Ozone (NOyO3) 4-channel chemiluminescence (CL) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. NOyO3 provides fast-response, specific, high precision, and calibrated measurements of nitrogen oxides and ozone at a spatial resolution of better than 100 m. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.", "links": [ { diff --git a/datasets/ATom_Organic_Aerossols_1795_1.json b/datasets/ATom_Organic_Aerossols_1795_1.json index 36545d40a6..d41330a345 100644 --- a/datasets/ATom_Organic_Aerossols_1795_1.json +++ b/datasets/ATom_Organic_Aerossols_1795_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Organic_Aerossols_1795_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aerosol Microphysical Properties package (AMP), black carbon mass content measured by the Single Particle Soot Photometer (NOAA SP2), and refractory and non-refractory aerosol composition measured by the Particle Analysis By Laser Mass Spectrometry (PALMS). Both observed and modeled data are provided at a 60-second temporal resolution. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/ATom_Ozonesonde_InstrumentData_1910_1.json b/datasets/ATom_Ozonesonde_InstrumentData_1910_1.json index 7a41452041..ab2f8e5921 100644 --- a/datasets/ATom_Ozonesonde_InstrumentData_1910_1.json +++ b/datasets/ATom_Ozonesonde_InstrumentData_1910_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Ozonesonde_InstrumentData_1910_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ozone measurements from the Ozonesonde instrument in Antarctica, Hawaii, and Fiji taken during the Atom-4 campaign. The Electrochemical Concentration Cell (ECC) Ozonesonde is a balloon-borne instrument that collects ozone concentrations paired with a radiosonde to collect additional meteorological info along a vertical profile (as a result, unlike other ATom data, this dataset is not associated with DC-8). The balloon can ascend to altitudes of 35 km before bursting. Ozone in the stratosphere helps reduce UV radiation that reaches Earth's surface; however, ozone at ground level can negatively influence respiratory health.", "links": [ { diff --git a/datasets/ATom_PALMS_Instrument_Data_1733_1.json b/datasets/ATom_PALMS_Instrument_Data_1733_1.json index bb1c942eb6..4f33804a3a 100644 --- a/datasets/ATom_PALMS_Instrument_Data_1733_1.json +++ b/datasets/ATom_PALMS_Instrument_Data_1733_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_PALMS_Instrument_Data_1733_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains single-particle aerosol composition as measured by the Particle Analysis by Laser Mass Spectrometry (PALMS) instrument during the four ATom campaigns from 2016-2018. Single aerosol particles are classified into several particle types, including: mixed sulfate/organic nitrate, biomass burning, elemental carbon, mineral/metallic, meteoric material, alkali salt, sea salt, heavy oil combustion, and others. Particle types are reported as raw number fractions and as absolute mass concentrations. PALMS measures aerosol composition for particles from diameter ~100 to 5000 nm, with most of the particle data in the size range ~150 to 3000 nm. Also included are absolute aerosol concentrations measured by a modified Laser Aerosol Spectrometer (LAS). Integrated number, surface area, and volume concentrations from LAS are reported over multiple size ranges.", "links": [ { diff --git a/datasets/ATom_PANTHER_Instrument_Data_1914_1.json b/datasets/ATom_PANTHER_Instrument_Data_1914_1.json index f336efdf34..04207aba95 100644 --- a/datasets/ATom_PANTHER_Instrument_Data_1914_1.json +++ b/datasets/ATom_PANTHER_Instrument_Data_1914_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_PANTHER_Instrument_Data_1914_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains measurements of various trace gases from the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) across the four ATom campaigns. PANTHER uses Electron Capture Detection and Gas Chromatography (ECD-GC) and Mass Selective Detection and Gas Chromatography (MSD-GC) to measure numerous trace gases, including methyl halides, HCFCs, PAN, N2O, SF6, CFC-12, CFC-11, Halon 1211, methyl chloroform, carbon tetrachloride.", "links": [ { diff --git a/datasets/ATom_PFP_Instrument_Data_1746_1.json b/datasets/ATom_PFP_Instrument_Data_1746_1.json index 83624ae0b8..f24d728c23 100644 --- a/datasets/ATom_PFP_Instrument_Data_1746_1.json +++ b/datasets/ATom_PFP_Instrument_Data_1746_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_PFP_Instrument_Data_1746_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides mole fractions of atmospheric trace gases measured by the Programmable Flask Package (PFP) Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The PFP whole air sampler provides a means of automated or manual filling of glass flasks. The sampler is designed to remove excess water vapor from the sampled air and compress it without contamination into ~1-liter volumes. These flasks are analyzed at the NOAA's Global Monitoring Division laboratory for trace gases and at the INSTAR's Staple Isotope Lab laboratory for isotopes of methane. Analysis of standardized PFP samples can measure more than 60 trace gases including N2O, SF6, H2, CS2, OCS, CO2, CH4, CO, CFCs, HCFCs, HFCs, Solvents, Methyl Halides, Hydrocarbons and Perfluorocarbons. The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.", "links": [ { diff --git a/datasets/ATom_Particulate_Iodine_1773_1.json b/datasets/ATom_Particulate_Iodine_1773_1.json index 7b811a62d5..588d2c9db4 100644 --- a/datasets/ATom_Particulate_Iodine_1773_1.json +++ b/datasets/ATom_Particulate_Iodine_1773_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Particulate_Iodine_1773_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides mass concentrations of particulate iodine as measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) during the first two deployments of the NASA Atmospheric Tomography airborne missions (ATom-1 and ATom-2) in 2016 and 2017, respectively. The data provided in this dataset result from a reanalysis of the initial HR-AMS data based on post-mission calibrations and are reported at 1-minute resolution. The dataset also includes the fractions of the main ions (I+, HI+, and I2+) that can be used to ascertain the oxidation state of iodine in particles. Each observation includes an air mass classification flag (tropospheric or stratospheric conditions) based on collocated in situ water vapor and ozone measurements and positional data from the HR-AMS data feed.", "links": [ { diff --git a/datasets/ATom_Photolysis_Rates_1651_1.json b/datasets/ATom_Photolysis_Rates_1651_1.json index e4ec22e68e..88f760b2c1 100644 --- a/datasets/ATom_Photolysis_Rates_1651_1.json +++ b/datasets/ATom_Photolysis_Rates_1651_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Photolysis_Rates_1651_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the results from nine global chemistry-climate or chemistry-transport models that estimated gridded values of atmospheric photolytic rates (J values) for ozone (O3), designated J-O1D, and nitrogen dioxide (NO2), designated J-NO2, under cloudy and clear sky scenarios. Each model produced global 4-D fields (latitude by longitude by pressure for 24 hours) for one day in mid-August 2016 (nominally) of results from two simulations: first using their standard treatment of clouds (all sky or cloudy) and a second with clouds and aerosols removed (clear sky). Model resolution ranges from 0.5 to 2.5 degrees. Observed J-O1D and J-NO2 values from the first ATom deployment (29 July - 23 August 2016) were collected with the Charged-coupled device Actinic Flux Spectroradiometer (CAFS) instrument. The ATom CAFS measurements are 3-second averages along the flight path for selected remote areas over the tropical and northern Pacific Ocean. Both all-sky (cloudy) and synthesized clear-sky J values are provided. Additional data are included for clouds and ozone column plus other cloudy and clear sky parameters for the same remote areas of the tropical and northern Pacific Ocean. These auxiliary data are provided for use with included MATLAB scripts to reproduce the plots and analyses performed in the related publication by Hall et al. (2018). Note that while the analyses in the related publication were limited to the Pacific basin, the global model data are archived with this dataset.", "links": [ { diff --git a/datasets/ATom_Picarro_Instrument_Data_1732_1.json b/datasets/ATom_Picarro_Instrument_Data_1732_1.json index d4e0642d0d..fc2d572891 100644 --- a/datasets/ATom_Picarro_Instrument_Data_1732_1.json +++ b/datasets/ATom_Picarro_Instrument_Data_1732_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Picarro_Instrument_Data_1732_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains atmospheric measurements of CO2, CH4, and CO mixing ratios made with a Picarro G2401 spectrometer during the four ATom campaigns. Picarro G2401 uses Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time-based measurement utilizing a near-infrared laser to measure a spectral signature of the molecule. For the ATom mission, the Picarro instrument was modified in the laboratory to operate across the full pressure altitude range of flight campaigns. The instrument was also modified to have a shorter measurement interval.", "links": [ { diff --git a/datasets/ATom_QCLS_Instrument_Data_V2_1932_2.json b/datasets/ATom_QCLS_Instrument_Data_V2_1932_2.json index 13bb495548..a4251a7c9e 100644 --- a/datasets/ATom_QCLS_Instrument_Data_V2_1932_2.json +++ b/datasets/ATom_QCLS_Instrument_Data_V2_1932_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_QCLS_Instrument_Data_V2_1932_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides atmospheric concentrations of CO2, CH4, CO, and N2O measured by the Harvard Quantum Cascade Laser System (QCLS) instruments during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The QCLS (DUAL and CO2) instrument package contains two separate optical assemblies and calibration systems, and a common data system and power supply. The QCLS DUAL instrument simultaneously measures CO, CH4, and N2O concentrations, in situ, using two thermoelectrically cooled pulsed-quantum cascade lasers light sources, a multiple pass absorption cell, and two liquid nitrogen-cooled solid-state detectors. The QCLS CO2 instrument measures CO2 concentrations in situ using a thermoelectrically cooled pulsed-quantum cascade laser light source, gas cells, and liquid nitrogen cooled solid-state detectors. The CO2 mixing ratio of air flowing through the sample gas cell is determined by measuring absorption from a single infrared transition line at 4.32 microns relative to a reference gas of known concentration.", "links": [ { diff --git a/datasets/ATom_Rad_Measurements_ARMAS_1906_1.json b/datasets/ATom_Rad_Measurements_ARMAS_1906_1.json index 9eb8838e7c..b240534e92 100644 --- a/datasets/ATom_Rad_Measurements_ARMAS_1906_1.json +++ b/datasets/ATom_Rad_Measurements_ARMAS_1906_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Rad_Measurements_ARMAS_1906_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2 (L2) absorbed radiation dose rates in silicon from the Automated Radiation Measurements for Aerospace Safety (ARMAS) system along ATom flight paths for the ATom-1 campaign conducted in July and August 2016. Absorbed dose rates measure how much energy is deposited in matter by ionizing radiation per unit time. The radiation sources can be from galactic cosmic rays, solar energetic particles, or Van Allen radiation belt energetic particles. Radiation can have adverse effects on human tissue and aerospace electronics, as well as profound effects on chemical species in the atmosphere, making them important to consider in atmospheric modeling and analyses. In this context, the derived ambient equivalent dose rates are also provided and relate the absorbed dose in human tissue to the effective biological damage of the radiation through a radiation weighting factor. In addition, visualizations of absorbed radiation dose for ATom-1 flight paths are included. The visualizations show the absorbed dose rates in silicon in the upper panel and the 3D representation of the flight in the bottom panel.", "links": [ { diff --git a/datasets/ATom_SAGA_Instrument_Data_1748_1.json b/datasets/ATom_SAGA_Instrument_Data_1748_1.json index 8b00bb502c..2902109424 100644 --- a/datasets/ATom_SAGA_Instrument_Data_1748_1.json +++ b/datasets/ATom_SAGA_Instrument_Data_1748_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_SAGA_Instrument_Data_1748_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soluble acidic gases and aerosols (SAGA) were collected with two related installations; a mist chamber/ion chromatography (MC/IC) system and a paired bulk aerosol system. The MC/IC system measures in situ atmospheric distributions of nitric acid (plus < 1 um NO3 aerosol) and fine (< 1 um) aerosol sulfate at an approximately 80-second interval. The paired bulk aerosol system collects particulates onto filters for subsequent analysis. Collected filters were first extracted with water to obtain the water-soluble (WS) constituents and then extracted again using methanol to collect the methanol soluble (MS) fraction. The light absorption of filtered extracts was measured from 300 to 700 nm. Ion chromatography on aqueous extracts of the bulk aerosol samples collected on Teflon filters were used to quantify soluble ions (Cl-, Br-, NO3-, SO42-, C2O42-, Na+, NH4+, K+, Ca+, and Mg+). The SAGA system is provided by the University of New Hampshire (UNH).", "links": [ { diff --git a/datasets/ATom_SO2_LIF_Instrument_Data_1890_1.json b/datasets/ATom_SO2_LIF_Instrument_Data_1890_1.json index 71f4cbe802..0c12210c8d 100644 --- a/datasets/ATom_SO2_LIF_Instrument_Data_1890_1.json +++ b/datasets/ATom_SO2_LIF_Instrument_Data_1890_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_SO2_LIF_Instrument_Data_1890_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides concentrations of sulfur dioxide (SO2) measured by the Laser Induced Fluorescence Instrumentation for Sulfur Dioxide (SO2-LIF) on the ATom-4 campaign in April and May 2018. The LIF-SO2 instrument detects SO2 at the single-part per trillion level using red-shifted laser-induced fluorescence. Measurements are reported at 1-second intervals along the flight paths. Sources of SO2 atmosphere from natural sources include volcanic eruptions and wildfires; however, most anthropogenic sources, such as fossil fuel combustion, arise. SO2 influences some negative health and environmental impacts and is an important precursor of aerosols in the nucleation of new particles globally.", "links": [ { diff --git a/datasets/ATom_SOAP_Instrument_Data_1898_1.json b/datasets/ATom_SOAP_Instrument_Data_1898_1.json index fdc5546253..0af7639078 100644 --- a/datasets/ATom_SOAP_Instrument_Data_1898_1.json +++ b/datasets/ATom_SOAP_Instrument_Data_1898_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_SOAP_Instrument_Data_1898_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains one-second aerosol extinction and absorption measurements from the Spectrometers for Optical Aerosol Properties (SOAP) instrument aboard the NASA DC-8 aircraft during the ATom-4 campaign that occurred in 2018. SOAP is a compact, low maintenance instrument that measures aerosol extinction and absorption at 532 nm. Aerosol extinction is measured by cavity ringdown spectroscopy and aerosol absorption by photoacoustic spectroscopy. Extinction is measured with sufficient precision and accuracy for the remote atmosphere. The absorption measurements are valid only in strongly absorbing cases, such as in dilute plumes from wildfire smoke. The absorption and extinction of visible light by aerosol particles is a major component of the earth's radiation budget, strongly affecting climate. Highly absorbing particles directly heat the atmosphere, while particles that scatter light tend to cool the atmosphere. Extinction is the sum of absorption and scattering; in most cases scattering represents >90% of extinction, with absorption making up the remainder. These aerosol-radiation interactions also alter air temperature and the rates of photochemical reactions.", "links": [ { diff --git a/datasets/ATom_SP2_Instrument_Data_1672_1.json b/datasets/ATom_SP2_Instrument_Data_1672_1.json index d96e1aa04a..6e03954c7c 100644 --- a/datasets/ATom_SP2_Instrument_Data_1672_1.json +++ b/datasets/ATom_SP2_Instrument_Data_1672_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_SP2_Instrument_Data_1672_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the refractory black carbon mass concentration at one-second resolution measured by the Single Particle Soot Photometer (NOAA SP2) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The SP2 is a laser-induced incandescence instrument primarily used for measuring the black carbon mass content of individual particles.", "links": [ { diff --git a/datasets/ATom_SP2_LAM_FeOx_MMR_1828_1.json b/datasets/ATom_SP2_LAM_FeOx_MMR_1828_1.json index f88d3e7772..160ff841c7 100644 --- a/datasets/ATom_SP2_LAM_FeOx_MMR_1828_1.json +++ b/datasets/ATom_SP2_LAM_FeOx_MMR_1828_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_SP2_LAM_FeOx_MMR_1828_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides mass mixing ratios and number density of light-absorbing metallic aerosols (LAM) in the size range 180-1290 nm obtained with the NOAA Single Particle Soot Photometer (SP2) during the four deployments of the NASA Atmospheric Tomography (ATom) airborne mission from 2016-2018. The NOAA SP2 detects light absorbing aerosols, such as black carbon (BC), via laser-induced incandescence to provide real-time in situ quantification of refractory aerosol mass and number density. The percent of LAM aerosols attributed to anthropogenic iron oxides (FeOx) by mass is also provided.", "links": [ { diff --git a/datasets/ATom_Simulated_Data_1597_1.json b/datasets/ATom_Simulated_Data_1597_1.json index f8467353ef..86f1c8e027 100644 --- a/datasets/ATom_Simulated_Data_1597_1.json +++ b/datasets/ATom_Simulated_Data_1597_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_Simulated_Data_1597_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.", "links": [ { diff --git a/datasets/ATom_TOGA_Instrument_Data_V2_1936_2.json b/datasets/ATom_TOGA_Instrument_Data_V2_1936_2.json index f4af1fca5f..e9e763429f 100644 --- a/datasets/ATom_TOGA_Instrument_Data_V2_1936_2.json +++ b/datasets/ATom_TOGA_Instrument_Data_V2_1936_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_TOGA_Instrument_Data_V2_1936_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides concentrations of volatile organic compounds (VOCs) measured by the Trace Organic Gas Analyzer (TOGA) during the four ATom campaigns. These data are relevant to the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. Specific data were obtained for radical precursors, tracers of anthropogenic and biogenic activities, tracers of urban and biomass combustion emissions, products of oxidative processing, precursors to aerosol formation, and compounds important for aerosol modification and transformation. TOGA measures a wide range of VOCs with high sensitivity (ppt or lower), frequency (2-minutes), accuracy (often 15% or better), and precision (<3%).", "links": [ { diff --git a/datasets/ATom_UCATS_Instrument_Data_1750_1.json b/datasets/ATom_UCATS_Instrument_Data_1750_1.json index 95774465a9..c2a3ab1a94 100644 --- a/datasets/ATom_UCATS_Instrument_Data_1750_1.json +++ b/datasets/ATom_UCATS_Instrument_Data_1750_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_UCATS_Instrument_Data_1750_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset, collected with the Unmanned Aircraft Systems (UAS) Chromatograph for Atmospheric Trace Species (UCATS), provides atmospheric concentrations of nitrous oxide (N2O), sulfur hexafluoride (SF6), methane (CH4), hydrogen (H2), carbon monoxide (CO), water vapor (H2O), and ozone (O3). The UCATS system is three different instruments in one enclosure: a two-channel chromatograph with electron capture detectors (one measures N2O and SF6, the other measures CH4, H2 and CO), a tunable diode laser instrument for H2O, and a dual-beam O3 photometer.", "links": [ { diff --git a/datasets/ATom_UHSAS_Data_1619_1.json b/datasets/ATom_UHSAS_Data_1619_1.json index 315984964d..7c26361947 100644 --- a/datasets/ATom_UHSAS_Data_1619_1.json +++ b/datasets/ATom_UHSAS_Data_1619_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_UHSAS_Data_1619_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides extensive calibration and in-flight performance data for two Ultra-High Sensitivity Aerosol Spectrometers (UHSAS) used for particle size distribution and volatility measurements during the NASA Atmospheric Tomography Mission (ATom) airborne campaign. UHSAS-1 was equipped with a compact thermodenuder operating at 300 degrees C and UHSAS-2 was operated without a thermodenuder to determine the number and volume fraction of volatile particles. Laboratory studies utilized aerosols from limonene ozonolysis (limon), atomization of ammonium sulfate (AS), and atomization of 2-diethylhexyl (dioctyl) sebacate (DOS). Data include: UHSAS detection efficiency, sizing calibration, performance at a range of pressures and at a range of thermodenuder temperatures, comparison of UHSAS-2 and condensation particle counter (CPC) particle number concentrations, comparisons of UHSAS-1 and UHSAS-2 for dry particle number concentration, surface area and volume collected onboard of a NASA DC-8 aircraft during August 2016, and dry aerosol size distributions for thermodenuded and non-thermodenuded instrument collected in February 2017.", "links": [ { diff --git a/datasets/ATom_WAS_Instrument_Data_1751_1.json b/datasets/ATom_WAS_Instrument_Data_1751_1.json index 94a7ec8996..6ef003f3d0 100644 --- a/datasets/ATom_WAS_Instrument_Data_1751_1.json +++ b/datasets/ATom_WAS_Instrument_Data_1751_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_WAS_Instrument_Data_1751_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides atmospheric concentrations of halocarbons and hydrocarbons measured by the UC-Irvine Whole Air Sampler (WAS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The analysis of samples from the UCI WAS provides measurements of more than 50 trace gases, including C2-C10 NMHCs, C1-C2 halocarbons, C1-C5 alkyl nitrates, and selected sulfur compounds. Species were identified and measured using an established technique of airborne whole air sampling followed by laboratory analysis using gas chromatography (GC) with flame ionization detection (FID), and mass spectrometric detection (MSD). The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.", "links": [ { diff --git a/datasets/ATom_merge_1581_1.5.json b/datasets/ATom_merge_1581_1.5.json index a45c1ea2fe..838b1c2557 100644 --- a/datasets/ATom_merge_1581_1.5.json +++ b/datasets/ATom_merge_1581_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_merge_1581_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data have been created for seven different sampling intervals. In the case of data obtained over longer time intervals (e.g. flask data), the merge files provide (weighted) averages to match the sampling intervals. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate. Profiles of the reactive gases will also provide critical information for the validation of satellite data, particularly in remote areas where in situ data is lacking. Complete aircraft flight information including, but not limited to, latitude, longitude, and altitude are also provided. This data release provides results from all instruments on all four ATom flight campaigns.", "links": [ { diff --git a/datasets/ATom_merge_V2_1925_2.0.json b/datasets/ATom_merge_V2_1925_2.0.json index ed92d22956..e284b62f7a 100644 --- a/datasets/ATom_merge_V2_1925_2.0.json +++ b/datasets/ATom_merge_V2_1925_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_merge_V2_1925_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data products have been created for seven different aggregation intervals (1 second, 10 seconds, and 5 instrument-specific intervals). In the case of data obtained over longer time intervals (e.g., flask data), the merge files provide (weighted) averages to match the sampling intervals. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.", "links": [ { diff --git a/datasets/ATom_nav_1613_1.json b/datasets/ATom_nav_1613_1.json index 91bf389440..217ad15403 100644 --- a/datasets/ATom_nav_1613_1.json +++ b/datasets/ATom_nav_1613_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ATom_nav_1613_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.", "links": [ { diff --git a/datasets/AUX_Dynamic_Open_4.0.json b/datasets/AUX_Dynamic_Open_4.0.json index 235dace471..986e6cd4ca 100644 --- a/datasets/AUX_Dynamic_Open_4.0.json +++ b/datasets/AUX_Dynamic_Open_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AUX_Dynamic_Open_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 2 ECMWF SMOS Auxiliary data product, openly available to all users, contains ECMWF data on the ISEA 4-9 DGG corresponding to SMOS half-orbit. It is used by both the ocean salinity and soil moisture operational processors to store the geophysical parameters from ECMWF forecasts. Access to other SMOS Level 1 and Level 2 "dynamic" and "static" auxiliary datasets is restricted to Cal/Val users. The detailed content of the SMOS Auxiliary Data Files (ADF) is described in the Products Specification documents available in the Resources section below.", "links": [ { diff --git a/datasets/AU_5DSno_1.json b/datasets/AU_5DSno_1.json index c0529c28eb..b5394da6cc 100644 --- a/datasets/AU_5DSno_1.json +++ b/datasets/AU_5DSno_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_5DSno_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This AMSR-E/AMSR2 Unified Level-3 (L3) data set provides 5-day maximum estimates of Snow Water Equivalent (SWE). SWE was derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the JAXA GCOM-W1 satellite. \n\nThe SWE data is rendered to an azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.\n\nNote: This data set uses JAXA AMSR2 Level-1R (L1R) input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 L1R products.", "links": [ { diff --git a/datasets/AU_DyOcn_1.json b/datasets/AU_DyOcn_1.json index d8b8d4f227..9fd575632a 100644 --- a/datasets/AU_DyOcn_1.json +++ b/datasets/AU_DyOcn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_DyOcn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified L3 Global Daily Ascending/Descending .25 x .25 deg Ocean Grids\ndata set (AU_DyOcn) reports daily estimates of water vapor, cloud liquid water content, and\nsurface wind speed over the ocean on a global 0.25\u00b0 \u00d7 0.25\u00b0 resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set.\n\nSea surface temperatures from the NOAA 1/4\u00b0 Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.", "links": [ { diff --git a/datasets/AU_DySno_1.json b/datasets/AU_DySno_1.json index 01ddc2ffa4..81ce7238a4 100644 --- a/datasets/AU_DySno_1.json +++ b/datasets/AU_DySno_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_DySno_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This AMSR-E/AMSR2 Unified Level-3 (L3) data set provides daily estimates of Snow Water Equivalent (SWE). SWE was derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the JAXA GCOM-W1 satellite. \n\nThe SWE data is rendered to an azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.\n\nNote: This data set uses JAXA AMSR2 Level-1R (L1R) input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 L1R products.", "links": [ { diff --git a/datasets/AU_DySno_NRT_R02_2.json b/datasets/AU_DySno_NRT_R02_2.json index 26ecacd84e..dbe31618c7 100644 --- a/datasets/AU_DySno_NRT_R02_2.json +++ b/datasets/AU_DySno_NRT_R02_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_DySno_NRT_R02_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Global Daily Snow Water Equivalent data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids). Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science.", "links": [ { diff --git a/datasets/AU_Land_1.json b/datasets/AU_Land_1.json index 1e74925146..e8b50c4c17 100644 --- a/datasets/AU_Land_1.json +++ b/datasets/AU_Land_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Land_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified Level-2B land product provides a long-term data record by combining AMSR-E and AMSR2 data. This data set includes surface soil moisture estimates derived from L1R brightness temperatures using the Normalized Polarization Difference algorithm (NPD) and the Single Channel Algorithm (SCA) along with ancillary information gridded to the 25 km Equal-Area Scalable Earth Grid (EASE-Grid).", "links": [ { diff --git a/datasets/AU_Land_NRT_R02_2.json b/datasets/AU_Land_NRT_R02_2.json index 57387bfeef..b404c753f3 100644 --- a/datasets/AU_Land_NRT_R02_2.json +++ b/datasets/AU_Land_NRT_R02_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Land_NRT_R02_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The GCOM-W1 NRT AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture product is a daily measurement of surface soil moisture produced by two retrieval algorithms using resampled Tb (Level-1R) data provided by JAXA: the Normalized Polarization Difference (NPD) algorithm developed by JPL and the Single Channel Algorithm (SCA) developed by USDA. Ancillary data include time, geolocation, and quality assessment. Data are stored in HDF-EOS5 and netCDF4 formats and are available via HTTPS from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/land/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. The AMSR SIPS produces AMSR2 standard science quality data products and they are available at the NSIDC DAAC. Note: This is the same algorithm that generates the corresponding standard science products in the AMSR SIPS. With this beta release, we are generating NRT products in both HDF-EOS5 and netCDF with CF metadata. Version 2 corrects these issues from the previous release: a boundary condition error that resulted in the failure of a small number of version 1 product files and an error in the number of low resolution scans processed which caused only the first half of each scan to be processed.", "links": [ { diff --git a/datasets/AU_MoOcn_1.json b/datasets/AU_MoOcn_1.json index 8a93916816..aa3dbecbd0 100644 --- a/datasets/AU_MoOcn_1.json +++ b/datasets/AU_MoOcn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_MoOcn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified L3 Global Monthly Ascending/Descending .25x.25 deg Ocean Grids data set is a gridded product that reports monthly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean. The data are derived from resampled Near Real-Time (NRT) Level-1R data provided by Japan Aerospace Exploration Agency (JAXA).\n\nSea surface temperatures from the NOAA 1/4\u00b0 Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.", "links": [ { diff --git a/datasets/AU_MoSno_1.json b/datasets/AU_MoSno_1.json index cd0e3a05f0..6346835e75 100644 --- a/datasets/AU_MoSno_1.json +++ b/datasets/AU_MoSno_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_MoSno_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This AMSR-E/AMSR2 Unified Level-3 (L3) data set provides monthly mean estimates of Snow Water Equivalent (SWE). SWE was derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the JAXA GCOM-W1 satellite. \n\nThe SWE data is rendered to an azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.\n\nNote: This data set uses JAXA AMSR2 Level-1R (L1R) input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 L1R products.", "links": [ { diff --git a/datasets/AU_Ocean_1.json b/datasets/AU_Ocean_1.json index 6e9f1eb777..ae61787113 100644 --- a/datasets/AU_Ocean_1.json +++ b/datasets/AU_Ocean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Ocean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This AMSR Unified global ocean data set reports integrated water vapor and cloud liquid water content in the atmospheric column, plus 10-meter sea surface wind speeds. The data are derived from AMSR-E and AMSR2 brightness temperature observations that have been resampled by the Japan Aerospace Exploration Agency (JAXA) to facilitate an intercalibrated (i.e., \u201cunified\u201d) AMSR-E/AMSR2 data record. Ancillary files, including product history, quality assessment (QA), and file-specific metadata are also available.", "links": [ { diff --git a/datasets/AU_Ocean_NRT_R01_1.json b/datasets/AU_Ocean_NRT_R01_1.json index 5af51341ea..e68645abdf 100644 --- a/datasets/AU_Ocean_NRT_R01_1.json +++ b/datasets/AU_Ocean_NRT_R01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Ocean_NRT_R01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The GCOM-W1 NRT AMSR2 Unified L2B Global Swath Ocean Products is a swath product containing global sea surface temperature over ocean, wind speed over ocean, water vapor over ocean and cloud liquid water over ocean, using resampled NRT Level-1R data provided by JAXA. This is the same algorithm that generates the corresponding standard science products in the AMSR SIPS. The NRT products are generated in HDF-EOS-5 augmented with netCDF-4/CF metadata and are available via HTTPS from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/ocean/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. The AMSR SIPS produces AMSR2 standard science quality data products, and they are available at the NSIDC DAAC.", "links": [ { diff --git a/datasets/AU_Rain_1.json b/datasets/AU_Rain_1.json index c5cf06ffdb..e3c4202811 100644 --- a/datasets/AU_Rain_1.json +++ b/datasets/AU_Rain_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Rain_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This AMSR-E/AMSR2 Unified Level-2B data set reports instantaneous surface precipitation rates and types (over land and ocean) and precipitation profiles (over ocean). The data are derived by applying the AMSR-E/AMSR2 unified algorithm to L1R data obtained by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments.", "links": [ { diff --git a/datasets/AU_Rain_NRT_R02_2.json b/datasets/AU_Rain_NRT_R02_2.json index 62b98ce810..8a2185f8a8 100644 --- a/datasets/AU_Rain_NRT_R02_2.json +++ b/datasets/AU_Rain_NRT_R02_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_Rain_NRT_R02_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The GCOM-W1 NRT AMSR2 Unified Global Swath Surface Precipitation GSFC Profiling Algorithm is a swath product containing global rain rate and type, calculated by the GPROF 2017 V2R rainfall retrieval algorithm using resampled NRT Level-1R data provided by JAXA. This is the same algorithm that generates the corresponding standard science products in the AMSR SIPS. The NRT products are generated in HDF-EOS-5 augmented with netCDF-4/CF metadata and are available via HTTPS from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rain/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. The AMSR SIPS produces AMSR2 standard science quality data products, and they are available at the NSIDC DAAC.", "links": [ { diff --git a/datasets/AU_SI12_1.json b/datasets/AU_SI12_1.json index 4da9d3a810..a8cf31c0ee 100644 --- a/datasets/AU_SI12_1.json +++ b/datasets/AU_SI12_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI12_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified Level-3 12.5 km product provides brightness temperatures, sea ice concentration, and snow depth over sea ice for the Northern and Southern Hemisphere, as well as sea ice motion for the Arctic. This data set includes daily brightness temperature fields for channels ranging from 18.7 GHz through 89.0 GHz, daily sea ice concentration fields, and daily sea ice concentration difference fields for ascending orbits, descending orbits, and full orbit daily averages. Snow depth over sea ice is provided as a five-day running average for the Arctic and Antarctic. Sea Ice motion is provided daily for tracking ice movement over consecutive days in the Arctic.\n\nNote: This product uses the Japan Aerospace Exploration Agency (JAXA) AMSR2 Level-1R input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 Level-1R products.", "links": [ { diff --git a/datasets/AU_SI12_NRT_R04_4.json b/datasets/AU_SI12_NRT_R04_4.json index 233fd7c754..1932a79741 100644 --- a/datasets/AU_SI12_NRT_R04_4.json +++ b/datasets/AU_SI12_NRT_R04_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI12_NRT_R04_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Daily 12.5 km Brightness Temperature & Sea Ice Concentration, Version 4 uses as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA). The Version 4 dataset uses the AMSR-U2 product generation algorithm with slight modifications for NRT product generation, same algorithm used to generation the standard, science quality, data that is available at the NSIDC DAAC. This Level-3 gridded product includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 12.5 km spatial resolution. Sea ice concentration and brightness temperatures include daily ascending averages, daily descending averages, and daily averages. Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/seaice12. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. These standard product, science quality, are available at the NSIDC DAAC: https://nsidc.org/", "links": [ { diff --git a/datasets/AU_SI25_1.json b/datasets/AU_SI25_1.json index 11f19f9c1e..4bffc52fd6 100644 --- a/datasets/AU_SI25_1.json +++ b/datasets/AU_SI25_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI25_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified Level-3 25 km product provides sea ice concentration derived from brightness temperatures using the NASA Team 2 (NT2) algorithm for the Northern and Southern Hemisphere. This data set includes six daily brightness temperature fields for channels ranging from 6.9 through 89.0 GHz, three daily sea ice concentration fields, and three daily sea ice concentration difference fields for ascending orbits, descending orbits, and full orbit daily averages. The sea ice concentration difference fields compare the NT2 algorithm with the Bootstrap algorithm. All fields are mapped to 25 km polar stereographic grids.\n\nNote: This product uses the Japan Aerospace Exploration Agency (JAXA) AMSR2 Level-1R input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 Level-1R products.", "links": [ { diff --git a/datasets/AU_SI25_NRT_R04_4.json b/datasets/AU_SI25_NRT_R04_4.json index c465ad94e0..36006195a4 100644 --- a/datasets/AU_SI25_NRT_R04_4.json +++ b/datasets/AU_SI25_NRT_R04_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI25_NRT_R04_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Daily 25 km Brightness Temperature & Sea Ice Concentration Polar Grids, Version 4 uses as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA). The Version 4 dataset uses the AMSR-U2 product generation algorithm with slight modifications for NRT product generation, same algorithm used to generation the standard, science quality, data that is available at the NSIDC DAAC. This Level-3 gridded product includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations. Data are mapped to a polar stereographic grid at 25 km spatial resolution. Sea ice concentration and brightness temperatures include daily ascending averages, daily descending averages, and daily averages. Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/seaice25. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. These standard product, science quality, are available at the NSIDC DAAC: https://nsidc.org/", "links": [ { diff --git a/datasets/AU_SI6_1.json b/datasets/AU_SI6_1.json index 0ba6a765b3..a735eec61e 100644 --- a/datasets/AU_SI6_1.json +++ b/datasets/AU_SI6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified Level-3 6.25 km product includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at a spatial resolution of 6.25 km for the Northern and Southern Hemispheres. This product uses the Japan Aerospace Exploration Agency (JAXA) AMSR2 Level-1R input brightness temperatures that are calibrated (unified) across the JAXA AMSR-E and AMSR2 Level-1R products.", "links": [ { diff --git a/datasets/AU_SI6_NRT_R04_4.json b/datasets/AU_SI6_NRT_R04_4.json index b18ae35260..8dbe7f3050 100644 --- a/datasets/AU_SI6_NRT_R04_4.json +++ b/datasets/AU_SI6_NRT_R04_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_SI6_NRT_R04_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Daily 6.25 km Polar Gridded 89 GHz Brightness Temperatures, Version 4 uses as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA). The Version 4 dataset uses the AMSR-U2 product generation algorithm with slight modifications for NRT product generation, same algorithm used to generation the standard, science quality, data that is available at the NSIDC DAAC. This Level-3 gridded product includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 6.25 km spatial resolution. This product is an intermediate product during processing of LANCE AMSR2 Level-3 sea ice products at 12.5 km and 25 km resolution. Data are stored in HDF-EOS5/netCDF-CF format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/seaice6. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. These standard product, science quality, are available at the NSIDC DAAC: https://nsidc.org/", "links": [ { diff --git a/datasets/AU_WkOcn_1.json b/datasets/AU_WkOcn_1.json index db92a38b70..f9cc5cdcf6 100644 --- a/datasets/AU_WkOcn_1.json +++ b/datasets/AU_WkOcn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AU_WkOcn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSR-E/AMSR2 Unified L3 Global Weekly Ascending/Descending .25 x .25 deg Ocean Grids data set (AU_WkOcn) reports weekly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean on a global 0.25\u00b0 \u00d7 0.25\u00b0 resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set.\n\nSea surface temperatures from the NOAA 1/4\u00b0 Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.", "links": [ { diff --git a/datasets/AV3_L1B_RDN_2356_1.json b/datasets/AV3_L1B_RDN_2356_1.json index e651599374..bb2aa3a307 100644 --- a/datasets/AV3_L1B_RDN_2356_1.json +++ b/datasets/AV3_L1B_RDN_2356_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AV3_L1B_RDN_2356_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 1B (L1B) calibrated radiance images as well as observational geometry and illumination parameters from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This spectrometer measures radiance from surface and atmosphere and is extremely similar in design to the orbital Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer. AVIRIS-3 has been designed to fly on a variety of aircraft platforms including the King Air B-200, Gulfstream III, Gulfstream V, and ER-2. For each flight line, two file types are included: calibrated radiance (RDN) and orthocorrected observation geometry and illumination (ORT) in netCDF format. Both file types include a geolocation lookup table (GLT) for georeferencing pixels in UTM and geographic coordinates. A band mask file indicates whether wavelengths were interpolated on a per pixel basis. In addition, ancillary files for each flight line are provided, including a quick look image in JPEG format and text files in YAML format that document processing algorithms and parameters used during production.", "links": [ { diff --git a/datasets/AV3_L2A_RFL_2357_1.json b/datasets/AV3_L2A_RFL_2357_1.json index 46944f9361..51a18dacb2 100644 --- a/datasets/AV3_L2A_RFL_2357_1.json +++ b/datasets/AV3_L2A_RFL_2357_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AV3_L2A_RFL_2357_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2A (L2A) surface reflectance images from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. Surface hemispherical directional reflectance was derived from calibrated radiance using an optimal estimation algorithm. For each flight line, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. In addition, ancillary files for each flight line are provided, including a quick look image in GeoTIFF format and text files in YAML format that document processing algorithms and parameters used during production.", "links": [ { diff --git a/datasets/AV3_L2B_GHG_2358_1.json b/datasets/AV3_L2B_GHG_2358_1.json index 4a35d57d9e..d9b1f304a0 100644 --- a/datasets/AV3_L2B_GHG_2358_1.json +++ b/datasets/AV3_L2B_GHG_2358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AV3_L2B_GHG_2358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2B (L2b) enhancements of greenhouse gasses (GHG) derived from imagery collected by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. Products include methane and carbon dioxide enhancements, each with per-pixel uncertainties and sensitivities to the background. Concentration enhancements are estimated from radiance measurements using a column-wise adaptive matched filter approach, which searches each pixel's radiance spectrum for deviations that are characteristic of a GHG's absorption spectrum. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm.", "links": [ { diff --git a/datasets/AVHRR18_G-NAVO-L2P-v1.0_1.0.json b/datasets/AVHRR18_G-NAVO-L2P-v1.0_1.0.json index 1b1a1fb54a..505866cb49 100644 --- a/datasets/AVHRR18_G-NAVO-L2P-v1.0_1.0.json +++ b/datasets/AVHRR18_G-NAVO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR18_G-NAVO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json b/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json index a896d209c8..006294c655 100644 --- a/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json +++ b/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR19_G-NAVO-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json b/datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json index a31f8345d0..aff5a52d13 100644 --- a/datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json +++ b/datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR19_L-NAVO-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.", "links": [ { diff --git a/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json b/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json index 60cf601f6a..9cda7abb96 100644 --- a/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json +++ b/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MA-STAR-L2P-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. MetOp-A launched on 19 October 2006 is the first in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTA-3US28", "links": [ { diff --git a/datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json b/datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json index a7f8906de1..0da8e9c60a 100644 --- a/datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json +++ b/datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MA-STAR-L3U-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MA-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json b/datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json index f98e9d1a06..60a862cc1f 100644 --- a/datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json +++ b/datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MB-STAR-L2P-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-B launched on 17 September 2012 is the second in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTB-3US28", "links": [ { diff --git a/datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json b/datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json index fa61aeb322..5a76243e12 100644 --- a/datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json +++ b/datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MB-STAR-L3U-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite B (Metop-B) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MB-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-B AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MB-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/AVHRRF_MC-STAR-L2P-v2.80_2.80.json b/datasets/AVHRRF_MC-STAR-L2P-v2.80_2.80.json index dedd3c1f15..4632a24da0 100644 --- a/datasets/AVHRRF_MC-STAR-L2P-v2.80_2.80.json +++ b/datasets/AVHRRF_MC-STAR-L2P-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MC-STAR-L2P-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-C launched on 7 November 2018 is the third and last in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTC-3US28", "links": [ { diff --git a/datasets/AVHRRF_MC-STAR-L3U-v2.80_2.80.json b/datasets/AVHRRF_MC-STAR-L3U-v2.80_2.80.json index c73a943661..615bdb9f8e 100644 --- a/datasets/AVHRRF_MC-STAR-L3U-v2.80_2.80.json +++ b/datasets/AVHRRF_MC-STAR-L3U-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRF_MC-STAR-L3U-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite C (Metop-C) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MC-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-C AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MC-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/AVHRRLocalAreaCoverageImagery10_6.0.json b/datasets/AVHRRLocalAreaCoverageImagery10_6.0.json index 2117018ee9..39aca47f25 100644 --- a/datasets/AVHRRLocalAreaCoverageImagery10_6.0.json +++ b/datasets/AVHRRLocalAreaCoverageImagery10_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRLocalAreaCoverageImagery10_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1B description\rThis collection is composed of AVHRR L1B products (1.1 km) reprocessed from the NOAA POES and Metop AVHRR sensors data acquired at the University of Dundee and University of Bern ground stations and from the ESA and University of Bern data historical archive.\rThe product format is the NOAA AVHRR Level 1B that combines the AVHRR data from the HRPT stream with ancillary information like Earth location and calibration data which can be applied by the user. Other appended parameters are time codes, quality indicators, solar and satellite angles and telemetry.\rTwo data collections cover Europe and the neighbouring regions in the period of 1 January 1981 to 31 December 2020 and the acquired data in the context of the 1-KM project in the \u201890s.\rDuring the early 1990\u2019s various groups, including the International Geosphere-Biosphere Programme (IGBP), the Commission of the European Communities (CEC), the Moderate Resolution Imaging Spectrometer (MODIS) Science Team and ESA concluded that a global land 1 KM AVHRR data set would have been crucial to study and develop algorithms for several land products for the Earth Observing System.\rUSGS, NOAA, ESA and other non-U.S. AVHRR receiving stations endorsed the initiative to collect a global land 1-km multi-temporal AVHRR data set over all land surfaces using NOAA's TIROS "afternoon" polar-orbiting satellite. On 1 April 1992, the project officially began up to the end of 1999 with the utilisation of 23 stations worldwide plus the NOAA local area coverage (LAC) on-board recorders. The global land 1-km AVHRR dataset is composed of 5 channels, raw AVHRR dataset at 1.1 km resolution from the NOAA-11 and NOAA-14 satellites covering land surfaces, inland water and coastal areas.\r\rLevel-1C Description\rThis data collection consists of measurements from the Advanced Very High Resolution Radiometer (AVHRR) at 1.1km full Local Area Coverage (LAC) resolution. It is based on the ESA AVHRR Level 1B European Data Set, a curated collection of AVHRR 1km data from 1981 to 2020 covering Europe, selected areas in Africa and the acquired data out-of-Europe in the context of the 1-KM project in the \u201890s (see the Level-1B description for details). The AVHRR LAC measurements were processed by the Remote Sensing Research Group of the University Bern, Switzerland. A landmark based navigation correction software adjusted time and satellite attitude to improve the georeferencing accuracy. The PyGAC software was used to convert the counts to reflectances for the visible and near-infrared channels 1, 2, 3A, and to brightness temperatures for the infrared channels 3B, 4, 5. The infrared calibration uses on-board calibration data and is satellite specific without cross-calibration between satellites. Due to the lack of on-board calibration data for the visible channels calculated coefficients from the CIMSS PATMOS-X project, version 2017r1, were used for the visible calibration aiming to minimize spectral differences among the various AVHRR sensors. \rThe data format is NetCDF. The calibrated AVHRR data are accompanied by coordinates, satellite and solar angles, additional metadata, and basic quality indicators. The NOAA nomenclature is used for the data record labelling it as a set of AVHRR L1C data.", "links": [ { diff --git a/datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json b/datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json index d99a9ed34d..d7d456a09a 100644 --- a/datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json +++ b/datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRMTA_G-NAVO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts. It was jointly established by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) with a contribution by the US National Oceanic and Atmospheric Administration (NOAA) of an AVHRR sensor identical to those flying on the family of Polar Orbiting Environmental Satellites (POES). AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json b/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json index 568369a15b..54b52d0301 100644 --- a/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json +++ b/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRMTA_G-NAVO-L2P-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-A (MetOp-A) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data", "links": [ { diff --git a/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json b/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json index ce025a4558..0494ba2415 100644 --- a/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json +++ b/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRMTB_G-NAVO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts. It was jointly established by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) with a contribution by the US National Oceanic and Atmospheric Administration (NOAA) of an AVHRR sensor identical to those flying on the family of Polar Orbiting Environmental Satellites (POES). AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json b/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json index d08b034380..39629d8579 100644 --- a/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json +++ b/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRMTB_G-NAVO-L2P-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-B (MetOp-B) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data", "links": [ { diff --git a/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json b/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json index 753b988ac3..8192881115 100644 --- a/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json +++ b/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRRMTC_G-NAVO-L2P-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-C (MetOp-C) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data", "links": [ { diff --git a/datasets/AVHRR_Fire_Products_1545_1.json b/datasets/AVHRR_Fire_Products_1545_1.json index 137e12acd0..bb9af6c18f 100644 --- a/datasets/AVHRR_Fire_Products_1545_1.json +++ b/datasets/AVHRR_Fire_Products_1545_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_Fire_Products_1545_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual forest fire burned area and daily hotspot products developed using data acquired from the Advanced Very-High-Resolution Radiometer (AVHRR) instruments carried aboard two NOAA polar-orbiting satellites (NOAA-11 and NOAA-14). The fire products were generated over 12 fire seasons (1st May - 31st October) from 1989-2000 across North America at 1-km resolution and subset to the ABoVE spatial domain of Alaska and Canada.", "links": [ { diff --git a/datasets/AVHRR_GLOBAL_10-DAY_COMPOSITES.json b/datasets/AVHRR_GLOBAL_10-DAY_COMPOSITES.json index 7bd281bda1..1724bad2c1 100644 --- a/datasets/AVHRR_GLOBAL_10-DAY_COMPOSITES.json +++ b/datasets/AVHRR_GLOBAL_10-DAY_COMPOSITES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_GLOBAL_10-DAY_COMPOSITES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Very High Resolution Radiometer (AVHRR) 1-km Global Land 10-Day Composites data set project is a component of the National Aeronautics and Space Administration (NASA) AVHRR Pathfinder Program. The project is a collaborative effort between the National Oceanic and Atmospheric Administration (NOAA), NASA, the U.S. Geological Survey (USGS), the European Space Agency (ESA), Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO), and 30 international ground receiving stations. The project represents an international effort to archive and distribute the 1-km AVHRR composites of the entire global land surface to scientific researchers and to the general public.\n\nThe data set is comprised of a time series of global 10-day normalized difference vegetation index composites. The composites are generated from radiometrically calibrated, atmospherically corrected, and geometrically corrected daily AVHRR observations. The time series begins in April 1992 and continues for specific time periods.", "links": [ { diff --git a/datasets/AVHRR_Imagery_1.json b/datasets/AVHRR_Imagery_1.json index 21a4116ebb..af151eb93e 100644 --- a/datasets/AVHRR_Imagery_1.json +++ b/datasets/AVHRR_Imagery_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_Imagery_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AVHRR satellite imagery of Eastern Antarctica, captured by the NOAA12 satellite.\n\nData have been collected since June of 1996, but not all locations have archives dating back to this time.\n\nImages are available of the following areas:\n\nPrincess Ragnhild Coast (West)\nPrincess Ragnhild Coast\nPrincess Ragnhild Coast (East)\nEnderby Land\nMawson region\nDavis - Prydz Bay\nWest Ice Shelf\nShackleton Ice Shelf\nCasey region\nSabrina Coast\nWilkes Coast\nDumont d'Urville - Mertz Gl.\nNinnis Glacier region\nCape Adare\nTerra Nova Bay\nRoss Ice Shelf\n \nHowever, this AVHRR sea-ice archival service was discontinued in 2015. Please contact the Bureau of Meteorology's Regional Manager for Antarctic Meteorology at tasrmam@bom.gov.au for any satellite archive support.", "links": [ { diff --git a/datasets/AVHRR_LST_826_1.json b/datasets/AVHRR_LST_826_1.json index fb926a69af..7004b27303 100644 --- a/datasets/AVHRR_LST_826_1.json +++ b/datasets/AVHRR_LST_826_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_LST_826_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994). The data were projected into Albers Equal Area and aggregated to 8 km spatial resolution. The data were cloud-filtered with CLAVR-1 algorithm (Stowe et al., 1999). The LST values were estimated with a split-window technique (Ulivieri et al., 1994) that takes advantage of differential absorption of the thermal infrared signal in bands 4 and 5. The emissivity of the surface was generated using a land cover classification map (Hansen et al., 2000) combined with the FAO soil map of Africa (FAO-UNESCO, 1977) and additional maps of tree, herbaceous, and bare soil percent cover (DeFries et al., 2000). Collateral products include cloud mask, time-of-scan, latitude and longitude, and land/water mask files.The data are in flat binary files. Each data file contains 1152 columns and 1152 rows, in signed integer format (2 bytes), with 8 km by 8 km spatial resolution. A unique map exists for each day and each night of the 6-year NOAA-14 lifetime. The data are best used to infer broad temporal and spatial trends rather than pixel-by-pixel values. ", "links": [ { diff --git a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json index 63348f528f..9c8e4f806d 100644 --- a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json +++ b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY (AVHRR-OI), available from September 1, 1981, but there is a companion SST product that includes microwave satellite data, available from June 2002.", "links": [ { diff --git a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json index a08420fff4..a4dcd237d7 100644 --- a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json +++ b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature dataset is produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships, buoys, and Argo floats above 5m depth), and the specific datasets employed may change over time. In the regions with sea-ice concentration higher than 30%, freezing points of seawater are used to generate proxy SSTs. A preliminary version of this dataset is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. The v2.1 (Huang et al. 2021) is updated from the previous AVHRR_OI-NCEI-L4-GLOB-v2.0 data. Major improvements include: 1) In-situ ship and buoy data changed from the NCEP Traditional Alphanumeric Codes (TAC) to the NCEI merged TAC + Binary Universal Form for the Representation (BUFR) data, with large increases of buoy data included to correct satellite SST biases; 2) Addition of Argo float observed SST data as well, for further correction of satellite SST biases; 3) Satellite input from the METOP-A and NOAA-19 to METOP-A and METOP-B, removing degraded satellite data; 4) Revised ship-buoy SST corrections for improved accuracy; and 5) Revised sea-ice-concentration to SST conversion to remove warm biases in the Arctic region. These updates only apply to data after January 1st, 2016. The data pre 2016 are still the same as v2.0 except for metadata upgrades. NCEI has panned to update the entire dataset from 1982 to fix the In-Situ data ingest and bias correction which exist prior 2016. ", "links": [ { diff --git a/datasets/AVHRR_ORBITAL_SEGMENTS.json b/datasets/AVHRR_ORBITAL_SEGMENTS.json index 03332d8457..31ab1e7bd3 100644 --- a/datasets/AVHRR_ORBITAL_SEGMENTS.json +++ b/datasets/AVHRR_ORBITAL_SEGMENTS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_ORBITAL_SEGMENTS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " The Advanced Very High Resolution Radiometer (AVHRR) 1-km Orbital\n Segments data set is a component of the National Aeronautics and\n Space Administration (NASA) AVHRR Pathfinder Program and contains\n global coverage of land masses at 1-kilometer resolution.\n\n The data set is the result of an international effort to acquire,\n process, and distribute AVHRR data of the entire global land surface\n to meet the needs of the international science community. The \n orbital segments are comprised of raw AVHRR scenes consisting of\n 5-channel, 10-bit, AVHRR data at 1.1-km resolution at nadir. The raw\n data are used to produce vegetation index composites; to support fire \n detection and cloud screening activities; to support research in\n atmospheric correction; to develop algorithms; and to support a host of\n research activities that may require the inclusion of raw AVHRR data.\n", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json b/datasets/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json index c0f3fc9ae9..545e8bd71a 100644 --- a/datasets/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A)satellite (launched 19 Oct 2006). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\ntime from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de\nMeteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved\nfrom the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm.\nAtmospheric profiles of water vapor and temperature from a numerical weather prediction model,\ntogether with a radiatiave transfer model, are used to correct the multispectral algorithm for\nregional and seasonal biases due to changing atmospheric conditions. This product is delivered at\nfull resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The\nproduct format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json index b2c8ebaae1..3f2e493489 100644 --- a/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is\nderived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json index fe08a37743..b873131a26 100644 --- a/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm.\nThis product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json b/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json index 6cc353b634..78666e88a5 100644 --- a/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) satellite (launched 17 Sep 2012). \r\n\r\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\r\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\r\ntime from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de\r\nMeteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved\r\nfrom the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm.\r\nAtmospheric profiles of water vapor and temperature from a numerical weather prediction model,\r\ntogether with a radiatiave transfer model, are used to correct the multispectral algorithm for\r\nregional and seasonal biases due to changing atmospheric conditions. This product is delivered at\r\nfull resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The\r\nproduct format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json index 50be1521b3..e5ac515a12 100644 --- a/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is\nderived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json index 0b50cb2f5b..2e907b2857 100644 --- a/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json +++ b/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\ntime from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-\nFrance/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system.\nNAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS.\nSST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm.\nThis product is delivered as four six hourly collated files per day on a regular 2km grid. The\nproduct format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json index b8065db8ec..eaad1a75fb 100644 --- a/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json +++ b/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA-19 platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/AVIRIS-Classic_L1B_Radiance_2155_1.json b/datasets/AVIRIS-Classic_L1B_Radiance_2155_1.json index 2a0ca0dbda..6e6c61d78f 100644 --- a/datasets/AVIRIS-Classic_L1B_Radiance_2155_1.json +++ b/datasets/AVIRIS-Classic_L1B_Radiance_2155_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS-Classic_L1B_Radiance_2155_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. Multiple file types are included for each flight line. The primary data files include: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), and parameters relating to the geometry of observation and illumination (obs). Additional files provide information on spectral (spc) and radiometric calibration (rcc, gain), spatial resolution (geo), aircraft and sensor position (eph, nav), deployment notes (info), and data processing (plog). Quicklook images (jpeg) and polygon outlines of imagery footprints (kmz) are provided for each flight line. The primary AVIRIS-Classic L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images, maps in Keyhole Markup Language (KML), and calibration files in binary and text formats.This archive currently includes data from 2006 - 2021. Additional L1B data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.", "links": [ { diff --git a/datasets/AVIRIS-Classic_L2_Reflectance_2154_1.json b/datasets/AVIRIS-Classic_L2_Reflectance_2154_1.json index eb5a791b4c..6d621ce8bb 100644 --- a/datasets/AVIRIS-Classic_L2_Reflectance_2154_1.json +++ b/datasets/AVIRIS-Classic_L2_Reflectance_2154_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS-Classic_L2_Reflectance_2154_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. This archive currently includes data from 2008 - 2020. Additional AVIRIS-Classic facility instrument L2 data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.", "links": [ { diff --git a/datasets/AVIRIS-NG_Data_Idaho_1533_1.json b/datasets/AVIRIS-NG_Data_Idaho_1533_1.json index ba3282a8b3..fdad866152 100644 --- a/datasets/AVIRIS-NG_Data_Idaho_1533_1.json +++ b/datasets/AVIRIS-NG_Data_Idaho_1533_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS-NG_Data_Idaho_1533_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument during flights over research sites in Idaho and California in 2014 and 2015. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at 1-meter spatial resolution. The data include 72 flight lines covering long-term research sites in the Reynolds Creek Experimental Watershed in southwestern Idaho and Hollister in southeastern Idaho. Several flight lines from a site in the Inyo National Forest near Big Pine, California are included.", "links": [ { diff --git a/datasets/AVIRIS-NG_L1B_radiance_2095_1.json b/datasets/AVIRIS-NG_L1B_radiance_2095_1.json index 80cc206f50..3c8b2a81cb 100644 --- a/datasets/AVIRIS-NG_L1B_radiance_2095_1.json +++ b/datasets/AVIRIS-NG_L1B_radiance_2095_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS-NG_L1B_radiance_2095_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, six file types are included: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), parameters relating to the geometry of observation and illumination (obs), and orthocorrected locations of each pixel (loc). In addition, ancillary files for the flight line are provided, including quick look images and polygon outlines of imagery footprints. The AVIRIS-NG L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images and maps in Keyhole Markup Language (KML). The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L1B data will be added as they become available. AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.", "links": [ { diff --git a/datasets/AVIRIS-NG_L2_Reflectance_2110_1.json b/datasets/AVIRIS-NG_L2_Reflectance_2110_1.json index 12c80bb65a..9f016bc244 100644 --- a/datasets/AVIRIS-NG_L2_Reflectance_2110_1.json +++ b/datasets/AVIRIS-NG_L2_Reflectance_2110_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS-NG_L2_Reflectance_2110_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L2 data will be added as they become available. The AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.", "links": [ { diff --git a/datasets/AVIRIS_FlightLine_Locator_2140_1.0.json b/datasets/AVIRIS_FlightLine_Locator_2140_1.0.json index 230106e423..ab05fcbc58 100644 --- a/datasets/AVIRIS_FlightLine_Locator_2140_1.0.json +++ b/datasets/AVIRIS_FlightLine_Locator_2140_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVIRIS_FlightLine_Locator_2140_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/AVISO_ADT.json b/datasets/AVISO_ADT.json index b79b8e573b..84ceb73058 100644 --- a/datasets/AVISO_ADT.json +++ b/datasets/AVISO_ADT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AVISO_ADT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contents: along-track sea surface heights above geoid; dynamic topography is\nthe sum of sea level anomaly (SLA) and mean dynamic topography (MDT, Rio05\nhere)\n\nUse: study of the general circulation (ocean gyres ...)\n\nThe data are global mono altimeter satellite products, homogeneous with other\nsatellites, available in near-real time and in delayed time in NetCDF format.\n\nIn delayed time, two types of products are available:\n- \"Ref\" (Reference) series: homogeneous datasets based on two satellites\n(Topex/Poseidon, Jason-1 + ERS, Envisat) with the same groundtrack. Sampling is\nstable in time.\n- \"Upd\" (Updated) series: up-to-date datasets with up to four satellites at a\ngiven time (adding GFO and/or Topex/Poseidon on its new orbit). Sampling and\nLong Wavelength Errors determination are improved, but quality of the series is\nnot homogeneous.\n\nRegional products with an improved quality are available in local areas\n(\"http://www.aviso.oceanobs.com/html/donnees/produits/hauteurs/regional/\")", "links": [ { diff --git a/datasets/AWI-EDMED_542_8.json b/datasets/AWI-EDMED_542_8.json index a0b35d75a7..56752d84f9 100644 --- a/datasets/AWI-EDMED_542_8.json +++ b/datasets/AWI-EDMED_542_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AWI-EDMED_542_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim of the aeromagnetic surveys in the Ross Sea and North Victoria Land\nare:\na) to develop a model on the break-up of this part of Gondwana\nb) to map the ocean-continent boundary\nc) to develop an idea about the evolution of the area since the break-up of\nGondwana\nd) to map the structures of the Transatlantic Mountains.\n\nThe data were sampled every 10 s, corresponding to 500 m distance.\n\nThe following instrument was used: PPM Geometics G 811.\n\nThe geographical coverage is as follows:\nabout 17000 km of aeromagnetic data have been collected in the Ross Sea and\nNorth Victoria Land, Antarctica.\n\nData are available on request, but with special arrangement.", "links": [ { diff --git a/datasets/A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0.json b/datasets/A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0.json index 2711bed1d7..bf962ed297 100644 --- a/datasets/A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0.json +++ b/datasets/A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biotic Database of Indo-Pacific Marine Mollusks provides access to nomenclatural, distribution, and ecological information on Indo-Pacific Mollusks. Georeferenced specimen records from ANSP and AMS related to these names are available for search through the OBIS global digital atlas. Nomenclatural, distribution, and ecological information assembled from the literature is available for search on the web. This database attempts to document all names that have ever been applied to marine molluscs in the tropical Indo-West Pacific. \n\nThis database provides information on the estimated 30,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. A future objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. \n\nThis database was compiled by teams at the Academy of Natural Sciences, the Australian Museum, the Mus\u00e9um National d' Histoire Naturelle, and the California Academy of Sciences, with support from the Alfred P. Sloan Foundation, the National Oceanographic Partnership Program, and the Australian Biological Resources Study. This database is part of the Ocean Biogeographic Information System.\n\nAs of 2006 May 19 the Database contains 84,147 names of all ranks, 72,597 species-group names, and 28,357 species names in current use, and 179,368 specimen records.", "links": [ { diff --git a/datasets/Absolutes_1.json b/datasets/Absolutes_1.json index 252a74bafe..f45b710343 100644 --- a/datasets/Absolutes_1.json +++ b/datasets/Absolutes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Absolutes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Final one minute average values of the absolute geomagnetic field in the north (X), east (Y) and vertical (Z) components in units of nanoTesla (nT).\n\nMagnetic variometer data have been collected at Macquarie Is. since 1952; Mawson since 1955; and Casey since 1988. Data were not digital from Macquarie Is. until late 1984; from Mawson until late 1986. They have been digital from Casey since 1988. Data that are currently available from the GA website are not complete, but improving. The status of data availability is available on the website.\n\nParticular magnetic elements can be chosen to be plotted or tabulated data are available.", "links": [ { diff --git a/datasets/Academ_Kurchatov_0.json b/datasets/Academ_Kurchatov_0.json index 38cd418598..1412f06832 100644 --- a/datasets/Academ_Kurchatov_0.json +++ b/datasets/Academ_Kurchatov_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Academ_Kurchatov_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the Akademik Kurchatov Russian research vessel in the Atlantic Ocean and Black Sea in 1988.", "links": [ { diff --git a/datasets/AcousticTrends_BlueFinLibrary_1.json b/datasets/AcousticTrends_BlueFinLibrary_1.json index f6da71c794..a01328dc6b 100644 --- a/datasets/AcousticTrends_BlueFinLibrary_1.json +++ b/datasets/AcousticTrends_BlueFinLibrary_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AcousticTrends_BlueFinLibrary_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This annotated library contains both a data set and a data product. \n\nThe data set contains a sub-sample of underwater recordings made around Antarctica from 2005-2017. These recordings were curated and sub-sampled from a variety of national and academic recording campaigns. Recordings were made using a variety of different instruments, and sub-samples span 11 different combinations of site and year. Spatial coverage of the recordings includes sites in the Western Antarctic Peninsula, Atlantic, Indian, and Pacific sectors. Temporal coverage of recordings covers a representative sample throughout each recording year for the years of 2005, 2013, 2014, 2015, and 2017. The focus is on low-frequency sounds of blue and fin whales, so curated recordings have been downsampled to sample rates of either 250, 500, 1000 or 2000 Hz. Recordings are all in 16-bit wav format. The file name of each wav file contains a timestamp with the date and time of the start of that file. Recordings are contained in the /wav/ subfolder for each site-year (e.g. Casey2014/wav). \n\nThe data product is in the form of annotations that describe the times within each WAV file that contain detections of blue and fin whale sounds. Each annotations are stored as a row in a tab-separated text file (with descriptive column headers), and each text file describes a particular type of sound. These annotation text files are formatted as Selection Tables that can be directly imported into the software program Raven Pro 1.5 (Cornell Bioacoustics Laboratory). \n\nFull description of the details of the creation and use of this dataset are described in the draft manuscript contained in the documentation folder.", "links": [ { diff --git a/datasets/Acoustic_Data_Cape_Floristic_2372_1.json b/datasets/Acoustic_Data_Cape_Floristic_2372_1.json index 7454fbe35f..d551bbc912 100644 --- a/datasets/Acoustic_Data_Cape_Floristic_2372_1.json +++ b/datasets/Acoustic_Data_Cape_Floristic_2372_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Acoustic_Data_Cape_Floristic_2372_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds in situ sound recordings from sites in Greater Cape Floristic Region (GCFR), South Africa from June to December 2023. The recordings were collected as part of the Biodiversity Survey of the Cape (BioSCape) project, a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. BioSoundSCape is a BioSCape subproject seeking to relate ground-based measures of bioacoustic diversity to remote imagery. AudioMoth recorders were deployed at sites for 4 to 10 days of data collection (median = 7), and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format with 16-bit digitization depth and a 48 kHz sampling rate. The recordings contain a wide range of environmental sounds such as biophony (e.g., birds, frogs, insects), anthropophony (e.g,. automobiles, airplanes) and geophony (e.g,. wind, rain). Sampling locations were stratified with respect to elevation, broad land use/land cover types, and time since wildfire disturbance. Most sites were within protected fynbos and Afromontane forest ecosystems. There were 538 sites in the wet season and 543 sites in the dry season, with most sites co-located between seasons. All sites were located within AVIRIS-NG hyperspectral acquisitions and 61% of sites were in LVIS lidar acquisitions. The dataset includes site information in tabular form and photographs of field sites.", "links": [ { diff --git a/datasets/Acoustic_Data_SonomaCounty_CA_2341_1.json b/datasets/Acoustic_Data_SonomaCounty_CA_2341_1.json index 1476870414..d6ab3d8133 100644 --- a/datasets/Acoustic_Data_SonomaCounty_CA_2341_1.json +++ b/datasets/Acoustic_Data_SonomaCounty_CA_2341_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Acoustic_Data_SonomaCounty_CA_2341_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds in situ sound recordings from sites in Sonoma County, California, USA as part of the Soundscapes to Landscapes citizen science project. Recordings were collected from 2017 to 2022 during the bird breeding season (mid-March thru mid-July). Sites (n=1399) were selected across the county; locations were stratified with respect to topographic position and broad land use/land cover types, such as forest, shrubland, herbaceous, urban, agriculture, and riparian areas. Two types of automated recorders were used: Android-based smartphones with attached microphones and AudioMoths. Recorders were deployed at sites for at least 3 days, and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format (.wav) with 16-bit digitization depth and 44.1 kHz or 48 kHz sampling rate for smartphone and AudioMoth recorders, respectively. The dataset also includes site information including site location when so permitted by landowners in tabular form and photographs of field sites.", "links": [ { diff --git a/datasets/Acoustic_seals_1.json b/datasets/Acoustic_seals_1.json index 8cb1ee3e14..ee173305a4 100644 --- a/datasets/Acoustic_seals_1.json +++ b/datasets/Acoustic_seals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Acoustic_seals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic surveying\nData from four acoustic surveys from the Aurora Australis from 1996-10-05 to 1996-10-31; 1997-10-09 to 1997-10-29; 1997-12-08 to 1998-01-06; and 1999-12-04 to 2001-01-16.\n \nSonobouys deployed off the back of the ship, half an hour recording duration samples made concurrently with Colin Southwells visual surveys. Numbers of leopard seal calls audible from recordings measured by acoustic analysis.\n \nThe fields in this dataset are:\nTape # = the tape number and date\nRecording # = Recording number\nBuoy # = Sonobuoy number\nBuoy Freq = Sonobuoy frequency\nLongitude S = Longitude\nDecimal Longitude S = Decimal Longitude\nLatitude E = Latitude\nDecimal Latitude E = Decimal Latitude\nGMT = Greenwich Mean Time\nLocal time = Local Time\nSerial Time = dd:mm:yy hh:mm\nShip Speed Kts\nICE Cover (/10) = Ice Cover in tenths\nIce % cover = Percentage of Ice Cover\n\nThick Ice: Ice Thickness 0 = 0; 1less than 2 cm; 2 = 2cm to 0.25m; 3= 0.25m to 0.5m; 4 = 0.5m - 1m; 5 greater than 1.0 m Ice Type: 1 = no information; 2 = grease or pancake; 3 = brash; 4 = floes first year; 5 = multiyear floes; 6 = first year rafted floes; 7 = multiyear rafted floes; 8 = mixed brash and 1st year floes; 9 = mixed brash and multiyear floes; 10 = icebergs; 11 = icebergs and brash; 12 = icebergs and 1st year floes; 13 = icebergs and multiyear floes; 14 = compacted pack ice; 15 = iceshelf; 16 = other; 17 = fast ice.\n \nFloe Width: 1 = less than 3 m; 2 = 3 - 10 m; 3 = 10 - 50m; 4 = 50 -100 m; 5 greater than 100 m. Weather: 1 = blue sky (0-20% cloud); 2 = partly cloudy (21-80%); 3 = cloudy (81-99%); 4 = overcast (100%); 5 = rain; 6 = mist; 7 = fog; 8 = fog patches; 9 = drizzle; 10 =snow; 11 = snow fog; 12 = rain fog.\n \nAlgae: 1 = clear; 2 = slight colour; 3 = medium colour; 4 = dark brown patches; 5 = all dark brown\n\nWater Depth m\nWind Speed Kts\nWind Direction\nAir temp degrees C\nRec Time = Duration of the recording made\nGain = Recording gain on the amplifier\n\nMammal Sounds? = Other mammal sounds. CS = unknown origin chain-saw like sound; P5/P6 = unknown origin pulsed sounds; NSL = unknown origin appears to be a new leopard seal sound; Wd = Weddell; LS = Leopard; KW = Killer Whale; RS = Ross\n \nLS Calls Total = Total number of leopard seal calls\nD = Total Low Descending trills\nH = Total High Double trills\nL = Total Low Double trills\nM = Total Medium Single trills\nO = Total Hoots with Single trills\nJuv LS = Total Juvenile Leopard seal calls\nNLS = Total New Leopard Seal Calls\nCS = Total Chain Saw Calls\nRS = Total Ross Seal Calls\nWd = Total Weddell seal Calls\nP2-P5 = Total Pulsed calls", "links": [ { diff --git a/datasets/Active_Fluorescence_2001_0.json b/datasets/Active_Fluorescence_2001_0.json index 479f052a8c..a5a1a569cf 100644 --- a/datasets/Active_Fluorescence_2001_0.json +++ b/datasets/Active_Fluorescence_2001_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Active_Fluorescence_2001_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements in the Gulf Stream off the East Coast of the US in 2001", "links": [ { diff --git a/datasets/Active_Layer_Thaw_Depths_1701_1.json b/datasets/Active_Layer_Thaw_Depths_1701_1.json index b92890f23a..2a0668f078 100644 --- a/datasets/Active_Layer_Thaw_Depths_1701_1.json +++ b/datasets/Active_Layer_Thaw_Depths_1701_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Active_Layer_Thaw_Depths_1701_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.", "links": [ { diff --git a/datasets/Adelie_Aerial_Photography_Casey20102011_1.json b/datasets/Adelie_Aerial_Photography_Casey20102011_1.json index 2de3361aca..eb2a0d979f 100644 --- a/datasets/Adelie_Aerial_Photography_Casey20102011_1.json +++ b/datasets/Adelie_Aerial_Photography_Casey20102011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Adelie_Aerial_Photography_Casey20102011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs were taken at 3 islands in the Cronk group and 3 islands in the Frazier group of the Windmill islands where occupancy surveys in 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations.\nPhotographs of the Cronk Island group were taken on the 2 January 2011. One flight was made along a northeast-southwest direction across the three main islands, Hollin, Midgley and Beall (see below). The flight started at 02:10:23 UTC and finished at 03:40:45 UTC. The SkyTraders crew were the flight and camera operators. The daily weather observations from Casey Station for 2 January 2011 were 14.0 hour of sunlight, winds from the North at 6-13 knots and 2/8 cloud cover.\n\nPhotographs of the Frazier Island group were taken on the 23 January 2011. \n\nAerial photos were taken from a CASA C212 airplane (VHA) flying at ~140 knots and ~750m altitude using a Nikon D200 camera with a 55 mm real lens which is converted to a 75 mm lens (including the focal length magnification factor of 1.5 for non-35mm format). The Nikon D200 camera was set to normal which allows for varied speed and aperture and was set on autofocus. A 3-second shutter closure interval was programmed using an external intervalometer. All photographs were recorded on the cameras internal memory card and downloaded after the flight was over.", "links": [ { diff --git a/datasets/Adelie_Aerial_Photography_Davis20092010_1.json b/datasets/Adelie_Aerial_Photography_Davis20092010_1.json index 555303031a..32ee9fd721 100644 --- a/datasets/Adelie_Aerial_Photography_Davis20092010_1.json +++ b/datasets/Adelie_Aerial_Photography_Davis20092010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Adelie_Aerial_Photography_Davis20092010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs were taken at 39 islands in the Vestfold and Rauer Islands regions where occupancy surveys in 2008-09 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations.\n\nA total of six flights between 18-23 November 2009 were required to cover the Vestfold and Rauer coastlines. \n\nThe first flight from 0355-0622 UTC on 18th November 2009 covered the southern Vestfolds (see download file).\n\nThe second flight from 0746-0930 UTC on the 18th November 2009 covered Long Peninsula (see download file).\n\nThe third flight from 0945-1132 UTC on the 19th November 2009 mostly covered the northern Vestfolds (Bandits, Mikkelson, Tryne, Wyatt Earp, but also covered Gardner (see download file).\n\nThe fourth flight from 0734-0946 UTC on the 21st November 2009 repeated the previous flight over the northern Vestfolds after preliminary stitching showed that the coverage was not as good as desired. Also, the flight lines for Tryne, Mikkelson and Wyatt Earp were moved to use the north-south flight lines. The opportunity was also taken to repeat a flight over Gardner and perform other tasks (visit 'Woop Woop', the plateau skiway and perform a low level LIDAR scan on the blue ice runway) (see download file).\n\nThe fifth flight from 1306-1556 UTC on the 21st November 2009 covered Hop and Filla Islands in the Rauers (see download file).\n\nThe sixth and final flight from 0828-0951 UTC on the 23rd November 2009 covered the remaining Rauer Islands including Forpost, Torckler, Varyarg, Lunnyy and Kryuchock Islands (see download file).\n\nVertical photos were taken along each flight line from a Squirrel AS350BA helicopter (VH-SES) flying at 80 knots and 750m altitude using a Hasselblad H3DII-50 camera with a 150 mm lens and 1/800th second shutter speed. A 3-second shutter closure interval was achieved using an SDK and intervalometer. The camera auto-focussed effectively at infinity using the software Phocus.", "links": [ { diff --git a/datasets/Adelie_Aerial_Photography_Davis20102011_1.json b/datasets/Adelie_Aerial_Photography_Davis20102011_1.json index 59e9cf42bc..710d2e6482 100644 --- a/datasets/Adelie_Aerial_Photography_Davis20102011_1.json +++ b/datasets/Adelie_Aerial_Photography_Davis20102011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Adelie_Aerial_Photography_Davis20102011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs were taken at 16 islands between the Rauer Islands and the Amery Ice Shelf where occupancy surveys in 2009-10 and 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations.\n\nThe survey was completed in a single mission from 09:53-13:44 UTC on the 20th November 2010. The flight was split into two parts and covered the Svenner and Steinnes islands first, with a stop in the Larsemann Hills for refueling at Progress I, then further surveying around Lichen Island. Weather conditions during the flight were sunny. This resulted in substantial areas being in shadow.\n\nPart 1 of the flight mission: Svenner, Svenner south-east, Svenner south and Steinnes islands\n\nVertical photos were taken along the flight lines from a Squirrel AS350BA helicopter (VH-SES) flying at 80 knots and 750m altitude using a Hasselblad H3DII-50 camera with a 150 mm lens and 1/800th second shutter speed. A 3-second shutter closure interval was achieved using an SDK and intervalometer. The camera auto-focussed effectively at infinity using the software Phocus.", "links": [ { diff --git a/datasets/Adelie_Colony_Maps_Prydz_81-82_1.json b/datasets/Adelie_Colony_Maps_Prydz_81-82_1.json index 7332699643..ddb31582cd 100644 --- a/datasets/Adelie_Colony_Maps_Prydz_81-82_1.json +++ b/datasets/Adelie_Colony_Maps_Prydz_81-82_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Adelie_Colony_Maps_Prydz_81-82_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises scanned copies of the boundaries of Adelie penguin breeding colonies and sections of island coastlines made from aerial photographs taken between 9-15 December 1981. The original tracings by Michael Whitehead were scanned by Colin Southwell.", "links": [ { diff --git a/datasets/Adelie_diet_BI_1.json b/datasets/Adelie_diet_BI_1.json index 02345f6b02..5d0d10bcc4 100644 --- a/datasets/Adelie_diet_BI_1.json +++ b/datasets/Adelie_diet_BI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Adelie_diet_BI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results from surveys on the feeding habits of Adelie Penguins (Pygoscelis adeliae) on Bechervaise Island, Mawson, Antarctica. Surveys have been conducted since 1991, and are ongoing to determine the diet composition and prey species of penguins.\n\nData for this project were compiled by Megan Tierney, as part of her PhD Thesis, and are presented in two excel spreadsheets.\n\nAlso provided in the Related URL section, is a link to a trophic database of \"A compilation of dietary and related data from the Southern Ocean\". This database contains a large amount of other publicly available diet related data collected as part of the Australian Antarctic program.", "links": [ { diff --git a/datasets/Aeolian_Processes_McMurdo.json b/datasets/Aeolian_Processes_McMurdo.json index 3369a0520e..12a996de9b 100644 --- a/datasets/Aeolian_Processes_McMurdo.json +++ b/datasets/Aeolian_Processes_McMurdo.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aeolian_Processes_McMurdo", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data collected during studies of boundary layer winds and\n surface characteristics. These field experiments were designed to:\n 1. Understand and quantify the partitioning of wind shear stress between\n surface and roughness elements on (a) rocky surfaces and (b) surfaces with\n scatted rocks and intervening sand surface.\n 2. Test the Raupach et al (1993) shear stress partitioning model to\n estimate the entrainment threshold on surfaces covered with isolated roughness\n elements\n 3. Quantify the spatial distribution of surface shear stress on surfaces\n with scatted rocks and an intervening sand surface.\n 4. Understand relations between shear stress partitioning and transport of\n sand.\n \n The dataset includes measurements of:\n - Boundary Layer winds and surface shear stress\n - Wind speed at 6 heights above the surface (6.00 m, 3.65 m, 2.22 m, 1.35 m,\n 0.82 m, 0.50 m wind direction at 6 m and 0.82 m, temperature at 3.65 m.\n - Surface shear stress using Irwin sensors (Wyatt and Nickling, 1997)\n - Sand mass transport rates at the Victoria Valley site with static (Nickling\n and McKenna Neuman, 1997) and automated sand traps.\n Saltation intensity with Sensit sensor at the Victoria Valley site (Gillette\n and Stockton, 1986)\n - Wind force on simulated roughness elements using the Guelph force balance\n (Gillies et al., 2000; Grant and Nickling, 1998; Wyatt and Nickling, 1997). \n \n Data were sampled every 1 second and averaged for 1, 5, and 10 minute\n intervals.\n \n Derived data include estimates of wind shear velocity (u*), aerodynamic\n roughness (zo)\n \n Surface characterization data: \n Information on rock cover and roughness element geometry, and sand grain size\n and sorting parameters for surface sand and sand in transport in the Victoria\n Valley is also available.\n \n Datasets available:\n Data were obtained for 2 sites located on the north side of Lake Fryxell and in\n the Victoria Valley. There is also Irwin sensor calibration data for 2 sites:\n Wright Valley and Victoria Lower Glacier, which includes wind profile and\n temperature measurements. \n Data cover the following periods:\n - Wright Valley: January 11-14, 2002\n - Lake Fryxell: January 15 - February 1, 2002; January 15 - February 3, 2003\n - Victoria Lower Glacier: January 11-13, 2003\n - Victoria Valley: January 15 - 31, 2003.\n \n Site locations are:\n - Lake Fryxell: 77 degrees 36.252 minutes; 163 degrees 07.827 minutes\n - Wright Valley: 77 degrees 31.363 minutes; 162 degrees 00.472 minutes\n - Victoria Valley: 77.366009935 degrees S, 162.320035048 degrees E\n \n These studies were funded by NSF grant OPP-0088136\n \n References cited\n \n Gillette, D.A. and Stockton, P.H., 1986. Mass momentum and kinetic energy\n fluxes of saltating particles. In: W.G. Nickling (Editor), Aeolian\n Geomorphology. Allen and Unwin, Boston, London, Sydney, pp. 35-56.\n \n Gillies, J.A., Lancaster, N., Nickling, W.G. and Crawley, D., 2000. Field\n determination of drag forces and shear stress partitioning effects for a desert\n shrub (Sarcobatus vermiculatus, Greasewood). Journal of Geophysical Research,\n Atmospheres, 105(D20): 24871-24880.\n \n Grant, P.F. and Nickling, W.G., 1998. Direct field measurement of wind drag on\n vegetation for application to windbreak design and monitoring. Land Degradation\n and Development, 9: 57-66.\n \n Nickling, W.G. and McKenna Neuman, C., 1997. Wind tunnel evaluation of a\n wedge-shaped aeolian sediment trap. Geomorphology, 18(3-4): 333-346. Wyatt,\n V.E. and Nickling, W.G., 1997. Drag and shear stress partioning in sparse\n desert creosote communities. Canadian Jornal of Earth Sciences, 34: 1486-1498.", "links": [ { diff --git a/datasets/Aeolus-CalVal-DAWN_DC8_1.json b/datasets/Aeolus-CalVal-DAWN_DC8_1.json index 5fd5662805..75445a6a3b 100644 --- a/datasets/Aeolus-CalVal-DAWN_DC8_1.json +++ b/datasets/Aeolus-CalVal-DAWN_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aeolus-CalVal-DAWN_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AEOLUS-CALVAL-DAWN_DC8_1 is the Aeolus CalVal DAWN (Doppler Aerosol WiNd) Lidar Wind Profiles data product. Data was collected using the DAWN instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. \r\n\r\nNASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products.", "links": [ { diff --git a/datasets/Aeolus-CalVal-Dropsondes_DC8_1.json b/datasets/Aeolus-CalVal-Dropsondes_DC8_1.json index c3dda0d64b..c4377004db 100644 --- a/datasets/Aeolus-CalVal-Dropsondes_DC8_1.json +++ b/datasets/Aeolus-CalVal-Dropsondes_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aeolus-CalVal-Dropsondes_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aeolus-CalVal-Dropsondes_DC8_1 is the Aeolus CalVal Dropsonde Profiles data product. Data was collected using Dropsondes from the Douglas (DC-8) Aircraft. Data collection for this product is complete. \r\n\r\nNASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products.", "links": [ { diff --git a/datasets/Aeolus-CalVal-HALO_DC8_1.json b/datasets/Aeolus-CalVal-HALO_DC8_1.json index 7bb7a3a6d5..eed88f856b 100644 --- a/datasets/Aeolus-CalVal-HALO_DC8_1.json +++ b/datasets/Aeolus-CalVal-HALO_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aeolus-CalVal-HALO_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aeolus-CalVal-HALO_DC8_1 is the Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images data product. Data was collected using the High Altitude Lidar Observatory (HALO) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. \r\n\r\nNASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future \r\ninternational Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products.", "links": [ { diff --git a/datasets/Aeolus-CalVal-MetNav_DC8_1.json b/datasets/Aeolus-CalVal-MetNav_DC8_1.json index 6c2e739553..61ad618889 100644 --- a/datasets/Aeolus-CalVal-MetNav_DC8_1.json +++ b/datasets/Aeolus-CalVal-MetNav_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aeolus-CalVal-MetNav_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aeolus-CalVal-MetNav_DC8_1 is the Aeolus CalVal Meteorological and Navigational data product. Data was collected using the Global Positioning System (GPS) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. \r\n\r\nNASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future \r\ninternational Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products.", "links": [ { diff --git a/datasets/Aerosol_Sulfate_LowermostStrat_1868_1.json b/datasets/Aerosol_Sulfate_LowermostStrat_1868_1.json index 236db881b7..051eb55e9f 100644 --- a/datasets/Aerosol_Sulfate_LowermostStrat_1868_1.json +++ b/datasets/Aerosol_Sulfate_LowermostStrat_1868_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aerosol_Sulfate_LowermostStrat_1868_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs. MAIA runs focused on data from ATom-4 deployment, and output includes aerosol formation rates, and ultrafine particle size distributions and number concentrations in the lowermost stratosphere (LMS). ATom 1-4 deployments included all four seasons from 2016 to 2018. This investigation sought to understand how new particle formation (NPF) can occur in the LMS, factors influencing the amount of NPF, and other potential sources of ultrafine aerosols in this region of the atmosphere. The data are provided in comma-separated value (CSV) format.", "links": [ { diff --git a/datasets/Aerosol_Sz_Dist_South_Pole_1.0.json b/datasets/Aerosol_Sz_Dist_South_Pole_1.0.json index f7d6055fba..c24131f971 100644 --- a/datasets/Aerosol_Sz_Dist_South_Pole_1.0.json +++ b/datasets/Aerosol_Sz_Dist_South_Pole_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aerosol_Sz_Dist_South_Pole_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the physical aerosol size distributions\n measured at the South Pole during December 1998 and December 2000.\n \n The size range covered by these measurements was 3 [nm] to 250 [nm] in\n 1998 and 3 [nm] to 2000 [nm] in 2000.\n \n For 1998 measurements, total particle concentration for Dp > ~ 3[nm]\n and concentrations for 3 [nm] < Dp < 10 [nm] is available from\n 12/01/1998 to 12/31/1998 except over 12/09/1998 ~ 12/13/1998. They\n measured by the prototype Ultrafine Condensation Particle Counter,\n equipped with Pulse Height Analysis (PHA-UCPC)\n \n Particle size distributions for 10 [nm] < Dp < 250 [nm] is available\n from 12/16/1998 to 12/31/1998. They were measured by a Scanning\n Mobility Particle Spectrometer.\n \n For 2000 measurements, total particle concentration for Dp > ~ 3[nm]\n and concentrations for 3 [nm] < Dp < 10 [nm] is available from\n 12/01/2000 to 12/29/2000 except over 12/22/2000 ~ 12/24/2000. They\n measured by the white-light 3025 Ultrafine Condensation Particle\n Counter, equipped with Pulse Height Analysis (PHA-UCPC)\n \n Particle size distributions for 10 [nm] < Dp < 250 [nm] is available\n from 12/01/2000 to 12/29/2000. They were measured by a Scanning\n Mobility Particle Spectrometer.\n \n A PMS LASAIR measured particle size distributions for 100 [nm] to 2000\n [nm] from 12/01/2000 to 12/29/2000.\n \n Typical data collection frequencies are ~ 5 minutes in all\n instruments.\n \n All length(size) units are in [um].\n \n Following are the meanings of the variables.\n concentration [#/cc]: number of particles in a cubic centimeter of\n air.\n \n surface area [um^2/cc]: surface area concentrations of particles,\n assuming all particles are sphere.\n \n volume [um^3/cc]: volume concentrations of particles, assuming all\n particles are sphere", "links": [ { diff --git a/datasets/Aerosol_char_and_snow_chem_TNB.json b/datasets/Aerosol_char_and_snow_chem_TNB.json index 00ef98cd68..5c7c7a6a89 100644 --- a/datasets/Aerosol_char_and_snow_chem_TNB.json +++ b/datasets/Aerosol_char_and_snow_chem_TNB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aerosol_char_and_snow_chem_TNB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic aerosol was sampled at Terra Nova Bay using an inertial spectrometer at high flow rate. This instrument can sample aerosol and deposit particles on a membrane filter with size separation. The density of single particles and average density vs. aerodynamic diameter has been evaluated. Chemical composition of aerosol particles was determined by analyzing samples taken on millipore filters by scanning electron microscope and x-ray energy spectrometer. The results from this investigation are such that for particles with radius > 0.5 micron, frequency of sea-salt increases when aerodynamic diameter decreases. An opposite behavior is displayed by crustal elements. A chlorine loss in sea-salt particles has been observed. The suggested mechanism for this loss is: H2SO4 2NaCl = Na2SO4 2HCl. Condensation nuclei (CN) concentrations were measured at Terra Nova Bay with an alcohol-based particle counter. In January 1989 the mean value for CN was 490. The concentrations of eight major ions (Cl-, NO-3, SO42-, Na , K , Ca2 , Mg , H ) were determined from fresh snow samples. These showed that precipitation is acidic, a fact depending on H2SO4, HCl and HNO3.", "links": [ { diff --git a/datasets/Aerosol_opt_char_at_BTN_station.json b/datasets/Aerosol_opt_char_at_BTN_station.json index 19305edb14..d4484261be 100644 --- a/datasets/Aerosol_opt_char_at_BTN_station.json +++ b/datasets/Aerosol_opt_char_at_BTN_station.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aerosol_opt_char_at_BTN_station", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements performed at BTN (Icaro Camp) in the austral summer 2001 - 2002\nwith the PREDE POM 01L sun-photometer. It detects direct solar radiative flux\nas well as diffuse at selected scattering angles and at six wavelengths.\nAerosol optical characteristics were derived making use of Nakajima inversion\ncode SKYRAD. Aerosol optical depth was evaluated at 6 channels centered at 315,\n400, 500, 870, 940, 1020 nm wavelength bands. The sampling time interval is\nabout 15 minutes. The air mass is also given. Data were collected under\ncloudless-sky conditions. An in situ radiometer calibration is also performed\nby means of a modified Langley plot.", "links": [ { diff --git a/datasets/Aerosol_opt_depths_at_BTN.json b/datasets/Aerosol_opt_depths_at_BTN.json index 5ab10f862a..e4491e59a4 100644 --- a/datasets/Aerosol_opt_depths_at_BTN.json +++ b/datasets/Aerosol_opt_depths_at_BTN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aerosol_opt_depths_at_BTN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements performed at BTN (Icaro Camp) in the austral summers 1988 and 1993\nwith the UVISIR-2 sun-photometer built at the FISBAT-Institute (cfr. References\nbelow). Aerosol optical depth was evaluated taking into account molecular\nscattering and gaseous absorption as H2O, O3 and NO2 (cfr. references below).\nAerosol optical depths were evaluated at 8 channels centered in the 400 - 1050\nnm wavelength range. Because each scanning has the physical meaning of an\ninstantaneous picture of the atmosphere (with the sun at elevation h), we use a\nsingle average time for each scanning . The scanning time interval is about\n1.5 minutes. The relative optical air mass is also given. Data was taken under\nclear-sky conditions. Legal maximum value of optical depth depends on\nturbidity daily conditions and wavelength, ranging from 0.03 and 0.15.All\nvalues are given with 3 digit. Missing data are indicated with a 999.000\nvalue.", "links": [ { diff --git a/datasets/AfriSAR_AGB_Maps_1681_1.json b/datasets/AfriSAR_AGB_Maps_1681_1.json index 15465135d6..1f7bf81aa6 100644 --- a/datasets/AfriSAR_AGB_Maps_1681_1.json +++ b/datasets/AfriSAR_AGB_Maps_1681_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AfriSAR_AGB_Maps_1681_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution.", "links": [ { diff --git a/datasets/AfriSAR_LVIS_Footprint_Cover_1591_1.json b/datasets/AfriSAR_LVIS_Footprint_Cover_1591_1.json index 0d0cc6541d..fd5580cd83 100644 --- a/datasets/AfriSAR_LVIS_Footprint_Cover_1591_1.json +++ b/datasets/AfriSAR_LVIS_Footprint_Cover_1591_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AfriSAR_LVIS_Footprint_Cover_1591_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle.", "links": [ { diff --git a/datasets/AfriSAR_Mondah_Field_Data_1580_1.json b/datasets/AfriSAR_Mondah_Field_Data_1580_1.json index ebd1bc9d2f..3385db6aa2 100644 --- a/datasets/AfriSAR_Mondah_Field_Data_1580_1.json +++ b/datasets/AfriSAR_Mondah_Field_Data_1580_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AfriSAR_Mondah_Field_Data_1580_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products.", "links": [ { diff --git a/datasets/African_Marine_Atlas.json b/datasets/African_Marine_Atlas.json index 5344bb26e5..fe9233bf4e 100644 --- a/datasets/African_Marine_Atlas.json +++ b/datasets/African_Marine_Atlas.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "African_Marine_Atlas", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The African Marine Atlas developed by the Ocean Data and Information Network for Africa (ODINAFRICA) was officially launched on 23 February 2007 at the IOC Project Office for International Oceanographic Data and Information Exchange (IODE) in Ostend, Belgium. \n \n The African Marine Atlas provides substantial maps, images, data and information to coastalama_screen_400x310.shkl.jpg resource managers, planners and decision-makers from various administrative institutions and specialized agencies in Africa. The Atlas will be of immense benefit to national institutions and a variety of users such as environmentalists, local administrators, park managers, scientific community, fishing cooperatives, tourists, hotel keepers, teachers, NGOs, the general public, and any other interested persons. It has over 800 downloadable data products derived from the fields of marine geo-sphere, hydrosphere, atmosphere, biosphere, geopolitical and the human socio-economic dimensions.\n \n The Atlas indicates areas of intense use along the coastline requiring careful management and provides potential foresight on likely consequences of specific decisions. Further, the Atlas indicates gaps in knowledge and information base, where additional efforts may be directed. The Atlas will also act in other ways as a guide to recreational opportunities and tourist attractions.\n \n In developing the Atlas, the main objective was to collate available geospatial datasets and information on the marine environment and to summarize it into an African Marine Atlas suite. \n \n The website is one of a set of Marine Atlas products that will include web data services, web mapping and an Atlas publication when completed.\n \n The Atlas was realized through intensive work between May 2006 and February 2007 by a team of 16 marine scientists and GIS experts from NODC\u2019s in Benin, Ghana, Kenya, Mauritania, Mauritius, Mozambique, Namibia, Senegal, Seychelles, South Africa, and Tanzania. International ocean data experts provided key inputs in data analysis. It is based on an extensive survey of coastal and marine data needs undertaken in early 2006 in all the countries participating in ODINAFRICA. \n \n Primary partners in this project were the United Nations Environment Programme (UNEP), and the African Coelecanth Ecosystem Programme (ACEP). UNEP will develop a clearinghouse and information system on coastal and marine resources of Eastern Africa from the regional atlas. The Atlas has brought great benefits to participating national institutions and Africa as a whole, by encouraging scientists to work together, learn new techniques, and build teams that will continue to regularly update the Atlas with national and local scale data sets.\n \n _____________________________________________________________________", "links": [ { diff --git a/datasets/African_Rainfall_Patterns_1263_1.json b/datasets/African_Rainfall_Patterns_1263_1.json index acd8c312bb..165b24b375 100644 --- a/datasets/African_Rainfall_Patterns_1263_1.json +++ b/datasets/African_Rainfall_Patterns_1263_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "African_Rainfall_Patterns_1263_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes rainfall distribution statistics over the African continent, including Madagascar. The rainfall estimates are based on data from the NASA Tropical Rainfall Measuring Mission (TRMM) measured between 1998 and 2012. Rainfall patterns were quantified using a gamma-based function and two Markov chain parameters with the aim to summarize the rainfall pattern to a small number of parameters and processes. These summary statistics are suitable for temporal downscaling.These data provide gridded (0.25 x 0.25-degree) estimates of 14-year mean monthly rainfall total amount (mm), frequency (count), intensity (mm/hr), and duration (hrs) of rainfall, as well as Markov chain and gamma-distribution parameters for use in temporal downscaling. The data are presented as a series of 12 netCDF (*.nc) files. ", "links": [ { diff --git a/datasets/Afrisar_LVIS_Biomass_VProfiles_1775_1.json b/datasets/Afrisar_LVIS_Biomass_VProfiles_1775_1.json index dcbd21c792..48155ffea8 100644 --- a/datasets/Afrisar_LVIS_Biomass_VProfiles_1775_1.json +++ b/datasets/Afrisar_LVIS_Biomass_VProfiles_1775_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Afrisar_LVIS_Biomass_VProfiles_1775_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world.", "links": [ { diff --git a/datasets/AgriFieldNet Competition Dataset_1.json b/datasets/AgriFieldNet Competition Dataset_1.json index 6543aefa0b..c830efd13d 100644 --- a/datasets/AgriFieldNet Competition Dataset_1.json +++ b/datasets/AgriFieldNet Competition Dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AgriFieldNet Competition Dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. There are 13 different classes in the dataset including Fallow land and 12 crop types of Wheat, Mustard, Lentil, Green pea, Sugarcane, Garlic, Maize, Gram, Coriander, Potato, Bersem, and Rice. The dataset is split to train and test collections as part of the AgriFieldNet India Competition. Ground reference data for this dataset is collected by IDinsight\u2019s [Data on Demand](https://www.idinsight.org/services/data-on-demand/) team. Radiant Earth Foundation carried out the training dataset curation and publication. This training dataset is generated through a grant from the Enabling Crop Analytics at Scale ([ECAAS](https://cropanalytics.net/)) Initiative funded by [The Bill & Melinda Gates Foundation](https://www.gatesfoundation.org/) and implemented by [Tetra Tech](https://www.tetratech.com/).", "links": [ { diff --git a/datasets/AirMOSS_Field_Data_Harvard_1677_1.json b/datasets/AirMOSS_Field_Data_Harvard_1677_1.json index 42e1eaa828..821cace541 100644 --- a/datasets/AirMOSS_Field_Data_Harvard_1677_1.json +++ b/datasets/AirMOSS_Field_Data_Harvard_1677_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_Field_Data_Harvard_1677_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_BERMS_1406_1.json b/datasets/AirMOSS_L1_Sigma0_BERMS_1406_1.json index c5c9f2b612..f3af18d42e 100644 --- a/datasets/AirMOSS_L1_Sigma0_BERMS_1406_1.json +++ b/datasets/AirMOSS_L1_Sigma0_BERMS_1406_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_BERMS_1406_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Chamel_1407_1.json b/datasets/AirMOSS_L1_Sigma0_Chamel_1407_1.json index 1b005570bc..9ae6b30435 100644 --- a/datasets/AirMOSS_L1_Sigma0_Chamel_1407_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Chamel_1407_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Chamel_1407_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_DukeFr_1408_1.json b/datasets/AirMOSS_L1_Sigma0_DukeFr_1408_1.json index 0780fe7a4b..270a25f1c6 100644 --- a/datasets/AirMOSS_L1_Sigma0_DukeFr_1408_1.json +++ b/datasets/AirMOSS_L1_Sigma0_DukeFr_1408_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_DukeFr_1408_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Harvrd_1409_1.json b/datasets/AirMOSS_L1_Sigma0_Harvrd_1409_1.json index 8d6bc3a8ce..b8ab0ef4d2 100644 --- a/datasets/AirMOSS_L1_Sigma0_Harvrd_1409_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Harvrd_1409_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Harvrd_1409_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Howlnd_1410_1.json b/datasets/AirMOSS_L1_Sigma0_Howlnd_1410_1.json index 44217834a6..bac0f7712f 100644 --- a/datasets/AirMOSS_L1_Sigma0_Howlnd_1410_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Howlnd_1410_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Howlnd_1410_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_LaSelv_1411_1.json b/datasets/AirMOSS_L1_Sigma0_LaSelv_1411_1.json index 931c2fb499..982d8db01e 100644 --- a/datasets/AirMOSS_L1_Sigma0_LaSelv_1411_1.json +++ b/datasets/AirMOSS_L1_Sigma0_LaSelv_1411_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_LaSelv_1411_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Metoli_1412_1.json b/datasets/AirMOSS_L1_Sigma0_Metoli_1412_1.json index ede8491de2..50adbf9994 100644 --- a/datasets/AirMOSS_L1_Sigma0_Metoli_1412_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Metoli_1412_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Metoli_1412_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Moisst_1413_1.json b/datasets/AirMOSS_L1_Sigma0_Moisst_1413_1.json index 671aed20f8..1deb8b08fe 100644 --- a/datasets/AirMOSS_L1_Sigma0_Moisst_1413_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Moisst_1413_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Moisst_1413_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_TonziR_1414_1.json b/datasets/AirMOSS_L1_Sigma0_TonziR_1414_1.json index 666758f5c0..8254f4195d 100644 --- a/datasets/AirMOSS_L1_Sigma0_TonziR_1414_1.json +++ b/datasets/AirMOSS_L1_Sigma0_TonziR_1414_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_TonziR_1414_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L1_Sigma0_Walnut_1415_1.json b/datasets/AirMOSS_L1_Sigma0_Walnut_1415_1.json index 1153aab272..51c1783530 100644 --- a/datasets/AirMOSS_L1_Sigma0_Walnut_1415_1.json +++ b/datasets/AirMOSS_L1_Sigma0_Walnut_1415_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L1_Sigma0_Walnut_1415_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.", "links": [ { diff --git a/datasets/AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1.json b/datasets/AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1.json index 58f6833adb..e4b7c39143 100644 --- a/datasets/AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1.json +++ b/datasets/AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site.", "links": [ { diff --git a/datasets/AirMOSS_L2_Carbon_Flux_1420_1.json b/datasets/AirMOSS_L2_Carbon_Flux_1420_1.json index cf12679e90..551bf1846e 100644 --- a/datasets/AirMOSS_L2_Carbon_Flux_1420_1.json +++ b/datasets/AirMOSS_L2_Carbon_Flux_1420_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L2_Carbon_Flux_1420_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University. Estimates of surface CO2 flux, sensible and latent heat fluxes, their corresponding uncertainties, and average wind speed and direction are provided for each of the 26 flights.", "links": [ { diff --git a/datasets/AirMOSS_L2_Inground_Soil_Moist_1416_1.json b/datasets/AirMOSS_L2_Inground_Soil_Moist_1416_1.json index e2e08ca908..7333b91e62 100644 --- a/datasets/AirMOSS_L2_Inground_Soil_Moist_1416_1.json +++ b/datasets/AirMOSS_L2_Inground_Soil_Moist_1416_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L2_Inground_Soil_Moist_1416_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data.", "links": [ { diff --git a/datasets/AirMOSS_L2_Precipitation_1417_1.json b/datasets/AirMOSS_L2_Precipitation_1417_1.json index 1ad3606c07..a7c9681c61 100644 --- a/datasets/AirMOSS_L2_Precipitation_1417_1.json +++ b/datasets/AirMOSS_L2_Precipitation_1417_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L2_Precipitation_1417_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data.", "links": [ { diff --git a/datasets/AirMOSS_L4_Daily_NEE_1422_1.json b/datasets/AirMOSS_L4_Daily_NEE_1422_1.json index ccb650d607..9a58baa150 100644 --- a/datasets/AirMOSS_L4_Daily_NEE_1422_1.json +++ b/datasets/AirMOSS_L4_Daily_NEE_1422_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L4_Daily_NEE_1422_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site. The AirMOSS L4 daily NEE were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.", "links": [ { diff --git a/datasets/AirMOSS_L4_RZ_Soil_Moisture_1421_1.json b/datasets/AirMOSS_L4_RZ_Soil_Moisture_1421_1.json index ffc6182528..d7090a8f10 100644 --- a/datasets/AirMOSS_L4_RZ_Soil_Moisture_1421_1.json +++ b/datasets/AirMOSS_L4_RZ_Soil_Moisture_1421_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L4_RZ_Soil_Moisture_1421_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here.", "links": [ { diff --git a/datasets/AirMOSS_L4_Regional_NEE_1423_1.json b/datasets/AirMOSS_L4_Regional_NEE_1423_1.json index b64b8bb2c3..60aeb4433b 100644 --- a/datasets/AirMOSS_L4_Regional_NEE_1423_1.json +++ b/datasets/AirMOSS_L4_Regional_NEE_1423_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMOSS_L4_Regional_NEE_1423_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.", "links": [ { diff --git a/datasets/AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index fa9bd4b919..3efeaac54d 100644 --- a/datasets/AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017.", "links": [ { diff --git a/datasets/AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6.json index 562fa7aec4..d73997e039 100644 --- a/datasets/AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. \r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017.", "links": [ { diff --git a/datasets/AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index 383262a8e2..72592b408a 100644 --- a/datasets/AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. \r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA\u2019s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015.", "links": [ { diff --git a/datasets/AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6.json index f68eb35a14..93060ad818 100644 --- a/datasets/AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. \r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA\u2019s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015.", "links": [ { diff --git a/datasets/AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6.json index 6588337b9e..bdf01ff3c9 100644 --- a/datasets/AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign Aug 2019.\r\rAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\rThis release of AirMSPI data contains all targets acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign. The NASA ER-2 with the AirMSPI instrument conducted flights from Aug 1 to Aug 21 and was based out of Armstrong Flight Research Center in Palmdale, California.", "links": [ { diff --git a/datasets/AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index a984b076de..a942383b65 100644 --- a/datasets/AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA\u2019s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016.", "links": [ { diff --git a/datasets/AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6.json index 98eb93951e..9bd40c1236 100644 --- a/datasets/AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA\u2019s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016.", "links": [ { diff --git a/datasets/AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index e6cfb91c0e..d625ec55fb 100644 --- a/datasets/AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external).", "links": [ { diff --git a/datasets/AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6.json index 42205bd2a8..bab5a5ed6f 100644 --- a/datasets/AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data are AirMSPI Terrain-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external).", "links": [ { diff --git a/datasets/AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5.json b/datasets/AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5.json index 0fbb559bee..97debbb9d9 100644 --- a/datasets/AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5.json +++ b/datasets/AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA\u2019s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013.", "links": [ { diff --git a/datasets/AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5.json b/datasets/AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5.json index 2165d3d3c4..1d53c8c399 100644 --- a/datasets/AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5.json +++ b/datasets/AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA\u2019s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013.", "links": [ { diff --git a/datasets/AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index 51cdee352d..48b4895188 100644 --- a/datasets/AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015.", "links": [ { diff --git a/datasets/AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6.json index 7342321952..096fb488e3 100644 --- a/datasets/AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign.\r\n\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015.", "links": [ { diff --git a/datasets/AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5.json b/datasets/AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5.json index 2de74925c3..cdd5d6e668 100644 --- a/datasets/AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5.json +++ b/datasets/AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign.\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format.\nThis release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013.", "links": [ { diff --git a/datasets/AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5.json b/datasets/AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5.json index 0df56fce50..aa4e355683 100644 --- a/datasets/AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5.json +++ b/datasets/AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign.\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\nThis release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013.", "links": [ { diff --git a/datasets/AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6.json index 9f41ead124..2b0f54affb 100644 --- a/datasets/AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign.\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data.", "links": [ { diff --git a/datasets/AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6.json b/datasets/AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6.json index 920571b864..8fc2ac605c 100644 --- a/datasets/AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6.json +++ b/datasets/AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign.\r\nAirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data.", "links": [ { diff --git a/datasets/AirSWOT_Orthomosaic_WaterMask_1655_1.json b/datasets/AirSWOT_Orthomosaic_WaterMask_1655_1.json index a908a1ade6..b2eb20f259 100644 --- a/datasets/AirSWOT_Orthomosaic_WaterMask_1655_1.json +++ b/datasets/AirSWOT_Orthomosaic_WaterMask_1655_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "AirSWOT_Orthomosaic_WaterMask_1655_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems.", "links": [ { diff --git a/datasets/Airborne_Insitu_Measurements_1784_1.json b/datasets/Airborne_Insitu_Measurements_1784_1.json index 1e243bc450..a653133cd5 100644 --- a/datasets/Airborne_Insitu_Measurements_1784_1.json +++ b/datasets/Airborne_Insitu_Measurements_1784_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Airborne_Insitu_Measurements_1784_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides results of selected in-situ measurements of airflow and aerosol particles collected during the following airborne campaigns: NASA Atmospheric Tomography (ATom), Saharan Aerosol Long-range Transport and Aerosol-Cloud-interaction Experiment (SALTRACE), and Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics (A-LIFE). The airborne campaigns were conducted between 2013-06-10 and 2018-05-21. Depending upon the aircraft instrumentation per flight and campaign, the data include aircraft position, relative humidity, temperature, pressure, angle of attack (AOA), the probe location, true and probe air speeds, and aerosol particle diameters as extracted from Cloud Imaging Probe (CIP) images for the ATom and A-LIFE flights. Also provided are the results of combining the airborne data with numerical modeling to simulate particle sampling efficiency. Simulations investigated how airflow around wing-mounted instruments affected sampling efficiency and the induced errors for different realistic flight conditions.", "links": [ { diff --git a/datasets/Airborne_radiotracers.json b/datasets/Airborne_radiotracers.json index ed04734804..acc66aa757 100644 --- a/datasets/Airborne_radiotracers.json +++ b/datasets/Airborne_radiotracers.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Airborne_radiotracers", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Natural radionuclides including 222Rn, 220Rn, 210Pb, 7Be have been used to\nexamine a large variety of relevant atmospheric processes. Routine measurements\nof these naturally occurring radionuclides in Antarctica. Zucchelli Station and\nat Campo Icaro, help to understand the atmospheric composition and its\nvariations. 222Rn, 220Rn are measured in situ with a dedicated low level alpha\nspectrometer working in continuous mode, with a time resolution of two hours.\n210Pb and 7Be are measured on aerosol filters sampled with a high volume device\nevery three days. Measurements are carried out in Bologna using HPGe\nspectrometers.", "links": [ { diff --git a/datasets/Akademik_Sergey_Vavilov_0.json b/datasets/Akademik_Sergey_Vavilov_0.json index 4039b85954..bb5bdedfc8 100644 --- a/datasets/Akademik_Sergey_Vavilov_0.json +++ b/datasets/Akademik_Sergey_Vavilov_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Akademik_Sergey_Vavilov_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Barents Sea north of Russia made during 1998 by the Russian research vessel, the Akademik Sergey Vavilov.", "links": [ { diff --git a/datasets/Alaska_Arctic_Tundra_Veg_Map_1353_1.json b/datasets/Alaska_Arctic_Tundra_Veg_Map_1353_1.json index 8deada1d32..8223c7737f 100644 --- a/datasets/Alaska_Arctic_Tundra_Veg_Map_1353_1.json +++ b/datasets/Alaska_Arctic_Tundra_Veg_Map_1353_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaska_Arctic_Tundra_Veg_Map_1353_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline.", "links": [ { diff --git a/datasets/Alaska_L4_WRF_STILT_Footprints_1544_1.json b/datasets/Alaska_L4_WRF_STILT_Footprints_1544_1.json index b1dbd88f40..4e2045b8d6 100644 --- a/datasets/Alaska_L4_WRF_STILT_Footprints_1544_1.json +++ b/datasets/Alaska_L4_WRF_STILT_Footprints_1544_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaska_L4_WRF_STILT_Footprints_1544_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location.", "links": [ { diff --git a/datasets/Alaska_L4_WRF_STILT_Particle_1571_1.json b/datasets/Alaska_L4_WRF_STILT_Particle_1571_1.json index b684530a6b..cb52d8e6ca 100644 --- a/datasets/Alaska_L4_WRF_STILT_Particle_1571_1.json +++ b/datasets/Alaska_L4_WRF_STILT_Particle_1571_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaska_L4_WRF_STILT_Particle_1571_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function.", "links": [ { diff --git a/datasets/Alaska_Lake_Pond_Maps_2134_1.json b/datasets/Alaska_Lake_Pond_Maps_2134_1.json index b2410e618b..229cd56f42 100644 --- a/datasets/Alaska_Lake_Pond_Maps_2134_1.json +++ b/datasets/Alaska_Lake_Pond_Maps_2134_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaska_Lake_Pond_Maps_2134_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format.", "links": [ { diff --git a/datasets/Alaska_Yukon_NDVI_1614_1.json b/datasets/Alaska_Yukon_NDVI_1614_1.json index 992eb71a85..50e33e3335 100644 --- a/datasets/Alaska_Yukon_NDVI_1614_1.json +++ b/datasets/Alaska_Yukon_NDVI_1614_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaska_Yukon_NDVI_1614_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values.", "links": [ { diff --git a/datasets/Alaskan_CH4_CO2_Fluxes_1316_1.json b/datasets/Alaskan_CH4_CO2_Fluxes_1316_1.json index 984e285b6e..2a544ad92c 100644 --- a/datasets/Alaskan_CH4_CO2_Fluxes_1316_1.json +++ b/datasets/Alaskan_CH4_CO2_Fluxes_1316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaskan_CH4_CO2_Fluxes_1316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides hourly atmospheric concentrations of methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) as mole fractions, from January 2012 to December 2014 measured at the CARVE flux tower in Fox, Alaska (17 km north of Fairbanks) as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). High-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (WRF- STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) were used to determine the influence region of the tower site and investigate the inter-annual and seasonal variability of regional fluxes of CO2 and CH4 in boreal Alaska using the tower observations. Modeled estimates of CH4, CO2, and CO background concentrations are provided. The WRF-STILT model \"footprints\" for the CARVE tower are provided with this data set.", "links": [ { diff --git a/datasets/Alaskan_CO2_Flux_1325_1.1.json b/datasets/Alaskan_CO2_Flux_1325_1.1.json index 2a21d8b5f8..0d4f345550 100644 --- a/datasets/Alaskan_CO2_Flux_1325_1.1.json +++ b/datasets/Alaskan_CO2_Flux_1325_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alaskan_CO2_Flux_1325_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports monthly averages of atmospheric CO2 concentration from satellite and airborne observations between 2009 and 2013 and simulated present and future monthly concentrations and land-atmosphere CO2 flux for periods between 1990 and 2200. Atmospheric CO2 concentration measurements were obtained from Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and NOAA Arctic Coast Guard (ACG) flights, the Greenhouse Gases Observing Satellite (GOSAT), and NOAA/ESRL vertical profile measurements at Poker Flat, Alaska (PFA). Present and future monthly CO2 concentrations and fluxes were simulated using the GEOS-Chem global tracer model and the Community Land Model, Version 4.5, for multiple regional flux and permafrost thaw scenarios.", "links": [ { diff --git a/datasets/Albedo_Boreal_North_America_1605_1.1.json b/datasets/Albedo_Boreal_North_America_1605_1.1.json index 837a41eec5..bb3043f64e 100644 --- a/datasets/Albedo_Boreal_North_America_1605_1.1.json +++ b/datasets/Albedo_Boreal_North_America_1605_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Albedo_Boreal_North_America_1605_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset.", "links": [ { diff --git a/datasets/Alder_Shrub_Soil_Alaska_V2_2300_2.json b/datasets/Alder_Shrub_Soil_Alaska_V2_2300_2.json index bd46404f7d..2ba59fbb3e 100644 --- a/datasets/Alder_Shrub_Soil_Alaska_V2_2300_2.json +++ b/datasets/Alder_Shrub_Soil_Alaska_V2_2300_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Alder_Shrub_Soil_Alaska_V2_2300_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11). The field data were collected during August in 2018 and 2019 to study the expansion of shrub cover, particularly alders (Alnus spp.) in tundra ecosystems and the potential impact of shrubs on soil properties. Samples were measured along transects at 5- to 10-m intervals. Soil samples were collected and analyzed in the laboratory. Vegetation variables include percent cover of mosses, lichens, graminoid species, shrubs, alder, birch (Betula spp.), and willow (Salix spp.) along with the biomass, size, and age structure of alder. An allometric model to estimate alder biomass was developed. Soil metrics include moisture content, conductivity, bulk density, carbon and nitrogen content and isotope ratios. The data include the maximum annual Normalized Difference Vegetation Index (NDVI) for 2019 and the trend in maximum NDVI for 2000-2020. This is version 2 of this dataset.The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Algal_Toxicity_Project_1.json b/datasets/Algal_Toxicity_Project_1.json index 03f87f0574..846fbbc325 100644 --- a/datasets/Algal_Toxicity_Project_1.json +++ b/datasets/Algal_Toxicity_Project_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Algal_Toxicity_Project_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Experiments were carried out at Casey Station over the summer of 2005-2006 to investigate declines in chlorophyll fluorescence following from exposure to seawater spiked with heavy metals. Chlorophyll fluorescence was measured using a pulse amplitude modulated (PAM) fluorometer. The PAM device was mounted to a robotic arm, which could be programmed using a laptop computer to automatically position the device at a constant height above 18 separate test chambers. The test chambers each contained 2130mL of metal-spiked seawater which was fanned by an electric motor across an aluminium sample holder (approximately 2.5cm x 2.5cm) containing a macroalgal specimen. The test chambers were placed in a tank and maintained at a constant temperature by circulating coolant water.\n\nRock-attached specimens of the species Desmerestia menziesii, Palmaria decipiens and Himantothallus grandifolius were collected either by divers or from the shallow nearshore from uncontaminated areas of the Casey region (~6-12m depth). Specimens of these species were exposed to single-toxicant test solutions containing copper, zinc or cadmium for durations ranging from 1.5-6.5d. A total of eighteen experiments were performed during the summer. Each experiment yielded a set of 2D image files that traced variations in fluorescence parameters over the duration.\n\nAll studied species demonstrated a decline in several fluorescence parameters including minimal (Fo`) and maximal fluorescence yield (Fm`) and, to a lesser extent, effective quantum yield (delta F/Fm` or, alternatively, Y(II)) following from several days' exposure to dissolved copper. D. menziesii and H. grandifolius also demonstrated a decline in fluorescence after exposure to zinc, albeit slower than copper, but not after exposure to cadmium. In contrast to the logarithmic decline observed following from copper exposure, the decline due to zinc toxicity occurred only after a brief increase in fluorescence at around 50h.\n\nData available:\nImage files taken hourly by the PAM device. These are sorted into folders for each experiment, with the folder title describing the experiment number, the species tested, the metal tested and the duration of the test. Each image file has the file extension *.pim and can be opened using the Imaging-WIN software package (also provided) here. Each image file is titled in the format : [Test Chamber number] - [date] - [24h time]. For example, 'T01-20060204-121539' corresponds to an image file taken from Test Chamber 1 on the 4th of February 2006 at 12h15m39s. In each folder, two other files are presented. The first is a *.pim file titled 'T01-darkadapted', and is an image file taken immediately before the beginning of the test and records the response of the Test Chamber 1 specimen to control water after being kept in total darkness for between 10-30min. Fluorescence parameters of dark-adapted specimens are often used as a measure of specimen health. The second file is a .txt file that describes the nominal concentrations of the test solutions in each chamber (at the time of posting this metadata, chemical analyses of water samples have not been completed).\n\nExcel spreadsheets. Also provided here are MS Excel spreadsheets for some (but not all) experiments (E1-E6). These spreadsheets were produced by arbitrarily designating specific 'zones' in the first image taken for each Test Chamber. The same zones were visually located in each subsequent image taken for that Test Chamber, and the Fm', Fo' and delta F/Fm` values for each zone were exported to the spreadsheet. These spreadsheets represent a first attempt at data analysis, although it is expected that the final approach will involve more complicated image editing software.\n\nPDF files. Finally, also provided are two *.pdf files which contain the scanned laboratory notebook compiled during the summer. This notebook contains the details of water sample labelling, as well as labelling of algal samples collected for associated projects during the summer. It also contains details of the dimensions of the PAM apparatus.\n\nSoftware. Installation package for ImagingWIN software, version 1.01k.\n\nJPEG files. Photographs showing the set-up of the PAM apparatus.\n\nThis work has been completed as part of ASAC projects 2201, 2566 and 2697 (ASAC_2201, ASAC_2256, ASAC_2697).", "links": [ { diff --git a/datasets/Aliens_in_Ant_Visitor_Numbers_1.json b/datasets/Aliens_in_Ant_Visitor_Numbers_1.json index dfd89ef02a..58661219e1 100644 --- a/datasets/Aliens_in_Ant_Visitor_Numbers_1.json +++ b/datasets/Aliens_in_Ant_Visitor_Numbers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aliens_in_Ant_Visitor_Numbers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "One Excel worksheet is provided. This contains estimates of the number of scientists (including their support personnel) landing at ice-free locations in Antarctica. Estimates were derived from station maximum and winter national program numbers for 2007-08 provided by the Council of Managers of National Antarctic Programs (COMNAP). Raw tourist data were provided by the International Association of Antarctic Tourism Operators (IAATO) and were filtered for duplicates and non-ice free landings. we do not have permission to make these data publicly available, contact IAATO directly(www.iaato.org) to request access.", "links": [ { diff --git a/datasets/Aliens_in_Antarctica_Invertebrates_2000_2013_1.json b/datasets/Aliens_in_Antarctica_Invertebrates_2000_2013_1.json index bc2a8c09c8..dcf5ae4958 100644 --- a/datasets/Aliens_in_Antarctica_Invertebrates_2000_2013_1.json +++ b/datasets/Aliens_in_Antarctica_Invertebrates_2000_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aliens_in_Antarctica_Invertebrates_2000_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify and identify alien invertebrate transfer to Antarctica our research utilised two methods. Firstly, we examined the Australian Antarctic Division's (AAD) alien invertebrate collection of samples from Australian Antarctic research stations, cargo handling facility, and supply ships. Secondly, we implemented a trapping regime at key locations and on supply ships during the 2012-13 shipping season. Furthermore, we utilised a trapping dataset from similar locations collected in 2002-2004. \n\nThe Collection\nSince 2000, the AAD has encouraged Antarctic expeditioners and staff to collect and record alien invertebrate incursions from its four Antarctic research stations, supply ships, a transport aircraft, the cargo facility in Hobart in the wharf precinct of Hobart, and its cargo warehouse in semi-rural Kingston, Tasmania, Australia. Furthermore in 2004, an electronic database for logging environmental incident reports was created. These reports instigate a chain of management response. Incident reports can be generated regardless of whether a physical specimen is collected. Alien invertebrate collection kits - colloquially known as critter kits, were dispatched to ships and stations by the AAD's Environmental Officer. The kits contained sample jars, collecting equipment, data capturing notebooks with defined fields (date, collector, location, notes) to record collection details, barcodes to enable identification of individual collection events and instructions for providing guidance to those not usually engaged in collection of invertebrates.\n\nAny specimens collected were returned to Australia along with collection information. We identified these specimens to the most resolved taxonomic level possible. Any records not paired with a physical specimen (i.e. an incident report with no collection) could not be formally identified and were therefore omitted from taxonomic analysis. The only exception was where the specimen was identified by the collector as a 'spider', 'fly', 'snail' or 'moth' which were categorised as Araneae, Diptera, Gastropoda, and Lepidoptera respectively. In these cases, it was deemed that the distinct form and familiarity of these invertebrates even to non-experts generated correct evaluations of the specimens to a coarse taxonomic level. During the 2012-13 season expeditioners were repeatedly briefed to be especially vigilant to search for and collect any invertebrates.\nAll specimens and incident reports were reviewed to determine vectors and location information. Vector categories were nominated as food, ship, aircraft, and various cargo types. Additional information associated with the specimen was used to determine the specific cargo type. Where invertebrates were 'hidden' in containers, 'trapped' or 'entangled' in cargo materials the vector was deemed 'container and packaging materials'. The supply ships and aircraft were considered vectors given they both travel south and attract invertebrates in their own right, via colours, lights and invertebrates windblown onto their surfaces. General location categories were: 'wharf/cargo facility', 'ships/aircraft', and the four research stations - Macquarie Island (54 degrees 30' S 158 degrees 57' E), Casey (66.28 degrees S, 110.52 degrees E), Davis (68.57 degrees S, 77.96 degrees E) and Mawson (67.60 degrees S, 62.86 degrees E). Samples with unknown vectors or undocumented locations were excluded from analyses. \n\nTrapping\nTwo types of traps were deployed on supply ships and at the cargo facility in 2012-13. Battery operated 8 watt ultra-violet light traps (Australian Entomological Supplies, Sydney, NSW) were complemented with colour pan traps constructed of yellow and white plastic plates 18 cm in diameter, smeared with Tangle Trap (R) brush-on, petroleum-based insect trap coating. These colours were chosen because they are the most attractive to targeted flying insects such as flies, wasps, aphids and thrips.\nTrapping was undertaken on two ships, which collectively undertook five voyages to Antarctica from Hobart from October to February 2012-13. We attempted to deploy traps at several times during the journey - leaving port, at sea, and approaching the destination (land). However, variable sea conditions among voyages influenced the frequency of trap deployment. Light traps were automatically activated by dark conditions and were illuminated for up to 12 hours at a time. The traps were placed in areas which were dark at night, and colour traps were placed in areas with access to the outdoors and proximity to food. At the cargo facility in Hobart, Australia, light and colour traps were deployed for approximately three consecutive days while the ship was in port undergoing cargo loading prior to departure for the Antarctic. During the course of the season, we deployed 39 light trap night for a total of 418 hours. Fifty-eight yellow and 58 white traps were exposed for a total of 7440 hours each.\nExpeditioners and staff were briefed prior to departure to encourage increased vigilance for ad hoc invertebrate collection at the cargo facility and on the supply ships.\n\nPrevious trapping data\nIn 2002-2004 trapping was undertaken at the Kingston cargo warehouse and the cargo facility in the spring and summer. Blue and yellow colour sticky traps were deployed for several weeks at a time. The quantity and identity of taxa from the 2002-04 trapping exercise were compared with our comparable trapping from 2012-13.", "links": [ { diff --git a/datasets/Aliens_in_Antarctica_seed_identifications_1.json b/datasets/Aliens_in_Antarctica_seed_identifications_1.json index 9e183ab26d..732e1a1d38 100644 --- a/datasets/Aliens_in_Antarctica_seed_identifications_1.json +++ b/datasets/Aliens_in_Antarctica_seed_identifications_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aliens_in_Antarctica_seed_identifications_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands. This file lists the plant seeds that were found in the samples.\n\nIdentification of the seeds was done mainly by comparing the seeds (or more often photographs of the seeds) with photographs of seeds in seed-atlases and in databases on the web (see the list below). Because often we had only a single specimen of a specific seed morphotype, we did not use any destructive methods (e.g. making cross-sections of the seed). All seeds have been stored, so they are available for further study.\n\nFor each identification a confidence level was given on a 4-point scale (0 = no identification available; 1 = low confidence in identification: it may be the taxon listed, but it would not be surprising if it was not; 2 = moderate confidence: we think it is the taxon indicated, but we may be wrong; and 3 = high confidence = we are convinced it is the taxon indicated).\n\nSometimes it was not possible to see if something was a seed or not. Whenever we had serious doubts about something being a seed, it was not counted as such. This way we may well have discarded (figuratively: all material has been kept) some seeds, but this will result at most in a somewhat conservative estimate of the propagule load of the samples. Equally we have discounted seeds that were seriously damaged, and thus not viable. Again in general we were fairly conservative in this matter.\n\nAll seeds were grouped in groups that were morphologically different (morphotypes), and for which we suggest they are different species (or groups of closely related species) . All morphotypes were given a unique number. Most seeds were identified more or less independently by several people. Subsequently differences in identification were checked and discussed, until some consensus was reached. Where no consensus was reached, identification was given at the taxonomic level where we agreed, and lower levels were given as unknown. For quite a number of seeds we did not arrive at an identification even at the family level.\n\n\nResources used for seed identification\n\nBotha C (2001) Common weeds of crops and gardens in South Africa. Ark grain crops institute. Potchefstroom\n\nCappers R T J, Bekker R M, Jans J E A. (2006) Digital seed Atlas of the Netherlands. Barkhuis Publishing. Groningen.\n\nCorner, E. J. H. (1976). Seeds of Dicotyledons. Cambridge University Press, Cambridge.\n\nKirkbride, J.H., Jr., C.R. Gunn, and M.J. Dallwitz. 2006. Family Guide for Fruits and Seeds, version 1.0.\nURL: https://nt.ars-grin.gov/SeedsFruits/keys/FrSdFam/Index.cfm. Accessed July-November 2009.\n\nMartin, A. C., Barkley, W. D. (1961). Seed Identification Manual. University of California Press.\n\nMillennium seed bank project (Kew) Seed identification database. URL: http://data.kew.org/sid/. Accessed July-November 2009.\n\nSeed ID Workshop. Department of Horticulture and Crop Science, The Ohio State University. URL: http://www.oardc.ohio-state.edu/seedid/ . Accessed July-November 2009.\n\nSeeds of Success Collections at the Bend Seed Extractory. URL: unknown - may be: https://www.blm.gov/programs/natural-resources/native-plant-communities/native-plant-and-seed-material-development/collection. Accessed July-November 2009.\n\nUBC Botanical Garden Seed Collection. URL: https://botanicalgarden.ubc.ca/research-collections/plant-collections/. Accessed July-November 2009.\n\nWebb C J, Simpson M J A (2001). Seeds of New Zealand gymnosperms and dicotyledons, Christchurch, N.Z. : Manuka Press.\n\n\nThe seeds were identified by\n\nDr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands\n\nDr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia.\n\nChris Ware, , Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia.\n\nDr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa", "links": [ { diff --git a/datasets/Aliens_in_Antarctica_survey_data_1.json b/datasets/Aliens_in_Antarctica_survey_data_1.json index 72c6fa3f22..212e277115 100644 --- a/datasets/Aliens_in_Antarctica_survey_data_1.json +++ b/datasets/Aliens_in_Antarctica_survey_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aliens_in_Antarctica_survey_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In principle all Antarctic visitors in the 2007/2008 southern summer season received a questionnaire called the General Visitor Survey (GVS) about previous use of their clothing and other equipment, and their travel pattern in the year before their Antarctic visit (pages 1 and 2 of the questionnaire Aliens_in_Antarctica_QUESTIONNAIRE_2.5.pdf).\n\nPassengers that were sampled for propagules also filled in the GVS questionnaire, but with a third page, with questions about the previous use of specific items of clothing and other gear. The data from this page is called the Visitor Clothing Survey (VCS).\n\nTo collect the data from the questionnaire forms these were optically scanned by a specialized company, and the results were sent to the investigators in spreadsheets. Some forms arrived only after the scanning was completed. From these we entered the data by hand.\n\nOn the packets with questionnaires and samples the name of the ship/airplane was written, as well as the date of collection of the data and/or samples.\n\nQuestionnaires were available in various languages, so most people could fill in a questionnaire in their own language.\n\nA total of ca. 5024 GVS forms were received. In addition to these, some 845 VCS questionnaires were received (file = Aliens_in_Antarctica_VCS_questionnaire_data.xls).\nOf the VCS questionnaire the first 2 pages were identical to the GVS form, and the data from the first 2 pages of all VCS forms were added to the GVS data (none of the visitors filled in both forms), bringing the total up to 5869.\n\nPersonnel\n\nThe data were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season.\nResponsible for the organisation of the data collecting were:\n\nDr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands\n\nDr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia.\n\nDr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK\n\nDr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France\n\nDr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa\n\nDr. S. Imura, National Institute of Polar Research, Tokyo, Japan\n\n\nDr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organisation of the data in digital form.", "links": [ { diff --git a/datasets/Aliens_in_Antarctica_visitor_data_1.json b/datasets/Aliens_in_Antarctica_visitor_data_1.json index 62f87b9302..f87e64c945 100644 --- a/datasets/Aliens_in_Antarctica_visitor_data_1.json +++ b/datasets/Aliens_in_Antarctica_visitor_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aliens_in_Antarctica_visitor_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands.\n\nThis dataset lists the number of seeds found on each visitor, as well as the number of different seed morphotypes (species) per visitor. In addition data on visitor characteristics are given, derived from the Visitor Clothing Survey (VCS) questionnaire data (see separate download link).\n\nSampling was done by cleaning out the outer clothing (jackets, outer trousers, hats, gloves), insulation layer (jerseys, fleece), backpacks, camera bags, daypacks, boots and shoes, and walking poles and camera tripods, using Philips FC 9154 Performer Animal Care vacuum cleaners. All material was collected in nylon mesh bags, placed just behind the suction opening. For all people performing the sampling a detailed instruction DVD was provided.\n\nEach sample in its mesh bag was placed a plastic bag, and put in an envelope, together with the matching questionnaire.\nSimilarly the dust bag used (a new dust bag was inserted in the vacuum cleaner for each person sampled) was put in a labelled plastic bag. Plastic bag, each page of the questionnaire, and the envelope were labelled with a barcode sticker, a different barcode for each sampled person. On the plastic bag with the mesh bag with the sample was indicated which item(s) was (were) sampled.\n\nAt the end of the field season all questionnaires and samples were returned to the Netherlands (samples collected from people travelling through South America), South Africa (people leaving from Cape Town), Japan (people travelling with the Japanese national program vessel), or Australia (people travelling from Australia or New Zealand). Here the samples were weighed, and sorted into plant seeds, other plant propagules (large fragments of moss, hepatics or lichens), invertebrate animal remains, and other material.\n\nWhenever possible all different items of clothing etc. were sampled separately. In this way separate samples per item were collected from 350 people.\n\nThe dataset lists the number of seeds found on separate items of clothing or other equipment per visitor, as well as the number of different seed morphotypes (species) per visitor. The number of seeds and number of species (morphotypes) is based on the results of the seed identifications (see metadata record Aliens_in_Antarctica_seed_identifications).\n\nItems that were sampled separately were:\n\nJ Outer Jacket\nT Outer trousers\nI Insulating layer\nH Headwear\nG Gloves/mittens\nF Outdoor footwear\nB Various bags\nS Camera tripods/walking sticks\n\n\nIn addition data on visitor characteristics are given, derived from the VCS questionnaire data (see metadata record Aliens_in_Antarctica_survey_data).\n\nPersonnel\n\nThe samples were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season.\nVolunteers were shown an instruction video on how to collect the samples.\n\nResponsible for the organisation of the data collecting were:\nDr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia.\nDr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan\n\nThe samples were sorted by\nDr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. K. Kiefer, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia.\nDr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa M. Tsujimoto, National Institute of Polar Research, Tokyo, Japan\n\nDr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organization of the data in digital form.", "links": [ { diff --git a/datasets/Amery_Ht_1968_1.json b/datasets/Amery_Ht_1968_1.json index 9ac9f0493b..d784bb1dd5 100644 --- a/datasets/Amery_Ht_1968_1.json +++ b/datasets/Amery_Ht_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Amery_Ht_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice shelf surface elevation data from an oversnow ground-based traverse along the centre of the Amery Ice Shelf from A509 (69.06 S, 72.15 E) to T4 (71.22 S, 69.48 E), including two transverse arms; between G1 (69.49 S, 71.72 E) and A119 (69.81 S, 73.28 E); and between T3 (70.79 S, 68.89 E) and T2 (71.00 S, 70.75 E) during the 1968 spring-summer season. More information can be found at the BEDMAP website.\n\nThe fields in this dataset are:\n\nMission ID\nLatitude\nLongitude\nIce Thickness\nSurface Elevation\nWater Column Thickness\nBed Elevation", "links": [ { diff --git a/datasets/Amery_Ht_88-89_1.json b/datasets/Amery_Ht_88-89_1.json index 86d5e4f369..60b7591da2 100644 --- a/datasets/Amery_Ht_88-89_1.json +++ b/datasets/Amery_Ht_88-89_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Amery_Ht_88-89_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Lambert Glacier - Amery Ice Shelf series of airborne (Squirrel helicopter and Twin Otter fixed wing) RES and surface elevation profiles were conducted over two summer seasons; 1988/89 and 1989/90. Altogether nearly 10,000 km of various flight paths were undertaken, operating out of Mawson (67.60 S, 62.88 E), Davis (68.58 S, 77.97 E), Dovers (70.22 S, 65.87 E) or Beaver Lake (70.80 S, 68.18 E).\n\nMore information can be found at the BEDMAP website.\n\nThe fields in this dataset are:\n\nmission_id (unique mission identifier) \nlatitude (decimal degrees) \nlongitude (decimal degrees) \nice_thickness (m) \nsurface_elevation (m) \nwater_column_thickness (m) \nbed_elevation (m) ", "links": [ { diff --git a/datasets/Annual_30m_AGB_1808_1.json b/datasets/Annual_30m_AGB_1808_1.json index d0033086e5..cb1701823e 100644 --- a/datasets/Annual_30m_AGB_1808_1.json +++ b/datasets/Annual_30m_AGB_1808_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Annual_30m_AGB_1808_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.", "links": [ { diff --git a/datasets/Annual_Burned_Area_Maps_1708_1.json b/datasets/Annual_Burned_Area_Maps_1708_1.json index 75bac160f6..d4afeb39ff 100644 --- a/datasets/Annual_Burned_Area_Maps_1708_1.json +++ b/datasets/Annual_Burned_Area_Maps_1708_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Annual_Burned_Area_Maps_1708_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI. The MODIS active fire product (MCD14) was used to define when fires occurred. Random Forest classifications were used to separate burned and unburned 30-m pixels with inputs of composites of Landsat indices and thermal bands, based on the pre- and post-fire values.", "links": [ { diff --git a/datasets/Annual_Landcover_ABoVE_1691_1.json b/datasets/Annual_Landcover_ABoVE_1691_1.json index f9f1382b67..9ee28aca68 100644 --- a/datasets/Annual_Landcover_ABoVE_1691_1.json +++ b/datasets/Annual_Landcover_ABoVE_1691_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Annual_Landcover_ABoVE_1691_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas.", "links": [ { diff --git a/datasets/Annual_Seasonality_Greenness_1698_1.json b/datasets/Annual_Seasonality_Greenness_1698_1.json index 2e731c79aa..8eb6dc7b5e 100644 --- a/datasets/Annual_Seasonality_Greenness_1698_1.json +++ b/datasets/Annual_Seasonality_Greenness_1698_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Annual_Seasonality_Greenness_1698_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite.", "links": [ { diff --git a/datasets/Annual_Thaw_Slump_1724_1.json b/datasets/Annual_Thaw_Slump_1724_1.json index 0f9549acb4..71476fe991 100644 --- a/datasets/Annual_Thaw_Slump_1724_1.json +++ b/datasets/Annual_Thaw_Slump_1724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Annual_Thaw_Slump_1724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red.", "links": [ { diff --git a/datasets/Antarctic_Meteorology_1.json b/datasets/Antarctic_Meteorology_1.json index 41071e145a..4ce8e9ba92 100644 --- a/datasets/Antarctic_Meteorology_1.json +++ b/datasets/Antarctic_Meteorology_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Antarctic_Meteorology_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record provides a listing of meteorological data collected in the Australian Antarctic Territory by members of the Australian Antarctic program (and it's predecessors) and the Bureau of Meteorology. The data have been obtained by manual observations and by automatic weather stations.\n\nAll data are available from the Bureau of Meteorology, and are considered to be the authoritative source of weather data in the Australian Antarctic Territory (as they have been quality checked). Raw data directly from the automatic weather stations at the stations is available at https://data.aad.gov.au/aws.\n\nThe data available here includes:\n\n- Automatic Weather Station data from 7 sites - Casey, Davis, Macquarie Island, Mawson, Wilkins, Davis Whoop Whoop, and Casey Skiway South. Data resolution varies, but is approximately every 30 minutes.\n\n- Daily weather data from 48 sites. Note - not all of these sites are still operational.\n\n- Synoptic weather data from 53 sites. Note - not all of these sites are still operational.\n\n- Terrestrial soil data from 4 sites. Note - not all of these sites are still operational.\n\n- Upper air data from 5 sites. Note - not all of these sites are still operational.\n\n- High resolution, 1 minute automatic weather station data from 7 sites - Casey, Davis, Macquarie Island, Mawson, Wilkins, Davis Whoop Whoop, and Casey Skiway South.\n\n- Daily and Synoptic data from a number of decommissioned sites.\n\n\n\n\nSite details of 24 sites. For full site listings, seeing the file for station details within each dataset (\"HM01X_StnDet\").\n\nMeteorology data from Wilkes Station, Antarctica 1960 - 1968 - data collected include: temperature (maximum and minimum; dry bulb; wet bulb; dew point), air pressure, wind (direction,speed and maximum gust; run (greater than 3 m)), phenomena, sunshine, cloud.\n\nMeteorology data from Casey Station (current) (300017), Antarctica 1989 ongoing, surface measurements - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: temperature (maximum and minimum; dry bulb), air pressure, wind (direction;speed), humidity, rainfall, sunshine, cloud, visibility. An AWS is now in operation at Casey station.\n\nMeteorology data from Davis Station (300000), Antarctica 1957 ongoing, surface measurements - location 68.5772 S, 77.9725 E, with a station height of 16.0m and a barometric height of 22.3m. - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: temperature (maximum and minimum; dry bulb; terrestrial minimum, soil temperature), air pressure, wind (direction, speed; run), rainfall, sunshine, cloud, humidity, visibility. An AWS is now in operation at Davis station.\n\nMeteorology data from Mawson Station (300001), Antarctica 1954 ongoing, surface measurements - location 67.6014 S, 62.8731 E, with a station height of 9.9m and a barometric height of 16.0m. Data collected include the following: temperature (maximum and minimum; dry bulb), air pressure, wind (direction,speed), humidity, cloud, rainfall, sunshine. An AWS is now in operation at Mawson station.\n\nMeteorology data from Macquarie Island Station (300004), 1948 ongoing, surface measurements - location 54.4997 S, 158.9522 E, with a station height of 6.0m, a barometric height of 8.3m and an aerodrome height of 6.0m. Data collected include the following: temperature (maximum and minimum; dry bulb; wet bulb; terrestrial minimum; soil 10cm,20cm,50cm,100cm), air pressure, wind (direction; speed; run), rainfall, sunshine, cloud, visibility, humidity, sea state, radiation. An AWS is now in operation at Macquarie Island station.\n\nMeteorology data from Heard Island (Atlas Cove) Station (300005), first installed 1948 - location 53.02 S, 73.39 E, with a station height of 3.0m, and a barometric height of 3.5m. Data collected include the following: temperature, air pressure, rainfall.\n\nMeteorology data from Heard Island (The Spit) Station (300028), installed 1992 - location 53.1069 S, 73.7211 E, with a station height of 12.0m and a barometric height of 12.5m. Data collected include the following: temperature (air and minimum terrestrial), air pressure, humidity, wind direction, sunshine, cloud.\n\nMeteorology data from Casey Station (current) (300017), Antarctica 1989 ongoing, upper atmosphere measurements - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: upper atmospheric temperature (via a radiosonde), upper atmospheric wind (using a wind find radar).\n\nMeteorology data from Davis Station (300000), Antarctica 1957 ongoing, upper atmosphere measurements - location 68.5772 S, 77.9725 E, with a station height of 16.0m and a barometric height of 22.3m. Data collected include the following: upper atmospheric temperature (using radiosonde), upper atmosphere wind (using wind find radar).\n\nMeteorology data from Mawson Station (300001), Antarctica 1954 ongoing, upper atmosphere measurements - location 67.6014 S, 62.8731 E, with a station height of 9.9m and a barometric height of 16.0m. Data collected include the following: upper atmosphere temperature and wind (using sounding processor and GPS).\n\nMeteorology data from Macquarie Island Station (300004), 1948 ongoing, upper atmosphere measurements - location 54.4997 S, 158.9522 E, with a station height of 6.0m, a barometric height of 8.3m and an aerodrome height of 6.0m. Data collected include the following: upper atmosphere temperature and wind (collected using wind find radar and radiosondes).\n\nMeteorology data from Knuckey Peaks Station (300009), 1975 - 1984 - location 67.8 S, 53.5 E.\n\nMeteorology data from Heard Island (Atlas Cove) Station (300005), first installed 1948, upper atmosphere measurements - location 53.02 S, 73.39 E, with a station height of 3.0m, and a barometric height of 3.5m. Data recorded include: upper atmosphere temperature, upper atmosphere wind.\n\nMeteorology data from Mount King Satellite of Mawson Station (300010), Antarctica, 1975 - 1984 - location 67.1 S, 52.5 E, with a station height of 112.5m. Data recorded include: temperature (dry bulb), air pressure, humidity, visibility, and some upper atmosphere measurements.\n\nMeteorology data from Lanyon Junction Station (300011), Antarctica 1983 to 1987 - location 66.3 S, 110.8667 E, with a station height of 470.0m. Observational records include: humidity charts, thermograph charts, pilot balloon flights, and surface observations.\n\nMeteorology data from Haupt Nunatak (Casey) Automatic Weather Station (site 300012), installed 1994 - located at 66.5819 S, 110.6939 E near Casey station, with a station height of 81.4m and a barometer height of 83.4m. Data recorded include: barometric pressure, wind direction, speed and gust, and air temperature.\n\nMeteorology data from Depot Peak site (300013), Antarctica, installed 1990 - location 69.05 S, 164.6 E, and has a station height of 1600 m. Instruments at the site include: barometer, cup anemometer and humicap (temperature and humidity).\n\nMeteorology data from Edgeworth David (Bunger Hills) Station (300014), Antarctica, 1986 to 1989 - location 66.25 S, 100.6036 E, with a station height of 6.0m and a barometric height of 7.0m.\n\nMeteorology data from Law Base Station (300015),Antarctica, 1989 - 1992 - location 69.4167 S, 76.5 E, with a station height of 77.0m.\n\nMeteorology data from Dovers Station (300016), Antarctica, 1988 to 1992 - located at 70.2333 S, 65.85 E, with a station height of 1058.0m and a barometric height of 1059.0m. Data recorded include: Air pressure, air temperature, humidity, wind speed and direction, cloud, visibility and upper atmosphere data.\n\nMeteorology data from Balaena Island Automatic Weather Station (300032), installed 1994 - location 66.017 S, 111.0833 E, 22.21 Nm NE of Casey, with a station height of 8.0m and a barometric height of 10m. Data collected from this AWS include: Wind speed and direction, wind gust, air temperature and barometric pressure.\n\nMeteorology data from Snyder Rocks Automatic Weather Station (300033), Antarctica, installed 1994 - located at 66.55 S, 107.75 E, with a station height of 40m and a barometric height of 42m. Data collected include: air temperature, barometric pressure, wind speed, direction and gust.\n\nMeteorology data from Law Dome Summit South Automatic Weather Station (300034), Antarctica, installed 1995 - location 66.717 S, 112.9333 E, with a station height of 1375.0 m. Data collected include: air pressure, air temperature, wind speed and direction.\n\nMeteorology data from Casey(old) Station, Antarctica 1969 - 1989. Data collected include: temperature (maximum and minimum; dry bulb; wet bulb; dew point), air pressure, wind (direction,speed and maximum gust; run (greater than 3 m)), phenomena, sunshine, cloud, radiation (global,diffuse).", "links": [ { diff --git a/datasets/Antarctic_subantarctic_insects_checklist_1954_1.json b/datasets/Antarctic_subantarctic_insects_checklist_1954_1.json index d1abe52ff2..019dbaffe4 100644 --- a/datasets/Antarctic_subantarctic_insects_checklist_1954_1.json +++ b/datasets/Antarctic_subantarctic_insects_checklist_1954_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Antarctic_subantarctic_insects_checklist_1954_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copied of a scanned document containing a check list from 1954 of known insect species from the Antarctic and sub-antarctic.\n\nTaken from the report:\n\nThis check list contains all known records of insect species from the Antarctic and Subantarctic with the exception of the subantarctic islands of New Zealand (a list of the major references to their insect fauna appears at the end of this volume).\n\nThe Antarctic region is most usefully defined as the area lying south of the Antarctic Convergence, the line along which the cold northward-moving antarctic surface water sinks beneath the warmer subantarctic water. Judged from this viewpoint, South Georgie, the South Orkney Islands, the South Shetland Islands, the South Sandwich Islands, Bouvet oya and Heard Island, all fall within the Antarctic region. The Falkland Islands, Iles de Kerguelen, Iles Crozet, the Prince Edward Islands and Macquarie Island lie between the Antarctic and Subtropical Convergences and are therefore subantarctic.\n\nScientific exploration in these regions has proceeded unevenly and spasmodically. Some islands (Heard Island and Macquarie Island) where parties have been stationed for long periods have been thoroughly searched for insects, others (Iles Crozet, South Orkney Islands, South Shetland Islands) where parties have been landed during the brief visits of expedition ships have been partially searched, whilst others (Bouvet oya) offer an untouched field.\n\nInsects from Ile Amsterdam and Ile Saint Paul, and several species of Siphonaptera recorded from birds outside the geographical limits cited, have been included for a knowledge of them is essential in considering possible new subantarctic species.\n\nThe Mallophaga as a group have been excluded from this list since their speciation and geographical distribution depends solely on that of their hosts and their inclusion would enlarge this list without adding greatly to its value.\n\nOrders, families and sub families are arranged according to current practice but genera and species are set out in alphabetical order within the various major groupings. Where a number of different Family names are in use one name may have been selected alternative names have been included in brackets. (Each case has been determined after considering the particular circumstances involved -- the terminology used in the most useful references to it, recent literature on its classification, etc). Where a cosmopolitan species has been recorded from Antarctic regions no attempt has been made to list all references to it and only the original description and the most important Antarctic records have been cited.\n\nAs it is likely that some omissions have occurred in this check list the author would appreciate being notified of any which are detected. The author would also like to record his thanks to D.J. Lee, School of Public Health and Tropical Medicine, University of Sydney, for his help and advice in the preparation of this report.\n\nIn a later paper it is hoped to discuss more fully the affinities, geographical and taxonomic, of the unique insect fauna of the Antarctic and subantarctic regions. The following analysis reveals the marked development of indigenous species in these unfavourable environments and shows the limited invasion from nearby continental areas. Of interest are the few cosmopolitan species that have succeeded in establishing themselves in the area.\n\nThis paper lists 233 species and 9 varieties included in 143 different genera. In addition 7 insects are listed which have not been described, but merely recorded as either ? genus ? species or genus ? sp.\n\nOf the 233 species recorded, 26 have been introduced into the region since they have either a cosmopolitan or very extensive distribution. Of the insects recorded from the South American islands 23 are found on various parts of the South American Continent and the majority show very strong affinities with the fauna of Patagonia and South Chile.\n\nThe insects of Iles de Kerguelen are for the most part indigenous and are in structure and habits archaic. Ile St. Paul has strong African affinities and Macquarie Island has a fauna similar to the subantarctic islands of New Zealand though more strongly modified.", "links": [ { diff --git a/datasets/Apero_0.json b/datasets/Apero_0.json index 9c66c8aac6..0aaaf9bd68 100644 --- a/datasets/Apero_0.json +++ b/datasets/Apero_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Apero_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The APERO project proposes a mechanistic approach to the biological carbon pump (export of surface biogenic carbon production and fate in the water column -200/2000m). APERO aims to reduce the gap between the amount of photosynthetically produced organic carbon transferred to the deep ocean and the metabolic demand for carbon in the water column. The project is built around a campaign with two oceanographic vessels in the Northeast Atlantic, at the level of the British permanent station PAP (58N, 16W). It lasted 40 days and took place in June and July of 2023, at the time of maximum particulate carbon export to the deep ocean. The three major contributions of APERO are the study of the role of small-scale dynamics (~1-10km) on the water column using autonomous platforms, imagery and innovative instrumentation, the construction of a comprehensive database based on the simultaneous multidisciplinary observation of all processes regulating the attenuation of carbon flux in the water column and the quantification of the fluxes associated with these processes. Relying on a significant international collaboration with the JETZON consortium (https://www.jetzon.org/) and an ambitious observation strategy, complemented by molecular biology and innovative modeling approaches, this study will contribute to a significant reduction of uncertainties on carbon storage by the ocean.", "links": [ { diff --git a/datasets/Aqua_AIRS_MODIS1km_IND_1.json b/datasets/Aqua_AIRS_MODIS1km_IND_1.json index 786dd33a35..8dbe2fd44e 100644 --- a/datasets/Aqua_AIRS_MODIS1km_IND_1.json +++ b/datasets/Aqua_AIRS_MODIS1km_IND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AIRS_MODIS1km_IND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " This dataset includes Aqua AIRS to MODIS 1-km collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).\n\nThe short name for this collections is Aqua_AIRS_MODIS1km_IND\n\n", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L1B_AMSR2_Format_TB_NA.json b/datasets/Aqua_AMSR-E_L1B_AMSR2_Format_TB_NA.json index db5d0a5f03..73a9c01c03 100644 --- a/datasets/Aqua_AMSR-E_L1B_AMSR2_Format_TB_NA.json +++ b/datasets/Aqua_AMSR-E_L1B_AMSR2_Format_TB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L1B_AMSR2_Format_TB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L1B AMSR2 Format Brightness Temperature dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. This product includes Brightness Temperature. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Brightness temperature is fundamental data to retrieve geophysical parameters. The physical quantity unit is Kelvin. For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format. L1B and L1R products also include information of data geolocation and quality. The provided format is HDF5. The current version of the product is \"Version 4\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L1B_TB_NA.json b/datasets/Aqua_AMSR-E_L1B_TB_NA.json index aa97c77c71..7c6024c8e4 100644 --- a/datasets/Aqua_AMSR-E_L1B_TB_NA.json +++ b/datasets/Aqua_AMSR-E_L1B_TB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L1B_TB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L1B Brightness Temperature dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. This product includes Brightness Temperature. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Brightness Temperature is fundamental data to retrieve geophysical parameters. Data is converted by the radiometric correction coefficients from observed sensor data of level 1A. It also contains the ancillary data stored in level 1A product. The physical quantity unit is Kelvin. For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format. The provided format is HDF4. The current version of the product is \"Version 3\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L1R_AMSR2_Format_TB_NA.json b/datasets/Aqua_AMSR-E_L1R_AMSR2_Format_TB_NA.json index 68afe6c4e0..72eaede6b9 100644 --- a/datasets/Aqua_AMSR-E_L1R_AMSR2_Format_TB_NA.json +++ b/datasets/Aqua_AMSR-E_L1R_AMSR2_Format_TB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L1R_AMSR2_Format_TB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L1R AMSR2 Format Brightness Temperature dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 1R AMSR2 Format Brightness Temperature data is converted from Level 1B data. Data are resampled with equal footprint size for each frequency. The physical quantity unit is Kelvin. For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format. L1B and L1R products also include information of data geolocation and quality. The provided format is HDF5. The current version of the product is \"Version 4\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_CLW_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_CLW_NA.json index f4703ebfa3..ce6d545ae0 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_CLW_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_CLW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_CLW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Cloud Liquid Water dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5 frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_PRC_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_PRC_NA.json index d751251f53..435c36fa13 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_PRC_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_PRC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_PRC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Precipitation dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Precipitation (PRC). Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/hr.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SIC_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SIC_NA.json index dfe100f855..a6ce42e248 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SIC_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SIC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_SIC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Sea Ice Concentration dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.This product includes Sea Ice Concentration (SIC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures.The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SMC_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SMC_NA.json index 159ebea089..f59920eabf 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SMC_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SMC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_SMC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Soil Moisture Content dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture Content (SMC). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SND_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SND_NA.json index 885efa9be7..ef29df6042 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SND_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SND_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_SND_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Snow Depth dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Snow Depth (SND). Compared with non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25 GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwelling radiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SST_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SST_NA.json index 9b8b9a4339..fd900bf15b 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SST_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Sea Surface Temperature dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SSW_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SSW_NA.json index ccb9eaea36..0d1a70fc40 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SSW_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_SSW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_SSW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Sea Surface Wind dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_TPW_NA.json b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_TPW_NA.json index 2ca338ac30..3282345e6d 100644 --- a/datasets/Aqua_AMSR-E_L2_AMSR2_Format_TPW_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AMSR2_Format_TPW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AMSR2_Format_TPW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 AMSR2 Format Total Precipitable Water dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Total Precipitable Water (TPW). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. If PWI is out of range of look-up table, the flag 'low accuracy' is added. The physical quantity unit is kg/m^2.Also, the quality flag for each observation point (Pixel Data Quality) is stored.The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 8\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_AP_NA.json b/datasets/Aqua_AMSR-E_L2_AP_NA.json index 46bb88b9a3..9f0d210c42 100644 --- a/datasets/Aqua_AMSR-E_L2_AP_NA.json +++ b/datasets/Aqua_AMSR-E_L2_AP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_AP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Amount of Precipitation dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Amount of Precipitation (AP). Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/hr. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 4\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_CLW_NA.json b/datasets/Aqua_AMSR-E_L2_CLW_NA.json index a50f19a522..fc85b0eb45 100644 --- a/datasets/Aqua_AMSR-E_L2_CLW_NA.json +++ b/datasets/Aqua_AMSR-E_L2_CLW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_CLW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Cloud Liquid Water dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 4\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_IC_NA.json b/datasets/Aqua_AMSR-E_L2_IC_NA.json index 85f50e0a35..2e95fa2740 100644 --- a/datasets/Aqua_AMSR-E_L2_IC_NA.json +++ b/datasets/Aqua_AMSR-E_L2_IC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_IC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Ice Concentration dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Ice Concentration (IC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene(defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_SM_NA.json b/datasets/Aqua_AMSR-E_L2_SM_NA.json index 77a9f25317..f738f066f7 100644 --- a/datasets/Aqua_AMSR-E_L2_SM_NA.json +++ b/datasets/Aqua_AMSR-E_L2_SM_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_SM_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Soil Moisture dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture (MC). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_SND_NA.json b/datasets/Aqua_AMSR-E_L2_SND_NA.json index 31a5bcacee..90d6a71ecd 100644 --- a/datasets/Aqua_AMSR-E_L2_SND_NA.json +++ b/datasets/Aqua_AMSR-E_L2_SND_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_SND_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Snow Water Equivalent dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Snow Water Equivalent (SND). Compared with non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25 GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwelling radiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_SST_NA.json b/datasets/Aqua_AMSR-E_L2_SST_NA.json index 7711daed4f..d524009feb 100644 --- a/datasets/Aqua_AMSR-E_L2_SST_NA.json +++ b/datasets/Aqua_AMSR-E_L2_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Sea Surface Temperature dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_SSW_NA.json b/datasets/Aqua_AMSR-E_L2_SSW_NA.json index 324bf59ae5..167acc914c 100644 --- a/datasets/Aqua_AMSR-E_L2_SSW_NA.json +++ b/datasets/Aqua_AMSR-E_L2_SSW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_SSW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Sea Surface Wind dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF5. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L2_WV_NA.json b/datasets/Aqua_AMSR-E_L2_WV_NA.json index 17fa35e862..253fd7dc05 100644 --- a/datasets/Aqua_AMSR-E_L2_WV_NA.json +++ b/datasets/Aqua_AMSR-E_L2_WV_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L2_WV_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L2 Water Vapor dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. If PWI is out of range of look-up table, the flag 'low accuracy' is added. The physical quantity unit is kg/m^2. Also, the quality flag for each observation point (Pixel Data Quality) is stored. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is \"Version 7\". The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.1deg_NA.json index 7622402418..bbeebb2132 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Cloud Liquid Water (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic(PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Integrated cloud liquid water (CLW) overwritten by latest data. CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.25deg_NA.json index 3e384f2745..fee34c503e 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_CLW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Cloud Liquid Water (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Integrated cloud liquid water (CLW) overwritten by latest data. CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.1deg_NA.json index ae8584faaa..9e0190e40f 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Cloud Liquid Water (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic(PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.25deg_NA.json index 5190db2b80..bde9d94fdd 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_CLW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Cloud Liquid Water (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.1deg_NA.json index 84199861b1..a7a1b8a3ea 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Precipitation (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Precipitation (PRC) overwritten by latest data. PRC is retrieved from combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.25deg_NA.json index b00ada887c..ece18941a8 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_PRC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Precipitation (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Precipitation (PRC) overwritten by latest data PRC is retrieved from combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.1deg_NA.json index 5fbfda990f..f8d50e2787 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Precipitation (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Precipitation(PRC). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.25deg_NA.json index dbe08232fd..ebe6004515 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_PRC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Precipitation (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Precipitation (PRC). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.1deg_NA.json index 0b2ad36162..65dcf4cfd3 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Ice Concentration (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Sea Ice Concentration (SIC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.25deg_NA.json index f7e7c299d9..6ee6929907 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SIC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Ice Concentration (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Sea Ice Concentration (SIC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\" The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.1deg_NA.json index 624d0b9a80..4a356931d3 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Ice Concentration (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Ice Concentration (SIC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.25deg_NA.json index 0782ed0e96..a419ae9490 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SIC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Ice Concentration (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Sea Ice Concentration (SIC) and Static information. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.1deg_NA.json index 0f12c605ea..c3e14994f2 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Soil Moisture Content (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes averaged Soil Moisture Content (SMC). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.25deg_NA.json index 9f47fb0cb2..fa576495a0 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SMC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Soil Moisture Content (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes averaged Soil Moisture Content (SMC). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.1deg_NA.json index 79425b4798..a078cd4ae0 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Soil Moisture Content (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Soil Moisture Content (SMC). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.25deg_NA.json index b99d6e8ba2..5b9b846c5b 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SMC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Soil Moisture Content (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Soil Moisture Content (SMC). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.1deg_NA.json index 3840433ed1..82713dd992 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Snow Depth (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Snow Depth (SND). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.25deg_NA.json index c8f394f66c..a44804d521 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SND_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Snow Depth (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Snow Depth (SND). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.1deg_NA.json index f0778a03d2..9ecf99ae27 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Snow Depth (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Snow Depth (SND) and Static information. The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.25deg_NA.json index 1652ef0ef9..d835a953db 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SND_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Snow Depth (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Snow Depth (SND) and Static information. The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.1deg_NA.json index 69f38698d9..0e46d8ca77 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Sea Surface Temperature (SST) overwritten by latest data. The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.25deg_NA.json index 136aa348fd..a436beb8f5 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SST_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Sea Surface Temperature (SST) overwritten by latest data. The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.1deg_NA.json index ebfdd4cff6..158a912af1 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Temperature (SST) and Static information. The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.25deg_NA.json index 33ca8f5e74..032feee376 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SST_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Temperature (SST) and Static information. The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.1deg_NA.json index 3fc8d8bf6e..9723b02933 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Wind (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes Sea Surface Wind (SSW) overwritten by latest data. SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.25deg_NA.json index 30fcac1e50..9badc3285d 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SSW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Wind (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Sea Surface Wind (SSW) overwritten by latest data. SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.1deg_NA.json index 46351175d2..562e19a3f2 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Wind (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Wind (SSW) and Static information. SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.25deg_NA.json index c95cd0c244..fe80e6367f 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_SSW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Sea Surface Wind (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Wind (SSW) and Static information. SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.1deg_NA.json index a6432a0f2e..6e2d41b963 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 10.65GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 10.65GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.25deg_NA.json index c88440d5a9..d849e3e17d 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 10.65GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 10.65GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.1deg_NA.json index c370131fd1..552395b6d0 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 10.65GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 10.65GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.25deg_NA.json index 5b25287009..4cd486dd52 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_10.65GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 10.65GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 10.65GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.1deg_NA.json index 0ce36ac18b..a33908aae0 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 18.7GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 18.7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.25deg_NA.json index 7972cffd3a..7589050800 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 18.7GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 18.7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.1deg_NA.json index 1b55fbbb54..76a5f8f02d 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 18.7GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 18.7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.25deg_NA.json index 48236f5654..d2694c6cdc 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_18.7GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 18.7GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 18.7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.1deg_NA.json index 533b996033..3e6a7e21e1 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 23.8GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 23.8GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.25deg_NA.json index 33f83f3d30..2004c39db3 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 23.8GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 23.8GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.1deg_NA.json index 15bd1f3f4e..12bcc891da 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 23.8GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 23.8GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.25deg_NA.json index 38ca538eae..08bae85bc7 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_23.8GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 23.8GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 23.8 GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.1deg_NA.json index f3b13e5b3d..c26289ed9e 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 36.5GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 36.5GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.25deg_NA.json index f786edaa1e..eb6f054480 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 36.5GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 36.5GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.1deg_NA.json index 03eeead2d1..422f75fd27 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 36.5GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 36.5GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.25deg_NA.json index 0288442d59..4fcd54107a 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_36.5GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 36.5GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 36.5GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.1deg_NA.json index 6d39bfc979..2101a8528b 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 6GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.25deg_NA.json index c1628f3b02..31919894fc 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 6GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.1deg_NA.json index 22325c6fff..8c36d43718 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 6GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.25deg_NA.json index 22c5ff379d..1f94adeae0 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_6GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 6GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.1deg_NA.json index f992f127a2..2d9ff6474c 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 89.0GHz Mean for Brightness Temperature (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 89.0GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.25deg_NA.json index 27b86a8298..df0250f418 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 89.0GHz Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 89.0GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.1deg_NA.json index 7e51ea866d..927ea6207d 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 89.0GHz Mean for Brightness Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 89.0GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.25deg_NA.json index 5037d502fe..a42a9d1d4e 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TB_89.0GHz_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format 89.0GHz Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 89.0GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 4\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.1deg_NA.json index 91b5e66392..1db1c80ad8 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Total Precipitable Water (1-Day, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes Total Precipitable Water (TPW) overwritten by latest data. PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.25deg_NA.json index d3d26c1400..2f4bad0f6b 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TPW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Total Precipitable Water (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Total Precipitable Water (TPW) overwritten by latest data. PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 day. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.1deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.1deg_NA.json index 6d4e9c4d91..e07478513d 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.1deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Total Precipitable Water (1-Month, 0.1 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Total Precipitable Water (TPW). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.1 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.25deg_NA.json index a1b056d541..330a3d63f4 100644 --- a/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AMSR2_Format_TPW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 AMSR2 Format Total Precipitable Water (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Total Precipitable Water (TPW). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The following Static information is included: Standard_Deviation: standard deviation value for each pixel. This item is only stored in monthly product. Average_Number: the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number: the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The spatial resolution is 0.25 deg. The statistical period is 1 month. The current version of the product is \"Version 8\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AP_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AP_1day_0.25deg_NA.json index 22258a02be..1a8eaace79 100644 --- a/datasets/Aqua_AMSR-E_L3_AP_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AP_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AP_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Amount of Precipitation (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 4\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_AP_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_AP_1month_0.25deg_NA.json index 50b03b48ff..bec866deef 100644 --- a/datasets/Aqua_AMSR-E_L3_AP_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_AP_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_AP_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Amount of Precipitation (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 4\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_CLW_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_CLW_1day_0.25deg_NA.json index 511b249232..f7dd4e7397 100644 --- a/datasets/Aqua_AMSR-E_L3_CLW_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_CLW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_CLW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Cloud Liquid Water (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 4\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_CLW_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_CLW_1month_0.25deg_NA.json index 2c1e168bd8..7d9845a8d9 100644 --- a/datasets/Aqua_AMSR-E_L3_CLW_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_CLW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_CLW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Cloud Liquid Water (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthyly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 4\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_IC_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_IC_1day_0.25deg_NA.json index c58294f41f..577acfaf3c 100644 --- a/datasets/Aqua_AMSR-E_L3_IC_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_IC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_IC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Ice Concentration (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_IC_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_IC_1month_0.25deg_NA.json index c18e013330..57528d505d 100644 --- a/datasets/Aqua_AMSR-E_L3_IC_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_IC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_IC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Ice Concentration (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SM_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SM_1day_0.25deg_NA.json index c9cecc1ead..fbf43ed91e 100644 --- a/datasets/Aqua_AMSR-E_L3_SM_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SM_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SM_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Soil Moisture (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SM_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SM_1month_0.25deg_NA.json index d9cb5c23bc..40841fed66 100644 --- a/datasets/Aqua_AMSR-E_L3_SM_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SM_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SM_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Soil Moisture (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SND_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SND_1day_0.25deg_NA.json index d0f37bee63..bb62f78585 100644 --- a/datasets/Aqua_AMSR-E_L3_SND_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SND_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SND_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Snow Water Equivalent (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA).Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes.AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25deg. The current version of the product is \"Version 7\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SND_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SND_1month_0.25deg_NA.json index f46b373282..72925f4c5d 100644 --- a/datasets/Aqua_AMSR-E_L3_SND_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SND_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SND_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Snow Water Equivalent (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SST_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SST_1day_0.25deg_NA.json index 30799e67f4..a3032ee0ee 100644 --- a/datasets/Aqua_AMSR-E_L3_SST_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SST_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SST_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Sea Surface Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SST_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SST_1month_0.25deg_NA.json index 11c71d3176..fd05a7ebce 100644 --- a/datasets/Aqua_AMSR-E_L3_SST_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SST_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SST_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Sea Surface Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SSW_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SSW_1day_0.25deg_NA.json index d64f824e43..51c8927b5b 100644 --- a/datasets/Aqua_AMSR-E_L3_SSW_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SSW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SSW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Sea Surface Wind (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_SSW_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_SSW_1month_0.25deg_NA.json index 6f0cc81455..32d7e5b3be 100644 --- a/datasets/Aqua_AMSR-E_L3_SSW_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_SSW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_SSW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Sea Surface Wind (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1day_0.25deg_NA.json index 9a626ebab1..a6b4c71bed 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_10.65GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 10.65GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1month_0.25deg_NA.json index 26fc3e2c86..58e6889ecc 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_10.65GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 10.65GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1day_0.25deg_NA.json index 46c29b79a2..a1f618789e 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_10.65GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 10.65GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1month_0.25deg_NA.json index 94255378bb..fb618ab564 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_10.65GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_10.65GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 10.65GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1day_0.25deg_NA.json index 89d1d13b6c..7804d11fbc 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_18.7GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 18.7GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1month_0.25deg_NA.json index f153d15344..db37877be3 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_18.7GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 18.7GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1day_0.25deg_NA.json index 6b8a67caba..3598d37947 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_18.7GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 18.7GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1month_0.25deg_NA.json index 297dc0ee2c..367d36ea2f 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_18.7GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_18.7GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 18.7GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1day_0.25deg_NA.json index 26370cf0e6..086d4940b7 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_23.8GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 23.8GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1month_0.25deg_NA.json index 4a11e79f44..adb61ac991 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_23.8GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 23.8GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1day_0.25deg_NA.json index 1f76c5fad6..241fa55f42 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_23.8GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 23.8GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1month_0.25deg_NA.json index ecf94cbcc7..577bbbb530 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_23.8GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_23.8GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 23.8GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1day_0.25deg_NA.json index ff2d5eebce..ec5e98254d 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_36.5GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 36.5GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1month_0.25deg_NA.json index d1f165017f..c46976ee24 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_36.5GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 36.5GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1day_0.25deg_NA.json index a98b587964..e5a142781a 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_36.5GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 36.5GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1month_0.25deg_NA.json index 2bdf1871ea..3ece1a7f3d 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_36.5GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_36.5GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 36.5GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1day_0.25deg_NA.json index 0480ff8918..6e11f8b3a2 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_6GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 6GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1month_0.25deg_NA.json index 4e1ab37f17..e6e17fd8b5 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_6GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_6GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 6GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1day_0.25deg_NA.json index 9c91cd07ce..7091a3dd4d 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_6GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 6GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes averaged Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1month_0.25deg_NA.json index 6037fb8033..efd34e2f19 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_6GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_6GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 6GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1day_0.25deg_NA.json index 822a67ff07..e3ece70537 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_89.0GHz-H_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 89.0GHz-H Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1month_0.25deg_NA.json index e04b1b2a59..9128bb357c 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-H_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_89.0GHz-H_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 89.0GHz-H Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1day_0.25deg_NA.json index 88c7d47bcf..e240390f35 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_89.0GHz-V_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 89.0GHz-V Mean for Brightness Temperature (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 89.0GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 day. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1month_0.25deg_NA.json index 05536ebace..51a20a8179 100644 --- a/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_TB_89.0GHz-V_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_TB_89.0GHz-V_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 89.0GHz-V Mean for Brightness Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Brightness Temperature at 89.0Hz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. The provided format is HDF4. The statistical period is 1 month. The current version of the product is \"Version 3\". The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_WV_1day_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_WV_1day_0.25deg_NA.json index fbe236036e..7525eb5df4 100644 --- a/datasets/Aqua_AMSR-E_L3_WV_1day_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_WV_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_WV_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Water Vapor (1-Day, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes daily mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The provided format is HDF4. The statistical period is 1 day. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/Aqua_AMSR-E_L3_WV_1month_0.25deg_NA.json b/datasets/Aqua_AMSR-E_L3_WV_1month_0.25deg_NA.json index 539f772d18..8a55d6f77e 100644 --- a/datasets/Aqua_AMSR-E_L3_WV_1month_0.25deg_NA.json +++ b/datasets/Aqua_AMSR-E_L3_WV_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Aqua_AMSR-E_L3_WV_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aqua/AMSR-E L3 Water Vapor (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes monthly mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is \"Version 7\". The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/ArabianSea_2011_0.json b/datasets/ArabianSea_2011_0.json index 7cad5f7ba3..42f832c4e4 100644 --- a/datasets/ArabianSea_2011_0.json +++ b/datasets/ArabianSea_2011_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ArabianSea_2011_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the monsoonal Arabian Sea in 2011.", "links": [ { diff --git a/datasets/Arc00_0.json b/datasets/Arc00_0.json index 6933a727aa..6c8c508998 100644 --- a/datasets/Arc00_0.json +++ b/datasets/Arc00_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arc00_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Beaufort and Chukchi seas in the Arctic north of Alaska during 2000.", "links": [ { diff --git a/datasets/ArcOD_2006B1.json b/datasets/ArcOD_2006B1.json index dc32375225..309613efb5 100644 --- a/datasets/ArcOD_2006B1.json +++ b/datasets/ArcOD_2006B1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ArcOD_2006B1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The species composition of Amphipoda (Crustacea: Malacostraca: Peracarida) of the Greenland shelf south of 65\u00b0N was investigated by means of 18 epibenthic samples over a sampling period of three years (2001, 2002, 2004). The samples were taken using a Rauschert sledge in depths between 106 and 251 m. In total, 62,205 specimens were identified belonging to 154 species. The amphipods from the South Greenland shelf represent in general a homogeneously distributed community with respect to evenness (J\u2019), diversity (H\u2019) and Hurlbert\u2019s rarefaction E (S500). Multivariate analyses of the species abundances divided the amphipods into a southeastern and southwestern fauna. Among the species most contributing to the separation between East and West, Hardametopa nasuta, Photis reinhardi and Phoxocephalus holboelli were identified. With respect to evenness and diversity, the amphipod community was stable over the three years. We used the WORMS database to present species in this metadata.", "links": [ { diff --git a/datasets/ArcticNET_0.json b/datasets/ArcticNET_0.json index 5234c2b19a..5a2b903839 100644 --- a/datasets/ArcticNET_0.json +++ b/datasets/ArcticNET_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ArcticNET_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in Hudson Bay on board the icebreaker C.C.G.S. Amundsen to gain knowledge on marine coastal ecosystems as part of the ArcticNet program in 2005 and 2010. ArcticNet is a Network of Centres of Excellence of Canada to study the impacts of climate change in the Canadian North.", "links": [ { diff --git a/datasets/ArcticTreeLine_Dendrometry_Env_2185_1.json b/datasets/ArcticTreeLine_Dendrometry_Env_2185_1.json index 425537c063..8d8982f7f2 100644 --- a/datasets/ArcticTreeLine_Dendrometry_Env_2185_1.json +++ b/datasets/ArcticTreeLine_Dendrometry_Env_2185_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ArcticTreeLine_Dendrometry_Env_2185_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/ArcticTreeLine_Spruce_CO2_WV_1948_1.json b/datasets/ArcticTreeLine_Spruce_CO2_WV_1948_1.json index b2851989ff..6983f09859 100644 --- a/datasets/ArcticTreeLine_Spruce_CO2_WV_1948_1.json +++ b/datasets/ArcticTreeLine_Spruce_CO2_WV_1948_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ArcticTreeLine_Spruce_CO2_WV_1948_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ measurements of needle-level gas-exchange and leaf traits from Picea glauca (white spruce) from a field site located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in northern Alaska, and from one study site located in Black Rock Forest, New York, USA. Measurements were collected with an open flow portable photosynthesis system (Li6400XT) and custom-built temperature-controlled cuvette. Respiration as a function of leaf temperature was measured continuously as the needle temperature was ramped from approximately 5 to 65 degrees C, at a constant rate of 1 degree C per minute. Additional data include tree diameter at breast height (dbh), leaf area, photosynthetic rate, intercellular C02, conductance to H20, tree height, and data from raw temperature curves. Results are reported on both a leaf area and leaf mass basis. The data are for the period 2018-06-06 to 2018-06-23 and are provided in comma-separated (CSV) format.", "links": [ { diff --git a/datasets/Arctic_Boreal_Burned_Area_V2_2328_2.json b/datasets/Arctic_Boreal_Burned_Area_V2_2328_2.json index 6b243cb089..15ef962f00 100644 --- a/datasets/Arctic_Boreal_Burned_Area_V2_2328_2.json +++ b/datasets/Arctic_Boreal_Burned_Area_V2_2328_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Boreal_Burned_Area_V2_2328_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual cumulative end-of-season burned area in circumpolar boreal forests and tundra for the years 2002-2022. The data were generated using the Arctic Boreal Burned Area (ABBA) version 2 algorithm with MODIS collection 6 products. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The annual MODIS Vegetation Continuous Fields (VCF) 250-m Collection 5.1 (MOD44B) product allowed for additional vegetation-dependent dNBR thresholds within the algorithm's processing steps. The spatial domain is circumpolar regions above 50 degrees north latitude. The data are provided in cloud-optimized GeoTIFF format with 463-m resolution.", "links": [ { diff --git a/datasets/Arctic_Boreal_CO2_Flux_1934_1.json b/datasets/Arctic_Boreal_CO2_Flux_1934_1.json index c36e95d253..99f7229aff 100644 --- a/datasets/Arctic_Boreal_CO2_Flux_1934_1.json +++ b/datasets/Arctic_Boreal_CO2_Flux_1934_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Boreal_CO2_Flux_1934_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Arctic-Boreal CO2 fluxes (ABCflux) dataset contains monthly aggregates of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity (GPP) and ecosystem respiration. Over 70 supporting variables describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. The data contained in this ABCflux dataset form a standardized monthly database of Arctic-Boreal CO2 fluxes (i.e., ABCflux Database) and include 244 sites and 6,309 monthly observations; 136 sites and 2,217 monthly observations represent tundra, and 108 sites and 4,092 observations represent the boreal biome. The data are for the period 1989 to 2020.", "links": [ { diff --git a/datasets/Arctic_Flux_629_1.json b/datasets/Arctic_Flux_629_1.json index 4ee4192115..cb614ec5da 100644 --- a/datasets/Arctic_Flux_629_1.json +++ b/datasets/Arctic_Flux_629_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Flux_629_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996.", "links": [ { diff --git a/datasets/Arctic_Network_Veg_plots_1542_1.json b/datasets/Arctic_Network_Veg_plots_1542_1.json index 4c87107881..a930ae1998 100644 --- a/datasets/Arctic_Network_Veg_plots_1542_1.json +++ b/datasets/Arctic_Network_Veg_plots_1542_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Network_Veg_plots_1542_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental, soil, and vegetation data collected at selected locations in the parks and preserves of the National Park Service (NPS) Arctic Network (ARCN) between 2002 and 2008. The ARCN includes five national parks and preserves in northern Alaska encompassing 19.5 million acres and represents some of the wildest, most undisturbed areas left on earth: The Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and the Noatak National Preserve. The sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions and were positioned on transects along toposequences within major physiographic units (riverine, lacustrine, lowland, upland, subalpine and alpine). Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources.", "links": [ { diff --git a/datasets/Arctic_RSWQ_0.json b/datasets/Arctic_RSWQ_0.json index 4781ce7f78..13b9b88d82 100644 --- a/datasets/Arctic_RSWQ_0.json +++ b/datasets/Arctic_RSWQ_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_RSWQ_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This research project seeks to jumpstart Arctic-COLORS while filling major gaps in our knowledge about the transformation of riverine water quality constituents from the ABoVE domain through estuaries to the near coastal environment. The project conducted field sampling in the Yukon River, delta and plume waters for three transects in spring, early and late summer, and acquisition of additional transect samples during similar flow regimes through our collaborators on the north slope of Alaska and Mackenzie River. Field measurements included a number of water quality parameters relevant to Arctic biogeochemical function and NASA products, including dissolved organic matter (DOM), particulate organic matter (POM), suspended particulate matter (SPM), chlorophyll-a, radiometry, in situ inherent optical properties, discrete dissolved and particle absorption, fluorescent DOM (FDOM), lignin phenols, HPLC pigments, bioavailability of dissolved organic carbon (DOC). Combined, our field sampling, algorithm development, hindcasting, and synthesis efforts will provide a foundation for a successful Arctic-COLORS campaign while providing critical new knowledge of transformations in estuarine systems. ", "links": [ { diff --git a/datasets/Arctic_Soil_Properties_2149_1.json b/datasets/Arctic_Soil_Properties_2149_1.json index d192c95002..5b09d0bc13 100644 --- a/datasets/Arctic_Soil_Properties_2149_1.json +++ b/datasets/Arctic_Soil_Properties_2149_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Soil_Properties_2149_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides lab-measured soil properties, including soil water matric potential, soil dielectric properties, soil electrical conductivity, corresponding soil moisture. The dataset also includes the basic soil physical properties such as soil organic matter, bulk density, porosity, fiber content, root biomass, and mineral texture. Soil samples were collected from August 21 to August 27, 2018, from the surface to permafrost table in soil pits at nine sites along the Dalton Highway in northern and central regions of Alaska. Permittivity and soil electrical conductivity measurements were conducted using METER TEROS 12 probes. Soil moisture measurements were made with a TEROS 21 probe. The measurements were conducted in the lab over the span of three years. The purpose of soil collection and lab measurements was to develop an integrated framework that relates the hydrological properties to dielectric properties of permafrost active layer soil in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.", "links": [ { diff --git a/datasets/Arctic_Vegetation_Maps_1323_1.json b/datasets/Arctic_Vegetation_Maps_1323_1.json index 68c113419f..012ef27215 100644 --- a/datasets/Arctic_Vegetation_Maps_1323_1.json +++ b/datasets/Arctic_Vegetation_Maps_1323_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Vegetation_Maps_1323_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the circumpolar Arctic tundra biome for the period 1982-2003. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, Arctic treeline, elevation, and substrate chemistry data. Vegetation indices, trends, and biomass estimate products for the circumpolar Arctic through 2010 are also provided.", "links": [ { diff --git a/datasets/Arctic_Wildlife_Refuge_Veg_Map_1384_1.json b/datasets/Arctic_Wildlife_Refuge_Veg_Map_1384_1.json index 17a8af3593..209af45378 100644 --- a/datasets/Arctic_Wildlife_Refuge_Veg_Map_1384_1.json +++ b/datasets/Arctic_Wildlife_Refuge_Veg_Map_1384_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Wildlife_Refuge_Veg_Map_1384_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing. The data used to derive the map cover the period 1982 to 1993.", "links": [ { diff --git a/datasets/Arctic_Winter_Respiration_v2_1762_2.1.json b/datasets/Arctic_Winter_Respiration_v2_1762_2.1.json index 35267dfd47..c56fd5cb58 100644 --- a/datasets/Arctic_Winter_Respiration_v2_1762_2.1.json +++ b/datasets/Arctic_Winter_Respiration_v2_1762_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arctic_Winter_Respiration_v2_1762_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides soil-surface carbon dioxide (CO2) efflux derived from measurements of soil respiration with forced diffusion (FD) chambers. Soil Respiration Stations (SRS) were installed at 11 boreal and tundra sites along a broad south-to-north transect starting from near Fairbanks in interior Alaska and extending to Atqasuk in northern Alaska. Each SRS measures soil respiration and ambient atmospheric CO2 concentrations with a forced diffusion (FD) chamber to derive soil CO2 flux. The SRS also measures soil CO2 concentrations and temperatures using instrumented chambers buried at 5, 10, and 15 cm depths in the soil profile. At the highest measurement frequency, data are collected hourly, and during the lowest winter frequency, every 48 hours. The data include flux values and running median filtered values from the two or three FD chambers at each site. Soil CO2 and temperature profile data (beginning June 2017) were collected beginning 2016-08-18 through 2023-09-02. This dataset updates four sites with extended temporal coverage. As of this publication, sampling is continuing, and new data will be added as available.", "links": [ { diff --git a/datasets/Arrigetch_Peaks_Veg_Plots_1358_1.json b/datasets/Arrigetch_Peaks_Veg_Plots_1358_1.json index 16da25e574..5202e5a02b 100644 --- a/datasets/Arrigetch_Peaks_Veg_Plots_1358_1.json +++ b/datasets/Arrigetch_Peaks_Veg_Plots_1358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Arrigetch_Peaks_Veg_Plots_1358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental and vegetation data collected between 1978 and 1981 from 439 study plots at Arrigetch Peaks research site, located in the Gates of the Arctic National Park and Preserve in the Endicott Mountains of the central Brooks Range, Alaska. Plots varied between 1 and 50 square meters in size and were located in 13 broad habitat types across the glaciated landscape. Environmental data include aspect, elevation, and cover of bare soil, rock, soil crust, and litter. Species data are described according to the Braun-Blanquet system. This product brings together for easy reference all the available information collected from the vegetation plots in the Arrigetch Peaks region of Alaska.", "links": [ { diff --git a/datasets/Atmospheric_CO2_California_1641_1.json b/datasets/Atmospheric_CO2_California_1641_1.json index 101c75212c..6aade6ec68 100644 --- a/datasets/Atmospheric_CO2_California_1641_1.json +++ b/datasets/Atmospheric_CO2_California_1641_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Atmospheric_CO2_California_1641_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of atmospheric CO2 concentrations, carbon isotopes d13C and D14C, and fossil fuel CO2 (ffCO2) estimates from nine observation sites in California over three month-long campaigns in separate seasons of 2014-2015. ffCO2 was quantified based on the CO2 concentration and D14C. Simulations of ffCO2 at the sites and times of the observations were conducted with the Vulcan v2.2 fossil fuel emissions estimate for 2002 and the Weather Research and Forecasting - Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) atmospheric model. The observed and simulated ffCO2 were incorporated into Bayesian inverse estimates of ffCO2 to calculate California's ffCO2 emissions during the campaign period.", "links": [ { diff --git a/datasets/Atqasuk_Veg_Plots_1371_1.json b/datasets/Atqasuk_Veg_Plots_1371_1.json index 539f6a05e0..1ace93156c 100644 --- a/datasets/Atqasuk_Veg_Plots_1371_1.json +++ b/datasets/Atqasuk_Veg_Plots_1371_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Atqasuk_Veg_Plots_1371_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation species abundance data collected in 1975 from 60 sites on the Arctic Coastal Plain near Atqasuk, Alaska, as well as environmental and species data for 31 of the sites that were revisited in 2000 and 2010. The study sites are located on Arctic tundra near the Meade River, about 60 miles southwest of Barrow. Data includes baseline plot information for vegetation and site factors for the study plots subjectively located in 9 plant communities. Specific attributes include: site characteristics such as altitude, slope, aspect, and topographic position; soil pH and organic layer depth; and dominant plant communities. This product brings together for easy reference all of the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors at the Atqasuk research sites and across Alaska.", "links": [ { diff --git a/datasets/B01_0.json b/datasets/B01_0.json index 5cd4ba80f2..bd45dda597 100644 --- a/datasets/B01_0.json +++ b/datasets/B01_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B01_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the Virginia coast during 2005.", "links": [ { diff --git a/datasets/B02_0.json b/datasets/B02_0.json index 336d8d919e..906623e7c4 100644 --- a/datasets/B02_0.json +++ b/datasets/B02_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B02_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the mid-Atlantic coastal region of the continental shelf in 2005.", "links": [ { diff --git a/datasets/B031_Band_1.0.json b/datasets/B031_Band_1.0.json index 53cde27a5b..7c6c6fbe70 100644 --- a/datasets/B031_Band_1.0.json +++ b/datasets/B031_Band_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_Band_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bands put on Ad\u00e9lie penguin chicks and adults, Ross Island, Antarctica, starting in 1996. Bands were attached at Cape Royds, Cape Bird, Cape Crozier, and Beaufort Island.", "links": [ { diff --git a/datasets/B031_ChickCon_1.0.json b/datasets/B031_ChickCon_1.0.json index 9554e4bcbb..8fa6947bc3 100644 --- a/datasets/B031_ChickCon_1.0.json +++ b/datasets/B031_ChickCon_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_ChickCon_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of chick flippers and mass taken at weekly intervals beginning 12/1996 (ongoing).", "links": [ { diff --git a/datasets/B031_chickcount_1.0.json b/datasets/B031_chickcount_1.0.json index cd6708f4c9..66ddc83a3d 100644 --- a/datasets/B031_chickcount_1.0.json +++ b/datasets/B031_chickcount_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_chickcount_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annual counts of Adelie penguin chicks at Capes Royds and Crozier, beginning in 1996 (ongoing).", "links": [ { diff --git a/datasets/B031_diet_1.0.json b/datasets/B031_diet_1.0.json index 45a644d97a..b503130014 100644 --- a/datasets/B031_diet_1.0.json +++ b/datasets/B031_diet_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_diet_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diet of Adelie Penguins at Capes Crozier and Royds, Ross Island, beginning in 1996 (ongoing).", "links": [ { diff --git a/datasets/B031_gls_1.0.json b/datasets/B031_gls_1.0.json index 767a88445f..1f9793687b 100644 --- a/datasets/B031_gls_1.0.json +++ b/datasets/B031_gls_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_gls_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geolocation data from Adelie Penguins, 2003-2006.", "links": [ { diff --git a/datasets/B031_resight_1.0.json b/datasets/B031_resight_1.0.json index 0143e607f8..133f18869d 100644 --- a/datasets/B031_resight_1.0.json +++ b/datasets/B031_resight_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_resight_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on resighting of banded Adelie penguins, Capes Crozier and Royds, Ross Island, Antarctica.", "links": [ { diff --git a/datasets/B031_sat_1.0.json b/datasets/B031_sat_1.0.json index a0634736e5..6c5ec434ce 100644 --- a/datasets/B031_sat_1.0.json +++ b/datasets/B031_sat_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_sat_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite positions from Adelie penguins, Ross Island, Antarctica.", "links": [ { diff --git a/datasets/B031_tdr_1.0.json b/datasets/B031_tdr_1.0.json index 41756b3510..45164b2968 100644 --- a/datasets/B031_tdr_1.0.json +++ b/datasets/B031_tdr_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_tdr_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diving data from Adelie penguins.", "links": [ { diff --git a/datasets/B031_wb_1.0.json b/datasets/B031_wb_1.0.json index cb269442f6..5782ac8f6c 100644 --- a/datasets/B031_wb_1.0.json +++ b/datasets/B031_wb_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B031_wb_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguin weighbridge (automatic penguin monitoring system) data from Capes Crozier and Royds (ongoing).", "links": [ { diff --git a/datasets/B03_0.json b/datasets/B03_0.json index 918fea630d..946581f3c2 100644 --- a/datasets/B03_0.json +++ b/datasets/B03_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B03_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the mid-Atlantic coastal region and Monterey Bay in 2005 and 2006.", "links": [ { diff --git a/datasets/B04_0.json b/datasets/B04_0.json index ffc2be692c..10da2ee8d5 100644 --- a/datasets/B04_0.json +++ b/datasets/B04_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B04_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the mid-Atlantic coastal region of the continental shelf in 2005 and 2006.", "links": [ { diff --git a/datasets/B05_0.json b/datasets/B05_0.json index 4c085accc9..3493a71b2b 100644 --- a/datasets/B05_0.json +++ b/datasets/B05_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B05_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the San Diego, Californian coast in 2007.", "links": [ { diff --git a/datasets/B06_0.json b/datasets/B06_0.json index a7e3c27ede..618a7b57cc 100644 --- a/datasets/B06_0.json +++ b/datasets/B06_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B06_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Gulf of Maine in 2008.", "links": [ { diff --git a/datasets/B07_0.json b/datasets/B07_0.json index 87ebfed59c..697a4587cc 100644 --- a/datasets/B07_0.json +++ b/datasets/B07_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B07_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements along the New Hampshire and Massachusetts coastal regions in 2009.", "links": [ { diff --git a/datasets/B08_0.json b/datasets/B08_0.json index 877eec9274..5acfa58646 100644 --- a/datasets/B08_0.json +++ b/datasets/B08_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B08_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken near Bermuda in 2009.", "links": [ { diff --git a/datasets/B09_0.json b/datasets/B09_0.json index 72a55a40a2..aaf8815e65 100644 --- a/datasets/B09_0.json +++ b/datasets/B09_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "B09_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken near Santa Barbara, California in 2009.", "links": [ { diff --git a/datasets/BAHAMAS2004_0.json b/datasets/BAHAMAS2004_0.json index c7cb4b98b4..6850abba5b 100644 --- a/datasets/BAHAMAS2004_0.json +++ b/datasets/BAHAMAS2004_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BAHAMAS2004_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Bahamas in 2004.", "links": [ { diff --git a/datasets/BANGSS_Ocean_1.json b/datasets/BANGSS_Ocean_1.json index 25930bd40d..8aac6b191c 100644 --- a/datasets/BANGSS_Ocean_1.json +++ b/datasets/BANGSS_Ocean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANGSS_Ocean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains CTD (conductivity, temperature, depth) data obtained from the Big ANtarctic Geological and Seismic Survey (BANGSS) 94/95 cruise of the Aurora Australis, during Feb - Apr 1995. 24 CTD casts were taken in the Prydz Bay region, as a supplement to the geology research program. This dataset is a subset of the whole cruise data.\n\nThe fields in this dataset are:\nPressure\nTemperature\nSigma-T\nSalinity\nGeopotential ANomaly\nSpecific volume Anomaly\nsamples\ndeviation\nconduction", "links": [ { diff --git a/datasets/BANZARE_logs_1.json b/datasets/BANZARE_logs_1.json index bb28bd3b18..2e76ebbdb5 100644 --- a/datasets/BANZARE_logs_1.json +++ b/datasets/BANZARE_logs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANZARE_logs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The British Australian (and) New Zealand Antarctic Research Expedition (BANZARE) was a research expedition into Antarctica between 1929 and 1931, involving two voyages over consecutive Austral summers.\n\nThis document describes the ship's log and station list taken from \nBiological Organisation and Station List by T. Harvey Johnston, BANZARE Reports, Series B, Vol I, Part 1, pages 1-48\n\n\nData are stored in an Access database.\n\nThe 5 tables are\n\nbanzare_noon_log_1929_1930 and banzare_noon_log_1930_1931\n\nnoon positions from page 46-47 - assumed log_date is local noon, latitude and longitude in decimals.\n\nbanzare_stations_1929_1930 and banzare_stations_1930_1931\n\nodate is station date (no time is given)\ndepth is echo depth (metres)\nlatg and long is refined positions using Google Earth and Kerguelen map on page 14\n \nfull_speed_nets_1930_1931 \nlog of full sped nets - see pages 40-44;\n \ntime is possibly UTC \ndistance is travel of ship when net is deployed\ndepth is possible depth of net in fathoms\ntow_speed is ship speed in knots", "links": [ { diff --git a/datasets/BANd0005_113.json b/datasets/BANd0005_113.json index 2298b6568b..82020e0fc9 100644 --- a/datasets/BANd0005_113.json +++ b/datasets/BANd0005_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0005_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA Weekly Global Vegetation Index (GVI maxima) dataset for Asia. The\ndataset represents the period from April 1982 to December 1989", "links": [ { diff --git a/datasets/BANd0009_113.json b/datasets/BANd0009_113.json index 96d497f567..95381c3985 100644 --- a/datasets/BANd0009_113.json +++ b/datasets/BANd0009_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0009_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Area Coverage (GAC) 5 band data for South East Asia derived\nfrom the NOAA Satellite AVHRR sensor Global Area Coverage (GAC)", "links": [ { diff --git a/datasets/BANd0016_113.json b/datasets/BANd0016_113.json index 1a8c8539a4..8981ab822c 100644 --- a/datasets/BANd0016_113.json +++ b/datasets/BANd0016_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0016_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA-11 AVHRR 3 band False Color Composites of the Kuwait region from\nJanuary 21, 1991 to March 1, 1991. Dataset includes 17 false color composites\nfrom imageries acquired over this period", "links": [ { diff --git a/datasets/BANd0018_113.json b/datasets/BANd0018_113.json index 1e9933d7e2..e11864f115 100644 --- a/datasets/BANd0018_113.json +++ b/datasets/BANd0018_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0018_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "District boundaries of India dataset prepared for FAO by Department of\n Energy and natural resources, University of Illinois. Includes\n coastlines, national-subnational boundaries, lakes, and islands", "links": [ { diff --git a/datasets/BANd0019_113.json b/datasets/BANd0019_113.json index 9a3a7859be..7537d4bd5b 100644 --- a/datasets/BANd0019_113.json +++ b/datasets/BANd0019_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0019_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "District boundaries of India & Pakistan (SATERT5G) with census data on\n maize, wheat, rice in INDATDBF.dbf and State boundaries of India &\n Pakistan (STATE08G)", "links": [ { diff --git a/datasets/BANd0020_113.json b/datasets/BANd0020_113.json index 939eae4801..737e44c849 100644 --- a/datasets/BANd0020_113.json +++ b/datasets/BANd0020_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0020_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National and Provincial boundaries of Burma compiled\n from the World Boundary Database II (WBD II)", "links": [ { diff --git a/datasets/BANd0021_113.json b/datasets/BANd0021_113.json index 7d0bf3c001..d7ef6461c6 100644 --- a/datasets/BANd0021_113.json +++ b/datasets/BANd0021_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0021_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers and Lakes of Burma compiled\n from the World Boundary Database II.", "links": [ { diff --git a/datasets/BANd0023_113.json b/datasets/BANd0023_113.json index 85aabcde3c..0d5a33e836 100644 --- a/datasets/BANd0023_113.json +++ b/datasets/BANd0023_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0023_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Burma at 100 meters contour interval\n produced from the Global Elevation ETOP05 dataset", "links": [ { diff --git a/datasets/BANd0024_113.json b/datasets/BANd0024_113.json index 491c2fd5ac..95cebbd8af 100644 --- a/datasets/BANd0024_113.json +++ b/datasets/BANd0024_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0024_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landset-5 TM 7 band Image of Tak, Burma on 29 January 1990. Scene ID #\n52160 - 031125, Path 131, Row 48", "links": [ { diff --git a/datasets/BANd0025_113.json b/datasets/BANd0025_113.json index 63e270cffc..586b1b3bb6 100644 --- a/datasets/BANd0025_113.json +++ b/datasets/BANd0025_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0025_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified Digital Forest Map of Myanmar together with National\nboundaries", "links": [ { diff --git a/datasets/BANd0038_113.json b/datasets/BANd0038_113.json index 60d034eec0..e3468b5374 100644 --- a/datasets/BANd0038_113.json +++ b/datasets/BANd0038_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0038_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National and Provincial boundaries of Bangladesh\ncompiled from the World Boundary Database II (WBD II)", "links": [ { diff --git a/datasets/BANd0039_113.json b/datasets/BANd0039_113.json index 645cf889d5..90153c1af0 100644 --- a/datasets/BANd0039_113.json +++ b/datasets/BANd0039_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0039_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers and Lakes of Bangladesh\n compiled from the World Boundary Database II (WBD II)", "links": [ { diff --git a/datasets/BANd0041_113.json b/datasets/BANd0041_113.json index 63aafe9a37..da2e5399a8 100644 --- a/datasets/BANd0041_113.json +++ b/datasets/BANd0041_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0041_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Bangladesh at 100 meters contour\n interval produced from the Global Elevation ETOPO5 dataset", "links": [ { diff --git a/datasets/BANd0049_113.json b/datasets/BANd0049_113.json index 63f3f54d3c..36f7e6c9d7 100644 --- a/datasets/BANd0049_113.json +++ b/datasets/BANd0049_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0049_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landset-5 TM 7 band Image of Nepal on 18 December 1989. Scene ID#\n52118 - 041144, Path 141, Raw 41", "links": [ { diff --git a/datasets/BANd0050_113.json b/datasets/BANd0050_113.json index 362dbe6b2a..22e5000e8a 100644 --- a/datasets/BANd0050_113.json +++ b/datasets/BANd0050_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0050_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Datasets of Nepal. Dataset consists of 11 parameters;\nGeology, Major Rivers, Roads, Elevation, Boundaries, Regions, Zones,\nDistrict, Protected areas, Precipitation, and Towns", "links": [ { diff --git a/datasets/BANd0051_113.json b/datasets/BANd0051_113.json index 25c87561c3..a7b673a7be 100644 --- a/datasets/BANd0051_113.json +++ b/datasets/BANd0051_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0051_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset of Sindhupalchol District, Nepal consisting 14 parameters\nDistrict/Panchayat (Old and New) boundaries, Settlements, Villages,\nRoads, Bridges, Rivers, Elevation, Land Utilization/Capability", "links": [ { diff --git a/datasets/BANd0052_113.json b/datasets/BANd0052_113.json index f18379a65e..05f45746d2 100644 --- a/datasets/BANd0052_113.json +++ b/datasets/BANd0052_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0052_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contours of Bagmati Zone, Nepal from GRID-Geneva", "links": [ { diff --git a/datasets/BANd0053_113.json b/datasets/BANd0053_113.json index 62a8bc4d09..48dfdf2f83 100644 --- a/datasets/BANd0053_113.json +++ b/datasets/BANd0053_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0053_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land Utilization and Land Capability Maps of Bagmati Zone, Nepal\ncompiled by GRID-Bangkok. The data is in several files (uncombined)\ncorresponding to various sectons in the Zone", "links": [ { diff --git a/datasets/BANd0054_113.json b/datasets/BANd0054_113.json index 93fbcc0aa0..38c19b0312 100644 --- a/datasets/BANd0054_113.json +++ b/datasets/BANd0054_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0054_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land Capability splited to 2 zones; 44, 45 in 100 Meters resolution", "links": [ { diff --git a/datasets/BANd0056_113.json b/datasets/BANd0056_113.json index e7dcab04c3..2d49361bb7 100644 --- a/datasets/BANd0056_113.json +++ b/datasets/BANd0056_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0056_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land Utilization splited to 2 zones; 44, 45 in 100 Meters resolution", "links": [ { diff --git a/datasets/BANd0062_113.json b/datasets/BANd0062_113.json index 2f8f251c73..76a05a8030 100644 --- a/datasets/BANd0062_113.json +++ b/datasets/BANd0062_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0062_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of National and Provincial boundaries of Laos compiled\n from the World Bandary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0063_113.json b/datasets/BANd0063_113.json index 6cd2ff0945..6b1ad239ab 100644 --- a/datasets/BANd0063_113.json +++ b/datasets/BANd0063_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0063_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of Hydrology consisting Rivers and Lakes of Laos compiled\n from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0065_113.json b/datasets/BANd0065_113.json index 9d9ddda93a..76828cf7f9 100644 --- a/datasets/BANd0065_113.json +++ b/datasets/BANd0065_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0065_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour Map of Laos at 100 meters contour interval\n produced from the Global Elevation ETOPO5 dataset.", "links": [ { diff --git a/datasets/BANd0066_113.json b/datasets/BANd0066_113.json index bcfaba05b3..a771ad1433 100644 --- a/datasets/BANd0066_113.json +++ b/datasets/BANd0066_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0066_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Basemap of Laos from the Atlas of Physical, Economic and\n Social Resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0067_113.json b/datasets/BANd0067_113.json index ee93bbd899..c62ffef910 100644 --- a/datasets/BANd0067_113.json +++ b/datasets/BANd0067_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0067_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Geology Map of Laos from the Atlas of Physical, Economic and\n Social Resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0068_113.json b/datasets/BANd0068_113.json index 92bd6a31ad..80e5d0028a 100644 --- a/datasets/BANd0068_113.json +++ b/datasets/BANd0068_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0068_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Surface Configuration Map of Laos from the Atlas of Physical,\n Economic and Social Resources of the lower Mekong Basin at 1:2 million\n scale.", "links": [ { diff --git a/datasets/BANd0069_113.json b/datasets/BANd0069_113.json index 3196fe353a..b9b76b4c66 100644 --- a/datasets/BANd0069_113.json +++ b/datasets/BANd0069_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0069_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Engineering Geology Mao of Laos from the Atlas of Physical,\n Economic and Social Resources of the lower Mekong Basin at 1:2 million\n scale.", "links": [ { diff --git a/datasets/BANd0071_113.json b/datasets/BANd0071_113.json index 30abcad6f1..a424f1a7c2 100644 --- a/datasets/BANd0071_113.json +++ b/datasets/BANd0071_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0071_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Soil Moisture Regimes Map of Laos from the Atlas of Physical,\n Economic and Social Resources of the lower Mekong Basin at 1:2 milion\n scale.", "links": [ { diff --git a/datasets/BANd0072_113.json b/datasets/BANd0072_113.json index 19d21855cb..55d0c846ba 100644 --- a/datasets/BANd0072_113.json +++ b/datasets/BANd0072_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0072_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Hydrogeology Map of Laos from the Atlas of Physical, Economic\n and Social Resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0073_113.json b/datasets/BANd0073_113.json index 7b251d0642..06712be2bc 100644 --- a/datasets/BANd0073_113.json +++ b/datasets/BANd0073_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0073_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Engineering Soils Map of Laos from the Atlas of Physical,\n Economic and Social Resources of the lower Mekong Basin at 1:2 million\n scale.", "links": [ { diff --git a/datasets/BANd0074_113.json b/datasets/BANd0074_113.json index cc97b8b391..6668e75632 100644 --- a/datasets/BANd0074_113.json +++ b/datasets/BANd0074_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0074_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Precipitation Map of Laos from the Atlas of Physical, Economic\n and Social Resources of the lower Mekong at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0075_113.json b/datasets/BANd0075_113.json index 455298524e..af934fdc90 100644 --- a/datasets/BANd0075_113.json +++ b/datasets/BANd0075_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0075_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Climatic Zones Map of Laos from the Atlas of Physical,\n Economic and Social Resources of the lower Mekong Basin at 1:2 million\n scale.", "links": [ { diff --git a/datasets/BANd0076_113.json b/datasets/BANd0076_113.json index 43d03cf3c5..c6d0079740 100644 --- a/datasets/BANd0076_113.json +++ b/datasets/BANd0076_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0076_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Roads, Railroads Map of Laos from the Operational Navigation\n Chart J-10 and K-10 at 1:1 million scale.", "links": [ { diff --git a/datasets/BANd0077_113.json b/datasets/BANd0077_113.json index 1d28a08054..630f07d074 100644 --- a/datasets/BANd0077_113.json +++ b/datasets/BANd0077_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0077_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Rivers, Lakes, Islands Map of Laos from the Operational\n Navigation Chart J-10 and K-10 at 1:1 million scale.", "links": [ { diff --git a/datasets/BANd0078_113.json b/datasets/BANd0078_113.json index 43c1a7feff..7042f2cb6a 100644 --- a/datasets/BANd0078_113.json +++ b/datasets/BANd0078_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0078_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Provincial Map of Laos from the Atlas of Physical, Economic\nand Social Resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0080_113.json b/datasets/BANd0080_113.json index 044f3613bf..0d5271da3f 100644 --- a/datasets/BANd0080_113.json +++ b/datasets/BANd0080_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0080_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landuse Map of Laos with national boundary from WBD II.", "links": [ { diff --git a/datasets/BANd0081_113.json b/datasets/BANd0081_113.json index 18715e1d17..85af15848d 100644 --- a/datasets/BANd0081_113.json +++ b/datasets/BANd0081_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0081_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Classification Map of Laos with national boundary.", "links": [ { diff --git a/datasets/BANd0084_113.json b/datasets/BANd0084_113.json index 3f9468fa7c..f551b5f824 100644 --- a/datasets/BANd0084_113.json +++ b/datasets/BANd0084_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0084_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecosystem Map of Western Samoa digitized by GRID-Bangkok from maps\nobtained from Western Samoa", "links": [ { diff --git a/datasets/BANd0085_113.json b/datasets/BANd0085_113.json index 7dbdfcf664..4195743fae 100644 --- a/datasets/BANd0085_113.json +++ b/datasets/BANd0085_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0085_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 4 bands (band 2-5) Image of Bhutan on 2nd January 1988.", "links": [ { diff --git a/datasets/BANd0086_113.json b/datasets/BANd0086_113.json index 0ef64b71ac..8472686642 100644 --- a/datasets/BANd0086_113.json +++ b/datasets/BANd0086_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0086_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5TM 4 bands (band 2-5) Image of Bhutan on 28 February 1989.", "links": [ { diff --git a/datasets/BANd0087_113.json b/datasets/BANd0087_113.json index 3ce366f7cd..a4c4eae8d8 100644 --- a/datasets/BANd0087_113.json +++ b/datasets/BANd0087_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0087_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of National and Provincial boundaries of Malaysia compiled\n from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0088_113.json b/datasets/BANd0088_113.json index d6ee33f9c4..5b757d43a1 100644 --- a/datasets/BANd0088_113.json +++ b/datasets/BANd0088_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0088_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of Hydrology consisting Rivers of Malaysia compiled from\n the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0090_113.json b/datasets/BANd0090_113.json index 2cea9d4541..809d8c4b58 100644 --- a/datasets/BANd0090_113.json +++ b/datasets/BANd0090_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0090_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour Map of Malaysia at 100 meters contour\n interval produced from the Global Elevation ETOPO5.", "links": [ { diff --git a/datasets/BANd0095_113.json b/datasets/BANd0095_113.json index 5aee56186b..57ca558809 100644 --- a/datasets/BANd0095_113.json +++ b/datasets/BANd0095_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0095_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of Hydrology consisting Rivers and Lakes of Philippines\n compiled from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0098_113.json b/datasets/BANd0098_113.json index 7f27e9c94e..871f5a1865 100644 --- a/datasets/BANd0098_113.json +++ b/datasets/BANd0098_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0098_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map of Hydrology consisting Rivers and Lakes of Indonesia\n compiled from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0100_113.json b/datasets/BANd0100_113.json index 320da90fab..da3a204f38 100644 --- a/datasets/BANd0100_113.json +++ b/datasets/BANd0100_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0100_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour Map of Indonesia at 100 meters contour\n interval produced from the Global Elevation ETOPO5 dataset.", "links": [ { diff --git a/datasets/BANd0101_113.json b/datasets/BANd0101_113.json index adc32faeb0..e25cac75b6 100644 --- a/datasets/BANd0101_113.json +++ b/datasets/BANd0101_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0101_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National and Provincial boundaries of Cambodia compiled\n from World Boundary Database II (WBDII).", "links": [ { diff --git a/datasets/BANd0102_113.json b/datasets/BANd0102_113.json index 82c287d870..774a2399b1 100644 --- a/datasets/BANd0102_113.json +++ b/datasets/BANd0102_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0102_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers and Lakes of Cambodia\n compiled from the World Boundary Database II - WBDII.", "links": [ { diff --git a/datasets/BANd0104_113.json b/datasets/BANd0104_113.json index 346ad9bbd5..59167c43b6 100644 --- a/datasets/BANd0104_113.json +++ b/datasets/BANd0104_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0104_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 MSS 5 band Image of Phnom Penh area, Central Cambodia on 25\n Nov 1984. Scene ID 85026902495, Path 126, Row 052.", "links": [ { diff --git a/datasets/BANd0105_113.json b/datasets/BANd0105_113.json index 16176e7287..ac11e2c4d6 100644 --- a/datasets/BANd0105_113.json +++ b/datasets/BANd0105_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0105_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 MSS 5 band Image of Phnom Penh area, Central Cambodia on 27\n Dec 1984. Scene ID 85030102501, Path 126, Row 052.", "links": [ { diff --git a/datasets/BANd0107_113.json b/datasets/BANd0107_113.json index 33183e9910..f071c7c745 100644 --- a/datasets/BANd0107_113.json +++ b/datasets/BANd0107_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0107_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Cambodia at 100 meters contour interval produced from the Global Elevation ETOPO5 dataset.", "links": [ { diff --git a/datasets/BANd0108_113.json b/datasets/BANd0108_113.json index 98e13711cd..ae63a9182a 100644 --- a/datasets/BANd0108_113.json +++ b/datasets/BANd0108_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0108_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Basemap of Cambodia from the Atlas of Physical, economic and\nsocial resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0109_113.json b/datasets/BANd0109_113.json index c776149669..a4d06c254f 100644 --- a/datasets/BANd0109_113.json +++ b/datasets/BANd0109_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0109_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Geology map of Cambodia from the Atlas of physical, economic\nand social resources of the lower Mekong Basin at 1:2million scale.", "links": [ { diff --git a/datasets/BANd0110_113.json b/datasets/BANd0110_113.json index 0fa732450c..1b15a1543f 100644 --- a/datasets/BANd0110_113.json +++ b/datasets/BANd0110_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0110_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Surface Configuration map of Cambodia from the Atlas of\n physical, economic and social resources of the lower Mekong Basin at\n 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0111_113.json b/datasets/BANd0111_113.json index d98309cb3e..5a36da1e31 100644 --- a/datasets/BANd0111_113.json +++ b/datasets/BANd0111_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0111_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Engineering Geology map of Cambodia from the Atlas of\nphysical, economic and social resources of the lower Mekong Basin at\n1:2 million scale", "links": [ { diff --git a/datasets/BANd0112_113.json b/datasets/BANd0112_113.json index 98f8fff204..8225d5adba 100644 --- a/datasets/BANd0112_113.json +++ b/datasets/BANd0112_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0112_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Soil Moisture Regimes map of Cambodia from the Atlas of\nphysical, economic and social resources of the lower Mekong Basin at\n1:2 million scale.", "links": [ { diff --git a/datasets/BANd0113_113.json b/datasets/BANd0113_113.json index 1a62038790..e3e40fde26 100644 --- a/datasets/BANd0113_113.json +++ b/datasets/BANd0113_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0113_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Hydrology map of Cambodia from the Atlas of physical, economic\nand social resources of the lower Mekong Basin at 1:2 million scale.", "links": [ { diff --git a/datasets/BANd0114_113.json b/datasets/BANd0114_113.json index 995d6e8cfe..07afeec2f6 100644 --- a/datasets/BANd0114_113.json +++ b/datasets/BANd0114_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0114_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Engineering Soil map of Cambodia from the Atlas of physical,\neconomic and social resources of the lower Mekong Basin at 1:2 million\nscale.", "links": [ { diff --git a/datasets/BANd0115_113.json b/datasets/BANd0115_113.json index 734ef1316e..2e22b0e7eb 100644 --- a/datasets/BANd0115_113.json +++ b/datasets/BANd0115_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0115_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Precipitation map of Cambodia from the Atlas of physical,\neconomic and social resources of the lower Mekong Basin at 1:2 million\nscale.", "links": [ { diff --git a/datasets/BANd0116_113.json b/datasets/BANd0116_113.json index 3afcbf7304..6cd8dfeff2 100644 --- a/datasets/BANd0116_113.json +++ b/datasets/BANd0116_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0116_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Climatic Zone map of Cambodia from the Atlas of physical,\neconomic and social resources of the lower Mekong Basin at 1:2 million\nscale.", "links": [ { diff --git a/datasets/BANd0117_113.json b/datasets/BANd0117_113.json index a2e464a966..84c3c24943 100644 --- a/datasets/BANd0117_113.json +++ b/datasets/BANd0117_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0117_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Roads, Railroads map of Cambodia from the Operational\nNavigation Chart J-10 and K-10 at 1:1million scale.", "links": [ { diff --git a/datasets/BANd0118_113.json b/datasets/BANd0118_113.json index 6b47a9fbc7..06b35cd594 100644 --- a/datasets/BANd0118_113.json +++ b/datasets/BANd0118_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0118_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Rivers, Lakes, Islands map of Cambodia from the Operational\nNavigation Chart J-10 and K-10 at 1:1 million scale.", "links": [ { diff --git a/datasets/BANd0119_113.json b/datasets/BANd0119_113.json index c3ee33f197..e175c8dc11 100644 --- a/datasets/BANd0119_113.json +++ b/datasets/BANd0119_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0119_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Provincial map of Cambodia from the Atlas of physical,\neconomic and social resources of the lower Mekong Basin at 1:2 million\nscale.", "links": [ { diff --git a/datasets/BANd0120_113.json b/datasets/BANd0120_113.json index 8de6d60d31..f7319eca6f 100644 --- a/datasets/BANd0120_113.json +++ b/datasets/BANd0120_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0120_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Drainage (Flooded area) map of Cambodia from the Atlas of\nphysical, economic and social resources of the lower Mekong Basin at\n1:2 million scale.", "links": [ { diff --git a/datasets/BANd0121_113.json b/datasets/BANd0121_113.json index 8e878a0d2b..9943505ea7 100644 --- a/datasets/BANd0121_113.json +++ b/datasets/BANd0121_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0121_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital District Centers map of Cambodia from TOPO map sheet at\n1:500,000 scale.", "links": [ { diff --git a/datasets/BANd0125_113.json b/datasets/BANd0125_113.json index 439a62933b..8ef68d846c 100644 --- a/datasets/BANd0125_113.json +++ b/datasets/BANd0125_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0125_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National Boundariesof Cambodia compiled from the USAID\nmap.", "links": [ { diff --git a/datasets/BANd0127_113.json b/datasets/BANd0127_113.json index 666fd3fb6f..e401685c84 100644 --- a/datasets/BANd0127_113.json +++ b/datasets/BANd0127_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0127_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Administrative Districts map of Thmarpouk province, Cambodia\ncompiled from the USAID map.", "links": [ { diff --git a/datasets/BANd0128_113.json b/datasets/BANd0128_113.json index 97af4fe1d7..0d8980beb8 100644 --- a/datasets/BANd0128_113.json +++ b/datasets/BANd0128_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0128_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Agricultural Soils map of Thmarpouk province, Cambodia compiled from\nthe Atlas of the Lower Mekong Basin.", "links": [ { diff --git a/datasets/BANd0129_113.json b/datasets/BANd0129_113.json index 905ab7809e..f38c3dea6f 100644 --- a/datasets/BANd0129_113.json +++ b/datasets/BANd0129_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0129_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dams and Reservoirs map of Thmarpouk province, Cambodia compiled from\nthe USAID map.", "links": [ { diff --git a/datasets/BANd0130_113.json b/datasets/BANd0130_113.json index 8f4d2dcfe9..11d4d7e96a 100644 --- a/datasets/BANd0130_113.json +++ b/datasets/BANd0130_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0130_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrology (Rivers) map of Thmarpouk province, Cambodia compiled from\nthe USAID map.", "links": [ { diff --git a/datasets/BANd0131_113.json b/datasets/BANd0131_113.json index e58ed44d0d..25fefa9a9b 100644 --- a/datasets/BANd0131_113.json +++ b/datasets/BANd0131_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0131_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Infrastructure (roads, railroads, bridges) map of Thmarpouk province,\nCambodia compiled from USAID map.", "links": [ { diff --git a/datasets/BANd0132_113.json b/datasets/BANd0132_113.json index 26c4eabbd3..e53280f78a 100644 --- a/datasets/BANd0132_113.json +++ b/datasets/BANd0132_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0132_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landuse map of Thmarpouk province, Cambodia from the Remote Sensing\nand Mapping Unit, Mekong Secretariat.", "links": [ { diff --git a/datasets/BANd0133_113.json b/datasets/BANd0133_113.json index 70acbf40c2..414e9fcdbd 100644 --- a/datasets/BANd0133_113.json +++ b/datasets/BANd0133_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0133_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mine fields map of Thmarpouk province, Cambodia compiled from the\nUSAID map.", "links": [ { diff --git a/datasets/BANd0134_113.json b/datasets/BANd0134_113.json index 5c64b92b00..9224550420 100644 --- a/datasets/BANd0134_113.json +++ b/datasets/BANd0134_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0134_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Map showing the proposed new villages of Thmarpouk province, Cambodia\ncompiled from the USAID map.", "links": [ { diff --git a/datasets/BANd0135_113.json b/datasets/BANd0135_113.json index 57203c3e67..cbf73c3f9c 100644 --- a/datasets/BANd0135_113.json +++ b/datasets/BANd0135_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0135_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Map showing settlements of Thmarpouk province, Cambodia compiled from\nthe USAID map.", "links": [ { diff --git a/datasets/BANd0136_113.json b/datasets/BANd0136_113.json index 4723f2dab8..ca886312cd 100644 --- a/datasets/BANd0136_113.json +++ b/datasets/BANd0136_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0136_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Images of Cambodia on 25 January 1992. Scene ID #\n52886 - 023746, Path 125, Row 51.", "links": [ { diff --git a/datasets/BANd0138_113.json b/datasets/BANd0138_113.json index d7afb6bc90..24c38c2002 100644 --- a/datasets/BANd0138_113.json +++ b/datasets/BANd0138_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0138_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of road system in Phnom Penh", "links": [ { diff --git a/datasets/BANd0139_113.json b/datasets/BANd0139_113.json index 6d10aae64b..2cdf954f74 100644 --- a/datasets/BANd0139_113.json +++ b/datasets/BANd0139_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0139_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of building in Phnom Penh.", "links": [ { diff --git a/datasets/BANd0146_113.json b/datasets/BANd0146_113.json index f8893c7063..566ee19c47 100644 --- a/datasets/BANd0146_113.json +++ b/datasets/BANd0146_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0146_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National and Provincial boundaries of Vietnam compiled\n from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0147_113.json b/datasets/BANd0147_113.json index 4245dc3dad..047d6892eb 100644 --- a/datasets/BANd0147_113.json +++ b/datasets/BANd0147_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0147_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers of Vietnam compiled from\n the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0149_113.json b/datasets/BANd0149_113.json index 0b9bec5593..3b278a5a0f 100644 --- a/datasets/BANd0149_113.json +++ b/datasets/BANd0149_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0149_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Vietnam at 100 meters contour\n interval produced from the Global Elevation ETOPO5 dataset.", "links": [ { diff --git a/datasets/BANd0150_113.json b/datasets/BANd0150_113.json index 92e6a35739..f0f30b92fc 100644 --- a/datasets/BANd0150_113.json +++ b/datasets/BANd0150_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0150_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Classification Map of Vietnam with national boundary.", "links": [ { diff --git a/datasets/BANd0151_113.json b/datasets/BANd0151_113.json index cb79bdce29..d44b3af743 100644 --- a/datasets/BANd0151_113.json +++ b/datasets/BANd0151_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0151_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Classfication Map of Vietnam with national boundary.", "links": [ { diff --git a/datasets/BANd0155_113.json b/datasets/BANd0155_113.json index d73c1b6dc4..1c1f360fb9 100644 --- a/datasets/BANd0155_113.json +++ b/datasets/BANd0155_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0155_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Non-forest classification of Landsat MSS images (1975, 79, 84,\n 85) of Chiang Mai Province in Northern Thailand illustrating the\n deforestration problem.", "links": [ { diff --git a/datasets/BANd0156_113.json b/datasets/BANd0156_113.json index c2e57d178e..7e5beb9c6f 100644 --- a/datasets/BANd0156_113.json +++ b/datasets/BANd0156_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0156_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Demonstration Images of Mae Klang Watershed developed as part of the\nGRID-Thailand case study on deforestration & soil erosion illustrating\nthe use of Universal Soil Loss Equation (USLE),", "links": [ { diff --git a/datasets/BANd0157_113.json b/datasets/BANd0157_113.json index 19d068cbdf..3c1f9921ec 100644 --- a/datasets/BANd0157_113.json +++ b/datasets/BANd0157_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0157_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-1 MSS Image of Thailand on 14 February 1973. Scene ID 1206 -\n 03230, Path 141, Row 47.", "links": [ { diff --git a/datasets/BANd0158_113.json b/datasets/BANd0158_113.json index b489cccbf0..feae6ecdbf 100644 --- a/datasets/BANd0158_113.json +++ b/datasets/BANd0158_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0158_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-2 MSS Image of Thailand on 18 May 1979. Scene ID 21516-03003,\n Path 141, Row 47.", "links": [ { diff --git a/datasets/BANd0159_113.json b/datasets/BANd0159_113.json index 64664fb32e..21e2ab6649 100644 --- a/datasets/BANd0159_113.json +++ b/datasets/BANd0159_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0159_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 MSS Image of Thailand on 27 October 1984. Scene ID 50240 -\n 03185, path 131, Row 47.", "links": [ { diff --git a/datasets/BANd0160_113.json b/datasets/BANd0160_113.json index 78c724b6ef..860d58b1a4 100644 --- a/datasets/BANd0160_113.json +++ b/datasets/BANd0160_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0160_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 MSS Image of Thailand on 5 April 1985. Scene ID 50400 -\n 03191, Path 131, Row 47.", "links": [ { diff --git a/datasets/BANd0161_113.json b/datasets/BANd0161_113.json index c632dd4919..301457f479 100644 --- a/datasets/BANd0161_113.json +++ b/datasets/BANd0161_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0161_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-4 MSS Image of Thailand on 25 December 1985. Scene ID 41258 -\n 03090, Path 131, Row 47.", "links": [ { diff --git a/datasets/BANd0163_113.json b/datasets/BANd0163_113.json index bad1e80d74..777e9280dd 100644 --- a/datasets/BANd0163_113.json +++ b/datasets/BANd0163_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0163_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Model (DEM) of Amphoe Pipun, Nakhon Si Thammarat,\n Thailand derived from the SPOT XS data of 9 February 1989.", "links": [ { diff --git a/datasets/BANd0164_113.json b/datasets/BANd0164_113.json index c4da3ff5d2..f8988cfa87 100644 --- a/datasets/BANd0164_113.json +++ b/datasets/BANd0164_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0164_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 channel raw Image of Bangkok on 9 December 1987. Scene\nID 51378-030624, Path 129, Row 51.", "links": [ { diff --git a/datasets/BANd0165_113.json b/datasets/BANd0165_113.json index 4242024ecb..cac3ceb39f 100644 --- a/datasets/BANd0165_113.json +++ b/datasets/BANd0165_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0165_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Thailand-Burman border on 14 February\n1990. Scene ID 52176-031004, Path 131, Row 46.", "links": [ { diff --git a/datasets/BANd0166_113.json b/datasets/BANd0166_113.json index 1a67b9259a..16baa7142c 100644 --- a/datasets/BANd0166_113.json +++ b/datasets/BANd0166_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0166_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Thailand on 1 June 1990. Scene ID\n52283-025345, Path 128, Row 55.", "links": [ { diff --git a/datasets/BANd0167_113.json b/datasets/BANd0167_113.json index bd63a9149f..c935ada037 100644 --- a/datasets/BANd0167_113.json +++ b/datasets/BANd0167_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0167_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Mae Hong Son area, Thailand - Burma\nborder on 25 March 1990. Scene ID 52215-031521, Path 132, Row 47.", "links": [ { diff --git a/datasets/BANd0168_113.json b/datasets/BANd0168_113.json index b69f54cb82..540b237b63 100644 --- a/datasets/BANd0168_113.json +++ b/datasets/BANd0168_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0168_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Mae Sot area, Thailand - Burma border on\n3 April 1990. Scene ID 52224-030921, Path 131, Row 48.", "links": [ { diff --git a/datasets/BANd0169_113.json b/datasets/BANd0169_113.json index 16caf0c007..4c37275ce7 100644 --- a/datasets/BANd0169_113.json +++ b/datasets/BANd0169_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0169_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Moul Mein area, Thailand - Burma border\non 3 April 1990. Scene ID 52224-030945, Path 131, Row 49.", "links": [ { diff --git a/datasets/BANd0170_113.json b/datasets/BANd0170_113.json index 0c94276a4b..2d2f5a7181 100644 --- a/datasets/BANd0170_113.json +++ b/datasets/BANd0170_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0170_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat MSS 5 band Image of Songkhla region, Thailand on 28 February\n 1973 obtained from the MSS Historical Archive at EROS Data Center,\n USA. Scene ID 8122003033500, Path 137, Row 55.", "links": [ { diff --git a/datasets/BANd0171_113.json b/datasets/BANd0171_113.json index f39b8ac279..e820571ddf 100644 --- a/datasets/BANd0171_113.json +++ b/datasets/BANd0171_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0171_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat MSS 5 band Image of Songkhla region, Thailand on 27 June 1991\n obtained from the MSS Historical Archive at EROS Data Center,\n USA. Scene ID unknown, Path 138, Row 55.", "links": [ { diff --git a/datasets/BANd0173_113.json b/datasets/BANd0173_113.json index dd773f5795..869c94c050 100644 --- a/datasets/BANd0173_113.json +++ b/datasets/BANd0173_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0173_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land classification of Songkhla lake region using Landsat MSS Image of\n30 March 1976 used in the ONEB-ILEC-GRID project on the study of Lake\nSongkhla region.", "links": [ { diff --git a/datasets/BANd0174_113.json b/datasets/BANd0174_113.json index 9df8d78c84..acd8fdf97a 100644 --- a/datasets/BANd0174_113.json +++ b/datasets/BANd0174_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0174_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land classification of Songkhla lake region using Landsat TM Image of\n20 September 1991 used in the ONEB-ILEC-GRID project on the study of\nLake Songkhla region.", "links": [ { diff --git a/datasets/BANd0175_113.json b/datasets/BANd0175_113.json index b930123d3a..8ebc95e089 100644 --- a/datasets/BANd0175_113.json +++ b/datasets/BANd0175_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0175_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified Digital Forest Map of Thailand together with National and\n Provincial boundaries.", "links": [ { diff --git a/datasets/BANd0176_113.json b/datasets/BANd0176_113.json index a6c8a19189..e8d33bd035 100644 --- a/datasets/BANd0176_113.json +++ b/datasets/BANd0176_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0176_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of National and Provincial boundaries of Thailand compiled\n from the World Boudary Database II (WBD II). Updated to 76 provinces\n from Royal Thai Survey Department Map.", "links": [ { diff --git a/datasets/BANd0177_113.json b/datasets/BANd0177_113.json index 6d77dbc9e7..dbd4c65cf9 100644 --- a/datasets/BANd0177_113.json +++ b/datasets/BANd0177_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0177_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers and Lakes of Thailand\n compiled from the World Boundary Database II (WBD II).", "links": [ { diff --git a/datasets/BANd0179_113.json b/datasets/BANd0179_113.json index 5137fb1bbc..54f62b7dbd 100644 --- a/datasets/BANd0179_113.json +++ b/datasets/BANd0179_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0179_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Thailand at 100 meters contour\ninterval produced from the Global Elevation ETOPO5 dataset.", "links": [ { diff --git a/datasets/BANd0181_113.json b/datasets/BANd0181_113.json index 77dfa28975..0cb4b75c97 100644 --- a/datasets/BANd0181_113.json +++ b/datasets/BANd0181_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0181_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Suratthani region, Thailand on 4 March\n1990. Scene ID 52194 - 030019, Path 129, Row 54.", "links": [ { diff --git a/datasets/BANd0182_113.json b/datasets/BANd0182_113.json index f542755392..e1944ec58b 100644 --- a/datasets/BANd0182_113.json +++ b/datasets/BANd0182_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0182_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 band Image of Thailand-Cambodia border on 27 March\n1992. Scene ID 52948-025018, Path 127, Row 52.", "links": [ { diff --git a/datasets/BANd0183_113.json b/datasets/BANd0183_113.json index 9f8e96eeed..4dd5ecf729 100644 --- a/datasets/BANd0183_113.json +++ b/datasets/BANd0183_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0183_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Area of Thailand 1991", "links": [ { diff --git a/datasets/BANd0184_113.json b/datasets/BANd0184_113.json index 9325f5475c..df2bd27d67 100644 --- a/datasets/BANd0184_113.json +++ b/datasets/BANd0184_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0184_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landuse Map of Thailand 197 classes", "links": [ { diff --git a/datasets/BANd0185_113.json b/datasets/BANd0185_113.json index d5c7794aa8..4f10f413a4 100644 --- a/datasets/BANd0185_113.json +++ b/datasets/BANd0185_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0185_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the modelled biomass density incorporating climatic / edhaphic\n/ geomorphological factors and the biomass density in the absence of\nhuman disturbance", "links": [ { diff --git a/datasets/BANd0188_113.json b/datasets/BANd0188_113.json index f7d8112f63..60f8fdb0e2 100644 --- a/datasets/BANd0188_113.json +++ b/datasets/BANd0188_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0188_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified image of the peninsular resgion of Thailand; Surat Thani,\nNakhon Si Thammarat and Krabi. Under the contract of TREES project\nwith JRC (Ispra) Italy. Path 129 Row 54", "links": [ { diff --git a/datasets/BANd0189_113.json b/datasets/BANd0189_113.json index 807c41dcfb..fd23e89cea 100644 --- a/datasets/BANd0189_113.json +++ b/datasets/BANd0189_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0189_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified image of the Northern Highland of Thailand, Myanmar and\nLaos (Golden Triangle). Under the contract of TREES project with JRC\n(Ispra) Italy. Path 131 Row 46", "links": [ { diff --git a/datasets/BANd0193_113.json b/datasets/BANd0193_113.json index 2cbf49503d..47163c63d7 100644 --- a/datasets/BANd0193_113.json +++ b/datasets/BANd0193_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0193_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 bands Image of Thailand on 17 Mar 1998. Path 130, Row\n49 Quad 9.", "links": [ { diff --git a/datasets/BANd0194_113.json b/datasets/BANd0194_113.json index 8bd61f6060..6553e1f86c 100644 --- a/datasets/BANd0194_113.json +++ b/datasets/BANd0194_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0194_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 bands Image of Thailand on 6 Mar 1994. Path 130, Row 50\nQuad 6", "links": [ { diff --git a/datasets/BANd0195_113.json b/datasets/BANd0195_113.json index 173ae26b96..408e8e788f 100644 --- a/datasets/BANd0195_113.json +++ b/datasets/BANd0195_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0195_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 bands Image of Thailand on 9 Mar 1995. Path 130, Row 50\nQuad 6", "links": [ { diff --git a/datasets/BANd0198_113.json b/datasets/BANd0198_113.json index b3806bfd3f..a1ffdeda53 100644 --- a/datasets/BANd0198_113.json +++ b/datasets/BANd0198_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0198_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified image of the Eastern part of Cambodia; Kratie and Stung\nTreng. Under the contract of TREES project with JRC (Ispra)\nItaly. Path 125 Row 51.", "links": [ { diff --git a/datasets/BANd0199_113.json b/datasets/BANd0199_113.json index 65f86930f5..26ed50cd0f 100644 --- a/datasets/BANd0199_113.json +++ b/datasets/BANd0199_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0199_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Classified image of the Western part of Cambodia. Under the contract\nof TREES project with JRC (Ispra) Italy. Path 127 Row 52", "links": [ { diff --git a/datasets/BANd0201_113.json b/datasets/BANd0201_113.json index 29553f8d5f..8e5a1ff9f8 100644 --- a/datasets/BANd0201_113.json +++ b/datasets/BANd0201_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0201_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nature Reserves in the Coastal Zone of Cambodia with names", "links": [ { diff --git a/datasets/BANd0202_113.json b/datasets/BANd0202_113.json index bce2964918..889aa94fd0 100644 --- a/datasets/BANd0202_113.json +++ b/datasets/BANd0202_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0202_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Depleted Mangrove Forest in Koh Kong of Cambodia. It is classified\ninto 2 classes; 1= Mangrove forest and 2 = Shrimp farm", "links": [ { diff --git a/datasets/BANd0203_113.json b/datasets/BANd0203_113.json index 9b931da26e..d4471c22e4 100644 --- a/datasets/BANd0203_113.json +++ b/datasets/BANd0203_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0203_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coral Reef of Cambodia classified into 4 classes", "links": [ { diff --git a/datasets/BANd0204_113.json b/datasets/BANd0204_113.json index ca3662f474..f195ebeb6d 100644 --- a/datasets/BANd0204_113.json +++ b/datasets/BANd0204_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0204_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "It shows the marine fish exploitation caught by the villagers of the\ncoastal provinces and actual yield in 1994 - 95", "links": [ { diff --git a/datasets/BANd0206_113.json b/datasets/BANd0206_113.json index 15e90910fa..0cae4f1ed2 100644 --- a/datasets/BANd0206_113.json +++ b/datasets/BANd0206_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0206_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-5 TM 7 bands Image of Nepal on 25 Feb 1997. Path 139, Row 42", "links": [ { diff --git a/datasets/BANd0207_113.json b/datasets/BANd0207_113.json index 5d1f548a42..e838211b9a 100644 --- a/datasets/BANd0207_113.json +++ b/datasets/BANd0207_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0207_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA AVHRR LAC Data (11 Feb '85) for the Philippines supplied by STAR\nprogram of AIT.", "links": [ { diff --git a/datasets/BANd0209_113.json b/datasets/BANd0209_113.json index 888d72d419..9621bcbd86 100644 --- a/datasets/BANd0209_113.json +++ b/datasets/BANd0209_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0209_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Contour map of Philippines at 100 meters contour\n interval produced from the global Elevation ETOPO5 dataset", "links": [ { diff --git a/datasets/BANd0210_113.json b/datasets/BANd0210_113.json index 54bbd215aa..c515e5ddbd 100644 --- a/datasets/BANd0210_113.json +++ b/datasets/BANd0210_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0210_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital map of Hydrology consisting Rivers and Lakes of Philippines\n compiled from the World Boundary Database II (WBDII)", "links": [ { diff --git a/datasets/BANd0213_113.json b/datasets/BANd0213_113.json index 7aed8d1537..95f7203a48 100644 --- a/datasets/BANd0213_113.json +++ b/datasets/BANd0213_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0213_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nature Reserves in the Coastal Zone of China with general sites\ninformation", "links": [ { diff --git a/datasets/BANd0214_113.json b/datasets/BANd0214_113.json index 5f432e0f4a..a784778cd9 100644 --- a/datasets/BANd0214_113.json +++ b/datasets/BANd0214_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0214_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coral Reef of China with names", "links": [ { diff --git a/datasets/BANd0216_113.json b/datasets/BANd0216_113.json index 0ec9af31e1..5e87215c98 100644 --- a/datasets/BANd0216_113.json +++ b/datasets/BANd0216_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0216_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "District boundaries of Vietnam", "links": [ { diff --git a/datasets/BANd0217_113.json b/datasets/BANd0217_113.json index 4a874a7c7b..c596b0099f 100644 --- a/datasets/BANd0217_113.json +++ b/datasets/BANd0217_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0217_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geological complex of Vietnam", "links": [ { diff --git a/datasets/BANd0218_113.json b/datasets/BANd0218_113.json index c84d50c4d2..f91a3ce396 100644 --- a/datasets/BANd0218_113.json +++ b/datasets/BANd0218_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0218_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Main rivers of Vietnam", "links": [ { diff --git a/datasets/BANd0219_113.json b/datasets/BANd0219_113.json index a5d74cdd85..56dbbcd98d 100644 --- a/datasets/BANd0219_113.json +++ b/datasets/BANd0219_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0219_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Main roads of Vietnam", "links": [ { diff --git a/datasets/BANd0221_113.json b/datasets/BANd0221_113.json index fe43c318e4..6daa8ef150 100644 --- a/datasets/BANd0221_113.json +++ b/datasets/BANd0221_113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BANd0221_113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The poster covers Asia Pacific region from Mongolia to New Zealand and\nfrom Cook Islands in the east to Iran in the west. Some cartographic\ndetail added.", "links": [ { diff --git a/datasets/BAROCLINIC_HRET14_14.json b/datasets/BAROCLINIC_HRET14_14.json index bb0d9ab9d1..75d421c3bf 100644 --- a/datasets/BAROCLINIC_HRET14_14.json +++ b/datasets/BAROCLINIC_HRET14_14.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BAROCLINIC_HRET14_14", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans.
\r\nThe harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys. The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions.
\r\nThis dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available.", "links": [ { diff --git a/datasets/BASIN_TCP_963_1.json b/datasets/BASIN_TCP_963_1.json index b5f775970e..8b5339be7f 100644 --- a/datasets/BASIN_TCP_963_1.json +++ b/datasets/BASIN_TCP_963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BASIN_TCP_963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file.The goal of this Terrestrial Carbon Processes (TCP) project is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems.", "links": [ { diff --git a/datasets/BAS_Soil_1.json b/datasets/BAS_Soil_1.json index 325316f194..096dde1bfb 100644 --- a/datasets/BAS_Soil_1.json +++ b/datasets/BAS_Soil_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BAS_Soil_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil water samples were collected from some of the sites used in ASAC project 2542 (ASAC_2542). Water was extracted from soil using vacuum tubes. They were sent to BAS and analysed for dissolved organic Nitrogen.\n\nThis dataset only contains a record of where the samples were collected, not data arising from the actual samples.\n\nSee the metadata record ASAC_2542 for further information.\n\nThe fields in this dataset are:\n\nSample Number\nStation\nLocation\nLatitude\nLongitude\nSampling\nNotes", "links": [ { diff --git a/datasets/BBOP_0.json b/datasets/BBOP_0.json index 8d199ba9a8..1770a47f1d 100644 --- a/datasets/BBOP_0.json +++ b/datasets/BBOP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BBOP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Bermuda Bio-Optics Project (BBOP) is a long term study of the factors contributing to the regulation of the underwater light field in the open ocean and the resulting biogeochemical impact. These studies are done, on average, once a month in conjunction with the Bermuda-Atlantic Time Series (BATS) in the Sargasso Sea.", "links": [ { diff --git a/datasets/BCFLEXPART_1.json b/datasets/BCFLEXPART_1.json index fa1556e05b..d8220b9f84 100644 --- a/datasets/BCFLEXPART_1.json +++ b/datasets/BCFLEXPART_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BCFLEXPART_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a global simulation of black carbon (BC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available. \n", "links": [ { diff --git a/datasets/BC_Aerosol_Dynamics_Alaska_1340_1.json b/datasets/BC_Aerosol_Dynamics_Alaska_1340_1.json index 36e222df20..8d5a349dc1 100644 --- a/datasets/BC_Aerosol_Dynamics_Alaska_1340_1.json +++ b/datasets/BC_Aerosol_Dynamics_Alaska_1340_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BC_Aerosol_Dynamics_Alaska_1340_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of the isotopic composition of black carbon and organic carbon aerosols collected at two locations in interior Alaska during the summer of 2013, as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The delta14C end member of fire aerosol was derived and linked to soil elemental and isotopic composition in Alaskan boreal forests. Soil and aerosol measurements were used to estimate average depth of burn in Alaska during the summer of 2013.", "links": [ { diff --git a/datasets/BDSNP_CMAQ_Model_1351_1.json b/datasets/BDSNP_CMAQ_Model_1351_1.json index a6fababefb..80e6c69862 100644 --- a/datasets/BDSNP_CMAQ_Model_1351_1.json +++ b/datasets/BDSNP_CMAQ_Model_1351_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BDSNP_CMAQ_Model_1351_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product provides: (1) the source code for the updated Berkeley-Dalhousie Soil Nitric Oxide (NO) Parameterization module (BDSNP, Version 1.0) as implemented with the Community Multi-scale Air Quality model (CMAQ, Version 5.0.2), (2) module input data from historical and new sources of maps for soil biome type, fertilizer, and arid and non-arid climates, and (3) sample CMAQ simulation outputs for three BDSNP module NO parameterizations (standard, historical, and newer inputs). The simulations use a 12-km spatial grid resolution for CMAQ modeling covering the conterminous United States for July 2011.", "links": [ { diff --git a/datasets/BENEFIT_0.json b/datasets/BENEFIT_0.json index 8da1d361e2..77a9670635 100644 --- a/datasets/BENEFIT_0.json +++ b/datasets/BENEFIT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BENEFIT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the Namibian and South African coasts between 2000 and 2002.", "links": [ { diff --git a/datasets/BEST_0.json b/datasets/BEST_0.json index 2ea6f0f3b6..f8e455b56a 100644 --- a/datasets/BEST_0.json +++ b/datasets/BEST_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BEST_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HLY0803 cruise of the USCG cutter Healy was an NSF funded cruise for the Bering Ecosystem Study (BEST) project that was focused on the impact of sea ice on the marine ecology of the region. In particular it focused on pathways of nutrients and organic matter that lead to the abundant upper trophic levels and valuable fisheries on the Bering Sea continental shelf. The cruise covered most of the eastern Bering Sea shelf from the Aleutian Islands to St. Lawrence Island with 177 unique stations that included CTD casts, bio-optics casts, MOCNESS tows, CALVet tows, bongo tows, multicore drops and sediment trap deployments.", "links": [ { diff --git a/datasets/BESTsed24.json b/datasets/BESTsed24.json index 36a9dd7d94..7446ce333b 100644 --- a/datasets/BESTsed24.json +++ b/datasets/BESTsed24.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BESTsed24", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of sediment and crayfish (Pacifastacus leniusculus) was\nconducted in order to satisfy monitoring requirements set forth in the\nCity of St. Helens National Discharge Elimination System (NPDES)\nPermit (Tetra Tech 1992). Samples were collected from five sites to\nevaluate the accumulation of dioxins and furans in sediment and\ncrayfish. Sediment and crayfish sampling primarily focused on\nlocations downriver from the location of the outfall pipe.\n\nSediment samples were collected and analyzed for seventeen\ndioxin/furan congeners, particle size distribution, total solids, and\ntotal organic carbon. All sediment data are presented on dry weight\nbasis and TOC-normalized values are also provided in the report.\nSampling station latitude and longitude were recorded from geographic\ncoordinates provided by a Trimble Navigation Global Positioning System\nreceiver.\n\nThe area of study was the Lower Columbia River-St. Helens.\n\nEach sediment sample consisted of a composite of at least four grab\nsamples. Surface sediments (top 2 cm) were transferred to a stainless\nsteel bowl and homogenized with a stainless steel spatula. The\nsamples were placed in jars and stored on ice except for the samples\ndesignated for TOC analysis. These samples were stored on dry ice.\n\nTarget analytes were seventeen dioxin and furan congeners.\nConventional analyses included particle size, total solids, and total\norganic carbon (TOC).\n\nAnalytical techniques included dioxins and furans (EPA Method 1613A),\nTOC (modified EPA Method 415.1), total solids (EPA Method 160.3.),\nparticle size (Puget Sound Estuary Program Protocols). All results\nare reported on a dry weight basis.\n\nThe information for this metadata was taken from the Columbia River\nBasin: Sediment Database Abstracts.", "links": [ { diff --git a/datasets/BESTsed25.json b/datasets/BESTsed25.json index 5deac9e2ae..941c486288 100644 --- a/datasets/BESTsed25.json +++ b/datasets/BESTsed25.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BESTsed25", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of sediment and crayfish (Pacifastacus leniusculus) was\nconducted in order to satisfy monitoring requirements set forth in the\nJames River Wauna Mill's National Discharge Elimination System (NPDES)\nPermit (Tetra Tech 1992). Samples were collected from five sites to\nevaluate the accumulation of dioxins and furans in sediment and\ncrayfish. Sediment and crayfish sampling primarily focused on\nlocations downriver from the location of the outfall pipe.\n\nSediment samples were collected and analyzed for seventeen\ndioxin/furan congeners, particle size distribution, total solids, and\ntotal organic carbon. Data are presented on a dry weight basis and\nTOC-normalized values are also provided in the report. Sampling\nstation latitude and longitude were recorded from geographic\ncoordinates provided by a Trimble Navigation Global Positioning System\nreceiver.\n\nThe area of study was the Lower Columbia River-Wauna.\n\nEach sediment sample consisted of a composite of at least four grab\nsamples. Surface sediments (top 2 cm) were transferred to a stainless\nsteel bowl and homogenized with a stainless steel spatula. The\nsamples were placed in jars and stored on ice except for the samples\ndesignated for TOC analysis. These samples were stored on dry ice.\n\nTarget analytes were Seventeen dioxin and furan congeners.\nConventional analyses included particle size, total solids, and total\norganic carbon (TOC).\n\nAnalytical techniques included Dioxins and furans (EPA Method 1613A),\nTOC (modified EPA Method 415.1), total solids (EPA Method 160.3.),\nparticle size (Puget Sound Estuary Program Protocols). All results\nare reported on a dry weight basis.\n\nThe information for this metadata was taken from the Columbia River\nBasin: Sediment Database Abstracts.", "links": [ { diff --git a/datasets/BFO_dsp01_ccrs_avhrr_landcover_589_1.json b/datasets/BFO_dsp01_ccrs_avhrr_landcover_589_1.json index 8a3d620f54..053d3318dc 100644 --- a/datasets/BFO_dsp01_ccrs_avhrr_landcover_589_1.json +++ b/datasets/BFO_dsp01_ccrs_avhrr_landcover_589_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp01_ccrs_avhrr_landcover_589_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters.", "links": [ { diff --git a/datasets/BFO_dsp01_ccrs_tm_landcover_588_1.json b/datasets/BFO_dsp01_ccrs_tm_landcover_588_1.json index c0cf47b670..da5d4a09b5 100644 --- a/datasets/BFO_dsp01_ccrs_tm_landcover_588_1.json +++ b/datasets/BFO_dsp01_ccrs_tm_landcover_588_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp01_ccrs_tm_landcover_588_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.", "links": [ { diff --git a/datasets/BFO_dsp04_ers_freeze-thaw_maps_590_1.json b/datasets/BFO_dsp04_ers_freeze-thaw_maps_590_1.json index 30678f7ad9..01bfc48829 100644 --- a/datasets/BFO_dsp04_ers_freeze-thaw_maps_590_1.json +++ b/datasets/BFO_dsp04_ers_freeze-thaw_maps_590_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp04_ers_freeze-thaw_maps_590_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data.", "links": [ { diff --git a/datasets/BFO_dsp05_ccrs_avhrr_npp_591_1.json b/datasets/BFO_dsp05_ccrs_avhrr_npp_591_1.json index 02f303e27e..f657e64354 100644 --- a/datasets/BFO_dsp05_ccrs_avhrr_npp_591_1.json +++ b/datasets/BFO_dsp05_ccrs_avhrr_npp_591_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp05_ccrs_avhrr_npp_591_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994. This document describes how the NPP is generated . The NPP data are stored in a binary image file. ", "links": [ { diff --git a/datasets/BFO_dsp06_casi_lai_cc_592_1.json b/datasets/BFO_dsp06_casi_lai_cc_592_1.json index 18bc4e0015..2faadbdfdb 100644 --- a/datasets/BFO_dsp06_casi_lai_cc_592_1.json +++ b/datasets/BFO_dsp06_casi_lai_cc_592_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp06_casi_lai_cc_592_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model. The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994. Additional high resolution LAI and canopy closure images were produced over the two black spruce flux tower sites using the FLIM-CLUS algorithm.", "links": [ { diff --git a/datasets/BFO_dsp08_polder_surface_params_594_1.json b/datasets/BFO_dsp08_polder_surface_params_594_1.json index 151158bb6b..adaa89c45e 100644 --- a/datasets/BFO_dsp08_polder_surface_params_594_1.json +++ b/datasets/BFO_dsp08_polder_surface_params_594_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp08_polder_surface_params_594_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains maps of surface reflectance and vegetation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area (SSA) during 1994. The POLDER imagery provided in this data set was collected on June 1 and July 21, 1994, from the NASA C-130 aircraft platform.", "links": [ { diff --git a/datasets/BFO_dsp09_moss_map_595_1.json b/datasets/BFO_dsp09_moss_map_595_1.json index 17f1bcaa1d..213b60b6ec 100644 --- a/datasets/BFO_dsp09_moss_map_595_1.json +++ b/datasets/BFO_dsp09_moss_map_595_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp09_moss_map_595_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type.", "links": [ { diff --git a/datasets/BFO_dsp09_sask_fire_map_596_1.json b/datasets/BFO_dsp09_sask_fire_map_596_1.json index 0e27848b6c..9fac892d9d 100644 --- a/datasets/BFO_dsp09_sask_fire_map_596_1.json +++ b/datasets/BFO_dsp09_sask_fire_map_596_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp09_sask_fire_map_596_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996. This data set was developed from a series of ARC/INFO export files of annual fire data compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.", "links": [ { diff --git a/datasets/BFO_dsp10_fpar_lai_1994_585_1.json b/datasets/BFO_dsp10_fpar_lai_1994_585_1.json index be84a3640b..2263c34581 100644 --- a/datasets/BFO_dsp10_fpar_lai_1994_585_1.json +++ b/datasets/BFO_dsp10_fpar_lai_1994_585_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_fpar_lai_1994_585_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.", "links": [ { diff --git a/datasets/BFO_dsp10_landcover_598_1.json b/datasets/BFO_dsp10_landcover_598_1.json index e75fa9ebcf..336915cfe5 100644 --- a/datasets/BFO_dsp10_landcover_598_1.json +++ b/datasets/BFO_dsp10_landcover_598_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_landcover_598_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.", "links": [ { diff --git a/datasets/BFO_dsp10_landcover_tm_mosaic_602_1.json b/datasets/BFO_dsp10_landcover_tm_mosaic_602_1.json index bd3fba46ab..cc612d6765 100644 --- a/datasets/BFO_dsp10_landcover_tm_mosaic_602_1.json +++ b/datasets/BFO_dsp10_landcover_tm_mosaic_602_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_landcover_tm_mosaic_602_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes).", "links": [ { diff --git a/datasets/BFO_dsp10_landcover_tm_reclassed_597_1.json b/datasets/BFO_dsp10_landcover_tm_reclassed_597_1.json index 5a0d7028f8..718381306e 100644 --- a/datasets/BFO_dsp10_landcover_tm_reclassed_597_1.json +++ b/datasets/BFO_dsp10_landcover_tm_reclassed_597_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_landcover_tm_reclassed_597_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes).", "links": [ { diff --git a/datasets/BFO_dsp10_moss_cover_599_1.json b/datasets/BFO_dsp10_moss_cover_599_1.json index 70e291fc26..b27a25bc29 100644 --- a/datasets/BFO_dsp10_moss_cover_599_1.json +++ b/datasets/BFO_dsp10_moss_cover_599_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_moss_cover_599_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 min, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.", "links": [ { diff --git a/datasets/BFO_dsp10_ndvi_600_1.json b/datasets/BFO_dsp10_ndvi_600_1.json index e4c5ce4084..527dfc39ad 100644 --- a/datasets/BFO_dsp10_ndvi_600_1.json +++ b/datasets/BFO_dsp10_ndvi_600_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_ndvi_600_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid. Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing. If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.", "links": [ { diff --git a/datasets/BFO_dsp10_peatlands_601_1.json b/datasets/BFO_dsp10_peatlands_601_1.json index dbde11519b..3af52781cb 100644 --- a/datasets/BFO_dsp10_peatlands_601_1.json +++ b/datasets/BFO_dsp10_peatlands_601_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_dsp10_peatlands_601_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids.", "links": [ { diff --git a/datasets/BFO_flx01_derived_data_603_1.json b/datasets/BFO_flx01_derived_data_603_1.json index 30ca6c2c20..eb48d2a52a 100644 --- a/datasets/BFO_flx01_derived_data_603_1.json +++ b/datasets/BFO_flx01_derived_data_603_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_flx01_derived_data_603_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site. The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets. The derived data covers the period from March 1994 through the end of 1998.", "links": [ { diff --git a/datasets/BFO_flx01_flux_met_temp_604_1.json b/datasets/BFO_flx01_flux_met_temp_604_1.json index 85e10369f4..60f231d5e8 100644 --- a/datasets/BFO_flx01_flux_met_temp_604_1.json +++ b/datasets/BFO_flx01_flux_met_temp_604_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_flx01_flux_met_temp_604_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998. Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.", "links": [ { diff --git a/datasets/BFO_flx03_area_avg_flux_586_1.json b/datasets/BFO_flx03_area_avg_flux_586_1.json index 9fd2a54380..f7d7a18000 100644 --- a/datasets/BFO_flx03_area_avg_flux_586_1.json +++ b/datasets/BFO_flx03_area_avg_flux_586_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_flx03_area_avg_flux_586_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Calculations of area-averaged fluxes using extracted flux data from BORIS.", "links": [ { diff --git a/datasets/BFO_flx04_nsa_burn_flux_587_1.json b/datasets/BFO_flx04_nsa_burn_flux_587_1.json index c07dc71c50..9560c5e718 100644 --- a/datasets/BFO_flx04_nsa_burn_flux_587_1.json +++ b/datasets/BFO_flx04_nsa_burn_flux_587_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_flx04_nsa_burn_flux_587_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930. At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000. Fluxes were measured using paired portable solar powered eddy flux systems. The data are part of an ongoing age sequence study that will result in year round eddy flux and meteorological measurements in seven sites that burned between 2 and 150 years ago.", "links": [ { diff --git a/datasets/BFO_hmet01_goes8_level2_srb_605_1.json b/datasets/BFO_hmet01_goes8_level2_srb_605_1.json index 831615615b..af68654075 100644 --- a/datasets/BFO_hmet01_goes8_level2_srb_605_1.json +++ b/datasets/BFO_hmet01_goes8_level2_srb_605_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_hmet01_goes8_level2_srb_605_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region. This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data. These GOES-8 Level-2 data cover the period from 12-Feb-1996 to 22-Oct-1996.", "links": [ { diff --git a/datasets/BFO_hmet01_ssmi_precip_606_1.json b/datasets/BFO_hmet01_ssmi_precip_606_1.json index 614240c1ec..5a86c04a0c 100644 --- a/datasets/BFO_hmet01_ssmi_precip_606_1.json +++ b/datasets/BFO_hmet01_ssmi_precip_606_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_hmet01_ssmi_precip_606_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images. The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.", "links": [ { diff --git a/datasets/BFO_hmet02_gridded_met_phase2-3_607_1.json b/datasets/BFO_hmet02_gridded_met_phase2-3_607_1.json index 95096e9562..d6cab3bc88 100644 --- a/datasets/BFO_hmet02_gridded_met_phase2-3_607_1.json +++ b/datasets/BFO_hmet02_gridded_met_phase2-3_607_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_hmet02_gridded_met_phase2-3_607_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. The meteorological variables in this data set are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.", "links": [ { diff --git a/datasets/BFO_hmet03_hourly_met_p1_608_1.json b/datasets/BFO_hmet03_hourly_met_p1_608_1.json index 81ae320f34..2c6852a92f 100644 --- a/datasets/BFO_hmet03_hourly_met_p1_608_1.json +++ b/datasets/BFO_hmet03_hourly_met_p1_608_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_hmet03_hourly_met_p1_608_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Point data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Meteorological variables of interest are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.", "links": [ { diff --git a/datasets/BFO_hmet04_src_nsa_96-98_609_1.json b/datasets/BFO_hmet04_src_nsa_96-98_609_1.json index 69d61cd3a5..40dd8fad67 100644 --- a/datasets/BFO_hmet04_src_nsa_96-98_609_1.json +++ b/datasets/BFO_hmet04_src_nsa_96-98_609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_hmet04_src_nsa_96-98_609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site. This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.", "links": [ { diff --git a/datasets/BFO_mod01_gridded_met_p3_daily_610_1.json b/datasets/BFO_mod01_gridded_met_p3_daily_610_1.json index e306c22a6b..2faa82d213 100644 --- a/datasets/BFO_mod01_gridded_met_p3_daily_610_1.json +++ b/datasets/BFO_mod01_gridded_met_p3_daily_610_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BFO_mod01_gridded_met_p3_daily_610_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.", "links": [ { diff --git a/datasets/BGC_glider_GNATS_1.json b/datasets/BGC_glider_GNATS_1.json index 8410d96bb2..e2eb243efe 100644 --- a/datasets/BGC_glider_GNATS_1.json +++ b/datasets/BGC_glider_GNATS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BGC_glider_GNATS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ocean biogeochemistry data from two Slocum gliders along the Gulf of Maine North Atlantic Time Series (GNATS) transect. The transect runs approximately east-west, with only a very minor change in latitude. The gliders are deployed on the western end of the transect, travel along the transect line to the eastern end, turn around and travel back along the transect to the western end, before being recovered. Each file contains data from one deployment (a glider \u201cmission\u201d), and thus contains both an eastbound and a westbound measurement of each variable. A full mission takes approximately 20 \u2013 30 days. The data are gridded by longitude (0.01\u00b0 intervals) and depth (1 m intervals). For more details on dataset preparation, see the Original Publication Citation and Data Processing Workflow below.", "links": [ { diff --git a/datasets/BILIM_0.json b/datasets/BILIM_0.json index 014ff9a3b6..48f0f0e46f 100644 --- a/datasets/BILIM_0.json +++ b/datasets/BILIM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BILIM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Turkish Seas during 1999 and 2000 by the Turkish research vessel, the Bilim.", "links": [ { diff --git a/datasets/BIO-GO-SHIP_0.json b/datasets/BIO-GO-SHIP_0.json index c4b172e9fe..d28b9de6e8 100644 --- a/datasets/BIO-GO-SHIP_0.json +++ b/datasets/BIO-GO-SHIP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIO-GO-SHIP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bio-GO-SHIP aims to become an international collaboration to measure, understand, and predict the distribution and biogeochemical role of pelagic plankton communities. The project leverages the global-reaching [GO-SHIP](https://www.go-ship.org/) platform and its complementary hydrographic measurements. The mission of Bio-GO-SHIP is to quantify the molecular diversity, size spectrum, chemical composition, and abundances of plankton communities across large spatial, vertical, and eventually temporal scales. This will be achieved through systematic, high-quality, and calibrated sampling of omics, plankton imaging, particle chemistry, and optical techniques as operational oceanographic tools. Integration with regular GO-SHIP measurements and their analyses of the physical and chemical environment will allow us to understand (and eventually predict) how plankton communities respondto ocean changes and how biological processes feeds backon carbon, oxygen and nutrient cycles.", "links": [ { diff --git a/datasets/BIOCOMPLEXITY_0.json b/datasets/BIOCOMPLEXITY_0.json index 52702cac08..a96acc81a9 100644 --- a/datasets/BIOCOMPLEXITY_0.json +++ b/datasets/BIOCOMPLEXITY_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOCOMPLEXITY_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Chesapeake Bay and Mid-Atlantic Bight during 2001 to 2004.", "links": [ { diff --git a/datasets/BIODIVERSITY_0.json b/datasets/BIODIVERSITY_0.json index 7db6b49976..cdf490cac9 100644 --- a/datasets/BIODIVERSITY_0.json +++ b/datasets/BIODIVERSITY_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIODIVERSITY_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Maine in 2007 under the BIODIVERSITY program.", "links": [ { diff --git a/datasets/BIOME_0.json b/datasets/BIOME_0.json index ef1fdf4188..0abd8db1f4 100644 --- a/datasets/BIOME_0.json +++ b/datasets/BIOME_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOME_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the Mid-Atlantic coastal and Chesapeake Bay outflow regions in 2005 and 2006.", "links": [ { diff --git a/datasets/BIOME_BGC_4_1_1_805_1.json b/datasets/BIOME_BGC_4_1_1_805_1.json index 0868e70be5..bcf8df53dd 100644 --- a/datasets/BIOME_BGC_4_1_1_805_1.json +++ b/datasets/BIOME_BGC_4_1_1_805_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOME_BGC_4_1_1_805_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles. Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include new leaf growth and old leaf litterfall, sunlight interception by leaves and penetration to the ground, precipitation routing to leaves and soil, snow accumulation and melting, drainage and runoff of soil water, evaporation of water from soil and wet leaves, transpiration of soil water through leaf stomata, photosynthetic fixation of carbon from CO2 in the air, uptake of nitrogen from the soil, distribution of carbon and nitrogen to growing plant parts, decomposition of fresh plant litter and old soil organic matter, plant mortality, and fire. The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions. A companion file with more information about Biome-BGC and its components is available. Biome-BGC, Version 4.1.1, was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, the University of Montana, Missoula, Montana, U.S.A. Additional information can be found on their web site at: http://www.ntsg.umt.edu/.", "links": [ { diff --git a/datasets/BIOME_BGC_m2_4_1_2_809_1.json b/datasets/BIOME_BGC_m2_4_1_2_809_1.json index 5cba9ae94d..6a7e78ab99 100644 --- a/datasets/BIOME_BGC_m2_4_1_2_809_1.json +++ b/datasets/BIOME_BGC_m2_4_1_2_809_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOME_BGC_m2_4_1_2_809_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of the following article: Law, B. E., O. J. Sun, J. Campbell, S. Van Tuyl, and P. E. Thornton. 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology, 9(4), 510-514. Abstract excerpt: Forest development following stand-replacing disturbance influences a variety of ecosystem processes including carbon exchange with the atmosphere. On a chronosequence of ponderosa pine (Pinius ponderosa var. Laws.) stands in central Oregon, U.S.A., we used biological measurements to estimate carbon storage in vegetation and soil pools, net primary productivity (NPP), and net ecosystem productivity (NEP) in relation to stand age. Measurements were made in 2000 on a suite of 12 ponderosa pine stands ranging in age from 9 to >300 years. Total ecosystem carbon storage and the fraction of ecosystem carbon in aboveground wood mass increased rapidly until 150-200 years and did not decline in older stands. Forest inventory data on 950 ponderosa pine plots in Oregon show that the greatest proportion of plots exist in stands ~100 years old, indicating that a majority of stands are approaching maximum carbon storage and net carbon uptake. Our data suggest that NEP averages ~70 g C m-2 year-1 for ponderosa pine forests in Oregon. About 85% of the total carbon storage in biomass on the survey plots exists in stands greater than 100 years, which has implications for managing forests for carbon sequestration. To investigate variation in carbon storage and fluxes with disturbance, simulation with process models requires a dynamic parameterization for biomass allocation that depends on stand age and should include a representation of competition between multiple plant functional types for space, water, and nutrients.", "links": [ { diff --git a/datasets/BIOME_BGC_m_4_1_1_806_1.json b/datasets/BIOME_BGC_m_4_1_1_806_1.json index 36c9dd5e5e..e349fa6dd0 100644 --- a/datasets/BIOME_BGC_m_4_1_1_806_1.json +++ b/datasets/BIOME_BGC_m_4_1_1_806_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOME_BGC_m_4_1_1_806_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of the following article: Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson, D. Hollinger, M. Falk, J. Chen, and J. P. Sparks. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222. Abstract: The effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year. The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.", "links": [ { diff --git a/datasets/BIOSCAPE_COASTAL_CARBON_0.json b/datasets/BIOSCAPE_COASTAL_CARBON_0.json index fede9dccb1..08fae13e56 100644 --- a/datasets/BIOSCAPE_COASTAL_CARBON_0.json +++ b/datasets/BIOSCAPE_COASTAL_CARBON_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOSCAPE_COASTAL_CARBON_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes the spectral (absorption and fluorescence) characteristics of colored dissolved organic matter (CDOM) coincident with field measurements of hyperspectral water remote sensing reflectance for improved remote sensing algorithm development. Samples were collected within three ecologically distinct but socio-economically vital bays surrounding South Africa's Greater Cape Floristic Region: St Helena Bay, Walker Bay, and Algoa Bay. Data from this and other elements of the BioSCape project can be found via the earthdata portal https://search.earthdata.nasa.gov/search?q=bioscape&fpj=BioSCape.", "links": [ { diff --git a/datasets/BIOSOPE_0.json b/datasets/BIOSOPE_0.json index b7fb2f0df4..01108f1d72 100644 --- a/datasets/BIOSOPE_0.json +++ b/datasets/BIOSOPE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIOSOPE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made during the BIOSOPE: Biogeochemistry and Optics South Pacific Experiment in 2004.", "links": [ { diff --git a/datasets/BIO_BURN_5X5_HAO_NAT_1.json b/datasets/BIO_BURN_5X5_HAO_NAT_1.json index ad2db3b84a..bcaf644def 100644 --- a/datasets/BIO_BURN_5X5_HAO_NAT_1.json +++ b/datasets/BIO_BURN_5X5_HAO_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIO_BURN_5X5_HAO_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BIO_MASS_5X5_HAO_NAT data set contains data representing the geographical and temporal distribution of total amount of biomass burned. The data were collected by Dr. Wei Min Hao and Mei-Huey Liu from ground stations located in Africa, Asia, and tropical America (Central and South America). Data are available for 1980. Each measurement consists of four parameters: amount of biomass burned in forest fires, amount of biomass burned in savanna fires, amount of fuel wood and agricultural residues burned, and total amount of biomass burned. Each granule consists of one year of data per region.", "links": [ { diff --git a/datasets/BIRDSCASEY0203_1.json b/datasets/BIRDSCASEY0203_1.json index 49e5b2ffc8..4ecae6f99d 100644 --- a/datasets/BIRDSCASEY0203_1.json +++ b/datasets/BIRDSCASEY0203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BIRDSCASEY0203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of bird nests, mostly snow petrels, mapped in the Windmill Islands during the 2002-2003 season. Location of nests were recorded with handheld GPS receivers connected to a pocket PC and stored as a shapefile using Arcpad (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in the detailed description of the shapefiles.\n\nTwo observers conducted the surveys using distinct methodologies, Frederique Olivier (FO) and Drew Lee (DL). Three separate nest location files (ArcView point shapefiles) were produced and correspond to each of the survey methodologies used. Methodology 1 was the use of 200*200 m grid squares in which exhaustive searches were conducted (FO). Methodology 2 was the use of 2 transects within each the 200*200 m grid squares; methodology 3 was the use of 4 small quadrats (ca 25 m) located within the 200*200m grid squares (DL). Nests mapped in a non-systematic manner (not following a specific methodology) are clearly identified within each dataset. Datasets were kept separate due to the uncertainties caused by GPS errors (the same nest may have different locations due to GPS error).\n\nThree separate shapefiles describe survey methodologies:\n- one polygon shapefile locates the 200*200 grid sites searched systematically (FO)\n- one polygon shapefile locates the small quadrats (DL)\n- one line shapefile locates line transects (DL)\n\nSpatial characteristics, date of survey, search effort, number of nests found and other parameters are recorded for the grid sites, transect and quadrats.\n\nSee the word document in the file download for more information.\n\nThis work has been completed as part of ASAC project 1219 (ASAC_1219).\n\nThe fields in this dataset are:\n\nSpecies\nActivity\nType\nEntrances\nSlope\nRemnants\nLatitude\nLongitude\nDate\nSnow\nEggchick\nCavitysize\nCavitydepth\nDistnn\nSubstrate\nComments\nSitedotID\nAspect\nFirstfred\nSystematic/Edge/Incidental\nRecordCode\n\nThe full dataset, including a word document providing further information about the dataset, is publicly available for download from the provided URL.\n\nAlso available for download from another URL is polygon data representing flying bird nesting areas. The polygon data was derived from the flying bird nest locations by the Australian Antarctic Data Centre for displaying on maps.", "links": [ { diff --git a/datasets/BLATM1B_1.json b/datasets/BLATM1B_1.json index bc8b7798f0..d57ba8a6dc 100644 --- a/datasets/BLATM1B_1.json +++ b/datasets/BLATM1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BLATM1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements of Arctic, Greenland, Antarctic, and Patagonia sea ice and ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.", "links": [ { diff --git a/datasets/BLATM2_1.json b/datasets/BLATM2_1.json index 934d6731ab..a45f644bed 100644 --- a/datasets/BLATM2_1.json +++ b/datasets/BLATM2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BLATM2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, as well as Greenland, Arctic, Patagonia, and Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.", "links": [ { diff --git a/datasets/BLVIS2_1.json b/datasets/BLVIS2_1.json index 88ad2c4156..9f80327ee8 100644 --- a/datasets/BLVIS2_1.json +++ b/datasets/BLVIS2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BLVIS2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation data over Greenland measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.", "links": [ { diff --git a/datasets/BMNA_33.json b/datasets/BMNA_33.json index 5edffc8223..c629bd9c8f 100644 --- a/datasets/BMNA_33.json +++ b/datasets/BMNA_33.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BMNA_33", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Butterflies and Moths of North America (BAMONA) project is ambitious effort to collect and provide access to quality-controlled data about butterflies and moths. The project is housed at Montana State University and directed by Kelly Lotts and Thomas Naberhaus. Our goal is to fill the needs of scientists and nature observers by bringing verified occurrence and life history data into one accessible location.\n\nBAMONA is a rich data source that grows daily. Citizen scientists of all ages and experience levels participate by taking photographs of butterflies and moths and then submitting their observations. Additional BAMONA data come from museum and personal collections, published literature, and professional lepidopterists. Quality control is provided by collaborating lepidopterists who serve as regional coordinators. Standardized data and metadata are stored in a database and accessible through the web site via checklists, species profiles, maps displaying point data, and other tools.\n\n(Source: www.butterfliesandmoths.org/about)", "links": [ { diff --git a/datasets/BMRGG_InventoryRocks_1.json b/datasets/BMRGG_InventoryRocks_1.json index d631029665..abd4d4f6d3 100644 --- a/datasets/BMRGG_InventoryRocks_1.json +++ b/datasets/BMRGG_InventoryRocks_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BMRGG_InventoryRocks_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Record provides details of rock specimens from Antarctic which are held in various institutions in Australia and which may be available to bona fide research workers. Those wishing to obtain material from these collections should contact the head of institution in question.\n\nThe fields in this dataset are:\nSample Numbers\nLocality\nRock Types\nReferences\nInstitution\nAddress", "links": [ { diff --git a/datasets/BNL_0.json b/datasets/BNL_0.json index 677cc96349..c685287f0e 100644 --- a/datasets/BNL_0.json +++ b/datasets/BNL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BNL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the Bahamas during 1998 by the Brookhaven National Laboratory (BNL).", "links": [ { diff --git a/datasets/BOA_0.json b/datasets/BOA_0.json index 5dbc7b5abc..37adcc7f3c 100644 --- a/datasets/BOA_0.json +++ b/datasets/BOA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements used to develop the Bio-Optical Algorithm (BOA), taken between 1991 and 1995 in the Northeast Pacific, North Atlantic, Gulf of Mexico, and Arabian Sea.", "links": [ { diff --git a/datasets/BOM_HRPTCAS.json b/datasets/BOM_HRPTCAS.json index 5ae029908d..5ff6b9f3f6 100644 --- a/datasets/BOM_HRPTCAS.json +++ b/datasets/BOM_HRPTCAS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOM_HRPTCAS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-HRPT dataset from the Bureau of Meteorology's Casey site\ncontains HRPT data as received from the NOAA polar orbiting\nspacecraft. The HRPT data stream is stored in the Australian\nSatellite Data Archive (ASDA) format which contains a Parameter-Value\nLanguage (PVL, ASCII) header and the full data stream for one pass\n(between 1500 and 6000 lines) at processing level 1A.", "links": [ { diff --git a/datasets/BOREAS_CDS_1350_1.json b/datasets/BOREAS_CDS_1350_1.json index 562720af5c..f8a4f1d5c0 100644 --- a/datasets/BOREAS_CDS_1350_1.json +++ b/datasets/BOREAS_CDS_1350_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOREAS_CDS_1350_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Boreal Ecosystem-Atmosphere Study (BOREAS) project information and data collected at selected sites in the boreal forest of Saskatchewan and Manitoba, Canada from 1993 through 1996. The data include surface, airborne, and satellite-based observations. Note that all of the data products on these CDs have been archived as separate BOREAS data sets by the ORNL DAAC and in many cases the published data are later versions. Users should search for BOREAS data among these individual data sets. These data were originally distributed on 12 CD-ROMs, but are now archived as 12 zip files to ensure historical completeness of the BOREAS data record.", "links": [ { diff --git a/datasets/BOREAS_RSS-03_Snapshots_289_2.json b/datasets/BOREAS_RSS-03_Snapshots_289_2.json index 6a58c27357..e724b8ca24 100644 --- a/datasets/BOREAS_RSS-03_Snapshots_289_2.json +++ b/datasets/BOREAS_RSS-03_Snapshots_289_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOREAS_RSS-03_Snapshots_289_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides images of boreal forests in central Canada collected over numerous tower and auxiliary sites during the BOREAS Intensive Field Campaigns (IFCs) in the Northern (NSA) and Southern Study Areas (SSA). The images were acquired by helicopter with VHS video cameras during the green-up, peak, and senescent stages of the growing season from May-September of 1994. These snapshots were generated from VHS imagery and converted to .jpg format.", "links": [ { diff --git a/datasets/BOREAS_SLICER_508_2.json b/datasets/BOREAS_SLICER_508_2.json index 6f5e7d6a0d..841a14a99b 100644 --- a/datasets/BOREAS_SLICER_508_2.json +++ b/datasets/BOREAS_SLICER_508_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOREAS_SLICER_508_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) data were acquired in support of BOReal Ecosystem-Atmosphere Study (BOREAS) at all of the Tower Flux (TF) sites in the Southern and Northern Study Areas (SSA and NSA, respectively) and along transects between the study areas. Data were acquired on 5 days between 18 and 30 July 1996. Each coverage of a tower site is typically 40 km in length, with a minimum of 3 and a maximum of 10 lines across each tower oriented in a variety of azimuths. The SLICER data were acquired simultaneously with Advanced Solid-State Array Spectroradiometer (ASAS) hyperspectral, multiview angle images. The SLICER Level 3 products consist of binary files for each flight line with a data record for each laser shot composed of 13 parameters and a 600-byte waveform that is the raw record of the back scatter laser energy reflected from Earth's surface.", "links": [ { diff --git a/datasets/BOUSSOLE_0.json b/datasets/BOUSSOLE_0.json index efd2803895..1401bbbb4e 100644 --- a/datasets/BOUSSOLE_0.json +++ b/datasets/BOUSSOLE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BOUSSOLE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the BOUSSOLE project is to establish a time series of optical properties in oceanic waters, in support to bio-optics research, to calibration of ocean color satellite observations, and to validation of the products derived from these observations. The bio-optics research as well as the match-up analyses and vicarious calibration experiments are performed based on the data set that is being built from the permanent marine optical buoy and monthly cruises. The site where the mooring is deployed and where the cruises are carried out is located in the Ligurian sea, one of the sub-basins of the Western Mediterranean sea. BOUSSOLE is a joint effort by multiple organizations and is funded and supported by the following agencies and academic or governmental institutes European Space Agency (ESA), Centre National d'Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences de l'Univers (INSU), National Aeronautics and Space Administration (NASA), University Pierre et Marie Curie (UPMC), and Observatoire Oceanologique de Villefranche-sur-Mer.", "links": [ { diff --git a/datasets/BRAZIL_0.json b/datasets/BRAZIL_0.json index 118a8db6b2..9f155ee3ed 100644 --- a/datasets/BRAZIL_0.json +++ b/datasets/BRAZIL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BRAZIL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Amazon River outflow region of the Atlantic Ocean off the coast of Brazil in 2002.", "links": [ { diff --git a/datasets/BRD_LSC002.json b/datasets/BRD_LSC002.json index 2bd8f8b152..eba6af55e5 100644 --- a/datasets/BRD_LSC002.json +++ b/datasets/BRD_LSC002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BRD_LSC002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two years of field work have been completed in the development of an\nindex of biotic integrity (IBI) for fish communities in the\nmiddle-to-upper Delaware River basin (Delaware Water Gap to Callicoon,\nNew York). Fish were collected in riffles and pools on 200 m segments\nof eight tributaries. Collections were made concurrently in the\nDelaware River mainstem within 0.5 mile (downstream) of the same\ntributary mouths, in three habitat types: riffles, deep pools, and\ninshore submerged vegetation zones. Quality control on species\nidentity, as well as length/weight/disease determination, has been\ncompleted on all specimens. A total of 15,673 fish were collected\n(7,655 in tributaries and 8,018 in mainstem habitats) representing 44\nspecies (36 in tributaries and 36 in mainstem habitats).\n\nFish community data (species richness, trophic composition, and\npopulation/health data) will be related to both water quality and land\nuse data to develop IBIs. Water quality data, including a dozen or\nmore physical, chemical, and biological parameters taken during the\nsame seasons/years by Delaware River Basin Commission personnel, are\ncurrently being indexed for use in the models. Land use data in four\ncategories (22 subcategories), obtained from the Anderson Level 2\ndatabase using GIS techniques, have been summarized for use in the\nmodels. Current work involves examination of the variance associated\nwith traditional fish metrics and the identification of alternative\nmetrics that may better explain the covariance with water quality and\nland use.\n\nThe Research and Development Laboratory-Wellsboro (RDL-W) is located\non 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory\nfacilities include 3 modern buildings, 8x200-foot concrete raceways, 3\nproduction wells, and support equipment.\n\nThe RDL-W conducts research for restoration of depleted fisheries and\nother aquatic biological resources. A diversified research program in\necology, conservation technology, genetics, and physiology emphases\nthe integration of laboratory and field studies to develop\nscientifically sound approaches to the management of aquatic\necosystems. Research is directed primarily towards development of\ninformation and technology to increase understanding of aquatic\necosystems in the northeastern United States and to assist client\nagencies to better manage these ecosystems and their biota. Technical\nassistance is provided to clients throughout the nation.", "links": [ { diff --git a/datasets/BRD_LSC003_MAHA.json b/datasets/BRD_LSC003_MAHA.json index ef96b4b1af..8421721d64 100644 --- a/datasets/BRD_LSC003_MAHA.json +++ b/datasets/BRD_LSC003_MAHA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BRD_LSC003_MAHA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The research establishes a national repository for fishieries data and\nmanagement practices to improve technical understanding of the status,\ntrends, causes, and effects of changes in native fish populations and\ntheir habitats and 2) increase investigations of fish contaminant\nimpacts, habitat losses, and control of exotics to restore depleted or\nendangered fishes.", "links": [ { diff --git a/datasets/BRD_LSC_AMERSHAD001.json b/datasets/BRD_LSC_AMERSHAD001.json index 176c52ee70..80f02e06ce 100644 --- a/datasets/BRD_LSC_AMERSHAD001.json +++ b/datasets/BRD_LSC_AMERSHAD001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BRD_LSC_AMERSHAD001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field evaluations of existing habitat suitability index (HSI) models\nfor spawning adults, eggs, and larvae of American shad (Alosa\nsapidissima) were conducted in 1990-1992; initial models for juveniles\nin nursery habitats were developed. Fish abundance in various\nhabitats of the upper Delaware River was quantified by (1) observation\nof adult spawning activity, (2) collection of eggs and larvae with\nmetered plankton and drift nets, and (3) enumeration of juveniles by\nunderwater observation and seining techniques. Regression analysis,\nprincipal component analysis, and range analysis were used to relate\nabundance to an array of physical habitat variables potentially\ninfluencing fish distributions.\n\nNo HSI model was previously developed for juvenile American shad in\nriverine habitats. Four physical habitat variables were correlated\nwith juvenile abundance: water temperature, dissolved oxygen\n(covariates), river depth, and turbidity. Regression analysis,\nprincipal component analysis, and range analysis were used to relate\nabundance to an array of physical habitat variables potentially\ninfluencing fish distributions.\n\n\nThe Research and Development Laboratory-Wellsboro (RDL-W) is located\non 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory\nfacilities include 3 modern buildings, 8x200-foot concrete raceways, 3\nproduction wells, and support equipment.\n\nCore Capabilities\n\nThe RDL-W conducts research for restoration of depleted fisheries and\nother aquatic biological resources. A diversified research program in\necology, conservation technology, genetics, and physiology emphases\nthe integration of laboratory and field studies to develop\nscientifically sound approaches to the management of aquatic\necosystems. Research is directed primarily towards development of\ninformation and technology to increase understanding of aquatic\necosystems in the northeastern United States and to assist client\nagencies to better manage these ecosystems and their biota. Technical\nassistance is provided to clients throughout the nation.", "links": [ { diff --git a/datasets/BRMCR2_1.json b/datasets/BRMCR2_1.json index ce2c05f023..c6bce6afc8 100644 --- a/datasets/BRMCR2_1.json +++ b/datasets/BRMCR2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BRMCR2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS).", "links": [ { diff --git a/datasets/BROKE-WEST_12kHZ_Bathy_Data_1.json b/datasets/BROKE-WEST_12kHZ_Bathy_Data_1.json index 09bbae1f6e..0361cfb943 100644 --- a/datasets/BROKE-WEST_12kHZ_Bathy_Data_1.json +++ b/datasets/BROKE-WEST_12kHZ_Bathy_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-WEST_12kHZ_Bathy_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Readme - Bathymetry Files Data for BROKE-WEST 2006\n\n1) Zipped folder contains .csv files created from each acoustics ev file for Transects 1 to 11.\n\n2) These files contain subsections of each transect of variable length (usually between 50 and 100 km).\n\n3) No data exists for files; Transect01_01 and 01_02 as the sea floor was greater than 5000m deep in these areas and was below the range set for the sounder.\n\n4) Each file contains 11 columns of data; Ping_date, Ping_time, Ping_milliseconds, Latitude, Longitude, Position_status, Depth, Line_Status, Ping_status, Altitude, GPS_UTC time.\n\n5) For practical purposes, the columns of interest will be Ping_date, Ping_time, Latitude, Longitude and Depth. Other columns are ancillary acoustics information and can be ignored. Line status should be 1 (meaning good) as sea floor was only picked when it could be easily defined. If the sea floor could not be visually defined or was deemed to uncertain, it was not picked in the echogram. Hence sea floor may not be totally contiguous.\n\n6) Depth of the sea floor was only defined for those areas deemed to be 'on transect', i.e. straight transects for acoustics survey purposes. Deviations from the transect, i.e. to pick up moorings, conduct target or routine trawls or visit nice looking bergs were deemed 'off transect' and were excluded from the analysis.\n\n7) Sea floor depth was primarly defined for the purposes of the acoustics analysis, i.e. exclusion from the echograms. Hence the values in the files are for the 'sea floor exclusion line' that is set above the true sea floor in order to exclude noise from the analysis. This means the sea floor depths in these files are likely to be an underestimate of the true depth. The uncertainty is likely to be of the order of 2 to 10m.\n\n8) Another source of error is that depth was calculated with values of absorption coefficient and sound speed set to default values derived from pre-cruise hydrographic data. One value for each parameter was applied to the whole data set. These values were; 0.028 dB/m (120 KhZ), 0.010 dB/m (38kHz), 0.041 dB/m (200 kHz), 0.0017 dB/m (12kHz - bathy sounder) for absorption coefficient and 1456 m/s for sound speed.\n\n9) These values will be recalculated from the oceanographic data derived during the voyage and applied to the data set during post-processing (forthcoming analyses for May-June 2006). Revision of these parameters may cause a slight shift in the calculated depths, although this is likely to be small. \n\n10) Reprocessing of the data may also result in more accurate bottom detection. This data should be available post June 2006 and will be sent to interested parties as soon as it is completed. \n\n11) Dataset was created by Esmee van Wijk.", "links": [ { diff --git a/datasets/BROKE-West_ACS_1.json b/datasets/BROKE-West_ACS_1.json index 9f6a8133d9..9301dfa7a9 100644 --- a/datasets/BROKE-West_ACS_1.json +++ b/datasets/BROKE-West_ACS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_ACS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Profiles of visible light absorption and attenuation coefficients were measured in the upper 100m of the water column.\n\nData Acquisition:\nThe Wetlabs ACS spectral absorption and attenuation meter was mounted on a deployment frame together with a Seabird pump, a Wetlabs DH-4 data logger and two battery packs. This set-up was as recommended in the Wetlabs manual. The logger was set to control the ACS once the on/off magnet had been inserted. The data acquisition program comprised 2 minutes delay time to allow the instrument to be deployed over the stern; 30 seconds warm-up time; 30 seconds flush time during which the pump was activated, and finally 12 minutes of data acquisition. Physically, the instrument was attached to the winch, the magnet was inserted as soon as permission to deploy had been obtained from the bridge, the instrument was lowered directly to 20m, until 1.5 minutes since insertion of the magnet. The instrument was then brought to just below the surface and lowered at 0.5m per second to a depth of 100m, then retrieved at the same speed. Once the instrument was back on deck the magnet was removed to prevent dry operation of the pump.\n\nThe data logger received an instrument-specific binary format data file for each deployment, with automatic sequential file numbering. These files were uploaded after each deployment. \n\nData Processing:\n\nThe Wetlabs software program WAP was used to extract ascii data from the binary files. This procedure included corrections for internal instrument temperature and the latest manufacturer's calibration for wavelength. Note that although daily calibrations were performed during the cruise, the manufacturer advised against using these calibrations as conditions were suboptimal (milli-Q water not fresh, environment not totally dry or well temperature-controlled).\n\nA matlab script, acs.m, written by the principal investigator, continues the data processing. Data recorded in air are discarded, remaining data are binned to 2m depth intervals, occasional spurious data with a discontinuity in absorption or attenuation spectra are removed, and a correction is applied to account for differences in ocean water temperature and salinity compared to the calibration conditions. This final step uses first-cut CTD data courtesy of the oceanography team (Bindoff et al).\n\nNot yet complete (as of 2006-03-10):\n\nRemaining spurious data need to be weeded out by hand. These include non-systematic quirks such as occurrence of bubbles or larger particles in the optical path.\n\nThe depth needs to be corrected for an offset of some 4m plus the difference between the pressure sensor location and the ACS-inlet location.\n\nDataset Format:\nFor each 100m profile, a single ascii file is available, comprising instrument calibration data and a time sequence of attenuation and absorption spectra. By placing each of the profile files from one cruise transect in a single directory, the acs.m routine can be applied to one leg at a time, yielding matlab fields of [station, depth (0:2m:100m), wavelength (87 wavelengths)]. The acs.m script includes details of which CTD station number refers to which ACS file number. This information is also supplied in the station log file jill_brokew_stations.xls.\n\nAcronyms Used:\nACS - Absorption (a) Attenuation (c) Spectral meter, produced by Wetlabs\nCTD - Conductivity, Temperature, Pressure.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_ADCP_1.json b/datasets/BROKE-West_ADCP_1.json index 50d2e6bdf0..d4a8d8a47f 100644 --- a/datasets/BROKE-West_ADCP_1.json +++ b/datasets/BROKE-West_ADCP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_ADCP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Acoustic Doppler Current Profiler (ADCP) data were acquired constantly over the duration of the Australian 2006 V3 BROKE-West survey. Data presented here are the results of 1/2 hour integrations of the cruise data from the start of the voyage in Fremantle, Australia, to the start of the return leg just north of Australia's Davis Station in Antarctica (-66.56S, 77.98E). North and eastward components of the current velocity are given for depths up to 300m below the surface along the ship track. \n \nData Acquisition: \n\nThe shipboard ADCP is a continuous broadband recording device that operates over the duration of the voyage, ensonifying the water column once a second. As the instrument is fixed to the ship, it has a range of approximately 250m deep. Data from the shipboard Ashtek 3 dimensional GPS system is used along with bottom tracking data (when the water is shallow enough i.e. less than 250m) and automatically integrated into ADCP ping data to provide absolute current velocities. \n\nData Processing:\n\nThe ship ADCP constantly and automatically collects and stores raw .rawdp binary files in ensembles of three minutes worth of pings. This is regularly automatically collated into larger .adp files containing data for several hours (200+ ensembles). This data are processed for use in analysis using specialist software provided by Mark Rosenberg (mark.rosenberg AT utas.edu.au) that integrates together data from the ADCP .adp files for periods (30 minutes in this case) over a give time (from cruise start to the 3-Mar-2006). This produces .any ASCII files. These ASCII files are read into the Matlab processing package using scripts provided by Sergeui Sokolov (sergeui.sokolov AT csiro.au) which then produces the .mat matlab data files covered by this metadata. ADCP data requires proper calibration with respect to ship motion, which were not carried out for this data set, and could cause significant change when processed properly after the voyage. \n\nDataset format:\n\nThe processed ADCP file is given in matlab .mat format. All 1/2 hour integrations of ADCP data for BROKE-West from 3 days (31-dec-2005) before departure from Fremantle, to the 3-Mar-2006 are included, each column in each matrix or array representing an individual 1/2 hour integration in chronological order. There are numerous gaps in the data that occurred when the ADCP crashed and was not immediately reset or when bad data prevented processing. The location can be identified by plotting a scatter plot of longitude vs latitude, and the times by plotting the julian date. \n\nThe matlab variables contained in the BROKE_West_ADCP.mat file are contained inside the adcp structure:\n\nlon: Longitude (decimal degrees)\nlat: Latitude (decimal degrees)\ntime: Each column gives the year month day and hour of collection of the corresponding columns in the other variables.\ndepth: Depth of each corresponding velocity value for each 1/2 profile. 60 fixed bin depths are given for each profile. (meters)\npress: As for depth but given in db. (db)\nu: Absolute current eastward component in ms-1 for each depth and profile.\nv: Absolute current northward component in ms-1 for each depth and profile.\nunav: Ship absolute eastward component in ms-1 for each profile\nvnav: Ship absolute northward component in ms-1 for each profile\njtime: Julian date for each profile (julian days)\nbadvals: Indexes of anomolous latitude and longitude values\n\nAcronyms used: \n\nADCP: Accoustic Doppler Current Profiler\nIASOS: Institute of Antarctic and Southern Ocean Studies\nCSIRO: Commonwealth Scientific and Industrial Research Organisation\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_CTD_Niskin_1.json b/datasets/BROKE-West_CTD_Niskin_1.json index 8bc9bcd8fc..f2fd55ed36 100644 --- a/datasets/BROKE-West_CTD_Niskin_1.json +++ b/datasets/BROKE-West_CTD_Niskin_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_CTD_Niskin_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3 litres of seawater were collected every 2nd CTD (conductivity, temperature and depth) cast on every CTD transect of the BROKE-West voyage. 7 CTD transects were completed on the BROKE-West voyage, all on southwards legs. Samples were collected at 6 depths in the top 200 m of the water column using niskin bottles. 2 litres were filtered through polycarbonate filters and 1 litre was filtered through a fibreglass filter. Chemical digestion of the polycarbonate filter enabled us to determine the particulate silicon concentration for each sample (using the nutrient autoanalyser onboard the Aurora Australis, see hydrochemistry section), fibreglass filters have been dried and stored for CHN analysis back on shore.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_CTD_RMT_1.json b/datasets/BROKE-West_CTD_RMT_1.json index 1b50e99b1b..51bdcfd7c6 100644 --- a/datasets/BROKE-West_CTD_RMT_1.json +++ b/datasets/BROKE-West_CTD_RMT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_CTD_RMT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CTD data were acquired when the RMT instrument was in the water. \n\nData Acquisition:\n\nThere is a FSI CTD sensor housed in a fibreglass box that is attached to the top bar of the RMT. The RMT software running in the aft control room establishes a Telnet connection to the aft control terminal server which connects to the CTD sensor using various hardware connections. Included are the calibration data for the CTD sensor that were used for the duration of the voyage.\n\nThe RMT software receives packet of CTD data and every second the most recent CTD data are written out to a data file. Additional information about the motor is also logged with the CTD data. \n\nData are only written to the data file when the net is in the water. The net in and out of water status is determined by the conductivity value. The net is deemed to be in the water when the conductivity averaged over a 10 second period is greater than 0. When the average value is less than 0 the net is deemed to be out of the water. New data files were automatically created for each trawl.\n\nData Processing:\n\n1. Adjustment of the net open time.\n\nIf the net did not open when first attempted then the net was 'jerked' open. This meant the winch operator adjusted the winch control so that it was at maximum speed and then turned it on for a very short time. This had the effect of dropping the net a short distance very quickly. This dislodges the net hook from its cradle and the net opens. The scientist responsible for the trawl would have noted the time in the trawl log book that the winch operator turned on the winch to jerk the net.\n\nThe data files will have started the 'net open' counter 10 seconds after the user clicks the 'Net Open' button. If this time did not match the time written in the trawl log book by the scientist, then the net open time in the CSV file was adjusted. The value in the 'Net Open Time' column will increment from the time the net started to open to the time that the net started to close.\n\nThe pressure was also plotted to ensure that the time written down in the log book was correct. When the net opens there is a visible change in the CTD pressure value received. The net 'flies' up as the drag in the water increases as the net opens. If the time noted was incorrect then the scientist responsible for the log book, So Kawaguchi, was notifed of the problem and the data file was not adjusted.\n\n2. Removing unused columns from the original log files.\n\nThe original log files that were produced by the RMT software were trimmed to remove any columns that did not pertain to the CTD data. These columns include the motor information and the ITI data. The ITI data gives information about the distance from the net to the ship but was not working for the duration of the BROKE-West voyage. This trimming was completed using a purpose built java application. This java class is part of the NOODLES source code.\n\nDataset Format:\n\nThe dataset is in a zip format. There is a .CSV file for each trawl, 125 in total.\nThere were 51 Routine trawls and 74 Target Trawls.\nThe file naming convention is as follows:\n\n[Routine/Target]NNN-rmt-2006-MM-DD.csv\n\nWhere,\n\nNNN is the trawl number from 001 to 124.\nMM is the month, 01 or 02\nDD is the day of the month.\n\nAlso included in the zip file are the calibration files for each of the CTD sensors and the current documentation on the RMT software.\n\nEach CSV file contains the following columns:\n\nDate (UTC)\nTime (UTC)\nShip Latitude (decimal degrees)\nShip Longitude (decimal degrees)\nConductivity (mS/cm)\nTemperature (Deg C)\nPressure (DBar)\nSalinity (PSU)\nSound Velocity (m/s)\nFluorometer (ug/L chlA)\nNet Open Time (mm:ss) If the net is not open this value will be 0, else the number of minutes and seconds since the net opened will be displayed.\n\nWhen the user clicks the 'Net Open' button there is a delay of 10 seconds before the net starts to open. The value displayed in the 'Net Open Time' column starts incrementing once this 10 seconds delay has passed. Similarly when the user clicks the 'Net Close' button there is a delay of 6 seconds before the net starts to close. Thus the counter stops once this 6 seconds has passed.\n\nAcronyms Used:\n\nCTD: Conductivity, Temperature, Pressure\nRMT: Rectangular Midwater Trawl\nCSV: Comma seperated value\nFSI: Falmouth Scientific Inc\nITI: Intelligent Trawl Interface\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_CTD_au0603_2.json b/datasets/BROKE-West_CTD_au0603_2.json index f9d798cddb..81d6567bdf 100644 --- a/datasets/BROKE-West_CTD_au0603_2.json +++ b/datasets/BROKE-West_CTD_au0603_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_CTD_au0603_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements around the 'BROKE-West' survey area along the Antarctic continental margin between 30 degrees and 80 degrees south were conducted aboard Aurora Australis cruise au0603 (voyage 3 2005/2006, 2nd January to 12th March 2006). A total of 120 CTD vertical profile stations were taken, most to within 15 m of the bottom. Over 2500 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate and ammonia), 18O, dissolved inorganic carbon, alkalinity, particulate organic carbon/nitrogen/silicate, dimethyl sulphide, and biological parameters, using a 24 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package, while near surface current profile data were collected by a ship mounted ADCP. Data from the array of ship's underway sensors are included in the data set. \n\nThis report describes the processing/calibration of the CTD and ADCP data, and details the data quality. An offset correction is derived for the underway sea surface temperature and salinity data, by comparison with near surface CTD data. LADCP data are not discussed in this report. Note that the data processor was not a cruise participant, thus this report does not describe all details of the shipboard field data collection or the problems encountered. CTD station positions are shown in Figures 1a and b, while CTD station information is summarised in Table 1. Niskin bottle sampling at each station is summarised in Table 2. (see word document detailed below for figures and tables)\n\nFurther information is available in a word document available as part of the download.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_Cetaceans_1.json b/datasets/BROKE-West_Cetaceans_1.json index 9c1a3bf1f1..c2969a3943 100644 --- a/datasets/BROKE-West_Cetaceans_1.json +++ b/datasets/BROKE-West_Cetaceans_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_Cetaceans_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observation on V3 commenced as the Aurora Australis departed Fremantle and concluded on the approach to Hobart. \n\nThe SOCEP research objective is to detect and document cetacean sightings and relevant environmental and other information throughout the voyage. The BROKE-West multidisciplinary voyage provides an opportunity to correlate sightings data with oceanographic and biology research conducted by other programs.\n\nSearch effort is conducted over a broad range of weather conditions. The majority of Antarctic species are medium to large whales, with cues that can be detected in relatively high Beaufort sea states up to and including Beaufort Sea State 7.\n\nObservers search for whales while ever light, weather and sea conditions are suitable unless the vessel is stopped (e.g. CTD stations) or traveling slowly (e.g. trawling).\n\nData are recorded using a laptop computer-based sighting program (Wincruz for Logger v3) that automatically logs under-way data from the ship's system including GPS position, ship course and speed, wind direction and speed, and also downloads time and date when required (F1 key).\n\nData Collection\nIn the preferred and highest level of (Full Effort) two observers are positioned on the port (Port) and starboard (Starboard) sides of the flying bridge (wheelhouse roof). The search area is an arc 180 degrees ahead to abeam of the vessel, primarily with the naked eye and augmented by the use of Fujinon 7x50 binoculars.\n\nA third observer (Tracker) is also stationed on the flying bridge. This person's role is to positively identify species, numbers and behaviour, particularly in the case of distant sightings, with the aid of Fujinon 25 x 150 binoculars (BigEyes). This team member also captures digital video footage of cetacean sightings when appropriate.\n\nThe fourth rostered team member, the Central Logger (CL) is located on the bridge and communicates with those on the flying bridge via hand-held radio transceiver. The role of the CL is to record all relevant data on the Logger laptop computer.\n\nWhen in sea ice, a fifth member of the team ('Duplicate Identifier') is rostered to collect sea ice digital still images and video, and enter ice data in the SeaIce page in Logger.\n\nThe CL monitors the effort activity and progressively updates as necessary general information such as search effort, observers, weather, sea conditions.\n\nSearch effort is dropped a lower level of effort (CAS Effort), if visibility is determined to be too poor for Full Effort due to some combination of adverse weather conditions that precluded detection of most species (i.e. strong winds, fog, and large swell, confused swell, high sea state). If conditions become too poor to survey, or if the ship is traveling slowly or stopped, the effort is terminated (Off Effort). At such times the CL is generally rostered to remain on the bridge to ensure that passing whales do not go unreported, and to alert the rest of the team when the ship begins transiting at speed again or if visibility improves.\n\nSightings\nWhen observers report whale sightings the CL enters the time, angle and distance from vessel, species identification, number of animals, sighting cue, behaviour and presence of ice and ancillary data. Cetaceans are identified to the lowest taxonomic level possible. Positive species identification is made only when there is certainty. Best, high and low estimates of group size are recorded for each sighting, and where more than one observer made an estimate, the final record is arrived at by consensus.\n\nPhotographic records of cetaceans (and other wildlife and habitat) are collected opportunistically using digital cameras.\n\nOther Wildlife\nSeal and penguin species are logged while in sea ice, and opportunistically elsewhere. Flying birds within 100 metres of the ship are logged half-hourly, and large flocks are logged when observed.\n\nOther Event\nOccurrences such as the sighting or marine debris are logged as they are observed.\n\nSea Ice Data\nSea ice observations are recorded in Logger every 10 minutes while in transit in sea ice unless the ship is stopped or transiting slowly. Sea ice data are based on observations within a 1km 90 degree radius of the ship on the port side. A buoy of known diameter is suspended just above the waterline in front of the bridge to assist with estimates of ice and snow thickness.\n\nSea ice still digital images are taken every 10 minutes while in transit in sea ice (unless transiting slowly), coinciding with SeaIce data recording in Logger.\n\nSea Ice continuous video is taken for ten minutes each half-hour, showing the bow and horizon.\n\nThe images and video assist in post cruise validation of sea ice thickness and assessment of the 1km radius for sea ice data collection. Sea ice habitat images are also captured when/where minke whales are sighted.\n\nAcronyms\n\n% Species 1\nPercentage of group made up by Species 1\n\n% Species 2\nPercentage of group made up by Species 2\n\n% Species 3\nPercentage of group made up by Species 3\n\nBearing\nBearing of sighting, in degrees, relative to the ship\n\nBeaufort\nSea state assessment using Beaufort Scale (1-12)\n\nBerg Count\nNo of icebergs 180 degrees ahead\n\nBest school size\nBest estimate of the number in group\n\nCasual observations (CAS)\nLower level of Effort e.g. fewer observers\n\nDuplicate Identifier\nPerson gathering/entering ice observations/images\n\nDynamics\nChanges to the pod's composition.\n\nEffort Status\nClassification of level of observation effort\n\nEnd Time\nTime sighting observation ended\n\nEst distance\nEstimated distance from ship in nm.\n\nFloe Size\nDescriptive of size/nature of ice flows\n\nFull effort\nHighest level of observation effort\n\nGlare strength\nClassification of glare as it effects visibility\n\nHabitat Bathymetry\nDetermined by reference to ship's chart\n\nHigh school size\nHighest estimate of the number in group\n\nIce Conc\nConcentration of ice, in tenths\n\nIce Thick\nIce thickness in cm\n\nIce Type\nDescriptive nature of ice\n\nImage File\nIdentification number allocated to image taken at time of data entry\n\nIn or Near Ice\nIce conditions where wildlife was sighted\n\nInitial cue\nWhat first drew the observer's attention to the sighting.\n\nLeft Glare\nLeft extremity of glare\n\nLow school size\nLowest estimate of the number in group\n\nMethod\nWhether sighting was made using naked eye, 7x50 binoculars or 25x150 (Big-eye) binoculars\n\nMinke Vis\nEstimate of the distance at which a minke whale blow could be seen in prevailing conditions\n\nNotes\nFor Recorder's additional information and comments\n\nObserver\nPerson reporting the sighting\n\nOpen Water\nOverall ice/water situation\n\nPort\nObserver monitoring the ocean on the port side\n\nPrimary Ice Obs.\nObservations of thickest ice type\n\nReaction\nThe animal's reaction to the ship\n\nRecorder\nPerson entering data into Logger\n\nRight Glare\nRight extremity of glare\n\nSecondary Ice Obs.\nObservations of second-thickest ice type\n\nSightability\nAssessment of overall viewing conditions\n\nSighting No\nProgressive numbering of whale sightings by Logger (default)\n\nSnow Thick\nSnow thickness in cm\n\nSnow Type\nDescriptive of snow on ice\n\nSpecies 1\nWhen multiple species are being reported, with the species in greatest number listed first\n\nStarboard\nObserver monitoring the ocean on the starboard side\n\nSwell Code\nDescriptive of ocean swell\n\nSwell Direction\nCompass direction from which swell moving.\n\nSwim direction\nAnimal's swim direction in degrees relative to the ship's heading\n\nTertiary Ice Obs\nObservations of third-thickest ice type\n\nTopog\nDescriptive of ice topography e.g. ridging\n\nTotal Ice Conc\nIce concentration in tenths\n\nTracker\nObserver using BigEyes binoculars to identify species, and assisting other observers generally\n\nWeather Code\nWeather conditions effecting visibility\n\nAn excel spreadsheet containing a full list of terms used in the observation logs is available for download from the URL given below.\n\nThis work was completed as part of ASAC projects 2253, 2655 and 2679 (ASAC_2253, ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_DMS_DMSP_1.json b/datasets/BROKE-West_DMS_DMSP_1.json index ac1a1a9446..b6b76fdc64 100644 --- a/datasets/BROKE-West_DMS_DMSP_1.json +++ b/datasets/BROKE-West_DMS_DMSP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_DMS_DMSP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data Acquisition:\n\nSampling was performed on seawater collected from CTDs and minicosm experiments. Sampling involved the collection of 250 mL of seawater from each Niskin bottle and minicosm sampled. 100 mL of this was fixed with 1 mL of concentrated hydrochloric acid (HCl). A second 100 mL sample was filtered through a 0.45 micron filter and then fixed with HCl. The remaining water was filtered and purged, with the volatile gases eluted being trapped on gold wool enclosed in glass tubes.\n\nData Analysis:\n\nAnalysis of the gold wool tubes involved heating the tubes to separate the dimethylsulphide (DMS) and then purge and trap followed by gas chromatography (GC) to give the DMS concentration of the seawater sample. The fixed water samples and filtered fixed water samples were basified and then the DMS formed during this process was purged, trapped and analysed by GC to determine the dissolved and particulate dimethylsulphoniopropionate (DMSP) concentrations. Analysis is expected to take approximately one year to complete.\n\nDataset Format: The data for the CTD sampling is in the following format - CTD Number; Niskin Bottle; DMS Concentration (nM); DMSP particulate concentration (nM); DMSP dissolved concentration (nM)\n\nThe data for the minicosm sampling is in the following format: Minicosm Number; Minicosm Day; Hour; Tank Number; DMS Concentration (nM); DMSP particulate concentration (nM); DMSP dissolved concentration (nM)\n\nAcronyms Used:\nCTD - conductivity, temperature, pressure\nDMS - dimethylsulphide \nDMSP - dimethylsulphoniopropionate\nDMSO - dimethylsulphoxide\nGC - gas chromatography\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_FRRF_1.json b/datasets/BROKE-West_FRRF_1.json index df16b43891..03c9f312cf 100644 --- a/datasets/BROKE-West_FRRF_1.json +++ b/datasets/BROKE-West_FRRF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_FRRF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At each CTD station the Fast Repetition Rate Fluorometer (FRRF) was carried out onto the trawl deck and shackled (+ cable tie) to the winch cable. When the crew in the aft control room were ready the PAR (Photosynthetically Active Radiation) cap was removed and the FRRF activated with the magnet. It was deployed at a rate of 0.3m/sec to 10m, stopped for 30sec, then the descent was continued to 100m at same rate where it was stopped for another 30 sec. The FRRF was then brought back up at 0.3m/sec to deck. Once on deck the FRRF was turned off, it was hosed down with hot fresh water and the PAR cap replaced. Underway data were collected from the flow-through system in the lab on all South/North transects. West to East legs were not surveyed. The FRRF data were downloaded after every Vertical Drop and at the end of the Underway legs. The post-processing and analysis of data will be carried out after the voyage. The Final dataset is in the form of a Binary file for each drop and Underway leg.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_Grazing_1.json b/datasets/BROKE-West_Grazing_1.json index 4d18434b07..1d3bd105e6 100644 --- a/datasets/BROKE-West_Grazing_1.json +++ b/datasets/BROKE-West_Grazing_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_Grazing_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data contain results from grazing dilution experiments conducted during BROKE-West. \n\nExperiments were conducted at 22 locations on the BROKE-West transect. \n\nData are presented in an excel spreadsheet containing sample collection information (longitude, latitude, UTC date and time, depth), experiment details (incubation time, dilution series), experiment results (chlorophyll a, bacterial concentrations, heterotrophic flagellate concentrations, phytoplankton concentrations, microzooplankton concentrations, geometric mean predator density, phytoplankton growth rates, microzooplankton grazing rates for bacteria and phytoplankton, bacterial growth rates). \n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_LADCP_1.json b/datasets/BROKE-West_LADCP_1.json index a0ec2bcbef..e284b79056 100644 --- a/datasets/BROKE-West_LADCP_1.json +++ b/datasets/BROKE-West_LADCP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_LADCP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lowered Acoustic Doppler Current Profiler (LADCP) data were acquired while the Conductivity Temperature Depth (CTD) sensor was in the water during the Australian 2006 V3 BROKE-west survey. \n\nData Acquisition: \n\nThe LADCP is mounted on the CTD frame and is lowered through the water column from surface to bottom on each CTD cast. During the cast upward and downward facing sensor heads ensonify the water column with four beams per head, collecting the data necessary to calculate the vertical velocity of the LADCP on the CTD frame, as well as the northward and eastward components of the current relative to the LADCP for the entire water column. Once the LADCP has been retrieved, the data collected in the cast are downloaded to a PC as two raw binary .adp files, one for the upward looking head and one for the downward. This occurs for each CTD cast. The only modification to a normal CTD cast procedure for the LADCP is a 5 minute pause within 50 m of the sea floor on the upcast. This gives the downward sensor time to gather enough data for later determination of relative bottom velocity.\n\nThe shipboard ADCP is a continuous recording device that operates over the duration of the voyage, ensonifying the water column once a second. It operates in a similar way to the LADCP, except that as it is fixed to the ship, it has only a range of approximately 250m deep. The ADCP data are necessary for final LADCP data processing. Similarly shipboard 10 seconds GPS records and CTD pressure data for the period of each cast is required for LADCP data processing. \n\nData Processing:\n\nOnce collected the upward and downward raw .adp LADCP files are subjected to fairly extensive processing using software written for the Matlab package, to produce the usable .mat data files given by this dataset. This software, written by Sergeui Sokolov (sergeui.sokolov AT csiro.au), and slightly modified for the 2005/06 V3 BROKE-west voyage by Andrew Meijers and Andreas Klocker combines the raw .adp files with the shipboard ADCP data, 10 second ship GPS data and CTD profile data. While the raw LADCP .adp files can be processed alone with minimal CTD data (date, start time, end time, start and end lat and long and max depth), they will only give current velocities relative to the CTDs frames motion. To gain an absolute profile the software identifies bottom and surface reflections, and uses this and ship ADCP and GPS data as boundary conditions for an integration of the velocity shear in the raw .adp files. The end result of processing is velocity in north and south components for each depth over the CTD cast. For more details refer to the above reference (Wijffels, et. al. 2005). \n\nDataset format:\n\nThe processed LADCP file (AU0603_LADCP_3_to_120.mat) is given in matlab .mat format, and before future processing with properly calibrated ADCP data, should be regarded as preliminary only. All CTD casts for BROKE-West are included, except for casts 1,2 and 119, where the LADCP was not used in the CTD cast. Casts 1 and 2 are not in the dataset, while 119 is represented by NaN (not a number) values. The absence of casts 1 and 2 from the data mean that care should be taken in attributing the data to the correct cast. Column one in each velocity matrix represents cast 3, not 1, and column 2 is cast 4 and so on up to column 118 representing CTD cast 120. On several casts the ADCP data were not available, meaning only part of the LADCP processing could be completed. This occurred for casts 5, 46, 91, 92, and 96, and data given here are unreferenced to a bottom velocity or ship track. Other errors occurred that meant that casts 68 and 115 could not be processed at all, and so data for these casts are represented by NaN values.\n\nCasts not present in dataset: 1,2\nCasts represented by NaN values: 68,115 and 119 \nLADCP data created without ADCP input on casts: 5,46,91,92,96 (warning unconstrained values) \n\nThe matlab variables contained in the file are:\n\nbindep: 20 depth levels in meters at which velocity data occurs for each profile. Each row of matrix represents a depth level, each column a CTD cast, ascending from cast 3 to 120. \ndate: Start date of each cast (UT) (year month day)\nlat: Start latitude of each cast (decimal degrees)\nlon: Start longitude of each cast (decimal degrees)\nstationno: Last 3 digits gives the CTD cast number\ntime: Start time of CTD cast (UT) of each cast (hours min sec)\nu_down: u (eastward) component of velocity in ms-1 for each bindepth and CTD cast, using only downward looking head data\nu_final: As for u_down but using data from both heads. This is the best estimate of velocity.\nu_up: As for u_down, but upward looking head data only.\nv_down: As for u_down, but northward component of velocity\nv_final: As for u_final, but northward component of velocity\nv_up: As for u_up, but northward component of velocity\nzbottom: Bottom depth in meters for each cast (m)\n\nAcronyms used: \n\nLADCP: Lowered Acoustic Doppler Current Profiler\nADCP: Acoustic Doppler Current Profiler\nCTD: Conductivity Temperature Depth\nIASOS: Institute of Antarctic and Southern Ocean Studies\nCSIRO: Commonwealth Scientific and Industrial Research Organisation\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_Protists_1.json b/datasets/BROKE-West_Protists_1.json index 9b80374fed..a5fa9e7f9e 100644 --- a/datasets/BROKE-West_Protists_1.json +++ b/datasets/BROKE-West_Protists_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_Protists_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BROKE-West survey was conducted from 30 degrees E and 80 degrees E between January and March 2006. It consisted of 1 east-west transect at the northernmost limit of the survey between 60 degrees S and 62 degrees S between the 10 and 19 of January, followed by 11 meridional transects separated by 5 degrees of longitude and extending from approximately 62 degrees S to the Antarctic continental shelf between the 19 January and 3 March.\n\nThis dataset details research undertaken to determine the identity, composition and abundance of protists in the survey area.\n\nSome explanations for terms used in the dataset are as follows:\n\n1. Group is the taxonomic Phylum, Class and occasionally Order to which the taxa belongs. The abbreviations are:\na. Diatom - Diatomophyceae/Bacillariophyceae. These are subdivided into the taxonomic Orders Centrales (centric) and Pennales (pennate)\nb. Chryso - Chrysophyceae\nc. Dino - Dinophyceae\nd. Eugleno - Euglenophyceae\ne. Hapto - Haptophyceae\nf. Prasino - Prasinophyceae\ng. Silico - Dictyochales (silicoflagellates)\nh. Choano - Craspedophyceae /Choanoflagellida\ni. Ciliate - Phylum Ciliophora\nj. Tintinnid - Phylum Ciliophora , Order Spirotrichea, Class Tintinnida\nk. Protozoa - refers to grouped rare taxa belonging to a number of Classes\n\n2. Autotroph/Heterotroph refers to the trophic status of the taxa, indicating whether they are autotrophic (plant) or heterotrophic (animal).\n\n3. CTD Station Number refers to the station at which samples were collected. Type indicates whether the samples were collected at the surface \"surf\" or at the point at which there was maximum chlorophyll fluorescence detected from the CTD flurometer trace \"ChlMax\".\n\nThe data are all cell concentration numbers.\n\nFor more information, see the other metadata records related to ASAC project 40 (ASAC_40), ASAC project 2655 (ASAC_2655) and ASAC project 2679 (ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_RMT_Fish_1.json b/datasets/BROKE-West_RMT_Fish_1.json index 6ac6eac672..d7b87928f5 100644 --- a/datasets/BROKE-West_RMT_Fish_1.json +++ b/datasets/BROKE-West_RMT_Fish_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_RMT_Fish_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset gives an overview of the fish (larvae) caught in the RMT 8+1 (Rectangular Midwater Trawl composed of nets with an 8 square metre and 1 square metre net opening surface area respectivly). For correct deployment procedure please see the'krill catches' document available for download at the URL given below.\n\nColumns\n'sheet' : 'Samples'\nStation number: Station number as attributed by Krill group\nTrawl type: Routine or Target Trawl\nStart Latitude: Latitudinal position at start of trawl (decimal notation)\nStart Longitude: Longitudinal position at start of trawl (decimal notation)\nStart Date: Date at the start of the trawl\nStart Time: Time (UTC) at start of the trawl\nEnd Latitude: Latitudinal position at end of trawl (decimal notation)\nEnd Longitude: Longitudinal position at end of trawl (decimal notation)\nEnd Date: Date at the end of the trawl\nEnd Time: Time (UTC) at the end of the trawl\nNet Size: Size of the net from which the sample was collected\nSample number: Individual Sample code as Used by anton van de Putte\nBar Code: bar code as used by AAD\nFamily: Taxonomic family to which the sample belongs\nSpecies: Name of species\nSpecies short: abbrivation of speciesname, format Genus species==Gen_spe example Electrona antarctica=Ele_ant\nSL:Standarlength of the specimen (mm)\nTL: Total length of the specimen (mm)\n\nPreservation: preservaion method of sample: Ethanol (sample stored in 100% ethanol), Formalin (sample stored in 4% formalin solution) -80 degrees (sample stored in deep frezer at -80 degrees C).\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_flow_cytometry_1.json b/datasets/BROKE-West_flow_cytometry_1.json index 7fccfaf2be..1b0059c154 100644 --- a/datasets/BROKE-West_flow_cytometry_1.json +++ b/datasets/BROKE-West_flow_cytometry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_flow_cytometry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains concentrations of phytoplankton, protozoa, total bacteria and metabolically active bacteria assessed by flow cytometry on transects 12, 1, 3, 5, 7, 9 and 11 of the BROKE-West survey of the Southern Ocean between January and March 2006. Only total bacterial concentrations were assessed for transect 11.\n \n Between 4 and 12 depths were sampled for marine microbes and concentrations were assessed using FACScan flowcytometer. Phytoplankton were identified and counted based on the autofluorescense of chlorophyll a when excited by the 488 nm laser of the FACScan. Protozoa were identified and counted after staining with the acid vacuole stain Lysotracker Green. Total bacteria were identified and counted using the cell permeant SYTO 13 nucleic stain. Metabolically active bacteria were identified and counted after staining for intracellular esterases with the esterase stain 6CFDA.\n\nThe fields in this dataset are:\n\nLatitude\nLongitude\nTransect Number\nCTD number, flow file\nDepth (m)\nTotal bacteria (per millilitre)\nActive bacteria (per millilitre)\nDead bacteria (per millilitre)\nProtozoa (per millilitre)\nPhytoplankton (per millilitre)\n\nThis work was completed as part of ASAC project 40 (ASAC_40).", "links": [ { diff --git a/datasets/BROKE-West_hydroacoustic_dataset_1.json b/datasets/BROKE-West_hydroacoustic_dataset_1.json index 40b9b155a0..a8901d4ff1 100644 --- a/datasets/BROKE-West_hydroacoustic_dataset_1.json +++ b/datasets/BROKE-West_hydroacoustic_dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_hydroacoustic_dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data files, processing templates and documentation relating to the BROKE-West multifrequency echosounder (acoustic) survey carried out from the RSV Aurora Australis in the austral summer of 2005/06 (ASAC project 2655). The primary aim of the acoustic survey was to describe the distribution and abundance of Antarctic krill (Euphausia superba) in CCAMLR Division 58.4.2. However, these data are also relevant for studies of other sound-scattering targets detected by the echosounder system, for example other pelagic taxa or the seafloor. \n\nThe dataset is a collection of *.csv data files, *.ev processing files and *.pdf documentation files, organised into 4 categories:\n\n1. Acoustic survey: data files relating to the transects undertaken for the acoustic survey 2. Acoustic data processing: metadata files, processing templates and documentation relating to the collection and processing of the acoustic data 3. Acoustic results: results arising from the processing of the raw data. The raw data are described in a separate metadata record - \"AAD Hydroacoustics hard disks - data collected from Southern Ocean cruises...\" 4. Ancillary data: additional non-acoustic data used during the processing of the acoustic data\n\nThe file \"data_fields.pdf\" lists and describes the fields in each of the *.csv data files.\n\nThe file \"processing_methods.pdf\" provides a synopsis of the methods by which the raw acoustic data were collected and processed.\n\nThe BROKE-West survey was conducted on voyage 3 of the Aurora Australis during the 2005-3006 season. It was intended to be a comprehensive biological and oceanographic survey of the region between 30 degrees and 80 degrees east.", "links": [ { diff --git a/datasets/BROKE-West_krill_IGR_1.json b/datasets/BROKE-West_krill_IGR_1.json index fa41ca1be9..3b133fc3fc 100644 --- a/datasets/BROKE-West_krill_IGR_1.json +++ b/datasets/BROKE-West_krill_IGR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_krill_IGR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Crustaceans grow or shrink in size as they moult. Length of discarded moults represent length of animals before their moulting events. Therefore, by measuring length of discarded moult and length of animal after moult, growth increments at the time of moult can be obtained. IGR is defined as the growth increment expressed as a proportion of pre-moult total length (TL). IGR can be converted into daily growth rate for a given value of TL by calculating absolute growth increment and dividing by an estimate of inter-moult period (IMP). \n\nThe IGR technique depends on the collection of live krill in good condition. Krill were caught with an RMT-8 net and individual freshly caught animals were randomly selected from the catch and immediately transferred to individual jars. They were then maintained onboard and checked regularly for moults for up to 5 days following capture. The experiments were run a flowthrough seawater system which used 250 ml jars with small holes to allow water exchange in a large flow-through tank of seawater maintained at ambient ocean temperature. No additional food was provided. The system allowed experiments with over 4000 krill. Each krill was checked daily after capture to ascertain whether it had moulted. If an animal had moulted, then the animal and its moult were collected and frozen in liquid nitrogen or at -85 degrees C to be measured back ashore. The growth rate will be estimated from the difference in length of the uropod of the moult and that of the whole post-moult krill.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_krill_larvae_1.json b/datasets/BROKE-West_krill_larvae_1.json index 52e30a091f..1fc28a456b 100644 --- a/datasets/BROKE-West_krill_larvae_1.json +++ b/datasets/BROKE-West_krill_larvae_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_krill_larvae_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected during the BROKE-West voyage of the 2005-2006 season.\n\nThey are numbers of krill larvae per cubic metre of water at each of the stations at which data were collected. The data cover three species of Antarctic krill - Euphausia crystallorophias, Euphausia superba and Thysanoessa macrura.\n\nThe superba data have been published in the Kawaguchi et al paper, \"Krill demography and large-scale distribution in the Western Indian Ocean sector of the Southern Ocean (CCAMLR Division 58.4.2) in Austral summer of 2006\". The data for the other species will be presented in a forthcoming paper by Kerrie Swadling.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_mm_acoustics_2.json b/datasets/BROKE-West_mm_acoustics_2.json index b8f33c5080..6c6322634d 100644 --- a/datasets/BROKE-West_mm_acoustics_2.json +++ b/datasets/BROKE-West_mm_acoustics_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_mm_acoustics_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data Acquisition:\n\nDIFAR (DIrectional Fixing And Ranging) 53D sonobuoys were deployed every 30 minutes of longitude during each of the north-south sampling transects as part of the acoustic survey for marine mammals. Sonobuoys were also deployed opportunistically when large numbers of whales (in particular minke whales) were sighted. Additionally, on the initial E-W transect (#12) sonobouys were deployed prior to the majority of CTD stations.\n\nThe VHF receiving system for the sonobuoys aboard the ship began with a 6 element YAGI antenna mounted atop the ship's mast. The sonobuoy's VHF signal output from the YAGI was amplified through an Advanced Receiver Research VHF amplifier and received on ICOM PCR-1000 VHF receivers modified to improve low frequency audio output. The audio signal passed through a low pass anti-alias filter (National Instruments analogue bessel SCXI module) and was recorded onto a laptop through a National Instruments E-series (model 6062E) sound card at a sampling rate of 48kHz. Difar sonobuoys have an effective audio response up to 2.5kHz before the low-pass filter roll-off starts. DIFAR bearing information is carried on 7.5 and 15kHz carrier frequencies. \n\nOnce sonobuoys were deployed, recordings were made for at least 70 minutes unless the sonobuoy failed or the signal was lost. During recordings at CTD stations, recordings were typically made for the length of time it took to complete the CTD (4 or more hours). \n\nData Processing:\n\nSignals were monitored in real-time during acquisition using Ishmael software (Dave Mellinger, http://www.bioacoustics.us/ishmael.html). A scrolling spectrogram (FFT size: 16384 samples, overlap: 50%, frequency range displayed: 0-1000 Hz, time scaling: 5 sec/cm) was monitored in real-time. Sounds of interest were clipped and the time and description were logged in the sonobuoy deployment data logs. Bearings to sounds were attained with a modified version of DiFarV (Mark McDonald, http://www.whaleacoustics.com ). Note that bearings to the ship noise given by DifarV are ~180 degrees off for an as yet undetermined reason (potentially deep cold water propagation effects), but the bearings to whale sounds and other sounds of interest are thought to be correct. This appears to be the case with a series of light bulb calibration tests I did, suggesting that bearings to other sounds are in fact, correct.\n\nAfter acquisition, recordings were also post-processed in Ishmael with two further passes, one examining 0-2.5kHz, and another monitoring 0-1kHz again, to ensure as many marine mammal sounds as possible were identified. Clips were also re-examined when necessary to ensure species were correctly identified.\n\nIn instances when apparently multiple whales were calling, calculated bearings were used to determine whether the sounds came from different bearings, and hence, different whales. \n\nDataset Format:\n\nThe dataset description is in an excel workbook, with a summary sheet at the front. The summary sheet has a single line summarising each sonobuoy deployment. The sonobuoy deployment data log sheets are separated by days when the deployment began. Each is marked by date - eg 01.10 is the 10th of January. Each deployment has an initial entry and the following rows are a running log of the sonobuoy recording session. The data sheets and the summary sheet are in the following format with column headers from left to right:\n\nObserver(real time/post-processing)Summary of the sounds that occurred within the sample (70 minutes)\nTotal recording length (in minutes)\nDate\nUTC time of deployment\nInitial latitude (decimal degrees)\nInitial Longitude (decimal degrees)\nDepth setting of sonobuoy hydrophone (90, 120, or 300m)\nNational Instruments sound card gain (0, 5, or 10 times)\nShip heading (true degrees)\nShip speed (knots)\nDistance of deployment from CTD location (if applicable)\nUTC time of events (applies mainly to log of events in sonobuoy deployment data log)\nSpecies or sound description (applies mainly to sonobuoy deployment data log)\nComments\nSonobuoy type\n\nRaw data files are stored on a series of external hard drives.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE-West_particulates_1.json b/datasets/BROKE-West_particulates_1.json index dfa98fa38a..07574e7086 100644 --- a/datasets/BROKE-West_particulates_1.json +++ b/datasets/BROKE-West_particulates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE-West_particulates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Particulates in the water were concentrated onto 25mm glass fibre filters. \n\nLight transmission and reflection through the filters was measured using a spectrophotometer to yield spectral absorption coefficients.\n\nData Acquisition:\n\nWater samples were taken from Niskin bottles mounted on the CTD rosette. Two or three depths were selected at each station, using the CTD fluorometer profile to identify the depth of maximum fluorescence and below the fluorescence maximum. One sample was always taken at 10m, provided water was available, as a reference depth for comparisons with satellite data (remote sensing international standard). Water sampling was carried out after other groups, leading to a considerable time delay of between half an hour and 3 hours, during which particulates are likely to have sedimented within the Niskin bottle, and algae photoadapted to the dark. In order to minimise problems of sedimentation, as large a sample as practical was taken. Often so little water remained in the Niskin bottle that the entire remnant was taken. Where less than one litre remained, leftover sample water was taken from the HPLC group. Water samples were filtered through 25mm diameter GF/F filters under a low vacuum (less than 5mmHg), in the dark. Filters were stored in tissue capsules in liquid nitrogen and transported to the lab for analysis after the cruise. Three water samples were filtered through GF/F filters under gravity, with 2 30ml pre-rinses to remove organic substances from the filter, and brought to the laboratory for further filtration through 0.2micron membrane filters.\n\nFilters were analysed in batches of 3 to 7, with all depths at each station being analysed within the same batch to ensure comparability. Filters were removed one batch at a time and place on ice in the dark. Once defrosted, the filters were placed upon a drop of filtered seawater in a clean petri dish and returned to cold, dark conditions. One by one, the filters were placed on a clean glass plate and scanned from 200 to 900nm in a spectrophotometer equipped with an integrating sphere. A fresh baseline was taken with each new batch using 2 blank filters from the same batch as the sample filters, soaked in filtered seawater. After scanning, the filters were placed on a filtration manifold, soaked in methanol for between 1 and 2 hours to extract pigments, and rinsed with filtered seawater. They were then scanned again against blanks soaked in methanol and rinsed in filtered seawater. \n\nData Processing:\n\nThe initial scan of total particulate matter, ap, and the second scan of non-pigmented particles, anp, were corrected for baseline wandering by setting the near-infrared absorption to zero.\n\nThis technique requires correction for enhanced scattering within the filter, which has been reported to vary with species. One dilution series was carried out at station 118 to allow calculation of the correction (beta-factor). Since it is debatable whether this factor will be applicable to all samples, no correction has been applied to the dataset. Potential users should contact JSchwarz for advice on this matter when using the data quantitatively.\n\nNot yet complete:\n\nComparison of the beta-factor calculated for station 118 with the literature values.\n\nComparison of phytoplankton populations from station 118 with those found at other stations to evaluate the applicability of the beta-factor.\n\nDataset Format:\n\nTwo files: phyto_absorp_brokew.txt and phyto_absorp_brokew_2.txt: covering stations 4 to 90 and 91 to 118, respectively. Note that not every station was sampled.\n\nFile format: Matlab-readable ascii text with 3 'header' lines:\nRow 1: col.1=-999, col.2 to end = ctd number\nRow 2: col.1=-999, col.2 to end = sample depth in metres\nRow 3: col.1=-999, col.2 to end = 1 for total absorption by particulates, 2 for absorption by non-pigmented particles\nRow 4 to end: col.1=wavelength in nanometres, col.2 to end = absorption coefficient corresponding to station, depth and type given in rows 1 to 3 of the same column.\n\nThis work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679).", "links": [ { diff --git a/datasets/BROKE_Documentation_Logs_1.json b/datasets/BROKE_Documentation_Logs_1.json index 1fc152706a..9337584fd1 100644 --- a/datasets/BROKE_Documentation_Logs_1.json +++ b/datasets/BROKE_Documentation_Logs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE_Documentation_Logs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of scanned logs and documentation from the BROKE cruise of the Aurora Australis in the 1995/1996 season.\n\nAvailable logs include:\n\nBROKE V4 1995/1996 Catch Composition - 2 Logs\nBROKE V4 1995/1996 Krill Larvae Log\nBROKE V4 1995/1996 Krill Morphometrics - 3 logs\nBROKE V4 1995/1996 Trawl Log\nBROKE V4 1995/1996 Wet Lab Log\n\nSee the logs for further details.", "links": [ { diff --git a/datasets/BROKE_Fish_Zooplankton_RM8_1.json b/datasets/BROKE_Fish_Zooplankton_RM8_1.json index bc42622d48..cd0a3bb69f 100644 --- a/datasets/BROKE_Fish_Zooplankton_RM8_1.json +++ b/datasets/BROKE_Fish_Zooplankton_RM8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE_Fish_Zooplankton_RM8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the abstracts of the referenced papers:\n\nDistribution patterns of pelagic fish, larvae and juveniles collected by RMT trawls during BROKE survey to CCAMLR Division 58.4.1 were investigated. Nearly 2000 individuals, weighing 1210 g, were collected from approximately 1.5 million cubic metres of the upper 200 m of ocean, supporting the theory that Antarctic ichthyoplankton has low biomass. The collection consisted mainly of P. antarcticum larvae and juveniles and E. antarctica sub-adults, with a range of other notothenioid fish and myctophids. Three distinct biogeographic zones, with characteristic ichthyo- and zooplankton assemblages, were identified. The Oceanic Zone was dominated by myctophids and, in the western reaches, the paralepidid N. coasti. The shelf break zone comprised of myctophids, and the juveniles of notothenioid fish. The shelf zone consisted of notothenioid juveniles and sub-adults. Characteristic water masses and associated zooplankton assemblages were found throughout these three zones. Analysis of fish stomach contents indicated feeding on locally abundant zooplankton taxa. There was niche-partitioning of prey taxa and size classes, between both sympatric species and between different ontogenetic stages. Fish distributions corresponded to known patterns, and extended the geographic range of several species.\n\n#####\n\nZooplankton data from routine 0-200 m oblique trawls were analysed using cluster analysis and non-metric multidimensional scaling to define the communities in Eastern Antarctica (80-150 E), their distribution patterns, indicator species, and species affinities. Three communities were defined based on routine trawls. The Main Oceanic Community comprising herbivorous copepods, chaetognaths, and the euphausiid Thysanoessa macrura dominated the area west of 120 E. The area east of 120 E was dominated by Salpa thompsoni. The third community located in the neritic zone was dominated by Euphausia crystallorophias. Antarctic krill Euphausia superba did not form a distinct community in its own right, unlike previous observations in Prydz Bay. Krill were distributed throughout most of the survey area but generally in higher abundances towards the shelf break. Overall, krill abundance was low compared with previous net surveys in Prydz Bay. Three main types of assemblages were identified based on target trawls. The first group was dominated by krill (mean 1149 individuals per 1000 cubic metres) which represented greater than 99% of Group 1 catches in terms of numbers and biomass. Group 2 comprised the bulk of target trawls and comprised a wide diversity of species typical of the main oceanic community, with a mean abundance approximately half of that observed in the routine trawls. The third group comprised trawls in the neritic zone dominated by E. crystallorophias. No salp-dominated aggregation was found. While E. superba did not dominate a distinct community geographically as seen in previous Prydz Bay surveys, it did dominate discrete layers or aggregations, showing that both horizontal and vertical separation of communities exist.\n\n#####\n\nThe download file contains the following documents:\n\n199596040Composition.csv\n199596040Density.csv\n199596040Biomass.csv", "links": [ { diff --git a/datasets/BROKE_Krill_Scans_1.json b/datasets/BROKE_Krill_Scans_1.json index a9ce928cb4..d89a28ee1e 100644 --- a/datasets/BROKE_Krill_Scans_1.json +++ b/datasets/BROKE_Krill_Scans_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE_Krill_Scans_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The download file contains files (Broke 1, Broke 2 and Broke 3) in three formats resulting from the scanning of three plots of BROKE transects with annotations about krill aggregation.\nThe tiff is the primary file from the scanning. The jpeg and pdf were created from the tiff for quick viewing.\n\nThe numbered points on the plots are trawl locations.\nThe annotations include information about krill aggregation from the echosounder and also information from the trawls. \nThe data contributed to the two papers listed in the references section.\n\nBROKE was a marine science cruise conducted by the Aurora Australis during the 1995-1996 season (voyage 4).", "links": [ { diff --git a/datasets/BROKE_at_sea_obs_1.json b/datasets/BROKE_at_sea_obs_1.json index 5431dd04d5..6ff00a3593 100644 --- a/datasets/BROKE_at_sea_obs_1.json +++ b/datasets/BROKE_at_sea_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE_at_sea_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of at sea observations made of icebergs, seabirds and whales on the BROKE voyage of the Aurora Australis during the 1995-1996 summer season.\n\nThe data are mostly text or csv files and document observations of icebergs, seabirds and whales, giving times and/or locations. Further supporting information may be included in the data download, or in other metadata records relating to the BROKE voyage (as opposed to the later BROKE-West voyage).\n\n", "links": [ { diff --git a/datasets/BROKE_ice_edges_1.json b/datasets/BROKE_ice_edges_1.json index 9e56d8803a..4cf107841b 100644 --- a/datasets/BROKE_ice_edges_1.json +++ b/datasets/BROKE_ice_edges_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BROKE_ice_edges_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Locations of ice edges on 18 north-south transects of the BROKE voyage of the Aurora Australis (AA V4 1995/96). Locations determined from direct observations by the seabird observers on board.\n\nThe fields in this dataset are:\n\nLatitude\nLongitude\nIce Conditions\nTransect", "links": [ { diff --git a/datasets/BUVN04L2_1.json b/datasets/BUVN04L2_1.json index 0b73f07448..2981fc928c 100644 --- a/datasets/BUVN04L2_1.json +++ b/datasets/BUVN04L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN04L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from Nimbus-4 Level-2 daily product (BUVN04L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe BUVN04L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from May 1970 through April 1976. The BUVN04L2 data product was used as input in creating the BUVN04L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/BUVN04L3zm_1.json b/datasets/BUVN04L3zm_1.json index 49bfdb7740..b8400daccd 100644 --- a/datasets/BUVN04L3zm_1.json +++ b/datasets/BUVN04L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN04L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from Nimbus-4 Level-3 monthly zonal mean (MZM) product (BUVN04L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 72 months of data from May 1970 through April 1976. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/BUVN4L1DCM_001.json b/datasets/BUVN4L1DCM_001.json index 578bac64fc..d136d4865e 100644 --- a/datasets/BUVN4L1DCM_001.json +++ b/datasets/BUVN4L1DCM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L1DCM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level-1 Dark Current Study Master Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current working data files, the difference is the working product data have been filtered.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data from the nighttime descending node. The data files consist of 140 4-byte word records which are blocked with up to 25 records. The average size of an orbit file is 480 kB.\n\nThis product was previously available from the NSSDC with the identifier ESAC-00045 (old ID 70-025A-05H).", "links": [ { diff --git a/datasets/BUVN4L1DCW_001.json b/datasets/BUVN4L1DCW_001.json index b6ac114f54..c7228d8487 100644 --- a/datasets/BUVN4L1DCW_001.json +++ b/datasets/BUVN4L1DCW_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L1DCW_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level-1 Dark Current Study Working Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current master data files, the difference is the working product data have been filtered.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data from the nighttime descending node. The data files consist of 140 4-byte word records which are blocked with up to 25 records. The average size of an orbit file is 375 kB.\n\nThis product was previously available from the NSSDC with the identifier ESAC-00054 (old ID 70-025A-05I).", "links": [ { diff --git a/datasets/BUVN4L1PDB_001.json b/datasets/BUVN4L1PDB_001.json index 42867f0756..39426e0841 100644 --- a/datasets/BUVN4L1PDB_001.json +++ b/datasets/BUVN4L1PDB_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L1PDB_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level-1 Radiance and Housekeeping Data in Telemetry Units collection contains the raw counts measured by the Backscatter Ultraviolet Spectrometer every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains ephemeris data, experiment subsystem status information, and spacecraft housekeeping and orbit data. This data collection was used to create the Level 1 Radiance U-Tape or RUT product.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The data files consist of 850 2-byte word records which are blocked in up to ten records. The first record in the file is the header record, followed by a series of data records, and ends with a trailer record. A typical orbit file is about 260 kB in size.\n\nThe BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.\n\nThis product was previously available from the NSSDC with the identifier ESAC-00024 (old ID 70-025A-05E).", "links": [ { diff --git a/datasets/BUVN4L1RUT_001.json b/datasets/BUVN4L1RUT_001.json index 11e1982d76..526250508f 100644 --- a/datasets/BUVN4L1RUT_001.json +++ b/datasets/BUVN4L1RUT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L1RUT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level-1 Radiance data collection was derived from the Primary Data Base (PDB) product and contains the calibrated and geolocated backscattered ultraviolet radiances measured every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains quality flags, dark current analyses of the data, orbital information, and housekeeping data.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The data files consist of 100 4-byte word records which are blocked with up to 25 records. The first record is the header record, followed by a series of data records, and ended with two trailer records. A typical orbit file is about 70 kB in size.\n\nThe BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.\n\nThis product was previously available from the NSSDC with the identifier ESAC-00055 (old ID 70-025A-05B).", "links": [ { diff --git a/datasets/BUVN4L2CPOZ_005.json b/datasets/BUVN4L2CPOZ_005.json index b5419f1114..45f16df760 100644 --- a/datasets/BUVN4L2CPOZ_005.json +++ b/datasets/BUVN4L2CPOZ_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L2CPOZ_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level 2 Compressed Ozone Profile Data collection or CPOZ contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). This product is a condensed version of the BUV High-Density Ozone Data Product or (HDBUV).\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one day of data (14 orbits). The files consist of data records each with seventy-two 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical daily file is about 100 kB in size.\n\nThe BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.\n\nThis product was previously available from the NSSDC as the Compressed Ozone Profile (CPOZ) Data with the identifier ESAC-00010 (old ID 70-025A-05P).", "links": [ { diff --git a/datasets/BUVN4L2HDBUV_005.json b/datasets/BUVN4L2HDBUV_005.json index e30fe32d5a..fc531c67cf 100644 --- a/datasets/BUVN4L2HDBUV_005.json +++ b/datasets/BUVN4L2HDBUV_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L2HDBUV_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level 2 High-Density Ozone Data collection contains the vertical distribtuion and total column amount of ozone, as well as the full set of ancillary information. Each file contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). The data collection also contains quality flags, orbital information, and housekeeping data.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The files consist of data records each with two hundred and seven 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical orbit file is about 96 kB in size.\n\nThe BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.\n\nThis product was previously available from the NSSDC as the Total and Profile Ozone Data (HDBUV) with the identifier ESAC-00030 (old ID 70-025A-05Q).", "links": [ { diff --git a/datasets/BUVN4L3ZMT_005.json b/datasets/BUVN4L3ZMT_005.json index c4df4b52c7..e522443801 100644 --- a/datasets/BUVN4L3ZMT_005.json +++ b/datasets/BUVN4L3ZMT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BUVN4L3ZMT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 BUV Level 3 Ozone Zonal Means collection or ZMT contains total ozone, reflectivities, and ozone mixing ratios averaged in 10 degree latitude zones centered from 80 to -80 degrees. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. In addition to the means, files also include the standard deviation, minimum and maximum values, as well as sample size.\n\nThe data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains monthly, weekly and daily zonal means, as well as quarterly means if it is the last month of the quarter. The files consist of data records each with one-hundred-eighty 4-byte words. Monthly, weekly, daily and quarterly means are distinguished by the seventh 4-byte word in the records. A typical file is about 380 kB in size.\n\nThe BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.\n\nThis product was previously available from the NSSDC as the Zonal Means File (ZMT) with the identifier ESAC-00039 (old ID 70-025A-05O).", "links": [ { diff --git a/datasets/Barrow_NGEE_Arctic_Veg_Plots_1505_1.json b/datasets/Barrow_NGEE_Arctic_Veg_Plots_1505_1.json index 4f28ea2d7b..6ea703f9fb 100644 --- a/datasets/Barrow_NGEE_Arctic_Veg_Plots_1505_1.json +++ b/datasets/Barrow_NGEE_Arctic_Veg_Plots_1505_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Barrow_NGEE_Arctic_Veg_Plots_1505_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation cover and environmental plot data collected on the Barrow Environmental Observatory (BEO), Barrow, Alaska in 2012. Forty-eight 1 x 1 m plots were established in homogenous plant communities along two perpendicular transects across ice wedge polygon geomorphic features on the BEO. Plots were distinguished as to their location within the polygons as center, edge, or trough. Vegetation data were originally collected by the U.S. Department of Energy (DOE) Next-Generation Ecosystem Experiment (NGEE) Arctic Project as part of a larger study to understand the response of Arctic terrestrial ecosystems to climate change.", "links": [ { diff --git a/datasets/Barrow_Tundra_Veg_Plots_1535_1.json b/datasets/Barrow_Tundra_Veg_Plots_1535_1.json index 0515001271..b3ab64a1fc 100644 --- a/datasets/Barrow_Tundra_Veg_Plots_1535_1.json +++ b/datasets/Barrow_Tundra_Veg_Plots_1535_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Barrow_Tundra_Veg_Plots_1535_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation cover and environmental plot data collected as part of the International Biological Program (IBP), U. S. Tundra Biome Program, in Barrow, Alaska in 1972. Forty-three (43) plots were assessed for estimated percent land cover by species and plot data including moisture, topographic position, slope, aspect, shape, and soil data. In 1999, 2008, and 2010, 33 of the same plots were resampled for these same measures as part of the IPY \"Back to the Future: Resampling old research sites to assess changes in high latitude terrestrial ecosystem structure and function\" project. The tundra at Barrow is considered coastal tundra located in the most northern region of North Slope and is characterized by various microtopographic features such as polygons, as well as many ponds and lakes.", "links": [ { diff --git a/datasets/Barter_Barrow_Veg_Plots_1534_1.json b/datasets/Barter_Barrow_Veg_Plots_1534_1.json index d14f5d2d3b..6cf1ac4ca6 100644 --- a/datasets/Barter_Barrow_Veg_Plots_1534_1.json +++ b/datasets/Barter_Barrow_Veg_Plots_1534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Barter_Barrow_Veg_Plots_1534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation cover and environmental plot and soil data collected at two U.S. Air Force sites at Barter Island (BI) and Point Barrow (B), on the coastal North Slope of Alaska, in 1994. At Barter Island, 31 plots, and 30 plots at Barrow, were subjectively located in 14 plant communities. The investigation was part of a larger study initiated by the United States Congress to provide an opportunity to enhance the stewardship of the natural and cultural resources land under Department of Defense jurisdiction. These two sites were characterized to build an inventory of the biotic communities to compare them to historic communities.", "links": [ { diff --git a/datasets/BenthicEcology_fromSpace_0.json b/datasets/BenthicEcology_fromSpace_0.json index 49d627227f..28d9732f8c 100644 --- a/datasets/BenthicEcology_fromSpace_0.json +++ b/datasets/BenthicEcology_fromSpace_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BenthicEcology_fromSpace_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Panama City and the Florida Keys in 2005 and 2006.", "links": [ { diff --git a/datasets/Ber95_0.json b/datasets/Ber95_0.json index 2c77b01754..e47082e607 100644 --- a/datasets/Ber95_0.json +++ b/datasets/Ber95_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ber95_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Bering Sea during 1996.", "links": [ { diff --git a/datasets/Ber96_0.json b/datasets/Ber96_0.json index 73feff081d..3a5c00cbea 100644 --- a/datasets/Ber96_0.json +++ b/datasets/Ber96_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ber96_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Bering Sea during 1995.", "links": [ { diff --git a/datasets/BeringSea_LaserFluoMon_0.json b/datasets/BeringSea_LaserFluoMon_0.json index 95bab7b670..c38f4858a9 100644 --- a/datasets/BeringSea_LaserFluoMon_0.json +++ b/datasets/BeringSea_LaserFluoMon_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BeringSea_LaserFluoMon_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Bering Sea by the RV Knorr in 2009.", "links": [ { diff --git a/datasets/Bermuda_Testbed_Mooring_0.json b/datasets/Bermuda_Testbed_Mooring_0.json index e15e27fdb1..749451cc64 100644 --- a/datasets/Bermuda_Testbed_Mooring_0.json +++ b/datasets/Bermuda_Testbed_Mooring_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Bermuda_Testbed_Mooring_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Bermuda Testbed Mooring in 1999.", "links": [ { diff --git a/datasets/BigEarthNet_1.json b/datasets/BigEarthNet_1.json index c3d5fa2802..d191e16d07 100644 --- a/datasets/BigEarthNet_1.json +++ b/datasets/BigEarthNet_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BigEarthNet_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided from the CORINE Land Cover database of the year 2018 (CLC 2018).", "links": [ { diff --git a/datasets/Big_Bend_0.json b/datasets/Big_Bend_0.json index 32b3a0db18..b34e38c62d 100644 --- a/datasets/Big_Bend_0.json +++ b/datasets/Big_Bend_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Big_Bend_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the Big Bend Wildlife Preservation area of the Florida Gulf Coast in 2010 and 2011.", "links": [ { diff --git a/datasets/Bio_optics_Chl_polarization_0.json b/datasets/Bio_optics_Chl_polarization_0.json index 80b974c332..9b2536af9c 100644 --- a/datasets/Bio_optics_Chl_polarization_0.json +++ b/datasets/Bio_optics_Chl_polarization_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Bio_optics_Chl_polarization_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bio_optics_Chl_polarization", "links": [ { diff --git a/datasets/Biogenic_CO2flux_SIF_SMUrF_1899_1.json b/datasets/Biogenic_CO2flux_SIF_SMUrF_1899_1.json index 6deba24fa4..3e4857b2c0 100644 --- a/datasets/Biogenic_CO2flux_SIF_SMUrF_1899_1.json +++ b/datasets/Biogenic_CO2flux_SIF_SMUrF_1899_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biogenic_CO2flux_SIF_SMUrF_1899_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates of biogenic CO2 flux components at 0.05 degree resolution from the Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (SMUrF) model. Estimates were produced for the following regions and periods: eastern and western CONUS (2010-2019), western Europe (2010-2014 and 2017-2018), eastern Asia, eastern China, eastern Australia, South America, and Central Africa (2017-2018). Modeled CO2 flux components include gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE). Four-day means of GPP are estimated from solar-induced fluorescence (SIF) and biome-specific GPP-SIF relationships. Daily estimates of Reco are included. In addition, GPP and Reco were downscaled to hourly estimates and used to generate hourly NEE. Uncertainties for 4-day GPP and daily Reco estimates are provided. The input data streams included 500 m MODIS-based annual land cover classification, 0.05 degree spatiotemporally contiguous SIF, above-ground biomass (AGB) from GlobBiomass, eddy-covariance (EC) flux measurements, and gridded products of air and soil temperatures.", "links": [ { diff --git a/datasets/Biology_Bunger_Hills_1977_1.json b/datasets/Biology_Bunger_Hills_1977_1.json index ee1f8712d6..2cd31c2744 100644 --- a/datasets/Biology_Bunger_Hills_1977_1.json +++ b/datasets/Biology_Bunger_Hills_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Bunger_Hills_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scanned copy of the title document.\n\nTaken from the abstract of the report:\n\nThe Bunger Hills, situated between latitudes 65 degrees, 51 minutes and 66 degrees 20 minutes South and longitudes 100 degrees and 101 degrees 30 minutes East, were visited by members of the Australian National Antarctic Research Expedition (ANARE) on the 2nd of March, 1977. Biological and geological samples were collected. This report presents a summary of the information obtained and reviews the earlier history and scientific work in the Bunger Hills by other nations.", "links": [ { diff --git a/datasets/Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1.json b/datasets/Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1.json index 5429b44530..ae21e6989d 100644 --- a/datasets/Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1.json +++ b/datasets/Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This log contains notes and hand drawn maps of Adelie Penguin Colonies/Rookeries in the Vestfold Hills, collected during November, 1973.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Adelie_Rookery_1957_Gardner_1.json b/datasets/Biology_Log_Adelie_Rookery_1957_Gardner_1.json index 75d4b1fd6a..29e1b1ef68 100644 --- a/datasets/Biology_Log_Adelie_Rookery_1957_Gardner_1.json +++ b/datasets/Biology_Log_Adelie_Rookery_1957_Gardner_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Adelie_Rookery_1957_Gardner_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This log contains observations made at an Adelie Penguin rookery at Gardner Island in 1957. At the time, Gardner Island was known as Breidneskollen.\n\nThe observations were made in December of 1957.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Antarctic_Petrel_Photos_1961_1962_1.json b/datasets/Biology_Log_Antarctic_Petrel_Photos_1961_1962_1.json index 61e60b096c..edb811227b 100644 --- a/datasets/Biology_Log_Antarctic_Petrel_Photos_1961_1962_1.json +++ b/datasets/Biology_Log_Antarctic_Petrel_Photos_1961_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Antarctic_Petrel_Photos_1961_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This log contains photographs of Antarctic Petrels taken in 1961-1962 at Ardery Island and Lewis Island.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_1968_1969_1.json b/datasets/Biology_Log_Casey_1968_1969_1.json index b69399492f..b053aeca92 100644 --- a/datasets/Biology_Log_Casey_1968_1969_1.json +++ b/datasets/Biology_Log_Casey_1968_1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_1968_1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains biological observations collected in the Casey region during the 1968-1969 season. Observations were made on a dog trip, of giant petrels, skuas, penguins, snow petrels, seals, wilson's storm petrels, and whales.\n\nA number of photographs are also included in the file.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_1972_1.json b/datasets/Biology_Log_Casey_1972_1.json index be61a9b30e..c60bd3496c 100644 --- a/datasets/Biology_Log_Casey_1972_1.json +++ b/datasets/Biology_Log_Casey_1972_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_1972_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains biological observations collected in the Casey region during 1972. Observations were made of giant petrels, skuas, penguins, snow petrels, leopard seals, elephant seals, wilson's storm petrels, Antarctic petrels, and whales.\n\nA number of photographs are also included in the file.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_1972_1987_1.json b/datasets/Biology_Log_Casey_1972_1987_1.json index cd9b06a7aa..ce942608a4 100644 --- a/datasets/Biology_Log_Casey_1972_1987_1.json +++ b/datasets/Biology_Log_Casey_1972_1987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_1972_1987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of observations of skuas collected in the Casey region between 1972 and 1987.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_1973_1.json b/datasets/Biology_Log_Casey_1973_1.json index 606929d649..a86cc8dae5 100644 --- a/datasets/Biology_Log_Casey_1973_1.json +++ b/datasets/Biology_Log_Casey_1973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_1973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of general biological observations collected in the Casey region during 1973. Observations were made of seals, whales, penguins, petrels, etc.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_1974_1.json b/datasets/Biology_Log_Casey_1974_1.json index 5738f3e9f1..591d77f84a 100644 --- a/datasets/Biology_Log_Casey_1974_1.json +++ b/datasets/Biology_Log_Casey_1974_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_1974_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of general biological observations collected in the Casey region during 1974. Observations were made of Antarctic Petrels, Wilson Storm Petrels, Snow Petrels, Silver Grey Petrels, Cape Pigeons, Pintado Petrels, Giant Petrels, Emperor Penguins, Adelie Penguins, Skuas, Crabeater Seals, Leopard Seals, Elephant Seals and Weddell Seals.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_Adelie_Penguins_1972_1988_1.json b/datasets/Biology_Log_Casey_Adelie_Penguins_1972_1988_1.json index 72aaef50d1..3184f18b5d 100644 --- a/datasets/Biology_Log_Casey_Adelie_Penguins_1972_1988_1.json +++ b/datasets/Biology_Log_Casey_Adelie_Penguins_1972_1988_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_Adelie_Penguins_1972_1988_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of observations collected in the Casey region between 1972 and 1988. Observations were made of Adelie penguins.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_Birds_1967_1968_1.json b/datasets/Biology_Log_Casey_Birds_1967_1968_1.json index 1550f38d8f..24e3d9e95c 100644 --- a/datasets/Biology_Log_Casey_Birds_1967_1968_1.json +++ b/datasets/Biology_Log_Casey_Birds_1967_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_Birds_1967_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains observations of Southern Giant Petrels, Penguins, Seals and Whales from the Nella Dan bird log collected in the Casey Region (Frazier Islands and Donovan Group) in the 1967-1968 season.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_Report_1972_1.json b/datasets/Biology_Log_Casey_Report_1972_1.json index f5126b6107..c28635b6aa 100644 --- a/datasets/Biology_Log_Casey_Report_1972_1.json +++ b/datasets/Biology_Log_Casey_Report_1972_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_Report_1972_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report on biological observations collected in the Casey region during 1972. Observations were made of giant petrels, skuas, penguins, snow petrels, leopard seals, elephant seals, wilson's storm petrels, Antarctic petrels, and whales.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Casey_Seals_Penguins_1972_1973_1.json b/datasets/Biology_Log_Casey_Seals_Penguins_1972_1973_1.json index 5989b86c20..71b18e48b1 100644 --- a/datasets/Biology_Log_Casey_Seals_Penguins_1972_1973_1.json +++ b/datasets/Biology_Log_Casey_Seals_Penguins_1972_1973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Casey_Seals_Penguins_1972_1973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of observations collected in the Casey region between 1972 and 1973. Observations were made of Seals (Weddell seals, Elephant seals, Leopard seals) and Adelie penguins.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1957_1.json b/datasets/Biology_Log_Davis_1957_1.json index 612c68cee0..384c4b973e 100644 --- a/datasets/Biology_Log_Davis_1957_1.json +++ b/datasets/Biology_Log_Davis_1957_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1957_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis region during 1957. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1958_1.json b/datasets/Biology_Log_Davis_1958_1.json index b6dd0c626c..ecc962b4e5 100644 --- a/datasets/Biology_Log_Davis_1958_1.json +++ b/datasets/Biology_Log_Davis_1958_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1958_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis region during 1958. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1959_1.json b/datasets/Biology_Log_Davis_1959_1.json index c329eb0d3c..7adbd50aee 100644 --- a/datasets/Biology_Log_Davis_1959_1.json +++ b/datasets/Biology_Log_Davis_1959_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1959_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis region during 1958. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1960_1.json b/datasets/Biology_Log_Davis_1960_1.json index 3d4e155630..75888dd9b0 100644 --- a/datasets/Biology_Log_Davis_1960_1.json +++ b/datasets/Biology_Log_Davis_1960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis region during 1960. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1961_1.json b/datasets/Biology_Log_Davis_1961_1.json index f024d33d83..82e491dd84 100644 --- a/datasets/Biology_Log_Davis_1961_1.json +++ b/datasets/Biology_Log_Davis_1961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis region during 1961. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_1969_1.json b/datasets/Biology_Log_Davis_1969_1.json index afc311d217..0fda1b0007 100644 --- a/datasets/Biology_Log_Davis_1969_1.json +++ b/datasets/Biology_Log_Davis_1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report and a log of biological observations made in the Davis region during 1969. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales.\n\nIt also includes information from earlier in the 1960s, including information on bird banding, and bird ordinance.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_Mawson_1954_1960_1.json b/datasets/Biology_Log_Davis_Mawson_1954_1960_1.json index 9e08dc5294..fdc96c4ab5 100644 --- a/datasets/Biology_Log_Davis_Mawson_1954_1960_1.json +++ b/datasets/Biology_Log_Davis_Mawson_1954_1960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_Mawson_1954_1960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations made in the Davis and Mawson regions between 1954 and 1960. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Davis_Report_1962_1.json b/datasets/Biology_Log_Davis_Report_1962_1.json index adaa5dd830..915efee96e 100644 --- a/datasets/Biology_Log_Davis_Report_1962_1.json +++ b/datasets/Biology_Log_Davis_Report_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Davis_Report_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report and a log of biological observations made in the Davis region during 1962. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Heard_Birds_1953_1.json b/datasets/Biology_Log_Heard_Birds_1953_1.json index d867c8eeee..d27562383a 100644 --- a/datasets/Biology_Log_Heard_Birds_1953_1.json +++ b/datasets/Biology_Log_Heard_Birds_1953_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Heard_Birds_1953_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report and a log of biological observations of birds made at Heard Island during 1953. It includes information on King Penguins, Macaroni Penguins, Adelie Penguins, Rockhopper Penguins, Gentoo Penguins, Light-Mantled Sooty Albatross, Storm Petrels, Dominican Gulls, Cape Pigeons, Skuas, etc.\n\nIt also includes information from earlier in the 1960s, including information on bird banding, and bird ordinance.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1950s_1.json b/datasets/Biology_Log_Mawson_1950s_1.json index 3601bc3a72..ba890e19e3 100644 --- a/datasets/Biology_Log_Mawson_1950s_1.json +++ b/datasets/Biology_Log_Mawson_1950s_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1950s_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report and a log of biological observations made at Mawson station during the 1950s, after the station was established in 1954. It contains observations of emperor Penguins, Adelie Penguins, Chinstrap Penguins, Giant Petrels, Cape Pigeons, Antarctic Petrels, Silver Grey Petrels, Snow Petrels, Wilson's Storm Petrels, McCormick Skuas, Dominican Gulls, Terns, Elephant Seals, Weddell Seals, Crabeater Seals and Leopard Seals.\n\nSome data are also provided for Davis Station.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1958_1962_1.json b/datasets/Biology_Log_Mawson_1958_1962_1.json index aec47edded..852e818798 100644 --- a/datasets/Biology_Log_Mawson_1958_1962_1.json +++ b/datasets/Biology_Log_Mawson_1958_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1958_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson, Davis and Wilkes stations between 1958 and 1962. The observations are primarily on flying birds (petrels, skuas, gulls), penguins and seals. The observed animals include: Snow Petrels, McCormick Skuas, Silver-Grey Petrels, Antarctic Petrels, Giant Petrels, Wilson's Storm Petrels, Cape Pigeons, Dominican Gulls, Crabeater Seals, Elephant Seals, Leopard Seals, Ross Seals, Weddell Seals, Emperor Penguins, Adelie Penguins, Chinstrap Penguins and Terns.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1971_1974_1.json b/datasets/Biology_Log_Mawson_1971_1974_1.json index 87e2b868df..09f658eb04 100644 --- a/datasets/Biology_Log_Mawson_1971_1974_1.json +++ b/datasets/Biology_Log_Mawson_1971_1974_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1971_1974_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson station between 1971 and 1974. The observed animals include: Wilson's Storm Petrels, Petrels, Giant Petrels, Skuas, Emperor Penguins, Snow Petrels, Silver Grey Petrels, Antarctic Petrel, Weddell Seals, Crabeater Seals, Leopard Seals, Elephant Seals, Ross Seals and Whales.\n\nThe log also includes a number of sea ice observations made at Mawson Station.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1977_1978_1.json b/datasets/Biology_Log_Mawson_1977_1978_1.json index 8a56eed801..7c339b22f0 100644 --- a/datasets/Biology_Log_Mawson_1977_1978_1.json +++ b/datasets/Biology_Log_Mawson_1977_1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1977_1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson station between 1977 and 1978. The observed animals include: Weddell Seals, Skuas, Snow Petrels, Wilson's Storm Petrels, Pintado Petrels, Giant Petrels, Crabeater Seals, \nElephant Seals, Leopard Seals and Adelie Penguins.\n\nThe log also includes a number of sea ice observations made at Mawson Station.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1980_1981_1.json b/datasets/Biology_Log_Mawson_1980_1981_1.json index e338151fe2..c42a0ee56c 100644 --- a/datasets/Biology_Log_Mawson_1980_1981_1.json +++ b/datasets/Biology_Log_Mawson_1980_1981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1980_1981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson station in 1980 and 1981. The logs include observations of adelie penguins, snow petrels, leopard seals, pintado petrels, skuas, antarctic petrels, wilson's storm petrels, southern giant petrels, dominican gulls, silver grey petrels, fulmars, killer whales, minke whales, elephant seals, sea spiders, crabeater seals and Antarctic terns.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_1982_1.json b/datasets/Biology_Log_Mawson_1982_1.json index ab7d05ca66..2e8da11b14 100644 --- a/datasets/Biology_Log_Mawson_1982_1.json +++ b/datasets/Biology_Log_Mawson_1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson station in 1982. The logs include observations of pintadoo petrels, emperor penguins, killer whales, elephant seals, leopard seals, minke whales, crabeater seals, adelie penguins, silver-grey petrels, wilson's storm petrels, antarctic petrels and skuas\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1.json b/datasets/Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1.json index d1a7a59c3f..8ef43db983 100644 --- a/datasets/Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1.json +++ b/datasets/Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations of Antarctic Petrels taken at Mawson Station between 1972 and 1990.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Fishing_1978_1985_1.json b/datasets/Biology_Log_Mawson_Fishing_1978_1985_1.json index 264bcb1f63..1e59771505 100644 --- a/datasets/Biology_Log_Mawson_Fishing_1978_1985_1.json +++ b/datasets/Biology_Log_Mawson_Fishing_1978_1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Fishing_1978_1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of fishing activities undertaken at Mawson station in 1979 and 1985.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1.json b/datasets/Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1.json index b3fb153d1a..cc0d69fed4 100644 --- a/datasets/Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1.json +++ b/datasets/Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report of bird banding undertaken on penguin and flying bird species at Macquarie Island and Mawson station from 1959-1965.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1.json b/datasets/Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1.json index fe852ba73e..025a670e55 100644 --- a/datasets/Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1.json +++ b/datasets/Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations of Pintardo Petrels taken at Mawson Station between 1972 and 1988.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Seals_1974_1979_1.json b/datasets/Biology_Log_Mawson_Seals_1974_1979_1.json index 57ff130b90..9df6e7f162 100644 --- a/datasets/Biology_Log_Mawson_Seals_1974_1979_1.json +++ b/datasets/Biology_Log_Mawson_Seals_1974_1979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Seals_1974_1979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations of Weddell Seals and Leopard Seals taken at Mawson Station between 1974 and 1979.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Skuas_1982_1990_1.json b/datasets/Biology_Log_Mawson_Skuas_1982_1990_1.json index beba670b23..772c42601d 100644 --- a/datasets/Biology_Log_Mawson_Skuas_1982_1990_1.json +++ b/datasets/Biology_Log_Mawson_Skuas_1982_1990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Skuas_1982_1990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations undertaken at Mawson station between 1982 and 1990. The logs comprise observations of skuas.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Mawson_Snow_Petrels_1971_1990_1.json b/datasets/Biology_Log_Mawson_Snow_Petrels_1971_1990_1.json index 7168aafe46..d94fc366b8 100644 --- a/datasets/Biology_Log_Mawson_Snow_Petrels_1971_1990_1.json +++ b/datasets/Biology_Log_Mawson_Snow_Petrels_1971_1990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Mawson_Snow_Petrels_1971_1990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of biological observations of Snow Petrels taken at Mawson Station between 1971 and 1990.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1961_1.json b/datasets/Biology_Log_Wilkes_1961_1.json index 3fcadffde0..68ca90fcbc 100644 --- a/datasets/Biology_Log_Wilkes_1961_1.json +++ b/datasets/Biology_Log_Wilkes_1961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report produced at Wilkes Station in 1961. Contributors to the report were R. Penney, D.F. Soucek, L. Jones and N. Orton. The report comprises data pertaining to:\nAdelie penguins\nEmperor penguins\nSilver-grey petrels\nAntarctic petrels\nCape pigeons\nGiant petrels\nSkuas\nSnow petrels\nWilson's storm petrels\nWeddell seals\nLeopard seals\nElephant seals\nRoss seals\nKiller whales\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1962_1.json b/datasets/Biology_Log_Wilkes_1962_1.json index e59b3258ed..367dcad360 100644 --- a/datasets/Biology_Log_Wilkes_1962_1.json +++ b/datasets/Biology_Log_Wilkes_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report produced at Wilkes Station in 19612 The report was compiled by N.M. Orton. The report comprises data pertaining to:\n\nAdelie penguins\nEmperor penguins\nSilver-grey petrels\nAntarctic petrels\nCape pigeons\nGiant petrels\nSkuas\nSnow petrels\nWilson's storm petrels\nWeddell seals\nLeopard seals\nElephant seals\nRoss seals\nKiller whales\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1962_1963_1.json b/datasets/Biology_Log_Wilkes_1962_1963_1.json index 4091ada04e..196e1ec52a 100644 --- a/datasets/Biology_Log_Wilkes_1962_1963_1.json +++ b/datasets/Biology_Log_Wilkes_1962_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1962_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains logs, papers and a report on biological observations at Wilkes station in 1962-1963.\n\nAdelie penguins\nEmperor penguins\nSilver-grey petrels\nAntarctic petrels\nCape pigeons\nGiant petrels\nSkuas\nSnow petrels\nWilson's storm petrels\nWeddell seals\nLeopard seals\nElephant seals\nRoss seals\nKiller whales\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1963_1.json b/datasets/Biology_Log_Wilkes_1963_1.json index 816bbba095..a1bf6493e1 100644 --- a/datasets/Biology_Log_Wilkes_1963_1.json +++ b/datasets/Biology_Log_Wilkes_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report Wilkes station in 1963. The data were collected in the Windmill Islands, at locations such as Lewis Island, Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island.\n\nThe report also contains meteorological observations, bird-banding data, and hand-drawn maps.\n\nThe observations were made of:\n\nAdelie penguins\nEmperor penguins\nSouth polar skuas\nGiant petrels\nCape pigeons\nSilver-grey petrels\nAntarctic petrels\nSnow petrels\nWilson's storm petrels\nTerns\nRoss seals\nCrabeater seals\nElephant seals\nWddell seals\nLeopard seals\nKiller whales.\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1964_1.json b/datasets/Biology_Log_Wilkes_1964_1.json index 82c7c27fc7..f0425034bf 100644 --- a/datasets/Biology_Log_Wilkes_1964_1.json +++ b/datasets/Biology_Log_Wilkes_1964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report Wilkes station in 1964. The data were collected by L.G. Murray in the Windmill Islands, at locations such as Lewis Island, Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island.\n\nThe report also contains meteorological observations, bird-banding data, thermistor calibration data and hand-drawn maps.\n\nThe observations were made of:\n\nAdelie penguins\nEmperor penguins\nSouth polar skuas\nGiant petrels\nCape pigeons\nSilver-grey petrels\nAntarctic petrels\nSnow petrels\nWilson's storm petrels\nTerns\nRoss seals\nCrabeater seals\nElephant seals\nWeddell seals\nLeopard seals\nKiller whales.\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1968_1.json b/datasets/Biology_Log_Wilkes_1968_1.json index 4d6210e34b..b0383e5d9f 100644 --- a/datasets/Biology_Log_Wilkes_1968_1.json +++ b/datasets/Biology_Log_Wilkes_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report from Wilkes station in 1968.\n\nAs well as a report, the file also contains correspondence and some banding data. Much of the information appears to relate to Adelie Penguins and South Polar Skuas.\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_1968_1969_1.json b/datasets/Biology_Log_Wilkes_1968_1969_1.json index 23a04d27aa..e12c109401 100644 --- a/datasets/Biology_Log_Wilkes_1968_1969_1.json +++ b/datasets/Biology_Log_Wilkes_1968_1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_1968_1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains biological observations collected in the Wilkes region during the 1968-1969 season. Observations were made of giant petrels, skuas, penguins, snow petrels, seals, wilson's storm petrels, Antarctic petrels, and whales.\n\nA number of photographs are also included in the file.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Ardery_1963_1.json b/datasets/Biology_Log_Wilkes_Ardery_1963_1.json index c15b961fa0..fefd510872 100644 --- a/datasets/Biology_Log_Wilkes_Ardery_1963_1.json +++ b/datasets/Biology_Log_Wilkes_Ardery_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Ardery_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report from Wilkes station in 1963. The report pertains specifically to a visit to Ardery Island in January, 1963 by F. Soucek.\n\nThe report contains biological observations, as well as an extract from a publication and some hand-drawn maps.\n\nThe observations were made of:\n\nSouth polar skuas\nGiant petrels\nCape pigeons\nSilver-grey petrels\nAntarctic petrels\nSnow petrels\nWilson's storm petrels\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Banding_1966_1.json b/datasets/Biology_Log_Wilkes_Banding_1966_1.json index 148c544d33..e4783786ac 100644 --- a/datasets/Biology_Log_Wilkes_Banding_1966_1.json +++ b/datasets/Biology_Log_Wilkes_Banding_1966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Banding_1966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a banding report Wilkes station in 1966.\n\nThe observations were made of:\n\nAdelie penguins\nSilver-grey petrels\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Banding_1968_1969_1.json b/datasets/Biology_Log_Wilkes_Banding_1968_1969_1.json index 82b269f2ae..a0bfcf6848 100644 --- a/datasets/Biology_Log_Wilkes_Banding_1968_1969_1.json +++ b/datasets/Biology_Log_Wilkes_Banding_1968_1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Banding_1968_1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report from Wilkes station in 1968-1969 detailing the banding program undertaken in the Windmill Islands. The document primarily relates to South Polar Skuas, but also mentions Wilson's Storm Petrels, and Snow Petrels.\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Bird_Banding_1962_1963_1.json b/datasets/Biology_Log_Wilkes_Bird_Banding_1962_1963_1.json index 57bc761f38..e4819ee02f 100644 --- a/datasets/Biology_Log_Wilkes_Bird_Banding_1962_1963_1.json +++ b/datasets/Biology_Log_Wilkes_Bird_Banding_1962_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Bird_Banding_1962_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a record of bird banding activities undertaken at Wilkes Station in 1962 and 1963. It also contains a log of observations made on animals in the area, as well as some hand-drawn maps. This document was compiled by F. Soucek.\n\nAnimals observed include:\nAdelie penguins\nMcCormick skuas\nWilson's storm petrels\nGiant petrels\nSilver grey petrels\nSnow petrels\nAntarctic petrels\nSnow petrels\nAntarctic terns\nCape pigeons\nEmperor penguins\nWeddell seals\nElephant seals\nLeopard seals\nCrabeater seals\nWhales\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Skuas_1957_1958_1.json b/datasets/Biology_Log_Wilkes_Skuas_1957_1958_1.json index 5bd2ae7b01..1e40a0ba6c 100644 --- a/datasets/Biology_Log_Wilkes_Skuas_1957_1958_1.json +++ b/datasets/Biology_Log_Wilkes_Skuas_1957_1958_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Skuas_1957_1958_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a map of banding stations for a distribution study of south polar skuas. The map is of the entire Antarctic continent and shows stations from the International Geophysical Year, 1957-1958 and from the US Navy Operation, Deep Freeze II, 1956-1957.\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Wildlife_Sightings_1963_1.json b/datasets/Biology_Log_Wilkes_Wildlife_Sightings_1963_1.json index bfdf77b5f8..ec496ac495 100644 --- a/datasets/Biology_Log_Wilkes_Wildlife_Sightings_1963_1.json +++ b/datasets/Biology_Log_Wilkes_Wildlife_Sightings_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Wildlife_Sightings_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of wildlife sightings made at Wilkes Station in 1963. Each sheet of the log is for a single month. The listed species include:\n\nSkuas\nAdelie penguins\nEmperor penguins\nGiant petrels\nWilson's storm petrels\nSilver grey petrels\nAntarctic petrels\nSnow petrels\nPintado\nWeddell seals\nElephant seals\nLeopard seals\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Wilkes_Zoology_1959_1961_1.json b/datasets/Biology_Log_Wilkes_Zoology_1959_1961_1.json index ed3b43f57e..760b5a02fb 100644 --- a/datasets/Biology_Log_Wilkes_Zoology_1959_1961_1.json +++ b/datasets/Biology_Log_Wilkes_Zoology_1959_1961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Wilkes_Zoology_1959_1961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a log of zoological observations made by Richard Penney at Wilkes station from 1959 to 1961. The observations were made in the Windmill Islands, at locations such as Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island.\n\nThe observations were made of, adelie penguins, emperor penguins, south polar skuas, giant petrels, cape pigeons, silver-grey petrels, antarctic petrels, snow petrels, wilson's storm petrels, terns, ross seals, crabeater seals, elephant seals, weddell seals, leopard seals and killer whales. Bird banding is also covered in the report.\n\nThe download file contains the official copy of the report, as well as Richard Penney's personal copy, which includes some handwritten notes.\n\nThe hard copy of the map has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Windmill_Islands_Fauna_1961_1962_1.json b/datasets/Biology_Log_Windmill_Islands_Fauna_1961_1962_1.json index 801266f594..737a1e5f1d 100644 --- a/datasets/Biology_Log_Windmill_Islands_Fauna_1961_1962_1.json +++ b/datasets/Biology_Log_Windmill_Islands_Fauna_1961_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Windmill_Islands_Fauna_1961_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report on the fauna of the Windmill Islands, 1961-1962. The file contains information on photographic records, banding data, and nest marking.\n\nSpecies included in the report are:\n\nAdelie penguins\nSilver grey petrels\nGiant petrels\nPintado petrels\nSnow petrels\nAntarctic petrels\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Windmill_Islands_Fleas_1961_1.json b/datasets/Biology_Log_Windmill_Islands_Fleas_1961_1.json index 9c260d9de8..bebfd08773 100644 --- a/datasets/Biology_Log_Windmill_Islands_Fleas_1961_1.json +++ b/datasets/Biology_Log_Windmill_Islands_Fleas_1961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Windmill_Islands_Fleas_1961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a report on bird fleas in the Windmill Islands in 1961. The report contains data on fleas collected, as well as general information on the bird fleas and how they affect silver grey petrels.\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Windmill_Islands_Photographs_1961_1962_1.json b/datasets/Biology_Log_Windmill_Islands_Photographs_1961_1962_1.json index 5fdee75497..316326313f 100644 --- a/datasets/Biology_Log_Windmill_Islands_Photographs_1961_1962_1.json +++ b/datasets/Biology_Log_Windmill_Islands_Photographs_1961_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Windmill_Islands_Photographs_1961_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This log contains general photographs of the Windmill Islands region, including photos of the Peterson Glacier, Cameron Island and Berkley Island. The photos were taken between December 1961 and January 1962.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Biology_Log_Windmill_Islands_Report_1962_1963_1.json b/datasets/Biology_Log_Windmill_Islands_Report_1962_1963_1.json index f2075c4845..568fa4fd40 100644 --- a/datasets/Biology_Log_Windmill_Islands_Report_1962_1963_1.json +++ b/datasets/Biology_Log_Windmill_Islands_Report_1962_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Biology_Log_Windmill_Islands_Report_1962_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains a biology report from the Windmill Islands in 1962-1963. The report was compiled by F. Soucek. Data were collected from Ardery Island and Wilkes Station.\n\nSpecies included in the report are:\n\nSilver grey petrels\nAntarctic petrels\nPintado petrels\nSnow petrels\n\n\nThe hard copy of the file has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/BlueFlux_AirborneObs_Florida_2327_1.json b/datasets/BlueFlux_AirborneObs_Florida_2327_1.json index de6905c81f..64b5dd167f 100644 --- a/datasets/BlueFlux_AirborneObs_Florida_2327_1.json +++ b/datasets/BlueFlux_AirborneObs_Florida_2327_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BlueFlux_AirborneObs_Florida_2327_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes airborne in situ measurements of greenhouse gas mixing ratios, meteorological parameters, and fluxes (CO2, CH4, latent heat fluxes, friction velocity, and convective velocity scale) calculated with wavelet transforms. CO2, CH4, CO, O3, and water vapor mixing ratios, and meteorological variables were obtained from a Beechcraft A90 King Air aircraft. Flights occurred on April 19-26 2022, October 14-20 2022, February 5-13 2023, and April 13-19 2023 as part of the BlueFlux campaign, funded by NASA's Carbon Monitoring System program. Measurements were made with several instruments, including a PICARRO 2401-m (0.5 Hz CO2/CH4/H2O/CO), PICARRO 2311-f (10 Hz CO2/CH4/H2O), NASA Rapid Ozone Experiment (ROZE, 10 Hz O3), and AIMMS-20 probe (3-D winds, meteorology, and aircraft location data). Flight lines span Everglades National Park (ENP) and Big Cypress National Preserve (BCNP) in southern Florida, USA. The measurements were used to calculate vertical fluxes of trace gases and heat via wavelet transform eddy covariance", "links": [ { diff --git a/datasets/BlueFlux_Tidal_River_Water_2333_1.json b/datasets/BlueFlux_Tidal_River_Water_2333_1.json index 4809b820b3..35b8a636db 100644 --- a/datasets/BlueFlux_Tidal_River_Water_2333_1.json +++ b/datasets/BlueFlux_Tidal_River_Water_2333_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BlueFlux_Tidal_River_Water_2333_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides dissolved carbon (dissolved inorganic carbon and dissolved organic carbon), greenhouse gases, dissolved organic matter optical, and hydrological (water temperature, pH, alkalinity, dissolved oxygen) data collected from the Shark and Harney tidal rivers in the Everglades, Florida, USA. The data were collected as part of the NASA Carbon Monitoring System (CMS) BlueFlux field campaigns over the 2022 wet season (October 2022) and 2023 dry season (March 2023). Data includes single-collection samples collected from sites along both rivers and samples collected by an autosampler at one site over multiple tidal cycles. The data are provided in comma-separated values (.csv) format.", "links": [ { diff --git a/datasets/Blue_Carbon_Tidal_Wetland_Maps_2091_1.json b/datasets/Blue_Carbon_Tidal_Wetland_Maps_2091_1.json index 5e47ddc788..784bfcfcb7 100644 --- a/datasets/Blue_Carbon_Tidal_Wetland_Maps_2091_1.json +++ b/datasets/Blue_Carbon_Tidal_Wetland_Maps_2091_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Blue_Carbon_Tidal_Wetland_Maps_2091_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production. The maps include current dominant wetland type, restoration category, potential removal of atmospheric greenhouse gases in units of mass carbon dioxide with estimates of uncertainty.", "links": [ { diff --git a/datasets/Bold_EPAHypoxia_0.json b/datasets/Bold_EPAHypoxia_0.json index c36c7d9b8d..aa3fb64097 100644 --- a/datasets/Bold_EPAHypoxia_0.json +++ b/datasets/Bold_EPAHypoxia_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Bold_EPAHypoxia_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the OSV Bold for the EPA studying hypoxia in the Gulf of Mexico during 2006 and 2007.", "links": [ { diff --git a/datasets/BorealForest_Greenness_Trends_2023_1.json b/datasets/BorealForest_Greenness_Trends_2023_1.json index 3d61dca025..a2cbcf473d 100644 --- a/datasets/BorealForest_Greenness_Trends_2023_1.json +++ b/datasets/BorealForest_Greenness_Trends_2023_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BorealForest_Greenness_Trends_2023_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides information on interannual trends in annual maximum vegetation greenness from 1985 to 2019 for recently undisturbed areas in the boreal forest biome. Multi-decadal changes in remotely sensed vegetation greenness provide evidence of an emerging boreal biome shift driven by climate warming. Annual maximum vegetation greenness was assessed at about 100,000 random sample locations using an ensemble of spectral vegetation indices (NDVI, EVI2, kNDVI, and NIRv) derived from Landsat products. The dataset provides raster data summarizing vegetation greenness trends for sample locations stratified by Ecological Land Unit in GeoTIFF format. These raster data span the circum-hemispheric boreal forest biome between 45 to 70 degrees north at 300 m resolution. Estimates of uncertainty were generated using Monte Carlo simulations. Interannual trends in annual maximum vegetation greenness from 1985 to 2019 and 2000 to 2019 are provided for sample locations with adequate data for time series analysis; these data are in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Boreal_AGB_Density_ICESat2_2186_1.json b/datasets/Boreal_AGB_Density_ICESat2_2186_1.json index d6934c1b09..63145fd13a 100644 --- a/datasets/Boreal_AGB_Density_ICESat2_2186_1.json +++ b/datasets/Boreal_AGB_Density_ICESat2_2186_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Boreal_AGB_Density_ICESat2_2186_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and \"msw_flag\" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.", "links": [ { diff --git a/datasets/Boreal_Arctic_Wetland_CH4_2351_1.json b/datasets/Boreal_Arctic_Wetland_CH4_2351_1.json index 5954998675..e247f7b387 100644 --- a/datasets/Boreal_Arctic_Wetland_CH4_2351_1.json +++ b/datasets/Boreal_Arctic_Wetland_CH4_2351_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Boreal_Arctic_Wetland_CH4_2351_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an upscaled estimate of Boreal-Arctic wetland CH4 emissions at a weekly time scale from 2002 to 2021 at 0.5 by 0.5-degree spatial resolution. Ground truth data on wetland CH4 emissions from eddy covariance towers (139 site years) and chambers (168 site years) were used to train and validate a causality-guided machine learning model. The trained model was then used to estimate CH4 emissions at grid cells that have wetlands and located above 44 degrees north. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/Boreal_CanopyCover_StandAge_2012_1.json b/datasets/Boreal_CanopyCover_StandAge_2012_1.json index a30bc82fb4..82ce04b6ed 100644 --- a/datasets/Boreal_CanopyCover_StandAge_2012_1.json +++ b/datasets/Boreal_CanopyCover_StandAge_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Boreal_CanopyCover_StandAge_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (*.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.", "links": [ { diff --git a/datasets/Boreal_Fire_Severity_Metrics_1520_1.json b/datasets/Boreal_Fire_Severity_Metrics_1520_1.json index 0dd618a754..8b22d39ded 100644 --- a/datasets/Boreal_Fire_Severity_Metrics_1520_1.json +++ b/datasets/Boreal_Fire_Severity_Metrics_1520_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Boreal_Fire_Severity_Metrics_1520_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides products characterizing immediate and longer-term ecosystem changes from fires in the circumpolar boreal forests of Northern Eurasia and North America. The data include fire intensity (fire radiative power; FRP), increase in spring albedo, decrease in tree cover, normalized burn ratio, normalized difference vegetation index, and land surface temperature, as well as three derived fire metrics: crown scorch, vegetation destruction, and fire-induced tree mortality. Longer-term changes are indicated by mean albedo determined 5-12 years after fires, mean percent decrease in tree cover 5-7 years after fires, and mean annual burned percentage. The data cover the period 2001-2013 and are provided at quarter, half, and one degree resolutions for boreal forests within the 40 to 80 degree North circumpolar region. The data were derived from a variety of sources including MODIS products, climate reanalysis data, and forest inventories. A data file with identified boreal forest area (pixels), as defined by climate and vegetation type, and a file with the defined North American and Eurasian boreal forest study regions are included.", "links": [ { diff --git a/datasets/Bot_Bibliography_1.json b/datasets/Bot_Bibliography_1.json index 8df390dbbf..28b4fe1347 100644 --- a/datasets/Bot_Bibliography_1.json +++ b/datasets/Bot_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Bot_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic Botanical Bibliography compiled by Dr Ron Lewis Smith of the British Antarctic Survey. There are 3,076 records in this bibliography.\n\nThe fields in this dataset are:\nyear\nauthor\ntitle\njournal", "links": [ { diff --git a/datasets/BowdoinBuoy_0.json b/datasets/BowdoinBuoy_0.json index bfe4d697e3..5fdbac31bf 100644 --- a/datasets/BowdoinBuoy_0.json +++ b/datasets/BowdoinBuoy_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BowdoinBuoy_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made from the Bowdoin buoy network and ECOHAB project since 2007 near Portland, Maine.", "links": [ { diff --git a/datasets/BurnedArea_Emissions_AK_YT_NWT_1812_2.json b/datasets/BurnedArea_Emissions_AK_YT_NWT_1812_2.json index a8e0e0c745..c194b5b5d7 100644 --- a/datasets/BurnedArea_Emissions_AK_YT_NWT_1812_2.json +++ b/datasets/BurnedArea_Emissions_AK_YT_NWT_1812_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "BurnedArea_Emissions_AK_YT_NWT_1812_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps.", "links": [ { diff --git a/datasets/Burned_Area_Depth_AK_CA_2063_1.json b/datasets/Burned_Area_Depth_AK_CA_2063_1.json index 9883dcd993..11757837de 100644 --- a/datasets/Burned_Area_Depth_AK_CA_2063_1.json +++ b/datasets/Burned_Area_Depth_AK_CA_2063_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Burned_Area_Depth_AK_CA_2063_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems.", "links": [ { diff --git a/datasets/Byrd-SipleDome-CO2-GICC05_1.json b/datasets/Byrd-SipleDome-CO2-GICC05_1.json index dc75e69f9d..4162b1bd67 100644 --- a/datasets/Byrd-SipleDome-CO2-GICC05_1.json +++ b/datasets/Byrd-SipleDome-CO2-GICC05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Byrd-SipleDome-CO2-GICC05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic ice cores provide clear evidence of a close coupling between variations in Antarctic temperature and the atmospheric concentration of CO2 during the glacial/interglacial cycles of at least the past 800-thousand years. Precise information on the relative timing of the temperature and CO2 changes can assist in refining our understanding of the physical processes involved in this coupling. Here, we focus on the last deglaciation, 19 000 to 11 000 yr before present, during which CO2 concentrations increased by ~80 parts per million by volume and Antarctic temperature increased by ~10 degrees C. \n\nUtilising a recently developed proxy for regional Antarctic temperature, derived from five near-coastal ice cores and two ice core CO2 records with high dating precision, we show that the increase in CO2 likely lagged the increase in regional Antarctic temperature by less than 400 yr and that even a short lead of CO2 over temperature cannot be excluded. This result, consistent for both CO2 records, implies a faster coupling between temperature and CO2 than previous estimates, which had permitted up to millennial-scale lags.\n\nThis work was done as part of project AAS 757.\n\nDESCRIPTION\nThe regional Antarctic temperature proxy data series is avaliable elsewhere: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/icecore/antarctica/antarctica2011iso.txt The locations and original references for the CO2 data and transfers to the GICC05 timescale are as follows:\n\nByrd\nLocation: 80 degrees 01'S 119 degrees 31'W\nElevation: 1530 m asl\nReference for transfer to GICC05 timescale: Pedro et al., Clim. Past. 8, 2012.\nReference for CO2 data: (1) Neftel, A., Oeschger, H., Staffelbach, T., and \nNature, 331, 609-11, doi:10.1038/331609a0, 1988.\n(2) Staffelbach, T., Stauffer, B., Sigg, A., and Oeschger, H.: CO2\nmeasurements from polar ice cores - More data from different\nsites, Tellus B, 43, 91-6, doi:10.1034/j.1600-0889.1991.t01-1-\n00003.x, 1991.\n\nSiple Dome\nLocation: 81 degrees 40'S 148 degrees 49'W\nElevation: 621 m asl\nReference for transfer to GICC05 timescale: Pedro et al., Clim. Past. 8, 2012.\nReference for CO2 data: \nAhn, J., Wahlen, M., Deck, B. L., Brook, E. J., Mayewski,\nP. A., Taylor, K. C., and White, J. W. C.: A record of atmospheric\nCO2 during the last 40,000 years from the Siple\nDome, Antarctica ice core, J. Geophys. Res., 109, D13305,\ndoi:10.1029/2003JD004415, 2004.", "links": [ { diff --git a/datasets/C-HARRIER_0.json b/datasets/C-HARRIER_0.json index 0d38775aaf..cdc7a4b2c5 100644 --- a/datasets/C-HARRIER_0.json +++ b/datasets/C-HARRIER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C-HARRIER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the C-HARRIER (Coastal High Acquisition Rate Radiometers for Innovative Environmental Research) project, which aims to study the coastal atmospheric and acquatic environments of Monterey Bay, Pinto Lake, and Lake Tahoe.", "links": [ { diff --git a/datasets/C1_PANA_STUC00GTD_1.json b/datasets/C1_PANA_STUC00GTD_1.json index dcb311cc88..6d0356eb32 100644 --- a/datasets/C1_PANA_STUC00GTD_1.json +++ b/datasets/C1_PANA_STUC00GTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C1_PANA_STUC00GTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is High resolution satellite carries two PAN sensors with 2.5m resolution and fore-aft stereo capability. The payload is designed to cater to applications in cartography, terrain modeling, cadastral mapping etc. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/C1_PANF_STUC00GTD_1.json b/datasets/C1_PANF_STUC00GTD_1.json index 1f4b063aeb..609edbd1a9 100644 --- a/datasets/C1_PANF_STUC00GTD_1.json +++ b/datasets/C1_PANF_STUC00GTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C1_PANF_STUC00GTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is High resolution satellite carries two PAN sensors with 2.5m resolution and fore-aft stereo capability. The payload is designed to cater to applications in cartography, terrain modeling, cadastral mapping etc. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/C5_0.json b/datasets/C5_0.json index 3540bcf0cd..d9550fbebd 100644 --- a/datasets/C5_0.json +++ b/datasets/C5_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C5_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the eastern US coast across the Gulf Stream in 2007.", "links": [ { diff --git a/datasets/CABARE_918_1.json b/datasets/CABARE_918_1.json index bc2644bbdc..c4a2129f5e 100644 --- a/datasets/CABARE_918_1.json +++ b/datasets/CABARE_918_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CABARE_918_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and 65-60 degrees W. Numerical maps of the natural landscape structure were prepared by digitizing existing 1:1,000,000 maps. Satellite data give information about the most recent modifications of the surface due to human activities. This mapping work was the first step of a mesoscale meteorological modeling program (Calvet et al., 1997) in forested and deforested Southwestern Amazonia (Rondonia, Brazil). This work was performed in the framework of a research program (CABARE) supported by the European Union, CEC Environment Program.Data are provided in ArcGIS ArcInfo grid ascii format for the following surface parameters:Elevation of terrain of the Rondonia region (altitude.txt)LANDSAT-derived vegetation classification of the Rondonia region in 1993-1994 (classify.txt)Soil classification of the Rondonia region (soil.txt)Sand and Clay of the Rondonia region (sand.txt and clay.txt)Vegetation classification of the Rondonia region from RADAMBRASIL (Macedo et al., 1979) (vegetation.txt)", "links": [ { diff --git a/datasets/CALIPSO-NVF_HSRL2_KingAir_Data_1.json b/datasets/CALIPSO-NVF_HSRL2_KingAir_Data_1.json index 802e490494..ad76dfbf10 100644 --- a/datasets/CALIPSO-NVF_HSRL2_KingAir_Data_1.json +++ b/datasets/CALIPSO-NVF_HSRL2_KingAir_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CALIPSO-NVF_HSRL2_KingAir_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CALIPSO Night Validation Flights (CALIPSO-NVF) airborne deployment was conducted in August 2022 out of Bermuda. The goal was to conduct a series of nighttime underflights of the CALIPSO satellite with the NASA Langley High Spectral Resolution Lidar (HSRL-2). Airborne measurements from the NASA Langley HSRL-2 instrument are essential for verifying the calibration accuracy of the CALIPSO lidar and for acquiring information on aerosol optical properties used for its aerosol profile retrievals. By flying under the CALIPSO ground track, HSRL-2 provides an independent measurement of lidar attenuated backscatter with a higher signal-to-noise ratio. To obtain this important validation dataset, the HSRL-2 was flown on board the LaRC B-200 King Air as CALIPSO passed within range of the aircraft. The western Atlantic Ocean was selected for CALIPSO-NVF to allow unobstructed, 45-minute flights along the satellite ground track. Five nighttime underflights were executed in total \u2013 four in cloud-free skies on August 7, 10, 12, and 17th, yielding ideal data from both instruments for calibration validation. The fifth flight on August 18th targeted measurements beneath cirrus to assess the accuracy of CALIPSO aerosol retrievals through high clouds at night, an important but previously unexplored validation target. Total research flight time was 17.7 hours, sampling 2,200 km along the CALIPSO ground track.", "links": [ { diff --git a/datasets/CAL_IIR_L1-Prov-V1-13_V1-13.json b/datasets/CAL_IIR_L1-Prov-V1-13_V1-13.json index 32a32c3a58..43a11c7c7c 100644 --- a/datasets/CAL_IIR_L1-Prov-V1-13_V1-13.json +++ b/datasets/CAL_IIR_L1-Prov-V1-13_V1-13.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L1-Prov-V1-13_V1-13", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L1-Prov-V1-13 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Infrared Imaging Radiometer Level 1B radiance data, Provisional Version 1-13. The version of this product was changed from 1-12 to 1-13 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nThe Imaging Infrared Radiometer (IIR) Level 1B data product contains a half orbit of geolocated, calibrated radiances. Image data are registered to a 1 km grid centered on the lidar track. The Level 1B data product is written in HDF. CALIPSO was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite is comprised of three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), IIR, and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.", "links": [ { diff --git a/datasets/CAL_IIR_L1-Standard-V2-00_V2-00.json b/datasets/CAL_IIR_L1-Standard-V2-00_V2-00.json index 904f338149..2130e7dec4 100644 --- a/datasets/CAL_IIR_L1-Standard-V2-00_V2-00.json +++ b/datasets/CAL_IIR_L1-Standard-V2-00_V2-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L1-Standard-V2-00_V2-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L1-Standard-V2-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Imaging Infrared Radiometer (IIR) Level 1B Radiance, Standard Version 2-00 data product. Data for this product was collected using the CALIPSO IIR instrument. Data collection for this product is ongoing. The IIR Level 1B data product contains a half orbit of geolocated, calibrated radiances. Image data are registered to a 1 km grid centered on the lidar track. The Level 1B data product is written in HDF.\r\rCALIPSO was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.", "links": [ { diff --git a/datasets/CAL_IIR_L1-Standard-V2-01_V2-01.json b/datasets/CAL_IIR_L1-Standard-V2-01_V2-01.json index 1c73a01853..dd7d9f5157 100644 --- a/datasets/CAL_IIR_L1-Standard-V2-01_V2-01.json +++ b/datasets/CAL_IIR_L1-Standard-V2-01_V2-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L1-Standard-V2-01_V2-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L1-Standard-V2-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Imaging Infrared Radiometer (IIR) Level 1B Radiance, Standard Version 2-01 data product. Data for this product was collected using the CALIPSO IIR instrument. Data collection for this product is ongoing. The IIR Level 1B data product contains a half orbit of geolocated, calibrated radiances. Image data are registered to a 1 km grid centered on the lidar track. The Level 1B data product is written in HDF. The version of this product was changed from 2-00 to 2-01 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nCALIPSO was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.", "links": [ { diff --git a/datasets/CAL_IIR_L2_Swath-Standard-V4-20_V4-20.json b/datasets/CAL_IIR_L2_Swath-Standard-V4-20_V4-20.json index 5d1bcd6268..2b876052c2 100644 --- a/datasets/CAL_IIR_L2_Swath-Standard-V4-20_V4-20.json +++ b/datasets/CAL_IIR_L2_Swath-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Swath-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Swath-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Swath, Version 4-20 data product. Data for this product was collected using the CALIPSO IIR instrument. This product contains emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the lidar track. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L2_Swath-Standard-V4-21_V4-21.json b/datasets/CAL_IIR_L2_Swath-Standard-V4-21_V4-21.json index 0830f880a2..4f0fc3992f 100644 --- a/datasets/CAL_IIR_L2_Swath-Standard-V4-21_V4-21.json +++ b/datasets/CAL_IIR_L2_Swath-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Swath-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Swath-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Swath, Version 4-21 data product. Data for this product was collected using the CALIPSO IIR instrument. Data collection for this product is ongoing. This product contains emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the lidar track. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L2_Swath-Standard-V4-51_V4-51.json b/datasets/CAL_IIR_L2_Swath-Standard-V4-51_V4-51.json index 283353e9f3..d11f764ebc 100644 --- a/datasets/CAL_IIR_L2_Swath-Standard-V4-51_V4-51.json +++ b/datasets/CAL_IIR_L2_Swath-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Swath-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Swath-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Swath, Version 4-51 data product. Data for this product was collected using the CALIPSO IIR instrument. This product contains emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the lidar track. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L2_Track-Standard-V4-20_V4-20.json b/datasets/CAL_IIR_L2_Track-Standard-V4-20_V4-20.json index f3650c970b..805c167aef 100644 --- a/datasets/CAL_IIR_L2_Track-Standard-V4-20_V4-20.json +++ b/datasets/CAL_IIR_L2_Track-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Track-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Track-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Track, Version 4-20 data product. Data for this product was collected using the CALIPSO IIR instrument. This product contains emissivity and cloud particle data related to pixels co-located to the lidar track. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L2_Track-Standard-V4-21_V4-21.json b/datasets/CAL_IIR_L2_Track-Standard-V4-21_V4-21.json index da7bd83de2..ca7f2a4651 100644 --- a/datasets/CAL_IIR_L2_Track-Standard-V4-21_V4-21.json +++ b/datasets/CAL_IIR_L2_Track-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Track-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Track-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Track, Version 4-21 data product. Data for this product was collected using the CALIPSO IIR instrument. Data collection for this product is ongoing. This product contains emissivity and cloud particle data related to pixels co-located to the lidar track. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L2_Track-Standard-V4-51_V4-51.json b/datasets/CAL_IIR_L2_Track-Standard-V4-51_V4-51.json index 650847f761..c7edbe3e45 100644 --- a/datasets/CAL_IIR_L2_Track-Standard-V4-51_V4-51.json +++ b/datasets/CAL_IIR_L2_Track-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L2_Track-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L2_Track-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Track, Version 4-51 data product. Data for this product was collected using the CALIPSO IIR instrument. This product contains emissivity and cloud particle data related to pixels co-located to the lidar track. The version of this product was changed to V4-51 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. The CALIPSO satellite comprises three instruments: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json b/datasets/CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json index 53d1702f8a..e9a22d8ef2 100644 --- a/datasets/CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json +++ b/datasets/CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) IIR Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Imaging Infrared Radiometer (IIR) instrument.\r\n\r\nThis product reports global distributions of IIR cloud effective radius, water path averages, and histograms on a uniform 2-dimensional (2D) spatial grid. This product is designed to follow the general guidance of the GEWEX Cloud Assessment. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. Cloud properties are reported for ice clouds, liquid water clouds, and high ice clouds of layer pressure lower than 440 hPa. All level 3 parameters are derived from the IIR version 4 level 2 track products, with the temporal extent averaging one month. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, Centre National d'Etudes Spatiales (CNES).", "links": [ { diff --git a/datasets/CAL_LID_L1-Standard-V4-10_V4-10.json b/datasets/CAL_LID_L1-Standard-V4-10_V4-10.json index de5a16e44c..f768937833 100644 --- a/datasets/CAL_LID_L1-Standard-V4-10_V4-10.json +++ b/datasets/CAL_LID_L1-Standard-V4-10_V4-10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L1-Standard-V4-10_V4-10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L1-Standard-V4-10 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 1B profile data, Version 4-10 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product version is complete.\r\n\r\nThe highest quality data products generated by the Data Management System (DMS) are referred to as Standard data products. Night and Day orbit segments are written to separate data files. A complete set of browse images, including orbit track maps, are generated and posted to the science data website. Standard data products are recommended for research studies and journal publications. The lidar Level 1B data product contains a half orbit (day or night) of calibrated and geolocated lidar profiles. The product contains data from all non-diagnostic instrument modes, including nominal science, depolarization gain ratio calibration, and boresight alignment. The lidar Level 1B product contains additional data not found in the Level 0 lidar input file, including post-processed ephemeris data, celestial data, and converted payload status data. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L1-Standard-V4-11_V4-11.json b/datasets/CAL_LID_L1-Standard-V4-11_V4-11.json index 1ac2aa9519..ea15909728 100644 --- a/datasets/CAL_LID_L1-Standard-V4-11_V4-11.json +++ b/datasets/CAL_LID_L1-Standard-V4-11_V4-11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L1-Standard-V4-11_V4-11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L1-Standard-V4-11 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 1B profile data, Version 4-10 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-10 to 4-11 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product version is ongoing.\r\n\r\nThe highest quality data products generated by the Data Management System (DMS) are referred to as Standard data products. Night and Day orbit segments are written to separate data files. A complete set of browse images, including orbit track maps, are generated and posted to the science data website. Standard data products are recommended for research studies and journal publications. The lidar Level 1B data product contains a half orbit (day or night) of calibrated and geolocated lidar profiles. The product contains data from all non-diagnostic instrument modes, including nominal science, depolarization gain ratio calibration, and boresight alignment. The lidar Level 1B product contains additional data not found in the Level 0 lidar input file, including post-processed ephemeris data, celestial data, and converted payload status data. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L1-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L1-Standard-V4-51_V4-51.json index 982fb66265..2249943502 100644 --- a/datasets/CAL_LID_L1-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L1-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L1-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L1-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 1B profile data, Version 4-51 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP Level 1B data product contains a half orbit (day or night) of calibrated and geolocated single-shot (highest resolution) lidar profiles, including 532 nm and 1064 nm attenuated backscatter and depolarization ratio at 532 nm. The product released contains data from nominal science mode measurements.", "links": [ { diff --git a/datasets/CAL_LID_L15-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L15-Standard-V1-00_V1-00.json index d032718b0a..27fc51ce64 100644 --- a/datasets/CAL_LID_L15-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L15-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L15-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L15-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 1.5 Profile, Version 1-00 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is complete.\r\n\r\nThis product is a continuous segment of calibrated, geolocated, cloud-cleared, and spatially averaged profiles of lidar attenuated backscatter. These profiles are derived via synthesizing the CALIPSO lidar Level 1B profile and lidar Level 2 5 km aerosol profile products with the lidar Level 2 Vertical Feature Mask (VFM) product. The lidar Level 1.5 standard data product is derived using standard Version 4.10 Level 1B and Version 4.20 Level 2 data products as input.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L15-Standard-V1-01_V1-01.json b/datasets/CAL_LID_L15-Standard-V1-01_V1-01.json index fd8edf8b5b..add85f5cc9 100644 --- a/datasets/CAL_LID_L15-Standard-V1-01_V1-01.json +++ b/datasets/CAL_LID_L15-Standard-V1-01_V1-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L15-Standard-V1-01_V1-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L15-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 1.5 Profile, Version 1-01 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 1-00 to 1-01 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nThis product is a continuous segment of calibrated, geolocated, cloud-cleared, and spatially averaged profiles of lidar attenuated backscatter. Data collection for this product is ongoing. These profiles are derived via synthesizing the CALIPSO lidar Level 1B profile and lidar Level 2 5 km aerosol profile products with the lidar Level 2 Vertical Feature Mask (VFM) product. The lidar Level 1.5 standard data product is derived using standard Version 4.11 Level 1B and Version 4.21 Level 2 data products as input.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-20_V4-20.json index 9e8c1b43a5..ccd4c20543 100644 --- a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_01kmCLay-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_01kmCLay-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to January 19, 2020. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-21_V4-21.json index 30def63fdc..be84b93d30 100644 --- a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_01kmCLay-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_01kmCLay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4.21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is ongoing. The version of this product was changed from 4.20 to 4.21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is complete.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51.json index c806959103..1a791d26e9 100644 --- a/datasets/CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_01kmCLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \r\n\r\nWithin this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products contain column descriptors associated with several layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmALay-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_05kmALay-Standard-V4-20_V4-20.json index 524531aad9..2b3050a812 100644 --- a/datasets/CAL_LID_L2_05kmALay-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_05kmALay-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmALay-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmALay-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer Data, Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to July 1, 2023. \r\n\r\nWithin the Lidar Aerosol Layer Product, there are two general classes of data:- Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each one of which is associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmALay-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_05kmALay-Standard-V4-21_V4-21.json index db56175502..b53e33f677 100644 --- a/datasets/CAL_LID_L2_05kmALay-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_05kmALay-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmALay-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmALay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer Data, Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is complete.\r\n\r\nWithin the Lidar Aerosol Layer Product, there are two general classes of data:- Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmALay-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_05kmALay-Standard-V4-51_V4-51.json index 4c48f5a4e1..a45ba6b0cf 100644 --- a/datasets/CAL_LID_L2_05kmALay-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_05kmALay-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmALay-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmALay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer Data, Version 4-51 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\r\n\r\nWithin this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-20_V4-20.json index 4ca3193ebd..f4446c1ce5 100644 --- a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmAPro-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmAPro-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Aerosol Profile, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to June 30, 2023.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-21_V4-21.json index 02ffb67a7f..65424164d9 100644 --- a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmAPro-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmAPro-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Aerosol Profile, Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-51_V4-51.json index 4ff1bc9438..d3e2b83f3d 100644 --- a/datasets/CAL_LID_L2_05kmAPro-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_05kmAPro-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmAPro-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmAPro-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Aerosol Profile, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, heretofore called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-20_V4-20.json index 9d328f925b..1ac9235acb 100644 --- a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCLay-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCLay-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 5 km Cloud Layer, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-21_V4-21.json index d7e5685aa1..16538a2d60 100644 --- a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCLay-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCLay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 5 km Cloud Layer, Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-51_V4-51.json index ff9394430d..33b5b75f93 100644 --- a/datasets/CAL_LID_L2_05kmCLay-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_05kmCLay-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCLay-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Layer Data, Version 4-51 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\r\n\r\nWithin this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-20_V4-20.json index 4964afdf97..b8968da3cf 100644 --- a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCPro-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCPro-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Cloud Profile, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-21_V4-21.json index 86ab548b19..4555d3df04 100644 --- a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCPro-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCPro-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Cloud Profile, Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-51_V4-51.json index 8c87473e48..8a7347cab1 100644 --- a/datasets/CAL_LID_L2_05kmCPro-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_05kmCPro-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmCPro-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmCPro-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Cloud Profile, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-20_V4-20.json index 5733d8c221..75ead841af 100644 --- a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmMLay-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmMLay-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 5 km Merged Layer, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to June 30, 2023. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-21_V4-21.json index 4320b26dc5..55fc8d46b4 100644 --- a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmMLay-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmMLay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 5 km Merged Layer, Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-51_V4-51.json index f2acb4bd63..29d6175d81 100644 --- a/datasets/CAL_LID_L2_05kmMLay-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_05kmMLay-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_05kmMLay-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_05kmMLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Merged (cloud + aerosol) Layer Data, Version 4-51 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\r\n\r\nWithin this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_333mMLay-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_333mMLay-Standard-V4-20_V4-20.json index 110e60cecf..99578d4c6c 100644 --- a/datasets/CAL_LID_L2_333mMLay-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_333mMLay-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_333mMLay-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_333mMLay-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1/3 km Merged Layer, Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_333mMLay-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_333mMLay-Standard-V4-21_V4-21.json index 9f6c3c1086..0241e8d761 100644 --- a/datasets/CAL_LID_L2_333mMLay-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_333mMLay-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_333mMLay-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_333mMLay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1/3 km Merged Layer, Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_333mMLay-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_333mMLay-Standard-V4-51_V4-51.json index 2da02cd745..4b9c0696d1 100644 --- a/datasets/CAL_LID_L2_333mMLay-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_333mMLay-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_333mMLay-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_333mMLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 333 m Merged (cloud + aerosol) Layer Data, Version 4-51 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\r\n\r\nWithin this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00.json index ff6a6607cc..3a3df585fd 100644 --- a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica. Data collection for this product is complete. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01.json b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01.json index de1c744dca..59d4fdf6be 100644 --- a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01.json +++ b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-01 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica. The version of this product was changed from 1-00 to 1-01 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is complete.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, Centre National d'\u00c9tudes Spatiales (CNES).", "links": [ { diff --git a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00_V2-00.json b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00_V2-00.json index 29bb81b8ca..e5d85c0b60 100644 --- a/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00_V2-00.json +++ b/datasets/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00_V2-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00_V2-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_BlowingSnow_Antarctica-Standard-V2-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 2-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00.json index 73a2c97847..32cbd3a797 100644 --- a/datasets/CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Greenland, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Greenland. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L2_PSCMask-Standard-V2-00_V2-00.json b/datasets/CAL_LID_L2_PSCMask-Standard-V2-00_V2-00.json index 53bb6939f6..d30746d115 100644 --- a/datasets/CAL_LID_L2_PSCMask-Standard-V2-00_V2-00.json +++ b/datasets/CAL_LID_L2_PSCMask-Standard-V2-00_V2-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_PSCMask-Standard-V2-00_V2-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Version 2 (V2) CALIPSO Lidar Level 2 Polar Stratospheric Clouds (PSC) data product ensemble describes the spatial distribution, optical properties, and composition of PSC layers observed by the CALIPSO lidar (CALIOP). The product contains profiles of PSC presence, composition, optical properties, and meteorological information on a uniform 5-km horizontal x 180-m vertical grid along CALIPSO orbit tracks. Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensable vapors HNO3 and H2O and a number of parameters from the Aura MLS V2 Derived Meteorological Products (DMPs) are also included in the V2 PSC data product ensemble.", "links": [ { diff --git a/datasets/CAL_LID_L2_VFM-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L2_VFM-Standard-V4-20_V4-20.json index e0ccc717b4..33da318b1e 100644 --- a/datasets/CAL_LID_L2_VFM-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L2_VFM-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_VFM-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_VFM-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_VFM-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L2_VFM-Standard-V4-21_V4-21.json index 8350999ba7..197d65ba21 100644 --- a/datasets/CAL_LID_L2_VFM-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L2_VFM-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_VFM-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_VFM-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L2_VFM-Standard-V4-51_V4-51.json b/datasets/CAL_LID_L2_VFM-Standard-V4-51_V4-51.json index 848ea656ac..3d944a4a20 100644 --- a/datasets/CAL_LID_L2_VFM-Standard-V4-51_V4-51.json +++ b/datasets/CAL_LID_L2_VFM-Standard-V4-51_V4-51.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L2_VFM-Standard-V4-51_V4-51", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L2_VFM-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \r\n\r\nThe CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, heretofore called the C-Train.", "links": [ { diff --git a/datasets/CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00.json index 70f5a4f5eb..89b4a8f267 100644 --- a/datasets/CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Cloud_Occurrence-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Cloud Occurrence Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The degradation of the laser energies that started in September 2016 had a negative impact on the product, and because of this, generation and distribution ended in December 2016. Updated Lidar Level 2 data products and changes to the Lidar Level 3 Cloud Occurrence algorithm will need to be completed before a new release of this product is released.\r\n\r\nThis product reports global distributions of clouds on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO level 2 data, with a temporal average of one month. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES.", "links": [ { diff --git a/datasets/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json index 514aa97c72..833e2a34fd 100644 --- a/datasets/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is complete.\r\n\r\nThis product is a reformatted version of the CALIPSO contribution to the GEWEX cloud assessment of global cloud datasets from satellites. The data submitted by the CALIPSO team for this project had to conform to a specific format: yearly netCDF files organized by parameter. To be compatible with another publicly orderable lidar level 3 CALIPSO aerosol and cloud products reported as monthly HDF files, this new lidar level 3 CALIPSO GEWEX cloud product was created. These files report global distributions of cloud amount and cloud top as averages and histograms on a uniform 2-dimensional (2D) spatial grid. All level 3 parameters are derived from the CALIPSO version 4. x Level 2, 5 km cloud merged layer products, with a temporal averaging of one month.\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00.json b/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00.json index 2b3576d566..562c073922 100644 --- a/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00.json +++ b/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Stratospheric_APro-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V1.00 product ended on July 1, 2020 to support a change in the operating system of the CALIPSO production clusters. The V1.01 data product covers July 1, 2020, to current. \r\n\r\nThe CALIPSO Lidar Level 3 stratospheric aerosol reports global distributions of 532nm total attenuated backscatter, extinction, attenuated scattering ratios, and stratospheric aerosol optical depths on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO version 4 level 1 and level 2 5 km merged layer and version 3 level 2 polar stratospheric cloud data products, with a temporal averaging of one month. The primary outputs are reported in terms of 1) background only and 2) all aerosol. All features identified by the level 2 algorithms have been removed for background only. Only aerosol layers are considered for all aerosols, while clouds and polar stratospheric clouds are removed. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01.json b/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01.json index 0602205adc..f637e43315 100644 --- a/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01.json +++ b/datasets/CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Stratospheric_APro-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-01 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 1-00 to 1-01 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.\r\n\r\nThe CALIPSO Lidar Level 3 stratospheric aerosol reports global distributions of 532nm total attenuated backscatter, extinction, attenuated scattering ratios, and stratospheric aerosol optical depths on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO version 4 level 1 and level 2 5 km merged layer and version 3 level 2 polar stratospheric cloud data products, with a temporal averaging of one month. The primary outputs are reported in terms of 1) background only and 2) all aerosol. All features identified by the level 2 algorithms have been removed for background only. Only aerosol layers are considered for all aerosols, while clouds and polar stratospheric clouds are removed. \r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20.json index b8b1ff8ca9..c902231b7e 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night):\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence,\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged,\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21_V4-21.json index d068bdf632..d37843e2fb 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is complete.\r\n\r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.21 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night):\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence,\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged,\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20.json index 9be9e6a528..69478a3287 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night)\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21_V4-21.json index 039cdd59ce..106357f5a8 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is ongoing.\r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.21 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night)\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'\u00e9tudes spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20.json index 6ec972871e..bcda8b148d 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Opaque Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data before averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night):\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D\u2019Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21_V4-21.json index bd209e8085..51678e46ad 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Opaque Data, Standard Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is ongoing.\r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12km. All level 3 parameters are derived from the version 4.21 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.\r\n\r\nDescription of the Four Sky Conditions (Day, Night):\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20.json index 76f43685e7..bcbe52c3cb 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Transparent Data, Standard Version 4-20 data product. Data is collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. \r\n\r\nThe CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, there are four different types of level 3 files produced, depending on the sky condition and the temporal coverage of the data before averaging: \r\n\r\nDescription of the Four Sky Conditions (Day, Night)\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence,\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged,\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in formation with five other satellites in the international \"A-Train\" (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the CALIOP, the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21_V4-21.json b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21_V4-21.json index ec52dc97d9..31cee46a02 100644 --- a/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21_V4-21.json +++ b/datasets/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21_V4-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21_V4-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Transparent Data, Standard Version 4-20 data product. Data collection, which is ongoing, is collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.21 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, there are four different types of level 3 files produced, depending on the sky condition and the temporal coverage of the data before averaging: \r\n\r\nDescription of the Four Sky Conditions (Day, Night)\r\n1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence,\r\n2) Cloud-Free: Only cloud-free level 2 columns are averaged,\r\n3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and\r\n4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged\r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in formation with five other satellites in the international \"A-Train\" (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the CALIOP, the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_125m-ValStage1-V3-01_V3-01.json b/datasets/CAL_WFC_L1_125m-ValStage1-V3-01_V3-01.json index d054238442..585a636fa3 100644 --- a/datasets/CAL_WFC_L1_125m-ValStage1-V3-01_V3-01.json +++ b/datasets/CAL_WFC_L1_125m-ValStage1-V3-01_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_125m-ValStage1-V3-01_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_125m-ValStage1-V3-01 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC) Level 1B 125m Native Science data, Validated Stage 1 Version 3-01. Data collection for this product is complete. Version 3.01 includes new metadata parameters and corrections to several minor software bugs. Specifically, the Orbit Number and Path Number metadata parameters are now included to facilitate improved subsetting capabilities. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During the normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The WFC Level 1B 125 m Native Science data product provides WFC radiance and reflectance measurements across just the central 5 km swath at 125 m resolution. No spatial interpolation is performed. CALIPSO was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite is comprised of three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the WFC. CALIPSO is a joint satellite mission between NASA and the French Agency, CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_125m-ValStage1-V3-02_V3-02.json b/datasets/CAL_WFC_L1_125m-ValStage1-V3-02_V3-02.json index 6cd6040ddb..7cc41d956c 100644 --- a/datasets/CAL_WFC_L1_125m-ValStage1-V3-02_V3-02.json +++ b/datasets/CAL_WFC_L1_125m-ValStage1-V3-02_V3-02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_125m-ValStage1-V3-02_V3-02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_125m-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC) Level 1B 125 m Native Science data, Validated Stage 1 Version 3-02. Data collection for this product is ongoing. Version 3.02 represents a transition of the Lidar, Imaging Infrared Radiometer (IIR), and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced, and very minor changes were observed between V3.01 and V3.02 as a result of the compiler and computer architecture differences. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The 1 km Native Science grid covers the full 61 km swath centered on the Lidar track. The 125 m Native Science grid contains only the central 5 km wide high-resolution portion of the WFC swath. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite is comprised of three instruments, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and WFC. CALIPSO is a joint satellite mission between NASA and the French Agency, CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01.json b/datasets/CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01.json index a67da64b10..de938bfa52 100644 --- a/datasets/CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01.json +++ b/datasets/CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_1Km-ValStage1-V3-01_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_1Km-ValStage1-V3-01 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1km Native Science data. Version 3.01 includes new metadata parameters and corrections to several minor software bugs. Specifically, the Orbit Number and Path Number metadata parameters are now included to facilitate improved subsetting capabilities. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 km Native Science grid covers the full 61 km swath centered on the Lidar track. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02.json b/datasets/CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02.json index 3e5706714a..2021fa9900 100644 --- a/datasets/CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02.json +++ b/datasets/CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_1Km-ValStage1-V3-02_V3-02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_1Km-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC), Level 1B 1 km Native Science data, Validated Stage 1 Version 3-02. Data collection for this product is ongoing. Version 3.02 represents a transition of the Lidar, Imaging Infrared Radiometer (IIR), and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced, and minor changes were observed between V3.01 and V3.02 due to the compiler and computer architecture differences. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During regular operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The WFC Level 1B 1 km Native Science grid covers the 61 km swath centered on the Lidar track. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), IIR, and WFC. CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01.json b/datasets/CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01.json index 91677cfa86..3591885958 100644 --- a/datasets/CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01.json +++ b/datasets/CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_IIR-ValStage1-V3-01_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_IIR-ValStage1-V3-01 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1 km Registered Science data. Version 3.01 includes new metadata parameters and corrections to several minor software bugs. Specifically, the Orbit Number and Path Number metadata parameters are now included to facilitate improved subsetting capabilities. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).", "links": [ { diff --git a/datasets/CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02.json b/datasets/CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02.json index 8783a52710..5096787de6 100644 --- a/datasets/CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02.json +++ b/datasets/CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAL_WFC_L1_IIR-ValStage1-V3-02_V3-02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAL_WFC_L1_IIR-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera Level 1B 1 km Registered Science data. Version 3.02 represents a transition of the Lidar, IIR, and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced and very minor changes were observed between V3.01 and V3.02 as a result of the compiler and computer architecture differences. The primary Wide Field Camera Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During the normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B 1 IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.", "links": [ { diff --git a/datasets/CAM5K30CFCLIM_003.json b/datasets/CAM5K30CFCLIM_003.json index 17d6b6139f..3cbfca4c71 100644 --- a/datasets/CAM5K30CFCLIM_003.json +++ b/datasets/CAM5K30CFCLIM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30CFCLIM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global coefficient climatology product (CAM5K30CFCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CFCLIM data product. This HSR algorithm is accessible in both MATLAB and FORTRAN programming languages, and it corresponds with the temporally equivalent CAM5K30EMCLIM emissivity data product. The HSR emissivity spectra for the same month each year and each unique combination of lab dataset version and number of Principal Components (PC)s are first computed independently and then combined via a weighted average. The weighted average over 2003 through 2021 (19 years) defines the weights by the number of samples from each unique combination. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).\n\nProvided in the CAM5K30CFCLIM product are variables for PCA coefficients, the weights and sample numbers of the climatology coefficients used in the average calculation, sets of the number of PCA coefficients, laboratory version numbers, latitude, longitude, and land flag information. PCA coefficients depend on the lab PC data version and the number of PCs used.", "links": [ { diff --git a/datasets/CAM5K30CF_002.json b/datasets/CAM5K30CF_002.json index 6a1724bada..5c8bef333d 100644 --- a/datasets/CAM5K30CF_002.json +++ b/datasets/CAM5K30CF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30CF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf).\n\nProvided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab PC data and number of PCs used.", "links": [ { diff --git a/datasets/CAM5K30CF_003.json b/datasets/CAM5K30CF_003.json index 453eacdab8..0e4d88d24b 100644 --- a/datasets/CAM5K30CF_003.json +++ b/datasets/CAM5K30CF_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30CF_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf).\r\n\r\nProvided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used.\r\n", "links": [ { diff --git a/datasets/CAM5K30COVCLIM_003.json b/datasets/CAM5K30COVCLIM_003.json index 17d5f7cb93..71972377bd 100644 --- a/datasets/CAM5K30COVCLIM_003.json +++ b/datasets/CAM5K30COVCLIM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30COVCLIM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global covariances climatology product (CAM5K30COVCLIM). The product is provided at 0.25 degree (~25 kilometer) resolution. The CAMEL covariance product includes the mean and variance of the covariance matrixes created for each month from 2003 through 2021 (19 years) on a 0.25 x 0.25 degree grid of 416 spectral points from the V003 CAMEL Emissivity product (CAM5K30EM). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). \n\nProvided in the CAM5K30COVCLIM product are variables for the mean and variance of the emissivity, latitude, longitude, spectral frequencies, and number of observations.\n", "links": [ { diff --git a/datasets/CAM5K30EMCLIM_003.json b/datasets/CAM5K30EMCLIM_003.json index f3791f4012..bb3b06fad3 100644 --- a/datasets/CAM5K30EMCLIM_003.json +++ b/datasets/CAM5K30EMCLIM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30EMCLIM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global emissivity climatology product (CAM5K30EMCLIM). This 0.05 degree (~5 kilometer) resolution product represents the mean emissivity from 2003 through 2021 (19 years). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).\n\nVariables provided in the CAM5K30EMCLIM product include latitude, longitude, wavelength, number of samples used to calculate climatology, CAMEL quality flag, snow fraction derived from MODIS (MOD10), and CAMEL Emissivity.", "links": [ { diff --git a/datasets/CAM5K30EM_002.json b/datasets/CAM5K30EM_002.json index 00bac3e4e5..97c885f94c 100644 --- a/datasets/CAM5K30EM_002.json +++ b/datasets/CAM5K30EM_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30EM_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. \n\nThe integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf).\n\nProvided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information.\n\n", "links": [ { diff --git a/datasets/CAM5K30EM_003.json b/datasets/CAM5K30EM_003.json index 502a4edfe3..2e221174c7 100644 --- a/datasets/CAM5K30EM_003.json +++ b/datasets/CAM5K30EM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30EM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information.\n", "links": [ { diff --git a/datasets/CAM5K30UCCLIM_003.json b/datasets/CAM5K30UCCLIM_003.json index 02748b684a..3d8f799d1d 100644 --- a/datasets/CAM5K30UCCLIM_003.json +++ b/datasets/CAM5K30UCCLIM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30UCCLIM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global uncertainty climatology product (CAM5K30UCCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The 13 hinge-point uncertainty climatology is computed by taking an average over each available month from 2003 through 2021 (19 years) and includes three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty climatology is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the CAM5K30EMCLIM data product.\n\nProvided in the CAM5K30UCCLIM product are variables for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, and total uncertainty quality flag information.", "links": [ { diff --git a/datasets/CAM5K30UC_002.json b/datasets/CAM5K30UC_002.json index 6a39d629cb..59a302286e 100644 --- a/datasets/CAM5K30UC_002.json +++ b/datasets/CAM5K30UC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30UC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product.\n\nProvided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.\n", "links": [ { diff --git a/datasets/CAM5K30UC_003.json b/datasets/CAM5K30UC_003.json index fbaedb204b..d200e956cf 100644 --- a/datasets/CAM5K30UC_003.json +++ b/datasets/CAM5K30UC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAM5K30UC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product.\n\nProvided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.\n", "links": [ { diff --git a/datasets/CAMEX4_ER2_MAS_1.json b/datasets/CAMEX4_ER2_MAS_1.json index 1aca5852b4..03d2105c65 100644 --- a/datasets/CAMEX4_ER2_MAS_1.json +++ b/datasets/CAMEX4_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMEX4_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Convection And Moisture EXperiment (CAMEX) 4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-fundend aircraft and surface remote sensing instrumentation. These aircraft flew over, through, and around selected hurricanes as they approached landfall in the Caribbean, Gulf of Mexico, and along the East Coast of the United States. This study yields high spatial and temporal information of hurricane structure, dynamics, and motion. The data set contains the measurements collected by the MAS instrument onboard the ER2 aircraft. The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995. The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees.", "links": [ { diff --git a/datasets/CAML_0.json b/datasets/CAML_0.json index 7ec6faca2a..293d856815 100644 --- a/datasets/CAML_0.json +++ b/datasets/CAML_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical, and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States. Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models. The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria incidence and proliferation. Data is provided in a comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/CAML_Project_Archive.CAML_DNA_Barcoding_1.json b/datasets/CAML_Project_Archive.CAML_DNA_Barcoding_1.json index 0198b98ca8..77565f8d8f 100644 --- a/datasets/CAML_Project_Archive.CAML_DNA_Barcoding_1.json +++ b/datasets/CAML_Project_Archive.CAML_DNA_Barcoding_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.CAML_DNA_Barcoding_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.CAML_South_America_1.json b/datasets/CAML_Project_Archive.CAML_South_America_1.json index c8c2fbe146..8664f25768 100644 --- a/datasets/CAML_Project_Archive.CAML_South_America_1.json +++ b/datasets/CAML_Project_Archive.CAML_South_America_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.CAML_South_America_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Data_Synthesis_1.json b/datasets/CAML_Project_Archive.Data_Synthesis_1.json index 083dda0e36..69c4495bc3 100644 --- a/datasets/CAML_Project_Archive.Data_Synthesis_1.json +++ b/datasets/CAML_Project_Archive.Data_Synthesis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Data_Synthesis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Documents_1.json b/datasets/CAML_Project_Archive.Documents_1.json index 0dab6e94ea..412be33ccd 100644 --- a/datasets/CAML_Project_Archive.Documents_1.json +++ b/datasets/CAML_Project_Archive.Documents_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Documents_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Media_Education_Outreach_1.json b/datasets/CAML_Project_Archive.Media_Education_Outreach_1.json index f94482ca7a..44b7fe9da2 100644 --- a/datasets/CAML_Project_Archive.Media_Education_Outreach_1.json +++ b/datasets/CAML_Project_Archive.Media_Education_Outreach_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Media_Education_Outreach_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Meetings_1.json b/datasets/CAML_Project_Archive.Meetings_1.json index ed88dbd898..2df7fe807f 100644 --- a/datasets/CAML_Project_Archive.Meetings_1.json +++ b/datasets/CAML_Project_Archive.Meetings_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Meetings_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Progress_Updates_1.json b/datasets/CAML_Project_Archive.Progress_Updates_1.json index ffc65498ac..19fb1325d3 100644 --- a/datasets/CAML_Project_Archive.Progress_Updates_1.json +++ b/datasets/CAML_Project_Archive.Progress_Updates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Progress_Updates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Sampling_Protocols_1.json b/datasets/CAML_Project_Archive.Sampling_Protocols_1.json index 81bc453c64..b4ef2ec1e1 100644 --- a/datasets/CAML_Project_Archive.Sampling_Protocols_1.json +++ b/datasets/CAML_Project_Archive.Sampling_Protocols_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Sampling_Protocols_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive.Ships_1.json b/datasets/CAML_Project_Archive.Ships_1.json index 69ee951f3b..4a796719ab 100644 --- a/datasets/CAML_Project_Archive.Ships_1.json +++ b/datasets/CAML_Project_Archive.Ships_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive.Ships_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAML_Project_Archive_1.json b/datasets/CAML_Project_Archive_1.json index cb7f5c4c60..3c63ee298c 100644 --- a/datasets/CAML_Project_Archive_1.json +++ b/datasets/CAML_Project_Archive_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAML_Project_Archive_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY).", "links": [ { diff --git a/datasets/CAMP2Ex-Cloud-Precip-Retrieval_1.json b/datasets/CAMP2Ex-Cloud-Precip-Retrieval_1.json index 0b7bfc8ebe..109ce7e4ea 100644 --- a/datasets/CAMP2Ex-Cloud-Precip-Retrieval_1.json +++ b/datasets/CAMP2Ex-Cloud-Precip-Retrieval_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex-Cloud-Precip-Retrieval_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex-Cloud-Precip-Retrieval_1 are cloud and precipitation retrievals derived from the Advanced Precipitation Radar 3 (APR-3) and Advanced Microwave Precipitation Radiometer (AMPR). Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete. CAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data_1.json b/datasets/CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data_1.json index 520150e6b4..0e25708220 100644 --- a/datasets/CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Aerosol_AircraftInSitu_Learjet_Data are in-situ aerosol measurements conducted onboard the SPEC Learjet aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete. \r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Aerosol_AircraftInSitu_P3_Data_1.json b/datasets/CAMP2Ex_Aerosol_AircraftInSitu_P3_Data_1.json index 66010edc4c..97df183a42 100644 --- a/datasets/CAMP2Ex_Aerosol_AircraftInSitu_P3_Data_1.json +++ b/datasets/CAMP2Ex_Aerosol_AircraftInSitu_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Aerosol_AircraftInSitu_P3_Data are in-situ aerosol measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data_1.json b/datasets/CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data_1.json index 71c1ba3ec3..463e1ad369 100644 --- a/datasets/CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data_1.json +++ b/datasets/CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Aerosol_AircraftRemoteSensing_P3_Data are remotely sensed aerosol measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data_1.json b/datasets/CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data_1.json index f0c734fbc6..0e127d843a 100644 --- a/datasets/CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Cloud_AircraftInSitu_Learjet_Data are in-situ cloud measurements conducted onboard the SPEC Learjet aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Cloud_AircraftInSitu_P3_Data_1.json b/datasets/CAMP2Ex_Cloud_AircraftInSitu_P3_Data_1.json index c009a624f6..c5f59ebf4a 100644 --- a/datasets/CAMP2Ex_Cloud_AircraftInSitu_P3_Data_1.json +++ b/datasets/CAMP2Ex_Cloud_AircraftInSitu_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Cloud_AircraftInSitu_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Cloud_AircraftInSitu_P3_Data are in-situ cloud measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Merge_Data_1.json b/datasets/CAMP2Ex_Merge_Data_1.json index b986c56d20..0965ab2774 100644 --- a/datasets/CAMP2Ex_Merge_Data_1.json +++ b/datasets/CAMP2Ex_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Merge_Data are pre-generated aircraft merge data files created utilizing data collected during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete. \r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data_1.json b/datasets/CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data_1.json index 357ffff235..176d7919e9 100644 --- a/datasets/CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_MetNav_AircraftInSitu_Learjet_Data are in-situ meteorological and navigational measurements conducted onboard the Learjet aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped with passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variables, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with the Filipino research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_MetNav_AircraftInSitu_P3_Data_1.json b/datasets/CAMP2Ex_MetNav_AircraftInSitu_P3_Data_1.json index 881ab97854..9d2f576199 100644 --- a/datasets/CAMP2Ex_MetNav_AircraftInSitu_P3_Data_1.json +++ b/datasets/CAMP2Ex_MetNav_AircraftInSitu_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_MetNav_AircraftInSitu_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_MetNav_AircraftInSitu_P3_Data are in-situ meteorological and navigational measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Miscellaneous_Data_1.json b/datasets/CAMP2Ex_Miscellaneous_Data_1.json index 34bd48ff88..c531fdf238 100644 --- a/datasets/CAMP2Ex_Miscellaneous_Data_1.json +++ b/datasets/CAMP2Ex_Miscellaneous_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Miscellaneous_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Miscellaneous_Data are miscellaneous data (satellite, model, analysis, merge data, etc.) collected during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete. \r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_Radiation_AircraftInSitu_P3_Data_1.json b/datasets/CAMP2Ex_Radiation_AircraftInSitu_P3_Data_1.json index 6b7bf9e21f..86fbba055d 100644 --- a/datasets/CAMP2Ex_Radiation_AircraftInSitu_P3_Data_1.json +++ b/datasets/CAMP2Ex_Radiation_AircraftInSitu_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_Radiation_AircraftInSitu_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_Radiation_AircraftInSitu_P3_Data are in-situ radiation measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMP2Ex_TraceGas_AircraftInSitu_P3_Data_1.json b/datasets/CAMP2Ex_TraceGas_AircraftInSitu_P3_Data_1.json index c32413ff35..f0587e4119 100644 --- a/datasets/CAMP2Ex_TraceGas_AircraftInSitu_P3_Data_1.json +++ b/datasets/CAMP2Ex_TraceGas_AircraftInSitu_P3_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAMP2Ex_TraceGas_AircraftInSitu_P3_Data are in-situ trace gas measurements conducted onboard the P-3 aircraft during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete.\r\n\r\nCAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA\u2019s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.", "links": [ { diff --git a/datasets/CAMREX_904_1.json b/datasets/CAMREX_904_1.json index f2cdb2a3e9..c22b4ccb41 100644 --- a/datasets/CAMREX_904_1.json +++ b/datasets/CAMREX_904_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAMREX_904_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of CAMREX (Carbon in the Amazon River Experiment) project which was conducted from 1982 through 1991, was been to define by mass balances and direct measurements those processes which control the distribution of bioactive elements (C, N, P and O) in the mainstem of the Amazon River in Brazil. The CAMREX dataset represents a time series unique in its length and detail for very large river systems. The central sampling strategy has been to obtain representative flux-weighted water samples for comprehensive chemical analysis and to make rate measurements over 18 different sites within a 2000 km reach of the Brazilian Amazon mainstem, including major intervening tributaries. Samples have now been collected on 13 different cruises (1982-1991) during contrasting hydrographic stages. Data or images are provided for (1) water chemistry, (2) daily river discharge, (3) monthly estimates for 1989 of some model drivers and structure including NPP, Evapotranspiration, Precipitation, Temperature, and AVHRR data, (4) daily precipitation, and (5) air temperature anomalies.The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. ", "links": [ { diff --git a/datasets/CARAFE_2016_2017_v2_2002_1.1.json b/datasets/CARAFE_2016_2017_v2_2002_1.1.json index 19fc50a61e..9a2ae20ddd 100644 --- a/datasets/CARAFE_2016_2017_v2_2002_1.1.json +++ b/datasets/CARAFE_2016_2017_v2_2002_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARAFE_2016_2017_v2_2002_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides airborne eddy covariance (EC) fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution collected during the NASA Carbon Airborne Flux Experiment (CARAFE) airborne 2016 and 2017 campaigns. CARAFE utilized the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation. Deployment occurred across the Mid-Atlantic Region for the period 2016-09-07 through 2016-09-26 and 2017-05-03 through 2017-05-26. The data also include downwelling radiation, water vapor, pressure, temperature, wind, and aircraft navigation data. Airborne EC can quantify surface fluxes at local to regional scales, potentially helping to bridge gaps between top-down and bottom-up flux estimates and offering novel insights into biophysical and biogeochemical processes.", "links": [ { diff --git a/datasets/CARIACO_0.json b/datasets/CARIACO_0.json index 6584603a0a..a304c8af18 100644 --- a/datasets/CARIACO_0.json +++ b/datasets/CARIACO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARIACO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since November 1995, the CARIACO Ocean Time Series program has been studying the relationship between surface biogeochemical processes and the vertical fluxes of carbon and nutrients in a continental margin setting influenced by seasonal upwelling. This tectonic depression, located on the continental shelf of Venezuela, shows marked seasonal and interannual variation in hydrography and primary production induced in part by the regular migration of the Intertropical Convergence Zone (ITCZ). Between December and May, the Cariaco Basin experiences dry, upwelling-favorable weather. This leads to primary production of ~1.4 gC m-2 d-1 (upper 100 m). From June to November is the rainy season, winds are weaker and upwelling is reduced; primary production falls to half the rate observed during upwelling. The ultimate goal of CARIACO is to understand how meteorological and upper ocean hydrographic conditions affect primary production, dissolved inorganic carbon, CO2 fugacity, bacterial productivity and respiration, and vertical particle fluxes, and how variation in these parameters are reflected in the basin's sedimentary record, which is well known for storing high-frequency global climate signals.", "links": [ { diff --git a/datasets/CARMIAAE_002.json b/datasets/CARMIAAE_002.json index 2fc34ee076..be369b9c7f 100644 --- a/datasets/CARMIAAE_002.json +++ b/datasets/CARMIAAE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARMIAAE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 Aerosol Product containing aerosol optical depth and particle type, with associated atmospheric data over the ICARTT_2004 theme.", "links": [ { diff --git a/datasets/CARMITST_002.json b/datasets/CARMITST_002.json index 2ec6386713..08ab4de944 100644 --- a/datasets/CARMITST_002.json +++ b/datasets/CARMITST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARMITST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 TOA/Cloud Stereo Product containing the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data over the ICARTT_2004 theme.", "links": [ { diff --git a/datasets/CARVE_Ecosystem_CH4_Flux_1558_1.json b/datasets/CARVE_Ecosystem_CH4_Flux_1558_1.json index a67f4e7d99..5561942a14 100644 --- a/datasets/CARVE_Ecosystem_CH4_Flux_1558_1.json +++ b/datasets/CARVE_Ecosystem_CH4_Flux_1558_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_Ecosystem_CH4_Flux_1558_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides methane flux estimates derived from airborne measurements collected over Alaska and the western Yukon Territory during the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) between 2012 and 2014. The state-scale methane fluxes were calculated using a combination of atmospheric profiles and lagrangian transport modeling. The methane flux estimates were used in a simple linear regression model to estimate the fluxes from the tundra and boreal ecosystems. Methane fluxes were also used with a combination of environmental variables to derive a statistical relationship between domain-wide flux and soil temperature. Soil temperature products from North American Regional Reanalysis and derived parameters from a Boltmann-Arrhenius model were used to model methane flux and related uncertainties within the domain at monthly and daily frequencies.", "links": [ { diff --git a/datasets/CARVE_L1_FTS_Spectra_1426_1.json b/datasets/CARVE_L1_FTS_Spectra_1426_1.json index e1fe5ce5d3..11877bafb4 100644 --- a/datasets/CARVE_L1_FTS_Spectra_1426_1.json +++ b/datasets/CARVE_L1_FTS_Spectra_1426_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L1_FTS_Spectra_1426_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level 1 spectral radiance data collected using the Fourier Transform Spectrometer (FTS) during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L1_FlightPath_1425_1.json b/datasets/CARVE_L1_FlightPath_1425_1.json index 5bca7fc10a..36758d21f6 100644 --- a/datasets/CARVE_L1_FlightPath_1425_1.json +++ b/datasets/CARVE_L1_FlightPath_1425_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L1_FlightPath_1425_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-frequency geolocation, time, height, pitch, roll, and heading information for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Digital Air Data System (DADS) onboard the aircraft and are presented at 1-second intervals throughout each flight. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are useful for matching aircraft position with the scientific data collected by other CARVE airborne instruments.", "links": [ { diff --git a/datasets/CARVE_L1_FlightPath_Winds_1427_1.json b/datasets/CARVE_L1_FlightPath_Winds_1427_1.json index 6a2ca6383f..fa8a34f174 100644 --- a/datasets/CARVE_L1_FlightPath_Winds_1427_1.json +++ b/datasets/CARVE_L1_FlightPath_Winds_1427_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L1_FlightPath_Winds_1427_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-frequency wind speed and direction data for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Aventech AIMMS-30 Airborne Wind Sensor onboard the aircraft and are presented at 1-second intervals throughout each flight. The Winds instrument was available for flights in year 2015 only. The measurements included in this data set are most useful when paired with the scientific data collected by other CARVE airborne instruments.", "links": [ { diff --git a/datasets/CARVE_L1_Ground_Flux_1424_1.json b/datasets/CARVE_L1_Ground_Flux_1424_1.json index 46382e61f3..c8c19d9a75 100644 --- a/datasets/CARVE_L1_Ground_Flux_1424_1.json +++ b/datasets/CARVE_L1_Ground_Flux_1424_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L1_Ground_Flux_1424_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides ground in situ flux and meteorological science data from fixed instruments at three eddy covariance tower sites located in the Alaskan Arctic tundra. Real and gap-filled observations of carbon dioxide, methane, water vapor, and latent energy flux in addition to standard meteorological and environmental variables are reported at half-hourly intervals between 2011 and 2015 for sites at Atqasuk, Barrow, and Ivotuk, Alaska. The three sites form a 300-km north-south transect on the North Slope of Alaska, each site representing distinct Arctic vegetation communities. These tower measurements create a long-term record of one of the largest, most volatile carbon stocks on the planet. Observations from these towers are being used to determine the seasonal and inter-annual patterns of CO2 and CH4 flux, and their relationship to changes in environmental factors.", "links": [ { diff --git a/datasets/CARVE_L1_Infrared_1428_1.json b/datasets/CARVE_L1_Infrared_1428_1.json index fd7a387c58..84117ee2a1 100644 --- a/datasets/CARVE_L1_Infrared_1428_1.json +++ b/datasets/CARVE_L1_Infrared_1428_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L1_Infrared_1428_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides earth referenced radiance counts measured by the Forward Looking Infrared (FLIR) camera aboard the CARVE aircraft between April 2013 and November 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The FLIR camera records images of the surface temperature while measuring concentrations of atmospheric carbon dioxide, methane, and ozone. Thermal images from the FLIR camera will be used to characterize land surfaces underlain by permafrost during specific phases in the freeze-thaw cycle. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_AtmosGas_Ground_1419_1.json b/datasets/CARVE_L2_AtmosGas_Ground_1419_1.json index 5f85c08102..90368a0e64 100644 --- a/datasets/CARVE_L2_AtmosGas_Ground_1419_1.json +++ b/datasets/CARVE_L2_AtmosGas_Ground_1419_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_AtmosGas_Ground_1419_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) dry air mole fractions and water vapor mole fractions (H2O) from continuous in situ measurements at the CARVE flux tower in Fox, Alaska between October 2011 and May 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Air was drawn from three different heights above the base of the tower (31.7 m, 17.1 m, and 4.9 m) and analyzed using a Picarro cavity ring-down spectrometer (CRDS). Measurements of ambient and sonic temperature, vertical and horizontal velocity, and atmospheric pressure are also included in the data set.", "links": [ { diff --git a/datasets/CARVE_L2_AtmosGas_Harvard_1403_1.json b/datasets/CARVE_L2_AtmosGas_Harvard_1403_1.json index 7dcfe83285..c656c9e167 100644 --- a/datasets/CARVE_L2_AtmosGas_Harvard_1403_1.json +++ b/datasets/CARVE_L2_AtmosGas_Harvard_1403_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_AtmosGas_Harvard_1403_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a four-species cavity ring-down spectrometer system (CRDS; Picarro Inc.) provided by Harvard University and are presented at 5-second intervals throughout each flight. The Harvard CRDS instrument only collected data in 2012-2014; no Harvard data are available for year 2015. Aircraft latitude, longitude, and altitude are also provided. CARVE flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_AtmosGas_Merge_1402_1.json b/datasets/CARVE_L2_AtmosGas_Merge_1402_1.json index 6cc83bec0b..509ee33e2f 100644 --- a/datasets/CARVE_L2_AtmosGas_Merge_1402_1.json +++ b/datasets/CARVE_L2_AtmosGas_Merge_1402_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_AtmosGas_Merge_1402_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). These data are merged and gap-filled outputs from two different cavity ring-down spectrometers (CRDS; Picarro Inc.) flown aboard the CARVE aircraft and are presented at 5-second intervals throughout each flight. Aircraft latitude, longitude, and altitude are also provided. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_AtmosGas_NOAA_1401_1.json b/datasets/CARVE_L2_AtmosGas_NOAA_1401_1.json index 82db18e7fe..1fafd0f502 100644 --- a/datasets/CARVE_L2_AtmosGas_NOAA_1401_1.json +++ b/datasets/CARVE_L2_AtmosGas_NOAA_1401_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_AtmosGas_NOAA_1401_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a cavity ring-down spectrometer (CRDS; Picarro Inc.) and are presented at 2-second intervals throughout each flight. Aircraft latitude, longitude, and altitude are also provided. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_FTS_ColumnGas_1429_1.json b/datasets/CARVE_L2_FTS_ColumnGas_1429_1.json index 3fa4c106c7..0b31896d15 100644 --- a/datasets/CARVE_L2_FTS_ColumnGas_1429_1.json +++ b/datasets/CARVE_L2_FTS_ColumnGas_1429_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_FTS_ColumnGas_1429_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides total vertical column O2, CO2, CH4, CO, and H2O, as well as dry-air columns of CO2, CH4, CO, and H2O from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data represent the Level 2 Quick Retrieval (L2QR) data product collected using the CARVE Fourier Transform Spectrometer (FTS). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_Flask_1404_1.json b/datasets/CARVE_L2_Flask_1404_1.json index 14f780825f..104ff5f829 100644 --- a/datasets/CARVE_L2_Flask_1404_1.json +++ b/datasets/CARVE_L2_Flask_1404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_Flask_1404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. \"concentrations\") from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The CARVE flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas abundances. The data were derived from laboratory measurements of whole air samples collected by a Programmable Flask Package (PFP) onboard the CARVE aircraft. Air samples were collected at strategic intervals to coincide with the overflight of a ground site of interest, or when interesting geophysical conditions were encountered. While most of these samples were collected near the surface in the planetary boundary layer (PBL), on almost every flight samples were also collected in the free troposphere. A minimum of 12 flask samples were collected per flight. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division in Boulder, CO, which also analyzes samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L2_Flask_Ground_1405_1.json b/datasets/CARVE_L2_Flask_Ground_1405_1.json index 29caad761a..a2263c02e3 100644 --- a/datasets/CARVE_L2_Flask_Ground_1405_1.json +++ b/datasets/CARVE_L2_Flask_Ground_1405_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L2_Flask_Ground_1405_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides atmospheric carbon dioxide, methane, carbon monoxide, molecular hydrogen, nitrous oxide, sulfur hexafluoride, and other trace gas mole fractions (i.e. \"concentrations\") from a flask sampling system at the CARVE flux tower in Fox, Alaska for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were derived from laboratory measurements of whole air samples collected by a Programmable Flask Package (PFP) from the top of the tower at 32 m above ground level during late evening multiple times per month since January 2012. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division in Boulder, CO, which also analyzes samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L4_WRF-STILT_Footprint_1431_1.1.json b/datasets/CARVE_L4_WRF-STILT_Footprint_1431_1.1.json index e4ada2e7e7..1b19a5e756 100644 --- a/datasets/CARVE_L4_WRF-STILT_Footprint_1431_1.1.json +++ b/datasets/CARVE_L4_WRF-STILT_Footprint_1431_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L4_WRF-STILT_Footprint_1431_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF footprint files packaged as a TAR/GZIP file. These aircraft and station positions were treated as receptors in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_L4_WRF-STILT_Particle_1430_1.1.json b/datasets/CARVE_L4_WRF-STILT_Particle_1430_1.1.json index 50de559d6b..09bb0349ac 100644 --- a/datasets/CARVE_L4_WRF-STILT_Particle_1430_1.1.json +++ b/datasets/CARVE_L4_WRF-STILT_Particle_1430_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_L4_WRF-STILT_Particle_1430_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) model inputs for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF files packaged as a TAR/GZIP file. These data correspond to WRF-STILT model footprint data also generated by the CARVE science team.", "links": [ { diff --git a/datasets/CARVE_Land_Thaw_State_1383_1.1.json b/datasets/CARVE_Land_Thaw_State_1383_1.1.json index c1ec1166f0..13c991f05c 100644 --- a/datasets/CARVE_Land_Thaw_State_1383_1.1.json +++ b/datasets/CARVE_Land_Thaw_State_1383_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_Land_Thaw_State_1383_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides daily 10 km resolution maps of the Alaskan and Arctic Boreal land surface state as either frozen, melting, or thawed. These data are generated from passive microwave radiometer observations made from 2003 through 2014 by the Advanced Microwave Scanning Radiometer (AMSR-E) and the Special Sensor Microwave Imager (SSM/I). Data products overlap with science data collections carried out during the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).", "links": [ { diff --git a/datasets/CARVE_Photos_1435_1.json b/datasets/CARVE_Photos_1435_1.json index 301bf01a7b..8aee8234e2 100644 --- a/datasets/CARVE_Photos_1435_1.json +++ b/datasets/CARVE_Photos_1435_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_Photos_1435_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains photos taken by scientists aboard the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.", "links": [ { diff --git a/datasets/CARVE_Reports_1434_1.json b/datasets/CARVE_Reports_1434_1.json index 6d96b7badd..3071ae5117 100644 --- a/datasets/CARVE_Reports_1434_1.json +++ b/datasets/CARVE_Reports_1434_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_Reports_1434_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes detailed daily flight reports from each of the airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The reports include plots of the flight path, altitude, wind and weather conditions, IR and visible light images, and initial analysis of the atmospheric gas concentrations encountered along the flight. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The CARVE measurements are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.", "links": [ { diff --git a/datasets/CARVE_Videos_1433_1.json b/datasets/CARVE_Videos_1433_1.json index cda82c89af..78bcb7f8f8 100644 --- a/datasets/CARVE_Videos_1433_1.json +++ b/datasets/CARVE_Videos_1433_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CARVE_Videos_1433_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains videos captured by a camera mounted on the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.", "links": [ { diff --git a/datasets/CAR_ARCTAS_BRDF_2.json b/datasets/CAR_ARCTAS_BRDF_2.json index 99c1a1e47f..0349540414 100644 --- a/datasets/CAR_ARCTAS_BRDF_2.json +++ b/datasets/CAR_ARCTAS_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ARCTAS_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research. The plan is for the combination of satellite and aircraft data to provide together powerful information for constraining and evaluating models of Arctic atmospheric composition and climate, and thus improve model projections of future change. \n\nThe first phase of ARCTAS was based in Fairbanks and Barrow, Alaska with some flights to Thule, Greenland in April and focused on thick aerosol layers known as “arctic haze.” The second phase followed in July based from Cold Lake, Alberta and the Northwest Territories focusing on the emissions from large boreal forest fires in northwest Canada. \n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_ARCTAS_L1C_1.json b/datasets/CAR_ARCTAS_L1C_1.json index fcc97a7454..1fa34ff03c 100644 --- a/datasets/CAR_ARCTAS_L1C_1.json +++ b/datasets/CAR_ARCTAS_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ARCTAS_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research. The plan is for the combination of satellite and aircraft data to provide together powerful information for constraining and evaluating models of Arctic atmospheric composition and climate, and thus improve model projections of future change.", "links": [ { diff --git a/datasets/CAR_ARMCAS_BRDF_2.json b/datasets/CAR_ARMCAS_BRDF_2.json index 443bae1ae8..011c4c4168 100644 --- a/datasets/CAR_ARMCAS_BRDF_2.json +++ b/datasets/CAR_ARMCAS_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ARMCAS_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic Radiation Measurement in Column Atmosphere-surface System (ARMCAS) was a collaborative research effort between the Cloud and Aerosol Research (CAR) Group, Department of Atmospheric Sciences, University of Washington (led by Professor Peter V. Hobbs) and Drs. Michael King and Si-Chee Tsay of NASA/Goddard. The field portion of ARMCAS was based out of Deadhorse, Alaska, from June 3-15, 1995. Flights of the University of Washington's Convair C-131A research aircraft and NASA's ER-2 aircraft took place over the tundra of the North Slope and over the partially ice-covered Beaufort Sea. Several of these flights were closely coordinated in order to provide simultaneous in situ and remote sensing measurements of arctic clouds.\n\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_ARMCAS_L1C_1.json b/datasets/CAR_ARMCAS_L1C_1.json index c739f84300..10e089f367 100644 --- a/datasets/CAR_ARMCAS_L1C_1.json +++ b/datasets/CAR_ARMCAS_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ARMCAS_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic Radiation Measurement in Column Atmosphere-surface System (ARMCAS) was a collaborative research effort between the Cloud and Aerosol Research (CAR) Group, Department of Atmospheric Sciences, University of Washington (led by Professor Peter V. Hobbs) and Drs. Michael King and Si-Chee Tsay of NASA/Goddard. The field portion of ARMCAS was based out of Deadhorse, Alaska, from June 3-15, 1995. Flights of the University of Washington's Convair C-131A research aircraft and NASA's ER-2 aircraft took place over the tundra of the North Slope and over the partially ice-covered Beaufort Sea. Several of these flights were closely coordinated in order to provide simultaneous in situ and remote sensing measurements of arctic clouds.", "links": [ { diff --git a/datasets/CAR_BRDF_709_1.json b/datasets/CAR_BRDF_709_1.json index f571b77796..09e1b05b89 100644 --- a/datasets/CAR_BRDF_709_1.json +++ b/datasets/CAR_BRDF_709_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_BRDF_709_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Absorption Radiometer (CAR) is an airborne multi-wavelength scanning radiometer that can perform several functions including determining the single scattering albedo of clouds at selected wavelengths in the visible and near-infrared; measuring the angular distribution of scattered radiation; measuring bidirectional reflectance of various surface types; and acquiring imagery of cloud and Earth surface features.", "links": [ { diff --git a/datasets/CAR_CLAMS_BRDF_2.json b/datasets/CAR_CLAMS_BRDF_2.json index 529b64afc0..5eb5a47906 100644 --- a/datasets/CAR_CLAMS_BRDF_2.json +++ b/datasets/CAR_CLAMS_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_CLAMS_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS is the Chesapeake Lighthouse and Aircraft Measurements for Satellites field campaign sponsored by CERES, MISR, MODIS-Atmospheres and the NASA/GEWEX Global Aerosol Climatology Project (GACP). The centerpiece of CLAMS is the Chesapeake Lighthouse sea platform 20 km east of Virginia Beach, at which NASA and NOAA make continuous, long-term measurements of radiation, meteorology, and ocean waves. Members of the CERES, MISR and MODIS instrument teams are collaborating to accomplish a common set of objectives tied to the validation of EOS data products. The CLAMS campaign took place in July-August 2001 to validate Terra data products from a shortwave closure experiment targeting clear (cloud-free) sky conditions and focused on obtaining:\n1. more accurate spectral and broadband radiative fluxes at the surface and within the atmosphere,\n2. characterization of ocean optics in the vicinity of the lighthouse, and\n3. description of the atmospheric aerosol amounts, micro-physical and optical properties, and their variability.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_CLAMS_L1C_1.json b/datasets/CAR_CLAMS_L1C_1.json index e97dc0ace1..673cbf57da 100644 --- a/datasets/CAR_CLAMS_L1C_1.json +++ b/datasets/CAR_CLAMS_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_CLAMS_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS is the Chesapeake Lighthouse and Aircraft Measurements for Satellites field campaign sponsored by CERES, MISR, MODIS-Atmospheres and the NASA/GEWEX Global Aerosol Climatology Project (GACP). The centerpiece of CLAMS is the Chesapeake Lighthouse sea platform 20 km east of Virginia Beach, at which NASA and NOAA make continuous, long-term measurements of radiation, meteorology, and ocean waves. Members of the CERES, MISR and MODIS instrument teams are collaborating to accomplish a common set of objectives tied to the validation of EOS data products. A first CLAMS campaign, currently being planned for July 2001 to validate Terra data products, is a shortwave closure experiment targeting clear (cloud-free) sky conditions and focused on obtaining:\n1. more accurate spectral and broadband radiative fluxes at the surface and within the atmosphere,\n2. characterization of ocean optics in the vicinity of the lighthouse, and\n3. description of the atmospheric aerosol amounts, micro-physical and optical properties, and their variability.", "links": [ { diff --git a/datasets/CAR_CLASIC_BRDF_2.json b/datasets/CAR_CLASIC_BRDF_2.json index 804a1d193d..c90cabb86f 100644 --- a/datasets/CAR_CLASIC_BRDF_2.json +++ b/datasets/CAR_CLASIC_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_CLASIC_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. CLASIC was conducted in the Southern Great Plains (SGP – a region comprising Kansas, Oklahoma, and Texas) of the United States during June 2007. The SGP site consists of in situ and remote-sensing instrument clusters arrayed across approximately 55,000 square miles (143,000 square kilometers) in north-central Oklahoma, making it the largest and most extensive climate research field site in the world. The CAR flew aboard Sky Research Jetstream-31 and measured spectral and angular distribution of scattered light by clouds and aerosols, and provided bidirectional reflectance of various surfaces, and imagery of cloud and Earth surface features. By making such diverse measurements, our goal is to widen the audience of potential end-users and to foster collaborations among campaign participants and with outside users.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_CLASIC_L1C_1.json b/datasets/CAR_CLASIC_L1C_1.json index b7f58d631e..02da50a6cf 100644 --- a/datasets/CAR_CLASIC_L1C_1.json +++ b/datasets/CAR_CLASIC_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_CLASIC_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. The CAR flew aboard Sky Research Jetstream-31 and measured spectral and angular distribution of scattered light by clouds and aerosols, and provided bidirectional reflectance of various surfaces, and imagery of cloud and Earth surface features. By making such diverse measurements, our goal is to widen the audience of potential end-users and to foster collaborations among campaign participants and with outside users.", "links": [ { diff --git a/datasets/CAR_DISCOVERAQ_BRDF_2.json b/datasets/CAR_DISCOVERAQ_BRDF_2.json index 019decf287..753a114b65 100644 --- a/datasets/CAR_DISCOVERAQ_BRDF_2.json +++ b/datasets/CAR_DISCOVERAQ_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_DISCOVERAQ_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVER-AQ, a NASA Earth Venture program funded mission, stands for Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality.\nIn recent years, progress in reaching air quality goals has begun to plateau for many locations. Furthermore, near-surface pollution is one of the most challenging problems for Earth observations from space. However, with an improved ability to monitor pollution from satellites from DISCOVER-AQ, scientists could make better air quality forecasts, more accurately determine the sources of pollutants in the air and more closely determine the fluctuations in emissions levels. In short, the more accurate data scientists have at hand, the better society is able to deal effectively with lingering pollution problems. During the DISCOVER-AQ mission in 2014, the CAR instrument was flown aboard NASA P-3 aircraft and obtained measurements of bidirectional reflectance distribution function (BRDF) at different scales over agricultural and urban areas in Colorado, USA. \n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_DISCOVERAQ_L1C_1.json b/datasets/CAR_DISCOVERAQ_L1C_1.json index d3272445ce..a7599f34df 100644 --- a/datasets/CAR_DISCOVERAQ_L1C_1.json +++ b/datasets/CAR_DISCOVERAQ_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_DISCOVERAQ_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVER-AQ, a NASA Earth Venture program funded mission, stands for Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality.\nIn recent years, progress in reaching air quality goals has begun to plateau for many locations. Furthermore, near-surface pollution is one of the most challenging problems for Earth observations from space. However, with an improved ability to monitor pollution from satellites from DISCOVER-AQ, scientists could make better air quality forecasts, more accurately determine the sources of pollutants in the air and more closely determine the fluctuations in emissions levels. In short, the more accurate data scientists have at hand, the better society is able to deal effectively with lingering pollution problems.", "links": [ { diff --git a/datasets/CAR_ECO3D_BRDF_2.json b/datasets/CAR_ECO3D_BRDF_2.json index b9becc734e..c21e781d4b 100644 --- a/datasets/CAR_ECO3D_BRDF_2.json +++ b/datasets/CAR_ECO3D_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ECO3D_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study provide critical measurements on 3-dimensional structure of vegetation, which is important for quantifying the amount of carbon stored in biomass. It promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.During the ECO-3D mission in 2011, the CAR instrument was flown aboard the NASA P-3 and obtained measurements of bidirectional reflectance distribution function (BRDF) over forests ranging from Boreal to tropical wetlands covering sites from Quebec to Southern Florida.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_ECO3D_L1C_1.json b/datasets/CAR_ECO3D_L1C_1.json index cbe5c31a85..e5c6146d4a 100644 --- a/datasets/CAR_ECO3D_L1C_1.json +++ b/datasets/CAR_ECO3D_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_ECO3D_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.", "links": [ { diff --git a/datasets/CAR_FIREACE_BRDF_2.json b/datasets/CAR_FIREACE_BRDF_2.json index 8184b2a3b7..9ea41eb165 100644 --- a/datasets/CAR_FIREACE_BRDF_2.json +++ b/datasets/CAR_FIREACE_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_FIREACE_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The scientific objectives of FIRE/ACE are to study impact of Arctic clouds on radiation exchange between surface, atmosphere, and space, and the influence of surface characteristics of sea ice, leads, and ice melt ponds on these clouds. FIRE/ACE will attempt to document, understand, and predict the Arctic cloud-radiation feedbacks, including changes in cloud fraction and vertical distribution, water vapor cloud content, cloud particle concentration and size, and cloud phase as atmospheric temperature and chemical composition change. FIRE/ACE uses the data to focus on improving current climate model simulations of the Arctic climate, especially with respect to clouds and their effects on the surface energy budget. In addition, FIRE/ACE addresses a number of scientific questions dealing with radiation, cloud microphysics, and atmospheric chemistry.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_FIREACE_L1C_1.json b/datasets/CAR_FIREACE_L1C_1.json index a0d119f063..efe884ddd2 100644 --- a/datasets/CAR_FIREACE_L1C_1.json +++ b/datasets/CAR_FIREACE_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_FIREACE_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The scientific objectives of FIRE/ACE are to study impact of Arctic clouds on radiation exchange between surface, atmosphere, and space, and the influence of surface characteristics of sea ice, leads, and ice melt ponds on these clouds. FIRE/ACE will attempt to document, understand, and predict the Arctic cloud-radiation feedbacks, including changes in cloud fraction and vertical distribution, water vapor cloud content, cloud particle concentration and size, and cloud phase as atmospheric temperature and chemical composition change. FIRE/ACE uses the data to focus on improving current climate model simulations of the Arctic climate, especially with respect to clouds and their effects on the surface energy budget. In addition, FIRE/ACE addresses a number of scientific questions dealing with radiation, cloud microphysics, and atmospheric chemistry.", "links": [ { diff --git a/datasets/CAR_INTEXB_BRDF_2.json b/datasets/CAR_INTEXB_BRDF_2.json index 5fb3e91f9a..db7832d6b8 100644 --- a/datasets/CAR_INTEXB_BRDF_2.json +++ b/datasets/CAR_INTEXB_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_INTEXB_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-B (Intercontinental Chemical Transport Experiment-Phase B) focuses on the long-range transport of pollution, global atmospheric photochemistry, and the effects of aerosols and clouds on radiation and climate. It has two phases: phase 1 of the study was performed in Mexico from March 1-20, 2006, and phase 2 was performed in April and May and focused on Asian City pollution outflow over the western Pacific.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_INTEXB_L1C_1.json b/datasets/CAR_INTEXB_L1C_1.json index 477950cfd3..919a6aeb9b 100644 --- a/datasets/CAR_INTEXB_L1C_1.json +++ b/datasets/CAR_INTEXB_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_INTEXB_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-B (Intercontinental Chemical Transport Experiment-Phase B) focuses on the long-range transport of pollution, global atmospheric photochemistry, and the effects of aerosols and clouds on radiation and climate. It has two phases: phase 1 of the study was performed in Mexico from March 1-20, 2006, and phase 2 was performed in April and May and focused on Asian City pollution outflow over the western Pacific.", "links": [ { diff --git a/datasets/CAR_KUWAITOILFIRE_BRDF_2.json b/datasets/CAR_KUWAITOILFIRE_BRDF_2.json index d5a6e8dd67..08fc4fd3ff 100644 --- a/datasets/CAR_KUWAITOILFIRE_BRDF_2.json +++ b/datasets/CAR_KUWAITOILFIRE_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_KUWAITOILFIRE_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols. This experiment was a part of an international research effort in response to an environmental crisis, when over 600 oil wells in Kuwait were ignited by Iraqi forces in 1991. The resulting fires produced large plumes of smoke that had significant effects on the Persian Gulf region but limited global effects. Between May 16 and June 12, 1991, the Kuwait Oil Fire Smoke Experiment (KOFSE) was conducted in the Persian Gulf Region. The purpose of KOFSE was to determine the chemical and physical nature of the smoke and to investigate its potential effects on air quality, weather, and climate.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_KUWAITOILFIRE_L1C_1.json b/datasets/CAR_KUWAITOILFIRE_L1C_1.json index 10029082d4..326a1a6d26 100644 --- a/datasets/CAR_KUWAITOILFIRE_L1C_1.json +++ b/datasets/CAR_KUWAITOILFIRE_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_KUWAITOILFIRE_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols.", "links": [ { diff --git a/datasets/CAR_LEADEX_BRDF_2.json b/datasets/CAR_LEADEX_BRDF_2.json index 81560bf58b..90d3517725 100644 --- a/datasets/CAR_LEADEX_BRDF_2.json +++ b/datasets/CAR_LEADEX_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_LEADEX_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR LEADEX mission measured bidirectional reflectance functions for four common arctic surfaces: snow covered sea ice, melt season sea ice, snow covered tundra, and tundra shortly after snowmelt. The measurements show how the reflectance differs amongst the mentioned arctic surfaces and provides insights into the variability of albedo in the arctic.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_LEADEX_L1C_1.json b/datasets/CAR_LEADEX_L1C_1.json index ea365c895f..8bf3d071d4 100644 --- a/datasets/CAR_LEADEX_L1C_1.json +++ b/datasets/CAR_LEADEX_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_LEADEX_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR LEADEX mission measured bidirectional reflectance functions for four common arctic surfaces: snow covered sea ice, melt season sea ice, snow covered tundra, and tundra shortly after snowmelt. The measurements show how the reflectance differs amongst the mentioned arctic surfaces and provides insights into the variability of albedo in the arctic.", "links": [ { diff --git a/datasets/CAR_SAFARI_BRDF_2.json b/datasets/CAR_SAFARI_BRDF_2.json index 48f83bad41..5f112fc543 100644 --- a/datasets/CAR_SAFARI_BRDF_2.json +++ b/datasets/CAR_SAFARI_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SAFARI_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Southern African Regional Science Initiative (SAFARI) 2000 is an international science field campaign aimed at developing a better understanding of the southern Africa earth-atmosphere-human system. The goal of SAFARI 2000 is to identify and understand the relationship between the physical, chemical, biological, and anthropogenic processes that underlie the biogeophysical and biogeochemical systems of southern Africa. Particular emphasis will be placed upon biogenic, pyrogenic, and anthropogenic emissions - their characterization and quantification, their transport and transformations in the atmosphere, their influence on regional climate and meteorology, their eventual deposition, and the effects of this deposition on ecosystems.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_SAFARI_L1C_1.json b/datasets/CAR_SAFARI_L1C_1.json index 7409a77d93..8badb177fa 100644 --- a/datasets/CAR_SAFARI_L1C_1.json +++ b/datasets/CAR_SAFARI_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SAFARI_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Southern African Regional Science Initiative (SAFARI) 2000 is an international science field campaign aimed at developing a better understanding of the southern Africa earth-atmosphere-human system. The goal of SAFARI 2000 is to identify and understand the relationship between the physical, chemical, biological, and anthropogenic processes that underlie the biogeophysical and biogeochemical systems of southern Africa. Particular emphasis will be placed upon biogenic, pyrogenic, and anthropogenic emissions - their characterization and quantification, their transport and transformations in the atmosphere, their influence on regional climate and meteorology, their eventual deposition, and the effects of this deposition on ecosystems.", "links": [ { diff --git a/datasets/CAR_SCARA_BRDF_2.json b/datasets/CAR_SCARA_BRDF_2.json index fd0e9c0cbc..4c82b3fe87 100644 --- a/datasets/CAR_SCARA_BRDF_2.json +++ b/datasets/CAR_SCARA_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SCARA_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-A campaign occurred in western Atlantic Ocean.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_SCARA_L1C_1.json b/datasets/CAR_SCARA_L1C_1.json index 9b7575164a..2e57393d61 100644 --- a/datasets/CAR_SCARA_L1C_1.json +++ b/datasets/CAR_SCARA_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SCARA_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in western Atlantic Ocean.", "links": [ { diff --git a/datasets/CAR_SCARB_BRDF_2.json b/datasets/CAR_SCARB_BRDF_2.json index 1c02a51667..1a6c5c4f01 100644 --- a/datasets/CAR_SCARB_BRDF_2.json +++ b/datasets/CAR_SCARB_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SCARB_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in Brazil.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_SCARB_L1C_1.json b/datasets/CAR_SCARB_L1C_1.json index a124487b7d..4245e9c707 100644 --- a/datasets/CAR_SCARB_L1C_1.json +++ b/datasets/CAR_SCARB_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SCARB_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in Brazil.", "links": [ { diff --git a/datasets/CAR_SKUKUZA_BRDF_2.json b/datasets/CAR_SKUKUZA_BRDF_2.json index ff54547e3a..eae3124187 100644 --- a/datasets/CAR_SKUKUZA_BRDF_2.json +++ b/datasets/CAR_SKUKUZA_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SKUKUZA_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA's Earth Observing System satellites.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_SKUKUZA_L1C_1.json b/datasets/CAR_SKUKUZA_L1C_1.json index 3434106739..f497c8d055 100644 --- a/datasets/CAR_SKUKUZA_L1C_1.json +++ b/datasets/CAR_SKUKUZA_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SKUKUZA_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA\u2019s Earth Observing System satellites.", "links": [ { diff --git a/datasets/CAR_SNOWEX17_BRDF_2.json b/datasets/CAR_SNOWEX17_BRDF_2.json index 9bfe8f9aee..83c936f0c2 100644 --- a/datasets/CAR_SNOWEX17_BRDF_2.json +++ b/datasets/CAR_SNOWEX17_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SNOWEX17_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent - currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world's water resources.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands. ", "links": [ { diff --git a/datasets/CAR_SNOWEX17_L1C_1.json b/datasets/CAR_SNOWEX17_L1C_1.json index 8a65733b40..bc3ae6c056 100644 --- a/datasets/CAR_SNOWEX17_L1C_1.json +++ b/datasets/CAR_SNOWEX17_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_SNOWEX17_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent \u2014 currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world\u2019s water resources.", "links": [ { diff --git a/datasets/CAR_TARFOX_BRDF_2.json b/datasets/CAR_TARFOX_BRDF_2.json index 6847ebe92b..086e5a9223 100644 --- a/datasets/CAR_TARFOX_BRDF_2.json +++ b/datasets/CAR_TARFOX_BRDF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_TARFOX_BRDF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR TARFOX mission collected data in the western Atlantic Ocean on the effects of tropospheric aerosols on radiation budgets in cloud free skies. The mission also measured the chemical, physical, and optical properties of aerosols. In July 1996, CAR data were collected aboard the University of Washington C-131A aircraft over the forested Great Dismal Swamp wetlands south of Norfolk, Virginia and the Atlantic Ocean approximately 340 km offshore of Richmond, Virginia.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.", "links": [ { diff --git a/datasets/CAR_TARFOX_L1C_1.json b/datasets/CAR_TARFOX_L1C_1.json index 1871dbdf3f..a8e0aceafa 100644 --- a/datasets/CAR_TARFOX_L1C_1.json +++ b/datasets/CAR_TARFOX_L1C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CAR_TARFOX_L1C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAR TARFOX mission collected data in the western Atlantic Ocean on the effects of tropospheric aerosols on radiation budgets in cloud free skies. The mission also measured the chemical, physical, and optical properties of aerosols.", "links": [ { diff --git a/datasets/CASES_0.json b/datasets/CASES_0.json index d76c4a0372..2cfd63ea71 100644 --- a/datasets/CASES_0.json +++ b/datasets/CASES_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CASES_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CASES program was carried out from September 2002 to August 2004. The objective of the CASES field expeditions was to perform an extensive sampling of the Southern Beaufort Sea and the Amundsen Gulf coastal shelves (from 67N to 76N and from 120W to 41W). Two different expeditions were held. The first expedition was conducted on board the CCGS Pierre Radisson between September 20th and October 14th, 2002 and was identified as leg 0. The second expedition was conducted on board the CCGS Amundsen between September 8th, 2003, and August 26th, 2004. This last expedition was divided into nine periods of six weeks (four weeks for leg 9) designated legs 1 to 9.The scientific program is focussing on a central hypothesis which states that the atmospheric, oceanic and hydrologic forcing of sea ice variability dictates the nature and magnitude of biogeochemical carbon fluxes on and at the edge of the Mackenzie Shelf. The Canadian-led projects studied: 1) Atmospheric and sea ice forcing of coastal circulation; 2) Ice-atmosphere interactions and biological linkages; 3) Light, nutrients, primary and export production in ice-free waters; 4) Microbial communities and heterotrophy; 5) Pelagic food web: structure, function and contaminants; 6) Organic and inorganic fluxes; 7) Benthic processes and carbon cycling; 8) Millennial-decadal variability in sea ice and carbon fluxes; 9) Coupled bio-physical models of the carbon flows on the Canadian Arctic Shelf (Simard et al., 2010).Reference: Simard, A., Rail, M.E., Gratton, Y. 2010. Distribution of temperature and salinity in the Beaufort Sea during the Canadian Arctic Shelf Exchange Study sampling expeditions 2002-2004. Report No R1187, INRS-ETE, Quebec (QC), 128p.", "links": [ { diff --git a/datasets/CATS-ISS_L1B_D-M7.1-V3-00_V3-00.json b/datasets/CATS-ISS_L1B_D-M7.1-V3-00_V3-00.json index c5edee832c..efe519d0cb 100644 --- a/datasets/CATS-ISS_L1B_D-M7.1-V3-00_V3-00.json +++ b/datasets/CATS-ISS_L1B_D-M7.1-V3-00_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L1B_D-M7.1-V3-00_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L1B_D-M7.1-V3-00 is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 1B Day Mode 7.1 Version 3-00 data product. The collection spans from February 10, 2015 through March 21, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. Level 1B data have been calibrated and annotated with ancillary meteorological data and processed to sensor units.", "links": [ { diff --git a/datasets/CATS-ISS_L1B_D-M7.2-V3-00_V3-00.json b/datasets/CATS-ISS_L1B_D-M7.2-V3-00_V3-00.json index 41f33bfcbe..99bb022a9b 100644 --- a/datasets/CATS-ISS_L1B_D-M7.2-V3-00_V3-00.json +++ b/datasets/CATS-ISS_L1B_D-M7.2-V3-00_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L1B_D-M7.2-V3-00_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L1B_D-M7.2-V3-00 is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 1B Day Mode 7.2 Version 3-00 data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L1B_N-M7.1-V3-00_V3-00.json b/datasets/CATS-ISS_L1B_N-M7.1-V3-00_V3-00.json index 3915441839..8ad4c2ae77 100644 --- a/datasets/CATS-ISS_L1B_N-M7.1-V3-00_V3-00.json +++ b/datasets/CATS-ISS_L1B_N-M7.1-V3-00_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L1B_N-M7.1-V3-00_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L1B_N-M7.1-V3-00 is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 1B Night Mode 7.1 Version 3-00 data product. The collection spans from February 10, 2015 through March 21, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. Level 1B data have been calibrated and annotated with ancillary meteorological data and processed to sensor units.", "links": [ { diff --git a/datasets/CATS-ISS_L1B_N-M7.2-V3-00_V3-00.json b/datasets/CATS-ISS_L1B_N-M7.2-V3-00_V3-00.json index 21bb860a27..2607c347e5 100644 --- a/datasets/CATS-ISS_L1B_N-M7.2-V3-00_V3-00.json +++ b/datasets/CATS-ISS_L1B_N-M7.2-V3-00_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L1B_N-M7.2-V3-00_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L1B_N-M7.2-V3-00 is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 1B Night Mode 7.2 Version 3-00 data product. The collection spans from March 25, 2015 through October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. Level 1B data have been calibrated and annotated with ancillary meteorological data and processed to sensor units.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmLay_V3-01.json b/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmLay_V3-01.json index 56dfe97e9f..c2c40f8174 100644 --- a/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmLay_V3-01.json +++ b/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmLay_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.1-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.1 Version 3-01 5 km Layer data product. This collection spans from February 20, 2015 to March 21, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmPro_V3-01.json b/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmPro_V3-01.json index 4c459aa9be..636056a33f 100644 --- a/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmPro_V3-01.json +++ b/datasets/CATS-ISS_L2O_D-M7.1-V3-01_05kmPro_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.1-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.1 Version 3-01 5 km Profile data product. This collection spans from February 10, 2015 to March 21, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmLay_V3-00.json b/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmLay_V3-00.json index 919df8ce98..0d9240f6c4 100644 --- a/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmLay_V3-00.json +++ b/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmLay_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.2-V3-00_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-00 5 km Layer data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters derived and are from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmPro_V3-00.json b/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmPro_V3-00.json index 3f2d4f2ed1..f47e36c21e 100644 --- a/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmPro_V3-00.json +++ b/datasets/CATS-ISS_L2O_D-M7.2-V3-00_05kmPro_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.2-V3-00_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-00 5 km Profile data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmLay_V3-01.json b/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmLay_V3-01.json index 550c8b5f06..8671a6184f 100644 --- a/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmLay_V3-01.json +++ b/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmLay_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.2-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-01 5 km Layer data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmPro_V3-01.json b/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmPro_V3-01.json index dd5cf9b392..decd4efb90 100644 --- a/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmPro_V3-01.json +++ b/datasets/CATS-ISS_L2O_D-M7.2-V3-01_05kmPro_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_D-M7.2-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-01 5 km Profile data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmLay_V3-01.json b/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmLay_V3-01.json index 60d969cc2e..ca6b03d3f7 100644 --- a/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmLay_V3-01.json +++ b/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmLay_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.1-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.1 Version 3-01 5 km Layer data product. This collection spans from February 12, 2015 to February 13, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmPro_V3-01.json b/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmPro_V3-01.json index 581e91071e..c7f468e8b9 100644 --- a/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmPro_V3-01.json +++ b/datasets/CATS-ISS_L2O_N-M7.1-V3-01_05kmPro_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.1-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.1 Version 3-01 5 km Profile data product. This collection spans from February 12, 2015 to February 13, 2015. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmLay_V3-00.json b/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmLay_V3-00.json index 3788399efa..278f24ae79 100644 --- a/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmLay_V3-00.json +++ b/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmLay_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.2-V3-00_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.2 Version 3-00 5 km Layer data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmPro_V3-00.json b/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmPro_V3-00.json index 1045d0fe5d..5d99045d61 100644 --- a/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmPro_V3-00.json +++ b/datasets/CATS-ISS_L2O_N-M7.2-V3-00_05kmPro_V3-00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro_V3-00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.2-V3-00_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.2 Version 3-00 5 km Profile data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmLay_V3-01.json b/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmLay_V3-01.json index 1767a94950..d43862bb6f 100644 --- a/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmLay_V3-01.json +++ b/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmLay_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.2-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.2 Version 3-01 5 km Layer data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmPro_V3-01.json b/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmPro_V3-01.json index 73b6b6a067..374c717a82 100644 --- a/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmPro_V3-01.json +++ b/datasets/CATS-ISS_L2O_N-M7.2-V3-01_05kmPro_V3-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro_V3-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CATS-ISS_L2O_N-M7.2-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night Mode 7.2 Version 3-01 5 km Profile data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.", "links": [ { diff --git a/datasets/CB4-MUX-L4-SR-1_NA.json b/datasets/CB4-MUX-L4-SR-1_NA.json index 2f1aa030ba..0b8e70fffe 100644 --- a/datasets/CB4-MUX-L4-SR-1_NA.json +++ b/datasets/CB4-MUX-L4-SR-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CB4-MUX-L4-SR-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4/MUX Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", "links": [ { diff --git a/datasets/CB4-WFI-L4-SR-1_NA.json b/datasets/CB4-WFI-L4-SR-1_NA.json index 6488a1ca07..fcaa2400aa 100644 --- a/datasets/CB4-WFI-L4-SR-1_NA.json +++ b/datasets/CB4-WFI-L4-SR-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CB4-WFI-L4-SR-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", "links": [ { diff --git a/datasets/CB4A-WFI-L4-SR-1_NA.json b/datasets/CB4A-WFI-L4-SR-1_NA.json index e9932abaa9..bb44e52fa6 100644 --- a/datasets/CB4A-WFI-L4-SR-1_NA.json +++ b/datasets/CB4A-WFI-L4-SR-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CB4A-WFI-L4-SR-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4A/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", "links": [ { diff --git a/datasets/CB4A-WPM-PCA-FUSED-1_NA.json b/datasets/CB4A-WPM-PCA-FUSED-1_NA.json index 4f3aa72cf5..52749febb0 100644 --- a/datasets/CB4A-WPM-PCA-FUSED-1_NA.json +++ b/datasets/CB4A-WPM-PCA-FUSED-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CB4A-WPM-PCA-FUSED-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains 2 meter high-resolution, RGB products, generated using the Principal Components Fusion (PCA) method, with values coded between 1 and 255, with 0 being reserved for 'No Data'. This product is derived from the original CBERS-4A WPM Level-4 Digital Number with 10 bit of quantization.", "links": [ { diff --git a/datasets/CBERS-WFI-8D-1_NA.json b/datasets/CBERS-WFI-8D-1_NA.json index d3ba6184f1..2486cfc6b4 100644 --- a/datasets/CBERS-WFI-8D-1_NA.json +++ b/datasets/CBERS-WFI-8D-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CBERS-WFI-8D-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Observation Data Cube generated from CBERS-4/WFI and CBERS-4A/WFI Level-4 SR products over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 8 days using the Least Cloud Cover First (LCF) best pixel approach.", "links": [ { diff --git a/datasets/CBERS4-MUX-2M-1_NA.json b/datasets/CBERS4-MUX-2M-1_NA.json index ba7d2bb1f0..a02d455614 100644 --- a/datasets/CBERS4-MUX-2M-1_NA.json +++ b/datasets/CBERS4-MUX-2M-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CBERS4-MUX-2M-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Observation Data Cube generated from CBERS-4/MUX Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 20 meters of spatial resolution, reprojected and cropped to BDC_MD grid Version 2 (BDC_MD V2), considering a temporal compositing function of 2 months using the Least Cloud Cover First (LCF) best pixel approach.", "links": [ { diff --git a/datasets/CBERS4-WFI-16D-2_NA.json b/datasets/CBERS4-WFI-16D-2_NA.json index 01427d9225..912467e62b 100644 --- a/datasets/CBERS4-WFI-16D-2_NA.json +++ b/datasets/CBERS4-WFI-16D-2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CBERS4-WFI-16D-2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Observation Data Cube generated from CBERS-4/WFI Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.", "links": [ { diff --git a/datasets/CBERS_DATA.json b/datasets/CBERS_DATA.json index 01e672572e..ef686fc4f7 100644 --- a/datasets/CBERS_DATA.json +++ b/datasets/CBERS_DATA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CBERS_DATA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "[Source: INPE Image Catalog web site, http://www.dgi.inpe.br/CDSR/ ]\n \nDGI/INPE's Image Database presently contains images cast by Landsat-1, Landsat-2, Landsat-3, Landsat-5, Landsat-7, CBERS2 and CBERS-2B (China-Brazil Environment Resources Satellite) satellites. \n \nAll images are fully cost free when requested (via Internet). These images are (by default) sent via FTP to the users for downloading. It can rather be recorded on a CD media and mailed to the user, what implies costs (media plus mailing) driving the user to register at the Users Assistance Service (ATUS) for tax payment and shipment procedures. \n \nUsers that order images on a CD media, will be granted with an FTP bonus dispatch for the requested images, by courtesy of ATUS. Users registered at the ATUS service are able to request any item in the Catalog ; users not acquainted to ATUS service can only request those items which are cost free. The '$' symbol will be showed attatched to the enclosing border of each (quicklook) charged item in the Catalog, in order to distinguish them form free ones.\n \nMore Information: http://www.cbers.inpe.br/", "links": [ { diff --git a/datasets/CCAMLR_statistical_areas_low_res_1.json b/datasets/CCAMLR_statistical_areas_low_res_1.json index e65297d860..05922797f5 100644 --- a/datasets/CCAMLR_statistical_areas_low_res_1.json +++ b/datasets/CCAMLR_statistical_areas_low_res_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CCAMLR_statistical_areas_low_res_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Statistical areas, subareas and divisions are used globally for the purpose of reporting fishery statistics. CCAMLR's Convention Area in the Southern Ocean is divided, for statistical purposes, into Area 48 (Atlantic Antarctic) between 70oW and 30oE, Area 58 (Indian Ocean Antarctic) between 30o and 150oE, and Area 88 (Pacific Antarctic) between 150oE and 70oW. These areas, which are further subdivided into subareas and divisions, are managed by CCAMLR. A global register of statistical areas, subareas and divisions is maintained by FAO http://www.fao.org/fishery/area/search/en. CCAMLR Secretariat (2013)", "links": [ { diff --git a/datasets/CCE-LTER_0.json b/datasets/CCE-LTER_0.json index f0a61cac9c..1402f8be34 100644 --- a/datasets/CCE-LTER_0.json +++ b/datasets/CCE-LTER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CCE-LTER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Long Term Ecological Research Network (LTER) California Current Ecosystem (CCE) program between 2006 and 2008.", "links": [ { diff --git a/datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json b/datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json index 1a068a04c3..6f849179ea 100644 --- a/datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json +++ b/datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CCMP_WINDS_10M6HR_L4_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a 6-hourly, 0.25 degree resolution, near-global gridded analysis of ocean surface vector winds from the Cross-Calibrated Multi-Platform (CCMP) project, produced by Remote Sensing Systems (RSS). CCMP is a combination of inter-calibrated 10 m ocean surface wind retrievals from multiple types of satellite microwave sensors and a background field from reanalysis. The wind retrievals are derived by RSS and include most of the wind-sensing U.S., Japanese, and European satellites flown to date. The background field is from ERA5 10m Neutral Stability winds. The result is a product that remains closely tied to the satellite retrievals where they are available and closely collocated in time and space. Data files are available in netCDF format, with one file per day. This time record is ongoing, with an expected latency of 2-3 months for new files.\r\n

\r\nVersion 3.1 updates include but are not limited to: (1) Improved performance and agreement with satellite winds at high wind speed, (2) Minimized spurious trends caused by the interaction between the amount of satellite measurements available and the satellite/model biases, and (3) improving the quality of the wind after 2012. \r\n

\r\nVersion 3.1 is produced and maintained by RSS with support from a NASA grant (ROSES proposal 17-OVWST-17-0023). Previous versions were funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program, with the original V1.0 led by Dr. Robert Atlas at Goddard Space Flight Center.", "links": [ { diff --git a/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json b/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json index 739088d3ba..1e3de51a93 100644 --- a/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json +++ b/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a monthly-mean, 0.25 degree resolution, near-global gridded analysis of ocean surface winds (wind speed, components, and anomalies) from the Cross-Calibrated Multi-Platform (CCMP) project. CCMP is a combination of inter-calibrated 10 m ocean surface wind retrievals from multiple types of satellite microwave sensors and a background field from reanalysis. The wind retrievals are derived by Remote Sensing Systems (RSS) and include most of the wind-sensing U.S., Japanese, and European satellites flown to date. The background field is from ERA5 10m Neutral Stability winds. The result is a product that remains closely tied to the satellite retrievals where they are available and closely collocated in time and space. Data files are available in netCDF format, with one file per month. \r\n

\r\nVersion 3.1 updates include but are not limited to: (1) Improved performance and agreement with satellite winds at high wind speed, (2) Minimized spurious trends caused by the interaction between the amount of satellite measurements available and the satellite/model biases, and (3) improving the quality of the wind after 2012. \r\n

\r\nVersion 3.1 is produced and maintained by RSS with support from a NASA grant (ROSES proposal 17-OVWST-17-0023). Previous versions were funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program, with the original V1.0 led by Dr. Robert Atlas at Goddard Space Flight Center.", "links": [ { diff --git a/datasets/CD01_BRAMS_907_1.json b/datasets/CD01_BRAMS_907_1.json index 0dd9e5442a..53b4bd7c40 100644 --- a/datasets/CD01_BRAMS_907_1.json +++ b/datasets/CD01_BRAMS_907_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD01_BRAMS_907_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a single NetCDF file containing simulated three dimensional winds and CO2 concentrations centered on the Tapajos National Forest in Brazil in August 2001. Winds (u, v, and w components) and CO2 concentrations were generated at 31 vertical levels at 1 km grid increment with the Brazilian version of Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The simulation ran from the 1st through the 15th of August 2001, which was concurrent with the Santarem Mesoscale Campaign. The data file is in NetCDF format.Mesoscale circulations and atmospheric CO2 variations were investigated over a heterogeneous landscape of forests, pastures, and large rivers during the Santarem Mesoscale Campaign (SMC) of August 2001 (Silva Dias et al., 2004). The atmospheric CO2 concentration variations were simulated using the Colorado State University Regional Atmospheric Modeling System with four nested grids that included a 1-km finest grid centered on the Tapajos National Forest. Surface CO2 fluxes were prescribed using idealized diurnal cycles over forest and pasture that derived from flux tower observations; while surface water CO2 efflux was prescribed using a value suggested by in situ measurements in the Amazon region (Lu et al., 2005). Simulation ran from 1 August through 15 August 2001, which was concurrent with the SMC. Evaluation against flux tower observations and Belterra meteorological tower measurements showed that the model captured the observed 2-m temperatures and 10-m winds reasonably well. At 57 m the model reproduced the daytime CO2 concentration better than the nighttime concentration but missed the observed early morning CO2 maxima, in part because of the difficulties of simulating stable nocturnal boundary conditions and subgrid-scale intra-canopy processes. The results also suggested that the topography, the differences in roughness length between water and land, the shape juxtaposition of Amazon and Tapajos Rivers, and the resulting horizontal and vertical wind shears all facilitated the generation of local mesoscale circulations. Possible mechanisms producing a low-level convergence (LLC) line near the east bank of the Tapajos River were explored. Under strong trade wind conditions, mechanical forcing is more important than thermal forcing in LLC formation. Persistent clouds near the east side of the Tapajos River may have a significant impact on observed ecosystem carbon flux and should be taken into account if tower fluxes are to be generalized to a larger region. ", "links": [ { diff --git a/datasets/CD01_CIRSAN_Meteorology_2001_1114_1.json b/datasets/CD01_CIRSAN_Meteorology_2001_1114_1.json index a78e648c1b..849bab570d 100644 --- a/datasets/CD01_CIRSAN_Meteorology_2001_1114_1.json +++ b/datasets/CD01_CIRSAN_Meteorology_2001_1114_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD01_CIRSAN_Meteorology_2001_1114_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological data collected around the confluence of the Tapajos River with the Amazon River in the Amazon Basin near Santarem, Brazil, in July and August 2001. Boundary layer and upper air measurements were collected with an acoustic sounder-sodar instrument, pilot balloons with optical theodolites, and radiosondes. Radiosondes also measured pressure, temperature, and relative humidity in addition to wind speed and direction. Measurements were made from five local stations at varying frequencies. There are 41 comma-delimited data files with this data set. Supporting information provided with the data set as companion files include: Weather forecasts: Weather forecasts were used to determine the presence of favorable conditions for the balloon flights during the CIRSAN experiment, as well as to help decide the radiosonde launch frequency. The daily observed and forecast weather descriptions for the study period (Weather_forecasts_Santarem.txt) are included. Satellite images: All the satellite images during the CIRSAN period are provided. This is a compilation of images from various instruments and satellite platforms. (See readme_sat.txt). There are 42 images in .gif format. CPTEC Analysis files: The CIRSAN measurement data were used in the CPTEC Global Analysis modeling activity. Model output results for the Pacific and South American region are provided in GRIB format. (See readme_GPSA.txt) ", "links": [ { diff --git a/datasets/CD02_Atmosphere_CO2_Isotopes_1011_1.json b/datasets/CD02_Atmosphere_CO2_Isotopes_1011_1.json index 9c2790fb43..4af1c2e847 100644 --- a/datasets/CD02_Atmosphere_CO2_Isotopes_1011_1.json +++ b/datasets/CD02_Atmosphere_CO2_Isotopes_1011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_Atmosphere_CO2_Isotopes_1011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports carbon and oxygen stable isotope ratios of atmospheric carbon dioxide (CO2) collected at several forest and pasture sites and in the free troposphere over Amazonia. There are three comma-delimited ASCII files with this data set.Atmospheric CO2 concentrations and isotope signatures were measured at ten different forest and pasture canopy sites across the states of Amazonas, Para, and Rondonia within the Brazilian Amazon between March 1999 and March 2004. Both daytime and nighttime profile samples were collected.Samples of CO2 in the troposphere were collected during aircraft flights over the Amazon/Tapajos Rivers, FLONA Tapajos, and pasture/agriculture areas during five days in May 2003 (wet season). Samples were analyzed for carbon and oxygen isotopes of atmospheric CO2. Flights ranged from low altitudes to above the diurnal tropospheric boundary layer.Measurements of carbon and oxygen stable isotope ratios of atmospheric carbon dioxide (CO2) are a powerful indicator of large-scale CO2 exchange on land across multiple spatial scales. Stable carbon isotope composition of leaf tissue and CO2 released by respiration (delta r) can be used as an estimate of changes in ecosystem isotopic discrimination that occur in response to seasonal and interannual changes in environmental conditions, and land-use change (forest-pasture conversion). Understanding of carbon dioxide stable isotope composition can play a central role in influencing our understanding of the extent to which terrestrial ecosystems are carbon sinks.", "links": [ { diff --git a/datasets/CD02_C_N_Isotopes_1097_1.json b/datasets/CD02_C_N_Isotopes_1097_1.json index ba9bd85973..d4781d6060 100644 --- a/datasets/CD02_C_N_Isotopes_1097_1.json +++ b/datasets/CD02_C_N_Isotopes_1097_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_C_N_Isotopes_1097_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports delta 13C/12C results for leaf tissues and atmospheric carbon dioxide (CO2), delta 15N/14N ratios for leaf tissue, and leaf carbon and nitrogen concentrations along a topographical gradient in old-growth forests in the ZF2 Reserve (km 34), Instituto Nacional de Pesquisas da Amazonia (INPA), near Manaus, Amazonas, Brazil. During the dry seasons of 2004 and 2006, leaves were sampled at various heights within the canopy and atmospheric air flask samples were also collected at various heights at three locations along this gradient. Also included are coincident meteorological, atmospheric CO2, and CO2 flux measurements from the plateau KM34 tower. There are 3 comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/CD02_C_N_O_Organic_983_1.json b/datasets/CD02_C_N_O_Organic_983_1.json index 76ddd0055d..907b6d9fc9 100644 --- a/datasets/CD02_C_N_O_Organic_983_1.json +++ b/datasets/CD02_C_N_O_Organic_983_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_C_N_O_Organic_983_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the measurement of stable carbon, nitrogen, and oxygen isotope ratios in organic material (plant, litter and soil samples) in forest canopy profiles and pasture (grasses and shrubs) as well as corresponding carbon and nitrogen tissue concentrations in a number of different sites across Brazil. The sampling design captured the temporal variation in rainfall over the course of several years. Carbon and nitrogen isotope ratios can act as a proxy for interpreting aspects of the carbon and nitrogen cycles in Amazonian rainforests. Data are in three comma-delimited ASCII files. ", "links": [ { diff --git a/datasets/CD02_Forest_Canopy_Structure_1009_1.json b/datasets/CD02_Forest_Canopy_Structure_1009_1.json index de4f6da046..315c68f44d 100644 --- a/datasets/CD02_Forest_Canopy_Structure_1009_1.json +++ b/datasets/CD02_Forest_Canopy_Structure_1009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_Forest_Canopy_Structure_1009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports on Leaf Area Index (LAI) and Specific Leaf Area (SLA) measurements collected from forest and pasture sites in or near the Tapajos National Forest (TNF), 80 km south of the city of Santarem, Para, Brazil. The collections were between October 1999 and June 2003 from tower sites accessed via the km 67 forest entrance. There are 2 comma-delimited ASCII data files with this data set, and 1 companion data file which provides site descriptions. ", "links": [ { diff --git a/datasets/CD02_Leaf_Level_Gas_Exchange_1010_1.json b/datasets/CD02_Leaf_Level_Gas_Exchange_1010_1.json index e99a3aac30..83a44e78a1 100644 --- a/datasets/CD02_Leaf_Level_Gas_Exchange_1010_1.json +++ b/datasets/CD02_Leaf_Level_Gas_Exchange_1010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_Leaf_Level_Gas_Exchange_1010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports leaf gas flux and leaf properties from samples collected from trees, liana, pasture saplings, and pasture grass located at eight different sampling locations in the states of Para (south of Santarem) and Amazonas (near Manaus) from November 1999 through December 2003. Data are reported on photosynthesis measurements, CO2 response curves, light response curves, humidity response curves, and stomatal responses to variations of the leaf-to-air water vapor mole fraction deficit. Leaf weight, carbon and nitrogen concentrations as well as stable isotope signatures for 13C and 15N are reported for a subset of the samples. There is one comma-delimited ASCII data file with this data set. ", "links": [ { diff --git a/datasets/CD02_Leaf_Water_Potential_1100_1.json b/datasets/CD02_Leaf_Water_Potential_1100_1.json index 4267f0a4c8..8c5bbe16d3 100644 --- a/datasets/CD02_Leaf_Water_Potential_1100_1.json +++ b/datasets/CD02_Leaf_Water_Potential_1100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_Leaf_Water_Potential_1100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data are reported for leaf water potential of leaves of seven species of trees and lianas from the primary forest at the km 67 Tower Site, Tapajos National Forest, and measurements of five sapling tree species and the grass Brachiaria brizantha from a pasture site located near the km 77 Pasture Tower Site, approximately 10 km from the primary forest site. The research area is situated within the Tapajos National Forest reserve, south of the city of Santarem, Para, Brazil. Measurements were made quarterly between March 2000 and March 2001. There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/CD02_O_H_Isotopes_1008_1.json b/datasets/CD02_O_H_Isotopes_1008_1.json index 14e4073184..665879d3c7 100644 --- a/datasets/CD02_O_H_Isotopes_1008_1.json +++ b/datasets/CD02_O_H_Isotopes_1008_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD02_O_H_Isotopes_1008_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the oxygen isotope signatures of water extracted from plant tissue (xylem from the stems and leaf tissue) and of atmospheric water vapor from twelve different sites (including both pasture and forest) throughout the Amazon region of Brazil. Samples were collected approximately every 4 months between 1999 and 2003 with additional samples collected monthly between January and May of 2003. In 2004 the collection of water samples from plant tissue continued at two sites, though water vapor collections were discontinued, and measurements of deuterium signatures were added to the analyses. In addition, water vapor from the troposphere was collected during a series of aircraft flights over the Tapajos National Forest in May of 2003 and analyzed for oxygen isotopes using the same methodology. There is one comma-delimited ASCII data file with this data set.", "links": [ { diff --git a/datasets/CD03_Ceilometer_Km67_942_1.json b/datasets/CD03_Ceilometer_Km67_942_1.json index 080bbde927..818eeab5ae 100644 --- a/datasets/CD03_Ceilometer_Km67_942_1.json +++ b/datasets/CD03_Ceilometer_Km67_942_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD03_Ceilometer_Km67_942_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Vaisala CT-25K ceilometer was installed at an old-growth forest site located at the km 67 Eddy Flux Tower site in the Tapajos National Forest, Para, Brazil, off Kilometer 67 of BR-163 south of Santarem in April 2001 and remained operational through December 2003, with reliable data being collected between May 2001 and June 2003.Annual, 2001 to 2003, 30-minute average cloud base and backscatter profile data and measurement statistics (sample count, variance, skewness, and kurtosis) are presented in 15 ASCII comma-delineated files. In addition, the cloud base values (m) and measurement statistics for the three reported cloud base levels have been consolidated in 3 annual comma-separated files.The ceilometer provides 15-second measurements of cloud base (three levels up to 7500 m), echo intensity, and a 30-m resolution backscatter profile. The ceilometer reports vertical visibility during periods when the sky is obscured but a cloud base is not detectable. The ceilometer was operational for a sufficient amount of time to examine wet-to-dry season variations in cloud cover fraction and cloud base height.", "links": [ { diff --git a/datasets/CD03_Mesoscale_Meteorology_944_1.json b/datasets/CD03_Mesoscale_Meteorology_944_1.json index ab83f12158..d9d0134854 100644 --- a/datasets/CD03_Mesoscale_Meteorology_944_1.json +++ b/datasets/CD03_Mesoscale_Meteorology_944_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD03_Mesoscale_Meteorology_944_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A mesoscale network has been set up in the Santarem region of Para, Brazil. This network consists of eight meteorological stations named Belterra, Km 117 (Fazenda Sr. Davi), Mojui, Jamaraqua, Guarana, Embrapa (Cacoal Grande), Vila Franca and Sudam (Curua Una). Belterra and Km117 stations have been almost continuously collecting data since August, 1998, respectively. Mojui, Jamaraqua, and Guarana have been collecting data since July, 2000. Embrapa, Vila Franca and Sudam stations have been collecting data since 2002. Data are presented in 52 individual comma-separated ASCII files. Each file contains data from one calendar year for one site; both site and year are identified clearly in the data file name and all files follow the same header information and organizational structure. Measurements include air temperature and pressure, wind speed and direction, relative humidity, downward solar radiation, and at some stations soil temperature and moisture.", "links": [ { diff --git a/datasets/CD03_Pasture_Flux_962_1.json b/datasets/CD03_Pasture_Flux_962_1.json index c740f3e999..0e7dda5210 100644 --- a/datasets/CD03_Pasture_Flux_962_1.json +++ b/datasets/CD03_Pasture_Flux_962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD03_Pasture_Flux_962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Eddy correlation and micrometeorological measurements began in 2001 and continued through 2005 at the pasture site at km 77 on BR-163 just south of the city of Santarem, Para, Brazil. Measurements included turbulent fluxes (momentum, heat, water vapor, and CO2) using the eddy covariance (EC) approach. Other measurements included the CO2 profile, air temperature, humidity, wind speed profile, downward and upward solar and terrestrial radiation, downward and upward photosynthetically active radiation (PAR), atmospheric pressure, rainfall, soil temperature, soil moisture, and soil heat flux. Data are presented in 5 comma-separated ASCII value (csv) files each corresponding roughly to one calendar year. At the beginning of the measurements, in September 2000, the field was a pasture. In November 2001, the pasture was burned, plowed, and planted in upland (non-irrigated) rice. Land use practices during the study period were recorded and are included in a table in Section 5 of this guide.The EC system was composed of a 3D sonic anemometer (ATI 3D) and an infrared analyzer (LICOR 6262) installed on a 20m tower in the agricultural field. The methodology to calculate the flux is described in detail in Sakai et al. (2004) and a companion file is included that describes in detail the formulae used to calculate the eddy flux variables (CD03_Pasture_Flux_Calculations.pdf). ", "links": [ { diff --git a/datasets/CD03_Tethered_Balloon_1108_1.json b/datasets/CD03_Tethered_Balloon_1108_1.json index dbd1071764..fb61b9aa6c 100644 --- a/datasets/CD03_Tethered_Balloon_1108_1.json +++ b/datasets/CD03_Tethered_Balloon_1108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD03_Tethered_Balloon_1108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of nocturnal meteorological profiles collected from tethered balloon platforms during July 2001, October 2001, and November 2003. Measurements were made near the pasture/agricultural tower site at km 77 on BR-163 just south of the city of Santarem, and the near the Tapajos National Forest, km 83 tower site, Santarem, Para, Brazil. Measurements collected include air temperature, wind speed and direction, and specific humidity. The 2003 measurements also included CO2 concentrations. Sites were near enough to allow comparison between sounding profiles and tower data. There are three comma-delimited ASCII files with this data set. Profiles were obtained from sunset until the first hours after sunrise. Each sounding provided information on temperature, humidity, horizontal wind magnitude and direction as the balloon went up and down. Typical soundings went up to 300 to 400 m. During most of the night, soundings were performed hourly. The balloon rose at a rate of 0.5 m per second in the first 100 m, and 2 m per second above it. The time between successive samplings was 10 s. Intensive periods of shallow, successive soundings were performed starting at dawn, to catch the early development of the convective boundary layer (CBL). These early morning soundings went up only to the capping inversion.", "links": [ { diff --git a/datasets/CD04_Biomass_990_1.json b/datasets/CD04_Biomass_990_1.json index 813b870612..48d370427a 100644 --- a/datasets/CD04_Biomass_990_1.json +++ b/datasets/CD04_Biomass_990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Biomass_990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of a biometric tree survey of a 19.25 ha area adjacent to the eddy flux tower at the km 83 logged forest tower site in Tapajos National Forest, Para, Brazil. The survey was done in March 2000. All measurements reported here were taken before the logging began. Diameters of all trees > 35 cm DBH within the 19.25 ha survey area were recorded and trees with DBH between 10 and 35 cm DBH were recorded along three transects with a total area of 2.3 ha (Miller et al., 2004). These data were used to calculate net ecosystem productivity (NEP) and the role of this forest as a carbon source or sink. Biometric data are reported in one comma-delimited ASCII file. ", "links": [ { diff --git a/datasets/CD04_CO2_Profiles_947_1.json b/datasets/CD04_CO2_Profiles_947_1.json index 7264115545..60e98af390 100644 --- a/datasets/CD04_CO2_Profiles_947_1.json +++ b/datasets/CD04_CO2_Profiles_947_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_CO2_Profiles_947_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric carbon dioxide profiles were measured at 12 levels up to 64 m at the km 83 logged tower site in Tapajos National Forest, Santarem, Para, Brazil. Data were collected over 3.5 years between June 2000-March 2004. An infra-red gas analyzer sequentially measured the concentration of CO2 at 12 heights (0.1, 0.35, 0.7, 1.4, 3, 6, 10.7, 20, 35, 40, 50, 64 m above the ground) on the tower every 48 minutes. The data, reported on a 30 minute interval, are provided in one single comma separated file.", "links": [ { diff --git a/datasets/CD04_Dendrometry_989_1.json b/datasets/CD04_Dendrometry_989_1.json index 11305f4dc9..81855cd920 100644 --- a/datasets/CD04_Dendrometry_989_1.json +++ b/datasets/CD04_Dendrometry_989_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Dendrometry_989_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A dendrometry study was conducted at the logged forest tower site, km 83 site, Tapajos National Forest, Para, Brazil over a period of 4 years following the implementation of a reduced impact logging management regime. Dendrometer bands were installed to measure diameter growth increments for 234 trees in an 18 ha plot adjacent to the eddy flux tower at the km 83 site. In addition to trees randomly selected for measurements within the plot prior to logging, a set of smaller diameter trees within or adjacent to gaps created during the logging treatment were added to the study in 2002. Selective logging is a major land use in the Amazon Basin. An accurate accounting of the effect of logging on regional carbon balances requires better information on the rates at which the logged forest recovers biomass. There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/CD04_LAI_992_1.json b/datasets/CD04_LAI_992_1.json index 45b437b23e..bc5db9b153 100644 --- a/datasets/CD04_LAI_992_1.json +++ b/datasets/CD04_LAI_992_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_LAI_992_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf area index was estimated in an 18 ha plot at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. The plot was adjacent to the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Thirty litter traps were placed at 25-m intervals along two east-west transects in the 18 ha block. Litter samples were collected biweekly from the traps and returned to the lab where they were sorted, air dried, and weighed. The leaf area of a subsample of air-dried leaves was determined using a computer scanner and image processing software. The subsample was then dried in an oven and the air-dried weights were corrected to oven-dried weight. The area of leaf litter collected during each sampling was calculated using the relationship between weight and area measured for the subsample (Goulden et al., 2004). There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/CD04_LAI_Estimates_1103_1.json b/datasets/CD04_LAI_Estimates_1103_1.json index a1d2b4c7de..6135227e30 100644 --- a/datasets/CD04_LAI_Estimates_1103_1.json +++ b/datasets/CD04_LAI_Estimates_1103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_LAI_Estimates_1103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains summary data for monthly leaf area index (LAI) and plant area index (PAI) at the km 83 Tower Site, in the Tapajos National Forest, Para, Brazil. LAI was estimated for hemispherical photographs of leaves collected between 2000 and 2003, using the histogram and gap-fraction analysis methods.There are two data files with this data set: one comma-delimited ASCII data file with this data set which contains the monthly summary LAI and PAI data, and one compressed (*.zip) file that contains hemispherical photo images (.bmp) for 2000-2001. The images include those taken pre-logging and post-logging at the measurement site for the purpose of comparing LAI. In addition, there is a companion file containing a program code developed for LAI analysis provided as an ASCII text file.", "links": [ { diff --git a/datasets/CD04_Leaf_Level_Gas_Exchange_1060_1.json b/datasets/CD04_Leaf_Level_Gas_Exchange_1060_1.json index 90c4ed0021..262ca4d16f 100644 --- a/datasets/CD04_Leaf_Level_Gas_Exchange_1060_1.json +++ b/datasets/CD04_Leaf_Level_Gas_Exchange_1060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Leaf_Level_Gas_Exchange_1060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of measurements of (1) leaf-level photosynthesis response curves for the effects of temperature, leaf age, warming, irradiation, and circadian rhythm and (2) leaf-level photorespiration rates at 30 and 37 degrees C. Measurements were made between June 2000 and February 2006 at the km 83 Logged Forest Tower site, the km 67 Primary Forest Tower site, and the control site at Seca Floresta, all in the Tapajos National Forest, Para, Brazil. There are 7 comma delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/CD04_Leaf_Litter_991_1.json b/datasets/CD04_Leaf_Litter_991_1.json index ac6bd038c2..de09fe9ca6 100644 --- a/datasets/CD04_Leaf_Litter_991_1.json +++ b/datasets/CD04_Leaf_Litter_991_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Leaf_Litter_991_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Above-ground litter productivity was measured in a 18 ha plot adjacent to the eddy flux tower at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. Thirty litter baskets distributed within the grid were visited bi-weekly (Goulden et al., 2004). Oven dry mass of leaves, wood, reproductive parts and miscellaneous components of the collected litter was determined for each collection. Collections covered a pre-harvest period (Sept 2000 - July 2001) and a post- harvest period (Aug 2001-Mar 2003). There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/CD04_Logging_Damage_1038_1.json b/datasets/CD04_Logging_Damage_1038_1.json index 02257cf7e2..6b2dfd1d61 100644 --- a/datasets/CD04_Logging_Damage_1038_1.json +++ b/datasets/CD04_Logging_Damage_1038_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Logging_Damage_1038_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of a survey of logging damage in a 18 ha plot (300 m N-S, 600 m E-W) east (upwind) of the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Data collected include type of damage, snap height, and log dimensions, as well as calculated biomass of stems and canopy either damaged or removed in logging. There are two comma-delimited data files with this data set. ", "links": [ { diff --git a/datasets/CD04_Meteorology_Fluxes_946_1.json b/datasets/CD04_Meteorology_Fluxes_946_1.json index b84a4b13b4..cf10315975 100644 --- a/datasets/CD04_Meteorology_Fluxes_946_1.json +++ b/datasets/CD04_Meteorology_Fluxes_946_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Meteorology_Fluxes_946_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tower flux measurements of carbon dioxide,water vapor, heat, and meteorological variables were obtained at the Tapajos National Forest, km 83 site, Santarem, Para, Brazil. For the period June 29, 2000 through March 11, 2004, 30-minute averaged and calculated quantities of fluxes of momentum, heat, water vapor, and carbon dioxide, storage of carbon dioxide in the air column, are reported. Data are reported in three comma separated files: (1) 30 minute-averages, (2) the daily (24 hour) averages, and (3) the monthly (calendar) averages.The variables measured on the 67 m tower relate to meteorology, soil moisture, respiration, fluxes of momentum, heat, water vapor, and carbon dioxide, and were used to calculate storage of carbon dioxide, Net Ecosystem Exchange, and Gross Primary Productivity. Most of the variables have not been gap filled. However, CO2 flux and storage have been filled to avoid biases in Net Ecosystem Exchange; a fill index flag is included to indicate which data points were filled. Variables derived from the filled variables (respiration, NEE, GPP) are essentially filled also. Net ecosystem exchange has been filtered for calm nighttime periods.", "links": [ { diff --git a/datasets/CD04_Soil_Moisture_Km83_979_1.json b/datasets/CD04_Soil_Moisture_Km83_979_1.json index e76d1f4357..5d4f21fbcd 100644 --- a/datasets/CD04_Soil_Moisture_Km83_979_1.json +++ b/datasets/CD04_Soil_Moisture_Km83_979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Soil_Moisture_Km83_979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports continuous high-resolution frequency-domain reflectometry measurements of soil moisture to 10 m depth and precipitation data near each of the two towers located at the km 83 tower site (logged forest site) in the Tapajos National Forest in the state of Para, Brazil. Measurements were made during 2002 and 2003. Soil moisture and precipitation data are provided in two comma-delimited ASCII files.The first tower was installed in an intact forest area at this site in June 2000 (the 'intact' tower) and instrumented for eddy flux and micrometerological measurements and operated 15 months prior to any logging in the area (Goulden et al., 2004; Miller et al., 2004; Rocha et al., 2004). In September 2001, the area adjacent to the tower was selectively logged (Bruno et al., 2006).The second tower (the 'gap tower' tower) was installed and operating in June 2002, 400 m east of the intact tower. The gap tower was installed in the middle of a 50 m x 50 m log landing.Soil moisture measurements were made in 10 m deep vertical pits (1 x 1 m2) approximately 20 m from the micrometerological tower sites in undisturbed forest patches. Reflectometers were inserted horizontally into shaft walls at 0.15, 0.30, 0.60, 1, 2, 3, 4, 6, 8, and 10 meters beneath the surface. These data were used to determine how soil moisture varies on diel, seasonal and multi-year timescales and to better understand the quantitative and mechanistic relationships between soil moisture and forest evapotranspiration.", "links": [ { diff --git a/datasets/CD04_Soil_Respiration_1039_1.json b/datasets/CD04_Soil_Respiration_1039_1.json index 053ee0014d..3a307c5b65 100644 --- a/datasets/CD04_Soil_Respiration_1039_1.json +++ b/datasets/CD04_Soil_Respiration_1039_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Soil_Respiration_1039_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports on the flux of carbon dioxide from logged forest soils near the eddy flux tower at the km 83 site, Para, Brazil. The automated soil respiration measurements were collected using 15 chambers, installed August 2001 in primary forest. Data were collected between December 19, 2001 and March 1, 2002. There is one comma-delimited data file with this data set. ", "links": [ { diff --git a/datasets/CD04_Tower_Flux_Gap_978_1.json b/datasets/CD04_Tower_Flux_Gap_978_1.json index e97da20cb2..72b4323cf6 100644 --- a/datasets/CD04_Tower_Flux_Gap_978_1.json +++ b/datasets/CD04_Tower_Flux_Gap_978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD04_Tower_Flux_Gap_978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports 30-minute values for above-canopy meteorology and fluxes of momentum, heat, and carbon dioxide, and within-canopy carbon dioxide and water vapor concentrations collected at 12 levels between 10 cm and 64 m at the tower located within a logging gap at km 83 Tower Site in the Tapajos National Forest, Para, Brazil. Data were collected over 1.5 years between June 3, 2002 and January 30, 2004. All of the data are contained in one comma separated file.Two towers are located at the km 83 site. The first tower was installed in an intact forest area at this site in June 2000 (the 'intact' tower). In September 2001, the area adjacent to the tower was selectively logged (Bruno et al., 2006). The second tower (the 'gap tower') was installed and operating in June 2002, 400 m east of the intact tower. The gap tower was installed in the middle of a 50 m x 50 m log landing. ", "links": [ { diff --git a/datasets/CD05_Fuel_Loads_1233_1.json b/datasets/CD05_Fuel_Loads_1233_1.json index d38281a1cc..4b3ab4577e 100644 --- a/datasets/CD05_Fuel_Loads_1233_1.json +++ b/datasets/CD05_Fuel_Loads_1233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD05_Fuel_Loads_1233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains estimates of understory fuel loads (forest litter) at six locations near Paragominas in Northeastern Amazonia. Samples were collected from three different forest conditions: primary forest, logged forest, and burned forest. Volumes and weights are provided by size and condition class based on the planar transect method of estimating understory fuel loads (Brown 1971). Means and standard errors are reported from 3 transects in each forest x condition class. There is one comma-delimited data file (.csv) with this data set.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. ", "links": [ { diff --git a/datasets/CD05_Micromet_1169_1.json b/datasets/CD05_Micromet_1169_1.json index f24401dd61..1bc900329c 100644 --- a/datasets/CD05_Micromet_1169_1.json +++ b/datasets/CD05_Micromet_1169_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD05_Micromet_1169_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports soil moisture expressed as volumetric water content (VWC), daily precipitation, air temperature, relative humidity, and dew point measurements conducted at the Seca Floresta site, km 67, Tapajos National Forest, Brazil. The measurements were part of the Rainfall Exclusion Experiment (REE) established to study the response of a humid Amazonian forest to severe drought.VWC was measured with continuous high-resolution frequency-domain reflectometry to 11-m depth in two 1-ha plots from 1999 to 2007. One plot was subjected to ~75 percent throughfall exclusion during the rainy season (exclusion) and another monitored under normal conditions (control). Daily precipitation was measured in the control plot and in a nearby clearing between 1999 and 2006 using wedge rain gauges. Air temperature, relative humidity, and dew point were measured along the vertical forest profile of the control and dry plots of the site between 2000 and 2003.There are three comma-delimited data files (.csv) with this data set.", "links": [ { diff --git a/datasets/CD05_REE_Fuel_Sticks_Moisture_1232_1.json b/datasets/CD05_REE_Fuel_Sticks_Moisture_1232_1.json index b0eed4a892..4b8fa866fd 100644 --- a/datasets/CD05_REE_Fuel_Sticks_Moisture_1232_1.json +++ b/datasets/CD05_REE_Fuel_Sticks_Moisture_1232_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD05_REE_Fuel_Sticks_Moisture_1232_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains moisture content measurements for fuel sticks located in the forest understory of the rainfall exclusion experimental site, Tapajos National Forest, Para, Brazil. The mean and standard errors are reported for control and treatment plot measurements. The measurements were taken on various dates and times of day between 1998 and 2000 during the dry season.The rainfall exclusion treatment began in late January 2000 and continued through December 2004. About 60% of throughfall (equivalent to approximately half the rainfall) was diverted from a 1-hectare plot (i.e., dry) using plastic panels installed in the understory. The comparable 1-hectare control plot (i.e., wet) was unaltered. The goal of this experiment was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad et al., 2002). There is one comma-separated (.csv) data file with this data set.", "links": [ { diff --git a/datasets/CD06_BGC_JiParana_1227_1.json b/datasets/CD06_BGC_JiParana_1227_1.json index 021d881ed4..30be79a950 100644 --- a/datasets/CD06_BGC_JiParana_1227_1.json +++ b/datasets/CD06_BGC_JiParana_1227_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_BGC_JiParana_1227_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides spatially extensive and temporally intensive surveys of the river biogeochemistry of the Ji-Parana River Basin in Western Amazonia, Rondonia, Brazil. The concentrations of major nutrient ions, dissolved organic and inorganic carbon, pH, temperature, dissolved oxygen, and conductivity were measured in Ji-Parana River and tributary samples at the defined seasonal or monthly intervals. Dominant landuse/landcover classes, slope, and soil cation exchange capacity are included for each of the extensive sampling locations derived from river basin and sub-basin characteristics.Water samples were collected from 1999 to 2003 along the main stem of the Ji-Parana River as well as from the major tributaries including the Urupa. Shapefiles with the boundaries of the major sub-basins of the study area as well as the location of the sample collection points are included for the intensive and extensive sampling campaigns as well as the Urupa River campaign. There are six comma-separated data files (.csv) and five compressed shapefiles (.zip) with this data set. ", "links": [ { diff --git a/datasets/CD06_C02_Exchange_1136_1.json b/datasets/CD06_C02_Exchange_1136_1.json index 877737ee8f..4be1ceba10 100644 --- a/datasets/CD06_C02_Exchange_1136_1.json +++ b/datasets/CD06_C02_Exchange_1136_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_C02_Exchange_1136_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of carbon dioxide flux rates (FCO2), gas transfer velocity (k), and partial pressures (pCO2) at 75 sites on rivers and streams of the Amazon River system in South America for the period beginning July 1, 2004, and ending January 23, 2007. Several fieldwork campaigns occurred between June 2004 and January 2007 in the Amazon River basin, with discharge conditions ranging from low to high flow. The sampled areas span the spectrum of chemical characteristics observed across the entire basin, including, for example, both low and high pH values and suspended sediment loads. There is one comma-delimited data file in this data set. ", "links": [ { diff --git a/datasets/CD06_C_Isotopes_1120_1.json b/datasets/CD06_C_Isotopes_1120_1.json index 9a2b270a75..289183422a 100644 --- a/datasets/CD06_C_Isotopes_1120_1.json +++ b/datasets/CD06_C_Isotopes_1120_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_C_Isotopes_1120_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes measurements of standard geochemical variables, dissolved CO2, dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), fine particulate organic carbon (FPOC), and coarse particulate organic carbon (CPOC) in samples taken from 60 Amazonian river locations across the Amazon Basin from 1991 to 2003 (Mayorga et al., 2005). The 14C and 13C isotopic composition of DIC was measured on samples collected between 1991 and 2003. The 14C composition of organic carbon fractions was measured on samples collected from 1995 through 1996. There are four comma-delimited data files with this data set. Note that site descriptions include a categorization of each site by topography according to the percentage of the drainage area above 1,000 m elevation (Mayorga et al., 2005). Only means of geochemical and carbon-fraction results are provided. Both individual 13C and 14C measurements and mean results are provided. ", "links": [ { diff --git a/datasets/CD06_Camrex_1086_1.json b/datasets/CD06_Camrex_1086_1.json index 2172c31751..e9014b848b 100644 --- a/datasets/CD06_Camrex_1086_1.json +++ b/datasets/CD06_Camrex_1086_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Camrex_1086_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-resolution (~500 m) gridded land and stream drainage direction maps for the Amazon River basin, excluding the Rio Tocantins basin. These maps are the result of a new topography-independent analysis method (Mayorga et al., 2005) using the vector river network from the Digital Chart of the World (DCW, Danko, 1992) to create a high-resolution flow direction map. The data products include (1) a stream network coverage with stream order assigned to each reach; (2) the basin boundaries of the major tributaries to the Amazon mainstem; (3) the mouths; and (4) the source points of these tributaries.There are 7 ESRI ArcGIS shapefiles provided in compressed *.zip format and 4 GeoTiff image files with this data set. ", "links": [ { diff --git a/datasets/CD06_Carbon_respiration_1125_1.json b/datasets/CD06_Carbon_respiration_1125_1.json index 1dd4c22926..45f5562e5d 100644 --- a/datasets/CD06_Carbon_respiration_1125_1.json +++ b/datasets/CD06_Carbon_respiration_1125_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Carbon_respiration_1125_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measured and calculated variables describing the carbon pools in river waters, CO2 respired from the water and total amount of CO2 evaded, dissolved oxygen isotopes (delta 18O-O2), and concentration of bacterial cells in river water. Samples were collected from 10 white-water rivers, two clear-water streams (one each in Amazonas and Acre), and two black-water rivers in Amazonas from July to September 2005, which coincided with a severe drought in the western and southern regions of the Amazon Basin (Zeng et al. 2008). Eight of these sites were resampled during August through September 2006 of the following year (no drought).", "links": [ { diff --git a/datasets/CD06_LULC_Map_JiParana_1087_1.json b/datasets/CD06_LULC_Map_JiParana_1087_1.json index cc41e9a26e..68a35d2dd9 100644 --- a/datasets/CD06_LULC_Map_JiParana_1087_1.json +++ b/datasets/CD06_LULC_Map_JiParana_1087_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_LULC_Map_JiParana_1087_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified. There is one GeoTiff file with this data set.", "links": [ { diff --git a/datasets/CD06_Landuse_Timeseries_JiParana_844_1.json b/datasets/CD06_Landuse_Timeseries_JiParana_844_1.json index aafa56c511..2ec18515ea 100644 --- a/datasets/CD06_Landuse_Timeseries_JiParana_844_1.json +++ b/datasets/CD06_Landuse_Timeseries_JiParana_844_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Landuse_Timeseries_JiParana_844_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains four land use/land cover maps (1986, 1992, 1996 and 2001) for the Ji-Parana River Basin, derived from the digital classification of 8 Landsat images obtained from The Tropical Rain Forest Information Center (TRFIC). ", "links": [ { diff --git a/datasets/CD06_Outgassing_1151_1.json b/datasets/CD06_Outgassing_1151_1.json index 010c2b8902..d654014a47 100644 --- a/datasets/CD06_Outgassing_1151_1.json +++ b/datasets/CD06_Outgassing_1151_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Outgassing_1151_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of monthly carbon dioxide (CO2) flux from the Amazon mainstem rivers, tributary stream networks, and their associated varzeas (floodplains). CO2 flux was calculated using two aggregation approaches: for defined river basins (data file #2) and for defined river reaches (figure 2). Flux was calculated from (1) estimated surface water area by month for the Amazon mainstem rivers, associated varzeas, and tributary stream networks, (2) mean daily partial pressures of CO2 (pCO2) concentrations for the mainstem rivers, and (3) calculated mean pCO2 values for the varzea waters. Mean monthly discharge data for 11 mainstem rivers are also included. There are five comma-delimited data files with this data set. Amazon mainstem is a region covering the Amazon/Solimoes River mainstem from 70 degrees W to 54 degrees W. Data from the Japanese Earth Resources Satellite-1 (JERS-1) L-band synthetic aperture radar were used to estimate the areal coverage and inundation status of rivers and floodplains over 100 m in width and compiled into mosaics for periods of high and low water. For each mosaic, the study area was classified into either flooded or non-flooded areas. Data for the seasonal and spatial distributions of pCO2 within each hydrographic region were utilized from over 1,800 samples taken on 13 Carbon in the Amazon River Experiment (CAMREX) expeditions at different water stages throughout a 2,000 km reach of the central Amazon mainstem, tributary, and floodplain waters (Degens et al., 1991, Devol et al., 1995, Richey et al., 1988).", "links": [ { diff --git a/datasets/CD06_Physical_Template_JiParana_1090_1.json b/datasets/CD06_Physical_Template_JiParana_1090_1.json index 9a63161378..80739a583a 100644 --- a/datasets/CD06_Physical_Template_JiParana_1090_1.json +++ b/datasets/CD06_Physical_Template_JiParana_1090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Physical_Template_JiParana_1090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains physical, hydrologic, political, demographic, and societal maps for the Ji-Parana River Basin, in the state of Rondonia, Brazil. These data were used as base information in subsequent investigations of land use/land cover, biogeochemistry, soils, and water balance processes (Ballester et al., 2003). This data set includes a Digital Elevation Model (DEM), river networks and morphometric characteristics of the region (derived from the DEM), and a number of social and demographic vector sets (roads as of 2001, county borders, population change from 1970-2000, and settlement projects). The DEM is provided in GeoTIFF format. Other files are provided as shapefiles. ", "links": [ { diff --git a/datasets/CD06_Soils_JiParana_1088_1.json b/datasets/CD06_Soils_JiParana_1088_1.json index ed94fc943d..c335fffb2f 100644 --- a/datasets/CD06_Soils_JiParana_1088_1.json +++ b/datasets/CD06_Soils_JiParana_1088_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Soils_JiParana_1088_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a digital map of soil orders for the Ji-Parana River Basin, in the state of Rondonia, Brazil (Western Amazonia). Soil orders were manually digitized from a 1:500,000 map from EMBRAPA originally published in 1983. Oxisols and Ultisols are the predominant soil types in the basin, encompassing 47% and 24% of the total drainage area, respectively. Entisols cover 14%, Alfisols 13% and Eptisols 2% of the basin (Ballester et al., 2003). One data file is provided in ESRI ArcGIS Shapefile format compressed into a single zip file (*.zip).", "links": [ { diff --git a/datasets/CD06_Water_Balance_JiParana_1132_1.json b/datasets/CD06_Water_Balance_JiParana_1132_1.json index c783b5b6f2..c71bde1926 100644 --- a/datasets/CD06_Water_Balance_JiParana_1132_1.json +++ b/datasets/CD06_Water_Balance_JiParana_1132_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD06_Water_Balance_JiParana_1132_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides simulated minimum, average, and maximum monthly rainfall, potential evapotranspiration, water deficit, and water surplus values for the Ji-Parana River basin, Rondonia, Brazil. The Thornthwaite-Mather climatological model integrated into a Geographic Information System (GIS) was used to derive the data by utilizing Advanced Very High Resolution Radar (AVHRR) images for temperatures, rainfall amounts from gauges within and around the basin, soil profiles, and land cover maps as model inputs. The monthly water balance for the Ji-Parana river basin is simulated from February 1995 through December 1996 (Victoria et al., 2007). Data are also provided from the Ji-Parana subbasin stations for total basin rainfall, basin discharge and basin evapotranspiration. This data was used to check the results of the water balance model. There are 2 comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/CD07_GOES_L3_Gridded_SRB_831_1.json b/datasets/CD07_GOES_L3_Gridded_SRB_831_1.json index d730b10eec..f6a5903b9c 100644 --- a/datasets/CD07_GOES_L3_Gridded_SRB_831_1.json +++ b/datasets/CD07_GOES_L3_Gridded_SRB_831_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD07_GOES_L3_Gridded_SRB_831_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LBA-Ecology CD-07 team collected and processed GOES-8 imager data over the LBA region to characterize the incoming radiation and precipitation rates at regional scales. This data set contains surface down-welling solar radiation, photosynthetically active radiation (PAR) and infrared radiation, as well as precipitation rates at 8x8-km and half-hourly resolutions. The data cover the time periods: 01Mar99-30Apr99 and 01Sep99-31Oct99. The data missing from the temporal series was filled using interpolation to create a continuous sequence of data for carbon modeling studies.", "links": [ { diff --git a/datasets/CD08_CWD_Res_and_Decomp_Manaus_911_1.json b/datasets/CD08_CWD_Res_and_Decomp_Manaus_911_1.json index e89cc12945..78c828c23b 100644 --- a/datasets/CD08_CWD_Res_and_Decomp_Manaus_911_1.json +++ b/datasets/CD08_CWD_Res_and_Decomp_Manaus_911_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_CWD_Res_and_Decomp_Manaus_911_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data sets contains data on coarse wood density, moisture content, respiration rates and decomposition rate constants in csv format from Manaus Brazil measured from 1/1/1996 through 12/31/1997. The data for respiration reports CO2 flux from coarse litter (trunks and large branches > 10 cm diameter) that was studied in central Amazon forests (Chambers et al. 2001). The respiration study took place during the transition from wet to dry season of 1997 (June-August),and sampling from the decomposition study (Chambers et al. 2000) was carried out during both the dry and wet seasons of 1996-97 (see below). Respiration rates varied over almost two orders of magnitude (1.003-0.014 micro g C g-1 C min-1, n=61), and were significantly correlated with wood density (r2adj = 0.42), and moisture content (r2adj = 0.39). Additional samples taken from a nearby pasture indicated that wood moisture content was the most important factor controlling respiration rates across sites (r2adj = 0.65). Based on average coarse litter wood density and moisture content, the mean long-term carbon loss rate due to respiration was estimated to be 0.13 yr-1 (range of 95% prediction interval (PI) = 0.11-0.15 yr-1).Decomposition rate constants are reported as mass loss fraction per year, for boles of 155 large dead trees (> 10 cm diameter) in central Amazon forests (Chambers et al. 2000). The measurements were carried out over a 2-year period (1996-1997) on permanent plots monitored by the Biological Dynamics of Forest Fragments Project (BDFFP) of the Smithsonian Institution (Lovejoy and Bierregaard 1990; Rankin-De Merona et al. 1992) and the Biomass and Nutrient Experiment (BIONTE) of the National Institute for Amazon Research (Instituto Nacional de Pesquisas da Amazonia-INPA). Mortality data from 21 hectares of permanent inventory plots, monitored for 10-15 years, were used to select dead trees for sampling. A single csv formatted data file includes dates when trees died, their diameter and breast height (DBH, i.e., at 1.3 m) and taxonomic information.Measured rate constants varied by over 1.5 orders of magnitude (0.015-0.67 /yr), averaged 0.19 /yr with predicted error averaging 0.026 /yr. Wood density and bole diameter were significantly and inversely correlated with rate constants. A tree of average biomass was predicted to decompose at 0.17 /yr.Understanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. These data, along with estimates of ecosystem leaf, live wood and soil respiration, were used to estimate total carbon balance as described in Chambers et al (2004).", "links": [ { diff --git a/datasets/CD08_C_Isotopes_Belowground_1025_1.json b/datasets/CD08_C_Isotopes_Belowground_1025_1.json index cea2f8b3f1..5641ec9ceb 100644 --- a/datasets/CD08_C_Isotopes_Belowground_1025_1.json +++ b/datasets/CD08_C_Isotopes_Belowground_1025_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_C_Isotopes_Belowground_1025_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains carbon isotope signatures from soil organic matter collected from the following sites: the forests of the ZF-2 INPA reserve approximately 80 km north of the city of Manaus, Amazon; the Tapajos National Forest approximately 83 km south of the city of Santarem, Para; and the Fazenda Vitoria, a ranch near the city of Paragominas, Para. Samples from the Fazenda Vitoria were from degraded and managed pasture sites as well as mature and secondary forests. In addition,carbon isotope signatures from roots sorted by size class, hand-picked from soil pits in the Flona Tapajos and Fazenda Vitoria, are included, as are carbon isotope signatures from soil gases from samples collected at the Fazenda Vitoria. There are 4 ASCII data files with this data set.", "links": [ { diff --git a/datasets/CD08_Ecosystem_Resp_Manaus_912_1.json b/datasets/CD08_Ecosystem_Resp_Manaus_912_1.json index d969e2dd4a..a9e51374a5 100644 --- a/datasets/CD08_Ecosystem_Resp_Manaus_912_1.json +++ b/datasets/CD08_Ecosystem_Resp_Manaus_912_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Ecosystem_Resp_Manaus_912_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf, live wood (tree stem), and soil respiration were measured along with additional environmental factors over a 1-yr period in a Central Amazon terra firme forest and are provided in this data set as three comma delimited data files. Investigations were carried out at an INPA reserve located along the ZF2 road at km 34 [LBA 34] on two 20 x 2500 m permanent forest inventory plots referred to as the Jacaranda plots (-2.6091 degrees S, -60.2093 degrees W). These long and narrow plots capture ecosystem variation associated with the undulating local topography. Leaf respiration measurements were also made at the tower located at ZF-2 road (km 14 [LBA 14]. Leaf respiration was measured during July and August 2001, woody respiration in August 2000 and June 2001, and soil respiration between July 2000 and June 2001 at 4 to 6-wk intervals.Understanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. These data were used to estimate ecosystem leaf, live wood and soil respiration with detailed information provided in Chambers et al. (2004).", "links": [ { diff --git a/datasets/CD08_Leaf_Isotopes_Manaus_1245_1.json b/datasets/CD08_Leaf_Isotopes_Manaus_1245_1.json index 22e714be8c..a57863ef68 100644 --- a/datasets/CD08_Leaf_Isotopes_Manaus_1245_1.json +++ b/datasets/CD08_Leaf_Isotopes_Manaus_1245_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Leaf_Isotopes_Manaus_1245_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for carbon (C), nitrogen (N), leaf area index (LAI), and carbon isotope ratio data (13C and 14C) of leaves sampled at the Manaus ZF2 Jacaranda transect area, Amazonas, Brazil, in 2001. Leaf tips and the petioles from the youngest and oldest leaves from a sampled branch were analyzed for nine different species. There is one comma-delimited data file (.csv) with this data set.", "links": [ { diff --git a/datasets/CD08_Radiocarbon_Dates_997_1.json b/datasets/CD08_Radiocarbon_Dates_997_1.json index 885437fb0f..d6da7060c7 100644 --- a/datasets/CD08_Radiocarbon_Dates_997_1.json +++ b/datasets/CD08_Radiocarbon_Dates_997_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Radiocarbon_Dates_997_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the ages and growth rates of trees determined by radiocarbon dating (14C) in three Amazonia forests. Tree samples were collected from permanent research plots at ZF2 km 34, Manaus, Amazonas, the Catuaba Experimental Farm, Acre, and the km 83 tower site (logged forest site) in the Tapajos National Forest, Para, between 2001-2003.Samples from 97 individual trees were either tree cores (Manaus and Acre) or a combination of tree cores and slabs cut from stems as part of the logging in the Tapajos National Forest (Para). Radiocarbon dating(14C)was used to determine the age and the mean diameter growth increment of samples from individual trees in various diameter size classes. These measurements can be used to verify and extend short-term diameter increment measurements done with dendrometers and to constrain models of tree demography.There is one comma-separated ASCII data file with this data set.", "links": [ { diff --git a/datasets/CD08_Radiocarbon_Dates_Manaus_996_1.json b/datasets/CD08_Radiocarbon_Dates_Manaus_996_1.json index ce21f76eb3..158960e558 100644 --- a/datasets/CD08_Radiocarbon_Dates_Manaus_996_1.json +++ b/datasets/CD08_Radiocarbon_Dates_Manaus_996_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Radiocarbon_Dates_Manaus_996_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the ages and growth rates of trees as determined by radiocarbon dating (14C), selected from a logging operation near the city of Itacoatiara, about 250 km east of Manaus, Brazil in 1997. Samples were collected from forty-four trees from 15 species with a basal diameter greater than 100 cm and prepared for radiocarbon dating by Accelerator Mass Spectrometry (AMS) at Lawrence Livermore National Laboratory. There is one comma-separated ASCII data file with this data set. ", "links": [ { diff --git a/datasets/CD08_Tree_Growth_Manaus_1194_1.json b/datasets/CD08_Tree_Growth_Manaus_1194_1.json index 85bb3f536a..4f0b2e6b4f 100644 --- a/datasets/CD08_Tree_Growth_Manaus_1194_1.json +++ b/datasets/CD08_Tree_Growth_Manaus_1194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Tree_Growth_Manaus_1194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides diameter at breast height (DBH) measurements made of trees in a dense terra-firme tropical moist forest at the ZF-2 Experimental Station, 90 km north of Manaus, Brazil. DBH was measured over two transects (East to West and North to South) which were established in 1996 by the Jacaranda Project (agreement between the National Institute for Research in the Amazon (INPA) and the Japan International Cooperation Agency, JICA). For each tree, a metal dendrometer band was fixed to the trunk and growth in circumference was measured monthly with digital calipers. The transects measured 20-m x 2500-m, and were stratified by plateau, slope, and baixio (lowland areas near small streams). Topography location, distance along the transect, height at which the band was installed, local tree name, and field notes are also provided in the data files. Measurements were taken between June 1999 and December 2001.", "links": [ { diff --git a/datasets/CD08_Tree_Inventory_Ducke_910_1.json b/datasets/CD08_Tree_Inventory_Ducke_910_1.json index 991ca96c7b..ea78544160 100644 --- a/datasets/CD08_Tree_Inventory_Ducke_910_1.json +++ b/datasets/CD08_Tree_Inventory_Ducke_910_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD08_Tree_Inventory_Ducke_910_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes in one data file the common names, base diameters, and calculated tree masses for almost 3,000 trees on a 5 hectare plot (20 x 2,500 m) located in the Ducke Reserve near Manaus, Brazil in the central Amazon. Measurements were taken during October-December 1999. All diameter measurements were taken at 1.3 meters in height (DBH), or above the buttresses or other stem anomalies. Forest structure characteristics such as biomass density, stem density, diameter class distribution, and taxonomic information at the family and perhaps genus level, can be derived from these data.", "links": [ { diff --git a/datasets/CD09_Soils_Veg_Tapajos_1104_1.json b/datasets/CD09_Soils_Veg_Tapajos_1104_1.json index db54bb5bc2..3ec9b2f42c 100644 --- a/datasets/CD09_Soils_Veg_Tapajos_1104_1.json +++ b/datasets/CD09_Soils_Veg_Tapajos_1104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD09_Soils_Veg_Tapajos_1104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of soil and vegetation surveys at four distinct areas within the Tapajos National Forest (TNF), 50 to 100 km south of Santarem, Para, Brazil, in November 1999. At 13 individual sites across the four areas, all located in primary forest, core soil samples at 10, 30 and 50 cm depths were collected and analyzed for dry mass, bulk density, texture, percentage carbon (C), percentage organic matter, and percentage nitrogen (N). At these 13 sites, vegetation was characterized for 250 m long by 10 m wide transects. Biomass was estimated for all stems over 10 cm DBH from allometric relationships for species, measured height, canopy dimension, and diameter. LAI was measured along the transect at 26 points with a LICOR LAI-2000. Canopy foliage samples were collected with a shotgun at dawn and leaf water potential was determined with a pressure chamber. Samples of foliage, wood, bark, fine roots, and litter were analyzed for %N, % C, delta 13C, and delta 15N. There are five comma-delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/CD10_Biometry_Tapajos_854_1.json b/datasets/CD10_Biometry_Tapajos_854_1.json index 0b730a8e76..2c93044914 100644 --- a/datasets/CD10_Biometry_Tapajos_854_1.json +++ b/datasets/CD10_Biometry_Tapajos_854_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_Biometry_Tapajos_854_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data sets contains a single text file which reports biometry measurements of the old-growth upland forest at the Parao Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from July 1999 through March 2005.To monitor tree woody increment, metal dendrometer bands (Figure 1) were placed on a sub-sample of 1000 trees in December 1999. The data set contains estimates of tree diameter at breast height (cm) based on caliper measurements made approximately every six weeks. The first column of data refers to the tree identification number. For a more detailed description of the biometry study refer to Rice et al. 2004.The data file contains a time series of DBH (cm) values from July 1999 through March 2005.", "links": [ { diff --git a/datasets/CD10_CO2_Profiles_Tapajos_855_1.json b/datasets/CD10_CO2_Profiles_Tapajos_855_1.json index 7014fe2e79..72bd996aed 100644 --- a/datasets/CD10_CO2_Profiles_Tapajos_855_1.json +++ b/datasets/CD10_CO2_Profiles_Tapajos_855_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_CO2_Profiles_Tapajos_855_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Eddy fluxes of CO2 and H2O are measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 analyzers and Campbell CSAT3 sonic anemometers. A third Licor gas analyzer measures (a) the CO2/H2O concentration profile (1 of 8 levels every 2 minutes) and (b) the instantaneous integrated canopy storage of CO2/H2O, using a design pulling air simultaneously through 8 inlets (once every 20 minutes). Comprehensive meteorological data (air temperature, PAR, net radiation, etc) are also included. Pressure and temperature of the Licor cells are controlled to 500 torr and 48 degrees C. Eddy licors are automatically zeroed every 2 hours and the profile licor every 20 minutes. All Licors are automatically calibrated with span gases (at 325, 400, and 475 ppm) every 6 hours.", "links": [ { diff --git a/datasets/CD10_CO_CO2_Maxaranguape_1012_1.json b/datasets/CD10_CO_CO2_Maxaranguape_1012_1.json index 90b8c7b8be..3decb3727f 100644 --- a/datasets/CD10_CO_CO2_Maxaranguape_1012_1.json +++ b/datasets/CD10_CO_CO2_Maxaranguape_1012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_CO_CO2_Maxaranguape_1012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the concentrations of carbon monoxide (CO) and carbon dioxide (CO2), wind direction, wind speed, and air temperature measured at the Maxaranguape Atmospheric Observatory in northeast Brazil, January 4, 2003 - December 27, 2006. The data are 30-minute averages. The concentrations observed at Maxaranguape are representative of upstream atmospheric boundary conditions for the Amazon basin and could be used in conjunction with Santarem data and other data sets to estimate regional budgets for these gasses (Kirchhoff et al., 2003). There is one comma-delimited ASCII text file with this data set. ", "links": [ { diff --git a/datasets/CD10_CO_Tapajos_856_1.json b/datasets/CD10_CO_Tapajos_856_1.json index d359434118..a32a8d609a 100644 --- a/datasets/CD10_CO_Tapajos_856_1.json +++ b/datasets/CD10_CO_Tapajos_856_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_CO_Tapajos_856_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a single comma separated text file of half-hourly average CO mixing ratios measured from 2001/04/18 to 2003/08/29 in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.CO concentrations were measured in air drawn from above the canopy top of tower (approx. 64 meters) using a TEI 48CTL instrument modified for increased stability and sensitivity. The sensor was frequently zeroed by passing ambient air over a CO oxidation catalyst. The span was checked 4 times daily by sampling calibration gases at 100 and 500 ppb. Time in the file is given in UTC (decimal date) at the start of each half hour interval.Associated meteorological parameters, CO2 concentrations and micrometerological fluxes are available in LBA-ECO CD-10 CO2 and H2O Eddy Flux Data at km 67 Tower Site, Tapajos National Forest.", "links": [ { diff --git a/datasets/CD10_CWD_Tapajos_858_1.json b/datasets/CD10_CWD_Tapajos_858_1.json index ae23d8130e..3fa8b03df6 100644 --- a/datasets/CD10_CWD_Tapajos_858_1.json +++ b/datasets/CD10_CWD_Tapajos_858_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_CWD_Tapajos_858_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data sets reports properties of fallen course woody debris in an old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from April 2001 through July 2001.Standing and Fallen coarse woody debris (CWD), or necromass were measured in a series of ecological plots at the km 67 eddy flux tower site in the Tapajos National Forest (Figure 2). The data set includes different size classes of debris measured in different plot sizes. Size classes were: 2-10cm (in 64 m2 subplots) , 10-30cm (in 1600 m2 subplots), 30cm (in 38400 m2 subplots), standing (in entire 50m by 1000m transects).", "links": [ { diff --git a/datasets/CD10_DBH_Tapajos_859_1.json b/datasets/CD10_DBH_Tapajos_859_1.json index 2a47000334..d696cadf4f 100644 --- a/datasets/CD10_DBH_Tapajos_859_1.json +++ b/datasets/CD10_DBH_Tapajos_859_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_DBH_Tapajos_859_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data sets reports diameter at breast height (DBH) measurements in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements were made periodically from July 1999 through August 2005.Trees with DBH >35cm were measured for ~2600 trees in four 5ha transects. Trees >10cm were measured in a smaller area (Rice et al., 2004). Measurements were made in 1999, 2001, and 2005. Trees are identified by local common names. A cross reference to scientific names is provided as a companion file.Coarse woody debris and litter samples and measurements were collected same area. See related data sets.", "links": [ { diff --git a/datasets/CD10_EddyFlux_Tapajos_860_1.json b/datasets/CD10_EddyFlux_Tapajos_860_1.json index e90bfa2267..44a1bc1cfc 100644 --- a/datasets/CD10_EddyFlux_Tapajos_860_1.json +++ b/datasets/CD10_EddyFlux_Tapajos_860_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_EddyFlux_Tapajos_860_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports eddy flux measurements of CO2 and H2O exchange and associated meteorological measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006.Eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers (Figure 1). Eddy-flux measurements were made at a sampling rate of 8 Hz and averaged over a 1 hour interval.. A comprehensive set of meteorological parameters (air temperature, pressure, PAR, net radiation, precipitation, etc) were also measured.Co-located measurements included a third Licor gas analyzer that measured (a) the CO2 and H2O concentration profiles at 8 levels in and above the canopy (1 level every 2 minutes) and (b) the instantaneous integrated canopy storage of CO2 and H2O, using a design that pulled air simultaneously through all 8 inlets (once every 20 minutes). See related data sets.With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.", "links": [ { diff --git a/datasets/CD10_H2O_Profiles_Tapajos_861_1.json b/datasets/CD10_H2O_Profiles_Tapajos_861_1.json index 950dae6a9c..343bc9293f 100644 --- a/datasets/CD10_H2O_Profiles_Tapajos_861_1.json +++ b/datasets/CD10_H2O_Profiles_Tapajos_861_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_H2O_Profiles_Tapajos_861_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a single text file which reports vertical profiles of H2O vapor concentrations measured at the Para Western (Santarem) - km 67, Primary Forest Tower Site (Figure 1). This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006.H2O concentrations were measured at 8 levels on the tower (62.2, 50, 39.4, 28.7, 19.6, 10.4, and 0.91 m). Sample air was drawn at 1000 sccm (standard cubic centimeters per minute) through 8 profile inlets in sequence (2 minutes at each level) and then a mixed air sample was simultaneously drawn from all 8 levels to obtain a total column integral (once every 20 minutes) and analyzed with an infrared gas analyzer (IRGA, LI-6262, Licor, Lincoln, NE). Data were averaged over a 1 hour interval. Calibration for H2O used two independent calibrations for the IRGA concentration measurements: (a) the nighttime relationship between ambient temperature measurements and sonic temperature measurements; (b) a chilled mirror dew point hygrometer mounted on the tower. See Appendix A of Hutyra (2007) for addition details about calibration methods. Co-located measurements included eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers. And a comprehensive set of meteorological parameters (air temperature, pressure, PAR, net radiation, precipitation, etc) were also measured. With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.", "links": [ { diff --git a/datasets/CD10_Litter_Tapajos_862_1.json b/datasets/CD10_Litter_Tapajos_862_1.json index 97800d8b6c..19c2964802 100644 --- a/datasets/CD10_Litter_Tapajos_862_1.json +++ b/datasets/CD10_Litter_Tapajos_862_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_Litter_Tapajos_862_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a single text file which reports litter type and mass in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from July 2000 through June 2005.Litter collection began in July 2000 using 40 circular, mesh screen traps (0.43 m diameter, 0.156 m2) randomly located throughout the 19.75-ha tree-survey area (Rice et al., 2004). Approximately every 14 days, litter was collected, sorted, oven dried at 60 degrees C, and weighed. The litterfall from each trap was sorted into (1) leaves, (2) fruits and flowers, (3) wood , <2 cm diameter, and (4) miscellaneous. Data values reported are the mean and standard error of the 40 mass measurements of each of the litter components and the combined total, that have been converted to the reporting units of Mg/ha/yr.", "links": [ { diff --git a/datasets/CD10_Temperature_Profiles_Tapajos_863_1.json b/datasets/CD10_Temperature_Profiles_Tapajos_863_1.json index 4744d89e93..bc93342be3 100644 --- a/datasets/CD10_Temperature_Profiles_Tapajos_863_1.json +++ b/datasets/CD10_Temperature_Profiles_Tapajos_863_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD10_Temperature_Profiles_Tapajos_863_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a single text file which reports temperature measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006 (Figure 1).Air temperature measurements were collected at 8 levels on the tower (61.9, 49.8, 39.1, 28.4, 18.3, 10.1, 2.8, and 0.6 m). Temperature measurements were made with aspirated thermistors (Met One 076B-4 aspiration with YSI 44032 thermistors) and averaged over a 1 hour interval.Co-located measurements included a Licor gas analyzer that measured (a) the CO2 and H2O concentration profiles at 8 levels in and above the canopy (1 level every 2 minutes), (b) the instantaneous integrated canopy storage of CO2 and H2O, using a design that pulled air simultaneously through all 8 inlets (once every 20 minutes), and (c) eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers. A comprehensive set of meteorological parameters (air pressure, PAR, net radiation, precipitation, etc) were also measured. See related data sets.With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.", "links": [ { diff --git a/datasets/CD11_Forest_Degradation_1118_1.json b/datasets/CD11_Forest_Degradation_1118_1.json index 73f513d297..4cee380483 100644 --- a/datasets/CD11_Forest_Degradation_1118_1.json +++ b/datasets/CD11_Forest_Degradation_1118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD11_Forest_Degradation_1118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of vegetation field surveys that measured tree height and diameter at breast height (DBH) in defined size classes at three study sites -- Santarem, Para; Paragominas, Para; and Alo Brasil, Mato Grosso, Brazil, from 2001-2003.At each site, plots and transects within plots, were defined that represented different types of logging and fire treatments, each including one primary forest plot used as a control. Along each transect all trees with more than 30 cm DBH were measured. Dead standing trees were also measured and classified in three classes of decomposition. A 4 m wide transect was used to measure individuals between 10 and 30 cm DBH. Six small subplots were set along each transect to measure regeneration individuals from 2-10 cm DBH and 0-2 cm DBH. DBH is also provided for stumps found in each of the logged forest plots. There are ten comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/CD15_Productivity_1167_1.json b/datasets/CD15_Productivity_1167_1.json index f24640d811..ef8eb3a56b 100644 --- a/datasets/CD15_Productivity_1167_1.json +++ b/datasets/CD15_Productivity_1167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD15_Productivity_1167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides mean leaf area index (LAI), dendrometry band measurements, and litterfall mass from samples collected at the km 67 research site, Topajos National Forest, Para, Brazil. Litterfall collections were from January 23, 2004 through December 3, 2004, dendrometer measurements were monthly between December 2003 and December 2004, and LAI measurements were collected from January 26, 2004 through November 3, 2004.All measurements were taken at the km 67 site in the Tapajos National Forest. This site is situated in an area of Amazonian primary tropical forest belonging to the municipality of Belterra, Para, Brazil. The forest is mostly evergreen with a few deciduous species. The canopy is characterized by large emergent trees up to 55-m tall, with a closed canopy at approximately 40-m; there are few indications of recent anthropogenic disturbance other than hunting trails. Measurement plots (50) were established along 4 transects at the site and within each plot, 5 subplots were established. The longest transect (25 m x 500 m) was the location of 20 (25 m x 25 m) plots. The other 3 transects (25 m x 250 m) contain 10 plots per transect. Note that the assignment of plots to transects is not provided.There are four comma-delimited data files (.csv) with this data set.", "links": [ { diff --git a/datasets/CD17_Forest_Survey_1254_1.json b/datasets/CD17_Forest_Survey_1254_1.json index 1bf2d980eb..1911250937 100644 --- a/datasets/CD17_Forest_Survey_1254_1.json +++ b/datasets/CD17_Forest_Survey_1254_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD17_Forest_Survey_1254_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for diameter at breast height (DBH), tree height, distance from tree stems to the furthest canopy element, and a species survey of secondary forests in Para and Rondonia, Brazil, from 2002-2003. The forest areas were defined as Type A and Type B stands. Measurements were made in the overstory, understory, and midstory of each stand. Type A stands were sampled intensively, with the goal of providing high-fidelity spatial information about the 3-dimensional structure of the stand. These stands were 60 x 60-m (0.36-ha) areas divided into 10 x 10-m grids of uniform clearing and abandonment history and were identifiable from Landsat images. Type B stands were sampled extensively, with the goal of providing unbiased estimates of biomass, along with some information about the vertical structure of the stand and of spatial variability. These stands were polygons of uniform clearing and afforestation history based on multitemporal Landsat imagery, and varied in size and shape. The Landsat files provide classified land cover for each scene and can be used as a time series to evaluate land cover change over time. Each Landsat file is a geolocated land cover map based on 30-m Landsat data. NOTE: There were additional files which could not be archived due to file problems. Data Quality Statement: The Data Center has determined that this data set has missing or incomplete data, metadata, or other documentation resulting in diminished usability of this product. Known Problems: Some unresolved issues remain where data values are inconsistent with the variable descriptions provided with the data set. The site identification and plot identification values are not consistently used in all three data files. The variables are not adequately described.", "links": [ { diff --git a/datasets/CD32_Fluxes_Brazil_1842_2.json b/datasets/CD32_Fluxes_Brazil_1842_2.json index 8e3ec10b62..52062d5dca 100644 --- a/datasets/CD32_Fluxes_Brazil_1842_2.json +++ b/datasets/CD32_Fluxes_Brazil_1842_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD32_Fluxes_Brazil_1842_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a compilation of carbon and energy eddy covariance flux, meteorology, radiation, canopy temperature, humidity, CO2 profiles and soil moisture and temperature profile data that were collected at nine towers across the Brazilian Amazon. Independent investigators provided the data from a variety of flux tower projects over the period 1999 thru 2006. This is Version 2 of the tower data compilation, where the data have been harmonized across projects, additional quality control checks were performed, and have been aggregated to hourly, daily, 16-day, and monthly timesteps. This integrated dataset is intended to facilitate integrative studies and data-model synthesis from a common reference point.", "links": [ { diff --git a/datasets/CD32_LBA_MIP_Drivers_1177_1.json b/datasets/CD32_LBA_MIP_Drivers_1177_1.json index 7c4683269e..a2db5e20dc 100644 --- a/datasets/CD32_LBA_MIP_Drivers_1177_1.json +++ b/datasets/CD32_LBA_MIP_Drivers_1177_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD32_LBA_MIP_Drivers_1177_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides gap-filled meteorological observations from nine Brazilian flux towers for periods between 1999 and 2006. The measurements include: air temperature, specific humidity, module of wind speed, downward long wave and shortwave radiation at the surface, surface pressure, precipitation, and carbon dioxide (CO2). These atmospheric data are provided at 1 hour time-steps. These data were used as the standardized forcing data input for the LBA Model Intercomparison Project (LBA-MIP).The LBA-MIP goal was to gain comparative understanding of ecosystem models that simulate energy, water, and CO2 fluxes over the LBA area. The task was to subject all the models to the same forcing and experimental protocol, and to compare the outputs. The protocol is provided as a companion file, lba_mip_protocol4.0_20100309.pdf.The source meteorological observations for the forcing data, from the nine Brazilian flux towers, were recently published as Saleska, et al. (2013). See related data sets. These source data were gap-filled according to the LBA-MIP standard protocol. Note that the CAX forest tower was not included in the MIP. See the companion file driver_data.pdf for additional gap-filling information.There are 34 data products with this data set and they are provided in both text (.txt) and ALMA-compliant NetCDF (.nc) formats. The files have been compressed into nine *.zip files according to site.", "links": [ { diff --git a/datasets/CD34_Amazon_Hyperion_1064_1.json b/datasets/CD34_Amazon_Hyperion_1064_1.json index 119d6134cd..9427ff5971 100644 --- a/datasets/CD34_Amazon_Hyperion_1064_1.json +++ b/datasets/CD34_Amazon_Hyperion_1064_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD34_Amazon_Hyperion_1064_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 20 multispectral surface reflectance images collected by the EO-1 satellite Hyperion sensor at 30-m resolution and covering the entire Amazon Basin for 2002 - 2005. All images were converted to GeoTiff format for distribution. The respective ENVI *.hdr files are included as companion files and contain image projection and band information.The selected multispectral images were processed using ENVI software as described in Chambers et al. (2009). Bands with uncalibrated wavelengths and those with low spectral response were removed leaving a spectral subset of generally 196 bands (some images have fewer). A cloud mask was developed using 2-d scatter plots of variable reflectance bands to highlight clouds as regions of interest (ROIs), allowing clouds and cloud edges to be masked. A de-streaking algorithm was then applied to the image to reduce variance in balance between the vertical columns. Apparent surface reflectance was calculated for this balanced image using the atmospheric correction algorithm ACORN in 1.5pb mode (AIG-LLC, Boulder, CO). The images (18 of the 20) were georeferenced using the corresponding Advanced Land Imager (ALI) satellite images.", "links": [ { diff --git a/datasets/CD34_Amazon_Landsat_1176_1.json b/datasets/CD34_Amazon_Landsat_1176_1.json index 7f5768c821..a1dc1fb683 100644 --- a/datasets/CD34_Amazon_Landsat_1176_1.json +++ b/datasets/CD34_Amazon_Landsat_1176_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD34_Amazon_Landsat_1176_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of fractional land cover analysis for nonphotosynthetic vegetation (NPV) from two Landsat images of Manaus, Brazil, for October 14, 2004, and for July 29, 2005. Both images are from Landsat 5, path 231, row 62. The Manauas area experienced a squall line with intense downbursts from January 16-18, 2005, that resulted in widespread blowdown and tree mortality. The pre- and post-disturbance Landsat images were obtained and processed using spectral mixture analysis (SMA) in order to investigate forest disturbance and tree mortatility resulting from the downburst. SMA was based on scene-derived end-members of green vegetation (GV, photosynthetically active vegetation), NPV ( wood, dead vegetation, and surface litter), soil, and shade obtained using a pixel purity index (PPI) algorithm (Negron-Juarez et al., 2010). Changes in NPV due to disturbance were calculated by subtracting the 2004 NPV image from the 2005 NPV image. This NPV difference image is provided. There are three image files (.tif) with this data set: The two Landsat images that were georectified and converted to reflectance values and the NPV difference image. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.KNOWN PROBLEMS: Four additional images were needed to make this data set complete but are unavailable. Specifically, the two images resulting from SMA as applied to the Landsat images collected on the 14th of October, 2004 and the 29th of July, 2005 to determine per-pixel fractional abundance of GV, NPV (wood, dead vegetation, and surface litter), soil, and shade and the 2004 NPV and 2005 NPV images that were used to derive the NPV changes image (which we do provide) (Negron-Juarez, et al., 2010).", "links": [ { diff --git a/datasets/CD36_SALDAS_1162_1.json b/datasets/CD36_SALDAS_1162_1.json index efddef0147..7947ee7aea 100644 --- a/datasets/CD36_SALDAS_1162_1.json +++ b/datasets/CD36_SALDAS_1162_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD36_SALDAS_1162_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides South American Land Data Assimilation System (SALDAS) forcing data including atmospheric fields necessary for land surface modeling for South America which are derived by combining modeled and observation based sources. The forcing data cover the entire continent of South America at 0.125 degree resolution and are built around the model-calculated values of air temperature, wind speed and specific humidity at two meters, surface pressure, downward shortwave and longwave surface radiation, and precipitation from the South American Regional Reanalysis (SARR). These SARR data (Aravequia et al. 2007), which were released in 2006 by INPE/CPTEC, are a medium-term, dynamically consistent, high-resolution (0.125 degree), high-frequency, atmospheric dataset covering South America. The forcing data are available at a 3-hourly time step for a 5-year period from 2000 to 2004. There are 60 monthly *.zip files with each zipped file containing ~240 3-hourly time step data files for that particular month in NetCDF format. Each zipped file is approximately one GB in size.", "links": [ { diff --git a/datasets/CD37_Biomass_Landsat_Glas_1145_1.json b/datasets/CD37_Biomass_Landsat_Glas_1145_1.json index ce381c0bc0..fe3dcb5e4a 100644 --- a/datasets/CD37_Biomass_Landsat_Glas_1145_1.json +++ b/datasets/CD37_Biomass_Landsat_Glas_1145_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CD37_Biomass_Landsat_Glas_1145_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides tree age, forest formation, and land cover classification maps, and estimates of landscape-level above-ground live woody biomass (AGLB) for secondary forests in Rondonia, Brazil. The Threshold Age Mapping Algorithm (TAMA) was applied to a densely spaced time series of Landsat images (1975 to 2003) to derive forest type and age classification maps. The AGLB of the secondary forest was estimated by combining the forest classification map with coincident biomass estimates from the Geoscience Laser Altimeter System (GLAS). There are five raster images and three comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/CDA_AR_GEO_J.ROSS_CLIMATOLOGY.json b/datasets/CDA_AR_GEO_J.ROSS_CLIMATOLOGY.json index feefeccb1c..c1acfe9fc9 100644 --- a/datasets/CDA_AR_GEO_J.ROSS_CLIMATOLOGY.json +++ b/datasets/CDA_AR_GEO_J.ROSS_CLIMATOLOGY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDA_AR_GEO_J.ROSS_CLIMATOLOGY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air temperature observations were carried out in Riscos Rink, James Ross Island\nduring 1995 and 1996. Mean annual air temperature, and freezing and thawing\nindices in 1996 were -6.8 degrees C, 274 and 240 degrees C days, respectively. \nThere are more than 100 freeze-thaw days in a year. These conditions favor the\ndevelopment of some kinds of periglacial landforms. The climatic\ngeomorphologic characteristics of James Ross Island are discussed based on\nfreezing and thawing indexes.\n\nThis study was carried out within a Joint Research Program of the\nInstituto Antartico Argentino and the Institute of Low Temperature\nScience, with the logistic support of the DNA and the Fuerza Aerea\nArgentina.", "links": [ { diff --git a/datasets/CDA_AR_GEO_MIOCENE_PLIOCENE_JRI.json b/datasets/CDA_AR_GEO_MIOCENE_PLIOCENE_JRI.json index d38dc8daf3..1d12dd44ee 100644 --- a/datasets/CDA_AR_GEO_MIOCENE_PLIOCENE_JRI.json +++ b/datasets/CDA_AR_GEO_MIOCENE_PLIOCENE_JRI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDA_AR_GEO_MIOCENE_PLIOCENE_JRI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Knowledge of the late Miocene - Pliocene climate of West Antarctica,\nrecorded by sedimentary units within the James Ross Island Volcanic\nGroup, is still fragmentary. Late Miocene glaciomarine deposits at the\nbase of the group in eastern James Ross Island (Hobbs Glacier\nFormation) and Late Pliocene (3 Ma) interglacial strata at its local\ntop on Cockburn Island (Cockburn Island Formation) have been studied\nextensively, but other Neogene sedimentary rocks on James Ross Island\nhave thus far not been considered in great detail. Here, we document\ntwo further occurrences of glaciomarine strata, included in an\nexpanded Hobbs Glacier Formation, which demonstrate the stratigraphic\ncomplexity of the James Ross Island Volcanic Group: reworked\ndiamictites intercalated within the volcanic sequence at Fiordo Bel?n,\nnorthern James Ross Island, are dated by 40Ar/39Ar and 87Sr/86Sr at\nc. 7 Ma (Late Miocene), but massive diamictites which underlie\nvolcanic rocks near Cape Gage, on eastern James Ross Island, yielded\nan Ar - Ar age of <3.1 Ma (Late Pliocene). These age assignments are\nconfirmed by benthic foraminiferal index species of the genus\nAmmoelphidiella. The geological setting and Cassidulina-dominated\nforaminiferal biofacies of the rocks at Fiordo Belen suggest\ndeposition in water depths of 150 - 200 m. The periglacial deposits\nand waterlain tills at Cape Gage were deposited at shallower depths\n(<100 m), as indicated by an abundance of the pectinid bivalve\n\"Zygochlamys\" anderssoni and the epibiotic foram Cibicides\nlobatulus. Macrofaunal and foraminiferal biofacies of glaciomarine and\ninterglacial deposits share many similarities, which suggests that\ntemperature is not the dominant factor in the distribution of late\nNeogene Antarctic biota. Approximately 10 m.y. of Miocene - Pliocene\nclimatic record is preserved within the rock sequence of the James\nRoss Island Volcanic Group. Prevailing glacial conditions were\npunctuated by interglacial conditions around 3 Ma.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSEOP_product_1.json b/datasets/CDDIS_DORIS_IDSEOP_product_1.json index 8cf13c9c4a..16415d552c 100644 --- a/datasets/CDDIS_DORIS_IDSEOP_product_1.json +++ b/datasets/CDDIS_DORIS_IDSEOP_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSEOP_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Earth Orientation Parameters Time Series Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network, including the time series of Earth orientation parameters (EOPs). The IDS Analysis Center Coordinator combines these solutions to produce an official IDS EOP product. The EOP time series are available in text format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDScumulativePositions_product_1.json b/datasets/CDDIS_DORIS_IDScumulativePositions_product_1.json index ff641d2728..68e34b1976 100644 --- a/datasets/CDDIS_DORIS_IDScumulativePositions_product_1.json +++ b/datasets/CDDIS_DORIS_IDScumulativePositions_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDScumulativePositions_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Cumulative Station Position Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute station position solutions for the DORIS beacons supporting the IDS network. The IDS Analysis Center Coordinator combines these individual AC solutions to generate a long-term DORIS position and velocity cumulative solution through a piecewise linear (position+velocity) model to describe the station motions. The cumulative position and velocity solution is obtained from the stacking of the weekly solution files and is then aligned to the current ITRF. The residuals of this stacking are of particular interest since they depict non-linear station motions.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSdpod_product_1.json b/datasets/CDDIS_DORIS_IDSdpod_product_1.json index fcb51a50bc..ab4ff7291d 100644 --- a/datasets/CDDIS_DORIS_IDSdpod_product_1.json +++ b/datasets/CDDIS_DORIS_IDSdpod_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSdpod_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Station Position Product for Precise Orbit Determination from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute station position solutions for the DORIS beacons supporting the IDS network. The IDS Analysis Center Coordinator combines these solutions to produce an official IDS product. This DPOD (DORIS extension of the ITRF for Precise Orbit Determination) solution is a set of coordinates and velocities of all the DORIS tracking stations for Precise Orbit Determination (POD) applications. The combined solution is generated in conjunction with official determination of the International Terrestrial Reference Frame. DPOD solutions are available in SINEX format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSgeocenter_product_1.json b/datasets/CDDIS_DORIS_IDSgeocenter_product_1.json index e1a9737cbd..7f2b54fca2 100644 --- a/datasets/CDDIS_DORIS_IDSgeocenter_product_1.json +++ b/datasets/CDDIS_DORIS_IDSgeocenter_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSgeocenter_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Geocenter Time Series Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network, including the time series of coordinates of the geocenter or the origin of the terrestrial reference frame. The IDS Analysis Center Coordinator combines these solutions to produce an official IDS geocenter product. The geocenter time series are available in text format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSionosphere_product_1.json b/datasets/CDDIS_DORIS_IDSionosphere_product_1.json index a823fbaa16..4da711b1e9 100644 --- a/datasets/CDDIS_DORIS_IDSionosphere_product_1.json +++ b/datasets/CDDIS_DORIS_IDSionosphere_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSionosphere_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Ionospheric Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network. These products include ionospheric products providing time derivatives of the Total Electron Content (TEC) obtained from the DORIS doppler data. These DORIS derived ionospheric products are available in text format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSorbit_products_1.json b/datasets/CDDIS_DORIS_IDSorbit_products_1.json index 385e320c2c..55f4babd55 100644 --- a/datasets/CDDIS_DORIS_IDSorbit_products_1.json +++ b/datasets/CDDIS_DORIS_IDSorbit_products_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSorbit_products_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) Satellite Orbit Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network. These products include orbits of satellites with DORIS receivers onboard. These orbit products are available in SP1 or SP3 orbit format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDStimeseriesPositions_product_1.json b/datasets/CDDIS_DORIS_IDStimeseriesPositions_product_1.json index 708a50f153..41f1446df1 100644 --- a/datasets/CDDIS_DORIS_IDStimeseriesPositions_product_1.json +++ b/datasets/CDDIS_DORIS_IDStimeseriesPositions_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDStimeseriesPositions_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) Station Position Time Series Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis and after producing the weekly SINEX files using the current ITRF, compute station position time series solutions for the DORIS beacons supporting the IDS network. The IDS Analysis Center Coordinator combines these individual AC solutions to generate the official IDS DORIS network time series solution in the IDS STCD (Station Coordinates Difference) format.", "links": [ { diff --git a/datasets/CDDIS_DORIS_IDSweeklyPositions_product_1.json b/datasets/CDDIS_DORIS_IDSweeklyPositions_product_1.json index d91c22f5bf..7fce50dc60 100644 --- a/datasets/CDDIS_DORIS_IDSweeklyPositions_product_1.json +++ b/datasets/CDDIS_DORIS_IDSweeklyPositions_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_IDSweeklyPositions_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) Weekly Station Position Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute weekly station position solutions for the DORIS beacons supporting the IDS network. The IDS Analysis Center Coordinator combines these individual AC solutions in a standard least-squares adjustment to generate the official IDS weekly combined station position solution.", "links": [ { diff --git a/datasets/CDDIS_DORIS_data_cycle_1.json b/datasets/CDDIS_DORIS_data_cycle_1.json index 12895eb238..e090c1a6df 100644 --- a/datasets/CDDIS_DORIS_data_cycle_1.json +++ b/datasets/CDDIS_DORIS_data_cycle_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_data_cycle_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.", "links": [ { diff --git a/datasets/CDDIS_DORIS_data_daily_1.json b/datasets/CDDIS_DORIS_data_daily_1.json index 9dd6740f4a..e3d7a371b3 100644 --- a/datasets/CDDIS_DORIS_data_daily_1.json +++ b/datasets/CDDIS_DORIS_data_daily_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_data_daily_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) Data (multi-day files) from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. The data records also contain information about any corrections that may have been applied during the processing phase, such as for the ionosphere, troposphere, and satellite center of mass, among others. Furthermore, meteorological measurements (e.g., temperature, relative humidity, ground pressure) recorded by instruments co-located with the ground-based beacons are included with the DORIS data and can be used to determine the tropospheric correction. DORIS data in RINEX format are supplied to the data center in daily files and are forwarded with a typical 1-day delay. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/DORIS/DORIS_data_holdings.html.", "links": [ { diff --git a/datasets/CDDIS_DORIS_data_multiday_1.json b/datasets/CDDIS_DORIS_data_multiday_1.json index c881b8d3c3..c97f808a9a 100644 --- a/datasets/CDDIS_DORIS_data_multiday_1.json +++ b/datasets/CDDIS_DORIS_data_multiday_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_data_multiday_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) Data (multi-day files) from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. The data records also contain information about any corrections that may have been applied during the processing phase, such as for the ionosphere, troposphere, and satellite center of mass, among others. Furthermore, meteorological measurements (e.g., temperature, relative humidity, ground pressure) recorded by instruments co-located with the ground-based beacons are included with the DORIS data and can be used to determine the tropospheric correction. DORIS data in the original format are also supplied to the data center in multi-day files, corresponding to the mission\u2019s data processing arc, and are forwarded approximately 20 days after the end of the last observation day contained in the file. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/DORIS/DORIS_data_holdings.html.", "links": [ { diff --git a/datasets/CDDIS_DORIS_data_rinex_1.json b/datasets/CDDIS_DORIS_data_rinex_1.json index 59b880ce05..d4ffdbc7ec 100644 --- a/datasets/CDDIS_DORIS_data_rinex_1.json +++ b/datasets/CDDIS_DORIS_data_rinex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_data_rinex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.", "links": [ { diff --git a/datasets/CDDIS_DORIS_information_1.json b/datasets/CDDIS_DORIS_information_1.json index 995c1370ed..7d51059286 100644 --- a/datasets/CDDIS_DORIS_information_1.json +++ b/datasets/CDDIS_DORIS_information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Data (multi-day files) from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite\u2019s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. The data records also contain information about any corrections that may have been applied during the processing phase, such as for the ionosphere, troposphere, and satellite center of mass, among others. Furthermore, meteorological measurements (e.g., temperature, relative humidity, ground pressure) recorded by instruments co-located with the ground-based beacons are included with the DORIS data and can be used to determine the tropospheric correction. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/DORIS/DORIS_data_and_product_archive.html.", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_geocenter_1.json b/datasets/CDDIS_DORIS_products_geocenter_1.json index 422e7ff133..2c71e7115d 100644 --- a/datasets/CDDIS_DORIS_products_geocenter_1.json +++ b/datasets/CDDIS_DORIS_products_geocenter_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_geocenter_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geocenter determination solutions derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_ionosphere_1.json b/datasets/CDDIS_DORIS_products_ionosphere_1.json index 6822d45cf7..a7f2e4fefa 100644 --- a/datasets/CDDIS_DORIS_products_ionosphere_1.json +++ b/datasets/CDDIS_DORIS_products_ionosphere_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_ionosphere_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionosphere correction values derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_orbit_1.json b/datasets/CDDIS_DORIS_products_orbit_1.json index 251400813a..2265b8fd8f 100644 --- a/datasets/CDDIS_DORIS_products_orbit_1.json +++ b/datasets/CDDIS_DORIS_products_orbit_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_orbit_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise satellite orbits derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_positions_1.json b/datasets/CDDIS_DORIS_products_positions_1.json index ab0ff3abbf..4500363373 100644 --- a/datasets/CDDIS_DORIS_products_positions_1.json +++ b/datasets/CDDIS_DORIS_products_positions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_positions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Station position and velocity solutions (weekly and cumulative) in Software INdependent EXchange (SINEX) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. The solutions include daily values of Earth Orientation Parameters (EOPs). These products are the generated by analysis centers in support of the International DORIS Service (IDS). Time series of station coordinate solutions in Station Coordinate Difference (STCD) are also generated by the IDS analysis centers. Weekly solutions represent the IDS contribution to the International Terrestrial Reference Frame (ITRF) determination.", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_quaternions_1.json b/datasets/CDDIS_DORIS_products_quaternions_1.json index 66385dc170..1a38a54614 100644 --- a/datasets/CDDIS_DORIS_products_quaternions_1.json +++ b/datasets/CDDIS_DORIS_products_quaternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_quaternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite attitude information from satellites with Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) receivers. Files include attitude quaternions for the body of the spacecraft and solar panel angular positions.", "links": [ { diff --git a/datasets/CDDIS_DORIS_products_stcd_1.json b/datasets/CDDIS_DORIS_products_stcd_1.json index 2923696f47..9b99f125e7 100644 --- a/datasets/CDDIS_DORIS_products_stcd_1.json +++ b/datasets/CDDIS_DORIS_products_stcd_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_DORIS_products_stcd_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Station position time series solutions in DORIS Station Coordinate Difference (STCD) format derived from analysis of Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) data. These products are the generated by analysis centers in support of the International DORIS Service (IDS).", "links": [ { diff --git a/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json b/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json index 67428c96f0..fdbc892a76 100644 --- a/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json +++ b/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GLONASS_daily_data_combinednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Combined Broadcast Ephemeris Data (daily files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS broadcast ephemeris files contain one day of GLONASS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per day. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GLONASS_daily_data_compactobs_1.json b/datasets/CDDIS_GLONASS_daily_data_compactobs_1.json index 488f78fe4f..e68379b487 100644 --- a/datasets/CDDIS_GLONASS_daily_data_compactobs_1.json +++ b/datasets/CDDIS_GLONASS_daily_data_compactobs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GLONASS_daily_data_compactobs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30-second sampling, daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS GNSS observation files (compact) contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GLONASS_daily_data_met_1.json b/datasets/CDDIS_GLONASS_daily_data_met_1.json index fe4338de8f..4214092ecb 100644 --- a/datasets/CDDIS_GLONASS_daily_data_met_1.json +++ b/datasets/CDDIS_GLONASS_daily_data_met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GLONASS_daily_data_met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Meteorological Data (daily, 24 hour files) from instruments co-located with Global Navigation Satellite System (GNSS) GLONASS receivers from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily meteorological data files contain one day of meteorological data (temperature, pressure, humidity, etc.) in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GLONASS_daily_data_obs_1.json b/datasets/CDDIS_GLONASS_daily_data_obs_1.json index d3621919df..39fb6ed03d 100644 --- a/datasets/CDDIS_GLONASS_daily_data_obs_1.json +++ b/datasets/CDDIS_GLONASS_daily_data_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GLONASS_daily_data_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System GLONASS Observation Data (30-second sampling, daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS GNSS observation files (un-compacted) contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GLONASS_daily_data_sum_1.json b/datasets/CDDIS_GLONASS_daily_data_sum_1.json index 19a0b1b5cf..0e38aa54d5 100644 --- a/datasets/CDDIS_GLONASS_daily_data_sum_1.json +++ b/datasets/CDDIS_GLONASS_daily_data_sum_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GLONASS_daily_data_sum_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Observation Summary Data (30-second sampling, daily files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily files contain summary information of one day of GPS or multi-GNSS observations (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Daily_Ionosphere_TEC_1.json b/datasets/CDDIS_GNSS_GD_Daily_Ionosphere_TEC_1.json index f40e515404..a003b3b44b 100644 --- a/datasets/CDDIS_GNSS_GD_Daily_Ionosphere_TEC_1.json +++ b/datasets/CDDIS_GNSS_GD_Daily_Ionosphere_TEC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Daily_Ionosphere_TEC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Developed at JPL, GUARDIAN is a near-real-time (NRT) ionospheric monitoring software (Martire et al.). Its main products are NRT total electronic content (TEC) time series, allowing users to explore ionospheric TEC perturbations due to natural and anthropogenic events on Earth. The NRT GUARDIAN time series are validated against well-established post-processing methods. Currently, time series are computed for more than 90 GNSS ground stations distributed around the Pacific Ring of Fire, which monitor the four main GNSS constellations (GPS, Galileo, BDS, and GLONASS).", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json index 7af8342168..f4c3970810 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains antenna phase center locations relative to the GLONASS satellite's center of mass. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json index 9d0824b2f2..8d04d86c87 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains file names and URLs to files containing antenna phase map data used in the real-time GLONASS POD processing. In particular, the IGS ANTEX file name used for the processing is provided. Additional meta data items may include information that identifies the real-time filter source populating the GLONASS real-time POD products (see \"pos\", \"quat\", and \"tdp\" products). The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json index aa18610c62..0189601a1d 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_EOP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of Earth orientation parameters from the IERS Bulletin A for the GLONASS constellation of satellites. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json index 876654280e..252c108fd9 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a high-rate time series of clock biases for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json index 53b851c45d..96a1157d7f 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json index 00d222131d..a75782a72b 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json index 5b11ee30a0..1e405f18ff 100644 --- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a high-rate time series of clock biases for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Centers_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Centers_1.json index 54e2a72a07..333a015fcd 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Centers_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Centers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Centers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product ontains antenna phase center locations relative to the GPS satellite's center of mass. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Map_Meta_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Map_Meta_1.json index ae8bc10595..c42e08dfe9 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Map_Meta_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Map_Meta_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_Antenna_Phase_Map_Meta_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains file names and URLs to files containing antenna phase map data used in the real-time GPS POD processing. In particular, the IGS ANTEX file name used for the processing is provided. Additional meta data items may include information that identifies the real-time filter source populating the GPS real-time POD products (see \"pos\", \"quat\", and \"tdp\" products). The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_EOP_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_EOP_1.json index 5bb36b26e5..ffcfbf827a 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_EOP_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_EOP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_EOP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of Earth orientation parameters from the IERS Bulletin A for the GPS constellation of satellites. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_1sec_clk_corr_1.json index 324bfee87d..efe886515d 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a high-rate time series of clock biases for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json index 6356eb4956..2a6642f9f3 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_Orbits_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_Orbits_1.json index 49980a30b8..dba60053f9 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_Orbits_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_Orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_POD_60sec_Orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_clk_corr_1.json index 363d0bb6cf..ef0625cc1c 100644 --- a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_60sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_GPS_Daily_POD_60sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1.json index 38f1cf889b..013f4e4592 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains antenna phase center locations relative to the Galileo satellite's center of mass. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json index 4254e0de61..83354645b6 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains file names and URLs to files containing antenna phase map data used in the real-time Galileo POD processing. In particular, the IGS ANTEX file name used for the processing is provided. Additional meta data items may include information that identifies the real-time filter source populating the Galileo real-time POD products (see \"pos\", \"quat\", and \"tdp\" products). The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json index 03f2a63494..5354ce6986 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_EOP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of Earth orientation parameters from the IERS Bulletin A for the Galileo constellation of satellites. The product is generated at JPL's Global Differential GPS Operations Centers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json index 7b097e8e73..86e32d6648 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json index f46d35a47e..f9d88f8d51 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json index ec7805000f..f8ce2c0b73 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json index b74801371f..e0e691aed6 100644 --- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json +++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json index 341cd98c34..69023bc1c5 100644 --- a/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a high-rate time series of clock biases for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json index 030247385d..a9c0bbda7c 100644 --- a/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json +++ b/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json index f8a363a857..744f34ccdf 100644 --- a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json index 587eb5a202..d4e036fe49 100644 --- a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json +++ b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GLONASS_POD_60sec_orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json index 1c05e6afe6..a9e5bb489e 100644 --- a/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GPS_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a high-rate time series of clock biases for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1.json b/datasets/CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1.json index 50f8c25fc1..5d178e76da 100644 --- a/datasets/CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1.json +++ b/datasets/CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json index e57cb23e3e..75d4d0238d 100644 --- a/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GPS_POD_60sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json b/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json index 1d087673af..10e603b477 100644 --- a/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json +++ b/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_GPS_POD_60sec_orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json index 2dfa5ce65f..1c1453c7b6 100644 --- a/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json b/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json index 1140d70993..6e309eb3ea 100644 --- a/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json +++ b/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of attitude quaternion components for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json index ef14309890..35ac08a768 100644 --- a/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json +++ b/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of clock biases for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json b/datasets/CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json index 1b9907d0bb..e53439e53a 100644 --- a/datasets/CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json +++ b/datasets/CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_Galileo_POD_60sec_orbits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains a time series of position and velocity components for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACERPFinal_product_1.json b/datasets/CDDIS_GNSS_IGSACERPFinal_product_1.json index b9a500e89c..55d4c37aa1 100644 --- a/datasets/CDDIS_GNSS_IGSACERPFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACERPFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACERPFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Earth Rotation Parameter (ERP) Product from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to generate GNSS-based ERP products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final ERP product. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit, clock, and ERP files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All ERP solution files utilize the IGS ERP file format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACIonosphereVTECRapid_product_1.json b/datasets/CDDIS_GNSS_IGSACIonosphereVTECRapid_product_1.json index 13e483a996..6f47e521ff 100644 --- a/datasets/CDDIS_GNSS_IGSACIonosphereVTECRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACIonosphereVTECRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACIonosphereVTECRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The AC VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they have an availability with a latency of 1-2 days.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACIonosphereVTEC_product_1.json b/datasets/CDDIS_GNSS_IGSACIonosphereVTEC_product_1.json index cff3c7f1aa..2da73de23f 100644 --- a/datasets/CDDIS_GNSS_IGSACIonosphereVTEC_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACIonosphereVTEC_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACIonosphereVTEC_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The AC VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they have an availability with a latency of 3-7 days.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACRFSSCfinal_product_1.json b/datasets/CDDIS_GNSS_IGSACRFSSCfinal_product_1.json index 178eae7f5d..e673d1ceca 100644 --- a/datasets/CDDIS_GNSS_IGSACRFSSCfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACRFSSCfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACRFSSCfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities (no covariance matrix) Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final AC products consist of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACRFfinal_product_1.json b/datasets/CDDIS_GNSS_IGSACRFfinal_product_1.json index 0b3cce578b..9a4ec73dd7 100644 --- a/datasets/CDDIS_GNSS_IGSACRFfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACRFfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACRFfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final AC products consist of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis and are available approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACTroposphereZPD_product_1.json b/datasets/CDDIS_GNSS_IGSACTroposphereZPD_product_1.json index aad9a214bb..6e4eaa0ad9 100644 --- a/datasets/CDDIS_GNSS_IGSACTroposphereZPD_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACTroposphereZPD_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACTroposphereZPD_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Troposphere Zenith Path Delay (ZPD) Product (daily files by station) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce troposphere ZPD estimates for stations of the IGS network. The primary\u00a0troposphere products\u00a0generated from ground-based GNSS data are estimates of total zenith path delay and north/east troposphere gradient. Ancillary measurements of surface pressure and temperature allow the extraction of precipitable water vapor from the total zenith path delay. The IGS Troposphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS troposphere ZPD estimates for many of the stations in the IGS network. The final AC products consist of daily files containing data from each observing station. All ZPD solution files utilize the Solution INdependent EXchange format for combination of TROpospheric estimates (SINEX_TRO) and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACclockFinal_product_1.json b/datasets/CDDIS_GNSS_IGSACclockFinal_product_1.json index 429ee3c7f7..ace94c6c3e 100644 --- a/datasets/CDDIS_GNSS_IGSACclockFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACclockFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACclockFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (30-second granularity, daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The AC clock products consist of daily station and satellite clock solution files, generated on a weekly basis with a delay of approximately 10 days (from the last day of the week). All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACorbitFinal_product_1.json b/datasets/CDDIS_GNSS_IGSACorbitFinal_product_1.json index 0204a55d95..20a0cfe1ca 100644 --- a/datasets/CDDIS_GNSS_IGSACorbitFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACorbitFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACorbitFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Orbit Product (daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined orbit products. The IGS AC orbit products consist of daily orbit files, generated on a weekly basis with a delay of approximately 10 days (from the last day of the week). All orbit solution files utilize the extended standard product-3 (SP3c) format and span 24 hours from 00:00 to 23:45 UTC. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSACsummaryFinal_product_1.json b/datasets/CDDIS_GNSS_IGSACsummaryFinal_product_1.json index 02fbcf8c33..fe5b4b24c7 100644 --- a/datasets/CDDIS_GNSS_IGSACsummaryFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSACsummaryFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSACsummaryFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final orbit, reference frame, combined satellite and receiver clock, and ERP products. The final AC products consist of daily orbit files, generated on a weekly basis with a delay of approximately 13 days from the last day of the week. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily final products.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSCMPsummaryUltraRapid_product_1.json b/datasets/CDDIS_GNSS_IGSCMPsummaryUltraRapid_product_1.json index ebbe9f35c0..d4883120d3 100644 --- a/datasets/CDDIS_GNSS_IGSCMPsummaryUltraRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSCMPsummaryUltraRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSCMPsummaryUltraRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS derived products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS ultra-rapid combined orbit and ERP products. The ultra-rapid orbit and ERP is a sub-daily solution, released four times per day, at 03:00, 09:00, 15:00, and 21:00 UTC (prior to GPS week 1267 they were released twice daily). The solution summary file details information about the generation of the ultra-rapid products and comparison with the individual AC solutions. The reduced latency on availability of these products allows for significantly improved orbit predictions and reduced errors for user applications.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSDCB_product_1.json b/datasets/CDDIS_GNSS_IGSDCB_product_1.json index de64d821f5..363a748382 100644 --- a/datasets/CDDIS_GNSS_IGSDCB_product_1.json +++ b/datasets/CDDIS_GNSS_IGSDCB_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSDCB_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of differential code biases (DCBs) from a network of ground-based Global Navigation Satellite System (GNSS) station and available from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. DCBs are the systematic errors, or biases, between two GNSS code observations at the same or different frequencies. DCBs are required for code-based positioning of GNSS receivers, extracting ionosphere total electron content (TEC), and other applications. Proper knowledge of DCBs is crucial to many navigation applications but also non-navigation applications such as ionospheric analysis and time transfer. With all of the new signals offered by modernized and new GNSS constellations, analysts now require a comprehensive multi-GNSS DCB product. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_differential_code_bias_product.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSDailyIonosphereVTECcomparison_product_1.json b/datasets/CDDIS_GNSS_IGSDailyIonosphereVTECcomparison_product_1.json index 76dfb9a4dd..b067cea79e 100644 --- a/datasets/CDDIS_GNSS_IGSDailyIonosphereVTECcomparison_product_1.json +++ b/datasets/CDDIS_GNSS_IGSDailyIonosphereVTECcomparison_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSDailyIonosphereVTECcomparison_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Ionosphere Vertical Total Electron Content (VTEC) comparison product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The comparison product is used to compare the IGS and AC solutions of generated VTEC maps.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSERPFinal_product_1.json b/datasets/CDDIS_GNSS_IGSERPFinal_product_1.json index 7dbdafe050..49214b0d79 100644 --- a/datasets/CDDIS_GNSS_IGSERPFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSERPFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSERPFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Earth Rotation Parameter (ERP) Product from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to generate GNSS-based ERP products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final ERP product. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit, clock, and ERP files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All ERP solution files utilize the IGS ERP file format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSERPRFcombinedFinal_product_1.json b/datasets/CDDIS_GNSS_IGSERPRFcombinedFinal_product_1.json index 993b48d006..844b044095 100644 --- a/datasets/CDDIS_GNSS_IGSERPRFcombinedFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSERPRFcombinedFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSERPRFcombinedFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Weekly Combined Station Position Earth Rotation Parameter (ERP) product (produced during the reference frame combination) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to generate GNSS-based ERP products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final ERP product. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit, clock, and ERP files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All ERP solution files utilize the IGS ERP file format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSERPRFcumulativeFinal_product_1.json b/datasets/CDDIS_GNSS_IGSERPRFcumulativeFinal_product_1.json index f70de56ce4..ce7203b0d0 100644 --- a/datasets/CDDIS_GNSS_IGSERPRFcumulativeFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSERPRFcumulativeFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSERPRFcumulativeFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Daily Cumulative Earth Rotation Parameter (ERP) product (since GPS week 0860, produced during the reference frame combination) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to generate GNSS-based ERP products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final ERP product. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit, clock, and ERP files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All ERP solution files utilize the IGS ERP file format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSERPRapid_product_1.json b/datasets/CDDIS_GNSS_IGSERPRapid_product_1.json index baa595cc19..73a54497b9 100644 --- a/datasets/CDDIS_GNSS_IGSERPRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSERPRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSERPRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Earth Rotation Product (ERP) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined ERP products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All IGS ERP files utilize the IGS ERP format and span 24 hours from 00:00 to 23:45 UTC. The IGS rapid products have a quality nearly comparable to that of the final products. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSERPUltraRapid_product_1.json b/datasets/CDDIS_GNSS_IGSERPUltraRapid_product_1.json index 0338e48b76..ce7d8ac33c 100644 --- a/datasets/CDDIS_GNSS_IGSERPUltraRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSERPUltraRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSERPUltraRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Ultra-Rapid Earth Rotation Product (ERP) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS Earth Rotation products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS ultra-rapid combined ERP products.\u00a0The ultra-rapid ERP combination is a sub-daily solution, released four times per day, at 03:00, 09:00, 15:00, and 21:00 UTC (prior to GPS week 1267 they were released twice daily). IGS ultra-rapid ERP files are to be used with the IGS ultra-rapid orbit solution files. All ERP solution files utilize the IGS ERP format. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSHRIonosphereVTEC_product_1.json b/datasets/CDDIS_GNSS_IGSHRIonosphereVTEC_product_1.json index 79f2de4a9c..bbe220ae99 100644 --- a/datasets/CDDIS_GNSS_IGSHRIonosphereVTEC_product_1.json +++ b/datasets/CDDIS_GNSS_IGSHRIonosphereVTEC_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSHRIonosphereVTEC_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System a high-rate Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The high-rate VTEC maps are computed with a resolution of every hour or every quarter hour in UT, 5 degrees in longitude and 2.5 degrees in latitude; they are available with a one day latency.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSIonosphereVTECPredicted_product_1.json b/datasets/CDDIS_GNSS_IGSIonosphereVTECPredicted_product_1.json index 457fe3a34d..d51c7da491 100644 --- a/datasets/CDDIS_GNSS_IGSIonosphereVTECPredicted_product_1.json +++ b/datasets/CDDIS_GNSS_IGSIonosphereVTECPredicted_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSIonosphereVTECPredicted_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Predicted Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The predicted VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they are available in a one and a two day predicted product set.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSIonosphereVTECRapid_product_1.json b/datasets/CDDIS_GNSS_IGSIonosphereVTECRapid_product_1.json index 8cd4f2bee5..a201c016b8 100644 --- a/datasets/CDDIS_GNSS_IGSIonosphereVTECRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSIonosphereVTECRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSIonosphereVTECRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The rapid VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they have an availability with a latency of 1-2 days.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSIonosphereVTECfinal_product_1.json b/datasets/CDDIS_GNSS_IGSIonosphereVTECfinal_product_1.json index 6c9471696f..c42aee996f 100644 --- a/datasets/CDDIS_GNSS_IGSIonosphereVTECfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSIonosphereVTECfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSIonosphereVTECfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The final VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they have an availability with a latency of 11 days.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSIonosphereVTECflux_product_1.json b/datasets/CDDIS_GNSS_IGSIonosphereVTECflux_product_1.json index a461169212..1da7f122be 100644 --- a/datasets/CDDIS_GNSS_IGSIonosphereVTECflux_product_1.json +++ b/datasets/CDDIS_GNSS_IGSIonosphereVTECflux_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSIonosphereVTECflux_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System a Ionosphere Vertical Total Electron Content (VTEC) fluctuation measurement product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. These fluctuations in TEC consists of a rate of TEC change index (ROTI) maps which are constructed with the grid of 2 degrees by 2 degrees resolution as a function of the magnetic local time and corrected magnetic latitude. GNSS data are used to determine ROTI maps, the standard deviation of rate of TEC change over a specified time span; ROTI can be used to describe irregularities in the ionosphere. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSIonosphereVTECvalidation_product_1.json b/datasets/CDDIS_GNSS_IGSIonosphereVTECvalidation_product_1.json index 688cd25fc4..5cda2ffbbf 100644 --- a/datasets/CDDIS_GNSS_IGSIonosphereVTECvalidation_product_1.json +++ b/datasets/CDDIS_GNSS_IGSIonosphereVTECvalidation_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSIonosphereVTECvalidation_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Ionosphere Vertical Total Electron Content (VTEC) comparison product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The validation products are used to compare the IGS and AC solutions of generated VTEC maps. There are three types of ionosphere product evaluation/validation products: 1) the upcwWWWW.YYv.Z files provide an evaluation of the final weekly combination solution of VTEC maps with the individual analysis center contributions; 2) the gpsgDDD0.YYi.Z files are provided by the Center for Orbit Determinate (CODE) at the Astronomical Institute at the University of Bern (AIUB) Switzerland; these files contain GPS broadcast ionosphere model for day YYDDD; and 3) the ckmgDDD0.YYi.Z products are computed by CODE using their Klobuchar model, best fitting CODE\u2019s final ionosphere solution, also available from the CDDIS.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSMGEX_product_1.json b/datasets/CDDIS_GNSS_IGSMGEX_product_1.json index 934ca73002..a5fd1085c9 100644 --- a/datasets/CDDIS_GNSS_IGSMGEX_product_1.json +++ b/datasets/CDDIS_GNSS_IGSMGEX_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSMGEX_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System satellite orbit products (daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. These products include satellite orbit, satellite and station clock, and station position solutions. They are generated multi-GNSS data in support of the IGS Multi-GNSS Experiment (MGEX). The observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS and downloaded by analysis centers who generate these MGEX products. More information about the MGEX products is available at: https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_mgex.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFITRResidualsFinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFITRResidualsFinal_product_1.json index e6109001b4..79fb2282b2 100644 --- a/datasets/CDDIS_GNSS_IGSRFITRResidualsFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFITRResidualsFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFITRResidualsFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product Residuals, between daily Analysis Center solutions and the current reference frame, available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. The final station position/velocities residual product consists of the residuals between the AC solutions and combined reference frame solution.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFResidualsFinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFResidualsFinal_product_1.json index de2dc80ee3..6d165cc421 100644 --- a/datasets/CDDIS_GNSS_IGSRFResidualsFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFResidualsFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFResidualsFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product Residuals available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. The final station position/velocities residual product consists of the residuals between the AC solutions and combined reference frame solution.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFSSCfinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFSSCfinal_product_1.json index ba930dd188..f12ab56afc 100644 --- a/datasets/CDDIS_GNSS_IGSRFSSCfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFSSCfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFSSCfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities (no covariance matrix) Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFSummaryFinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFSummaryFinal_product_1.json index adbd34634f..724237b74b 100644 --- a/datasets/CDDIS_GNSS_IGSRFSummaryFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFSummaryFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFSummaryFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Summary Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFcumlativeFinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFcumlativeFinal_product_1.json index 367d0849cb..0b8d8c916e 100644 --- a/datasets/CDDIS_GNSS_IGSRFcumlativeFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFcumlativeFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFcumlativeFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The cumulative final product can contain several position and velocities sets per station over specific time periods to model discontinuities. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFcumlativeResidualsFinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFcumlativeResidualsFinal_product_1.json index 69bc0bad1d..2617b77cb9 100644 --- a/datasets/CDDIS_GNSS_IGSRFcumlativeResidualsFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFcumlativeResidualsFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFcumlativeResidualsFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product Residuals available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. The final station position/velocities residual product consists of the residuals between the AC solutions and cumulative reference frame solution.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFcumulativeSSCfinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFcumulativeSSCfinal_product_1.json index 659f8d5727..e8d4c14cbe 100644 --- a/datasets/CDDIS_GNSS_IGSRFcumulativeSSCfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFcumulativeSSCfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFcumulativeSSCfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Cumulative Station Positions/Velocities (no covariance matrix) Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRFfinal_product_1.json b/datasets/CDDIS_GNSS_IGSRFfinal_product_1.json index c07b019c95..37d6611f3b 100644 --- a/datasets/CDDIS_GNSS_IGSRFfinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRFfinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRFfinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRTClock_product_1.json b/datasets/CDDIS_GNSS_IGSRTClock_product_1.json index b4b7661722..29da76332b 100644 --- a/datasets/CDDIS_GNSS_IGSRTClock_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRTClock_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRTClock_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System satellite and receiver clock combination product (30-second granularity, daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The CDDIS provides access to products generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. IGS analysis centers (ACs) access GNSS real-time data streams to produce GNSS satellite and ground receiver clock values in real-time. The product streams are combination solutions generated by processing individual real-time solutions from participating IGS Real-time ACs. The IGS Real-Time Analysis Center Coordinator (RTACC) uses these individual AC solutions to generate this real-time IGS combined satellite and receiver clock product. The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. This clock solution is a batch combination based on daily clock submissions by these IGS real-time analysis centers and have been provided since February 2009, shortly after real-time streams were routinely available through the IGS Real-Time Pilot Project and prior to the availability of real-time product streams. Clock solution files consist of decoded clock results from the real time stream at 30-second intervals. This combination is a daily solution available approximately one to three days after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRTRapidComparisonSummary_product_1.json b/datasets/CDDIS_GNSS_IGSRTRapidComparisonSummary_product_1.json index ec730febf4..67ab52155c 100644 --- a/datasets/CDDIS_GNSS_IGSRTRapidComparisonSummary_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRTRapidComparisonSummary_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRTRapidComparisonSummary_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of a summary comparing the International GNSS Service (IGS) Real-Time Service (RTS) orbit and clock products from all analysis center and combination streams with the IGS rapid products. Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The CDDIS provides access to products generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. The product streams are combination solutions generated by processing individual Real Time solutions from participating IGS Real-time Analysis Centers. The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. The solution summary files consist of orbit and clock comparisons of all the AC and combination streams against IGS rapid solution. These products have been provided in support of the IGS RTS (previously Real-Time Pilot Project) since February 2009, prior to the availability of real-time product streams. This summary of the combination product is a daily report available approximately one to three days after the end of the previous UTC day.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRTStreamClock_product_1.json b/datasets/CDDIS_GNSS_IGSRTStreamClock_product_1.json index ba48fd81ed..1510f8528a 100644 --- a/datasets/CDDIS_GNSS_IGSRTStreamClock_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRTStreamClock_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRTStreamClock_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System satellite and receiver clock products (10-second granularity, daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. These clock products are generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. The product streams are combination solutions generated by processing individual real time solutions from participating IGS Real-time Analysis Centers (ACs). The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. This derived product solution is one of the RTS solutions generated by decoding the real-time product streams. These files use the real-time data streams that are referred to the satellite center-of-mass (CoM). These clock products have been provided in support of the IGS Real-Time Service (previously Real-Time Pilot Project) since February 2009, prior to the availability of real-time product streams. This combination is a daily solution available approximately one to three days after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRTStreamOrbit_product_1.json b/datasets/CDDIS_GNSS_IGSRTStreamOrbit_product_1.json index 23d8f2b713..a7aede97d6 100644 --- a/datasets/CDDIS_GNSS_IGSRTStreamOrbit_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRTStreamOrbit_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRTStreamOrbit_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System satellite orbit products (daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure.These orbit products are generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. The product streams are combination solutions generated by processing individual real time solutions from participating IGS Real-time Analysis Centers (ACs). The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. This derived product solution is one of the RTS solutions generated by decoding the real-time product streams. These files use the real-time data streams that are referred to the satellite center-of-mass (CoM). These orbit products have been provided in support of the IGS Real-Time Service (previously Real-Time Pilot Project) since February 2009, prior to the availability of real-time product streams. This combination is a daily solution available approximately one to three days after the end of the previous UTC day. All orbit solution files utilize the SP3 format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSRTSummary_product_1.json b/datasets/CDDIS_GNSS_IGSRTSummary_product_1.json index 31367aa539..067b6804af 100644 --- a/datasets/CDDIS_GNSS_IGSRTSummary_product_1.json +++ b/datasets/CDDIS_GNSS_IGSRTSummary_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSRTSummary_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System satellite and receiver clock combination product (30-second granularity, daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The CDDIS provides access to products generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. IGS analysis centers (ACs) access GNSS real-time data streams to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. The product streams are combination solutions generated by processing individual real-time solutions from participating IGS Real-time ACs. The IGS Real-Time Analysis Center Coordinator (RTACC) uses these individual AC solutions to generate this real-time IGS combined satellite and receiver clock product. The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. The solution summary files consist of reports on the combination analysis and the comparison with the IGS rapid solution product. This combination summary is a daily file available approximately one to three days after the end of the previous UTC day.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSTroposphereZPD_product_1.json b/datasets/CDDIS_GNSS_IGSTroposphereZPD_product_1.json index f7edc0ed39..265b0b9c49 100644 --- a/datasets/CDDIS_GNSS_IGSTroposphereZPD_product_1.json +++ b/datasets/CDDIS_GNSS_IGSTroposphereZPD_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSTroposphereZPD_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Troposphere Zenith Path Delay (ZPD) Product (daily files by station) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce troposphere ZPD estimates for stations of the IGS network. The primary\u00a0troposphere products\u00a0generated from ground-based GNSS data are estimates of total zenith path delay and north/east troposphere gradient. Ancillary measurements of surface pressure and temperature allow the extraction of precipitable water vapor from the total zenith path delay. The IGS Troposphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS troposphere ZPD estimates for many of the stations in the IGS network. The final products consist of daily files containing data from each observing station. All ZPD solution files utilize the Solution INdependent EXchange format for combination of TROpospheric estimates (SINEX_TRO) and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSTroposphereZPDv1_product_1.json b/datasets/CDDIS_GNSS_IGSTroposphereZPDv1_product_1.json index 788d3291d9..39eb3a0a5c 100644 --- a/datasets/CDDIS_GNSS_IGSTroposphereZPDv1_product_1.json +++ b/datasets/CDDIS_GNSS_IGSTroposphereZPDv1_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSTroposphereZPDv1_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of version 1 of the Global Navigation Satellite System Final Troposphere Zenith Path Delay (ZPD) Product (daily files by station) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce troposphere ZPD estimates for stations of the IGS network. The primary\u00a0troposphere products\u00a0generated from ground-based GNSS data are estimates of total zenith path delay and north/east troposphere gradient. Ancillary measurements of surface pressure and temperature allow the extraction of precipitable water vapor from the total zenith path delay. The IGS Troposphere Analysis Center Coordinator (ACC) used these individual AC solutions to generate the official version 1 of the IGS troposphere ZPD estimates for many of the stations in the IGS network. The final products consisted of daily files containing data from each observing station. All ZPD solution files utilize the Solution INdependent EXchange format for combination of TROpospheric estimates (SINEX_TRO) and span 24 hours from 00:00 to 23:45 UTC. In late 2006, a new version of the troposphere combination has been generated by the IGS Troposphere Analysis Center Coordinator. More information about the new version can be found under collection CDDIS_GNSS_IGSTroposphereZPD_product.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSclock30Final_product_1.json b/datasets/CDDIS_GNSS_IGSclock30Final_product_1.json index acdb3559e5..259964dc1d 100644 --- a/datasets/CDDIS_GNSS_IGSclock30Final_product_1.json +++ b/datasets/CDDIS_GNSS_IGSclock30Final_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSclock30Final_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (30-second granularity, daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSclock5Final_product_1.json b/datasets/CDDIS_GNSS_IGSclock5Final_product_1.json index 80824d7d46..282054f9b4 100644 --- a/datasets/CDDIS_GNSS_IGSclock5Final_product_1.json +++ b/datasets/CDDIS_GNSS_IGSclock5Final_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSclock5Final_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (5-minute granularity, daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSclockComparisonFinal_product_1.json b/datasets/CDDIS_GNSS_IGSclockComparisonFinal_product_1.json index 1059ed0139..f822e0d0b5 100644 --- a/datasets/CDDIS_GNSS_IGSclockComparisonFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSclockComparisonFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSclockComparisonFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Clock Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the final combined clock products and comparison with the individual AC solutions.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSclockComparisonRapid_product_1.json b/datasets/CDDIS_GNSS_IGSclockComparisonRapid_product_1.json index 490159d284..2f6cef2fc6 100644 --- a/datasets/CDDIS_GNSS_IGSclockComparisonRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSclockComparisonRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSclockComparisonRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Clock Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined satellite and receiver clock products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily rapid combined clock products and comparison with the individual AC solutions. The reduced latency on availability of these products allows for significantly improved orbit predictions and reduced errors for user applications.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSclockRapid_product_1.json b/datasets/CDDIS_GNSS_IGSclockRapid_product_1.json index 087bb56b53..fdff67a34b 100644 --- a/datasets/CDDIS_GNSS_IGSclockRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSclockRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSclockRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Satellite and Receiver Clock Product (30-second granularity, daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined satellite and receiver clock products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSorbitFinal_product_1.json b/datasets/CDDIS_GNSS_IGSorbitFinal_product_1.json index 66535b7ed4..d112d6375a 100644 --- a/datasets/CDDIS_GNSS_IGSorbitFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSorbitFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSorbitFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Orbit Product (daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined orbit products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All orbit solution files utilize the extended standard product-3 (SP3c) format and span 24 hours from 00:00 to 23:45 UTC. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSorbitRapid_product_1.json b/datasets/CDDIS_GNSS_IGSorbitRapid_product_1.json index 01ed622254..2f9c339954 100644 --- a/datasets/CDDIS_GNSS_IGSorbitRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSorbitRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSorbitRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Orbit Product (daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined orbit products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All orbit solution files utilize the extended standard product-3 (SP3c) format and span 24 hours from 00:00 to 23:45 UTC. The IGS rapid products have a quality nearly comparable to that of the final products. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products. ", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSorbitUltraRapid_product_1.json b/datasets/CDDIS_GNSS_IGSorbitUltraRapid_product_1.json index f41a520190..ea40397cb9 100644 --- a/datasets/CDDIS_GNSS_IGSorbitUltraRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSorbitUltraRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSorbitUltraRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Ultra-Rapid Orbit Product (daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS ultra-rapid combined orbit products. The ultra-rapid orbit and clock combination is a sub-daily solution, released four times per day, at 03:00, 09:00, 15:00, and 21:00 UTC (prior to GPS week 1267 they were released twice daily). In this way the average age of the predictions is reduced to 6 hours (compared to 36 hours for the old IGS predicted products and 9 hours for the twice-daily ultra-rapid solutions). IGS ultra-rapid orbit files contain 48 hours of tabulated orbital ephemerides, and the start/stop epochs continuously shift by 6 hours with each update. The first 24 hours of each IGS ultra-rapid orbit are based on the most recent GNSS observational data from the IGS hourly tracking network. At the time of release, the observed orbits have an initial latency of 3 hours. The next 24 hours of each file are predicted orbits, extrapolated from the observed orbits. The orbits within each ultra-rapid product file are, however, continuous at the boundary between the observed and predicted parts. Normally, the predicted orbits between 3 and 9 hours into the second half of each ultra-rapid orbit file are most relevant for true real time applications. All orbit solution files utilize the extended standard product-3 (SP3c) format. The reduced latency on availability of these products allows for significantly improved orbit predictions and reduced errors for user applications.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSsummaryFinal_product_1.json b/datasets/CDDIS_GNSS_IGSsummaryFinal_product_1.json index 4766ca5e03..ae3ca399bb 100644 --- a/datasets/CDDIS_GNSS_IGSsummaryFinal_product_1.json +++ b/datasets/CDDIS_GNSS_IGSsummaryFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSsummaryFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Final Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final orbit, reference frame, combined satellite and receiver clock, and ERP products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily final products.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSsummaryRapid_product_1.json b/datasets/CDDIS_GNSS_IGSsummaryRapid_product_1.json index 2b322da76f..627506123f 100644 --- a/datasets/CDDIS_GNSS_IGSsummaryRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSsummaryRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSsummaryRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined orbit, satellite and receiver clock, and ERP products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily rapid products.", "links": [ { diff --git a/datasets/CDDIS_GNSS_IGSsummaryUltraRapid_product_1.json b/datasets/CDDIS_GNSS_IGSsummaryUltraRapid_product_1.json index 7d02d2adaa..06dc78b31f 100644 --- a/datasets/CDDIS_GNSS_IGSsummaryUltraRapid_product_1.json +++ b/datasets/CDDIS_GNSS_IGSsummaryUltraRapid_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_IGSsummaryUltraRapid_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Global Navigation Satellite System Rapid Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS derived products. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS ultra-rapid combined orbit and ERP products. The ultra-rapid orbit and ERP is a sub-daily solution, released four times per day, at 03:00, 09:00, 15:00, and 21:00 UTC (prior to GPS week 1267 they were released twice daily). The solution summary file details information about the generation of the daily rapid products. The reduced latency on availability of these products allows for significantly improved orbit predictions and reduced errors for user applications.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_1.json b/datasets/CDDIS_GNSS_daily_data_1.json index 1ee8bfecfb..55a0cb615d 100644 --- a/datasets/CDDIS_GNSS_daily_data_1.json +++ b/datasets/CDDIS_GNSS_daily_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS observation files (un-compacted) contain one day of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_beidounav_1.json b/datasets/CDDIS_GNSS_daily_data_beidounav_1.json index 9970b520e7..14be3d1d78 100644 --- a/datasets/CDDIS_GNSS_daily_data_beidounav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_beidounav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_beidounav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Beidou Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily Beidou broadcast ephemeris files contain one day of Beidou broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_combinednav_1.json b/datasets/CDDIS_GNSS_daily_data_combinednav_1.json index 9f47023be2..d2b56e62cd 100644 --- a/datasets/CDDIS_GNSS_daily_data_combinednav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_combinednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_combinednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Combined Broadcast Ephemeris Data (daily files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS broadcast ephemeris files contain one day of mixed GNSS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_compactobs_1.json b/datasets/CDDIS_GNSS_daily_data_compactobs_1.json index e0a431592c..b2a61eb7c0 100644 --- a/datasets/CDDIS_GNSS_daily_data_compactobs_1.json +++ b/datasets/CDDIS_GNSS_daily_data_compactobs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_compactobs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Compact Observation Data (30-second sampling, daily, 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS observation files (compact) contain one day of GPS or multi-GNSS observation (30-second sampling) data in compact RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_galileonav_1.json b/datasets/CDDIS_GNSS_daily_data_galileonav_1.json index fa2b72d795..c3744d7c22 100644 --- a/datasets/CDDIS_GNSS_daily_data_galileonav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_galileonav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_galileonav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Galileo Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily Galileo broadcast ephemeris files contain one day of Galileo broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json b/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json index 45e50cdd30..84736ace00 100644 --- a/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_glonassnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLObal NAvigation Satellite System (GLONASS) Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS broadcast ephemeris files contain one day of GLONASS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json b/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json index 9f36abfc62..ef83773bd0 100644 --- a/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_gpsnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GPS Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GPS broadcast ephemeris files contain one day of GPS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_irnssnav_1.json b/datasets/CDDIS_GNSS_daily_data_irnssnav_1.json index 9cf2b01d74..cc7fec4cda 100644 --- a/datasets/CDDIS_GNSS_daily_data_irnssnav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_irnssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_irnssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Indian Regional Navigation Satellite System (IRNSS) Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily IRNSS broadcast ephemeris files contain one day of IRNSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_met_1.json b/datasets/CDDIS_GNSS_daily_data_met_1.json index bda69f1108..e53862a808 100644 --- a/datasets/CDDIS_GNSS_daily_data_met_1.json +++ b/datasets/CDDIS_GNSS_daily_data_met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Meteorological Data (daily, 24 hour files) from instruments co-located with Global Navigation Satellite System (GNSS) receivers from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily meteorological data files contain one day of meteorological data (temperature, pressure, humidity, etc.) in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_mixednav_1.json b/datasets/CDDIS_GNSS_daily_data_mixednav_1.json index 782206c46e..88872e1edc 100644 --- a/datasets/CDDIS_GNSS_daily_data_mixednav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_mixednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_mixednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Mixed Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS broadcast ephemeris files contain one day of mixed multi-GNSS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_obs_1.json b/datasets/CDDIS_GNSS_daily_data_obs_1.json index b964e40794..597a572a44 100644 --- a/datasets/CDDIS_GNSS_daily_data_obs_1.json +++ b/datasets/CDDIS_GNSS_daily_data_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (30-second sampling, daily 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS observation files (un-compacted) contain one day of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_qzssnav_1.json b/datasets/CDDIS_GNSS_daily_data_qzssnav_1.json index f279110de4..d50c35fd03 100644 --- a/datasets/CDDIS_GNSS_daily_data_qzssnav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_qzssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_qzssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Quasi-Zenith Satellite System (QZSS) Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily QZSS broadcast ephemeris files contain one day of QZSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_sbasnav_1.json b/datasets/CDDIS_GNSS_daily_data_sbasnav_1.json index 38d11c6db8..05dab4f026 100644 --- a/datasets/CDDIS_GNSS_daily_data_sbasnav_1.json +++ b/datasets/CDDIS_GNSS_daily_data_sbasnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_sbasnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Satellite-Based Augmentation System (SBAS) Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily SBAS broadcast ephemeris files contain one day of SBAS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_daily_data_sum_1.json b/datasets/CDDIS_GNSS_daily_data_sum_1.json index 95853938e4..2225ebb736 100644 --- a/datasets/CDDIS_GNSS_daily_data_sum_1.json +++ b/datasets/CDDIS_GNSS_daily_data_sum_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_daily_data_sum_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Summary Data (30-second sampling, daily files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily files contain summary information of one day of GPS or multi-GNSS observations (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_glonass_daily_g_1.json b/datasets/CDDIS_GNSS_glonass_daily_g_1.json index 4ba47beee4..14a6860543 100644 --- a/datasets/CDDIS_GNSS_glonass_daily_g_1.json +++ b/datasets/CDDIS_GNSS_glonass_daily_g_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_glonass_daily_g_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GNSS receivers collect the signals from orbiting satellites to determine their location in three dimensions and calculate precise time. GNSS receivers detect, decode, and process both pseudorange (code) and phase transmitted by the GNSS satellites. The satellites transmit the ranging codes on two or more radio-frequency carriers, allowing the locations of GNSS receivers to be determined with varying degrees of accuracy, depending on the receiver and post-processing of the data. The receivers also calculate current local time to high precision facilitating time synchronization applications.\n\nThis dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Combined Broadcast Ephemeris Data (daily files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS broadcast ephemeris files contain one day of GLONASS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per day.", "links": [ { diff --git a/datasets/CDDIS_GNSS_glonass_daily_obs_data_1.json b/datasets/CDDIS_GNSS_glonass_daily_obs_data_1.json index 28959a3e52..80fed1b90d 100644 --- a/datasets/CDDIS_GNSS_glonass_daily_obs_data_1.json +++ b/datasets/CDDIS_GNSS_glonass_daily_obs_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_glonass_daily_obs_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GNSS receivers collect the signals from orbiting satellites to determine their location in three dimensions and calculate precise time. GNSS receivers detect, decode, and process both pseudorange (code) and phase transmitted by the GNSS satellites. The satellites transmit the ranging codes on two or more radio-frequency carriers, allowing the locations of GNSS receivers to be determined with varying degrees of accuracy, depending on the receiver and post-processing of the data. The receivers also calculate current local time to high precision facilitating time synchronization applications.\n\nThis dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30 second sampling, daily, 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS compact observation data files contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_1.json b/datasets/CDDIS_GNSS_highrate_data_1.json index 2b67ca90b7..279d464a39 100644 --- a/datasets/CDDIS_GNSS_highrate_data_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_beidounav_1.json b/datasets/CDDIS_GNSS_highrate_data_beidounav_1.json index 2adb6504cd..2425341aea 100644 --- a/datasets/CDDIS_GNSS_highrate_data_beidounav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_beidounav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_beidounav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Beidou Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The high-rate (1-second) Beidou broadcast ephemeris files contain 15 minutes of Beidou broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json b/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json index 1b4ef43654..eb795deb38 100644 --- a/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_compactobs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (1-second sampling, sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GNSS observation files (compact) contain 15 minutes of GPS or multi-GNSS observation (1-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json b/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json index 4fc494562e..3cb4d7c683 100644 --- a/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_galileonav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Galileo Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly Galileo broadcast ephemeris files contain 15 minutes of Galileo broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json b/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json index 2371550e26..8a7df5a612 100644 --- a/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_glonassnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLObal NAvigation Satellite System (GLONASS) Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GLONASS broadcast ephemeris files contain 15 minutes of GLONASS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json b/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json index c55aaac10b..02d424d830 100644 --- a/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_gpsnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GPS Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GPS broadcast ephemeris files contain 15 minutes of GPS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_irnssnav_1.json b/datasets/CDDIS_GNSS_highrate_data_irnssnav_1.json index 7e04e26690..e3b498df7b 100644 --- a/datasets/CDDIS_GNSS_highrate_data_irnssnav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_irnssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_irnssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Indian Regional Navigation Satellite System (IRNSS) Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The high-rate (1-second) IRNSS broadcast ephemeris files contain 15 minutes of IRNSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_met_1.json b/datasets/CDDIS_GNSS_highrate_data_met_1.json index 2efef8a8de..a27cde2eb4 100644 --- a/datasets/CDDIS_GNSS_highrate_data_met_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Meteorological Data (sub-hourly files) from instruments co-located with Global Navigation Satellite System (GNSS) receivers from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly meteorological data files contain 15 minutes of meteorological data (temperature, pressure, humidity, etc.) in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json b/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json index 033e991673..d2d417df0e 100644 --- a/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_mixednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Mixed Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GNSS broadcast ephemeris files contain 15 minutes of mixed multi-GNSS navigation (1-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_obs_1.json b/datasets/CDDIS_GNSS_highrate_data_obs_1.json index 16cdb99e4a..e504c4dc80 100644 --- a/datasets/CDDIS_GNSS_highrate_data_obs_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (1-second sampling, sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GNSS observation files (un-compacted) contain 15 minutes of GPS or multi-GNSS observation (1-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_qzssnav_1.json b/datasets/CDDIS_GNSS_highrate_data_qzssnav_1.json index ead82cfbc1..d936c4e6c8 100644 --- a/datasets/CDDIS_GNSS_highrate_data_qzssnav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_qzssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_qzssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Quasi-Zenith Satellite System (QZSS) Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly QZSS broadcast ephemeris files contain 15 minutes of QZSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_highrate_data_sbasnav_1.json b/datasets/CDDIS_GNSS_highrate_data_sbasnav_1.json index 168600337e..80145e0997 100644 --- a/datasets/CDDIS_GNSS_highrate_data_sbasnav_1.json +++ b/datasets/CDDIS_GNSS_highrate_data_sbasnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_highrate_data_sbasnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Satellite-Based Augmentation System (SBAS) Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly SBAS broadcast ephemeris files contain 15 minutes of SBAS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_1.json b/datasets/CDDIS_GNSS_hourly_data_1.json index caa91ce3e9..5cd66265dd 100644 --- a/datasets/CDDIS_GNSS_hourly_data_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Navigation Satellite System (GNSS) daily 30-second sampled data available from the Crustal Dynamics Data Information System (CDDIS). Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs) are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure; CDDIS began archiving data from these systems in 2011. These data include hourly files of observation (30-second sampling), broadcast ephemeris, meteorological messages in RINEX format as well as other files (e.g., hourly meteorological data) from a global network of permanent ground-based receivers.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_beidounav_1.json b/datasets/CDDIS_GNSS_hourly_data_beidounav_1.json index a040c451a7..e424c294cb 100644 --- a/datasets/CDDIS_GNSS_hourly_data_beidounav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_beidounav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_beidounav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Beidou Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly Beidou broadcast ephemeris files contain one day of Beidou broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json b/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json index 6134077d81..526ddcb77d 100644 --- a/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_combinednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Combined Broadcast Ephemeris Data (hourly files of all distinct navigation messages received in one day) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GNSS broadcast ephemeris files contain one day of mixed GNSS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json b/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json index 73022fa02d..66d5f42cde 100644 --- a/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_compactobs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (30-second sampling, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GNSS observation files (compact) contain one hour of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per hour per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json b/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json index 554ec20c55..b879913699 100644 --- a/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_galileonav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Galileo Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly Galileo broadcast ephemeris files contain one day of Galileo broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json b/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json index 9ab4f0e3ac..d5c6a287dc 100644 --- a/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_glonassnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLObal NAvigation Satellite System (GLONASS) Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GLONASS broadcast ephemeris files contain one day of GLONASS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json b/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json index 02bcf7a5d1..50e8397eff 100644 --- a/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_gpsnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GPS Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GPS broadcast ephemeris files contain one day of GPS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_irnssnav_1.json b/datasets/CDDIS_GNSS_hourly_data_irnssnav_1.json index 6387ba2023..c9a941e3eb 100644 --- a/datasets/CDDIS_GNSS_hourly_data_irnssnav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_irnssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_irnssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Indian Regional Navigation Satellite System (IRNSS) Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly IRNSS broadcast ephemeris files contain one hour of IRNSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_met_1.json b/datasets/CDDIS_GNSS_hourly_data_met_1.json index 492c02b44b..7c9161cfe9 100644 --- a/datasets/CDDIS_GNSS_hourly_data_met_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Meteorological Data (hourly, 24 hour files) from instruments co-located with Global Navigation Satellite System (GNSS) receivers from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly meteorological data files contain one day of meteorological data (temperature, pressure, humidity, etc.) in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_mixednav_1.json b/datasets/CDDIS_GNSS_hourly_data_mixednav_1.json index 85f954466b..7a4fe2108e 100644 --- a/datasets/CDDIS_GNSS_hourly_data_mixednav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_mixednav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_mixednav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Mixed Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GNSS broadcast ephemeris files contain one hour of mixed multi-GNSS navigation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_obs_1.json b/datasets/CDDIS_GNSS_hourly_data_obs_1.json index d52792ea7d..d2f384a886 100644 --- a/datasets/CDDIS_GNSS_hourly_data_obs_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (30-second sampling, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GNSS observation files (un-compacted) contain one hour of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per hour per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_qzssnav_1.json b/datasets/CDDIS_GNSS_hourly_data_qzssnav_1.json index 2cdb06a2b3..17dbbefea3 100644 --- a/datasets/CDDIS_GNSS_hourly_data_qzssnav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_qzssnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_qzssnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Quasi-Zenith Satellite System (QZSS) Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly QZSS broadcast ephemeris files contain one day of QZSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_hourly_data_sbasnav_1.json b/datasets/CDDIS_GNSS_hourly_data_sbasnav_1.json index 59bcc887ea..a804c9b6df 100644 --- a/datasets/CDDIS_GNSS_hourly_data_sbasnav_1.json +++ b/datasets/CDDIS_GNSS_hourly_data_sbasnav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_hourly_data_sbasnav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Satellite-Based Augmentation System (SBAS) Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly SBAS broadcast ephemeris files contain one day of SBAS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.", "links": [ { diff --git a/datasets/CDDIS_GNSS_information_1.json b/datasets/CDDIS_GNSS_information_1.json index c12810f382..792544d909 100644 --- a/datasets/CDDIS_GNSS_information_1.json +++ b/datasets/CDDIS_GNSS_information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of supporting information for use of ground-based Global Navigation Satellite System (GNSS) data and products from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. More information about these data and products are available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/GNSS_data_and_product_archive.html.\r\n", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_IGS20_1.json b/datasets/CDDIS_GNSS_products_IGS20_1.json index 316f346336..d41bd336c6 100644 --- a/datasets/CDDIS_GNSS_products_IGS20_1.json +++ b/datasets/CDDIS_GNSS_products_IGS20_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_IGS20_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data-derived products are the International GNSS Service (IGS) Analysis Centers' (AC) contribution to the International Terrestrial Reference Frame (ITRF) 2020.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_clocks_final_1.json b/datasets/CDDIS_GNSS_products_clocks_final_1.json index d056f2d0ca..d688ed2f92 100644 --- a/datasets/CDDIS_GNSS_products_clocks_final_1.json +++ b/datasets/CDDIS_GNSS_products_clocks_final_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_clocks_final_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS final clock product (weekly).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_clocks_rapid_1.json b/datasets/CDDIS_GNSS_products_clocks_rapid_1.json index 907b066034..aff2d6bc99 100644 --- a/datasets/CDDIS_GNSS_products_clocks_rapid_1.json +++ b/datasets/CDDIS_GNSS_products_clocks_rapid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_clocks_rapid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS rapid clock product (daily).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_clocks_realtime_1.json b/datasets/CDDIS_GNSS_products_clocks_realtime_1.json index 0480cee3ce..fef07224f3 100644 --- a/datasets/CDDIS_GNSS_products_clocks_realtime_1.json +++ b/datasets/CDDIS_GNSS_products_clocks_realtime_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_clocks_realtime_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_erp_1.json b/datasets/CDDIS_GNSS_products_erp_1.json index 5e36b36b6b..c106c0eee3 100644 --- a/datasets/CDDIS_GNSS_products_erp_1.json +++ b/datasets/CDDIS_GNSS_products_erp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_erp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Rotation Parameters (ERPs) derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS ERP product (weekly).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_ionosphere_final_1.json b/datasets/CDDIS_GNSS_products_ionosphere_final_1.json index 2d5b9fa5ea..17637a6d76 100644 --- a/datasets/CDDIS_GNSS_products_ionosphere_final_1.json +++ b/datasets/CDDIS_GNSS_products_ionosphere_final_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_ionosphere_final_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and receiver clock products derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS analysis coordinator to form the official IGS real-time clock product (daily). This product is used for comparison purposes.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_ionosphere_predicted_1.json b/datasets/CDDIS_GNSS_products_ionosphere_predicted_1.json index 0f04e46916..0ed0fb167a 100644 --- a/datasets/CDDIS_GNSS_products_ionosphere_predicted_1.json +++ b/datasets/CDDIS_GNSS_products_ionosphere_predicted_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_ionosphere_predicted_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS predicted ionopsphere product (daily).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_ionosphere_rapid_1.json b/datasets/CDDIS_GNSS_products_ionosphere_rapid_1.json index 1ca3e08549..fdd0b19cf7 100644 --- a/datasets/CDDIS_GNSS_products_ionosphere_rapid_1.json +++ b/datasets/CDDIS_GNSS_products_ionosphere_rapid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_ionosphere_rapid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionosphere Total Electron Content (TEC) grids derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS ionosphere analysis coordinator to form the official IGS rapid ionosphere product (daily).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_orbit_final_1.json b/datasets/CDDIS_GNSS_products_orbit_final_1.json index 8942ed152b..910a7ddc64 100644 --- a/datasets/CDDIS_GNSS_products_orbit_final_1.json +++ b/datasets/CDDIS_GNSS_products_orbit_final_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_orbit_final_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The IGS Final products are the basis for the IGS reference frame and are intended for those applications demanding high consistency and quality.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_orbit_rapid_1.json b/datasets/CDDIS_GNSS_products_orbit_rapid_1.json index dd12ed18b2..23f56cd20b 100644 --- a/datasets/CDDIS_GNSS_products_orbit_rapid_1.json +++ b/datasets/CDDIS_GNSS_products_orbit_rapid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_orbit_rapid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_orbit_realtime_1.json b/datasets/CDDIS_GNSS_products_orbit_realtime_1.json index 2d422b63b0..474f15e88b 100644 --- a/datasets/CDDIS_GNSS_products_orbit_realtime_1.json +++ b/datasets/CDDIS_GNSS_products_orbit_realtime_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_orbit_realtime_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The orbits derived from real-time data streams are used for comparison purposes.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_orbit_ultrarapid_1.json b/datasets/CDDIS_GNSS_products_orbit_ultrarapid_1.json index e8c08610b4..6b62732f23 100644 --- a/datasets/CDDIS_GNSS_products_orbit_ultrarapid_1.json +++ b/datasets/CDDIS_GNSS_products_orbit_ultrarapid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_orbit_ultrarapid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise satellite orbits derived from analysis of Global Navigation Satellite System (GNSS) data. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. These orbits are determined sub-daily (ultra-rapid generation), daily (rapid generation), and weekly (final IGS product). The IGS Analysis Center Coordinator retrieves these individual solutions and generates the official IGS combined orbit products. The orbits generated by the individual ACs and the combination products generated by the ACCs are available at the CDDIS. These orbits can be used to determine precise coordinates of the observing stations, gravity field parameters, and Earth orientation parameters. The Ultra-rapid products are available for real time and near real time use and include predicted orbit information.", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_positions_1.json b/datasets/CDDIS_GNSS_products_positions_1.json index 65244cd6b7..368cecf36b 100644 --- a/datasets/CDDIS_GNSS_products_positions_1.json +++ b/datasets/CDDIS_GNSS_products_positions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_positions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Weekly station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS reference frame coordinator to form the official IGS station position product (weekly).", "links": [ { diff --git a/datasets/CDDIS_GNSS_products_troposphere_1.json b/datasets/CDDIS_GNSS_products_troposphere_1.json index 7dfbb33831..c156c4ab89 100644 --- a/datasets/CDDIS_GNSS_products_troposphere_1.json +++ b/datasets/CDDIS_GNSS_products_troposphere_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_products_troposphere_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Troposphere Zenith Path Delay (ZPD) values by site derived from analysis of Global Navigation Satellite System (GNSS) data. These products are the generated by analysis centers in support of the International GNSS Service (IGS) and combined by the IGS troposphere analysis coordinator to form the official IGS troposphere product (daily).", "links": [ { diff --git a/datasets/CDDIS_GNSS_realtime_data_1.json b/datasets/CDDIS_GNSS_realtime_data_1.json index ceddb61878..9d9aa1e6be 100644 --- a/datasets/CDDIS_GNSS_realtime_data_1.json +++ b/datasets/CDDIS_GNSS_realtime_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_realtime_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Navigation Satellite System (GNSS) real-time 1 to multi-second sampled data available from the Crustal Dynamics Data Information System (CDDIS). Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS real-time data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs) are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure; CDDIS began streaming real-time data from these systems in 2015. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format.", "links": [ { diff --git a/datasets/CDDIS_GNSS_satellite_data_1.json b/datasets/CDDIS_GNSS_satellite_data_1.json index b571abdea9..9aa732003a 100644 --- a/datasets/CDDIS_GNSS_satellite_data_1.json +++ b/datasets/CDDIS_GNSS_satellite_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_GNSS_satellite_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Navigation Satellite System (GNSS) data consists of the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS) (plus other international systems) data sets. The Global Positioning System, developed by the U.S. Department of Defense, has been fully operational since 1994. GPS consists of a constellation of 24 satellites and three active spares each traveling in a 12 hour circular orbit, 20,200 kilometers above the Earth. The satellites are positioned so that six are observable nearly 100 percent of the time from any point on the Earth. The GLObal NAvigation Satellite System (GLONASS), managed and deployed by the Russian Federation, is similar to the U. S. Global Positioning System (GPS) in terms of the satellite constellation, orbits, and signal structure. GNSS receivers detect, decode, and process signals from the GNSS satellites. The satellites transmit the ranging codes on two radio-frequency carriers, allowing the locations of GNSS r", "links": [ { diff --git a/datasets/CDDIS_General_Information_1.json b/datasets/CDDIS_General_Information_1.json index 552501aa2d..4971af1ffd 100644 --- a/datasets/CDDIS_General_Information_1.json +++ b/datasets/CDDIS_General_Information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_General_Information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Crustal Dynamics Data Information System (CDDIS) supports the space geodesy and geodynamics community through NASA's Space Geodesy Project as well as NASA's Earth Science Enterprise. The CDDIS was established in 1982 at NASA's Goddard Space Flight Center as a dedicated data bank to archive and distribute space geodesy related data sets. Today, the CDDIS archives and distributes mainly Global Navigation Satellite Systems (GNSS, currently Global Positioning System GPS and GLObal NAvigation Satellite System GLONASS), laser ranging (both to artificial satellites, SLR, and lunar, LLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS) data for an ever increasing user community of geophysicists. The CDDIS serves as a global data center for the International GNSS Service (IGS) since 1992, the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), International DORIS Service (IDS), and the International Earth Rotation and Reference Systems Service (IERS). General information, including summary reports, data set documentation, etc., are available through the CDDIS archive.", "links": [ { diff --git a/datasets/CDDIS_IERSrspcEOP_product_1.json b/datasets/CDDIS_IERSrspcEOP_product_1.json index ee8dc9c56d..f6c892f6e8 100644 --- a/datasets/CDDIS_IERSrspcEOP_product_1.json +++ b/datasets/CDDIS_IERSrspcEOP_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_IERSrspcEOP_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IERS Rapid Service/Prediction Center is the product center of the International Earth Rotation and Reference Systems Service responsible for providing Earth orientation parameters (EOPs) on a rapid turnaround basis. This service is primarily intended for real-time users and others needing the highest quality EOP information sooner than is available in the IERS final series (Bulletin B) published by the IERS Earth Orientation Center, which is based at the Observatoire de Paris. This Center is an activity of the Earth Orientation (EO) Department at the U.S. Naval Observatory (USNO). \r\n\r\nThe CDDIS is providing a backup mirror of the IERS Rapid Service / Prediction Center (RS/PC) daily and Bulletin A EOP solutions and other Earth orientation results. Daily EOP solutions (including finals.daily, finals2000A.daily and gpsrapid.daily) should be uploaded here at 18:00 UTC each day, and Bulletin A EOP data should by available by 20:00 UTC on Thursdays. Three subdirectories, eop0300utc, eop0900utc, and eop2100utc, are updated daily with intermediate, sub-daily versions of these files, finals.daily, final2000A.daily, and gpsrapid.daily.", "links": [ { diff --git a/datasets/CDDIS_LLR_data_1.json b/datasets/CDDIS_LLR_data_1.json index be2491d26a..51ffb92f9a 100644 --- a/datasets/CDDIS_LLR_data_1.json +++ b/datasets/CDDIS_LLR_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_LLR_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lunar Laser Ranging (LLR) measures the distance between the Earth and the Moon using laser ranging. A short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the moon. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Apollo astronauts and Russian rovers deployed laser ranging retroreflector arrays on the lunar surface which continue to yield fundamental scientific data. Analysis of Lunar Laser Ranging (LLR) data provides information on the lunar orbit, rotation, solid-body tides, and retroreflector locations.", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_coseismic_offsets_1.json b/datasets/CDDIS_MEASURES_products_coseismic_offsets_1.json index 4c3b3e0ac4..227ac3b08e 100644 --- a/datasets/CDDIS_MEASURES_products_coseismic_offsets_1.json +++ b/datasets/CDDIS_MEASURES_products_coseismic_offsets_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_coseismic_offsets_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_daily_time_series_1.json b/datasets/CDDIS_MEASURES_products_daily_time_series_1.json index 7adfbfaa5a..d242189a03 100644 --- a/datasets/CDDIS_MEASURES_products_daily_time_series_1.json +++ b/datasets/CDDIS_MEASURES_products_daily_time_series_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_daily_time_series_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are daily geodetic displacement time series (compressed). They are combined, cleaned and filtered, GIPSY-GAMIT long-term time series of Continuous Global Navigation Satellite System (CGNSS) station positions (global and regional) in the latest version of ITRF\r\n", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_daily_tropo_delay_1.json b/datasets/CDDIS_MEASURES_products_daily_tropo_delay_1.json index 4d02bb8549..ce4ac2abed 100644 --- a/datasets/CDDIS_MEASURES_products_daily_tropo_delay_1.json +++ b/datasets/CDDIS_MEASURES_products_daily_tropo_delay_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_daily_tropo_delay_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These GNSS data products are long-term time series of troposphere delay (5-minute resolution) at geodetic stations, necessarily estimated during position time series production.\r\n\r\n", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_discplacement_grids_1.json b/datasets/CDDIS_MEASURES_products_discplacement_grids_1.json index a0750a1e2f..b2cfb74f57 100644 --- a/datasets/CDDIS_MEASURES_products_discplacement_grids_1.json +++ b/datasets/CDDIS_MEASURES_products_discplacement_grids_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_discplacement_grids_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. ", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_earthquake_displacement_1.json b/datasets/CDDIS_MEASURES_products_earthquake_displacement_1.json index 963c4dff3b..eb7f44c667 100644 --- a/datasets/CDDIS_MEASURES_products_earthquake_displacement_1.json +++ b/datasets/CDDIS_MEASURES_products_earthquake_displacement_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_earthquake_displacement_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These products consist of high-rate displacements at a rate of 1 sample per second or greater. They are used to measure the ground motions when an earthquake occurs.", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_strain_rate_grids_1.json b/datasets/CDDIS_MEASURES_products_strain_rate_grids_1.json index 346c59835a..92216739ee 100644 --- a/datasets/CDDIS_MEASURES_products_strain_rate_grids_1.json +++ b/datasets/CDDIS_MEASURES_products_strain_rate_grids_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_strain_rate_grids_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_transients_1.json b/datasets/CDDIS_MEASURES_products_transients_1.json index 5953d54f57..6e08fcd3a0 100644 --- a/datasets/CDDIS_MEASURES_products_transients_1.json +++ b/datasets/CDDIS_MEASURES_products_transients_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_transients_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products catalog plate boundary aseismic transient deformation with focus in Cascadia, cataloging and parameterizing transient deformation in tectonically active areas known for aseismic transient motion such as episodic tremor and slip (ETS), first discovered in Japan and Cascadia.\r\n", "links": [ { diff --git a/datasets/CDDIS_MEASURES_products_water_storage_1.json b/datasets/CDDIS_MEASURES_products_water_storage_1.json index 6aac5407f4..50b6448d55 100644 --- a/datasets/CDDIS_MEASURES_products_water_storage_1.json +++ b/datasets/CDDIS_MEASURES_products_water_storage_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEASURES_products_water_storage_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are grids of changes in total water storage over the continental U.S.; continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.\r\n", "links": [ { diff --git a/datasets/CDDIS_MEaSURES_products_velocities_1.json b/datasets/CDDIS_MEaSURES_products_velocities_1.json index 4b0111d377..b49128ef72 100644 --- a/datasets/CDDIS_MEaSURES_products_velocities_1.json +++ b/datasets/CDDIS_MEaSURES_products_velocities_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MEaSURES_products_velocities_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA\u2019s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.", "links": [ { diff --git a/datasets/CDDIS_MISC_information_1.json b/datasets/CDDIS_MISC_information_1.json index 5295d7cea6..9e726fea6a 100644 --- a/datasets/CDDIS_MISC_information_1.json +++ b/datasets/CDDIS_MISC_information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_MISC_information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Miscellaneous documentation, information, and ancillary data to be used with GNSS, laser ranging, VLBI and DORIS data and analyzed products available through the CDDIS.", "links": [ { diff --git a/datasets/CDDIS_SLR_DATA_MONTHLYSUM_FR_1.json b/datasets/CDDIS_SLR_DATA_MONTHLYSUM_FR_1.json index 89585a76cf..c0d8f12e04 100644 --- a/datasets/CDDIS_SLR_DATA_MONTHLYSUM_FR_1.json +++ b/datasets/CDDIS_SLR_DATA_MONTHLYSUM_FR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_DATA_MONTHLYSUM_FR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Full-rate data include all valid satellite returns and are thus larger in volume; these data are not routinely provided by all stations in the laser tracking network. Full-rate data are useful for both engineering evaluation and scientific applications (e.g., studying the performance of retroreflectors, discerning satellite signatures, understanding the statistical nature of satellite returns, calibration of satellite targets, validating system quality of laser station co-locations, etc.). Although many of these studies are of an engineering nature, the results have an important impact on the quality of the scientific output. Full-rate data are transmitted in daily files containing all data received in the previous 24-hour period. The CDDIS then updates monthly, satellite-specific files from these daily files. The summary files summarize the data passes of the monthly full-rate data files.\n\nCRD format started testing in 2008 and became operational in January 2011. ILRS/CSTG formats were used for normal point data from 1976 through 2011.", "links": [ { diff --git a/datasets/CDDIS_SLR_ILRSorbitAC_product_1.json b/datasets/CDDIS_SLR_ILRSorbitAC_product_1.json index 1a70dca155..165e11d4e1 100644 --- a/datasets/CDDIS_SLR_ILRSorbitAC_product_1.json +++ b/datasets/CDDIS_SLR_ILRSorbitAC_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_ILRSorbitAC_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Satellite Laser Ranging Final Orbit Product (weekly files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise orbits identifying the position and velocity of satellites equipped with retroreflectors. The individual ILRS AC solutions are used by the Analysis Center Coordinators (ACC) to generate the official ILRS final combined orbit products, as well as backup combination. The final products are considered the most consistent and highest quality ILRS solutions; they consist of weekly orbit files, generated on a weekly basis with a typical delay of 3 days. All orbit solution files utilize the extended standard product-3 (SP3) format and span 7 days from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_SLR_ILRSorbitFinal_product_1.json b/datasets/CDDIS_SLR_ILRSorbitFinal_product_1.json index 3ead850335..63a52f43a9 100644 --- a/datasets/CDDIS_SLR_ILRSorbitFinal_product_1.json +++ b/datasets/CDDIS_SLR_ILRSorbitFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_ILRSorbitFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Satellite Laser Ranging Final Orbit Product (weekly files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise orbits identifying the position and velocity of satellites equipped with retroreflectors. The ILRS Analysis Center Coordinators (ACC) uses these individual AC solutions to generate the official ILRS final combined orbit products, as well as backup combination. The final products are considered the most consistent and highest quality ILRS solutions; they consist of weekly orbit files, generated on a weekly basis with a typical delay of 3 days. All orbit solution files utilize the extended standard product-3 (SP3) format and span 7 days from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_SLR_ILRSpositionERPAC_product_1.json b/datasets/CDDIS_SLR_ILRSpositionERPAC_product_1.json index aba68e7b7b..2d27ba2b36 100644 --- a/datasets/CDDIS_SLR_ILRSpositionERPAC_product_1.json +++ b/datasets/CDDIS_SLR_ILRSpositionERPAC_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_ILRSpositionERPAC_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Satellite Laser Ranging Analysis Center (AC) Station Position plus ERP Product (daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The ACs also generate Earth Orientation Parameters from the SLR data. These individual AC solutions are used by the ILRS Analysis Center Coordinators (ACC) to generate the official ILRS final combined station position plus ERP products, as well as backup combination. The final products are considered the most consistent and highest quality ILRS solutions; they consist of daily station position/ERP files, generated on a daily basis with a typical delay of 2 days. All station position/ERP solution files utilize the Software Independent Exchange (SINEX) format and span 1 day from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_SLR_ILRSpositionERPFinal_product_1.json b/datasets/CDDIS_SLR_ILRSpositionERPFinal_product_1.json index 826e278436..a6e0f5df68 100644 --- a/datasets/CDDIS_SLR_ILRSpositionERPFinal_product_1.json +++ b/datasets/CDDIS_SLR_ILRSpositionERPFinal_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_ILRSpositionERPFinal_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Satellite Laser Ranging Final Station Position plus ERP Product (daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The ACs also generate Earth Orientation Parameters from the SLR data. The ILRS Analysis Center Coordinators (ACC) uses these individual AC solutions to generate the official ILRS final combined station position plus ERP products, as well as backup combination. The final products are considered the most consistent and highest quality ILRS solutions; they consist of daily station position/ERP files, generated on a daily basis with a typical delay of 2 days. All station position/ERP solution files utilize the Software Independent Exchange (SINEX) format and span 1 day from 00:00 to 23:45 UTC.", "links": [ { diff --git a/datasets/CDDIS_SLR_ILRSpredictedOrbit_product_1.json b/datasets/CDDIS_SLR_ILRSpredictedOrbit_product_1.json index 5050c670cd..1565ca8178 100644 --- a/datasets/CDDIS_SLR_ILRSpredictedOrbit_product_1.json +++ b/datasets/CDDIS_SLR_ILRSpredictedOrbit_product_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_ILRSpredictedOrbit_product_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This derived product set consists of Satellite Laser Ranging Predicted Orbit Product (daily files in Consolidated Prediction Format, or CPF) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. ", "links": [ { diff --git a/datasets/CDDIS_SLR_LROLR_LOLA_full_rate_1.json b/datasets/CDDIS_SLR_LROLR_LOLA_full_rate_1.json index b2f83512ff..fdea920916 100644 --- a/datasets/CDDIS_SLR_LROLR_LOLA_full_rate_1.json +++ b/datasets/CDDIS_SLR_LROLR_LOLA_full_rate_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_LROLR_LOLA_full_rate_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lunar Orbiter Laser Altimeter (LOLA) one-way laser ranging full rate data. These files contain the full rate data (all ranges collected) as delivered from the ground stations participating in one way ranging. Each file is an aggregate of full rate data collected for every station on a particular day. Note that this does not constitute the official data delivered by the LOLA mission; for these data, please visit the LOLA Planetary Data System listed in the reference. The ground station only data may be useful for those who wish to do their own transmit-receive pairing from onboard spacecraft data.", "links": [ { diff --git a/datasets/CDDIS_SLR_Mail_ILRS_info_1.json b/datasets/CDDIS_SLR_Mail_ILRS_info_1.json index a617f03eee..c44eaa37d0 100644 --- a/datasets/CDDIS_SLR_Mail_ILRS_info_1.json +++ b/datasets/CDDIS_SLR_Mail_ILRS_info_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_Mail_ILRS_info_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SLRMail is a mail exploder to distribute general information about ILRS related activities to the ILRS community. Each individual message is automatically numbered sequentially and archived for reference.", "links": [ { diff --git a/datasets/CDDIS_SLR_Report_ILRS_reports_1.json b/datasets/CDDIS_SLR_Report_ILRS_reports_1.json index ee2a6a8e87..24d403b8cf 100644 --- a/datasets/CDDIS_SLR_Report_ILRS_reports_1.json +++ b/datasets/CDDIS_SLR_Report_ILRS_reports_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_Report_ILRS_reports_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SLReport is a mail exploder to distribute regular mission related reports (e.g., campaign status reports, weekly LAGEOS reports, etc.). Each individual message is automatically numbered sequentially and archived for reference.", "links": [ { diff --git a/datasets/CDDIS_SLR_SLRF2020_1.json b/datasets/CDDIS_SLR_SLRF2020_1.json index 50fc186e60..1f28ee563a 100644 --- a/datasets/CDDIS_SLR_SLRF2020_1.json +++ b/datasets/CDDIS_SLR_SLRF2020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_SLRF2020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Expanded set of SLR station positions and velocities in the ITRF2020 frame, includes historical sites NOT part of ITRF2020 and some very recently installed sites that came online in 2022. A small number of sites require special treatment with the addition of corrections to their \"mean\" positions (in this file) from the ITRS-distributed PSD model, due to \"events\" (e.g. earthquakes) or changes at the site. Users must apply these corrections cumulatively, to the linearly propagated positions from this file, by themselves. For more details, s/w and relevant correction files please visit the official ITRS site on ITRF2020 at: https://itrf.ign.fr/en/solutions/ITRF2020", "links": [ { diff --git a/datasets/CDDIS_SLR_SLRF2020_DHF_1.json b/datasets/CDDIS_SLR_SLRF2020_DHF_1.json index 94305c06e0..c40a65cf2c 100644 --- a/datasets/CDDIS_SLR_SLRF2020_DHF_1.json +++ b/datasets/CDDIS_SLR_SLRF2020_DHF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_SLRF2020_DHF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Corrections to SLR tracking data collected from various tables on CDDIS, resolutions from the ILRS/ASC (AWG) meetings, the T2L2 @ Jason-2 project (July 2008 to December 2017), the final results of the ILRS Station Systematic Error Monitoring--SSEM project, amended with results from its 2023 extension as an ongoing project, SSEM-X.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_1.json b/datasets/CDDIS_SLR_data_1.json index 204246f518..772aec4d4f 100644 --- a/datasets/CDDIS_SLR_data_1.json +++ b/datasets/CDDIS_SLR_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In Satellite Laser Ranging (SLR), a short pulse of coherent light generated by a laser (Light Amplification by Stimulated Emission of Radiation) is transmitted in a narrow beam to illuminate corner cube retroreflectors on the satellite. The return signal, typically a few photons, is collected by a telescope and the time-of-flight is measured. Using information about the satellite's orbit, the time-of-flight, and the speed of light, the location of the ranging station can be determined. Similar data acquired by another station, many kilometers distant from the first, or on a different continent, can be used to determine the distance between stations to precisions of centimeters or better. Repetitive measurements over months and years yield the change in distance, or the motion of the Earth's crust.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_daily_fr_1.json b/datasets/CDDIS_SLR_data_daily_fr_1.json index fd4e1d50bd..cb114c76e2 100644 --- a/datasets/CDDIS_SLR_data_daily_fr_1.json +++ b/datasets/CDDIS_SLR_data_daily_fr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_daily_fr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (full-rate, daily 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The daily SLR full-rate observation files contain data received in the previous 24-hour period from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Full-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_daily_npt_1.json b/datasets/CDDIS_SLR_data_daily_npt_1.json index 619651246a..70663f359a 100644 --- a/datasets/CDDIS_SLR_data_daily_npt_1.json +++ b/datasets/CDDIS_SLR_data_daily_npt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_daily_npt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (normal points, daily 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The daily SLR normal point observation files contain data received in the previous 24-hour period from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Normal_point_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_hourly_npt_1.json b/datasets/CDDIS_SLR_data_hourly_npt_1.json index 2a1b54bed0..e875f037a6 100644 --- a/datasets/CDDIS_SLR_data_hourly_npt_1.json +++ b/datasets/CDDIS_SLR_data_hourly_npt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_hourly_npt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (normal points, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The daily SLR normal point observation files contain one hour of SLR data received in the previous one hour period from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Normal_point_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_monthly_fr_1.json b/datasets/CDDIS_SLR_data_monthly_fr_1.json index 8823f779e4..f09661923b 100644 --- a/datasets/CDDIS_SLR_data_monthly_fr_1.json +++ b/datasets/CDDIS_SLR_data_monthly_fr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_monthly_fr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (full-rate, monthly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The monthly SLR full-rate observation files contain data received in the month from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Full-rate_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_monthly_npt_1.json b/datasets/CDDIS_SLR_data_monthly_npt_1.json index f690cb0cb3..f2459acc17 100644 --- a/datasets/CDDIS_SLR_data_monthly_npt_1.json +++ b/datasets/CDDIS_SLR_data_monthly_npt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_monthly_npt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (normal points, monthly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The monthly SLR normal point observation files contain one month of SLR data from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Normal_point_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_data_monthlysum_npt_1.json b/datasets/CDDIS_SLR_data_monthlysum_npt_1.json index a6923304a1..2da9c7f12e 100644 --- a/datasets/CDDIS_SLR_data_monthlysum_npt_1.json +++ b/datasets/CDDIS_SLR_data_monthlysum_npt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_data_monthlysum_npt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data summary (normal points, monthly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The monthly SLR normal point observation summary files report on one month of SLR data from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Normal_point_data.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_information_1.json b/datasets/CDDIS_SLR_information_1.json index 1d58ebcf48..4b59877945 100644 --- a/datasets/CDDIS_SLR_information_1.json +++ b/datasets/CDDIS_SLR_information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (normal points, daily 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/SLR_data_and_product_archive.html.", "links": [ { diff --git a/datasets/CDDIS_SLR_predictions_1.json b/datasets/CDDIS_SLR_predictions_1.json index 9d004c72fb..f2b753c139 100644 --- a/datasets/CDDIS_SLR_predictions_1.json +++ b/datasets/CDDIS_SLR_predictions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_predictions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Predicted satellite orbits for Satellite Laser Ranging (SLR) tracking of satellites equipped with corner cube retroreflectors. SLR stations download these prediction files and coordinate tracking schedules for satellite acquisition. The predicted orbit files typically contain orbit information for multiple days and are issued on a daily or sub-daily basis.", "links": [ { diff --git a/datasets/CDDIS_SLR_products_ITRF2020_REPRO2020_1.json b/datasets/CDDIS_SLR_products_ITRF2020_REPRO2020_1.json index af9bc4a69c..4da0a511a9 100644 --- a/datasets/CDDIS_SLR_products_ITRF2020_REPRO2020_1.json +++ b/datasets/CDDIS_SLR_products_ITRF2020_REPRO2020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_products_ITRF2020_REPRO2020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\u201cThe ILRS contribution to ITRF2020 consists of a pair of time series of weekly and bi-weekly station position estimates along with daily and 3-day averaged Earth Orientation Parameters (X-pole, Y-pole and excess Length-Of-Day (LOD)) estimated over 7-day arcs (1993.0 \u2013 2021.0) and 15-day arcs for the period 1983.0-1993.0, aligned to the calendar weeks (Sunday to Saturday), starting from January 1983. Each solution is obtained through the combination of loosely constrained weekly/biweekly solutions submitted by each of the seven official ILRS Analysis Centers. Both, the individual and combined solutions have followed strict standards agreed upon within the ILRS Analysis Standing Committee (ASC) to provide ITRS products of the highest possible quality.\u201d (The ILRS contribution to ITRF2020, E. C. Pavlis (GESTAR II/UMBC & NASA Goddard 61A) and V. Luceri (e-GEOS S.p.A., ASI/CGS))", "links": [ { diff --git a/datasets/CDDIS_SLR_products_orbit_1.json b/datasets/CDDIS_SLR_products_orbit_1.json index 8c803a3f40..d906eaa082 100644 --- a/datasets/CDDIS_SLR_products_orbit_1.json +++ b/datasets/CDDIS_SLR_products_orbit_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_products_orbit_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SLR Satellite Orbit solutions available from the Crustal Dynamics Data Information System (CDDIS). Precise Orbit Determination (POD) solutions in Standard Product 3 (SP3) format derived from analysis of Satellite Laser Ranging (SLR) data. These products are the generated by analysis centers in support of the International Laser Ranging Service (ILRS) and combined by the ILRS analysis coordinator to form the official ILRS orbit product (weekly), available approximately 5 days after the end of the solution week.", "links": [ { diff --git a/datasets/CDDIS_SLR_products_poseop_1.json b/datasets/CDDIS_SLR_products_poseop_1.json index 2ac1f450c3..cd704473cf 100644 --- a/datasets/CDDIS_SLR_products_poseop_1.json +++ b/datasets/CDDIS_SLR_products_poseop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_SLR_products_poseop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Station position and Earth Orientation Parameters (EOPs) solutions derived from analysis of Satellite Laser Ranging (SLR) data. These products are the generated by analysis centers in support of the International Laser Ranging Service (ILRS) and combined by the ILRS analysis coordinator to form the official ILRS EOP and station position product (daily and weekly).", "links": [ { diff --git a/datasets/CDDIS_VLBI_daily_ind_soln_DSNX_1.json b/datasets/CDDIS_VLBI_daily_ind_soln_DSNX_1.json index a0a6051845..93172578e2 100644 --- a/datasets/CDDIS_VLBI_daily_ind_soln_DSNX_1.json +++ b/datasets/CDDIS_VLBI_daily_ind_soln_DSNX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_daily_ind_soln_DSNX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily solution files are an analysis product that provides estimates of Earth orientation and site positions for each 24-hour session, the covariance matrix of the estimates, and decomposed normal equations. The solution files are in SINEX format. The SINEX product files are available on the same frequency as the EOP-S products: 24 hours after each new session data base is available.", "links": [ { diff --git a/datasets/CDDIS_VLBI_daily_int_soln_1.json b/datasets/CDDIS_VLBI_daily_int_soln_1.json index 57dc1fa948..d1144b1006 100644 --- a/datasets/CDDIS_VLBI_daily_int_soln_1.json +++ b/datasets/CDDIS_VLBI_daily_int_soln_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_daily_int_soln_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily Intensive solution files in SINEX format are analysis products that are mainly designed for combination, for both within the IVS and with other space techniques, since they contain datum-free normal equations (decomposed normal equations). Secondly, they allow to estimate UT1 for each 1-hour Intensive session applying a selected datum. Thirdly, the files contain estimates for all parameters (site positions, polar motion, UT1-TAI, and LOD), but these parameters, except UT1-TAI, have been tightly constrained to a priori values. The files are in SINEX format providing also the covariance matrix of the estimates. The SINEX product files are available on the same frequency as the EOP-I products: 24 hours after each new session data base is available.", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_1.json b/datasets/CDDIS_VLBI_data_1.json index 61f59f4b50..524012de61 100644 --- a/datasets/CDDIS_VLBI_data_1.json +++ b/datasets/CDDIS_VLBI_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very Long Baseline Interferometry (VLBI) is a geometric technique: it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. VLBI precisely locates points on Earth by comparing radio signals from a quasar radio source located in deep space received at different times at different Earth ground stations. This method gives relative positions of the Earth stations. VLBI data can be used to calculate information on Earth rotation, length of day, polar motion, baseline and velocities of stations. Products generated by VLBI contribute to research in many areas, including solid Earth, tides, studies of the vertical, fundamental astronomy, and VLBI technique improvement.", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_SWIN_1.json b/datasets/CDDIS_VLBI_data_SWIN_1.json index 0748564e0d..40e819deda 100644 --- a/datasets/CDDIS_VLBI_data_SWIN_1.json +++ b/datasets/CDDIS_VLBI_data_SWIN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_SWIN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SWIN files contain the VLBI fringe visibilities of an observing session (24 hours long or 1 hour long). The files are created by the International VLBI Service for Geodesy and Astrometry (IVS) correlation centers and constitute the raw output of the Distributed FX-style (DiFX) software correlator running on a Swinburne supercomputer. These data form the basis for the fringe fitting process and can also be used to make source maps of the observed quasars. ", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_aux_1.json b/datasets/CDDIS_VLBI_data_aux_1.json index 3319e83d2a..8ad89002c8 100644 --- a/datasets/CDDIS_VLBI_data_aux_1.json +++ b/datasets/CDDIS_VLBI_data_aux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_aux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Very Long Baseline Interferometry (VLBI) auxiliary ASCII files provided by the International VLBI Service for Geodesy and Astrometry (IVS) include schedules, notes, and session log files.", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_db_1.json b/datasets/CDDIS_VLBI_data_db_1.json index ee1b44230b..2972d1d07b 100644 --- a/datasets/CDDIS_VLBI_data_db_1.json +++ b/datasets/CDDIS_VLBI_data_db_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_db_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very Long Baseline Interferometry (VLBI) binary files provided by the International VLBI Service for Geodesy and Astrometry (IVS) in vgosDB format.", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_ngs_1.json b/datasets/CDDIS_VLBI_data_ngs_1.json index 55c4381a9d..be29a0e12b 100644 --- a/datasets/CDDIS_VLBI_data_ngs_1.json +++ b/datasets/CDDIS_VLBI_data_ngs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_ngs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very Long Baseline Interferometry (VLBI) ASCII files in the NGS card format. Very Long Baseline Interferometry (VLBI) auxiliary ASCII files provided by the International VLBI Service for Geodesy and Astrometry (IVS) include schedules, notes, and session log files.", "links": [ { diff --git a/datasets/CDDIS_VLBI_data_vgosDB_1.json b/datasets/CDDIS_VLBI_data_vgosDB_1.json index a4f602295c..f729210fe8 100644 --- a/datasets/CDDIS_VLBI_data_vgosDB_1.json +++ b/datasets/CDDIS_VLBI_data_vgosDB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_data_vgosDB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CDDIS collection is composed of geodetic, Very Long Baseline Array (VLBI) level 2 observational and supporting data (including observations, standard deviations, station coordinates, and more) and derived products which are stored and exchanged in a format named vgosDB, which is the International VLBI Service for Geodesy and Astrometry (IVS) standard format for storing, transmitting, and archiving VLBI data. vgosDB datasets are comprised of NetCDF and ASCII files which contain almost all the information that is required to process a single VLBI session (typically 24-hours of data per single session).", "links": [ { diff --git a/datasets/CDDIS_VLBI_global_soln_trf_1.json b/datasets/CDDIS_VLBI_global_soln_trf_1.json index 3494cdcc96..50e1645825 100644 --- a/datasets/CDDIS_VLBI_global_soln_trf_1.json +++ b/datasets/CDDIS_VLBI_global_soln_trf_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_global_soln_trf_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terrestrial Reference Frame product consists of a set of station positions, velocities, and correlations. A minimum of three years of data is used in each solution. The TRF operational product is available on the IVS Data Centers approximately quarterly.", "links": [ { diff --git a/datasets/CDDIS_VLBI_information_1.json b/datasets/CDDIS_VLBI_information_1.json index 1a66252a6c..eb182e5d32 100644 --- a/datasets/CDDIS_VLBI_information_1.json +++ b/datasets/CDDIS_VLBI_information_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_information_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very Long Baseline Interferometry (VLBI) is a geometric technique: it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. VLBI precisely locates points on Earth by comparing radio signals from a quasar radio source located in deep space received at different times at different Earth ground stations. This method gives relative positions of the Earth stations. VLBI data can be used to calculate information on Earth rotation, length of day, polar motion, baseline and velocities of stations. Products generated by VLBI contribute to research in many areas, including solid Earth, tides, studies of the vertical, fundamental astronomy, and VLBI technique improvement. More information about these data and products are available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/VLBI/VLBI_data_and_product_archive.html.", "links": [ { diff --git a/datasets/CDDIS_VLBI_product_EOPI_1.json b/datasets/CDDIS_VLBI_product_EOPI_1.json index 2793856e63..98476243f5 100644 --- a/datasets/CDDIS_VLBI_product_EOPI_1.json +++ b/datasets/CDDIS_VLBI_product_EOPI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_product_EOPI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These derived data products are intensive (1-hour experiments) Earth orientation parameter (EOPI) solutions obtained with Very Long Baseline Interferometry (VLBI). The CDDIS archive contains EOPI solutions provided by various analysis centers of the International VLBI Service for Geodesy and Astrometry (IVS). The VLBI EOPI series products includes one for each Universal Time (UT1) intensive session with a minimum of one year of data. The operational EOPI product is available at IVS Data Centers 24 hours after the Intensive data become available.", "links": [ { diff --git a/datasets/CDDIS_VLBI_product_trf_1.json b/datasets/CDDIS_VLBI_product_trf_1.json index 590ee41afc..d5d5ab2454 100644 --- a/datasets/CDDIS_VLBI_product_trf_1.json +++ b/datasets/CDDIS_VLBI_product_trf_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_product_trf_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial Reference Frame (TRF) product derived from the analysis of Very Long Baseline Interferometry (VLBI) data. The Terrestrial Reference Frame product includes station positions, velocities, and correlations. A minimum of three years of data are used in each solution. The TRF operational product is available quarterly at International VLBI Service for Geodesy and Astrometry (IVS) Data Centers.", "links": [ { diff --git a/datasets/CDDIS_VLBI_products_crf_1.json b/datasets/CDDIS_VLBI_products_crf_1.json index 45b90d5d00..7be08ebcbb 100644 --- a/datasets/CDDIS_VLBI_products_crf_1.json +++ b/datasets/CDDIS_VLBI_products_crf_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_products_crf_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Celestial Reference Frame (CRF) product derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS CRF product.", "links": [ { diff --git a/datasets/CDDIS_VLBI_products_eop_1.json b/datasets/CDDIS_VLBI_products_eop_1.json index f1ede6ba7f..537aab99fd 100644 --- a/datasets/CDDIS_VLBI_products_eop_1.json +++ b/datasets/CDDIS_VLBI_products_eop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_products_eop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These derived data products are intensive (1-hour experiments) and series Earth orientation parameter (EOPI and EOPS, respectively) solutions obtained with Very Long Baseline Interferometry (VLBI). The CDDIS archive contains EOPI solutions provided by various analysis centers of the International VLBI Service for Geodesy and Astrometry (IVS). The VLBI EOP products include one for each Universal Time (UT1) intensive session with a minimum of one year of data. The operational EOP products are available at IVS Data Centers 24 hours after the Intensive data become available.", "links": [ { diff --git a/datasets/CDDIS_VLBI_products_positions_1.json b/datasets/CDDIS_VLBI_products_positions_1.json index 5c9f0175e7..ff26e0b222 100644 --- a/datasets/CDDIS_VLBI_products_positions_1.json +++ b/datasets/CDDIS_VLBI_products_positions_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_products_positions_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Station positions and velocity solutions in Software INdependent EXchange (SINEX) format derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS) and combined by the IVS analysis coordinator to form the official IVS station position product.", "links": [ { diff --git a/datasets/CDDIS_VLBI_products_troposphere_1.json b/datasets/CDDIS_VLBI_products_troposphere_1.json index a081281564..ed466c2a9f 100644 --- a/datasets/CDDIS_VLBI_products_troposphere_1.json +++ b/datasets/CDDIS_VLBI_products_troposphere_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_products_troposphere_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Troposphere Zenith Path Delay (ZPD) values derived from analysis of Very Long Baseline Interferometry (VLBI) data. These products are the generated by analysis centers in support of the International VLBI Service for Geodesy and Astrometry (IVS).", "links": [ { diff --git a/datasets/CDDIS_VLBI_session_eops_1.json b/datasets/CDDIS_VLBI_session_eops_1.json index 49405cbf77..c7d6cc7db8 100644 --- a/datasets/CDDIS_VLBI_session_eops_1.json +++ b/datasets/CDDIS_VLBI_session_eops_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDDIS_VLBI_session_eops_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Session Series EOP product is a series of EOP results, one for each geodetic session. Data are irregularly spaced and there are multiple results for days on which there were simultaneous sessions. Each series includes a minimum of one year of results. The operational EOP-S product is available on the IVS Data Centers 24 hours after availability of each new session data base.", "links": [ { diff --git a/datasets/CDIAC_AEROSOL_TRENDS93.json b/datasets/CDIAC_AEROSOL_TRENDS93.json index f88a436bf5..90d0d21df6 100644 --- a/datasets/CDIAC_AEROSOL_TRENDS93.json +++ b/datasets/CDIAC_AEROSOL_TRENDS93.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_AEROSOL_TRENDS93", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of direct solar irradiance have been carried out since\n 1977 at each of four baseline atmospheric monitoring stations operated\n by NOAA/CMDL. The four stations are at:\n \n Barrow, Alaska (1977-1992)\n Mauna Loa, Hawaii (1977-1992)\n Samoa, Cape Matatula (1977-1992)\n South Pole, Antarctica (1977-1992)\n \n Monitoring is done by means of a wideband pyrheliometer. Measured\n values are compared with results of solar irradiance calculations to\n derive aerosol optical depth (AOD), defined as the aerosol component\n of the exponent in the exponential decrease in solar beam intensity as\n the beam passes through the atmosphere. The data are presented as\n monthly anomalies in relation to a baseline comprised of all AOD\n values from the nonvolcanic years at a given site, with mean seasonal\n variation removed.\n \n \n Please use the following dataset citation:\n \n Dutton, E.G. 1994. \"Aerosol optical depth measurements from four\n NOAA/CMDL monitoring sites\", pp. 484-492. In T.A. Boden, D.P.\n Kaiser, R.J. Sepanski, and F.W. Stoss (eds.), Trends '93: A Compendium\n of Data on Global Change. ORNL/CDIAC-65. Carbon Dioxide Information\n Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, USA.\n \n CDIAC has provided an anonymous FTP area to all data files, retrieval codes,\n and descriptive files for all data available in TRENDS. The FTP address is\n CDIAC.ESD.ORNL.GOV and 128.219.24.36 and input your email address as the\n password. The data bases are arranged as subdirectories in /pub/trends93/trace\n that correspond to major chapter headings in TRENDS. The data files are\n arranged as xxxx.yyy where xxxx is the name of the station, country, site,\n region, or principle investigator and yyy is the page number in TRENDS '93\n (example: maunaloa.19 refers to the Mauna Loa CO2 dataset tabulated on page\n 19 of TRENDS '93).\n \n \"ftp://cdiac.esd.ornl.gov/pub/trends93/\"", "links": [ { diff --git a/datasets/CDIAC_DB1004.json b/datasets/CDIAC_DB1004.json index 7dc64ee961..479a62be00 100644 --- a/datasets/CDIAC_DB1004.json +++ b/datasets/CDIAC_DB1004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_DB1004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alaskan Historical Climatology Network (HCN) database is a\ncompanion to the Historical Climatology Network (HCN) database for the\ncontiguous United States (CDIAC NDP-019/R3). The Alaskan HCN contains\nmonthly temperature (minimum, maximum, and mean) and total monthly\nprecipitation data for 47 Alaskan stations. The data were derived from\na variety of sources including the National Climatic Data Center\n(NOAA/NCDC) archives, the state climatologist for Alaska, and\npublished literature. The period of record varies by station. The\nlongest record is for the Sitka Magnetic Observatory (beginning in\n1828), and most records extend through 1990.\n\nUnlike the HCN database (NDP-019/R3) for the continuous United States,\nadjustments have not been made to these climate records for\ntime-of-observation differences, instrument changes, or station\nmoves. Users of these data are urged to review information given in\nthe station history file in order to identify stations with suitable\nrecords for their applications.\n\nThe data are contained in three files: a data file containing all four\nclimate variables: monthly minimum, maximum, and mean temperatures,\nand total monthly precipitation; a station history file; and, a\nstation inventory file.\n\nak_hcn.dat - Alaskan HCN Data File (1.64 Mb)\nak_hcn.his - Alaskan HCN Station History File (148 kb)\nak_hcn.sta - Alaskan HCN Station Inventory File (4.0 kb)\n\n\nThe Alaskan HCN Data File consists of station and date information,\ntemperature and precipitation data, monthly data flags (quality,\nlocation), and annual data values.\n\nThe Alaskan HCN Station History File consists of station information\n(number, name, location), station flags (quality, instrument), and\ntimes of observations.\n\n\nThe Alaskan HCN database was contributed to CDIAC by: T.R. Karl,\nR.G. Baldwin, M.G. Burgin, D.R. Easterling, R.W. Knight, and\nP.Y. Hughes of the NOAA/National Climatic Data Center (NCDC) in\nAsheville, NC.", "links": [ { diff --git a/datasets/CDIAC_DB1012.json b/datasets/CDIAC_DB1012.json index cabd893161..a2204f285c 100644 --- a/datasets/CDIAC_DB1012.json +++ b/datasets/CDIAC_DB1012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_DB1012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database (DB-1012) contains estimates of the net flux of\natmospheric-soil carbon dioxide (CO2) produced by the Amiotte Sucjet\nand Probst model(1993) and the associated bicarbonate river flux\n(HCO3-). The data are referenced to a 1 degree by 1 degree global\ngrid.\n\nThe work was done at the Centre National de la Recherche Scientifique\n(CNRS) of Strasborg Cedex, France with the support of the Environment\nProgramme of the European Communities to model the spatial\ndistribution of atmospheric-soil CO2 consumption by chemical\nweathering of continental rocks. The result of the study is the\ndatabase of CO2 consumption and transport of bicarbonate from rivers\nto the ocean in moles per kilometer squared per year (mol km2/yr).\n\nAmiotte Suchet and Probst developed a model that calculates the flux\nof atmospheric-soil CO2 consumed by chemical erosion of continental\nrock (i.e., rock weathering) and the bicarbonate river transfer to the\nocean. The model is based on a set of empirical relationships between\nFCO2 and the drainage (runoff) on the major rock types outcropping on\nthe continents. The model assumes that the consumption of atmospheric\nCO2 by continential weathering is primarily influenced by drainage,\nand the different types of rocks outcropping the continents.\n\nThe estimates of flux in the model are the result of four processes:\nthe identification of the empirical relationships between FCO2 and\ndrainage for major rock types;, the development of a model (GEM-CO2)\nto estimate FCO2 and FHCO3-; the validation of GEM-CVO2 using three\ncase studies; and the global application of GEM-CO2.\n\nIn Phase I, rock types used to identify empirical relationships\ninclude: plutonic & metamorphic; sands & snadstones; acid volcanic;\nevaporitic; basalts; shales; and carbonate.\n\nIn Phase III, the GEM-CO2 model results were validated using three\nlarge river basins: the Amazon and Cingo basins in tropical equatorial\nclimates, and the Garonne (France) in temperate climate.\n\nIn Phase IV, the model results were applied to a global grid. For\neach grid cell, a mean lithology was determined using lithological and\nsoil maps published by the FAO-UNESCO (1971, 1975, 1976, 1978, and\n1981) for each continent. The drainage intensity was calculated after\nWilmott (1985) using mean monthly precipitation data supplied by NCAR.\n\nThe DB-1012 consists of 4 files: a README file; estimates of CO2 and\nHCO3 flux in a global grid (64,800 cells), an exported ARC/INFO (TM\nVersion 7) map, and a FORTRAN 77 program to read the data.\n\nCDIAC has provided an anonymous FTP area to all data files, retrieval\ncodes, and descriptive files for the DB-1012 dataset.\n\nThe FTP address is\n\n\"ftp://cdiac.esd.ornl.gov\"\n\nand input your email address as the password.\n\nThe DB-1012 data are located in 'ftp://cdiac.esd.ornl.gov/pub/db1012'.", "links": [ { diff --git a/datasets/CDIAC_NDP043C.json b/datasets/CDIAC_NDP043C.json index 180213c568..33dd414684 100644 --- a/datasets/CDIAC_NDP043C.json +++ b/datasets/CDIAC_NDP043C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP043C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "[Adapted from the online documentation]\n\nThe Numeric Data Package (NDP-043C) consists of a digital data base\nthat may be used to identify coastlines along the U.S. West Coast that\nare at risk to sea-level rise. This data base integrates point, line,\nand polygon data for the U.S. West Coast into 0.25 degree latitude by\n0.25 degree longitude grid cells and into 1:2,000,000 digitized line\nsegments that can be used by raster or vector geographic information\nsystems (GIS) as well as by non-GIS data bases. Each coastal grid cell\nand line segment contains data variables from the following seven data\nsets: elevation, geology, geomorphology, sea-level trends, shoreline\ndisplacement (erosion/accretion), tidal ranges, and wave heights.\nThese variables may be used to calculate a Coastal Vulnerability Index\n(CVI).\n\nTwo other Coastal Hazards Databases are available from CDIAC:\n\nCoastal Hazards Database for the U.S. East Coast\n\"http://cdiac.esd.ornl.gov/ndps/ndp043a.html\"\n\nCoastal Hazards Database for the U.S. Gulf Cost\n\"http://cdiac.esd.ornl.gov/ndps/ndp043b.html\"\n\nThe data set is available free of charge as a numeric data package\n(NDP) from the Carbon Dioxide Information Analysis Center. The NDP\nconsists of 21 data files including ASCII, ARC/INFO export files,\nFORTRAN, SAS, and documentation files.\n\nCDIAC has provided an anonymous FTP area to all data files, retrieval\ncodes, and descriptive files for the NDP's that are presently\navailable. The FTP address for the ndp043c database is:\n\n\"ftp://cdiac.esd.ornl.gov/pub/ndp043c\"\n\n\nor via anonymous ftp to:\nftp cdiac.esd.ornl.gov\nlogin as \"anonymous\", enter email as password\ncd pub/ndp043c\n\nNDP043C can also be obtained via the WWW:\n\n\"http://cdiac.esd.ornl.gov/ndps/ndp043c.html\"\n\nFull documentation is available online at:\n\"http://cdiac.esd.ornl.gov/epubs/ndp/ndp043c/43c.htm\"", "links": [ { diff --git a/datasets/CDIAC_NDP072_ORNL_CDIAC-120.json b/datasets/CDIAC_NDP072_ORNL_CDIAC-120.json index edd68974b6..b68fac2b58 100644 --- a/datasets/CDIAC_NDP072_ORNL_CDIAC-120.json +++ b/datasets/CDIAC_NDP072_ORNL_CDIAC-120.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP072_ORNL/CDIAC-120", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Numeric Data Package NDP-072 replaces the database DB-1018 previously\navailable from CDIAC. This data base contains enhancements, additional\nquality control and corrections to the data in DB-1018. NDP-072 is a\nmulti-parameter database generated to aid in a statistically rigorous\nsynthesis of research results on the response by woody plants to\nincreased atmospheric CO2 levels. Eighty-four independent\nCO2-enrichment studies, covering 65 species and 35 response\nparameters, met the necessary criteria for inclusion in the database,\nreporting mean response, sample size and variance of the response\n(either as standard deviation or standard error). The data were\nretrieved from published literature and, in a few instances, from\nunpublished reports. The effects of environmental factors (e.g.,\ndrought, heat, ozone, ultraviolet-B radiation), and the effects of\nexperimental conditions (e.g., duration of CO2 exposure, pot size,\ntype of CO2 exposure facility) on plant responses to elevated CO2\nlevels can be explored with this database. The database consists of a\n26-field data file of CO2-exposure experiment responses by woody\nplants, a paper-reference file, a paper-comment file and SAS (and\nFORTRAN-77 codes to read the data file.\n\nThe database and full documentation is available from:\n\"http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html\"", "links": [ { diff --git a/datasets/CDIAC_NDP073.json b/datasets/CDIAC_NDP073.json index 57b9bb6913..2b5e574f2e 100644 --- a/datasets/CDIAC_NDP073.json +++ b/datasets/CDIAC_NDP073.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP073", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Numeric Data Package NDP-073 is a multiparameter database of\nresponses by herbaceous vegetation to increased atmospheric CO2 levels\ncompiled from the literature. Seventy-eight independent CO2-enrichment\nstudies, covering 53 species and 26 response parameters, reported mean\nresponse, sample size, and variance of the response (either as\nstandard deviation or standard error). An additional 43 studies,\ncovering 25 species and 6 response parameters, did not report\nvariances. This numeric data package accompanies the Carbon Dioxide\nInformation Analysis Center's (CDIAC's) NDP- 072\n(\"http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html\"), which\nprovides similar information for woody vegetation.\n\nFor more information, see:\n\"http://cdiac.esd.ornl.gov/epubs/ndp/ndp073/ndp073.html\"", "links": [ { diff --git a/datasets/CDIAC_NDP41_220_2.json b/datasets/CDIAC_NDP41_220_2.json index 77b1f49490..2e8d97e593 100644 --- a/datasets/CDIAC_NDP41_220_2.json +++ b/datasets/CDIAC_NDP41_220_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP41_220_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea level pressure stations, and 1800 station pressure stations. Each station has at least 10 years of data, 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara Desert, Greenland, and Antarctica.", "links": [ { diff --git a/datasets/CDIAC_NDP43A.json b/datasets/CDIAC_NDP43A.json index d7c1339fa9..c4212feaaa 100644 --- a/datasets/CDIAC_NDP43A.json +++ b/datasets/CDIAC_NDP43A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP43A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NDP presents data on coastal geology, geomorphology, elevation, erosion, wave heights, tide ranges, and sea levels for the U.S. east coast. These data may be used either by nongeographic database management systems or by raster or vector geographic information systems (GISs). The database integrates several data sets (originally obtained as point, line, and polygon data) for the east coast into 0.25\u00b0-latitude by 0.25\u00b0-longitude grid cells. Each coastal grid cell contains 28 data variables. This NDP may be used to predict the response of coastal zones on the U.S. east coast to changes in local or global sea levels. Information on the geologic, geomorphic, and erosional states of the coast provides the basic data needed to predict the behavior of the coastal zone into the far future. Thus, these data may be seen as providing a baseline for the calculation of the relative vulnerability of the east coast to projected sea-level rises. This data will also be useful to research, educational, governmental, and private organizations interested in the present and future vulnerability of coastal areas to erosion and inundation. The data are in 13 files, the largest of which is 1.42 MB; the entire data base takes up 3.29 MB, excluding the ARC/INFOTM files.", "links": [ { diff --git a/datasets/CDIAC_NDP43B.json b/datasets/CDIAC_NDP43B.json index 08d2bcba04..a72b916039 100644 --- a/datasets/CDIAC_NDP43B.json +++ b/datasets/CDIAC_NDP43B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_NDP43B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Numeric Data Package (NDP) contains a digital database that\ndescribes the U.S. Gulf Coast. The database integrates point, line,\nand polygon data for the U.S. Gulf Coast into 0.25 latitude by 0.25\nlongitude grid cells and into 1:2,000,000 digitized line segments that\ncan be used by raster or vector geographic information systems (GIS)\nas well as non-GIS database systems. Each coastal grid cell and/or\nline segment contains data on elevation, geology, geomorphology,\nsea-level trends, shoreline displacement (erosion/accretion), tidal\nrange, and wave heights. The database identifies seven of 22 variables\nas relative-risk variables to assess coastal vulnerability. The data\ncan be used to create a coastal vulnerability index for each grid cell\nand/or line segment. The database and corresponding coastal\nvulnerability indices may be used to identify coastal zones that are\nat risk from coastal erosion or possible changes in sea level. The\ndata are contained in five groups, available as ARC/INFO export files\nand as flat ASCII files for a total of 10 files, each less than 2 MB.\n\nThis NDP is related to NDP-043A \"Coastal Hazards Data Base for the\nU.S. East Coast\" submitted by the same investigators as NDP-043B.\n\nAll CDIAC numerical data packages include copies of pertinent literature\ndiscussing the data, summaries discussing the background, source and scope of\nthe data, as well as applications, limitations and restrictions of the data.", "links": [ { diff --git a/datasets/CDIAC_TR051.json b/datasets/CDIAC_TR051.json index a677482ce0..e3352d7bd4 100644 --- a/datasets/CDIAC_TR051.json +++ b/datasets/CDIAC_TR051.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDIAC_TR051", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eischeid Surface Rain Gauge Observations data set consists of\nan inventory of the stations used for the climatology, total monthly\nprecipitation data for 5,328 stations and gridded seasonal\nprecipitation anomalies (in mm) for the period 1851-1989. The data\nwere interpolated to a 4 deg latitude by 5 deg longitude grid\nextending from 60 S to 80 N. The total volume of the data set is\n9.6 Mbytes and is available by ftp.\n\nThe full documentation for this database and all data files are\navailable via CDIAC's world wide web site at\n\"http://cdiac.esd.ornl.gov/ndps/tr051.html\"\n\nThe data files are also available via anonymous FTP. FTP to\n'cdiac.esd.ornl.gov' or 128.219.24.36, enter 'anonymous' as your\nuser id and input your email address as the password. Then change\ndirectories to pub/tr051.\n\"ftp://cdiac.esd.ornl.gov/pub/tr051\"", "links": [ { diff --git a/datasets/CDMO_acemet01-12.02m.json b/datasets/CDMO_acemet01-12.02m.json index 35aa47bfcc..da4b4c1eb5 100644 --- a/datasets/CDMO_acemet01-12.02m.json +++ b/datasets/CDMO_acemet01-12.02m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acemet01-12.02m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR)\nfrom at least one location within or adjacent to the reserve. Data are collected every 5 seconds\nand averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and\ndaily (1440 minutes) intervals. The parameters collected within these intervals are: averages,\nmaximums and minimums of air temperature, relative humidity, barometric pressure, wind speed,\nwind direction, precipitation and photosynthetically active solar radiation.", "links": [ { diff --git a/datasets/CDMO_acemet01-12.03m.json b/datasets/CDMO_acemet01-12.03m.json index cb181f31be..c248c09e77 100644 --- a/datasets/CDMO_acemet01-12.03m.json +++ b/datasets/CDMO_acemet01-12.03m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acemet01-12.03m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological monitoring is conducted at 26 National Estuarine Research\nReserves (NERR) from at least one location within or adjacent to the reserve. Data are collected\nevery 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60\nminutes) and daily (1440 minutes) intervals. The parameters collected within these intervals\nare: averages, maximums and minimums of air temperature, relative humidity, barometric pressure,\nwind speed, wind direction, precipitation and photosynthetically active solar radiation", "links": [ { diff --git a/datasets/CDMO_acemet01-12.04m.json b/datasets/CDMO_acemet01-12.04m.json index 21bcc4907a..760d300bf4 100644 --- a/datasets/CDMO_acemet01-12.04m.json +++ b/datasets/CDMO_acemet01-12.04m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acemet01-12.04m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological monitoring is conducted at 26 National Estuarine Research\n Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected\n every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60\n minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals\n are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure,\n wind speed, wind direction, precipitation and photosynthetically active solar radiation", "links": [ { diff --git a/datasets/CDMO_acemet03-12.01m.json b/datasets/CDMO_acemet03-12.01m.json index 6d089e9286..98b897809d 100644 --- a/datasets/CDMO_acemet03-12.01m.json +++ b/datasets/CDMO_acemet03-12.01m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acemet03-12.01m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological monitoring is conducted at 26 National Estuarine Research\nReserves (NERR) from at least one location within or adjacent to the reserve. Data are collected\nevery 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60\nminutes) and daily (1440 minutes) intervals. The parameters collected within these intervals\nare: averages, maximums and minimums of air temperature, relative humidity, barometric pressure,\nwind speed, wind direction, precipitation and photosynthetically active solar radiation", "links": [ { diff --git a/datasets/CDMO_acenut01-12.02m.json b/datasets/CDMO_acenut01-12.02m.json index 51c4031717..c396ec0714 100644 --- a/datasets/CDMO_acenut01-12.02m.json +++ b/datasets/CDMO_acenut01-12.02m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acenut01-12.02m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological monitoring is conducted at 26 National Estuarine Research\nReserves (NERR) from at least one location within or adjacent to the reserve. Data are collected\nevery 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60\nminutes) and daily (1440 minutes) intervals. The parameters collected within these intervals\nare: averages, maximums and minimums of air temperature, relative humidity, barometric pressure,\nwind speed, wind direction, precipitation and photosynthetically active solar radiation.\n\n\n", "links": [ { diff --git a/datasets/CDMO_acenut01-12.03m.json b/datasets/CDMO_acenut01-12.03m.json index 33223bc4a2..ee38f532ab 100644 --- a/datasets/CDMO_acenut01-12.03m.json +++ b/datasets/CDMO_acenut01-12.03m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acenut01-12.03m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from\nfour locations within or adjacent to the reserve on a monthly basis of the following parameters:\northophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect\nadditional parameters which are available by searching the Yearly Files directory. \n\n", "links": [ { diff --git a/datasets/CDMO_acenut01-12.04m.json b/datasets/CDMO_acenut01-12.04m.json index 4408f98aaf..bc7fc1bda9 100644 --- a/datasets/CDMO_acenut01-12.04m.json +++ b/datasets/CDMO_acenut01-12.04m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acenut01-12.04m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from\n four locations within or adjacent to the reserve on a monthly basis of the following parameters:\n orthophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect\n additional parameters which are available by searching the Yearly Files directory. ", "links": [ { diff --git a/datasets/CDMO_acewq01-12.00m.json b/datasets/CDMO_acewq01-12.00m.json index 1dec68e752..e989565990 100644 --- a/datasets/CDMO_acewq01-12.00m.json +++ b/datasets/CDMO_acewq01-12.00m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.00m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.01m.json b/datasets/CDMO_acewq01-12.01m.json index d13eddf306..a7a8d00c17 100644 --- a/datasets/CDMO_acewq01-12.01m.json +++ b/datasets/CDMO_acewq01-12.01m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.01m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.02m.json b/datasets/CDMO_acewq01-12.02m.json index b8d5772860..5831e122a5 100644 --- a/datasets/CDMO_acewq01-12.02m.json +++ b/datasets/CDMO_acewq01-12.02m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.02m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.04m.json b/datasets/CDMO_acewq01-12.04m.json index 25375f18ff..004bb84f58 100644 --- a/datasets/CDMO_acewq01-12.04m.json +++ b/datasets/CDMO_acewq01-12.04m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.04m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.96m.json b/datasets/CDMO_acewq01-12.96m.json index e7ec4f4d51..f8b994d3f9 100644 --- a/datasets/CDMO_acewq01-12.96m.json +++ b/datasets/CDMO_acewq01-12.96m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.96m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)\n at four locations within or adjacent to the reserve. The following parameters are collected at\n least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation,\n dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers\n will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.97m.json b/datasets/CDMO_acewq01-12.97m.json index 26e9a5bd08..aeddbe4d18 100644 --- a/datasets/CDMO_acewq01-12.97m.json +++ b/datasets/CDMO_acewq01-12.97m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.97m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research\n Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters\n are collected at least every 30 minutes: water temperature, specific conductivity, salinity,\n percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water\n quality data loggers will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.98m.json b/datasets/CDMO_acewq01-12.98m.json index 3ed66016fb..442e32ff92 100644 --- a/datasets/CDMO_acewq01-12.98m.json +++ b/datasets/CDMO_acewq01-12.98m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.98m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)\n at four locations within or adjacent to the reserve. The following parameters are collected at\n least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation,\n dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers\n will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq01-12.99m.json b/datasets/CDMO_acewq01-12.99m.json index 26d9bb0e2c..403835f5ef 100644 --- a/datasets/CDMO_acewq01-12.99m.json +++ b/datasets/CDMO_acewq01-12.99m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq01-12.99m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)\n at four locations within or adjacent to the reserve. The following parameters are collected at\n least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation,\n dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers\n will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CDMO_acewq03-12.95m.json b/datasets/CDMO_acewq03-12.95m.json index c4e93bfebc..a6cd065930 100644 --- a/datasets/CDMO_acewq03-12.95m.json +++ b/datasets/CDMO_acewq03-12.95m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CDMO_acewq03-12.95m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)\n at four locations within or adjacent to the reserve. The following parameters are collected at\n least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation,\n dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers\n will be deployed from a known depth from the bottom at each site.", "links": [ { diff --git a/datasets/CE1d0023_173.json b/datasets/CE1d0023_173.json index 962355f01f..aab491ae2f 100644 --- a/datasets/CE1d0023_173.json +++ b/datasets/CE1d0023_173.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CE1d0023_173", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage represents polygon features that describe the administrative\nboundaries down to Mohtamadeyas.\nOriginal Map name: Administrative boundaries of Mohtamadeyas\nDate of production: not mentioned\nDate collection: 1989", "links": [ { diff --git a/datasets/CE1d0029_173.json b/datasets/CE1d0029_173.json index bbcc17f783..8d67947ed1 100644 --- a/datasets/CE1d0029_173.json +++ b/datasets/CE1d0029_173.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CE1d0029_173", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage represents polygons that describe the\nagroclimatological zones.\n\nOriginating center: Natural Resource Authority in Amman", "links": [ { diff --git a/datasets/CE1d0038_173.json b/datasets/CE1d0038_173.json index c074b70f61..f0695af265 100644 --- a/datasets/CE1d0038_173.json +++ b/datasets/CE1d0038_173.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CE1d0038_173", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage represents polygon features that describe the administrative\nboundaries.\n\nOriginating center: Natural Resource Authority in Amman", "links": [ { diff --git a/datasets/CE1d0043_173.json b/datasets/CE1d0043_173.json index 8c05c18d45..616433cbd6 100644 --- a/datasets/CE1d0043_173.json +++ b/datasets/CE1d0043_173.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CE1d0043_173", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage represents polygon features that describe the administrative\nboundaries.\nOriginal Map name: Carte Administrative.\n\nOriginating center: Division de la Cartographie - Direction de la\nconservation Fonciere et des Traveaux Topographiques", "links": [ { diff --git a/datasets/CEAMARC-200708_V3_IYGPT_2.json b/datasets/CEAMARC-200708_V3_IYGPT_2.json index fbe0debcb2..b12182bae1 100644 --- a/datasets/CEAMARC-200708_V3_IYGPT_2.json +++ b/datasets/CEAMARC-200708_V3_IYGPT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC-200708_V3_IYGPT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Australia's Census of Antarctic Marine Life project.\n\nThis project is a part of the international \"Census of Antarctic Marine Life\" (CAML) which was conducted during the International Polar Year. It was a collaborative contribution by Australia and France to understand the biodiversity of the oceans surrounding Antarctica, with particular emphasis on the fishes of the eastern part of the Australian Antarctic Territory. The biodiversity data, when added to that obtained by all other nations participating in the CAML, serves as a robust reference for future examinations of the health of the Southern Ocean, and assists in the conservation and management of the region.\n\nField sampling for this project was undertaken in the 2007/08 season, commencing in December and finishing in February 2008. Three ships surveyed the area with a range of traditional and modern sampling gear, including IYGPT (International Young Gadoid Pelagic Trawl (marine science equipment)) gear.", "links": [ { diff --git a/datasets/CEAMARC-200708_V3_MARINE_SEDIMENT_SAMPLES_1.json b/datasets/CEAMARC-200708_V3_MARINE_SEDIMENT_SAMPLES_1.json index bc99fb70a0..23aa06410b 100644 --- a/datasets/CEAMARC-200708_V3_MARINE_SEDIMENT_SAMPLES_1.json +++ b/datasets/CEAMARC-200708_V3_MARINE_SEDIMENT_SAMPLES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC-200708_V3_MARINE_SEDIMENT_SAMPLES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine sediment samples were obtained from box corer, Smith-MacIntyre and Van Veen grabs.\n\nSamples were named by:\n1. CEAMARC site (e.g. 16)\n2. Instrument (e.g. box corer = BC; Smith-MacIntyre = GRSM; Van Veen = GRVV) 3. Sequence of sample at each site (e.g. first sample = 01; second sample = 02) So 16BC02 is the second sample at CEAMARC site 16, using the box corer.\n\nFrom each successful sample, a sub-sample was obtained:\n1. 200 g surface scrape (labelled A)\n2. short (20 cm) push core (labelled B)\n3. bulk (labelled Bulk)\n4. rocks-only (labelled Rocks)\ne.g. 16BC02A is a 200 g surface scrape subsample from 16BC02.\n 16BC02B is a push core subsample from 16BC02\n 16BC02Bulk is a bulk sediment subsample from 16BC02.\n 16BC02Rocks is a rocks-only subsample from 16BC02.\n\nPost-cruise analyses:\n1. Grain size\n2. Total organic carbon\n3. Total organic nitrogen\n4. Carbon and nitrogen isotopes\n5. Biogenic silica and carbonate\n6. Physical properties of cores\n7. Zircon dating\n8. X-rays for infauna and sedimentary structures\n\nAdded by Alix Post - March 2010:\n\nSeabed samples were collected from 52 sites across the George V Shelf. Most samples were collected with a box corer (BC), though more gravelly sediments required a Smith-McIntyre (GRSM) or Van-Veen grab (GRVV) as indicated by the station name in the spreadsheet. A small volume of sediment was frozen following collection and later analysed for organic carbon and nitrogen content, in addition to carbon and nitrogen isotopes. Organic carbon and nitrogen values are express as percent of the total sediment, and have been corrected back to the total sediment volume. Isotopic values are expressed as values per mil.\n\nWhere sufficient volume of sediment was collected, a mini-core was pushed into the sediment to provide a depth profile of the sample, and a bulk surface sample was also taken. Surface sediment samples analysed for sieve grainsize, calcium carbonate and biogenic silica content. All values are expressed as percentage values. The naming convention of the samples describes the type of gear used and the nature of the sediment analysed: e.g. 01BC01Bulk is a bulk sediment sample collected with a box core; 38GRVV02B/0-1 is a slice taken from 0 to 1 cm at the top of a van veen grab.", "links": [ { diff --git a/datasets/CEAMARC_200708_V3_MARINE_VIDEO_SAMPLES_1.json b/datasets/CEAMARC_200708_V3_MARINE_VIDEO_SAMPLES_1.json index cc17cd70ff..3a7f796075 100644 --- a/datasets/CEAMARC_200708_V3_MARINE_VIDEO_SAMPLES_1.json +++ b/datasets/CEAMARC_200708_V3_MARINE_VIDEO_SAMPLES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_200708_V3_MARINE_VIDEO_SAMPLES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underwater video samples were obtained from the Deep Underwater Camera II (DUCII) system. Data are in mpeg video format.\n\nSamples were named by:\n1. CEAMARC site (e.g. 16)\n2. Instrument (e.g. camera = CAM)\n3. Sequence of deployments through the survey overall (e.g. first deployment = 01; second deployment = 02) e.g. 09CAM05 is the fifth camera deployment of the survey overall, and was at CEAMARC site 09.\n\nPost-cruise analyses:\n15 second logging of seabed geology and biology (species, class, order, whatever is significant for the habitat) directly into GNAV software for overlay into a GIS.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708030_BATHYMETRY_1.json b/datasets/CEAMARC_CASO_200708030_BATHYMETRY_1.json index 21b716b34b..8b1369fc88 100644 --- a/datasets/CEAMARC_CASO_200708030_BATHYMETRY_1.json +++ b/datasets/CEAMARC_CASO_200708030_BATHYMETRY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708030_BATHYMETRY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetry data was collected using a Simrad EK60 echosounder. The sample data have been corrected for the relative locations of GPS antenna, transducers and waterline. A sound-speed value of 1500 m/s was applied when calculating depth.\n\nThe seafloor depth itself was defined firstly as the depth of the sounder-detected bottom minus 10m (contact Simrad for more information about their bottom-detection algorithm), and then modified manually where necessary to ensure that the line followed the seafloor as perceived by eye from the echogram. This is therefore a subjective process, and the true seafloor depth may vary from the perceived depth by several hundred metres in the worst cases. The greatest uncertainties are typically at greater depths, e.g. greater than 1000 m.\n\nThis seafloor depth line therefore refers to the approximate depth (not range from transducer) of the seafloor less 10 m, i.e. 10 m should be added to the 'depth' values in the *.CSV file to give the 'true' seafloor depth.\n\nDepths greater than 5000 m are not available due to the 12 kHz data not being logged any deeper than this.\n\nThese data are preliminary and subject to change.\n\nBathymetry data was exported during the voyage by Belinda Ronai. Post voyage enquiries however should be directed to Toby Jarvis.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES_1.json b/datasets/CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES_1.json index a8aa338369..cc24d2e57b 100644 --- a/datasets/CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES_1.json +++ b/datasets/CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data describe the locations, dates, time, etc where biogeochemistry data were collected on the CEAMARC-CASO cruise in the 2007/2008 Antarctic season.\n\nSee the CEAMARC-CASO events metadata record for further information.\n\nSample codes are not descriptive. \nCEMARC/CASO column have underway data (no link to group site) as well as the CEAMARC and CASO sampling locations.\nEvents are recorded by number and the associated type of sample taken. \nCTD - 0.4 um filtered water sample. \nBox corer - diatom scrape.\nBeam Trawl AAD - sponge sample.\nPHY - phytoplankton sample taken from inline surface seawater system.\nVan Veen grab - sediment scrape.\nWAT - surface water sample passed through 0.4 um filter.\nDescription column explains the samples in more detail - eg information on what size fraction the phytoplankton were filtered at.\nLitres column describes the volume of water that was filtered.\nDepth is in metres.\nTime is local time.\nTemperature is degrees C.\nStorage location was for shipboard use only. The \"other\" column details any extra information that may be useful to the sample for example #2153 refers to a sample id code that the French CEAMARC group was using to code for their samples.\n\nOur aim for this voyage was to collect surface phytoplankton and water samples across a transect of the Southern Ocean, and to collect benthic sponge and coral samples in Antarctica, to (i) measure the Ge/Si and Si isotope composition to construct a nutrient profile across the Southern Ocean, and to test and calibrate these parameters as proxies for silica utilisation; and (ii) measure the B isotope composition to test the potential of biogenic silica to be used as a seawater pH proxy. \n\nWe collected phytoplankton, sponges, diatom sediment scrapes and water samples at strategic locations to ensure that the entire water column was surveyed. The data that were collected were used in collaboration with palaeoenvironmental data from sediment cores and experimental culture experiments on diatoms and sponges to gain a better understanding of historical distributions of Silicon and pH in the Southern Ocean.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1.json b/datasets/CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1.json index 6d83ec301e..07a1d8773a 100644 --- a/datasets/CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1.json +++ b/datasets/CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok.\n\nThe R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script.\n\nHowever, all output files are here for every CEAMARC event. Filenames contain a reference to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1.json b/datasets/CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1.json index ba22593849..10dc9ce5b3 100644 --- a/datasets/CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling strategy:\nSamples from trawls or sledges are sieved on the trawl deck then sorted in the wet lab per taxonomic group. Sorting may vary from high taxonomic levels (order, family) to specific ones according to expertise on board.\n\nFor some taxa, sampling includes:\nup to 10 voucher specimens with a unique batch number;\nphotos;\ntissue samples in 80% ethanol for DNA analysis (Barcoding and Phylogeny); 30 samples minimum for population genetics (for abundant species); sampling for isotopic measures; fish chromosomes preparations; primary fish cell lines and cryopreservation of fish tissues for permanent cell lines\n\nThe database was intended to contain information about stations, events, gear, all material collected and associated samples listed above. currently only contains information on material collected and samples.\n\nData was recorded on log sheets then transcribed into an Oracle database called cabo. Tailor made user interace for entering data. No export functionality. SQL database dump has been provided but there was no-one on the voyage to elaborate on the structure, this was promised post voyage along with some simple data exports to match the log sheets, so we have access to the data without the unfriendly database.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1.json b/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1.json index d8203015eb..6fd279ae34 100644 --- a/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous underway measurements of sea surface (7 metres depth)dissolved gasses (co2, o2, argon, nitrogen)by quadrupole mass spectrometry (Electron Impact Mass Spectrometry - EIMS).\n\nASCII encoded. 1 file per 24 hours. Naming convention: YYMMDD. Excel readable format. Column data (0/0 refers to ion mass, 7 ION masses detected in total): Cycle Date Time RelTime[s] '0/0' '0/1' '0/2' '0/3' '0/4' '0/5' '0/6' '0/7' '1/0' '2/0' '2/1' '2/2' '2/3' '2/4' '2/5' '2/6' '2/7'\n\nMeasurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1.json b/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1.json index 6d18ad1756..e6109fef09 100644 --- a/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous underway measurements of sea surface (7 metres depth)and atmospheric carbon dioxide. \nData format\n.txt extension comma delimited files. 1 file per 24 hours. Naming similar to AA03607_001-0000 (voyage_julian day_HH:MM). Excel readable format. 58 columns of data.\n\nMeasurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_EVENTS_1.json b/datasets/CEAMARC_CASO_200708_V3_EVENTS_1.json index bcd7336329..e4c42e171c 100644 --- a/datasets/CEAMARC_CASO_200708_V3_EVENTS_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_EVENTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_EVENTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two components. The first component is an even log for all station and instrument deployements. The second component is a log where start and end bottom times need to be recorded for instruments for example the benthic trawl. There is one file for each of the logs. Both logs need to be ideally merged into one to have one data source of event information. The start and end bottom times need to ideally go into the event logging system on the ship.\n\n1) Event log for stations and all instrument deployments\n\nStations and instrument deployments were recorded (including failures) over the progress of the voyage to provide a summary of all work carried out over voyage and and assigned an Event ID number for referencing data associated with these events.\n\nData_Format \n\nData was initially recorded in the ship board PostgreSQL database. Data was exported as a comma delimited file 'events.csv' at the end of the voyage.\n\nColumn 1 - Setcode (voyage identifier of the form 200708030 meaning year 2007-08, voyage 3)\n\nColumn 2 - Voyage Code (text voyage identifier)\n\nColumn 3 - Transect ID (transect identifier, no transects were identified this voyage)\n\nColumn 4 - Station ID (Station identifier, blank for events not associated with a station, CEAMARC project stations are pre-pended with 'CEAMARC', CASo stations are pre-pended with 'CASO', sampling near icebergs for trace metals pre-pended with 'ICEBERG', woCE SR3 transect sampling pre-pended with 'SR3').\n\nColumn 5 - Event ID (unique ID across voyage for individual events)\n\nColumn 6 - Event Type (usually the instrument deployed, self explanatory. One event type 'Plankton Water Sample' refers to mass water sampling undertaken for genomics work).\n\nColumn 7 - User Reference (id used by individual scientists to reference their data for this event. If left blank they are using the auto assigned event id from this table).\n\nColumn 8 - Start Timestamp (start timestamp of the event in UTC).\n\nColumn 9 - Start Latitude (start latitude of the event from the ship gps)\n\nColumn 10 - Start Longitude (start longitude of the event from the ship gps)\n\nColumn 11 - Start Bottom Depth (bottom depth at the start time of the event in metres from EK60 sounder bathymetry export)\n\nColumn 12 - End Timestamp (end timestamp of the event in UTC)\n\nColumn 13 - End Latitude (end latitude of the event from the ship gps)\n\nColumn 14 - End Longitude (end longitude of the event from the ship gps)\n\nColumn 15 - Duration (duration of the event in hours)\n\nColumn 16 - End Bottom Depth (bottom depth at the end time of the event in metres from EK60 sounder bathymetry export)\n\nColumn 17 - Min bottom Depth (minimum bottom depth encountered over event period from EK60 sounder bathymetry export)\n\nColumn 17 - Avg Bottom Depth (average bottom depth encountered over event period from EK60 sounder bathymetry export)\n\nColumn 18 - Max Bottom Depth (maximum bottom depth encountered over event period from EK60 sounder bathymetry export)\n\nColumn 19 - Author (person who entered event details into logging system)\n\nColumn 20 - Notes (notes peculiar to the event, may be blank)\n\n2) Log of instrument bottom times.\n\nExcel spreadsheet 'Trawl_log_18_Jan_08_final.xls'\n\nColumn A - Station number, these are all CEAMARC station numbers, matching stations in the event log pre-pended by 'CEAMARC'.\n\nColumn B - Event ID (matching event log, sometimes blank as this log an contain entries on intended events that did not get carried out for some reason or another)\n\nColumn C - Trawl Name (labelled trawl name, actually event type as the log started off with just trawl start/end bottom times, but was expanded to encompass other event types like grabs etc.)\n\nColumn D - Date of the event.\n\nColumn E - Ship Speed (in knots from displays of gps speed).\n\nColumn F - Time instrument hit the water in utc\n\nColumn G - Time instrument reached the bottom in utc.\n\nColumn H - Time instrument left the bottom (i.e. hauling started) in utc.\n\nColumn I - Time instrument on the deck (ie out of the water)\n\nColumn J - Depth in meters read of EK60 sounder display (could be any time during event).\n\nColumn K - Comments pertaining to the event.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_IMAGES_1.json b/datasets/CEAMARC_CASO_200708_V3_IMAGES_1.json index 24922844f4..d83609b4a5 100644 --- a/datasets/CEAMARC_CASO_200708_V3_IMAGES_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_IMAGES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_IMAGES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Image data (both stills and video) collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods, file descriptions and an AMSA (Australian Maritime Safety Authority) report.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_KRILL_2.json b/datasets/CEAMARC_CASO_200708_V3_KRILL_2.json index 6788a3fdb4..4d8b879a47 100644 --- a/datasets/CEAMARC_CASO_200708_V3_KRILL_2.json +++ b/datasets/CEAMARC_CASO_200708_V3_KRILL_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_KRILL_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Krill data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions.", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_MINERALOGY_1.json b/datasets/CEAMARC_CASO_200708_V3_MINERALOGY_1.json index 5eb9be9d48..86a85fefad 100644 --- a/datasets/CEAMARC_CASO_200708_V3_MINERALOGY_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_MINERALOGY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_MINERALOGY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mineralogy data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions.\n\nTaken from the \"Methods\" document in the download file:\n\nCEAMARC MINERALOGY METHODS\nMargaret Lindsay \nAugust 2009 \n\nMineralogy sampling method:\n(numbers in brackets refer to image below)\nIndividual bags containing the samples taken during the CEAMARC 2007/08 voyage (1) were emptied in to a sorting tray and slightly defrosted to enable the biota to be separated and sorted in to like biota (2). Taxonomic samples were selected to represent different species. The taxonomy sample was moved onto the bench and allocated a STD barcode, a photo was taken (3) and the image number, barcode and 'identification' of the biota was recorded. From the taxonomy sample a small (larger than 0.05g) sample of the individual was dissected, weighed (4) and bagged separately, this sub-sample became the 'mineralogy sample' that were sent to Damien Gore at Macquarie University on 21/05/2009 for mineralogy analysis by Damien Gore and Peter Johnston.\n \n\nSamples were tracked using the Sample Tracking Database (located \\\\aad.gov.au\\files\\HIRP\\new-shared-hirp\\30 Samples tracking + LIMS (Lab Inf Management Sys)\\Sample Tracking Database\\HIRP STD Working). The key barcodes are:\nThe nally bin's containing the CEAMARC samples are located in reefer 1 (-20 C) (barcode 11919). The original CEAMARC samples (parent container) are in nally bins 14762 and 14759. The taxonomy samples are in a nally barcoded as 70469 (contains 10 bags). The mineralogy samples are in a nally bin barcoded 70472 (contains three bags) and are currently at Macquarie University for mineralogy analysis.\n\n\nData was entered during the lab process into the spreadsheet file - Sub sampling taxonomy and mineralogy.xls the details of the spreadsheets contents;\n\nThe list below describes each column in the 'Taxonomy and Mineralogy', 'bamboo coral' and 'other analyses' sheets from the excel file - Sub sampling taxonomy and mineralogy.xls (location described in G:\\CEAMARC\\CEAMARC MINERALOGY FILE DESCRIPTIONS.doc)\n\nDate sampled\nDate that the taxonomic samples were dissected to obtain the mineralogy samples\n\nParent barcode\nSTD barcode for the nally bin that the samples are located in\n\nSite barcode\nSTD barcode for the CEAMARC site and deployment \n\nCEAMARC site number\nCEAMARC voyage sample site number\n\nCEAMARC event number\nThe CEAMARC voyage event number is the sampling devices deployment number, related to CEAMARC site number \n\nTaxonomy bag barcode\nSTD barcode for the bag that contains the taxonomy samples\n\nImage number\nThe image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy)\n\nSub sample barcode (for taxonomy)\nThe STD barcode allocated to the taxonomy sample\n\nAnalyses label for mineralogy\nThe number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample.\n\nAnalysis sample weight\nThe weight in grams of the dissected part that is the mineralogy sample.\n\nMineralogy bag barcode\nSTD barcode for the bag that contains the mineralogy samples\n\nIdentification\nBiota sample identification eg. Gorgonian, bryozoan, ophiuroids\n\nMineralogy sample size\nRelative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample.\n\nTaxonomy sample size\nRelative size of sample small sample; medium sample or large sample (suitable for further analysis).\n\n\nThe 'KRILL' sheet in the above excel file has the following columns; \n\nDate sub sampled\nDate that the taxonomic samples were dissected to obtain the mineralogy samples\n\nSample details\nSample code used to label the krill sample\n\nTaxonomy bag barcode\nSTD barcode for the bag that contains the taxonomy samples\n\nImage number\nThe image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy)\n\nSub sample barcode (for taxonomy)\nThe STD barcode allocated to the taxonomy sample\n\nAnalyses label for mineralogy\nThe number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample.\n\nAnalysis sample weight\nThe weight in grams of the dissected part that is the mineralogy sample.\n\nMineralogy bag barcode\nSTD barcode for the bag that contains the mineralogy samples\n\nIdentification\nBiota sample identification eg. Gorgonian, bryozoan, ophiuroids\n\nMineralogy sample size\nRelative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample.\n\nTaxonomy sample size\nRelative size of sample small sample; medium sample or large sample (suitable for further analysis).\n\nVoyage\nThe ANARE Voyage number and year is expressed as V4 02/03\n\nStation\nStation number that the samples were obtained from\n\nDate\nDate that the samples were taken during the voyage\n\nTime\nTime that the samples were taken during the voyage\n\nLocation\nLocation that the samples were taken from during the voyage\n\nNet\nThe RMT 8 and 1 were used to collect the krill\n\nDepth\nThe depth that the samples were obtained from (25 meters)\n\n\nTotal mineralogy samples\n1033 mineralogy samples + 15 bamboo coral samples (+ 12 krill samples) = 1060 samples ", "links": [ { diff --git a/datasets/CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1.json b/datasets/CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1.json index d33d59e00d..2f02d853ed 100644 --- a/datasets/CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1.json +++ b/datasets/CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrochemistry of surface water. Parameters measured=salinity, oxygen, co2, oxygen isotope species, nutrients.\n\nAll data have been stored in a single excel file.\n\nMeasurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information.", "links": [ { diff --git a/datasets/CEAMARC_CASO_AAV30708_Biogeochemistry_1.json b/datasets/CEAMARC_CASO_AAV30708_Biogeochemistry_1.json index e9d5516e05..6f37d03724 100644 --- a/datasets/CEAMARC_CASO_AAV30708_Biogeochemistry_1.json +++ b/datasets/CEAMARC_CASO_AAV30708_Biogeochemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_CASO_AAV30708_Biogeochemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total carbon dioxide and total alkalinity analysis of niskin bottle samples collected on CTD casts.\n\nAll data have been stored in a single excel file.\n\nMeasurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information.", "links": [ { diff --git a/datasets/CEAMARC_Diatom_Absolute_Abundance_1.json b/datasets/CEAMARC_Diatom_Absolute_Abundance_1.json index 9f983603ea..371171a7aa 100644 --- a/datasets/CEAMARC_Diatom_Absolute_Abundance_1.json +++ b/datasets/CEAMARC_Diatom_Absolute_Abundance_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_Diatom_Absolute_Abundance_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data provides the absolute abundance of diatom valves from cores recovered from the George V coast as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission of 2007-2008. Data are presented as valves/gram dry weight of sediment. All samples analyzed were core top samples, however no age constraints have been established. Chaetoceros resting spores were included in the absolute abundance calculations. Slides were prepared following Rathburn et al 1997.\n", "links": [ { diff --git a/datasets/CEAMARC_Diatom_Abundance_1.json b/datasets/CEAMARC_Diatom_Abundance_1.json index 0d904f42ce..31d6ed662b 100644 --- a/datasets/CEAMARC_Diatom_Abundance_1.json +++ b/datasets/CEAMARC_Diatom_Abundance_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_Diatom_Abundance_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the abundance of diatom species found in the surface sediments from cores collected as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission. The cores were collected from the George V basin along the Antarctic coast. Latitude, longitude and water depth data are included for each site. Sediments were prepared following standard diatom preparation techniques (Rathburn et al 1997).", "links": [ { diff --git a/datasets/CEAMARC_all_events_1.json b/datasets/CEAMARC_all_events_1.json index fa96bf5943..ed4bad7fe6 100644 --- a/datasets/CEAMARC_all_events_1.json +++ b/datasets/CEAMARC_all_events_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEAMARC_all_events_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copies of the event logs/station lists taken from the Aurora Australis, Astrolabe and Umitaka Maru during their CEAMARC cruises (collaborative East Antarctic Marine Census).", "links": [ { diff --git a/datasets/CEDAR_Imager.json b/datasets/CEDAR_Imager.json index c06ea1a6f0..892c2f75bf 100644 --- a/datasets/CEDAR_Imager.json +++ b/datasets/CEDAR_Imager.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEDAR_Imager", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR)\nData Base at NCAR/HAO holds data collected from airglow imagers and\nall-sky cameras. None of the imager data are in digital form in the\nCEDAR Data Base and must be obtained from the contact person. Video\ntapes from the imager at Millstone Hill are in the CEDAR Data Base.\n\nOther data are as follows:\n1. Utah State University CCD imager data from October 6-23, 1993 which\nmeasured nightglow emissions over Hawaii (20N, 155W). Data are\navailable from Michael Taylor. The Utah State University CCD Imager is\noperated by the Utah State University with support from the NSF.\n\n2. Boston University Mobile Ionospheric Observatory (MIO) imaging\nsystem which operated from July 1987 to June 1989, and the CEDAR\nimager which started in September 1989. Both imagers are located at\nMillstone Hill (42.6N, 71.5W). Video tapes from 1987-1994 are\navailable in the CEDAR Data Base. The contact person is Michael\nMendillo. The CEDAR imager is operated at Millstone Hill by Boston\nUniversity with support from the NSF.\n\n3. All-sky camera data at Qaanaaq, Greenland (77.5N, 69.2W), at\nLongyearbyen, Sweden (78.2N,15.4E), at Ny Alesund, Svalbard (78.9N,\n12.0E), and at Nord, Greenland (91.6N, 16.6W). These all-sky cameras\nare operated by the Air Force Research Laboratory (AFRL) at Hanscom\nAFB. All of the film data are available from Katsura Fukui at\nAFRL. The Qaanaaq and Nord all-sky cameras are operated by the Danish\nMeteorological Institute and owned by the U.S. Air Force Research\nLaboratory at Hanscom, AFB, MA. The Ny Alesund all-sky camera is\noperated by the University of Oslo and owned by the US AFRL.\n\nThe CEDAR Data Base is accessible through the WWW and ftp, but users\nmust have a valid access form, available from the WWW or ftp (see\nAccess and Use constraints) or contact Barbara Emery\n(emery@ucar.edu). See the WWW site for additional information on\naccessing the data and Rules of the Road procedures.\nhttp://cedarweb.hao.ucar.edu/wiki/index.php/Data_Services:Rules_of_the_Road", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Dome_C-Antarctica.json b/datasets/CEOS_CalVal_Test_Site-Dome_C-Antarctica.json index 02ec742054..90db8c66d2 100644 --- a/datasets/CEOS_CalVal_Test_Site-Dome_C-Antarctica.json +++ b/datasets/CEOS_CalVal_Test_Site-Dome_C-Antarctica.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Dome_C-Antarctica", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\r\n\r\nBackground:\r\n\r\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\r\nRequirement:\r\n\r\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\r\n\r\nInstrumented Sites:\r\nDome C, Antarctica is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Dunhuang-China.json b/datasets/CEOS_CalVal_Test_Site-Dunhuang-China.json index 148b538715..4fd63f25cb 100644 --- a/datasets/CEOS_CalVal_Test_Site-Dunhuang-China.json +++ b/datasets/CEOS_CalVal_Test_Site-Dunhuang-China.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Dunhuang-China", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nDunhuang, China, is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Frenchman_Flat-USA.json b/datasets/CEOS_CalVal_Test_Site-Frenchman_Flat-USA.json index 8b27d0a31a..47e554614f 100644 --- a/datasets/CEOS_CalVal_Test_Site-Frenchman_Flat-USA.json +++ b/datasets/CEOS_CalVal_Test_Site-Frenchman_Flat-USA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Frenchman_Flat-USA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nFrenchman Flat, USA is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Ivanpah_Playa-USA.json b/datasets/CEOS_CalVal_Test_Site-Ivanpah_Playa-USA.json index 4ddcf2c617..89dbf3e7f4 100644 --- a/datasets/CEOS_CalVal_Test_Site-Ivanpah_Playa-USA.json +++ b/datasets/CEOS_CalVal_Test_Site-Ivanpah_Playa-USA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Ivanpah_Playa-USA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nIvanpah Playa, USA is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-La_Crau-France.json b/datasets/CEOS_CalVal_Test_Site-La_Crau-France.json index 69b0d2d318..08014bbc4a 100644 --- a/datasets/CEOS_CalVal_Test_Site-La_Crau-France.json +++ b/datasets/CEOS_CalVal_Test_Site-La_Crau-France.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-La_Crau-France", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nLa Crau, France is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Libya1.json b/datasets/CEOS_CalVal_Test_Site-Libya1.json index c0639bdf59..9417ead163 100644 --- a/datasets/CEOS_CalVal_Test_Site-Libya1.json +++ b/datasets/CEOS_CalVal_Test_Site-Libya1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Libya1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nLibya 4 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Libya4.json b/datasets/CEOS_CalVal_Test_Site-Libya4.json index 0cbd5988b8..edb6a41596 100644 --- a/datasets/CEOS_CalVal_Test_Site-Libya4.json +++ b/datasets/CEOS_CalVal_Test_Site-Libya4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Libya4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nLibya 4 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Mauritania1.json b/datasets/CEOS_CalVal_Test_Site-Mauritania1.json index 4a97f4c6fb..ac1eb845ca 100644 --- a/datasets/CEOS_CalVal_Test_Site-Mauritania1.json +++ b/datasets/CEOS_CalVal_Test_Site-Mauritania1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Mauritania1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nMauritania 1 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Negev-Southern_Israel.json b/datasets/CEOS_CalVal_Test_Site-Negev-Southern_Israel.json index aecdc55a42..51f1316a42 100644 --- a/datasets/CEOS_CalVal_Test_Site-Negev-Southern_Israel.json +++ b/datasets/CEOS_CalVal_Test_Site-Negev-Southern_Israel.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Negev-Southern_Israel", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nNegev, Southern Israel is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Railroad_Valley_Playa-USA.json b/datasets/CEOS_CalVal_Test_Site-Railroad_Valley_Playa-USA.json index 4f4ddffcbe..5e03e8373f 100644 --- a/datasets/CEOS_CalVal_Test_Site-Railroad_Valley_Playa-USA.json +++ b/datasets/CEOS_CalVal_Test_Site-Railroad_Valley_Playa-USA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Railroad_Valley_Playa-USA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nRailroad Valley Playa, USA is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Site-Tuz_Golu-Turkey.json b/datasets/CEOS_CalVal_Test_Site-Tuz_Golu-Turkey.json index b93b031aaf..a42f06088e 100644 --- a/datasets/CEOS_CalVal_Test_Site-Tuz_Golu-Turkey.json +++ b/datasets/CEOS_CalVal_Test_Site-Tuz_Golu-Turkey.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Site-Tuz_Golu-Turkey", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nTuz Golu, Turkey is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Sites-Algeria3.json b/datasets/CEOS_CalVal_Test_Sites-Algeria3.json index 4f507dbe40..cbb4399066 100644 --- a/datasets/CEOS_CalVal_Test_Sites-Algeria3.json +++ b/datasets/CEOS_CalVal_Test_Sites-Algeria3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Sites-Algeria3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nAlgeria 3 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Sites-Algeria5.json b/datasets/CEOS_CalVal_Test_Sites-Algeria5.json index 4a3c289c21..8be5728de1 100644 --- a/datasets/CEOS_CalVal_Test_Sites-Algeria5.json +++ b/datasets/CEOS_CalVal_Test_Sites-Algeria5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Sites-Algeria5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nAlgeria 5 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CEOS_CalVal_Test_Sites-Mauritania2.json b/datasets/CEOS_CalVal_Test_Sites-Mauritania2.json index 5168b70986..2ab2220636 100644 --- a/datasets/CEOS_CalVal_Test_Sites-Mauritania2.json +++ b/datasets/CEOS_CalVal_Test_Sites-Mauritania2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CEOS_CalVal_Test_Sites-Mauritania2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nMauritania 2 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "links": [ { diff --git a/datasets/CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1.json b/datasets/CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1.json index 2a558f7ded..0e81f0c6fd 100644 --- a/datasets/CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1.json +++ b/datasets/CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1 is the Clouds and the Earth's Radiant Energy System (CERES) NASA Energy and Water cycle Study (NEWS) A-Train Integrated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), loudSat Cloudsat, CERES, and Moderate-Resolution Imaging Spectroradiometer (MODIS) (CCCM) Merged Release B1 data product. Data was collected using the CALIOP on CALIPSO and CERES Flight Model 3 (FM3), CERES Scanner, MODIS, and Cloudsat on Aqua. Data collection for this product is complete. \r\n\r\nThe CER-NEWS_CCCM_Aqua-FM3-MODIS-CAL-CS_RelB1 contains the current CERES CCCM data. The CALIPSO-CloudSat-CERES-MODIS (CCCM) data set integrates measurements from the CALIPSO CALIOP instrument, CloudSat Cloud Profiling Radar (CPR), CERES, and the MODIS data. The cloud and aerosol properties from CALIOP and cloud properties from the CPR are matched to a MODIS pixel and then an Aqua CERES footprint. The product contains only the CERES footprint in each scan with the highest CALIPSO and CloudSat ground track coverage. The product consists of all cloud and aerosol properties derived from MODIS radiances included in the Single Scanner Footprint (SSF) product and computed irradiances included in the Cloud Radiative Swath (CRS) product. Two sets of SSF variables are included in the CCCM data. One set covers the entire CERES footprint, and the other set is only over the CALIOP and CPR ground track. CERES-derived top-of-atmosphere (TOA) shortwave (SW), longwave (LW), and window (WN) irradiances by angular distribution models are also included. In addition, irradiance profiles computed by a radiative transfer model using MODIS, CALIOP, and CPR-derived aerosol, clouds, and surface properties are included in the product. Furthermore, MODIS-derived cloud properties from the algorithm incorporating CALIOP and CPR cloud information are included. \r\n\r\nMODIS-derived cloud properties and TOA irradiances derived from CERES radiances are produced by the same algorithm that produces CERES SSF and CRS products. However, the CCCM product should not be considered a climate data record since various input data product versions and algorithm modifications will occur during the measurement period. The scan and packet numbers unique to the CERES footprint provide the means to match the data to other CERES products, although the CCCM product contains more near-nadir CERES footprints compared with SSF and CRS products. The resulting HDF granule contains 24 hours of data.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. \r\n\r\nThe CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1.json b/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1.json index a1d1fb090b..60f25d081a 100644 --- a/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1.json +++ b/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1 is the Clouds and the Earth's Radiant Energy System (CERES) and Multi-angle Imaging SpectroRadiometer (MISR) Along-Track Footprint Radiances, Fluxes, and Clouds Terra-Flight Model 1 (FM1) Version 1 data set. Data was collected using the MODIS, FM1, FM2, MISR, and CERES Scanner instruments on the Terra platform. Data collection for this product is complete. \r\n\r\nCERES-MISR-MODIS_SSF-SSFM_Terra-FM1_1 is the CERES and MISR Along-Track Footprint Radiances, Fluxes, and Clouds Terra-FM1 data set. It integrates measurements from CERES, MISR, and MODIS. The resulting data granule contains two files: an hour of instantaneous CERES SSF data, which includes MODIS data, the matching SSFM daytime measurements (solar zenith angle < 90 deg), and MISR radiances associated with along-track CERES SSF data. Data is only available for along-track days within the temporal coverage.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1.json b/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1.json index aee247649b..ccb392907b 100644 --- a/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1.json +++ b/datasets/CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1 is the Clouds and the Earth's Radiant Energy System (CERES) and Multi-angle Imaging SpectroRadiometer (MISR) Along-Track Footprint Radiances, Fluxes, and Clouds Terra-Flight Model 2 (FM2) Version 1 data set. Data was collected using the MODIS, FM1, FM2, MISR, and CERES Scanner instruments on the Terra platform. Data collection for this product is complete. \r\n\r\nCERES-MISR-MODIS_SSF-SSFM_Terra-FM2_1 integrates measurements from CERES, MISR, and MODIS. The resulting data granule contains two files: an hour of instantaneous CERES SSF data, which includes MODIS data, the matching SSFM daytime measurements (solar zenith angle < 90 deg), and MISR radiances associated with along-track CERES SSF data. Data is only available for along-track days within the temporal coverage.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES_EBAF-TOA_Edition4.1.json b/datasets/CERES_EBAF-TOA_Edition4.1.json index a83319ef47..42361912b9 100644 --- a/datasets/CERES_EBAF-TOA_Edition4.1.json +++ b/datasets/CERES_EBAF-TOA_Edition4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES_EBAF-TOA_Edition4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES_EBAF-TOA_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms. Data collection for this product is ongoing.\r\n\r\nCERES_EBAF-TOA_Edition4.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) alongside TOA fluxes. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both Earth Observing System (EOS) Terra and Aqua satellites as well as geostationary satellites to more fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. \r\n\r\nCERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES_EBAF-TOA_Edition4.2.json b/datasets/CERES_EBAF-TOA_Edition4.2.json index 5cdd4957c1..a871a68710 100644 --- a/datasets/CERES_EBAF-TOA_Edition4.2.json +++ b/datasets/CERES_EBAF-TOA_Edition4.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES_EBAF-TOA_Edition4.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES_EBAF-TOA_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms. Data collection for this product is ongoing.\r\n\r\nCERES_EBAF-TOA_Edition4.2 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from high-resolution imager data alongside TOA fluxes. The Moderate-Resolution Imaging Spectroradiometer (MODIS) imagers Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) are used for NOAA-20. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard Earth Observing System (EOS) Terra and Aqua and NOAA-20 satellites and geostationary satellites to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. Only Terra data is used from March 2000 to June 2002; Terra and Aqua are combined from July 2002 until March 2022; and only NOAA-20 is used after March 2022. A correction created from an overlap period with time periods when both Terra and Aqua are available is used to adjust the single satellite periods.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES_EBAF_Edition4.1.json b/datasets/CERES_EBAF_Edition4.1.json index 35e65df859..2831761f89 100644 --- a/datasets/CERES_EBAF_Edition4.1.json +++ b/datasets/CERES_EBAF_Edition4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES_EBAF_Edition4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES_EBAF_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms. Data collection for this product is ongoing.\r\n\r\nCERES_EBAF_Edition4.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. It also provides computed monthly mean surface radiative fluxes consistent with the CERES EBAF-TOA product and some basic cloud properties derived from MODIS. Cloud Radiative Effects are provided at both the TOA and surface as determined using a cloud-free profile in the Fu-Liou Radiative Transfer Model (RTM). Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both EOS Terra and Aqua satellites, as well as geostationary satellites, to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CERES_EBAF_Edition4.2.json b/datasets/CERES_EBAF_Edition4.2.json index e3dc2078ee..b23aad073d 100644 --- a/datasets/CERES_EBAF_Edition4.2.json +++ b/datasets/CERES_EBAF_Edition4.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CERES_EBAF_Edition4.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CERES_EBAF_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms for various periods. Data collection for this product is ongoing.\r\n\r\nCERES_EBAF_Edition4.2 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. It also provides computed monthly mean surface radiative fluxes consistent with the CERES EBAF-TOA product and some basic cloud properties derived from colocated imagers. Cloud Radiative Effects are provided at both the TOA and surface as determined using a cloud-free profile in the Fu-Liou Radiative Transfer Model (RTM). Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both EOS Terra and Aqua satellites and NOAA-20, as well as geostationary satellites, to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Aqua-FM3_Edition1-CV.json b/datasets/CER_BDS_Aqua-FM3_Edition1-CV.json index 2d2b96eb92..58f1a2acc4 100644 --- a/datasets/CER_BDS_Aqua-FM3_Edition1-CV.json +++ b/datasets/CER_BDS_Aqua-FM3_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Aqua-FM3_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Aqua-FM3_Edition1-CV is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Aqua Flight Model 3 (FM3) Edition1-CV data product, which was collected using the CERES-FM3 instrument on the Aqua platform. This data product is intended only to be used for instrument validation purposes and is, therefore, not suited for science publications. Data collection for this product is ongoing. \r\n\r\nNote: Edition 1-CV data are only for instrument validation and not suited for science publications.\r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Aqua-FM3_Edition4.json b/datasets/CER_BDS_Aqua-FM3_Edition4.json index 96c883dc61..0716a80dd8 100644 --- a/datasets/CER_BDS_Aqua-FM3_Edition4.json +++ b/datasets/CER_BDS_Aqua-FM3_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Aqua-FM3_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Aqua-FM3_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Aqua Flight Model 3 (FM3) Edition 4 data product, which is collected using the CERES-FM3 instrument on the Aqua platform. CER_BDS_Aqua-FM3_Edition4 includes geolocated and calibrated Top-of-Atmosphere (TOA) filtered radiances and other instrument data. Data collection for this product is ongoing. \r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Aqua-FM4_Edition1-CV.json b/datasets/CER_BDS_Aqua-FM4_Edition1-CV.json index 96b869a446..81a5976554 100644 --- a/datasets/CER_BDS_Aqua-FM4_Edition1-CV.json +++ b/datasets/CER_BDS_Aqua-FM4_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Aqua-FM4_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Aqua-FM4_Edition1-CV is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Aqua Flight Model 4 (FM4) Edition 1-CV data product, which is collected using the CERES-FM4 instrument on the Aqua platform. This data product is intended only to be used for instrument validation purposes and is, therefore, not suited for science publications. Data collection for this product is complete. \r\n\r\nNote: Edition 1-CV data are only for instrument validation and not suited for science publications.\r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Aqua-FM4_Edition4.json b/datasets/CER_BDS_Aqua-FM4_Edition4.json index dd6bd21f16..38428ae7b2 100644 --- a/datasets/CER_BDS_Aqua-FM4_Edition4.json +++ b/datasets/CER_BDS_Aqua-FM4_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Aqua-FM4_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Aqua-FM4_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Aqua Flight Model 4 (FM4) Edition 4 data product, which is collected using the CERES-FM4 instrument on the Aqua platform. CER_BDS_Aqua-FM3_Edition4 includes geolocated and calibrated TOA-filtered radiances and other instrument data. Data collection for this product is complete. \r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_J01-FM6_Edition1-CV.json b/datasets/CER_BDS_J01-FM6_Edition1-CV.json index cf85ceece7..a7517cc573 100644 --- a/datasets/CER_BDS_J01-FM6_Edition1-CV.json +++ b/datasets/CER_BDS_J01-FM6_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_J01-FM6_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_J01-FM6_Edition1-CV is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Joint Polar Satellite System 1 (NOAA-20) Flight Model 6 (FM6) Edition1-CV data product. Data collection for this product is ongoing. \r\n\r\nNote: Edition 1-CV data are only for instrument validation and not suited for science publications.\r\n\r\nCER_BDS_J01-FM6_Edition1-CV is CERES geolocated and calibrated Top-of-Atmosphere (TOA) filtered radiances and other instrument data. Edition1-CV data are for instrument validation and are not suited for science publications. Each CERES BDS data product contains twenty-four hours of Level-1b data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples of regular and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data and the geolocated converted science and instrument data. The BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_NOAA20-FM6_Edition1.json b/datasets/CER_BDS_NOAA20-FM6_Edition1.json index 9de2e8da12..711985840d 100644 --- a/datasets/CER_BDS_NOAA20-FM6_Edition1.json +++ b/datasets/CER_BDS_NOAA20-FM6_Edition1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_NOAA20-FM6_Edition1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_NOAA20-FM6_Edition1 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Joint Polar Satellite System 1 (JPSS-1) Flight Model 6 (FM6) Edition1data product. Data collection for this product is ongoing. \r\n\r\nCER_BDS_NOAA20-FM6_Edition1 data are CERES geolocated and calibrated Top of Atmosphere (TOA) filtered radiances and other instrument data. Each CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data and the geolocated converted science and instrument data. The BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_NPP-FM5_Edition1-CV.json b/datasets/CER_BDS_NPP-FM5_Edition1-CV.json index 62bcf55ab9..b1cefe2e2a 100644 --- a/datasets/CER_BDS_NPP-FM5_Edition1-CV.json +++ b/datasets/CER_BDS_NPP-FM5_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_NPP-FM5_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_NPP-FM5_Edition1-CV is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Suomi National Polar-orbiting Partnership (NPP) Flight Model 5 (FM5) Edition 1-CV data product. Data collection for this product is ongoing. \r\n\r\nPlease note that Edition1-CV data are for instrument validation and unsuitable for science publications.\r\n\r\nCER_BDS_NPP-FM5_Edition1-CV data are CERES geolocated and calibrated Top-of Atmosphere (TOA) filtered radiances and other instrument data. Each CERES BDS data product contains twenty-four hours of Level-1B data for a CERES scanner instrument mounted on a spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) science and instrument data and the geolocated converted science and instrument data. It also has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_NPP-FM5_Edition2.json b/datasets/CER_BDS_NPP-FM5_Edition2.json index 64f740a46f..7d3ef2ea55 100644 --- a/datasets/CER_BDS_NPP-FM5_Edition2.json +++ b/datasets/CER_BDS_NPP-FM5_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_NPP-FM5_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Edition2 CERES BiDirectional Scans (BDS) for Flight Model 5 (FM5) on the Suomi NPP spacecraft (CER_BDS_NPP-FM5_Edition2) data are CERES geolocated and calibrated TOA filtered radiances and other instrument data. Each Cloud and the Earth's Radiant Energy System (CERES) BiDirectional Scans (BDS) data product contains twenty-four hours of Level-1b data for a CERES scanner instrument mounted on a spacecraft. The BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data and the geolocated converted science and instrument data. The BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_TRMM-PFM_Edition1.json b/datasets/CER_BDS_TRMM-PFM_Edition1.json index 820d11e3ed..c94a821e7e 100644 --- a/datasets/CER_BDS_TRMM-PFM_Edition1.json +++ b/datasets/CER_BDS_TRMM-PFM_Edition1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_TRMM-PFM_Edition1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_TRMM-PFM_Edition1 is the Clouds and the Earth's Radiant Energy System (CERES) BiDirectional Scans (BDS) Tropical Rainfall Measuring Mission (TRMM) Edition 1 data product. Data collection for this product is complete. \r\n\r\nCER_BDS_TRMM-PFM_Edition1 data are CERES geolocated and calibrated Top-of-Atmosphere (TOA) filtered radiances and other instrument data. Each CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) science and instrument data and the geolocated converted science and instrument data. Further, BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto-flight model (PFM), was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Terra-FM1_Edition1-CV.json b/datasets/CER_BDS_Terra-FM1_Edition1-CV.json index 4efdc1e6f2..12662a5207 100644 --- a/datasets/CER_BDS_Terra-FM1_Edition1-CV.json +++ b/datasets/CER_BDS_Terra-FM1_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Terra-FM1_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Terra-FM1_Edition1-CVis the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Terra Flight Model 1 (FM1) Edition1-CV data product, which was collected using the CERES-FM1 instrument on the Terra platform. This data product is intended only to be used for instrument validation purposes and is, therefore, not suited for science publications. Data collection for this product is ongoing. \r\n\r\nNote: Edition 1-CV data are only for instrument validation and not suited for science publications.\r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS has additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Terra-FM1_Edition4.json b/datasets/CER_BDS_Terra-FM1_Edition4.json index d21beb11e6..2e80d214d5 100644 --- a/datasets/CER_BDS_Terra-FM1_Edition4.json +++ b/datasets/CER_BDS_Terra-FM1_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Terra-FM1_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Terra-FM1_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Terra Flight Model 1 (FM1) Edition 4 data product, which is collected using the CERES-FM1 instrument on the Terra platform. CER_BDS_Terra-FM1_Edition4 includes geolocated and calibrated Top of the Atmosphere (TOA) filtered radiances and other instrument data. Data collection for this product is ongoing. \r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\n\r\nThe CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Terra-FM2_Edition1-CV.json b/datasets/CER_BDS_Terra-FM2_Edition1-CV.json index ef1a732d6f..30670d6f14 100644 --- a/datasets/CER_BDS_Terra-FM2_Edition1-CV.json +++ b/datasets/CER_BDS_Terra-FM2_Edition1-CV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Terra-FM2_Edition1-CV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Terra-FM2_Edition1-CV is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Terra Flight Model 2 (FM2) Edition1-CV data product, which was collected using the CERES-FM2 instrument on the Terra platform. This data product is intended only to be used for instrument validation purposes and is, therefore, not suited for science publications. Data collection for this product is ongoing. \r\n\r\nNote: Edition 1-CV data are only for instrument validation and not suited for science publications.\r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_BDS_Terra-FM2_Edition4.json b/datasets/CER_BDS_Terra-FM2_Edition4.json index 185c6595ca..4bc794b81e 100644 --- a/datasets/CER_BDS_Terra-FM2_Edition4.json +++ b/datasets/CER_BDS_Terra-FM2_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_BDS_Terra-FM2_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_BDS_Terra-FM2_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Terra Flight Model 2 (FM2) Edition 4 data product, which is collected using the CERES-FM2 instrument on the Terra platform. CER_BDS_Terra-FM2_Edition4 includes geolocated and calibrated Top of the Atmosphere (TOA) filtered radiances and other instrument data. Data collection for this product is ongoing. \r\n\r\nEach CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1.json b/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1.json index 06d3992456..5af153d882 100644 --- a/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1.json +++ b/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD1 is the Clouds and the Earth's Radiant Energy System (CERES) A-Train Integrated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat Cloudsat, CERES, and Moderate-Resolution Imaging Spectroradiometer (MODIS) (CCCM) Merged Release D1 data product. Data was collected using the CALIOP on CALIPSO, Cloudsat Cloud Profiling Radar (CPR), CERES Flight Model 3 (FM3), CERES Scanner, and MODIS on Aqua. \r\n\r\nThe CALIPSO-CloudSat-CERES-MODIS (CCCM) data set integrates measurements from the CALIPSO CALIOP instrument, CloudSat Cloud Profiling Radar (CPR), CERES, and the MODIS data. The cloud and aerosol properties from CALIOP and cloud properties from the CPR are matched to a MODIS pixel and then an Aqua CERES footprint. The product contains only the CERES footprint in each scan with the highest CALIPSO and CloudSat ground track coverage. The product consists of all cloud and aerosol properties derived from MODIS radiances included in the Single Scanner Footprint (SSF) product and computed irradiances included in the Cloud Radiative Swath (CRS) product. Two sets of SSF variables are included in the CCCM data. One set covers the entire CERES footprint, and the other set is only over the CALIOP and CPR ground track. CERES-derived top-of-atmosphere (TOA) shortwave (SW), longwave (LW), and window (WN) irradiances by angular distribution models are also included. In addition, irradiance profiles computed by a radiative transfer model using MODIS, CALIOP, and CPR-derived aerosol, clouds, and surface properties are included in the product. Furthermore, MODIS-derived cloud properties from the algorithm incorporating CALIOP instrument data and CPR cloud information are also included. \r\n\r\nMODIS-derived cloud properties and TOA irradiances derived from CERES radiances are produced by the same algorithm that produces CERES SSF and CRS products. However, the CCCM product should not be considered a climate data record since various input data product versions and algorithm modifications will occur during the measurement period. The scan and packet numbers unique to the CERES footprint provide the means to match the data to other CERES products, although the CCCM product contains more near-nadir CERES footprints compared with SSF and CRS products. The resulting HDF granule contains 24 hours of data.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the Proto-Flight Model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments, Flight Models 1 and 2 (FM1 and FM2), were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments, Flight Models 3 and 4 (FM3 and FM4), were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES Flight Model 5 (FM5) instrument was launched onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite on October 28, 2011. The newest CERES instrument, Flight Model 6 (FM6), was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\r\n\r\nRelD1 incorporates the latest CERES SSF algorithms and CALIPSO and CloudSat data versions. An additional input is the CloudSat 2C-ICE product. This version also includes single short CALIPSO cloud information.", "links": [ { diff --git a/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2.json b/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2.json index 6287384bbb..7896e70277 100644 --- a/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2.json +++ b/datasets/CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CCCM_Aqua-FM3-MODIS-CAL-CS_RelD2 is Release D2 of a highly fused Level 2 data product that uses multiple satellites and instruments in the Afternoon Train or A-Train to produce high-resolution vertical computed atmosphere fluxes. From Aqua, the Clouds and the Earth's Radiant Energy System (CERES) Flight Model 3 (FM3) and Moderate-Resolution Imaging Spectroradiometer (MODIS); from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP); and from CloudSat, the Cloud Profiling Radar (CPR) instruments are used in the product. The CCCM product name indicates the CALIPSO, CloudSat, CERES, and MODIS merged data synergy. The cloud and aerosol properties from CALIOP and cloud properties from the CPR are used to create high-resolution vertical profiles and sixteen horizontal groupings of cloud and aerosol that are then matched to a MODIS pixel and then convolved into Aqua CERES footprints. The product contains only the CERES footprint in each scan, which has the highest CALIPSO and CloudSat ground track coverage. The high-resolution information is used to compute within-atmosphere irradiance profiles using the Fu-Liou radiation transfer model (RTM). Four assumptions are used in the RTM: Total-sky, clear-sky (no clouds, but aerosol), pristine (no clouds or aerosols), and total-sky, no aerosol. The product also contains variables from the Single Scanner Footprint (SSF) product, including CERES-derived top-of-atmosphere (TOA) shortwave (SW), longwave (LW), and window (WN) irradiances obtained using angular distribution models and computed irradiances included in the Cloud Radiative Swath (CRS) product based on cloud and aerosol properties derived only from MODIS radiances. Two sets of SSF variables are included in the CCCM data. One set uses imager data covering the entire CERES footprint, and the other set only uses imager pixel data that matches with the CALIOP and CPR ground track. However, the CCCM product should not be considered a climate data record since various input data product versions and algorithm modifications will occur during the measurement period. The scan and packet numbers unique to the CERES footprint provide the means to match the data to other CERES products, although the CCCM product contains more near-nadir CERES footprints that are not in the standard SSF, CER_SSF_Aqua-FM3-MODIS_Edition4A, and CRS, CER_CRS_Aqua-FM3-MODIS_Edition2C, products. The resulting daily HDF granule contains 24 hours of data along the satellite track covering the globe. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The CERES instrument, Flight Models 3 (FM3), was launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. CloudSat was selected as a NASA Earth System Science Pathfinder satellite mission in 1999 to provide observations necessary to advance our understanding of cloud abundance, distribution, structure, and radiative properties. It also launched on April 28, 2006.", "links": [ { diff --git a/datasets/CER_CRS1deg-Hour_Aqua-MODIS_Edition4A.json b/datasets/CER_CRS1deg-Hour_Aqua-MODIS_Edition4A.json index 76d212816b..ab6cbc859f 100644 --- a/datasets/CER_CRS1deg-Hour_Aqua-MODIS_Edition4A.json +++ b/datasets/CER_CRS1deg-Hour_Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS1deg-Hour_Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS1deg-Hour_Aqua-MODIS_Edition4A is the Aqua Clouds and the Earth's Radiant Energy System (CERES) Level 3 computed flux Edition4A data product. The Cloud and Radiative Swath One Degree (CRS1deg) Hour provides data hourly on a 1-degree latitude and longitude global grid from the Aqua CERES Flight Model 3 (FM3) or FM4 CER_CRS_Aqua-FM3-MODIS_Edition4A instantaneous footprint data. The data provides instantaneous averages of computed top of atmosphere (ToA) (0.01-hPa), within the atmosphere (850-, 500-, 200-, and 70-hPa), and surface fluxes (Wm-2). Four assumptions are used in the Fu-Liou radiative transfer model (RTM): Total-sky, clear-sky (no clouds, but aerosol), pristine (no clouds or aerosols), and total-sky, no aerosol as described in Scott 2022. Variables from these footprints are then assigned to a grid box and linearly averaged.\r\n\r\nThe data has been processed from January 1, 2018, to December 31, 2022, and is available in daily netCDF4 files. This single satellite product uses the primary CERES instrument in cross-track mode. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels: total, shortwave, and window. Longwave fluxes are obtained by subtracting shortwave from the total.\r\n \r\nA few variables from the SSF have been included in this product, such as observed CERES shortwave and longwave ToA fluxes and cloud fraction. MODIS measurements are not directly used in this dataset. The MODIS data is used during SSF processing to obtain cloud properties. These cloud properties are then used to compute the CRS fluxes.\r\nStill, this product should be used in conjunction with the CER_SSF1deg-Hour_Aqua-MODIS_Edition4A product, which has additional cloud and aerosol properties. The CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A is another related product that averages the cloud properties to the one-degree grid before performing the Fu-Liou RTM calculations. The SYN1deg-1Hour product also brings in cloud properties derived from geostationary imagers between 60 N and 60 S to provide information to provide a complete diurnal cycle.\r\n", "links": [ { diff --git a/datasets/CER_CRS1deg-Hour_Terra-MODIS_Edition4A.json b/datasets/CER_CRS1deg-Hour_Terra-MODIS_Edition4A.json index 7423601d59..c3e83d85b4 100644 --- a/datasets/CER_CRS1deg-Hour_Terra-MODIS_Edition4A.json +++ b/datasets/CER_CRS1deg-Hour_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS1deg-Hour_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS1deg-Hour_Terra-MODIS_Edition4A is the Terra Clouds and the Earth's Radiant Energy System (CERES) Level 3 computed flux Edition4A data product. The Cloud and Radiative Swath One Degree (CRS1deg) Hour provides data hourly on a 1-degree latitude and longitude global grid from the Terra CERES Flight Model 1 (FM1) or FM2 CER_CRS_Terra-FM1-MODIS_Edition4A instantaneous footprint data. The data provides instantaneous averages of computed top of atmosphere (ToA) (0.01-hPa), within the atmosphere (850-, 500-, 200-, and 70-hPa), and surface fluxes (Wm-2). Four assumptions are used in the Fu-Liou radiative transfer model (RTM): Total-sky, clear-sky (no clouds, but aerosol), pristine (no clouds or aerosols), and total-sky, no aerosol as described in Scott 2022. Variables from these footprints are then assigned to a grid box and linearly averaged.\r\n\r\nThe data has been processed from January 1, 2018, to December 31, 2022, and is available in daily netCDF4 files. This single satellite product uses the primary CERES instrument in cross-track mode. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels: total, shortwave, and window. Longwave fluxes are obtained by subtracting shortwave from the total.\r\n \r\nA few variables from the SSF have been included in this product, such as observed CERES shortwave and longwave ToA fluxes and cloud fraction. MODIS measurements are not directly used in this dataset. The MODIS data is used during SSF processing to obtain cloud properties. These cloud properties are then used to compute the CRS fluxes.\r\nStill, this product should be used in conjunction with the CER_SSF1deg-Hour_Terra-MODIS_Edition4A product, which has additional cloud and aerosol properties. The CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A is another related product that averages the cloud properties to the one-degree grid before performing the Fu-Liou RTM calculations. The SYN1deg-1Hour product also brings in cloud properties derived from geostationary imagers between 60 N and 60 S to provide information to provide a complete diurnal cycle.\r\n", "links": [ { diff --git a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2B.json b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2B.json index fa5f674b72..491beaff03 100644 --- a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2B.json +++ b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Aqua-FM3-MODIS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Aqua-FM3-MODIS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Aqua-Flight Model 3 (FM3) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2B data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Data collection for this product is complete. Note that more recent (2006) CRS Ed2C fields for untuned SW (upper left for all-sky globe and lower right for clear-sky ocean) show a bit more bias than does an average of the earlier (2002-2005) Clouds and Radiative Swath (CRS) Ed2B. CRS Ed2C (Ed2B) biases are evaluated for SSF Ed2C (Ed2B) observations, and those Single Scanner Footprint (SSF) observations do not include recent \"Rev1\" adjustments to observations.\r\n\r\nThe CRS product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS contains all of the CERES SSF product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2C.json b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2C.json index 4a22724179..3635442eb7 100644 --- a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2C.json +++ b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition2C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Aqua-FM3-MODIS_Edition2C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Aqua-FM3-MODIS_Edition2c is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Aqua-Flight Model 3 (FM3) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2C data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Data collection for this product is complete. Note that more recent (2006) CRS Ed2C fields for untuned SW (upper left for all-sky globe and lower right for clear-sky ocean) show a bit more bias than does an average of the earlier (2002-2005) Clouds and Radiative Swath (CRS) Ed2B. CRS Ed2C (Ed2B) biases are evaluated concerning SSF Ed2C (Ed2B) observations, and those Single Scanner Footprint (SSF) observations do not include recent \"Rev1\" adjustments to observations.\r\n\r\nThe CRS product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS has all of the CERES SSF product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition4A.json b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition4A.json index ef935641f4..0c3782dc87 100644 --- a/datasets/CER_CRS_Aqua-FM3-MODIS_Edition4A.json +++ b/datasets/CER_CRS_Aqua-FM3-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Aqua-FM3-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Aqua-FM3-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Aqua Flight Model 3 (FM3) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition4A data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Please note that only a few variables from the SSF have been included, and this product should be used in conjunction with the CER_SSF_Aqua-FM3-MODIS_Edition4A product.\r\n\r\nThe Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS includes geolocation, geometry, packet identification, minimal cloud properties, and TOA fluxes from the CERES SSF product. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at six levels in the atmosphere: the surface, 850-, 500-, 200-, 70-, and 0.01-hPa for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Aqua-FM4-MODIS_Edition2B.json b/datasets/CER_CRS_Aqua-FM4-MODIS_Edition2B.json index 236392854a..05d72a60bf 100644 --- a/datasets/CER_CRS_Aqua-FM4-MODIS_Edition2B.json +++ b/datasets/CER_CRS_Aqua-FM4-MODIS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Aqua-FM4-MODIS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Aqua-FM4-MODIS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Aqua-Flight Model 4 (FM4) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2B data product, which was collected using the CERES-FM4 instrument on the Aqua platform. Data collection for this product is complete. \r\n\r\nThe CRS product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS contains all the CERES Single Scanner Footprint (SSF)product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_TRMM-PFM-VIRS_Edition2C.json b/datasets/CER_CRS_TRMM-PFM-VIRS_Edition2C.json index 1df6f0f821..e665c43e39 100644 --- a/datasets/CER_CRS_TRMM-PFM-VIRS_Edition2C.json +++ b/datasets/CER_CRS_TRMM-PFM-VIRS_Edition2C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_TRMM-PFM-VIRS_Edition2C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_TRMM-PFM-VIRS_Edition2C is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Tropical Rainfall Measuring Mission (TRMM) Edition2C data product, which was collected using the CERES-proto flight model (PFM) instrument on the Tropical Rainfall Measuring Mission (TRMM) platform. Data collection for this product is complete.\r\n\r\nThe CER_CRS_TRMM-PFM-VIRS_Edition2C data product is computed Top-of-Atmosphere (TOA)/surface/profile fluxes using Moderate-Resolution Imaging Spectroradiometer (MODIS) clouds and aerosols from Single Scanner Footprint (SSF) obtained from the TRMM PFM instrument. The Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. CRS contains all of the CERES SSF product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto-flight model (PFM), was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2B.json b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2B.json index 596b549b4e..43eca5843a 100644 --- a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2B.json +++ b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Terra-FM1-MODIS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Terra-FM1-MODIS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 1 (FM1) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2B data product, which was collected using the CERES-FM1 instrument on the Terra platform. Data collection for this product is complete. Please note that for a full record, this product should be used in conjunction with the CER_CRS_Terra-FM1-MODIS_Edition2F and CER_CRS_Terra-FM1-MODIS_Edition2G products.\r\n\r\nThe Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS contains all of the CERES SSF product data. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2F.json b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2F.json index 1da67aab12..ee9d445b9d 100644 --- a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2F.json +++ b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Terra-FM1-MODIS_Edition2F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Terra-FM1-MODIS_Edition2F is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 1 (FM1) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2F data product, which was collected using the CERES-FM1 instrument on the Terra platform. Data collection for this product is complete. Please note that for a full record, this product should be used in conjunction with the CER_CRS_Terra-FM1-MODIS_Edition2B and CER_CRS_Terra-FM1-MODIS_Edition2G products.\r\n\r\nThe Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS has all of the CERES SSF product data. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2G.json b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2G.json index 26d08f89c3..fbca3bfdd6 100644 --- a/datasets/CER_CRS_Terra-FM1-MODIS_Edition2G.json +++ b/datasets/CER_CRS_Terra-FM1-MODIS_Edition2G.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Terra-FM1-MODIS_Edition2G", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Terra-FM1-MODIS_Edition2G is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 1 (FM1) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2G data product, which was collected using the CERES-FM1 instrument on the Terra platform. Data collection for this product is complete. Please note that for a full record, this product should be used in conjunction with the CER_CRS_Terra-FM1-MODIS_Edition2B and CER_CRS_Terra-FM1-MODIS_Edition2F products.\r\n\r\nThe Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS contains all of the CERES SSF product data. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Terra-FM1-MODIS_Edition4A.json b/datasets/CER_CRS_Terra-FM1-MODIS_Edition4A.json index 029f2d2f1f..122f9d23b7 100644 --- a/datasets/CER_CRS_Terra-FM1-MODIS_Edition4A.json +++ b/datasets/CER_CRS_Terra-FM1-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Terra-FM1-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Terra-FM1-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 1 (FM1) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition4A data product, which was collected using the CERES-FM1 instrument on the Terra platform. Please note that only a few variables from the SSF have been included, and this product should be used in conjunction with the CER_SSF_Terra-FM1-MODIS_Edition4A product.\r\n\r\nThe Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS contains geolocation, geometry, packet identification, minimal cloud properties, and TOA fluxes from the CERES SSF product. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at six levels in the atmosphere: the surface, 850-, 500-, 200-, 70-, and 0.01-hPa for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CRS_Terra-FM2-MODIS_Edition2B.json b/datasets/CER_CRS_Terra-FM2-MODIS_Edition2B.json index ed0deefb1b..38f025edcf 100644 --- a/datasets/CER_CRS_Terra-FM2-MODIS_Edition2B.json +++ b/datasets/CER_CRS_Terra-FM2-MODIS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CRS_Terra-FM2-MODIS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CRS_Terra-FM2-MODIS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 2 (FM2) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2B data product, which was collected using the CERES-FM2 instrument on the Terra platform. The CERES-FM2 instrument collected data in this collection on the Terra platform. The collection for this product is complete. \r\n\r\nThe CRS product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS contains all the CERES Single Scanner Footprint (SSF) product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A.json b/datasets/CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A.json index 83974cba9f..f586916738 100644 --- a/datasets/CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A.json +++ b/datasets/CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES)- Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) and hourly geostationary cloud properties stratified by the International Satellite Cloud Climatology Project (ISCCP) cloud types for day and night Edition 4A data product. Data collection is ongoing. \r\n\r\nThe CERES-MODIS-VIIRS and hourly geostationary cloud properties (CldTypHist) data product contain monthly and one-hourly gridded regional mean cloud properties as a function of 18 cloud types, where the cloud properties are stratified by pressure, optical depth, and phase. Data is available day and night. The CldTypHist product combines cloud properties from Terra-MODIS (10:30 AM local equator crossing time LECT), NOAA20-VIIRS (1:30 PM LECT), and geostationary satellites (GEO) to provide the most diurnally complete product. The GEO cloud properties have been normalized with MODIS for diurnal consistency. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. Likewise, CERES-VIIRS cloud properties are not the official NASA VIIRS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived and VIIRS-derived cloud properties provide coverage from pole to pole. The hourly GEO cloud properties come from five satellites at 8km nominal resolution with coverage limited to equatorward of 60 degrees. The GEO cloud retrievals incorporate additional channels as they become available on improved geostationary satellites that replaced earlier ones in the time period. The geostationary calibration is normalized to Terra-MODIS. Each CldTypHist file covers a single month.\r\n\r\nCERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_CldTypHist_GEO-MODIS_Edition4A.json b/datasets/CER_CldTypHist_GEO-MODIS_Edition4A.json index 692cc6e1aa..cb9b111cc3 100644 --- a/datasets/CER_CldTypHist_GEO-MODIS_Edition4A.json +++ b/datasets/CER_CldTypHist_GEO-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_CldTypHist_GEO-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_CldTypHist_GEO-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES)- Moderate-Resolution Imaging Spectroradiometer (MODIS) and hourly geostationary cloud properties stratified by the International Satellite Cloud Climatology Project (ISCCP) cloud types for day and night Edition 4A data product. Data collection is ongoing. \r\n\r\nThe CERES-MODIS and hourly geostationary cloud properties (CldTypHist) data product contain monthly and one-hourly gridded regional mean cloud properties as a function of 18 cloud types, where the cloud properties are stratified by pressure, optical depth, and phase. Data is available day and night. The CldTypHist product combines cloud properties from Terra-MODIS (10:30 AM local equator crossing time LECT), Aqua-MODIS (1:30 PM LECT), and geostationary satellites (GEO) to provide the most diurnally complete product. The GEO cloud properties have been normalized with MODIS for diurnal consistency. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties provide coverage from pole to pole. The hourly GEO cloud properties come from five satellites at 8km nominal resolution with coverage limited to equatorward of 60 degrees. The GEO cloud retrievals incorporate additional channels as they become available on improved geostationary satellites that replaced earlier ones in the time period. The geostationary calibration is normalized to Terra-MODIS. Each CldTypHist file covers a single month.\r\n\r\nCERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_Aqua-Xtrk_Edition4.json b/datasets/CER_ES4_Aqua-Xtrk_Edition4.json index e5df7b510a..c972c8b780 100644 --- a/datasets/CER_ES4_Aqua-Xtrk_Edition4.json +++ b/datasets/CER_ES4_Aqua-Xtrk_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_Aqua-Xtrk_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES4_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Aqua Crosstrack Edition4 data product, which was collected using the CERES-FM3 and CERES-FM4 instruments on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time averaged CERES data for a single satellite using measurements from the primary crosstrack instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the TOA from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE.\r\n\r\nCERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, protoflight model (PFM), was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_NOAA20-FM6_Edition1.json b/datasets/CER_ES4_NOAA20-FM6_Edition1.json index b3fae6d1b2..88998efd04 100644 --- a/datasets/CER_ES4_NOAA20-FM6_Edition1.json +++ b/datasets/CER_ES4_NOAA20-FM6_Edition1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_NOAA20-FM6_Edition1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES4_NOAA20-FM6_Edition1 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Geographical Averages NOAA-20 FM6 Edition1, data product. The CERES instrument TOA fluxes use algorithms identical to those used by ERBE, averaged regionally (2.5-degree, 5-degree, and 10-degree grid), zonally (2.5-degree, 5-degree, and 10-degree) and globally. The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time averaged CERES data for a single scanner instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and long-wave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is like the algorithm used for ERBE. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_NPP-FM5_Edition2.json b/datasets/CER_ES4_NPP-FM5_Edition2.json index 9760e32096..6bf97b2aa2 100644 --- a/datasets/CER_ES4_NPP-FM5_Edition2.json +++ b/datasets/CER_ES4_NPP-FM5_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_NPP-FM5_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for the Earth Radiation Budget Experiment (ERBE).\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_TRMM-PFM_Edition2.json b/datasets/CER_ES4_TRMM-PFM_Edition2.json index 064d9d78bd..3b59531654 100644 --- a/datasets/CER_ES4_TRMM-PFM_Edition2.json +++ b/datasets/CER_ES4_TRMM-PFM_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_TRMM-PFM_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES4_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Geographical Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES PFM instrument on the TRMM platform. Data collection for this product is complete. \r\n\r\nCER_ES4_TRMM-PFM_Edition2 data are CERES instrument Top-of-the-Atmosphere (TOA) fluxes that used algorithms identical to those used by ERBE, averaged regionally, zonally, and globally. The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged CERES data for a single scanner instrument. The ES-4 is also produced for combinations of scanner instruments. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and long-wave (LW) fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_Terra+Aqua_Edition4.json b/datasets/CER_ES4_Terra+Aqua_Edition4.json index c2b85acb5b..7105bff3ec 100644 --- a/datasets/CER_ES4_Terra+Aqua_Edition4.json +++ b/datasets/CER_ES4_Terra+Aqua_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_Terra+Aqua_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES4_Terra+Aqua_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Terra and Aqua Cross-track Edition4 data product. Data for this product is collected by Flight Model 1 (FM1) and FM2 on Terra and FM3 and FM4 on Aqua. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged CERES data for both the Terra and Aqua satellites using measurements from the primary cross-track instrument on each platform. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave (SW) and longwave (LW) fluxes at the TOA from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES4_Terra-Xtrk_Edition4.json b/datasets/CER_ES4_Terra-Xtrk_Edition4.json index 1224fcd37a..58fe343081 100644 --- a/datasets/CER_ES4_Terra-Xtrk_Edition4.json +++ b/datasets/CER_ES4_Terra-Xtrk_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES4_Terra-Xtrk_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES4_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM1) and FM2 instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the TOA from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_Aqua-FM3_Edition4.json b/datasets/CER_ES8_Aqua-FM3_Edition4.json index cfad269002..5aab328d43 100644 --- a/datasets/CER_ES8_Aqua-FM3_Edition4.json +++ b/datasets/CER_ES8_Aqua-FM3_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_Aqua-FM3_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_Aqua-FM3_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Instantaneous Top-of-the-Atmosphere (TOA) Estimates Aqua Flight Model 3 (FM3) Edition 4 data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Footprint TOA Fluxes (ES-8) product contains 24 hours of instantaneous CERES data for a single scanner instrument, FM3, on the Aqua spacecraft. The ES-8 contains filtered radiances recorded every 0.01-second for the total (TOT), shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW and LW radiances at spacecraft altitude are converted to TOA fluxes with a scene identification algorithm and Angular Distribution Models (ADMs), which are \"like\" those used for the ERBE. The TOA fluxes, scene identification, and angular geometry are included in the ES-8.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_Aqua-FM4_Edition4.json b/datasets/CER_ES8_Aqua-FM4_Edition4.json index fc57b0d6ec..960caf191a 100644 --- a/datasets/CER_ES8_Aqua-FM4_Edition4.json +++ b/datasets/CER_ES8_Aqua-FM4_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_Aqua-FM4_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_Aqua-FM4_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Instantaneous Top-of-the-Atmosphere (TOA) Estimates Aqua Flight Model 4 (FM4) Edition 4 data product, which was collected using the CERES-FM4 instrument on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe ERBE-like Footprint TOA Fluxes (ES-8) product contains 24 hours of instantaneous CERES data for a single scanner instrument, FM3, on the Aqua spacecraft. The ES-8 contains filtered radiances recorded every 0.01-second for the total (TOT), shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW and LW radiances at spacecraft altitude are converted to TOA fluxes with a scene identification algorithm and Angular Distribution Models (ADMs), which are \"like\" those used for the ERBE. The TOA fluxes, scene identification, and angular geometry are included in the ES-8.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_NOAA20-FM6_Edition1.json b/datasets/CER_ES8_NOAA20-FM6_Edition1.json index 0b8005289b..f05a6d4a8b 100644 --- a/datasets/CER_ES8_NOAA20-FM6_Edition1.json +++ b/datasets/CER_ES8_NOAA20-FM6_Edition1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_NOAA20-FM6_Edition1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_NOAA20-FM6_Edition1 data are ERBE-like instantaneous TOA estimates. Edition1 data are for instrument validation and are not suited for science publications. The Clouds and the Earth's Radiant Energy System (CERES) ES-8 data product contains a 24-hour, single-satellite, instantaneous view of scanner fluxes at the top-of-atmosphere (TOA) reduced from spacecraft altitude unfiltered radiances using Earth Radiation Budget Experiment (ERBE) scanner Inversion algorithms and the ERBE shortwave (SW) and longwave (LW) Angular Distribution Models (ADMs). The ES-8 data include the total (TOT), SW, LW, and window (WN) channel radiometric data; SW, LW, and WN unfiltered radiance values; and the ERBE scene identification for each measurement. These data are organized according to the CERES 3.3-second scan into 6.6-second records. As long as there is one valid scanner measurement within a record, the ES-8 record will be generated. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_NPP-FM5_Edition2.json b/datasets/CER_ES8_NPP-FM5_Edition2.json index 2ecbcf9718..9305bdd3eb 100644 --- a/datasets/CER_ES8_NPP-FM5_Edition2.json +++ b/datasets/CER_ES8_NPP-FM5_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_NPP-FM5_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ERBE-like Footprint TOA Fluxes (ES-8) product contains 24 hours of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument, Flight Model 5 (FM5) on the Suomi NPP spacecraft. The ES-8 contains filtered radiances recorded every 0.01-second for the total (TOT), shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW and LW radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes with a scene identification algorithm and Angular Distribution Models (ADMs), which are \"like\" those used for the Earth Radiation Budget Experiment (ERBE). The TOA fluxes, scene identification, and angular geometry are included in the ES-8.\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_TRMM-PFM_Edition2.json b/datasets/CER_ES8_TRMM-PFM_Edition2.json index 1ce73fb116..5031d32ee2 100644 --- a/datasets/CER_ES8_TRMM-PFM_Edition2.json +++ b/datasets/CER_ES8_TRMM-PFM_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_TRMM-PFM_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Instantaneous Top of Atmosphere (TOA) Estimates Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. The CERES-PFM instrument collected data for this product on the TRMM platform. Data collection for this product is complete.\r\n\r\nCERES ES-8 data product contains a 24-hour, single-satellite, instantaneous view of scanner fluxes at the top-of-atmosphere (TOA) reduced from spacecraft altitude unfiltered radiances using ERBE scanner Inversion algorithms and the ERBE shortwave (SW) and long-wave (LW) Angular Distribution Models (ADMs). The ES-8 data include the total (TOT), SW, LW, and window (WN) channel radiometric data; SW, LW, and WN unfiltered radiance values; and the ERBE scene identification for each measurement. These data are organized according to the CERES 3.3-second scan into 6.6-second records. As long as there was one valid scanner measurement within a record, the ES-8 record was generated. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_Terra-FM1_Edition4.json b/datasets/CER_ES8_Terra-FM1_Edition4.json index 6ea33ddbe4..c8f9aedfca 100644 --- a/datasets/CER_ES8_Terra-FM1_Edition4.json +++ b/datasets/CER_ES8_Terra-FM1_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_Terra-FM1_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_Terra-FM1_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Instantaneous Top-of-the-Atmosphere (TOA) Estimates Terra Flight Model 1 (FM1) Edition 4 data product, which was collected using the CERES-FM1 instrument on the Terra platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Footprint TOA Fluxes (ES-8) product contains 24 hours of instantaneous CERES data for a single scanner instrument, FM3, on the Aqua spacecraft. The ES-8 contains filtered radiances recorded every 0.01 second for the total (TOT), shortwave (SW), and window (WN) channels and the unfiltered SW, long-wave (LW), and WN radiances. The SW and LW radiances at spacecraft altitude are converted to TOA fluxes with a scene identification algorithm and Angular Distribution Models (ADMs), which are \"like\" those used for the ERBE. The TOA fluxes, scene identification, and angular geometry are included in the ES-8.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES8_Terra-FM2_Edition4.json b/datasets/CER_ES8_Terra-FM2_Edition4.json index cb063962d1..1f334ae0f0 100644 --- a/datasets/CER_ES8_Terra-FM2_Edition4.json +++ b/datasets/CER_ES8_Terra-FM2_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES8_Terra-FM2_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES8_Terra-FM2_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Instantaneous Top-of-the-Atmosphere (TOA) Estimates Terra Flight Model 2 (FM2) Edition 4 data product, which was collected using the CERES-FM2 instrument on the Terra platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Footprint TOA Fluxes (ES-8) product contains 24 hours of instantaneous CERES data for a single scanner instrument, FM3, on the Aqua spacecraft. The ES-8 contains filtered radiances recorded every 0.01-second for the total (TOT), shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW and LW radiances at spacecraft altitude are converted to TOA fluxes with a scene identification algorithm and Angular Distribution Models (ADMs), which are \"like\" those used for the ERBE. The TOA fluxes, scene identification, and angular geometry are included in the ES-8.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_Aqua-Xtrk_Edition4.json b/datasets/CER_ES9_Aqua-Xtrk_Edition4.json index 64c7a60b0c..1bf34d56a5 100644 --- a/datasets/CER_ES9_Aqua-Xtrk_Edition4.json +++ b/datasets/CER_ES9_Aqua-Xtrk_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_Aqua-Xtrk_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES9_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Aqua Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM3) and FM4 instruments on the Aqua platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Monthly Regional Averages (ES-9) products contain a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_NOAA20-FM6_Edition1.json b/datasets/CER_ES9_NOAA20-FM6_Edition1.json index 97018a332a..ef5f6c64ab 100644 --- a/datasets/CER_ES9_NOAA20-FM6_Edition1.json +++ b/datasets/CER_ES9_NOAA20-FM6_Edition1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_NOAA20-FM6_Edition1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES9_NOAA20-FM6_Edition1, CERES ERBE-like Monthly Regional Averages NOAA-20 FM6 Edition 1, contains TOA fluxes from the Clouds and the Earth's Radiant Energy System (CERES) instrument using algorithms identical to those used by ERBE, regional averages of instantaneous footprint TOA fluxes only for the hours of satellite overpass (from ES-8 Level 2 product). \r\nThe ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged CERES data for a single scanner instrument. The ES-9 is also produced for combinations of scanner instruments. All instantaneous shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is like the algorithm used for the Earth Radiation Budget Experiment (ERBE). The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. T The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_NPP-FM5_Edition2.json b/datasets/CER_ES9_NPP-FM5_Edition2.json index 9bbc2e30c2..d0aadc0e78 100644 --- a/datasets/CER_ES9_NPP-FM5_Edition2.json +++ b/datasets/CER_ES9_NPP-FM5_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_NPP-FM5_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for the Earth Radiation Budget Experiment (ERBE). The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes.\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_TRMM-PFM_Edition2.json b/datasets/CER_ES9_TRMM-PFM_Edition2.json index dd0014590a..5dc1c33d0e 100644 --- a/datasets/CER_ES9_TRMM-PFM_Edition2.json +++ b/datasets/CER_ES9_TRMM-PFM_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_TRMM-PFM_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES9_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Regional Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES-PFM on the Tropical Rainfall Measuring Mission (TRMM) platform. Data collection for this product is complete. \r\n\r\nCER_ES9_TRMM-PFM_Edition2 data are CERES instrument Top-of-the-Atmosphere (TOA) fluxes that used algorithms identical to those used by ERBE, regional averages of instantaneous footprint TOA fluxes only for the hours of satellite overpass (from ES-8 Level 2 product). The ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged CERES data for a single scanner instrument. The ES-9 is also produced for combinations of scanner instruments. All instantaneous short-wave and long-wave (LW) fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is like the algorithm used for the ERBE. The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_Terra+Aqua_Edition4.json b/datasets/CER_ES9_Terra+Aqua_Edition4.json index 23b606f440..32e2c1d13a 100644 --- a/datasets/CER_ES9_Terra+Aqua_Edition4.json +++ b/datasets/CER_ES9_Terra+Aqua_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_Terra+Aqua_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES9_Terra+Aqua_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Terra and Aqua Cross-track Edition4 data product. Data for this product is collected through the CERES-Flight Model 1 (FM1) and FM2 on the Terra platform and FM3 and FM4 on the Aqua platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged CERES data for both the Terra and Aqua satellites using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave (LW) fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ES9_Terra-Xtrk_Edition4.json b/datasets/CER_ES9_Terra-Xtrk_Edition4.json index d54cbfbc6d..05a9de7083 100644 --- a/datasets/CER_ES9_Terra-Xtrk_Edition4.json +++ b/datasets/CER_ES9_Terra-Xtrk_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ES9_Terra-Xtrk_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ES9_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES Flight Model 1 (FM1) and FM2 instruments on the Terra platform. Data collection for this product is ongoing.\r\n\r\nThe ERBE-like Monthly Regional Averages (ES-9) products contain a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_Aqua-FM4-MODIS_Edition2B.json b/datasets/CER_FSW_Aqua-FM4-MODIS_Edition2B.json index eba20a1200..f922a6636b 100644 --- a/datasets/CER_FSW_Aqua-FM4-MODIS_Edition2B.json +++ b/datasets/CER_FSW_Aqua-FM4-MODIS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_Aqua-FM4-MODIS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_Aqua-FM4-MODIS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Fixed Swath Width (FSW) Monthly Gridded Single Satellite Fluxes (SSF) and Clouds Aqua Flight Model 4 (FM4) Edition 2B data product. Data was collected using the CERES Scanner and FM4 on the Aqua platform. Data collection for this product is complete. \r\n\r\nCER_FSW_Aqua-FM4-MODIS_Edition2B includes legacy data covering regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmospheric (profile)], associated TOA observed fluxes, and cloud parameters only for the hours of satellite overpass (from the Clouds and Radiative Swath (CRS) level l2 product). The Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains a month of space and time-averaged CERES data for a single scanner instrument. The FSW is also produced for combinations of scanner instruments. All instantaneous fluxes from the CERES CRS product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_TRMM-PFM-VIRS_Edition2C.json b/datasets/CER_FSW_TRMM-PFM-VIRS_Edition2C.json index 6814f618a7..4b617d0dcb 100644 --- a/datasets/CER_FSW_TRMM-PFM-VIRS_Edition2C.json +++ b/datasets/CER_FSW_TRMM-PFM-VIRS_Edition2C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_TRMM-PFM-VIRS_Edition2C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_TRMM-PFM-VIRS_Edition2C is the Clouds and the Earth's Radiant Energy System (CERES) Fixed Swath Width (FSW) Monthly Gridded Single Satellite Fluxes (SSF) and Clouds Tropical Rainfall Measuring Mission (TRMM) Edition 2C data product. Data was collected by the CERES Scanner and CERES protoflight model (PFM) on the Tropical Rainfall Measuring Mission (TRMM) platform. Data collection for this product is complete.\r\n\r\nCER_FSW_TRMM-PFM-VIRS_Edition2C includes legacy data covering regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmospheric (profile)], associated TOA observed fluxes, and cloud parameters only for the hours of satellite overpass (from the Clouds and Radiative Swath (CRS) level2 product). The Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains a month of space and time averaged CERES data for a single scanner instrument. The FSW is also produced for combinations of scanner instruments. All instantaneous fluxes from the CERES CRS product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2C.json b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2C.json index 8a61553e03..fe62bb69bb 100644 --- a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2C.json +++ b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_Terra-FM1-MODIS_Edition2C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_Terra-FM1-MODIS_Edition2C is the Clouds and the Earth's Radiant Energy System (CERES) Fixed Swath Width (FSW) Monthly Gridded Single Satellite Fluxes and Clouds Terra Flight Model (FM1) Edition 2C data product, which was collected using the CERES-FM1 and the CERES Scanner instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmosphere (profile)], associated TOA observed fluxes and cloud parameters only for the hours of the satellite overpass. The FSW product contains a month of space and time-averaged CERES data for a single scanner instrument. All instantaneous fluxes from the CERES Clouds and Radiative Swath (CRS) product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and the TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2F.json b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2F.json index a418e70013..9eb58b4811 100644 --- a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2F.json +++ b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_Terra-FM1-MODIS_Edition2F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_Terra-FM1-MODIS_Edition2F is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Gridded Radiative Fluxes and Clouds Terra Flight Model 1 (FM1) Edition2F data product, which was collected using the CERES-FM1 and the CERES Scanner instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmosphere (profile)], associated TOA observed fluxes, and cloud parameters only for the hours of the satellite overpass. The FSW product contains a month of space and time-averaged CERES data for a single scanner instrument. All instantaneous fluxes from the CERES Clouds and Radiative Swath (CRS) product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and the TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2G.json b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2G.json index 7fd71d393a..93d6ef5f51 100644 --- a/datasets/CER_FSW_Terra-FM1-MODIS_Edition2G.json +++ b/datasets/CER_FSW_Terra-FM1-MODIS_Edition2G.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_Terra-FM1-MODIS_Edition2G", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_Terra-FM1-MODIS_Edition2G is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Gridded Radiative Fluxes and Clouds Terra Flight Model (FM1) Edition2G data product, which was collected using the CERES-FM1 and the CERES Scanner instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmosphere (profile)], associated TOA observed fluxes, and cloud parameters only for the hours of the satellite overpass. The FSW product contains a month of space and time-averaged CERES data for a single scanner instrument. All instantaneous fluxes from the CERES Clouds and Radiative Swath (CRS) product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and the TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FSW_Terra-FM2-MODIS_Edition2C.json b/datasets/CER_FSW_Terra-FM2-MODIS_Edition2C.json index 0fcf563790..6fb5514fb3 100644 --- a/datasets/CER_FSW_Terra-FM2-MODIS_Edition2C.json +++ b/datasets/CER_FSW_Terra-FM2-MODIS_Edition2C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FSW_Terra-FM2-MODIS_Edition2C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FSW_Terra-FM2-MODIS_Edition2C is the Clouds and the Earth's Radiant Energy System (CERES) Fixed Swath Width (FSW) Monthly Gridded Single Satellite Fluxes (SSF) and Clouds Terra Flight Model 2 (FM2) Edition 2C data product. Data was collected by CERES FM2 and the CERES Scanner on Terra. Data collection for this product is complete. \r\n\r\nCER_FSW_Terra-FM2-MODIS_Edition2C includes legacy data covering regional averages of instantaneous footprint computed fluxes [Top-of-the-Atmosphere (TOA), surface, and in-atmospheric (profile)], associated TOA observed fluxes, and cloud parameters only for the hours of satellite overpass (from the Clouds and Radiative Swath (CRS) level 2 product). The Monthly Gridded Radiative Fluxes and Clouds (FSW) product contains a month of space and time-averaged CERES data for a single scanner instrument. The FSW is also produced for combinations of scanner instruments. All instantaneous fluxes from the CERES CRS product for a month are sorted by 1-degree spatial regions and by the Universal Time (UT) hour of observation. The mean of the instantaneous fluxes for a given region-hour bin is determined and recorded on the FSW, along with other flux statistics and scene information. The mean adjusted fluxes at the four atmospheric levels defined by CRS are also included for both clear-sky and total-sky scenes. In addition, four cloud height categories are defined by dividing the atmosphere into four intervals with boundaries at the surface: 700-, 500-, 300-hPa, and TOA. The cloud layers from CRS are put into one of the cloud height categories and averaged over the region. The cloud properties are also column averaged and included on the FSW.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B.json b/datasets/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B.json index 549e3b2bfd..68c80d3b72 100644 --- a/datasets/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B.json +++ b/datasets/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1B data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. Data collection for this product is ongoing. \r\n\r\nCER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B provides the monthly mean daytime CERES fluxes and CERES-VIIRS cloud properties that have been spatially gridded into 1\u00b0 regions along both the NOAA-20 ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Day Edition1B product inputs Single Scanner Footprint (SSF) Edition1B footprint data. Within each footprint, all 1-km pixel-level VIIRS-retrieved cloud properties are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The VIIRS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A.json index 951240b9e4..de4930bad1 100644 --- a/datasets/CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Daily Daytime Mean Regionally Averaged Terra and Aqua Top-of-Atmosphere (TOA) Fluxes and associated cloud properties stratified by the optical depth and effective pressure Edition4A data product. Data was collected using CERES Flight Model 1 (FM1), FM2, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and CERES-FM3, FM4, and MODIS on Aqua. Data collection is ongoing. \r\n\r\nCER_FluxByCldTyp-Day_Terra-Aqua-MODIS_Edition4A provides instantaneous daytime CERES fluxes and CERES-MODIS cloud properties that have been spatially gridded into 1\u00b0 regions along both the Terra and Aqua ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Day Edition4A product inputs Single Scanner Footprint (SSF) Edition4A footprint data. All 1-km pixel-level MODIS-retrieved cloud properties within each footprint are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The MODIS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a single day.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B.json b/datasets/CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B.json index 47c24cd6c4..912c206c91 100644 --- a/datasets/CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B.json +++ b/datasets/CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1B data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. Data collection for this product is ongoing. \r\n\r\nCER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1B provides the monthly mean daytime CERES fluxes and CERES-VIIRS cloud properties that have been spatially gridded into 1\u00b0 regions along both the Terra and Aqua ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Month Edition1B product inputs Single Scanner Footprint (SSF) Edition1B footprint data. Within each footprint, all 1-km pixel-level VIIRS-retrieved cloud properties are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The VIIRS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a single day.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A.json index d8defe0003..e349c85540 100644 --- a/datasets/CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged Terra and Aqua Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 4A data product. Data was collected using CERES Flight Model 1 (FM1), FM2, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and FM3, FM4, and MODIS on Aqua. Data collection for this product is ongoing. \r\n\r\nCER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A provides the monthly mean daytime CERES fluxes and CERES-Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud properties that have been spatially gridded into 1\u00b0 regions along both the Terra and Aqua ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Month Edition4A product inputs Single Scanner Footprint (SSF) Edition4A footprint data. All 1-km pixel-level MODIS-retrieved cloud properties within each footprint are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The MODIS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a single day.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GMS05_FD_V02.json b/datasets/CER_GEO_Ed4_GMS05_FD_V02.json index 2cf906e5b1..41cf5ca970 100644 --- a/datasets/CER_GEO_Ed4_GMS05_FD_V02.json +++ b/datasets/CER_GEO_Ed4_GMS05_FD_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GMS05_FD_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GMS05_FD_V02 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 GMS-5 over the Full Disk (FD) Version 2 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer instrument on the GMS-5 platform.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GMS-5 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE08_NH_V01.json b/datasets/CER_GEO_Ed4_GOE08_NH_V01.json index 2eef238915..54d3bc531c 100644 --- a/datasets/CER_GEO_Ed4_GOE08_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE08_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE08_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE08_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 8 (GOES-8) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-8 Imager on the GOES-8 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-8 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE08_SH_V01.json b/datasets/CER_GEO_Ed4_GOE08_SH_V01.json index 6e6b0d4ef1..4ac39a56d7 100644 --- a/datasets/CER_GEO_Ed4_GOE08_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE08_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE08_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE08_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 8 (GOES-8) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-8 Imager on the GOES-8 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-8 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE09_NH_V01.json b/datasets/CER_GEO_Ed4_GOE09_NH_V01.json index 3699798bf9..1f0f0ce4a1 100644 --- a/datasets/CER_GEO_Ed4_GOE09_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE09_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE09_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE09_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 9 (GOES-9) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-9 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-9 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE09_SH_V01.json b/datasets/CER_GEO_Ed4_GOE09_SH_V01.json index b84c4477be..830b925d12 100644 --- a/datasets/CER_GEO_Ed4_GOE09_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE09_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE09_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE09_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 9 (GOES-9) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-9 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-9 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE10_NH_V01.json b/datasets/CER_GEO_Ed4_GOE10_NH_V01.json index df57071a38..942f4f765e 100644 --- a/datasets/CER_GEO_Ed4_GOE10_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE10_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE10_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE10_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 10 (GOES-10) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-10 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-10 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE10_SH_V01.json b/datasets/CER_GEO_Ed4_GOE10_SH_V01.json index 9d103927f5..6757a11114 100644 --- a/datasets/CER_GEO_Ed4_GOE10_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE10_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE10_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE10_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 10 (GOES-10) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-10 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-10 geostationary satellite imager data using the Langley Research Center's (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE11_NH_V01.json b/datasets/CER_GEO_Ed4_GOE11_NH_V01.json index 57b054e8cb..53f3499600 100644 --- a/datasets/CER_GEO_Ed4_GOE11_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE11_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE11_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE11_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 11 (GOES-11) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-11 Imager on the GOES-11 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-11 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE11_SH_V01.json b/datasets/CER_GEO_Ed4_GOE11_SH_V01.json index e4de28f1d5..00eb676a90 100644 --- a/datasets/CER_GEO_Ed4_GOE11_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE11_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE11_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE11_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 11 (GOES-11) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-11 Imager on the GOES-11 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-11 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE12_NH_V01.json b/datasets/CER_GEO_Ed4_GOE12_NH_V01.json index 5f81b8fad6..8266ea84ab 100644 --- a/datasets/CER_GEO_Ed4_GOE12_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE12_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE12_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE12_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 12 (GOES-12) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-12 Imager on the GOES-12 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-12 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE12_SH_V01.json b/datasets/CER_GEO_Ed4_GOE12_SH_V01.json index 4e31b2460d..c5d6a4d26d 100644 --- a/datasets/CER_GEO_Ed4_GOE12_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE12_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE12_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE12_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 12 (GOES-12) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-12 Imager on the GOES-12 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-12 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE13_NH_V01.2.json b/datasets/CER_GEO_Ed4_GOE13_NH_V01.2.json index fabc932c87..09b123b840 100644 --- a/datasets/CER_GEO_Ed4_GOE13_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE13_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE13_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE13_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 13 (GOES-13) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the GOES-13 Imager on the GOES-13 Platform.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-13 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms in support of the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE13_NH_V01.json b/datasets/CER_GEO_Ed4_GOE13_NH_V01.json index bfe9d7f1e7..2557e7817c 100644 --- a/datasets/CER_GEO_Ed4_GOE13_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE13_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE13_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE13_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 13 (GOES-13) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-13 Imager on the GOES-13 platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-13 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE13_SH_V01.2.json b/datasets/CER_GEO_Ed4_GOE13_SH_V01.2.json index 1ea63e8a64..1b45ecb2dc 100644 --- a/datasets/CER_GEO_Ed4_GOE13_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE13_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE13_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE13_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 13 (GOES-13) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the GOES-13 Imager on the GOES-13 Platform.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-13 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE13_SH_V01.json b/datasets/CER_GEO_Ed4_GOE13_SH_V01.json index 840f0941f2..b27c850a77 100644 --- a/datasets/CER_GEO_Ed4_GOE13_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE13_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE13_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE13_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 13 (GOES-13) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-13 Imager on the GOES-13 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-13 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE14_NH_V01.json b/datasets/CER_GEO_Ed4_GOE14_NH_V01.json index 8ebaf1dafe..93c30550c9 100644 --- a/datasets/CER_GEO_Ed4_GOE14_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE14_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE14_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE14_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 14 (GOES-14) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-14 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-14 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE14_SH_V01.json b/datasets/CER_GEO_Ed4_GOE14_SH_V01.json index c2e319b77c..3b74d99259 100644 --- a/datasets/CER_GEO_Ed4_GOE14_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE14_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE14_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE14_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 14 (GOES-14) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-I-M Imager on the GOES-14 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-14 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE15_NH_V01.2.json b/datasets/CER_GEO_Ed4_GOE15_NH_V01.2.json index 0eaf44d823..c208a1c5ce 100644 --- a/datasets/CER_GEO_Ed4_GOE15_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE15_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE15_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE15_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-15 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE15_NH_V01.json b/datasets/CER_GEO_Ed4_GOE15_NH_V01.json index 394d903a26..151b5be82d 100644 --- a/datasets/CER_GEO_Ed4_GOE15_NH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE15_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE15_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE15_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-15 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE15_SH_V01.2.json b/datasets/CER_GEO_Ed4_GOE15_SH_V01.2.json index aaa4aa6767..574b86a7ee 100644 --- a/datasets/CER_GEO_Ed4_GOE15_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE15_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE15_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE15_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform. Data collection for this product is complete.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-15 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE15_SH_V01.json b/datasets/CER_GEO_Ed4_GOE15_SH_V01.json index d6b51e365c..8398807c5a 100644 --- a/datasets/CER_GEO_Ed4_GOE15_SH_V01.json +++ b/datasets/CER_GEO_Ed4_GOE15_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE15_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE15_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-15 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE16_NH_V01.2.json b/datasets/CER_GEO_Ed4_GOE16_NH_V01.2.json index d42ffe0cec..32cb41c6d4 100644 --- a/datasets/CER_GEO_Ed4_GOE16_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE16_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE16_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE16_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the GOES-16 Imager on the GOES-16 Platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE16_SH_V01.2.json b/datasets/CER_GEO_Ed4_GOE16_SH_V01.2.json index 060aefe13d..7c2a045b3d 100644 --- a/datasets/CER_GEO_Ed4_GOE16_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE16_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE16_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE16_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the GOES-16 Imager on the GOES-16 Platform. Data collection for this product is in progress.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE17_NH_V01.2.json b/datasets/CER_GEO_Ed4_GOE17_NH_V01.2.json index e46637c66f..5f6ae0faa3 100644 --- a/datasets/CER_GEO_Ed4_GOE17_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE17_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE17_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE17_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 17 (GOES-17) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using an Imager on the GOES-17 Platform. Data collection for this product is in progress. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-17 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE17_SH_V01.2.json b/datasets/CER_GEO_Ed4_GOE17_SH_V01.2.json index 9a0d0b97d5..d71f670f9b 100644 --- a/datasets/CER_GEO_Ed4_GOE17_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE17_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE17_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE17_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 17 (GOES-17) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Baseline Imager on the GOES-17 Platform. Data collection for this product is in progress. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-17 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE18_NH_V01.2.json b/datasets/CER_GEO_Ed4_GOE18_NH_V01.2.json index e9b7d9b0ef..a793f57108 100644 --- a/datasets/CER_GEO_Ed4_GOE18_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE18_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE18_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE18_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 18 (GOES-18) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Advanced Baseline Imager on the GOES-18 Platform. Data collection for this product is in progress. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-18 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_GOE18_SH_V01.2.json b/datasets/CER_GEO_Ed4_GOE18_SH_V01.2.json index 64cc129b35..1960add947 100644 --- a/datasets/CER_GEO_Ed4_GOE18_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_GOE18_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_GOE18_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_GOE18_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 18 (GOES-18) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Baseline Imager on the GOES-18 Platform. Data collection for this product is in progress. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from GOES-18 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_HIM08_NH_V01.2.json b/datasets/CER_GEO_Ed4_HIM08_NH_V01.2.json index 91a6f1675d..58e92ed1ac 100644 --- a/datasets/CER_GEO_Ed4_HIM08_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_HIM08_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_HIM08_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_HIM08_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Himawari-8 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Advanced Himawari Imager (AHI) Instrument on the Himawari-8 platform. Data collection for this product is in progress. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Himawari-8 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_HIM08_SH_V01.2.json b/datasets/CER_GEO_Ed4_HIM08_SH_V01.2.json index 9f83ed99e4..d1d34551a9 100644 --- a/datasets/CER_GEO_Ed4_HIM08_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_HIM08_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_HIM08_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_HIM08_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Himawari-8 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Himawari Imager (AHI) Instrument on the Himawari-8 platform. Data collection for this product is in progress.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Himawari-8 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_HIM09_NH_V01.2.json b/datasets/CER_GEO_Ed4_HIM09_NH_V01.2.json index 882eb808e6..90b71917a8 100644 --- a/datasets/CER_GEO_Ed4_HIM09_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_HIM09_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_HIM09_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_HIM09_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Himawari-9 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Advanced Himawari Imager (AHI) Instrument on the Himawari-9 platform. Data collection for this product is in progress. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Himawari-9 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_HIM09_SH_V01.2.json b/datasets/CER_GEO_Ed4_HIM09_SH_V01.2.json index 3b1c5e2b8b..a548d0e62f 100644 --- a/datasets/CER_GEO_Ed4_HIM09_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_HIM09_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_HIM09_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_HIM09_SH_V01.2 is the Satellite Cloud and Radiation Property Retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Himawari-9 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Himawari Imager (AHI) Instrument on the Himawari-9 platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. There are no changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Himawari-9 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET05_FD_V02.json b/datasets/CER_GEO_Ed4_MET05_FD_V02.json index 8a389277b5..7842d992dc 100644 --- a/datasets/CER_GEO_Ed4_MET05_FD_V02.json +++ b/datasets/CER_GEO_Ed4_MET05_FD_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET05_FD_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET05_FD_V02 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-5 over the Full Disk (FD) Version 2 data product. Data was collected using the Meteosat Visible Infra-Red Imager instrument on the Meteosat-5 platform.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-5 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET07_FD_V02.json b/datasets/CER_GEO_Ed4_MET07_FD_V02.json index 2fe98ed4e5..34e853dbd3 100644 --- a/datasets/CER_GEO_Ed4_MET07_FD_V02.json +++ b/datasets/CER_GEO_Ed4_MET07_FD_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET07_FD_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET07_FD_V02 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-7 over the Full Disk (FD) Version 2 data product. Data was collected using the Meteosat Visible Infra-Red Imager instrument on the Meteosat-7 platform.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-7 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET08_NH_V01.2.json b/datasets/CER_GEO_Ed4_MET08_NH_V01.2.json index 03598ea755..54563adb35 100644 --- a/datasets/CER_GEO_Ed4_MET08_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET08_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET08_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET08_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-8 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-8 platform. Data collection for this product is in progress. \r\n\r\nNote: Version 1.2 covers the period when the satellite is moved to 41\u00b0 E.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-8 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET08_NH_V01.json b/datasets/CER_GEO_Ed4_MET08_NH_V01.json index 5c428e7eed..dd2f8abb78 100644 --- a/datasets/CER_GEO_Ed4_MET08_NH_V01.json +++ b/datasets/CER_GEO_Ed4_MET08_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET08_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET08_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-8 over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-8 platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-8 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET08_SH_V01.2.json b/datasets/CER_GEO_Ed4_MET08_SH_V01.2.json index f648cd44cc..6ac356bb9f 100644 --- a/datasets/CER_GEO_Ed4_MET08_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET08_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET08_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET08_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-8 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-8 platform. Data collection for this product is in progress.\r\n\r\nNote: Version 1.2 covers when the satellite moves to 41\u00b0 E.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-8 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET08_SH_V01.json b/datasets/CER_GEO_Ed4_MET08_SH_V01.json index bbf1c1ace1..92538eb1bf 100644 --- a/datasets/CER_GEO_Ed4_MET08_SH_V01.json +++ b/datasets/CER_GEO_Ed4_MET08_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET08_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET08_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-8 over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-8 platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-8 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 9 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET09_NH_V01.2.json b/datasets/CER_GEO_Ed4_MET09_NH_V01.2.json index 1bdcf81263..927d728667 100644 --- a/datasets/CER_GEO_Ed4_MET09_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET09_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET09_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET09_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-9 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-9 platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-9 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET09_NH_V01.json b/datasets/CER_GEO_Ed4_MET09_NH_V01.json index ab26fd0031..c865d48f32 100644 --- a/datasets/CER_GEO_Ed4_MET09_NH_V01.json +++ b/datasets/CER_GEO_Ed4_MET09_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET09_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET09_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-9 over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-9 platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-9 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET09_SH_V01.2.json b/datasets/CER_GEO_Ed4_MET09_SH_V01.2.json index 77bb62d349..57cdbb2e04 100644 --- a/datasets/CER_GEO_Ed4_MET09_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET09_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET09_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET09_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-9 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-9 platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set is comprised of cloud micro-physical and radiation properties derived hourly from Meteosat-9 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms in support of the Clouds and the Earth's Radiant Energy System (CERES) project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET09_SH_V01.json b/datasets/CER_GEO_Ed4_MET09_SH_V01.json index 63390e31a3..857722bb54 100644 --- a/datasets/CER_GEO_Ed4_MET09_SH_V01.json +++ b/datasets/CER_GEO_Ed4_MET09_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET09_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET09_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-9 over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-9 platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-9 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 9 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET10_NH_V01.2.json b/datasets/CER_GEO_Ed4_MET10_NH_V01.2.json index 6445b4f5c3..7dcbbf0ea1 100644 --- a/datasets/CER_GEO_Ed4_MET10_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET10_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET10_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET10_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-10 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-10 platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-1 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET10_NH_V01.json b/datasets/CER_GEO_Ed4_MET10_NH_V01.json index 4ade2c98fa..f8231f837f 100644 --- a/datasets/CER_GEO_Ed4_MET10_NH_V01.json +++ b/datasets/CER_GEO_Ed4_MET10_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET10_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET10_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-10 over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-10 platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-10 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET10_SH_V01.2.json b/datasets/CER_GEO_Ed4_MET10_SH_V01.2.json index c56b4f4662..ab49080ad6 100644 --- a/datasets/CER_GEO_Ed4_MET10_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET10_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET10_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET10_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-10 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-10 platform. \r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-10 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 9 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET10_SH_V01.json b/datasets/CER_GEO_Ed4_MET10_SH_V01.json index f79b2631ed..4314dcdba0 100644 --- a/datasets/CER_GEO_Ed4_MET10_SH_V01.json +++ b/datasets/CER_GEO_Ed4_MET10_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET10_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET10_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-10 over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-10 platform. Data collection for this product is complete.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-10 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 9 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET11_NH_V01.2.json b/datasets/CER_GEO_Ed4_MET11_NH_V01.2.json index d38c25cef4..61c0ed05cb 100644 --- a/datasets/CER_GEO_Ed4_MET11_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET11_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET11_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET11_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-11 over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-11 platform. Data collection for this product is in progress. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-11 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MET11_SH_V01.2.json b/datasets/CER_GEO_Ed4_MET11_SH_V01.2.json index 72b5cad9cf..5627ec1ff6 100644 --- a/datasets/CER_GEO_Ed4_MET11_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MET11_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MET11_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MET11_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Meteosat-11 over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Instrument on the Meteosat-11 platform. Data collection for this product is in progress.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from Meteosat-11 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS01_NH_V01.json b/datasets/CER_GEO_Ed4_MTS01_NH_V01.json index b3784fa2ed..bd20bc1a6a 100644 --- a/datasets/CER_GEO_Ed4_MTS01_NH_V01.json +++ b/datasets/CER_GEO_Ed4_MTS01_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS01_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS01_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 1 Replacement (MTSAT-1R) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on the Multi-functional Transport Satellite 1 Replacement (MTSAT-1R) platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from MTSAT-1R geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS01_SH_V01.json b/datasets/CER_GEO_Ed4_MTS01_SH_V01.json index be4ba9181d..a100bc0e59 100644 --- a/datasets/CER_GEO_Ed4_MTS01_SH_V01.json +++ b/datasets/CER_GEO_Ed4_MTS01_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS01_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS01_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 1 Replacement (MTSAT-1R) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on the Multi-functional Transport Satellite 1 Replacement (MTSAT-1R) platform. Data collection for this product is complete.\r\n\r\nThis data set is comprised of cloud micro-physical and radiation properties derived hourly from MTSAT-1R geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms in support of the Clouds and the Earth's Radiant Energy System (CERES) project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 3 km resolution (at nadir) and are sub-sampled to 6 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS02_NH_V01.2.json b/datasets/CER_GEO_Ed4_MTS02_NH_V01.2.json index 112dab3601..14613ea414 100644 --- a/datasets/CER_GEO_Ed4_MTS02_NH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MTS02_NH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS02_NH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS02_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 2 Replacement (MTSAT-2R) over the Northern Hemisphere (NH) Version 1.2 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on the Multi-functional Transport Satellite 2 (MTSAT-2) platform.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from MTSAT-2 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS02_NH_V01.json b/datasets/CER_GEO_Ed4_MTS02_NH_V01.json index eb25f0d50b..fb58d6ce63 100644 --- a/datasets/CER_GEO_Ed4_MTS02_NH_V01.json +++ b/datasets/CER_GEO_Ed4_MTS02_NH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS02_NH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS02_NH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 2 Replacement (MTSAT-2R) over the Northern Hemisphere (NH) Version 1.0 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on the Multi-functional Transport Satellite 2 (MTSAT-2) platform. Data collection for this product is complete. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from MTSAT-2 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS02_SH_V01.2.json b/datasets/CER_GEO_Ed4_MTS02_SH_V01.2.json index f10c1092fa..843a95057c 100644 --- a/datasets/CER_GEO_Ed4_MTS02_SH_V01.2.json +++ b/datasets/CER_GEO_Ed4_MTS02_SH_V01.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS02_SH_V01.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS02_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 2 Replacement (MTSAT-2R) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on the Multi-functional Transport Satellite 2 (MTSAT-2) platform. Data collection for this product is in progress.\r\n\r\nNote: Version 1.2 is identical to version 1.0. No changes in the retrieval algorithm.\r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from MTSAT-2 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud micro-physical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_GEO_Ed4_MTS02_SH_V01.json b/datasets/CER_GEO_Ed4_MTS02_SH_V01.json index 48a1acd6b2..e84968ba93 100644 --- a/datasets/CER_GEO_Ed4_MTS02_SH_V01.json +++ b/datasets/CER_GEO_Ed4_MTS02_SH_V01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_GEO_Ed4_MTS02_SH_V01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_GEO_Ed4_MTS02_SH_V01 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Multi-functional Transport Satellite 2 Replacement (MTSAT-2R) over the Southern Hemisphere (SH) Version 1.0 data product. Data was collected using the Visible and Infrared Spin Scan Radiometer (VISSR) Instrument on The Multi-functional Transport Satellite 2 (MTSAT-2) platform. \r\n\r\nThis data set comprises cloud micro-physical and radiation properties derived hourly from MTSAT-2 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. Each active geostationary satellite's cloud microphysical and radiation properties are merged to create hourly global cloud properties that estimate fluxes between CERES instrument measurements to account for the changing diurnal cycle. The data set is arranged as files for each hour and in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A.json index da0b63db27..f539d9412d 100644 --- a/datasets/CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Day_Aqua-FM3-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) Day 2 like Format Daytime Aqua Flight Model 3 (FM3) Edition data product, which was collected using the CERES-FM3 and MODIS instruments on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A.json index 61a9c80b6a..2cba166004 100644 --- a/datasets/CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Day_Aqua-FM4-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP)-Day2-like Format Daytime Aqua Flight Model 4 (FM4) Edition 3A data product, which was collected using the CERES FM-4 and MODIS instruments on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Mrg) data products contain monthly and monthly 3-hourly (GMT-based) gridded regional mean cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. The merged (Mrg) product combines daytime cloud properties from Terra-MODIS (10:30 AM local equator crossing time LECT), Aqua-MODIS (1:30 PM LECT), and geostationary satellites (GEO) to provide the most diurnally complete daytime ISCCP-D2like product. The GEO cloud properties have been normalized with MODIS for diurnal consistency. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties provide coverage from pole to pole. The 3-hourly GMT-based GEO cloud properties come from five satellites at 8 km nominal resolution with limited coverage. The GEO daytime cloud retrievals incorporate only a visible and IR channel common to all geostationary satellites for spatial consistency. The geostationary calibration is normalized to Terra-MODIS. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A.json index afe20d4b4b..3752d0e321 100644 --- a/datasets/CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Day_Terra-FM1-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) Day 2 like Format Daytime Terra Flight Model (FM1) Edition 3A data product, which was collected using the CERES-FM1 and MODIS instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A.json index b20830e577..23bf0b14f6 100644 --- a/datasets/CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Day_Terra-FM2-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP)-Day 2 like Format Daytime Terra Flight Model (FM2) Edition 3A data product, which was collected using the CERES-FM2 and MODIS instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-GEO_DAY_Edition3A.json b/datasets/CER_ISCCP-D2like-GEO_DAY_Edition3A.json index 609122d36f..fe95a96a4b 100644 --- a/datasets/CER_ISCCP-D2like-GEO_DAY_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-GEO_DAY_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-GEO_DAY_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-GEO_DAY_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP)-D2like Format Daytime Edition3A data product. Data collection for this product is complete. \r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-GEO) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean geostationary satellite (GEO) cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. The ISCCP-D2like-GEO product is a 5-satellite, daytime 3-hourly GMT, 8-km nominal resolution, geostationary-only cloud product limited to . The ISCCP-D2like-GEO is a daytime-only product, where the cloud retrievals incorporate only the visible and IR channels common to all geostationary satellites for spatial consistency. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, protoflight model (PFM), was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A.json b/datasets/CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A.json index fc7cbed2ba..3fd04fa529 100644 --- a/datasets/CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Mrg_GEO-MODIS-DAY_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Satellite (GEO) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) \u2013 Day 2like Format Daytime Edition3A data product. This product is a merge of data from the following platforms and instruments: Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat Operational Programme 10 (METEOSAT-10); Japanese Advanced Meteorological Imager (JAMI) on The Multi-functional Transport Satellite 2 (MTSAT-2); SEVIRI on METEOSAT-9; Visible and Infrared Spin Scan Radiometer (GMS Series) on (VISSR-GMS) on Geostationary Meteorological Satellite-5 (GMS-5); SEVIRI on METEOSAT-8; Geostationary Operational Environmental Satellite (GOES) I-M IMAGER on Geostationary Operational Environmental Satellite 9 (GOES-9); GOES-11 IMAGER on GOES-11; GOES N-P IMAGER on GOES-13; GOES-8 IMAGER on GOES-8; GOES I-M IMAGER on GOES-10; SEVIRI on METEOSAT-7; MODIS on Terra; GOES N-P IMAGER on GOES-14; MVIRI on METEOSAT-5; GOES-12 IMAGER on GOES-12; GOES-15 IMAGER on GOES-15; MODIS on Aqua; JAMI on Multi-functional Transport Satellite 1 Replacement (MTSAT-1R). Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Mrg) data products contain monthly and monthly 3-hourly (GMT-based) gridded regional mean cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. The merged (Mrg) product combines daytime cloud properties from Terra-MODIS (10:30 AM local equator crossing time LECT), Aqua-MODIS (1:30 PM LECT), and geostationary satellites (GEO) to provide the most diurnally complete daytime ISCCP-D2like product. The GEO cloud properties have been normalized with MODIS for diurnal consistency. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived cloud properties provide coverage from pole to pole. The 3-hourly GMT-based GEO cloud properties come from five satellites at 8 km nominal resolution with limited coverage. The GEO daytime cloud retrievals incorporate only a visible and IR channel common to all geostationary satellites for spatial consistency. The geostationary calibration is normalized to Terra-MODIS. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A.json index ddef76243e..d0c24a801a 100644 --- a/datasets/CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Nit_Aqua-FM3-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) Day 2 like Format Nighttime Aqua Flight Model 3 (FM3) Edition 3A data product, which was collected using the CERES-FM3 and MODIS instruments on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals, also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A.json index 385a0bd871..a0f36b68bc 100644 --- a/datasets/CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Nit_Aqua-FM4-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP)-Day 2 like Format Nighttime Aqua FM4 Edition 3A data product, which was collected using the CERES-FM4 and MODIS instruments on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals, also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A.json index eaf09f61ce..cf260606af 100644 --- a/datasets/CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Nit_Terra-FM1-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) Day 2 like Format Nighttime Terra Flight Model 1 (FM1) Edition 3A data product, which was collected using the CERES-FM1 and MODIS instruments on the Terra platform. Data collection for this product is complete.\r\n\r\nThe Monthly Gridded Cloud Averages (ISCCP-D2like-Day) data product contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals, also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A.json b/datasets/CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A.json index dbacbb438c..d157156e67 100644 --- a/datasets/CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A.json +++ b/datasets/CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A is the Clouds and the Earth's Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Retrievals in International Satellite Cloud Climatology Project (ISCCP) Day 2-like Format Nighttime Terra Flight Model 2 (FM2) Edition 3A data product, which was collected using the CERES-FM2 and MODIS instruments on the Terra platform and MODIS on the Aqua platform. Data collection for this product is complete.\r\n\r\nCER_ISCCP-D2like-Nit_Terra-FM2-MODIS_Edition3A contains monthly and monthly 3-hourly (GMT-based) gridded regional mean CERES MODIS-derived cloud properties as a function of 18 cloud types, similar to the ISCCP D2 product, where the cloud properties are stratified by pressure, optical depth, and phase. There are separate daytime and nighttime data sets for both Terra-MODIS and Aqua-MODIS. The retrievals, and therefore the quality, are different for each data set. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals, also available in other CERES products. The CERES MODIS-derived cloud properties have coverage from pole to pole. For these MODIS-based ISCCP-D2like products, the cloud fractions for 42 cloud types, similar to the ISCCP D1 product, are also available. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) data product is the input to this product. Each ISCCP-D2like file covers a single month.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Day_Aqua-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Day_Aqua-MODIS_Edition4A.json index b718b55cd9..5b744849ca 100644 --- a/datasets/CER_SSF1deg-Day_Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Day_Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Day_Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Day_Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Daily Aqua Edition4A data product, which was collected using the CERES-Flight Model 3 (FM3) and FM4 instruments on the Aqua platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint one degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B.json b/datasets/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B.json index 4ea6bdc304..824a59f632 100644 --- a/datasets/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B.json +++ b/datasets/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Day_NPP-VIIRS_Edition2A.json b/datasets/CER_SSF1deg-Day_NPP-VIIRS_Edition2A.json index f2242481f3..bfa798b4bb 100644 --- a/datasets/CER_SSF1deg-Day_NPP-VIIRS_Edition2A.json +++ b/datasets/CER_SSF1deg-Day_NPP-VIIRS_Edition2A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Day_NPP-VIIRS_Edition2A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Day_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Daily SUOMI National Polar-orbiting Partnership (NPP) Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi NPP platform. Data collection for this product is ongoing.\r\n\r\nThe CERES Single Scanner Footprint One Degree (SSF1deg) Daily product provides daily averages of regional constant meteorology temporally interpolated CERES TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the Solar Radiation and Climate Experiment (SORCE), Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Day_Terra-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Day_Terra-MODIS_Edition4A.json index fbede1b9bd..01f04cd599 100644 --- a/datasets/CER_SSF1deg-Day_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Day_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Day_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Day_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Daily Terra Edition 4A data product, which was collected using the CERES-Flight Model (FM1) and FM2 instruments on the Terra platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint one degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Hour_Aqua-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Hour_Aqua-MODIS_Edition4A.json index f23b6fd59f..94e3a7df83 100644 --- a/datasets/CER_SSF1deg-Hour_Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Hour_Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Hour_Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Hour_Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Regionally Averaged Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Hourly Aqua Edition 4A data product, which was collected using the CERES Flight Model 3 (FM3), FM4, and MODIS instruments on the Aqua platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B.json b/datasets/CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B.json index f9453af226..506d0b4d4e 100644 --- a/datasets/CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B.json +++ b/datasets/CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Hour provides regional averages of CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Hour_NPP-VIIRS_Edition2A.json b/datasets/CER_SSF1deg-Hour_NPP-VIIRS_Edition2A.json index c4fee39b35..8bf0656ac5 100644 --- a/datasets/CER_SSF1deg-Hour_NPP-VIIRS_Edition2A.json +++ b/datasets/CER_SSF1deg-Hour_NPP-VIIRS_Edition2A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Hour_NPP-VIIRS_Edition2A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Hour_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Regionally Averaged Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Hourly Suomi National Polar-orbiting Partnership (NPP) Visible-Infrared Imager-Radiometer Suite (VIIRS) Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and VIIRS instruments on the Suomi NPP platform. Data collection for this product is in progress.\r\n\r\nThe CERES SSF1deg Hour product provides regional averages of CERES TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Hour_Terra-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Hour_Terra-MODIS_Edition4A.json index 075574a581..7069bfeb79 100644 --- a/datasets/CER_SSF1deg-Hour_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Hour_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Hour_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Regionally Averaged Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Hourly Terra Edition4A data product, which was collected using the CERES Flight Model 1 (FM1) and FM2 instruments on the Terra platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Month_Aqua-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Month_Aqua-MODIS_Edition4A.json index db986e50ad..5ab1e9045d 100644 --- a/datasets/CER_SSF1deg-Month_Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Month_Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Month_Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Month_Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Aqua Edition4A data product, which was collected using the CERES Flight Model 3 (FM3) and FM4 instruments on the Aqua platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B.json b/datasets/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B.json index 1fa406b56a..fc097e2b72 100644 --- a/datasets/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B.json +++ b/datasets/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Month provides monthly averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. One-degree zonally and global averaged values for the parameters are also provided. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Month_NPP-VIIRS_Edition2A.json b/datasets/CER_SSF1deg-Month_NPP-VIIRS_Edition2A.json index f068295038..53f1cdf492 100644 --- a/datasets/CER_SSF1deg-Month_NPP-VIIRS_Edition2A.json +++ b/datasets/CER_SSF1deg-Month_NPP-VIIRS_Edition2A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Month_NPP-VIIRS_Edition2A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Month_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Time-Interpolated Top-of-Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi National Polar-orbiting Partnership (NPP) platform. Data collection for this product is in progress.\r\n\r\nCERES SSF1deg Month provides monthly averages of regional constant meteorology temporally interpolated CERES TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. One-degree zonally and global averaged values for the parameters are also provided. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the Solar Radiation and Climate Experiment (SORCE) Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF1deg-Month_Terra-MODIS_Edition4A.json b/datasets/CER_SSF1deg-Month_Terra-MODIS_Edition4A.json index 772670f896..7359fbb234 100644 --- a/datasets/CER_SSF1deg-Month_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SSF1deg-Month_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF1deg-Month_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using the CERES Flight Model 1 (FM1), FM2, and MODIS instruments on the Terra platform. Data collection for this product is in progress.\r\n\r\nCERES Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_Aqua-FM3-MODIS_Edition4A.json b/datasets/CER_SSF_Aqua-FM3-MODIS_Edition4A.json index 470bf53a34..7fc23b9fba 100644 --- a/datasets/CER_SSF_Aqua-FM3-MODIS_Edition4A.json +++ b/datasets/CER_SSF_Aqua-FM3-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_Aqua-FM3-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_Aqua-FM3-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) Top-of-the-Atmosphere (TOA)/Surface Fluxes, Clouds and Aerosols Aqua-Flight Model 3 (FM3) Edition4A data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Data collection for this product is in progress.\r\n\r\nCERES SSF TOA/Surface Fluxes are data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on the Tropical Measuring Mission (TRMM), Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi- National Polar-orbiting Partnership (NPP). Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to TOA fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. On the SSF, only footprints with adequate imager coverage are included, which is much less than the complete set of footprints on the CERES ES-8 product.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_Aqua-FM4-MODIS_Edition4A.json b/datasets/CER_SSF_Aqua-FM4-MODIS_Edition4A.json index 7c78fefd5d..d6077352f3 100644 --- a/datasets/CER_SSF_Aqua-FM4-MODIS_Edition4A.json +++ b/datasets/CER_SSF_Aqua-FM4-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_Aqua-FM4-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_Aqua-FM4-MODIS_Edition4A is the CERES Single Scanner Footprint (SSF) TOA/Surface Fluxes, Clouds and Aerosols Aqua-Flight Model 4 (FM4) Edition4A data product. This data was obtained from the CERES-FM4 Instrument on the Aqua platform. Data collection for this product is complete.\r\n\r\nThe Single Scanner Footprint (SSF) TOA/Surface Fluxes, Clouds, and Aerosols product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM, Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi-NPP. Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. \r\n\r\nFor each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. On the SSF, only footprints with adequate imager coverage are included, which is much less than the full set of footprints on the CERES ES-8 product.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_NOAA20-FM6-VIIRS_Edition1B.json b/datasets/CER_SSF_NOAA20-FM6-VIIRS_Edition1B.json index 8f53fd5de4..8ff327bbad 100644 --- a/datasets/CER_SSF_NOAA20-FM6-VIIRS_Edition1B.json +++ b/datasets/CER_SSF_NOAA20-FM6-VIIRS_Edition1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_NOAA20-FM6-VIIRS_Edition1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_NOAA20-FM6_Edition1B data are Clouds, and the Earth's Radiant Energy System (CERES) observed Top of Atmosphere (TOA) fluxes, Moderate Resolution Imaging Spectroradiometer (MODIS) clouds and aerosols, and parameterized surface fluxes. Data collection for this product is in progress. The TOA/Single Scanner Footprint (SSF) product contains one hour of instantaneous CERES data for a single scanner instrument. SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as a Visible/Infrared Scanner (VIRS) on Tropical Rainfall Measuring Mission (TRMM) or MODIS on Terra and Aqua or Visible Infrared Imaging Radiometer Suite (VIIRS) on SUOMI National Polar-orbiting Partnership (S-NPP) and NOAA-20. \r\n\r\nScene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, SSF contains the number of cloud layers, and for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to TOA fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_NOAA20-FM6-VIIRS_Edition1B, which is much less than the full set of footprints on the CERES ES-8 product. \r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (Flight Model 1 (FM1) and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the S-NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_NPP-FM5-VIIRS_Edition2A.json b/datasets/CER_SSF_NPP-FM5-VIIRS_Edition2A.json index 8cf3ad5944..9df7483f3d 100644 --- a/datasets/CER_SSF_NPP-FM5-VIIRS_Edition2A.json +++ b/datasets/CER_SSF_NPP-FM5-VIIRS_Edition2A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_NPP-FM5-VIIRS_Edition2A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_NPP-FM5_Edition2A data have CERES observed TOA fluxes, MODIS clouds and aerosols, and parameterized surface fluxes. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as a Visible/Infrared Scanner (VIRS) on TRMM, Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, or Visible Infrared Imaging Radiometer Suite (VIIRS) on S-NPP and NOAA-20. Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1, the SSF, which is much less than the full set of footprints on the CERES ES-8 product. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_TRMM-PFM-VIRS_Edition2B.json b/datasets/CER_SSF_TRMM-PFM-VIRS_Edition2B.json index 7a6b3f463e..1a2b7ba403 100644 --- a/datasets/CER_SSF_TRMM-PFM-VIRS_Edition2B.json +++ b/datasets/CER_SSF_TRMM-PFM-VIRS_Edition2B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_TRMM-PFM-VIRS_Edition2B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_TRMM-PFM-VIRS_Edition2B is the Clouds and the Earth's Radiant Energy System (CERES) Scanner Footprint (SSF) Top-of-Atmosphere (TOA)/Surface Fluxes, Clouds, and Aerosols Tropical Rainfall Measuring Mission (TRMM)-protoflight model (PFM) Edition2B data product. Data was collected using the CERES PFM instrument on both the TRMM platform. Data collection for this product is complete.\r\n\r\nCER_SSF_TRMM-PFM-VIRS_Edition2B contains one hour of instantaneous CERES data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Visible/Infrared Scanner (VIRS) on TRMM, MODIS on Terra and Aqua, and VIIRS on Suomi-NPP. Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. On the SSF, only footprints with adequate imager coverage are included, which is much less than the full set of footprints on the CERES ES-8 product.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_Terra-FM1-MODIS_Edition4A.json b/datasets/CER_SSF_Terra-FM1-MODIS_Edition4A.json index e1ae25c4c1..c40046201e 100644 --- a/datasets/CER_SSF_Terra-FM1-MODIS_Edition4A.json +++ b/datasets/CER_SSF_Terra-FM1-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_Terra-FM1-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_Terra-FM1-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) Top-of-the-Atmosphere (TOA)/Surface Fluxes, Clouds and Aerosols Terra- Flight Model 1 (FM1) Edition 4A data product, which was collected using the CERES-FM1 instrument on the Terra platform. Data collection for this product is in progress.\r\n\r\nCERES SSF TOA/Surface Fluxes are data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on the Tropical Measuring Mission (TRMM), Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi- National Polar-orbiting Partnership (NPP). Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to TOA fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. On the SSF, only footprints with adequate imager coverage are included, which is much less than the full set of footprints on the CERES ES-8 product.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SSF_Terra-FM2-MODIS_Edition4A.json b/datasets/CER_SSF_Terra-FM2-MODIS_Edition4A.json index 41e6dfb8e6..15032e4e0f 100644 --- a/datasets/CER_SSF_Terra-FM2-MODIS_Edition4A.json +++ b/datasets/CER_SSF_Terra-FM2-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SSF_Terra-FM2-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SSF_Terra-FM2-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) Top-of-the-Atmosphere (TOA)/Surface Fluxes, Clouds and Aerosols Terra- Flight Model 2 (FM2) Edition 4A data product, which was collected using the CERES-FM2 instrument on the Terra platform. Data collection for this product is in progress.\r\n\r\nCERES SSF TOA/Surface Fluxes are data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on the Tropical Measuring Mission (TRMM), Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi- National Polar-orbiting Partnership (NPP). Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and, for each layer, the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES-filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to TOA fluxes based on the imager-defined scene. These TOA fluxes are used to estimate surface fluxes. On the SSF, only footprints with adequate imager coverage are included, which is much less than the full set of footprints on the CERES ES-8 product.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A.json index a1083d0ef0..087db774fe 100644 --- a/datasets/CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4, CERES Scanner, and MODIS on Aqua. Data collection for this product is ongoing.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-1Hour_Terra-MODIS_Edition4A.json b/datasets/CER_SYN1deg-1Hour_Terra-MODIS_Edition4A.json index 6d9812af6e..b7ebae302b 100644 --- a/datasets/CER_SYN1deg-1Hour_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-1Hour_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-1Hour_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-1Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites, CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra. Data collection for this product is complete.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS and geostationary satellite cloud properties along with atmospheric profiles provided by GMAO. The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A.json b/datasets/CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A.json index e743438c5e..197a428222 100644 --- a/datasets/CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A.json +++ b/datasets/CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-1Hour_Terra-NOAA20-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6, and VIIRS on NOAA-20. Data collection for this product is ongoing.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-1Hour_Terra-NPP_Edition1A.json b/datasets/CER_SYN1deg-1Hour_Terra-NPP_Edition1A.json index a6256d22a3..5801c666df 100644 --- a/datasets/CER_SYN1deg-1Hour_Terra-NPP_Edition1A.json +++ b/datasets/CER_SYN1deg-1Hour_Terra-NPP_Edition1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-1Hour_Terra-NPP_Edition1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-1Hour_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds and Aerosols One-Hourly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition 1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on SUOMI-NPP. Data collection for this product is complete. \r\n\r\nThe CERES Synoptic Radiative Fluxes and Clouds (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the long-wave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A.json index 337295f95b..0a3596ed40 100644 --- a/datasets/CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra-Aqua Edition4A data product. The instruments and platforms used to collect this data include Imaging Radiometers on the Geostationary Satellites platform; CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and CERES FM3, CERES FM4, CERES Scanner, and MODIS on Aqua. Data collection for this product is in progress.\r\n\r\nCERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-3Hour_Terra-MODIS_Edition4A.json b/datasets/CER_SYN1deg-3Hour_Terra-MODIS_Edition4A.json index 58b9c84668..60c06f326d 100644 --- a/datasets/CER_SYN1deg-3Hour_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-3Hour_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-3Hour_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-3Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra Edition4A data product, which was collected using Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra. Data collection for this product is complete.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-3Hour_Terra-NPP_Edition1A.json b/datasets/CER_SYN1deg-3Hour_Terra-NPP_Edition1A.json index f6cfaf521e..5d57a0f976 100644 --- a/datasets/CER_SYN1deg-3Hour_Terra-NPP_Edition1A.json +++ b/datasets/CER_SYN1deg-3Hour_Terra-NPP_Edition1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-3Hour_Terra-NPP_Edition1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-3Hour_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Three-Hourly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using several instruments on multiple platforms including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on NPP. Data collection for this product is complete.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg)products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A.json index 5d80c87d0c..31aea3565a 100644 --- a/datasets/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua. Data collection for this product is ongoing.\r\n\r\nThe CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. \r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Day_Terra-MODIS_Edition4A.json b/datasets/CER_SYN1deg-Day_Terra-MODIS_Edition4A.json index 573e03bace..35cabb3596 100644 --- a/datasets/CER_SYN1deg-Day_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-Day_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Day_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Day_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra Edition4A data product. Data was collected using CERES Imaging Radiometers on Geostationary Satellites as well as CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra. Data collection for this product is complete. \r\n\r\nNote: It is highly recommended to use this product (CER_SYN1deg-Day_Terra-MODIS_Edition4A) in conjunction with CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A when doing science-quality research. \r\n\r\nThe CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. \r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Day_Terra-NOAA20_Edition4A.json b/datasets/CER_SYN1deg-Day_Terra-NOAA20_Edition4A.json index 83d870a8b0..61233ec2a0 100644 --- a/datasets/CER_SYN1deg-Day_Terra-NOAA20_Edition4A.json +++ b/datasets/CER_SYN1deg-Day_Terra-NOAA20_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Day_Terra-NOAA20_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Day_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20. Data collection for this product is ongoing.\r\n\r\nThe CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident imager-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. \r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Daily means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Day_Terra-NPP_Edition1A.json b/datasets/CER_SYN1deg-Day_Terra-NPP_Edition1A.json index 47c2b4a0e0..56d8a9ec74 100644 --- a/datasets/CER_SYN1deg-Day_Terra-NPP_Edition1A.json +++ b/datasets/CER_SYN1deg-Day_Terra-NPP_Edition1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Day_Terra-NPP_Edition1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Day_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on Suomi-NPP. Data collection for this product is complete.\r\n\r\nThe CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a daily temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. \r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A.json index 09f0161290..226aa41c78 100644 --- a/datasets/CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua. Data collection for this product is ongoing. \r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a monthly-averaged one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-MHour_Terra-MODIS_Edition4A.json b/datasets/CER_SYN1deg-MHour_Terra-MODIS_Edition4A.json index 578e8c94c3..5113cab2f8 100644 --- a/datasets/CER_SYN1deg-MHour_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-MHour_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-MHour_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-MHour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra Edition4A data product. Data was collected using the CERES Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra platform. Data collection for this product is complete.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, and surface fluxes and computed fluxes that have been adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are made for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique is used to ensure GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-MHour_Terra-NOAA20_Edition4A.json b/datasets/CER_SYN1deg-MHour_Terra-NOAA20_Edition4A.json index ebeb40d963..fbad4e9865 100644 --- a/datasets/CER_SYN1deg-MHour_Terra-NOAA20_Edition4A.json +++ b/datasets/CER_SYN1deg-MHour_Terra-NOAA20_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-MHour_Terra-NOAA20_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-MHour_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6 CERES Scanner, and VIIRS on NOAA-20. Data collection for this product is ongoing. \r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-MHour_Terra-NPP_Edition1A.json b/datasets/CER_SYN1deg-MHour_Terra-NPP_Edition1A.json index 027a61961d..ce7f4e72cd 100644 --- a/datasets/CER_SYN1deg-MHour_Terra-NPP_Edition1A.json +++ b/datasets/CER_SYN1deg-MHour_Terra-NPP_Edition1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-MHour_Terra-NPP_Edition1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-MHour_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (NPP). Data collection for this product is complete.\r\n\r\nThe CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the long-wave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A.json b/datasets/CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A.json index 177005ba1e..20fdffcd12 100644 --- a/datasets/CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-Aqua Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM3, FM4, and MODIS on Aqua. Data collection for this product is ongoing.\r\n\r\nCERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Month_Terra-MODIS_Edition4A.json b/datasets/CER_SYN1deg-Month_Terra-MODIS_Edition4A.json index 2d417e33c0..48ae4e1128 100644 --- a/datasets/CER_SYN1deg-Month_Terra-MODIS_Edition4A.json +++ b/datasets/CER_SYN1deg-Month_Terra-MODIS_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Month_Terra-MODIS_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using Imaging Radiometers on Geostationary Satellites platform as well as CERES Flight Model 1 (FM1), CERES FM2, and MODIS on Terra. Data collection for this product is complete.\r\n\r\nCERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Month_Terra-NOAA20_Edition4A.json b/datasets/CER_SYN1deg-Month_Terra-NOAA20_Edition4A.json index f30ae947fa..3d9b522002 100644 --- a/datasets/CER_SYN1deg-Month_Terra-NOAA20_Edition4A.json +++ b/datasets/CER_SYN1deg-Month_Terra-NOAA20_Edition4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Month_Terra-NOAA20_Edition4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Month_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20. Data collection for this product is ongoing.\r\n\r\nCERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, VIIRS, and geostationary satellite cloud properties along with atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1\u00b0-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nCERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a critical Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CER_SYN1deg-Month_Terra-NPP_Edition1A.json b/datasets/CER_SYN1deg-Month_Terra-NPP_Edition1A.json index 18d5435ac7..c4d674d6da 100644 --- a/datasets/CER_SYN1deg-Month_Terra-NPP_Edition1A.json +++ b/datasets/CER_SYN1deg-Month_Terra-NPP_Edition1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CER_SYN1deg-Month_Terra-NPP_Edition1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CER_SYN1deg-Month_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on NPP. Data collection for this product is complete. \r\n\r\nThe CERES SYN1deg products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly temporal resolution on 1\u00b0-regional, zonal, and global spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.\r\n\r\nThe CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5\u00b0x5 \u00b0 latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1\u00b0 equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.\r\n\r\nCERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.", "links": [ { diff --git a/datasets/CFL_0.json b/datasets/CFL_0.json index b253e60ef4..a0213a39f1 100644 --- a/datasets/CFL_0.json +++ b/datasets/CFL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CFL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken within the Cape Bathurst flaw lead on board the icebreaker C.C.G.S. Amundsen to examine how physical changes affect biological processes in the flaw lead\u00a0through an entire annual cycle (October 2007 - August 2008). The circumpolar flaw lead occurs each year when the central pack ice moves away from the coastal fast ice creating an area of open water called a flaw lead.", "links": [ { diff --git a/datasets/CH-OG-1-GPS-30S_0.0.json b/datasets/CH-OG-1-GPS-30S_0.0.json index cb034f7ed0..d5f8981ea9 100644 --- a/datasets/CH-OG-1-GPS-30S_0.0.json +++ b/datasets/CH-OG-1-GPS-30S_0.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH-OG-1-GPS-30S_0.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises GPS ground data of a sample rate of 30 sec, generated\n by decoding and sampling GPS high rate ground data. This raw data passed no\n quality control. The data are given in the Rinex 2.1 format.", "links": [ { diff --git a/datasets/CH4_Aircraft_STILT_footprints_1300_1.json b/datasets/CH4_Aircraft_STILT_footprints_1300_1.json index 76905add0b..641cf05329 100644 --- a/datasets/CH4_Aircraft_STILT_footprints_1300_1.json +++ b/datasets/CH4_Aircraft_STILT_footprints_1300_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH4_Aircraft_STILT_footprints_1300_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of (1) year-round measurements of methane (CH4) flux along with soil and air temperatures at five eddy covariance towers at sites located in the Alaskan Arctic tundra from June 2013 to December 2014 and (2) airborne CH4 and ozone (O3) measurements collected during Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight campaigns for years 2012 through 2014. The included site-level flux data at half-hourly intervals were calculated following standard eddy covariance data processing procedures. Also reported are daily mean methane flux, soil temperature with depth, and air temperature for each tower site. Also identified for each flux tower site were the \"zero curtain\" periods of extended cold when soil temperatures were poised near 0 degrees C. The reported CARVE airborne CH4 and O3 data were aggregated horizontally at 5 km intervals. Measurement heights are reported. These aircraft positions were treated as receptors in a Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled with meteorology fields from the polar variant of the Weather and Research Forecasting model (WRF), in order to model the land surface influence on the aircraft-observed methane concentrations. The summed land surface influence on the aircraft data at each position is reported. For each airborne measurement, 2D surface influence fields (i.e. footprints) at two different spatial resolutions were derived using the WRF-STILT simulations. These gridded footprints are provided as netCDF formatted files. Regional C-CH4 fluxes were calculated from the CARVE CH4 data and footprints for the period 2012-2014 and are also included with this data set. Acknowledgements: Data collection efforts were funded by NSF ARCSS project \"Methane Loss From Arctic\" (ARCSS #1204263; http://www.nsf.gov/awardsearch/showAward?AWD_ID=1204263) and by NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).", "links": [ { diff --git a/datasets/CH4_CO2_WaterBodies_YK_Delta_2178_1.json b/datasets/CH4_CO2_WaterBodies_YK_Delta_2178_1.json index 0b60cb2532..3eb2cb90db 100644 --- a/datasets/CH4_CO2_WaterBodies_YK_Delta_2178_1.json +++ b/datasets/CH4_CO2_WaterBodies_YK_Delta_2178_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH4_CO2_WaterBodies_YK_Delta_2178_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers. The observations include ~300 samples of surface water dissolved gases collected in July 2016-2019 from the central region of the YK Delta, Alaska. A machine learning model was used to generate estimated fluxes. Model inputs include Sentinel-2 MSI with derived normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), an Arctic digital elevation model (DEM) with derived slope and flow accumulation, Sentinel-1 C-band July and December VV and VH composites, and a landcover map. Waterbody size, shape, and reflectance were determined using object-based image analysis in Google Earth Engine. Landscape-level input data were averaged in non-nested sub-basins calculated using the System for Automated Geoscientific Analyses (SAGA) \"channel network\" algorithm at three threshold sizes. Cross validation was used to tune and select variables for gradient boosting models. The trained gradient boosting models were then used to predict dissolved methane and carbon dioxide in all waterbodies (~17,000) in the region. These aquatic concentrations were converted to fluxes using an average gas transfer velocity from observations (0.33 m/d). The data are provided in GeoTIFF and shapefile formats.", "links": [ { diff --git a/datasets/CH4_Flux_BigTrail_Goldstream_1778_1.json b/datasets/CH4_Flux_BigTrail_Goldstream_1778_1.json index 55c59f0473..deb84baf77 100644 --- a/datasets/CH4_Flux_BigTrail_Goldstream_1778_1.json +++ b/datasets/CH4_Flux_BigTrail_Goldstream_1778_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH4_Flux_BigTrail_Goldstream_1778_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG).", "links": [ { diff --git a/datasets/CH4_Fluxes_ThermokarstLakes_AK_1870_1.json b/datasets/CH4_Fluxes_ThermokarstLakes_AK_1870_1.json index 18e0e20e17..f4b8f46e71 100644 --- a/datasets/CH4_Fluxes_ThermokarstLakes_AK_1870_1.json +++ b/datasets/CH4_Fluxes_ThermokarstLakes_AK_1870_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH4_Fluxes_ThermokarstLakes_AK_1870_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides methane fluxes from hot-spot and non-hot spot differing surfaces at Big Trail Lake (BTL) in the Goldstream Valley near Fairbanks, AK, USA. Measurements were taken at a remotely-sensed methane hotspot on the shoreline of a pond, adjacent to BTL with a Los Gatos Ultra-Portable Greenhouse Gas Analyzer (UGGA), and from various non-hotspot surfaces representative of the broader thermokarst lake ecosystem with bucket chambers. All data were collected between 2019-07-04 and 2019-12-04 during the daytime hours of 09:35-17:32 local time. A ground-based CH4 enhancement survey was performed on 2019-07-06 between 13:25-17:15 Alaska Daylight Time (AKDT), approximately two hours following an AVIRIS-NG overflight and hotspot detection at the Eastside Pond. Methane flux is reported in units of both mmol CH4 m-2 hr-1 and mg CH4 m-2 d-1. Flux errors are quantified for each", "links": [ { diff --git a/datasets/CH4_Plume_AVIRIS-NG_1727_1.json b/datasets/CH4_Plume_AVIRIS-NG_1727_1.json index 72e4836c35..cf74b7b6a2 100644 --- a/datasets/CH4_Plume_AVIRIS-NG_1727_1.json +++ b/datasets/CH4_Plume_AVIRIS-NG_1727_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CH4_Plume_AVIRIS-NG_1727_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey. The survey was designed to cover at least 60% of the methane point source infrastructure in California guided by the Vista-CA dataset of identified locations of potential methane emitting facilities and infrastructure in three primary sectors (energy, agriculture, and waste). The purpose of the survey was to detect, quantify, and attribute point source emissions to specific infrastructure elements to improve the scientific understanding of regional methane budgets and to inform policy and planning activities that reduce methane emissions.", "links": [ { diff --git a/datasets/CHELTON_SEASAT_SASS_L3_1.json b/datasets/CHELTON_SEASAT_SASS_L3_1.json index e9418bdb76..945e7188d5 100644 --- a/datasets/CHELTON_SEASAT_SASS_L3_1.json +++ b/datasets/CHELTON_SEASAT_SASS_L3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CHELTON_SEASAT_SASS_L3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model.", "links": [ { diff --git a/datasets/CHEMTAX_1.json b/datasets/CHEMTAX_1.json index deaeee0c32..44d882c30f 100644 --- a/datasets/CHEMTAX_1.json +++ b/datasets/CHEMTAX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CHEMTAX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CHEMTAX V1.95 \nThis program was written by Chris Boucher, assisted by Harry Higgins and Simon Wright, for the Australian Antarctic Division. \nIt is a stand-alone program that takes input from a Microsoft Excel worksheet. \nIt calculates the taxonomic composition of phytoplankton populations based on pigment data and a table of the expected taxonomic composition and pigment:chl a ratios entered by the operator. \nIt is based on CHEMTAX V1, which was a MATLAB script written by Mark Mackey (CSIRO) and published in Mackey et al (1996). \nThe zip folder contains Chemtax.exe, Chemtax2.dll, Testrun195.xls, PicoDataWorkup.xls (example), CHEMTAXHelper for V195.xlm. \nAlso included are two Word files (Chemtax 195 Instructions.doc, and Chaxmanw.rtf, which is the manual for Version 1).The latter manual contains details on the algorithms used in Chemtax, which are unchanged, but the operating instructions in that manual are superseded by those in Chemtax 195 Instructions.doc. \n\nPlease note: CHEMTAX must not be used as a black box. It will not deduce what taxa are in the water. The user must input the expected taxa and their expected pigment composition, then CHEMTAX will calculate the contributions of each taxon to the total in each sample. It is imperative that the user understands the function of CHEMTAX, and the taxonomic distribution of pigments (including the potential ambiguities) if useful data are to be obtained. \nA detailed strategy for applying CHEMTAX (and interpreting pigment data in general) is given in Higgins et al (2011). \nAn example of combining CHEMTAX with other data is given in Wright et al (2010).\nHiggins H.W., Wright S. W., Schluter L. (2011). Quantitative Interpretation of Chemotaxonomic Pigment Data, Chapter 6, Phytoplankton Pigments: Characterization, Chemotaxonomy and Applications in Oceanography, Suzanne Roy, Einar Skarstad Egeland, Geir Johnsen and Carole Anne Llewellyn (eds.) Cambridge University Press.\n\nWright, SW, van den Enden, RL, Pearce, I, Davidson, AT, Scott FJ, Westwood, KJ (2010). Phytoplankton community structure and stocks in the Southern Ocean (30 - 80 degrees E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep-Sea Research II 57, 758-778\n\nA CHEMTAX User Forum has been set up at http://groups.google.com/forum/#!forum/chemtax_users.\n\nRegistration:\n\nAfter downloading the files, please email the enclosed registration form to Simon.Wright@aad.gov.au with CHEMTAX in the title. Please note that Simon is semi-retired and may not respond immediately.", "links": [ { diff --git a/datasets/CIESIN0122.json b/datasets/CIESIN0122.json index 297cff95fe..03656597a5 100644 --- a/datasets/CIESIN0122.json +++ b/datasets/CIESIN0122.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN0122", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Africa Real Time Environmental Monitoring Information\nSystem (ARTEMIS)\" is part of the FAO's use of satellite remote\nsensing techniques to improve the surveillance and forecasting\ncapabilities of its Global Information and Early Warning System\n(GIEWS). ARTEMIS was developed as a result of close technical\ncooperation between the FAO and the NASA Goddard Space Flight\nCenter, the University of Reading in the United Kingdom, and the\nNational Aerospace Laboratory of the Netherlands. Since August\n1988, the ARTEMIS system has been delivering the following\nproducts on a routine basis: ten-day and monthly cold cloud\nduration maps for the continent of Africa and the Near East\n(resolution 7.6 km); ten-day and monthly estimated rainfall maps\nfor the Southern Sahara, the Sahel, Sudan, and the tropical\ncountries of West Africa (resolution 7.6 km); ten-day and\nmonthly composite vegetation index maps for Africa, and the Near\nEast.\n\nIn addition to these databases, ARTEMIS contains a ten-year\nvegetation index archive on a ten-day and monthly basis,\ndeveloped jointly by NASA GSFC and the FAO Remote Sensing\nCentre. This archive allows for early assessment of current crop\ngrowing conditions by comparison with known situations in the\npast.\nLANGUAGE:\n\nEnglish\nACCESS/AVAILABILITY:\n\nARTEMIS data products are available in photographic and digital\nformats. Analyzed infomation is communicated in bulletins and\npublications of Global Information and Early Warning System\n(GIEWS) and Emergency Centre for Locust Operations (ECLO) of FAO.\n\nFor making ARTEMIS data available in a timely manner to users,\nmore and more use is currently being made of electronic mail.", "links": [ { diff --git a/datasets/CIESIN_AfSIS_CLIMATE_ECV2014_2014.00.json b/datasets/CIESIN_AfSIS_CLIMATE_ECV2014_2014.00.json index cd7037801e..7162864ef2 100644 --- a/datasets/CIESIN_AfSIS_CLIMATE_ECV2014_2014.00.json +++ b/datasets/CIESIN_AfSIS_CLIMATE_ECV2014_2014.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_CLIMATE_ECV2014_2014.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Climate Collection's Essential Climate Variable (ECV) Soil Moisture data set contains rasters with the following calculations: time series average, time series monthly averages, and annual averages. These Africa continent-wide rasters were created using the soil moisture data for the period 1978-2010 provided by the European Space Agency (ESA) Soil Moisture Climate Change Initiative (CCI) project. The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated \nby AfSIS when observations are available and provided by ESA at http://www.esa-soilmoisture-cci.org.\n\nThe data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01.json b/datasets/CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01.json index b07daf2885..be5daa4700 100644 --- a/datasets/CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01.json +++ b/datasets/CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Climate Collection's Tropical Rainfall Measurement Mission (TRMM) data set contains rasters with the following calculations: time series average, time series Modified Fournier index (MFI), time series average number of rainy days, annual averages, annual MFI, and annual average number of rainy days, for precipitation. These Africa continent-wide calculations use the TRMM observations obtained by the National Aeronautics and Space Administration (NASA). The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_CLIMATE_WC2013_2013.00.json b/datasets/CIESIN_AfSIS_CLIMATE_WC2013_2013.00.json index f054774def..84ee5cc349 100644 --- a/datasets/CIESIN_AfSIS_CLIMATE_WC2013_2013.00.json +++ b/datasets/CIESIN_AfSIS_CLIMATE_WC2013_2013.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_CLIMATE_WC2013_2013.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Climate Collection's WorldClim data set contains rasters with the following calculations: time series average for BIO1 temperature as well as time series average and time series Modified Fournier Index (MFI) for BIO12 precipitation. These Africa continent-wide calculations use the temperature and precipitation data for the period 1950-2000 created by WorldClim. The rasters contain interpolated weather station data with a spatial resolution of 1 kilometer, and are updated by AfSIS using data provided by WorldClim at http://www.worldclim.org.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_ALB2012_2012.00.json b/datasets/CIESIN_AfSIS_MODIS_ALB2012_2012.00.json index 5610b32653..58f6823af4 100644 --- a/datasets/CIESIN_AfSIS_MODIS_ALB2012_2012.00.json +++ b/datasets/CIESIN_AfSIS_MODIS_ALB2012_2012.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_ALB2012_2012.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Albedo data set contains rasters with the following calculations: time series average, time series standard deviation, and time series variance for white sky and black sky albedo. These Africa continent-wide calculations use surface reflectance data obtained by the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 16-day temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00.json b/datasets/CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00.json index 6a87204f2f..7a0e579a43 100644 --- a/datasets/CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00.json +++ b/datasets/CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets contain rasters with the following calculations: time series average, time series standard deviation, and time series variance for LAI and FPAR. These Africa continent-wide calculations for surface photosynthesis use observations from the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_LCT2012_2012.00.json b/datasets/CIESIN_AfSIS_MODIS_LCT2012_2012.00.json index 7d27b0ccc9..a04eea45e7 100644 --- a/datasets/CIESIN_AfSIS_MODIS_LCT2012_2012.00.json +++ b/datasets/CIESIN_AfSIS_MODIS_LCT2012_2012.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_LCT2012_2012.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Cover Type 2 data set is constructed for the continent of Africa using observations from the National Aeronautics and Space Administration (NASA) MODIS MCD12Q1 product. The grids have an annual temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_LST201404_2014.04.json b/datasets/CIESIN_AfSIS_MODIS_LST201404_2014.04.json index 9bf6fcffe6..fbfcbf4900 100644 --- a/datasets/CIESIN_AfSIS_MODIS_LST201404_2014.04.json +++ b/datasets/CIESIN_AfSIS_MODIS_LST201404_2014.04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_LST201404_2014.04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Surface Temperature data set contains rasters with the following calculations: time series average and time series monthly averages for day and night. These Africa continent-wide calculations use observations from the National Aeronautics and Space Administration (NASA) MODIS MYD11A2 product. The rasters have an 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_PP2012_2014.00.json b/datasets/CIESIN_AfSIS_MODIS_PP2012_2014.00.json index db4370e594..67aca00ad0 100644 --- a/datasets/CIESIN_AfSIS_MODIS_PP2012_2014.00.json +++ b/datasets/CIESIN_AfSIS_MODIS_PP2012_2014.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_PP2012_2014.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Primary Productivity data set contains rasters with the following calculations: time series average, time series variance, and annual averages for Net Primary Productivity (NPP) and Gross Primary Productivity (GPP). These Africa continent-wide calculations for vegetation productivity use observations from the National Aeronautics and Space Administration (NASA) MODIS MOD17A3 product. The rasters have a annual temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_AfSIS_MODIS_VEGIN201404_2014.04.json b/datasets/CIESIN_AfSIS_MODIS_VEGIN201404_2014.04.json index 0387c5439e..1a765c2d1d 100644 --- a/datasets/CIESIN_AfSIS_MODIS_VEGIN201404_2014.04.json +++ b/datasets/CIESIN_AfSIS_MODIS_VEGIN201404_2014.04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_AfSIS_MODIS_VEGIN201404_2014.04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Vegetation Indices data set contains rasters with the following calculations: time series average and time series monthly average for the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Reflectance Band 1, Near-Infrared Reflectance Band 2, Blue Reflectance Band 3, and Mid-Infrared Reflectance Band 7. These Africa continent-wide calculations for vegetation indices and surface reflectances use data from the National Aeronautics and Space Administration (NASA) MODIS MOD13Q1 product. The rasters have a 16-day temporal resolution, a spatial resolution of 250 meters, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.\n\n The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_CYCLONE_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_CYCLONE_HFD_1.00.json index 4268b2fd41..4f14cea41b 100644 --- a/datasets/CIESIN_CHRR_NDH_CYCLONE_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_CYCLONE_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_CYCLONE_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Cyclone Hazard Frequency and Distribution is a 2.5 minute grid based on more than 1,600 storm tracks for the period 1 January 1980 through 31 December 2000 for the Atlantic, Pacific, and Indian Oceans that were assembled and modeled at UNEP/GRID-Geneva PreView. Windspeeds around storm tracks were modeled using Holland's model (1997) to assess the grid cells likely to have been exposed to high wind levels. Post-modeling, the cells were divided into deciles, 10 classes consisting of approximately equal number of grid cells. The higher the value of the grid cell, the higher the decile ranking and the greater the frequency of the hazard relative to other cells. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, United Nations Environment Programme Global Resource Information Database Geneva (UNEP/GRID-Geneva), and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_CYCLONE_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_CYCLONE_MRD_1.00.json index a0070f2d07..0aca137ab9 100644 --- a/datasets/CIESIN_CHRR_NDH_CYCLONE_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_CYCLONE_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_CYCLONE_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Cyclone Mortality Risks and Distribution is a 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_CYCLONE_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_CYCLONE_PELRD_1.00.json index 5c0eaa4f8b..416bc8a6f3 100644 --- a/datasets/CIESIN_CHRR_NDH_CYCLONE_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_CYCLONE_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_CYCLONE_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_CYCLONE_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_CYCLONE_TELRD_1.00.json index 834d4cec7c..a3ff31aeb1 100644 --- a/datasets/CIESIN_CHRR_NDH_CYCLONE_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_CYCLONE_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_CYCLONE_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Cyclone Total Economic Loss Risk Deciles is a 2.5 minute grid of global cyclone total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP is spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using population distribution data. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell as determined from Gridded Population of the World, Version 3 (GPWv3) data. Once a GDP value is determined on a per grid cell basis, then the regionally variable loss rate, as derived from the historical records of EM-DAT, is used to determine the total economic loss risks posed to a grid cell by cyclone hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_DROUGHT_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_DROUGHT_HFD_1.00.json index 2b033b6947..248be117bd 100644 --- a/datasets/CIESIN_CHRR_NDH_DROUGHT_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_DROUGHT_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_DROUGHT_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_DROUGHT_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_DROUGHT_MRD_1.00.json index 18a96ba409..b4d983d510 100644 --- a/datasets/CIESIN_CHRR_NDH_DROUGHT_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_DROUGHT_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_DROUGHT_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_DROUGHT_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_DROUGHT_PELRD_1.00.json index 5204d8c713..32d70cf691 100644 --- a/datasets/CIESIN_CHRR_NDH_DROUGHT_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_DROUGHT_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_DROUGHT_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Drought Proportional Economic Loss Risk Deciles is a 2.5 minute grid of drought hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_DROUGHT_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_DROUGHT_TELRD_1.00.json index 4438817a8c..ee51d3081b 100644 --- a/datasets/CIESIN_CHRR_NDH_DROUGHT_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_DROUGHT_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_DROUGHT_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Drought Total Economic Loss Risk Deciles is a 2.5 minute grid of global drought total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP is spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by drought hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_EQUAKE_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_EQUAKE_HFD_1.00.json index 096b635c8f..4390bb5b8d 100644 --- a/datasets/CIESIN_CHRR_NDH_EQUAKE_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_EQUAKE_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_EQUAKE_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Earthquake Hazard Frequency and Distribution is a 2.5 minute grid utilizing Advanced National Seismic System (ANSS) Earthquake Catalog data of actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is calculated for each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid cell value in the final output, the higher the relative frequency of hazard posed by earthquakes. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_EQUAKE_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_EQUAKE_MRD_1.00.json index 2595df27f2..b5edb634d3 100644 --- a/datasets/CIESIN_CHRR_NDH_EQUAKE_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_EQUAKE_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_EQUAKE_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Earthquake Mortality Risks and Distribution is a 2.5 minute grid of global earthquake mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provides a baseline estimate of population per grid cell from which to estimate potential mortality risks due to earthquake hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the distribution of earthquake hazard are obtained from the Global Earthquake Hazard Distribution-peak ground acceleration data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of earthquake-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_EQUAKE_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_EQUAKE_PELRD_1.00.json index ab6d241d71..dc5f663dc3 100644 --- a/datasets/CIESIN_CHRR_NDH_EQUAKE_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_EQUAKE_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_EQUAKE_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_EQUAKE_PGA_1.00.json b/datasets/CIESIN_CHRR_NDH_EQUAKE_PGA_1.00.json index 0af56f67f4..87e72bf4d9 100644 --- a/datasets/CIESIN_CHRR_NDH_EQUAKE_PGA_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_EQUAKE_PGA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_EQUAKE_PGA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Earthquake Hazard Distribution - Peak Ground Acceleration is a 2.5 minute grid of global earthquake hazards developed using Global Seismic Hazard Program (GSHAP) data that incorporate expert opinion in predicting localities where there exists a 10 percent chance of exceeding a peak ground acceleration (pga) of 2 meters per second per second (approximately one-fifth of surface gravitational acceleration) in a 50 year time span. For the purpose of identifying hazard hotspots, values of 2 meters per second per second and less were excluded from analysis. The resulting ranges of pga values were classified into deciles, 10 classes of approximately an equal number of grid cells. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_EQUAKE_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_EQUAKE_TELRD_1.00.json index 104fc826b3..b7188024d3 100644 --- a/datasets/CIESIN_CHRR_NDH_EQUAKE_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_EQUAKE_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_EQUAKE_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data population distributions. A per capita contribution value is determined within each subnational Unit, and then this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_FLOOD_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_FLOOD_HFD_1.00.json index 00437504ed..bce49738f0 100644 --- a/datasets/CIESIN_CHRR_NDH_FLOOD_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_FLOOD_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_FLOOD_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_FLOOD_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_FLOOD_MRD_1.00.json index ea819f2e23..7e3036ae42 100644 --- a/datasets/CIESIN_CHRR_NDH_FLOOD_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_FLOOD_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_FLOOD_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_FLOOD_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_FLOOD_PELRD_1.00.json index ca7b27c36c..a949e017d8 100644 --- a/datasets/CIESIN_CHRR_NDH_FLOOD_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_FLOOD_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_FLOOD_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_FLOOD_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_FLOOD_TELRD_1.00.json index 3e541bb687..4e390dabd7 100644 --- a/datasets/CIESIN_CHRR_NDH_FLOOD_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_FLOOD_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_FLOOD_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_LSLIDE_HD_1.00.json b/datasets/CIESIN_CHRR_NDH_LSLIDE_HD_1.00.json index bbbd829c48..44d3cca677 100644 --- a/datasets/CIESIN_CHRR_NDH_LSLIDE_HD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_LSLIDE_HD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_LSLIDE_HD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values less than or equal to 4 are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other data sets, value 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_LSLIDE_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_LSLIDE_MRD_1.00.json index 88521673f4..40b1929de0 100644 --- a/datasets/CIESIN_CHRR_NDH_LSLIDE_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_LSLIDE_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_LSLIDE_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_LSLIDE_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_LSLIDE_PELRD_1.00.json index beb2e3b222..6a53075823 100644 --- a/datasets/CIESIN_CHRR_NDH_LSLIDE_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_LSLIDE_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_LSLIDE_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_LSLIDE_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_LSLIDE_TELRD_1.00.json index e661f7ddbd..a9031b24be 100644 --- a/datasets/CIESIN_CHRR_NDH_LSLIDE_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_LSLIDE_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_LSLIDE_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_MULTI_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_MULTI_HFD_1.00.json index 258f52b198..bf56b8c7ef 100644 --- a/datasets/CIESIN_CHRR_NDH_MULTI_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_MULTI_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_MULTI_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Multihazard Frequency and Distribution is a 2.5 minute grid presenting a simple multihazard index based solely on summated single-hazard decile values. The hazards of interest include cyclones, droughts, earthquakes, floods, landslides, and volcanoes. This data set is further enriched by the inclusion of data pertaining to population, Gross Domestic Product (GDP), and transportation infrastructure. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_MULTI_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_MULTI_MRD_1.00.json index e98b8ce6fb..f543d821f0 100644 --- a/datasets/CIESIN_CHRR_NDH_MULTI_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_MULTI_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_MULTI_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Multihazard Mortality Risks and Distribution is a 2.5 minute grid identifying and characterizing the nature of multihazard risk at the global scale. For this study, multihazard considers the hazards posed by cyclones, droughts, earthquakes, floods, landslides and volcanoes. The specific hazards are grouped into the following hazard categories: drought (drought), seismic (earthquakes and volcanoes), and hydro (cyclones, floods, and landslides). Each grid cell is assessed for each hazard category; and is considered at high risk or not at high risk. Treated as a binary value, the at-risk values of the hazards categories function as a 3-digit index of multihazard.\n\n\n\n\n\nFor each of the hazard category combinations, aggregate analyses determine the total population, area, and length of major transportation features, as well as, the value of Gross Domestic Product (GDP) and agricultural GDP. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_MULTI_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_MULTI_PELRD_1.00.json index 62b8e263a9..8c160098c2 100644 --- a/datasets/CIESIN_CHRR_NDH_MULTI_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_MULTI_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_MULTI_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical Unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category.\n\n\n\nFor each of the six hazards considered, a binary risk surface is constructed utilizing the three most-at-risk deciles of each hazard's global proportional economic loss risks data set (deciles 8-10). Each of the category risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those locations that are at higher-risk from multihazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_MULTI_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_MULTI_TELRD_1.00.json index 162dde3359..2a56ba2b6f 100644 --- a/datasets/CIESIN_CHRR_NDH_MULTI_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_MULTI_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_MULTI_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational Units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_VOLCANO_HFD_1.00.json b/datasets/CIESIN_CHRR_NDH_VOLCANO_HFD_1.00.json index d0c9dbf96e..9466723389 100644 --- a/datasets/CIESIN_CHRR_NDH_VOLCANO_HFD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_VOLCANO_HFD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_VOLCANO_HFD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Volcano Hazard Frequency and Distribution is a 2.5 minute gridded data set based upon the National Geophysical Data Center (NGDC) Volcano Database spanning the period of 79 through 2000. This database includes nearly 4,000 volcanic events categorized as moderate or above (values 2 through 8) according to the Volcano Explosivity Index (VEI). Most volcanoes are georeferenced to the nearest tenth or hundredth of a degree with a few to the nearest thousandth of a degree. To produce the final output, the frequency of a volcanic hazard is computed for each grid cell, with the data set consequently being classified into deciles (10 classes of approximately equal number of grid cells). The higher the grid cell value in the final output, the higher the relative frequency of hazard posed by volcanoes. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_VOLCANO_MRD_1.00.json b/datasets/CIESIN_CHRR_NDH_VOLCANO_MRD_1.00.json index 5811a6d11a..b60e5af8ef 100644 --- a/datasets/CIESIN_CHRR_NDH_VOLCANO_MRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_VOLCANO_MRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_VOLCANO_MRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Volcano Mortality Risks and Distribution is a 2.5 minute grid representing global volcano mortality risks. The data set was constructed using historical hazard-specific mortality loss data from the Emergency Events Database (EM-DAT) maintained by the Centre for Research on the Epidemiology of Disasters (CRED), subnational year 2000 population estimates from Gridded Population of the World, Version 3 (GPWv3), and volcano hazard data from the Global Volcano Hazard Frequency and Distribution data set. Estimates were made as to the mortality numbers associated with volcano hazard. In turn, these mortality estimates were classified into deciles, 10 class of an approximately equal number of grid cells of increasing mortality risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00.json index 65fee44a4d..3c21fdf299 100644 --- a/datasets/CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Volcano Proportional Economic Loss Risk Deciles is a 2.5 minute grid of volcano hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00.json b/datasets/CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00.json index 56d7a54288..482f26fcb4 100644 --- a/datasets/CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00.json +++ b/datasets/CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Volcano Total Economic Loss Risk Deciles is a 2.5 minute grid of global volcano total economic loss risks. First, subnational distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where applicable, the proportional contribution of subnational Units are determined following the methodology of Sachs et al. (2003) and these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational Unit. Once a national GDP has been spatially stratified into the smallest administrative Units available, it is further distributed based upon Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each Unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1980_SAS_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1980_SAS_1.00.json index 5a02a425b0..133a65ab94 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1980_SAS_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1980_SAS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1980_SAS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing Unit, value of the Unit, tenure and occupancy status in housing Unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_CBS_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_CBS_1.00.json index 6b1963af08..fe695b38f6 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_CBS_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_CBS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_CBS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing Units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_EMF_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_EMF_1.00.json index e2bb80ea14..0f13616960 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_EMF_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_EMF_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_EMF_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Enhanced Migration Files portion of the Archive of Census Related Products (ACRP) contains migration data derived from the U.S. Census Bureau's Summary Tape File (STP-28). The data includes counts by race and hispanic origin within each county, county to county, and state to state. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_PBF_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_PBF_1.00.json index b943aae3b3..0eb68150ca 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_PBF_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_PBF_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_PBF_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_SAS_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_SAS_1.00.json index c19dd19074..2392ef834b 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_SAS_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_SAS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_SAS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_SEF_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_SEF_1.00.json index 1678c63ddd..d21a2344c9 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_SEF_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_SEF_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_SEF_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Standard Extract Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3A database. The population data includes age\n\ndistribution, education levels, ethnicity, income distribution, labor force status and family structure, while the housing data embodies size and state of the housing Unit, value of the Unit, water, sewage, heating, and monthly owner costs. The data are available by county, county subdivisions, place within-county, tract, block numbering area (bna), and blockgroup. Each file contains a unique POLygon IDentification (POLID) field which matches a similar field in the corresponding TIGER-based boundary file. Tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_SI_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_SI_1.00.json index da0929e8b8..2d6ab283c7 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_SI_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_SI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_SI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Street Intersections portion of the Archive of Census Related Products (ACRP) contains the latitude and longitude of street intersections for each county in the United States. The data includes the names of both streets that intersect, as well as the location and unique node number for streets which frequently intersect each other. This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_STF1B_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_STF1B_1.00.json index ffd9017833..d59c50f1d4 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_STF1B_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_STF1B_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_STF1B_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Summary Tape File (STF1B) portion of the Archive of Census Related Products (ACRP) contains population and housing data, along with additional demographic data for the U.S. The population data includes age, race, sex, marital status, Hispanic origin, and household type, while the housing data encompasses tenure, number of Units, value, number of rooms per Unit, and the use of the Unit. The data are available by county, county subdivision, place within county, tract/block numbering area (bna), blockgroup, and block. This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1990_ZIPEF_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1990_ZIPEF_1.00.json index 82504ce0f2..75ac1439e3 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1990_ZIPEF_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1990_ZIPEF_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1990_ZIPEF_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1990 Zip Equivalency Files portion of the Archive of Census Related Products (ACRP) contains population and housing data derived from the U.S. Census Bureau's 1990 STF3B header file. The data are available at the census block and census blockgroup levels, and encompass 5-digit zip codes, POLygon IDentification (POLID) block fields that correspond to the POLID of the Tiger-based boundary file, and population centroids. This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_1992_BF_1.00.json b/datasets/CIESIN_SEDAC_ACRP_1992_BF_1.00.json index 11fc2709c3..c76badd152 100644 --- a/datasets/CIESIN_SEDAC_ACRP_1992_BF_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_1992_BF_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_1992_BF_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1992 Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 1992 boundary data from the U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) system. The data have been processed to create the state boundary files for county, county subdivision (mcd), place, 1990 tract/block numbering area, blockgroup, and 1980 tract and mcd's (if available). The tract and blockgroup files are grouped by MSA (Metropolitan Statistical Area) and CMSA (Consolidated Metropolitan Statistical Area). County boundary files have also been created for each census block, distinguished by its unique POLygon IDentification (POLID) field, matching the corresponding POLID in the 1990 Summary Tape File (STF) data. This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ACRP_PUMS_1.00.json b/datasets/CIESIN_SEDAC_ACRP_PUMS_1.00.json index 9122e586e8..c08f534757 100644 --- a/datasets/CIESIN_SEDAC_ACRP_PUMS_1.00.json +++ b/datasets/CIESIN_SEDAC_ACRP_PUMS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ACRP_PUMS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Public Use Microdata Samples (PUMS) are computer-accessible files containing records for a sample of housing Units, with information on the characteristics of each housing Unit and the people in it for 1940-1990. Within the limits of sample size and geographical detail, these files allow users to prepare virtually any tabulations they require. Each datafile is documented in a codebook containing a data dictionary and supporting appendix information. Electronic versions for the codebooks are only available for the 1980 and 1990 datafiles. Identifying information has been removed to protect the confidentiality of the respondents. PUMS is produced by the United States Census Bureau (USCB) and is distributed by USCB, Inter-university Consortium for Political and Social Research (ICPSR), and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AGLANDS_CROP2000_1.00.json b/datasets/CIESIN_SEDAC_AGLANDS_CROP2000_1.00.json index 98c0d67a68..041a0fc525 100644 --- a/datasets/CIESIN_SEDAC_AGLANDS_CROP2000_1.00.json +++ b/datasets/CIESIN_SEDAC_AGLANDS_CROP2000_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AGLANDS_CROP2000_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Croplands data set represents the proportion of land areas used as cropland (land used for the cultivation of food) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensors were combined with agricultural inventory data to create a global data set. The visual presentation of these data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data were compiled by Navin Ramankutty et al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AGLANDS_PAS2000_1.00.json b/datasets/CIESIN_SEDAC_AGLANDS_PAS2000_1.00.json index eaf17b4c13..06276396f3 100644 --- a/datasets/CIESIN_SEDAC_AGLANDS_PAS2000_1.00.json +++ b/datasets/CIESIN_SEDAC_AGLANDS_PAS2000_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AGLANDS_PAS2000_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Pastures data set represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensors were combined with agricultural inventory data to create a global data set. The visual presentation of these data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data were compiled by Navin Ramankutty et al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ANTHROMES_v1_1.00.json b/datasets/CIESIN_SEDAC_ANTHROMES_v1_1.00.json index 1cd447472c..485d1094fc 100644 --- a/datasets/CIESIN_SEDAC_ANTHROMES_v1_1.00.json +++ b/datasets/CIESIN_SEDAC_ANTHROMES_v1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ANTHROMES_v1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Biomes of the World, Version 1 data set describes globally-significant ecological patterns within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture, urbanization, forestry and other land uses. Conventional biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate. Now that humans have fundamentally altered global patterns of ecosystem form, process, and biodiversity, anthropogenic biomes provide a contemporary view of the terrestrial biosphere in its human-altered form. Anthropogenic biomes may also be termed \"anthromes\" to distinguish them from conventional biome systems, or \"human biomes\" (a simpler but less precise term). This data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1700_2.00.json b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1700_2.00.json index ad3b1a3810..0dc4ab558a 100644 --- a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1700_2.00.json +++ b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1700_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ANTHROMES_v2_1700_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1700. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1800_2.00.json b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1800_2.00.json index 2c5f35d92c..5eef4a1a8e 100644 --- a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1800_2.00.json +++ b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1800_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ANTHROMES_v2_1800_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1800. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1900_2.00.json b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1900_2.00.json index f4a33bee2b..ed0a0c0973 100644 --- a/datasets/CIESIN_SEDAC_ANTHROMES_v2_1900_2.00.json +++ b/datasets/CIESIN_SEDAC_ANTHROMES_v2_1900_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ANTHROMES_v2_1900_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1900. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ANTHROMES_v2_2000_2.00.json b/datasets/CIESIN_SEDAC_ANTHROMES_v2_2000_2.00.json index dcca0d8732..52852ed191 100644 --- a/datasets/CIESIN_SEDAC_ANTHROMES_v2_2000_2.00.json +++ b/datasets/CIESIN_SEDAC_ANTHROMES_v2_2000_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ANTHROMES_v2_2000_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 2000. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_CTMAP_2003_2018_1.00.json b/datasets/CIESIN_SEDAC_AQDH_CTMAP_2003_2018_1.00.json index b247efe8fe..6ff21050f5 100644 --- a/datasets/CIESIN_SEDAC_AQDH_CTMAP_2003_2018_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_CTMAP_2003_2018_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_CTMAP_2003_2018_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Country Trends in Major Air Pollutants data set is a framework of public-health-focused air quality indicators that quantifies over 200 countries' trends in exposure to Particulate Matter (PM2.5), Ozone (O3), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Volatile Organic Compounds (VOCs). Pollutant concentration data are derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Composition Reanalysis 4 (EAC4) data sets, freely available from the Copernicus Atmospheric Monitoring Services' Atmospheric Data Store (https://ads.atmosphere.copernicus.eu). CIESIN's Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 was used in the population weighting algorithm.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10_1.10.json b/datasets/CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10_1.10.json index 79be7737b7..959d8de4bf 100644 --- a/datasets/CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10_1.10.json +++ b/datasets/CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10_1.10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_DANO2_US_1KM_1.10_1.10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.00.json b/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.00.json index 766c42eb8c..90bb5ce492 100644 --- a/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10_1.10.json b/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10_1.10.json index ab26866e06..67d465db56 100644 --- a/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10_1.10.json +++ b/datasets/CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10_1.10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_DAO3_US_1KM_1.10_1.10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains estimates of ozone concentrations at a high resolution spatially (1-km grid cells) and temporally (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically-weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages. In version 1.10, we have enhanced the completeness of daily O3 predictions by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, we used inverse distance weighting interpolation to fill the missing grid cells. Other missing daily O3 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily 8-hour maximum and annual O3 predictions allow public health researchers to respectively estimate the short- and long-term effects of O3 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for O3. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.0.json b/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.0.json index 7a667ab80c..fc46843bc8 100644 --- a/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.0.json +++ b/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10_1.10.json b/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10_1.10.json index 9a8ff0cedb..7738ec3c70 100644 --- a/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10_1.10.json +++ b/datasets/CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10_1.10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_DAPM25_US_1KM_1.10_1.10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set includes predictions of PM2.5 concentration in grid cells at a resolution of 1-km for the years 2000-2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, and others. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_NO2_US_1KM_1.00.json b/datasets/CIESIN_SEDAC_AQDH_NO2_US_1KM_1.00.json index ac852e94d4..91e9544e84 100644 --- a/datasets/CIESIN_SEDAC_AQDH_NO2_US_1KM_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_NO2_US_1KM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_NO2_US_1KM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1 km x 1 km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_PM25COM_US_1KM_1.00.json b/datasets/CIESIN_SEDAC_AQDH_PM25COM_US_1KM_1.00.json index ee3aab6306..b7cc44e004 100644 --- a/datasets/CIESIN_SEDAC_AQDH_PM25COM_US_1KM_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_PM25COM_US_1KM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_PM25COM_US_1KM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 microgram per cubic meter (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are microgram per cubic meter. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00.json b/datasets/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00.json index 03fdaf15de..2506cfa7ec 100644 --- a/datasets/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, public health, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, and U.S. Census Bureau American Community Survey (ACS). The data are particularly useful for studies on rural populations who may lack air monitoring sites. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by making it easier to use these data in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.\n", "links": [ { diff --git a/datasets/CIESIN_SEDAC_AQDH_TRACE_US_1KM_1.00.json b/datasets/CIESIN_SEDAC_AQDH_TRACE_US_1KM_1.00.json index c826a2cb09..cb2ba0897a 100644 --- a/datasets/CIESIN_SEDAC_AQDH_TRACE_US_1KM_1.00.json +++ b/datasets/CIESIN_SEDAC_AQDH_TRACE_US_1KM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_AQDH_TRACE_US_1KM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of trace elements concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and a high resolution (1km x 1km grid cells) in non-urban areas, for the years 2000 to 2019. Particulate matter with an aerodynamic diameter of less than 2.5 microgram per cubic meter (PM2.5) is a human silent killer of millions worldwide, and contains many trace elements (TEs). Understanding the relative toxicity is largely limited by the lack of data. In this work, ensembles of machine learning models were used to generate approximately 163 billion predictions estimating annual mean PM2.5 TEs, namely Bromine (Br), Calcium (Ca), Copper (Cu), Iron (Fe), Potassium (K), Nickel (Ni), Lead (Pb), Silicon (Si), Vanadium (V), and Zinc (Zn). The monitored data from approximately 600 locations were integrated with more than 160 predictors, such as time and location, satellite observations, composite predictors, meteorological covariates, and many novel land use variables using several machine learning algorithms and ensemble methods. Multiple machine-learning models were developed covering urban areas and non-urban areas. Their predictions were then ensembled using either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA), or Super-Learners. The overall best model R-squared values for the test sets ranged from 0.79 for Copper to 0.88 for Zinc in non-urban areas. In urban areas, the R-squared model values ranged from 0.80 for Copper to 0.88 for Zinc. The Coordinate Reference System (CRS) used in the predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components TEs are nanograms per cubic meter. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_ADM_GIS_1990_1.01.json b/datasets/CIESIN_SEDAC_CD_ADM_GIS_1990_1.01.json index b3d5e42d21..cd85858356 100644 --- a/datasets/CIESIN_SEDAC_CD_ADM_GIS_1990_1.01.json +++ b/datasets/CIESIN_SEDAC_CD_ADM_GIS_1990_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_ADM_GIS_1990_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This data set is produced in collaboration with the Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_ADM_GIS_JUL90_1.00.json b/datasets/CIESIN_SEDAC_CD_ADM_GIS_JUL90_1.00.json index 5350713c56..92f07c5d1e 100644 --- a/datasets/CIESIN_SEDAC_CD_ADM_GIS_JUL90_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_ADM_GIS_JUL90_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_ADM_GIS_JUL90_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China Administrative Regions GIS Data: 1:1M, County Level, 1 July 1990 consists of geographic boundary data for the administrative regions of China as of 1 July 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This data set is produced in collaboration with the Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_AGENDA21_1.00.json b/datasets/CIESIN_SEDAC_CD_AGENDA21_1.00.json index f4aef791e1..40617a94f2 100644 --- a/datasets/CIESIN_SEDAC_CD_AGENDA21_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_AGENDA21_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_AGENDA21_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Priority Programme for China's Agenda 21 consists of full-text program descriptions supporting China's economic and social development. The descriptions represent 69 programs covering legislation, policy, education, agriculture, environment, energy, transportation, regional development, population, health, and global change research. Each description includes project scope, background, objectives, activities, inputs, and benefits. This data set is produced in collaboration with the Administrative Center for China's Agenda 21 (ACCA21), United Nations Development Programme (UNDP), and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_BIB_ADM_GEOG_1.00.json b/datasets/CIESIN_SEDAC_CD_BIB_ADM_GEOG_1.00.json index e002a9df52..3f234d4efc 100644 --- a/datasets/CIESIN_SEDAC_CD_BIB_ADM_GEOG_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_BIB_ADM_GEOG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_BIB_ADM_GEOG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Bibliography of Chinese Administrative Geography is a historical collection of bibliographic information on 75 published books describing the administrative geography of China. The information resides in a searchable database and includes title, author/editor, subject, spatial (national, provincial, local) and temporal coverage, publisher, description, and language, as well as location of the reference, for works published during the 1949-1994 period. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, Universities Service Center at the Chinese University of Hong Kong, and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_CTY_ECON_YRBK_1.00.json b/datasets/CIESIN_SEDAC_CD_CTY_ECON_YRBK_1.00.json index 4e640b29fe..10d03f23a0 100644 --- a/datasets/CIESIN_SEDAC_CD_CTY_ECON_YRBK_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_CTY_ECON_YRBK_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_CTY_ECON_YRBK_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_CTY_POPAG_GIS_1.00.json b/datasets/CIESIN_SEDAC_CD_CTY_POPAG_GIS_1.00.json index 56e66363f4..3507c6c130 100644 --- a/datasets/CIESIN_SEDAC_CD_CTY_POPAG_GIS_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_CTY_POPAG_GIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_CTY_POPAG_GIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_FUNDGIS_1.00.json b/datasets/CIESIN_SEDAC_CD_FUNDGIS_1.00.json index 3d2fa1b9b5..5d9a952fae 100644 --- a/datasets/CIESIN_SEDAC_CD_FUNDGIS_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_FUNDGIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_FUNDGIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M).\r\n\r\nThis data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_GBCODES_1.00.json b/datasets/CIESIN_SEDAC_CD_GBCODES_1.00.json index cebfc7aa7a..ab52a3b3f9 100644 --- a/datasets/CIESIN_SEDAC_CD_GBCODES_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_GBCODES_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_GBCODES_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GuoBiao (GB) Codes for the Administrative Divisions of the People's Republic of China consists of geographic codes for the administrative divisions of China. The data includes provinces (autonomous regions, municipalities directly under the Central Government), prefectures (prefecture-level cities, autonomous prefectures, leagues), and counties (districts, county-level cities, autonomous counties, banners) for 1 January 1982 to 31 December 1992.\n\nThis data set is produced in collaboration with the Chinese Academy of Surveying and Mapping (CASM), University of Washington as part of the China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_HOS_EPI_50-85_1.00.json b/datasets/CIESIN_SEDAC_CD_HOS_EPI_50-85_1.00.json index 25f088a95b..7745587975 100644 --- a/datasets/CIESIN_SEDAC_CD_HOS_EPI_50-85_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_HOS_EPI_50-85_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_HOS_EPI_50-85_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China Dimensions Data Collection: Chinese County-Level Data on Hospitals and Epidemiology Stations, 1950-1985 consists of hospital and epidemiological station data for the administrative regions of China from 1950 to 1985. The data includes name and years of operation at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_MAPS_BIBDB_1.00.json b/datasets/CIESIN_SEDAC_CD_MAPS_BIBDB_1.00.json index 6664843e70..7aa5e0d1b1 100644 --- a/datasets/CIESIN_SEDAC_CD_MAPS_BIBDB_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_MAPS_BIBDB_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_MAPS_BIBDB_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CD_STAT_1949-1990_1.00.json b/datasets/CIESIN_SEDAC_CD_STAT_1949-1990_1.00.json index 7c9205f7fc..0b28e4d26f 100644 --- a/datasets/CIESIN_SEDAC_CD_STAT_1949-1990_1.00.json +++ b/datasets/CIESIN_SEDAC_CD_STAT_1949-1990_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CD_STAT_1949-1990_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This data set is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_2004EVI_2004.00.json b/datasets/CIESIN_SEDAC_CESIC_2004EVI_2004.00.json index b73f673f92..19f8f6a141 100644 --- a/datasets/CIESIN_SEDAC_CESIC_2004EVI_2004.00.json +++ b/datasets/CIESIN_SEDAC_CESIC_2004EVI_2004.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_2004EVI_2004.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2004 Environmental Vulnerability Index (EVI) portion of the Compendium of Environmental Sustainability Indicator Collections contains 111 variables for 235 countries and territories. This index is designed to be used with economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. It was developed through consultation and collaborations with countries, institutions and experts across the globe by the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Programme (UNEP) and their partners. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_2006NFA_2006.00.json b/datasets/CIESIN_SEDAC_CESIC_2006NFA_2006.00.json index 35f065d9b7..cec47e6184 100644 --- a/datasets/CIESIN_SEDAC_CESIC_2006NFA_2006.00.json +++ b/datasets/CIESIN_SEDAC_CESIC_2006NFA_2006.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_2006NFA_2006.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2006 National Footprint Accounts (NFA) portion of the Compendium of Environmental Sustainability Indicator Collections, version 1.1 is a data set that measures how much land and water area a human population requires to produce the resources it consumes and to absorb its wastes under prevailing technology and management. It includes Footprints for cropland, grazing land, carbon, nuclear, forest, built-up and fishing ground for 147 countries. It also identifies countries that are considered to have ecological deficits and reserves. These data are drawn from the National Footprint Accounts, 2006 Edition, produced by the Global Footprint Network and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_ANC_1.00.json b/datasets/CIESIN_SEDAC_CESIC_ANC_1.00.json index c58f577284..e8b96dd332 100644 --- a/datasets/CIESIN_SEDAC_CESIC_ANC_1.00.json +++ b/datasets/CIESIN_SEDAC_CESIC_ANC_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_ANC_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ancillary Data portion of the Compendium of Environmental Sustainability Indicator Collections contains 38 variables (time series data on population and gross domestic product as well as region codes, land area, and waterbody area) for 238 countries. The data are taken from the UN Population Division, the World Bank, the CIA Factbook, and CIESIN's Gridded Population of the World, and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_COMPLETE_V11_1.01.json b/datasets/CIESIN_SEDAC_CESIC_COMPLETE_V11_1.01.json index 15d102c327..6df2ab1544 100644 --- a/datasets/CIESIN_SEDAC_CESIC_COMPLETE_V11_1.01.json +++ b/datasets/CIESIN_SEDAC_CESIC_COMPLETE_V11_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_COMPLETE_V11_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Compendium of Environmental Sustainability Indicator Collections, Version 1.1 contains 426 indicators for 239 countries from five major environmental sustainability indicator efforts: the 2006 Environmental Performance Index (EPI), 2005 Environmental Sustainability Index (ESI), 2004 Environmental Vulnerability Index (EVI), the Rio to Johannesburg Dashboard, the Wellbeing of Nations, and 2006 National Footprint Accounts. It also incorporates 38 ancillary variables such as region name, dummy variables for landlocked countries and small island states, population, GDP, and land area. The collection is compiled and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_RIOJO_1.00.json b/datasets/CIESIN_SEDAC_CESIC_RIOJO_1.00.json index 440dbd5c9a..bcad2fa8e4 100644 --- a/datasets/CIESIN_SEDAC_CESIC_RIOJO_1.00.json +++ b/datasets/CIESIN_SEDAC_CESIC_RIOJO_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_RIOJO_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Rio to Johannesburg Dashboard of Sustainable Development Indicators portion of the Compendium of Environmental Sustainability Indicator Collections contains 35 Commission on Sustainable Development (CSD) indicators for 202 countries. Commonly known as the RioJo Dashboard, indicators are from the CSD Thematic Framework from the Rio Summit (1992 UN conference on the Environment and Development) to the time of the Johannesburg Summit in 2000. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CESIC_WELLBEING_1.00.json b/datasets/CIESIN_SEDAC_CESIC_WELLBEING_1.00.json index 83127e4073..47d74360e9 100644 --- a/datasets/CIESIN_SEDAC_CESIC_WELLBEING_1.00.json +++ b/datasets/CIESIN_SEDAC_CESIC_WELLBEING_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CESIC_WELLBEING_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Wellbeing of Nations portion of the Compendium of Environmental Sustainability Indicator Collections contains a subset of 123 variables assembled from the Wellbeing of Nations, which assesses human and ecosystem wellbeing for 183 countries. The variables selected include both raw data and processed indicators and indices created by the report's author, Robert Prescott-Allen. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CLIMMIG_ACMI_BILMIGPROJ_1.00.json b/datasets/CIESIN_SEDAC_CLIMMIG_ACMI_BILMIGPROJ_1.00.json index 07987cccfb..25d5e2135d 100644 --- a/datasets/CIESIN_SEDAC_CLIMMIG_ACMI_BILMIGPROJ_1.00.json +++ b/datasets/CIESIN_SEDAC_CLIMMIG_ACMI_BILMIGPROJ_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CLIMMIG_ACMI_BILMIGPROJ_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Climate Mobility Initiative (ACMI) Bilateral Migration Projections project bilateral migration flows at 5-year intervals from 2015 to 2050 for a combination of 2 sets of Shared Socioeconomic Pathways (SSPs) scenarios and 3 sets of Representative Concentration Pathways (RCPs) scenarios. The unit of analysis for the projections are directed migration corridors from an origin country to a receiving country in the African continent. There are 46 African countries and thus 2,070 unique directed corridors. These data underpin the African Shifts Report, both produced by the ACMI enabled by the Global Centre for Climate Mobility (GCCM). The ACMI was launched in September 2021 as a joint initiative of the GCCM, the African Union Commission (AUC), the United Nations System (UN), and the World Bank.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CLIMMIG_GASPMP18SR_1.00.json b/datasets/CIESIN_SEDAC_CLIMMIG_GASPMP18SR_1.00.json index 35e4c2d358..41000c1486 100644 --- a/datasets/CIESIN_SEDAC_CLIMMIG_GASPMP18SR_1.00.json +++ b/datasets/CIESIN_SEDAC_CLIMMIG_GASPMP18SR_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CLIMMIG_GASPMP18SR_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Groundswell Africa Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050 data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in five-year increments, of population distribution and internal climate-related and other migration for West Africa and the Lake Victoria Basin. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on water availability, crop productivity, and pasturelands (where cropping does not occur), as well as flood impacts, from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Four scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, a more inclusive development scenario combining SSP2 and RCP8.5, and an optimistic scenario combining SSP2 and RCP2.6. Each scenario provides an ensemble average of four model runs combining different climate impact models as well as confidence intervals to better capture uncertainties. The modeling work was funded and developed jointly with The World Bank.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CLIMMIG_GSPMP18SR_1.00.json b/datasets/CIESIN_SEDAC_CLIMMIG_GSPMP18SR_1.00.json index b5da9b798e..3315c15d40 100644 --- a/datasets/CIESIN_SEDAC_CLIMMIG_GSPMP18SR_1.00.json +++ b/datasets/CIESIN_SEDAC_CLIMMIG_GSPMP18SR_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CLIMMIG_GSPMP18SR_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050, data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in ten-year increments, of population distribution and internal climate-related and other migration. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on crop productivity and water availability from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Three scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, and a more inclusive development scenario combining SSP2 and RCP8.5, and each scenario represents an ensemble of four model runs combining different climate impact models. The modeling work was funded and developed jointly with The World Bank, and covers most World Bank client countries, with reports released in 2018 and 2021 that address different regions and provide full methodological details.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00.json b/datasets/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00.json index 536af65ddf..f556afa438 100644 --- a/datasets/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00.json +++ b/datasets/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CROPCLIM_GISS_DB_1.00.json b/datasets/CIESIN_SEDAC_CROPCLIM_GISS_DB_1.00.json index 8da08e60bb..077457c170 100644 --- a/datasets/CIESIN_SEDAC_CROPCLIM_GISS_DB_1.00.json +++ b/datasets/CIESIN_SEDAC_CROPCLIM_GISS_DB_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CROPCLIM_GISS_DB_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_CRV_USCRPC_2040-2049_1.00.json b/datasets/CIESIN_SEDAC_CRV_USCRPC_2040-2049_1.00.json index d5ba8a38ec..d716ab4e49 100644 --- a/datasets/CIESIN_SEDAC_CRV_USCRPC_2040-2049_1.00.json +++ b/datasets/CIESIN_SEDAC_CRV_USCRPC_2040-2049_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_CRV_USCRPC_2040-2049_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Climate Risk Projections by County, 2040-2049 data set contains a projection for 2040-2049 risk for the entire contiguous U.S. at the county level with a novel climate risk index integrating multiple hazards, exposures and vulnerabilities. Multiple hazards such as weather and climate are characterized as a frequency of heat wave, cold spells, drought, and heavy precipitation events along with anomalies of temperature and precipitation using high resolution (4 km) downscaled climate projections. Exposure is characterized by projections of population, infrastructure, and built surfaces prone to multiple hazards including sea level rise and storm surges. Vulnerability is characterized by projections of demographic groups most sensitive to climate hazards. This approach can guide planners in targeting counties at most risk and where adaptation strategies to reduce exposure or protect vulnerable populations might be best applied.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_DEDC_ACE_V2_2.00.json b/datasets/CIESIN_SEDAC_DEDC_ACE_V2_2.00.json index e7df1f1b09..30edf525cf 100644 --- a/datasets/CIESIN_SEDAC_DEDC_ACE_V2_2.00.json +++ b/datasets/CIESIN_SEDAC_DEDC_ACE_V2_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_DEDC_ACE_V2_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Altimeter Corrected Elevations, Version 2 (ACE2) data set, is the Global Digital Elevation Model (GDEM) created by using multi-mission Satellite Radar Altimetry with the Shuttle Radar Topography Mission (SRTM). It was created by synergistically merging the SRTM data set with Satellite Radar Altimetry within the region bounded by 60\u00ef\u00bf\u00bdN and 60\u00ef\u00bf\u00bdS. Over the areas lying outside the SRTM latitude limits, other sources have been used including Global Observations to Benefit the Environment (GLOBE) and the original Altimeter Corrected Elevations (ACE) Digital Elevation Model (DEM), together with new matrices derived from reprocessing the European Remote Sensing (ERS-1) Geodetic Mission data set with an enhanced re-tracking system, and the inclusion of data from other satellites. ACE2 was developed at resolutions of 3, 9 and 30 arc-seconds, and 5 arc-minutes. The data are distributed in little-endian format as 15 degree by 15 degree tiles, with the file name referring to the southwestern edge of the southwestern most pixel.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ENERGY_NPPCLA_1.00.json b/datasets/CIESIN_SEDAC_ENERGY_NPPCLA_1.00.json index cb2c8c7674..eb7d5ff393 100644 --- a/datasets/CIESIN_SEDAC_ENERGY_NPPCLA_1.00.json +++ b/datasets/CIESIN_SEDAC_ENERGY_NPPCLA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ENERGY_NPPCLA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates data set consists of country-level estimates of total, urban, and rural populations and land area, country-wide, that are in proximity to a nuclear power plant. This data set was created using a global data set of point locations of nuclear power plants, with buffer zones at 30km, 75km, 150km, 300km, 600km, and 1200km, and the Global Population Count Grid Time Series Estimates, Version 1 to estimate the population within each buffer zone for the years 1990, 2000, and 2010. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) Land and Geographic Unit Area Grids were used to estimate land area within each buffer zone. The GRUMPv1 Urban Extents Grid was used to further delineate population and land area estimates within urban and rural areas. All grids used for population, land area, and urban mask were of 1 km (30 arc-second) resolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ENERGY_NPPL_1.00.json b/datasets/CIESIN_SEDAC_ENERGY_NPPL_1.00.json index 5ef3e30cd2..3bfd183496 100644 --- a/datasets/CIESIN_SEDAC_ENERGY_NPPL_1.00.json +++ b/datasets/CIESIN_SEDAC_ENERGY_NPPL_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ENERGY_NPPL_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2006_2006.00.json b/datasets/CIESIN_SEDAC_EPI_2006_2006.00.json index 9fac5d9858..4de1e94164 100644 --- a/datasets/CIESIN_SEDAC_EPI_2006_2006.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2006_2006.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2006_2006.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Pilot 2006 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using sixteen indicators tracked in six well-established policy categories: Environmental Health, Air Quality, Water Resources, Productive Natural Resources, Biodiversity and Habitat, and Sustainable Energy. The Pilot 2006 EPI utilizes a proximity-to-target methodology focused on a core set of environmental outcomes linked to policy goals for which every government should be held accountable. By identifying specific targets and measuring how close each country comes to them, the EPI provides a factual foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The Pilot 2006 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2008_2008.00.json b/datasets/CIESIN_SEDAC_EPI_2008_2008.00.json index c2d07bef66..e013656f9a 100644 --- a/datasets/CIESIN_SEDAC_EPI_2008_2008.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2008_2008.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2008_2008.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents \u00ef\u00bf\u00bdat target\u00ef\u00bf\u00bd). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2010_2010.00.json b/datasets/CIESIN_SEDAC_EPI_2010_2010.00.json index ef01ca4f8c..ddef87899d 100644 --- a/datasets/CIESIN_SEDAC_EPI_2010_2010.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2010_2010.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2010_2010.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI\u00ef\u00bf\u00bds proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2012_2012.00.json b/datasets/CIESIN_SEDAC_EPI_2012_2012.00.json index c8870932d9..8413267346 100644 --- a/datasets/CIESIN_SEDAC_EPI_2012_2012.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2012_2012.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2012_2012.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups.\n\n\n\n\n\nThe Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. \n\n\n\n\n\nThe 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2014_2014.00.json b/datasets/CIESIN_SEDAC_EPI_2014_2014.00.json index 5fadb639bd..57244fea70 100644 --- a/datasets/CIESIN_SEDAC_EPI_2014_2014.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2014_2014.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2014_2014.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2014 EPI is at http://epi.yale.edu/.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2016_2016.00.json b/datasets/CIESIN_SEDAC_EPI_2016_2016.00.json index 3ad3d6bf7a..81b2ff96df 100644 --- a/datasets/CIESIN_SEDAC_EPI_2016_2016.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2016_2016.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2016_2016.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2016 Environmental Performance Index (EPI) ranks 180 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2016 EPI and component scores, backcast EPI scores for 1950-2016, and time-series source data. The 2016 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 23, 2016. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and Yale Data-Driven Environmental Solutions Group, Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2016 EPI is at https://epi.yale.edu.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2018_2018.00.json b/datasets/CIESIN_SEDAC_EPI_2018_2018.00.json index 068356efb0..99ec57e8d7 100644 --- a/datasets/CIESIN_SEDAC_EPI_2018_2018.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2018_2018.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2018_2018.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2018 Environmental Performance Index (EPI) ranks 180 countries on 24 performance indicators in the following 10 issue categories: air quality, water and sanitation, heavy metals, biodiversity and habitat, forests, fisheries, climate and energy, air pollution, water resources, and agriculture. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2018 EPI, component scores, and time-series source data. The 2018 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum in January 2018. It is the result of collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2018 EPI is at https://epi.envirocenter.yale.edu/.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2020_2020.00.json b/datasets/CIESIN_SEDAC_EPI_2020_2020.00.json index 8c03d6c374..9f6be02489 100644 --- a/datasets/CIESIN_SEDAC_EPI_2020_2020.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2020_2020.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2020_2020.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2020 Environmental Performance Index (EPI) ranks 180 countries on 32 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, climate change, pollution emissions, agriculture, and water resources. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2020 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2020 EPI is at https://epi.yale.edu/.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_EPI_2022_2022.00.json b/datasets/CIESIN_SEDAC_EPI_2022_2022.00.json index 960045246b..26d1f0ce76 100644 --- a/datasets/CIESIN_SEDAC_EPI_2022_2022.00.json +++ b/datasets/CIESIN_SEDAC_EPI_2022_2022.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_EPI_2022_2022.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2022 Environmental Performance Index (EPI) ranks 180 countries on 40 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, acid rain, agriculture, water resources, and climate change mitigation. These categories track performance and progress on three broad policy objectives, environmental health, ecosystem vitality, and climate change. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2022 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ESI_2000_2000.00.json b/datasets/CIESIN_SEDAC_ESI_2000_2000.00.json index fe457862d9..c65ce79eac 100644 --- a/datasets/CIESIN_SEDAC_ESI_2000_2000.00.json +++ b/datasets/CIESIN_SEDAC_ESI_2000_2000.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ESI_2000_2000.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ESI_2001_2001.00.json b/datasets/CIESIN_SEDAC_ESI_2001_2001.00.json index 86d645aa2a..ba749206f9 100644 --- a/datasets/CIESIN_SEDAC_ESI_2001_2001.00.json +++ b/datasets/CIESIN_SEDAC_ESI_2001_2001.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ESI_2001_2001.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ESI_2002_2002.00.json b/datasets/CIESIN_SEDAC_ESI_2002_2002.00.json index 476061a56a..877ff8008c 100644 --- a/datasets/CIESIN_SEDAC_ESI_2002_2002.00.json +++ b/datasets/CIESIN_SEDAC_ESI_2002_2002.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ESI_2002_2002.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ESI_2005_2005.00.json b/datasets/CIESIN_SEDAC_ESI_2005_2005.00.json index 0ab2ae4427..e956d61554 100644 --- a/datasets/CIESIN_SEDAC_ESI_2005_2005.00.json +++ b/datasets/CIESIN_SEDAC_ESI_2005_2005.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ESI_2005_2005.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying data sets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FERMANv1_NAPP_1.00.json b/datasets/CIESIN_SEDAC_FERMANv1_NAPP_1.00.json index 6f74e47d97..8cfcd31908 100644 --- a/datasets/CIESIN_SEDAC_FERMANv1_NAPP_1.00.json +++ b/datasets/CIESIN_SEDAC_FERMANv1_NAPP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FERMANv1_NAPP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nitrogen Fertilizer Application data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) \"Fertilizer Use by Crop 2002\" statistics database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FERMANv1_NMAN_1.00.json b/datasets/CIESIN_SEDAC_FERMANv1_NMAN_1.00.json index aaa24d4573..a6ece5674c 100644 --- a/datasets/CIESIN_SEDAC_FERMANv1_NMAN_1.00.json +++ b/datasets/CIESIN_SEDAC_FERMANv1_NMAN_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FERMANv1_NMAN_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nitrogen in Manure Production data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen manure produced and present on the landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FERMANv1_PAPP_1.00.json b/datasets/CIESIN_SEDAC_FERMANv1_PAPP_1.00.json index 1906823714..ee26dd3920 100644 --- a/datasets/CIESIN_SEDAC_FERMANv1_PAPP_1.00.json +++ b/datasets/CIESIN_SEDAC_FERMANv1_PAPP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FERMANv1_PAPP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Phosphorus Fertilizer Application data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus fertilizer nutrients applied to croplands. The national-level phosphorus fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) \"Fertilizer Use by Crop 2002\" statistics database that is available by request from the Food and Agriculture Organization (FAO).The number of crop-specific fertilizer application rates reported for each country ranged from 2 crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FERMANv1_PESTG_V1.01_1.01.json b/datasets/CIESIN_SEDAC_FERMANv1_PESTG_V1.01_1.01.json index bf315f0af8..0e31166cd3 100644 --- a/datasets/CIESIN_SEDAC_FERMANv1_PESTG_V1.01_1.01.json +++ b/datasets/CIESIN_SEDAC_FERMANv1_PESTG_V1.01_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FERMANv1_PESTG_V1.01_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Pesticide Grids (PEST-CHEMGRIDS), Version 1.01 data set contains 20 of the most-used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 arc-minute resolution (about 10 km at the equator), estimated in year 2015, and then projected to 2020 and 2025. To estimate the global application rates of specific active ingredients, spatial statistical methods were used to re-analyze the U.S. Geological Survey Pesticide National Synthesis Project (USGS/PNSP) and the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) pesticide databases, along with other public inventories including globally gridded data of soil physical properties, hydro-climatic variables, agricultural quantities, and socioeconomic indices. The application rate (APR) of each active ingredient on each crop is in kilogram per hectare per year (kg/ha-year), and the harvest area of each crop is in hectare (ha). The data set also includes 200 data quality index maps corresponding to each active ingredient on each crop, as well as maps of the 10 dominant crops and 4 aggregated crop classes. Version 1.01 includes data in GeoTIFF and netCDF formats.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FERMANv1_PMAN_1.00.json b/datasets/CIESIN_SEDAC_FERMANv1_PMAN_1.00.json index 86991ece71..fbe50c36e5 100644 --- a/datasets/CIESIN_SEDAC_FERMANv1_PMAN_1.00.json +++ b/datasets/CIESIN_SEDAC_FERMANv1_PMAN_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FERMANv1_PMAN_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Phosphorus in Manure Production data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorous in manure produced and present on the landscape. The manure production at grid cell level was computed based on livestock population and nutrient excretion rates. The livestock population per grid cell was computed by multiplying the density values from FAO Gridded Livestock of the World by the area of grid cell. Spatially explicit manure produced and present on landscape is derived by combining the number of livestock heads and the nutrient excretion rate. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017_1.00.json b/datasets/CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017_1.00.json index ba13d7dd5e..cf09b0d640 100644 --- a/datasets/CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017_1.00.json +++ b/datasets/CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FOOD_CROPSTAT_1900-2017_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Twentieth Century Crop Statistics, 1900-2017 data set consists of national or subnational maize and wheat production, yield, and harvested area statistics for all available years for the period 1900-2017. It combines a new digitization of crop statistics from Italy, Spain, Indonesia, China, Mexico, Uruguay, Chile, Sweden, and Morocco with existing, publicly available, digitized data sets from India, Australia, the United States, Canada, Southern Brazil, Argentina, England, Austria, Belgium, Croatia, Czech Republic, Finland, Germany, Spain, Portugal, France, the Netherlands, and South Africa. All Units are converted to hectares (ha) for Units of harvested areas, tonnes for Units of production, and tonnes/ha for Units of yield. A ratio of 1/36.744 is used to convert wheat bushels to tonnes, and a value of 1/39.368 is used to convert maize bushels to tonnes. In all cases, the Harvest_year reported in the data set is the harvest year for the crop.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_FOOD_FIH_V1_1.00.json b/datasets/CIESIN_SEDAC_FOOD_FIH_V1_1.00.json index 850b988fe2..9aa1175c08 100644 --- a/datasets/CIESIN_SEDAC_FOOD_FIH_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_FOOD_FIH_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_FOOD_FIH_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00.json b/datasets/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00.json index 4d652a5961..80c0cb4cef 100644 --- a/datasets/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00.json +++ b/datasets/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing Units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00.json b/datasets/CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00.json index 80e40d30a2..42da631d46 100644 --- a/datasets/CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00.json +++ b/datasets/CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00.json b/datasets/CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00.json index e294207532..c2281b4292 100644 --- a/datasets/CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00.json +++ b/datasets/CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00.json b/datasets/CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00.json index 6ec8ab1009..d1638008d9 100644 --- a/datasets/CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00.json +++ b/datasets/CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00.json b/datasets/CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00.json index 716a1f503b..70a15f7cb1 100644 --- a/datasets/CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00.json +++ b/datasets/CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json b/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json index aebcaf7089..f8815b6070 100644 --- a/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json +++ b/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GHSL_PBSMOD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json index b3eec462f3..2839666e61 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_CENTROIDS_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Centroids consists of estimates of human population counts and densities for the years 1990, 1995, 2000, 2005, 2010, and 2015 by administrative Unit centroid location. The centroids are based on the 399,781 input administrative Units used in GPWv3. In addition to population counts and variables, the centroids have associated administrative Unit names and the land area of contained within the administrative Unit. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json index e0371114a9..9ad7692ad8 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_COASTLINES_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Coastlines are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GPWv3. The coastlines are designed for cartographic use with the GPWv3 population raster data sets. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json index bd12c97e21..3345729c8e 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_LANDGEOG_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived..GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json index 7a5745df48..2291d8627a 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_NADMINBND_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GPWv3 on a per-country/territory basis. The National Boundaries data are derived from the pixels as polygons and thus have rectilinear boundaries at large scale. Note that the polygons that outline the countries and territories are not official representations; rather, they represent the area covered by the statistical data as provided. The national/territorial boundaries are designed for cartographic use with the GPWv3 population raster data sets. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_NATIDEN_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_NATIDEN_3.00.json index 0c4912a7a7..80e7af05b7 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_NATIDEN_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_NATIDEN_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_NATIDEN_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same country or territory have the same value. Note that the countries and territories are not official representations of countries boundaries; rather, they represent the area covered by the statistical data as provided. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_POPCOUNTFE_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_POPCOUNTFE_3.00.json index 6abdfcdd59..9749bdbb2c 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_POPCOUNTFE_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_POPCOUNTFE_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_POPCOUNTFE_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid, Future Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population counts that the grids are derived from are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_POPCOUNT_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_POPCOUNT_3.00.json index cb172e184b..1b3f593196 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_POPCOUNT_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_POPCOUNT_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_POPCOUNT_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_POPDENSFE_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_POPDENSFE_3.00.json index 6e31ee408d..19ec17a01a 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_POPDENSFE_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_POPDENSFE_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_POPDENSFE_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, Future EstimatesFuture Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_POPDENS_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_POPDENS_3.00.json index e93ac4dade..6f5221ddc3 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_POPDENS_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_POPDENS_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_POPDENS_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv3_SUBADBND_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_SUBADBND_3.00.json index d1ee230732..abc0ceaabb 100644 --- a/datasets/CIESIN_SEDAC_GPWv3_SUBADBND_3.00.json +++ b/datasets/CIESIN_SEDAC_GPWv3_SUBADBND_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv3_SUBADBND_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 3 (GPWv3): Subnational Administrative Boundaries are the basis of the population data products in GPWv3. Due to copyright restrictions, only maps of the subnational administrative boundaries are available, the underlying data cannot be released. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json index 084b318ef4..e3030f0429 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11 consists of UN WPP-adjusted population estimates and densities for the years 2000, 2005, 2010, 2015 and 2020, as well as the basic demographic characteristics (age and sex) for the year 2010. The data set also includes administrative name, land and water area, and data context by administrative Unit center point (centroid) location. The center points are based on approximately 13.5 million input administrative Units used in GPWv4, therefore, these files require hardware and software that can read large amounts of data into memory.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11_4.11.json index a9c7f78a0c..ac1b5ea765 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_APCT_WPP_2015_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 consists of estimates of human population (number of persons per pixel) consistent with national censuses and population registers with respect to relative spatial distribution, but adjusted to match the 2015 Revision of the United Nation's World Population Prospects (UN WPP) country totals for the years 2000, 2005, 2010, 2015, and 2020.\u00ef\u00bf\u00bdA proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second adjusted count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11_4.11.json index d3b1bb4f16..0e1f8f3122 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_APDENS_WPP_2015_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers with respect to relative spatial distribution, but adjusted to match the 2015 Revision of the United Nation's World Population Prospects (UN WPP) country totals, for the years 2000, 2005, 2011, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign UN WPP-adjusted population counts to 30 arc-second grid cells. The density rasters were created by dividing the UN WPP-adjusted population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second adjusted count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_BDC_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_BDC_R11_4.11.json index 0fa188cfed..f633c4f498 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_BDC_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_BDC_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_BDC_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_DQI_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_DQI_R11_4.11.json index 0b4de17d4c..38a260261d 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_DQI_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_DQI_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_DQI_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11 consists of three data layers created to provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data. The Data Context raster explains pixels with a \"0\" population estimate in the population count and density rasters based on information included in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Water Mask raster distinguishes between pixels that are completely water and/or ice (Total Water Pixels), pixels that contain water and land (Partial Water Pixels), pixels that are completely land (Total Land Pixels), and pixels that are completely ocean water (Ocean Pixels). The Mean Administrative Unit Area raster represents the mean input Unit size in square kilometers and provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density rasters are created. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11_4.11.json index 95ed064517..28d704196e 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_LANDH2OAREA_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Land and Water Area, Revision 11 consists of two rasters that measure surface areas of land and water in square kilometers per pixel. The Land Area raster provides estimates of the land area, excluding permanent ice and water, within each pixel, and was used to calculate the population density rasters. The Water Area raster provides estimates of the water area (permanent ice and water) within each pixel. The sum of land area and water area of a pixel equals the total surface area of that pixel. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_NATIDEN_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_NATIDEN_R11_4.11.json index 353e83f440..afa530250e 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_NATIDEN_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_NATIDEN_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_NATIDEN_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 is a raster representation of nation-states in GPWv4 for use in aggregating population data. This data set was produced from the input census Units which were used to create a raster surface where pixels that cover the same census data source (most often a country or territory) have the same value. Note that these data are not official representations of country boundaries; rather, they represent the area covered by the input data. In cases where multiple countries overlapped a given pixel (e.g. on national borders), the pixels were assigned the country code of the input data set which made up the majority of the land area. The data file was produced as a global raster at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions. Each level of aggregation results in the loss of one or more countries with areas smaller than the cell size of the final raster. Rasters of all resolutions were also converted to polygon shapefiles.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_POPCOUNT_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_POPCOUNT_R11_4.11.json index 8043ec1e79..646268cf9c 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_POPCOUNT_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_POPCOUNT_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_POPCOUNT_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 consists of estimates of human population (number of persons per pixel), consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GPWv4_POPDENS_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_POPDENS_R11_4.11.json index 86e3ceafda..8f6b225d15 100644 --- a/datasets/CIESIN_SEDAC_GPWv4_POPDENS_R11_4.11.json +++ b/datasets/CIESIN_SEDAC_GPWv4_POPDENS_R11_4.11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GPWv4_POPDENS_R11_4.11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020.\u00ef\u00bf\u00bdA proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRANDv1_DAMS_1.01.json b/datasets/CIESIN_SEDAC_GRANDv1_DAMS_1.01.json index 754de130bf..705676da4d 100644 --- a/datasets/CIESIN_SEDAC_GRANDv1_DAMS_1.01.json +++ b/datasets/CIESIN_SEDAC_GRANDv1_DAMS_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRANDv1_DAMS_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json b/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json index eaa6536ccc..f7e6c033f3 100644 --- a/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json +++ b/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GROADS_CCRDSv1_1.00.json b/datasets/CIESIN_SEDAC_GROADS_CCRDSv1_1.00.json index 93f9a877bb..c096b6930d 100644 --- a/datasets/CIESIN_SEDAC_GROADS_CCRDSv1_1.00.json +++ b/datasets/CIESIN_SEDAC_GROADS_CCRDSv1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GROADS_CCRDSv1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GROADS_V1_1.00.json b/datasets/CIESIN_SEDAC_GROADS_V1_1.00.json index 1356442ffc..41d3f7bb4b 100644 --- a/datasets/CIESIN_SEDAC_GROADS_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_GROADS_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GROADS_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_COASTLINES_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_COASTLINES_1.00.json index 02b00a3c60..1775658a0c 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_COASTLINES_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_COASTLINES_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_COASTLINES_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Coastlines data are derived from the land area grids to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 that fall along waterbodies. The coastlines are designed for cartographic use with the GRUMPv1 population raster data sets. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json b/datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json index f26276f39b..e6aaceeb64 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_EXT02_1.02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_EXT_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_EXT_1.00.json index 5552d43e4c..b6b383ae7a 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_EXT_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_EXT_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_EXT_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json index dcd35c6f3f..392569a5ad 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_LGUAG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json index 3f6f4ecc22..33d20f0fc5 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 on a per-country/territory basis. They are derived from the pixels as polygons and thus have rectilinear boundaries at a large scale. The polygons that outline the countries and territories are not official representations; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json index b3a62e7f5f..28b7d24c8a 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same nation or territory have the same value. The countries and territories are not official representations of country boundaries; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00.json index 79b74be28d..c4a512551d 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid estimates human population for the years 1990, 1995, and 2000 by 30 arc-second (1 km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_POPDENS_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_POPDENS_1.00.json index b6fde36555..a3d2503c3e 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_POPDENS_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_POPDENS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_POPDENS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid estimates population per square km for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values to grid cells. The population count grids are divided by the land area grid to produce population density grids. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01.json b/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01.json index 63f70efc23..3544dabdb6 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 is an updated version of the Settlement Points, Version 1 (v1) used in the original population reallocation. Revision 01 includes improved geospatial location for selected settlements, as well as new georeferenced settlements. Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the CUNY Institute for Demographic Research (CIDR).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json index a2142d27ea..6c98a2428d 100644 --- a/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json +++ b/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_GRUMPv1_STLMNT_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points contains geospatial location for selected settlements. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_HANPP_1.00.json b/datasets/CIESIN_SEDAC_HANPP_1.00.json index 976dad7693..c710f88f41 100644 --- a/datasets/CIESIN_SEDAC_HANPP_1.00.json +++ b/datasets/CIESIN_SEDAC_HANPP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_HANPP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HANPP Collection: Global Patterns in Human Appropriation of Net Primary Productivity (HANPP) represents a digital map of human appropriation of net primary productivity measured in Units of elemental carbon on a one-quarter degree global grid. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. The data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_HANPP_CPROD_1.00.json b/datasets/CIESIN_SEDAC_HANPP_CPROD_1.00.json index cb8b6f72a9..829247adbd 100644 --- a/datasets/CIESIN_SEDAC_HANPP_CPROD_1.00.json +++ b/datasets/CIESIN_SEDAC_HANPP_CPROD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_HANPP_CPROD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HANPP Collection: Human Appropriation of Net Primary Productivity (HANPP) by Country and Product contains tabular data on carbon-equivalents of consumption by country and by type of product. The data were compiled from national-level FAO statistics on consumption of products such as vegetables, meat, paper, and wood. HANPP represents the amount of carbon required to derive food and fibre products consumed by humans including organic matter that is lost during harvesting and processing. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems. These tabular data were used to allocate country level NPP consumption to a spatial surface of NPP consumption (Global Patterns in Human Appropriation of Net Primary Productivity), which is part of this collection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_HANPP_NPP_1.00.json b/datasets/CIESIN_SEDAC_HANPP_NPP_1.00.json index cf5a68f796..ef1e9eca0a 100644 --- a/datasets/CIESIN_SEDAC_HANPP_NPP_1.00.json +++ b/datasets/CIESIN_SEDAC_HANPP_NPP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_HANPP_NPP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HANPP Collection: Global Patterns in Net Primary Productivity (NPP) maps the net amount of solar energy converted to plant organic matter through photosynthesis. NPP is measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_HANPP_PERCENT_1.00.json b/datasets/CIESIN_SEDAC_HANPP_PERCENT_1.00.json index 6f7af68b8c..5ad1174f44 100644 --- a/datasets/CIESIN_SEDAC_HANPP_PERCENT_1.00.json +++ b/datasets/CIESIN_SEDAC_HANPP_PERCENT_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_HANPP_PERCENT_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity represents a map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_HASO2_SO2_1850-2005_2.86.json b/datasets/CIESIN_SEDAC_HASO2_SO2_1850-2005_2.86.json index b99e3bcf1d..fbc3594022 100644 --- a/datasets/CIESIN_SEDAC_HASO2_SO2_1850-2005_2.86.json +++ b/datasets/CIESIN_SEDAC_HASO2_SO2_1850-2005_2.86.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_HASO2_SO2_1850-2005_2.86", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Anthropogenic Sulfur Dioxide Emissions, 1850-2005: National and Regional Data Set by Source Category, Version 2.86 provides annual estimates of anthropogenic global and regional Sulfur Dioxide (SO2) emissions spanning the period 1850-2005 using a bottom-up mass balance method, calibrated to country-level inventory data. It includes emissions by country and by source category (coal, petroleum, biomass combustion, smelting, fuel processing, and other processes). This data set is developed at the Pacific Northwest National Laboratory (PNNL) and the maps are produced at the Center for International Earth Science Information Network (CIESIN). The data and maps created using the data set are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ICWQ_ACHLOA_1.00.json b/datasets/CIESIN_SEDAC_ICWQ_ACHLOA_1.00.json index 55524d18d3..fc5207c908 100644 --- a/datasets/CIESIN_SEDAC_ICWQ_ACHLOA_1.00.json +++ b/datasets/CIESIN_SEDAC_ICWQ_ACHLOA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ICWQ_ACHLOA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Annual Chlorophyll-a Concentrations component of the Indicators of Coastal Water Quality Collection consists of gridded satellite measurements of chlorophyll-a concentrations (in nanogram/cubic meter) in a band extending between 10 and 100 km from the shoreline. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grids are based on annual composites of SeaWiFS satellite data provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and GeoEye in the form of HDF files at a resolution of 5 arc-minutes (approximately 9 x 9 km at the equator). The source files are true-color images generated from sub-sampled, calibrated, Rayleigh-corrected level-2 data, which are derived from raw radiance counts by applying sensor calibration, atmospheric corrections, and bio-optical algorithms. To arrive at chlorophyll-a concentrations, radiance counts were converted using the conversion formula provided as part of the original data files. The gridding is done by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ICWQ_ANC_1.00.json b/datasets/CIESIN_SEDAC_ICWQ_ANC_1.00.json index 78b861387d..04ac9d54f1 100644 --- a/datasets/CIESIN_SEDAC_ICWQ_ANC_1.00.json +++ b/datasets/CIESIN_SEDAC_ICWQ_ANC_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ICWQ_ANC_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular \"Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007\" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ICWQ_CCHLOA_1.00.json b/datasets/CIESIN_SEDAC_ICWQ_CCHLOA_1.00.json index 6328ecb682..983124927c 100644 --- a/datasets/CIESIN_SEDAC_ICWQ_CCHLOA_1.00.json +++ b/datasets/CIESIN_SEDAC_ICWQ_CCHLOA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ICWQ_CCHLOA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of the chlorophyll-a concentrations for each grid cell, derived from the \"Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007\" data set. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table are linked to a sequence grid from the \"Indicators of Coastal Water Quality: Ancillary Data\" collection to facilitate the mapping of the trend values for selected countries and areas of interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_INDIA_AWCA_1.00.json b/datasets/CIESIN_SEDAC_INDIA_AWCA_1.00.json index 98dfd0c6d6..1862d9db42 100644 --- a/datasets/CIESIN_SEDAC_INDIA_AWCA_1.00.json +++ b/datasets/CIESIN_SEDAC_INDIA_AWCA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_INDIA_AWCA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The India Annual Winter Cropped Area, 2001 - 2016 consists of annual winter cropped areas for most of India (except the Northeastern states) from 2000-2001 to 2015-2016. This data set utilizes the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; spatial resolution: 250m) for the winter growing season (October-March). The methodology uses an automated algorithm identifying the EVI peak in each pixel for each year and linearly scales the EVI value between 0% and 100% cropped area within that particular pixel. Maps were then resampled to 1 km and were validated using high-resolution QuickBird, RapidEye, SkySat, and WorldView-2 images spanning 2008 to 2016 across 11 different agricultural regions of India. The spatial resolution of the data set is 1 km, resampled from 250m. The data are distributed as GeoTIFF and NetCDF files and are in WGS 84 projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_INDIA_CENSUS_2011_1.00.json b/datasets/CIESIN_SEDAC_INDIA_CENSUS_2011_1.00.json index f97f3e0c0d..fb8b548455 100644 --- a/datasets/CIESIN_SEDAC_INDIA_CENSUS_2011_1.00.json +++ b/datasets/CIESIN_SEDAC_INDIA_CENSUS_2011_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_INDIA_CENSUS_2011_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spatial Data from the 2011 India Census contains gridded estimates of India population at a resolution of 1 kilometer along with two spatial renderings of urban areas, one based on the official tabulations of population and settlement type (statutory town, outgrowth, census town), and the second, remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. This data set includes a constructed hybrid representation of the urban settlement continuum by cross-classifying the census and remotely-sensed data.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_INDIA_VILSOCIOECON_1.00.json b/datasets/CIESIN_SEDAC_INDIA_VILSOCIOECON_1.00.json index b089cf1983..923f7702b1 100644 --- a/datasets/CIESIN_SEDAC_INDIA_VILSOCIOECON_1.00.json +++ b/datasets/CIESIN_SEDAC_INDIA_VILSOCIOECON_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_INDIA_VILSOCIOECON_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is a compilation of the finest level of administrative boundaries in India (village/town-level) and over 200 socio-economic variables collected during the Indian Census in 1991 and 2001. This data set was developed by digitizing village/town level boundaries from the official analog maps published by the Survey of India for 2001. This data set also utilized tabular data for 1991 and 2001 from the Primary Census Abstract (PCA) and Village Directory (VD) data series of the Indian census. The data are in UTM 44N projection and are distributed primarily as shapefiles. Separate files are provided for each of the 28 states (number of states during 1991 and 2001 census) and combined Union Territories for 1991 and 2001.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_BASELINE_1.00.json b/datasets/CIESIN_SEDAC_IPCC_BASELINE_1.00.json index da68270f01..3c06024f7d 100644 --- a/datasets/CIESIN_SEDAC_IPCC_BASELINE_1.00.json +++ b/datasets/CIESIN_SEDAC_IPCC_BASELINE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_BASELINE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land cover, land use, agriculture, food, energy and biodiversity data . This dataset was collated by IPCC from a variety of sources such as The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7_0.3.7.json b/datasets/CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7_0.3.7.json index 8bf6fcadae..7bba058f34 100644 --- a/datasets/CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7_0.3.7.json +++ b/datasets/CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7_0.3.7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_GRI_MIDYR_0.3.7_0.3.7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_IS92_V11_1.01.json b/datasets/CIESIN_SEDAC_IPCC_IS92_V11_1.01.json index 4d1abb3e0f..0e16429c16 100644 --- a/datasets/CIESIN_SEDAC_IPCC_IS92_V11_1.01.json +++ b/datasets/CIESIN_SEDAC_IPCC_IS92_V11_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_IS92_V11_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The data set was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_OBSERVED_1.00.json b/datasets/CIESIN_SEDAC_IPCC_OBSERVED_1.00.json index 1fc93756e5..c3587fc77c 100644 --- a/datasets/CIESIN_SEDAC_IPCC_OBSERVED_1.00.json +++ b/datasets/CIESIN_SEDAC_IPCC_OBSERVED_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_OBSERVED_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a \"change consistent with warming\" or a \"change not consistent with warming\", based on information from the underlying studies.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_OBSERVED_AR5_2.01.json b/datasets/CIESIN_SEDAC_IPCC_OBSERVED_AR5_2.01.json index e1c29b5f02..6d76206765 100644 --- a/datasets/CIESIN_SEDAC_IPCC_OBSERVED_AR5_2.01.json +++ b/datasets/CIESIN_SEDAC_IPCC_OBSERVED_AR5_2.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_OBSERVED_AR5_2.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change Fifth Assessment Report (AR5) Observed Climate Change Impacts Database, Version 2.01 contains observed responses to climate change across a wide range of systems as well as regions. These responses include systems for which climate change has played a major role in observed changes, regional-scale impacts where climate change has played a minor role, and sub-regional impacts. Impacts on physical, biological, and human systems were differentiated, and the area impacted can vary from specific locations to broad areas such as a major river basin.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_SAGDVCC_1.00.json b/datasets/CIESIN_SEDAC_IPCC_SAGDVCC_1.00.json index c434a1afaa..97d017800e 100644 --- a/datasets/CIESIN_SEDAC_IPCC_SAGDVCC_1.00.json +++ b/datasets/CIESIN_SEDAC_IPCC_SAGDVCC_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_SAGDVCC_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Synthetic Assessment of Global Distribution of Vulnerability to Climate Change: Maps and Data, 2005, 2050, and 2100 data set consist of maps and vulnerability index to climate change of 100 countries based on the Vulnerability-Resilience Indicator Model (VRIM), which not only presents sensitivity to climate change stresses but allows the division of indicators into components that reflects sensitivity and adaptive capacity. It was produced in collaboration with the Wesleyan University, Joint Global Change Research Institute, University of Illinois and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_SRES_1X1EM_1.00.json b/datasets/CIESIN_SEDAC_IPCC_SRES_1X1EM_1.00.json index 0c94dc8e2c..a8b5dfdcc9 100644 --- a/datasets/CIESIN_SEDAC_IPCC_SRES_1X1EM_1.00.json +++ b/datasets/CIESIN_SEDAC_IPCC_SRES_1X1EM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_SRES_1X1EM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) 1x1 Degree Gridded Emissions Dataset consists of global gridded emissions for greenhouse gases (GHGs) projected every 10 years beginning in 1990 through 2100. The grids are produced for reactive gases Methane (CH4), Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Non-Methane Volatile Organic Compounds (NMVOC), along with Sulfur Dioxide (SO2), based on the IPCC SRES Emissions Scenarios Data Set Version 1.1. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_SRES_EMSC_V11_1.01.json b/datasets/CIESIN_SEDAC_IPCC_SRES_EMSC_V11_1.01.json index 71f83e2c5d..9637c1117e 100644 --- a/datasets/CIESIN_SEDAC_IPCC_SRES_EMSC_V11_1.01.json +++ b/datasets/CIESIN_SEDAC_IPCC_SRES_EMSC_V11_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_SRES_EMSC_V11_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) Emissions Scenarios Dataset Version 1.1 consists of 40 global and regional greenhouse gases (GHGs) and sulfur emissions scenarios projected every 10 years beginning in 1990 through 2100. The scenarios are based on extensive assessment of driving forces and emissions in the scenario literature, alternative modeling approaches, and an open process that solicited wide participation and feedback. The scenarios provide the basis for future assessments of climate change and possible response strategies. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_IPCC_SRES_FLGASES_1.00.json b/datasets/CIESIN_SEDAC_IPCC_SRES_FLGASES_1.00.json index b5ccc75914..42f5a0387e 100644 --- a/datasets/CIESIN_SEDAC_IPCC_SRES_FLGASES_1.00.json +++ b/datasets/CIESIN_SEDAC_IPCC_SRES_FLGASES_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_IPCC_SRES_FLGASES_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset consists of global and regional emissions of Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulfur Hexafluoride (SF6), Cholorfluorocarbons (CFCs) and Hydrochlorofluorocarbons (HCFCs) projected every 10 years beginning in 1990 through 2100. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LECZ_DELTA_V1_1.00.json b/datasets/CIESIN_SEDAC_LECZ_DELTA_V1_1.00.json index 81113f077c..badda2688a 100644 --- a/datasets/CIESIN_SEDAC_LECZ_DELTA_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_LECZ_DELTA_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LECZ_DELTA_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1 data set provides country-level estimates of urban, quasi-urban, rural, and total population (count), land area (square kilometers), and built-up areas in river delta- and non-delta contexts for 246 statistical areas (countries and other UN-recognized territories) for the years 1990, 2000, 2014 and 2015. The population estimates are disaggregated such that compounding risk factors including elevation, settlement patterns, and delta zones can be cross-examined. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that without significant adaptation and mitigation action, risk to coastal commUnities will increase at least one order of magnitude by 2100, placing people, property, and environmental resources at greater risk. Greater-risk zones were then generated: 1) the global extent of two low-elevation zones contiguous to the coast, one bounded by an upper elevation of 10m (LECZ10), and one by an upper elevation of 5m (LECZ05); 2) the extent of the world's major deltas; 3) the distribution of people and built-up area around the world; 4) the extents of urban centers around the world. The data are layered spatially, along with political and land/water boundaries, allowing the densities and quantities of population and built-up area, as well as levels of urbanization (defined as the share of population living in \"urban centers\") to be estimated for any country or region, both inside and outside the LECZs and deltas, and at two points in time (1990 and 2015). In using such estimates of populations living in 5m and 10m LECZs and outside of LECZs, policymakers can make informed decisions based on perceived exposure and vulnerability to potential damages from sea level rise.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LECZ_SLRIRAMSARWII_1.00.json b/datasets/CIESIN_SEDAC_LECZ_SLRIRAMSARWII_1.00.json index d5439666c3..6f6cfbf556 100644 --- a/datasets/CIESIN_SEDAC_LECZ_SLRIRAMSARWII_1.00.json +++ b/datasets/CIESIN_SEDAC_LECZ_SLRIRAMSARWII_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LECZ_SLRIRAMSARWII_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sea Level Rise Impacts on Ramsar Wetlands of International Importance data set represents the results of an analysis using the boundaries for Ramsar sites designated under the Ramsar Convention on Wetlands and intersecting them with different elevation zones in the coastal zone to assess area and percent area that would become inundated under 1 and 2 meter sea level rise scenarios. This data set provides results for 613 sites with defined boundaries that were found to intersect with the 0-5m above mean sea level coastal zone, defined by NASA Shuttle Radar Topography Mission (SRTM) elevation data. In addition to assessing the degree of risk of inundation, the data set provides population density and percent of land that is urban within the site and within 1km and 5km buffers surrounding the site. The data set also reports on infant mortality rates within 1km and 5km buffers around the site, as a measure of poverty levels that may affect adaptive capacity.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LECZ_URPEV1_1.00.json b/datasets/CIESIN_SEDAC_LECZ_URPEV1_1.00.json index 908fea99fc..275aeffa9e 100644 --- a/datasets/CIESIN_SEDAC_LECZ_URPEV1_1.00.json +++ b/datasets/CIESIN_SEDAC_LECZ_URPEV1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LECZ_URPEV1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LECZ_URPLAEV2_2.00.json b/datasets/CIESIN_SEDAC_LECZ_URPLAEV2_2.00.json index 2214d85c20..10174f25f8 100644 --- a/datasets/CIESIN_SEDAC_LECZ_URPLAEV2_2.00.json +++ b/datasets/CIESIN_SEDAC_LECZ_URPLAEV2_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LECZ_URPLAEV2_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census Units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LECZ_URPLAEV3_3.00.json b/datasets/CIESIN_SEDAC_LECZ_URPLAEV3_3.00.json index 21cc1db96c..e36d802b99 100644 --- a/datasets/CIESIN_SEDAC_LECZ_URPLAEV3_3.00.json +++ b/datasets/CIESIN_SEDAC_LECZ_URPLAEV3_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LECZ_URPLAEV3_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper \"Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter\" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_CAMVEGLCCCS_1.00.json b/datasets/CIESIN_SEDAC_LULC_CAMVEGLCCCS_1.00.json index 3938d1e6aa..20f3f3edbb 100644 --- a/datasets/CIESIN_SEDAC_LULC_CAMVEGLCCCS_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_CAMVEGLCCCS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_CAMVEGLCCCS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Central American Vegetation/Land Cover Classification and Conservation Status consists of GIS coverages of vegetation classes (forests, woodlands, savannas, shrubs, grasslands, wetlands, rocks, sand, soils, inland waters, parks and reserves) for Central America, derived from 1-kilometer resolution Advanced Very High Resolution Radiometer (AVHRR) imagery. This data set is produced by Proyecto Ambiental Regional de Centroamerica/Central America Protected Areas Systems (PROARCA/CAPAS), a conservation partnership of the Central American Commission on Environment and Development (CCAD), U.S. Agency for International Development (USAID), International Resources Group, Ltd. (IRG), The Nature Conservancy (TNC), Winrock International (WI), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_DPI_1.00.json b/datasets/CIESIN_SEDAC_LULC_DPI_1.00.json index 4c9ed05c13..6e6fcc7e29 100644 --- a/datasets/CIESIN_SEDAC_LULC_DPI_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_DPI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_DPI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Development Potential Indices (DPIs) data set contains thirteen sector-level DPIs for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). The DPI for each sector represents land suitability that accounts for both resource potential and development feasibility. Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of current and planned development, and examined for uncertainty and sensitivity. The DPIs can be used to identify lands with current favorable economic and physical conditions for individual sector expansion and assist in planning for sector and cumulative development across the globe.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_DT1_V1_1.00.json b/datasets/CIESIN_SEDAC_LULC_DT1_V1_1.00.json index e31fc895d0..1502d5d043 100644 --- a/datasets/CIESIN_SEDAC_LULC_DT1_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_DT1_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_DT1_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Development Threat Index data set is a terrestrial global, future development threat map based on combining these resources: agricultural expansion, urban expansion, conventional oil and gas, unconventional oil and gas, coal, mining, biofuels, solar, and wind. Each threat ranked potential development from 0-100 with 100 indicating the highest potential for future development of the resource and were produced at a 50 square kilometer (km2) grid cell resolution, excluding all cells overlapping Antarctica and those with >50% considered marine.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_HMTS_1.00.json b/datasets/CIESIN_SEDAC_LULC_HMTS_1.00.json index e0dde789f9..49b71318d9 100644 --- a/datasets/CIESIN_SEDAC_LULC_HMTS_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_HMTS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_HMTS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Modification of Terrestrial Systems data set provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_MANGROVES_2000_1.00.json b/datasets/CIESIN_SEDAC_LULC_MANGROVES_2000_1.00.json index c204660f11..69a8ad2036 100644 --- a/datasets/CIESIN_SEDAC_LULC_MANGROVES_2000_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_MANGROVES_2000_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_MANGROVES_2000_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Mangrove Forests Distribution, 2000 data set is a compilation of the extent of mangroves forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques. The data are available at 30-m spatial resolution. The total area of mangroves in the year 2000 was estimated at 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LULC_PUEXPANS_2030_1.00.json b/datasets/CIESIN_SEDAC_LULC_PUEXPANS_2030_1.00.json index 996789e194..4b231478f8 100644 --- a/datasets/CIESIN_SEDAC_LULC_PUEXPANS_2030_1.00.json +++ b/datasets/CIESIN_SEDAC_LULC_PUEXPANS_2030_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LULC_PUEXPANS_2030_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Grid of Probabilities of Urban Expansion to 2030 presents spatially explicit probabilistic forecasts of global urban land cover change from 2000 to 2030 at a 2.5 arc-minute resolution. For each grid cell that is non-urban in 2000, a Monte-Carlo model assigned a probability of becoming urban by the year 2030. The authors first extracted urban extent circa 2000 from the NASA MODIS Land Cover Type Product Version 5, which provides a conservative estimate of global urban land cover. The authors then used population densities from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) to create the population density driver map. They estimated the amount of new urban land in each United Nations region by 2030 in a Monte-Carlo fashion based on present empirical distribution of regional urban population densities and probability density functions of projected regional population and GDP values for 2030. To facilitate integration with other data products, CIESIN reprojected the data from Goode's Homolosine to Geographic WGS84 projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_HF_GEOG_2.00.json b/datasets/CIESIN_SEDAC_LWP2_HF_GEOG_2.00.json index 181b089277..83cefd7790 100644 --- a/datasets/CIESIN_SEDAC_LWP2_HF_GEOG_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_HF_GEOG_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_HF_GEOG_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_HF_IGHP_2.00.json b/datasets/CIESIN_SEDAC_LWP2_HF_IGHP_2.00.json index a3ddd6bed7..3a5a5b7b52 100644 --- a/datasets/CIESIN_SEDAC_LWP2_HF_IGHP_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_HF_IGHP_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_HF_IGHP_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_HII_GEOG_2.00.json b/datasets/CIESIN_SEDAC_LWP2_HII_GEOG_2.00.json index 7c76856859..72e5bdd108 100644 --- a/datasets/CIESIN_SEDAC_LWP2_HII_GEOG_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_HII_GEOG_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_HII_GEOG_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_HII_IGHP_2.00.json b/datasets/CIESIN_SEDAC_LWP2_HII_IGHP_2.00.json index 8e52276a4c..8aa443f88a 100644 --- a/datasets/CIESIN_SEDAC_LWP2_HII_IGHP_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_HII_IGHP_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_HII_IGHP_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_LW_GEOG_2.00.json b/datasets/CIESIN_SEDAC_LWP2_LW_GEOG_2.00.json index 715aaa614f..593da0dd33 100644 --- a/datasets/CIESIN_SEDAC_LWP2_LW_GEOG_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_LW_GEOG_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_LW_GEOG_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Last of the Wild Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is derived from the LWP-2 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP2_LW_IGHP_2.00.json b/datasets/CIESIN_SEDAC_LWP2_LW_IGHP_2.00.json index e2495cfce3..473be9b049 100644 --- a/datasets/CIESIN_SEDAC_LWP2_LW_IGHP_2.00.json +++ b/datasets/CIESIN_SEDAC_LWP2_LW_IGHP_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP2_LW_IGHP_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Last of the Wild Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is derived from the LWP-2 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP3_HF_1993_2018.00.json b/datasets/CIESIN_SEDAC_LWP3_HF_1993_2018.00.json index 3551c27387..fc4315941d 100644 --- a/datasets/CIESIN_SEDAC_LWP3_HF_1993_2018.00.json +++ b/datasets/CIESIN_SEDAC_LWP3_HF_1993_2018.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP3_HF_1993_2018.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1993 Human Footprint, 2018 Release provides a global map of the cumulative human pressure on the environment in 1993, at a spatial resolution of ~1 km. The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways. The 1993 Human Footprint was developed to allow inter-comparability with the 2009 Human Footprint. The data set is produced by Venter et.al., and is available in the Mollweide projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP3_HF_2009_2018.00.json b/datasets/CIESIN_SEDAC_LWP3_HF_2009_2018.00.json index a3a9f28b26..1671da8fe2 100644 --- a/datasets/CIESIN_SEDAC_LWP3_HF_2009_2018.00.json +++ b/datasets/CIESIN_SEDAC_LWP3_HF_2009_2018.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP3_HF_2009_2018.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2009 Human Footprint, 2018 Release provides a global map of the cumulative human pressure on the environment in 2009, at a spatial resolution of ~1 km. The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways. The data set is produced by Venter et.al., and is available in the Mollweide projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_HFP_GEOG_1.00.json b/datasets/CIESIN_SEDAC_LWP_HFP_GEOG_1.00.json index 1203eb8761..592e6986ec 100644 --- a/datasets/CIESIN_SEDAC_LWP_HFP_GEOG_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_HFP_GEOG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_HFP_GEOG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Footprint Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density, population settlements), human land use and infrastructure (built up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_HFP_IGHP_1.00.json b/datasets/CIESIN_SEDAC_LWP_HFP_IGHP_1.00.json index 1385904f29..1ba2b7b892 100644 --- a/datasets/CIESIN_SEDAC_LWP_HFP_IGHP_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_HFP_IGHP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_HFP_IGHP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Footprint Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density, population settlements), human land use and infrastructure (built up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_LW_GEOG_1.00.json b/datasets/CIESIN_SEDAC_LWP_LW_GEOG_1.00.json index 0e455704d9..458618ee2d 100644 --- a/datasets/CIESIN_SEDAC_LWP_LW_GEOG_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_LW_GEOG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_LW_GEOG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Last of the Wild Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is derived from the LWP-1 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_LW_IGHP_1.00.json b/datasets/CIESIN_SEDAC_LWP_LW_IGHP_1.00.json index 9518af0e25..b4c13f3262 100644 --- a/datasets/CIESIN_SEDAC_LWP_LW_IGHP_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_LW_IGHP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_LW_IGHP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Last of the Wild Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is derived from the LWP-1 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest polygons of more than 5 square kilometers within each biome by realm are selected and identified. The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_TOPWA_GEOG_1.00.json b/datasets/CIESIN_SEDAC_LWP_TOPWA_GEOG_1.00.json index c540d512f1..0892843f61 100644 --- a/datasets/CIESIN_SEDAC_LWP_TOPWA_GEOG_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_TOPWA_GEOG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_TOPWA_GEOG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Top One Percent Wild Areas Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is derived from the LWP-1 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-1; not wild >1). The top 1% of the wild areas within each biome by realm are selected and identified. The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_LWP_TOPWA_IGHP_1.00.json b/datasets/CIESIN_SEDAC_LWP_TOPWA_IGHP_1.00.json index cafec99cd9..8b727c2889 100644 --- a/datasets/CIESIN_SEDAC_LWP_TOPWA_IGHP_1.00.json +++ b/datasets/CIESIN_SEDAC_LWP_TOPWA_IGHP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_LWP_TOPWA_IGHP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Top One Percent Wild Areas Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is derived from the LWP-1 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-1; not wild >1). The top 1% of the wild areas within each biome by realm are selected and identified. The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_BIODIVERSITY_1.00.json b/datasets/CIESIN_SEDAC_MA_BIODIVERSITY_1.00.json index d9bf29fcbc..dc50a2cd81 100644 --- a/datasets/CIESIN_SEDAC_MA_BIODIVERSITY_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_BIODIVERSITY_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_BIODIVERSITY_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Biodiversity provides data and information on amphibians, disease agents (extent and distribution of infectious and parasitic diseases), drylands (cattle, sheep and goats, and pasture), islands (fishing pressure, sewage pollution index and tourism), loss of natural land cover (biomes and realms), polar population, species distribution models, and terrestrial ecoregions and realms. Biodiversity is defined as the variability among living organisms from all sources, including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are a part. The original information was received from multiple sources that include the International Union for Conservation of Nature (IUCN, formerly the World Conservation Union), the World Wildlife Fund (WWF), the History Database of the Global Environment (HYDE) of Netherlands Environmental Assessment Agency (PBL), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board NASA satellites Terra and Aqua. Through the Convention on Biological Diversity, United Nations Convention to Combat Desertification, Ramsar Convention on Wetlands, and the Convention on Migratory Species, the data were also designed to meet the needs of stakeholders in the business, civil and native commUnities.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_CLIMLC_1.00.json b/datasets/CIESIN_SEDAC_MA_CLIMLC_1.00.json index 9f43c412da..a27b93935c 100644 --- a/datasets/CIESIN_SEDAC_MA_CLIMLC_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_CLIMLC_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_CLIMLC_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Climate and Land Cover provides data and information on global gridded climatological variables, global land cover maps, and national and international protected areas. The climatological data include the Climatic Research Unit (CRU) monthly terrestrial surface temperature and precipitation grids at 0.5 degree resolution from 1901-1996, along with temperature and precipitation grids at 10-minute resolution from the CRU and the International Water Management Institute (IWMI). The Global Land Cover 2000 (GLC2000) was produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre (JRC), which contained two levels of land cover information; detailed and regionally optimized land cover for each continent, and a less thematically detailed global land cover that harmonizes regions into one consistent product. The maps were based on daily data from the Vegetation 1 sensor on-board SPOT 4, though mapping of regions required the use of data from additional Earth observing sensors to resolve specific issues. The international and national protected areas were generated from the World Database of Protected Areas for the year 2000.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_ECOSYSTEMS_1.00.json b/datasets/CIESIN_SEDAC_MA_ECOSYSTEMS_1.00.json index 32f6f428f2..524bb5e07e 100644 --- a/datasets/CIESIN_SEDAC_MA_ECOSYSTEMS_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_ECOSYSTEMS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_ECOSYSTEMS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Ecosystems provides data and information on the extent and classification of ecosystems circa 2000, including coastal, cultivated, forest and woodlands, inland water bodies, islands, marine, mountains (elevation), polar, and urban. The data set also includes socioeconomic reporting Units and the location of regional MA projects. The data were used in a number of different ways in the assessment, contributing to an understanding of how humans have altered ecosystems, how changes in ecosystem services have affected human well-being, and how ecosystem changes may affect people in future decades.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_POPULATION_1.00.json b/datasets/CIESIN_SEDAC_MA_POPULATION_1.00.json index 65fb4d1e97..0bbc2f864a 100644 --- a/datasets/CIESIN_SEDAC_MA_POPULATION_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_POPULATION_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_POPULATION_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Population provides data and information on baseline population as one of the drivers of ecosystem change. The data was used in estimating the magnitude of regional pressures on ecosystems. The MA Population data sets include Gridded Population of the World (GPW) Version 3, population grids from the Alpha version of the Global Rural-Urban Mapping Project (GRUMP), Global Subnational Infant Mortality Rates (Alpha version), and Global Subnational Prevalence of Child Malnutrition (Alpha version).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_RLCC_1.00.json b/datasets/CIESIN_SEDAC_MA_RLCC_1.00.json index f15bc1362e..edda56e129 100644 --- a/datasets/CIESIN_SEDAC_MA_RLCC_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_RLCC_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_RLCC_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Rapid Land Cover Change provides data and information on global and regional land cover change in raster format for agriculture (cropland increase and disease), deforestation (forest mask and hotspots), desertification (hotspot areas of degraded land and degradation types), and fires (most frequent and exceptional fires). Urbanization data are in vector format for cities with the highest rate of change (1995-2015), largest cities (2000), and the overlay of these two data layers for the year 2000. This assessment identified the need to synthesize what is known about areas of rapid land cover change around the world in order to evaluate how the provision of ecosystem goods and services has changed.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_MA_SCENARIOS_1.00.json b/datasets/CIESIN_SEDAC_MA_SCENARIOS_1.00.json index f162ee2b54..b36c6f6447 100644 --- a/datasets/CIESIN_SEDAC_MA_SCENARIOS_1.00.json +++ b/datasets/CIESIN_SEDAC_MA_SCENARIOS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_MA_SCENARIOS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millennium Ecosystem Assessment: MA Scenarios provides data and information on population, income, cereal production and consumption, meat production and consumption, land cover, water stress, water availability, acidification and nitrogen deposition. These scenarios provide useful insight into the complex factors that drive ecosystem change, estimating the magnitude of regional pressures on ecosystems and critical uncertainties that could undermine sustainable development. They also provide an understanding of the importance of institutions and values as the long-range outlook for the world's ecosystems depends on the course taken by global and regional development in the coming decades. The integration of changing ecosystem conditions into the global scenarios was taken as both effects and causes.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NAGDC_PLACEIII_3.00.json b/datasets/CIESIN_SEDAC_NAGDC_PLACEIII_3.00.json index 9cc1382307..47f1c49e86 100644 --- a/datasets/CIESIN_SEDAC_NAGDC_PLACEIII_3.00.json +++ b/datasets/CIESIN_SEDAC_NAGDC_PLACEIII_3.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NAGDC_PLACEIII_3.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NAGDC_PLACEII_2.00.json b/datasets/CIESIN_SEDAC_NAGDC_PLACEII_2.00.json index f9d61f5c79..fcd9d36b8a 100644 --- a/datasets/CIESIN_SEDAC_NAGDC_PLACEII_2.00.json +++ b/datasets/CIESIN_SEDAC_NAGDC_PLACEII_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NAGDC_PLACEII_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 2 (PLACE II) data set contains estimates of national-level aggregations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, a compendium of nearly 300 variables for 228 countries. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NAGDC_PLACEIV_4.00.json b/datasets/CIESIN_SEDAC_NAGDC_PLACEIV_4.00.json index 37ec9a2358..871bede130 100644 --- a/datasets/CIESIN_SEDAC_NAGDC_PLACEIV_4.00.json +++ b/datasets/CIESIN_SEDAC_NAGDC_PLACEIV_4.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NAGDC_PLACEIV_4.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 4 (PLACE IV) provides measures of population (head counts) and land area (square kilometers) as totals and by urban and rural designation, within multiple biophysical themes for 248 statistical areas (countries and other territories recognized by the United Nations (UN)), UN geographic regions and subregions, and World Bank economic classifications. It improves upon previous versions by providing these estimates at both the national level, and where possible, at subnational administrative level 1 for the years 2000, 2005, 2010, 2015, and 2020, and by 5-year and broad age groups for the year 2010.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NAGDC_PLACE_1.00.json b/datasets/CIESIN_SEDAC_NAGDC_PLACE_1.00.json index 63abd79bcb..cc75e6a638 100644 --- a/datasets/CIESIN_SEDAC_NAGDC_PLACE_1.00.json +++ b/datasets/CIESIN_SEDAC_NAGDC_PLACE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NAGDC_PLACE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates (PLACE) data set contains estimates of national-level aggregations of territorial extent and population size by biome, climate zone, coastal proximity, elevation and slope, a compendium of nearly 300 variables for 222 countries. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_2010_2010.00.json b/datasets/CIESIN_SEDAC_NRMI_2010_2010.00.json index 95f49184a3..36d35902f7 100644 --- a/datasets/CIESIN_SEDAC_NRMI_2010_2010.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_2010_2010.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_2010_2010.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Management Index (NRMI), 2010 Release is a composite index for 157 countries derived from the average of four proximity-to-target indicators for eco-region protection (weighted average percentage of biomes under protected status), access to improved sanitation, access to improved water and child mortality. The 2010 release of the NRMI includes time series NRMIs for 2006, 2007, 2008, 2009 and 2010. Note that the NRMIs for 2006-2008 provided in this release are not directly comparable to those found in the 2006-2008 releases of the NRMI owing to changes in data and methods. The data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP), Yale University.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_2011_2011.00.json b/datasets/CIESIN_SEDAC_NRMI_2011_2011.00.json index 185c612f52..2e1eb2871b 100644 --- a/datasets/CIESIN_SEDAC_NRMI_2011_2011.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_2011_2011.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_2011_2011.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Management Index (NRMI), 2011 Release is a composite index for 174 countries derived from the average of four proximity-to-target indicators for eco-region protection (weighted average percentage of biomes under protected status), access to improved sanitation, access to improved water and child mortality. The 2011 release of the NRMI includes a consistent time series of NRMIs for 2006 to 2011. In addition, the 2011 release includes two new indicators that will eventually supplant the NRMI: a Natural Resource Protection Indicator (NRPI) that is solely composed of the eco-region protection indicator, and a Child Health Indicator (CHI), which is an unweighted average of the proximtiy-to-target scores for access to water, access to sanitation, and child mortality. The data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP), Yale University.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI12_2012.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI12_2012.00.json index afe79de9af..e9ba533f62 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI12_2012.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI12_2012.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI12_2012.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2012 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 235 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 175 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2012 release includes a consistent time series of NRPIs and CHIs for 2006 to 2012.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI13_2013.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI13_2013.00.json index 5c241b30bf..9a2a1a0074 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI13_2013.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI13_2013.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI13_2013.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2013 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2013 release includes a consistent time series of NRPIs and CHIs for 2006 to 2013.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI14_2014.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI14_2014.00.json index 6384f25b1d..80429866ff 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI14_2014.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI14_2014.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI14_2014.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2014 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2014 release includes a consistent time series of NRPIs and CHIs for 2006 to 2014.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI15_2015.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI15_2015.00.json index c7e6ec3d2a..e336bc4ae1 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI15_2015.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI15_2015.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI15_2015.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2015 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2015 release includes a consistent time series of NRPIs and CHIs for 2010 to 2015.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI16_2016.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI16_2016.00.json index bba240827a..16a80ca6ee 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI16_2016.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI16_2016.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI16_2016.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2016 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 237 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 190 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2016 release includes a consistent time series of NRPI scores for 2012-2016 and CHI scores for 2010 to 2016.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI17_2017.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI17_2017.00.json index 3f1c3f4bbd..9e63c33ebc 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI17_2017.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI17_2017.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI17_2017.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2017 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 234 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 199 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2017 release includes a consistent time series of NRPI scores for 2013-2017 and CHI scores for 2010 to 2017.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI18_2018.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI18_2018.00.json index 76bac41497..c01fa8b407 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI18_2018.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI18_2018.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI18_2018.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2018 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 234 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 199 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2018 release includes a consistent time series of NRPI scores for 2014-2018 and CHI scores for 2010 to 2018.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI19_2019.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI19_2019.00.json index c6536d5a92..3180662b14 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI19_2019.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI19_2019.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI19_2019.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2019 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 234 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 195 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2019 release includes a consistent time series of NRPI scores for 2015 to 2019 and CHI scores for 2010 to 2018.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI20_2020.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI20_2020.00.json index bcebbcaeba..2a00f8893b 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI20_2020.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI20_2020.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI20_2020.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2020 Release, is produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 250 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 194 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2020 release includes a consistent time series of NRPI scores for 2010 to 2020 and CHI scores for 2010 to 2019.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI21_2021.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI21_2021.00.json index a66eb1b7e8..909d1d11a3 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI21_2021.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI21_2021.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI21_2021.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2021 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 135 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 194 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2021 release includes a consistent time series of NRPI scores for 2017 to 2021 and CHI scores for 2010 to 2020.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI22_2022.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI22_2022.00.json index 0d20f6cb58..5dea9943d0 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI22_2022.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI22_2022.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI22_2022.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2022 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 220 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 195 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation together with child mortality rates. The 2022 release includes a consistent time series of NRPI scores for 2010 to 2022 and CHI scores for 2010 to 2020.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_NRMI_NRPCHI23_2023.00.json b/datasets/CIESIN_SEDAC_NRMI_NRPCHI23_2023.00.json index e57357833c..bcd55bc5e0 100644 --- a/datasets/CIESIN_SEDAC_NRMI_NRPCHI23_2023.00.json +++ b/datasets/CIESIN_SEDAC_NRMI_NRPCHI23_2023.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_NRMI_NRPCHI23_2023.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natural Resource Protection and Child Health Indicators, 2023 Release, is produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 76 countries which are eligible for MCC funding and is using the 2022 Environmental Performance Index. The CHI is a composite index for 198 countries derived from the average of three proximity-to-target scores for at least basic access to water, at least basic access to sanitation, and child mortality rates. The 2023 release includes a consistent time series of NRPI scores for 2019 to 2022 and CHI scores for 2010 to 2022.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_LACPOP_1.00.json b/datasets/CIESIN_SEDAC_PD_LACPOP_1.00.json index d337a34d6d..805cf6e9df 100644 --- a/datasets/CIESIN_SEDAC_PD_LACPOP_1.00.json +++ b/datasets/CIESIN_SEDAC_PD_LACPOP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_LACPOP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Latin America and the Caribbean Population Time Series data set provides total population estimates using spatially consistent and comparable Units for Latin American municipalities or equivalent administrative Units for the years 1990 and 2000. The data set consists of two vector polygon layers: one layer displays population estimates for subnational administrative Units in 1990 and 2000, including population counts, density, and percent change, at the municipality level or equivalent (level 2); a second layer summarizes this information at the country level (level 0).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_NETMIG_1970_2000_1.00.json b/datasets/CIESIN_SEDAC_PD_NETMIG_1970_2000_1.00.json index a1d9f342dd..f3a4031584 100644 --- a/datasets/CIESIN_SEDAC_PD_NETMIG_1970_2000_1.00.json +++ b/datasets/CIESIN_SEDAC_PD_NETMIG_1970_2000_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_NETMIG_1970_2000_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_POPCGTSE_1.00.json b/datasets/CIESIN_SEDAC_PD_POPCGTSE_1.00.json index 2bb3053db8..b25d0675d5 100644 --- a/datasets/CIESIN_SEDAC_PD_POPCGTSE_1.00.json +++ b/datasets/CIESIN_SEDAC_PD_POPCGTSE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_POPCGTSE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_POPDGTSE_1.00.json b/datasets/CIESIN_SEDAC_PD_POPDGTSE_1.00.json index a82cfbf051..a8263bedd6 100644 --- a/datasets/CIESIN_SEDAC_PD_POPDGTSE_1.00.json +++ b/datasets/CIESIN_SEDAC_PD_POPDGTSE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_POPDGTSE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_PPUSCASRCSSP_1.00.json b/datasets/CIESIN_SEDAC_PD_PPUSCASRCSSP_1.00.json index 93472541d2..c233525b8f 100644 --- a/datasets/CIESIN_SEDAC_PD_PPUSCASRCSSP_1.00.json +++ b/datasets/CIESIN_SEDAC_PD_PPUSCASRCSSP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_PPUSCASRCSSP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_SSPBSYR_1_8th_1.01.json b/datasets/CIESIN_SEDAC_PD_SSPBSYR_1_8th_1.01.json index a6c47fad14..9cbdb92991 100644 --- a/datasets/CIESIN_SEDAC_PD_SSPBSYR_1_8th_1.01.json +++ b/datasets/CIESIN_SEDAC_PD_SSPBSYR_1_8th_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_SSPBSYR_1_8th_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total population data for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes), consistent both quantitatively and qualitatively with the SSPs. Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PD_SSPBSYR_1km_1.01.json b/datasets/CIESIN_SEDAC_PD_SSPBSYR_1km_1.01.json index 6e3fbca510..432f14ee0b 100644 --- a/datasets/CIESIN_SEDAC_PD_SSPBSYR_1km_1.01.json +++ b/datasets/CIESIN_SEDAC_PD_SSPBSYR_1km_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PD_SSPBSYR_1km_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total \npopulaton for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds), consistent both quantitatively and qualitatively with the SSPs. This 1-km data set is a downscaled version of the one-eighth degree (7.5 arc-minutes) data published in Jones and O'Neill (2016). \nThe downscaling methods were published in Gao (2017). Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the \nassessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel \non Climate Change (IPCC) Sixth Assessment Report (AR6).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_ALLOMSSF3_alpha.json b/datasets/CIESIN_SEDAC_PEND_ALLOMSSF3_alpha.json index add96c34fa..0211d076aa 100644 --- a/datasets/CIESIN_SEDAC_PEND_ALLOMSSF3_alpha.json +++ b/datasets/CIESIN_SEDAC_PEND_ALLOMSSF3_alpha.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_ALLOMSSF3_alpha", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Population Grids (Summary File 3), 2000: Alabama, Louisiana, and Mississippi, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the year 2000 census. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (income, poverty, education, housing age). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_ALLOMSTXSF1_alpha.json b/datasets/CIESIN_SEDAC_PEND_ALLOMSTXSF1_alpha.json index 6343829411..2624810951 100644 --- a/datasets/CIESIN_SEDAC_PEND_ALLOMSTXSF1_alpha.json +++ b/datasets/CIESIN_SEDAC_PEND_ALLOMSTXSF1_alpha.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_ALLOMSTXSF1_alpha", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Population Grids (Summary File 1), 2000: Alabama, Louisiana, Mississippi and Texas, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the year 2000 census. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_GDIS_1.00.json b/datasets/CIESIN_SEDAC_PEND_GDIS_1.00.json index bc3eb4ef57..9a9cecfee9 100644 --- a/datasets/CIESIN_SEDAC_PEND_GDIS_1.00.json +++ b/datasets/CIESIN_SEDAC_PEND_GDIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_GDIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_HSF1_alpha.json b/datasets/CIESIN_SEDAC_PEND_HSF1_alpha.json index 4b45e03691..5362e8640b 100644 --- a/datasets/CIESIN_SEDAC_PEND_HSF1_alpha.json +++ b/datasets/CIESIN_SEDAC_PEND_HSF1_alpha.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_HSF1_alpha", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Population Grids (Summary File 1), 2000: Houston Metropolitan Statistical Area, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_NOSF1_alpha.json b/datasets/CIESIN_SEDAC_PEND_NOSF1_alpha.json index 1e4a64aad1..8222b2c7fc 100644 --- a/datasets/CIESIN_SEDAC_PEND_NOSF1_alpha.json +++ b/datasets/CIESIN_SEDAC_PEND_NOSF1_alpha.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_NOSF1_alpha", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "U.S. Population Grids (Summary File 1), 2000: New Orleans Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PEND_NOSF3_alpha.json b/datasets/CIESIN_SEDAC_PEND_NOSF3_alpha.json index 9a0aa22d91..2cb5f45b81 100644 --- a/datasets/CIESIN_SEDAC_PEND_NOSF3_alpha.json +++ b/datasets/CIESIN_SEDAC_PEND_NOSF3_alpha.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PEND_NOSF3_alpha", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Population Grids (Summary File 3), 2000: New Orleans Metropolitan Statistical Area, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the year 2000 census. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (income, poverty, education, housing age). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_GRDI_2010_2020_1.00.json b/datasets/CIESIN_SEDAC_PMP_GRDI_2010_2020_1.00.json index 0264fdc245..370c0e8aae 100644 --- a/datasets/CIESIN_SEDAC_PMP_GRDI_2010_2020_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_GRDI_2010_2020_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_GRDI_2010_2020_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_GSPCM_1.00.json b/datasets/CIESIN_SEDAC_PMP_GSPCM_1.00.json index 599bddc175..b6ff6bdeb4 100644 --- a/datasets/CIESIN_SEDAC_PMP_GSPCM_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_GSPCM_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_GSPCM_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_IMR_1.00.json b/datasets/CIESIN_SEDAC_PMP_IMR_1.00.json index c2fd13a96f..11313ac513 100644 --- a/datasets/CIESIN_SEDAC_PMP_IMR_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_IMR_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_IMR_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational Units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 Units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json b/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json index 319ca01c4a..e8c6e92a85 100644 --- a/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json +++ b/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_IMR_V2.01_2.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_PFSCS_1.00.json b/datasets/CIESIN_SEDAC_PMP_PFSCS_1.00.json index 518a08332e..495172e33b 100644 --- a/datasets/CIESIN_SEDAC_PMP_PFSCS_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_PFSCS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_PFSCS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Poverty Mapping Project: Poverty and Food Security Case Studies data set consists of small area estimates of poverty, inequality, food security and related measures for subnational administrative Units in Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). The data set was originally produced by CIAT, International Maize and Wheat Improvement Center (CIMMYT), International Livestock Research Institute (ILRI), International Food Policy Research Institute (IFPRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), and International Institute for Tropical Agriculture (IITA).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_SAEPI_1.00.json b/datasets/CIESIN_SEDAC_PMP_SAEPI_1.00.json index 5fd76f95bd..83cb876344 100644 --- a/datasets/CIESIN_SEDAC_PMP_SAEPI_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_SAEPI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_SAEPI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative Units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic Unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_PMP_UBN_1.00.json b/datasets/CIESIN_SEDAC_PMP_UBN_1.00.json index b6593d982a..ff323604f9 100644 --- a/datasets/CIESIN_SEDAC_PMP_UBN_1.00.json +++ b/datasets/CIESIN_SEDAC_PMP_UBN_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_PMP_UBN_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Poverty Mapping Project: Unsatisfied Basic Needs data set consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative Units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN), Economic Commission for Latin America and the Caribbean (ECLAC), and Centro Internacional de Agricultura Tropical (CIAT).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_APM25_URBAN_1.00.json b/datasets/CIESIN_SEDAC_SDEI_APM25_URBAN_1.00.json index 91fdf97794..ea09e0a27d 100644 --- a/datasets/CIESIN_SEDAC_SDEI_APM25_URBAN_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_APM25_URBAN_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_APM25_URBAN_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016, consists of mean concentrations of particulate matter (PM2.5) for countries and urban areas. The PM2.5 data are from the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. The urban areas are from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02, and its time series runs from 1998 to 2016. The country averages are population-weighted such that concentrations in populated areas count more toward the country average than concentrations in less populated areas, and its time series runs from 2008 to 2015.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GEHE_1.00.json b/datasets/CIESIN_SEDAC_SDEI_GEHE_1.00.json index e0b2f7ce99..45050a4e76 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GEHE_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_GEHE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GEHE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016 data set provides global 0.05 degrees (~5 km) gridded annual counts of the number of days where the maximum Wet Bulb Globe Temperature (WBGTmax) exceeded dangerous hot-humid heat thresholds for the period 1983 to 2016. The thresholds are based on the International Standards Organization (ISO) criteria for occupational heat-related risk, defined as days where WBGTmax > 28, 30, and 32 degrees Celsius. This data set also includes the annual rate of change in the number of extreme humid-heat days that exceeded these thresholds. GEHE has a wide array of applications for mapping and quantifying extreme humid-heat dynamics over a 34-year time period, and is the highest resolution data set of its kind to date. GEHE provides scientific researchers and decision makers from a wide range of arenas, including climate change, public and occupational health, urban planning and design, hazards risk reduction, and food security, insights into how humid-heat has impacted human and environmental systems worldwide. The data set can be used to pinpoint how changes in extreme humid-heat impact human health and well-being, as well as ecological systems, across scales of analysis, from local, to national, to global.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GFA_GRACE_1.00.json b/datasets/CIESIN_SEDAC_SDEI_GFA_GRACE_1.00.json index d8a8f23c71..6ef4769194 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GFA_GRACE_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_GFA_GRACE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GFA_GRACE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE), 2002-2016, is a global gridded data set at a spatial resolution of 0.5 degrees that presents trends (rate of change measured in centimeters per year) in freshwater availability based on data obtained from 2002 to 2016 by NASA GRACE. Terrestrial water availability storage is the sum of groundwater, soil moisture, snow and ice, surface waters, and wet biomass, expressed as an equivalent height of water. GRACE measures changes in the terrestrial water cycle by assessing small changes in Earth's gravity field. This observation-based assessment of how the world's water cycle is responding to human impacts and climate variations provides an important tool for evaluating and predicting emerging threats to water and food security.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GFED4_CLTAB_1.00.json b/datasets/CIESIN_SEDAC_SDEI_GFED4_CLTAB_1.00.json index 4cfd0d752c..ca2b01e27d 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GFED4_CLTAB_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_GFED4_CLTAB_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GFED4_CLTAB_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Fire Emissions Indicators, Country-Level Tabular Data: 1997-2015 contains country tabulations from 1997 to 2015 for the total area burned (hectares) and total carbon content (tons). The annual total area burned is for all fire types per country. There are two groups of total carbon content (TCC), annual totals for all six fire types per country and annual totals for each of six fire types per country which include Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forest fires.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GFED4_GRIDS_1.00.json b/datasets/CIESIN_SEDAC_SDEI_GFED4_GRIDS_1.00.json index 034d6a75b0..6815940e2b 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GFED4_GRIDS_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_GFED4_GRIDS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GFED4_GRIDS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Fire Emissions Indicators, Grids: 1997-2015 contain a time-series of rasters from 1997 to 2015 for total area burned (hectares) and total carbon content (tons). The annual total area burned raster is the sum of monthly rasters, which are products of the Cell Area and Burn Fraction (fraction of the cell area burned in the month). There are two groups of total carbon content (TCC) rasters, annual totals for all fire types and annual totals for each of six fire types which include Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forest fires. The annual TCC raster for all fire types is the sum of monthly carbon emission rasters. The annual TCC raster for each fire type is the product of Dry Matter, Burn Fraction, and Fire Type Contribution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03_4.03.json b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03_4.03.json index 07741a5dfe..16936bdc47 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03_4.03.json +++ b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03_4.03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GWRPM25_MMSAOD_4GL03_4.03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms\n\nincluding the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json index 9ec7ff6673..39f24dc357 100644 --- a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json +++ b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Annual PM2.5 Grids from MODIS, MISR, SeaWiFS and VIIRS Aerosol Optical Depth (AOD), 1998-2022, V5.GL.04 consists of annual concentrations (micrograms per cubic meter) of all composition (i.e. total) ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4, along with the Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The GEOS-Chem chemical transport model is used to initially relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database and available regional networks to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF and netCDF files and are in WGS84 projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json b/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json index 3f83687895..daed532b37 100644 --- a/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_LST2013_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Summer Land Surface Temperature (LST) Grids, 2013, represent daytime maximum temperature and nighttime minimum temperature in degree Celsius at a spatial resolution of 30 arc-seconds (~1km) during summer months of the northern and southern hemisphere for the year 2013. The grids are produced using Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime LST 8-day composite data (MYD11A2). For most regions, the LST grids provide the daytime maximum (1:30 p.m. overpass) and nighttime minimum (1:30 a.m. overpass) LST values for each grid cell from a 40-day time-span during July-August (Julian days 185-224) 2013 in the northern hemisphere and January-February (Julian days 001-040) 2013 in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_NO2_1.00.json b/datasets/CIESIN_SEDAC_SDEI_NO2_1.00.json index 48875098cf..9b0e4e33e4 100644 --- a/datasets/CIESIN_SEDAC_SDEI_NO2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_NO2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_NO2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global 3-Year Running Mean Ground-Level Nitrogen Dioxide (NO2) Grids from GOME, SCIAMACHY and GOME-2 represent a series of three-year running mean grids (1996-2012) of ground level NO2 that were derived from Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment-2 (GOME-2) satellite retrievals. For each satellite-derived NO2 source, the relationship between satellite observations of tropospheric NO2 column densities and the NO2 concentrations at ground level relevant to human exposure is simulated, using the Goddard Earth Observing System chemical transport model (GEOS-Chem) to produce a mean NO2 concentration raster grid. The grid cell resolution is six arc-minutes (0.1 degree, or approximately 10 km at the equator) covering the global land surface.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_UHE_1.00.json b/datasets/CIESIN_SEDAC_SDEI_UHE_1.00.json index 49193e807c..41337a4175 100644 --- a/datasets/CIESIN_SEDAC_SDEI_UHE_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_UHE_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_UHE_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016 data set contains a high-resolution, longitudinal global record of geolocated urban extreme heat events and urban population exposure estimates for more than 10,000 urban settlements worldwide for 1983-2016. Urban extreme heat events and urban population exposure are identified for each urban settlement in the data record for five combined temperature-humidity thresholds: two-day or longer periods where the daily maximum Heat Index (HImax) > 40.6 \u00ef\u00bf\u00bdC; one-day or longer periods where HImax > 46.1 \u00ef\u00bf\u00bdC; and one day or longer periods where the daily maximum Wet Bulb Globe Temperature (WBGTmax) > 28 \u00ef\u00bf\u00bdC, 30 \u00ef\u00bf\u00bdC, and 32 \u00ef\u00bf\u00bdC. The WBGTmax thresholds follow the International Standards Organization (ISO) criteria for risk of occupational heat related heat illness, whereas the HImax thresholds follow the U.S. National Weather Services' definition for an excessive heat warning. For each criteria, across urban settlements worldwide, the data set also contains the duration, intensity, and severity of each urban extreme heat event.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_UHI2013_1.00.json b/datasets/CIESIN_SEDAC_SDEI_UHI2013_1.00.json index 0f49db5ce6..4aca71ec23 100644 --- a/datasets/CIESIN_SEDAC_SDEI_UHI2013_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_UHI2013_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_UHI2013_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC\u00ef\u00bf\u00bds Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC\u00ef\u00bf\u00bds Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_VDL_1.00.json b/datasets/CIESIN_SEDAC_SDEI_VDL_1.00.json index 76355785f1..0a5d77b0ad 100644 --- a/datasets/CIESIN_SEDAC_SDEI_VDL_1.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_VDL_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_VDL_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Plus DMSP Change in Lights (VIIRS+DMSP dLIGHT) data set fuses nighttime lights imagery from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with a stable night light composite from the next generation Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band to map the spatial distribution and temporal evolution of global nighttime lights between 1992 and 2015. The product visualizes changes in both brightness and extent of nocturnal low lights over two decades while minimizing the spatial overextent (overglow) and bright saturation that compromise the DMSP-OLS composites. The map product utilizes annual DMSP-OLS stable lights composites, produced by the NOAA Earth Observation Group and archived at the NOAA National Geophysical Data Center (NGDC), in a tri-temporal global change map. To achieve greater spatial resolution and radiometric accuracy, the DMSP-OLS composites are co-registered and fused with the 2015 VIIRS annual composite from NGDC. The final product therefore retains the spatial detail and dynamic range of the VIIRS product, and the decadal change information from DMSP-OLS images.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDEI_YCEOUHI_V4_4.00.json b/datasets/CIESIN_SEDAC_SDEI_YCEOUHI_V4_4.00.json index 157f044979..ed01b9d972 100644 --- a/datasets/CIESIN_SEDAC_SDEI_YCEOUHI_V4_4.00.json +++ b/datasets/CIESIN_SEDAC_SDEI_YCEOUHI_V4_4.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDEI_YCEOUHI_V4_4.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018 includes annual, summertime, and wintertime Surface Urban Heat Island (SUHI) intensities for daytime and nighttime for over 10,000 global urban extents. This global SUHI data set was created using the Simplified Urban-Extent (SUE) algorithm and is available at the pixel and urban cluster-levels (i.e. at the level of larger urban agglomerations). Monthly composites are also available as urban cluster means. A summary of older versions, including changes from the data set created and analyzed in the originally published manuscript (Chakraborty and Lee, 2019) can be found on the YCEO Global Surface UHI Explorer website (https://yceo.yale.edu/research/global-surface-uhi-explorer).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDGI_ACCELEC_2023_2023.00.json b/datasets/CIESIN_SEDAC_SDGI_ACCELEC_2023_2023.00.json index 37f6d223b4..fc0743fecf 100644 --- a/datasets/CIESIN_SEDAC_SDGI_ACCELEC_2023_2023.00.json +++ b/datasets/CIESIN_SEDAC_SDGI_ACCELEC_2023_2023.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDGI_ACCELEC_2023_2023.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SDG Indicator 7.1.1: Access to Electricity, 2023 Release data set, part of the Sustainable Development Goal Indicators (SDGI) collection, measures the proportion of the population with access to electricity for a given statistical area. UN SDG 7 is \"ensure access to affordable, reliable, sustainable and modern energy for all\". Tracking SDG 7: The Energy Progress Report estimated that in 2019, 759 million people around the world lacked access to electricity. Moreover, due to current policies and the detrimental effects of the COVID-19 crisis, it is predicted that by 2030, 660 million people will still not have access to electricity, with a majority of these people residing in Sub-Saharan Africa. As one measure of progress towards SDG 7, the UN agreed upon SDG indicator 7.1.1. The indicator was computed as the proportion of WorldPop gridded population located within illuminated areas defined by annual VIIRS Nighttime Lights Version 2 (VNL V2) data. The SDG indicator 7.1.1 data set provides estimates for the proportion of population with access to electricity for 206 countries and 45,979 level 2 subnational Units. The data set is available at both national and level 2 subnational resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDGI_RAI_2023_2023.00.json b/datasets/CIESIN_SEDAC_SDGI_RAI_2023_2023.00.json index c36e859504..7001e8b6f9 100644 --- a/datasets/CIESIN_SEDAC_SDGI_RAI_2023_2023.00.json +++ b/datasets/CIESIN_SEDAC_SDGI_RAI_2023_2023.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDGI_RAI_2023_2023.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SDG Indicator 9.1.1: The Rural Access Index (RAI), 2023 Release data set, part of the SDGI collection, measures the proportion of the rural population who live within 2 kilometers of an all-season road for a given statistical area. UN SDG 9 is \"build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\". Addressing inadequate access to roads, especially in rural areas, is critical to achieving SDG 9. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, sustainable transportation helps to eliminate poverty, promote food security, improve access to key health services, increase trade competitiveness, and bolster human rights. As one measure of progress towards SDG 9, the UN has established SDG indicator 9.1.1. The indicator was computed as the proportion of WorldPop gridded population within 2 kilometers to an OpenStreetMap (OSM) all-season road. The SDG indicator 9.1.1 data set provides estimates for the proportion of the rural population with access to all-season roads for 209 countries and 45,073 subnational Units. The data set is available at both national and level 2 subnational resolutions.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDGI_UAPT_2023_2023.00.json b/datasets/CIESIN_SEDAC_SDGI_UAPT_2023_2023.00.json index 4a15211bb1..f8262a4869 100644 --- a/datasets/CIESIN_SEDAC_SDGI_UAPT_2023_2023.00.json +++ b/datasets/CIESIN_SEDAC_SDGI_UAPT_2023_2023.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDGI_UAPT_2023_2023.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release, part of the SDGI collection, measures the proportion of the population in a city that has convenient access to public transport. UN SDG 11 is \"make cities and human settlements inclusive, safe, resilient and sustainable\". Improving access to public transport services is integral to achieving the objectives of SDG 11. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, \"only about half the world's urban population have convenient access to public transport\". The report highlights that access to sustainable transport can help reduce food insecurity, boost economies, empower women, and connect people to key health, education, and financial services. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.2.1. The indicator was computed as the proportion of WorldPop gridded population within either 0.5 kilometer walking distance to a low-capacity OpenStreetMap (OSM) public transport point or 1 kilometer walking distance to a high-capacity OSM public transport point. Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (UCDB). The SDG indicator 11.2.1 data set provides estimates for the proportion of population with convenient access to public transport for 5,749 urban centers across 178 countries.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDGI_UPSAE_2023_2023.00.json b/datasets/CIESIN_SEDAC_SDGI_UPSAE_2023_2023.00.json index 6ef9eca40e..02c1dec3a4 100644 --- a/datasets/CIESIN_SEDAC_SDGI_UPSAE_2023_2023.00.json +++ b/datasets/CIESIN_SEDAC_SDGI_UPSAE_2023_2023.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDGI_UPSAE_2023_2023.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is \"make cities and human settlements inclusive, safe, resilient and sustainable\". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDP_CGDP_A1A2B1B2_1.00.json b/datasets/CIESIN_SEDAC_SDP_CGDP_A1A2B1B2_1.00.json index ded721ade0..47b49bdad6 100644 --- a/datasets/CIESIN_SEDAC_SDP_CGDP_A1A2B1B2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDP_CGDP_A1A2B1B2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDP_CGDP_A1A2B1B2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDP_CPOP_A1B1A2_1.00.json b/datasets/CIESIN_SEDAC_SDP_CPOP_A1B1A2_1.00.json index 93fabbfdc6..7ac9211f8e 100644 --- a/datasets/CIESIN_SEDAC_SDP_CPOP_A1B1A2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDP_CPOP_A1B1A2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDP_CPOP_A1B1A2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA \"rapid\" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA \"slow\" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDP_CPOP_B2_1.00.json b/datasets/CIESIN_SEDAC_SDP_CPOP_B2_1.00.json index d95b239321..9226743ee6 100644 --- a/datasets/CIESIN_SEDAC_SDP_CPOP_B2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDP_CPOP_B2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDP_CPOP_B2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDP_GGDP_B2_1.00.json b/datasets/CIESIN_SEDAC_SDP_GGDP_B2_1.00.json index 706f1297c8..53067804ad 100644 --- a/datasets/CIESIN_SEDAC_SDP_GGDP_B2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDP_GGDP_B2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDP_GGDP_B2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SDP_GPOP_B2_1.00.json b/datasets/CIESIN_SEDAC_SDP_GPOP_B2_1.00.json index a7bfa056ae..8912d87597 100644 --- a/datasets/CIESIN_SEDAC_SDP_GPOP_B2_1.00.json +++ b/datasets/CIESIN_SEDAC_SDP_GPOP_B2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SDP_GPOP_B2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per Unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SPATIALECON_GECON4_4.00.json b/datasets/CIESIN_SEDAC_SPATIALECON_GECON4_4.00.json index e1a933b3d4..bdbe505306 100644 --- a/datasets/CIESIN_SEDAC_SPATIALECON_GECON4_4.00.json +++ b/datasets/CIESIN_SEDAC_SPATIALECON_GECON4_4.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SPATIALECON_GECON4_4.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Gridded Geographically Based Economic Data (G-Econ), Version 4 contains derived one degree grid cells of Gross Domestic Product (GDP) data in Grid and ASCII formats for both Market Exchange Rate (MER) and Purchasing Power Parity (PPP) for the years 1990, 1995, 2000 and 2005. MER is the exchange rate between local and U.S. dollar currencies for a given time period established by the market. PPP is the exchange rate between a country's currency and U.S. dollars adjusted to reflect the actual cost in U.S. dollars of purchasing a standardized market basket of goods in that country using the country's currency. The original data from the G-Econ Project at Yale University is also available in tabular format and includes latitude and longitude geographic coordinates of the grid cells, area of grid cells, as well as country names, distance to coast, elevation, vegetation, population, precipitation and temperature.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SPATIALECON_LGII_V1_1.00.json b/datasets/CIESIN_SEDAC_SPATIALECON_LGII_V1_1.00.json index c986e213ed..076b769105 100644 --- a/datasets/CIESIN_SEDAC_SPATIALECON_LGII_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_SPATIALECON_LGII_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SPATIALECON_LGII_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1 data set contains Gini-coefficients of inequality for 234 countries and territories from 1992 to 2013. The measurement Unit is the Gini-Coefficient (Range: 0-1), with higher values representing higher inequality. These measures are constructed using worldwide geospatial satellite data on nighttime lights emission as a proxy for economic prosperity, matched with varying sources of data on geo-located population counts. The nighttime lights data were supplied by the National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Information (NCEI), Earth Observation Group (EOG), and Operational Linescan System (OLS) instruments. The population data used consisted of CIESIN's Gridded Population of the World (GPW) collection, and the Oak Ridge National Laboratory (ORNL) LandScan (LSC) data set. The nighttime lights and population data were combined to produce an array of geospatially-informed Gini-coefficients, which were then weighted to optimize their correlation with a benchmark - specifically, the Standardized World Income Inequality Database (SWIID), to generate a parsimonious composite inequality metric.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SPECIES_AMP_RICH15_2015.00.json b/datasets/CIESIN_SEDAC_SPECIES_AMP_RICH15_2015.00.json index 54d28827ea..ee622afe97 100644 --- a/datasets/CIESIN_SEDAC_SPECIES_AMP_RICH15_2015.00.json +++ b/datasets/CIESIN_SEDAC_SPECIES_AMP_RICH15_2015.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SPECIES_AMP_RICH15_2015.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2015 Release of the Global Amphibian Richness Grids data set of the Gridded Species Distribution collection are aggregations of the presence grids data for the entire class, individual families, and International Union for the Conservation of Nature (IUCN) Red List status categories. The data are available in 30 arc-second (~1 km) resolutions. The grid cell values represent the number of species in a particular class, family or IUCN threatened category. The input vector layers are based on the IUCN Red List and the grids are compiled by the Columbia University Center for International Earth Science Information Network (CIESIN). The data from IUCN were downloaded in April 2013.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SPECIES_MAM_RICH15_2015.00.json b/datasets/CIESIN_SEDAC_SPECIES_MAM_RICH15_2015.00.json index 1270f6f5c4..26b69261c7 100644 --- a/datasets/CIESIN_SEDAC_SPECIES_MAM_RICH15_2015.00.json +++ b/datasets/CIESIN_SEDAC_SPECIES_MAM_RICH15_2015.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SPECIES_MAM_RICH15_2015.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2015 Release of the Global Mammal Richness Grids data set of the Gridded Species Distribution collection are aggregations of the presence grids data for the entire class, individual families, and International Union for the Conservation of Nature (IUCN) Red List status categories. The data are available in 30 arc-second (~1 km) resolutions. The grid cell values represent the number of species in a particular class, family or IUCN threatened category. The input vector layers are based on the IUCN Red List and the grids are compiled by the Columbia University Center for International Earth Science Information Network (CIESIN). The data from IUCN were downloaded in April 2013.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSF_EPANPLSPDCMv2_2.00.json b/datasets/CIESIN_SEDAC_SSF_EPANPLSPDCMv2_2.00.json index 85d8dd51e1..6d9846d145 100644 --- a/datasets/CIESIN_SEDAC_SSF_EPANPLSPDCMv2_2.00.json +++ b/datasets/CIESIN_SEDAC_SSF_EPANPLSPDCMv2_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSF_EPANPLSPDCMv2_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Environmental Protection Agency (EPA) National Priorities List (NPL) Sites Point Data with CIESIN Modifications, Version 2 is a modified version of the 2014 EPA NPL list. It includes all the sites that are proposed, currently on, or deleted from the Final NPL as of February 27, 2014. CIESIN has fixed eleven of the original coordinates by correcting latitude or longitude coordinates. It contains the point locations, including the eleven corrections, for 1,747 U.S. hazardous waste sites on the National Priorities List (NPL) of EPA's Comprehensive Environmental Response, Compensation, and Liability Information System (CERCLIS) for the fifty states, Puerto Rico, and 4 other territorial areas plus the now independent Palau, Federated States of Micronesia. The sites in CERCLIS are also known as Superfund Sites. The NPL is intended primarily to guide the EPA in determining which sites warrant further investigation.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSF_HWSPDCMv2_2.00.json b/datasets/CIESIN_SEDAC_SSF_HWSPDCMv2_2.00.json index 13f5e4d2cf..dcfe61347d 100644 --- a/datasets/CIESIN_SEDAC_SSF_HWSPDCMv2_2.00.json +++ b/datasets/CIESIN_SEDAC_SSF_HWSPDCMv2_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSF_HWSPDCMv2_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been requested.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSF_HWSPDv2_2.00.json b/datasets/CIESIN_SEDAC_SSF_HWSPDv2_2.00.json index 7fd205e36d..3c618eff54 100644 --- a/datasets/CIESIN_SEDAC_SSF_HWSPDv2_2.00.json +++ b/datasets/CIESIN_SEDAC_SSF_HWSPDv2_2.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSF_HWSPDv2_2.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, Version 2 consists of 2,080 polygons for selected hazardous waste sites that were compiled on May 26, 2010. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. The hazardous waste site boundaries maintained by the Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) contain NPL and non-NPL hazardous waste site boundaries for which health assessments or consultations have been requested.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP_1.00.json b/datasets/CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP_1.00.json index 3c897800d8..f685f22032 100644 --- a/datasets/CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP_1.00.json +++ b/datasets/CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSP_1-8thDULEPBYGSSP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00.json b/datasets/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00.json index 52a8e4c886..0b06ea57f8 100644 --- a/datasets/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00.json +++ b/datasets/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade. The algorithm uses an iterative process to collect any overflows from already highly-developed 1-km grid cells, and then allocates them to 1-km grid cells that are not yet fully developed. This iterative process repeats itself until all 1/8-degree amounts of urban land expansion are allocated to 1-km grid cells with no overflow. The downscaling process is applied decade by decade throughout the 21st century for each urban land expansion scenario. The final product is a set of global maps displaying the 1-km fraction of urban land, updated at decadal intervals throughout the 21st century, for five different urban land expansion scenarios consistent with the Shared Socioeconomic Pathways (SSPs).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSP_GSNLITDB_V1_1.00.json b/datasets/CIESIN_SEDAC_SSP_GSNLITDB_V1_1.00.json index 448801069f..cde78fc3f0 100644 --- a/datasets/CIESIN_SEDAC_SSP_GSNLITDB_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_SSP_GSNLITDB_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSP_GSNLITDB_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_SSP_LITDB_V1_1.00.json b/datasets/CIESIN_SEDAC_SSP_LITDB_V1_1.00.json index c6fa91ec9c..2ba868744e 100644 --- a/datasets/CIESIN_SEDAC_SSP_LITDB_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_SSP_LITDB_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_SSP_LITDB_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs. The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ULANDSAT_GMIS_V1_1.00.json b/datasets/CIESIN_SEDAC_ULANDSAT_GMIS_V1_1.00.json index 8883cfde9d..7f55b00a2f 100644 --- a/datasets/CIESIN_SEDAC_ULANDSAT_GMIS_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_ULANDSAT_GMIS_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ULANDSAT_GMIS_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of man-made impervious cover to be derived from the GLS data for 2010 and is a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ULANDSAT_HBASE_V1_1.00.json b/datasets/CIESIN_SEDAC_ULANDSAT_HBASE_V1_1.00.json index 515d7156f6..d4eae6c040 100644 --- a/datasets/CIESIN_SEDAC_ULANDSAT_HBASE_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_ULANDSAT_HBASE_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ULANDSAT_HBASE_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Human Built-up And Settlement Extent (HBASE) Dataset from Landsat is a global map of HBASE derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The HBASE dataset consists of two layers: 1) the HBASE mask; and 2) the pixel-wise probability of HBASE. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of urban extent to be derived from the GLS data for 2010 and is a companion dataset to the Global Man-made Impervious Surface (GMIS) dataset. The HBASE mask was created for post-processing of the GMIS dataset, but can also be utilized by users needing a binary map. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT_1.00.json b/datasets/CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT_1.00.json index c1e3ba8d96..f8469b0803 100644 --- a/datasets/CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT_1.00.json +++ b/datasets/CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_ULANDSAT_URBAN_LANDSAT_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Urban Landsat: Cities from Space data set contains images for 66 urban areas and the raw, underlying data for 28 of these places. Each image shows a Landsat false color composite in UTM projection. The R/G/B layers correspond to TM/ETM+ bands 7/4/2. Each pixel is 30x30 meters in area and most images are 30x30 km in area. A 2% linear stretch has been applied to the images. The Landsat data files contain six reflected bands of calibrated exoatmospheric reflectance stored in ENVI band sequential (BSQ) format. Geographic coordinates are included in the header files. The data files contain 1000x1000x6 4 byte floating point numbers as indicated in the header files.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_90SF1MSA_1.00.json b/datasets/CIESIN_SEDAC_USCG_90SF1MSA_1.00.json index 9a92debab6..2d230cd3a8 100644 --- a/datasets/CIESIN_SEDAC_USCG_90SF1MSA_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_90SF1MSA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_90SF1MSA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 1), 1990: Metropolitan Statistical Areas data set contains grids of demographic and socioeconomic data from the year 1990 U.S. census in ASCII andGeoTIFF formats for 39 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 1990 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_90SF1_1.00.json b/datasets/CIESIN_SEDAC_USCG_90SF1_1.00.json index ff9fdd5d44..902cf865ff 100644 --- a/datasets/CIESIN_SEDAC_USCG_90SF1_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_90SF1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_90SF1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 1), 1990 data set contains grids of demographic and socioeconomic data from the year 1990 U.S. census in ASCII and geotiff formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 1990 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_90SF3MSA_1.00.json b/datasets/CIESIN_SEDAC_USCG_90SF3MSA_1.00.json index fa80b7566f..c3078b60de 100644 --- a/datasets/CIESIN_SEDAC_USCG_90SF3MSA_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_90SF3MSA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_90SF3MSA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 3), 1990: Metropolitan Statistical Areas data set contains grids of demographic and socioeconomic data from the year 1990 U.S. Census in ASCII and GeoTIFF formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 1990 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN)", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_90SF3_1.00.json b/datasets/CIESIN_SEDAC_USCG_90SF3_1.00.json index 839df04f4e..7ac71e7cf8 100644 --- a/datasets/CIESIN_SEDAC_USCG_90SF3_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_90SF3_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_90SF3_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 3), 1990 data set contains grids of demographic and socioeconomic data from the year 1990 U.S. census in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 1990 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json b/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json index c32eaee8cb..577fb8a31d 100644 --- a/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_SF12010_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json b/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json index 39be3406c9..52a7acc85d 100644 --- a/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_SF1MSA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "U.S. Census Grids (Summary File 1), 2000: Metropolitan Statistical Areas contain grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and geotiff formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_SF1_1.00.json b/datasets/CIESIN_SEDAC_USCG_SF1_1.00.json index e6e62b0ac3..aba9442ae0 100644 --- a/datasets/CIESIN_SEDAC_USCG_SF1_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_SF1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_SF1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 1), 2000 data set contains grids of demographic and socioeconomic data from the year 2000 U.S. Census in ASCII and geotiff formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_SF3MSA_1.00.json b/datasets/CIESIN_SEDAC_USCG_SF3MSA_1.00.json index caa2a3f9b9..b23d973e7e 100644 --- a/datasets/CIESIN_SEDAC_USCG_SF3MSA_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_SF3MSA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_SF3MSA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 3), 2000: Metropolitan Statistical Areas data set contains grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and GeoTIFF formats for 50 metropolitan statistical areas with at least one million in population. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_SF3_1.00.json b/datasets/CIESIN_SEDAC_USCG_SF3_1.00.json index 23660da274..ef570e041b 100644 --- a/datasets/CIESIN_SEDAC_USCG_SF3_1.00.json +++ b/datasets/CIESIN_SEDAC_USCG_SF3_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_SF3_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Census Grids (Summary File 3), 2000 data set contains grids of demographic and socioeconomic data from the year 2000 U.S. census in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USCG_USSVIG01_1.01.json b/datasets/CIESIN_SEDAC_USCG_USSVIG01_1.01.json index 783a0fc868..b4f00581ea 100644 --- a/datasets/CIESIN_SEDAC_USCG_USSVIG01_1.01.json +++ b/datasets/CIESIN_SEDAC_USCG_USSVIG01_1.01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USCG_USSVIG01_1.01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Social Vulnerability Index Grids, Revision 01 data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, 2018, and 2020. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 kilometer spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4 (GPWv4) Revision 11 data sets, a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable commUnities by ranking them on these inputs across the U.S.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USPAT_BSCATTER_1993_2020_1.00.json b/datasets/CIESIN_SEDAC_USPAT_BSCATTER_1993_2020_1.00.json index a486879023..e0ca8ca977 100644 --- a/datasets/CIESIN_SEDAC_USPAT_BSCATTER_1993_2020_1.00.json +++ b/datasets/CIESIN_SEDAC_USPAT_BSCATTER_1993_2020_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USPAT_BSCATTER_1993_2020_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USPAT_DESLUIS_1.00.json b/datasets/CIESIN_SEDAC_USPAT_DESLUIS_1.00.json index d1080d052d..0c66b6245e 100644 --- a/datasets/CIESIN_SEDAC_USPAT_DESLUIS_1.00.json +++ b/datasets/CIESIN_SEDAC_USPAT_DESLUIS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USPAT_DESLUIS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Dar es Salaam Land Use and Informal Settlement Data Set represents urban land use and consolidation of informal settlements for the years 1982, 1992, 1998, and 2002, in Dar es Salaam, Tanzania. The land use categories are informal settlement areas, planned residential areas, ocean and estuaries, vacant and agriculture lands, and other urban features such as industrial or recreation areas. The data are based on the World Geodetic System spheroid of 1984 and use the Universal Transverse Mercator Zone 37 South projection.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json b/datasets/CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json index 547e7592f6..eddd976988 100644 --- a/datasets/CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USPAT_GUPPD_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Urban Polygons and Points Dataset (GUPPD), Version 1 is a global data set of 123,034 urban settlements with place names and population for the years 1975-2030 in five-year increments. The data set builds on and expands the European Commission, Joint Research Centre's (JRC) 2015 Global Human Settlement (GHS) Urban Centre Database (UCDB). The JRC Settlement Model (GHS-SMOD) data set includes a hierarchy of urban settlements, from urban centre (level 30), to dense urban cluster (level 23), to semi-dense urban cluster (level 22). The UCDB only includes level 30, whereas the GUPPDv1 adds levels 23 and 22, and uses open data sources to both check and validate the names that JRC assigned to its UCDB polygons and to label the newly added settlements. The methodology described in the documentation was able to consistently label a greater percentage of UCDB polygons than were previously labeled by JRC.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USPAT_HUP_1.00.json b/datasets/CIESIN_SEDAC_USPAT_HUP_1.00.json index 3d6e4b7000..66f3b0bebd 100644 --- a/datasets/CIESIN_SEDAC_USPAT_HUP_1.00.json +++ b/datasets/CIESIN_SEDAC_USPAT_HUP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USPAT_HUP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Historical Urban Population, 3700 BC - AD 2000, originally developed by the Yale School of Forestry & Environmental Studies, is the first spatially explicit global data set containing location and size of urban populations over the last 6,000 years. The data set was created by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data. Each data point consists of a city name, latitude, longitude, year, population, and a reliability ranking to assess the geographic uncertainty of each data point. Despite spatial and temporal gaps, no other geocoded data set at this resolution exists. It can therefore be used to investigate long-term historical urbanization trends and patterns, evaluate the current era of urbanization, and build a richer record of urban population through history.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_USPAT_USUEXT2015_1.00.json b/datasets/CIESIN_SEDAC_USPAT_USUEXT2015_1.00.json index c5566ab2e3..4bc3b75559 100644 --- a/datasets/CIESIN_SEDAC_USPAT_USUEXT2015_1.00.json +++ b/datasets/CIESIN_SEDAC_USPAT_USUEXT2015_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_USPAT_USUEXT2015_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When applied to the combination of daytime spectral and nighttime lights satellite data, the machine learning methods achieved high accuracy at an intermediate-resolution of 500 meters at large spatial scales. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). Although several machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and the Ensemble of RF, GBM, and NN (ESB), the highest accuracy results were achieved with NN, and those results were used to delineate the urban extents in this data set.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_CCPROD_1.00.json b/datasets/CIESIN_SEDAC_WACVM_CCPROD_1.00.json index ee6d105379..0e3b447004 100644 --- a/datasets/CIESIN_SEDAC_WACVM_CCPROD_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_CCPROD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_CCPROD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Commercial Crop Production, 2000 data set includes 5-minute rasters of crop production in metric tons per grid cell for five higher-value export crops in West Africa: cocoa, bananas, coconut, palm oil, and rubber. Commercial crops are economically valuable to the countries of West Africa, and some are at high risk due to sea level rise and storm surge impacts. The crop production rasters are derived from the harvested area and yield rasters in the M3-Crops data collection (Monfreda et al., 2008) which includes geographic distributions for 175 crops.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_DEFOR200012_1.00.json b/datasets/CIESIN_SEDAC_WACVM_DEFOR200012_1.00.json index 106c5ca477..76959ca99d 100644 --- a/datasets/CIESIN_SEDAC_WACVM_DEFOR200012_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_DEFOR200012_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_DEFOR200012_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Deforestation, 2000-2012, layer was created from the Global Forest Change data set which represents global tree cover extent, loss, and gain mapped for the period from 2000 to 2012 at a spatial resolution of 30m, with loss allocated annually. The Global Forest Change data set defines trees as all vegetation taller than 5m in height. Forest loss (deforestation) is defined as a stand-replacement disturbance, or a change from a forest to non-forest state. The data on forest loss were aggregated to a one kilometer resolution and subsetted to 200 kilometers from the coast to produce this data set. Raster values represent the percentage of the grid cell area that experienced forest cover loss from 2000 to 2012.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_DHS_1.00.json b/datasets/CIESIN_SEDAC_WACVM_DHS_1.00.json index 12345a89ca..21f7ef806d 100644 --- a/datasets/CIESIN_SEDAC_WACVM_DHS_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_DHS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_DHS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Demographic and Health Survey Data Sets present grids of maternal education levels and household wealth based on Demographic and Health Survey (DHS) cluster level data for ten West African countries. While the maternal education levels are comparable across countries, owing to different underlying indicators, the household wealth index is not. Education can directly influence risk perception, skills and knowledge and indirectly reduce poverty, improve health, and promote access to information and resources. When facing natural hazards or climate risks, educated individuals, households, and societies are assumed to be more empowered and more adaptive in their response to, preparation for, and recovery from disasters. Education is a key background indicator that helps contextualize a country's health and development situation. The household wealth index is a composite measure of a household's cumulative living standard. The wealth index is calculated using easy-to-collect data on a household's ownership of selected assets, such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities. Bayesian spatial interpolation methods were employed to create country level grids based on DHS cluster point data for each country. Data are from the following dates by country: Benin (2006), Cameroon (2011), Cote d'Ivoire (2012), Ghana (2008), Guinea (2012), Liberia (2011), Nigeria (2010), Sierra Leone (2008), and Togo (1998).", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_ECONSI_1.00.json b/datasets/CIESIN_SEDAC_WACVM_ECONSI_1.00.json index fed6424dc3..98802ed23c 100644 --- a/datasets/CIESIN_SEDAC_WACVM_ECONSI_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_ECONSI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_ECONSI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Economic Systems Index is a composite index based on several spatial indicators, including gridded Gross Domestic Product (GDP), nighttime lights as a proxy for urban built-up and industrial areas, and cocoa, coconut, palm oil, rubber, and banana production in metric tons. It covers the coastal region of West Africa within 200 km of the coast. Population growth in the coastal zone is mostly a function of migration related to coastal economic activities; this indicator provides insights into highly exposed coastal areas that not only have high levels of economic activity but also high population growth and migration.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PD_1.00.json b/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PD_1.00.json index de2882fbbd..bde61cff61 100644 --- a/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PD_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PD_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_GPWv4PR_PD_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: GPW Version 4 Population Density, Preliminary Release 1, 2010, represents the number of persons per square kilometer, and was calculated by dividing an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster for the West Africa region by a land area raster and cropping the result to within 200 kilometers of the coast. GPW provides globally consistent and spatially explicit human population information and data for use in research, policy making, and communications. This is a gridded (raster) data product that renders global population data at the scale and extent required to demonstrate the spatial relationship of human populations and the environment across the globe. The gridded data set is constructed from national or subnational input Units (usually administrative Units) of varying resolutions. The native grid cell resolution of GPWv4 is 30 arc-second, or ~1 km at the equator.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PG_1.00.json b/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PG_1.00.json index 08233e34d1..3cf576a680 100644 --- a/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PG_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_GPWv4PR_PG_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_GPWv4PR_PG_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: GPW Version 4 Population Growth, Preliminary Release 1, 2000-2010, represents positive or negative growth in the number of persons per grid cell, and was calculated by subtracting an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2000 population count raster for the West Africa region from an unreleased working version of the GPWv4 year 2010 population count raster and cropping the result to within 200 kilometers of the coast. GPW provides globally consistent and spatially explicit human population information and data for use in research, policy making, and communications. This is a gridded (raster) data product that renders global population data at the scale and extent needed to demonstrate the spatial relationship of human populations and the environment globally. The gridded data set is constructed from national or subnational input Units (usually administrative Units) of varying resolutions. The native grid cell resolution of GPWv4 is 30 arc-second, or ~1 km at the equator.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_GSSPEPP_1.00.json b/datasets/CIESIN_SEDAC_WACVM_GSSPEPP_1.00.json index b95dd625e0..a18dac0c4c 100644 --- a/datasets/CIESIN_SEDAC_WACVM_GSSPEPP_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_GSSPEPP_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_GSSPEPP_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the \"defenselessness\" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_JRCMA_1.00.json b/datasets/CIESIN_SEDAC_WACVM_JRCMA_1.00.json index cdc106fe73..2732f37486 100644 --- a/datasets/CIESIN_SEDAC_WACVM_JRCMA_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_JRCMA_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_JRCMA_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Subset of JRC Map of Accessibility data set is a 30 arc-second raster of travel time to major cities in West Africa within 200 kilometers of the coast. Extensive literature shows that road networks and market accessibility play an important role in development and access to health care and other social services. Greater spatial isolation is assumed to produce higher vulnerability to climate stressors. Market accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake, and ocean) based travel. A team at the Joint Research Centre (JRC) in Ispra, Italy, created a global raster of accessibility using a cost-distance algorithm which computes the \"cost\" (in Units of time) of traveling between two locations on a regular raster grid. The raster grid cells contain values which represent the cost required to travel across them, hence this raster grid is often termed a friction-surface. The friction-surface contains information on the transport network, and environmental and political factors that affect travel times between locations. Transport networks can include road and rail networks, navigable rivers, and shipping lanes. The locations of interest are termed targets, and in the case of this data set, the targets are cities with a population of 50,000 or greater in the year 2000.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_MFD2000_POL_1.00.json b/datasets/CIESIN_SEDAC_WACVM_MFD2000_POL_1.00.json index 8320813d5b..9f0744ffd7 100644 --- a/datasets/CIESIN_SEDAC_WACVM_MFD2000_POL_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_MFD2000_POL_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_MFD2000_POL_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Mangrove Forests Distribution, 2000 Polygon data set was derived from the 30m resolution NASA Socioeconomic Data and Applications Center (SEDAC) Global Mangrove Forests Distribution, 2000 raster data set. It represents the distribution of mangrove forests within 200 kilometers of the coast. The global raster data set is a compilation of the extent of mangrove forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_OSM_ROADS_1.00.json b/datasets/CIESIN_SEDAC_WACVM_OSM_ROADS_1.00.json index 10b5d1ed5f..dc1ea9f7be 100644 --- a/datasets/CIESIN_SEDAC_WACVM_OSM_ROADS_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_OSM_ROADS_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_OSM_ROADS_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Subset of OpenStreetMap (OSM) Roads data set includes roads within 200 kilometers of the coast and was extracted from the full OSM data set in March 2014. OSM is a global crowdsourced road and street map and is continually being updated.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_PGLF_200813_1.00.json b/datasets/CIESIN_SEDAC_WACVM_PGLF_200813_1.00.json index 15592285dd..15eedefff4 100644 --- a/datasets/CIESIN_SEDAC_WACVM_PGLF_200813_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_PGLF_200813_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_PGLF_200813_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013 data set consists of two layers: points representing the location of conflict events with fatalities within 200 kilometers from the coast during the time period from 2008 to 2013, and a raster layer created from the points using a kernel density interpolation of the number of fatalities. These layers were created from the Armed Conflict Location and Event Dataset (ACLED), which codes the dates and locations of all reported political violence events in over 50 developing countries. Political violence includes events that occur within civil wars and periods of instability. Armed conflict reduces human security and increases the sensitivity of populations to climate stressors.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_POPPROJ_203050_1.00.json b/datasets/CIESIN_SEDAC_WACVM_POPPROJ_203050_1.00.json index 066dc45ff2..e0b3ee9eca 100644 --- a/datasets/CIESIN_SEDAC_WACVM_POPPROJ_203050_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_POPPROJ_203050_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_POPPROJ_203050_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010_1.00.json b/datasets/CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010_1.00.json index d3f7a5e19b..b6eec13bb6 100644 --- a/datasets/CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_SDMSPOLSNLEA_2010_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Subset of DMSP-OLS Nighttime Lights for Economic Activity, 2010 data set is based on Version 4 of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) Nighttime Lights Time Series, 2010 annual global composite of radiance lights inter-calibrated to the digital number (DN) values of gain 55 for satellite F16 (2006). These data are commonly used for identifying human settlements and economic activity. The DNs are on a Unitless scale ranging from 0 (no light) to 4,000 (greatest light intensity). This data set is a proxy for economic activity within 200 kilometers of the coast of West Africa. The resolution of the grid is 30 arc-second, or approximately 1 km at the equator. The data were provided courtesy of Christopher Elvidge and Kimberly Baugh of the Earth Observation Group, NOAA National Geophysical Data Center (NGDC), where image and data processing were performed.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_SMAMAMP_RICH_2015.00.json b/datasets/CIESIN_SEDAC_WACVM_SMAMAMP_RICH_2015.00.json index 648e83c4fa..b66ba8dc16 100644 --- a/datasets/CIESIN_SEDAC_WACVM_SMAMAMP_RICH_2015.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_SMAMAMP_RICH_2015.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_SMAMAMP_RICH_2015.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Subset of Global Mammal and Amphibian Richness Grids, 2015 Release was extracted from the NASA Socioeconomic Data and Applications Center (SEDAC) Gridded Species Distribution collection created from vector data files acquired from the International Union for Conservation of Nature (IUCN) Red List collection. Threatened species include any species classified as vulnerable, endangered, or critically endangered. The data represent the density of threatened mammals and amphibians within 200 kilometers of the coast at a one kilometer resolution.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_SUBSETACE2_1.00.json b/datasets/CIESIN_SEDAC_WACVM_SUBSETACE2_1.00.json index 9d9f2dc2a9..ed2f40cbfd 100644 --- a/datasets/CIESIN_SEDAC_WACVM_SUBSETACE2_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_SUBSETACE2_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_SUBSETACE2_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Subset of High and Low Resolution Altimeter Corrected Elevations 2 (ACE2) data set consists of extracts from the 3 arc-second (high) and 30 arc-second (low) resolution ACE2 data product packaged as GeoTIFFs. It includes values within 200 kilometers of the coast. ACE2 was created by synergistically merging the Shuttle Radar Topography Mission (SRTM) data set with Satellite Radar Altimetry within the region bounded by 60 degrees N and 60 degrees S. The primary altimetry data set used in the generation of this data set was the European Remote Sensing (ERS-1) Geodetic mission, which due to its small across track spacing presents a uniquely dense spatial distribution of tracks altimetry data over land surfaces. Data from ERS-2 and the Environmental Satellite (Envisat) Ku-band are included where appropriate. All of the altimeter data have been reprocessed using the Berry Expert System to retrack the waveforms. Over 11,000,000,000 SRTM pixels were adjusted using this unique network of control arcs of altimeter derived height data. In rainforest regions, extensive investigation has shown that the altimetry data returns ground values, whereas the SRTM signal bounces off the canopy. Thus ACE2 does a better job of providing ground values than SRTM in forested coastal regions of West Africa.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WACVM_SVI_1.00.json b/datasets/CIESIN_SEDAC_WACVM_SVI_1.00.json index b1f1b382f5..ffbdc12e22 100644 --- a/datasets/CIESIN_SEDAC_WACVM_SVI_1.00.json +++ b/datasets/CIESIN_SEDAC_WACVM_SVI_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WACVM_SVI_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.", "links": [ { diff --git a/datasets/CIESIN_SEDAC_WATER_WSIM_GLDAS_V1_1.00.json b/datasets/CIESIN_SEDAC_WATER_WSIM_GLDAS_V1_1.00.json index 80c069d70b..e9a4026cfc 100644 --- a/datasets/CIESIN_SEDAC_WATER_WSIM_GLDAS_V1_1.00.json +++ b/datasets/CIESIN_SEDAC_WATER_WSIM_GLDAS_V1_1.00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CIESIN_SEDAC_WATER_WSIM_GLDAS_V1_1.00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1 data set identifies and characterizes surpluses and deficits of freshwater, and the parameters determining these anomalies, at monthly intervals over the period January 1948 to December 2014. The data set uses the land surface model outputs from NASA's Global Land Data Assimilation System, covering the global extent, to generate anomaly values for the following parameters at a gridded resolution of 0.25 degrees: temperature, precipitation, soil moisture, potential minus actual evapotranspiration, runoff, total blue water (flow-accumulated runoff), composite index of water surplus, and composite index of water deficits. These data are provided in terms of return periods, scientific Units, and standardized (normalized) anomalies, and are computed over 1-month, 3-month, 6-month, and 12-month temporal periods of accumulation, referred to as integration periods. Anomaly values are present in terms of return periods with respect to a fitted Generalized Extreme Value (GEV) probability distribution function over a historical baseline period of January 1950 to December 2009, at a global spatial resolution of 0.25 degrees over the monthly, 3-month, 6-month, and 12-month periods of integration. Parameter values (location, scale, shape) of the fitted GEV probability distribution, which are fit separately for each calendar month, are distributed per parameter for each integration period.", "links": [ { diff --git a/datasets/CL07LC_1.json b/datasets/CL07LC_1.json index f20feb1cb3..779382ac65 100644 --- a/datasets/CL07LC_1.json +++ b/datasets/CL07LC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07LC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.", "links": [ { diff --git a/datasets/CL07PLBK_1.json b/datasets/CL07PLBK_1.json index 80b11869aa..46b7b96e58 100644 --- a/datasets/CL07PLBK_1.json +++ b/datasets/CL07PLBK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07PLBK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).", "links": [ { diff --git a/datasets/CL07PLTB_1.json b/datasets/CL07PLTB_1.json index 08a105defb..66b001369d 100644 --- a/datasets/CL07PLTB_1.json +++ b/datasets/CL07PLTB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07PLTB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).", "links": [ { diff --git a/datasets/CL07SM_1.json b/datasets/CL07SM_1.json index eaf484b9bf..65c64b07e6 100644 --- a/datasets/CL07SM_1.json +++ b/datasets/CL07SM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07SM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).", "links": [ { diff --git a/datasets/CL07ST_1.json b/datasets/CL07ST_1.json index 07689c3057..00690cf0e8 100644 --- a/datasets/CL07ST_1.json +++ b/datasets/CL07ST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07ST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil texture data obtained for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). The original data were extracted from a multi-layer soil characteristics database for the conterminous United States called CONUS-Soil and generated for the regional study area. Data are representative of the conditions present in the regional study area during the general timeline of the CLASIC07 campaign.", "links": [ { diff --git a/datasets/CL07VWC_1.json b/datasets/CL07VWC_1.json index 50bd57157b..59bb235ae0 100644 --- a/datasets/CL07VWC_1.json +++ b/datasets/CL07VWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07VWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.", "links": [ { diff --git a/datasets/CL07V_1.json b/datasets/CL07V_1.json index b550eaa6d0..4ac51b2622 100644 --- a/datasets/CL07V_1.json +++ b/datasets/CL07V_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CL07V_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.", "links": [ { diff --git a/datasets/CLAMS_CERES_CHESLIGHT_SONDE_1.json b/datasets/CLAMS_CERES_CHESLIGHT_SONDE_1.json index e6e06aef9d..78591ed9af 100644 --- a/datasets/CLAMS_CERES_CHESLIGHT_SONDE_1.json +++ b/datasets/CLAMS_CERES_CHESLIGHT_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLAMS_CERES_CHESLIGHT_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS_CERES_CHESLIGHT_SONDE data were collected during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment.The CLAMS_CERES_CHESLIGHT_SONDE data set was collected by Vaisala RS-80 Radiosonde at the Chesapeake Lighthouse, which is a Coast Guard platform located 25 km East of Virginia (near the mouth of the Chesapeake Bay). It is located outside of the surf zone and far enough away from shore to make it an excellent validation site for space-borne retrievals of cloud and aerosol microphysics. The platform itself is small enough (25x25 meters) that the usual island effect associated with oceanic sites is negligible. Aerosol climatology at this location indicates optical depths and Angstrom exponents that are consistent with polluted urban aerosols, which is not surprising given its close proximity to Virginia Beach and Norfolk, VA. These urban aerosols will also have an impact on the marine clouds at the site. Not all air masses at Chesapeake Lighthouse are polluted, however, as Easterly winds from occasional synoptic systems and frequent sea breezes provide a marine aerosol source. Hence, the Chesapeake Lighthouse is an excellent location to study the impact of anthropogenic aerosols on cloud microphysical properties (i.e., the indirect effect).", "links": [ { diff --git a/datasets/CLAMS_CV580_CAR_1.json b/datasets/CLAMS_CV580_CAR_1.json index 5cef13a49d..300514bfe1 100644 --- a/datasets/CLAMS_CV580_CAR_1.json +++ b/datasets/CLAMS_CV580_CAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLAMS_CV580_CAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS_CV580_CAR data were collected during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment.The Cloud Absorption Radiometer (CAR) instrument is an airborne multi-wavelength scanning radiometer, designed to operate from a mounted position aboard various aircraft, including the nose cone of the University of Washington's Convair CV-580. Developed by Dr. Michael King at NASA Goddard Space Flight Center, the CAR instrument measures radiance for 190 degrees, and total view at 1 degree field of view resolution. Instrument functions include: * acquiring imagery of Earth surface and Cloud features* single scattering albedo determination of clouds* angular distribution measuring of scattered radiation* bidirectional reflectance measuring of various surface types", "links": [ { diff --git a/datasets/CLAMS_ER2_MAS_1.json b/datasets/CLAMS_ER2_MAS_1.json index defb8cc826..71f5ca1a01 100644 --- a/datasets/CLAMS_ER2_MAS_1.json +++ b/datasets/CLAMS_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLAMS_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS_ER2_MAS data were collected during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment with the objective to combine long term ocean spectral surface observations with satellite and aircraft measurements to enhance our knowledge of ocean surface reflections and aerosols.The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. Data acquired by the MAS help to define, develop, and test algorithms for the Moderate Resolution Imaging Spectroradiometer (MODIS), a key sensor of NASA's Earth Observing System (EOS). The MODIS program emphasizes the use of remotely sensed data to monitor variation in environmental conditions for assessing both natural and human-induced global change.The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. Pre-1995 the digitizer was configured for each mission to record a pre-selected group of 12 bands during the flight. For most of these missions the digitizer was configured to record four 10-bit channels and seven 8-bit channels. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995.The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees.", "links": [ { diff --git a/datasets/CLAMS_MODIS_L2_AEROSOL_PRODUCTS_1.json b/datasets/CLAMS_MODIS_L2_AEROSOL_PRODUCTS_1.json index 8f34615f42..00fcc0f714 100644 --- a/datasets/CLAMS_MODIS_L2_AEROSOL_PRODUCTS_1.json +++ b/datasets/CLAMS_MODIS_L2_AEROSOL_PRODUCTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLAMS_MODIS_L2_AEROSOL_PRODUCTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLAMS_MODIS_L2_AEROSOL_PRODUCTS data were collected during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment.The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data products give the ambient aerosol optical thickness over the oceans and over a portion of the continents. The aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. Daily Level 2 data were produced at the spatial resolution of a 10??10 1-km (at nadir)-pixel array. The MODIS_CLAMS_L2_AEROSOL_PRODUCTS cover the time period from July 10, 2001 (Julian day 191) to August 2, 2001 (Julian day 214) over the spatial region nomally from -106 degree longitude to -42 degree longitude and from 57 degree N latitude to 18 degree N latitude.The MODIS instrument provides high radiometric sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 ??m to 14.4 ??m. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m and the remaining 29 bands at 1,000 m. A ??55-degree scanning pattern at the EOS orbit of 705 km achieves a 2,330-km swath and provides global coverage approximately every two days.", "links": [ { diff --git a/datasets/CLAMS_UWASH_CONVAIR_DATA_1.json b/datasets/CLAMS_UWASH_CONVAIR_DATA_1.json index 984d2ed1e8..303360b7fa 100644 --- a/datasets/CLAMS_UWASH_CONVAIR_DATA_1.json +++ b/datasets/CLAMS_UWASH_CONVAIR_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLAMS_UWASH_CONVAIR_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Un. Washington Convair-580 Aerosol, radiation, chemical, and meteorological data products for the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign in ASCII format", "links": [ { diff --git a/datasets/CLDCR_L2_VIIRS_SNPP_1.json b/datasets/CLDCR_L2_VIIRS_SNPP_1.json index fca20f7bfa..89991421b4 100644 --- a/datasets/CLDCR_L2_VIIRS_SNPP_1.json +++ b/datasets/CLDCR_L2_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDCR_L2_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/Suomi-NPP Cirrus Reflectance 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. An indication of shadows affecting the scene is also provided. Radiometrically-accurate radiances are required, thus holes in the Cloud Mask will appear wherever the input radiances are incomplete or of poor quality assurance.\r\n\r\nFor more information consult Product Page at: \r\nhttps://cimss.ssec.wisc.edu/MVCM/", "links": [ { diff --git a/datasets/CLDMSK_L2_MODIS_Aqua_1.json b/datasets/CLDMSK_L2_MODIS_Aqua_1.json index b6b82805b3..66927411ed 100644 --- a/datasets/CLDMSK_L2_MODIS_Aqua_1.json +++ b/datasets/CLDMSK_L2_MODIS_Aqua_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_MODIS_Aqua_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS-VIIRS Cloud Mask (MVCM) is designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS MVCM product does not use an algorithm identical to that used in the standard MODIS product (MOD35/MYD35). The MVCM-MODIS Cloud Mask product is Aqua MOIDS Level-2, 5-Min Swath product generated at 1000 m (at nadir) spatial resolution. The algorithm employs a series of visible through infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. Radiometrically-accurate radiances are required, thus holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality.\r\n\r\nFor more information consult Product Page at: \r\nhttps://cimss.ssec.wisc.edu/MVCM/", "links": [ { diff --git a/datasets/CLDMSK_L2_VIIRS_NOAA20_1.json b/datasets/CLDMSK_L2_VIIRS_NOAA20_1.json index cb1b26bdfd..03cd7e6701 100644 --- a/datasets/CLDMSK_L2_VIIRS_NOAA20_1.json +++ b/datasets/CLDMSK_L2_VIIRS_NOAA20_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_VIIRS_NOAA20_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m product Mask is one of three continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA20 is the shortname for the NOAA-20 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product\u2019s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the NOAA-20 VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the NOAA-20 VIIRS L2 Cloud mask is 750 meters.\r\n\r\nFor more information consult Product Page at: \r\nhttps://cimss.ssec.wisc.edu/MVCM/", "links": [ { diff --git a/datasets/CLDMSK_L2_VIIRS_NOAA20_NRT_1.json b/datasets/CLDMSK_L2_VIIRS_NOAA20_NRT_1.json index 8e89dc1f46..f151f71a70 100644 --- a/datasets/CLDMSK_L2_VIIRS_NOAA20_NRT_1.json +++ b/datasets/CLDMSK_L2_VIIRS_NOAA20_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_VIIRS_NOAA20_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA Level-2 (L2) Cloud Mask is one of two continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA20_NRT is the shortname for the NOAA-20 VIIRS Near Real-time incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from SNPP and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product\u2019s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the NOAA-20 VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the NOAA-20 VIIRS L2 Cloud mask is 750 meters.", "links": [ { diff --git a/datasets/CLDMSK_L2_VIIRS_NOAA21_1.json b/datasets/CLDMSK_L2_VIIRS_NOAA21_1.json index 513c35bc17..94a84ba954 100644 --- a/datasets/CLDMSK_L2_VIIRS_NOAA21_1.json +++ b/datasets/CLDMSK_L2_VIIRS_NOAA21_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_VIIRS_NOAA21_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Oceanic and Atmospheric Administration (NOAA-21) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA Level-2 (L2) Cloud Mask (VIIRS/NOAA21 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m) product is one of four continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA21 is the short-name for the NOAA-21 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. The MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product\u2019s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the NOAA-21 VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the NOAA-21 VIIRS L2 Cloud mask is 750 meters. The L2 netCDF product is acquired and processed every 6 minutes.\r\n\r\nFor more information consult Product Page at: \r\nhttps://cimss.ssec.wisc.edu/MVCM/", "links": [ { diff --git a/datasets/CLDMSK_L2_VIIRS_SNPP_1.json b/datasets/CLDMSK_L2_VIIRS_SNPP_1.json index f11df99f57..da6f350ccf 100644 --- a/datasets/CLDMSK_L2_VIIRS_SNPP_1.json +++ b/datasets/CLDMSK_L2_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/Suomi-NPP Cloud Mask 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. An indication of shadows affecting the scene is also provided. Radiometrically-accurate radiances are required, thus holes in the Cloud Mask will appear wherever the input radiances are incomplete or of poor quality assurance.\r\n\r\nFor more information consult Product Page at: \r\nhttps://cimss.ssec.wisc.edu/MVCM/", "links": [ { diff --git a/datasets/CLDMSK_L2_VIIRS_SNPP_NRT_1.json b/datasets/CLDMSK_L2_VIIRS_SNPP_NRT_1.json index 95aa00422a..c9fbd19fb2 100644 --- a/datasets/CLDMSK_L2_VIIRS_SNPP_NRT_1.json +++ b/datasets/CLDMSK_L2_VIIRS_SNPP_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDMSK_L2_VIIRS_SNPP_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA Level-2 (L2) Cloud Mask is one of two continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_SNPP is the shortname for the SNPP VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from SNPP and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product\u2019s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the SNPP VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the SNPP VIIRS L2 Cloud mask is 750 meters.", "links": [ { diff --git a/datasets/CLDPROPCOSP_D3_MODIS_Aqua_1.1.json b/datasets/CLDPROPCOSP_D3_MODIS_Aqua_1.1.json index 796c9c2722..8fca86a1bd 100644 --- a/datasets/CLDPROPCOSP_D3_MODIS_Aqua_1.1.json +++ b/datasets/CLDPROPCOSP_D3_MODIS_Aqua_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_D3_MODIS_Aqua_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. \nProvided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from July 4, 2002 and includes 365 granules each calendar year.", "links": [ { diff --git a/datasets/CLDPROPCOSP_D3_VIIRS_NOAA20_1.1.json b/datasets/CLDPROPCOSP_D3_VIIRS_NOAA20_1.1.json index c32105e4b7..5462408663 100644 --- a/datasets/CLDPROPCOSP_D3_VIIRS_NOAA20_1.1.json +++ b/datasets/CLDPROPCOSP_D3_VIIRS_NOAA20_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_D3_VIIRS_NOAA20_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_NOAA20. It contains VIIRS NOAA-20 cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from February 17, 2018 and includes 365 granules each calendar year.", "links": [ { diff --git a/datasets/CLDPROPCOSP_D3_VIIRS_SNPP_1.1.json b/datasets/CLDPROPCOSP_D3_VIIRS_SNPP_1.1.json index 8ab7935d29..b56598c2bb 100644 --- a/datasets/CLDPROPCOSP_D3_VIIRS_SNPP_1.1.json +++ b/datasets/CLDPROPCOSP_D3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_D3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. \nProvided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2012 and includes 365 granules each calendar year.", "links": [ { diff --git a/datasets/CLDPROPCOSP_M3_MODIS_Aqua_1.1.json b/datasets/CLDPROPCOSP_M3_MODIS_Aqua_1.1.json index 6d3fb8c995..9d4dc370dd 100644 --- a/datasets/CLDPROPCOSP_M3_MODIS_Aqua_1.1.json +++ b/datasets/CLDPROPCOSP_M3_MODIS_Aqua_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_M3_MODIS_Aqua_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. \nProvided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from August 1, 2002 and includes 12 granules each calendar year.", "links": [ { diff --git a/datasets/CLDPROPCOSP_M3_VIIRS_NOAA20_1.1.json b/datasets/CLDPROPCOSP_M3_VIIRS_NOAA20_1.1.json index 723b74b632..45bfebad24 100644 --- a/datasets/CLDPROPCOSP_M3_VIIRS_NOAA20_1.1.json +++ b/datasets/CLDPROPCOSP_M3_VIIRS_NOAA20_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_M3_VIIRS_NOAA20_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_NOAA20. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. \nProvided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 monthly statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2018 and includes 12 granules each calendar year. ", "links": [ { diff --git a/datasets/CLDPROPCOSP_M3_VIIRS_SNPP_1.1.json b/datasets/CLDPROPCOSP_M3_VIIRS_SNPP_1.1.json index fd7d6169f1..e7819f5493 100644 --- a/datasets/CLDPROPCOSP_M3_VIIRS_SNPP_1.1.json +++ b/datasets/CLDPROPCOSP_M3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROPCOSP_M3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The \u201cCOSP\u201d acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. \nProvided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters).\n\nConsult the CLDPROPCOSP User Guide for details regarding how the L3 monthly statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2012 and includes 12 granules each calendar year.", "links": [ { diff --git a/datasets/CLDPROP_D3_MODIS_Aqua_1.1.json b/datasets/CLDPROP_D3_MODIS_Aqua_1.1.json index 8724ed0b29..f1ceed9977 100644 --- a/datasets/CLDPROP_D3_MODIS_Aqua_1.1.json +++ b/datasets/CLDPROP_D3_MODIS_Aqua_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_D3_MODIS_Aqua_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Properties Level-3 gridded product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.", "links": [ { diff --git a/datasets/CLDPROP_D3_VIIRS_NOAA20_1.1.json b/datasets/CLDPROP_D3_VIIRS_NOAA20_1.1.json index 2390f2b152..256f7f4b5f 100644 --- a/datasets/CLDPROP_D3_VIIRS_NOAA20_1.1.json +++ b/datasets/CLDPROP_D3_VIIRS_NOAA20_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_D3_VIIRS_NOAA20_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Properties Level 3 daily, 1x1 degree grid product, shortname CLDPROP_D3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. The Cloud Properties in this product includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters. This product ensures continuity of approach through a common algorithm that is applicable to both MODIS and VIIRS data by leveraging only those spectral channels that are common to both instruments.\r\n\r\nFor more information, visit product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_D3_VIIRS_NOAA20", "links": [ { diff --git a/datasets/CLDPROP_D3_VIIRS_SNPP_1.1.json b/datasets/CLDPROP_D3_VIIRS_SNPP_1.1.json index fa34315e00..f6220ce890 100644 --- a/datasets/CLDPROP_D3_VIIRS_SNPP_1.1.json +++ b/datasets/CLDPROP_D3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_D3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Cloud Properties Level 3 daily, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.", "links": [ { diff --git a/datasets/CLDPROP_L2_MODIS_Aqua_1.1.json b/datasets/CLDPROP_L2_MODIS_Aqua_1.1.json index 1777d0bbdd..57e1946fb5 100644 --- a/datasets/CLDPROP_L2_MODIS_Aqua_1.1.json +++ b/datasets/CLDPROP_L2_MODIS_Aqua_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_L2_MODIS_Aqua_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Cloud Properties 5-min L2 Swath 1km product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS Cloud Properties product does not use algorithms identical to those used in the standard MODIS product (MOD06/MYD06). The product consists of cloud optical and physical parameters derived using observations in visible through infrared spectral channels. MODIS infrared channels that are common with VIIRS are primarily used to derive cloud-top temperature, cloud-top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. The MODIS solar reflectances channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The MODIS Cloud Properties product is a Level-2 product generated at 1 km (at nadir) spatial resolution.\r\n\r\nThe current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties\u2019 thermodynamic phase that caused erroneous liquid water cloud phase results.", "links": [ { diff --git a/datasets/CLDPROP_L2_VIIRS_NOAA20_1.1.json b/datasets/CLDPROP_L2_VIIRS_NOAA20_1.1.json index a0f6914ab4..e1501c3b09 100644 --- a/datasets/CLDPROP_L2_VIIRS_NOAA20_1.1.json +++ b/datasets/CLDPROP_L2_VIIRS_NOAA20_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_L2_VIIRS_NOAA20_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Properties 6-min L2 Swath 750m product is a continuity product similar to its counterpart product from the Suomi National Polar-orbiting Partnership (SNPP) VIIRS. Judiciously leveraging a common set of spectral channels, they help sustain the long-term records of both MODIS and VIIRS heritages. A commonly applicable algorithm to both MODIS and VIIRS inputs is the hallmark of this continuity approach. CLDPROP_L2_VIIRS_NOAA20 is the shortname for the NOAA20 VIIRS incarnation of the orbital swath-based Cloud Properties continuity product.\r\n\r\nFor more information, visit product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_L2_VIIRS_NOAA20", "links": [ { diff --git a/datasets/CLDPROP_L2_VIIRS_SNPP_1.1.json b/datasets/CLDPROP_L2_VIIRS_SNPP_1.1.json index 70c6b2a3f7..d923ec24d4 100644 --- a/datasets/CLDPROP_L2_VIIRS_SNPP_1.1.json +++ b/datasets/CLDPROP_L2_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_L2_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Cloud Properties 6-min L2 Swath 750m product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. The VIIRS Cloud Properties product consists of cloud optical and physical parameters. These parameters are derived using observations in visible through infrared spectral channels. VIIRS infrared channel radiances are primarily used to derive cloud top temperature, cloud top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque) and cloud fraction under both daytime and nighttime conditions. The VIIRS solar reflectance channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The VIIRS Cloud Properties product is a Level-2 product generated at 750 m (at nadir) spatial resolution.\r\n\r\nThe current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties\u2019 thermodynamic phase that caused erroneous liquid water cloud phase results.", "links": [ { diff --git a/datasets/CLDPROP_M3_MODIS_Aqua_1.1.json b/datasets/CLDPROP_M3_MODIS_Aqua_1.1.json index 8ce640bfbe..b45f2dd16b 100644 --- a/datasets/CLDPROP_M3_MODIS_Aqua_1.1.json +++ b/datasets/CLDPROP_M3_MODIS_Aqua_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_M3_MODIS_Aqua_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Properties Level-3 gridded product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.", "links": [ { diff --git a/datasets/CLDPROP_M3_VIIRS_NOAA20_1.1.json b/datasets/CLDPROP_M3_VIIRS_NOAA20_1.1.json index d4d2c42ae6..ad8a5fd034 100644 --- a/datasets/CLDPROP_M3_VIIRS_NOAA20_1.1.json +++ b/datasets/CLDPROP_M3_VIIRS_NOAA20_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_M3_VIIRS_NOAA20_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 Cloud Properties Level 3 monthly, 1x1 degree grid product, shortname CLDPROP_M3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDPROP is used to represent Cloud Properties, which includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters. This product ensures continuity of approach through a common algorithm that is applicable to both MODIS and VIIRS data by leveraging only those spectral channels that are common to both instruments.\r\n\r\nFor more information, visit product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_M3_VIIRS_NOAA20", "links": [ { diff --git a/datasets/CLDPROP_M3_VIIRS_SNPP_1.1.json b/datasets/CLDPROP_M3_VIIRS_SNPP_1.1.json index 685cd9c5cf..680e28117e 100644 --- a/datasets/CLDPROP_M3_VIIRS_SNPP_1.1.json +++ b/datasets/CLDPROP_M3_VIIRS_SNPP_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLDPROP_M3_VIIRS_SNPP_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Cloud Properties Level 3 monthly, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.", "links": [ { diff --git a/datasets/CLIMATE_IMAGE_ATLAS.json b/datasets/CLIMATE_IMAGE_ATLAS.json index 6866cc339e..65138fc7b6 100644 --- a/datasets/CLIMATE_IMAGE_ATLAS.json +++ b/datasets/CLIMATE_IMAGE_ATLAS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLIMATE_IMAGE_ATLAS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Color-shaded and contoured images of global gridded instrumental data\n have been produced as a computer-based atlas and is available via ftp\n and as a CD-ROM. The data consists of images depicting anomaly maps of\n surface temperature, sea level pressure, 500-mb geopotential heights,\n and percentages of reference period precipitation. Monthly, seasonal,\n and annual composites are available, in either cylindrical,\n equidistant, or northern and southern hemisphere polar\n projections. Temperature maps are from 1854 to 1991, precipitation\n maps from 1851 to 1989, sea level pressure maps from 1899 to 1991 and\n 500 mb height maps from 1946 to 1991. Documentation is available as\n README files at the FTP site and on the CD-ROM (Bradley, et al. 1994).\n \n The data consists of the following:\n \n Temperature Data\n ----------------\n The temperature data are distributed on a 5 degree latitude by 5\n degree longitude grid with 2592 (36 by 72) points in the grid. The\n data are in the form of monthly, seasonal, and annual anomalies to\n 0.01 degrees C, expressed as departures from a 1950-1979 reference\n period mean. The data are derived from the following:\n \n 1) land-based monthly station surface air temperatures from January 1854\n through December 1991 (Jones, et al. 1991).\n \n 2) the Comprehensive Ocean-Atmosphere Data Set (COADS) gridded (2 lat by 2\n long) monthly sea surface temperatures from January 1854 through December 1986\n (Woddruff et al. 1987).\n \n 3) the United Kingdom Meteorological Office (UKMO) gridded (1 lat by 1 long)\n monthly sea surface temperature dataset with data from January 1987\n through December 1991 (Bottomley et al. 1990).\n \n Precipitation Data\n ------------------\n The precipitation data are distributed on a 4 lat by 5 long grid. There are\n 2736 (38 by 72) points in the grid. The data are in the form of seasonal and\n annual percentages of the reference period (1951-1970) mean precipitation\n interpolated onto the grid. The orginal source is monthly station precipitation\n records (1851-1989) from Eischeid et al. (1991).\n \n Sea Level Pressure Data\n -----------------------\n The sea level pressure data are distributed on a 5 lat by 5 long grid. There\n are 2520 (35 by 72) points in the grid. The data are in the form of monthly,\n seasonal, and annual anomalies to 0.1 mb. The anomalies are calculated as\n departures from a 1951-1980 reference period mean for the Northern Hemisphere\n and a 1974-1989 reference period mean for the Southern Hemisphere. There is no\n sea level pressure data between 15 North and 10 South. The original source is\n from NCAR (Jenne 1975) for the periods 1899-1991 and 1973-1989.\n \n 500 mb Geopotential Height Data\n -------------------------------\n The 500-mb height data are distributed on a 5 lat by 5 long grid. There are\n 2520 (35 by 72) points in the grid. The data are in the form of monthly,\n seasonal, and annual anomalies to 1 m. The anomalies are calculated as\n departures from a 1951-1980 reference period mean for the Northern Hemisphere\n and a 1974-1989 reference period mean for the Southern Hemisphere. There are no\n height data between 15 North and 10 South. The original source of the data are\n as follows:\n \n 1) National Meteorological Center (NMC) Northern Hemisphere octagonal grid data\n (Jenne 1975) from a compact disc (CD-ROM) produced jointly by the University of\n Washington and NCAR. The data is from January 1946 through June 1989.\n \n 2) files of Northern Hemisphere and Southern Hemisphere gridded 500-mb heights\n (5 lat by 5 long) from NCAR. The files contain data from April 1973 through\n December 1991.\n \n All of the data described have been produced as Graphic Interchange\n Format (GIF) image files (1024 x 822 pixels, 256 color). Shareware for\n viewing the GIF images (PC, MAC or X-window workstations) are also\n available at the FTP site. All of the maps were produced using NCAR\n Graphics Version 3.00.\n \n The Atlas is also available as a CD-ROM. The CD-ROM contains the image\n and documentation files and shareware for viewing the GIF\n images. Software is for PC, MAC or X-Window workstations. The CD-ROM\n is available from Frank Keimig, Department of Geology and Geography,\n University of Massachussetts, Amherst, MA 01375 (email:\n frank@geo.umass.edu).\n \n More information is available from:\n \"http://cdiac.esd.ornl.gov/ndps/db1003.html\"\n \n NOTE:\n The Eischied, et al. precipitation data set is available at:\n \"http://cdiac.esd.ornl.gov/ndps/tr051.html\"", "links": [ { diff --git a/datasets/CLIVAR_0.json b/datasets/CLIVAR_0.json index 55b564518d..1a232bda8a 100644 --- a/datasets/CLIVAR_0.json +++ b/datasets/CLIVAR_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLIVAR_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Climate Variability and Predictability (CLIVAR)", "links": [ { diff --git a/datasets/CLIVAR_Chlorophyll_1.json b/datasets/CLIVAR_Chlorophyll_1.json index ed9690220e..cb454897c4 100644 --- a/datasets/CLIVAR_Chlorophyll_1.json +++ b/datasets/CLIVAR_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CLIVAR_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll a data collected on the CLIVAR (Climate Variability) cruise of the Aurora Australis in the 2001-2002 season. Data were collected from October to December of 2001 along the CLIVAR transect.\n\nThese data were collected as part of ASAC project 40 (ASAC_40) - The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms.", "links": [ { diff --git a/datasets/CMAQ-N_Module_1661_1.json b/datasets/CMAQ-N_Module_1661_1.json index 394e4ac35b..ddc6769350 100644 --- a/datasets/CMAQ-N_Module_1661_1.json +++ b/datasets/CMAQ-N_Module_1661_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMAQ-N_Module_1661_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product provides source code, input data files, and example model outputs for a new mechanistic soil nitrogen (N) module in-line with the Community Multiscale Air Quality (CMAQ) model 5.1 to simulate nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O), and ammonia (NH3) soil emissions. The modeling domain covers the continental USA plus portions of northern Mexico and southern Canada, extending from 25 degrees north to 52 degrees north.The simulations use a 12-km spatial grid resolution. Input data are from high-quality reference sources for year 2011. Example model output data are provided for one day, April 21, 2011.", "links": [ { diff --git a/datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json b/datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json index 27cf32abeb..f2e5e5f67f 100644 --- a/datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json +++ b/datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMC0.1deg-CMC-L4-GLOB-v3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the Advanced Very High Resolution Radiometer (AVHRR) from NOAA-18,19, the European Meteorological Operational-A (METOP-A) and Operational-B (METOP-B), and microwave data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json b/datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json index 082a56b8aa..af897993ea 100644 --- a/datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json +++ b/datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMC0.2deg-CMC-L4-GLOB-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/CMRMIAAE_2.json b/datasets/CMRMIAAE_2.json index 7d7ef28041..5ab30ac4c6 100644 --- a/datasets/CMRMIAAE_2.json +++ b/datasets/CMRMIAAE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMRMIAAE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 Aerosol Data containing aerosol optical depth, ancillary meteorological data, and related parameters on a 17.6 km grid for the CMARE_2004 theme.", "links": [ { diff --git a/datasets/CMRMIALS_2.json b/datasets/CMRMIALS_2.json index b2524e04ce..450f4dcca1 100644 --- a/datasets/CMRMIALS_2.json +++ b/datasets/CMRMIALS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMRMIALS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 Land Surface Data containing albedo and BRF data for the CMARE_2004 theme", "links": [ { diff --git a/datasets/CMRMIGEO_2.json b/datasets/CMRMIGEO_2.json index 0a1ab823dc..07c1d30c1e 100644 --- a/datasets/CMRMIGEO_2.json +++ b/datasets/CMRMIGEO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMRMIGEO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Geometric Parameters containing the geometric parameters which measure the sun and view angles at the reference ellipsoid for the CMARE_2004 theme", "links": [ { diff --git a/datasets/CMRMITST_2.json b/datasets/CMRMITST_2.json index d1c93d22b9..5553bc2538 100644 --- a/datasets/CMRMITST_2.json +++ b/datasets/CMRMITST_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMRMITST_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 TOA/Cloud Stereo Data containing stereoscopically-derived cloud mask and cloud height, and reflecting level reference altitude for the CMARE_2004 theme", "links": [ { diff --git a/datasets/CMSFluxFire_1.json b/datasets/CMSFluxFire_1.json index e024776bb4..f238224bd6 100644 --- a/datasets/CMSFluxFire_1.json +++ b/datasets/CMSFluxFire_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFire_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Fires.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxFire_2.json b/datasets/CMSFluxFire_2.json index 061ab15c89..2c14f939e1 100644 --- a/datasets/CMSFluxFire_2.json +++ b/datasets/CMSFluxFire_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFire_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Fires.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxFirepost_1.json b/datasets/CMSFluxFirepost_1.json index 021ace8c30..b58e4e5bf5 100644 --- a/datasets/CMSFluxFirepost_1.json +++ b/datasets/CMSFluxFirepost_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFirepost_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Fires.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxFossilFuelPrior_2.json b/datasets/CMSFluxFossilFuelPrior_2.json index 86233b7b64..8c425f06cb 100644 --- a/datasets/CMSFluxFossilFuelPrior_2.json +++ b/datasets/CMSFluxFossilFuelPrior_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFossilFuelPrior_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Fossil Fuel Prior.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxFossilFuelPrior_3.json b/datasets/CMSFluxFossilFuelPrior_3.json index 43213310b6..65b5c1ddc3 100644 --- a/datasets/CMSFluxFossilFuelPrior_3.json +++ b/datasets/CMSFluxFossilFuelPrior_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFossilFuelPrior_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Prior for the Fossil Fuel Carbon Flux.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxFossilfuel_1.json b/datasets/CMSFluxFossilfuel_1.json index 447a2027b5..89c0c4d0b5 100644 --- a/datasets/CMSFluxFossilfuel_1.json +++ b/datasets/CMSFluxFossilfuel_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxFossilfuel_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Fossil Fuel.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxLandPrior_3.json b/datasets/CMSFluxLandPrior_3.json index 394759b5d9..6e9ed7c12b 100644 --- a/datasets/CMSFluxLandPrior_3.json +++ b/datasets/CMSFluxLandPrior_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxLandPrior_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Prior for the Land Carbon Flux.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxMISC_1.json b/datasets/CMSFluxMISC_1.json index e7666d8095..5cea85c1a8 100644 --- a/datasets/CMSFluxMISC_1.json +++ b/datasets/CMSFluxMISC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxMISC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxNBEPrior_2.json b/datasets/CMSFluxNBEPrior_2.json index 65d08df0b3..5242d3ef28 100644 --- a/datasets/CMSFluxNBEPrior_2.json +++ b/datasets/CMSFluxNBEPrior_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxNBEPrior_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux from the Net Biome Exchange Prior.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxNBE_2.json b/datasets/CMSFluxNBE_2.json index 7e948142ee..d409c318b7 100644 --- a/datasets/CMSFluxNBE_2.json +++ b/datasets/CMSFluxNBE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxNBE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux from the Net Biome Exchange.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxNBE_3.json b/datasets/CMSFluxNBE_3.json index 725d939e80..9181c11efd 100644 --- a/datasets/CMSFluxNBE_3.json +++ b/datasets/CMSFluxNBE_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxNBE_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Posterior Net Biome Exchange (NBE).\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxNEE_1.json b/datasets/CMSFluxNEE_1.json index cc0662c644..5e2657f013 100644 --- a/datasets/CMSFluxNEE_1.json +++ b/datasets/CMSFluxNEE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxNEE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux from the Net Ecosystem Exchange.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxOceanPrior_2.json b/datasets/CMSFluxOceanPrior_2.json index 59200b3599..60ec4c9270 100644 --- a/datasets/CMSFluxOceanPrior_2.json +++ b/datasets/CMSFluxOceanPrior_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxOceanPrior_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Ocean Carbon Prior.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxOceanPrior_3.json b/datasets/CMSFluxOceanPrior_3.json index d2e7bc9b05..b8e383c11f 100644 --- a/datasets/CMSFluxOceanPrior_3.json +++ b/datasets/CMSFluxOceanPrior_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxOceanPrior_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Prior for the Carbon Flux for Ocean.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxOcean_1.json b/datasets/CMSFluxOcean_1.json index 49c030490f..859fb9c3e7 100644 --- a/datasets/CMSFluxOcean_1.json +++ b/datasets/CMSFluxOcean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxOcean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Ocean Carbon.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxOcean_3.json b/datasets/CMSFluxOcean_3.json index a0f364506f..4f76409769 100644 --- a/datasets/CMSFluxOcean_3.json +++ b/datasets/CMSFluxOcean_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxOcean_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Posterior Carbon Flux for the Ocean.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxTotalPrior_3.json b/datasets/CMSFluxTotalPrior_3.json index 9de7fff660..026538edf7 100644 --- a/datasets/CMSFluxTotalPrior_3.json +++ b/datasets/CMSFluxTotalPrior_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxTotalPrior_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Prior for Total Carbon Flux.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxTotal_2.json b/datasets/CMSFluxTotal_2.json index f3a261f1f5..04703514bb 100644 --- a/datasets/CMSFluxTotal_2.json +++ b/datasets/CMSFluxTotal_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxTotal_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Posterior Total Carbon.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxTotal_3.json b/datasets/CMSFluxTotal_3.json index 5190702d0a..b332d12ddd 100644 --- a/datasets/CMSFluxTotal_3.json +++ b/datasets/CMSFluxTotal_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxTotal_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Posterior Total Carbon.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxTotalpost_1.json b/datasets/CMSFluxTotalpost_1.json index 736f6c86f1..ba64852c87 100644 --- a/datasets/CMSFluxTotalpost_1.json +++ b/datasets/CMSFluxTotalpost_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxTotalpost_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Posterior Total Carbon.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSFluxTotalprior_1.json b/datasets/CMSFluxTotalprior_1.json index edd9fc5306..66056b69ca 100644 --- a/datasets/CMSFluxTotalprior_1.json +++ b/datasets/CMSFluxTotalprior_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSFluxTotalprior_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Carbon Flux for Prior Total Carbon.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSGCH4F_1.json b/datasets/CMSGCH4F_1.json index bd6164edd8..713143635d 100644 --- a/datasets/CMSGCH4F_1.json +++ b/datasets/CMSGCH4F_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSGCH4F_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global methane fluxes optimized with GOSAT data for 2010-2018. It is supported by the Carbon Monitoring System project. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeHuronPPM_1.json b/datasets/CMSLakeHuronPPM_1.json index 57eed68dc7..bc68d1e57d 100644 --- a/datasets/CMSLakeHuronPPM_1.json +++ b/datasets/CMSLakeHuronPPM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeHuronPPM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeHuronPPY_1.json b/datasets/CMSLakeHuronPPY_1.json index b31faa8a9b..17e1e112db 100644 --- a/datasets/CMSLakeHuronPPY_1.json +++ b/datasets/CMSLakeHuronPPY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeHuronPPY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeMichiganPPM_1.json b/datasets/CMSLakeMichiganPPM_1.json index f10b5b6b26..13d216384e 100644 --- a/datasets/CMSLakeMichiganPPM_1.json +++ b/datasets/CMSLakeMichiganPPM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeMichiganPPM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeMichiganPPY_1.json b/datasets/CMSLakeMichiganPPY_1.json index 93bb2217fe..9368640264 100644 --- a/datasets/CMSLakeMichiganPPY_1.json +++ b/datasets/CMSLakeMichiganPPY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeMichiganPPY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeSuperiorPPM_1.json b/datasets/CMSLakeSuperiorPPM_1.json index bea62c10e3..91e237301b 100644 --- a/datasets/CMSLakeSuperiorPPM_1.json +++ b/datasets/CMSLakeSuperiorPPM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeSuperiorPPM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMSLakeSuperiorPPY_1.json b/datasets/CMSLakeSuperiorPPY_1.json index 775bf148a2..f48cb33816 100644 --- a/datasets/CMSLakeSuperiorPPY_1.json +++ b/datasets/CMSLakeSuperiorPPY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMSLakeSuperiorPPY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_AGB_Landcover_Indonesia_1645_1.json b/datasets/CMS_AGB_Landcover_Indonesia_1645_1.json index a6ab5648b3..7a03df1931 100644 --- a/datasets/CMS_AGB_Landcover_Indonesia_1645_1.json +++ b/datasets/CMS_AGB_Landcover_Indonesia_1645_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_AGB_Landcover_Indonesia_1645_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.", "links": [ { diff --git a/datasets/CMS_AGB_NW_USA_1719_1.json b/datasets/CMS_AGB_NW_USA_1719_1.json index 1f644c86a4..b8c574eaee 100644 --- a/datasets/CMS_AGB_NW_USA_1719_1.json +++ b/datasets/CMS_AGB_NW_USA_1719_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_AGB_NW_USA_1719_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.", "links": [ { diff --git a/datasets/CMS_CH4_FLX_CA_1.json b/datasets/CMS_CH4_FLX_CA_1.json index 3f9fd106ff..6be659ae81 100644 --- a/datasets/CMS_CH4_FLX_CA_1.json +++ b/datasets/CMS_CH4_FLX_CA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CH4_FLX_CA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. A related data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. The Canadian emissions are concentrated in Alberta (gas production and processing) and the Mexican emissions are concentrated along the east coast (oil production). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CH4_FLX_MX_1.json b/datasets/CMS_CH4_FLX_MX_1.json index 3b196c5368..f64e7abb64 100644 --- a/datasets/CMS_CH4_FLX_MX_1.json +++ b/datasets/CMS_CH4_FLX_MX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CH4_FLX_MX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. A related data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. The Mexican emissions are concentrated along the east coast (oil production) and the Canadian emissions are concentrated in Alberta (gas production and processing). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CH4_FLX_NAD_1.json b/datasets/CMS_CH4_FLX_NAD_1.json index 20a8dee738..135cd3a118 100644 --- a/datasets/CMS_CH4_FLX_NAD_1.json +++ b/datasets/CMS_CH4_FLX_NAD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CH4_FLX_NAD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors. The emission sources are separated into 12 different sectors as follows: Total, Oil/Gas, Coal, Cows, Waste (Landfills+ Wastewater), Biofuel, Rice, Other Anthropogenic, Biomass Burning, Wetlands, Soil Absorption, Other Natural. More details about the algorithm and error characterization can be found in Turner, Jacob, Wecht, et al. 2015.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CH4_FLX_NA_1.json b/datasets/CMS_CH4_FLX_NA_1.json index dcc902310e..1ac1add5d7 100644 --- a/datasets/CMS_CH4_FLX_NA_1.json +++ b/datasets/CMS_CH4_FLX_NA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CH4_FLX_NA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An error was found in this product; therefore, it has been deleted. Please use the CMS Methane (CH4) Flux for North America Daily product (CMS_CH4_FLX_NAD) in its place.\n\nThe CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations . The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors. The emission sources are separated into 9 different sectors as follows: Total, Wetlands, Livestock, Oil/Gas, Waste (Landfills wastewater), Coal, Rice, Open Fires, and Other. More details about the algorithm and error characterization can be found in (Turner, Jacob, Wecht, et al. 2015).\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CO2_Fluxes_TBMO_1315_1.json b/datasets/CMS_CO2_Fluxes_TBMO_1315_1.json index e81cea95f8..2a8d66d25f 100644 --- a/datasets/CMS_CO2_Fluxes_TBMO_1315_1.json +++ b/datasets/CMS_CO2_Fluxes_TBMO_1315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CO2_Fluxes_TBMO_1315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global, gridded, model-derived net ecosystem exchange (NEE) of CO2 flux between the land and atmosphere at 3-hourly time steps over seven years (2004-2010) at three different spatial resolutions: 0.5 x 0.5 degree, 2.0 x 2.5 degrees, and 4.0 x 5.0 degrees (latitude/longitude). The 3-hourly data were derived from monthly NEE outputs of 15 global land surface models and four ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).", "links": [ { diff --git a/datasets/CMS_CONUS_Biomass_1752_1.json b/datasets/CMS_CONUS_Biomass_1752_1.json index 4c771a90eb..4232749139 100644 --- a/datasets/CMS_CONUS_Biomass_1752_1.json +++ b/datasets/CMS_CONUS_Biomass_1752_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CONUS_Biomass_1752_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.", "links": [ { diff --git a/datasets/CMS_CTL_NA_GOSAT_FOOTPRINTS_1.json b/datasets/CMS_CTL_NA_GOSAT_FOOTPRINTS_1.json index 8cb7a8a7cd..fa6463005d 100644 --- a/datasets/CMS_CTL_NA_GOSAT_FOOTPRINTS_1.json +++ b/datasets/CMS_CTL_NA_GOSAT_FOOTPRINTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CTL_NA_GOSAT_FOOTPRINTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CTL_NA_OCO2_FOOTPRINTS_1.json b/datasets/CMS_CTL_NA_OCO2_FOOTPRINTS_1.json index 04248b43dd..291a0e3128 100644 --- a/datasets/CMS_CTL_NA_OCO2_FOOTPRINTS_1.json +++ b/datasets/CMS_CTL_NA_OCO2_FOOTPRINTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CTL_NA_OCO2_FOOTPRINTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CTL_NA_TCCON_FOOTPRINTS_1.json b/datasets/CMS_CTL_NA_TCCON_FOOTPRINTS_1.json index 987250c9f5..3fea1e9fee 100644 --- a/datasets/CMS_CTL_NA_TCCON_FOOTPRINTS_1.json +++ b/datasets/CMS_CTL_NA_TCCON_FOOTPRINTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CTL_NA_TCCON_FOOTPRINTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the TCCON ground network. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_CTL_SA_OCO2_FOOTPRINTS_1.json b/datasets/CMS_CTL_SA_OCO2_FOOTPRINTS_1.json index 55c2524345..e171465411 100644 --- a/datasets/CMS_CTL_SA_OCO2_FOOTPRINTS_1.json +++ b/datasets/CMS_CTL_SA_OCO2_FOOTPRINTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_CTL_SA_OCO2_FOOTPRINTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_Coastal_Wetland_Resilience_1839_1.json b/datasets/CMS_Coastal_Wetland_Resilience_1839_1.json index 48488f00b7..914a91a133 100644 --- a/datasets/CMS_Coastal_Wetland_Resilience_1839_1.json +++ b/datasets/CMS_Coastal_Wetland_Resilience_1839_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Coastal_Wetland_Resilience_1839_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides information about the resilience of tidal wetlands to sea-level rise under three scenarios of global change. With rising seas, regularly inundated tidal wetlands may persist by vertical accretion of sediments (vertical resilience) and/or by migrating inland (lateral resilience), but local and regional conditions constrain these options. This dataset provides a vertical resilience index (VR) for coastal wetlands at 30 m resolution across the continental US predicted for 2100. The VR index was computed for current sea levels, local tidal dynamics, and coastal topography. It was also calculated for future sea levels predicted for 2100 by three IPCC Realized Concentration Pathway (RCP) scenarios: 2.5, 4.5, and 8.5. Moreover, the VR index incorporates estimated rates of sediment accretion. Relevant to lateral resiliency, the data include current and future tidal areas identified by mapping mean higher high water spring tide locations under the RCP scenarios. A shapefile outlining watershed units with tidal wetlands is included along with land cover classes for these areas for 1996 and 2011.", "links": [ { diff --git a/datasets/CMS_DARTE_V2_1735_2.json b/datasets/CMS_DARTE_V2_1735_2.json index 27e95848a7..2d13843ac3 100644 --- a/datasets/CMS_DARTE_V2_1735_2.json +++ b/datasets/CMS_DARTE_V2_1735_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_DARTE_V2_1735_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.", "links": [ { diff --git a/datasets/CMS_Daily_ET_MexFlux_1309_1.json b/datasets/CMS_Daily_ET_MexFlux_1309_1.json index 56a79551ba..8f7d1143c2 100644 --- a/datasets/CMS_Daily_ET_MexFlux_1309_1.json +++ b/datasets/CMS_Daily_ET_MexFlux_1309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Daily_ET_MexFlux_1309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides daily average observations for evapotranspiration (measured and gap-filled), precipitation, net radiation, soil water content, air temperature, vapor pressure deficit, and normalized vegetation index (NDVI) from two water-limited shrubland sites for years 2008-2010. Both sites are located in the northwest part of Mexico and are part of the MexFlux network.", "links": [ { diff --git a/datasets/CMS_EFT_CONUS_1659_1.json b/datasets/CMS_EFT_CONUS_1659_1.json index 150e70d5de..d014059fdb 100644 --- a/datasets/CMS_EFT_CONUS_1659_1.json +++ b/datasets/CMS_EFT_CONUS_1659_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_EFT_CONUS_1659_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.", "links": [ { diff --git a/datasets/CMS_Fire_Weather_Data_AK_1509_1.json b/datasets/CMS_Fire_Weather_Data_AK_1509_1.json index 2de2b2905e..3d7f9206a2 100644 --- a/datasets/CMS_Fire_Weather_Data_AK_1509_1.json +++ b/datasets/CMS_Fire_Weather_Data_AK_1509_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Fire_Weather_Data_AK_1509_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.", "links": [ { diff --git a/datasets/CMS_FluxEstimates_Aircraft_CO2_2336_1.json b/datasets/CMS_FluxEstimates_Aircraft_CO2_2336_1.json index f150110edf..c152799546 100644 --- a/datasets/CMS_FluxEstimates_Aircraft_CO2_2336_1.json +++ b/datasets/CMS_FluxEstimates_Aircraft_CO2_2336_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_FluxEstimates_Aircraft_CO2_2336_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded surface-atmosphere CO2 fluxes over North America from April 8 to November 18 during 2018 and 2019. Net ecosystem exchange (NEE) was estimated by the CMS-Flux-NA CO2 inversion system by assimilating in situ CO2 measurements and/or Orbiting Carbon Observatory (OCO-2) column-averaged CO2 retrievals. These data, along with imposed diurnal NEE variations, fossil fuel emissions, biomass burning, and biofuel emissions, are provided at 3-hour temporal resolution. The modeled co-samples of CO2 observed for aircraft flights are included for model evaluation. The data are provided in NetCDF version 4 format.", "links": [ { diff --git a/datasets/CMS_Forest_Carbon_Fluxes_1313_1.json b/datasets/CMS_Forest_Carbon_Fluxes_1313_1.json index 7c6e58d68c..77cc7951b4 100644 --- a/datasets/CMS_Forest_Carbon_Fluxes_1313_1.json +++ b/datasets/CMS_Forest_Carbon_Fluxes_1313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Forest_Carbon_Fluxes_1313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides maps of estimated carbon in forests of the 48 continental states of the US for the years 2005-2010. Carbon (termed committed carbon) stocks were estimated for forest aboveground biomass, belowground biomass, standing dead stems, and litter for the year 2005. Carbon emissions were estimated from land use conversion to agriculture, insect damage, logging, wind, and weather events in the forests for the years 2006 - 2010. Committed net carbon flux was estimated as the sum of carbon emissions and sequestration. The maps are provided at 100-m spatial resolution in GeoTIFF format. Average annual carbon estimates, by US county, for (1) emissions for the multiple disturbance sources, (2) sequestration, and (3) the committed net carbon flux are provided in an ESRI shapefile.", "links": [ { diff --git a/datasets/CMS_Forest_Carbon_Maryland_1660_1.json b/datasets/CMS_Forest_Carbon_Maryland_1660_1.json index 9ab6b72c15..de22b19c40 100644 --- a/datasets/CMS_Forest_Carbon_Maryland_1660_1.json +++ b/datasets/CMS_Forest_Carbon_Maryland_1660_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Forest_Carbon_Maryland_1660_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the National Land Cover Database (NLCD), and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.", "links": [ { diff --git a/datasets/CMS_Forest_Productivity_1221_1.json b/datasets/CMS_Forest_Productivity_1221_1.json index 39324e621e..4c4e0b3362 100644 --- a/datasets/CMS_Forest_Productivity_1221_1.json +++ b/datasets/CMS_Forest_Productivity_1221_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Forest_Productivity_1221_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice: This data set and guide were updated on June 30, 2014 to correct an error in the reported units. The data values were not changed.Spatially-gridded estimates of above ground biomass (AGB), net primary productivity (NPP), and net ecosystem productivity (NEP) are provided for forested areas of the conterminous United States (CONUS). Estimates of uncertainty are also provided for AGB and NEP. These data were derived by using Forest Inventory and Analysis (FIA) data to constrain forest growth rates in a Carnegie-Ames-Stanford Approach (CASA) carbon-cycle process model. Note that the data set does not include data for forests in the Northern Prairie States region (NPS; see Figure 3). These data provide a detailed estimate of carbon sources and sinks from recent forest disturbance and recovery across regions and forest types of the US.The data are presented as a series of ten NetCDF v4 (*.nc4) files at two spatial scales (1-degree and 5-km spatial resolution) for the nominal year of 2005.", "links": [ { diff --git a/datasets/CMS_GO_CH4_SEC_TDYC_NA_1.json b/datasets/CMS_GO_CH4_SEC_TDYC_NA_1.json index d67b502bbf..232e62b5ae 100644 --- a/datasets/CMS_GO_CH4_SEC_TDYC_NA_1.json +++ b/datasets/CMS_GO_CH4_SEC_TDYC_NA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_GO_CH4_SEC_TDYC_NA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Methane emissions are provided by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), En- vironment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecolog\u00eda y Cambio Clim\u00e1tico (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM).\n", "links": [ { diff --git a/datasets/CMS_Global_Cropland_Carbon_1279_1.json b/datasets/CMS_Global_Cropland_Carbon_1279_1.json index b0467f5cf8..c519f3265e 100644 --- a/datasets/CMS_Global_Cropland_Carbon_1279_1.json +++ b/datasets/CMS_Global_Cropland_Carbon_1279_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Cropland_Carbon_1279_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global estimates of carbon fluxes associate with annual crop net primary production (NPP) and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange (NCE) derived from these carbon fluxes. NCE estimates are for the combined crop plant harvest and consumption/expiration of fodder by livestock and of food by humans. Estimation of carbon uptake and release from global agricultural production and consumption required compilation and analysis of inventory data from various sources for the years 2005-2011. The flux estimates were spatially distributed to a global 0.05-degree resolution grid using MODIS land cover data. The quantities of carbon flux in each gridcell are represented in two ways: (1) where the quantities of carbon distributed to each gridcell were divided by the total gridcell area, resulting in average carbon fluxes per unit of total area (g C/m2/yr), and (2), where annual carbon fluxes associated with a source were summed over all types for the gridcell (Mg C/yr). The total surface area of the grid cells is provided.There are eight data files in netCDF format (.nc4) with this data set -- two files (per area and per gridcell) for each of the four flux source types. Data for all years are in each *.nc4 file.", "links": [ { diff --git a/datasets/CMS_Global_Fire_Atlas_1642_1.json b/datasets/CMS_Global_Fire_Atlas_1642_1.json index a9fd006576..f3ca7d2602 100644 --- a/datasets/CMS_Global_Fire_Atlas_1642_1.json +++ b/datasets/CMS_Global_Fire_Atlas_1642_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Fire_Atlas_1642_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product. The algorithm identified 13.3 million individual fires (>=21 ha or 0.21 km2; the size of one MODIS pixel) over the 2003-2016 study period.", "links": [ { diff --git a/datasets/CMS_Global_Forest_AGC_2180_1.json b/datasets/CMS_Global_Forest_AGC_2180_1.json index 0ebc7858f2..e21fbf1e5b 100644 --- a/datasets/CMS_Global_Forest_AGC_2180_1.json +++ b/datasets/CMS_Global_Forest_AGC_2180_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Forest_AGC_2180_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions. A total of 3.77 billion lidar samples were used to generate gridded canopy height histograms that were then linked to ED simulations of canopy height and carbon dynamics during ecosystem succession. This process constrained representation of contemporary forest conditions and associated carbon stocks and fluxes in the model. Inputs that drove these simulations included meteorology, carbon dioxide levels, and soil properties. The data are provided in cloud-optimized GeoTIFF format.", "links": [ { diff --git a/datasets/CMS_Global_Forest_Age_2345_1.json b/datasets/CMS_Global_Forest_Age_2345_1.json index ca13f7e849..9654633af8 100644 --- a/datasets/CMS_Global_Forest_Age_2345_1.json +++ b/datasets/CMS_Global_Forest_Age_2345_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Forest_Age_2345_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.", "links": [ { diff --git a/datasets/CMS_Global_Livestock_CH4_CO2_1329_2.json b/datasets/CMS_Global_Livestock_CH4_CO2_1329_2.json index bbd9ee1d6f..481ff5ebd6 100644 --- a/datasets/CMS_Global_Livestock_CH4_CO2_1329_2.json +++ b/datasets/CMS_Global_Livestock_CH4_CO2_1329_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Livestock_CH4_CO2_1329_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global annual carbon flux estimates, at 0.05-degree resolution, associated with livestock feed intake, manure, manure management, respiration, and enteric fermentation, summed over all livestock types. These fluxes can be summed across multiple grid cells to obtain totals for any given areas. These 2000-2013 flux estimates were based on livestock populations reported by the Food and Agriculture Organization (FAO) and the United States Department of Agriculture National Agricultural Statistics Service (USDA NASS), on coefficients provided by the Intergovernmental Panel on Climate Change (IPCC), and on additional coefficients developed by the authors.", "links": [ { diff --git a/datasets/CMS_Global_Mangrove_Forest_Ht_2251_1.json b/datasets/CMS_Global_Mangrove_Forest_Ht_2251_1.json index 6ad4230478..76cbbc575c 100644 --- a/datasets/CMS_Global_Mangrove_Forest_Ht_2251_1.json +++ b/datasets/CMS_Global_Mangrove_Forest_Ht_2251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Mangrove_Forest_Ht_2251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models. Also provided are Lidar estimates of canopy height based on the GEDI instrument, which were used for training and validation of the TanDEM-X estimates of forest height. The coverage of these data follows Global Mangrove Watch's mangrove extent maps. These spatially explicit maps of mangrove canopy height can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (>60 m) that surpass maximum heights of other forest types. Maps are provided in cloud optimized GeoTIFF format, and mangrove heights for individual GEDI tiles are compiled in a comma separated values (CSV) files.", "links": [ { diff --git a/datasets/CMS_Global_Mangrove_Loss_1768_1.json b/datasets/CMS_Global_Mangrove_Loss_1768_1.json index b337263d5b..729be0cc1b 100644 --- a/datasets/CMS_Global_Mangrove_Loss_1768_1.json +++ b/datasets/CMS_Global_Mangrove_Loss_1768_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Mangrove_Loss_1768_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.", "links": [ { diff --git a/datasets/CMS_Global_Map_Mangrove_Canopy_1665_1.3.json b/datasets/CMS_Global_Map_Mangrove_Canopy_1665_1.3.json index af03045e8a..75be852e0e 100644 --- a/datasets/CMS_Global_Map_Mangrove_Canopy_1665_1.3.json +++ b/datasets/CMS_Global_Map_Mangrove_Canopy_1665_1.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Map_Mangrove_Canopy_1665_1.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.", "links": [ { diff --git a/datasets/CMS_Global_Monthly_Wetland_CH4_1502_1.json b/datasets/CMS_Global_Monthly_Wetland_CH4_1502_1.json index aa49fc5a8e..399b6387f2 100644 --- a/datasets/CMS_Global_Monthly_Wetland_CH4_1502_1.json +++ b/datasets/CMS_Global_Monthly_Wetland_CH4_1502_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Monthly_Wetland_CH4_1502_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution. Two model output data products are included in WetCHARTs v1.0: an output from the full ensemble for 2009-2010 and an output from a limited subset for 2001-2015. The intended use of the products is as a process-informed wetland CH4 emission and uncertainty data set for atmospheric chemistry and transport modelling (WetCHARTs).", "links": [ { diff --git a/datasets/CMS_Global_Soil_Respiration_1736_1.json b/datasets/CMS_Global_Soil_Respiration_1736_1.json index 3852620e72..86bbcc2567 100644 --- a/datasets/CMS_Global_Soil_Respiration_1736_1.json +++ b/datasets/CMS_Global_Soil_Respiration_1736_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Global_Soil_Respiration_1736_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.", "links": [ { diff --git a/datasets/CMS_Great_Basin_Biomass_1755_1.json b/datasets/CMS_Great_Basin_Biomass_1755_1.json index 0a2e11a273..e9ed15bba7 100644 --- a/datasets/CMS_Great_Basin_Biomass_1755_1.json +++ b/datasets/CMS_Great_Basin_Biomass_1755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Great_Basin_Biomass_1755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map. The estimates of biomass were based on a linear relationship with pinyon-juniper canopy cover and crown-based allometrics developed from field data in Nevada and Idaho. Canopy cover was estimated from remote sensing by using annual composites of Landsat imagery, which were temporally segmented with the LandTrendr algorithm, along with biologically-relevant climate variables, and topographic indices in a Random Forest regression model. Models of canopy cover were trained from semi-automatic extraction of tree crowns from 2011 - 2013 high resolution imagery (1 m) from the National Agriculture Imagery Program, which were validated with photo interpretation. Maps of the standard deviation of biomass estimates from decision trees in the Random Forest model are provided as an indicator of uncertainty. Biomass estimates were calibrated to estimates from the Forest Inventory and Analysis program (FIA) on an annual basis and corrections applied.", "links": [ { diff --git a/datasets/CMS_HR_MNA_CH4_FLUX_1.json b/datasets/CMS_HR_MNA_CH4_FLUX_1.json index 24ff3fc3e3..bb52a48906 100644 --- a/datasets/CMS_HR_MNA_CH4_FLUX_1.json +++ b/datasets/CMS_HR_MNA_CH4_FLUX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_HR_MNA_CH4_FLUX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_Landcover_Indonesia_1838_1.json b/datasets/CMS_Landcover_Indonesia_1838_1.json index d9d533e35e..4399554bb3 100644 --- a/datasets/CMS_Landcover_Indonesia_1838_1.json +++ b/datasets/CMS_Landcover_Indonesia_1838_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Landcover_Indonesia_1838_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.", "links": [ { diff --git a/datasets/CMS_Landscapes_Brazil_Forests_1301_1.json b/datasets/CMS_Landscapes_Brazil_Forests_1301_1.json index dd07d87de0..9eaf26908b 100644 --- a/datasets/CMS_Landscapes_Brazil_Forests_1301_1.json +++ b/datasets/CMS_Landscapes_Brazil_Forests_1301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Landscapes_Brazil_Forests_1301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status. These biophysical measurements were made at Fazenda Cauaxi during 2012 and 2014 and at the Fazenda Nova Neonita during 2013.", "links": [ { diff --git a/datasets/CMS_Landscapes_Brazil_LiDAR_1302_1.json b/datasets/CMS_Landscapes_Brazil_LiDAR_1302_1.json index ef471ffbd7..3ebb8e5696 100644 --- a/datasets/CMS_Landscapes_Brazil_LiDAR_1302_1.json +++ b/datasets/CMS_Landscapes_Brazil_LiDAR_1302_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Landscapes_Brazil_LiDAR_1302_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014. Shapefiles showing the LiDAR/DTM coverage areas are also provided for each of the areas.", "links": [ { diff --git a/datasets/CMS_LiDAR_AGB_California_1537_1.json b/datasets/CMS_LiDAR_AGB_California_1537_1.json index 33ac191a69..76b153dd0e 100644 --- a/datasets/CMS_LiDAR_AGB_California_1537_1.json +++ b/datasets/CMS_LiDAR_AGB_California_1537_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_AGB_California_1537_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized \"remote sensing aware\" allometric equation to these individual trees. Aboveground biomass estimates and their uncertainties for each study area are provided in per-tree and gridded format. The canopy height models used for the tree detection and biomass estimation are also provided.", "links": [ { diff --git a/datasets/CMS_LiDAR_AGB_PEF_2012_1318_1.json b/datasets/CMS_LiDAR_AGB_PEF_2012_1318_1.json index a5da2ae081..25a4fe6835 100644 --- a/datasets/CMS_LiDAR_AGB_PEF_2012_1318_1.json +++ b/datasets/CMS_LiDAR_AGB_PEF_2012_1318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_AGB_PEF_2012_1318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF. The estimates were produced through a novel modeling approach that accommodates temporal misalignment between field measurements and remotely sensed data by including multiple time-indexed measurements at plot locations to estimate changes in AGB.", "links": [ { diff --git a/datasets/CMS_LiDAR_Biomass_CanHt_Sonoma_1523_1.json b/datasets/CMS_LiDAR_Biomass_CanHt_Sonoma_1523_1.json index 05417e59ab..ec19efa083 100644 --- a/datasets/CMS_LiDAR_Biomass_CanHt_Sonoma_1523_1.json +++ b/datasets/CMS_LiDAR_Biomass_CanHt_Sonoma_1523_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_Biomass_CanHt_Sonoma_1523_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.", "links": [ { diff --git a/datasets/CMS_LiDAR_Biomass_MD_PA_DE_1538_2.json b/datasets/CMS_LiDAR_Biomass_MD_PA_DE_1538_2.json index 5a2e7ecb6f..6a83f2fb02 100644 --- a/datasets/CMS_LiDAR_Biomass_MD_PA_DE_1538_2.json +++ b/datasets/CMS_LiDAR_Biomass_MD_PA_DE_1538_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_Biomass_MD_PA_DE_1538_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.", "links": [ { diff --git a/datasets/CMS_LiDAR_Indonesia_1518_1.json b/datasets/CMS_LiDAR_Indonesia_1518_1.json index af77a85b1c..ed01d5e363 100644 --- a/datasets/CMS_LiDAR_Indonesia_1518_1.json +++ b/datasets/CMS_LiDAR_Indonesia_1518_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_Indonesia_1518_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.", "links": [ { diff --git a/datasets/CMS_LiDAR_Point_Cloud_Zambezi_1521_1.json b/datasets/CMS_LiDAR_Point_Cloud_Zambezi_1521_1.json index 1077bd693e..0c21ee39ba 100644 --- a/datasets/CMS_LiDAR_Point_Cloud_Zambezi_1521_1.json +++ b/datasets/CMS_LiDAR_Point_Cloud_Zambezi_1521_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_Point_Cloud_Zambezi_1521_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.", "links": [ { diff --git a/datasets/CMS_LiDAR_Products_Indonesia_1540_1.json b/datasets/CMS_LiDAR_Products_Indonesia_1540_1.json index c6eb6b4950..cac6ec47ef 100644 --- a/datasets/CMS_LiDAR_Products_Indonesia_1540_1.json +++ b/datasets/CMS_LiDAR_Products_Indonesia_1540_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_LiDAR_Products_Indonesia_1540_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia. The data were produced as part of an effort to improve a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.", "links": [ { diff --git a/datasets/CMS_Mangrove_Biomass_Zambezi_1522_1.json b/datasets/CMS_Mangrove_Biomass_Zambezi_1522_1.json index d6908881f2..c678fd3623 100644 --- a/datasets/CMS_Mangrove_Biomass_Zambezi_1522_1.json +++ b/datasets/CMS_Mangrove_Biomass_Zambezi_1522_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Mangrove_Biomass_Zambezi_1522_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013. Aboveground biomass estimates for the larger area of mangrove coverage within the delta are based on (1) the plot level data and (2) canopy structure data derived from airborne LiDAR surveys in 2014. The high-resolution canopy height model for the delta region derived from the airborne LiDAR data is also included.", "links": [ { diff --git a/datasets/CMS_Mangrove_CanHt_Stand_Age_1377_1.json b/datasets/CMS_Mangrove_CanHt_Stand_Age_1377_1.json index 24d3386f04..ec2e7eb6ff 100644 --- a/datasets/CMS_Mangrove_CanHt_Stand_Age_1377_1.json +++ b/datasets/CMS_Mangrove_CanHt_Stand_Age_1377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Mangrove_CanHt_Stand_Age_1377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.", "links": [ { diff --git a/datasets/CMS_Mangrove_Canopy_Height_1327_1.json b/datasets/CMS_Mangrove_Canopy_Height_1327_1.json index 590fd1b6fd..828e64aebe 100644 --- a/datasets/CMS_Mangrove_Canopy_Height_1327_1.json +++ b/datasets/CMS_Mangrove_Canopy_Height_1327_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Mangrove_Canopy_Height_1327_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.", "links": [ { diff --git a/datasets/CMS_Mangrove_Canopy_Ht_Zambezi_1357_1.json b/datasets/CMS_Mangrove_Canopy_Ht_Zambezi_1357_1.json index 32eb842775..9df9edc536 100644 --- a/datasets/CMS_Mangrove_Canopy_Ht_Zambezi_1357_1.json +++ b/datasets/CMS_Mangrove_Canopy_Ht_Zambezi_1357_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Mangrove_Canopy_Ht_Zambezi_1357_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery. The data cover the period 2011-10-14 to 2014-05-06.", "links": [ { diff --git a/datasets/CMS_Mangrove_Cover_1670_1.1.json b/datasets/CMS_Mangrove_Cover_1670_1.1.json index 54cd8a0743..29367d71e2 100644 --- a/datasets/CMS_Mangrove_Cover_1670_1.1.json +++ b/datasets/CMS_Mangrove_Cover_1670_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Mangrove_Cover_1670_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.", "links": [ { diff --git a/datasets/CMS_Maryland_AGB_Canopy_1320_1.json b/datasets/CMS_Maryland_AGB_Canopy_1320_1.json index 5ae6bd88d0..d2d2490512 100644 --- a/datasets/CMS_Maryland_AGB_Canopy_1320_1.json +++ b/datasets/CMS_Maryland_AGB_Canopy_1320_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Maryland_AGB_Canopy_1320_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.", "links": [ { diff --git a/datasets/CMS_Methane_Emissions_Boston_1291_1.json b/datasets/CMS_Methane_Emissions_Boston_1291_1.json index 5b7bac53e2..fd2650b00d 100644 --- a/datasets/CMS_Methane_Emissions_Boston_1291_1.json +++ b/datasets/CMS_Methane_Emissions_Boston_1291_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Methane_Emissions_Boston_1291_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides average hourly measured, modeled enhancements, and background methane (CH4) concentrations, atmospheric ethane (C2H6) measurements, prior CH4 flux fields by sector, and a spatial reconstruction of natural gas (NG) consumption in Boston, Massachusetts and the surrounding region. Atmospheric CH4 concentrations were measured continuously from September 2012 through August 2013 at four locations and atmospheric ethane was measured continuously for several months during 2012-2014 at one location. Spatial models of prior CH4 emissions and natural gas consumption are given for an ~18,000 km^2 area centered on Boston, MA.", "links": [ { diff --git a/datasets/CMS_Monthly_CO2_Gulf_of_Mexico_1668_1.json b/datasets/CMS_Monthly_CO2_Gulf_of_Mexico_1668_1.json index be830c021e..75d56dbe8d 100644 --- a/datasets/CMS_Monthly_CO2_Gulf_of_Mexico_1668_1.json +++ b/datasets/CMS_Monthly_CO2_Gulf_of_Mexico_1668_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Monthly_CO2_Gulf_of_Mexico_1668_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of Mexico for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations. Estimates of CO2 flux were derived from estimates of seawater pCO2, wind fields, and atmospheric pCO2.", "links": [ { diff --git a/datasets/CMS_OCE_BGC_CCS_1.json b/datasets/CMS_OCE_BGC_CCS_1.json index 926bd8fcb1..cc751b2d42 100644 --- a/datasets/CMS_OCE_BGC_CCS_1.json +++ b/datasets/CMS_OCE_BGC_CCS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_OCE_BGC_CCS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coupled physical-biogeochemical ocean model (the MITgcm with BLING biogeochemistry) is a least squares fit to all available ocean observations in the region of the California Current System. This is accomplished iteratively through the adjoint method, using the methodology developed by the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO). The result is a physically realistic estimate of the ocean state. The model domain extends from 28N to 40N and from 130W to 114W. It has a 1/16-degree horizontal resolution (~7km) and 72 vertical levels.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/CMS_Pantropical_Forest_Biomass_1337_1.json b/datasets/CMS_Pantropical_Forest_Biomass_1337_1.json index ddb4e64e45..4cfc21bc15 100644 --- a/datasets/CMS_Pantropical_Forest_Biomass_1337_1.json +++ b/datasets/CMS_Pantropical_Forest_Biomass_1337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Pantropical_Forest_Biomass_1337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of pre-deforestation aboveground live woody biomass (AGLB) at 30-m resolution for deforested areas of tropical America, tropical Africa, and tropical Asia for the year 2000. The biomass estimates are only for areas where deforestation occurred during the period 2000 through 2012. These estimates represent biomass loss over this time period and can be used to derive average annual carbon emissions from tropical deforestation.", "links": [ { diff --git a/datasets/CMS_Pennsylvania_Tree_Cover_1334_1.1.json b/datasets/CMS_Pennsylvania_Tree_Cover_1334_1.1.json index 2599cec934..67f9bae601 100644 --- a/datasets/CMS_Pennsylvania_Tree_Cover_1334_1.1.json +++ b/datasets/CMS_Pennsylvania_Tree_Cover_1334_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Pennsylvania_Tree_Cover_1334_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.", "links": [ { diff --git a/datasets/CMS_Pilot_Biomass_1257_1.json b/datasets/CMS_Pilot_Biomass_1257_1.json index c1f6cf29d7..fe55ab66d8 100644 --- a/datasets/CMS_Pilot_Biomass_1257_1.json +++ b/datasets/CMS_Pilot_Biomass_1257_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Pilot_Biomass_1257_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies.These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site).The data are presented as a series of 11 GeoTIFF (*.tif) files.", "links": [ { diff --git a/datasets/CMS_SABGOM_Model_Simulations_1510_1.json b/datasets/CMS_SABGOM_Model_Simulations_1510_1.json index 9c8d1b749e..c6d34499ee 100644 --- a/datasets/CMS_SABGOM_Model_Simulations_1510_1.json +++ b/datasets/CMS_SABGOM_Model_Simulations_1510_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_SABGOM_Model_Simulations_1510_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of Mexico simulated by the South Atlantic Bight and Gulf of Mexico (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of Mexico to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.", "links": [ { diff --git a/datasets/CMS_SOC_Mexico_1754_1.json b/datasets/CMS_SOC_Mexico_1754_1.json index c1ea15dbd1..1ed41c46ae 100644 --- a/datasets/CMS_SOC_Mexico_1754_1.json +++ b/datasets/CMS_SOC_Mexico_1754_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_SOC_Mexico_1754_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.", "links": [ { diff --git a/datasets/CMS_SOC_Mexico_CONUS_1737_1.json b/datasets/CMS_SOC_Mexico_CONUS_1737_1.json index c9a4ae0230..52e4b698cf 100644 --- a/datasets/CMS_SOC_Mexico_CONUS_1737_1.json +++ b/datasets/CMS_SOC_Mexico_CONUS_1737_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_SOC_Mexico_CONUS_1737_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction. Predictions of gridded SOC and uncertainty based on multiple bulk density (BD) pedotransfer functions (PFTs) are also included. The second set of gridded SOC estimates, for the period 2009-2011, were derived from two fully independent validation field datasets from across both countries. Note that the same environmental variables and modeling approach used for the first set of estimates were applied to the second set to assess the models' sensitivity to multiple SOC data sources. The SOC field data for the first set of estimates are provided in this dataset and the other data sources, including the two independent validation field datasets, are referenced.", "links": [ { diff --git a/datasets/CMS_SST_GPP_Mexico_1310_1.json b/datasets/CMS_SST_GPP_Mexico_1310_1.json index 5990b00878..c031a24307 100644 --- a/datasets/CMS_SST_GPP_Mexico_1310_1.json +++ b/datasets/CMS_SST_GPP_Mexico_1310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_SST_GPP_Mexico_1310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean. The terrestrial transects, in order from North to South, West to East included Punta Colonet (three sites-PC1, PC2, PC3), Punta Abreojos (two sites-PA1, PA2), and Magdalena Bay (three sites-MB1, MB2, MB3).", "links": [ { diff --git a/datasets/CMS_Simulated_SIF_NiwotRidge_1720_1.json b/datasets/CMS_Simulated_SIF_NiwotRidge_1720_1.json index 871025bdd4..6edbd42b6c 100644 --- a/datasets/CMS_Simulated_SIF_NiwotRidge_1720_1.json +++ b/datasets/CMS_Simulated_SIF_NiwotRidge_1720_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Simulated_SIF_NiwotRidge_1720_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.", "links": [ { diff --git a/datasets/CMS_Soil_CO2_Efflux_1298_1.json b/datasets/CMS_Soil_CO2_Efflux_1298_1.json index 9b759f25b3..629efcf935 100644 --- a/datasets/CMS_Soil_CO2_Efflux_1298_1.json +++ b/datasets/CMS_Soil_CO2_Efflux_1298_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_Soil_CO2_Efflux_1298_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.", "links": [ { diff --git a/datasets/CMS_WFEIS_CONUS-AK_1306_1.json b/datasets/CMS_WFEIS_CONUS-AK_1306_1.json index 523acd84ff..7a8f90fb0a 100644 --- a/datasets/CMS_WFEIS_CONUS-AK_1306_1.json +++ b/datasets/CMS_WFEIS_CONUS-AK_1306_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_WFEIS_CONUS-AK_1306_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.", "links": [ { diff --git a/datasets/CMS_WRF_Footprints_CO2_Signals_1381_1.json b/datasets/CMS_WRF_Footprints_CO2_Signals_1381_1.json index 799f564a68..c9d19854f6 100644 --- a/datasets/CMS_WRF_Footprints_CO2_Signals_1381_1.json +++ b/datasets/CMS_WRF_Footprints_CO2_Signals_1381_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_WRF_Footprints_CO2_Signals_1381_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes. Data is also provided for the land surface in the larger modeling domain outside California. CO2 signals refer to the local enhancement or depletion in atmospheric CO2 concentration caused by fossil fuel emissions or biospheric exchange occurring within the region.", "links": [ { diff --git a/datasets/CMS_WRF_Model_Products_1338_1.json b/datasets/CMS_WRF_Model_Products_1338_1.json index 1bceaa4493..6609eec094 100644 --- a/datasets/CMS_WRF_Model_Products_1338_1.json +++ b/datasets/CMS_WRF_Model_Products_1338_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CMS_WRF_Model_Products_1338_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.", "links": [ { diff --git a/datasets/CNDA-ESP_ANT94-0905_LIQ_05.json b/datasets/CNDA-ESP_ANT94-0905_LIQ_05.json index 999e31dc81..c649191116 100644 --- a/datasets/CNDA-ESP_ANT94-0905_LIQ_05.json +++ b/datasets/CNDA-ESP_ANT94-0905_LIQ_05.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNDA-ESP_ANT94-0905_LIQ_05", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In English:\n \n At the beginning of the 1990's our ecophysiological research on Antarctic\n lichens was focussed on adaptations to cope with low temperatures. We assumed\n that low temperatures should play an important limiting role in the growth of\n the Maritime Antarctic tundra, which is made up of lichens and to a lesser\n extent of other cryptogams and two species of vascular plants. In different\n expeditions to the South Shetland Islands, mostly to the Spanish Antarctic Base\n on Livingston island, we carried out extensive field measurements of gas\n exchange of representative species of the tundra under natural conditions. We\n completed these studies with experiments under controlled conditions in the\n laboratory, exploring the photosynthetic response of these species to light and\n temperature. We combined gas exchange measurements with chlorophyll\n fluorescence analyses, with anatomical and ultra structural observations, and\n with photosynthetic pigments and relations studies. Some of the main specific\n goals were the comparisons between Antarctic and European populations of\n certain cosmopolitan lichen species, the tolerance to the simultaneous stresses\n of high irradiance and low temperatures, and the estimation of the primary\n production of some lichens during the austral summer. We concluded from these\n studies that the Antarctic populations were relatively less productive, that\n both lichens and vascular plants were remarkably resistant to the combination\n of high irradiances and low temperatures, and that, surprisingly, the austral\n summer was a period of negative carbon balance for some lichens, which required\n low temperatures to refrain respiration in order to reach a positive carbon\n balance.\n \n In our opinion, the ecological success of lichens in Antarctica is related not\n only to the fact that they are well adapted to low temperatures but also to the\n fact that they can exploit brief, unpredictable, favorable periods during the\n austral spring and autumn, which it is not the case for vascular plants. These\n studies left at least two open questions: why are the Antarctic populations so\n unproductive? And could the temperatures be involved in the limited growth of\n the Antarctic tundra through their interactions with biogeochemical cycles? The\n answer to these question is the main goal of our research towards the end of\n the 90's. Some preliminary results obtained during the 1996/1997 expedition\n pointed to nutrient availability as an important factor determining maximum\n rates of photosynthesis and, consequently, potential primary production.\n Comparisons between characteristic species of the tundra with species growing\n in the vicinities of penguin colonies or bird perches, which are local sources\n of nitrogen and phosphorus, confirmed to some extent the overlooked importance\n of nutrients versus the more commonly addressed role of low temperatures as\n direct determinant of primary production in terrestrial ecosystems of the\n maritime Antarctica.\n \n En Espanol:\n \n Al comienzo de los anos 90 nuestra investigacion ecofisiologica en liquenes\n antarticos estaba focalizada en las capacidades adaptativas a las bajas\n temperaturas. Asumimos que las bajas temperaturas jugarian un importante papel\n limitador en el crecimiento de la tundra antartica maritima, la cual esta\n formada por liquenes y por una menor cantidad de otras criptogamas y dos\n especies de plantas vasculares. En diferentes expediciones a las islas Shetland\n del Sur, la mayoria a la base antartica espanola de la isla Livingston,\n llevamos a cabo numerosas medidas de campo de intercambio de gases de especies\n representativas de la tundra bajo condiciones naturales. Completamos estos\n estudios con experimentos bajo condiciones controladas de laboratorio,\n explorando la respuesta fotosintetica de estas especies a la luz y la\n temperatura. Combinamos las medidas de intercambio de gases con analisis de\n fluorescencia en clorofila, con observaciones anatomicas y ultra estructurales,\n y con pigmentos fotosinteticos y estudios de relaciones. Algunos de los\n principales objetivos especificos fueron las comparaciones entre poblaciones\n Antarticas y europeas de ciertas especies de liquenes cosmopolitas, la\n tolerancia a la presion simultanea de alta irradiancia y bajas temperaturas, y\n la estimacion de la produccion primaria de algunos liquenes durante el verano\n austral. De estos estudios concluimos que las poblaciones antarticas eran\n relativamente poco productivas, que tanto liquenes como plantas vasculares eran\n remarcablemente resistentes a la combinacion de altas irradiancias y bajas\n temperaturas, y que, sorprendentemente, el verano austral era un periodo\n negativo de balance de carbono para algunos liquenes, los cuales requerian\n bajas temperaturas para abstenerse de respirar y asi alcanzar un balance de\n carbono positivo.\n \n En nuestra opinion el exito ecologico de los liquenes en la Antartida esta\n relacionado no solo con la realidad de que estan bien adaptados a las bajas\n temperaturas sino tambien a que ellos pueden aprovechar los breves e\n impredecibles, periodos favorables durante la primavera austral y el otono, lo\n cual no es el caso de las plantas vasculares. Estos estudios dejan al menos dos\n preguntas abiertas. ?Por que son las poblaciones antarticas tan poco\n productivas? Y ?podria la temperatura estar implicada en el crecimiento de la\n tundra antartica a traves de sus interacciones con los ciclos bioquimicos? Las\n respuestas a estas preguntas es el principal objetivo de nuestra investigacion\n hacia el final de los anos 90. Algunos resultados preliminares obtenidos\n durante la expedicion 1996/1997 apuntaban a la disponibilidad de nutrientes\n como un factor determinante del maximo indice de fotosintesis y,\n consecuentemente potencial de produccion primaria. Comparaciones entre especies\n caracteristicas de tundra con especies creciendo en las inmediaciones de las\n colonias de pinguinos o pedestales de pajaros, los cuales son fuentes locales\n de nitrogeno y fosforo, confirmaron hasta cierto punto la infravalorada\n importancia de los nutrientes contra el mas comunmente papel dirigido de las\n bajas temperaturas como determinante directo de la produccion primaria in\n ecosistemas terrestres de la Antartida maritima.", "links": [ { diff --git a/datasets/CNDP_HES_20230103_CHALLENGE_ALS_1.0.json b/datasets/CNDP_HES_20230103_CHALLENGE_ALS_1.0.json index 4f2e1c3f56..825c758923 100644 --- a/datasets/CNDP_HES_20230103_CHALLENGE_ALS_1.0.json +++ b/datasets/CNDP_HES_20230103_CHALLENGE_ALS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNDP_HES_20230103_CHALLENGE_ALS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this sampling is to know the biodiversity of the Antarctic algae communities (macroalgae and microalgae) and their temporal changes along the South Shetland Islands and the Antarctic Peninsula. Another objective of the sampling is to know the molecular biology of certain species of the red algae group and its nuclear patterns. For all this, it is necessary to carry out sampling both in the intertidal zone and in the sublitoral zone.\r\nFor this study, a total of 54 stations have been sampled. For intertidal communities, 25 x 25cm squares were taken with three replicates per community and a sample was obtained for each different species found throughout the season. For diatoms in the intertidal zone, three sediment falcon tubes were taken from the beach break area.\r\nSamples for each species were also collected within the sublitoral zone and in addition to the target species for the molecular study.\r\nSamples for each different species were also obtained in the sublitoral area and sampled in addition to the target species for molecular study. Diatoms were obtained by extracting sediment during diving or using multicore and gravity core, in which the first centimetres of sediment were obtained in a falcon tube. On the other hand, samples of epiphyte diatoms, found on benthic animals such as starfish or tunicates, were taken by scraping and later preserved in 70% alcohol.\r\nA total of 218 samples of diatoms have been obtained and frozen at -20\u00baC for preservation. Those taken from sediment and animals have been kept in the refrigerator at 4\u00baC.\r\nA total of 39 samples from squares have been taken. These samples have been classified by taxa at species level and weighed in wet weight.\r\nQualitative biodiversity samples have been 351. These have been stored in zip bags at -20\u00baC.\r\nThe samples for molecular studies have been 37 and preserved in three ways each sample; frozen, in Silica gel and in Carnoy (Solution of Ethanol and Glacial Acetic).\r\nAnalyses and calculations of these results will be carried out later in the Antarctic campaign.", "links": [ { diff --git a/datasets/CNDP_JCI_20220103_EPOLAAR_CAM_1.0.json b/datasets/CNDP_JCI_20220103_EPOLAAR_CAM_1.0.json index bbe553ef60..c2cbd746b0 100644 --- a/datasets/CNDP_JCI_20220103_EPOLAAR_CAM_1.0.json +++ b/datasets/CNDP_JCI_20220103_EPOLAAR_CAM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNDP_JCI_20220103_EPOLAAR_CAM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island in 2022", "links": [ { diff --git a/datasets/CNDP_JCI_20240101_TRIPOLI_CAM_1.0.json b/datasets/CNDP_JCI_20240101_TRIPOLI_CAM_1.0.json index 2d503c6c19..a5538df755 100644 --- a/datasets/CNDP_JCI_20240101_TRIPOLI_CAM_1.0.json +++ b/datasets/CNDP_JCI_20240101_TRIPOLI_CAM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNDP_JCI_20240101_TRIPOLI_CAM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island since 2023", "links": [ { diff --git a/datasets/CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4.json b/datasets/CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4.json index 99ae5af42b..7563b32c46 100644 --- a/datasets/CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4.json +++ b/datasets/CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNES_http__cnes.fr_ark_68059_0b222c7948a7ffa8035b3053b4b3ad30_IDN_1.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "By succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by Cnes, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; Cnes serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it delivers. From June 2016, Jason-3 is the reference altimetry mission.The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform.\nBy succeeding Topex/Poseidon, Jason-1 and Jason-2, Jason-3 extends the high-precision ocean altimetry data record to support climate monitoring, operational oceanography and seasonal forecasting. Jason-3 is the result of a joint effort by CNES, NASA, EUMETSAT and NOAA, pursuing a heritage that has been keeping the oceans under close watch for more than 20 years. Partnership is as for Jason-2, but the operational agencies (NOAA and EUMETSAT) take the lead; CNES serves as the system coordinator and all partners -including NASA- support science team activities. By continuing long-term operational oceanography observations, Jason-3 is a key element of the constellation of altimetry satellites in the years ahead. From June 2016, Jason-3 is the reference mission for the altimetry constellation. Succeeding Jason-2, it boasts a number of enhancements to its systems and in processing of the data it will deliver. The satellite is built around a PROTEUS bus carrying a typical suite of altimetry mission instruments that acquires highly accurate measurements of ocean surface height to extend the data record compiled by Topex/Poseidon, Jason-1 and Jason-2. The orbit is the traditional T/P-Jason orbit -non-sun-synchronous, 1336 km, 66° inclination on a Proteus platform.\n[http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html] [http://www.aviso.altimetry.fr/en/missions/future-missions/jason-3.html]", "links": [ { diff --git a/datasets/CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2.json b/datasets/CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2.json index 2cd9e6ef6b..33774a98f2 100644 --- a/datasets/CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2.json +++ b/datasets/CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example).\nRadar echoes over land surfaces are hampered by interfering reflections due to water, vegetation and topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. If the surface is flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. In other cases, elevated topography sometimes prevents the altimeter to lock on the water surface, leading to less valid data than over flat areas. The time series available in Hydroweb are constructed using Jason-2 and Saral GDRs. The basic data used for rivers are the 20 or 40Hz data(\u201chigh rate\u201d data).To construct river water level time series, we need to define virtual stations corresponding to the intersection of the satellite track with the river. For that purpose, we select for each cycle a rectangular \u201cwindow\u201d taking into account all available along track high rate altimetry data over the river area. The coordinate of the virtual station is defined as the barycenter of the selected data within the \u201cwindow\u201d. After rigourous data editing, all available high rate data of a given cycle are geographically averaged. At least two high rate data are needed for averaging otherwise no mean height is provided. Scattering of high rate data with respect to the mean height defines the uncertainty associated with the mean height.The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some virtual stations on rivers. Other virtual stations are monitored on a research mode basis.\n[https://www.theia-land.fr/fr/projets/hydroweb]", "links": [ { diff --git a/datasets/CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2.json b/datasets/CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2.json index 3197416540..f42bfd30a0 100644 --- a/datasets/CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2.json +++ b/datasets/CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example).\nAltimetry missions used are repetitive, i.e. the satellite overflow the same point at a given time interval (10, 17 or 35 days depending on the satellite). The satellite does not deviate from more than +/-1 km across its track. A given lake can be overflown by several satellites, with potentially several passes. The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some lakes. Other lakes are also monitored on a research mode basis.\n[https://www.theia-land.fr/fr/projets/hydroweb]", "links": [ { diff --git a/datasets/CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6.json b/datasets/CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6.json index 54dae166c6..5ac587d7e2 100644 --- a/datasets/CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6.json +++ b/datasets/CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 flies the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing is integrated into the CNES ground segment \"SALP\" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description.\nThe JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 will fly the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing will be integrated into the CNES ground segment \"SALP\" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description.\n The data described here are part of the European Directive INSPIRE. [http://smsc.cnes.fr/JASON2/] [http://smsc.cnes.fr/JASON2/]", "links": [ { diff --git a/datasets/CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5.json b/datasets/CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5.json index 737f6b9702..9f35e3c173 100644 --- a/datasets/CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5.json +++ b/datasets/CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES \"SALP\" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site. The level 2 data stored at CNES are those addressed in this description. \nJASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES \"SALP\" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site.The level 2 data stored at CNES are those addressed in this description.\n[http://smsc.cnes.fr/JASON/index.htm] [http://smsc.cnes.fr/JASON/index.htm]", "links": [ { diff --git a/datasets/CNNADC_1999_ARCTIC_MAP.json b/datasets/CNNADC_1999_ARCTIC_MAP.json index 6be36a5f05..c2574b0032 100644 --- a/datasets/CNNADC_1999_ARCTIC_MAP.json +++ b/datasets/CNNADC_1999_ARCTIC_MAP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNNADC_1999_ARCTIC_MAP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is maps of Arctic Ocean area,their scales are 1:5000000,1:10000000 and 1:40000000.", "links": [ { diff --git a/datasets/CNNADC_2006_ZhongshanStation_Antarctica.json b/datasets/CNNADC_2006_ZhongshanStation_Antarctica.json index 658d37403b..b3fd02e81d 100644 --- a/datasets/CNNADC_2006_ZhongshanStation_Antarctica.json +++ b/datasets/CNNADC_2006_ZhongshanStation_Antarctica.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNNADC_2006_ZhongshanStation_Antarctica", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Laseaman hills earth tide data from March to November 2006 by using Lacoste ET gravimeter.", "links": [ { diff --git a/datasets/CNNADC_2006_ZhongshanStation_Antarctica_2006.json b/datasets/CNNADC_2006_ZhongshanStation_Antarctica_2006.json index e479ea59c3..6789862cc1 100644 --- a/datasets/CNNADC_2006_ZhongshanStation_Antarctica_2006.json +++ b/datasets/CNNADC_2006_ZhongshanStation_Antarctica_2006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CNNADC_2006_ZhongshanStation_Antarctica_2006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Laseaman hill's earth tide data from March to November 2006 by using Lacoste ET gravimeter.", "links": [ { diff --git a/datasets/CO2Fluxes_Arctic_Boreal_Domain_2377_1.json b/datasets/CO2Fluxes_Arctic_Boreal_Domain_2377_1.json index 003b3ea69d..dfa8df830d 100644 --- a/datasets/CO2Fluxes_Arctic_Boreal_Domain_2377_1.json +++ b/datasets/CO2Fluxes_Arctic_Boreal_Domain_2377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CO2Fluxes_Arctic_Boreal_Domain_2377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem CO2 exchange (NEE) across the circumpolar terrestrial Arctic-boreal region at a 1-km spatial resolution. Monthly CO2 flux data from 2001 to 2020 were generated using terrestrial eddy covariance and chamber CO2 flux observations, combined with geospatial meteorological, remote sensing, topographical and soil data, all within a random forest modeling framework. Aggregated average annual NEE, average annual NEE with direct fire emissions added based on the Global Fire Emissions Database (GFED) product, and temporal trends in annual NEE rasters over 2002-2020 are also included. The data are provided in NetCDF and GeoTIFF formats.", "links": [ { diff --git a/datasets/COASTAL_0.json b/datasets/COASTAL_0.json index 395e1ddc6e..7c3534f50d 100644 --- a/datasets/COASTAL_0.json +++ b/datasets/COASTAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COASTAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the Eastern Seaboard of the United States, North Atlantic Bight, and Gulf Stream between 2000 and 2010.", "links": [ { diff --git a/datasets/COMEX_AJAX_CO2_CH4_2347_1.json b/datasets/COMEX_AJAX_CO2_CH4_2347_1.json index aa466d3d43..3e5d358087 100644 --- a/datasets/COMEX_AJAX_CO2_CH4_2347_1.json +++ b/datasets/COMEX_AJAX_CO2_CH4_2347_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COMEX_AJAX_CO2_CH4_2347_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides information to access NASA Earthdata published flight data and flight information collected by the Alpha Jet Atmospheric eXperiment (AJAX) and associated with the COMEX project in 2014-2015. The file lists information for COMEX-related datasets that has been subsetted from AJAX collections archived through NASA's Atmospheric Science Data Center. AJAX data are not otherwise replicated in this dataset. AJAX is a partnership between NASA's Ames Research Center and H211, L.L.C., which conducted in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. During COMEX data collection, a Picarro greenhouse gas (GHG) sensor was mounted on an Alpha Jet, a tactical strike fighter developed by Dassault-Breguet and Dornier through a German-French NATO collaboration. The GHG sensor made repeat measurements in California and Nevada. In situ data included measurements of CO2, CH4, and H2O at 2 Hz or CH4 and H2O at 10 Hz with a strategy of characterizing atmospheric structure over ocean and land, and vertical profiles to at least 5000 m. Ancillary data, including O3, formaldehyde, and meteorological profiles, were also collected. This dataset provides filenames, spatiotemporal bounds, and download URLs for accessing these in situ data. This information is provided in comma separated values (CSV) format.", "links": [ { diff --git a/datasets/COMEX_AVIRIS_Classic_Flights_2343_1.json b/datasets/COMEX_AVIRIS_Classic_Flights_2343_1.json index 133025812e..9631f06cdb 100644 --- a/datasets/COMEX_AVIRIS_Classic_Flights_2343_1.json +++ b/datasets/COMEX_AVIRIS_Classic_Flights_2343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COMEX_AVIRIS_Classic_Flights_2343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-Classic Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014. AVIRIS-Classic Facility Instrument data are otherwise not replicated in this dataset. The COMEX Project utilized several measurement capabilities including the AVIRIS-Classic airborne facility instrument data to demonstrate that methane emissions associated with fossil fuel production activities in the Los Angeles, California area were of sufficient magnitude and size for space-based observations. These lists of the associated COMEX flights from the AVIRIS-Classic Facility Instrument provide flight lines and access information for the Level 1B Calibrated Radiance data and the Level 2 Calibrated Reflectance data.", "links": [ { diff --git a/datasets/COMEX_AVIRIS_NG_Flights_2342_1.json b/datasets/COMEX_AVIRIS_NG_Flights_2342_1.json index 109edfcde9..426eb2cb0b 100644 --- a/datasets/COMEX_AVIRIS_NG_Flights_2342_1.json +++ b/datasets/COMEX_AVIRIS_NG_Flights_2342_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COMEX_AVIRIS_NG_Flights_2342_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-NG Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014. AVIRIS-NG Facility Instrument data are otherwise not replicated in this dataset. The COMEX Project utilized several measurement capabilities including the AVIRIS-NG airborne facility instrument data to demonstrate that methane emissions associated with fossil fuel production activities in the Los Angeles, California area were of sufficient magnitude and size for space-based observations. These lists of the associated COMEX flights from the AVIRIS-NG Facility Instrument provide flight lines and access information for the Level 1B Calibrated Radiance data and the Level 2 Calibrated Reflectance data.", "links": [ { diff --git a/datasets/COMEX_LongwaveInfrared_Imagery_2331_1.json b/datasets/COMEX_LongwaveInfrared_Imagery_2331_1.json index 84b05a34db..7dde19bc15 100644 --- a/datasets/COMEX_LongwaveInfrared_Imagery_2331_1.json +++ b/datasets/COMEX_LongwaveInfrared_Imagery_2331_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COMEX_LongwaveInfrared_Imagery_2331_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides calibrated at-sensor radiance, retrieved surface brightness temperature, and adaptive coherence estimator (ACE) detection imagery of methane, and a limited number of auxiliary gases collected with the Aerospace Corporation's Mako airborne longwave-IR hyperspectral imager flown during July 22-25, 2014 over a variety of methane generating sites in southern and central California (CA), U.S. These sites included animal husbandry and oil/gas production facilities. Specific study areas included the Coal Oil Point marine seep field off of Goleta, CA, the Kern River oil field complex at Bakersfield, CA, and the extensive stockyards in Chino, CA. The Kern River complex was acquired at 1-m ground sampling distance (GSD), while the other study areas were at 2-m GSD. Levels 1-3 data include single whisk data cubes (L1); at-sensor radiance and sensor performance (L2); surface brightness temperature and ACE detections for specific gases (L3). The data were collected in support of the NASA/ESA COMEX (CO2 and Methane EXperiment) campaign. The data are provided in ENVI and comma separated values (CSV) formats. Quicklook images are included for flight lines and molecule specific detections.", "links": [ { diff --git a/datasets/COOA_0.json b/datasets/COOA_0.json index 960c429c15..448583cda6 100644 --- a/datasets/COOA_0.json +++ b/datasets/COOA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COOA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Gulf of Maine between 2008 and 2009 made by the Coastal Observing Center at UNH (sometimes called COOA).", "links": [ { diff --git a/datasets/CORAL_0.json b/datasets/CORAL_0.json index f1278e87c3..d0852e0df4 100644 --- a/datasets/CORAL_0.json +++ b/datasets/CORAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CORAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CORAL experiment is a Earth Venture Suborbital-2 (EVS-2) mission designed to provide an extensive, uniform picture of coral reef composition through the use of the Portable Remote Imaging Spectrometer (PRISM) instrument aboard the Tempus Applied Solutions Gulfstream-IV (G-IV) aircraft combined with a variety of in situ data to identify reef composition and model primary production. The CORAL experiment covers the Mariana Islands, Palau, portions of the Great Barrier Reef, and the Main Hawaiian Islands.INTERNAL LINKSCORAL-PRISM Browser (Aircraft data)EXTERNAL LINKSBIOS CORAL Page", "links": [ { diff --git a/datasets/COROAS-AVHRR.json b/datasets/COROAS-AVHRR.json index 6024072d6e..4ba0bdd757 100644 --- a/datasets/COROAS-AVHRR.json +++ b/datasets/COROAS-AVHRR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COROAS-AVHRR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data consisting of AVHRR five channels from satellites NOAA-11 and\nNOAA-12 and Sea Surface Temperature derived from brightness\ntemperature files through NOAA algorithms. Exchange of data after\nJanuary 1995. Due to system limitation, files are 512 lines x 512\npixels per line, 8 bits resolution.", "links": [ { diff --git a/datasets/CORONA_SATELLITE_PHOTOS.json b/datasets/CORONA_SATELLITE_PHOTOS.json index 66d59a412f..8408dff095 100644 --- a/datasets/CORONA_SATELLITE_PHOTOS.json +++ b/datasets/CORONA_SATELLITE_PHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CORONA_SATELLITE_PHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth\u2019s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis.\n\nThe images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995.\n\nThe first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications.\n\nThe intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data.\n\nA variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30\u00b0 with one camera looking forward and the other looking aft.\n\nThe original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery.\n\nMathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.", "links": [ { diff --git a/datasets/COSMO-SkyMed.full.archive.and.tasking_8.0.json b/datasets/COSMO-SkyMed.full.archive.and.tasking_8.0.json index 6b9f7b6d99..5c00946164 100644 --- a/datasets/COSMO-SkyMed.full.archive.and.tasking_8.0.json +++ b/datasets/COSMO-SkyMed.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COSMO-SkyMed.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The archive and new tasking X-band SAR products are available from COSMO-Skymed (CSK) and COSMO-SkyMed Second Generation (CSG) missions in ScanSAR and Stripmap modes, right and left looking acquisition (20 to 60\u00b0 incidence angle). COSMO-SkyMed modes: Acquisition Mode\t/\tSingle look Resolution [Az. X. Rg, SCS] (m)\t/\tScene size [Az. X. Rg] (km)\t/\tPolarisation\t/\tScene duration (seconds)\t/\tNumber of looks\t/\tMultilook resolution (m)\t/\tGeolocation accuracy \u00b13 s (m)\t// \t/\t/\t/\t/\t/\t[DGM, GEC, GTC]\t// Stripmap Himage\t\t/\t2.6 x 3\t/\t40 x 40\t/\tSingle: HH, HV, VH, VV\t/\t7\t/\t3\t/\t5\t/\t25\t// Stripmap PingPong\t/\t9.7 x 11\t/\t30 x 30\t/\tAlternate: HH/VV, HH/HV, VV/VH\t/\t6\t/\t3\t/\t20\t/\t25\t// ScanSAR Wide\t/\t23 x 13.5\t/\t100 x 100\t/\tSingle: HH, HV, VH, VV\t/\t15\t/\t4 - 9\t/\t30\t/\t30\t// ScanSAR Huge\t/\t38 x 13.5\t/\t200 x 200\t/\tSingle: HH, HV, VH, VV\t/\t30\t/\t25 - 66\t/\t100\t/\t100\t// COSMO-Skymed Second Generation Modes: Acquisition Mode\t/\tSingle look Resolution [Az. X. Rg, SCS] (m)\t/\tScene size [Az. X. Rg] (km)\t/\tPolarisation\t/\tScene duration (seconds)\t/\tNumber of looks\t/\tMultilook resolution (m)\t/\tGeolocation accuracy \u00b13 s (m)\t// \t/\t/\t/\t/\t/\t[DGM, GEC, GTC]\t// Stripmap\t/\t3 x 3\t/\t40 x 40\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t7\t/\t2 x 2; 4 x 4\t/\t6 x 7 ; 11 x 14\t/\t3.75 \t// Stripmap PingPong\t/\t12 x 5\t/\t30 x 30\t/\tAlternate (HH/VV, HH/VH-HV/VV)\t/\t6\t/\t1 x 2; 2 x 5\t/\t12 x 10; 23 x 26 /\t12\t// QuadPol\t/\t3 x 3\t/\t40 x 15\t/\tQuad(HH+HV+VV+VH)\t/\tn/a\t/\t2 x 2; 4 x 4\t/\t6 x 7 ; 11 x 14 /\t3.75 \t// ScanSAR 1\t/\t20 x 4\t/\t100 x 100\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t15\t/\t1 x 3; 1 x 5 ; 2 x 8\t/\t20 x 14; 23 x 27; 35 x 40\t/\t12\t// ScanSAR 2\t/\t40 x 6\t/\t200 x 200\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t30\t/\t1 x 4; 1 x 7; 3 x 16\t/\t40 x 27; 47 x 54; 115 x 135\t/\t12\t// Following Processing Levels are available, for both CSK and CSG: - SCS (Level 1A, Single-look Complex Slant): data in complex format, in slant range projection (the sensor's natural acquisition projection) and zero doppler projection, weighted and radiometrically equalised; the coverage corresponds to the full resolution area illuminated by the SAR instrument - DGM (Level 1B, Detected Ground Multi-look): product obtained detecting, multi-looking and projecting the Single-look Complex Slant data onto a grid regular in ground: it contains focused data, amplitude detected, optionally despeckled by multi-looking approach, radiometrically equalised and represented in ground/azimuth projection - GEC (Level 1C, Geocoded Ellipsoid Corrected): focused data, amplitude detected, optionally despeckled by multi-looking approach, geolocated on the reference ellipsoid and represented in a uniform preselected cartographic presentation. Any geometric correction derived by usage of terrain model isn't applied to this product by default - GTC (Level 1D, Geocoded Terrain Corrected): focused data, fully calibrated with the usage of terrain model, amplitude detected, optionally despeckled by multi-looking approach, geolocated on a DEM and represented in a uniform preselected cartographic presentation. The image scene is located and accurately rectified onto a map projection, through the use of Ground Control Points (GCPs) and Digital Elevation Model (DEM); it differs from GEC for the use of the DEM (instead of reference ellipsoid) for the accurate conversion from slant to ground range and to approximate the real earth surface The list of available data can be retrieved using the _$$CLEOS COSMO-SkyMed products catalogue$$ https://www.cleos.earth/ . User registration is requested to navigate the catalogue.", "links": [ { diff --git a/datasets/COWVR_STPH8_L1_TSDR_V10.0_10.0.json b/datasets/COWVR_STPH8_L1_TSDR_V10.0_10.0.json index c62a1f9feb..fc196967f2 100644 --- a/datasets/COWVR_STPH8_L1_TSDR_V10.0_10.0.json +++ b/datasets/COWVR_STPH8_L1_TSDR_V10.0_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COWVR_STPH8_L1_TSDR_V10.0_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 18.7 GHz, 23.8 GHz, and 34.5 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. Its swath width is 1012 km and spatial resolution is <35 km. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbers of the project team prior to release\n

\nThe COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.", "links": [ { diff --git a/datasets/COWVR_STPH8_L2_EDR_V10.0_10.0.json b/datasets/COWVR_STPH8_L2_EDR_V10.0_10.0.json index 37d2ef0339..cc4f63ab6a 100644 --- a/datasets/COWVR_STPH8_L2_EDR_V10.0_10.0.json +++ b/datasets/COWVR_STPH8_L2_EDR_V10.0_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "COWVR_STPH8_L2_EDR_V10.0_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes satellite-based observations of geolocated surface wind vectors, precipitable water vapor, and integrated cloud liquid water, as well as the microwave brightness temperatures used to derive them. Theses measurements make up the environmental data record (EDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), beginning in January 2022 with forward-streaming to PO.DAAC. Data over the satellite swath are available in HDF5 format with roughly one file per hour (the ISS orbit period is ~90 minutes), and coverage shown in the thumbnail is for a full day. Spatial resolution is roughly 35 km. The file metadata formats may be different than what an average user is familiar with \u2013 please see the User Guide to learn more. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.\n

\nThe COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.", "links": [ { diff --git a/datasets/CPEXAW-ADM-Aeolus_1.json b/datasets/CPEXAW-ADM-Aeolus_1.json index ab172e8b2e..0d4028019e 100644 --- a/datasets/CPEXAW-ADM-Aeolus_1.json +++ b/datasets/CPEXAW-ADM-Aeolus_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXAW-ADM-Aeolus_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXAW-ADM-Aeolus_1 is the ESA ADM-Aeolus Datasets for the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) sub-orbital campaign. Data collection for this product is complete.\r\n\r\nThe Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include:\r\n\u2022 Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic;\r\n\u2022 Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ;\r\n\u2022 Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ;\r\n\u2022 Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic.\r\nIn order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC).", "links": [ { diff --git a/datasets/CPEXAW-DAWN_DC8_1.json b/datasets/CPEXAW-DAWN_DC8_1.json index 7bd339aeff..fee923212c 100644 --- a/datasets/CPEXAW-DAWN_DC8_1.json +++ b/datasets/CPEXAW-DAWN_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXAW-DAWN_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXAW-DAWN_DC8_1 are the Doppler Aerosol WiNd lidar (DAWN) image and NetCDF data files collected during the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) onboard the DC-8 aircraft. Data collection for this product is complete.\r\n\r\nThe Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include:\r\n\u2022 Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic;\r\n\u2022 Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ;\r\n\u2022 Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ;\r\n\u2022 Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic.\r\nIn order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC).", "links": [ { diff --git a/datasets/CPEXAW-Dropsondes_1.json b/datasets/CPEXAW-Dropsondes_1.json index bc2191d8d4..09018b00c6 100644 --- a/datasets/CPEXAW-Dropsondes_1.json +++ b/datasets/CPEXAW-Dropsondes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXAW-Dropsondes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXAW-Dropsondes_1 is the dropsonde data files collected during the Convective Processes Experiment - Aerosols & Winds (CPEX-AW). Data collection for this product is complete.\r\n\r\nThe Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include:\r\n\u2022 Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic;\r\n\u2022 Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ;\r\n\u2022 Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ;\r\n\u2022 Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic.\r\nIn order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC).", "links": [ { diff --git a/datasets/CPEXAW-HALO_DC8_1.json b/datasets/CPEXAW-HALO_DC8_1.json index cf8e7bd6af..e8bd9b2ee1 100644 --- a/datasets/CPEXAW-HALO_DC8_1.json +++ b/datasets/CPEXAW-HALO_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXAW-HALO_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXAW-HALO_DC8_1 is the High Altitude Lidar Observatory (HALO) image and h5 data files collected during the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) onboard the DC-8 aircraft. Data collection for this product is complete.\r\n\r\nThe Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include:\r\n\u2022 Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic;\r\n\u2022 Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ;\r\n\u2022 Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ;\r\n\u2022 Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic.\r\nIn order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC).", "links": [ { diff --git a/datasets/CPEXCV-DAWN_DC8_1.json b/datasets/CPEXCV-DAWN_DC8_1.json index 7a3b97ac43..4e7a100198 100644 --- a/datasets/CPEXCV-DAWN_DC8_1.json +++ b/datasets/CPEXCV-DAWN_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXCV-DAWN_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXCV-DAWN_DC8_1 are the Doppler Aerosol WiNd lidar (DAWN) image and NetCDF data files collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data collection for this product is complete.\r\n\r\nSeeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX \u2013 Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region.\r\n\r\nCPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements.\r\n\r\nTo achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.", "links": [ { diff --git a/datasets/CPEXCV-Dropsondes_1.json b/datasets/CPEXCV-Dropsondes_1.json index b96b5f67a0..c47edc12d3 100644 --- a/datasets/CPEXCV-Dropsondes_1.json +++ b/datasets/CPEXCV-Dropsondes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXCV-Dropsondes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXCV-Dropsondes_1 is the dropsonde data files collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV). Data collection for this product is complete.\r\n\r\nSeeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX \u2013 Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region.\r\n\r\nCPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements.\r\n\r\nTo achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.", "links": [ { diff --git a/datasets/CPEXCV-HALO_DC8_1.json b/datasets/CPEXCV-HALO_DC8_1.json index ae30d87175..ab0bd3a4d5 100644 --- a/datasets/CPEXCV-HALO_DC8_1.json +++ b/datasets/CPEXCV-HALO_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXCV-HALO_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXCV-HALO_DC8_1 is the High Altitude Lidar Observatory (HALO) image and h5 data files collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data collection for this product is complete.\r\n\r\nSeeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX \u2013 Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region.\r\n\r\nCPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements.\r\n\r\nTo achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.", "links": [ { diff --git a/datasets/CPEXCV_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/CPEXCV_Cloud_AircraftInSitu_DC8_Data_1.json index 1a99c010be..2c7121f35b 100644 --- a/datasets/CPEXCV_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/CPEXCV_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXCV_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXCV_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data from the Cloud and Aerosol Spectrometer (CAS) instrument is featured in this collection. Data collection for this product is complete.\r\n\r\nSeeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX \u2013 Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. CPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements. To achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.", "links": [ { diff --git a/datasets/CPEXCV_Merge_Data_1.json b/datasets/CPEXCV_Merge_Data_1.json index 5ee530435f..e0b458f390 100644 --- a/datasets/CPEXCV_Merge_Data_1.json +++ b/datasets/CPEXCV_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEXCV_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPEXCV_Merge_DC8_Data are pre-generated aircraft merge data files created utilizing data collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data collection for this product is complete. Seeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX \u2013 Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. CPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements. To achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment.", "links": [ { diff --git a/datasets/CPEX_DAWN_DC8_1.json b/datasets/CPEX_DAWN_DC8_1.json index 885b85e2e0..064d583c9f 100644 --- a/datasets/CPEX_DAWN_DC8_1.json +++ b/datasets/CPEX_DAWN_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPEX_DAWN_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 25 May \u2013 24 June 2017, NASA funded and conducted the Convective Processes Experiment (CPEX) which was based out of Ft. Lauderdale, FL and used a suite of instruments aboard a NASA DC-8 aircraft to investigate convective process and circulations over tropical waters. A main objective of CPEX was to obtain a comprehensive set of temperature, humidity and, particularly, wind observations in the vicinity of scattered and organized deep convection in all phases of the convective life cycle.\r\n\r\nThe featured instrument of the airborne campaign was NASA\u2019s Doppler Aerosol WiNd (DAWN) lidar but also included dropsondes, the Airborne Second Generation Precipitation Radar (APR-2), the High Altitude MMIC Sounding Radiometer (HAMSR), the Microwave Temperature and Humidity Profiler (MTHP), and the Microwave Atmospheric Sounder for Cubesat (MASC).\r\n\r\nIn total, the CPEX campaign flew 16 missions over the Atlantic Ocean, Caribbean Sea and the Gulf of Mexico and included missions investigating undisturbed conditions, scattered convection, organized convection and the environment of a tropical storm. The DAWN (and Dropsonde) wind measurement collected during CPEX have provided a unique set of wind profiles to be used in analysis and model assimilation and prediction studies. \r\n\r\nCPEX also utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 300 dropsondes to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements.", "links": [ { diff --git a/datasets/CPL_ABL_Top_Height_1825_1.json b/datasets/CPL_ABL_Top_Height_1825_1.json index d7fbe4d45d..9e0cb85dcf 100644 --- a/datasets/CPL_ABL_Top_Height_1825_1.json +++ b/datasets/CPL_ABL_Top_Height_1825_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CPL_ABL_Top_Height_1825_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds.", "links": [ { diff --git a/datasets/CRP_0.json b/datasets/CRP_0.json index 09b7a9a795..e89a6e1e56 100644 --- a/datasets/CRP_0.json +++ b/datasets/CRP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CRP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coastal marine system of the Gulf of Alaska (GoA) is connected hydrologically, biogeochemically and biologically with the upriver systems of the Copper River basin. Glacially weathered rock yields highly reactive particulate iron (Fe) into rivers that yields an important flux of bioavailable iron to the open ocean. North Pacific deep water is extremely nutrient-rich, and upwelling of deep water in estuaries and at river plumes results in very high biological productivity. The world-renowned fisheries in the vicinity of the Copper River region of the GoA thrive, in part, due to pristine riparian and lacustrine habitats for spawning and rearing. Pacific salmon spawn in the upper reaches of coastal watersheds, and their progeny spend a significant amount of time in freshwater habitats before migrating to the ocean. Prior to making the transition to a fully marine lifestyle, salmon smolts benefit from the enhanced biological productivity at plumes and within estuaries.The coastal GoA region is currently experiencing rapid and accelerating climate change as manifested by rapid recession of glaciers; climate models predict up to a 40% increase in river runoff from Alaska rivers by 2050. Over the coming decades an increase in glacier-dominated river discharge is likely, followed by decreases as glaciers recede. In addition, there will be a change in the seasonality of river discharge. Changes in freshwater discharge are likely to alter the flux of reactive particulate Fe, as well as dissolved organic and inorganic carbon (DIC and DOC) from glacier-dominated rivers, as well as the nitrate flux to surface water from estuarine upwelling, with cascading effects throughout the ecosystem. Furthermore, the freshwater supply of dissolved organic nitrogen (DON) and nitrate may increase over time due to recolonization of deglaciated watersheds by opportunistic nitrogen-fixing plants. New habitats for salmon and other members of the headwater ecosystem are likely to become available as glaciers retreat and as permafrost melts in the upper watershed. Conversely, decreased permafrost and decreased river flows may lead to the loss of habitat as freshwater sources dry seasonally or permanently. In addition, the positive or negative feedbacks to rising atmospheric CO2 concentrations, which are responsible for the warming and the subsequent melting of the glaciers, have not been addressed. As landscapes become ice free, the evolution of vegetation on these areas may act as net C sinks./The specific changes that will be manifested in the Copper River watershed and associated marine systems are difficult to predict and monitor. Using NASA products and a combination of remote sensing and field-based studies, this project seeks to establish a framework to document and monitor physical, biogeochemical biological changes in the coastal Gulf of Alaska adjacent to the Copper River.", "links": [ { diff --git a/datasets/CSA_ortho_1.json b/datasets/CSA_ortho_1.json index 6a87ca66fd..3c1166b477 100644 --- a/datasets/CSA_ortho_1.json +++ b/datasets/CSA_ortho_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSA_ortho_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthophoto is a rectified georeferenced corrected image of the Casey Station Area. Distortions due to relief and camera have been removed. This orthophoto is shown in a map which is available from the SCAR Map Catalogue.", "links": [ { diff --git a/datasets/CSIRO_AR_GASLAB_1.json b/datasets/CSIRO_AR_GASLAB_1.json index 75c4e9e920..81341d4a0c 100644 --- a/datasets/CSIRO_AR_GASLAB_1.json +++ b/datasets/CSIRO_AR_GASLAB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_AR_GASLAB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Australian Antarctic Division project #124 monitors the background level of major greenhouse gases, and related species (carbon dioxide, methane, carbon monoxide, nitrous oxide, hydrogen, and carbon dioxide isotopes, oxygen), at a number of Antarctic sites.\n\nSamples of air are collected and returned to CSIRO Atmospheric Research for analysis. Radiocarbon and oxygen are measured by international collaborators.\n\nApproximately 4 samples are collected from each station per month.\n\nThe greenhouse gases released by human activity and most implicated in global climate change, are long lived and well mixed in the atmosphere. The Antarctic regions, remote from industrial and land plant activity are ideally located to measure result of global changes in the gases. The CSIRO sampling network represents the most comprehensive, long running Southern hemisphere program. With continuing innovation in measurement and interpretive models, it is ideally positioned to detect possible climate induced regional changes in carbon uptake, as well as monitor global changes. It also provides essential background information to the new challenge of monitoring integrated emissions from the Australian continent.\n\nData from this project have also been incorporated into State of the Environment Indicator 11, Atmospheric concentrations of greenhouse gas species. See the link below for further details.\n\nThe download file contains both the individual flask data measurements and also monthly means derived from these. The monthly mean data are presented in the State Of Environment indicator linked below. The monthly mean files are labelled sss_mm.xxx where sss is the site code and xxx is the species identifier. An example for Cape Grim for Methane would be cga_mm.ch4.\n\nA number of readme files are also provided in the download for further information. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nConcentrations of CO2, CO, CH4, H2, and N2O, and the isotopes 13C and 18O in CO2, have been made in flask air samples collected at ~2 week intervals at Mawson, Casey, and Macquarie Island.\nIn addition, at Macquarie Island, continuous CO2 measurements and sampling for the O2/N2 ratio and the 14C isotope of CO2 were made.\nThe data have been calibrated and quality controlled for incorporation into global data sets, for use in detecting spatial and temporal trends and in model inversions to infer fluxes.", "links": [ { diff --git a/datasets/CSIRO_Albatross_fish.json b/datasets/CSIRO_Albatross_fish.json index 6248ef7882..668e1bb6ca 100644 --- a/datasets/CSIRO_Albatross_fish.json +++ b/datasets/CSIRO_Albatross_fish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_Albatross_fish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "7, four- to five-day cruises were undertaken using the vessel\n \"Jacqueline D\" in Albatross Bay, Gulf of Carpentaria between August\n 1986 and November 1988, using a random stratified trawl survey to\n measure fish species composition and abundance. Four depth zones\n between 7 and 45 m were sampled during both day and\n night. Approximately 890,000 fish of 237 species were collected, of\n which the bulk were made up of 25 species. The dominant families were\n Leignathidae, Haemulidae and Clupeidae, with Sciaenidae and Dasyatidae\n important at night. Leiognathus bindus was the most abundant species,\n while Caranx bucculentus was the most frequently caught (96% of all\n trawls). The suite of fishes was separately analysed for occurrence of\n prawn predators. This metadata record is sourced from 'MarLIN', the\n CSIRO Marine Laboratories Information Network.\n \n Information was obtained from\n http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1621 .\n The originating project was the Tropical Fish Ecology Project: Gulf of\n Carpenteria studies. The Tropical Fish Ecology project over this time\n period carried out work on fish as peneid prawn predators in\n Albatross Bay and the Embley Estuary, and as tiger prawn predators at\n Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the\n biology of tuna baitfish in the Solomon Islands, Kiribati, and the\n Maldives (work commissioned by ACIAR).", "links": [ { diff --git a/datasets/CSIRO_Albatross_primaryprod.json b/datasets/CSIRO_Albatross_primaryprod.json index 89bd6cbbbd..fdfb15c029 100644 --- a/datasets/CSIRO_Albatross_primaryprod.json +++ b/datasets/CSIRO_Albatross_primaryprod.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_Albatross_primaryprod", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly sampling from 1986 to 1992 at 4 stations was carried out in\n Albatross Bay, Gulf of Carpentaria, plus one year with 4 sampling\n times at 20 stations. Primary productivity in the water column was\n measured. This metadata record is sourced from 'MarLIN', the CSIRO\n Marine Laboratories Information Network.\n \n Information was obtained from:\n http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1664 .\n The originating project was the Tropical Fish Ecology Project: Gulf of\n Carpenteria studies. The Tropical Fish Ecology project over this time\n period carried out work on fish as peneid prawn predators in\n Albatross Bay and the Embley Estuary, and as tiger prawn predators at\n Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the\n biology of tuna baitfish in the Solomon Islands, Kiribati, and the\n Maldives (work commissioned by ACIAR).", "links": [ { diff --git a/datasets/CSIRO_PortLincoln.json b/datasets/CSIRO_PortLincoln.json index b110ef72a7..a55cc84d95 100644 --- a/datasets/CSIRO_PortLincoln.json +++ b/datasets/CSIRO_PortLincoln.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_PortLincoln", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the result of a biological baseline survey of\nthe port region of Port Lincoln, South Australia, carried out in\nMay-June 1996 by CSIRO Marine Research Centre for Research on\nIntroduced Marine Pests (CRIMP). Collection methods employed include\npylon scrapings, sediment cores, crab traps, plankton nets, and\nqualitative visual inspection and photographs (both still and\nvideo). Voucher specimens have been incorporated into collections of\nCMR, Hobart. Taxonomic groups surveyed include marine invertebrates,\nfishes, phytoplankton, macroalgae, and marine vegetation. This dataset\nforms part of a series of Port Surveys conducted by CRIMP over the\nperiod 1996 to present. This metadata record is sourced from 'MarLIN',\nthe CSIRO Marine Laboratories Information Network. Additional\ninformation for this dataset may be available via the original MarLIN\nmetadata entry.", "links": [ { diff --git a/datasets/CSIRO_adultprawn.json b/datasets/CSIRO_adultprawn.json index bac30ded5a..09cbea894c 100644 --- a/datasets/CSIRO_adultprawn.json +++ b/datasets/CSIRO_adultprawn.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_adultprawn", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adult prawn species, size, sex, reproductive stage, moult stage, and\n parasites were measured at 20 stations in Albatross Bay, Gulf of\n Carpentaria. Sampling was carried out monthly between 1986 and\n 1992. This metadata record is sourced from 'MarLIN', the CSIRO Marine\n Laboratories Information Networ\n \n Information was obtained from\n http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1361", "links": [ { diff --git a/datasets/CSIRO_phytoplankton.json b/datasets/CSIRO_phytoplankton.json index b4d4ece7e9..e8a4768c65 100644 --- a/datasets/CSIRO_phytoplankton.json +++ b/datasets/CSIRO_phytoplankton.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_phytoplankton", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly cruises were carried out between March 1986 and April 1992, at\n four stations in Albatross Bay, Gulf of Carpentaria. Phytoplankton\n taxonomic groups were identified. This metadata record is sourced from\n 'MarLIN', the CSIRO Marine Laboratories Information Network.\n \n Information was obtained from:\n http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1582.\n The originating project was the Tropical Fish Ecology Project: Gulf of\n Carpenteria studies. The Tropical Fish Ecology project over this time\n period carried out work on fish as peneid prawn predators in Albatross\n Bay and the Embley Estuary, and as tiger prawn predators at Groote\n Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of\n tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work\n commissioned by ACIAR).", "links": [ { diff --git a/datasets/CSIRO_portland.json b/datasets/CSIRO_portland.json index 0d2e100857..6dc93746bf 100644 --- a/datasets/CSIRO_portland.json +++ b/datasets/CSIRO_portland.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSIRO_portland", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the result of a biological baseline survey of\nthe port region of Portland, Victoria, carried out in April-May 1996\nby CSIRO Marine Research Centre for Research on Introduced Marine\nPests (CRIMP). Collection methods employed include pylon scrapings,\nsediment cores, crab traps, plankton nets, and qualitative visual\ninspection and photographs (both still and video). Voucher specimens\nhave been incorporated into collections of the Museum of Victoria and\nCMR, Hobart. Taxonomic groups surveyed include marine invertebrates,\nfishes, phytoplankton, macroalgae, and marine vegetation. This dataset\nforms part of a series of Port Surveys conducted by CRIMP over the\nperiod 1996 to present. This metadata record is sourced from 'MarLIN',\nthe CSIRO Marine Laboratories Information Network. Additional\ninformation for this dataset may be available via the original MarLIN\nmetadata entry (see on-line links).", "links": [ { diff --git a/datasets/CSU Synthetic Attribution Benchmark Dataset_1.json b/datasets/CSU Synthetic Attribution Benchmark Dataset_1.json index d191becaea..2d4e2e3d8e 100644 --- a/datasets/CSU Synthetic Attribution Benchmark Dataset_1.json +++ b/datasets/CSU Synthetic Attribution Benchmark Dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSU Synthetic Attribution Benchmark Dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a synthetic dataset that can be used by users that are interested in benchmarking methods of explainable artificial intelligence (XAI) for geoscientific applications. The dataset is specifically inspired from a climate forecasting setting (seasonal timescales) where the task is to predict regional climate variability given global climate information lagged in time. The dataset consists of a synthetic input X (series of 2D arrays of random fields drawn from a multivariate normal distribution) and a synthetic output Y (scalar series) generated by using a nonlinear function F: R^d -> R.

The synthetic input aims to represent temporally independent realizations of anomalous global fields of sea surface temperature, the synthetic output series represents some type of regional climate variability that is of interest (temperature, precipitation totals, etc.) and the function F is a simplification of the climate system.

Since the nonlinear function F that is used to generate the output given the input is known, we also derive and provide the attribution of each output value to the corresponding input features. Using this synthetic dataset users can train any AI model to predict Y given X and then implement XAI methods to interpret it. Based on the \u201cground truth\u201d of attribution of F the user can assess the faithfulness of any XAI method.

NOTE: the spatial configuration of the observations in the NetCDF database file conform to the planetocentric coordinate system (89.5N - 89.5S, 0.5E - 359.5E), where longitude is measured in the positive heading east from the prime meridian.", "links": [ { diff --git a/datasets/CSU_fueltreatment_Fontainebleauwildfirestudy.json b/datasets/CSU_fueltreatment_Fontainebleauwildfirestudy.json index 3da2f2c141..8818fa9db1 100644 --- a/datasets/CSU_fueltreatment_Fontainebleauwildfirestudy.json +++ b/datasets/CSU_fueltreatment_Fontainebleauwildfirestudy.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSU_fueltreatment_Fontainebleauwildfirestudy", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are from the 1999 Fontainebleau wildfire that burned into an area that\n had previously been treated with 3 prescribed fires (1988, 1992, and 1998) in\n the Mississippi Sandhill Crane National Wildlife Refuge. Nine plots were\n established in both the treated area and an adjacent untreated area. Data\n collected describe stand conditions and fire severity at each plot.\n \n The data were collected to assess the effect of repeated prescribed burn\n treatments on stand conditions and subsequent wildfire severity.", "links": [ { diff --git a/datasets/CSU_fueltreatment_HiMeadow.json b/datasets/CSU_fueltreatment_HiMeadow.json index ccdcce9b09..ba5e49f935 100644 --- a/datasets/CSU_fueltreatment_HiMeadow.json +++ b/datasets/CSU_fueltreatment_HiMeadow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSU_fueltreatment_HiMeadow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are from the 2000 Hi Meadow wildfire that burned into an area of the\n Pike National Forest that had received extensive fuel treatments since 1990\n that included mechanical thinning and prescribed burning. Twelve plot pairs\n were established that straddled the fuel treatment boundaries. Data collected\n describe stand conditions and fire severity at each plot.\n \n The data were collected to assess the effect of the fuel treatments on stand\n conditions and subsequent wildfire severity.", "links": [ { diff --git a/datasets/CSU_fueltreatments_megramwildfire.json b/datasets/CSU_fueltreatments_megramwildfire.json index dff79e950e..6e9bb5d2dc 100644 --- a/datasets/CSU_fueltreatments_megramwildfire.json +++ b/datasets/CSU_fueltreatments_megramwildfire.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CSU_fueltreatments_megramwildfire", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are from the 1999 Megram wildfire that burned into an area of the Six Rivers National Forest that had been affected by a blowdown event in the winter of 1995-96. Surface fuels reduction in a portion of the blowdown area was accomplished via yarding and burning in 1997. Eleven plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot.", "links": [ { diff --git a/datasets/CS_Bibliography_1.json b/datasets/CS_Bibliography_1.json index b91af2c407..0b380ddf51 100644 --- a/datasets/CS_Bibliography_1.json +++ b/datasets/CS_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CS_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of references relating to contaminated sites from the Antarctic and subantarctic regions, dating from 1992 to 2003. The bibliography was compiled by Colin Davis, and contains 17 references.", "links": [ { diff --git a/datasets/CS_ortho_1.json b/datasets/CS_ortho_1.json index cab99c2d50..e06a8efc9b 100644 --- a/datasets/CS_ortho_1.json +++ b/datasets/CS_ortho_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CS_ortho_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthophoto is a rectified georeferenced corrected image of Casey Station. Distortions due to relief and camera have been removed. This orthophoto is shown in a map which is available from the SCAR Map Catalogue via the provided link.", "links": [ { diff --git a/datasets/CStocks_Greenness_Mangroves_MX_1853_1.json b/datasets/CStocks_Greenness_Mangroves_MX_1853_1.json index 03c938068c..da41765fbf 100644 --- a/datasets/CStocks_Greenness_Mangroves_MX_1853_1.json +++ b/datasets/CStocks_Greenness_Mangroves_MX_1853_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CStocks_Greenness_Mangroves_MX_1853_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.", "links": [ { diff --git a/datasets/CUSTARD_0.json b/datasets/CUSTARD_0.json index 45d8335d19..3b7d21bdff 100644 --- a/datasets/CUSTARD_0.json +++ b/datasets/CUSTARD_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CUSTARD_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface ocean is home to billions of microscopic plants called phytoplankton which produce organic matter in the surface ocean using sunlight and carbon dioxide. When they die many of them sink, taking this carbon into the deep ocean, where it may be stored for hundreds to thousands of years, which helps keep our climate the way it is today.In this project we will tackle this by making new observations in a remote region of the Southern Ocean using an exciting combination of robotic vehicles and sophisticated new sensors. We will make new observations of how much carbon the ocean takes up in this key motorway junction of the Southern Ocean. We will examine the processes that control the uptake of carbon and its fate, in particular how seasonal availability of nutrients can affect the make-up of the phytoplankton which changes the depth to which carbon sinks before being dissolved.", "links": [ { diff --git a/datasets/CV-580_710_1.json b/datasets/CV-580_710_1.json index 65e1d38e39..890099732c 100644 --- a/datasets/CV-580_710_1.json +++ b/datasets/CV-580_710_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CV-580_710_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud and Aerosol Research Group (CARG) of the University of Washington participated in the SAFARI-2000 Dry Season Aircraft campaign with their Convair-580 research aircraft. This campaign covered five countries in southern Africa from 10 August through 18 September 2000. Various types of measurements were obtained on the thirty-one research flights of the Convair-580 in SAFARI-2000, to study their relationships to simultaneous measurements from satellites (particularly Terra), other research aircraft, and SAFARI-2000 ground-based measurements and activities.", "links": [ { diff --git a/datasets/CV4A Kenya Crop Type Competition_1.json b/datasets/CV4A Kenya Crop Type Competition_1.json index 4f3220005f..64e64cf8b6 100644 --- a/datasets/CV4A Kenya Crop Type Competition_1.json +++ b/datasets/CV4A Kenya Crop Type Competition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CV4A Kenya Crop Type Competition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was produced as part of the [Crop Type Detection competition](https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition) at the [Computer Vision for Agriculture (CV4A) Workshop](https://www.cv4gc.org/cv4a2020/) at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellites.\n

\nThe ground reference data were collected by the PlantVillage team, and Radiant Earth Foundation curated the training dataset after inspecting and selecting more than 4,000 fields from the original ground reference data. The dataset has been split into training and test sets (3,286 in the train and 1,402 in the test).\n

\nThe dataset is cataloged in four tiles. These tiles are smaller than the original Sentinel-2 tile that has been clipped and chipped to the geographical area that labels have been collected.\n

\nEach tile has a) 13 multi-band observations throughout the growing season. Each observation includes 12 bands from Sentinel-2 L2A product, and a cloud probability layer. The twelve bands are [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12]. The cloud probability layer is a product of the Sentinel-2 atmospheric correction algorithm (Sen2Cor) and provides an estimated cloud probability (0-100%) per pixel. All of the bands are mapped to a common 10 m spatial resolution grid.; b) A raster layer indicating the crop ID for the fields in the training set; and c) A raster layer indicating field IDs for the fields (both training and test sets). Fields with a crop ID of 0 are the test fields.", "links": [ { diff --git a/datasets/CWA_Aeromag_Data.json b/datasets/CWA_Aeromag_Data.json index e18092e166..dd916ac735 100644 --- a/datasets/CWA_Aeromag_Data.json +++ b/datasets/CWA_Aeromag_Data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CWA_Aeromag_Data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aeromagnetic data presented here are one data set collected as a National\nScience Foundation (NSF) project entitled, \"Lithospheric controls on the\nbehavior of the West Antarctic ice sheet.\" This was a multi-institutional\nproject that includes the Institute for Geophysics of the University of Texas\n(UTIG) at Austin, the Lamont-Doherty Earth Observatory (LDEO), and the U. S.\nGeological Survey (USGS).", "links": [ { diff --git a/datasets/CWIC_REG_1.0.json b/datasets/CWIC_REG_1.0.json index 882bc3c30d..377b5d0e9c 100644 --- a/datasets/CWIC_REG_1.0.json +++ b/datasets/CWIC_REG_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CWIC_REG_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The collection represents browse images and metadata for systematically georeferenced Radarsat-2 Synthetic Aperture Radar(SAR) satellite scenes. The browse scenes are not geometrically enhanced using ground control points, but are systematically corrected using sensor parameters. Full resolution precision geocoded scenes(corrected using ground control points) which correspond to the browse images can be ordered from MacDonald Dettwiler and Associates Ltd., Vancouver, Canada. Metadata discovery is achieved using the online catalog http://neodf.nrcan.gc.ca OR by using the CWIC OGC CSW service URL : http://cwic.csiss.gmu.edu/cwicv1/discovery. The imaging frequency is C Band SAR : 5405.0000 MHz. RADARSAT-2 is in a polar, sun-synchronous orbit with a period of approximately 101 minutes. The RADARSAT-2 orbit\nwill be maintained at +\\/- 1 km in across track direction. This orbit maintenance is suitable for InSAR data collection. The geo-location accuracy of RADARSAT-2 products varies with product type. It is currently estimated at +\\/- 30 m for Standard beam products. The revisit period for RADARSAT-2 depends on the beam mode, incidence angle and geographic location of\nthe area of interest. In general, revisit is more frequent at the poles than the equator and the wider swath modes have higher revisit than t\nhe narrow swath modes.", "links": [ { diff --git a/datasets/CWIC_REG_RCM_1.0.json b/datasets/CWIC_REG_RCM_1.0.json index 5d577af631..af2b5cadfc 100644 --- a/datasets/CWIC_REG_RCM_1.0.json +++ b/datasets/CWIC_REG_RCM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CWIC_REG_RCM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The collection represents products and metadata for georeferenced Radarsat Constellation Mission ( RCM ) satellite scenes. Metadata discovery and product ordering is achieved using the online catalog https://www.eodms-sgdot.nrcan-rncan.gc.ca/index-en.html OR by using the CWIC OpenSearch OSDD : http://cwic.csiss.gmu.edu/cwicv1/discovery. \n\n ", "links": [ { diff --git a/datasets/CWIC_REG_Radarsat-1_1.0.json b/datasets/CWIC_REG_Radarsat-1_1.0.json index 4a906dc340..9244b76026 100644 --- a/datasets/CWIC_REG_Radarsat-1_1.0.json +++ b/datasets/CWIC_REG_Radarsat-1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CWIC_REG_Radarsat-1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The collection represents browse images and metadata for systematically georeferenced Radarsat-1 Synthetic Aperture Radar(SAR) satellite scenes. The browse scenes are not geometrically enhanced using ground control points, but are systematically corrected using sensor parameters. Full resolution precision geocoded scenes(corrected using ground control points) which correspond to the browse images can be ordered from MacDonald Dettwiler and Associates Ltd., Vancouver, Canada. Metadata discovery is achieved using the online catalog https://neodf.nrcan.gc.ca/neodf_cat3 OR by using the CWIC OGC CSW service URL : http://cwic.csiss.gmu.edu/cwicv1/discovery. \nRadarsat-1 operates at 5.3 GHz. (C-Band). It is in a sun-synchronous orbit. Image resolution is in the range 8-100 meters.", "links": [ { diff --git a/datasets/CYGNSS_L1_CAL_RAW_IF_V1.0_1.0.json b/datasets/CYGNSS_L1_CAL_RAW_IF_V1.0_1.0.json index e94f55de6a..934f92e3d7 100644 --- a/datasets/CYGNSS_L1_CAL_RAW_IF_V1.0_1.0.json +++ b/datasets/CYGNSS_L1_CAL_RAW_IF_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_CAL_RAW_IF_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS Level 1 Calibrated Raw IF Version 1.0 dataset is produced by the CYGNSS Science Team of the University of Michigan, and it contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.\r\n

\r\nThis product includes several established signal coherence detectors, including the power-ratio Pratio, complex zero-Doppler delay waveform and full entropy Efull, and a novel fast entropy detector Efast. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Several scattered signal strength products are included: Signal-to-Noise Ratio SNR, reflected power Pg, reflectivity \u0393, and Normalized Bistatic Radar Cross-Section NBRCS. Each of these products is derived using a coherent integration time of Tc = 1 ms and incoherent integration times of Ninc = 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (\u2206\u03c4 = 1/16 chip) and Doppler (\u2206f= 50 Hz) resolution. This suite of coherence detection methods can be used to detect the presence of small inland water bodies. ", "links": [ { diff --git a/datasets/CYGNSS_L1_CDR_V1.0_1.0.json b/datasets/CYGNSS_L1_CDR_V1.0_1.0.json index 6a958a8336..7aadfe4a84 100644 --- a/datasets/CYGNSS_L1_CDR_V1.0_1.0.json +++ b/datasets/CYGNSS_L1_CDR_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_CDR_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 1.0 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 2 months, depending on the availability of the MERRA wind speed reanalysis. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provide in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. The CDR exhibits improved calibration accuracy and stability over v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. By comparison, the v2.1 SDR L1 algorithm assumes a constant GPS transmit power, and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v2.1 SDR L1 data, so there is also some loss of samples that were present in v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.", "links": [ { diff --git a/datasets/CYGNSS_L1_CDR_V1.1_1.1.json b/datasets/CYGNSS_L1_CDR_V1.1_1.1.json index 5a191cbe07..47da2a93a1 100644 --- a/datasets/CYGNSS_L1_CDR_V1.1_1.1.json +++ b/datasets/CYGNSS_L1_CDR_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_CDR_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 1.1 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 month, depending on the availability of the MERRA wind speed reanalysis. The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L1C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.0 SDR L1 data, so there is also some loss of samples that were present in v3.0.", "links": [ { diff --git a/datasets/CYGNSS_L1_CDR_V1.2_1.2.json b/datasets/CYGNSS_L1_CDR_V1.2_1.2.json index ee939699f8..bac64156b4 100644 --- a/datasets/CYGNSS_L1_CDR_V1.2_1.2.json +++ b/datasets/CYGNSS_L1_CDR_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_CDR_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 1.2 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 week. The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L1C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.1 SDR L1 data, so there is also some loss of samples that were present in v3.1.", "links": [ { diff --git a/datasets/CYGNSS_L1_FULL_DDM_1.0.json b/datasets/CYGNSS_L1_FULL_DDM_1.0.json index 8874f4d2c7..513115d4ef 100644 --- a/datasets/CYGNSS_L1_FULL_DDM_1.0.json +++ b/datasets/CYGNSS_L1_FULL_DDM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_FULL_DDM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.", "links": [ { diff --git a/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json b/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json index df1cbaf4cf..1052b64c98 100644 --- a/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json +++ b/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_FULL_DDM_V3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 3.0 (v3.0) Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest. This version supersedes the Full DDM Version 1.0 (https://doi.org/10.5067/CYGNS-L1FDD) for data retrieved during or after August 2018. For data retrieved prior to August 2018, users will need to continue using the Full DDM Version 1.0. This version links the Full DDMs to the CYGNSS v3.0 L1 files (https://doi.org/10.5067/CYGNS-L1X30) whereas the version 1.0 Full DDM linked the Full DDMs to the CYGNSS v2.1 L1 files (https://doi.org/10.5067/CYGNS-L1X21). The calibration of the Full DDMs has not been modified for this release.", "links": [ { diff --git a/datasets/CYGNSS_L1_RAW_IF_1.0.json b/datasets/CYGNSS_L1_RAW_IF_1.0.json index ab5c6ea7f3..bac487b843 100644 --- a/datasets/CYGNSS_L1_RAW_IF_1.0.json +++ b/datasets/CYGNSS_L1_RAW_IF_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_RAW_IF_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Raw Intermediate Frequency (IF) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), digitizes the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna). The Raw IF data included in this data record are the raw sensor counts, retrieved prior to any digital signal processing, thus providing the highest possible resolution in delay and doppler space allowing for the construction of high resolution Delay Doppler Map (DDM) data. Raw IF data records are 30-90 sec in duration, with 60 sec being typical, and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.", "links": [ { diff --git a/datasets/CYGNSS_L1_V2.1_2.1.json b/datasets/CYGNSS_L1_V2.1_2.1.json index bb80c9048b..4461aeccdc 100644 --- a/datasets/CYGNSS_L1_V2.1_2.1.json +++ b/datasets/CYGNSS_L1_V2.1_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_V2.1_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) data is now available when the CYGNSS satellites are rolled away from nadir during orbital high beta-angle periods, resulting in a significant amount of additional data; 2) correction to coordinate frames result in more accurate estimates of receiver antenna gain at the specular point; 3) improved calibration for analog-to-digital conversion results in better consistency between CYGNSS satellites measurements at nearly the same location and time; 4) improved GPS EIRP and transmit antenna pattern calibration results in significantly reduced PRN-dependence in the observables; 5) improved estimation of the location of the specular point within the DDM; 6) an altitude-dependent scattering area is used to normalize the scattering cross section (v2.0 used a simpler scattering area model that varied with incidence and azimuth angles but not altitude); 7) corrections added for noise floor-dependent biases in scattering cross section and leading edge slope of delay waveform observed in the v2.0 data. Users should also note that the receiver antenna pattern calibration is not applied per-DDM-bin in this v2.1 release.", "links": [ { diff --git a/datasets/CYGNSS_L1_V3.0_3.0.json b/datasets/CYGNSS_L1_V3.0_3.0.json index 87ebeaa434..594be189a3 100644 --- a/datasets/CYGNSS_L1_V3.0_3.0.json +++ b/datasets/CYGNSS_L1_V3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_V3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21 . Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.0 data: 1) the transmitted GPS signal strength in the direction of the DDM scattering surface is determined in real time from measurements of the direct signal from the GPS satellite to the CYGNSS navigation receiver, allowing for the BRCS calibration to be corrected for variations in GPS transmit power; 2) the NBRCS has been validated using comparisons with a large population of modeled values derived from coincident ocean surface roughness spectra produced by the NOAA WAVEWATCH-3 oceanographic wave model; 3) L1 calibration parameters have been adjusted to produce a best fit to the model population.", "links": [ { diff --git a/datasets/CYGNSS_L1_V3.1_3.1.json b/datasets/CYGNSS_L1_V3.1_3.1.json index 309a4064fb..e1a5935702 100644 --- a/datasets/CYGNSS_L1_V3.1_3.1.json +++ b/datasets/CYGNSS_L1_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.1 data: The CYGNSS science antenna gain patterns have been adjusted to improve the accuracy of the ocean surface scattering cross section (a.k.a. the NBRCS) calibration. They are adjusted so that the annual average observed NBRCS matches the model-predicted average as derived from Wavewatch-3 estimates of the surface roughness with the appropriate spectral tail extension added to the roughness spectrum. The adjustment is made independently at each position in the science antenna pattern. A correction for coarse quantization effects by the on-board digital processor has also been added. This reduces the effects of radio frequency interference, which appeared as calibration biases in the v3.0 L1 NBRCS and retrieval biases in the v3.0 L2 wind speed that were persistent at certain locations.", "links": [ { diff --git a/datasets/CYGNSS_L1_V3.2_3.2.json b/datasets/CYGNSS_L1_V3.2_3.2.json index 3014f6fab3..a04e1383fb 100644 --- a/datasets/CYGNSS_L1_V3.2_3.2.json +++ b/datasets/CYGNSS_L1_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L1_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time.

\r\nThe correction for coarse quantization effects that was implemented in v3.1 for the signal portion of the DDM has been updated to include a correction to the noise floor portion of the DDM. This update is found to improve the sensitivity to soil moisture over land and to have a minimal effect on the sensitivity to wind speed over ocean. An update is made to the correction for the temperature dependence of the receiver electronics. This update reduces slow variations in calibration bias associated with a ~60 day oscillation in the mean temperature of the satellites. L1 variables over land and ocean are now combined in common netcdf data files, with additional details added regarding the specular point calculation over land. Nadir (science) antenna pattern and NBRCS rescaling has been updated to improve the inter-satellite consistency of the L1 calibration.

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.", "links": [ { diff --git a/datasets/CYGNSS_L2_CDR_V1.0_1.0.json b/datasets/CYGNSS_L2_CDR_V1.0_1.0.json index 895d684e2b..90a70fa6f9 100644 --- a/datasets/CYGNSS_L2_CDR_V1.0_1.0.json +++ b/datasets/CYGNSS_L2_CDR_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_CDR_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 2 months (or better) from the last recorded measurement time. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.", "links": [ { diff --git a/datasets/CYGNSS_L2_CDR_V1.1_1.1.json b/datasets/CYGNSS_L2_CDR_V1.1_1.1.json index 29ad55c511..295bb09df4 100644 --- a/datasets/CYGNSS_L2_CDR_V1.1_1.1.json +++ b/datasets/CYGNSS_L2_CDR_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_CDR_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.1 CDR represents is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L2X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L2C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.", "links": [ { diff --git a/datasets/CYGNSS_L2_CDR_V1.2_1.2.json b/datasets/CYGNSS_L2_CDR_V1.2_1.2.json index 77758c22b3..d62f5e7d81 100644 --- a/datasets/CYGNSS_L2_CDR_V1.2_1.2.json +++ b/datasets/CYGNSS_L2_CDR_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_CDR_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json index 1c73b5d729..98ff428352 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the first release, Version 1.0, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.0 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.0 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C10.", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json index 7ae9591993..77ef28acb6 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the first release, Version 1.1, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.1 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.1 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C11.", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json index 0a8432f374..c18836b7bc 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the third release, Version 1.2, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 6-7 day latency from the Delay Doppler Mapping Instrument (DDMI) aboard the Cyclone Global Navigation Satellite System (CYGNSS) constellation. CYGNSS is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.2 ocean surface wind speed estimates with the auxiliary parameters provided by the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS . Additional information on the CYGNSS L2 CDR v1.2 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C12 .", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json b/datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json index eec3f4209b..98cd9e0140 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Only one netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. Version 1.0 represents the first release. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. The 35 degree orbital inclination allows CYGNSS to measure ocean surface winds between approximately 38 degrees North and 38 degrees South latitude using an innovative combination of all-weather performance Global Positioning System (GPS) L-band ocean surface reflectometry to penetrate the clouds and heavy precipitation. The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), which uses data assimilation to combine all available in situ and satellite observation data with an initial estimate of the atmospheric state, provided by a global atmospheric model. Since the MERRA-2 data is only updated on monthly intervals, this corresponding heat flux dataset is likewise updated on a monthly interval to reflect the latest data available from MERRA-2, thus accounting for measurement latency, with respect to CYGNSS observables, ranging from 1 to 2 months. The data from this release compares well with in situ buoy data, including: Kuroshio Extension Observatory (KEO), National Data Buoy Center (NDBC), Ocean Sustained Interdisciplinary Time-series Environment observation System (OceanSITES), Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and the Tropical Atmosphere Ocean (TAO) array. As this marks only the first data release, future work is expected to provide comparisons and validation with various field campaigns (e.g., PISTON, CAMP2Ex) as well as more buoy data, especially at higher flux estimates.", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json b/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json index a1907092ca..90dc2e6126 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 2.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 2.0 represents the second release of this product, which now uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.1 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). Version 1.0 used CYGNSS L2 SDR Version 2.1 surface wind speeds and NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. As a result of the CYGNSS constellation coverage, this data is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.", "links": [ { diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json b/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json index 8a0f714073..c024e6caf4 100644 --- a/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json +++ b/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_SURFACE_FLUX_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS level 2 ocean surface heat flux science data record version 3.2 dataset is provided as a service to the oceanographic and meteorological research communities on behalf of the CYGNSS Science Team in direct collaboration with the Cyclone Global Navigation Satellite System (CYGNSS) Mission. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. \r\n

\r\nThis dataset provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 3.2 uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.2 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. This dataset is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.", "links": [ { diff --git a/datasets/CYGNSS_L2_V2.1_2.1.json b/datasets/CYGNSS_L2_V2.1_2.1.json index 4fb79e5666..c83566325b 100644 --- a/datasets/CYGNSS_L2_V2.1_2.1.json +++ b/datasets/CYGNSS_L2_V2.1_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_V2.1_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 2.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) revised Geophysical Model Functions (GMFs) for both Fully Developed Seas (FDS) and Young Seas with Limited Fetch conditions, to be consistent with the calibration changes made to the v2.1 Level 1 science data products.; 2) Revised covariance matrix between DDMA and LES versions of the FDS wind speed retrieval, used by the minimum variance estimator, resulting from changes made to the v2.1 Level 1 science data products; 3) Revised debiasing algorithm coefficients used by the FDS L2 retrieval algorithm, resulting from changes made to the v2.1 Level 2 science data products; 4) revised quality control (Q/C) flags related to the required level of consistency between DDMA and LES versions of the FDS wind speed retrieval (the errors in the two retrievals are now less correlated so larger discrepancies are allowed; if either retrieval is not available, the sample receives a fatal Q/C flag); 5) new Q/C flag related to the block type of the GPS satellite which provided the transmitted signal. Samples using block II-F signals receive a fatal Q/C flag due to the higher level of uncertainty in their radiated power; 6) revised wind speed uncertainty values as a function of RCG and wind speed, plus a new dependence of the uncertainty on GPS block type to reflect the higher uncertainty in GPS radiated power for block II-F satellites.", "links": [ { diff --git a/datasets/CYGNSS_L2_V3.0_3.0.json b/datasets/CYGNSS_L2_V3.0_3.0.json index 80211627ed..efbffbd4b1 100644 --- a/datasets/CYGNSS_L2_V3.0_3.0.json +++ b/datasets/CYGNSS_L2_V3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_V3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 3.0 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L2X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.0 data: 1) the changes to calibration and validation of the Level 1 Normalized Bistatic Radar Cross Section (NBRCS) necessitated updates to the Geophysical Model Functions (GMFs) used to retrieve wind speed; 2) the GMF for fully developed seas (FDS) conditions was generating using matchups between NBRCS measurements and coincident wind speeds produced by NASAs Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis model; 3) the GMF for young seas with limited fetch (YSLF) was generated using matchups between NBRCS and coincident wind speeds produced by NOAAs Hurricane Weather Research and Forecast (HWRF) System; 4) YSLF wind speed is a tapered linear combination of wind speeds derived from the FDS and YSLF GMFs, where the taper gives more weight to FDS at low wind speeds and more to YSLF at high wind speeds and accounts for the transition from FDS to YSLF sea state conditions near cyclonic storms; 5) re-introduces measurements using transmissions from previously discarded GPS satellite block types; in prior versions, Block II-F was completely discarded due to large variations in GPS transmit power. The real time transmit power monitoring and correction implemented in Level 1 v3.0 data now allows Block II-F signals to be used.", "links": [ { diff --git a/datasets/CYGNSS_L2_V3.1_3.1.json b/datasets/CYGNSS_L2_V3.1_3.1.json index 0b3c567d63..e6f65ccfd8 100644 --- a/datasets/CYGNSS_L2_V3.1_3.1.json +++ b/datasets/CYGNSS_L2_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 3.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L2X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.1 data: The L2 Geophysical Model Functions (GMFs) that map L1 observables to ocean surface wind speed were rederived to be consistent with the v3.1 L1 calibration. The method used for deriving the GMFs is the same as for v3.0. A new correction has been added to both the Fully Developed Seas (FDS) and Young Seas Limited Fetch (YSLF) wind speed products that is a function of the Significant Wave Height (SWH) of the ocean surface. The correction is based on an observed correlation between the wind speed error and SWH. The SWH value used by the correction algorithm is the ERA5 reanalysis product, coincident in space and time with a CYGNSS measurement. The FDS and YSLF retrieval algorithms are otherwise the same as v3.0.", "links": [ { diff --git a/datasets/CYGNSS_L2_V3.2_3.2.json b/datasets/CYGNSS_L2_V3.2_3.2.json index 9e707c1585..9b5dae3234 100644 --- a/datasets/CYGNSS_L2_V3.2_3.2.json +++ b/datasets/CYGNSS_L2_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L2_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2 CYGNSS level 2 science data record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L2X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time.

\r\n\r\nThe L2 Geophysical Model Function (GMF) that maps L1 observables to ocean surface wind speed and the Significant Wave Height (SWH) second order correction to the wind speed retrievals were rederived to be consistent with the v3.2 L1 calibration. The method used for deriving the GMF and SWH correction is the same as for v3.1. An additional swell wave correction has been added to better account for the long wave dependence at low wind speeds. The FDS and YSLF retrieval algorithms are otherwise the same as v3.1. The v3.2 L2 YSLF wind speed is now designated as an intermediate product and should not be used \u2018as is\u2019. Additional quality control filters have been added to the Level 3 gridded product derived from the L2 YSLF wind speed to detect and remove outlier L2 samples, and use of the L3 product is recommended.

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.", "links": [ { diff --git a/datasets/CYGNSS_L3_CDR_V1.0_1.0.json b/datasets/CYGNSS_L3_CDR_V1.0_1.0.json index 81e411817d..cd829fce6e 100644 --- a/datasets/CYGNSS_L3_CDR_V1.0_1.0.json +++ b/datasets/CYGNSS_L3_CDR_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_CDR_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.", "links": [ { diff --git a/datasets/CYGNSS_L3_CDR_V1.1_1.1.json b/datasets/CYGNSS_L3_CDR_V1.1_1.1.json index 6d68f38f56..0a28575bea 100644 --- a/datasets/CYGNSS_L3_CDR_V1.1_1.1.json +++ b/datasets/CYGNSS_L3_CDR_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_CDR_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.", "links": [ { diff --git a/datasets/CYGNSS_L3_CDR_V1.2_1.2.json b/datasets/CYGNSS_L3_CDR_V1.2_1.2.json index 5fe15bcb2d..3ec58dee64 100644 --- a/datasets/CYGNSS_L3_CDR_V1.2_1.2.json +++ b/datasets/CYGNSS_L3_CDR_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_CDR_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 5 days latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L3X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L3C11 ), which was derived from SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.", "links": [ { diff --git a/datasets/CYGNSS_L3_MICROPLASTIC_V1.0_1.0.json b/datasets/CYGNSS_L3_MICROPLASTIC_V1.0_1.0.json index b085cd5a7b..fe643fa0c1 100644 --- a/datasets/CYGNSS_L3_MICROPLASTIC_V1.0_1.0.json +++ b/datasets/CYGNSS_L3_MICROPLASTIC_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MICROPLASTIC_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 1.0 CYGNSS level 3 ocean microplastic concentration data record, which provides 18 netCDF files, each containing one month of daily gridded maps of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatiotemporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed).", "links": [ { diff --git a/datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json b/datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json index d04ec2184d..4129bf0028 100644 --- a/datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MICROPLASTIC_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan.\r\nCYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.\r\n

\r\nThis dataset contains the version 3.2 CYGNSS Level 3 ocean microplastic concentration data record, which provides daily netCDF files, each file containing a gridded map of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatial temporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed). Version 3.2 uses CYGNSS MSS measurements that are derived from updated v3.2 Level 1 scattering cross section data and has updated the parameterizations in the data processing algorithm to use v3.2 data correctly.", "links": [ { diff --git a/datasets/CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1.json b/datasets/CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1.json index 1de988d18d..ebefcff13a 100644 --- a/datasets/CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1.json +++ b/datasets/CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2.1 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid. \r\n

\r\nL3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the September 1, 2024 through the present with an approximate latency between 2 and 24 hours. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis. \r\n

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\r\n", "links": [ { diff --git a/datasets/CYGNSS_L3_MRG_NRT_V3.2_3.2.json b/datasets/CYGNSS_L3_MRG_NRT_V3.2_3.2.json index 0611418852..dff9435219 100644 --- a/datasets/CYGNSS_L3_MRG_NRT_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_MRG_NRT_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MRG_NRT_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid.\r\n

\r\nL3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the June 11, 2024 through the present with an approximate latency between 2 and 24 hours . A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis.\r\n

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement", "links": [ { diff --git a/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json b/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json index 22134759f6..7bd6d1c5b0 100644 --- a/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json +++ b/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MRG_V3.2.1_3.2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2.1 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. \r\n

\r\nL3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis.\r\n

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.", "links": [ { diff --git a/datasets/CYGNSS_L3_MRG_V3.2_3.2.json b/datasets/CYGNSS_L3_MRG_V3.2_3.2.json index 4ffe5e9fb3..b017ac4310 100644 --- a/datasets/CYGNSS_L3_MRG_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_MRG_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_MRG_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 YSLF winds for a region surrounding a given tropical cyclone (TC), with L3 FDS winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation.

\r\n\r\nL3 MRG is a new product which combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netcdf files are output on a storm-by-storm basis.

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.", "links": [ { diff --git a/datasets/CYGNSS_L3_S1.0_1.0.json b/datasets/CYGNSS_L3_S1.0_1.0.json index 94a91f6072..75f7ebb3c0 100644 --- a/datasets/CYGNSS_L3_S1.0_1.0.json +++ b/datasets/CYGNSS_L3_S1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_S1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Storm centric coordinates are derived from the National Hurricane Center (NHC) Best Track dataset to produce a 6 hourly wind speed averaging window. A single netCDF-4 data file is produced for each storm. Each storm is uniquely identified by the year, storm basin, and a storm number. This dataset is intended for historical storm analysis, and as such, this dataset is periodically updated based on the availability of the NHC Best Track storm center information that is typically made available in April for the previous year's hurricane season. SCG files are produced for named storms, as defined by the NHC, that reach hurricane strength (i.e., having a maximum sustained wind speed of at least 65 knots). Due to the dependency on NHC Best Track data, the SCG files produced in this dataset are confined to storms in the Northern Hemisphere within the North Atlantic and East Pacific ocean regions. Wind speed inputs are provided by the CYGNSS Level 2 SDR Version 3.0 (https://doi.org/10.5067/CYGNS-L2X30 ).", "links": [ { diff --git a/datasets/CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0.json b/datasets/CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0.json index 8fe3c542db..59114fe3ed 100644 --- a/datasets/CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0.json +++ b/datasets/CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR. The soil moisture algorithm uses collocated soil moisture retrievals from SMAP to calibrate CYGNSS observations from the same day. For a given location, a linear relationship between the SMAP soil moisture and CYGNSS reflectivity is determined and used to transform the CYGNSS observations into soil moisture. The data are archived in daily files in netCDF-4 format. Two soil moisture variables report the volumetric water content in units of cm3/cm3. The variable SM_subdaily includes up to four soil moisture estimates per day. Another variable SM_daily provides a daily average. The time series covers the period from March 2017 to present.", "links": [ { diff --git a/datasets/CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2.json b/datasets/CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2.json index d7ad43a372..6864594676 100644 --- a/datasets/CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.\r\n

\r\nThe soil moisture retrieval algorithm is an update of the previous version developed by UCAR-CU using a linear regression of CYGNSS angle-normalized effective surface reflectivity trained against collocated SMAP soil moisture during the calibration period 8/1/2018 to 11/15/2023. The data are archived in daily files in netCDF-4 format. Volumetric soil moisture water content in units of cm3/cm3 is provided with two gridding resolutions, 9x9 km and 36x36 km. The variable SM_subdaily contains data reported in six hour intervals. The variable SM_daily provides a daily average. The time series covers the period from August 2018 to present.", "links": [ { diff --git a/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2.json b/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2.json index 856de861a8..29333b091d 100644 --- a/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. \n

\nThis dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.\n

\nThis product is recommended for operational use. For science applications, we recommend the use of the Berkeley-RWAWC monthly product instead: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 Note that the daily product consist of maps constructed using the most recent 31 days of data to rapidly capture surface water dynamics without relying on historical data. While the oldest data within this 31 day-period is weighted less and replaced by newer observations as they become available, extreme flood events may still be detected with a delay due to the incorporation of prior days\u2019 data into the algorithm. The incorporation of older data is necessary to maintain the spatial scale. ", "links": [ { diff --git a/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1.json b/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1.json index b26de6fa0d..323dec3cae 100644 --- a/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1.json +++ b/datasets/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. \r\n

\r\nThis dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X31), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present.", "links": [ { diff --git a/datasets/CYGNSS_L3_V2.1_2.1.json b/datasets/CYGNSS_L3_V2.1_2.1.json index 2772d765ee..43d5dfa450 100644 --- a/datasets/CYGNSS_L3_V2.1_2.1.json +++ b/datasets/CYGNSS_L3_V2.1_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_V2.1_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1", "links": [ { diff --git a/datasets/CYGNSS_L3_V3.0_3.0.json b/datasets/CYGNSS_L3_V3.0_3.0.json index 1142b82f34..f4ff1f70ab 100644 --- a/datasets/CYGNSS_L3_V3.0_3.0.json +++ b/datasets/CYGNSS_L3_V3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_V3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 3.0 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L3X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.0 release inherits all improvements made to the version 3.0 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.0 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X30.", "links": [ { diff --git a/datasets/CYGNSS_L3_V3.1_3.1.json b/datasets/CYGNSS_L3_V3.1_3.1.json index df1be045fa..5bd6ed8c94 100644 --- a/datasets/CYGNSS_L3_V3.1_3.1.json +++ b/datasets/CYGNSS_L3_V3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_V3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 3.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L3X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.1 release inherits all improvements made to the version 3.1 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.1 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X31.", "links": [ { diff --git a/datasets/CYGNSS_L3_V3.2_3.2.json b/datasets/CYGNSS_L3_V3.2_3.2.json index e91e385c8d..4f5750f527 100644 --- a/datasets/CYGNSS_L3_V3.2_3.2.json +++ b/datasets/CYGNSS_L3_V3.2_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_L3_V3.2_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the version 3.2 CYGNSS level 3 science data record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.1; https://doi.org/10.5067/CYGNS-L3X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).

\r\nThe v3.2 L3 gridded wind speed product inherits the v3.2 L2 FDS data as input at the same temporal and spatial resolution as the Level 2 data, sampled on consistent 0.2 by 0.2 degree latitude by longitude grid cells. The L3 gridding algorithm is unchanged. Range Corrected Gain (RCG) has been added to the L3 netcdf files as a new data field.

\r\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.", "links": [ { diff --git a/datasets/CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2.json b/datasets/CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2.json index 827cd16858..cf5c569bee 100644 --- a/datasets/CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2.json +++ b/datasets/CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).", "links": [ { diff --git a/datasets/CZCS_L0_1.json b/datasets/CZCS_L0_1.json index 8c81312698..a5d5d983b8 100644 --- a/datasets/CZCS_L0_1.json +++ b/datasets/CZCS_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L1_1.json b/datasets/CZCS_L1_1.json index 98d4aa77c0..63b7fea73d 100644 --- a/datasets/CZCS_L1_1.json +++ b/datasets/CZCS_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to\nthe measurement of ocean color and flown on a spacecraft. Although other instruments\nflown on other spacecraft had sensed ocean color, their spectral bands, spatial\nresolution and dynamic range were optimized for land or meteorological use and had\nlimited sensitivity in this area, whereas in CZCS, every parameter was optimized for\nuse over water to the exclusion of any other type of sensing. CZCS had six spectral\nbands, four of which were used primarily for ocean color. These were of a 20 nanometer\nbandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered\nat 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5\nmicrometer region and sensed emitted thermal radiance for derivation of equivalent\nblack body temperature. (This thermal band failed within the first year of the mission,\nand so was not used in the global processing effort.) Bands 1-4 were preset to view\nwater only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L1_2.json b/datasets/CZCS_L1_2.json index ecaf58477b..cc1754af55 100644 --- a/datasets/CZCS_L1_2.json +++ b/datasets/CZCS_L1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L2_OC_2014.json b/datasets/CZCS_L2_OC_2014.json index 752e94df58..60e34c35f0 100644 --- a/datasets/CZCS_L2_OC_2014.json +++ b/datasets/CZCS_L2_OC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L2_OC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to\nthe measurement of ocean color and flown on a spacecraft. Although other instruments\nflown on other spacecraft had sensed ocean color, their spectral bands, spatial\nresolution and dynamic range were optimized for land or meteorological use and had\nlimited sensitivity in this area, whereas in CZCS, every parameter was optimized for\nuse over water to the exclusion of any other type of sensing. CZCS had six spectral\nbands, four of which were used primarily for ocean color. These were of a 20 nanometer\nbandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered\nat 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5\nmicrometer region and sensed emitted thermal radiance for derivation of equivalent\nblack body temperature. (This thermal band failed within the first year of the mission,\nand so was not used in the global processing effort.) Bands 1-4 were preset to view\nwater only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L2_OC_2022.0.json b/datasets/CZCS_L2_OC_2022.0.json index b3882471d7..e301769d1c 100644 --- a/datasets/CZCS_L2_OC_2022.0.json +++ b/datasets/CZCS_L2_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L2_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_CHL_2014.json b/datasets/CZCS_L3b_CHL_2014.json index a2bc2390af..225a2cb496 100644 --- a/datasets/CZCS_L3b_CHL_2014.json +++ b/datasets/CZCS_L3b_CHL_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_CHL_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_CHL_2022.0.json b/datasets/CZCS_L3b_CHL_2022.0.json index 96a71dbfae..80f0136654 100644 --- a/datasets/CZCS_L3b_CHL_2022.0.json +++ b/datasets/CZCS_L3b_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_KD_2014.json b/datasets/CZCS_L3b_KD_2014.json index 139981804e..45f1947d15 100644 --- a/datasets/CZCS_L3b_KD_2014.json +++ b/datasets/CZCS_L3b_KD_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_KD_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_KD_2022.0.json b/datasets/CZCS_L3b_KD_2022.0.json index 5fe3a264c1..e66f5d3536 100644 --- a/datasets/CZCS_L3b_KD_2022.0.json +++ b/datasets/CZCS_L3b_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_PIC_2022.0.json b/datasets/CZCS_L3b_PIC_2022.0.json index f46c0638b3..084a1b7981 100644 --- a/datasets/CZCS_L3b_PIC_2022.0.json +++ b/datasets/CZCS_L3b_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_POC_2022.0.json b/datasets/CZCS_L3b_POC_2022.0.json index 66fcf4aff0..8708bc6615 100644 --- a/datasets/CZCS_L3b_POC_2022.0.json +++ b/datasets/CZCS_L3b_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_RRS_2014.json b/datasets/CZCS_L3b_RRS_2014.json index 8035b5e430..e3908df5ce 100644 --- a/datasets/CZCS_L3b_RRS_2014.json +++ b/datasets/CZCS_L3b_RRS_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_RRS_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3b_RRS_2022.0.json b/datasets/CZCS_L3b_RRS_2022.0.json index 07a9dfac11..7acdb064c2 100644 --- a/datasets/CZCS_L3b_RRS_2022.0.json +++ b/datasets/CZCS_L3b_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3b_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_CHL_2014.json b/datasets/CZCS_L3m_CHL_2014.json index c22fa9fc69..9b1fd5fad5 100644 --- a/datasets/CZCS_L3m_CHL_2014.json +++ b/datasets/CZCS_L3m_CHL_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_CHL_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_CHL_2022.0.json b/datasets/CZCS_L3m_CHL_2022.0.json index a0fcbfdc39..2dba86ad75 100644 --- a/datasets/CZCS_L3m_CHL_2022.0.json +++ b/datasets/CZCS_L3m_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_KD_2014.json b/datasets/CZCS_L3m_KD_2014.json index bcad372362..13b0d2916a 100644 --- a/datasets/CZCS_L3m_KD_2014.json +++ b/datasets/CZCS_L3m_KD_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_KD_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_KD_2022.0.json b/datasets/CZCS_L3m_KD_2022.0.json index c356015af2..7a911e7839 100644 --- a/datasets/CZCS_L3m_KD_2022.0.json +++ b/datasets/CZCS_L3m_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_PIC_2022.0.json b/datasets/CZCS_L3m_PIC_2022.0.json index a0860af02a..f78518c71f 100644 --- a/datasets/CZCS_L3m_PIC_2022.0.json +++ b/datasets/CZCS_L3m_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_POC_2022.0.json b/datasets/CZCS_L3m_POC_2022.0.json index 234bab2e6f..4fd32fbe7c 100644 --- a/datasets/CZCS_L3m_POC_2022.0.json +++ b/datasets/CZCS_L3m_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_RRS_2014.json b/datasets/CZCS_L3m_RRS_2014.json index bd168f2e80..59cfb69561 100644 --- a/datasets/CZCS_L3m_RRS_2014.json +++ b/datasets/CZCS_L3m_RRS_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_RRS_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZCS_L3m_RRS_2022.0.json b/datasets/CZCS_L3m_RRS_2022.0.json index daaa16fe21..3e658dbb4a 100644 --- a/datasets/CZCS_L3m_RRS_2022.0.json +++ b/datasets/CZCS_L3m_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZCS_L3m_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", "links": [ { diff --git a/datasets/CZM_moris_algonquin_hubline_lng_arc.json b/datasets/CZM_moris_algonquin_hubline_lng_arc.json index 8b875b6f13..e8fadd0a6e 100644 --- a/datasets/CZM_moris_algonquin_hubline_lng_arc.json +++ b/datasets/CZM_moris_algonquin_hubline_lng_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CZM_moris_algonquin_hubline_lng_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS layer shows the Hubline, an approximately 29.5 mile natural gas pipeline constructed primarily in the ocean along the coast of Massachusetts between Beverly and Weymouth. The route travels in a southerly direction through the communities of Salem, Beverly, Marblehead, Swampscott, Lynn, Nahant, Winthrop, Boston, Hull, Quincy, and Weymouth. This dataset represents an as-built location of the pipeline. Original survey for the bottom position of the pipeline was established by a combination of surface position of the installation vessel using DGPS, diver's surveys, multibeam surveys, and sidescan surveys. The project was surveyed in accordance with the USACOE's minimum standards and techniques as defined in the engineering manual EM 1110-2-1003.", "links": [ { diff --git a/datasets/C_Bibliography_1.json b/datasets/C_Bibliography_1.json index 82a43a771e..bf3c407445 100644 --- a/datasets/C_Bibliography_1.json +++ b/datasets/C_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of references relating to Collembola from the Antarctic and subantarctic regions, dating from 1876 to 2004. The bibliography was compiled by Penny Greenslade, and contains 105 references.", "links": [ { diff --git a/datasets/C_FluxStocks_CLM5_DART_WestUS_1856_1.json b/datasets/C_FluxStocks_CLM5_DART_WestUS_1856_1.json index f906a0ba9d..b3a728c2bb 100644 --- a/datasets/C_FluxStocks_CLM5_DART_WestUS_1856_1.json +++ b/datasets/C_FluxStocks_CLM5_DART_WestUS_1856_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C_FluxStocks_CLM5_DART_WestUS_1856_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a \"free\" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) \"assimilation\" runs using the land surface data assimilation system (CLM5-DART). In assimilation runs, the CLM5 vegetation state is constrained by remotely sensed observations of leaf area index and aboveground biomass, which influenced biomass stocks and carbon fluxes.", "links": [ { diff --git a/datasets/C_Pools_Fluxes_CONUS_1837_1.json b/datasets/C_Pools_Fluxes_CONUS_1837_1.json index bcd196ac89..0aac54f81b 100644 --- a/datasets/C_Pools_Fluxes_CONUS_1837_1.json +++ b/datasets/C_Pools_Fluxes_CONUS_1837_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "C_Pools_Fluxes_CONUS_1837_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon. Carbon fluxes include gross primary production (GPP), net primary production (NPP), net biome exchange, autotrophic respiration, and heterotrophic respiration. The modeled estimates are provided as monthly averages over the 16-year period, 2001 through 2016. The data were derived from the CARbon DAta MOdel fraMework (CARDAMOM) that included climate data, and above and below ground biomass maps of CONUS for the years 2005, 2010, 2015 and 2016 as input data sources to this model-data fusion framework. The input data were integrated into the CARDAMOM model to constrain on the terrestrial carbon and to specifically attribute changes of forest carbon stocks and spatial distributions of carbon emissions and removals across forested lands. United States Forest Service's Forest Inventory and Analysis (FIA) plot data were used to train models for the prediction of forest above-ground biomass (AGB).", "links": [ { diff --git a/datasets/CaTS_0.json b/datasets/CaTS_0.json index 469c5cf44a..e00ed0ceb8 100644 --- a/datasets/CaTS_0.json +++ b/datasets/CaTS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CaTS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The department of Marine Sciences at the University of Puerto Rico maintained the Caribbean Time Series (CaTS) between 1993 and 2007. Sampling took place approximately monthly at a station 26 nautical miles southwest of Puerto Rico.", "links": [ { diff --git a/datasets/CalCOFI_0.json b/datasets/CalCOFI_0.json index 2ce4eb67dd..9cefdb1c8d 100644 --- a/datasets/CalCOFI_0.json +++ b/datasets/CalCOFI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CalCOFI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The California Cooperative Oceanic Fisheries Investigations (CalCOFI)", "links": [ { diff --git a/datasets/California_2002_0.json b/datasets/California_2002_0.json index 5a38b60871..668d2a23ec 100644 --- a/datasets/California_2002_0.json +++ b/datasets/California_2002_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "California_2002_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the California coast in 2002.", "links": [ { diff --git a/datasets/CanSIS_Regional_Soils_1347_2.json b/datasets/CanSIS_Regional_Soils_1347_2.json index 518ec5b109..b4dcffa37d 100644 --- a/datasets/CanSIS_Regional_Soils_1347_2.json +++ b/datasets/CanSIS_Regional_Soils_1347_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CanSIS_Regional_Soils_1347_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soils data from the Canada Soil Information System (CanSIS) in ESRI Shapefile format for the provinces of Saskatchewan and Manitoba. They are provided as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science GIS data collection program. Attribute tables provide the various soil data for the polygons. There is one attribute table for Saskatchewan and one for Manitoba. This data product may be useful to someone who is interested in studying this area at a regional scale.", "links": [ { diff --git a/datasets/Canada_Boreal_Forest_Greenness_1587_1.json b/datasets/Canada_Boreal_Forest_Greenness_1587_1.json index 493e162142..4ee2ee0efd 100644 --- a/datasets/Canada_Boreal_Forest_Greenness_1587_1.json +++ b/datasets/Canada_Boreal_Forest_Greenness_1587_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Canada_Boreal_Forest_Greenness_1587_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel.", "links": [ { diff --git a/datasets/Canadian_West_Arctic_Veg_Plots_1543_1.json b/datasets/Canadian_West_Arctic_Veg_Plots_1543_1.json index 4c3d0e931b..d7ffd16db8 100644 --- a/datasets/Canadian_West_Arctic_Veg_Plots_1543_1.json +++ b/datasets/Canadian_West_Arctic_Veg_Plots_1543_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Canadian_West_Arctic_Veg_Plots_1543_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation, soil, and plot characteristics for 154 study plots located at three sites across the Richardson Mountains, Northwest Territories (NWT), and the British Mountains, Yukon Territory (YT). Study sites in the NWT included areas near Canoe Lake and Divided Lake; the study site in the YT was near Trout Lake. Specific attributes include dominant vegetation, species cover, and the physical characteristics of the plot areas. A soil pit was dug at each plot and the physical and chemical characteristics were determined for soil horizons. The data were collected in June, July, and August of 1965 and July and August of 1966.", "links": [ { diff --git a/datasets/CapeHatteras2010_0.json b/datasets/CapeHatteras2010_0.json index f8c195bea2..887604340e 100644 --- a/datasets/CapeHatteras2010_0.json +++ b/datasets/CapeHatteras2010_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CapeHatteras2010_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Cape Hatteras in 2010.", "links": [ { diff --git a/datasets/Cape_Darnley_Bloom_1.json b/datasets/Cape_Darnley_Bloom_1.json index b19926c282..e77bbe1520 100644 --- a/datasets/Cape_Darnley_Bloom_1.json +++ b/datasets/Cape_Darnley_Bloom_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cape_Darnley_Bloom_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data relate to a large-scale early-autumn phytoplankton bloom that occurred off Cape Darnley, East Antarctica, in March 2012. The bloom was detected by Dr Jan Lieser (Antarctic Climate and Ecosystems Cooperative Research Centre, ACE-CRC) through MODIS satellite and was opportunistically sampled from RSV Aurora Australis using the uncontaminated seawater line. Samples were analysed for protist species and abundances using light and scanning electron microscopy, and pigment analyses were conducted using high performance liquid chromatography. Additional water samples were taken for dissolved nutrient analyses. \n\nSpecific details of the files are:\n\nCape Darnley Protist Counts\nSamples were preserved with 1 % vol:vol Lugols iodine and stored in glass bottles in the dark at 4 degrees C. Protists were identified and counted using phase and Nomarski interference optics using Olympus IX71 and IX81 inverted microscopes at 400X to 640X magnification. Bright field optics were also used to discriminate taxa that contained chloroplasts. Protistan taxa were counted in 20 randomly chosen fields of view, except for highly abundant taxa that were counted in a subset of the field of view defined by an ocular quadrant (Whipple grid). Cell biovolumes and carbon conversion statistics were used to calculate the cell biomass of protistan taxa/groups.\n\nCape Darnley Fluorometer Calibration\nFluorometer measurements from the ships underway system were calibrated using chlorophyll a readings determined through high performance liquid chromatography. A linear relationship was established between fluorometer v HPLC chlorophyll a measurements at the same sites. The linear equation was then used to convert all underway fluorometry data from the voyage.\n\nCape Darnley Bloom HPLC Pigments CHEMTAX summary\nMajor phytoplankton groups at each site determined through analysis of pigments using high performance liquid chromatography and CHEMTAX. Methods were according to that of Wright et al. (2010).\n\nCape Darnley Bloom Nutrients\nDissolved nutrient concentrations. Samples were analysed by the Department of Primary Industries, Parks, Water and Environment, 18 St. Johns Avenue, Newtown, Tasmania 7008.\n\nCape Darnley Underway Data VOYAGE_04_0_201112\nRaw underway data from Aurora Australis in the bloom region\n\nCape Darnley Underway Data Maps\nMaps of the underway data in the bloom region", "links": [ { diff --git a/datasets/Carbon_Estuaries_0.json b/datasets/Carbon_Estuaries_0.json index 292f88273c..fe594a654c 100644 --- a/datasets/Carbon_Estuaries_0.json +++ b/datasets/Carbon_Estuaries_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Carbon_Estuaries_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Response of Carbon Cycling to Climatic and Anthropogenic Perturbations in Two North American Subtropical Estuaries, near Tampa Bay and Biscayne Bay. Collaboration with USF, Upenn, and FIU.", "links": [ { diff --git a/datasets/Carbon_Transport_MS_River_0.json b/datasets/Carbon_Transport_MS_River_0.json index c02c2ed45f..1ceb86fa25 100644 --- a/datasets/Carbon_Transport_MS_River_0.json +++ b/datasets/Carbon_Transport_MS_River_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Carbon_Transport_MS_River_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the Mississippi River outflow region in the Gulf of Mexico between 2001 and 2003.", "links": [ { diff --git a/datasets/Carbon_UAV_0.json b/datasets/Carbon_UAV_0.json index 592063974d..497533b176 100644 --- a/datasets/Carbon_UAV_0.json +++ b/datasets/Carbon_UAV_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Carbon_UAV_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the High Resolution Assessment of Carbon Dynamics in Seagrass and Coral Reef Biomes, in the Florida Keys.", "links": [ { diff --git a/datasets/Cartagena_Station_0.json b/datasets/Cartagena_Station_0.json index 127a80898c..60c65f6de1 100644 --- a/datasets/Cartagena_Station_0.json +++ b/datasets/Cartagena_Station_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cartagena_Station_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antares Cartagena station is located 10 km offshore, across from the Cartagena Bay/Caribbean Sea at 75° 36'W, 10° 22'N in Cartagena, Colombia. Activities in the station are coordinated by the General Maritime Directorate for the Caribbean Oceanographic and Hydrographic Research Center (CIOH).Activities at Antares Cartagena Station started in 2008 when the CIOH joined the ANTARES monitoring network (see http://www.antares.ws/?p=station.php&st=7). The CIOH has measured in situ CTD temprature, pressure, salinity, oxygen and fluorescence. Additionally, water samples are taken at discrete depths with Niskin bottles and temperature, salinity, oxygen and pH are measured through multiparameter probe.Assays for determining chlorophyll, planktonic community analysis, determination of nutrients (nitrate, nitrite, ammonium, orthophosphates and silicates) and SST, are performed in CIOH's laboratory which is accredited under NTC ISO / IEC 17025:2001 for physic-chemical analysis of marine and estuarine waters.", "links": [ { diff --git a/datasets/CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0.json b/datasets/CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0.json index 1bf07def23..0cfbb46fff 100644 --- a/datasets/CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0.json +++ b/datasets/CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CartoSat-1 (also known as IRS-P5) archive products are available as PAN-Aft (backward), PAN-Fore (forward) and Stereo (PAN-Aft and PAN-Fore). - Sensor: PAN - Products: PAN-Aft (backward), PAN-Fore (forward), Stereo (PAN-Aft+PAN-Fore) - Type: Panchromatic - Resolution (m): 2.5 - Coverage (km x km): 27 x 27 - System or radiometrically corrected - Ortho corrected (DN) - Neustralitz archive: 2007 - 2016 - Global archive: 2005 - 2019 Note: - Resolution 2.5 m. - Coverage 27 km x 27 km. - System or radiometrically corrected. For Ortho corrected products: If unavailable, user has to supply ground control information and DEM in suitable quality, - For Stereo ortho corrected: only one of the datasets will be ortho corrected. Euro-Maps 3D is a homogeneous, 5 m spaced digital surface model (DSM) semi-automatically derived from 2.5 m in-flight stereo data provided by IRS-P5 CartoSat-1 and developed in cooperation with the German Aerospace Center, DLR. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. In addition, the final product includes detailed flanking information consisting of several pixel-based quality and traceability layers also including an ortho layer. Product Overview: - Post spacing: 5m - Spatial reference system: DD, UTM or other projections on WGS84 - Height reference system: EGM96 - Absolute vertical accuracy: LE90 5-10 m - Absolute Horizontal Accuracy: CE90 5-10 m - Relative vertical accuracy: LE90 2.5 m - File format: GeoTIFF, 16 bit - Tiling: 0.5\u00b0 x 0.5\u00b0 - Ortho Layer Pixel Size: 2.5 m The CartoSat-1 products and Euro-Maps 3D are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. \u2018Cartosat-1 archive\u2019 collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) as well as Euro-Maps 3D DSM products can be requested by contacting GAF user support to check the readiness since no catalogue is available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.", "links": [ { diff --git a/datasets/Cartosat-1.Euro-Maps.3D_7.0.json b/datasets/Cartosat-1.Euro-Maps.3D_7.0.json index 4fdeaae9f1..521685d3ff 100644 --- a/datasets/Cartosat-1.Euro-Maps.3D_7.0.json +++ b/datasets/Cartosat-1.Euro-Maps.3D_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cartosat-1.Euro-Maps.3D_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage.\r\rThe Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers:\r\rThe dsm layer (*_dsm.tif) contains the elevation heights as a geocoded raster file\rThe source layer (*_src.tif) contains information about the data source for each height value/pixel\rThe number layer (*_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM\rThe quality layer (*_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications\rThe accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel.\rThe ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types.\r\rEO-SIP product type\tDescription\rPAN_PAM_3O\tIRS-P5 Cartosat-1 ortho image\rDSM_DEM_3D\tIRS-P5 Cartosat-1 DSM", "links": [ { diff --git a/datasets/Casey_Tide_Gauges_2.json b/datasets/Casey_Tide_Gauges_2.json index b7ebe19723..1053454eb3 100644 --- a/datasets/Casey_Tide_Gauges_2.json +++ b/datasets/Casey_Tide_Gauges_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Casey_Tide_Gauges_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Over time there have been a number of tide gauges deployed at Casey Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1996 to 2007. More recent data are described elsewhere.\n\nOld Tide Gauge 2 (TG002_Old)\nOldtg02 is a download from the first gauge submerged deployed at Casey in 1992.\nThis gauge was lost but later recovered standing upright in the mud.\nThe gauge overwrote its memory and stopped.\nThe record runs from 02/04/97 to 08/09/99.\nIt is highly probable that the position of the gauge was stable during this period.\nThere is data from the same period from gauge TG06.\n\nTide Gauge 2 (TG002)\nThese folders contain data downloaded from the redeployed gauge TG02.\nTG02 was redeployed in November 2003.\nThe Record runs from 12/11/03 to 4/3/05.\nIt is expected that data will be downloaded from this gauge for the next 4-5 years.\n\nThis gauge was deployed after the previously deployed gauge ran out of battery energy. \nThere is therefore a substantial gap in the record prior to 12/11/03.\n\nTide Gauge 6 (TG006)\nTg06 was deployed at Casey in March 1996.\nThe battery became exhausted in June 2003.\nThe gauge was replaced by TG02 in Novenber 2003.\nThere is therefore a gap in the data between June and November 2003.\n\nTide Gauges 33, 34 and 36 (TG033, TG034, TGA001, TG036)\nThere are two wharf pressure sensors at Casey separated vertically by 2.007 m.\nThere is also a barometer in the wharf hut.\nThe files in this folder are from the old tide gauge data loggers.\nThere are three loggers,\n\nTG33 records pressures from lower water pressure gauge as 30 second average values (absolute pressure mbar). \n It also records wharf tube water temperatures. \n This logger also streams 30sec average pressure. \n\nTG34 records pressures from upper water pressure gauge.\n This logger also streams 30sec average values as and 10minute average water pressure data.\n\t\nTGA01 (and later replaced by TG36) records air pressure as 10 minute average values in mbar.\n\n\nFurther documentation from the old metadata records:\nDocumentation dated 2001-03-07 \nCasey Submerged Tide Gauge\n\nThe gauge used at Casey was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania.\nIt was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid.\nThe gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature.\nThe overall accuracy of the system is better than +/- 3 mm for a known water density.\nData is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit, a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved.\nOne of these of these gauges was deployed at Casey in early 1992 in a mooring in Geoffrey Bay. \nThe mooring was apparently moved by sea ice and was later found, but the gauge is missing.\nA new mooring, one which was originally made for Harry Burton for use in one of the Vestfold Hills lakes, was taken by ship to Casey and was placed in Geoffrey Bay using a collection of 200 litre fuel drum to float the mooring into position.\nA new gauge was deployed in March 1996.\nThe gauge was lowered into position with the holding grab wired closed to check that the device fitted in the mooring. The gauge became jammed so was left in situ with the grab preventing access to downloading. In April that year Roger Handsworth attached weights to the floating ropes of the grab to sink them out of the way of the freezing surface water.\nDivers located the mooring and gauge in late 1997 and 22 months of tidal records were retrieved.\nThe gauge was restarted to clear the memory and allow another two years of data to be collected without any problems from a small software bug.\nConversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC .\n\nLevelling\nIn December 1997 a set of water level observations were made by the station leader. These observations have been sent to National Tidal Facility, Flinders University, SA to derive a value for mean sea level.\n\nDocumentation dated 2008-10-17\nThere is one submerged bottom mounted gauge at Casey. (TG02)\n\nThe wharf based tide gauge installation at Casey has been upgraded with 2 Campbell Scientific CR1000 dataloggers.\nOne logger (Main) receives signals from two wharf installed submerged Paroscientific Digiquartz pressure sensors and a barometer. The other logger (Backup) receives signals from only the two submerged sensors.\nPressures are recorded in hPa, temperatures from the Digiquartz sensors in degrees C and temperatures from thermistors in the water column in unscaled A/D values.\n\nThe two submerged pressure sensors are separated vertically by 2.007 metre.\nThe backup logger streams 30 second average pressure values from both submerged sensors.\nThe main datalogger records 3 pressure and 6 temperatures and controls the water heaters.\n\t", "links": [ { diff --git a/datasets/Catlin_Arctic_Survey_0.json b/datasets/Catlin_Arctic_Survey_0.json index 579c4a1d37..0a70413afe 100644 --- a/datasets/Catlin_Arctic_Survey_0.json +++ b/datasets/Catlin_Arctic_Survey_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Catlin_Arctic_Survey_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Arctic Ocean by the RV Catlin in 2011.", "links": [ { diff --git a/datasets/ChesBay_0.json b/datasets/ChesBay_0.json index 447f911654..47b253edb8 100644 --- a/datasets/ChesBay_0.json +++ b/datasets/ChesBay_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ChesBay_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality measurements taken in the Chesapeake Bay region of the United States as a joint effort between NASA GSFC and Johns Hopkins University.", "links": [ { diff --git a/datasets/Chesapeake Land Cover_1.json b/datasets/Chesapeake Land Cover_1.json index ae55205ed8..d2cb9a854c 100644 --- a/datasets/Chesapeake Land Cover_1.json +++ b/datasets/Chesapeake Land Cover_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake Land Cover_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas.", "links": [ { diff --git a/datasets/Chesapeake_Bay_DataFlow_0.json b/datasets/Chesapeake_Bay_DataFlow_0.json index d05d8ffb44..1970fb9c16 100644 --- a/datasets/Chesapeake_Bay_DataFlow_0.json +++ b/datasets/Chesapeake_Bay_DataFlow_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake_Bay_DataFlow_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We are collecting and analyzing biological, chemical, and physical variables in and above the water at target sites and in the lab, looking for hyperspectral proxies that covarying with pollutants. This project is applying an AI model to address water quality, using datasets collected around the Bay in combination with remotely sensed data during targeted field work to support the need to more effectively sort through disparate data sets to identify areas of poor water quality that result in shellfish bed closure.", "links": [ { diff --git a/datasets/Chesapeake_Bay_Helicopter_0.json b/datasets/Chesapeake_Bay_Helicopter_0.json index ecfe0a425d..92e6924d0b 100644 --- a/datasets/Chesapeake_Bay_Helicopter_0.json +++ b/datasets/Chesapeake_Bay_Helicopter_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake_Bay_Helicopter_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data will be provided for above water radiance measurements in the hyperspectral and multi-spectral polarization modes using a system containing a hyperspectral imager (Cubert, Germany) and multi-spectral polarimetric imaging camera (Teledyne Dalsa) with additional color filters. For this experiment data will be provided for the system installed on the helicopter at 10 stations in Aug 2022 over Chesapeake Bay at 4 heights from 60 to 750 m. Complimentary data from handheld GER spectroradiometer and ac-s flowthrough measurements near same stations with 1-2 hours delay from the boat will be also provided. Similar data were submitted to SeaBASS as a part of the VIIRS validation experiment and can be found under the DOI: 10.5067/SeaBASS/VIIRS_VALIDATION/DATA001.", "links": [ { diff --git a/datasets/Chesapeake_Bay_Plume_0.json b/datasets/Chesapeake_Bay_Plume_0.json index b5c4aa8fe2..b5562d035d 100644 --- a/datasets/Chesapeake_Bay_Plume_0.json +++ b/datasets/Chesapeake_Bay_Plume_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake_Bay_Plume_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken at the mouth of the Chesapeake Bay during a plume event.", "links": [ { diff --git a/datasets/Chesapeake_Bay_Water_Quality_0.json b/datasets/Chesapeake_Bay_Water_Quality_0.json index fc56dea03a..456eace45c 100644 --- a/datasets/Chesapeake_Bay_Water_Quality_0.json +++ b/datasets/Chesapeake_Bay_Water_Quality_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake_Bay_Water_Quality_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This use-inspired NASA AIST project collects biological, chemical, and physical variables in and above the water at Chesapeake Bay sites for analysis within the lab. These ground-truth data are then used for data labeling, in combination with remotely sensed data, within a machine learning model trained to identify water quality challenges of resource managers that could result in shellfish bed closures, for example.", "links": [ { diff --git a/datasets/Chesapeake_Light_Tower_0.json b/datasets/Chesapeake_Light_Tower_0.json index 5bb38ba1b4..3699e265bb 100644 --- a/datasets/Chesapeake_Light_Tower_0.json +++ b/datasets/Chesapeake_Light_Tower_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chesapeake_Light_Tower_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Chesapeake Light Tower in 2000 and 2003-2007.", "links": [ { diff --git a/datasets/Chlorophyll_Fluorescence_Data_1785_1.json b/datasets/Chlorophyll_Fluorescence_Data_1785_1.json index 4aa98be73e..cded5fd5cf 100644 --- a/datasets/Chlorophyll_Fluorescence_Data_1785_1.json +++ b/datasets/Chlorophyll_Fluorescence_Data_1785_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Chlorophyll_Fluorescence_Data_1785_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the results of in situ measurements of needle-level chlorophyll fluorescence (ChlF) obtained from a pulse amplitude modulated (PAM) fluorometer from evergreen needleleaf forested sites one in Alaska and one in Idaho. Measured light-adapted minimal fluorescence (Ft) is reported as the quantum yield of fluorescence and light-adapted variable fluorescence over maximal fluorescence (Fv/Fm) and is reported as the quantum yield of photosystem II. Also reported for both sites are two modeled irradiance products: (1) the top-of-canopy instantaneous irradiance (W/m2) and (2) needle-level irradiance (W/m2) that was modeled to account for shadow casting and canopy orientation in modulating direct radiation. Both products were modeled to be contemporaneous with ChlF observations. At the Idaho site only, needle-level irradiance (W/m2) was measured in situ with a handheld pyranometer. The Alaska field site is located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in Northern Alaska. Thirty-six Picea glauca (white spruce) trees were sampled on 2017-07-07 to 2017-07-08. The Idaho field site is located in a montane forest near McCall, Idaho. Ten selected Abies grandis (grand fir) trees were sampled on 2019-07-05 to 2019-07-06. Measurement of needle-level ChlF occurred during clear-sky conditions such that the canopies experienced a broad range of variability in sunlit-shading patterns across the day during these near-solstice periods.", "links": [ { diff --git a/datasets/CircumArctic_Trends_Hotspots_2322_1.json b/datasets/CircumArctic_Trends_Hotspots_2322_1.json index 06f23d086b..3410dbb4a8 100644 --- a/datasets/CircumArctic_Trends_Hotspots_2322_1.json +++ b/datasets/CircumArctic_Trends_Hotspots_2322_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CircumArctic_Trends_Hotspots_2322_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of trends in temperature, moisture, and vegetation changes over the circumpolar Arctic. Time series trends were measured by the Theil-Sen slope and associated p-values for a variety of variables including 2-meter air temperature, precipitation, soil moisture, non-frozen season days, permafrost active layer thickness, snow cover, vapor pressure deficit, land surface water fraction, normalized difference vegetation index (NDVI), and vegetation optical depth. Trends were measured annually and over specific seasons of spring (March to May), summer (June to August), autumn (September to November) and winter (December to February), and for the 1980-2020 and 1997-2020 time periods, depending on the variable and original data availability. Emerging hotspots of change were identified for the same variables and seasons, but only over the 1997-2020 period. In addition, a multivariate ranking was used to create combined hotspot layers to show areas of substantial changes in the thermal environment, moisture, and vegetation; these themes reflect landscape changes considered to be detrimental (e.g., a threat) to ecosystems and human populations. Ancillary files provide the boundaries of study regions, Brown permafrost regions, and a land cover product. The data are provided in cloud optimized GeoTIFF (COG) and shapefile formats.", "links": [ { diff --git a/datasets/CliVEC_0.json b/datasets/CliVEC_0.json index d33f8fca26..b1ababd517 100644 --- a/datasets/CliVEC_0.json +++ b/datasets/CliVEC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CliVEC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Title: The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) - NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.", "links": [ { diff --git a/datasets/Climate_Normals_Modern_LGM_AK_1663_1.json b/datasets/Climate_Normals_Modern_LGM_AK_1663_1.json index 37759ba995..ca3788cce1 100644 --- a/datasets/Climate_Normals_Modern_LGM_AK_1663_1.json +++ b/datasets/Climate_Normals_Modern_LGM_AK_1663_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Climate_Normals_Modern_LGM_AK_1663_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides two 30-year climate normal data products for conditions during the last glacial maximum (LGM; ~18,000 years ago) and a modern time period (1975-2005) for the entire state of Alaska. The first set of products are monthly climate variable averages at 60 m resolution, including: minimum, maximum, and average temperatures, total precipitation, total surface radiation, rain, snow, potential evapotranspiration (PET), actual evapotranspiration (AET), and water deficit. The second set of products are annual summary climate variable averages for the same variables (excepting average temperature and rain) at 60m, 120m, 240m, 800m, 1km, 2km, 3km, 4km, 5km, 10km and 12km resolutions. The 30-year climate normal monthly averages were derived by topographically downscaling climate variables from existing coarse-resolution general circulation model outputs combined with local weather station data and digital surface models for Alaska for both the LGM and modern time periods at 60 m resolution. From this baseline, monthly averages for total surface radiation, rain, snow, potential evapotranspiration, actual evapotranspiration, and water deficit were also modeled. The annual averages are coarser resolution upsampled versions of the 60 m resolution monthly average data.", "links": [ { diff --git a/datasets/Clone_Libraries_1.json b/datasets/Clone_Libraries_1.json index a0ae285a73..894a4e9332 100644 --- a/datasets/Clone_Libraries_1.json +++ b/datasets/Clone_Libraries_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Clone_Libraries_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two 16S rDNA clone libraries, one from a Brown Bay sample and one from an O'Brien Bay sample were generated. These samples were originally collected as part of ASAC project 868 and the microbiology of the samples is now being investigated as part of ASAC 1228.\n\nTwo data files are included in the download. Both are in \"fasta\" format, a text-based format for representing either nucleotide sequences or peptide sequences, in which base pairs or amino acids are represented using single-letter codes.\n\nFurther information about the dataset can also be found in the referenced paper.", "links": [ { diff --git a/datasets/Cloud to Street - Microsoft flood dataset_1.json b/datasets/Cloud to Street - Microsoft flood dataset_1.json index c5a7392fd4..6f790e0655 100644 --- a/datasets/Cloud to Street - Microsoft flood dataset_1.json +++ b/datasets/Cloud to Street - Microsoft flood dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cloud to Street - Microsoft flood dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The C2S-MS Floods Dataset is a dataset of global flood events with labeled Sentinel-1 & Sentinel-2 pairs. There are 900 sets (1800 total) of near-coincident Sentinel-1 and Sentinel-2 chips (512 x 512 pixels) from 18 global flood events. Each chip contains a water label for both Sentinel-1 and Sentinel-2, as well as a cloud/cloud shadow mask for Sentinel-2. The dataset was constructed by Cloud to Street in collaboration with and funded by the Microsoft Planetary Computer team.", "links": [ { diff --git a/datasets/CoJet_0.json b/datasets/CoJet_0.json index 3bc5c5b4ad..e5e114c805 100644 --- a/datasets/CoJet_0.json +++ b/datasets/CoJet_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CoJet_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made as a part of the NRL-sponsored study of the dynamics of coastal buoyancy jets (CoJet) in the Gulf of Mexico during 2001 and 2002.", "links": [ { diff --git a/datasets/Coastal_Carbon_Budget_NE_US_1594_1.json b/datasets/Coastal_Carbon_Budget_NE_US_1594_1.json index f1b387cef7..64c9ca2f48 100644 --- a/datasets/Coastal_Carbon_Budget_NE_US_1594_1.json +++ b/datasets/Coastal_Carbon_Budget_NE_US_1594_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Coastal_Carbon_Budget_NE_US_1594_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains best estimates and uncertainties for mean annual fluxes of inorganic, organic, and total (organic + inorganic) carbon in tidal wetlands, estuaries and shelf waters of eastern North America, which is defined by the coastline running between the tip of the Scotian Peninsula (Canada) and the southern tip of Florida (USA). The data are provided on a per-unit-area basis and as spatially integrated values for each of the three ecosystem types (tidal wetlands, estuaries, and shelf waters) and the entire coastal ecosystem (tidal wetlands + estuaries + shelf waters) as well as for three geographic subregions (the Gulf of Maine, the Mid-Atlantic Bight, and the South Atlantic Bight) and the entire Eastern North America domain (Gulf of Maine + Mid-Atlantic Bight + South Atlantic Bight). The data include the net uptake from the atmosphere by the three ecosystems; burial in tidal wetland soils, estuarine sediments, and continental shelf sediments; riverine input from land to estuaries; and the net lateral advective transports from ecosystem to ecosystem. In addition, heterotrophic respiration (HR), net primary production (NPP), and net ecosystem production (NEP) estimates were computed for each ecosystem. The fluxes were derived using a variety of sources and are estimates for average conditions over the past decades from data covering roughly the period 1976-01-01 to 2017-12-31.", "links": [ { diff --git a/datasets/Coastal_US_Elevation_Data_1844_1.json b/datasets/Coastal_US_Elevation_Data_1844_1.json index 34c5f17115..f6c8b5fcf1 100644 --- a/datasets/Coastal_US_Elevation_Data_1844_1.json +++ b/datasets/Coastal_US_Elevation_Data_1844_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Coastal_US_Elevation_Data_1844_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of the elevation of coastal wetlands relative to tidal ranges for the conterminous United States (CONUS) at 30 m resolution for 2010. It also includes maps of tidal amplitude, relative sea-level rise for the period 1983-2001, and maps for coastal lands and low marsh areas based on the probability of being below the mean higher high tide water line for spring tides (MHHWS). Uncertainty layers for elevation maps are also provided.", "links": [ { diff --git a/datasets/Coccolithophore_Fluxes_SAZ_1.json b/datasets/Coccolithophore_Fluxes_SAZ_1.json index d5f9301338..9acaa35d91 100644 --- a/datasets/Coccolithophore_Fluxes_SAZ_1.json +++ b/datasets/Coccolithophore_Fluxes_SAZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Coccolithophore_Fluxes_SAZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coccolithophore fluxes were investigated over a one-year period (2001-02) at the southern Antarctic Zone in the Australian Sector of the Southern Ocean at the site of the Southern\nOcean Iron Release Experiment (SOIREE) near 61\u00b0S, 140\u00b0E. Two vertically moored sediment traps were deployed at 2000 and 3700 m below sea-level during a period of 10 months. In these data sets we present the results on the temporal and vertical variability of total coccolith flux, species composition and seasonal changes in coccolith weights of E. huxleyi populations estimated using circularly polarised micrographs analysed with C-Calcita software. A description of the field experiment, diatom and biogeochemical fluxes can be found in Rigual-Hern\u00e1ndez et al. (2015), while a detailed description of sample processing and counting of coccolithophores can be found in Rigual-Hern\u00e1ndez et al. (2018). Moreover, an explanation of the estimation of Emiliania huxleyi coccoliths using C-Calcita software can be also found in Rigual-Hernandez et al. (2018). \nCoccolithophore assemblages captured by the traps were nearly monospecific for Emiliania huxleyi morphotype B/C. Coccolith fluxes showed strong seasonal cycle at both sediment trap depths. The maximum coccolith export occurred during summer and was divided into two peaks in early January (2.2 x 109 coccoliths m-2 d-1 at 2000 m) and in mid-February (9.8 x 108 coccoliths m-2 d-1). Coccolith flux was very low in winter (down to ~7 x 107 coccoliths m-2 d-1). Coccolith fluxes in the deeper trap (3700 m) followed a similar pattern to that in the 2000 m trap with a delay of about one sampling interval. Coccoliths intercepted by the traps exhibited a weight and length reduction during summer. The annual coccolith weight at both sediment traps was 2.11 plus or minus 0.96 and 2.13 plus or minus 0.91 pg at 2000 m and 3700 m, respectively. Our coccolith mass estimation was consistent with previous reports for morphotype B/C in other regions of the Southern Ocean. \n\nData available: two excel files containing sampling dates and depths, raw counts, relative abundance and fluxes (coccoliths m-2 d-1) of the coccolithophore species, and morphometric measurements of Emiliania huxleyi coccoliths made with C-Calcita software. \nEach file contains four spreadsheets: raw coccolith counts, relative abundance of coccolithophore species and coccolith flux of each coccolithophore species identified and E. huxleyi morphometrics. \n\nDetailed information of the column headings is provided below.\n\nCup \u2013 Cup (=sample) number\nDepth \u2013 vertical location of the sediment trap in meters below the surface \nMid-point date - Mid date of the sampling interval\nLength (days) \u2013 number of days the cup was open", "links": [ { diff --git a/datasets/Coccolithophore_Fluxes_SAZ_2009-2012_1.json b/datasets/Coccolithophore_Fluxes_SAZ_2009-2012_1.json index ec4cc4383c..4e9dfa3bc4 100644 --- a/datasets/Coccolithophore_Fluxes_SAZ_2009-2012_1.json +++ b/datasets/Coccolithophore_Fluxes_SAZ_2009-2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Coccolithophore_Fluxes_SAZ_2009-2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coccolithophore fluxes were investigated over a one-year period at two sites of the Subantarctic Zone in the Australian and New Zealand Sectors of the Southern Ocean. The samples from the Australian SAZ were retrieved at the SOTS observatory, which lies in the SAZ (near 47\u00b0S, 142\u00b0E), approximately 500 km south west of Tasmania. SOTS was instrumented with three moored platforms: (i) a surface tower buoy that performs meteorological measurements (the Southern Ocean Flux Station - SOFS); (ii) a surface mixed layer mooring equipped with an automated water sampler) and nutrient, carbon and biological measurement sensors (the Pulse mooring); and (iii) a bottom-tethered deep sediment trap mooring that collects sinking particle fluxes for diverse biogeochemical studies (the SAZ mooring). The samples from New Zealand came from the deep-ocean SAM mooring deployed in Subantarctic waters south east of New Zealand (46\u00b040\u2019S, 178\u2019 30\u00b0E), and was equipped with sediment traps and a suite of sensors. Here, we report the coccolith sinking assemblages captured by sediment traps at ~1000, 2000 and 3800 m depth for a year from August 2011 until July 2012 at the SOTS observatory and a sediment trap at ~1500 m depth for a year from November 2009 until October 2010 at the SAM site. \n\nA description of the field experiment, sample treatment, determination of total CaCO3 content, and estimation of coccolith and coccosphere fluxes can be found in Rigual-Hern\u00e1ndez et al. (2020a) and Rigual-Hern\u00e1ndez et al. (2020b). \n\nData available: two excel files (one for each station) containing sampling dates and depths, relative abundance of coccolith sinking assemblages, and coccolith, coccosphere and total CaCO3 fluxes. \n\nDetailed information of the column headings is provided below.\n\nCup \u2013 Cup (=sample) number\nDepth \u2013 vertical location of the sediment trap in meters below the surface \nMid-point date - Mid date of the sampling interval\nDuration (days) \u2013 number of days the cup was open", "links": [ { diff --git a/datasets/Cold_Water_Corals_2.0.json b/datasets/Cold_Water_Corals_2.0.json index 439028d2ed..f8a88609f9 100644 --- a/datasets/Cold_Water_Corals_2.0.json +++ b/datasets/Cold_Water_Corals_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cold_Water_Corals_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat Coverage: Seamounts", "links": [ { diff --git a/datasets/Continuous_Lifeform_Maps_CONUS_1809_1.json b/datasets/Continuous_Lifeform_Maps_CONUS_1809_1.json index 762f2a9138..6ae0e5fa93 100644 --- a/datasets/Continuous_Lifeform_Maps_CONUS_1809_1.json +++ b/datasets/Continuous_Lifeform_Maps_CONUS_1809_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Continuous_Lifeform_Maps_CONUS_1809_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS). The estimates were derived using quantile regression forest models and indicate the percent of ground covered by a vertical projection of each lifeform class ranging from 0 to 100 percent. Model input data included Landsat surface reflectance (SR) data and 165 airborne LiDAR datasets covering eight of the eleven terrestrial biomes of the conterminous U.S. and Alaska. Eighty-six of the LiDAR acquisitions are part of the NASA Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) airborne imager data collection; the remaining 79 sites were acquired by the National Science Foundation's National Ecological Observatory Network Airborne Observation Platform (NEON AOP). Acquisitions were selected based on the availability of the SR data for each G-LiHT and NEON dataset. The data are annual estimates from 1984 to 2018 and were tiled (425 tiles) using the CONUS Landsat Analysis Ready Data (ARD) grid scheme. Data are provided in GeoTIFF format.", "links": [ { diff --git a/datasets/Copepods_1.json b/datasets/Copepods_1.json index 3190ad2b4c..01fd97841f 100644 --- a/datasets/Copepods_1.json +++ b/datasets/Copepods_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Copepods_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains samples collected at O'Gorman Rocks and Ellis Fjord near Davis station from December 1997 to March 1998. Depth-stratified zooplankton samples were obtained for determination of zooplankton abundance and biomass. Water samples were collected for the determination of chlorophyll a concentration, protist identification and abundance, and the concentration of particulate and dissolved organic carbon. Sediment trap material was collected for the analysis of faecal pellets (identification and CHN analyses), protist identification and abundance, and the measurement of particulate organic carbon concentration. Zooplankton grazing experiments were performed in the laboratory at Davis station and zooplankton were also collected for CHN analyses. Data from this project arose from projects ASAC 963 and ASAC 2229.", "links": [ { diff --git a/datasets/CosRay_Notes_Charts_1959-1986_1.json b/datasets/CosRay_Notes_Charts_1959-1986_1.json index 1dda5b9ddc..1bcedeab77 100644 --- a/datasets/CosRay_Notes_Charts_1959-1986_1.json +++ b/datasets/CosRay_Notes_Charts_1959-1986_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CosRay_Notes_Charts_1959-1986_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Several boxes of paper records belonging to Dr Peter Fenton (a cosmic ray physicist based at the University of Tasmania), were provided to the Australian Antarctic Data Centre by his daughter, Dr Gwen Fenton. The paper records included charts of data collected from Australian Antarctic stations between 1959 and 1986, as well as some explanatory notes, correspondence and administration records.\n\nDr Peter Fenton also had an older brother, Dr Geoff Fenton, who was also a cosmic ray physicist based at the University of Tasmania.\n\nThe download file contains scanned pdfs of the following items:\n\nCharts_1959-1974a.pdf\nCharts_1959-1974b.pdf\nCorrespondence_1986.pdf\nExplanatory_Notes_1962-1971.pdf\nProgram_Description_1986.pdf", "links": [ { diff --git a/datasets/Cosmic_Ray_NM_1.json b/datasets/Cosmic_Ray_NM_1.json index a71d0cbba0..6f1d00a3b2 100644 --- a/datasets/Cosmic_Ray_NM_1.json +++ b/datasets/Cosmic_Ray_NM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cosmic_Ray_NM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly neutron monitor data both raw data and corrected for atmospheric pressure variations. Data are presently, routinely recorded at Mawson and Kingston, using super neutron monitors. Old data are also available from Wilkes and Casey Stations using standard IGY neutron monitors during earlier operational periods. Details of recording monitors and periods of operation are available on request.", "links": [ { diff --git a/datasets/Cosmic_Ray_PW_1.json b/datasets/Cosmic_Ray_PW_1.json index 3f474ebdeb..7cbc6d63af 100644 --- a/datasets/Cosmic_Ray_PW_1.json +++ b/datasets/Cosmic_Ray_PW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cosmic_Ray_PW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Records of atmospheric pressure and wind speed used in conjunction with cosmic ray data recorded at corresponding Antarctic cosmic ray observatories. Data has been collected from Australian Antarctic cosmic ray observatories during operational times at Macquarie Island, Casey, Wilkes and Mawson. Individual observatory operating periods are available on request.", "links": [ { diff --git a/datasets/Cosmic_Ray_SM_1.json b/datasets/Cosmic_Ray_SM_1.json index 0e629155a3..cce9e146ef 100644 --- a/datasets/Cosmic_Ray_SM_1.json +++ b/datasets/Cosmic_Ray_SM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cosmic_Ray_SM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data have been collected from muon detectors in cosmic ray observatories/laboratories at Macquarie Island, Wilkes, Casey and Mawson. Data have been collected from varied detectors and detecting systems, specific operational times and detector information are available on request.\n \nThis dataset contains normalised counting rate records from the combined north/south pointing proportional counter telescopes P2 and P3 in low and high zenith angle modes, corrected for atmospheric pressure variation. No correction has been made for height or temperature of the 125mb level resulting in a significant seasonal variation but no diurnal variation at Antarctic latitudes.\n\nThe telescope output consists of the sum of the 2-fold coincidence counting rates. Coincidence counts from individual counter pairs are summed into thirteen possible arrival zenith angles and two arrival directions (north or south). The low zenith data are hourly sums of coincidences from the seven lowest zenith angles of arrival (34 degrees -51 degrees) and the high zenith data are hourly sums of coincidences of the remaining six zenith angles of arrival (55 degrees -79 degrees).\n\nThe accidental rate is removed from the total coincidence rate using a resolving time difference method (See Jacklyn R M and Duldig M L, 20th Int. Cosmic Ray Conf. Papers, Moscow, Vol 4, pp. 380-383, 1987).\n\nFurther information is also provided in ANARE Research Notes 102, 50 Years of cosmic ray research in Tasmania. A copy of this research note is available for download from the provided URL.", "links": [ { diff --git a/datasets/Cosmic_Ray_UGM_1.json b/datasets/Cosmic_Ray_UGM_1.json index fb04da0bd4..5c26437b5e 100644 --- a/datasets/Cosmic_Ray_UGM_1.json +++ b/datasets/Cosmic_Ray_UGM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cosmic_Ray_UGM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly muon data collected from detectors housed in an underground (11 m.w.e.) vault at Mawson. Data are recorded from two viewing directions:\n(a) North - Zenith 24 deg., Azimuth 330 deg (P6, P7) and \n(b) South-West - Zenith 40 deg, Azimuth 205 deg (P9, P10).\nData are available in raw form or count rates that have been corrected for atmospheric pressure variations.\nDetecting methods range from geiger counter telescopes, installed in 1972, to proportional counter telescopes, installed in 1982 and still in operation.\n \nThis data contain normalised counting rate records from the combined north pointing proportional counter telescopes P6 and P7 and the southwest pointing proportional counter telescopes P9 and P10, corrected for atmospheric pressure variation.\n\nThe telescope output consists of the sum of the 2-fold coincidence counting rates from two identical modules.\n\nThe accidental rate is removed from the total coincidence rate using a resolving time difference method (See Jacklyn R M and Duldig M L, 20th Int. Cosmic Ray Conf. Papers, Moscow, Vol 4, pp. 380-383, 1987).\n\nFurther information is also provided in ANARE Research Notes 102, 50 Years of cosmic ray research in Tasmania. A copy of this research note is available for download from the provided URL.", "links": [ { diff --git a/datasets/CosmoSkyMed_9.0.json b/datasets/CosmoSkyMed_9.0.json index 8a965c0ae5..4ba4984983 100644 --- a/datasets/CosmoSkyMed_9.0.json +++ b/datasets/CosmoSkyMed_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CosmoSkyMed_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The COSMO-SkyMed ESA archive collection is a dataset of COSMO-SkyMed products that ESA collected over the years with worldwide coverage. The dataset regularly grows as ESA collects new products. The following list delineates the characteristics of the SAR measurement modes that are disseminated under ESA Third Party Missions (TPM). - STRIPMAP HIMAGE (HIM): achieving medium resolution (3m x 3m single look), wide swath imaging (swath extension \u226540 km) . - STRIPMAP PINGPONG (SPP): achieving medium resolution (15 m)), medium swath imaging (swath \u226530 km) with two radar polarization's selectable among HH, HV, VH and VV. - SCANSAR WIDE (SCW): achieving radar imaging with swath extension of 100x100 km2 and a spatial resolution of 30x30 m2. - SCANSAR HUGE (SCH): achieving radar imaging with swath extension of 200x200 km2 and a spatial resolution selectable of 100x100 m2. Processing Levels: - Level 1A - Single-look Complex Slant (SCSB and SCSU) : RAW data focused in slant range-azimuth projection, that is the sensor natural acquisition projection; product contains In-Phase and Quadrature of the focused data, weighted and radiometrically equalised. The processing of the 1A_SCSU product differs from that of the 1A_SCSB product for the following features: a non-weighted processing is performed, which means that windowing isn't applied on the processed bandwidth; radiometric equalisation (in terms of compensation of the range antenna pattern and incidence angle) is not performed; hence only compensation of the antenna transmitter gain and receiver attenuation and range spreading loss is applied.\u2022 Level 1B - Detected Ground Multi-look (DGM): product obtained detecting, multi-looking and projecting the Single-look Complex Slant data onto a grid regular in ground. Spotlight Mode products are not multi-looked - Level 1C - Geocoded Ellipsoid Corrected (GEC) and Level 1D - Geocoded Terrain Corrected (GTC): Obtained projecting the Level 1A product onto a regular grid in a chosen cartographic reference system. In case of Lev 1C the surface is the earth ellipsoid while for the Lev 1D a DEM (Digital Elevation Model) is used to approximate the real earth surface. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/CosmoSkyMed/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/Country_SOC_Latin_America_1615_1.json b/datasets/Country_SOC_Latin_America_1615_1.json index ac53886147..d2d7b6cc05 100644 --- a/datasets/Country_SOC_Latin_America_1615_1.json +++ b/datasets/Country_SOC_Latin_America_1615_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Country_SOC_Latin_America_1615_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.", "links": [ { diff --git a/datasets/CramerLeemans_416_1.json b/datasets/CramerLeemans_416_1.json index 1c0f681f4d..5e3a494e6d 100644 --- a/datasets/CramerLeemans_416_1.json +++ b/datasets/CramerLeemans_416_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CramerLeemans_416_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database is a major update of the Leemans and Cramer database. It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution. It was generated from a large data base, using the partial thin-plate splining algorithm.", "links": [ { diff --git a/datasets/Cropland_Carbon_Fluxes_2125_1.json b/datasets/Cropland_Carbon_Fluxes_2125_1.json index d5916cc899..284753ccef 100644 --- a/datasets/Cropland_Carbon_Fluxes_2125_1.json +++ b/datasets/Cropland_Carbon_Fluxes_2125_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cropland_Carbon_Fluxes_2125_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily estimates of carbon fluxes in croplands derived from the \"ecosys\" model covering a portion of the Midwestern US (Illinois, Indiana, and Iowa) at county-level resolution from 2001-2018. Ecosys simulates water, energy, carbon, and nutrient cycles simultaneously for various ecosystems, including agricultural systems at up to hourly resolution. Estimates include: gross primary productivity (GPP), net primary productivity (NPP), autotrophic respiration (Ra), heterotrophic respiration (Rh), or net ecosystem exchange (NEE). Data were generated by the ecosys model constrained by observational data, including USDA crop yield from USDA National Agricultural Statistics Service, and a remote-sensing-based SLOPE GPP product. Model performance was evaluated using observations from AmeriFlux towers at agricultural sites within the study area. Agriculture in the US Midwest produces significant quantities of corn and soybeans, which are key elements to the global food supply. The data are provided in shapefile format.", "links": [ { diff --git a/datasets/Crops_SIF_VegIndices_IL_NE_2136_1.json b/datasets/Crops_SIF_VegIndices_IL_NE_2136_1.json index 92b614ea57..127a69727f 100644 --- a/datasets/Crops_SIF_VegIndices_IL_NE_2136_1.json +++ b/datasets/Crops_SIF_VegIndices_IL_NE_2136_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Crops_SIF_VegIndices_IL_NE_2136_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/CryoSat.products_NA.json b/datasets/CryoSat.products_NA.json index 99784b6faa..25ced2be05 100644 --- a/datasets/CryoSat.products_NA.json +++ b/datasets/CryoSat.products_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CryoSat.products_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CryoSat's primary payload is the SAR/Interferometric Radar Altimeter (SIRAL) (https://earth.esa.int/eogateway/instruments/siral) which has extended capabilities to meet the measurement requirements for ice-sheet elevation and sea-ice freeboard. CryoSat also carries three star trackers for measuring the orientation of the baseline. In addition, a radio receiver called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS) and a small laser retroreflector ensures that CryoSat's position will be accurately tracked. More detailed information on CryoSat instruments is available on the CryoSat mission page. The following CryoSat datasets are available and distributed to registered users: Level 1B and L2 Ice products: FDM, LRM, SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO EOLIS Gridded Products Detailed information concerning each of the above datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products) and in the news item: CryoSat Ocean Products now open to scientific community (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/cryosat/news/-/asset_publisher/47bD/content/cryosat-ocean-products-now-open-to-scientific-community). CryoSat Level 1B altimetric products contain time and geo-location information as well as SIRAL measurements in engineering units. Calibration corrections are included and have been applied to the window delay computations. In Offline products, geophysical corrections are computed from Analysis Auxiliary Data Files (ADFs), whereas in FDM products corrections are computed for Forecast ADFs. All corrections are included in the data products and therefore the range can be calculated by taking into account the surface type. The Offline Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after data acquisition and are principally dedicated to glaciologists working on sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean products, generated 2-3 hours after data acquisition, and fulfill the needs of some ocean operational services. Level 2 products contain the time of measurement, the geo-location and the height of the surface. IOP and GOP are outputs of the CryoSat Ocean Processor. These products are dedicated to the study of ocean surfaces, and provided specifically for the needs of the oceanographic community. IOP are generated 2-3 days after data sensing acquisition and use the DORIS Preliminary Orbit. GOP are typically generated 30 days after data sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections are computed from the Analysis ADFs, however following the oceanographic convention the corrections are available but not directly applied to the range (as for FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that are easily accessible to new communities of non-altimeter experts and end users. The Cryo-TEMPO datasets include dedicated products over five thematic areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water, together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing dedicated to each thematic area. Throughout the project, the products will be constantly evolved, and validated by a group of Thematic Users, thus ensuring optimal relevance and impact for the intended target communities. More information on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html). The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users.The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. This dataset will further the ability of the community to analyse and understand trends across the Greenland Ice Sheet margin, Antarctica and several mountain glaciers and ice caps around the world.", "links": [ { diff --git a/datasets/CubeSat_Arctic_Boreal_LakeArea_1667_1.json b/datasets/CubeSat_Arctic_Boreal_LakeArea_1667_1.json index e3ce381c6d..452fcf9aba 100644 --- a/datasets/CubeSat_Arctic_Boreal_LakeArea_1667_1.json +++ b/datasets/CubeSat_Arctic_Boreal_LakeArea_1667_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "CubeSat_Arctic_Boreal_LakeArea_1667_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides near-daily lake area timeseries for 85,358 lakes across four study areas in Northern Canada and Alaska, USA, between May 1 and October 1, 2017. These lake area estimates were produced using digital images from newly developed Planet Labs CubeSats, small satellites with a 4-band (blue, green, red, near-infrared) camera payload. In constellation, CubeSats collected imagery at very high spatial (3-5m) and temporal (near-daily) resolution. From the imagery, each lake's mean, minimum, and maximum areas and seasonal dynamism were derived. The dataset covers four Arctic-Boreal regions: the Yukon Flats Basin (YFB) in eastern interior Alaska, and the Mackenzie River Valley (MRV), Canadian Shield Transect (CST), and Hudson Bay Lowland (HBL) in Canada.", "links": [ { diff --git a/datasets/Cyanate_0.json b/datasets/Cyanate_0.json index ba1db4fc48..24d21bcd8b 100644 --- a/datasets/Cyanate_0.json +++ b/datasets/Cyanate_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Cyanate_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of Cyanate and CDOM made in the mid-Atlantic Bight by researchers at NASA's Ocean Ecology Lab's Field Support Group.", "links": [ { diff --git a/datasets/D.Parmelee_QuatGeo_Erebus_Holocene_cosmogenic_1.json b/datasets/D.Parmelee_QuatGeo_Erebus_Holocene_cosmogenic_1.json index 1bd0408f0f..ced9856ebd 100644 --- a/datasets/D.Parmelee_QuatGeo_Erebus_Holocene_cosmogenic_1.json +++ b/datasets/D.Parmelee_QuatGeo_Erebus_Holocene_cosmogenic_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "D.Parmelee_QuatGeo_Erebus_Holocene_cosmogenic_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ages of recent effusive eruptions on Erebus volcano, Antarctica are poorly known. Published 40Ar/39Ar ages of the 10 youngest ?post-caldera? lava flows are unreliable because of the young ages of the flows (<10 ka) and the presence of excess 40Ar. Here we use cosmogenic 3He and 36Cl to provide new ages for the 10 youngest flows and 3 older summit flows, including a newly recognized flow distin- guished by its exposure age. Estimated eruption ages of the post-caldera flows, assuming no erosion or prior snow cover, range from 4.52 � 0.08 ka to 8.50 � 0.19 ka, using Lifton et al. (2014) to scale cosmogenic production rates. If the older Lal (1991)/Stone (2000) model is used to scale production rates, calculated ages are older by 16e25%. Helium-3 and chlorine-36 exposure ages measured on the same samples show excellent agreement. Helium-3 ages measured on clinopyroxene and olivine from the same samples are discordant, probably due in part to lower-than-expected 3He production rates in the Fe-rich olivine. Close agreement of multiple clinopyroxene 3He ages from each flow indicates that the effects of past snow coverage on the exposure ages have been minimal.\nThe new cosmogenic ages differ considerably from published 40Ar/39Ar and 36Cl ages and reveal that the post-caldera flows were erupted during relatively brief periods of effusive activity spread over an interval of ~4 ka. The average eruption rate over this interval is estimated to be 0.01 km3/ka. Because the last eruption was at least 4 ka ago, and the longest repose interval between the 10 youngest eruptions is ~1 ka, we consider the most recent period of effusive activity to have ended.", "links": [ { diff --git a/datasets/DAVIS_STP_1.json b/datasets/DAVIS_STP_1.json index 7d7f0ab390..ad634b2cae 100644 --- a/datasets/DAVIS_STP_1.json +++ b/datasets/DAVIS_STP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DAVIS_STP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Untreated, macerated wastewater effluent has been discharged to the sea at Davis Station since 2005, when the old wastewater treatment infrastructure was removed. This environmental assessment was instigated to guide the choice of the most suitable wastewater treatment facility at Davis. The assessment will support decisions that enable Australia to meet the standards set for the discharge of wastewaters in Antarctica in national legislation (Waste Management Regulations of the Antarctic Treaty Environmental Protection Act - ATEP) and to meet international commitments (the Madrid Protocol) and to meet Australia's aspirations to be a leader in Antarctic environmental protection. \nThe overall objective was to provide environmental information in support of an operational infrastructure project to upgrade wastewater treatment at Davis. This information is required to ensure that the upgrade satisfies national legislation (ATEP/Waste Management Regulations), international commitments (the Madrid Protocol) and maintain the AAD's status as an international leader in environmental management. The specific objectives were to:\n1.\tWastewater properties: Determine the properties of discharged wastewater (contaminant levels, toxicity, microbiological hazards) as the basis for recommendations on the required level of treatment and provide further consideration of what might constitute adequate dilution and dispersal for discharge to the nearshore marine environment\n2.\tDispersal and dilution characteristics of marine environment: Assess the dispersing characteristics of the immediate nearshore marine environment in the vicinity of Davis Station to determine whether conditions at the existing site of effluent discharge are adequate to meet the ATEP requirement of initial dilution and rapid dispersal.\n3.\tEnvironmental impacts: Describe the nature and extent of impacts to the marine environment associated with present wastewater discharge practices at Davis and determine whether wastewater discharge practices have adversely affected the local environment.\n4.\tEvaluate treatment options: Evaluate the different levels of treatment required to mitigate and/or prevent various environmental impacts and reduce environmental risks.", "links": [ { diff --git a/datasets/DAVIS_STP_Biota_1.json b/datasets/DAVIS_STP_Biota_1.json index 84c3ad8093..e121453c75 100644 --- a/datasets/DAVIS_STP_Biota_1.json +++ b/datasets/DAVIS_STP_Biota_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DAVIS_STP_Biota_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Untreated, macerated wastewater effluent has been discharged to the sea at Davis Station since 2005, when the old wastewater treatment infrastructure was removed. This environmental assessment was instigated to guide the choice of the most suitable wastewater treatment facility at Davis. The assessment will support decisions that enable Australia to meet the standards set for the discharge of wastewaters in Antarctica in national legislation (Waste Management Regulations of the Antarctic Treaty Environmental Protection Act - ATEP) and to meet international commitments (the Madrid Protocol) and to meet Australia's aspirations to be a leader in Antarctic environmental protection. \nThe overall objective was to provide environmental information in support of an operational infrastructure project to upgrade wastewater treatment at Davis. This information is required to ensure that the upgrade satisfies national legislation (ATEP/Waste Management Regulations), international commitments (the Madrid Protocol) and maintain the AAD's status as an international leader in environmental management. The specific objectives were to:\n1. Wastewater properties: Determine the properties of discharged wastewater (contaminant levels, toxicity, microbiological hazards) as the basis for recommendations on the required level of treatment and provide further consideration of what might constitute adequate dilution and dispersal for discharge to the nearshore marine environment\n2. Dispersal and dilution characteristics of marine environment: Assess the dispersing characteristics of the immediate nearshore marine environment in the vicinity of Davis Station to determine whether conditions at the existing site of effluent discharge are adequate to meet the ATEP requirement of initial dilution and rapid dispersal.\n3. Environmental impacts: Describe the nature and extent of impacts to the marine environment associated with present wastewater discharge practices at Davis and determine whether wastewater discharge practices have adversely affected the local environment.\n4. Evaluate treatment options: Evaluate the different levels of treatment required to mitigate and/or prevent various environmental impacts and reduce environmental risks.", "links": [ { diff --git a/datasets/DAVIS_STP_Chemistry_1.json b/datasets/DAVIS_STP_Chemistry_1.json index 0ac285bf84..dd2ba263bd 100644 --- a/datasets/DAVIS_STP_Chemistry_1.json +++ b/datasets/DAVIS_STP_Chemistry_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DAVIS_STP_Chemistry_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Untreated, macerated wastewater effluent has been discharged to the sea at Davis Station since 2005, when the old wastewater treatment infrastructure was removed. This environmental assessment was instigated to guide the choice of the most suitable wastewater treatment facility at Davis. The assessment will support decisions that enable Australia to meet the standards set for the discharge of wastewaters in Antarctica in national legislation (Waste Management Regulations of the Antarctic Treaty Environmental Protection Act - ATEP) and to meet international commitments (the Madrid Protocol) and to meet Australia's aspirations to be a leader in Antarctic environmental protection. \nThe overall objective was to provide environmental information in support of an operational infrastructure project to upgrade wastewater treatment at Davis. This information is required to ensure that the upgrade satisfies national legislation (ATEP/Waste Management Regulations), international commitments (the Madrid Protocol) and maintain the AAD's status as an international leader in environmental management. The specific objectives were to:\n1. Wastewater properties: Determine the properties of discharged wastewater (contaminant levels, toxicity, microbiological hazards) as the basis for recommendations on the required level of treatment and provide further consideration of what might constitute adequate dilution and dispersal for discharge to the nearshore marine environment\n2. Dispersal and dilution characteristics of marine environment: Assess the dispersing characteristics of the immediate nearshore marine environment in the vicinity of Davis Station to determine whether conditions at the existing site of effluent discharge are adequate to meet the ATEP requirement of initial dilution and rapid dispersal.\n3. Environmental impacts: Describe the nature and extent of impacts to the marine environment associated with present wastewater discharge practices at Davis and determine whether wastewater discharge practices have adversely affected the local environment.\n4. Evaluate treatment options: Evaluate the different levels of treatment required to mitigate and/or prevent various environmental impacts and reduce environmental risks.", "links": [ { diff --git a/datasets/DAVIS_STP_HabitatSurveys_1.json b/datasets/DAVIS_STP_HabitatSurveys_1.json index 7cbacc8e59..fabc323a8d 100644 --- a/datasets/DAVIS_STP_HabitatSurveys_1.json +++ b/datasets/DAVIS_STP_HabitatSurveys_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DAVIS_STP_HabitatSurveys_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat surveys of the sea floor were conducted by divers at sites around Davis Station, East Antarctica, as part of the Davis Sewage Treatment Project during the 2009/10 austral summer (AAS 3217 - metadata parent record = DAVIS_STP). \n\nFour non-overlapping and haphazardly placed 25m long habitat surveys were conducted at each site, generally 2 on each side of the boat and at least 10m apart. One diver used a weighted tape measure to lay out a transect and survey information was communicated to the surface over voice comms and recorded by the dive supervisor. The diver moved along the transect recording the substrate and type of biological cover (if present), starting at the 0cm point. Each point of transition to a different type of substrata or biological cover was recorded. Patches of substrata or biological cover that constituted less than a 2cm length of the transect were not counted as transitions and were not recorded. The depth at the beginning and end of the transect was also recorded.", "links": [ { diff --git a/datasets/DAVIS_STP_Macrofauna_1.json b/datasets/DAVIS_STP_Macrofauna_1.json index a17fcd408b..555643b9b1 100644 --- a/datasets/DAVIS_STP_Macrofauna_1.json +++ b/datasets/DAVIS_STP_Macrofauna_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DAVIS_STP_Macrofauna_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine macrofaunal invertebrate community composition data from 30 sites sampled by divers around Davis Station, East Antarctica, for the Davis Sewage Treatment Project during the 2009/10 summer. \nSoft-sediment infauna were sampled using a core of PVC tubing (15cm long x 10cm diameter) pushed 10cm into the sediment. Lids were placed on the top and, after some digging, on the bottom of the core and the sealed core was extracted from the sediment. In the lab, core contents were rinsed through a 0.5mm sieve and the remaining infauna and other material preserved in 8% buffered formalin in seawater with Biebrich Scarlet stain (4 cores per plot) or 96% ethanol (1 core per plot). Infauna were sorted from the surrounding matrix under a binocular microscope, counted and identified to species, when possible, or allocated to a morphospecies category.", "links": [ { diff --git a/datasets/DB_Antarctic_Artefacts_1.json b/datasets/DB_Antarctic_Artefacts_1.json index 1cd0dabe39..9cbe28b8ea 100644 --- a/datasets/DB_Antarctic_Artefacts_1.json +++ b/datasets/DB_Antarctic_Artefacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Antarctic_Artefacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic Artefacts database contains a record of items that have either been recorded in-situ in Antarctica, or have been retrieved and returned to Australia for storage.\n\nThe artefacts are organised on a collection basis, and currently the archive centres around three main collections:\n\nAntarctic Division Library Artefacts\nCape Denison Artefacts\nHeard Island Artefacts\n\nMany artefacts have accompanying images in the AAD's Image Antarctica.\n\nThe original library artefacts database has been incorporated into this database.\n\nSee also the Antarctic Artefacts Bibliography metadata record.", "links": [ { diff --git a/datasets/DB_Argos_PTT_Tracking_1.json b/datasets/DB_Argos_PTT_Tracking_1.json index 4b43c8026c..c409a96f40 100644 --- a/datasets/DB_Argos_PTT_Tracking_1.json +++ b/datasets/DB_Argos_PTT_Tracking_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Argos_PTT_Tracking_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A repository of all ARGOS satellite messages from 1982 to present. Trackers have been used on AWS stations, buoys and numerous species of whales, seals and seabirds.\n\nARGOS is a means of sending data back from PTT devices - Position Tracking Terminals. However, the subject does not necessarily have to be moving - as in the case of the Automatic Weather Stations (AWS), which use ARGOS for relaying meteorological data back to Australia.\n\nAnimal species that have been or are currently monitored by the Australian Antarctic Program using the ARGOS system include:\n\nGrey-headed Albatross\nBlack-browed Albatross\nLight mantled sooty albatross\nAustralian Fur Seal\nAntarctic Fur Seal\nWeddell Seal\nRoss seal\nCrabeater seal\nSouthern Elephant Seal\nEmperor Penguin\nKing Penguin\nMacaroni Penguin\nAdelie Penguin\nPygmy Blue Whale\n\nLocations in which the ARGOS system is/was being used by the Australian Antarctic Program are:\n\nAdmiralty Bay\nAlbatross Island\nAlmagro\nAuster Rookery\nBechervaise Island\nCape Gantheaume\nCaroline Cove\nCasey\nDavis\nDiego Ramirez\nDumont d'Urville, Base\nEdmonson Point\nIldefonso\nInexpressible Island\nMacquarie Island\nMagnetic Island\nPedra Branca\nScullin Monolith\nShirley Island\nSpit Bay\nTaylor Rookery\nUfs Island\n\nEach day, data is retrieved via telnet client from the ARGOS site in France. A batch process parses the data files and inserts into the Data Centre database by 0800 local time. End-users can subscribe to an email describing the recent data uploads. \n\nWeb-based tools are provided to filter the data by bounding box, time span and type of message quality. Finally a optional velocity filter can be applied to remove spurious positions that should not be reachable by that particular species. For example, seal data can be filtered for positions that would require speeds in excess of 10 km/hr. The same tool ascribes species, gender, age class and breeding status to each set of data. \n\nA separate control allows the filtered data to be published to the general public and/or to OBIS and GBIF via web services.\n\nOutput products include maps, excel spreadsheets and KML files for mapping data on Google Earth.", "links": [ { diff --git a/datasets/DB_Historic_WoV_1.json b/datasets/DB_Historic_WoV_1.json index 72952b6a95..53746bd23b 100644 --- a/datasets/DB_Historic_WoV_1.json +++ b/datasets/DB_Historic_WoV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Historic_WoV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ship-based observations of birds, seals and whales from the original 'ANARE Bird Log' books have been recovered into a single repository of sightings and associated abiotic information. ANARE (Australian National Antarctic Research Expeditions) is the historic acronym for these voyages. A few voyages have been included that were not part of ANARE but have Australian observers or volunteer\nobservers.\n\nVoyages start from the 1947/48 austral season up to 1982/83 with an average of 3 voyages per season. There are a few voyages where there is no data. It is not known if either no bird observations were undertaken during this period or that the bird logs exist if observations were undertaken.\n\nCurrent counts are birds, seals and whales\n\nObserving platforms include the following ships - Wyatt Earp, Tottan, River Fitzroy, Norsel, Kista Dan, Thala Dan, Magga Dan, Nella Dan, Lady Franklin and Nanok S and a single voyage from the private yacht Solo.\n\nThe quality and quantity of abiotic data associated with observations such as air temperature, sea ice cover etc vary immensely from voyage to voyage. Where possible this data has been entered.\n\nThis dataset contains very little information on estimates of survey effort and cannot be used to derive useful presence/absence spatial coverages of species during this period. It is purely sighting data only.", "links": [ { diff --git a/datasets/DB_Marine_Debris_1.json b/datasets/DB_Marine_Debris_1.json index 619d558d32..b019a3605f 100644 --- a/datasets/DB_Marine_Debris_1.json +++ b/datasets/DB_Marine_Debris_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Marine_Debris_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine debris records from beaches on Heard and Macquarie Islands and floating debris spotted on voyages.\n\nData were collected by observers surveying beaches either methodically or opportunistically, and by observers spotting debris as it floated past ships.\n\nThe data were originally collated into a searchable database, but the application is no longer supported by the Australian Antarctic Data Centre. An extract of the data is attached to this metadata record. The extract is in Excel format, and each worksheet is a copy of a database table.", "links": [ { diff --git a/datasets/DB_Trophic_1.json b/datasets/DB_Trophic_1.json index 7b3dec70f0..908f612844 100644 --- a/datasets/DB_Trophic_1.json +++ b/datasets/DB_Trophic_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Trophic_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2018-08-10 - these data have been superseded by a new metadata record and dataset - see the provided URL for more details.\n\nThis record describes a compilation of trophic data from across the Southern Ocean. Data have been drawn from published literature, existing trophic data collections, AADC data sets, and unpublished collections. The database comprises two principal tables. The first table relates to direct sampling methods of dietary assessment, including gut, scat, and bolus content analyses, stomach flushing, and observed feeding. The second table is a compilation of stable isotope values. Each record in these two tables includes details such as the location and date of sampling, predator size and mass, prey size and mass, and estimates of dietary importance. Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/).\n\nThe schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data.\n\nA range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source. Apparent errors that could not be resolved were marked as such in the QUALITY_FLAG column, with the reason in the NOTES column.\n\nNotes on names\n'Sp.' indicates unidentified members of a genus (e.g. 'Pachyptila sp.'). For unidentified taxa at other taxonomic levels, the taxonomic name has been used (e.g. Amphipoda, Myctophidae, Decapoda). Uncertain species identifications (e.g. 'Notothenia rossii?' or 'Gymnoscopelus cf. piabilis') were assigned the genus name (e.g. 'Notothenia sp.'). Original names were retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) were recorded with each matched taxon.\n\nGrouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The prey_is_aggregate column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with 0 in the prey_is_aggregate column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with a 1 in the prey_is_aggregate column. See the notes on prey_is_aggregate, below.\n\n\nSchema: Diet sample table\n\n- LINK_ID: The unique identifier of this record\n- SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3\n- LOCATION: The name of the location at which the data was collected.\n- WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)\n- EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)\n- SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)\n- NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)\n- OBSERVATION_DATE_START: The start of the sampling period (UTC)\n- OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003)\n- ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable)\n- ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable)\n- DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable)\n- DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable)\n- PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source\n- PREDATOR_NAME: The scientific name of the predator (corrected, if necessary).\n- PREDATOR_COMMON_NAME: The common name of the predator (from the WoRMS taxonomic register)\n- PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register\n- PREDATOR_LIFE_STAGE: Life stage of the predator. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites\n- PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable. e.g. 'brooding', 'chick rearing', 'nonbreeding', 'posthatching'\n- PREDATOR_SEX: Sex of the predator. 'male', 'female', 'both', or 'unknown'\n- PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20.\n- PREDATOR_TOTAL_COUNT: The total number of predators sampled. If (say) 50 predators were caught but only 20 analysed, this column will contain 50.\n- PREDATOR_SAMPLE_COUNT: The identifier of this predator sample. PREDATOR_SAMPLE_ID values are unique within a source (i.e. - SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or population, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will often report detailed prey information for a large sample, and also report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season).\n- PREDATOR_SIZE_MIN: The minimum size of the predators in the sample\n- PREDATOR_SIZE_MAX: The maximum size of the predators in the sample\n- PREDATOR_SIZE_MEAN: The mean size of the predators in the sample\n- PREDATOR_SIZE_SD: The standard deviation of the size of the predators in the sample\n- PREDATOR_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm'\n- PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. 'total length', 'standard length')\n- PREDATOR_MASS_MIN: The minimum mass of the predators in the sample\n- PREDATOR_MASS_MAX: The maximum mass of the predators in the sample\n- PREDATOR_MASS_MEAN: The mean mass of the predators in the sample\n- PREDATOR_MASS_SD: The standard deviation of the mass of the predators in the sample\n- PREDATOR_MASS_UNITS: The units of mass (e.g. 'g' or 'kg')\n- PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents (blank implies total body weight). Current values 'g', 'kg', 't'\n- PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source.\n- PREY_NAME: The scientific name of the prey item (corrected, if necessary).\n- PREY_COMMON_NAME: The common name of the prey item (from the WoRMS taxonomic register)\n- PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register\n- PREY_IS_AGGREGATE: 'Y' indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses. If there no entry in this column, it means that this information is not included anywhere else in the database and can be used freely when aggregating over taxonomic groups, for example\n- PREY_LIFE_STAGE: Life stage of the prey. e.g. 'adult', 'chick', 'larva'\n- PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: NOT the total number of individuals of this prey type, unless all individuals in the sample were measured)\n- PREY_SIZE_MIN: The minimum size of the prey in the sample\n- PREY_SIZE_MAX: The maximum size of the prey in the sample\n- PREY_SIZE_MEAN: The mean size of the prey in the sample\n- PREY_SIZE_SD: The standard deviation of the size of the prey in the sample\n- PREY_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm'\n- PREY_SIZE_NOTES: Notes on the prey size information, including a definition of what the size value represents (e.g. 'total length', 'standard length')\n- PREY_MASS_MIN: The minimum mass of the prey in the sample\n- PREY_MASS_MAX: The maximum mass of the prey in the sample\n- PREY_MASS_MEAN: The mean mass of the prey in the sample\n- PREY_MASS_SD: The standard deviation of the mass of the prey in the sample\n- PREY_MASS_UNITS: The units of mass. Current values 'mg', 'g', 'kg'\n- PREY_MASS_NOTES: Notes on the prey mass information, including a definition of what the mass value represents (blank implies total body weight)\n- FRACTION_DIET_BY_WEIGHT: The fraction (by weight) of the predator diet that this prey type made up (e.g. if Euphausia superba contributed 50% of the total mass of prey items, this value would be 0.5). Many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999\n- FRACTION_DIET_BY_PREY_ITEMS: The fraction (by number) of prey items that this prey type made up (e.g. if 1000 Euphausia superba were found out of a total of 2000 prey items, this value would be 0.5). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999\n- FRACTION_OCCURRENCE: The number of times this prey item occurred in a predator sample, as a fraction of the number of non-empty samples (e.g. if Euphausia superba occurred in half of the non-empty stomachs examined, this value would be 0.5). Empty stomachs are ignored for the purposes of calculating fraction of occurrence. For gut content analyses (and any other study types where 'no prey' can occur in a sample), the fraction of empty stomachs is also given (using prey_name 'None' - e.g. if predator_total_count was 10 and 3 stomachs were empty, this will be 0.3). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999\n- QUALITATIVE_DIETARY_IMPORTANCE: Qualitative description of the dietary importance of this prey item (e.g. from comments about certain prey in the discussion text of an article), if numeric values have not been given. Current values are 'none', 'incidental', 'minor', 'major', 'almost exclusive', 'exclusive'\n- CONSUMPTION_RATE_MIN: The minimum consumption rate of this prey item\n- CONSUMPTION_RATE_MAX: The maximum consumption rate of this prey item\n- CONSUMPTION_RATE_MEAN: The mean consumption rate of this prey item\n- CONSUMPTION_RATE_SD: The standard deviation of the consumption rate of this prey item\n- CONSUMPTION_RATE_UNITS: The units of consumption rate (e.g. 'kg/day')\n- CONSUMPTION_RATE_NOTES: Notes about the consumption rate estimates\n- IDENTIFICATION_METHOD: How this dietary information was gathered. Multiple values can potentially be entered (separated by commas). Current values include 'scat content' (contents of scats), 'stomach flushing' (physical sampling of the stomach contents by flushing the contents out with water), 'stomach content' (physical sampling of the stomach contents from a dead animal), 'regurgitate content' (physical sampling of the contents of forced or spontaneous regurgitations), 'observed predation', 'bolus content' (physical sampling of the contents of boluses), 'nest detritus', 'unknown'\n- QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data\n- IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source.\n- NOTES: Any other notes\n- LAST_MODIFIED: The date of last modification of this record\n\n\nSchema: Isotope data table\n\n- RECORD_ID: The unique identifier of this record\n- SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3\n- LOCATION: The name of the location at which the data was collected.\n- WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)\n- EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)\n- SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)\n- NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)\n- OBSERVATION_DATE_START: The start of the sampling period (UTC)\n- OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003)\n- ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable)\n- ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable)\n- DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable)\n- DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable)\n- TAXON_NAME_ORIGINAL: The name of the taxon, as it appeared in the original source.\n- TAXON_NAME: The scientific name of the taxon (corrected, if necessary).\n- TAXON_COMMON_NAME: The common name of the taxon (from the WoRMS taxonomic register)\n- TAXON_APHIA_ID: The numeric identifier of the taxon in the WoRMS taxonomic register\n- TAXON_LIFE_STAGE: Life stage of the taxon. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites\n- TAXON_BREEDING_STAGE: Stage of the breeding season of the taxon, if applicable. e.g. 'lactating', 'weaning', 'chick rearing'\n- TAXON_SEX: Sex of the taxon. 'male', 'female', 'both', or 'unknown'\n- TAXON_SAMPLE_COUNT: The number of samples from which size and stable isotope measurements were made\n- TAXON_SIZE_MIN: The minimum size of the individuals in the sample\n- TAXON_SIZE_MAX: The maximum size of the individuals in the sample\n- TAXON_SIZE_MEAN: The mean size of the individuals in the sample\n- TAXON_SIZE_SD: The standard deviation of the size of the individuals in the sample\n- TAXON_SIZE_UNITS: The units of size. Current values 'mm', 'm'\n- TAXON_SIZE_NOTES: Notes on the size information, including a definition of what the size value represents (e.g. 'total length', 'standard length')\n- TAXON_MASS_MIN: The minimum mass of the individuals in the sample\n- TAXON_MASS_MAX: The maximum mass of the individuals in the sample\n- TAXON_MASS_MEAN: The mean mass of the individuals in the sample\n- TAXON_MASS_SD: The standard deviation of the mass of the individuals in the sample\n- TAXON_MASS_UNITS: The units of mass. e.g. 'g', 'kg'\n- TAXON_MASS_NOTES: Notes on the taxon mass information, including a definition of what the mass value represents (blank implies total body weight)\n- DELTA_13C_MEAN: The mean of the d13C values from the sample (permil;)\n- DELTA_13C_VARIABILITY_VALUE: The variability of the d13C values from the sample\n- DELTA_13C_VARIABILITY_TYPE: The variability type that the DELTA_13C_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error)\n- DELTA_15N_MEAN: The mean of the d15N values from the sample (permil;)\n- DELTA_15N_VARIABILITY_VALUE: The variability of the d15N values from the sample\n- DELTA_15N_VARIABILITY_TYPE: The variability type that the DELTA_15N_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error)\n- C_N_RATIO_MEAN: The mean of the C:N ratio values from the sample, expressed as a molar percentage\n- C_N_RATIO_VARIABILITY_VALUE: The variability of the C:N ratio values from the sample\n- C_N_RATIO_VARIABILITY_TYPE: The variability type that the C_N_RATIO_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error)\n- ISOTOPES_CARBONATES_EXTRACTED: Were carbonates extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown)\n- ISOTOPES_LIPIDS_EXTRACTED: Were lipids extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown)\n- ISOTOPES_BODY_PART_USED: Which part of the organism was sampled?\n- QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data\n- IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source.\n- NOTES: Any other notes\n- LAST_MODIFIED: The date of last modification of this record", "links": [ { diff --git a/datasets/DB_Voyages_1.json b/datasets/DB_Voyages_1.json index 0e80d0c6b3..42cf602e62 100644 --- a/datasets/DB_Voyages_1.json +++ b/datasets/DB_Voyages_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DB_Voyages_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A register of all voyages that contribute to the science of the Australian Antarctic Programme. It includes voyages that opportunistically collect marine data while underway.\n\nDetails have been gleaned from historic paper records, publications, voyage situation reports and reports from marine science cruises.\n\nProducts linked to each voyage include a map, voyage schedule and a list of any science related activities on the voyage.\n\nThe application links to various external resources within the Antarctic Division such as daily shipping reports, passenger lists and various sets of data.\n\nNOTE - Support for this application was put \"on hold\" after the 2013/2014 season.\n Hence, only voyages up until that season are included in the database. This decision may be revisited at some time in the future.", "links": [ { diff --git a/datasets/DC3_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_Aerosol_AircraftInSitu_DC8_Data_1.json index e1209ba799..082b3976a5 100644 --- a/datasets/DC3_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Aerosol_AircraftInSitu_DC8_Data are in-situ aerosol data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data_1.json b/datasets/DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data_1.json index eb08a3e1e6..38c239c90b 100644 --- a/datasets/DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data_1.json +++ b/datasets/DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data are in-situ aerosol data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data_1.json index badd4b6b96..e6a91abe3a 100644 --- a/datasets/DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Aerosol_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ aerosol data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_AircraftRemoteSensing_DIAL_DC8_Data_1.json b/datasets/DC3_AircraftRemoteSensing_DIAL_DC8_Data_1.json index 909b2eec6b..48b4d33f1d 100644 --- a/datasets/DC3_AircraftRemoteSensing_DIAL_DC8_Data_1.json +++ b/datasets/DC3_AircraftRemoteSensing_DIAL_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_AircraftRemoteSensing_DIAL_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_AircraftRemoteSensing_DIAL_DC8_Data are remotely sensed data collected by the Differential Absorption Lidar (DIAL) onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_Cloud_AircraftInSitu_DC8_Data_1.json index a6e165d324..b64a2f3c7e 100644 --- a/datasets/DC3_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Cloud_AircraftInSitu_Data are in-situ cloud data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data_1.json index 6bd3cdc560..f4b9a4a64c 100644 --- a/datasets/DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ cloud data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Merge_Data_1.json b/datasets/DC3_Merge_Data_1.json index 5487520357..893fa005da 100644 --- a/datasets/DC3_Merge_Data_1.json +++ b/datasets/DC3_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Merge_Data are pre-generated merge data files collected during the Deep Convective Clouds and Chemistry (DC3) field campaign. This product contains merged data products collected from instruments onboard the DC-8, NSF/NCAR GV-HIAPER, and DLR-Falcon aircrafts. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_MetNav_AircraftInSitu_DC8_Data_1.json index 8b638dd458..5aa050618c 100644 --- a/datasets/DC3_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_MetNav_AircraftInSitu_DC8_Data are in-situ meteorological and navigational data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloud Physics Lidar (CPL) onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_MetNav_AircraftInSitu_DLR-Falcon_Data_1.json b/datasets/DC3_MetNav_AircraftInSitu_DLR-Falcon_Data_1.json index 1a9d1aa2ab..8a575cc671 100644 --- a/datasets/DC3_MetNav_AircraftInSitu_DLR-Falcon_Data_1.json +++ b/datasets/DC3_MetNav_AircraftInSitu_DLR-Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_MetNav_AircraftInSitu_DLR-Falcon_Data are meteorological and navigational data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data_1.json index 57d908060c..390837c888 100644 --- a/datasets/DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_MetNav_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ meteorological and navigational data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Miscellaneous_DC8_Data_1.json b/datasets/DC3_Miscellaneous_DC8_Data_1.json index b05589a427..52b00e0062 100644 --- a/datasets/DC3_Miscellaneous_DC8_Data_1.json +++ b/datasets/DC3_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Miscellaneous_DC8_Data are miscellaneous data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. This product features data from the Global Forecast System (GFS) model. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Miscellaneous_DLR-Falcon_Data_1.json b/datasets/DC3_Miscellaneous_DLR-Falcon_Data_1.json index 001c2bb925..d8fb371a89 100644 --- a/datasets/DC3_Miscellaneous_DLR-Falcon_Data_1.json +++ b/datasets/DC3_Miscellaneous_DLR-Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Miscellaneous_DLR-Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Miscellaneous_DLR-Falcon_Data are miscellaneous data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. This product features data from the Global Forecast System (GFS) model. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Miscellaneous_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_Miscellaneous_NSF-GV-HIAPER_Data_1.json index ae0f7e2713..db574cee8f 100644 --- a/datasets/DC3_Miscellaneous_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_Miscellaneous_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Miscellaneous_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Miscellaneous_NSF-GV-HIAPER_Data are miscellaneous data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. This product features data from the Global Forecast System (GFS) model. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_Radiation_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_Radiation_AircraftInSitu_DC8_Data_1.json index 91a2f98734..359d31fc48 100644 --- a/datasets/DC3_Radiation_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_Radiation_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_Radiation_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_Radiation_AircraftInSitu_DC8_Data are in-situ radiation data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_TraceGas_AircraftInSitu_DC8_Data_1.json index eaa7694016..3385f547dd 100644 --- a/datasets/DC3_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_TraceGas_AircraftInSitu_DC8_Data are in-situ trace gas data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data_1.json b/datasets/DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data_1.json index 3d3ba7d6f7..0435b60b56 100644 --- a/datasets/DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data_1.json +++ b/datasets/DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data are in-situ trace gas data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data_1.json index 5b0f7cc3b8..c0be82a551 100644 --- a/datasets/DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_TraceGas_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ trace gas data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/DC3_jValue_AircraftInSitu_DC8_Data_1.json index 9aee376780..5f2c4776e8 100644 --- a/datasets/DC3_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/DC3_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_jValue_AircraftInSitu_DC8_Data are photolysis rate (j value) data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data_1.json b/datasets/DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data_1.json index 308d9d4cae..4f45126539 100644 --- a/datasets/DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data_1.json +++ b/datasets/DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC3_jValue_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ photolysis rate data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.\r\n\r\nThe Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.\r\nDC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.\r\nIn addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.", "links": [ { diff --git a/datasets/DCOTSS-Aircraft-Data_1.json b/datasets/DCOTSS-Aircraft-Data_1.json index 0a28ca9fda..8d621af01a 100644 --- a/datasets/DCOTSS-Aircraft-Data_1.json +++ b/datasets/DCOTSS-Aircraft-Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DCOTSS-Aircraft-Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DCOTSS-Aircraft-Data features the aircraft data collected during the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. These data products were collected via a variety of instrumentation onboard the NASA ER-2 aircraft, including: Advanced Whole Air Sampler (AWAS), ERA5,GFS, and GEOS-5 Analysis Fields , Meteorological Measurement System (MMS), Particle Analysis by Laser Mass Spectrometry \u2013 Next Generation (PALMS-NG), UAS Chromatography for Atmospheric Trace Species (UCATS), DCOTSS Printed Optical Particle Spectrometer (DPOPS), Rapid Ozone Experiment (ROZE), Harvard Water Vapor (HWV), Compact Airborne Nitrogen diOxide Experiment (CANOE), Compact Airborne Formaldehyde Experiment (CAF\u00c9), Harvard Halogens Experiment (HAL), and Harvard University Picarro Cavity Ringdown Spectrometer (HUPCRS). Data collection for this product is ongoing and currently only features the first deployment.\r\n\r\nEach summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA\u2019s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.", "links": [ { diff --git a/datasets/DCOTSS-Balloon-Data_1.json b/datasets/DCOTSS-Balloon-Data_1.json index 907d3e8b0b..fb30958814 100644 --- a/datasets/DCOTSS-Balloon-Data_1.json +++ b/datasets/DCOTSS-Balloon-Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DCOTSS-Balloon-Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DCOTSS-Balloon-Data features the balloon data collected during the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. DCOTSS-Ozone-H2O features balloon flights that include both ozone and water vapor trace gas measurements while DCOTSS-Ozone features balloons with only ozone measurements. Balloons were launched from the following locations: Boulder, CO; Salina, KS: Corpus Christi, TX; and Grand Forks, ND. Data collection for this product is ongoing and currently only features the first deployment. \r\n\r\nEach summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA\u2019s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.", "links": [ { diff --git a/datasets/DCOTSS-Model-Output_1.json b/datasets/DCOTSS-Model-Output_1.json index 55be9c02dc..28e28cbca8 100644 --- a/datasets/DCOTSS-Model-Output_1.json +++ b/datasets/DCOTSS-Model-Output_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DCOTSS-Model-Output_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DCOTSS-Model-Output features numerical model output for the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. Featured in this product are trajectory calculations, convection-permitting model simulations, and chemistry model output. Air parcel trajectories are computed using the TRAJ3D trajectory model. Two types of trajectory products are created: flight trajectories and overshoot trajectories. Flight trajectories will be initialized every second along each Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) flight track and run backwards for up to 10 days. Overshoot trajectories will be initialized in overshoot volumes identified from both GridRad radar and GOES satellite data every 10 minutes and run forward for up to 5 days. Convection allowing model simulations are carried out using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). These will aid in the evaluation of aircraft observations and evaluate the ability of numerical models to represent overshooting convection and transport. Photodissociation frequencies (J values) will also be computed using a radiative transfer model of the UV and Visible (UV/Vis) spectral regions. Data collection for this product is ongoing and currently only features the first deployment.\r\n\r\nEach summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The DCOTSS field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA\u2019s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.", "links": [ { diff --git a/datasets/DCOTSS-Radar-Satellite-Data_1.json b/datasets/DCOTSS-Radar-Satellite-Data_1.json index 050ba2129e..bb3710ed71 100644 --- a/datasets/DCOTSS-Radar-Satellite-Data_1.json +++ b/datasets/DCOTSS-Radar-Satellite-Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DCOTSS-Radar-Satellite-Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DCOTSS-Radar-Satellite-Data feature the radar and satellite data products for the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. Featured in this product are NEXRAD WSR-88D radar products and GOES-16 and GOES-17 geostationary satellite imagery and derived products. NEXRAD GridRad data were produced at a 10-minute frequency across the contiguous United States to support forecasting and flight planning activities. GridRad data also include volumes of radar reflectivity at horizontal polarization and radial velocity spectrum width, which were primarily used to identify tropopause-overshooting convection. Identified GridRad overshoots, which rely upon ERA5 tropopause heights, are included in separate daily files.\r\nAlso included are GOES-16 and GOES-17 satellite products. Tropopause-overshooting convection is also identified using GOES visible and infrared geostationary satellite imagery. These products include derived cloud top altitude, convective overshoot probability, and visible texture rating product and are produced in 10-minute intervals. The product domain extends over North America and encompasses most of Mexico and Canada. The satellite overshoot products complement the GridRad products and enable an understanding of overshooting that occurs outside the NEXRAD network. Data collection for this product is ongoing and currently only features the first deployment.\r\n\r\nEach summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA\u2019s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.", "links": [ { diff --git a/datasets/DCOTSS-Reports_1.json b/datasets/DCOTSS-Reports_1.json index a574ed918b..e281306320 100644 --- a/datasets/DCOTSS-Reports_1.json +++ b/datasets/DCOTSS-Reports_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DCOTSS-Reports_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DCOTSS-Reports features important reports and documentation that support the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. Featured in this product are mission scientist reports, forecasting and flight planning briefings, and pilot reports. A flight report is required to be submitted for all SMD aircraft flights. Mission scientists submit two types of reports: daily reports and science flight summary reports. Daily reports provide high-level summaries of daily weather conditions and forecasts and the flight tasking decisions and options for the ER-2 for the current and next several days. The science flight summary reports are produced after each flight and provides a summary of instrument operating status, the forecasting and planning sequence leading into the mission, and a description of significant events that occurred including problems related to the aircraft or instruments, weather conditions during flight and key observations related to the mission science objectives. Data collection for this product is complete.\r\n\r\nEach summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA\u2019s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.", "links": [ { diff --git a/datasets/DECKLAB_0.json b/datasets/DECKLAB_0.json index 741d8c1498..a5658320a9 100644 --- a/datasets/DECKLAB_0.json +++ b/datasets/DECKLAB_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DECKLAB_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the western African coast in 1997.", "links": [ { diff --git a/datasets/DEVOTE_Aerosol_AircraftInSitu_B200_Data_1.json b/datasets/DEVOTE_Aerosol_AircraftInSitu_B200_Data_1.json index f6cb25533f..b4b48c1c8f 100644 --- a/datasets/DEVOTE_Aerosol_AircraftInSitu_B200_Data_1.json +++ b/datasets/DEVOTE_Aerosol_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DEVOTE_Aerosol_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEVOTE_Aerosol_AircraftInSitu_B200_Data are in-situ aerosol data collected onboard the B-200 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data from the Polarized Imaging Nephelometer (PI-Neph), Particle Soot Absorption Photometer (PSAP), Aerodynamic Particle Sizer (APS), Condensation Particle Counter (CPC), Nephelometers, Optical Particle Counter (OPC), Scanning Mobility Particle Sizer (SMPS), and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) are included in this product. Data collection is complete.\r\n\r\nThe Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.\r\n\r\nDEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.\r\n\r\nDEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.", "links": [ { diff --git a/datasets/DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data_1.json b/datasets/DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data_1.json index 6be71f4850..6a8f96b075 100644 --- a/datasets/DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data_1.json +++ b/datasets/DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DEVOTE_AircraftRemoteSensing_UC12_HSRL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEVOTE_ AircraftRemoteSensing_UC12_HSRL_Data are remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data collection is complete.\r\n\r\nThe Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.\r\n\r\nDEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.\r\n\r\nDEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.", "links": [ { diff --git a/datasets/DEVOTE_AircraftRemoteSensing_UC12_RSP_Data_1.json b/datasets/DEVOTE_AircraftRemoteSensing_UC12_RSP_Data_1.json index 533e0b6c4c..e52fe8ec09 100644 --- a/datasets/DEVOTE_AircraftRemoteSensing_UC12_RSP_Data_1.json +++ b/datasets/DEVOTE_AircraftRemoteSensing_UC12_RSP_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEVOTE_AircraftRemoteSensing_UC12_RSP_Data are remotely sensed data collected via the Research Scanning Polarimeter (RSP) onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data collection is complete.\r\n\r\nThe Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.\r\n\r\nDEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.\r\n\r\nDEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.", "links": [ { diff --git a/datasets/DEVOTE_MetNav_AircraftInSitu_B200_Data_1.json b/datasets/DEVOTE_MetNav_AircraftInSitu_B200_Data_1.json index bebf729825..d28e1f9e3e 100644 --- a/datasets/DEVOTE_MetNav_AircraftInSitu_B200_Data_1.json +++ b/datasets/DEVOTE_MetNav_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DEVOTE_MetNav_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEVOTE_MetNav_AircraftInSitu_B200_Data are in-situ meteorological and navigational data collected onboard the B-200 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data from the NAV420 CrossBow Inertial Measurement Unit (IMU) Navigational Data and 2 Diode Laser Hygrometer (DLH) instruments are included in this product. Data collection is complete.\r\n\r\nThe Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.\r\n\r\nDEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the DLH, and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.\r\n\r\nDEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.", "links": [ { diff --git a/datasets/DEVOTE_MetNav_AircraftInSitu_UC12_Data_1.json b/datasets/DEVOTE_MetNav_AircraftInSitu_UC12_Data_1.json index 0ed379d793..287f3173f2 100644 --- a/datasets/DEVOTE_MetNav_AircraftInSitu_UC12_Data_1.json +++ b/datasets/DEVOTE_MetNav_AircraftInSitu_UC12_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DEVOTE_MetNav_AircraftInSitu_UC12_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEVOTE_MetNav_AircraftInSitu_UC12_Data are in-situ meteorological and navigational data collected onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data from the Applanix POSAV is included in this product. Data collection is complete.\r\n\r\nThe Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.\r\n\r\nDEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.\r\n\r\nDEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.", "links": [ { diff --git a/datasets/DFO_Canada_Time_Series_0.json b/datasets/DFO_Canada_Time_Series_0.json index f955aad595..8f899972e9 100644 --- a/datasets/DFO_Canada_Time_Series_0.json +++ b/datasets/DFO_Canada_Time_Series_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DFO_Canada_Time_Series_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This time series of chl data was obtained from the BioChem archive run by the Department of Fisheries and Oceans, Canada. Water sampling and chlorophyll analysis methods are described in the Mitchell et al. (2002) protocol document accompanying the data.DFO (2014). BioChem: database of biological and chemical oceanographic data. Department of Fisheries and Oceans, Canada.http://isdm.gc.ca/biochem/biochem-eng.htm", "links": [ { diff --git a/datasets/DGT-Heavymetals-Casey03-04_1.json b/datasets/DGT-Heavymetals-Casey03-04_1.json index 9143acc27b..6b0a69b00d 100644 --- a/datasets/DGT-Heavymetals-Casey03-04_1.json +++ b/datasets/DGT-Heavymetals-Casey03-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DGT-Heavymetals-Casey03-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The concentration of heavy metals in seawater at four sites around Casey was determined via Diffusive Gradients in Thin films (DGT) loggers attached to experimental mesocosms suspended below the sea ice. Data are the concentration of heavy metals in micrograms per litre (ug/l), equivalent to parts per billion (ppb)/litre Two loggers were attached to each mesocosm (perforated 20 litre food buckets) at each site; one at the top and one at the bottom of each mesocosm. Mesocosms were suspended two to three metres below the bottom edge of the sea ice through a 1 metre diameter hole and were periodically raised to the surface for short periods (~1 hour). This experiment was part of the short-term biomonitoring program for the Thala Valley Tip Clean-up at Casey during summer 2003/04. During Runs 1 and 2 of the experiment mesocosms were deployed at Brown Bay Inner (S66 16.811 E110 32.475), Brown Bay Outer (S66 16.811 E110 32.526), McGrady Cove (S66 16.556 E110 34.392) and O'Brien Bay 1 (S66 18.730 E110 30.810). In Run 3 mesocosm were deployed in open water with no sea ice covering at Brown Bay Inner (S66 16.807 E110 32.556), Brown Bay Outer (S66 16.805 E110 32.607), McGrady Cove (S66 16.520 E110 34.257) and O'Brien Bay (S66 17.607 E110 31.247). \n\nThese data were collected as part of ASAC project 2201 (ASAC_2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities).\n\nSee also other metadata records by Glenn Johnstone for related information.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data_1.json index 98ff9bd63d..9aea2c5536 100644 --- a/datasets/DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the PSAP, APS, CPC, CCN Counter, Nephelometer/PI-Neph, LAS, PILS, Ion Chromatographs, PILS/Total Organic Carbon Analyzer (TOC), SMPS, SP2 and UHSAS. This data product contains data for only the California deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data_1.json b/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data_1.json index f78f6d5c87..20df594c01 100644 --- a/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data_1.json +++ b/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_AircraftRemoteSensing_B200_ACAM_Data contains remotely sensed data collected by the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B-200 aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data_1.json b/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data_1.json index 9d7566fed4..08d35e1b6b 100644 --- a/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data_1.json +++ b/datasets/DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_AircraftRemoteSensing_B200_HSRL_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL-2) onboard NASA's B-200 aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Analysis_Ancillary_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Analysis_Ancillary_Data_1.json index e46cf1ec6b..73382dd367 100644 --- a/datasets/DISCOVERAQ_California_Ground_Analysis_Ancillary_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Analysis_Ancillary_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Analysis_Ancillary_Data contains data collected at ancillary ground sites during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P3-B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Bakersfield_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Bakersfield_Data_1.json index 8a1fee2447..2baddbe0fa 100644 --- a/datasets/DISCOVERAQ_California_Ground_Bakersfield_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Bakersfield_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Bakersfield_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Bakersfield_Data contains data collected at the Bakersfield ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_CARB_Data_1.json b/datasets/DISCOVERAQ_California_Ground_CARB_Data_1.json index 588bcf7cf4..7cbaebc27b 100644 --- a/datasets/DISCOVERAQ_California_Ground_CARB_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_CARB_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_CARB_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_CARB_Data contains data collected by the California Air Resources Board (CARB) at various ground sites throughout the study area, including Fresno, Oildale, and Shafter as part of the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Fresno_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Fresno_Data_1.json index e83263d968..9f0f772fb4 100644 --- a/datasets/DISCOVERAQ_California_Ground_Fresno_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Fresno_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Fresno_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Fresno_Data contains data collected at the Fresno ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Huron_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Huron_Data_1.json index 9fa4fa5c9c..56fa91fbdc 100644 --- a/datasets/DISCOVERAQ_California_Ground_Huron_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Huron_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Huron_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Huron_Data contains data collected at the Huron ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Oildale_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Oildale_Data_1.json index 3c2d424e2b..02bff694e8 100644 --- a/datasets/DISCOVERAQ_California_Ground_Oildale_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Oildale_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Oildale_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Oildale_Data contains data collected at the Oildale ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Pandora_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Pandora_Data_1.json index ab35741efd..92331fae78 100644 --- a/datasets/DISCOVERAQ_California_Ground_Pandora_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Pandora_Data contains all of the Pandora instrumentation data collected during the DISCOVER-AQ field study. Contained in this dataset are column measurements of NO2 and O3. Pandoras were situated at various ground sites across the study area, including Arvin-DiGiorgio, Bakersfield, Corcoran, Fresno, Hanford, Huron, Madera, Parlier, Porterville, Shafter, Tranquility and Visalia Airport. This data product contains only data from the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_Porterville_Data_1.json b/datasets/DISCOVERAQ_California_Ground_Porterville_Data_1.json index d7529cf209..493444820b 100644 --- a/datasets/DISCOVERAQ_California_Ground_Porterville_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_Porterville_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_Porterville_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_Porterville_Data contains data collected at the Porterville ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ground_SJV_Data_1.json b/datasets/DISCOVERAQ_California_Ground_SJV_Data_1.json index fc8d0cacfe..56343dbf65 100644 --- a/datasets/DISCOVERAQ_California_Ground_SJV_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ground_SJV_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ground_SJV_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ground_SJV_Data contains data collected by the San Joaquin Valley (SJV) Air Pollution Control District at ground sites around the study area, including Bakersfield, Clovis, Corcoran, Fresno, Hanford, Huron, Madera, Parlier, Porterville, Tranquility and Visalia Airport as part of the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Merge_Data_1.json b/datasets/DISCOVERAQ_California_Merge_Data_1.json index d593dc21aa..95d836fc38 100644 --- a/datasets/DISCOVERAQ_California_Merge_Data_1.json +++ b/datasets/DISCOVERAQ_California_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Merge_Data contains pre-generated merged data files created from measurements obtained onboard the P-3B aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data_1.json b/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data_1.json index 4b0a318f0a..a2ca7a5cb3 100644 --- a/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data_1.json +++ b/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_MetNav_AircraftInSitu_B200_Data contains in situ meteorological and navigational data collected onboard NASA's B-200 aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This product contains the B-200 navigational data collected via the APPLANIX. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).", "links": [ { diff --git a/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data_1.json index 62bf0b52bb..24e620f7ba 100644 --- a/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_MetNav_AircraftInSitu_P3B_Data contains in situ meteorological and navigational data collected onboard NASA's P-3B aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This product features navigational data for the P-3B aircraft, along with data from the DLH. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_Ozonesondes_Data_1.json b/datasets/DISCOVERAQ_California_Ozonesondes_Data_1.json index 6d9ed12df5..e01953f9bd 100644 --- a/datasets/DISCOVERAQ_California_Ozonesondes_Data_1.json +++ b/datasets/DISCOVERAQ_California_Ozonesondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_Ozonesondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_Ozonesondes_Data contains data collected via ozonesonde lauches at the Porterville ground site during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data_1.json index 64ff09d938..0ef896cc72 100644 --- a/datasets/DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_California_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the California (San Joaquin Valley) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, Picarro G2103, and PTR-MS. This data product contains only data from the California deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data_1.json index 0e2e0cec50..740c2c1660 100644 --- a/datasets/DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the 3-Wavelength Particle Soot Absorption Photometer Manufactured by Radiance Research (PSAP), Aerodynamic Particle Sizer (APS), Condensation Particle Counter (CPC), Nephelometer, Launch Abort System (LAS), Particle-Into-Liquid Sampler (PILS), Scanning Mobility Particle Sizer (SMPS), Single Particle Soot Photometer (SP2), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), Cloud and Aerosol Spectrometer (CAS), Cloud Imaging Probe (CIP), and Theory of change (TOC). This data product contains data for only the Colorado deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data_1.json b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data_1.json index 31ad6b0b1a..70fd146912 100644 --- a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_ACAM_Data contains remotely sensed data collected by the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Denver deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data_1.json b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data_1.json index 5c8aaf984f..46144ab5bb 100644 --- a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_GCAS_Data contains remotely sensed data collected by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data_1.json b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data_1.json index 3f588f11e0..2c1b92b32f 100644 --- a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL-2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar-2 (HSRL-2) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json index a05cd17b0f..7e66de20ba 100644 --- a/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_AircraftRemoteSensing_Falcon_GeoTASO_Data contains remotely sensed data collected by the Geostationary Trace gas and Aerosol Optimization (GeoTASO) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_BAOTower_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_BAOTower_Data_1.json index 82198c1e3d..cfb33aec42 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_BAOTower_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_BAOTower_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_BAOTower_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_BAOTower_Data contains data collected at the BAO Tower ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_CDPHE_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_CDPHE_Data_1.json index aee9d034c4..2abea44b76 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_CDPHE_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_CDPHE_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_CDPHE_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_CDPHE_Data contains data collected by the Colorado Department of Public Health and Environment (CDPHE) at ground sites around the study area, including Chatfield Park, Denver-LaCasa, Fort Collins, NREL-Golden, Aurora-East, Boulder, Denver-CAMP, Denver-I25, Rocky Flats, Welch, and Weld Co. Tower as part of the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_EPA_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_EPA_Data_1.json index 7fcea4a970..0bb52759ef 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_EPA_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_EPA_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_EPA_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_EPA_Data contains data collected by the Environmental Protection Agency (EPA) at ground sites around the study area, including Chatfield Park, Fort Collins, NREL-Golden, and Denver-I25 as part of the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Denver deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_FortCollins_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_FortCollins_Data_1.json index 0adfbf68ce..d9bdc9804a 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_FortCollins_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_FortCollins_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_FortCollins_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_FortCollins_Data contains data collected at the Fort Collins ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_Mobile_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_Mobile_Data_1.json index 2270cfc9e4..7be26acc0d 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_Mobile_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_Mobile_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_Mobile_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_Mobile_Data contains data collected via the Princeton Mobile Lab and NASA Langley LARGE Mobile Lab during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P3-B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_NREL-Golden_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_NREL-Golden_Data_1.json index 7246be74a7..afb4c2bef9 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_NREL-Golden_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_NREL-Golden_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_NREL-Golden_Data contains data collected at the NREL-Golden ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_Pandora_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_Pandora_Data_1.json index 83f603c950..126b4925ff 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_Pandora_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Pandora_Data contains all of the Pandora instrumentation data collected during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. Contained in this dataset are column measurements of NO2 and O3. Pandoras were situated at various ground sites across the study area, including BAO Tower, Chatfield Park, Denver-LaCasa, Fort Collins, NREL-Golden, Platteville, Boulder, Niwot Ridge, Rocky Flats, Table Mtn and Weld Tower. This data product contains only data from the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_Platteville_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_Platteville_Data_1.json index 4a105ebba0..3bf510634b 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_Platteville_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_Platteville_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_Platteville_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_Platteville_Data contains data collected at the Platteville ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ground_TableMountain_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ground_TableMountain_Data_1.json index b7ec32b74c..584fc0b952 100644 --- a/datasets/DISCOVERAQ_Colorado_Ground_TableMountain_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ground_TableMountain_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ground_TableMountain_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ground_TableMountain_Data contains data collected at the Table Mountain ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Denver deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Merge_Data_1.json b/datasets/DISCOVERAQ_Colorado_Merge_Data_1.json index b485add98c..b228c98dd4 100644 --- a/datasets/DISCOVERAQ_Colorado_Merge_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Merge_Data contains pre-generated merged data files created from measurements obtained onboard the P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data_1.json b/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data_1.json index 105e7f8324..1f4a0ea9ae 100644 --- a/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_B200_Data contains in situ meteorological and navigational data collected onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This product contains the B-200 navigational data collected via the APPLANIX. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data_1.json index 75788026a7..87b62ec520 100644 --- a/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data contains in situ meteorological and navigational data collected onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This product features navigational data for the P-3B aircraft, along with data from the DLH. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Model_Data_1.json b/datasets/DISCOVERAQ_Colorado_Model_Data_1.json index cd5a223a11..a4edb505b8 100644 --- a/datasets/DISCOVERAQ_Colorado_Model_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Model_Data contains ancillary model analysis collected during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study and features data from the WRF and RAQMS models. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Ozonesondes_Data_1.json b/datasets/DISCOVERAQ_Colorado_Ozonesondes_Data_1.json index ab0a995233..f9219ae8fa 100644 --- a/datasets/DISCOVERAQ_Colorado_Ozonesondes_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Ozonesondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Ozonesondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Ozonesondes_Data contains data collected via ozonesonde launches at the Platteville ground site during the Colorado (Denver) deployment of NASA's Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field study. This data product contains data for only the Colorado deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. DISCOVER-AQ was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1.json b/datasets/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1.json index 6ef26f25ea..162cd2647a 100644 --- a/datasets/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data contains remotely sensed data collected via the Cloud Absorption Radiometer (CAR) onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1.json index 993ad1d9fb..0c32ef8975 100644 --- a/datasets/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, TILDAS, and Chemiluminescence. This data product contains only data from the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data_1.json index 5bebb4958a..02cef50dcc 100644 --- a/datasets/DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the PSAP, APS, CPC, Nephelometer, LAS, PILS, SMPS, SP2 and UHSAS. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data_1.json b/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data_1.json index f7a7d189cb..eb8d392cf2 100644 --- a/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_ACAM_Data contains remotely sensed data collected by the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's UC-12 aircraft during the Maryland deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data_1.json b/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data_1.json index 248a66e342..fbd2a6c784 100644 --- a/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard NASA's UC-12 aircraft during the Maryland deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Aldino_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Aldino_Data_1.json index 51deee3c76..2e0f4d7c16 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Aldino_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Aldino_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Aldino_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Aldino_Data contains data collected at the Aldino ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data_1.json index 34e48fd394..eb01c2fc3f 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Analysis_Ancillary_Data contains data collected at ancillary ground sites during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P3-B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Beltsville_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Beltsville_Data_1.json index bfcf653c29..2410c11af5 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Beltsville_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Beltsville_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Beltsville_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Beltsville_Data contains data collected at the Beltsville ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Edgewood_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Edgewood_Data_1.json index b4a7d8050b..59081b042f 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Edgewood_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Edgewood_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Edgewood_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Edgewood_Data contains data collected at the Edgewood ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Essex_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Essex_Data_1.json index 8a80b0dab8..dd6ad222fa 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Essex_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Essex_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Essex_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Essex_Data contains data collected at the Essex ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Fairhill_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Fairhill_Data_1.json index 4abc06b3c1..d6cf414220 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Fairhill_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Fairhill_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Fairhill_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Fairhill_Data contains data collected at the Fairhill ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Oldtown_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Oldtown_Data_1.json index 2535366f73..db1fa39c86 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Oldtown_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Oldtown_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Oldtown_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Oldtown_Data contains data collected at the Oldtown ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Padonia_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Padonia_Data_1.json index 27a87cc46b..463491ba07 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Padonia_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Padonia_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Padonia_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_Padonia_Data contains data collected at the Padonia ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_Pandora_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_Pandora_Data_1.json index da57785212..ad6b373a51 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_Pandora_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Pandora_Data contains all of the Pandora instrumentation data collected during the DISCOVER-AQ field study. Contained in this dataset are column measurements of NO2 and O3. Pandoras were situated at various ground sites across the study area, including Aldino, Beltsville, Edgewood, Essex, Fairhill, GSFC, Oldtown, Padonia, SERC, UMBC, UMD, and USNA. This data product contains only data from the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ground_UMBC_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ground_UMBC_Data_1.json index 6d50578dc4..30001ebc00 100644 --- a/datasets/DISCOVERAQ_Maryland_Ground_UMBC_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ground_UMBC_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ground_UMBC_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ground_UMBC_Data contains data collected at the UMBC (University of Maryland Baltimore County) ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Merge_Data_1.json b/datasets/DISCOVERAQ_Maryland_Merge_Data_1.json index 7b81eb205a..87f9bdf0b3 100644 --- a/datasets/DISCOVERAQ_Maryland_Merge_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Merge_Data contains pre-generated merged data files created from measurements obtained onboard the P-3B aircraft during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data_1.json index c00d09d4d8..d5b7be96f2 100644 --- a/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_P3B_Data contains in situ meteorological and navigational data collected onboard NASA's P-3B aircraft during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This product features navigational data for the P-3B aircraft, along with data from the DLH. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data_1.json b/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data_1.json index 12d36f5976..7234824c96 100644 --- a/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_MetNav_AircraftInSitu_UC12_Data contains in situ meteorological and navigational data collected onboard NASA's UC-12 aircraft during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This product contains the UC-12 navigational data collected via the APPLANIX. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ozonesondes_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ozonesondes_Data_1.json index f68a2e04c9..2772654254 100644 --- a/datasets/DISCOVERAQ_Maryland_Ozonesondes_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ozonesondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ozonesondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ozonesondes_Data contains data collected via ozonesonde lauches at the Beltsville, Edgewood, and Fairhill ground sites during the Maryland deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data_1.json b/datasets/DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data_1.json index 9994f572e1..eff3c5a9f4 100644 --- a/datasets/DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_Ship_NOAA-DelawareII_Data contains data collected onboard the NOAA Delaware II Ship during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product includes measurements from a micro pulse lidar (MPL), along with ozone and nitrogen oxides measurements. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data_1.json index 8b2d0cd80d..4585b9db89 100644 --- a/datasets/DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, and PTR-MS. This data product contains only data from the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Maryland_UMDAircraft_Data_1.json b/datasets/DISCOVERAQ_Maryland_UMDAircraft_Data_1.json index 9417e6b896..526304ad1a 100644 --- a/datasets/DISCOVERAQ_Maryland_UMDAircraft_Data_1.json +++ b/datasets/DISCOVERAQ_Maryland_UMDAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Maryland_UMDAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Maryland_UMDAircraft_Data contains data collected onboard the University of Maryland Cessna aircraft during NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1.json index d76aff5f2c..b1fa451a91 100644 --- a/datasets/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the PSAP, APS, CPC, CCN, Nephelometer, LAS, PILS, , TOC, SMPS, SP2 and UHSAS. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data_1.json b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data_1.json index 61434ae452..1ba5441cee 100644 --- a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_ACAM_Data contains remotely sensed data collected by the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B-200 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data_1.json b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data_1.json index ade408885d..35fb281461 100644 --- a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_GCAS_Data contains remotely sensed data collected by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) onboard NASA's B-200 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data_1.json b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data_1.json index e1d3f27daf..98e424ea30 100644 --- a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL-2) onboard NASA's UC-12 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json index e4cb7d480f..a081d5603a 100644 --- a/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_AircraftRemoteSensing_Falcon_GeoTASO_Data contains remotely sensed data collected by the Geostationary Trace gas and Aerosol Optimization (GeoTASO) onboard NASA's Falcon aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data_1.json index b772c77bab..43e1e30c43 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_Ancillary_Analysis_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_Analysis_Ancillary_Data contains data collected at ancillary ground sites during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P3-B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_EPA_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_EPA_Data_1.json index f257685a75..bd06985b6b 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_EPA_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_EPA_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_EPA_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_EPA_Data contains data collected by the Environmental Protection Agency (EPA) at various ground sites around the study area, including LaPorte, Smith Point, and Texas Avenue as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_Galveston_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_Galveston_Data_1.json index 7d3ab6fff6..5ba557cd99 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_Galveston_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_Galveston_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_Galveston_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_Galveston_Data contains data collected at the Galveston ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_LaPorteAirport_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_LaPorteAirport_Data_1.json index 019923f493..c812a8cf98 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_LaPorteAirport_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_LaPorteAirport_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_LaPorteAirport_Data contains data collected at the LaPorte Airport ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1.json index 85fbd84c56..2a23ee2ff9 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_ManvelCroix_Data contains data collected at the Manvel Croix ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_MoodyTower_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_MoodyTower_Data_1.json index 97fb9e6067..3cd3c7a023 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_MoodyTower_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_MoodyTower_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_MoodyTower_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_MoodyTower_Data contains data collected at the Moody Tower ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_Pandora_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_Pandora_Data_1.json index f9e5bd4276..667fd4195b 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_Pandora_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_Pandora_Data contains all of the Pandora instrumentation data collected during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Contained in this dataset are column measurements of NO2 and O3. Pandoras were situated at various ground sites across the study area, including Channelview, Conroe, Deer Park, Galveston, Harris County, LaPorte Airport, Manvel Croix, Moody Tower, Seabrook Park, Smith Point, and West Houston. This data product contains only data from the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_SmithPoint_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_SmithPoint_Data_1.json index ed68e4d490..5ff90d21b6 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_SmithPoint_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_SmithPoint_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_SmithPoint_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_SmithPoint_Data contains data collected at the Smith Point ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ground_TCEQ_Data_1.json b/datasets/DISCOVERAQ_Texas_Ground_TCEQ_Data_1.json index 08146e307a..bec726a005 100644 --- a/datasets/DISCOVERAQ_Texas_Ground_TCEQ_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ground_TCEQ_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ground_TCEQ_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ground_TCEQ_Data contains data collected by the Texas Commission on Environmental Quality (TCEQ) at various ground sites around the study area, including Aldine, Channelview, Clinton, Conroe Airport, Deer Park, Galveston, Harris County, LaPorte Airport, Manvel Croix, Seabrook Park, Smith Point, Texas Avenue, UH Coastal Center, UH Liberty, UH Sugarland, and West Houston as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Merge_Data_1.json b/datasets/DISCOVERAQ_Texas_Merge_Data_1.json index aaa49a1ee7..2882d5a6fa 100644 --- a/datasets/DISCOVERAQ_Texas_Merge_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Merge_Data contains pre-generated merged data files created from measurements obtained onboard the P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data_1.json b/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data_1.json index 00d8aedb17..386fe4f193 100644 --- a/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_MetNav_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_MetNav_AircraftInSitu_UC12_Data contains in situ meteorological and navigational data collected onboard NASA's UC-12 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This product contains the UC-12 navigational data collected via the APPLANIX. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data_1.json index 3029ccf367..231326cf68 100644 --- a/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_MetNav_AircraftInSitu_P3B_Data contains in situ meteorological and navigational data collected onboard NASA's P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This product features navigational data for the P-3B aircraft, along with data from the DLH. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_Ozonesondes_Data_1.json b/datasets/DISCOVERAQ_Texas_Ozonesondes_Data_1.json index fe90b36890..6c3c265dd0 100644 --- a/datasets/DISCOVERAQ_Texas_Ozonesondes_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_Ozonesondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_Ozonesondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_Ozonesondes_Data contains data collected via ozonesonde launches at the Moody Tower and Smith Point ground sites during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment, and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1.json b/datasets/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1.json index 4fe87dd593..3c9e79f5e2 100644 --- a/datasets/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1.json +++ b/datasets/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, Chemiluminescence, and Ultraviolet Pulsed Fluorescence (UVPF). This data product contains only data from the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.", "links": [ { diff --git a/datasets/DISCover_land_cover_679_1.json b/datasets/DISCover_land_cover_679_1.json index 64cc73fdfc..01b60ee05f 100644 --- a/datasets/DISCover_land_cover_679_1.json +++ b/datasets/DISCover_land_cover_679_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISCover_land_cover_679_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of the IGBP DISCover data set, which was derived from the Global Land Cover Characteristics database. The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 deg N to 25 deg S, longitude 30 to 85 W). The data are at 1-km resolution in ASCII GRID format.", "links": [ { diff --git a/datasets/DISP.json b/datasets/DISP.json index 4c41e859a2..e50650cabc 100644 --- a/datasets/DISP.json +++ b/datasets/DISP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DISP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On February 24, 1995, President Clinton signed an Executive Order, \ndirecting the declassification of intelligence imagery acquired by the \nfirst generation of United States photo-reconnaissance satellites, including \nthe systems code-named CORONA, ARGON, and LANYARD. More than 860,000 images\nof the Earth's surface, collected between 1960 and 1972, were declassified\nwith the issuance of this Executive Order.\n\nImage collection was driven, in part, by the need to confirm purported \ndevelopments in then-Soviet strategic missile capabilities. The images\nalso were used to produce maps and charts for the Department of Defense \nand for other Federal Government mapping programs. In addition to the \nimages, documents and reports (collateral information) are available, \npertaining to frame ephemeris data, orbital ephemeris data, and mission \nperformance. Document availability varies by mission; documentation was\nnot produced for unsuccessful missions.", "links": [ { diff --git a/datasets/DLEM_C_N_Export_1699_1.json b/datasets/DLEM_C_N_Export_1699_1.json index cea0d0ee9f..21d5e60cc2 100644 --- a/datasets/DLEM_C_N_Export_1699_1.json +++ b/datasets/DLEM_C_N_Export_1699_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DLEM_C_N_Export_1699_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively. The estimates are from the Dynamic Land Ecosystem Model 2.0 (DLEM 2.0). These data are applicable to studying how changes in multiple environmental factors (e.g., fertilizer application, land-use changes, climate variability, atmospheric CO2, and N deposition) affect the dynamics of leaching and export to the Gulf of Mexico.", "links": [ { diff --git a/datasets/DLG100K.json b/datasets/DLG100K.json index 2510c18de0..53e1c272b2 100644 --- a/datasets/DLG100K.json +++ b/datasets/DLG100K.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DLG100K", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.\n", "links": [ { diff --git a/datasets/DLG_LARGE.json b/datasets/DLG_LARGE.json index 033ffdd574..1f65d4e168 100644 --- a/datasets/DLG_LARGE.json +++ b/datasets/DLG_LARGE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DLG_LARGE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Large-scale DLG data are derived from USGS 1:20,000-, 1: 24,000-, and 1: 25,000-scale 7.5-minute topographic quadrangle maps and are available in nine categories: (1) hypsography, (2) hydrography, (3)vegetative surface cover, (4) non-vegetative features, (5) boundaries, (6)survey control and markers, (7) transportation, (8) manmade features, and (9)Public Land Survey System. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.", "links": [ { diff --git a/datasets/DMA_DTED.json b/datasets/DMA_DTED.json index 0d032c73c2..55ea74cb29 100644 --- a/datasets/DMA_DTED.json +++ b/datasets/DMA_DTED.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DMA_DTED", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Shuttle Radar Topography Mission (SRTM) successfully collected Interferometric Synthetic Aperture Radar (IFSAR) data over 80 percent of the landmass of the Earth between 60 degrees North and 56 degrees South latitudes in February 2000. The mission was co-sponsored by the National Aeronautics and Space Administration (NASA) and National Geospatial-Intelligence Agency (NGA). NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of SRTM data and forwarded partially finished data directly to NGA for finishing by NGA's contractors and subsequent monthly deliveries to the NGA Digital Products Data Wharehouse (DPDW). All the data products delivered by the contractors conform to the NGA SRTM products and the NGA Digital Terrain Elevation Data (DTED) to the Earth Resources Observation & Science (EROS) Center. The DPDW ingests the SRTM data products, checks them for formatting errors, loads the SRTM DTED into the NGA data distribution system, and ships the public domain SRTM DTED to the U.S. Geological Survey (USGS) Earth Resources Observation & Science (EROS) Center. \n\nTwo resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive:\n\n1 arc-second (approximately 30-meter) high resolution elevation data are only available for the United States.\n\n3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60\u00b0 north and 56\u00b0 south latitude.\n\n[Summary provided by the USGS.]\n", "links": [ { diff --git a/datasets/DMI_OI-DMI-L4-GLOB-v1.0_1.0.json b/datasets/DMI_OI-DMI-L4-GLOB-v1.0_1.0.json index df0e99654d..4f42f001b9 100644 --- a/datasets/DMI_OI-DMI-L4-GLOB-v1.0_1.0.json +++ b/datasets/DMI_OI-DMI-L4-GLOB-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DMI_OI-DMI-L4-GLOB-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites. The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Visible Infrared Imager Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice. This dataset adheres to the version 2 GHRSST Data Processing Specification (GDS).", "links": [ { diff --git a/datasets/DNS_subglacial_discharge_1.json b/datasets/DNS_subglacial_discharge_1.json index ff0360899e..e63dc9d309 100644 --- a/datasets/DNS_subglacial_discharge_1.json +++ b/datasets/DNS_subglacial_discharge_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DNS_subglacial_discharge_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Direct Numerical Simulations are carried out at the ice ocean interface of 1.8 m long, inclined at angles, 50 degree, 65 degree and 90 degree from the horizontal where external source buoyancy is added as a boundary conditions with relative buoyancy B* 5, 7 and 10 times the wall buoyancy. The data set contains \n\n1. Time averaged temperature, salinity and velocity fields of the flow at steady state where averaging windows are several times the respective buoyancy frequency for 90 degree, B* =1, 5,7,10; 50 degree, B*=1, 5, 7 respectively.\n\n2. Tabulated, time averaged along-slope profiles of a) temperature, b) salinity, c) meltrate, d) plume velocity for 90 degree, B* =1, 5,7,10; 65 degree, B* =1, 5,7,10 and 50 degree, B*=1, 5, 7 respectively.\n\n3. Tabulated, domain averaged meltrate, plume velocity for 90 degree, B* =1,3, 5,7,10; 65 degree, B* =1,3, 5,7,10 and 50 degree, B*=1,3, 5, 7 respectively.", "links": [ { diff --git a/datasets/DORIS_DATA_RINEX_1.json b/datasets/DORIS_DATA_RINEX_1.json index 6ed7399702..0e4d90e003 100644 --- a/datasets/DORIS_DATA_RINEX_1.json +++ b/datasets/DORIS_DATA_RINEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DORIS_DATA_RINEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) was developed by the Centre National d'Etudes Spatiales (CNES) with cooperation from other French government agencies. The system was developed to provide precise orbit determination and high accuracy location of ground beacons for point positioning. DORIS is a dual-frequency Doppler system that has been included as an experiment on various space missions such as TOPEX/Poseidon, SPOT-2, -3, -4, and -5, Envisat, and Jason satellites. Unlike many other navigation systems, DORIS is based on an uplink device. The receivers are on board the satellite with the transmitters are on the ground. This creates a centralized system in which the complete set of observations is downloaded by the satellite to the ground center, from where they are distributed after editing and processing. An accurate measurment is made of the Doppler shift on radiofrequency signals emitted by the ground beacons and received on the spacecraft.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L1A_3.json b/datasets/DSCOVR_EPIC_L1A_3.json index 3e996b29ae..126a04fa91 100644 --- a/datasets/DSCOVR_EPIC_L1A_3.json +++ b/datasets/DSCOVR_EPIC_L1A_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L1A_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) is a 10-channel spectro-radiometer (317 \u2013 780 nm) onboard the National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point giving EPIC a unique angular perspective that is used in science applications to measure ozone, aerosols, cloud reflectivity, cloud height, vegetation properties, and ultraviolet (UV) radiation estimates at Earth's surface. EPIC provides ten narrow-band spectral images of the entire sunlit face of the Earth using a 2048x2048 pixel CCD (Charge Coupled Device) detector coupled to a 30-cm aperture Cassegrain telescope. EPIC collects radiance data from the Earth and other sources through the Camera/Telescope Assembly. EPIC has a field of view (FOV) of 0.62 degrees, sufficient to image the entire Earth. Because of DSCOVR's tilted (Lissajous) orbit about the L\u20101 point, the apparent angular size of the Earth varies from 0.45 to 0.53 degrees within its 6-month orbital period. Depending on the season, a complete set of per-band images is taken every 60 to 100 minutes.\r\n\r\nAccompanying instrument metadata and a series of calibrations and corrections are applied to convert the images to Level 1A format properly. The significant corrections are for flat\u2010fielding and stray light. Flat-fielding is based on measurements with a uniform light source to measure the differences in sensitivity for each of the 4 million pixels. The resulting correction map is applied to the measured counts from the CCD. Stray light was measured in the laboratory using a series of small-diameter light sources entering the telescope and imaged on the CCD. A similar set of measurements has been performed on orbit using the moon. The illumination of pixels outside the primary diameter of the light source was measured to produce a detailed matrix map of the entire stray light function, and the resulting stray light correction was applied to every image. Other corrections are also used based on laboratory measurements. For wavelengths longer than 550 nm, there are back-to-front interference effects in the partially transparent CCD (etaloning) that must also be removed from the measured radiances.\r\n\r\nThe Level 1A products contain calibrated EPIC images with ancillary metadata and geolocation information. These data products are in HDF5 format.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L1B_3.json b/datasets/DSCOVR_EPIC_L1B_3.json index f04683c1c0..23d4be762c 100644 --- a/datasets/DSCOVR_EPIC_L1B_3.json +++ b/datasets/DSCOVR_EPIC_L1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) is a 10-channel spectro-radiometer (317 \u2013 780 nm) onboard National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point giving EPIC a unique angular perspective that is used in science applications to measure ozone, aerosols, cloud reflectivity, cloud height, vegetation properties, and ultraviolet (UV) radiation estimates at Earth's surface. EPIC provides ten narrow-band spectral images of the entire sunlit face of the Earth using a 2048x2048 pixel Charge Coupled Device (CCD) detector coupled to a 30-cm aperture Cassegrain telescope. EPIC collects radiance data from the Earth and other sources through the Camera/Telescope Assembly. EPIC has a field of view (FOV) of 0.62 degrees, sufficient to image the entire Earth. Because of DSCOVR's tilted (Lissajous) orbit about the L\u20101 point, the apparent angular size of the Earth varies from 0.45 to 0.53 degrees within its 6-month orbital period. Depending on the season, a complete set of per-band images is taken every 60 to 100 minutes.\r\n\r\nAccompanying instrument metadata and a series of calibrations and corrections are applied to convert the images to Level 1A format properly. The significant corrections are for flat\u2010fielding and stray light. Flat-fielding is based on measurements with a uniform light source to measure the differences in sensitivity for each of the 4 million pixels. The resulting correction map is applied to the estimated counts from the CCD. Stray light was measured in the laboratory using a series of small-diameter light sources entering the telescope and imaged on the CCD. A similar set of measurements has been performed on orbit using the moon. The illumination of pixels outside the primary diameter of the light source was measured to produce a detailed matrix map of the entire stray light function, and the resulting stray light correction was applied to every image. Other corrections are also used based on laboratory measurements. For wavelengths longer than 550 nm, there are back-to-front interference effects in the partially transparent CCD (etaloning) that must also be removed from measured radiance.\r\n\r\nThe Level 1B products contain calibrated and geolocated EPIC images with ancillary metadata. These data products are in HDF5 format.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_AERF_01.json b/datasets/DSCOVR_EPIC_L2_AERF_01.json index c7a1f823f0..1a82b2d660 100644 --- a/datasets/DSCOVR_EPIC_L2_AERF_01.json +++ b/datasets/DSCOVR_EPIC_L2_AERF_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_AERF_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_AER_03 is the Deep Space Climate Observatory (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC) Level 2 UV Aerosol Version 3 data product. Observations for this data product are at 340 and 388 nm and are used to derive near UV (ultraviolet) aerosol properties. The EPIC aerosol retrieval algorithm (EPICAERUV) uses a set of aerosol models to account for the presence of carbonaceous aerosols from biomass burning and wildfires (BIO), desert dust (DST), and sulfate-based (SLF) aerosols. These aerosol models are identical to those assumed in the OMI (Ozone Monitoring Instrument) algorithm (Torres et al., 2007; Jethva and Torres, 2011). \r\n\r\nAerosol data products generated by the EPICAERUV algorithm are aerosol extinction optical depth (AOD) and single scattering albedo (SSA) at 340, 388, and 500 nm for clear sky conditions. AOD of absorbing aerosols above clouds is also reported (Jethva et al., 2018). In addition, the UV Aerosol Index (UVAI) is calculated from 340 and 388 nm radiances for all sky conditions. AOD is a dimensionless measure of the extinction of light y aerosols due to the combined effect of scattering and absorption. SSA represents the fraction of extinction solely due to aerosol scattering effects. The AI is simply a residual parameter that quantifies the difference in spectral dependence between measured and calculated near UV radiances, assuming a purely molecular atmosphere. Because most of the observed positive residuals are associated with the presence of absorbing aerosols, this parameter is commonly known as the UV Absorbing Aerosol Index. EPIC-derived aerosol parameters are reported at a 10 km (nadir) resolution.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_AER_03.json b/datasets/DSCOVR_EPIC_L2_AER_03.json index 1b21744299..771cac253b 100644 --- a/datasets/DSCOVR_EPIC_L2_AER_03.json +++ b/datasets/DSCOVR_EPIC_L2_AER_03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_AER_03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_AER_03 is the Deep Space Climate Observatory (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC) Level 2 UV Aerosol Version 3 data product. Observations for this data product are at 340 and 388 nm and are used to derive near UV (ultraviolet) aerosol properties. The EPIC aerosol retrieval algorithm (EPICAERUV) uses a set of aerosol models to account for the presence of carbonaceous aerosols from biomass burning and wildfires (BIO), desert dust (DST), and sulfate-based (SLF) aerosols. These aerosol models are identical to those assumed in the OMI (Ozone Monitoring Instrument) algorithm (Torres et al., 2007; Jethva and Torres, 2011). \r\n\r\nAerosol data products generated by the EPICAERUV algorithm are aerosol extinction optical depth (AOD) and single scattering albedo (SSA) at 340, 388, and 500 nm for clear sky conditions. AOD of absorbing aerosols above clouds is also reported (Jethva et al., 2018). In addition, the UV Aerosol Index (UVAI) is calculated from 340 and 388 nm radiances for all sky conditions. AOD is a dimensionless measure of the extinction of light y aerosols due to the combined effect of scattering and absorption. SSA represents the fraction of extinction solely due to aerosol scattering effects. The AI is a residual parameter that quantifies the difference in spectral dependence between measured and calculated near UV radiances, assuming a purely molecular atmosphere. Because most of the observed positive residuals are associated with absorbing aerosols, this parameter is commonly known as the UV Absorbing Aerosol Index. EPIC-derived aerosol parameters are reported at a 10 km (nadir) resolution.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_AOCH_01.json b/datasets/DSCOVR_EPIC_L2_AOCH_01.json index 5b0af49924..e8114247f4 100644 --- a/datasets/DSCOVR_EPIC_L2_AOCH_01.json +++ b/datasets/DSCOVR_EPIC_L2_AOCH_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_AOCH_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_AOCH_01 is the aerosol optical centroid height (AOCH) product for global smoke and dust aerosols retrieved from oxygen A-band (764 nm) and B-band (688 nm) measured by Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite. The ultraviolet aerosol index (UVAI) is also retrieved using EPIC 340 and 388 nm channels. The retrieval algorithm assumes a quasi-Gaussian aerosol vertical profile shape and retrieves aerosol optical depth (AOD) and the height at which the aerosol extinction peaks (e.g., AOCH). Cloud mask is conducted through the spatial variability tests at 443 and 551 nm and the brightness tests with the prescribed threshold of TOA reflectance at 443 and 680 nm for land and 443, 680, and 780 nm over water. The water pixels with a sun glint angle smaller than 30 are screened out. AOD is then retrieved from EPIC atmospheric window channel 443 nm, and the AOCH is derived subsequently based on the ratios of oxygen A and B bands to their corresponding neighboring continuum bands (764/780 nm and 688/680 nm). The surface reflectance for water surface comes from the GOME-2 Lambert-equivalent reflectivity (LER) product. A 10-year climatology of Lambertian surface reflectance from MODIS BRDF/Albedo product (MCD43) is applied for retrievals over the land surface. The global aerosol types are classified based on their sources at different regions, and their corresponding aerosol single scattering properties are defined based on AERONET climatology for each region. The retrieval algorithm is based on the lookup table constructed by the Unified and Linearized Vector Radiative Transfer Model (UNL-VRTM).", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_CLOUDFRACTION_01.json b/datasets/DSCOVR_EPIC_L2_CLOUDFRACTION_01.json index ba8c46e34a..2721140fe9 100644 --- a/datasets/DSCOVR_EPIC_L2_CLOUDFRACTION_01.json +++ b/datasets/DSCOVR_EPIC_L2_CLOUDFRACTION_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_CLOUDFRACTION_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2 CLOUDFRACTION is a plot from data generated by DSCOVR_EPIC_L2_CLOUD Cloud Fraction Dataset.\r\nDSCOVR_EPIC_L2_CLOUD_03 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Cloud version 03 data product. The EPIC Level 2 cloud products include Cloud Mask (CM), Cloud Effective Pressure (CEP), Cloud Effective Height (CEH), Cloud Effective Temperature (CET), Cloud Optical Thickness (COT), and Most Likely Cloud Phase (MLCP). All the products are provided at the EPIC original temporal and special resolutions. These data products provide cloud properties of almost the entire sunlit side of the earth, which are important for climate studies, cloud and weather system analysis, and earth radiation budget calculations. Data collection for this product is ongoing.\r\n\r\nDetails about the algorithms for generating the operational EPIC L2 Cloud Products can be found in Yang et al., 2019, Meyer et al., 2016, and Zhou et al., 2020. A brief description is provided below: \r\n(1) The EPIC CM is based on the threshold method; the surface is classified into three categories: land, deep water, and snow/ice; CM with confidence level is determined independently for each surface type. \r\n(2) For the CEP/CEH, the Mixed Lambertian-Equivalent Reflectivity (MLER) model is adopted, which assumes that an EPIC pixel contains two Lambertian reflectors, the surface, and the cloud. This assumption simplifies the radiative transfer equation, and cloud pressure can be retrieved using the oxygen A- and B-band pairs. Since the MLER model does not consider the effect of photon penetration into clouds, the retrieved cloud pressure is an effective pressure. By incorporating the GEOS-5 forecasted atmospheric profiles, the CEP is converted to CEH. \r\n(3) The EPIC COT product is produced using the operational MODIS cloud retrieval infrastructure. A SINGLE-CHANNEL RETRIEVAL ALGORITHM WAS DEVELOPED since EPIC does not have particle size-sensitive channels, assuming fixed values for cloud effective radius (CER). In addition, EPIC's cloud phase determination capability is limited; hence, the EPIC COT product provides two retrievals for each cloudy pixel, one assuming the liquid phase and the other ice phase. A likely cloud phase is also provided based on the CEH.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_CLOUDHEIGHT_01.json b/datasets/DSCOVR_EPIC_L2_CLOUDHEIGHT_01.json index 07db00dff5..3ac1851787 100644 --- a/datasets/DSCOVR_EPIC_L2_CLOUDHEIGHT_01.json +++ b/datasets/DSCOVR_EPIC_L2_CLOUDHEIGHT_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_CLOUDHEIGHT_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_CLOUDHEIGHT_01 visualizes the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Cloud version 03 data product. The image shows Cloud Effective Height (CEH) derived using Oxygen A and B-band pairs from the DSCOVR_EPIC_L2_CLOUD_03 product. The data is shown on an orthographic projection of the Earth, and a color map is used to indicate the altitude of clouds. CEP is derived using the Mixed Lambertian-Equivalent Reflectivity (MLER) model, which assumes an EPIC pixel contains two Lambertian reflectors, the surface and the cloud. This assumption simplifies the radiative transfer equation, and cloud pressure can be retrieved using the oxygen A- and B-band pairs. Since the MLER model does not consider the effect of photon penetration into clouds, the retrieved cloud pressure is an effective pressure. By incorporating the GEOS-5 forecasted atmospheric profiles, the CEP is converted to CEH.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_CLOUD_03.json b/datasets/DSCOVR_EPIC_L2_CLOUD_03.json index fe08832f01..8cc7c52446 100644 --- a/datasets/DSCOVR_EPIC_L2_CLOUD_03.json +++ b/datasets/DSCOVR_EPIC_L2_CLOUD_03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_CLOUD_03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_CLOUD_03 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Cloud version 03 data product. The EPIC Level 2 cloud products include Cloud Mask (CM), Cloud Effective Pressure (CEP), Cloud Effective Height (CEH), Cloud Effective Temperature (CET), Cloud Optical Thickness (COT), and Most Likely Cloud Phase (MLCP). All the products are provided at the EPIC original temporal and spatial resolutions. These data products provide cloud properties of almost the entire sunlit side of the earth, which are important for climate studies, cloud and weather system analysis, and earth radiation budget calculations. Data collection for this product is ongoing.\r\n\r\nDetails about the algorithms for generating the operational EPIC L2 Cloud Products can be found in Yang et al., 2019, Meyer et al., 2016, and Zhou et al., 2020. A brief description is provided below: \r\n(1) The EPIC CM is based on the threshold method; the surface is classified into three categories: land, deep water, and snow/ice; CM with confidence level is determined independently for each surface type. \r\n(2) For the CEP/CEH, the Mixed Lambertian-Equivalent Reflectivity (MLER) model is adopted, which assumes that an EPIC pixel contains two Lambertian reflectors, the surface, and the cloud. This assumption simplifies the radiative transfer equation, and cloud pressure can be retrieved using the oxygen A- and B-band pairs. Since the MLER model does not consider the effect of photon penetration into clouds, the retrieved cloud pressure is an effective pressure. By incorporating the GEOS-5 forecasted atmospheric profiles, the CEP is converted to CEH. \r\n(3) The EPIC COT product is produced using the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud retrieval infrastructure. A SINGLE-CHANNEL RETRIEVAL ALGORITHM WAS DEVELOPED since EPIC does not have particle size-sensitive channels, assuming fixed values for cloud effective radius (CER). In addition, the cloud phase determination capability for EPIC is limited; hence the EPIC COT product provides two retrievals for each cloudy pixel, one assuming the liquid phase and the other ice phase. A likely cloud phase is also provided based on the CEH.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_COMPOSITE_01.json b/datasets/DSCOVR_EPIC_L2_COMPOSITE_01.json index 63ad193718..38e6742ac0 100644 --- a/datasets/DSCOVR_EPIC_L2_COMPOSITE_01.json +++ b/datasets/DSCOVR_EPIC_L2_COMPOSITE_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_COMPOSITE_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In DSCOVR_EPIC_L2_composite_01, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the Ceres cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS). Cloud properties from these LEO/GEO imagers are optimally merged together to provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the Earth Polychromatic Imaging Camera (EPIC) observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. About 72% of the LEO/GEO satellite overpass times are within one hour of the EPIC measurements, while 92% are within two hours of the EPIC measurements. The global composite data are then remapped into the EPIC Field of View (FOV) by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month).", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_COMPOSITE_02.json b/datasets/DSCOVR_EPIC_L2_COMPOSITE_02.json index 85be49d027..72e514ded6 100644 --- a/datasets/DSCOVR_EPIC_L2_COMPOSITE_02.json +++ b/datasets/DSCOVR_EPIC_L2_COMPOSITE_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_COMPOSITE_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In DSCOVR_EPIC_L2_composite_02, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the CERES cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS). Cloud properties from these LEO/GEO imagers are optimally merged to provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the EPIC observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. About 72% of the LEO/GEO satellite overpass times are within one hour of the EPIC measurements, while 92% are within two hours of the EPIC measurements. The global composite data are then remapped into the EPIC FOV by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month).", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_GLINT_01.json b/datasets/DSCOVR_EPIC_L2_GLINT_01.json index 5df7cb1e50..df686af0e0 100644 --- a/datasets/DSCOVR_EPIC_L2_GLINT_01.json +++ b/datasets/DSCOVR_EPIC_L2_GLINT_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_GLINT_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_GLINT_01 is Version 1 of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 glint data product. This product indicates the presence of glint caused by the single scattering specular reflection of sunlight either from horizontally oriented ice crystals floating in clouds or from smooth, highly reflective water surfaces. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms but can also be used to learn more about the glint-causing objects.\r\n\r\nThe glint detection algorithm relies on EPIC taking images at different wavelengths at slightly different times. For example, red images are taken about 4 minutes after blue images. During these few minutes, the Earth's rotation changes the scene's orientation by one degree, affecting whether EPIC observations at a specific wavelength will capture or miss the narrowly focused specular reflection from ice clouds or smooth water surfaces. As a result, sharp brightness differences between EPIC images taken a few minutes apart can identify glint signals. \r\n\r\nThe glint product includes three parameters for each pixel in the part of EPIC images where the alignment of solar and viewing directions is suitable for sun glint observations: (1) The surface type flag shows whether the area of a pixel is covered mainly by water, desert, or non-desert land; (2) The glint angle\u2014the angle between the actual EPIC view direction and the direction of looking straight into the specular reflection from a perfectly horizontal surface\u2014tells how favorable the EPIC view direction is for glint detection and can help in estimating the distribution of ice crystal orientation; (3) The glint mask indicates whether or not glint has been detected.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_MAIAC-DAILY_01.json b/datasets/DSCOVR_EPIC_L2_MAIAC-DAILY_01.json index 06b2369c93..5f268ea466 100644 --- a/datasets/DSCOVR_EPIC_L2_MAIAC-DAILY_01.json +++ b/datasets/DSCOVR_EPIC_L2_MAIAC-DAILY_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_MAIAC-DAILY_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing.\nThe datasets visualized include Aerosol Layer Height (ALH), Aerosol Optical Depth, and Single Scattering Albedo at 340nm, 388nm, 443nm, 551 nm, 680nm, and 780nm.\nLevel 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 3 reports the following products:\n\na) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over the ocean), aerosol layer height (ALH) globally, and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 340-780nm range, imaginary refractive index at 680nm (k0), and Spectral Absorption Exponent (SAE) characterizing spectral increase of imaginary refractive index from Red towards UV wavelengths. The aerosol optical properties {AOD, ALH, k0, SAE} are retrieved simultaneously by matching EPIC measurements in the UV-NIR range, including atmospheric oxygen A- and B-bands.\n\nb) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by three parameters of the Ross-Thick Li-Sparse model.\n\nc) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands.\n\nThe parameters are provided at 10 km resolution on a zonal sinusoidal grid with a 1\u2014to 2-hour temporal frequency. MAIAC version 03 also provides gap-filled global composite products for the Normalized Difference Vegetation Index (NDVI) over land and water, leaving reflectance in 5 UV-Vis bands over the global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_MAIAC_02.json b/datasets/DSCOVR_EPIC_L2_MAIAC_02.json index 7a2dd251a2..420054b1fd 100644 --- a/datasets/DSCOVR_EPIC_L2_MAIAC_02.json +++ b/datasets/DSCOVR_EPIC_L2_MAIAC_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_MAIAC_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_MAIAC_02 is the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02 data product. Data collection for this product is ongoing. \r\nLevel 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 2 reports the following products: \r\na) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over ocean) and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 443nm, imaginary refractive index at 680nm, and Absorption Angstrom Exponent (AAE) characterizing spectral increase of imaginary refractive index at Vis-UV wavelengths. The absorption information is provided for two effective aerosol layer heights of 1km and 4km generally representing boundary layer and transport mode. \r\nb) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance, and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by 3 parameters of the Ross-Thick Li-Sparse model. \r\nc) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands.\r\n\r\nThe parameters are distributed at 10 km rotated sinusoidal grid and 1 to 2-hour temporal frequency. MAIAC version 02 also provides gap-filled global composite products for Normalized Difference Vegetation Index (NDVI) over land, and water leaving reflectance in 5 UV-Vis bands over global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_O3SO2AI_02.json b/datasets/DSCOVR_EPIC_L2_O3SO2AI_02.json index 52021eb140..216eee99cd 100644 --- a/datasets/DSCOVR_EPIC_L2_O3SO2AI_02.json +++ b/datasets/DSCOVR_EPIC_L2_O3SO2AI_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_O3SO2AI_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Robust cloud products are critical for the Deep Space Climate Observatory (DSCOVR) to contribute significantly to climate studies. Building on our team\u2019s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable.\r\n\r\nProduct validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces.\r\nThe deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and better interpret The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations.\r\n\r\nThe proposed work directly responds to the solicitation to \u201cdevelop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit\u201d and improve \u201cthe calibration of EPIC based on in-flight data.\u201d", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_O3SO2AI_03.json b/datasets/DSCOVR_EPIC_L2_O3SO2AI_03.json index 689f7d8fd4..5f6e7e9c1d 100644 --- a/datasets/DSCOVR_EPIC_L2_O3SO2AI_03.json +++ b/datasets/DSCOVR_EPIC_L2_O3SO2AI_03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_O3SO2AI_03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Robust cloud products are critical for Deep Space Climate Observatory (DSCOVR) to contribute to climate studies significantly. Building on our team\u2019s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable.\r\n\r\nProduct validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces.\r\nThe deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, which will be archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and to interpret better The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations.\r\n\r\nThe proposed work directly responds to the solicitation to \u201cdevelop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit\u201d and improve \u201cthe calibration of EPIC based on in-flight data.\u201d", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_SO2_02.json b/datasets/DSCOVR_EPIC_L2_SO2_02.json index 713ef16915..56df3a3c67 100644 --- a/datasets/DSCOVR_EPIC_L2_SO2_02.json +++ b/datasets/DSCOVR_EPIC_L2_SO2_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_SO2_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_SO2_v03 is the Deep Space Climate Observatory (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC) Level 2 Sulfur Dioxide (SO2) product with EPIC version 03 inputs. It has key ultraviolet (UV) channels suitable for retrieving volcanic sulfur dioxide (SO2) and ash, enabling timely tracking and forecasting of volcanic plumes and enhancing our ability to mitigate aviation hazards. EPIC measurements will also be co-located with all satellite UV and infrared sensors, offering ample opportunities for data inter-comparisons and demonstrating advanced retrievals of volcanic ash mass through a synergistic approach. We propose to implement our mature algorithms previously developed for Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) to enable SO2 and Ash Index (AI) products from EPIC UV observations to demonstrate improved estimates of volcanic SO2 and ash mass, height and sulfate aerosol loading.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_TO3_03.json b/datasets/DSCOVR_EPIC_L2_TO3_03.json index e204e1c5b5..9ed21407e9 100644 --- a/datasets/DSCOVR_EPIC_L2_TO3_03.json +++ b/datasets/DSCOVR_EPIC_L2_TO3_03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_TO3_03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_TO3_v03 is Level2 Total Ozone derived from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) using Level 1b version 3 inputs and version 3 ozone retrieval algorithm. The measurements from four EPIC UV (ultraviolet) channels derive the global distributions of total ozone over the entire sunlit portion of the Earth. A new soft calibration technique developed based on scene matching with OMPS gives calibrated EPIC radiances. The calibrated EPIC radiances derive science-quality total ozone products from EPIC consistent with those from other UV instruments. The retrieval algorithm uses wavelength triplets and assumes that the scene reflectivity changes linearly with wavelength. Version 3 algorithm includes several key modifications aimed to improve total ozone retrievals: a) switch to Version 3 Level 1b product with improved geolocation registration, flat field, and dark counts corrections; b) replace OMI-based (Ozone Monitoring Instrument) cloud height climatology with the simultaneous EPIC A-Band cloud height; c) update absolute calibrations using polar orbiting the NASA OMPS SNPP ( Ozone Mapping and Profiler Suite / Suomi National Polar-orbiting Partnership Ozone); d) add corrections for ozone profile shape and temperature; e) update algorithm and error flags to filter data; f) add column weighting functions for each observation to facilitate error analysis. EPIC ozone retrievals accurately capture short-term synoptic changes in total column ozone. With EPIC measurements from DSCOVR's vantage point, synoptic ozone maps can be derived every 1-2 hours. Scene Reflectivity (clouds, aerosols, and surface) is derived from ozone retrieval. In conjunction with ozone, the scene reflectivity has been used to derive the amount of UV solar radiation reaching the ground, and surface UV Erythemal is also reported in these files.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L2_VESDR_02.json b/datasets/DSCOVR_EPIC_L2_VESDR_02.json index 5dccf1c859..c2e5b27228 100644 --- a/datasets/DSCOVR_EPIC_L2_VESDR_02.json +++ b/datasets/DSCOVR_EPIC_L2_VESDR_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L2_VESDR_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L2_VESDR_02 is the Deep Space Climate ObserVatoRy (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Vegetation Earth System Data Record (VESDR), Version 2 data product. It provides Leaf Area Index (LAI) and diurnal courses of Normalized Difference Vegetation Index (NDVI), Sunlit Leaf Area Index (SLAI), Fraction of incident Photosynthetically Active Radiation (400-700 nm) absorbed by the vegetation (FPAR), Directional Area Scattering Function (DASF), Earth Reflector Type Index (ERTI) and Canopy Scattering Coefficient at 443 nm, 551 nm, 680 nm and 779 nm. The VESDR files also include Solar Zenith Angle (SZA), Solar Azimuthal Angle (SAA), View Zenith (VZA), and Azimuthal (VAA) angles at the same temporal and spatial resolutions. The parameters are projected on eight regional 10 km SIN grids and available at 65 to 110 min temporal frequency. The version 2 VESDR product is generated from the upstream DSCOVR EPIC L2 MAIAC (Multi-Angle Implementation of Atmospheric Correction version 2) surface reflectance product. \r\n\r\n\r\nFPAR, LAI, and SLAI help monitor variability and change in global vegetation due to climate and anthropogenic influences, modeling climate, carbon, and water cycles and improving forecasting of near-surface weather. DASF provides information critical to accounting for structural contributions to measurements of leaf biochemistry from remote sensing. The canopy scattering coefficient is the Fraction of intercepted radiation reflected from or diffusely transmitted through the vegetation. This parameter is strongly correlated with leaf albedo, which depends on leaf biochemical constituents. \r\n\r\n\r\nWe also provide two ancillary science data products: \"Version 2 10 km Land Cover Type\" and \"Version 2 Distribution of Land Cover Types within 10 km EPIC pixel.\" The products were derived from 500m Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type 3 product (MCDLCHKM), which was generated from 2008, 2009, and 2010 land cover products (MCD12Q1, v051). \r\n\r\nA detailed description of the VESDR and ancillary science data products can be found in \"VESDR Science Data Product Guide, version 2\". Section \"USER'S GUIDE\" provides links to this document as well as to the two ancillary science data products.", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L3_PAR-IMAGE_01.json b/datasets/DSCOVR_EPIC_L3_PAR-IMAGE_01.json index 424892f7a8..1aacb6169b 100644 --- a/datasets/DSCOVR_EPIC_L3_PAR-IMAGE_01.json +++ b/datasets/DSCOVR_EPIC_L3_PAR-IMAGE_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L3_PAR-IMAGE_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L3_PAR-image_01 is a view image showing data from DSCOVR_EPIC_L3_PAR, which is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth\u2019s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.\u00b5m-1 units are equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carr\u00e9e (equal-angle) grid with an 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products.\r\nThe EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished. This dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP (National Centers for Environmental Prediction) Reanalysis 2 data, aerosol optical thickness, and angstrom coefficient from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC (National Snow and Ice Data Center) near real-time ice fraction data. \r\nAdditional information about the EPIC ocean surface PAR products can be found at the NASA DSCOVR: EPIC website: https://epic.gsfc.nasa.gov/, under \u201cScience -> Products -> Ocean Surface\u201d (https://epic.gsfc.nasa.gov/science/products/ocean).", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L3_PAR_01.json b/datasets/DSCOVR_EPIC_L3_PAR_01.json index 9e18556f34..3fef068a69 100644 --- a/datasets/DSCOVR_EPIC_L3_PAR_01.json +++ b/datasets/DSCOVR_EPIC_L3_PAR_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L3_PAR_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_EPIC_L3_PAR_01 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth\u2019s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.\u00b5m-1 units are equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carr\u00e9e (equal-angle) grid with an 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products.\r\nThe EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished. This dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP (National Centers for Environmental Prediction) Reanalysis 2 data, aerosol optical thickness, and angstrom coefficient from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC (National Snow and Ice Data Center) near real-time ice fraction data. \r\nAdditional information about the EPIC ocean surface PAR products can be found at the NASA DSCOVR: EPIC website: https://epic.gsfc.nasa.gov/, under \u201cScience -> Products -> Ocean Surface\u201d (https://epic.gsfc.nasa.gov/science/products/ocean).", "links": [ { diff --git a/datasets/DSCOVR_EPIC_L4_TrO3_01.json b/datasets/DSCOVR_EPIC_L4_TrO3_01.json index 82b6b7b507..e9697432a8 100644 --- a/datasets/DSCOVR_EPIC_L4_TrO3_01.json +++ b/datasets/DSCOVR_EPIC_L4_TrO3_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_EPIC_L4_TrO3_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EPIC Tropospheric Ozone Data Product\r\n\r\nThe Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) spacecraft provides measurements of Earth-reflected radiances from the entire sunlit portion of the Earth. The measurements from four EPIC UV (Ultraviolet) channels reconstruct global distributions of total ozone. The tropospheric ozone columns (TCO) are then derived by subtracting independently measured stratospheric ozone columns from the EPIC total ozone. TCO data product files report gridded synoptic maps of TCO measured over the sunlit portion of the Earth disk on a 1-2 hour basis. Sampling times for these hourly TCO data files are the same as for the EPIC L2 total ozone product. Version 1.0 of the TCO product is based on Version 3 of the EPIC L1 product and the Version 3 Total Ozone Column Product. The stratospheric columns were derived from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) ozone fields (Gelaro et al., 2017).\r\nIn contrast to the EPIC total ozone maps that are reported at a high spatial resolution of 18 \u00d7 18 km2 near the center of the image, the TCO maps are spatially averaged over several EPIC pixels and written on a regular spatial grid (1\u00b0 latitude x 1\u00b0 longitude). Kramarova et al. (2021) describe the EPIC TCO product and its evaluation against independent sonde and satellite measurements. Table 1 lists all of the variables included in the TCO product files. Ozone arrays in the product files are integrated vertical columns in Dobson Units (DU; 1 DU = 2.69\u00d71020 molecules m-2).\r\n\r\nFilename Convention\r\n\r\nThe TCO product files are formatted HDF5 and represent a Level-4 (L4) product. The filenames have the following naming convention:\r\n\r\n\u201dDSCOVR_EPIC_L4_TrO3_01_YYYYMMDDHHMMSS_03.h5\u201d \r\n\r\nWhere \u201cTrO3\u201d means tropospheric column ozone, \u201c01\u201d means that this is version 01 for this product, \u201cYYYYMMDDHHMMSS\u201d is the UTC measurement time with \u201cYYYY\u201d for year (2015-present), \u201cMM\u201d for month (01-12), \u201cDD\u201d for day of the month (1-31), and \u201cHHMMSS\u201d denotes hours-minutes-seconds, and \u201c03\u201d signifies that v3 L1b measurements were used to derive the EPIC total ozone and consequently TCO.\r\n\r\nColumn Weighting Function Adjustment\r\n\r\nThere are two TCO gridded arrays in each hourly data file for the user to choose from; one is denoted TroposphericColumnOzone, and the other is TroposphericColumnOzoneAdjusted. The latter TCO array includes an adjustment to correct for reduced sensitivity of the EPIC UV measurements in detecting ozone in the low troposphere/boundary layer. The adjustment depended on latitude and season and was derived using simulated tropospheric ozone from the GEOS-Replay model (Strode et al. 2020) constrained by the MERRA-2 meteorology through the replay method. Our analysis (Kramarova et al., 2021) indicated that the adjusted TCO array is more accurate and precise. \r\n\r\nFlagging Bad Data\r\n\r\nKramarova et al. (2021) note that the preferred EPIC total ozone measurements used for scientific study are those where the L2 \u201cAlgorithmFlag\u201d parameter equals 1, 101, or 111. In this TCO product, we have included only L2 total ozone pixels with these algorithm flag values. The TCO product files provide a gridded version of the AlgorithmFlag parameter as a comparison reference. Still, it is not needed by the user for applying data quality filtering.\r\n\r\nAnother parameter in the EPIC L2 total ozone files for filtering questionable data is the \u201cErrorFlag.\u201d The TCO product files include a gridded version of this ErrorFlag parameter that the user should apply. Only TCO-gridded pixels with an ErrorFlag value of zero should be used.\r\n\r\nTCO measurements at high satellite-look angles and/or high solar zenith angles should also be filtered out for analysis. The TCO files include a gridded version of the satellite look angle and the solar zenith angle denoted as \u201cSatelliteLookAngle\u201d and \u201cSolarZenithAngle,\u201d respectively. For scientific applications, users should filter TCO array data and use only pixels with SatelliteLookAngle and SolarZenithAngle < 70\u00b0 to avoid retrieval errors near the Earth view edge. \r\n\r\nIn summary, filtering the TCO arrays is optional, but for scientific analysis, we recommend applying the following two filters: \r\n(1) filter out all gridded pixels where ErrorFlag \u2260 0; \r\n(2) filter out all pixels where SatelliteLookAngle or SolarZenithAngle > 70\u00b0.\r\n\r\nSummary of the Derivation of the tropospheric column ozone product\r\n\r\nWe briefly summarize the derivation of EPIC TCO, stratospheric column ozone, and tropopause pressure. An independent measure of the stratospheric column ozone is needed to derive EPIC TCO. We use MERRA-2 ozone fields (Gelaro et al., 2017) to derive stratospheric ozone columns subtracted from EPIC total ozone (TOZ) to obtain TCO. The MERRA-2 data assimilation system ingests Aura OMI (Ozone Monitoring Instrument) v8.5 total ozone and MLS (Microwave Limb Sounder) v4.2 stratospheric ozone profiles to produce global synoptic maps of profile ozone from the surface to the top of the atmosphere; for our analyses, we use MERRA-2 ozone profiles reported every three hours (0, 3, 6, \u2026, 21 UTC) at a resolution of 0.625\u00b0 longitude \u00d7 0.5\u00b0 latitude. MERRA-2 ozone profiles were integrated vertically from the top of the atmosphere down to tropopause pressure to derive maps of stratospheric column ozone. Tropopause pressure was determined from MERRA-2 re-analyses using standard PV-\u03b8 definition (2.5 PVU and 380K). The resulting maps of stratospheric column ozone at 3-hour intervals from MERRA-2 were then space-time collocated with EPIC footprints and subtracted from the EPIC total ozone, thus producing daily global maps of residual TCO sampled at the precise EPIC pixel times. These tropospheric ozone measurements were further binned to 1\u00b0 latitude x 1\u00b0 longitude resolution. \r\n\r\nReferences\r\n\r\nGelaro, R., W. McCarty, M.J. Su\u00e1rez, R. Todling, A. Molod, L. Takacs, C.A. Randles, A. Darmenov, M.G. Bosilovich, R. Reichle, K. Wargan, L. Coy, R. Cullather, C. Draper, S. Akella, V. Buchard, A. Conaty, A.M. da Silva, W. Gu, G. Kim, R. Koster, R. Lucchesi, D. Merkova, J.E. Nielsen, G. Partyka, S. Pawson, W. Putman, M. Rienecker, S.D. Schubert, M. Sienkiewicz, and B. Zhao, The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419\u20135454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.\r\n\r\nKramarova N. A., J. R. Ziemke, L.-K. Huang, J. R. Herman, K. Wargan, C. J. Seftor, G. J. Labow, and L. D. Oman, Evaluation of Version 3 total and tropospheric ozone columns from EPIC on DSCOVR for studying regional-scale ozone variations, Front. Rem. Sens., in review, 2021.\r\n\r\nTable 1. List of parameters and data arrays in the EPIC tropospheric ozone hourly product files. The left column lists the variable name, the second column lists the variable description and units, and the third column lists the variable data type and dimensions.\r\nProduct Variable Name\tDescription and units\tData Type and Dimensions\r\nNadirLatitude\tNadir latitude in degrees\tReal*4 number\r\nNadirLongitude\tNadir longitude in degrees\tReal*4 number\r\nLatitude\tCenter latitude of grid-point in degrees\tReal*4 array with 180 elements\r\nLongitude\tCenter longitude of grid-point in degrees\tReal*4 array with 360 elements\r\nTroposphericColumnOzone\tTropospheric column ozone in Dobson Units\tReal*4 array with dimensions 360 \u00d7 180\r\nTroposphericColumnOzoneAdjusted\tTropospheric column ozone with BL adjustment in Dobson Units\tReal*4 array with dimensions 360 \u00d7 180\r\nStratosphericColumnOzone\tStratospheric column ozone in Dobson Units\tReal*4 array with dimensions 360 \u00d7 180\r\nTotalColumnOzone\tTotal column ozone in Dobson Units\tReal*4 array with dimensions 360 \u00d7 180\r\nReflectivity\tReflectivity (no units)\tReal*4 array with dimensions 360 \u00d7 180\r\nRadiativeCloudFraction\tRadiative cloud fraction (no units)\tReal*4 array with dimensions 360 \u00d7 180\r\nTropopausePressure\tTropopause pressure in units hPa\tReal*4 array with dimensions 360 \u00d7 180\r\nCWF1\tColumn weighting function for layer 1 (506.6-1013.3 hPa)\tReal*4 array with dimensions 360 \u00d7 180\r\nErrorFlag\tError flag for TCO data\tReal*4 array with dimensions 360 \u00d7 180\r\nAlgorithmFlag\tAlgorithm flag for TCO data\tReal*4 array with dimensions 360 \u00d7 180\r\nSatelliteLookAngle\tSatellite Look Angle in degrees\tReal*4 array with dimensions 360 \u00d7 180\r\nSolarZenithAngle\tSolar Zenith Angle in degrees\tReal*4 array with dimensions 360 \u00d7 180", "links": [ { diff --git a/datasets/DSCOVR_NISTAR_L1A_3.json b/datasets/DSCOVR_NISTAR_L1A_3.json index 6155985cbd..bdca135f72 100644 --- a/datasets/DSCOVR_NISTAR_L1A_3.json +++ b/datasets/DSCOVR_NISTAR_L1A_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_NISTAR_L1A_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_NISTAR_L1A is the Deep Space Climate Observatory (DSCOVR) National Institute of Standards & Technology Advanced Radiometer (NISTAR) Level 1A Radiance, Version 3 data product. \r\n\r\nNISTAR is a 4-band radiometer onboard THE National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point, from which vantage it continuously measures the reflected and emitted radiances of the sunlit face of the Earth. These measurements provide an accurate energy balance measurement that improves our understanding of the Earth's radiation budget.\r\n\r\nNISTAR employs three electrical substitution radiometers and a photodiode to measure reflected sunlight and infrared emission from the Earth. NISTAR measures the absolute irradiance integrated over the entire sunlit face of Earth in four broadband channels minute-by-minute. NISTAR has a 1\u00ba field of view (FOV) that acts as one large pixel that encompasses the entire sunlit side of the Earth and a 7\u00ba field of regard.\r\n\r\nThe four measurement bands and their uses are: \r\n\r\n1) Total Radiation \u2013 0.2 \u00b5m to 100 \u00b5m: total radiant power in the UV, visible, and infrared wavelengths emerging from Earth.\r\n2) Total Solar Reflected \u2013 0.2 \u00b5m to 4 \u00b5m: reflected solar radiance in UV, visible, and near-infrared wavelengths from Earth.\r\n3) Near Infrared Solar Reflected \u2013 0.7 \u00b5m to 4 \u00b5m: reflected near-infrared solar radiation from Earth.\r\n4) Photodiode \u2013 0.2 \u00b5m to 1.1 \u00b5m: tracks the stability of the filters and verifies co-alignment of NISTAR and EPIC.\r\n\r\nThe Level 1A products have been converted to engineering units but retain one-to-one associations with the items in the raw telemetry from which they were derived. These data products are in HDF5 format.", "links": [ { diff --git a/datasets/DSCOVR_NISTAR_L1B_3.json b/datasets/DSCOVR_NISTAR_L1B_3.json index 72ede5c0bd..421c4689b6 100644 --- a/datasets/DSCOVR_NISTAR_L1B_3.json +++ b/datasets/DSCOVR_NISTAR_L1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_NISTAR_L1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_NISTAR_L1B_3 is the Deep Space Climate Observatory (DSCOVR) National Institute of Standards & Technology Advanced Radiometer (NISTAR) Level 1B version 3 data product. \r\n\r\nNISTAR is a 4-band radiometer onboard the National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point, from which vantage it continuously measures the reflected and emitted radiances of the sunlit face of the Earth. These measurements provide an accurate energy balance measurement that improves our understanding of the Earth's radiation budget.\r\n\r\nNISTAR employs three electrical substitution radiometers and a photodiode to measure reflected sunlight and infrared emission from the Earth. NISTAR measures the absolute irradiance integrated over the entire sunlit face of Earth in four broadband channels minute-by-minute. NISTAR has a 1\u00ba field of view (FOV), one large pixel encompassing the whole sunlit side of the Earth, and a 7\u00ba field of regard.\r\n\r\nThe four measurement bands and their uses are: \r\n\r\n1) Total Radiation \u2013 0.2 \u00b5m to 100 \u00b5m: total radiant power in the UV, visible, and infrared wavelengths emerging from Earth.\r\n2) Total Solar Reflected \u2013 0.2 \u00b5m to 4 \u00b5m: reflected solar radiance in UV, visible, and near-infrared wavelengths from Earth.\r\n3) Near Infrared Solar Reflected \u2013 0.7 \u00b5m to 4 \u00b5m: reflected near-infrared solar radiation from Earth.\r\n4) Photodiode \u2013 0.2 \u00b5m to 1.1 \u00b5m: tracks the stability of the filters and verifies co-alignment of NISTAR and EPIC.\r\n\r\nThese Level 1B products are the irradiance values computed from Level 1A data collected while the instrument was aimed at the Earth. These data products are in HDF5 format.", "links": [ { diff --git a/datasets/DSCOVR_NISTAR_L1B_FILTERED_3.json b/datasets/DSCOVR_NISTAR_L1B_FILTERED_3.json index 1c6a691bc8..0db88fe143 100644 --- a/datasets/DSCOVR_NISTAR_L1B_FILTERED_3.json +++ b/datasets/DSCOVR_NISTAR_L1B_FILTERED_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_NISTAR_L1B_FILTERED_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DSCOVR_NISTAR_L1B_FILTERED_3 is the Deep Space Climate Observatory (DSCOVR) National Institute of Standards & Technology Advanced Radiometer (NISTAR) Level 1B Radiance Filtered, Version 3 data product. \r\n\r\nNISTAR is a 4-band radiometer onboard the National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point, from which vantage it continuously measures the reflected and emitted radiances of the sunlit face of the Earth. These measurements provide an accurate energy balance measurement that improves our understanding of the Earth\u2019s radiation budget.\r\n\r\nNISTAR employs three electrical substitution radiometers and a photodiode to measure reflected sunlight and infrared emission from the Earth. NISTAR measures the absolute irradiance integrated over the entire sunlit face of Earth in four broadband channels minute-by-minute. NISTAR has a 1\u00ba field of view (FOV), one large pixel that encompasses the entire sunlit side of the Earth, and a 7\u00ba field of regard.\r\n\r\nThe four measurement bands and their uses are: \r\n\r\n1) Total Radiation \u2013 0.2 \u00b5m to 100 \u00b5m: total radiant power in the ultraviolet (UV), visible, and infrared wavelengths emerging from Earth.\r\n2) Total Solar Reflected \u2013 0.2 \u00b5m to 4 \u00b5m: reflected solar radiance in UV, visible, and near-infrared wavelengths from Earth.\r\n3) Near Infrared Solar Reflected \u2013 0.7 \u00b5m to 4 \u00b5m: reflected near-infrared solar radiation from Earth.\r\n4) Photodiode \u2013 0.2 \u00b5m to 1.1 \u00b5m: tracks the stability of the filters and to verify co-alignment of NISTAR and Earth Polychromatic Imaging Camera (EPIC).\r\n\r\nThese Level 1B products are the irradiance values computed from Level 1A data collected while the instrument was aimed at the Earth. These data products are in HDF5 format.", "links": [ { diff --git a/datasets/DSCOVR_NISTAR_L2_FLX_01.json b/datasets/DSCOVR_NISTAR_L2_FLX_01.json index 2939ba7583..600777d540 100644 --- a/datasets/DSCOVR_NISTAR_L2_FLX_01.json +++ b/datasets/DSCOVR_NISTAR_L2_FLX_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSCOVR_NISTAR_L2_FLX_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Deep Space Climate Observatory (DSCOVR) DSCOVR National Institute of Standards and Technology Advanced Radiometer (NISTAR) was explicitly designed to measure the global daytime radiation budget for an entire hemisphere using active cavity radiometers for three channels: total (0.2 - 100 um), SW (0.2 - 4.0 um), and near-infrared (0.7 - 4.0 um). To derive the Earth Radiation Budget (ERB) from NISTAR measurements, the Short Wave (SW) radiances need to be unfiltered first before they can be subtracted from the total to yield the Long Wave (LW) (4 - 100 um) radiances. Additionally, the Earth's surface and atmosphere are anisotropic reflectors and emitters, resulting in a relatively complex variation of radiance leaving the Earth as a function of viewing and illumination. Converting radiance to flux requires using angular distribution models (ADMs) to account for the emittance and reflectance anisotropies. \r\n\r\nThe anisotropies are characterized for all Earth Polychromatic Imaging Camera (EPIC) pixels by using the Clouds and the Earth's Radiant Energy System (CERES) empirical angular distribution models (ADMs), which are functions of scene types which are defined using many variables including surface type, cloud amount, cloud phase, and optical depth, and water vapor. EPIC composite product is used to provide accurate scene-type information. The EPIC composites are generated from cloud property retrievals from Low Earth Orbit/Geostationary Equatorial Orbit (LEO/GEO) imagers mapped into the EPIC pixels. The EPIC composite also includes ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections. The anisotropies at the EPIC-pixel are then used to calculate the global mean SW and LW anisotropic factors, then convert the NISTAR SW and LW radiances to fluxes. This product contains the time series of the daytime Earth radiation budget derived from the NISTAR measurements.", "links": [ { diff --git a/datasets/DSS_age_scale_1.json b/datasets/DSS_age_scale_1.json index 9d5ccaf3b9..6293780c3a 100644 --- a/datasets/DSS_age_scale_1.json +++ b/datasets/DSS_age_scale_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSS_age_scale_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Records of methane, and oxygen isotopes (ice and air) for Law Dome DSS (Dome Summit South) core through the last deglaciation (9000-19000 before present).\n\nData are obtained from the entire DSS core, collected between late 1987 and early 1993.\n\nData comprise air composition measurements (d18Oair (per mille) and methane composition (ppbv)) and water isotope data (per mille) from the Law Dome core with age scale matched to the GRIP (Greenland) ice core. Default age scale (yr BP, 1950) gives best age scale - LDmin age scale gives a minimum age limiting case that is not the preferred dating.\n\nSupporting information is provided in a pdf document available both in the dataset for download, and the online Science site.\n\nThis work was completed as part of ASAC project 757.\n\nThe work is also related to the Greenland Ice Core Project (GRIP), and the Greenland Ice Sheet Project Two (GISP2).\n\nThe last deglaciation was marked by large, hemispheric, millennial-scale climate variations: the Bolling-Allerod and Younger Dryas periods in the north, and the Antarctic Cold Reversal in the south. A chronology from the high-accumulation Law Dome East Antarctic ice core constrains the relative timing of these two events and provides strong evidence that the cooling at the start of the Antarctic Cold Reversal did not follow the abrupt warming during the northern Bolling transition around 14,500 years ago. This result suggests that southern changes are not a direct response to abrupt changes in North Atlantic thermohaline circulation, as is assumed in the conventional picture of a hemispheric temperature seesaw.\n\nPublic Summary from project 757:\nPrediction of future climate change requires knowledge of past changes. Polar snow forms an archive of environmental conditions that is accessible by drilling and analysing ice cores. This project uses ice core data to reconstruct and study records, including past temperature and atmospheric composition, to improve understanding of the climate system.\n\nThe fields in this dataset are:\n\n(Note: Ages are all BP (1950); the two scenarios only give identical gas-ages at tie-points.)\n\nSample Mid Depth (metres)\nSample Length (metres)\nDelta 18O air (ppt)\nSample Mid Gas Age (Default)\nDelta Age (Default) (years)\nSample Mid Gas Age (LDmin)\nDelta Age (LDmin) (years)\nCH4 (ppbv)\nDepth (metres)\nAge (Default)\nAge (LDmin)\nDelta 18 O (ppt)", "links": [ { diff --git a/datasets/DSS_ice_core_c-axis_fabrics_and_grain_size_1.json b/datasets/DSS_ice_core_c-axis_fabrics_and_grain_size_1.json index 6cc1838fc3..636245b378 100644 --- a/datasets/DSS_ice_core_c-axis_fabrics_and_grain_size_1.json +++ b/datasets/DSS_ice_core_c-axis_fabrics_and_grain_size_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DSS_ice_core_c-axis_fabrics_and_grain_size_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Here we present ice crystallographic c-axis orientation and grain size data from the Dome Summit South (DSS) ice core drilled 4.7 km SSW of the summit of Law Dome, East Antarctica (66.770S, 112.80E). The 1195.9m ice core was drilled by the the Australian Antarctic Division during the austral summers of 1987-88 to 1992-93. These data are from 185 individual thin sections obtained between a depth of 117m below the surface and the bottom of the DSS core at a depth of 1196m. The median number of c-axis orientations recorded in each thin section was 100, with values ranging from 5 through to 111 orientations.\n\nThe fields in this dataset are (for CSV format):\n\nname - the name of the ice core section\nlongitude - the longitude of the location of the DSS core (decimal degrees)\nlatitude - the latitude of the location of the DSS core (decimal degrees)\nnumber_of_c_axes - the number of c-axes in the section\ndepth_actual - the actual depth of the thin section from the ice sheet surface to the top of the core section (metres)\ndepth_ice_equivalent - actual depth converted to an ice equivalent depth (metres)\nsection_orientation - thin section orientation (\"vertical\" or \"horizontal\")\nmean_horz_grain_area - arithmetic mean grain area (mm^2)\ngrain_index - the identifier of the data point within the section\ncolatitude - c-axis orientation colatitude (degrees)\nazimuth - c-axis orientation azimuth (degrees)\n\n(For Matlab format):\n\nname - the name of the ice core section\nc_cartesian - [N x 3] array of cartesian c-axis unit vectors. N is the number of orientations in a given section\nc_polar - [N x 2] array of polar c-axis vectors; colatitude and azimuth (degree)\ndepth_actual - actual depth of the thin section from the ice sheet surface to the top of the core section (metres)\ndepth_ice_equivalent - actual depth converted to an ice equivalent depth (metres)\nmean_horz_grain_area - mean grain area measured from horizontal thin sections (perpendicular to the vertical ice core axis; mm^2)\norientation_tensor_a - eigenvalues, a, of the 2nd order orientation tensor\norientation_tensor_V - eigenvectors, V, of the 2nd order orientation tensor", "links": [ { diff --git a/datasets/DUSTFLEXPART_1.json b/datasets/DUSTFLEXPART_1.json index 80d734ff2a..0c1f3ce6fa 100644 --- a/datasets/DUSTFLEXPART_1.json +++ b/datasets/DUSTFLEXPART_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DUSTFLEXPART_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a global simulation of mineral dust aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available. \n", "links": [ { diff --git a/datasets/Daily_FineParticulateMatter_AK_2157_1.json b/datasets/Daily_FineParticulateMatter_AK_2157_1.json index 2628029fd4..d95bdaffbd 100644 --- a/datasets/Daily_FineParticulateMatter_AK_2157_1.json +++ b/datasets/Daily_FineParticulateMatter_AK_2157_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daily_FineParticulateMatter_AK_2157_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015. The data were created in a modeling process utilizing the Wildland Fire Emissions Inventory System (WFEIS), the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, and multiple models including the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The data are provided in GeoTIFF format.", "links": [ { diff --git a/datasets/Dairy_Methane_CA_V1-2_1902_1.2.json b/datasets/Dairy_Methane_CA_V1-2_1902_1.2.json index c82ffe0aaf..920a49e017 100644 --- a/datasets/Dairy_Methane_CA_V1-2_1902_1.2.json +++ b/datasets/Dairy_Methane_CA_V1-2_1902_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dairy_Methane_CA_V1-2_1902_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of methane (CH4) emissions from dairies in California at a resolution of 0.1 degrees (~ 10 km x 10 km) for the year 2019. The mapped sources of dairy CH4 emissions are enteric fermentation and manure management reported in gigagrams per square km per year (Gg km-2 y-1). The sum of the two sources is also provided. These data are in the succession of Vista California (Vista-CA) spatial datasets that have identified and classified potential methane source emitters in California and were created utilizing an assortment of publicly available data sources from local, state, and federal agencies. This dataset can serve as a planning tool for mitigation, a prior for atmospheric observation-based emissions estimates, attribution of emissions to a specific facility, and to validate CH4 emissions reductions from management changes.", "links": [ { diff --git a/datasets/Dalberg Data Insights Crop Type Uganda_1.json b/datasets/Dalberg Data Insights Crop Type Uganda_1.json index 8b016ff2c8..f57176563d 100644 --- a/datasets/Dalberg Data Insights Crop Type Uganda_1.json +++ b/datasets/Dalberg Data Insights Crop Type Uganda_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dalberg Data Insights Crop Type Uganda_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains crop types and field boundaries along with other metadata collected in a campaign run by Dalberg Data Insights in the end of September 2017, as close as possible to the harvest period of 2017. GeoODKapps were used to collect approximately four points per field to get widest coverage during two field campaigns.\n

\nPost ground data collection, Radiant Earth Foundation conducted a quality control of the polygons using Sentinel-2 imagery of the growing season as well as Google basemap imagery, and removed several polygons that overlapped with infrastructure or built-up areas. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item).", "links": [ { diff --git a/datasets/Dall_Sheep_Population_Dynamics_1640_1.json b/datasets/Dall_Sheep_Population_Dynamics_1640_1.json index 4e852190b0..13a89b3f08 100644 --- a/datasets/Dall_Sheep_Population_Dynamics_1640_1.json +++ b/datasets/Dall_Sheep_Population_Dynamics_1640_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dall_Sheep_Population_Dynamics_1640_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range.", "links": [ { diff --git a/datasets/Dall_Sheep_Snowpack_1602_1.json b/datasets/Dall_Sheep_Snowpack_1602_1.json index b04929d519..2d1e007cb8 100644 --- a/datasets/Dall_Sheep_Snowpack_1602_1.json +++ b/datasets/Dall_Sheep_Snowpack_1602_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dall_Sheep_Snowpack_1602_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008.", "links": [ { diff --git a/datasets/Davis_2009_Aerial_Photography_1.json b/datasets/Davis_2009_Aerial_Photography_1.json index cc33ca8630..8ec2b8fec9 100644 --- a/datasets/Davis_2009_Aerial_Photography_1.json +++ b/datasets/Davis_2009_Aerial_Photography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_2009_Aerial_Photography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High resolution digital aerial photography of Adelie penguin colonies, Davis Station, Heidemann Valley, and other various areas, LIDAR scanning of portions of the Vestfold Hills, Rauer Islands and sea ice in front of the Amery Ice Shelf, conducted from 2009/11/17 to 2009/11/23.\n\nSome of the aerial photography has been conducted in support of various AAS projects:\n\nAAS 3012 (ASAC_3012)\nAAS 2722 (ASAC_2722)\nAAS 1034 (ASAC_1034)\nAAS 3130 (ASAC_3130)\n\nA short list of the work carried out:\n\n- Long duration over water/sea ice flights for the purposes of \"Investigation of physical and biological processes in the Antarctic sea ice zone during spring using in situ, aircraft and underwater observations\".\n\n- Over-flights at 750m over specific islands in the Vestfold Hills and Rauer Islands known to hold Adelie colonies.\n\n- Transects of flights were performed over Davis station, at 500m altitude, taking photos and LIDAR measurements.\n\n- The evaluation of the APPLS equipment (camera, LIDAR, electronics, software) was performed and in parallel to the other tasks.\n\n- Production a digital elevation model of the Heidemann Bay Area.\n\n- Aerial photography / LIDAR of moss beds in the Vestfold Hills area.\n\n- The Marine Plain area, south east of Davis, was mapped using LIDAR and aerial imagery for the purposes of general Antarctic information.\n\n- The Vestfold Lakes, particularly Lake Druzby, Watts Lake, Lake Nicholson and Crooked Lake provide interesting aerial imagery.\n\n- The opportunity was taken to visit the plateau skiway (at 'Woop woop') and estimate the effort in opening the skiway later in the season.\n\n- Fly over and photograph the length of the resupply fuel hose from the AA to the shore.\n\n- The Russian 'Progress 1 and 2', and Chinese Zhong Shan stations were over flown and aerial imagery collected.\n\nTaken from the report:\n\nThis document describes the results of the use of the APPLS (Aerial Photographic Pyrometer Laser System) at Davis during resupply 2009/2010 (November 17 to 24, 2009). This document is primarily for Science Technical Support use.\nPortions of the report can be used to provide information on the results obtained to other parts of AAD.", "links": [ { diff --git a/datasets/Davis_2010_Aerial_Photography_November_1.json b/datasets/Davis_2010_Aerial_Photography_November_1.json index a6b5bcd6fc..3c1f84bcfd 100644 --- a/datasets/Davis_2010_Aerial_Photography_November_1.json +++ b/datasets/Davis_2010_Aerial_Photography_November_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_2010_Aerial_Photography_November_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the report:\n\nThis document describes the results of the use of the APPLS (Aerial Photography Pyrometer LiDAR System) during underway science (sea ice) on the way to Davis, and later at Davis during resupply 2010/2011 (November 16 to 20, 2010). This document is primarily for Science Technical Support use. Portions of the report can be used to provide information on the results obtained to other parts of AAD.\n\nSome of this aerial photography has also been conducted in support of various AAS projects:\n\nAAS 3012 (ASAC_3012)\nAAS 3113 (ASAC_3113)\nAAS 2205 (ASAC_2205)\nAAS 2425 (ASAC_2425)\nAAS 3154 (ASAC_3154)\nAAS 3189 (ASAC_3189)\n\nA short list of the work carried out:\n\n- 3012, 3113\nThis activity involved long duration over water/sea ice flights for the purposes of \"Investigation of physical and biological processes in the Antarctic sea ice zone during spring using in-situ, aircraft and underwater observations\".\nThis activity was scheduled for prior to Davis, over pack ice far from shore. \nTwo science specific flights were made, and one opportunistic (sea ice reconnaissance), for a total of 5 hours 19 minutes of data collection for dedicated science\n\n- 2205\nPriority 1 - Adelie Penguin Census Survey on the Islands in the Davis vicinity\nThis task was a repeat of aerial census of Adelie penguins, conducted in 2009/2010 with coordinated ground counts of specific islands/colonies on Gardner, Magnetic, Lugg and Turner Islands. The ground counts were performed at the same time as the aerial survey, to compare aerial versus ground counts.\nPersonnel from the CEMP Penguin Monitoring Program (Colin Southwell, Barbara Wienecke) performed ground counts coordinated with the flying on two days. \nThe Flight lines were initially done on 2010/11/18 in bright sunlight, and then repeated on 2010/11/20 during overcast weather to compare the different image quality due to lack of shadows cast by the penguins.\nPriority 2 - Aerial photographic survey of the Svenner Group Islands\nFlights over Adelie Penguin colonies were performed at 750m, using 150mm lens, and then only over the islands known to host Adelie colonies. \nFlying time total = 5 hours, 51 minutes\n\n- 2425\nThis task was to survey the Woop Woop Skiway, over an area of 320 square kilometres. Due to time constraints, only every 2nd line was flown after consultation with AAD Air-operations (Steve Daw and Matt Filipowski).\nFlying time total = 4 hours 25 minutes\n\n- 3154\nThis task was to capture an aerial photograph of a Hawker Island Giant Petrel colony, being monitored by nest cameras. A run was conducted on 2010/11/19 in bright sunlight and also repeated on 2010/11/20 in flat light.\nFlying time total = 22 minutes\n\n- 3189\nThis task was to survey potential sites, in the Vestfold Hills near Davis, for a Nuclear Test Ban Treaty monitoring installation.\nFlying time total = 29 minutes", "links": [ { diff --git a/datasets/Davis_33MHz_Meteor_Radar_1.json b/datasets/Davis_33MHz_Meteor_Radar_1.json index 861f36695c..4d5f42c9e6 100644 --- a/datasets/Davis_33MHz_Meteor_Radar_1.json +++ b/datasets/Davis_33MHz_Meteor_Radar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_33MHz_Meteor_Radar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the characteristics of meteor detections from a 33MHz meteor detection radar operating at Davis station, Antarctica. The direction of arrival and radial velocity of these meteor detections can be used to infer average wind speed in the height range 75-105 km (depending on the season). Meteor detection data also includes signal power, decay time and the echo range. The experiment runs continuously, with the exception of data transfers and downtime for maintenance. \n\nData collection began in January 2005.\n\nInitial operation used single dipole receive antennas that had low end-on sensitivity (to the NE and SW). These antennas were upgraded to crossed dipoles in early 2008 such that all receive directions could be observed.\n\nThe data is stored in two formats. One contains records corresponding to individual meteor detections (with a 'met' file type). The other contains inferred hourly wind velocity estimates for the mesosphere, lower thermosphere region (with a 'vel' file extension'.\n\nData are stored using a binary format designed by the radar manufacturer Atmospheric Radar Systems (ATRAD). The radar PI or ATRAD can be contacted for instructions on converting the data file format.", "links": [ { diff --git a/datasets/Davis_55MHz_Meteor_Radar_1.json b/datasets/Davis_55MHz_Meteor_Radar_1.json index dab34cad98..02f6a763bb 100644 --- a/datasets/Davis_55MHz_Meteor_Radar_1.json +++ b/datasets/Davis_55MHz_Meteor_Radar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_55MHz_Meteor_Radar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the characteristics of sporadic meteor detections at 55MHz above Davis, Antarctica and the wind speed and direction in the middle atmosphere derived from those detections. \n\nThe capability to make these measurements is an add-on to the Davis 55MHz MST radar which is used when experiment schedules allow. As such, the duration of operation in this mode has varied through the life of the instrument. \n\nThe Davis 55 MHz atmospheric radar can be run in a meteor detection mode by selecting an alternate set of transmitting and receiving antennas. These consist of a single circularly polarized transmitting antenna and five linear polarized receiving antennas arranged in a \u2018Mills Cross\u2019 configuration. \n\nIn meteor mode, circularly polarized pulses are transmitted at a high repetition rate and the received signal is sampled at ranges sensitive to returns from the altitude range of 80-110 km approximately. If a meteor trail is present in the antenna field of view, increases of power of duration less than are second can be detected. The range is calculated from the pulse transit time and the direction of arrival is inferred from the relative phases of the signals at each receive antenna. Data files with a \u2018.met\u2019 extension contain the analysed data products from these detections and these include:\nEvent start time \u2013 The time of the detection\nRange \u2013 The distance from the radar to the meteor trail\nSNR \u2013 The signal to noise ratio of the detection\nAngle of arrival \u2013 The azimuth and zenith angles of the direction from the radar to the meteor trail\nDecay time \u2013 The exponential decay time of the detected signal (and its error)\nDiffusion coefficient \u2013 An inferred trail diffusion coefficient (and its error)\nRadial velocity \u2013 The speed with which the trail was moving toward or away (positive) from the radar (and its error)\nPhase differences \u2013 The mean phase differences for each pair combination of the five antennas.\n\nIf enough meteors are detected, it is possible to infer a horizontal wind field at the height of the detections. This is done my assuming the wind flows without divergence or convergence in the vicinity of the radar over a selected averaging interval. Horizontal and vertical components of the wind are derived in this way and stored with their heights. These data are stored in files with a \u2018.vel\u2019 extension. \n\nData collection began in 2003 and is ongoing within scheduling constraints.\n\nProject History:\nThe operation of the Davis 55MHz meteor detection radar began with:\nProject 2529 \u2013 \u2018A Meteor Radar for Measuring Mesospheric and Lower Thermospheric Winds and Temperatures at Davis Station\u2019. 2004/05 to 2008/09.\n\nTechnical History:\nSummer 2002/03 \u2013 The 55MHz meteor antenna array was constructed as part of the MST radar installation using antennas, cabling and switching provided by ATRAD and the University of Adelaide. \nChanges to the MST radar transmitter and beam steering unit since that time have not affected the 55MHz meteor detection operation.", "links": [ { diff --git a/datasets/Davis_Aerodrome_Project_1.json b/datasets/Davis_Aerodrome_Project_1.json index f70f7f47b3..ce31ed3249 100644 --- a/datasets/Davis_Aerodrome_Project_1.json +++ b/datasets/Davis_Aerodrome_Project_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Aerodrome_Project_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Davis Aerodrome Project (DAP) has collected a range of terrestrial survey data over the previous field seasons to support aerodrome and Davis masterplan design. \n\nThis data has been collected by a number of different methods, and extends across the current Davis Station, location of the proposed station masterplan site, aerodrome footprint and supporting infrastructure (Ridge Site) and previous sites considered for the aerodrome (Heidemann Valley, Adams Flat). \n\nAdditional information on datasets is provided within the child records.", "links": [ { diff --git a/datasets/Davis_Annual_Report_1985_1.json b/datasets/Davis_Annual_Report_1985_1.json index 8bbe097c60..61d9c1b754 100644 --- a/datasets/Davis_Annual_Report_1985_1.json +++ b/datasets/Davis_Annual_Report_1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Annual_Report_1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a scanned copy of the annual report on the scientific work undertaken at Davis Station in 1985. The report was written by J.B. Gallagher.\n\nParaphrased from the introduction:\n\nThis report describes the work undertaken at Davis from January 1985 to November 1985.\n\nAims of the program:\n\n1) Description of the seasonal circulation patterns within Ellis Fjord. This was done by measuring the salinity temperature profiles at selected stations down the length of the fjord, its entrance and the sea.\n\n2) Origin, age, circulation and mixing rates of the meromictic basin. The deep waters of the meromictic basin have a salinity of approximately 1.4 the salinity of sea water. It's possible that this may be due to old sea water left behind after an isostatic uplift with subsequent concentration through evaporation.\n\nCalculation of mixing rates and description of the water circulation will explain why the basin is so stable.\n\n3) Recent sedimentation rates within the meromictic basin. Sediment was collected from 110m for 137Cs analysis - the peak in the profile should indicate the age of that layer of sediment as 1963, during which atmospheric bomb tests released large quantities of 137Cs into the atmosphere.\n\n4) Biogeochemistry of sulphur, iron, manganese, aluminium and carbon within the meromictic basin. The deep waters of the meromictic basin are anoxic (from 45m). This gave an ideal opportunity to study the cycling of the above redox active elements.\n\n5) Collection and processing of water for trace element analysis and organometallics.", "links": [ { diff --git a/datasets/Davis_MFSA_Radar_3.json b/datasets/Davis_MFSA_Radar_3.json index f1417921dd..5826037161 100644 --- a/datasets/Davis_MFSA_Radar_3.json +++ b/datasets/Davis_MFSA_Radar_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_MFSA_Radar_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains wind speed and direction in the middle atmosphere above Davis, Antarctica. The radar runs continuously. Data are collected and stored every two minutes (excluding downtime for maintenance) for heights in the approximate range 50km to 110km at 2km intervals. Analysis of the source data yields parameters describing the strength and character of the radar echo and, when certain acceptance criteria are met, the wind speed. The requirement to meet acceptance criteria results in a data rate that may be less than the sampling rate. \n\nData collection began in 1994 and is ongoing.\n\nChanges to the polarisation of the transmitted radar pulse are used to maximise the range over which winds can be obtained, using the differing reflection and absorption characteristics of the ordinary and extraordinary modes. These have the potential to yield measures of electron density over the sampled height range. A combination of technical difficulties and unknown issues have meant that these data have not yielded results that are considered reliable.\n\nThe data set is standalone. MFSA stands for Medium Frequency Spaced Array. This is in reference to the transmitted frequency and the antenna configuration needed for Full Correlation Analysis (FCA).\n\nProject History:\nThe operation of the MFSA radar has supported a number of Antarctic Science Research projects. These include:\nProject 674 \u2013 \u2018Dynamical coupling in the Antarctic middle atmosphere\u2019. 1993/94 to 2011/12\nProject 4025 \u2013 \u2018Gravity wave drag parameterization in climate models\u2019. 2012/13 to 2016/17\nProject 4445 \u2013 \u2018High-latitude gravity wave processes and their parameterization in climate models\u2019 2017/18 to 2020/21\nFurther details of the outcomes of these projects can be found on the Antarctic Division website www.aad.gov.au. The data have also contributed to Australian and international research activities beyond these projects. Similar radars are/have been operated by other countries at Antarctic stations including Syowa (Japan), Rothera (UK), McMurdo (USA) and Scott Base (NZ).\n\nTechnical History:\nSummer 1993/94 \u2013 New radar equipment and existing equipment previously operating at Mawson station were installed at Davis. Operation and data collection began on 11 April 1994. This mode of operation used 8-bit digitisation of received signals, ongoing gain adjustment and polarization changes between (rather than within) data accumulation cycles.\nSummer 2000/2001 \u2013 An upgrade to the receiver and digitiser system removed the need for gain adjustment and introduced 12 bit digitisation. Polarisation changes within data accumulation cycles became possible. Operation began on 7 November 2000.\nSummer 2003/2004 - Replaced \u2018TOMCO\u2019 transmitter with 3rd generation ATRAD transmitter. \nSummer 2008/9 \u2013 Container laboratory replaced.\nFeb 2012 \u2013 Transmit antenna replaced. Replacement allows tap changes to improve tuning of antenna impedance.\n\n", "links": [ { diff --git a/datasets/Davis_MST_Radar_1.json b/datasets/Davis_MST_Radar_1.json index 36150a8dfb..c4947bd654 100644 --- a/datasets/Davis_MST_Radar_1.json +++ b/datasets/Davis_MST_Radar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_MST_Radar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains wind speed, direction and associated information in the troposphere, lower stratosphere and mesosphere above Davis, Antarctica. The radar runs continuously. Data are collected and stored approximately every minute (excluding downtime for maintenance) over height ranges and resolutions that are determined by experiment parameters. Analysis of the source data yields parameters describing the radial velocity, and radar echo strength and character for defined radar beam pointing directions. \n\nRadar beams are formed by phasing the entire 12x12 antenna array. Beam directions are chosen from vertical or off-vertical pointing to the north, south, east or west. Their zenith angle is typically 14 or 7 degrees depending on the upgrade status of the radar. The capability to form groups of 4x6 antennas existed prior to an upgrade in the beam steering system. Pulse shape and duration is configurable as is the pulse repetition frequency (within the design limits of the radar).\n\nSampled height ranges typically encompass the troposphere and lower stratosphere (e.g. 2-15 km) or the mesosphere (e.g. 70-90 km). The resolution and extent of the sampled range are limited by acquisition memory. Pulse repetition frequencies are sometimes chosen to \u2018range-alias\u2019 height ranges to lower sampled ranges. Coherent averaging of the echoes from successive pulses is possible.\n\nCombinations of these options are chosen for particular observing modes. These are described in \u2018experiment\u2019 configurations. These typically have a duration of less than a minute and repeat on cycles of 6-8 minutes (configurable).\n\nThese configuration options yield a variety of data files.\n\nPostanalysis of experiment data files can be configured on the radar. Wind-field fits and averages can be formed in this way. A data quality control algorithm was sometimes applied in this way to yield data files with \u2018_clean\u2019 in their name.\n\nData collection began in 2003 and is ongoing. Files are named using their start date (and time), an experiment tag and an extension describing their format. Technical issues with the radar have affected the quality of some data sets as will be described below. \n\nMST stands for Mesosphere Stratosphere Troposphere, which refer to the atmospheric regions that the radar can sense (although not all year round). The radar is also sometimes referred to as a \u2018VHF radar\u2019 due to the 55MHz radar frequency which is in the VHF band.\n\nProject History:\nThe operation of the MST radar has supported a number of Antarctic Science Research projects. These include:\nProject 2325 \u2013 \u2018VHF Radar Studies of the Antarctic Mesosphere, Stratosphere and Troposphere\u2019. 2002/03 to 2011/12.\nProject 4025 \u2013 \u2018Gravity wave drag parameterization in climate models\u2019. 2012/13 to 2016/17\nProject 4445 \u2013 \u2018High-latitude gravity wave processes and their parameterization in climate models\u2019 2017/18 to 2020/21\nProject 674 \u2013 \u2018Dynamical coupling in the Antarctic middle atmosphere\u2019 2002/03 to 2011/12.\nProject 737 \u2013 \u2018Lidar studies of atmospheric dynamics, composition and climatology\u2019. 2002/3 to 2011/12.\nProject 2529 \u2013 \u2018A Meteor Radar for Measuring Mesospheric and Lower Thermospheric Winds and Temperatures at Davis Station\u2019. 2004/05 to 2008/09.\nProject 2668 \u2013 \u2018Investigations of the Antarctic Mesosphere and Lower Thermosphere using satellite data\u2019. 2005/06 to 2011/12.\nProject 3140 \u2013 \u2018Dynamical Variability of the Lower Atmosphere\u2019. 2009/10 to 2011/12.\n\nFurther details of the outcomes of these projects can be found on the Antarctic Division website www.aad.gov.au. \n\nTechnical History:\nSummer 2002/03 \u2013 Installation of 144 antenna phased array (in hybrid Doppler-spaced antenna configuration), single power amplifier transmitter system, transceiver and control system. Array tuning issue identified. Separate add-on meteor detection radar system was also installed at this time. \nSummer 2003/04 \u2013 Attempt to upgrade transmitter system to full power was delayed by technical faults. Temporary installation of ATRAD supplied transmitter.\nSummer 2004/05 \u2013 Replacement transmitter, transceiver and control system installed. Beam steering unit upgraded to decrease \u2018clutter\u2019 near zero Hz.\nSummer 2008/09 \u2013 Contact grease inserted into connectors in attempt to alleviate clutter problems.\nSummer 2010/11 \u2013 Bias in tropospheric wind results obtained using hybrid array and FCA analysis identified. Doppler observations shown to compare favourably to radiosondes. \nSummer 2012/13 \u2013 Antenna array reconfigured to Doppler only. Beam Steering unit replaced with Power combiner-splitter/beam steering unit combination.\nWinter 2013 \u2013 High voltage power supply failure. Radar operated through winter on low power solid state preamplifiers.\nSummer 2013/14 \u2013 High voltage, 50V and heater current power supplies upgraded.\nSummer 2014/15 \u2013 Beam steering unit relay switch timing error was identified and found to cause relay damage. Some sticking relays replaced after timing changed. \nSummer 2016/17 \u2013 Beam steering unit relay replacement completed.", "links": [ { diff --git a/datasets/Davis_Radiosonde_Stratospheric_Gravity_Wave_parameters_2001_to_2012_1.json b/datasets/Davis_Radiosonde_Stratospheric_Gravity_Wave_parameters_2001_to_2012_1.json index f202d2c17e..b7a5e73a26 100644 --- a/datasets/Davis_Radiosonde_Stratospheric_Gravity_Wave_parameters_2001_to_2012_1.json +++ b/datasets/Davis_Radiosonde_Stratospheric_Gravity_Wave_parameters_2001_to_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Radiosonde_Stratospheric_Gravity_Wave_parameters_2001_to_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Davis radiosonde data was analysed to identify stratospheric (15-31 km altitude) inertial gravity waves as described in the publication Murphy et al (2014). This NetCDF file contains the gravity wave parameters used in that publication.\n\nParameters are described within the NetCDF file.\n\nMurphy, D. J., S. P. Alexander, A. R. Klekociuk, P. T. Love, and R. A. Vincent (2014), Radiosonde observations of gravity waves in the lower stratosphere over Davis, Antarctica, J. Geophys. Res. Atmos.,\n119, 11,973\u201311,996, doi:10.1002/2014JD022448.", "links": [ { diff --git a/datasets/Davis_STP_Chemistry_Methods_1.json b/datasets/Davis_STP_Chemistry_Methods_1.json index 1c9f9eb647..691e235e8c 100644 --- a/datasets/Davis_STP_Chemistry_Methods_1.json +++ b/datasets/Davis_STP_Chemistry_Methods_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_STP_Chemistry_Methods_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This document details the analytical methods used to determine a range of chemical and physical properties of the wastewater effluent from Davis Station and of marine sediments collected from shallow waters (5 to 25 m) around the Vestfold Hills and Davis Station. This was done as part of a study examining the environmental impacts of the Davis Station wastewater outfall in early 2010.\n\nSee Davis_STP metadata record for further information on the project.", "links": [ { diff --git a/datasets/Davis_Station_1.json b/datasets/Davis_Station_1.json index fcba0f424f..a082213b86 100644 --- a/datasets/Davis_Station_1.json +++ b/datasets/Davis_Station_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Station_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents topographic features of Davis Station, Antarctica.\nThe mapped features include coastline, contours, spot heights, high water mark (the 0.4 m contour was used as the high water mark) and station infrastructure (buildings, masts, aerials, tanks, pipes and other structures).\n\nThe data are included in the data available for download from the provided URL.\n\nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 16.\nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.\n\nChanges have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added.\nAs a result the data available for download from the provided URL is updated with new data having different Dataset_id(s).", "links": [ { diff --git a/datasets/Davis_Tide_Gauges_2.json b/datasets/Davis_Tide_Gauges_2.json index cd1fd7cb9c..8aaf9f08b7 100644 --- a/datasets/Davis_Tide_Gauges_2.json +++ b/datasets/Davis_Tide_Gauges_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Tide_Gauges_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Over time there have been a number of tide gauges deployed at Davis Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1993 to 2017. More recent data are described elsewhere.\n\nTide Gauge 3 (TG003)\nThis folder contains the following folders:-\nearly_tg_files \n \n\tmonthly_tg_files\n\tmonthly download files from the submerged tide gauge at Davis deployed in March 1993.\n\tThese files are ASCII hexadecimal files. They need to be converted to decimal.\n\tThe resultant values are absolute seawater pressures in mbar.\n\t\n\tRemaining files are downloaded in normal format obtained directly from tide gauge.\n\t\nraw\n\tmemory images from submerged tide gauge. file extension is memory bank number. \n\tThese files are processed by a utility called tgxtract.exe which creates files in same format as those in old_tidedata folder.\n\tThese file have extension .srt. They are then converted to decimal pressure values.\n\noutput\n\toutput file (.srt) which have been sent to BoM.\n\nTide Gauge 6 (TG006)\nThis folder contains the following folders:-\n\nraw\n\tmemory images from submerged tide gauge. file extension is memory bank number. \n\tThese files are processed by a utility called tgxtract.exe which creates files in same format as original download format.\n\tThese file have extension .srt. \n\tThese files are ASCII hexadecimal files. They need to be converted to decimal.\n\tThe resultant values are absolute seawater pressures in mbar.\n\n\noutput\n\toutput file (.srt) which have been sent to BoM.\n\nTide Gauge 12 (TG012) and Tide Gauge 12i (TG012i)\n\nDocumentation notes from the older metadata records:\nDocumentation dated 2001-03-07\nDavis Submerged Tide Gauge \n\nThe gauge used at Davis was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania . It was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid. The gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature. The overall accuracy of the system is better than +/- 3 mm for a known water density. Data is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit, a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved. One of these of these gauges was deployed at Davis in early 1993 in a mooring in ???? bay. Data has been retrieved from these gauges irregularly since then. The records are complete since deployment except for a few days in late 1995. The loss was caused by a fault in the software which allows directory entries to overwrites data when the directory memory has been filled. Conversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC . As the current gauge is expected to require a new battery soon, a new mooring has been placed close to the original. A new gauge is at Davis ready to be deployed as time permits.\nLevelling\n\nLevelling of the gauge at Davis was done by installing a temporary pressure type gauge in shallow water and recording sealevel for 10 days. The temporary gauge was precisely levelled to a permanent benchmark. The temporary gauge was then calibrated using a known height of seawater from the bay at the same temperature as the water in the bay. The density of the seawater was accurately measured. This work, in conjunction with the tidal records from the submerged gauge have enabled a MSL for Davis to be established. Permanent Tide Gauge. No suitable sites for an Aquatrak type gauge at Davis have been identified.\n\nDocumentation dated 2008-10-17\nThere are two submerged tide gauges at Davis. One is soon to be removed to have its battery replaced.\nThese gauges record pressure and temperature values. The download software only formats these records to produce 10 minute average presure values (hPa) and unscaled temperature values.\n", "links": [ { diff --git a/datasets/Davis_USU_Camera_GW_parameters_1.json b/datasets/Davis_USU_Camera_GW_parameters_1.json index 661e688aab..4adb0ab35b 100644 --- a/datasets/Davis_USU_Camera_GW_parameters_1.json +++ b/datasets/Davis_USU_Camera_GW_parameters_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_USU_Camera_GW_parameters_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains atmospheric gravity-wave parameters obtained from images of the infrared emissions from the Meinel band from the night sky above Davis, Antarctica. The camera that provides the images runs continuously during darkness, over intervals set by a yearly almanac of start and stop times. Images are collected and stored approximately every ten seconds. They record the emission intensity over much of the sky. Continuous daylight through summer limits the observation interval to between February and October.\n\nThe camera was first installed in the summer of 2011/12 and started operation 2012.\n\nThe gravity wave parameters described here were obtained with the following procedure:\n- manual identification of clear sky intervals in the image data,\n- star removal, flat fielding, and projection of the images onto a linear-scale geographic grid,\n- application of a two dimensional FFT algorithm to extract the parameters of wavelike features from image sequences.\nMore information on this can be obtained from Garcia et al. (1997) and the project investigators.\n\nThe gravity wave parameters obtained are horizontal wavelength, wave propagation direction and wave horizontal phase velocity.\n\nCurrently only two years worth of processed data are available - 2012, 2013.\n\nReferences:\nGarcia, Taylor and Kelley, Two dimensional spectral analysis of mesospheric airglow image data, Applied Optics, 36, 7374-7385 (1997)", "links": [ { diff --git a/datasets/Davis_USU_Infrared_Camera_3.json b/datasets/Davis_USU_Infrared_Camera_3.json index 817626b8ff..c0ac33b054 100644 --- a/datasets/Davis_USU_Infrared_Camera_3.json +++ b/datasets/Davis_USU_Infrared_Camera_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_USU_Infrared_Camera_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains images of the infrared emissions from the Meinel band from the night sky above Davis, Antarctica. \n\nThe camera runs continuously during darkness, over intervals set by a yearly almanac of start and stop times. Images of resolution 320x256 pixels are collected and stored approximately every ten seconds. They record the emission intensity over much of the sky. \n\nContinuous daylight through summer limits the observation interval to between February and October.\n\nThe camera was first installed in the summer of 2011/12 and started operation 2012.", "links": [ { diff --git a/datasets/Davis_Winter_Report_1989_1.json b/datasets/Davis_Winter_Report_1989_1.json index 70d6c4a903..787aa28a57 100644 --- a/datasets/Davis_Winter_Report_1989_1.json +++ b/datasets/Davis_Winter_Report_1989_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_Winter_Report_1989_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a scanned copy of the report written by Simon Townsend on work undertaken at Davis Station during the wintering year of 1989.\n\nThe report covers the following topics:\n\n- Tierny Drainage System\n - The hypersaline density current hypothesis tested\n - Ellis Fjord temperature and salinity data\n - Ellis Fjord long-term instrument deployment\n - Water tracer experiment\n - Organic Lake\n - Ellis Fjord in-situ chlorophyll-a profiles\n - Appendices: Platypus notes, Platypus software, Seabird instrument notes, assessment of Chelsea suspended solids meter, winches for biological use, advise under-ice instrument deployment.", "links": [ { diff --git a/datasets/Davis_biology_report_1982_1.json b/datasets/Davis_biology_report_1982_1.json index d9d6a478bc..6fc0a1b330 100644 --- a/datasets/Davis_biology_report_1982_1.json +++ b/datasets/Davis_biology_report_1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_biology_report_1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the biology report for Davis Station, 1982, prepared by Mark Tucker.\n\nA hardcopy of the report and field books are available in the Australian Antarctic Division library, and pdf copies of the report and field books are available for download at the provided URLs.\n\nIntroduction\n\nThe year biology programme for the 1982 season was divided amongst three persons into Phytoplankton, Chlorophyll, Invertebrates and Fish. As the zoologist, I will therefore concentrate on the animal, aspect.\n\nThe aims of this programme as outlined in the ARPAC approved \"A survey of the inshore marine area of Davis\" are:\n\n1) A systematic investigation to determine the flora and fauna of the marine inshore environment.\n2) To explain their distribution and abundance in response to environmental variables.\n\nThe first aim can be divided into two categories:\n\n1) Wide range collection of the benthic, planktonic, pelagic and epontic faunas from the inshore waters of the Vestfold Hills.\n2) Quantitative examination of the seasonal and distributional changes of the more common species.\n\nMost of the wide range collecting of the benthos and to a certain extent the plankton was carried out over the 81/81 summer. Collections were made from as far north as the Wyatt Earp islands and in the south near the Sorsdal Glacier. As wide a coverage as possible of the Vestfolds was made plus a visit to the Rauer group on one occasion. The planktonic fauna was collected throughout the year on a monthly basis from three sites from January 82 to December 82 while the pelagic and epontic faunas were collected monthly from the same sites after fast ice formation - April to December. Additions were made to the benthic collections throughout the year if any previously uncollected or interesting specimens were observed.\n\nThese collections have culminated in over 150 species. I would expect the total number of different species to be around 200 once all are identified. Representatives of all the species collected will be returned to Biology, Kingston, for reference for future workers in the marine invertebrate field.\n\nThe second aim, the quantitative examination, was carried out over a 12 month period from January 82 to December 82 at three sites - A, B and C (figure 1). These sites were selected on the criteria of depth, proximity to Davis and most importantly sediment types. Site A is 9m deep with a sandy bottom and a few odd rocks. It has a relatively low (5% or less) macrophytic cover. Site B is 20m deep with a mud bottom and zero macrophytes while site C is 15m deep with a rocky bottom and scattered pockets of sand and shell fragments etc. and 5-10% macrophyte cover. Sites A and B are relatively flat while C is situated on quite a steep slope. Sediment samples have been retained from each site to enable particle size analysis for more accurate descriptions of the sediment types.\n\nSeveral zooplankton, sediment inhabiting and macroscopic benthic species were monitored on a monthly basis for the year. Fish were sampled at sites A and C while the epontic community was sampled after ice formation at all three sites.\n\nThe environmental variables measured were ice and snow thickness, tide, hours of daylight, salinity, nutrients, water temperature plus chlorophyll data and phytoplankton numbers. These variables are to be used in statistical analysis as a means of explaining the abundance and distribution of the species studied.", "links": [ { diff --git a/datasets/Davis_multibeam_grids_2.json b/datasets/Davis_multibeam_grids_2.json index 3499fd6def..ae93cb7477 100644 --- a/datasets/Davis_multibeam_grids_2.json +++ b/datasets/Davis_multibeam_grids_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_multibeam_grids_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From February to March 2010, Geoscience Australia (GA) conducted a multibeam sonar survey of the coastal waters of the Vestfold Hills in the Australian Antarctic Territory. The survey was conducted jointly with Australian Antarctic Division (AAD) and the Deployable Geospatial Survey Team (DGST) of the Royal Australian Navy. The survey was aimed primarily at understanding the character of the sea floor around Davis Station to better inform studies of the benthic biota and the possible impacts of the Davis sewage outfall. DGST were involved to ensure that the bathymetric data could be used to update and extend the nautical charts of the Davis area.\n \nThe survey was conducted using GA's Kongsberg EM3002D multibeam echo sounder and C-Nav Differential GPS system mounted on the AAD work boat Howard Burton. Sixteen under water videos were also collected using the GA Raytech camera system and 3 grabs were also collected to compliment an intensive sampling program by AAD divers and a sampling program conducted in the 1990's by University of Tasmania (Franklin, 1996).\n\nAn area of 42 km2 was surveyed intensively immediately off Davis and additional survey lines were run to Long Fjord in the north and to Crooked Fjord and the Sorsdal Glacier in the south. The main survey area had between 150% and 200% coverage as the seabed was esonified from opposing angles to resolve and provide detail to the numerous features of the seafloor such as rocky reefs, iceberg scours, boulders, anchor chain drag marks and grounded icebergs. The new high resolution data provided detailed maps of sea bed morphology and texture classification to complement sample data. Sixteen video transects were collected and 3 grab samples collected in water too deep for the Australian Antarctic Division Diving program.\n \nNew high resolution bathymetric grids have been prepared for scientific use and further processing for hydrographic charting is ongoing. A new sea floor geomorphic map has been prepared using the multibeam data, preliminary video and sampling data.\n\nThe project was a component of Australian Antarctic Science (AAS) Project 2201 - Natural Variability and Human Induced Change on Antarctic Nearshore Marine Benthic Communities.\n\nIn 2011, Dr Phil O'Brien provided to the Australian Antarctic Data Centre the following interim data:\n75 cm multibeam data in CARIS format; and \na 4 metre resolution bathymetric grid and an image of the sea floor, both derived from the 75 cm multibeam data.\nThis data was made available for download from this metadata record.\n\nIn August 2013, Geoscience Australia released 2 metre resolution bathymetric and backscatter grids after further processing of the multibeam data. The bathymetry and backscatter data have now been fully processed checked and validated by Geoscience Australia and supersede the interim data. The interim data has been archived by the Australian Antarctic Data Centre. The 2 metre resolution grids and final report are available for download from the Geoscience Australia website.", "links": [ { diff --git a/datasets/Davis_seabed_geomorphic_map_1.json b/datasets/Davis_seabed_geomorphic_map_1.json index e01a03871b..236192ebc5 100644 --- a/datasets/Davis_seabed_geomorphic_map_1.json +++ b/datasets/Davis_seabed_geomorphic_map_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Davis_seabed_geomorphic_map_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Davis Coastal Seabed Mapping Survey, Antarctica (GA-4301 / AAS2201 / HI468) was conducted on the Australian Antarctic Division workboat Howard Burton during February-March 2010 as a component of Australian Antarctic Science (AAS) Project 2201 - Natural Variability and Human Induced Change on Antarctic Nearshore Marine Benthic Communities. \nThe survey was undertaken as a collaboration between Geoscience Australia, the Australian Antarctic Division and the Australian Hydrographic Service (Royal Australian Navy). The survey acquired multibeam bathymetry and backscatter datasets from the nearshore region of the Vestfold Hills around Davis Station, Antarctica. These datasets are described by the metadata record with ID Davis_multibeam_grids.\nThis dataset comprises an interpreted geomorphic map produced for the central survey area using multibeam bathymetry and backscatter grids and their derivatives (e.g. slope, contours). Six geomorphic units; basin, valley, embayment, pediment, bedrock outcrop and scarp were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Polygons were created using a combination of automatic extraction and manual digitisation in ArcGIS. \nFor further information on the geomorphic mapping methods and a description of each unit, please refer to OBrien P.E., Smith J., Stark J.S., Johnstone G., Riddle M., Franklin D. (2015) Submarine geomorphology and sea floor processes along the coast of Vestfold Hills, East Antarctica, from multibeam bathymetry and video data. Antarctic Science 27:566-586.\nThis metadata record was created using information in Geoscience Australia's metadata record at \nhttp://www.ga.gov.au/metadata-gateway/metadata/record/89984/", "links": [ { diff --git a/datasets/Daymet_Annual_V4R1_2130_4.1.json b/datasets/Daymet_Annual_V4R1_2130_4.1.json index d6c3813595..fbf5f7f0f9 100644 --- a/datasets/Daymet_Annual_V4R1_2130_4.1.json +++ b/datasets/Daymet_Annual_V4R1_2130_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_Annual_V4R1_2130_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable. Each data file is provided as a single year by variable and covers the same period of record as the Daymet V4 R1 daily data. The annual climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Separate annual files are provided for the land areas of continental North America, Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (*.nc) and Cloud Optimized GeoTIFF (*.tif) file formats. In Version 4 R1, all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.", "links": [ { diff --git a/datasets/Daymet_Daily_V4R1_2129_4.1.json b/datasets/Daymet_Daily_V4R1_2129_4.1.json index ac16696687..a9c077c173 100644 --- a/datasets/Daymet_Daily_V4R1_2129_4.1.json +++ b/datasets/Daymet_Daily_V4R1_2129_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_Daily_V4R1_2129_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.", "links": [ { diff --git a/datasets/Daymet_Monthly_V4R1_2131_4.1.json b/datasets/Daymet_Monthly_V4R1_2131_4.1.json index f4e396f6b7..9076a37667 100644 --- a/datasets/Daymet_Monthly_V4R1_2131_4.1.json +++ b/datasets/Daymet_Monthly_V4R1_2131_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_Monthly_V4R1_2131_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable. Each data file is yearly by variable with 12 monthly time steps and covers the same period of record as the Daymet V4 R1 daily data. The monthly climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America, Hawaii, and Puerto Rico. Separate monthly files are provided for the land areas of continental North America (Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (*.nc) and Cloud-Optimized GeoTIFF (*.tif) formats. In Version 4 R1 (ver 4.1), all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.", "links": [ { diff --git a/datasets/Daymet_SubDaily_Puerto_Rico_1977_1.json b/datasets/Daymet_SubDaily_Puerto_Rico_1977_1.json index e25d94e6e1..b8597eb7c1 100644 --- a/datasets/Daymet_SubDaily_Puerto_Rico_1977_1.json +++ b/datasets/Daymet_SubDaily_Puerto_Rico_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_SubDaily_Puerto_Rico_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To support high spatial- and temporal-resolution land surface modeling, this dataset provides 3-hourly time step historic weather forcing at 1-km spatial resolution for Puerto Rico and surrounding islands. The latest Daymet V4 data provides gridded historic daily weather observation at 1-km spatial resolution from 1950 to present. Using sub-daily temporal information from two meteorological reanalysis datasets (GSWP3 and NARR), Daymet was further temporally downscaled to 3-hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each sub-daily time step from GSWP3 and NARR while maintaining the total and average values from Daymet at each day. These result in two blended datasets: 1950-2014 Daymet-GSWP3 and 1979-2019 Daymet-NARR. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.", "links": [ { diff --git a/datasets/Daymet_V4_Daily_MonthlyLatency_1904_1.json b/datasets/Daymet_V4_Daily_MonthlyLatency_1904_1.json index ceec86a7cc..dc75090bd1 100644 --- a/datasets/Daymet_V4_Daily_MonthlyLatency_1904_1.json +++ b/datasets/Daymet_V4_Daily_MonthlyLatency_1904_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_V4_Daily_MonthlyLatency_1904_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Daymet Version 4 daily data on a monthly cycle as 1-km gridded estimates of daily weather variables for minimum temperature (tmin), maximum temperature (tmax), precipitation (prcp), shortwave radiation (srad), vapor pressure (vp), snow water equivalent (swe), and day length. Data are derived from the Daymet version 4 software where the primary inputs are daily observations of near-surface maximum and minimum air temperature and daily total precipitation from weather stations. The main algorithm to estimate primary Daymet variables (tmax, tmin, and prcp) at each Daymet grid is based on a combination of interpolation and extrapolation, using inputs from multiple weather stations and weights that reflect the spatial and temporal relationships between a Daymet grid and the surrounding weather stations. Secondary variables (srad, vp, and swe) are derived from the primary variables (tmax, tmin, and prcp) based on atmospheric theory and empirical relationships. The day length (dayl) estimate is based on geographic location and time of year. Data are available for the Continental North America, Puerto Rico, and Hawaii as separate spatial layers in a Lambert Conformal Conic projection and are distributed in standardized Climate and Forecast (CF)-compliant netCDF file formats.", "links": [ { diff --git a/datasets/Daymet_xval_V4R1_2132_4.1.json b/datasets/Daymet_xval_V4R1_2132_4.1.json index 27d8ddea4c..520196c17c 100644 --- a/datasets/Daymet_xval_V4R1_2132_4.1.json +++ b/datasets/Daymet_xval_V4R1_2132_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Daymet_xval_V4R1_2132_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset reports the station-level daily weather observation data and the corresponding cross-validation results for three Daymet model parameters: minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) across continental North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Each data file contains the daily observations and cross-validation results for one parameter for each modeled region and each year, that is, from 1980 to the current calendar year for stations across continental North America and Hawaii and from 1950 to the current year for Puerto Rico. Also included are corresponding station metadata files listing every surface weather station used in Daymet processing for each parameter, region, and year and containing the station name, station identification, latitude, and longitude. The data are provided in netCDF and text formats. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.", "links": [ { diff --git a/datasets/Decadal_LULC_India_1336_1.json b/datasets/Decadal_LULC_India_1336_1.json index 95edf9c313..76b75c8a10 100644 --- a/datasets/Decadal_LULC_India_1336_1.json +++ b/datasets/Decadal_LULC_India_1336_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Decadal_LULC_India_1336_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation. The data were classified according to the International Geosphere-Biosphere Programme (IGBP) classification scheme.", "links": [ { diff --git a/datasets/Decadal_Water_Maps_1324_1.1.json b/datasets/Decadal_Water_Maps_1324_1.1.json index e685ad2671..59021c95c8 100644 --- a/datasets/Decadal_Water_Maps_1324_1.1.json +++ b/datasets/Decadal_Water_Maps_1324_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Decadal_Water_Maps_1324_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period.", "links": [ { diff --git a/datasets/DeciduousFractionl_CanopyCover_2296_1.json b/datasets/DeciduousFractionl_CanopyCover_2296_1.json index 77a925ed99..81bcaa3c17 100644 --- a/datasets/DeciduousFractionl_CanopyCover_2296_1.json +++ b/datasets/DeciduousFractionl_CanopyCover_2296_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeciduousFractionl_CanopyCover_2296_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format.", "links": [ { diff --git a/datasets/Declassified_Satellite_Imagery_2_2002.json b/datasets/Declassified_Satellite_Imagery_2_2002.json index a1d284f20c..69d004912a 100644 --- a/datasets/Declassified_Satellite_Imagery_2_2002.json +++ b/datasets/Declassified_Satellite_Imagery_2_2002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Declassified_Satellite_Imagery_2_2002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Declassified satellite images provide an important worldwide record of land-surface change. With the success of the first release of classified satellite photography in 1995, images from U.S. military intelligence satellites KH-7 and KH-9 were declassified in accordance with Executive Order 12951 in 2002. The data were originally used for cartographic information and reconnaissance for U.S. intelligence agencies. Since the images could be of historical value for global change research and were no longer critical to national security, the collection was made available to the public.\n\nKeyhole (KH) satellite systems KH-7 and KH-9 acquired photographs of the Earth\u2019s surface with a telescopic camera system and transported the exposed film through the use of recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications.\n\nThe KH-7 surveillance system was a high resolution imaging system that was operational from July 1963 to June 1967. Approximately 18,000 black-and-white images and 230 color images are available from the 38 missions flown during this program. Key features for this program were larger area of coverage and improved ground resolution. The cameras acquired imagery in continuous lengthwise sweeps of the terrain. KH-7 images are 9 inches wide, vary in length from 4 inches to 500 feet long, and have a resolution of 2 to 4 feet.\n\nThe KH-9 mapping program was operational from March 1973 to October 1980 and was designed to support mapping requirements and exact positioning of geographical points for the military. This was accomplished by using image overlap for stereo coverage and by using a camera system with a reseau grid to correct image distortion. The KH-9 framing cameras produced 9 x 18 inch imagery at a resolution of 20-30 feet. Approximately 29,000 mapping images were acquired from 12 missions. \n\nThe original film sources are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery.", "links": [ { diff --git a/datasets/Del_Ches_Bay_Fluorescence_0.json b/datasets/Del_Ches_Bay_Fluorescence_0.json index 3633fe95d7..957a6d4458 100644 --- a/datasets/Del_Ches_Bay_Fluorescence_0.json +++ b/datasets/Del_Ches_Bay_Fluorescence_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Del_Ches_Bay_Fluorescence_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Chesapeake Bay and off the Delaware coast in 2008.", "links": [ { diff --git a/datasets/DeltaX_ADCP_Measurements_V2_2081_2.json b/datasets/DeltaX_ADCP_Measurements_V2_2081_2.json index c285b11539..c44e251388 100644 --- a/datasets/DeltaX_ADCP_Measurements_V2_2081_2.json +++ b/datasets/DeltaX_ADCP_Measurements_V2_2081_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_ADCP_Measurements_V2_2081_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides river discharge measurements collected at selected locations in the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Delta-X 2021 field efforts from 2021-03-25 to 2021-04-11 (spring) and 2021-08-16 to 2021-09-25 (fall). Channel surveys were conducted with a Teledyne RiverPro acoustic doppler current profiler (ADCP) or a Sontek M9 RiverSurveyor ADCP on selected wide channels (>100 m wide) and a few selected narrow channels (approximately 10 m wide) near the Delta-X intensive study sites. River discharge was measured on cross-channel transects. Reported data include bathymetry, discharge (m3 s-1), and flow velocity.", "links": [ { diff --git a/datasets/DeltaX_AGB_AGN_V2_2237_2.json b/datasets/DeltaX_AGB_AGN_V2_2237_2.json index c175ac2b4d..63f2c2803b 100644 --- a/datasets/DeltaX_AGB_AGN_V2_2237_2.json +++ b/datasets/DeltaX_AGB_AGN_V2_2237_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_AGB_AGN_V2_2237_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains total aboveground biomass (AGB) and necromass (AGN), and total carbon, total nitrogen, and total phosphorus content of aboveground biomass (AGB) and necromass (AGN) samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana during 2021. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. All AGB and AGN plant material within each plot was clipped at soil level, stored in plastic bags, and transported to the laboratory for further analyses. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. These data cover the period 2021-03-19 to 2021-03-31 (spring) and 2021-08-19 to 2021-08-27 (fall).", "links": [ { diff --git a/datasets/DeltaX_ANUGA_AtchafalayaBasin_2306_1.json b/datasets/DeltaX_ANUGA_AtchafalayaBasin_2306_1.json index 48b5cd9a87..1ad3116da1 100644 --- a/datasets/DeltaX_ANUGA_AtchafalayaBasin_2306_1.json +++ b/datasets/DeltaX_ANUGA_AtchafalayaBasin_2306_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_ANUGA_AtchafalayaBasin_2306_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides ANUGA hydrodynamic modeling results and input run-scripts for the Atchafalaya basin in the Mississippi River Delta in southern Louisiana, USA, during three windows of time corresponding to the Delta-X and Pre-Delta-X field campaigns in fall 2016, spring 2021, and fall 2021. ANUGA is a 2D depth-integrated hydrodynamic model which uses the Finite Volume Method (FVM) to numerically solve the shallow water momentum and continuity equations for fluid flow in broad-scale geophysical systems. Each iteration of the model was extensively calibrated using a database of in-situ and remotely-sensed observations, including about 54 water level gauges, numerous water surface profiles collected by AirSWOT or lidar, and water level change measurements derived from UAVSAR. The model was forced using observational data collected from NOAA and USGS, and the model mesh was specifically designed to capture channel-island connectivity using high-resolution Planet Labs imagery spanning over a decade. In total, over a month of simulation outputs are included in this dataset, covering different seasons and hydrological conditions in the Atchafalaya and Wax Lake Delta systems. These model outputs can be leveraged with other Delta-X datasets to provide contextual information about water levels or flow velocities at different times or locations within the Atchafalaya basin, and the model codes provided can be used to simulate additional time periods for further analysis in this region. Model outputs are presented in NetCDF (*.nc) format and run-scripts are in Python (*.py) or contained in compressed (*.zip) file format.", "links": [ { diff --git a/datasets/DeltaX_ANUGA_Hydrodynamics_MRD_2310_1.json b/datasets/DeltaX_ANUGA_Hydrodynamics_MRD_2310_1.json index e73fea71f2..9ef2fcfa45 100644 --- a/datasets/DeltaX_ANUGA_Hydrodynamics_MRD_2310_1.json +++ b/datasets/DeltaX_ANUGA_Hydrodynamics_MRD_2310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_ANUGA_Hydrodynamics_MRD_2310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises the primary inputs and outputs from the ANUGA hydrodynamic model for spring 2021 (2021-03-20 to 2021-04-04). These dates align with the 2021 Delta-X Spring Campaign. Data cover the Atchafalaya and Terrebonne basins of the Mississippi River Delta in southern Louisiana, USA. ANUGA is a 2D depth-integrated hydrodynamic model which uses the Finite Volume Method (FVM) to numerically solve the shallow water momentum and continuity equations for fluid flow in broad-scale geophysical systems. The inputs consist of a modified digital elevation (DEM) model, a spatial classification of the friction coefficient modified, and the model's unstructured grid with boundary condition locations. The model's outputs include two weeks of predictions of water levels and mean horizontal velocities at each mesh node at a 30-minute time step. Outputs are provided in NetCDF format, and inputs are provided in GeoTIFF and comma separated values (CSV) format. Also included are MP4 videos (*.mp4) that provide visual summaries of the outputs.", "links": [ { diff --git a/datasets/DeltaX_BGB_BGN_V2_2238_2.json b/datasets/DeltaX_BGB_BGN_V2_2238_2.json index a0b4b101be..09f0871d46 100644 --- a/datasets/DeltaX_BGB_BGN_V2_2238_2.json +++ b/datasets/DeltaX_BGB_BGN_V2_2238_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_BGB_BGN_V2_2238_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains total belowground biomass (BGB) and necromass (BGN), and total carbon, total nitrogen, and total phosphorus content of samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in southeastern coastal Louisiana, U.S., during March and August 2021. The data were collected during the Delta-X Spring and Fall deployments. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. Root biomass samples were collected using a gouge soil auger.", "links": [ { diff --git a/datasets/DeltaX_BG_Root_Stable_Isotopes_2193_1.json b/datasets/DeltaX_BG_Root_Stable_Isotopes_2193_1.json index 337254da9d..a835c6a930 100644 --- a/datasets/DeltaX_BG_Root_Stable_Isotopes_2193_1.json +++ b/datasets/DeltaX_BG_Root_Stable_Isotopes_2193_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_BG_Root_Stable_Isotopes_2193_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains carbon-13 (13C) and nitrogen-15 (15N) isotopic signatures of belowground root biomass samples from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, U.S., during August 2021. The data were collected during the Delta-X Fall deployment. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. Root biomass samples were collected using a gouge soil auger. Bulk isotopic signatures in living fine roots were measured with a mass spectrometer coupled to an elemental analyzer.", "links": [ { diff --git a/datasets/DeltaX_DEM_MRD_LA_2181_1.json b/datasets/DeltaX_DEM_MRD_LA_2181_1.json index a93a2d9f38..0a89a2e99a 100644 --- a/datasets/DeltaX_DEM_MRD_LA_2181_1.json +++ b/datasets/DeltaX_DEM_MRD_LA_2181_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_DEM_MRD_LA_2181_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an updated digital elevation model (DEM) for the Atchafalaya and Terrebonne basins in coastal Louisiana, USA. The DEM is updated from the Pre-Delta-X DEM and extended to the full Delta-X study area. This DEM was developed from multiple data sources, including sonar data collected during Pre-Delta-X and Delta-X campaigns, bathymetric data from the Coastal Protection and Restoration Authority System-Wide Assessment and Monitoring System (CPRA SWAMP), and NOAA, and topography from the National Elevation Dataset and LiDAR from US Geological Survey (USGS). The provided data layers include the DEM, a binary water/land mask, data source flags, and eight layers with analysis weighting factors for each pixel. Elevation values are provided in meters with respect to the North American Vertical Datum of 1988 (NAVD88). The weighting factors indicate how each data source contributed to this multisource DEM. The data are provided in cloud-optimized GeoTIFF (CoG) format.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_294_Terrebonne_2303_1.json b/datasets/DeltaX_Delft3D_294_Terrebonne_2303_1.json index 2ab80f5081..b8ceb1e7ea 100644 --- a/datasets/DeltaX_Delft3D_294_Terrebonne_2303_1.json +++ b/datasets/DeltaX_Delft3D_294_Terrebonne_2303_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_294_Terrebonne_2303_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the intensive site 294 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_322_Terrebonne_2312_1.json b/datasets/DeltaX_Delft3D_322_Terrebonne_2312_1.json index 5958c727f0..2b4c719a42 100644 --- a/datasets/DeltaX_Delft3D_322_Terrebonne_2312_1.json +++ b/datasets/DeltaX_Delft3D_322_Terrebonne_2312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_322_Terrebonne_2312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the intensive site 322 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF and ENVI formats.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_396_Terrebonne_2314_1.json b/datasets/DeltaX_Delft3D_396_Terrebonne_2314_1.json index d1b60c9f3e..200353a7aa 100644 --- a/datasets/DeltaX_Delft3D_396_Terrebonne_2314_1.json +++ b/datasets/DeltaX_Delft3D_396_Terrebonne_2314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_396_Terrebonne_2314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the intensive site 396 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_399_Terrebonne_2313_1.json b/datasets/DeltaX_Delft3D_399_Terrebonne_2313_1.json index 8bd9ed012b..dadd3308fb 100644 --- a/datasets/DeltaX_Delft3D_399_Terrebonne_2313_1.json +++ b/datasets/DeltaX_Delft3D_399_Terrebonne_2313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_399_Terrebonne_2313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the intensive site 399 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_421_Terrebonne_2304_1.json b/datasets/DeltaX_Delft3D_421_Terrebonne_2304_1.json index 9485869b89..4c5eaca121 100644 --- a/datasets/DeltaX_Delft3D_421_Terrebonne_2304_1.json +++ b/datasets/DeltaX_Delft3D_421_Terrebonne_2304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_421_Terrebonne_2304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the intensive site 421 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_Atchafalaya_MRD_2302_1.json b/datasets/DeltaX_Delft3D_Atchafalaya_MRD_2302_1.json index 39588653b3..3c9cee2c07 100644 --- a/datasets/DeltaX_Delft3D_Atchafalaya_MRD_2302_1.json +++ b/datasets/DeltaX_Delft3D_Atchafalaya_MRD_2302_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_Atchafalaya_MRD_2302_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the Atchafalaya Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport. Bottom friction was calibrated using AirSWOT water elevation data, while sediment parameters were calibrated using AVIRIS-NG Total Suspended Solids (TSS) data. All files required to run the simulations are included. Model output of water levels, velocity, and depth-averaged sediment concentration are provided for both campaigns as netCDF files. The dataset includes a netCDF file containing the annual inorganic mass accumulation rates derived from simulations.", "links": [ { diff --git a/datasets/DeltaX_Delft3D_Terrebonne_MRD_2301_1.json b/datasets/DeltaX_Delft3D_Terrebonne_MRD_2301_1.json index a804f0c087..5fe3a5f63d 100644 --- a/datasets/DeltaX_Delft3D_Terrebonne_MRD_2301_1.json +++ b/datasets/DeltaX_Delft3D_Terrebonne_MRD_2301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Delft3D_Terrebonne_MRD_2301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Delft3D model of the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport. Bottom friction was calibrated using AirSWOT water elevation data, while sediment parameters were calibrated using AVIRIS-NG Total Suspended Solids (TSS) data. All files required to run the simulations are included. The model's output of water levels, velocity, and depth-averaged sediment concentrations are provided for both campaigns as netCDF files. The dataset includes a netCDF file containing the annual inorganic mass accumulation rates derived through a storms analysis and modelling.", "links": [ { diff --git a/datasets/DeltaX_Ecogeomorphic_Products_2108_1.json b/datasets/DeltaX_Ecogeomorphic_Products_2108_1.json index 4ce720fd4b..eb29c260ba 100644 --- a/datasets/DeltaX_Ecogeomorphic_Products_2108_1.json +++ b/datasets/DeltaX_Ecogeomorphic_Products_2108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Ecogeomorphic_Products_2108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product delineates the Mississippi River Delta (MRD) landscape into distinct ecogeomorphic cells, which are small contiguous areas of land with similar ecological and geomorphological characteristics. The study area is the Atchafalaya and Terrebonne basins of the MRD in southern Louisiana, U.S., which was the focus of NASA's 2021 Delta-X campaign. Each ecogeomorphic cell is a small homogeneous area of similar vegetation and elevation (or bathymetry). A \"cell\" typically consists of a cluster of contiguous pixels in a raster image, although cells of single pixels are present. The elevation was derived from the USGS Digital Elevation Model, and the vegetation was characterized by its spectral signature as measured by near infrared (NIR) reflectance and normalized difference vegetation index (NDVI). NIR and NDVI were computed from Sentinel-2 images acquired January through September 2021. The data are provided in shapefile and GeoTIFF formats. The vector shapefiles contain the distinct ecogeomorphic cells as polygons with unique labels (i.e., ID number). A raster image of these ecogeomorphic cells provided wherein the pixel values are the polygon labels from the shapefiles. The GeoTIFFs hold the mean and standard deviations of bathymetry, NIR, and NDVI spectral indices within each ecogeomorphic region (polygon). The raster data are provided with a spatial resolution of 0.000045 degrees (approximately 5 meters).", "links": [ { diff --git a/datasets/DeltaX_Feldspar_Sediment_V3_2290_3.json b/datasets/DeltaX_Feldspar_Sediment_V3_2290_3.json index e86c3c0093..e8fdf8227c 100644 --- a/datasets/DeltaX_Feldspar_Sediment_V3_2290_3.json +++ b/datasets/DeltaX_Feldspar_Sediment_V3_2290_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Feldspar_Sediment_V3_2290_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides elevation, hydrogeomorphic zone classification, soil carbon content, bulk density, organic matter content, and sediment accretion measurements collected at feldspar stations established near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). Feldspar stations were established to capture recent sediment deposition rates across hydrogeomorphic zones defined as discrete surface elevation ranges relative to NAVD88 (e.g., subtidal < -0.04 m, intertidal -0.04 m to 0.30 m, and supratidal > 0.30 m). Hydrogeomorphic zones classification was based on marsh surface elevations extracted from the U.S. Geological Survey (USGS) Atchafalaya 2 project LiDAR Survey 2012 digital elevation model and field GPS measurements in November - December 2020. Between two and three feldspar stations were deployed approximately 25 and 50 meters from the main channel to represent existing hydrogeomorphic zones in brackish and saline emergent marsh vegetation, tidal freshwater emergent marshes, and forested swamps. Cryocore technique was used to determine recent sediment deposition. Soil samples were collected to determine organic and inorganic fractions and organic carbon content. This dataset is from the Delta-X field studies conducted during Fall 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022, and Spring 2023. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/DeltaX_Foliar_Stable_Isotopes_2194_1.json b/datasets/DeltaX_Foliar_Stable_Isotopes_2194_1.json index 73ad2ab845..bd6a7dc2ce 100644 --- a/datasets/DeltaX_Foliar_Stable_Isotopes_2194_1.json +++ b/datasets/DeltaX_Foliar_Stable_Isotopes_2194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Foliar_Stable_Isotopes_2194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains foliar tissue C and N bulk isotopic signatures (delta 13C, delta 15N) of dominant wetland herbaceous species collected at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins in coastal Louisiana. Five of the sites are from the Coastwide Reference Monitoring System (CRMS) and one site is the Mike Island Site in the Wax Lake Delta (WLD). For the herbaceous wetland sites, Aboveground biomass (AGB) was harvested inside duplicate plots (0.25 m2), located 5 m apart at each sampling station. All plant material within each plot was clipped at soil level, stored in plastic bags, and transported to the laboratory for further analyses. In the lab, plant tissue (foliar) C and N bulk isotopic signatures were analyzed for two dominant plant species from each site using a Thermo Scientific Delta V Plus CF-IRMS coupled to a Carlo-Erba 1108 elemental analyzer via a ConFlo IV interface (Thermo Fisher Scientific, Waltham, MA, USA). The data were collected during 2021-08-19 to 2021-08-27 during the Delta-X Fall 2021 deployment.", "links": [ { diff --git a/datasets/DeltaX_H2O_Surface_Elevation_2086_1.json b/datasets/DeltaX_H2O_Surface_Elevation_2086_1.json index ed70281702..583562e375 100644 --- a/datasets/DeltaX_H2O_Surface_Elevation_2086_1.json +++ b/datasets/DeltaX_H2O_Surface_Elevation_2086_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_H2O_Surface_Elevation_2086_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains water surface elevations collected from boat surveys performed on August 24 and September 22-25, 2021, across the Atchafalaya and Terrebonne basins in the Mississippi River Delta (MRD) floodplain, during the Delta-X Fall 2021 deployment. To perform the surveys, a Global Navigation Satellite System (GNSS) antenna (Septentrio receiver) was mounted on the side of the boat on a pole directly above the depth sounder. This GNSS antenna recorded observations of elevation continuously at 1 Hz on all field days. These data were post-processed using precise point positioning (PPP) and converted to water surface elevation by subtracting the height of the antenna above the water surface when it was mounted on the boat to provide an estimate of water surface elevation.The data are limited to times when the boat was moving slowly enough such that its speed didn't affect the height of the antenna above the water. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/DeltaX_Herb_WetlandSoil_V3_2239_3.json b/datasets/DeltaX_Herb_WetlandSoil_V3_2239_3.json index 9b474217d0..1b0aa787f5 100644 --- a/datasets/DeltaX_Herb_WetlandSoil_V3_2239_3.json +++ b/datasets/DeltaX_Herb_WetlandSoil_V3_2239_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Herb_WetlandSoil_V3_2239_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains properties of soil core samples for herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana for the period 2021-03-21 to 2021-04-02 and on 2021-08-19. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins. Five sites were adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is in the Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish and saline ecosystems. Soil properties include bulk density, organic matter content, total densities of carbon, nitrogen, phosphorus, along with 13C and 15N isotopic signatures. The data are provided in comma-separated values (.csv) format.", "links": [ { diff --git a/datasets/DeltaX_Insitu_POC_2073_1.json b/datasets/DeltaX_Insitu_POC_2073_1.json index 4ccba9df35..03b9a538ab 100644 --- a/datasets/DeltaX_Insitu_POC_2073_1.json +++ b/datasets/DeltaX_Insitu_POC_2073_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Insitu_POC_2073_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of particulate organic carbon (POC) concentrations made on water samples collected during 2021 in surface waters of the Atchafalaya River and Terrebonne Basins, portions of the Mississippi River Delta in coastal Louisiana. Water samples were collected at ~0.5 m depth from surface during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts. Field sampling was paused on August 25 and resumed on September 13 due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study sites. Water quality changes in this dataset caused by the hurricane are expected to be minimal. Samples were collected in multiple channels of varying width (from a few meters to >100 m) near Delta-X intensive study sites, in open bays and lakes, and a few locations in the nearshore Gulf of Mexico. For each sample, the water sample volume was filtered (in triplicate) through 25-mm glass microfiber (GF/F) filters to retain the suspended particles. The amount of organic carbon retained on each filter was measured using an elemental carbon, hydrogen and nitrogen (CHN) analyzer and normalized by the volume of sample water filtered. The reported values in this dataset include the mean and standard deviation of POC measurements from three replicate samples collected at each site.", "links": [ { diff --git a/datasets/DeltaX_Insitu_Reflectance_V3_2153_3.json b/datasets/DeltaX_Insitu_Reflectance_V3_2153_3.json index 3576c10d2d..00bba179f3 100644 --- a/datasets/DeltaX_Insitu_Reflectance_V3_2153_3.json +++ b/datasets/DeltaX_Insitu_Reflectance_V3_2153_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Insitu_Reflectance_V3_2153_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes above water measurements of remote-sensing reflectance measured in situ at field sampling stations during the Delta-X 2021 field efforts. Measurements were collected in the Atchafalaya River and Terrebonne Basins on the southern coast of Louisiana from 2021-03-25 to 2021-04-22 (spring) and from 2021-08-14 to 2021-09-24 (fall). Field sampling was paused on 2021-08-25 and resumed on 2021-09-13 due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Reflectance was measured near-simultaneously with collection of field samples and in-water sediment parameters in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at a few locations in the nearshore Gulf of Mexico. For each in situ collection, a handheld Portable SpectroRadiometer (PSR-1100f, Spectral Evolution) was used to measure radiance from: (a) a highly reflective (>99% reflectance) Lambertian Spectralon panel (b) from the sky, measured at 40 degrees from the solar zenith and at 135 degrees from the sun azimuthal plane, (c) from the water, measured at 40 degrees from nadir and at 135 degrees from the sun azimuthal plane. These measurements were used to calculate remote-sensing reflectance and the water-leaving radiance relative to downwelling irradiance, including a correction for the influence of reflected skylight. In Version 3, the data files with this dataset replace and update the data files in Version 2. A minor update was made to the code used to calculate the remote sensing reflectance (Rrs). These data are provided in comma-separated values (.csv) format.", "links": [ { diff --git a/datasets/DeltaX_Insitu_WQ_Indicators_V2_2080_2.json b/datasets/DeltaX_Insitu_WQ_Indicators_V2_2080_2.json index 9955324741..4e35836c75 100644 --- a/datasets/DeltaX_Insitu_WQ_Indicators_V2_2080_2.json +++ b/datasets/DeltaX_Insitu_WQ_Indicators_V2_2080_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Insitu_WQ_Indicators_V2_2080_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ measurements of water temperature (degrees C), salinity (PSU), turbidity (FNU), and chlorophyll-a fluorescence (RFU) in surface of the Atchafalaya River and Terrebonne Basins during the Spring (2021-03-25 to 2021-04-22) and Fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Measurements were collected in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at a few locations in the nearshore Gulf of Mexico using either a YSI ProDSS water quality probe or a YSI EXO3 water quality probe.", "links": [ { diff --git a/datasets/DeltaX_Island_Channel_Model_2106_1.json b/datasets/DeltaX_Island_Channel_Model_2106_1.json index 2449a8fed4..3a34b08dbc 100644 --- a/datasets/DeltaX_Island_Channel_Model_2106_1.json +++ b/datasets/DeltaX_Island_Channel_Model_2106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Island_Channel_Model_2106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes model code and output for a model that simulates changes in islands and small water channels of river delta systems in response to dynamics of sediment deposit, erosion, and changing water levels. Simulations demonstrate developmental cycles of secondary channels and how sediment dynamics can allow islands to build land vertically to keep pace with rising sea levels rather than passively drowning. The model was applied to the Mississippi River Delta as part of NASA's Delta-X project. Simulations were run for other river deltas, including the Amazon, Brahmputra, Danube, Magdalena, Nile, Orinoco, Parana, Rhine-Meuse, and Rhone rivers. The model code is provided in text format for MATLAB software. Files demonstrating initial model conditions and outputs are provided in binary MATLAB as well as NetCDF version 4 format.", "links": [ { diff --git a/datasets/DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1.json b/datasets/DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1.json index 520c94db0e..2d6cf9c0cf 100644 --- a/datasets/DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1.json +++ b/datasets/DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains UAVSAR Level 1B (L1B) interferometric products for Delta-X flight lines acquired during the 2021 Spring (2021-03-27 to 2021-04-18) and Fall (2021-09-03 to 2021-09-13) deployments. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. For this dataset, a set of nearest-neighbor (NN), NN+1, and NN+2 co-registered VV-polarization interferograms were generated from the quad-polarization SLC stack level-1 (L1) product using a combination of the InSAR Scientific Computing Environment (ISCE), the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU), and previously developed python code. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. The data are provided in non-georeferenced ENVI file format and include interferometric amplitude, wrapped interferometric phase, interferometric coherence, and unwrapped interferometric phase products. Geometry files for each flight line per field campaign with latitude, longitude, height and incidence angle information are also included. The goal of this campaign was to measure water-level changes throughout wetlands, and these data may be used to generate time series of water levels. The data are provided in ENVI format.", "links": [ { diff --git a/datasets/DeltaX_L1_AVIRIS_Radiance_1987_1.json b/datasets/DeltaX_L1_AVIRIS_Radiance_1987_1.json index 0027759ed8..8563653c5e 100644 --- a/datasets/DeltaX_L1_AVIRIS_Radiance_1987_1.json +++ b/datasets/DeltaX_L1_AVIRIS_Radiance_1987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L1_AVIRIS_Radiance_1987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 1B (L1B) radiance products from NASA's Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) instrument acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA during two deployments; spring and fall of 2021. All flights were flown on a Dynamic Aviation King Air B200. There are a combined 200 total flight lines for the spring and fall 2021 deployments; spring 2021 had 75 flight lines, fall 2021 had 175 flight lines. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Level 1B data are orthorectified calibrated radiance values in units of spectral radiance in which raw digital numbers (DNs) are translated to units of radiant intensity measured at the sensor. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution, dependent on aircraft altitude. Additional flight line files include band information of observational geometry and illumination parameters, as well as geographic pixel locations and elevation. These L1B data are provided in ENVI file format. AVIRIS-NG Cal/Val, Level 2 and Level 3 products for the Pre-Delta-X and Delta-X missions are provided in related datasets.", "links": [ { diff --git a/datasets/DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1.json b/datasets/DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1.json index 215f52afd4..dae21b47de 100644 --- a/datasets/DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1.json +++ b/datasets/DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains UAVSAR Level 1 (L1) Single Look Complex (SLC) stack products for Delta-X flight lines acquired during 2021-03-27 to 2021-04-18 (spring) and 2021-09-03 to 2021-09-13 (fall). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. These repeat pass SLC stack co-registered time series data were used as the underlying data for higher level data products. These higher level products provide a time series of water level changes and address a goal of the Delta-X campaign to measure water-level changes throughout wetlands. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. These L1 data contain slant range single look complex (SLC), latitude/longitude/height, look vector, doppler, and metadata files. The data are provided in SLC stack format (*.slc) with associated annotation (*.ann), latitude-longitude-height (*.llh), look vector (*.lkv), and Doppler centroid-slant range (*.dop) files. The single look complex (SLC) stacks are in the HH, HV, VH, and VV polarizations. The same area was sampled at approximately 30-minute intervals. The SLCs are not corrected for residual baseline (BU).", "links": [ { diff --git a/datasets/DeltaX_L1b_AirSWOT_1996_1.1.json b/datasets/DeltaX_L1b_AirSWOT_1996_1.1.json index c6c2df287d..4b0faf8f99 100644 --- a/datasets/DeltaX_L1b_AirSWOT_1996_1.1.json +++ b/datasets/DeltaX_L1b_AirSWOT_1996_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L1b_AirSWOT_1996_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains AirSWOT interferogram products collected during the 2021 Delta-X Campaign over the Atchafalaya and Terrebonne Basins of the Mississippi River Delta, Louisiana, USA from 2021-03-26 to 2021-04-18 (Spring) and 2021-08-21 to 2021-09-12 (Fall). AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. AirSWOT elevation data is useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions. The AirSWOT Level 1B (L1B) data products represent interferogram data in the radar coordinate system, not in georeferenced map coordinates. This is an earlier stage of data processing which is used to generate the later Level 2 and Level 3 data products which will contain georeferenced water heights and water height profiles for river channels in each basin. The data are provided in binary and text file formats.", "links": [ { diff --git a/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2.json b/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2.json index 203d46dc91..5fda6cd6fe 100644 --- a/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2.json +++ b/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided as file sets for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Files are presented as compressed (*.zip) files, containing binary ENVI image and header files. Only land pixels were corrected and mask files for the mosaic file coverage showing presence/absence of water are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise.", "links": [ { diff --git a/datasets/DeltaX_L2_AVIRIS_Reflectance_1988_1.json b/datasets/DeltaX_L2_AVIRIS_Reflectance_1988_1.json index f1d404ec49..6f650c5d24 100644 --- a/datasets/DeltaX_L2_AVIRIS_Reflectance_1988_1.json +++ b/datasets/DeltaX_L2_AVIRIS_Reflectance_1988_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L2_AVIRIS_Reflectance_1988_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 (L2) atmospherically corrected surface reflectance data acquired from NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over regions of interest in the Atchafalaya and Terrebonne basins on the southern coast of Louisiana, United States. Data were collected as part of the Delta-X Spring and Fall 2021 deployments that occurred from 2021-03-27 to 2021-04-06 and from 2021-08-18 to 2021-08-25. Additionally, L2 data from flights flown specifically to capture the Significant Event of Hurricane Ida are provided. This includes 56 files from flights conducted following Hurricane Ida from 2021-09-23 to 2021-09-25. Hurricane Ida made landfall over this region on 2021-08-29. AVIRIS-NG is a pushbroom spectral mapping system with a high signal-to-noise ratio (SNR) designed for high performance imaging spectroscopy. AVIRIS-NG measures the wavelength range from 380 nm to 2510 nm with 5-nm sampling resolution. For this dataset, spatial resolution varies from 3.8-5.4 meters. For this campaign, the AVIRIS-NG instrument was deployed on the Dynamic Aviation King Air B200 platform. This dataset represents one part of a multisensor airborne sampling campaign conducted by different aircraft teams for the Delta-X Campaign. Data are provided in ENVI file format.", "links": [ { diff --git a/datasets/DeltaX_L2_AirSWOT_WaterElev_V3_2350_3.json b/datasets/DeltaX_L2_AirSWOT_WaterElev_V3_2350_3.json index 76174d6d68..b9f74a697c 100644 --- a/datasets/DeltaX_L2_AirSWOT_WaterElev_V3_2350_3.json +++ b/datasets/DeltaX_L2_AirSWOT_WaterElev_V3_2350_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L2_AirSWOT_WaterElev_V3_2350_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2 (L2) AirSWOT geocoded products, including estimated water surface elevation. The AirSWOT instrument is a Ka-band interferometer and for this study is flown on the King Air B200 platform. Data were collected during the DeltaX airborne campaign over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA. Flights occurred during the Delta-X Spring 2021 deployment from 2021-03-26 to 2021-04-18 and the Delta-X Fall 2021 deployment from 2021-08-21 to 2021-09-12. AirSWOT is capable of producing high resolution (3.6 m) digital elevation models over land and water bodies using near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The instrument includes six antennas that form multiple baseline pairs for along-track and across-track interferometry. AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions and is an airborne calibration and validation instrument for the Surface Water and Ocean Topography (SWOT) satellite. This Version 3 dataset provides updated data files due to an updated Calumet survey that changed the water level by 0.138 m. This resulted in all the AirSWOT water levels changing by that same amount. For these L2 products, only the estimated water surface elevation in respect to the WGS84 ellipsoid surface, and estimated height above the NAVD88 (GEOID12B) vertical datum files changed. Note that data acquired on September 1 and September 5, 2021 do not meet the expected MAE in-situ comparison and should be used with caution. This dataset contains cloud optimized GeoTIFF rasters in UTM map coordinates for each flight line. In addition, a text file provides basic metadata, including flight line ID, start and end UTC times of data acquisition, processor version number, and the date and time of different processing stages.", "links": [ { diff --git a/datasets/DeltaX_L2_UAVSAR_WaterLevels_2057_1.1.json b/datasets/DeltaX_L2_UAVSAR_WaterLevels_2057_1.1.json index 55f0196e3f..aaa7d78aaa 100644 --- a/datasets/DeltaX_L2_UAVSAR_WaterLevels_2057_1.1.json +++ b/datasets/DeltaX_L2_UAVSAR_WaterLevels_2057_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L2_UAVSAR_WaterLevels_2057_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains georeferenced UAVSAR Level 2 (L2) interferometric products for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. This dataset provides water-level change observations throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD), and it may be used to generate time series analysis. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. The data include interferogram phase, interferogram amplitude, unwrapped interferogram phase, and coherence products. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm. Geometry files for each flight line per field campaign with latitude, longitude, height and incidence angle information are also included. The data are provided in ENVI format.", "links": [ { diff --git a/datasets/DeltaX_L3_AVIRIS-NG_AGB_V2_2138_2.json b/datasets/DeltaX_L3_AVIRIS-NG_AGB_V2_2138_2.json index 32ded17c47..06e2e45836 100644 --- a/datasets/DeltaX_L3_AVIRIS-NG_AGB_V2_2138_2.json +++ b/datasets/DeltaX_L3_AVIRIS-NG_AGB_V2_2138_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L3_AVIRIS-NG_AGB_V2_2138_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes high-resolution (~5 m) gridded estimates of herbaceous aboveground biomass (AGB) for the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, USA, for the fall and spring seasons of 2021. AGB, quantified as dry biomass in Mg per hectare, was estimated from Bidirectional Reflectance Distribution Function (BRDF)-adjusted surface reflectance products from NASA's Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) acquired over the study area in April and August 2021. The BRDF-adjusted reflectance was derived from hemispherical-directional surface reflectance with atmospheric correction. A machine learning model to estimate AGB was generated by comparing local pixel reflectance spectra with coincident in-situ samples of herbaceous vegetation AGB. This model was then scaled to the AVIRIS-NG mosaic imagery to map herbaceous AGB across the Atchafalaya and Terrebonne Basins. The Delta-X campaign conducted both airborne (remote sensing) and field (in situ) measurements to measure hydrology, water quality (e.g., total suspended solids (TSS)), and vegetation structure. These data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise. The data are provided in cloud optimized GeoTIFF (COG) format. This Version 2 dataset replaces the files provided in Version 1.", "links": [ { diff --git a/datasets/DeltaX_L3_AVIRIS-NG_Veg_Types_2352_1.json b/datasets/DeltaX_L3_AVIRIS-NG_Veg_Types_2352_1.json index 4672eb7f60..3420dfeff6 100644 --- a/datasets/DeltaX_L3_AVIRIS-NG_Veg_Types_2352_1.json +++ b/datasets/DeltaX_L3_AVIRIS-NG_Veg_Types_2352_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L3_AVIRIS-NG_Veg_Types_2352_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of vegetation types for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S., derived from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) imagery acquired during spring and fall of 2021 for the Delta-X campaign. Vegetation types were classified from Level-2B BRDF-adjusted surface reflectance. Local pixel reflectance spectra coincident with herbaceous vegetation field samples and vegetation plot data from Louisiana's Coastwide Reference Monitoring System were used to generate a machine learning-based model to classify vegetation types. This model was then applied to the AVIRIS-NG mosaic imagery to map vegetation types across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF (COG) format.", "links": [ { diff --git a/datasets/DeltaX_L3_AVIRIS-NG_Water_V3_2152_3.json b/datasets/DeltaX_L3_AVIRIS-NG_Water_V3_2152_3.json index 05d5445eda..10b59a8f88 100644 --- a/datasets/DeltaX_L3_AVIRIS-NG_Water_V3_2152_3.json +++ b/datasets/DeltaX_L3_AVIRIS-NG_Water_V3_2152_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L3_AVIRIS-NG_Water_V3_2152_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta (MRD) in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). AVIRIS-NG imagery was collected from March 27-April 6 (spring) and August 20-25 (fall), 2021, as part of the 2021 Delta-X campaign. Algorithms for TSS and turbidity estimation were developed using in-situ remote-sensing reflectance measured at field sampling stations paired with in-situ measures of turbidity from a water quality probe and TSS from water samples. Using the in-situ data, a partial least squares regression (PLSR) model was developed for each AVIRIS-NG wavelength. A subset of the in-situ data, collected during relatively clear AVIRIS-NG overflights, was held out to validate the PLSR model. The PLSR algorithm was then applied to AVIRIS-NG imagery to retrieve TSS and turbidity across the study area. The measurement units for TSS and turbidity estimates are mg L-1 and Formazin Nephelometric Units (FNU), respectively, and the spatial resolution is 3.8 to 5.4 m as determined by the AVIRIS-NG imagery. The dataset includes binary cloud and water masks. These data quantify the mesoscale (i.e., on the order of 1 ha) patterns of soil accretion that control land loss and gain and predict the resilience of deltaic floodplains under projected relative sea-level rise. Gridded estimates are provided in netCDF format, and regression coefficients are included in a comma-separated values (CSV) file. This is Version 3 of this dataset. All previously released data were updated to the latest available versions.", "links": [ { diff --git a/datasets/DeltaX_L3_AirSWOT_WaterElev_V2_2349_2.json b/datasets/DeltaX_L3_AirSWOT_WaterElev_V2_2349_2.json index 9a98c6f613..2244ac478f 100644 --- a/datasets/DeltaX_L3_AirSWOT_WaterElev_V2_2349_2.json +++ b/datasets/DeltaX_L3_AirSWOT_WaterElev_V2_2349_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L3_AirSWOT_WaterElev_V2_2349_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains water surface elevations at selected point locations generated from the AirSWOT data collected during the Spring and Fall 2021 Delta-X deployments over the Atchafalaya and Terrebonne basins in Louisiana, USA. AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The Level 3 (L3) data were created by masking land areas out of the AirSWOT Level 2 products, then filtering and averaging to the AirSWOT heights to produce water surface elevations at selected points throughout the scene. The AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions. AirSWOT performance in the floodplain was limited by the presence of vegetation and the very small slope characteristic of two dimensional floodplain discharge. Therefore, the bulk of the AirSWOT data collections were targeted at the larger channels, since the channel discharge provides the necessary boundary conditions for potential overflow to islands and floodplains. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/DeltaX_L3_UAVSAR_WaterLevels_2058_1.1.json b/datasets/DeltaX_L3_UAVSAR_WaterLevels_2058_1.1.json index 6dcdd26015..080ac7c0db 100644 --- a/datasets/DeltaX_L3_UAVSAR_WaterLevels_2058_1.1.json +++ b/datasets/DeltaX_L3_UAVSAR_WaterLevels_2058_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_L3_UAVSAR_WaterLevels_2058_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains georeferenced InSAR-derived water level change maps for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. Water-level change observations are provided throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. There are three types of gridded products available: temporalcoherence (which provide an index measuring quality of phase unwrapping ranging from 0 (poor) to 1 (correctly unwrapped)), waterlevelchange in centimeters (which provide cumulative changes in water levels at approximately 30-minute intervals), and waterlevelchange_ramp in centimeters (which provide a 2-dimensional linear trend in water-level estimates not related to changing water levels). The water-level change maps were estimated using the phase unwrapping corrected interferograms generated for nearest-neighbor (NN), NN+1, and NN+2 pairs for data acquired within a single flight (one day). This analysis was done for all flight lines. Water level changes are relative to the first sampling flight for that study area. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm.", "links": [ { diff --git a/datasets/DeltaX_LandAccretionMap_WLD_2308_1.json b/datasets/DeltaX_LandAccretionMap_WLD_2308_1.json index 7b35150dbb..7a3f1f7428 100644 --- a/datasets/DeltaX_LandAccretionMap_WLD_2308_1.json +++ b/datasets/DeltaX_LandAccretionMap_WLD_2308_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_LandAccretionMap_WLD_2308_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides sediment transport and land accretion model results at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana, USA. Data were simulated over the Delta-X Spring 2021 (2021-03-21 to 2021-04-03) and Fall 2021 (2021-08-14 to 2021-08-27) campaigns and the results are presented as annualized land accretion rate map. The model results for these two short-term campaigns are used to calculate the 1-year upscale land accretion rate at WLD in post-processing, which is also provided in this dataset. Model results for these two short-term campaigns were derived using inputs from an ANUGA hydrodynamic model. The Matlab sediment transport and land accretion model used to derive these data employs sediment transport theory that models floc behavior using a non-cohesive sediment transport framework. Data are presented in NetCDF (*.nc) format.", "links": [ { diff --git a/datasets/DeltaX_LandAccretion_WLD_2309_1.json b/datasets/DeltaX_LandAccretion_WLD_2309_1.json index 30ad435fc1..8b7e2c60a9 100644 --- a/datasets/DeltaX_LandAccretion_WLD_2309_1.json +++ b/datasets/DeltaX_LandAccretion_WLD_2309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_LandAccretion_WLD_2309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Matlab sediment transport and land accretion model at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana. The data include the Matlab scripts that solve the advection and Exner equations to simulate the suspended sediment transport and accretion at WLD. The model requires modeled flow information from a separate ANUGA hydrodynamic model as inputs. For this study, ANUGA modeled flow information from the Delta-X Spring and Fall 2021 campaigns were used as inputs. The ANUGA output files are converted to variables used by this Matlab model using pre-processing tools. The main code calculates suspended sediment fluxes and accretion rates of mud and sand as a function of space and time. The cumulative sediment accretion from each campaign was then used to estimate an annualized land accretion map using a weighted-average formula presented. The final product, the one-yr upscaled land accretion map, is archived as a separate dataset.", "links": [ { diff --git a/datasets/DeltaX_MarshAccretion_NUMAR_2354_1.json b/datasets/DeltaX_MarshAccretion_NUMAR_2354_1.json index 58bc07b6ed..4a8e2aa1a8 100644 --- a/datasets/DeltaX_MarshAccretion_NUMAR_2354_1.json +++ b/datasets/DeltaX_MarshAccretion_NUMAR_2354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_MarshAccretion_NUMAR_2354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides input data and model code to run the Marsh Accretion Rates (NUMAR) process model used to predict soil accretion rates and chemical properties for marsh sites in the Mississippi River Delta. NUMAR is a modification of the NUMAN model by Chen and Twilley (1999) that was developed for mangrove environments. This dataset provides Python code, input data in comma separated values (CSV) format, and documentation for installing and running the model in Portable Document Format (PDF).", "links": [ { diff --git a/datasets/DeltaX_NUMAR_Soil_Accretion_2368_1.json b/datasets/DeltaX_NUMAR_Soil_Accretion_2368_1.json index 29b3287692..9332230fb8 100644 --- a/datasets/DeltaX_NUMAR_Soil_Accretion_2368_1.json +++ b/datasets/DeltaX_NUMAR_Soil_Accretion_2368_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_NUMAR_Soil_Accretion_2368_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds modeled estimates of soil accretion for the Atchafalaya and Terrebonne basins in the Mississippi River Delta of coastal Louisiana, U.S. Soil accretion was predicted from 2021-2100 using the Numerical Understanding of Marsh Accretion Resilience (NUMAR) model. This process-based model is an adaptation of the NUMAN model that was modified for marsh environments. The input parameters were aggregated within ecogeomorphic cells, areas of similar vegetation and elevation. The dataset includes spatially explicit input values, description of important parameters, and a shapefile of model outputs.", "links": [ { diff --git a/datasets/DeltaX_Particle_Size_LISST_V2_2077_2.json b/datasets/DeltaX_Particle_Size_LISST_V2_2077_2.json index d70bbb9b32..b6328a62b9 100644 --- a/datasets/DeltaX_Particle_Size_LISST_V2_2077_2.json +++ b/datasets/DeltaX_Particle_Size_LISST_V2_2077_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Particle_Size_LISST_V2_2077_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ measurements of beam attenuation coefficient at 670 nm, average suspended particle size, particle size distribution, and water temperature in surface waters (~0.5 m) of the Atchafalaya and Terrebonne Basins on the southern coast of Louisiana. The field studies were conducted in the Spring and Fall in support of the Delta-X mission and include measurements made in 2021 during March 25 - April 22 and August 14 - September 24. Measurements were made using the Sequoia Scientific Laser In-Situ Scattering and Transmissometer instrument (LISST-200X) in multiple channels of varying width (from a few meters to >100m), near Delta-X intensive study sites and in open bays and lakes and at a few locations in the nearshore Gulf of Mexico. The data are provided in comma-separated (CSV) format.", "links": [ { diff --git a/datasets/DeltaX_RTK_Elevation_2071_1.json b/datasets/DeltaX_RTK_Elevation_2071_1.json index d109fc6da7..66404b1833 100644 --- a/datasets/DeltaX_RTK_Elevation_2071_1.json +++ b/datasets/DeltaX_RTK_Elevation_2071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_RTK_Elevation_2071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides real-time kinematic (RTK) GPS elevation measurements, along with horizontal and vertical precision errors, obtained along transects near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). The data were collected during the Delta-X Spring Campaign from 2021-03-24 to 2021-04-02. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/DeltaX_Sediment_Grain_Size_V2_2135_2.json b/datasets/DeltaX_Sediment_Grain_Size_V2_2135_2.json index e575db4d9e..285e6e05ed 100644 --- a/datasets/DeltaX_Sediment_Grain_Size_V2_2135_2.json +++ b/datasets/DeltaX_Sediment_Grain_Size_V2_2135_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Sediment_Grain_Size_V2_2135_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides sediment concentration and grain size distribution measurements from suspended and bed sediment samples collected in the Atchafalaya and Terrebonne River Basins as part of the Delta-X Spring campaign between March 24 to April 2, 2021 and Delta-X Fall campaign between August 17-25, 2021. During the field campaign, samples were collected in the main distributary channels and the interior of Mike Island in the Wax Lake Delta, Louisiana and at site CRMS0421 inside the Terrebonne River Basin. Sediment samples were collected from a boat using a Van Dorn sampler (for suspended sediment samples) or a Ponar bed sampler (for bed samples). Suspended sediment samples were collected from a boat drifting at approximately the same velocity as the water flow. One sample was collected per drift. Bed samples were collected in a similar fashion. Data includes measurements of sediment grain size, total sediment concentration, as well as water temperature, velocity, salinity, and depth. This Version 2 includes the initial release of Fall 2021 data and an update to the Version 1 (Spring 2021) data file in which an error in the data was resolved.", "links": [ { diff --git a/datasets/DeltaX_Sonar_Bathymetry_2085_1.json b/datasets/DeltaX_Sonar_Bathymetry_2085_1.json index 23e0f651f0..a6528b32af 100644 --- a/datasets/DeltaX_Sonar_Bathymetry_2085_1.json +++ b/datasets/DeltaX_Sonar_Bathymetry_2085_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Sonar_Bathymetry_2085_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes bathymetry data for water channels in a portion of the Mississippi River Delta (MRD) of coastal Louisiana. The data were collected using sonar during field efforts of the Delta-X Campaign taking place during 2021. In situ continuous surveys of channel bathymetry were conducted in the Atchafalaya and Terrebonne basins using either a Lowrance HDS-Live Fish Finder with Active Imaging 3-in-1 Transducer or a SonarMite Echo Sounder mounted on the side or back of the research boat. The sounder depth observations were delineated by the date, time, and location. These bathymetry measurements were used to generate a digital elevation model (DEM) through interpolation with ancillary DEMs. The data are provided in comma-separated values (CSV) format. A map of data collection routes is provided in compressed keyhole markup language (KMZ).", "links": [ { diff --git a/datasets/DeltaX_TSS_Concentration_V2_2075_2.json b/datasets/DeltaX_TSS_Concentration_V2_2075_2.json index a607e75574..5f5150ca74 100644 --- a/datasets/DeltaX_TSS_Concentration_V2_2075_2.json +++ b/datasets/DeltaX_TSS_Concentration_V2_2075_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_TSS_Concentration_V2_2075_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of total suspended solids concentrations (TSS) of surface waters in the Atchafalaya River and Terrebonne Basins during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Samples were collected from ~0.5 m of surface in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at few locations in the nearshore Gulf of Mexico. For each collection, the water sample volume was filtered, and the suspended particles retained on the filter were weighed after drying. The TSS concentration was calculated as the difference of the filter weight (before and after filtration) divided by the volume filtered.", "links": [ { diff --git a/datasets/DeltaX_TotalSubsidenceRate_MRD_2307_1.json b/datasets/DeltaX_TotalSubsidenceRate_MRD_2307_1.json index 54d9b332de..33e32aadf3 100644 --- a/datasets/DeltaX_TotalSubsidenceRate_MRD_2307_1.json +++ b/datasets/DeltaX_TotalSubsidenceRate_MRD_2307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_TotalSubsidenceRate_MRD_2307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of land subsidence rates for the Delta-X domain area within the Atchafalaya and Terrebonne basins for 2021. The study area is a portion of the Mississippi River Delta in coastal Louisiana, U.S. The total subsidence is calculated as the sum of deep and shallow vertical elevation change rates. The deep subsidence rate is based on information from the Coastal Protection and Restoration Authority (CPRA) of Louisiana, documented in the Phase-4 subsidence trend report prepared for and provided by CPRA (2022). The shallow subsidence is calculated for the Delta-X study area by interpolation of publicly available data provided by CPRA for their coast-wide estimation of shallow subsidence in the 2023 Coastal Master Plan. The total subsidence rates and the estimated uncertainty in the total subsidence rates are provided as separate files in cloud optimized GeoTIFF (COG) format at 30-m (0.0003 decimal degrees) resolution.", "links": [ { diff --git a/datasets/DeltaX_Turbidity_Data_V4_2241_4.json b/datasets/DeltaX_Turbidity_Data_V4_2241_4.json index 833a6178ba..5db9566f89 100644 --- a/datasets/DeltaX_Turbidity_Data_V4_2241_4.json +++ b/datasets/DeltaX_Turbidity_Data_V4_2241_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Turbidity_Data_V4_2241_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides turbidity measurements with co-located water and air pressure and temperature measurements in portions of the Mississippi River Delta, coastal Louisiana, US. Data were collected at five sites in Atchafalaya River Basin in Spring (2021-03-24 to 2021-04-02) and eight sites in the Atchafalaya River and Terrebonne Basins in Fall 2021 (2021-08-16 to 2021-08-27). In order to sample various hydrodynamic conditions, sensors were deployed at island edges, island interior, and other portions of wetlands. Sensors recorded turbidity, absolute pressure, and temperature. The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain. The data are provided in comma-separated values (CSV) files.", "links": [ { diff --git a/datasets/DeltaX_UAVSAR_L3_ChannelMap_V2_2344_2.json b/datasets/DeltaX_UAVSAR_L3_ChannelMap_V2_2344_2.json index 478135faff..7a7d319560 100644 --- a/datasets/DeltaX_UAVSAR_L3_ChannelMap_V2_2344_2.json +++ b/datasets/DeltaX_UAVSAR_L3_ChannelMap_V2_2344_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_UAVSAR_L3_ChannelMap_V2_2344_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of water channels for the Atchafalaya and Terrebonne basins of the Mississippi River Delta in Louisiana, U.S.A. The data show channels with open water that are as narrow as 10 m. These channel estimates were generated from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Level 1B interferometric products in radar coordinates acquired in the Spring and Fall Delta-X deployments of 2021, which have a spatial resolution of approximately 6 m. UAVSAR is a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft. The data are provided in cloud-optimized GeoTIFF format. The channel estimates can be used to define open water paths in hydrodynamic models and to evaluate model performance.", "links": [ { diff --git a/datasets/DeltaX_Vegetation_Structure_V2_2240_2.json b/datasets/DeltaX_Vegetation_Structure_V2_2240_2.json index d15ff811e4..3cfaa034c8 100644 --- a/datasets/DeltaX_Vegetation_Structure_V2_2240_2.json +++ b/datasets/DeltaX_Vegetation_Structure_V2_2240_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DeltaX_Vegetation_Structure_V2_2240_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides mean stem diameter, mean height, dominant species, hydrogeomorphic zone (HGM), and stem density for vegetation in herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana. The data were collected between 2021-03-21 to 2021-03-31 during the Delta-X Spring 2021 deployment, and from 2021-08-19 to 2021-08-27 during the Fall deployment. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins. Five of the sites were adjacent to sites from the Coastwide Reference Monitoring System (CRMS), and the other site was in the Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. At each herbaceous wetland site, duplicate sampling stations (30 m apart) were established parallel to the wetland edge at 25 and 50 m within the intertidal zone to capture within site variability in vegetation dynamics and soil properties. The data are provided in comma-separated values (*.csv) format.", "links": [ { diff --git a/datasets/Dendrophenology_Eastern_US_1369_1.json b/datasets/Dendrophenology_Eastern_US_1369_1.json index 3aea1183d1..0ad915a22b 100644 --- a/datasets/Dendrophenology_Eastern_US_1369_1.json +++ b/datasets/Dendrophenology_Eastern_US_1369_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dendrophenology_Eastern_US_1369_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a 30-year record of Landsat TM and ETM+ derived forest phenology and the results of tree ring analyses for annual wood production and nitrogen and carbon isotopic composition at 113 selected forested sites in the eastern United States. The sites are located in four national parks: Prince William Forest Park (PRWI), Harpers Ferry National Historical Park (HAFE), Catoctin Mountain Park (CATO), and Great Smoky Mountains National Park (GRSM). Phenology and tree ring data cover 1984-2013.", "links": [ { diff --git a/datasets/Diatom_transects_1.json b/datasets/Diatom_transects_1.json index 28e3a9a639..4460b93849 100644 --- a/datasets/Diatom_transects_1.json +++ b/datasets/Diatom_transects_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatom_transects_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment samples were collected from four locations within the Windmill Islands (Cloyd Island, Odbert Island, Shannon Bay and Brown Bay). Within each location three parallel transects were created, with samples taken at set depths along each transect. At the time of collection, both surface and benthic irradiance levels were measured, and the % of surface irradiance that reached the sediment-water interface was calculated. Samples were analysed for benthic diatom abundances (expressed as relative abundances), and grain-size (expressed as % of total weight).\n\nThe diatom spreadsheet (diatom_data)lists the relative abundance of benthic species. The abbreviation used to identify species are explained in the separate file called sp_list. Samples are identified XTYZ where X is the first letter of the location, Y indicates the sampling position along the transect and z indicates the transect (a, b or c). The benthic sheet is the relative abundances of benthic species. The greater than 2% sheet lists all the species that reach abundances greater than2% in at least 1 sample. The table sheet has the same info as greater than 2% but arranged by the individual locations. In this sheet (tables), measurements in m represent the depth of the water column overlying the position where the sediment samples were collected. (ie it was at different locations, not different water depths in the one spot).\n\nSampling positions reflect increasing depth. At Brown Bay and Odbert Island, sediment samples were collected below water columns/water depths of 1, 2, 4, 8 and 12 m. At Cloyd Island, samples were collected from 4,6,8 and 12 m water depths. At Shannon Bay samples were collected from 2, 4, 8, and 12 m water depths.\n\nDetails of the environmental parameters examined (grainsize and light) are given in the file labelled 'env_data'\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nThe fields in this dataset are:\n\nDiatom Spreadsheet\n\nSpecies\nSite\nLocation\nTransect\nDepth (m)\n\nEnvironmental Data Spreadsheet\n\nLocation\nTransect\nDepth (m)\nGrain size\nGravel\nSand\nMud\nLight", "links": [ { diff --git a/datasets/Diatoms_bbg_1.json b/datasets/Diatoms_bbg_1.json index 17b4a0108f..6b7a13eb32 100644 --- a/datasets/Diatoms_bbg_1.json +++ b/datasets/Diatoms_bbg_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatoms_bbg_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment samples were collected from nine points along 3 parallel transects within the contaminated Brown Bay. The diatom spreadsheet (diatom_data) contains both initial diatom counts and the relative abundance of benthic species. The abbreviation used to identify species are explained in the separate file called sp_list. Metal, Total Purgeable Hydrocarbons (TPH), and grain-size data are all presented as separate files.\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip-site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nbbg_lat spreadsheet\n\nSite\nLatitude\nLongitude\nEasting\nNorthing\n\nDiatoms spreadsheet\n\nSpecies\nSite\nAbundance\nTransect\n\nMetals Spreadsheet\n\nSample\nAntimony\nArsenic\nCadmium\nChromium\nCopper\nIron\nLead\nManganese\nMercury\nNickel\nSilver\nTin\nZinc\nTotal Organic Carbon\nEasting\nNorthing\n\nTPH Spreadsheet\nSite\nTotal Purgeable Hydrocarbons\nFraction of Purgeable Hydrocarbons", "links": [ { diff --git a/datasets/Diatoms_long_core_1.json b/datasets/Diatoms_long_core_1.json index d700cc69b5..ba4c8fbed2 100644 --- a/datasets/Diatoms_long_core_1.json +++ b/datasets/Diatoms_long_core_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatoms_long_core_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A sediment core was collected from the western side of Pidgeon Island, (66.3216 S, 110.445 E) at a water depth of 82.0 m. This sediment core (PG 1411-2) was recovered using a release-controlled piston corer, with a length of 3 m, using the coring technique described in Melles et al., (1994). The total core length was 240 cm. This core was stored in the dark, at 0 degrees C until required. Samples were taken for diatom analyses and radiocarbon (14C) dating. Prior to sub-sampling the core was split in half, along its length. One half was used for sampling, the other kept intact and stored at IASOS (University of Tasmania). To reduce potential contamination, resulting from the disturbance of sediments during the core-splitting procedure, a thin layer of sediment was removed from the exposed surface immediately prior to sampling.\n\nIn order to obtain samples for diatom analysis, a toothpick was inserted into the core segment, and used to gouge a small amount of sediment from the middle of the core. Samples for diatom analyses were initially collected every 5 mm, however, sampling frequency progressively decreased down the core. Samples for radiocarbon data consisted of at least 1 cm 3 of sediment, collected from the middle of the core. These samples were collected from between 0-1 cm, 12-13 cm, 59-60 cm, 77-78 cm, 117-118 cm, and 229-230 cm depth.\n\nDiatom data are presented as raw counts, benthic abundances, the ratio of benthic to plankton species, and as the benthic index. Calculated ages (in years) are also given for all samples. The sedimentological core log is given as a powerpoint presentation.\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nBenthic %\nPlanktonic %\nDepth (cm)\nAge (years)\nRadiocarbon Age\nCorrected Age\nBenthic Index", "links": [ { diff --git a/datasets/Diatoms_seasonal_var_1.json b/datasets/Diatoms_seasonal_var_1.json index f0561a63bf..52937e770c 100644 --- a/datasets/Diatoms_seasonal_var_1.json +++ b/datasets/Diatoms_seasonal_var_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatoms_seasonal_var_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment samples were collected with an Eckamn grab from four locations within the Windmill Islands (Herring Island, O'Connor Island, Shannon Bay and Brown Bay). A weekly sampling program was performed over a 10 week period, however not all locations could be accessed each time due to sea-ice conditions. All samples were collected at an 8 m water depth. Preliminary analysis of fortnightly samples are presented here. Diatom data are given as relative abundances of benthic diatom species. The abbreviations used to identify species are explained in the accompanying file sp_list.\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nAbundance\nBenthic\nDate\nLocation", "links": [ { diff --git a/datasets/Diatoms_short_cores_1.json b/datasets/Diatoms_short_cores_1.json index ad6f89dcdf..1658ba5c35 100644 --- a/datasets/Diatoms_short_cores_1.json +++ b/datasets/Diatoms_short_cores_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatoms_short_cores_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ten sediment cores were collected from 3 marine bays in the Windmill Islands. Two cores were collected in Sparkes Bay, one in Shannon Bay, and seven in Brown Bay. Only diatom data are presented here, however Pb210 and metal analyses have also been undertaken - contact Ian Snape (ian.snape@aad.gov.au) for more information regarding this.\n\nThe diatom spreadsheet (diatom_data) lists the relative abundance of benthic species. The abbreviation used to identify species are explained in the separate file called sp_list. Each core has been saved as a separate file. The STE cores were collected from within a couple of meters of each other. These cores were collected in close proximity to a tip site at one end of Brown Bay. BBMid was collected from the middle of the bay, while BB Outer 1 and 2 were collected from the outer regions of this bay, and thus represent the greatest distance from the tip site. Unless otherwise stated, the lowest number within each core represents the youngest sample.\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nAbundance\nBenthic", "links": [ { diff --git a/datasets/Diatoms_sre2_1.json b/datasets/Diatoms_sre2_1.json index 76d989bb7a..132f965e25 100644 --- a/datasets/Diatoms_sre2_1.json +++ b/datasets/Diatoms_sre2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Diatoms_sre2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Full title:\n\nDiatom and associated data for a manipulative field experiment which translocated control and contaminated sediments between locations within the Windmill Islands, Antarctica.\n\nA manipulative field experiment was performed to assess the effects of heavy metals and petroleum hydrocarbons on benthic diatom communities in the Windmill Islands. Three treatments were used (control, metal contaminated, and petroleum hydrocarbon contaminated), with replicates of each treatment deployed at three locations (Sparkes Bay, Brown Bay and O'Brien Bay). The datasets associated with this experiment include the concentrations of metals within the sediments as well as diatom data (raw counts, and the relative abundance of benthic species).\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip-site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nSpecies\nArsenic\nCadmium\nCopper\nLead\nSilver\nZinc\nConcentration\nLocation\nTreatment\nAbundance\nBenthic\nSite", "links": [ { diff --git a/datasets/Dissolved_Gases_Alaska_Rivers_2360_1.json b/datasets/Dissolved_Gases_Alaska_Rivers_2360_1.json index b423dfa5fd..e412084505 100644 --- a/datasets/Dissolved_Gases_Alaska_Rivers_2360_1.json +++ b/datasets/Dissolved_Gases_Alaska_Rivers_2360_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dissolved_Gases_Alaska_Rivers_2360_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides dissolved carbon dioxide (CO2) and methane (CH4) concentrations alongside their stable and radiocarbon isotopic compositions within the Arctic Sagavanirktok and Kuparuk River watersheds located on the North Slope of Alaska. The data were collected during the spring, fall, and summer seasons in 2022. In field separation of the bulk gaseous components (N2, CO2, and CH4) from the liquid phase was achieved using a degassing membrane contactor. Laboratory isotopic analyses were conducted at the W. M. Keck Carbon Cycle Accelerator Mass Spectrometer facility at UC Irvine. This collection aims to provide insights into the seasonal dynamics of greenhouse gas emissions in these critical Arctic environments, thereby contributing valuable information for climate change research and monitoring programs. The data are provided in comma separated values (CSV) format.", "links": [ { diff --git a/datasets/Disturbance_Biomass_Maps_1679_1.json b/datasets/Disturbance_Biomass_Maps_1679_1.json index 0125252123..a12118504b 100644 --- a/datasets/Disturbance_Biomass_Maps_1679_1.json +++ b/datasets/Disturbance_Biomass_Maps_1679_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Disturbance_Biomass_Maps_1679_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides derived disturbance history and predicted annual forest biomass maps at 30-m resolution for six selected Landsat scenes across the Conterminous United States (CONUS) for the period 1985-2014. The focus sites are in the following states: Colorado, Maine, Minnesota, Oregon, Pennsylvania, and South Carolina. These scenes were selected to represent a wide range of forest ecosystems, which ensured that a diversity of forest type groups and forest change processes (e.g., harvest, fire, insects, and urbanization) were included. Disturbance history was derived from a Landsat time-series for each site. Each disturbance is represented by year of detection, duration, and magnitude. The cause of the disturbance was not identified. Forest biomass was measured at field plots within each of the six sites and combined with airborne LiDAR data from each site to create land validation maps. Site biomass at 30-m resolution was estimated by developing Random Forest models that include site disturbance history with the land validation maps.", "links": [ { diff --git a/datasets/Dive_data_PhD_G.Roncon_IMAS_2019_1.json b/datasets/Dive_data_PhD_G.Roncon_IMAS_2019_1.json index 86dc727d56..6e3bc9c168 100644 --- a/datasets/Dive_data_PhD_G.Roncon_IMAS_2019_1.json +++ b/datasets/Dive_data_PhD_G.Roncon_IMAS_2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dive_data_PhD_G.Roncon_IMAS_2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study was carried out by Giulia Roncon as part of her PhD at IMAS. The study employed both archival and contemporary diving data, collected by six species of marine predators (three penguins and three seal species) from the Eastern Antarctic sector of the Southern Ocean, to clarify key questions, such as (i) are there differences and/or commonalities regarding the diving physiology and ecology of marine predators, and (ii) what are the main determinants and constrains that characterise the underwater behaviour of air-breathing vertebrates.\n\nThis dataset is a compilation of data of several different studies carried out by different research teams in various locations and at various times. All TDRs were archival loggers that had to be retrieved to obtain the data. Thus, the animals had to be captured twice (deployment and retrieval). Details about the types of tags are listed in the dataset.\n\nSpecies used in the study were:\nAdelie Penguins\nEmperor Penguins\nKing Penguins\nFur Seals\nSouthern Elephant Seals\nWeddell Seals", "links": [ { diff --git a/datasets/Dogs_1.json b/datasets/Dogs_1.json index 6169486225..f6375ca259 100644 --- a/datasets/Dogs_1.json +++ b/datasets/Dogs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dogs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dog/sledging tracks in the Mawson region between 1954-1970. Mapped at 1:3250000 and the Casey region in 1967 mapped at 1:1250000.", "links": [ { diff --git a/datasets/Dolche-Vita_0.json b/datasets/Dolche-Vita_0.json index 6ba0253c43..df0b436222 100644 --- a/datasets/Dolche-Vita_0.json +++ b/datasets/Dolche-Vita_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dolche-Vita_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the northern Adriatic Sea in 2003.", "links": [ { diff --git a/datasets/DomeA_AWS_1 .json b/datasets/DomeA_AWS_1 .json index 55d7270f33..c6c62342e4 100644 --- a/datasets/DomeA_AWS_1 .json +++ b/datasets/DomeA_AWS_1 .json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "DomeA_AWS_1 ", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Automatic Weather Station data obtained at Dome A (Argus), Antarctica.\n\nData from this AWS are currently available from 2005-2017. At this stage the AWS remains operational, and the data record will be updated as data become available. \n\nThe surface elevation of Dome Argus (Dome A) is 4093 m, i.e., at one of the highest place in Antarctica. Our Automatic Weather Station has been deployed near one end of an elongated ridge (about 60 km long and 10 km wide). At this location the thickness of the glacial ice sheet is in excess of 3000 m, overlaying the subglacial Gamburtsev Mountains. We consider this location as a potential site to collect an ice core to provide a record of past climate and atmospheric gas composition going back more than one million years.\n\nThe Dome A AWS measures:\n- wind speed (knots)\n- air temperature - with sensors mounted on mast arms at 1 m, 2 m and 4 m above the snow surface (degrees Celsius) \n- snow temperature at 0.1 m, 1 m, 3 m and 10 m depth\n- atmospheric pressure (hPa)\n- wind direction (degrees)\n- incoming solar radiation (MJ per square metre)\n- relative humidity (percent)\n- snow-fall rate.\n(We note that sensor heights decrease as precipitation settles at the site.)\n\nTime is given in UTC.", "links": [ { diff --git a/datasets/Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1.json b/datasets/Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1.json index c51b8594ed..dac3721644 100644 --- a/datasets/Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1.json +++ b/datasets/Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in \u201cDeep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images\u201d (Chew et al., 2020).", "links": [ { diff --git a/datasets/Dunne_545_1.json b/datasets/Dunne_545_1.json index db6487c9d2..fa98ccf70b 100644 --- a/datasets/Dunne_545_1.json +++ b/datasets/Dunne_545_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Dunne_545_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil.", "links": [ { diff --git a/datasets/E06_OCM_GAC_STGO00GND_1.0.json b/datasets/E06_OCM_GAC_STGO00GND_1.0.json index 00b34041fb..39282831a2 100644 --- a/datasets/E06_OCM_GAC_STGO00GND_1.0.json +++ b/datasets/E06_OCM_GAC_STGO00GND_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "E06_OCM_GAC_STGO00GND_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main objectives of E06 are to study surface winds and ocean surface strata, observation of chlorophyll concentrations, monitoring of phytoplankton blooms, study of atmospheric aerosols and suspended sediments in the water. This has global coverage for every 2 days and sun glint free data for every 13 days.", "links": [ { diff --git a/datasets/E06_OCM_LAC_STGO00GND_1.0.json b/datasets/E06_OCM_LAC_STGO00GND_1.0.json index c6b9b924d5..f522d44727 100644 --- a/datasets/E06_OCM_LAC_STGO00GND_1.0.json +++ b/datasets/E06_OCM_LAC_STGO00GND_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "E06_OCM_LAC_STGO00GND_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main objectives of E06 are to study surface winds and ocean surface strata, observation of chlorophyll concentrations, monitoring of phytoplankton blooms, study of atmospheric aerosols and suspended sediments in the water. ", "links": [ { diff --git a/datasets/EANET.json b/datasets/EANET.json index bb08da941a..8690db04ba 100644 --- a/datasets/EANET.json +++ b/datasets/EANET.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EANET", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rapid industrialization in the East Asian countries has helped in achieving economic growth. Along with industrialization, primary energy consumption has also rapidly increased in East Asia. In 2002, total primary energy consumption in East Asia was 2.5 billion tons (oil equivalent). The major energy source in East Asia is coal, accounting for 38% of the total in 2002. Oil and natural gas follow at a rate of 33% and 8.7% respectively. The combustion of these fossil fuels is the main source of air pollutants such as sulfur dioxide and nitrogen oxides released into the atmosphere. East Asia\u0081fs total primary energy consumption in 2030 is estimated to be 4.7 billion tons (oil equivalent), twice large than in 2002 (international Energy Agency (IEA), World Energy Outlook 2004). If there is no efficient control, the emission of air pollutants will also increase.\n\nSulfur and nitric acids are recognized as major causes of atmospheric acidification. Sulfur dioxide and nitrogen oxides emitted from the burning of coal and oil react in the atmosphere to form sulfuric acid and nitric acid that are deposited on the earth. Sulfuric acid is one of the most important components used to evaluate acid deposition. In some major cities in East Asia the annual deposition of sulfate amounts to more that 100 kg/ha. Sulfuric acid is not only deposited with precipitation in the cities but also transported together with sulfur dioxide and sulfate as well as other acids to surrounding areas and may affect our natural ecosystems.\n\nAcid deposition can cause various effects on the ecosystems through acidification of soil and waters as well as damage to buildings and cultural heritage through corrosion of metals, concrete and stone. In order to assess the adverse effects on the ecosystem, it is necessary to identify dose-effect relationship of acid and eutrophic substances in environment. It is also important to quantify the effects on ecosystems, estimate the necessary amount of reduction of emission, and consider the most cost-effective policy options. Determination of emission reduction target may require the identification of the threshold level of acidic and eutrophic substances that do not cause any adverse effect on ecosystems.\n\nAcid deposition is not limited by national boundaries and therefore cooperation at the regional and international level is required to effectively address this problem. In Europe, it was successfully achieved through the activities under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). As pointed out in Agenda 21 adopted by the United Nations Conference on Environment and Development in June 1992, \"the programs (in Europe and North America) need to be continued and enhanced, and their experience needs to be shared with other regions of the world\". The Acid Deposition Monitoring Network in East Asia (EANET) was established as a regional cooperative initiative to promote efforts for environmental sustainability and protection of human health in the East Asian region.", "links": [ { diff --git a/datasets/EARTH_CRUST_AEDD_PAC_MAR_GEOL1.json b/datasets/EARTH_CRUST_AEDD_PAC_MAR_GEOL1.json index 132743af95..375bb28ce0 100644 --- a/datasets/EARTH_CRUST_AEDD_PAC_MAR_GEOL1.json +++ b/datasets/EARTH_CRUST_AEDD_PAC_MAR_GEOL1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_AEDD_PAC_MAR_GEOL1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Summary level describes each U.S. Geological Survey, Office of\nPacific Marine Geology cruise. Inventory level describes each\nindividual sampling activity. Sample log details tests on each\nsample. Data level contains results of tests and assessments\n(geologic, engineering, biological).\nData is from the Pacific Ocean and Arctic Ocean basins.\nData base is mostly complete for region offshore of central\nand northern California. Interactive access from outside the\nfacility is limited at present.", "links": [ { diff --git a/datasets/EARTH_CRUST_AK_PETROGRAPH_THIN1.json b/datasets/EARTH_CRUST_AK_PETROGRAPH_THIN1.json index b11b67dda4..f52ddfb2e9 100644 --- a/datasets/EARTH_CRUST_AK_PETROGRAPH_THIN1.json +++ b/datasets/EARTH_CRUST_AK_PETROGRAPH_THIN1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_AK_PETROGRAPH_THIN1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of petrographic thin sections made from rock samples collected by\nUSGS field geologists in Alaska. Many of the sections have corresponding\ndescriptions cards; thin sections number 30,000.\n\nWritten requests or appointment only. Access to materials restricted to\non-site use for non-USGS employees.", "links": [ { diff --git a/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json b/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json index 64a8f9f5c5..6be9998332 100644 --- a/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json +++ b/datasets/EARTH_CRUST_AUS_BMR_Min_Loc_DB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_AUS_BMR_Min_Loc_DB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Mineral Occurrence Location Database provides\n information on mineral occurrence and deposit locations in\n Australia. Currently, data covers 52% of Australia by area.\n The data base contains the name of mineral occurrences with the\n geographical coordinates of each occurrence. The spatial\n resolution: varies from 10m to 10 km, mainly about 500m. Sources\n of the data are named, such as maps, bibliographies, or\n correspondence. Commodities associated with the occurrence are\n delineated. The data is about 32 Megabytes and is rectified to\n standard coordinates. There are charges for the data. Order\n form and information about the data set are available from\n B. Elliott, Project Manager, Mineral Databases (telephone\n 06-2499502) or BMR Publication Sales (fax 06-2499982).", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_AK_NOTEBOOKS1.json b/datasets/EARTH_CRUST_USGS_AK_NOTEBOOKS1.json index 260698888e..1258d2594b 100644 --- a/datasets/EARTH_CRUST_USGS_AK_NOTEBOOKS1.json +++ b/datasets/EARTH_CRUST_USGS_AK_NOTEBOOKS1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_AK_NOTEBOOKS1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These notebooks are the original field records of geologic observations made\nby USGS geologists working in Alaska. They contain field stations, sample\nnumbers, rock types and descriptions, terrain conditions and outcrop sketches. \nSome also report topographic measurements, daily weather, and camp life. A few\npersonal diaries of the early explorers are preserved.\n\nThe notebooks may be viewed by written requests or appointment only. Access to\ncertain materials may be restricted for non-government employees. Microfilm\nfor these records is stored in Menlo Park, California. The data consists of\n3,600 notebooks.", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json b/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json index 14e5949089..c1d8b6a596 100644 --- a/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json +++ b/datasets/EARTH_CRUST_USGS_COAL_NCRDS_DB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_COAL_NCRDS_DB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The basic National Coal Resources Data System (NCRDS) allows users to interactively retrieve information on coal quantity and quality and to build new resource data from ongoing research on the geology of coal by the U.S. Geological Survey and state agencies. \n \nThe NCRDS is an automated system. Data bases accessed contain some proprietary data. Access to non-U.S. Geological Survey users is limited to nonproprietary data. NCRDS is a user-oriented computerized storage, retrieval, and display system devised by the U.S. Geological Survey to assess the quantity and quality of national coal resources. The U.S. Geological Survey has initiated a 5- to 10-year program to provide point-source coverage for the coal-bearing rocks in the U.S. Cooperative projects with many state geologic agencies have been funded to supplement U.S. Geological Survey work and to amass the volume of data required to assess U.S. coal resources. Currently, files containing summary areal coal tonnage estimates and proximate/ultimate chemical analyses and point-located major, minor, and trace-element analyses are available. The point-source files are used to calculate resource estimates and to depict trends in the occurrence and chemical characteristics of coal. Primary data for measurements, coal-bed outcrop patterns, burned, channeled, and mined-out areas, and geochemical analyses. System software can calculate coal resource estimates, generate overburden or interburden distribution, and delineate areas of coal with selected quality parameters (for example, >1 percent sulfur, <50 ppm Zinc) within specified boundaries, (for example, county, quadrangle, or lease tract). Data may be displayed descriptively by preformated tables, user-determined listings, and selected statistics or graphically by two-and three-dimensional diagrams, trend surfaces, isoline maps, and stratigraphic sections.\n \nThe system runs on a SUN Network of servers and workstations located in Reston, VA. and Denver, CO.", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_GeoNames.json b/datasets/EARTH_CRUST_USGS_GeoNames.json index d02052a204..7781ced215 100644 --- a/datasets/EARTH_CRUST_USGS_GeoNames.json +++ b/datasets/EARTH_CRUST_USGS_GeoNames.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_GeoNames", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data base contains an annotated index lexicon of formal\ngeologic nomenclature of the United States, territories, and\npossessions, with data on location, geologic age, U.S. Geological\nSurvey usage, lithology, geologic province, thickness at type\nsection, location of type section, and naming reference for each\ngeologic unit.", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json b/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json index 1f48792940..b14c540e30 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_GEOCHEM1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_NPRA_GEOCHEM1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive\nwilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began\nsome geological surveying in this area in the early 1900's, and the U.S. Navy\nbegan geological and geophysical surveys and drilling in 1945 to appraise the\npetroleum potential of the Reserve. \n\nGEOCHEMICAL DATA\nIncludes microfilm reels of Phase I, II, and III geochemical analyses of well\ncores.\n\nSee also the NPPRA legacy data\narchive:http://energy.cr.usgs.gov/", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json b/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json index 13b58c81fc..3f3087ffe1 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_GEO_RPTS1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_NPRA_GEO_RPTS1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive\nwilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began\nsome geological surveying in this area in the early 1900's, and the U.S. Navy\nbegan geological and geophysical surveys and drilling in 1945 to appraise the\npetroleum potential of the Reserve. \n\nGEOPHYSICAL AND GEOLOGICAL REPORTS\nA variety of reports are available from the USGS summarizing and interpreting\ngeophysical and geological data about the NPRA.\n\nSee the NPRA Legacy Data Archive:\nhttp://energy.cr.usgs.gov/", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json b/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json index 3eca0e2aa2..865bb3e82e 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_SEISMIC1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_NPRA_SEISMIC1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive\n wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began\n some geological surveying in this area in the early 1900's, and the U.S. Navy\n began geological and geophysical surveys and drilling in 1945 to appraise the\n petroleum potential of the Reserve. Information on surveys prior to 1955 may\n be obtained from the Branch of Alaskan Geology at:\n Alaska Technical Data Unit\n Mail Stop 48\n U.S. Geological Survey\n 345 Middlefield Road\n Menlo Park, CA 94025.\n \n SEISMIC DATA:\n Common Depth Point (CDP) seismic reflection data and documentation covering\n about 13,000 miles for 1972-81 are available from USGS. Full-scale (5 in./sec)\n sections are available for most of the lines, which were shot at either 6-,\n 12-, or 24-fold multiplicity. Data sets include index maps, shot-point location\n maps, seismic sections and velocity analyses.\n \n MAJOR DATA SETS:\n CDP seismic reflection data\n Reprocessed seismic sections\n Seismic reflection field tapes\n Processed field tapes\n Miscellaneous Geophysical data tapes\n Barrow data\n Stacking velocities\n \n \n Anciliary Data for the Seismic Data:\n \n TIME, VELOCITY, DEPTH DATA\n This file contains time, velocity and depth (TVD) information for up to 25\n identified seismic horizons. Position information includes line number, shot-\n point number, latitude and longitude for most of 1972-1981. These data were\n generated by Petroleum Information for 1981. The data are preliminary and are\n from Terra Tech (contractor).\n \n SHOT-POINT LOCATION DATA\n This file contains position information for shot locations during 1972-1981.\n The file was created by National Geophysical Data Center (NGDC) from TVD data\n and other shot-point tapes.\n \n ELEVATION DATA\n Elevation data for the National Petroleum Reserve in Alaska includes elevation,\n northing and easting information for 1972-1979. This file was created by Tetra\n Tech (contractor for USGS) and contains position information, including line\n number, shot point, latitude and longitude.", "links": [ { diff --git a/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json b/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json index 1d3aaabe05..016ecb326a 100644 --- a/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json +++ b/datasets/EARTH_CRUST_USGS_NPRA_WELL_LOGS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_CRUST_USGS_NPRA_WELL_LOGS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the primitive\n wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began\n some geological surveying in this area in the early 1900's, and the U.S. Navy\n began geological and geophysical surveys and drilling in 1945 to appraise the\n petroleum potential of the Reserve. Information on surveys prior to 1955 may\n be obtained from the Branch of Alaskan Geology, Alaska Technical Data Unit,\n Mail Stop 48, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA\n 94025.\n \n WELL LOG DATA\n Well logs and associated information are available from USGS. These data deal\n primarily with NPRA exploration and development since the drilling of the South\n Barrow No. 5 Well in 1955. Well log formats include Schlumberger LISLOG,\n DRESSER, and DATOUT. Included with some well log data sets are auxiliary\n information such as drilling history of the wells and velocity check-shot\n surveys. Well core analyses include porosity, permeability and fluid saturation\n measurements.\n \n MAJOR DATA SETS:\n Well logs; Digitized well logs; Well core analyses; Seismic velocity surveys;\n Synthetic seismograms; Palynology/Micropaleontology reports.", "links": [ { diff --git a/datasets/EARTH_INT_AUS_BMR_AIR_MAG_GAMMA.json b/datasets/EARTH_INT_AUS_BMR_AIR_MAG_GAMMA.json index 91a3cde048..1b010618ef 100644 --- a/datasets/EARTH_INT_AUS_BMR_AIR_MAG_GAMMA.json +++ b/datasets/EARTH_INT_AUS_BMR_AIR_MAG_GAMMA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_AUS_BMR_AIR_MAG_GAMMA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Digital Airborne Geophysical Data set consists of\nreconnaissance measurements of magnetic field anomalies and land\nsurface emission of gamma rays in the range 0.3-3.0 MeV.\nStacked total magnetic intensity profiles with each profile base\nthe least squares straight line fit to the navigation. Radiometric\nProfiles are stacked profiles of radiometric total count and total\ncount radiometric contours. Potassium, Uranium and Thorium Profiles\nare stacked profiles of radiometric K, U, and Th counts. TMI Contours\nare Total magnetic intensity contours. The Radiometric contours\nusually are at a scale of 1:250000. The Gamma-ray spectrometer\ncontours are total count contours. The Magnetic digital data is\nposition located processed magnetic digital data. The Gamma-ray\nspectrometer digital data is position located processed gamma-ray\ndigital data for K, U, Th, and total count channels. Gridded magnetic\ndigital data is available in processed form. The data is available in\nASCII format. The positional accuracy varies from 1 metre to 100\nmetres. To order stacked profiles and maps, contact BMR Copy Service.\nFor digital data, contact the Airborne Section (telephone 06-2499223\nor fax 06-2499986).", "links": [ { diff --git a/datasets/EARTH_INT_AUS_BMR_EARTHQUAKE_DB.json b/datasets/EARTH_INT_AUS_BMR_EARTHQUAKE_DB.json index 22fd708023..7e9b861d8b 100644 --- a/datasets/EARTH_INT_AUS_BMR_EARTHQUAKE_DB.json +++ b/datasets/EARTH_INT_AUS_BMR_EARTHQUAKE_DB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_AUS_BMR_EARTHQUAKE_DB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BMR-ASC World Earthquake Database is maintained by the Australian\nBureau of Mineral Resources (BMR). It lists the Hypocentres of world\nearthquakes from 1904-1989 and Australian earthquakes from 1873-1991\nwhere the magnitude is 4 or greater. Data is also obtained from the\nInternational Seismological Centre, UK and the USGS. Aside from\ndate and time and the hypocenter parameters, the data base provides\nfocal depth, magnitude, and describes its effects. To order data by\nFAX, Fax to 06-2499969.", "links": [ { diff --git a/datasets/EARTH_INT_AUS_BMR_GEOCHRON1.json b/datasets/EARTH_INT_AUS_BMR_GEOCHRON1.json index 6212202b12..45c0695a63 100644 --- a/datasets/EARTH_INT_AUS_BMR_GEOCHRON1.json +++ b/datasets/EARTH_INT_AUS_BMR_GEOCHRON1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_AUS_BMR_GEOCHRON1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Database of Australian Geochronology includes Main tables\non SAMPLES, K_AR, AR40_39, RB_SR, SM_ND, ZIRCON, SHRIMP, REFERENCES and\nSTOREBOXES. The table SAMPLES includes attributes for sample no.,\nstratigraphic unit, rock type, country, state, region, AMG reference and\ndecimal latitude and longitude. K-Ar, Ar-Ar, Rb-Sr, Sm-Nd, Pb-Pb zircon\nand Pb-Pb ion microprobe (SHRIMP, mostly zircon) tables contain dating\nresults. Table REFERENCES contains bibliographic references relevant to\nsamples &/or results. The data is Australia-wide, mainly from hardrock\nareas. In the future, possibly Antarctica and PNG samples will be added.\nThe Relational Database is on Magnetic Disk. The interchange format is\nASCII. The entire data base is about 10 Megabytes.", "links": [ { diff --git a/datasets/EARTH_INT_AUS_BMR_GRAVITY_DB1.json b/datasets/EARTH_INT_AUS_BMR_GRAVITY_DB1.json index 988c34624d..5a4cdca1f3 100644 --- a/datasets/EARTH_INT_AUS_BMR_GRAVITY_DB1.json +++ b/datasets/EARTH_INT_AUS_BMR_GRAVITY_DB1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_AUS_BMR_GRAVITY_DB1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian National Gravity Database contains point located\nobserved gravity and height values over Australia and its continental\nshelf. Here, observed gravity is the acceleration due to gravity\nmeasured at the location measured on an 11 km grid or finer. Ground\nelevation is the elevation of the observation point on the Australian\nHeight Datum measured on an 11 km grid or finer. The whole data base\nis about 100 Megabytes. Some of the data have been provided by State\nGovernment Departments, universities and private exploration\ncompanies. Copies of the database on magnetic tapes are available for\n$7500. Copies of the database on 1:1M sheet on disc are available for\n$350. A Bouguer anomaly map dyeline is $25. Order with a written\nrequest to BMR or fax (06)2488420 for data and fax (06)2472728 for\nmaps.", "links": [ { diff --git a/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json b/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json index 9e93dbc4d2..3e1079f6a1 100644 --- a/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json +++ b/datasets/EARTH_INT_USGS_NPRA_GAMMA_MAG1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_USGS_NPRA_GAMMA_MAG1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Petroleum Reserve in Alaska (NPRA) is located in the\n primitive wilderness of Alaska's North Slope. The U.S. Geological\n Survey (USGS) began some geological surveying in this area in the eary\n 1900's, and the U.S. Navy began geological and geophysical surveys and\n drilling in 1945 to appraise the petroleum potential of the Reserve.\n Information on surveys prior to 1955 may be obtained from the Branch of\n Alaskan Geology, Alaska Technical Data Unit, Mail Stop 48, U.S. Geological\n Survey, 345 Middlefield Road, Menlo Park, CA 94025.\n \n AERIAL GAMMA RAY AND MAGNETIC DATA\n Radiometric and magnetic profiles from 1977 are available from USGS.\n Aerial data were recorded at 1-sec intgervals from a helicopter about\n 800 feet above the terrain with average ground speed of 100m/hr.\n Included with the data set are 5 index maps, 2 record location maps, 2\n residual total magnetic-field profile maps, and an interpreted\n depth-to-basement map.\n \n These files are available as Open-File Report 95-835.\n \"http://pubs.usgs.gov/of/1995/ofr-95-0835/\"", "links": [ { diff --git a/datasets/EARTH_INT_USGS_S_AK_EARTHQUAKES.json b/datasets/EARTH_INT_USGS_S_AK_EARTHQUAKES.json index 46a33b7ead..a4b828dd0a 100644 --- a/datasets/EARTH_INT_USGS_S_AK_EARTHQUAKES.json +++ b/datasets/EARTH_INT_USGS_S_AK_EARTHQUAKES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_INT_USGS_S_AK_EARTHQUAKES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Between 1994 and 1999, the Alaska Volcano Observatory (AVO) seismic monitoring\n program underwent significant changes with networks added at new volcanoes\n during each summer from 1995 through 1999. The existing network at Katmai\n ?Valley of Ten Thousand Smokes (VTTS) was repaired in 1995, and new networks\n were installed at Makushin (1996), Akutan (1996), Pavlof (1996), Katmai - south\n (1996), Aniakchak (1997), Shishaldin (1997), Katmai - north (1998), Westdahl,\n (1998), Great Sitkin (1999) and Kanaga (1999). These networks added to AVO's\n existing seismograph networks in the Cook Inlet area and increased the number\n of AVO seismograph stations from 46 sites and 57 components in 1994 to 121\n sites and 155 components in 1999. The 1995?1999 seismic network expansion\n increased the number of volcanoes monitored in real-time from 4 to 22,\n including Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano,\n Mount Snowy, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount\n Mageik, Mount Martin, Aniakchak Crater, Pavlof Volcano, Mount Dutton, Isanotski\n volcano, Shisaldin Volcano, Fisher Caldera, Westdahl volcano, Akutan volcano,\n Makushin Volcano, Great Sitkin volcano, and Kanaga Volcano (see Figures 1-15).\n The network expansion also increased the number of earthquakes located from\n about 600 per year in 1994 and 1995 to about 3000 per year between 1997 and\n 1999.\n \n Highlights of the catalog period include: 1) a large volcanogenic seismic swarm\n at Akutan volcano in March and April 1996 (Lu and others, 2000); 2) an eruption\n at Pavlof Volcano in fall 1996 (Garces and others, 2000; McNutt and others,\n 2000); 3) an earthquake swarm at Iliamna volcano between September and December\n 1996; 4) an earthquake swarm at Mount Mageik in October 1996 (Jolly and McNutt,\n 1999); 5) an earthquake swarm located at shallow depth near Strandline Lake; 6)\n a strong swarm of earthquakes near Becharof Lake; 7) precursory seismicity and\n an eruption at Shishaldin Volcano in April 1999 that included a 5.2 ML\n earthquake and aftershock sequence (Moran and others, in press; Thompson and\n others, in press). The 1996 calendar year is also notable as the seismicity\n rate was very high, especially in the fall when 3 separate areas (Strandline\n Lake, Iliamna Volcano, and several of the Katmai volcanoes) experienced high\n rates of located earthquakes.\n \n This catalog covers the period from January 1, 1994, through December 31,1999,\n and includes: 1) earthquake origin times, hypocenters, and magnitudes with\n summary statistics describing the earthquake location quality; 2) a description\n of instruments deployed in the field and their locations and magnifications; 3)\n a description of earthquake detection, recording, analysis, and data archival;\n 4) velocity models used for earthquake locations; 5) phase arrival times\n recorded at individual stations; and 6) a summary of daily station usage from\n throughout the report period. We have made calculated hypocenters, station\n locations, system magnifications, velocity models, and phase arrival\n information available for download via computer network as a compressed Unix\n \n tar file.", "links": [ { diff --git a/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json b/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json index fa5940dc98..5f4aa87a79 100644 --- a/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json +++ b/datasets/EARTH_LAND_NBS_GLACIER_TERMINUS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_NBS_GLACIER_TERMINUS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glacier terminus positions data are provided by the project on glacier\ndynamics in relation to climate. The terminus positions are surveyed\nby field crews or derived from aerial and oblique photos of glaciers\nfrom 1887 to the present in Glacier National Park, Montana, USA.", "links": [ { diff --git a/datasets/EARTH_LAND_UAK_GI_Permafrost1.json b/datasets/EARTH_LAND_UAK_GI_Permafrost1.json index 99a416196f..540654ecdd 100644 --- a/datasets/EARTH_LAND_UAK_GI_Permafrost1.json +++ b/datasets/EARTH_LAND_UAK_GI_Permafrost1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_UAK_GI_Permafrost1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (PTDAK) includes handwritten temperature logs from drill holes in\n permafrost. The time series consists of temperatures versus time for active\n layer and permafrost. Data are stored on ERROM's. Transect from Prudoe Bay to\n Glenallen, ANWR and other sites in Alaska.", "links": [ { diff --git a/datasets/EARTH_LAND_USFWS_AK_Wildlife1.json b/datasets/EARTH_LAND_USFWS_AK_Wildlife1.json index ddaefaf09f..52dad7c65c 100644 --- a/datasets/EARTH_LAND_USFWS_AK_Wildlife1.json +++ b/datasets/EARTH_LAND_USFWS_AK_Wildlife1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USFWS_AK_Wildlife1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic National Wildlife Refuge (ANWR) is the most northern and one of the\n largest Refuges within America's National Wildlife Refuge System. The Arctic\n Refuge is managed by the U.S. Fish and Wildlife Service, U.S. Department of the\n Interior. The USFWS ANWR site contains information and data on wildlife,\n habitat, and people in the Arctic Refuge.\n \n The Refuge is home to more than 160 bird species, 36 kinds of land mammals,\n nine marine mammal species, and 36 types of fish.\n \n The ANWR geological and land surface databases are available from the Alaska\n Geospatial Data Clearinghouse (AGDC) at\n \"http://agdc.usgs.gov/data/projects/anwr/webhtml/\"\n \n The data consists of political boundaries and coastlines, land cover and\n vegetation maps, rivers, streams and wetlands, elevation contours, satellite\n images (Landsat-MSS and Landsat-TM), surficial geology, and coastal bathymetry\n as well as Alaska statewide land characterization and geospatial data.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_B_Landcover1.json b/datasets/EARTH_LAND_USGS_AK_B_Landcover1.json index 867045e6ac..fb56db4c02 100644 --- a/datasets/EARTH_LAND_USGS_AK_B_Landcover1.json +++ b/datasets/EARTH_LAND_USGS_AK_B_Landcover1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_B_Landcover1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation and land cover for Beechey Point 1:250,000-scale topographic quadrangle. Derived from a single Landsat MSS scene. Printed map: AK Vegetation and Land Cover Series l-0211. Digital raster data set also available. Seven categories. Data stored as 50 meter UTM-referenced pixels.\n\nInformation available as map or digital data set. Map unit coverage within each township printed on back of map. Map printed using Scitex laser printer. Documentation - Walker, D.A. and W. Acevedo, 1987, Vegetation and a Landsat-derived Land Cover Map of the Beechey Point Quadrangle, Arctic Coastal Plain, Alaska, U.S. Army Cold Regions Research and Engineering Laboratory, CRREL Report 87-5, 63 p. \n", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Bristol_Bay.json b/datasets/EARTH_LAND_USGS_AK_Bristol_Bay.json index d7faf3e1f2..95338920ed 100644 --- a/datasets/EARTH_LAND_USGS_AK_Bristol_Bay.json +++ b/datasets/EARTH_LAND_USGS_AK_Bristol_Bay.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Bristol_Bay", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This digital data set includes vegetation cover classification derived from\n Landsat MSS data and can be keyed on a 1:250,000 quadrangle basis. Spatial\n referencing is by 50 meter grid cell size. The data source is Landsat MSS data\n (73 records), storage required varies by storage medium and selected area. The\n file structure is sequential. Data are available on: 9-track, 800 bpi, 1600\n bpi, 6250 bpi, unlabeled, unblocked, fixed record length tape and 8' floppy\n disk. Subsets and custom formats are available; documentation is also\n available. The data is organized by 7 1/2 ' or 15 ' quads.\n General area covered: Bristol Bay region in Alaska.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Colville1.json b/datasets/EARTH_LAND_USGS_AK_Colville1.json index c91d7a7790..30451d23a5 100644 --- a/datasets/EARTH_LAND_USGS_AK_Colville1.json +++ b/datasets/EARTH_LAND_USGS_AK_Colville1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Colville1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vector and digital data from the Colville River area in Alaska. Data contains\n land cover classifications derived from Landsat MSS data, aerial photography,\n and National Wetlands Inventory (no DEM data). Data can be keyed on a U.S.\n Geological Survey quadrangle basis. Spatial referencing is by 50 meter grid\n cells. Data source is Landsat MSS data (4 records). Storage required varies\n by storage medium and selected area. The file structure is sequential.\n \n Subsets and custom formats as well as limited documentation are available.\n \n The data is organized by 7 1/2 ' or 15 ' quads. covering the area from 70\n degrees 15' North to 70 degrees 30' North and from 150 degrees 15' west to 151\n degrees 30' west.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_HI_ALT_PHOT.json b/datasets/EARTH_LAND_USGS_AK_HI_ALT_PHOT.json index ba119d5b1f..2bce655c54 100644 --- a/datasets/EARTH_LAND_USGS_AK_HI_ALT_PHOT.json +++ b/datasets/EARTH_LAND_USGS_AK_HI_ALT_PHOT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_HI_ALT_PHOT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "[From GeoData Center Home Page descriptions,\n \"http://www.gi.alaska.edu/alaska-satellite-facility/geodata-center\"]\n \n The GeoData Center is the browse facility for the state copy of the AHAP\n collection, which covers approximately 95% of the State of Alaska in 1:60,000\n color infrared (CIR) and 1:120,000 black and white (B&W) photography. The data\n reside in 10\" film format. Approximately 70,000 frames of photography were\n acquired between 1978 and 1986.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Iditarod1.json b/datasets/EARTH_LAND_USGS_AK_Iditarod1.json index cf7c9288b3..0a9f5e0a7a 100644 --- a/datasets/EARTH_LAND_USGS_AK_Iditarod1.json +++ b/datasets/EARTH_LAND_USGS_AK_Iditarod1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Iditarod1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since the early 1980's the EROS Alaska Field Office (AFO) has been involved in\n the acquisition, classification, and analysis of digital land cover data over\n the State of Alaska, and to a limited extent, northwestern Canada and Wrangle\n Island, Russia. The digital data currently covers approximately 77% of the land\n and water within the boundaries of the State of Alaska. These data are\n currently being made available via the AFO web site to land managers and\n researchers who may be interested in land cover conditions over various\n portions of Alaska. The land cover maps as a result of digital analysis of\n Landsat multispectral scanner, Landsat thematic mapper, and SPOT multispectral\n scanner satellite data. Some data however, are missing from the database due to\n data degradation on storage media or loss of data tapes, although a limited\n number of hard copy map products may be in existence, for example, U.S. Forest\n Service land cover data for southeast Alaska. The land cover data are stored\n online in a series of directories and are available via the web. Each directory\n name is indicative of the area for which the land cover data exists. Some\n directories connote a U.S. Geological Survey 1:250,000 quadrangle (for example,\n anchorage_int), while others indicate a particular project area (for example,\n anwr represents Arctic National Wildlife Refuge) or the agency responsible for\n producing the data. For example, nowitna.fws indicates that the data were\n produced for the U.S. Fish and Wildlife Service. Directories with an '_int'\n suffix denote that the data correspond to the Interim Landcover Mapping\n project. Each directory contains five files with the following extensions:\n .bil, .blw, .hdr, .prj, and .stx. As a group, one or more of the files can be\n read by most image analysis and GIS software packages. The .bil file contains\n the binary raster land cover data and the others are ASCII files. The .bil file\n does not contain any header or footer records, and can be easily imported into\n image processing software by using the information contained in the .blw and\n .hdr files. The .blw file provides pixel size information as well as the upper\n left corner coordinate. The .hdr contains number of rows and columns of the\n data set, as well as band format (band interleave). The .prj file gives\n projection information, and the .stx file gives information on minimum/maximum/\n mean values of the data. Projection parameters for the data sets are either in\n Universal Transverse Projection (UTM) or Alaska Albers Equal Area Conic.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Innoko1.json b/datasets/EARTH_LAND_USGS_AK_Innoko1.json index 7862efcd18..38ab07317d 100644 --- a/datasets/EARTH_LAND_USGS_AK_Innoko1.json +++ b/datasets/EARTH_LAND_USGS_AK_Innoko1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Innoko1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Innoko National Wildlife Refuge digital data sets contain land cover\n classifications derived from Landsat MSS data, and elevation, slope and aspect\n data derived from DEM data. Data can be keyed on a U.S. Geological Survey\n 1:250,000 quadrangle basis. Spatial referencing is from 50 meter grid cells\n and data source is Landsat MSS data and Digital Elevation Model (DEM) data. \n This data set contains 113 records. The amount of storage required varies by\n storage medium and selected area. The file structure is sequential. Data are\n available online.\n \n The data is organized by 7 1/2 ' or 15 ' quads. General area covered: 62 to 64\n north to 155 to 45' to 160 to 15' west.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json b/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json index 30a5501672..bb16e52990 100644 --- a/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json +++ b/datasets/EARTH_LAND_USGS_AK_Koyukuk1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Koyukuk1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital data is given on the Koyukuk National Wildlife Refuge in west-central\n Alaska. The data set contains information on land status, lake and streams\n digitized at 1:63,360, and wildlife ditribution digitized at 1:250,000. Also\n included are digitized land cover, slope, a scale of elevation, and aspect from\n EROS Data Center; data are digitized from U.S. Geological Survey quadrangle\n maps and updated periodically. The amount of storage required is unknown. \n Data are available on 9-track, 800 bpi, 1600 bpi, unlabeled, AMS variable\n block, or DLG 3 fixed block in binary or ASCII, variable record length tape, 5\n 1/4 inch floppy disk, and cassette. Subsets and custom formats are available.\n The data dictionary has been completed for this record. The data is organized\n by 7 1/2 ' or 15 ' quads.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json b/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json index 5fca0be32e..2e10aedd17 100644 --- a/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json +++ b/datasets/EARTH_LAND_USGS_AK_NOAA_AVHRR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_NOAA_AVHRR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This digital data set contains selected NOAA 6, 7, 8 and 9\nAdvanced Very High Resolution Radiometer (AVHRR) imagery of\nAlaska; AVHRR is carried on NOAA's polar orbiting satellites.\nSpatial referencing is 1.1 km at nadir. Data source is National\nOceanic and Atmospheric Administration (NOAA). The data set\nincludes 47 records with estimated growth rate of 100 records per\nyear. Storage required varies by storage medium and selected\nscene. The file structure is sequential. Data are available\non 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked,\nBCD, fixed record length tape. Subsets and customs formats are\navailable. Limited documentation is available. Data is organized\nby 7 1/2 ' or 15 ' quads. Uses include fuel mapping, vegetation\nmonitoring, large area mosaic, and monitoring of ice/snow dynamics.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json b/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json index e5b952a9bb..e1f030d3d3 100644 --- a/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json +++ b/datasets/EARTH_LAND_USGS_AK_NPRA_veg1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_NPRA_veg1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A vegetation/land cover raster digital data set for the entire\n National Petroleum Reserve in Alaska (NPR-A) was generated from\n Landsat multispectral data sets. Included are eleven categories\n of vegetation and land cover which are derived from all or\n portions of 10 Landsat MSS scenes. The data set covers all or\n part of thirteen 1:250,000-scale topographic quadrangles. Data\n are stored in 50 meter pixels and registered to a UTM base.\n A full NPR-A mosaic as well as the 1:250,000 topographic series.\n Data are available in two forms: a digital mosaic of (1) the\n entire NPR-A coverage, split into two pieces each and registered\n to a separate UTM zone, or (2) for each 1:250,000-scale topo\n quadrangle area within the NPR-A. This file is too large to\n remain online. It is stored on magnetic tape at Moffett Field,\n CA.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AK_Wildlif_Ref1.json b/datasets/EARTH_LAND_USGS_AK_Wildlif_Ref1.json index 90222743de..e4552b83be 100644 --- a/datasets/EARTH_LAND_USGS_AK_Wildlif_Ref1.json +++ b/datasets/EARTH_LAND_USGS_AK_Wildlif_Ref1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AK_Wildlif_Ref1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital land cover and terrain data of the Arctic National Wildlife Refuge\n (ANWR) were prduced by the U.S. Geological Survey (USGS) Earth Resources\n Observation Systems Field Office, Anchorage, Alaska for the U.S. Fish and\n Wildlife Service (USFWS). These and other environmental data, were incorporated\n into the USFWS geographic information system to prepare a comprehensive\n conservation plan for the ANWR and an environmental impact statement which\n addresses oil and gas development in the Arctic Coastal Plain, and to assist\n research of the Porcupine Caribou herd.\n \n The data set contains land cover classification derived from Landsat MSS data,\n and elevation, slope and aspect data derived from DEM data. Data can be keyed\n on a U.S. Geological Survey 1:250,000 quadrangle basis. Spatial referencing is\n by 50 meter grid cells. Data source is Landsat MSS data, Digital Elevation\n Model (DEM) data, containing 299 records and the storage required varies by\n storage medium and selected area; file structure is sequential. Limited\n documentation and users guide are available. The data is organized by 7 1/2 '\n or 15 ' quads.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_ALASKA_FOSSILS1.json b/datasets/EARTH_LAND_USGS_ALASKA_FOSSILS1.json index 742bf1b3c3..4ea44c2319 100644 --- a/datasets/EARTH_LAND_USGS_ALASKA_FOSSILS1.json +++ b/datasets/EARTH_LAND_USGS_ALASKA_FOSSILS1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_ALASKA_FOSSILS1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data base consists of a compilation of reports made by the U.S. Geological\nSurvey Branch of Paleontology and Stratigraphy concerning the identification of\nfossils collected in Alaska. Data includes fossil type and age, sample\nlocality, collector, author, and date of report. Reports are grouped together\nby year, but are not indexed.\n\nWritten requests or appointment only. Permission for access by non-U.S.\nGeological Survey employees must be obtained in writing from the U.S.\nGeological Survey Branch of Paleontology and Stratigraphy. Data consists of 65\nnotebooks.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_ALASKA_GEODETIC.json b/datasets/EARTH_LAND_USGS_ALASKA_GEODETIC.json index dc54ff1ca6..9747419f60 100644 --- a/datasets/EARTH_LAND_USGS_ALASKA_GEODETIC.json +++ b/datasets/EARTH_LAND_USGS_ALASKA_GEODETIC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_ALASKA_GEODETIC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Positional (horizontal) central data and elevational\n (vertical) control data for the state of Alaska. Data may\n include description of control points and 'to-reach' information.\n It is issued in 1/2 ' or 15 ' quads, states, and the 1:250,000 topographic\n series. These maps are not yet available in digital form.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_AMES_AIR_PHOTOS.json b/datasets/EARTH_LAND_USGS_AMES_AIR_PHOTOS.json index 3c8ad2a851..f87501da0a 100644 --- a/datasets/EARTH_LAND_USGS_AMES_AIR_PHOTOS.json +++ b/datasets/EARTH_LAND_USGS_AMES_AIR_PHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_AMES_AIR_PHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aerial photography inventoried by the Pilot Land Data System \n(PLDS) at NASA AMES Research Center has been transferred to the USGS\nEROS Data Center. The photos were obtained from cameras mounted on\nhigh and medium altitude aircraft based at the NASA Ames Research\nCenter. Several cameras with varying focal lengths, lenses and film\nformats are used, but the Wild RC-10 camera with a focal length of 152\nmillimeters and a 9 by 9 inch film format is most common. The positive\ntransparencies are typically used for ancillary ground checks in\nconjunctions with digital processing for the same sites. The aircraft\nflights, specifically requested by scientists performing approved\nresearch, often simultaneously collect data using other sensors on \nboard (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared \nMultispectral Scanners). High altitude color infrared photography is\nused regularly by government agencies for such applications as crop\nyield forecasting, timber inventory and defoliation assessment, water\nresource management, land use surveys, water pollution monitoring, and\nnatural disaster assessment.\n\nTo order, specify the latitude and longitude of interest. You will then be\ngiven a list of photos available for that location. In some cases, \"flight\nbooks\" are available at EDC that describe the nature of the mission during\nwhich the photos were taken and other attribute information. The customer\nservice personnel have access to these books for those photo sets for which\nthe books exist.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_DEM_AK1.json b/datasets/EARTH_LAND_USGS_DEM_AK1.json index c4748b51f1..d2e12f363d 100644 --- a/datasets/EARTH_LAND_USGS_DEM_AK1.json +++ b/datasets/EARTH_LAND_USGS_DEM_AK1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_DEM_AK1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains up to nine types of digital elevation data: 1-1 degree\n blocks, 2-1 degree x 3 degree mosaic of elevation (latitude/longitude\n coordinate system), 3-1 degree x 3 degree mosaic of slope, 4-1 degree x 3\n degree mosaic of aspect (latitude/longitude coordinate system), 5-1 degree x 3\n degree mosaic of filtered elevation (5 x 5 filter), 6-1 degree x 3 degree\n mosaic of elevation (UTM registered), 7-1 degree x 3 degree mosaic of slope\n (UTM registered), 8-1 degree x 3 degree mosaic of aspect (UTM registered), 9-1\n degree x 3 degree mosaic of shaded relief (latitude/longitude coordinate\n system). Data coverage is from 1982 to present with work ongoing. Data source\n is 1:250,000 scale Defense Mapping Agency Digital Terrain Series. The data set\n currently contains 966 records with estimated growth of 5-15 records per year.\n Storage required varies by selection on area size. Data are available on:\n 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, or BCD tape.\n Subsets on the main file and custom formats as well as limited documentation is\n available.\n \n Data is organized by 7 1/2 ' or 15 ' quads. This data is intended to be used\n for land cover analysis, wildlife refuge studies, drainage analysis, and land\n use planning.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json b/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json index 95953a1e3b..2a6ef6d2b2 100644 --- a/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json +++ b/datasets/EARTH_LAND_USGS_EDC_AK_Landsat.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_EDC_AK_Landsat", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw unregistered Landsat digital data covering\nmost of Alaska. Data obtained from EROS Data Center in Sioux\nFalls, South Dakota. Data acquired from 1980 and is ongoing.\nSome Landsat scenes date back to 1972. The data set currently\nhas 585 records with a growth chart at 5-10 records per year.\nThe amount of storage required varies by medium used or full\nscene or subscene selection; the file structure is sequential.\nSpatial referencing of data is by 57 x 59 meter grid cell\nsize-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi,\n6250 bpi, unlabeled, unblocked, BCD, fixed record length tape.\nSubsets and custom formats are available. Limited documentation\nis available. The data is organized in 7 1/2 ' or 15 ' quads.\nData is used for false color composites, land cover analysis,\ngeologic analysis, hydrogeologic analysis, land use planning,\nbasis for update of topographic maps, production of image maps.", "links": [ { diff --git a/datasets/EARTH_LAND_USGS_Water_PKFIL.json b/datasets/EARTH_LAND_USGS_Water_PKFIL.json index b8ff7eaa76..3e9d249631 100644 --- a/datasets/EARTH_LAND_USGS_Water_PKFIL.json +++ b/datasets/EARTH_LAND_USGS_Water_PKFIL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EARTH_LAND_USGS_Water_PKFIL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Originally available as hard copy publication, the Annual Peak Discharge and\n Stage of US Surface Water data will be made available to the public via the\n World Wide Web. The URL of the data set is to be announced. For more\n information, please contact the U.S. Geological Survey, Water Resources\n Division.\n \n The Peak Flow File (PKFIL) of the U.S. Geological Survey/Water Resources\n Division contains data on Annual maximum (peak) streamflow (discharge) and gage\n height (stage) values at surface water sites in the U.S. These data are\n published annually on a state basis in water resources data reports.", "links": [ { diff --git a/datasets/ECA011.json b/datasets/ECA011.json index 990a56c5dd..6343a3aebe 100644 --- a/datasets/ECA011.json +++ b/datasets/ECA011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hexachlorobenzene (HCB), heptachlor, \u03b1- and \u03b3- HCH and heptachlor epoxide were identified in air, seawater, sea ice, and snow. Samples were collected during the austral winter (September-October 2001) and summer (January-February 2002) along a transect in the Western Antarctic Peninsula. By comparison with previous studie they concluded HCB and HCH levels declined over the past 20 years, with a half-life of 3 28 years in Antarctic air. However, they observed that heptachlor epoxide levels did not decrease in Antarctic air over the past decade, possibly due to continued use of heptachlor in the southern hemisphere. They detected peak heptachlor concentrations in air coincident with air masses moving into the region from lower latitudes. Levels of lindane were 1.2-200 times higher in annual sea ice and snow compared to \u03b1 HCH, likely due to greater atmospheric input of \u03b3-HCH. On the basis of the ratio of \u03b1/\u03b3-HCH <1 in Antarctic air, sea ice and snow they concluded that there is a predominance of influx of lindane versus technical HCH to the regional environment. However, they also observed that the \u03b1/\u03b3-HCH in seawater was >1, likely due to more rapid microbial degradation of \u03b3- versus \u03b1-HCH. Also this study concluded that the water/air fugacity ratios for HCHs demonstrate continued atmospheric influx of HCHs to coastal Antarctic seas, particularly during late summer", "links": [ { diff --git a/datasets/ECA012.json b/datasets/ECA012.json index cd8d90e872..e29f123523 100644 --- a/datasets/ECA012.json +++ b/datasets/ECA012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The spatial distribution of \u03b1-HCH and the net direction of air/water gas exchange were determined between November 1997 and February 1998. Air and water samples were collected between South Atlantic Ocean (South Africa) and Antarctica SANAE Base (70\u00b0S, 3\u00b0E). The \u03b1-HCH concentrations in air and surface water were much lower than in Arctic regions, consistent with the historically lower usage of technical HCH in the Southern Hemisphere. The water/air fugacity ratios of \u03b1-HCH were lower than or equal to 1.0, indicating steady state or net deposition conditions. One analysis of the enantiomeric fractionation was also made The results showed that the \u03b1-HCH in water was enantioselectively metabolized and that the two isomers [(-)\u03b1-HCH and (+)\u03b1-HCH] in the air boundary layer reflected those in surface water, showing the bidirectional nature of gas exchange.", "links": [ { diff --git a/datasets/ECA014.json b/datasets/ECA014.json index 79596d45c4..7776f77d49 100644 --- a/datasets/ECA014.json +++ b/datasets/ECA014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To study the transport of POPs from the northern hemisphere to the southern, cruises were carried out collecting aerosol and surface water samples where different classes of organic pollutants were determined. The content of polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), 1,1-dichloro-2,2-bis(4-chlorophenyl)ethene (4,4\u2032-DDE), and polyaromatic hydrocarbons (PAHs) were determined from the island of Texel (The 29 Netherlands) to Walvis Bay (Namibia) and Cape Town (South Africa).The concentrations of HCB range from 2 to 9 pg L-1 in water and from 56 to 145 pg m-3 in air.\n Concentrations of 4,4\u2019-DDE in water ranged from 0.3 to 1.4 pg L-1, which is similar to the values found in previous studies carried out in the North Atlantic (0.4\u20130.6 pg L-1). Atmospheric 4,4\u2019-DDE concentrations range from 0.1 to 0.9 pg m-3 were somewhat smaller than the values of 1.3\u20136.3 pg m-3 observed in the same area during one cruise carried out in April 1990.\n During the same cruises the contents of polycyclic aromatic hydrocarbons (PAHs) and one emerging class of pollutants (polychlorinated naphthalenes, PCNs) were determined. The highest PAH concentrations occurred in the European samples, and in samples close to West Africa and South Africa. Consistently low PAH concentrations were measured in the southern hemisphere open ocean samples (190-680 pg/m3). Concentrations showed a diurnal cycle, the day/night ratios of phenanthrene, 1-methylphenanthrene and fluoranthene were typically ~1.5-2.5:1. The mechanisms causing this pattern are not understood at present, but dynamic environmental processes are implicated. The highest PCN concentrations occurred in the European samples, but high values were also detected off the West African coast, and in the sample taken closest to South Africa.", "links": [ { diff --git a/datasets/ECA023.json b/datasets/ECA023.json index f435387415..c317570e42 100644 --- a/datasets/ECA023.json +++ b/datasets/ECA023.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA023", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic continent does not have stream\u2013river drainage systems, Antarctic lakes are thus the main sinks for water and solutes from the surrounding environment. Depending on their origin, the presence of a perennial ice cover, exposed rocks and soils in the watershed, seabirds and distance from the sea, the water may show very different characteristics \u2013 from almost distilled to salt-rich brine which does not freeze in winter.\nThis dataset regards the accumulation flux profiles and temporal trends of organochlorine pesticides such as DDT and HCH in two lake cores from King George Island, West Antarctica. In the lake core sediments with glacier melt water input, the accumulation flux of DDT shows an abnormal peak around the 1980s in addition to the expected one in the 1960s. In the lake core sediments without glacier melt water input, the accumulation flux of DDT shows a gradual decline trend after the peak in 1960s. This striking difference in the DDT flux profiles between the two lake cores is most likely caused by the regional climate warming and the resulted discharge of the DDT stored in the Antarctic ice cap into the lakes in the Antarctic glacier frontier, as already reported in 1996 for PCBs.", "links": [ { diff --git a/datasets/ECA051.json b/datasets/ECA051.json index 19f702ff44..dced79254b 100644 --- a/datasets/ECA051.json +++ b/datasets/ECA051.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA051", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim of the present work is to characterize the local atmospheric emissions levels and compare them to the component derived from global pollution in a remote site at South Hemisphere (Admiralty Bay located at King George Island in Antarctic Peninsula). Airborne particles, snow and soil/sediments samples were analyzed. Local-produced atmospheric aerosol dispersion was estimated for metals originated by fossil fuel burning from the permanent scientific stations using a simplified Gaussian model. Validation of atmospheric dispersion was established by in situ measurements. Soluble and insoluble particles deposited in freshly snow and airborne particles were analyzed by PIXE (Particle Induced X-Ray Emission) for the determination of the elemental mass concentration and to obtain the Mass Median Aerodynamic Diameter (MMAD). The results showed significant correlation between the concentration of atmospheric aerosol and the freshly deposited particles in the snow, and permitted an estimate of the atmospheric snow deposition factor for K, Cu, Zn, Fe, Pb, and Ti. Results of long-term aerosol data compilation suggest that besides the local aerosol sources, the continental atmospheric transport of airborne particles is not significantly affected by the airborne particles produced by local human impacts at King George Island.", "links": [ { diff --git a/datasets/ECA060.json b/datasets/ECA060.json index 17a6bbb240..df2e387e2a 100644 --- a/datasets/ECA060.json +++ b/datasets/ECA060.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECA060", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The concentrations of total mercury (HgT) and three bio-essential elements (phosphor, potassium, sodium) were analyzed in Antarctic seal hairs from a lake core spanning the past 2000 years and collected from King George Island (63823VS, 57800VW), West Antarctica. The HgT concentration shows a significant fluctuation while the levels of the three bio-essential elements remain almost constant. The rise and fall of the HgT concentration in the seal hairs are found to be closely coincided with ancient activities of gold and silver mining using Hg-amalgamation process around the world, especially in the Southern Hemisphere. Two profiles of HgT in other two lake cores, one affected by seal excrements and the other by penguin droppings, from the same region are similar to the one in seal hairs. The Hg concentration profile in the seal hairs is significantly correlated with the one in a peat bog of Southern Chile near King George Island. Since Hg is existent mainly at the form of methyl-mercury in seal hairs, this correlation supports a relationship and link between atmospheric mercury concentration and methyl-mercury production. Comparing with samples from American and European continents, the Antarctic seal hairs provide an archive of total mercury concentration in surface seawater of the South Ocean less affected by regional human activities, and this archive may provide a good reference for assessing the global Hg emissions, depositions and recycling in the past thousand years.", "links": [ { diff --git a/datasets/ECCO_L4_ANCILLARY_DATA_V4R4_V4r4.json b/datasets/ECCO_L4_ANCILLARY_DATA_V4R4_V4r4.json index 6a8377e876..ebf77623a1 100644 --- a/datasets/ECCO_L4_ANCILLARY_DATA_V4R4_V4r4.json +++ b/datasets/ECCO_L4_ANCILLARY_DATA_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_ANCILLARY_DATA_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds].", "links": [ { diff --git a/datasets/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4.json index 7dcacfa093..a4bcf3af4d 100644 --- a/datasets/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4.json index 125315fc8b..6c254a5b76 100644 --- a/datasets/ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4.json index b2191dc07e..94cf56837f 100644 --- a/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 8ae9773fb5..ecb2e7b299 100644 --- a/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4.json index cd66463835..44ef2140b2 100644 --- a/datasets/ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4.json index 956377fab3..c24c6075f2 100644 --- a/datasets/ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4.json index c80f39ad32..09865a5624 100644 --- a/datasets/ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged Gent-McWilliams ocean bolus velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 82da13d445..2acb7fb9b1 100644 --- a/datasets/ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged Gent-McWilliams ocean bolus ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4.json index f1b4da739b..d2aee69eff 100644 --- a/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4.json index 35c137ba21..3cb0e1b940 100644 --- a/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json index 20ff7b4c2b..91852a7d90 100644 --- a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json index b046beb8cb..0a69a924a8 100644 --- a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4.json index 8e94336b6c..987b64d7dc 100644 --- a/datasets/ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4.json index 1010526d46..ab78492ea2 100644 --- a/datasets/ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index 313613f393..f62fdf11f3 100644 --- a/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index f1d49449ac..31818b9068 100644 --- a/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4.json b/datasets/ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4.json index d79fdf00ef..5c02bd8731 100644 --- a/datasets/ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4.json +++ b/datasets/ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides geometric parameters for the regular 0.5-degree lat-lon grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides and the horizontal and vertical coordinates of grid cell centers and corners. Additional information related to the global domain geometry (e.g., bathymetry and land/ocean masks) are also included. However, users should note these domain geometry fields are approximations because they have been interpolated from the ECCO lat-lon-cap 90 (llc90) native model grid. Users interested in exact budget closure calculations for volume, heat, salt, or momentum should use ECCO fields provided on the llc90 grid. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4.json b/datasets/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4.json index 7433260e7b..3e1f573871 100644 --- a/datasets/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4.json +++ b/datasets/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json b/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json index c62a29c38d..f9c3eb0f25 100644 --- a/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json index 5dfd720099..46b0b385b6 100644 --- a/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json index 755a2908e7..9d27e700de 100644 --- a/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4.json index b2778db91e..029f8322e7 100644 --- a/datasets/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4.json index 73a7484cc5..d4e1b79a2b 100644 --- a/datasets/ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json index 4f3145b947..a4268c094e 100644 --- a/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index a3e79c9a8e..29f235218b 100644 --- a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 2f60fe7d8c..c33dd6bc1a 100644 --- a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json index 727a9ecd06..d5d368a5e8 100644 --- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json index 72a4c10d67..70e0858cde 100644 --- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4.json index f46f02b948..28c61b6eed 100644 --- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 378d6199c3..15d87cc60d 100644 --- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json index 5fcb445b0a..2c2d429abc 100644 --- a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json index 920104d842..b7575bce16 100644 --- a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json index 316527c0c4..621cafb125 100644 --- a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json index ed5ddc3fb2..53013a9e82 100644 --- a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json index b03784b139..019c8c1ad8 100644 --- a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json index 2bef0412ca..df79aed5f5 100644 --- a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json index 3ae1b91620..79226f611d 100644 --- a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 9e8ba927c8..2b19c7e711 100644 --- a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json index 99d295a9a6..b29069d43d 100644 --- a/datasets/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json index 485ba602dd..60fbb7486a 100644 --- a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json index d085263559..b1f6fb8ae6 100644 --- a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json index a040a9a517..817cc9568e 100644 --- a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json index d970e84d0f..df7c8e6fe1 100644 --- a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index 88f25ccad1..d7f10f0b3c 100644 --- a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index ccd36fbfba..a352f77fa3 100644 --- a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index e3a2919ca7..07bc0a8020 100644 --- a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 6e216564e6..6bc0f2b46d 100644 --- a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index 5cd91cb0b0..8a51cd5448 100644 --- a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 09d6c907fc..1f3e92b200 100644 --- a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4.json index 8fd9864587..9673721a23 100644 --- a/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 3d2586d0c8..d8168b1d26 100644 --- a/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4.json index 000d2e5f58..87bd33592f 100644 --- a/datasets/ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4.json index 8ba1ecda46..fdaa80b01f 100644 --- a/datasets/ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4.json index 6bc734e0e6..7f1d046d51 100644 --- a/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 7aab8041e2..6a6f1501e0 100644 --- a/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json index d78b8f342d..276e8d40e2 100644 --- a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json index a47198161e..0ebb608f76 100644 --- a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4.json index a69bfc0d6b..727fe06422 100644 --- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json index ce74a2d33f..40542430af 100644 --- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4_V4r4.json index 5b5ac2b6b0..4d06e85fb6 100644 --- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 234c12cc67..ff7675732e 100644 --- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json index 527dd5c687..0d5b96ef4b 100644 --- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index afa953f7fe..4422a9422f 100644 --- a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index a6407d518f..25d8919d8d 100644 --- a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json index 778ce224f2..f881cd6334 100644 --- a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json index c805c9911b..c90567e794 100644 --- a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json index 7f3d7ba7ec..13e3318a78 100644 --- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json index b93144b86e..f942e46bc9 100644 --- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json index d3f2262d2b..40b7b9e98c 100644 --- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json index d48fa0b13f..1f6fea22ee 100644 --- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json index 1845c2d3ff..f82f336a63 100644 --- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4B_V4r4b.json index 85ec633581..ebf612cefc 100644 --- a/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_05DEG_DAILY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4_V4r4.json index bdb2719728..2b435ab6fa 100644 --- a/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SSH_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4B_V4r4b.json index 350fa00b46..c4062e5a16 100644 --- a/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_05DEG_MONTHLY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4.json index 5fa0a0c33f..fee7d429c9 100644 --- a/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json index 28a80037b4..cfaa404645 100644 --- a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json index eea2a0c78c..f86e2a542b 100644 --- a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json index b61db5ea73..0ee10ac730 100644 --- a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json +++ b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 3629ec9290..10d2d320c6 100644 --- a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json index c91a06e81b..6b2205600b 100644 --- a/datasets/ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include dynamic sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; dynamic sea surface temperature (SST) from satellite radiometers [AVHRR], dynamic sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4.json index 509febbdda..c3e7fefbc8 100644 --- a/datasets/ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_STRESS_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_STRESS_05DEG_MONTHLY_V4R4_V4r4.json index 20521a7db4..5ec8a94e40 100644 --- a/datasets/ECCO_L4_STRESS_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_STRESS_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_STRESS_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json index bb8cf28a9a..0828c0be99 100644 --- a/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json index cd980ad317..61d5899b96 100644 --- a/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json index 7ffbd9de1e..003dc07b45 100644 --- a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json index 1104bcddbe..7f5dcf4e4c 100644 --- a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4_V4r4.json index 9d953d34ed..883f691560 100644 --- a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json index 6123471e97..088bb91907 100644 --- a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json +++ b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json index b9cb6d29a8..e5593d46c4 100644 --- a/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json +++ b/datasets/ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.", "links": [ { diff --git a/datasets/ECMWF_DATA_ARCHIVE.json b/datasets/ECMWF_DATA_ARCHIVE.json index a4c613a079..fc830786ea 100644 --- a/datasets/ECMWF_DATA_ARCHIVE.json +++ b/datasets/ECMWF_DATA_ARCHIVE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECMWF_DATA_ARCHIVE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Catalogue of ECMWF model archived data and products This\n catalogue describes the ECMWF archive according to the daily\n operational production. It does not reflect the past archive structure\n in detail. The catalogue is also available in pdf format.\n \n - Operational atmospheric model daily\n - Operational atmospheric model monthly\n - Operational wave model daily\n - Operational wave model monthly\n - Ensemble Prediction System (EPS) atmospheric\n - Ensemble Prediction System (EPS) wave\n - Seasonal forecast\n - Re-analysis (ERA) atmospheric model daily\n - Re-analysis (ERA) atmospheric model monthly\n - Re-analysis (ERA) wave model daily\n - Observational\n \n Access the ECMWF Data Catalogue:\n http://www.ecmwf.int\n \n [Summary Extracted from the ECMWF Homepage]\n", "links": [ { diff --git a/datasets/ECMWF_DEMETER.json b/datasets/ECMWF_DEMETER.json index 6af030d3c3..2000712430 100644 --- a/datasets/ECMWF_DEMETER.json +++ b/datasets/ECMWF_DEMETER.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECMWF_DEMETER", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "[Excerpt from: \nPalmer, T.N., A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, P. Délécluse, M. Déqué, E. Díez, F. J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi, J.-F. Guérémy, R. Hagedorn, M. Hoshen, N. Keenlyside, M. Latif, A. Lazar, E. Maisonnave, V. Marletto, A. P. Morse, B. Orfila, P. Rogel, J.-M. Terres and M. C. Thomson, Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER), ECMWF Technical Memorandum 434, 2004 ]\n\nThe DEMETER project (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) was conceived, and funded under the European Union Vth Framework Environment Programme. The principal aim of DEMETER was to advance the concept of multi- model ensemble prediction by installing a number of state-of-the-art global coupled ocean-atmosphere models on a single supercomputer, and to produce a series of six-month multi-model ensemble hindcasts with common archiving and common diagnostic software. Such a strategy posed substantial technical problems, as well as more mundane but nevertheless important issues (e.g. on agreeing units in which model variables were archived).", "links": [ { diff --git a/datasets/ECMWF_OPERATIONAL_WAVE.json b/datasets/ECMWF_OPERATIONAL_WAVE.json index f204c24344..dbc73a86bf 100644 --- a/datasets/ECMWF_OPERATIONAL_WAVE.json +++ b/datasets/ECMWF_OPERATIONAL_WAVE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECMWF_OPERATIONAL_WAVE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data sets contain data at the resolution of the data\nassimilation and forecast system in operational use at ECMWF. Since the\nresolution and internal representation of the archive may vary according to\nchanges in ECMWF's operational practice, data services associated with these\ndata sets include the provision of interpolation to requested resolutions and\nrepresentation forms.\n\nFour data sets are separately supported:\n\nAnalysis\n - Global wave analysis\n - Mediterranean wave analysis\n\nForecast\n - Global wave forecast\n - Mediterranean wave forecast\n\nAccess the ECMWF Wave Data Sets:\n\nhttp://apps.ecmwf.int/archive-catalogue/?class=od", "links": [ { diff --git a/datasets/ECMWF_OPER_EPS.json b/datasets/ECMWF_OPER_EPS.json index da2162c570..2031ae1f3a 100644 --- a/datasets/ECMWF_OPER_EPS.json +++ b/datasets/ECMWF_OPER_EPS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECMWF_OPER_EPS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data sets contain data at the resolution of the ensemble prediction\n forecast system in operational use at ECMWF. Since the resolution and internal\n representation of the archive may vary according to changes in ECMWF's\n operational practice, data services associated with these data sets include the\n provision of interpolation to requested resolutions and representation forms.\n \n Five data sets are separately supported:\n \n - Control Forecast\n + Surface ensemble\n + Pressure level ensemble\n + Model level ensemble\n + Wave model ensemble\n \n \n - Perturbed Forecast\n + Surface ensemble perturbed forecasts\n + Pressure level ensemble perturbed forecasts\n + Wave model ensemble\n \n [Summary Extracted from the ECMWF home page]", "links": [ { diff --git a/datasets/ECMWF_WCRP_TOGA.json b/datasets/ECMWF_WCRP_TOGA.json index 47645c86a4..fb0e9f127d 100644 --- a/datasets/ECMWF_WCRP_TOGA.json +++ b/datasets/ECMWF_WCRP_TOGA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECMWF_WCRP_TOGA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ECMWF created and maintains an archive of level III-A atmospheric data\n in support of projects associated with the World Climate Research\n Program (WCRP). This archive is directly interpolated from the ECMWF\n operational, full resolution, surface and pressure level data. It is\n accommodated the 10 year period beginning 1 January 1985, fulfilling\n ECMWF's role as a Tropical Ocean and Global Atmosphere (TOGA) Level\n III Atmospheric Data Centre.\n \n The Level III-A archive is subdivided into three classes of data sets:\n \n * Basic Level III\n * Supplementary Fields\n * Extension\n \n The data sets are based on quantities analyzed or computed within the\n ECMWF data assimilation scheme or from forecasts based on these\n analyses.\n \n The Basic Data Set contain selected analysed values in a compact form\n at a resolution of 2.5 degree x 2.5degree. They are particularly suitable for\n users with limited data processing resources. Derived quantities\n (fluxes, etc.) are not included, but can in principle be calculated\n from the data provided in the data sets.\n \n The Supplementary Fields Data Set contains additional surface data,\n fluxes and net radiation data derived from short-range forecasts used\n as first-guess data for the analyses. Most of the fields in this data\n set contain values accumulated over the first 6 (or 12) hours of the\n forecast. The exceptions, total cloud cover fields, contain\n instantaneous 6 (or 12) hour forecast values. This is a subset of the\n operational first-guess surface data.\n \n The Extension Data Set contains additional surface data, fluxes, net\n radiation data and precipitation derived from 24-hour forecast\n values. All the fields in this data set contain values accumulated\n between time step 12 and time step 36 of the forecast.\n \n The archive is currently maintained using the WMO FM 92-IX Ext GRIB\n (grid in binary) form of data representation, with ECMWF local\n versions of GRIB Table 2. All fields of data are global within the\n archive.\n \n A full extraction service is supported, enabling users to obtain\n sub-areas of data and data at various resolutions on regular Gaussian\n or latitude/longitude grids, or as spherical harmonics with selected\n triangular truncation. All extracted data are delivered using the GRIB\n representation.\n \n [Summary Extracted from the ECMWF home page]", "links": [ { diff --git a/datasets/ECO1BATT_001.json b/datasets/ECO1BATT_001.json index 5effa74978..be0e92a11e 100644 --- a/datasets/ECO1BATT_001.json +++ b/datasets/ECO1BATT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO1BATT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO1BATT Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS. The data are provided in 1 second intervals by the ISS, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data.\n\nThe ECO1BATT Version 1 data product contains layers of corrected and uncorrected attitude quaternions, spacecraft ephemeris data including Earth-centered inertial (ECI) position and velocity, and associated time elements. \n\nKnown Issues\n\n-\tCannot perform spatial query on ECO1BATT in NASA Earthdata Search: ECO1BATT does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO1BATT data products by orbit number instead.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO1BGEO_001.json b/datasets/ECO1BGEO_001.json index eb06a4eeb0..2841590479 100644 --- a/datasets/ECO1BGEO_001.json +++ b/datasets/ECO1BGEO_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO1BGEO_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO1BGEO Version 1 data product provides the geolocation information for the radiance values retrieved in the ECO1BRAD Version 1 data product. The ECO1BGEO data product should be used to georeference the ECO1BRAD, ECO2CLD, ECO2LSTE, ECO3ANCQA, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO1BGEO data product is provided as swath data.\n\nThe ECO1BGEO data product contains data layers for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water.\n\nKnown Issues\n\n-\tGeolocation accuracy: In cases where scenes were not successfully matched with the ortho-base, the geolocation error is significantly larger, with the worst-case geolocation error for uncorrected data being at 7 kilometers (km). Within the metadata of the ECO1BGEO file, if the field \"L1GEOMetadata/OrbitCorrectionPerformed\" is \"True,\" the data was corrected, and geolocation accuracy should be better than 50 m. If this is \"False,\" then the data was processed without correcting the geolocation and will have up to 7 km geolocation error.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO1BMAPRAD_001.json b/datasets/ECO1BMAPRAD_001.json index 618c6a99d9..ce504f4382 100644 --- a/datasets/ECO1BMAPRAD_001.json +++ b/datasets/ECO1BMAPRAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO1BMAPRAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO1BMAPRAD Version 1 data product combines the at-sensor calibrated radiance values retrieved for the ECO1BRAD data product and the geolocation information provided in the ECO1BGEO data product to produce a geotagged, resampled radiance product. The ECO1BMAPRAD data product is produced as a map registered product that is in a rotated geographic projection with a spatial resolution of 70 meters (m). The ECO1BMAPRAD data product accounts for the overlap and variable pixel size in the ECO1BRAD data product.\n\nThe ECO1BMAPRAD Version 1 data product contains data layers including the radiance values for the five thermal infrared (TIR) bands, digital number (DN) values for the shortwave infrared (SWIR) band, associated data quality indicators, latitude and longitude values, solar and view geometry information, and surface height.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tResampled data: The data has been resampled, so users interested in working with data closest to that acquired by the instrument may want to work with the swath products. \n\n-\tMissing scan data: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see section 3.3.2 of the User Guide (https://lpdaac.usgs.gov/documents/1323/ECO1B_User_Guide_V1.pdf).\n\n-\tCold bias: ECOSTRESS Level-1 Radiance data shows high correlation with in-situ ground measurements (R2 = 0.99 in all bands). Currently, ECOSTRESS has a cold bias of approximately 0.7 Kelvin (K), which will be corrected through calibration in future data releases.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO1BRAD_001.json b/datasets/ECO1BRAD_001.json index 3ddc6d445c..1ae2a0cfcf 100644 --- a/datasets/ECO1BRAD_001.json +++ b/datasets/ECO1BRAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO1BRAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO1BRAD Version 1 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) to 90 m at the edge of the swath. The ECO1BRAD data product is provided as swath data and does not contain geolocation information. The corresponding ECO1BGEO data product is required to georeference the ECO1BRAD data product. The geographic coverage of acquisitions for the ECO1BRAD Version 1 data product extends to areas outside of those indicated on the coverage map. However, corresponding higher-level products over these areas are not available at this time.\n\nThe ECO1BRAD Version 1 data product contains layers of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data.\n\nKnown Issues\n\n-\tCannot perform spatial query on ECO1BRAD in NASA Earthdata Search: ECO1BRAD does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO1BRAD data products by orbit number instead.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tMissing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see section 3.3.2 of the User Guide (https://lpdaac.usgs.gov/documents/1323/ECO1B_User_Guide_V1.pdf).\n\n-\tScan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in section 3.2 of the User Guide (https://lpdaac.usgs.gov/documents/1323/ECO1B_User_Guide_V1.pdf).\n\n-\tScan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in section 3.4 of the User Guide (https://lpdaac.usgs.gov/documents/1323/ECO1B_User_Guide_V1.pdf).\n\n-\tCold bias: ECOSTRESS Level-1 Radiance data shows high correlation with in-situ ground measurements (R2 = 0.99 in all bands). Currently, ECOSTRESS has a cold bias of approximately 0.7 Kelvin (K), which will be corrected through calibration in future data releases.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n", "links": [ { diff --git a/datasets/ECO2CLD_001.json b/datasets/ECO2CLD_001.json index 209977ae26..080e0c8147 100644 --- a/datasets/ECO2CLD_001.json +++ b/datasets/ECO2CLD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO2CLD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach. The details of the algorithm are provided in the Algorithm Theoretical Basis Document (ATBD). The corresponding ECO1BGEO data product is required to georeference the ECO2CLD data product.\n\nThe ECO2CLD Version 1 data product contains a single cloud mask layer. Information on how to interpret the bit fields in the cloud mask is provided in section 3.1 of the User Guide.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO2LSTE_001.json b/datasets/ECO2LSTE_001.json index 0d5f5db09b..6a00b568ba 100644 --- a/datasets/ECO2LSTE_001.json +++ b/datasets/ECO2LSTE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO2LSTE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.\n\nThe ECO2LSTE Version 1 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO2LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO2LSTE is provided as swath data and has a spatial resolution of 70 meters (m). The corresponding ECO1BGEO data product is required to georeference the ECO2LSTE data product.\n\nThe ECO2LSTE Version 1 data product contains layers of LST, emissivity for bands 1 through 5, quality control for LST&E, LST error, emissivity error for bands 1 through 5, wideband emissivity, and Precipitable Water Vapor (PWV).\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO3ANCQA_001.json b/datasets/ECO3ANCQA_001.json index 5d80735b9e..b0cacac54d 100644 --- a/datasets/ECO3ANCQA_001.json +++ b/datasets/ECO3ANCQA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO3ANCQA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product.\n\nThe ECO3ANCQA Version 1 data product contains layers of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask.\n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO3ETALEXIU_001.json b/datasets/ECO3ETALEXIU_001.json index 7dcf5bf5f7..4d3cf33c44 100644 --- a/datasets/ECO3ETALEXIU_001.json +++ b/datasets/ECO3ETALEXIU_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO3ETALEXIU_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe United States Department of Agriculture (USDA) ECO3ETALEXIU Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXIU data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (disALEXI). Described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/332/ECO3ETALEXIU_ATBD_V1.pdf), disALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. Many approaches exist for mapping ET spatially; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. The output ET is generated on a UTM grid at a spatial resolution of 30 meters.\n\nThe ECO3ETALEXIU Version 1 data product contains layers of daily ET, ET uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format.\n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO3ETALEXI_001.json b/datasets/ECO3ETALEXI_001.json index 12acdf12c6..a438a96388 100644 --- a/datasets/ECO3ETALEXI_001.json +++ b/datasets/ECO3ETALEXI_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO3ETALEXI_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe NASA Jet Propulsion Laboratory (JPL) ECO3ETALEXI Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXI data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI). Described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1000/ECO3ETALEXI_ATBD_V1.pdf), DisALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. There are many approaches for spatially mapping ET; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes and has now been evaluated in comparison with micrometeorological flux tower observations over crop, forest, grassland, wetland, and semiarid desert sites. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. ECO3ETALEXI is available for CONUS at 70-meter (m) pixel resolution.\n\nThe ECO3ETALEXI Version 1 data product contains layers of daily ET, ET uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format.\n\nKnown Issues \n\n- Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n- Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n- Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO3ETPTJPL_001.json b/datasets/ECO3ETPTJPL_001.json index 6b3ad25fd8..7fa5f5e1ae 100644 --- a/datasets/ECO3ETPTJPL_001.json +++ b/datasets/ECO3ETPTJPL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO3ETPTJPL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nECO3ETPTJPL Version 1 is a Level 3 (L3) product that provides evapotranspiration (ET) generated from data acquired by the ECOSTRESS radiometer instrument according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/335/ECO3ETPTJPL_ATBD_V1.pdf). The ET product is generated from the Level 2 data products for surface temperature and emissivity, the Level 1 geolocation information, and a significant number of ancillary data inputs from other sources. ET is set by various controls, including radiative and atmospheric demand, and environmental sensitivity, productivity, vegetation physiology, and phenology. PT-JPL is best utilized for natural ecosystems. The L3 ET product is used for creating the Level 4 products, Evaporative Stress Index (ESI) and Water Use Efficiency (WUE).\n\nThe ECO3ETPTJPL Version 1 data product contains layers of instantaneous ET, daily ET, canopy transpiration, soil evaporation, ET uncertainty, and interception evaporation. \n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO4ESIALEXIU_001.json b/datasets/ECO4ESIALEXIU_001.json index b7c2a24492..fed44a2fe6 100644 --- a/datasets/ECO4ESIALEXIU_001.json +++ b/datasets/ECO4ESIALEXIU_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO4ESIALEXIU_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe United States Department of Agriculture (USDA) ECO4ESIALEXIU Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/340/ECO4ESIALEXIU_ATBD_V1.pdf). ESI applications include indicating agricultural drought and observing vegetation stress. The dis-ALEXI USDA ESI product is generated on a UTM grid at a spatial resolution of 30 meters.\n\nThe ECO4ESIALEXIU Version 1 data product contains layers of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.\n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO4ESIALEXI_001.json b/datasets/ECO4ESIALEXI_001.json index 3d618aa7f8..57046a98fa 100644 --- a/datasets/ECO4ESIALEXI_001.json +++ b/datasets/ECO4ESIALEXI_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO4ESIALEXI_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe NASA Jet Propulsion Laboratory (JPL) ECO4ESIALEXI Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1002/ECO4ESIALEXI_ATBD_V1.pdf). ESI applications include indicating agricultural drought and observing vegetation stress. ECO4ESIALEXI is available for CONUS at 70-meter (m) pixel resolution.\n\nThe ECO4ESIALEXI Version 1 data product contains layers of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.\n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO4ESIPTJPL_001.json b/datasets/ECO4ESIPTJPL_001.json index ee88f39cd0..9de5ed5ebe 100644 --- a/datasets/ECO4ESIPTJPL_001.json +++ b/datasets/ECO4ESIPTJPL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO4ESIPTJPL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe ECO4ESIPTJPL Version 1 data product provides Evaporative Stress Index (ESI) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 (ESI_PT-JPL) Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/342/ECO4ESIPTJPL_ATBD_V1.pdf). The ESI product is derived from the ratio of the Level 3 actual evapotranspiration (ET) to potential ET (PET) calculated as part of the algorithm. The ESI is an indicator of potential drought and plant water stress emphasizing areas of sub-optimal plant productivity.\n\nThe ECO4ESIPTJPL Version 1 data product contains layers of ESI and PET.\n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO4WUE_001.json b/datasets/ECO4WUE_001.json index 04631199ef..af2bdad865 100644 --- a/datasets/ECO4WUE_001.json +++ b/datasets/ECO4WUE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO4WUE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science)\n\nThe ECO4WUE Version 1 data product provides Water Use Efficiency (WUE) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 WUE Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/346/ECO4WUE_ATBD_V1.pdf). WUE is the ratio of carbon stored by plants to water evaporated by plants. This ratio is given as grams of carbon stored per kilogram of water evaporated over the course of the day from sunrise to sunset on the day when the ECOSTRESS granule was acquired.\n\nThe ECO4WUE Version 1 data product contains a single layer of water use efficiency. \n\nKnown Issues\n\n-\tData acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECOA_0.json b/datasets/ECOA_0.json index f526f22bc3..ee20bf57f8 100644 --- a/datasets/ECOA_0.json +++ b/datasets/ECOA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECOA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "East Coast Ocean Acidification cruise", "links": [ { diff --git a/datasets/ECOHAB_0.json b/datasets/ECOHAB_0.json index 6bffb89127..ba95c4e188 100644 --- a/datasets/ECOHAB_0.json +++ b/datasets/ECOHAB_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECOHAB_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ECOHAB is a peer-reviewed, national, competitive program that funds regional-scale and targeted studies. Regional ecosystem investigations of the causes and impacts of HABs leading to development of model-based operational ecological forecasting capabilities in areas with severe, recurrent blooms are a high priority.", "links": [ { diff --git a/datasets/ECOMON_0.json b/datasets/ECOMON_0.json index b7ffec769f..078f54e0da 100644 --- a/datasets/ECOMON_0.json +++ b/datasets/ECOMON_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECOMON_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EcoMon is the NOAA Northeast Fisheries Science Center Ecosystem Monitoring program for the Northeast U.S. continental shelf. EcoMon main objective is hydrography and zooplankton sampling along the Mid-Atlantic and New England coasts . Funding for the project was provided by the NOAA JPSS PGRR program (https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/proving-grounds). Additional information for this project can be found in Turner et al., 2021 (https://www.sciencedirect.com/science/article/abs/pii/S0034425721004491).", "links": [ { diff --git a/datasets/ECO_L1B_ATT_002.json b/datasets/ECO_L1B_ATT_002.json index 61c6cc1e68..dabecb6ade 100644 --- a/datasets/ECO_L1B_ATT_002.json +++ b/datasets/ECO_L1B_ATT_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L1B_ATT_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Swath Attitude and Ephemeris Instantaneous Level 1B Global (ECO_L1B_ATT) Version 2 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS. The data are provided in 1 second intervals, and each product file contains vectors from the duration of the orbit. \nThe ECO_L1B_ATT Version 2 data product contains layers of attitude and ephemeris data generated by the ISS, which are used to start the geolocation process. These layers also include Earth-centered inertial (ECI) position and velocity, and associated time elements distributed in HDF5 format.\n\nKnown Issues\n\n-\tCannot perform spatial query on ECO_L1B_ATT in NASA Earthdata Search: ECO_L1B_ATT does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO_L1B_ATT data products by orbit number instead.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L1B_GEO_002.json b/datasets/ECO_L1B_GEO_002.json index b5cc7982cd..eff7c31d1d 100644 --- a/datasets/ECO_L1B_GEO_002.json +++ b/datasets/ECO_L1B_GEO_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L1B_GEO_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Swath Geolocation Instantaneous Level 1B Global (ECO_L1B_GEO) Version 2 data product provides the geolocation information for the radiance values retrieved in the ECO_L1B_RAD (https://doi.org/10.5067/ecostress/eco_l1b_rad.002) Version 2 data product. The geolocation product gives geo-tagging to each of the radiance pixels. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO_L1B_GEO data product is provided as swath data.\n\nThe ECO_L1B_GEO data product contains data layers for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water distributed in HDF5 format.\n\nImprovements/Changes from Previous Versions\n\n-\tIf the initial co-registration is of poor quality or fails, up to four retries are attempted using modified parameters to match the scene. See Section 4.2 of the User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\nKnown Issues\n\n-\tGeolocation accuracy: In cases where scenes were not successfully matched with the ortho-base, the geolocation error is significantly larger; the worst-case geolocation error for uncorrected data is 7 kilometers (km). Within the metadata of the ECO_L1B_GEO file, if the field \"L1GEOMetadata/OrbitCorrectionPerformed\" is \"True\", the data was corrected, and geolocation accuracy should be better than 50 m. If this field is \"False\", then the data was processed without correcting the geolocation and will have up to 7 km geolocation error.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L1B_RAD_002.json b/datasets/ECO_L1B_RAD_002.json index f77eab40ab..8c62cce52f 100644 --- a/datasets/ECO_L1B_RAD_002.json +++ b/datasets/ECO_L1B_RAD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L1B_RAD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Swath Top of Atmosphere Calibrated Radiance Instantaneous L1B Global 70 m (ECO_L1B_RAD) Version 2 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) and 90 m at the edge of the swath. The ECO_L1B_RAD data product is provided as swath data and does not contain geolocation information. The corresponding ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L1B_RAD data product. The geographic coverage of acquisitions for the ECO_L1B_RAD Version 2 data product extends to areas outside of those indicated on the coverage map. \nThe ECO_L1B_RAD Version 2 data product contains layers of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data distributed in HDF5 format.\n\nImprovements/Changes from Previous Versions\n\n-\tA radiance calibration has been applied to correct the ~1K cold bias that has been observed in previous studies. These values are described in Section 3 of the User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\nKnown Issues\n\n-\tCannot perform spatial query on ECO_L1B_RAD in NASA Earthdata Search: ECO_L1B_RAD does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO_L1B_RAD data products by orbit number instead.\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tMissing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tScan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf). \n\n-\tScan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf). \n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L1CG_RAD_002.json b/datasets/ECO_L1CG_RAD_002.json index 9d73656e4b..892560e374 100644 --- a/datasets/ECO_L1CG_RAD_002.json +++ b/datasets/ECO_L1CG_RAD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L1CG_RAD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Gridded Top of Atmosphere Calibrated Radiance Instantaneous Level 1C Global 70 m (ECO_L1CG_RAD) Version 2 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. This product is a gridded version of the ECO_L1B_RAD (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_RAD.002) Version 2 data product that has been resampled by nearest neighbor, projected to a globally snapped 0.0006\u00b0 grid, and repackaged as the ECO_L1CG_RAD data product.\nThe ECO_L1CG_RAD Version 2 data product contains 12 layers distributed in an HDF5 format file containing radiance values for the five TIR bands, associated data quality indicators, and cloud and water masks. \n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tMissing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tScan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tScan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L1CT_RAD_002.json b/datasets/ECO_L1CT_RAD_002.json index 75fa8da392..0414aef9f3 100644 --- a/datasets/ECO_L1CT_RAD_002.json +++ b/datasets/ECO_L1CT_RAD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L1CT_RAD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Tiled Top of Atmosphere Calibrated Radiance Instantaneous Level 1 Global 70 m (ECO_L1CT_RAD) Version 2 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. This tiled data product is generated from the ECO_L1CG_RAD (https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data product using a modified version of the Military Grid Reference System (MGRS) (https://hls.gsfc.nasa.gov/products-description/tiling-system/), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each ECOSTRESS pixel can be assumed to remain at the same location at each timestep within a tile.\nThe ECO_L1CT_RAD Version 2 data product contains 12 layers distributed in Cloud Optimized GeoTIFF (COG) format consisting of separate files containing five TIR bands, associated data quality indicators, and cloud and water masks. \n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tMissing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tScan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tScan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the ECO_L1B_RAD User Guide (https://lpdaac.usgs.gov/documents/1491/ECO1B_User_Guide_V2.pdf).\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L2G_CLOUD_002.json b/datasets/ECO_L2G_CLOUD_002.json index 12442678df..81e0d4fcdd 100644 --- a/datasets/ECO_L2G_CLOUD_002.json +++ b/datasets/ECO_L2G_CLOUD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2G_CLOUD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Gridded Cloud Mask Instantaneous L2 Global 70 m (ECO_L2G_CLOUD) Version 2 data product is derived using a single-channel Bayesian cloud threshold with a look-up-table (LUT) approach. The ECO_L2G_CLOUD product provides a cloud mask that can be used to determine cloud cover for accurate land surface temperature and evapotranspiration estimation. This data product is a gridded version of the ECO_L2_CLOUD Version 2 product that was resampled using nearest neighbor, projected to a globally snapped 0.0006\u00b0 grid, and repackaged as the ECO_L2G_CLOUD Version 2 data product.\nThe ECO_L2G_CLOUD Version 2 data product contains two cloud mask layers: cloud confidence and final cloud mask. Information on how to interpret the cloud confidence and cloud mask layers is provided in Table 7 of the ECO_L2_CLOUD Version 2 User Guide. \n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L2G_LSTE_002.json b/datasets/ECO_L2G_LSTE_002.json index 9dca309224..4c72c6132c 100644 --- a/datasets/ECO_L2G_LSTE_002.json +++ b/datasets/ECO_L2G_LSTE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2G_LSTE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Gridded Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2G_LSTE) Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO_L2G_LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. This data product is a gridded version of the ECO_L2_LSTE (https://doi.org/10.5067/ECOSTRESS/ECO_L2_LSTE.002) Version 2 data product that was resampled using nearest neighbor, projected to a globally snapped 0.0006\u00b0 grid, and repackaged as the ECO_L2G_LSTE data product. The ECO_L2G_LSTE product is provided as gridded data and has a spatial resolution of 70 meters (m). The ECO_L2G_LSTE Version 2 data product contains 8 layers distributed in an HDF5 format file including LST, LST error, wideband emissivity, height, view zenith angle, quality flags, and cloud and water masks.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tData alert: All users of ECOSTRESS L2 v002 products (ECO_L2T_LSTE, ECO_L2_LSTE, ECO_L2G_LSTE) should be aware that the cloud mask information previously available in the Quality Control (QC) layer in v001, is not available in the v002 QC layer. Instead, users should be using the \u2018cloud_mask\u2019 layer in the L2 LSTE product, or the cloud information in the standard cloud mask products (ECO_L2_CLOUD, ECO_L2T_CLOUD, ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide). For v002, the information described by the mandatory QA flags in the QC bit mask can be found in Section 2.4 of the User Guide (https://lpdaac.usgs.gov/documents/1574/ECOL2_User_Guide_V2.pdf).", "links": [ { diff --git a/datasets/ECO_L2T_LSTE_002.json b/datasets/ECO_L2T_LSTE_002.json index 641c1d2e0d..e70d0eab35 100644 --- a/datasets/ECO_L2T_LSTE_002.json +++ b/datasets/ECO_L2T_LSTE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2T_LSTE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS [website](https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO_L2T_LSTE data product was derived using a physics-based Temperature/Emissivity Separation (TES) algorithm. This tiled data product is subset from the ECO_L2G_LSTE data product using a modified version of the Military Grid Reference System (MGRS) which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.\n\nThe ECO_L2T_LSTE Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains seven layers including LST, LST error, wideband emissivity, quality flags, height, and cloud and water masks.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tData alert: All users of ECOSTRESS L2 v002 products (ECO_L2T_LSTE, ECO_L2_LSTE, ECO_L2G_LSTE) should be aware that the cloud mask information previously available in the Quality Control (QC) layer in v001, is not available in the v002 QC layer. Instead, users should be using the \u2018cloud_mask\u2019 layer in the L2 LSTE product, or the cloud information in the standard cloud mask products (ECO_L2_CLOUD, ECO_L2T_CLOUD, ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide). For v002, the information described by the mandatory QA flags in the QC bit mask can be found in Section 2.4 of the User Guide (https://lpdaac.usgs.gov/documents/1574/ECOL2_User_Guide_V2.pdf).\n\n", "links": [ { diff --git a/datasets/ECO_L2T_STARS_002.json b/datasets/ECO_L2T_STARS_002.json index e012286e35..15ce06154b 100644 --- a/datasets/ECO_L2T_STARS_002.json +++ b/datasets/ECO_L2T_STARS_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2T_STARS_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) and Harmonized Landsat Sentinel (HLS) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.\n\nThe ECO_L2T_STARS Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each band is distributed as a separate COG. This product contains four layers including NDVI, NDVI uncertainty, albedo, and albedo uncertainty. The ECO_L2T_STARS ancillary NDVI and albedo product is only generated for corresponding daytime ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) Version 2 tiles.\n\nThe ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) ( https://lpdaac.usgs.gov/product_search/?query=viirs&status=Operational&view=list&sort=title) and Harmonized Landsat Sentinel (HLS) ( https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) ( https://doi.org/10.5067/VIIRS/VNP09GA.001) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.\n\nThe ECO_L2T_STARS Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format using a modified version of the Military Grid Reference System (MGRS) ( https://hls.gsfc.nasa.gov/products-description/tiling-system/), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each band is distributed as a separate COG. This product contains four layers including NDVI, NDVI uncertainty, albedo, and albedo uncertainty. The ECO_L2T_STARS ancillary NDVI and albedo product is only generated for corresponding daytime ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) ( https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002) Version 2 tiles. \n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L2_CLOUD_002.json b/datasets/ECO_L2_CLOUD_002.json index 0d05b5a98e..54d4b8b221 100644 --- a/datasets/ECO_L2_CLOUD_002.json +++ b/datasets/ECO_L2_CLOUD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2_CLOUD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Swath Cloud Mask Instantaneous L2 Global 70 m (ECO_L2_CLOUD) Version 2 data product is derived using a single-channel Bayesian cloud threshold with a look-up-table (LUT) approach. The ECOSTRESS Level 2 cloud product provides a cloud mask that can be used to determine cloud cover for accurate land surface temperature and evapotranspiration estimation. The corresponding ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L2_CLOUD data product.\n \nThe ECO_L2_CLOUD Version 2 data product contains two cloud mask layers: Brightness temperature LUT test and Final cloud mask. Information on how to interpret the bit fields in the cloud mask is provided in Table 7 of the User Guide.\n\nImprovements/Changes from Previous Versions\n\n-\tECO_L2_CLOUD Version 2 data product algorithm has been revamped to have a more rigorous single-channel Bayesian cloud threshold with a look-up-table (LUT). See Section 3.1 of the User Guide (https://lpdaac.usgs.gov/documents/1574/ECOL2_User_Guide_V2.pdf).\n\n-\tAddition of brightness temperature LUT test and brightness temperature difference test data layers.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L2_LSTE_002.json b/datasets/ECO_L2_LSTE_002.json index f86357c175..b824bc48c8 100644 --- a/datasets/ECO_L2_LSTE_002.json +++ b/datasets/ECO_L2_LSTE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L2_LSTE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).\nThe ECOSTRESS Swath Land Surface Temperature and Emissivity Instantaneous L2 Global 70 m (ECO_L2_LSTE) Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO_L2_LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO_L2_LSTE is provided as swath data and has a spatial resolution of 70 meters (m). The corresponding ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L2_LSTE data product.\nThe ECO_L2_LSTE Version 2 data product contains layers of LST, emissivity for bands 1 through 5, quality control for LST&E, LST error, emissivity error for bands 1 through 5, wideband emissivity, Precipitable Water Vapor (PWV), cloud mask, and water mask.\n\nImprovements/Changes from Previous Versions\n\n-\tAddition of cloud mask and water mask layers.\n\n-\tAddition of ECOSTRESS Gridded Land Surface Temperature and Emissivity Instantaneous L2 Global 70 m v002 (ECO_L2G_LSTE.002 (https://doi.org/10.5067/ECOSTRESS/ECO_L2G_LSTE.002)) and ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous L2 Global 70 m v002 (ECO_L2T_LSTE.002 (https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002)) data products.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tData alert: All users of ECOSTRESS L2 v002 products (ECO_L2T_LSTE, ECO_L2_LSTE, ECO_L2G_LSTE) should be aware that the cloud mask information previously available in the Quality Control (QC) layer in v001, is not available in the v002 QC layer. Instead, users should be using the \u2018cloud_mask\u2019 layer in the L2 LSTE product, or the cloud information in the standard cloud mask products (ECO_L2_CLOUD, ECO_L2T_CLOUD, ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see Section 3 of the User Guide). For v002, the information described by the mandatory QA flags in the QC bit mask can be found in Section 2.4 of the User Guide (https://lpdaac.usgs.gov/documents/1574/ECOL2_User_Guide_V2.pdf).", "links": [ { diff --git a/datasets/ECO_L3G_JET_002.json b/datasets/ECO_L3G_JET_002.json index b09a185a76..a03ce7e90b 100644 --- a/datasets/ECO_L3G_JET_002.json +++ b/datasets/ECO_L3G_JET_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3G_JET_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Gridded Evapotranspiration Instantaneous and Daytime L3 Global 70 m (ECO_L3G_JET) Version 2 data product provides instantaneous canopy transpiration, leaf surface evaporation, and soil moisture evaporation using the Priestley-Taylor formula. This data product is mosaicked from the L3 tiled JET (ECO_L3T_JET (https://doi.org/10.5067/ECOSTRESS/ECO_L3T_JET.002)) product, projected to a globally snapped 0.0006\u00b0 grid, and has a spatial resolution of 70 meters (m).\nThe ECO_L3G_JET Version 2 data product contains 12 layers distributed in an HDF5 format file including ETdaily, ETinstUncertainty, PTJPLSMinst, STICinst, MOD16inst, BESSinst, STICcanopy, PTJPLSMcanopy, PTJPLSMinterception, PTJPLSMsoil, cloud mask, and water mask.\n\nImprovements/Changes from Previous Versions\n\n-\tThis product utilizes a modified version of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model from the ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m ([ECO3ETPTJPL](https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001)) Version 1 data product which incorporates soil moisture as an added constraint (PT-JPL-SM). In addition to PT-JPL-SM, this data product includes the outputs from other models not included in ECO3ETPTJPL v001, details on what is included can be found in Section 5.4 of the [User Guide](https://lpdaac.usgs.gov/documents/1655/ECO_L1C-4_Grid_Tile_User_Guide_V2.pdf).\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L3G_MET_002.json b/datasets/ECO_L3G_MET_002.json index 92d6f22111..a807056309 100644 --- a/datasets/ECO_L3G_MET_002.json +++ b/datasets/ECO_L3G_MET_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3G_MET_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Gridded Downscaled Meteorology Instantaneous L3 Global 70 m (ECO_L3G_MET) Version 2 data product provides instantaneous near-surface air temperature (Ta) and relative humidity (RH) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and Ta or RH from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. This data product is mosaicked from the L3 tiled MET (ECO_L3T_MET (https://doi.org/10.5067/ECOSTRESS/ECO_L3T_MET.002)) product, projected to a globally snapped 0.0006\u00b0 grid, and has a spatial resolution of 70 meters (m).\n\nThe ECO_L3G_MET Version 2 data product contains four layers distributed in an HDF5 format file including Ta, RH, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L3G_SEB_002.json b/datasets/ECO_L3G_SEB_002.json index aba6be1078..cc1688f069 100644 --- a/datasets/ECO_L3G_SEB_002.json +++ b/datasets/ECO_L3G_SEB_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3G_SEB_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Gridded Surface Energy Balance Instantaneous L3 Global 70 m (ECO_L3G_SEB) Version 2 data product provides estimated incoming surface radiation (Rg) and net radiation (Rn) aligned with each daytime ECOSTRESS overpass. The Rg was generated using the Forest Light Environmental Simulator (FLiES) radiative transfer model implemented in an artificial neural network using Cloud Optical Thickness (COT) and Aerosol Optical Thickness (AOT) from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) along with albedo from ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 as variables. The Rg output from the FLiES model was bias corrected to Rg from GEOS-FP. The Rn is an output from the Breathing Earth System Simulator (BESS) algorithm. This data product is mosaicked from the L3 tiled SEB (ECO_L3T_SEB (https://doi.org/10.5067/ECOSTRESS/ECO_L3T_SEB.002)) product, projected to a globally snapped 0.0006\u00b0 grid, and has a spatial resolution of 70 meters (m).\n\nThe ECO_L3G_SEB Version 2 data product contains four layers distributed in an HDF5 format file including Rg, Rn, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tMissing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the \u2018cloud mask\u2019 layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).", "links": [ { diff --git a/datasets/ECO_L3G_SM_002.json b/datasets/ECO_L3G_SM_002.json index 8762b8152b..1ca110b0f3 100644 --- a/datasets/ECO_L3G_SM_002.json +++ b/datasets/ECO_L3G_SM_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3G_SM_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.\n\nThe ECOSTRESS Gridded Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3G_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. This data product is mosaicked from the L3 tiled SM (ECO_L3T_SM) product, is projected to a globally snapped 0.0006\u00b0 grid, and has a spatial resolution of 70 m.\n\nThe ECO_L3G_SM Version 2 data product contains three layers distributed in an HDF5 file including SM, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tMissing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the \u2018cloud mask\u2019 layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).", "links": [ { diff --git a/datasets/ECO_L3T_JET_002.json b/datasets/ECO_L3T_JET_002.json index f5a1278cf3..18b971c59a 100644 --- a/datasets/ECO_L3T_JET_002.json +++ b/datasets/ECO_L3T_JET_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3T_JET_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Evapotranspiration Instantaneous and Daytime L3 Global 70 m (ECO_L3T_JET) Version 2 data product provides instantaneous canopy transpiration, leaf surface evaporation, and soil moisture evaporation using the Priestley-Taylor formula. This data product is tiled using a modified version of the Military Grid Reference System (MGRS (https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.\n\nThe ECO_L3T_JET Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains 12 layers including ETdaily, ETinstUncertainty, PTJPLSMinst, STICinst, MOD16inst, BESSinst, STICcanopy, PTJPLSMcanopy, PTJPLSMinterception, PTJPLSMsoil, cloud mask, and water mask.\n\nImprovements/Changes from Previous Versions\n\n-\tThis product utilizes a modified version of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model from the ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m (ECO3ETPTJPL (https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001)) Version 1 data product which incorporates soil moisture as an added constraint (PT-JPL-SM). In addition to PT-JPL-SM, this data product includes the outputs from other models not included in ECO3ETPTJPL v001, details on what is included can be found in Section 5.4 of the User Guide (https://lpdaac.usgs.gov/documents/1655/ECO_L1C-4_Grid_Tile_User_Guide_V2.pdf).\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n", "links": [ { diff --git a/datasets/ECO_L3T_MET_002.json b/datasets/ECO_L3T_MET_002.json index 9c0528ac43..4248dc7865 100644 --- a/datasets/ECO_L3T_MET_002.json +++ b/datasets/ECO_L3T_MET_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3T_MET_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Downscaled Meteorology Instantaneous L3 Global 70 m (ECO_L3T_MET) Version 2 data product provides instantaneous near-surface air temperature (Ta) and relative humidity (RH) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and Ta or RH from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled meteorology estimates are recorded into the ECO_L3T_MET data product and tiled using a modified version of the Military Grid Reference System (MGRS (https://hls.gsfc.nasa.gov/products-description/tiling-system/)) which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.\n\nThe ECO_L3T_MET Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format with each data layer distributed as a separate COG. This product contains four layers including Ta, RH, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L3T_SEB_002.json b/datasets/ECO_L3T_SEB_002.json index 5800ff2b91..5f37b03ec4 100644 --- a/datasets/ECO_L3T_SEB_002.json +++ b/datasets/ECO_L3T_SEB_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3T_SEB_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Surface Energy Balance Instantaneous L3 Global 70 m (ECO_L3T_SEB) Version 2 data product provides estimated incoming surface radiation (Rg) and net radiation (Rn) aligned with each daytime ECOSTRESS overpass. The Rg was generated using the Forest Light Environmental Simulator (FLiES) radiative transfer model implemented in an artificial neural network using Cloud Optical Thickness (COT) and Aerosol Optical Thickness (AOT) from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) along with albedo from ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS (https://doi.org/10.5067/ECOSTRESS/ECO_L2T_STARS.002)) Version 2 as variables. The Rg output from the FLiES model was bias corrected to Rg from GEOS-FP. The Rn is an output from the Breathing Earth System Simulator (BESS) algorithm. This data product is tiled using a modified version of the Military Grid Reference System (MGRS (https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.\n\nThe ECO_L3T_SEB Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format with each data layer distributed as a separate COG. This product contains four layers including Rg, Rn, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tMissing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the \u2018cloud mask\u2019 layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).\n", "links": [ { diff --git a/datasets/ECO_L3T_SM_002.json b/datasets/ECO_L3T_SM_002.json index 907d9d1553..bbe57625f9 100644 --- a/datasets/ECO_L3T_SM_002.json +++ b/datasets/ECO_L3T_SM_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L3T_SM_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.\nThe ECOSTRESS Tiled Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3T_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled soil moisture estimates are recorded into the ECO_L3T_SM data product and tiled using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.\nThe ECO_L3T_SM Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains three layers including SM, cloud mask, and water mask.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n\n-\tMissing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the \u2018cloud mask\u2019 layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).", "links": [ { diff --git a/datasets/ECO_L4G_ESI_002.json b/datasets/ECO_L4G_ESI_002.json index c2148ebaaa..abbff8a48b 100644 --- a/datasets/ECO_L4G_ESI_002.json +++ b/datasets/ECO_L4G_ESI_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L4G_ESI_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.\n\nThe ECOSTRESS Gridded Evaporative Stress Index PT-JPL Instantaneous L4 Global 70 m (ECO_L4G_ESI) Version 2 data product uses the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) model to generate estimates of both actual and potential instantaneous evapotranspiration (ET). The potential evapotranspiration (PET) estimate represents the maximum expected ET if there were no water stress to plants on the ground. The ratio of the actual ET estimate to the PET estimate forms an index representing the water stress of plants.\n\nThe ECO_L4G_ESI Version 2 data product is available globally and is projected to a globally snapped 0.0006\u00b0 grid with a 70 meter spatial resolution and is distributed in HDF5. Each granule contains layers of Evaporative Stress Index (ESI), PET, cloud mask, and water mask. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L4G_WUE_002.json b/datasets/ECO_L4G_WUE_002.json index 955ca6b36c..d3ae42c55b 100644 --- a/datasets/ECO_L4G_WUE_002.json +++ b/datasets/ECO_L4G_WUE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L4G_WUE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.\n\nThe ECOSTRESS Gridded Water Use Efficiency Instantaneous L4 Global 70 m (ECO_L4G_WUE) Version 2 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE, the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants. \n\nThe ECO_L4G_WUE Version 2 data product is available globally and projected to a globally snapped 0.0006\u00b0 grid with a 70 meter spatial resolution and is distributed in HDF5. Each granule contains layers of Water Use Efficiency (WUE), Water Gross Primary Production (GPP), cloud mask, and water mask. A low-resolution browse is also available showing daily WUE as a stretched image with a color ramp in JPEG format.\n\nKnown Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L4T_ESI_002.json b/datasets/ECO_L4T_ESI_002.json index ce4d92a5cb..9239d0f4a1 100644 --- a/datasets/ECO_L4T_ESI_002.json +++ b/datasets/ECO_L4T_ESI_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L4T_ESI_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.\n\nThe ECOSTRESS Tiled Evaporative Stress Index PT-JPL Instantaneous L4 Global 70 m (ECO_L4T_ESI) Version 2 data product uses the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) model to generate estimates of both actual and potential instantaneous evapotranspiration (ET). The potential evapotranspiration (PET) estimate represents the maximum expected ET if there were no water stress to plants on the ground. The ratio of the actual ET estimate to the PET estimate forms an index representing the water stress of plants.\n\nThe ECO_L4T_ESI Version 2 data product is available globally in Cloud Optimized GeoTIFF (COG) format and uses a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each granule contains separate COG files for each layer: Evaporative Stress Index (ESI), PET, and cloud mask, and water mask. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.\n\n Known Issues\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECO_L4T_WUE_002.json b/datasets/ECO_L4T_WUE_002.json index f3a692f4e8..c36318138b 100644 --- a/datasets/ECO_L4T_WUE_002.json +++ b/datasets/ECO_L4T_WUE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECO_L4T_WUE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science).\n\nThe ECOSTRESS Tiled Water Use Efficiency Instantaneous L4 Global 70 m (ECO_L4T_WUE) Version 2 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE, the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants. \n\nThe ECO_L4T_WUE Version 2 data product is available globally in Cloud Optimized GeoTIFF (COG) format and uses a modified version of the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each granule contains separate COG files for each layer: Water Use Efficiency (WUE), Water Gross Primary Production, cloud mask, and water mask. A low-resolution browse is also available showing WUE as a stretched image with a color ramp in JPEG format.\n\n**Known Issues**\n\n-\tData acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.\n\n-\tData acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.\n\n-\tData acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.", "links": [ { diff --git a/datasets/ECS_0.json b/datasets/ECS_0.json index a7416999b2..abe433cb1f 100644 --- a/datasets/ECS_0.json +++ b/datasets/ECS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ECS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "East China Sea (ECS) measurements spanning 1997 and 1998.", "links": [ { diff --git a/datasets/EDA_v2_1.json b/datasets/EDA_v2_1.json index f45ff251d1..b9e43b512b 100644 --- a/datasets/EDA_v2_1.json +++ b/datasets/EDA_v2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EDA_v2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report on the physical environment-based classification for the whole Antarctic Continent. This classification built on the success Landcare Research scientists have achieved in developing a classification of New Zealand's terrestrial environments (Land Environments of New Zealand or LENZ - Leathwick et al. 2002b). The classification was designed to provide a data-derived, spatially explicit delineation of environmental variables in Antarctica, to be used for a range of management activities including identification of priority sites for protection, environmental monitoring, and assessment of risks associated with human activities.\n\nDownload contents include PDF of the report and vector and raster shapefiles.\n\nEnvironmental parameters used\n\nAir temperature (mean annual and seasonal), wind, mean annual wind speed, solar radiation, period of year with normal diurnal pattern, slope, land and ice cover, geology.\n\n\nDescriptive labels have been assigned to each environment classified within the resulting raster data layer. There are as follows;\n\nA. Antarctic Peninsula northern geologic\nB. Antarctic Peninsula mid-northern latitudes geologic\nC. Antarctic Peninsula southern geologic\nD. East Antarctic coastal geologic\nE. Antarctic Peninsula and Alexander Island main ice fields and glaciers\nF. Larsen Ice Shelf\nG. Antarctic Peninsula offshore island geologic\nH. East Antarctic low latitude glacier tongues\nI. East Antarctic ice shelves\nJ. Southern latitude coastal fringe ice shelves and floating glaciers\nK. Northern latitude ice shelves\nL. Continental coastal-zone ice sheet\nM. Continental mid-latitude sloping ice\nN. East Antarctic inland ice sheet\nO. West Antarctic Ice Sheet\nP. Ross and Ronne-Filchner ice shelves\nQ. East Antarctic high interior ice sheet\nR. Transantarctic Mountains geologic\nS. McMurdo - South Victoria Land geologic\nT. Inland continental geologic\nU. North Victoria Land geologic\n\nThe sizes of the environments are extremely varied - from 3.7 million square kilometres down to a comparatively small 966 square kilometres.", "links": [ { diff --git a/datasets/EDDIES_0.json b/datasets/EDDIES_0.json index a4094c5a4c..199fc9c095 100644 --- a/datasets/EDDIES_0.json +++ b/datasets/EDDIES_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EDDIES_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the EDDIES program near Bermuda in the Atlantic Ocean from 2004 and 2005.", "links": [ { diff --git a/datasets/EDM_SA_Vegetation_1149_1.json b/datasets/EDM_SA_Vegetation_1149_1.json index 89f59fd0d3..7125ed2da6 100644 --- a/datasets/EDM_SA_Vegetation_1149_1.json +++ b/datasets/EDM_SA_Vegetation_1149_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EDM_SA_Vegetation_1149_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988, averaged into a single year (Moorcroft et al., 2001). Data for the three estimates are provided in both ASCII text and in NetCDF formatted files. ED is an individual-based terrestrial ecosystem model that predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP and evapotranspiration) from climate, soil, and land-use inputs. The model consists of integrated sub-models governing processes such as leaf-level physiology, plant allocation, allometry, phenology, dispersal, the effects of fire disturbances, and below-ground sub-models for soil carbon dynamics and hydrology. Using a new method for scaling-up it is possible to predict ED's large-scale behavior without simulating the fate of every plant individually. ED is used to examine how climate and edaphic factors, natural disturbances, and human land-use practices affect ecosystem structure and fluxes. This data set contains six zip files which each uncompress into six unique subdirectories. Each subdirectory is described in detail in the Model Product Description section of this document. Installation and execution instructions are provided in the Model Documentation and User's Guide section of this document. ", "links": [ { diff --git a/datasets/EDM_US_Carbon_1160_1.json b/datasets/EDM_US_Carbon_1160_1.json index 4cc0bb8c6d..d930b30532 100644 --- a/datasets/EDM_US_Carbon_1160_1.json +++ b/datasets/EDM_US_Carbon_1160_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EDM_US_Carbon_1160_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.", "links": [ { diff --git a/datasets/EF_Data_Mexico_1693_1.json b/datasets/EF_Data_Mexico_1693_1.json index f4b3d34fa6..40e11fa205 100644 --- a/datasets/EF_Data_Mexico_1693_1.json +++ b/datasets/EF_Data_Mexico_1693_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EF_Data_Mexico_1693_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.", "links": [ { diff --git a/datasets/EGEE3_0.json b/datasets/EGEE3_0.json index 567793faa5..6bea6f2e0b 100644 --- a/datasets/EGEE3_0.json +++ b/datasets/EGEE3_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EGEE3_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Guinea off of west-central Africa in 2006 as part of the third cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).", "links": [ { diff --git a/datasets/EGEE5_0.json b/datasets/EGEE5_0.json index 550d86fb35..95b732adcb 100644 --- a/datasets/EGEE5_0.json +++ b/datasets/EGEE5_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EGEE5_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Guinea off of west-central Africa in 2007 as part of the fifth cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).", "links": [ { diff --git a/datasets/EIC12.json b/datasets/EIC12.json index 69ee8c3aaa..156939a7dc 100644 --- a/datasets/EIC12.json +++ b/datasets/EIC12.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EIC12", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimate of the vulnerability of surface waters in Wales to\nacidification from the effects of atmospheric pollution. Estimates\nmade on the basis of the sensitivity of receptor soils geology,\nwatercourses and vegetation, each of which are recorded by\ndigitization from published map sources.", "links": [ { diff --git a/datasets/EKAMSAT_Pilot_ASTRAL_0.json b/datasets/EKAMSAT_Pilot_ASTRAL_0.json index 59e71cf284..869e7ca4c8 100644 --- a/datasets/EKAMSAT_Pilot_ASTRAL_0.json +++ b/datasets/EKAMSAT_Pilot_ASTRAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EKAMSAT_Pilot_ASTRAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Enhancing Knowledge of the Arabian Sea Marine environment through Science and Advanced Training (EKAMSAT) is a collaborative Indo-US field campaign funded by the Ministry of Earth Sciences, Govt. of India and the Office of Naval Research, USA, focused on the acquisition of contemporary oceanographic and atmospheric datasets deemed critical for improving the predictive skills of operational monsoon models. The atmospheric and oceanographic datasets acquired will be used primarily to examine how oceanographic conditions such as recent SST increases, enhanced stratification, formation of boundary layers, a recurring warm water pool and other changes in the Arabian Sea are influencing the onset, intensity and length of the Indian monsoon. The campaign commenced with a pilot study in June 2023 in preparation for subsequent full-fledged field campaigns in May-June of 2024-2025. This and the following cruises target atmospheric and oceanographic measurements supplemented with limited bio-optical and biogeochemical observations to advance understanding of the influence of seasonally evolving mixed layer on biological productivity and biogeochemical cycling in northern Arabian Sea. Microtops data available at https://aeronet.gsfc.nasa.gov/new_web/cruises_v3/Roger_Revelle_23_0.html .", "links": [ { diff --git a/datasets/ELOKA001_1.json b/datasets/ELOKA001_1.json index d59b2391b2..f623266ed5 100644 --- a/datasets/ELOKA001_1.json +++ b/datasets/ELOKA001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ELOKA001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set highlights the research conducted by the Narwhal Tusk Research Project in Baffin Bay, between Canada and Greenland. Content includes laboratory and field studies directly investigating the physical and dental properties of the narwhal tusk, narwhal behavior, and an examination of the field expeditions and collected interviews from Inuit community members.", "links": [ { diff --git a/datasets/ELOKA033_1.json b/datasets/ELOKA033_1.json index 38cf8ffbc7..acfcd75924 100644 --- a/datasets/ELOKA033_1.json +++ b/datasets/ELOKA033_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ELOKA033_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This atlas showcases Arctic communities actively involved in observing social and environmental change. It was designed to highlight the many community-based monitoring (CBM) and traditional knowledge (TK) initiatives across the circumpolar region.", "links": [ { diff --git a/datasets/ELOKA037_1.json b/datasets/ELOKA037_1.json index 53ad07b8d6..641d9a3dde 100644 --- a/datasets/ELOKA037_1.json +++ b/datasets/ELOKA037_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ELOKA037_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cillaput: Our Weather, Yukon-Kuskowim Delta Weather Station Network provides access to current weather conditions in the villages of Chevak and Kotlik located in the Yukon-Kuskokwim Delta region of western Alaska. Measurements are collected hourly and include air temperature, humidity, wind speed, rainfall, solar radiation, and soil and surface temperatures. The weather stations were installed in late 2017 through a partnership between the Chevak Traditional Council, the US Geological Survey, and collaboration with the US Forest Service. The Cillaput data application displays the hourly conditions and graphs showing the past 24 hours of data. Users may download the data record by completing the user registration form on this page.", "links": [ { diff --git a/datasets/ELOKA039_1.json b/datasets/ELOKA039_1.json index 669cff3210..2d09978905 100644 --- a/datasets/ELOKA039_1.json +++ b/datasets/ELOKA039_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ELOKA039_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Atlas focuses on sharing the knowledge, wisdom, and culture of one of the Indigenous Nations of Siberia, Russian Federation, the Evenki, who live in Iyengra, Russia, and the surrounding taiga. Access the atlas here, or read more about the Evenki here.", "links": [ { diff --git a/datasets/EM27_XCO2_XCH4_XCO_AK_1831_1.json b/datasets/EM27_XCO2_XCH4_XCO_AK_1831_1.json index 630b98fbbf..ecb213c06f 100644 --- a/datasets/EM27_XCO2_XCH4_XCO_AK_1831_1.json +++ b/datasets/EM27_XCO2_XCH4_XCO_AK_1831_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EM27_XCO2_XCH4_XCO_AK_1831_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides ground-based column-averaged dry mole fractions (DMFs) of CO2 (xco2), CO (xco), CH4 (xch4), and N2O (xn2o) to supplement satellite-based observations of carbon dynamics of northern boreal ecosystems. Measurements were conducted with Bruker EM27/SUN Fourier transform spectrometers (FTS) at the University of Alaska Fairbanks (UAF) and two sites on the edges of the Tanana Flats wetlands to the south from 2016-08-04 to 2019-10-31. Single detectors were used during the first campaign at UAF in 2017, then two instruments were updated to dual detectors in early 2018 to allow retrieval of xco and xn2o. Data from additional FTS instruments, operated by Los Alamos National Laboratories (LANL), Karlsruhe Institute of Technology (KIT), and Jet Propulsion Laboratory (JPL), employed in these campaigns are included.", "links": [ { diff --git a/datasets/EMITL1BATT_001.json b/datasets/EMITL1BATT_001.json index 769f1307c2..b2d3cae3e2 100644 --- a/datasets/EMITL1BATT_001.json +++ b/datasets/EMITL1BATT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL1BATT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS). During its one-year mission, EMIT will take mineralogical measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. A map of the regions being investigated can be found on the EMIT website. The EMIT Level 1B Corrected Spacecraft Attitude and Ephemeris (EMITL1BATT) Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS, including Earth-centered inertial (ECI) position and velocity, and associated time elements. The data are provided in 1 second intervals, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data. The data for each EMITL1BATT granule are delivered in a single Network Common Data Format 4 (netCDF-4) file.", "links": [ { diff --git a/datasets/EMITL1BRAD_001.json b/datasets/EMITL1BRAD_001.json index 5c149a366f..b27ebbec7b 100644 --- a/datasets/EMITL1BRAD_001.json +++ b/datasets/EMITL1BRAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL1BRAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\n\nThe EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation (EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. Each EMITL1BRAD granule consists of two Network Common Data Format Version 4 (netCDF-4) files at a spatial resolution of 60 meters (m): Radiance (EMIT_L1B_RAD) and Observation (EMIT_L1B_OBS). The Radiance file contains the at-sensor radiance measurements of 285 bands with a spectral range of 381-2493 nanometers (nm) and with a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Observation file contains viewing and solar geometries, timing, topographic, and other information related to the observation.\n\nEach NetCDF4 file holds a location group containing geometric lookup tables (GLT), which are orthorectified images that provide relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT\u2019s spatial resolution.\n\nEach granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.", "links": [ { diff --git a/datasets/EMITL2ARFL_001.json b/datasets/EMITL2ARFL_001.json index 1a6b70a84d..6154d487ea 100644 --- a/datasets/EMITL2ARFL_001.json +++ b/datasets/EMITL2ARFL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2ARFL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\n\nThe EMIT Level 2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) Version 1 data product provides surface reflectance data in a spatially raw, non-orthocorrected format. Each EMITL2ARFL granule consists of three Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Reflectance (EMIT_L2A_RFL), Reflectance Uncertainty (EMIT_L2A_RFLUNCERT), and Reflectance Mask (EMIT_L2A_MASK). The Reflectance file contains surface reflectance maps of 285 bands with a spectral range of 381-2493 nanometers (nm) at a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Reflectance Uncertainty file contains uncertainty estimates about the reflectance captured as per-pixel, per-band, posterior standard deviations. The Reflectance Mask file contains six binary flag bands and two data bands. The binary flag bands identify the presence of features including clouds, water, and spacecraft which indicate if a pixel should be excluded from analysis. The data bands contain estimates of aerosol optical depth (AOD) and water vapor.\n\nEach NetCDF4 file holds a location group containing a geometric lookup table (GLT) which is an orthorectified image that provides relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files will also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT\u2019s spatial resolution.\n\nEach granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.\n\nKnown Issues:\n\n- Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n\n- Possible Reflectance Discrepancies: Due to changes in computational architecture, EMITL2ARFL reflectance data produced after December 4, 2024, with Software Build 010621 and onward may show discrepancies in reflectance of up to 0.8% in extreme cases in some wavelengths as compared to values in previously processed data. These discrepancies are generally lower than 0.8% and well within estimated uncertainties. Between earlier builds and Build 010621, neither resulting output should be interpreted as more \u2018correct\u2019 than the other, as their results are simply convergence differences from an optimization search. Most users are unlikely to observe the impact.", "links": [ { diff --git a/datasets/EMITL2BCH4ENH_001.json b/datasets/EMITL2BCH4ENH_001.json index e81cece9d1..0c9e52cd5a 100644 --- a/datasets/EMITL2BCH4ENH_001.json +++ b/datasets/EMITL2BCH4ENH_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2BCH4ENH_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\n\r\nIn addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Methane Enhancement Data (EMITL2BCH4ENH) Version 1 data product is a total vertical column enhancement estimate of methane in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCH4ENH provides per-pixel methane enhancement data used to identify methane plume complexes. The initial release of the EMITL2BCH4ENH data product will only include granules where methane plume complexes have been identified. Each granule contains one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m): Methane Enhancement (EMIT_L2B_CH4ENH). The EMITL2BCH4ENH file contains methane enhancement data based primarily on EMITL1BRAD radiance values.\r\n\r\nEach granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.", "links": [ { diff --git a/datasets/EMITL2BCH4PLM_001.json b/datasets/EMITL2BCH4PLM_001.json index 2beb70f417..3ed4a97cc2 100644 --- a/datasets/EMITL2BCH4PLM_001.json +++ b/datasets/EMITL2BCH4PLM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2BCH4PLM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\n\r\nIn addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Estimated Methane Plume Complexes (EMITL2BCH4PLM) Version 1 data product provides estimated methane plume complexes in parts per million meter (ppm m) along with uncertainty data. The EMITL2BCH4PLM data product will only be generated where methane plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCH4ENH data undergo a manual review (or identification and confirmation) process before being designated as a plume complex. For more information on the manual review process, see Section 4.2.2 of the EMIT GHG Algorithm Theoretical Basis Document (ATBD). Each EMITL2BCH4PLM granule is sized to a specific plume complex but may cross multiple EMITL2BCH4ENH granules. A list of source EMITL2BCH4ENH granules is included in the GeoTIFF file metadata as well as the GeoJSON file. Each EMITL2BCH4PLM granule contains two files: one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m) and one GeoJSON file. The EMITL2BCH4PLM COG file contains a raster image of a methane plume complex extracted from EMITL2BCH4ENH v001 data. The EMITL2BCH4PLM GeoJSON file contains a vector outline of the plume complex, a list of source scenes, coordinates of the maximum enhancement values, and the uncertainty of the plume complex.", "links": [ { diff --git a/datasets/EMITL2BCO2ENH_001.json b/datasets/EMITL2BCO2ENH_001.json index 896d402681..eb28c69b13 100644 --- a/datasets/EMITL2BCO2ENH_001.json +++ b/datasets/EMITL2BCO2ENH_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2BCO2ENH_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\n\r\nIn addition to its primary objective described above, EMIT has demonstrated the capacity to characterize carbon dioxide CO2 and methane CH4 point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 1 data product is a total vertical column enhancement estimate of CO2 in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel CO2 enhancement data used to identify CO2 plume complexes. The initial release of the EMITL2BCO2ENH data product will only include granules where CO2 plume complexes have been identified. Each granule contains one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH). The EMITL2BCO2ENH COG file contains methane enhancement data based primarily on EMITL1BRAD radiance values.\r\n\r\nEach granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.", "links": [ { diff --git a/datasets/EMITL2BCO2PLM_001.json b/datasets/EMITL2BCO2PLM_001.json index c28a6e856d..0cce3277c7 100644 --- a/datasets/EMITL2BCO2PLM_001.json +++ b/datasets/EMITL2BCO2PLM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2BCO2PLM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\n\r\nIn addition to its primary objective described above, EMIT has demonstrated the capacity to characterize carbon dioxide CO2 and methane CH4 point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Estimated Carbon Dioxide Plume Complexes (EMITL2BCO2PLM) Version 1 data product provides estimated carbon dioxide plume complexes in parts per million meter (ppm m) along with uncertainty data. The EMITL2BCO2PLM data product will only be generated where carbon dioxide plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCO2ENH data undergo a manual review (or identification and confirmation) process before being designated as a plume complex. For more information on the manual review process, see Section 4.2.2 of the EMIT GHG Algorithm Theoretical Basis Document (ATBD). Each EMITL2BCO2PLM granule is sized to a specific plume complex but may cross multiple EMITL2BCO2ENH granules. A list of EMITL2BCO2ENH source granules is included in the GeoTIFF file metadata as well as the GeoJSON file. Each EMITL2BCO2PLM granule contains two files: one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m) and one GeoJSON file. The EMITL2BCO2PLM COG file contains a raster image of a carbon dioxide plume complex extracted from EMITL2BCO2ENH v001 data. The EMITL2BCO2PLM GeoJSON file contains a vector outline of the plume complex, a list of source scenes, coordinates of the maximum enhancement values, and the uncertainty of the plume complex.", "links": [ { diff --git a/datasets/EMITL2BMIN_001.json b/datasets/EMITL2BMIN_001.json index 0c30705e60..3dc67b8219 100644 --- a/datasets/EMITL2BMIN_001.json +++ b/datasets/EMITL2BMIN_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL2BMIN_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\n\r\nThe EMIT Level 2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) Version 1 data product provides estimated mineral identification and band depths in a spatially raw, non-orthocorrected format. Each EMITL2BMIN granule contains two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Mineral Identification (EMIT_L2B_MIN) and Mineral Uncertainty (EMIT_L2B_MINUNCERT). The EMIT_L2B_MIN file contains the band depth (the depth of the identified spectral feature) and the identified mineral for each pixel. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur. These estimates are generated using the Tetracorder system (code) and are based on EMITL2ARFL reflectance values. The EMIT_L2B_MINUNCERT file provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the EMITL2ARFL data product. The band depth uncertainties are presented as standard deviations. The fit score for each mineral identification is also provided as the coefficient of determination (r2) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories is available in the emit-sds-l2b repository and will be available as subsequent data products.\r\n\r\nThe EMITL2BMIN data product includes a total of 19 Science Dataset (SDS) layers. There are four layers for each of the Spectral Groups (Group 1 and Group 2): Mineral Identification, Band Depth, Band Depth Uncertainties, and Fit Score. Additional layers consist of geometric lookup table (GLT) x values, GLT y values, latitude, longitude, elevation, associated spectral library record, mineral name, URL for the spectral library description, spectral group, spectral library, and spectral group index. A browse image with Group 1 Band Depth, Group 2 Band Depth, Group 1 Band Depth Uncertainty, and Group 2 Band Depth Uncertainty is also included.\r\n\r\nEach granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.\r\n\r\nDisclaimer\r\nThis product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the Algorithm Theoretical Basis Document (ATBD) for more details.", "links": [ { diff --git a/datasets/EMITL3ASA_001.json b/datasets/EMITL3ASA_001.json index 27e8cee76a..c4769a1f47 100644 --- a/datasets/EMITL3ASA_001.json +++ b/datasets/EMITL3ASA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL3ASA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.\r\nThe EMIT Level 3 Aggregated Mineral Spectral Abundance and Uncertainty (EMITL3ASA) Version 1 data product provides an aggregated mineral spectral abundance of the 10 minerals that are the focus of the EMIT mission. These minerals, referred to as the EMIT-10 minerals, are calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. The EMITL3ASA granule consists of one network Common Data Format 4 (netCDF-4) file at a spatial resolution of 0.5 degrees. The data in EMITL3ASA relies heavily on the EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) data. Using the EMITL2BMIN data, aggregated spectral abundance (ASA) is calculated for each of the EMIT-10 minerals as the simple average of relevant 60 m pixels within each 0.5 degree grid cell in the EMITL3ASA product, after controlling for the estimated fractional cover of bare soil within the pixel. \r\nThe EMITL3ASA data product contains 20 Science Dataset (SDS) layers. There are two layers for each of the EMIT-10 minerals: mineral spectral abundance and mineral spectral abundance uncertainty. The latitude and longitude layers contain the coordinates for the upper left corner of each pixel.", "links": [ { diff --git a/datasets/EMITL4ESM_001.json b/datasets/EMITL4ESM_001.json index 82bca533c8..f9e34d441f 100644 --- a/datasets/EMITL4ESM_001.json +++ b/datasets/EMITL4ESM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EMITL4ESM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal. \r\n\r\nThe EMIT Level 4 Earth System Model (EMITL4ESM) Version 1 data product provides radiative forcing outputs, along with other ancillary outputs generated from different Earth System Models (ESMs). ESMs are complex models that integrate relevant physical, chemical, biological, and human components to simulate multiple aspects of large-scale systems on Earth. Multiple models, input mineral maps, meteorology inputs, and emissions/concentration scenarios are examined for the model runs contained within this data product. Models currently utilized include the Community Earth System Model 2 (CESM2) and the Goddard Institute for Space Studies (GISS) model. Some ESM runs utilize reference surface mineral maps from the literature dating back to 2007; others rely on the EMIT L3 Aggregated Mineral Spectral Abundance and Uncertainty 0.5 Deg (EMITL3ASA) data as inputs. \r\n\r\nEach EMITL4ESM granule represents a single ESM run with a Network Common Data Format 4 (netCDF-4) file for each variable. A total of 12 Science Dataset (SDS) layers or variables are provided for each model run. For some SDS layers or variables, multiple layers based on inclusion of model minerology inputs are provided in their netCDF files. The layers/variables table below details which variables contain the extra layers. Metadata flags for Earth System Model, Resolution, Surface Mineral Map, External Meteorology, Time Period, and Emissions/Concentration Scenario indicate the key parameters for each granule. A table outlining each variable in detail can be found in the EMIT Science Data System Level 4 repository. ", "links": [ { diff --git a/datasets/EN1_MDSI_MER_FRS_1P_4.json b/datasets/EN1_MDSI_MER_FRS_1P_4.json index 73acbc5387..a3dbb607cb 100644 --- a/datasets/EN1_MDSI_MER_FRS_1P_4.json +++ b/datasets/EN1_MDSI_MER_FRS_1P_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EN1_MDSI_MER_FRS_1P_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012.\r\n\r\nThe 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_FRS_1P is the short-name for the MERIS Level-1 full resolution, full swath, geolocated and calibrated top-of-atmosphere (TOA) radiance product. This product contains the TOA upwelling spectral radiance measurements. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance for each pixel. Each pixel\u2019s in-band solar irradiance is computed by integrating the reference solar spectrum with the band-pass of each pixel. The Level-1 product contains 22 data files: 15 files contain radiances for each band (one band per file) along with associated error estimates, and 7 annotation data files. It also includes a Manifest file that provides metadata information describing the product.", "links": [ { diff --git a/datasets/EN1_MDSI_MER_FRS_2P_4.json b/datasets/EN1_MDSI_MER_FRS_2P_4.json index 09c94a90ee..8d30f21e14 100644 --- a/datasets/EN1_MDSI_MER_FRS_2P_4.json +++ b/datasets/EN1_MDSI_MER_FRS_2P_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EN1_MDSI_MER_FRS_2P_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012.\r\n\r\nThe 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_FRS_2P is the short-name for the MERIS Level-2 full resolution, geophysical product for ocean, land, and atmosphere. This Level-2 product comes in a netCDF4 package that contains both instrument and science measurements, and a Manifest file that provides metadata information describing the product. Each Level-2 product contains 64 measurement files that break down thus: 13 files containing water-leaving reflectance, 13 files containing land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measuring atmospheric gas - M11 and M15), and several files containing additional measurements on ocean, land, and atmosphere parameters.", "links": [ { diff --git a/datasets/EN1_MDSI_MER_RR__1P_4.json b/datasets/EN1_MDSI_MER_RR__1P_4.json index 295abedef2..a1fb7a2740 100644 --- a/datasets/EN1_MDSI_MER_RR__1P_4.json +++ b/datasets/EN1_MDSI_MER_RR__1P_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EN1_MDSI_MER_RR__1P_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012.\r\n\r\nThe 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_RR__1P is the short-name for the MERIS Level-1 reduced resolution, geolocated and calibrated top-of-atmosphere (TOA) radiance product. This product contains the TOA upwelling spectral radiance measurements at reduced resolution. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance for each pixel. Each pixel\u2019s in-band solar irradiance is computed by integrating the reference solar spectrum with the band-pass of each pixel. The Level-1 product contains 22 data files: 15 files contain radiances for each band (one band per file) along with associated error estimates, and 7 annotation data files. It also includes a Manifest file that provides metadata information describing the product.", "links": [ { diff --git a/datasets/EN1_MDSI_MER_RR__2P_4.json b/datasets/EN1_MDSI_MER_RR__2P_4.json index bda47631c5..fba7ba937b 100644 --- a/datasets/EN1_MDSI_MER_RR__2P_4.json +++ b/datasets/EN1_MDSI_MER_RR__2P_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EN1_MDSI_MER_RR__2P_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012.\r\n\r\nThe 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_RR__2P is the short-name for the MERIS Level-2 reduced resolution, geophysical product for ocean, land, and atmosphere. This Level-2 product comes in a netCDF4 package that contains both instrument and science measurements, and a Manifest file that provides metadata information describing the product. Each Level-2 product contains 64 measurement files that break down thus: 13 files containing water-leaving reflectance, 13 files containing land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measuring atmospheric gas - M11 and M15), and several files containing additional measurements on ocean, land, and atmospheric parameters and annotation.", "links": [ { diff --git a/datasets/ENVISAT.ASA.APM_1P_9.0.json b/datasets/ENVISAT.ASA.APM_1P_9.0.json index 39e7ae38a0..ed3c8fd952 100644 --- a/datasets/ENVISAT.ASA.APM_1P_9.0.json +++ b/datasets/ENVISAT.ASA.APM_1P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.APM_1P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ASAR Alternating Polarization Medium Resolution Image product has been generated from Level 0 data collected when the instrument was in Alternating Polarisation Mode. The product has lower geometric resolution but higher radiometric resolution than ASA_APP and contains one or two co-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). This product has been processed using the SPECAN algorithm and contains radiometric resolution good enough for ice applications and covers a continuous area along the imaging swath. The ASAR AP L0 full mission data archive has been bulk processed to Level 1 (ASA_APM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m ground range x 150 m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.APP_1P_8.0.json b/datasets/ENVISAT.ASA.APP_1P_8.0.json index c8513b6461..09d72562dc 100644 --- a/datasets/ENVISAT.ASA.APP_1P_8.0.json +++ b/datasets/ENVISAT.ASA.APP_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.APP_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ASAR Alternating Polarisation Mode Precision product is generated from Level 0 data collected when the instrument is in Alternating Polarisation Mode (7 possible swaths). The product contains two CO-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). This is a stand-alone multi-look, ground range, narrow swath digital image generated using the SPECAN algorithm and the most up to date auxiliary information available at the time of processing. Engineering corrections and relative calibration (antenna elevation gain, range spreading loss) are applied to compensate for well-understood sources of system variability. Generation of this product uses a technique to allow half the looks of an image to be acquired in horizontal polarisation and the other half in vertical polarisation and processed to 30-m resolution (with the exception of IS1). Absolute calibration parameters are available depending on external calibration activities and are provided in the product annotations. Spatial Resolution: 30 m ground range x 30 m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.APS_1P_8.0.json b/datasets/ENVISAT.ASA.APS_1P_8.0.json index 47986b9901..98711d57bb 100644 --- a/datasets/ENVISAT.ASA.APS_1P_8.0.json +++ b/datasets/ENVISAT.ASA.APS_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.APS_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is a complex, slant-range, digital image generated from Level 0 data collected when the instrument is in Alternating Polarisation mode. (7 possible swaths). It contains two CO-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). In addition, the product uses the Range Doppler algorithm and the most up to date processing parameters available at the time of processing. It can be used to derive higher level products for SAR image quality assessment, calibration and interferometric applications, if allowed by the instrument acquisition. A minimum number of corrections and interpolations are performed on the data in order to allow the end-user maximum freedom to derive higher level products. Complex output data is retained to avoid loss of information. Absolute calibration parameters are available depending on external calibration activities and are provided in the product annotations. Spatial Resolution: approximately 8m slant range x approximately 4m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.GM1_1P_9.0.json b/datasets/ENVISAT.ASA.GM1_1P_9.0.json index e22e7b9f08..fbc5466c82 100644 --- a/datasets/ENVISAT.ASA.GM1_1P_9.0.json +++ b/datasets/ENVISAT.ASA.GM1_1P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.GM1_1P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product has been generated from Level 0 data collected when the instrument was in Global Monitoring Mode. One product covers a full orbit. The product includes slant range to ground range corrections. This strip-line product is the standard for ASAR Global Monitoring Mode. It is processed to approximately 1 km resolution using the SPECAN algorithm. The swath width is approximately 400 km. The ASAR GM L0 full mission data archive has been bulk processed to Level 1 (ASA_GM1_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 1 km ground range x 1 km azimuth.", "links": [ { diff --git a/datasets/ENVISAT.ASA.IMM_1P_9.0.json b/datasets/ENVISAT.ASA.IMM_1P_9.0.json index b1b44e2c75..3dd2745351 100644 --- a/datasets/ENVISAT.ASA.IMM_1P_9.0.json +++ b/datasets/ENVISAT.ASA.IMM_1P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.IMM_1P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ASAR Medium Resolution strip-line product has been generated from Level 0 data collected when the instrument was in Image Mode. This product has lower resolution but higher radiometric resolution than the ASA_IMP. The product covers a continuous area along the imaging swath and features an ENL (radiometric resolution) good enough for ice applications. It is intended to perform applications-oriented analysis on large scale phenomena and multi-temporal imaging. This product provides a continuation of the ERS-SAR Image Mode data. The ASAR IM L0 full mission data archive has been bulk processed to Level 1 (ASA_IMM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m ground range x 150 m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.IMP_1P_8.0.json b/datasets/ENVISAT.ASA.IMP_1P_8.0.json index 20b90ef2f2..c1f9148482 100644 --- a/datasets/ENVISAT.ASA.IMP_1P_8.0.json +++ b/datasets/ENVISAT.ASA.IMP_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.IMP_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a multi-look, ground range, digital Precision Image generated from Level 0 data collected when the instrument was in Image Mode (7 possible swaths HH or VV polarisation). The product includes slant range to ground range correction. It is for users wishing to perform applications-oriented analysis and applies to multi-temporal imaging and to derive backscattering coefficients. The stand-alone image is generated using the Range/Doppler algorithm. The processing uses up to date (at time of processing) auxiliary parameters corrected for antenna elevation gain, and range spreading loss. Engineering corrections and relative calibration are applied to compensate for well-understood sources of system variability. Absolute calibration parameters, when available (depending on external calibration activities) are provided in the product annotations. This product provides a continuation of the ERS-SAR_PRI product. Spatial Resolution: 30 m ground range x 30 m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.IMS_1P_8.0.json b/datasets/ENVISAT.ASA.IMS_1P_8.0.json index 4921ce8d1b..3f2ca038c4 100644 --- a/datasets/ENVISAT.ASA.IMS_1P_8.0.json +++ b/datasets/ENVISAT.ASA.IMS_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.IMS_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product represents a single-look, complex, slant-range, digital image generated from Level 0 ASAR data collected when the instrument is in Image Mode. Seven possible swaths in HH or VV polarisation are available. The product is primarily intended for use in SAR quality assessment and calibration or applications requiring complex SAR images such as interferometry, and can be used to derive higher level products. The spatial coverage is about 100 km along track per 56- 100 km across track, and the radiometric resolution is 1 look in azimuth, 1 look in range. The file size is 741 Mbytes. It is worth highlighting that Azimuth pixel spacing depends on Earth-Satellite relative velocity and actual PRF and slant range pixel spacing is given by ASAR sampling frequency (19.208 Mhz). Auxiliary data include: Orbit state vector, Time correlation parameters, Main Processing parameters ADS, Doppler Centroid ADS, Chirp ADS, Antenna Elevation Pattern ADS, Geolocation Grid ADS, SQ ADS. Spatial Resolution: approximately 8m slant range x approximately 4m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.IM__0P_9.0.json b/datasets/ENVISAT.ASA.IM__0P_9.0.json index 70fa96d912..a8cfb48ec0 100644 --- a/datasets/ENVISAT.ASA.IM__0P_9.0.json +++ b/datasets/ENVISAT.ASA.IM__0P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.IM__0P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASAR Image Mode source packets Level 0 data product offers Level 0 data for possible images processing on an other processing site. It includes some mandatory information for SAR processing. The Image Mode Level 0 product consists of time-ordered Annotated Instrument Source Packets (AISPs) collected by the instrument in Image Mode. The echo samples contained in the AISPs are compressed to 4 bits/sample using Flexible Block Adaptive Quantisation (FBAQ). This is a high-rate, narrow swath mode so data is only acquired for partial orbit segments and may be from one of seven possible image swaths. The Level 0 product is produced systematically for all data acquired within this mode. This product provides a continuation of the ERS-SAR_RAW product.", "links": [ { diff --git a/datasets/ENVISAT.ASA.WSM_1P_10.0.json b/datasets/ENVISAT.ASA.WSM_1P_10.0.json index c9780a1c0f..19c65b1a55 100644 --- a/datasets/ENVISAT.ASA.WSM_1P_10.0.json +++ b/datasets/ENVISAT.ASA.WSM_1P_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WSM_1P_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This strip-line product has been generated from Level 0 data collected when the instrument was in Wide Swath Mode. The product includes slant range to ground range corrections and it covers a continuous area along the imaging swath. It is intended to perform applications-oriented analysis on large scale phenomena over a wide region and for multi-temporal imaging. This is the standard product for ASAR Wide Swath Mode. The ASAR WS L0 full mission data archive has been bulk processed to Level 1 (ASA_WSM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m slant range x 150 m azimuth.", "links": [ { diff --git a/datasets/ENVISAT.ASA.WSS_1P_8.0.json b/datasets/ENVISAT.ASA.WSS_1P_8.0.json index d504af5f7c..0000ad5b1b 100644 --- a/datasets/ENVISAT.ASA.WSS_1P_8.0.json +++ b/datasets/ENVISAT.ASA.WSS_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WSS_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-1B data product offered by ESA from the ASAR Wide-Swath Mode (WS) is the multi-look detected product (ASA_WSM_1P), intended to support applications that exploit intensity data. In order to support the development of new applications with the ASAR ScanSAR data, a WSM product providing phase information has been developed and implemented in the ESA ASAR processor, the Wide-Swath Single-Look complex product (ASA_WSS_1P). This product is mainly used for INSAR applications based either on wide-swath/wide-swath pairs or wide-swath/image mode pairs, applications of ocean current mapping, large-area ocean wave retrievals, and atmospheric water vapour characterisation. It shall be mentioned that the standard ESA WSS product is based on the prototype WSS processor developed by Polimi/Poliba, which has also been used to generate prototype products for testing the potential and preparing the exploitation of the WSS product. The ESA ASA_WSS_1P product is available as a standard Envisat ASAR product. The ASA_WSS_1P product format is slightly different from other ASAR products since: - there are 5 different MDSs, one per sub-swath - a "Doppler Grid" ADS has been included to support ocean current mapping applications - there are 5 records in the MPP ADS, one per sub-swath - there are 5 records in the SQ ADS, one per sub-swath Other key characteristics of the ASA_WSS_1P product are summarised below: - processing is fully phase preserving - data in the MDSs is sampled in a common grid both in range and in azimuth - standard product is 60 sec long with 80 m az. pixel spacing - auxiliary timeline information has been added in the Main Processing Parameters ADS - elevation antenna pattern correction is applied by default (although the product is a single-look complex) - Spatial Resolution: approximately 8 m slant range x 80 m azimuth", "links": [ { diff --git a/datasets/ENVISAT.ASA.WS__0P_9.0.json b/datasets/ENVISAT.ASA.WS__0P_9.0.json index 876569c6cb..705081afdc 100644 --- a/datasets/ENVISAT.ASA.WS__0P_9.0.json +++ b/datasets/ENVISAT.ASA.WS__0P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WS__0P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WS Mode Level 0 product consists of time-ordered AISPs collected while the instrument was is in Wide Swath Mode. The echo samples in the AISPs have been compressed to 4 bits/sample using FBAQ. This is a high-rate, wide swath (ScanSAR) mode so data is only acquired for partial orbit segments and is composed of data from five image swaths (SS1 to SS5). The Level 0 product is produced systematically for all data acquired within this mode. The objective of this product is to offer Level 0 data for possible images processing on another processing site. It includes mandatory information for SAR processing. Data Size: 400 km across track x 400 km along track", "links": [ { diff --git a/datasets/ENVISAT.ASA.WVI_1P_7.0.json b/datasets/ENVISAT.ASA.WVI_1P_7.0.json index 6bec0b0099..9b35efde76 100644 --- a/datasets/ENVISAT.ASA.WVI_1P_7.0.json +++ b/datasets/ENVISAT.ASA.WVI_1P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WVI_1P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the basic Level 1B ASAR Wave Mode product, including up to 400 single-look, complex, slant range, imagettes generated from Level 0 data, and up to 400 imagette power spectra computed using the cross-spectra methodology.\r\rThe auxiliary parameters used are the most up to date at the time of processing. A minimum number of corrections and interpolations are performed in order to allow the end-user maximum freedom to derive higher level products. Complex output data is retained to avoid loss of information. Absolute calibration parameters, when available (depending on external calibration activities), are provided in the product annotations.\r\rImagette power spectrum is equivalent to the ERS UWA (Near Real Time) product with revisited algorithm (cross-spectra) taking into account the higher quality of the SLC imagette. Note that starting from an SLC imagette, the generation of an ERS UWA-type product might be ensured by a simple look detection and summation.\r\rThis product provides a continuation of the ERS SAR Wave Mode data.\r\rThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of processing and are available in Envisat format.\r\rImagette Spatial Resolution: 20 m ground range x 20 m azimuth.\r\rCross Spectra Output: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "links": [ { diff --git a/datasets/ENVISAT.ASA.WVS_1P_7.0.json b/datasets/ENVISAT.ASA.WVS_1P_7.0.json index 538b781430..5650102019 100644 --- a/datasets/ENVISAT.ASA.WVS_1P_7.0.json +++ b/datasets/ENVISAT.ASA.WVS_1P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WVS_1P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ASAR Wave Mode product is extracted from the combined SLC and Cross Spectra product,(_$$ASA_WVI_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-slc-and-cross-spectra-imagettes-l1-asa_wvi_1p-), which is generated from data collected when the instrument was in Wave Mode using the Cross Spectra methodology.\r\rThe product is meant for Meteo users. The spatial coverage is up to 20 spectra acquired every 100 km, with a minimum coverage of 5km x 5km.\r\rThe file size has a maximum of 0.2 Mbytes. Auxiliary data include Orbit state vector, Time correlation parameters, Wave Processing parameters ADS, Wave Geolocation ADS, and SQ ADS.\r\r\rThe product provides a continuation of the ERS -SAR Wave Mode data. \r\rThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of .processing and are available in Envisat format.\r\rOutput: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "links": [ { diff --git a/datasets/ENVISAT.ASA.WVW_2P_7.0.json b/datasets/ENVISAT.ASA.WVW_2P_7.0.json index 6891e6f28b..ab3e07a5bb 100644 --- a/datasets/ENVISAT.ASA.WVW_2P_7.0.json +++ b/datasets/ENVISAT.ASA.WVW_2P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.ASA.WVW_2P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ASAR Wave Mode product is created by inverting the cross-spectra which is computed from inter-look processing of the SLC wave imagettes in order to derive the directional ocean product ocean wave spectra. \rAuxiliary ADSs included with the product remains the same as for the ASAR Wave Mode Cross-Spectra product (_$$ASA_WVS_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-imagette-cross-spectra-l1-asa_wvs_p- ).\r\rThe output follows the format of the Envisat ASAR Level 1B Wave Mode Cross-Spectra Imagette (_$$ASA_WVS_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-imagette-cross-spectra-l1-asa_wvs_p- ) product. This is done in order to be compatible with the ground segment products of Envisat ASAR.\r \rThis product provides a continuation of the ERS SAR Wave Mode data.\r\rThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of processing and are available in Envisat format.\r\r\rOutput: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "links": [ { diff --git a/datasets/ENVISAT.DOR.DOP_1P_5.0.json b/datasets/ENVISAT.DOR.DOP_1P_5.0.json index 78081eb779..39e464c52f 100644 --- a/datasets/ENVISAT.DOR.DOP_1P_5.0.json +++ b/datasets/ENVISAT.DOR.DOP_1P_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.DOR.DOP_1P_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product was generated by the Centre de Traitement Doris Poseidon (CTDP), 3 days after sensing and stored into the F-PAC archive. The file size is 0.5 Mbytes per orbit.", "links": [ { diff --git a/datasets/ENVISAT.DOR.VOR_AX_7.0.json b/datasets/ENVISAT.DOR.VOR_AX_7.0.json index a965c14b4f..d3c610c47b 100644 --- a/datasets/ENVISAT.DOR.VOR_AX_7.0.json +++ b/datasets/ENVISAT.DOR.VOR_AX_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.DOR.VOR_AX_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The latest version of the Envisat DORIS Precise Orbit product (DOR_VOR_AX) was generated by the Centre de Traitement Doris Poseidon (CTDP) using the Geophysical Data Records F standards (GDR-F). The product is used to obtain the satellite orbital parameters (latitude, longitude, height and height rate) by using orbit computation routines. The most significant changes related to the GDR-F standards concern the new ocean tide model (FES2014) and the updated Terrestrial Reference Frame (ITRF2014). The new standards significantly improve all Precise Orbit Determination (POD) metrics with respect to GDR-E: the mean difference and variance of Sea Surface Height (SSH) at crossovers is slightly reduced. The DOR_VOR_AX product adopts the Envisat format, and the size is 0.2 Mbytes per orbit. Users are recommended to apply the GDR-F version, but the previous datasets are still available (i.e. GDR-D and GDR-E versions). See further details in the readme file (https://earth.esa.int/eogateway/documents/20142/37627/Readme-file-for-Envisat-DORIS-POD.pdf/e94f32f0-3776-788b-abaf-3901ad26440c) for Envisat DORIS Precise Orbit Determination files. https://earth.esa.int/eogateway/documents/20142/1565579/DOR-VOR-AX-Description.png", "links": [ { diff --git a/datasets/ENVISAT.GOM.LIM_1P_6.0.json b/datasets/ENVISAT.GOM.LIM_1P_6.0.json index 3f6696fc5e..af724eab6d 100644 --- a/datasets/ENVISAT.GOM.LIM_1P_6.0.json +++ b/datasets/ENVISAT.GOM.LIM_1P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.GOM.LIM_1P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product describes localised calibrated upper and lower background limb spectra (flat-field corrected, with and without stray light). Coverage is as follows: - Elevation range: C25+62 deg to +68 deg - Azimuth range: +90 deg to +190 deg (with respect to the flight direction). The file size is Mbytes/occultation, depending on the duration of the occultation.", "links": [ { diff --git a/datasets/ENVISAT.GOM.NL__2P_6.0.json b/datasets/ENVISAT.GOM.NL__2P_6.0.json index 52c95eaf21..4e8bc79d38 100644 --- a/datasets/ENVISAT.GOM.NL__2P_6.0.json +++ b/datasets/ENVISAT.GOM.NL__2P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.GOM.NL__2P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product describes atmospheric constituents profiles. In particular the vertical and line density profiles of ozone, NO2, NO3, O2, H2O, air, aerosols, temperature, turbulence. Coverage is as follows: - Elevation range: +62 deg to +68 deg - Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The file size is 1 Mbyte/occultation, depending on the duration of the occultation.", "links": [ { diff --git a/datasets/ENVISAT.GOM.TRA_1P_6.0.json b/datasets/ENVISAT.GOM.TRA_1P_6.0.json index c5e116e899..44b7cf6eef 100644 --- a/datasets/ENVISAT.GOM.TRA_1P_6.0.json +++ b/datasets/ENVISAT.GOM.TRA_1P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.GOM.TRA_1P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product describes the geolocated and calibrated transmission spectra products, containing the full transmission and the covariance spectra needed for the Level 2 processing. Coverage is as follows: - Elevation range: +62 deg to +68 deg - Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The file size is 1 MB/occultation, depending on the duration of the occultation.", "links": [ { diff --git a/datasets/ENVISAT.GOM_EXT_2P_6.0.json b/datasets/ENVISAT.GOM_EXT_2P_6.0.json index eae7722fd6..7fd8b7d246 100644 --- a/datasets/ENVISAT.GOM_EXT_2P_6.0.json +++ b/datasets/ENVISAT.GOM_EXT_2P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.GOM_EXT_2P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Re-computed transmission spectra corrected for scintillation and dilution effects, before and after inversion. Coverage is as follows: - Elevation range: +62 deg to +68 deg - Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The file size is 1 Mbyte/occultation, depending on the duration of the occultation.", "links": [ { diff --git a/datasets/ENVISAT.MIP.NL__1P_5.0.json b/datasets/ENVISAT.MIP.NL__1P_5.0.json index 412d88701c..157c9db7e6 100644 --- a/datasets/ENVISAT.MIP.NL__1P_5.0.json +++ b/datasets/ENVISAT.MIP.NL__1P_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.MIP.NL__1P_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This MIPAS Level 1 data product covers the geo-located, spectrally and radiometrically calibrated limb emission spectra in the 685-2410 cm-1 wave number range. It comprises 5 bands: 685-980 cm-1, 1010-1180 cm-1, 1205-1510 cm-1, 1560-1760 cm-1, 1810-2410 cm-1 and covers the following spatial ranges: -Tangent height range: 5 to 170 km -Pointing range: (azimuth pointing range relative to satellite velocity vector): 160 deg - 195 deg (rearward anti-flight direction); 80 deg - 110 deg (sideward anti-Sun direction) The instantaneous field of view (IFOV) is 0.05230 (elevation) x 0.5230 (azimuth) deg. The length of measurement cell for an individual height step is approximately 300-500 km (dependent on tangent height and optical properties of the atmosphere). The spectral resolution spans from 0.030 to 0.035 cm-1, with a radiometric sensitivity of 4.2 to 50 nW / cm-1 / sr / cm2. The resolution range of the dataset is: 3 km (vertical) x 30 km (horizontal) at the tangent point. Please consult the Product Quality Readme - https://earth.esa.int/documents/700255/3711375/Read_Me_File_MIP_NL__1PY_ESA-EOPG-EBA-TN-1+issue1.1.pdf - file for MIPAS Level 1b IPF 8.03 before using the data.", "links": [ { diff --git a/datasets/ENVISAT.MIP.NL__2P_5.0.json b/datasets/ENVISAT.MIP.NL__2P_5.0.json index 8af54b266f..31069856a5 100644 --- a/datasets/ENVISAT.MIP.NL__2P_5.0.json +++ b/datasets/ENVISAT.MIP.NL__2P_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.MIP.NL__2P_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This MIPAS Level 2 data product describes localised vertical profiles of pressure, temperature and 21 target species (H2O, O3, HNO3, CH4, N2O, NO2, CFC-11, ClONO2, N2O5, CFC-12, COF2, CCL4, HCN, CFC-14, HCFC-22, C2H2, C2H6, COCl2, CH3Cl, OCS and HDO).\rIt has a global coverage of Earth's stratosphere and mesosphere at all latitudes and longitudes. The vertical resolution of p, T and VMR profiles varies from 3 to 4 km, whereas the horizontal resolution is approximately 300 km to 500 km along track. This depends on the tangent height range and optical properties of the atmosphere. Auxiliary data include spectroscopic data, microwindows data, validation data, initial guess p, T and trace gas VMR profiles.\rThe resolution range of the dataset is: 3 km (vertical) x 30 km (horizontal) at the tangent point.\rThe latest reprocessed MIPAS Level 2 data (v8.22) is available as \r1)\tStandard products (MIPAS_2PS): \rA complete product containing 22 MIPAS L2 chemical species covering a single orbit and single species providing information generally needed by data users.\r\r2)\tExtended products (MIPAS_2PE): \rA complete product containing 22 MIPAS L2 chemical species covering a single orbit and single species intended for diagnostics and expert users who need complete information about the retrieval process.\rBoth products are available in NetCDF format\rPlease refer to the MIPAS L2 v8.22 _$$Product Quality Readme file$$ https://earth.esa.int/eogateway/documents/20142/37627/README_V8_issue_1.0_20201221.pdf for further details.", "links": [ { diff --git a/datasets/ENVISAT.RA2.GDR_2P_7.0.json b/datasets/ENVISAT.RA2.GDR_2P_7.0.json index 660a463f25..bd982f2a47 100644 --- a/datasets/ENVISAT.RA2.GDR_2P_7.0.json +++ b/datasets/ENVISAT.RA2.GDR_2P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.RA2.GDR_2P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a RA-2 Geophysical Data Record (GDR) Full Mission Reprocessing (FMR) v3 product containing radar range and orbital altitude, wind speed, wave height, water vapour from the MWR and geophysical corrections. This FMR follows the first Envisat Altimetry reprocessing Version (V2.1) completed in 2012. The GDR and S-GDR data products were reprocessed for all cycles from 6 to 113 (May 2002 to April 2012) into a homogeneous standard in NetCDF format (close to Sentinel-3). For many aspects, the V3.0 reprocessed data are better than the previous dataset: - In terms of available and valid data, the coverage is better, notably thanks to a better availability of MWR data at the beginning of the mission - In terms of performance at cross-overs, the quality is improved: the annual signal and average of Mean SSH is decreased, as well as the standard deviation - The new MWR characteristics were shown to improve largely the global quality of data. As well as the new tide model, the new MSS and the new orbit standard - The Global and regional Mean Sea Level trend is very weakly impacted though the effort was put, this time, on the mesoscale restitution, rather than long term drift, as during V2.1 reprocessing Please consult the Envisat RA-2/MWR Product Quality Readme file pdf before using the data.\rThe creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. \rUsers are therefore strongly encouraged to make use of these new datasets for optimal results. \rThe records are aimed at different user communities and include the following datasets:\r1.\t_$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry\r2.\t_$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry\r3.\t_$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere\r4.\t_$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water\r5.\t_$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice\r6.\t_$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography\r7.\t_$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves\r8.\t_$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice\r", "links": [ { diff --git a/datasets/ENVISAT.RA2.MWS_2P_7.0.json b/datasets/ENVISAT.RA2.MWS_2P_7.0.json index cb498868c5..27d33c5c8b 100644 --- a/datasets/ENVISAT.RA2.MWS_2P_7.0.json +++ b/datasets/ENVISAT.RA2.MWS_2P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT.RA2.MWS_2P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a RA-2 Sensor and Geophysical Data Record (SGDR) Full Mission Reprocessing (FMR) v3 product. This FMR follows the first Envisat Altimetry reprocessing Version (V2.1) completed in 2012. The GDR and S-GDR data products were reprocessed for all cycles from 6 to 113 (May 2002 to April 2012) into a homogeneous standard in NetCDF format (close to Sentinel-3). The Sensor Data Record (SGDR) Product from RA-2/MWR includes the data in the GDR product (https://earth.esa.int/eogateway/catalog/envisat-ra-2-geophysical-data-record-gdr-ra2_gdr__2p-) (RA-2 geophysical data, MWR data) and also RA-2 averaged waveforms (18Hz) and RA-2 individual waveforms (1800Hz). This product is a continuation of ERS RA data. This data product has a coverage of 1 pass, pole-pole, a spatial sampling of about 390 m along track and a size of 31 to 40 MB, depending on presence of individual waveforms. The radiometric accuracy is 0.2 dB and auxiliary data include: Orbit state vectors (DORIS, FOS), RA2 and MWR characterisation data, Platform attitude, Gain calibration, USO frequency, ECMWF data, time relation, leap second, Ionospheric corrections, geoid, mean sea surface, slope data, and tide model (ocean, earth, loading, pole). Please consult the Envisat RA-2/MWR Product Quality Readme file before using the data.\rThe creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. \rUsers are therefore strongly encouraged to make use of these new datasets for optimal results. \rThe records are aimed at different user communities and include the following datasets:\r1.\t_$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry\r2.\t_$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry\r3.\t_$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere\r4.\t_$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water\r5.\t_$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice\r6.\t_$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography\r7.\t_$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves\r8.\t_$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice\r", "links": [ { diff --git a/datasets/ENVISAT_SCIAMACHY_SIF_1871_1.json b/datasets/ENVISAT_SCIAMACHY_SIF_1871_1.json index 38111ada3f..dca0565ebc 100644 --- a/datasets/ENVISAT_SCIAMACHY_SIF_1871_1.json +++ b/datasets/ENVISAT_SCIAMACHY_SIF_1871_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ENVISAT_SCIAMACHY_SIF_1871_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on the European Space Agency's (ESA's) Environmental satellite (Envisat) with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. SCIAMACHY covers global land between approximately 70 and -57 degrees latitude on an orbital basis at a resolution of approximately 30 km x 240 km. Data are provided for the period from 2003-01-01 to 2012-04-08. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data.", "links": [ { diff --git a/datasets/EO:EUM:CM:METOP:ASCSZFR02_2014-10-07.json b/datasets/EO:EUM:CM:METOP:ASCSZFR02_2014-10-07.json index b3478cbd45..1e09d46a3a 100644 --- a/datasets/EO:EUM:CM:METOP:ASCSZFR02_2014-10-07.json +++ b/datasets/EO:EUM:CM:METOP:ASCSZFR02_2014-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:METOP:ASCSZFR02_2014-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled at full resolution (SZF). Normalized radar cross section (NRCS) of the Earth surface together with measurement time, location (latitude and longitude) and geometrical information (incidence and azimuth angles). The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. This product is also available at 12.5 and 25 km Swath Grids. This is a Fundamental Climate Data Record (FCDR). ", "links": [ { diff --git a/datasets/EO:EUM:CM:METOP:ASCSZOR02_2014-10-07.json b/datasets/EO:EUM:CM:METOP:ASCSZOR02_2014-10-07.json index 137642fe8c..4fe7f10034 100644 --- a/datasets/EO:EUM:CM:METOP:ASCSZOR02_2014-10-07.json +++ b/datasets/EO:EUM:CM:METOP:ASCSZOR02_2014-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:METOP:ASCSZOR02_2014-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled at 25 km Swath Grid (SZO). Normalized radar cross section (NRCS) triplets of the Earth surface together with measurement time, location (latitude and longitude) and geometrical information (incidence and azimuth angles). The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. This product is also available at full resolution and at 12.5 km Swath Grid. This is a Fundamental Climate Data Record (FCDR). ", "links": [ { diff --git a/datasets/EO:EUM:CM:METOP:ASCSZRR02_2014-10-07.json b/datasets/EO:EUM:CM:METOP:ASCSZRR02_2014-10-07.json index 0f51ca366e..b530659ee5 100644 --- a/datasets/EO:EUM:CM:METOP:ASCSZRR02_2014-10-07.json +++ b/datasets/EO:EUM:CM:METOP:ASCSZRR02_2014-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:METOP:ASCSZRR02_2014-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled at 12.5 km Swath Grid (SZR). Normalized radar cross section (NRCS) triplets of the Earth surface together with measurement time, location (latitude and longitude) and geometrical information (incidence and azimuth angles). The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. This product is also available at full resolution and at 25 km Swath Grid. This is a Fundamental Climate Data Record (FCDR). ", "links": [ { diff --git a/datasets/EO:EUM:CM:MSG:MSGAMVE0100_2015-06-01.json b/datasets/EO:EUM:CM:MSG:MSGAMVE0100_2015-06-01.json index 15b6a1b5da..9465c9f632 100644 --- a/datasets/EO:EUM:CM:MSG:MSGAMVE0100_2015-06-01.json +++ b/datasets/EO:EUM:CM:MSG:MSGAMVE0100_2015-06-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:MSG:MSGAMVE0100_2015-06-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the first release of the reprocessed SEVIRI Atmospheric Motion Vectors at all heights below the tropopause, derived from 4 channels (Visual 0.8, Water Vapour 6.2, Water Vapour 7.3, Infrared 10.8), all combined into one product. Vectors are derived by tracking the motion of clouds and other atmospheric constituents as water vapour patterns. The height assignment of the AMVs is calculated using the Cross-Correlation Contribution (CCC) function to determine the pixels that contribute the most to the vectors. An AMV product contains more than 30000 vectors depending of the time of the day. The final AMV product is BUFR encoded 3-hourly at every third quarter of the hour (e.g. 00:45, 01:45 ...). Note that the reprocessing was done using the latest version of the EUMETSAT software (Version 1.5.3, 2013) ingesting original level 1.5 SEVIRI images and the ECMWF ERA-interim as a as a forecast input re-analysis data. This is a Thematic Climate Data Record (TCDR).", "links": [ { diff --git a/datasets/EO:EUM:CM:MSG:MSGASRE0100_2015-06-01.json b/datasets/EO:EUM:CM:MSG:MSGASRE0100_2015-06-01.json index 334b89fbc3..c817afb260 100644 --- a/datasets/EO:EUM:CM:MSG:MSGASRE0100_2015-06-01.json +++ b/datasets/EO:EUM:CM:MSG:MSGASRE0100_2015-06-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:MSG:MSGASRE0100_2015-06-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the first release of the reprocessed SEVIRI All-Sky Radiances (ASR) product. The ASR product contains information on mean brightness temperatures (16x16 pixels so around 50km at nadir) from all thermal (e.g. infrared and water vapour) channels. It includes both clear and cloudy sky brightness temperatures. The ASR product also contains the fraction of clear sky and the solar zenith angle. The final ASR product is BUFR encoded 3-hourly at every third quarter of the hour (e.g. 00:45, 01:45 ...).Note that the reprocessing was done using the latest version of the EUMETSAT software (Version 1.5.3, 2013) ingesting original level 1.5 SEVIRI images and the ECMWF ERA-interim as a as a forecast input re-analysis data.", "links": [ { diff --git a/datasets/EO:EUM:CM:MSG:MSGCSKR0100_2015-06-01.json b/datasets/EO:EUM:CM:MSG:MSGCSKR0100_2015-06-01.json index 0328debf35..36c83e7bf3 100644 --- a/datasets/EO:EUM:CM:MSG:MSGCSKR0100_2015-06-01.json +++ b/datasets/EO:EUM:CM:MSG:MSGCSKR0100_2015-06-01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:CM:MSG:MSGCSKR0100_2015-06-01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the first release of the reprocessed SEVIRI Clear-Sky Radiances (CSR) product. The CSR product is a subset of the information derived during the Scenes Analysis processing. The product provides the brightness temperature for a subset of the MSG channels averaged over all pixels within a processing segment (16x16 pixels) which have been identified as clear (a minimum of 7 clear pixels are needed to compute the average). For the channel WV6.2 the CSR is also derived for areas containing low-level clouds. The CSR product also contains the fraction of clear sky and the solar zenith angle. The final CSR product is BUFR encoded 3-hourly at every third quarter of the hour (e.g. 00:45, 01:45 ...). Note that the reprocessing was done using the latest version of the EUMETSAT software (Version 1.5.3, 2013) ingesting original level 1.5 SEVIRI images and the ECMWF ERA-interim as a as a forecast input re-analysis data.", "links": [ { diff --git a/datasets/EO:EUM:DAT:DMSP:GBLSIC_2010-08-04.json b/datasets/EO:EUM:DAT:DMSP:GBLSIC_2010-08-04.json index 69223da19d..ac609e18d6 100644 --- a/datasets/EO:EUM:DAT:DMSP:GBLSIC_2010-08-04.json +++ b/datasets/EO:EUM:DAT:DMSP:GBLSIC_2010-08-04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:DMSP:GBLSIC_2010-08-04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily averaged fractional ice cover in percentage, processed from passive microwave satellite data (SSMIS) over the polar regions. Sea ice concentration and its uncertainties are calculated from swath observations, and averaged and gridded to the daily fields. Better than using this archived NRT sea ice concentration product, please use the reprocessed sea ice concentration data record v2.0 (EO:EUM:DAT:MULT:OSI-450).", "links": [ { diff --git a/datasets/EO:EUM:DAT:GOES:OSIDDLI_2011-10-07.json b/datasets/EO:EUM:DAT:GOES:OSIDDLI_2011-10-07.json index f34f8f1651..28501d30a2 100644 --- a/datasets/EO:EUM:DAT:GOES:OSIDDLI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:GOES:OSIDDLI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:GOES:OSIDDLI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the Downward Longwave Irradiance reaching the Earth surface, derived from the geostationary satellite GOES-E, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a bulk parameterization that uses NWP model outputs to calculate a clear sky Downward Longwave Irradiance (DLI), corrected according to satellite derived cloud information. The daily value is the integration of all the hourly values in the UT day.", "links": [ { diff --git a/datasets/EO:EUM:DAT:GOES:OSIDSSI_2011-10-07.json b/datasets/EO:EUM:DAT:GOES:OSIDSSI_2011-10-07.json index ce4dd7f42a..625bedc469 100644 --- a/datasets/EO:EUM:DAT:GOES:OSIDSSI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:GOES:OSIDSSI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:GOES:OSIDSSI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the solar irradiance reaching the Earth surface, derived from the geostationary satellite GOES-E, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. The daily value is the integration of all the hourly values in the UT day.", "links": [ { diff --git a/datasets/EO:EUM:DAT:GOES:OSIHDLI_2011-10-07.json b/datasets/EO:EUM:DAT:GOES:OSIHDLI_2011-10-07.json index 683d1a3970..ab391e0ced 100644 --- a/datasets/EO:EUM:DAT:GOES:OSIHDLI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:GOES:OSIHDLI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:GOES:OSIHDLI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the Downward Longwave Irradiance reaching the Earth surface, derived from the geostationary satellite GOES-E, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a bulk parameterization that uses NWP model outputs to calculate a clear sky Downward Longwave Irradiance (DLI), corrected according to satellite derived cloud information. An essential point is the calculation of products interpolated at rounded UT hours. A radiative flux calculated on a satellite image is not homogeneous in time (the pixel time varies from north to south, of about 24 minutes for GOES-E data).", "links": [ { diff --git a/datasets/EO:EUM:DAT:GOES:OSIHSSI_2011-10-07.json b/datasets/EO:EUM:DAT:GOES:OSIHSSI_2011-10-07.json index 59a476ebca..54b7758f9d 100644 --- a/datasets/EO:EUM:DAT:GOES:OSIHSSI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:GOES:OSIHSSI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:GOES:OSIHSSI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the solar irradiance reaching the Earth surface, derived from the geostationary satellite GOES-E, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a physical parameterization applied separately to every pixel of a satellite image to derive an instantaneous field of the Solar Surface Irradiance. An essential point is the calculation of products interpolated at rounded UT hours. A radiative flux calculated on a satellite image is not homogeneous in time (the pixel time varies from north to south, of about 24 minutes for GOES-E data).", "links": [ { diff --git a/datasets/EO:EUM:DAT:GOES:OSIHSST_2011-10-07.json b/datasets/EO:EUM:DAT:GOES:OSIHSST_2011-10-07.json index 61839add59..ac2c16e1cf 100644 --- a/datasets/EO:EUM:DAT:GOES:OSIHSST_2011-10-07.json +++ b/datasets/EO:EUM:DAT:GOES:OSIHSST_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:GOES:OSIHSST_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly sub-skin Sea Surface Temperature product derived from GOES-East at 75\u00b0E longitude, covering 60S-60N and 135W-15W and re-projected on a 0.05\u00b0 regular grid. ", "links": [ { diff --git a/datasets/EO:EUM:DAT:METEOSAT:OSIDDLI_2011-10-07.json b/datasets/EO:EUM:DAT:METEOSAT:OSIDDLI_2011-10-07.json index 9f496bf12e..91084a941d 100644 --- a/datasets/EO:EUM:DAT:METEOSAT:OSIDDLI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:METEOSAT:OSIDDLI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METEOSAT:OSIDDLI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the Downward Longwave Irradiance reaching the Earth surface, derived from the geostationary satellite Meteosat, derived at present from the 0.6\u00b5m visible channel of SEVIRI, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a bulk parameterization that uses NWP model outputs to calculate a clear sky Downward Longwave Irradiance (DLI), corrected according to satellite derived cloud information. The daily value is the integration of all the hourly values in the UT day.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METEOSAT:OSIDSSI_2011-10-07.json b/datasets/EO:EUM:DAT:METEOSAT:OSIDSSI_2011-10-07.json index 26a5ecfcc1..7ab540509a 100644 --- a/datasets/EO:EUM:DAT:METEOSAT:OSIDSSI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:METEOSAT:OSIDSSI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METEOSAT:OSIDSSI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the solar irradiance reaching the Earth surface, derived from the geostationary satellite Meteosat, derived at present from the 0.6\u00b5m visible channel of SEVIRI, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. The daily value is the integration of all the hourly values in the UT day.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METEOSAT:OSIHDLI_2011-10-07.json b/datasets/EO:EUM:DAT:METEOSAT:OSIHDLI_2011-10-07.json index e5a319c182..95c7b95ea0 100644 --- a/datasets/EO:EUM:DAT:METEOSAT:OSIHDLI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:METEOSAT:OSIHDLI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METEOSAT:OSIHDLI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the Downward Longwave Irradiance reaching the Earth surface, derived from the geostationary satellite Meteosat, derived at present from the 0.6\u00b5m visible channel of SEVIRI, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a bulk parameterization that uses NWP model outputs to calculate a clear sky Downward Longwave Irradiance (DLI), corrected according to satellite derived cloud information. An essential point is the calculation of products interpolated at rounded UT hours. A radiative flux calculated on a satellite image is not homogeneous in time (the pixel time varies from north to south, of about 12 minutes for Meteosat data).", "links": [ { diff --git a/datasets/EO:EUM:DAT:METEOSAT:OSIHSSI_2011-10-07.json b/datasets/EO:EUM:DAT:METEOSAT:OSIHSSI_2011-10-07.json index fe4adc8703..1e09a92958 100644 --- a/datasets/EO:EUM:DAT:METEOSAT:OSIHSSI_2011-10-07.json +++ b/datasets/EO:EUM:DAT:METEOSAT:OSIHSSI_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METEOSAT:OSIHSSI_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimation of the solar irradiance reaching the Earth surface, derived from the geostationary satellite Meteosat, derived at present from the 0.6\u00b5m visible channel of SEVIRI, produced by remapping over a 0.05\u00b0 regular grid and expressed in W/m2. Algorithm is a physical parameterization applied separately to every pixel of a satellite image to derive an instantaneous field of the Solar Surface Irradiance. An essential point is the calculation of products interpolated at rounded UT hours. A radiative flux calculated on a satellite image is not homogeneous in time (the pixel time varies from north to south, of about 12 minutes for Meteosat data).", "links": [ { diff --git a/datasets/EO:EUM:DAT:METEOSAT:OSIHSST_2011-10-07.json b/datasets/EO:EUM:DAT:METEOSAT:OSIHSST_2011-10-07.json index 1166d6b183..102e8dcc43 100644 --- a/datasets/EO:EUM:DAT:METEOSAT:OSIHSST_2011-10-07.json +++ b/datasets/EO:EUM:DAT:METEOSAT:OSIHSST_2011-10-07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METEOSAT:OSIHSST_2011-10-07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly sub-skin Sea Surface Temperature product derived from Meteosat at 0\u00b0 longitude, covering 60S-60N and 60W-60E and re-projected on a 0.05\u00b0 regular grid.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:ASCAT12_2010-09-06.json b/datasets/EO:EUM:DAT:METOP:ASCAT12_2010-09-06.json index 41d512d95f..d7a8c350f1 100644 --- a/datasets/EO:EUM:DAT:METOP:ASCAT12_2010-09-06.json +++ b/datasets/EO:EUM:DAT:METOP:ASCAT12_2010-09-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:ASCAT12_2010-09-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASCAT Wind Product contains measurements of the wind direction and wind speed at 10 m above the sea surface. The measurements are obtained through the processing of scatterometer data originating from the ASCAT instrument on EUMETSAT's Metop satellite, as described in the ASCAT Wind Product User Manual. Note that up until 2011-02-28, a wind-only version of this product in BUFR and netCDF formats was available via EUMETCast and the EUMETSAT Data Centre. From that date on, the wind values can be found in BUFR and netCDF formats in the ASCAT multi-parameter products (collection reference: EO:EUM:DAT:METOP:OAS012 and EO:EUM:DAT:METOP:OAS025). It is recommended that the reprocessed ASCAT winds data records (EO:EUM:DAT:METOP:OSI-150-B, EO:EUM:DAT:METOP:OSI-150-A) are used instead of this archived NRT product.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:ASCAT25_2010-09-06.json b/datasets/EO:EUM:DAT:METOP:ASCAT25_2010-09-06.json index 15b413e126..fde8451b8c 100644 --- a/datasets/EO:EUM:DAT:METOP:ASCAT25_2010-09-06.json +++ b/datasets/EO:EUM:DAT:METOP:ASCAT25_2010-09-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:ASCAT25_2010-09-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASCAT Wind Product contains measurements of the wind direction and wind speed at 10 m above the sea surface. The measurements are obtained through the processing of scatterometer data originating from the ASCAT instrument on EUMETSAT's Metop satellite, as described in the ASCAT Wind Product User Manual. Note that up until 2011-02-28, a wind-only version of this product in BUFR and netCDF formats was available via EUMETCast and the EUMETSAT Data Centre. From that date on, the wind values can be found in BUFR format in the ASCAT multi-parameter products (collection reference: EO:EUM:DAT:METOP:OAS012 and EO:EUM:DAT:METOP:OAS025). It is recommended that the reprocessed ASCAT winds data records (EO:EUM:DAT:METOP:OSI-150-B, EO:EUM:DAT:METOP:OSI-150-A) are used instead of this archived NRT product.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:ASCSZF1B_2010-09-21.json b/datasets/EO:EUM:DAT:METOP:ASCSZF1B_2010-09-21.json index 92819c6fdd..5d0fbd0527 100644 --- a/datasets/EO:EUM:DAT:METOP:ASCSZF1B_2010-09-21.json +++ b/datasets/EO:EUM:DAT:METOP:ASCSZF1B_2010-09-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:ASCSZF1B_2010-09-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. This product consists of geo-located radar backscatter values along the six ASCAT beams. The different beam measurements are not collocated into a regular swath grid and the individual measurements are not spatially averaged. The resolution of each of the 255 backscatter values per each beam varies slightly along the beam, but it is approximately 10km (in the along beam direction) x 25 km (across the beam). This product is usually referred to as 'ASCAT Level 1B Full resolution product'. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:ASCSZO1B_2010-09-21.json b/datasets/EO:EUM:DAT:METOP:ASCSZO1B_2010-09-21.json index b8abe9ae3d..a1ac7bfed1 100644 --- a/datasets/EO:EUM:DAT:METOP:ASCSZO1B_2010-09-21.json +++ b/datasets/EO:EUM:DAT:METOP:ASCSZO1B_2010-09-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:ASCSZO1B_2010-09-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. The product is available from the archive in 2 different spatial resolutions; 25 km and 12.5 km. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information. Near real-time distribution discontinued on 29/09/2015 but the product contents are now available in the corresponding Level 2 product 'ASCAT Soil Moisture at 25 km Swath Grid'.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:ASCSZR1B_2010-09-21.json b/datasets/EO:EUM:DAT:METOP:ASCSZR1B_2010-09-21.json index 2d1b995df9..078181dd0e 100644 --- a/datasets/EO:EUM:DAT:METOP:ASCSZR1B_2010-09-21.json +++ b/datasets/EO:EUM:DAT:METOP:ASCSZR1B_2010-09-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:ASCSZR1B_2010-09-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. The product is available from the archive in 2 different spatial resolutions; 25 km and 12.5 km. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information. Near real-time distribution discontinued on 29/09/2015 but the product contents are now available in the corresponding Level 2 product 'ASCAT Soil Moisture at 12.5 km Swath Grid'.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:GLB-SST_2010-08-05.json b/datasets/EO:EUM:DAT:METOP:GLB-SST_2010-08-05.json index c368243247..e778e9e659 100644 --- a/datasets/EO:EUM:DAT:METOP:GLB-SST_2010-08-05.json +++ b/datasets/EO:EUM:DAT:METOP:GLB-SST_2010-08-05.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:GLB-SST_2010-08-05", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Metop/AVHRR sub-skin Sea Surface Temperature (GBL SST) is a 12 hourly synthesis on a 0.05\u00b0 global grid.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:IASI-SST_2014-12-11.json b/datasets/EO:EUM:DAT:METOP:IASI-SST_2014-12-11.json index 23cfba3206..ce8e32c622 100644 --- a/datasets/EO:EUM:DAT:METOP:IASI-SST_2014-12-11.json +++ b/datasets/EO:EUM:DAT:METOP:IASI-SST_2014-12-11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:IASI-SST_2014-12-11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a full resolution skin SST product based on Metop IASI data, in satellite projection from a resolution of 12km at nadir to 40km. The product format is compliant with the Data Specification (GDS) version 2 from the Group for High Resolution Sea Surface Temperatures (GHRSST).", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:OSI-104_2011-09-28.json b/datasets/EO:EUM:DAT:METOP:OSI-104_2011-09-28.json index 63cedab8ca..67c9e1399f 100644 --- a/datasets/EO:EUM:DAT:METOP:OSI-104_2011-09-28.json +++ b/datasets/EO:EUM:DAT:METOP:OSI-104_2011-09-28.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:OSI-104_2011-09-28", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Equivalent neutral 10m winds over the global oceans, with specific sampling to provide as many observations as possible near the coasts. Better than using this archived NRT product, please use the reprocessed ASCAT winds data records (EO:EUM:DAT:METOP:OSI-150-A, EO:EUM:DAT:METOP:OSI-150-B).", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:SOMO12_2010-06-21.json b/datasets/EO:EUM:DAT:METOP:SOMO12_2010-06-21.json index 21b1ed809b..f60fee5820 100644 --- a/datasets/EO:EUM:DAT:METOP:SOMO12_2010-06-21.json +++ b/datasets/EO:EUM:DAT:METOP:SOMO12_2010-06-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:SOMO12_2010-06-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Surface Soil Moisture L2 product is derived from the Advanced SCATterometer (ASCAT) data and given in swath geometry. This product provides an estimate of the water saturation of the 5 cm topsoil layer, in relative units between 0 and 100 [%]. The algorithm used to derive this parameter is based on a linear relationship of soil moisture and scatterometer backscatter and uses change detection techniques to eliminate the contributions of vegetation, land cover and surface topography, considered invariant from year to year. Seasonal vegetation effects are modelled by exploiting the multiple viewing capabilities of ASCAT. The processor has been developed by the Institute of Photogrammetry and Remote Sensing of the Vienna University of Technology. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information.", "links": [ { diff --git a/datasets/EO:EUM:DAT:METOP:SOMO25_2010-06-21.json b/datasets/EO:EUM:DAT:METOP:SOMO25_2010-06-21.json index 78f39b098d..91ddddee69 100644 --- a/datasets/EO:EUM:DAT:METOP:SOMO25_2010-06-21.json +++ b/datasets/EO:EUM:DAT:METOP:SOMO25_2010-06-21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:METOP:SOMO25_2010-06-21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Surface Soil Moisture L2 product is derived from the Advanced SCATterometer (ASCAT) data and given in swath geometry. This product provides an estimate of the water saturation of the 5 cm topsoil layer, in relative units between 0 and 100 [%]. The algorithm used to derive this parameter is based on a linear relationship of soil moisture and scatterometer backscatter and uses change detection techniques to eliminate the contributions of vegetation, land cover and surface topography, considered invariant from year to year. Seasonal vegetation effects are modelled by exploiting the multiple viewing capabilities of ASCAT. The processor has been developed by the Institute of Photogrammetry and Remote Sensing of the Vienna University of Technology. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:AHL-DLI_2011-11-29.json b/datasets/EO:EUM:DAT:MULT:AHL-DLI_2011-11-29.json index b58269ea15..2f35dd58a1 100644 --- a/datasets/EO:EUM:DAT:MULT:AHL-DLI_2011-11-29.json +++ b/datasets/EO:EUM:DAT:MULT:AHL-DLI_2011-11-29.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:AHL-DLI_2011-11-29", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily averaged estimation of the Downward Longwave Irradiance reaching the Earth surface, derived from AVHRR on NOAA and Metop polar orbiting satellites. The product covers the Atlantic High Latitudes, is delivered on a 5km polar stereographic grid and expressed in W/m2.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:AHL-SSI_2011-11-29.json b/datasets/EO:EUM:DAT:MULT:AHL-SSI_2011-11-29.json index 42ca9b7581..cb4c0f34ee 100644 --- a/datasets/EO:EUM:DAT:MULT:AHL-SSI_2011-11-29.json +++ b/datasets/EO:EUM:DAT:MULT:AHL-SSI_2011-11-29.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:AHL-SSI_2011-11-29", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily averaged estimation of the Surface Shortwave Irradiance reaching the Earth surface, derived from AVHRR on NOAA and Metop polar orbiting satellites. The product covers the Atlantic High Latitudes, is delivered on a 5km polar stereographic grid and expressed in W/m2.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:AHL-SST_2011-11-29.json b/datasets/EO:EUM:DAT:MULT:AHL-SST_2011-11-29.json index 74048f22d3..d84cad968d 100644 --- a/datasets/EO:EUM:DAT:MULT:AHL-SST_2011-11-29.json +++ b/datasets/EO:EUM:DAT:MULT:AHL-SST_2011-11-29.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:AHL-SST_2011-11-29", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Calculation of underskin temperature (\u00b0C) with multispectral algorithm. The product covers the Atlantic High Latitudes and is delivered twice daily on a 5km polar stereographic grid.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:GBL-LR-SIDR_2017-05-30.json b/datasets/EO:EUM:DAT:MULT:GBL-LR-SIDR_2017-05-30.json index 1b78b0a9c0..549cb8711e 100644 --- a/datasets/EO:EUM:DAT:MULT:GBL-LR-SIDR_2017-05-30.json +++ b/datasets/EO:EUM:DAT:MULT:GBL-LR-SIDR_2017-05-30.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:GBL-LR-SIDR_2017-05-30", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Low Resolution Sea Ice Drift product covers both Northern Hemisphere (NH) and Southern Hemisphere (SH), all year round. Ice motion vectors with a time span of 48 hours are estimated by an advanced cross-correlation method (the Continuous MCC, CMCC) from pairs of passive and active microwave satellite images. Several single-sensor products are available, as well as a merged (multi-sensor) product. Maps of uncertainties are embedded in the product files. Due to higher atmospheric wetness and sea ice surface melting, it is more challenging to track ice motion during summer. Accordingly, the NH product files distributed between 1 May and 30 September have larger uncertainties and more interpolated vectors. The same holds for the SH product files between 1 November and 30 March.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:GBLSIED_2017-05-30.json b/datasets/EO:EUM:DAT:MULT:GBLSIED_2017-05-30.json index db69414a13..b27d005728 100644 --- a/datasets/EO:EUM:DAT:MULT:GBLSIED_2017-05-30.json +++ b/datasets/EO:EUM:DAT:MULT:GBLSIED_2017-05-30.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:GBLSIED_2017-05-30", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sea Ice Edge product is a classification product (open water/open ice/closed ice) and covers both the Northern and Southern Hemisphere. The sea ice edge is derived from passive microwave and active microwave sensors using a multi sensor methods with a Bayesian approach to combine the different sensors.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:GBLSITY_2017-05-30.json b/datasets/EO:EUM:DAT:MULT:GBLSITY_2017-05-30.json index afd8042e29..a2eeebbeb8 100644 --- a/datasets/EO:EUM:DAT:MULT:GBLSITY_2017-05-30.json +++ b/datasets/EO:EUM:DAT:MULT:GBLSITY_2017-05-30.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:GBLSITY_2017-05-30", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sea Ice Type product is a classification product (multiyear ice/first-year ice) and covers both the Northern Hemisphere (NH) and Southern Hemisphere (SH). The sea ice type is derived from passive microwave and active microwave sensors using a multi sensor methods with a Bayesian approach to combine the different sensors. At present, it is not possible to do an ice type classification during summer from the processed channels. Accordingly, the NH ice type product files distributed between mid-May and 30 September do not contain any valid classification. Similarly for the SH, at present no ice type classification has been defined and SH product files do not contain valid classification.", "links": [ { diff --git a/datasets/EO:EUM:DAT:MULT:OSSTNAR_2013-11-20.json b/datasets/EO:EUM:DAT:MULT:OSSTNAR_2013-11-20.json index e133bbfd09..5accd7d19f 100644 --- a/datasets/EO:EUM:DAT:MULT:OSSTNAR_2013-11-20.json +++ b/datasets/EO:EUM:DAT:MULT:OSSTNAR_2013-11-20.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:MULT:OSSTNAR_2013-11-20", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAR product consists in Metop/AVHRR and SNPP/VIIRS derived sub-skin Sea Surface Temperature over North Atlantic and European Seas at 2 km resolution, four times a day.", "links": [ { diff --git a/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05.json b/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05.json index ddaa8aa961..c9917bd0c4 100644 --- a/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05.json +++ b/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SLSTR SST has a spatial resolution of 1km at nadir. All Sentinel-3 NRT products are available at pick-up point in less than 3h. Skin Sea Surface Temperature following the GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ . Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.", "links": [ { diff --git a/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05.json b/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05.json index a94ff3bb83..d28db4a772 100644 --- a/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05.json +++ b/datasets/EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SLSTR SST has a spatial resolution of 1km at nadir. All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days. Skin Sea Surface Temperature following the GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ . Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.", "links": [ { diff --git a/datasets/EOLE1_001.json b/datasets/EOLE1_001.json index 4f2ba000b9..c40d105534 100644 --- a/datasets/EOLE1_001.json +++ b/datasets/EOLE1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EOLE1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit. Data from each orbit are contained in a single record and consist of a heading giving the orbit number, the number of balloons contacted, and a control number. Following the heading, the balloon number, date of observation, location, and ambient temperature and pressure are listed. A maximum of 25 balloon contacts may appear in a single record. Empty records with no balloon contacts have been omitted. These data were obtained from balloons near 200 mbar and are for the region between 30 deg S and 60 deg S. The upper level wind speed and direction can be generated from these data by comparing individual balloon locations obtained from successive orbits. Eole, also known as the Cooperative Application Satellite (CAS-A), was the the second French experimental relay and meteorological satellite and the first launched by NASA under a cooperative agreement with the Centre National d'Etudes Spatiales (CNES).", "links": [ { diff --git a/datasets/EOSWEBSTER_CLIMCALC_NE_US.json b/datasets/EOSWEBSTER_CLIMCALC_NE_US.json index 6f8a0df02f..267722e9e2 100644 --- a/datasets/EOSWEBSTER_CLIMCALC_NE_US.json +++ b/datasets/EOSWEBSTER_CLIMCALC_NE_US.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EOSWEBSTER_CLIMCALC_NE_US", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLIMCALC is a simple model of physical and chemical climate for the northeasten United States (New York and New England) that can be incorporated into a geographic information system (GIS) for integration with ecosystem models presented. The variables include average maximum and minimum daily temperature, precipitation, humidity, and solar radiation, all at a monthly time step, as well as annual wet and dry deposition of sulfur and nitrogen. Regressions on latitude, longitude, and elevation are fitted to regional data bases of these variables The equations are combined with a digital elevation model (DEM) of the region to generate GIS coverages of each variableresults are from a model of atmospheric deposition called CLIMCALC. Spatial patterns of atmospheric deposition across the northeastern United States were evaluated and summarized in a simple model as a function of elevation and geographic position within the region. For wet deposition, 3-11 yr of annual concentration data for the major ions in precipitation were obtained from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) for 26 sites within the region. Concentration trends were evaluated by regression of annual mean concentrations against latitude and longitude. For nitrate, sulfate, and ammonium concentrations, a more than twofold linear decrease occurs from western New York and Pennsylvania to eastern Maine. These trends were combined with regional and elevational trends or precipitation amount, obtained from 30-yr records of annual precipitation at >300 weather stations, to provide long-term patterns of wet deposition. Regional trends of dry deposition of N and S compounds were determined using 2-3 yrs of particle and gas concentration data collected by the National Dry Deposition Network (NDDN) and several other sources, in combination with estimates of deposition velocities. Contrary to wet deposition trends, the dominant air concentration trends were steep decreases from south to north, creating regional decreases in total deposition (wet + dry) from the southwest to the northeast. This contrast between wet and dry deposition trends suggests that within the northeast the two deposition forms are received in different proportions from different source areas, wet deposited materials primarily from areas to the west and dry deposited materials primarily from urban areas along the southern edge of the region. The equations generated describing spatial patterns of wet and dry depositions within the region were entered into a geographic information system (GIS) containing a digital elevation model (DEM) in order to develop spatially explicit predictions of atmospheric deposition for the region.\n", "links": [ { diff --git a/datasets/EOSWEBSTER_US_County_Data.json b/datasets/EOSWEBSTER_US_County_Data.json index 927a0f7411..79e33d75bc 100644 --- a/datasets/EOSWEBSTER_US_County_Data.json +++ b/datasets/EOSWEBSTER_US_County_Data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EOSWEBSTER_US_County_Data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Annual crop data from 1972 to 1998 are now available on\n EOS-WEBSTER. These data are county-based acreage, production, and\n yield estimates published by the National Agricultural Statistics\n Service. We also provide county level livestock, geography,\n agricultural management, and soil properties derived from datasets\n from the early 1990s.\n \n The National Agricultural Statistics Service (NASS), the statistical\n arm of the U.S. Department of Agriculture, publishes U.S., state, and\n county level agricultural statistics for many commodities and data\n series. In response to our users requests, EOS-WEBSTER now provides 27\n years of crop statistics, which can be subset temporally and/or\n spatially. All data are at the county scale, and are only for the\n conterminous US (48 states + DC). There are 3111 counties in the\n database. The list includes 43 cities that are classified as\n counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in\n Virginia.\n \n In addition, a collection of livestock, geography, agricultural\n practices, and soil properties variables for 1992 is available through\n EOS-WEBSTER. These datasets were assembled during the mid-1990's to\n provide driving variables for an assessment of greenhouse gas\n production from US agriculture using the DNDC agro-ecosystem model\n [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776;\n Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data\n (except nitrogen fertilizer use) were all derived from publicly\n available, national databases. Each dataset has a separate DIF.\n \n The US County data has been divided into seven datasets.\n \n US County Data Datasets:\n \n 1) Agricultural Management\n 2) Crop Data (NASS Crop data)\n 3) Crop Summary (NASS Crop data)\n 4) Geography and Population\n 5) Land Use\n 6) Livestock Populations\n 7) Soil Properties\n", "links": [ { diff --git a/datasets/EPA0175.json b/datasets/EPA0175.json index a0947b80db..d5b3690cc9 100644 --- a/datasets/EPA0175.json +++ b/datasets/EPA0175.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EPA0175", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"National Water Quality Assessment Program (NAWQA) Home\nPage\" is an Internet resource that provides information on\nresearch dealing with water quality in the United States. This\nhome page provides links to NAWQA activities, selected\npublications, a bibliography, and summaries of current research\nprojects.\n\nThe NAWQA program is designed to assess historical, current, and\nfuture water-quality conditions in representative river basins\nand aquifers nationwide. One of the primary objectives of the\nprogram is to describe relations between natural factors, human\nactivities, and water quality conditions and to define those\nfactors that most affect water quality in different parts of the\nNation. The linkage of water quality to environmental processes\nis of fundamental importance to water-resource managers,\nplanners, and policy makers. It provides a strong and unbiased\nbasis for better decision making by those responsible for making\ndecisions that affect our water resources, including the United\nStates Congress, Federal, State, and local agencies,\nenvironmental groups, and industry. Information from the NAWQA\nProgram also will be useful for guiding research, monitoring,\nand regulatory activities in cost effective ways.\n LANGUAGE:\n\nEnglish\n ACCESS/AVAILABILITY:\n\nData Center: National Water Quality Assessment Program\nDissemination Media: Online\nFile Format:\n\nSize:\nMemory Requirements:\nOperating System:\nHardware Required:\nSoftware Required:\nAvailability Status: On Request\nDocumentation Available:", "links": [ { diff --git a/datasets/EPA_AQA.json b/datasets/EPA_AQA.json index 4afc5c3eb3..73f0230678 100644 --- a/datasets/EPA_AQA.json +++ b/datasets/EPA_AQA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EPA_AQA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Air Quality Atlas is a collection of maps prepared by the Air Quality\nAnalysis Section in the Region 6 office of the U.S. Environmental Protection\nAgency (EPA). The atlas presents a spatial analysis of air quality in EPA\nRegion 6 for 1996, focusing on the six criteria pollutants for which the EPA\nhas set primary and secondary standards to protect public health and welfare.\nThese standards, defined as the National Ambient Air Quality Standards (NAAQS),\nhave been set for the following six pollutants: lead, nitrogen dioxide, carbon\nmonoxide, sulfur dioxide, ozone, and small particles less than or equal to 10\nmicrons in aerodynamic diameter (PM-10). The primary standards are set to\nprotect public health, and the secondary standards are set to protect public\nwelfare, such as buildings, forests, and agricultural crops. The primary and\nsecondary standards are currently identical for all of the criteria pollutants\nexcept sulfur dioxide. The sulfur dioxide secondary standard is based on a\nthree hour averaging time, while the primary standard is based on both 24-hour\nand annual averaging times.\n\nThe maps show Region 6 air quality levels referenced against the standards set\nfor the six criteria pollutants. The legend for each map, except for the two\nexceedance day maps, was constructed to show the following information: (1) The\nblue shade depicts levels less than 10% of the standard; (2) the green shade\ndepicts levels between 10-50% of the standard; (3) the gray shade depicts\nlevels between 50-90% of the standard; (4) the yellow shade depicts levels\nwithin 10% of the standard; and (5) the red shade depicts levels over the\nstandard. Counties not shaded (white) either do not contain monitors, or their\nmonitors did not achieve a data capture rate of at least 75% (exception - all\nozone site data were reported). The data used to compose each map were obtained\nfrom the EPA's Aerometric Information Retrieval System (AIRS) data base.\n\nAnalysis of the maps reveals that all Region 6 monitors recorded concentrations\nbelow the NAAQS set for lead, nitrogen dioxide, and sulfur dioxide. Indeed, a\nsignificant amount of areas in Region 6 recorded maximum concentrations well\nbelow these standards. Additional map analysis shows that one Region 6 county\n(El Paso) contained monitors recording measurements above the carbon monoxide\n8-hour standard, that two Region 6 counties (El Paso and Dona Ana) contained\nmonitors recording measurements above the PM-10 standards, and that every state\nexcept Arkansas had at least one monitor with values above the ozone standard.\nFollowing each map displaying the 1996 Region 6 status of particulate and ozone\nair quality is a map showing the number of days per county in which a monitor\nrecorded concentrations above the PM-10 or ozone standards.\n\n[Summary provided by the EPA.]", "links": [ { diff --git a/datasets/EPEA_0.json b/datasets/EPEA_0.json index 5fa4a5f44f..59c9f4560f 100644 --- a/datasets/EPEA_0.json +++ b/datasets/EPEA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EPEA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The EPEA (Estación Permanente de Estudios Ambientales) time series station was started in 2000 and since 2003 belongs to ANTARES (www.antares.ws), a network of Latin American time series stations whose main goal is the study of long-term changes in coastal ecosystems to distinguish those due to natural variability from those due to external perurbations (anthropogenic effects).Different research groups at the INIDEP (the National Institute of Fisheries Research and Development of Argentina) sample at the EPEA station, monitoring chemical, environmental and bio-optical variables as well as the bacterioplankton, phytoplankton, zooplankton, and the icthyoplankton communities. EPEA station is located on the Argentine shelf (38°28'S, 57°41'W), 27.0 nautical miles from Mar del Plata city and 13.5 nautical miles from the coast and has a depth of 50m. EPEA is characterized by a temperate regime, with annual sea surface temperatures between 10°C and 21°C and salinity values ranging between 33.5 and 34.1. Occasionally the site can receive less salty waters coming from the North, influenced by the La Plata River, driving salinity values to less than 31.0. Its oceanographic regime is described as the transition between high salinity coastal waters to the medium shelf (Guerrero et al., 1997). ", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_NF_Edition2.json b/datasets/ERBE_S10N_WFOV_NF_Edition2.json index 0d57c61c3f..c4fdf09dec 100644 --- a/datasets/ERBE_S10N_WFOV_NF_Edition2.json +++ b/datasets/ERBE_S10N_WFOV_NF_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_NF_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_NF_Edition2 is the Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 2 in Native Format data product. Data collection for this product is complete.\r\n\r\nThe reprocessed ERBE S10N_WFOV ERBS Edition2 data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite. Instantaneous TOA fluxes from the ERBE/ERBS S7 product were spatially averaged on a 5\u00b0 and 10\u00b0 equal-angle grid using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5\u00b0 and 10\u00b0 region observed during the month. The major differences between Edition2 and the original release are in the monthly mean fluxes with (1) the incorporation of stochastic quality assurance algorithms for filtering out monthly-mean data with excessive temporal sample errors and (2) a self-consistent usage of the WFOV data in selecting scene-dependent directional models for temporal interpolation of the ERBE WFOV instantaneous gridded data.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_NF_Edition3.json b/datasets/ERBE_S10N_WFOV_NF_Edition3.json index e41cfc8c9d..150d3f0fc5 100644 --- a/datasets/ERBE_S10N_WFOV_NF_Edition3.json +++ b/datasets/ERBE_S10N_WFOV_NF_Edition3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_NF_Edition3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_NF_Edition3 is the Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 3 in Native Format data product. Data collection for this product is complete.\r\n\r\nThis data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite (ERBS). Instantaneous TOA fluxes were spatially averaged on 5\u00b0 and 10\u00b0 equal-angle grids using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5\u00b0 and 10\u00b0 region observed during the month. The main difference between Edition3 and Edition2 releases is in the treatment of TOA radiative fluxes resulting from changes in the ERBE non-scanner processing algorithm to account for decay in satellite altitude over the data period.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4.1.json b/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4.1.json index 1b77ef944b..73907bb072 100644 --- a/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4.1.json +++ b/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) area average time series through Wide-field-of-view nonscanner abroad Earth Radiation Budget Satellite Edition 4.1 data product. Understanding the mean and variability of the Earth\u2019s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date.\r\n\r\nThe ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4_4.json b/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4_4.json index 986ec96f59..5e94224ba2 100644 --- a/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4_4.json +++ b/datasets/ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries_Edition4_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the mean and variability of the Earth\u2019s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and CERES, that span nearly 30 years to date.\r\n\r\nThe proposed project utilizes knowledge gained in the last 10 years through CERES data analyses and apply the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.1.json b/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.1.json index 9556dc393e..143c4c8650 100644 --- a/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.1.json +++ b/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_SF_ERBS_Regional is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) Wide-field-of-view Nonscanner Observations Edition 4.1 data product. Understanding the mean and variability of the Earth's radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date.\r\nThe ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.json b/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.json index 2c3b003e1c..87907fcba6 100644 --- a/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.json +++ b/datasets/ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_ERBS_Regional_Edition4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the mean and variability of the Earth\u2019s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and CERES, that span nearly 30 years to date.\r\n\r\nThe proposed project utilizes knowledge gained in the last 10 years through CERES data analyses and apply the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_Edition2.json b/datasets/ERBE_S10N_WFOV_SF_Edition2.json index 3918f8d511..b796e440ca 100644 --- a/datasets/ERBE_S10N_WFOV_SF_Edition2.json +++ b/datasets/ERBE_S10N_WFOV_SF_Edition2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_Edition2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_SF_Edition2 is the Earth Radiation Budget Experiment (ERBE) S-10N (Non-scanner-only) Wide Field of View (WFOV) Shape Factor (SF) Radiant Flux and Albedo Edition 2 in Native Format data product. Data collection for this product is complete.\r\n\r\nThis product resulted from the reprocessed ERBE S10N_WFOV ERBS Edition2 data product. It contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite (ERBS). Instantaneous Top-of-Atmosphere (TOA) fluxes from the ERBE/ERBS S7 product were spatially averaged on a 5\u00b0 and 10\u00b0 equal-angle grid using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5\u00b0 and 10\u00b0 region observed during the month. The major differences between Edition2 and the original release were in the monthly mean fluxes with (1) the incorporation of stochastic quality assurance algorithms for filtering out monthly-mean data with excessive temporal sample errors and (2) a self-consistent usage of the WFOV data in selecting scene-dependent directional models for temporal interpolation of the ERBE WFOV instantaneous gridded data.", "links": [ { diff --git a/datasets/ERBE_S10N_WFOV_SF_Edition3.json b/datasets/ERBE_S10N_WFOV_SF_Edition3.json index e75bfcf79c..fc877ab792 100644 --- a/datasets/ERBE_S10N_WFOV_SF_Edition3.json +++ b/datasets/ERBE_S10N_WFOV_SF_Edition3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFOV_SF_Edition3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFOV_SF_Edition3 is the Earth Radiation Budget Experiment (ERBE) S-10N (Non-scanner-only) Wide Field of View (WFOV) Shape Filter (SF) Radiant Flux and Albedo Edition 3 in Native Format data product. Data collection for this product is complete.\r\n\r\nThis data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite (ERBS). Instantaneous TOA fluxes were spatially averaged on 5\u00b0 and 10\u00b0 equal-angle grids using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5\u00b0 and 10\u00b0 region observed during the month. The main difference between Edition3 and Edition2 releases is in the treatment of TOA radiative fluxes resulting from changes in the ERBE non-scanner processing algorithm to account for decay in satellite altitude over the data period.", "links": [ { diff --git a/datasets/ERBE_S10N_WFV_NF_NAT_1.json b/datasets/ERBE_S10N_WFV_NF_NAT_1.json index 64ac2fb7b7..edadd7e961 100644 --- a/datasets/ERBE_S10N_WFV_NF_NAT_1.json +++ b/datasets/ERBE_S10N_WFV_NF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFV_NF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFV_NF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10N (Non-scanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Earth Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called WFOV and the latter the medium field-of-view (MFOV) channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S10N_WFV_SF_NAT_1.json b/datasets/ERBE_S10N_WFV_SF_NAT_1.json index 6d30f2d9b4..6898319e10 100644 --- a/datasets/ERBE_S10N_WFV_SF_NAT_1.json +++ b/datasets/ERBE_S10N_WFV_SF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10N_WFV_SF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10N_WFV_SF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10N (Non-scanner-only) Wide Field of View (WFOV) Shape Factor (SF) Earth Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called WFOV and the latter the medium field-of-view (MFOV) channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S10_MFOV_NF_NAT_1.json b/datasets/ERBE_S10_MFOV_NF_NAT_1.json index 32651517cc..889690796b 100644 --- a/datasets/ERBE_S10_MFOV_NF_NAT_1.json +++ b/datasets/ERBE_S10_MFOV_NF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10_MFOV_NF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10_MFOV_NF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10 Medium Field of View (MFOV) Numerical Filter (NF) Radiant Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter MFOV channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S10_MFOV_SF_NAT_1.json b/datasets/ERBE_S10_MFOV_SF_NAT_1.json index 591622b98b..7b25e7b78a 100644 --- a/datasets/ERBE_S10_MFOV_SF_NAT_1.json +++ b/datasets/ERBE_S10_MFOV_SF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10_MFOV_SF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10_MFOV_SF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10 Medium Field of View (MFOV) Shape Factor (SF) Radiant Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter MFOV channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S10_WFOV_NF_NAT_1.json b/datasets/ERBE_S10_WFOV_NF_NAT_1.json index 6466e4efa0..6c3dadc2f0 100644 --- a/datasets/ERBE_S10_WFOV_NF_NAT_1.json +++ b/datasets/ERBE_S10_WFOV_NF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10_WFOV_NF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10_WFOV_NF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10 Wide Field of View (WFOV) Numerical Filter (NF) Earth Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called WFOV and the latter the medium field-of-view (MFOV) channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S10_WFOV_SF_NAT_1.json b/datasets/ERBE_S10_WFOV_SF_NAT_1.json index 53b2b62582..d82f14abe9 100644 --- a/datasets/ERBE_S10_WFOV_SF_NAT_1.json +++ b/datasets/ERBE_S10_WFOV_SF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S10_WFOV_SF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S10_WFOV_SF_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-10 Wide Field of View (WFOV) Shape Factor (SF) Earth Flux and Albedo data product. Data collection for this product is complete. It is available in the Native (NAT) Format.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument package contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called WFOV and the latter the medium field-of-view (MFOV) channels. The solar monitor was a direct descendant of the Solar Maximum Mission's Active Cavity Radiometer Irradiance Monitor detector. Due to the concern for spectral flatness and high accuracy, all five of the channels were active cavity radiometers. The MFOV (medium-field-of-view) SF (shape factor) S-10 contained inverted daily, monthly hourly, and monthly averages of shortwave and long-wave radiant fluxes at the top-of-the-atmosphere for one month. This data set was produced for each of the satellites (ERBS and NOAA-9) and the combination of satellites, which were operational during the data month. The values for this data set were derived using the shape factor technique (Smith et al. 1986). As described in the Earth Radiant Fluxes and Albedo, Scanner S-9, Non-scanner S-10/S-10N User's Guide, the data contains a 30 byte header, 67 scale factors which were used to scale the data in the first record, and 26 scale factors which were used to scale the data in the second record. The data set also contained two records for each processed region. The first record was of fixed length (990 words) and contained averaged data. The second record was of variable length and contained individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (978th word of record one) by the number of values stored for each hour box (38 for the non-scanner).", "links": [ { diff --git a/datasets/ERBE_S4GN_WFOV_NF_1.json b/datasets/ERBE_S4GN_WFOV_NF_1.json index c00e988393..feb94c576a 100644 --- a/datasets/ERBE_S4GN_WFOV_NF_1.json +++ b/datasets/ERBE_S4GN_WFOV_NF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4GN_WFOV_NF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4GN_WFOV_NF_1 is the Earth Radiation Budget Experiment (ERBE) S-4GN (Nons-canner) Wide Field of View Numerical Filter (NF) 5.0 degree Regional Averages data product. Data collection for this product is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each carried both a scanner and a non-scanner instrument package. The ERBE S-4G product contained the same time and space averages of all the individual estimates of radiant flux at the top-of-the-atmosphere for one month and one spacecraft or combination of spacecraft as the S-4N product. The difference between the two products was that S-4N is arranged by region, with all parameters for a region grouped together, while S-4GN presented gridded data, with all regions for a given parameter grouped together. The S-4GN data set consisted of non-scanner data processed without scene identification information from the scanner and with the numerical filter cross track enhancement.", "links": [ { diff --git a/datasets/ERBE_S4GN_WFOV_NF_ZG_1.json b/datasets/ERBE_S4GN_WFOV_NF_ZG_1.json index dd39ff52ae..85844726bd 100644 --- a/datasets/ERBE_S4GN_WFOV_NF_ZG_1.json +++ b/datasets/ERBE_S4GN_WFOV_NF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4GN_WFOV_NF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4GN_WFOV_NF_ZG data set contains Earth Radiation Budget Experiment (ERBE) S-4GN (Nonscanner-only) Wide Field of View Numerical Filter (NF) 5.0 and 10.0 degree Zonal and Global Averages in Hierarchical Data Format.The Earth Radiation Budget Experiment (ERBE) is a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments fly on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each carries both a scanner and a nonscanner instrument package.The ERBE S-4G product contains the same time and space averages of all the individual estimates of radiant flux at the top-of-the-atmosphere for one month and one spacecraft or combination of spacecraft as the S-4N product. The difference between the two products is that S-4N is arranged by region, with all parameters for a region grouped together, while S-4GN presents gridded data, with all regions for a given parameter grouped together.The S-4GN data set consists of nonscanner data processed without scene identification information from the scanner and with the numerical filter cross track enhancement.", "links": [ { diff --git a/datasets/ERBE_S4GN_WFOV_SF_1.json b/datasets/ERBE_S4GN_WFOV_SF_1.json index 45c1988de0..12c1a627a1 100644 --- a/datasets/ERBE_S4GN_WFOV_SF_1.json +++ b/datasets/ERBE_S4GN_WFOV_SF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4GN_WFOV_SF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4GN_WFOV_SF_1 is the Earth Radiation Budget Experiment (ERBE) S-4GN (Non-scanner) Wide Field of View Shape Factor (SF) 10.0 degree Regional Averages data set, which is in Hierarchical Data Format (HDF). Data collection for this data set is complete. \r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each carried both a scanner and a non-scanner instrument package. The ERBE S-4G product contained the same time and space averages of all the individual estimates of radiant flux at the top-of-the-atmosphere (TOA) for one month and one spacecraft or combination of spacecraft as the S-4N product. The difference between the two products was that S-4N was arranged by region, with all parameters for a region grouped together, while S-4GN presented gridded data, with all regions for a given parameter grouped together. The S-4GN data set consisted of non-scanner data processed without scene identification information from the scanner and with the numerical filter cross track enhancement.", "links": [ { diff --git a/datasets/ERBE_S4GN_WFOV_SF_ZG_1.json b/datasets/ERBE_S4GN_WFOV_SF_ZG_1.json index 4b54e5b169..9b7c3d085a 100644 --- a/datasets/ERBE_S4GN_WFOV_SF_ZG_1.json +++ b/datasets/ERBE_S4GN_WFOV_SF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4GN_WFOV_SF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4GN_WFOV_SF_ZG_1 is the Earth Radiation Budget Experiment (ERBE) S-4GN (Non-scanner) Wide Field of View Shape Factor (SF) 10.0 degree Zonal and Global Averages data set, which is in Hierarchical Data Format (HDF). Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each carried both a scanner and a non-scanner instrument package. The ERBE S-4G product contained the same time and space averages of all the individual estimates of radiant flux at the top-of-the-atmosphere (TOA) for one month and one spacecraft or combination of spacecraft as the S-4N product. The difference between the two products was that S-4N was arranged by region, with all parameters for a region grouped together, while S-4GN presented gridded data, with all regions for a given parameter grouped together. The S-4GN data set consisted of non-scanner data processed without scene identification information from the scanner and with the numerical filter cross track enhancement.", "links": [ { diff --git a/datasets/ERBE_S4GN_WFV_NF_N10_1.json b/datasets/ERBE_S4GN_WFV_NF_N10_1.json index ca0feb96e9..1cba6f0750 100644 --- a/datasets/ERBE_S4GN_WFV_NF_N10_1.json +++ b/datasets/ERBE_S4GN_WFV_NF_N10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4GN_WFV_NF_N10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4GN_WFV_NF_N10_1 is the Earth Radiation Budget Experiment (ERBE) S-4GN (Non-scanner) Wide Field of View Numerical Filter 5 deg. nested 10 deg. Regional Averages data set, which is in Hierarchical Data Format (HDF). Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each carried both a scanner and a non-scanner instrument package. The ERBE S-4G product contained the same time and space averages of all the individual estimates of radiant flux at the top-of-the-atmosphere (TOA) for one month and one spacecraft or combination of spacecraft as the S-4N product. The difference between the two products was that S-4N was arranged by region, with all parameters for a region grouped together, while S-4GN presented gridded data, with all regions for a given parameter grouped together. The S-4GN data set consisted of non-scanner data processed without scene identification information from the scanner and with the numerical filter cross track enhancement.", "links": [ { diff --git a/datasets/ERBE_S4G_MFOV_NF_1.json b/datasets/ERBE_S4G_MFOV_NF_1.json index 299e91bcae..6daa9265f9 100644 --- a/datasets/ERBE_S4G_MFOV_NF_1.json +++ b/datasets/ERBE_S4G_MFOV_NF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_MFOV_NF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_MFOV_NF_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Medium Field of View (MFOV) Numerical Filter (NF) 5 degree Regional Averages data product. Data collection for this product is complete. It consists of non-scanner, medium field-of-view data, processed using the numerical filter data reduction technique and averaged to a 5 degree grid scale. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nERBE is a multi-satellite system that was designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former were called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which was sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. ERBE S-4G MFOV product was available as a combination of the ERBS and NOAA-9 spacecraft. Products were archived as a combination of ERBS and NOAA-9 from February 1985 through October 1986. MFOV measurements from NOAA-10 have not been archived.", "links": [ { diff --git a/datasets/ERBE_S4G_MFOV_NF_N10_1.json b/datasets/ERBE_S4G_MFOV_NF_N10_1.json index f34ca52a4a..61ceeb407b 100644 --- a/datasets/ERBE_S4G_MFOV_NF_N10_1.json +++ b/datasets/ERBE_S4G_MFOV_NF_N10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_MFOV_NF_N10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_MFOV_NF_N10_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Medium data product. Data collection for this product is complete. It consists of non-scanner, medium field-of-view data, processed using the numerical filter data reduction technique and averaged to a 5 degree grid scale nested with area weighting to 10 degree regions. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nERBE is a multi-satellite system that was designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former were called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which was sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. ERBE S-4G MFOV product was available as a combination of the ERBS and NOAA-9 spacecraft. Products were archived as a combination of ERBS and NOAA-9 from February 1985 through October 1986. MFOV measurements from NOAA-10 have not been archived.", "links": [ { diff --git a/datasets/ERBE_S4G_MFOV_NF_ZG_1.json b/datasets/ERBE_S4G_MFOV_NF_ZG_1.json index 5132d2645e..7599568fc8 100644 --- a/datasets/ERBE_S4G_MFOV_NF_ZG_1.json +++ b/datasets/ERBE_S4G_MFOV_NF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_MFOV_NF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_MFOV_NF_ZG_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Medium Field of View (MFOV) Numerical Filter (NF) Zonal and Global Averages in HDF data product. Data collection for this product is complete. It consists of non-scanner, medium field-of-view data, processed using the numerical filter data reduction technique. The data are averaged over latitudinal bands(zones) as well as on a global level in which each parameter is averaged over the entire globe. The zonal averages are available in both 5.0 and 10.0 degree resolutions. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nERBE is a multi-satellite system that was designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former were called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which was sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. ERBE S-4G MFOV product was available as a combination of the ERBS and NOAA-9 spacecraft. Products were archived as a combination of ERBS and NOAA-9 from February 1985 through October 1986. MFOV measurements from NOAA-10 have not been archived.", "links": [ { diff --git a/datasets/ERBE_S4G_MFOV_SF_1.json b/datasets/ERBE_S4G_MFOV_SF_1.json index 6456d45559..e3d3e054da 100644 --- a/datasets/ERBE_S4G_MFOV_SF_1.json +++ b/datasets/ERBE_S4G_MFOV_SF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_MFOV_SF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_MFOV_SF_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Medium Field of View (MFOV) Shape Factor (SF) 10 degree Regional Averages in HDF data product. Data collection for this product is complete. This data set consists of non-scanner, medium field-of-view data, which was processed using the shape factor data reduction technique and averaged to a 10.0 degree grid scale. Monthly (day), monthly (hour), daily, and monthly hourly averages were determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_MFOV_SF_ZG_1.json b/datasets/ERBE_S4G_MFOV_SF_ZG_1.json index 8059103901..f7f01b8cba 100644 --- a/datasets/ERBE_S4G_MFOV_SF_ZG_1.json +++ b/datasets/ERBE_S4G_MFOV_SF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_MFOV_SF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_MFOV_SF_ZG_1 is the Earth Radiation Budget Experiment (ERBE) Non-scanner S-4G Medium-field of View (MFOV) Shape Factor (SF) Zonal and Global Averages data product. Data collection for this product is complete. This data set consists of non-scanner, medium field-of-view data, which was processed using the shape factor data reduction technique. The data are averaged over latitudinal bands(zones) as well as on a global level in which each parameter is averaged over the entire globe. The zonal averages are available in 10.0 degree resolution. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_SC_2.5_1.json b/datasets/ERBE_S4G_SC_2.5_1.json index 567f3a5efc..f5072f081f 100644 --- a/datasets/ERBE_S4G_SC_2.5_1.json +++ b/datasets/ERBE_S4G_SC_2.5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_SC_2.5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_SC_2.5_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Scanner (SC) 2.5 degree Regional Averages data set. It contains Earth Radiation Budget Experiment (ERBE) S-4G Scanner (SC) 2.5 degrees Regional Averages in Hierarchical Data Format. Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave (0.2 to 5 microns), longwave (5 to 50 microns) and total waveband radiation (.2 to 50 microns). Each detector normally scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. They were located at the focal point of a f/1.84 Cassegrain telescope, whose aluminum-coated mirrors were overcoated to enhance ultraviolet reflectivity. The total channel had no filter; therefore it absorbed all wavelengths. The shortwave channel was a fused silica filter which transmitted only shortwave radiation. The longwave channel was a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. To enhance the spectral flatness of the detectors, each thermistor chip was coated with a thin layer of black paint. The effective field of view of the scanner was 3 degrees. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_SC_NEST10_1.json b/datasets/ERBE_S4G_SC_NEST10_1.json index 0861d6e71d..56de74035c 100644 --- a/datasets/ERBE_S4G_SC_NEST10_1.json +++ b/datasets/ERBE_S4G_SC_NEST10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_SC_NEST10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_SC_NEST10_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Scanner (SC) 5 degree nested to 10 degree Regional Averages data set, which in in Hierarchical Data Format. Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave (0.2 to 5 microns), longwave (5 to 50 microns) and total waveband radiation (.2 to 50 microns). Each detector normally scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. They were located at the focal point of a f/1.84 Cassegrain telescope, whose aluminum-coated mirrors were overcoated to enhance ultraviolet reflectivity. The total channel had no filter; therefore it absorbed all wavelengths. The shortwave channel was a fused silica filter which transmitted only shortwave radiation. The longwave channel was a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. To enhance the spectral flatness of the detectors, each thermistor chip was coated with a thin layer of black paint. The effective field of view of the scanner was 3 degrees. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_SC_NEST5_1.json b/datasets/ERBE_S4G_SC_NEST5_1.json index bbf1793703..290b60bdbd 100644 --- a/datasets/ERBE_S4G_SC_NEST5_1.json +++ b/datasets/ERBE_S4G_SC_NEST5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_SC_NEST5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_SC_NEST5_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Scanner (SC) 2.5 degree nested to 5 degree Regional Averages data set it is in Hierarchical Data Format. Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave (0.2 to 5 microns), longwave (5 to 50 microns) and total waveband radiation (.2 to 50 microns). Each detector normally scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. They were located at the focal point of a f/1.84 Cassegrain telescope, whose aluminum-coated mirrors were overcoated to enhance ultraviolet reflectivity. The total channel had no filter; therefore it absorbed all wavelengths. The shortwave channel was a fused silica filter which transmitted only shortwave radiation. The longwave channel was a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. To enhance the spectral flatness of the detectors, each thermistor chip was coated with a thin layer of black paint. The effective field of view of the scanner was 3 degrees. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_SC_ZG_1.json b/datasets/ERBE_S4G_SC_ZG_1.json index 755eddcfd9..e895d77779 100644 --- a/datasets/ERBE_S4G_SC_ZG_1.json +++ b/datasets/ERBE_S4G_SC_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_SC_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_SC_ZG_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Scanner (SC) 2.5, 5, 10 degrees Zonal and Global Regional Averages data set. It is in Hierarchical Data Format. Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave (0.2 to 5 microns), longwave (5 to 50 microns) and total waveband radiation (.2 to 50 microns). Each detector normally scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. They were located at the focal point of a f/1.84 Cassegrain telescope, whose aluminum-coated mirrors were overcoated to enhance ultraviolet reflectivity. The total channel had no filter; therefore it absorbed all wavelengths. The shortwave channel was a fused silica filter which transmitted only shortwave radiation. The longwave channel was a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. To enhance the spectral flatness of the detectors, each thermistor chip was coated with a thin layer of black paint. The effective field of view of the scanner was 3 degrees. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_WFOV_NF_1.json b/datasets/ERBE_S4G_WFOV_NF_1.json index bbe149ab64..c0b044aea7 100644 --- a/datasets/ERBE_S4G_WFOV_NF_1.json +++ b/datasets/ERBE_S4G_WFOV_NF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_WFOV_NF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_WFOV_NF_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Wide Field of View (WFOV) Numerical Filter (NF) 5 degree Regional Averages data product. Data collection for this product is complete. This data set consists of non-scanner, wide field-of-view data, which was processed using the numerical filter data reduction technique and averaged to a 5 degree grid scale. Monthly (day), monthly (hour), daily, and monthly hourly averages were determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_WFOV_NF_N10_1.json b/datasets/ERBE_S4G_WFOV_NF_N10_1.json index 0223222794..c9d6f2ff9f 100644 --- a/datasets/ERBE_S4G_WFOV_NF_N10_1.json +++ b/datasets/ERBE_S4G_WFOV_NF_N10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_WFOV_NF_N10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_WFOV_NF_N10_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Nonscanner,Wide Field of View (WFOV) Numerical Filter (NF) 5 degree nested to 10 degree Regional Averages data set. It is in HDF format. Data collection for this data set is complete. \r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave (0.2 to 5 microns), longwave (5 to 50 microns) and total waveband radiation (.2 to 50 microns). Each detector normally scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. They were located at the focal point of a f/1.84 Cassegrain telescope, whose aluminum-coated mirrors were overcoated to enhance ultraviolet reflectivity. The total channel had no filter; therefore it absorbed all wavelengths. The shortwave channel was a fused silica filter which transmitted only shortwave radiation. The longwave channel was a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. To enhance the spectral flatness of the detectors, each thermistor chip was coated with a thin layer of black paint. The effective field of view of the scanner was 3 degrees. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_WFOV_NF_ZG_1.json b/datasets/ERBE_S4G_WFOV_NF_ZG_1.json index b4c091bcc5..0c0f7bb48d 100644 --- a/datasets/ERBE_S4G_WFOV_NF_ZG_1.json +++ b/datasets/ERBE_S4G_WFOV_NF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_WFOV_NF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_WFOV_NF_ZG_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Wide Field of View (WFOV) Numerical Filter (NF) 5 and 10 degree Zonal and Global Averages in HDF data product. Data collection for this product is complete. This data set consists of non-scanner, wide field-of-view data, which was processed using the numerical filter data reduction technique. The data were averaged over latitudinal bands(zones) as well as on a global level in which each parameter is averaged over the entire globe. The zonal averages are available in 5.0 and 10.0 degree resolutions. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_WFOV_SF_1.json b/datasets/ERBE_S4G_WFOV_SF_1.json index fee8906586..374e9f888e 100644 --- a/datasets/ERBE_S4G_WFOV_SF_1.json +++ b/datasets/ERBE_S4G_WFOV_SF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_WFOV_SF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_WFOV_SF_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Wide Field of View (WFOV) Shape Factor (SF) 10 degree Regional Averages in HDF data product. Data collection for this product is complete. The data set consists of non-scanner, wide field-of-view data, processed using the shape factor data reduction technique and averaged to a 10.0 degree grid scale. Monthly (day), monthly (hour), daily, and monthly hourly averages were determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4G_WFOV_SF_ZG_1.json b/datasets/ERBE_S4G_WFOV_SF_ZG_1.json index 8b5da1a141..b6bbf1e586 100644 --- a/datasets/ERBE_S4G_WFOV_SF_ZG_1.json +++ b/datasets/ERBE_S4G_WFOV_SF_ZG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4G_WFOV_SF_ZG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4G_WFOV_SF_ZG_1 is the Earth Radiation Budget Experiment (ERBE) S-4G Non-scanner, Wide Field of View (WFOV) Shape Factor (SF) 10 degree Zonal and Global Averages in HDF data product. Data collection for this product is complete. The data set consists of non-scanner, wide field-of-view data, which was processed using the shape factor data reduction technique. The data were averaged over latitudinal bands(zones) as well as on a global level in which each parameter is averaged over the entire globe. The zonal averages are available in 10.0 degree resolution. Monthly (day), monthly (hour), daily, and monthly hourly averages were determined for each region. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nEarth Radiation Budget Experiment (ERBE) was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which is sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. The ERBE S-4G WFOV product was available as a combination of all operational spacecraft. Products have been archived from November 1984 - January 1985 and June 1989 - February 1990 for ERBS; February 1985 - October 1986 for ERBS/NOAA-9; November 1986 - January 1987 for ERBS/NOAA-9/NOAA-10; and February 1987 - May 1989 for ERBS/NOAA-10. The various combinations of the satellites reflected the actual duration of the scanners.", "links": [ { diff --git a/datasets/ERBE_S4N_NAT_1.json b/datasets/ERBE_S4N_NAT_1.json index e482e856bd..4e2f8894a8 100644 --- a/datasets/ERBE_S4N_NAT_1.json +++ b/datasets/ERBE_S4N_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4N_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4N_NAT_1 is the Earth Radiation Budget Experiment (ERBE) Nonscanner Regional, Zonal, and Global Averages S-4N data in native format data set, which contains averages of flux and albedo on regional, zonal, and global scales for non-scanner data processed without scanner scene identification information. Data collection for this data set is complete. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The S-4N contained averages of flux and albedo on regional, zonal, and global scales for non-scanner data. It was available as a combination of all operational spacecraft (ERBS, NOAA-9 and NOAA-10) for the nons-canner wide field-of-view (WFOV) data. The S-4N is a multi-file product which has three files of WFOV numerical filter and three files of WFOV shape factor data. Monthly (day), monthly (hour), daily, and monthly hourly averages are determined for each region.", "links": [ { diff --git a/datasets/ERBE_S4_NAT_1.json b/datasets/ERBE_S4_NAT_1.json index 171f38ed03..5f77c345ed 100644 --- a/datasets/ERBE_S4_NAT_1.json +++ b/datasets/ERBE_S4_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S4_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S4_NAT_1 is the Earth Radiation Budget Experiment (ERBE) Regional, Zonal, and Global Averages S-4 data in native format data set, which contains space and time averages of flux and albedo on regional, zonal, and global scales for both scanner and non-scanner data in native format. Data collection for this collection is complete. The data are represented as 8-bit, 16-bit, and 32-bit integers.\r\n\r\nERBE is a multi-satellite system that was designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites, NOAA-9 and NOAA-10. NOAA-9 and NOAA-10 provided global coverage and the ERBS provided coverage between 67.5 degrees north and south latitude. Each satellite carried both a scanner and a non-scanner instrument package. The non-scanner instrument contained four Earth-viewing channels and a solar monitor. The Earth-viewing channels had two spatial resolutions: a horizon-to-horizon view of the Earth, and a field-of-view limited to about 1000 km in diameter. The former was called the wide field-of-view (WFOV) and the latter the medium field of view (MFOV) channels. For each of the two fields of view, there was a total spectral channel which was sensitive to all wavelengths and a shortwave channel which used a high purity, fused silica filter dome to transmit only the shortwave radiation from 0.2 to 5 microns. Because of the concern for spectral flatness and high accuracy, all five channels on the non-scanner package were active cavity radiometers. The ERBE S-4G product contained averages of radiant flux and albedo on regional, zonal, and global scales. The data for the S-4G product were arranged by parameter values. ERBE S-4G MFOV product was available as a combination of the ERBS and NOAA-9 spacecraft. Products were archived as a combination of ERBS and NOAA-9 from February 1985 through October 1986. MFOV measurements from NOAA-10 have not been archived.", "links": [ { diff --git a/datasets/ERBE_S7_NAT_1.json b/datasets/ERBE_S7_NAT_1.json index a10b20b4b1..f52b76dea4 100644 --- a/datasets/ERBE_S7_NAT_1.json +++ b/datasets/ERBE_S7_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S7_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S7_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-7 Monthly Medium-Wide Data Tape (MWDT) data set, which is in Native (NAT) format. The MWDT (S-7) product contains a condensed version of the non-scanner data that were found on a monthly set of Processed Archival Tapes (PAT), except that the shortwave estimates of the radiant exitance at the top-of-atmosphere (TOA) were based on the mostly cloudy over ocean bidirectional model. The MWDT product then provided a consistent data set of non-scanner TOA estimates which were not dependent on the operational status of the ERBE scanner instrument.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The S-8 contained all satellite and viewing geometry, and all scanner and non-scanner radiometric measurements in engineering units with flags defining their validity. It also contained quantities such as scanner measurements corrected to flat spectral responses, the scene identified for each scanner pixel, the estimate of radiant flux at the top-of-the-atmosphere (TOA) for each scanner pixel, and the estimates of the radiant fluxes from the non-scanner measurements. The data were for a 24-hour period and one satellite. If all three satellites were operational on the same day, three separate S-8s were required for a full set of ERBE data. The data period started at Greenwich midnight (zero Universal Time) and continued for 24 hours and the period was divided into 16-second intervals.", "links": [ { diff --git a/datasets/ERBE_S8_NAT_1.json b/datasets/ERBE_S8_NAT_1.json index 50ac2c2ea6..5647e1a271 100644 --- a/datasets/ERBE_S8_NAT_1.json +++ b/datasets/ERBE_S8_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S8_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S8_NAT_1 is the Earth Radiation Budget Experiment (ERBE) S-8 Processed Archival Tape data set, which is in Native (NAT) Format. The PAT (S-8) contains ERBE scanner and non-scanner radiometric measurements for one day and one satellite. Estimates of the flux at the Top-of-Atmosphere (TOA) based on these measurements are also included. Data collection for this data set is complete.\r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The S-8 contained all satellite and viewing geometry, and all scanner and non-scanner radiometric measurements in engineering units with flags defining their validity. It also contained quantities such as scanner measurements corrected to flat spectral responses, the scene identified for each scanner pixel, the estimate of radiant flux at TOA for each scanner pixel, and the estimates of the radiant fluxes from the non-scanner measurements. The data were for a 24-hour period and one satellite. If all three satellites were operational on the same day, three separate S-8s were required for a full set of ERBE data. The data period started at Greenwich midnight (zero Universal Time) and continued for 24 hours and the period was divided into 16-second intervals.", "links": [ { diff --git a/datasets/ERBE_S9_NAT_1.json b/datasets/ERBE_S9_NAT_1.json index a05aa17221..2060036812 100644 --- a/datasets/ERBE_S9_NAT_1.json +++ b/datasets/ERBE_S9_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_S9_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_S9_NAT is the Earth Radiation Budget Experiment (ERBE) S-9 Scanner Radiant Flux data set. It contains inverted daily, monthly hourly, and monthly averages of shortwave (SWF) and longwave (LWF) radiant fluxes at the top-of-atmosphere (TOA) for ERBE scanner data in native format for one month. Data collection for this data set is complete. \r\n\r\nERBE was a multi-satellite system designed to measure the Earth's radiation budget. The ERBE instruments flew on a mid-inclination National Aeronautics and Space Administration (NASA) satellite Earth Radiation Budget Satellite (ERBS) and two sun-synchronous National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-9 and NOAA-10). Each satellite carried both a scanner and a non-scanner instrument package. The scanner instrument package contained three detectors to measure shortwave, longwave, and total waveband radiation. Each detector scanned the Earth perpendicular to the satellite ground-track from horizon-to-horizon. The detectors were thermistors which used space views on every scan as a reference point to guard against drift. The total channel had no filter, so it absorbed all wavelength. The shortwave channel had a fused silica filter which transmitted only shortwave radiation. The longwave channel had a multilayer filter on a diamond substrate to reject shortwave energy and accept longwave. \r\n\r\nThe S-9 contained inverted daily, monthly hourly, and monthly averages of shortwave and longwave radiant fluxes at the top-of-the atmosphere which were averaged into 2.5 degree regions. A S-9 data set was produced for each satellite (ERBS, NOAA-9, and NOOA-10) and the combination of satellites which were operational during the data month. The data set contained a 30 byte header, 67 scale factors - which were used to scale the data in the first record, and 26 scale factors - which were used to scale the data in the second record. The data set also contained two types of records for each processed region. The first record was of fixed length (1860 words) and contained averaged data. The second record was of variable length containing individual hour box estimates. The length of the second record, in words, was calculated by multiplying the number of hour boxes (1846th word of record one) by the number of values passed by hour box which is 32 for the scanner.", "links": [ { diff --git a/datasets/ERBE_TSI_ERBS_NAT_1.json b/datasets/ERBE_TSI_ERBS_NAT_1.json index b33092f3af..c41f5baf04 100644 --- a/datasets/ERBE_TSI_ERBS_NAT_1.json +++ b/datasets/ERBE_TSI_ERBS_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERBE_TSI_ERBS_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERBE_TSI_ERBS_NAT is the Earth Radiation Budget Experiment (ERBE) Total Solar Irradiance (TSI) from the Earth Radiation Budget Satellite in Native Format data set. Data collection for this product is complete.\r\n\r\nThe goal of the ERBE was to produce monthly averages of longwave and shortwave radiation parameters on the Earth at regional to global scales. Preflight mission analysis lead to a three spacecraft system to provide the geographic and temporal sampling required to meet this goal. Three, nearly identical, sets of instruments were built and launched on three separate spacecraft. These instruments differed principally in the spacecraft interface electronics and in the field-of-view limiters for the non-scanner instruments that were required due to differences in the spacecraft orbit altitudes. \r\n\r\nThe ERBS spacecraft was launched by Space Shuttle Challenger in October 1984 and was the first spacecraft to carry ERBE instruments into orbit. ERBS was designed and built by Ball Aerospace Systems under contract to NASA Goddard Space Flight Center (GSFC), and ERBS was the first spacecraft dedicated to NASA science experiments to be launched by the Space Shuttle. ERBS carried the Stratospheric Aerosols and Gas Experiment II (SAGE II) in addition to the ERBE instruments. \r\n\r\nThe Payload Operation and Control Center (POCC) at GSFC directed operations of the ERBS spacecraft as well as the ERBE and SAGE II instruments and employed both ground stations and the Tracking and Data Relay Satellite System (TDRSS) network. Spacecraft and instrument telemetry data were received at GSFC where the data were processed by the Information Processing Division that provided ERBE and SAGE II experiment data to the NASA Langley Research Center (LaRC). \r\n\r\nThe second and third spacecraft that launched with ERBE instruments were the Television Infrared Radiometer Orbiting Satellite (TIROS) N-class spacecraft, which was a part of the NOAA operational meteorological satellite series. The NOAA-9 and NOAA-10 spacecraft were launched in December 1984 and September 1986, respectively. The NOAA spacecraft included other instruments, such as the Advanced Very High Resolution Radiometer (AVHRR) and the High-Resolution Infrared Radiometer Sounder (HIRS), which provided NOAA with data for near-real-time weather forecasting. Both spacecraft were in nearly Sun-synchronous orbits. At launch equator-crossing times for the NOAA-9 and NOAA-10 orbits were 1420 UT (ascending) and 1930 UT (descending), respectively, where UT denotes universal time. The Satellite Operations and Control Center (SOCC) at the National Environmental Satellite and Data Information Service (NESDIS) operated the NOAA spacecraft. \r\n\r\nNOAA provided telemetry data and generated ERBE data for LaRC. From 1984 through 1994, TSI values were obtained from the solar monitor on the ERBS non-scanner. The individual TSI values represented orbital averages of the instantaneous measurements which were corrected for the angle between the instrument optical axis and the Sun and which were normalized to the mean Earth/Sun distance. At least once every 2 weeks, the Sun was observed by the monitor for several 64-second measurement intervals. Each interval was separated into two 32-second periods. During the first period, the Sun drifted across the 9.2-degree non-occulted field of view, and its radiation field is measured. During the second period, a low-emittance shutter, representative of a near-zero irradiance source, was cycled into the field of view, and the low irradiance from the back of the shutter was measured. The resulting measurements from the two different periods were used to define the irradiance, using the model that is described in Characteristics of the Earth Radiation Budget Experiment Solar Monitors by R. B. Lee III, B. R. Barkstrom, and R. D. Cess.\r\n\r\nTypically, two to eight values of the irradiance were determined during an orbit. Considering that these irradiance values were derived typically during a single orbit for a few minutes, the averaged irradiance values represented an almost instantaneous level, and not a daily average.", "links": [ { diff --git a/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json b/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json index 33020b328d..287cf6c9b5 100644 --- a/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json +++ b/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-1 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP.", "links": [ { diff --git a/datasets/ERS-1_L0_1.json b/datasets/ERS-1_L0_1.json index 1425ba6c33..21cbd2a869 100644 --- a/datasets/ERS-1_L0_1.json +++ b/datasets/ERS-1_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-1_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERS-1 Standard Beam Level 0 Frame", "links": [ { diff --git a/datasets/ERS-1_L1_1.json b/datasets/ERS-1_L1_1.json index 640e102e75..ba32fc14d7 100644 --- a/datasets/ERS-1_L1_1.json +++ b/datasets/ERS-1_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-1_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERS-1 Standard Beam Data Level 1", "links": [ { diff --git a/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json b/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json index 97e430ad11..24ba7f7a11 100644 --- a/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json +++ b/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-2 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP. For more information, please visit: http://www.scp.byu.edu/docs/ERS_user_notes.html", "links": [ { diff --git a/datasets/ERS-2_L0_1.json b/datasets/ERS-2_L0_1.json index 4ef6c6061d..26615d042d 100644 --- a/datasets/ERS-2_L0_1.json +++ b/datasets/ERS-2_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-2_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERS-2 Standard Beam Data Level 0", "links": [ { diff --git a/datasets/ERS-2_L1_1.json b/datasets/ERS-2_L1_1.json index d10ef74d95..a57cdc8a72 100644 --- a/datasets/ERS-2_L1_1.json +++ b/datasets/ERS-2_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS-2_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERS-2 Standard Beam Data Level 1", "links": [ { diff --git a/datasets/ERS.ASPS20_7.0.json b/datasets/ERS.ASPS20_7.0.json index 90f3695f11..8a43548379 100644 --- a/datasets/ERS.ASPS20_7.0.json +++ b/datasets/ERS.ASPS20_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.ASPS20_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ASPS Level 2 products contain, for each node: the radar backscattering sigma nought for the three beams of the instrument, the four aliased wind solutions (Rank 1-4 wind vector) and the de-aliased wind vector flag, the sea-ice probability and sea-ice flag, the YAW quality flag.\r\rThe wind retrieval is performed with the CMOD5N geophysical model function derived by ECMWF to compute the neutral winds rather than 10 m winds.\r\rASPS L2.0 High resolution products are provided with a spatial resolution of 25x25 km and a grid spacing of 12.5 km.\r\rASPS L2.0 Nominal resolution products are provided with a spatial resolution of 50x50 km and a grid spacing of 25 km.\r\rOne product covers one orbit from ascending node crossing.\r\rPlease consult the _$$Product Quality Readme$$ https://earth.esa.int/eogateway/documents/20142/37627/ERS_WS_Readme-ENVI-GSOP-EOGD-QD-15-0130_issue1.2.pdf file before using the ERS ASPS data.", "links": [ { diff --git a/datasets/ERS.GOM.L1_5.0.json b/datasets/ERS.GOM.L1_5.0.json index 1713e1b73e..a78f487c83 100644 --- a/datasets/ERS.GOM.L1_5.0.json +++ b/datasets/ERS.GOM.L1_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.GOM.L1_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOME Level 1 products contain Earthshine radiance at the Top of the Atmosphere and solar irradiance spectra. They were generated by DLR on behalf of the European Space Agency with Level 1 GOME Data Processor (GDP-L1) starting from the Extracted GOME Calibration (EGOC) Level 0 data files. Originally raw detector signals (binary Analog to Digital Converted units) of the science measurements plus calibration constants were provided (dataset version 4 and lower), but following the end of the operational phase of the ERS-2 mission (2 July 2011), as part of ESA's post-operational algorithm improvement activities (Coldewey-Egbers et al., 2018), the GOME Level 1 data type was entirely revised and a dataset of fully calibrated and ready to use data was generated with GOME processor version 5.1. The version 5.1 data bring relevant quality improvements for the revised calibration approach, compensating aging and instrument degradation, and provide enhanced accessibility. The version 5.1 data are in NetCDF format and differ fundamentally from the previous GOME Level 1 data, having the Envisat proprietary format and basically containing Level 1a data where a dedicated extraction software tool had to be applied by end user to obtain spectrally and radiometrically calibrated radiances (including the correction for polarisation, leakage current and stray light). Such calibrations are now applied to the version 5.1 L1b data product in the standard processing. Users of GOME Level 1 products are strongly recommended to migrate to the latest reprocessed dataset. Please consult the GOME Product Quality Readme file before using the data. (https://earth.esa.int/eogateway/documents/20142/37627/GOME-TCWV-Product-sQuality-Readme-File.pdf)", "links": [ { diff --git a/datasets/ERS.GOM.L2_4.0.json b/datasets/ERS.GOM.L2_4.0.json index 40c0bed5aa..d435562860 100644 --- a/datasets/ERS.GOM.L2_4.0.json +++ b/datasets/ERS.GOM.L2_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.GOM.L2_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOME Level 2 products were generated by DLR on behalf of the European Space Agency, and are the end result of the Level 1 to 2 reprocessing campaign of GOME Level 1 version 4 data with Level 2 GOME Data Processor (GDP) version 5.0 (HDF-5 format). The GOME Level 2 data product comprises the product header, total column densities of ozone and nitrogen dioxide and their associated errors, cloud properties and selected geo-location information, diagnostics from the Level 1 to 2 algorithms and a small amount of statistical information.", "links": [ { diff --git a/datasets/ERS.ORB.POD_4.0.json b/datasets/ERS.ORB.POD_4.0.json index 4aee70e7c1..0797cd1caa 100644 --- a/datasets/ERS.ORB.POD_4.0.json +++ b/datasets/ERS.ORB.POD_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.ORB.POD_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The precise orbit results from a data reduction process in which all available tracking data (Single-Lens Reflex, radar altimeter crossovers, PRARE range and Doppler data) and most accurate correction, transformation and dynamical models are taken into account and in which high level numerical procedures are applied. These orbits are "optimal" achievable representations of the real orbital motion under the circumstances of tracking situation and the "state of the art" model situation. The precise orbit product for the ERS satellites are the satellite ephemeris (position and velocity vector) including time tag, given in a well-defined reference frame, together with the nominal satellite attitude information and a radial orbit correction. Several orbit solutions are currently distributed: A new set of ORB POD (Precise Orbit Determination - REAPER v2) computed with the most updated model standards for the complete ERS-1 and ERS-2 mission. A previous set of ORB POD (REAPER v1) data already available on the ESA dissemination site since 2014, covering the ERS-1 full mission and the ERS-2 mission up to July 2003. ORB PRC which is the original Precise Orbit dataset computed during the ERS mission operations for ERS-1 and ERS-2. In the new POD dataset (REAPER v2) for the ERS-1 and ERS-2 missions, two different orbit solutions are provided together with the combined solution to be used for processing of the radar altimeter measurements and the determination of geodetic/geophysical products: those computed by DEOS (Delft Institute of Earth Observation and Space Systems), and those generated by ESOC (European Space Operations Centre) using different software (GEODYN and NAPEOS respectively). Careful evaluation of the various solutions of REAPER v2 has shown that the DEOS solution for both ERS-1 and ERS-2 has the best performance and is recommended to be used as reference. See the ERS Orbit Validation Report (https://earth.esa.int/eogateway/documents/20142/37627/ERS-Orbit-Validation-Report.pdf). For the previous version of the POD data set (REAPER v1), with ERS-2 mission data only up to 2003, three different orbit solutions together with the combined solution are available. These precise orbits for ERS-1 and ERS-2 have been computed at DEOS, ESOC, and GFZ (Deutschen GeoForschungsZentrums) using different software and different altimeter databases. Combined solutions have been created using three individual solutions for each satellite. All orbits were derived using consistent models in the same LPOD2005 terrestrial reference frame. These new orbit solutions show notable improvement with respect to DGME04 orbits (Scharroo and Visser, 1998). Thus, RMS crossover differences of new orbits improved by 4-9 mm. Careful evaluation of the various solutions has shown that the combined solution for both ERS-1 and ERS-2 has the best performance. All POD orbit files (REAPER v1/v2) are available in SP3c format (ftp://igscb.jpl.nasa.gov/igscb/data/format/sp3c.txt).", "links": [ { diff --git a/datasets/ERS.SSM_7.0.json b/datasets/ERS.SSM_7.0.json index 974d0eb6a2..103b507915 100644 --- a/datasets/ERS.SSM_7.0.json +++ b/datasets/ERS.SSM_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.SSM_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface soil moisture records are derived from the backscatter coefficient measured by the Scatterometer on-board the European Remote Sensing satellite (ERS-2) using the Technische Universit\u00e4t (TU) Wien soil moisture retrieval algorithm called WARP (WAter Retrieval Package).\r\rIn the WARP algorithm, the relative surface soil moisture estimates, given in degree of saturation Sd, range between 0% and 100% are derived by scaling the normalized backscatter between the lowest/highest backscatter values corresponding to the driest/wettest soil conditions.\r\rSurface Soil Moisture - Time Series product:\r\rThe products generated are the surface soil moisture time series, where for each grid point defined in a DGG (Discrete Global Grid) is stored the time series of soil moisture and its noise, the surface state flag, the geolocation and the satellite parameters.\r\rThe spatial resolution of the products is about 25 km x 25 km (high resolution) or 50 km x 50 km (nominal resolution) geo-referenced on the WARP grid. The location of the points can be viewed interactively with the tool _$$DGG Point Locator$$ https://dgg.geo.tuwien.ac.at/ .\r\rSurface Soil Moisture - Orbit product:\r\rIn addition to WARP, a second software package, referred to as WARP orbit, was developed in response to the strong demand of soil moisture estimates in satellite orbit geometry.\rThe Level 2 soil moisture orbit product contains a series of Level 1 data information, such as the backscatter, the incidence angle and the azimuth angle for each triplet together with the surface soil moisture and its noise, normalized backscatter at 40\u00b0 incidence angle, parameters useful for soil moisture, the geolocation and the satellite parameters. The soil moisture orbit product is available in two spatial resolutions with different spatial sampling distances:\r\rSpatial sampling on a regular 12.5 km grid in orbit geometry with a spatial resolution of about 25 km x 25 km (High resolution)\rSpatial sampling on a regular 25 km grid in orbit geometry with a spatial resolution of about 50 km x 50 km (Nominal resolution)\rThe spatial resolution is defined by the Hamming window function, which is used for re-sample of raw backscatter measurements to the orbit grid in the Level-1 ground processor.\r\rPlease consult the Product Quality _$$Readme$$ https://earth.esa.int/eogateway/documents/20142/37627/ERS_WS_Soil_Moisture_Readme-ESA-EOPG-EBA-TN-2_issue1.0.pdf file before using the ERS-2 Surface Soil Moisture data.", "links": [ { diff --git a/datasets/ERS.UWI_6.0.json b/datasets/ERS.UWI_6.0.json index 127320e2f4..810971ed56 100644 --- a/datasets/ERS.UWI_6.0.json +++ b/datasets/ERS.UWI_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS.UWI_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ERS data reprocessed with the ASPS facility is also available in the UWI format to maintain the compatibility with the FD (Fast Delivery) products. The ASPS UWI product is organised in frames of 500 x 500 km providing the radar backscattering sigma nought for the three beams of the instrument plus the wind speed and direction. The wind retrieval is performed with the CMOD5N geophysical model function derived by ECMWF to compute the neutral winds rather than 10m winds. ASPS UWI products are provided with a spatial resolution of 50x50km and a grid spacing of 25 km. One product covers one orbit from ascending node crossing. Please consult the Product Quality Readme file (https://earth.esa.int/eogateway/documents/20142/37627/ERS-WS-Product-Quality-Readmefile-ENVI-GSOP-EOGD-QD-15-0130-issue1.2.pdf) before using the ERS ASPS data.", "links": [ { diff --git a/datasets/ERS2_GOME_SIF_1758_1.json b/datasets/ERS2_GOME_SIF_1758_1.json index bb7460f0bc..b35db1c5e5 100644 --- a/datasets/ERS2_GOME_SIF_1758_1.json +++ b/datasets/ERS2_GOME_SIF_1758_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS2_GOME_SIF_1758_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 Solar-Induced Fluorescence (SIF) of Chlorophyll estimates derived from the Global Ozone Monitoring Experiment (GOME) instrument on the European Space Agency's (ESA's) European Remote-Sensing 2 (ERS-2) satellite. Each file contains daily raw and bias-adjusted solar-induced fluorescence on an orbital basis (land pixels only), at a resolution of 40 km x 320 km, along with quality control information and ancillary data. Data is provided for the period from 19950701 to 20030622. The GOME SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation.", "links": [ { diff --git a/datasets/ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0.json b/datasets/ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0.json index e86e1b015a..7911c3b35d 100644 --- a/datasets/ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0.json +++ b/datasets/ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ERS-1/2 ATSR Level 1B Brightness Temperature/Radiance products (RBT) contain top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, when available, on a 1-km pixel grid. The visible channels are only available for the ATSR-2 instrument.\rValues for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information.\rThe ATSR-1 and ATSR-2 products [ER1_AT_1_RBT and ER2_AT_1_RBT], in NetCDF format stemming from the 4th ATSR reprocessing, are precursors of Envisat AATSR and Sentinel-3 SLSTR data. They have replaced the former L1B products [AT1_TOA_1P and AT2_TOA_1P] in Envisat format from the 3rd reprocessing. \rUsers with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products, and consult the ERS _$$ATSR Product Notice Readme document$$ https://earth.esa.int/eogateway/documents/20142/37627/ATSR-Level-1B-ERn-AT-1-RBT-Product-Notices-Readme.pdf \rThe processing updates that have been put in place and the expected scientific improvements for the ERS ATSR 4th reprocessing data have been outlined in full in the _$$User Documentation for (A)ATSR 4th Reprocessing Products$$ https://earth.esa.int/documents/20142/37627/QA4EO-VEG-OQC-MEM-4538_User_Documentation_for__A_ATSR_4th_Reprocessing_Level_1.pdf .", "links": [ { diff --git a/datasets/ERS_ALT_2M_6.0.json b/datasets/ERS_ALT_2M_6.0.json index 26b7157faf..f86950c10d 100644 --- a/datasets/ERS_ALT_2M_6.0.json +++ b/datasets/ERS_ALT_2M_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_ALT_2M_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a RA Meteo product containing only the 1 Hz parameters for altimeter (surface range, satellite altitude, wind speed and significant wave height at nadir) and MWR/MWS data (brightness temperature at 23.8 GHz and 36.5 GHz, water vapour content, liquid water content) used to correct altimeter measurements. It also contains the full geophysical corrections. This product corresponds to a subset of the REAPER GDR product (ERS_ALT_2_). The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains only the low rate of 1Hz data. The REAPER Meteo (ERS_ALT_2M) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: the product format; which is NetCDF (more details can be found in the Product Handbook https://earth.esa.int/eogateway/documents/20142/37627/reaper-product-handbook-for-ers-altimetry-reprocessed-products.pdf), and not PDS the product is delivered based on orbit acquisition and not per pass (pole-to-pole) This product is extended through Envisat RA-2 data\rThe creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. \rUsers are therefore strongly encouraged to make use of these new datasets for optimal results. \rThe records are aimed at different user communities and include the following datasets:\r1.\t_$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry\r2.\t_$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry\r3.\t_$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere\r4.\t_$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water\r5.\t_$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice\r6.\t_$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography\r7.\t_$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves\r8.\t_$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice\r", "links": [ { diff --git a/datasets/ERS_ALT_2S_6.0.json b/datasets/ERS_ALT_2S_6.0.json index 9120430171..5f12ff0327 100644 --- a/datasets/ERS_ALT_2S_6.0.json +++ b/datasets/ERS_ALT_2S_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_ALT_2S_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a RA Geophysical Data Record (GDR) product containing radar range, orbital altitude, wind speed, wave height and water vapour from the ATSR/MWR as well as geophysical corrections. The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains two data rates: a low rate of 1Hz and a high rate of 20Hz. Most 1Hz data is also represented at 20Hz, while microwave radiometer (ATSR/MWR) data and the atmospheric and geophysical corrections are only given at 1 Hz. The REAPER GDR (ERS_ALT_2_) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: The product format; which is NetCDF (more details can be found in the Product Handbook, and not PDS The product is delivered based on orbit acquisition and not per pass (pole-to-pole). This product is extended through Envisat RA-2 data.\rThe creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. \rUsers are therefore strongly encouraged to make use of these new datasets for optimal results. \rThe records are aimed at different user communities and include the following datasets:\r1.\t_$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry\r2.\t_$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry\r3.\t_$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere\r4.\t_$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water\r5.\t_$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice\r6.\t_$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography\r7.\t_$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves\r8.\t_$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice\r", "links": [ { diff --git a/datasets/ERS_ALT_2__6.0.json b/datasets/ERS_ALT_2__6.0.json index 6327169279..fcd982274b 100644 --- a/datasets/ERS_ALT_2__6.0.json +++ b/datasets/ERS_ALT_2__6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_ALT_2__6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a RA Geophysical Data Record (GDR) product containing radar range, orbital altitude, wind speed, wave height and water vapour from the ATSR/MWR as well as geophysical corrections. The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains two data rates: a low rate of 1Hz and a high rate of 20Hz. Most 1Hz data is also represented at 20Hz, while microwave radiometer (ATSR/MWR) data and the atmospheric and geophysical corrections are only given at 1 Hz. The REAPER GDR (ERS_ALT_2_) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: The product format; which is NetCDF (more details can be found in the Product Handbook, and not PDS The product is delivered based on orbit acquisition and not per pass (pole-to-pole). This product is extended through Envisat RA-2 data.\rThe creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. \rUsers are therefore strongly encouraged to make use of these new datasets for optimal results. \rThe records are aimed at different user communities and include the following datasets:\r1.\t_$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry\r2.\t_$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry\r3.\t_$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere\r4.\t_$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water\r5.\t_$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice\r6.\t_$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography\r7.\t_$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves\r8.\t_$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice\r", "links": [ { diff --git a/datasets/ERS_CONT_500_ANT_1.json b/datasets/ERS_CONT_500_ANT_1.json index 3288204d05..bc563b3291 100644 --- a/datasets/ERS_CONT_500_ANT_1.json +++ b/datasets/ERS_CONT_500_ANT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_CONT_500_ANT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "500 metre interval contours of the Antarctic continent derived from slope corrected orthometric heights that were captured using European Remote Sensing (ERS) radar altimetry.\n\nESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields.\n\nERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades.\n\nThe ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011.", "links": [ { diff --git a/datasets/ERS_CONT_MERGED_AMERY_1.json b/datasets/ERS_CONT_MERGED_AMERY_1.json index 365684b18b..9b3c4e7d7b 100644 --- a/datasets/ERS_CONT_MERGED_AMERY_1.json +++ b/datasets/ERS_CONT_MERGED_AMERY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_CONT_MERGED_AMERY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contours for the Amery Region map published by the Australian Antarctic Data Centre in November 2002 (see link below).\nThis contour data were derived from Russian space photography, ERS-1 and ERS-2 Radar altimeter data (BKG, Germany) and the Antarctic Digital Database, Version 2. Refer to the contour source diagram - digital data (refer to metadata record ERS_CONT_SOURCE_AMERY) or view map (see link below). The contour interval is 500 metres from 500 to 3000 metres. There are also 200 metre contours.\n\nESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields.\n\nERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades.\n\nThe ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011.", "links": [ { diff --git a/datasets/ERS_CONT_SOURCE_AMERY_1.json b/datasets/ERS_CONT_SOURCE_AMERY_1.json index 848a63ff7c..088c327165 100644 --- a/datasets/ERS_CONT_SOURCE_AMERY_1.json +++ b/datasets/ERS_CONT_SOURCE_AMERY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_CONT_SOURCE_AMERY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This polygon shapefile was used in the contour source diagram on the Amery Region Map published by the Australian Antarctic Data Centre in November 2002 (see link).\n\nThe contours used in the map were derived from a number of different data sources:\n1 - Russian Space Photography, ERS-1 Radar Altimeter data and digitised from 1:1 million scale maps produced by National Mapping Australia;\n2 - Antarctic Digital Database Version 2;\n3 - ERS-1 and ERS-2 Radar Altimeter data (BKG, Germany).\n\nThis shapefile shows in which part of the map each source was used.", "links": [ { diff --git a/datasets/ERS_DTM_1.json b/datasets/ERS_DTM_1.json index 1d76262008..4993b9184b 100644 --- a/datasets/ERS_DTM_1.json +++ b/datasets/ERS_DTM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_DTM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data generated from slope corrected orthometric heights derived from ERS radar altimetry as described in the paper 'A Digital Terrain Ice Model of Antarctica derived by ERS Radar Altimeter Data' by J. Ihde, J. Eck, U. Schirmer.\n\nThe data products (and their metadata records): the original point data as a shapefile (GRI_ORT_SLC_FIN); a shapefile showing data and no data areas for the original point data (ERS_REL_ANT); a triangulated irregular network (TIN) generated from the point data (ERS_DTM_TIN_ANT); 500 m interval contours interpolated from the TIN (ERS_CONT_500_ANT); a raster grid with 5 km cell size interpolated from the TIN (ERS_DTM_GRID_ANT); a contour shapefile for the Amery Region map published by the Australian Antarctic Data Centre in November 2002 - contours sourced from ERS radar altimetry, the Antarctic Digital Database Version 2 and Russian space photography (ERS_CONT_MERGED_AMERY); a shapefile used for the contour source diagram for the Amery Region map (ERS_CONT_SOURCE_AMERY).\n\nESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields.\n\nERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades.\n\nThe ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011.", "links": [ { diff --git a/datasets/ERS_DTM_GRID_ANT_1.json b/datasets/ERS_DTM_GRID_ANT_1.json index 51774d19bb..4397bca5dd 100644 --- a/datasets/ERS_DTM_GRID_ANT_1.json +++ b/datasets/ERS_DTM_GRID_ANT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_DTM_GRID_ANT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESRI formatted raster grid of the Antarctic continental terrain, derived from ERS radar altimeter data. \nThe data is in a Polar Stereographic projection with true scale at 71 degrees South.\nThe grid has 'no data' cells in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast.\n\nESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields.\n\nERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades.\n\nThe ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011.", "links": [ { diff --git a/datasets/ERS_DTM_TIN_ANT_1.json b/datasets/ERS_DTM_TIN_ANT_1.json index 87715470fa..018143392d 100644 --- a/datasets/ERS_DTM_TIN_ANT_1.json +++ b/datasets/ERS_DTM_TIN_ANT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ERS_DTM_TIN_ANT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An ESRI formatted triangular irregular network (TIN) of the Antarctic continental terrain, derived from ERS radar altimeter data. The data is in a Polar Stereographic projection with true scale at 71 degrees South. The TIN is unreliable in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast.\n\nESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields.\n\nERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades.\n\nThe ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011.", "links": [ { diff --git a/datasets/ESA_Orthorectified_Map_oriented_Level1_products_6.0.json b/datasets/ESA_Orthorectified_Map_oriented_Level1_products_6.0.json index c64162ef26..081f2e6169 100644 --- a/datasets/ESA_Orthorectified_Map_oriented_Level1_products_6.0.json +++ b/datasets/ESA_Orthorectified_Map_oriented_Level1_products_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESA_Orthorectified_Map_oriented_Level1_products_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Orthorectified Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\rThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. \rThe collection consists of data products of the following type: \r\rMES_GEC_1P: Geocoded Ellipsoid GCP Corrected Level 1 MOS-1/1B MESSR products which are the default products generated by the MOS MESSR processor in all cases (where possible), with usage of the latest set of LANDSAT improved GCP (Ground Control Points). These are orthorectified map-oriented products, corresponding to the old MOS-1/1B MES_ORT_1P products with geolocation improvements. \r \r\rMESSR Instrument Characteristics\rBand\tWavelength Range (nm)\tSpatial Resolution (m)\tSwath Width (km)\r1 (VIS)\t510 \u2013 690\t50\t100\r2 (VIS)\t610 \u2013 690\t50\t100\r3 (NIR)\t720 \u2013 800\t50\t100\r4 (NIR)\t800 \u2013 1100\t50\t100", "links": [ { diff --git a/datasets/ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0.json b/datasets/ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0.json index 1bbc869796..c6fecf7c26 100644 --- a/datasets/ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0.json +++ b/datasets/ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA System Corrected (Level 1) MOS-1/1B VTIR Products collection is composed of MOS-1/1B VTIR (Visible and Thermal Infrared Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\rThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the MOS Product Format Specification for further details.\rThe collection consists of data products of the following type:\r\rVTI_SYC_1P: System corrected Level 1 MOS-1/1B VTIR products in EO-SIP format. \r\rBand\tWavelength Range (\u00b5m)\tSpatial Resolution (km)\tSwath Width (km)\r1 (VIS)\t0.5 \u2013 0.7\t 0.9\t 1500\r2 (TIR)\t6.0 \u2013 7.0\t 2.7\t 1500\r3 (TIR)\t10.5 \u2013 11.5\t 2.7\t 1500\r4 (TIR)\t11.5 \u2013 12.5\t 2.7\t 1500", "links": [ { diff --git a/datasets/ESA_System_corrected_map_oriented_Level_1_products_6.0.json b/datasets/ESA_System_corrected_map_oriented_Level_1_products_6.0.json index 32b5d13cb0..a23784d652 100644 --- a/datasets/ESA_System_corrected_map_oriented_Level_1_products_6.0.json +++ b/datasets/ESA_System_corrected_map_oriented_Level_1_products_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESA_System_corrected_map_oriented_Level_1_products_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA System Corrected Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\rThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. \rThe collection consists of data products of the following type:\r\rMES_GES_1P: Geocoded Ellipsoid System Corrected Level 1 MOS-1/1B MESSR products as generated by the MOS MESSR processor where the generation of MES_GEC_1P products is not possible. These replace the old MES_SYC_1P products. \r\rMESSR Instrument Characteristics\rBand\tWavelength Range (nm)\tSpatial Resolution (m)\tSwath Width (km)\r1 (VIS)\t510 \u2013 690\t50\t100\r2 (VIS)\t610 \u2013 690\t50\t100\r3 (NIR)\t720 \u2013 800\t50\t100\r4 (NIR)\t800 \u2013 1100\t50\t100", "links": [ { diff --git a/datasets/ESCAPE_0.json b/datasets/ESCAPE_0.json index 13ac7a10e0..ad704eae7f 100644 --- a/datasets/ESCAPE_0.json +++ b/datasets/ESCAPE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESCAPE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken off The Netherlands in the North Sea in 1998.", "links": [ { diff --git a/datasets/ESMRN5IM_001.json b/datasets/ESMRN5IM_001.json index 24c6879e6b..c802fe6e9a 100644 --- a/datasets/ESMRN5IM_001.json +++ b/datasets/ESMRN5IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESMRN5IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESMRN5IM is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips. Each frame contains a geographic grid and two groups of three parallel strips of imagery, each containing one-half the orbital data. The spatial coverage is identical in each group, but each strip has a different dynamic range for its gray scale: 100-200 K, 190-270 K, and 250-300 K, respectively. The spatial resolution is 25 x 25 km near nadir, degrading to 160 km cross-track by 45 km down-track at the ends of the scan. The images are saved as JPEG 2000 digital files. About 2 weeks of images are archived into a TAR file. Additional information can be found in \"The Nimbus 5 User's Guide.\"\n\nThe primary objectives of the ESMR experiment were: (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture. To accomplish these objectives, the ESMR was capable of continuous global mapping of the 1.55-cm (19.36 GHz) microwave radiation emitted by the earth/atmosphere system, and could function even in the presence of cloud conditions that block conventional satellite infrared sensors. The ESMR instrument made measurements from Dec. 11, 1972 until May 16, 1977.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00192 (old ID 72-097A-04C).", "links": [ { diff --git a/datasets/ESMRN5L1_001.json b/datasets/ESMRN5L1_001.json index 0421144122..40f82a25e6 100644 --- a/datasets/ESMRN5L1_001.json +++ b/datasets/ESMRN5L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESMRN5L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESMRN5L1 is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) Level 1 Calibrated Brightness Temperature product and contains calibrated radiances expressed in units of brightness temperature measured at 19.35 GHz. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as the Calibrated Brightness Temperature Tapes (CBTT). The data are archived in their original IBM binary proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-5 satellite was successfully launched on December 11, 1972. The ESMR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The ESMR instrument objectives were (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture.\n\nThe ESMR Principal Investigator was Dr. Thomas T. Wilheit, Jr. from NASA Goddard Space Flight Center. The Nimbus-5 ESMR data are available from December 11, 1972 (day of year 346) through May 16, 1977 (day of year 136)\n\nThis product was previously available from the NSSDC with the identifier ESAD-00219 (old ID 72-097A-04A).", "links": [ { diff --git a/datasets/ESMRN6IM_001.json b/datasets/ESMRN6IM_001.json index 6aac3cc063..64688f46cd 100644 --- a/datasets/ESMRN6IM_001.json +++ b/datasets/ESMRN6IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ESMRN6IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESMRN6IM is the Nimbus-6 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips (both positives and negatives). Each frame contains two sets with a geographic grid and either 10 (F = full scale) or 5 (P1,P2 = partial scale) parallel strips of imagery, each containing one-half of an orbit swath (ascending on left, descending on right). The spatial coverage is identical in each set, but each swath strip has a different dynamic range and polarization. The spatial resolution is about 20 x 45 km near nadir. The images are saved as TIFF digital files. About 5-10 months worth of images are archived into a ZIP file. Additional information about ESMR can be found in \"The Nimbus 6 User's Guide.\"\n\nThe primary objectives of the ESMR experiment were: (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture. To accomplish these objectives, the ESMR was capable of continuous global mapping of the 0.81 cm (37.0 GHz) microwave radiation emitted by the earth/atmosphere system, using both horizontal and vertical polarized components. The ESMR instrument performance was satisfactory until September 15, 1976, when the horizontal channel failed. Another ESMR instrument was flown on Nimbus 5.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00201 (old ID 75-052A-03B).", "links": [ { diff --git a/datasets/ETM1999-2003.json b/datasets/ETM1999-2003.json index 463045407a..aedc8fe878 100644 --- a/datasets/ETM1999-2003.json +++ b/datasets/ETM1999-2003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ETM1999-2003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge.\n", "links": [ { diff --git a/datasets/ETM_PAN.json b/datasets/ETM_PAN.json index 2344c7c844..d139731ed9 100644 --- a/datasets/ETM_PAN.json +++ b/datasets/ETM_PAN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ETM_PAN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Single scene Tri-Decadal Global Landsat Orthorectified MSS, TM, ETM+, and ETM+ Pan-sharpened data, which may be browsed, searched, and downloaded through EarthExplorer or the USGS Global Visualization Viewer (Glovis).\n\nGround control points are fixed, and images have been registered to the Universal Transverse Mercator (UTM) map projection and coordinate system and the World Geodetic System 1984 (WGS84) datum. All image bands have been individually resampled, using a nearest neighbor algorithm. Positional accuracy on the final image product has a Root Mean Square Error of better than 100 meters (MSS) and 50 meters (TM and ETM+). The Landsat data were acquired and processed through a National Aeronautics and Space Administration (NASA) contract with Earth Satellite Corporation, Rockville, Maryland, and are part of NASA's Scientific Data Purchase program.\n", "links": [ { diff --git a/datasets/EUCFe_0.json b/datasets/EUCFe_0.json index bc583f9544..e122db69a5 100644 --- a/datasets/EUCFe_0.json +++ b/datasets/EUCFe_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EUCFe_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the RV Kilo Moana in 2006 of the EUCFe (Iron in the Equatorial Undercurrent) in 2006.", "links": [ { diff --git a/datasets/EUMELI_0.json b/datasets/EUMELI_0.json index 3da618d09e..25f460cda6 100644 --- a/datasets/EUMELI_0.json +++ b/datasets/EUMELI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EUMELI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the EUMELI program, a component of FRANCE-JGOFS (Joint Global Ocean Flux Study), to study ocean fluxes in eutrophic, mesotrophic and oligotrophic waters.", "links": [ { diff --git a/datasets/EWSG1-NAVO-L2P-v01_1.0.json b/datasets/EWSG1-NAVO-L2P-v01_1.0.json index 8692c466b6..c399a56b41 100644 --- a/datasets/EWSG1-NAVO-L2P-v01_1.0.json +++ b/datasets/EWSG1-NAVO-L2P-v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EWSG1-NAVO-L2P-v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature produced by The Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System \u2013 Geostationary satellite (EWS-G1). The EWS-G1, formerly GOES-13, is the first Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0\u00b0 West longitude in January 2018 and fully operational since September 8, 2020, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G1 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degree. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 48 half-hour granules per day. The data will be continually updated with 24 hours latency. ", "links": [ { diff --git a/datasets/EWSG2-NAVO-L2P-v01_1.0.json b/datasets/EWSG2-NAVO-L2P-v01_1.0.json index 15b9ac8586..b4016d8e87 100644 --- a/datasets/EWSG2-NAVO-L2P-v01_1.0.json +++ b/datasets/EWSG2-NAVO-L2P-v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EWSG2-NAVO-L2P-v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature dataset produced by the Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System \u2013 Geostationary satellite (EWS-G2). The EWS-G2, formerly GOES-15, is the second Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0\u00b0 West longitude in September 2023 and fully operational since December 3, 2023, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G2 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degrees. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 104 partial disks per day. The data will be continually updated with 24 hours latency.", "links": [ { diff --git a/datasets/EXP7L1TRTALL_001.json b/datasets/EXP7L1TRTALL_001.json index b098776c01..f4126567a5 100644 --- a/datasets/EXP7L1TRTALL_001.json +++ b/datasets/EXP7L1TRTALL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EXP7L1TRTALL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Explorer-7 Thermal Radiation Experiment Temperature Values from All Sensors product contains temperature readings from all five bolometers in order to measure solar, reflected and terrestrial radiation. There are two files for the entire mission (Oct. 19, 1959 to April 16, 1960 and April 16, 1960 to June 4, 1960. Note there is no geolocation information included with these data. The data were originally written on IBM 7094 machines on magnetic tapes. The data have been restored and are archived in their original IBM 36-bit word binary format.\n\nThe Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961.\n\nThe Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00249 (old ID 59-009A-01B).", "links": [ { diff --git a/datasets/EXP7L1TRTWHT_001.json b/datasets/EXP7L1TRTWHT_001.json index 28d420e926..6e122a7a15 100644 --- a/datasets/EXP7L1TRTWHT_001.json +++ b/datasets/EXP7L1TRTWHT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EXP7L1TRTWHT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Explorer-7 Thermal Radiation Experiment Selected White Sensor Temperature (Nighttime) Values product contains the temperatures measured by the white sensor at night. The white sensor was designed to measure terrestrial radiation. There is a single file for the entire mission (Nov. 15, 1959 to May 24, 1960). The data were originally written on IBM 7094 machines to magnetic tapes. In addition to the temperature values, the file contains radiance, geolocation and orbit information. The data have been restored and are archived in their original IBM EBCDIC text format.\n\nThe Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961.\n\nThe Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00248 (old ID 59-009A-01A).", "links": [ { diff --git a/datasets/EXPORTS_0.json b/datasets/EXPORTS_0.json index 6e129a9e8d..89533ed761 100644 --- a/datasets/EXPORTS_0.json +++ b/datasets/EXPORTS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EXPORTS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EXport Processes in the Ocean from RemoTeSensing (EXPORTS) is a NASA-led and NSF co-funded science project aiming to understand export and fate of upper ocean net primary production (NPP) using satellite remote sensing, state of the art ocean field measurements, and numerical models. EXPORTS lead a pre-EXPORTS modeling and data-mining activity, followed by two major oceanographic expeditions: EXPORTS North Pacific (EXPORTSNP) and EXPORTS North Atlantic (EXPORTSNA); and it is currently on a final project phase of synthesis and modeling. \n\nThe EXPORTSNP deployment was conducted at Ocean Station Papa (Station P, nominally 50N, 145W) operated by Canada's Line P time-series sampling program. The EXPORTS 2018 field deployment consisted of four major components: 1) the R/V Roger Revelle (cruise id= RR1813) functioned as the Process Ship, sampling BGC stocks and fluxes, ecological abundances and rates, and optical properties following a Lagrangian float; 2) the R/V Sally Ride(cruise id=SR1012) was the Survey Ship and characterized spatial variability about the Process Ship on scales from about 1 to 100 km; 4) a heterogeneous array of AUV platforms was deployed to set the spatial center of the sampling program, to provide horizontal spatial and high-temporal information, and to extend the temporal presence in the area; and 4) a long-term sampling presence was created, tying the ship-based observations to climatically relevant time and space scales using BGC floats and partnerships with ongoing research programs. \n\nThe EXPORTSNA deployment was conducted at the Porcupine Abyssal Plain (Station PAP, nominally) in collaboration with the PAP Sustained Observatory (PAP-SO) which is a sustained, multidisciplinary observatory in the North Atlantic coordinated by the National Oceanography Centre, Southampton. Similar to the North Pacific filed mission, EXPORTSNA consisted of Process Ship represented by the RRS James Cook (cruise id = JC214), a Survey Ship represented by the RRS Discovery (cruise id = DY131), over 40 autonomous assets, and long-term collaboration and observations using BGC floats and partnerships with ongoing research programs. Among the assets, the user will find data from 3 different glider missions (SG219, SG237, SL305), a lagrangian floats (LF092), Neutrally Buoyant Sediment Traps (NBST) floats, Wirewalker, TZEK, and Minions. EXPORTS partnered with the Ocean Twilight Zone (OTZ) program with a third vessel, the R/V Sarmiento de Gamboa (cruise id= SG2105) that join them at the PAP station. EXPORTS funded data were collected aboard SG2105 as well, however, all the data collected is served under the OTZ_WHOI SeaBASS experiment and cruise SG2105. For additional information about the EXPORTS field experiments please refer to Siegel et al., 2021. \n\nEXPORTS data funded under NSF can be found in BCO-DMO: https://www.bco-dmo.org/program/757397 \n\nTo find information about all the data collected under EXPORTS and their data repositories and availability, please visit: https://sites.google.com/view/oceanexports/home", "links": [ { diff --git a/datasets/EarthCAREAuxiliary_3.0.json b/datasets/EarthCAREAuxiliary_3.0.json index 7937038662..761dafe2da 100644 --- a/datasets/EarthCAREAuxiliary_3.0.json +++ b/datasets/EarthCAREAuxiliary_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREAuxiliary_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EarthCARE data products encompass essential supporting auxiliary (AUX) and orbit data critical for accurate sensor data processing and analysis. \r\rAUX data includes datasets used outside the primary Space Segment stream to apply corrections to sensor data. This comprises previously derived calibration parameters, ground control data, and digital elevation data. Calibration parameters ensure measurement accuracy, while ground control data aids in data validation, and digital elevation data enables precise geolocation. \r\rOrbit data consists of on-board satellite data and orbital information. For EarthCARE, this includes Reconstructed Orbit and Attitude Files, which provide detailed satellite positioning and orientation information. \r\rThe integration of AUX and orbit data into EarthCARE's data processing workflow ensures the production of high-quality, scientifically valuable datasets for atmospheric research, climate modeling, and environmental monitoring.", "links": [ { diff --git a/datasets/EarthCAREL0L1Products_4.0.json b/datasets/EarthCAREL0L1Products_4.0.json index aa4947df93..00ad9ac89c 100644 --- a/datasets/EarthCAREL0L1Products_4.0.json +++ b/datasets/EarthCAREL0L1Products_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL0L1Products_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection is restricted, and contains the following data products:\r\r\u00b7 Level 0: Annotated Raw Instrument Source Packets\r\rThese packets contain unprocessed data as generated by EarthCARE's instruments, annotated with basic metadata in front of each packet.\r\r\u00b7 Level 1b: Fully Calibrated and Geolocated Instrument Measurements\r\rLevel 1b products are fully processed, calibrated, and geolocated measurements from EarthCARE's instruments. Each measurement is aligned with the native instrument grid. For the Broadband Radiometer (BBR), measurements are also spatially integrated to various ground pixel sizes.\r\r\u00b7 Level 1c (MSI only): MSI Level 1b Data Interpolated to a Common Spatial Grid\r\rSpecifically for the Multi-Spectral Imager (MSI), Level 1c data involves interpolating Level 1b measurements onto a standardized spatial grid that is consistent across all MSI bands. This grid closely matches the spacing used in MSI Level 1b data.\r\r\u00b7 Level 1d: Joint Standard Grid (JSG) for All Instruments and ECMWF Meteorological Fields.\r\rLevel 1d data provide a spatial grid to enable easy collocation and synergistic use of the data from all EarthCARE instruments, named the "joint standard grid." Additionally, this level incorporates ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological fields limited to the EarthCARE swath, enabling comprehensive analysis and modelling of atmospheric conditions within the satellite's coverage area.", "links": [ { diff --git a/datasets/EarthCAREL1InstChecked_5.0.json b/datasets/EarthCAREL1InstChecked_5.0.json index b144f741f2..0805896119 100644 --- a/datasets/EarthCAREL1InstChecked_5.0.json +++ b/datasets/EarthCAREL1InstChecked_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL1InstChecked_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection is restricted, and contains the following data products:\r\r\u00b7 Level 1b: Fully Calibrated and Geolocated Instrument Science Measurements\r\rLevel 1b data represents the fully processed, calibrated, and geolocated measurements from EarthCARE's instruments. Each measurement is aligned with the native instrument grid. For the Broadband Radiometer (BBR), measurements are also spatially integrated to various groundpixel sizes.\r\r\u00b7 Level 1c (MSI only): MSI Level 1b Data Interpolated to a Common Spatial Grid\r\rSpecifically for the Multi-Spectral Imager (MSI), Level 1c data involves interpolating Level 1b measurements onto a standardized spatial grid that is consistent across all MSI bands. This grid closely matches the spacing used in MSI Level 1b data.\r\r\u00b7 Level 1d: Joint Standard Grid (JSG) for All Instruments with ECMWF Meteorological Fields.\r\rLevel 1d data provides a spatial grid to enable easy collocation and synergistic use of the data from all EarthCARE instruments, named the "joint standard grid." Additionally, this level incorporates ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological fields limited to the EarthCARE swath, enabling comprehensive analysis and modeling of atmospheric conditions within the satellite's coverage area.", "links": [ { diff --git a/datasets/EarthCAREL1Validated_3.0.json b/datasets/EarthCAREL1Validated_3.0.json index b67e03cb79..024d633c05 100644 --- a/datasets/EarthCAREL1Validated_3.0.json +++ b/datasets/EarthCAREL1Validated_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL1Validated_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection contains the following data products: \r\rLevel 1b: Fully Calibrated and Geolocated Instrument Science Measurements \r\rLevel 1b data represents the fully processed, calibrated, and geolocated measurements from EarthCARE's instruments. Each measurement is aligned with the native instrument grid. For the Broadband Radiometer (BBR), measurements are also spatially integrated to various groundpixel sizes. \r\rLevel 1c (MSI only): MSI Level 1b Data Interpolated to a Common Spatial Grid \r\rSpecifically for the Multi-Spectral Imager (MSI), Level 1c data involves interpolating Level 1b measurements onto a standardized spatial grid that is consistent across all MSI bands. This grid closely matches the spacing used in MSI Level 1b data. \r\rLevel 1d: Joint Standard Grid (JSG) for All Instruments with ECMWF Meteorological Fields. \r\rLevel 1d data provides a spatial grid to enable easy collocation and synergistic use of the data from all EarthCARE instruments, named the "joint standard grid." Additionally, this level incorporates ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological fields limited to the EarthCARE swath, enabling comprehensive analysis and modelling of atmospheric conditions within the satellite's coverage area. \r\r \r\rCPR level 1b: C-NOM products is generated and provided by JAXA. This product is used as input, in combination with the X-MET aux file, for different processors in the EarthCARE production chain. \r \r\rAUX_MET_1D: meteorological analysis and forecast fields X-MET provided by ECMWF. This product is used as input, in combination with the C-NOM product, for different processors in the EarthCARE production chain.", "links": [ { diff --git a/datasets/EarthCAREL2InstChecked_4.0.json b/datasets/EarthCAREL2InstChecked_4.0.json index 4db06ad8b8..1ce37c18b6 100644 --- a/datasets/EarthCAREL2InstChecked_4.0.json +++ b/datasets/EarthCAREL2InstChecked_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL2InstChecked_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection is restricted, and contains the following data products:\r\r\u00b7 Level 2a: Single-Instrument Geophysical Products\r\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena.\r\r\u00b7 Level 2b: Synergistic Geophysical Products\r\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/EarthCAREL2Products_4.0.json b/datasets/EarthCAREL2Products_4.0.json index c80c4f30ab..20c821d661 100644 --- a/datasets/EarthCAREL2Products_4.0.json +++ b/datasets/EarthCAREL2Products_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL2Products_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection contains the following data products: \r\rLevel 2a: Single-Instrument Geophysical Products \r\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. \r\rLevel 2b: Synergistic Geophysical Products \r\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/EarthCAREL2Validated_3.0.json b/datasets/EarthCAREL2Validated_3.0.json index 37605867ee..f3663f8cf1 100644 --- a/datasets/EarthCAREL2Validated_3.0.json +++ b/datasets/EarthCAREL2Validated_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREL2Validated_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection contains the following data products:\r\rLevel 2a: Single-Instrument Geophysical Products\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena.\rLevel 2b: Synergistic Geophysical Products\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/EarthCAREOrbitData_2.0.json b/datasets/EarthCAREOrbitData_2.0.json index e997a13fb7..60443d3866 100644 --- a/datasets/EarthCAREOrbitData_2.0.json +++ b/datasets/EarthCAREOrbitData_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EarthCAREOrbitData_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EarthCARE data products encompass essential supporting auxiliary (AUX) and orbit data critical for accurate sensor data processing and analysis. \r\rOrbit data consists of on-board satellite data and orbital information predicted or determined by the Flight Operations Segment (FOS). For EarthCARE, this includes Reconstructed Orbit and Attitude Files, which provide detailed satellite positioning and orientation information. \r\rThe integration of AUX and orbit data into EarthCARE's data processing workflow ensures the production of high-quality, scientifically valuable datasets for atmospheric research, climate modeling, and environmental monitoring.", "links": [ { diff --git a/datasets/East Africa Agricultural Field Centers_1.json b/datasets/East Africa Agricultural Field Centers_1.json index d7ec78cfc1..a1302b7fcf 100644 --- a/datasets/East Africa Agricultural Field Centers_1.json +++ b/datasets/East Africa Agricultural Field Centers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "East Africa Agricultural Field Centers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nGeoreferenced crop yield prediction is a valuable tool for agronomists and policymakers. One challenge with many existing datasets is that of location accuracy. GPS locations for fields can end up offset from the true location due to sensor inaccuracies or from locations being collected at the edges of fields rather than the field centers. This makes it harder to connect remote-sensed data to the yield values. The goal of this project was to produce a method that can help correct these location offsets by finding the most probable field center given an input location.\n", "links": [ { diff --git a/datasets/EastAnglia10YearMean_549_1.json b/datasets/EastAnglia10YearMean_549_1.json index 001ddb103d..d9283a657e 100644 --- a/datasets/EastAnglia10YearMean_549_1.json +++ b/datasets/EastAnglia10YearMean_549_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EastAnglia10YearMean_549_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of decade-mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degree lat/lon for a range of variables: precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.", "links": [ { diff --git a/datasets/EastAnglia30YearMean_550_1.json b/datasets/EastAnglia30YearMean_550_1.json index 5108aedee8..497c8c0c55 100644 --- a/datasets/EastAnglia30YearMean_550_1.json +++ b/datasets/EastAnglia30YearMean_550_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EastAnglia30YearMean_550_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of 30-year mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degree lat/lon for a range of variables: precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency.", "links": [ { diff --git a/datasets/EastAngliaClimate_542_1.json b/datasets/EastAngliaClimate_542_1.json index 8421f15dd3..fb21e31ded 100644 --- a/datasets/EastAngliaClimate_542_1.json +++ b/datasets/EastAngliaClimate_542_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EastAngliaClimate_542_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A data set of mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degrees lat/lon for a range of variables: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed.", "links": [ { diff --git a/datasets/EastAngliaPrecip_417_1.json b/datasets/EastAngliaPrecip_417_1.json index 593fbadf31..439f70c0d0 100644 --- a/datasets/EastAngliaPrecip_417_1.json +++ b/datasets/EastAngliaPrecip_417_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EastAngliaPrecip_417_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An historical monthly precipitation data set for global land areas from 1900 to January 1, 1999, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).", "links": [ { diff --git a/datasets/Ecosystem_Map_SRD_PAD_1947_1.json b/datasets/Ecosystem_Map_SRD_PAD_1947_1.json index 79820a4ca9..f7691e3313 100644 --- a/datasets/Ecosystem_Map_SRD_PAD_1947_1.json +++ b/datasets/Ecosystem_Map_SRD_PAD_1947_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ecosystem_Map_SRD_PAD_1947_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.", "links": [ { diff --git a/datasets/Ecotoxicology_1.json b/datasets/Ecotoxicology_1.json index f1d9bc2b5d..377d8d9335 100644 --- a/datasets/Ecotoxicology_1.json +++ b/datasets/Ecotoxicology_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ecotoxicology_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from AAS (ASAC) Project 2933.\n\nSee the child records for access to the datasets.\n\nPublic \nWhile it is generally thought that Antarctic organisms are highly sensitive to pollution, there is little data to support or disprove this. Such data is essential if realistic environmental guidelines, which take into account unique physical, biological and chemical characteristics of the Antarctic environment, are to be developed. Factors that modify bioavailability, and the effects of common contaminants on a range of Antarctic organisms from micro-algae to macro-invertebrates will be examined. Risk assessment techniques developed will provide the scientific basis for prioritising contaminated site remediation activities in marine environments, and will contribute to the development of guidelines specific to Antarctica.\n\nProject objectives:\n1. Develop and use toxicity tests to characterise the responses of a range of Antarctic marine invertebrates, micro- and macro-algae to common inorganic and organic contaminants.\n\n2. To examine factors controlling bioavailability and the influence of physical, chemical and biological properties unique to the Antarctic environment on the bioavailability and toxicity of contaminants to biota.\n\n3. To compare the response of Antarctic biota to analogous species in Arctic, temperate and tropical environments in order to determine the applicability of using toxicity data and environmental guidelines developed in other regions of the world for use in the Antarctic.\n\n4. Develop a suite of standard bioassay techniques using Antarctic species to assess the toxicity of mixtures of contaminants (aqueous and sediment-bound) including tip leachates, sewage effluents and contaminated sediments.\n\n5. To establish risk assessment models to predict the potential hazards associated with contaminated sites in Antarctica to marine biota, and to develop Water and Sediment Quality Guidelines for Antarctica to set as targets for the remediation of contaminated marine environments. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nDue to logistical constraints, only a short field season (5 weeks) was conducted at Casey in 2008/09 and no berths were allocated solely to this project. A team of 6 scientists worked together on an intensive marine sampling program under TRENZ (AAS project 2948, CI Stark) in support of 5 different AAS projects, including this one. The lack of adequate personnel dedicated to this project and the limited time that we were allocated on station hindered progress and meant that no experiments on Antarctic organisms were able to be conducted in situ. The airlink was however successfully used to transport marine invertebrates collected at Casey and held in seawater at 0degC back to Hobart on 3 separate flights. These invertebrates are currently being maintained in the cold water ecotoxicology aquarium facilities at Kingston. Once they are sorted and where possible established in cultures, they will be used in toxicity tests.\n\nProgress against specific objects are:\n1) Much effort and time has been put towards the husbandry and culture of the collected Antarctic marine invertebrates. Some species are now successfully breeding in the laboratory providing new generations and sensitive juvenile stages of invertebrates to work with in toxicity tests. This culturing capability, if able to be developed, will hugely extend opportunities for carrying out research for this project, by giving us access to live material over the winter months and during summer when berths to or space on station in Antarctica is limited. Toxicity tests using some of the common amphipods and gastropods collected in the 0809 season at Casey will commence shortly at Kingston.\n2) Toxicity tests to commence shortly using invertebrates collected in the 0809 season now being maintained in the Ecotoxicology aquarium will focus on interactions and potentially synergistic effects of contaminants along with other environmental stressors including increases in temperature and decreases in salinity associated with predicted environmental changes in response to climate change.\n3) A phD candidate has recently started on this project and is currently reviewing all available literature on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes.\n4) Invertebrates collected in the 0809 season that are being maintained in the Ecotoxicology aquarium will be screened in toxicity tests to commence shortly. Methods will then be developed using the most suitable and sensitive species to form the basis of standard bioassay procedures that can be used to test mixtures such as sewage effluents and tip leachates in the upcoming season.\n5) The establishment of risk assessment models and Environmental Quality Guidelines for Antarctica is a long term goal of this project when data from the first 4 objectives can be synthesised and hence has not yet been addressed. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nObjectives 1 and 2: Metal effects on the behaviour and survival of three marine invertebrate species were investigated during the field season. Two replicate toxicity tests were conducted on the larvae of sea urchin Sterechinus neumayeri where combined effects of metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) were to be investigated on developmental success. However, due to lower than optimal fertilisation success, both tests were terminated before any meaningful results could be derived.\n\nFour tests were conducted on the adult amphipod, Paramorea walkeri. Two replicate tests investigated combined metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) effects, and two tests investigated the effects of copper, cadmium, lead, zinc and nickel exposure at ambient sea water temperature of -1 degrees Celsius.\n\nOne test was conducted with the micro-gastropod Skenella paludionoides being exposed to copper, cadmium, lead, zinc and nickel at ambient sea water temperature.\n\nThe larvae of bivalve Laternula sp. were also investigated as a potential test organism for metal toxicity. Strip spawning was conducted a number of times, however, this technique did not provide adequate levels of fertilisation success and as such, the toxicity tests on larval development were not completed.\n\nObjective 3: A phD candidate working on this project is in the process of compiling a review of all available date on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. This literature review will form a major component of her thesis' first chapter\n\nObjective 4: Methods for Standard bioassay procedures were developed using the most suitable and sensitive species, the amphipod Paramoera walkeri and the microgastropod Skenella paludionoides. These standard tests were then used to assess the toxicity of sewage effluent at Davis Station (in conjunction with project 3217).\n\nObjective 5: Toxicity tests on sewage effluent were conducted as part of a risk assessment to determine hazards associated with the current discharge. The determined toxicity of the sewage effluent will provide a basis for guideline recommendations on the required level of treatment and on what constitutes an adequate or 'safe' dilution factor for dispersal of the effluent discharge to the near shore marine environment.", "links": [ { diff --git a/datasets/Effect_Environment_Moose_1739_1.json b/datasets/Effect_Environment_Moose_1739_1.json index 94318828d1..72459be953 100644 --- a/datasets/Effect_Environment_Moose_1739_1.json +++ b/datasets/Effect_Environment_Moose_1739_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Effect_Environment_Moose_1739_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.", "links": [ { diff --git a/datasets/Elephant_seal_toothfish_interaction_1.json b/datasets/Elephant_seal_toothfish_interaction_1.json index f6b164d61d..700d9ac178 100644 --- a/datasets/Elephant_seal_toothfish_interaction_1.json +++ b/datasets/Elephant_seal_toothfish_interaction_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Elephant_seal_toothfish_interaction_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Edited version of a video showing three elephant seals interacting with a toothfish longline.\n\nTaken from the abstract of the referenced paper:\n\nHumans have devised fishing technologies that compete with marine predators for fish resources world-wide. One such fishery for the Patagonian toothfish (Dissostichus eleginoides) has developed interactions with a range of predators, some of which are marine mammals capable of diving to extreme depths for extended periods. A deep-sea camera system deployed within a toothfish fishery operating in the Southern Ocean acquired the first-ever video footage of an extreme-diver, the southern elephant seal (Mirounga leonina), depredating catch from longlines set at depths in excess of 1000m. The interactions recorded were non-lethal, however independent fisheries observer reports confirm elephant seal-longline interactions can be lethal. The seals behaviour of depredating catch at depth during the line soak-period differs to other surface-breathing species and thus presents a unique challenge to mitigate their by-catch. Deployments of deep-sea cameras on exploratory fishing gear prior to licencing and permit approvals would gather valuable information regarding the nature of interactions between deep diving/dwelling marine species and longline fisheries operating at bathypelagic depths. Furthermore, the positive identification by sex and age class of species interacting with commercial fisheries would assist in formulating management plans and mitigation strategies founded on species-specific life-history strategies.", "links": [ { diff --git a/datasets/Emperor_Peterson_1.json b/datasets/Emperor_Peterson_1.json index dce11db7b1..1182313868 100644 --- a/datasets/Emperor_Peterson_1.json +++ b/datasets/Emperor_Peterson_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Emperor_Peterson_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The exact location of an Emperor Penguin Colony on Peterson Bank continually changes due to the changing ice conditions of where the colony is situated.\n\nThe location confirmed on the 3rd of November 1994 on fast ice at Peterson Bank was 65.9333 S, 110.2 E, 41km NNW of Australia's Casey Station. The location was recorded by Ward Bremmers during a helicopter flight involved in the resupply operations from an ice-bound ship to Casey Station.\n\nThe presence of chicks was confirmed on landing and an approximate count estimated chick numbers at 2000 with at least 1000 adults present. Many foraging animals were also observed in transit in the surrounding area. Approximately 100 dead chicks, ranging in age from a few weeks to 3 months old, were observed during a casual check in the immediate vicinity.\n\nThe colony lies on fast ice amid grounded bergs in Peterson Bank. The surrounding icebergs are widely spaced (1-2km), so the colony site is relatively unsheltered from the prevailing easterly gales. The sea-ice thickness at the colony sites was 7-8m, suggesting the ice had been stable for the previous three or our seasons. However, during a second visit to the site on 24 December 1994, the ice at the colony site was breaking up, and 200 chicks in the process of moulting were observed drifting on a large ice floe.\n\nOn the 24 of April in 1995, a large group of Adults on new ice amid grounded bergs in the Peterson Bank was sighted, suggesting that the colony was reforming.\n\nThe fields in this dataset are:\n\nDate\nLatitude\nLongitude\nNumber of Adults\nNumber of Chicks\nDead Chicks\nComments", "links": [ { diff --git a/datasets/End_of_Season_Snow_Depth_1702_1.json b/datasets/End_of_Season_Snow_Depth_1702_1.json index 21be3c6f3c..bf4a062982 100644 --- a/datasets/End_of_Season_Snow_Depth_1702_1.json +++ b/datasets/End_of_Season_Snow_Depth_1702_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "End_of_Season_Snow_Depth_1702_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 20,582 snow depth measurements collected at six sites near Fairbanks, Alaska, USA. Measurements were made during March or April from 2014-2019. The sites were located at or near Goldstream, Creamer's Field, APEX, the Permafrost Tunnel and Farmer's Loop. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.", "links": [ { diff --git a/datasets/Enderby_Ht_1.json b/datasets/Enderby_Ht_1.json index c6b5ba03c3..d709bd2bac 100644 --- a/datasets/Enderby_Ht_1.json +++ b/datasets/Enderby_Ht_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Enderby_Ht_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet surface elevation data from an oversnow ground-based traverse to Knuckey Peaks (67.90 S, 53.53 E) from GE2 (68.65 S, 61.97 E) near Mawson Station (67.60 S, 62.88 E) during the 1975/76 summer season.\n\nThe printouts from the doppler positioning used to precisely fix the position of the observation points are archived at the Australian Antarctic Division.\n\nAll logbooks have been archived at the Australian Antarctic Division.\n\nCopies of the document details forms for the logbooks is available for download from the provided URL.", "links": [ { diff --git a/datasets/Environmental_Disturbances_AK_1705_1.json b/datasets/Environmental_Disturbances_AK_1705_1.json index 8eec7458d5..1f861bf7e7 100644 --- a/datasets/Environmental_Disturbances_AK_1705_1.json +++ b/datasets/Environmental_Disturbances_AK_1705_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Environmental_Disturbances_AK_1705_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides descriptions and photos of environmental conditions that impacted availability to subsistence resources by residents in nine rural communities within the Yukon River basin of Interior Alaska. The data (photos) were collected by citizens (harvesters) residing in the communities while engaged in subsistence harvesting activities. The data include descriptions of the environmental condition captured in the photo, photo date, an explanation of how the condition influenced travel and access to resources, the subsistence activity when the photo was taken, effects of the environmental condition on the participant's safety, and the participant's observations regarding frequency and extent of the condition. A sensitivity metric was derived that incorporated the adaptive capacity of the participants to environmental conditions. The observations are for the period February 2016 - June 2017.", "links": [ { diff --git a/datasets/Environmental_Kerguelen_Plateau_1955_2012_1.json b/datasets/Environmental_Kerguelen_Plateau_1955_2012_1.json index 925b83bbf4..2fbd903cc7 100644 --- a/datasets/Environmental_Kerguelen_Plateau_1955_2012_1.json +++ b/datasets/Environmental_Kerguelen_Plateau_1955_2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Environmental_Kerguelen_Plateau_1955_2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Environmental variables in the region of the Kerguelen Plateau compiled from different sources and provided in the ascii raster format. Mean surface and seafloor temperature, salinity and their respective amplitude data are available on the time coverage 1955-2012 and over five decades: 1955 to 1964, 1965 to 1974, 1975 to 1984, 1985 to 1994 and 1995 to 2012. N/A was set as the no data reference.\nFuture projections are provided for several parameters: they were modified after the Bio-ORACLE database (Tyberghein et al. 2012). They are based on three IPCC scenarii (B1, AIB, A2) for years 2100 and 2200 (IPCC, 4th report).", "links": [ { diff --git a/datasets/Environmental_data_Southern_Ocean_1.json b/datasets/Environmental_data_Southern_Ocean_1.json index 4356c4f021..23dc5075fb 100644 --- a/datasets/Environmental_data_Southern_Ocean_1.json +++ b/datasets/Environmental_data_Southern_Ocean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Environmental_data_Southern_Ocean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Environmental descriptors that are available for the study area (-180 degrees W/+180 degrees E; -45 degrees/-78 degrees S) and for the following periods: 1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2012. They were compiled from different sources and transformed to the same grid resolution of 0.1 degree pixel. We also provide future projections for environmental descriptors established based on the Bio-Orable database (Tyberghein et al. 2012). They come from IPCC scenarii (B1, AIB, A2) for years 2100 and 2200 (IPCC, 4th report).", "links": [ { diff --git a/datasets/EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT_8.0.json b/datasets/EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT_8.0.json index e5480f3dd5..81bb725a5e 100644 --- a/datasets/EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT_8.0.json +++ b/datasets/EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Envisat AATSR Level 1B Brightness Temperature/Radiance product (RBT) contains top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, on a 1-km pixel grid. Values for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information. This AATSR product [ENV_AT_1_RBT] in NetCDF format stemming from the 4th AATSR reprocessing, is a continuation of ERS ATSR data and a precursor of Sentinel-3 SLSTR data. It has replaced the former L1B product [ATS_TOA_1P] in Envisat format from the 3rd reprocessing. Users with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products. The 4th reprocessing of ENVISAT AATSR data was completed in 2022; the processing updates that have been put in place and the expected scientific improvements have been outlined in full in the _$$User Documentation for (A)ATSR 4th Reprocessing Products$$ https://earth.esa.int/documents/20142/37627/QA4EO-VEG-OQC-MEM-4538_User_Documentation_for__A_ATSR_4th_Reprocessing_Level_1.pdf .", "links": [ { diff --git a/datasets/Erosion_Vegetation_Yukon_1616_1.json b/datasets/Erosion_Vegetation_Yukon_1616_1.json index b35774715e..0f68f438d2 100644 --- a/datasets/Erosion_Vegetation_Yukon_1616_1.json +++ b/datasets/Erosion_Vegetation_Yukon_1616_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Erosion_Vegetation_Yukon_1616_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products. Image classification used the Normalized Differenced Vegetation Index (NDVI) with an NDVI threshold of 0.2 to differentiate vegetated from non-vegetated pixels. Images were assigned to one of seven or eight multiyear intervals, within the 1984-2017 overall range, for each study area. Time intervals vary by study site. Change detection identified shifts from one time interval to the next: changes from vegetated to non-vegetated classes were considered riverbank erosion and changes from non-vegetated to vegetated classes were considered vegetation colonization.", "links": [ { diff --git a/datasets/Estimated_Biomass_Stock_Amazon_1648_1.json b/datasets/Estimated_Biomass_Stock_Amazon_1648_1.json index eb031ce8d8..ed3443cbe9 100644 --- a/datasets/Estimated_Biomass_Stock_Amazon_1648_1.json +++ b/datasets/Estimated_Biomass_Stock_Amazon_1648_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Estimated_Biomass_Stock_Amazon_1648_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas. The PALSAR data across the three focal areas was combined and used in a GBM model to predict forest aboveground biomass across the entire Paragominas municipality.", "links": [ { diff --git a/datasets/Eurasia_Biomass_1278_1.json b/datasets/Eurasia_Biomass_1278_1.json index ef9ac22327..b4d79b6887 100644 --- a/datasets/Eurasia_Biomass_1278_1.json +++ b/datasets/Eurasia_Biomass_1278_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Eurasia_Biomass_1278_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of eastern and western Eurasia, roughly between 50 and 70 degrees N. The study focused on within-growing-season data, i.e. leaf-on conditions.The AGB estimates were derived from a series of models that first related ground-based measured biomass to airborne data collected with an Optech Airborne Laser Terrain Mapper (ALTM) 3100, and a second set of models that related the airborne estimates of biomass to Geoscience Laser Altimeter System (GLAS) LiDAR canopy structure measurements. The ground, airborne, and GLAS measurements were used to formulate the models needed to generate biomass predictions for western Eurasia. Eastern Eurasia employed a two-phase approach relating field measurements directly to the GLAS measurements without the airborne intermediary. The GLAS LiDAR biomass estimates were extrapolated by land cover types and ecoregions across the entire biome area.The study compiled remotely sensed forest structure data collected in June of 2005 and 2006 from the GLAS LiDAR instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite and from an Optech Airborne Laser Terrain Mapper (ALTM) 3100 airborne instrument flown in Southeast Norway over both the ground plots and the ICESat GLAS flight path. For a consistent biome-level analysis, ecoregions contained within the boreal forest biome were identified by the World Wildlife Fund's (WWF) ecoregion map of the world (Olson et al., 2001). MODIS MOD12Q1 land cover products (2004) were used to identify land cover types for stratification purposes within eco-regions. The ground-based measurements are not provided with this data set.", "links": [ { diff --git a/datasets/Eurobis_2_24 Feb 2004 (Version 2.1).json b/datasets/Eurobis_2_24 Feb 2004 (Version 2.1).json index 80f2bc7422..cfcf3fb20c 100644 --- a/datasets/Eurobis_2_24 Feb 2004 (Version 2.1).json +++ b/datasets/Eurobis_2_24 Feb 2004 (Version 2.1).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Eurobis_2_24 Feb 2004 (Version 2.1)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AlgaeBase is a database of information on algae that includes terrestrial, marine and freshwater organisms. At present, the data for the marine algae, particularly seaweeds, are the most complete.\n\nAlgaeBase is often a compromise of taxonomic opinions that may or may not reflect your particular conclusions. Feel free to use the information and images included on the AlgaeBase web site, but do please cite AlgaeBase in your publications or presentations. This helps to raise money in order to continue maintenance of the service. Please also realise that AlgaeBase is made available in an incomplete form and is purely meant as a aid to taxonomic studies and not a definitive source in its own right. You should always check the information included prior to use.\n\n[Source: The information provided in the summary was extracted from the MarBEF Data System at \"http://www.marbef.org/data/eurobisproviders.php\"]", "links": [ { diff --git a/datasets/Eurobis_505_1.json b/datasets/Eurobis_505_1.json index 68b99137e9..b25c1ecc59 100644 --- a/datasets/Eurobis_505_1.json +++ b/datasets/Eurobis_505_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Eurobis_505_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data which produced the publications: Rees, H. L. et al. (1999) and Rees, H. L. et al. (2000). See references below.\n \n Size reference: 69 stations sampled, 2735 distribution records\n \n [Source: The information provided in the summary was extracted from the MarBEF Data System at \"http://www.marbef.org/data/eurobisproviders.php\"]", "links": [ { diff --git a/datasets/Eurobis_618_1.json b/datasets/Eurobis_618_1.json index 0d6af91874..0fef8a174a 100644 --- a/datasets/Eurobis_618_1.json +++ b/datasets/Eurobis_618_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Eurobis_618_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine Benthic data on benthos at station 014 in Kiel Bay representing 1,144 distribution records of 56 taxa from 1 station in Kiel Bay. \n \n [Source: The information provided in the summary was extracted from the MarBEF Data System at \"http://www.marbef.org/data/eurobisproviders.php\"]", "links": [ { diff --git a/datasets/Eyes on the Ground Image Data_1.json b/datasets/Eyes on the Ground Image Data_1.json index 5d77e88f75..2d45c178a3 100644 --- a/datasets/Eyes on the Ground Image Data_1.json +++ b/datasets/Eyes on the Ground Image Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Eyes on the Ground Image Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 'Eyes on the Ground' project ([lacunafund.org](https://lacunafund.org/ag2020awards/)) is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework (Ceballos, Kramer and Robles, 2019, [https://doi.org/10.1016/j.deveng.2019.100042](https://doi.org/10.1016/j.deveng.2019.100042)). This is a unique dataset of georeferenced crop images along with labels on input use, crop management, phenology, crop damage, and yields, collected across 8 counties in Kenya.The research leading to this dataset was undertaken as part of the CGIAR research program on Policies, Institutions and Markets (PIM)", "links": [ { diff --git a/datasets/FAO_AGL.json b/datasets/FAO_AGL.json index 56f8731f2b..0f0e8bbf99 100644 --- a/datasets/FAO_AGL.json +++ b/datasets/FAO_AGL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAO_AGL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Food and Agricultural Organization of the United Nations (FAO)/AGL\n World River Sediment Yields Database\n \n The World River Sediment Yields database contains data on annual\n sediment yields in worldwide rivers and reservoirs, searchable by\n river, country and continent. The database was compiled from\n different sources by HR Wallingford, UK, on behalf of the FAO Land and\n Water Development Division. It is currently in a test phase.\n \n The database allows its user to enter the name of the river, the\n country, or the continent for which they would like to see summary\n sedimentation data. From this data, you can discover explanations of\n the data and complete sedimentation records\n \n Data URL: \"http://www.fao.org/ag/AGL/aglw/sediment/default.asp\"\n Information taken from \"http://www.fao.org/ag/AGL/aglw/sediment/default.asp\"", "links": [ { diff --git a/datasets/FAO_FIGIS.json b/datasets/FAO_FIGIS.json index 892d95b3d3..d134c069fa 100644 --- a/datasets/FAO_FIGIS.json +++ b/datasets/FAO_FIGIS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAO_FIGIS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FAO's major program on Fisheries aims to promote sustainable\ndevelopment of responsible fisheries and contribute to food\nsecurity. To implement this major program, the Fisheries Department\nfocuses its activities, through programs in Fishery Resources, Fishery\nPolicy, Fishery Industries and Fishery Information on three\nmedium-term strategic objectives, including promotion of responsible\nfisheries sector management at the global, regional and national\nlevels, promotion of increased contribution of responsible fisheries\nand aquaculture to world food supplies and food security, and global\nmonitoring and strategic analysis of fisheries\n\nThe FAO Fisheries Global Information System is a global network of\nintegrated fisheries information. FIGIS is a work in progress -\nsections are currently under development.\n\nValuable information can be accessed on topics such as aquatic\nspecies, marine resources, marine fisheries, and fishing technology.\nSoon you will be able to access databases on trade and marketing,\naquaculture, inland fisheries, and fisheries issues.\n\nhttp://www.fao.org/fishery/figis", "links": [ { diff --git a/datasets/FAOd0008_148.json b/datasets/FAOd0008_148.json index bb16614ba2..822c3b2200 100644 --- a/datasets/FAOd0008_148.json +++ b/datasets/FAOd0008_148.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAOd0008_148", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1990 a Map of World Soil Resources was completed at scale 1:25.000.000,\ngeneralized from the FAO/UNESCO Soil Map or the World at scale 1:5.000.000\n(FAO, 1971 - 1981).\n\nThe map was issued on the occasion of the 14th International Congress of Soil\nScience held in Kyoto, Japan in 1990. Since then new material has become\navailable, the FAO/UNESCO Soil Map of the World has been partly updated under\nthe SOTER Programme and the FAO legend has been replaced by the World Reference\nBase for Soil Resources (WRB). In 1998 the latter was adopted by the\nInternational Union of Soil Sciences as the standard for soil correlation and\nnomenclature. In the light of these new developments it was decided to prepare\nan updated version of the generalized Map of the World Soil Resources at\n1:25.000.000.\n\nThe updating exercise covered:\n\n- the switch from the original map projection to a Flat Polar Quartic\nprojection\n- the conversion of the FAO legend into the WRB classification\n- the incorporation of additional soil data obtained from new or revised soil\nmap sources\n- the matching, when possible of soil unit boundaries with major landforms", "links": [ { diff --git a/datasets/FAOd0018_148.json b/datasets/FAOd0018_148.json index 937d909ab6..e472162c92 100644 --- a/datasets/FAOd0018_148.json +++ b/datasets/FAOd0018_148.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAOd0018_148", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CD-ROM is released in conjunction with World Soil Resources Reports No.\n94: \"Lecture Notes on the Major Soils of the World\". In addition to the\ncomplete (hyperlinked) text of the book, it contains many additional pictures,\na slideshow with a virtual tour of soils and landscapes and a typical soil\nprofile for each of the thirty reference soil groups of the World Reference\nBase for Soil Resources. In total more than 550 slides and pictures illustrate\nthe lecture notes.\n\n\"http://www.fao.org/icatalog/search/dett.asp?aries_id=102985\"", "links": [ { diff --git a/datasets/FAOd0019_148.json b/datasets/FAOd0019_148.json index f426c3af21..44086a69a9 100644 --- a/datasets/FAOd0019_148.json +++ b/datasets/FAOd0019_148.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAOd0019_148", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CD-ROM contains the Digital Soil Map of the World in various formats,\nverctor as well as raster, supported by most GIS software. The base material is\nthe FAO/UNESCO Soil Map of the World at an original scale of 1:5 million.\nPrograms and data files give tabular country information on soil\ncharacteristics and derived soil properties from the map are included, such as\npH, organic carbon content and soil moisture storage capability. In addition\nprograms and data files are included that display derived soil properties. The\nrevision included the adding of a number of user-friendly ArcView files\nallowing the display of dominant soils by continent and the inclusion of the\nupdate of the image of the WRB World Soil Resources Map.\n\n\"http://www.fao.org/icatalog/search/dett.asp?aries_id=103540\"", "links": [ { diff --git a/datasets/FAOd0020_148.json b/datasets/FAOd0020_148.json index 8303df4653..e8b8fa71c7 100644 --- a/datasets/FAOd0020_148.json +++ b/datasets/FAOd0020_148.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAOd0020_148", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrological Basins of Africa, with major basins and sub basins,\nautomatically derived from USGS topographic data with some manual\ncorrections in flat areas. Current version completed March 2000.", "links": [ { diff --git a/datasets/FAUNA_PENGUIN_COLONY_1.json b/datasets/FAUNA_PENGUIN_COLONY_1.json index b053d54cb1..995bb48ea6 100644 --- a/datasets/FAUNA_PENGUIN_COLONY_1.json +++ b/datasets/FAUNA_PENGUIN_COLONY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FAUNA_PENGUIN_COLONY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a census of penguin colony counts from the year 1900 in the Antarctic region. It forms part of the Inventory of Antarctic seabird breeding sites within the Antarctic and subantarctic islands. The Antarctic and subantarctic fauna database (seabirds) is a database detailing the distribution and abundance of breeding localities for Antarctic and Subantarctic seabirds. Each species' compilation was produced by members of the SCAR Bird Biology Subcommittee. \n\nThis separate metadata record has been created beacause it represents only the penguin colony counts that have been published to OBIS. Note: The Year (not day or month) date is only relevent in this dataset. The positions that have been published to OBIS include latitude and longitude positions that were not included within the original dataset. The latitude and longitude positions that were not noted by the observer have been created from the locality given by the observer using the Antarctic Composite Gazetteer.\n\nTwo spreadsheets are available for download, from the URL given below. The original, unmodified spreadsheet is available, as well as a corrected spreadsheet. In the corrected spreadsheet, the AADC has attempted to reconcile the poorly presented localities into a single column. It is possible that some of these localities may not be correct.\n\nThe fields in this dataset are:\n \nSCAR Number\nSpecies\nRegion\nLocality\nLongitude\nLatitude\nNumber of Colonies\nNumber of Pairs\nType and accuracy of count\nData Date\nReferences\nRemarks\n\nThese data are further referenced in ANARE Research Notes 9 - see reference below.", "links": [ { diff --git a/datasets/FDRforAltimetry_6.0.json b/datasets/FDRforAltimetry_6.0.json index 26935d7ff4..718628f5c5 100644 --- a/datasets/FDRforAltimetry_6.0.json +++ b/datasets/FDRforAltimetry_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FDRforAltimetry_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ .\rThe Fundamental Data Record for Altimetry V1 products contain Level 0 and Level 1 altimeter-related parameters including calibrated radar waveforms and supplementary instrumental parameters describing the altimeter operating status and configuration through the satellite lifetime.\r\rThe data record consists of data for the ERS-1, ERS-2 and Envisat missions for the period ranging from 1991 to 2012, and bases on the Level 1 data obtained from previous ERS REAPER and ENVISAT V3.0 reprocessing efforts incorporating new algorithms, flags, and corrections to enhance the accuracy and reliability of the data.\r\rFor many aspects, the Altimetry FDR product has improved compared to the existing individual mission datasets:\r\rNew neural-network waveform classification, surface type classification, distance to shoreline and surface flag based on GSHHG\rInstrumental calibration information directly available in the product\rImproved Orbit solutions\rCorrection of REAPER drawbacks (i.e., time jumps and negative waveforms)\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/FDRforAtmosphericCompositionATMOSL1B_4.0.json b/datasets/FDRforAtmosphericCompositionATMOSL1B_4.0.json index f25d33b127..e788a7fc66 100644 --- a/datasets/FDRforAtmosphericCompositionATMOSL1B_4.0.json +++ b/datasets/FDRforAtmosphericCompositionATMOSL1B_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FDRforAtmosphericCompositionATMOSL1B_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fundamental Data Record (FDR) for Atmospheric Composition UVN Level 1b v.1.0 dataset is a cross-instrument Level-1 product [ATMOS__L1B] generated in 2023 and resulting from the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ .\rThe FDR contains selected Earth Observation Level 1b parameters (irradiance/reflectance) from the nadir-looking measurements of the ERS-2 GOME and Envisat SCIAMACHY missions for the period ranging from 1995 to 2012. \rThe data record offers harmonised cross-calibrated spectra, essential for subsequent trace gas retrieval. The focus lies on spectral windows in the Ultraviolet-Visible-Near Infrared regions the retrieval of critical atmospheric constituents like ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) column densities, alongside cloud parameters in the NIR spectrum.\rFor many aspects, the FDR product has improved compared to the existing individual mission datasets:\r\u2022\tGOME solar irradiances are harmonised using a validated SCIAMACHY solar reference spectrum, solving the problem of the fast-changing etalon present in the original GOME Level 1b data; \r\u2022\tReflectances for both GOME and SCIAMACHY are provided in the FDR product. GOME reflectances are harmonised to degradation-corrected SCIAMACHY values, using collocated data from the CEOS PIC sites;\r\u2022\tSCIAMACHY data are scaled to the lowest integration time within the spectral band using high-frequency PMD measurements from the same wavelength range. This simplifies the use of the SCIAMACHY spectra which were split in a complex cluster structure (with own integration time) in the original Level 1b data;\r\u2022\tThe harmonization process applied mitigates the viewing angle dependency observed in the UV spectral region for GOME data;\r\u2022\tUncertainties are provided.\r\r\rEach FDR product covers three FDRs (irradiance/reflectance for UV-VIS-NIR) for a single day within the same product including information from the individual ERS-2 GOME and Envisat SCIAMACHY orbits therein.\r\rFDR has been generated in two formats: Level 1A and Level 1B targeting expert users and nominal applications respectively. The Level 1A [ATMOS__L1A] data include additional parameters such as harmonisation factors, PMD, and polarisation data extracted from the original mission Level 1 products. The ATMOS__L1A dataset is not part of the nominal dissemination to users. In case of specific requirements, please contact _$$EOHelp$$ http://esatellus.service-now.com/csp?id=esa_simple_request&sys_id=f27b38f9dbdffe40e3cedb11ce961958 .\r\r\rThe FDR4ATMOS products should be regarded as experimental due to the innovative approach and the current use of a limited-sized test dataset to investigate the impact of harmonization on the Level 2 target species, specifically SO2, O3 and NO2. Presently, this analysis is being carried out within follow-on activities.\r\rOne of the main aspects of the project was the characterization of Level 1 uncertainties for both instruments, based on metrological best practices. The following documents are provided:\r\r1.\tGeneral guidance on a metrological approach to Fundamental Data Records (FDR) -> link TBC\r2.\tUncertainty Characterisation document -> link TBC\r3.\tEffect tables -> link TBC\r4.\tNetCDF files containing example uncertainty propagation analysis and spectral error correlation matrices for SCIAMACHY (Atlantic and Mauretania scene for 2003 and 2010) and GOME (Atlantic scene for 2003) links TBC reflectance_uncertainty_example_FDR4ATMOS_GOME.nc\rreflectance_uncertainty_example_FDR4ATMOS_SCIA.nc\r\rThe FDR V1 is currently being extended to include the MetOp GOME-2 series.\r\rAll the new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \r\rPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .", "links": [ { diff --git a/datasets/FDRforRadiometry_5.0.json b/datasets/FDRforRadiometry_5.0.json index 13e6beea7f..191c3c623f 100644 --- a/datasets/FDRforRadiometry_5.0.json +++ b/datasets/FDRforRadiometry_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FDRforRadiometry_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ .\rThe Fundamental Data Record for Radiometry V1 products contain intercalibrated Top of the Atmosphere brightness temperatures at 23.8 and 36.5 GHz. The collection covers data for the ERS-1, ERS-2 and Envisat missions, and is built upon a new processing of Level 0 data, incorporating numerous improvements in terms of algorithms, flagging procedures, and corrections.\r\rCompared to existing datasets, the Radiometry FDR demonstrates notable improvements in several aspects:\r\rNew solutions for instrumental effects (ERS Reflector loss, Skyhorn, and Sidelobe corrections)\rNative sampling rate of 7Hz with enhanced coverage\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/FEDMAC_AEROSOLS.json b/datasets/FEDMAC_AEROSOLS.json index b7bf7beb2c..214054a97f 100644 --- a/datasets/FEDMAC_AEROSOLS.json +++ b/datasets/FEDMAC_AEROSOLS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FEDMAC_AEROSOLS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC):\n Aerosol Optical Thickness\n \n The Biospheric Sciences Branch (formerly Earth Resources Branch)\n within the Laboratory for Terrestrial Physics at NASA's Goddard Space\n Flight Center and associated University investigators are involved in\n a research program entitled Forest Ecosystem Dynamics (FED) which is\n fundamentally concerned with vegetation change of forest ecosystems at\n local to regional spatial scales (100 to 10,000 meters) and temporal\n scales ranging from monthly to decadal periods (10 to 100 years). The\n nature and extent of the impacts of these changes, as well as the\n feedbacks to global climate, may be addressed through modeling the\n interactions of the vegetation, soil, and energy components of the\n boreal ecosystem.\n \n Measurement of atmospheric attenuation and hence estimate of the\n aerosol optical thickness were made in the Northern Experimental\n Forest (NEF) in Howland, Maine, with sunphotometers. This parameter is\n useful in calibration and correction of other measurements made with\n remote sensing instruments at FED sites. Measurements were made with the\n eight channel sun-photometer named SXM-2 (440, 522, 613, 672, 781, 871\n and 1030 nm with 10 nm FWHM) located on the ground. It tracks the\n sun automatically using a 4 quadrant detector. The detector is a\n silicon photodiode which is kept at a constant temperature. The\n instrument has a 1.5 degree field-of-view.\n \n The FED Home Page is at: \"https://forest.gsfc.nasa.gov/\".\n", "links": [ { diff --git a/datasets/FEDMAC_ALPS.json b/datasets/FEDMAC_ALPS.json index 1607a18975..711c5570ee 100644 --- a/datasets/FEDMAC_ALPS.json +++ b/datasets/FEDMAC_ALPS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FEDMAC_ALPS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC):\n Airborne Laser Polarization Experiment\n \n The Biospheric Sciences Branch (formerly Earth Resources Branch)\n within the Laboratory for Terrestrial Physics at NASA's Goddard Space\n Flight Center and associated University investigators are involved in\n a research program entitled Forest Ecosystem Dynamics (FED) which is\n fundamentally concerned with vegetation change of forest ecosystems at\n local to regional spatial scales (100 to 10,000 meters) and temporal\n scales ranging from monthly to decadal periods (10 to 100 years). The\n nature and extent of the impacts of these changes, as well as the\n feedbacks to global climate, may be addressed through modeling the\n interactions of the vegetation, soil, and energy components of the\n boreal ecosystem.\n \n A new remote sensing instrument, the Airborne Laser Polarization\n Sensor (ALPS), mounted on a helicopter, was used to make multispectral\n radiometric and polarization measurements of the Earth's surface using\n a polarized laser light source. The ALPS system consists of a pulsed,\n polarized laser source, an optical receiver package, a video camera and\n recorder, and data acquisition and analysis hardware and software. The\n choice of laser wavelengths is limited to frequencies from the\n ultraviolet to the near-infrared by the photo-cathode response of the\n selected photo multiplier tube (PMT) detectors. Twelve PMTs were used\n corresponding to the 12 channels of data: Channels 1,2,3,4,9 & 10 have\n 1090 nm bandpass filters. The reminder are for 532 nm. Channels 9 and\n 11 have no polarization filters.\n \n For each wavelength, polarization filters are mounted in front of each\n PMT at angles relative to the transmitted polarization. A pulsed (7 ns)\n Nd:YAG laser is employed. It operates in the infrared at 1060 nm and\n the visible at 532 nm. The 532 nm green wavelength can be seen near\n the center of the TV screen as it hits the surface in most cases. This\n is used for ground truth correlation. The spot is about 20 cm in\n diameter from 100 meters altitude.\n \n In these data for ALPS Experiment for the FED MAC 90, the file\n tabulation refers to data files taken on September 9 and 11. A\n standard VHS video tape is available (the master tapes are recorded at\n the SP speed on Super-VHS). The first half of this tape is from a\n camera coaxial with the laser transmission. Time on the tape\n correspond to file times while oral comments on the tape supplement\n the general comments. The second half of the tape consists primarily\n of site descriptive narration on the ground and some pictures of the\n helicopter setup.\n\n The FED Home Page is at: \"https://forest.gsfc.nasa.gov/\".\n", "links": [ { diff --git a/datasets/FEWS_precip_711_1.json b/datasets/FEWS_precip_711_1.json index 3b2f37198e..5b9469e07e 100644 --- a/datasets/FEWS_precip_711_1.json +++ b/datasets/FEWS_precip_711_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FEWS_precip_711_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate (RFE) data for Africa since 1995. The FEWSNET project was established with the goal of reducing the incidence of drought- or flood-induced famine by providing decision makers with timely and accurate information on conditions that may require intervention. RFE data for continental Africa for 1999, 2000, and 2001 were downloaded the from the African Data Dissemination Service (ADDS) site and were subset for southern Africa by the SAFARI 2000 data group. The RFE 1.0 algorithm, implemented from 1995 to 2000, uses an interpolation method to combine Meteosat and Global Telecommunication System (GTS) data, and warm cloud information for the 10-day estimations. The 30-minute geostationary Meteosat-7 satellite infrared data are used to estimate convective rainfall from areas where cloud top temperatures are less than 235K. The RFE 2.0 algorithm, implemented as of January 1, 2001, uses additional techniques to better estimate precipitation while continuing the use of cold cloud duration and station rainfall data.", "links": [ { diff --git a/datasets/FIA_Forest_Biomass_Estimates_1873_1.json b/datasets/FIA_Forest_Biomass_Estimates_1873_1.json index 2c8d99764a..7f5b996939 100644 --- a/datasets/FIA_Forest_Biomass_Estimates_1873_1.json +++ b/datasets/FIA_Forest_Biomass_Estimates_1873_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIA_Forest_Biomass_Estimates_1873_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates.", "links": [ { diff --git a/datasets/FIFE_CD_V3_130_1.json b/datasets/FIFE_CD_V3_130_1.json index e06d0a202c..02628d5995 100644 --- a/datasets/FIFE_CD_V3_130_1.json +++ b/datasets/FIFE_CD_V3_130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIFE_CD_V3_130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides aircraft-based NS001 Thematic Mapper Simulator (TMS) images of the study area associated with The First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) project conducted on the Konza Prairie in Kansas. The images were acquired during June 1987 to August 1989. The images in this data set were originally provided on the FIFE CD-ROM Volume 3.", "links": [ { diff --git a/datasets/FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data_1.json b/datasets/FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data_1.json index b8677f896a..8cccf024a6 100644 --- a/datasets/FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data_1.json +++ b/datasets/FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data are remotely sensed data collected by the Cloud Physics Lidar (CPL) onboard the ER-2 aircraft during FIREX-AQ. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_Aerosol_AircraftInSitu_DC8_Data_1.json index 382c974220..2279660a83 100644 --- a/datasets/FIREXAQ_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Aerosol_AircraftInSitu_DC8_Data are in-situ aerosol measurements collected onboard the DC-8 aircraft during FIREX-AQ. This product features data collected by the AMS, CPC, PSAP, CCN, CDP, Nephelometers, and a variety of other in-situ instrumentation. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Aerosol_AircraftInSitu_N48_Data_1.json b/datasets/FIREXAQ_Aerosol_AircraftInSitu_N48_Data_1.json index c641a4d4b4..0f23077dda 100644 --- a/datasets/FIREXAQ_Aerosol_AircraftInSitu_N48_Data_1.json +++ b/datasets/FIREXAQ_Aerosol_AircraftInSitu_N48_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Aerosol_AircraftInSitu_N48_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Aerosol_AircraftInSitu_N48_Data are in situ aerosol data collected onboard the NOAA-CHEM Twin Otter during FIREX-AQ. This product includes data collected by the UHSAS, AMS, CLAP, and PILS instruments. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Analysis_Data_1.json b/datasets/FIREXAQ_Analysis_Data_1.json index 4d2c4ec31f..ba040acadb 100644 --- a/datasets/FIREXAQ_Analysis_Data_1.json +++ b/datasets/FIREXAQ_Analysis_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Analysis_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Analysis_Data are supplementary analysis and ancillary data collected during FIREX-AQ. This product includes plume ratios, and supplementary datasets regarding the wildfires sampled during the campaign. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts. Data collection is complete.", "links": [ { diff --git a/datasets/FIREXAQ_Analysis_N48_Data_1.json b/datasets/FIREXAQ_Analysis_N48_Data_1.json index eb0a8f9eaa..67982a5cf0 100644 --- a/datasets/FIREXAQ_Analysis_N48_Data_1.json +++ b/datasets/FIREXAQ_Analysis_N48_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Analysis_N48_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Analysis_N48_Data are supplementary smoke age data collected during FIREX-AQ. This product is derived from a variety of models, including the NAM, GFS, and HRRR and corresponds to the NOAA-CHEM Twin Otter. Data collection for this product is complete. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_Cloud_AircraftInSitu_DC8_Data_1.json index b27f3911fd..4784d916b6 100644 --- a/datasets/FIREXAQ_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Cloud_AircraftInSitu_DC8_Data are in-situ cloud measurements collected onboard the DC8 aircraft during FIREX-AQ. This product features data collected by the CDP, CPSPD, and CAPS. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Ground_InSitu_Data_1.json b/datasets/FIREXAQ_Ground_InSitu_Data_1.json index 8fb9aa005c..eeb3fddaf2 100644 --- a/datasets/FIREXAQ_Ground_InSitu_Data_1.json +++ b/datasets/FIREXAQ_Ground_InSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Ground_InSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Ground_InSitu_Data are in-situ ground measurements collected during FIREX-AQ. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data_1.json b/datasets/FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data_1.json index 368b43652b..ff159a3248 100644 --- a/datasets/FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data_1.json +++ b/datasets/FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data are remotely sensed data collected by the High-Spectral Resolution Lidar (HSRL) onboard the DC-8 aircraft during FIREX-AQ. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Merge_Data_2.json b/datasets/FIREXAQ_Merge_Data_2.json index d593e82030..9910e7a871 100644 --- a/datasets/FIREXAQ_Merge_Data_2.json +++ b/datasets/FIREXAQ_Merge_Data_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Merge_Data_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Merge_Data are pre-generated merge data files collected during FIREX-AQ. These files contain merged data products collected onboard the DC-8 aircraft. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts. Data collection is complete.", "links": [ { diff --git a/datasets/FIREXAQ_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_MetNav_AircraftInSitu_DC8_Data_1.json index 2baff08e58..d4090a8fcd 100644 --- a/datasets/FIREXAQ_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_MetNav_AircraftInSitu_DC8_Data are in-situ meteorological and navigational data collected onboard the DC-8 aircraft during FIREX-AQ. This product features the navigational information for the DC-8 aircraft, along with data collected by the MMS, LGR, and DLH. Data collection for this product is complete. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1.json b/datasets/FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1.json index 52f0c3f3df..af4542ea46 100644 --- a/datasets/FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1.json +++ b/datasets/FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_MetNav_AircraftInSitu_ER2_Data_1 are meteorological and navigational data collected onboard the Earth Resources-2 (ER-2) aircraft during the Fire Influence on Regional to Global Environments Experiment - Air Quality (FIREX-AQ) Campaign. Completed during summer 2019, FIREX-AQ used a combination of instrumented airplanes, satellites, and ground-based instrumentation. Specifically, data was collected by the NASA Airborne Science Data Telemetry (NASDAT) System on the ER-2 platform. Data collection for this product is complete. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe FIREX-AQ campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_MetNav_AircraftInSitu_N48_Data_1.json b/datasets/FIREXAQ_MetNav_AircraftInSitu_N48_Data_1.json index c98f4b19a8..75cccb327f 100644 --- a/datasets/FIREXAQ_MetNav_AircraftInSitu_N48_Data_1.json +++ b/datasets/FIREXAQ_MetNav_AircraftInSitu_N48_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_MetNav_AircraftInSitu_N48_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_MetNav_AircraftInSitu_N48_Data are in situ meteorological and navigational data collected onboard the NOAA-CHEM Twin Otter during FIREX-AQ. This product includes data collected via GPS and meteorology probes. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_N46_Data_1.json b/datasets/FIREXAQ_N46_Data_1.json index 3613287150..be348d0909 100644 --- a/datasets/FIREXAQ_N46_Data_1.json +++ b/datasets/FIREXAQ_N46_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_N46_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_N46_Data are the wind profile and fire temperature data collected onboard the NOAA-MET Twin Otter during FIREX-AQ. This product includes data collected by the Nighttime Fire Observations eXperiment (NightFOX) UAS and wind lidar. Data collection for this product is complete. This data was collected by the NOAA Chemical Sciences Laboratory (CSL).\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Radiation_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_Radiation_AircraftInSitu_DC8_Data_1.json index c1d3b34c5f..e5ed5ccdf5 100644 --- a/datasets/FIREXAQ_Radiation_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_Radiation_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Radiation_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Radiation_AircraftInSitu_DC8_Data are in-situ radiation measurements conducted onboard the DC8 aircraft during FIREX-AQ. This product features data from the CCD-based Actinic Flux Spectroradiometer (CAFS) instrument. Data collection for this product is complete. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_Satellite_Data_2.json b/datasets/FIREXAQ_Satellite_Data_2.json index 155aadab5a..41089f357d 100644 --- a/datasets/FIREXAQ_Satellite_Data_2.json +++ b/datasets/FIREXAQ_Satellite_Data_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_Satellite_Data_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_Satellite_Data are supplementary satellite and related ancillary data collected during FIREX-AQ. This product includes data from the VIIRS, GOES-16, and GOES-17 satellites. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts. Data collection is complete.", "links": [ { diff --git a/datasets/FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data_1.json b/datasets/FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data_1.json index d8425a1f67..f6d3293557 100644 --- a/datasets/FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data_1.json +++ b/datasets/FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_SurfaceMobile_Aerodyne_InSitu_Data are in-situ measurements collected via the Aerodyne mobile platform during Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ). Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_SurfaceMobile_CARB_InSitu_Data_1.json b/datasets/FIREXAQ_SurfaceMobile_CARB_InSitu_Data_1.json index 71d1013744..9aec1fb561 100644 --- a/datasets/FIREXAQ_SurfaceMobile_CARB_InSitu_Data_1.json +++ b/datasets/FIREXAQ_SurfaceMobile_CARB_InSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_SurfaceMobile_CARB_InSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_SurfaceMobile_CARB_InSitu_Data are in-situ measurements collected via the California Air Resources Board (CARB) mobile lab during Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ). Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_SurfaceMobile_MACH2_InSitu_Data_1.json b/datasets/FIREXAQ_SurfaceMobile_MACH2_InSitu_Data_1.json index f07d03d209..de3d958a0f 100644 --- a/datasets/FIREXAQ_SurfaceMobile_MACH2_InSitu_Data_1.json +++ b/datasets/FIREXAQ_SurfaceMobile_MACH2_InSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_SurfaceMobile_MACH2_InSitu_Data are in-situ measurements collected via the NASA Langley Aerosol Research Group mobile platform (MACH2) during FIREX-AQ. Instruments included on this platform include the Aerodyne CAPS, APS, OPS, MAAP, and a variety of other instrumentation. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftInSitu_DC8_Data_1.json index 4be36a534f..3395ac269e 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_TraceGas_AircraftInSitu_DC8_Data are in-situ trace gas measurements conducted onboard the DC8 aircraft during FIREX-AQ. This product features data collected from the TOGA, WAS, DACOM. CAMS, PTR-MS and LGR instruments. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftInSitu_N48_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftInSitu_N48_Data_1.json index 1984e14ce2..a4ecd7d222 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftInSitu_N48_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftInSitu_N48_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftInSitu_N48_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_TraceGas_AircraftInSitu_N48_Data are in situ trace gas data collected onboard the NOAA-CHEM Twin Otter during FIREX-AQ. This product features data collected by chemiluminescence, and the PIcarro, CIMS, and GC-ToF-MS instrumentation. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data_1.json index 61cbf78277..3d1b6778dd 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_TraceGas_AircraftRemoteSensing_DC8_Data are remotely sensed trace gas measurements conducted onboard the DC8 aircraft during FIREX-AQ. This product features data collected by the DOAS instrument. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data_1.json index 7e47b6443d..9a3cee36fb 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_GCAS_Data are remotely sensed data collected by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) onboard the ER-2 aircraft during FIREX-AQ. \r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data_1.json index b86c60aed3..8ee1248b23 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_NASTI_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_ TraceGasAircraftRemoteSensing_ER2_NASTI_Data are remotely sensed measurements collected by the National Polar-Orbiting Operational Environmental Satellite System Airborne Sounder Testbed-Interferometer (NAST-I) onboard the ER-2 aircraft during FIREX-AQ. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data_1.json b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data_1.json index 77a7cef6b1..40856988bd 100644 --- a/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data_1.json +++ b/datasets/FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_TraceGas_AircraftRemoteSensing_ER2_SHIS_Data are remotely sensed measurements collected by the Scanning High-Resolution Interferometer Sounder (S-HIS) onboard the ER-2 aircraft. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. A ground-based mobile lab provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species.\r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/FIREXAQ_jValue_AircraftInSitu_DC8_Data_1.json index 798cbe0aa5..2804085f62 100644 --- a/datasets/FIREXAQ_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/FIREXAQ_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_jValue_AircraftInSitu_DC8_Data are in-situ photolysis rate (J-value) measurements conducted onboard the DC8 aircraft during FIREX-AQ. This product features data from the CAFS instrument. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIREXAQ_jValue_AircraftInSitu_N48_Data_1.json b/datasets/FIREXAQ_jValue_AircraftInSitu_N48_Data_1.json index 64d06db0c7..636f876c7a 100644 --- a/datasets/FIREXAQ_jValue_AircraftInSitu_N48_Data_1.json +++ b/datasets/FIREXAQ_jValue_AircraftInSitu_N48_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIREXAQ_jValue_AircraftInSitu_N48_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIREXAQ_jValue_AircraftInSitu_N48_Data are in situ photolysis rate (j value) data collected onboard the NOAA-CHEM Twin Otter aircraft during FIREX-AQ. Data collection for this product is complete.\r\n\r\nCompleted during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA\u2019s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. \r\n\r\nThe Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite\u2019s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.", "links": [ { diff --git a/datasets/FIRE_ACE_AMPR_1.json b/datasets/FIRE_ACE_AMPR_1.json index 2c6ca03f28..0f76b0dc15 100644 --- a/datasets/FIRE_ACE_AMPR_1.json +++ b/datasets/FIRE_ACE_AMPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_AMPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data consists of data provided by the Advanced Microwave Precipitation Radiometer (AMPR) flown onboard the ER2 aircraft during the FIRE ACE field campaign.The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_ACE_C130_CFD_1.json b/datasets/FIRE_ACE_C130_CFD_1.json index a57bdd8765..c1669bb351 100644 --- a/datasets/FIRE_ACE_C130_CFD_1.json +++ b/datasets/FIRE_ACE_C130_CFD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_C130_CFD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. Aerosol data obtained by Colorado State University during May 1998 on the NCAR C-130 research flights as part of the First ISCCP Regional Experiment (FIRE3) Arctic Cloud Experiment (ACE) flown onboard the NCAR C-130 aircraft during the FIRE ACE field campaign. The data are in ASCII format. The primary measurements were of ice nuclei and condensation nuclei.", "links": [ { diff --git a/datasets/FIRE_ACE_C130_RAMS_1.json b/datasets/FIRE_ACE_C130_RAMS_1.json index 9892eb44cf..5ceb274906 100644 --- a/datasets/FIRE_ACE_C130_RAMS_1.json +++ b/datasets/FIRE_ACE_C130_RAMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_C130_RAMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of data provided by Scripps' Radiation Measurement System (RAMS) flown onboard the NCAR C-130 aircraft during the FIRE ACE field campaign.The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_ACE_ER2_MAS_1.json b/datasets/FIRE_ACE_ER2_MAS_1.json index 15c9ab9b5b..4cd1763f59 100644 --- a/datasets/FIRE_ACE_ER2_MAS_1.json +++ b/datasets/FIRE_ACE_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of radiance measurements from the NASA ER2 Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) during the First ISCCP Regional Experiment (FIRE) Arctic Cloud Experiment (ACE).The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_ACE_ER2_MIR_1.json b/datasets/FIRE_ACE_ER2_MIR_1.json index 63ad684b1d..e84d6f36bf 100644 --- a/datasets/FIRE_ACE_ER2_MIR_1.json +++ b/datasets/FIRE_ACE_ER2_MIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_ER2_MIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.FIRE ACE Millimeter-wave Imaging Radiometer flown aboard the ER2.", "links": [ { diff --git a/datasets/FIRE_ACE_SHIP_SSFR_1.json b/datasets/FIRE_ACE_SHIP_SSFR_1.json index 133eb4ec11..a8f11b8707 100644 --- a/datasets/FIRE_ACE_SHIP_SSFR_1.json +++ b/datasets/FIRE_ACE_SHIP_SSFR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_SHIP_SSFR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.First ISCCP Regional Experiment (FIRE) Artic Cloud Experiment (ACE) Solar Spectral Flux Radiometer (SSFR) in NetCDF format.", "links": [ { diff --git a/datasets/FIRE_ACE_UTRECHT_BALLOON_1.json b/datasets/FIRE_ACE_UTRECHT_BALLOON_1.json index b58999fb4f..0afdf54afd 100644 --- a/datasets/FIRE_ACE_UTRECHT_BALLOON_1.json +++ b/datasets/FIRE_ACE_UTRECHT_BALLOON_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_UTRECHT_BALLOON_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. First ISCCP Regional Experiment - Arctic Cloud Experiment Utrecht University Tethered Balloon.", "links": [ { diff --git a/datasets/FIRE_ACE_UTRECHT_TOWER_1.json b/datasets/FIRE_ACE_UTRECHT_TOWER_1.json index efd9a0091f..4e27501494 100644 --- a/datasets/FIRE_ACE_UTRECHT_TOWER_1.json +++ b/datasets/FIRE_ACE_UTRECHT_TOWER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_UTRECHT_TOWER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_ACE_UWCV580_GMETER_1.json b/datasets/FIRE_ACE_UWCV580_GMETER_1.json index d6aa69dc3b..b045a84179 100644 --- a/datasets/FIRE_ACE_UWCV580_GMETER_1.json +++ b/datasets/FIRE_ACE_UWCV580_GMETER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_UWCV580_GMETER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of light scattering measurements provided by the 4-channel nephelometer g-meter instrument flown onboard the University of Washington's CV580 aircraft during the FIRE ACE field campaign.The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_ACE_UWCV580_UWA_1.json b/datasets/FIRE_ACE_UWCV580_UWA_1.json index 31ee49eed9..4a3d9e10b7 100644 --- a/datasets/FIRE_ACE_UWCV580_UWA_1.json +++ b/datasets/FIRE_ACE_UWCV580_UWA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ACE_UWCV580_UWA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of measurements provided by the University of Washington instruments flown onboard their CV580 aircraft during the FIR ACE/SHEBA field campaign.The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.", "links": [ { diff --git a/datasets/FIRE_AX_CMS_LWFLUX_1.json b/datasets/FIRE_AX_CMS_LWFLUX_1.json index 9721cec0eb..e5fb921b58 100644 --- a/datasets/FIRE_AX_CMS_LWFLUX_1.json +++ b/datasets/FIRE_AX_CMS_LWFLUX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_LWFLUX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are the calculated downward longwave flux at the surface derived from METEOSAT observations. The file naming convention is: raDDMMYYsxx.fis_tmpwhere DDMMYY is the date and xx = slot numberMean time (UT) is obtained from the slot number overthe ASTEX region by the formula: UT = (xx/2) - 0.17These files are: I2 pixels, 188 pixels/row, 163 rows. Each pixel has a spatial resolution of 0.08 degrees.The units of flux are Wm^-2.", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SOLAR_DY_1.json b/datasets/FIRE_AX_CMS_SOLAR_DY_1.json index 713cb195d7..8b531bf13e 100644 --- a/datasets/FIRE_AX_CMS_SOLAR_DY_1.json +++ b/datasets/FIRE_AX_CMS_SOLAR_DY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SOLAR_DY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution clouddata.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the daily solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esqDDMMYYx.fis where DDMMYY is the dateThese files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SOLAR_HR_1.json b/datasets/FIRE_AX_CMS_SOLAR_HR_1.json index 0410e53780..373a44224c 100644 --- a/datasets/FIRE_AX_CMS_SOLAR_HR_1.json +++ b/datasets/FIRE_AX_CMS_SOLAR_HR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SOLAR_HR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the hourly solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esDDMMYYsxx.fiswhere DDMMYY is the date and xx = slot numberMean time (UT) is obtained from the slot number overthe ASTEX region by the formula: UT = (xx/2) - 0.17These files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SOLAR_MN_1.json b/datasets/FIRE_AX_CMS_SOLAR_MN_1.json index 967553a5ab..5574c2c53b 100644 --- a/datasets/FIRE_AX_CMS_SOLAR_MN_1.json +++ b/datasets/FIRE_AX_CMS_SOLAR_MN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SOLAR_MN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the monthly solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esmxx.fis where xx is the month number of 1992.These files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SOLAR_WK_1.json b/datasets/FIRE_AX_CMS_SOLAR_WK_1.json index cfcb8d2460..3a6d667854 100644 --- a/datasets/FIRE_AX_CMS_SOLAR_WK_1.json +++ b/datasets/FIRE_AX_CMS_SOLAR_WK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SOLAR_WK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the monthly solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esmxx.fis where xx is the month number of 1992.These files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SST_DAY_1.json b/datasets/FIRE_AX_CMS_SST_DAY_1.json index d63753238d..d55db09e41 100644 --- a/datasets/FIRE_AX_CMS_SST_DAY_1.json +++ b/datasets/FIRE_AX_CMS_SST_DAY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SST_DAY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each missioncombined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are daily temperature fields from observations made by theNOAA 11 and 12 polar orbiting satellites.", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SST_FNTS_1.json b/datasets/FIRE_AX_CMS_SST_FNTS_1.json index 421e3e464d..0eca4dfe6d 100644 --- a/datasets/FIRE_AX_CMS_SST_FNTS_1.json +++ b/datasets/FIRE_AX_CMS_SST_FNTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SST_FNTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each missioncombined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are weekly front intensity fields from observations made by the NOAA 11 and 12 polar orbiting satellites. The file namingconvention is: fasteseXX.fiswhere XX = week number in 1992These files are: I1 pixels, 752 pixels/row, 652 rows. Each pixelhas a spatial resolution of 0.02 degrees.The units are: front intensity [degree C/5km]", "links": [ { diff --git a/datasets/FIRE_AX_CMS_SST_WEEK_1.json b/datasets/FIRE_AX_CMS_SST_WEEK_1.json index 4c7a6cb473..29b4849996 100644 --- a/datasets/FIRE_AX_CMS_SST_WEEK_1.json +++ b/datasets/FIRE_AX_CMS_SST_WEEK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CMS_SST_WEEK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are weekly temperature fields from observations made by the NOAA 11 and 12 polar orbiting satellites. The file naming convention is: thseXXYYZZ.fiswhere XX = week number in 1992 YY = jo (jour) day nu (nuit) night ma (matin) morning ZZ = 11 NOAA-11 12 NOAA-12These files are: I1 pixels, 752 pixels/row, 652 rows. Each pixel has a spatial resolution of 0.02 degrees.The units are: temperature [degree C] (offset by 10 deg.)", "links": [ { diff --git a/datasets/FIRE_AX_CSU_CEILOM_1.json b/datasets/FIRE_AX_CSU_CEILOM_1.json index bd9b80be62..649c2dfde5 100644 --- a/datasets/FIRE_AX_CSU_CEILOM_1.json +++ b/datasets/FIRE_AX_CSU_CEILOM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CSU_CEILOM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987) a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observationswith modeling studies to investigate the cloud properties and physical processes of the cloud system.The Belfort Laser Ceilometer was operated during FIRE ASTEX on Porto Santo, Madeira. It utilized a 20 watt near-infrared Gallium-Arsenide laser operating at a wavelength of 0.91 microns to detect cloud base height. It employed 1024 range gates which yield a vertical resolution of 25 feet up to a maximum range of 25,600 feet. The fields of view of the transmitter and receiver are approximately 1 degree.The ceilometer used a measured noise level to determine a count (-1,0,1) which is then summed for each gate. This histogram is the basic output from which the cloud base height is estimated.", "links": [ { diff --git a/datasets/FIRE_AX_CSU_MET_SFC_1.json b/datasets/FIRE_AX_CSU_MET_SFC_1.json index 549e65d579..77a1c54609 100644 --- a/datasets/FIRE_AX_CSU_MET_SFC_1.json +++ b/datasets/FIRE_AX_CSU_MET_SFC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CSU_MET_SFC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987) a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud system.The surface radiation and meteorological data collection employed data loggers from Campbell Scientific Inc. The Campbell Model 207 unit measured temperature and relative humidity using two sensors combined into one probe. Wind speed and direction were monitored using a propeller anemometer manufactured by R.M. Young. Irradiance (solar total and near infrared) were measured using an Eppley Pyranometer and infrared irradiance and dome and sink temperatures were measured using an Epply Pyrgeometer.", "links": [ { diff --git a/datasets/FIRE_AX_CSU_PRT6_1.json b/datasets/FIRE_AX_CSU_PRT6_1.json index 331732c25c..d17ed2eea4 100644 --- a/datasets/FIRE_AX_CSU_PRT6_1.json +++ b/datasets/FIRE_AX_CSU_PRT6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CSU_PRT6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basicunderstanding of the interaction of physical processes in determining lifecycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, andhigher space and time resolution cloud data.To-date, four intensive field-observation periods were planned andexecuted: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987) a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud system.The PRT-6 radiometer is a chopped bolometer which can passively senseinfrared targets within the spectral range of 2 to 20 microns. The radiometer is configured to accept optics with either a 2 or 20 degree field of view. The output is a voltage signal sampled at a frequency of 1/0.1 sec. The average of the sampled voltage was recorded every 10 seconds, which is nominally linear with respect to the incident radiant power.For the FIRE ASTEX deployment the instrument was configured with a field of view of 2 degrees and made use of an interference filter. This filter effectively limited the spectral bandpass to 885 to 945 inverse centimeters. Most of the measurements were made with the instrument pointing vertically upward, although for brief intervals zenith angles of 15, 30, 45, 60, and 75 degrees were also utilized.", "links": [ { diff --git a/datasets/FIRE_AX_CSU_WNDPRFS_1.json b/datasets/FIRE_AX_CSU_WNDPRFS_1.json index 44dbb10f8a..1193348011 100644 --- a/datasets/FIRE_AX_CSU_WNDPRFS_1.json +++ b/datasets/FIRE_AX_CSU_WNDPRFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_CSU_WNDPRFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987) a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud system.The CSU wind profiler is a five beam wind profiler with high and low modes of operation. The wind profiler is a clear air doppler radar and operates at a frequency of 404.37 MHz. It operated with a height resolution of 250m and measured radial velocities up to about 15km.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_BASIC_1.json b/datasets/FIRE_AX_ECMWF_BASIC_1.json index 1476d7a222..80fe19f9ea 100644 --- a/datasets/FIRE_AX_ECMWF_BASIC_1.json +++ b/datasets/FIRE_AX_ECMWF_BASIC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_BASIC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from Santa Maria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost none of the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing researchthat makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude and longitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are best compared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Units Field ID# range Abbrev.------------- ------ ----- ----- ----- -----BASIC Z 129 I Geopotential m^2/s^2 T 130 I Temperature K Q 133 I Specific humidity kg/kg U 131 I U[ eastward]-velocity m/s V 132 I V[northward]-velocity m/s W 135 I Vertical velocity Pa/s(lat/lon range: 85W to 15E, 70N to 10N)(levels: 1010,1000,975,950,925,900,875,850,825,800,775,750,700,650,600,550,500,400,300,200,100 hPa)The ECMWF field abbreviation, ID#, field description and units are taken directly from ECMWF Code Table 2, in case you ever need to consult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_CLOUDS_1.json b/datasets/FIRE_AX_ECMWF_CLOUDS_1.json index ef97de6b9c..aae81519b1 100644 --- a/datasets/FIRE_AX_ECMWF_CLOUDS_1.json +++ b/datasets/FIRE_AX_ECMWF_CLOUDS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_CLOUDS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from SantaMaria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost noneof the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing research that makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude and longitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are best compared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Units field ID# range abbrev.----------- ------ ----- ----- ----- -----CLOUDS CLW 212 I Cloud liquid water kg/kg CF 213 I Cloud fraction 0-1(lat/lon range: 35W to 05W, 20N to 45N)(levels: 1010,1000,975,950,925,900,875,850,825,800,775,750,700,650,600,550,500,400,300,200,100 HPa)The ECMWF field abbreviation, ID#, field description and units aretaken directly from ECMWF Code Table 2, in case you ever need toconsult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_DIAG_1.json b/datasets/FIRE_AX_ECMWF_DIAG_1.json index 3376e2e491..fc32ec3397 100644 --- a/datasets/FIRE_AX_ECMWF_DIAG_1.json +++ b/datasets/FIRE_AX_ECMWF_DIAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_DIAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from Santa Maria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost none of the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing researchthat makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude and longitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are best compared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Unitsfield ID# rangeabbrev.------------ ------ ----- ----- ----- -----DIAGNOSTIC DHR 214 A Diabatic heating by radiation K/sDHVD 215 A Diab. heat. by vert. diffusion K/sDHCC 216 A Diab. heat. by cu. convection K/sDHLC 217 A Diab. heat. by lg-scale condens.K/sVDZW 218 A Vert. diffusion of zonal wind m^2/s^3VDMW 219 A Vert. diffusion of meri. wind m^2/s^3EWGD 220 A E-W gravity wave drag m^2/s^3NSGD 221 A N-S gravity wave drag m^2/s^3CTZW 222 A Convective tend. of zonal wind m^2/s^3CTMW 223 A Convective tend. of meri. wind m^2/s^3VDH 224 A Vertical diffusion of humidity kg/(kg s)HTCC 225 A Humid. tend. by cu. convection kg/(kg s)HTLC 226 A Humid. tend. by lg-scale cond. kg/(kg s)ATT 228 A Adiabatic tend. of temperature K/sATH 229 A Adiabatic tend. of humidity kg/(kg s)ATZW 230 A Adiabatic tend. of zonal wind m/s^2ATMW 231 A Adiabatic tend. of meri. wind m/s^2232 A(lat/lon range: 35W to 05W, 20N to 45N)(levels: 1010,1000,975,950,925,900,875,850,825,800,775,750,700)The ECMWF field abbreviation, ID#, field description and units are taken directly from ECMWF Code Table 2, in case you ever need to consult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_MEANW_1.json b/datasets/FIRE_AX_ECMWF_MEANW_1.json index 8d16d34c5f..01bf1fbf28 100644 --- a/datasets/FIRE_AX_ECMWF_MEANW_1.json +++ b/datasets/FIRE_AX_ECMWF_MEANW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_MEANW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from Santa Maria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost none of the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing researchthat makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude and longitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are best compared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Units field ID# range abbrev.----------- ------ ----- ----- ----- -----MEANW MVV 232 A Mean vertical velocity Pa/s(lat/lon range: 85W to 15E, 70N to 10N)(levels: 1010,1000,975,950,925,900,875,850,825,800,775,750,700,650,600,550,500,400,300,200,100 hPa)The ECMWF field abbreviation, ID#, field description and units aretaken directly from ECMWF Code Table 2, in case you ever need toconsult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_SFDIAG_1.json b/datasets/FIRE_AX_ECMWF_SFDIAG_1.json index aef9d0e932..aecf2b94c8 100644 --- a/datasets/FIRE_AX_ECMWF_SFDIAG_1.json +++ b/datasets/FIRE_AX_ECMWF_SFDIAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_SFDIAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from Santa Maria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost none of the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing researchthat makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude and longitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are bestcompared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Units field ID# range abbrev.----------- ------ ----- ----- ----- -----SURFACE DIAG LSP 142 A Large scale precipitation m/(6 hr) CP 143 A Convective precipitation m/(6 hr) BLD 145 A Boundary layer dissipation W/m^2 SSHF 146 A Surface sensible heat flux W/m^2 SLHF 147 A Surface latent heat flux W/m^2 TCC 164 I Total cloud cover 0-1 10U 165 I 10 meter u m/s 10V 166 I 10 meter v m/s 2T 167 I 2 meter temperature K 2D 168 I 2 meter dewpoint temperature K SSR 176 A Surface solar radiation W/m^2 STR 177 A Surface thermal radiation W/m^2 TSR 178 A Top solar radiation W/m^2 TTR 179 A Top thermal radiation W/m^2 EWSS 180 A U-stress N/m^2 NSSS 181 A V-stress N/m^2 E 182 A Evaporation m (H2O) CCC 185 I Convective cloud cover 0-1 LCC 186 I Low cloud cover 0-1 MCC 187 I Medium cloud cover 0-1 HCC 188 I High cloud cover 0-1 TSRU 208 I Top solar radiation upward W/m^2 TTRU 209 I Top thermal radiation upward W/m^2 TSUC 210 I Top solar radiation upward clear sky(lat/lon range: 35W to 05W, 20N to 45N; at surface pressure)The ECMWF field abbreviation, ID#, field description and units are taken directly from ECMWF Code Table 2, in case you ever need to consult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ECMWF_SURFCE_1.json b/datasets/FIRE_AX_ECMWF_SURFCE_1.json index d7e0396af6..af161f66f1 100644 --- a/datasets/FIRE_AX_ECMWF_SURFCE_1.json +++ b/datasets/FIRE_AX_ECMWF_SURFCE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ECMWF_SURFCE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A special set of analysis products for the Atlantic Stratocumulus Transition Experiment (ASTEX) region during June 1-28, 1992 was prepared by Ernst Klinker and Tony Hollingsworth of the European Centre for Medium-range Forecasting (ECMWF), and reformatted by Chris Bretherton of Univ. of Washington. These analyses, or more correctly initializations and very short range forecasts using the ECMWF T213L30 operational model, incorporate routine observations from the global network and special soundings from ASTEX that were sent to ECMWFduring ASTEX via the GTS telecommunication system. About 650 special soundings were incorporated, including nearly all soundings from Santa Maria, Porto Santo, and the French ship Le Suroit, most of the soundings taken on the Valdivia and Malcolm Baldridge, and almost none of the soundings from the Oceanus. Surface reports from the research ships were also incorporated into the analyses after the first week of the experiment. Aircraft soundings were not included in the analyses. ECMWF has requested that anyone making use of this data set acknowledge them, and that those investigators publishing researchthat makes more than casual use of this data set contact Ernst Klinker or Tony Hollingsworth.The data have been decoded by Chris Bretherton into ASCII files, one for each horizontal field at a given level and base time. All data have the same horizontal resolution of 1.25 degrees in latitude andlongitude and correspond to base (initialization) times of 00, 06, 12, or 18Z. Different fields have different lat/lon ranges and sets of available vertical levels, as tabulated below. Also, some fields are instantaneous (I) while others are accumulated (A) over the first 6 hours of a forecast initialized at the base time. This is tabulated in the 'time range' column below. Instantaneous fields are bestcompared with data at the base time, while accumulated fields are best compared with data three hours after the base time.Data Set Name ECMWF ECMWF Time Field Units field ID# range abbrev.----------- ------ ----- ----- ----- -----SURFACE SP 134 I Surface pressure Pa ST 139 I [Sea] surface temperature K(lat/lon range: 85W to 15E, 70N to 10N; at surface pressure)The ECMWF field abbreviation, ID#, field description and units are taken directly from ECMWF Code Table 2, in case you ever need to consult with ECMWF about this data set.", "links": [ { diff --git a/datasets/FIRE_AX_ER2_LIDAR_1.json b/datasets/FIRE_AX_ER2_LIDAR_1.json index 2c7ff13648..1656f0d15a 100644 --- a/datasets/FIRE_AX_ER2_LIDAR_1.json +++ b/datasets/FIRE_AX_ER2_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ER2_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The development of parameterizations requires an understanding of the processes that generate, maintain, and dissipate boundary layer clouds. This development is currently impeded by lack of understanding of the transition from stratocumulus clouds to trade cumulus clouds and the factors that control cloud type and amount in the boundary layer. ASTEX was designed to address key issues related to stratocumulus to trade cumulus transition and mode selection. ASTEX involved intensive measurements from several platforms operating from June 1-28, 1992 in the area of the Azores and Madeira Islands. The purpose was to study how the transition and mode selection are effected by 1) cloud-top entrainment instability, 2) diurnal decoupling and clearing due to solar absorption, 3) patchy drizzle and a transition to horizontally inhomogeneous clouds through decoupling, 4) mesoscale variability in cloud thickness and associated mesoscale circulations, and 5) episodic strong subsidence lowering the inversion below the LCL. Detailed descriptions of the scientific goals of ASTEX are in the FIRE Phase II: Research plan (1989) and in the ASTEX Operations Plan (1992). The Cloud Lidar System (CLS) instrument was flown aboard the NASA ER-2 airplane. This instrument was used to determine cloud altitudes. Information pertaining to the number of cloud layers detected; the heights of the boundaries for up to 5 cloud layers; geo-physical location information; and time were recorded.Four channels of data were recorded. The first channel recorded wave lengths at 532 nanometers in the parallel plane. The second channel recorded wave lengths of 532 nanometers in the perpendicular plane. The third channel recorded wavelengths of 1064 nanometers total. The forth channel was a linear amplifier which received the digitized signal from one of the three previously mentioned CLS detectors. The data are organized so that there is a single header record for the file. This header record is followed by a series of pairs of records. The first record of each pair contains the CLS calibrated data and the second record of the pair contains the CLS analyzed data.", "links": [ { diff --git a/datasets/FIRE_AX_ER2_MAS_1.json b/datasets/FIRE_AX_ER2_MAS_1.json index c4468f38e9..187240d59d 100644 --- a/datasets/FIRE_AX_ER2_MAS_1.json +++ b/datasets/FIRE_AX_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The MODIS Airbourne Simulator (MAS) is a modified Daedalus Wildfire scanning spectrometer which flies on a NASA ER-2 and provides spectral information similar to that which will be provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), scheduled to be launched on the EOS-AM platform in 1998 (King et al. 1992). The principal investigators for the MAS are Dr. Michael King (NASA/GSFC, Greenbelt MD), and Dr. Paul Menzel (NOAA/NESDIS, Madison WI).In January 1992, the modified Wildfire instrument was converted to MAS configuration. In June 1992 the MAS was flown over portions of the Atlantic Ocean in the region of the Azores during the ASTEX experiment. Although the MAS instrument is a 50 band spectrometer, the data system used in this experiment could only record 12 channels (at 8-bit resolution). The MAS spectrometer acquires high spatial resolution imagery in the wavelength range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range, and the digitizer can be configured to collect data from any 12 of these bands. The digitizer was configured with four 10-bit channels and seven 8-bit channels. The MAS spectrometer was mated to a scanner subassembly which collected image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. The data granules were written using the self documenting file storage format provided through the netCDF interface routines included in the HDF libraries.", "links": [ { diff --git a/datasets/FIRE_AX_ERS1_ALTIMTR_1.json b/datasets/FIRE_AX_ERS1_ALTIMTR_1.json index dfc2d456e6..1da26e953f 100644 --- a/datasets/FIRE_AX_ERS1_ALTIMTR_1.json +++ b/datasets/FIRE_AX_ERS1_ALTIMTR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ERS1_ALTIMTR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_ERS1_MCRWRAD_1.json b/datasets/FIRE_AX_ERS1_MCRWRAD_1.json index 66dbc54d8f..e435b803e5 100644 --- a/datasets/FIRE_AX_ERS1_MCRWRAD_1.json +++ b/datasets/FIRE_AX_ERS1_MCRWRAD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ERS1_MCRWRAD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_ERS1_SCTRMTR_1.json b/datasets/FIRE_AX_ERS1_SCTRMTR_1.json index b541b2a8f4..e966240e4e 100644 --- a/datasets/FIRE_AX_ERS1_SCTRMTR_1.json +++ b/datasets/FIRE_AX_ERS1_SCTRMTR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ERS1_SCTRMTR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The wind scatterometer aboard ERS-1 scans a 300km wide zone, situated 300km right of the satellite track. Orbital data are given for each orbit (number 1 to 501), starting from the orbit node (10:30 solar time for the descending orbit at the equator). The complete cycle duration is 35 days. Data from the ASTEX domain have been extracted for June, 1992 from the fast delivery product tapes provided by ESA. The raw data have been processed by ESA, using an algorithm (CMOD2) which has has revealed to fail in a number of cases. Itresults in particular in erroneous wind direction (180deg ambiguit\\ y). These data thus cannot be used without a careful examination of their coherence.", "links": [ { diff --git a/datasets/FIRE_AX_ISCCP_DX_1.json b/datasets/FIRE_AX_ISCCP_DX_1.json index 9a35e6975d..82f250417e 100644 --- a/datasets/FIRE_AX_ISCCP_DX_1.json +++ b/datasets/FIRE_AX_ISCCP_DX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_ISCCP_DX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. A subset of the ISCCP Stage DX Cloud Product (Revised Algorithm) are included for the FIRE ASTEX region.", "links": [ { diff --git a/datasets/FIRE_AX_MAGE_OCN_AIR_1.json b/datasets/FIRE_AX_MAGE_OCN_AIR_1.json index e1e96ccbac..6480f5dd59 100644 --- a/datasets/FIRE_AX_MAGE_OCN_AIR_1.json +++ b/datasets/FIRE_AX_MAGE_OCN_AIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_MAGE_OCN_AIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The ASTEX/MAGE experiment is a multinational effort to improve our capability for studying cloud-chemistry interactions and the air/sea fluxes that affect them. The primary purpose of ASTEX (with which MAGE collaborated) was to study the factors influencing the formation and dissipation of marine clouds. The specific goals of the MAGE atmospheric chemistry experiment in ASTEX included:- Develop and test a Lagrangian strategy for studying chemical and meteorological evolution in a tagged airmass, using ships, balloons, and aircraft.- Develop and test new techniques for estimating trace-gas and aerosol fluxes across the air/sea interface by comparison with traditional approaches.- Evaluate the impact of marine and continental aerosols on the formation and dissipation of stratocumulus clouds.- Compare the impacts of natural and anthropogenic sulphur, halogens, and hydrocarbons on marine aerosol chemistry.- Gain experience with multi-national and multi-agency field experiments as a means for addressing global tropospheric chemistry issues.Data were derived directly from ion chromatograms recorded from samples collected on the ship and stored in liquid nitrogen for later analysis. Concentrations were calculated from the standard concentration and the peak height ratio of the standard and ambient isotopomers in the ion chromatograms. Uncertainties were estimated from a propagation of errors calculation which considers estimated error in the standard concentration and signal-to-noise derived error.", "links": [ { diff --git a/datasets/FIRE_AX_MAGE_OCN_MET_1.json b/datasets/FIRE_AX_MAGE_OCN_MET_1.json index 842727a505..ac37809b2b 100644 --- a/datasets/FIRE_AX_MAGE_OCN_MET_1.json +++ b/datasets/FIRE_AX_MAGE_OCN_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_MAGE_OCN_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The Atlantic Stratocumulus Transition Experiment (ASTEX)/Marine Aerosol Gas Exchange (MAGE) experiment is a multinational effort to improve our capability for studying cloud-chemistry interactions and the air/sea fluxes that affect them. The primary purpose of ASTEX (with which MAGE collaborated) was to study the factors influencing the formation and dissipation of marine clouds. The specific goals of the MAGE atmospheric chemistry experiment in ASTEX included:\r\n- Develop and test a Lagrangian strategy for studying chemical and meteorological evolution in a tagged airmass, using ships, balloons, and aircraft.\r\n- Develop and test new techniques for estimating trace-gas and aerosol fluxes across the air/sea interface by comparison with traditional approaches.\r\n- Evaluate the impact of marine and continental aerosols on the formation and dissipation of stratocumulus clouds.\r\n- Compare the impacts of natural and anthropogenic sulphur, halogens, and hydrocarbons on marine aerosol chemistry.\r\n- Gain experience with multi-national and multi-agency field experiments as a means for addressing global tropospheric chemistry issues.", "links": [ { diff --git a/datasets/FIRE_AX_MAGE_OCN_SEA_1.json b/datasets/FIRE_AX_MAGE_OCN_SEA_1.json index e7adb0bb0b..ac6cf6dc65 100644 --- a/datasets/FIRE_AX_MAGE_OCN_SEA_1.json +++ b/datasets/FIRE_AX_MAGE_OCN_SEA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_MAGE_OCN_SEA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The ASTEX/MAGE experiment is a multinational effort to improve our capability for studying cloud-chemistry interactions and the air/sea fluxes that affect them. The primary purpose of ASTEX (with which MAGE collaborated) was to study the factors influencing the formation and dissipation of marine clouds. The specific goals of the MAGE atmospheric chemistry experiment in ASTEX included:- Develop and test a Lagrangian strategy for studying chemical and meteorological evolution in a tagged airmass, using ships, balloons, and aircraft.- Develop and test new techniques for estimating trace-gas and aerosol fluxes across the air/sea interface by comparison with traditional approaches.- Evaluate the impact of marine and continental aerosols on the formation and dissipation of stratocumulus clouds.- Compare the impacts of natural and anthropogenic sulphur, halogens, and hydrocarbons on marine aerosol chemistry.- Gain experience with multi-national and multi-agency field experiments as a means for addressing global tropospheric chemistry issues.Data were derived directly from ion chromatograms recorded from the ship and stored in liquid nitrogen for later analysis. Concentrations were calculated from the standard concentrations and the peak height ratio of the standard and ambient isotopomers in the ion chromatograms.", "links": [ { diff --git a/datasets/FIRE_AX_MAGE_TETROON_1.json b/datasets/FIRE_AX_MAGE_TETROON_1.json index 8731ad318e..5e10e9c5ea 100644 --- a/datasets/FIRE_AX_MAGE_TETROON_1.json +++ b/datasets/FIRE_AX_MAGE_TETROON_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_MAGE_TETROON_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The ASTEX/MAGE experiment is a multinational effort to improve our capability for studying cloud-chemistry interactions and the air/sea fluxes that affect them. The primary purpose of ASTEX (with which MAGE collaborated) was to study the factors influencing the formation and dissipation of marine clouds. The specific goals of the MAGE atmospheric chemistry experiment in ASTEX included:- Develop and test a Lagrangian strategy for studying chemical and meteorological evolution in a tagged airmass, using ships, balloons, and aircraft.- Develop and test new techniques for estimating trace-gas and aerosol fluxes across the air/sea interface by comparison with traditional approaches.- Evaluate the impact of marine and continental aerosols on the formation and dissipation of stratocumulus clouds.- Compare the impacts of natural and anthropogenic sulphur, halogens, and hydrocarbons on marine aerosol chemistry.- Gain experience with multi-national and multi-agency field experiments as a means for addressing global tropospheric chemistry issues. The North Carolina State University tetroons were launched from the ship Oceanus in support of the FIRE-ASTEX observational program, conducted in the eastern North Atlantic during the month of June 1992. Special constant density balloons were launched and then tracked for 48 hours -- with the idea that they were tracking a single parcel of air. The parameter #sats gives the number of GPS satellites available for positioning. Four satellites are necessary to determine altitude, otherwise the last available altitude from four satellites is assumed to remain constant, so that the horizontal location can be triangulated from three satellites. An altitude is always given in the file, so care should be taken as to its use.Each tetroon attempted to fix its location once every 5 minutes of operation. Each tetroon was given an offset in transmission time during the 5 minute period in which to transmit its location in order to allow all tetroons to broadcast on the same frequency. The time at which a position fix was made and the relevant location information is provided for each tetroon. In the event no fix was possible due to the satellite constellation configuration, number or signal strength, no position was transmitted. Each position was retransmitted at 1/2 hour increments for seven hours to attempt to obtain the maximumamount of data, even for periods when aircraft were not in the area of transmission.", "links": [ { diff --git a/datasets/FIRE_AX_MALBAL_SONDE_1.json b/datasets/FIRE_AX_MALBAL_SONDE_1.json index 4466761387..fa79ad6a0c 100644 --- a/datasets/FIRE_AX_MALBAL_SONDE_1.json +++ b/datasets/FIRE_AX_MALBAL_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_MALBAL_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.Radiosonde data were collected during the FIRE ASTEX for time period June 7, 1992 through June 28, 1992 from the Malcolm Baldridge (ship).There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar.Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more that 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dew point, mixing ratio and wind speedand direction.", "links": [ { diff --git a/datasets/FIRE_AX_METEOSAT_1.json b/datasets/FIRE_AX_METEOSAT_1.json index 407cf2fee0..02a7071461 100644 --- a/datasets/FIRE_AX_METEOSAT_1.json +++ b/datasets/FIRE_AX_METEOSAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_METEOSAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. A subset of the METEOSAT data are included for the FIRE Atlantic Stratocumulus Transition Experiment (ASTEX) region.", "links": [ { diff --git a/datasets/FIRE_AX_OCEANS_SONDE_1.json b/datasets/FIRE_AX_OCEANS_SONDE_1.json index 469a926905..7f28d034d1 100644 --- a/datasets/FIRE_AX_OCEANS_SONDE_1.json +++ b/datasets/FIRE_AX_OCEANS_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_OCEANS_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. Radiosonde data were collected during the FIRE Atlantic Stratocumulus Transition Experiment (ASTEX) for time period June 3, 1992 through June 23, 1992 from the Oceanus (ship). There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar. Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more than 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dewpoint, mixing ratio and wind speed and direction.", "links": [ { diff --git a/datasets/FIRE_AX_PORSAN_SONDE_1.json b/datasets/FIRE_AX_PORSAN_SONDE_1.json index 643bbfc7b7..7a2d3e18f1 100644 --- a/datasets/FIRE_AX_PORSAN_SONDE_1.json +++ b/datasets/FIRE_AX_PORSAN_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PORSAN_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. Radiosonde data were collected during the FIRE Atlantic Stratocumulus Transition Experiment (ASTEX) for time period June 3, 1992 through June 23, 1992 from the Oceanus (ship). There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar. Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more than 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dew point, mixing ratio and wind speed and direction.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_CEIL_MAL_1.json b/datasets/FIRE_AX_PSU_CEIL_MAL_1.json index ad4ffc4456..6aecabe2ae 100644 --- a/datasets/FIRE_AX_PSU_CEIL_MAL_1.json +++ b/datasets/FIRE_AX_PSU_CEIL_MAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_CEIL_MAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_CEIL_SAN_1.json b/datasets/FIRE_AX_PSU_CEIL_SAN_1.json index 32f3b03612..25c9f97835 100644 --- a/datasets/FIRE_AX_PSU_CEIL_SAN_1.json +++ b/datasets/FIRE_AX_PSU_CEIL_SAN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_CEIL_SAN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_CEIL_VAL_1.json b/datasets/FIRE_AX_PSU_CEIL_VAL_1.json index 5fb07b2715..53aea6a436 100644 --- a/datasets/FIRE_AX_PSU_CEIL_VAL_1.json +++ b/datasets/FIRE_AX_PSU_CEIL_VAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_CEIL_VAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_H2OVAP_1.json b/datasets/FIRE_AX_PSU_H2OVAP_1.json index 5714e8bcbf..a5dfa7d234 100644 --- a/datasets/FIRE_AX_PSU_H2OVAP_1.json +++ b/datasets/FIRE_AX_PSU_H2OVAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_H2OVAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_MALBAL_1.json b/datasets/FIRE_AX_PSU_MALBAL_1.json index 953c1fa895..aec4e09713 100644 --- a/datasets/FIRE_AX_PSU_MALBAL_1.json +++ b/datasets/FIRE_AX_PSU_MALBAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_MALBAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_PSU_WND_MAL_1.json b/datasets/FIRE_AX_PSU_WND_MAL_1.json index 342513b703..b40992b00e 100644 --- a/datasets/FIRE_AX_PSU_WND_MAL_1.json +++ b/datasets/FIRE_AX_PSU_WND_MAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_PSU_WND_MAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.", "links": [ { diff --git a/datasets/FIRE_AX_RWSONDE_LVL1_1.json b/datasets/FIRE_AX_RWSONDE_LVL1_1.json index c971c7fe25..4cd5c15819 100644 --- a/datasets/FIRE_AX_RWSONDE_LVL1_1.json +++ b/datasets/FIRE_AX_RWSONDE_LVL1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_RWSONDE_LVL1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. During the period from June 1 to June 29, 1992, 203 soundings were obtained. At present two forms of data exist - Level I and Level II. Level I are the raw data produced in real time by the software of the Atmospheric Instrumentation Research (AIR) radiosonde system. These data are at irregular pressure and height levels. Level II data consist of processed thermodynamic and wind data at a uniform resolution of 10m, which essentially retains the highest possible vertical resolution in the original data. The Level II thermodynamic data seem to be reasonably free of errors; however, as mentioned in Schubert et. al., (1992) the wind data requires additional filtering before use.", "links": [ { diff --git a/datasets/FIRE_AX_RWSONDE_LVL2_1.json b/datasets/FIRE_AX_RWSONDE_LVL2_1.json index fd433004e3..d28d35f0b3 100644 --- a/datasets/FIRE_AX_RWSONDE_LVL2_1.json +++ b/datasets/FIRE_AX_RWSONDE_LVL2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_RWSONDE_LVL2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. During the period from June 1 to June 29, 1992, 203 soundings were obtained. At present two forms of data exist - Level I and Level II. Level I are the raw data produced in real time by the software of the Atmospheric Instrumentation Research (AIR) radiosonde system. These data are at irregular pressure and height levels. Level II data consist of processed thermodynamic and wind data at a uniform resolution of 10m, which essentially retains the highest possible vertical resolution in the original data. The Level II thermodynamic data seem to be reasonably free of errors; however, as mentioned in Schubert et. al., (1992) the wind data requires additional filtering before use.", "links": [ { diff --git a/datasets/FIRE_AX_SANMAR_SONDE_1.json b/datasets/FIRE_AX_SANMAR_SONDE_1.json index 9f87006b6a..cef3590363 100644 --- a/datasets/FIRE_AX_SANMAR_SONDE_1.json +++ b/datasets/FIRE_AX_SANMAR_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SANMAR_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.Radiosonde data were collected during the FIRE ASTEX for time period June 1, 1992 through June 28, 1992 from the Santa Maria. There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar.Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more that 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dew point, mixing ratio and wind speedand direction.", "links": [ { diff --git a/datasets/FIRE_AX_SFC_FUNCHAL_1.json b/datasets/FIRE_AX_SFC_FUNCHAL_1.json index bd95de1137..0d02f748e5 100644 --- a/datasets/FIRE_AX_SFC_FUNCHAL_1.json +++ b/datasets/FIRE_AX_SFC_FUNCHAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SFC_FUNCHAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set contains sounding measurements taken in Funchal, Madeiras during June, 1987-1992. Six files contain 00Z data and five files contain 12Z data.", "links": [ { diff --git a/datasets/FIRE_AX_SFC_IMAU_1.json b/datasets/FIRE_AX_SFC_IMAU_1.json index 7ded9088d2..57e64c29db 100644 --- a/datasets/FIRE_AX_SFC_IMAU_1.json +++ b/datasets/FIRE_AX_SFC_IMAU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SFC_IMAU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These data were collected by the University of Utrecht (The Netherlands) during ASTEX experimental campaign, June 1992, at the surface site of Santa Maria (36.99 N; 25.17W; ASL=50M).Every file contains the following variables:-time (UTC): Universal Time Coordinated time.Data were taken every 2 minutes.-T6(C): Temperature at 6 meters.Accuracy of the temperature sensor 0.2 C-T2(C): Temperature at 2 meters.Accuracy of the temperature sensor 0.2 C-rh6(%): Relative humidity at 6 meters.Accuracy of the relative humidity sensor 2 %.Above 90% the measurements are less accurate.Highest value measured by the sensor: 95%.-rh2(%): Relative humidity at 2 meters. Accuracy of the relative humidity sensor 2 %.Above 90% the measurements lose accuracy.Highest value measured by the sensor: 99%.-ff6(m/s) Wind speed at 6 meters.Accuracy of the sensor 0.2 m/s.-dd(deg) Wind direction at 6 meters.Accuracy of the sensor 4 deg.-fsin(W/m2) Incoming shortwave radiation at 1.5 meters.Pyranometer measures the irradiance between 305 to 2800 nm with a precision 2 W/m2.-fsou(W/m2) Outcoming shortwave radiation at 1.5 meters.Pyranometer measures the irradiance between 305 to 2800 nm with a precision 2 W/m2.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_ARAT_FLT_1.json b/datasets/FIRE_AX_SOF_ARAT_FLT_1.json index 8ee78d6b5e..cbfac5b23b 100644 --- a/datasets/FIRE_AX_SOF_ARAT_FLT_1.json +++ b/datasets/FIRE_AX_SOF_ARAT_FLT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_ARAT_FLT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse. The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic and atmospheric structures responsible for spatial inhomogeneity of fluxes. The FOKKER F27 aircraft with flux measurement package and the airborne Lidar Leandre was used during ASTEX. The FOKKER 27 ARAT capabilities were as follows: * Turbulence measurements of wind, temperature and moisture. Fast response sensors located on a nose boom 5m long, which measured - attack and sideslip angles by mobile vanes and by a five hole probe (Rosemound 858). - true airspeed by a Pitot probe - temperature by a fast response INSU probe - humidity by a Lyman-alpha humidity meter * Mean state sensors - Rosemount temperature probe - Reverse-flow temperature probe - General Eastern dew point sensor * Aerosols and cloud microphysics - 1-D drop size measurements from 0-6000 microns by four Knollenberg sensors - 2-D sensor OAP 2DC for drop sizes between 25 and 800 microns * Liquid water content - Johnson-Williams sensors * Radiative measurements, up- and downward - Longwave (14-40 microns) Eppley radiometers - Shortwave (0.2-2.8 microns) Eppley radiometers - Radiances (7.8-14 microns) Barnes PRT5 radiometers * Chemical measurements(isokinetic veins) * Pointint backscatter lidar (Leandre) * Directional reflectances meausrements (POLDER- Polarized Direct Reflectance)", "links": [ { diff --git a/datasets/FIRE_AX_SOF_ARAT_TRB_1.json b/datasets/FIRE_AX_SOF_ARAT_TRB_1.json index 6424574c96..0c3bdabbc0 100644 --- a/datasets/FIRE_AX_SOF_ARAT_TRB_1.json +++ b/datasets/FIRE_AX_SOF_ARAT_TRB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_ARAT_TRB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse. The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic and atmospheric structures responsible for spatial inhomogeneity of fluxes. The FOKKER F27 aircraft with flux measurement package and the airborne Lidar Leandre was used during ASTEX. The FOKKER 27 ARAT capabilities were as follows: * Turbulence measurements of wind, temperature and moisture. Fast response sensors located on a nose boom 5m long, which measured - attack and sideslip angles by mobile vanes and by a five hole probe (Rosemound 858). - true airspeed by a Pitot probe - temperature by a fast response INSU probe - humidity by a Lyman-alpha humidity meter * Mean state sensors - Rosemount temperature probe - Reverse-flow temperature probe - General Eastern dew point sensor * Aerosols and cloud microphysics - 1-D drop size measurements from 0-6000 microns by four Knollenberg sensors - 2-D sensor OAP 2DC for drop sizes between 25 and 800 microns * Liquid water content - Johnson-Williams sensors * Radiative measurements, up- and downward - Longwave (14-40 microns) Eppley radiometers - Shortwave (0.2-2.8 microns) Eppley radiometers - Radiances (7.8-14 microns) Barnes PRT5 radiometers * Chemical measurements(isokinetic veins) * Pointint backscatter lidar (Leandre) * Directional reflectances meausrements (POLDER- Polarized Direct Reflectance)", "links": [ { diff --git a/datasets/FIRE_AX_SOF_BUOY_DFT_1.json b/datasets/FIRE_AX_SOF_BUOY_DFT_1.json index 08ab13b8b6..cca6f6b805 100644 --- a/datasets/FIRE_AX_SOF_BUOY_DFT_1.json +++ b/datasets/FIRE_AX_SOF_BUOY_DFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_BUOY_DFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.Five drifting buoys (CMM) with bathymetric chains (100 m) provided surface measurements of sea surface temperature, pressure and wind.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_BUOY_SPR_1.json b/datasets/FIRE_AX_SOF_BUOY_SPR_1.json index 8a6b22c1d6..447bc5b63c 100644 --- a/datasets/FIRE_AX_SOF_BUOY_SPR_1.json +++ b/datasets/FIRE_AX_SOF_BUOY_SPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_BUOY_SPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse. The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic and atmospheric structures responsible for spatial inhomogeneity of fluxes.A wave buoy (IFREMER) was used to obtain the wave spectrum (not directional measurements). This buoy was drogued to have a slow speed displacement.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_PTU_1.json b/datasets/FIRE_AX_SOF_PTU_1.json index aa52986bfa..41d093c0f5 100644 --- a/datasets/FIRE_AX_SOF_PTU_1.json +++ b/datasets/FIRE_AX_SOF_PTU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_PTU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.This data set contains radiosounding measurements of pressure, temperature and humidity at selected points (B) and radiosounding measurements of wind at selected points (C).", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_BUCK_1.json b/datasets/FIRE_AX_SOF_SUR_BUCK_1.json index 5af0d28c88..63d2234df8 100644 --- a/datasets/FIRE_AX_SOF_SUR_BUCK_1.json +++ b/datasets/FIRE_AX_SOF_SUR_BUCK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_BUCK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.The data provided were collected via a trailing thermistor with bucketmeasurements. The thermistor data have been calibrated but not quality controlled.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_DRAK_1.json b/datasets/FIRE_AX_SOF_SUR_DRAK_1.json index 645fd21821..05026296b7 100644 --- a/datasets/FIRE_AX_SOF_SUR_DRAK_1.json +++ b/datasets/FIRE_AX_SOF_SUR_DRAK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_DRAK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.Data were collected using a DRAKKAR, an upward pointing, two channel microwave radiometer. Its channels are 23.8 and 36.5 GHz, and the antenna aperture is about 15 degrees. It was developed at CRPE based upon the the ATSR/M (ERS-1/MWR) design. Its basic sampling was 0.5 seconds during ASTEX. Calibration was performed prior to the campaign and verified using a cold load on June 12, 1995, and verified again after return to France.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_HYD_1.json b/datasets/FIRE_AX_SOF_SUR_HYD_1.json index 6622f9dcc0..1f19e2bc51 100644 --- a/datasets/FIRE_AX_SOF_SUR_HYD_1.json +++ b/datasets/FIRE_AX_SOF_SUR_HYD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_HYD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.The parameters of this dataset were derived from spectral ambient noise at 19 kHz. Underwater sound was measured by a hydrophone hanging to a small buoy, at a few kilometers from the ship Le Suroit during June 1992.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_MET_1.json b/datasets/FIRE_AX_SOF_SUR_MET_1.json index 24a801c4b2..c4df9b2b7a 100644 --- a/datasets/FIRE_AX_SOF_SUR_MET_1.json +++ b/datasets/FIRE_AX_SOF_SUR_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_RAD_1.json b/datasets/FIRE_AX_SOF_SUR_RAD_1.json index e9caeb2b98..b68b3eb14f 100644 --- a/datasets/FIRE_AX_SOF_SUR_RAD_1.json +++ b/datasets/FIRE_AX_SOF_SUR_RAD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_RAD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.This data set contains the radiation and pressure measurements collected on Le Suroit during Astex.", "links": [ { diff --git a/datasets/FIRE_AX_SOF_SUR_TBAL_1.json b/datasets/FIRE_AX_SOF_SUR_TBAL_1.json index 98f3792958..90c627189e 100644 --- a/datasets/FIRE_AX_SOF_SUR_TBAL_1.json +++ b/datasets/FIRE_AX_SOF_SUR_TBAL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SOF_SUR_TBAL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SOFIA (Surface of the Ocean, Fluxes and Interaction with the Atmosphere) is a research program carried out by French groups from the Centre de Recherches en Physique de l'Environnement (CRPE), Laboratoire l'Aerologie (LA)-Toulouse, Centre de Meteorologie Marine (CMM)-Brest, Institut Francais de Rechercher sur la Mer (IFREMER)-Brest, Service d'Aeronomie-Paris, and Laboratoire de Meteorologie Dynamique (LMD)-Palaiseau with cooperation from Centre National de Recherche Meteorologique (CNRM)-Toulouse.The scientific objective of SOFIA during ASTEX was the study of energy transfer (heat, humidity and momentum fluxes) between the sea surface and the atmospheric boundary layer at scales ranging from the local scale to the mesoscale (50 km). The general concept of the program was to develop a measurement strategy based on nested boxes in which instrumentation would be used to estimate and quantify fluxes. These instruments, from which flux estimates at different scales would be measured, were used in connection with satellite measurements to understand and, hence, to validate the satellite integration of fluxes, particularly in the presence of mesoscale oceanic andatmospheric structures responsible for spatial inhomogeneity of fluxes.", "links": [ { diff --git a/datasets/FIRE_AX_SUROIT_SONDE_1.json b/datasets/FIRE_AX_SUROIT_SONDE_1.json index 5f4ad1a0e4..74b6262181 100644 --- a/datasets/FIRE_AX_SUROIT_SONDE_1.json +++ b/datasets/FIRE_AX_SUROIT_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_SUROIT_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.Radiosonde data were collected during the FIRE ASTEX for time period June 1, 1992 through June 20, 1992 from the Le Suroit (ship). There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar.Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more that 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dew point, mixing ratio and wind speedand direction.", "links": [ { diff --git a/datasets/FIRE_AX_UKMO_C130_1.json b/datasets/FIRE_AX_UKMO_C130_1.json index 527860a5c7..a2d16341b7 100644 --- a/datasets/FIRE_AX_UKMO_C130_1.json +++ b/datasets/FIRE_AX_UKMO_C130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_UKMO_C130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. These data were collected by the United Kingdom Meteorological Office (UKMO) from the Meteorological Research Flight C-130 Aircraft. This data set is a 1 to 64 Hertz time series data set.", "links": [ { diff --git a/datasets/FIRE_AX_UW_C131A_1.json b/datasets/FIRE_AX_UW_C131A_1.json index 4f42e757f3..f5bb4de69b 100644 --- a/datasets/FIRE_AX_UW_C131A_1.json +++ b/datasets/FIRE_AX_UW_C131A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_UW_C131A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To date, four intensive field observation (IFO) periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The development of parameterizations requires an understanding of the processes that generate, maintain, and dissipate boundary layer clouds. This development is currently impeded by lack of understanding of the transition from stratocumulus clouds to trade cumulus clouds and the factors that control cloud type and amount in the boundary layer. The Atlantic Stratocumulus Transition EXperiment (ASTEX) was designed to address key issues related to stratocumulus to trade cumulus transition and mode selection. ASTEX involved intensive measurements from several platforms operating from 1-28 June 1992 in the area of the Azores and Madeira Islands. The purpose was to study how the transition and mode selection are effected by 1) cloud-top entrainment instability, 2) diurnal decoupling and clearing due to solar absorption, 3) patchy drizzle and a transition to horizontally inhomogeneous clouds through decoupling, 4) mesoscale variability in cloud thickness and associated mesoscale circulations, and 5) episodic strong subsidence lowering the inversion below the LCL. Detailed descriptions of the scientific goals of ASTEX are in the FIRE Phase II: Research plan (1989) and in the ASTEX Operations Plan (1992). The University of Washington Convair data are best considered raw at this point and should be validated by comparing with data collected from other platforms where possible if high accuracy is desired. Of the three measures of liquid water content available from the Convair, the Johnson-Williams (JW) hot-wire probe is considered the most readily usable, although there is a significant drift in the output that should be accounted for. The Forward Scattering Spectrometer Probe (FSSP) measured the liquid water content using optical scattering principles.", "links": [ { diff --git a/datasets/FIRE_AX_UW_DSCRT_1.json b/datasets/FIRE_AX_UW_DSCRT_1.json index 5944f61a85..b09cf023bb 100644 --- a/datasets/FIRE_AX_UW_DSCRT_1.json +++ b/datasets/FIRE_AX_UW_DSCRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_UW_DSCRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The development of parameterizations requires an understanding of the processes that generate, maintain, and dissipate boundary layer clouds. This development is currently impeded by lack of understanding of the transition from stratocumulus clouds to trade cumulus clouds and the factors that control cloud type and amount in the boundary layer. The Atlantic Stratocumulus Transition EXperiment (ASTEX) was designed to address key issues related to stratocumulus to trade cumulus transition and mode selection. ASTEX involved intensive measurements from several platforms operating from 1-28 June 1992 in the area of the Azores and Madeira Islands. The purpose was to study how the transition and mode selection are effected by 1) cloud-top entrainment instability, 2) diurnal decoupling and clearing due to solar absorption, 3) patchy drizzle and a transition to horizontally inhomogeneous clouds through decoupling, 4) mesoscale variability in cloud thickness and associated mesoscale circulations, and 5) episodic strong subsidence lowering the inversion below the LCL. Detailed descriptions of the scientific goals of ASTEX are in the FIRE Phase II: Research plan (1989) and in the ASTEX Operations Plan (1992).This ASCII formatted data set includes data collected aboard the University of Washington's Corsair 131A airplane. Several different probes were used to gather data on the liquid water content of clouds, the droplet radius/diameter, and condensation nuclei measurements. All sulfur parameter measurements were made using filter methods.", "links": [ { diff --git a/datasets/FIRE_AX_UW_GERB_10HZ_1.json b/datasets/FIRE_AX_UW_GERB_10HZ_1.json index d6721acf2d..00fe9cbffd 100644 --- a/datasets/FIRE_AX_UW_GERB_10HZ_1.json +++ b/datasets/FIRE_AX_UW_GERB_10HZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_UW_GERB_10HZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The development of parameterizations requires an understanding of the processes that generate, maintain, and dissipate boundary layer clouds. This development is currently impeded by lack of understanding of the transition from stratocumulus clouds to trade cumulus clouds and the factors that control cloud type and amount in the boundary layer. The Atlantic Stratocumulus Transition EXperiment (ASTEX) was designed to address key issues related to stratocumulus to trade cumulus transition and mode selection. ASTEX involved intensive measurements from several platforms operating from 1-28 June 1992 in the area of the Azores and Madeira Islands. The purpose was to study how the transition and mode selection are effected by 1) cloud-top entrainment instability, 2) diurnal decoupling and clearing due to solar absorption, 3) patchy drizzle and a transition to horizontally inhomogeneous clouds through decoupling, 4) mesoscale variability in cloud thickness and associated mesoscale circulations, and 5) episodic strong subsidence lowering the inversion below the LCL. Detailed descriptions of the scientific goals of ASTEX are in the FIRE Phase II: Research plan (1989) and in the ASTEX Operations Plan (1992).This ASCII formatted data set includes data collected aboard the University of Washington's Corsair 131A airplane. The cloud microphysics probe (PVM-100A) was used to gather data on cloud liquid water content, particle surface area, and effective droplet radius. Please refer to the reference authored by H. Gerber to obtain information on how the raw data were reduced to produce this data set.", "links": [ { diff --git a/datasets/FIRE_AX_UW_GERB_1HZ_1.json b/datasets/FIRE_AX_UW_GERB_1HZ_1.json index 7e046242fb..25a80f48f1 100644 --- a/datasets/FIRE_AX_UW_GERB_1HZ_1.json +++ b/datasets/FIRE_AX_UW_GERB_1HZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_UW_GERB_1HZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The development of parameterizations requires an understanding of the processes that generate, maintain, and dissipate boundary layer clouds. This development is currently impeded by lack of understanding of the transition from stratocumulus clouds to trade cumulus clouds and the factors that control cloud type and amount in the boundary layer. The Atlantic Stratocumulus Transition EXperiment (ASTEX) was designed to address key issues related to stratocumulus to trade cumulus transition and mode selection. ASTEX involved intensive measurements from several platforms operating from 1-28 June 1992 in the area of the Azores and Madeira Islands. The purpose was to study how the transition and mode selection are effected by 1) cloud-top entrainment instability, 2) diurnal decoupling and clearing due to solar absorption, 3) patchy drizzle and a transition to horizontally inhomogeneous clouds through decoupling, 4) mesoscale variability in cloud thickness and associated mesoscale circulations, and 5) episodic strong subsidence lowering the inversion below the LCL. Detailed descriptions of the scientific goals of ASTEX are in the FIRE Phase II: Research plan (1989) and in the ASTEX Operations Plan (1992).This ASCII formatted data set includes data collected aboard the University of Washington's Corsair 131A airplane. The cloud microphysics probe (PVM-100A) was used to gather data on cloud liquid water content, particle surface area, and effective droplet radius. Please refer to the reference authored by H. Gerber to obtain information on how the raw data were reduced to produce this data set.", "links": [ { diff --git a/datasets/FIRE_AX_VALDIV_SONDE_1.json b/datasets/FIRE_AX_VALDIV_SONDE_1.json index 9fc66c9f05..9d8290bdfb 100644 --- a/datasets/FIRE_AX_VALDIV_SONDE_1.json +++ b/datasets/FIRE_AX_VALDIV_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_AX_VALDIV_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.Radiosonde data were collected during the FIRE ASTEX for time period May 28, 1992 through June 22, 1992 from the Valdivia (ship). There are 3 sets of interpolated sounding data. They are 5-second, 20-meter, and 2-millibar.Each file contains a 5-line header. The first line is the site name (up to 16 characters), the next line is the latitude and longitude at the time of launch, the third contains the date-time group at launch in YYMMDDHHMM format. Lines 4 and 5 describe the data to follow, which comprise no more that 1500 additional lines. The data are: minutes, seconds past launch, ascent rate, height, pressure, temperature, relative humidity, dew point, mixing ratio and wind speedand direction.", "links": [ { diff --git a/datasets/FIRE_CI1_CSU_SABRE_1.json b/datasets/FIRE_CI1_CSU_SABRE_1.json index b83bea15ee..14832cafa8 100644 --- a/datasets/FIRE_CI1_CSU_SABRE_1.json +++ b/datasets/FIRE_CI1_CSU_SABRE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_CSU_SABRE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological and radiometric data from the National Center for Atmospheric Research (NCAR) Sabreliner aircraft that was collected during the 1986 First ISCCP Regional Experiment (FIRE) Cirrus Intensive Field-Observation (IFO). The NCAR Sabreliner research aircraft is a Rockwell International Sabreliner Model 60 aircraft, a low-wind twin-jet monoplane. The NCAR instrumentation that measured the data described above consisted of: \r\n1. Aircraft Position, Velocity and Attitude -- Litton LTN-51 INS (Inertial Navigation System) \r\n2. Static Pressure -- Rosemount Model 1201F1 Pressure Transducer (Fuselage Port) \r\n3. Temperatures -- Rosemount Type 102 Non-dieced and Dieced Sensors (with Rosemount Model 510BH Amplifiers) \r\n4. Dew Point and Humidity -- EG&G Model 137-C3 Dew Point Hygrometers -- NCAR Model LA-3 Lyman-alpha Hygrometer \r\n5. Flow Angle Sensors -- Rosemount Model 858 Gust Probe -- Rosemount Model 1221FVL Differential Pressure Transducer \r\n6. Cloud Physics -- Rosemount 871A Icing Rate Detector \r\n7. Radiation Irradiances -- Shortwave Radiation (.3 - 2.8 microns): Research Aviation Facility (RAF) Modified Epply Model PSP Pyranometers -- Near Infrared Radiation (.7 - 2.8 microns): RAF Modified Epply Model Precision Spectral Pyranometer (PSP) Pyranometers -- Infrared Radiation (4 - 50 microns): RAF Modified Epply Model Precision Infrared Radiometer (PIR) Pyrgeometers", "links": [ { diff --git a/datasets/FIRE_CI1_ER2_LIDAR_1.json b/datasets/FIRE_CI1_ER2_LIDAR_1.json index ff112d5a85..52131e18ee 100644 --- a/datasets/FIRE_CI1_ER2_LIDAR_1.json +++ b/datasets/FIRE_CI1_ER2_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_ER2_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. This data set contains cloud top height and ground height calculations from the NASA ER-2 Cloud LIDAR System (CLS) during the Wisconsin FIRE experiment in October, 1986.", "links": [ { diff --git a/datasets/FIRE_CI1_ER2_RAD_1.json b/datasets/FIRE_CI1_ER2_RAD_1.json index 2470c70324..f2ac55f6f1 100644 --- a/datasets/FIRE_CI1_ER2_RAD_1.json +++ b/datasets/FIRE_CI1_ER2_RAD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_ER2_RAD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Infrared radiation measurements from NASA ER-2 aircraft-based instruments during the FIRE Cirrus IFO, October/November 1986. 1) Narrow field-of-view nadir radiances and brightness temperatures, 6.6 and 10.4 um wavelength channels; 2) upwelling and downwelling hemispherical broadband solar fluxes; 3) net upwelling hemispherical fluxes, broadband thermal infrared.", "links": [ { diff --git a/datasets/FIRE_CI1_ISCCP_DX_1.json b/datasets/FIRE_CI1_ISCCP_DX_1.json index e2b38e4ce4..c45939926d 100644 --- a/datasets/FIRE_CI1_ISCCP_DX_1.json +++ b/datasets/FIRE_CI1_ISCCP_DX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_ISCCP_DX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. A subset of the ISCCP Stage DX Cloud Product (Revised Algorithm) are included for the FIRE Cirrus 1 region.", "links": [ { diff --git a/datasets/FIRE_CI1_KINGAIR_1.json b/datasets/FIRE_CI1_KINGAIR_1.json index e2ad717082..5bfd49404e 100644 --- a/datasets/FIRE_CI1_KINGAIR_1.json +++ b/datasets/FIRE_CI1_KINGAIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_KINGAIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. Cirrus IFO-I was conducted from October 13 to November 2, 1986 in central Wisconsin. The NCAR King Air aircraft measured radiation andmicrophysical properties of the cloud layers, in addition to temperature, moisture, and air motions.", "links": [ { diff --git a/datasets/FIRE_CI1_LARC8_LIDAR_1.json b/datasets/FIRE_CI1_LARC8_LIDAR_1.json index 6dc6d3e002..9127756145 100644 --- a/datasets/FIRE_CI1_LARC8_LIDAR_1.json +++ b/datasets/FIRE_CI1_LARC8_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_LARC8_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Langley Research Center (LARC) Cloud Lidar is a dual-channel polarization sensitive lidar using a frequency doubled Nd: YAG laser as a linearly polarized transmitter and an eight inch Cassegrainian telescope as a receiver. Backscattered laser light collected by the receiver is collimated, directed through a half wave plate, and then passed through polarizing optics which decompose the signal into two components, one parallel and the other perpendicular to the polarization plane of the transmitted beam. Separate amplification and digitization paths are employed for each component, resulting in two arrays of back scatter data for each measured laser pulse. The LARC Cloud Lidar is designed for optimum cloud monitoring operations at altitudes between 3 km and 18 km. To prevent saturation of the detectors at lower altitudes, a gating circuit is used to delay the activation of the first dynode in the Photomultiplier (PMT). The PMT is brought to full sensitivity only after this delay time has elapsed.", "links": [ { diff --git a/datasets/FIRE_CI1_RAWINSONDES_1.json b/datasets/FIRE_CI1_RAWINSONDES_1.json index 8e471c0d8e..173b712d13 100644 --- a/datasets/FIRE_CI1_RAWINSONDES_1.json +++ b/datasets/FIRE_CI1_RAWINSONDES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_RAWINSONDES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Rawinsonde data for the 1986 FIRE Cirrus IFO. Includes data from seven (7) National Weather Service stations at Green Bay, WI (72645); St. Cloud (72655) and International Falls (72747), MN; Peoria, IL (72532); Omaha, NE (72553); and Flint (72637) and Sault Ste. Marie (72734), MI and three special stations located at Plattville (100), Fort McCoy (200) and Wausau (300), WI.", "links": [ { diff --git a/datasets/FIRE_CI1_SABRELINER_1.json b/datasets/FIRE_CI1_SABRELINER_1.json index 43a9a519c3..a90fa9bf8a 100644 --- a/datasets/FIRE_CI1_SABRELINER_1.json +++ b/datasets/FIRE_CI1_SABRELINER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SABRELINER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Cirrus IFO-I was conducted from October 13 to November 2, 1986 in central Wisconsin. The NCAR Sabreliner aircraft measured radiation and microphysical properties of the cloud layers, in addition to temperature, moisture, and air motions.", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_ALASKA_1.json b/datasets/FIRE_CI1_SRB_ALASKA_1.json index bbb62521d4..83491ac195 100644 --- a/datasets/FIRE_CI1_SRB_ALASKA_1.json +++ b/datasets/FIRE_CI1_SRB_ALASKA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_ALASKA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. Results from ISCCP analysis of B3 radiance data (sampled to 25 km). Unlike the standard ISCCP product, these data are reported at original pixel resolution and contain detailed information about the algorithm decision. Data covers the region from: 55 degrees North to 90 degrees North (+55 to +90), 175 degrees West to 135 degrees West (-175 to -135). (Polar projection covers 55S - 90S or 55N - 90N, maximum; Midlatitude maps cover 55S to 55N, maximum.)Spatially sampled imaging data. Nominal spatial resolution is 25 km. Pixel field of view is 4 km (NOAA data). Earth location (latitude +/- 90 degrees, longitude +/- 180 degrees) is obtained for each pixel from ancillary data.", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_CANADA_1.json b/datasets/FIRE_CI1_SRB_CANADA_1.json index 102921418a..3745bfdddc 100644 --- a/datasets/FIRE_CI1_SRB_CANADA_1.json +++ b/datasets/FIRE_CI1_SRB_CANADA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_CANADA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. Results from ISCCP analysis of B3 radiance data (sampled to 25 km). Unlike the standard ISCCP product, these data are reported at original pixel resolution and contain detailed information about the algorithm decision. Data covers the region from: 40 degrees North to 90 degrees North (+40 to +90), 70 degrees West to 110 degrees West (-70 to -110). (Polar projection covers 55S - 90S or 55N - 90N, maximum; Midlatitude maps cover 55S to 55N, maximum.)Spatially sampled imaging data. Nominal spatial resolution is 25 km. Pixel field of view is 4 km (NOAA data). Earth location (latitude +/- 90 degrees, longitude +/- 180 degrees) is obtained for each pixel from ancillary data.", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_LW_1.json b/datasets/FIRE_CI1_SRB_LW_1.json index 0a1fe33f35..0a05227a4c 100644 --- a/datasets/FIRE_CI1_SRB_LW_1.json +++ b/datasets/FIRE_CI1_SRB_LW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_LW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data contain down-welled global longwave hemispherical radiation taken during the Wisconsin FIRE/SRB experiment. The data set consists of measurements taken every minute for 5 ground stations during each 24 hour day. The data include values from October 12 through November 2, 1986.The number, location, latitude, and longitude of the FIRE/SRB WISCONSIN sites are:NUMBER LOCATION LATITUDE(DEG N) LONGITUDE(DEG W)1 FT. MCCOY 43.96 90.762 STEVENS POINT 44.55 89.533 BARABOO 43.52 89.774 ADAMS COUNTY 43.97 89.805 WAUTOMA 44.04 89.30", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_SO_POLE_1.json b/datasets/FIRE_CI1_SRB_SO_POLE_1.json index ba138b0939..cf4e31fcc9 100644 --- a/datasets/FIRE_CI1_SRB_SO_POLE_1.json +++ b/datasets/FIRE_CI1_SRB_SO_POLE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_SO_POLE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Results from ISCCP analysis of B3 radiance data (sampled to 25 km). Unlike the standard ISCCP product, these data are reported at original pixel resolution and contain detailed information about the algorithm decision.Data covers the region from: 55 degrees South to 90 degrees South (-55 to -90), 180 degrees West to 180 degrees East (-180 to +180). (Polar projection covers 55S - 90S or 55N - 90N, maximum; Midlatitude maps cover 55S to 55N, maximum.)Spatially sampled imaging data. Nominal spatial resolution is 25 km. Pixel field of view is 4 km (NOAA data). Earth location (latitude +/- 90 degrees, longitude +/- 180 degrees) is obtained for each pixel from ancillary data.", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_SWITZ_1.json b/datasets/FIRE_CI1_SRB_SWITZ_1.json index 1cad04cca0..dba0b99215 100644 --- a/datasets/FIRE_CI1_SRB_SWITZ_1.json +++ b/datasets/FIRE_CI1_SRB_SWITZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_SWITZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiment (FIRE) Cirrus 1 Surface Radiation Budget (SRB) Data over Switzerland show results from the International Satellite Cloud Climatology Project (ISCCP) analysis of B3 radiance data (sampled to 25 km). Unlike the standard ISCCP product, these data are reported at the original pixel resolution and contain detailed information about the algorithm decision. Data covers the region from: 30 degrees North to 55 degrees North (+30 to +55), 40 degrees West to 40 degrees East (-40 to +40). (Polar projection covers 55S - 90S or 55N - 90N, maximum; Midlatitude maps cover 55S to 55N, maximum.) The nominal spatial resolution is 25 km for the spatially sampled imaging data. The pixel field of view is 4 km (National Oceanic and Atmospheric Administration (NOAA) data). Earth location (latitude +/- 90 degrees, longitude +/- 180 degrees) is obtained for each pixel from ancillary data.", "links": [ { diff --git a/datasets/FIRE_CI1_SRB_SW_1.json b/datasets/FIRE_CI1_SRB_SW_1.json index f0d54757d3..7c52aad218 100644 --- a/datasets/FIRE_CI1_SRB_SW_1.json +++ b/datasets/FIRE_CI1_SRB_SW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_SRB_SW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data contain down-welled global shortwave hemispherical radiation taken during the Wisconsin FIRE/SRB experiment. The data set consists of measurement\\ s taken every minute for 17 ground stations during the daylight hours. The data include values from October 12 through November 2, 1986.The number, location, latitude, and longitude of the FIRE/SRB WISCONSIN sites are: NUMBER LOCATION LATITUDE(DEG N) LONGITUDE(DEG W) 1 FT. MCCOY 43.96 90.76 2 STEVENS POINT 44.55 89.53 3 BARABOO 43.52 89.77 4 ADAMS COUNTY 43.97 89.80 5 WAUTOMA 44.04 89.30 6 WAUSAU 44.92 89.62 7 ARLINGTON 43.33 89.37 8 PORTAGE 43.56 89.48 9 REEDSBURG 43.53 89.97 10 PLAIN 43.28 90.04 11 TRI-COUNTY 43.21 90.19 12 DODGEVILLE 42.99 90.15 13 MT. HOREB 43.00 89.74 14 ARENA 43.16 89.91 15 SAUK CITY 43.30 89.74 16 MIDDLETON 43.11 89.53 17 MADISON 43.13 89.32", "links": [ { diff --git a/datasets/FIRE_CI1_TOVS_1.json b/datasets/FIRE_CI1_TOVS_1.json index 6609a98c4a..08628251e5 100644 --- a/datasets/FIRE_CI1_TOVS_1.json +++ b/datasets/FIRE_CI1_TOVS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI1_TOVS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiment (FIRE) Cirrus 1 TIROS Operational Vertical Sounder (TOVS) Data includes temperature soundings from the TOVS sensor on NOAA-9 and -10 satellites. Soundings were made for each pixel over the cirrus Intensive Field Observations (IFO) network. Sounding information contains both standard levels and layer means. Cloud top heights and cloud amount/emissivity also are included. Temperature soundings were made at the overpass times of the NOAA-9 and NOAA-10 satellites when the FIRE network (Wisconsin) was in view of the satellite. There is a maximum of 4 observation times per day.", "links": [ { diff --git a/datasets/FIRE_CI2_CITATN_1HZ_1.json b/datasets/FIRE_CI2_CITATN_1HZ_1.json index 1b29e15c9b..024d379ba2 100644 --- a/datasets/FIRE_CI2_CITATN_1HZ_1.json +++ b/datasets/FIRE_CI2_CITATN_1HZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CITATN_1HZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The University of North Dakota owns and operates a Cessna Citation II aircraft (N77ND) for the purpose of atmospheric research. This aircraft type has a number of design and performance characteristics which make it an ideal platform for a wide range of atmospheric studies. A series of structural modifications have been made to the basic airplane. These include the following: pylons under the wing tips for a variety of probes in the undisturbed air flow away from the fuselage; a nose boom for wind measurement; a heated radome to prevent ice accumulation on the nose area; special mounts for upward and downward looking radiometers; side-facing camera mounts for time-lapse cameras; optically-flat glass windows for photography; and an airinlet port for air sampling inside the pressurized cabin. The research instrumentation available on the Citation for the second Cirrus IFO is described below.The basic instrumentation package measured temperature, dew point temperature, pressure, wind and cloud microphysical characteristics along with aircraft position, altitude and performance parameters. The three-dimensional wind field is derived from measurements of acceleration, pitch, roll and yaw combined with angles of attack and sideslip and indicated airspeed. The aircraft parameters were supplied by an LTN-76 inertial navigation system and a Global PositioningSystem (GPS). Turbulence intensity can be derived from differential pressure transducers and INS accelerometer outputs.Cloud microphysics were sampled with PMS 1D-P, 2D-C, 1D-C and FSSP probes, and a continuous formvar replicator from DRI. A number of gas and aerosol sampling instruments were available. These included fast response O3 and NO2 monitors and a condensation nuclei counter.A forward-looking video camera was used to provide a visual record of flight conditions. The data were sampled at various rates from 1 to 24 sec-1. The sampling is controlled by the on-board computer system which also displayed the data in real time in graphic and alphanumeric formats while recording them on magnetic tape.", "links": [ { diff --git a/datasets/FIRE_CI2_CITATN_24HZ_1.json b/datasets/FIRE_CI2_CITATN_24HZ_1.json index 9221fae841..02a014f813 100644 --- a/datasets/FIRE_CI2_CITATN_24HZ_1.json +++ b/datasets/FIRE_CI2_CITATN_24HZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CITATN_24HZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The University of North Dakota owns and operates a Cessna Citation II aircraft (N77ND) for the purpose of atmospheric research. This aircraft type has a number of design and performance characteristics which make it an ideal platform for a wide range of atmospheric studies. A series of structural modifications have been made to the basic airplane. These include the following: pylons under the wing tips for a variety of probes in the undisturbed air flow away from the fuselage; a nose boom for wind measurement; a heated radome to prevent ice accumulation on the nose area; special mounts for upward and downward looking radiometers; side-facing camera mounts for time-lapse cameras; optically-flat glass windows for photography; and an air inlet port for air sampling inside the pressurized cabin. The research instrumentation available on the Citation for the second Cirrus IFO is described below.For more information about the UND Citation see http://cumulus.atmos.und.edu/The data contained in the following file is 24 hertz data from the UND Citation II Weather Research Aircraft. Each record has 24 values of sixteen variables.The data order is:Number Variable Units24 values Date yymmdd24 values Time seconds from midnight24 values Pitotpressure, Nose millibars24 values Pitotpressure, Wing millibars24 values Vertical acceleration meters/second/second24 values Vertical wind meters/second24 values Static pressure millibars24 values Air temperature, Rosemount Celcius24 values True heading degrees24 values Wind direction degrees24 values Wind velocity meters/second24 values Angle of attack degrees24 values Angle of sideslip degrees24 values Replicator film speed24 values Replicator frame count24 values Replicator event mark", "links": [ { diff --git a/datasets/FIRE_CI2_CITATN_5SEC_1.json b/datasets/FIRE_CI2_CITATN_5SEC_1.json index bf0ea495d3..656e932240 100644 --- a/datasets/FIRE_CI2_CITATN_5SEC_1.json +++ b/datasets/FIRE_CI2_CITATN_5SEC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CITATN_5SEC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIRE_CI2_CITATN_5SEC data are First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Cirrus 2 University of North Dakota Citation Aircraft 5 Second Data in Native format. The First ISCCP Regional Experiments were designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE were to: improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles; and investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. Four intensive field-observation periods were planned and executed: a cirrus Intensive Field Observation (IFO) (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). \r\n\r\nEach mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The University of North Dakota owns and operates a Cessna Citation II aircraft (N77ND) for the purpose of atmospheric research. This aircraft type has a number of design and performance characteristics which make it an ideal platform for a wide range of atmospheric studies. A series of structural modifications have been made to the basic airplane. These include the following: pylons under the wing tips for a variety of probes in the undisturbed air flow away from the fuselage; a nose boom for wind measurement; a heated radome to prevent ice accumulation on the nose area; special mounts for upward and downward looking radiometers; side-facing camera mounts for time-lapse cameras; optically-flat glass windows for photography; and an air inlet port for air sampling inside the pressurized cabin. The research instrumentation available on the Citation for the second Cirrus IFO is described below. \r\n\r\nThe data contained in the following file is 1/5 hertz data from the UND Citation II Weather Research Aircraft. Each record has one value of one hundred and thirteen variables. The data order is Number Variable Units:\r\n1. Date yymmdd; \r\n2. True Air Speed meters/sec; \r\n3. INS Ground Speed meters/sec; \r\n4. INS Wind velocity meters/second; \r\n5. Calculated Wind velocity meters/second; \r\n6. INS Wind direction degrees; \r\n7. Calculated Wind direction degrees; \r\n8. INS True heading degrees; \r\n9. Magnetic heading degrees; \r\n10. Drift angle degrees; \r\n11. Pitch angle degrees; \r\n12. Roll angle degrees; \r\n13. Static pressure millibars; \r\n14. INS Altitude meters; \r\n15. Pressure Altitude meters; \r\n16. Vertical acceleration meters/second/second; \r\n17. Air temperature, Rosemount Celsius; \r\n18. Air temperature, Reverse Flow Celsius; \r\n19. Dew Point Celsius; \r\n20. Attack millibars; \r\n21. SideSlip millibars; \r\n22. J. W. Liquid Water grams/cubic-meter; \r\n23. Pitotpressure, Nose millibars; \r\n24. Pitotpressure, Wing millibars; \r\n25. Ice detector volts; \r\n26. Ice detector Liquid WAter grams/cubic-meter; \r\n27. Angle of attack degrees; \r\n28. INS Latitude degrees; \r\n29. INS Longitude degrees; \r\n30. Vertical velocity meters/second; \r\n31. Vertical wind meters/second; \r\n32. Angle of sideslip degrees; \r\n33. Vapor Pressure millibars; \r\n34. Mixing Ratio grams/kilogram; \r\n35. Temperature, Reverse Flow (uncorrected) Celsius; \r\n36. Equivalent Potential Temperature kelvin; \r\n37. Potential Temperature kelvin; \r\n38. Virtual Potential Temperature kelvin; \r\n39. Total Mixing Ratio grams/kilogram; \r\n40. Wet Equivalent Potential Temperature kelvin; \r\n41. Vertical Wind Angle degrees; \r\n42. VOR degrees; \r\n43. DME Nautical Miles; \r\n44. Heater Flag Volts; \r\n45. Engine RPM Per Cent; \r\n46. INS Track Angle degrees; \r\n47. Mach Number; \r\n48. Time Seconds from midnight; \r\n49. Cabin Altitude meters; \r\n50. Virtual Temperature kelvin; \r\n51. Ozone ppbv; \r\n52. Film Speed Number; \r\n53. Frame Count meters; \r\n54. Event Marker Volts; \r\n55. NO2. ppbv; \r\n56. Condensation Nucle; \r\n57. Relative Humidity Per Cent; \r\n58. Condensation Nuclei Temperature Celsius; \r\n59. GPS Time Seconds from midnight; \r\n60. GPS Latitude degrees; \r\n61. GPS Longitude degrees; \r\n62. GPS Altitude meters; \r\n63. GPS Velocity North meters/second; \r\n64. GPS Velocity East meters/second; \r\n65. GPS Ground Speed meters/second; \r\n66. GPS Track Angle degrees; \r\n67. Condensation Nuclei Flow Rate liters/minute; \r\n68. FSSP Counts Number; \r\n69. FSSP Concentration Number/ml; \r\n70. FSSP Liquid Water grams/cubic-meter; \r\n71. FSSP Mean Diameter microns; \r\n72. FSSP Mean Volume Diameter microns; \r\n73. FSSP Median Diameter microns; \r\n74. FSSP Median Volume Diameter microns; \r\n75. FSSP Mode microns; \r\n76. FSSP Skew microns; \r\n77. FSSP Standard Deviation, Mean Diameter microns; \r\n78. FSSP Standard Deviation, Mean Volume Diameter microns; \r\n79. OneDC Counts Number; \r\n80. OneDC Concentration number/liter; \r\n81. OneDC Mass grams/cubic-meter; \r\n82. OneDC Mean Diameter microns; \r\n83. OneDC Mean Volume Diameter microns; \r\n84. OneDC Median Diameter microns; \r\n85. OneDC Median Volume Diameter microns; \r\n86. OneDC Mode microns; \r\n87. OneDC Standard Deviation, Mean Diameter microns; \r\n88. OneDC Standard Deviation, Mean Volume Diameter microns; \r\n89. OneDP Counts Number; \r\n90. OneDP Concentration number/liter; \r\n91. OneDP Mass grams/cubic-meter; \r\n92. OneDP Mean Diameter microns; \r\n93. OneDP Mean Volume Diameter microns; \r\n94. OneDP Median Diameter microns; \r\n95. OneDP Median Volume Diameter microns;\r\n96. OneDP Mode microns; \r\n97. OneDP Standard Deviation, Mean Diameter microns; \r\n98. OneDP Standard Deviation, Mean Volume Diameter microns; \r\n99. TwoDC Counts Number; \r\n100. TwoDC Concentration number/liter; \r\n101. TwoDC Mass grams/cubic-meter; \r\n102. TwoDC Mean Diameter microns; \r\n103. TwoDC Mean Volume Diameter microns; \r\n104. TwoDC Median Diameter microns; \r\n105. TwoDC Median Volume Diameter microns; \r\n106. TwoDC Mode microns; \r\n107. TwoDC Standard Deviation, Mean Diameter microns; \r\n108. TwoDC Standard Deviation, Mean Volume Diameter microns; \r\n109. TwoDC Shadow-Or number/liter; \r\n110. OneDC Skew microns; \r\n111. OneDP Skew microns; \r\n112. TwoDC Skew microns; \r\n113. Corrected Latitude degrees; \r\n114. Corrected Longitude degrees.", "links": [ { diff --git a/datasets/FIRE_CI2_CITATN_IWC_1.json b/datasets/FIRE_CI2_CITATN_IWC_1.json index 60ca426420..66d8869732 100644 --- a/datasets/FIRE_CI2_CITATN_IWC_1.json +++ b/datasets/FIRE_CI2_CITATN_IWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CITATN_IWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The University of North Dakota owns and operates a Cessna Citation II aircraft (N77ND) for the purpose of atmospheric research. This aircraft type has a number of design and performance characteristics which make it an ideal platform for a wide range of atmospheric studies. A series of structural modifications have been made to the basic airplane. These include the following: pylons under the wing tips for a variety of probes in the undisturbed air flow away from the fuselage; a nose boom for wind measurement; a heated radome to prevent ice accumulation on the nose area; special mounts for upward and downward looking radiometers; side-facing camera mounts for time-lapse cameras; optically-flat glass windows for photography; and an air inlet port for air sampling inside the pressurized cabin. The research instrumentation available on the Citation for the second Cirrus IFO is described below. For more information about the UND Citation see http://cumulus.atmos.und.edu/ Cloud microphysical measurements were derived from data taken by the University of North Dakota Citation aircraft PMS 2D-C and 2D-P probes during the FIRE Cirrus IFO - II. Following are a list of parameters collected: VARIABLE DESCRIPTION UNITS ------------------------------------------------------------------------------- IT1,IT2 MEASUREMENT TIME INTERVAL HH/MM/SS PS STATIC PRESSURE mb TEMP AMBIENT TEMPERATURES degrees C ALT PRESSURE ALTITUDE m USTAR VERTICAL VELOCITY NEEDED TO KEEP THE cm/s RELATIVE HUMIDITY CONSTANT DBARM MEDIAN PARTICLE MASS WEIGHTED DIAMETER cm DMAX MAXIMUM PARTICLE DIAMETER cm W1 DIFFUSIONAL GROWTH RATE IN CHANNEL 1 g/sec W2 DIFFUSIONAL GROWTH RATE IN CHANNEL 2 g/sec W3 DIFFUSIONAL GROWTH RATE IN CHANNEL 3 g/sec W4 DIFFUSIONAL GROWTH RATE IN CHANNEL 4 g/sec WTOT TOTAL DIFFUSTIONAL GROWTH RATE g/sec DT8 DEPLETION TIME (8 micron droplets) sec DT12 DEPLETION TIME (12 micron droplets) sec TMASS1 IWC IN CHANNEL 1 g/m^3 TMASS2 IWC IN CHANNEL 2 g/m^3 DPTC DEW POINT TEMPERATURE (EG&G) degreesC RH RELATIVE HUMIDITY (EG&G) % RIWC ICE WATER CONTENT g/m^3 XM1 ICE WATER CONTENT BASED ON SNOW HABIT g/m^3 XM2 ICE WATER CONTENT BASED ON SMALL SNOW g/m^3 HABIT XM3 ICE WATER CONTENT BASED ON LARGE SNOW g/m^3 HABIT R PRECIPITATION RATE mm/hr DBZ RADAR REFLECTIVITY FACTOR decibels VBAR MEAN REFLECTIVITY WEIGHTED WITH THE cm/s TERMINAL VELOCITY TTCONC TOTAL PARTICLE CONCENTRATION #/L CBIN1 PARTICLE CONCENTRATION WITHIN THE RANGE LE 200 #/L CBIN2 PARTICLE CONCENTRATION WITHIN 200-500 #/L THE RANGE CBIN3 PARTICLE CONCENTRATION WITHIN THE 500-800 #/L RANGE CBIN4 PARTICLE CONCENTRATION WITHIN THE #/L RANGE GT 800 CE8 COLLECTION EFFICIENCY (8 micron none droplets) CE12 COLLECTION EFFICIENCY (12 micron none droplets) TMASS3 IWC IN CHANNEL 3 g/m^3 TMASS4 IWC IN CHANNEL 4 g/m^3 TIMP # OF CRYSTAL-CRYSTAL COLUMNS sec^(1-) RHORH WATER VAPOR DENSITY g/cm^3 SI SUPERSATURATION WITH RESPECT TO ICE % SW SUPERSATURATION WITH RESPECT TO WATER % LAMBDA COEFFICIENTS USED TO FIT THE EQUATION #/cm^3 NZERO N=N0*EXP(-LAMBDA*D) #/L/mm RSQ COEFFICIENT OF THE FIT none ICP PROBE TYPE (C OR P) none", "links": [ { diff --git a/datasets/FIRE_CI2_CITATN_PMS_1.json b/datasets/FIRE_CI2_CITATN_PMS_1.json index ba0959951d..09f52fd8ef 100644 --- a/datasets/FIRE_CI2_CITATN_PMS_1.json +++ b/datasets/FIRE_CI2_CITATN_PMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CITATN_PMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The University of North Dakota owns and operates a Cessna Citation II aircraft (N77ND) for the purpose of atmospheric research. This aircraft type has a number of design and performance characteristics which make it an ideal platform for a wide range of atmospheric studies. A series of structural modifications have been made to the basic airplane. These include the following: pylons under the wing tips for a variety of probes in the undisturbed air flow away from the fuselage; a nose boom for wind measurement; a heated radome to prevent ice accumulation on the nose area; special mounts for upward and downward looking radiometers; side-facing camera mounts for time-lapse cameras; optically-flat glass windows for photography; and an air inlet port for air sampling inside the pressurized cabin. The research instrumentation available on the Citation for the second Cirrus IFO is described below.The basic instrumentation package measured temperature, dew point temperature, pressure, wind and cloud microphysical characteristic along with aircraft position, altitude and performance parameters. The three-dimensional wind field is derived from measurements of acceleration, pitch, roll and yaw combined with angles of attack and sideslip and indicated airspeed. The aircraft parameters were supplied by an LTN-76 inertial navigation system and a Global Positioning System (GPS). Turbulence intensity can be derived from differential pressure transducers and INS accelerometer outputs. Cloud microphysical measurements were made with an array of Particle Measuring Systems probes (FSSP, 1D-C,2D-C,1D-P) mounted on the wing-tip pylons. These probes measure concentrations and sizes of particles from one micrometer to several millimeters in diameter. In addition there were probes to measure both liquid water content and icing rate. Several gas and aerosol sampling instruments were available. These include fast response O3 and NO2 monitors, and a condensation nuclei counter. A forward or side-looking video camera was also used to provide a visual record of flight conditions. The data were sampled at various rate from 1 to 24 sec-1. The sampling is controlled by the on-board computer system which also displayed the data in real time in graphic and alphanumeric formats while recording them on magnetic tape.", "links": [ { diff --git a/datasets/FIRE_CI2_CLASS_SONDE_1.json b/datasets/FIRE_CI2_CLASS_SONDE_1.json index 459ed76c34..5dc9a54d5c 100644 --- a/datasets/FIRE_CI2_CLASS_SONDE_1.json +++ b/datasets/FIRE_CI2_CLASS_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CLASS_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Cross-chain LORAN Atmospheric Sounding System (CLASS) sonde data were collected in four locations: Arkansas City, KS; Coffeyville, KS: Iola, KS; and Muskegee, OK.", "links": [ { diff --git a/datasets/FIRE_CI2_CSU_PRT6_1.json b/datasets/FIRE_CI2_CSU_PRT6_1.json index 0b5142baa0..d3aae34326 100644 --- a/datasets/FIRE_CI2_CSU_PRT6_1.json +++ b/datasets/FIRE_CI2_CSU_PRT6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CSU_PRT6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Colorado State radiometer data set was produced by the Department of Atmospheric Sciences of CSU as part of the FIRE Phase II Cirrus Intensive Field Observations (IFO) conducted in Coffeyville, Kansas. The CSU PRT-6 data were collected during the period from Nov. 18, 1991 (day 322) to Dec. 7, 1991 (day 341) at the Parsons KG&E Power Plant, Parsons, Kansas (37 deg. 18 min. N and 95 deg. 07 min.W). The PRT-6 is an all-purpose chopped bolometer. It was operated with a 2 degree field of view pointing vertically upward. The filter employed narrowed the spectral band to ranges from about 885 to 945 inversecentimeters (the infrared window region). The PRT-6 was not run in continuous mode. When operating, data were sampled every 5 seconds. Please note that there are temporal gaps in the data.", "links": [ { diff --git a/datasets/FIRE_CI2_CSU_SONDES_1.json b/datasets/FIRE_CI2_CSU_SONDES_1.json index 81b82b2556..8a3bea2132 100644 --- a/datasets/FIRE_CI2_CSU_SONDES_1.json +++ b/datasets/FIRE_CI2_CSU_SONDES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CSU_SONDES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The CSU sonde data were generated in support of the FIRE Phase II Cirrus observation field experiment held in Coffeyville, Kansas during the period from 13 Nov. to 06 Dec. 1991 at the Parsons KG&E Power Plant. The data were collected at 37 deg. 18 min. N and 95 deg. 07 min. W, with a vertical resolution usually of roughly 5-10 m. They were provided to allow a calculation of an approximate location of the sonde.", "links": [ { diff --git a/datasets/FIRE_CI2_CSU_STN1_1.json b/datasets/FIRE_CI2_CSU_STN1_1.json index 901d9ac8ab..e97e430c21 100644 --- a/datasets/FIRE_CI2_CSU_STN1_1.json +++ b/datasets/FIRE_CI2_CSU_STN1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CSU_STN1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The CSU Station 1 surface radiation data set was produced by the Atmospheric Sciences Division of CSU in support of the FIRE Phase II Cirrus IFO conducted in Coffeyville, Kansas. CSU Station 1 point data were collected every 2 minutes for the period from Nov. 11, 1991 (day 315) to Dec. 8, 1991 (day 342) at the Parsons KG&E Power Plant Parsons, Kansas (37 deg. 18 min. N and 95 deg. 07 min. W). NOTE: The 2 minute values were instantaneous readings.", "links": [ { diff --git a/datasets/FIRE_CI2_CSU_STN2_1.json b/datasets/FIRE_CI2_CSU_STN2_1.json index 84c7379072..fd0673893f 100644 --- a/datasets/FIRE_CI2_CSU_STN2_1.json +++ b/datasets/FIRE_CI2_CSU_STN2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CSU_STN2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The CSU Station 2 surface radiation data set was collected every 2 minutes for the period from Nov. 13, 1991 through Dec. 8, 1991 at the Tri-City Airport, Parsons, Kansas (37 deg. 20 min. N, 95 deg. 30 min. 30 sec. W.) NOTE: the 2 minute values were instantaneous readings.", "links": [ { diff --git a/datasets/FIRE_CI2_CSU_WNDPRFS_1.json b/datasets/FIRE_CI2_CSU_WNDPRFS_1.json index 55815e19c7..4801e1fe07 100644 --- a/datasets/FIRE_CI2_CSU_WNDPRFS_1.json +++ b/datasets/FIRE_CI2_CSU_WNDPRFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_CSU_WNDPRFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Colorado State University (CSU) wind profiler data set was produced by the Department of Atmospheric Sciences of CSU as part of the FIRE Phase II Cirrus Intensive Field Observations (IFO) conducted in Coffeyville, Kansas. The CSU wind profiler data were collected during the period from Nov. 12, 1991 to Dec. 7, 1991 at the Parsons KG&E Power Plant, Parsons, Kansas (37 deg. 18 min. N and 95 deg. 07 min. W).", "links": [ { diff --git a/datasets/FIRE_CI2_DOPLR_LIDAR_1.json b/datasets/FIRE_CI2_DOPLR_LIDAR_1.json index 2c865e96aa..f1200e56e4 100644 --- a/datasets/FIRE_CI2_DOPLR_LIDAR_1.json +++ b/datasets/FIRE_CI2_DOPLR_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_DOPLR_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13-November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29-July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13-December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1-June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Doppler lidar data set includes wind profiles derived by the VAD method for the FIRE-II top 5 priority days (21,25,28,30 of Nov. 1991, and Dec. 5, 1991). Vertical profiles of the horizontal wind speed and direction were acquired by the lidar using a classical method commonly referred to as the VAD technique, where VAD stands for Velocity Azimuth Display.The Doppler lidar experiment objective was to obtain lidar measurements of relative backscatter signal intensity and radial velocity from cirrus clouds to study their microphysical and radiative properties. This data set provides vertical profiles (approx. 1.5 - 20.0 km agl).", "links": [ { diff --git a/datasets/FIRE_CI2_ER2_LIDAR_1.json b/datasets/FIRE_CI2_ER2_LIDAR_1.json index ee22d17595..7f2a521eb7 100644 --- a/datasets/FIRE_CI2_ER2_LIDAR_1.json +++ b/datasets/FIRE_CI2_ER2_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_ER2_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. Operations Plan (1992). The Cloud Lidar System (CLS) instrument was flown aboard the NASA ER-2 airplane. This instrument was used to determine cloud altitudes. Information pertaining to the number of cloud layers detected; the heights of the boundaries for up to 5 cloud layers; geo-physical location information; and time were recorded. Four channels of data were recorded. The first channel recorded wavelengths at 532 nanometers in the parallel plane. The second channel recorded wavelengths of 532 nanometers in the perpendicular plane. The third channel recorded wavelengths of 1064 nanometers total. The forth channel was a linear amplifier which received the digitized signal from one of the three previously mentioned CLS detectors. The data are organized so that there is a single header record for the file. This header record is followed by a series of pairs of records.The first record of each pair contains the CLS calibrated data and the second record of the pair contains the CLS analyzed data.", "links": [ { diff --git a/datasets/FIRE_CI2_ER2_MAS_1.json b/datasets/FIRE_CI2_ER2_MAS_1.json index 340d5b28d3..54eb226baf 100644 --- a/datasets/FIRE_CI2_ER2_MAS_1.json +++ b/datasets/FIRE_CI2_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The MODIS Airbourne Simulator (MAS) is a modified Daedalus Wildfire scanning spectrometer which flies on a NASA ER-2 and provides spectral information similar to that which will be provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), scheduled to be launched on the EOS-AM platform in 1998 (King et al. 1992). The principal investigators for the MAS are Dr. Michael King (NASA/GSFC, Greenbelt MD), and Dr. Paul Menzel (NOAA/NESDIS, Madison WI). In November/December 1991, the modified Wildfire instrument was flown during the FIRE Cirrus-II experiment onboard a NASA ER-2 in coordination with other aircraft and satellites over the Coffeyville, KS field site as well as the Texas and Louisiana Gulf coast. The MAS spectrometer acquires high spatial resolution imagery in the wavelength range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range, and the digitizer can be configured to collect data from any 12 of these bands. The digitizer was configured with four 10-bit channels and seven 8-bit channels. The MAS spectrometer was mated to a scanner subassembly which collected image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. The data granules were written using the self documenting file storage format provided through the netCDF interface routines included in the HDF libraries.", "links": [ { diff --git a/datasets/FIRE_CI2_ETL_RADAR_1.json b/datasets/FIRE_CI2_ETL_RADAR_1.json index 5db78c12d2..c51c1daf92 100644 --- a/datasets/FIRE_CI2_ETL_RADAR_1.json +++ b/datasets/FIRE_CI2_ETL_RADAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_ETL_RADAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The National Oceanic and Atmospheric Administration (NOAA) Environmental Technology Laboratory (ETL) Doppler radar was used during the Fire Cirrus II experiment in Coffeyville, Kansas to document the structural, kinematic, microphysical and turbulent properties of climatically important cirrus cloud systems. Data were collected from November 13, 1991 through November 29, 1991.", "links": [ { diff --git a/datasets/FIRE_CI2_HIS_1.json b/datasets/FIRE_CI2_HIS_1.json index 8b7af2bdef..3be27933c2 100644 --- a/datasets/FIRE_CI2_HIS_1.json +++ b/datasets/FIRE_CI2_HIS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_HIS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The High-resolution Interferometer Sounder (HIS) was flown on board the NASA ER-2 aircraft during FIRE Cirrus Phase II in Coffeyville, Kansas. The HIS measured upwelling calibrated radiances and was positioned to to capture a nadir view along the ER-2 flight tracks.", "links": [ { diff --git a/datasets/FIRE_CI2_HSRL_1.json b/datasets/FIRE_CI2_HSRL_1.json index 426a95b9b3..25a0b4443c 100644 --- a/datasets/FIRE_CI2_HSRL_1.json +++ b/datasets/FIRE_CI2_HSRL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_HSRL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set contains images of cirrus clouds advected over the HSRL during FIRE Cirrus 2 in Coffeyville, Kansas. These images consist of both the lidar backscatter and the depolarization ratio of backscatter radiation.", "links": [ { diff --git a/datasets/FIRE_CI2_ISCCP_DX_1.json b/datasets/FIRE_CI2_ISCCP_DX_1.json index be339a81ff..45626473ff 100644 --- a/datasets/FIRE_CI2_ISCCP_DX_1.json +++ b/datasets/FIRE_CI2_ISCCP_DX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_ISCCP_DX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.A subset of the ISCCP Stage DX Cloud Product (Revised Algorithm) are included for the FIRE Cirrus 2 region.", "links": [ { diff --git a/datasets/FIRE_CI2_KINGAIR_1.json b/datasets/FIRE_CI2_KINGAIR_1.json index 5895789202..b48f629c3e 100644 --- a/datasets/FIRE_CI2_KINGAIR_1.json +++ b/datasets/FIRE_CI2_KINGAIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_KINGAIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. Cirrus IFO-II was conducted from November 9 to December 8, 1991 in Coffeyville, Kansas. The NCAR King Air aircraft measured radiation and microphysical properties of the cloud layers, in addition to temperature, moisture, and air motions.", "links": [ { diff --git a/datasets/FIRE_CI2_KINGAIR_2D_1.json b/datasets/FIRE_CI2_KINGAIR_2D_1.json index 51fd03d4a5..acc38d9d87 100644 --- a/datasets/FIRE_CI2_KINGAIR_2D_1.json +++ b/datasets/FIRE_CI2_KINGAIR_2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_KINGAIR_2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The PMS 2D-C and 2D-P probes illuminate a linear array of photodiodes with a He-Ne laser. As a particle passes through this focused beam, a shadow image is cast on the diodes and a count of the total number of occulted diodes represents the particle size. The data are organized on a single flight basis, for both the King Air and the Sabreliner. Relevant portions of the header from the raw binary files are included. Each data file contains processed concentration data based on habit type and area ratio.", "links": [ { diff --git a/datasets/FIRE_CI2_KINGAIR_IWC_1.json b/datasets/FIRE_CI2_KINGAIR_IWC_1.json index b4107968eb..4e9344961e 100644 --- a/datasets/FIRE_CI2_KINGAIR_IWC_1.json +++ b/datasets/FIRE_CI2_KINGAIR_IWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_KINGAIR_IWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR aircraft during FIRE II. The 2D-C data are converted to size spectra according to the guidelines given in Heymsfield and Baumgardner (1985, Bull. Amer. Meteoro. Soc.), where one element is added to the size of a particle along the the flight direction to account for the probe's intrinsic start-up time. Size is determined as the maximum dimension ($D_{max}$) along the flight direction or optical array axis. The nominal size resolution for the Sabreliner 2D probe is 50 microns/per shadowed optical array element, for the King Air is 25 microns/bin. Sample area (SA) is derived using the depth of field estimates reported by Knollenberg (1970). Particles are binned into 32 size categories, nonuniformly spaced with higher resolution in the smaller classes. Particles within each size bin are subdivided into 10 ``area ratio (AR)'' bins, where AR represents the ratio of particle area to the area of discs of diameter $D_{max}$. The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR Sabreliner during FIRE II. The derivation of the microphysical parameters is outlined in the later reference to Heymsfield (1977). The vertical velocity is the steady-state velocity in cm s-1 to keep the relative humidity at it's currently measured value. Differential growth rate represents the growth rate of the particle population of different sizes at the current relative humidity. The Total differential growth rate is the sum of the growth rate in all channels. The assumptions used for the IWC calculations are reported in Heymsfield; also, generic size to mass equations are used. Precipitation rate is calculated from particle size and terminal velocity data, integrated over the size spectrum. Concentration data are as derived above. Number of crystal-crystal collisions are derived from the data reported by Hindman and the crystal terminal velocities. Water vapor density andsupersaturation information in this data set should not be used--it is unreliable. Curve fits to the data using least squares methods are provided. VARIABLE DESCRIPTION UNITS ------------------------------------------------------------------------------- IT1, ITMEASUREMENT TIME INTERVAL HH/MM/SS PS STATIC PRESSURE mb TEMP AMBIENT TEMPERATURE degreesC ALT PRESSURE ALTITUDE m USTAR VERTICAL VELOCITY NEEDED TO KEEP THE cm/s RELATIVE HUMIDITY CONSTANT DBARM MEDIAN PARTICLE MASS WEIGHTED DIAMETER cm DMAX MAXIMUM PARTICLE DIAMETER cm W1 DIFFUSIONAL GROWTH RATE IN CHANNEL 1 g/sec W2 DIFFUSIONAL GROWTH RATE IN CHANNEL 2 g/sec W3 DIFFUSIONAL GROWTH RATE IN CHANNEL 3 g/sec W4 DIFFUSIONAL GROWTH RATE IN CHANNEL 4 g/sec WTOT TOTAL DIFFUSTIONAL GROWTH RATE g/sec DT8 DEPLETION TIME (8 micron droplets) sec DT12 DEPLETION TIME (12 micron droplets) sec TMASS1 IWC IN CHANNEL 1 g/m^3 TMASS2 IWC IN CHANNEL 2 g/m^3 DPTC DEW POINT TEMPERATURE (EG&G) degrees C RH RELATIVE HUMIDITY (EG&G) % RIWC ICE WATER CONTENT g/m^3 XM1 ICE WATER CONTENT BASED ON SNOW HABIT g/m^3 XM2 ICE WATER CONTENT BASED ON SMALL g/m^3 SNOW HABIT XM3 ICE WATER CONTENT BASED ON LARGE g/m^3 SNOW HABIT R PRECIPITATION RATE mm/hr DBZ RADAR REFLECTIVITY FACTOR decibels VBAR MEAN REFLECTIVITY WEIGHTED WITH THE cm/s TERMINAL VELOCITY TTCONC TOTAL PARTICLE CONCENTRATION #/L CBIN1 PARTICLE CONCENTRATION WITHIN THE #/L RANGE LE 200 CBIN2 PARTICLE CONCENTRATION WITHIN THE 200-500 #/L RANGE CBIN3 PARTICLE CONCENTRATION WITHIN THE 500-800 #/L RANGE CBIN4 PARTICLE CONCENTRATION WITHIN THE #/L RANGE GT 800 CE8 COLLECTION EFFICIENCY (8 micron none droplets) CE12 COLLECTION EFFICIENCY (12 micron none droplets) TMASS3 IWC IN CHANNEL 3 g/m^3 TMASS4 IWC IN CHANNEL 4 g/m^3 TIMP # OF CRYSTAL-CRYSTAL COLUMNS sec^(1-) RHORH WATER VAPOR DENSITY g/cm^3 SI SUPERSATURATION WITH RESPECT TO ICE % SW SUPERSATURATION WITH RESPECT TO WATER % LAMBDA COEFFICIENTS USED TO FIT THE EQUATION #/cm^3 NZERO N=N0*EXP(-LAMBDA*D) #/L/mm RSQ COEFFICIENT OF THE FIT ICP PROBE TYPE (C OR P) none", "links": [ { diff --git a/datasets/FIRE_CI2_LARC8_LIDAR_1.json b/datasets/FIRE_CI2_LARC8_LIDAR_1.json index f3c471bad6..0e6df23ef4 100644 --- a/datasets/FIRE_CI2_LARC8_LIDAR_1.json +++ b/datasets/FIRE_CI2_LARC8_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_LARC8_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIRE_CI2_LARC8_LIDAR is the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiments (FIRE) Cirrus Phase II Langley Research Center (LARC) Eight Inch Lidar data product. It was designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE were to: seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles ; and investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). \r\n\r\nEach mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Langley Research Center (LARC) Cloud Lidar is a dual-channel polarization sensitive lidar using a frequency doubled Nd: YAG laser as a linearly polarized transmitter and an eight inch Cassegrainian telescope as a receiver. Backscattered laser light collected by the receiver is collimated, directed through a half wave plate, and then passed through polarizing optics which decompose the signal into two components, one parallel and the other perpendicular to the polarization plane of the transmitted beam. Separate amplification and digitization paths are employed for each component, resulting in two arrays of back scatter data for each measured laser pulse. The LARC Cloud Lidar is designed for optimum cloud monitoring operations at altitudes between 3 km and 18 km. To prevent saturation of the detectors at lower altitudes, a gating circuit is used to delay the activation of the first dynode in the Photomultiplier (PMT). The PMT is brought to full sensitivity only after this delay time has elapsed.", "links": [ { diff --git a/datasets/FIRE_CI2_MAPS_1.json b/datasets/FIRE_CI2_MAPS_1.json index a3c0f0c0f5..ab8186d24a 100644 --- a/datasets/FIRE_CI2_MAPS_1.json +++ b/datasets/FIRE_CI2_MAPS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_MAPS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIRE_CI2_MAPS is the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Cirrus Phase II Mesoscale Analysis and Prediction System data product. This product was designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE were to: seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles; and investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). \r\n\r\nEach mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.MAPS refers to both a data analysis system and a numerical forecast model developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). The analysis system combines profiler, ACARS, surface and radiosonde data with the previous 3 hour MAPS model forecast to generate an analysis every 3 hours. The parameters available are Pressure, Montgomery Streamfunction, Virtual Potential Temperature, Condensation Pressure, and Wind Speed.", "links": [ { diff --git a/datasets/FIRE_CI2_NOAA_WNDPFS_1.json b/datasets/FIRE_CI2_NOAA_WNDPFS_1.json index 6d705978e2..19a403f89a 100644 --- a/datasets/FIRE_CI2_NOAA_WNDPFS_1.json +++ b/datasets/FIRE_CI2_NOAA_WNDPFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_NOAA_WNDPFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The NOAA wind profiles were collected during the period from Nov. 13, 1991 to Dec. 7 1991. The original data were stored in the Enhanced Binary Universal Form (EBUF) format. These data files have been reformatted and are provided (in ASCII format) by the Langley DAAC.", "links": [ { diff --git a/datasets/FIRE_CI2_NWS_IN_SND_1.json b/datasets/FIRE_CI2_NWS_IN_SND_1.json index fc1f9d5e14..049b8dffd9 100644 --- a/datasets/FIRE_CI2_NWS_IN_SND_1.json +++ b/datasets/FIRE_CI2_NWS_IN_SND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_NWS_IN_SND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The FIRE_CI2_NWS_IN_SND data set was collected for the period Nov. 13, 1991 to Dec. 7, 1991. Each granule has multiple ASCII data files. Data were collected from 17 different National Weather Service (NWS) sites. These sites are: (ABQ) Albuquerque, NM; (AMA) Amarillo, TX; (DDC) Dodge City, KS; (DEN) Denver, CO; (DRT) Del Rio, TX; (ELP) El Paso, TX; (GGG) Longview, TX; (LBF) North Platte, NE; (LIT) North Little Rock, AR; (MAF) Midland, TX; (OMA) Omaha, NE; (OUN) Norman, OK; (PAH) Paducah, KY; (PIA) Peoria, IL; (SEP) Stephenville, TX; (TOP)Topeka, KS; and (UMN) Monett, MO.", "links": [ { diff --git a/datasets/FIRE_CI2_NWS_OUT_SND_1.json b/datasets/FIRE_CI2_NWS_OUT_SND_1.json index 685341a178..1c1f942659 100644 --- a/datasets/FIRE_CI2_NWS_OUT_SND_1.json +++ b/datasets/FIRE_CI2_NWS_OUT_SND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_NWS_OUT_SND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The FIRE_CI2_NWS_OUT_SND data set was collected for the period Nov. 20 1991 to Dec. 7, 1991. Each granule has multiple ASCII data files. Data were collected from 31 different National Weather Service (NWS) sites. These stations are: (BIS) Bismarck, ND; (BNA) Nashville, TN; (BOI) Boise, ID; (CKL) Centreville, AL; (CRP) Corpus Christi, TX; (DAY) Dayton, OH; (DRA) Desert Rock, NV; (ELY) Ely, NV; (FNT) Flint,MI; (GEG) Spokane, WA; (GGW) Glasgow, MT; (GJT) Grand Junction, CO; (GRB) Green Bay, WI; (GTF) Great Falls, MT; (HON) Huron, SD; (HTS) Huntington, WV; (INL) International Falls, MN; (INW) Winslow, AZ; (JAN) Jackson, MS; (LCH) Lake Charles, LA; (LND) Lander, WY; (MFR) Medford, OR; (NKX) San Diego, CA; (OAK) Oakland, CA; (RAP) Rapid City, SD; (SIL) Slidell, LA; (SLC) Salt Lake City, UT; (SLE) Salem, OR; (STC) St. Cloud, MN; (TUS) Tucson, AZ; and (WMC) Winnemucca, NV.", "links": [ { diff --git a/datasets/FIRE_CI2_PAMS_1.json b/datasets/FIRE_CI2_PAMS_1.json index dac1654e7c..67a40c27b3 100644 --- a/datasets/FIRE_CI2_PAMS_1.json +++ b/datasets/FIRE_CI2_PAMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_PAMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The PAMS data set was collected during the FIRE Cirrus Phase II experiment from Nov. 13, 1991 to Dec. 7, 1991 at six sites. There are 25 data files for each of 6 sites where PAMS data were collected.", "links": [ { diff --git a/datasets/FIRE_CI2_RAMAN_LIDAR_1.json b/datasets/FIRE_CI2_RAMAN_LIDAR_1.json index 84d3ac7bb3..330cbf9856 100644 --- a/datasets/FIRE_CI2_RAMAN_LIDAR_1.json +++ b/datasets/FIRE_CI2_RAMAN_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_RAMAN_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The GSFC Raman Lidar water vapor mixing ratio (wvmr) data with altitudes and times were collected for the period from 13 Nov 1991 to07 Dec 1991. Data were collected at night and consists of a series of one minute profiles. Data are summed for one minute in the detectors and saved to a file. For the 10 minute averaged data, the data are summed for 10 minutes before the calculations are performed. Each profile has a 75 meter resolution from 0.4135 to 10.299 kilometers. Zero (0) km means sea level. The site altitude is 0.229 km and thefirst data point is at 0.1845 km above ground level.", "links": [ { diff --git a/datasets/FIRE_CI2_SABRELINER_1.json b/datasets/FIRE_CI2_SABRELINER_1.json index bea11ad56a..082da9b437 100644 --- a/datasets/FIRE_CI2_SABRELINER_1.json +++ b/datasets/FIRE_CI2_SABRELINER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_SABRELINER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Cirrus IFO-II was conducted from November 9 to December 8, 1991 in Coffeyville, Kansas. The NCAR Sabreliner aircraft measured radiation and microphysical properties of the cloud layers, in addition to temperature, moisture, and air motions.", "links": [ { diff --git a/datasets/FIRE_CI2_SABRLNR_2D_1.json b/datasets/FIRE_CI2_SABRLNR_2D_1.json index 771c2c7a7e..852d5563f5 100644 --- a/datasets/FIRE_CI2_SABRLNR_2D_1.json +++ b/datasets/FIRE_CI2_SABRLNR_2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_SABRLNR_2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The data are organized on a single flight basis, for both the King Air and the Sabreliner. Relevant portions of the header from the raw binary files are included. Each data file contains processed concentration data based on habit type and area ratio.", "links": [ { diff --git a/datasets/FIRE_CI2_SABRLNR_IWC_1.json b/datasets/FIRE_CI2_SABRLNR_IWC_1.json index 3c7ea1ff51..61030cfba8 100644 --- a/datasets/FIRE_CI2_SABRLNR_IWC_1.json +++ b/datasets/FIRE_CI2_SABRLNR_IWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_SABRLNR_IWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR aircraft during FIRE II. The 2D-C data are converted to size spectra according to the guidelines given in Heymsfield and Baumgardner (1985, Bull. Amer. Meteoro. Soc.), where one element is added to the size of a particle along the the flight direction to account for the probe's intrinsic start-up time. Size is determined as the maximum dimension ($D_{max}$) along the flight direction or optical array axis. The nominal size resolution for the Sabreliner 2D probe is 50 microns/per shadowed optical array element, for the King Air is 25 microns/bin. Sample area (SA) is derived using the depth of field estimates reported by Knollenberg (1970). Particles are binned into 32 size categories, nonuniformly spaced with higher resolution in the smaller classes. Particles within each size bin are subdivided into 10 ``area ratio (AR)'' bins, where AR represents the ratio of particle area to the area of discs of diameter $D_{max} The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR Sabreliner during FIRE II. The derivation of the microphysical parameters is outlined in the later reference to Heymsfield (1977). The vertical velocity is the steady-state velocity in cm s-1 to keep the relative humidity at it's currently measured value. Differential growth rate represents the growth rate of the particle population of different sizes at the current relative humidity. The Total differential growth rate is thesum of the growth rate in all channels. The assumptions used for the IWC calculations are reported in Heymsfield; also, generic size to mass equations are used. Precipitation rate is calculated from particle size and terminal velocity data, integrated over the size spectrum. Concentration data are as derived above. Number of crystal-crystal collisions are derived from the data reported by Hindman and the crystal terminal velocities. Water vapor density andsupersaturation information in this data set should not be used--it is unreliable. Curve fits to the data using least squares methods are provided. VARIABLE DESCRIPTION UNITS ------------------------------------------------------------------------------- IT1,IT2 MEASUREMENT TIME INTERVAL HH/MM/SS PS STATIC PRESSURE mb TEMP AMBIENT TEMPERATURE degreesC ALT PRESSURE ALTITUDE m USTAR VERTICAL VELOCITY NEEDED TO KEEP THE cm/s RELATIVE HUMIDITY CONSTANT DBARM MEDIAN PARTICLE MASS WEIGHTED DIAMETER cm DMAX MAXIMUM PARTICLE DIAMETER cm W1 DIFFUSIONAL GROWTH RATE IN CHANNEL 1 g/sec W2 DIFFUSIONAL GROWTH RATE IN CHANNEL 2 g/sec W3 DIFFUSIONAL GROWTH RATE IN CHANNEL 3 g/sec W4 DIFFUSIONAL GROWTH RATE IN CHANNEL 4 g/sec WTOT TOTAL DIFFUSTIONAL GROWTH RATE g/sec DT8 DEPLETION TIME (8 micron droplets) sec DT12 DEPLETION TIME (12 micron droplets) sec TMASS1 IWC IN CHANNEL 1 g/m^3 TMASS2 IWC IN CHANNEL 2 g/m^3 DPTC DEW POINT TEMPERATURE (EG&G) degreesC RH RELATIVE HUMIDITY (EG&G) % RIWC ICE WATER CONTENT g/m^3 XM1 ICE WATER CONTENT BASED ON SNOW HABIT g/m^3 XM2 ICE WATER CONTENT BASED ON SMALL SNOW g/m^3 HABIT XM3 ICE WATER CONTENT BASED ON LARGE SNOW g/m^3 HABIT R PRECIPITATION RATE mm/hr DBZ RADAR REFLECTIVITY FACTOR decibels VBAR MEAN REFLECTIVITY WEIGHTED WITH THE cm/s TERMINAL VELOCITY TTCONC TOTAL PARTICLE CONCENTRATION #/L CBIN1 PARTICLE CONCENTRATION WITHIN THE RANGE 200#/L LE CBIN2 PARTICLE CONCENTRATION WITHIN THE RANGE #/L 200-500 CBIN3 PARTICLE CONCENTRATION WITHIN THE RANGE 500-800 #/L CBIN4 PARTICLE CONCENTRATION WITHIN THE RANGE 800 #/L GT CE8 COLLECTION EFFICIENCY (8 micron none droplets) CE12 COLLECTION EFFICIENCY (12 micron none droplets) TMASS3 IWC IN CHANNEL 3 g/m^3 TMASS4 IWC IN CHANNEL 4 g/m^3 TIMP # OF CRYSTAL-CRYSTAL COLUMNS sec^(1-) RHORH WATER VAPOR DENSITY g/cm^3 SI SUPERSATURATION WITH RESPECT TO ICE % SW SUPERSATURATION WITH RESPECT TO WATER % LAMBDA COEFFICIENTS USED TO FIT THE EQUATION #/cm^3 NZERO N=N0*EXP(-LAMBDA*D) #/L/mm RSQ COEFFICIENT OF THE FIT none ICP PROBE TYPE (C OR P) none", "links": [ { diff --git a/datasets/FIRE_CI2_SPECT_SIRIS_1.json b/datasets/FIRE_CI2_SPECT_SIRIS_1.json index 05462bdea6..84b0371db4 100644 --- a/datasets/FIRE_CI2_SPECT_SIRIS_1.json +++ b/datasets/FIRE_CI2_SPECT_SIRIS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_SPECT_SIRIS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.SPECTRE/SIRIS high spectral resolution observations were obtained at Coffeyville, Kansas in November - December 1991. The SIRIS instrument has been previously flown for balloon-borne studies of stratospheric chemistry relevant to the ozone cycles. It is a modified version of a Bomem continuously scanning Fourier transform spectrometer, operating in emission mode. The following instrument parameters were applicable for the Coffeyville SPECTRE campaign. The field-of-view, 0.5 degrees full width at half-maximum, was directed towards the zenith, except for a day when limb were recorded. The highest emission-mode spectral resolution recorded during SPECTRE was taken by SIRIS 0.06 cm-1, apodized. Scan times varied from one to a few minutes, depending onthe resolution. The instrument was run at ambient temperature, withthe Si:Ga detectors at liquid helium (LHe) temperature. Data are limited by photon noise from the emission from the instrument and from the atmosphere itself. Therefore data were recorded with two different width bandpasses: 1) narrow bandpass cooled filters in channels 1-4, which reduces the background noise, yielding higher signal-to-noise; and 2) wide band in channel 5 for more complete spectral coverage.It was the goal of SPECTRE to acquire clear-sky radiance spectra under a variety of temperature and water vapor conditions.", "links": [ { diff --git a/datasets/FIRE_CI2_UTAH_PDL_1.json b/datasets/FIRE_CI2_UTAH_PDL_1.json index c0c14470e0..692b264653 100644 --- a/datasets/FIRE_CI2_UTAH_PDL_1.json +++ b/datasets/FIRE_CI2_UTAH_PDL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_UTAH_PDL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Lidar returned signal in arbitrary units, raw data, background subtracted, Minimum value = 0, Maximum value = 25600, Scaling Factor = 100 A description of the lidar is given in the following paper: Sassen, K., 1994: Advances in polarization diversity lidar for cloud remote sensing, Proc. IEEE, 82, 1907-1914", "links": [ { diff --git a/datasets/FIRE_CI2_VIL_RTI_1.json b/datasets/FIRE_CI2_VIL_RTI_1.json index 44d899088b..0e2b41c221 100644 --- a/datasets/FIRE_CI2_VIL_RTI_1.json +++ b/datasets/FIRE_CI2_VIL_RTI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_VIL_RTI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set contains altitude vs. time images (RTIs) of cirrus clouds collected during FIRE Cirrus 2 at Coffeyville, Kansas. The images were sampled at 5 km intervals in the cross wind scans.", "links": [ { diff --git a/datasets/FIRE_CI2_VIL_SCAN_1.json b/datasets/FIRE_CI2_VIL_SCAN_1.json index e49a36f106..180851578d 100644 --- a/datasets/FIRE_CI2_VIL_SCAN_1.json +++ b/datasets/FIRE_CI2_VIL_SCAN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_CI2_VIL_SCAN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set contains images of cirrus cloud scans of 120 km extent both along the wind and across the wind (at the cirrus clouds heights). These images were collected during FIRE Cirrus 2 in Coffeyville, Kansas.", "links": [ { diff --git a/datasets/FIRE_ETO_UTAH_PDL_1.json b/datasets/FIRE_ETO_UTAH_PDL_1.json index 5d6695f43f..04b7e9a379 100644 --- a/datasets/FIRE_ETO_UTAH_PDL_1.json +++ b/datasets/FIRE_ETO_UTAH_PDL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_ETO_UTAH_PDL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Lidar returned signal in arbitrary units, raw data, background subtracted, Minimum value = 0, Maximum value = 25600, Scaling Factor = 100 A description of the lidar is given in the following paper: Sassen, K., 1994: Advances in polarization diversity lidar for cloud remote sensing, Proc. IEEE, 82, 1907-1914", "links": [ { diff --git a/datasets/FIRE_MS_CEILOM_CLASS_1.json b/datasets/FIRE_MS_CEILOM_CLASS_1.json index ec62e639ff..514323c3de 100644 --- a/datasets/FIRE_MS_CEILOM_CLASS_1.json +++ b/datasets/FIRE_MS_CEILOM_CLASS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_CEILOM_CLASS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These data were collected during the FIRE Marine Stratocumulus experiment on San Nicolas Island, California. They are as follows: cloud base height data measured with a ceilometer; processed CLASS sounding (CSD) data up to 2 kilometers (thermodynamic data only), raw CSD recorded at 3.3 second intervals (thermodynamic data only), and raw CSD at 10 second intervals (thermodynamic and wind data).", "links": [ { diff --git a/datasets/FIRE_MS_CSU_RADSURF_1.json b/datasets/FIRE_MS_CSU_RADSURF_1.json index ff83cfb9db..3081f00dbe 100644 --- a/datasets/FIRE_MS_CSU_RADSURF_1.json +++ b/datasets/FIRE_MS_CSU_RADSURF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_CSU_RADSURF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The Colorado State University surface radiation data set was collected from the radiometric ground station on San Nicolas Island, California. These data were collected during the FIRE marine stratocumulus IFO. The data consists of ten minute averages of wind speed, wind direction, air temperature, relative humidity and shortwave (.3 - 2.8 microns), near IR (.7 - 2.8 microns), and longwave (4 - 50 microns) radiation.", "links": [ { diff --git a/datasets/FIRE_MS_CSU_TBALLOON_1.json b/datasets/FIRE_MS_CSU_TBALLOON_1.json index 6990ed7315..8e6915196c 100644 --- a/datasets/FIRE_MS_CSU_TBALLOON_1.json +++ b/datasets/FIRE_MS_CSU_TBALLOON_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_CSU_TBALLOON_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set was collected from the Colorado State University tethered platform. The balloon was flown during the FIRE Marine Stratocumulus IFO on San Nicolas Island. The data set has been interpolated to the times of the cloud microphysics data, which has a five second interval between points.", "links": [ { diff --git a/datasets/FIRE_MS_ELECTRA_1.json b/datasets/FIRE_MS_ELECTRA_1.json index aaa0e14717..f425c90331 100644 --- a/datasets/FIRE_MS_ELECTRA_1.json +++ b/datasets/FIRE_MS_ELECTRA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_ELECTRA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Data were collected from the NCAR Electra aircraft during the FIRE Marine Stratocumulus experiment in July 1987. The data were produced by the NCAR Research Aviation Facility (RAF) Data Management Group, with the GENPRO-II data processing software. The format of these data include a header file and a data file which corresponds to all or part of a particular aircraft flight.", "links": [ { diff --git a/datasets/FIRE_MS_ER2_LIDAR_1.json b/datasets/FIRE_MS_ER2_LIDAR_1.json index 6085e07e86..7296155c79 100644 --- a/datasets/FIRE_MS_ER2_LIDAR_1.json +++ b/datasets/FIRE_MS_ER2_LIDAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_ER2_LIDAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. This data set contains cloud top height and ground height calculations from the NASA ER-2 Cloud Lidar System (CLS). These data were collected during the FIRE Marine Stratocumulus experiment in July 1987; the parameters collected included time, position, and plane height. Undetected cloud tops and ground heights are signified by values of -9.9 after decoding.", "links": [ { diff --git a/datasets/FIRE_MS_ISCCP_DX_1.json b/datasets/FIRE_MS_ISCCP_DX_1.json index a382485e4a..94118293e4 100644 --- a/datasets/FIRE_MS_ISCCP_DX_1.json +++ b/datasets/FIRE_MS_ISCCP_DX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_ISCCP_DX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. A subset of the ISCCP Stage DX Cloud Product (Revised Algorithm) are included for the FIRE Marine Stratocumulus region.", "links": [ { diff --git a/datasets/FIRE_MS_MCRW_RAD_1.json b/datasets/FIRE_MS_MCRW_RAD_1.json index ac7db29443..b58cb339d1 100644 --- a/datasets/FIRE_MS_MCRW_RAD_1.json +++ b/datasets/FIRE_MS_MCRW_RAD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_MCRW_RAD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.Microwave radiometer with steerable antenna was used for the measurement of column amounts of liquid water in clouds, and precipitable water vapor in the atmosphere. Antenna was directed to the zenith during FIRE I.Operating frequencies: 20.6, 31.65, 90.0 GHzSpatial resolution: 2.5 deg antenna beamwidth (44m @ 1.0 km range)Temporal resolution: 60 secEstimated accuracies Liquid water: +/- 10 percent or better (absolute) Noise level +/- .025 mm Water vapor: 0.08 cm rms relative to radiosonde (Vapor and liquid data retrievals were from 20.6 and 31.65 GHz data only)Radiometer location: San Nicolas Island, northwestern tip 33.27N, 119.58W", "links": [ { diff --git a/datasets/FIRE_MS_NOAAWNDS_1.json b/datasets/FIRE_MS_NOAAWNDS_1.json index b866900dcf..cf0913b4bf 100644 --- a/datasets/FIRE_MS_NOAAWNDS_1.json +++ b/datasets/FIRE_MS_NOAAWNDS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_NOAAWNDS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.There are three types of NOAA wind profiler data, all have been splined to a 25-meter vertical resolution and a 1-hour temporal resolution. Parameters include potential temperature derived from the CLASS (CSU, Steve Cox) radiosonde (100 to 2300 M above sea level), smoothed merged Pennsylvania State University (PSU) sodar and profiler wind speeds and directions (300 to 2075 M above sea level) and derived Richardson Numbers from these data (325-2050 M MSL).", "links": [ { diff --git a/datasets/FIRE_MS_UKMO_C130_1.json b/datasets/FIRE_MS_UKMO_C130_1.json index abe3117cb3..22ad8d722d 100644 --- a/datasets/FIRE_MS_UKMO_C130_1.json +++ b/datasets/FIRE_MS_UKMO_C130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FIRE_MS_UKMO_C130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. These data were collected by the United Kingdom Meteorological Office (UKMO) from the Meteorological Research Flight C-130 Aircraft. This data set is a 16 HZ time series data set.", "links": [ { diff --git a/datasets/FLASH_SSF_Aqua-FM3-MODIS_Version4A.json b/datasets/FLASH_SSF_Aqua-FM3-MODIS_Version4A.json index 263f1b84ec..735d37f862 100644 --- a/datasets/FLASH_SSF_Aqua-FM3-MODIS_Version4A.json +++ b/datasets/FLASH_SSF_Aqua-FM3-MODIS_Version4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_SSF_Aqua-FM3-MODIS_Version4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_SSF_Aqua-FM3-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) Aqua-FM3-MODIS data in HDF Version 4A data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. Data collection for this product is in progress.\r\n\r\nFLASHFlux data are a product line of the CERES project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1A.json b/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1A.json index 091546359a..49f0ab8ef9 100644 --- a/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1A.json +++ b/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_SSF_NOAA20-FM6-VIIRS_Version1A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_SSF_NOAA20-FM6-VIIRS_Version1A data are near real-time CERES observed TOA fluxes, clouds, and parameterized surface fluxes, not officially calibrated. The Fast Longwave and SHortwave Flux (FLASHFlux) data are a product line of the Clouds and the Earth's Radiant Energy Systems (CERES) project designed for processing and release of top-of-atmosphere (TOA) and surface radiative fluxes within one week of CERES instrument measurement. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality.FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a week of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Fast Longwave And SHortwave Fluxes (FLASHFlux) data for a single Clouds and the Earth's Radiant Energy Systems (CERES) scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 satellite and meteorological and ozone information from The Goddard Earth Observing System GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher imager resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and incoming NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. CERES instrument Flight Model 5 (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The latest CERES instrument (FM6) was launched on board NOAA-20 on November 18, 2017.", "links": [ { diff --git a/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1B.json b/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1B.json index 1c96dd211e..3fa4950dfc 100644 --- a/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1B.json +++ b/datasets/FLASH_SSF_NOAA20-FM6-VIIRS_Version1B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_SSF_NOAA20-FM6-VIIRS_Version1B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_SSF_NOAA20-FM6-VIIRS_Version1B data are near real-time CERES observed TOA fluxes, clouds, and parameterized surface fluxes, not officially calibrated. The Fast Longwave and SHortwave Flux (FLASHFlux) data are a product line of the Clouds and the Earth's Radiant Energy Systems (CERES) project designed for processing and release of top-of-atmosphere (TOA) and surface radiative fluxes within one week of CERES instrument measurement. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality.FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a week of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Fast Longwave And SHortwave Fluxes (FLASHFlux) data for a single Clouds and the Earth's Radiant Energy Systems (CERES) scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 satellite and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and incoming NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. CERES instrument Flight Model 5 (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The latest CERES instrument (FM6) was launched on board NOAA-20 on November 18, 2017.", "links": [ { diff --git a/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4A.json b/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4A.json index 9fa5f83387..2e3822008d 100644 --- a/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4A.json +++ b/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_SSF_Terra-FM1-MODIS_Version4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_SSF_Terra-FM1-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. \r\n\r\nFLASHFlux data are a product line of the CERES project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument, Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4B.json b/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4B.json index 44bb9b97a0..03abf51ace 100644 --- a/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4B.json +++ b/datasets/FLASH_SSF_Terra-FM1-MODIS_Version4B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_SSF_Terra-FM1-MODIS_Version4B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_SSF_Terra-FM1-MODIS_Version4B is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in netCDF Version 4B data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. \r\n\r\nFLASHFlux data are a product line of the CERES project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-5 IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument, Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/FLASH_TISA_Terra-Aqua_Version4A.json b/datasets/FLASH_TISA_Terra-Aqua_Version4A.json index 91d6dc76ba..35e13e80a1 100644 --- a/datasets/FLASH_TISA_Terra-Aqua_Version4A.json +++ b/datasets/FLASH_TISA_Terra-Aqua_Version4A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_TISA_Terra-Aqua_Version4A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_TISA_Terra-Aqua_Version4a is the Fast Longwave And SHortwave Fluxes (FLASHFlux) Daily Gridded Single Satellite Top-of-Atmosphere (TOA) and Surfaces/Clouds data in HDF Version 4A data product. This product contains low latency (< 7 days from observations) combined Terra and Aqua FLASHFlux Single Scanner Footprint (SSF) globally gridded TOA and parameterized surface radiative fluxes for applied science uses. Data collection for this product is in progress.\r\n\r\nFLASHFlux data are a product line of the Clouds and the Earth's Radiant Energy Systems (CERES) project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument, Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/FLASH_TISA_Terra-NOAA20_Version4B.json b/datasets/FLASH_TISA_Terra-NOAA20_Version4B.json index 21b3b78735..6db81df0e9 100644 --- a/datasets/FLASH_TISA_Terra-NOAA20_Version4B.json +++ b/datasets/FLASH_TISA_Terra-NOAA20_Version4B.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_TISA_Terra-NOAA20_Version4B", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_TISA_Terra-NOAA20_Version4B is the Fast Longwave And SHortwave Fluxes (FLASHFlux) Daily Gridded Top-of-Atmosphere (TOA) and Surfaces/Clouds Version 4B data product. This product contains low latency (< 7 days from observations) combined Terra and NOAA-20 FLASHFlux Single Scanner Footprint (SSF) globally gridded TOA and parameterized surface radiative fluxes for applied science uses. Data collection for this product is in progress.\r\n\r\nFLASHFlux data are a product line of the Clouds and the Earth's Radiant Energy Systems (CERES) project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra, Aqua, and NOAA-20 spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). NOAA-20 SSF combines instantaneous CERES data with scene information from the Visible Infrared Imaging Radiometer Suite (VIIRS) with GEOS-5 ADAS. Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. CERES instrument Flight Model 5 (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite and the CERES instrument (FM6) was launched on board NOAA's next generation of polar-orbiting satellites on November 18, 2017.", "links": [ { diff --git a/datasets/FLASH_TISA_Terra-NOAA20_Version4C.json b/datasets/FLASH_TISA_Terra-NOAA20_Version4C.json index 65c9a3f334..765c41d2cb 100644 --- a/datasets/FLASH_TISA_Terra-NOAA20_Version4C.json +++ b/datasets/FLASH_TISA_Terra-NOAA20_Version4C.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLASH_TISA_Terra-NOAA20_Version4C", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLASH_TISA_Terra-NOAA20_Version4C is the Fast Longwave And SHortwave Fluxes (FLASHFlux) Daily Gridded Top-of-Atmosphere (TOA) and Surfaces/Clouds Version 4C data product. This product contains low latency (< 7 days from observations) combined Terra and NOAA-20 FLASHFlux Single Scanner Footprint (SSF) globally gridded TOA and parameterized surface radiative fluxes for applied science uses. Data collection for this product is in progress.\r\n\r\nFLASHFlux data are a product line of the Clouds and the Earth's Radiant Energy Systems (CERES) project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra, Aqua, and NOAA-20 spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-IT Atmospheric Data Assimilation System. NOAA-20 SSF combines instantaneous CERES data with scene information from the Visible Infrared Imaging Radiometer Suite (VIIRS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. CERES instrument Flight Model 5 (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite and the CERES instrument (FM6) was launched on board NOAA's next generation of polar-orbiting satellites on November 18, 2017.", "links": [ { diff --git a/datasets/FLDAS_NOAH01_CP_GL_M_001.json b/datasets/FLDAS_NOAH01_CP_GL_M_001.json index 535e738b3f..0ca1742c61 100644 --- a/datasets/FLDAS_NOAH01_CP_GL_M_001.json +++ b/datasets/FLDAS_NOAH01_CP_GL_M_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLDAS_NOAH01_CP_GL_M_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). \n\nThe simulation was forced by a combination of the Global Data Assimilation System (GDAS) data and Climate Hazards Group InfraRed Precipitation with Station Preliminary (CHIRPS-PRELIM) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit, restarted from CHIRPS-FINAL of the previous month. The simulation was initialized on January 1, 2019 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. ", "links": [ { diff --git a/datasets/FLDAS_NOAH01_C_GL_MA_001.json b/datasets/FLDAS_NOAH01_C_GL_MA_001.json index de9ef79df4..cbceb21f07 100644 --- a/datasets/FLDAS_NOAH01_C_GL_MA_001.json +++ b/datasets/FLDAS_NOAH01_C_GL_MA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLDAS_NOAH01_C_GL_MA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly anomaly data set contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of monthly files, each representing how the month compares to the 35-year monthly climatology from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data. The data are in 0.10 degree resolution and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly anomaly datasets will no longer be available and have been superseded by the global monthly anomaly dataset. \n\nMore information about the monthly FLDAS can be found from the dataset landing page for FLDAS_NOAH01_C_GL_M_001 and the FLDAS README document. \n\nIn November 2020, all FLDAS data were\u00a0post-processed with the MOD44W MODIS land mask. \u00a0Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.\u00a0 The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect\u00a0the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\u00a0", "links": [ { diff --git a/datasets/FLDAS_NOAH01_C_GL_MC_001.json b/datasets/FLDAS_NOAH01_C_GL_MC_001.json index ac390d0a4b..bb97027445 100644 --- a/datasets/FLDAS_NOAH01_C_GL_MC_001.json +++ b/datasets/FLDAS_NOAH01_C_GL_MC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLDAS_NOAH01_C_GL_MC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly climatology dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of 12 monthly files, each representing the monthly data averaged over 35 years from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data. The data are in 0.10 degree resolution and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly climatology datasets will no longer be available and have been superseded by the global monthly climatology dataset. \n\nMore information about the monthly FLDAS can be found from the dataset landing page for FLDAS_NOAH01_C_GL_M_001 and the FLDAS README document.\n\nIn November 2020, all FLDAS data were\u00a0post-processed with the MOD44W MODIS land mask. \u00a0Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.\u00a0 The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect\u00a0the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\u00a0", "links": [ { diff --git a/datasets/FLDAS_NOAH01_C_GL_M_001.json b/datasets/FLDAS_NOAH01_C_GL_M_001.json index c63b91f42f..ba7b4a6bcc 100644 --- a/datasets/FLDAS_NOAH01_C_GL_M_001.json +++ b/datasets/FLDAS_NOAH01_C_GL_M_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLDAS_NOAH01_C_GL_M_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly datasets will no longer be available and have been superseded by the global monthly dataset. \n\nThe simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit.\n\nThe simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.\n\nIn November 2020, all FLDAS data were\u00a0post-processed with the MOD44W MODIS land mask. \u00a0Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.\u00a0 The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect\u00a0the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\u00a0 ", "links": [ { diff --git a/datasets/FLDAS_NOAHMP001_G_CA_D_001.json b/datasets/FLDAS_NOAHMP001_G_CA_D_001.json index 6baa1db1e2..138aa0d248 100644 --- a/datasets/FLDAS_NOAHMP001_G_CA_D_001.json +++ b/datasets/FLDAS_NOAHMP001_G_CA_D_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLDAS_NOAHMP001_G_CA_D_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land surface parameters simulated by the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System version 2 (FLDAS2) Central Asia model. The FLDAS2 Central Asia model is a custom instance of the NASA Land Information System that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The data are produced using the Noah Multi-Parameterization (Noah-MP) version 4.0.1 Land Surface Model (LSM) forced by Global Data Assimilation System (GDAS) meteorological data.\n\nThe FLDAS2 Central Asia dataset is produced daily with a one-day latency. Data are available from October 1, 2000 to present. The dataset contains 27 parameters at a 0.01 degree spatial resolution over the Central Asia region (30-100\u00b0E, 21-56\u00b0N). \n", "links": [ { diff --git a/datasets/FLEXPART_Influence_Functions_2018_1.json b/datasets/FLEXPART_Influence_Functions_2018_1.json index 621566b6fb..6325abc5d3 100644 --- a/datasets/FLEXPART_Influence_Functions_2018_1.json +++ b/datasets/FLEXPART_Influence_Functions_2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLEXPART_Influence_Functions_2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a set of Lagrangian particle dispersion simulations of carbon dioxide concentrations using the FLEXible PARTicle (FLEXPART) model. FLEXPART quantified the source-receptor relationships, so-called \"influence functions\", in a backward mode. The simulations were constructed for five Atmospheric Carbon and Transport America (ACT-America) deployments over the eastern U.S. that occurred in 2016-2019. Each receptor of the influence function is the 30-second or 10-minute interval along flight tracks, characterized by a box with boundaries between the maximum and minimum latitude/longitude as well as between the maximum and minimum altitudes during the interval. Each receptor box released 5,000 particles and simulated their transport and dispersion backward for 10 or 20 days. The simulations were driven by 27-km meteorology provided by the WRF-Chem simulation or by ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Background levels of carbon dioxide were obtained from CarbonTracker and OCO-2 v9 MIP. The data are provided in netCDF and FLEXPART binary formats.", "links": [ { diff --git a/datasets/FLOODAMA_903_1.json b/datasets/FLOODAMA_903_1.json index 7e9e2f4c95..9b61f5433f 100644 --- a/datasets/FLOODAMA_903_1.json +++ b/datasets/FLOODAMA_903_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLOODAMA_903_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a digital mosaic of the Amazon River floodplain that was compiled using Landsat TM images. This mosaic was planned in July 1995 as an activity of the EOS-IDS Project that was developed with cooperation among INPE, CENA, University of Washington in Seattle (UW), University of California in Santa Barbara (UCSB), and NASA. The mosaic is composed by 29 Landsat TM images covering a period from 1986 to 1995 that were selected with minimum cloud cover and within the July to September high water season of the Amazon River. These images were geometrically corrected using ground control points extracted from topographic charts and image charts at the 1:250,000 scale. In addition, these images were radiometrically rectified to 231/062 (Manaus region) TM image using the method developed by Hall et al. (1991). The radiometric rectification applied had a good performance for bands 3, 5, and 7, for most of the scenes. For bands 1 and 2 the radiometric rectification was limited, especially for scenes with intense haze. Nevertheless, the overall performance of radiometric normalization allowed the production of a uniform data set for the entire Brazilian Amazon River mainstem floodplain. The mosaic was then built using the best bands (rectified or non-rectified) of the TM images with 90 meter spatial resolution. The mosaic data are provided in geoTIFF-formatted files, rectified and geocoded, for six TM bands (1 to 5 and 7) with 90-meter spatial resolution. The mosaic is divided in two parts: Part 1, from the mouth of the Amazon river in Brazil to the Brazil/Peru boundary and Part 2, from the Brazil/Peru boundary to its spring.There is also a 500-meter resolution mosaic covering all the Amazon river (from spring to the mouth) with geoTIFF-formatted data files for TM bands 3, 4, and 5. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. ", "links": [ { diff --git a/datasets/FLUAMAZON_896_1.json b/datasets/FLUAMAZON_896_1.json index c71578465f..b8073577ed 100644 --- a/datasets/FLUAMAZON_896_1.json +++ b/datasets/FLUAMAZON_896_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLUAMAZON_896_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FLUAMAZON Experiment data set includes meteorological data collected with radiosondes to examine the moisture flux from the northern coast of South America (near the mouth of the Amazon River) into central Amazonia. The measurements were collected from November 23, to December 21, 1989 during the period of transition between the dry and humid seasons in the region. Some of the studies performed with data from FLUAMAZON were related to the atmospheric thermodynamic structure over Amazonia. During FLUAMAZON, radiosonde measurements were made simultaneously in five different locations: Alcantara, Belem, Oiapoque, Manaus, and Alta Floresta. ASCII text data files for each location have been compiled and compressed into site-specific zipped files.", "links": [ { diff --git a/datasets/FLUXNET_Canada_1335_1.json b/datasets/FLUXNET_Canada_1335_1.json index 5d4fa9341c..a22f0baa07 100644 --- a/datasets/FLUXNET_Canada_1335_1.json +++ b/datasets/FLUXNET_Canada_1335_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FLUXNET_Canada_1335_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLUXNET Canada is a Fluxnet research network comprised of the Fluxnet-Canada Research Network (FCRN) and the Canadian Carbon Program (CCP) operating from 1993 through 2014. It was a national research network of university and government scientists studying the influence of climate and disturbance on carbon cycling along an east-west transect of Canadian forest and peat land ecosystems. The data provided are measured and modeled results as obtained from the site investigators. They were not standardized and quality-controlled. Data include: atmospheric carbon dioxide (CO2) and water vapor fluxes and many ancillary meteorological variables; soil CO2 efflux and soil moisture; stable carbon isotopes; site soil and vegetation characteristics, plus documentation and descriptions for the 32 tower sites across 12 flux research stations. The time period is from 1993 - 2014; most reported data for a site does not cover the entire period.", "links": [ { diff --git a/datasets/FRAMNESCOAST_1.json b/datasets/FRAMNESCOAST_1.json index d6ad79e88f..9cc0ece64f 100644 --- a/datasets/FRAMNESCOAST_1.json +++ b/datasets/FRAMNESCOAST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FRAMNESCOAST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Photogrammetric Map Data and Orthophotography of the Framnes Mountain Coastal Region taken from 1:45000, 1:10000 and 1:20000 1996/97, aerial photography and 1988/89 SPOT scenes. Data Layers consist of: coast, contour, geology (erratics only), human, lake, spot_height, topoline and topopoly.", "links": [ { diff --git a/datasets/FRM4SOC2_0.json b/datasets/FRM4SOC2_0.json index 10e6cb5823..be7675e6df 100644 --- a/datasets/FRM4SOC2_0.json +++ b/datasets/FRM4SOC2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FRM4SOC2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FRM4SOC2 is a funded by the European Commission as part of the Copernicus Programme and implemented by EUMETSAT to promote the adoption of FRM principles across the OC community towards enhancing satellite product validation and algorithm development.", "links": [ { diff --git a/datasets/FRONT_0.json b/datasets/FRONT_0.json index 49d3bc73cd..bcf95b1730 100644 --- a/datasets/FRONT_0.json +++ b/datasets/FRONT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FRONT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Ocean Partnership Program (NOPP) Front Resolving Observational Network with Telemetry (FRONT) site, New England, measurements made between 2000 and 2002.", "links": [ { diff --git a/datasets/FSNRAD_L2_VIIRS_CRIS_NOAA20_2.json b/datasets/FSNRAD_L2_VIIRS_CRIS_NOAA20_2.json index 9f2b3f1e64..393bc67dd4 100644 --- a/datasets/FSNRAD_L2_VIIRS_CRIS_NOAA20_2.json +++ b/datasets/FSNRAD_L2_VIIRS_CRIS_NOAA20_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FSNRAD_L2_VIIRS_CRIS_NOAA20_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with the High Resolution Infrared Radiation Sounder (HIRS) and the Moderate resolution Imaging Spectroradiometer (MODIS) and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution). These spectral bands are similar to the MODIS spectral bands. The fusion radiances, and look-up tables required to compute the related brightness temperatures, are stored in compressed NetCDF4 files of 6-minutes duration.\r\n\r\nThis L2 NOAA-20 VIIRS+CrIS product release relates to Version-2.0 (v2.0) collection, which has undergone some improvements over its previous version. In the v2.0 Fusion product, scanlines are checked for quality instead of the entire input granule as was done in the previous version of this product. Such a process has helped salvage and use granules with continuous blocks of good data with good calibration. The v2.0 product also includes a couple of improvements to the VIIRS-CrIS collocation. The first relates to how VIIRS scan sync loss events are addressed while the other correctly characterizes VIIRS pixels that should have been identified as falling within a CrIS Field-of-View. A final improvement in the v2.0 product attempts to correct an artifact detected over warm, dry surfaces in the water vapor channels that are derived using the MODIS Band-27 and -28 response functions that apparently manifest signs of surface features that should not exist for these channels. Check the User Guide for further details on these improvements.\r\n\r\nThe L2 VIIRS+CrIS Fusion product has a horizontal pixel size of 750 m, which is the native VIIRS moderate-resolution (M) band pixel-size. Consult the VIIRS+CrIS Fusion product User Guide (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/VIIRSCrISFusionUserGuidev1.11.pdf) for additional information regarding this product\u2019s algorithm, file format, global and data-field attributes, quality control flags, etc. ", "links": [ { diff --git a/datasets/FSNRAD_L2_VIIRS_CRIS_SNPP_2.json b/datasets/FSNRAD_L2_VIIRS_CRIS_SNPP_2.json index 11e69a79ae..50ee51aae8 100644 --- a/datasets/FSNRAD_L2_VIIRS_CRIS_SNPP_2.json +++ b/datasets/FSNRAD_L2_VIIRS_CRIS_SNPP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FSNRAD_L2_VIIRS_CRIS_SNPP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with HIRS and MODIS and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution). These spectral bands are similar to the MODIS spectral bands.\r\n\r\nThis L2 SNPP VIIRS+CrIS product release relates to Version-2.0 (v2.0) collection, which has undergone some improvements over its previous version. In the v2.0 Fusion product, scanlines are checked for quality instead of the entire input granule as was done in the previous version of this product. Such a process has helped salvage and use granules with continuous blocks of good data with good calibration. The v2.0 product also includes a couple of improvements to the VIIRS-CrIS collocation. The first relates to how VIIRS scan sync loss events are addressed while the other correctly characterizes VIIRS pixels that should have been identified as falling within a CrIS Field-of-View. A final improvement in the v2.0 product attempts to correct an artifact detected over warm, dry surfaces in the water vapor channels that are derived using the MODIS Band-27 and -28 response functions that apparently manifest signs of surface features that should not exist for these channels. Check the User Guide for further details on these improvements.\r\n\r\nThe fusion radiances, and look-up tables required to compute the related brightness temperatures, are stored in compressed NetCDF4 files of 6-minutes duration.\r\nThe L2 VIIRS+CRiS Fusion product has a horizontal pixel size of 750m, which is the native VIIRS moderate-resolution (M) band pixel-size. \r\n\r\nConsult the VIIRS+CRiS Fusion product User Guide for additional information regarding this product\u2019s algorithm, file format, global and data-field attributes, quality control flags, etc. ", "links": [ { diff --git a/datasets/FSSCat.products_5.0.json b/datasets/FSSCat.products_5.0.json index a2cce03122..c1f6013539 100644 --- a/datasets/FSSCat.products_5.0.json +++ b/datasets/FSSCat.products_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FSSCat.products_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FSSCat collection provides hyperspectral data coverage over a number of locations around the world, as measured by the HyperScout 2 sensor.\r\rThe FSSCat hyperspectral data products are comprised of 50 spectral bands, covering a spectral range of 450 \u2013 950 nm with a spectral resolution of 18 nm (at FWHM). Imagery is available with an along-track ground sampling distance (GSD) of 75 m. To ensure a high degree of radiometric accuracy, HyperScout 2 data are validated through comparison with Sentinel-2 data products.\r\rThe processing level of the data is L1C \u2013 calibrated top-of-atmosphere radiance, reflectance or brightness temperature. The raster type of the L1C data product is a GRID \u2013 a 2D or 3D raster where the (geo)location of the data is uniquely defined by the upper left pixel location of the raster and the pixel size of the raster, and the projection parameters of the raster (if georeferenced). The third dimension can e.g. be a spectral or third spatial dimension.\r\rThe L-1C VNIR data product includes a hyperspectral cube of TOA reflectance in the VNIR range, as well as relevant meta-data that adheres to EDAP's best practice guidelines. This product consists of georeferenced and ortho-rectified image tiles that contain spectral reflectance data at the top-of-the-atmosphere. Each image tile contains radiometrically corrected and ortho-rectified band images that are projected onto a map, as well as geolocation information and the coordinate system used. Additionally, each image pixel provides TOA spectral reflectance data in scaled integers, conversion coefficients for spectral radiance units, viewing and solar zenith and azimuth angles, and quality flags.", "links": [ { diff --git a/datasets/F_Bibliography_1.json b/datasets/F_Bibliography_1.json index 5f7a8225f1..29363fb8db 100644 --- a/datasets/F_Bibliography_1.json +++ b/datasets/F_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "F_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of references relating to flora from the Antarctic and subantarctic regions, dating from 1859 to 2002. The bibliography was compiled by Dana Bergstrom, and contains 993 references.", "links": [ { diff --git a/datasets/FieldData_Alaska_Tundra_2177_1.json b/datasets/FieldData_Alaska_Tundra_2177_1.json index 76aebf1db9..51b7328787 100644 --- a/datasets/FieldData_Alaska_Tundra_2177_1.json +++ b/datasets/FieldData_Alaska_Tundra_2177_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FieldData_Alaska_Tundra_2177_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset. The variables include active layer thickness, vegetation cover (by plant functional types), soil moisture and temperatures, as well as the wildfire history. SATFiD provides a unique lens into various long-term ecological processes within the tundra (such as the fire-permafrost-vegetation interactions) under a rapidly changing climate.", "links": [ { diff --git a/datasets/Field_Measurements_868_1.json b/datasets/Field_Measurements_868_1.json index 99f3d15df5..50e57d2c46 100644 --- a/datasets/Field_Measurements_868_1.json +++ b/datasets/Field_Measurements_868_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Field_Measurements_868_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot project gathered field data for selected EOS Land Validation Sites in North America from 1999 to 2003. Data collected and derived for varying intervals at the BigFoot sites and archived with this data set include FPAR, nitrogen content, allometry equations, root biomass, LAI, tree biomass, soil respiration, NPP, landcover images, and vegetation inventories.Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, and deciduous broadleaf forest; desert grassland and shrubland. The project collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle at the sites listed in Table 1. Companion files include documentation of measurement data, site and plot locations (Figure 2), and plot photographs for the SEVI and TUND sites (Figure 3).BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellites Terra and Aqua ( http://landval.gsfc.nasa.gov/MODIS/index.php ), was used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP), and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high-resolution remote-sensing data, and ecosystem process models at six flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 1-km MODIS pixels) surrounding the CO2 flux towers located at six of the nine BigFoot sites. The sampling design allowed the Project to examine scales and spatial patterns of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.", "links": [ { diff --git a/datasets/Fire_Emissions_Indonesia_2118_1.json b/datasets/Fire_Emissions_Indonesia_2118_1.json index 55eac55189..b80b68cdbf 100644 --- a/datasets/Fire_Emissions_Indonesia_2118_1.json +++ b/datasets/Fire_Emissions_Indonesia_2118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Fire_Emissions_Indonesia_2118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations. Specifically, the Ce values of peatland, tropical forest, cropland, or savanna and grassland were derived from fire radiative power (FRP) and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE for each 0.1-degree interval was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution (10 mins) Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution (375 m) VIIRS. This new dataset was named the Fused AHI-VIIRS based fire Emissions (FAVE). Fire emissions data are provided in comma-separated values (CSV) format with one file per month from July 2015 to December 2020. Each file includes variables of fire observation time, fire geographic location, classification, fire radiative energy, various fire emissions and related standard deviations.", "links": [ { diff --git a/datasets/Fire_Emissions_NWT_1561_1.json b/datasets/Fire_Emissions_NWT_1561_1.json index 00e7070e8d..b6396ba5b4 100644 --- a/datasets/Fire_Emissions_NWT_1561_1.json +++ b/datasets/Fire_Emissions_NWT_1561_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Fire_Emissions_NWT_1561_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.", "links": [ { diff --git a/datasets/Fire_Ignitions_Locations_AK_CA_2316_1.json b/datasets/Fire_Ignitions_Locations_AK_CA_2316_1.json index e47f26389f..c1e23d8695 100644 --- a/datasets/Fire_Ignitions_Locations_AK_CA_2316_1.json +++ b/datasets/Fire_Ignitions_Locations_AK_CA_2316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Fire_Ignitions_Locations_AK_CA_2316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats.", "links": [ { diff --git a/datasets/Flight_Environment_Parameters_1909_1.json b/datasets/Flight_Environment_Parameters_1909_1.json index 2a2a60aa3e..64e11a3950 100644 --- a/datasets/Flight_Environment_Parameters_1909_1.json +++ b/datasets/Flight_Environment_Parameters_1909_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Flight_Environment_Parameters_1909_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains flight dynamics and environmental parameters (often referred to as housekeeping) specific to the DC-8 aircraft as collected from an assortment of instruments across all four ATom campaigns flown from 2016 through 2018. Measurements include aircraft position, altitude, speed, wind parameters, air temperature, and atmospheric and cabin pressure. These data can be used to understand the interior and exterior conditions and positioning of the DC-8 aircraft at 1-second resolution.", "links": [ { diff --git a/datasets/Flora1_1.json b/datasets/Flora1_1.json index a5eab311a5..59aae06468 100644 --- a/datasets/Flora1_1.json +++ b/datasets/Flora1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Flora1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic Biodiversity database is a database including collection records of plants collected from the Antarctic, subantarctic islands and other areas around the world. You can search for collections and enter, edit or delete collections. An administration page is available for approving entered, edited or deleted collections and for entering, editing or deleting things such as new flora types, new collectors, etc.", "links": [ { diff --git a/datasets/FluxSat_GPP_FPAR_1835_2.json b/datasets/FluxSat_GPP_FPAR_1835_2.json index e3e60ecbfa..7634589448 100644 --- a/datasets/FluxSat_GPP_FPAR_1835_2.json +++ b/datasets/FluxSat_GPP_FPAR_1835_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "FluxSat_GPP_FPAR_1835_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05-degree resolution for the period 2000-03-01 to the recent past. The GPP was derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua satellites using the MCD43C4v006 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product as input to neural networks that were used to globally upscale GPP estimated from selected FLUXNET 2015 eddy covariance tower sites. Additional data will be added periodically.", "links": [ { diff --git a/datasets/Flux_Tower_Zona_Veg_Plots_1546_1.json b/datasets/Flux_Tower_Zona_Veg_Plots_1546_1.json index b281b47f74..bde38b4345 100644 --- a/datasets/Flux_Tower_Zona_Veg_Plots_1546_1.json +++ b/datasets/Flux_Tower_Zona_Veg_Plots_1546_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Flux_Tower_Zona_Veg_Plots_1546_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation, environmental, and soil data collected from plots located in the footprints of eddy covariance flux towers along a 300 km north-south latitudinal gradient from Barrow, to Atqasuk, and to Ivotuk across the North Slope of Alaska in 2014. Within each of the five flux tower footprints, 1x1-m quadrats were placed subjectively within widespread habitat or micro-habitat types to map the dominant vegetation communities and site environmental factors. Specific attributes included species cover data and environmental, soil and spectral data (active layer thaw depth, moss layer depth, organic horizon layer depth, standing water depth, soil moisture status, vegetation height, LAI).", "links": [ { diff --git a/datasets/Fluxnet_site_DB_1530_1.json b/datasets/Fluxnet_site_DB_1530_1.json index 5cf3747b06..228ffacc28 100644 --- a/datasets/Fluxnet_site_DB_1530_1.json +++ b/datasets/Fluxnet_site_DB_1530_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Fluxnet_site_DB_1530_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. This dataset provides information from the ORNL DAAC-hosted FLUXNET site database which was discontinued in 2016. The files provided contain a list of investigators associated with each tower site, site locations and environmental data, and a bibliography of papers that used FLUXNET data. For more up to date information on FLUXNET sites, see http://fluxnet.fluxdata.org/.", "links": [ { diff --git a/datasets/Fluxnet_website_archive_copy_1549_1.json b/datasets/Fluxnet_website_archive_copy_1549_1.json index 5422a9422e..f70d3b00bd 100644 --- a/datasets/Fluxnet_website_archive_copy_1549_1.json +++ b/datasets/Fluxnet_website_archive_copy_1549_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Fluxnet_website_archive_copy_1549_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains an archived copy of the fluxnet.ornl.gov website as of September 2017. This archived website is provided for informational purposes only. The last updates to the website and the underlying database were made in October 2016. Support for the Fluxnet project and website was transitioned to http://fluxnet.fluxdata.org in September 2017. Please see http://fluxnet.fluxdata.org/ for information on site locations, data availability, and to add or update a flux tower site.", "links": [ { diff --git a/datasets/ForestHt_Biomass_GEDI_TDX_2298_1.json b/datasets/ForestHt_Biomass_GEDI_TDX_2298_1.json index 4b905017a4..09240b1fe9 100644 --- a/datasets/ForestHt_Biomass_GEDI_TDX_2298_1.json +++ b/datasets/ForestHt_Biomass_GEDI_TDX_2298_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ForestHt_Biomass_GEDI_TDX_2298_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes maps of canopy height and aboveground biomass at spatial resolutions of 25 m and 100 m for Mexico, Gabon, French Guiana, and the Amazon Basin. The GEDI-TanDEM-X (GTDX) fusion maps were created by combining data from NASA's Global Ecosystem Dynamics Investigation (GEDI) Version 2 footprint data (from 2019-04-18 to 2021-08-18) and TanDEM-X (abbreviated as TDX) Interferometric Synthetic Aperture Radar (InSAR) images (from 2011-01-06 to 2020-12-31). The GTDX canopy height maps were generated by using the TDX coherence maps to invert the TDX height and subsequently using GEDI canopy height as reference data to calibrate the inverted height. The GTDX aboveground biomass maps were produced based on a generalized hierarchical model-based (GHMB) framework that utilizes GEDI biomass as training data to establish models for estimating biomass based on the GTDX canopy height. The dataset also includes maps of canopy height uncertainty, biomass uncertainty, and ancillary data including a regional modeling parameter and forest disturbance. The uncertainty of GTDX canopy height was estimated for each pixel by propagating the GEDI-TDX model error to each GTDX pixel prediction. The uncertainty of GTDX aboveground biomass was estimated by considering the error in both the GEDI footprint biomass data and the GEDI-TDX model, and then applying it to each GTDX biomass pixel prediction. The regional model parameter indicates the size of the analysis window (2 to 50 km or country wide) used for each pixel. The forest disturbance information identifies pixels where disturbance occurred between 2011 and 2020, and provides the year of last disturbance.", "links": [ { diff --git a/datasets/Forest_AGB_NW_USA_1766_1.json b/datasets/Forest_AGB_NW_USA_1766_1.json index 83ed12b667..f964da457f 100644 --- a/datasets/Forest_AGB_NW_USA_1766_1.json +++ b/datasets/Forest_AGB_NW_USA_1766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_AGB_NW_USA_1766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.", "links": [ { diff --git a/datasets/Forest_Carbon_Priority_1803_1.json b/datasets/Forest_Carbon_Priority_1803_1.json index e2620d50d5..c9784ecdb0 100644 --- a/datasets/Forest_Carbon_Priority_1803_1.json +++ b/datasets/Forest_Carbon_Priority_1803_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Carbon_Priority_1803_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides related gridded outputs of future modeled forest carbon sequestration priority and related species richness and habitat suitability for the western United States. The primary dataset is of the ranking of forest lands in the western U.S. for preservation based on the ability of these lands to sequester carbon over the coming century. The preservation ranking was derived from the results of simulations of future potential forest net ecosystem productivity (NEP) and vulnerability to drought and wildfire, as modeled from 2020 to 2099 at 4 km x 4 km resolution using a modified version of the Community Land Model (CLM 4.5). In addition, data files of potential forest NEP ranking and the forest vulnerability ranking are also provided. Co-located data of species richness for amphibians, birds, mammals, and reptiles are included to illustrate habitat suitability in relation to forest carbon preservation rankings. There are two files for each vertebrate class, one reflecting all western U.S. species included in the USGS GAP Analysis Project and a second for the subset of species listed as threatened or endangered by the U.S. Fish and Wildlife Service. Establishing this forest carbon preservation priority ranking for forest lands in the western U.S. will help guide the conservation of land for climate change mitigation activities and improved harvest management in the region.", "links": [ { diff --git a/datasets/Forest_Disturbance_Intensity_2059_1.json b/datasets/Forest_Disturbance_Intensity_2059_1.json index f10be0dec0..7f8ad464b0 100644 --- a/datasets/Forest_Disturbance_Intensity_2059_1.json +++ b/datasets/Forest_Disturbance_Intensity_2059_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Disturbance_Intensity_2059_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates of forest disturbance intensity for the conterminous United States from 1986 to 2015. It quantifies the severity/intensity of forest disturbances at 30 m resolution using time series Landsat observations and the vegetation change tracker algorithm. For each disturbance event mapped at a pixel location, the percentage of basal area removal (PBAR) after the disturbance event was estimated based on spectral changes derived from Landsat data and Random Forest models calibrated using field measurements collected by the US Forest Service Forest Inventory and Analysis (FIA) Program. This dataset complements and extends the North American Forest Dynamics-NASA Earth eXchange (NAFD-NEX) US Forest Disturbance History dataset and the NAFD-ATT Forest Canopy Cover Loss dataset from 1986-2010 to 1986-2015. Data are provided in Cloud Optimized GeoTIFF (*.tif) format.", "links": [ { diff --git a/datasets/Forest_Inventory_Acre_Brazil_1654_1.json b/datasets/Forest_Inventory_Acre_Brazil_1654_1.json index 80fd00f318..4255e39e08 100644 --- a/datasets/Forest_Inventory_Acre_Brazil_1654_1.json +++ b/datasets/Forest_Inventory_Acre_Brazil_1654_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Inventory_Acre_Brazil_1654_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of diameter at breast height (DBH) and species identification at four forest sites in the eastern side of Acre, Brazil including Bonal (A), Catuaba (B), Humaita (C) and Transacreana (D). The inventory locations include forests burned in 2005 and 2010 and nearby unburned areas. Inventory surveys were conducted in October and December 2017.", "links": [ { diff --git a/datasets/Forest_Inventory_Brazil_2007_1.json b/datasets/Forest_Inventory_Brazil_2007_1.json index 98f241bf95..bdf2dcf910 100644 --- a/datasets/Forest_Inventory_Brazil_2007_1.json +++ b/datasets/Forest_Inventory_Brazil_2007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Inventory_Brazil_2007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories. Also included for each tree are the family, common and scientific names, coordinates, canopy position, crown radius, and for dead trees, the decomposition status. Aboveground biomass estimate is available for selected sites. The data are provided in comma-separated values (CSV) and shapefile formats. Sampling methodology for each site and year is described in companion files.", "links": [ { diff --git a/datasets/Forest_Inventory_Data_Brazil_1563_1.json b/datasets/Forest_Inventory_Data_Brazil_1563_1.json index 86813ba261..cbeec455da 100644 --- a/datasets/Forest_Inventory_Data_Brazil_1563_1.json +++ b/datasets/Forest_Inventory_Data_Brazil_1563_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Inventory_Data_Brazil_1563_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements for diameter at breast height (DBH) and species identification of trees for inventories taken at five tropical forest sites in Acre state, Brazil, in the southwestern Amazon region. The sites included one in a forest reserve (Reserva Bonal) and four within forest fragments situated on private property. The inventory sites included forests burned in 2005 and 2010 and also unburned forests. Surveys were conducted in July and August 2014.", "links": [ { diff --git a/datasets/Forest_Inventory_Tapajos_1552_1.json b/datasets/Forest_Inventory_Tapajos_1552_1.json index 32d8cbd48c..857d193c06 100644 --- a/datasets/Forest_Inventory_Tapajos_1552_1.json +++ b/datasets/Forest_Inventory_Tapajos_1552_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forest_Inventory_Tapajos_1552_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides tree inventory, tree height, diameter at breast height (DBH), and estimated crown measurements from 30 plots located in the Tapajos National Forest, Para, Brazil collected in September 2010. The plots were located in primary forest, primary forest subject to reduced-impact selective logging (PFL) between 1999 and 2003, and secondary forest (SF) with different age and disturbance histories. Plots were centered on GLAS (the Geoscience Laser Altimeter System) LiDAR instrument footprints selected along two sensor acquisition tracks spanning a wide range in vertical structure and aboveground biomass.", "links": [ { diff --git a/datasets/Forested_Areas_Amazonas_Brazil_1515_1.json b/datasets/Forested_Areas_Amazonas_Brazil_1515_1.json index 646ed9ecb2..57e466369e 100644 --- a/datasets/Forested_Areas_Amazonas_Brazil_1515_1.json +++ b/datasets/Forested_Areas_Amazonas_Brazil_1515_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forested_Areas_Amazonas_Brazil_1515_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the K34 tower site in the Cuieiras Biological Reserve, over forest inventory plots in the Adolpho Ducke Forest Reserve, and over sites of the Biological Dynamics of Forest Fragments Project (BDFFP) in Rio Preto da Eva municipality near Manaus, Amazonas, Brazil during June 2008. The surveys encompass the K34 eddy flux tower managed through the Large-scale Biosphere-Atmosphere Experiment in Amazonia, forest inventory plots managed by the Programa de Pesquisa em Biodiversidade (PPBio), and sites managed by the BDFFP. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.", "links": [ { diff --git a/datasets/Forested_Areas_Para_Brazil_1514_1.json b/datasets/Forested_Areas_Para_Brazil_1514_1.json index fdda6de2d7..33e129cc60 100644 --- a/datasets/Forested_Areas_Para_Brazil_1514_1.json +++ b/datasets/Forested_Areas_Para_Brazil_1514_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Forested_Areas_Para_Brazil_1514_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the Tapajos National Forest in Belterra municipality, Para, Brazil during late June and early July 2008. The surveys encompass the K67 and K83 eddy flux towers and a deforestation chronosequence managed through the Large-Scale Biosphere-Atmosphere Experiment in Amazonia providing long-term flux measurements of carbon dioxide. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.", "links": [ { diff --git a/datasets/Frac_FuelComponent_Maps_Tundra_1761_1.json b/datasets/Frac_FuelComponent_Maps_Tundra_1761_1.json index 2ca2d1c0ce..353a2e23bf 100644 --- a/datasets/Frac_FuelComponent_Maps_Tundra_1761_1.json +++ b/datasets/Frac_FuelComponent_Maps_Tundra_1761_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Frac_FuelComponent_Maps_Tundra_1761_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra.", "links": [ { diff --git a/datasets/Freeze_Thaw_NorthernHemisphere_2323_1.json b/datasets/Freeze_Thaw_NorthernHemisphere_2323_1.json index 8ee1818951..fde4ee1a8a 100644 --- a/datasets/Freeze_Thaw_NorthernHemisphere_2323_1.json +++ b/datasets/Freeze_Thaw_NorthernHemisphere_2323_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Freeze_Thaw_NorthernHemisphere_2323_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a probabilistic freeze/thaw (FT) data record from 2016 to 2020 for the Northern Hemisphere derived using a deep learning model (U-Net). The model was informed by satellite multi-frequency microwave brightness temperature retrievals from the NASA SMAP (Soil Moisture Active Passive) and JAXA AMSR2 (Advanced Microwave Scanning Radiometer 2) radiometers, and trained using daily soil temperature observations from Northern Hemisphere weather stations and global reanalysis data (ERA-5). Unlike other available FT data records that provide only a binary classification of frozen or non-frozen conditions, this product includes both binary FT and continuous variable estimates of the probability of thawed conditions. This product is designed to complement other established binary FT data records, including the NASA FT Earth System Data Record and SMAP Level 3 FT operational products, by providing a probabilistic FT variable with enhanced accuracy and sensitivity to near-surface (<=5 cm depth) soil FT condition. The data are provided in cloud optimized GeoTIFF (COG) format.", "links": [ { diff --git a/datasets/Frost_Boils_Veg_Plots_1361_1.json b/datasets/Frost_Boils_Veg_Plots_1361_1.json index 8495591955..fb74011ae5 100644 --- a/datasets/Frost_Boils_Veg_Plots_1361_1.json +++ b/datasets/Frost_Boils_Veg_Plots_1361_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Frost_Boils_Veg_Plots_1361_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes the environment, soil, and vegetation on nonsorted circles and earth hummocks at seven study sites along a N-S-transect from the Arctic Ocean to the Arctic Foothills based on data collected from 2000 to 2006. The study sites are located along the Dalton Highway, beginning in Prudhoe Bay, on the North Slope of Alaska. These frost-boil features are important landscape components of the arctic tundra. Data include the baseline plot information for vegetation, soils, and site factors for 117 study plots subjectively located in areas of homogeneous, representative vegetation on frost-heave features surrounding stable tundra. Nine community types were identified in three bioclimate subzones. Vegetation was classified according to the Braun-Blanquet system.", "links": [ { diff --git a/datasets/Ft_Lauderdale_0.json b/datasets/Ft_Lauderdale_0.json index d02d564af4..e66b7ab36c 100644 --- a/datasets/Ft_Lauderdale_0.json +++ b/datasets/Ft_Lauderdale_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ft_Lauderdale_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the Lady Pamela research vessel off the south Florida coast near Fort Lauderdale in 2003.", "links": [ { diff --git a/datasets/G00472_1.json b/datasets/G00472_1.json index 99f074b5b3..b47b06f54c 100644 --- a/datasets/G00472_1.json +++ b/datasets/G00472_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00472_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Glacier Photograph Collection is a database of digital photographs of glaciers from around the world, some dating back to the mid-19th century, that provide an historical reference for glacier extent. As of August 2021, the database contains over 25,000 photographs.\n\nFurther information can be found on the Glacier Photo Collection Product Web Site.", "links": [ { diff --git a/datasets/G00486_1.json b/datasets/G00486_1.json index c2ed44a557..4416ec98f4 100644 --- a/datasets/G00486_1.json +++ b/datasets/G00486_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00486_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These charts show ice extent and concentration three times weekly during the ice season, for all lakes except Ontario, from the 1973/74 ice season through the 2001/2002 ice season. Most charts are annotated to indicate data sources. The Straits of Mackinac, Green Bay, and Sault Ste. Marie are enlarged as insets; and in some cases, provided as separate charts. From 1973/74 through 1986/87, charts were prepared by the National Weather Service Forecast Office in Ann Arbor, Michigan. Beginning with the 1987/88 season, the National Ice Center (formerly known as the Navy/NOAA Joint Ice Center) in Suitland, Maryland prepared the charts. Charts are available as GIF files.", "links": [ { diff --git a/datasets/G00799_1.json b/datasets/G00799_1.json index 7e15018a4d..e668daf388 100644 --- a/datasets/G00799_1.json +++ b/datasets/G00799_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00799_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides monthly sea ice concentration for the Arctic from 1901 to 1995 and for the Southern Oceans from 1973 to 1990 on a standard 1-degree grid (cylindrical projection) to provide a relatively uniform set of sea ice extent for all longitudes. The data are in ASCII format and are available via FTP.", "links": [ { diff --git a/datasets/G00801_1.json b/datasets/G00801_1.json index 7e3995960c..4bec555c9f 100644 --- a/datasets/G00801_1.json +++ b/datasets/G00801_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00801_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily maximum and minimum temperatures for 25 stations around the Great Lakes, 1897 to 1983, were given to NSIDC by the NOAA Great Lakes Environmental Research Laboratory (GLERL), Ann Arbor, MI. Daily data can be used to produce daily maximum, minimum, and mean temperatures, and seasonal accumulations of freezing and thawing degree days. The statistical data are archived in ASCII text files transcribed from 25 reels of 35 mm microfilm, one roll per station. Microfilm rolls are the appendices to Assel (1980), with updates covering 1978 to 1983 spliced to the end of each microfilm roll. Data sources include U.S. Department of Commerce summaries of meteorological data for Minnesota, Michigan, Wisconsin, Illinois, Pennsylvania, and New York, and the monthly meteorological observations published by Environment Canada.", "links": [ { diff --git a/datasets/G00802_1.json b/datasets/G00802_1.json index ef61ea9eb8..48797d1310 100644 --- a/datasets/G00802_1.json +++ b/datasets/G00802_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00802_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Radiation transmittance (ratio of transmitted to incident radiation) through clear ice, refrozen slush ice, and brash ice, from ice surface to ice-water interface in the 400 to 600 nanometer range (photosynthetically active range), was measured at two freshwater lakes: Silver Lake, near Ann Arbor, Michigan and Whitefish Point on Lake Superior. Data available include surface and under-ice sensor readings, date and time of observation, ice thickness, water depth, distance of under-ice sensor from ice bottom surface, and site number/location.", "links": [ { diff --git a/datasets/G00803_1.json b/datasets/G00803_1.json index 52e70f244b..e81c20a9e6 100644 --- a/datasets/G00803_1.json +++ b/datasets/G00803_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00803_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the winters of 1965/66 through 1976/77, NOAA/Great Lakes Environmental Research Laboratory (GLERL) collected weekly ice thickness and stratigraphy data at up to 90 stations per year on the Great Lakes. Data include station name, latitude, longitude and period of record as well as thickness of up to six ice layers, total ice thickness, snow depth (on top of ice), snow condition, ice condition, and ice type code.", "links": [ { diff --git a/datasets/G00804_1.json b/datasets/G00804_1.json index 460f403ebf..2d7ac798f9 100644 --- a/datasets/G00804_1.json +++ b/datasets/G00804_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00804_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of ice concentration grids from 1960 to 1979 for the Great Lakes. Over 2800 charts were digitized to produce this data base. The data were given to NSIDC by the NOAA Great Lakes Environmental Research Laboratory (GLERL), Ann Arbor, MI.", "links": [ { diff --git a/datasets/G00805_1.json b/datasets/G00805_1.json index 2d35b1b4c6..67a149e83d 100644 --- a/datasets/G00805_1.json +++ b/datasets/G00805_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00805_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data consist of ice observations from U.S. Coast Guard vessels operating on the Great Lakes and from Coast Guard shore stations reported via teletype messages and ice logging forms. Observations include ice thickness and concentration, weather conditions, and ice breaking activity. Data from 1961/1962 through 1966/1967 have been processed to a standard format and sorted by year and stations, and are available via FTP as ASCII files, one for each of the five lakes.", "links": [ { diff --git a/datasets/G00945_1.json b/datasets/G00945_1.json index 203cb106f2..da9f6b51be 100644 --- a/datasets/G00945_1.json +++ b/datasets/G00945_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G00945_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily visual ice observations taken yearly from 1 November to 30 April at NOAA/National Ocean Service water level gauge sites in the Great Lakes Basin from 1956 to the 1997. Not all gauge sites have reported each season; a list of sites with coordinates and reporting years is included with the data set. The longest records tend to be at river stations such as St. Clair, St. Marys, Detroit, and St. Lawrence. Daily observations are coded (open water, solid ice, honeycombed ice, slush ice, windrowed ice, drifting ice, or ice gorge), as well as first and last reported ice for each season.", "links": [ { diff --git a/datasets/G01170_1.json b/datasets/G01170_1.json index 9fd30facc8..ce9c94bbe4 100644 --- a/datasets/G01170_1.json +++ b/datasets/G01170_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01170_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Former Soviet Union Hydrological Snow Surveys are based on observations made by personnel at 1,345 sites throughout the Former Soviet Union between 1966 and 1990, and over 200 of those sites between 1991 and 1996. These observations include snow depths at World Meteorological Organization (WMO) stations and snow depth and snow water equivalent measured over a nearby snow course transect. The station snow depth measurements are a ten-day average of individual snow depth measurements. The transect snow depth data are the spatial average of 100 to 200 individual measuring points. The transect snow water equivalent is the spatial average of 20 individual measuring points. Data were acquired from the Institute of Geography, Russian Academy of Sciences Moscow, and data were digitized in Russia under the supervision of Professor Alexander Krenke.", "links": [ { diff --git a/datasets/G01171_1.json b/datasets/G01171_1.json index 159af5e63c..aafb93e1aa 100644 --- a/datasets/G01171_1.json +++ b/datasets/G01171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides observations of end of month snow depth, snow density, and snow water equivalent from three river basins in Central Asia: Amu Darya, Sir Darya, and Naryn. Temporal coverage varies for each snow point, with the longest station record extending from 1932 through 1990.", "links": [ { diff --git a/datasets/G01174_1.json b/datasets/G01174_1.json index d3c69a6bf9..39d83bc3ce 100644 --- a/datasets/G01174_1.json +++ b/datasets/G01174_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01174_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the number of days of snow cover in days per year, and three 10-day snow depth means per month in centimeters from stations across Estonia. The days of snow cover data extend from 1891 through 1994, while the snow depth means extend from 1891 through 1990. Some stations for some years have two data entries, one for a protected collection area, and one for an exposed collection area.", "links": [ { diff --git a/datasets/G01358_1.json b/datasets/G01358_1.json index c7589ad714..393e7ad794 100644 --- a/datasets/G01358_1.json +++ b/datasets/G01358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thirty-four drift tracks in the Arctic Ocean pack ice are collected in a unified tabular data format, one file per track. Data are from drifting ships, manned research stations on ice floes (ice islands) and data buoys. Track names are FRAM (ship, 1893 to 1896), NP-01 through NP-20 (Soviet North Pole stations on ice floes, 1937, 1950, 1954 to 1970), IGY-A and IGY-B (International Geophysical Year ice camps, 1957 to 59), T-3 (Fletcher's Ice Island, 1959 to 1970), ARLIS-II (Arctic Research Laboratory Ice Station II ice camp, 1961 to 1965), BTAE (British Transarctic Expedition, 1968 to 1969), seven buoys deployed during the AIDJEX (Arctic Ice Dynamics Joint Experiment) pilot study (1972), TEGG (Austrian ship Tegetthoff, 1872 to 1873) and St. Anna (Russian ship, 1912 to 1914).", "links": [ { diff --git a/datasets/G01359_1.json b/datasets/G01359_1.json index f40416f685..5f6626aad9 100644 --- a/datasets/G01359_1.json +++ b/datasets/G01359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of moored Upward Looking Sonar (ULS) data from 14 stations in the Weddell Sea. Parameters in the processed data files are water pressure, water temperature, draft, and a flag to indicate if the instrument is under ice. Raw data files contain additional parameters. These data were contributed by the Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany, in 1999. Data are available via ftp.", "links": [ { diff --git a/datasets/G01375_1.json b/datasets/G01375_1.json index 50ae4e957d..3b2cfe6278 100644 --- a/datasets/G01375_1.json +++ b/datasets/G01375_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01375_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The inventory includes 5,297 Glaciers from west Greenland between 59 to 71 degrees latitude north and 43 to 53 degrees longitude west. The glacier data basin division is based on a simplified version of the World Glacier Monitoring Service (WGMS) system. Data are based on 1:250000 maps, Landsat imagery, and aerial photos that were taken between 1948 and 1985.", "links": [ { diff --git a/datasets/G01377_1.json b/datasets/G01377_1.json index bf872eaed5..7ac77ceb14 100644 --- a/datasets/G01377_1.json +++ b/datasets/G01377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Lake and River Ice Phenology Database contains freeze and thaw/breakup dates as well as other descriptive ice cover data for 865 lakes and rivers in the Northern Hemisphere. Of the 542 water bodies that have records longer than 19 years, 370 of them are in North America and 172 are in Eurasia. 249 lakes and rivers have records longer than 50 years, and 66 have records longer than 100 years. A few water bodies have data available prior to 1845. This database, with water bodies distributed around the Northern Hemisphere, allows for the analysis of broad spatial patterns as well as long-term temporal patterns.", "links": [ { diff --git a/datasets/G01378_1.json b/datasets/G01378_1.json index b8f1bfa236..6fbd541631 100644 --- a/datasets/G01378_1.json +++ b/datasets/G01378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of glacier outline, laser altimetry profile, and surface elevation change data for 46 glaciers in Alaska and British Columbia, Canada, measured with an airborne laser altimetry system. Six glaciers in the Alaska Range of central Alaska, two glaciers in the Wrangell Mountains of southcentral Alaska, 11 glaciers in the Chugach Mountains of southcentral Alaska, five glaciers in the Chigmit Mountains of southcentral Alaska, 13 glaciers in the Kenai Mountains of southcentral Alaska (comprising the Harding Icefield), one glacier in the St. Elias Mountains of southeast Alaska, one glacier in the Takhinsha Mountains of southeast Alaska, and seven glaciers in the Coast Mountains of southeast Alaska and British Columbia were profiled between 1994 and 2001. Surface elevation profiles are accurate to about 0.3 m. Long-term elevation changes can be estimated by comparison of these profiles with existing maps.", "links": [ { diff --git a/datasets/G01938_1.json b/datasets/G01938_1.json index e415c7d28e..58f40eeffa 100644 --- a/datasets/G01938_1.json +++ b/datasets/G01938_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01938_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Arctic Meteorology and Climate Atlas is part of the NOAA@NSIDC Environmental Working Group (EWG) Atlases data collection.\n\nThe Arctic Meteorology and Climate Atlas was developed in the late 1990s by specialists from the Arctic and Antarctic Research Institute (AARI), St. Petersburg, Russia, the University of Washington, Seattle, and the National Snow and Ice Data Center, University of Colorado, Boulder. The Atlas contains three main sections: a history section, a Primer, and a data section. The history section summarizes Arctic exploration from both Russian and U.S. vantage points. It includes a condensed translation of an AARI publication detailing the Russian North Pole drifting station program, as well as a photo gallery from the North Pole stations. The Primer provides introductory information for newcomers to arctic meteorology. The data section contains gridded fields of meteorological parameters. These maps of air temperature, sea level pressure, precipitation, cloud cover, and snow and solar radiation from drifting and coastal stations can be browsed using an included viewer. Meteorological station data from Russian and other sources, newly released at the time the atlas was published, is included as well. In addition, the Atlas includes several English translations of Russian technical documents, and a glossary of meteorological terms in English and Russian. See the User Guide for a more complete listing of contents. The online User Guide includes an important Addendum and Errata section that is not included in the documentation that accompanies the Atlas download.", "links": [ { diff --git a/datasets/G01961_1.json b/datasets/G01961_1.json index 1dc66439b3..7dcb9ca687 100644 --- a/datasets/G01961_1.json +++ b/datasets/G01961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Environmental Working Group Joint U.S.-Russian Atlas of the Arctic Ocean is part of the NOAA@NSIDC Environmental Working Group (EWG) Atlases data collection.\n\nThe Environmental Working Group (EWG) was established in 1995 under the framework of the U.S.-Russian Joint Commission on Economic and Technological Cooperation. The EWG Arctic Climatology Group took on the task of compiling digital data on arctic regions. This atlas of the Arctic Ocean was developed by specialists from the Environmental Research Institute of Michigan with Russian and U.S. partners. Separate volumes for winter and summer have file names G01961a and G01961b respectively. More than 1.3 million individual temperature and salinity observations collected from Russian and western drifting stations, ice breakers, and airborne expeditions were used to develop the products contained in the winter volume. The primary products of the Atlas are gridded mean fields for decadal periods (1950s,1960s, 1970s, 1980s) of temperature, salinity, density and dynamic height, Atlantic water layer depth, and temperature and salinity profiles and transects. The original individual observations that were used to derive these fields are not provided with the Atlas and are not available. Note that the Polar Science Center Hydrographic Climatology (PHC) ocean database (version 3.0) is available from the Polar Science Center, Applied Physics Laboratory, University of Washington. This is a global gridded database with a high-quality description of arctic seas achieved by merging data from several sources, including data from the Environmental Working Group Joint U.S.-Russian Atlas of the Arctic Ocean. The PHC or later versions may be more suitable for your research. As of January 2023, contact Michael Steele, Applied Physics Laboratory, 1013 NE 40th Street, Seattle, WA 98105 if you are interested in learning more about the PHC.", "links": [ { diff --git a/datasets/G01962_1.json b/datasets/G01962_1.json index 0dae1bc33d..28c16b0cb6 100644 --- a/datasets/G01962_1.json +++ b/datasets/G01962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G01962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Environmental Working Group Joint U.S.-Russian Arctic Sea Ice Atlas is part of the NOAA@NSIDC Environmental Working Group (EWG) Atlases data collection.\nThe EWG Joint U.S.-Russian Arctic Sea Ice Atlas was developed by U.S. and Russian partners in the late 1990s. It is based on observations collected over the period 1950 through 1994 from satellite data, ice stations, icebreakers, and airborne ice surveys. Additionally, U.S. submarines operating in the Arctic over the period from 1977 through 1993 collected data used for a previously classified ice climatology. The Atlas contains four main sections: an introduction to Arctic sea ice, a section that describes primary sea ice data sets and analysis methods, a section with a graphical atlas containing two-dimensional color-coded ice charts and graphical products, and an Arctic sea ice data section. Note: The Russian chart component of this product has been replaced and updated by Sea Ice Charts of the Russian Arctic in Gridded Format, 1933-2006 and the U.S chart component by National Ice Center Arctic Sea Ice Charts and Climatologies in Gridded Format, 1972-2007 and U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format.", "links": [ { diff --git a/datasets/G02139_1.json b/datasets/G02139_1.json index 73a973abe0..ece4208126 100644 --- a/datasets/G02139_1.json +++ b/datasets/G02139_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02139_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of Upward Looking Sonar (ULS) data from 11 moorings in the Greenland Sea. Parameters in the processed data files include ice draft, water pressure, and water temperature. Raw data files with sonar travel time, and files with draft frequency of occurrence, are available as well. A single statistical file for each mooring summarizes that mooring's record. These data were contributed by the Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany, in 2002 and 2004, as a contribution to the World Climate Research Programme's Arctic Climate System Study/Climate and Cryosphere (ACSYS/CliC) Project. Data are available via FTP.\n\nNSIDC strongly encourages you to register as a user of this data product. As a registered user, you will be notified of updates and corrections. When registering, please include the title of this data set, AWI Moored ULS Data, Greenland Sea and Fram Strait, 1991-2002.", "links": [ { diff --git a/datasets/G02159_1.json b/datasets/G02159_1.json index 3bf2f464ca..1581274cbd 100644 --- a/datasets/G02159_1.json +++ b/datasets/G02159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Visible band imagery from high-resolution satellites were acquired over four Arctic Ocean sites (three in 1999) during the summers of 1999, 2000, and 2001. The sites were within the median extent of the perennial ice pack. Imagery was analyzed using supervised maximum likelihood classification to derive either two (water and ice) or three (pond, open water, and ice) surface classes. Clouds were masked by hand. The data set consists of tables of pond coverage and size statistics for 500 m square cells within 10 km square images (image resolution is 1 meter), along with the surface type maps called Image Derived Products (IDPs) from which the pond statistics were derived. A total of 101 images over the three summers and four sites were used for pond statistics, out of a total of 1056 images acquired. The images are irregularly spaced in time.\n\nData are stored in Microsoft Excel format and ASCII text, image files are stored as GeoTIFF binary images, browse images in PNG, and JPG image files, and are available from August 1999 and generally for May into September for 2000 and 2001 via FTP.", "links": [ { diff --git a/datasets/G02164_1.json b/datasets/G02164_1.json index e6c102fde6..2889797f83 100644 --- a/datasets/G02164_1.json +++ b/datasets/G02164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains precipitation data originally recorded in log books at 65 Russian coastal and island meteorological stations, and later digitized at the Arctic and Antarctic Research Institute (AARI), St. Petersburg, Russia, under the direction of Vladimir Radionov. Records from most stations begin in 1940 and contain daily precipitation amounts in mm.", "links": [ { diff --git a/datasets/G02171_1.json b/datasets/G02171_1.json index b8fe15005c..f20c306702 100644 --- a/datasets/G02171_1.json +++ b/datasets/G02171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Canadian Ice Service (CIS) produces digital Arctic regional sea ice charts for marine navigation, forecasting, and climate research. The ice charts are created through the manual analysis of in situ, satellite, and aerial reconnaissance data. The ice charts have information on ice concentration, stage of development, and ice form, following World Meteorological Organization terminology. This digital record of sea ice charts begin in 2006 and cover the following regions of the Canadian Arctic: Northern Canadian waters (Western Arctic, Eastern Arctic, and Hudson Bay) and Southern Canadian waters (Great Lakes and East Coast). Each regional shapefile (.shp) (encoded in SIGRID-3 format) and associated metadata file (.xml) are combined into a tar archive file (.tar) for distribution. All data are available via FTP.", "links": [ { diff --git a/datasets/G02174_1.json b/datasets/G02174_1.json index 2b240e7e0c..236e2f07a9 100644 --- a/datasets/G02174_1.json +++ b/datasets/G02174_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02174_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides temperature and precipitation data from 298 meteorological stations in the Northern Tien Shan and Pamir Mountain Ranges of Central Asia, specifically from stations in Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. The period of record covered by each station is variable, however, most stations have almost 100 years of observations with the earliest record from 1879 and the latest from 2003. The data are stored as tab-delimited ASCII text format, Microsoft Excel, and PDF, and are availabe via FTP.", "links": [ { diff --git a/datasets/G02175_1.json b/datasets/G02175_1.json index bc2b03f895..49f50a4cbf 100644 --- a/datasets/G02175_1.json +++ b/datasets/G02175_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02175_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This silent film documents student hiking trips conducted by the University of Colorado at Boulder in the Rocky Mountains, Colorado, USA during the summers of 1938-1942. The hikes took place in various locations west of Boulder, including Rocky Mountain National Park, Indian Peaks Wilderness, and Roosevelt National Forest. The film contains rare historical footage of the Rocky Mountains, including Arapaho Glacier and Fair Glacier.", "links": [ { diff --git a/datasets/G02178_1.json b/datasets/G02178_1.json index 06be46f754..5b4bed1425 100644 --- a/datasets/G02178_1.json +++ b/datasets/G02178_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02178_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Barnes Ice Cap data set contains survey measurements of a network of 43 stakes along a 10 km flow line on the northeast flank of the south dome of the Barnes Ice Cap. The measurements are of mass balance, surface velocity, and surface elevation. They were taken over a period of time from 1970 to 1984. \n\nThe data set came from a hard copy computer printout containing raw data as well as processed quantities. This printout was scanned and digitized into a PDF file. This PDF file was put through Optical Character Recognition (OCR) software and saved as another PDF file. The resultant PDF file is human readable and all values are correct when viewed in an Adobe PDF reader. However, if you copy the contents and paste them into another application there may be errors in the values as the OCR process did not accurately compute all characters correctly. If you copy the data values into another application for analysis, double check the values against what is in the PDF file. The data are available via FTP.", "links": [ { diff --git a/datasets/G02183_1.json b/datasets/G02183_1.json index 06c987eb25..0075a4d4bc 100644 --- a/datasets/G02183_1.json +++ b/datasets/G02183_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02183_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project described in this documentary was a pilot study conducted in 1972 in preparation for the AIDJEX main experiment of 1975 to 1976.\u00a0The study included a main camp on drifting sea ice in the Beaufort Sea north of Alaska along with two satellite camps forming a station triangle with a 100 km side length.\u00a0A detailed description of the observational program and a running account of the results can be found in the AIDJEX Bulletin series published between 1970 and the end of the project in 1978.\u00a0The Polar Science Center at the University of Washington maintains an AIDJEX electronic library. It includes downloadable copies of the contents of all 40 AIDJEX Bulletins, AIDJEX Operations Manuals for the Pilot Study and the Main Experiment, and other resources.\u00a0\n\nThe film was produced by Hannes Zell and Dieter Wittich of Vienna, Austria under an arrangement with the AIDJEX Project Office at the University of Washington.\u00a0The transfer of the original 16 mm film to electronic medium was performed by Victory Studios of Seattle, Washington, USA.\u00a0The digital copy was donated to NSIDC by Dr. Norbert Untersteiner, AIDJEX Project Director.", "links": [ { diff --git a/datasets/G02191_1.json b/datasets/G02191_1.json index a9385d569d..057c898426 100644 --- a/datasets/G02191_1.json +++ b/datasets/G02191_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02191_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains Upward Looking Sonar (ULS) profiles of the underside of the Arctic pack ice along three transects whose total length is 777 nautical miles. The data were obtained by the USS Gurnard (SSN-662), a U.S. Navy submarine, on a traverse of the AIDJEX Main Experiment area in the Beaufort Sea from 07 April 1976 to 10 April 1976. The sea ice thickness derived from the ULS is given in feet.\n\nThe data are in a single ASCII text file: Aidjex_04_1976_uls.txt. The data in this text file are not formatted into columns; all data are presented in one long row separated by spaces. Little is known about the format of the file, so caution should be used when working with the data. NSIDC is providing this data as part of our effort to preserve historical data. The data file begins with nine values that appear to be header information. These nine values include latitude and longitude values along with other unknown values. After the header, there are approximately 2100 measurements of what NSIDC believes is sea ice thickness in feet, however it is unclear how often these measurements were taken. After these 2100 values, another header of nine values occurs followed again by 2100 measurements. The file continues in this pattern through the remainder of the file. Users with information about the contents of the file are encouraged to contact NSIDC User Services.\n\nTwo supporting documents that provide some background have been scanned and included as PDF files. These are AIDJEX_ULS_background.pdf and AIDJEX_ULS_format.pdf. \n\nThese data are available via FTP. \n\nNote: These data are in a raw format with unknown fields and are being provided as is for preservation purposes. A processed version of the data are available in the Submarine Upward Looking Sonar Ice Draft Profile Data and Statistics data set.", "links": [ { diff --git a/datasets/G02195_1.json b/datasets/G02195_1.json index cb081698de..d6f7ca29cc 100644 --- a/datasets/G02195_1.json +++ b/datasets/G02195_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02195_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files in this data set contain monthly mean landfast sea ice data gathered from both Russian Arctic and Antarctic Research Institute (AARI) and Canadian Ice Service (CIS) sources. Data are in the form of percent of area covered, within a longitude-latitude grid with 0.2\u00b0 resolution. Details on processing and treatment are given in the contributor's PhD thesis (K\u00f6nig, 2007). The data are provided in NetCDF format. \n\nThe time span over which data are available is split into three ranges: for 1953-1967 there are only AARI data, for 1968-1990 both AARI and CIS data are available, and from 1991-1998 only CIS data are available. There are a total of six files: two for the 1953-1967 data, two for the 1968-1990 data, and two for the 1991-1998 data. There are two files for each range because the files with \"_noNaN\" in their names contain \"-1000\" as the missing value, and the other files use \"nan\" as the missing value. Otherwise, the data in those files are identical. \n\nK\u00f6nig obtained AARI data from NSIDC data set AARI 10-Day Arctic Ocean EASE-Grid Sea Ice Observations. NSIDC has replaced that data set with Sea Ice Charts of the Russian Arctic in Gridded Format, 1933-2006 (AARI, 2007). That data set was edited and compiled by V. Smolyanitsky, V. Borodachev, A. Mahoney, F. Fetterer, and R. Barry (https://nsidc.org/data/g02176).\n\nNotice to Data Users: The documentation for this data set was provided solely by the contributor, Christof S. K\u00f6nig, and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.", "links": [ { diff --git a/datasets/G02196_1.json b/datasets/G02196_1.json index 854b370cf7..73c06b57bc 100644 --- a/datasets/G02196_1.json +++ b/datasets/G02196_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02196_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this program was to collect data relevant to developing year-round transportation capabilities in the Arctic Ocean. The US Maritime Administration sponsored this multi-year program to define environmental conditions in the Bering, Chukchi, and Beaufort Seas; to obtain data to improve design criteria for ice-worthy ships and offshore structures; and to demonstrate the operational feasibility of commercial icebreaking ships along possible future Arctic marine routes. The research was performed using the US Coast Guard Polar Star and Polar Sea ice-class ships, which were at the time the world's most powerful non-nuclear icebreakers and the only US ships capable of mid-winter Arctic operations.\n\nThe items in this data set are PDFs of Arctic Marine Transportation reports with embedded data, along with a PDF of the Achievement Record Brochure (Achievement_Record_1979_1984_Brochure.pdf), and JPEG images providing a historical context of the program. The 15 JPEG images and a PDF of accompanying captions (G02196_images_captions.pdf) are located in the images directory. \n\nThe PDF reports, an Executive Summary (Executive_Summary_Arctic_Marine_Transportation_Program.pdf), and the Appendix to the Executive Summary (Executive_Summary_Arctic_Marine_Transportation_Program_Appendix_A_List_of_Reports.pdf) are located in the Arctic_Marine_Transportation_Reports directory. Note that page 33 of the Executive Summary is missing. The Appendix to the Executive Summary contains an index of reports included in this data set. This index lists 64 reports; however, out of these 64, the following reports were never included and their location is unknown: 1, 4, 36, 37, 60, 61, and 62. Also note that reports 35 and 44 come in two parts.\n\nThe data cover the years 1979 to 1986 and were collected from ships in the Bering, Chukchi, and Beaufort Seas. Data are available via FTP.\n\nThis data is being provided as is. NOAA@NSIDC believes these data to be of value, but is unable to provide documentation. If you have information about this data set that others would find useful, please contact NSIDC User Services.", "links": [ { diff --git a/datasets/G02199_1.json b/datasets/G02199_1.json index 515234cca8..d6e80f96d4 100644 --- a/datasets/G02199_1.json +++ b/datasets/G02199_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02199_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are daily dust count observations taken in College-Fairbanks, Alaska from 23 March 1933 to 29 August 1933. The data are part of a larger collection titled \"Second International Polar Year Records, 1931-1936, Department of Terrestrial Magnetism, Carnegie Institute of Washington.\" Within this larger collection, the data are identified as \"Series 1: College-Fairbanks IPY Station Records and Data, 1932-1934: Subseries C: Auroral and Meteorological Records and Data, 1932-1933: Dust Count Observations, March 1933 - August 1933.\"\n\nThe data are provided in a PDF copy of the handwritten entries (Dust_Count_Observations_March1933_to_August1933.pdf). Two supporting files are also included in this data set. The first is a copy of the handwritten data transcribed to a Microsoft Excel spreadsheet (Dust_Count_Observations_March1933_to_August1933.xls). The second is a PDF document that explains the larger collection (DTM_Collection_Description.pdf).\n\nThe entries were recorded using an Aitken Dust Counter. Each entry includes up to 10 counts per day with measurements of wind, clouds, and visibility. The handwritten copy has the most complete data, as some of the handwritten notes were not transcribed into the computer spreadsheet. For example, handwritten notes concerning problems with the counter itself were not transcribed into the computer spreadsheet.\n\nThe data are available via FTP. \n\nNOAA@NSIDC believes these data to be of value but is unable to provide documentation. If you have information about this data set that others would find useful, please contact NSIDC User Services.", "links": [ { diff --git a/datasets/G02203_1.json b/datasets/G02203_1.json index b89cfca1d0..791bdf9a66 100644 --- a/datasets/G02203_1.json +++ b/datasets/G02203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G02203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These charts, created by the Danish Meteorological Institute (DMI), provide observed and inferred sea ice extent for each summer month from 1893 to 1956. From 1893 to 1956, the Danish Meteorological Institute (DMI) created charts of observed and inferred sea ice extent for each summer month. These charts are based on compiled observations of ice conditions reported by a variable network of national organizations, shore-based observers, scientific expeditions, and ships as detailed in each report; in cases where no observations were available, the lead mapmakers extrapolated further ice cover using their knowledge of ice movement. Except for where direct observations are indicated, caution is advised in using the charts\u2019 ice edge because there is no way to quantify the assumptions used in extrapolating ice edge or the error involved in this method. See the note on reliability for further discussion of potential error. The charts were scanned at the Icelandic Meteorological Office (IMO) and are being made available here as a service and in cooperation with DMI and other contributors. In all, there are 266 image files containing 291 images.

\n

For a gridded data set derived from this product, see the Arctic Sea Ice Concentration and Extent from Danish Meteorological Institute Sea Ice Charts, 1901-1956data set

", "links": [ { diff --git a/datasets/G10002_1.json b/datasets/G10002_1.json index c8bd090dd8..2e0f5a1526 100644 --- a/datasets/G10002_1.json +++ b/datasets/G10002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of glacier regime parameters observed between 1945 and 2003. Data include annual mass balances, ablation, accumulation, and equilibrium-line altitude of mountain and subpolar glaciers outside the two major ice sheets. All available sources of information, such as publications, archived data, and personal communications have been collected, and include time series of more than 300 glaciers. Data have been digitized and quality checked.", "links": [ { diff --git a/datasets/G10004_1.json b/datasets/G10004_1.json index 5bdd45ed45..e84e36c9cb 100644 --- a/datasets/G10004_1.json +++ b/datasets/G10004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is comprised of scientific field study notebooks from geologist Carl S. Benson describing his traverses of Greenland from 1952 to 1955. The notebooks contain data on Greenland snow accumulation, snow temperature, stratigrapy, ice sheet facies, and snow densification. Dr. Benson's notebooks also include a supplementary 1956 snow accumulation study done by the U.S. Air Force. The notebooks have been scanned and put into PDF format. In addition, a compendium of Greenland snow accumulation data, compiled by Dr. Benson in 1986, is included that spans 1911 to 1981. It is in ASCII text format.\n\nDuring a four-year period from 1952 through 1955, Carl Benson, along with many other individuals and several other organizations, dug and studied 146 snow pits and made 288 supplementary snow hardness profiles with a ramsonde instrument along a 1100 mile traverse in Northwest Greenland (Benson 1962). For each exposed pit, temperature, density, ram hardness, and grain size were measured. The data in the notebooks include a listing of all pit locations, a summary matrix of data collected at each station; accumulation data adjusted for Fall 1954 and Fall 1955 reference horizons; average accumulation for all stations; integrated ram hardness (snow density); descriptive stratigraphy, pit profile temperature and density; and pit and core temperature, density, ram hardness, and stratigraphy for each location. Benson's notebooks for 1952 through 1954 were scanned at NSIDC and are available via FTP. As of January 2013, the 1955 notebooks have not been digitized.", "links": [ { diff --git a/datasets/G10007_1.json b/datasets/G10007_1.json index 7772616b0b..3dea463439 100644 --- a/datasets/G10007_1.json +++ b/datasets/G10007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of Arctic sea ice extent and concentration from 1901 to 1956 created from a collection of historic, hand-drawn sea ice charts from the Danish Meteorological Institute (DMI). These were used to create estimates for each summer month from 1901 to 1956 by manual, subjective interpretation of scanned versions of the charts. These estimates, shown in colored-coded fields, are available as GIS shapefiles, gridded NetCDF files, and browse image files.", "links": [ { diff --git a/datasets/G10008_1.json b/datasets/G10008_1.json index def128bae7..20a657f6d2 100644 --- a/datasets/G10008_1.json +++ b/datasets/G10008_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10008_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ClimoBase is a collection of surface climate measurements collected in Northern Canada by Dr. Wayne Rouse between 1984 and 1998 in three locations: Churchill, Manitoba; Marantz Lake, Manitoba; and Inuvik, Northwest Territories. These data are comprised of surface-climate measurements, including solar time, wind speed, wind direction, dry-bulb, wet-bulb, and vapor pressure in 24 sites focused at the three Northern Canadian locations. The sites were chosen to include a variety of terrains in the study: sedge fen wetland, willow-birch wetland, lichen-heath, bedrock boulders/heath, spruce- tamarack forest, tundra lake, creek, various (e.g. a basin: sedge, willow, lichen-heath, forest, etc.), sparse vegetation (short grass/sedge, heath spp.), and coastal marsh (tall grass, sandy soils). The measurements were taken in increments ranging from seasonally to every 15 minutes. In all, 177 different variables were measured and recorded. The data are valuable due to their unique and consistent nature.", "links": [ { diff --git a/datasets/G10010_2.json b/datasets/G10010_2.json index f308e13c6a..44c88a2ccd 100644 --- a/datasets/G10010_2.json +++ b/datasets/G10010_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10010_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observations from historical sources are the basis of this monthly gridded sea ice concentration product that begins in 1850. In 1979, these sources give way to a single source: concentration from satellite passive microwave data. The historical observations come in many forms: ship observations, compilations by naval oceanographers, analyses by national ice services, and others. Monthly sea ice concentration is given in a 1/4 degree latitude by 1/4 degree longitude grid. In addition to the concentration array, for each month a corresponding source array indicates where each of 16 possible sources is used. The file of concentration and source arrays conforms to NetCDF-4 standards.", "links": [ { diff --git a/datasets/G10021_1.json b/datasets/G10021_1.json index d517ee58af..2d221bffe1 100644 --- a/datasets/G10021_1.json +++ b/datasets/G10021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data provide daily snow, temperature, and precipitation data for North America from 1959 to 2009. They are gridded to a 1-degree latitude by 1-degree longitude resolution to create a 114 x 58 grid for each day of data.", "links": [ { diff --git a/datasets/G10027_1.json b/datasets/G10027_1.json index 6e598e3447..5edd92ef89 100644 --- a/datasets/G10027_1.json +++ b/datasets/G10027_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10027_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.", "links": [ { diff --git a/datasets/G10029_1.json b/datasets/G10029_1.json index ce145d34c9..b28ad27fc8 100644 --- a/datasets/G10029_1.json +++ b/datasets/G10029_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10029_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily gridded lake ice concentration for the Laurentian Great Lakes from the NOAA Great Lakes Environmental Research Laboratory (GLERL). The data are provided as gridded ASCII text files and shapefiles along with corresponding browse image files in .jpg format.", "links": [ { diff --git a/datasets/G10030_1.json b/datasets/G10030_1.json index 67f9aa0825..73487fa246 100644 --- a/datasets/G10030_1.json +++ b/datasets/G10030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Office of Naval Research (ONR) Sea State Departmental Research Initiative (DRI) field campaign was conducted during autumn of 2015 in the Beaufort Sea in order to better understand how waves and ice interact as Arctic ice advances in late autumn. Data collection took place under four sampling modes: wave experiments, ice stations, flux stations, and ship surveys. This data set provides curated data from this field campaign in NetCDF data files.", "links": [ { diff --git a/datasets/G10040_1.json b/datasets/G10040_1.json index 32d3700a5c..9c65101e51 100644 --- a/datasets/G10040_1.json +++ b/datasets/G10040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set captures changes in glacier covered area across the state of Alaska for the period 1985 to 2020.The data set includes 18 biannual shapefiles each for overall glacier covered area, supraglacial debris area, and debris-free glacier covered area.", "links": [ { diff --git a/datasets/G10042_1.json b/datasets/G10042_1.json index c6d33b30a7..dc7e12d997 100644 --- a/datasets/G10042_1.json +++ b/datasets/G10042_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G10042_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains photographs of camps on drifting sea ice from the early 1970s along with a few aerial photographs of the drifting ice station T-3. Most of the photos were taken during a pilot study conducted in 1972 in preparation for the AIDJEX main experiment of 1975 to 1976. There are 83 photos in JPEG format with captions available for 60 of them, which are listed in an accompanying Excel (.xlsx) file. The photos were taken by Tom Marlar of the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL). Pat Martin took the aerial photographs.\n\nThe pilot study included a main camp on drifting sea ice in the Beaufort Sea north of Alaska along with two satellite camps forming a station triangle with a 100 km side length. Additional details on the AIDJEX experiment can be found on the NOAA@NSIDC AIDJEX web site. Also, a detailed description of the observational program and a running account of the results can be found in the AIDJEX Bulletin series published between 1970 and the end of the project in 1978. The Polar Science Center at the University of Washington maintains an AIDJEX electronic library at http://psc.apl.washington.edu/nonwp_projects/aidjex/. It includes downloadable copies of the contents of all 40 AIDJEX Bulletins, AIDJEX Operations Manuals for the Pilot Study and the Main Experiment, and other resources.\n\nThese photographs existed as 8\u201d x 10\u201d prints in the analog collection of material at NSIDC, and were scanned under the direction of the NSIDC archivist around 2007. The captions come from text that was written on the prints. Some captions may have been added to at a later date. \n\nAt least four of the photographs are not from AIDJEX, but are aerial photographs of Fletcher\u2019s Ice Island, or T-3. In 1972, when the AIDJEX pilot study was taking place in the Beaufort Sea, T-3 was north of the Canadian Archipelago and on its way East, as explained in the caption for AIDJEX_1972_002.jpg. According to the captions, the T-3 photos were taken in 1974. We believe that these photographs, like other aerial shots, were taken by Pat Martin, and included with other 8\u201d x 10\u201d prints that may have been sent to NSIDC by personnel at CRREL.\n\nThe track of T-3, as well as data from T-3 and other drifting ice stations, can be found on the EWG Arctic Meteorology and Climate Atlas", "links": [ { diff --git a/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json b/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json index d42f09f9a2..861eb678e0 100644 --- a/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json +++ b/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G18-ABI-L2P-ACSPO-v2.90_2.90", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI onboard the G18 satellite. NOAA\u2019s G18 (aka, GOES-T pre-launch) was launched on March 1, 2022, replacing the G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.

\r\nG18/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with a spatial resolution of 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P is produced in netCDF4 GDS2 format, with 24 granules per day, and a total data volume 0.8 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).

\r\nPixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at Documents tab under How-To section. The ACSPO G18 ABI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/G18-ABI-L3C-ACSPO-v2.90 \r\n\r\n", "links": [ { diff --git a/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json b/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json index a267954ea9..a5e0958460 100644 --- a/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json +++ b/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G18-ABI-L3C-ACSPO-v2.90_2.90", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA\u2019s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.

\r\nThe G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.

\r\nThe ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). \r\n\r\n\r\n", "links": [ { diff --git a/datasets/G5NR_1.json b/datasets/G5NR_1.json index 03e1c899e7..061fc9cd50 100644 --- a/datasets/G5NR_1.json +++ b/datasets/G5NR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "G5NR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This specific GEOS-5 model configuration used to perform a two-year global,\r\nnon-hydrostatic mesoscale simulation for the period 2005-2007 at 7-km (3.5-km in the future) horizontal resolution.\r\nBecause this simulation is intended to serve as a reference Nature Run for Observing System\r\nSimulation Experiments (OSSEs, e.g., Errico et al., 2012) it will be referred to as the 7-km GEOS-5\r\nNature Run or 7-km G5NR. This simulation has been performed with the Ganymed version of GEOS-\r\n5, more specifically with CVS Tag wmp-Ganymed-4_0_BETA8.\r\nIn addition to standard meteorological parameters (wind, temperature, moisture, surface pressure),\r\nthis simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), O3, CO\r\nand CO2. This model simulation is driven by prescribed sea-surface temperature and sea-ice, as well\r\nas surface emissions and uptake of aerosols and trace gases, including daily volcanic and biomass\r\nburning emissions, biogenic sources and sinks of CO2, and high-resolution inventories of\r\nanthropogenic sources.The simulation is performed at a horizontal resolution of 7 km using a cubed-sphere horizontal\r\ngrid with 72 vertical levels, extending up to to 0.01 hPa (~ 80 km). For user convenience, all data\r\nproducts are generated on two logically rectangular longitude-latitude grids: a full-resolution\r\n0.0625o grid that approximately matches the native cubed-sphere resolution, and another 0.5o\r\nreduced-resolution grid. The majority of the full-resolution data products are instantaneous with\r\nsome fields being time-averaged. The reduced-resolution datasets are mostly time-averaged, with\r\nsome fields being instantaneous. Hourly data intervals are used for the reduced-resolution datasets,\r\nwhile 30-minute intervals are used for the full-resolution products. All full-resolution output is on\r\nthe model\u2019s native 72-layer hybrid sigma-pressure vertical grid, while the reduced-resolution\r\noutput is given on native vertical levels and on 48 pressure surfaces extending up to 0.02 hPa.\r\nSection 4 presents additional details on horizontal and vertical grids. ", "links": [ { diff --git a/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json b/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json index 700e9ec1ea..c5383591e8 100644 --- a/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json +++ b/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GAMSSA_28km-ABOM-L4-GLOB-v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00250.json b/datasets/GB-NERC-BAS-AEDC-00250.json index d37b495306..35cf0e0336 100644 --- a/datasets/GB-NERC-BAS-AEDC-00250.json +++ b/datasets/GB-NERC-BAS-AEDC-00250.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00250", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This CASE (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00251.json b/datasets/GB-NERC-BAS-AEDC-00251.json index b4006e7bbe..e084708f1c 100644 --- a/datasets/GB-NERC-BAS-AEDC-00251.json +++ b/datasets/GB-NERC-BAS-AEDC-00251.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00251", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This case (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00260.json b/datasets/GB-NERC-BAS-AEDC-00260.json index 5063688efb..880f830f2c 100644 --- a/datasets/GB-NERC-BAS-AEDC-00260.json +++ b/datasets/GB-NERC-BAS-AEDC-00260.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00260", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three plant species, the leafy liverwort Cephaloziella varians and the angiosperms Deschampsia antarctica and Colobanthus quitensis, were sampled from 12 islands across a 1480 km latitudinal gradient from South Georgia through to Adelaide Island. Samples were collected to determine the abundance of dark septate fungi in Antarctic plant and soil communities and the effects of these organisms on plant growth. Where the target species were found in sufficient numbers to allow sampling, it proved possible to collect at least 10 samples of each species. At least 10 soil samples were collected from each site where Deschampsia was found. Plants, with intact roots and soil, were transported back to the UK using cool and frozen stowage. Additionally, intact live plants were transported to the UK in an illuminated cabinet. Seeds of the two key species (Deschampsia antarctica and Colobanthus quitensis) were also collected at Bird Island and South Georgia. As the exact months of t\n he data collection were not provided, and the metadata standard requires a YYYY-MM-DD format, this dataset has been dated as 1st January for start date, and 31st December for stop date.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00262.json b/datasets/GB-NERC-BAS-AEDC-00262.json index 24f1e2e51d..0718ad0976 100644 --- a/datasets/GB-NERC-BAS-AEDC-00262.json +++ b/datasets/GB-NERC-BAS-AEDC-00262.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00262", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study investigated the status of dark septate (\"DS\") fungi in Antarctic plant and soil communities, with the aim of determining the abundance of DS fungi in plant roots and rhizoids, their taxonomic affinities and their symbiotic status. Abundances of fungal hyphae were recorded in roots and rhizoids, and fungi were isolated and identified. Sequencing of ITS (internal transcribed spacer) regions of rDNA indicated that some isolates share taxonomic affinities with fungi of known symbiotic status. Synthesis experiments assessed the effects of DS fungal isolates, including H. ericae, on the growth and nutrient balance of their host plants. Seeds of Deschampsia antarctica and Colobanthus quitensis were collected for use in ecophysiological experiments.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00272.json b/datasets/GB-NERC-BAS-AEDC-00272.json index 92473c142d..37da31a79f 100644 --- a/datasets/GB-NERC-BAS-AEDC-00272.json +++ b/datasets/GB-NERC-BAS-AEDC-00272.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00272", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00273.json b/datasets/GB-NERC-BAS-AEDC-00273.json index 908d84026e..4121dc5741 100644 --- a/datasets/GB-NERC-BAS-AEDC-00273.json +++ b/datasets/GB-NERC-BAS-AEDC-00273.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00273", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00276.json b/datasets/GB-NERC-BAS-AEDC-00276.json index e7f8abe8c8..5b7edc69a3 100644 --- a/datasets/GB-NERC-BAS-AEDC-00276.json +++ b/datasets/GB-NERC-BAS-AEDC-00276.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00276", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling was undertaken within the West Scotia Sea in an attempt to identify the boundary between the Pacific and Bouvet mantle domains and so understand, quantify and document the flow of mantle - which is important for understanding global geodynamics The JR77 cruise aimed to acquire rock samples to constrain the history of the mantle beneath the Scotia Sea, from which the oceanic crust was derived by melting. Twenty days of rock dredging were conducted at fourteen sites in five main areas. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. The cruise also (remarkably) recovered fresh mantle peridotite nodules from the West Scotia Ridge, the first of its type - to our knowledge - from the world's ocean ridge system.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00277.json b/datasets/GB-NERC-BAS-AEDC-00277.json index 99ace01782..8d6eb5fe9c 100644 --- a/datasets/GB-NERC-BAS-AEDC-00277.json +++ b/datasets/GB-NERC-BAS-AEDC-00277.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00277", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The plan was to map the spreading centre using the swath bathymetry system, and then to use this map to locate the best dredging sites. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00278.json b/datasets/GB-NERC-BAS-AEDC-00278.json index 80600c42d5..93811d7f20 100644 --- a/datasets/GB-NERC-BAS-AEDC-00278.json +++ b/datasets/GB-NERC-BAS-AEDC-00278.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00278", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The fieldwork involved mapping the spreading centre using swath bathymetry, and then using this information to locate the best dredging sites. This meant successfully imaging a significant area of hitherto unsurveyed oceanic crust and recovering rocks at 13 dredge sites. The new bathymetric maps add considerably to knowledge of the West Scotia Ridge.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00279.json b/datasets/GB-NERC-BAS-AEDC-00279.json index 552463dff9..2764519647 100644 --- a/datasets/GB-NERC-BAS-AEDC-00279.json +++ b/datasets/GB-NERC-BAS-AEDC-00279.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00279", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The initial aim of this project was to carry out a higher resolution geochemical study of mantle flow using existing samples. This confirmed flow from the Bouvet domain into the East Scotia Sea and placed constraints on flow pathways. The second stage was to sample further within the West Scotia Sea and to use elemental and isotope (Sr, Nd, Pb, Hf) analyses to fingerprint mantle provenance. The results were used to locate and investigate the nature of the Pacific-South Atlantic mantle domain boundary and thus to contribute to the understanding and quantification of global upper mantle fluxes.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00284.json b/datasets/GB-NERC-BAS-AEDC-00284.json index da0abc7c6c..66d388eb80 100644 --- a/datasets/GB-NERC-BAS-AEDC-00284.json +++ b/datasets/GB-NERC-BAS-AEDC-00284.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00284", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New instrumentation was deployed in the Antarctic Peninsula region to monitor conditions occurring in the region of near-space surrounding the Earth. The opportunity was taken to link into a NASA satellite mission occurring at the same time and with similar goals - to study the dynamics of the Earth-Sun system at a location where the two systems are finely balanced. The experiments have been used to interpret the changes in plasma composition at the same point in space due to solar weather events. A refurbished VLF Doppler receiver was installed at Rothera to measure plasmaspheric electron concentration. The electron number density was determined from analysis of the 15 minute integration providing group delay times, Doppler shift and arrival bearing of whistler-mode signals, of man-made transmissions, from MSK format transmitters from north east America. If you would like more information about the VLF Doppler receiver data that is still being routinely collected a\n t Rothera please contact the Antarctic Environmental Data Centre (&AEDC&) at the British Antarctic Survey.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00289.json b/datasets/GB-NERC-BAS-AEDC-00289.json index 7e6df8bce9..b3e1d30750 100644 --- a/datasets/GB-NERC-BAS-AEDC-00289.json +++ b/datasets/GB-NERC-BAS-AEDC-00289.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00289", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. Successful coring and examination of sediments now on and immediately beneath the sea floor, which provided the deforming bed of the former ice stream, enhanced our understanding\n of conditions beneath ice streams.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00290.json b/datasets/GB-NERC-BAS-AEDC-00290.json index 50b3dc5f5d..4e3a59e944 100644 --- a/datasets/GB-NERC-BAS-AEDC-00290.json +++ b/datasets/GB-NERC-BAS-AEDC-00290.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00290", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. The mapping of streamlined sedimentary bedforms on the outer shelf has allowed the dimensions of a former fast-flowing ice stream present at the Last Glacial Maximum to be defin\n ed. This, in turn, enabled estimates of the past magnitude of ice flow through this glacial system to be calculated. Data was collected using Kongsberg-Simrad EM120 multibeam swath bathymetry and a TOPAS sub-bottom profiler. EM120 data was processed using the Kongsberg-Simrad bathymetric processing package &NEPTUNE&. These ice flux estimates were compared with computer-model reconstructions of former ice-sheet dynamics as a robust test of model performance.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00293.json b/datasets/GB-NERC-BAS-AEDC-00293.json index b4c8211038..441b522cba 100644 --- a/datasets/GB-NERC-BAS-AEDC-00293.json +++ b/datasets/GB-NERC-BAS-AEDC-00293.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00293", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Three micro-power Automatic Weather Stations with two sonic ranging sensors were deployed at field-sites situated at Rothschild Island, Latady Island and Smyley Island in January 2005. The Automatic Weather Stations instruments included a wind vane and two humicaps on the mast and two sonic ranging sensors mounted on separate horizontal scaffold poles.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00294.json b/datasets/GB-NERC-BAS-AEDC-00294.json index 494a587ef2..1e04ada138 100644 --- a/datasets/GB-NERC-BAS-AEDC-00294.json +++ b/datasets/GB-NERC-BAS-AEDC-00294.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00294", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Ground truth measurements in the form of snow/ice cores were obtained in 2006 at three sites, Rothschild Island, Latady Island and Smyley Island, where Automatic Weather Stations had been deployed in the previous season. At both the Rothschild Island and Smyley Island sites the AWS, due to an unprecedented amount of snowfall, had been buried therefore two cores, 8m and 12m in length, were obtained from the approximate position of the AWS, in addition to the sampling of a snow pit. At the Latady Island site the top 60cm of the 5m AWS was protruding above the surface, again, due to an unprecedented amount of snowfall. A diagonally descending trench was dug to recover the AWS and two cores were collected at this site. Photographs of the expedition showing \n the ground layout, the situation of the cores and what was done when they were gathered are available and stored with the data.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00296.json b/datasets/GB-NERC-BAS-AEDC-00296.json index eb5486b8a0..29466992ec 100644 --- a/datasets/GB-NERC-BAS-AEDC-00296.json +++ b/datasets/GB-NERC-BAS-AEDC-00296.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00296", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Correction, Verification and Context, of Satellite-Derived Elevation Changes on the Antarctic Peninsula CVaCS-DECAP The aim of the project was to measure the various factors that affect altitude of snow surfaces in Antarctica, in order to validate data from satellite altimeters. In particular, it aimed for a better understanding of the factors affecting snowpack compaction rates, by accurate measurement of compaction over a period of several years. At four sites on the Antarctic Peninsula during the 2004-2005 austral summer ice cores were drilled to reveal the history of snowfall, and how the snow gets denser as it is crushed. Loggers designed to measure the compaction of snow were installed in boreholes, these sensors took a measurement every hour and are sensitive to downward movements of less than a millimetre. Automatic weather stations, sonic snow rangers and thermistor strings were also installed at each site, measuring the snow arriving at hourly intervals. A\n network of stakes was surveyed by GPS to provide horizontal strain rates, of the glacier, at each location. The flow away from the sites was compared with the snowfall from the ice cores to show up any imbalance.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00311.json b/datasets/GB-NERC-BAS-AEDC-00311.json index 5c475983d1..b7c040d64f 100644 --- a/datasets/GB-NERC-BAS-AEDC-00311.json +++ b/datasets/GB-NERC-BAS-AEDC-00311.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00311", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During field work in 2001 over 1600 specimens were collected from four main fossil plant assemblages: the ''Nordenksjold flora'' from the Cross Valley Formation of Late Palaeocene age; and 3 floras from La Meseta Formation i) Flora2 from the Valle De Las Focas allomember, ~late Early Eocene, ii) Wiman Flora, Acantilados allomember, late Early/mid Eocene, iii) Cucullaea 1, Cuculleae 1 allomember Flora, early Late Eocene. In addition smaller collections of fossils from other parts of the La Meseta Formation were collected. The work concentrated on the Late Palaeocene and the Cuculleae 1 floras as these were the best preserved and had sufficient morphotypes for climate analysis. In the Late Palaeocene flora 36 angiosperm leaf morphotypes were identified, along with 2 pteridophytes (ferns), and podocarp and araucarian conifers. Discovery of several new leaf types indicates that the Tertiary floras from Antarctica were more diverse than previously thought.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00312.json b/datasets/GB-NERC-BAS-AEDC-00312.json index 3224b1aa70..b1538984f3 100644 --- a/datasets/GB-NERC-BAS-AEDC-00312.json +++ b/datasets/GB-NERC-BAS-AEDC-00312.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00312", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fossils from Palaeogene strata on Seymour Island, Antarctic Peninsula, were studied to determine the nature of vegetation response to the fundamental change from greenhouse to icehouse climates in Antarctica. Palaeoclimate data was derived using CLAMP (Climate Leaf Analysis Multivariate Program) and several Leaf Margin Analysis (LMA) techniques based on the physiognomic properties of the leaves. Climate interpretation of the fossils produced new data on terrestrial climate change at high latitudes and were used to test and validate climate models, and to establish whether climate-induced changes in biodiversity occurred in a gradual or punctuated manner.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00342.json b/datasets/GB-NERC-BAS-AEDC-00342.json index 83b05368c4..6eabfb2e8d 100644 --- a/datasets/GB-NERC-BAS-AEDC-00342.json +++ b/datasets/GB-NERC-BAS-AEDC-00342.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00342", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seismic reflection data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: Geometrics Geode, 48 channels, active source (explosives). Five single-fold lines. Line length between 7.7 and 2.5 km. In addition, fold increased to 4 for the central part of one line (over the lake itself). Dataset also includes data from a single shallow seismic refraction experiment.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00343.json b/datasets/GB-NERC-BAS-AEDC-00343.json index 4c2bacc377..e611555e1c 100644 --- a/datasets/GB-NERC-BAS-AEDC-00343.json +++ b/datasets/GB-NERC-BAS-AEDC-00343.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00343", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geographical Positioning System (GPS) data recorded in the region of Subglacial Lake Ellsworth. Recording instruments: Leica geodetic receivers. Four locations with continuous data records; all other locations (~70) occupied for short periods (mostly 1 hour).", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00344.json b/datasets/GB-NERC-BAS-AEDC-00344.json index 58e483eae0..b4be98c00e 100644 --- a/datasets/GB-NERC-BAS-AEDC-00344.json +++ b/datasets/GB-NERC-BAS-AEDC-00344.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00344", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Radar data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: the British Antarctic Survey's (BAS) DELORES 1 and DELORES II radar systems. Line length between 1 and 45 km. Simultaneous GPS data acquired with Leica geodetic GPS receiver at 1 sec intervals.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00347.json b/datasets/GB-NERC-BAS-AEDC-00347.json index 16c2184089..7d0cf4cd15 100644 --- a/datasets/GB-NERC-BAS-AEDC-00347.json +++ b/datasets/GB-NERC-BAS-AEDC-00347.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00347", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: HOBO Weather Station (HOBO AWS) recording wind speed, wind direction, temperature, pressure, humidity, solar radiation. HOBO - registered trademark of the Onset Computer Corporation", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00348.json b/datasets/GB-NERC-BAS-AEDC-00348.json index c94616cf6b..1114fba0af 100644 --- a/datasets/GB-NERC-BAS-AEDC-00348.json +++ b/datasets/GB-NERC-BAS-AEDC-00348.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00348", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Expect no core to remain once analysis has been completed.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00349.json b/datasets/GB-NERC-BAS-AEDC-00349.json index 5866229c75..d9861843ef 100644 --- a/datasets/GB-NERC-BAS-AEDC-00349.json +++ b/datasets/GB-NERC-BAS-AEDC-00349.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00349", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Density data is complete. Accumulation and chemical analysis is in progress.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00350.json b/datasets/GB-NERC-BAS-AEDC-00350.json index 7daa988d2a..bb9a4e9130 100644 --- a/datasets/GB-NERC-BAS-AEDC-00350.json +++ b/datasets/GB-NERC-BAS-AEDC-00350.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00350", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity data acquired in the region of Subglacial Lake Ellsworth. Instrument Lacoste and Romberg land gravity meter. Drift control primarily contained within the local area. Single, one-way tie to international gravity base station network (Rothera) Single survey line ~30 km long. Station spacing 2 km, except for 240 m spacing over the lake. Position, elevation, ice- and water-thickness data exist for each station.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00351.json b/datasets/GB-NERC-BAS-AEDC-00351.json index 4bd746e32c..30f78a2fd6 100644 --- a/datasets/GB-NERC-BAS-AEDC-00351.json +++ b/datasets/GB-NERC-BAS-AEDC-00351.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00351", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurement of temperature at the base of a 20-m deep borehole in the region of Subglacial Lake Ellsworth. Resistance of two calibrated thermistors measured at the base of a 20 m deep borehole.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00361.json b/datasets/GB-NERC-BAS-AEDC-00361.json index a5847dad6e..ac33d36f83 100644 --- a/datasets/GB-NERC-BAS-AEDC-00361.json +++ b/datasets/GB-NERC-BAS-AEDC-00361.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00361", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Approximatively 1MB of ice temperature data acquired during the RABID Project. Measured on a thermistor cable with 10 sensors located at depths between 15 m and 300 m below the surface. Collected between November 2004 and February 2006.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00367.json b/datasets/GB-NERC-BAS-AEDC-00367.json index 2175f64261..ae23f26b9e 100644 --- a/datasets/GB-NERC-BAS-AEDC-00367.json +++ b/datasets/GB-NERC-BAS-AEDC-00367.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00367", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital time series data collected for monitoring of drilling during the RABID Project. Water temperature, pressure, flow. Drill depth and hose tension. Instrumentation: SENSORS Flow meter - Kobold Instruments Ltd, L25 axial turbine flow meter. Water pressure - Omega Engineering Ltd, PX222-250GV pressure transducer Water level - GEMS 4000KGB100M2KJ Range 0-10bG immersible pressure transducer Water temperate - Omega Engineering Ltd, K2017 PT100 ceramic element thermometer Hose tension(Load Cell) - Omega Engineering Ltd, LCCB-2K load cell Hose speed and depth - Red Lion, rotary pulse generator LSQS0200 Additional water temperature - FishTag and TinyTalk data loggers", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00368.json b/datasets/GB-NERC-BAS-AEDC-00368.json index 97a8e86307..8056884ed7 100644 --- a/datasets/GB-NERC-BAS-AEDC-00368.json +++ b/datasets/GB-NERC-BAS-AEDC-00368.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00368", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS positions from sensors monitoring ice flow during the RABID Project (Leica and Trimble receivers). Five stations on the ice stream, plus one on slow-moving adjacent ice sheet (Fletcher Promontory), and one on a nunatak (unofficial name &Tolly''s Heel&) in the Ellsworth Mountains. Sensors: Leica 1200 GPS receivers Trimble 5200 GPS receivers Trimble 4000 GPS receivers", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00369.json b/datasets/GB-NERC-BAS-AEDC-00369.json index e416d9954a..612d05b778 100644 --- a/datasets/GB-NERC-BAS-AEDC-00369.json +++ b/datasets/GB-NERC-BAS-AEDC-00369.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00369", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital seismic reflection data (BISON 9024 seismograph) acquired during the RABID Project. Data collected using 24 channels, active source (explosives). Four single-fold lines. Line length 3.6 km. Instrumentation Data logger: BISON 9024 seismograph Sensors: OYO-Geospace geophones (100 Hz natural frequency)", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00371.json b/datasets/GB-NERC-BAS-AEDC-00371.json index d1b19f290b..d762ed04ba 100644 --- a/datasets/GB-NERC-BAS-AEDC-00371.json +++ b/datasets/GB-NERC-BAS-AEDC-00371.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00371", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sections of ice core acquired from upper 100 m of the ice stream during the RABID Project. Retrieved using hot-water corer. Cores taken at selected depths in two adjacent holes. Core section length = up to 4 m. Number of core sections = 6. Total length = 20.8 m. Instrumentation: Ice cores drilled using hot-water ice-coring technique.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00373.json b/datasets/GB-NERC-BAS-AEDC-00373.json index c566452023..2527b009a7 100644 --- a/datasets/GB-NERC-BAS-AEDC-00373.json +++ b/datasets/GB-NERC-BAS-AEDC-00373.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00373", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground-Penetrating Radar (GPR) data acquired during the RABID Project with a Mala GPR.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00374.json b/datasets/GB-NERC-BAS-AEDC-00374.json index 1f40c3fcfb..877747186e 100644 --- a/datasets/GB-NERC-BAS-AEDC-00374.json +++ b/datasets/GB-NERC-BAS-AEDC-00374.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00374", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Weather data acquired on Rutford Ice Stream during the RABID Project. Wind speed, wind direction, temperature, pressure, humidity, solar radiation recored with an HOBO AWS (Automatic Weather Station: data logger & sensors );", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00396.json b/datasets/GB-NERC-BAS-AEDC-00396.json index 71673e0a78..47747eacaf 100644 --- a/datasets/GB-NERC-BAS-AEDC-00396.json +++ b/datasets/GB-NERC-BAS-AEDC-00396.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00396", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. \n", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00400.json b/datasets/GB-NERC-BAS-AEDC-00400.json index 973f01a8da..6d7d09e6c6 100644 --- a/datasets/GB-NERC-BAS-AEDC-00400.json +++ b/datasets/GB-NERC-BAS-AEDC-00400.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00400", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Initial work during the 2001/2002 field season commenced with reconnaissance and sampling in northeast Palmer Land. Over a two month period, outcrop from the Welch Mountains to the Eternity Range was visited, the geology described, and mafic dyke samples collected for analysis. This was followed by a further two months based on the ship HMS Endurance, carrying out helicopter assisted sampling of numerous islands and coastal localities along the western and eastern margin of northern Graham Land. Approximately 200 (400kg of dyke and host rock at Palmer land and 80kg at nine localities in Graham Land) rock samples were collected.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00401.json b/datasets/GB-NERC-BAS-AEDC-00401.json index a3882914a0..9ec2adc03a 100644 --- a/datasets/GB-NERC-BAS-AEDC-00401.json +++ b/datasets/GB-NERC-BAS-AEDC-00401.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00401", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The chemistry of mafic volcanic rocks and minor intrusions erupted on continents can be used to define the composition and history of subcontinental asthenospheric and lithospheric mantle domains. We have produced new and collated published data for Antarctica in order to identify mantle domains beneath the continent. Suitable material archived at the British Antarctic Survey, Cambridge, the result of previous geological research, was sampled and prepared for petrographic and geochemical analysis in the intervening period between field collection and sample arrival in the United Kingdom. Field information, petrography and raw geochemical data obtained from XRF (X-ray fluorescence), ICPMS (Inductively coupled plasma-mass spectrometer), TIMS (Thermal Ionization Mass Spectrometer), Ar/Ar analysis and Electron Microprobe analysis of rock samples collected from Palmer Land and Graham Land was used to define a geochemical profile of crust/mantle architecture beneath the An\n tarctic Peninsula.", "links": [ { diff --git a/datasets/GB-NERC-BAS-AEDC-00423.json b/datasets/GB-NERC-BAS-AEDC-00423.json index d1c9dd58df..aed34e8064 100644 --- a/datasets/GB-NERC-BAS-AEDC-00423.json +++ b/datasets/GB-NERC-BAS-AEDC-00423.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-AEDC-00423", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. The aim was to sample air that had passed over land (the Peninsula), sea (Bellingshausen and Weddell) or ice (the plateau) and compare the size and quantity of ice crystals transported. Data collected using our own Automatic Weather Station (AWS), also an ADAS tether sonde system, some radiosondes, a sensor and logger attached to the ice-crystal replicator system and an Ice Nucleus chamber. The collection was made during a month in January, February 2002 East of Weatherheaven.", "links": [ { diff --git a/datasets/GB-NERC-BAS-PDC-00499.json b/datasets/GB-NERC-BAS-PDC-00499.json index 0978b3cea1..d908da1278 100644 --- a/datasets/GB-NERC-BAS-PDC-00499.json +++ b/datasets/GB-NERC-BAS-PDC-00499.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-PDC-00499", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. \n\n Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC.\n", "links": [ { diff --git a/datasets/GB-NERC-BAS-PDC-00500.json b/datasets/GB-NERC-BAS-PDC-00500.json index 18a1f41920..d7a85979ca 100644 --- a/datasets/GB-NERC-BAS-PDC-00500.json +++ b/datasets/GB-NERC-BAS-PDC-00500.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GB-NERC-BAS-PDC-00500", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. \n\nOur knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC.\n", "links": [ { diff --git a/datasets/GCAM_Land_Cover_2005-2095_1216_1.json b/datasets/GCAM_Land_Cover_2005-2095_1216_1.json index b489b902cf..a514feb0cc 100644 --- a/datasets/GCAM_Land_Cover_2005-2095_1216_1.json +++ b/datasets/GCAM_Land_Cover_2005-2095_1216_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCAM_Land_Cover_2005-2095_1216_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided.Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100.These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario.Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference). ", "links": [ { diff --git a/datasets/GCIP-GREDS.json b/datasets/GCIP-GREDS.json index 66f7fa791f..448f04dc4c 100644 --- a/datasets/GCIP-GREDS.json +++ b/datasets/GCIP-GREDS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCIP-GREDS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets on this compact disc are a compilation of several geographic\n reference data sets of interest to the global-change research community. The\n data sets were chosen with input from the Global Energy and Water Cycle\n Experiment (GEWEX) Continental-Scale International Project (GCIP) Data\n Committee and the GCIP Hydrometeorology and Atmospheric Subpanels. The data\n sets include: locations and periods of record for stream gages, reservoir\n gages, and meteorological stations; a 500-meter-resolution digital elevation\n model; grid-node locations for the Eta numerical weather-prediction model; and\n digital map data sets of geology, land use, streams, large reservoirs, average\n annual runoff, average annual precipitation, average annual temperature,\n average annual heating and cooling degree days, hydrologic units, and state and\n county boundaries. Also included are digital index maps for LANDSAT scenes,\n and for the U.S. Geological Survey 1:250,000, 1:100,000, and 1:24,000-scale map\n series. Most of the data sets cover the conterminous United States; the\n digital elevation model also includes part of southern Canada. The stream and\n reservoir gage and meteorological station files cover all states having area\n within the Mississippi River Basin plus that part of the Mississippi River\n Basin lying within Canada. Several data-base retrievals were processed by\n state, therefore many sites outside the Mississippi River Basin are included. \n \n See: \"http://nsdi.usgs.gov\" for a complete desciption of metadata and browse\n images.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA.json b/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA.json index baaa2f847a..9b0a63ba08 100644 --- a/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is Observation DN value observed by SGLI-IRS Radiometer (Short Wavelength Infrared (SWI: 1.05 micrometer to 2.21 micrometer, 4 channels) and Thermal Infrared (TIR: 10.8 micrometer, 12.0 micrometer, 2 channels)) are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km also 250 m are available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA.json b/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA.json index 7153819c73..6a37885e06 100644 --- a/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1A_SWI_and_TIR_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is Observation DN value observed by SGLI-IRS Radiometer (Short Wavelength Infrared (SWI: 1.05 micrometer to 2.21 micrometer, 4 channels) and Thermal Infrared (TIR: 10.8 micrometer, 12.0 micrometer, 2 channels)) are stored for each band as image data. The provided format is HDF5. The spatial resolution is 250 m also 1 km is available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel.The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA.json b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA.json index 0f5af2f355..e87940cf17 100644 --- a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Visible and Near Infrared (Non-Polarization) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is the intensity of reflected light scattered and absorbed by the atmosphere and the earth\u00e2\u0080\u0099s surface using Non Polarized observation observed by Visible and Near Infrared Radiometer. Observation DN value are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km. 250 m is also available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_250m_NA.json b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_250m_NA.json index 19f21007c6..5a79fda914 100644 --- a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_NP_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Visible and Near Infrared (Non-Polarization) (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is the intensity of reflected light scattered and absorbed by the atmosphere and the earth\u00e2\u0080\u0099s surface using Non Polarized observation. Observation DN value are stored for each band as image data. The provided format is HDF5. The spatial resolution is 250 m. 1 km is also available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_PL_1km_NA.json b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_PL_1km_NA.json index b75402d240..c9910d7be8 100644 --- a/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_PL_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_PL_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1A_Visible_and_Near_Infrared_PL_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Visible and Near Infrared (Polarization) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is the intensity of reflected light scattered and absorbed by the atmosphere and the earth\u00e2\u0080\u0099s surface using VNR-PL (P1: 673.5 \u00ce\u00bcm, P2: 868.5 \u00ce\u00bcm, 2 channels) (Polarized observation). Observation DN value are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_1km_NA.json b/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_1km_NA.json index 10fe94ae5f..50bc5f6014 100644 --- a/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1B_SWI_and_TIR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1B products are using the data contained in Level 1A products as inputs, and the following processes are applied on the input data, calculation of spectral radiance, re-sampling of geometric correction, data and observation data to L1B, Reference Coordinate System, calculation of land/water flag, creation of quality information. This product is top of atmosphere radiance SI (Scaled Integer) data, using the Level 1A product as input. The provided format is HDF5. The spatial resolution is 1 km. 250 m is also available. Radiometric correction is stored. The geometries are projected to L1B reference coordinates commonly for VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10 pixels interval are stored commonly for band. On the other hand, since the precise satellite position in observed pixels is varied depending on band, the satellite azimuth angle and the satellite zenith angle are stored by 10 pixels interval for each band. The stored geometric information is the center position of the pixel. In addition, The current version of the product is Version 2. QA flag corresponding to the observation image is appended to Level 1B product.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_250m_NA.json b/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_250m_NA.json index 25110f00c4..8a6c9ca50b 100644 --- a/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L1B_SWI_and_TIR_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1B_SWI_and_TIR_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1B products are using the data contained in Level 1A products as inputs, and the following processes are applied on the input data, calculation of spectral radiance, re-sampling of geometric correction, data and observation data to L1B, Reference Coordinate System, calculation of land/water flag, creation of quality information. This product is top of atmosphere radiance SI (Scaled Integer) data, using the Level 1A product as input. The provided format is HDF5. The spatial resolution is 250 m. 1 km is also available. Radiometric correction is stored. The geometries are projected to L1B reference coordinates commonly for VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10 pixels interval are stored commonly for band. On the other hand, since the precise satellite position in observed pixels is varied depending on band, the satellite azimuth angle and the satellite zenith angle are stored by 10 pixels interval for each band. The stored geometric information is the center position of the pixel. In addition, The current version of the product is Version 2. QA flag corresponding to the observation image is appended to Level 1B product.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_1km_NA.json b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_1km_NA.json index 97911ccd88..e6c5c86bb0 100644 --- a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1B Visible and Near Infrared (Non-Polarization) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1B 1km products are defined two products because the resolution for land and coastal observation is 250 m, and the resolution for open ocean observation is 1 km. One is using the data contained in Level 1A 1km resolution products as inputs, and the following processes are applied on the input data, calculation of spectral radiance, re-sampling of geometric correction, data and observation data to L1B, Reference Coordinate System, calculation of land/water flag, creation of quality information. The other is using Level 1B data Level 1B products as inputs, and the following processes are applied on the input data: low resolution (1000 m) re-sampling of high resolution images, filling of missing data in high resolution images with contiguous low resolution images and re-calculation of various information related to L1B products. This product is top of atmosphere radiance SI (Scaled Integer) data, using the Level 1A product as input. The provided format is HDF5. The spatial resolution is 1 km. 250 m is also available. Radiometric correction is stored. The geometries are projected to L1B reference coordinates commonly for VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10 pixels interval are stored commonly for band. On the other hand, since the precise satellite position in observed pixels is varied depending on band, the satellite azimuth angle and the satellite zenith angle are stored by 10 pixels interval for each band. The stored geometric information is the center position of the pixel. In addition, The current version of the product is Version 2. QA flag corresponding to the observation image is appended to Level1B product.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_250m_NA.json b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_250m_NA.json index 7341c0aaab..625b3ffb6f 100644 --- a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_NP_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1B Visible and Near Infrared (Non-Polarization) (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1B products are using the data contained in Level 1A products as inputs, and the following processes are applied on the input data, calculation of spectral radiance, re-sampling of geometric correction, data and observation data to L1B, Reference Coordinate System, calculation of land/water flag, creation of quality information. This product is top of atmosphere radiance SI (Scaled Integer) data, using the Level 1A product as input. The provided format is HDF5. The spatial resolution is 250 m. 1 km is also available. Radiometric correction is stored. The geometries are projected to L1B reference coordinates commonly for VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10 pixels interval are stored commonly for band. On the other hand, since the precise satellite position in observed pixels is varied depending on band, the satellite azimuth angle and the satellite zenith angle are stored by 10 pixels interval for each band. The stored geometric information is the center position of the pixel. In addition, The current version of the product is Version 2. QA flag corresponding to the observation image is appended to Level 1B product.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_PL_1km_NA.json b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_PL_1km_NA.json index 887f7d7e00..659300e18d 100644 --- a/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_PL_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_PL_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L1B_Visible_and_Near_Infrared_PL_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L1B Visible and Near Infrared (Polarization) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1B products are using the data contained in Level 1A products as inputs, and the following processes are applied on the input data, calculation of spectral radiance, re-sampling of geometric correction, data and observation data to L1B, Reference Coordinate System, calculation of land/water flag, creation of quality information. This product is top of atmosphere radiance SI (Scaled Integer) data, using the Level 1A product as input. The provided format is HDF5. The spatial resolution is 1km. Radiometric correction is stored. The geometries are projected to L1B reference coordinates commonly for VNR-NP and IRS, and the ground observation position in each band are same. Therefore, as geometric information, latitude, longitude, solar azimuth angle and solar zenith angle of 10 pixels interval are stored commonly for band. On the other hand, since the precise satellite position in observed pixels is varied depending on band, the satellite azimuth angle and the satellite zenith angle are stored by 10 pixels interval for each band. The stored geometric information is the center position of the pixel. In addition, The current version of the product is Version 2. QA flag corresponding to the observation image is appended to Level1B product.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_AGB_NA.json b/datasets/GCOM-C_SGLI_L2_AGB_NA.json index 3f3803bd74..d556bf3fc6 100644 --- a/datasets/GCOM-C_SGLI_L2_AGB_NA.json +++ b/datasets/GCOM-C_SGLI_L2_AGB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_AGB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Above Ground Biomass and Vegetation Roughness Index dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data. This dataset includes Above Ground Biomass (AGB), Vegetation Roughness Index (VRI) and quality flag (QA_Flag). AGB is the volume of aboveground biomass shown in dry weight and estimated using two sets of the red and near-infrared channel data observed from nadir and slant viewing direction by SGLI sensor. The physical quantity unit is t/ha. VRI is the index expressing 3D structural information of vegetation (the unevenness changes in spatial distribution of canopy density). It is related to both the area ratio of shadows produced by the vegetation canopy in the sensor's field of view and the vegetation coverage. It is a parameter for calculating AGB. The physical quantity unit is dimensionless. The QA_flag shows flag of quality and observation condition. The provided format is HDF5. The spatial resolution is 250 m. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available, but please note that the QA_Flag data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_ARNP_NA.json b/datasets/GCOM-C_SGLI_L2_ARNP_NA.json index 8a34271543..5c3dd4b8c1 100644 --- a/datasets/GCOM-C_SGLI_L2_ARNP_NA.json +++ b/datasets/GCOM-C_SGLI_L2_ARNP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_ARNP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 AeRosol properties using Numerical Prediction dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The contents are listed in the following:AROT: Aerosol Optical Thickness over land and ocean at 500 nm (dimensionless).ARAE: Angstrom Exponent over land and ocean at 500 nm and 380 nm (dimensionless).ASSA: Single Scattering Albedo over land and ocean at 380 nm (dimensionless).AROT_uncertainty, AROT_uncertainty, AROT_uncertainty: The uncertainties of AROT, ARAE and ASSA, respectively (dimensionless).The utilized aerosol optical model for the retrieval is the same for both over land and ocean, and the coefficients are based on the skyradiometer observations. While the particle shapes, real parts of complex refraction indices, and size distributions of large and small particles are assumed to be fixed, the fraction of small particles and complex refraction indices (in terms of SSA) vary. The algorithm assumes reasonable aerosol parameters for the SGLI observations and derives the contents of this dataset.The provided format is HDF5. The spatial resolution is 1 km. The projection method is EQA. The spatial coverage is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_CLFG_1km_NA.json b/datasets/GCOM-C_SGLI_L2_CLFG_1km_NA.json index 08164fbbc6..33960736ec 100644 --- a/datasets/GCOM-C_SGLI_L2_CLFG_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_CLFG_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_CLFG_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Cloud Flag Classification (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The CLFG dataset includes Clear Confidence Level (0~1, 1 is clear), Cloud Inhomogeneity, phase, and the relevant information.The cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm (CAPCOM) are utilized, and Nakajima et al. 2019 (https://doi.org/10.1186/s40645-019-0295-9 ) describe the methodologies in detail.The provided format is HDF5. The spatial resolution is 1 km. The projection method is EQA. The spatial coverage is \"Tile\". The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_CLFG_250m_NA.json b/datasets/GCOM-C_SGLI_L2_CLFG_250m_NA.json index a26634656c..8857f285bf 100644 --- a/datasets/GCOM-C_SGLI_L2_CLFG_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_CLFG_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_CLFG_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Cloud Flag Classification (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The CLFG dataset includes Clear Confidence Level (0~1, 1 is clear), Cloud Inhomogeneity, phase, and the relevant information.The cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm (CAPCOM) are utilized, and Nakajima et al. 2019 (https://doi.org/10.1186/s40645-019-0295-9 ) describe the methodologies in detail.The provided format is HDF5. The spatial resolution is 250m . The projection method is EQA. The spatial coverage is \"Tile\". The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_CLPR_NA.json b/datasets/GCOM-C_SGLI_L2_CLPR_NA.json index b9bb22ef88..e8e69ce82d 100644 --- a/datasets/GCOM-C_SGLI_L2_CLPR_NA.json +++ b/datasets/GCOM-C_SGLI_L2_CLPR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_CLPR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Cloud properties dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The contents are listed in the following:CLOT_W and CLOR_W: Optical thickness (unitless) and effective radius of water cloud droplets (mm), respectively.CLOT_I are CLER_I: Optical thickness (unitless) and effective radius of ice cloud droplets (mm), respectively.CLTT and CLTH: Temperature and Height of the cloud top layer (kelvin and km), respectively.CLTYPE: Cloud discrimination flag including the classification of cloud type and phase (unitless)The cloud properties are retrieved with the Comprehensive Analysis Program for Cloud Optical Measurements (CAPCOM), initially developed for the GLI mission. The current algorithm is optimized for the SGLI characteristics and uses L2 Cloud flag classification.The provided format is HDF5. The spatial resolution is 1 km. The projection method is EQA. The spatial coverage is Tile. The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_IWPR_1km_NA.json b/datasets/GCOM-C_SGLI_L2_IWPR_1km_NA.json index fcdae5bb04..0ac4814850 100644 --- a/datasets/GCOM-C_SGLI_L2_IWPR_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_IWPR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_IWPR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 In-water properties (Chl-a TSM CDOM) (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit sattelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Chlorophyll-a concentration (CHLA), Total suspended matter concentration (TSM) and Colored dissolved organic matter (CDOM).CHLA is the concentration of the green pigment in phytoplankton in sea surface level. The physical quantity unit is mg/m^3.TSM is the dry weight of suspended matter in a unit volume of surface water which is the sum of organic such as phytoplankton and inorganic such as soil. The physical quantity unit is g/m^3.CDOM is the light absorption coefficient of organics dissolved in surface water at 412 nm. The physical quantity unit is m-1.The product also includes QA_Flag data with the same spatial resolution as the auxiliary data. The provided format is HDF5. The Spatial resolution is 250 m. The projection method is L1B reference coordinates. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available, but please note that the \"QA_Flag\" data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_IWPR_250m_NA.json b/datasets/GCOM-C_SGLI_L2_IWPR_250m_NA.json index e411cb7d23..ec726b67af 100644 --- a/datasets/GCOM-C_SGLI_L2_IWPR_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_IWPR_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_IWPR_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 In-water properties (Chl-a TSM CDOM) (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit sattelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Chlorophyll-a concentration (CHLA), Total suspended matter concentration (TSM) and Colored dissolved organic matter (CDOM).CHLA data is the concentration of the green pigment in phytoplankton in sea surface level. The physical quantity unit is mg/m^3.TSM data is the dry weight of suspended matter in a unit volume of surface water which is the sum of organic such as phytoplankton and inorganic such as soil. The physical quantity unit is g/m^3.CDOM data is the light absorption coefficient of organics dissolved in surface water at 412 nm. The physical quantity unit is m-1.The product also includes QA_Flag data with the same spatial resolution as the auxiliary data. The provided format is HDF5. The Spatial resolution is 250 m. The projection method is L1B reference coordinates. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available, but please note that the \"QA_Flag\" data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_LAI_NA.json b/datasets/GCOM-C_SGLI_L2_LAI_NA.json index de1254b9c8..3eb41321a7 100644 --- a/datasets/GCOM-C_SGLI_L2_LAI_NA.json +++ b/datasets/GCOM-C_SGLI_L2_LAI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_LAI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Leaf Area Index dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit sattelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Leaf Area Index (LAI), LAI for understory vegetation (Overstory_LAI) and quality flag (QA_Flag). FAPAR is the proportion of the effectively absorbed solar radiation by green vegetation in the photosynthetically active wavelength (the spectral region from 400 to 700 nm). It corresponds to the instantaneous FAPAR at the observation time of the GCOM-C satellite. The physical quantity is dimensionless. LAI is one half of the total green leaf area per unit ground surface area. It includes all overstory and understory green vegetation. The physical quantity is m^2/m^2. Overstory_LAI is LAI for overstory green vegetation (like leaves on trees). It excludes LAI for understory green vegetation like grasses. The physical quantity is m^2/m^2. The QA_flag shows flag of quality and observation condition.The provided format is HDF5. The spatial resolution is 250 m. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_LST_NA.json b/datasets/GCOM-C_SGLI_L2_LST_NA.json index 452704744c..fc680827ce 100644 --- a/datasets/GCOM-C_SGLI_L2_LST_NA.json +++ b/datasets/GCOM-C_SGLI_L2_LST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_LST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Land surface temperature dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit sattelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Land Surface Emissivity at TI01 (E01), Land Surface Emissivity at TI02 (E02), Land Surface Temperature (LST), and quality flag (QA_flag). E01 is land surface emissivity at TI01 estimated using the geometric corrected TOA radiance data as input. The physical quantiy unit is None. E02 is land surface emissivity at TI02 estimated using the geometric corrected TOA radiance data as input. The physical quantiy unit is None. LST is the temperature of terrestrial land surface estimated using the geometric corrected TOA radiance data as input by semi-analytical approach based on the radiative transfer simulation at two thermal infrared bands and the Split-Window method. The physical quantiy units is Kelvin.The QA_flag shows flag of quality and observation condition.The provided format is HDF5. The spatial resolution is 250m. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available, but please note that the \"QA_Flag\" data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_LTOA_1km_NA.json b/datasets/GCOM-C_SGLI_L2_LTOA_1km_NA.json index e897b18813..99d7db4702 100644 --- a/datasets/GCOM-C_SGLI_L2_LTOA_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_LTOA_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_LTOA_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Top of atmosphere radiance (1km) is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA).GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level 1B or Level 2 products and ancillary data such as meteorological data and various additional data related to the physical quantity. This dataset includes Top of atmosphere radiance (or reflectance by using the other conversion coefficients) of each band (Lt_VN01-11, Lt_SW01-04 and Lt_TI01-02), Land water flag, observation geometries, and QA flag.As the tile product of precise geometric corrected top of atmosphere (TOA) radiance, it corrects the parallax due to postural change or altitude using the parameter PGCP with positional variation estimated using GCP. The physical quantity unit is W/m^2/um/sr (or dimensionless for the reflectance).Land water flag is simply from the land water flag from L1B. The observation geometries include solar and satellite zenith and azimuth angles, and observation time in hour.The QA_flag shows flag of quality and condition of the geometric correction.The provided format is HDF5. The spatial resolution is 1 km. The projection method is EQA and the generation unit is \"Tile\". The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_LTOA_250m_NA.json b/datasets/GCOM-C_SGLI_L2_LTOA_250m_NA.json index fdeaf4892f..579a983b7e 100644 --- a/datasets/GCOM-C_SGLI_L2_LTOA_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_LTOA_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_LTOA_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Top of atmosphere radiance (250m) is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA).GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level 1B or Level 2 products and ancillary data such as meteorological data and various additional data related to the physical quantity. This dataset includes Top of atmosphere radiance (or reflectance by using the other conversion coefficients) of each band (Lt_VN01-11, Lt_SW01-04 and Lt_TI01-02), Land water flag, observation geometries, and QA flag. As the tile product of precise geometric corrected top of atmosphere (TOA) radiance, it corrects the parallax due to postural change or altitude using the parameter PGCP with positional variation estimated using GCP. The physical quantity unit is W/m^2/um/sr (or dimensionless for the reflectance).Land water flag is simply from the land water flag from L1B. The observation geometries include solar and satellite zenith and azimuth angles, and observation time in hour.The QA_flag shows flag of quality and condition of the geometric correction.The provided format is HDF5. The spatial resolution is 250 m. The projection method is EQA and the generation unit is \"Tile\".The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_NWLR_1km_NA.json b/datasets/GCOM-C_SGLI_L2_NWLR_1km_NA.json index 1059dc94d5..ac1f029041 100644 --- a/datasets/GCOM-C_SGLI_L2_NWLR_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_NWLR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_NWLR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Normalized water leaving radiance and aerosol parameters and PAR (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Normalized water leaving radiance (NWLR), TAUA, and Photosynthetically available radiation (PAR).NWLR data is the upwelling radiance just above the sea surface at 380, 412, 443, 490, 530, 565, and 670 nm, with the sun at the zenith, at the average distance from the earth to the sun (1 AU). It is corrected for the viewing angle dependence and for the effects of the non-isotropic distribution of the in-water light field. The NWLR data can convert into Rrs (Remote Sensing Reflectance) by the parameters of itself. The physical quantity unit is W/m^2/str/um.TAUA (\u00cf\u0084A) data is the aerosol optical thickness at 670 and 865 nm when the atmospheric correction algorithm of ocean color estimated the NWLR data. Note that 'TAUA' and 'AROT_ocean in GCOM-C/SGLI L2 Aerosol over the ocean-land aerosol by near ultra violet (dio:xxxx)' differ in algorithm.PAR data is the photon flux density which is potentially available to plant for photosynthesis within the visible wavelength range of 400 to 700 nm over ocean. The physical quantity unit is Ein/m^2/day.The provided format is HDF5. The Spatial resolution is 1 km. The projection method is L1B reference coordinates. The generation unit is Scene.The current version of the product is Version 3. The Version 2 is also available, but please note that the \"QA_Flag\" data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_NWLR_250m_NA.json b/datasets/GCOM-C_SGLI_L2_NWLR_250m_NA.json index 4c734c9c89..a582b41a6b 100644 --- a/datasets/GCOM-C_SGLI_L2_NWLR_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_NWLR_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_NWLR_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Normalized water leaving radiance and aerosol parameters and PAR (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Normalized water leaving radiance (NWLR), TAUA, and Photosynthetically available radiation (PAR). NWLR data is the upwelling radiance just above the sea surface at 380, 412, 443, 490, 530, 565, and 670 nm, with the sun at the zenith, at the average distance from the earth to the sun (1 AU). It is corrected for the viewing angle dependence and for the effects of the non-isotropic distribution of the in-water light field. The NWLR data can convert into Rrs (Remote Sensing Reflectance) by the parameters of itself. The physical quantity unit is W/m^2/str/um. TAUA (\u00cf\u0084A) data is the aerosol optical thickness at 670 and 865 nm when the atmospheric correction algorithm of ocean color estimated the NWLR data. Note that 'TAUA' and 'AROT_ocean in GCOM-C/SGLI L2 Aerosol over the ocean-land aerosol by near ultra violet (dio:xxxx)' differ in algorithm. The physical quantity is dimensionless. PAR data is the photon flux density which is potentially available to plant for photosynthesis within the visible wavelength range of 400 to 700 nm over ocean. The physical quantity unit is Ein/m^2/day. The product also includes QA_Flag data with the same spatial resolution as the auxiliary data. The provided format is HDF5. The Spatial resolution is 250 m. The projection method is L1B reference coordinates. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available, but please note that the \"QA_Flag\" data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_OKID_1km_NA.json b/datasets/GCOM-C_SGLI_L2_OKID_1km_NA.json index 87a7133c2e..43c81609be 100644 --- a/datasets/GCOM-C_SGLI_L2_OKID_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_OKID_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_OKID_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 OKhotsk sea-ice Distribution (1km) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA).GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity. This dataset includes Okhotsk sea-ice Distribution (OKID). OKID data is the classification flags of cloud, land, open water and snow and sea ice in Okhotsk Sea, northern of Japan. The flags were determined using typical characteristics of reflectance based on the radiative transfer simulations. The physical quantity unit is dimensionless. This products are stored only 30 days. The provided format is HDF5. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_OKID_250m_NA.json b/datasets/GCOM-C_SGLI_L2_OKID_250m_NA.json index 258bf0cfa5..9b5a33f419 100644 --- a/datasets/GCOM-C_SGLI_L2_OKID_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_OKID_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_OKID_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 OKhotsk sea-ice Distribution (250m) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity. This dataset includes Okhotsk sea-ice Distribution (OKID). OKID data is the classification flags of cloud, land, open water and snow and sea ice in Okhotsk Sea, northern of Japan. The flags were determined using typical characteristics of reflectance based on the radiative transfer simulations. The physical quantity unit is dimensionless. This products are stored only 30 days. The provided format is HDF5. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_RSRF_NA.json b/datasets/GCOM-C_SGLI_L2_RSRF_NA.json index aa047ad74a..a1999c2e91 100644 --- a/datasets/GCOM-C_SGLI_L2_RSRF_NA.json +++ b/datasets/GCOM-C_SGLI_L2_RSRF_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_RSRF_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Land atmospheric corrected reflectance is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity. The product is the atmospherically corrected reflectance equivalent to that of the land surface removing the effect of scattering/absorption of light caused by gas molecules and aerosol particles in the atmosphere. BRDF is not corrected in the daily product. This dataset includes surface reflectance of each bands (Rs_SW01-04, Rs_VN01-Rs_VN11, Rs_PI01-02), TOA brightness temperature of TI01 and TI02 sensors (Tb_TI01, 02), daily mean of shortwave radiation (SWR) , daily photosynthetically active radiation (PAR), particle optical thickness at 500 nm (Tau_500), angstrom exponent of particles (Angstrom), observation geometries, Land water flag, and quality flag. Rs_VN01-11, Rs_VN08P, Rs_VN11P, Rs_SW_01-04, PI01-02 are surface reflectance of the SGLI channels. PI01 and PI02 are I components of the Stokes vector. VN08P and VN11P are Surface reflectance of co-registered for PI01 and PI02 respectively. The physical quantity unit is dimensionless. Tb_TI01, 02 is TOA brightness temperature of TI01 and TI02 sensors. The physical quantity unit is Kelvin.SWR is Daily mean of shortwave radiation in W/m^2.PAR is Daily Photosynthetically Active Radiation in Ein/m^2/day.Tau_500 is particle optical thickness at 500 nm.Angstrom is Angstrom exponent of particles. The physical quantity unit is dimensionless.Satellite/solar zenith/azimuth angles, and observation time from LTOA, in degree.Land_water_flag is Land water flag from LTOA.QA_flag is quality flag indicating the observation condition. Bit-14 (non-polarization) and 15 (polarization) of QA_flag are set when the grid is recovered by the pre-day\u00e2\u0080\u0099s BRDF table. Available data on the target day can be identified by selecting the pixel of the Bit-14 (15 for the polarization channels)=0. The provided format is HDF5. The Spatial resolution of Rs_PI01, Rs_PI02, Rs_SW02, Rs_SW04, Rs_VN08P and Rs_VN11P are 1 km. Others are 250 m. The projection method is EQA. The data projection type is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SICE_1km_NA.json b/datasets/GCOM-C_SGLI_L2_SICE_1km_NA.json index 4d97404705..24b2fea813 100644 --- a/datasets/GCOM-C_SGLI_L2_SICE_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SICE_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SICE_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Snow and Ice Cover Extent (1km) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity.This dataset includes Snow and Ice Cover Extent (SICE).SICE data is the classification flags of cloud, land, open water and snow and sea ice. The flags were determined using typical characteristics of reflectance based on the radiative transfer simulations. The physical quantity unit is dimensionless. The provided format is HDF5. The projection method is EQA and the generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SICE_250m_NA.json b/datasets/GCOM-C_SGLI_L2_SICE_250m_NA.json index db8a64450d..f81064dc01 100644 --- a/datasets/GCOM-C_SGLI_L2_SICE_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SICE_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SICE_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Snow and Ice Cover Extent (1km) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity.This dataset includes Snow and Ice Cover Extent (SICE).SICE data is the classification flags of cloud, land, open water and snow and sea ice. The flags were determined using typical characteristics of reflectance based on the radiative transfer simulations. The physical quantity unit is dimensionless. The provided format is HDF5. The projection method is EQA and the generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SIPR_1km_NA.json b/datasets/GCOM-C_SGLI_L2_SIPR_1km_NA.json index 4b49c650f6..c85a7ef307 100644 --- a/datasets/GCOM-C_SGLI_L2_SIPR_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SIPR_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SIPR_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Snow and Ice Physical Properties (1km) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level 1B or Level 2 products and ancillary data such as meteorological data and various additional data related to the physical quantity.This dataset includes Snow Grain size of Shallow Layer (SGSL), Snow and Ice Surface Temperature (SIST) and QA flag.SGSL data is the optical equivalent snow grain size of shallow layer estimated based on neural network method using observed radiance in the visible and near infrared region. The physical quantity unit is micro-meter.SIST data is the skin temperature of snow and ice estimated based on split-window method using observed brightness temperature in the thermal infrared region. The physical quantity unit is Kelvin.QA_flag shows the flag of estimation quality and retrieval conditions. The provided format is HDF5. The projection method is EQA and the generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SIPR_250m_NA.json b/datasets/GCOM-C_SGLI_L2_SIPR_250m_NA.json index 0d8a1a4391..a17cd758a0 100644 --- a/datasets/GCOM-C_SGLI_L2_SIPR_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SIPR_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SIPR_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Snow and Ice Physical Properties (250m) dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level 1B or Level 2 products and ancillary data such as meteorological data and various additional data related to the physical quantity.This dataset includes Snow Grain size of Shallow Layer (SGSL), Snow and Ice Surface Temperature (SIST) and QA flag.SGSL data is the optical equivalent snow grain size of shallow layer estimated based on neural network method using observed radiance in the visible and near infrared region. The physical quantity unit is micro-meter.SIST data is the skin temperature of snow and ice estimated based on split-window method using observed brightness temperature in the thermal infrared region. The physical quantity unit is Kelvin.QA_flag shows the flag of estimation quality and retrieval conditions. The provided format is HDF5. The projection method is EQA and the generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SST_1km_NA.json b/datasets/GCOM-C_SGLI_L2_SST_1km_NA.json index df4d0c9eb6..5572845226 100644 --- a/datasets/GCOM-C_SGLI_L2_SST_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SST_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SST_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Sea Surface Temperature (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Sea Surface Temperature (SST) and Cloud probability (Cloud_probability).SST data is the temperature of sea surface. The physical quantity unit is degree.Cloud Probability data is possibly affected by clouds for each SST pixel. The unit is %.The package includes not only this product but also QA_Flag data with the same spatial resolution as the auxiliary data. The provided format is HDF5. The Spatial resolution is 1 km. The projection method is L1B reference coordinates. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available, but please note that the QA_Flag data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_SST_250m_NA.json b/datasets/GCOM-C_SGLI_L2_SST_250m_NA.json index 355cc1f542..8066bb49c5 100644 --- a/datasets/GCOM-C_SGLI_L2_SST_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_SST_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_SST_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Sea Surface Temperature (250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be products composed of physical quantity data that is calculated based on Level 1B products and meteorological data, as well as various additional data related to the physical quantity data.This dataset includes Sea Surface Temperature (SST) and Cloud probability (Cloud_probability).SST data is the temperature of sea surface. The physical quantity unit is degree.Cloud Probability data is possibly affected by clouds for each SST pixel. The physical quantity unit is %.The package includes not only this product but also QA_Flag data with the same spatial resolution as the auxiliary data. The provided format is HDF5. The Spatial resolution is 250 m. The projection method is L1B reference coordinates. The generation unit is Scene. The current version of the product is Version 3. The Version 2 is also available, but please note that the QA_Flag data has been changed.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_VGI_NA.json b/datasets/GCOM-C_SGLI_L2_VGI_NA.json index c25bc8d20a..12dc51e5df 100644 --- a/datasets/GCOM-C_SGLI_L2_VGI_NA.json +++ b/datasets/GCOM-C_SGLI_L2_VGI_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_VGI_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Vegetation Indices dataset is obtained from the SGLI sensor onboard GCOM-C produced by the Japan Aerospace Exploration Agency (JAXA). The product is the vegetation indices and shadow index. GCOM-C is sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017 and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products are defined to be granule scene and global (tile) data of physical quantity derived from Level-1B or Level-2 products and ancillary data such as meteorological data and various additional data related to the physical quantity.This dataset includes Enhanced Vegetation Index (EVI), Normalized Defferemce Difference Vegetation Index (NDVI), Shadow Index (SDI), and quality flag (QA_flag). NDVI is a simple normalized index that can be used to indicate the activity of vegetation making use of the reflectance at red and near-infrared wavelengths where the reflectance of vegetation exhibits a steep increase like a step function. The physical quantity is dimensionless. EVI is an improved version of vegetation indices designed to enhance the vegetation signal in high density vegetation area. The physical quantity is dimensionless. SDI is the fraction of shadow generated by conformation of vegetation (shadow area proportion within a pixel) and is estimated from SW03 surface reflectance. SDI reflects the canopy shape and density. The physical quantity is dimensionless. The QA_flag shows flag of quality and observation condition. The provided format is HDF5. The Spatial resolution is 250 m. The projection method is EQA. The data projection type is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_global-ARNP_NA.json b/datasets/GCOM-C_SGLI_L2_global-ARNP_NA.json index fee4085a3d..9b7f6fff3f 100644 --- a/datasets/GCOM-C_SGLI_L2_global-ARNP_NA.json +++ b/datasets/GCOM-C_SGLI_L2_global-ARNP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_global-ARNP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Global-AeRosol properties using Numerical Prediction dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA).GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The contents are listed in the following:AROT: Aerosol Optical Thickness over land and ocean at 500 nm (dimensionless).ARAE: Angstrom Exponent over land and ocean at 500 nm and 380 nm (dimensionless).ASSA: Single Scattering Albedo over land and ocean at 380 nm (dimensionless).AROT_uncertainty, AROT_uncertainty, AROT_uncertainty: The uncertainties of AROT, ARAE and ASSA, respectively (dimensionless).The utilized aerosol optical model for the retrieval is the same for both over land and ocean, and the coefficients are based on the skyradiometer observations. While the particle shapes, real parts of complex refraction indices, and size distributions of large and small particles are assumed to be fixed, the fraction of small particles and complex refraction indices (in terms of SSA) vary. The algorithm assumes reasonable aerosol parameters for the SGLI observations and derives the contents of this dataset.The provided format is HDF5. The spatial and temporal resolutions are 1/24 deg and daily, respectively. The projection method is EQA. The spatial coverage is Global. The current version of the product is Version 3. The Version 2 is also available, note that the QA_Flag data has been updated.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_global-LCLR_NA.json b/datasets/GCOM-C_SGLI_L2_global-LCLR_NA.json index 9a30ccf65e..b62d11c403 100644 --- a/datasets/GCOM-C_SGLI_L2_global-LCLR_NA.json +++ b/datasets/GCOM-C_SGLI_L2_global-LCLR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_global-LCLR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Global Top of atmosphere radiance is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data.The contents are each band's Top of atmosphere radiance (W/m^2/str/mm) and Land water flag.This product is generated from the Level1B (1 km) daily products. The provided format is HDF5. The spatial and temporal resolutions is are 1/24 degree and daily, respectively. The projection method is EQA. The spatial coverage is Global. The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_global-LTOA_NA.json b/datasets/GCOM-C_SGLI_L2_global-LTOA_NA.json index f9f3cdb734..635c0d52b0 100644 --- a/datasets/GCOM-C_SGLI_L2_global-LTOA_NA.json +++ b/datasets/GCOM-C_SGLI_L2_global-LTOA_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_global-LTOA_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Global Top of atmosphere radiance (clear sky) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is a Sun-synchronous sub-recurrent orbit satellite launched on December 23, 2017. It mounts SGLI sensor to observe long-term geophysical conditions based on 28 variables, including aerosol and vegetation, out of four Earth subsystems: atmosphere, land, ocean, and cryosphere. These data are helpful to improve the global warming prediction accuracy. The SGLI has a swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 products consist of derived physical variables based on Level 1B products, such as meteorological data. The contents are each band's Top of atmosphere radiance (W/m^2/str/mm) and Land water flag. TOA radiances are initially derived from daily 1-km resolution TOA radiance and cloud flag products, which spatial coverage is Tile. Consequently, this product is generated from the clear (cloud-free) pixels. The provided format is HDF5. The spatial and temporal resolutions are 1/24 deg and daily, respectively. The projection method is EQA. The spatial coverage is Global. The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-AGB_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-AGB_1month_250m_NA.json index ad3e6de8ac..8983655ccd 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-AGB_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-AGB_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-AGB_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Above Ground Biomass (AGB) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Above Ground Biomass (AGB). AGB is the volume of aboveground biomass shown in dry weight and estimated using two sets of the red and near-infrared channel data observed from nadir and slant viewing direction by SGLI sensor. The physical quantity unit is t/ha.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile.The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-AGB_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-AGB_8days_250m_NA.json index cf9a1e8fc2..035b5f7ac6 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-AGB_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-AGB_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-AGB_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Above Ground Biomass (AGB) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Above Ground Biomass (AGB). AGB is the volume of aboveground biomass shown in dry weight and estimated using two sets of the red and near-infrared channel data observed from nadir and slant viewing direction by SGLI sensor. The physical quantity unit is t/ha.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of data used (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-EVI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-EVI_1month_250m_NA.json index afa67fc9d5..9b46defa0c 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-EVI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-EVI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-EVI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Enhanced Vegetation Index (EVI) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Enhanced Vegetation Index (EVI). EVI is an improved version of vegetation indices designed to enhance the vegetation signal in high density vegetation area. The physical quantity is dimensionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-EVI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-EVI_8days_250m_NA.json index 605df2f91e..7be8dea25d 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-EVI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-EVI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-EVI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Enhanced Vegetation Index (EVI) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Enhanced Vegetation Index (EVI). EVI is an improved version of vegetation indices designed to enhance the vegetation signal in high density vegetation area. The physical quantity is dimensionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_1month_250m_NA.json index 9c0d53014f..6ea0c1ada9 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-FAPAR_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Fraction of absorbed PAR (FAPAR) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The physical quantity unit is dimensionless.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_8days_250m_NA.json index 934f028cec..2eba01294a 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-FAPAR_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-FAPAR_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Fraction of absorbed PAR (FAPAR) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and QA_flag. The physical quantity unit is dimensionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOI_1month_250m_NA.json index a0d7ce6016..e1fc8b5e51 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOI) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. The Geometry data of IRS sensor is stored. This dataset includes of following SDs:GEOI_Date is Date identification of surface reflectance [unit:none]GEOI_Ninput is Input data number of surface reflectance [unit:none]GEOI_Nused is Used data number of surface reflectance [unit:none]GEOI_QA_flag is quality flag about the statistics and BRDF regression. [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOI_8days_250m_NA.json index 40c3784293..c7e49a5408 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOI) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. The Geometry data of IRS sensor is stored. This dataset includes of following SDs: GEOI_Date is Date identification of RSRF/GEOI [unit:none]GEOI_Ninput is Input data number of RSRF/GEOI [unit:none]GEOI_Nused is Used data number of RSRF/GEOI [unit:none]GEOI_QA_flag is Level-3 8-bit flag of RSRF/GEOI [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOP_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOP_1month_250m_NA.json index 24903e35b6..95befdbbd9 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOP_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOP_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOP_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOP) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.The Geometry data of VNR-PL sensor is stored. This dataset includes of following SDs:GEOP_Date is Date identification of surface reflectance [unit:none]GEOP_Ninput is Input data number of surface reflectance [unit:none]GEOP_Nused is Used data number of surface reflectance [unit:none]GEOP_QA_flag is quality flag about the statistics and BRDF regression. [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOP_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOP_8days_250m_NA.json index 71c35c450c..caa376c9de 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOP_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOP_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOP_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOP) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. The Geometry data of VNR-PL sensor is stored. This dataset includes of following SDs: GEOP_Date is Date identification of RSRF/GEOP [unit:none]GEOP_Ninput is Input data number of RSRF/GEOP [unit:none]GEOP_Nused is Used data number of RSRF/GEOP [unit:none]GEOP_QA_flag is Level-3 8-bit flag of RSRF/GEOP [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOV_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOV_1month_250m_NA.json index b3ceab4e84..9b334cc9ac 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOV_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOV_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOV_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOV) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. The Geometry data of VNR-NP sensor is stored. This dataset includes of following SDs:GEOI_Date is Date identification of surface reflectance [unit:none]GEOI_Ninput is Input data number of surface reflectance [unit:none]GEOI_Nused is Used data number of surface reflectance [unit:none]GEOI_QA_flag is quality flag about the statistics and BRDF regression. [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-GEOV_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-GEOV_8days_250m_NA.json index b82d4aa2a4..e6efd63fa0 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-GEOV_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-GEOV_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-GEOV_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (GEOV) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. The Geometry data of VNR-NP sensor is stored. This dataset includes of following SDs:GEOI_Date is Date identification of surface reflectance [unit:none]GEOI_Ninput is Input data number of surface reflectance [unit:none]GEOI_Nused is Used data number of surface reflectance [unit:none]GEOI_QA_flag is quality flag about the statistics and BRDF regression. [unit:none]Relative_azimuth_AVE is Normal of Relative azimuth angle [degree]Relative_azimuth_MAX is Maximum of Relative azimuth angle [degree]Relative_azimuth_MIN is Minimum of Relative azimuth angle [degree]Sensor_zenith_AVE is Normal of Sensor zenith angle [degree]Sensor_zenith_MAX is Maximum of Sensor zenith angle [degree]Sensor_zenith_MIN is Minimum of Sensor zenith angle [degree]Solar_zenith_AVE is Normal of Solar zenith angle [degree]Solar_zenith_MAX is Maximum of Sensor zenith angle [degree]Solar_zenith_MIN is Minimum of Solar zenith angle [degree]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LAI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LAI_1month_250m_NA.json index 2e22115095..b931547120 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LAI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LAI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LAI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Leaf Area Index (LAI) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes LAI: Leaf Area Index. The physical quantity unit is dimensionless.To retrieve LAI, the processing algorithm generates a lookup table (LUT) indicating the relationship between LAI, FAPAR, land surface reflectance and crown reflectance using FLiES that performs 3-dimensional radiative transfer simulations, in relation to arbitrary solar zenith angle, satellite zenith angle and relative azimuth angle. The SGLI atmospherically collected land surface reflectance (RSRF) data and geometry data (solar zenith angle, solar azimuth angle, satellite zenith angle and satellite azimuth angle) are used as inputs. The past 7 days of SGLI data are also used to get the data for multi angle direction.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LAI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LAI_8days_250m_NA.json index 823a367e55..21bb80d20c 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LAI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LAI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LAI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Leaf Area Index (LAI) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes LAI: Leaf Area Index. The physical quantity unit is dimensionless.To retrieve LAI, the processing algorithm generates a lookup table (LUT) indicating the relationship between LAI, FAPAR, land surface reflectance and crown reflectance using FLiES that performs 3-dimensional radiative transfer simulations, in relation to arbitrary solar zenith angle, satellite zenith angle and relative azimuth angle. The SGLI atmospherically collected land surface reflectance (RSRF) data and geometry data (solar zenith angle, solar azimuth angle, satellite zenith angle and satellite azimuth angle) are used as inputs. The past 7 days of SGLI data are also used to get the data for multi angle direction. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LST_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LST_1month_250m_NA.json index 3ee76fbe89..64c243dad2 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LST_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LST_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LST_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land surface temperature (LST) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LST_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LST_8days_250m_NA.json index 556aedb5aa..306419a6b9 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LST_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LST_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LST_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land surface temperature (LST) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LTOA_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LTOA_1month_250m_NA.json index cfa6a8b7e1..62b115a341 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LTOA_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LTOA_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LTOA_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-TOA radiance (1-Month,250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.Using the Level2 tile product of precise geometric corrected TOA radiance as input, the clear region mosaic data for 8-days or 1-month is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. This dataset includes Top of atmosphere radiance of each bands and Land water flag. Using the Level2 tile product of precise geometric corrected TOA radiance (Daily, Tile, 250 m resolution) as input, the clear region mosaic data for 1-month is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. Maximum absolute NDVI composite method is used for mosaicking process. In this method output data store the TOA radiance of a single observation day on which the value of |NDVI-alpha| becomes the maximum during the temporal interval (1-month).The physical quantity unit is W/m^2/str/mm. The provided format is HDF5.The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 2. The Version 3 is also available", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-LTOA_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-LTOA_8days_250m_NA.json index 38bac53a3a..8a0edc23e1 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-LTOA_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-LTOA_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-LTOA_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-TOA radiance (8-Days,250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Using the Level 2 tile product of precise geometric corrected TOA radiance as input, the clear region mosaic data for 8-days or 1-month is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. This dataset includes Top of atmosphere radiance of each bands and Land water flag. Using the Level2 tile product of precise geometric corrected TOA radiance (Daily, Tile, 250 m resolution) as input, the clear region mosaic data for 8-days is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. Maximum absolute NDVI composite method is used for mosaicking process. In this method output data store the TOA radiance of a single observation day on which the value of |NDVI-alpha| becomes the maximum during the temporal interval (8-days).The physical quantity unit is W/m^2/str/mm. The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-NDVI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-NDVI_1month_250m_NA.json index 764cc51768..af07c88567 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-NDVI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-NDVI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-NDVI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Normalized Difference Vegetation Index (NDVI) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes Normalized Difference Vegetation Index (NDVI). The physical quantity unit is dimensionless.The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-NDVI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-NDVI_8days_250m_NA.json index c2833d5f28..ecc5d1f141 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-NDVI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-NDVI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-NDVI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Normalized Difference Vegetation Index (NDVI) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes Normalized Difference Vegetation Index (NDVI). The physical quantity unit is dimensionless. The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RN08_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RN08_1month_1km_NA.json index adfcab1c43..245e783e21 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RN08_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RN08_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RN08_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RN08) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN08P_AVE is Average surface reflectance [unit: none] Rs_VN08P_Date is Date identification of surface reflectance [unit: none] Rs_VN08P_MAX is Maximum of surface reflectance [unit: none] Rs_VN08P_MIN is Minimum of surface reflectance [unit: none] Rs_VN08P_Ninput is Input data number of surface reflectance [unit: none] Rs_VN08P_Nused is Used data number of surface reflectance [unit: none] Rs_VN08P_QA_flag is quality flag about the statistics and BRDF regression. [unit: none] Rs_VN08P_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN08P_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RN08_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RN08_8days_1km_NA.json index f7e5d5265a..5ce8d992e0 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RN08_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RN08_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RN08_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RN08) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN08P_AVE is Normal surface reflectance [unit: none] Rs_VN08P_Date is Date identification of surface reflectance [unit: none] Rs_VN08P_MAX is Maximum of surface reflectance [unit: none] Rs_VN08P_MIN is Minimum of surface reflectance [unit: none] Rs_VN08P_Ninput is Input data number of surface reflectance [unit: none] Rs_VN08P_Nused is Used data number of surface reflectance [unit: none] Rs_VN08P_QA_flag is quality flag about the statistics and BRDF regression. [unit: none] Rs_VN08P_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN08P_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RN11_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RN11_1month_1km_NA.json index b288171ab8..2aecc1f2d6 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RN11_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RN11_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RN11_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RN11) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN11P_AVE is Average surface reflectance [unit: none] Rs_VN11P_Date is Date identification of surface reflectance [unit: none] Rs_VN11P_MAX is Maximum of surface reflectance [unit: none] Rs_VN11P_MIN is Minimum of surface reflectance [unit: none] Rs_VN11P_Ninput is Input data number of surface reflectance [unit: none] Rs_VN11P_Nused is Used data number of surface reflectance [unit: none] Rs_VN11P_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN11P_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN11P_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RN11_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RN11_8days_1km_NA.json index 72ec0558af..f6f2b3e13b 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RN11_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RN11_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RN11_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RN11) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN11P_AVE is Average surface reflectance [unit: none] Rs_VN11P_Date is Date identification of surface reflectance [unit: none] Rs_VN11P_MAX is Maximum of surface reflectance [unit: none] Rs_VN11P_MIN is Minimum of surface reflectance [unit: none] Rs_VN11P_Ninput is Input data number of surface reflectance [unit: none] Rs_VN11P_Nused is Used data number of surface reflectance [unit: none] Rs_VN11P_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN11P_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN11P_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RP01_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RP01_1month_1km_NA.json index 2d0ddbafe3..2492f969d1 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RP01_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RP01_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RP01_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RP01) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RP01_AVE is Average surface reflectance [unit: none] Rs_RP01_Date is Date identification of surface reflectance [unit: none] Rs_RP01_MAX is Maximum of surface reflectance [unit: none] Rs_RP01_MIN is Minimum of surface reflectance [unit: none] Rs_RP01_Ninput is Input data number of surface reflectance [unit: none] Rs_RP01_Nused is Used data number of surface reflectance [unit: none] Rs_RP01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RP01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RP01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RP01_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RP01_8days_1km_NA.json index bf81a99d48..a4adb0b7ca 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RP01_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RP01_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RP01_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RP01) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RP01_AVE is Average surface reflectance [unit: none] Rs_RP01_Date is Date identification of surface reflectance [unit: none] Rs_RP01_MAX is Maximum of surface reflectance [unit: none] Rs_RP01_MIN is Minimum of surface reflectance [unit: none] Rs_RP01_Ninput is Input data number of surface reflectance [unit: none] Rs_RP01_Nused is Used data number of surface reflectance [unit: none] Rs_RP01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RP01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RP01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RP02_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RP02_1month_1km_NA.json index 885423c368..a7831cc50b 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RP02_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RP02_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RP02_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RP02) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RP02_AVE is Average surface reflectance [unit: none] Rs_RP02_Date is Date identification of surface reflectance [unit: none] Rs_RP02_MAX is Maximum of surface reflectance [unit: none] Rs_RP02_MIN is Minimum of surface reflectance [unit: none] Rs_RP02_Ninput is Input data number of surface reflectance [unit: none] Rs_RP02_Nused is Used data number of surface reflectance [unit: none] Rs_RP02_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RP02_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RP02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RP02_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RP02_8days_1km_NA.json index 1bbf86ea1d..82c68b3e8e 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RP02_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RP02_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RP02_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RP02) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RP02_AVE is Average surface reflectance [unit: none] Rs_RP02_Date is Date identification of surface reflectance [unit: none] Rs_RP02_MAX is Maximum of surface reflectance [unit: none] Rs_RP02_MIN is Minimum of surface reflectance [unit: none] Rs_RP02_Ninput is Input data number of surface reflectance [unit: none] Rs_RP02_Nused is Used data number of surface reflectance [unit: none] Rs_RP02_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RP02_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RP02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS01_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS01_1month_1km_NA.json index 8b527eaab3..42435c36ee 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS01_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS01_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS01_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS01) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS01_AVE is Average surface reflectance [unit: none] Rs_RS01_Date is Date identification of surface reflectance [unit: none] Rs_RS01_MAX is Maximum of surface reflectance [unit: none] Rs_RS01_MIN is Minimum of surface reflectance [unit: none] Rs_RS01_Ninput is Input data number of surface reflectance [unit: none] Rs_RS01_Nused is Used data number of surface reflectance [unit: none] Rs_RS01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS01_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS01_8days_1km_NA.json index bd08332ac5..a9cd80a315 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS01_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS01_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS01_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS01) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS01_AVE is Average surface reflectance [unit: none] Rs_RS01_Date is Date identification of surface reflectance [unit: none] Rs_RS01_MAX is Maximum of surface reflectance [unit: none] Rs_RS01_MIN is Minimum of surface reflectance [unit: none] Rs_RS01_Ninput is Input data number of surface reflectance [unit: none] Rs_RS01_Nused is Used data number of surface reflectance [unit: none] Rs_RS01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS02_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS02_1month_1km_NA.json index e3c52373fe..10bae28c54 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS02_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS02_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS02_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS02) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS02_AVE is Average surface reflectance [unit: none] Rs_RS02_Date is Date identification of surface reflectance [unit: none] Rs_RS02_MAX is Maximum of surface reflectance [unit: none] Rs_RS02_MIN is Minimum of surface reflectance [unit: none] Rs_RS02_Ninput is Input data number of surface reflectance [unit: none] Rs_RS02_Nused is Used data number of surface reflectance [unit: none] Rs_RS02_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS02_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS02_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS02_8days_1km_NA.json index a5366048f3..391f50d5f3 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS02_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS02_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS02_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS02) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS02_AVE is Average surface reflectance [unit: none] Rs_RS02_Date is Date identification of surface reflectance [unit: none] Rs_RS02_MAX is Maximum of surface reflectance [unit: none] Rs_RS02_MIN is Minimum of surface reflectance [unit: none] Rs_RS02_Ninput is Input data number of surface reflectance [unit: none] Rs_RS02_Nused is Used data number of surface reflectance [unit: none] Rs_RS02_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS02_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS03_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS03_1month_250m_NA.json index 459089c5ff..f745ce84ee 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS03_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS03_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS03_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS03) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS03_AVE is Average surface reflectance [unit: none] Rs_RS03_Date is Date identification of surface reflectance [unit: none] Rs_RS03_MAX is Maximum of surface reflectance [unit: none] Rs_RS03_MIN is Minimum of surface reflectance [unit: none] Rs_RS03_Ninput is Input data number of surface reflectance [unit: none] Rs_RS03_Nused is Used data number of surface reflectance [unit: none] Rs_RS03_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS03_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS03_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS03_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS03_8days_250m_NA.json index 66537dfb71..4ab92d6aff 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS03_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS03_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS03_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS03) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS03_AVE is Average surface reflectance [unit: none] Rs_RS03_Date is Date identification of surface reflectance [unit: none] Rs_RS03_MAX is Maximum of surface reflectance [unit: none] Rs_RS03_MIN is Minimum of surface reflectance [unit: none] Rs_RS03_Ninput is Input data number of surface reflectance [unit: none] Rs_RS03_Nused is Used data number of surface reflectance [unit: none] Rs_RS03_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS03_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS03_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS04_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS04_1month_1km_NA.json index c0b1bb3cf2..ffc87c3af6 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS04_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS04_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS04_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS04) (1-Month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS04_AVE is Average surface reflectance [unit: none] Rs_RS04_Date is Date identification of surface reflectance [unit: none] Rs_RS04_MAX is Maximum of surface reflectance [unit: none] Rs_RS04_MIN is Minimum of surface reflectance [unit: none] Rs_RS04_Ninput is Input data number of surface reflectance [unit: none] Rs_RS04_Nused is Used data number of surface reflectance [unit: none] Rs_RS04_QA_flag is quality flag about the statistics and BRDF regression[unit: none] Rs_RS04_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS04_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RS04_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RS04_8days_1km_NA.json index 2ea22c53cb..e3bdf3c268 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RS04_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RS04_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RS04_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RS04) (8-Days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_RS04_AVE is Average surface reflectance [unit: none] Rs_RS04_Date is Date identification of surface reflectance [unit: none] Rs_RS04_MAX is Maximum of surface reflectance [unit: none] Rs_RS04_MIN is Minimum of surface reflectance [unit: none] Rs_RS04_Ninput is Input data number of surface reflectance [unit: none] Rs_RS04_Nused is Used data number of surface reflectance [unit: none] Rs_RS04_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_RS04_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_RS04_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RT01_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RT01_1month_250m_NA.json index 1f324dc961..7eb78994bd 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RT01_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RT01_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RT01_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RT01) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients.GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Tb_TI01_AVE is Normalized surface reflectance [unit: Kelvin]Tb_TI01_Date is Date identification of surface reflectance [unit: none]Tb_TI01_MAX is Maximum of surface reflectance [unit: Kelvin]Tb_TI01_MIN is Minimum of surface reflectance [unit: Kelvin]Tb_TI01_Ninput is Input data number of surface reflectance [unit: none]Tb_TI01_Nused is Used data number of surface reflectance [unit: none]Tb_TI01_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Tb_TI01_RMS is RMS of BRDF model regression of surface reflectance [unit: Kelvin]Tb_TI01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RT01_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RT01_8days_250m_NA.json index 0c2019faa2..b0668866aa 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RT01_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RT01_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RT01_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RT01) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Tb_TI01_AVE is Normalized surface reflectance [unit: Kelvin]Tb_TI01_Date is Date identification of surface reflectance [unit: none]Tb_TI01_MAX is Maximum of surface reflectance [unit: Kelvin]Tb_TI01_MIN is Minimum of surface reflectance [unit: Kelvin]Tb_TI01_Ninput is Input data number of surface reflectance [unit: none]Tb_TI01_Nused is Used data number of surface reflectance [unit: none]Tb_TI01_QA_flag is quality flag about the statistics and BRDF regression. [unit: none]Tb_TI01_RMS is RMS of BRDF model regression of surface reflectance [unit: Kelvin]Tb_TI01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RT02_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RT02_1month_250m_NA.json index b080355746..710ab844c6 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RT02_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RT02_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RT02_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RT02) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients.GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Tb_TI02_AVE is Normalized surface reflectance [unit: Kelvin]Tb_TI02_Date is Date identification of surface reflectance [unit: none]Tb_TI02_MAX is Maximum of surface reflectance [unit: Kelvin]Tb_TI02_MIN is Minimum of surface reflectance [unit: Kelvin]Tb_TI02_Ninput is Input data number of surface reflectance [unit: none]Tb_TI02_Nused is Used data number of surface reflectance [unit: none]Tb_TI02_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Tb_TI02_RMS is RMS of BRDF model regression of surface reflectance [unit: Kelvin]Tb_TI02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RT02_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RT02_8days_250m_NA.json index 42b26b9f48..aab7eeefbb 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RT02_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RT02_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RT02_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RT02) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Tb_TI02_AVE is Normalized surface reflectance [unit: Kelvin]Tb_TI02_Date is Date identification of surface reflectance [unit: none]Tb_TI02_MAX is Maximum of surface reflectance [unit: Kelvin]Tb_TI02_MIN is Minimum of surface reflectance [unit: Kelvin]Tb_TI02_Ninput is Input data number of surface reflectance [unit: none]Tb_TI02_Nused is Used data number of surface reflectance [unit: none]Tb_TI02_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Tb_TI02_RMS is RMS of BRDF model regression of surface reflectance [unit: Kelvin]Tb_TI02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV01_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV01_1month_250m_NA.json index b05875cc9c..ccfb4c6a7b 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV01_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV01_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV01_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV01) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN01_AVE is Average surface reflectance [unit: none] Rs_VN01_Date is Date identification of surface reflectance [unit: none] Rs_VN01_MAX is Maximum of surface reflectance [unit: none] Rs_VN01_MIN is Minimum of surface reflectance [unit: none] Rs_VN01_Ninput is Input data number of surface reflectance [unit: none] Rs_VN01_Nused is Used data number of surface reflectance [unit: none] Rs_VN01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV01_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV01_8days_250m_NA.json index 892b3170b2..d365781246 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV01_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV01_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV01_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RV01) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN01_AVE is Normalized surface reflectance [unit: none]Rs_VN01_Date is Date identification of surface reflectance [unit: none]Rs_VN01_MAX is Maximum of surface reflectance [unit: none]Rs_VN01_MIN is Minimum of surface reflectance [unit: none]Rs_VN01_Ninput is Input data number of surface reflectance [unit: none]Rs_VN01_Nused is Used data number of surface reflectance [unit: none]Rs_VN01_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN01_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV02_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV02_1month_250m_NA.json index 7f0711d782..efb6ffd68b 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV02_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV02_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV02_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV02) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN02_AVE is Average surface reflectance [unit: none] Rs_VN02_Date is Date identification of surface reflectance [unit: none] Rs_VN02_MAX is Maximum of surface reflectance [unit: none] Rs_VN02_MIN is Minimum of surface reflectance [unit: none] Rs_VN02_Ninput is Input data number of surface reflectance [unit: none] Rs_VN02_Nused is Used data number of surface reflectance [unit: none] Rs_VN02_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN02_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV02_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV02_8days_250m_NA.json index db905e383a..50ce49ccf3 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV02_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV02_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV02_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV02) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN02_AVE is Normalized surface reflectance [unit: none]Rs_VN02_Date is Date identification of surface reflectance [unit: none]Rs_VN02_MAX is Maximum of surface reflectance [unit: none]Rs_VN02_MIN is Minimum of surface reflectance [unit: none]Rs_VN02_Ninput is Input data number of surface reflectance [unit: none]Rs_VN02_Nused is Used data number of surface reflectance [unit: none]Rs_VN02_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN02_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN02_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV03_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV03_1month_250m_NA.json index 8e556f9492..4a8bb91d94 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV03_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV03_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV03_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RV03) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN01_AVE is Average surface reflectance [unit: none] Rs_VN01_Date is Date identification of surface reflectance [unit: none] Rs_VN01_MAX is Maximum of surface reflectance [unit: none] Rs_VN01_MIN is Minimum of surface reflectance [unit: none] Rs_VN01_Ninput is Input data number of surface reflectance [unit: none] Rs_VN01_Nused is Used data number of surface reflectance [unit: none] Rs_VN01_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN01_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN01_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV03_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV03_8days_250m_NA.json index d0f0599971..5ed59f6305 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV03_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV03_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV03_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RV03) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN03_AVE is Normalized surface reflectance [unit: none]Rs_VN03_Date is Date identificator of surface reflectance [unit: none]Rs_VN03_MAX is Maximum of surface reflectance [unit: none]Rs_VN03_MIN is Minimum of surface reflectance [unit: none]Rs_VN03_Ninput is Input data number of surface reflectance [unit: none]Rs_VN03_Nused is Used data number of surface reflectance [unit: none]Rs_VN03_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN03_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN03_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV04_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV04_1month_250m_NA.json index 69c48966b6..54dabe0660 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV04_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV04_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV04_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV04) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN04_AVE is Average surface reflectance [unit: none] Rs_VN04_Date is Date identification of surface reflectance [unit: none] Rs_VN04_MAX is Maximum of surface reflectance [unit: none] Rs_VN04_MIN is Minimum of surface reflectance [unit: none] Rs_VN04_Ninput is Input data number of surface reflectance [unit: none] Rs_VN04_Nused is Used data number of surface reflectance [unit: none] Rs_VN04_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN04_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN04_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV04_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV04_8days_250m_NA.json index 0c2c96d908..1eb5c6789b 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV04_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV04_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV04_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance(RV04) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN04_AVE is Normalized surface reflectance [unit: none]Rs_VN04_Date is Date identification of surface reflectance [unit: none]Rs_VN04_MAX is Maximum of surface reflectance [unit: none]Rs_VN04_MIN is Minimum of surface reflectance [unit: none]Rs_VN04_Ninput is Input data number of surface reflectance [unit: none]Rs_VN04_Nused is Used data number of surface reflectance [unit: none]Rs_VN04_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN04_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN04_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV05_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV05_1month_250m_NA.json index dc69b19f64..d3fa708ac2 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV05_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV05_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV05_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV05) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN05_AVE is Average surface reflectance [unit: none] Rs_VN05_Date is Date identification of surface reflectance [unit: none] Rs_VN05_MAX is Maximum of surface reflectance [unit: none] Rs_VN05_MIN is Minimum of surface reflectance [unit: none] Rs_VN05_Ninput is Input data number of surface reflectance [unit: none] Rs_VN05_Nused is Used data number of surface reflectance [unit: none] Rs_VN05_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN05_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN05_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV05_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV05_8days_250m_NA.json index a5b060c939..3f9c5d99a1 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV05_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV05_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV05_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV05) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN05_AVE is Normalized surface reflectance [unit: none]Rs_VN05_Date is Date identification of surface reflectance [unit: none]Rs_VN05_MAX is Maximum of surface reflectance [unit: none]Rs_VN05_MIN is Minimum of surface reflectance [unit: none]Rs_VN05_Ninput is Input data number of surface reflectance [unit: none]Rs_VN05_Nused is Used data number of surface reflectance [unit: none]Rs_VN05_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN05_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN05_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV06_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV06_1month_250m_NA.json index ea47a1a9a7..7673a133d3 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV06_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV06_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV06_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV06) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN06_AVE is Average surface reflectance [unit: none] Rs_VN06_Date is Date identification of surface reflectance [unit: none] Rs_VN06_MAX is Maximum of surface reflectance [unit: none] Rs_VN06_MIN is Minimum of surface reflectance [unit: none] Rs_VN06_Ninput is Input data number of surface reflectance [unit: none] Rs_VN06_Nused is Used data number of surface reflectance [unit: none] Rs_VN06_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN06_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN06_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV06_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV06_8days_250m_NA.json index 2fa002b9f2..e4cb454a90 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV06_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV06_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV06_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV06) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN06_AVE is Normalized surface reflectance [unit: none]Rs_VN06_Date is Date identification of surface reflectance [unit: none]Rs_VN06_MAX is Maximum of surface reflectance [unit: none]Rs_VN06_MIN is Minimum of surface reflectance [unit: none]Rs_VN06_Ninput is Input data number of surface reflectance [unit: none]Rs_VN06_Nused is Used data number of surface reflectance [unit: none]Rs_VN06_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN06_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN06_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV07_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV07_1month_250m_NA.json index bb6366fada..ab23e5a1d1 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV07_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV07_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV07_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV07) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN07_AVE is Average surface reflectance [unit: none] Rs_VN07_Date is Date identification of surface reflectance [unit: none] Rs_VN07_MAX is Maximum of surface reflectance [unit: none] Rs_VN07_MIN is Minimum of surface reflectance [unit: none] Rs_VN07_Ninput is Input data number of surface reflectance [unit: none] Rs_VN07_Nused is Used data number of surface reflectance [unit: none] Rs_VN07_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN07_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN07_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV07_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV07_8days_250m_NA.json index ddec54f10c..5de562f772 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV07_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV07_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV07_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV07) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN07_AVE is Normalized surface reflectance [unit: none]Rs_VN07_Date is Date identification of surface reflectance [unit: none]Rs_VN07_MAX is Maximum of surface reflectance [unit: none]Rs_VN07_MIN is Minimum of surface reflectance [unit: none]Rs_VN07_Ninput is Input data number of surface reflectance [unit: none]Rs_VN07_Nused is Used data number of surface reflectance [unit: none]Rs_VN07_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN07_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN07_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV08_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV08_1month_250m_NA.json index 0f70f42e37..374518beb6 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV08_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV08_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV08_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV08) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN08_AVE is Average surface reflectance [unit: none] Rs_VN08_Date is Date identification of surface reflectance [unit: none] Rs_VN08_MAX is Maximum of surface reflectance [unit: none] Rs_VN08_MIN is Minimum of surface reflectance [unit: none] Rs_VN08_Ninput is Input data number of surface reflectance [unit: none] Rs_VN08_Nused is Used data number of surface reflectance [unit: none] Rs_VN08_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN08_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN08_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV08_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV08_8days_250m_NA.json index b059a17b7a..f23a81939c 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV08_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV08_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV08_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV08) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN08_AVE is Normalized surface reflectance [unit: none]Rs_VN08_Date is Date identification of surface reflectance [unit: none]Rs_VN08_MAX is Maximum of surface reflectance[unit: none]Rs_VN08_MIN is Minimum of surface reflectance [unit: none]Rs_VN08_Ninput is Input data number of surface reflectance [unit: none]Rs_VN08_Nused is Used data number of surface reflectance [unit: none]Rs_VN08_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN08_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN08_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV09_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV09_1month_250m_NA.json index 9aa2a186a9..1e19e2a6fa 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV09_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV09_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV09_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV09) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN09_AVE is Average surface reflectance [unit: none] Rs_VN09_Date is Date identification of surface reflectance [unit: none] Rs_VN09_MAX is Maximum of surface reflectance [unit: none] Rs_VN09_MIN is Minimum of surface reflectance [unit: none] Rs_VN09_Ninput is Input data number of surface reflectance [unit: none] Rs_VN09_Nused is Used data number of surface reflectance [unit: none] Rs_VN09_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN09_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN09_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV09_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV09_8days_250m_NA.json index 7fb5372585..55933210bb 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV09_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV09_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV09_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV09) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN09_AVE is Normalized surface reflectance [unit: none]Rs_VN09_Date is Date identification of surface reflectance [unit: none]Rs_VN09_MAX is Maximum of surface reflectance [unit: none]Rs_VN09_MIN is Minimum of surface reflectance [unit: none]Rs_VN09_Ninput is Input data number of surface reflectance [unit: none]Rs_VN09_Nused is Used data number of surface reflectance [unit: none]Rs_VN09_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN09_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN09_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV10_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV10_1month_250m_NA.json index bcafd30360..78bb8f5f93 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV10_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV10_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV10_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV10) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN10_AVE is Average surface reflectance [unit: none] Rs_VN10_Date is Date identification of surface reflectance [unit: none] Rs_VN10_MAX is Maximum of surface reflectance [unit: none] Rs_VN10_MIN is Minimum of surface reflectance [unit: none] Rs_VN10_Ninput is Input data number of surface reflectance [unit: none] Rs_VN10_Nused is Used data number of surface reflectance [unit: none] Rs_VN10_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN10_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN10_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV10_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV10_8days_250m_NA.json index 7e62312207..ff0e12b19d 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV10_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV10_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV10_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV10) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN10_AVE is Normalized surface reflectance [unit: none]Rs_VN10_Date is Date identification of surface reflectance [unit: none]Rs_VN10_MAX is Maximum of surface reflectance [unit: none]Rs_VN10_MIN is Minimum of surface reflectance [unit: none]Rs_VN10_Ninput is Input data number of surface reflectance [unit: none]Rs_VN10_Nused is Used data number of surface reflectance [unit: none]Rs_VN10_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN10_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN10_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV11_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV11_1month_250m_NA.json index ac74704014..b38af953f2 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV11_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV11_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV11_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV11) (1-Month, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level 2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 1-Month processing, the land surface reflectance data of the past 1 month, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN11_AVE is Average surface reflectance [unit: none] Rs_VN11_Date is Date identification of surface reflectance [unit: none] Rs_VN11_MAX is Maximum of surface reflectance [unit: none] Rs_VN11_MIN is Minimum of surface reflectance [unit: none] Rs_VN11_Ninput is Input data number of surface reflectance [unit: none] Rs_VN11_Nused is Used data number of surface reflectance [unit: none] Rs_VN11_QA_flag is quality flag about the statistics and BRDF regression [unit: none] Rs_VN11_RMS is root-mean-square (RMS) of BRDF model regression of surface reflectance [unit: none] Rs_VN11_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none] The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-RV11_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-RV11_8days_250m_NA.json index c0c120a227..20f003f490 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-RV11_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-RV11_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-RV11_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Land Surface Reflectance (RV11) (8-Days, 250m) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). Using the Level2 atmospheric corrected land surface reflectance tile product as input, BRDF correction is performed for the land surface in clear region using data of multiple days, and the reflectance equivalent to that at nadir view direction is stored in the output product. The region definition and spatial resolutions of the output product are kept the input data. In 8-days processing, the land surface reflectance data of the past 24 days, respectively are fitted to generate appropriate BRDF models with the correction coefficients. GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes of following SDs (Center Wavelength is correspond to channel name as same as L1B): Rs_VN11_AVE is Normalized surface reflectance [unit: none]Rs_VN11_Date is Date identification of surface reflectance [unit: none]Rs_VN11_MAX is Maximum of surface reflectance [unit: none]Rs_VN11_MIN is Minimum of surface reflectance [unit: none]Rs_VN11_Ninput is Input data number of surface reflectance [unit: none]Rs_VN11_Nused is Used data number of surface reflectance [unit: none]Rs_VN11_QA_flag is quality flag about the statistics and BRDF regression [unit: none]Rs_VN11_RMS is RMS of BRDF model regression of surface reflectance [unit: none]Rs_VN11_c0-2 are c0, c1, and c2 coefficients of surface reflectance [unit: none]The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is \"Tile\". The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SDI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SDI_1month_250m_NA.json index ed0970788e..ddda5a32cd 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SDI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SDI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SDI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Shadow Index (SI) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band.This dataset includes SI: Shadow Index. SDI is the fraction of shadow generated by conformation of vegetation (areal occupation within a pixel) and is estimated with regression equation. The physical quantity unit is dimentionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SDI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SDI_8days_250m_NA.json index 69c029a448..1125bb161e 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SDI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SDI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SDI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Shadow Index (SI) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes SI: Shadow Index. SDI is the fraction of shadow generated by conformation of vegetation (areal occupation within a pixel) and is estimated with regression equation. The physical quantity unit is dimensionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SGSL_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SGSL_1month_1km_NA.json index b823c44996..849f9e87c8 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SGSL_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SGSL_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SGSL_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Snow grain size of shallow layer (1-month, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes Snow grain size of shallow layer. To estimate the snow grain size from the radiance, their relationship is learned as a nonlinear function using the neural network training, and the snow grain size of shallow layer is estimated based on the observed radiance by nonlinear optimal estimation method. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SGSL_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SGSL_8days_1km_NA.json index 4bb4d1a1e7..a9dfcfacf1 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SGSL_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SGSL_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SGSL_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Snow grain size of shallow layer (8-days, 1km) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. This dataset includes Snow grain size of shallow layer. To estimate the snow grain size from the radiance, their relationship is learned as a nonlinear function using the neural network training, and the snow grain size of shallow layer is estimated based on the observed radiance by nonlinear optimal estimation method. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SICE_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SICE_1month_1km_NA.json index 9e2258ffd2..fd21645df8 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SICE_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SICE_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SICE_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Snow and Ice cover extent (1-month, 1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes SICE_Nsnow1: Number of snow or ice cover, SICE_Nsnow2: Number of snow with vegetation or bare ice, SICE_Nsnow3: Number of melting snow. Their physical quantity unit is dimensionless. Using the Level2 product as input, it calculates and outputs the temporal statistics of 8-days. The region definition and spatial resolutions of the output product are kept those of input data.The physical quantity unit is micrometer. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SICE_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SICE_8days_1km_NA.json index 574ac22ab7..ab0bbb664a 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SICE_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SICE_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SICE_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Snow and Ice cover extent (8-days, 1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes SICE_Nsnow1: Number of snow or ice cover, SICE_Nsnow2: Number of snow with vegetation or bare ice, SICE_Nsnow3: Number of melting snow. Their physical quantity unit is dimensionless. Using the Level2 product as input, it calculates and outputs the temporal statistics of 8-days. The region definition and spatial resolutions of the output product are kept those of input data.The physical quantity unit is micrometer. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SIST_1month_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SIST_1month_1km_NA.json index 0f4f003678..433c5239bc 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SIST_1month_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SIST_1month_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SIST_1month_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Snow and ice surface temperature (1-month,1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes SIST: Snow and ice surface temperature based on a model snow. Using the Level2 product as input, it calculates and outputs the temporal statistics of 1 month. The region definition and spatial resolutions of the output product are kept those of input data.The physical quantity unit is Kelvin. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-SIST_8days_1km_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-SIST_8days_1km_NA.json index 78e30641ae..bb93abd439 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-SIST_8days_1km_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-SIST_8days_1km_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-SIST_8days_1km_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Snow and ice surface temperature (8-Days,1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes SIST: Snow and ice surface temperature based on a model snow.Using the Level2 product as input, it calculates and outputs the temporal statistics of 8-days. The region definition and spatial resolutions of the output product are kept those of input data.Physical quantity unit is Kelvin. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-VRI_1month_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-VRI_1month_250m_NA.json index 403da61a40..c6445f5991 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-VRI_1month_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-VRI_1month_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-VRI_1month_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Vegetation Roughness Index (VRI) (1-Month,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes VRI: Vegetation Roughness Index. The physical quantity unit is dimensionless.The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 1 month also 8 days statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L2_statistics-VRI_8days_250m_NA.json b/datasets/GCOM-C_SGLI_L2_statistics-VRI_8days_250m_NA.json index 5d407e9afc..6dd45750f9 100644 --- a/datasets/GCOM-C_SGLI_L2_statistics-VRI_8days_250m_NA.json +++ b/datasets/GCOM-C_SGLI_L2_statistics-VRI_8days_250m_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L2_statistics-VRI_8days_250m_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L2 Statistics-Vegetation Roughness Index (VRI) (8-Days,250m) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes VRI: Vegetation Roughness Index. The physical quantity unit is dimensionless. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 250 m. The statistical period is 8 days also 1 month statistics is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 2.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AGB_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AGB_1day_1-24deg_NA.json index 0b4fcbb728..73faa24db8 100644 --- a/datasets/GCOM-C_SGLI_L3B_AGB_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AGB_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AGB_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Above Ground Biomass (AGB) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AGB_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AGB_1month_1-24deg_NA.json index d5f5586fef..6b23c9e3b0 100644 --- a/datasets/GCOM-C_SGLI_L3B_AGB_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AGB_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AGB_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Above Ground Biomass (AGB) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AGB_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AGB_8days_1-24deg_NA.json index 2f3561aeb4..f447d2fe73 100644 --- a/datasets/GCOM-C_SGLI_L3B_AGB_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AGB_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AGB_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Above Ground Biomass (AGB) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_ARAE_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_ARAE_1day_1-12deg_NA.json index db8ef6561d..ea59725363 100644 --- a/datasets/GCOM-C_SGLI_L3B_ARAE_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_ARAE_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_ARAE_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol angstrom exponent (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable.The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol angstrom exponent over ocean (AAEO), L3 Binned Aerosol angstrom exponent over ocean over land (near UV) (AAEL) and L3 Binned Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_ARAE_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_ARAE_1month_1-12deg_NA.json index 0ba9fcac29..df20d042eb 100644 --- a/datasets/GCOM-C_SGLI_L3B_ARAE_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_ARAE_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_ARAE_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol angstrom exponent (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable.The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol angstrom exponent over ocean (AAEO), L3 Binned Aerosol angstrom exponent over ocean over land (near UV) (AAEL) and L3 Binned Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_ARAE_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_ARAE_8days_1-12deg_NA.json index e5d26dbea4..dfae729159 100644 --- a/datasets/GCOM-C_SGLI_L3B_ARAE_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_ARAE_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_ARAE_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol angstrom exponent (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable. The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol angstrom exponent over ocean (AAEO), L3 Binned Aerosol angstrom exponent over ocean over land (near UV) (AAEL) and L3 Binned Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AROT_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AROT_1day_1-12deg_NA.json index bcc0541f8a..70672b39bf 100644 --- a/datasets/GCOM-C_SGLI_L3B_AROT_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AROT_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AROT_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol optical thickness over ocean (AOTO), L3 Binned Aerosol optical thickness over land (near UV) (AOTL) and L3 Binned Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AROT_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AROT_1month_1-12deg_NA.json index 3fc8671f0e..7870aa8482 100644 --- a/datasets/GCOM-C_SGLI_L3B_AROT_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AROT_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AROT_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol optical thickness over ocean (AOTO), L3 Binned Aerosol optical thickness over land (near UV) (AOTL) and L3 Binned Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_AROT_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_AROT_8days_1-12deg_NA.json index 99fd3a2e07..db832c5d28 100644 --- a/datasets/GCOM-C_SGLI_L3B_AROT_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_AROT_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_AROT_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Binned Aerosol optical thickness over ocean (AOTO), L3 Binned Aerosol optical thickness over land (near UV) (AOTL) and L3 Binned Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_ASSA_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_ASSA_1day_1-12deg_NA.json index 62ad08d484..16c363809d 100644 --- a/datasets/GCOM-C_SGLI_L3B_ASSA_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_ASSA_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_ASSA_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol Single Scattering Albedo over land (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol Single Scattering Albedo over land. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_ASSA_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_ASSA_8days_1-12deg_NA.json index 3620c65b2e..c0c52b3e78 100644 --- a/datasets/GCOM-C_SGLI_L3B_ASSA_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_ASSA_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_ASSA_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Aerosol Single Scattering Albedo over land (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes aerosol Single Scattering Albedo over land. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CDOM_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CDOM_1day_1-24deg_NA.json index ba0a55a7e6..cbd1fbd167 100644 --- a/datasets/GCOM-C_SGLI_L3B_CDOM_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CDOM_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CDOM_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Colored dissolved organic matter (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes colored dissolved organic matter.The physical quantity unit is m-1. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CDOM_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CDOM_1month_1-24deg_NA.json index d111751474..5fab8536bc 100644 --- a/datasets/GCOM-C_SGLI_L3B_CDOM_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CDOM_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CDOM_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Colored dissolved organic matter (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes colored dissolved organic matter.The physical quantity unit is m-1. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CDOM_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CDOM_8days_1-24deg_NA.json index b54e9e3634..b712ba6b9c 100644 --- a/datasets/GCOM-C_SGLI_L3B_CDOM_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CDOM_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CDOM_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Colored dissolved organic matter (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes colored dissolved organic matter.The physical quantity unit is m-1. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CERW_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CERW_1day_1-12deg_NA.json index f5659f7baf..91523b3297 100644 --- a/datasets/GCOM-C_SGLI_L3B_CERW_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CERW_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CERW_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud effective radius (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CERW_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CERW_1month_1-12deg_NA.json index 97ed638f95..1f06f3179c 100644 --- a/datasets/GCOM-C_SGLI_L3B_CERW_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CERW_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CERW_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud effective radius (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CERW_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CERW_8days_1-12deg_NA.json index 6326531211..8c36aaa596 100644 --- a/datasets/GCOM-C_SGLI_L3B_CERW_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CERW_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CERW_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud effective radius (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR1_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR1_1day_1-12deg_NA.json index ab6b34f98b..9554ec3a33 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR1_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR1_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR1_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR1) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR1_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR1_1month_1-12deg_NA.json index a88b6eea9e..478b55f6bd 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR1_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR1_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR1_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR1) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR1_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR1_8days_1-12deg_NA.json index 98b404fb83..5a173427cd 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR1_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR1_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR1_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR1) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR2_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR2_1day_1-12deg_NA.json index b37b170e39..945a50c17b 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR2_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR2_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR2_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR2) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR2_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR2_1month_1-12deg_NA.json index a63993347f..928a58b4de 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR2_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR2_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR2_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR2) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR2_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR2_8days_1-12deg_NA.json index 511948251c..c08982b426 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR2_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR2_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR2_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR2) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR3_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR3_1day_1-12deg_NA.json index 2ef435b1a1..59c8b6eea8 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR3_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR3_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR3_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR3) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR3_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR3_1month_1-12deg_NA.json index c8f2c0b176..02714ec458 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR3_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR3_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR3_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR3) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR3_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR3_8days_1-12deg_NA.json index 327ec6fec7..905d4b4016 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR3_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR3_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR3_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR3) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR4_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR4_1day_1-12deg_NA.json index 29dfd052a6..192998f8e0 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR4_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR4_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR4_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR4) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR4_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR4_1month_1-12deg_NA.json index ccbba2f238..7b6337c9c7 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR4_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR4_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR4_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR4) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR4_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR4_8days_1-12deg_NA.json index 0122f86a63..d6398447ba 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR4_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR4_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR4_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR4) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR5_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR5_1day_1-12deg_NA.json index 7484b3fc60..1f93c51acb 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR5_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR5_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR5_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR5) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR5_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR5_1month_1-12deg_NA.json index 74218cbcd1..9f5973b66d 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR5_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR5_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR5_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR5) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection. This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR5_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR5_8days_1-12deg_NA.json index 362484d908..ac58f4148f 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR5_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR5_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR5_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR5) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection. This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR6_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR6_1day_1-12deg_NA.json index 85fc458325..cd520c8540 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR6_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR6_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR6_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR6) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR6_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR6_1month_1-12deg_NA.json index 2174ddff8c..4d51bca083 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR6_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR6_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR6_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR6) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR6_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR6_8days_1-12deg_NA.json index d103d6c146..f17ef99017 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR6_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR6_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR6_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR6) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR7_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR7_1day_1-12deg_NA.json index c5314ffc71..440bd24201 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR7_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR7_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR7_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR7) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR7_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR7_1month_1-12deg_NA.json index 16a4bea9ca..16c25be17a 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR7_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR7_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR7_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR7) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR7_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR7_8days_1-12deg_NA.json index 657fd3dd0f..94ba29bfd2 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR7_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR7_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR7_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR7) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR8_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR8_1day_1-12deg_NA.json index f779c95580..246508d12d 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR8_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR8_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR8_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR8) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-8 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR8_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR8_1month_1-12deg_NA.json index 055db7a30d..98eb6f1cef 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR8_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR8_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR8_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR8) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR8_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR8_8days_1-12deg_NA.json index 551f5ec112..cda8d1fbd9 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR8_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR8_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR8_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR8) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-8 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR9_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR9_1day_1-12deg_NA.json index 4febeb8efb..297dcb0167 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR9_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR9_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR9_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR9) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR9_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR9_1month_1-12deg_NA.json index a6a106ccc2..43bf5595e5 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR9_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR9_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR9_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR9) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFR9_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFR9_8days_1-12deg_NA.json index 0cc62b7018..3c155d895a 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFR9_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFR9_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFR9_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFR9) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRA_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRA_1day_1-12deg_NA.json index 562bdeca2a..3ea7a020f9 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRA_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRA_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRA_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRA) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRA_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRA_1month_1-12deg_NA.json index 422838194d..e1ba1889d4 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRA_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRA_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRA_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRA) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRA_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRA_8days_1-12deg_NA.json index aa240b71c0..2b06fa44d8 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRA_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRA_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRA_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRA) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRH_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRH_1day_1-12deg_NA.json index ac6fdf61c6..a9f0e522f9 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRH_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRH_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRH_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRH) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRH_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRH_1month_1-12deg_NA.json index 5150f0ed7d..4fa447539a 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRH_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRH_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRH_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRH) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRH_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRH_8days_1-12deg_NA.json index 5938e55517..05483fa88f 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRH_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRH_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRH_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRH) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRL_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRL_1day_1-12deg_NA.json index 7b7f176e96..de7eed4560 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRL_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRL_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRL_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRL) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRL_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRL_1month_1-12deg_NA.json index 6bbc15441a..3f83159b3b 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRL_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRL_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRL_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRL) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRL_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRL_8days_1-12deg_NA.json index 34ef0f4e2c..d1543606a6 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRL_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRL_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRL_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRL) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRM_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRM_1day_1-12deg_NA.json index aad85db4c6..c3cc04420a 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRM_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRM_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRM_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRM) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRM_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRM_1month_1-12deg_NA.json index 18c0a3b5b8..a0b5bcfe26 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRM_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRM_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRM_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRM) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CFRM_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CFRM_8days_1-12deg_NA.json index b284be77c7..9621b321f4 100644 --- a/datasets/GCOM-C_SGLI_L3B_CFRM_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CFRM_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CFRM_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Classified cloud fraction (CFRM) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CHLA_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CHLA_1day_1-24deg_NA.json index 6b5f541ddf..dc43d6c09e 100644 --- a/datasets/GCOM-C_SGLI_L3B_CHLA_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CHLA_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CHLA_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Chlorophyll-a concentration (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes chlorophyll-a concentration.The physical quantity unit is mg/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree.The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CHLA_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CHLA_1month_1-24deg_NA.json index 440ccee84c..aaaa4bcbce 100644 --- a/datasets/GCOM-C_SGLI_L3B_CHLA_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CHLA_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CHLA_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Chlorophyll-a concentration (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes chlorophyll-a concentration. The physical quantity unit is mg/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree.The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CHLA_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CHLA_8days_1-24deg_NA.json index fe266e7cda..3baf1644ab 100644 --- a/datasets/GCOM-C_SGLI_L3B_CHLA_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CHLA_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CHLA_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Chlorophyll-a concentration (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes chlorophyll-a concentration. The physical quantity unit is mg/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTH_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTH_1day_1-12deg_NA.json index b22babe776..6a60ec8581 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTH_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTH_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTH_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top height (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top height. The physical quantity unit is km. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTH_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTH_1month_1-12deg_NA.json index 5a08993f78..c815109d71 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTH_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTH_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTH_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top height (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top height. The physical quantity unit is km. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTH_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTH_8days_1-12deg_NA.json index 0d09e9399e..b07be6b38c 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTH_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTH_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTH_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top height (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top height. The physical quantity unit is km. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTT_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTT_1day_1-12deg_NA.json index 567182ecca..d13c6db5e4 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTT_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTT_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTT_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top temperature (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTT_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTT_1month_1-12deg_NA.json index 0660a6ad8c..9d0df087b7 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTT_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTT_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTT_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top temperature (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_CLTT_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_CLTT_8days_1-12deg_NA.json index 9bf3d208bc..26728c27be 100644 --- a/datasets/GCOM-C_SGLI_L3B_CLTT_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_CLTT_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_CLTT_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Cloud top temperature (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTI_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTI_1day_1-12deg_NA.json index e6c20be6dc..5d78a08c28 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTI_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTI_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTI_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Ice cloud optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTI_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTI_1month_1-12deg_NA.json index 145ac1431f..48c5920f51 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTI_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTI_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTI_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Ice cloud optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTI_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTI_8days_1-12deg_NA.json index f0c6fa7e61..650138b8b1 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTI_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTI_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTI_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Ice cloud optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTW_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTW_1day_1-12deg_NA.json index e3b35d6b40..9e05637574 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTW_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTW_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTW_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTW_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTW_1month_1-12deg_NA.json index bf01d69738..9e5d6392d2 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTW_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTW_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTW_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_COTW_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3B_COTW_8days_1-12deg_NA.json index 1a7aebe709..84a07d6ba5 100644 --- a/datasets/GCOM-C_SGLI_L3B_COTW_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_COTW_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_COTW_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Water cloud optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/12 deg with EQA projection.This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_EVI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_EVI_1day_1-24deg_NA.json index b41b7baaed..08defaef30 100644 --- a/datasets/GCOM-C_SGLI_L3B_EVI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_EVI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_EVI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Enhanced Vegetation Index (EVI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_EVI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_EVI_1month_1-24deg_NA.json index fbb96ce77a..aec03161ed 100644 --- a/datasets/GCOM-C_SGLI_L3B_EVI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_EVI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_EVI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Enhanced Vegetation Index (EVI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_EVI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_EVI_8days_1-24deg_NA.json index 1830b02d36..57604962d2 100644 --- a/datasets/GCOM-C_SGLI_L3B_EVI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_EVI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_EVI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Enhanced Vegetation Index (EVI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_FPAR_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_FPAR_1day_1-24deg_NA.json index 0296ed7c7a..5ed35a5b60 100644 --- a/datasets/GCOM-C_SGLI_L3B_FPAR_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_FPAR_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_FPAR_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Fraction of absorbed PAR (FAPAR) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_FPAR_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_FPAR_1month_1-24deg_NA.json index 5b3bfd6687..33a2694a63 100644 --- a/datasets/GCOM-C_SGLI_L3B_FPAR_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_FPAR_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_FPAR_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Fraction of absorbed PAR (FAPAR) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_FPAR_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_FPAR_8days_1-24deg_NA.json index 4aeb79dc5d..10b60dec0e 100644 --- a/datasets/GCOM-C_SGLI_L3B_FPAR_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_FPAR_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_FPAR_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Fraction of absorbed PAR (FAPAR) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOI_1day_1-24deg_NA.json index 1ce38e0371..a5d89eb6b3 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOI) (1-day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of IRS sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days 1 month statistics are available. The projection method is EQA. The generation unit is Global.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOI_1month_1-24deg_NA.json index 8a891a520c..ddfd77535f 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of IRS sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOI_8days_1-24deg_NA.json index a0c0cc3be1..7e3667d6bb 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-NP sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOP_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOP_1day_1-24deg_NA.json index b3810d52b7..d45b0272a6 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOP_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOP_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOP_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOP) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-PL sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOP_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOP_1month_1-24deg_NA.json index 33cce2ac57..91f7551854 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOP_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOP_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOP_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOP) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-PL sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOP_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOP_8days_1-24deg_NA.json index 410f4a1f53..2b45c5dff0 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOP_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOP_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOP_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOP) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-PL sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOV_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOV_1day_1-24deg_NA.json index 9b0b91b7d4..7180827684 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOV_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOV_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOV_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOV) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-NP sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOV_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOV_1month_1-24deg_NA.json index 81555bed8f..c90c5f42b1 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOV_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOV_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOV_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOV) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-NP sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_GEOV_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_GEOV_8days_1-24deg_NA.json index 7fd6314707..b60d843441 100644 --- a/datasets/GCOM-C_SGLI_L3B_GEOV_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_GEOV_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_GEOV_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOV) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-NP sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L380_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L380_1day_1-24deg_NA.json index 383c2f0498..d0906409be 100644 --- a/datasets/GCOM-C_SGLI_L3B_L380_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L380_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L380_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L380) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 380 nm (NWLR_380) and QA_Flag.The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L380_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L380_1month_1-24deg_NA.json index fe17c37e78..5a95709b01 100644 --- a/datasets/GCOM-C_SGLI_L3B_L380_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L380_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L380_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L380) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 380 nm (NWLR_380) and QA_Flag. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The physical quantity unit is W/m^2/str/um. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L380_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L380_8days_1-24deg_NA.json index b7c05dcb2b..2580d0221f 100644 --- a/datasets/GCOM-C_SGLI_L3B_L380_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L380_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L380_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L380) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 380 nm (NWLR_380) and QA_Flag. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The physical quantity unit is W/m^2/str/um. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L412_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L412_1day_1-24deg_NA.json index d9476020f2..b0dc77a735 100644 --- a/datasets/GCOM-C_SGLI_L3B_L412_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L412_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L412_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L412) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 412 nm (NWLR_412) and QA_Flag.The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L412_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L412_1month_1-24deg_NA.json index 3d8ec9ef75..9c0f7a1f1c 100644 --- a/datasets/GCOM-C_SGLI_L3B_L412_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L412_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L412_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L412) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 412 nm (NWLR_412) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L412_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L412_8days_1-24deg_NA.json index 4ace792398..deff7a155f 100644 --- a/datasets/GCOM-C_SGLI_L3B_L412_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L412_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L412_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L412) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 412 nm (NWLR_412) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L443_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L443_1day_1-24deg_NA.json index 94844f30a6..f06ec6db2a 100644 --- a/datasets/GCOM-C_SGLI_L3B_L443_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L443_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L443_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L443) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 443 nm (NWLR_443) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L443_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L443_1month_1-24deg_NA.json index abbbb2e2c1..65933bc433 100644 --- a/datasets/GCOM-C_SGLI_L3B_L443_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L443_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L443_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L443) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 443 nm (NWLR_443) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L443_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L443_8days_1-24deg_NA.json index cf5c7f9923..fd846dfea7 100644 --- a/datasets/GCOM-C_SGLI_L3B_L443_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L443_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L443_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L443) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 443 nm (NWLR_443) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L490_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L490_1day_1-24deg_NA.json index 504aeac98a..dd463a5889 100644 --- a/datasets/GCOM-C_SGLI_L3B_L490_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L490_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L490_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L490) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 490 nm (NWLR_490) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L490_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L490_1month_1-24deg_NA.json index 168c431394..6ee71a3938 100644 --- a/datasets/GCOM-C_SGLI_L3B_L490_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L490_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L490_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L490) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 490 nm (NWLR_490) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L490_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L490_8days_1-24deg_NA.json index e15c664341..4999009949 100644 --- a/datasets/GCOM-C_SGLI_L3B_L490_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L490_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L490_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L490) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 490 nm (NWLR_490) and QA_Flag. The physical quantity unit is W/m2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L530_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L530_1day_1-24deg_NA.json index 8d28788fce..39dbfa10f7 100644 --- a/datasets/GCOM-C_SGLI_L3B_L530_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L530_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L530_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L530) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 530 nm (NWLR_530) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L530_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L530_1month_1-24deg_NA.json index cbc2c2067b..ce035c8d65 100644 --- a/datasets/GCOM-C_SGLI_L3B_L530_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L530_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L530_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L530) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 530 nm (NWLR_530) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L530_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L530_8days_1-24deg_NA.json index a6c8c68b4f..0dec8fa79f 100644 --- a/datasets/GCOM-C_SGLI_L3B_L530_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L530_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L530_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L530) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes the upwelling radiance just above the sea surface at 530 nm (NWLR_530) and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L565_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L565_1day_1-24deg_NA.json index 40b49204f9..7da6ef81be 100644 --- a/datasets/GCOM-C_SGLI_L3B_L565_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L565_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L565_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L565) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L565_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L565_1month_1-24deg_NA.json index f12a772be0..8e4c0cb94a 100644 --- a/datasets/GCOM-C_SGLI_L3B_L565_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L565_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L565_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L565) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L565_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L565_8days_1-24deg_NA.json index 8b8ed5ea8c..3cacb90a1f 100644 --- a/datasets/GCOM-C_SGLI_L3B_L565_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L565_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L565_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L565) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L670_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L670_1day_1-24deg_NA.json index 527fd69e21..fb5c0a6eae 100644 --- a/datasets/GCOM-C_SGLI_L3B_L670_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L670_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L670_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L565) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L670_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L670_1month_1-24deg_NA.json index e359d844f1..3527532376 100644 --- a/datasets/GCOM-C_SGLI_L3B_L670_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L670_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L670_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L670) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_670: the upwelling radiance just above the sea surface at 670 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_L670_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_L670_8days_1-24deg_NA.json index b518a8bc89..80913c19b7 100644 --- a/datasets/GCOM-C_SGLI_L3B_L670_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_L670_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_L670_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized water leaving radiance (NWLR L670) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes NWLR_670: the upwelling radiance just above the sea surface at 670 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LAI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LAI_1day_1-24deg_NA.json index 65ce2948eb..1e6421804e 100644 --- a/datasets/GCOM-C_SGLI_L3B_LAI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LAI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LAI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Leaf Area Index (LAI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LAI: Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LAI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LAI_1month_1-24deg_NA.json index a6c5252465..768223197e 100644 --- a/datasets/GCOM-C_SGLI_L3B_LAI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LAI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LAI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Leaf Area Index (LAI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LAI: Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LAI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LAI_8days_1-24deg_NA.json index 7aaf1927b9..cb7b57efe0 100644 --- a/datasets/GCOM-C_SGLI_L3B_LAI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LAI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LAI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Leaf Area Index (LAI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LAI: Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LST_1day_1-24deg_NA.json index b815e5508e..a3951546e9 100644 --- a/datasets/GCOM-C_SGLI_L3B_LST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Land surface temperature (LST) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LST_1month_1-24deg_NA.json index e9cf072d6a..496895fc98 100644 --- a/datasets/GCOM-C_SGLI_L3B_LST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Land surface temperature (LST) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_LST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_LST_8days_1-24deg_NA.json index 4a88e2c417..84db155ff3 100644 --- a/datasets/GCOM-C_SGLI_L3B_LST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_LST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_LST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Land surface temperature (LST) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_NDVI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_NDVI_1day_1-24deg_NA.json index ba6395fb7b..3766f9c326 100644 --- a/datasets/GCOM-C_SGLI_L3B_NDVI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_NDVI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_NDVI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized Difference Vegetation Index (NDVI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_NDVI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_NDVI_1month_1-24deg_NA.json index 3e35bb7812..791c561e96 100644 --- a/datasets/GCOM-C_SGLI_L3B_NDVI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_NDVI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_NDVI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized Difference Vegetation Index (NDVI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_NDVI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_NDVI_8days_1-24deg_NA.json index d1cd7b07af..437d14935a 100644 --- a/datasets/GCOM-C_SGLI_L3B_NDVI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_NDVI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_NDVI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Normalized Difference Vegetation Index (NDVI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_PAR_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_PAR_1day_1-24deg_NA.json index 67108cbcc8..ad8a31450c 100644 --- a/datasets/GCOM-C_SGLI_L3B_PAR_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_PAR_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_PAR_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Photosynthetically available radiation (PAR) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_PAR_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_PAR_1month_1-24deg_NA.json index ecc5f4d43a..90ac2a2ccd 100644 --- a/datasets/GCOM-C_SGLI_L3B_PAR_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_PAR_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_PAR_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Photosynthetically available radiation (PAR) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_PAR_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_PAR_8days_1-24deg_NA.json index d15b68f970..ab572fbe83 100644 --- a/datasets/GCOM-C_SGLI_L3B_PAR_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_PAR_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_PAR_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Photosynthetically available radiation (PAR) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN08_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN08_1day_1-24deg_NA.json index 0e3bfd80b8..91fb4801e9 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN08_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN08_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN08_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN08) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN08_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN08_1month_1-24deg_NA.json index a4bf2ff63d..484e1be3bb 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN08_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN08_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN08_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN08) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN08_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN08_8days_1-24deg_NA.json index 1e138054ca..aaf9c5eff2 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN08_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN08_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN08_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN08) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN11_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN11_1day_1-24deg_NA.json index 763740191e..6ffc1b7826 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN11_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN11_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN11_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN11) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes Reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN11_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN11_1month_1-24deg_NA.json index 458c3008b9..25af132cfd 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN11_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN11_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN11_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN11) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RN11_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RN11_8days_1-24deg_NA.json index e8788c9489..977cef9704 100644 --- a/datasets/GCOM-C_SGLI_L3B_RN11_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RN11_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RN11_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RN11) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes Reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP01_1day_1-24deg_NA.json index 4de6d7405b..8a4608db4d 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP01_1month_1-24deg_NA.json index e0a3574492..1410bca023 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP01_8days_1-24deg_NA.json index acef49c949..339a7cdf2e 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP02_1day_1-24deg_NA.json index 60fa1eca2c..4157b46815 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP02_1month_1-24deg_NA.json index fa648e4eb9..f9a99e88cd 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RP02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RP02_8days_1-24deg_NA.json index cf51a16a61..8824867723 100644 --- a/datasets/GCOM-C_SGLI_L3B_RP02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RP02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RP02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RP02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS01_1day_1-24deg_NA.json index b5649b79b7..5cccf7ad62 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS01_1month_1-24deg_NA.json index 9b2eda257b..93f1821d9e 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS01_8days_1-24deg_NA.json index b831785475..69139ac4f6 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS02_1day_1-24deg_NA.json index 754600e0d3..20b94ac83d 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS02_1month_1-24deg_NA.json index c0db0c985f..d5a7dd6dd5 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS02_8days_1-24deg_NA.json index a6d1e761fe..1f9d08b8f1 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RS02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS03_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS03_1day_1-24deg_NA.json index e528170292..8c5273ecd6 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS03_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS03_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS03_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS03) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS03_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS03_1month_1-24deg_NA.json index 1dab54aa76..23c87e7e8f 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS03_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS03_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS03_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS03) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS03_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS03_8days_1-24deg_NA.json index 41373f8726..ef42b05ad5 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS03_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS03_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS03_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS03) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RS03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS04_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS04_1day_1-24deg_NA.json index 4a6cf277ef..349748f4a8 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS04_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS04_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS04_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS04) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS04_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS04_1month_1-24deg_NA.json index bb57242fd9..356dc3d13d 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS04_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS04_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS04_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS04) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of SW04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RS04_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RS04_8days_1-24deg_NA.json index d3ef58338c..939c1b42e5 100644 --- a/datasets/GCOM-C_SGLI_L3B_RS04_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RS04_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RS04_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RS04) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RS04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT01_1day_1-24deg_NA.json index 73861e3929..ce1acec08b 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT01_1month_1-24deg_NA.json index d3844fe605..a9ac41141a 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT01_8days_1-24deg_NA.json index 78f7e4b949..7039109dc6 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT02_1day_1-24deg_NA.json index 3062b76a3c..d6bb401d58 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T2 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT02_1month_1-24deg_NA.json index 6c0fd27873..3443783e5a 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T2 (Center Wavelength is 12.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RT02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RT02_8days_1-24deg_NA.json index c129aed720..604965923d 100644 --- a/datasets/GCOM-C_SGLI_L3B_RT02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RT02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RT02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RT02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of T2 (Center Wavelength is 12.8 micro m) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV01_1day_1-24deg_NA.json index 56e26ac20e..eff8ddb4c0 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV01_1month_1-24deg_NA.json index fb8accd4d9..4b83d2223a 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV01_8days_1-24deg_NA.json index a9717ab1dc..5a0d75c733 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV02_1day_1-24deg_NA.json index 26810faeb6..8c4dd28c6e 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV02_1month_1-24deg_NA.json index eefb651842..28b5db784c 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV02_8days_1-24deg_NA.json index 5736348021..8173d20bcb 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV03_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV03_1day_1-24deg_NA.json index f19b2b2448..3c671332de 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV03_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV03_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV03_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV03) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV03_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV03_1month_1-24deg_NA.json index 6e3f7ac9aa..d40efbf91f 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV03_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV03_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV03_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV03) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV03_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV03_8days_1-24deg_NA.json index 923afbb9ad..1535eb246a 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV03_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV03_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV03_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV03) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV04_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV04_1day_1-24deg_NA.json index 577ec5d01d..710de000bd 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV04_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV04_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV04_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV04) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV04_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV04_1month_1-24deg_NA.json index ecc01db6fb..69b0f493bb 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV04_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV04_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV04_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV04) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV04_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV04_8days_1-24deg_NA.json index 8e55d07611..dc4988893b 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV04_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV04_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV04_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV04) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV05_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV05_1day_1-24deg_NA.json index 0e2ad14d7b..216c4d1107 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV05_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV05_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV05_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV05) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV05_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV05_1month_1-24deg_NA.json index b21a5a4cae..53f29c19e1 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV05_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV05_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV05_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV05) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV05_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV05_8days_1-24deg_NA.json index d529f8fa95..2b50632040 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV05_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV05_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV05_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV05) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV06_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV06_1day_1-24deg_NA.json index 879732d367..c407c54bba 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV06_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV06_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV06_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV06) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV06_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV06_1month_1-24deg_NA.json index e063a4806b..f2e15884a3 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV06_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV06_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV06_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV06) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV06_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV06_8days_1-24deg_NA.json index c78f7fabc5..54667ae077 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV06_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV06_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV06_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV06) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV07_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV07_1day_1-24deg_NA.json index d59c96b15c..b7de5479f4 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV07_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV07_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV07_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV07) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV07_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV07_1month_1-24deg_NA.json index 464caaf14f..f493b5e671 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV07_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV07_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV07_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV07) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV07_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV07_8days_1-24deg_NA.json index 1eb9b98ec1..14b8d6f848 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV07_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV07_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV07_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV07) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV08_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV08_1day_1-24deg_NA.json index 275cce0750..b29323395d 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV08_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV08_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV08_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV08) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV08_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV08_1month_1-24deg_NA.json index f1a863d6f2..ae3e6a3bbe 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV08_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV08_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV08_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV08) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV08_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV08_8days_1-24deg_NA.json index 772c374cdd..ae409c886c 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV08_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV08_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV08_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV08) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV09_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV09_1day_1-24deg_NA.json index d8dff4223b..c2ce729dc9 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV09_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV09_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV09_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV09) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV09_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV09_1month_1-24deg_NA.json index 8d056331b1..919e0d5a50 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV09_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV09_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV09_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV09) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV09_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV09_8days_1-24deg_NA.json index f92b8adb89..5c5afd9838 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV09_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV09_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV09_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV09) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV10_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV10_1day_1-24deg_NA.json index 9caae9a2dc..d9088227a3 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV10_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV10_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV10_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV10) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV10_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV10_1month_1-24deg_NA.json index 68d6b41768..629f8260c7 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV10_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV10_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV10_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV10) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV10_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV10_8days_1-24deg_NA.json index f9b83745a2..2c272cdbec 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV10_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV10_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV10_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV10) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV11_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV11_1day_1-24deg_NA.json index 2b8617b131..26506764d5 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV11_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV11_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV11_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV11) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV11_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV11_1month_1-24deg_NA.json index 3b25900528..5945abdc36 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV11_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV11_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV11_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV11) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_RV11_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_RV11_8days_1-24deg_NA.json index 620361d7b7..4b4edbed22 100644 --- a/datasets/GCOM-C_SGLI_L3B_RV11_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_RV11_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_RV11_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (RV11) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SDI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SDI_1day_1-24deg_NA.json index b4c1e74fef..f3dc81bea9 100644 --- a/datasets/GCOM-C_SGLI_L3B_SDI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SDI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SDI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Shadow index (SI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SDI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SDI_1month_1-24deg_NA.json index 0b3ae9bf38..e5f2f84146 100644 --- a/datasets/GCOM-C_SGLI_L3B_SDI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SDI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SDI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Shadow index (SI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SDI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SDI_8days_1-24deg_NA.json index 43f7719a2a..37cf463fba 100644 --- a/datasets/GCOM-C_SGLI_L3B_SDI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SDI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SDI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Shadow index (SI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SGSL_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SGSL_1day_1-24deg_NA.json index 6a35af75d2..45bb6c89b1 100644 --- a/datasets/GCOM-C_SGLI_L3B_SGSL_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SGSL_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SGSL_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow grain size of shallow layer (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SGSL_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SGSL_1month_1-24deg_NA.json index 98e3c5069c..c15bf5bbc7 100644 --- a/datasets/GCOM-C_SGLI_L3B_SGSL_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SGSL_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SGSL_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow grain size of shallow layer (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SGSL_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SGSL_8days_1-24deg_NA.json index cf76a84d8a..a2fdef7f38 100644 --- a/datasets/GCOM-C_SGLI_L3B_SGSL_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SGSL_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SGSL_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow grain size of shallow layer (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SICE_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SICE_1day_1-24deg_NA.json index 9a1129c84e..b986a828cf 100644 --- a/datasets/GCOM-C_SGLI_L3B_SICE_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SICE_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SICE_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and Ice Cover Extent (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SICE_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SICE_1month_1-24deg_NA.json index 775d99fc5e..68880f906b 100644 --- a/datasets/GCOM-C_SGLI_L3B_SICE_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SICE_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SICE_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and Ice Cover Extent (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SICE_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SICE_8days_1-24deg_NA.json index 9cc5e144bb..fb636cc955 100644 --- a/datasets/GCOM-C_SGLI_L3B_SICE_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SICE_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SICE_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and Ice Cover Extent (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection. This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SIST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SIST_1day_1-24deg_NA.json index 9f51ee07c9..b95a8697f1 100644 --- a/datasets/GCOM-C_SGLI_L3B_SIST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SIST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SIST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and ice surface temperature (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SIST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SIST_1month_1-24deg_NA.json index 7a2c4ddace..9e975ca9de 100644 --- a/datasets/GCOM-C_SGLI_L3B_SIST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SIST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SIST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and ice surface temperature (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SIST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SIST_8days_1-24deg_NA.json index 8564fb47ee..569f7f4cac 100644 --- a/datasets/GCOM-C_SGLI_L3B_SIST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SIST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SIST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Snow and ice surface temperature (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SST_1day_1-24deg_NA.json index 5697ecee8e..8e18a54249 100644 --- a/datasets/GCOM-C_SGLI_L3B_SST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Sea surface temperature (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes sea surface temperature. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree.The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SST_1month_1-24deg_NA.json index 2d550eec8e..6b4fd2e089 100644 --- a/datasets/GCOM-C_SGLI_L3B_SST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Sea surface temperature (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes sea surface temperature. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA, EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_SST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_SST_8days_1-24deg_NA.json index 162b0981da..ef7b0b96af 100644 --- a/datasets/GCOM-C_SGLI_L3B_SST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_SST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_SST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Sea surface temperature (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes sea surface temperature. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T670_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T670_1day_1-24deg_NA.json index 391ed482fe..a30afb0746 100644 --- a/datasets/GCOM-C_SGLI_L3B_T670_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T670_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T670_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T670) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T670_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T670_1month_1-24deg_NA.json index fa71f4e28e..336519ca4c 100644 --- a/datasets/GCOM-C_SGLI_L3B_T670_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T670_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T670_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T670) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T670_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T670_8days_1-24deg_NA.json index 7f4a3c2ff9..54b4b6ba05 100644 --- a/datasets/GCOM-C_SGLI_L3B_T670_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T670_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T670_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T670) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T865_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T865_1day_1-24deg_NA.json index 4d2236390f..cb54436c64 100644 --- a/datasets/GCOM-C_SGLI_L3B_T865_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T865_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T865_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T865) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T865_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T865_1month_1-24deg_NA.json index 93f3cb67c2..8c6ee80e1f 100644 --- a/datasets/GCOM-C_SGLI_L3B_T865_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T865_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T865_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T865) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_T865_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_T865_8days_1-24deg_NA.json index 2032c334d9..964bbd2c69 100644 --- a/datasets/GCOM-C_SGLI_L3B_T865_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_T865_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_T865_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T865) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865.0. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_TSM_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_TSM_1day_1-24deg_NA.json index 1ed7afb3a5..f6fbb0c9fd 100644 --- a/datasets/GCOM-C_SGLI_L3B_TSM_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_TSM_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_TSM_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Suspended solid concentration (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes suspended concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_TSM_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_TSM_1month_1-24deg_NA.json index 53790f891e..2230f161c0 100644 --- a/datasets/GCOM-C_SGLI_L3B_TSM_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_TSM_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_TSM_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Suspended solid concentration (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes total suspended solid concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_TSM_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_TSM_8days_1-24deg_NA.json index 3da0930db3..745ff08134 100644 --- a/datasets/GCOM-C_SGLI_L3B_TSM_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_TSM_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_TSM_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Suspended solid concentration (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes total suspended solid concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_VRI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_VRI_1day_1-24deg_NA.json index ed6826c78d..d6651a7493 100644 --- a/datasets/GCOM-C_SGLI_L3B_VRI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_VRI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_VRI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Vegetation Roughness Index (VRI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes VRI: Vegetation Roughness Index. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_VRI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_VRI_1month_1-24deg_NA.json index 27fe73e291..e19b5d0832 100644 --- a/datasets/GCOM-C_SGLI_L3B_VRI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_VRI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_VRI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Vegetation Roughness Index (VRI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes VRI: Vegetation Roughness Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3B_VRI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3B_VRI_8days_1-24deg_NA.json index 8d81645e46..d30963cebc 100644 --- a/datasets/GCOM-C_SGLI_L3B_VRI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3B_VRI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3B_VRI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Binned Vegetation Roughness Index (VRI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes VRI: Vegetation Roughness Index. Physical quantity unit is dimensionless. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AGB_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AGB_1day_1-24deg_NA.json index af312e7929..964b75e1cf 100644 --- a/datasets/GCOM-C_SGLI_L3M_AGB_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AGB_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AGB_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Above Ground Biomass (AGB) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AGB_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AGB_1month_1-24deg_NA.json index aa8a85f222..2cad287d63 100644 --- a/datasets/GCOM-C_SGLI_L3M_AGB_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AGB_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AGB_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Above Ground Biomass (AGB) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE) and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AGB_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AGB_8days_1-24deg_NA.json index 929db6ae20..a1e538f10c 100644 --- a/datasets/GCOM-C_SGLI_L3M_AGB_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AGB_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AGB_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Above Ground Biomass (AGB) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes AGB: Above Ground Biomass and QA_flag. Physical quantity unit is t/ha. The stored statistics values are average (AVE) and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_ARAE_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_ARAE_1day_1-12deg_NA.json index ffc26568f4..b792e73d49 100644 --- a/datasets/GCOM-C_SGLI_L3M_ARAE_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_ARAE_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_ARAE_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol angstrom exponent (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable.The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol angstrom exponent over ocean (AAEO), L3 Map Aerosol angstrom exponent over land (near UV) (AAEL) and L3 Map Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_ARAE_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_ARAE_1month_1-12deg_NA.json index 4593a9918c..dd4fd19849 100644 --- a/datasets/GCOM-C_SGLI_L3M_ARAE_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_ARAE_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_ARAE_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol angstrom exponent (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable.The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol angstrom exponent over ocean (AAEO), L3 Map Aerosol angstrom exponent over land (near UV) (AAEL) and L3 Map Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_ARAE_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_ARAE_8days_1-12deg_NA.json index a15701868f..9a8f3b6699 100644 --- a/datasets/GCOM-C_SGLI_L3M_ARAE_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_ARAE_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_ARAE_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol angstrom exponent (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes aerosol angstrom exponent. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). A common aerosol optical model is used for the retrieval over ocean and land, and the model is determined based on the skyradiometer observation data. While fixing the particle shape, real part of complex refraction index and size distributions of large and small particle, the fraction of small particle and complex refraction index (in terms of SSA) are assumed to be variable.The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol angstrom exponent over ocean (AAEO), L3 Map Aerosol angstrom exponent over land (near UV) (AAEL) and L3 Map Aerosol angstrom exponent over land (Polarization) (AAEP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AROT_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AROT_1day_1-12deg_NA.json index e58966efbc..5cc954bef6 100644 --- a/datasets/GCOM-C_SGLI_L3M_AROT_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AROT_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AROT_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol optical thickness over ocean (AOTO), L3 Map Aerosol optical thickness over land (near UV) (AOTL) and L3 Map Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AROT_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AROT_1month_1-12deg_NA.json index 37a6de0a60..6670061e47 100644 --- a/datasets/GCOM-C_SGLI_L3M_AROT_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AROT_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AROT_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol optical thickness over ocean (AOTO), L3 Map Aerosol optical thickness over land (near UV) (AOTL) and L3 Map Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_AROT_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_AROT_8days_1-12deg_NA.json index 6d168e268b..c8a2513587 100644 --- a/datasets/GCOM-C_SGLI_L3M_AROT_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_AROT_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_AROT_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes aerosol optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. This product integrates L3 Map Aerosol optical thickness over ocean (AOTO), L3 Map Aerosol optical thickness over land (near UV) (AOTL) and L3 Map Aerosol optical thickness over land (Polarization) (AOTP) from version 3.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_ASSA_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_ASSA_1day_1-12deg_NA.json index b1c08c67ed..908acfd22c 100644 --- a/datasets/GCOM-C_SGLI_L3M_ASSA_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_ASSA_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_ASSA_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol Single Scattering Albedo over land (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes aerosol Single Scattering Albedo over land. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_ASSA_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_ASSA_8days_1-12deg_NA.json index 64a6148042..cc32c7c39c 100644 --- a/datasets/GCOM-C_SGLI_L3M_ASSA_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_ASSA_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_ASSA_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Aerosol Single Scattering Albedo over land (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes aerosol Single Scattering Albedo over land. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CDOM_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CDOM_1day_1-24deg_NA.json index 0676c7edcf..f425e20154 100644 --- a/datasets/GCOM-C_SGLI_L3M_CDOM_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CDOM_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CDOM_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Colored dissolved organic matter (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes colored dissolved organic matter. The physical quantity unit is m-1. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CDOM_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CDOM_1month_1-24deg_NA.json index 8075273046..a29d0ac11d 100644 --- a/datasets/GCOM-C_SGLI_L3M_CDOM_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CDOM_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CDOM_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Colored dissolved organic matter (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes colored dissolved organic matter. The physical quantity unit is m-1. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CDOM_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CDOM_8days_1-24deg_NA.json index c030b304c2..09f245500b 100644 --- a/datasets/GCOM-C_SGLI_L3M_CDOM_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CDOM_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CDOM_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Colored dissolved organic matter (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes colored dissolved organic matter. The physical quantity unit is m-1. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CERW_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CERW_1day_1-12deg_NA.json index 44d8910ad5..c396edfce6 100644 --- a/datasets/GCOM-C_SGLI_L3M_CERW_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CERW_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CERW_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud effective radius (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CERW_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CERW_1month_1-12deg_NA.json index 7ef083fb5b..542269dcca 100644 --- a/datasets/GCOM-C_SGLI_L3M_CERW_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CERW_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CERW_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud effective radius (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CERW_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CERW_8days_1-12deg_NA.json index ba3962c150..ff622f6c70 100644 --- a/datasets/GCOM-C_SGLI_L3M_CERW_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CERW_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CERW_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud effective radius (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR1_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR1_1day_1-12deg_NA.json index e17097a0a8..5dca9f63e8 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR1_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR1_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR1_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR1) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR1_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR1_1month_1-12deg_NA.json index 4e6964cb51..16543757fb 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR1_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR1_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR1_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR1) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR1_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR1_8days_1-12deg_NA.json index 9a440fb67b..ed824f6290 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR1_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR1_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR1_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR1) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-1 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR2_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR2_1day_1-12deg_NA.json index a9b4d7cf90..7ee36ed607 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR2_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR2_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR2_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR2) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product.This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR2_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR2_1month_1-12deg_NA.json index cfec370cdf..814bd2a544 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR2_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR2_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR2_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR2) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR2_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR2_8days_1-12deg_NA.json index 11245a734f..492698934c 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR2_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR2_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR2_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR2) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-2 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR3_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR3_1day_1-12deg_NA.json index e1b6874ed1..2854301775 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR3_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR3_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR3_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR3) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR3_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR3_1month_1-12deg_NA.json index be6135bd99..542788626f 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR3_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR3_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR3_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR3) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR3_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR3_8days_1-12deg_NA.json index bb97f91343..65988469fa 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR3_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR3_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR3_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR3) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR4_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR4_1day_1-12deg_NA.json index 5ffc24bbc6..1d0c8c50e5 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR4_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR4_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR4_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR4) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR4_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR4_1month_1-12deg_NA.json index 20aae27108..0c41f96afe 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR4_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR4_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR4_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR4) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR4_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR4_8days_1-12deg_NA.json index c42e31d1e2..a858c44b49 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR4_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR4_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR4_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR4) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-4 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus)The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR5_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR5_1day_1-12deg_NA.json index 7975d0ee35..07a1838a75 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR5_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR5_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR5_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR5) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR5_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR5_1month_1-12deg_NA.json index 249c7562e2..c3f4758f5a 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR5_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR5_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR5_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR5) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR5_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR5_8days_1-12deg_NA.json index b7f10ee83f..bb7a7fc378 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR5_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR5_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR5_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR5) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-5 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR6_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR6_1day_1-12deg_NA.json index d2f47dc93d..d40d724b2c 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR6_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR6_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR6_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR6) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR6_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR6_1month_1-12deg_NA.json index 48f84afd58..5487c25e51 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR6_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR6_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR6_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR6) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR6_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR6_8days_1-12deg_NA.json index cd8456c353..0e6b062b8a 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR6_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR6_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR6_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR6) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-6 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR7_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR7_1day_1-12deg_NA.json index b286d91920..4ebf5b6492 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR7_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR7_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR7_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR7) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR7_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR7_1month_1-12deg_NA.json index 5d1512a405..3ea2fbfcb2 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR7_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR7_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR7_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR7) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product.This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR7_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR7_8days_1-12deg_NA.json index b2b4ed6a44..cbe2051008 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR7_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR7_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR7_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR7) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR8_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR8_1day_1-12deg_NA.json index 59a999bc76..9a15f3dab0 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR8_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR8_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR8_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR8) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-8 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR8_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR8_1month_1-12deg_NA.json index b179b410a6..339d575007 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR8_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR8_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR8_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR8) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR8_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR8_8days_1-12deg_NA.json index bd13d1ad63..937837cb8b 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR8_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR8_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR8_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR8) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-8 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR9_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR9_1day_1-12deg_NA.json index 081836c420..0c7361d322 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR9_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR9_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR9_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR9) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR9_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR9_1month_1-12deg_NA.json index 5cbaae6ee4..2d85c85be0 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR9_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR9_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR9_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR9) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFR9_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFR9_8days_1-12deg_NA.json index 2987c1bc0d..7712bf8f33 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFR9_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFR9_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFR9_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFR9) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-9 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRA_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRA_1day_1-12deg_NA.json index 7c7ff58369..ee343558e8 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRA_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRA_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRA_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRA) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRA_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRA_1month_1-12deg_NA.json index d2fdce1283..1f818999ef 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRA_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRA_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRA_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRA) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRA_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRA_8days_1-12deg_NA.json index 5d9ab2fc21..38a4f43112 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRA_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRA_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRA_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRA) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-A (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRH_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRH_1day_1-12deg_NA.json index 83e7289145..8fa0fc3f64 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRH_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRH_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRH_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRH) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRH_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRH_1month_1-12deg_NA.json index 9fbbb957df..b7c68efc13 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRH_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRH_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRH_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRH) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRH_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRH_8days_1-12deg_NA.json index dd93f0a965..31e964a130 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRH_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRH_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRH_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRH) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-H (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRL_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRL_1day_1-12deg_NA.json index 4096ff266a..f6bfa45dd4 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRL_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRL_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRL_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRL) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRL_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRL_1month_1-12deg_NA.json index 44a50e249a..1f12d5ee5d 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRL_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRL_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRL_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRL) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRL_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRL_8days_1-12deg_NA.json index 3b1f8031e2..89f3111a4a 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRL_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRL_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRL_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRL) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-L (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRM_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRM_1day_1-12deg_NA.json index 79dfa77691..54c5f07be6 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRM_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRM_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRM_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRM) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRM_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRM_1month_1-12deg_NA.json index ca85d5b2de..bc69a9632f 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRM_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRM_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRM_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRM) (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CFRM_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CFRM_8days_1-12deg_NA.json index cacbbef56e..bbbaf8ecf3 100644 --- a/datasets/GCOM-C_SGLI_L3M_CFRM_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CFRM_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CFRM_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Classified cloud fraction (CFRM) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-M (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CHLA_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CHLA_1day_1-24deg_NA.json index cc7d496ba1..1988dd0c85 100644 --- a/datasets/GCOM-C_SGLI_L3M_CHLA_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CHLA_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CHLA_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Chlorophyll-a concentration (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product.This dataset includes chlorophyll-a concentration. The physical quantity unit is mg/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CHLA_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CHLA_1month_1-24deg_NA.json index b705837cc0..4984156bf9 100644 --- a/datasets/GCOM-C_SGLI_L3M_CHLA_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CHLA_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CHLA_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Chlorophyll-a concentration (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product.This dataset includes chlorophyll-a concentration. The physical quantity unit is mg/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CHLA_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CHLA_8days_1-24deg_NA.json index 5f009fa13d..9d360969c9 100644 --- a/datasets/GCOM-C_SGLI_L3M_CHLA_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CHLA_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CHLA_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Chlorophyll-a concentration (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product.This dataset includes chlorophyll-a concentration. The physical quantity unit is mg/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTH_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTH_1day_1-12deg_NA.json index e11607201d..efcd7366be 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTH_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTH_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTH_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top height (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product.This dataset includes cloud top height. The physical quantity unit is km. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTH_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTH_1month_1-12deg_NA.json index 14bf4c57f0..9e47ee988b 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTH_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTH_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTH_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top height (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes cloud top height. The physical quantity unit is km. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTH_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTH_8days_1-12deg_NA.json index ca37146851..e1a2338f3a 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTH_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTH_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTH_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top height (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes cloud top height. The physical quantity unit is km. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTT_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTT_1day_1-12deg_NA.json index 8dd3a62ac0..2734bf4834 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTT_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTT_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTT_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top temperature (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTT_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTT_1month_1-12deg_NA.json index af25d71a90..1ae4c9dde4 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTT_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTT_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTT_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top temperature (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_CLTT_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_CLTT_8days_1-12deg_NA.json index 87b3871ce9..740f0975fe 100644 --- a/datasets/GCOM-C_SGLI_L3M_CLTT_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_CLTT_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_CLTT_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Cloud top temperature (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes cloud top temperature. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTI_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTI_1day_1-12deg_NA.json index 1e5d3ce66f..03ea3bdbd8 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTI_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTI_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTI_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Ice cloud optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTI_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTI_1month_1-12deg_NA.json index 9c1dda030d..81df20fbfd 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTI_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTI_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTI_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Ice cloud optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTI_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTI_8days_1-12deg_NA.json index af6460d6c9..591eec0b0a 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTI_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTI_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTI_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Ice cloud optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes ice cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTW_1day_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTW_1day_1-12deg_NA.json index 50283a8322..4aa084f2fa 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTW_1day_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTW_1day_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTW_1day_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud optical thickness (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTW_1month_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTW_1month_1-12deg_NA.json index fd27728ea2..0b8ce72b6c 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTW_1month_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTW_1month_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTW_1month_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud optical thickness (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_COTW_8days_1-12deg_NA.json b/datasets/GCOM-C_SGLI_L3M_COTW_8days_1-12deg_NA.json index 53ec09159c..496739c84a 100644 --- a/datasets/GCOM-C_SGLI_L3M_COTW_8days_1-12deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_COTW_8days_1-12deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_COTW_8days_1-12deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Water cloud optical thickness (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes water cloud optical thickness. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_EVI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_EVI_1day_1-24deg_NA.json index 7d2ded44a5..afa79cbabc 100644 --- a/datasets/GCOM-C_SGLI_L3M_EVI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_EVI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_EVI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Enhanced Vegetation Index (EVI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_EVI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_EVI_1month_1-24deg_NA.json index e5d9b63603..cf155fa1ae 100644 --- a/datasets/GCOM-C_SGLI_L3M_EVI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_EVI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_EVI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Enhanced Vegetation Index (EVI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_EVI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_EVI_8days_1-24deg_NA.json index 10724c168e..c5dc90fc0a 100644 --- a/datasets/GCOM-C_SGLI_L3M_EVI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_EVI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_EVI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Enhanced Vegetation Index (EVI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes EVI: Enhanced Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_FPAR_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_FPAR_1day_1-24deg_NA.json index 6de375e256..5954591a71 100644 --- a/datasets/GCOM-C_SGLI_L3M_FPAR_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_FPAR_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_FPAR_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Fraction of absorbed PAR (FAPAR) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_FPAR_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_FPAR_1month_1-24deg_NA.json index 2d86892aa7..a6c56856e6 100644 --- a/datasets/GCOM-C_SGLI_L3M_FPAR_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_FPAR_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_FPAR_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Fraction of absorbed PAR (FAPAR) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_FPAR_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_FPAR_8days_1-24deg_NA.json index 3f6af3597d..2678f47b28 100644 --- a/datasets/GCOM-C_SGLI_L3M_FPAR_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_FPAR_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_FPAR_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Fraction of absorbed PAR (FAPAR) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes FAPAR: Fraction of Absorbed Photosynthetically Active Radiation and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L380_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L380_1day_1-24deg_NA.json index 7e3f0c38c4..d0fa1a3e62 100644 --- a/datasets/GCOM-C_SGLI_L3M_L380_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L380_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L380_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L380) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_380: the upwelling radiance just above the sea surface at 380 nm and QA_Flag.The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L380_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L380_1month_1-24deg_NA.json index b6a8bc49d0..9056b0540a 100644 --- a/datasets/GCOM-C_SGLI_L3M_L380_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L380_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L380_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L380) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_380: the upwelling radiance just above the sea surface at 380 nm and QA_Flag. The stored statistics values are average (AVE) and quality flag (QA_flag). The physical quantity unit is W/m^2/str/um. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L380_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L380_8days_1-24deg_NA.json index c8a95b5534..35ed491e41 100644 --- a/datasets/GCOM-C_SGLI_L3M_L380_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L380_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L380_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L380) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product.This dataset includes NWLR_380: the upwelling radiance just above the sea surface at 380 nm and QA_Flag. The stored statistics values are average (AVE) and quality flag (QA_flag). The physical quantity unit is W/m^2/str/um. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L412_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L412_1day_1-24deg_NA.json index e8df883074..6f16dde1f4 100644 --- a/datasets/GCOM-C_SGLI_L3M_L412_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L412_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L412_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L412) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_412: the upwelling radiance just above the sea surface at 412 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L412_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L412_1month_1-24deg_NA.json index db2d550962..08ec7f25db 100644 --- a/datasets/GCOM-C_SGLI_L3M_L412_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L412_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L412_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L412) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_412: the upwelling radiance just above the sea surface at 412 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L412_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L412_8days_1-24deg_NA.json index 106beb2c57..6a71219424 100644 --- a/datasets/GCOM-C_SGLI_L3M_L412_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L412_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L412_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L412) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_412: the upwelling radiance just above the sea surface at 412 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L443_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L443_1day_1-24deg_NA.json index 2456ee35a7..1ef3d81525 100644 --- a/datasets/GCOM-C_SGLI_L3M_L443_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L443_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L443_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L443) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_443: the upwelling radiance just above the sea surface at 443 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L443_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L443_1month_1-24deg_NA.json index eecb32acfd..d2148a4355 100644 --- a/datasets/GCOM-C_SGLI_L3M_L443_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L443_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L443_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L443) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_443: the upwelling radiance just above the sea surface at 443 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L443_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L443_8days_1-24deg_NA.json index f49565e39b..636ac98a49 100644 --- a/datasets/GCOM-C_SGLI_L3M_L443_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L443_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L443_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L443) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_443: the upwelling radiance just above the sea surface at 443 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L490_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L490_1day_1-24deg_NA.json index fd03fc04b8..84a44b3605 100644 --- a/datasets/GCOM-C_SGLI_L3M_L490_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L490_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L490_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L490) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_490: the upwelling radiance just above the sea surface at 490 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L490_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L490_1month_1-24deg_NA.json index b3dd6bd900..b49a21202f 100644 --- a/datasets/GCOM-C_SGLI_L3M_L490_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L490_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L490_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L490) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_490: the upwelling radiance just above the sea surface at 490 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L490_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L490_8days_1-24deg_NA.json index 945a7a4c0e..93bec01c6d 100644 --- a/datasets/GCOM-C_SGLI_L3M_L490_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L490_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L490_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L490) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_490: the upwelling radiance just above the sea surface at 490 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L530_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L530_1day_1-24deg_NA.json index dd2aa09b92..a1fa7f9552 100644 --- a/datasets/GCOM-C_SGLI_L3M_L530_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L530_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L530_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L530) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_530: the upwelling radiance just above the sea surface at 530 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L530_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L530_1month_1-24deg_NA.json index eb860f4bce..1b97c1ef72 100644 --- a/datasets/GCOM-C_SGLI_L3M_L530_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L530_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L530_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L530) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_530: the upwelling radiance just above the sea surface at 530 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L530_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L530_8days_1-24deg_NA.json index 40046e2d46..200fbe9efe 100644 --- a/datasets/GCOM-C_SGLI_L3M_L530_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L530_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L530_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L530) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_530: the upwelling radiance just above the sea surface at 530 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L565_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L565_1day_1-24deg_NA.json index 58409d6900..a5d6be1dca 100644 --- a/datasets/GCOM-C_SGLI_L3M_L565_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L565_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L565_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L565) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L565_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L565_1month_1-24deg_NA.json index 87f19c10a2..4f8b13f49b 100644 --- a/datasets/GCOM-C_SGLI_L3M_L565_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L565_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L565_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L565) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L565_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L565_8days_1-24deg_NA.json index e1c2954943..43e5f744de 100644 --- a/datasets/GCOM-C_SGLI_L3M_L565_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L565_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L565_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L565) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L670_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L670_1day_1-24deg_NA.json index c72be52ba2..2a2e0987fc 100644 --- a/datasets/GCOM-C_SGLI_L3M_L670_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L670_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L670_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L565) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NWLR_565: the upwelling radiance just above the sea surface at 565 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L670_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L670_1month_1-24deg_NA.json index 718d85b71c..6fe4c89f82 100644 --- a/datasets/GCOM-C_SGLI_L3M_L670_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L670_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L670_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L670) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NWLR_670: the upwelling radiance just above the sea surface at 670 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_L670_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_L670_8days_1-24deg_NA.json index 14e1d1853d..d9736f30c6 100644 --- a/datasets/GCOM-C_SGLI_L3M_L670_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_L670_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_L670_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized water leaving radiance (NWLR L670) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NWLR_670: the upwelling radiance just above the sea surface at 670 nm and QA_Flag. The physical quantity unit is W/m^2/str/um. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LAI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LAI_1day_1-24deg_NA.json index 33a5c755f9..a2b6cc8f01 100644 --- a/datasets/GCOM-C_SGLI_L3M_LAI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LAI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LAI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Leaf Area Index (LAI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes LAI : Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LAI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LAI_1month_1-24deg_NA.json index b8084b15be..c09c1e9c6b 100644 --- a/datasets/GCOM-C_SGLI_L3M_LAI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LAI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LAI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Leaf Area Index (LAI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes LAI: Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LAI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LAI_8days_1-24deg_NA.json index d9f51f3e38..114d873c86 100644 --- a/datasets/GCOM-C_SGLI_L3M_LAI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LAI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LAI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Leaf Area Index (LAI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes LAI: Leaf Area Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LST_1day_1-24deg_NA.json index 82e183f7e8..257f8f8d35 100644 --- a/datasets/GCOM-C_SGLI_L3M_LST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Land surface temperature (LST) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LST_1month_1-24deg_NA.json index 62a324ca8c..ba963b6cf6 100644 --- a/datasets/GCOM-C_SGLI_L3M_LST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Land surface temperature (LST) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_LST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_LST_8days_1-24deg_NA.json index b11055ae3f..afec34cd0e 100644 --- a/datasets/GCOM-C_SGLI_L3M_LST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_LST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_LST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Land surface temperature (LST) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_NDVI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_NDVI_1day_1-24deg_NA.json index 5ff83a71b9..6339c0ffb1 100644 --- a/datasets/GCOM-C_SGLI_L3M_NDVI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_NDVI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_NDVI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized Difference Vegetation Index (NDVI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_NDVI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_NDVI_1month_1-24deg_NA.json index 4827d30524..23fd9fd14f 100644 --- a/datasets/GCOM-C_SGLI_L3M_NDVI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_NDVI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_NDVI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized Difference Vegetation Index (NDVI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_NDVI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_NDVI_8days_1-24deg_NA.json index 891f1dea5e..f6b66ec0d1 100644 --- a/datasets/GCOM-C_SGLI_L3M_NDVI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_NDVI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_NDVI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Normalized Difference Vegetation Index (NDVI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_PAR_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_PAR_1day_1-24deg_NA.json index 08fe7056fa..518a47a69c 100644 --- a/datasets/GCOM-C_SGLI_L3M_PAR_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_PAR_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_PAR_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Photosynthetically available radiation (PAR) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_PAR_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_PAR_1month_1-24deg_NA.json index d7e3e1fa00..04de01d79c 100644 --- a/datasets/GCOM-C_SGLI_L3M_PAR_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_PAR_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_PAR_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Photosynthetically available radiation (PAR) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_PAR_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_PAR_8days_1-24deg_NA.json index 79266ad9cd..12e5c49523 100644 --- a/datasets/GCOM-C_SGLI_L3M_PAR_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_PAR_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_PAR_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Photosynthetically available radiation (PAR) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAI_1day_1-24deg_NA.json index 28d44a7ac5..5811dc1919 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes relative zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAI_1month_1-24deg_NA.json index 70b3685fc9..c52073e3ff 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes relative zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAI_8days_1-24deg_NA.json index 7101dcd6b4..2a4d83896d 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes relative zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAP_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAP_1day_1-24deg_NA.json index 398edb0772..3bf73870ed 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAP_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAP_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAP_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAP) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes relative zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAP_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAP_1month_1-24deg_NA.json index 01961a9004..decb8bb8fc 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAP_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAP_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAP_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAP) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes relative zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAP_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAP_8days_1-24deg_NA.json index 805ad99002..a773986608 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAP_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAP_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAP_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAP) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes relative zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAV_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAV_1day_1-24deg_NA.json index 1ecefc0a2d..447123eef2 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAV_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAV_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAV_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAV) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes relative zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAV_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAV_1month_1-24deg_NA.json index 2a89d79b2a..8eb95b2c04 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAV_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAV_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAV_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAV) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes relative zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RLAV_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RLAV_8days_1-24deg_NA.json index 88853715c6..39b936d534 100644 --- a/datasets/GCOM-C_SGLI_L3M_RLAV_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RLAV_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RLAV_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RLAV) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes relative zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN08_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN08_1day_1-24deg_NA.json index fe93776388..406f3b2162 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN08_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN08_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN08_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN08) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN08_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN08_1month_1-24deg_NA.json index adada943f0..81d0b69633 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN08_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN08_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN08_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN08) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN08_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN08_8days_1-24deg_NA.json index 51fa30ce3d..5521048f5d 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN08_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN08_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN08_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN08) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 8 co-registered for VNR-PL (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN11_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN11_1day_1-24deg_NA.json index 7b54952467..a73ba0768e 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN11_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN11_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN11_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN11) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN11_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN11_1month_1-24deg_NA.json index d2f9ddf23f..1fc2b6f34f 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN11_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN11_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN11_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN11) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RN11_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RN11_8days_1-24deg_NA.json index 071d23f78e..ca9acd41c3 100644 --- a/datasets/GCOM-C_SGLI_L3M_RN11_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RN11_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RN11_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN11) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP01_1day_1-24deg_NA.json index 296cab9c96..fa1ccd8f45 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP01_1month_1-24deg_NA.json index d96db9b5f3..f5c921f1c5 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP01_8days_1-24deg_NA.json index fca45a437a..7a94e85191 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 01 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP02_1day_1-24deg_NA.json index 28b456f65f..9ce8fa9c61 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP02_1month_1-24deg_NA.json index 36e4f90222..34c0b3622e 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RP02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RP02_8days_1-24deg_NA.json index 857c4d30df..aa79337dcc 100644 --- a/datasets/GCOM-C_SGLI_L3M_RP02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RP02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RP02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RP02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of VNR-PL Band 02 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS01_1day_1-24deg_NA.json index ee0261900f..4aa9b09b30 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS01_1month_1-24deg_NA.json index 65540b0eee..df8d04adae 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS01_8days_1-24deg_NA.json index dcc8fbc173..af4952d8c3 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of SW01 (Center Wavelength is 1050 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS02_1day_1-24deg_NA.json index cd3a235cb4..516ff50070 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of SW02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS02_1month_1-24deg_NA.json index 1195b5ebd0..aae11bd57f 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of SW02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS02_8days_1-24deg_NA.json index b6a581b168..0be6bc9200 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RS02 (Center Wavelength is 1380 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS03_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS03_1day_1-24deg_NA.json index 9d01700b4a..789d9787b4 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS03_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS03_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS03_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS03) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of SW03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS03_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS03_1month_1-24deg_NA.json index d16b9f9c36..d24914bfd2 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS03_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS03_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS03_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS03) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of SW03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS03_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS03_8days_1-24deg_NA.json index 5ad57bea0f..06fad40b33 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS03_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS03_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS03_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS03) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RS03 (Center Wavelength is 1630 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS04_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS04_1day_1-24deg_NA.json index eed2d75ae5..4ab0d7657d 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS04_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS04_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS04_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS04) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of SW04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS04_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS04_1month_1-24deg_NA.json index 0872bd827a..df4397d720 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS04_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS04_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS04_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS04) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of SW04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RS04_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RS04_8days_1-24deg_NA.json index a058d44856..2035ca7f61 100644 --- a/datasets/GCOM-C_SGLI_L3M_RS04_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RS04_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RS04_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RS04) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RS04 (Center Wavelength is 2210 nm) and QA_Flag. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT01_1day_1-24deg_NA.json index 79ae642560..6d8961f1ea 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT01_1month_1-24deg_NA.json index 4d53e86d95..9ff08f34c0 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT01_8days_1-24deg_NA.json index e1bfccade4..3fcd85d5c2 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of T1 (Center Wavelength is 10.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT02_1day_1-24deg_NA.json index 1d0c76ed5a..c2d8b564a6 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of T2 (Center Wavelength is 12.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT02_1month_1-24deg_NA.json index 9875ee4930..a92b4038e6 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of T2 (Center Wavelength is 12.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RT02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RT02_8days_1-24deg_NA.json index 6c7d1da874..b8aa7fdc40 100644 --- a/datasets/GCOM-C_SGLI_L3M_RT02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RT02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RT02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RT02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of T2 (Center Wavelength is 12.8 micro m) and QA_Flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV01_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV01_1day_1-24deg_NA.json index f0d9873dd0..5cffe79bd4 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV01_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV01_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV01_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV01) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV01_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV01_1month_1-24deg_NA.json index 410b149164..186daf3430 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV01_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV01_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV01_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV01) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV01_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV01_8days_1-24deg_NA.json index 9be72a4453..770c2ba698 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV01_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV01_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV01_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV01) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV01 (Center Wavelength is 380 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV02_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV02_1day_1-24deg_NA.json index 6a013fe5e8..cff384b1c7 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV02_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV02_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV02_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV02) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV02_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV02_1month_1-24deg_NA.json index f101a7393e..f822e599ec 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV02_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV02_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV02_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV02) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV02_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV02_8days_1-24deg_NA.json index f06e8393c1..827b214a7e 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV02_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV02_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV02_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV02) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV02 (Center Wavelength is 412 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV03_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV03_1day_1-24deg_NA.json index 28bf4e4425..ac6b43b25b 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV03_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV03_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV03_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV03) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV03_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV03_1month_1-24deg_NA.json index fe1c7236a6..3f81048958 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV03_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV03_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV03_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV03) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV03_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV03_8days_1-24deg_NA.json index d941d12da3..eaf5cbc8cc 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV03_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV03_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV03_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV03) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV03 (Center Wavelength is 443 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV04_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV04_1day_1-24deg_NA.json index 4b0ffc7a92..55f16d22d1 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV04_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV04_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV04_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV04) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV04_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV04_1month_1-24deg_NA.json index 10437dd840..61ea1a8f5c 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV04_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV04_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV04_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV04) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satelite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV04_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV04_8days_1-24deg_NA.json index 3075cd7007..f75190a705 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV04_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV04_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV04_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV04) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV04 (Center Wavelength is 490 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV05_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV05_1day_1-24deg_NA.json index d59f40ee43..27139bbbf0 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV05_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV05_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV05_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV05) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV05_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV05_1month_1-24deg_NA.json index 684122564c..9f75d1ae3a 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV05_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV05_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV05_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV05) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV05_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV05_8days_1-24deg_NA.json index c8a2e8fd6d..01706c03ba 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV05_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV05_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV05_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV05) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV05 (Center Wavelength is 530 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV06_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV06_1day_1-24deg_NA.json index e0c74b2665..8f5ca94324 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV06_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV06_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV06_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV06) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV06_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV06_1month_1-24deg_NA.json index f19c74063d..ac427dc208 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV06_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV06_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV06_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV06) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV06_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV06_8days_1-24deg_NA.json index ba269737e6..e5ccc703de 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV06_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV06_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV06_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV06) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV06 (Center Wavelength is 565 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV07_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV07_1day_1-24deg_NA.json index b50296f317..03e4474bae 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV07_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV07_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV07_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV07) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV07_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV07_1month_1-24deg_NA.json index e0ccc461f0..71a2e9b3fb 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV07_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV07_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV07_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV07) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV07_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV07_8days_1-24deg_NA.json index a934523dc1..e207e17fc2 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV07_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV07_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV07_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV07) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV07 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV08_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV08_1day_1-24deg_NA.json index 1bb8d56e55..1fcc9d9d67 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV08_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV08_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV08_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV08) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV08_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV08_1month_1-24deg_NA.json index 3e89265ed4..0e3ed5670e 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV08_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV08_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV08_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV08) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV08_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV08_8days_1-24deg_NA.json index f13dbf227c..94a0441d48 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV08_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV08_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV08_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV08) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV08 (Center Wavelength is 673.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV09_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV09_1day_1-24deg_NA.json index 962eb76d44..420d7697e1 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV09_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV09_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV09_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV09) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV09_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV09_1month_1-24deg_NA.json index 53a72cf985..620eb45100 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV09_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV09_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV09_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV09) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV09_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV09_8days_1-24deg_NA.json index 746a17aebf..c9d77c7027 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV09_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV09_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV09_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV09) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV09 (Center Wavelength is 763 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV10_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV10_1day_1-24deg_NA.json index 05c27c689a..fc5c0e3635 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV10_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV10_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV10_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV10) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV10_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV10_1month_1-24deg_NA.json index 22c09b497a..c12fd26a16 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV10_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV10_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV10_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV10) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV10_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV10_8days_1-24deg_NA.json index d7853bb580..d648711cfc 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV10_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV10_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV10_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV10) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV10 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV11_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV11_1day_1-24deg_NA.json index 0b728b8abd..1bd150e8a2 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV11_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV11_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV11_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV11) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV11_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV11_1month_1-24deg_NA.json index 586ab5a850..a94e61ae89 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV11_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV11_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV11_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV11) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_RV11_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_RV11_8days_1-24deg_NA.json index f7642e6619..1e973aad3f 100644 --- a/datasets/GCOM-C_SGLI_L3M_RV11_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_RV11_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_RV11_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RV11) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes Surface reflectance of RV11 (Center Wavelength is 868.5 nm) and QA_Flag. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SDI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SDI_1day_1-24deg_NA.json index 9ee8009046..6e689c54d8 100644 --- a/datasets/GCOM-C_SGLI_L3M_SDI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SDI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SDI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Shadow index (SI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SDI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SDI_1month_1-24deg_NA.json index bf08041aba..57a10551f0 100644 --- a/datasets/GCOM-C_SGLI_L3M_SDI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SDI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SDI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Shadow index (SI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SDI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SDI_8days_1-24deg_NA.json index 850fb7c1a2..25361ee35a 100644 --- a/datasets/GCOM-C_SGLI_L3M_SDI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SDI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SDI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Shadow index (SI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes SI: Shadow Index and QA_flag. Physical quantity unit is dimensionless. The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SGSL_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SGSL_1day_1-24deg_NA.json index e7a0dfc793..17cc880aa3 100644 --- a/datasets/GCOM-C_SGLI_L3M_SGSL_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SGSL_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SGSL_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow grain size of shallow layer (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SGSL_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SGSL_1month_1-24deg_NA.json index 48072bde04..24caf015ff 100644 --- a/datasets/GCOM-C_SGLI_L3M_SGSL_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SGSL_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SGSL_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow grain size of shallow layer (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SGSL_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SGSL_8days_1-24deg_NA.json index 9681a09a12..ffb6e48871 100644 --- a/datasets/GCOM-C_SGLI_L3M_SGSL_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SGSL_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SGSL_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow grain size of shallow layer (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes snow grain size of shallow layer. Physical quantity unit is micrometer. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SICE_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SICE_1day_1-24deg_NA.json index 74f96608e5..1127a5bf3f 100644 --- a/datasets/GCOM-C_SGLI_L3M_SICE_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SICE_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SICE_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and Ice Cover Extent (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SICE_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SICE_1month_1-24deg_NA.json index 42d59c934f..a102486d1d 100644 --- a/datasets/GCOM-C_SGLI_L3M_SICE_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SICE_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SICE_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and Ice Cover Extent (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SICE_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SICE_8days_1-24deg_NA.json index 0e30cd9b65..dbb3361d4f 100644 --- a/datasets/GCOM-C_SGLI_L3M_SICE_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SICE_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SICE_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and Ice Cover Extent (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes snow and ice cover extent covering global. It distinguishes snow, ice, cloud and several types of ground surface focusing on the cryospheric region based on the difference in reflectance characteristics by ground surface varieties. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SIST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SIST_1day_1-24deg_NA.json index 553ecfe97e..927f2b57e2 100644 --- a/datasets/GCOM-C_SGLI_L3M_SIST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SIST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SIST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and ice surface temperature (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SIST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SIST_1month_1-24deg_NA.json index 1c09523881..ff0239ca73 100644 --- a/datasets/GCOM-C_SGLI_L3M_SIST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SIST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SIST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and ice surface temperature (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SIST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SIST_8days_1-24deg_NA.json index 16a3845c45..cbe6ebf793 100644 --- a/datasets/GCOM-C_SGLI_L3M_SIST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SIST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SIST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Snow and ice surface temperature (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes snow and ice surface temperature based on a model snow. Physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZI_1day_1-24deg_NA.json index 678e771994..67159580b7 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes solar zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZI_1month_1-24deg_NA.json index 5404370886..122cd63ce1 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes solar zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZI_8days_1-24deg_NA.json index 7b53effc01..9b5761fcea 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes solar zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZP_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZP_1day_1-24deg_NA.json index 2a16b53271..60578c545c 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZP_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZP_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZP_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZP) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes solar zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZP_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZP_1month_1-24deg_NA.json index 3b7532d3f9..92094b0974 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZP_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZP_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZP_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZP) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes solar zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZP_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZP_8days_1-24deg_NA.json index 7e691d5114..da475090f5 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZP_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZP_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZP_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZP) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes solar zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZV_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZV_1day_1-24deg_NA.json index 4e1e36d588..14d6677a73 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZV_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZV_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZV_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZV) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes solar zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZV_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZV_1month_1-24deg_NA.json index e48668f34a..a893419a3b 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZV_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZV_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZV_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZV) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes solar zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SLZV_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SLZV_8days_1-24deg_NA.json index 1645e403b9..826c32d228 100644 --- a/datasets/GCOM-C_SGLI_L3M_SLZV_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SLZV_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SLZV_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZV) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes solar zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZI_1day_1-24deg_NA.json index 4abc06d7f9..042638e581 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes sensor zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZI_1month_1-24deg_NA.json index ace5c7563e..bab15a099c 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes sensor zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZI_8days_1-24deg_NA.json index a0f3cd465f..86575a5d09 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes sensor zenith angle of IRS sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZP_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZP_1day_1-24deg_NA.json index d79dee8154..dfed01f9cf 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZP_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZP_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZP_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZP) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes sensor zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZP_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZP_1month_1-24deg_NA.json index 2c1f8e6732..8c63b29cb3 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZP_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZP_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZP_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZP) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes sensor zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZP_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZP_8days_1-24deg_NA.json index 6bae09d56b..c6221a6a35 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZP_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZP_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZP_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZP) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes sensor zenith angle of VNR-PL sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZV_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZV_1day_1-24deg_NA.json index 4a8be2e814..19d958bea1 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZV_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZV_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZV_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZV) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes sensor zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZV_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZV_1month_1-24deg_NA.json index 3f8729a8b0..954cd0f4a4 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZV_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZV_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZV_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SLZV) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes sensor zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SNZV_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SNZV_8days_1-24deg_NA.json index 4bd5e8c883..9af38ea51d 100644 --- a/datasets/GCOM-C_SGLI_L3M_SNZV_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SNZV_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SNZV_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (SNZV) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes sensor zenith angle of VNR-NP sensor. The data unit is degree. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SST_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SST_1day_1-24deg_NA.json index 95bd51353f..27b970a839 100644 --- a/datasets/GCOM-C_SGLI_L3M_SST_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SST_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SST_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Sea surface temperature (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes sea surface temperature. The stored statistics values are average (AVE) and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SST_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SST_1month_1-24deg_NA.json index 38b6076eb8..a55e8cb5c7 100644 --- a/datasets/GCOM-C_SGLI_L3M_SST_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SST_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SST_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Sea surface temperature (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes sea surface temperature. The stored statistics values are average (AVE) and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQA, EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_SST_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_SST_8days_1-24deg_NA.json index b7ac110021..cd3ddb7e92 100644 --- a/datasets/GCOM-C_SGLI_L3M_SST_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_SST_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_SST_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Sea surface temperature (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes sea surface temperature. The stored statistics values are average (AVE) and quality flag (QA_flag). Physical quantity unit is degree. The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T670_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T670_1day_1-24deg_NA.json index 09a5a01911..38fa5a02cc 100644 --- a/datasets/GCOM-C_SGLI_L3M_T670_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T670_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T670_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T670) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T670_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T670_1month_1-24deg_NA.json index 9b24fdef08..49cf2b8e79 100644 --- a/datasets/GCOM-C_SGLI_L3M_T670_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T670_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T670_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T670) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T670_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T670_8days_1-24deg_NA.json index 91dccc1121..552d548de2 100644 --- a/datasets/GCOM-C_SGLI_L3M_T670_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T670_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T670_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T670) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes TAUA_670: Aerosol Optical Thickness (TauA) at 673.5. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T865_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T865_1day_1-24deg_NA.json index 80edbe9e54..e35d6d91b9 100644 --- a/datasets/GCOM-C_SGLI_L3M_T865_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T865_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T865_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T865) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T865_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T865_1month_1-24deg_NA.json index faf1f891bc..426719d05a 100644 --- a/datasets/GCOM-C_SGLI_L3M_T865_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T865_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T865_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T865) (1-Month,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_T865_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_T865_8days_1-24deg_NA.json index aa8254afcb..2c72f7f5a2 100644 --- a/datasets/GCOM-C_SGLI_L3M_T865_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_T865_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_T865_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Atmospheric Correction Parameter (ACP T865) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865.0. The physical quantity unit is nm. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_TSM_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_TSM_1day_1-24deg_NA.json index b179e643d9..53f4cbc342 100644 --- a/datasets/GCOM-C_SGLI_L3M_TSM_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_TSM_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_TSM_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Suspended solid concentration (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes suspended concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_TSM_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_TSM_1month_1-24deg_NA.json index 01a4b4cef7..74070492b5 100644 --- a/datasets/GCOM-C_SGLI_L3M_TSM_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_TSM_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_TSM_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Suspended solid concentration (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes suspended concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_TSM_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_TSM_8days_1-24deg_NA.json index 54e76ad9dc..3230b50b11 100644 --- a/datasets/GCOM-C_SGLI_L3M_TSM_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_TSM_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_TSM_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Suspended solid concentration (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes suspended concentration. The physical quantity unit is g/m^3. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_VRI_1day_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_VRI_1day_1-24deg_NA.json index 38f65ae918..0c6739dd5a 100644 --- a/datasets/GCOM-C_SGLI_L3M_VRI_1day_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_VRI_1day_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_VRI_1day_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Vegetation Roughness Index (VRI) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes VRI: Vegetation Roughness Index. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_VRI_1month_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_VRI_1month_1-24deg_NA.json index 1843a1b5f3..0af7a9897c 100644 --- a/datasets/GCOM-C_SGLI_L3M_VRI_1month_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_VRI_1month_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_VRI_1month_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Vegetation Roughness Index (VRI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes VRI: Vegetation Roughness Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag).The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-C_SGLI_L3M_VRI_8days_1-24deg_NA.json b/datasets/GCOM-C_SGLI_L3M_VRI_8days_1-24deg_NA.json index ee987f3a45..00f9e97613 100644 --- a/datasets/GCOM-C_SGLI_L3M_VRI_8days_1-24deg_NA.json +++ b/datasets/GCOM-C_SGLI_L3M_VRI_8days_1-24deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-C_SGLI_L3M_VRI_8days_1-24deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-C/SGLI L3 Map Vegetation Roughness Index (VRI) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes VRI: Vegetation Roughness Index. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L1B_TB_NA.json b/datasets/GCOM-W_AMSR2_L1B_TB_NA.json index 9834f0de58..8f8d27d515 100644 --- a/datasets/GCOM-W_AMSR2_L1B_TB_NA.json +++ b/datasets/GCOM-W_AMSR2_L1B_TB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L1B_TB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L1B Brightness Temperature dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 1B product uses Level 1A product as the input and converting digital output values from the sensor to brightness temperatures. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power. This product is the Level 1B brightness temperatures which converted from digital output values from the sensor. The physical quantity unit is Kelvin. The provided format is HDF5. The physical quantity unit is Kelvin. The sampling resolution is 5-50km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is Scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L1R_TB_NA.json b/datasets/GCOM-W_AMSR2_L1R_TB_NA.json index 2e21eb9272..b0cdf7c359 100644 --- a/datasets/GCOM-W_AMSR2_L1R_TB_NA.json +++ b/datasets/GCOM-W_AMSR2_L1R_TB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L1R_TB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L1R Brightness Temperature dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. This product is The Level 1R brightness data which resampled the Level 1B brightness temperature data in proportion to the low-frequency resolution. Resampling takes place by overlaying the original brightness temperature data with weighted parameters (resampling coefficients) calculated using the Backus-Gilbert Method. The center latitude and longitude of data is adjusted to the 89 GHz A channel receiver data. The provided format is HDF5. The physical quantity unit is Kelvin. The Sampling resolution are 5-50km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_CLW_NA.json b/datasets/GCOM-W_AMSR2_L2_CLW_NA.json index 2e98d7f4f9..e130979a23 100644 --- a/datasets/GCOM-W_AMSR2_L2_CLW_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_CLW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_CLW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 Cloud Liquid Water dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Cloud liquid water (CLW), amount of vertically accumulated cloud droplet in the atmosphere, defined as amount of water per unit area. Coverage of the product is over the ocean only and Vertically integrated (columnar) cloud liquid water. Sea ice and precipitating areas are expected. The physical quantity unit is [kg/m2]. Cloud liquid water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and total precipitable water. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 15 km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_PRC_NA.json b/datasets/GCOM-W_AMSR2_L2_PRC_NA.json index 4639d90103..cff3428cf8 100644 --- a/datasets/GCOM-W_AMSR2_L2_PRC_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_PRC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_PRC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Precipitation dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Precipitation (PRC). Although \"precipitation\" is usually defined as amount of water, which reaches the surface of the earth from atmosphere as rain and/or snow, microwave imager's precipitation products provide amount of rain (surface rainfall). Coverage of the product is global, and unit is [mm/h]. Accuracy of rainfall estimate over land, however tends to be lower than that over the ocean. Method of rainfall estimate used in AMSR2 precipitation product is also applied to \"Global Rainfall Watch\" system, which distributes global rainfall map in near-real-time. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 15 km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_SIC_NA.json b/datasets/GCOM-W_AMSR2_L2_SIC_NA.json index 84521f5559..cb1991084d 100644 --- a/datasets/GCOM-W_AMSR2_L2_SIC_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_SIC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_SIC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Sea Ice Concentration dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Sea Ice Concentration (SIC), percentage of sea ice coverage within target ocean area. Coverage of the product is over the ocean around Arctic and Antarctic Sea, and unit is [%]. There is no sea ice within pixel area when sea ice concentration is 0%, and all pixel area is covered by sea ice when it shows 100%. We can learn distribution of sea ice immediately using this product, and it will become more and more important because of recent decrease of Arctic sea ice cover. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 15 km. The current version of the product is Version 3. The Version 2 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_SMC_NA.json b/datasets/GCOM-W_AMSR2_L2_SMC_NA.json index 250e554781..8119a0a58b 100644 --- a/datasets/GCOM-W_AMSR2_L2_SMC_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_SMC_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_SMC_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Soil Moisture Content dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Soil Moisture Content (SMC), amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 50 km. The current version of the product is Version 3. The Version 2 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_SND_NA.json b/datasets/GCOM-W_AMSR2_L2_SND_NA.json index d212f899d6..6121e93914 100644 --- a/datasets/GCOM-W_AMSR2_L2_SND_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_SND_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_SND_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Snow Depth dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Snow Depth (SND), depth of snow cover over land surface. Coverage of the product is over land only, and unit is [cm]. We do not provide snow cover over the sea ice surface. Snow depth parameter is closely related to climate variation, and this product enables us to figure out changes in distribution of global snow cover. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 30 km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_SST_NA.json b/datasets/GCOM-W_AMSR2_L2_SST_NA.json index 0bd6d56aec..619b572618 100644 --- a/datasets/GCOM-W_AMSR2_L2_SST_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_SST_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_SST_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Sea Surface Temperature dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Sea Surface Temperature (SST), temperature of water at ocean surface. Coverage of the product is over the ocean only, and unit is [degree]. Its horizontal resolution is about 20-30 km and coarser than that of optical instruments. Microwave radiometer, however, can observe ocean surface through clouds, and monitor continuous change of sea surface temperature over the ocean where few clear region can be found in specific areas or seasons. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 50 km. The current version of the product is Version 4. The Version 3 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_SSW_NA.json b/datasets/GCOM-W_AMSR2_L2_SSW_NA.json index 7966e91af8..a1121803c0 100644 --- a/datasets/GCOM-W_AMSR2_L2_SSW_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_SSW_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_SSW_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Sea Surface Wind Speed dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Sea Surface Wind Speed (SSW). Coverage of the product is over the ocean only, and unit is [m/s]. Microwave radiometer can observe wind speed under the clouds, but it is difficult to estimate where there is rainfall and will be missing value. Wind speed around tropical cyclones, however, is important parameter in weather forecast, and we provide research product, which includes wind speed in rainy area over the ocean, as all-weather wind speed research product. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 15 km. The current version of the product is Version 4. The Version 3 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L2_WV_NA.json b/datasets/GCOM-W_AMSR2_L2_WV_NA.json index cdc493ec85..8555663eda 100644 --- a/datasets/GCOM-W_AMSR2_L2_WV_NA.json +++ b/datasets/GCOM-W_AMSR2_L2_WV_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L2_WV_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L2 Integrated Water Vapor dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. Level 2 products calculates various geophysical parameters related to water using the Level 1B or 1R products as inputs. This dataset includes Integrated Water Vapor (WV), amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. The Pixel_Data_Quality is quality flag stored for each observation point. The provided format is HDF5. The Sampling resolution is 15 km. The current version of the product is Version 2. The Version 1 is also available. The generation unit is scene (defined as a half orbit).", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.1deg_NA.json index a6c8d3ae77..d19feaec10 100644 --- a/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_CLW_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Cloud Liquid Water (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Cloud liquid water (CLW), amount of vertically accumulated cloud droplet in the atmosphere, defined as amount of water per unit area. Coverage of the product is over the ocean only and Vertically integrated (columnar) cloud liquid water. Sea ice and precipitating areas are expected. The physical quantity unit is [kg/m2]. Cloud liquid water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and total precipitable water. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.25deg_NA.json index 854cc630f2..6d4ee96e49 100644 --- a/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_CLW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_CLW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Cloud Liquid Water (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Cloud liquid water (CLW), amount of vertically accumulated cloud droplet in the atmosphere, defined as amount of water per unit area. Coverage of the product is over the ocean only and Vertically integrated (columnar) cloud liquid water. Sea ice and precipitating areas are expected. The physical quantity unit is [kg/m2]. Cloud liquid water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and total precipitable water. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.1deg_NA.json index 9d71373dad..16aca7c434 100644 --- a/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_CLW_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Cloud Liquid Water (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Cloud liquid water (CLW), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). CLW is amount of vertically accumulated cloud droplet in the atmosphere, defined as amount of water per unit area. Coverage of the product is over the ocean only and Vertically integrated (columnar) cloud liquid water. Sea ice and precipitating areas are expected. The physical quantity unit is [kg/m2]. Cloud liquid water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and total precipitable water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.25deg_NA.json index 1571e68edb..161d43767b 100644 --- a/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_CLW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_CLW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Cloud Liquid Water (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Cloud liquid water (CLW), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). CLW is amount of vertically accumulated cloud droplet in the atmosphere, defined as amount of water per unit area. Coverage of the product is over the ocean only and Vertically integrated (columnar) cloud liquid water. Sea ice and precipitating areas are expected. The physical quantity unit is [kg/m2]. Cloud liquid water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and total precipitable water. Standard_Deviation is standarad deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.1deg_NA.json index 08a500f2f4..70266b93f5 100644 --- a/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_PRC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Precipitation (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Precipitation (PRC) overwritten by latest data. Although \"precipitation\" is usually defined as amount of water, which reaches the surface of the earth from atmosphere as rain and/or snow, microwave imager's precipitation products provide amount of rain (surface rainfall). Coverage of the product is global, and unit is [mm/h]. Accuracy of rainfall estimate over land, however tends to be lower than that over the ocean. Method of rainfall estimate used in AMSR2 precipitation product is also applied to \"Global Rainfall Watch\" system, which distributes global rainfall map in near-real-time. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.25deg_NA.json index 6418e212d0..bb40cf14dc 100644 --- a/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_PRC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_PRC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Precipitation (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Precipitation (PRC) overwritten by latest data. Although \"precipitation\" is usually defined as amount of water, which reaches the surface of the earth from atmosphere as rain and/or snow, microwave imager's precipitation products provide amount of rain (surface rainfall). Coverage of the product is global, and unit is [mm/h]. Accuracy of rainfall estimate over land, however tends to be lower than that over the ocean. Method of rainfall estimate used in AMSR2 precipitation product is also applied to \"Global Rainfall Watch\" system, which distributes global rainfall map in near-real-time. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.1deg_NA.json index c3e0e80686..8132ca63f2 100644 --- a/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_PRC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Precipitation (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Precipitation (PRC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Although \"precipitation\" is usually defined as amount of water, which reaches the surface of the earth from atmosphere as rain and/or snow, microwave imager's precipitation products provide amount of rain (surface rainfall). Coverage of the product is global, and unit is [mm/h]. Accuracy of rainfall estimate over land, however tends to be lower than that over the ocean. Method of rainfall estimate used in AMSR2 precipitation product is also applied to \"Global Rainfall Watch\" system, which distributes global rainfall map in near-real-time. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.25deg_NA.json index 172f7e62ff..f35011c9cc 100644 --- a/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_PRC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_PRC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Precipitation (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Precipitation (PRC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Although \"precipitation\" is usually defined as amount of water, which reaches the surface of the earth from atmosphere as rain and/or snow, microwave imager's precipitation products provide amount of rain (surface rainfall). Coverage of the product is global, and unit is [mm/hr]. Accuracy of rainfall estimate over land, however tends to be lower than that over the ocean. Method of rainfall estimate used in AMSR2 precipitation product is also applied to \"Global Rainfall Watch\" system, which distributes global rainfall map in near-real-time. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.1deg_NA.json index 1921b22109..48c292698b 100644 --- a/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SIC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Ice Concentration (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Sea Ice Concentration (SIC), percentage of sea ice coverage within target ocean area. Coverage of the product is over the ocean around Arctic and Antarctic Sea, and unit is [%]. There is no sea ice within pixel area when sea ice concentration is 0 %, and all pixel area is covered by sea ice when it shows 100 %. We can learn distribution of sea ice immediately using this product, and it will become more and more important because of recent decrease of Arctic sea ice cover. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 3. The Version 2 is also available.. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.25deg_NA.json index 2e8221b738..3d4ce4ef7c 100644 --- a/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SIC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SIC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Ice Concentration (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Sea Ice Concentration (SIC), percentage of sea ice coverage within target ocean area. Coverage of the product is over the ocean around Arctic and Antarctic Sea, and unit is [%]. There is no sea ice within pixel area when sea ice concentration is 0 %, and all pixel area is covered by sea ice when it shows 100 %. We can learn distribution of sea ice immediately using this product, and it will become more and more important because of recent decrease of Arctic sea ice cover. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.1deg_NA.json index e5c315234d..7018ced2d2 100644 --- a/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SIC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Ice Concentration (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Sea Ice Concentration (SIC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SIC is percentage of sea ice coverage within target ocean area. Coverage of the product is over the ocean around Arctic and Antarctic Sea, and unit is [%]. There is no sea ice within pixel area when sea ice concentration is 0 %, and all pixel area is covered by sea ice when it shows 100 %. We can learn distribution of sea ice immediately using this product, and it will become more and more important because of recent decrease of Arctic sea ice cover. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid).The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.25deg_NA.json index d048fa23e0..b5158641f6 100644 --- a/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SIC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SIC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Ice Concentration (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Sea Ice Concentration (SIC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SIC is percentage of sea ice coverage within target ocean area. Coverage of the product is over the ocean around Arctic and Antarctic Sea, and unit is [%]. There is no sea ice within pixel area when sea ice concentration is 0 %, and all pixel area is covered by sea ice when it shows 100 %. We can learn distribution of sea ice immediately using this product, and it will become more and more important because of recent decrease of Arctic sea ice cover. Standard_Deviation is standara\\d deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.1deg_NA.json index 55372bf6fe..5292c02702 100644 --- a/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SMC_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Soil Moisture Content (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Soil Moisture Content (SMC), amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.25deg_NA.json index 3b95080cdf..8a90c60b25 100644 --- a/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SMC_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SMC_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Soil Moisture Content (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Soil Moisture Content (SMC), amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 3. The Version 2 is also available. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.1deg_NA.json index 09813799c3..82f59f98e0 100644 --- a/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SMC_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Soil Moisture Content (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Soil Moisture Content (SMC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SMC is amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.25deg_NA.json index 5932ded569..0afc7291b0 100644 --- a/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SMC_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SMC_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Soil Moisture Content (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Soil Moisture Content (SMC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SMC is amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SND_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SND_1day_0.1deg_NA.json index a09a1b698c..8faa381e35 100644 --- a/datasets/GCOM-W_AMSR2_L3_SND_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SND_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SND_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Snow Depth (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Snow Depth (SND), depth of snow cover over land surface. Coverage of the product is over land only, and unit is [cm]. We do not provide snow cover over the sea ice surface. Snow depth parameter is closely related to climate variation, and this product enables us to figure out changes in distribution of global snow cover. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SND_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SND_1day_0.25deg_NA.json index f11b09f5c8..ec1d7299c3 100644 --- a/datasets/GCOM-W_AMSR2_L3_SND_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SND_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SND_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Snow Depth (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes averaged Snow Depth (SND), depth of snow cover over land surface. Coverage of the product is over land only, and unit is [cm]. We do not provide snow cover over the sea ice surface. Snow depth parameter is closely related to climate variation, and this product enables us to figure out changes in distribution of global snow cover. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SND_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SND_1month_0.1deg_NA.json index 0962cf40da..927e928e99 100644 --- a/datasets/GCOM-W_AMSR2_L3_SND_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SND_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SND_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Snow Depth (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Snow Depth (SND), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SND is depth of snow cover over land surface. Coverage of the product is over land only, and unit is [cm]. We do not provide snow cover over the sea ice surface. Snow depth parameter is closely related to climate variation, and this product enables us to figure out changes in distribution of global snow cover. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SND_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SND_1month_0.25deg_NA.json index a8dcb8569f..5589a521d2 100644 --- a/datasets/GCOM-W_AMSR2_L3_SND_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SND_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SND_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Snow Depth (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Snow Depth (SND), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SND is depth of snow cover over land surface. Coverage of the product is over land only, and unit is [cm]. We do not provide snow cover over the sea ice surface. Snow depth parameter is closely related to climate variation, and this product enables us to figure out changes in distribution of global snow cover. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SST_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SST_1day_0.1deg_NA.json index 88ba1a5222..723e69ca8a 100644 --- a/datasets/GCOM-W_AMSR2_L3_SST_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SST_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SST_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Temperature (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Sea Surface Temperature (SST) overwritten by latest data, temperature of water at ocean surface. Coverage of the product is over the ocean only, and unit is [degree]. Its horizontal resolution is about 20-30 km and coarser than that of optical instruments. Microwave radiometer, however, can observe ocean surface through clouds, and monitor continuous change of sea surface temperature over the ocean where few clear region can be found in specific areas or seasons. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1 degree grid. The statistical period is 1 day. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SST_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SST_1day_0.25deg_NA.json index 82fa80a440..330f940699 100644 --- a/datasets/GCOM-W_AMSR2_L3_SST_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SST_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SST_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Temperature (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Sea Surface Temperature (SST) overwritten by latest data, temperature of water at ocean surface. Coverage of the product is over the ocean only, and unit is [degree]. Its horizontal resolution is about 20-30 km and coarser than that of optical instruments. Microwave radiometer, however, can observe ocean surface through clouds, and monitor continuous change of sea surface temperature over the ocean where few clear region can be found in specific areas or seasons. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SST_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SST_1month_0.1deg_NA.json index 378fb027b1..34d05e41b2 100644 --- a/datasets/GCOM-W_AMSR2_L3_SST_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SST_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SST_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Temperature (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes MonthMean Sea Surface Temperature (SST), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SST is temperature of water at ocean surface. Coverage of the product is over the ocean only, and unit is [degree]. Its horizontal resolution is about 20-30 km and coarser than that of optical instruments. Microwave radiometer, however, can observe ocean surface through clouds, and monitor continuous change of sea surface temperature over the ocean where few clear region can be found in specific areas or seasons. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SST_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SST_1month_0.25deg_NA.json index 3a5a705c28..0f67e515cc 100644 --- a/datasets/GCOM-W_AMSR2_L3_SST_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SST_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SST_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Temperature (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes MonthMean Sea Surface Temperature (SST), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SST is temperature of water at ocean surface. Coverage of the product is over the ocean only, and unit is [degree]. Its horizontal resolution is about 20-30 km and coarser than that of optical instruments. Microwave radiometer, however, can observe ocean surface through clouds, and monitor continuous change of sea surface temperature over the ocean where few clear region can be found in specific areas or seasons. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.1deg_NA.json index 2d71c8bcb9..438f7f3626 100644 --- a/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SSW_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Wind Speed (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Sea Surface Wind Speed (SSW) overwritten by latest data. Coverage of the product is over the ocean only, and unit is [m/s]. Microwave radiometer can observe wind speed under the clouds, but it is difficult to estimate where there is rainfall and will be missing value. Wind speed around tropical cyclones, however, is important parameter in weather forecast, and we provide research product, which includes wind speed in rainy area over the ocean, as all-weather wind speed research product. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.25deg_NA.json index b40679b232..6215dfe4e2 100644 --- a/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SSW_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SSW_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Wind Speed (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Sea Surface Wind Speed (SSW) overwritten by latest data. Coverage of the product is over the ocean only, and unit is [m/s]. Microwave radiometer can observe wind speed under the clouds, but it is difficult to estimate where there is rainfall and will be missing value. Wind speed around tropical cyclones, however, is important parameter in weather forecast, and we provide research product, which includes wind speed in rainy area over the ocean, as all-weather wind speed research product. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.1deg_NA.json index ae65516673..3b235f29a4 100644 --- a/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SSW_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Wind Speed (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Sea Surface Wind Speed (SSW), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Coverage of SSW is over the ocean only, and unit is [m/s]. Microwave radiometer can observe wind speed under the clouds, but it is difficult to estimate where there is rainfall and will be missing value. Wind speed around tropical cyclones, however, is important parameter in weather forecast, and we provide research product, which includes wind speed in rainy area over the ocean, as all-weather wind speed research product. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.25deg_NA.json index 9acf8cb06b..ba93055898 100644 --- a/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_SSW_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_SSW_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Sea Surface Wind Speed (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Sea Surface Wind Speed (SSW), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Coverage of SSW is over the ocean only, and unit is [m/s]. Microwave radiometer can observe wind speed under the clouds, but it is difficult to estimate where there is rainfall and will be missing value. Wind speed around tropical cyclones, however, is important parameter in weather forecast, and we provide research product, which includes wind speed in rainy area over the ocean, as all-weather wind speed research product. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 4. The Version 3 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T06_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T06_1day_0.1deg_NA.json index 572162da19..1250e0f1f2 100644 --- a/datasets/GCOM-W_AMSR2_L3_T06_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T06_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T06_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (6Ghz) (1-Day,0.1_deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T06_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T06_1day_0.25deg_NA.json index b31803ae1b..1ea67c5c8c 100644 --- a/datasets/GCOM-W_AMSR2_L3_T06_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T06_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T06_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (6Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T06_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T06_1month_0.1deg_NA.json index bb96a2120a..17d765ebcd 100644 --- a/datasets/GCOM-W_AMSR2_L3_T06_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T06_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T06_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (6Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T06_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T06_1month_0.25deg_NA.json index 5bf62b16d8..b5f168bfce 100644 --- a/datasets/GCOM-W_AMSR2_L3_T06_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T06_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T06_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (6Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 6GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T07_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T07_1day_0.1deg_NA.json index 1c967af4d0..38714c2bcd 100644 --- a/datasets/GCOM-W_AMSR2_L3_T07_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T07_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T07_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (7Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T07_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T07_1day_0.25deg_NA.json index 7e44393c7b..8fca38bb86 100644 --- a/datasets/GCOM-W_AMSR2_L3_T07_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T07_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T07_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (7Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T07_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T07_1month_0.1deg_NA.json index 89dbfe7c7d..0eeeaee3e5 100644 --- a/datasets/GCOM-W_AMSR2_L3_T07_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T07_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T07_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (7Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T07_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T07_1month_0.25deg_NA.json index 004f3424cb..646f69af99 100644 --- a/datasets/GCOM-W_AMSR2_L3_T07_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T07_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T07_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (7Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 7GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T10_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T10_1day_0.1deg_NA.json index 8c29d858f9..989b22703a 100644 --- a/datasets/GCOM-W_AMSR2_L3_T10_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T10_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T10_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (10Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 10GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T10_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T10_1day_0.25deg_NA.json index 5c785561c9..8ed458094a 100644 --- a/datasets/GCOM-W_AMSR2_L3_T10_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T10_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T10_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (10Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 10GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T10_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T10_1month_0.1deg_NA.json index 05bf5be53c..9a4f120e0b 100644 --- a/datasets/GCOM-W_AMSR2_L3_T10_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T10_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T10_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (10Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 10GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T10_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T10_1month_0.25deg_NA.json index ad7d4869ba..859b602755 100644 --- a/datasets/GCOM-W_AMSR2_L3_T10_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T10_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T10_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (10Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 10GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T18_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T18_1day_0.1deg_NA.json index b39c00f156..9ab45d732c 100644 --- a/datasets/GCOM-W_AMSR2_L3_T18_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T18_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T18_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (18Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 18GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T18_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T18_1day_0.25deg_NA.json index 02ec186d92..8f63f0d748 100644 --- a/datasets/GCOM-W_AMSR2_L3_T18_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T18_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T18_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (18Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 18GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable , nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T18_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T18_1month_0.1deg_NA.json index 64f4f830b5..2d5ce25106 100644 --- a/datasets/GCOM-W_AMSR2_L3_T18_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T18_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T18_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (18Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 18GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T18_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T18_1month_0.25deg_NA.json index e74ea59c43..4a9e4719c7 100644 --- a/datasets/GCOM-W_AMSR2_L3_T18_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T18_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T18_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (18Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 18GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T23_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T23_1day_0.1deg_NA.json index 50fe85ecca..9290d9cafb 100644 --- a/datasets/GCOM-W_AMSR2_L3_T23_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T23_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T23_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (23Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 23GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T23_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T23_1day_0.25deg_NA.json index fd888093af..f7da0eff6e 100644 --- a/datasets/GCOM-W_AMSR2_L3_T23_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T23_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T23_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (23Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 23GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T23_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T23_1month_0.1deg_NA.json index 64e62f7530..5cdda23bd7 100644 --- a/datasets/GCOM-W_AMSR2_L3_T23_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T23_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T23_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (23Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 23GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T23_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T23_1month_0.25deg_NA.json index 31756d2be3..a686ae1520 100644 --- a/datasets/GCOM-W_AMSR2_L3_T23_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T23_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T23_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (23Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 23GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T36_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T36_1day_0.1deg_NA.json index 19c4be859e..e5981d132c 100644 --- a/datasets/GCOM-W_AMSR2_L3_T36_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T36_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T36_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (36Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 36GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T36_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T36_1day_0.25deg_NA.json index 5cbecde31a..e3e44922a3 100644 --- a/datasets/GCOM-W_AMSR2_L3_T36_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T36_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T36_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (36Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 36GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T36_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T36_1month_0.1deg_NA.json index 2b17e869b4..fe3c8d50d0 100644 --- a/datasets/GCOM-W_AMSR2_L3_T36_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T36_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T36_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (36Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 36GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T36_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T36_1month_0.25deg_NA.json index 2f837fc904..fcc457cb27 100644 --- a/datasets/GCOM-W_AMSR2_L3_T36_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T36_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T36_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (36Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 36GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T89_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T89_1day_0.1deg_NA.json index 05d5cb1393..988cd81b36 100644 --- a/datasets/GCOM-W_AMSR2_L3_T89_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T89_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T89_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (89Ghz) (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 89GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T89_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T89_1day_0.25deg_NA.json index 1c9b9fca34..2f8ecc2d0b 100644 --- a/datasets/GCOM-W_AMSR2_L3_T89_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T89_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T89_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (89Ghz) (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 89GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)) Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel is stored. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T89_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T89_1month_0.1deg_NA.json index 8beec381d1..6ef1d21405 100644 --- a/datasets/GCOM-W_AMSR2_L3_T89_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T89_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T89_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (89Ghz) (1-Month,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 89GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_T89_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_T89_1month_0.25deg_NA.json index 61132c6a20..dcc2cf03f2 100644 --- a/datasets/GCOM-W_AMSR2_L3_T89_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_T89_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_T89_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Brightness Temperature (89Ghz) (1-Month,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This product includes averaged Brightness Temperature at 89GHz (Brightness_Temperature_(H), Brightness_Temperature_(H)), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). Brightness Temperature is calculated from sensor counts and is proportional to the power of observed electromagnetic wave. They are expressed by the physical temperature of blackbody, which can emit the electromagnetic wave with the same power, and unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately. Missing value is 65535. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are 65531 to 65534. It is outside observation swath data. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR and PS. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA.json index 8d43f96dbe..2a380daac8 100644 --- a/datasets/GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_WV_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Day,0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Integrated Water Vapor (WV) overwritten by latest data, amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA.json index 5bb11a4753..cf01017955 100644 --- a/datasets/GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_WV_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Day,0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Integrated Water Vapor (WV) overwritten by latest data, amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Missing value is -32768. When there is no geophysical data within observation swath, this value is set up when computing neither the case where the amount of geophysics is incomputable, nor the amount of geophysics. Error values are -32761 to -32767. It is outside observation swath data. The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 day. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA.json b/datasets/GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA.json index e28c653958..ea53f60fc5 100644 --- a/datasets/GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_WV_1month_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes MonthMean Integrated Water Vapor (WV), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). WV is amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA.json b/datasets/GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA.json index 79f0c7f267..7dd4bf5570 100644 --- a/datasets/GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA.json +++ b/datasets/GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCOM-W_AMSR2_L3_WV_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GCOM-W/AMSR2 L3 Integrated Water Vapor (1-Month, 0.25 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the \"A-Train\" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth\u00e2\u0080\u0099s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Integrated Water Vapor (WV), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). WV is amount of vertically accumulated water vapor (H2O in gaseous state) in the atmosphere, and defined as amount of water per unit area. Coverage of the product is over the ocean only, and unit is [kg/m2]. Total precipitable water is one of essential hydrological parameters describing state of the atmosphere along with precipitation and cloud liquid water. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine \"Geophysical Data\". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.25degree grid. The statistical period is 1 month. The current version of the product is Version 2. The Version 1 is also available. The projection method is EQR. The generation unit is global.", "links": [ { diff --git a/datasets/GCRP-DDS-10.json b/datasets/GCRP-DDS-10.json index 58e9edc76d..949dfdfbb7 100644 --- a/datasets/GCRP-DDS-10.json +++ b/datasets/GCRP-DDS-10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCRP-DDS-10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information about the Modern Average Global Sea-Surface Temperature (USGS data\nseries DDS-10) Data Set archived at the USGS Global Change Research Program is\navailable via FTP:\n\n\"ftp://geochange.er.usgs.gov in /pub/magsst\"\n\nor via World Wide Web:\n\"http://pubs.usgs.gov/dds/dds10/magsst.html\"\n\nDirections on how to obtain the CD-ROM or access it on-line are made available\non the WWW site. The following information about the data set was provided by\nthe data center contact:\n\n The purpose of this data set is to provide Paleoclimate researchers with a\ntool for estimating the average seasonal variation in sea- surface temperature\n(SST) through out the modern world ocean and for estimating the modern monthly\nand weekly sea-surface temperature at any given oceanic location. It is\nexpected that these data will be compared with temperature estimates derived\nfrom geological proxy measures such as faunal census analyses and stable\nisotopic analyses. The results can then be used to constrain general\ncirculation models of climate change.\n\n The data contained in this data set are derived from the NOAA Advanced Very\nHigh Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR\nMCSST), which are obtainable from the Distributed Active Archive Center at the\nJet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain\nweekly images of SST from October 1981 through December 1990 in nine regions of\nthe world ocean: North Atlantic, Eastern North Atlantic, South Atlantic,\nAgulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and\nNorthwest Pacific.\n\n This data set represents the results of calculations carried out on the NOAA\ndata and also contains the source code of the programs that made the\ncalculations. The objective was to derive the average sea- surface temperature\nof each month and week throughout the whole 10-year series, meaning, for\nexample, that data from January of each year would be averaged together. The\nresult is 12 monthly and 52 weekly images for each of the oceanic regions.\nAveraging the images in this way tends to reduce the number of grid cells that\nlack valid data and to suppress interannual variability.\n\n As ancillary data, the ETOPO5 global gridded elevation and bathymetry data\n(Edwards, 1989) were interpolated to the resolution of the SST data; the\ninterpolated topographic data are included. The images are provided in three\nformats: a modified form of run-length encoding (MRLE), Graphics Interchange\nFormat (GIF), and Macintosh PICT format.\n\n Also included in the data set are programs that can retrieve seasonal\ntemperature profiles at user-specified locations and that can decompress the\ndata files. These nongraphical SST retrieval programs are provided in versions\nfor UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are\nincluded for users of UNIX with the X Window System, 80386-based PC's, and\nMacintosh computers.\n", "links": [ { diff --git a/datasets/GCRW_DEM_2016_1793_1.json b/datasets/GCRW_DEM_2016_1793_1.json index 6d7bb1abb0..ec8915a2c1 100644 --- a/datasets/GCRW_DEM_2016_1793_1.json +++ b/datasets/GCRW_DEM_2016_1793_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GCRW_DEM_2016_1793_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.", "links": [ { diff --git a/datasets/GE01_MSI_L1B_1.json b/datasets/GE01_MSI_L1B_1.json index a4a3c20e94..68bcf40848 100644 --- a/datasets/GE01_MSI_L1B_1.json +++ b/datasets/GE01_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GE01_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GeoEye-1 Level 1B Multispectral 4-Band L1B Satellite Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The imagery has a spatial resolution of 1.84m at nadir (1.65m before summer 2013) and has a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/GE01_Pan_L1B_1.json b/datasets/GE01_Pan_L1B_1.json index 6538877da8..88d502bb53 100644 --- a/datasets/GE01_Pan_L1B_1.json +++ b/datasets/GE01_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GE01_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GeoEye-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m at nadir (0.41m before summer 2013) and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/GEDI01_B_002.json b/datasets/GEDI01_B_002.json index ddb5d50c3a..ac919c9e21 100644 --- a/datasets/GEDI01_B_002.json +++ b/datasets/GEDI01_B_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI01_B_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.\r\n\r\nThe GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.\r\n\r\nThe GEDI01_B data product contains 85 layers for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary.", "links": [ { diff --git a/datasets/GEDI02_A_002.json b/datasets/GEDI02_A_002.json index c280c61be2..71de263fc5 100644 --- a/datasets/GEDI02_A_002.json +++ b/datasets/GEDI02_A_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI02_A_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.\r\n\r\nThe purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.\r\n\r\nThe GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.\r\n", "links": [ { diff --git a/datasets/GEDI02_B_002.json b/datasets/GEDI02_B_002.json index e7e8283348..474e56ed59 100644 --- a/datasets/GEDI02_B_002.json +++ b/datasets/GEDI02_B_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI02_B_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.\r\n\r\nThe purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.\r\n\r\nThe GEDI02_B data product contains 96 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary.", "links": [ { diff --git a/datasets/GEDI_Fusion_Structure_2236_1.json b/datasets/GEDI_Fusion_Structure_2236_1.json index d24fe243f8..9dbd37a347 100644 --- a/datasets/GEDI_Fusion_Structure_2236_1.json +++ b/datasets/GEDI_Fusion_Structure_2236_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_Fusion_Structure_2236_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides eight GEDI forest structure metrics relevant to wildlife habitat modeling and biodiversity assessments at 30-m resolutions across Washington, Oregon, Idaho, Montana, Wyoming, and Colorado. The metrics characterize canopy height, strata densities, and canopy cover. The data were derived using random forest modeling and prediction frameworks. The models created were also hindcasted using 2019 and 2020 GEDI footprints back to 2016 on annual time steps leveraging continuous Landsat spectral and disturbance information, Sentinel-1 backscatter metrics and ratios, topographic information, and bioclimatic variables. Machine learning data fusion approaches were used to scale-up structure information provided by the novel space-borne Global Ecosystems Dynamics Investigation (GEDI) waveform lidar sensor to continuous extents using additional satellite-based continuous earth observation data. GEDI provides a consistent sample of forest structure information at 25-m diameter footprints at near-global extents, providing a valuable source of reference information to drive continuous mapping efforts.", "links": [ { diff --git a/datasets/GEDI_HighQuality_Shots_Rasters_2339_1.json b/datasets/GEDI_HighQuality_Shots_Rasters_2339_1.json index 5f6a885201..b6f457ee19 100644 --- a/datasets/GEDI_HighQuality_Shots_Rasters_2339_1.json +++ b/datasets/GEDI_HighQuality_Shots_Rasters_2339_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_HighQuality_Shots_Rasters_2339_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.", "links": [ { diff --git a/datasets/GEDI_ICESAT2_Global_Veg_Height_2294_1.json b/datasets/GEDI_ICESAT2_Global_Veg_Height_2294_1.json index 1a497a3335..e77c37c6df 100644 --- a/datasets/GEDI_ICESAT2_Global_Veg_Height_2294_1.json +++ b/datasets/GEDI_ICESAT2_Global_Veg_Height_2294_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_ICESAT2_Global_Veg_Height_2294_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global rasters of relative height metrics for vegetation from Global Ecosystem Dynamics Investigation (GEDI) L2A data and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) L3A ATL08 data at 100-, 200-, 500-, and 1000-m spatial resolutions. The metrics include the relative heights RH98, RH90, RH75, and RH50, corresponding to the height at which the respective 98th, 90th, 75th, and 50th percentile of returned energy is reached relative to the ground. These metrics provide measures of vegetation canopy height and structure. The different relative height metrics were intercalibrated over the overlap area (50 - 52 degrees N). GEDI data were collected from 2019-2022, and ICESat2 data were from 2019-2021. The data are provided in cloud optimized GeoTIFF format.", "links": [ { diff --git a/datasets/GEDI_L3_LandSurface_Metrics_V2_1952_2.json b/datasets/GEDI_L3_LandSurface_Metrics_V2_1952_2.json index f8be03d105..ce85f90c6a 100644 --- a/datasets/GEDI_L3_LandSurface_Metrics_V2_1952_2.json +++ b/datasets/GEDI_L3_LandSurface_Metrics_V2_1952_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L3_LandSurface_Metrics_V2_1952_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1-km x 1-km grid cells globally within -52 and 52 degrees latitude. These L3 gridded products were derived from Level 2 (L2) geolocated laser footprint return profile metrics from the GEDI instrument onboard the International Space Station (ISS). Canopy height is provided as the mean height (in meters) above the ground of the received waveform signal that was the first reflection off the top of the canopy (RH100). Ground elevation is provided as the mean elevation (in meters) of the center of the lowest waveform mode relative to the WGS84 reference ellipsoid. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This dataset version uses Version 2 of the input L2 data, which includes improved geolocation of the footprints as well as a modified method to predict an optimum algorithm setting group.", "links": [ { diff --git a/datasets/GEDI_L4A_AGB_Density_GW_2028_1.1.json b/datasets/GEDI_L4A_AGB_Density_GW_2028_1.1.json index 05ea8e061c..91f003a608 100644 --- a/datasets/GEDI_L4A_AGB_Density_GW_2028_1.1.json +++ b/datasets/GEDI_L4A_AGB_Density_GW_2028_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L4A_AGB_Density_GW_2028_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the mission weeks 19, 32, 34 and 38 (a.k.a. Golden Weeks). These weeks cover the range of instrument operating conditions important for calibration and validation of geolocation algorithms, and also include GEDI orbits that are coincident with underflights acquired by the LVIS (Land, Vegetation, and Ice Sensor) airborne lidar instrument. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFT) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).", "links": [ { diff --git a/datasets/GEDI_L4A_AGB_Density_V2_1_2056_2.1.json b/datasets/GEDI_L4A_AGB_Density_V2_1_2056_2.1.json index 20855d6e18..f22caaeba1 100644 --- a/datasets/GEDI_L4A_AGB_Density_V2_1_2056_2.1.json +++ b/datasets/GEDI_L4A_AGB_Density_V2_1_2056_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L4A_AGB_Density_V2_1_2056_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2023-03-16. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products.", "links": [ { diff --git a/datasets/GEDI_L4B_Country_Biomass_2321_1.json b/datasets/GEDI_L4B_Country_Biomass_2321_1.json index 1b080e839d..f81cd767f7 100644 --- a/datasets/GEDI_L4B_Country_Biomass_2321_1.json +++ b/datasets/GEDI_L4B_Country_Biomass_2321_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L4B_Country_Biomass_2321_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S). For comparison purposes, this dataset also includes national-scale National Forest Inventory (NFI) estimates of AGBD from the 2020 Global Forest Resources Assessment (FRA) published by the Food and Agriculture Organization (FAO, 2020) of the United Nations.The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth's surface. GEDI was launched on December 5, 2018, and is attached to the International Space Station (ISS). The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which consist of ~25 m footprint samples spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. The data are provided in comma-separated value (CSV) format.", "links": [ { diff --git a/datasets/GEDI_L4B_Gridded_Biomass_V2_1_2299_2.1.json b/datasets/GEDI_L4B_Gridded_Biomass_V2_1_2299_2.1.json index 0d1e2dbca8..3a6a1d9cce 100644 --- a/datasets/GEDI_L4B_Gridded_Biomass_V2_1_2299_2.1.json +++ b/datasets/GEDI_L4B_Gridded_Biomass_V2_1_2299_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L4B_Gridded_Biomass_V2_1_2299_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.", "links": [ { diff --git a/datasets/GEDI_L4C_WSCI_2338_2.json b/datasets/GEDI_L4C_WSCI_2338_2.json index 9dfd9e50d8..b7696da392 100644 --- a/datasets/GEDI_L4C_WSCI_2338_2.json +++ b/datasets/GEDI_L4C_WSCI_2338_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEDI_L4C_WSCI_2338_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) Version 2 predictions of the Waveform Structural Complexity Index (WSCI) and estimates of prediction intervals for each footprint estimate at 95% confidence. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI04_C is the same as in the GEDI02_A product. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-17 to 2023-03-16. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint WSCI was derived from XGBoost regression models relating simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to a 3D structural complexity metric calculated from matched Airborne laser Scanning (ALS) point clouds. Four global WSCI models were trained on a plant functional type (PFT) basis (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the WSCI estimates, including the associated uncertainty metrics, quality flags, and other information about the GEDI L2A waveform for this selected algorithm setting group. Additional model outputs include WSCI predictions for each of the six GEDI L2A algorithm setting groups and associated prediction intervals. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. The data are provided in HDF5 format.", "links": [ { diff --git a/datasets/GEMIR_GPS_ANU_1.json b/datasets/GEMIR_GPS_ANU_1.json index a8ad01601e..8621a16861 100644 --- a/datasets/GEMIR_GPS_ANU_1.json +++ b/datasets/GEMIR_GPS_ANU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEMIR_GPS_ANU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present-day observed change in sea level is the sum of several factors, including the continuing readjustment of the crust to the past redistribution of the surface ice-water load and any present-day melting of the Antarctic ice sheet. Constraints can be provided on both of these contributions by measuring the magnitude of the crustal rebound using the Global Positioning System. By combining the measurements with other estimates of sea-level change, it becomes possible to separate the two contributions. This will lead to both improved mass balance models for the ice-ocean system and improved understanding of present sea-level change.\n\nGPS Data:\n\nGPS data collected at sites near the Lambert Glacier. Ashtech Z-12 GPS receivers, 24 hr data files, 30 s sampling, 10 deg cutoff elevation, Ashtech cone-shaped radome used.\n\n1998\n----\n\nBeaver Lake (BVLK): days of year 012-083 (024 and 028 data corrupted)\n\n1999\n----\n\nBeaver Lake (BVLK): days of year 022-058.\n\n2000\n----\n\nBeaver Lake (BVLK):037-044, 308-331.\nDaltons Corner (DALT): 045-072.\nLanding Bluff (LDBF): 346-366.\n\n2001\n----\n\nBeaver Lake (BVLK): 008-028.\nDalton Corner (DALT): 008-026.\nLanding Bluff (LDBF): 001-108, 114, 123, 343-365.\n\n2002\n----\n\nBeaver Lake (BVLK): 006-082\nLanding Bluff (LDBF): 001\nDalton Corner (DALT): started recording day 010.\n\n\nBVLK: 002,006-092,103,263-264,274\nLDBF: 001-107, 128-130,322-365\nDALT: 010-030,343-365\nKOMS (Komsomolskiy Peak): 362-365\n\n2003\n----\n\nBVLK: 001-102, 358-365\nLDBF: 001-104, 331-343\nDALT: 001-105, 260-306, 353-365\nKOMS: 001-108, 271-342\n\n2004\n----\n\nBVLK: 2004 data and archive report\nLDBF: 2004 data and archive report\nDALT: site not visited during last field season\nKOMS: site not visited during last field season\n\n2005\n----\n\nBVLK: 070-241\nDALT: 1101-1111, 2431-3261\nLDBF: 0011-3651\n\n2006\n----\n\nLDBF: 0011-3651\n\n2007\n----\n\nDALT: 0170-1120\nLDBF: 0011-0991\nRICH (Richardson Lake, Enderby Land): 0060-1290\n\n\nDiagnostic Data:\nThe diagnostic data are updated on a daily basis for those sites which have active satellite communication facilities. For other sites, the data are only retrieved on an opportunistic basis as it has to be physically downloaded from the machine. Diagnostic data are only available from 2000 onwards.\n\nDiagnostic data were collected at a test site, davi, located at Davis during 2000. The equipment was subsequently removed and redeployed in the PCMs after 2000. We did not install a GPS receiver at Davis; only diagnostic data were collected by the test installation.\n\nFurther information about the Richardson Lake site is available from the provided URL.\n\nThis work was completed as part of ASAC project 1112 - Crustal rebound in the Lambert Glacier area.\n\nThe fields in this dataset are:\nYear\nMonth\nDay\nHour\nMinute\nSecond\nBattery Voltages (bat1, bat2, bat3)\nSolar Panel Voltage switched across a 10 ohm resistor (pvp1, pvp2, pvp5)\nFuel Cell Input Voltage (no longer used)\nInternal Temperature (temp1, temp2, temp3)\nExternal Temperature (temp4)\nBattery Status (good, low, flat, sick, dead), connected to Fuel Cell (FC) - y/n\nHeater Status (on - y/n - if hibernating, shows hhh)\n \nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nSuccessful automated operation of remote GPS installations in Antarctica and transmission of the data via satellite communications led to the accurate estimation of uplift rates at several sites in the Prince Charles Mountains and Enderby Land. These velocities help interpret mass balance changes as estimated from space gravity. The project was finalised and most equipment was removed. One site has not been visited since 2004 and the equipment still needs to be retrieved.", "links": [ { diff --git a/datasets/GEO-CAPE_0.json b/datasets/GEO-CAPE_0.json index 81a9b15195..b4942f5f11 100644 --- a/datasets/GEO-CAPE_0.json +++ b/datasets/GEO-CAPE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEO-CAPE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEO-CAPE : Geostationary Coastal and Air Pollution Events", "links": [ { diff --git a/datasets/GEO-CAPE_GOCI_0.json b/datasets/GEO-CAPE_GOCI_0.json index 470a7c2ecd..4c14a8f539 100644 --- a/datasets/GEO-CAPE_GOCI_0.json +++ b/datasets/GEO-CAPE_GOCI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEO-CAPE_GOCI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEO-CAPE is the Geostationary Coastal and Air Pollution Events program with a focus on the Geostationary Ocean Color Imager (GOCI).", "links": [ { diff --git a/datasets/GEOLST4KHR_002.json b/datasets/GEOLST4KHR_002.json index 407bab45f2..f8d2ab88e7 100644 --- a/datasets/GEOLST4KHR_002.json +++ b/datasets/GEOLST4KHR_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOLST4KHR_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) GEOLST4KHR version 2 swath product provides per-pixel Land Surface Temperature (LST) with a spatial resolution of 4,000 meters (m). The product is produced daily in hourly increments using data acquired from Geostationary Operational Environmental Satellite (GOES) 8 and 10 through 17 satellites for the years 2000 through 2023. The GEOLST4KHR product provides LST values for both North and South America. The GEOLST4KHR data product utilizes the Modern-Era Retrospective analysis for Research and Applications Version 2 / Radiative Transfer for TIROS Operational Vertical Sounder (MERRA-2/RTTOV) Single-Channel Emissivity-Combined ASTER and MODIS Emissivity over Land (CAMEL) algorithm.\n\nThe GEOLST4KHR product provides layers for cloud mask, latitude, longitude, land surface temperature, and land surface temperature error. A low-resolution browse is also available showing land surface temperature as an RGB (red, green, blue) image in JPEG format.", "links": [ { diff --git a/datasets/GEOS FP_1.json b/datasets/GEOS FP_1.json index 292fa6728c..bf7b38ee87 100644 --- a/datasets/GEOS FP_1.json +++ b/datasets/GEOS FP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS FP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS FP Atmospheric Data Assimilation System (GEOS ADAS) uses an analysis\r\ndeveloped jointly with NOAA\u2019s National Centers for Environmental Prediction (NCEP), which\r\nallows the Global Modeling and Assimilation Office (GMAO) to take advantage of the\r\ndevelopments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The\r\nGEOS AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics\r\npackages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF)\r\nincluding the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI\r\nanalysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to\r\nfacilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002;\r\nDerber et al., 2003). The GSI implementation for GEOS FP incorporates a set of recursive filters\r\nthat produce approximately Gaussian smoothing kernels and isotropic correlation functions.\r\nThe GEOS ADAS is documented in Rienecker et al. (2008). More recent updates to the model\r\nare presented in Molod et al. (2011). The GEOS system actively assimilates roughly 2 \u00b4 106\r\nobservations for each analysis, including about 7.5 \u00b4 105 AIRS radiance data. The input stream\r\nis roughly twice this volume, but because of the large volume, the data are thinned\r\ncommensurate with the analysis grid to reduce the computational burden. Data are also rejected\r\nfrom the analysis through quality control procedures designed to detect, for example, the\r\npresence of cloud.\r\nTo minimize the spurious periodic perturbations of the analysis, GEOS FP uses the Incremental\r\nAnalysis Update (IAU) technique developed by Bloom et al. (1996).", "links": [ { diff --git a/datasets/GEOS-3_ALT_GDR_1.json b/datasets/GEOS-3_ALT_GDR_1.json index 60bad9ddab..22419771be 100644 --- a/datasets/GEOS-3_ALT_GDR_1.json +++ b/datasets/GEOS-3_ALT_GDR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS-3_ALT_GDR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of Geos-3 altimeter measurements produced by NOAA/NODC/Laboratory for Satellite Altimetry. The dataset contains 5,006,956 altimetric sea surface heights and supporting information such as sea state, wind speed, Schwiderski ocean tide height, and Cartwright solid-tide height. Corrections for altimeter bias, wet and dry troposheric delays, and electromagnetic bias are not included. The corrections in this dataset (tides and even orbit height) are old and not very accurate. This dataset should only be used by those with an expertise in altimetry. Measurements are compressed to a rate of 1 per second using a trim mean filter. Data values are written in binary format.", "links": [ { diff --git a/datasets/GEOS2OBSINPUTINTL_001.json b/datasets/GEOS2OBSINPUTINTL_001.json index c1ff0e064a..a5c47ee6e5 100644 --- a/datasets/GEOS2OBSINPUTINTL_001.json +++ b/datasets/GEOS2OBSINPUTINTL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS2OBSINPUTINTL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEOS2OBSINPUTINTL is the optical beacon system data product which contains reduced raw geodetic optical observations obtained by various international camera systems. These data were used as input to the Quality Control Program to create the product called the International Optical Beacon Pass Summary Data. The optical beacon system, used for geometric geodesy studies, consisted of four xenon flash tubes programmed to flash sequentially, in a series of five or seven flashes. Data are available for the time period from 1968-02-20 to 1968-10-03 in a single file with 1689 data records where each is a line of ASCII text.\\n\\nThe principal investigator for the Optical Beacon System experiment was R. E. Williston from APL. A previous version of this instrument flew on the first GEOS-1 satellite.", "links": [ { diff --git a/datasets/GEOS3STST_001.json b/datasets/GEOS3STST_001.json index 9a692ffdd1..3b04be38f1 100644 --- a/datasets/GEOS3STST_001.json +++ b/datasets/GEOS3STST_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS3STST_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEOS3STST is the satellite-to-satellite tracking data product which contains observations, obtained from the S-band transponders on GEOS 3 relayed by the ATS 6 spacecraft to various ground stations, used for geodetic studies. Data are available for the time period from 1975-04-13 to 1976-04-28 in sixteen files, written in ASCII text, where each measurement is recorded as two lines of text.\\n\\nThe principal investigator for the Satellite-to-Satellite Tracking experiment was Indalecio Y. Galicinao from NASA/GSFC.", "links": [ { diff --git a/datasets/GEOSAT-1.and.2.ESA.archive_9.0.json b/datasets/GEOSAT-1.and.2.ESA.archive_9.0.json index e9e6f779f8..63b6096bf6 100644 --- a/datasets/GEOSAT-1.and.2.ESA.archive_9.0.json +++ b/datasets/GEOSAT-1.and.2.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOSAT-1.and.2.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEOSAT 1 and 2 collection is composed of products acquired by the GEOSAT 1 and GEOSAT 2 Spanish satellites. The collection regularly grows as ESA collects new products.\rGEOSAT-1 standard products offered are:\r\u2022 SL6_22P: SLIM6, 22m spatial resolution, from bank P\r\u2022 SL6_22S: SLIM6, 22m spatial resolution, from bank S\r\u2022 SL6_22T: SLIM6, 22m spatial resolution, 2 banks merged together\r\rGEOSAT-1 products are available in two different processing levels:\r\u2022 Level 1R: All 3 Spectral channels combined into a band-registered image using L0R data. Geopositioned product based on rigorous sensor model. Coefficients derived from internal and external satellite orientation parameters coming from telemetry and appended to metadata.\r\u2022 Level 1T: data Orthorectified to sub-pixel accuracy (10 meters RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m). The use of the GCPs, it is not automatic, as it is done manually, which gives greater precision. (GCPs by human operators).\r\rGEOSAT-2 standard products offered are:\r\u2022 Pan-sharpened (HRA_PSH four-band image, HRA_PS3 321 Natural Colours, HRA_PS4 432 False Colours): a four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserve all spectral features of the multispectral bands, so it should not be used for radiometric purposes.\r\u2022 Panchromatic (HRA_PAN): a single-band image coming from the panchromatic sensor.HRA_MS4: Multispectral (HRA_MS4): a four-band image coming for the multispectral sensor, with band co-registration.\r\u2022 Bundle (HRA_PM4): a five-band image contains the panchromatic and multispectral products packaged together, with band co-registration.\r\u2022 Stereo Pair (HRA_STP): The image products obtained from two acquisitions of the same target performed from different viewpoints in the same pass by using the agility feature of the platform. It can be provided as a pair of pan sharpened or panchromatic images.\r\rGEOSAT-2 products are available in two different processing levels:\r\u2022 Level 1B: A calibrated and radiometrically corrected product, but not resampled. The product includes the Rational Polynomial Coefficients (RPC), the metadata with gain and bias values for each band, needed to convert the digital numbers into radiances at pixel level, and information about geographic projection (EPGS), corners geolocation, etc.\r\u2022 Level 1C: A calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid. The geometric information is contained in the GeoTIFF tags.\rSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/GEOSAT/ available on the Third Party Missions Dissemination Service.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/GEOSAT-2.Portugal.Coverage_7.0.json b/datasets/GEOSAT-2.Portugal.Coverage_7.0.json index 74e37a8b44..cfc70f8b6c 100644 --- a/datasets/GEOSAT-2.Portugal.Coverage_7.0.json +++ b/datasets/GEOSAT-2.Portugal.Coverage_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOSAT-2.Portugal.Coverage_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Description\rGEOSAT-2 Portugal coverage is a collection of a 2021`s data over the Portugal area, including islands. The available dataset hasve a cloud cover less thaen 10%, and is acquired up to 1m resolution with Geometric accuracy <6m CE90 based on Copernicus DEM @10m. in tThe following acquisition modesproduct types are available:\r\u2022\tPan-sharpened (4 bands, 321 Natural Colours or 432 False Colours): A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not prereserves all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Resolution 1m; Bands: All, R-G-B or Ni-R-G\r\u2022\tBundle: Panchromatic (1m resolution) + Multispectral bands (4m resolution): five-band image containing the panchromatic and multispectral products packaged together, with band co-registration.\r\rThe available processing level is L1C orthorectified: a calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid.\r\rProduct Type HRA_PM4_1C , HRA_PSH_1C Processing Level and Spatial Resolution\r\tL1B (native)\tL1C (ortho)\rPan-sharpened\t1.0m\t1.0m\rBundle (PAN+MS)\t1.0m (P), 4.0m (MS)\t1.0m (P), 4.0m(MS)\r \rDetails", "links": [ { diff --git a/datasets/GEOSAT2SpainCoverage10_11.0.json b/datasets/GEOSAT2SpainCoverage10_11.0.json index e8c34eca46..9cb3b6bbd3 100644 --- a/datasets/GEOSAT2SpainCoverage10_11.0.json +++ b/datasets/GEOSAT2SpainCoverage10_11.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOSAT2SpainCoverage10_11.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOSAT-2 Spain Coverage collection consists of two separate coverages of Spain, including the Balearic and Canary islands, acquired by GEOSAT-2 between March and November of 2021 and 2022, respectively.\rThe available imagery have a geolocation accuracy of < 4 m RMSE, a cloud cover percentage of < 10 %, and were acquired at an off-nadir angle from -30\u00b0 to 30\u00b0.\r \rSpatial coverage of the 2021 collection.\rThe following product types are available:\r\t\u2022 Pan-sharpened: A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserves all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Resolution 1 m; Bands: All.\r\t\u2022 Bundle: Panchromatic (1 m resolution) + Multispectral bands (4 m resolution): five-band image containing the panchromatic and multispectral products packaged together, with band co-registration.\rThe available processing level is L1C orthorectified: a calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid.\rProduct Type\t\tSpatial Resolution\rPan-sharpened\t\t1.0 m\rBundle (PAN + MS)\t1.0 m (PAN), 4.0 m (MS)", "links": [ { diff --git a/datasets/GEOS_CASAGFED_3H_NEE_2.json b/datasets/GEOS_CASAGFED_3H_NEE_2.json index 305524fecf..4504ff6d51 100644 --- a/datasets/GEOS_CASAGFED_3H_NEE_2.json +++ b/datasets/GEOS_CASAGFED_3H_NEE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_3H_NEE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE)\nof Carbon derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOS_CASAGFED_3H_NEE_3.json b/datasets/GEOS_CASAGFED_3H_NEE_3.json index b8f1d68ea3..b408946961 100644 --- a/datasets/GEOS_CASAGFED_3H_NEE_3.json +++ b/datasets/GEOS_CASAGFED_3H_NEE_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_3H_NEE_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE)\nof Carbon derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOS_CASAGFED_D_FIRE_2.json b/datasets/GEOS_CASAGFED_D_FIRE_2.json index 728b03b1e3..5a9c01d1bf 100644 --- a/datasets/GEOS_CASAGFED_D_FIRE_2.json +++ b/datasets/GEOS_CASAGFED_D_FIRE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_D_FIRE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides Daily average wildfire emissions (FIRE) and\nfuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOS_CASAGFED_D_FIRE_3.json b/datasets/GEOS_CASAGFED_D_FIRE_3.json index f898cfdac6..2e296ad770 100644 --- a/datasets/GEOS_CASAGFED_D_FIRE_3.json +++ b/datasets/GEOS_CASAGFED_D_FIRE_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_D_FIRE_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides Daily average wildfire emissions (FIRE) and\nfuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOS_CASAGFED_M_FLUX_2.json b/datasets/GEOS_CASAGFED_M_FLUX_2.json index 17c7add79d..1705f76432 100644 --- a/datasets/GEOS_CASAGFED_M_FLUX_2.json +++ b/datasets/GEOS_CASAGFED_M_FLUX_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_M_FLUX_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and\nfuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOS_CASAGFED_M_FLUX_3.json b/datasets/GEOS_CASAGFED_M_FLUX_3.json index 858180fb4c..5c4293afff 100644 --- a/datasets/GEOS_CASAGFED_M_FLUX_3.json +++ b/datasets/GEOS_CASAGFED_M_FLUX_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOS_CASAGFED_M_FLUX_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and\nfuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-\nGFED3) model.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GEOTRACES_0.json b/datasets/GEOTRACES_0.json index 129c12db7d..8f79ae2cef 100644 --- a/datasets/GEOTRACES_0.json +++ b/datasets/GEOTRACES_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEOTRACES_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the GEOTRACES program of Biogeochemical Cycles of Trace Elements and their Isotopes.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_1.json b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_1.json index f72433ada9..fde95b7c6d 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Ancillary_3hrly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEX SRB Integrated Product (Rel-4) Ancillary 3-Hourly contains the global fields of meteorology, clouds and other ancillary data that serves as the inputs to the GEWEX SRB Integrated Product (Rel-4) Longwave algorithm, although some are applicable to the Shortwave product as well. Included are surface skin temperature, near-surface meteorology, Fu-Liou longwave algorithm based bands of surface emissivity, snow and ice amount, total column precipitable water, total column ozone, total cloud properties, and three-level water and ice cloud properties. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. These data are available in NetCDF-4.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly_1.json b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly_1.json index e901400665..418a65a966 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Ancillary_3hrly_landonly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Release-4) Ancillary 3-Hourly Land-only data product. It contains the global land-only fields of meteorology, clouds and other ancillary data that serves as the inputs to the GEWEX SRB Integrated Product (Rel-4) Longwave algorithm, although some are applicable to the Shortwave product as well. Included are surface skin temperature, near-surface meteorology, Fu-Liou longwave algorithm based bands of surface emissivity, snow and ice amount, total column precipitable water, total column ozone, total cloud properties, and three-level water and ice cloud properties. The temporal range is January 1983 through December 1987, with the ends bound by input constraints. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly_1.json b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly_1.json index 15ffd00a2e..954f1e9d81 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Ancillary_3hrly_oceanonly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Ancillary 3-Hourly Ocean-only product. It contains the global ocean-only fields of meteorology, clouds and other ancillary data that serves as the inputs to the GEWEX SRB Integrated Product (Rel-4) Longwave algorithm, although some are applicable to the Shortwave product as well. Included are surface skin temperature, near-surface meteorology, Fu-Liou longwave algorithm based bands of surface emissivity, snow and ice amount, total column precipitable water, total column ozone, total cloud properties, and three-level water and ice cloud properties. The temporal range is January 2010 through June 2017, with the ends bound by input constraints. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrly_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrly_1.json index 14f755c69b..fa3dc702d3 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrly_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_3hrly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_3hrly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave 3-hourly data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. In addition to the fluxes a day/night flag and a status flag of filled cloud properties are also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc_1.json index 0a003c81bf..8b8fd6a508 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_landonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave 3-Hourly Monthly Average by UTC Land-only Fluxes data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with land-only fluxes due to a missing key input from the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. These are temporally averaged on UTC. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc_1.json index c97184a64f..856a48cc01 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_oceanonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Release-4) Longwave 3-Hourly Monthly Average by UTC (also known as \"diurnal\"). This product contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with ocean-only fluxes due to a missing key input from the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. These data are averaged by UTC from 3-hourly values.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc_1.json index 7f6651413b..bb29c7a6d1 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_3hrlymonthly_utc is theGlobal Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave 3-hourly Monthly Average (also known as diurnal average) by UTC data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. These data are averaged by UTC from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc_1.json index 7ebd097265..53cb935f7c 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_daily_landonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by UTC Land-only data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with land-only fluxes due to a missing key input from the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. These data are averaged by UTC from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc_1.json index 579bd82aea..b17b8ec744 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc is the GEWEXSRB_Rel4-IP_Longwave_daily_oceanonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by UTC Ocean-only Fluxes data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with ocean-only fluxes due to a missing key input from the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. These data are averaged by UTC from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_utc_1.json index 6b092f2fe5..50af211b73 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_daily_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_daily_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_daily_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by UTC data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. These data are averaged by UTC from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc_1.json index 874c51e4f6..982e60c4ca 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_monthly_landonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by UTC data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with land-only fluxes due to a missing key input from the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_local_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_local_1.json index da701ce62b..080d3d0129 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_local_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_monthly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_monthly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by Local data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. The monthly averages are computed by local solar time. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc_1.json index c548c39bf4..7a9f6d2539 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_monthly_oceanonly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by UTC data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with ocean-only fluxes due to a missing key input for the main data set . The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_utc_1.json index 2283b5b19a..6459dd41be 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Longwave_monthly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Longwave_monthly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Longwave_monthly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by UTC data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky and pristine-sky upward and downward fluxes at: tropopause, 200hPa, 500hPa and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrly_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrly_1.json index 2be3a1802d..996c8f1550 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrly_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_3hrly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_3hrly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave 3-hourly data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, solar zenith angle, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc_1.json index f7c081eccd..77a12756a5 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_3hrlymonthly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave 3-Hourly Monthly Average by UTC (also known as diurnal) data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. These data are averaged from UTC 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_local_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_local_1.json index ac404ec204..fdd7ddffaf 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_local_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_daily_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_daily_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave Daily Average by local data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. These data are averaged from local solar time 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_utc_1.json index 13c14dea2d..a515aba19f 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_daily_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_daily_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_daily_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave Daily Average by UTC data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. These data are averaged from UTC 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_local_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_local_1.json index 19caef48cc..ac446d8f21 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_local_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_monthly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_monthly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave Monthly Average by local data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. These data are averaged from local solar time daily averages. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_utc_1.json b/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_utc_1.json index e41842a79c..351ae20cd2 100644 --- a/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_utc_1.json +++ b/datasets/GEWEXSRB_Rel4-IP_Shortwave_monthly_utc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4-IP_Shortwave_monthly_utc is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Shortwave Monthly Average by UTC data product. It contains global fields of 11 shortwave surface and Top of Atmosphere (TOA), radiative parameters derived with the Shortwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky and pristine-sky TOA upward fluxes, incoming (downward) TOA flux, and all-sky, clear-sky and pristine-sky upward and downward fluxes at surface. Surface PAR, cloud fraction, average solar zenith angle and a status flag of filled fluxes are also included. Inputs to the shortwave algorithm are cloud and radiance information from International Satellite Cloud Climatology Project (ISCCP) HXS, total column precipitable water from ISCCP nnHIRS, Total Solar Irradiance from SORCE TIM, ozone from ISCCP, and MAC V1 aerosol amounts and radiative properties. The temporal range is July 1983 through June 2017. These data are averaged from UTC daily averages. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly_1.json index d4a2d96f99..6f586474b5 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_landonly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave 3-Hourly Land-only data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with land-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. In addition to the fluxes, a day/night flag and a status flag of filled cloud properties are also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly_1.json index ad95084d89..a863e5113f 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_3hrly_oceanonly is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave 3-Hourly data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with ocean-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. In addition to the fluxes, a day/night flag and a status flag of filled cloud properties are also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local_1.json index 9a056292ba..731e7cbf02 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_daily_landonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by Local Land-only data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with land-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. These data are averaged by local solar time from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1.json index f6eb199ce0..f1e6c39b6d 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_daily_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by Local data product. It contains global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is known as Release 4-Integrated Product. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, LandFlux meteorology, and MERRA-2 conditionally. The temporal range is January 1988 through December 2009, with the ends bound by input constraints. The daily averages are computed by local solar time. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1.json index 203d1f43c5..e3dae64a02 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by local Ocean-only Fluxes data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with ocean-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. These data are averaged by local solar time from 3-hourly values. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1.json index ddffdbae95..16856ada3f 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by local data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with land-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. These are temporally averaged on local solar time. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1.json b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1.json index 13e13ba56b..a03ec2e255 100644 --- a/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1.json +++ b/datasets/GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by local Ocean-only data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with ocean-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. Averaging is done by local solar time. Data collection for this product is complete.", "links": [ { diff --git a/datasets/GFCC30FCC_001.json b/datasets/GFCC30FCC_001.json index 167517836f..152ba49e51 100644 --- a/datasets/GFCC30FCC_001.json +++ b/datasets/GFCC30FCC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFCC30FCC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. \n\nEach forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf).\n", "links": [ { diff --git a/datasets/GFCC30SR_001.json b/datasets/GFCC30SR_001.json index bcdc5dd47c..9d49fb178f 100644 --- a/datasets/GFCC30SR_001.json +++ b/datasets/GFCC30SR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFCC30SR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available \u201cleaf-on\u201d date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during \u201cleaf-off\u201d seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection.\n\nFor each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). \n", "links": [ { diff --git a/datasets/GFCC30TC_003.json b/datasets/GFCC30TC_003.json index 39a4eb6913..315489d908 100644 --- a/datasets/GFCC30TC_003.json +++ b/datasets/GFCC30TC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFCC30TC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf).\n", "links": [ { diff --git a/datasets/GFCC30WC_001.json b/datasets/GFCC30WC_001.json index b19a3f5cb3..8a9b95f620 100644 --- a/datasets/GFCC30WC_001.json +++ b/datasets/GFCC30WC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFCC30WC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", "links": [ { diff --git a/datasets/GFEI_CH4_1.json b/datasets/GFEI_CH4_1.json index eb9747c51d..90ffc9db0c 100644 --- a/datasets/GFEI_CH4_1.json +++ b/datasets/GFEI_CH4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFEI_CH4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries.\n The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A.\n The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/GFOI_Boreno_Island.json b/datasets/GFOI_Boreno_Island.json index dfa2748a0b..a90ea13606 100644 --- a/datasets/GFOI_Boreno_Island.json +++ b/datasets/GFOI_Boreno_Island.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFOI_Boreno_Island", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).\n", "links": [ { diff --git a/datasets/GFSAD1KCD_001.json b/datasets/GFSAD1KCD_001.json index 10b0c199c0..80426fd536 100644 --- a/datasets/GFSAD1KCD_001.json +++ b/datasets/GFSAD1KCD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD1KCD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data.\n\nThe GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012.\n", "links": [ { diff --git a/datasets/GFSAD1KCM_001.json b/datasets/GFSAD1KCM_001.json index 37a417d459..f2abb98383 100644 --- a/datasets/GFSAD1KCM_001.json +++ b/datasets/GFSAD1KCM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD1KCM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data.\n\nThe GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012.\n", "links": [ { diff --git a/datasets/GFSAD30AFCE_001.json b/datasets/GFSAD30AFCE_001.json index 60e4149289..6909cbe1f0 100644 --- a/datasets/GFSAD30AFCE_001.json +++ b/datasets/GFSAD30AFCE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30AFCE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30AUNZCNMOCE_001.json b/datasets/GFSAD30AUNZCNMOCE_001.json index a0deee531e..913c0e6e13 100644 --- a/datasets/GFSAD30AUNZCNMOCE_001.json +++ b/datasets/GFSAD30AUNZCNMOCE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30AUNZCNMOCE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30EUCEARUMECE_001.json b/datasets/GFSAD30EUCEARUMECE_001.json index d3806672dd..37943ac9fb 100644 --- a/datasets/GFSAD30EUCEARUMECE_001.json +++ b/datasets/GFSAD30EUCEARUMECE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30EUCEARUMECE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30NACE_001.json b/datasets/GFSAD30NACE_001.json index f705d60f36..337381b029 100644 --- a/datasets/GFSAD30NACE_001.json +++ b/datasets/GFSAD30NACE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30NACE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30SAAFGIRCE_001.json b/datasets/GFSAD30SAAFGIRCE_001.json index 4984adecd9..2431a9e78a 100644 --- a/datasets/GFSAD30SAAFGIRCE_001.json +++ b/datasets/GFSAD30SAAFGIRCE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30SAAFGIRCE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30SACE_001.json b/datasets/GFSAD30SACE_001.json index b38b902179..24d5c1280d 100644 --- a/datasets/GFSAD30SACE_001.json +++ b/datasets/GFSAD30SACE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30SACE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.", "links": [ { diff --git a/datasets/GFSAD30SEACE_001.json b/datasets/GFSAD30SEACE_001.json index d3bb0d725c..f318361070 100644 --- a/datasets/GFSAD30SEACE_001.json +++ b/datasets/GFSAD30SEACE_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30SEACE_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.\n", "links": [ { diff --git a/datasets/GFSAD30VAL_001.json b/datasets/GFSAD30VAL_001.json index 85174364aa..9aa0bcf15f 100644 --- a/datasets/GFSAD30VAL_001.json +++ b/datasets/GFSAD30VAL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GFSAD30VAL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. The accuracy assessment and validation process utilizes a cluster of 3 by 3 pixels of 30 meter data to resample the product to 90 meter resolution. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe.", "links": [ { diff --git a/datasets/GGD200_1.json b/datasets/GGD200_1.json index c5afc7db34..6e67ea4fee 100644 --- a/datasets/GGD200_1.json +++ b/datasets/GGD200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a database of the permafrost and geothermal conditions of the oil and gas deposits of Western Siberia. Data were taken from 736 plots, each having from one to ten wells. The data set includes soil and rock temperatures at 20, 50, 100, 200, 300, 400, 500, 1000, and 3000 meters; depth of the bedding of the top and bottom of permafrost layers; size of the thermal flows in the subpermafrost; and thickness of frozen layers and underlying thawed layers. Additional information includes the geographical coordinates of the sites, the air temperature, permafrost-geothermal geological sections, maps of thermal flows, and the distribution of the temperatures at each depth (down to 5000 meters). The data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD222_1.json b/datasets/GGD222_1.json index f98cf0e269..a1832732d5 100644 --- a/datasets/GGD222_1.json +++ b/datasets/GGD222_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD222_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil temperature, soil moisture, thaw depth, and snow depth data collected at test sites near Barrow, Alaska, during the following years.\n\nSoil temperature data - 1963-1966, 1993 Soil moisture data - 1963 Thaw depth - 1962-1968, 1991-1993 Snow depth - 1963-1964 This study focused on characterizing the active soil layer at Barrow, and determining the relationships between and among these physical properties at permafrost sites in the Arctic.\n\nThis site is U1 of the IPA's Circumpolar Active Layer Monitoring (CALM) Program and later measurements are available at the CALM Web site.", "links": [ { diff --git a/datasets/GGD223_1.json b/datasets/GGD223_1.json index 1be553a866..cfd44bb2af 100644 --- a/datasets/GGD223_1.json +++ b/datasets/GGD223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The methods utilized by the U.S. Geological Survey to measure subsurface temperatures have evolved considerably over the years. Although some of the early measurements were obtained using thermistor strings frozen into permafrost, the vast majority of the measurements were made in fluid-filled holes using a custom temperature sensor. A typical sensor used in Alaska prior to 1989 consisted of a series-parallel network of 20 thermistors; see Sass et al. [1971] for a more detailed description. During a logging experiment, the resistance of the thermistor network was determined using a Wheatstone bridge prior to 1967. After that time, a 4-wire resistance measurement was made using a commercial 5.5-digit multimeter (DMM). Before 1984, boreholes were logged in the 'incremental' or 'stop-and-go' modes; the vertical spacing of the measurements was typically 3-15 m. Beginning in 1984, the depth/resistance measurements were automatically stored on magnetic tape, allowing boreholes to be logged in the 'continuous' mode; the typical data spacing for the continuous temperature logs was 0.3 m (1 ft). Many of the Alaskan boreholes were re-logged several times to quantify the thermal disturbance caused by drilling the holes (see Lachenbruch and Brewer [1959]). A review of current temperature measuring techniques used by the USGS in the polar regions is given by Clow et al. [1996]. Data from 1950-1989 are included on the CAPS CD-ROM Version 1.0, June 1998.", "links": [ { diff --git a/datasets/GGD239_1.json b/datasets/GGD239_1.json index 6bebfbf230..60e2cca0fb 100644 --- a/datasets/GGD239_1.json +++ b/datasets/GGD239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data have been collected from an Arctic desert site (latitude 78o57'29N, longitude 12o27'42E), Broeggerhalvoya in western Spitsbergen, 10 km NW from Ny Alesund, 45 m above sea level, 2 km from the shore. This is a low relief tip of a bedrock peninsula covered with several meters of glacial drift and reworked raised beach ridges. The measurements are obtained in the site of well developed patterned ground, sorted polygons, where the influence of plants, including thermal insulation and transpiration, is negligible. The 1985-1986 period was average. Mean annual air temperature was -6.6 C, 0.4 C colder than the long-term (1975-1990) mean, but well within the mean variability. Mean winter air temperature is relatively warm (mean of coldest month, February, is -14.6 C). Annual precipitation was 17 % greater than the ong-term mean (372 mm); however, the number of rain-on-snow events was less (3) than average (5.5). Overall, the reference period is close to long-term averages. \n \nA program of automated soil temperature recordings was initiated in the summer of 1984, at a patterned ground field site Thermistors were placed approximately 0.1 m apart in an epoxy-filled PVC rod (18 mm outside diameter), buried in the center of a fine-grained domain of a sorted circle, down to 1.14 m below the ground surface. The data presented here covers 7/1/85-7/1/86, once a day (6 am), at two levels (0.0 m, 1.145 m below surface). The resolution of the thermistors is 0.004 C, and the accuracy is estimated to be 0.02 C near 0 C. Missing data accounts for less than 7 %. The gaps are filled with simple average of the beginning and end of the gap values. For a detailed description of the field site and data analysis see Putkonen (1997) and Hallet and Prestrud (1986). These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD23_1.json b/datasets/GGD23_1.json index 42cdeb17a0..f0412dc7f8 100644 --- a/datasets/GGD23_1.json +++ b/datasets/GGD23_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD23_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains active-layer and permafrost temperatures from Sisimiut, west Greenland, recorded from 18 sensors at depths of 0.25 m, 0.5 m, 0.75 m, 1 m, 1.25 m, 1.5 m, 1.75 m, 2 m, 2.5 m, 3 m, 3.5 m, 4 m, 4.5 m, 5 m, 6 m, 7 m, 8 m, and 9 m below the surface. Snow depth, snow extent, and surface air temperature were also recorded. Thermometers recorded temperatures once a day from September 1967 to August 1982; however, this data set only contains bi-weekly averages. Data are in tab-delimited ASCII text format and are available via FTP.", "links": [ { diff --git a/datasets/GGD249_1.json b/datasets/GGD249_1.json index 584fe603c9..10df5d97c2 100644 --- a/datasets/GGD249_1.json +++ b/datasets/GGD249_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD249_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow and soil temperature records for January 1988 - May 1996 are presented. Included are snow depth and weight measurements, snow density (calculated), active layer depth in the frost tubes, weight of wet and dried soil samples from unknown depth within the active layer (water content calculated), and soil temperature at the surface (0.05 cm) and to the depths of 3 to 4 meters at 3 sites. The sites are 1) on a road covered by 1 m of gravel underlain by clay; 2) outside a building on piles, (sensors are placed 1 to 2 m from the building wall); and 3) under the building between piles. In addition, air temperature was measured inside the building or between the piles (documentation is not clear on this point.) There are several gaps in temperature measurements (January 1991 to May 1992). These data are presented on the CAPS CD-ROM version 1.0, June 1998.\n\nAir temperature, wind direction, and temperature were measured at 5, 20, 50, 100, 150, and 200 cm below the tundra surface at an undisturbed site; and at 5, 20, 50, 100, 150, 200 cm, and 3 m and 8 m below the concrete surface of a building. Incoming radiation, outgoing radiation, temperature of the heat flux instrument, global radiation, heat flux, wind speed, wind speed maximum, average wind speed, and temperature inside the building were measured since 1993 with data loggers. All data are recorded for July 1987 - February 1996.", "links": [ { diff --git a/datasets/GGD272_1.json b/datasets/GGD272_1.json index 98a1da3caa..f04b6c8999 100644 --- a/datasets/GGD272_1.json +++ b/datasets/GGD272_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD272_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "U.S. pedon data on the CAPS Version 1.0 CD-ROM, June 1998, are a sample of the pedon data contained on a CD-ROM produced by the National Soil Survey Center - Soil Survey Laboratory(NSSC-SSL). The data include recent pedons from analyses for soil characterization and research within the National Cooperative Soil Survey. Less-than-complete characterization data are available for many pedons because only selected measurements were planned or because the planned measurements are not yet complete. This database is dynamic-- data for additional pedons are added as they are sampled and analyzed, other information is updated as pedons are classified, suspect measurements are rerun and replaced, and errors are found and corrected. The data on the NSSC-SSL CD-ROM represent a 'snapshot' of the database. The database includes pedons that represent the central concept of a soil series, pedons that represent the central concept of a map unit but not of a series, and pedons sampled to bracket a range of properties within a series or landscape. Thus, attribute data for some data elements may be incomplete or missing for certain portions of the United States. In instances where data are unavailable, a mask should be used to exclude the area from the analysis. For research purposes, all data are retained in the database. Users unfamiliar with a given soil or set of data may want to consult with a research soil scientist at the National Soil Survey Center. A research soil scientist can be reached by telephone at (402) 437-5006, or by writing the Soil Survey Laboratory Head, National Soil Survey Center, Natural Resources Conservation Service, Federal Building, Room 152, 100 Centennial Mall North, Lincoln, NE 68508-3866 USA. Pedons on the CAPS Version 1.0 CD-ROM cover areas in Russia (60 deg 37 min N to 69 deg 27 min N; 159 deg 07 min E to 161 deg 33 min\nE) and in Alaska (62 deg to 68 deg N; 135 deg to 149 deg W).", "links": [ { diff --git a/datasets/GGD311_1.json b/datasets/GGD311_1.json index 253a8179bf..00ebaee693 100644 --- a/datasets/GGD311_1.json +++ b/datasets/GGD311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pedons included here represent Cryosolic (permafrost-affected) soils from across the Canadian North from Baffin Island in the east, to the lower Mackenzie Valley and northern Yukon in the west, and to Ellesmere Island in the High Arctic. Pedon locations are Pangnirtung Pass, Baffin Island, N.W.T. (8 pedons); Inuvik area, N.W.T. (2 pedons); Mackenzie Delta, N.W.T. (2 pedons); Tanquary Fiord, Ellesmere Island, N.W.T. (4 pedons); Lake Hazen, Ellesmere Island, N.W.T. (4 pedons); Eagle Plains, northern Yukon (3 pedons); Dawson City area, central Yukon (2 pedons). \n\nCryosolic soils, according to the Canadian soil classification, are either mineral or organic materials that have permafrost either within 1 m of the surface (Static and Organic Cryosols) or within 2 m (Turbic Cryosols) if more than one-third of the pedon has been strongly cryoturbated, as indicated by disrupted, mixed, or broken horizons. They have a mean annual temperature below 0 degree C. In the soil profile descriptions, the perennially frozen (permafrost) soil horizons are identified by the letter 'z'. The descriptions and nomenclature used to describe these pedons are according to - Expert Committee on Soil Survey. 1983. The Canada Soil Information System, Manual for describing soil in the field. Agriculture Canada, Ottawa, Canada. Agriculture Canada Expert Committee on Soil Survey. 1987. The Canadian System of Soil Classification. (2nd ed.) Research Branch, Agriculture Canada, Ottawa, Canada. The methods for laboratory analysis are according to - Sheldrick, B.H. (editor). 1984. Analytical Methods Manual. 1984. Land Resource Research Institute, Agriculture Canada,\nOttawa, Canada.\n\nAdditional information relating to these pedons can be obtain by contacting Charles Tarnocai, Agriculture and Agri-Food Canada, Research Branch (ECORC), K.W. Neatby Building, Rm. 1135, 960 Carling Avenue, OTTAWA, Canada, K1A 0C6; Tel.- (613) 759-1857; Fax- (613) 759-1937; E-mail- tarnocaict@em.agr.ca. The data file on the CAPS Version 1.0 CD-ROM contains laboratory analyses of the soil samples, including chemical, physical, mineralogical (clay mineralogy when applicable), and particle size distribution analyses.", "links": [ { diff --git a/datasets/GGD316_1.json b/datasets/GGD316_1.json index e68f079ff7..e473ebcea6 100644 --- a/datasets/GGD316_1.json +++ b/datasets/GGD316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This catalog of boreholes from across Russia and Mongolia includes those published in papers and monographs as well as other literature of limited circulation. The 122 boreholes were used to derive a characterization of the Russian territory according to eight geocryological regions. Five boreholes are included for Mongolia. Data from these boreholes were used in the generation of the Circum-arctic Map of Permafrost and Ground-Ice Conditions (Brown et al., 1997). Data obtained from various sources as noted within each borehole entry. The time period varies for each borehole, but is primarily from the late 1980s to early 1990s. Observation methods include 'Standard logging', a combined natural gamma logging, electric logging and well caliper logging; 'Geothermal observations' which demonstrate the thickness of layer with the temperature below zero (data of Yakutsk Permafrost Institute, Siberian Branch, Academy of Sciences of the USSR); visual observations on ice-content in the core, and depth of appearance of fresh water table; thermologging of the boreholes (studies of 'PGO Yakutskgeologia'); and electric, well caliper and thermal logging in pioneer and exploratory oil and gas wells ('PGO Lenaneftegasgeologia' studies). The permafrost base is exposed by a number of adjacent boreholes; interval of fluctuations of permafrost depth is shown. The data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD318_2.json b/datasets/GGD318_2.json index a89d0b71d8..e8d506ab91 100644 --- a/datasets/GGD318_2.json +++ b/datasets/GGD318_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD318_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Circum-Arctic permafrost and ground ice map is available via ftp in ESRI Shapefile format and Equal-Area Scalable Earth Grid (EASE-Grid) format. See the Format section for an explanation of the files provided via FTP.\n\nThe circumpolar permafrost and ground ice data contribute to a unified international data set that depicts the distribution and properties of permafrost and ground ice in the Northern Hemisphere (20\u00b0N to 90\u00b0N). The re-gridded data set shows discontinuous, sporadic, or isolated permafrost boundaries. Permafrost extent is estimated in percent area (90-100 percent, 50-90 percent, 10-50 percent, <10 percent, and no permafrost). Relative abundance of ground ice in the upper 20 m is estimated in percent volume (>20 percent, 10-20 percent, <10 percent, and 0 percent). The data set also contains the location of subsea and relict permafrost. the gridded data are gridded at 12.5 km, 25 km, and 0.5 degree resolution. The shapefiles were derived from the original 1:10,000,000 paper map (Brown et al. 1997)\n\nPermafrost, or permanently frozen ground, is ground (soil, sediment, or rock) that remains at or below 0\u00b0C for at least two years (Permafrost Subcommittee, 1988). It occurs both on land and beneath offshore arctic continental shelves, and underlies about 22 percent of the Earth's land surface.\n\nFor more information on the creation of the original map, see Heginbottom et al. (1993). The original paper map also includes information on the relative abundance of ice wedges, massive ice bodies and Pingos, ranges of permafrost temperature and thickness (Brown et al. 1997).", "links": [ { diff --git a/datasets/GGD332_1.json b/datasets/GGD332_1.json index 2001159f70..89ba818055 100644 --- a/datasets/GGD332_1.json +++ b/datasets/GGD332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Location and description of some geocryological boreholes in Mongolia. Data include latitude, longitude, location, depth of permafrost top and bottom, and mean annual soil temperature. These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD353_6.json b/datasets/GGD353_6.json index 1d3cc58e92..0f8ea62a57 100644 --- a/datasets/GGD353_6.json +++ b/datasets/GGD353_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD353_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project involves measuring regional and site variability in maximum annual active layer development and vertical surface movement over permafrost, and monitoring sites over time in order to observe trends. The project records maximum thaw penetration, maximum heave and subsidence, late season snow depths, current depth of thaw, elevation, and soil properties. Some sites are twinned with soil- and air-temperature recording equipment.\n\nThe project includes about 60 monitoring stations extending from Fort Simpson, Canada, in the upper Mackenzie River valley to the Beaufort Sea coast at North Head, Richards Island, Canada. Ten of the sites are part of the IPA's Circumpolar Active Layer Monitoring (CALM) Program. CALM site numbers are in parentheses after the site names: North Head (C3), Taglu (C4), Lousy Point (C5), Reindeer Depot (C7), Rengleng River (C8), Mountain River (C9), Norman Wells (C11), Ochre River (C13), Willowlake River (C14), and Fort Simpson (C15). See the CALM Program Web page for geographic coordinates and site history for all CALM sites.\n\nThese data are the property of the people of Canada and the responsibility of the Geological Survey of Canada. If published, adequate acknowledgment is expected. Please contact F. M. Nixon regarding use of the data set or access to the extended data.", "links": [ { diff --git a/datasets/GGD402_1.json b/datasets/GGD402_1.json index 3411cabaf0..7ea75ac0cf 100644 --- a/datasets/GGD402_1.json +++ b/datasets/GGD402_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD402_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database of selected borehole records from the Yamal Peninsula, Russia, contains environmental descriptions (textual and numerical) of the units on the index map, and relevant borehole data. The Index Map of Yamal Peninsula (VSEGINGEO-Earth Cryosphere Institute SB RAS; PI - Prof.E.S.Melnikov) was originally compiled at a scale of 1 to 1,000,000, as 'The Map of Natural Complexes of West Siberia for the Purpose of Geocryological Prediction and Planning of Nature-Protection Measures for the Mass Construction, 1 to 1 mln' (1991) by E.S.Melnikov and N.G.Moskalenko (eds.). It was taken as a base map for nature-protection regionalization. Environmental 'regions', 'sub-regions', 'landscapes' and localities' shown on a landscape map are merged into the nature-protection regions. The map was compiled by interpreting more than 1000 satellite images and aerial photos as well as from analysis of field data from several institutions. Dominating components of the landscape, composition of the surface deposits, geocryological conditions and natural protection of ground water were considered while distinguishing the Nature-Protection Regions within the limits of Environmental Regions (Melnikov, 1988). The map is supplied with relevant databases, containing the following information - number of regions and landscape type; category of resiliency; category of the ground water protection; vegetation type; geological and geocryological structure to the depth of 10-15 m; ice content (of lenses and of macro-inclusions separately); thickness of seasonally frozen and seasonally thawed layers; ground temperature; contemporary exogenic geological (periglacial) processes; and the area affected by these processes.\n\nThe 55 nature-protection regions of Yamal Peninsula generalize information. To approve the ranges of geocryological and cryolithological characteristics, 160 boreholes were retrieved out of the database containing more than 4000 boreholes data obtained in 1977-1990 by Fundamentproekt Design Institute (Moscow, Russia; PI - Dr.sci.M.A.Minkin) at Kharasavey and Bovanenkovo gas fields and along the pipelines Yamal-Ukhta and Yamal-Uzhgorod. The boreholes have reference to geographical coordinates (latitude and longitude), as well as to the nature-protection region numbers shown on the Index Map. A total of 21 units are covered by borehole data, 5-8 boreholes in each unit, covering most typical conditions\n\nThe original database consisted of 3 relational tables. The first table includes category of resiliency; locality type description; landscape type description; ground-ice content, water saturation, cryogenic structure, macro-ground-ice content; vegetation types; seasonally frozen and seasonally thawed layer depths; ground temperature at 10 m; exogenic geological\n processes an their paragenesis and combinations; and degree of the surface disturbance. The second relational table contains layer-by-layer description of the lithological section types. The third table for the boreholes includes the description of topography around the borehole; types of geological profiles through the active layer and depths down to the permafrost table; ground temperature at 10-m depth (close to the depth of zero annual amplitude in the area); macro-ice content; and salinity of permafrost. These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD499_1.json b/datasets/GGD499_1.json index 4c1d70ee7c..a674f105a5 100644 --- a/datasets/GGD499_1.json +++ b/datasets/GGD499_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD499_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes observations of the permafrost temperatures in the Inner Tien Shan were started in 1986 by Kazakhstan Alpine Permafrost Laboratory. Observations are carried out on more than 40 boreholes, at altitudes between 3300-4200 m. The depths of the boreholes vary from 30 to 600 m. The boreholes are located in both loose (moraines) sediments and bedrock. Several boreholes are situated in the territory of the 'Kumtor' goldmine. The geocryological conditions of goldmine 'Kumtor' and nearby territory have been discussed in scientific reports 1988, 1989 and articles (see references). Two boreholes were drilled in body of glacier 'Davydov' and located in the central and lateral parts of the glacier (depth - 30 m). A third borehole passed through the glacier, moraine and bedrock to a depth of 600 m. In the Kumtor and Taragai valleys, permafrost temperature in 14 boreholes from 25 to 50 m depth, between 3300-3750 m ASL were observed. The distance between outermost boreholes is about 40 km.\n\nTemperature measurements in 9 geological prospecting adits [tunnels] (lengthwise 1500-1900 m) located in the four neighboring valleys (altitudes from 3920 to 4010 m) were carried out. At the same sites, but in natural conditions, the thermal conductivity of the bedrock was determined by the cylindrical sounding method. Grain size, soil moisture content, cryogenic structure and depth of seasonal thaw were also obtained from 15 pits located in differing altitudinal levels and exposures. At two further sites, ground temperatures measurements at depths of 0, 2, 5, 10, 15, 20 and 40 cm were taken every hour during daylight hours every 5 days over a two year period. Air temperature, wind velocity and duration of daylight were measured at the same time as the ground temperature measurements. These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD503_1.json b/datasets/GGD503_1.json index 70da090e6c..89643eb92b 100644 --- a/datasets/GGD503_1.json +++ b/datasets/GGD503_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD503_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precision temperature measurements have been made in some 150 deep wells and holes drilled in the course of natural resource exploration in the permafrost regions of Northern Canada. In most cases, holes were logged by lowering a probe containing a regions of Northern Canada. In most cases, holes were logged by lowering a probe containing a thermistor incrementally down the well, in other cases multi-thermistor cables were left in the holes and periodic measurements taken. In the 1990's, a few holes were logged by a automatic quasi- continuous logging system. Most holes were logged annually for 5-10 years after drilling completion, and measured temperatures show the disturbance due to drilling and the gradual recovery to near-undisturbed conditions. Some holes in the collection are of depth less than 125 m. Permafrost thicknesses are estimated at each well or hole from the depth of the 0 degree Celsius isotherm. This data collection provides the highest quality of permafrost temperature and permafrost thickness information available for Northern Canada. Other data are the large number of downhole temperature and permafrost thickness estimates taken during commercial well logging of petroleum exploration wells, and are by nature of lesser quality. These data are not included in this data set, but references to compilations of this data are provided. A short text (2000 words), tables of site locations and permafrost thicknesses with small-scale maps, and an extensive bibliography accompany the data collection. The file structure and contents of each file are well described. The text is sufficient to locate the data of interest, and the file description is adequate for a user to recover the parameters of interest. The data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD611_1.json b/datasets/GGD611_1.json index 2cf57a6941..5c6ff8bb4f 100644 --- a/datasets/GGD611_1.json +++ b/datasets/GGD611_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD611_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides air temperature (1.5 m above ground surface) data from the Kanchanjunga Himal, eastern Nepal. Air temperature was monitored from November 1998 to November 1999 at three locations (Tengkoma, Lhonak, and Ghunsa) at altitudes of 3410, 4750 and 6012 m ASL. Although temperature was measured at one-hour intervals, only daily mean values are provided.", "links": [ { diff --git a/datasets/GGD622_1.json b/datasets/GGD622_1.json index 0df2be7d6e..1d1843fff7 100644 --- a/datasets/GGD622_1.json +++ b/datasets/GGD622_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD622_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 76 active-layer depth measurements (cm) of the Vaisje\u00e4ggi palsa bog, Finland, from 08 September 1993 to 14 October 2002. Data were collected from a single location at 69 deg 49'16.6' N, 27 deg 10'17.1' E. Data also contain snow depth (cm) when snow cover was present. Data are in tab-delimited ASCII text format, and are available via ftp.", "links": [ { diff --git a/datasets/GGD623_1.json b/datasets/GGD623_1.json index 374743d901..00262a6777 100644 --- a/datasets/GGD623_1.json +++ b/datasets/GGD623_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD623_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thaw depths and water depths were monitored at 1 m to 2 m intervals along a 255-m transect across an area of discontinuous and degrading permafrost on the Tanana Flats south of Fairbanks, Alaska. Measurements were taken once a year in late August from 1995 to 2002 to show effects of winter snow depths, climate warming, and vegetation and wetland creation-surface subsidence. Data are in a single tab-delimited ASCII text file, available via FTP.", "links": [ { diff --git a/datasets/GGD632_1.json b/datasets/GGD632_1.json index 8f13e75871..f5d6d8724c 100644 --- a/datasets/GGD632_1.json +++ b/datasets/GGD632_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD632_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains active-layer and permafrost temperatures from two stations in Soendre Stroemfjord, Greenland. Snow depth and snow extent were also recorded. Thermometers at Station A (67 deg N, 50.8 deg W, 50 m asl) recorded temperatures once a day from September 1967 to February 1976. Thermometers at Station B (67 deg N, 50.8 deg W, 38 m asl) recorded temperatures once a day from September 1967 to August 1970; however, only bi-weekly averages are given for Station B. Data are in tab-delimited ASCII text format and are available via FTP.", "links": [ { diff --git a/datasets/GGD641_2.json b/datasets/GGD641_2.json index 57f8be43e8..576f60491b 100644 --- a/datasets/GGD641_2.json +++ b/datasets/GGD641_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD641_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains near-surface (< 5 cm) soil freeze/thaw status on snow-free and snow-covered land surfaces over the Arctic terrestrial drainage basin. The near-surface soil freeze/thaw status is determined by using passive-microwave remote sensing data over snow-free land and a numerical model over snow-covered land. Data are projected to a 25 km x 25 km Northern Hemisphere EASE-Grid. Version 2 of this data set greatly extends the temporal coverage and makes use of data from SMMR as well as SSM/I. Data are from October 1978 to June 2004. Data are in ASCII text format and are available via FTP.", "links": [ { diff --git a/datasets/GGD642_1.json b/datasets/GGD642_1.json index 4bbce09531..2e1ca8e19c 100644 --- a/datasets/GGD642_1.json +++ b/datasets/GGD642_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD642_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thaw-depth data were collected annually in August from 21 August 1979 to 18 August 1999, from 400-m, 600-m, and tundra transects at Illisarvik lake, Richards Island, Northwest Territories, Canada. Coordinates are 69 deg 30 min N, 134 deg 32 min W. Data are in tab-delimited ASCII text format, and are available via FTP.", "links": [ { diff --git a/datasets/GGD646_1.json b/datasets/GGD646_1.json index 70f6c892ab..db976dd9d5 100644 --- a/datasets/GGD646_1.json +++ b/datasets/GGD646_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD646_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil temperature data from three deep boreholes in the Ob River valley in Russia. Boreholes were drilled in 1967 (VK-1615), 1977 (ZS-124), and 1980 (ZS-124a) in discontinuous permafrost of approximately 70 m depth. Borehole VK-1615 was sampled to a depth of 100 m between 1971 and 2002, ZS-124 was sampled monthly to a depth of 13 m between 1978 and 1980, and ZS-124a was sampled monthly to a depth of 33.7 m between 1980 and 2002.", "links": [ { diff --git a/datasets/GGD648_1.json b/datasets/GGD648_1.json index edd7cbb838..5c819a4614 100644 --- a/datasets/GGD648_1.json +++ b/datasets/GGD648_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD648_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map of Geocryology and Geocryological Zonation of Mongolia was derived from the National Atlas of Mongolia (Sodnom and Yanshin, 1990). The data set depicts the distribution and general properties of permafrost and seasonally frozen ground and locations of specific cryogenic phenomena in Mongolia. Two plates were digitized. One plate, at a scale of 1:12,000,000, depicts four general geocryological zones: continuous and discontinuous permafrost, insular and sparsely insular permafrost, sporadic permafrost, and seasonally frozen ground. The second plate, at a scale of 1:4,500,000, depicts 14 different terrain classifications determined according to elevation, mean annual air temperature, permafrost thickness and thaw depth, and seasonal frozen ground freeze depth. The locations of six specific cryogenic phenomena are also included: perennial frost mounds, icings, thermokarst, cryogenic landslides, solifluction, and cryogenic planation. Data are available via FTP as ESRI shapefiles.", "links": [ { diff --git a/datasets/GGD651_1.json b/datasets/GGD651_1.json index 9b9b91fcc0..e3626c6ff4 100644 --- a/datasets/GGD651_1.json +++ b/datasets/GGD651_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD651_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains mean, median, minimum and maximum freeze and thaw depths for each year from \n1901 to 2002 on the 25 km resolution Equal-Area Scalable Earth Grid (EASE-Grid) for areas north of 50 \ndeg. Freeze and thaw depths are estimated using a variant of the Stefan solution using an edaphic factor \nand freezing or thawing indices as inputs. The edaphic factor is estimated based on different land surface \ntypes; the freezing and thawing indices are from Northern Hemisphere EASE-Grid annual freezing \nand thawing indices, 1901 - 2002 (Zhang, et al. 2005).\n\nTwo ASCII files are available for each year for freeze depth and thaw depth, respectively. Each file is \napproximately 25.6 MB in size. In addition, there is one 10.5 MB ASCII file defining the latitude and longitude coordinates for each grid point. The data set is available via FTP as three compressed files.", "links": [ { diff --git a/datasets/GGD700_1.json b/datasets/GGD700_1.json index e815571221..31422b8733 100644 --- a/datasets/GGD700_1.json +++ b/datasets/GGD700_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD700_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four groups of borehole data from the Qinghai-Xizang (Tibet) Plateau are presented. \n1) Boreholes at three sites, with sand surface, natural surface, and near a sand dune, at 66 Road Station - 1994 and 1995 measurements to about 17 meters. \n2) Borehole temperatures at Borehole CK123 - 1979, 1984, 1994 measurements to 60 meters.\n3) Borehole temperatures at five sites in Fenghuoshan Station area - 1962, 1967, 1980, 1984, 1989, 1994, 1995 measurements to 35 meters. \n4) Boreholes at Xidatan-Kunlun Pass area - 1994 and 1995 measurements to 17.5 meters; 1994 and 1995 measurements to 25 meters; and 1975, 1976, 1979, 1985, 1989, 1994 and 1995 to 30 meters. Data provided by Wang Shaoling and Cheng Guodong, Lanzhou Institute of Glaciology and Geocryology. Some of these data are presented on the CAPS Version 1.0 CD-ROM, June 1998.", "links": [ { diff --git a/datasets/GGD906_1.json b/datasets/GGD906_1.json index 887b7ede7c..e3dfce322c 100644 --- a/datasets/GGD906_1.json +++ b/datasets/GGD906_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GGD906_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily air, water, and soil temperature, wind speed, vapor pressure, and the sum of global radiation and unfrozen precipitation data from the Toolik Lake area of Alaska between 1998 and 2002. The data includes readings from two sites: the Toolik Arctic Long Term Ecological Research (LTER) Tundra site and the nearby Toolik Tussock Experimental plots site that includes soil measurements from fertilized and unfertilized greenhouse and 'shadehouse' areas.\n\nData loggers recorded soil temperatures at various intervals down to 150 cm. Air temperatures were recorded between 1 and 5 meters. The data consist of 28 comma-delimited ASCII text files, and are available via ftp. Data files for each site contain slightly different meteorological parameters. This data set is a subset of the more comprehensive data set, Monitoring and Manipulation of Tundra Response to Climate Change, Arctic LTER, Toolik Lake, Alaska. \n\nThe research project was funded by the Arctic System Sciences (ARCSS) Program, grant number OPP-9810222.", "links": [ { diff --git a/datasets/GHGSat.archive.and.tasking_7.0.json b/datasets/GHGSat.archive.and.tasking_7.0.json index 38114aa797..a3e98a4e16 100644 --- a/datasets/GHGSat.archive.and.tasking_7.0.json +++ b/datasets/GHGSat.archive.and.tasking_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GHGSat.archive.and.tasking_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GHGSat data produce measures of vertical column densities of greenhouse gas emissions (currently CH4, but eventually CO2), provided on a pre-defined area of 12 km x 12 km, from the full sensor field-of-view.\rGHGSat Catalogue and New Collect data are available in three different data types:\r\u2022\tSingle Observation: a single observation of a scene.\r\u2022\tMonthly Monitoring: guaranteed 12 successful observations in a year over a given site (once per month or flexible best effort cadence depending on weather).\r\u2022\tWeekly Monitoring: guaranteed 52 successful observations in a year over a given site (once a week or flexible best effort cadence based on weather), to accommodate large & persistent monitoring needs.\rData are provided as an Emissions package containing the following products:\r\u2022\tAbundance dataset (Level 2): Set of per-pixel abundances in excess of the local background (ppb) for a single species, and per-pixel measurement error expressed as a standard deviation for a single site on a single satellite pass. Data format is 16-bit GeoTIFF.\r\u2022\tConcentration Maps (Level 2): High readability pseudocolour map combining surface reflectance, and column density expressed in ppb for a single species in PNG format. The relevant abundance dataset is provided as well.\r\u2022\tEmission Rates (Level 4): Instantaneous rate for a detected emission from a targeted source estimated using abundance datasets from a single satellite pass and applying dispersion modelling techniques. The delivered product includes the emission rate estimate with uncertainty and key dispersion parameters (in CSV format) as well as the abundance dataset used for the emission estimate. This product is only delivered in the Emissions package if an emission is detected within the abundance dataset. The Level 2 products will be delivered regardless of whether or not an emission is detected.\rThe properties of the available products are summarised in the following table:\rBand(s) / Beam Mode(s) and Polarisation\tSWIR\t(1635-1675 nm), multiple bands, unpolarised\rSpatial Resolution\t<30 m\rScene Size\t12 km x 12 km\rSpecies Measured\tCH4\rGeometric Corrections\tRadial distortion, perspective projection\rRadiometric Corrections\tDetector pixel response, ghosting, spectral response, atmospheric correction including trace gas modelling and surface reflectance\r\rDetails about the data provision, data access conditions and quota assignment procedure are described in the GHGSat _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/GHGSat-Terms-of-Applicability.pdf .", "links": [ { diff --git a/datasets/GHISACASIA_001.json b/datasets/GHISACASIA_001.json index 3614f14771..b36e64dbcb 100644 --- a/datasets/GHISACASIA_001.json +++ b/datasets/GHISACASIA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GHISACASIA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy.\r\n\r\nThe GHISA for Central Asia (GHISACASIA) Version 1 product provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia. The GHISA hyperspectral library for the two irrigated areas was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired in 2007 and ASD (Analytical Spectral Devices, Inc.) Spectroradiometer data acquired in 2006 and 2007. GHISACASIA is extracted from three Hyperion hyperspectral images and several thousands of field ASD Spectroradiometer data. Measurements were taken from 1,232 randomly chosen points scattered across the two farm sites throughout the growing season. All the processing algorithms are coded in Statistical Analysis System (SAS) format and available for download.\r\n\r\nProvided in the .xlsx files are the spectral library including image information, plot IDs, study area, instrument, Julian or acquisition date, and crop type labels for Central Asia sample fields.\r\n", "links": [ { diff --git a/datasets/GHISACONUS_001.json b/datasets/GHISACONUS_001.json index d29b07752b..1d3a0ce41b 100644 --- a/datasets/GHISACONUS_001.json +++ b/datasets/GHISACONUS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GHISACONUS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy.\r\n\r\nThe GHISA for the Conterminous United States (GHISACONUS) Version 1 product provides dominant crop data in different growth stages for various agroecological zones (AEZs) of the United States. The GHISA hyperspectral library of the five major agricultural crops (e.g., winter wheat, rice, corn, soybeans, and cotton) for CONUS was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired from 2008 through 2015 from different AEZs of CONUS using the United States Department of Agriculture (USDA) Cropland Data Layer (CDL) as reference data..\r\n\r\nGHISACONUS is comprised of seven AEZs throughout the United States covering the major agricultural crops in six different growth stages: emergence/very early vegetative (Emerge VEarly), early and mid vegetative (Early Mid), late vegetative (Late), critical, maturing/senescence (Mature Senesc), and harvest. The crop growth stage data were derived using crop calendars generated by the Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison.\r\n\r\nProvided in the CSV file is the spectral library including image information, geographic coordinates, corresponding agroecological zone, crop type labels, and crop growth stage labels for the United States.\r\n", "links": [ { diff --git a/datasets/GIMMS3g_NDVI_Trends_1275_1.json b/datasets/GIMMS3g_NDVI_Trends_1275_1.json index 4214cc45b2..5ca657cf86 100644 --- a/datasets/GIMMS3g_NDVI_Trends_1275_1.json +++ b/datasets/GIMMS3g_NDVI_Trends_1275_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GIMMS3g_NDVI_Trends_1275_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides normalized difference vegetation index (NDVI) data for the arctic growing season derived primarily with data from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard several NOAA satellites over the years 1982 through 2012. The NDVI data, which show vegetation activity, were averaged annually for the arctic growing season (GS; June, July and August). The products include the annual GS-NDVI values and the results of a cumulative GS-NDVI time series trends analysis. The data are circumpolar in coverage at 8-km resolution and limited to greater than 20 degrees N.These normalized difference vegetation index (NDVI) trends were calculated using the third generation data set from the Global Inventory Modeling and Mapping Studies (GIMMS 3g). GIMMS 3g improves on its predecessor (GIMMS g) in three important ways. First, GIMMS 3g integrates data from NOAA-17 and 18 satellites to lengthen its record. Second, it addresses the spatial discontinuity north of 72 degrees N, by using SeaWiFS, in addition to SPOT VGT, to calibrate between the second and third versions of the AVHRR sensor (AVHRR/2 and AVHRR/3). Finally, the GIMMS 3g algorithm incorporates improved snowmelt detection and is calibrated based on data from the shorter, arctic growing season (May-September) rather than the entire year (January-December). ", "links": [ { diff --git a/datasets/GISS-CMIP5_1.json b/datasets/GISS-CMIP5_1.json index 8a14cadcf9..2f9520932c 100644 --- a/datasets/GISS-CMIP5_1.json +++ b/datasets/GISS-CMIP5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GISS-CMIP5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980-2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics.", "links": [ { diff --git a/datasets/GIS_EastAngliaClimateMonthly_551_1.json b/datasets/GIS_EastAngliaClimateMonthly_551_1.json index ffc0000135..822d272806 100644 --- a/datasets/GIS_EastAngliaClimateMonthly_551_1.json +++ b/datasets/GIS_EastAngliaClimateMonthly_551_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GIS_EastAngliaClimateMonthly_551_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 0.5 degree lat/lon data set of monthly surface climate over global land areas, excluding Antarctica. Primary variables are interpolated directly from station time-series: precipitation, mean temperature and diurnal temperature range.", "links": [ { diff --git a/datasets/GLAH01_033.json b/datasets/GLAH01_033.json index 7402b8f64b..4e814a7f0d 100644 --- a/datasets/GLAH01_033.json +++ b/datasets/GLAH01_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH01_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH02_033.json b/datasets/GLAH02_033.json index c264170ec1..4ac5f54c0a 100644 --- a/datasets/GLAH02_033.json +++ b/datasets/GLAH02_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH02_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH03_033.json b/datasets/GLAH03_033.json index 95f7c80a73..1002aa1da3 100644 --- a/datasets/GLAH03_033.json +++ b/datasets/GLAH03_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH03_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.", "links": [ { diff --git a/datasets/GLAH04_033.json b/datasets/GLAH04_033.json index 2d743c99f8..47f72bdf26 100644 --- a/datasets/GLAH04_033.json +++ b/datasets/GLAH04_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH04_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.", "links": [ { diff --git a/datasets/GLAH05_034.json b/datasets/GLAH05_034.json index 16eb8bc938..be68b63d0d 100644 --- a/datasets/GLAH05_034.json +++ b/datasets/GLAH05_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH05_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH06_034.json b/datasets/GLAH06_034.json index 15ca139efe..27241c03b6 100644 --- a/datasets/GLAH06_034.json +++ b/datasets/GLAH06_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH06_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH07_033.json b/datasets/GLAH07_033.json index 86de3a12bd..33c13cf6e5 100644 --- a/datasets/GLAH07_033.json +++ b/datasets/GLAH07_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH07_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH08_033.json b/datasets/GLAH08_033.json index 80f9c1d506..073214aacb 100644 --- a/datasets/GLAH08_033.json +++ b/datasets/GLAH08_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH08_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH09_033.json b/datasets/GLAH09_033.json index bfa6a6e28b..5dad57170d 100644 --- a/datasets/GLAH09_033.json +++ b/datasets/GLAH09_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH09_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH10_033.json b/datasets/GLAH10_033.json index 104ecdfb8f..dd4dfea822 100644 --- a/datasets/GLAH10_033.json +++ b/datasets/GLAH10_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH10_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH11_033.json b/datasets/GLAH11_033.json index 8cd886769d..f47cbf980e 100644 --- a/datasets/GLAH11_033.json +++ b/datasets/GLAH11_033.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH11_033", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH12_034.json b/datasets/GLAH12_034.json index d0f2b142b0..528b8028d5 100644 --- a/datasets/GLAH12_034.json +++ b/datasets/GLAH12_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH12_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH13_034.json b/datasets/GLAH13_034.json index a5862c6939..80b4dc4b31 100644 --- a/datasets/GLAH13_034.json +++ b/datasets/GLAH13_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH13_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH14_034.json b/datasets/GLAH14_034.json index bf386debf0..71dd55d805 100644 --- a/datasets/GLAH14_034.json +++ b/datasets/GLAH14_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH14_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLAH15_034.json b/datasets/GLAH15_034.json index 923966318f..706d4439e3 100644 --- a/datasets/GLAH15_034.json +++ b/datasets/GLAH15_034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLAH15_034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "links": [ { diff --git a/datasets/GLCHMK_001.json b/datasets/GLCHMK_001.json index 1dbff5c204..e90f07e97d 100644 --- a/datasets/GLCHMK_001.json +++ b/datasets/GLCHMK_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLCHMK_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Canopy Height Model Keyhole Markup Language (KML) data product (GLCHMK) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA\u2019s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. \r\n\r\nGLCHMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format. ", "links": [ { diff --git a/datasets/GLCHMT_001.json b/datasets/GLCHMT_001.json index 761b919f13..1182c2bb0f 100644 --- a/datasets/GLCHMT_001.json +++ b/datasets/GLCHMT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLCHMT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Canopy Height Model data product (GLCHMT) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA\u2019s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.\r\n\r\nGLCHMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format. \r\n", "links": [ { diff --git a/datasets/GLDAS_CLM10SUBP_3H_001.json b/datasets/GLDAS_CLM10SUBP_3H_001.json index b0649f0980..623194ea53 100644 --- a/datasets/GLDAS_CLM10SUBP_3H_001.json +++ b/datasets/GLDAS_CLM10SUBP_3H_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLM10SUBP_3H_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "With the upgraded Land Surface Models (LSMs) and updated forcing data sets, the GLDAS version 2.1 (GLDAS-2.1) production stream serves as a replacement for GLDAS-001. The entire GLDAS-001 collection from January 1979 through March 2020 was decommissioned on June 30, 2020 and removed from the GES DISC system. However, the replacement for GLDAS-001 monthly and 3-hourly 1.0 x 1.0 degree products from CLM Land Surface Model currently are not available yet. Once their replacement data products become available, the DOIs of GLDAS-001 CLM data products will direct to the GLDAS-2.1 CLM data products.\n\nThis data set contains a series of land surface parameters simulated from the Common Land Model (CLM) V2.0 model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0 degree resolution and range from January 1979 to present. The temporal resolution is 3-hourly. \n\nThis simulation was forced by a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET). The simulation was initialized on 1 January 1979 using soil moisture and other state fields from a GLDAS/CLM model climatology for that day of the year. \n\nWGRIB or another GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor more information, please see the README document.", "links": [ { diff --git a/datasets/GLDAS_CLSM025_DA1_D_2.2.json b/datasets/GLDAS_CLSM025_DA1_D_2.2.json index a0179fcb8d..35b287ba32 100644 --- a/datasets/GLDAS_CLSM025_DA1_D_2.2.json +++ b/datasets/GLDAS_CLSM025_DA1_D_2.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM025_DA1_D_2.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.2 is new to the GES DISC archive and currently includes a main product from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and Climate Experiment (GRACE-DA) from February 2003 to present. The GLDAS-2.2 data are available in two production streams: one with GRACE data assimilation outputs (the main production stream), and one without GRACE-DA (the early production stream). Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency. Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive. \n\nThe GLDAS-2.2 GRACE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System. The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields.\n\nThe GLDAS-2.2 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_CLSM025_DA1_D_EP_2.2.json b/datasets/GLDAS_CLSM025_DA1_D_EP_2.2.json index 4af9182f4e..c0b2b53e1c 100644 --- a/datasets/GLDAS_CLSM025_DA1_D_EP_2.2.json +++ b/datasets/GLDAS_CLSM025_DA1_D_EP_2.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM025_DA1_D_EP_2.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.2 is new to the GES DISC archive and currently includes a main product from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and Climate Experiment (GRACE-DA) from February 2003 to present. The GLDAS-2.2 data are available in two production streams: one with GRACE data assimilation outputs (the main production stream), and one without GRACE-DA (the early production stream). Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency. Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive. \n\nThis data product is an Early Product for GLDAS-2.2 Catchment Land Surface Model daily 0.25 x 0.25 degree with GRACE-DA. \n\nThe GLDAS-2.2 GARCE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System. The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields.\n\nThe GLDAS-2.2 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_CLSM025_D_2.0.json b/datasets/GLDAS_CLSM025_D_2.0.json index d7f3f15b80..5741499810 100644 --- a/datasets/GLDAS_CLSM025_D_2.0.json +++ b/datasets/GLDAS_CLSM025_D_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM025_D_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 0.25 degree daily, contains a series of land surface parameters simulated from the Catchment Land Surface Model 3.6, and currently covers from January 1948 to December 2014.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products. \n\nThe GLDAS-2.0 data are archived and distributed in netCDF format.", "links": [ { diff --git a/datasets/GLDAS_CLSM10_3H_2.0.json b/datasets/GLDAS_CLSM10_3H_2.0.json index 8aa0b61832..09dc15cdca 100644 --- a/datasets/GLDAS_CLSM10_3H_2.0.json +++ b/datasets/GLDAS_CLSM10_3H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_3H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 1.0 degree 3-hourly, contains a series of land surface variables simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format. \n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_CLSM10_3H_2.1.json b/datasets/GLDAS_CLSM10_3H_2.1.json index 3ee6c71c7c..1cd171f1c3 100644 --- a/datasets/GLDAS_CLSM10_3H_2.1.json +++ b/datasets/GLDAS_CLSM10_3H_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_3H_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is for GLDAS-2.1 Catchment 3-hourly 1.0 degree data from the main production stream. It was simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. \n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n", "links": [ { diff --git a/datasets/GLDAS_CLSM10_3H_EP_2.1.json b/datasets/GLDAS_CLSM10_3H_EP_2.1.json index eb36dbecf8..11a5820ea1 100644 --- a/datasets/GLDAS_CLSM10_3H_EP_2.1.json +++ b/datasets/GLDAS_CLSM10_3H_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_3H_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Catchment 1.0 degree 3-hourly dataset. \n\nThe GLDAS-2.1 3 hourly 1.0 degree product was simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. \n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_CLSM10_M_2.0.json b/datasets/GLDAS_CLSM10_M_2.0.json index 4b583b7dbf..ca83947886 100644 --- a/datasets/GLDAS_CLSM10_M_2.0.json +++ b/datasets/GLDAS_CLSM10_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_CLSM10_M_2.1.json b/datasets/GLDAS_CLSM10_M_2.1.json index f09e61052f..dc15ea1941 100644 --- a/datasets/GLDAS_CLSM10_M_2.1.json +++ b/datasets/GLDAS_CLSM10_M_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_M_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is for GLDAS-2.1 Catchment monthly 1.0 degree data from the main production stream. It was generated through temporal averaging of GLDAS-2.1 3-hourly data simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. \n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_CLSM10_M_EP_2.1.json b/datasets/GLDAS_CLSM10_M_EP_2.1.json index 401794cb28..e68670f48c 100644 --- a/datasets/GLDAS_CLSM10_M_EP_2.1.json +++ b/datasets/GLDAS_CLSM10_M_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_CLSM10_M_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Catchment 1.0 degree monthly dataset. \n\nThe GLDAS-2.1 monthly 1.0 degree product was generated through temporal averaging of GLDAS-2.1 3-hourly data simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. \n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_NOAH025_3H_2.0.json b/datasets/GLDAS_NOAH025_3H_2.0.json index 546268def6..9c5aca6c21 100644 --- a/datasets/GLDAS_NOAH025_3H_2.0.json +++ b/datasets/GLDAS_NOAH025_3H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_3H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data product, GLDAS-2.0 0.25 degree 3-hourly, was reprocessed and replaced its previous data product on November 27, 2019. The data product contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). \n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n", "links": [ { diff --git a/datasets/GLDAS_NOAH025_3H_2.1.json b/datasets/GLDAS_NOAH025_3H_2.1.json index bd7c6bcfa7..e0f7f0ecc6 100644 --- a/datasets/GLDAS_NOAH025_3H_2.1.json +++ b/datasets/GLDAS_NOAH025_3H_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_3H_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive.\n\nThis data product, reprocessed in January 2020, is for GLDAS-2.1 Noah 3-hourly 0.25 degree data from the main production stream and it is a replacement to its previous version.\n\nThe 3-hourly data product was simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n", "links": [ { diff --git a/datasets/GLDAS_NOAH025_3H_EP_2.1.json b/datasets/GLDAS_NOAH025_3H_EP_2.1.json index 76650dca36..cf3cbce75e 100644 --- a/datasets/GLDAS_NOAH025_3H_EP_2.1.json +++ b/datasets/GLDAS_NOAH025_3H_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_3H_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Noah 0.25 degree 3-hourly dataset. \n\nThe 3-hourly data product was simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_NOAH025_M_2.0.json b/datasets/GLDAS_NOAH025_M_2.0.json index 01d4d7b8fd..4c9cc77049 100644 --- a/datasets/GLDAS_NOAH025_M_2.0.json +++ b/datasets/GLDAS_NOAH025_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data product, GLDAS-2.0 0.25 degree monthly, was reprocessed and replaced its previous data product on November 19, 2019. The data product was generated through temporal averaging of the reprocessed 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The land mask was modified to accommodate the river routing scheme included in the simulations in the fall 2019 update. \n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_NOAH025_M_2.1.json b/datasets/GLDAS_NOAH025_M_2.1.json index 9e9bf1184a..718541f09e 100644 --- a/datasets/GLDAS_NOAH025_M_2.1.json +++ b/datasets/GLDAS_NOAH025_M_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_M_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product, reprocessed in January 2020, is GLDAS-2.1 Noah monthly 0.25 degree data from the main production stream and it is a replacement to its previous version. \n\nThe monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency’s AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n", "links": [ { diff --git a/datasets/GLDAS_NOAH025_M_EP_2.1.json b/datasets/GLDAS_NOAH025_M_EP_2.1.json index 831fcfa6e5..3fdac15e7b 100644 --- a/datasets/GLDAS_NOAH025_M_EP_2.1.json +++ b/datasets/GLDAS_NOAH025_M_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH025_M_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Noah 0.25 degree monthly dataset. \n\nThe monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency’s AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_NOAH10_3H_2.0.json b/datasets/GLDAS_NOAH10_3H_2.0.json index c02477e419..534beb8740 100644 --- a/datasets/GLDAS_NOAH10_3H_2.0.json +++ b/datasets/GLDAS_NOAH10_3H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_3H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data product, GLDAS-2.0 1.0 degree 3-hourly, was reprocessed and replaced its previous data product on November 22, 2019. The data product contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The land mask was modified to accommodate the river routing scheme included in the simulations in the fall 2019 update. \n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_NOAH10_3H_2.1.json b/datasets/GLDAS_NOAH10_3H_2.1.json index 98a2e1f7f1..5bdc53e4c6 100644 --- a/datasets/GLDAS_NOAH10_3H_2.1.json +++ b/datasets/GLDAS_NOAH10_3H_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_3H_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product, reprocessed in January 2020, is for GLDAS-2.1 Noah 3-hourly 1.0 degree data from the main production stream and it is a replacement to its previous version.\n\nThe 3-hourly data product was simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_NOAH10_3H_EP_2.1.json b/datasets/GLDAS_NOAH10_3H_EP_2.1.json index 879bfd0d5c..78e5c8ef14 100644 --- a/datasets/GLDAS_NOAH10_3H_EP_2.1.json +++ b/datasets/GLDAS_NOAH10_3H_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_3H_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Noah 1.0 degree 3-hourly dataset. \n\nThe 3-hourly data product was simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_NOAH10_M_2.0.json b/datasets/GLDAS_NOAH10_M_2.0.json index 7ef128c12f..088f58ca46 100644 --- a/datasets/GLDAS_NOAH10_M_2.0.json +++ b/datasets/GLDAS_NOAH10_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data product, GLDAS-2.0 1.0 degree monthly, was reprocessed and replaced its previous data product on November 19, 2019. The data product was generated through temporal averaging of the reprocessed 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The land mask was modified to accommodate the river routing scheme included in the simulations in the fall 2019 update. \n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n", "links": [ { diff --git a/datasets/GLDAS_NOAH10_M_2.1.json b/datasets/GLDAS_NOAH10_M_2.1.json index e7bfb9b43f..8a0bf26e3c 100644 --- a/datasets/GLDAS_NOAH10_M_2.1.json +++ b/datasets/GLDAS_NOAH10_M_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_M_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products. \n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product, reprocessed in January 2020, is for GLDAS-2.1 Noah monthly 1.0 degree data from the main production stream and it is a replacement to its previous version.\n\nThe monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_NOAH10_M_EP_2.1.json b/datasets/GLDAS_NOAH10_M_EP_2.1.json index 72851a1ff5..bb3fe2750f 100644 --- a/datasets/GLDAS_NOAH10_M_EP_2.1.json +++ b/datasets/GLDAS_NOAH10_M_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_NOAH10_M_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 Noah 1.0 degree monthly dataset. \n\nThe monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_VIC10_3H_2.0.json b/datasets/GLDAS_VIC10_3H_2.0.json index f92b49f2f5..c99458b978 100644 --- a/datasets/GLDAS_VIC10_3H_2.0.json +++ b/datasets/GLDAS_VIC10_3H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_3H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 VIC 3-hourly 1.0 degree, contains a series of land surface variables simulated simulated with the Variable Infiltration Capacity (VIC) 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_VIC10_3H_2.1.json b/datasets/GLDAS_VIC10_3H_2.1.json index 3bd057b749..34032b1fc1 100644 --- a/datasets/GLDAS_VIC10_3H_2.1.json +++ b/datasets/GLDAS_VIC10_3H_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_3H_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is for GLDAS-2.1 VIC 3-hourly 1.0 degree data from the main production stream. It was simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data. ", "links": [ { diff --git a/datasets/GLDAS_VIC10_3H_EP_2.1.json b/datasets/GLDAS_VIC10_3H_EP_2.1.json index e870024841..ca08095bca 100644 --- a/datasets/GLDAS_VIC10_3H_EP_2.1.json +++ b/datasets/GLDAS_VIC10_3H_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_3H_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 VIC 1.0 degree 3-hourly dataset. \n\nThe GLDAS-2.1 VIC 1.0 degree 3-hourly data product was simulated with the VIC Land Surface Model 4.1.2 in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDAS_VIC10_M_2.0.json b/datasets/GLDAS_VIC10_M_2.0.json index b2078ef61d..a994005901 100644 --- a/datasets/GLDAS_VIC10_M_2.0.json +++ b/datasets/GLDAS_VIC10_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 VIC monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in NetCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n\n\n", "links": [ { diff --git a/datasets/GLDAS_VIC10_M_2.1.json b/datasets/GLDAS_VIC10_M_2.1.json index e0db74e27f..2f0001cfdd 100644 --- a/datasets/GLDAS_VIC10_M_2.1.json +++ b/datasets/GLDAS_VIC10_M_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_M_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is for GLDAS-2.1 VIC monthly 1.0 degree data from the main production stream. It was generated through temporal averaging of GLDAS-2.1 3-hourly data simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.", "links": [ { diff --git a/datasets/GLDAS_VIC10_M_EP_2.1.json b/datasets/GLDAS_VIC10_M_EP_2.1.json index aceec6acc6..323297bde9 100644 --- a/datasets/GLDAS_VIC10_M_EP_2.1.json +++ b/datasets/GLDAS_VIC10_M_EP_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDAS_VIC10_M_EP_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. \n\nThis data product is an Early Product for GLDAS-2.1 VIC 1.0 degree monthly dataset. \n\nThe GLDAS-2.1 VIC 1.0 degree monthly data product, was generated through temporal averaging of GLDAS-2.1 VIC 3-hourly data simulated with the VIC Land Surface Model 4.1.2 in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present.\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.\n\nThe GLDAS-2.1 products supersede their corresponding GLDAS-1 products.\n\nThe GLDAS-2.1 data are archived and distributed in NetCDF format.", "links": [ { diff --git a/datasets/GLDSMT_001.json b/datasets/GLDSMT_001.json index 47157ffd5e..91c9c03e86 100644 --- a/datasets/GLDSMT_001.json +++ b/datasets/GLDSMT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDSMT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission (https://gliht.gsfc.nasa.gov/) is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over CONUS, Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Digital Surface Model data product (GLDSMT) is to provide LiDAR-derived visualizations of elevation above bare earth. GLDSMT data is offered in multiple formats, including Digital Surface Model, Mean, Aspect, Rugosity, and Slope.\r\n\r\nGLDSMT data are processed as multiple raster data products (GeoTIFFs) at a nominal 1 meter spatial resolution over locally defined areas. A low resolution browse is also provided showing the digital surface model with a color map applied in PNG format. \r\n", "links": [ { diff --git a/datasets/GLDTMK_001.json b/datasets/GLDTMK_001.json index ecbfc8d7ed..eda861e58b 100644 --- a/datasets/GLDTMK_001.json +++ b/datasets/GLDTMK_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDTMK_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission (https://gliht.gsfc.nasa.gov/) utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Digital Terrain Model Keyhole Markup Language (KML) data product (GLDTMK) is to provide LiDAR-derived bare earth elevation, aspect and slope on the EGM96 Geopotential Model. Scientists at NASA\u2019s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and the collection will continue to grow as aerial campaigns are flown and processed. \r\n\r\nGLDTMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the digital terrain with a color map applied in JPEG format. ", "links": [ { diff --git a/datasets/GLDTMT_001.json b/datasets/GLDTMT_001.json index 62cfe77bb4..4fa5874417 100644 --- a/datasets/GLDTMT_001.json +++ b/datasets/GLDTMT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLDTMT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission (https://gliht.gsfc.nasa.gov/) utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\n\nThe purpose of G-LiHT\u2019s Digital Terrain Model data product (GLDTMT) is to provide LiDAR-derived bare earth elevation, aspect and slope on the EGM96 Geopotential Model. Scientists at NASA\u2019s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.\n\nGLDTMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the digital terrain with a color map applied in JPEG format. \n\n", "links": [ { diff --git a/datasets/GLE_Bibliography_1.json b/datasets/GLE_Bibliography_1.json index ab2fcddc36..76787ab193 100644 --- a/datasets/GLE_Bibliography_1.json +++ b/datasets/GLE_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLE_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This bibliography contains references to Ground Level Enhancements (GLE) - rare but powerful radiation storms from the sun. The compilers would appreciate lists of missed and new items for inclusion. These should be sent to the dataofficer at the Australian Antarctic Data Centre at the contact details listed below.\n\nThe fields in this dataset are:\nyear\nauthor\ntitle\njournal", "links": [ { diff --git a/datasets/GLE_Database_1.json b/datasets/GLE_Database_1.json index a47e391771..6bfddda71d 100644 --- a/datasets/GLE_Database_1.json +++ b/datasets/GLE_Database_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLE_Database_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOTE: This database has been taken offline, as it was no longer maintained. A copy of the data that was stored in the database is available for download from the provided URL.\n\nThe database holds all known cosmic ray data from the worldwide network of observatories that observed Ground Level Enhancements.\n\nThe Cosmic Ray program contributes to our understanding of the radiation environment in space near the Earth. Radiation constantly bombards the Earth from space and is measurable at and below the surface of the Earth. The high energy particles are detected at the Mawson cosmic ray laboratory with large detectors located on the surface and in an underground vault. This is the only system of its type in polar regions and gives a unique view of the radiation effects. Variations in the radiation are constantly monitored. The sun plays a major role in generating the changes. The radiation levels are important to spacecraft and crew and to high altitude aircraft flying on polar routes. There is also some evidence that the radiation may influence climate.", "links": [ { diff --git a/datasets/GLFC_FishHabitatDatabase.json b/datasets/GLFC_FishHabitatDatabase.json index df8a1edd97..e1a28cac33 100644 --- a/datasets/GLFC_FishHabitatDatabase.json +++ b/datasets/GLFC_FishHabitatDatabase.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLFC_FishHabitatDatabase", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fish Habitat Database is a synthesis of extensive information on habitat\nrequirements and characteristics of selected Great Lakes fish species.\nSponsored by the Great Lakes Fishery Commission through its Habitat Advisory\nBoard, the Fish Habitat Database was developed in response to a need for\nhabitat information on Great Lakes fish species. This need was identified in\npart in the Joint Strategic Plan for Management of the Great Lakes Fisheries.\nIn addition, natural resource managers from environmental agencies indicated a\nneed for fish habitat data in order to develop and implement Fish Management\nPlans, Lakewide Management Plans, and Remedial Action Plans that recognize fish\nas an integral component of the Great Lakes ecosystem. The database potentially\ncontains habitat information for 18 selected fish species at five stages of\ntheir life and in six bodies of water. This information was obtained primarily\nfrom the U. S. Fish and Wildlife Service's Habitat Suitability Index Models and\nother data where available. This version of the database is not the final\nproduct. In fact, information gaps are present in this version. The database is\nintended to be an ongoing effort which will need maintenance as new needs are\nidentified and new information is discovered. The Great Lakes Fishery\nCommission is seeking an individual or agency that will be able to take over\nthis role on behalf of other users in the Great Lakes basin.", "links": [ { diff --git a/datasets/GLHYANC_001.json b/datasets/GLHYANC_001.json index a5ab22d55b..3f64a0eb87 100644 --- a/datasets/GLHYANC_001.json +++ b/datasets/GLHYANC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLHYANC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Hyperspectral Ancillary data product (GLHYANC) is to provide information related to aircraft attitude and altitude, view and solar angles, and other ancillary reflectance and radiance data. \r\n\r\nGLHYANC data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas.\r\n", "links": [ { diff --git a/datasets/GLHYVI_001.json b/datasets/GLHYVI_001.json index 9022074037..c2571e2121 100644 --- a/datasets/GLHYVI_001.json +++ b/datasets/GLHYVI_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLHYVI_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Hyperspectral Vegetative Indices data product (GLHYVI) is to provide vegetative, stress, and other index data in 44 science dataset layers. Included in the product are vegetative indices such as Normalized Difference Vegetation Index (NDVI), Triangular Vegetation Index (TVI), Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Difference Vegetation Index (DVI). Stress indices include, but are not limited to, Carter Stress, Gitelson and Merzlyac Stress, Maccioni Stress, and Vogelmann Stress.\r\n\r\nGLHYVI data are processed as a raster data product (GeoTIFF) at 1 meter spatial resolution over locally defined areas. A browse image displaying NDVI is also included.\r\n", "links": [ { diff --git a/datasets/GLLIDARPC_001.json b/datasets/GLLIDARPC_001.json index 3bb48e706b..76b5f201ba 100644 --- a/datasets/GLLIDARPC_001.json +++ b/datasets/GLLIDARPC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLLIDARPC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s LiDAR Point Cloud data product (GLLIDARPC) is to provide high-density individual LiDAR return data, including 3D coordinates, classified ground returns, Above Ground Level (AGL) heights, and LiDAR apparent reflectance. \r\n\r\nGLLIDARPC data are processed as a LAS Version 1.1 binary format specified by the American Society for Photogrammetry and Remote Sensing (ASPRS). The point cloud includes a density of more than 10 points per square meter. A low resolution browse is also provided showing the LiDAR Point Cloud as an Inverse Data Weighted (IDW) interpolation in PNG format. \r\n", "links": [ { diff --git a/datasets/GLMETRICS_001.json b/datasets/GLMETRICS_001.json index 853cb8b964..abe39b9121 100644 --- a/datasets/GLMETRICS_001.json +++ b/datasets/GLMETRICS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLMETRICS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\n\nThe purpose of G-LiHT\u2019s Metrics data product (GLMETRICS) is to provide extensive lidar height and density metrics and return statistics in more than 80 science data set layers. Included in the product are mean, standard deviation, and percentile information for ground, tree, and shrub data. Some flights also contain Canopy Height Model (CHM) and Digital Terrain Model (DTM) returns. The total number of metrics layers varies by flight or campaign.\n\nGLMETRICS data are processed as a raster data product (GeoTIFF) at a 13 meter spatial resolution over locally defined areas. \n\n", "links": [ { diff --git a/datasets/GLOBEC_0.json b/datasets/GLOBEC_0.json index 2a4dc7d372..61f09f2f51 100644 --- a/datasets/GLOBEC_0.json +++ b/datasets/GLOBEC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLOBEC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Ocean Ecosystem Dynamics (GLOBEC) optical measurements.", "links": [ { diff --git a/datasets/GLOBEC_059_UK_009.json b/datasets/GLOBEC_059_UK_009.json index 6c575cf749..3de437ac54 100644 --- a/datasets/GLOBEC_059_UK_009.json +++ b/datasets/GLOBEC_059_UK_009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLOBEC_059_UK_009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim is to establish a numerical model system providing\na robust 3-dimensional physical environment within which\necosystem and zooplankton models of different structure\nand complexity will be compared and assessed. The\nprinciple aims are: to provide a hydrodynamic/ecological\ntestbed for development and testing of models of zooplankton\ndynamics; to formally compare existing models of ecosystem\ndynamics in the testbed and evaluate performance against\narchived data; to identify important processes and scales\nof interaction for Irish Sea zooplankton populations and\nto determine the optimal complexity of marine hydrodynamic\nand ecosystem models necessary to describe zooplankton\ndynamics in the Irish Sea.", "links": [ { diff --git a/datasets/GLOBIO_barents.json b/datasets/GLOBIO_barents.json index d5491b35c4..16f1145c5d 100644 --- a/datasets/GLOBIO_barents.json +++ b/datasets/GLOBIO_barents.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLOBIO_barents", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract - Impact of human activities in the Barents region using the\nGLOBIO methodology. Distance impact from Infrastructure and defined in\nthe GLOBIO report\n\nPurpose - To provide policy makers with a tool to help assess the\nlikelihood of environmental impacts in the Barents Region\n\nFormat - Windows NT Version 5.0 (Build 2195) Service Pack 2; ESRI\nArcInfo 8.1.0.415\n\nRaw data are the B1000 dataset from the Barents GIT and National\nMapping agency over the Barents region\n\nGrid Cell - Row Count 5192\n Cell Count 6436\n\nMap Projection - Albers Conical Equal Area", "links": [ { diff --git a/datasets/GLORTHO_001.json b/datasets/GLORTHO_001.json index 465049ad5c..8fa7f48afd 100644 --- a/datasets/GLORTHO_001.json +++ b/datasets/GLORTHO_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLORTHO_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Aerial Orthomosaic data product (GLORTHO) is to provide orthorectified high-resolution aerial photography. This data is provided as a supplement to other G-LiHT data products.\r\n\r\nGLORTHO data are processed as a raster data product (GeoTIFF) at 1 inch spatial resolution over locally defined areas. A low resolution browse is also provided with a color map applied in PNG format. \r\n\r\n", "links": [ { diff --git a/datasets/GLRADS_001.json b/datasets/GLRADS_001.json index 008ac35918..632d9fa0cb 100644 --- a/datasets/GLRADS_001.json +++ b/datasets/GLRADS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLRADS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Hyperspectral Radiance data product (GLRADS) is to provide high-resolution radiance data, ranging in wavelength from 418 to 920 nanometers across 114 spectral bands. Radiance data is computed as the ratio between observed upwelling radiance and downwelling hemispheric irradiance, then corrected for differences in cross-track illumination and Bidirectional Reflectance Distribution Function (BRDF) using an empirically derived multiplier. At a nominal flying height of 335 m above ground level (AGL), the at-sensor radiance is a close approximation of surface radiance. \r\n\r\nGLRADS data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas. A low-resolution browse is also provided with a color map applied in PNG format. \r\n\r\n", "links": [ { diff --git a/datasets/GLREFL_001.json b/datasets/GLREFL_001.json index a642062fc2..de446ff288 100644 --- a/datasets/GLREFL_001.json +++ b/datasets/GLREFL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLREFL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\r\n\r\nThe purpose of G-LiHT\u2019s Hyperspectral Reflectance data product (GLREFL) is to provide high-resolution reflectance data, ranging in wavelength from 418 to 920 nanometers across 114 spectral ranges. Reflectance data is computed as the ratio between observed upwelling radiance and downwelling hemispheric irradiance and corrected for differences in cross-track illumination and Bidirectional Reflectance Distribution Function (BRDF) using an empirically derived multiplier. At a nominal flying height of 335 m above ground level (AGL), the at-sensor reflectance is a close approximation of surface reflectance. \r\n\r\nGLREFL data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1-meter spatial resolution over locally defined areas. A low-resolution browse is also provided with a color map applied in PNG format. \r\n", "links": [ { diff --git a/datasets/GLSC_CraneCreek_2A_Survey.json b/datasets/GLSC_CraneCreek_2A_Survey.json index e2fea4306b..8af38f9569 100644 --- a/datasets/GLSC_CraneCreek_2A_Survey.json +++ b/datasets/GLSC_CraneCreek_2A_Survey.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_CraneCreek_2A_Survey", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains all of the elevation points used to model the ground\nsurface in Pool 2A at the Ottawa National\nWildlife Refuge.\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex. \n\nThis data set was used to generate a topographic/bathymetric surface that will\nallow modelling of different biotic and abiotic conditions.", "links": [ { diff --git a/datasets/GLSC_CraneCreek_2B_SurveyPts.json b/datasets/GLSC_CraneCreek_2B_SurveyPts.json index 815e2c5200..af3acf67f6 100644 --- a/datasets/GLSC_CraneCreek_2B_SurveyPts.json +++ b/datasets/GLSC_CraneCreek_2B_SurveyPts.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_CraneCreek_2B_SurveyPts", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains all of the elevation points used to model the ground\nsurface in Pool 2B at the Ottawa National\nWildlife Refuge.\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex. \n\nThis data set was used to generate a topographic/bathymetric surface that will\nallow modelling of different biotic and abiotic conditions.", "links": [ { diff --git a/datasets/GLSC_cranecreek2atin_sub3.txt.json b/datasets/GLSC_cranecreek2atin_sub3.txt.json index abeeae6ff6..43dcaa83bc 100644 --- a/datasets/GLSC_cranecreek2atin_sub3.txt.json +++ b/datasets/GLSC_cranecreek2atin_sub3.txt.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_cranecreek2atin_sub3.txt", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains an interpolated surface for Pool 2A at the Ottawa\nNational Wildlife Refuge.\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex.", "links": [ { diff --git a/datasets/GLSC_cranecreek_2btinsub3.json b/datasets/GLSC_cranecreek_2btinsub3.json index 1e0e4cff39..3b5bfdbb44 100644 --- a/datasets/GLSC_cranecreek_2btinsub3.json +++ b/datasets/GLSC_cranecreek_2btinsub3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_cranecreek_2btinsub3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains an interpolated surface for Pool 2B at the Ottawa\nNational Wildlife Refuge.\n\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex.", "links": [ { diff --git a/datasets/GLSC_cranecreekbathtopoGRID.json b/datasets/GLSC_cranecreekbathtopoGRID.json index 8f90a42d40..2b537c16ce 100644 --- a/datasets/GLSC_cranecreekbathtopoGRID.json +++ b/datasets/GLSC_cranecreekbathtopoGRID.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_cranecreekbathtopoGRID", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains an interpolated surface for Crane Creek at the Ottawa\nNational Wildlife Refuge.\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex.", "links": [ { diff --git a/datasets/GLSC_cranecreekbathtopopoints.json b/datasets/GLSC_cranecreekbathtopopoints.json index 80830e31c7..e360753ebb 100644 --- a/datasets/GLSC_cranecreekbathtopopoints.json +++ b/datasets/GLSC_cranecreekbathtopopoints.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLSC_cranecreekbathtopopoints", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains all of the elevation points used to model the ground\nsurface in Crane Creek at the Ottawa National Wildlife Refuge.\n\nOver 95% of the original wetland habitats along the U.S. shoreline of western\nLake Erie have been lost since the 1860s. Most of the remaining coastal\nwetlands have been isolated by earthen dikes and no longer provide many of the\nfunctions of coastal wetlands (e.g., fish habitat). Unfortunately, most of the\nfew remaining undiked wetlands are severely degraded. They remain\nhydrologically connected to the lake, but the wetland vegetation that provides\nvital fish habitat is sufficiently degraded to negatively impact the\napproximately 43 species of Great Lakes fish that use wetland habitats. The\n2003 EPA project initiated the restoration of the drowned-river mouth wetlands\nat Crane Creek, a small stream flowing into western Lake Erie. Since\nhistorical descriptions of the study site suggest a much broader expanse of\nwetlands than just the 345 ha (852 ac) in and near the Crane Creek channel, the\ndiked wetlands bordering the creek were examined to evaluate options for\nlong-term ecological restoration. The research focused on both reestablishing\nwetland vegetation near Crane Creek and exploring whether diked wetlands can be\nfunctionally restored by hydrologically reconnecting them to Lake Erie,\neffectively adding habitat to the lake. This work addressed many of the high\npriority research areas identified by the 1998 EPA Science Advisory Board\nreport on marsh management and restored critical coastal wetland habitat,\nidentified as a special focus area in the Great Lakes Strategy and recently\nidentified as a priority by the Council of Great Lakes Governors. One aspect\nof this research was to characterize the bathymetry and topography throughout\nthe wetland complex.", "links": [ { diff --git a/datasets/GLS_1975.json b/datasets/GLS_1975.json index 9cec9f0361..a88a60ef0e 100644 --- a/datasets/GLS_1975.json +++ b/datasets/GLS_1975.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLS_1975", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Survey 1975 images were acquired from 1972 to 1987 by Landsat 1-5 MSS. Landsat 4-5 data were used to fill gaps in the Landsat 1-3 data.\n \nThe U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.", "links": [ { diff --git a/datasets/GLS_1990.json b/datasets/GLS_1990.json index 3623280a15..e72d120141 100644 --- a/datasets/GLS_1990.json +++ b/datasets/GLS_1990.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLS_1990", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Survey 1990 images were acquired from 1987 to 1997 by Landsat 4-5 TM.\nThe U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.", "links": [ { diff --git a/datasets/GLS_2005.json b/datasets/GLS_2005.json index 0e988e581d..b567f9e170 100644 --- a/datasets/GLS_2005.json +++ b/datasets/GLS_2005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLS_2005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Survey 2005 images were acquired from 2003 - 2008 by Landsat 7 ETM+, Landsat 5 Thematic Mapper (TM) and EO-1 ALI.\n\nThe U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.", "links": [ { diff --git a/datasets/GLS_2005_islands.json b/datasets/GLS_2005_islands.json index 1bfd20bdcb..51b6e6000e 100644 --- a/datasets/GLS_2005_islands.json +++ b/datasets/GLS_2005_islands.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLS_2005_islands", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Survey 2005 images were acquired from 2003 to 2008 by Landsat 7 ETM+, EO1 ALI, and Landsat 5 Thematic Mapper (TM).\n\nThe U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.\n", "links": [ { diff --git a/datasets/GLS_2010.json b/datasets/GLS_2010.json index 5131b847c2..fe40511cee 100644 --- a/datasets/GLS_2010.json +++ b/datasets/GLS_2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLS_2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Survey 2010 images were acquired from 2008 to 2011 by Landsat 7 ETM+ and Landsat 5 Thematic Mapper (TM).\n\nThe U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.", "links": [ { diff --git a/datasets/GLTRAJECTORY_001.json b/datasets/GLTRAJECTORY_001.json index 8fa942b8dd..0b98a67066 100644 --- a/datasets/GLTRAJECTORY_001.json +++ b/datasets/GLTRAJECTORY_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLTRAJECTORY_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.\n\nThe purpose of G-LiHT\u2019s Trajectory data product (GLTRAJECTORY) is to provide aircraft location and orientation to support and supplement other G-LiHT data products.\n\nGLTRAJECTORY data are processed as a Google Earth overlay Keyhole Markup Language (KML) file over the extent of an entire flight path. A low resolution browse is also provided to show the flight path. \n", "links": [ { diff --git a/datasets/GLanCE30_001.json b/datasets/GLanCE30_001.json index 77210898eb..f33616da19 100644 --- a/datasets/GLanCE30_001.json +++ b/datasets/GLanCE30_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GLanCE30_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, the North American, South American and European continents are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. \r\n\r\nThe GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.\r\n", "links": [ { diff --git a/datasets/GMAO-CMIP5_1.json b/datasets/GMAO-CMIP5_1.json index 739663e642..32e897610c 100644 --- a/datasets/GMAO-CMIP5_1.json +++ b/datasets/GMAO-CMIP5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GMAO-CMIP5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Studies of change and variations on decadal timescales are essential for planning satellite missions that seek to improve our understanding of linkages among various components of the Earth System. Decadal predictions using a version of the GEOS-5 AOGCM were contributed to the CMIP5 project. The dataset include a three-member ensemble initialized on December 1 of each year from 1960 to 2010. These data are available, with the designation NASA GMAO, from the CMIP5 Archive at NASA NCCS.", "links": [ { diff --git a/datasets/GMAO_M2SCREAM_INST3_CHEM_1.json b/datasets/GMAO_M2SCREAM_INST3_CHEM_1.json index 43c63f1b3b..de1fd16cc3 100644 --- a/datasets/GMAO_M2SCREAM_INST3_CHEM_1.json +++ b/datasets/GMAO_M2SCREAM_INST3_CHEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GMAO_M2SCREAM_INST3_CHEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA\u2019s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem. Assimilated fields are provided globally at 0.5\u00b0 by 0.625\u00b0 resolution at three-hourly frequencies from 2004/09/01 to 2024/09/30. Assimilation uncertainties for each of the assimilated constituents are calculated from the CoDAS statistical output (Wargan et al., 2022) and provided as global full-resolution three-dimensional monthly files.\nData product updates in March 2024, as a result of Aurora MLS \u201cduty cycle\u201d of 190-GHz measurements, include reduced availability of H2O, N2O and HNO3 retrievals resulting in expected M2-SCREAM data quality degradation. However, preliminary analysis shows that the GEOS CoDAS handles the reduced temporal data coverage well, indicating that the GEOS model accurately propagates information from past observations. \nData product updates in June 2024 resulting from MLS version upgrade to v5.0 include discontinuities in assimilated H2O (throughout the stratosphere) and N2O (in the lower stratosphere). To note: MLS water vapor is about 0.5 ppmv lower in v5.0, and the vertical range of assimilated N2O data is 100 hPa, extended down from 68 hPa. GMAO is not aware of discontinuities in HCl, HNO3, and ozone related to the version switch. \n", "links": [ { diff --git a/datasets/GMAO_M2SCREAM_MONTH_UNCERT_1.json b/datasets/GMAO_M2SCREAM_MONTH_UNCERT_1.json index a15fc8c68b..b7cbfa85e4 100644 --- a/datasets/GMAO_M2SCREAM_MONTH_UNCERT_1.json +++ b/datasets/GMAO_M2SCREAM_MONTH_UNCERT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GMAO_M2SCREAM_MONTH_UNCERT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA\u2019s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem. Assimilated fields are provided globally at 0.5\u00b0 by 0.625\u00b0 resolution at three-hourly frequencies from 2004/09/01 to 2024/09/30. Assimilation uncertainties for each of the assimilated constituents are calculated from the CoDAS statistical output (Wargan et al., 2022) and provided as global full-resolution three-dimensional monthly files.\nData product updates in March 2024, as a result of Aura MLS \u201cduty cycle\u201d of 190-GHz measurements, include reduced availability of H2O, N2O and HNO3 retrievals resulting in expected M2-SCREAM data quality degradation. However, preliminary analysis shows that the GEOS CoDAS handles the reduced temporal data coverage well, indicating that the GEOS model accurately propagates information from past observations. \nData product updates in June 2024 resulting from MLS version upgrade to v5.0 include discontinuities in assimilated H2O (throughout the stratosphere) and N2O (in the lower stratosphere). To note: MLS water vapor is about 0.5 ppmv lower in v5.0, and the vertical range of assimilated N2O data is 100 hPa, extended down from 68 hPa. GMAO is not aware of discontinuities in HCl, HNO3, and ozone related to the version switch. ", "links": [ { diff --git a/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json b/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json index 3119aca3b1..df97564c6a 100644 --- a/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json +++ b/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GMI-REMSS-L3U-v8.2a_8.2a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.", "links": [ { diff --git a/datasets/GNATS_0.json b/datasets/GNATS_0.json index 162e6ed1c7..b26279a0e2 100644 --- a/datasets/GNATS_0.json +++ b/datasets/GNATS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GNATS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gulf of Maine (GoM) is a highly productive shelf sea that constitutes a large part of the N.E. US Continental Shelf. We have run a time series across the GoM for the last 8 years known as GNATS (Gulf of Maine North Atlantic Time Series). It consists of monthly, cross-Gulf sampling on ships of opportunity, during clear-sky days, so that we are assured concurrent measurements from ship and satellite (ocean color, SST). The power of this strategy is seen in our 95% success rate for being at sea during clear, high quality overpasses (randomly, one would expect a success rate of ~10% due to the GoM cloud climatology). We then can extrapolate our large shipboard data set of carbon cycle parameters to regional scales using synoptic remote sensing. GNATS includes a suite of carbon-specific standing stocks and rate measurements (e.g. POC, PIC [calcite], DOC, primary productivity, and calcification) plus hydrographic, chemical and optical measurements. Through coordinated ship/satellite measurements, we can constrain the major carbon production terms of the Gulf, follow their monthly variation using synoptic remote sensing, and regionally tune satellite algorithms. GNATS documents not only marine carbon pools, but it includes carbon supplied from the terrestrial watershed; this is why the Gulf is optically-dominated by Case II waters. We propose to A) continue GNATS, coordinated ship and satellite measurements for another 3 years, B) provide monthly, regional estimates of the standing stock and production terms for the various particulate and dissolved carbon fractions based on satellite ocean color observations and C) perform a statistical comparison of photoadaptive parameters in the Mid-Atlantic Bight and GoM to examine how broadly we can extrapolate these results along the NE U.S. Continental Shelf. Deliverables of this work will be: ship-based quantification of the various components of the carbon cycle in the GoM (standing stocks of POC, PIC, DOC plus primary production/calcification rates), an improved DOC algorithm, tuning of satellite carbon algorithms for the NE Continental Shelf, and documentation of the long- term biogeochemical and ecological changes occurring in the GoM carbon cycle. Quantification of the variability in the composition and concentration of dissolved and particulate carbon over a wide range of temporal and spatial scales is the first step towards understanding the role of coastal ecosystems in the global carbon cycle.", "links": [ { diff --git a/datasets/GNVd0188_104.json b/datasets/GNVd0188_104.json index 0e1c271e42..a615b53143 100644 --- a/datasets/GNVd0188_104.json +++ b/datasets/GNVd0188_104.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GNVd0188_104", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PLEASE NOTE: This is an updated release of the Africa 30 arc second DEM. Comments from users of this data set are welcome. Please contact Dean Gesch (gesch@dg1.cr.usgs.gov) or Sue Jenson (jenson@dg1.cr.usgs.gov).\n \nA digital elevation model (DEM) consists of a sampled array of elevations for ground positions that are normally spaced at regular intervals. To meet the needs of the geospatial data user community for regional and continental scale elevation data, the staff at the U.S. Geological Survey's EROS Data Center (EDC) are developing DEM's at a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). These data are being made available to the public via electronic distribution and hard media. As of July, 1996 data are available for Africa, Antarctica, Asia, Europe, and North America. Data sets for South America, Australia, New Zealand, the islands of southeast Asia, and Greenland are under development and are scheduled for release before the end of 1996.", "links": [ { diff --git a/datasets/GNVd0189_104.json b/datasets/GNVd0189_104.json index fd9c102769..e836933466 100644 --- a/datasets/GNVd0189_104.json +++ b/datasets/GNVd0189_104.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GNVd0189_104", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PLEASE NOTE: This is a beta release of the Antarctica DEM. If any data\nanomalies are noticed, please send an E-mail to either Mike Oimoen at:\noimoen@dgl.cr.usgs.gov, or Sue Jenson at: jenson@dg1.cr.usgs.gov. \nWe will look into them, and they may be addressed in the next release.\n \nThe Antarctica data is provided in two projections.\nAntarctic DEM in geographic (lat/lon) coordinates: 30 arc-second spacing\n-ant_dem_lkms1 UL = 180 W, 60S. LR = 90W, 90S (3600 rows x 10800 \ncolumns)\n-ant_dem_lkms2 UL = 90 W, 60S. LR = 0W, 90S (3600 rows x 10800 columns)\n-ant_dem_1kms3 UL = 0 W, 60S. LR = 90E, 90S (3600 rows x 10800 columns)\n-ant_dem_1kms4 UL = 90 E, 60S. LR = 180E, 90S (3600 rows x 10800 columns)\n \nAntarctic DEM in polar stereographic coordinates (meters)\n-ant_dem_1kmps UL = -2700000 x 2699000. LR = 2699000 x -2700000 (5400 rows x\n5400 columns)\n \nNote: Both DEMs are referenced to the WGS84 ellipsoid. The standard latitude of\nthe polar stereographic DEM is 71S, and it central meridian is 0.\n \nData Organization: Data are distributed as 16-bit straight raster image files\nin a latitude/ longitude coordinate system, and also in a polar stereographic\ncoordinate system.\n \nImage files are identified by the .bil.gz extension. Each image file of the\nAntarctica data set is compressed using the GNU \"gzip\" utility. If you do not\nhave access to gzip, the FTP server will uncompress the file as you retrieve\nit. To do this, simply leave off the \".gz\" extension when retrieving the file\n(NOTE: This option is not available through MOSAIC). For example, to retrieve\nthe file \"af_1k_dem1.bil.gz\" without compression just use \"get\naf_dem_lks1.bil\". Note that the uncompressed files are typically five times\nlarger than the compressed versions and so will take five times longer to\ntransmit. The gzip program is available via anonymous FTP at the following\nsites: prep.ai.mit.edu:/pub/gnuwuarchive.wustl.edu:/systems/gnu\n \nEach image file is accompanied by five ancillary files (header file, world\nfile, statistics file, coordinate file, and data descriptor \nrecord ). The format of each ancillary file is described below:", "links": [ { diff --git a/datasets/GNVd0190_104.json b/datasets/GNVd0190_104.json index f2b4cc8fdd..15d04f9c88 100644 --- a/datasets/GNVd0190_104.json +++ b/datasets/GNVd0190_104.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GNVd0190_104", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The European 30 arc-second DEM was compiled from varied data sources. The primary source was a generalization of the Level 1 Digital Terrain Elevation Data. Digital Terrain Elevation Data (DTED) is a 1 degree by 1 degree dataset produced by the US Defense Mapping Agency (DMA) \nthat contains digital data in the form of a uniform matrix of terrain elevation values for most parts of the world. It was originally designed to provide basic quantitative data for military training, planning and operating systems that require terrain elevation, slope and related information. This includes applications such as modeling \nthe influence of terrain on radar line-of-sight, automatic height determination, terrain modeling etc.", "links": [ { diff --git a/datasets/GO-BGC_0.json b/datasets/GO-BGC_0.json index 5557d677ed..88517b01d0 100644 --- a/datasets/GO-BGC_0.json +++ b/datasets/GO-BGC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GO-BGC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ocean Biogeochemistry (GO-BGC) Array is a project funded by the US National Science Foundation (NSF Award 1946578 ) to build a global network of chemical and biological sensors that will monitor ocean health. This grant is being used to build and deploy 500 robotic ocean-monitoring floats around the globe as part of NSF\u2019s Mid-scale Research Infrastructure-2 program. This network of floats is collecting data on the chemistry and the biology of the ocean from the surface to a depth of 2,000 meters, augmenting the existing Argo array that monitors ocean temperature and salinity. The GO-BGC Array is led by Director Ken Johnson and administered by the Monterey Bay Aquarium Research Institute. For questions specific to the HPLC/POC/PON data submitted to SeaBASS please contact Josh Plant at jplant@mbari.org.", "links": [ { diff --git a/datasets/GO-SHIP_0.json b/datasets/GO-SHIP_0.json index a515674be3..b48c0321df 100644 --- a/datasets/GO-SHIP_0.json +++ b/datasets/GO-SHIP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GO-SHIP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the GO-SHIP (Global Ocean Ship-based Hydrographic Investigations Program) project, which is a network of sustained hydrographic sections, supporting physical oceanography, the carbon cycle, and marine biogeochemistry and ecosystems.", "links": [ { diff --git a/datasets/GOA97_0.json b/datasets/GOA97_0.json index 4fef324fb8..15072ea642 100644 --- a/datasets/GOA97_0.json +++ b/datasets/GOA97_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOA97_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Gulf of Alaska during 1997.", "links": [ { diff --git a/datasets/GOCAL_0.json b/datasets/GOCAL_0.json index b6892c2e00..773d4b4e5c 100644 --- a/datasets/GOCAL_0.json +++ b/datasets/GOCAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOCAL was an internationally coordinated long-term sampling program in the Gulf of California, designed to examine the temporal and spatial variability in the biogeochemical properties of the region. This program includes Drs. S. Alvarez Borrego, R. Lara Lara, G. Gaxiola and H. Maske from the Centro de Investigacion Cientifica y de Educacion Secondaria de Ensenada (CICESE), Ensenada, Mexico, Dr. E. Valdez from the University of Sonora, Hermosillo, Mexico, Drs. J Mueller and C. Trees of San Diego State University, San Diego, CA, Dr. Ron Zaneveld, Dr. Scott Pegau, and Andrew Barnard, Oregon State University, Corvallis, OR. Components of this program were funded by the NASA SIMBIOS initiative", "links": [ { diff --git a/datasets/GOCE_Global_Gravity_Field_Models_and_Grids_6.0.json b/datasets/GOCE_Global_Gravity_Field_Models_and_Grids_6.0.json index c5ea27ab9b..0a6ebbdcde 100644 --- a/datasets/GOCE_Global_Gravity_Field_Models_and_Grids_6.0.json +++ b/datasets/GOCE_Global_Gravity_Field_Models_and_Grids_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_Global_Gravity_Field_Models_and_Grids_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains gravity gradient and gravity anomalies grids at ground level, at satellite height. In addition it contains the GOCE gravity field models (EGM_GOC_2,EGM_GCF_2) and their covariance matrices (EGM_GVC_2): Gridded Gravity gradients and anomalies at ground level: GO_CONS_GRC_SPW_2__20091101T000000_20111231T235959_0001.TGZ GO_CONS_GRC_SPW_2__20091101T055147_20120731T222822_0001.TGZ GO_CONS_GRC_SPW_2__20091101T055226_20131020T033415_0002.TGZ GO_CONS_GRC_SPW_2__20091009T000000_20131021T000000_0201.TGZ Latest baseline is: GO_CONS_GRC_SPW_2__20091009T000000_20131021T000000_0201.TGZ Gridded Gravity gradients and anomalies at satellite height: GO_CONS_GRD_SPW_2__20091101T055147_20100630T180254_0001.TGZ GO_CONS_GRD_SPW_2__20091101T055147_20120731T222822_0001.TGZ GO_CONS_GRD_SPW_2__20091101T055226_20131020T033415_0002.TGZ GO_CONS_GRD_SPW_2__20091009T000000_20131021T000000_0201.TGZ Latest baseline is: GO_CONS_GRD_SPW_2__20091009T000000_20131021T000000_0201.TGZ As output from the ESA-funded GOCE+ GeoExplore project, GOCE gravity gradients were combined with heterogeneous other satellite gravity information to derive a combined set of gravity gradients complementing (near)-surface data sets spanning all together scales from global down to 5 km. The data is useful for various geophysical applications and demonstrate their utility to complement additional data sources (e.g., magnetic, seismic) to enhance geophysical modelling and exploration. The GOCE+ GeoExplore project is funded by ESA through the Support To Science Element (STSE) and was undertaken as a collaboration of the Deutsches Geod\u00e4tisches Forschungsinstitut (DGFI), Munich, DE, the Christian-Albrechts-Universit\u00e4t zu Kiel, the Geological Survey of Norway (NGU), Trondheim, Norway, TNO, the Netherlands and the University of West Bohemia, Plzen, CZ. Read more about gravity gradients and how GOCE delivered them in this Nature article: Satellite gravity gradient grids for geophysics (https://www.nature.com/articles/srep21050) View images of the GOCE original gravity gradients and gradients with topographic reduction grids (https://earth.esa.int/eogateway/missions/goce/data/goce-gravity-gradients-grids-map). Available Data GRIDS File Type: GGG_225 Gridded data - full Gravity Gradients, at 225 km and 255 km with and without topographic correction: Computed from GOCE/GRACE gradients lower orbit phase February 2010 - October 2013 File Type: GGG_255 Gridded data - full Gravity Gradients, at 225 km and 255 km with and without topographic correction: Computed from GOCE/GRACE gradients nominal orbit phase February 2010 - October 2013 File Type: TGG_255 Gridded data - full Gravity Gradients, at 225 km and 255 km with and without topographic correction: Gravity gradient grids from topography at fixed height of 225/255 km above ellipsoid given in LNOF (Local North Oriented Frame) File Type: TGG_225 Gridded data - full Gravity Gradients, at 225 km and 255 km with and without topographic correction: File Type: TGG_225 Gridded data - full Gravity Gradients, at 225 km and 255 km with and without topographic correction: Gravity gradient grids from topography at fixed height of 225/255 km above ellipsoid given in LNOF (Local North Oriented Frame) MAPS File Type: Vij_225km_Patch_n.jpg Maps of Gravity Gradients with and without topographic corrections: Maps of grids from lower orbit phase with topographic correction from ETOPO1 File Type: Vij_225km_Patch_n.jpg Maps of Gravity Gradients with and without topographic corrections: Maps of the original grids from lower orbit phase without topographic correction ALONG-ORBIT File Type: GGC_GRF Full Gravity Gradients, along-orbit, in GRF and TRF reference frames. A detailed description is provided in the data set user manual: Combined gradients from GRACE (long wavelengths) & GOCE (measurement band) in the GRF (Gradiometer Reference Frame) File Type: GGC_TRF Full Gravity Gradients, along-orbit, in GRF and TRF reference frames. A detailed description is provided in the data set user manual: Combined gradients from GRACE (long wavelengths) & GOCE (measurement band) rotated from GRF to TRF (Terrestrial Reference Frame: North, West, Up) Direct Solution First Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20100110T235959_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20100110T235959_0002.TGZ Second Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20100630T235959_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20100630T235959_0001.TGZ Third Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20110419T235959_0001.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20110419T235959_0001.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GCF_2__20091101T000000_20110419T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Fourth Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20120801T060000_0001.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20120801T060000_0002.TGZ Fifth Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20131020T235959_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20131020T235959_0001.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GOC_2__20091101T000000_20131020T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Sixth Generation Product: GO_CONS_EGM_GOC_2__20091009T000000_20131020T235959_0201.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091009T000000_20131020T235959_0201.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GOC_2__20091009T000000_20131020T235959_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Release 6 gravity model validation report (https://earth.esa.int/eogateway/documents/20142/37627/Release-6-gravity-model-validation-report-GO-TN-HPF-GS-0337-1.0.pdf) GO-TN-HPF-GS-0337_1.0 - Rel6_Validation_Report.pdf Time-Wise solution First Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20100111T000000_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20100111T000000_0002.TGZ Second Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20100705T235500_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20100705T235500_0001.TGZ Third Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20110430T235959_0001.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20110430T235959_0001.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GCF_2__20091101T000000_20110430T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Fourth Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20120618T235959_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20120618T235959_0001.TGZ Fifth Generation Product: GO_CONS_EGM_GOC_2__20091101T000000_20131021T000000_0002.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091101T000000_20131021T000000_0001.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GOC_2__20091101T000000_20131021T000000_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Sixth Generation Product: GO_CONS_EGM_GOC_2__20091009T000000_20131021T000000_0201.TGZ Variance/Covariance matrix: GO_CONS_EGM_GVC_2__20091009T000000_20131021T000000_0202.TGZ Coefficients (ICGEM format): GO_CONS_EGM_GOC_2__20091009T000000_20131021T000000_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Combined gravity field GOCE model plus Antarctic and Arctic data (ICGEM format): GO_CONS_EGM_GOC_2__20091009T000000_20160119T235959_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime) Release 6 gravity model validation report (https://earth.esa.int/eogateway/documents/20142/37627/Release-6-gravity-model-validation-report-GO-TN-HPF-GS-0337-1.0.pdf) GO-TN-HPF-GS-0337_1.0 - Rel6_Validation_Report.pdf", "links": [ { diff --git a/datasets/GOCE_Level_1_6.0.json b/datasets/GOCE_Level_1_6.0.json index ed576ffdb0..d3ce958477 100644 --- a/datasets/GOCE_Level_1_6.0.json +++ b/datasets/GOCE_Level_1_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_Level_1_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains the GOCE L1b data of the gradiometer, the star trackers, the GPS receiver, the magnetometers, magnetotorquers and the DFACS data of each accelerometer of the gradiometer. EGG_NOM_1b: latest baseline _0202 SST_NOM_1b: latest baseline _000x (always take the highest number available) ACC_DFx_1b: latest baseline _0001 (x=1:6) MGM_GOx_1b: latest baseline _0001 (x=1:3) MTR_GOC_1b: latest baseline _0001 SST_RIN_1b: latest baseline _000x (always take the highest number available) STR_VC2_1b: latest baseline _000x (always take the highest number available) STR_VC3_1b:latest baseline _000x (always take the highest number available)", "links": [ { diff --git a/datasets/GOCE_Level_2_6.0.json b/datasets/GOCE_Level_2_6.0.json index ae4ad90c6a..f4c83a3456 100644 --- a/datasets/GOCE_Level_2_6.0.json +++ b/datasets/GOCE_Level_2_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_Level_2_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains GOCE level 2 data: Gravity Gradients in the gradiometer reference frame (EGG_NOM_2), in the terrestrial reference frame (EGG_TRF_2), GPS receiver derived precise science orbits (SST_PSO_2) and the non-tidal time variable gravity field potential with respect to a mean value in terms of a spherical harmonic series determined from atmospheric and oceanic mass variations as well as from a GRACE monthly gravity field time series (SST_AUX_2). EGG_NOM_2_: latest baseline: _0203 EGG_TRF_2_: latest baseline _0101 SST_AUX_2_: latest baseline _0001 SST_PSO_2_: latest baseline _0201", "links": [ { diff --git a/datasets/GOCE_TEC_and_ROTI_6.0.json b/datasets/GOCE_TEC_and_ROTI_6.0.json index 178d44bd61..de67f73d09 100644 --- a/datasets/GOCE_TEC_and_ROTI_6.0.json +++ b/datasets/GOCE_TEC_and_ROTI_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_TEC_and_ROTI_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOCE total electron content (TEC) and rate of TEC index (ROTI) data.", "links": [ { diff --git a/datasets/GOCE_Telemetry_6.0.json b/datasets/GOCE_Telemetry_6.0.json index 487b90b383..56135ee740 100644 --- a/datasets/GOCE_Telemetry_6.0.json +++ b/datasets/GOCE_Telemetry_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_Telemetry_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains all GOCE platform and instruments telemetry. For details see http://eo-virtual-archive1.esa.int/products/GOCE_BACKUP/MUST_TLM/GOCE_TLM_packets_description.xlsx.", "links": [ { diff --git a/datasets/GOCE_Thermosphere_Data_6.0.json b/datasets/GOCE_Thermosphere_Data_6.0.json index 1f82455f89..bd1844da96 100644 --- a/datasets/GOCE_Thermosphere_Data_6.0.json +++ b/datasets/GOCE_Thermosphere_Data_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCE_Thermosphere_Data_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thermospheric density and crosswind data products derived from GOCE data. latest baseline _0200 The GOCE+ Air Density and Wind Retrieval using GOCE Data project produced a dataset of thermospheric density and crosswind data products which were derived from ion thruster activation data from GOCE telemetry. The data was combined with the mission's accelerometer and star camera data products. The products provide data continuity and extend the accelerometer-derived thermosphere density data sets from the CHAMP and GRACE missions. The resulting density and wind observations are made available in the form of time series and grids. These data can be applied in investigations of solar-terrestrial physics, as well as for the improvement and validation of models used in space operations. Funded by ESA through the Support To Science Element (STSE) of ESA's Earth Observation Envelope Programme, supporting the science applications of ESA's Living Planet programme, the project was a partnership between TU Delft, CNES and Hypersonic Technology G\u00f6ttingen. Dataset History Date: 18/04/2019 Change: - Time series data v2.0, covering the whole mission - Updated data set user manual - New satellite geometry and aerodynamic model (http://thermosphere.tudelft.nl/) - New vertical wind field - New data for the deorbit phase, (GPS+ACC and GPS-only versions) Reason: Updated satellite models and additional data Date: 14/07/2016 Change: - Time series data v1.5, covering the whole mission - Updated data set user manual Reason: Removal of noisy data Date: 31/07/2014 Change: - Time series data v1.4, covering the whole mission - Gridded data, now including error estimates - Updated data set user manual - Updated validation report; Updated ATBD Reason: Full GOCE dataset available Date: 28/09/2013 Change: - Version 1.3 density/winds timeseries and gridded data released - User manual updated to v1.3 Reason: Bug fix and other changes Date: 04/09/2013 - Version 1.2 density/winds timeseries and gridded data released, with user manual Reason: First public data release of thermospheric density/winds data", "links": [ { diff --git a/datasets/GOCI_2013_0.json b/datasets/GOCI_2013_0.json index 8945a8546c..6128ee4f53 100644 --- a/datasets/GOCI_2013_0.json +++ b/datasets/GOCI_2013_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCI_2013_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the East China Sea in 2013 to validate the South Korean GOCI instrument.", "links": [ { diff --git a/datasets/GOCI_L1_1.json b/datasets/GOCI_L1_1.json index e8914a6270..4e6aa4a19f 100644 --- a/datasets/GOCI_L1_1.json +++ b/datasets/GOCI_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCI_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Ocean Color Imager (GOCI) is one of the three payloads onboard the Communication,Ocean and Meteorological Satellite (COMS). It acquires data in 8 spectral bands (6 visible, 2 NIR) witha spatial resolution of about 500m over the Korean sea. The ocean data products that can be derivedfrom the measurements are mainly the chlorophyll concentration, the optical diffuse attenuationcoefficients, the concentration of dissolved organic material or yellow substance, and the concentrationof suspended particles in the near-surface zone of the sea. In operational oceanography, satellite deriveddata products are used in conjunction with numerical models and in situ measurements to provide forecastingand now casting of the ocean state. Such information is of genuine interest for many categories of users.", "links": [ { diff --git a/datasets/GOCI_L2_OC_2014.json b/datasets/GOCI_L2_OC_2014.json index ce36828cb1..0fb9fe9992 100644 --- a/datasets/GOCI_L2_OC_2014.json +++ b/datasets/GOCI_L2_OC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOCI_L2_OC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Ocean Color Imager (GOCI) is one of the three payloads onboard the Communication,Ocean and Meteorological Satellite (COMS). It acquires data in 8 spectral bands (6 visible, 2 NIR) witha spatial resolution of about 500m over the Korean sea. The ocean data products that can be derivedfrom the measurements are mainly the chlorophyll concentration, the optical diffuse attenuationcoefficients, the concentration of dissolved organic material or yellow substance, and the concentrationof suspended particles in the near-surface zone of the sea. In operational oceanography, satellite deriveddata products are used in conjunction with numerical models and in situ measurements to provide forecastingand now casting of the ocean state. Such information is of genuine interest for many categories of users.", "links": [ { diff --git a/datasets/GOC_0.json b/datasets/GOC_0.json index 7dcb7bb93e..62db65a168 100644 --- a/datasets/GOC_0.json +++ b/datasets/GOC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Baja California and the Gulf of California in 2003.", "links": [ { diff --git a/datasets/GOES13-OSISAF-L3C-v1.0_1.json b/datasets/GOES13-OSISAF-L3C-v1.0_1.json index 5eb43b26f5..f2664e9e45 100644 --- a/datasets/GOES13-OSISAF-L3C-v1.0_1.json +++ b/datasets/GOES13-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOES13-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\ntime from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo-\nFrance/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system.\nSST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral\nalgorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible\nin daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical\nweather prediction model, together with a radiatiave transfer model, are used to correct the\nmultispectral algorithm for regional and seasonal biases due to changing atmospheric conditions.\nEvery 30 minutes slot is processed at full satellite resolution. The operational products are then\nproduced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields\nobtained by aggregating 30 minute SST data available in one hour time, and the priority being\ngiven to the value the closest in time to the product nominal hour. The product format is compliant\nwith the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/GOES13-OSPO-L2P-v1.0_1.0.json b/datasets/GOES13-OSPO-L2P-v1.0_1.0.json index f612e569fe..ccaa28b3c2 100644 --- a/datasets/GOES13-OSPO-L2P-v1.0_1.0.json +++ b/datasets/GOES13-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOES13-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/GOES15-OSPO-L2P-v1.0_1.0.json b/datasets/GOES15-OSPO-L2P-v1.0_1.0.json index 3faa9e26a8..cada044ce0 100644 --- a/datasets/GOES15-OSPO-L2P-v1.0_1.0.json +++ b/datasets/GOES15-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOES15-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-15 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/GOES16-L2-CMI-1_NA.json b/datasets/GOES16-L2-CMI-1_NA.json index 884b2f9f79..3a0e714087 100644 --- a/datasets/GOES16-L2-CMI-1_NA.json +++ b/datasets/GOES16-L2-CMI-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOES16-L2-CMI-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-16 Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery provides 16 spectral bands with high temporal resolution over the American continent. The significance of the GOES-16 satellite for Brazil and South America lies in its location at longitude -75\u00b0, allowing it to offer comprehensive coverage of the continent and the oceanic regions of the Pacific and Atlantic. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions ranging from 500m to 2km. This collection encompasses images acquired by the GOES-16 satellite (GOES-East) in full-disk mode, depicting nearly full coverage of the Western Hemisphere in a circular image. Important: note that other modes, such as CONUS and MESOSCALE, are not included in this collection. Cloud and Moisture Imagery product (CMIP) files are generated for each of the 16 ABI reflective and emissive bands. The collection captures CMIP product files into individual STAC Items for each observation from the GOES-16 satellite. It includes the original and full-resolution CMIP NetCDF files generated by INPE's GOES-R receive station. There is also a version for band 02, which originally has a resolution of 500m, degraded to 1km. For more information, refer to the Beginner\u2019s Guide to GOES-R Series Data (https://www.goesr.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf), GOES-R Series Product Definition and Users Guide: Volume 5 (Level 2A+ Products) (https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) and the ABI Bands Quick Information Guides (https://www.goes-r.gov/mission/ABI-bands-quick-info.html).", "links": [ { diff --git a/datasets/GOES16-SST-OSISAF-L3C-v1.0_1.0.json b/datasets/GOES16-SST-OSISAF-L3C-v1.0_1.0.json index aa2966bc03..1cc2283c07 100644 --- a/datasets/GOES16-SST-OSISAF-L3C-v1.0_1.0.json +++ b/datasets/GOES16-SST-OSISAF-L3C-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOES16-SST-OSISAF-L3C-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data is regional and part of the Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset covering the America Region based on retrievals from the Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-16 (GOES-16). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES-16 in the Eastern position. GOES-16 Imager level 1 data are acquired at M\u00e9t\u00e9o-France/Centre de M\u00e9t\u00e9orologie Spatiale (CMS) through the EUMETSAT/EUMETCast system. The GOES-16 ABI enables daytime SST calculations (whereas, previously, GOES East SST was restricted to nighttime conditions). The L3C SST is derived from a three-band (centered at 8.4, 10.3, and 12.3 um) algorithm. The ABI split-window configuration features three bands instead of the two found in heritage sensors (GOES-13). The 8.5-um is used in conjunction with the 10.3-um and 12.3-um bands for improved thin cirrus detection as well as for better atmospheric moisture correction in relatively dry atmospheres. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Each 10-minute observation interval is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating the available10-minute SST data into hourly files-hour time, with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/GOMECC_0.json b/datasets/GOMECC_0.json index 62ac9c7ca2..3af6790627 100644 --- a/datasets/GOMECC_0.json +++ b/datasets/GOMECC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMECC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gulf of Mexico and East Coast Carbon Cruise (GOMECC)", "links": [ { diff --git a/datasets/GOMEX_0.json b/datasets/GOMEX_0.json index 062d3b1dfe..ab03c0be0b 100644 --- a/datasets/GOMEX_0.json +++ b/datasets/GOMEX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMEX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Gulf of Mexico along the Florida and Louisiana coasts in 1993.", "links": [ { diff --git a/datasets/GOME_Evl_ClimateProd_TCWV_4.0.json b/datasets/GOME_Evl_ClimateProd_TCWV_4.0.json index ad5623d702..fc7d6248ca 100644 --- a/datasets/GOME_Evl_ClimateProd_TCWV_4.0.json +++ b/datasets/GOME_Evl_ClimateProd_TCWV_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOME_Evl_ClimateProd_TCWV_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOME Total Column Water Vapour (TCWV) Climate product was generated by the Max Planck Institute for Chemistry (MPIC), and the German Aerospace Center (DLR) within the ESA GOME-Evolution project. It is a Level 3 type product containing homogenized time-series of the global distribution of TCWV spanning over more than two decades (1995-2015). The data is provided as single netCDF file, containing monthly mean TCWV (units kg/m2) with 1-degree resolution, and is based on measurements from the satellite instruments ERS-2 GOME, Envisat SCIAMACHY, and MetOp-A GOME-2. Details are available in the paper by Beirle et al, 2018,. Please also consult the GOME TCWV Product Quality Readme file before using the data. (https://earth.esa.int/eogateway/documents/20142/37627/GOME-TCWV-Product-sQuality-Readme-File.pdf)", "links": [ { diff --git a/datasets/GOME_MINDS_NO2_1.1.json b/datasets/GOME_MINDS_NO2_1.1.json index 816833f33f..008d1bfde3 100644 --- a/datasets/GOME_MINDS_NO2_1.1.json +++ b/datasets/GOME_MINDS_NO2_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOME_MINDS_NO2_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2003), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.", "links": [ { diff --git a/datasets/GOMI2AE_002.json b/datasets/GOMI2AE_002.json index fe13dafaa1..6cbd0057fd 100644 --- a/datasets/GOMI2AE_002.json +++ b/datasets/GOMI2AE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMI2AE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Aerosol Product subset for the GoMACCS region V002 contains Aerosol optical depth and particle type, with associated atmospheric data.", "links": [ { diff --git a/datasets/GOMI2LS_002.json b/datasets/GOMI2LS_002.json index f346c19a87..3ecc8a69ad 100644 --- a/datasets/GOMI2LS_002.json +++ b/datasets/GOMI2LS_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMI2LS_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Land Surface Product subset for the GoMACCS region V002 contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); associated radiation parameters; and terrain-referenced geometric parameters.", "links": [ { diff --git a/datasets/GOMI2ST_002.json b/datasets/GOMI2ST_002.json index 00d0f5d418..8930a0338f 100644 --- a/datasets/GOMI2ST_002.json +++ b/datasets/GOMI2ST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMI2ST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Stereo Product subset for the GoMACCS region V002 contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.", "links": [ { diff --git a/datasets/GOMIB2E_003.json b/datasets/GOMIB2E_003.json index f8ceedeac1..380890c2be 100644 --- a/datasets/GOMIB2E_003.json +++ b/datasets/GOMIB2E_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMIB2E_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Product subset for the GoMACCS region V003 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/GOMIB2T_003.json b/datasets/GOMIB2T_003.json index 267c89412c..573f1e7245 100644 --- a/datasets/GOMIB2T_003.json +++ b/datasets/GOMIB2T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMIB2T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Product subset for the GoMACCS region V003 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/GOMIGEO_002.json b/datasets/GOMIGEO_002.json index e9ebe42b5e..b2d00d9b9c 100644 --- a/datasets/GOMIGEO_002.json +++ b/datasets/GOMIGEO_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOMIGEO_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters subset for the GoMACCS region V002 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid.", "links": [ { diff --git a/datasets/GOM_0.json b/datasets/GOM_0.json index 29f5971221..de92a2c23b 100644 --- a/datasets/GOM_0.json +++ b/datasets/GOM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gulf of Mexico measurements made in 1994 and 1997.", "links": [ { diff --git a/datasets/GOM_Oil_Spill_0.json b/datasets/GOM_Oil_Spill_0.json index e880ce6307..901cd4e0c9 100644 --- a/datasets/GOM_Oil_Spill_0.json +++ b/datasets/GOM_Oil_Spill_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOM_Oil_Spill_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Mexico in 2010 during the Deepwater Horizon Oil Spill.", "links": [ { diff --git a/datasets/GOSAT-2.TANSO.FTS-2.and.CAI-2.ESA.archive_3.0.json b/datasets/GOSAT-2.TANSO.FTS-2.and.CAI-2.ESA.archive_3.0.json index dacd739e7f..2552f1465f 100644 --- a/datasets/GOSAT-2.TANSO.FTS-2.and.CAI-2.ESA.archive_3.0.json +++ b/datasets/GOSAT-2.TANSO.FTS-2.and.CAI-2.ESA.archive_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOSAT-2.TANSO.FTS-2.and.CAI-2.ESA.archive_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TANSO-FTS-2 (Thermal And Near infrared Sensor for carbon Observation - Fourier Transform Spectrometer-2) instrument is an high-resolution 5-bands (NIR and TIR) spectrometer which allows the observation of reflective and emissive radiative energy from Earth's surface and the atmosphere for the measurement of atmospheric chemistry and greenhouse gases.\rThe TANSO-CAI-2 (Thermal And Near infrared Sensor for carbon Observation - Cloud and Aerosol Imager-2) instrument is a push-broom radiometer in the spectral ranges of ultraviolet (UV), visible (VIS), Near Infrared (NIR) and SWIR (5 bands observe in the forward direction and 5 in backwards direction, with LOS tilted by 20 degrees) for the observation of aerosols and cloud optical properties and for monitoring of air pollution.\rThe GOSAT-2 available products are:\r\u2022\tFTS-2 Level 1A products contain interferogram data observed by FTS-2, together with geometric information of observation points and various telemetry. In addition, data from an optical camera (CAM) near the observation time are also stored. Two different products for day and night observations. Common data contain common information for SWIR/TIR including CAM data; SWIR data contain information from SWIR band; TIR data contain information from TIR band.\r\u2022\tFTS-2 level 1B products contain spectrum data, which are generated by Fourier transformation and other corrections to raw interferogram data in L1A. The sampled CAM data near the observation time are also stored. Two different products for day and night observations. Common data contain common information for SWIR/TIR including CAM data; SWIR products for SWIR spectrum data before and after sensitivity correction; TIR products for TIR spectrum data after sensitivity correction using blackbody and deep space calibration data and after correction of finite field of view.\r\u2022\tFTS-2 NearRealTime products: FTS-2 data are first processed with predicted orbit file and made immediately available: NRT product does not include monitor camera image, best-estimate pointing-location, and target point classification but is available on the ESA server 5 hours after sensing. After a few days (usually 3 days), data is reprocessed with definitive orbit file and sent as a consolidated product.\r\u2022\tFTS-2 Level 2 products: \u201cColumn-averaged Dry-air Mole Fraction\u201d products store column-averaged dry-air mole fraction of atmospheric gases retrieved by using Band 1-3 spectral radiance data in TANSO-FTS-2 L1B; \u201cChlorophyll Fluorescence and Proxy Method (FTS-2_02_SWPR)\u201d products store solar induced chlorophyll fluorescence data retrieved from Band 1 spectral radiance data in L1B Product as well as column-averaged dry-air mole fraction of atmospheric gases retrieved from Band 2 and 3 spectral radiance data in L1B Product. Both products are obtained by using the fill-physic maximum a posteriori (MAP) method and under the assumption of clear-sky condition.\r\u2022\tCAI-2 Level 1A products contain uncorrected image data of TANSO-CAI-2, which is stored as digital number together with telemetry of geometric information at observation point, orbit and attitude data, temperature, etc. One scene is defined as a satellite revolution data starting from ascending node to the next ascending node. Common data contain common information for both Forward looking and Backward looking; FWD products contain information for Forward looking bands, from 1 to 5; BWD products contain information for Backward looking bands, from 6 to 10.\r\u2022\tCAI-2 Level 1B products contain spectral radiance data per pixel converted from TANSO-CAI-2 L1A Products. Band-to-band registration of each forward- and backward- viewing band is applied; ortho-correction is performed to observation location data based on an earth ellipsoid model using digital elevation model data.\r\u2022\tCAI-2 Level 2 products: Cloud Discrimination Products stores clear-sky confidence levels per pixel, which are calculated by combining the results of threshold tests for multiple features such as reflectance ratio and Normalised Difference Vegetation Index (NDVI), obtained from spectral radiance data in GOSAT-2 TANSO-CAI-2 L1B Product. This product also stores cloud status bit data, in which results of individual threshold tests and quality flags are summarised.\r\rThe full ESA archive and newly acquired/systematically processed GOSAT-2 FTS-2 and CAI-2 products are (ESA collection name versus JAXA product name):\r\u2022\tFTS-2 L1A Common day (FTS-2_1A_COMMON_DAY)\r\u2022\tFTS-2 L1A Common night (FTS-2_1A_COMMON_NIGHT)\r\u2022\tFTS-2 L1A SWIR day (FTS-2_1A_SWIR_DAY)\r\u2022\tFTS-2 L1A TIR day (FTS-2_1A_TIR_DAY)\r\u2022\tFTS-2 L1A TIR night (FTS-2_1A_TIR_NIGHT)\r\u2022\tFTS-2 L1B Common day (FTS-2_1B_COMMON_DAY)\r\u2022\tFTS-2 L1B Common night (FTS-2_1B_COMMON_NIGHT)\r\u2022\tFTS-2 L1B SWIR day (FTS-2_1B_SWIR_DAY)\r\u2022\tFTS-2 L1B TIR day (FTS-2_1B_TIR_DAY)\r\u2022\tFTS-2 L1B TIR night (FTS-2_1B_TIR_NIGHT)\r\u2022\tFTS-2 L2 Column-averaged Dry-air Mole Fraction (FTS-2_0)\r\u2022\tFTS-2 L2 Chlorophyll Fluorescence and Proxy Method (FTS-2_02_SWPR)\r\u2022\tCAI-2 L1A Common (CAI-2_1A_COMMON)\r\u2022\tCAI-2 L1A Forward viewing (CAI-2_1A_FWD)\r\u2022\tCAI-2 L1A Backward viewing (CAI-2_1A_BWD)\r\u2022\tCAI-2 L1B (CAI-2_1B)", "links": [ { diff --git a/datasets/GOSAT.TANSO-FTS.CAI.full.archive.and.new.products_NA.json b/datasets/GOSAT.TANSO-FTS.CAI.full.archive.and.new.products_NA.json index 2954bff408..954f74db16 100644 --- a/datasets/GOSAT.TANSO-FTS.CAI.full.archive.and.new.products_NA.json +++ b/datasets/GOSAT.TANSO-FTS.CAI.full.archive.and.new.products_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOSAT.TANSO-FTS.CAI.full.archive.and.new.products_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TANSO-FTS instrument on-board GOSAT satellite features high optical throughput, fine spectral resolution, and a wide spectral coverage (from VIS to TIR in four bands). The reflective radiative energy is covered by the VIS and SWIR (Shortwave Infrared) ranges, while the emissive portion of radiation from Earth's surface and the atmosphere is covered by the MWIR (Midwave Infrared) and TIR (Thermal Infrared) ranges. These spectra include the absorption lines of greenhouse gases such as carbon dioxide (CO2) and methane (CH4). The TANSO-CAI instrument on-board GOSAT satellite is a radiometer in the spectral ranges of ultraviolet (UV), visible, and SWIR to correct cloud and aerosol interference. The imager has continuous spatial coverage, a wider field of view, and higher spatial resolution than the FTS in order to detect the aerosol spatial distribution and cloud coverage. Using the multispectral bands, the spectral characteristics of the aerosol scattering can be retrieved together with optical thickness. In addition, the UV-band range observations provide the aerosol data over land. With the FTS spectra, imager data, and the retrieval algorithm to remove cloud and aerosol contamination, the column density of the gases can be the column density of the gases can be retrieved with an accuracy of 1%. The full ESA archive and newly acquired/systematically processed GOSAT FTS and CAI products are available in the following processing levels: - FTS Observation mode 1 L1B, day (FTS_OB1D_1) - FTS Observation mode 1 L1B, night (FTS_OB1N_1) - FTS Special Observation L1B, day (FTS_SPOD_1) - FTS Special Observation L1B, night (FTS_SPON_1) - FTS L2 CO2 profile, TIR (FTS_P01T_2) - FTS L2 CH4 profile, TIR (FTS_P02T_2) - FTS L2 CH4 column amount, SWIR (FTS_C02S_2) - FTS L2 CO2 column amount, SWIR (FTS_C01S_2) - FTS L2 H2O column amount, SWIR (FTS_C03S_2) - FTS L3 global CO2 distribution, SWIR (FTS_C01S_3) - FTS L3 global CH4 distribution, SWIR (FTS_C02S_3) - FTS L4A global CO2 flux, annual in text format (FTS_F01M4A) - FTS L4A global CO2 flux, annual in netCDF format (FTS_F03M4A) - FTS L4A global CO2 distribution (FTS_P01M4B) - FTS L4A global CH4 flux, annual in text format (FTS_F02M4A) - FTS L4A global CH4 flux, annual in netCDF format (FTS_F04M4A) - FTS L4A global CH4 distribution (FTS_P02M4B) - CAI L1B data (CAI_TRB0_1) - CAI L1B+ (CAI_TRBP_1) - CAI L2 cloud flag (CAI_CLDM_2) - CAI L3 global reflect. distrib. clear sky (CAI_TRCF_3) - CAI L3 global radiance distrib. all pixels (CAI_TRCL_3) - CAI L3 global NDVI (CAI_NDVI_3) All products are made available as soon as processed and received from JAXA. To satisfy NearRealTime requirements, ESA also provides access to the FTS L1X products, which are the NRT version of FTS L1B products. The main difference between L1X and L1B is that L1X does not include CAM data, best-estimate pointing-location, and target point classification, but most of all the L1X products are available on the ESA server between 2 and 5 hours after acquisition. The L1X products remains on the FTP server for 5 days, the time for the corresponding L1B to be available. A document describing the differences between L1X and L1B products is listed in the available resources. For more details on products, please refer to below product specifications.", "links": [ { diff --git a/datasets/GOSAT.TANSO.FTS.and.CAI.ESA.archive_3.0.json b/datasets/GOSAT.TANSO.FTS.and.CAI.ESA.archive_3.0.json index d860107f15..90d487387e 100644 --- a/datasets/GOSAT.TANSO.FTS.and.CAI.ESA.archive_3.0.json +++ b/datasets/GOSAT.TANSO.FTS.and.CAI.ESA.archive_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOSAT.TANSO.FTS.and.CAI.ESA.archive_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TANSO-FTS instrument on-board GOSAT satellite features high optical throughput, fine spectral resolution, and a wide spectral coverage (from VIS to TIR in four bands). The reflective radiative energy is covered by the VIS and SWIR (Shortwave Infrared) ranges, while the emissive portion of radiation from Earth's surface and the atmosphere is covered by the MWIR (Midwave Infrared) and TIR (Thermal Infrared) ranges. These spectra include the absorption lines of greenhouse gases such as carbon dioxide (CO2) and methane (CH4).\rThe TANSO-CAI instrument on-board GOSAT satellite is a radiometer in the spectral ranges of ultraviolet (UV), visible, and SWIR to correct cloud and aerosol interference. The imager has continuous spatial coverage, a wider field of view, and higher spatial resolution than the FTS in order to detect the aerosol spatial distribution and cloud coverage. Using the multispectral bands, the spectral characteristics of the aerosol scattering can be retrieved together with optical thickness. In addition, the UV-band range observations provide the aerosol data over land. With the FTS spectra, imager data, and the retrieval algorithm to remove cloud and aerosol contamination, the column density of the gases can be retrieved with an accuracy of 1%.\rThe full ESA archive and newly acquired/systematically processed GOSAT FTS and CAI products are available in the following processing levels:\r\u2022\tFTS Observation mode 1 L1B, day (FTS_OB1D_1)\r\u2022\tFTS Observation mode 1 L1B, night (FTS_OB1N_1)\r\u2022\tFTS Special Observation L1B, day (FTS_SPOD_1)\r\u2022\tFTS Special Observation L1B, night (FTS_SPON_1)\r\u2022\tFTS L2 CO2 profile, TIR (FTS_P01T_2)\r\u2022\tFTS L2 CH4 profile, TIR (FTS_P02T_2)\r\u2022\tFTS L2 CH4 column amount, SWIR (FTS_C02S_2)\r\u2022\tFTS L2 CO2 column amount, SWIR (FTS_C01S_2)\r\u2022\tFTS L2 H2O column amount, SWIR (FTS_C03S_2)\r\u2022\tFTS L3 global CO2 distribution, SWIR (FTS_C01S_3)\r\u2022\tFTS L3 global CH4 distribution, SWIR (FTS_C02S_3)\r\u2022\tFTS L4A global CO2 flux, annual in text format (FTS_F01M4A)\r\u2022\tFTS L4A global CO2 flux, annual in netCDF format (FTS_F03M4A)\r\u2022\tFTS L4A global CO2 distribution (FTS_P01M4B)\r\u2022\tFTS L4A global CH4 flux, annual in text format (FTS_F02M4A)\r\u2022\tFTS L4A global CH4 flux, annual in netCDF format (FTS_F04M4A)\r\u2022\tFTS L4A global CH4 distribution (FTS_P02M4B)\r\u2022\tCAI L1B data (CAI_TRB0_1)\r\u2022\tCAI L1B+ (CAI_TRBP_1)\r\u2022\tCAI L2 cloud flag (CAI_CLDM_2)\r\u2022\tCAI L3 global reflect. distrib. clear sky (CAI_TRCF_3)\r\u2022\tCAI L3 global radiance distrib. all pixels (CAI_TRCL_3)\r\u2022\tCAI L3 global NDVI (CAI_NDVI_3)\rAll products are made available as soon as processed and received from JAXA.\r\rTo satisfy NearRealTime requirements, ESA also provides access to the FTS L1X products, which are the NRT version of FTS L1B products. The main difference between L1X and L1B is that L1X does not include CAM data, best-estimate pointing-location, and target point classification, but most of all the L1X products are available on the ESA server between 2 and 5 hours after acquisition. The L1X products remain on the dissemination server for 5 days, the time for the corresponding L1B to be available. A document describing the differences between L1X and L1B products is listed in the available resources.\r\rFor more details on products, please refer to below product specifications.", "links": [ { diff --git a/datasets/GOSAT2.TANSO-FTS2.CAI2.full.archive.and.new.products_NA.json b/datasets/GOSAT2.TANSO-FTS2.CAI2.full.archive.and.new.products_NA.json index 0e6c0d8d63..0b41dd05fd 100644 --- a/datasets/GOSAT2.TANSO-FTS2.CAI2.full.archive.and.new.products_NA.json +++ b/datasets/GOSAT2.TANSO-FTS2.CAI2.full.archive.and.new.products_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GOSAT2.TANSO-FTS2.CAI2.full.archive.and.new.products_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TANSO-FTS-2 (Thermal And Near infrared Sensor for carbon Observation - Fourier Transform Spectrometer-2) instrument is an high-resolution 5-bands (NIR and TIR) spectrometer which allows the observation of reflective and emissive radiative energy from Earth's surface and the atmosphere for the measurement of atmospheric chemistry and greenhouse gases. The TANSO-CAI-2 (Thermal And Near infrared Sensor for carbon Observation - Cloud and Aerosol Imager-2) instrument is a push-broom radiometer in the spectral ranges of ultraviolet (UV), visible (VIS), Near Infrared (NIR) and SWIR (5 bands observe in the forward direction and 5 in backwards direction, with LOS tilted of 20 degrees) for the observation of aerosols and clouds optical properties and for monitoring of air pollution The GOSAT-2 FTS-2 available products are: - FTS-2 Level 1A products contain interferogram data observed by FTS-2, together with geometric information of observation points and various telemetry. In addition, data from an optical camera (CAM) near the observation time are also stored. Two different products for day and night observations. Common data contain common information for SWIR/TIR including CAM data; SWIR data contain information from SWIR band; TIR data contain information from TIR band; - FTS-2 level 1B products contain spectrum data, which are generated by Fourier transformation and other corrections to raw interferogram data in L1A. The sampled CAM data near the observation time are also stored. Two different products for day and night observations. Common data contain common information for SWIR/TIR including CAM data; SWIR products for SWIR spectrum data before and after sensitivity correction; TIR products for TIR spectrum data after sensitivity correction using blackbody and deep space calibration data and after correction of finite field of view. - FTS-2 NearRealTime products: FTS-2 data are first processed with predicted orbit file and made immediately available: NRT product does not include monitor camera image, best-estimate pointing-location, and target point classification but is available on the ESA server 5 hours after sensing. After a few days (usually 3 days), data is reprocessed with definitive orbit file and sent as consolidated product. - FTS-2 Level 2 products: Column-averaged Dry-air Mole Fraction\" products store column-averaged dry-air mole fraction of atmospheric gases retrieved by using Band 1-3 spectral radiance data in TANSO-FTS-2 L1B; \"Chlorophyll Fluorescence and Proxy Method (FTS-2_02_SWPR)\" products store solar induced chlorophyll fluorescence data retrieved from Band 1 spectral radiance data in L1B Product as well as column-averaged dry-air mole fraction of atmospheric gases retrieved from Band 2 and 3 spectral radiance data in L1B Product. Both products are obtained by using the fill-physic maximum a posteriori (MAP) method and under the assumption of of clear-sky condition - CAI-2 Level 1A products contain uncorrected image data of TANSO-CAI-2, which is stored as digital number together with telemetry of geometric information at observation point, orbit and attitude data, temperature, etc. One scene is defined as a satellite revolution data starting from ascending node to the next ascending node. Common data contain common information for both Forward looking and Backward looking; FWD products contain information for Forward looking bands, from 1 to 5; BWD products contain information for Backward looking bands, from 6 to 10. - CAI-2 Level 1B products contain spectral radiance data per pixel converted from TANSO-CAI-2 L1A Products. Band-to-band registration of each forward- and backward- viewing band is applied; ortho-correction is performed to observation location data based on an earth ellipsoid model using digital elevation model data. - CAI-2 Level 2 products: Cloud Discrimination Products stores clear-sky confidence levels per pixel, which are calculated by combining the results of threshold tests for multiple features such as reflectance ratio and Normalized Difference Vegetation Index (NDVI), obtained from spectral radiance data in GOSAT-2 TANSO-CAI-2 L1B Product. This product also stores cloud status bit data, in which results of individual threshold tests and quality flags are summarized. The GOSAT-2 FTS-2 available products are: The full ESA archive and newly acquired/systematically processed GOSAT2 FTS-2 and CAI-2 products are (ESA collection name versus JAXA product name): - FTS-2 L1A Common day (FTS-2_1A_COMMON_DAY) - FTS-2 L1A Common night (FTS-2_1A_COMMON_NIGHT) - FTS-2 L1A SWIR day (FTS-2_1A_SWIR_DAY) - FTS-2 L1A TIR day (FTS-2_1A_TIR_DAY) - FTS-2 L1A TIR night (FTS-2_1A_TIR_NIGHT) - FTS-2 L1B Common day (FTS-2_1B_COMMON_DAY) - FTS-2 L1B Common night (FTS-2_1B_COMMON_NIGHT) - FTS-2 L1B SWIR day (FTS-2_1B_SWIR_DAY) - FTS-2 L1B TIR day (FTS-2_1B_TIR_DAY) - FTS-2 L1B TIR night (FTS-2_1B_TIR_NIGHT) - FTS-2 L2 Column-averaged Dry-air Mole Fraction (FTS-2_0) - FTS-2 L2 Chlorophyll Fluorescence and Proxy Method (FTS-2_02_SWPR) - CAI-2 L1A Common (CAI-2_1A_COMMON) - CAI-2 L1A Forward viewing (CAI-2_1A_FWD) - CAI-2 L1A Backward viewing (CAI-2_1A_BWD) - CAI-2 L1B (CAI-2_1B) All products are made available as soon as processed and received from JAXA. To satisfy NearRealTime requirements, ESA also provides access to the FTS L1X products, which are the NRT version of FTS L1B products. The main difference between L1X and L1B is that L1X does not include CAM data, best-estimate pointing-location, and target point classification, but most of all the L1X products are available on the ESA server between 2 and 5 hours after acquisition. The L1X products remains on the FTP server for 5 days, the time for the corresponding L1B to be available. A document describing the differences between L1X and L1B products is listed in the available resources. For more details on products, please refer to below product specifications.", "links": [ { diff --git a/datasets/GPCPDAY_3.1.json b/datasets/GPCPDAY_3.1.json index 9a849a8f2d..71384e4d9c 100644 --- a/datasets/GPCPDAY_3.1.json +++ b/datasets/GPCPDAY_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPCPDAY_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This version has been superseded by Version 3.2 (DOI: 10.5067/MEASURES/GPCP/DATA305).\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Programme. As the follow-on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.1) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly. All GPCPV3 products will be internally consistent. The Daily product spans June 2000-December 2019. Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S. The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box. In addition to the final precipitation field, probability of liquid precipitation estimates are provided globally.", "links": [ { diff --git a/datasets/GPCPDAY_3.2.json b/datasets/GPCPDAY_3.2.json index 2a493b4aed..546484a426 100644 --- a/datasets/GPCPDAY_3.2.json +++ b/datasets/GPCPDAY_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPCPDAY_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 3.2 is the current version. Older versions have been superseded by Version 3.2.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program. As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly. All GPCPV3 products will be internally consistent. The Daily product spans June 2000-December 2020. Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S. The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box. In addition to the final precipitation field, probability of liquid phase estimates are provided globally.", "links": [ { diff --git a/datasets/GPCPMON_3.1.json b/datasets/GPCPMON_3.1.json index 510d923c23..fc0a099c9e 100644 --- a/datasets/GPCPMON_3.1.json +++ b/datasets/GPCPMON_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPCPMON_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This version has been superseded by Version 3.2 (DOI: 10.5067/MEASURES/GPCP/DATA304).\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Experiment (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program. As the follow on to the GPCP Version 2.X products, GPCP Version 3 (GPCP V3.1) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCPV3 suite currently consists the 0.5-degree monthly. Additional products are planned, namely a 0.5\u00b0 daily product for the entire record from 1983 to the (delayed) present and a 0.1\u00b0 3-hourly product from 1998 to the (delayed) present. All GPCPV3 products will be internally consistent. The monthly product spans 1983 - 2019. Inputs consist of the GPROF SSMI/SSMIS orbit files that are used to calibrate the PERSIANN-CDR IR-based precipitation in the span 60\u00b0N-S. Outside of 58\u00b0N-S, TOVS/AIRS estimates, calibrated by the IR estimates and the GPCC gauge analyses are used. This satellite-only estimate is then merged with GPCC gauge analyses over land to compute the final product. In addition to the final precipitation field, ancillary precipitation and error estimates are provided.", "links": [ { diff --git a/datasets/GPCPMON_3.2.json b/datasets/GPCPMON_3.2.json index 46e0a68d8e..18451bdba3 100644 --- a/datasets/GPCPMON_3.2.json +++ b/datasets/GPCPMON_3.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPCPMON_3.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 3.2 is the current version. Older versions have been superseded by Version 3.2.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Experiment (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program. As the follow on to the GPCP Version 2.X products, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCPV3 suite currently consists the 0.5-degree monthly and daily products. A follow-on 0.1-degree 3-hourly is expected. All GPCPV3 products will be internally consistent. The monthly product spans 1983 - 2020. Inputs consist of the GPROF SSMI/SSMIS orbit files that are used to calibrate the PERSIANN-CDR IR-based precipitation in the span 60\u00b0N-S, which are in turn calibrated to the monthly 2.5-degree METH product. The METH-GPROF-adjusted PERSIANN-CDR IR estimates are then climatologically adjusted to the blended TCC/MCTG. Outside of 58\u00b0N-S, TOVS/AIRS estimates, adjusted climatologically to the MCTG, are used. The PERSIANN-CDR / TOVS/AIRS estimates are then merged in the region 35\u00b0N-S-58\u00b0N-S, which are then merged with GPCC gauge analyses over land to obtain the final product. In addition to the final precipitation field, ancillary precipitation and error estimates are provided.", "links": [ { diff --git a/datasets/GPCP_precip_712_1.json b/datasets/GPCP_precip_712_1.json index 015f8ee7e0..2175948df4 100644 --- a/datasets/GPCP_precip_712_1.json +++ b/datasets/GPCP_precip_712_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPCP_precip_712_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Climatology Project (GPCP) is an international project designed to provide improved long-record estimates of precipitation over the globe. The general approach is to combine the precipitation information available from several sources into a final merged product that takes advantage of the strengths of each data type. The GPCP has promoted the development of an analysis procedure for blending the various estimates together to produce the necessary global gridded precipitation fields. The currently operational procedure is based on Huffman et al. (1995) and has been used to produce the GPCP Version 2 Combined Precipitation Data Set, covering the period January 1979 through the present. The primary product in the Version 2 data set is a combined observation-only data set, that is, a gridded analysis based on gauge measurements and satellite estimates of rainfall.", "links": [ { diff --git a/datasets/GPM_1AGMI_07.json b/datasets/GPM_1AGMI_07.json index 3682ac0e4a..6fed1a13f6 100644 --- a/datasets/GPM_1AGMI_07.json +++ b/datasets/GPM_1AGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1AGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 1AGMI product contains unaltered data directly from the Global Microwave Imager (GMI) aboard the GPM core satellite. The GMI is a multi-channel, conical- scanning, microwave radiometer. \n \nIf there is enough bandwidth, the entire circle of GMI samples will be sent down. The 1AGMI product's swaths 4 and 5 contain all of the samples that are sent down. Later products only use the subset of these data that contains the Earth view, hot load, and cold sky samples.", "links": [ { diff --git a/datasets/GPM_1ATMI_07.json b/datasets/GPM_1ATMI_07.json index 8d3cf881a2..89a36dc57d 100644 --- a/datasets/GPM_1ATMI_07.json +++ b/datasets/GPM_1ATMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1ATMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A11\n\n Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.\n\nThe 1ATMI product contains science and housekeeping sensor count data directly from the TRMM Microwave Imager (TMI) Instrument aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 33 MB. There are 16 files of TMI 1A data produced per day.\n\nSpatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.\n\n", "links": [ { diff --git a/datasets/GPM_1AVIRS_07.json b/datasets/GPM_1AVIRS_07.json index 72b3232378..6b0e5a1e94 100644 --- a/datasets/GPM_1AVIRS_07.json +++ b/datasets/GPM_1AVIRS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1AVIRS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A01\n\n Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.\n\nThe 1AVIRS product contains science and housekeeping sensor count data directly from the Visible and Infrared Scanner (VIRS) aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 131 MB. There are 16 files of VIRS 1A data produced per day.\n\nThe Visible and Infrared Scanner (VIRS) is a five-channel visible/infrared radiometer, which builds on the heritage of the Advanced Very High Resolution Radiometer (AVHRR) instrument flown aboard the NOAA series of Polar-Orbiting Operational Environmental Satellites (POES). The VIRS detects radiation at 1 visible, 2 near infrared and 2 thermal infrared wavelengths, allowing determination of cloud coverage, cloud top height and temperature, and precipitation indices. The central wavelengths for the VIRS channels are 0.63, 1.60,3.75, 10.8, and 12.0 microns. All channels are in operation during the daytime, but only channels 3, 4 and 5 operate during the nighttime.\n\nSpatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation\n\n", "links": [ { diff --git a/datasets/GPM_1BGMI_07.json b/datasets/GPM_1BGMI_07.json index b721112b68..7dbe27f1e1 100644 --- a/datasets/GPM_1BGMI_07.json +++ b/datasets/GPM_1BGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1BGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 1BGMI algorithm uses a non-linear three-point in-flight calibration to derive antenna temperature (Ta) and convert Ta to Tb using GMI antenna pattern corrections. The four-point calibration, which utilizes noise diode measurements, is used to monitor the sensor non-linearty. The noise diode measurements also provide a hot load back-up calibration in case hot load measurements are lost. Details are in the GMI ATBD. The 1BGMI algorithm and software transform Level 0 counts into geolocated and calibrated brightness temperatures (Tb) for 13 GMI channels.", "links": [ { diff --git a/datasets/GPM_1BPR_07.json b/datasets/GPM_1BPR_07.json index 6c4b7d5a0f..45757ec542 100644 --- a/datasets/GPM_1BPR_07.json +++ b/datasets/GPM_1BPR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1BPR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B21,1C21\n\n Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.\nThe TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation.\n \nThe 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. Periodically the input-output characteristics are measured using an internal calibration loop for the IF unit and later receiver stages. To make an absolute calibration, an Active Radar Calibrator (ARC) is placed at Kansai Branch of CRL and overall system gain of the PR is being measured every 2 months. Using the transfer function based on the above internal and external calibrations, the PR received power is obtained. Note that the value assumes that the signal follows the Rayleigh fading, so if the fading characteristics of a scatter is different, a small bias error may occur (within 1 or 2 dB).\n\t\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km", "links": [ { diff --git a/datasets/GPM_1BTMI_07.json b/datasets/GPM_1BTMI_07.json index 771300470a..be0888a623 100644 --- a/datasets/GPM_1BTMI_07.json +++ b/datasets/GPM_1BTMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1BTMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B11\n\n Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.\n\nThis dataset contains TRMM Micrwave Imager (TMI) L1B calibrated radiances in terms of Brightness Temperatures.\n\nThe TMI calibration algorithm (1B11) converts the radiometer counts to antenna temperatures by applying a linear relationship of the form Ta = c1 + c2 x count. The coefficients are provided by the instrument contractor. Antenna temperatures are corrected for cross-polarization and spill over to produce brightness temperatures (Tb), but no antenna beam pattern correction or sample to pixel averaging are performed. Temperatures are provided at 104 scan positions for the low frequency channels and 208 scan positions at 85 GHz. There are four samples per pixel (3 -dB beamwidth) at 10 GHz, two samples at 19, 22, and 37 GHz, and one sample per pixel for the 85 GHz.\nData Flow Description\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: ~13 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: ~13 km\n", "links": [ { diff --git a/datasets/GPM_1BVIRS_07.json b/datasets/GPM_1BVIRS_07.json index 2d64180f3c..6435e7eae7 100644 --- a/datasets/GPM_1BVIRS_07.json +++ b/datasets/GPM_1BVIRS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1BVIRS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B01\n\n Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.\n\nThis TRMM Visible and Infrared Scanner (VIRS) Level 1B Calibrated Radiance Product (1B01) contains calibrated radiances and auxiliary geolocation information from the five channels of the VIRS instrument, for each pixel of each scan. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the VIRS measurements. A file contains a single orbit of data with a file size of about 95 MB. The EOSDIS \"swath\" structure is used to accommodate the actual geophysical data arrays. There are 16 files of VIRS 1B01 data produced per day.\n\nFor channels 1 and 2, Level 1B radiances are derived from the Level 1A (1A01) sensor counts by computing calibration parameters (gain and offset) derived from the counts registered during space and solar and/or lunar views. New calibration parameters are produced every one to four weeks. Channels 3, 4, and 5 are calibrated using the internal blackbody and the space view. These calibration parameters, together with a quadratic term determined pre-launch, are used to generate a counts vs. radiance curve for each band, which is then used to convert the earth-view pixel counts to spectral radiances.\n\nGeolocation and channel data are written out for each pixel along the scan, whereas the time stamp, scan status (containing scan quality information), navigation, calibration coefficients, and solar/satellite geometry are specified on a per-scan basis. There are in general 18026 scans along the orbit pre-boost and 18223 post-boost, with each scan consisting of 261 pixels. The scan width is about 720 km pre-boost and 833 km post-boost.\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 720 km; Horizontal Resolution: 2.2 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 833 km; Horizontal Resolution: 2.4 km\n\t", "links": [ { diff --git a/datasets/GPM_1CAQUAAMSRE_07.json b/datasets/GPM_1CAQUAAMSRE_07.json index 802f6b04a9..ba3a6feaff 100644 --- a/datasets/GPM_1CAQUAAMSRE_07.json +++ b/datasets/GPM_1CAQUAAMSRE_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CAQUAAMSRE_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF08SSMI_07.json b/datasets/GPM_1CF08SSMI_07.json index 1adea0909c..b5c5f13d9d 100644 --- a/datasets/GPM_1CF08SSMI_07.json +++ b/datasets/GPM_1CF08SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF08SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF10SSMI_07.json b/datasets/GPM_1CF10SSMI_07.json index 605f8ffcd7..52ba80e111 100644 --- a/datasets/GPM_1CF10SSMI_07.json +++ b/datasets/GPM_1CF10SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF10SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF11SSMI_07.json b/datasets/GPM_1CF11SSMI_07.json index 4e8c037af3..106fc3025e 100644 --- a/datasets/GPM_1CF11SSMI_07.json +++ b/datasets/GPM_1CF11SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF11SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF13SSMI_07.json b/datasets/GPM_1CF13SSMI_07.json index f67e0e855b..a5c301b082 100644 --- a/datasets/GPM_1CF13SSMI_07.json +++ b/datasets/GPM_1CF13SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF13SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF14SSMI_07.json b/datasets/GPM_1CF14SSMI_07.json index 582aab55f6..3bd56eb879 100644 --- a/datasets/GPM_1CF14SSMI_07.json +++ b/datasets/GPM_1CF14SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF14SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF15SSMI_07.json b/datasets/GPM_1CF15SSMI_07.json index 98cf2701d6..5e4e8d2222 100644 --- a/datasets/GPM_1CF15SSMI_07.json +++ b/datasets/GPM_1CF15SSMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF15SSMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF16SSMIS_07.json b/datasets/GPM_1CF16SSMIS_07.json index 2e9978345b..ec92ad7403 100644 --- a/datasets/GPM_1CF16SSMIS_07.json +++ b/datasets/GPM_1CF16SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF16SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF17SSMIS_07.json b/datasets/GPM_1CF17SSMIS_07.json index 92dfa3783e..2117e00a00 100644 --- a/datasets/GPM_1CF17SSMIS_07.json +++ b/datasets/GPM_1CF17SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF17SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF18SSMIS_07.json b/datasets/GPM_1CF18SSMIS_07.json index 8f04333aef..2697f38f42 100644 --- a/datasets/GPM_1CF18SSMIS_07.json +++ b/datasets/GPM_1CF18SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF18SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CF19SSMIS_07.json b/datasets/GPM_1CF19SSMIS_07.json index 956f9037b7..4d90083169 100644 --- a/datasets/GPM_1CF19SSMIS_07.json +++ b/datasets/GPM_1CF19SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CF19SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_1CGCOMW1AMSR2_07.json b/datasets/GPM_1CGCOMW1AMSR2_07.json index 7ef30ab8c5..2083cf92d7 100644 --- a/datasets/GPM_1CGCOMW1AMSR2_07.json +++ b/datasets/GPM_1CGCOMW1AMSR2_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CGCOMW1AMSR2_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CGPMGMI_07.json b/datasets/GPM_1CGPMGMI_07.json index b673d9af83..2e6b7306d0 100644 --- a/datasets/GPM_1CGPMGMI_07.json +++ b/datasets/GPM_1CGPMGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CGPMGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CGMI contains common calibrated brightness temperatures from the GMI passive microwave instrument flown on the GPM satellite. 1C-R GMI is a remapped version of 1CGMI which is explained at the end of this section. Swath S1 has 9 channels which are similar to TRMM TMI (10V 10H 19V 19H 23V 37V 37H 89V 89H). Swath S2 has 4 channels similar to AMSU-B (166V 166H 183+/-3V 183+/-8V). Data for both swaths is observed in the same revolution of the instrument.", "links": [ { diff --git a/datasets/GPM_1CGPMGMI_R_07.json b/datasets/GPM_1CGPMGMI_R_07.json index 1755ce9794..fda841d1ac 100644 --- a/datasets/GPM_1CGPMGMI_R_07.json +++ b/datasets/GPM_1CGPMGMI_R_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CGPMGMI_R_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CGMI contains common calibrated brightness temperatures from the GMI passive microwave instrument flown on the GPM satellite. 1C-R GMI is a remapped version of 1CGMI which is explained at the end of this section. Swath S1 has 9 channels which are similar to TRMM TMI (10V 10H 19V 19H 23V 37V 37H 89V 89H). Swath S2 has 4 channels similar to AMSU-B (166V 166H 183+/-3V 183+/-8V). Data for both swaths is observed in the same revolution of the instrument.", "links": [ { diff --git a/datasets/GPM_1CMETOPAMHS_07.json b/datasets/GPM_1CMETOPAMHS_07.json index 03fd07e4d5..7a9a454a40 100644 --- a/datasets/GPM_1CMETOPAMHS_07.json +++ b/datasets/GPM_1CMETOPAMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CMETOPAMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CMETOPBMHS_07.json b/datasets/GPM_1CMETOPBMHS_07.json index b4b79aa059..033309ee54 100644 --- a/datasets/GPM_1CMETOPBMHS_07.json +++ b/datasets/GPM_1CMETOPBMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CMETOPBMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CMETOPCMHS_07.json b/datasets/GPM_1CMETOPCMHS_07.json index 5a6092eb64..45ac319bbf 100644 --- a/datasets/GPM_1CMETOPCMHS_07.json +++ b/datasets/GPM_1CMETOPCMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CMETOPCMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CMT1SAPHIR_07.json b/datasets/GPM_1CMT1SAPHIR_07.json index fd3f5da8de..4614795ec2 100644 --- a/datasets/GPM_1CMT1SAPHIR_07.json +++ b/datasets/GPM_1CMT1SAPHIR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CMT1SAPHIR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CNOAA15AMSUB_07.json b/datasets/GPM_1CNOAA15AMSUB_07.json index 43f79da038..ef1fb54e30 100644 --- a/datasets/GPM_1CNOAA15AMSUB_07.json +++ b/datasets/GPM_1CNOAA15AMSUB_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA15AMSUB_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. \n\n 1CAMSUB contains common calibrated brightness temperature from the AMSU-B passive microwave instrument flown on the NOAA satellites. Swath S1 is the only swath and has 5 channels (89.0 +/- 0.9 GHz, 150.0 +/- 0.9 GHz, 183.31 +/- 1 GHz, 183.31 +/- 3 GHz, and 183.31 +/- 7 GHz) AMSU-B is very similar to MHS. The scan period is 2.667s. S1 is the only swath, containing observations sampled 90 times along the scan. ", "links": [ { diff --git a/datasets/GPM_1CNOAA16AMSUB_07.json b/datasets/GPM_1CNOAA16AMSUB_07.json index bb1caad8e0..4d221da408 100644 --- a/datasets/GPM_1CNOAA16AMSUB_07.json +++ b/datasets/GPM_1CNOAA16AMSUB_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA16AMSUB_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. \n\n 1CAMSUB contains common calibrated brightness temperature from the AMSU-B passive microwave instrument flown on the NOAA satellites. Swath S1 is the only swath and has 5 channels (89.0 +/- 0.9 GHz, 150.0 +/- 0.9 GHz, 183.31 +/- 1 GHz, 183.31 +/- 3 GHz, and 183.31 +/- 7 GHz) AMSU-B is very similar to MHS. The scan period is 2.667s. S1 is the only swath, containing observations sampled 90 times along the scan.", "links": [ { diff --git a/datasets/GPM_1CNOAA17AMSUB_07.json b/datasets/GPM_1CNOAA17AMSUB_07.json index 47ebb4862c..2e67c70837 100644 --- a/datasets/GPM_1CNOAA17AMSUB_07.json +++ b/datasets/GPM_1CNOAA17AMSUB_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA17AMSUB_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. \n\n 1CAMSUB contains common calibrated brightness temperature from the AMSU-B passive microwave instrument flown on the NOAA satellites. Swath S1 is the only swath and has 5 channels (89.0 +/- 0.9 GHz, 150.0 +/- 0.9 GHz, 183.31 +/- 1 GHz, 183.31 +/- 3 GHz, and 183.31 +/- 7 GHz) AMSU-B is very similar to MHS. The scan period is 2.667s. S1 is the only swath, containing observations sampled 90 times along the scan. ", "links": [ { diff --git a/datasets/GPM_1CNOAA18MHS_07.json b/datasets/GPM_1CNOAA18MHS_07.json index 9af42942f9..a1a6502bc8 100644 --- a/datasets/GPM_1CNOAA18MHS_07.json +++ b/datasets/GPM_1CNOAA18MHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA18MHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CMHS contains common calibrated brightness temperature from the MHS passive microwave instrument flown on the NOAA and METOPS satellites. Swath S1 is the only swath and has 5 channels (89.0GHzV, 157.0GHzV, 183.3GHz+/-250MHzH, 183.3GHz+/- 500MHzH, and 190.3 GHzV). MHS is very similar to AMSU-B. The scan period is 2.667s.", "links": [ { diff --git a/datasets/GPM_1CNOAA19MHS_07.json b/datasets/GPM_1CNOAA19MHS_07.json index 9bcb204cf7..43bff4b06f 100644 --- a/datasets/GPM_1CNOAA19MHS_07.json +++ b/datasets/GPM_1CNOAA19MHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA19MHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.", "links": [ { diff --git a/datasets/GPM_1CNOAA20ATMS_07.json b/datasets/GPM_1CNOAA20ATMS_07.json index 0782f3dd4a..801fd397c2 100644 --- a/datasets/GPM_1CNOAA20ATMS_07.json +++ b/datasets/GPM_1CNOAA20ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA20ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CATMS contains common calibrated brightness temperature from the ATMS passive microwave instrument flown on the Suomi NPP satellite and JPSS satellites. ATMS is approximatly a combination of the AMSU-A channels and the MHS channels, to the total of 22 channels. ATMS rotates 3 scans per 8 seconds. 1CATMS contains 4 swaths, one for each band K, A(Ka), W, and G.\n\n", "links": [ { diff --git a/datasets/GPM_1CNOAA21ATMS_07.json b/datasets/GPM_1CNOAA21ATMS_07.json index de62cb34d9..5e0b92a7b4 100644 --- a/datasets/GPM_1CNOAA21ATMS_07.json +++ b/datasets/GPM_1CNOAA21ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNOAA21ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n1CATMS contains common calibrated brightness temperature from the ATMS passive microwave instrument flown on the Suomi NPP satellite and JPSS satellites. ATMS is approximatly a combination of the AMSU-A channels and the MHS channels, to the total of 22 channels. ATMS rotates 3 scans per 8 seconds. 1CATMS contains 4 swaths, one for each band K, A(Ka), W, and G.\n\n", "links": [ { diff --git a/datasets/GPM_1CNPPATMS_07.json b/datasets/GPM_1CNPPATMS_07.json index 78d362c3b1..7791c27632 100644 --- a/datasets/GPM_1CNPPATMS_07.json +++ b/datasets/GPM_1CNPPATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CNPPATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CATMS contains common calibrated brightness temperature from the ATMS passive microwave instrument flown on the Suomi NPP satellite and JPSS satellites. ATMS is approximately a combination of the AMSU-A channels and the MHS channels. ATMS rotates 3 scans per 8 seconds. ATMS has the following 22 channels: Ch GHz Pol 1 23.8 QV 2 31.4 QV 3 50.3 QH 4 51.76 QH 5 52.8 QH 6 53.596+-0.115 QH 7 54.4 QH 8 54.94 QH 9 55.5 QH 10 fo = 57.29 QH 11 fo+-0.3222+-0.217 QH 12 fo+-0.3222+-0.048 QH 13 fo+-0.3222+-0.022 QH 14 fo+-0.3222+-0.010 QH 15 fo+-0.3222+-0.0045 QH 16 88.2 QV 17 165.5 QH 18 183.31+-7 QH 19 183.31+-4.5 QH 20 183.31+-3 QH 21 183.31+-1.8 QH 22 183.31+-1 QH\n\nQV means quasi-vertical; the polarization vector is parallel to the scan plane at nadir. QH meansquasi-horizontal polarization. Note on geolocation and 1C swaths: The BeamLatitude and BeamLongitude in ATMSBASE have a band dimension of 5. Lat and lon is for channels 1,2,3,16,17. Each 1C swath will contain one band: 1C swath Band IEEE GHz Ch geo Chs in band 1 K 18-26.5 1 1 2 A(Ka) 26.5-40 2 2 3 W 75-110 16 16 4 G 110-300 17 17-22 Note that channels 3-15 are NOT included in the 1C product. 1C ATMS contains 4 swaths, one for each band K, A(Ka), W, and G.", "links": [ { diff --git a/datasets/GPM_1CTRMMTMI_07.json b/datasets/GPM_1CTRMMTMI_07.json index 14168edca3..e8cce194cd 100644 --- a/datasets/GPM_1CTRMMTMI_07.json +++ b/datasets/GPM_1CTRMMTMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_1CTRMMTMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.", "links": [ { diff --git a/datasets/GPM_2ADPRENV_07.json b/datasets/GPM_2ADPRENV_07.json index 12f5e8eac0..cac0d7f0a2 100644 --- a/datasets/GPM_2ADPRENV_07.json +++ b/datasets/GPM_2ADPRENV_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2ADPRENV_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\nThis is environmental data that includes the profiles of atmospheric parameters assumed in the L2 retrieval algorithm.\n\nThis GPM data type provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products derived from the: \n+ the Ku-band frequency over a wide swath (245 km);\n+ the Ka-band frequency over a narrow swath (125 km), and\n+ the dual-frequency data over the narrow swath. \n\nThe Ka-band results are further divided into the standard and high-sensitivity estimates. In the standard sensitivity mode, the fields of view within the inner swath are matched to those of the Ku-band. Data from these matched-beam Ku- and Ka-band fields of view are used to derive the dual-frequency precipitation products. The retrievals are performed at each radar range bin along the slant path of the radar instrument field of view (IFOV). \n\nThe dual-frequency retrieval benefits from having co-aligned measurements at Ku- and Ka-bands. Data from these measurements are used to infer properties of the particle size distribution, which are expected to lead to improved estimates of rainfall rate and equivalent liquid water content. Dual-frequency data are expected to improve the capability to discriminate among water, ice, and mixed-phase hydrometeors as a function of height. This capability is particularly important in convective storms where a bright-band signature, associated with mixed-phase hydrometeors, is usually not detectable. In addition, the different attenuation rates of the Ku- and Ka-bands allow differential attenuation techniques to be used to estimate the path integrated attenuation. The high-sensitivity Ka-band channel is expected to have 6 dB greater sensitivity than the Ku- and standard Ka-band channels and to provide enhanced detection capabilities at the light rainfall rates.", "links": [ { diff --git a/datasets/GPM_2ADPR_07.json b/datasets/GPM_2ADPR_07.json index bfc3352386..c2e0d31cd7 100644 --- a/datasets/GPM_2ADPR_07.json +++ b/datasets/GPM_2ADPR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2ADPR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\n2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a narrow swath (125 km), and those derived from the dual-frequency data over the narrow swath. The Ka-band results are further divided into the standard and high-sensitivity estimates. In the standard sensitivity mode, the fields of view within the inner swath are matched to those of the Ku-band. Data from these matched-beam Ku- and Ka-band fields of view are used to derive the dual-frequency precipitation products. The retrievals are performed at each radar range bin along the slant path of the radar instrument field of view (IFOV).", "links": [ { diff --git a/datasets/GPM_2AGPROFAQUAAMSRE_CLIM_07.json b/datasets/GPM_2AGPROFAQUAAMSRE_CLIM_07.json index 49d805def9..9fbc68e257 100644 --- a/datasets/GPM_2AGPROFAQUAAMSRE_CLIM_07.json +++ b/datasets/GPM_2AGPROFAQUAAMSRE_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFAQUAAMSRE_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: \n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (Aqua)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF08SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF08SSMI_CLIM_07.json index dc5537757b..36e3482b96 100644 --- a/datasets/GPM_2AGPROFF08SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF08SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF08SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF10SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF10SSMI_CLIM_07.json index 4bd4b03050..d1d67de83a 100644 --- a/datasets/GPM_2AGPROFF10SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF10SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF10SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF11SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF11SSMI_CLIM_07.json index dbd1af6da7..4a75cd7ef5 100644 --- a/datasets/GPM_2AGPROFF11SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF11SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF11SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF13SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF13SSMI_CLIM_07.json index 9191403529..94c59aec7b 100644 --- a/datasets/GPM_2AGPROFF13SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF13SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF13SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF14SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF14SSMI_CLIM_07.json index c790131bc4..4162bea91d 100644 --- a/datasets/GPM_2AGPROFF14SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF14SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF14SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF15SSMI_CLIM_07.json b/datasets/GPM_2AGPROFF15SSMI_CLIM_07.json index 75e620c434..86db777fd6 100644 --- a/datasets/GPM_2AGPROFF15SSMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFF15SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF15SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF16SSMIS_07.json b/datasets/GPM_2AGPROFF16SSMIS_07.json index 0a9498f345..ec930440ef 100644 --- a/datasets/GPM_2AGPROFF16SSMIS_07.json +++ b/datasets/GPM_2AGPROFF16SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF16SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF16SSMIS_CLIM_07.json b/datasets/GPM_2AGPROFF16SSMIS_CLIM_07.json index 8003afdddc..a6345920ae 100644 --- a/datasets/GPM_2AGPROFF16SSMIS_CLIM_07.json +++ b/datasets/GPM_2AGPROFF16SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF16SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF17SSMIS_07.json b/datasets/GPM_2AGPROFF17SSMIS_07.json index c1fd0ab5ca..c7f0f1689d 100644 --- a/datasets/GPM_2AGPROFF17SSMIS_07.json +++ b/datasets/GPM_2AGPROFF17SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF17SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF17SSMIS_CLIM_07.json b/datasets/GPM_2AGPROFF17SSMIS_CLIM_07.json index 53cbc79b96..9e1e277f2d 100644 --- a/datasets/GPM_2AGPROFF17SSMIS_CLIM_07.json +++ b/datasets/GPM_2AGPROFF17SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF17SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF18SSMIS_07.json b/datasets/GPM_2AGPROFF18SSMIS_07.json index 41b3dbae07..882409d709 100644 --- a/datasets/GPM_2AGPROFF18SSMIS_07.json +++ b/datasets/GPM_2AGPROFF18SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF18SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF18SSMIS_CLIM_07.json b/datasets/GPM_2AGPROFF18SSMIS_CLIM_07.json index b837b68b26..200a6e6cc3 100644 --- a/datasets/GPM_2AGPROFF18SSMIS_CLIM_07.json +++ b/datasets/GPM_2AGPROFF18SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF18SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFF19SSMIS_CLIM_07.json b/datasets/GPM_2AGPROFF19SSMIS_CLIM_07.json index 82734db626..7353435b02 100644 --- a/datasets/GPM_2AGPROFF19SSMIS_CLIM_07.json +++ b/datasets/GPM_2AGPROFF19SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFF19SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFGCOMW1AMSR2_07.json b/datasets/GPM_2AGPROFGCOMW1AMSR2_07.json index afaa1cd3f0..7e098f8fc7 100644 --- a/datasets/GPM_2AGPROFGCOMW1AMSR2_07.json +++ b/datasets/GPM_2AGPROFGCOMW1AMSR2_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFGCOMW1AMSR2_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFGCOMW1AMSR2_CLIM_07.json b/datasets/GPM_2AGPROFGCOMW1AMSR2_CLIM_07.json index 7197c1cc78..910c602672 100644 --- a/datasets/GPM_2AGPROFGCOMW1AMSR2_CLIM_07.json +++ b/datasets/GPM_2AGPROFGCOMW1AMSR2_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFGCOMW1AMSR2_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: \n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFGPMGMI_07.json b/datasets/GPM_2AGPROFGPMGMI_07.json index 1c0d208958..cc6ef04b7d 100644 --- a/datasets/GPM_2AGPROFGPMGMI_07.json +++ b/datasets/GPM_2AGPROFGPMGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFGPMGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n", "links": [ { diff --git a/datasets/GPM_2AGPROFGPMGMI_CLIM_07.json b/datasets/GPM_2AGPROFGPMGMI_CLIM_07.json index 475a97b822..5f9ad4304a 100644 --- a/datasets/GPM_2AGPROFGPMGMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFGPMGMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFGPMGMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: \n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFMETOPAMHS_CLIM_07.json b/datasets/GPM_2AGPROFMETOPAMHS_CLIM_07.json index ef2fce9474..6d0cf3a880 100644 --- a/datasets/GPM_2AGPROFMETOPAMHS_CLIM_07.json +++ b/datasets/GPM_2AGPROFMETOPAMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFMETOPAMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFMETOPBMHS_07.json b/datasets/GPM_2AGPROFMETOPBMHS_07.json index 073237f454..f1f50eb3db 100644 --- a/datasets/GPM_2AGPROFMETOPBMHS_07.json +++ b/datasets/GPM_2AGPROFMETOPBMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFMETOPBMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT", "links": [ { diff --git a/datasets/GPM_2AGPROFMETOPBMHS_CLIM_07.json b/datasets/GPM_2AGPROFMETOPBMHS_CLIM_07.json index 41806e7f36..f7a7709041 100644 --- a/datasets/GPM_2AGPROFMETOPBMHS_CLIM_07.json +++ b/datasets/GPM_2AGPROFMETOPBMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFMETOPBMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT", "links": [ { diff --git a/datasets/GPM_2AGPROFMETOPCMHS_07.json b/datasets/GPM_2AGPROFMETOPCMHS_07.json index 6196eb24c7..f2ebd7fe40 100644 --- a/datasets/GPM_2AGPROFMETOPCMHS_07.json +++ b/datasets/GPM_2AGPROFMETOPCMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFMETOPCMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A;B;C), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT", "links": [ { diff --git a/datasets/GPM_2AGPROFMETOPCMHS_CLIM_07.json b/datasets/GPM_2AGPROFMETOPCMHS_CLIM_07.json index 83448f05d7..dfd55e1f7b 100644 --- a/datasets/GPM_2AGPROFMETOPCMHS_CLIM_07.json +++ b/datasets/GPM_2AGPROFMETOPCMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFMETOPCMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A;B;C), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA15AMSUB_CLIM_07.json b/datasets/GPM_2AGPROFNOAA15AMSUB_CLIM_07.json index f4e98df3f6..f8d6b3355a 100644 --- a/datasets/GPM_2AGPROFNOAA15AMSUB_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA15AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA15AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty\n", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA16AMSUB_CLIM_07.json b/datasets/GPM_2AGPROFNOAA16AMSUB_CLIM_07.json index 2d57e5d5ac..a4f0f6d608 100644 --- a/datasets/GPM_2AGPROFNOAA16AMSUB_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA16AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA16AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty\n", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA17AMSUB_CLIM_07.json b/datasets/GPM_2AGPROFNOAA17AMSUB_CLIM_07.json index d205bd45d7..d50e5ca2eb 100644 --- a/datasets/GPM_2AGPROFNOAA17AMSUB_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA17AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA17AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty\n\n", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA18MHS_CLIM_07.json b/datasets/GPM_2AGPROFNOAA18MHS_CLIM_07.json index f3b97b943c..c7c477eba2 100644 --- a/datasets/GPM_2AGPROFNOAA18MHS_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA18MHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA18MHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA19MHS_07.json b/datasets/GPM_2AGPROFNOAA19MHS_07.json index f05d5e29b3..62b5fee790 100644 --- a/datasets/GPM_2AGPROFNOAA19MHS_07.json +++ b/datasets/GPM_2AGPROFNOAA19MHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA19MHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA19MHS_CLIM_07.json b/datasets/GPM_2AGPROFNOAA19MHS_CLIM_07.json index 8b4509967c..b65171b178 100644 --- a/datasets/GPM_2AGPROFNOAA19MHS_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA19MHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA19MHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA20ATMS_07.json b/datasets/GPM_2AGPROFNOAA20ATMS_07.json index 966894247c..5fb84b5b8d 100644 --- a/datasets/GPM_2AGPROFNOAA20ATMS_07.json +++ b/datasets/GPM_2AGPROFNOAA20ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA20ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA20ATMS_CLIM_07.json b/datasets/GPM_2AGPROFNOAA20ATMS_CLIM_07.json index 175012e401..af9fde9216 100644 --- a/datasets/GPM_2AGPROFNOAA20ATMS_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA20ATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA20ATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA21ATMS_07.json b/datasets/GPM_2AGPROFNOAA21ATMS_07.json index 58c0b3d69a..1ecd9169ea 100644 --- a/datasets/GPM_2AGPROFNOAA21ATMS_07.json +++ b/datasets/GPM_2AGPROFNOAA21ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA21ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-21). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNOAA21ATMS_CLIM_07.json b/datasets/GPM_2AGPROFNOAA21ATMS_CLIM_07.json index 1a7f75df3f..04ce60a180 100644 --- a/datasets/GPM_2AGPROFNOAA21ATMS_CLIM_07.json +++ b/datasets/GPM_2AGPROFNOAA21ATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNOAA21ATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-21). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.", "links": [ { diff --git a/datasets/GPM_2AGPROFNPPATMS_07.json b/datasets/GPM_2AGPROFNPPATMS_07.json index 3ac8b9a114..fa7422e8f5 100644 --- a/datasets/GPM_2AGPROFNPPATMS_07.json +++ b/datasets/GPM_2AGPROFNPPATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNPPATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n\n", "links": [ { diff --git a/datasets/GPM_2AGPROFNPPATMS_CLIM_07.json b/datasets/GPM_2AGPROFNPPATMS_CLIM_07.json index 8f9f2ede38..f7d75cee9a 100644 --- a/datasets/GPM_2AGPROFNPPATMS_CLIM_07.json +++ b/datasets/GPM_2AGPROFNPPATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFNPPATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n\n", "links": [ { diff --git a/datasets/GPM_2AGPROFTRMMTMI_CLIM_07.json b/datasets/GPM_2AGPROFTRMMTMI_CLIM_07.json index 4cacf27ad5..16c22c656d 100644 --- a/datasets/GPM_2AGPROFTRMMTMI_CLIM_07.json +++ b/datasets/GPM_2AGPROFTRMMTMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AGPROFTRMMTMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_2A12\n Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19)\n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n", "links": [ { diff --git a/datasets/GPM_2AKaENV_07.json b/datasets/GPM_2AKaENV_07.json index 18824456fb..47dd9d0eb7 100644 --- a/datasets/GPM_2AKaENV_07.json +++ b/datasets/GPM_2AKaENV_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AKaENV_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\nThis is environmental data that includes the profiles of atmospheric parameters assumed in the L2 retrieval algorithm.\n\nThe 2AKa algorithm provides precipitation estimates from the Ka radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. The product contains two swaths of data corresponding to the scans of the Ka radar. \n\nThe first swath contains matched scans (MS), which are intended to be co-aligned with the Ku-band instantaneous fields of view (IFOV). The second swath contains the high-sensitivity scans (HS), which are interleaved between the Ku/Ka-MS swaths. Both swaths are narrow and centered within the interior of the Ku swath. \n\nThis is a single-frequency retrieval of precipitation; no information from the Ku radar is used. The retrievals are performed at each radar range bin along the slant path of the radar IFOV for each swath. This is a single-frequency retrieval that relies on Ka-band data only. While the 2ADPR dual-frequency retrieval should give better overall estimates, that algorithm requires co-aligned Ku-band data. This 2AKa product will be produced independently and would not be impacted by any operational issues with the Ku-band radar. The high sensitivity to smaller hydrometeors should result in precipitation estimates in lighter precipitation than the Ku-only data.", "links": [ { diff --git a/datasets/GPM_2AKa_07.json b/datasets/GPM_2AKa_07.json index d9629762de..2e82127d10 100644 --- a/datasets/GPM_2AKa_07.json +++ b/datasets/GPM_2AKa_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AKa_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n The 2AKa algorithm provides precipitation estimates from the Ka radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. The product contains two swaths of data corresponding to the scans of the Ka radar. The first swath contains matched scans (MS), which are intended to be co-aligned with the Ku-band instantaneous fields of view (IFOV). The second swath contains the high-sensitivity scans (HS), which are interleaved between the Ku/Ka-MS swaths. Both swaths are narrow and centered within the interior of the Ku swath. This is a single-frequency retrieval of precipitation; no information from the Ku radar is used. The retrievals are performed at each radar range bin along the slant path of the radar IFOV for each swath.\n\t\n This is a single-frequency retrieval that relies on Ka-band data only. While the dual-frequency retrieval should give better overall estimates, that algorithm requires co-aligned Ku-band data. This 2AKa product will be produced independently and would not be impacted by any operational issues with the Ku-band radar. The high sensitivity to smaller hydrometeors should result in precipitation estimates in lighter precipitation then the Ku-only data.\n\n It is important to note the difference between the single- and the dual-frequency (DF) algorithms. While this 2AKa dataset is a single-frequency retrieval, the DF algorithm employs both KuPR and KaPR L1B standard products as inputs. The DF algorithm cannot be executed unless both L1B products are available. Pixels observed by DPR can be categorized into three types: pixels in the inner swath of normal scans (observed both by KuPR and KaPR), pixels in the outer swath of normal scans (observed only by KuPR), and pixels in the interleaved scans (observed only by KaPR in the high-sensitivity mode). The KuPR algorithm is executed for pixels in both inner and outer swaths of normal scans. The KaPR algorithm is executed for pixels in the inner swath of normal scans and in the interleaved scans. The DF algorithm is executed for pixels of all the three kinds.\n\n", "links": [ { diff --git a/datasets/GPM_2AKuENV_07.json b/datasets/GPM_2AKuENV_07.json index 4a5d22e149..b219950ec2 100644 --- a/datasets/GPM_2AKuENV_07.json +++ b/datasets/GPM_2AKuENV_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AKuENV_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\nThis is environmental data that includes the profiles of atmospheric parameters assumed in the L2 retrieval algorithm. \n\nThe 2AKu algorithm provides precipitation estimates from the Ku radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. The product contains one swath of data corresponding to the scans of the Ku radar. This is a single-frequency retrieval of precipitation; no information from the Ka radar is used. The retrievals are performed at each radar range bin along the slant path of the radar IFOV. This is a single-frequency retrieval that relies on Ku-band data only. While the 2ADPR dual-frequency retrieval should give better overall estimates, that algorithm requires co-aligned Ka-band data. This 2AKu product will be produced independently and would not be impacted by any operational issues with the Ka-band radar.", "links": [ { diff --git a/datasets/GPM_2AKu_07.json b/datasets/GPM_2AKu_07.json index 079ca6fef4..224f5ead12 100644 --- a/datasets/GPM_2AKu_07.json +++ b/datasets/GPM_2AKu_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2AKu_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n The 2AKu algorithm is a single-frequency retrieval that relies on Ku-band data only, and provides precipitation estimates from the Ku radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. \n\n The Ku Level-2A product, 2AKu, \u201dKu precipitation,\u201d is written as a 1 swath structure. The swath is NS, normal scans.\n\n Since the Ku-band channel of the Dual-Frequency Precipitaiton Radar (DPR) is very similar to the TRMM PR, the principal challenge in the development of the DPR level 2 algorithms is to combine the new Ka-band data with the Ku-band data.\n\t\n It is important to note the difference between the single- and the dual-frequency (DF) algorithms. While this 2AKu dataset is a single-frequency retrieval, the DF algorithm employs both KuPR and KaPR L1B standard products as inputs. The DF algorithm cannot be executed unless both L1B products are available. Pixels observed by DPR can be categorized into three types: pixels in the inner swath of normal scans (observed both by KuPR and KaPR), pixels in the outer swath of normal scans (observed only by KuPR), and pixels in the interleaved scans (observed only by KaPR in the high-sensitivity mode). The KuPR algorithm is executed for pixels in both inner and outer swaths of normal scans. The KaPR algorithm is executed for pixels in the inner swath of normal scans and in the interleaved scans. The DF algorithm is executed for pixels of all the three kinds. \n\n", "links": [ { diff --git a/datasets/GPM_2APRPSMT1SAPHIR_06.json b/datasets/GPM_2APRPSMT1SAPHIR_06.json index be7343d405..e2e93fbb92 100644 --- a/datasets/GPM_2APRPSMT1SAPHIR_06.json +++ b/datasets/GPM_2APRPSMT1SAPHIR_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2APRPSMT1SAPHIR_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.\n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.\n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval. \n\n", "links": [ { diff --git a/datasets/GPM_2APRPSMT1SAPHIR_CLIM_06.json b/datasets/GPM_2APRPSMT1SAPHIR_CLIM_06.json index f89add8c44..8cae1ff1a9 100644 --- a/datasets/GPM_2APRPSMT1SAPHIR_CLIM_06.json +++ b/datasets/GPM_2APRPSMT1SAPHIR_CLIM_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2APRPSMT1SAPHIR_CLIM_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.\n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.\n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval.\n\n", "links": [ { diff --git a/datasets/GPM_2APR_07.json b/datasets/GPM_2APR_07.json index 78186e4f89..3283e65d88 100644 --- a/datasets/GPM_2APR_07.json +++ b/datasets/GPM_2APR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2APR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new, GPM-like, format for TRMM Precipitation Radar L2 data that now incorporates what was known as 2A21, 2A23 and 2A25 datasets.\n\nThe primary purpose of 2A21 is to compute the path-integrated attenuation (PIA) using the surface reference technique (SRT). The surface reference technique rests on the assumption that the difference between the measurements of the normalized surface cross section outside and within the rain provides an estimate of the PIA. The secondary purpose of 2A21 is to compute the normalized radar cross sections (\"sigma-not\" or Normalized Radar Cross-Sectin (NRTS)) of the surface under rain-free conditions. \n\nMain objectives of 2A23 are as follows:\n(a) Detection of bright band (BB) and determination of the height of BB, the\nstrength of BB, and the width (i.e. thickness) of BB when BB exists.\n(b) Classification of rain type into the following three categories:\n- stratiform,\n - convective,\n - other,\nwhere \"other\" means (ice) cloud only and/or maybe noise.\n (c) Detection of shallow isolated and shallow non-isolated.\n (d) Output of Rain/No-rain flag.\n (e) Computation of the estimated height of freezing level.\n (f) Output of the height of storm top. \n\nThe objectives of 2A25 are to correct for the rain attenuation in measured radar reflectivity (Zm) and to estimate the instantaneous three-dimensional distribution of rain from the TRMM Precipitation Radar (PR) data. The estimated vertical profiles of attenuation-corrected radar reflectivity factor (Ze) and rainfall rate (R) are given at each resolution cell of the PR. The estimated rainfall rate at the actual surface height and the average rainfall rate between the two predefined altitudes (2 and 4 km) are also calculated for each beam position.\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km", "links": [ { diff --git a/datasets/GPM_2BCMB_07.json b/datasets/GPM_2BCMB_07.json index 53b779e1d9..2ad71dce97 100644 --- a/datasets/GPM_2BCMB_07.json +++ b/datasets/GPM_2BCMB_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2BCMB_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThis is a precipitation product created from the combination of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) instruments.\n\nThe 2BCMB product uses data from the Dual-Frequency Precipitation Radar and GMI, determining the precipitation structure that best fits the combined data from these instruments. It is the Level 2 DPR and GMI Combined precipitation product that contains the data acquired by the GPM instruments in one orbit, or granule. It is written as a two-swath structure. The first swath, NS (normal scan), contains 49 rays per scan that match the KuPR rays. It is calculated from the KuPR and GMI data. The second swath, MS (matched scan), contains 25 rays per scan that match the 25 KaPR rays. It is calculated from the KuPR, KaPR, and GMI data.", "links": [ { diff --git a/datasets/GPM_2BCMB_TRMM_07.json b/datasets/GPM_2BCMB_TRMM_07.json index 272aecf588..af9d267a6b 100644 --- a/datasets/GPM_2BCMB_TRMM_07.json +++ b/datasets/GPM_2BCMB_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2BCMB_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_2B31\n\nThis is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing. \n\nGombined Radar-Radiometer Algorithm performs two basic functions: \n\nFirst, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital. \n\nSecond, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to \u201ccross-calibrate\u201d rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations.\n\nThe cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation \napplications. \n", "links": [ { diff --git a/datasets/GPM_2HCSH_07.json b/datasets/GPM_2HCSH_07.json index 4f572a795e..602c83a57f 100644 --- a/datasets/GPM_2HCSH_07.json +++ b/datasets/GPM_2HCSH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2HCSH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\nThe Convective Stratiform Heating (2HCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and are superseded by Version 07.\n", "links": [ { diff --git a/datasets/GPM_2HCSH_TRMM_07.json b/datasets/GPM_2HCSH_TRMM_07.json index 756d16589d..f3d0ea46ca 100644 --- a/datasets/GPM_2HCSH_TRMM_07.json +++ b/datasets/GPM_2HCSH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2HCSH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a new (GPM-formated) TRMM product. The equivalent old TRMM legacy product is TRMM_2H31.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm.\n\n", "links": [ { diff --git a/datasets/GPM_2HSLH_07.json b/datasets/GPM_2HSLH_07.json index 69dcc0593c..e181329b54 100644 --- a/datasets/GPM_2HSLH_07.json +++ b/datasets/GPM_2HSLH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2HSLH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 6B of these data were introduced in July, 2020. Please, see documentation tab for release notes.\n\n Latent heating variables are retrieved utilizing two separate algorithms for tropics and for mid-latitudes. First, location of each GPM KuPR pixel is assigned to either tropics or mid-latitudes, depending on monthly maps of precipitation types determined in a similar manner as described in Takayabu (2008). Then, three dimensional convective latent heating are retrieved, Q1-QR (Q1R), and Q2, applying either tropical/mid-latitude algorithms to precipitation data observed from GPM DPR (KuPR). Here, Q1 and Q2 are apparent heat source and apparent moisture sink, respectively, introduced by Yanai et al. (1973), and QR is radiative heating of the atmosphere.", "links": [ { diff --git a/datasets/GPM_2HSLH_TRMM_07.json b/datasets/GPM_2HSLH_TRMM_07.json index 44d667a64e..02b7e740d3 100644 --- a/datasets/GPM_2HSLH_TRMM_07.json +++ b/datasets/GPM_2HSLH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_2HSLH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.\n\n", "links": [ { diff --git a/datasets/GPM_3CMB_07.json b/datasets/GPM_3CMB_07.json index c060d70d3b..5d476e49f2 100644 --- a/datasets/GPM_3CMB_07.json +++ b/datasets/GPM_3CMB_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3CMB_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\nThis is a precipitation product created from the combination of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) instruments.", "links": [ { diff --git a/datasets/GPM_3CMB_DAY_07.json b/datasets/GPM_3CMB_DAY_07.json index 65e3c4fd11..52a2f6f3b7 100644 --- a/datasets/GPM_3CMB_DAY_07.json +++ b/datasets/GPM_3CMB_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3CMB_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n. \n\nThis is a precipitation product created from the combination of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) instruments.", "links": [ { diff --git a/datasets/GPM_3CMB_TRMM_07.json b/datasets/GPM_3CMB_TRMM_07.json index e7c286c780..7f0661903d 100644 --- a/datasets/GPM_3CMB_TRMM_07.json +++ b/datasets/GPM_3CMB_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3CMB_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3B31\n\nThis is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing.\nGombined Radar-Radiometer Algorithm performs two basic functions:\n\nFirst, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital.\n\nSecond, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to \u201ccross-calibrate\u201d rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations.\n\nThe cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation\napplications. \n", "links": [ { diff --git a/datasets/GPM_3CMB_TRMM_DAY_07.json b/datasets/GPM_3CMB_TRMM_DAY_07.json index c14cfbbe2a..27ccc4ac31 100644 --- a/datasets/GPM_3CMB_TRMM_DAY_07.json +++ b/datasets/GPM_3CMB_TRMM_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3CMB_TRMM_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nThis is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing. \nGombined Radar-Radiometer Algorithm performs two basic functions: \n\nFirst, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital.\n\nSecond, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to \u201ccross-calibrate\u201d rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations.\n\nThe cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation\napplications. \n", "links": [ { diff --git a/datasets/GPM_3DPRD_07.json b/datasets/GPM_3DPRD_07.json index b22e68eb29..be2521ca04 100644 --- a/datasets/GPM_3DPRD_07.json +++ b/datasets/GPM_3DPRD_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3DPRD_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n. \n\nThe precipitation estimates in the 3DPRD product are a subset of those in the full daily 3DPR product; the retrieval estimates are the same. Since this is a subset, the product is smaller, and Level 3 DPR products present the user with summary information over daily and monthly time periods. These gridded products are in a convenient gridded form and can be used easily in comparisons with other satellite and ground data. \n\n\t\tThe Level 3 DPR joint algorithm subsets precipitation estimates from the Level 3 daily products. In addition, it adds time information from Level 2 instantaneous data to give a date/time for the last measurement in each grid box. The product contains one 0.25 x 25 km grid with separate indices for the ascending and descending parts of the GPM orbit.", "links": [ { diff --git a/datasets/GPM_3DPR_07.json b/datasets/GPM_3DPR_07.json index c37d273cde..9c454f0f6e 100644 --- a/datasets/GPM_3DPR_07.json +++ b/datasets/GPM_3DPR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3DPR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n. \n\nThe Level 3 DPR products present the user with summary information over daily and monthly time periods. These gridded products are in a convenient gridded form and can be used easily in comparisons with other satellite and ground data.\n\n The Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. There are two grid resolutions: 5.0 degrees and 25 kms. For each grid box, the core statistics are the number of measurements, mean, and standard deviation. Most variables are also conditioned on surface type and precipitation type with other three-dimensional fields adding the height above the ellipsoid. Unless otherwise specified, the means are conditioned on precipitation being present (rain rate > 0). For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product.", "links": [ { diff --git a/datasets/GPM_3DPR_ASC_07.json b/datasets/GPM_3DPR_ASC_07.json index 9736901244..2fd5401b33 100644 --- a/datasets/GPM_3DPR_ASC_07.json +++ b/datasets/GPM_3DPR_ASC_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3DPR_ASC_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n. \n\nThe Level 3 DPR products present the user with summary information over daily and monthly time periods. These gridded products are in a convenient gridded form and can be used easily in comparisons with other satellite and ground data.\n\n\t\tThe Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. There are two grid resolutions: 5.0 degrees and 25 kms. For each grid box, the core statistics are the number of measurements, mean, and standard deviation. Most variables are also conditioned on surface type and precipitation type with other three-dimensional fields adding the height above the ellipsoid. Unless otherwise specified, the means are conditioned on precipitation being present (rain rate > 0). For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product.", "links": [ { diff --git a/datasets/GPM_3DPR_DES_07.json b/datasets/GPM_3DPR_DES_07.json index c92d015973..0bf7055945 100644 --- a/datasets/GPM_3DPR_DES_07.json +++ b/datasets/GPM_3DPR_DES_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3DPR_DES_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n. \n\nThe Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. There are two grid resolutions: 5.0 degrees and 25 kms. For each grid box, the core statistics are the number of measurements, mean, and standard deviation. Most variables are also conditioned on surface type and precipitation type with other three-dimensional fields adding the height above the ellipsoid. Unless otherwise specified, the means are conditioned on precipitation being present (rain rate > 0). For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product. The Level 3 DPR products present the user with summary information over daily and monthly time periods. These gridded products are in a convenient gridded form and can be used easily in comparisons with other satellite and ground data.", "links": [ { diff --git a/datasets/GPM_3GCSH_07.json b/datasets/GPM_3GCSH_07.json index 12094a1c56..225c5e8f49 100644 --- a/datasets/GPM_3GCSH_07.json +++ b/datasets/GPM_3GCSH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GCSH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Convective Stratiform Heating (3GCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and are superseded by Version 07.\n", "links": [ { diff --git a/datasets/GPM_3GCSH_TRMM_07.json b/datasets/GPM_3GCSH_TRMM_07.json index a8c1cd6d44..ec9c33b596 100644 --- a/datasets/GPM_3GCSH_TRMM_07.json +++ b/datasets/GPM_3GCSH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GCSH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy product TRMM_3G31.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm.\n\nThe Gridded Orbital Spectral Latent Heating is actually one orbit gridded onto a global map with 0.25x0.25 degree grid cell size. These latent heating profiles from the TRMM Precipitation Radar (PR) rain. The granule temporal size is one orbit.", "links": [ { diff --git a/datasets/GPM_3GPROFAQUAAMSRE_CLIM_07.json b/datasets/GPM_3GPROFAQUAAMSRE_CLIM_07.json index 34084538d6..83e436f2fb 100644 --- a/datasets/GPM_3GPROFAQUAAMSRE_CLIM_07.json +++ b/datasets/GPM_3GPROFAQUAAMSRE_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFAQUAAMSRE_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFAQUAAMSRE_DAY_CLIM_07.json b/datasets/GPM_3GPROFAQUAAMSRE_DAY_CLIM_07.json index 49098103af..798f397f4e 100644 --- a/datasets/GPM_3GPROFAQUAAMSRE_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFAQUAAMSRE_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFAQUAAMSRE_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF08SSMI_CLIM_07.json b/datasets/GPM_3GPROFF08SSMI_CLIM_07.json index 7d0eedb0b2..16b8fd5ef4 100644 --- a/datasets/GPM_3GPROFF08SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF08SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF08SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF08SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF08SSMI_DAY_CLIM_07.json index 64e00352e4..82d7976b25 100644 --- a/datasets/GPM_3GPROFF08SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF08SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF08SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF10SSMI_CLIM_07.json b/datasets/GPM_3GPROFF10SSMI_CLIM_07.json index 37074d81da..5bfaeffd2b 100644 --- a/datasets/GPM_3GPROFF10SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF10SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF10SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF10SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF10SSMI_DAY_CLIM_07.json index fb9da374d6..81bda0077d 100644 --- a/datasets/GPM_3GPROFF10SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF10SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF10SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF11SSMI_CLIM_07.json b/datasets/GPM_3GPROFF11SSMI_CLIM_07.json index a86eeb3144..1f65c1e475 100644 --- a/datasets/GPM_3GPROFF11SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF11SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF11SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF11SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF11SSMI_DAY_CLIM_07.json index 48d97f953c..b3b1634e67 100644 --- a/datasets/GPM_3GPROFF11SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF11SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF11SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF13SSMI_CLIM_07.json b/datasets/GPM_3GPROFF13SSMI_CLIM_07.json index 63ec871532..b5c6ea360d 100644 --- a/datasets/GPM_3GPROFF13SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF13SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF13SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF13SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF13SSMI_DAY_CLIM_07.json index 569c4dd8be..4e897228ec 100644 --- a/datasets/GPM_3GPROFF13SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF13SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF13SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF14SSMI_CLIM_07.json b/datasets/GPM_3GPROFF14SSMI_CLIM_07.json index 2c2db051a2..23a1e6db32 100644 --- a/datasets/GPM_3GPROFF14SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF14SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF14SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF14SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF14SSMI_DAY_CLIM_07.json index c36a26ecd7..4b8f4fc3d7 100644 --- a/datasets/GPM_3GPROFF14SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF14SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF14SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF15SSMI_CLIM_07.json b/datasets/GPM_3GPROFF15SSMI_CLIM_07.json index a9041c3152..3deaf5b9a9 100644 --- a/datasets/GPM_3GPROFF15SSMI_CLIM_07.json +++ b/datasets/GPM_3GPROFF15SSMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF15SSMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF15SSMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFF15SSMI_DAY_CLIM_07.json index eb5e32ac03..7d3a517e4d 100644 --- a/datasets/GPM_3GPROFF15SSMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF15SSMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF15SSMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF16SSMIS_07.json b/datasets/GPM_3GPROFF16SSMIS_07.json index 0c3d31502a..b5e10e05cd 100644 --- a/datasets/GPM_3GPROFF16SSMIS_07.json +++ b/datasets/GPM_3GPROFF16SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF16SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF16SSMIS_CLIM_07.json b/datasets/GPM_3GPROFF16SSMIS_CLIM_07.json index aacaf226ce..26bc346945 100644 --- a/datasets/GPM_3GPROFF16SSMIS_CLIM_07.json +++ b/datasets/GPM_3GPROFF16SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF16SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF16SSMIS_DAY_07.json b/datasets/GPM_3GPROFF16SSMIS_DAY_07.json index 6671654260..33d2f04f8a 100644 --- a/datasets/GPM_3GPROFF16SSMIS_DAY_07.json +++ b/datasets/GPM_3GPROFF16SSMIS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF16SSMIS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF16SSMIS_DAY_CLIM_07.json b/datasets/GPM_3GPROFF16SSMIS_DAY_CLIM_07.json index d8ddb902f6..cc0f29490d 100644 --- a/datasets/GPM_3GPROFF16SSMIS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF16SSMIS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF16SSMIS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF17SSMIS_07.json b/datasets/GPM_3GPROFF17SSMIS_07.json index 33cb7c21d1..d0d88a287c 100644 --- a/datasets/GPM_3GPROFF17SSMIS_07.json +++ b/datasets/GPM_3GPROFF17SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF17SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF17SSMIS_CLIM_07.json b/datasets/GPM_3GPROFF17SSMIS_CLIM_07.json index 617cca5519..eebe45eeca 100644 --- a/datasets/GPM_3GPROFF17SSMIS_CLIM_07.json +++ b/datasets/GPM_3GPROFF17SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF17SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF17SSMIS_DAY_07.json b/datasets/GPM_3GPROFF17SSMIS_DAY_07.json index 1dead4085e..225375cfb2 100644 --- a/datasets/GPM_3GPROFF17SSMIS_DAY_07.json +++ b/datasets/GPM_3GPROFF17SSMIS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF17SSMIS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF17SSMIS_DAY_CLIM_07.json b/datasets/GPM_3GPROFF17SSMIS_DAY_CLIM_07.json index 93b351a0d6..ead4eba66f 100644 --- a/datasets/GPM_3GPROFF17SSMIS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF17SSMIS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF17SSMIS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF18SSMIS_07.json b/datasets/GPM_3GPROFF18SSMIS_07.json index ae9d4f8f5e..cb118b4b7a 100644 --- a/datasets/GPM_3GPROFF18SSMIS_07.json +++ b/datasets/GPM_3GPROFF18SSMIS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF18SSMIS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF18SSMIS_CLIM_07.json b/datasets/GPM_3GPROFF18SSMIS_CLIM_07.json index 236ffdb959..18fef51c01 100644 --- a/datasets/GPM_3GPROFF18SSMIS_CLIM_07.json +++ b/datasets/GPM_3GPROFF18SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF18SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF18SSMIS_DAY_07.json b/datasets/GPM_3GPROFF18SSMIS_DAY_07.json index d60e3cbd55..38907e6e75 100644 --- a/datasets/GPM_3GPROFF18SSMIS_DAY_07.json +++ b/datasets/GPM_3GPROFF18SSMIS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF18SSMIS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF18SSMIS_DAY_CLIM_07.json b/datasets/GPM_3GPROFF18SSMIS_DAY_CLIM_07.json index f3455ca54d..fe15ba889c 100644 --- a/datasets/GPM_3GPROFF18SSMIS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF18SSMIS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF18SSMIS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF19SSMIS_CLIM_07.json b/datasets/GPM_3GPROFF19SSMIS_CLIM_07.json index 6d76d5f34a..3add33f17e 100644 --- a/datasets/GPM_3GPROFF19SSMIS_CLIM_07.json +++ b/datasets/GPM_3GPROFF19SSMIS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF19SSMIS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFF19SSMIS_DAY_CLIM_07.json b/datasets/GPM_3GPROFF19SSMIS_DAY_CLIM_07.json index fd61a26daa..08aeca3feb 100644 --- a/datasets/GPM_3GPROFF19SSMIS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFF19SSMIS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFF19SSMIS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGCOMW1AMSR2_07.json b/datasets/GPM_3GPROFGCOMW1AMSR2_07.json index 314f00a89b..b4851f5da3 100644 --- a/datasets/GPM_3GPROFGCOMW1AMSR2_07.json +++ b/datasets/GPM_3GPROFGCOMW1AMSR2_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGCOMW1AMSR2_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGCOMW1AMSR2_CLIM_07.json b/datasets/GPM_3GPROFGCOMW1AMSR2_CLIM_07.json index ae56418747..b405581483 100644 --- a/datasets/GPM_3GPROFGCOMW1AMSR2_CLIM_07.json +++ b/datasets/GPM_3GPROFGCOMW1AMSR2_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGCOMW1AMSR2_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_07.json b/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_07.json index e1c9dc195f..595bb6b8d4 100644 --- a/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_07.json +++ b/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGCOMW1AMSR2_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_CLIM_07.json b/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_CLIM_07.json index c6ac31e7fa..0746771406 100644 --- a/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFGCOMW1AMSR2_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGCOMW1AMSR2_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGPMGMI_07.json b/datasets/GPM_3GPROFGPMGMI_07.json index 0cf52b47c1..ba0787c04b 100644 --- a/datasets/GPM_3GPROFGPMGMI_07.json +++ b/datasets/GPM_3GPROFGPMGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGPMGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGPMGMI_CLIM_07.json b/datasets/GPM_3GPROFGPMGMI_CLIM_07.json index 9671282cd9..6d0eff1a93 100644 --- a/datasets/GPM_3GPROFGPMGMI_CLIM_07.json +++ b/datasets/GPM_3GPROFGPMGMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGPMGMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGPMGMI_DAY_07.json b/datasets/GPM_3GPROFGPMGMI_DAY_07.json index ac36e925cb..47d4b8936d 100644 --- a/datasets/GPM_3GPROFGPMGMI_DAY_07.json +++ b/datasets/GPM_3GPROFGPMGMI_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGPMGMI_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFGPMGMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFGPMGMI_DAY_CLIM_07.json index 53521ba53d..b34a7c9f1b 100644 --- a/datasets/GPM_3GPROFGPMGMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFGPMGMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFGPMGMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPAMHS_CLIM_07.json b/datasets/GPM_3GPROFMETOPAMHS_CLIM_07.json index ddf8cf8464..5ccb3b4f35 100644 --- a/datasets/GPM_3GPROFMETOPAMHS_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPAMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPAMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPAMHS_DAY_CLIM_07.json b/datasets/GPM_3GPROFMETOPAMHS_DAY_CLIM_07.json index dad29e2c41..74c61ad0f6 100644 --- a/datasets/GPM_3GPROFMETOPAMHS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPAMHS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPAMHS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n\nThe purpose of the 3GPROF algorithm is to provide monthly and daily mean precipitation and related retrieved parameters from the Level 2 GPROF precipitation profiling algorithm for the GPM core and constellation satellites. Each 3GPROF product contains global 0.25 degree x 0.25 degree gridded monthly/daily means. Because this product is an accumulation of the Level 2 retrieval products, much more information is available via the GPROF Level 2 documentation.\n", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPBMHS_07.json b/datasets/GPM_3GPROFMETOPBMHS_07.json index 262b932e2d..610c09b08c 100644 --- a/datasets/GPM_3GPROFMETOPBMHS_07.json +++ b/datasets/GPM_3GPROFMETOPBMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPBMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPBMHS_CLIM_07.json b/datasets/GPM_3GPROFMETOPBMHS_CLIM_07.json index 4bcd37f5dc..54970a1488 100644 --- a/datasets/GPM_3GPROFMETOPBMHS_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPBMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPBMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPBMHS_DAY_07.json b/datasets/GPM_3GPROFMETOPBMHS_DAY_07.json index 8a360e6ca1..f34f4bea1b 100644 --- a/datasets/GPM_3GPROFMETOPBMHS_DAY_07.json +++ b/datasets/GPM_3GPROFMETOPBMHS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPBMHS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPBMHS_DAY_CLIM_07.json b/datasets/GPM_3GPROFMETOPBMHS_DAY_CLIM_07.json index a398f318cd..6efea590f9 100644 --- a/datasets/GPM_3GPROFMETOPBMHS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPBMHS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPBMHS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPCMHS_07.json b/datasets/GPM_3GPROFMETOPCMHS_07.json index e2948fa19a..b2a968daeb 100644 --- a/datasets/GPM_3GPROFMETOPCMHS_07.json +++ b/datasets/GPM_3GPROFMETOPCMHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPCMHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPCMHS_CLIM_07.json b/datasets/GPM_3GPROFMETOPCMHS_CLIM_07.json index 0e48b194e3..7b4a1f8e55 100644 --- a/datasets/GPM_3GPROFMETOPCMHS_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPCMHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPCMHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPCMHS_DAY_07.json b/datasets/GPM_3GPROFMETOPCMHS_DAY_07.json index fbe7e7d7c6..8a68311251 100644 --- a/datasets/GPM_3GPROFMETOPCMHS_DAY_07.json +++ b/datasets/GPM_3GPROFMETOPCMHS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPCMHS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFMETOPCMHS_DAY_CLIM_07.json b/datasets/GPM_3GPROFMETOPCMHS_DAY_CLIM_07.json index c06851967d..066d7eeecb 100644 --- a/datasets/GPM_3GPROFMETOPCMHS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFMETOPCMHS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFMETOPCMHS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA15AMSUB_CLIM_07.json b/datasets/GPM_3GPROFNOAA15AMSUB_CLIM_07.json index dc727b774a..c5241fcfa1 100644 --- a/datasets/GPM_3GPROFNOAA15AMSUB_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA15AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA15AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA15AMSUB_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA15AMSUB_DAY_CLIM_07.json index e642bb9822..75824c0d07 100644 --- a/datasets/GPM_3GPROFNOAA15AMSUB_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA15AMSUB_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA15AMSUB_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA16AMSUB_CLIM_07.json b/datasets/GPM_3GPROFNOAA16AMSUB_CLIM_07.json index 2215ca0e1f..b8c4d53005 100644 --- a/datasets/GPM_3GPROFNOAA16AMSUB_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA16AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA16AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA16AMSUB_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA16AMSUB_DAY_CLIM_07.json index 4d9d341078..d812f41b63 100644 --- a/datasets/GPM_3GPROFNOAA16AMSUB_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA16AMSUB_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA16AMSUB_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA17AMSUB_CLIM_07.json b/datasets/GPM_3GPROFNOAA17AMSUB_CLIM_07.json index c3131ce2a3..f423da806d 100644 --- a/datasets/GPM_3GPROFNOAA17AMSUB_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA17AMSUB_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA17AMSUB_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA17AMSUB_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA17AMSUB_DAY_CLIM_07.json index 6fa73c54fe..a7fdc3b409 100644 --- a/datasets/GPM_3GPROFNOAA17AMSUB_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA17AMSUB_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA17AMSUB_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json b/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json index 422ee4c320..9b98183695 100644 --- a/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA18MHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA18MHS_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA18MHS_DAY_CLIM_07.json index 586581918d..b19d7dbec5 100644 --- a/datasets/GPM_3GPROFNOAA18MHS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA18MHS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA18MHS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA19MHS_07.json b/datasets/GPM_3GPROFNOAA19MHS_07.json index 09cd1a78fc..26cf1ebafe 100644 --- a/datasets/GPM_3GPROFNOAA19MHS_07.json +++ b/datasets/GPM_3GPROFNOAA19MHS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA19MHS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA19MHS_CLIM_07.json b/datasets/GPM_3GPROFNOAA19MHS_CLIM_07.json index 9a473acb75..b0d399bff2 100644 --- a/datasets/GPM_3GPROFNOAA19MHS_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA19MHS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA19MHS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA19MHS_DAY_07.json b/datasets/GPM_3GPROFNOAA19MHS_DAY_07.json index 206e9c0994..f21b11b503 100644 --- a/datasets/GPM_3GPROFNOAA19MHS_DAY_07.json +++ b/datasets/GPM_3GPROFNOAA19MHS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA19MHS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA19MHS_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA19MHS_DAY_CLIM_07.json index 73c4cab885..9c2c714c77 100644 --- a/datasets/GPM_3GPROFNOAA19MHS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA19MHS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA19MHS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA20ATMS_07.json b/datasets/GPM_3GPROFNOAA20ATMS_07.json index 5a5b4b8241..39c2ec574c 100644 --- a/datasets/GPM_3GPROFNOAA20ATMS_07.json +++ b/datasets/GPM_3GPROFNOAA20ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA20ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA20ATMS_CLIM_07.json b/datasets/GPM_3GPROFNOAA20ATMS_CLIM_07.json index b103f3f750..4da1af4703 100644 --- a/datasets/GPM_3GPROFNOAA20ATMS_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA20ATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA20ATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA20ATMS_DAY_07.json b/datasets/GPM_3GPROFNOAA20ATMS_DAY_07.json index 8b84e497c0..00f85ee433 100644 --- a/datasets/GPM_3GPROFNOAA20ATMS_DAY_07.json +++ b/datasets/GPM_3GPROFNOAA20ATMS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA20ATMS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA20ATMS_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA20ATMS_DAY_CLIM_07.json index 0ee6735929..0fb3e292e3 100644 --- a/datasets/GPM_3GPROFNOAA20ATMS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA20ATMS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA20ATMS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA21ATMS_07.json b/datasets/GPM_3GPROFNOAA21ATMS_07.json index 9a9b5fad1d..a1d5dd086c 100644 --- a/datasets/GPM_3GPROFNOAA21ATMS_07.json +++ b/datasets/GPM_3GPROFNOAA21ATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA21ATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA21ATMS_CLIM_07.json b/datasets/GPM_3GPROFNOAA21ATMS_CLIM_07.json index 0e8331fb49..3b5e257570 100644 --- a/datasets/GPM_3GPROFNOAA21ATMS_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA21ATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA21ATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA21ATMS_DAY_07.json b/datasets/GPM_3GPROFNOAA21ATMS_DAY_07.json index 4fb86420ba..a8b9b33e51 100644 --- a/datasets/GPM_3GPROFNOAA21ATMS_DAY_07.json +++ b/datasets/GPM_3GPROFNOAA21ATMS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA21ATMS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNOAA21ATMS_DAY_CLIM_07.json b/datasets/GPM_3GPROFNOAA21ATMS_DAY_CLIM_07.json index 85c1522150..a0717125cc 100644 --- a/datasets/GPM_3GPROFNOAA21ATMS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA21ATMS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNOAA21ATMS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNPPATMS_07.json b/datasets/GPM_3GPROFNPPATMS_07.json index ed900256e2..b7cce2e05f 100644 --- a/datasets/GPM_3GPROFNPPATMS_07.json +++ b/datasets/GPM_3GPROFNPPATMS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNPPATMS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNPPATMS_CLIM_07.json b/datasets/GPM_3GPROFNPPATMS_CLIM_07.json index 387516edfc..6c787ee87a 100644 --- a/datasets/GPM_3GPROFNPPATMS_CLIM_07.json +++ b/datasets/GPM_3GPROFNPPATMS_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNPPATMS_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNPPATMS_DAY_07.json b/datasets/GPM_3GPROFNPPATMS_DAY_07.json index b893cf6966..b520922714 100644 --- a/datasets/GPM_3GPROFNPPATMS_DAY_07.json +++ b/datasets/GPM_3GPROFNPPATMS_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNPPATMS_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFNPPATMS_DAY_CLIM_07.json b/datasets/GPM_3GPROFNPPATMS_DAY_CLIM_07.json index afbb0bb3c6..1d172c010f 100644 --- a/datasets/GPM_3GPROFNPPATMS_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFNPPATMS_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFNPPATMS_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFTRMMTMI_CLIM_07.json b/datasets/GPM_3GPROFTRMMTMI_CLIM_07.json index e80d67ad0b..c216ca2adc 100644 --- a/datasets/GPM_3GPROFTRMMTMI_CLIM_07.json +++ b/datasets/GPM_3GPROFTRMMTMI_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFTRMMTMI_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A12,3A11\n Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GPROFTRMMTMI_DAY_CLIM_07.json b/datasets/GPM_3GPROFTRMMTMI_DAY_CLIM_07.json index 7e8543e125..80735959f5 100644 --- a/datasets/GPM_3GPROFTRMMTMI_DAY_CLIM_07.json +++ b/datasets/GPM_3GPROFTRMMTMI_DAY_CLIM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GPROFTRMMTMI_DAY_CLIM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. \n\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.", "links": [ { diff --git a/datasets/GPM_3GSLH_07.json b/datasets/GPM_3GSLH_07.json index 67239659cb..5c8b547d76 100644 --- a/datasets/GPM_3GSLH_07.json +++ b/datasets/GPM_3GSLH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GSLH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\nThe Gridded Orbital Spectral Latent Heating (3GSLH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.", "links": [ { diff --git a/datasets/GPM_3GSLH_TRMM_07.json b/datasets/GPM_3GSLH_TRMM_07.json index 5b847eacec..65aee13432 100644 --- a/datasets/GPM_3GSLH_TRMM_07.json +++ b/datasets/GPM_3GSLH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3GSLH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3G25\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.\n\nThe Gridded Orbital Spectral Latent Heating is actually one orbit gridded onto a global map with 0.5 degree x 0.5 degree grid cell size. These latent heating profiles from the TRMM Precipitation Radar (PR) rain. The granule temporal size is one orbit.", "links": [ { diff --git a/datasets/GPM_3HCSH_07.json b/datasets/GPM_3HCSH_07.json index 2e4aec596f..69cc8641f2 100644 --- a/datasets/GPM_3HCSH_07.json +++ b/datasets/GPM_3HCSH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HCSH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gridded Convective Stratiform Heating (3HCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and are superseded by Version 07.\n", "links": [ { diff --git a/datasets/GPM_3HCSH_TRMM_07.json b/datasets/GPM_3HCSH_TRMM_07.json index 6407860e0d..89292b13d4 100644 --- a/datasets/GPM_3HCSH_TRMM_07.json +++ b/datasets/GPM_3HCSH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HCSH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy TRMM_3H31\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm.\n\nMonthly Spectral Latent Heating produces 0.25x0.25 degree grid of latent heating profiles from the TRMM PR rain. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n", "links": [ { diff --git a/datasets/GPM_3HSLH_07.json b/datasets/GPM_3HSLH_07.json index f8a6f81d0d..4612ee0e19 100644 --- a/datasets/GPM_3HSLH_07.json +++ b/datasets/GPM_3HSLH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HSLH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\nThe Gridded Spectral Latent Heating (3HSLH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.", "links": [ { diff --git a/datasets/GPM_3HSLH_DAY_07.json b/datasets/GPM_3HSLH_DAY_07.json index 1ace51803e..25a3124e58 100644 --- a/datasets/GPM_3HSLH_DAY_07.json +++ b/datasets/GPM_3HSLH_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HSLH_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\nThe Gridded Spectral Latent Heating (3HSLH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.", "links": [ { diff --git a/datasets/GPM_3HSLH_TRMM_07.json b/datasets/GPM_3HSLH_TRMM_07.json index f20abed561..c99fa94439 100644 --- a/datasets/GPM_3HSLH_TRMM_07.json +++ b/datasets/GPM_3HSLH_TRMM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HSLH_TRMM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3H25\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.\n\nMonthly Spectral Latent Heating produces 0.5 degree x 0.5 degree grid of latent heating profiles from the TRMM PR rain. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n", "links": [ { diff --git a/datasets/GPM_3HSLH_TRMM_DAY_07.json b/datasets/GPM_3HSLH_TRMM_DAY_07.json index 48f2f6ad8f..b91fece1a7 100644 --- a/datasets/GPM_3HSLH_TRMM_DAY_07.json +++ b/datasets/GPM_3HSLH_TRMM_DAY_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3HSLH_TRMM_DAY_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nEstimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall.\n\nThe method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u2014convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u2014were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean\u2013Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles.\n\nBecause observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.\n\nDaily Spectral Latent Heating produces 0.5 degree x 0.5 degree grid of latent heating profiles from the TRMM PR rain. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n", "links": [ { diff --git a/datasets/GPM_3IMERGDE_06.json b/datasets/GPM_3IMERGDE_06.json index 3b06209428..893b83d18f 100644 --- a/datasets/GPM_3IMERGDE_06.json +++ b/datasets/GPM_3IMERGDE_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGDE_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nVersion 06 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06.\n\nThis dataset is the GPM Level 3 IMERG *Early* Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily accumulated precipitation. The dataset is produced at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) by simply summing the valid precipitation retrievals for the day in GPM_3IMERGHHE and giving the result in (mm). The latency of the derived Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL). \n\nIn the original IMERG algorithm, the precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean and tropical land to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the CMORPH-KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe CMORPH-KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours of the vertically integrated vapor (TQV) provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~3.5 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nCurrently, the near-real-time Early and Late half-hourly estimates have no concluding calibration, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitationCal, is the data field of choice for most users.\n\n\nThe following describes the derivation of the Daily in more details.\n\nThe daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 0.5-hourly source data are in mm/hr, a factor of 0.5 is applied to the sum. Thus, for every grid cell we have \nPdaily = 0.5 * SUM{Pi * 1[Pi valid]}, i=[1,Nf]\nPdaily_cnt = SUM{1[Pi valid]}\n\nwhere:\nPdaily - Daily accumulation (mm)\nPi - 0.5-hourly input, in (mm/hr)\nNf - Number of 0.5-hourly files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\n\nOn occasion, the 0.5-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which\n Pdaily_cnt less than Nf.\n\n\nThere are various ways the accumulated daily error could be estimated from the source 0.5-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 0.5-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 0.5 is finally applied:\n\nPerr_daily = 0.5 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_daily\t- Magnitude of the daily accumulated error power, (mm)\nNcnt_err\t- The counts for the error variable\n\nThus computed Perr_daily represents the worst case scenario that assumes the error in the 0.5-hourly source data, which is given in mm/hr, is accumulating within the 0.5-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate:\n\nrms_err = { (Perr_daily/0.5) ^2 / Ncnt_err }^0.5\t(mm/hr)\n\n\nThis estimate assumes that the error given in the 0.5-hourly files is representative of the error of the rainfall rate (mm/hr) within the 0.5-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGDE_07.json b/datasets/GPM_3IMERGDE_07.json index da8646c0ca..27f95201df 100644 --- a/datasets/GPM_3IMERGDE_07.json +++ b/datasets/GPM_3IMERGDE_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGDE_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1\u00b0 every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG *Early* Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before \"07\", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable \"precipitation\", and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL). \n\n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have \n\nPdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi - half-hourly input, in (mm/hr)\nNf - Number of half-hourly files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGDF_07.json b/datasets/GPM_3IMERGDF_07.json index ee5b5facfd..6c8611c405 100644 --- a/datasets/GPM_3IMERGDF_07.json +++ b/datasets/GPM_3IMERGDF_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGDF_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1\u00b0 every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG *Final* Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before \"07\", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge dataset, variable \"precipitation\", and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived *Final* Daily product depends on the delivery of the IMERG *Final* Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. \n\n\n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have \n\nPdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi - half-hourly input, in (mm/hr)\nNf - Number of half-hourly files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n", "links": [ { diff --git a/datasets/GPM_3IMERGDL_06.json b/datasets/GPM_3IMERGDL_06.json index 2933c93ed7..de91dcb160 100644 --- a/datasets/GPM_3IMERGDL_06.json +++ b/datasets/GPM_3IMERGDL_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGDL_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nVersion 06 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06.\n\nThis dataset is the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily accumulated precipitation. The dataset is produced at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) by simply summing the valid precipitation retrievals for the day in GPM_3IMERGHHL and giving the result in (mm). The latency of the derived late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 12 hours, the daily should appear about 12 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL). \n\nIn the original IMERG algorithm, the precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean and tropical land to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the CMORPH-KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe CMORPH-KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours of the vertically integrated vapor (TQV) provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~3.5 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nCurrently, the near-real-time Early and Late half-hourly estimates have no concluding calibration, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitationCal, is the data field of choice for most users.\n\n\nThe following describes the derivation of the Daily in more details.\n\nThe daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 0.5-hourly source data are in mm/hr, a factor of 0.5 is applied to the sum. Thus, for every grid cell we have \n\nPdaily = 0.5 * SUM{Pi * 1[Pi valid]}, i=[1,Nf]\nPdaily_cnt = SUM{1[Pi valid]}\n\nwhere:\nPdaily - Daily accumulation (mm)\nPi - 0.5-hourly input, in (mm/hr)\nNf - Number of 0.5-hourly files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\n\nOn occasion, the 0.5-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which\n Pdaily_cnt less than Nf.\n\n\nThere are various ways the accumulated daily error could be estimated from the source 0.5-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 0.5-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 0.5 is finally applied:\n\nPerr_daily = 0.5 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_daily\t- Magnitude of the daily accumulated error power, (mm)\nNcnt_err\t- The counts for the error variable\n\nThus computed Perr_daily represents the worst case scenario that assumes the error in the 0.5-hourly source data, which is given in mm/hr, is accumulating within the 0.5-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate:\n\nrms_err = { (Perr_daily/0.5) ^2 / Ncnt_err }^0.5\t(mm/hr)\n\n\nThis estimate assumes that the error given in the 0.5-hourly files is representative of the error of the rainfall rate (mm/hr) within the 0.5-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n\n\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGDL_07.json b/datasets/GPM_3IMERGDL_07.json index da7a006ac7..02b9e7383b 100644 --- a/datasets/GPM_3IMERGDL_07.json +++ b/datasets/GPM_3IMERGDL_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGDL_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1\u00b0 every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before \"07\", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable \"precipitation\", and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived Late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 14 hours, the daily should appear no earlier than 14 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL). \n\n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have \n\nPdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi - half-hourly input, in (mm/hr)\nNf - Number of half-hourly files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGHHE_06.json b/datasets/GPM_3IMERGHHE_06.json index 0d14366f83..bf65b2c28c 100644 --- a/datasets/GPM_3IMERGHHE_06.json +++ b/datasets/GPM_3IMERGHHE_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGHHE_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nMinor Version 06B is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06B.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean and tropical land to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the CMORPH-KF morphing (quasi-Lagrangian time interpolation) scheme. \nThe CMORPH-KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours of the vertically integrated vapor (TQV) provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~3.5 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nCurrently, the near-real-time Early and Late half-hourly estimates have no concluding calibration, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitationCal, is the data field of choice for most users.\n\nBriefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward morphed\" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 hours).\n\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGHHE_07.json b/datasets/GPM_3IMERGHHE_07.json index c918286f64..2a94ab0af2 100644 --- a/datasets/GPM_3IMERGHHE_07.json +++ b/datasets/GPM_3IMERGHHE_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGHHE_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. ", "links": [ { diff --git a/datasets/GPM_3IMERGHHL_06.json b/datasets/GPM_3IMERGHHL_06.json index 32d58526de..82599c8ceb 100644 --- a/datasets/GPM_3IMERGHHL_06.json +++ b/datasets/GPM_3IMERGHHL_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGHHL_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nMinor Version 06B is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06B.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean and tropical land to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the CMORPH-KF morphing (quasi-Lagrangian time interpolation) scheme. \nThe CMORPH-KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours of the vertically integrated vapor (TQV) provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~3.5 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nCurrently, the near-real-time Early and Late half-hourly estimates have no concluding calibration, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitationCal, is the data field of choice for most users.\n\nBriefly describing the Late Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 14 hours).\n\n\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGHHL_07.json b/datasets/GPM_3IMERGHHL_07.json index 57b6cc7611..1cf98a87b7 100644 --- a/datasets/GPM_3IMERGHHL_07.json +++ b/datasets/GPM_3IMERGHHL_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGHHL_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nVersion 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. \n\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGHH_07.json b/datasets/GPM_3IMERGHH_07.json index 1605ff523d..4334dfb7ed 100644 --- a/datasets/GPM_3IMERGHH_07.json +++ b/datasets/GPM_3IMERGHH_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGHH_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nBriefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).\n\n", "links": [ { diff --git a/datasets/GPM_3IMERGM_07.json b/datasets/GPM_3IMERGM_07.json index 92616c38d6..b550e97be0 100644 --- a/datasets/GPM_3IMERGM_07.json +++ b/datasets/GPM_3IMERGM_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3IMERGM_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nBriefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).", "links": [ { diff --git a/datasets/GPM_3PRD_07.json b/datasets/GPM_3PRD_07.json index a0c567168c..2e8b4ea6f5 100644 --- a/datasets/GPM_3PRD_07.json +++ b/datasets/GPM_3PRD_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PRD_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThis is the GPM-like formatted TRMM Precipitation Radar (PR) daily gridded data, first released with the \"V8\" TRMM reprocessing. The daily radar grid data is new for TRMM nomenclature and is introduced for consistency with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor was 3A25 which was a monthly radar statistics.\n\nThis product consists of daily statistics of the PR measurements at (0.25x0.25) degrees horizontal resolution.\n\nThe objective of the algorithm is to calculate various daily statistics from the level 2 PR\noutput products. Four types of statistics are calculated:\n1. Probabilities of occurrence (count values)\n2. Means and standard deviations\nIn all cases, the statistics are conditioned on the presence of rain or some other quantity such\nas the presence of stratiform rain or the presence of a bright-band. For example, to compute\nthe unconditioned mean rain rate, the conditional mean must be multiplied by the probability\nof rain which, in turn is calculated from the ratio of rain counts to the total number of\nobservations in the box of interest. \n\nThe grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells. \n\t", "links": [ { diff --git a/datasets/GPM_3PRPSMT1SAPHIR_06.json b/datasets/GPM_3PRPSMT1SAPHIR_06.json index f7cb889d4a..c30d1fff9e 100644 --- a/datasets/GPM_3PRPSMT1SAPHIR_06.json +++ b/datasets/GPM_3PRPSMT1SAPHIR_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PRPSMT1SAPHIR_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.\n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.\n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval.\n\n", "links": [ { diff --git a/datasets/GPM_3PRPSMT1SAPHIR_CLIM_06.json b/datasets/GPM_3PRPSMT1SAPHIR_CLIM_06.json index 07ac9e576e..33d321ae2d 100644 --- a/datasets/GPM_3PRPSMT1SAPHIR_CLIM_06.json +++ b/datasets/GPM_3PRPSMT1SAPHIR_CLIM_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PRPSMT1SAPHIR_CLIM_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. \n\n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.\n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model\ndata) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval.\n\n", "links": [ { diff --git a/datasets/GPM_3PRPSMT1SAPHIR_DAY_06.json b/datasets/GPM_3PRPSMT1SAPHIR_DAY_06.json index d4baebb513..4d6715419d 100644 --- a/datasets/GPM_3PRPSMT1SAPHIR_DAY_06.json +++ b/datasets/GPM_3PRPSMT1SAPHIR_DAY_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PRPSMT1SAPHIR_DAY_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.\n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products. \n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval.\n\n", "links": [ { diff --git a/datasets/GPM_3PRPSMT1SAPHIR_DAY_CLIM_06.json b/datasets/GPM_3PRPSMT1SAPHIR_DAY_CLIM_06.json index 791f4b24ce..393c8a4c7f 100644 --- a/datasets/GPM_3PRPSMT1SAPHIR_DAY_CLIM_06.json +++ b/datasets/GPM_3PRPSMT1SAPHIR_DAY_CLIM_06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PRPSMT1SAPHIR_DAY_CLIM_06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. \n\nThe Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.\n\nFundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).\n\nThe algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the \u2018fit\u2019 of the observations to the database provided. A quality flag is also provided, with any bad data generating a \u2018missing flag\u2019 in the retrieval.\n\n", "links": [ { diff --git a/datasets/GPM_3PR_07.json b/datasets/GPM_3PR_07.json index 4b14eb4163..a147151db2 100644 --- a/datasets/GPM_3PR_07.json +++ b/datasets/GPM_3PR_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PR_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThis is the GPM-like formatted TRMM Precipitation Radar (PR) monthly gridded data, first released with the \"V8\" TRMM reprocessing. The TRMM radar Level 3 grids are now consistent with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor of this dataset was the monthly radar statistics 3A25.\n\nThis product consists of monthly statistics of the PR measurements at 0.25x0.25 degrees, and monthly histograms and statistics at 5x5 degrees, horizontal resolution.\n\nThe objective of the algorithm is to calculate various daily statistics from the level 2 PR\noutput products. Four types of statistics are calculated:\n1. Probabilities of occurrence (count values)\n2. Means and standard deviations\n3. Histograms\n4. Correlation coefficients\nIn all cases, the statistics are conditioned on the presence of rain or some other quantity such\nas the presence of stratiform rain or the presence of a bright-band. For example, to compute\nthe unconditioned mean rain rate, the conditional mean must be multiplied by the probability\nof rain which, in turn is calculated from the ratio of rain counts to the total number of\nobservations in the box of interest.\n\nThe grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n\n\t", "links": [ { diff --git a/datasets/GPM_3PR_ASC_07.json b/datasets/GPM_3PR_ASC_07.json index 11de8d80b7..d8298e669a 100644 --- a/datasets/GPM_3PR_ASC_07.json +++ b/datasets/GPM_3PR_ASC_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PR_ASC_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThis product consists of Ascending daily statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.25 degrees x 0.25 degrees) horizontal resolution. The low resolution grids are in the Planetary Grid 1 structure and include 1) mean and standard deviation of the rain rate, reflectivity, path-integrated attenuation (PIA), storm height, Xi, bright band height and the NUBF (Non-Uniform Beam Filling) correction; 2) rain fractions; 3) histograms of the storm height, bright-band height, snow-ice layer, reflectivity, rain rate, path-attenuation and NUBF correction; 4) correlation coefficients. The high resolution grids are in the Planetary Grid 2 structure and contain mean rain rate along with standard deviation and rain fractions.\n\nThe grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n\t", "links": [ { diff --git a/datasets/GPM_3PR_DES_07.json b/datasets/GPM_3PR_DES_07.json index f835471d3b..f871e8e1e6 100644 --- a/datasets/GPM_3PR_DES_07.json +++ b/datasets/GPM_3PR_DES_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_3PR_DES_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n\nVersion 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThis product consists of Descending daily statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.25 degrees x 0.25 degrees) horizontal resolution. The low resolution grids are in the Planetary Grid 1 structure and include 1) mean and standard deviation of the rain rate, reflectivity, path-integrated attenuation (PIA), storm height, Xi, bright band height and the NUBF (Non-Uniform Beam Filling) correction; 2) rain fractions; 3) histograms of the storm height, bright-band height, snow-ice layer, reflectivity, rain rate, path-attenuation and NUBF correction; 4) correlation coefficients. The high resolution grids are in the Planetary Grid 2 structure and contain mean rain rate along with standard deviation and rain fractions.\n\nThe grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the \u00b140 degrees of latitudes are padded with empty grid cells.\n\t", "links": [ { diff --git a/datasets/GPM_BASEGPMGMI_07.json b/datasets/GPM_BASEGPMGMI_07.json index 41b604563c..849a02c612 100644 --- a/datasets/GPM_BASEGPMGMI_07.json +++ b/datasets/GPM_BASEGPMGMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_BASEGPMGMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.\n\nGMI is a multi-channel, conical- scanning, microwave radiometer. \nThe BASEGPMGMI product contains unaltered data directly from the Global Microwave Imager (GMI) aboard the GPM core satellite. It is the standard GMI calibration product with full precision of all physical fields. It contains one full orbit with no overlaps to other orbits in the production, although up to 200 overlap scans may be used for multi-scan calibration in the process. \n \nIf there is enough bandwidth, the entire circle of GMI samples will be sent down. The BASEGPMGMI product's swaths 4 and 5 contain all of the samples that are sent down. Later products only use the subset of these data that contains the Earth view, hot load, and cold sky samples.", "links": [ { diff --git a/datasets/GPM_BASEGPMGMI_RSS_07.json b/datasets/GPM_BASEGPMGMI_RSS_07.json index c42a334898..37bee86257 100644 --- a/datasets/GPM_BASEGPMGMI_RSS_07.json +++ b/datasets/GPM_BASEGPMGMI_RSS_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_BASEGPMGMI_RSS_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.\n\nThis GPM GMI dataset contains \"GMI Antenna Temperatures\", and is written as a multi-Swath Structure. Swath S1 has channels 1-9: 10V 10H 19V 19H 23V 37V 37H 89V 89H. Swath S2 has channels 10-13: 166V 166H 183+/-3V 183+/-8V. GMIBASERSS is the standard GMI calibration product with full precision of all physical fields. It contains one full orbit plus 200 overlap scans from previous orbit and 200 overlap scans from the post orbit.", "links": [ { diff --git a/datasets/GPM_BASEGPMGMI_XCAL_07.json b/datasets/GPM_BASEGPMGMI_XCAL_07.json index d2dbf6dfc5..4c315064a4 100644 --- a/datasets/GPM_BASEGPMGMI_XCAL_07.json +++ b/datasets/GPM_BASEGPMGMI_XCAL_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_BASEGPMGMI_XCAL_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.\n\nConsistent rainfall retrievals from each instrument mandate the basic sensor radiance measurements (brightness temperature, Tb) to be consistently inter-calibrated.\n\nFundamental to this concept is the existence of the GPM Microwave Imager (GMI), a conically scanning multifrequency radiometer in non-sun-synchronous orbit, which serves as a radiometric transfer standard for the other passive microwave sensors on cooperative (polar orbiting) constellation satellites. This GPM inter-satellite calibration process is known as XCAL.\n\n GMIBASEXCAL is the standard GMI calibration product with reduced precision of all physical fields. It consists of one full orbit with no overlaps to other orbits in the production although up to 200 overlap scans may be used for multi-scan calibration in the process. \n\nThis GPM GMI product contains \"GMI Antenna Temperatures\", and is written as a multi-Swath Structure. Swath S1 has channels 1-9: 10V 10H 19V 19H 23V 37V 37H 89V 89H. Swath S2 has channels 10-13: 166V 166H 183+/-3V 183+/-8V. S3 S4 are full rotation versions of S1 S2. ", "links": [ { diff --git a/datasets/GPM_BASETRMMTMI_07.json b/datasets/GPM_BASETRMMTMI_07.json index d7fdeeaa12..6a2ff51b3f 100644 --- a/datasets/GPM_BASETRMMTMI_07.json +++ b/datasets/GPM_BASETRMMTMI_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_BASETRMMTMI_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.\n \n\nThe BASETRMMTMI product contains unaltered data directly from the TRMM Microwave Imager (TMI) aboard the TRMM satellite.\n\nIt has has two purposes: To repackage the raw satellite data from binary Consultative Committee for Space Data Systems (CCSDS) packets to Hierarchical Data Format (HDF5), and to geolocate the sample data. The counts in the product are the raw counts taken directly from the packets created by the TRMM Microwave Imager (TMI) instrument. There is no calibration or interpretation performed at the BASETRMMTMI level. \n\n\t", "links": [ { diff --git a/datasets/GPM_DPR_DPR_DD2_NA.json b/datasets/GPM_DPR_DPR_DD2_NA.json index 9168617c3e..8c07da5330 100644 --- a/datasets/GPM_DPR_DPR_DD2_NA.json +++ b/datasets/GPM_DPR_DPR_DD2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_DPR_DD2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR DPR Environment Auxiliary dataset is produced by the Japan Aerospace Exploration Agency (JAXA). Environment Auxiliary (ENV) is Meteorological analysis data used as an input data to the level 2 processing shown. They are the Japanese Global Analysis model data (GANAL) used to provide atmospheric environmental conditions.In the current algorithm formulation, only the analysis data such as analysis data, must be integrated from an external source during combined algorithm processing. Analysis data are required to produce initial estimations of environmental parameters such as total precipitable water, TPWanal, cloud liquid water path, CLWPanal, surface skin temperature, Tsfcanal, and 10m altitude wind speed, U10manal. The current algorithm design requires space-time interpolation of these data from the Japanese Meteorological agency\u00e2\u0080\u0099s (JMA) global analysis (GANAL) during standard algorithm processing. The data are interpolated to the DPR footprint/range bin locations and overpass times in the Vertical Profile Submodule (VER) of the Level 2 Radar Algorithm and then output. For near realtime processing, the JMA forecast fields, but if these fields are not received in time for any reason, the climate value data are substituted for the JMA analysis/forecast data in the VER processing.Main parameters: Air temperature, Air pressure, Water vapor, Cloud liquid waterSwath width: 245 kmResolution: 5 km(horizontal), 125/250 m(vertical)The generation unit is orbit. The current version of the product is Version 7. The Version 6 is also available.", "links": [ { diff --git a/datasets/GPM_DPR_KaPR_DA2_NA.json b/datasets/GPM_DPR_KaPR_DA2_NA.json index 5c9ab7c675..37fb1a2aaa 100644 --- a/datasets/GPM_DPR_KaPR_DA2_NA.json +++ b/datasets/GPM_DPR_KaPR_DA2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KaPR_DA2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR KaPR Environment Auxiliary dataset is produced by the Japan Aerospace Exploration Agency (JAXA).Environment Auxiliary (ENV) is Meteorological analysis data used as an input data to the level 2 processing shown. They are the Japanese Global Analysis model data (GANAL) used to provide atmospheric environmental conditions.In the current algorithm formulation, only the analysis data such as analysis data, must be integrated from an external source during combined algorithm processing. Analysis data are required to produce initial estimations of environmental parameters such as total precipitable water, TPWanal, cloud liquid water path, CLWPanal, surface skin temperature, Tsfcanal, and 10m altitude wind speed, U10manal. The current algorithm design requires space-time interpolation of these data from the Japanese Meteorological agency\u00e2\u0080\u0099s (JMA) global analysis (GANAL) during standard algorithm processing. The data are interpolated to the DPR footprint/range bin locations and overpass times in the Vertical Profile Submodule (VER) of the Level 2 Radar Algorithm and then output. For near realtime processing, the JMA forecast fields, but if these fields are not received in time for any reason, the climate value data are substituted for the JMA analysis/forecast data in the VER processing.Main parameters: Air temperature, Air pressure, Water vapor, Cloud liquid waterSwath width: 125 km (245 km since May 21, 2018)Resolution: 5 km(horizontal), 125/250 m(vertical)The generation unit is orbit. The current version of the product is Version 7. The Version 6 is also available.", "links": [ { diff --git a/datasets/GPM_DPR_KaPR_L1B_DAB_NA.json b/datasets/GPM_DPR_KaPR_L1B_DAB_NA.json index 9d0248b939..b8bdfc6339 100644 --- a/datasets/GPM_DPR_KaPR_L1B_DAB_NA.json +++ b/datasets/GPM_DPR_KaPR_L1B_DAB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KaPR_L1B_DAB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR/KaPR L1B Received Power dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries DPR and a GPM Microwave Imager (GMI). Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days. The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively.In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles). In DPR level 1B standard processing, a level 1A product is read as input data and a product containing a received power profile and geometric information, such as observation positions, is outputted. During the course of processing, radiometric correction is carried out, missing data is processed based on missing data information, scan time is corrected and geometric calculation of the time, latitude, longitude, and height of each piece of scan data in each range bin is performed.The provided format is HDF5. The Sampling resolution are 5km(horizontal) and 125/250m(vertical). The current version of the product is Version 7. The Version 5 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_KaPR_L2_DA2_NA.json b/datasets/GPM_DPR_KaPR_L2_DA2_NA.json index 7f105d843d..c2a9ae6f7d 100644 --- a/datasets/GPM_DPR_KaPR_L2_DA2_NA.json +++ b/datasets/GPM_DPR_KaPR_L2_DA2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KaPR_L2_DA2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR/KaPR L2 Precipitation dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer.Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\".GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7- 10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles). The level 2 processing algorithm of the dual frequency precipitation radar additionally uses received power value profiles observed by KuPR and KaPR to estimate a precipitation intensity profile. It also estimates precipitation type, precipitation top height, and bright band height. Level 2 algorithm input data is a level 1 DPR product (calibrated radar echo power value profile), and level 2 algorithm output data is a level 2 product (precipitation intensity profile).The provided format is HDF5. The Sampling resolution are 5 km(horizontal) and 125 m(vertical). The current version of the product is Version 7. The Version 6 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_KuPR_DU2_NA.json b/datasets/GPM_DPR_KuPR_DU2_NA.json index 860a73793a..51b379163d 100644 --- a/datasets/GPM_DPR_KuPR_DU2_NA.json +++ b/datasets/GPM_DPR_KuPR_DU2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KuPR_DU2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR KuPR Environment Auxiliary dataset is produced by the Japan Aerospace Exploration Agency (JAXA). Environment Auxiliary (ENV) is Meteorological analysis data used as an input data to the level 2 processing shown. They are the Japanese Global Analysis model data (GANAL) used to provide atmospheric environmental conditions.In the current algorithm formulation, only the analysis data such as analysis data, must be integrated from an external source during combined algorithm processing. Analysis data are required to produce initial estimations of environmental parameters such as total precipitable water, TPWanal, cloud liquid water path, CLWPanal, surface skin temperature, Tsfcanal, and 10m altitude wind speed, U10manal. The current algorithm design requires space-time interpolation of these data from the Japanese Meteorological agency's (JMA) global analysis (GANAL) during standard algorithm processing. The data are interpolated to the DPR footprint/range bin locations and overpass times in the Vertical Profile Submodule (VER) of the Level 2 Radar Algorithm and then output. For near realtime processing, the JMA forecast fields, but if these fields are not received in time for any reason, the climate value data are substituted for the JMA analysis/forecast data in the VER processing.Main parameters: Air temperature, Air pressure, Water vapor, Cloud liquid waterSwath width: 245 kmResolution: 5 km(horizontal), 125m(vertical)The generation unit is orbit. The current version of the product is Version 7. The Version 6 is also available.", "links": [ { diff --git a/datasets/GPM_DPR_KuPR_L1B_DUB_NA.json b/datasets/GPM_DPR_KuPR_L1B_DUB_NA.json index 2922215d59..14ac151dad 100644 --- a/datasets/GPM_DPR_KuPR_L1B_DUB_NA.json +++ b/datasets/GPM_DPR_KuPR_L1B_DUB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KuPR_L1B_DUB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR/KuPR L1B Received Power dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).GPM Core Satellite carries DPR and a GPM Microwave Imager (GMI). Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7- 10 days. The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).In DPR level 1B standard processing, a level 1A product is read as input data and a product containing a received power profile and geometric information, such as observation positions, is outputted. During the course of processing, radiometric correction is carried out, missing data is processed based on missing data information, scan time is corrected and geometric calculation of the time, latitude, longitude, and height of each piece of scan data in each range bin is performed.The provided format is HDF5. The Sampling resolution are 5 km (horizontal) and 125 m (vertical). The current version of the product is Version 7. The Version 5 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_KuPR_L2_DU2_NA.json b/datasets/GPM_DPR_KuPR_L2_DU2_NA.json index 6bf719cb01..06377066c3 100644 --- a/datasets/GPM_DPR_KuPR_L2_DU2_NA.json +++ b/datasets/GPM_DPR_KuPR_L2_DU2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_KuPR_L2_DU2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR/KuPR L2 Precipitation dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer.Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days. The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles). The level 2 processing algorithm of the dual frequency precipitation radar additionally uses received power value profiles observed by KuPR and KaPR to estimate a precipitation intensity profile. It also estimates precipitation type, precipitation top height, and bright band height. Level 2 algorithm input data is a level 1 DPR product (calibrated radar echo power value profile), and level 2 algorithm output data is a level 2 product (precipitation intensity profile).The provided format is HDF5. The Sampling resolution are 5km(horizontal) and 125m/250m(vertical). The current version of the product is Version 7. The Version 6 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_L2_DD2_NA.json b/datasets/GPM_DPR_L2_DD2_NA.json index e9805afc31..db7c96c33b 100644 --- a/datasets/GPM_DPR_L2_DD2_NA.json +++ b/datasets/GPM_DPR_L2_DD2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L2_DD2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L2 Precipitation dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer. Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles). The level 2 processing algorithm of the dual frequency precipitation radar additionally uses received power value profiles observed by KuPR and KaPR to estimate a precipitation intensity profile. It also estimates precipitation type, precipitation top height, and bright band height. Level 2 algorithm input data is a level 1 DPR product (calibrated radar echo power value profile), and level 2 algorithm output data is a level 2 product (precipitation intensity profile).The provided format is HDF5. The Sampling resolution are 5 km(horizontal) and 125m/250 m(vertical). The current version of the product is Version 7. The Version 6 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_L2_SLP_NA.json b/datasets/GPM_DPR_L2_SLP_NA.json index 09af7b8b8e..9a064f9ebe 100644 --- a/datasets/GPM_DPR_L2_SLP_NA.json +++ b/datasets/GPM_DPR_L2_SLP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L2_SLP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L2 Spectral Latent Heating dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries DPR and a GPM Microwave Imager (GMI). Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles). The provided format is HDF5. The Sampling resolution are 5 km(horizontal) and 250 m(vertical). The current version of the product is Version 7A. The Version 6 is also available. The generation unit is one orbit.", "links": [ { diff --git a/datasets/GPM_DPR_L3_D3D_1day_0.1deg_NA.json b/datasets/GPM_DPR_L3_D3D_1day_0.1deg_NA.json index 5e7abd6df2..3a5ab61d31 100644 --- a/datasets/GPM_DPR_L3_D3D_1day_0.1deg_NA.json +++ b/datasets/GPM_DPR_L3_D3D_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L3_D3D_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L3 Precipitaion (1-Day,0.1deg) dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer. Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).The Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. Unless otherwise specified, the means are conditioned on precipitation being present. For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product. The Level 3 DPR products present the user with summary information over daily and monthly time periods. The provided format is TEXT. The Sampling resolution are 0.1 degree grid (Daily TEXT). The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/GPM_DPR_L3_D3M_1month_0.25deg_NA.json b/datasets/GPM_DPR_L3_D3M_1month_0.25deg_NA.json index a79bbde6ac..4dc2c9523a 100644 --- a/datasets/GPM_DPR_L3_D3M_1month_0.25deg_NA.json +++ b/datasets/GPM_DPR_L3_D3M_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L3_D3M_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L3 Precipitation (1-Month,0.25deg) dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer. Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).The Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. Unless otherwise specified, the means are conditioned on precipitation being present. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product. The Level 3 DPR products present the user with summary information over daily and monthly time periods. The provided format is HDF5. The Sampling resolution is 0.25 degree grid (Monthly HDF). The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/GPM_DPR_L3_D3Q_1day_0.25deg_NA.json b/datasets/GPM_DPR_L3_D3Q_1day_0.25deg_NA.json index f4d2885eed..a5b8751b69 100644 --- a/datasets/GPM_DPR_L3_D3Q_1day_0.25deg_NA.json +++ b/datasets/GPM_DPR_L3_D3Q_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L3_D3Q_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L3 Precipitation (1-Day,0.25deg) dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer. Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\". GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).The Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. Unless otherwise specified, the means are conditioned on precipitation being present. For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product. The Level 3 DPR products present the user with summary information over daily and monthly time periods. The provided format is HDF5. The Sampling resolution is 0.25 degree grid (Daily HDF5). The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/GPM_DPR_L3_SLG_0.5deg_NA.json b/datasets/GPM_DPR_L3_SLG_0.5deg_NA.json index 86c267bbae..be62f32254 100644 --- a/datasets/GPM_DPR_L3_SLG_0.5deg_NA.json +++ b/datasets/GPM_DPR_L3_SLG_0.5deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L3_SLG_0.5deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L3 Gridded Orbital Spectral Latent Heating (0.5deg) dataset is obtained from the DPR sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries Dual-frequency Precipitation Radar (DPR) and a microwave radiometer. Main purposes of the GPM are \"Comprehension of horizontal and vertical structure of precipitation systems\", \"Acquisition of precipitation particles information\" and \"Improvement of accuracy of precipitation by constellation satellites\".GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).In Gridded Orbital Spectral Latent Heating (3GSLH) products, latent heating of 0.5degree grid and vertical distribution of Q1-QR and Q2 are obtained from DPR rainfall. The provided format is HDF5. The sampling resolution are 0.5degree grid. The current version of the product is Version 7A. The generation unit is orbit.", "links": [ { diff --git a/datasets/GPM_DPR_L3_SLM_1month_0.5deg_NA.json b/datasets/GPM_DPR_L3_SLM_1month_0.5deg_NA.json index be0017bea7..862391f4b0 100644 --- a/datasets/GPM_DPR_L3_SLM_1month_0.5deg_NA.json +++ b/datasets/GPM_DPR_L3_SLM_1month_0.5deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_DPR_L3_SLM_1month_0.5deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPM/DPR L3 Spectral Latent Heating (1-Month,0.5deg) dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard GPM and produced by the Japan Aerospace Exploration Agency (JAXA). GPM Core Satellite carries DPR and a GPM Microwave Imager (GMI). Main purposes of the GPM are 'Comprehension of horizontal and vertical structure of precipitation systems', 'Acquisition of precipitation particles information' and 'Improvement of accuracy of precipitation by constellation satellite'.GPM Core Satellite can observe the range from the south latitude about 65 degrees to the north latitude about 65 degrees, and flies Non-Sun-synchronous Circular Orbit at about the 407 km altitude. To keep the altitude, the Core Satellite does the orbit maintenance maneuver. The interval is about 7-10 days.The swaths of DPR instrument are 125 and 245 km (78 and 152 mile) (245 km since May 21, 2018) for a Ka-band precipitation radar (KaPR) and a Ku-band precipitation radar (KuPR) respectively. In addition, simultaneous measurements are done at the overlapping of Ka/Ku-bands of the DPR. The GMI instrument is a conical-scanning multi-channel microwave radiometer covering a swath of 904 km (565 miles).In the Monthly Spectral Latent Heating (3HSLH), latent heating of 0.5degree grid and vertical distribution of Q1-QR and Q2 are obtained from DPR rainfall products. The provided format is HDF5. The Sampling resolution are 0.5degree grid. The statistical period is 1 month. The generation unit is global. The current version of the product is Version 7A.", "links": [ { diff --git a/datasets/GPM_IMERG_LandSeaMask_2.json b/datasets/GPM_IMERG_LandSeaMask_2.json index cf45bb0adc..4ccfdb67e1 100644 --- a/datasets/GPM_IMERG_LandSeaMask_2.json +++ b/datasets/GPM_IMERG_LandSeaMask_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_IMERG_LandSeaMask_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.\n\nThis land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Global Change Data Center, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions.\n\nUsers should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data.", "links": [ { diff --git a/datasets/GPM_MERGIR_1.json b/datasets/GPM_MERGIR_1.json index 749ccd04bd..e6a1b269bc 100644 --- a/datasets/GPM_MERGIR_1.json +++ b/datasets/GPM_MERGIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_MERGIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data originate from NOAA/NCEP.\n\nThe NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. It has been the intention to extend the record back to TRMM epoch, and some data are available from 1998. However, currently, the continuous record starts from February, 2000.\n\nThe leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers).\n\nThe data contain globally-merged (60S-60N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16, METEOSAT-5/7/8/9/10, and GMS-5/MTSat-1R/2/Himawari-8).\n\nThe data have been corrected for \"zenith angle dependence\", i.e. IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects. This correction allows for the merging of the IR data from the various geostationary satellites with greatly reduced discontinuities at their boundaries. Some residual differences among the data exist since the IR channels aboard the various spacecraft have slightly different characteristics and no intercalibration among the sensors has been performed. NOAA are in the process of performing such an intercalibration, although this effect is considerably smaller than the zenith angle effects. As well, parallax correction is applied, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops being displaced away from their actual geographic location when viewed along a slanted path.\n\n\nThe NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface. \n\n\nSimilarities with the original\n-----------------------------\nAs in the original binaries, every netCDF-4 file covers one hour, and contains two half-hourly grids, at 4-km grid cell resolution. \n\n\nDifferences from the original\n-----------------------------\n1. The data in the netCDF-4 files are already converted to real (float) values of Brightness Temperatures in Kelvin. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines. \n\n2. To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South).\n\nThe data and time units are reflected in the corresponding \"units\" attributes, and grid dimensions are described by longitude (\"lon\"), latitude (\"lat\") and \"time\" vectors. Thus, any CF-compliant tool should automatically understand the setup in the data files and the starting time for each half-hourly grid. Even without such tools, simple \"ncdump\" or \"h5dump\" command line tools will easily disclose the netCDF-4 files configuration.\n\n\nAcknowledgements\n------------------\nThe creation of the original data at NOAA/NCEP is supported by funding from the NOAA Office of Global Programs for the Global Precipitation Climatology Project (GPCP) and by NASA via the Tropical Rainfall Measuring Mission (TRMM). \n\nThe permanent archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) Program. \n\n\n", "links": [ { diff --git a/datasets/GPM_PRL1KA_07.json b/datasets/GPM_PRL1KA_07.json index c67a2fef27..4c4d45d124 100644 --- a/datasets/GPM_PRL1KA_07.json +++ b/datasets/GPM_PRL1KA_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_PRL1KA_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThis product contains the calibrated received power from the Ka-band of the Dual-frequency Precipitation Radar (DPR) aboard the core satellite of the Global Precipitation Measurement (GPM) mission.\n\nOne of the reasons for adding the Ka-band frequency (35.5 GHz) channel to the DPR is to provide information on the drop-size distribution that can be obtained from non-Rayleigh scattering effects at the higher frequency. Another reason for the new Ka-band channel is to provide more accurate estimates of the phase-transition height in precipitating systems. This information is very important not only in increasing the accuracy of rain rate estimation by the DPR itself, but in improving rain estimation by passive microwave radiometers.\n\nThe Ka-band Radar has a more complex scanning geometry, defined by two modes: Matched (MS) and High-sensitivity (HS). In the first type of scan (MS), the Ka Radar beams are matched to the central 25 beams of the Ku Radar, providing a swath of 120 km (within the 245km swath of the Ku Radar). In the second type of scan (HS), the Ka is operated in the high-sensitivity mode to detect light rain and snow. In this case, its beams are interlaced within the matched scan pattern, resulting in 49 cross-track beams which, however, still cover the 120-km swath. \n", "links": [ { diff --git a/datasets/GPM_PRL1KU_07.json b/datasets/GPM_PRL1KU_07.json index fbfcb304be..5d92a3a9bd 100644 --- a/datasets/GPM_PRL1KU_07.json +++ b/datasets/GPM_PRL1KU_07.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPM_PRL1KU_07", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.\n\nThis product contains the calibrated received power from the Ku-band Radar of the Dual-frequency Precipitation Radar (DPR) aboard the core satellite of the Global Precipitation Measurement (GPM) mission.\n\nThe Ku-radar scan pattern is simpler than that of the Ka-band Radar, and is similar to the TRMM PR. It only has \"Normal Scan\" (NS) swath consisting of 49 footprints cross-track in a scan and the footprint size is about 5 km in diameter. The scan swath is 245 km. \n", "links": [ { diff --git a/datasets/GPP_CONUS_TROPOMI_1875_1.json b/datasets/GPP_CONUS_TROPOMI_1875_1.json index 53e21e1098..dc3dbe15d1 100644 --- a/datasets/GPP_CONUS_TROPOMI_1875_1.json +++ b/datasets/GPP_CONUS_TROPOMI_1875_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPP_CONUS_TROPOMI_1875_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites. Knowledge of the spatiotemporal patterns of GPP is necessary for understanding regional and global carbon budgets. Broad-scale estimates of GPP have typically relied upon carbon cycle models linking spatial patterns of vegetation with remotely sensed environmental data. SIF provides a means to directly estimate photosynthetic activity, and therefore, GPP. Recent deployments of satellite platforms that measure SIF provide near-real-time measurements and represent a breakthrough in measuring GPP on a global scale. Regular SIF measurements can detect spatially explicit ecosystem-level responses to climate events such as drought and flooding. This dataset includes spatially explicit estimates of GPP (g m-2 d-1), uncertainty in GPP, and related TROPOMI SIF measurements (mW m-2 sr-1 nm-1) at 500-m resolution. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/GPP_COS_Conductance_SoilFluxes_2324_1.json b/datasets/GPP_COS_Conductance_SoilFluxes_2324_1.json index 2af54ccfe7..a30171219b 100644 --- a/datasets/GPP_COS_Conductance_SoilFluxes_2324_1.json +++ b/datasets/GPP_COS_Conductance_SoilFluxes_2324_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPP_COS_Conductance_SoilFluxes_2324_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast. The data are separated by plant functional type (PFT). Fluxes have dimensions of latitude, longitude, time, and plant functional type. Model output spans 53N to 90N latitude and 180W to 180E longitude over years 2000 to 2020. The data are provided in NetCDF version 4 format.", "links": [ { diff --git a/datasets/GPP_MODIS_Alaska_Canada_2024_1.json b/datasets/GPP_MODIS_Alaska_Canada_2024_1.json index 84e045828b..a6f952a7a7 100644 --- a/datasets/GPP_MODIS_Alaska_Canada_2024_1.json +++ b/datasets/GPP_MODIS_Alaska_Canada_2024_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPP_MODIS_Alaska_Canada_2024_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.", "links": [ { diff --git a/datasets/GPP_surfaces_749_1.json b/datasets/GPP_surfaces_749_1.json index deb63361dd..12a06105b9 100644 --- a/datasets/GPP_surfaces_749_1.json +++ b/datasets/GPP_surfaces_749_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPP_surfaces_749_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.", "links": [ { diff --git a/datasets/GPROF_precip_716_1.json b/datasets/GPROF_precip_716_1.json index 1bfc43426c..6b75680ace 100644 --- a/datasets/GPROF_precip_716_1.json +++ b/datasets/GPROF_precip_716_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPROF_precip_716_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPROF 6.0 Pentads data set contains 5-day (pentad) averages of the GPROF 6.0 Gridded Orbits. The GPROF(Goddard Profiling Algorithm) data set contains a suite of 9 products providing instantaneous, gridded values of precipitation totals for each granule of the SSM/I (Special Sensor Microwave/Imager) data over the roughly 14-year period July 1987 through the present. Even though there have been at least two satellites for the entire period, sampling is sufficiently sparse that the data are averaged for pentads, then the pentads are smoothed with a 1-2-3-2-1 time-weighting. The last two pentads are unevenly weighted since the last (or last two) pentads in the average are not yet available. Consequently, the last two pentads must be recomputed when the next pentad becomes available.The data set prepared for SAFARI cover the years 1999, 2000, and 2001.The main refereed citations for the data set are Kummerow et al. (1996)and Olson et al. (1999)", "links": [ { diff --git a/datasets/GPR_MACCA_ANARE53_1.json b/datasets/GPR_MACCA_ANARE53_1.json index 6c0c5850f2..6905ee91a7 100644 --- a/datasets/GPR_MACCA_ANARE53_1.json +++ b/datasets/GPR_MACCA_ANARE53_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPR_MACCA_ANARE53_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPR data collected at Macquarie Island at three locations, at the station area, above the abandoned tip and around the ionosonde hut. The instrument used was Ramac GPR with 250 MHz antennas. The station data are positioned, the other two data sets are not, only description of location is available. Data are in .rad and .rd3 format.", "links": [ { diff --git a/datasets/GPR_miscellaneous_1.json b/datasets/GPR_miscellaneous_1.json index ce858e5695..57a1d794d8 100644 --- a/datasets/GPR_miscellaneous_1.json +++ b/datasets/GPR_miscellaneous_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPR_miscellaneous_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Old data collected by Jared Pettersson, and found by the Australian Antarctic Data Centre. Note, this metadata record has not been produced by the scientist in question, but by the Australian Antarctic Data Centre. Therefore we cannot guarantee its quality.\n\nThe information below has been copied from word documents found within the dataset:\n\nSummary of work done by Jared Pettersson on Casey - Old Casey road GPR survey.\n\n- Established goals of survey\no To image bedrock height and confirm if old Casey is situated on an island or a peninsula\no Gain as much information about the subsurface to try and give insights into the movement/location of road material placed in previous years\no Bring data together to make it useful for operations to make any decisions for alternative road options.\n\n- Reviewed work done by Eva Papp and extract any useful information from her work.\n- Locate all GPR lines and import into Gradix processing software.\n- Due to the inconsistency between surveyed length of GPR lines and that recorded by the GPR, length corrections had to be made.\n- Process GPR profiles for bedrock interface and internal layering (see paper for details)\n- Extract information from the profiles, ie depth to bedrock.\n- Convert these data points (distance along profile and depth to layer boundary) to a format for input to a GIS system.\n-Construct 3D model in Surfer for visualization to aid in interpretation and provide a simple way of displaying extracted data. Write sections in proposed paper, ie survey details, data processing and some interpretation.\n\nSummary of work done by Jared Pettersson on Loken Moraine GPR survey.\n\n- Established goals of survey\no To image bedrock height, basal debris layer, internal ice structure and any features that may allude to the moraines development\no Produce a paper on the use of GPR for investigating moraine structure/development\n\n- Reviewed work done by Eva Papp and extract any useful information from her work.\n- Locate all GPR lines and import into Gradix processing software.\n- Process GPR profiles for bedrock interface and internal layering (see paper for details)\n- Travelled to Newcastle to meet with Ian Goodwin to discuss the paper and work through data. Discussed future plans for the ITASE traverse, and ran through operation of GPR with Ian.\n- Write sections in proposed paper, ie survey details, data processing and some interpretation.\n- Produced preliminary graphics for paper (GPR profiles and location maps)\n\n\nSummary of work done by Jared Pettersson on the Wilkes GPR survey.\n\n- Goals of survey\no Establish if tip extents could be imaged by GPR as well as any possible volume calculations of the tip material\no Map any cultural features in the subsurface (buildings, barrels etc) to aid in future waste management plans.\no Integrate GPR data into a GIS system.\n\n- Reviewed work done by Eva Papp and extract any useful information from her work.\n- Locate all GPR lines and import into Gradix processing software.\n- Process GPR profiles for bedrock interface and internal layering (see paper for details)\n- Extract information from the profiles, ie lateral extents of tip/buildings and buried debris.\n- Convert these data points (distance along profile and depth to layer boundary) to a format suitable for input to a GIS system.\n- Write sections in proposed paper, ie survey details, data processing and some interpretation.", "links": [ { diff --git a/datasets/GPSROZPBLA_1.json b/datasets/GPSROZPBLA_1.json index 4b79833290..12085c5dfa 100644 --- a/datasets/GPSROZPBLA_1.json +++ b/datasets/GPSROZPBLA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPSROZPBLA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset has been superseded by version 2. It provides an annual average of a global planetary boundary layer (PBL) height climatology derived from the COSMIC/FORMOSAT-3 and TerraSAR-X Global Positioning System (GPS) radio occultation (RO) measurements from June 2006 to December 2015. \n\n\nThe COSMIC/FORMOSAT-3 mission consists of a six-satellite constellation launched in 2006. Each satellite carries the IGOR GPS receiver and is equipped with fore and aft looking antenna to track both setting and rising occultations. The constellation provides globally distributed measurements across different local times. The TerraSAR-X (TSX) is a X-band SAR imaging satellite with GPS RO being a secondary measurement. It also carries an IGOR receiver and has been collecting GPS RO measurements since 2011. The instrument tracks the L-band microwave signal broadcast by a GPS satellite in a limb-viewing geometry. The IGOR receivers on COSMIC and TSX are capable of tracking the GPS signals in open loop through the middle to lower troposphere, which is essential for obtaining data with high quality for PBL height estimation, especially at low latitudes. The refractivity profiles from COSMIC and TSX form the basis for this PBL height product.\n\n\n\nFor each occultation, the PBL height is calculated as the height where the vertical gradient of the refractivity (dN/dz) is minimum. This algorithm is designed to locate the height where a large vertical change in refractivity occurs, corresponding to the transition from the free troposphere to the PBL. More details can be found in Ao et al. (2012). Each PBL height is associated with a time (starting time of the occultation) and location (latitude and longitude of the tangent point at the minimum altitude). The PBL height data are then binned into 2 degree x 2 degree latitude/longitude regions and averaged to produce the mean and standard deviation values in the climatology products. The refractivity profile has a vertical resolution of about 200 m and represents an along path horizontal averaging of ~100 km. Thus, occultations with tangent points near the coast may represent averaging over both land and ocean and should be interpreted with care. \n\nThe refractivity gradient method used here is not the only method that can be used to estimate the PBL height. Other algorithms have been proposed, including looking at \"breakpoint\" instead of minimum gradient, wavelet covariance transform, and using variables like bending angles or specific humidity instead of refractivity. However, the basic principle is the same. The difference between the different algorithms is small where the PBL is well-defined, with a strong capping inversion.", "links": [ { diff --git a/datasets/GPSROZPBLA_2.json b/datasets/GPSROZPBLA_2.json index f9548f8857..74a98db32d 100644 --- a/datasets/GPSROZPBLA_2.json +++ b/datasets/GPSROZPBLA_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPSROZPBLA_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an annual average climatology of planetary boundary layer (PBL) height derived from COSMIC/FORMOSAT-3, TerraSAR-X, KOMPSAT-5, and PAZ Global Positioning System (GPS) radio occultation (RO) measurements. The COSMIC/FORMOSAT-3 mission consists of a six-satellite constellation launched in 2006. Each satellite carries an Integrated GPS Occultation Receiver (IGOR) GPS receiver and is equipped with fore and aft looking antenna to track both setting and rising occultations. The constellation provides globally distributed measurements across different local times. The instrument tracks the L-band microwave signal broadcast by a GPS satellite in a limb-viewing geometry. The IGOR receivers are capable of tracking the GPS signals in open loop through the middle to lower troposphere, which is essential for obtaining data with high quality for PBL height estimation, especially at low latitudes. The refractivity profiles form the basis for this PBL height product. For each occultation, the PBL height is calculated as the height where the vertical gradient of the refractivity (dN/dz) is minimum. This algorithm is designed to locate the height where a large vertical change in refractivity occurs, corresponding to the transition from the free troposphere to the PBL. More details can be found in Ao et al. (2012). This is the latest version of this collection which supercedes previous versions.", "links": [ { diff --git a/datasets/GPSROZPBLS_1.json b/datasets/GPSROZPBLS_1.json index e9718fadc3..a79d0a3c06 100644 --- a/datasets/GPSROZPBLS_1.json +++ b/datasets/GPSROZPBLS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPSROZPBLS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset has been superseded by version 2. It provides seasonal averages of a global planetary boundary layer (PBL) height climatology derived from the COSMIC/FORMOSAT-3 and TerraSAR-X Global Positioning System (GPS) radio occultation (RO) measurements from June 2006 to December 2015. \n\n\nThe COSMIC/FORMOSAT-3 mission consists of a six-satellite constellation launched in 2006. Each satellite carries the IGOR GPS receiver and is equipped with fore and aft looking antenna to track both setting and rising occultations. The constellation provides globally distributed measurements across different local times. The TerraSAR-X (TSX) is a X-band SAR imaging satellite with GPS RO being a secondary measurement. It also carries an IGOR receiver and has been collecting GPS RO measurements since 2011. The instrument tracks the L-band microwave signal broadcast by a GPS satellite in a limb-viewing geometry. The IGOR receivers on COSMIC and TSX are capable of tracking the GPS signals in open loop through the middle to lower troposphere, which is essential for obtaining data with high quality for PBL height estimation, especially at low latitudes. The refractivity profiles from COSMIC and TSX form the basis for this PBL height product.\n\nFor each occultation, the PBL height is calculated as the height where the vertical gradient of the refractivity (dN/dz) is minimum. This algorithm is designed to locate the height where a large vertical change in refractivity occurs, corresponding to the transition from the free troposphere to the PBL. More details can be found in Ao et al. (2012). Each PBL height is associated with a time (starting time of the occultation) and location (latitude and longitude of the tangent point at the minimum altitude). The PBL height data are then binned into 2 degree x 2 degree latitude/longitude regions and averaged to produce the mean and standard deviation values in the climatology products. The refractivity profile has a vertical resolution of about 200 m and represents an along path horizontal averaging of ~100 km. Thus, occultations with tangent points near the coast may represent averaging over both land and ocean and should be interpreted with care. \n\nThe refractivity gradient method used here is not the only method that can be used to estimate the PBL height. Other algorithms have been proposed, including looking at \"breakpoint\" instead of minimum gradient, wavelet covariance transform, and using variables like bending angles or specific humidity instead of refractivity. However, the basic principle is the same. The difference between the different algorithms is small where the PBL is well-defined, with a strong capping inversion.", "links": [ { diff --git a/datasets/GPSROZPBLS_2.json b/datasets/GPSROZPBLS_2.json index 8bb2925086..c27ef187ff 100644 --- a/datasets/GPSROZPBLS_2.json +++ b/datasets/GPSROZPBLS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GPSROZPBLS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a seasonal average climatology of global planetary boundary layer (PBL) height derived from COSMIC/FORMOSAT-3, TerraSAR-X, KOMPSAT-5, and PAZ Global Positioning System (GPS) radio occultation (RO) measurements. The COSMIC/FORMOSAT-3 mission consists of a six-satellite constellation launched in 2006. Each satellite carries an Integrated GPS Occultation Receiver (IGOR) GPS receiver and is equipped with fore and aft looking antenna to track both setting and rising occultations. The constellation provides globally distributed measurements across different local times. The instrument tracks the L-band microwave signal broadcast by a GPS satellite in a limb-viewing geometry. The IGOR receivers are capable of tracking the GPS signals in open loop through the middle to lower troposphere, which is essential for obtaining data with high quality for PBL height estimation, especially at low latitudes. The refractivity profiles form the basis for this PBL height product. For each occultation, the PBL height is calculated as the height where the vertical gradient of the refractivity (dN/dz) is minimum. This algorithm is designed to locate the height where a large vertical change in refractivity occurs, corresponding to the transition from the free troposphere to the PBL. More details can be found in Ao et al. (2012). This is the latest version of this collection which supersedes previous versions.", "links": [ { diff --git a/datasets/GP_Bibliography_1.json b/datasets/GP_Bibliography_1.json index f06ef957a1..6ce009af5a 100644 --- a/datasets/GP_Bibliography_1.json +++ b/datasets/GP_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GP_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Giant Petrels Bibliography compiled by Donna Patterson of the SCAR Bird Biology Subgroup, contains 113 records.\n\nThe fields in this dataset are:\nyear\nauthor\ntitle\njournal\npetrel", "links": [ { diff --git a/datasets/GRACE-A.and.GRACE-B.Level1B.Level1Bcombined.Level2_4.0.json b/datasets/GRACE-A.and.GRACE-B.Level1B.Level1Bcombined.Level2_4.0.json index 8b0c26514d..d6f303b9db 100644 --- a/datasets/GRACE-A.and.GRACE-B.Level1B.Level1Bcombined.Level2_4.0.json +++ b/datasets/GRACE-A.and.GRACE-B.Level1B.Level1Bcombined.Level2_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE-A.and.GRACE-B.Level1B.Level1Bcombined.Level2_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1A Data Products are the result of a non-destructive processing applied to the Level-0 data at NASA/JPL. The sensor calibration factors are applied in order to convert the binary encoded measurements to engineering units. Where necessary, time tag integer second ambiguity is resolved and data are time tagged to the respective satellite receiver clock time. Editing and quality control flags are added, and the data is reformatted for further processing. The Level-1A data are reversible to Level-0, except for the bad data packets. This level also includes the ancillary data products needed for processing to the next data level. The Level-1B Data Products are the result of a possibly destructive, or irreversible, processing applied to both the Level-1A and Level-0 data at NASA/JPL. The data are correctly time-tagged, and data sample rate is reduced from the higher rates of the previous levels. Collectively, the processing from Level-0 to Level-1B is called the Level-1 Processing. This level also includes the ancillary data products generated during this processing, and the additional data needed for further processing. The Level-2 data products include the static and time-variable (monthly) gravity field and related data products derived from the application of Level-2 processing at GFZ, UTCSR and JPL to the previous level data products. This level also includes the ancillary data products such as GFZ's Level-1B short-term atmosphere and ocean de-aliasing product (AOD1B) generated during this processing. GRACE-A and GRACE-B Level-1B Data Product \u2022 Satellite clock solution [GA-OG-1B-CLKDAT, GB-OG-1B-CLKDAT, GRACE CLKDAT]: Offset of the satellite receiver clock relative to GPS time, obtained by linear fit to raw on-board clock offset estimates. \u2022 GPS flight data [GA-OG-1B-GPSDAT, GB-OG-1B-GPSDAT, GRACE GPSDAT]: Preprocessed and calibrated GPS code and phase tracking data edited and decimated from instrument high-rate (10 s (code) or 1 s (phase)) to low-rate (10 s) samples for science use (1 file per day, level-1 format) \u2022 Accelerometer Housekeeping data [GA-OG-1B-ACCHKP, GB-OG-1B-ACCHKP, GRACE ACCHKP]: Accelerometer proof-mass bias voltages, capacitive sensor outputs, instrument control unit (ICU) and sensor unit (SU) temperatures, reference voltages, primary and secondary power supply voltages (1 file per day, level-1 format). \u2022 Accelerometer data [GA-OG-1B-ACCDAT, GB-OG-1B-ACCDAT, GRACE ACCDAT]: Preprocessed and calibrated Level-1B accelerometer data edited and decimated from instrument high-rate (0.1 s) to low-rate (1s) samples for science use (1 file per day, level-1 format). \u2022 Intermediate clock solution [GA-OG-1B-INTCLK, GB-OG-1B-INTCLK, GRACE INTCLK]: derived with GIPSY POD software (300 s sample rate) (1 file per day, GIPSY format) \u2022 Instrument processing unit (IPU) Housekeeping data [GA-OG-1B-IPUHKP, GB-OG-1B-IPUHKP, GRACE IPUHKP]: edited and decimated from high-rate (TBD s) to low-rate (TBD s) samples for science use (1 file per day, level-1 format) \u2022 Spacecraft Mass Housekeeping data [GA-OG-1B-MASDAT, GB-OG-1B-MASDAT, GRACE MASDAT]: Level 1B Data as a function of time \u2022 GPS navigation solution data [GA-OG-1B-NAVSOL, GB-OG-1B-NAVSOL, GRACE NAVSOL]: edited and decimated from instrument high-rate (60 s) to low-rate (30 s) samples for science use (1 file per day, level-1 format) \u2022 OBDH time mapping to GPS time Housekeeping data [GA-OG-1B-OBDHTM, GB-OG-1B-OBDHTM, GRACE OBDHTM]: On-board data handling (OBDH) time mapping data (OBDH time to receiver time \u2022 Star camera data [GA-OG-1B-SCAATT, GB-OG-1B-SCAATT, GRACE SCAATT]: Preprocessed and calibrated star camera quaternion data edited and decimated from instrument high-rate (1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) \u2022 Thruster activation Housekeeping data [GA-OG-1B-THRDAT, GB-OG-1B-THRDAT, GRACE THRDAT]: GN2 thruster data used for attitude (10 mN) and orbit (40 mN) control \u2022 GN2 tank temperature and pressure Housekeeping data [GA-OG-1B-TNKDAT, GB-OG-1B-TNKDAT, GRACE TNKDAT]: GN2 tank temperature and pressure data \u2022 Oscillator frequency data [GA-OG-1B-USODAT, GB-OG-1B-USODAT, GRACE USODAT]: derived from POD productGRACE-A and GRACE-B Combined Level-1B Data Product \u2022 Preprocessed and calibrated k-band ranging data [GA-OG-1B-KBRDAT, GB-OG-1B-KBRDAT, GRACE KBRDAT]: range, range-rate and range-acceleration data edited and decimated from instrument high-rate (0.1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) \u2022 Atmosphere and Ocean De-aliasing Product [GA-OG-1B-ATMOCN, GB-OG-1B-ATMOCN, GRACE ATMOCN]: GRACE Atmosphere and Ocean De-aliasing Product GRACE Level-2 Data Product \u2022 GAC [GA-OG-_2-GAC, GB-OG-_2-GAC, GRACE GAC]: Combination of non-tidal atmosphere and ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) \u2022 GCM [GA-OG-_2-GCM, GB-OG-_2-GCM, GRACE GCM]: Spherical harmonic coefficients and standard deviations of the long-term static gravity field estimated by combination of GRACE satellite instrument data and other information for a dedicated time span (multiple years) and spatial resolution (1 file per time span, level-2 format) \u2022 GAB [GA-OG-_2-GAB, GB-OG-_2-GAB, GRACE GAB]: Non-tidal ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) \u2022 GAD [GA-OG-_2-GAD, GB-OG-_2-GAD, GRACE GAD]: bottom pressure product - combination of surface pressure and ocean (over the oceans, and zero over land). Spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) \u2022 GSM [GA-OG-_2-GSM, GB-OG-_2-GSM, GRACE GSM]: Spherical harmonic coefficients and standard deviations of the static gravity field estimated from GRACE satellite instrument data only for a dedicated time span (e.g. weekly, monthly, multiple years) and spatial resolution (1 file per time span, level-2 format).", "links": [ { diff --git a/datasets/GRACEDADM_CLSM0125US_7D_4.0.json b/datasets/GRACEDADM_CLSM0125US_7D_4.0.json index 35c44ea776..22a472a2b0 100644 --- a/datasets/GRACEDADM_CLSM0125US_7D_4.0.json +++ b/datasets/GRACEDADM_CLSM0125US_7D_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEDADM_CLSM0125US_7D_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.\n\nThis data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) U.S. Version 4.0 data product and supersedes the GRACE-DA-DM Version 2.0. \n\nThe GRACE-DA-DM U.S. V4.0 is based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). This simulation used the latest GRACE RL06 (GRACE; 2002-2017) and GRACE Follow On (GRACE-FO; 2018-present) Mascon solutions version 2, at 0.25 degree resolution, from the University of Texas at Austin (Save et al., 2016; Save, 2020). The CLSM soil parameters were updated to address a soil moisture dry limit issue found near Zapata, Texas. Because the root zone soil moisture frequently reaches the dry limit there, drought conditions are often \u201cnormal\u201d when the area should be in drought. The new soil parameters resolved the issue, and the root zone soil moisture now matches closely the in-situ observation near Zapata. In the data assimilation, the baseline for Terrestrial Water Storage anomaly computation was updated to the 2003-2019 mean, whereas previous simulations used the 2003-2016 mean. The percentile computation was switched to a 7-day moving average climatology, instead of monthly, to improve the temporal transition of drought/wetness conditions.\n\nThe GRACE-DA-DM V1.0 was created by the stand alone CLSM (an older version) using the GRACE-Tellus 1 degree data from the Center for Space Research at University of Texas. The GRACE data assimilation (DA) is executed on a grid-to-grid basis in V2.0, while a basin scale average was used in V1.0 (Zaitchik et al. 2008). The V2.0 data were reprocessed (on June 14, 2017), using the GRACE RL05 Mascon solutions version 1 data set from UT CSR, for the entire period from April 1, 2002 to June 5, 2017. The reprocessing included fixes in the DA and increased the bedrock depth by 3 meters to enhance the drought indicator calculations. \n\nThe GRACE-DA-DM U.S. V4.0 uses the same configuration as the V2.0 for the DA scheme and increased bedrock depth, with the updates previously mentioned, thus supersedes the previous versions. \n\nThe GRACE-DA-DM U.S. V4.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.125 x 0.125 degree over North America and range from April 1, 2002 to present (with a 3-6 months latency). The data are archived in NetCDF format.", "links": [ { diff --git a/datasets/GRACEDADM_CLSM025GL_7D_3.0.json b/datasets/GRACEDADM_CLSM025GL_7D_3.0.json index 7f660c6830..017e0bf099 100644 --- a/datasets/GRACEDADM_CLSM025GL_7D_3.0.json +++ b/datasets/GRACEDADM_CLSM025GL_7D_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEDADM_CLSM025GL_7D_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.\n\nThis data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) Global Version 3.0 from a global GRACE and GRACE-FO data assimilation and drought indicator product generation (Li et al., 2019). It varies from the other GRACE-DA-DM products which are from the U.S. GRACE-based drought indicator product generation (Houborg et al., 2012).\nThe GRACE-DA-DM Global V3.0 is similar to the GRACE-DA-DM U.S. V4.0 product. Both products are based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). GRACE-DA-DM Global V3.0 drought indicator maps are derived from the GLDAS_CLSM025_DA1_D product, at 0.25 degree resolution, forced by ECMWF meteorological data, and assimilated RL06 GRACE and GRACE-FO data from the University of Texas at Austin (Save et al., 2016; Save, 2020). The GRACE-DA-DM U.S. V4.0 is at 0.125 degree, which is based on a model simulation (not published at GES DISC) forced by NLDAS-2 meteorological data and assimilated with RL06 GRACE/GRACE-FO data. \nMore information on GRACE-DA-DM U.S. V4.0 and previous versions of the data can be found in the README.\n\nThe GRACE-DA-DM Global V3.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.25 x 0.25 degree with global coverage (60S, 180W, 90N, 180E), and a temporal range from February 2003 to present (with a 3-6 month latency). The data are archived in NetCDF format.\n\nThe GRACE-DA-DM is an operational project which produces groundwater and soil moisture drought indicators each week. The operational data is available weekly with a 2-9 day latency from the NASA GRACE project home page found under the Documentation tab. The GRACE-DA-DM data distributed here at GESDISC is the final archive version, which is generated after the latest GRACE-FO data are available.", "links": [ { diff --git a/datasets/GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4.json b/datasets/GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4.json index 91374cc433..9e18ded718 100644 --- a/datasets/GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4.json +++ b/datasets/GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. The GRACE-FO Level-1A data contains telemetry data that has been converted to engineering units, from which Level-1B data products are derived. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).", "links": [ { diff --git a/datasets/GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4.json b/datasets/GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4.json index 289eadfeb2..8ba84c3ac8 100644 --- a/datasets/GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4.json +++ b/datasets/GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. The GRACE-FO Level-1B data provide all necessary inputs to derive monthly time variations in the Earth gravity field. Level-1B data are also used for GRACE orbit and mean gravity field determination. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).", "links": [ { diff --git a/datasets/GRACEFO_L2_CSR_MONTHLY_0063_6.3.json b/datasets/GRACEFO_L2_CSR_MONTHLY_0063_6.3.json index f6468ccedf..e6495801c2 100644 --- a/datasets/GRACEFO_L2_CSR_MONTHLY_0063_6.3.json +++ b/datasets/GRACEFO_L2_CSR_MONTHLY_0063_6.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEFO_L2_CSR_MONTHLY_0063_6.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas (at Austin) Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission. \n

\nThis GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.", "links": [ { diff --git a/datasets/GRACEFO_L2_GFZ_MONTHLY_0063_6.3.json b/datasets/GRACEFO_L2_GFZ_MONTHLY_0063_6.3.json index 4dce81bde0..c8beefa56e 100644 --- a/datasets/GRACEFO_L2_GFZ_MONTHLY_0063_6.3.json +++ b/datasets/GRACEFO_L2_GFZ_MONTHLY_0063_6.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEFO_L2_GFZ_MONTHLY_0063_6.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. Refer to the mission page for more information. \n

\nThis GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-deadband operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.", "links": [ { diff --git a/datasets/GRACEFO_L2_JPL_MONTHLY_0063_6.3.json b/datasets/GRACEFO_L2_JPL_MONTHLY_0063_6.3.json index 3a29ad81ec..5dceb72c0f 100644 --- a/datasets/GRACEFO_L2_JPL_MONTHLY_0063_6.3.json +++ b/datasets/GRACEFO_L2_JPL_MONTHLY_0063_6.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACEFO_L2_JPL_MONTHLY_0063_6.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission. \n

\nThis GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.", "links": [ { diff --git a/datasets/GRACE_ABPR_FO_L2_V1.0_1.0.json b/datasets/GRACE_ABPR_FO_L2_V1.0_1.0.json index 4f53d6b25c..63e41c4950 100644 --- a/datasets/GRACE_ABPR_FO_L2_V1.0_1.0.json +++ b/datasets/GRACE_ABPR_FO_L2_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_ABPR_FO_L2_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the first year of hourly ocean bottom pressure measurements at the North Pole from the Arctic Bottom Pressure Recorder - Follow On (ABPR-FO) deployed in August 2022. Based around a Paroscientific Digiquartz pressure sensor, the ABPR-FO is designed and powered to collect and transmit data for 5 years. The 5-year record will be collected, quality-controlled and updated at PO.DAAC on a yearly basis. The full time series is provided as a single netCDF file, and reports both bottom pressure (reported as liquid water equivalent thickness) and temperature. This dataset aims to serve as in situ validation for GRACE-FO data products in the central Arctic. This project is funded by NASA\u2019s GRACE and GRACE-FO Science Team and supported by PONANT Science. ", "links": [ { diff --git a/datasets/GRACE_AOD1B_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_AOD1B_GRAV_GFZ_RL06_6.0.json index 958fd3480a..330ac340de 100644 --- a/datasets/GRACE_AOD1B_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_AOD1B_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_AOD1B_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRACE Atmosphere and Ocean De-aliasing dataset contains spherical harmonic coefficients of combined barotropic or baroclinic sea level and vertical integrated pressure variations at 6-hour sample rate. It is used as a correction product for the Level 2 GRACE datasets.", "links": [ { diff --git a/datasets/GRACE_GAA_L2_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_GAA_L2_GRAV_GFZ_RL06_6.0.json index 2fa6e2a9e7..976e9f3d12 100644 --- a/datasets/GRACE_GAA_L2_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_GAA_L2_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAA_L2_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAA_L2_GRAV_JPL_RL06_6.0.json b/datasets/GRACE_GAA_L2_GRAV_JPL_RL06_6.0.json index 698ca45660..e6987bf78a 100644 --- a/datasets/GRACE_GAA_L2_GRAV_JPL_RL06_6.0.json +++ b/datasets/GRACE_GAA_L2_GRAV_JPL_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAA_L2_GRAV_JPL_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAB_L2_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_GAB_L2_GRAV_GFZ_RL06_6.0.json index 88e1bff0c5..f926e31767 100644 --- a/datasets/GRACE_GAB_L2_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_GAB_L2_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAB_L2_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAB_L2_GRAV_JPL_RL06_6.0.json b/datasets/GRACE_GAB_L2_GRAV_JPL_RL06_6.0.json index 14b0f6cfc9..fe964c1500 100644 --- a/datasets/GRACE_GAB_L2_GRAV_JPL_RL06_6.0.json +++ b/datasets/GRACE_GAB_L2_GRAV_JPL_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAB_L2_GRAV_JPL_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAC_L2_GRAV_CSR_RL06_6.0.json b/datasets/GRACE_GAC_L2_GRAV_CSR_RL06_6.0.json index 7df07b01f9..82bd66563c 100644 --- a/datasets/GRACE_GAC_L2_GRAV_CSR_RL06_6.0.json +++ b/datasets/GRACE_GAC_L2_GRAV_CSR_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAC_L2_GRAV_CSR_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAC_L2_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_GAC_L2_GRAV_GFZ_RL06_6.0.json index 0a1c1d0ed7..530d8ed86f 100644 --- a/datasets/GRACE_GAC_L2_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_GAC_L2_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAC_L2_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAC_L2_GRAV_JPL_RL06_6.0.json b/datasets/GRACE_GAC_L2_GRAV_JPL_RL06_6.0.json index 613cd9558d..6c263586d5 100644 --- a/datasets/GRACE_GAC_L2_GRAV_JPL_RL06_6.0.json +++ b/datasets/GRACE_GAC_L2_GRAV_JPL_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAC_L2_GRAV_JPL_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json b/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json index d03c88c33f..b395d25bf9 100644 --- a/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json +++ b/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAD_L2_GRAV_CSR_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAD_L2_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_GAD_L2_GRAV_GFZ_RL06_6.0.json index b3a949ea7b..5ea0befed1 100644 --- a/datasets/GRACE_GAD_L2_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_GAD_L2_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAD_L2_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GAD_L2_GRAV_JPL_RL06_6.0.json b/datasets/GRACE_GAD_L2_GRAV_JPL_RL06_6.0.json index 1eebc4ff8d..13dbbd4c5b 100644 --- a/datasets/GRACE_GAD_L2_GRAV_JPL_RL06_6.0.json +++ b/datasets/GRACE_GAD_L2_GRAV_JPL_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GAD_L2_GRAV_JPL_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GSM_L2_GRAV_CSR_RL06_6.0.json b/datasets/GRACE_GSM_L2_GRAV_CSR_RL06_6.0.json index b83465f02a..2bf48e0dbe 100644 --- a/datasets/GRACE_GSM_L2_GRAV_CSR_RL06_6.0.json +++ b/datasets/GRACE_GSM_L2_GRAV_CSR_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GSM_L2_GRAV_CSR_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GSM_L2_GRAV_GFZ_RL06_6.0.json b/datasets/GRACE_GSM_L2_GRAV_GFZ_RL06_6.0.json index 3978b37a88..d61167866d 100644 --- a/datasets/GRACE_GSM_L2_GRAV_GFZ_RL06_6.0.json +++ b/datasets/GRACE_GSM_L2_GRAV_GFZ_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GSM_L2_GRAV_GFZ_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_GSM_L2_GRAV_JPL_RL06_6.0.json b/datasets/GRACE_GSM_L2_GRAV_JPL_RL06_6.0.json index 49213a375f..259fd8e854 100644 --- a/datasets/GRACE_GSM_L2_GRAV_JPL_RL06_6.0.json +++ b/datasets/GRACE_GSM_L2_GRAV_JPL_RL06_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_GSM_L2_GRAV_JPL_RL06_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.", "links": [ { diff --git a/datasets/GRACE_L1B_GRAV_JPL_RL02_2.json b/datasets/GRACE_L1B_GRAV_JPL_RL02_2.json index ed72c121eb..39648d32a5 100644 --- a/datasets/GRACE_L1B_GRAV_JPL_RL02_2.json +++ b/datasets/GRACE_L1B_GRAV_JPL_RL02_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_L1B_GRAV_JPL_RL02_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)", "links": [ { diff --git a/datasets/GRACE_L1B_GRAV_JPL_RL03_3.json b/datasets/GRACE_L1B_GRAV_JPL_RL03_3.json index 81da10523b..69751765ca 100644 --- a/datasets/GRACE_L1B_GRAV_JPL_RL03_3.json +++ b/datasets/GRACE_L1B_GRAV_JPL_RL03_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRACE_L1B_GRAV_JPL_RL03_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)The GRACE Level-1B RL03 data consists only of updated spacecraft attitude (SCA1B) and K-band inter-satellite ranging (KBR1B) data. All other Level-1B were not changed and it is recommended to use the RL02 products with the updated RL03 KBR1B and SCA1B products. The RL03 SCA1B data were corrected for a stellar aberration error in the onboard star tracker software and incorrect data weighting in the star tracker combination software. For the RL03 SCA1B data a new software module was developed that uses Kalman filtering, field of view error modeling, relative alignment adjustment and the inclusion of angular spacecraft body acceleration measurements from the ACC instrument. This new processing resulted in a significant reduction in high frequency noise and the elimination of jumps during transitions between dual and single star tracker operation. The KBR1B product is updated as well because the KBR antenna phase center range correction, range rate correction and range acceleration are computed using the spacecraft attitude information (SCA1B). Only these three correction values were updated in the KBR1B product. All other entries in the KBR1B remained the same.", "links": [ { diff --git a/datasets/GRAVCD-npra.json b/datasets/GRAVCD-npra.json index 940f618cc3..4a54b2d49c 100644 --- a/datasets/GRAVCD-npra.json +++ b/datasets/GRAVCD-npra.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRAVCD-npra", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of a 2 CD-ROM set from NOAA's National Geophysical Data Center entitled Land and Marine Gravity Data - 1999 Edition.\n\nThe gravity station data (53,520 records) were gathered by various governmental organizations (and academia) using a variety of methods. This data base was received in November 1980. Principal gravity parameters include Free-air Anomalies and Simple Bouguer Anomalies (no terrain correction applied). The observed gravity values are referenced to the International Gravity Standardization Net 1971 (IGSN 71). The gravity anomaly computation uses the Geodetic Reference System 1967 (GRS 67) theoretical gravity formula. The data are randomly distributed within the boundaries of the National Petroleum Reserve-Alaska (NPRA).", "links": [ { diff --git a/datasets/GRAVITY_LD_WL_1967_1986_CSV_1.json b/datasets/GRAVITY_LD_WL_1967_1986_CSV_1.json index 376d1786f7..f46733028e 100644 --- a/datasets/GRAVITY_LD_WL_1967_1986_CSV_1.json +++ b/datasets/GRAVITY_LD_WL_1967_1986_CSV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRAVITY_LD_WL_1967_1986_CSV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity data collected from the Australian Antarctic Territory and subantarctic between 1967 and 1986. Data are mostly from the Casey region.\n\nThe download file contains a large number of csv files, as well as a number of explanatory documents.", "links": [ { diff --git a/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3.json b/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3.json index 3cb1896be1..db53a91deb 100644 --- a/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3.json +++ b/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model.\n

\nThe Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models).", "links": [ { diff --git a/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3.json b/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3.json index f129995df9..a9b8ba6366 100644 --- a/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3.json +++ b/datasets/GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model.\n

\nThe Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models).", "links": [ { diff --git a/datasets/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json b/datasets/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json index b69ab04591..3f1fda5e0a 100644 --- a/datasets/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json +++ b/datasets/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.", "links": [ { diff --git a/datasets/GRID-INPE.json b/datasets/GRID-INPE.json index 5d4ca7d8a9..9ad526b638 100644 --- a/datasets/GRID-INPE.json +++ b/datasets/GRID-INPE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GRID-INPE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a collection of data-sets held by GRID-INPE. Please contact\n the technical contact for further details and data-set breakdown.\n \n GRID-INPE is a cooperating center to UNEP's Global Resource\n Information Database. Grid is a system of cooperating centers within\n the United Nations Environmental Programme that is dedicated to making\n environmental information more readily accessible to environmental\n analysis as well as to international and national decision makers. Its\n mission is to provide timely and reliable geo-referenced environmental\n information. Besides acquiring and disseminating integrated,\n spatially-referenced environmental data, GRID provides\n decision-support services to environmental analysts and international\n and national decision makers, and fosters the use of geographic\n information systems (GIS) and satellite image processing (IP) as tools\n for environmental analysis.", "links": [ { diff --git a/datasets/GSI_ABSOLUT_GRAVITY_ANT.json b/datasets/GSI_ABSOLUT_GRAVITY_ANT.json index 96804ddb40..7ecfcd7b35 100644 --- a/datasets/GSI_ABSOLUT_GRAVITY_ANT.json +++ b/datasets/GSI_ABSOLUT_GRAVITY_ANT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSI_ABSOLUT_GRAVITY_ANT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IAGBN aims to distribute gravity points worldwide and construct a network on which gravity observation is based. There are two kinds of points: A is a point set up in regions with stable crustal structure, and B is a point set up in regions where crustal activity is expected. Syowa Station in Antarctica was among the 36 A points. McMurdo Station of the U.S. is the only point in Antarctica other than Syowa Station that is classified as A. Introduced GSI in 1980, the upcast-type absolute gravity meter (GA60) generally called the Sakuma type, was used in this survey. The 36th JARE (1994) conducted observation using FG5 that the GSI introduced in 1992. Because FG5 measures gravity in a free-fall system, it is characterized by the ability to conduct automatic continuous measurement and allow for many measurements.", "links": [ { diff --git a/datasets/GSI_JARE_TOPOMAPS.json b/datasets/GSI_JARE_TOPOMAPS.json index 536b6addc7..843b8aeb1d 100644 --- a/datasets/GSI_JARE_TOPOMAPS.json +++ b/datasets/GSI_JARE_TOPOMAPS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSI_JARE_TOPOMAPS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of 1:50,000 topographic maps which cover most areas of the Sor-Rondane Mountains, with 21 sheets. The contour interval is 20 m. All maps have been digitalized into raster data and are available in TIFF format.", "links": [ { diff --git a/datasets/GSJ-DAM.json b/datasets/GSJ-DAM.json index 23dfd6a6f3..3edf879086 100644 --- a/datasets/GSJ-DAM.json +++ b/datasets/GSJ-DAM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSJ-DAM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geological Survey of Japan has carried out developments on the\nexploration and analysis techniques in aeromagnetic survey since 1964,\nwhen the research on aeromagnetic exploration was begun on full\nscale. And since 1969, explorations for various purposes as well as\ninvestigations for assessing the deposit of hydrocarbon resources in\nthe continental shelf area surrounding Japan have been carried\nout. The results were already published as the Aerial Aeromagnetic Map\nseries, and the data were stored in magnetic media in the form of file\ngroups with unified formats.", "links": [ { diff --git a/datasets/GSMNP_Vegetation_Structure_R1_1286_1.2.json b/datasets/GSMNP_Vegetation_Structure_R1_1286_1.2.json index 488defa67a..32e63f6cb4 100644 --- a/datasets/GSMNP_Vegetation_Structure_R1_1286_1.2.json +++ b/datasets/GSMNP_Vegetation_Structure_R1_1286_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSMNP_Vegetation_Structure_R1_1286_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides multiple-return LiDAR-derived vegetation canopy structure at 30-meter spatial resolution for the Great Smoky Mountains National Park (GSMNP). Canopy characteristics were analyzed using high resolution three-dimensional point cloud measurements gathered between February-April 2011 for Tennessee and during March-April 2005 for North Carolina sections of the park. Vegetation types were mapped by grouping areas of similar canopy structure. The map was compared and validated against existing vegetation maps for the park.", "links": [ { diff --git a/datasets/GSMaP_Hourly_NA.json b/datasets/GSMaP_Hourly_NA.json index 01653e8293..3e91838d59 100644 --- a/datasets/GSMaP_Hourly_NA.json +++ b/datasets/GSMaP_Hourly_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSMaP_Hourly_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GSMaP Hourly dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM), other GPM constellation satellites, and Geostationary satellites produced by the Japan Aerospace Exploration Agency (JAXA).The GSMaP is generated based on a multi-satellite algorithm under the GPM mission, and the accuracy has been improved by DPR data and information. It offers a map of global precipitation by combining: estimated precipitation based on multiple microwave radiometers (imager/sounder) and cloud moving information obtained from geostationary infrared (IR) data.The GSMaP algorithm can be roughly divided into the following three algorithms: microwave imager (MWI) algorithm, microwave sounder (MWS) algorithm, and microwave-Infrared (IR) combined (MVK) algorithm. A global satellite mapping of precipitation can be subject to standard processing or near real-time processing.In standard processing, hourly observation data is processed then the data is averaged monthly. Near real-time processing provides a higher data frequency than standard processing (every hour). The provided formats are HDF5, text, GeoTIFF and NetCDF. The Sampling resolution is 0.1 degree grid. The projection method is EQR.The statistical period is 1 hourly. The current version of the product is Version 5. The Version 4 is also available. The generation unit is global.", "links": [ { diff --git a/datasets/GSMaP_Monthly_NA.json b/datasets/GSMaP_Monthly_NA.json index 604ee1e18a..512b465a9b 100644 --- a/datasets/GSMaP_Monthly_NA.json +++ b/datasets/GSMaP_Monthly_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSMaP_Monthly_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GSMaP Monthly dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM), other GPM constellation satellites, and Geostationary satellites produced by the Japan Aerospace Exploration Agency (JAXA). The GSMaP is generated based on a multi-satellite algorithm under the GPM mission, and the accuracy has been improved by DPR data and information. It offers a map of global precipitation by combining: estimated precipitation based on multiple microwave radiometers (imager/sounder) and cloud moving information obtained from geostationary infrared (IR) data.The GSMaP algorithm can be roughly divided into the following three algorithms: microwave imager (MWI) algorithm, microwave sounder (MWS) algorithm, and microwave-Infrared (IR) combined (MVK) algorithm. A global satellite mapping of precipitation can be subject to standard processing or near real-time processing. In standard processing, hourly observation data is processed and data is averaged monthly. Near real-time processing provides a higher data frequency than standard processing (every hour).The provided format is HDF5, GeoTIFF and NetCDF. The Sampling resolution is 0.1degree grid. The projection method is EQR. The statistical period is 1 monthly. The current version of the product is Version 5. The Version 4 is also available. The generation unit is global.", "links": [ { diff --git a/datasets/GSSTFMC_2c.json b/datasets/GSSTFMC_2c.json index 6b66eeb6d4..d9ba2ea4f0 100644 --- a/datasets/GSSTFMC_2c.json +++ b/datasets/GSSTFMC_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFMC_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nGSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). However, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the \"artifactitious\" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. \n\nGSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful \"Rice Cooker Theory\" by Shie (2010a,b), i.e., \"To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality\" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c. \n\nThis is the Monthly Climatology product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. Starting with Version 2c, there is only one set of Combined data, \"Set1\". The Monthly Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\". A finer resolution, 0.25 deg, of this product has been released as Version 3.\n \nThe monthly temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans.\n\nThe short name for this product is GSSTFMC.\n", "links": [ { diff --git a/datasets/GSSTFMC_3.json b/datasets/GSSTFMC_3.json index 9eab5c1432..0473f3fbc8 100644 --- a/datasets/GSSTFMC_3.json +++ b/datasets/GSSTFMC_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFMC_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is the fine resolution version of the previously released GSSTFMC.2c. \n\nThis is the Monthly Climatology product; data are projected to equidistant Grid that covers the globe at 0.25x00.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nStarting from previous Version 2c, these data have only one set of Combined data, \"Set1\". The Monthly Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\". \n\nStarting with this Version 3, the \"WB\" variable, \"lowest 500-m precipitable water\" has been discontinued. \n \nThe short name for this product is GSSTFMC.\n", "links": [ { diff --git a/datasets/GSSTFM_2c.json b/datasets/GSSTFM_2c.json index ebf2c723d8..87aee6727d 100644 --- a/datasets/GSSTFM_2c.json +++ b/datasets/GSSTFM_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFM_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nGSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). However, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the \"artifactitious\" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. \n\nGSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful \"Rice Cooker Theory\" by Shie (2010a,b), i.e., \"To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality\" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c.\n \n This is the Monthly product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. The monthly product is a result of averaging of a month worth of daily GSSTF2c files. Starting with Version 2c, there is only one set of Combined data, \"Set1\". A finer resolution, 0.25 deg, of this product has been released as Version 3.\n\n The monthly temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans.\n\n The short name for this product is GSSTFM.\n", "links": [ { diff --git a/datasets/GSSTFM_3.json b/datasets/GSSTFM_3.json index 543f5aa9a9..a4d085f2a9 100644 --- a/datasets/GSSTFM_3.json +++ b/datasets/GSSTFM_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFM_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is the fine resolution version of the previously released GSSTFM.2c. \n\nThis is the Monthly product; data are projected to equidistant Grid that covers the globe at 0.25x00.25 degree cell size, resulting in data arrays of 1440x720 size. The monthly product is a result of averaging of a month worth of daily GSSTF3 files. Starting with Version 3, the \"WB\" variable, \"lowest 500-m precipitable water\" has been discontinued. \n\nThe monthly temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans.\n\n The short name for this product is GSSTFM.\n", "links": [ { diff --git a/datasets/GSSTFM_NCEP_2c.json b/datasets/GSSTFM_NCEP_2c.json index 782a1d3c8d..2d0477c4fb 100644 --- a/datasets/GSSTFM_NCEP_2c.json +++ b/datasets/GSSTFM_NCEP_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFM_NCEP_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Monthly product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. \n \nThe short name for this product is GSSTFM_NCEP.\n", "links": [ { diff --git a/datasets/GSSTFM_NCEP_3.json b/datasets/GSSTFM_NCEP_3.json index c6c52015e8..fe6ec98336 100644 --- a/datasets/GSSTFM_NCEP_3.json +++ b/datasets/GSSTFM_NCEP_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFM_NCEP_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version 3 Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Monthly product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. \n \nThe short name for this product is GSSTFM_NCEP.\n", "links": [ { diff --git a/datasets/GSSTFSC_2c.json b/datasets/GSSTFSC_2c.json index a8a0335ec9..8767717772 100644 --- a/datasets/GSSTFSC_2c.json +++ b/datasets/GSSTFSC_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFSC_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is the Seasonal Climatology product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. The seasonal product is a result of averaging of three consecutive months: December-February, March-May, June-August, and September-November. There is one HDF-EOS5 file per season. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nStarting with Version 2c, there is only one set of Combined data, \"Set1\". The Seasonal Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\".\n \nThe seasonal temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans.\n\nThe short name for this product is GSSTFSC.\n", "links": [ { diff --git a/datasets/GSSTFSC_3.json b/datasets/GSSTFSC_3.json index 2ccf1ca4d7..3ff53a56ef 100644 --- a/datasets/GSSTFSC_3.json +++ b/datasets/GSSTFSC_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFSC_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is the fine resolution version of the previously released GSSTFSC.2c. \n\nThis is the Seasonal Climatology product; data are projected to equidistant Grid that covers the globe at 0.25x00.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nStarting from previous Version 2c, these data have only one set of Combined data, \"Set1\". The Seasonal Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\".\n\nStarting with this Version 3, the \"WB\" variable, \"lowest 500-m precipitable water\" has been discontinued. \n \nThe short name for this product is GSSTFSC.\n", "links": [ { diff --git a/datasets/GSSTFYC_2c.json b/datasets/GSSTFYC_2c.json index 6ee7f18229..5274533671 100644 --- a/datasets/GSSTFYC_2c.json +++ b/datasets/GSSTFYC_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFYC_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nGSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). \n\nHowever, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the \"artifactitious\" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. \n\nGSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful \"Rice Cooker Theory\" by Shie (2010a,b), i.e., \"To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality\" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c. \n\nThis is the Yearly Climatology product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. Starting with Version 2c, there is only one set of Combined data, \"Set1\". The Monthly Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\". A finer resolution, 0.25 deg, of this product has been released as Version 3.\n \nThe yearly temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans.\n\nThe short name for this product is GSSTFYC.\n", "links": [ { diff --git a/datasets/GSSTFYC_3.json b/datasets/GSSTFYC_3.json index 3f11f9a650..873b6f2b4a 100644 --- a/datasets/GSSTFYC_3.json +++ b/datasets/GSSTFYC_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTFYC_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is the fine resolution version of the previously released GSSTFYC.2c. \n\nThis is the Yearly Climatology product; data are projected to equidistant Grid that covers the globe at 0.25x00.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nStarting from previous Version 2c, these data have only one set of Combined data, \"Set1\". The Yearly Climatology HDF-EOS5 file also contains one extra grid of NCEP Climatology, \"NCEP\". \n\nStarting with this Version 3, the \"WB\" variable, \"lowest 500-m precipitable water\" has been discontinued. \n \nThe short name for this product is GSSTFYC.\n", "links": [ { diff --git a/datasets/GSSTF_2c.json b/datasets/GSSTF_2c.json index ef14b9d911..ce54721117 100644 --- a/datasets/GSSTF_2c.json +++ b/datasets/GSSTF_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF2c) Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nGSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). However, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the \"artifactitious\" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. \n\nGSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful \"Rice Cooker Theory\" by Shie (2010a,b), i.e., \"To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality\" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c. \n\nThis is the Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. \n\nA finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe GSSTF, Version 2c, daily fluxes have first been produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15). Then, the Combined daily fluxes are produced by averaging (equally weighted) over available flux data/files from various satellites. These Combined daily flux data are considered as the \"final\" GSSTF, Version 2c, and are stored in this HDF-EOS5 collection.\n \nThere are only one set of GSSTF, Version 2c, Combined data, \"Set1\" \n \nThe \"individual\" daily flux data files, produced for each individual satellite, are also available in HDF-EOS5, although from different collections:\n GSSTF_Fxx_2c, where Fxx are the individual satellites (F08, F10, etc..)\n \n The input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. Relevant to this MEaSUREs project, these are converted to HDF-EOS5, and are stored in the GSSTF_NCEP_2c collection. \n \n Please use these products with care and proper citations, i.e., properly indicating your applications with, e.g., \"using the combined 2001 data file of Set1\" or \"using the 2001 F13 data file\". \n \n APPENDIX SET1\n ---------------\n The following list summarizes individual satellites used to produce the Combined SET1. \n \n (1) Y1987/:\n F08/\n 1987/07-12: F08 (Note: 1987/12 is filled with missing value due to data scarcity)\n \n (2) Y1988/:\n F08/\n 1988/01-12: F08\n \n (3) Y1989/:\n F08/\n 1989/01-12: F08\n \n (4) Y1990:\n F08/ F10/\n 1990/01-12: F08 (Note: F10 started in 1990/12, but N/A due to data scarcity)\n \n (5) Y1991/:\n F08/ F10/\n 1991/01-12: F08+F10\n \n (6) Y1992/:\n F10/ F11/\n 1992/01-12: F10+F11\n \n (7) Y1993/:\n F10/ F11/\n 1993/01-12: F10+F11\n \n (8) Y1994/:\n F10/ F11/\n 1994/01-12: F10+F11\n \n (9) Y1995/:\n F10/ F11/ F13/\n 1995/01-12: 01-04: F10+F11\n 05-12: F10+F11+F13\n \n (10) Y1996/:\n F10/ F11/ F13/\n 1996/01-12: F10+F11+F13\n \n (11) Y1997/:\n F10/ F11/ F13/ F14/\n 1997/01-12: 01-04: F10+F11+F13\n 05/01-11/14: F10+F11+F13+F14\n 11/15-12/31: F11+F13+F14\n \n (12) Y1998/:\n F11/ F13/ F14/\n 1998/01-12: F11+F13+F14\n \n (13) Y1999/:\n F11/ F13/ F14/\n 1999/01-12: F11+F13+F14\n \n (14) Y2000/:\n F11/ F13/ F14/ F15/\n 2000/01-12: 01/01-05/16: F11+F13+F14+F15\n 05/17-12/31: F13+F14+F15\n \n (15) Y2001/:\n F13/ F14/ F15/\n 2001/01-12: F13+F14+F15\n \n (16) Y2002/:\n F13/ F14/ F15/\n 2002/01-12: F13+F14+F15\n \n (17) Y2003/:\n F13/ F14/ F15/\n 2003/01-12: F13+F14+F15\n \n (18) Y2004/:\n F13/ F14/ F15/\n 2004/01-12: F13+F14+F15\n \n (19) Y2005/:\n F13/ F14/ F15/\n 2005/01-12: F13+F14+F15\n \n (20) Y2006/:\n F13/ F14/ F15/\n 2006/01-12: F13+F14 \n \n (21) Y2007/:\n F13/ F14/ F15/\n 2007/01-12: F13+F14 \n \n (22) Y2008/:\n F13/ F14/ F15/\n 2008/01-12: 01-07: F13+F14 \n 08-12: F13 \n \n Special notes:\n \n (a) For Y2006, Y2007 and Y2008, the current Combined daily data files do not include the F15 Individual daily data files due to problematic calibration in F15. The Combined daily files will be updated for those three years once an improved set of Individual daily data files are produced using corrected and updated SSM/I F15 input files.\n \n (b) The current Combined daily data files are produced with at most 4 combined satellites,\n i.e., F10, F11, F13 and F14 for May-Nov 1997,\n and F11, F13, F14 and F15 for Jan-May 2000.\n", "links": [ { diff --git a/datasets/GSSTF_3.json b/datasets/GSSTF_3.json index 9054388adb..0633af1ca0 100644 --- a/datasets/GSSTF_3.json +++ b/datasets/GSSTF_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 (GSSTF3) Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis suite of GSSTF version 3 products is the 0.25x0.25 deg resolution version of the GSSTF 2c collections. It does not contain, however, the \"WB\" variable - 'lowest 500-m precipitable water' (g/cm**2). \n\nThis is the Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nAs in previous versions, the daily fluxes have first been produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15). Then, the Combined daily fluxes are produced by averaging (equally weighted) over available flux data/files from various satellites. These Combined daily flux data are considered as the \"final\" GSSTF, Version 3, and are stored in this HDF-EOS5 collection.\n \n There are only one set of GSSTF, Version 3, Combined data, \"Set1\" \n \n The \"individual\" daily flux data files, produced for each individual satellite, are also available in HDF-EOS5, although from different collections:\n GSSTF_Fxx_3, where Fxx are the individual satellites (F08, F10, etc..)\n \n The input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. Relevant to this MEaSUREs project, these are converted to HDF-EOS5, and are stored in the GSSTF_NCEP_3 collection. \n \n Please use these products with care and proper citations, i.e., properly indicating your applications with, e.g., \"using the combined 2001 data file of Set1\" or \"using the 2001 F13 data file\". \n \n APPENDIX SET1\n ---------------\n The following list summarizes individual satellites used to produce the Combined SET1. \n \n (1) Y1987/:\n F08/\n 1987/07-12: F08 (Note: 1987/12 is filled with missing value due to data scarcity)\n \n (2) Y1988/:\n F08/\n 1988/01-12: F08\n \n (3) Y1989/:\n F08/\n 1989/01-12: F08\n \n (4) Y1990:\n F08/ F10/\n 1990/01-12: F08 (Note: F10 started in 1990/12, but N/A due to data scarcity)\n \n (5) Y1991/:\n F08/ F10/\n 1991/01-12: F08+F10\n \n (6) Y1992/:\n F10/ F11/\n 1992/01-12: F10+F11\n \n (7) Y1993/:\n F10/ F11/\n 1993/01-12: F10+F11\n \n (8) Y1994/:\n F10/ F11/\n 1994/01-12: F10+F11\n \n (9) Y1995/:\n F10/ F11/ F13/\n 1995/01-12: 01-04: F10+F11\n 05-12: F10+F11+F13\n \n (10) Y1996/:\n F10/ F11/ F13/\n 1996/01-12: F10+F11+F13\n \n (11) Y1997/:\n F10/ F11/ F13/ F14/\n 1997/01-12: 01-04: F10+F11+F13\n 05/01-11/14: F10+F11+F13+F14\n 11/15-12/31: F11+F13+F14\n \n (12) Y1998/:\n F11/ F13/ F14/\n 1998/01-12: F11+F13+F14\n \n (13) Y1999/:\n F11/ F13/ F14/\n 1999/01-12: F11+F13+F14\n \n (14) Y2000/:\n F11/ F13/ F14/ F15/\n 2000/01-12: 01/01-05/16: F11+F13+F14+F15\n 05/17-12/31: F13+F14+F15\n \n (15) Y2001/:\n F13/ F14/ F15/\n 2001/01-12: F13+F14+F15\n \n (16) Y2002/:\n F13/ F14/ F15/\n 2002/01-12: F13+F14+F15\n \n (17) Y2003/:\n F13/ F14/ F15/\n 2003/01-12: F13+F14+F15\n \n (18) Y2004/:\n F13/ F14/ F15/\n 2004/01-12: F13+F14+F15\n \n (19) Y2005/:\n F13/ F14/ F15/\n 2005/01-12: F13+F14+F15\n \n (20) Y2006/:\n F13/ F14/ F15/\n 2006/01-12: F13+F14 \n \n (21) Y2007/:\n F13/ F14/ F15/\n 2007/01-12: F13+F14 \n \n (22) Y2008/:\n F13/ F14/ F15/\n 2008/01-12: 01-07: F13+F14 \n 08-12: F13 \n \n Special notes:\n \n (a) For Y2006, Y2007 and Y2008, the current Combined daily data files do not include the F15 Individual daily data files due to problematic calibration in F15. The Combined daily files will be updated for those three years once an improved set of Individual daily data files are produced using corrected and updated SSM/I F15 input files.\n \n (b) The current Combined daily data files are produced with at most 4 combined satellites,\n i.e., F10, F11, F13 and F14 for May-Nov 1997,\n and F11, F13, F14 and F15 for Jan-May 2000.\n", "links": [ { diff --git a/datasets/GSSTF_F08_2c.json b/datasets/GSSTF_F08_2c.json index 68f512987e..665998e9ab 100644 --- a/datasets/GSSTF_F08_2c.json +++ b/datasets/GSSTF_F08_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F08_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c). A finer resolution, 0.25 deg, of this product has been released as Version 3.\n \n The short name of this data set is GSSTF_F08.\n", "links": [ { diff --git a/datasets/GSSTF_F08_3.json b/datasets/GSSTF_F08_3.json index cce002d7ba..00fec12cc4 100644 --- a/datasets/GSSTF_F08_3.json +++ b/datasets/GSSTF_F08_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F08_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \n The short name of this data set is GSSTF_F08.\n ", "links": [ { diff --git a/datasets/GSSTF_F10_2c.json b/datasets/GSSTF_F10_2c.json index 553d54682d..d89a83763b 100644 --- a/datasets/GSSTF_F10_2c.json +++ b/datasets/GSSTF_F10_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F10_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c).\n \nThe short name for this data set is GSSTF_F10.\n ", "links": [ { diff --git a/datasets/GSSTF_F10_3.json b/datasets/GSSTF_F10_3.json index e65af85b3d..a31340b1a0 100644 --- a/datasets/GSSTF_F10_3.json +++ b/datasets/GSSTF_F10_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F10_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size.\n \n The daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \n The short name of this data set is GSSTF_F10.\n ", "links": [ { diff --git a/datasets/GSSTF_F11_2c.json b/datasets/GSSTF_F11_2c.json index f7b98c0b77..35526e5901 100644 --- a/datasets/GSSTF_F11_2c.json +++ b/datasets/GSSTF_F11_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F11_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c).\n\nThe short name for this data set is GSSTF_F11.\n", "links": [ { diff --git a/datasets/GSSTF_F11_3.json b/datasets/GSSTF_F11_3.json index a144d72eda..566434a3e7 100644 --- a/datasets/GSSTF_F11_3.json +++ b/datasets/GSSTF_F11_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F11_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \nThe short name of this data set is GSSTF_F11.\n", "links": [ { diff --git a/datasets/GSSTF_F13_2c.json b/datasets/GSSTF_F13_2c.json index 50d3d619b6..78991a11f8 100644 --- a/datasets/GSSTF_F13_2c.json +++ b/datasets/GSSTF_F13_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F13_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c).\n \nThe short name for this dataset is GSSTF_F13.\n", "links": [ { diff --git a/datasets/GSSTF_F13_3.json b/datasets/GSSTF_F13_3.json index f9d2fadec5..dee4a66c3d 100644 --- a/datasets/GSSTF_F13_3.json +++ b/datasets/GSSTF_F13_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F13_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \nThe short name of this data set is GSSTF_F13.\n", "links": [ { diff --git a/datasets/GSSTF_F14_2c.json b/datasets/GSSTF_F14_2c.json index 31835713c3..0949a8184a 100644 --- a/datasets/GSSTF_F14_2c.json +++ b/datasets/GSSTF_F14_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F14_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c).\n \nThe short name for this data set is GSSTF_F14.\n", "links": [ { diff --git a/datasets/GSSTF_F14_3.json b/datasets/GSSTF_F14_3.json index b6cd3fdd87..619fade268 100644 --- a/datasets/GSSTF_F14_3.json +++ b/datasets/GSSTF_F14_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F14_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \nThe short name of this data set is GSSTF_F14.", "links": [ { diff --git a/datasets/GSSTF_F15_2c.json b/datasets/GSSTF_F15_2c.json index 57dd811766..527f3f0ab5 100644 --- a/datasets/GSSTF_F15_2c.json +++ b/datasets/GSSTF_F15_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F15_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF 2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_2c).\n \nThe short name for this data set is GSSTF_F15.\n", "links": [ { diff --git a/datasets/GSSTF_F15_3.json b/datasets/GSSTF_F15_3.json index 4c1cb93cdd..12c3b43f76 100644 --- a/datasets/GSSTF_F15_3.json +++ b/datasets/GSSTF_F15_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_F15_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nThe daily fluxes are produced for each individual available SSM/I satellite tapes (e.g., F11, F13, F14 and F15), and then serve as input to the Combined daily fluxes (GSSTF_3).\n \nThe short name of this data set is GSSTF_F15.\n", "links": [ { diff --git a/datasets/GSSTF_NCEP_2c.json b/datasets/GSSTF_NCEP_2c.json index c71d61ae14..80994dd216 100644 --- a/datasets/GSSTF_NCEP_2c.json +++ b/datasets/GSSTF_NCEP_2c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_NCEP_2c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF2c) Dataset recently produced through a MEaSURES funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. \n\nThe input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. \n \nThe short name for this product is GSSTF_NCEP.\n", "links": [ { diff --git a/datasets/GSSTF_NCEP_3.json b/datasets/GSSTF_NCEP_3.json index a4ec0a5ee7..6331c538dd 100644 --- a/datasets/GSSTF_NCEP_3.json +++ b/datasets/GSSTF_NCEP_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GSSTF_NCEP_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are the Goddard Satellite-based Surface Turbulent Fluxes Version 3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. This HDF-EOS5 dataset is part of the MEaSUREs project. \n\nThis is a Daily product; data are projected to equidistant Grid that covers the globe at 0.25x0.25 degree cell size, resulting in data arrays of 1440x720 size. \n\nData gap: Daily GSSTF_NCEP files are missing for October 21-22,26-28, in 1990.\n\nThe input data sets used for this recent GSSTF production include the upgraded and improved datasets such as the Special Sensor Microwave Imager (SSM/I) Version-6 (V6) product of brightness temperature [Tb], total precipitable water [W], and wind speed [U] produced by the Wentz of Remote Sensing Systems (RSS), as well as the NCEP/DOE Reanalysis-2 (R2) product of sea skin temperature [SKT], 2-meter air temperature [Tair], and sea level pressure [SLP]. \n \nThe short name for this product is GSSTF_NCEP.\n", "links": [ { diff --git a/datasets/GVHRRATS6IMIR_001.json b/datasets/GVHRRATS6IMIR_001.json index 8ae8cc2520..42ca53e492 100644 --- a/datasets/GVHRRATS6IMIR_001.json +++ b/datasets/GVHRRATS6IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GVHRRATS6IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GVHRRATS6IMIR is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Infrared Images on 70mm Film data product from the sixth Applications Technology Satellite (ATS-6). This set of IR imagery (10.5 to 12.5 micrometer, with an 11 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date.\n\nThe ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00092 (old ID 74-039A-08B).", "links": [ { diff --git a/datasets/GVHRRATS6IMVIS_001.json b/datasets/GVHRRATS6IMVIS_001.json index b15bf31f17..fae87d0460 100644 --- a/datasets/GVHRRATS6IMVIS_001.json +++ b/datasets/GVHRRATS6IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GVHRRATS6IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GVHRRATS6IMVIS is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Visible Images on Film data product from the sixth Applications Technology Satellite (ATS-6). This set of visible imagery (0.55 to 0.75 micrometer, with a 5.5 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date.\n\nThe ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00047 (old ID 74-039A-08A).", "links": [ { diff --git a/datasets/GVdem_2008_3.json b/datasets/GVdem_2008_3.json index 4e9d414f66..379d5bffc5 100644 --- a/datasets/GVdem_2008_3.json +++ b/datasets/GVdem_2008_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GVdem_2008_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises Digital Elevation Models (DEMs) of varying resolutions for the George V and Terre Adelie continental margin, derived by incorporating all available singlebeam and multibeam point depth data into ESRI ArcGIS grids. The purpose was to provide revised DEMs for Census of Antarctic Marine Life (CAML) researchers who required accurate, high-resolution depth models for correlating seabed biota data against the physical environment. The DEM processing method utilised all individual multibeam and singlebeam depth points converted to geographic xyz (long/lat/depth) ASCII files. In addition, an ArcGIS line shapefile of the East Antarctic coastline showing the grounding lines of coastal glaciers and floating ice shelves, was converted to a xyz ASCII file with 0 m as the depth value. Land elevation data utilised the Radarsat Antarctic Mapping Project (RAMP) 200 m DEM data converted to xyz ASCII data. All depth, land and coastline ASCII files were input to Fledermaus 3DEditor visualisation software for removal of noisy data. The cleaned point data were then binned into a gridded surface using Fledermaus DMagic software, resulting in a 0.001-arcdegree (~100 m) resolution DEM with holes where no input data exists. ArcGIS Topogrid software was used to interpolate across the holes to output a full-coverage DEM. ArcGIS was used to produce the additional 0.0025-arcdegree (~250 m) and 0.005-arcdegree (~500 m) resolution grids. Full processing details can be viewed in: Beaman, R.J., O'Brien, P.E., Post, A.L., De Santis, L., 2011. A new high-resolution bathymetry model for the Terre Adelie and George V continental margin, East Antarctica. Antarctic Science 23(1), 95-103. doi:10.1017/S095410201000074X", "links": [ { diff --git a/datasets/GWELDMO_003.json b/datasets/GWELDMO_003.json index e2a2af8520..dc97726e05 100644 --- a/datasets/GWELDMO_003.json +++ b/datasets/GWELDMO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDMO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.\r\n\r\nProvided in the GWELDMO product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.", "links": [ { diff --git a/datasets/GWELDMO_031.json b/datasets/GWELDMO_031.json index 0b813b0a4d..67b1877ce3 100644 --- a/datasets/GWELDMO_031.json +++ b/datasets/GWELDMO_031.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDMO_031", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3.1 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 1985, 1990, and 2000 epochs. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.\r\n\r\nProvided in the GWELDMO product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.\r\n\r\nVersion 3.1 products use Landsat Collection 1 products as input and have improved per-pixel cloud mask, new quality data, improved calibration information, and improved product metadata that enable view and solar geometry calculations.", "links": [ { diff --git a/datasets/GWELDMO_032.json b/datasets/GWELDMO_032.json index 43e465d4e8..10cafc2359 100644 --- a/datasets/GWELDMO_032.json +++ b/datasets/GWELDMO_032.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDMO_032", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3.2 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 2005 epoch. GWELD products are generated from all available Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provides a consistent data source to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.\r\n\r\nProvided in the GWELDMO product are layers for surface reflectance bands 1-5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.\r\n\r\nGWELD Version 3.2 products now use Landsat Collection 2 products as input while previous GWELD versions use Landsat Collection 1. Additionally, the Landsat FMask layer, CFMask_State, was adopted as the cloud mask replacing the DT_Cloud_State and ACCA_State layers.", "links": [ { diff --git a/datasets/GWELDYR_003.json b/datasets/GWELDYR_003.json index d46b0465fb..85d9918b05 100644 --- a/datasets/GWELDYR_003.json +++ b/datasets/GWELDYR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDYR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid\r\n\r\nProvided in the GWELDYR product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.", "links": [ { diff --git a/datasets/GWELDYR_031.json b/datasets/GWELDYR_031.json index 4d61f79f07..984980d651 100644 --- a/datasets/GWELDYR_031.json +++ b/datasets/GWELDYR_031.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDYR_031", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3.1 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 1985, 1990, and 2000 epochs. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.\r\n\r\nProvided in the GWELDYR product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.\r\n\r\nVersion 3.1 products use Landsat Collection 1 products as input and have improved per-pixel cloud mask, new quality data, improved calibration information, and improved product metadata that enable view and solar geometry calculations.", "links": [ { diff --git a/datasets/GWELDYR_032.json b/datasets/GWELDYR_032.json index 20481993ae..b197b8baa8 100644 --- a/datasets/GWELDYR_032.json +++ b/datasets/GWELDYR_032.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GWELDYR_032", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3.2 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 2005 epoch. GWELD products are generated from all available Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provides a consistent data source to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.\r\n\r\nThe GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.\r\n\r\nProvided in the GWELDYR product are layers for surface reflectance bands 1-5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.\r\n\r\nGWELD Version 3.2 products now use Landsat Collection 2 products as input while previous GWELD versions use Landsat Collection 1. Additionally, the Landsat FMask layer, CFMask_State, was adopted as the cloud mask replacing the DT_Cloud_State and ACCA_State layers.", "links": [ { diff --git a/datasets/GasEx_0.json b/datasets/GasEx_0.json index 4207a8d479..7efb812913 100644 --- a/datasets/GasEx_0.json +++ b/datasets/GasEx_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GasEx_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GasEx experiments took place as several different cruises, e.g. GasEx I, GasEx II and GasEx III, also known as the Southern Ocean GasEx.", "links": [ { diff --git a/datasets/Gaus5k_1.json b/datasets/Gaus5k_1.json index 778e0b8a66..60d9b9a5e8 100644 --- a/datasets/Gaus5k_1.json +++ b/datasets/Gaus5k_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Gaus5k_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Gaussberg 1:5000 Topographic Dataset details features in the Gaussberg area. The Gaussberg falls within Wilhelm II land. The database contains all natural features. Attributes are held for line, point and polygon features. The dataset includes five metre contours. Conforms to the SCAR Feature Catalogue.", "links": [ { diff --git a/datasets/GePCO_0.json b/datasets/GePCO_0.json index ce4ad4a12a..30611d81d3 100644 --- a/datasets/GePCO_0.json +++ b/datasets/GePCO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GePCO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken during 2001 under the Geochemistry, Phytoplankton, and Color of the Ocean (GePCO) program.", "links": [ { diff --git a/datasets/GeoEye-1.ESA.archive_9.0.json b/datasets/GeoEye-1.ESA.archive_9.0.json index 7eb51976d4..8e36472b2e 100644 --- a/datasets/GeoEye-1.ESA.archive_9.0.json +++ b/datasets/GeoEye-1.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GeoEye-1.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GeoEye-1 archive collection consists of GeoEye-1 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years.\r\rPanchromatic (up to 40 cm resolution) and 4-Bands (up to 1.65 m resolution) products are available. The 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR1).\r\rThe processing levels are:\r\rSTANDARD (2A): normalised for topographic relief\rView Ready Standard (OR2A): ready for orthorectification\rView Ready Stereo: collected in-track for stereo viewing and manipulation\rMap-Ready (Ortho) 1:12,000 Orthorectified: additional processing unnecessary.\rSpatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service.\rThe following table summarises the offered product types\r\rEO-SIP product type\tBand Combination\tDescription\rGIS_4B__2A\t4-Band (4B)\t4-Band Standard/ 4-Band Ortho Ready Standard\rGIS_4B__MP\t4-Band (4B)\t4-Band Map Scale Ortho\rGIS_4B__OR\t4-Band (4B)\t4-Band Ortho Ready Stereo\rGIS_PAN_2A\tPanchromatic (PAN)\tPanchromatic Standard/ Panchromatic Ortho Ready Standard\rGIS_PAN_MP\tPanchromatic (PAN)\tPanchromatic Map Scale Ortho\rGIS_PAN_OR\tPanchromatic (PAN)\tPanchromatic Ortho Ready Stereo\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/GeoEye-1.full.archive.and.tasking_8.0.json b/datasets/GeoEye-1.full.archive.and.tasking_8.0.json index 030fea0a3d..de749c989b 100644 --- a/datasets/GeoEye-1.full.archive.and.tasking_8.0.json +++ b/datasets/GeoEye-1.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GeoEye-1.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GeoEye-1 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4 and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rIn particular, GeoEye-1 offers archive and tasking panchromatic products up to 0.41 m GSD resolution and Multispectral products up to 1.65 m GSD resolution.\r\rBand Combination\tData Processing Level\tResolutions\rPanchromatic and 4-bands\tStandard (2A) / View Ready Standard (OR2A)\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap-Ready (Ortho) 1:12,000 Orthorectified\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\r \r\rThe options for 4-Bands are the following:\r\r4-Band Multispectral (BLUE, GREEN, RED, NIR1)\r4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1)\r4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1)\r3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED)\r3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1).\rNative 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well-reconstructed details.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json b/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json index 9bacf2bd5e..7f3988425a 100644 --- a/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json +++ b/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. \nThe Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis uses both daytime and nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json b/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json index f57345e854..11971c5756 100644 --- a/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json +++ b/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/Geology_NPCMs_1.json b/datasets/Geology_NPCMs_1.json index b4d4cc2009..d5a41e08d1 100644 --- a/datasets/Geology_NPCMs_1.json +++ b/datasets/Geology_NPCMs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Geology_NPCMs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A dataset describing Australian Geological activities in the Northern Prince Charles Mountains from 1987 to 1996. The data are stored in an excel spreadsheet and contains information such as dates, base of operations, field leader, individual geologists, their field of speciality, localities visited and publications, theses or reports arising from the research.", "links": [ { diff --git a/datasets/GeomagneticObs_1.json b/datasets/GeomagneticObs_1.json index 7bf6d6d309..d1c466baf9 100644 --- a/datasets/GeomagneticObs_1.json +++ b/datasets/GeomagneticObs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GeomagneticObs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geomagnetic Observatories at Mawson and Macquarie Island, magnetic secular variation information from Davis and Casey, magnetic repeat stations in AAT and Heard Is (former observatories Wilkes, Heard).\n\nThe geomagnetic elements X, Y and Z are the components of the vector field in the Geographic North, Geographic East and Vertical directions. They are in a cartesian coordinate system. The magnetic field is completely defined by three independent components such as X,Y and Z. It can also be expressed in polar coordinates as D,F,I where D is the declination, I is the inclination and F is the magnitude of the vector field. There is one other component used: H. This is the horizontal component. H,D and Z or D,H and F are also commonly used to define the magnetic field.\n\nData stored at Geoscience Australia (GA).\n\nThis is part of ASAC project 760.\n\nThe fields in this dataset are:\n\nDate\nX (nT)\nY (nT)\nZ (nT)", "links": [ { diff --git a/datasets/Geosat-1.Full.archive.and.tasking_6.0.json b/datasets/Geosat-1.Full.archive.and.tasking_6.0.json index b50932f02c..9f6257ec2d 100644 --- a/datasets/Geosat-1.Full.archive.and.tasking_6.0.json +++ b/datasets/Geosat-1.Full.archive.and.tasking_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Geosat-1.Full.archive.and.tasking_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEOSAT-1 full archive and new tasking products are available at 22 m resolution in two processing levels.\r\rL1R (Basic Geopositioned): All 3 spectral channels combined into a band-registered image. Geopositioned product based on sensor model. Coefficients derived from satellite orientation parameters coming from telemetry and appended to metadata\rL1T (L1R orthorectified): Orthorectified to sub-pixel accuracy (10 metres RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m)\rGEOSAT-1 products are provided in DIMAP format. The image products are delivered in the TIFF and GeoTIFF image formats by default. All products can be provided in False Colours (R,G,NIR) or Natural Colours (R, G, Synthetic Blue).\r\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rThe list of available archived data can be retrieved using the GEOSAT catalogue (https://catalogue.geosat.space/cscda/extcat/)\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. \r\rThe list of available archived data can be retrieved using the Deimos catalogue (http://www.deimos-imaging.com/catalogue).", "links": [ { diff --git a/datasets/Geosat-2.Full.archive.and.tasking_8.0.json b/datasets/Geosat-2.Full.archive.and.tasking_8.0.json index 90d09e588c..b175f2d548 100644 --- a/datasets/Geosat-2.Full.archive.and.tasking_8.0.json +++ b/datasets/Geosat-2.Full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Geosat-2.Full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GEOSAT-2 full archive and new tasking products are available in different bands combinations:\r\rPan-sharpened (4 bands, 321 Natural Colours or 432 False Colours): A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserves all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Resolution 1m (L1B), 0.75m (L1C) or 0.40m (L1D); Bands: All, R-G-B or Ni-R-G\rPanchromatic: Single-band image coming from the panchromatic sensor. Resolution 1m (L1B) or 0.75m (L1C)\rMultispectral: Four-band image coming for the multispectral sensor, with band co-registration. Resolution 4m (L1B) or 3m (L1C)\rBundle: Panchromatic + Multispectral bands: five-band image containing the panchromatic and multispectral products packaged together, with band co-registration. Resolution 1m+4m (L1B), 0.75m+3m (L1C) or 0.40m+1.6m (L1D);\rAnd in addition\r Stereo Pair: Obtained from two acquisitions of the same target performed from different viewpoints in the same pass by using the agility feature of the platform. It can be provided as a pair of pan-sharpened or panchromatic images.\r\rGEOSAT-2 full archive and new tasking products are available at up to 0.4m resolution as:\r\rL1 SR Pan-sharpened (4 bands, 321 Natural Colours or 432 False Colours): A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserve all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Enhanced GSD from AI based techniques. Resolution 0.4m enhanced ortho; Bands: All, R-G-B or Ni-R-G\rL1SR Bundle: Panchromatic + Multispectral bands: five-band image containing the panchromatic and multispectral products packaged together, with band co-registration. Enhanced GSD from AI based techniques. Resolution 0.4m (P), 1.6m (MS) enhanced ortho. \r\rThe image products are delivered in GeoTIFF image format by default. JPEG-2000 format is also available on demand.\r\rAvailable processing levels are ortho-ready L1B (not resampled) and ortho L1C (orthorectified and resampled). \rIn addition, for Pan-sharpened and Bundle, also L1D (enhanced ortho) super-resolution products are available: based on artificial intelligence, this technology increases the original resolution and detail of an image without losing quality with respect to the original product\r\r\r \t Processing Level and Spatial Resolution\t Spectral Bands\rProduct Type\r L1B (orthoready)\t L1C (ortho)\t L1D (Enhanced Ortho)\r\rPan-sharpened\t1.0m\t 0.75m\t 0.40m\t All\t R, G, B\tNI, R, G\rPan\t 1.0m\t 0.75m\t \t Only Pan band\rMS\t 4.0m\t 3.00m\t \t Only MS band\rBundle (PAN+MS) 1.0m (P), 4.0m (MS)\t0.75m (P), 3.0m(MS)\t0.40m (P), 1.6m(MS) All\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/GlacioTraverseProgram1985_1.json b/datasets/GlacioTraverseProgram1985_1.json index 7bae24e23d..035ea73b24 100644 --- a/datasets/GlacioTraverseProgram1985_1.json +++ b/datasets/GlacioTraverseProgram1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GlacioTraverseProgram1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two major traverse programs were run out of Casey in 1985 over the Law Dome and Wilkes Land area. The first (Autumn/Winter) traverse ran from the 24th of March to 14th of June. The second (Spring) traverse ran from the 10th of September to the 3rd of January. Both traverses covered similar areas, visiting the A0, GC and GD series of snow canes.\n\nNumerous measurements were taken during the traverses in various quantities, including snow accumulation, sastrugi observations, wind direction and speed, air temperature, barometric pressure, snow hardness, snow density, gravity, snow temperature, and stratigraphy and isotope observations from shallow drilled cores.\n\nAll log books from these traverses are archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/GloSSAC_1.1.json b/datasets/GloSSAC_1.1.json index 547f896594..6d4e214bfb 100644 --- a/datasets/GloSSAC_1.1.json +++ b/datasets/GloSSAC_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 38-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GloSSAC_2.0.json b/datasets/GloSSAC_2.0.json index 944d35185f..678e615e3c 100644 --- a/datasets/GloSSAC_2.0.json +++ b/datasets/GloSSAC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 40-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GloSSAC_2.1.json b/datasets/GloSSAC_2.1.json index d9c436a62a..bee18dab7b 100644 --- a/datasets/GloSSAC_2.1.json +++ b/datasets/GloSSAC_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 42-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GloSSAC_2.2.json b/datasets/GloSSAC_2.2.json index d45ce9fcef..a040f7ead5 100644 --- a/datasets/GloSSAC_2.2.json +++ b/datasets/GloSSAC_2.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_2.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 43-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GloSSAC_2.21.json b/datasets/GloSSAC_2.21.json index 24e429a78b..e76b95e2cd 100644 --- a/datasets/GloSSAC_2.21.json +++ b/datasets/GloSSAC_2.21.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_2.21", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 44-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GloSSAC_2.22.json b/datasets/GloSSAC_2.22.json index 1f4e32aa42..74e2fe5f51 100644 --- a/datasets/GloSSAC_2.22.json +++ b/datasets/GloSSAC_2.22.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GloSSAC_2.22", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 44-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "links": [ { diff --git a/datasets/GlobFireCarbon_1.json b/datasets/GlobFireCarbon_1.json index 3c2c52f9e9..8e1884848f 100644 --- a/datasets/GlobFireCarbon_1.json +++ b/datasets/GlobFireCarbon_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GlobFireCarbon_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations.\n\nThe NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", "links": [ { diff --git a/datasets/Global_Biomass_1950-2010_1296_1.json b/datasets/Global_Biomass_1950-2010_1296_1.json index 90cefa7bac..a6f8ed93e7 100644 --- a/datasets/Global_Biomass_1950-2010_1296_1.json +++ b/datasets/Global_Biomass_1950-2010_1296_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Biomass_1950-2010_1296_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE. Data of different assessments are to the extent possible harmonized to reflect both forest area and other wooded land, to be comparable between countries and assessments.", "links": [ { diff --git a/datasets/Global_CDOM_0.json b/datasets/Global_CDOM_0.json index 0246ba4b82..c69b9e9b06 100644 --- a/datasets/Global_CDOM_0.json +++ b/datasets/Global_CDOM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_CDOM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of CDOM (colored dissolved organic matter) in the central equatorial Pacific Ocean in 2005 and 2006.", "links": [ { diff --git a/datasets/Global_Clumping_Index_1531_1.json b/datasets/Global_Clumping_Index_1531_1.json index b259b92d56..f8c5499d8a 100644 --- a/datasets/Global_Clumping_Index_1531_1.json +++ b/datasets/Global_Clumping_Index_1531_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Clumping_Index_1531_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global clumping index (CI) data for 2006 derived from the MODIS Bidirectional Reflectance Distribution Function (BRDF) data product. Clumping index is a key structural parameter of plant canopies which represents the degree of foliage grouping within distinct canopy structures relative to a random distribution. The data are provided at substantially higher resolution (500-m) than existing clumping index data products.", "links": [ { diff --git a/datasets/Global_Forest_Cover.json b/datasets/Global_Forest_Cover.json index 3a381dacc0..c4b018f7ce 100644 --- a/datasets/Global_Forest_Cover.json +++ b/datasets/Global_Forest_Cover.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Forest_Cover", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mounting global concern over the conservation status of the world's\n biodiversity, especially at ecosystem and species levels, has led\n tocalls for increasing the extent of protected areas and for\n identifying priority areas for conservation. Although most decisions\n to establish protected areas are made at the national level,\n international perspectives are necessary both to assess the status of\n ecosystems occurring in more than one country and to target the use\n ofinternational resources. Species and ecosystems are not contained by\n political boundaries, and international cooperation is essential to\n ensure their preservation.\n \n One means of establishing priorities for conservation is analysis of\n the degree to which existing networks of protected areas are\n representative of the full range of ecosystems and species. At the\n national level, detailed ecosystem or vegetation classifications can\n provide a basis for assessing the representativeness of the existing\n protected areas network. Provided that the data are available it is\n also possible to carry out a study of this nature on aglobal level. Up\n until now the data were not available.\n \n Several studies have highlighted the status of forest protection and\n decline for particular regions using some version of ecological zones\n (e.g. Lysenko et al., 1995, Mackinnon, 1996). Although the FAO have\n compiled data on forest resources globally (e.g. FAO 1995), the\n methodology used has differed between developed and developing\n countries. A more uniform approach to the forests of the different\n regions of the world is called for (Paivinen 1996). The World\n Conservation Monitoring Centre (WCMC) recently produced an analysis of\n protection of ecological zones in the tropics (Murray et al. 1996),\n using the system of ecofloristic zonesdeveloped for FAO. In 1996\n Iremonger et al. wrote a global analysis; the extent of current forest\n cover in each ecological zone was determined and the protection of\n that existing forest cover was assessed (FAO,1997). This latter study\n was possible because WCMC had completed a digital map of the world's\n forests at a clear enough resolution for the work. However, in the\n study the forest was not subdivided into different forest types, and\n an overlay of ecological zones coverages was used as a surrogate for\n these. The assumption in that study was that vegetationoccurring in\n different ecological zones belongs to different ecological types.\n \n The present study builds upon the work carried out byIremonger et\n al. (1997). The digital world forest coverage was subdivided into\n different broad forest types, and the ecological zones coverages were\n used as an overlay to define in even more detail the ecological\n variants of each forest type. The differences in scales and\n resolutions upon which the forest data sets and the ecological\n zones data sets were based, meant that the results of combining zones\n and forest types was not always meaningful. A more in-depth analysis\n of the reasons for the results obtained would be very useful and\n eliminate misleading combinations (e.g., thorn forest in the Tropical\n wet ecological zone) that may seem like rare and unique forest\n variants.\n \n Having created such detailed forest coverages for eachregion of the\n world, it was possible to compare forest area to the population\n figures. Some preliminary test extrapolations were attempted.", "links": [ { diff --git a/datasets/Global_Freshwater_CH4Emissions_2253_1.json b/datasets/Global_Freshwater_CH4Emissions_2253_1.json index c8c815ae99..a27f5a2eed 100644 --- a/datasets/Global_Freshwater_CH4Emissions_2253_1.json +++ b/datasets/Global_Freshwater_CH4Emissions_2253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Freshwater_CH4Emissions_2253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.", "links": [ { diff --git a/datasets/Global_Hydrologic_Soil_Group_1566_1.json b/datasets/Global_Hydrologic_Soil_Group_1566_1.json index 556c9ff107..f9b56c7665 100644 --- a/datasets/Global_Hydrologic_Soil_Group_1566_1.json +++ b/datasets/Global_Hydrologic_Soil_Group_1566_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Hydrologic_Soil_Group_1566_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.", "links": [ { diff --git a/datasets/Global_Lakes_Methane_2008_1.json b/datasets/Global_Lakes_Methane_2008_1.json index 16afdffdf2..63831ee3be 100644 --- a/datasets/Global_Lakes_Methane_2008_1.json +++ b/datasets/Global_Lakes_Methane_2008_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Lakes_Methane_2008_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global gridded information on lake surface area and open water CH4 emissions at a resolution of 0.25-degree x 0.25-degree for an annual climatology representative of the average conditions from 2003 to 2015. A compilation of flux data from 575 individual lake systems and 893 aggregated flux values were used, and each flux measurement was classified into one of seven ecoclimatic types. Ice-cover-regulated emission seasonality was derived from satellite microwave observations of ice cover phenology and freeze-thaw dynamics. Global lake area was determined from the merger of HydroLAKES and Climate Change Initiative Inland-Water (CCI-IW) remote-sensing data, and lakes were classified into ecoclimatic regions to facilitate linking these types with ecosystem-specific CH4 measurements in the flux compilation. Exploratory estimates of fluxes associated with ice melt and with spring and fall water-column turnover are also included. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/Global_Landslide_Exposure_Maps_1.0.json b/datasets/Global_Landslide_Exposure_Maps_1.0.json index 220c402ee4..71932f6474 100644 --- a/datasets/Global_Landslide_Exposure_Maps_1.0.json +++ b/datasets/Global_Landslide_Exposure_Maps_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Landslide_Exposure_Maps_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite\u2010based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a \u201cnowcast\u201d is issued to indicate the times and places where landslides are more probable.\n\nThis archive contains GeoTIFF Rasters that are a 16-year average (beginning of 2001 - end of 2016). The spatial coverage is from 72\u00b0N to 60\u00b0S latitude, and 180\u00b0W to 180\u00b0E longitude, based on IMERG Ver06B from the aforementioned time interval. The provided global maps of exposure to landslide hazards, are at a 30x30 arc-second resolution. These maps show the estimated exposure of population, roads, and critical infrastructure (hospitals/clinics, schools, fuel stations, power stations & distribution facilities) to landslide hazard, as modeled by the NASA LHASA model.\n\nThe data collection consists of eight files, covering the aforementioned spatial and temporal ranges, totaling approximately 20.3 GB (~2.5 GB each):\n (1): Landslide hazard (annual average; Units: Nowcasts.yr-1)\n (2): Landslide hazard (annual standard deviation; Units: Nowcasts.yr-1)\n (3): Population exposure (annual average; Units: Person-Nowcasts. yr-1. km-2)\n (4): Population exposure (annual standard deviation; Units: Person-Nowcasts. yr-1. km-2)\n (5): Road exposure (annual average; Units: Nowcasts.km.yr-1.km-2)\n (6): Road exposure (annual standard deviation; Units: Nowcasts.km.yr-1.km-2)\n (7): Critical infrastructure exposure (annual average; Units: Nowcasts.element.yr-1.km-2)\n (8): Critical infrastructure exposure (annual standard deviation; Units: Nowcasts.element.yr-1.km-2)\n", "links": [ { diff --git a/datasets/Global_Landslide_Nowcast_1.1.json b/datasets/Global_Landslide_Nowcast_1.1.json index 99925a99b7..597414b461 100644 --- a/datasets/Global_Landslide_Nowcast_1.1.json +++ b/datasets/Global_Landslide_Nowcast_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Landslide_Nowcast_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite\u2010based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a \u201cnowcast\u201d is issued to indicate the times and places where landslides are more probable. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run and spatial coverage is from 60\u00b0N to 60\u00b0S .", "links": [ { diff --git a/datasets/Global_Landslide_Nowcast_2.0.0.json b/datasets/Global_Landslide_Nowcast_2.0.0.json index ec0f6a6703..68bffa6803 100644 --- a/datasets/Global_Landslide_Nowcast_2.0.0.json +++ b/datasets/Global_Landslide_Nowcast_2.0.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Landslide_Nowcast_2.0.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Landslide Nowcast addresses the need for real-time situational awareness of landslide hazard. The Landslide Hazard Assessment for Situational Awareness model (LHASA) combines satellite rainfall estimates from the Global Precipitation Measurement mission (GPM) with soil moisture estimates from the Soil Moisture Active Passive (SMAP) satellite and other factors to produce a map of locations where rainfall-triggered landslide activity is probable. Due to the latency of the rainfall data, the nowcast is a near-real time product with a minimum latency of 5 hours. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run.\nThe Global Landslide Nowcast version 2.0.0 retains replaces the heuristic decision tree from version 1.0 with a machine learning model. Instead of merging all factors other than precipitation into a susceptibility map, LHASA 2.0 takes in each variable as a separate input layer. The most important change is the replacement of the categorical nowcast with a probabilistic output. This will enable users to adjust the threshold to suit their specific application and geographic location.\n", "links": [ { diff --git a/datasets/Global_Litter_Carbon_Nutrients_1244_1.json b/datasets/Global_Litter_Carbon_Nutrients_1244_1.json index 5d1c441ff2..b8c067c51a 100644 --- a/datasets/Global_Litter_Carbon_Nutrients_1244_1.json +++ b/datasets/Global_Litter_Carbon_Nutrients_1244_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Litter_Carbon_Nutrients_1244_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations. ", "links": [ { diff --git a/datasets/Global_Maps_C_Density_2010_1763_1.json b/datasets/Global_Maps_C_Density_2010_1763_1.json index fd297aa98f..a4a268f342 100644 --- a/datasets/Global_Maps_C_Density_2010_1763_1.json +++ b/datasets/Global_Maps_C_Density_2010_1763_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Maps_C_Density_2010_1763_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.", "links": [ { diff --git a/datasets/Global_Microbial_Biomass_C_N_P_1264_1.json b/datasets/Global_Microbial_Biomass_C_N_P_1264_1.json index d6f86d6caa..272a64efaf 100644 --- a/datasets/Global_Microbial_Biomass_C_N_P_1264_1.json +++ b/datasets/Global_Microbial_Biomass_C_N_P_1264_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Microbial_Biomass_C_N_P_1264_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two *.csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations.There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication.The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file. ", "links": [ { diff --git a/datasets/Global_Monthly_GPP_1789_1.json b/datasets/Global_Monthly_GPP_1789_1.json index 6df654e0c6..7050eb3422 100644 --- a/datasets/Global_Monthly_GPP_1789_1.json +++ b/datasets/Global_Monthly_GPP_1789_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Monthly_GPP_1789_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global monthly average gross primary productivity (GPP; g carbon/m2/d) modeled at 8 km spatial resolution for each of the 35 years from 1982-2016. GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data. Optimized LUE was extrapolated to a consistent 8 km resolution global grid using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing LUE and GPP. Global gridded long-term daily GPP was derived using the optimized LUE, Global Inventory Modeling and Mapping Studies (GIMMS3g) canopy fraction of photosynthetically active radiation (FPAR), and Modern-Era Retrospective analysis for Research and Applications, Version 2, (MERRA-2) meteorological information. These data will improve satellite-based estimation and understanding of GPP using a refined LUE model framework.", "links": [ { diff --git a/datasets/Global_Phosphorus_Dist_Map_1223_1.json b/datasets/Global_Phosphorus_Dist_Map_1223_1.json index 264c8d55c4..74664887ff 100644 --- a/datasets/Global_Phosphorus_Dist_Map_1223_1.json +++ b/datasets/Global_Phosphorus_Dist_Map_1223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Phosphorus_Dist_Map_1223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set. ", "links": [ { diff --git a/datasets/Global_Phosphorus_Hedley_Fract_1230_1.json b/datasets/Global_Phosphorus_Hedley_Fract_1230_1.json index 1ea2e3ff90..4992df40a8 100644 --- a/datasets/Global_Phosphorus_Hedley_Fract_1230_1.json +++ b/datasets/Global_Phosphorus_Hedley_Fract_1230_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Phosphorus_Hedley_Fract_1230_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites. ", "links": [ { diff --git a/datasets/Global_RTSG_Flux_1078_1.json b/datasets/Global_RTSG_Flux_1078_1.json index 67ae8b8a83..5fc3fa2dbe 100644 --- a/datasets/Global_RTSG_Flux_1078_1.json +++ b/datasets/Global_RTSG_Flux_1078_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_RTSG_Flux_1078_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file. ", "links": [ { diff --git a/datasets/Global_Reservoirs_Methane_1918_1.json b/datasets/Global_Reservoirs_Methane_1918_1.json index 1d88ad9c63..ea8535a0ff 100644 --- a/datasets/Global_Reservoirs_Methane_1918_1.json +++ b/datasets/Global_Reservoirs_Methane_1918_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Reservoirs_Methane_1918_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes global maps of methane (CH4) emissions from inland dam-reservoir systems at 0.25-degree spatial resolution. Daily emission rates (as grams of CH4 per day per total area of grid cell) were estimated for boreal, temperate, and subtropical-tropical eco-climatic domains and total emissions. The annual duration of the emission season is based on freeze-thaw cycles of these water bodies as applicable. In addition, the dataset includes the total fractional area of reservoirs in each grid cell. These estimates will promote understanding of the current and future role of reservoirs in the global CH4 budget and guide efforts to mitigate reservoir-related CH4 emissions. These emission estimates are climatological; one daily value for each day of year (n=365) is provided for each grid cell. Modeled estimates were based on daily mean inputs, averaged over 2002 to 2015.", "links": [ { diff --git a/datasets/Global_Riverine_N2O_Emissions_1791_1.json b/datasets/Global_Riverine_N2O_Emissions_1791_1.json index 3fb84ee931..95bbe6e316 100644 --- a/datasets/Global_Riverine_N2O_Emissions_1791_1.json +++ b/datasets/Global_Riverine_N2O_Emissions_1791_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Riverine_N2O_Emissions_1791_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe.", "links": [ { diff --git a/datasets/Global_SIF_OCO2_MODIS_1863_2.json b/datasets/Global_SIF_OCO2_MODIS_1863_2.json index 4f7085bcd1..670cd8e26c 100644 --- a/datasets/Global_SIF_OCO2_MODIS_1863_2.json +++ b/datasets/Global_SIF_OCO2_MODIS_1863_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_SIF_OCO2_MODIS_1863_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides spatially-contiguous global mean daily solar-induced chlorophyll fluorescence (SIF) estimates at 0.05 degree (approximately 5 km at the equator) spatial and 16-day temporal resolution from September 2014 through July 2020. This product was derived from Orbiting Carbon Observatory-2 (OCO-2) SIF observations and produced by training an artificial neural network (ANN) on the native OCO-2 SIF observations and MODIS BRDF-corrected seven-band surface reflectance along OCO-2's orbits. The trained ANN model was then applied to predict mean daily SIF (mW/m2/nm/sr) in OCO-2's gap regions based on MODIS reflectance and landcover. This framework was stratified by biomes and 16-day time steps. This dataset's high resolution and global contiguous coverage will greatly enhance the synergy between satellite SIF and photosynthesis measured on the ground at consistent spatial scales. Potential applications of this dataset include advancing dynamic drought monitoring and mitigation, informing agricultural planning and yield estimation, and providing a benchmark for upcoming satellite missions with SIF capabilities at higher spatial resolutions.", "links": [ { diff --git a/datasets/Global_Salt_Marsh_Change_2122_1.json b/datasets/Global_Salt_Marsh_Change_2122_1.json index 7286ada499..7303675de5 100644 --- a/datasets/Global_Salt_Marsh_Change_2122_1.json +++ b/datasets/Global_Salt_Marsh_Change_2122_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Salt_Marsh_Change_2122_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes. The data are provided in cloud-optimized GeoTIFF format.", "links": [ { diff --git a/datasets/Global_Soil_Regolith_Sediment_1304_1.json b/datasets/Global_Soil_Regolith_Sediment_1304_1.json index e68dd21b22..b8e4e139d6 100644 --- a/datasets/Global_Soil_Regolith_Sediment_1304_1.json +++ b/datasets/Global_Soil_Regolith_Sediment_1304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Soil_Regolith_Sediment_1304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-resolution estimates of the thickness of the permeable layers above bedrock (soil, regolith, and sedimentary deposits) within a global 30-arcsecond (~1-km) grid using the best available data for topography, climate, and geology as input. These data are modeled to represent estimated thicknesses by landform type for the geological present.", "links": [ { diff --git a/datasets/Global_Veg_Greenness_GIMMS_3G_2187_1.json b/datasets/Global_Veg_Greenness_GIMMS_3G_2187_1.json index e67d5a0aaf..e82a775156 100644 --- a/datasets/Global_Veg_Greenness_GIMMS_3G_2187_1.json +++ b/datasets/Global_Veg_Greenness_GIMMS_3G_2187_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Global_Veg_Greenness_GIMMS_3G_2187_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/Globalsoil_ESM.json b/datasets/Globalsoil_ESM.json index 49d5ea9e9e..ad7eda41c0 100644 --- a/datasets/Globalsoil_ESM.json +++ b/datasets/Globalsoil_ESM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Globalsoil_ESM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications as well. GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e. taxotransfer rules) and the polygon linkage method to derive the spatial distribution of soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: area-weighting method, the dominant soil type method and the dominant binned soil attribute method. In the released gridded dataset, we used the area-weighting method as it will meet the demands of most applications. The dataset can be also aggregate to a lower resolution. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m).\n", "links": [ { diff --git a/datasets/GoMA-Platts_Bank_Aerial_Survey.json b/datasets/GoMA-Platts_Bank_Aerial_Survey.json index e1256eb502..da5ecb12b6 100644 --- a/datasets/GoMA-Platts_Bank_Aerial_Survey.json +++ b/datasets/GoMA-Platts_Bank_Aerial_Survey.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GoMA-Platts_Bank_Aerial_Survey", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The study area is located 50 km from shore in the western Gulf of Maine and covers 1672 km2, including Platts Bank, Three Dory Ridge and surrounding deep water. Platts Bank (43\u00b010\u0092N, 069\u00b040\u0092W) is a glacial deposit composed primarily of sand and gravel. When defined by the 100 m isobath, the bank is approximately 15 km in its longest dimension and has an area <140 km2. \n\nAerial surveys were flown on ten days from July 11 to 29, 2005 to record the distribution and relative abundance of marine mammals, birds and large fish. Surveys were typically conducted in the morning or early afternoon and consisted of six transects, each 46 km long oriented on an East-West axis to minimize interference from reflected sunlight. Survey legs were flown at 185 km/hr and an altitude of 230 m using a high-wing, twin-engine aircraft. Observation effort (two observers) was concentrated from both sides of the plane perpendicular to the flight path. To estimate the distances of sightings of mammals and fish from the plane\u0092s flight path, sightings were binned into five groupings corresponding to 15 degrees of arc from 15\u00b0 (the area directly beneath the plane was not visible) to 90\u00b0. When species identification or number of individuals was uncertain, search effort was interrupted while the plane circled to confirm identifications and number of individuals and to obtain a more precise location. Birds were recorded only within a 170 m strip on each side of the aircraft (15\u00b0 to 45\u00b0 of arc) during the survey legs. Sightings of birds continued when the plane circled for closer inspection of mammals and fish, but these data were not used in analyses since this would bias bird sightings towards areas where cetaceans were concentrated. Data were recorded by a dedicated data recorder directly onto a computer using software that recorded the time and location from the GPS navigation system aboard the plane at regular intervals throughout the flight and for each recorded sighting.", "links": [ { diff --git a/datasets/GozMmlpH2O_1.json b/datasets/GozMmlpH2O_1.json index 2a36b80786..fc5cbc5786 100644 --- a/datasets/GozMmlpH2O_1.json +++ b/datasets/GozMmlpH2O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozMmlpH2O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Merged Data for Water Vapor 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpH2O) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged H2O data are from the following satellite instruments: HALOE (v19; 1991 - 2005), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for H2O is from 147 to 0.01 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpH2O.\n\nThe GozMmlpH2O merged data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozMmlpHCl_1.json b/datasets/GozMmlpHCl_1.json index 3ff369b67e..98f2106624 100644 --- a/datasets/GozMmlpHCl_1.json +++ b/datasets/GozMmlpHCl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozMmlpHCl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Merged Data for Hydrogen Chloride 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpHCl) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged HCl data are from the following satellite instruments: HALOE (v19; 1991 - 2005), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for HCl is from 147 to 0.5 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpHCl.\n\nThe GozMmlpHCl merged data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozMmlpHNO3_1.json b/datasets/GozMmlpHNO3_1.json index fea5215be9..daa58d953e 100644 --- a/datasets/GozMmlpHNO3_1.json +++ b/datasets/GozMmlpHNO3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozMmlpHNO3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Merged Data for Nitric Acid 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpHNO3) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged HNO3 data are from the following satellite instruments: UARS MLS (v6; 1991 - 1997), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for HNO3 is from 147 to 1 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpHNO3.\n\nThe GozMmlpHNO3 merged data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozMmlpN2O_1.json b/datasets/GozMmlpN2O_1.json index f7444abcce..7a43fabff3 100644 --- a/datasets/GozMmlpN2O_1.json +++ b/datasets/GozMmlpN2O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozMmlpN2O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Merged Data for Nitrous Oxide 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpN2O) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged N2O data are from the following satellite instruments: ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for N2O is from 147 to 0.5 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpN2O.\n\nThe GozMmlpN2O merged data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozMmlpO3_1.json b/datasets/GozMmlpO3_1.json index 8a77734480..92d4b3b20f 100644 --- a/datasets/GozMmlpO3_1.json +++ b/datasets/GozMmlpO3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozMmlpO3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Merged Data for Ozone 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpO3) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged O3 data are from the following satellite instruments: SAGE I (v5.9_rev; 1979-1981), SAGE II (v6.2; 1984-2005), HALOE (v19; 1991-2005), UARS MLS (v5; 1991-1997), ACE-FTS (v2.2; 2004-onward), Aura MLS (v2.2; 2004 onward) others as validation (e.g., SAGE III, v4.0; 2002-2005). The vertical pressure range for O3 is from 147 to 0.5 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpO3.\n\nThe GozMmlpO3 merged data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpH2O_1.json b/datasets/GozSmlpH2O_1.json index 29d5388bac..54fff4f4df 100644 --- a/datasets/GozSmlpH2O_1.json +++ b/datasets/GozSmlpH2O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpH2O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Water Vapor 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpH2O) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from original Level 2 satellite instruments and products. The source H2O data are from the following satellite instruments: HALOE (v19; 1991-2005), UARS MLS (v5; 1991-1997), ACE-FTS (v2.2; 2004-onward), Aura MLS (v2.2; 2004 onward). The vertical pressure range for H2O is from 147 to 0.01 hPa. The source data are used to create a merged product contained in a separate data product with the short name GozMmlpH2O.\n\nThe GozSmlpH2O source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpHCl_1.json b/datasets/GozSmlpHCl_1.json index 2e6e2b4398..4a1243b4f9 100644 --- a/datasets/GozSmlpHCl_1.json +++ b/datasets/GozSmlpHCl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpHCl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Hydrogen Chloride 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpHCl) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from original Level 2 satellite instruments and products. The source HCl data are from the following satellite instruments: HALOE (v19; 1991 - 2005), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for HCl is from 147 to 0.5 hPa. The source data are used to create a merged product contained in a separate data product with the short name GozMmlpHCl.\n\nThe GozSmlpHCl source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpHNO3_1.json b/datasets/GozSmlpHNO3_1.json index bd83b0eba8..4cfe3d458b 100644 --- a/datasets/GozSmlpHNO3_1.json +++ b/datasets/GozSmlpHNO3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpHNO3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Nitric Acid 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpHNO3) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from original Level 2 satellite instruments and products. The source HNO3 data are from the following satellite instruments: UARS MLS (v6; 1991 - 1997), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for HNO3 is from 147 to 1 hPa. source data are used to create a merged product contained in a separate data product with the short name GozMmlpHNO3.\n\nThe GozSmlpHNO3 source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpN2O_1.json b/datasets/GozSmlpN2O_1.json index 3fd9066983..41b7ef2f25 100644 --- a/datasets/GozSmlpN2O_1.json +++ b/datasets/GozSmlpN2O_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpN2O_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Nitrous Oxide 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpN2O) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from original Level 2 satellite instruments and products. The source N2O data are from the following satellite instruments: ACE-FTS (v2.2; 2004-onward) and Aura MLS (v2.2; 2004 onward). The vertical pressure range for N2O is from 147 to 0.5 hPa. The source data are used to create a merged product contained in a separate data product with the short name GozMmlpN2O.\n\nThe GozSmlpN2O source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpO3_1.json b/datasets/GozSmlpO3_1.json index 6f1a7f9f13..bfa210d4a1 100644 --- a/datasets/GozSmlpO3_1.json +++ b/datasets/GozSmlpO3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpO3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Ozone 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpO3) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from original Level 2 satellite instruments and products. The source O3 data are from the following satellite instruments: SAGE I (v5.9_rev; 1979-1981), SAGE II (v6.2; 1984-2005), HALOE (v19; 1991-2005), UARS MLS (v5; 1991-1997), ACE-FTS (v2.2; 2004-onward), Aura MLS (v2.2; 2004 onward) + others as validation (e.g., SAGE III, v4.0; 2002-2005). The vertical pressure range for O3 is from 147 to 0.5 hPa. The source data are used to create a merged product contained in a separate data product with the short name GozMmlpO3.\n\nThe GozSmlpO3 source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/GozSmlpT_1.json b/datasets/GozSmlpT_1.json index b9f98e5e6e..5976346858 100644 --- a/datasets/GozSmlpT_1.json +++ b/datasets/GozSmlpT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GozSmlpT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOZCARDS Source Data for Temperature 1 month L4 10 degree Zonal Averages on a Vertical Pressure Grid product (GozSmlpT) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated from the original products. The source Temperature data are from the GMAO MERRA model product DAS 3d analyzed state MAI6NVANA (v5.2.0; 1979 - onward). The vertical pressure range for Temperature is from 1000 to 0.015 hPa.\n\nThe GozSmlpT source data are distributed in netCDF4 format.", "links": [ { diff --git a/datasets/Great African Food Company Crop Type Tanzania_1.json b/datasets/Great African Food Company Crop Type Tanzania_1.json index 9fbea00b68..57ce2f9647 100644 --- a/datasets/Great African Food Company Crop Type Tanzania_1.json +++ b/datasets/Great African Food Company Crop Type Tanzania_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Great African Food Company Crop Type Tanzania_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains field boundaries and crop types from farms in Tanzania. Great African Food Company used Farmforce app to collect a point within each field, and recorded other properties including area of the field.\n

\nRadiant Earth Foundation team used the point measurements from the ground data collection and the area of each field overlaid on satellite imagery (multiple Sentinel-2 scenes during the growing season, and Google basemap) to draw the polygons for each field. These polygons do not cover the entirety of the field, and are always enclosed within the field. Therefore, they should not be used for field boundary detection, rather as reference polygons for crop type classification. Data points that were not clear if they belong to a neighboring farm (e.g. the point was on the edge of two farms)were removed from the dataset. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item).", "links": [ { diff --git a/datasets/Great_Belt_0.json b/datasets/Great_Belt_0.json index 0551206732..0b0d69a0a8 100644 --- a/datasets/Great_Belt_0.json +++ b/datasets/Great_Belt_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Great_Belt_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Great Belt research cruise investigated the Great Southern Coccolithophore Belt in the Southern Ocean.", "links": [ { diff --git a/datasets/Great_Lakes_0.json b/datasets/Great_Lakes_0.json index ca254841b3..834b3845e1 100644 --- a/datasets/Great_Lakes_0.json +++ b/datasets/Great_Lakes_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Great_Lakes_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality measurements taken in the Great Lakes region of the United States.", "links": [ { diff --git a/datasets/Great_Slave_Lake_Ecosystem_Map_1695_1.json b/datasets/Great_Slave_Lake_Ecosystem_Map_1695_1.json index 812a3bc908..341b345936 100644 --- a/datasets/Great_Slave_Lake_Ecosystem_Map_1695_1.json +++ b/datasets/Great_Slave_Lake_Ecosystem_Map_1695_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Great_Slave_Lake_Ecosystem_Map_1695_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area.", "links": [ { diff --git a/datasets/GreenBay_0.json b/datasets/GreenBay_0.json index 3488bcfca0..aba208dd2e 100644 --- a/datasets/GreenBay_0.json +++ b/datasets/GreenBay_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GreenBay_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Green Bay, Wisconsin in 2010.", "links": [ { diff --git a/datasets/Gridded_Biomass_Africa_1777_1.json b/datasets/Gridded_Biomass_Africa_1777_1.json index eab5c852f2..5a9c8eb96c 100644 --- a/datasets/Gridded_Biomass_Africa_1777_1.json +++ b/datasets/Gridded_Biomass_Africa_1777_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Gridded_Biomass_Africa_1777_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of woody (tree and shrub) cover and biomass across Sub-Saharan Africa at a resolution of 1 km for the period 2000-2004. Canopy cover observations and remote-sensing data related to woody vegetation were used to predict woody cover across Africa. Predicted woody cover, canopy height, and tree allometry were used to estimate woody biomass for Sub-Saharan Africa. Canopy cover observations were assembled from field measurements and Google Earth imagery collected from 2000-2004. Remote-sensing data related to the structural attributes of woody vegetation were derived from MODIS optical data and Q-SCAT (Quick Scatterometer) microwave measurements. Canopy height estimates were derived from spaceborne lidar and tree allometry equations were retrieved from GlobAllomeTree.", "links": [ { diff --git a/datasets/GroundMSPI_ACEPOL_Radiance_Data_9.json b/datasets/GroundMSPI_ACEPOL_Radiance_Data_9.json index eb279a4318..acac3f7458 100644 --- a/datasets/GroundMSPI_ACEPOL_Radiance_Data_9.json +++ b/datasets/GroundMSPI_ACEPOL_Radiance_Data_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GroundMSPI_ACEPOL_Radiance_Data_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GroundMSPI_ACEPOL_Radiance_Data are GroundMSPI radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. \r\n\r\nGroundMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format.\r\n\r\nThis release of GroundMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. GroundMSPI provided support for the instruments aboard the ER-2, acquiring data on October 25 and November 7, 2017.", "links": [ { diff --git a/datasets/GulfCarbon_0.json b/datasets/GulfCarbon_0.json index 8dea059af1..c59052c8f8 100644 --- a/datasets/GulfCarbon_0.json +++ b/datasets/GulfCarbon_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GulfCarbon_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite Assessment of CO2 Distribution, Variability and Flux and Understanding of Control Mechanisms in a River Dominated Ocean Margin", "links": [ { diff --git a/datasets/GulfOfMaine_0.json b/datasets/GulfOfMaine_0.json index d1c71be18f..dd1b6d666d 100644 --- a/datasets/GulfOfMaine_0.json +++ b/datasets/GulfOfMaine_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "GulfOfMaine_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Gulf Of Maine during 2008 and 2009.", "links": [ { diff --git a/datasets/Gulf_Of_Maine_0.json b/datasets/Gulf_Of_Maine_0.json index 4240f25eac..8524622074 100644 --- a/datasets/Gulf_Of_Maine_0.json +++ b/datasets/Gulf_Of_Maine_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Gulf_Of_Maine_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Maine between 2004 and 2007.", "links": [ { diff --git a/datasets/Gulf_of_Maine_Nutrients_0.json b/datasets/Gulf_of_Maine_Nutrients_0.json index 1294fe9087..3fe96f66ea 100644 --- a/datasets/Gulf_of_Maine_Nutrients_0.json +++ b/datasets/Gulf_of_Maine_Nutrients_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Gulf_of_Maine_Nutrients_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of nutrients from the Gulf of Maine region from 1933 to 1991.", "links": [ { diff --git a/datasets/Gulf_of_Maine_Optics_0.json b/datasets/Gulf_of_Maine_Optics_0.json index 3302df775e..712bc4b8bd 100644 --- a/datasets/Gulf_of_Maine_Optics_0.json +++ b/datasets/Gulf_of_Maine_Optics_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Gulf_of_Maine_Optics_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of optics from the Gulf of Maine region spanning 1979 to 1996.", "links": [ { diff --git a/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json b/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json index 86172fb487..37fbe31b53 100644 --- a/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json +++ b/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H09-AHI-L2P-ACSPO-v2.90_2.90", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.

\r\nThe H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).

\r\nPixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90\r\n", "links": [ { diff --git a/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json b/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json index e6aa82aa98..d1aa2866fd 100644 --- a/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json +++ b/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H09-AHI-L3C-ACSPO-v2.90_2.90", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The H09-AHI-L3C-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.

\r\nThe H09-AHI-L3C-ACSPO-v2.90 dataset is a gridded version of the ACSPO H09-AHI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/AHI_H09-STAR-L2P-v2.90). The L3C (Level 3 Collated) data is mapped on 0.02-deg lat-lon grid and outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.

\r\nThe ACSPO H09/AHI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA for DM instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).\r\n\r\n", "links": [ { diff --git a/datasets/H3ZFC12MEXT_007.json b/datasets/H3ZFC12MEXT_007.json index 118c9262db..ae108c95a7 100644 --- a/datasets/H3ZFC12MEXT_007.json +++ b/datasets/H3ZFC12MEXT_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFC12MEXT_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Extinction at 12.1 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC12MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFC8MEXT_007.json b/datasets/H3ZFC8MEXT_007.json index 6ec2e3c118..f7e19396d6 100644 --- a/datasets/H3ZFC8MEXT_007.json +++ b/datasets/H3ZFC8MEXT_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFC8MEXT_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Extinction at 8.3 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC8MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCCFC11_007.json b/datasets/H3ZFCCFC11_007.json index 8ef82d9b70..4d692d2996 100644 --- a/datasets/H3ZFCCFC11_007.json +++ b/datasets/H3ZFCCFC11_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCCFC11_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Chlorofluorocarbon-11 (CFC-11) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC11) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-11 data is 316 to 17.8 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCCFC12_007.json b/datasets/H3ZFCCFC12_007.json index ce48951844..da36fbcb77 100644 --- a/datasets/H3ZFCCFC12_007.json +++ b/datasets/H3ZFCCFC12_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCCFC12_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Chlorofluorocarbon-12 (CFC-12) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC12) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-12 data is 316 to 8.3 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCCLONO2_007.json b/datasets/H3ZFCCLONO2_007.json index 73fc2e72ee..082542a3e5 100644 --- a/datasets/H3ZFCCLONO2_007.json +++ b/datasets/H3ZFCCLONO2_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCCLONO2_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Chlorine Nitrate (ClONO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCLONO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCGPH_007.json b/datasets/H3ZFCGPH_007.json index ab061c64e3..b8d7584cdd 100644 --- a/datasets/H3ZFCGPH_007.json +++ b/datasets/H3ZFCGPH_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCGPH_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Geopotential Height Zonal Fourier Coefficients\" version 7 data product (H3ZFCGPH) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.01 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCH2O_007.json b/datasets/H3ZFCH2O_007.json index 8539205377..2605361763 100644 --- a/datasets/H3ZFCH2O_007.json +++ b/datasets/H3ZFCH2O_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCH2O_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Water Vapor (H2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCH2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 200 to 10 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCHNO3_007.json b/datasets/H3ZFCHNO3_007.json index 98549323cf..6d2d97405f 100644 --- a/datasets/H3ZFCHNO3_007.json +++ b/datasets/H3ZFCHNO3_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCHNO3_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Nitric Acid (HNO3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCHNO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCN2O5_007.json b/datasets/H3ZFCN2O5_007.json index ea23e39a53..21e9fde1e1 100644 --- a/datasets/H3ZFCN2O5_007.json +++ b/datasets/H3ZFCN2O5_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCN2O5_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Dinitrogen Pentoxide (N2O5) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 82.5 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCN2O_007.json b/datasets/H3ZFCN2O_007.json index 99b0601b81..ec9ceba805 100644 --- a/datasets/H3ZFCN2O_007.json +++ b/datasets/H3ZFCN2O_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCN2O_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Nitrous Oxide (N2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCNO2_007.json b/datasets/H3ZFCNO2_007.json index 3e7ab3a376..a8ecc99b46 100644 --- a/datasets/H3ZFCNO2_007.json +++ b/datasets/H3ZFCNO2_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCNO2_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Nitrogen Dioxide (NO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCNO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCO3_007.json b/datasets/H3ZFCO3_007.json index 8baa64059f..2ed7fc26e1 100644 --- a/datasets/H3ZFCO3_007.json +++ b/datasets/H3ZFCO3_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCO3_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Ozone (O3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 422 to 0.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/H3ZFCT_007.json b/datasets/H3ZFCT_007.json index a83ee302e6..867e5d57c0 100644 --- a/datasets/H3ZFCT_007.json +++ b/datasets/H3ZFCT_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "H3ZFCT_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 3 Temperature Zonal Fourier Coefficients\" version 7 data product (H3ZFCT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.0042 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.", "links": [ { diff --git a/datasets/HABITATCASEY0203_2.json b/datasets/HABITATCASEY0203_2.json index af98a03d66..e9cbf30240 100644 --- a/datasets/HABITATCASEY0203_2.json +++ b/datasets/HABITATCASEY0203_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HABITATCASEY0203_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is available on the geomorphology of areas surrounding Australian Antarctic stations. This type of information is generally collected during geological surveys. This metadata record gathers a range of descriptive geomorphological information of various nature: \n\n-Habitat surveys were conducted in the season 2002-2003 in the Windmill Islands in parallel with bird nest mapping (reported in metadata record BIRDSCASEY0203) in order to study selection of nest sites by a range of species. Habitat was described in the survey sites searched for bird nests following various methods (described in BIRDSCASEY0203). Information is stored as GIS files (Arcview 3.2)\n-polygon shapefile gathering all the geomorphological units.\n-line shapefile describing habitat along transects used for searching bird nests\n-polygon shapefile describing habitat in small 25*25m quadrats used for searching bird nests\n\n-A collection of 1309 digital photos showing the sites searched for bird nests indexed by grid site number. Plus another set of 194 photos showing region of the Windmill Islands or bird nests more in detail\n-A set of Digital Elevation Models (DEM) covering the entire Windmill Islands area generated separately for 18 regions.\n-200m*200m grid created from the coverage of ice-free areas (Aerial photography 93-94) providing site numbers for the photographic database\n-A series of Black and White aerial Photos (500 m, Zeiss, 1994) scanned at high resolution for the purpose of substrate study.\n\nSee the word document in the file download for more information.\n\nThis work has been completed as part of ASAC project 1219 (ASAC_1219).\n\nThe fields in this dataset are:\n\nDate\nBoulderbig\nBouldsmall\nBaresubst\nMorsed\nScree\nSnowcover\nPermice\nSlope\nAspect\nPhotonumber\nSitedotid\nComments", "links": [ { diff --git a/datasets/HABITATMAWSON04-05_1.json b/datasets/HABITATMAWSON04-05_1.json index d6e71949e5..aab77b0867 100644 --- a/datasets/HABITATMAWSON04-05_1.json +++ b/datasets/HABITATMAWSON04-05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HABITATMAWSON04-05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is available on the geomorphology of areas surrounding Australian Antarctic stations. This type of information is generally collected during geological surveys. This metadata record gathers a range of descriptive geomorphological information of various nature: \n\n-Habitat surveys were conducted in the season 2004-2005 in the Mawson area in parallel with bird nest mapping (reported in metadata record SNPEMAWSON0405) in order to study selection of nest sites by a range of species. Habitat was described in the survey sites searched for bird nests following various methods (described in). Information is stored as GIS files (Arcview 3.2 or ArcGIS):\n-polygon shapefile gathering all the geomorphological units describing % substrate cover\n-A collection of digital photos showing the sites searched for bird nests, most if them indexed by grid site number. The grid sites numbers are located in a shapefile of 200*200m sites, below)\n-A set of Digital Elevation Models (DEM) covering the entire Mawson area generated for 5 separate regions and the derived slope, aspect, aspect to the prevailing winds, convexity raster files at a 10m resolution\n-200m*200m grid created from the coverage of ice-free areas (from Aerial photography) providing site boundaries and numbers for the photographic database\n-A series of Black and White and colour aerial Photos scanned at high resolution for the purpose of substrate study and associated 3D images.\n\nThis work has been completed as part of ASAC project 2704 (ASAC_2704).\n\nThe main fields in this dataset are:\n\nDate\nBoulderbig\nBouldsmall\nBaresubst\nMorsed\nScree\nSnowcover\nPermice\nSlope\nAspect\nSitedotid\nComments", "links": [ { diff --git a/datasets/HAB_0.json b/datasets/HAB_0.json index 097cf89765..36f4bbf274 100644 --- a/datasets/HAB_0.json +++ b/datasets/HAB_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAB_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of Harmful Algal Blooms (HABs) in the Gulf of Mexico, Chesapeake Bay, and Great Lake regions during 2006.", "links": [ { diff --git a/datasets/HALO_LiDAR_AOP_ML_Heights_1833_1.json b/datasets/HALO_LiDAR_AOP_ML_Heights_1833_1.json index b0ca4cdc40..8b575f9d11 100644 --- a/datasets/HALO_LiDAR_AOP_ML_Heights_1833_1.json +++ b/datasets/HALO_LiDAR_AOP_ML_Heights_1833_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HALO_LiDAR_AOP_ML_Heights_1833_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (*.pdf) format.", "links": [ { diff --git a/datasets/HAQES_NA_PM25_BC_1.json b/datasets/HAQES_NA_PM25_BC_1.json index 5ec22fa5cc..3744bd4f58 100644 --- a/datasets/HAQES_NA_PM25_BC_1.json +++ b/datasets/HAQES_NA_PM25_BC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_BC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). \n", "links": [ { diff --git a/datasets/HAQES_NA_PM25_BC_CENSUS_1.json b/datasets/HAQES_NA_PM25_BC_CENSUS_1.json index 4e4ba82674..2fb61de2e0 100644 --- a/datasets/HAQES_NA_PM25_BC_CENSUS_1.json +++ b/datasets/HAQES_NA_PM25_BC_CENSUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_BC_CENSUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQES_NA_PM25_BC_COUNTY_1.json b/datasets/HAQES_NA_PM25_BC_COUNTY_1.json index 11a8294473..1e9d7dbac1 100644 --- a/datasets/HAQES_NA_PM25_BC_COUNTY_1.json +++ b/datasets/HAQES_NA_PM25_BC_COUNTY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_BC_COUNTY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the county level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQES_NA_PM25_OC_1.json b/datasets/HAQES_NA_PM25_OC_1.json index c7a09d3b7b..574f6285cd 100644 --- a/datasets/HAQES_NA_PM25_OC_1.json +++ b/datasets/HAQES_NA_PM25_OC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_OC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQES_NA_PM25_OC_CENSUS_1.json b/datasets/HAQES_NA_PM25_OC_CENSUS_1.json index 09d9b49e77..665439c593 100644 --- a/datasets/HAQES_NA_PM25_OC_CENSUS_1.json +++ b/datasets/HAQES_NA_PM25_OC_CENSUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_OC_CENSUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the census level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQES_NA_PM25_OC_COUNTY_1.json b/datasets/HAQES_NA_PM25_OC_COUNTY_1.json index 0843af9af6..80b11dcea0 100644 --- a/datasets/HAQES_NA_PM25_OC_COUNTY_1.json +++ b/datasets/HAQES_NA_PM25_OC_COUNTY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_OC_COUNTY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the county level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n", "links": [ { diff --git a/datasets/HAQES_NA_PM25_TOT_1.json b/datasets/HAQES_NA_PM25_TOT_1.json index 9379b9c2e5..3cedfcfa9e 100644 --- a/datasets/HAQES_NA_PM25_TOT_1.json +++ b/datasets/HAQES_NA_PM25_TOT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_TOT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). \n", "links": [ { diff --git a/datasets/HAQES_NA_PM25_TOT_CENSUS_1.json b/datasets/HAQES_NA_PM25_TOT_CENSUS_1.json index 1567bee44d..81a2194db2 100644 --- a/datasets/HAQES_NA_PM25_TOT_CENSUS_1.json +++ b/datasets/HAQES_NA_PM25_TOT_CENSUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_TOT_CENSUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the census level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQES_NA_PM25_TOT_COUNTY_1.json b/datasets/HAQES_NA_PM25_TOT_COUNTY_1.json index d80bc95943..dbe39a3787 100644 --- a/datasets/HAQES_NA_PM25_TOT_COUNTY_1.json +++ b/datasets/HAQES_NA_PM25_TOT_COUNTY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQES_NA_PM25_TOT_COUNTY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the county level over the continental United States (CONUS). \n\nThe Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). ", "links": [ { diff --git a/datasets/HAQ_TROPOMI_NO2_CONUS_A_L3_2.4.json b/datasets/HAQ_TROPOMI_NO2_CONUS_A_L3_2.4.json index f8d4eecb16..ae1e4bac39 100644 --- a/datasets/HAQ_TROPOMI_NO2_CONUS_A_L3_2.4.json +++ b/datasets/HAQ_TROPOMI_NO2_CONUS_A_L3_2.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQ_TROPOMI_NO2_CONUS_A_L3_2.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01\u02da x 0.01\u02da (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in 2019 and continues to the present. \n\nThis L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.\n\nNO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. ", "links": [ { diff --git a/datasets/HAQ_TROPOMI_NO2_CONUS_M_L3_2.4.json b/datasets/HAQ_TROPOMI_NO2_CONUS_M_L3_2.4.json index d728c3dca4..213aa66d11 100644 --- a/datasets/HAQ_TROPOMI_NO2_CONUS_M_L3_2.4.json +++ b/datasets/HAQ_TROPOMI_NO2_CONUS_M_L3_2.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQ_TROPOMI_NO2_CONUS_M_L3_2.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01\u02da x 0.01\u02da (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in May 2018 and continues to the present. \n\nThis L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.\n\nNO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. ", "links": [ { diff --git a/datasets/HAQ_TROPOMI_NO2_CONUS_S_L3_2.4.json b/datasets/HAQ_TROPOMI_NO2_CONUS_S_L3_2.4.json index 979072f055..a463a247d6 100644 --- a/datasets/HAQ_TROPOMI_NO2_CONUS_S_L3_2.4.json +++ b/datasets/HAQ_TROPOMI_NO2_CONUS_S_L3_2.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQ_TROPOMI_NO2_CONUS_S_L3_2.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides level 3 seasonal averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01\u02da x 0.01\u02da (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in June-August 2018 and continues to the present. \n\nThis L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.\n\nNO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. ", "links": [ { diff --git a/datasets/HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4.json b/datasets/HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4.json index 2349c26503..71eb0b55c6 100644 --- a/datasets/HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4.json +++ b/datasets/HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1\u02da x 0.1\u02da (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. \n\nThis L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.\n\nNO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. ", "links": [ { diff --git a/datasets/HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4.json b/datasets/HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4.json index 2799578a79..5dfd7d1c8c 100644 --- a/datasets/HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4.json +++ b/datasets/HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1\u02da x 0.1\u02da (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. \n\nThis L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.\n\nNO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. ", "links": [ { diff --git a/datasets/HAWKEYE_L1_1.json b/datasets/HAWKEYE_L1_1.json index 65df51a9b6..4dcc1d5662 100644 --- a/datasets/HAWKEYE_L1_1.json +++ b/datasets/HAWKEYE_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAWKEYE_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide high quality, high resolution imagery (120 meter) of the open ocean, coastal zones, lakes, estuaries and land features. This ability provides a valuable complement to the lower resolution measurements from previous missions like SeaWiFS, MODIS and VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and is funded by the Moore Foundation under a grant for the Sustained Ocean Color Observations with Nanosatellites (SOCON).", "links": [ { diff --git a/datasets/HAWKEYE_L2_OC_2022.0.json b/datasets/HAWKEYE_L2_OC_2022.0.json index f33211f69e..6ae90c80a5 100644 --- a/datasets/HAWKEYE_L2_OC_2022.0.json +++ b/datasets/HAWKEYE_L2_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HAWKEYE_L2_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide high quality, high resolution imagery (120 meter) of the open ocean, coastal zones, lakes, estuaries and land features. This ability provides a valuable complement to the lower resolution measurements from previous missions like SeaWiFS, MODIS and VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and is funded by the Moore Foundation under a grant for the Sustained Ocean Color Observations with Nanosatellites (SOCON).", "links": [ { diff --git a/datasets/HCDN_810_1.json b/datasets/HCDN_810_1.json index facb12a93b..a5811d48f9 100644 --- a/datasets/HCDN_810_1.json +++ b/datasets/HCDN_810_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HCDN_810_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990.The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999.Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/HDDS_Baseline_Adhoc.json b/datasets/HDDS_Baseline_Adhoc.json index 9050362170..139226fb75 100644 --- a/datasets/HDDS_Baseline_Adhoc.json +++ b/datasets/HDDS_Baseline_Adhoc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HDDS_Baseline_Adhoc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) Emergency Operations, in support of the Department of Homeland Security, provides imagery and resources for use in disaster preparations, rescue and relief operations, damage assessments, and reconstruction efforts. A variety of products, however ,not limited to, include: multiple types of satellite and aerial imagery, maps, products, presentations and data source documents.", "links": [ { diff --git a/datasets/HE_DEM_5MIN.json b/datasets/HE_DEM_5MIN.json index 876b6c8f86..a4316794ac 100644 --- a/datasets/HE_DEM_5MIN.json +++ b/datasets/HE_DEM_5MIN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HE_DEM_5MIN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following text was abstracted from Bruce Gittings' Digital Elevation\nData Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'. The catalogue is a\ncomprehensive source of information on digital elevation data and should be\nretrieved in its entirity for additional information.\n\n\n Global land and seafloor elevations exist... in ASCII on IBM-formatted\nfloppy disk as a 5 degree quad at 5 arc second resolution for $75 or a one\ndegree quad at 12 arc second resolution for $195 (designate the SW corner of\nthe required quad in each case). Data may be redistributed for non commercial\npurposes only. The following data are available for each USGS 7.5' quadrangle.\n Data is arranged and sold by layers. Files are in AutoCAD format. Data is\nunder copyright.\n\n Basic roads.............. $80 Enhanced roads.......... $100\n Double line roads....... $150 Geographic names......... $40\n County Lines............. $80 Township Range and Section\n Lines... $80\n Contours................ $160 Terrain Relief Grid..... $160\n Quicksurf Compatible x,y,z ascii... $160", "links": [ { diff --git a/datasets/HI176_hydrographic_survey_1.json b/datasets/HI176_hydrographic_survey_1.json index 0a4e8b690c..7f8721e8ed 100644 --- a/datasets/HI176_hydrographic_survey_1.json +++ b/datasets/HI176_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI176_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI176 at Macquarie Island in December 1993. The main survey area was adjacent to the north-east coast between North Head and The Nuggets. Survey lines were also followed part way down the west coast of the island and in the vicinity of Judge and Clerk Islets and Bishop and Clerk Islets. \nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LT A.J.Withers.\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI242_hydrographic_survey_1.json b/datasets/HI242_hydrographic_survey_1.json index 2af89f705e..4b8430075d 100644 --- a/datasets/HI242_hydrographic_survey_1.json +++ b/datasets/HI242_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI242_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI242 at Macquarie Island in November and December 1996. The main survey areas were Buckles Bay and Hasselborough Bay. Survey lines were also followed from Elliott Reef down the west coast to Langdon Bay and down the east coast to Buckles Bay.\nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LT M.A.R.Matthews.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI256_hydrographic_survey_1.json b/datasets/HI256_hydrographic_survey_1.json index f21e1d9b29..9b9d27a50a 100644 --- a/datasets/HI256_hydrographic_survey_1.json +++ b/datasets/HI256_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI256_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI256 at Casey, February to March 1997. The survey areas were north-west of the station near the Frazier Islands and Donovan Islands and south-west of the station between Beall Island and Holl Island.\nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LT M.A.R.Matthews.\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI290_hydrographic_survey_1.json b/datasets/HI290_hydrographic_survey_1.json index 48ef543b6b..f7c6512dca 100644 --- a/datasets/HI290_hydrographic_survey_1.json +++ b/datasets/HI290_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI290_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI290 at Heard Island, February to March 1997.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LT R.D.Bowden.\nThe spatial extent given in this metadata record is that of Heard Island as the spatial extent of the survey is unknown to the Australian Antarctic Data Centre.\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI320_hydrographic_survey_1.json b/datasets/HI320_hydrographic_survey_1.json index 8dd1d16d56..95e81945a8 100644 --- a/datasets/HI320_hydrographic_survey_1.json +++ b/datasets/HI320_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI320_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI320 at Commonwealth Bay, December 2000 to January 2001. The survey area was the north and north-west coast of Cape Denison, including Boat Harbour. Some survey lines were also followed further out in Commonwealth Bay, including in the vicinity of Mackellar Islands.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was conducted by LCDR J.Daetz.\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI333_hydrographic_survey_1.json b/datasets/HI333_hydrographic_survey_1.json index 934945e8c0..a419863b3b 100644 --- a/datasets/HI333_hydrographic_survey_1.json +++ b/datasets/HI333_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI333_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI333 at Corinthian Bay, Heard Island, March 2001.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by G.K.Colledge.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI350_hydrographic_survey_1.json b/datasets/HI350_hydrographic_survey_1.json index 586d93c1f9..7a4b33e9ea 100644 --- a/datasets/HI350_hydrographic_survey_1.json +++ b/datasets/HI350_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI350_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI350 at Mawson, January to March 2002. The survey area was north of the station between Williams Rocks and Parallactic Islands and also at Moller Bank.\nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LCDR M.Pounder.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI364_hydrographic_survey_1.json b/datasets/HI364_hydrographic_survey_1.json index dee04d1ca3..daa04d8b8f 100644 --- a/datasets/HI364_hydrographic_survey_1.json +++ b/datasets/HI364_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI364_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI364 at Mawson, January to March 2003. The survey areas were north-west and south-west of the station.\nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by LCDR M.B.Rigby.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI468_hydrographic_survey_1.json b/datasets/HI468_hydrographic_survey_1.json index 7bd1723185..37d8d4c7e2 100644 --- a/datasets/HI468_hydrographic_survey_1.json +++ b/datasets/HI468_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI468_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI468 at Davis, January to March 2010. The survey was conducted jointly with Geoscience Australia and the Australian Antarctic Division. The main survey area was near Davis but additional survey lines were followed to Long Fjord to the north and to Crooked Fjord and the Sorsdal Glacier in the south.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available from the Australian Antarctic Data Centre by request.\nThe RAN Australian Hydrographic Service team was lead by LCDR R.D.Bowden.\n\nThe data are not suitable for navigation.\n\nGeoscience Australia produced bathymetric and backscatter gridded datasets from the survey data which are available via the metadata record 'Coastal seabed mapping survey, Vestfold Hills, Antarctica, February-March 2010' with Entry ID: Davis_multibeam_grids.\n\nThe Australian Antarctic Division produced two bathymetric maps from the survey data. See Related URLs in this metadata record.", "links": [ { diff --git a/datasets/HI514_hydrographic_survey_1.json b/datasets/HI514_hydrographic_survey_1.json index c0411c0c1c..7a806427b2 100644 --- a/datasets/HI514_hydrographic_survey_1.json +++ b/datasets/HI514_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI514_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI514 at Mawson, February to March 2012. \nThe areas surveyed were the entrance to Horseshoe Harbour and the western side of West Arm. There is also a single line of soundings east of Evans Island.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download (see a Related URL).\nThe vertical datum of the soundings is Lowest Astronomical Tide, 0.83 metres below Mean Sea Level.\nThe survey was lead by LT C.E.Diplock.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI545_hydrographic_survey_1.json b/datasets/HI545_hydrographic_survey_1.json index 1cf7375fa3..91a3e4edc2 100644 --- a/datasets/HI545_hydrographic_survey_1.json +++ b/datasets/HI545_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI545_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI545 at Casey, December 2013 to January 2014. The survey areas were Newcomb Bay and O'Brien Bay. A multibeam sonar system was used.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available from the Australian Antarctic Data Centre on request.\nThe survey was lead by LT P.S.Waring.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI560_hydrographic_survey_1.json b/datasets/HI560_hydrographic_survey_1.json index 7b47c644a8..dda1572451 100644 --- a/datasets/HI560_hydrographic_survey_1.json +++ b/datasets/HI560_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI560_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted hydrographic survey HI560 at Casey, December 2014 to February 2015. The survey was conducted jointly with Geoscience Australia.\nThe survey area was offshore from Clark Peninsula south to Beall Island, but not including Newcomb Bay and O'Brien Bay which were surveyed in 2013/14 (see the metadata record with ID HI545_hydrographic_survey). A multibeam sonar system was used.\nThe survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office.\nThe RAN Australian Hydrographic Service team was lead by LCDR G.A.Walker.\n\nThe data are not suitable for navigation.", "links": [ { diff --git a/datasets/HI607_hydrographic_survey_1.json b/datasets/HI607_hydrographic_survey_1.json index 552334a675..2ef00d7e51 100644 --- a/datasets/HI607_hydrographic_survey_1.json +++ b/datasets/HI607_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI607_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This terrestrial dataset was collected at Ursula Harris\u2019s behest by Craig Hamilton and a Naval Survey team on 09 January 2018 when sea conditions prevented the team from taking bathymetric measurements. This survey was intended to fill gaps in the existing Mawson Station survey data and includes 29 previously unrecorded features comprised of bollards, HF towers, flagpoles, masts, antennae, ionosonde transmitter and receiver, the Mawson Signpost and the Douglas Mawson Bust.", "links": [ { diff --git a/datasets/HI634_hydrographic_survey_1.json b/datasets/HI634_hydrographic_survey_1.json index 95e85260c4..f56e13fc58 100644 --- a/datasets/HI634_hydrographic_survey_1.json +++ b/datasets/HI634_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI634_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division identified areas that required hydrographic surveying. \n(See map available in the download at \\Plans and Instructions\\HPS Supplied Data\\davis_plan_2019_2020 version 5.1.pdf and a shapefile of the identified areas at FSD\\ArcGIS\\Pink V2\\AOI_Unproject_wgs84.shp)\n \nA team from the Maritime Geospatial Warfare Unit, of the Australian Hydrographic Service, was at Davis in early February 2020. Single beam and side scanning survey data was collected on the water, beach profiles collected and rock data. \n \nSingle beam and side scanning survey data\nAreas A, D, F, H, I, J and K were ice free.\nArea J was further broken down into four areas, J1, J2, J3 and J4.\nAreas A, D and F were thoroughly surveyed with 10m mainline spacing with 20m X-line spacing.\nAreas I, J3 and J4 were surveyed but due to time constraints were surveyed at approximately 40m line spacing to provide 200% sea floor coverage with the SSS to detect any features dangerous to navigation with one shoal detected in area I which is mentioned in Section I.\nArea H was too shallow to survey at any other time except high tide and it was decided to focus on other areas as the survey of this area would not value add to the required results of the survey.\nArea J1, J2 and K were not surveyed due to time constraints.\n \nRTK corrections or access to the CORS network couldn't be made to the CEESCOPE survey system. Instead positioning during the survey was recorded exclusively with the NovaTel GNSS 850 Antenna. No post processing was conducted. The team wasn't able to determine why the CEESCOPE was unable to connect to the CORS network or Base Station to gain RTK corrections, despite considerable effort spent problem solving and conducting a number of trials. Tide data collected was applied to the data and all tidal information is explained in section F of the report.\n \nA map showing the surveyed areas can be found in the report. \nRaw data in caris format is available from the Australian Hydrographic Office (AHO). Sounding data, stored as a shapefile, is available as a download file.\n\nBeach profiles\nSites were also surveyed with 5m line spacing to maximise seafloor coverage, at 5 beach locations, 4 in area A and 1 in Area F. ArcGIS projects and PDF documents displaying the depth data and significant rocks are included in the download. Please note the ArcGIS projects do not include the AHO chart, due to distribution restrictions on digital charts. It is included in the PDF documents. These documents refer to images taken from the survey boat and spreadsheets displaying gradients data.\n \nRock data\nA shapefile recording conspicuous rocks as well as photographs is available for downloading.\n \nBench mark positions were reclaimed using Trimble R10 and post processed with AUSPOS.\n \nAbbreviations used in the download directories \nROS = Report of Survey, \nFSD = Final Survey Data\n \nA detailed report can be found at /ROS/\n\nProjection\u2026\u2026..\u2026...\u2026...\u2026\u2026\u2026\u2026.\u2026.\u2026\u2026..Universal Transverse Mercator (UTM) Zone 43 South\nHorizontal Datum\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026World Geodetic System 1984 (WGS84)\nVertical Datum\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.....Approximated Lowest Astronomical Tide (LAT)\nSounding Depths.\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Metres (m)\n \nSurvey Date\u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026\u2026\u2026.6th - 18th Feb 2020\nBathymetric Accuracy Horizontal\u2026\u2026\u2026\u2026\u2026\u00b1 0.8m\nBathymetric Accuracy Vertical\u2026\u2026\u2026\u2026\u2026\u2026\u00b10.46m\nSounding Density\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..2m Surface\nChart Reference\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026AUS 451, 602\u200b\n\nITRF 2014 and GRS80 were utilised for static observations of bench marks and levelling to the tide pole for establishment of approximate LAT. Hypack v19.1.11.0 which was used to gather all bathymetric data does not have the option to use the ITRF datum and the WGS84 Datum was used.", "links": [ { diff --git a/datasets/HICO_L0_1.json b/datasets/HICO_L0_1.json index ea72b5c657..a9106f706a 100644 --- a/datasets/HICO_L0_1.json +++ b/datasets/HICO_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HICO_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", "links": [ { diff --git a/datasets/HICO_L1_2.json b/datasets/HICO_L1_2.json index 16af289e62..50d43c3420 100644 --- a/datasets/HICO_L1_2.json +++ b/datasets/HICO_L1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HICO_L1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", "links": [ { diff --git a/datasets/HICO_L2_OC_2022.0.json b/datasets/HICO_L2_OC_2022.0.json index e31be4ac5b..1507ae3538 100644 --- a/datasets/HICO_L2_OC_2022.0.json +++ b/datasets/HICO_L2_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HICO_L2_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", "links": [ { diff --git a/datasets/HIC_NMOrthos_1.json b/datasets/HIC_NMOrthos_1.json index 7aa5ff233b..ded24ea22d 100644 --- a/datasets/HIC_NMOrthos_1.json +++ b/datasets/HIC_NMOrthos_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIC_NMOrthos_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthophoto is a rectified georeferenced image of the Heard Island Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric Hasselblad and Linhof cameras (focal length unknown).\n\nThe photos are between 17 MB and 193 MB each, and are in tiff format with associated world files.", "links": [ { diff --git a/datasets/HIC_PHOTO_NMOrtho_TopoMapping_1.json b/datasets/HIC_PHOTO_NMOrtho_TopoMapping_1.json index 14b9d313f0..ab17da53c9 100644 --- a/datasets/HIC_PHOTO_NMOrtho_TopoMapping_1.json +++ b/datasets/HIC_PHOTO_NMOrtho_TopoMapping_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIC_PHOTO_NMOrtho_TopoMapping_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Heard Island Topographic Data was mapped from Ortho-rectified non-metric photography. The data consists of Coastline, Glacier, Lagoon, Offshore Rocks, Water Storage and Watercourse datasets digitised from the photography, all of which are available for download at the url given below.", "links": [ { diff --git a/datasets/HIMI_Demersal_Fish_1.json b/datasets/HIMI_Demersal_Fish_1.json index 0d44ddc184..413fe68235 100644 --- a/datasets/HIMI_Demersal_Fish_1.json +++ b/datasets/HIMI_Demersal_Fish_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIMI_Demersal_Fish_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains demersal fish data from 181 trawls from the HIMS 1989-90 (AADC-0075), FISHOG 1991-92 (AADC-00080) and THIRST 1993-94 (AADC-00073) surveys as well as a selection of matching environmental data, most obtained from the Polar_Environmental_Data AADC dataset. The dataset IDs above contain the full metadata for each survey and many environmental layers. The fish data has been modified from the data in the Historical Fish Data database in the following ways:\n1) Fish abundances are those recorded in the trawl logs (i.e. not limited to the ~200 measured and weighed for abundant species)\n2) Trawls noted as having issues in log books or without complete coverage of the selected environmental covariates were omitted\n3) Species that were rare (present at less than 8 sites) were omitted, leaving 15 species.\n\nThe dataset consists of a spreadsheets called fish.csv. It contains fish abundance records and associated environmental covariates for each haul\n\nBelow are descriptions of field headers in the fish.csv file as well as source, units and resolution for environmental covariates\n\nColumn Name Description Units Resolution Source\n1 Champsocephalus_gunnari Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n2 Lepidonotothen_squamifrons Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n3 Channichthys_rhinoceratus Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n4 Dissostichus_eleginoides Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n5 Macrourus_holotrachys Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n6 Paradiplospinus_gracilis Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n7 Bathyraja_eatonii Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n8 Zanclorhynchus_spinifer Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n9 Lycodapu_antarcticus Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n10 Bathyraja_murrayi Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n11 Lepidonotothen_mizops Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n12 Muraenolepis_sp Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n13 Paraliparis_operculosus Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n14 Gobionotothen_acuta Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n15 Melanostigma_gelatinosum Species abundance Count NA AAD Historical Fish Database checked against trawl logs\n16 Latitude Latitude of trawl midpoint Decimal degree NA Midpoint of trawl from trawl logs\n17 Longitude Longitude of trawl midpoint Decimal degree NA Trawl logs\n18 Geomorphology Geomorphology Bank, Plateau, Plateau_slope 0.1 degree Polar_Environmental_Data\n19 Season Season/year Autumn_1990, Summer_1992, Spring_1993 NA Trawl logs\n20 Seafloor_temperature Average temperature near seafloor from CAISOM model Degrees C 0.1 degree Polar_Environmental_Data\n21 SST_spatial_gradient Spatial gradient of sst_summer_climatology (from MODIS 2002-2010) Degrees C/ km 0.1 degree Polar_Environmental_Data\n22 SST_summer_climatology Average austral summer SST (from MODIS 2002-2010) Degrees C 0.1 degree Polar_Environmental_Data\n23 Seafloor_salinity Average bottom salinity PSU 0.5 degree CSIRO Atlas of Regional Seas: www.cmar.csiro.au/cars\n24 Seafloor_salinity_SD Standard deviation of bottom salinity PSU 0.5 degree CSIRO Atlas of Regional Seas: www.cmar.csiro.au/cars\n25 Surface_temperature Average of daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)*\n26 Surface_temperature_var Variance in daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)*\n27 Surface_temperature_var_ds Variance after removing seasonal cycle (detrended) in daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)*\n28 Surface_temperature_trend Slope of linear regression over timeseries Degrees C/ year 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)*\n29 Chlorophyll_a_yearly_mean Standard deviation of annual chla mean mg/m3 0.2 degree mean of yearly mean chl-a (1997-2010) from corrected L3 SeaWiFs data ^\n30 Chlorophyll_a_yearly_SD Average of annual chla mean mg/m3 0.2 degree mean of yearly mean chl-a (1997-2010) from corrected L3 SeaWiFs data ^\n31 Haul_depth Average depth of haul m NA Trawl logs\n32 Bathymetry_slope Seafloor slope Degrees 0.1 degree Polar_Environmental_Data\n33 Seafloor_current_speed Average current speed near seafloor from CAISOM model m/s2 0.1 degree Polar_Environmental_Data\n34 Chlorophyll_a_summer_climatology Average austral summer chla concentration mg/m3 0.1 degree Polar_Environmental_Data\n35 Haul_Index Haul Identifier NA AAD Historical Fish Database \n \n^ Daily SeaWiFs values corrected for the Southern Ocean using: Johnson, R., et al. (2013). \"Three improved satellite chlorophyll algorithms for the Southern Ocean.\" Journal of Geophysical Research: Oceans 118(7): 3694-3703.\n*NOAA High Resolution SST data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/ \n* Reynolds, Richard W., Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496. Reynolds, Richard W., Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.", "links": [ { diff --git a/datasets/HIMI_Fisheries_Sectors_1.json b/datasets/HIMI_Fisheries_Sectors_1.json index c0de3a59dd..5d7ced1b46 100644 --- a/datasets/HIMI_Fisheries_Sectors_1.json +++ b/datasets/HIMI_Fisheries_Sectors_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIMI_Fisheries_Sectors_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset covers the area of the Australian Exclusive Economic Zone (EEZ) around Heard Island and McDonald Islands (HIMI). \nThe Fisheries sector areas were originally created by Dick Williams, former Fisheries biologist at the Australian Antarctic Division for the HIMI fishery, to define areas of research fishing for the first longline vessels to fish at HIMI in 2003 and 2004.\nThis dataset consists of a polygon shapefile representing the sector areas and a map displaying the sector areas. Each polygon has the attributes polygon number and area in square kilometres. The dataset was created in December 2015 using current boundaries as listed in the Quality section of this record.", "links": [ { diff --git a/datasets/HIMI_RSTS_Strata_1.json b/datasets/HIMI_RSTS_Strata_1.json index d24f2be955..cd2d50ac02 100644 --- a/datasets/HIMI_RSTS_Strata_1.json +++ b/datasets/HIMI_RSTS_Strata_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIMI_RSTS_Strata_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A trawl survey is conducted each year at Heard Island and McDonald Islands (HIMI) to assess the abundance and biology of fish and invertebrate species. The survey provides information for input into the stock assessments for the two main fished species, Patagonian toothfish (Dissostichus eleginoides), and mackerel icefish (Champsocephalus gunnari). In addition, it provides information on biodiversity and bycatch species from the fishery. \n\nNine strata were defined as areas for sampling during the annual Random Stratified Trawl Survey (RSTS) conducted on board an industry vessel. The area of the plateau down to 1000 metres depth was divided into nine strata, each covering an area of similar depth and/or fish abundance. A number of randomly allocated stations (between 10 and 30) are sampled in each stratum during every survey to assess the abundance of juvenile and adult toothfish on the shallow and deep parts of the Heard Island Plateau (300 to 1000 metres depth) and to assess the abundance of mackerel icefish on the Heard Island Plateau. \n\nAlthough the number and boundaries of strata have been adjusted over the years, they have been consistent since 2002 (Welsford et al. 2006).\n\nThis dataset consists of a polygon shapefile representing the strata and a map displaying the strata.", "links": [ { diff --git a/datasets/HIMI_seabed_areas_1.json b/datasets/HIMI_seabed_areas_1.json index 0e2ebc796b..fb64246b8d 100644 --- a/datasets/HIMI_seabed_areas_1.json +++ b/datasets/HIMI_seabed_areas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIMI_seabed_areas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a spreadsheet with planimetric areas of the seabed within the Heard Island and McDonald Islands Marine Reserve and adjacent Conservation Zone. The areas are provided for one hundred metre depth ranges and are given in square kilometres.\nThe areas were calculated for the Wildlife Conservation and Fisheries research group at the Australian Antarctic Division.\nDepth data was sourced from a bathymetric grid of the Kerguelen Plateau by R.J.Beaman of James Cook University, Australia and P.E.O'Brien of Geoscience Australia and published by Geoscience Australia. See a Related URL below for a link to the metadata record describing the bathymetric grid.\nThe Marine Reserve and Conservation Zone boundaries were sourced from the Australian Government's Australian Marine Parks Division. See the provided URL for a link to the department's website.", "links": [ { diff --git a/datasets/HIMYCO_1.json b/datasets/HIMYCO_1.json index 571e6fdfe6..320662d743 100644 --- a/datasets/HIMYCO_1.json +++ b/datasets/HIMYCO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIMYCO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Personnel\n\nYVES FRENOT 1, DANA M. BERGSTROM 2, J.C. GLOAGUEN 3, R. TAVENARD 4 and D.G. STRULLU 4\n\n1 UMR CNRS 6553 Ecobio, Universit de Rennes 1, Station Biologique, 35380 Paimpont, France,\n2 Australian Antarctic Division, Channel Highway, Kingston, Tasmania 7050, Australia,\n3 UMR CNRS 6553 Ecobbio, Universit de Rennes 1, campus de Beaulieu, 35042 Rennes cedex, France,\n4 Lab. de Biologie et Physiologie V gtales, Universit d'Angers, 2 Bd Lavoisier, 49045 Angers cedex, France.\n\nSummary\n\nRoots of nine vascular plant species collected from subantarctic Heard Island were examined for mycorrhizae. Most of these species showed associations with vesicular-arbuscular mycorrhizae or dark septate mycorrhizae. The degree of root infection varied considerably within the sites, appearing to have an inverse relationship with the availability of nutrients in soil. As mycorrhizae are known to play an important role in the nutrient uptake by host-plants, the results suggest that mycorrhizae influence the capacities of plants to colonise in cold and low-nutrient environments such as subantarctic glacier forelands.\n\nDetails of Sampling sites\nPlant samples were collected in the nine following sites from the eastern side of the island :\n\n1 Unstable Feldmark (Site 1) - 53 6'47.5S-73 42'55.5E, 100m a.s.l.: sheltered east side of a moraine, just under a crest. Vegetation dominated by Pringlea antiscorbutica with low cover (less than 20%) and sparse individuals of Poa kerguelensis, Colobanthus kerguelensis and small cushions of Azorella selago. The total vegetation cover did not exceeded 40%. The mineral soil was coarse.\n\n2 Open cushion carpet (Site 2) - 53 6'45.6S-73 43'07.6E, 43 m a.s.l.: gentle slope (3) at the bottom of a morainic slope, oriented east, with low vegetation cover (less than 40%) dominated by Azorella selago cushions. Poa kerguelensis, Colobanthus kerguelensis and bryophytes were also present. Soil was mineral. Presence of some burrows of petrels.\n\n3 Closed cushion carpet (Site 3) - 53 6'43.6S-73 43'13.1E, 29 m a.s.l.: flat area covered with large cushions of Azorella selago which were coalesced into extensive carpets. Bryophytes were locally developed at the bottom of cushions. Soil was mineral between the cushion but peat accumulated under the vegetation. Few burrows of petrels were prs were present.\n\n4 Pringlea hebfield slope (Site 4) - 53 6'32.3S-73 43'13.4E, 23 m a.s.l.: Morainic slope (20) oriented east, with a pure stand of Kerguelen cabbage, Pringlea antiscorbutica (greater than 80 % cover). Soil was organic and deep (greater than 50 cm).\n\n5 Wet biotic vegetation (Site 5) - 53 6'39.3S-73 43'22.8E, 19 m a.s.l.: flat area occupied by several ponds (1-5 m in area). The plant community showed the highest species richness, including Acaena magellanica, Poa cookii, Deschampsia antarctica, Callitriche antarctica, Azorella selago, Colobanthus kerguelensis and numerous bryophytes. Soil was peaty in concave areas and more mineral elsewhere. This site was occasionally visited by fur seals or King Penguins during the moult.\n\n6 Maritime biotic vegetation (Site 6) - 53 6'34.2S-73 43'25.7E, 15 m a.s.l.: coastal area characterised by tussocks of Poa cookii and Azorella selago cushions forming a chaotic microrelief. Callitriche antarctica grew at the bottom of tussocks. Soil was mainly sandy.\n\n7 Stephenson glacier forelands (Site 7) - 53 5'54.8S-73 41'40.2E, 4 m a.s;l.: flat area near the proglacial lake. Poa annua grew either in close communities where it was dominant (other species being Poa kerguelensis, Deschampsia antarctica, Azorella selago, Callitriche antarctica and Pringlea antiscorbutica), or in open communities where it grew as sparse individuals. Soil was mineral and, in some places, very rich in fine particles (thixotropy).\n\n8 Winston glacier forelands (Site 8) - 53 9'20.6S-73 38'30.8E, 8 m a.s.l.: Mossy seepage areas near snout of the Winston Glacier. P. annua grew in a stream-line on a very young morainic deposit, with Acaena magellanica, Montia fontana and liverworts.\n\n9 Skua Beach (Site 9) - 53 5'18.8S-73 40'38.9E, 5 m a.s.l.: On moraine outwash plain approximately 200m inland, at seaward edge of extensive area of moss flushes (with Poa annua, Pringlea antiscorbutica, Deschampsia antarctica, Montia fontana, Acaena magellanica) growing along braided streams aided streams and coalescing to form large expanses of wet vegetation. This area was under ice in 1947.\n\nThis metadata record is part of ASAC project 1015 (ASAC_1015). ASAC project 1015 forms part of the Regional Sensitivity to Climate Change (RiSCC) program.\n\nSee Publication/Reference for citation of a paper which includes the data described by this metadata record.\nThe paper is available for download from the provided URL. See also Access Constraints.", "links": [ { diff --git a/datasets/HIR3SCOL_007.json b/datasets/HIR3SCOL_007.json index d9c28eed6d..3b5177737c 100644 --- a/datasets/HIR3SCOL_007.json +++ b/datasets/HIR3SCOL_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIR3SCOL_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HIR3SCOL is the EOS High Resolution Dynamics Limb Sounder (HIRDLS/Aura) level 3 daily gridded 1 x 1 deg. stratospheric columns of NO2 (nitrogen dioxide) data product. The data are gridded at 1 x 1 degree resolution from +80 to -64 degrees latitude. The stratospheric column is computed from data at 57 to 1.0 hPa. The product consists of one file spanning the entire ~3 year HIRDLS mission from January 22, 2005 through March 17, 2008. Users of the HIR3SCOL data product should read the Version 7 HIRDLS Data Description and Quality document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains one grid object with data fields, attributes, and metadata.", "links": [ { diff --git a/datasets/HIRDLS2_007.json b/datasets/HIRDLS2_007.json index 13bfa5990e..424fc549a1 100644 --- a/datasets/HIRDLS2_007.json +++ b/datasets/HIRDLS2_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIRDLS2_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"HIRDLS/Aura Level 2 Geophysical Parameters\" data product (HIRDLS2) contains an entire day's worth of Level-2 vertical profiles of O3, HNO3, H2O, CFC-11, CFC-12, N2O, NO2, N2O5, ClONO2, temperature, geopotential height, and aerosol extinction at 12.1 and 8.3 microns, as well as cloud top pressure. HIRDLS measured infrared emissions in 21 channels ranging from 6.12 to 17.76 microns in the upper troposphere, stratosphere and mesosphere. Data are available for the ~3 year mission from January 29, 2005 until March 17, 2008. Observations of the Earth's atmosphere were only made from the far azimuth scan (away from sun side) resulting in limited data coverage from +80 to -64 degrees latitude. The useful vertical range of the data depends on the measured species, and are provided on 24 levels per decade of pressure corresponding to about 1 km vertical resolution. The current and final version of this product is 7. Of the original targeted species, only CH4 was not retrieved in this version.\n\nThe data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a single swath object with one day of data (measured species and species precision), geolocation fields (e.g. time, latitude, longitude, pressure), and swath attributes, along with file level metadata. Each file contains approximately 5600 profile scans.", "links": [ { diff --git a/datasets/HIRMLS3IWC_002.json b/datasets/HIRMLS3IWC_002.json index 270b915048..3b4573779b 100644 --- a/datasets/HIRMLS3IWC_002.json +++ b/datasets/HIRMLS3IWC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIRMLS3IWC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HIRMLS3IWC is the Joint EOS High Resolution Dynamics Limb Sounder (HIRDLS) and Microwave Limb Sounder (MLS) monthly 10 degreee lat x 20 degreee lon gridded product for ice water content (IWC) data. This is version 2 released to the public, with the original input coming from v3.3 MLS and v7 HIRDLS. The grid spatial coverage is near-global (-80 to +90 degrees latitude). The product contains HIRDLS and MLS IWC data for the time of the HIRDLS mission from February 1, 2005 through December 31, 2007. The useful vertical range of the data is from 215 to 82 hPa for both HIRDLS and MLS, and the vertical resolution is about 1.5 km for HIRDLS and 3 km for MLS. Users of the HIRMLS3IWC data product should read the Version 2 HIRDLS-MLS Level 3 IWC Data Description and Quality document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains two grid objects (one with HIRDLS data, the other with MLS data), each with a set of data fields, attributes, and metadata. Each grid contains data fields with IWC values, and the HIRDLS grid includes data fields with volume density, cloud top pressure and frequency of clouds. Time, latitude and vertical pressure information are also included in each grid.", "links": [ { diff --git a/datasets/HIRSN6IM_001.json b/datasets/HIRSN6IM_001.json index f5f1fd2fa3..a53d7e1852 100644 --- a/datasets/HIRSN6IM_001.json +++ b/datasets/HIRSN6IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIRSN6IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HIRSN6IM data product consists of images of brightness temperatures on 70 mm film strips from the Nimbus-6 High Resolution Infrared Radiation Sounder. Each display contains black and white images at either full vertical scale (F) or partial vertical scale (P). A full scale mode image will contain one orbit of data or 125 minutes of data, while a partial scale mode image will contain twice the vertical scale and thus requires two images for an orbit of data (the last 60 minutes on the first image, P1 and the remaining data on the second image, P2). There are 10 channels (swaths) for an orbit on each image, with a header identifying the channel (1-1, 6-6, etc.). An 18-step gray scale is found at the bottom. Time and geographic information is encoded in the center of the image. Conversion from the 18-step gray scale to brightness temperatures can be found in a table in each of the first six volumes of \"The Nimbus 6 Data Catalog.\"\n\nThe HIRS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 ITPR experiment. HIRS was a multi-channel filter radiometer with a Cassegrain telescope before the chopper assembly. The instrument scans in the cross track direction with 21 scans on each side of the subtrack point with about 30 km x 55 km resolution at nadir. HIRS measured radiances primarily in five spectral regions: (1) seven channels near the 15-micrometer CO2 absorption band, (2) two channels (11.1 and 3.7 micrometers) in the IR window, (3) two channels (8.2 and 6.7 micrometers) in the water vapor absorption band, (4) five channels in the 4.3-micrometer band, and (5) one channel in the visible 0.69-micrometer region.\n\nThe HIRS Principal Investigator was Mr. W. L. Smith from the NOAA National Environmental Satellite Service. The Nimbus-6 HIRS data are available from August 17, 1975 (day of year 229) through March 4, 1976 (day of year 238).\n\nThese data were previously archived at NASA NSSDC under the entry ID ESAD-00094 (old id 75-052A-02A).", "links": [ { diff --git a/datasets/HIRSN6L1GARP_001.json b/datasets/HIRSN6L1GARP_001.json index 07b2db00ab..8d33d50bb9 100644 --- a/datasets/HIRSN6L1GARP_001.json +++ b/datasets/HIRSN6L1GARP_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIRSN6L1GARP_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-6 High Resolution Infrared Radiometer (HIRS) Level 1 Calibrated Radiances for the Global Atmospheric Research Program (GARP) data product contains daily infrared radiances. The HIRS was designed to measure surface temperature and albedo, temperature and H2O profiles, cloud liquid water content, cloud amount and outgoing longwave fluxes in the infrared. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, also referred to as a binary TAP file, and contain one orbit of measurements.\n\nThe HIRS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 ITPR experiment. HIRS was a multi-channel filter radiometer with a Cassegrain telescope before the chopper assembly. The instrument scans in the cross track direction with 21 scans on each side of the subtrack point with about 30 km x 55 km resolution at nadir. HIRS measured radiances primarily in five spectral regions: (1) seven channels near the 15-micrometer CO2 absorption band, (2) two channels (11.1 and 3.7 micrometers) in the IR window, (3) two channels (8.2 and 6.7 micrometers) in the water vapor absorption band, (4) five channels in the 4.3-micrometer band, and (5) one channel in the visible 0.69-micrometer region.\n\nThe HIRS Principal Investigator was Mr. W. L. Smith from the NOAA National Environmental Satellite Service. The Nimbus-6 HIRS data are available from August 17, 1975 (day of year 229) through March 4, 1976 (day of year 238).\n\nThese data were previously archived at NASA NSSDC under the entry ID ESAD-00017 together with the merged retrieval data set).", "links": [ { diff --git a/datasets/HIVE_0.json b/datasets/HIVE_0.json index d53fc278e4..f132e6005a 100644 --- a/datasets/HIVE_0.json +++ b/datasets/HIVE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HIVE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Gulf of Alaska and Bering Sea during 1997 and 1998.", "links": [ { diff --git a/datasets/HI_Bibliography_1.json b/datasets/HI_Bibliography_1.json index 525dd50964..9b680f9351 100644 --- a/datasets/HI_Bibliography_1.json +++ b/datasets/HI_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heard Island Bibliography compiled by Evlyn Barrett, (now deceased), contains 573 records. The bibliography has not been updated since 2002, and should not be considered a complete record of publications related to Heard Island.", "links": [ { diff --git a/datasets/HI_VEG_NON_ORTHO_VEGMAP_1.json b/datasets/HI_VEG_NON_ORTHO_VEGMAP_1.json index 7ea59840a0..32e528345f 100644 --- a/datasets/HI_VEG_NON_ORTHO_VEGMAP_1.json +++ b/datasets/HI_VEG_NON_ORTHO_VEGMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_NON_ORTHO_VEGMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The majority of the coastal areas of Heard Island have had their vegetation mapped using orthophotos (HI_VEG_ORTHO_VEGMAP). For the remaining areas, five sets of shapefiles cover the vegetation mapping for five small areas in the Atlas Cove/north coast region: Pageos Moraine/Kildalkey Head, Azorella Peninsula, Hoseason Beach, Saddle Point and Cape Bidlingmaier. These areas were poorly covered by aerial photography, and their vegetation was mapped at low resolution using five separate non-rectified airphotos and satellite images - hence not able to be included as part of the Heard Island georeferenced vegetation dataset. This will be fixed and the data amended. Until this time, the data represent an 'interim' situation of low resolution vegetation mapping for these areas.\n\nFor each of the five areas there are four shapefiles covering vegetation polygons, 35mm photo locations, aerial photos or satellite images used, and reliability. There is also a folder containing 64 x 35mm scanned photos which were used when vegetation mapping (as in HI_VEG_PHOTOS_VEGMAP).\n\nA list of the unrectified aerial photos and satellite imagery used for the vegetation mapping, with locations, is as follows:\n\nAzorella Peninsula\nheard_mosaic_1991_edit (extract of SPOT 1988/1991 image)\n\nPageos Moraine/Kildalkey Head\nL713609706112001sub (extract of LANDSAT 2001 image)\n\nHoseason Beach\nantc1206_r1_f13 (1987 aerial photo)\n\nSaddle Point\ncasc9500_f38_part (extract of 1980 aerial photo)\n\nCape Bidlingmaier\ncompton_shag (extract of DigitalGlobe 2003 image).", "links": [ { diff --git a/datasets/HI_VEG_ORTHOPHOTOS_VEGMAP_1.json b/datasets/HI_VEG_ORTHOPHOTOS_VEGMAP_1.json index 0b2b3bbd57..1d8f066d30 100644 --- a/datasets/HI_VEG_ORTHOPHOTOS_VEGMAP_1.json +++ b/datasets/HI_VEG_ORTHOPHOTOS_VEGMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_ORTHOPHOTOS_VEGMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The vegetation mapping project used a set of 1987 and 1980 orthophotos and orthophoto mosaics for mapping and screen-digitising (see 'Quality' section). Contact AADC for access to these. \nA shapefile of orthophoto coverage shows polygons for the areas where each orthophoto was used for mapping. This is an essential guide for use when matching the vegetation polygons to the correct orthophotos.\n\nA list of the orthophotos and orthophoto mosaics used for the vegetation mapping, with locations, is as follows: ('orthoantc' signifies 1987 series, 'orthocasc' signifies 1980 series. Run numbers are not included. Frame numbers are signified by 'f').\n\nGilchrist Beach\northoantc1206_f50, f51, f52, f53\n\nFairchild Beach\northoantc1209_f209, f210, f212\n\nSkua Beach/Stephenson Moraine\northoantc1209_f217, f218, f221, f223, f226\n\nScarlet Hill\northoantc1202_f32, f34, f36\n\nSkua/Stephenson Moraine\northoantc_f226\n\nSpit north and Spit south\northoantc_f230, f232, f233, f240, f242\northocasc9495_f15\n\nPaddick Valley\northoantc_f251, f252\northocasc9495_f17\n\nWinston Lagoon/Capsize Beach\northoantc1209_f260, f261, f263\northoantc1206_f247\n\nSouth Barrier/Lambeth Bluff\northoantc1209_f263, f264, f266, f267, f268, f269, f271\n\nLavett Bluff\northoantc1209_f282\northoantc1207_f030, f032, f033\n\nLong Beach\northoantc1209_f285, f287, f289, f291, f293, f295\northoantc1207_f20-f29 (mosaic)\n\nCape Arkona/Cape Pillar\northoantc1209_f303-f315 (mosaic)\northoantc1208_f046, f047\n\nHenderson Bluff\northoantc1209_f320\n\nWalsh Bluff\northoantc1209_f327-f328 (mosaic)\northoantc1208_f11-f17 (mosaic)\n\nCape Gazert\northocasc9495_f63\n\nLaurens Peninsula/Atlas Cove\nphoto_mosaic_laurens_or (mosaic of nine casc9495 frames covering\nLaurens Peninsula and Atlas Cove area).", "links": [ { diff --git a/datasets/HI_VEG_ORTHO_VEGMAP_1.json b/datasets/HI_VEG_ORTHO_VEGMAP_1.json index b79846ed1c..01bb64c83e 100644 --- a/datasets/HI_VEG_ORTHO_VEGMAP_1.json +++ b/datasets/HI_VEG_ORTHO_VEGMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_ORTHO_VEGMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation mapping from orthophotos derived from non-metric photography for the north eastern, south eastern, south, west and north west coastal areas of Heard Island.\n\nMore information about the vegetation mapping process is documented in a pdf report entitled 'Notes for Heard Island Vegetation Mapping project 2002-2006' available for download at the provided URL. The report is an updated version of the December 2004 pdf report entitled 'Heard Island Vegetation Mapping Report'.\n\nThe vegetation mapping project was undertaken between 1986 and 1988 (field mapping), 2002 and 2005 (digitising) and 2003-2004 (limited field checking). The data for the mapping done on orthophotos are contained in two shapefiles as follows:\n\n- Vegetation mapping polygons.\n- Reliability (comments on quality of orthophoto coverage, areas of overlapping cover, etc).\n\nAssociated shapefiles are as follows:\n- Aerial photo coverage, showing polygons for the areas where each orthophoto was used for mapping (see metadata record with ID HI_VEG_ORTHOPHOTOS_VEGMAP).\n- Photo locations of 35mm photos used during mapping (see metadata record with ID HI_VEG_PHOTOS_VEGMAP).", "links": [ { diff --git a/datasets/HI_VEG_OVERALL_1.json b/datasets/HI_VEG_OVERALL_1.json index 3fdbdd0643..a830303ac9 100644 --- a/datasets/HI_VEG_OVERALL_1.json +++ b/datasets/HI_VEG_OVERALL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_OVERALL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The production of a detailed vegetation dataset which can act as a sound baseline for detection of change has been a longterm process and has now been completed. There are seven 'child' metadata records, (a) to (g), under the 'parent DIF' (Heard Island: baseline vegetation data for monitoring longterm change, HI_VEG_OVERALL) as follows:\n\n(a) Vegetation mapping from orthophotos (HI_VEG_ORTHO_VEGMAP).\n(b) Vegetation mapping from non-rectified aerial photographs and satellite imagery with accompanying data (HI_VEG_NON_ORTHO_VEGMAP).\n(c) Orthophotos used for vegetation mapping (HI_VEG_ORTHOPHOTOS_VEGMAP).\n(d) 35mm photos (oblique aerial and terrestrial) used for vegetation mapping (HI_VEG_PHOTOS_VEGMAP).\n(e) 35mm photos (terrestrial) from fixed photo-points, 1986-2000 (HI_VEG_PHOTOPOINTS_1).\n(f) 35mm photos (terrestrial) from fixed photo-points, 2003-2004 (HI_VEG_PHOTOPOINTS_2).\n(g) Vegetation 'signature points' with accompanying 35mm terrestrial photos (HI_VEG_VEGSIGNATURES).\n\nMetadata records (a) to (d) describe the data directly associated with the vegetation mapping project. Records (e) and (f) describe two time-series of photo-monitoring images from a set of fixed photo-points. Record (g) describes a set of 'vegetation signature points' for use in remote sensing analysis. The metadata record 'Heard Island: Terrestrial Biology: Documenting vegetation change on Heard Island' (ASAC_1181) is also relevant to this vegetation dataset.\n\nData collection and analysis have utilised the Australian Antarctic Data Centre's Digital Elevation Model (DEM) of Heard Island and field data from several intensive field seasons (1986-87, 1987-88, 2000-01, 2003-04). Field data collection initially involved mapping of vegetation communities on non-rectified aerial photos and overlays and setting up an extensive series of fixed photo-points for future change detection. Since 2000, fieldwork has also included collection of ground control for orthorectification of aerial photography and satellite imagery (ASAC_1181).\n\nThe vegetation communities for the majority of the island have been re-mapped on 1986 and 1980 orthophotos and orthophoto mosaics, and screen-digitised using ArcGIS software. The remaining five small vegetated areas of the north coast have not yet been mapped on orthophotos. They have been provisionally mapped using non-rectified airphotos and satellite images, using the same GIS software. Currently the data, and the images, have been included as a separate metadata record as they are not yet geo-referenced.\n\nA brief description of the data covered by each 'child' metadata record is as follows:\n\n(a) Vegetation mapping from orthophotos (HI_VEG_ORTHO_VEGMAP).\n\ni) A shapefile of vegetation polygons covering 13 community types, for most of the island. Vegetation was mapped on orthophotos and orthophoto mosaics (see c) below). The area covered extends from Gilchrist Beach (northeast) to Spit Bay (east) to Long Beach (south), Cape Arkona to Gazert (west) to Laurens Peninsula (northwest) to Atlas Cove area (north). The remaining sections of the north coast are covered in (b) below.\nii) A shapefile of polygons indicating reliability of the data.\niii) A pdf report detailing the methods used during vegetation mapping and other information.\n\n(b) Vegetation mapping from non-rectified aerial photographs and satellite imagery with accompanying data (HI_VEG_NON_ORTHO_VEGMAP).\n\ni) The two non-rectified airphotos and three satellite images used for mapping the five areas not covered by HI_VEG_ORTHO_VEGMAP. These areas are Azorella Peninsula, Pageos Moraine/Kildalkey Head, Hoseason Beach, Saddle Point and Cape Bidlingmaier (see AADC).\nii) Four shapefiles for each of the five areas, covering vegetation, reliability, aerial photo and satellite image coverage, and 35mm photo locations.\niii) A total of 64 x 35mm scanned photos showing general landscape and vegetation of the five areas.\n\n(c) Orthophotos used for vegetation mapping (HI_VEG_ORTHOPHOTOS_VEGMAP).\n\ni) A shapefile of polygons indicating which orthophotos and orthophoto mosaics were used for the mapping of each area.\nii) The orthophotos and orthophoto mosaics used for mapping (see AADC).\n\n(d) 35mm photos (oblique aerial and terrestrial) used for vegetation mapping (HI_VEG_PHOTOS_VEGMAP).\n\ni) A shapefile showing the approximate locations of the 581 x 35mm photos used as a &ground-check& resource during mapping.\nii) A total of 581 x 35mm scanned photos, taken from helicopters, on the ground, and from ships offshore, showing general landscape and vegetation of all areas mapped from orthophotos. These images (along with those in metadata record (b)) are useful for gaining an overall familiarisation of Heard Island coastal terrain and vegetation.\n\n(e) 35mm photos (terrestrial) from fixed photo-points, 1986-2000 (HI_VEG_PHOTOPOINTS_1).\n\ni) Two shapefiles of 35mm photo locations (differential and hand-held GPS) of the majority of the 35mm fixed photo-point photos which are located in the eastern half of the island (none from the northern part of the island are included).\nii) A total of 334 x 35mm scanned terrestrial photos of landscapes and vegetation taken all around the island between 1986 and 2000 (fixed photo-point photos). For use as baseline data for long-term photo-monitoring of vegetation and landscape change. Permission for use needed from researcher.\niii) A word document with metadata for the images.\niv) An excel spreadsheet with additional information on the images.\n\n(f) 35mm photos (terrestrial) from fixed photo-points, 2003-2004 (HI_VEG_PHOTOPOINTS_2).\n\ni) Two shapefiles of 35mm photo locations (differential and hand-held GPS) of the majority of the 35mm fixed photo-point photos which are located in the eastern half of the island (none from the northern part of the island are included). As for (e) above. These represent the fixed photopoint photos taken in 2003-2004.\nii) Seven folders containing 1633 x 35mm scanned photos of landscapes and vegetation taken in the eastern half of the island in 2003-2004. The majority are re-takes of the fixed photo-point images in metadata record (e), expanded from single images into panoramas. Others represent new photo-points to fill gaps in coverage. For use as baseline data for long-term photo-monitoring of vegetation and landscape change. Permission for use needed from researcher.\niii) A word document with metadata for the images.\n\n(g) Vegetation 'signature points' with accompanying 35mm terrestrial photos (HI_VEG_VEGSIGNATURES).\n\ni) A shapefile of 35mm photo locations of the terrestrial 'vegetation signature' photos.\nii) A total of 34 x 35mm scanned photos showing 'vegetation signatures' for elected vegetation communities, for use in remote sensing analysis.\niii) A word document with metadata for the images.", "links": [ { diff --git a/datasets/HI_VEG_PHOTOPOINTS_1.json b/datasets/HI_VEG_PHOTOPOINTS_1.json index e03310f5f6..03503cd0e1 100644 --- a/datasets/HI_VEG_PHOTOPOINTS_1.json +++ b/datasets/HI_VEG_PHOTOPOINTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_PHOTOPOINTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 35mm photos were taken by Dr Jenny Scott in December 1986-January 1987, October 1987-February 1988 and October 2000. The photos represent the first time series of a longterm fixed-point photo-monitoring project. They serve as a baseline for documenting vegetation and landscape change between this time-frame and when photos were re-taken in 2003-04 (HI_VEG_PHOTOPOINTS_2), and for the future.\n\nArea covered: eastern and southern Heard Island from Fairchild Beach (Compton Lagoon) to Long Beach, and northwestern and northern Heard Island from Cape Gazert to Saddle Point including Laurens Peninsula. The majority of images in southern and eastern Heard Island were re-taken by Scott in 2003-2004 (HI_VEG_PHOTOPOINTS_2). None of the images from northwest and northern Heard Island were re-taken in 2003-2004.\n\nNumber of images: 334 jpg images, less than 4 MB each. The images are also available at high resolution (approx. 4000 dpi), with each image approximately 66 MB; and as A4 contact sheets with 6 high-res images to a sheet, eg. for printing to use in the field. All the high res images, including contact sheets, total 47 GBs.\n\nFurther information on image labelling etc is available in metadata notes (word doc) and an excel spreadsheet, both accompanying the images.", "links": [ { diff --git a/datasets/HI_VEG_PHOTOPOINTS_2_1.json b/datasets/HI_VEG_PHOTOPOINTS_2_1.json index 19ea7eb677..a3ab01c910 100644 --- a/datasets/HI_VEG_PHOTOPOINTS_2_1.json +++ b/datasets/HI_VEG_PHOTOPOINTS_2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_PHOTOPOINTS_2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 35mm photos were taken by Dr Jenny Scott between December 2003 and February 2004 using two Nikon FM-2 SLR cameras with 50mm and 28mm lenses. The photos are part of a longterm fixed-point photo-monitoring project, started as part of ASAC Project 1181 (ASAC_1181), and the majority of image locations are the same as for the photo series taken by Scott between 1986-2000 (HI_VEG_PHOTOPOINTS_1). The photos serve as a baseline for documenting vegetation and landscape change, both between the two existing time series 1986-2000 and 2003-04, and in the future.\n\nArea covered: eastern and southern Heard Island from Fairchild Beach (Compton Lagoon) to Long Beach.\n\nNumber of images: 1633, ranging between 1-3 MB. Images are stored in seven zip files, according to location. For more information, see metadata notes (word doc) accompanying the images.\n\nZip files cover the locations as follows:\n- Fairchild Beach (FB). 181 images. Includes north side of Brown Lagoon.\n- Skua Beach (SK). 313 images. Includes North Skua and Scarlet Hill.\n- Stephenson Moraine (SP). 143 images.\n- Spit North (SN) and SpitSouth (SS). 309 images.\n- Paddick Valley (PV) and Winston Lagoon east (WE). 203 images.\n- Capsize Beach (CB) and Winston Lagoon west (WL). 193 images.\n- Long Beach, Lavett Bluff, Lambeth Bluff (LB). 291 images.\n\nFor each main location (ie each zip-file) there are folders with 28mm images (wide-angle) and folders with 50mm images (standard view) of basically the same series of photo-points. They are called eg. 'Skua 28mm', 'Skua 50mm'. The 28mm images can be viewed first to give an overall impression, and the 50mm images of selected sites can be viewed to give greater detail. The photo-points are arranged in approximate numerical order in each series of 28mm folders and 50mm folders. Note that the 28mm and 50mm series do not always match up completely; not all image series are repeated with 50mm and several images may be 50mm only. The majority of photo-points consist of a series of images (both 28mm and 50mm) forming a panorama progressing from left to right.\n\nPhoto-point location shapefiles:\n\nThere are two shapefiles showing location of DGPS photo-points and handheld GPS photo-points. The handheld GPS unit was used on several occasions when the DGPS system was unavailable. Image labels eg. JJS-SK-001 and JJS_SK_001 signify the same photo-point. For information on labelling, see metadata notes (word doc) accompanying the images.", "links": [ { diff --git a/datasets/HI_VEG_PHOTOS_VEGMAP_1.json b/datasets/HI_VEG_PHOTOS_VEGMAP_1.json index 38e5283fc8..92c71607b7 100644 --- a/datasets/HI_VEG_PHOTOS_VEGMAP_1.json +++ b/datasets/HI_VEG_PHOTOS_VEGMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_PHOTOS_VEGMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These photos consist of 581 scanned 35mm colour slides (each approximately 2.2 MB) taken on Heard Island by Dr Jenny Scott (JJS) between December 1986 and February 2004. Areas covered are Gilchrist Beach in the northeast, to Spit Bay in the east, to Long Beach in the south, to Cape Gazert in the west, to Atlas Cove and Laurens Peninsula in the northwest. An equivalent set of 35mm photos for the remaining areas along the north coast is included in metadata record HI_VEG_NON_ORTHO_VEGMAP. Photos are either oblique aerial (taken from helicopter), terrestrial or ship-based. Although they were taken specifically to use for interpretation when finalising the vegetation mapping (HI_VEG_ORTHO_VEGMAP), they can also be useful for fieldwork planning of any sort, as they give an overview of terrain and topography covering most of the coastal and near-coastal areas of the island.\n\nShapefile - photo locations\nOne shapefile shows the approximate location and direction of the camera for each image. The location pointers were digitised as vectors with the start of the line being the approximate location of the camera and the end of the line providing the direction (hint: convert to arrows for ease of reference). Attributes include image name and file source, location, date of capture, where photo was taken from (aerial, ground, ship offshore), ID of original JJS slide, and comments (re quality of image, whether location approximate, etc).", "links": [ { diff --git a/datasets/HI_VEG_VEGSIGNATURES_1.json b/datasets/HI_VEG_VEGSIGNATURES_1.json index dfc06a87f4..3a677dfd2e 100644 --- a/datasets/HI_VEG_VEGSIGNATURES_1.json +++ b/datasets/HI_VEG_VEGSIGNATURES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_VEG_VEGSIGNATURES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data consist of 35 'Vegetation Signature' geo-located points and 34 x 35mm terrestrial photos (scanned images) of the vegetation at the actual points, for use in remote sensing research. The points were collected by Dr Jenny Scott and are located in eastern and southern Heard Island. The dataset is very limited and does not cover all vegetation categories.\n\nAll data points and images were collected in summer 2003-04 and all images were taken with 50mm (standard view) lens. There are 34 images and matching data points from four locations; Paddick Valley (south coast); and three locations at Skua Beach on the northeast coast (Scarlet Hill, 'Ranunculus Bluffs' and 'Sooty Valley'). There is one additional data point without a matching image (total data points 35).\n\nThere are 34 scanned photos and a point location shapefile. Each photo is labelled individually.\n\nMost of the sites were in the centre of a patch of similar vegetation at least 10 x 10m in diameter (some may be smaller, some larger; this was not done rigidly). The notebook in each image indicates the exact point where the DGPS reading was taken.\n\nNot all vegetation categories were sampled. It was the intention to do this, but lack of time prevented it - these images represent a partial sample of all vegetation categories.\n\nSee metadata notes (word doc) accompanying the images, for additional information and explanation of image locations and veg categories.", "links": [ { diff --git a/datasets/HI_animaltracks_ARGOS_1.json b/datasets/HI_animaltracks_ARGOS_1.json index d3a9c3a779..4f86bf17b1 100644 --- a/datasets/HI_animaltracks_ARGOS_1.json +++ b/datasets/HI_animaltracks_ARGOS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HI_animaltracks_ARGOS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A major goal of a research expedition by the Australian Antarctic Division over the summer of (2003/04) in the Southern Ocean off Heard Island was to answer some of the questions needed to determine what level of exploitation of Southern Ocean fisheries is sustainable. The use of novel equipment, cutting edge technology and some adept logistical co-ordination allowed the Aurora Australis, on the Southern Ocean, to catch the prey of the predators of Heard Island.\n\nThis work was accomplished by placing satellite trackers on animals at Heard Island, and then, using the ARGOS system, monitoring their activities in the Southern Ocean around the island. The Aurora Australis assisted in the monitoring and tracking of the animals by searching the areas the animals were foraging for prey species.\n\nThe animals tracked in this experiment were:\n\nLight-mantled sooty albatrosses\nblack-browed albatrosses\nking penguins\nmacaroni penguins\nAntarctic fur seals\n\nThe columns in this data file are:\n\nindividual_id - the identifier of the individual animal\nspecies - the species name of that animal\npttid - the identifier of the PTT tracker deployed on that animal\ndeployment_longitude - the longitude at which the tracker was deployed\ndeployment_latitude - the latitude at which the tracker was deployed\nobservation_date - the date (ISO8601 format) of the position observation\nyear, month, day, time, time_zone - as per the observation_date, but in separate columns\nlocationclass - the ARGOS location class of the position (see http://www.argos-system.org/manual/3-location/34_location_classes.htm; value -3 corresponds to a \"Z\" class, value -2 to \"B\", value -1 to \"A\")\nlatitude - the latitude of the position observation\nlongitude - the longitude of the position observation", "links": [ { diff --git a/datasets/HLSL30_2.0.json b/datasets/HLSL30_2.0.json index 676685be6b..8f23a5c671 100644 --- a/datasets/HLSL30_2.0.json +++ b/datasets/HLSL30_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HLSL30_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.\r\n\r\nThe HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS)(https://hls.gsfc.nasa.gov/products-description/tiling-system/) tiling system, and thus are \u201cstackable\u201d for time series analysis.\r\n\r\nThe HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.", "links": [ { diff --git a/datasets/HLSS30_2.0.json b/datasets/HLSS30_2.0.json index 374a1652df..b976e40d5d 100644 --- a/datasets/HLSS30_2.0.json +++ b/datasets/HLSS30_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HLSS30_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. \r\n\r\nThe HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) (https://hls.gsfc.nasa.gov/products-description/tiling-system/) tiling system, and thus are \u201cstackable\u201d for time series analysis.\r\n\r\nThe HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.\r\n", "links": [ { diff --git a/datasets/HMA2_DCG_SMB_1.json b/datasets/HMA2_DCG_SMB_1.json index 0da8b5fc64..444cd72950 100644 --- a/datasets/HMA2_DCG_SMB_1.json +++ b/datasets/HMA2_DCG_SMB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_DCG_SMB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal.\n\nSMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1.\n\nThis data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.", "links": [ { diff --git a/datasets/HMA2_DDSMET_1.json b/datasets/HMA2_DDSMET_1.json index a9ae1ef441..10ac2164b0 100644 --- a/datasets/HMA2_DDSMET_1.json +++ b/datasets/HMA2_DDSMET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_DDSMET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions \u2013 36 km, 12 km, and 4 km \u2013 and 9 pressure levels spanning 1000 hPa \u2013 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.", "links": [ { diff --git a/datasets/HMA2_DSPAT_1.json b/datasets/HMA2_DSPAT_1.json index 4d7e4ade50..e31cd3efcf 100644 --- a/datasets/HMA2_DSPAT_1.json +++ b/datasets/HMA2_DSPAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_DSPAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5\u00b0 resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model.\n\nProjections are provided for two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5 8.5. A historical model run from 1990 through 2014 is also available.", "links": [ { diff --git a/datasets/HMA2_FGP_1.json b/datasets/HMA2_FGP_1.json index 515e1b5029..bf923a4199 100644 --- a/datasets/HMA2_FGP_1.json +++ b/datasets/HMA2_FGP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_FGP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Flood Geomorphic Potential (FGP) at 30 m resolution for the High Mountain Asia region and 8 m resolution over Nepal. FGP is a digital elevation model-derived index that provides high-resolution flood mapping based on bankfull elevations, defined in terms of river widths, and elevation differences between points under examination and the closest bankfull elevations in the river network.", "links": [ { diff --git a/datasets/HMA2_GFTP_1.json b/datasets/HMA2_GFTP_1.json index 623d35b4d6..3de1e06cbe 100644 --- a/datasets/HMA2_GFTP_1.json +++ b/datasets/HMA2_GFTP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_GFTP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 \u2013 31 Dec 2016.\n\nThe data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.", "links": [ { diff --git a/datasets/HMA2_GGP_1.json b/datasets/HMA2_GGP_1.json index d78088580f..d44d918e44 100644 --- a/datasets/HMA2_GGP_1.json +++ b/datasets/HMA2_GGP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_GGP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises results from a hybrid glacier evolution model that uses the mass balance module of the Python Glacier Evolution Model (PyGEM) and the glacier dynamics module of the Open Global Glacier Model (OGGM). Output parameters include projections of glacier mass change, fixed runoff, and various mass balance components at regionally aggregated and glacier scales.", "links": [ { diff --git a/datasets/HMA2_HFD_1.json b/datasets/HMA2_HFD_1.json index e96fdc5e01..5c1c9ea1b0 100644 --- a/datasets/HMA2_HFD_1.json +++ b/datasets/HMA2_HFD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_HFD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This High Mountain Asia (HMA) data set contains hydrological flow directions at 5 arc-minute resolution for the headwaters of the Amu Darya and Indus River basins. The domain spans parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan. Flow directions are reported in deterministic eight (D8) format.\n\nThe data were developed to support the University of New Hampshire Water Balance Model and the \"High Mountain Asia CMIP6 Monthly and Yearly Water Balance Projections, 2016-2099 for Parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan, Version 1\" data set.", "links": [ { diff --git a/datasets/HMA2_LHI_1.json b/datasets/HMA2_LHI_1.json index 09b817d56f..3cc7e66d7b 100644 --- a/datasets/HMA2_LHI_1.json +++ b/datasets/HMA2_LHI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_LHI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set projects the daily hazard of rainfall-triggered landslides in the High Mountain Asia region from 2015 through 2100, at 5 km resolution. Projections are provided for two Shared Socioeconomic Pathways (SSPs)\u2014SSP2-4.5 and SSP5 8.5\u2014based on downscaled temperature and precipitation projections from a 30-member ensemble climate model.\n\nLandslide hazard is represented by a landslide hazard indicator (LHI), computed with a machine learning (ML) model trained on historical temperatures and precipitation from 1990 through 2019 and a catalog of documented landslides.\n\nTwo historical LHI data sets are also available: the ML model LHIs generated for 1990 through 2019; and retrodicted LHIs computed by inputting downscaled temperatures and precipitation for 1990 through 2014 to the ensemble climate model.", "links": [ { diff --git a/datasets/HMA2_MATCHA_1.json b/datasets/HMA2_MATCHA_1.json index 021b9acb29..1e20288415 100644 --- a/datasets/HMA2_MATCHA_1.json +++ b/datasets/HMA2_MATCHA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_MATCHA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019.\n\nTwo-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa.\n\nAlso known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables.\n\nThe simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.", "links": [ { diff --git a/datasets/HMA2_MTLI_1.json b/datasets/HMA2_MTLI_1.json index f258bbdb00..89e53f52e9 100644 --- a/datasets/HMA2_MTLI_1.json +++ b/datasets/HMA2_MTLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_MTLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The transboundary Pumpqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumpqu River flows. The river is named the Arun River once it enters Nepal. Five large hydropower projects (in total about 3,163 MW) are currently under construction or are planned for the Arun River valley. Rainfall and earthquake-induced landslides, landslide dammed lakes, and landslide-induced glacial lake outburst floods pose major risks to the smooth operation of these projects. This data set is a multitemporal landslide inventory covering the whole Pumpqu/Arun River basin. It was generated in support of the World Bank\u2019s Risk Assessment of Landslides in the Upper Arun Hydropower Project.", "links": [ { diff --git a/datasets/HMA2_NLSMR_1.json b/datasets/HMA2_NLSMR_1.json index a8b2621775..f4f72d9ce2 100644 --- a/datasets/HMA2_NLSMR_1.json +++ b/datasets/HMA2_NLSMR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_NLSMR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a water budget reanalysis for the High Mountain Asia (HMA) region spanning the years 2003 through 2020. Estimates are provided for more than 30 parameters, including storages; fluxes; snow depth, extent, and snow water equivalent; temperature (land surface, soil, snow, and ice); surface albedo; soil moisture; evapotranspiration; and streamflow.\n\nThe data were generated using the Noah Multi-Parameterization (Noah-MP) land surface model (Version 4.0.1), driven by precipitation estimates and hydrological inputs developed specifically for HMA.", "links": [ { diff --git a/datasets/HMA2_WBP_1.json b/datasets/HMA2_WBP_1.json index 9ed6c26580..7abebba77d 100644 --- a/datasets/HMA2_WBP_1.json +++ b/datasets/HMA2_WBP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA2_WBP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This High Mountain Asia (HMA) data set comprises a suite of monthly and yearly water balance model (WBM) projections for the years 2016 \u2013 2099, covering parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan (primarily the headwaters of the Amu Darya and Indus River basins).\n\nProjections are available for 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5). The data were generated using the University of New Hampshire WBM.\n\nA historical run is also available for the years 1980 through 2018, using as input ERA5 reanalysis temperature data and ensemble precipitation estimates.", "links": [ { diff --git a/datasets/HMA_AWS_1.json b/datasets/HMA_AWS_1.json index 66d47131d2..c1a13d61d7 100644 --- a/datasets/HMA_AWS_1.json +++ b/datasets/HMA_AWS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_AWS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).", "links": [ { diff --git a/datasets/HMA_DEM8m_AT_1.json b/datasets/HMA_DEM8m_AT_1.json index 8ac2d229a3..3c5f2f5d4a 100644 --- a/datasets/HMA_DEM8m_AT_1.json +++ b/datasets/HMA_DEM8m_AT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_DEM8m_AT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.", "links": [ { diff --git a/datasets/HMA_DEM8m_CT_1.json b/datasets/HMA_DEM8m_CT_1.json index 390a722c3f..429de45f3a 100644 --- a/datasets/HMA_DEM8m_CT_1.json +++ b/datasets/HMA_DEM8m_CT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_DEM8m_CT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.", "links": [ { diff --git a/datasets/HMA_DEM8m_MOS_1.json b/datasets/HMA_DEM8m_MOS_1.json index 9e9d9e6f11..e93a68eadd 100644 --- a/datasets/HMA_DEM8m_MOS_1.json +++ b/datasets/HMA_DEM8m_MOS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_DEM8m_MOS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.", "links": [ { diff --git a/datasets/HMA_DM_6H_1.json b/datasets/HMA_DM_6H_1.json index 9ad5c30bcd..ee9915e28d 100644 --- a/datasets/HMA_DM_6H_1.json +++ b/datasets/HMA_DM_6H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_DM_6H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides downscaled six-hourly atmospheric forcings from European Centre for Medium-Range Weather Forecasts (ECMWF) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation from 2003 to 2019 at a spatial resolution of ~1km across High Mountain Asia.", "links": [ { diff --git a/datasets/HMA_DTE_1.json b/datasets/HMA_DTE_1.json index feaca75f04..8ef8c3e15a 100644 --- a/datasets/HMA_DTE_1.json +++ b/datasets/HMA_DTE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_DTE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes spatially distributed estimates of the debris thickness and sub-debris melt enhancement factors for every debris-covered glacier in the Randolph Glacier Inventory\nVersion 6, excluding the ice sheets and Antarctic Periphery. The debris thickness estimates are derived using a novel approach that uses a combination of sub-debris melt inversion and surface temperature inversion methods. The sub-debris melt enhancement factors are estimated from the debris thickness using debris thickness-melt curves normalized by estimates of the clean-ice melt.", "links": [ { diff --git a/datasets/HMA_EAPrecip_FLOR_1.json b/datasets/HMA_EAPrecip_FLOR_1.json index d4d1cb6be9..970d94a877 100644 --- a/datasets/HMA_EAPrecip_FLOR_1.json +++ b/datasets/HMA_EAPrecip_FLOR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_EAPrecip_FLOR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes three climate simulations of daily precipitation over the Himalayan region for summer and winter, covering different time periods: two 30-member ensemble simulations spanning 40-year time periods in the 20th century (1961-2000) and 21st century (2061-2100), and a present-day climate simulation from 1982 to 2017 nudged to reanalysis winds. These precipitation estimates were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of the CM2.5 model (GFDL FLOR).", "links": [ { diff --git a/datasets/HMA_FreezeThawMelt_ASCAT_1.json b/datasets/HMA_FreezeThawMelt_ASCAT_1.json index d93687250a..3654dc8a5d 100644 --- a/datasets/HMA_FreezeThawMelt_ASCAT_1.json +++ b/datasets/HMA_FreezeThawMelt_ASCAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_FreezeThawMelt_ASCAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains bulk landscape frozen or thawed status over seasonally frozen land, as well as snowmelt status over glacierized areas for the High Mountain Asia region. Daily Freeze/Thaw/Melt (F/T/M) status is derived from vertically polarized (V-pol) C-band (5.255 GHz) backscatter measurements that were acquired by the Advanced Scatterometer (ASCAT) on EUMETSAT Metop-A and Metop-B satellites. Swath-ordered observations are spatially enhanced using the Scatterometer Image Reconstruction (SIR) algorithm, posted on Earth-fixed 4.45 km grids, and interpolated to a daily product from the original 3-day A.M. overpasses.", "links": [ { diff --git a/datasets/HMA_GLI_1.json b/datasets/HMA_GLI_1.json index 3713c6c02c..e807923928 100644 --- a/datasets/HMA_GLI_1.json +++ b/datasets/HMA_GLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_GLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains polygons of glacial lake extent on a near-global scale, averaged over five multi-year periods between 1990 and 2018.", "links": [ { diff --git a/datasets/HMA_GL_RCPR_1.json b/datasets/HMA_GL_RCPR_1.json index 9e92478747..a8f8e7cb3d 100644 --- a/datasets/HMA_GL_RCPR_1.json +++ b/datasets/HMA_GL_RCPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_GL_RCPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.", "links": [ { diff --git a/datasets/HMA_GL_RCP_1.json b/datasets/HMA_GL_RCP_1.json index 5dd30c0904..57cdfac29f 100644 --- a/datasets/HMA_GL_RCP_1.json +++ b/datasets/HMA_GL_RCP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_GL_RCP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises results from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.", "links": [ { diff --git a/datasets/HMA_GSM_1.json b/datasets/HMA_GSM_1.json index 468ef52e11..61e0ae0ebd 100644 --- a/datasets/HMA_GSM_1.json +++ b/datasets/HMA_GSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_GSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images. The duration of annual glacier surface melt was determined for 105,432 mapped glaciers (83,102 km2 glacierized area) during the calendar years 2017-2020.", "links": [ { diff --git a/datasets/HMA_GlacierAvg_dH_1.json b/datasets/HMA_GlacierAvg_dH_1.json index d2fffc9010..b034300787 100644 --- a/datasets/HMA_GlacierAvg_dH_1.json +++ b/datasets/HMA_GlacierAvg_dH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_GlacierAvg_dH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains average thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.", "links": [ { diff --git a/datasets/HMA_Glacier_dH_1.json b/datasets/HMA_Glacier_dH_1.json index 55f5978d48..b9665d2b11 100644 --- a/datasets/HMA_Glacier_dH_1.json +++ b/datasets/HMA_Glacier_dH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_Glacier_dH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains gridded thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from KH-9 HEXAGON and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.", "links": [ { diff --git a/datasets/HMA_Glacier_dH_Mosaics_1.json b/datasets/HMA_Glacier_dH_Mosaics_1.json index a27d2939de..c5c7f625da 100644 --- a/datasets/HMA_Glacier_dH_Mosaics_1.json +++ b/datasets/HMA_Glacier_dH_Mosaics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_Glacier_dH_Mosaics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains thickness change mosaics that include approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.", "links": [ { diff --git a/datasets/HMA_LIS_LandSurfaceHydro_1.json b/datasets/HMA_LIS_LandSurfaceHydro_1.json index c32a819000..962605bef1 100644 --- a/datasets/HMA_LIS_LandSurfaceHydro_1.json +++ b/datasets/HMA_LIS_LandSurfaceHydro_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_LIS_LandSurfaceHydro_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data provided in this data set are simulated using the Noah-Multiparameterization Land Surface Model (Noah-MP LSM) Version 3.6 within the NASA Land Information System (LIS) Version 7.2. The data files contain estimates of water, energy fluxes, and land surface states for the High Mountain Asia (HMA) region.", "links": [ { diff --git a/datasets/HMA_LS_Cat_2.json b/datasets/HMA_LS_Cat_2.json index 7b43b82d98..557d62e91d 100644 --- a/datasets/HMA_LS_Cat_2.json +++ b/datasets/HMA_LS_Cat_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_LS_Cat_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is an inventory of some 2800 landslides that occurred in the High Mountain Asia (HMA) study area between 5 January 2007 and 31 December 2018 (plus one event from 28 January 1990). The catalog includes dates and locations of landslides, plus additional characteristics such as event triggers, country, length and area of the slide, and the number of injuries and fatalities.\n\nThe events in this catalog represent an HMA-specific subset of the Cooperative Open Online Landslide Repository (COOLR), a project that was created to build a more robust, publicly available inventory of landslides by supplementing data in the NASA Global Landslide Catalog with citizen science reports.", "links": [ { diff --git a/datasets/HMA_MAR3_5_1.json b/datasets/HMA_MAR3_5_1.json index 7fb35cef29..a184aaab93 100644 --- a/datasets/HMA_MAR3_5_1.json +++ b/datasets/HMA_MAR3_5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_MAR3_5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides modeled surface and atmospheric fields from the Mod\u00e8le Atmosph\u00e9rique R\u00e9gionale (MAR) regional climate model (version 3.5) over the Himalayan region at 10 km spatial resolution. Modeled parameters include surface mass and energy balance components, near-surface atmospheric properties, and snowpack properties.", "links": [ { diff --git a/datasets/HMA_MTLI_1.json b/datasets/HMA_MTLI_1.json index 7ec7cd1fe9..899de3aa1c 100644 --- a/datasets/HMA_MTLI_1.json +++ b/datasets/HMA_MTLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_MTLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The mountains of Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, extreme rainfall and infrastructure development. As a landlocked country, Nepal relies on proper functioning of major transportation networks such as the highways to sustain and improve the livelihoods of the population. Every year there are reports of landslides blocking the highways, especially during the rainy season; however, the frequency and location of landslides along the highway corridors are not well reported. RapidEye satellite imagery was used to create annual landslide initiation point inventories along three important highways in Nepal: the Arniko, Karnali, and Pasang Lhamu highway.", "links": [ { diff --git a/datasets/HMA_OptDepth_1.json b/datasets/HMA_OptDepth_1.json index 1abecff2a3..42129e2d46 100644 --- a/datasets/HMA_OptDepth_1.json +++ b/datasets/HMA_OptDepth_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_OptDepth_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly mean MODIS Level 3 data from aboard the Aqua and Terra satellites. The parameters provided in this data set are aerosol optical depth (AOD) and Angstrom exponent (AE) at a spatial resolution of 1\u00ba by 1\u00ba.", "links": [ { diff --git a/datasets/HMA_Precip_3B42_1.json b/datasets/HMA_Precip_3B42_1.json index da28b68a70..9cab367248 100644 --- a/datasets/HMA_Precip_3B42_1.json +++ b/datasets/HMA_Precip_3B42_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_Precip_3B42_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were calculated from 3B42 version 7 daily satellite precipitation estimates.", "links": [ { diff --git a/datasets/HMA_Precip_FLOR_1.json b/datasets/HMA_Precip_FLOR_1.json index 49066cc9ef..1ecf953aed 100644 --- a/datasets/HMA_Precip_FLOR_1.json +++ b/datasets/HMA_Precip_FLOR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_Precip_FLOR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of CM2.5 (GFDL FLOR).", "links": [ { diff --git a/datasets/HMA_RCMO_1H_1.json b/datasets/HMA_RCMO_1H_1.json index ef01070c5e..2844debf75 100644 --- a/datasets/HMA_RCMO_1H_1.json +++ b/datasets/HMA_RCMO_1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_RCMO_1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains either hourly accumulated or hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.", "links": [ { diff --git a/datasets/HMA_RCMO_6H_1.json b/datasets/HMA_RCMO_6H_1.json index 89b0162db6..cc1726a0d4 100644 --- a/datasets/HMA_RCMO_6H_1.json +++ b/datasets/HMA_RCMO_6H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_RCMO_6H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains either 6-hourly accumulated or 6-hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.", "links": [ { diff --git a/datasets/HMA_RCMO_D_1.json b/datasets/HMA_RCMO_D_1.json index 304b80b796..6bc8cb8e46 100644 --- a/datasets/HMA_RCMO_D_1.json +++ b/datasets/HMA_RCMO_D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_RCMO_D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains either daily averaged or daily accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.", "links": [ { diff --git a/datasets/HMA_RCMO_M_1.json b/datasets/HMA_RCMO_M_1.json index e06d02be15..e9e8ed1c3f 100644 --- a/datasets/HMA_RCMO_M_1.json +++ b/datasets/HMA_RCMO_M_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_RCMO_M_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains either monthly averaged or monthly accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.", "links": [ { diff --git a/datasets/HMA_SBRF_1.json b/datasets/HMA_SBRF_1.json index d4e525f9e6..a246d7335a 100644 --- a/datasets/HMA_SBRF_1.json +++ b/datasets/HMA_SBRF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_SBRF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow bidirectional reflectance factor (BRF) between 350 and 2500 nm collected on the Yala Glacier on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD).", "links": [ { diff --git a/datasets/HMA_SDI_1.json b/datasets/HMA_SDI_1.json index b033299af2..b6a527f26c 100644 --- a/datasets/HMA_SDI_1.json +++ b/datasets/HMA_SDI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_SDI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains thermal-dome-corrected downward shortwave irradiance at the bottom of atmosphere, measured by the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) and collected by the International Centre for Integrated Mountain Development (ICIMOD).", "links": [ { diff --git a/datasets/HMA_SR_D_1.json b/datasets/HMA_SR_D_1.json index a152c361f3..d023e2d82d 100644 --- a/datasets/HMA_SR_D_1.json +++ b/datasets/HMA_SR_D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_SR_D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snowpack plays a significant role in the hydrologic cycle over High Mountain Asia (HMA). As a vital water resource, the distribution of snowpack volume also impacts the water availability for downstream populations. To assess the regional water balance, it is important to characterize the spatio-temporal distribution of water storage in the HMA snowpack.\nThis HMA snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow covered area (fSCA), snow depth (SD), etc.", "links": [ { diff --git a/datasets/HMA_STParams_1.json b/datasets/HMA_STParams_1.json index fa8cacc415..7231f73698 100644 --- a/datasets/HMA_STParams_1.json +++ b/datasets/HMA_STParams_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_STParams_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides daily-averaged NASA Land Information System (LIS) output at a spatial resolution of 1 km. LIS was driven by uncorrected Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) data, using the Noah Multiparameterization Land Surface Model (Noah-MP). Modeled parameters include snow water equivalent (SWE), snow depth, surface temperature, and soil temperature profile.", "links": [ { diff --git a/datasets/HMA_Snowfield_1.json b/datasets/HMA_Snowfield_1.json index 689fb038b7..2fde10630a 100644 --- a/datasets/HMA_Snowfield_1.json +++ b/datasets/HMA_Snowfield_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HMA_Snowfield_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of several different snow properties, including reflectance at 1310 nm, specific surface area, and optical mean radius, collected on the Yala Glacier, Nepal. These data were collected on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD) using the IceCube instrument.", "links": [ { diff --git a/datasets/HOMAGE_GGFO_L4_GOMA_Monthly_v01_1.0.json b/datasets/HOMAGE_GGFO_L4_GOMA_Monthly_v01_1.0.json index 5c4cace152..0c92b640cb 100644 --- a/datasets/HOMAGE_GGFO_L4_GOMA_Monthly_v01_1.0.json +++ b/datasets/HOMAGE_GGFO_L4_GOMA_Monthly_v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HOMAGE_GGFO_L4_GOMA_Monthly_v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.", "links": [ { diff --git a/datasets/HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0.json b/datasets/HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0.json index 59e918b818..dce25827fc 100644 --- a/datasets/HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0.json +++ b/datasets/HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); \r\nInstitute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. \r\nThe data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution\u2019s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.", "links": [ { diff --git a/datasets/HOT_0.json b/datasets/HOT_0.json index 2b62721b01..15017a499c 100644 --- a/datasets/HOT_0.json +++ b/datasets/HOT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HOT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scientists working on the Hawaii Ocean Time-series (HOT) program have been making repeated observations of the hydrography, chemistry and biology of the water column at a station north of Oahu, Hawaii since October 1988. The objective of this research is to provide a comprehensive description of the ocean at a site representative of the North Pacific subtropical gyre. Cruises are made approximately once per month to the deep-water Station ALOHA (A Long-Term Oligotrophic Habitat Assessment) located 100 km north of Oahu, Hawaii. Measurements of the thermohaline structure, water column chemistry, currents, optical properties, primary production, plankton community structure, and rates of particle export are made on each cruise.", "links": [ { diff --git a/datasets/HRAC_Precip_1.json b/datasets/HRAC_Precip_1.json index f7a231ac2d..65f9cf911d 100644 --- a/datasets/HRAC_Precip_1.json +++ b/datasets/HRAC_Precip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRAC_Precip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a dataset that enhances the TMPA monthly product (3B43) in its accuracy and spatial resolution, in hydrometeorological applications. About 9,200 gauge measurement are used to compare with the 3B43 product at 0.25\u00b0 x 0.25\u00b0 spatial resolution across the CONUS. Observed is a strong relationship between the bias and land surface elevation, in which 3B43 underestimates the true precipitation at the elevations above 1,500 m amsl. Satellite data is resampled to elevation data at ~1km grid size and applied a correction function to reduce bias in the data. Accordingly, a High-Resolution Altitude-Corrected product is constructed, based on 3B43 and covering the entire CONUS at 1-km resolution. This product is verified against 9,200 gauges across the country. The results showed a substantial improvement in the satellite-gauge data accuracy as well as spatial resolution. \n", "links": [ { diff --git a/datasets/HRIRN1IM_001.json b/datasets/HRIRN1IM_001.json index f398081eaa..73a715d9d9 100644 --- a/datasets/HRIRN1IM_001.json +++ b/datasets/HRIRN1IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN1IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN1IM is the Nimbus-1 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the north pole to the south pole. The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus I Users' Guide.\"\n\nThe HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-1 satellite and was operational from August 28, 1964 through September 22, 1964.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00135 (old ID 64-052A-03B).", "links": [ { diff --git a/datasets/HRIRN1L1_001.json b/datasets/HRIRN1L1_001.json index c195a9036e..d0789f3e59 100644 --- a/datasets/HRIRN1L1_001.json +++ b/datasets/HRIRN1L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN1L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN1L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-1 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. he data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-1 satellite and was operational from August 28, 1964 through September 22, 1964 when the spacecraft malfunctioned.\n\nMeasurements taken during daytime do not reveal true surface temperaturessince the radiometer operates in the 3.5 to 4.1 micron region, and reflectedsolar radiation is added to emitted surface radiation. However, reflected sunlight in this spectral region does not saturate the radiometer output and usable pictures can be made.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00209 (old ID 64-052A-03A).", "links": [ { diff --git a/datasets/HRIRN2IM_001.json b/datasets/HRIRN2IM_001.json index 71f67ef132..35a9ce7c3b 100644 --- a/datasets/HRIRN2IM_001.json +++ b/datasets/HRIRN2IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN2IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN2IM is the Nimbus-2 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the north pole to the south pole. The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus II Users' Guide.\n\n\"The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. This HRIR instrument was launched on the Nimbus-2 satellite and was operational from May 15, 1966 through November 15, 1966.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00226 (old ID 66-040A-03B).", "links": [ { diff --git a/datasets/HRIRN2L1_001.json b/datasets/HRIRN2L1_001.json index 1c6c1819f9..4de9bba1cd 100644 --- a/datasets/HRIRN2L1_001.json +++ b/datasets/HRIRN2L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN2L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN2L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-2 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-2 satellite and was operational from May 16, 1966 through November 15, 1966.\n\nMeasurements taken during daytime do not reveal true surface temperatures since the radiometer operates in the 3.5 to 4.1 micron region, and reflected solar radiation is added to emitted surface radiation. However, reflected sunlight in this spectral region does not saturate the radiometer output and usable pictures can be made.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00108 (old ID 66-040A-03A).", "links": [ { diff --git a/datasets/HRIRN3IM_001.json b/datasets/HRIRN3IM_001.json index c8031cd304..bb337ca788 100644 --- a/datasets/HRIRN3IM_001.json +++ b/datasets/HRIRN3IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN3IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN3IM is the Nimbus-3 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital daytime (0.7 to 1.3 microns) and nighttime (3.4 to 4.2 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the south pole to the north pole (day) and the north pole to the south pole (night). The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus III Users' Guide.\"\n\nThe HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 22, 1969 through January 31, 1970.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00223 (old ID 69-037A-02B).", "links": [ { diff --git a/datasets/HRIRN3L1_001.json b/datasets/HRIRN3L1_001.json index 7021d65764..2b763779d3 100644 --- a/datasets/HRIRN3L1_001.json +++ b/datasets/HRIRN3L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRIRN3L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRIRN3L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-3 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR flown on Nimbus-3 was modified to allow nighttime and daytime cloud cover mapping by use of dual band-pass filter which transmits 0.7 to 1.3 micron, and 3.4 to 4.2 micron radiation. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 14, 1966 through July 22, 1969. Nighttime operation was made in the 3.4 to 4.2 micron near infrared region. Daytime operation was based on the predominance of reflected solar energy in the 0.7 to 1.3 micron region. Change-over from nighttime to daytime operation was accomplished automatically (or by ground station command), by actuating a relay in the early stages of the radiometer electronics. The system gain was reduced in the daytime mode to compensate for the higher energy levels.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00222 (old ID 69-037A-02C).", "links": [ { diff --git a/datasets/HRO.json b/datasets/HRO.json index 32a08d0d34..cb80f1be4e 100644 --- a/datasets/HRO.json +++ b/datasets/HRO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map.\n\nA digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel.\n", "links": [ { diff --git a/datasets/HRTS-II_ATLAS.json b/datasets/HRTS-II_ATLAS.json index 86340ad8f0..feaef270f3 100644 --- a/datasets/HRTS-II_ATLAS.json +++ b/datasets/HRTS-II_ATLAS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HRTS-II_ATLAS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An ultraviolet spectral Atlas of a sunspot with high spectral and\nspatial resolution in the wavelength region 1190 - 1730 A is\npresented. The sunspot was observed with the High Resolution Telescope\nand Spectrograph (HRTS). The HRTS instrument was built at the U.S.\nNaval Research Laboratory (NRL), Washington, D.C. (Bartoe and\nBrueckner, 1975). The instrument combines high spatial, spectral, and\ntime resolution with an extensive wavelength and angular\ncoverage. This makes HRTS particularly well suited for studies of fine\nstructure and mass flows in the upper solar atmosphere. HRTS has\nflown six times on rockets between 1975 and 1989 and as a part of\nSpacelab 2 in 1985.\n\nThe spectrograms used for the Atlas are from the second HRTS rocket\nflight, known as HRTS II, flown on 13 February 1978 aboard a Black\nBrant VC rocket (NASA Flight 21.042) at White Sands, New Mexico.\nDuring the rocket flight the slit was oriented radially from the solar\ndisc center through the active region McMath 15139, including a\nsunspot, and across the solar limb. The Solar Pointing Aerobee Rocket\nControl System (SPARCS) kept the spectrograph slit positioned on the\nsolar surface during the observing time of 4.2 minutes. The spatial\nresolution on this flight was 2 arcsec with a time resolution from 0.2\n- 20.2 sec.\n\nThe HRTS spectra were recorded on Eastman Kodak 101-01 photographic\nfilm. Microphotometry of the spectrograms has been carried out at the\nInstitute of Theoretical Astrophysics in Oslo. The data reduction\nincludes correcting the spectral images for geometrical distortion,\nFourier filtering to remove high frequency noise, transformation to\nabsolute calibrated solar intensity and calibration of the wavelength\nscale.\n\nThe absolute intensity calibration was obtained by comparing relative\nintensity scans of a quiet solar region with absolute intensities from\nthe Skylab S082B calibration rocket, CALROC The resulting absolute\nintensities are accurate to within 30% (rms).\n\nThe wavelength scale was established using solar lines from neutral\nand singly ionized atoms as reference lines. From this wavelength\nscale velocities accurate to 2 km/s can be measured over the entire\nwavelength range. The measured velocities are, however, relative to\nthe average velocity in the chromosphere where the reference lines are\nformed.\n\nThe Atlas contains spectra of three different areas in the sunspot and\nalso of an active region and a quiet region. The selected areas are\naveraged over several arcsec, ranging from 3.5 arcsec in the sunspot\nto 18 arcsec in the quiet region. The transition region lines in the\nAtlas show the most extreme example known of downflowing gas above a\nsunspot, a phenomenon which seems to be commonly occurring in sunspots.\n\nOne of the selected areas in the sunspot is a light bridge crossing\nthe spot. This is the most interesting sunspot region where the\ncontinuum radiation is enhanced and measurable throughout the HRTS\nspectral range. A number of lines appear which do not occur in the\nregular sunspot spectrum.\n\nThe Atlas is available in a machine readable form together with an IDL\nprogram to interactively measure linewidths, total intensities and\nsolar wavelengths. See: http://zeus.nascom.nasa.gov/~pbrekke/HRTS/", "links": [ { diff --git a/datasets/HUC250k.json b/datasets/HUC250k.json index 5aefecaa50..28d0ffa955 100644 --- a/datasets/HUC250k.json +++ b/datasets/HUC250k.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HUC250k", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geographic Information Retrieval and Analysis System (GIRAS) was\ndeveloped in the mid 70s to put into digital form a number of data layers which\nwere of interest to the USGS. One of these data layers was the Hydrologic\nUnits. The map is based on the Hydrologic Unit Maps published by the U.S.\nGeological Survey Office of Water Data Coordination, together with the list\ndescriptions and name of region, subregion, accounting units, and cataloging\nunit. The hydrologic units are encoded with an eight- digit number that\nindicates the hydrologic region (first two digits), hydrologic subregion\n(second two digits), accounting unit (third two digits), and cataloging unit\n(fourth two digits).\n\nThe data produced by GIRAS was originally collected at a scale of 1: 250K. Some\nareas, notably major cities in the west, were recompiled at a scale of 1: 100K.\nIn order to join the data together and use the data in a geographic information\nsystem (GIS) the data were processed in the ARC/INFO GUS software package.\nWithin the GIS, the data were edge matched and the neatline boundaries between\nmaps were removed to create a single data set for the conterminous United\nStates.\n\nThis data set was compiled originally to provide the National Water Quality\nAssessment (NAWQA) study units with an intermediate- scale river basin boundary\nfor extracting other GIS data layers. The data can also be used for\nillustration purposes at intermediate or small scales (1:250,000 to 1:2\nmillion).\n\n[Summary provided by EPA]", "links": [ { diff --git a/datasets/HWSD_1247_1.json b/datasets/HWSD_1247_1.json index 1fda37286c..091f540d8c 100644 --- a/datasets/HWSD_1247_1.json +++ b/datasets/HWSD_1247_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HWSD_1247_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2 The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles. The data are presented as a series of 27 NetCDF v3/v4 (*.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000.", "links": [ { diff --git a/datasets/HYCODE_LEO-15_0.json b/datasets/HYCODE_LEO-15_0.json index 716abcd1fd..a15d16aeee 100644 --- a/datasets/HYCODE_LEO-15_0.json +++ b/datasets/HYCODE_LEO-15_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HYCODE_LEO-15_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Hyperspectral Coastal Ocean Dynamics Experiment (HyCoDE) LEO-15 station off the Atlantic Coast of New Jersey.", "links": [ { diff --git a/datasets/Happy_Valley_Veg_Plots_1354_1.json b/datasets/Happy_Valley_Veg_Plots_1354_1.json index 7312a09ee1..3a26f1ae0a 100644 --- a/datasets/Happy_Valley_Veg_Plots_1354_1.json +++ b/datasets/Happy_Valley_Veg_Plots_1354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Happy_Valley_Veg_Plots_1354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental, soil, and vegetation data collected in July 1994 from 56 study plots at the Happy Valley research site, located along the Sagavanirktok River in a glaciated valley of the northern Arctic Foothills of the Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 17 plant communities that occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools, soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Happy Valley region and across Alaska.", "links": [ { diff --git a/datasets/Health_seals_1.json b/datasets/Health_seals_1.json index f26212b4cd..032cae773b 100644 --- a/datasets/Health_seals_1.json +++ b/datasets/Health_seals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Health_seals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples from 35 seals have been collected for serum biochemistry analysis.\nScats from 20 animals have been collected for parasitology. Estimated weights and morphometric measurements from 35 animals have been collected.\n\nThe data for this project are presented in a number of excel worksheets. In addition, a word document is also included in the download file which fully explains each spreadsheet. A precis of that word document is copied below.\n\nHaematology\n\nData from haematological analysis performed on fresh whole blood collected from leopard seals between 27.12.1999-22.01.2002 and Weddell seals between 02.01.2001-21.01.2002 off Davis Station in the Austral summer seasons of 1999/2000, 2000/2001 and 2001/2002.\n\nSerum protein electrophoresis (SPE)\n\nData from SPE analysis performed on serum (stored at -80 degrees C) collected from leopard seals between 27.12.1999-22.01.2002 and Weddell seals between 02.01.2001-21.01.2002 off Davis Station in the Austral summer seasons of 1999/2000, 2000/2001 and 2001/2002.\n\nSerum Biochemistry\n\nData from biochemistry analysis performed on serum (stored at -80 degrees C) collected from leopard seals between 27.12.1999-22.01.2002 and Weddell seals between 02.01.2001-21.01.2002 off Davis Station in the Austral summer seasons of 1999/2000, 2000/2001 and 2001/2002.\n\nTrace element and heavy metal analysis\n\nData from trace element and heavy metal analysis performed on serum (-80 degrees C), fur, frozen (-20 degrees C) and formalin (10%) fixed tissues, plasma (-80 degrees C), EDTA plasma (-80 degrees C), washed red blood cells (-80 degrees C) and urine (-20 degrees C) collected from leopard seals between 27.12.1999-17.02.2002 and Weddell seals between 02.01.2001-21.01.2002 off Davis Station in the Austral summer seasons of 1999/2000, 2000/2001 and 2001/2002 using inductively coupled plasma mass spectroscopy.\n\nThe spreadsheet is organised into six worksheets:\n\n1.Serum (micro g/L)\n2.Fur (micro g/g dry weight)\n3.Frozen tissues (micro g/g dry weight)\n4.Plasma and RBC (red blood cells) (micro g/L)\n5.Urine (micro g/L)\n6.Formalin tissues (micro g/g dry weight)\n\nFaecal Parasites\n\nData from faecal flotation in saturated salt solution performed on fresh and frozen (- 20 degrees C) scats collected from leopard seals between 23.11.1999-17.02.2002 and Weddell seals between 06.12.2000-16.01.2002 off Davis Station in the Austral summer seasons of 1999/2000, 2000/2001 and 2001/2002.\n\nThe fields in this dataset are:\n\nGlucose\nUrea\nCreatinine\nFibrinogen\nProtein\nAlbumin\nGlobulin\nBilirubin\nALP\nAST\nALT\nCreatinine Kinase\nCholesterol\nCalcium\nPhosphate\nSodium\nPotassium\nChloride\nBicarbonate\nAnion Gap\nAmylase\nLipase\nDate\nFaeces\nCestode eggs\nAscarid Eggs\nPCV\nWCC\nNeutrophil\nEosinophil\nBasophil\nLymphocyte\nMonocyte\nBand Neutrophil\nSerum\nMagnesium\nAluminium\nVandium\nChromium\nManganese\nIron\nCobalt\nNickel\nCopper\nZinc\nArsenic\nSelenium\nCadmium\nMercury\nLead\nBismuth\nDate", "links": [ { diff --git a/datasets/Heard_2008_Images_1.json b/datasets/Heard_2008_Images_1.json index 3739c96f8f..a6aa10c57d 100644 --- a/datasets/Heard_2008_Images_1.json +++ b/datasets/Heard_2008_Images_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_2008_Images_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In December 2008, the RSV Aurora Australis had an opportunity to visit Heard Island and McDonald Islands. A number of activities took place and included:\n\n- An aerial survey (16th December) from the north coast of Red Island (west end) to the end of The Spit. The helicopter flew approximately 1.5 km offshore and at an altitude of about 1900 ft. Then it flew directly to the west coast and surveyed from Henderson Bluff to Kildalke. Video, stereo photos and photos of wildlife colonies and areas of interest were taken. \n- An aerial survey (17th December) from The Spit travelling along the south coast. Then repeating the aerial survey of the 16th. The survey of the 16th was in dull light and was repeated on the 17th when light was better. \n- A ship-based survey from Atlas Roads to the north coast of The Spit was made on the 16th December and from Atlas Roads to Red Island on the 17th December. Stereo photos, photos of wildlife and named features and video were taken about 2nm offshore.\n- A team of people visited Atlas Cove and assessed the huts and ruins, took stereo photos of heritage items and elephant seals.\n- A small team flew around the Island and assessed and photographed the hut sites.\n- On the 17th December, the RSV Aurora Australis sailed past McDonald Islands. Visibility was poor so only a few photos were taken. \n- Water samples were taken for AAS Project 2899.\n\nMore information is included in documents and spreadsheets, including some GPS locations of where photographs were taken, by whom and photo descriptions.", "links": [ { diff --git a/datasets/Heard_2016_glaciers_1.json b/datasets/Heard_2016_glaciers_1.json index 2879771284..d90f458d4a 100644 --- a/datasets/Heard_2016_glaciers_1.json +++ b/datasets/Heard_2016_glaciers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_2016_glaciers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding of changes in the extent of the Heard Island glaciers has been derived from comparison of brief journal accounts, photographs and drawings made during the early sealing period and later, as scientists began to explore this remote island, from published reports, photographs, satellite images and eventually mass-balance studies. The fluctuations of these glaciers have previously been discussed by Budd and Stephenson (1970), Allison and Keage (1986), Budd (2000), Ruddell (2006), Donoghue (2009), and Cogley et al (2014). \nThis report examines two newly acquired satellite images from 2012 and 2014. What is unique about these images is that in both cases the images are near cloud free and include the entire island. This is unusual for this mountainous island. These images provide the first chance to complete a full inventory of the island's glaciers over both years (2012 and 2014) from a single point in time. Complete satellite inventories of the island have only previously been attempted in 1988 and 2008 - in each case this required the use images over several years and different sources to capture the entire island.\nThis report also includes an inventory of additional data sets and data resources that have been used to calculate the length and areas of Heard Island glaciers between 1947 and 2014.", "links": [ { diff --git a/datasets/Heard_86-87_Report_1.json b/datasets/Heard_86-87_Report_1.json index 35876096d0..0a99e236aa 100644 --- a/datasets/Heard_86-87_Report_1.json +++ b/datasets/Heard_86-87_Report_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_86-87_Report_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heard Island Expedition, 16 November 1986 to 21 January 1987, report written by Rod Ledingham, Officer in Charge.\n\nTaken from the report:\n\nThe 1986-87 expedition was the second in a series of three consecutive expeditions planned to conduct new scientific work and to check on changes since the early wintering years from 1948-1954 and more recent sporadic visits by various government and private expeditions. We were dropped off at Heard Island on the 14th November 1986 by the Nella Dan.\n\nThe main thrust of this expedition was originally to have been geological but this was later expanded to cover biology and archaeology. Transport was provided by three Hughes 500 helicopters, old faithfuls VH-BAD piloted by John Robertson and VH-BAG piloted by Doug Crossan, and a new arrive from NZ, VH-HED flown by Phillip Turner, to provide speedy access to all areas of rock, either coastal or at high altitude on the mountain. Of particular interest to the geologists were the lavas of the January 1985 Big Ben eruption spotted by observers including Dick Williams, on the French vessel Marion Dufresne.\n\nDespite some initial doubts about the possibility of flying, or even holding, aircraft at Heard for any length of time, and numerous relatively minor problems with weather and wind blown volcanic sand, the operation went very well and a great deal of new ground was covered, including several flights to the summit of Big Ben and the discovery of a new active crater and the expedition lava flows on the south-western slopes at Cape Arkona.\n\nTwo geologists accompanied the expedition, Jane Barling and Graeme Wheller. Geological mapping of the whole island was carried out by Jane where access was not too difficult or dangerous. Jane had previously worked on the samples brought back from Long Ridge and the summit by the Heard Island Expedition (private) on Anaconda II in 1983. The original map produced by Ainsworth in 1947 will be greatly improved when the material has been studied in more detail. The second geologist Graeme studied the relationships of the more recent lavas and attempted to get samples from the summit vent. The failure to do so was somewhat ameliorated by the finding of the new lava which it appears had emanated from the summit vent pipe and samples of summit rock were therefore available from 700m above Cape Arkona.\n\nFurther information about the botanical and biological work is available in the report.", "links": [ { diff --git a/datasets/Heard_Archaeology_1986_1987_1.json b/datasets/Heard_Archaeology_1986_1987_1.json index 3edca9bc6d..3e94046343 100644 --- a/datasets/Heard_Archaeology_1986_1987_1.json +++ b/datasets/Heard_Archaeology_1986_1987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_Archaeology_1986_1987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "See the downloadable report for more details.\n\nHistorical context of Heard Island (taken from the report)\nThere is some controversy as to when Heard Island was first sighted and when the first landing occurred. Initially, Captain John J. Heard of the American barque Oriental and after whom the island was named, was credited with having first sighted Heard Island in November 1853, during his voyage from Boston to Melbourne using the great circle route. The explanation offered for such a late discovery of Heard Island when the nearby Kerguelen Islands had already been discovered as long ago as 1772 was based on a combination of two factors. Increased interest in travelling to Australia during the gold rush years of the 1850s and the suggestion by Maury, the Director of the US Naval Observatory, that the use of the great circle route might result in faster passages meant that more vessels were travelling further to the south after they passed the Cape of Good Hope. That these factors had an impact on the sighting of Heard Island is borne out by the number of vessels that reported the presence of Heard and the nearby McDonald Islands between 1853 and 1855. Captain McDonald of the English sealer, Samarang, saw Heard Island and discovered the McDonald Islands in January, 1854. Three further sightings of Heard Island were made by British vessels in the latter part of that year.\n\nDownload the report for more...", "links": [ { diff --git a/datasets/Heard_Geology_1948_1.json b/datasets/Heard_Geology_1948_1.json index 8b94900254..081353645f 100644 --- a/datasets/Heard_Geology_1948_1.json +++ b/datasets/Heard_Geology_1948_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_Geology_1948_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the scanned dataset:\n\n1) Summary\n- Some observations on the geomorphology have been recorded.\n- Xenolithic ejectamenta collected from the tuffs of Rogers Head and Rogers Head Peninsula have been described.\n\n2) Introduction\nHeard Island is in the South Indian Ocean in Latitude 53 S, Longitude 73.5 E, and some 2,400 miles south-west of Fremantle, Western Australia. It is a volcanic island, 25 miles long and about 10 miles wide, with its main axis E.S.E. It has rarely been visited, owing to its being situated in one of the stormiest regions in the world.\nDuring the summer of 1947, an expedition was despatched to Heard Island with the object of landing a party to spend about twelve months there.\nThe writer worked in collaboration with the Magnetician, Mr N.G. Chamberlain, Bureau of Mineral Resources, and was able to undertake a little geological work. The results of this work are submitted in the following notes.\n\n3) Geomorphology\nHeard Island has been formed by the accumulation of material extruded from several volcanic vents. These volcanoes are on a submarine ridge which extends north to Kerguelen, and possibly south-west to the Antarctic continent.\nThe sketch outline of Heard Island, shows that the island is elongated in an east-south-easterly direction and along a probable fissure.\nThe main volcano of the island is Kaiser Wilhelm Peak, named by Drygalski in 1908. This peak is some 10,000 feet high, and consists of a cone partly surrounded by a caldera. Adventive or parasitic cones are situated on its flanks.\nTowards the south-west the island narrows to a long tapering spit, covered with sand and beach boulders. To the north-east of Kaiser Wilhelm Peak are the volcanic peaks (approximately 2,000 feet high) of Cape Laurens Peninsula, Rogers Head Peninsula and Cave Bay, separated from the main island mass by the Atlas Cove Plain.\n\nThe work was carried out by Mr J.F. Ivanac (geologist).", "links": [ { diff --git a/datasets/Heard_Is_Birds_Stomachs_1951_1.json b/datasets/Heard_Is_Birds_Stomachs_1951_1.json index 656b0e9461..429c3671d9 100644 --- a/datasets/Heard_Is_Birds_Stomachs_1951_1.json +++ b/datasets/Heard_Is_Birds_Stomachs_1951_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_Is_Birds_Stomachs_1951_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The documents available for downloading are:\n \nA scanned copy of a notebook from data collection/analysis\nTwo annotated copies of a typed list detailing the contents of the stomachs of bird species from Heard Island Iles de Kerguelen.\n \nSome correspondence is also included with the stomach content lists.\n \nThe typed lists refer to data collected in 1950, whereas the notebook refers to data collected in 1951.", "links": [ { diff --git a/datasets/Heard_Is_Pelecanoides_Old_1.json b/datasets/Heard_Is_Pelecanoides_Old_1.json index d577f9f50f..aa82084992 100644 --- a/datasets/Heard_Is_Pelecanoides_Old_1.json +++ b/datasets/Heard_Is_Pelecanoides_Old_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_Is_Pelecanoides_Old_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data tables were scanned by Fiona Gleadow. The data relate to diving petrels (Pelecanoides) from Heard Island, and generally appear to be measurements of body parts (weight, wing, tail, beak, tarsus, toe) on males and females, as well as measurements of eggs (weight, length and width).", "links": [ { diff --git a/datasets/Heard_Island_digitising_2009_1.json b/datasets/Heard_Island_digitising_2009_1.json index 0edd32dfd0..28e3d89a47 100644 --- a/datasets/Heard_Island_digitising_2009_1.json +++ b/datasets/Heard_Island_digitising_2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_Island_digitising_2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digitising of the coastline, glacier extents, water features, wildlife, and human footprints of the Heard Island Remote Sensing Project, 2009. This GIS data has Dataset_id = 273 and is available for downloading under the heading Heard and McDonald Islands (see url below).\n\nThe coastline and glacier data available for download was updated in May 2014 after the correction of dates in the Date of Capture field which records the date of capture of the image from which the digitising was done. The coastline of the offshore rocks which were large enough to map as polygons was added to the coastline data.", "links": [ { diff --git a/datasets/Heard_RadarSat_georef_1.json b/datasets/Heard_RadarSat_georef_1.json index 84079d1347..ae786f3cd8 100644 --- a/datasets/Heard_RadarSat_georef_1.json +++ b/datasets/Heard_RadarSat_georef_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_RadarSat_georef_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim of the project was to derive a number of control points that could be used to georeference two Radarsat scenes over Heard Island. Control points were derived from aerial photography covering various locations around the island, namely: Cape Gazert, Atlas Cove, Brown Lagoon, Manning Lagoon and Winston Lagoon. \nERDAS Imagine with OrthoBase Pro photogrammatric software was used to ortho-rectify the aerial photography and extract values for the derived control points. ERDAS Imagine OrthoRadar was used to georeference the Radarsat images.\nThe measurements taken from the aerial photography have been described in an earlier report.", "links": [ { diff --git a/datasets/Heard_SPOT_georef_1.json b/datasets/Heard_SPOT_georef_1.json index 2f936a668d..08f103252c 100644 --- a/datasets/Heard_SPOT_georef_1.json +++ b/datasets/Heard_SPOT_georef_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_SPOT_georef_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aim of this project was to determine orientation parameters for two SPOT scenes over Heard Island using the control derived for the Radarsat Georeferencing project.\nERDAS Imagine with OrthoBase Pro photogrammetric software was used to georeference the SPOT scenes.", "links": [ { diff --git a/datasets/Heard_WorldView-1_23MAR08_1.json b/datasets/Heard_WorldView-1_23MAR08_1.json index 2be8bed2f1..3e61cd3be5 100644 --- a/datasets/Heard_WorldView-1_23MAR08_1.json +++ b/datasets/Heard_WorldView-1_23MAR08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_WorldView-1_23MAR08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-1 image of Heard Island (23 March 2008) that was purchased by the Australian Antarctic Division (AAD) and the University of Tasmania (UTAS) in June 2008 has to be geometrically corrected to match the Quickbird and IKONOS imagery in the Australian Antarctic Data Centre (AADC) satellite image catalogue. In addition, the WorldView-1 imagery contains two separate image strips that cover the whole island. These strips were acquired at slightly different times from different angles during the satellite overpass. The discrepancy in acquisition angle has resulted in a geometric offset between the two image strips. These two image strips were orthorectified with a 10 m RADARSAT DEM (2002). The orthorectified images were then merged into a single image mosaic for the whole island.\n\nThis work was completed as part of ASAC project 2939 (ASAC_2939).", "links": [ { diff --git a/datasets/Heard_data_snapshot_1901-2002_1.json b/datasets/Heard_data_snapshot_1901-2002_1.json index b994e57e7e..235df9a63e 100644 --- a/datasets/Heard_data_snapshot_1901-2002_1.json +++ b/datasets/Heard_data_snapshot_1901-2002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_data_snapshot_1901-2002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The snapshot (originally produced on CD for a conference) was produced by the Australian Antarctic Data Centre for distribution to Heard Island expeditioners in the 2003/2004 season. The snapshot contained all publicly available data held by the Australian Antarctic Data Centre related to Heard Island at the time of production. The snapshot also contained all metadata held by the AADC at the time of production.\n\nFurthermore, information is also included from:\n\nAADC's gazetteer\nbiodiversity database\nsatellite image archive\ngis shapefiles\nheard island wilderness reserve management plan\n\nFinally, freely available software needed to browse some of the data are also included.\n", "links": [ { diff --git a/datasets/Heard_lichens_1980_1.json b/datasets/Heard_lichens_1980_1.json index 91fc658196..f1720a8095 100644 --- a/datasets/Heard_lichens_1980_1.json +++ b/datasets/Heard_lichens_1980_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_lichens_1980_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A description of lichen samples collected from Heard Island during March of 1980. The samples were mostly collected by John Jenkin, but some other collectors were also used. On return to Australia, the samples were lodged with the Australian Antarctic Division Herbarium (Code- ADT) under the control of Rod Seppelt. The samples are distinguishable within the herbarium by their 3 digit code.\n\nThe dataset details the date each collection was made on, as well as an approximate descriptive location. Unless otherwise specified, all samples were collected by John Jenkin.", "links": [ { diff --git a/datasets/Heard_veg_survey_86-88_1.json b/datasets/Heard_veg_survey_86-88_1.json index edea956aec..312e5a2ab1 100644 --- a/datasets/Heard_veg_survey_86-88_1.json +++ b/datasets/Heard_veg_survey_86-88_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heard_veg_survey_86-88_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation surveys were conducted on Heard Island during the 1986/87 and 1987/88 Australian National Antarctic Research Expeditions (ANARE). A stratified sampling approach was adopted. Given the limited time available for sampling, quadrats were placed to sample the bryophytic component of Hughes (1987) six visually recognizable broad vascular plant community categories as well as sampling distinct landscape features such as coastal areas, moraines, scoria cones, and lava fields. Ideally, this stratification would have ensured that the major environmental gradients on the island were detected. A total of 475, 1 x 1 m quadrats were surveyed during the 8-wk 1986/87 field period. Two hundred and fifty quadrats were randomly selected within 25 (10 x 10 m) sites. One hundred and eighty quadrats were positioned on transects over distinct landscape features. The remaining 45 quadrats were randomly located in visually different areas in isolated localities.\n\nAccess to Heard Island is logistically difficult. Field time for our survey was short. Travel by foot was slow due to rough terrain and the use of helicopters was restricted by unfavorable weather conditions. Field work was conducted in three major ice-free areas on the island: the northwest areas encompassing Laurens Peninsula, Azorella Peninsula, and Mt. Drygalski; the eastern Spit Bay area; and the southern Long Beach area. The number of quadrats in each area reflects the time available (Laurens Peninsula, 119; Azorella Peninsula, 40; Mt. Drygalski, 10; Spit Bay, 250; Long Beach, 45; other areas 11.\n\nIn each quadrat the following habitat characteristics were noted: location (mapped); geomorphological features (sand, moraine, clinker lava, and lava flow), and general notes on topography; altitude; general slope of the quadrat (irregularities in clinker lava sites made slope difficult to assess); aspect; unconsolidated substrate depth to bedrock or a maximum of 100 cm, using a 1-cm-diameter metal probe (this may be an organic base such as peat or an inorganic substrate such as moraine); availability of water, rated on a subjective five-point exponential scale ranging from 1 (very dry) to 5 (surface free water); exposure to wind, rated on a subjective five-point scale ranging from 1 (very exposed) to 5 (very protected); availability of light, rated on a five-point subjective scale ranging from 1 (exposed to full light conditions) to 5 (deep shade).\n\nIn each quadrat, cover values using the Braun-Blanquet (1932) scale were recorded for all vascular plants, bryophytes (as a collective unit), bare ground, and rock. Notes on individual cover values for major bryophyte taxa were taken, and samples of bryophyte taxa were collected for identification.\n\nThis work now falls under the auspices of the RiSCC project (ASAC_1015).\n\nThe fields in this dataset are:\n\nRegion\nSite\nFormation\nEnviron\nAltitude (m)\nSpecies", "links": [ { diff --git a/datasets/Heavymetals-Gamms-Casey03-04_1.json b/datasets/Heavymetals-Gamms-Casey03-04_1.json index 3c2e0ece8b..f042a9555c 100644 --- a/datasets/Heavymetals-Gamms-Casey03-04_1.json +++ b/datasets/Heavymetals-Gamms-Casey03-04_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Heavymetals-Gamms-Casey03-04_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The heavy metal content of whole Paramoera walkeri (Eusiridae, Amphipoda) were measured from specimens collected and deployed in experimental mesocosms around Casey station during the summer of 2003/04. Data are the parts per million (ppm) concentrations of 45 heavy metals measured via acid digestion and ICP-MS analysis. P.walkeri were collected from an intertidal area on the northern side of O'Brien Bay and deployed in mesocosms (perforated sample jars housed within perforated 20 litre food buckets) suspended approximately three metres below the sea ice at four sites; two potentially impacted sites in Brown Bay and two control sites, O'Brien Bay and McGrady Cove. The experiment was run on three occasions during the summer each lasting two weeks.\n\nThese data were collected as part of ASAC project 2201 (ASAC_2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities).\n\nSee also other metadata records by Glenn Johnstone for related information.", "links": [ { diff --git a/datasets/HighRes_ClimateData_Western_US_1682_1.json b/datasets/HighRes_ClimateData_Western_US_1682_1.json index 7964ab621c..845818ce14 100644 --- a/datasets/HighRes_ClimateData_Western_US_1682_1.json +++ b/datasets/HighRes_ClimateData_Western_US_1682_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HighRes_ClimateData_Western_US_1682_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides sub-daily, high-resolution, climate data inputs including temperature, precipitation, near surface specific humidity, incoming short-wave radiation, and near-surface wind speed over 11 states of the western USA. States included are Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. These data were derived for use in the Community Land Model (CLM v4.5) and are at 3-hourly temporal and 4 x 4 km spatial resolutions for the 1979 through 2015 time period. The source for observational data was METDATA (now called GRIDMET), at a daily resolution. Modeling efforts using these data estimated annual carbon stocks, fluxes, and productivity across the western United States.", "links": [ { diff --git a/datasets/High_Res_Tidal_Marsh_Veg_1609_1.json b/datasets/High_Res_Tidal_Marsh_Veg_1609_1.json index 6f4851aed9..869aa9d6d5 100644 --- a/datasets/High_Res_Tidal_Marsh_Veg_1609_1.json +++ b/datasets/High_Res_Tidal_Marsh_Veg_1609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "High_Res_Tidal_Marsh_Veg_1609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.", "links": [ { diff --git a/datasets/Historic_S2K_Website_1765_1.json b/datasets/Historic_S2K_Website_1765_1.json index 64fb23cc33..7dc57ef889 100644 --- a/datasets/Historic_S2K_Website_1765_1.json +++ b/datasets/Historic_S2K_Website_1765_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Historic_S2K_Website_1765_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains an archived copy of the Safari 2000 Project website as of October 2008. This archived website is provided for informational purposes only. No updates to the website and associated content have been made since January of 2008. The database that once provided content for this website was transitioned to text and is included herein. SAFARI 2000 was an international regional science initiative developed for southern Africa to explore, study and address linkages between land-atmosphere processes and the relationship of biogenic, pyrogenic or anthropogenic emissions and the consequences of their deposition to the functioning of the biogeophysical and biogeochemical systems of southern Africa. This initiative was built around a number of on-going, already funded activities by NASA, the international community and African nations in the southern African region.", "links": [ { diff --git a/datasets/HistoricalLai_584_1.json b/datasets/HistoricalLai_584_1.json index e15cfb6742..a24aa0babe 100644 --- a/datasets/HistoricalLai_584_1.json +++ b/datasets/HistoricalLai_584_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HistoricalLai_584_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf Area Index (LAI) data from the scientific literature, 1932-2000, have been compiled at the ORNL DAAC to support model development and EOS MODIS product validation. Like net primary productivity (NPP), leaf area index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange.", "links": [ { diff --git a/datasets/Historical_Fish_data_1.json b/datasets/Historical_Fish_data_1.json index fe6d0bdd9f..21476f5722 100644 --- a/datasets/Historical_Fish_data_1.json +++ b/datasets/Historical_Fish_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Historical_Fish_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises of an Access Database of compiled historical fish data from the following voyages and field surveys:\n\nFish biological and stomach contents data - Casey 1988\nInshore Marine Fish of the Vestfold Hills Antarctica, 1983-1984\nMacquarie Island Fisheries, 1994-1995\nAurora Australis Voyage 7.2 (HIMS) 1989-90 Heard Island Fish Data\nAurora Australis Voyage 6 (AAMBER2) 1990-91 Pelagic Fish Data\nAurora Australis Voyage 6 (FISHOG) 1991-92 Heard Island Fish Data\nAurora Australis Voyage 1 (THIRST) 1993-94 Demersal Fish Data\n\nSee the child records for more details about the individual voyages or field surveys.", "links": [ { diff --git a/datasets/Historical_Lake_Shorelines_AK_1859_1.json b/datasets/Historical_Lake_Shorelines_AK_1859_1.json index ed05a1957b..1a35a96198 100644 --- a/datasets/Historical_Lake_Shorelines_AK_1859_1.json +++ b/datasets/Historical_Lake_Shorelines_AK_1859_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Historical_Lake_Shorelines_AK_1859_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes maps of historical lake shorelines with derived lake areas in the southern portion of the Goldstream Valley and the surrounding landscape north of Fairbanks, Alaska, USA. Historical lake margins were mapped for 1949, 1967, and 1985 using 1 m aerial photographs available through U.S. Geological Survey Earth Explorer, and for 2009 using 2.5 m SPOT satellite image mosaics. The study area was a 214 km2 area of Pleistocene-aged yedoma permafrost in the southern portion of the Goldstream Valley. An increasing number of thermokarst lakes and ponds, from 130–275 per year, were identified over the entire study period. Anthropogenic lakes, formed by mining peat, gravel, and gold concentrated in the northwestern extent of Goldstream Valley, were excluded.", "links": [ { diff --git a/datasets/Horn_Island_0.json b/datasets/Horn_Island_0.json index f61ab6d90e..8a6ddb0e59 100644 --- a/datasets/Horn_Island_0.json +++ b/datasets/Horn_Island_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Horn_Island_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the northern part of the Gulf of Mexico near Horn Island in 2003.", "links": [ { diff --git a/datasets/HourlyUrban_GreenhouseGases_US_1916_2.json b/datasets/HourlyUrban_GreenhouseGases_US_1916_2.json index 888d7af954..d60c265aad 100644 --- a/datasets/HourlyUrban_GreenhouseGases_US_1916_2.json +++ b/datasets/HourlyUrban_GreenhouseGases_US_1916_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "HourlyUrban_GreenhouseGases_US_1916_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides hourly urban greenhouse gas measurements for cities in the CO2 Urban Synthesis and Analysis (CO2-USA) Data Synthesis Network for 2000 to 2019. Measurements include carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations measured at hourly intervals at multiple sites within the U.S. cities of Boston, Indianapolis, Los Angeles, Portland, Salt Lake City, San Francisco, and Washington DC/Baltimore, and Toronto, Canada.", "links": [ { diff --git a/datasets/Hudson_River_Fluorescence_0.json b/datasets/Hudson_River_Fluorescence_0.json index 7e0f9632e2..72ec9a65cb 100644 --- a/datasets/Hudson_River_Fluorescence_0.json +++ b/datasets/Hudson_River_Fluorescence_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Hudson_River_Fluorescence_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the Hudson River and its outflow region in 2007.", "links": [ { diff --git a/datasets/Hydroprofiler_0.json b/datasets/Hydroprofiler_0.json index d985f83dac..88472d5a14 100644 --- a/datasets/Hydroprofiler_0.json +++ b/datasets/Hydroprofiler_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Hydroprofiler_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from Monterey Bay during 2011 by a hydroprofiler.", "links": [ { diff --git a/datasets/IAKST1B_1.json b/datasets/IAKST1B_1.json index 9cf556e768..0711ceb91d 100644 --- a/datasets/IAKST1B_1.json +++ b/datasets/IAKST1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IAKST1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. For flights with the NASA DC-8 aircraft, the National Suborbital Research Center (NSRC) operates the instrument and creates the data product. For flights with the NASA P-3 and other aircraft, the instrument is operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.", "links": [ { diff --git a/datasets/IAKST1B_2.json b/datasets/IAKST1B_2.json index 058e935e4b..179d63e76b 100644 --- a/datasets/IAKST1B_2.json +++ b/datasets/IAKST1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IAKST1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. The instrument is operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.", "links": [ { diff --git a/datasets/IAPRS1B_1.json b/datasets/IAPRS1B_1.json index ce35b4bd0a..2643d33238 100644 --- a/datasets/IAPRS1B_1.json +++ b/datasets/IAPRS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IAPRS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains static pressure values for Antarctica using the Paroscientific Digiquartz Transmitter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IASI_SST_METOP_A-OSISAF-L2P-v1.0_1.json b/datasets/IASI_SST_METOP_A-OSISAF-L2P-v1.0_1.json index 7e8c2ef887..478d77bb8b 100644 --- a/datasets/IASI_SST_METOP_A-OSISAF-L2P-v1.0_1.json +++ b/datasets/IASI_SST_METOP_A-OSISAF-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IASI_SST_METOP_A-OSISAF-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-A (MetOp-A）satellite （launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.", "links": [ { diff --git a/datasets/IASI_SST_METOP_B-OSISAF-L2P-v1.0_1.json b/datasets/IASI_SST_METOP_B-OSISAF-L2P-v1.0_1.json index b1f8a1b094..49fc177ae4 100644 --- a/datasets/IASI_SST_METOP_B-OSISAF-L2P-v1.0_1.json +++ b/datasets/IASI_SST_METOP_B-OSISAF-L2P-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IASI_SST_METOP_B-OSISAF-L2P-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-B (MetOp-B)satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.", "links": [ { diff --git a/datasets/ICEPAR_1.json b/datasets/ICEPAR_1.json index 4162c491bd..4210715935 100644 --- a/datasets/ICEPAR_1.json +++ b/datasets/ICEPAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICEPAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data comprise images (encapsulated postscript and PNG formats) showing the integrated solar irradiance exposure of sea ice. The exposure value for ice at a given grid point was calculated by computing the motion trajectory of that patch of ice across the autumn/winter season (1-March to 1-November). Daily motion data were obtained from the National Snow and Ice Data Center (http://nsidc.org/data/nsidc-0116.html). The integrated radiation exposure was then calculated using daily estimates of downward solar flux from the NCEP/NCAR re-analyses. The values shown in the images are cumulative photosynthetically active radiation expressed in W-days/m^2.\n \nPlease contact the data custodian before using these data.\n \nThis work was done as part of ASAC project 2943 (ASAC_2943). See the link below for public details about the project.", "links": [ { diff --git a/datasets/ICESCAPE_0.json b/datasets/ICESCAPE_0.json index a2c1131fca..846bc37416 100644 --- a/datasets/ICESCAPE_0.json +++ b/datasets/ICESCAPE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICESCAPE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) was a multi-year NASA shipborne project. The bulk of the research took place in the Beaufort and Chukchi Seas in the summers of 2010 and 2011.", "links": [ { diff --git a/datasets/ICESheet_Antarctic_474.json b/datasets/ICESheet_Antarctic_474.json index 5548739f00..cb4da9eddd 100644 --- a/datasets/ICESheet_Antarctic_474.json +++ b/datasets/ICESheet_Antarctic_474.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICESheet_Antarctic_474", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The East Antarctic ice sheet has played a fundamental part in modulating climate and sea level during the past 30 million years. Understanding its history is crucial to evaluating its future behaviour and response to global warming. Airborne ice-penetrating radar studies now reveal a fjord-like landscape beneath several kilometres of ice in the East Antarctic Aurora subglacial basin. The data confirm, and provide a new constraint on, the magnitude and dynamics of the oscillations of the East Antarctic ice sheet during the late Cenozoic, which had previously been supported only by marine cores.", "links": [ { diff --git a/datasets/ICEVOLC_FlowerKahn2020_1.json b/datasets/ICEVOLC_FlowerKahn2020_1.json index f3b4eb8083..ed4af41e92 100644 --- a/datasets/ICEVOLC_FlowerKahn2020_1.json +++ b/datasets/ICEVOLC_FlowerKahn2020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICEVOLC_FlowerKahn2020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises MISR-derived output from a comprehensive analysis of Icelandic volcano eruptions (Eyjafjallajokull 2010, Grimsvotn 2011, Holuhraun 2014-2015). The data presented here are analyzed and discussed in the following paper: Flower, V.J.B., and R.A. Kahn, 2020. The evolution of Icelandic volcano emissions, as observed from space in the era of NASA\u2019s Earth Observing System (EOS). J. Geophys. Res. Atmosph. (in press).\r\nThe data is subdivided by volcano of origin, date and MISR orbit number. Within each case folder there are up to 11 files relating to an individual MISR overpass. Files include plume height records (from both the red and blue spectral bands) derived from the MISR INteractive eXplorer (MINX) program, displayed in: map view, downwind profile plot (along with the associated wind vectors retrieved at plume elevation), a histogram of retrieved plume heights and a text file containing the digital plume height values. An additional JPG is included delineating the plume analysis region, start point for assessing downwind distance, and input wind direction used to initialize the MINX retrieval. A final two files are generated from the MISR Research Aerosol (RA) retrieval algorithm (Limbacher, J.A., and R.A. Kahn, 2014. MISR Research-Aerosol-Algorithm: Refinements For Dark Water Retrievals. Atm. Meas. Tech. 7, 1-19, doi:10.5194/amt-7-1-2014). These files include the RA model output in HDF5, and an associated JPG of key derived variables (e.g. Aerosol Optical Depth, Angstrom Exponent, Single Scattering Albedo, Fraction of Non-Spherical components, model uncertainty classifications and example camera views). \r\nFile numbers per folder vary depending on the retrieval conditions of specific observations. RA plume retrievals are limited when cloud cover was widespread or the solar radiance was insufficient to run the RA. In these cases the RA files are not included in the individual folders.", "links": [ { diff --git a/datasets/ICEYE.ESA.Archive_8.0.json b/datasets/ICEYE.ESA.Archive_8.0.json index 3a45d1d32d..55fb0fba55 100644 --- a/datasets/ICEYE.ESA.Archive_8.0.json +++ b/datasets/ICEYE.ESA.Archive_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICEYE.ESA.Archive_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ICEYE ESA archive collection consists of ICEYE Level 1 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Three different modes are available: \u2022\tSpot: with a slant resolution of 50 cm in range by 25 cm in azimuth that translated into the ground generates a ground resolution of 1 m over an area of 5 km x 5 km. Due to multi-looking, speckle noise is significantly reduced. \u2022\tStrip: the ground swath is 30 x 50 km2 and the ground range resolution is 3 m. \u2022\tScan: a large area (100km x 100kmis acquired with ground resolution of 15m. Two different processing levels: \u2022\tSingle Look Complex (SLC): Level 1A geo-referenced product and stored in the satellite's native image acquisition geometry (the slant imaging plane) \u2022\tGround Range Detected (GRD): Level 1B product; detected, multi-looked and projected to ground range using an Earth ellipsoid model; the image coordinates are oriented along the flight direction and along the ground range; no image rotation to a map coordinate system is performed, interpolation artefacts not introduced. The following table defines the offered product types EO-SIP product type\tMode\tProcessing level XN_SM__SLC\tStrip\tSingle Look Complex (SLC) - Level 1A XN_SM__GRD\tStrip\tGround Range Detected (GRD) - Level 1B XN_SL__SLC\tSpot\tSingle Look Complex (SLC) - Level 1A XN_SL__GRD\tSpot\tGround Range Detected (GRD) - Level 1B XN_SR__GRD\tScan\tGround Range Detected (GRD) - Level 1B", "links": [ { diff --git a/datasets/ICEYE_9.0.json b/datasets/ICEYE_9.0.json index 435bbf7a17..6dd02da776 100644 --- a/datasets/ICEYE_9.0.json +++ b/datasets/ICEYE_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICEYE_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ICEYE full archive and new tasking products are available in Strip, Spot, SLEA (Spot Extended Area), Scan, and Dwell modes:\r\t\u2022\tStrip instrument mode: the ground swath is illuminated with a continuous sequence of pulses while the antenna beam is fixed in its orientation. This results in a long image strip parallel to the flight direction: the transmitted pulse bandwidth is adjusted to always achieve a ground range resolution of 3 m\r\t\u2022\tSpot instrument mode: the radar beam is steered to illuminate a fixed point to increase the illumination time, resulting in an extended Synthetic aperture length, which improves the azimuth resolution. Spot mode uses a 300 MHz pulse bandwidth and provides a slant plane image with a resolution of 0.5 m (range) by 0.25 m (azimuth); when translated into the ground, the products has 1 m resolution covering an area of 5 km x 5 km. Due to multi-looking, speckle noise is significantly reduced\r\t\u2022\tAs an evolution of Spot mode, SLEA (Spot Extended Area) products are available with the same resolution of Spot data but a scene size of 15 km x 15 km\r\t\u2022\tScan Instrument mode: the phased array antenna is used to create multiple beams in the elevation direction which allows to acquire a large area (100km x 100km) with resolution better than 15m. To achieve the finest image quality of its Scan image, ICEYE employs a TOPSAR technique, which brings major benefits over the quality of the images obtained with conventional SCANSAR imaging. With the 2-dimensional electronic beam steering, TOPSAR ensures the maximum radar power distribution in the scene, providing uniform image quality.\r\t\u2022\tDwell mode: with the satellite staring at the same location for up to 25 seconds, Dwell mode is a very long Spot mode SAR collection. This yields a very fine azimuth resolution and highly-reduced speckle. The 25 second collection time allows the acquired image stack to be reconstructed as a video to give insight into the movement of objects.\rTwo different processing levels can be requested:\r\t\u2022\tSingle Look Complex (SLC): Single Look Complex (SLC) Level 1a products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and the scenes are stored in the satellite's native image acquisition geometry which is the slant-range-by-azimuth imaging plane and with zero-Doppler SAR coordinates. The pixels are spaced equidistant in azimuth and in slant range. The products include a single look in each dimension using the full transmit signal bandwidth and consist of complex magnitude value samples preserving both amplitude and phase information. No radiometric artefacts induced by spatial resampling or geocoding. The product is provided in Hierarchical Data Format (HDF5) plus a xml file with selected metadata\r\t\u2022\tGround Range Detected (GRD): Ground Range Detected (GRD) Level 1b products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The image coordinates are oriented along the flight direction and along the ground range. Pixel values represent detected magnitude, the phase information is lost. The resulting product has approximately square spatial resolution pixels and square pixel spacing with reduced speckle due to the multi-look processing at the cost of worse spatial resolution. No image rotation to a map coordinate system has been performed and interpolation artefacts are thus avoided. The product is provided in GeoTiff plus a xml file with selected metadata.\r\t\t\t\tStrip\t\tSpot\t\tSLEA\t\tScan\t\tDwell\rGround range resolution (GRD)\t3 m\t\t1 m\t\t1 m\t\t15 m\t\t1 m\rGround azimuth resolution (GRD)\t3 m\t\t1 m\t\t1 m\t\t15 m\t\t1 m\rSlant range resolution (SLC)\t0.5 m - 2.5 m\t0.5 m\t\t0.5 m\t \t\t\t0.5 m\rSlant azimuth resolution (SLC)\t3 m\t\t0.25 m\t\t1 m\t \t\t\t0.05 m\rScene size (W x L)\t\t30 x 50 km2\t5 x 5 km2\t15 x 15 km2\t100 x 100 km2\t5 x 5 km2\rIncident angle\t\t\t15 - 30\u00b0\t20 - 35\u00b0\t20 - 35\u00b0\t21 - 29\u00b0\t20 - 35\u00b0\rPolarisation\tVV\r\rAll details about the data provision, data access conditions and quota assignment procedure are described in the _$$ICEYE Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/ICEYE-Terms-Of-Applicability.pdf .\rIn addition, ICEYE has released a _$$public catalogue$$ https://www.iceye.com/lp/iceye-18000-public-archive that contains nearly 18,000 thumbnails under a creative common license of radar images acquired with ICEYE's SAR satellite constellation all around the world from 2019 until October 2020. Access to the catalogue requires registration.", "links": [ { diff --git a/datasets/ICE_RADAR_DATA_AMERY_1.json b/datasets/ICE_RADAR_DATA_AMERY_1.json index bf4816e404..e8566e98cb 100644 --- a/datasets/ICE_RADAR_DATA_AMERY_1.json +++ b/datasets/ICE_RADAR_DATA_AMERY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICE_RADAR_DATA_AMERY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains ASCII lat/long records extracted from the binary data.\n\nThe binary data are ice radar soundings at 150 MHz from Aircraft flown at about 100 knots.\n\nThis covers the area around Gillock Island to look at the grounding zone between the ice shelf and Gillock Island.\n\nThe Radar unit was built by the Science and Technical Support group of the Australian Antarctic Division.\n\nThis data are part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP) for continental mapping of the Australian continent (Geoscience Australia).\n\nSee also the other metadata record for ice radar data.\n\nThe files in this dataset are:\n\nASCII lat/long records:\n\nRecord\nTime (UTC)\nLatitude\nLongitude", "links": [ { diff --git a/datasets/ICE_RADAR_DATA_GILLOCK_1.json b/datasets/ICE_RADAR_DATA_GILLOCK_1.json index d488f98128..75b29a34c6 100644 --- a/datasets/ICE_RADAR_DATA_GILLOCK_1.json +++ b/datasets/ICE_RADAR_DATA_GILLOCK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICE_RADAR_DATA_GILLOCK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains ASCII lat/long records extracted from the binary data.\n\nThe binary data are ice radar soundings at 150 MHz from Aircraft flown at about 100 knots.\n\nThis covers the area around Gillock Island to look at the grounding zone between the ice shelf and Gillock Island.\n\nThe Radar unit was built by the Science and Technical Support group of the Australian Antarctic Division.\n\nThis data are part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP) for continental mapping of the Australian continent (Geoscience Australia).\n\nSee also the other metadata record for ice radar data.\n\nThe files in this dataset are:\n\nASCII lat/long records:\n\nRecord\nTime (UTC)\nLatitude\nLongitude", "links": [ { diff --git a/datasets/ICE_RADAR_DATA_PRIORITY_1.json b/datasets/ICE_RADAR_DATA_PRIORITY_1.json index 308e7c2e59..f37a431a9a 100644 --- a/datasets/ICE_RADAR_DATA_PRIORITY_1.json +++ b/datasets/ICE_RADAR_DATA_PRIORITY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICE_RADAR_DATA_PRIORITY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data contains ASCII lat/long records extracted from the binary data.\n\nThe binary data are ice radar soundings at 150 MHz from Aircraft flown at about 100 knots.\n\nThis covers the priority flights in the Amery Ice Shelf area to look at the grounding zone between the ice shelf and Gillock Island.\n\nThe Radar unit was built by the Science and Technical Support group of the Australian Antarctic Division.\n\nThis data are part of the Australian Antarctica and Southern Ocean Profiling Project (AASOPP) for continental mapping of the Australian continent.\n\nSee also the other metadata record for ice radar data.\n\nThe files in this dataset are:\n\nASCII lat/long records:\n\nRecord\nTime (UTC)\nLatitude\nLongitude", "links": [ { diff --git a/datasets/ICIMOD_KATHMANDU.json b/datasets/ICIMOD_KATHMANDU.json index af8b690560..315b6d4886 100644 --- a/datasets/ICIMOD_KATHMANDU.json +++ b/datasets/ICIMOD_KATHMANDU.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICIMOD_KATHMANDU", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital data of Administrative Boundaries of Kathmandu Valley:\n\n- Districts and Village Development Committee from 1997 map.\n\n- Demographic data from 1991 census", "links": [ { diff --git a/datasets/ICId0001_202.json b/datasets/ICId0001_202.json index 1ced7c6b01..8d1258b180 100644 --- a/datasets/ICId0001_202.json +++ b/datasets/ICId0001_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0001_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Various GIS datasets on Jhikhu Khola Watershed\n (see members for details)\n \n Members informations:\n Attached Vector(s):\n MemberID: 1\n Vector Name: Soil map of Arunachal Pradesh\n Source Map Name: Soil association map of Arunachal Pradesh\n Source Map Scale: 250000\n Source Map Date: ?\n Projection: polyconic\n Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal\n Projection_meas: meters\n Feature_type: polygons\n Legend_file: lugen.avl\n Vector \n \n The soil resource inventory was carried out following a three\n tier approach viz. image interpretation, soil survey and chemical\n analysis and GIS application for thematic mapping and\n interpretation of database for developing a land use plan.\n The soil association maps on 1:250,000 scale were digitised\n toposheetwise (14 toposheets) using polyconic projection to\n bring out the state soil map. Various thematic maps\n were generated using 'reclassification' techniques and area\n calculation was carried out using 'map analysis' tools.\n \n Members informations:\n Attached Vector(s):\n MemberID: 2\n Vector Name: Soil map of Himachal Pradesh\n Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal\n Feature_type: polygon\n \n Members informations:\n Attached Vector(s):\n MemberID: 3\n Vector Name: Soil map of Jammu&Kashmir\n Projection: transverse mercator\n Projection_desc: orig: 87 / 0, sc.: 0.9999, Ell: Everest1830/Nepal\n Projection_meas: meters\n Feature_type: polygon\n Legend_file: lugen.avl\n \n Members informations:\n Attached Vector(s):\n MemberID: 4\n Vector Name: Soil map of Uttar Pradesh\n Feature_type: polygon\n Vector \n \n \n Attached Image(s):\n Member ID: 5\n Image Name: Orthophoto mosaic\n Image Projection: Nepal zone87\n Image Source name: camera\n Image Resolution: 1m\n Image Number of Rows: 12001\n Image Number of Columns: 15201\n Image Number of Bits: 8\n Image \n Mosaic of digital orthophotos, 1m resolution,\n The orthophotos have been prepared from 1996 aerial photographs\n 1:20000, scanned at 600dpi, using GPS control points and the DEM\n Accuracy: 10-20m horizontal RMS; maximum errors ca. 50 (absolute)\n resp. 100m (relative vs the drainage)\n \n Members informations:\n Attached Vector(s):\n MemberID: 6\n Vector Name: Land systems\n Source Map Name: Land systems map\n Source Map Scale: 20000\n Source Map Date: 1905-06-12\n Projection: Nepal 87\n Feature_type: polygon\n Vector \n Land system classification, soil types\n \n Members informations:\n Attached Vector(s):\n MemberID: 7\n Vector Name: Roads\n Source Map Name: GPS\n Source Map Scale: -\n Source Map Date: 1998/2000\n Projection: Nepal 87\n Feature_type: lines\n Vector \n Road network surveyed by differential GPS\n \n Attached Raster(s):\n Member_ID: 8\n Raster Name: Land Capability Evaluation\n Raster Name: Land systems, DEM\n Raster Scale: 20000\n Raster Date: 1905-06-12\n Raster Projection: Nepal 87\n Raster Resolution: 20\n Number of Rows: 651\n Number of Columns: 801\n Number of Bits: 8\n Raster \n Land capability evaluation according to refined LRMP-method\n \n Members informations:\n Attached Vector(s):\n MemberID: 9\n Vector Name: Drainage\n Source Map Name: Jhikhu Khola Base Map\n Source Map Scale: 20000\n Source Map Date: 1905-06-12\n Projection: Nepal87\n Feature_type: lines\n Vector \n Drainage Network; contains some substantial geometric\n distortions mainly in the upper parts\n \n Members informations:\n Attached Vector(s):\n MemberID: 10\n Vector Name: Contours\n Source Map Name: Jhikhu Khola Base map\n Source Map Scale: 20000\n Source Map Date: 1905-06-12\n Projection: Nepal 87\n Projection_meas: meters\n Feature_type: lines\n Vector \n Contours (25m interval), contains some substantial geometric\n distortions mainly in the upper parts\n \n Members informations:\n Attached Vector(s):\n MemberID: 11\n Vector Name: VDC boundaries\n Source Map Name: Jhikhu Khola Base Map\n Source Map Scale: 20000\n Source Map Date: 1905-06-12\n Projection: Nepal 87\n Feature_type: polygon\n Vector \n VDC (Village Development Committee) boundaries\n \n Members informations:\n Attached Vector(s):\n MemberID: 12\n Vector Name: settlements\n Source Map Name: Jhikhu Khola Basemap\n Source Map Scale: 20000\n Source Map Date: 1905-06-12\n Projection: Nepal 87\n Feature_type: point\n Vector \n Location and names of settlements", "links": [ { diff --git a/datasets/ICId0005_202.json b/datasets/ICId0005_202.json index a1c729ae9c..99511061f3 100644 --- a/datasets/ICId0005_202.json +++ b/datasets/ICId0005_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0005_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the recent past, there has been continuing growth in using GIS and related\n technologies by many organizations engaged in planning and management of the\n Kathmandu Valley. As a result, the demand for accurate and homogenous spatial\n data of the Valley has been realized by government as well as research and\n development organizations.\n \n This study attempts to build a comprehensive GIS Database of the Kathmandu\n Valley with an aim to bridge the important data gaps in the Valley. The study\n employs a fresh approach in constructing a GIS database with the available maps\n and integrates many different kinds of satellite imageries. The maps presented\n in this publication visualize the different scenarios and raise the awareness\n of exiting digital database. The application presented in this publication\n shall increase awareness about the usefulness of digital database and\n demonstrate what can be achieved with the GIS and related technologies. The\n database thus developed shall improve the availability of information of the\n Kathmandu Valley and assist different stakeholders engaged in planning and\n management of the Valley.\n \n Furthermore, the study advocates a building block approach to development,\n management and revision of database in a complementary way and it hopes to\n avoid duplication of efforts in costly production of digital data. The study\n hopes to sensitise senior executives and decision-makers about the need for a\n sound policy on database sharing, development and standards. Such a policy, at\n the national level known as National Spatial Database Infrastructure (NSDI)\n should evolve in order to benefit from the prevailing GIS technology. In using\n GIS and related technologies, the study facilitated the establishment of\n Spatial Data Infrastructure of the Kathmandu Valley in a concrete manner.\n \n \n Members informations:\n Attached Vector(s):\n MemberID: 1\n Vector Name: Contours\n Source Map Name: topo sheets\n Source Map Scale: 25000\n Source Map Date: 1905-06-17\n Projection: transverse mercator\n Projection_desc: origin 87E/ 0N, false easting=900000, scale=0.9999\n Projection_meas: Meter\n Feature_type: lines\n Vector \n Contours digitized from topo sheets\n \n Members informations:\n Attached Vector(s):\n MemberID: 2\n Vector Name: Roads\n Source Map Name: topo sheet\n Source Map Scale: 25000\n Source Map Date: 1905-06-17\n Projection: see member1\n Feature_type: lines\n Vector \n Road Network\n \n Members informations:\n Attached Vector(s):\n MemberID: 3\n Vector Name: Drainage\n Source Map Name: topo sheets\n Source Map Scale: 25000\n Source Map Date: 1905-06-17\n Projection: see member 1\n Feature_type: lines\n Vector \n Drainage Network\n \n Members informations:\n Attached Vector(s):\n MemberID: 4\n Vector Name: Land use 78\n Source Map Name: LRMP\n Source Map Scale: 50000\n Source Map Date: 1905-05-31\n Feature_type: polygon\n Vector \n Land use\n \n Members informations:\n Attached Vector(s):\n MemberID: 5\n Vector Name: Land use 1995\n Source Map Name: topo sheet\n Source Map Scale: 25000\n Source Map Date: 1905-06-17\n Feature_type: polygon\n Vector \n Land cover\n \n \n Members informations:\n Attached Vector(s):\n MemberID: 6\n Vector Name: Administrative boundaries\n Source Map Name: topo sheet\n Source Map Scale: 25000\n Source Map Date: 1905-06-17\n Feature_type: polygon\n Vector \n District and VDC boundaries and various socio-economic data\n \n Attached Report(s)\n Member ID: 7\n Report Name: Kathmandu Valley GIS database\n Report Authors: B. Shrestha & S. Pradhan\n Report Publisher: ICIMOD\n Report Date: 2000-02-01\n Report \n Report", "links": [ { diff --git a/datasets/ICId0012_202.json b/datasets/ICId0012_202.json index b118ab761a..89457ba6a0 100644 --- a/datasets/ICId0012_202.json +++ b/datasets/ICId0012_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0012_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atlas of district-based indicators on poverty and deprivation,\n socio-economic development, women's empowerment, and Natural resource\n endowment", "links": [ { diff --git a/datasets/ICId0013_202.json b/datasets/ICId0013_202.json index a9f3f68a8a..bc25a97318 100644 --- a/datasets/ICId0013_202.json +++ b/datasets/ICId0013_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0013_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averages of Temperature, Precipitation, Humidity, Sunshine\n etc. have been interpolated spatially from Meteo station data. Also\n contains some hydrographic charts and data.", "links": [ { diff --git a/datasets/ICId0015_202.json b/datasets/ICId0015_202.json index c6347698e8..da4dcaf8cd 100644 --- a/datasets/ICId0015_202.json +++ b/datasets/ICId0015_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0015_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inventory of glaciers and glacial lakes from aerial photographs, topo\n sheets of different years, and satellite images has been\n prepared. Potentially dangerous lakes (GLOF: Glacial Lake Outburst\n Floods) will be identified based on air phoitographs and field work.", "links": [ { diff --git a/datasets/ICId0016_202.json b/datasets/ICId0016_202.json index ae7c0502e6..db16c71d1d 100644 --- a/datasets/ICId0016_202.json +++ b/datasets/ICId0016_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0016_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "IRS 1D LISS3 109-50 of 12 July 1998 Satellite image", "links": [ { diff --git a/datasets/ICId0017_202.json b/datasets/ICId0017_202.json index 41c9559a6b..d27a67ebd3 100644 --- a/datasets/ICId0017_202.json +++ b/datasets/ICId0017_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0017_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Various datasets on land use and population", "links": [ { diff --git a/datasets/ICId0018_202.json b/datasets/ICId0018_202.json index b1e231df51..1dc8866291 100644 --- a/datasets/ICId0018_202.json +++ b/datasets/ICId0018_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0018_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "IRS WiFS coverage of most of the HKH region\n \n Attached Image(s):\n Member ID: 1\n Image Name: 86-42 of 30 Sep 96\n Image Resolution: 188\n Image Number of Rows: 4489\n Image Number of Columns: 4904\n Image Number of Bits: 8\n Image \n Satellite Image\n \n Attached Image(s):\n Member ID: 2\n Image Name: 086-047_961024\n Image Resolution: 188\n Image Number of Rows: 4492\n Image Number of Columns: 4918\n Image Number of Bits: 8\n Image \n sat. image\n \n Attached Image(s):\n Member ID: 3\n Image Name: 086-052_961024\n Image Resolution: 188\n Image Number of Rows: 4494\n Image Number of Columns: 4929\n Image Number of Bits: 8\n Image \n sat. image\n \n Attached Image(s):\n Member ID: 4\n Image Name: 092-042_970122\n Image Resolution: 188\n Image Number of Rows: 4351\n Image Number of Columns: 4892\n Image Number of Bits: 8\n Image \n sat. image\n \n Attached Image(s):\n Member ID: 5\n Image Name: 092-047_961030\n Image Resolution: 188\n Image Number of Rows: 4492\n Image Number of Columns: 4917\n Image Number of Bits: 8\n Image \n satellite image\n \n Attached Image(s):\n Member ID: 6\n Image Name: 092-052_961205\n Image Resolution: 188\n Image Number of Rows: 4358\n Image Number of Columns: 4899\n Image Number of Bits: 8\n Image \n sat. image\n \n Attached Image(s):\n Member ID: 7\n Image Name: 098-042_961024\n Image Resolution: 188\n Image Number of Rows: 4349\n Image Number of Columns: 4891\n Image Number of Bits: 8\n Image \n sat. img.\n \n Attached Image(s):\n Member ID: 8\n Image Name: 098-047_980531\n Image Resolution: 188\n Image Number of Rows: 4350\n Image Number of Columns: 4760\n Image Number of Bits: 8\n Image \n sat. img\n \n Attached Image(s):\n Member ID: 9\n Image Name: 098-052_961012\n Image Resolution: 188\n Image Number of Rows: 4494\n Image Number of Columns: 4929\n Image Number of Bits: 8\n Image \n sat.img.\n \n Attached Image(s):\n Member ID: 10\n Image Name: 104-049_981104\n Image Resolution: 188\n Image Number of Rows: 4359\n Image Number of Columns: 4726\n Image Number of Bits: 8\n Image \n sat. img.\n \n Attached Image(s):\n Member ID: 11\n Image Name: 104-052_961018\n Image Resolution: 188\n Image Number of Rows: 4494\n Image Number of Columns: 4929\n Image Number of Bits: 8\n Image \n sat.img.\n \n Attached Image(s):\n Member ID: 12\n Image Name: 110-052_961129\n Image Resolution: 188\n Image Number of Rows: 4354\n Image Number of Columns: 4923\n Image Number of Bits: 8\n Image \n sat.img.\n \n Attached Image(s):\n Member ID: 13\n Image Name: 110-057_980209\n Image Resolution: 188\n Image Number of Rows: 4337\n Image Number of Columns: 4862\n Image Number of Bits: 8\n Image \n sat.img.\n \n Attached Image(s):\n Member ID: 14\n Image Name: 116-052_961205\n Image Resolution: 188\n Image Number of Rows: 4353\n Image Number of Columns: 4910\n Image Number of Bits: 8\n Image \n sat.img.\n \n Attached Image(s):\n Member ID: 15\n Image Name: 116-055_990226\n Image Resolution: 188\n Image Number of Rows: 4350\n Image Number of Columns: 4791\n Image Number of Bits: 8\n Image \n sat.image\n \n Attached Image(s):\n Member ID: 16\n Image Name: 116-062_970404\n Image Resolution: 188\n Image Number of Rows: 4365\n Image Number of Columns: 4934\n Image Number of Bits: 8\n Image \n sat.image\n \n Attached Image(s):\n Member ID: 17\n Image Name: Mosaic\n Image Projection: Albers Equal-Area\n Image Resolution: ?\n Image Number of Rows: ?\n Image Number of Columns: ?\n Image Number of Bits: 8\n Image \n Geometrically controlled Mosaic of all 16 images,\n radiometrically not adjusted", "links": [ { diff --git a/datasets/ICId0019_202.json b/datasets/ICId0019_202.json index ca2e4bb435..49cbdc9c2e 100644 --- a/datasets/ICId0019_202.json +++ b/datasets/ICId0019_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0019_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat TM scenes of Winter 98/99\n\nAttached Image(s):\n Member ID: 1\nImage Name: 137-041_990116\nImage Resolution: 30\nImage Number of Rows: 5728\nImage Number of Columns: 6920\nImage Number of Bits: 8\nImage \nTM image\n\nAttached Image(s):\n Member ID: 2\nImage Name: 138-041_981104\nImage Resolution: 30\nImage Number of Rows: 5728\nImage Number of Columns: 6920\nImage Number of Bits: 8\nImage \nTM image\n\nAttached Image(s):\n Member ID: 3\nImage Name: 139-041_981229 (Quadrant 4 only)\nImage Resolution: 30\nImage Number of Rows: 2944\nImage Number of Columns: 3500\nImage Number of Bits: 7*8\nImage \nSatellite image", "links": [ { diff --git a/datasets/ICId0020_202.json b/datasets/ICId0020_202.json index beed09e0a2..3265ef8347 100644 --- a/datasets/ICId0020_202.json +++ b/datasets/ICId0020_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0020_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Supervised classification of IRS WiFS data;\n IGBP legend", "links": [ { diff --git a/datasets/ICId0021_202.json b/datasets/ICId0021_202.json index 4e86e24b91..df242e9b0f 100644 --- a/datasets/ICId0021_202.json +++ b/datasets/ICId0021_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0021_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "IRS Panchromatic image of Kathmandu Valley", "links": [ { diff --git a/datasets/ICId0023_202.json b/datasets/ICId0023_202.json index 1031b1951e..0657fb1fec 100644 --- a/datasets/ICId0023_202.json +++ b/datasets/ICId0023_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0023_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat TM 140-040 of 22 Sep 1992 Satellite image", "links": [ { diff --git a/datasets/ICId0028_202.json b/datasets/ICId0028_202.json index 1f9362f42e..8dd1fe7e88 100644 --- a/datasets/ICId0028_202.json +++ b/datasets/ICId0028_202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICId0028_202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat TM 141-41 Q2 of 24 Jan 89 Satellite image", "links": [ { diff --git a/datasets/ICO_Casey_1.json b/datasets/ICO_Casey_1.json index 44191d2716..92d3bd0fd3 100644 --- a/datasets/ICO_Casey_1.json +++ b/datasets/ICO_Casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICO_Casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In-situ chemical oxidation (ICO) is a remediation technology that involves the addition of chemicals to the substrate that degrade contaminants through oxidation processes. This series of field experiments conducted at the Old Casey Powerhouse/Workshop investigate the potential for the use of ICO technology in Antarctica on petroleum hydrocarbon contaminated sediments.\n\nSurface application was made using 12.5% sodium hyperchlorite, 6.25% sodium hydrechlorite, 30% hydrogen peroxide and Fentons Reagent (sodium hypchlorite with an iron catalyst) on five separate areas of petroleum hydrocarbon contaminated sediments. Sampling was conducted before and after chemical application from the top soil section (0 - 5 cm) and at depth (10 - 15 cm).\n\nThe data are stored in an excel file.\n\nThis work was completed as part of ASAC project 1163 (ASAC_1163).\n\nThe spreadsheet is divided up as follows:\n\nThe first 51 sheets are the raw GC-FID data for the 99/00 field season, labelled by sample name. These sheets use the same format as the radiometric GC-FID spreadsheet in the metadata record entitled 'Mineralisation results using 14C octadecane at a range of temperatures'. Sample name format consists of a location or experiment indicator (CW=Casey Workshop, BR= Small-scale field trial), the year the sample was collected (00=2000), the sample type (S=Soil) and a sequence number.\n\nSUMMARY and PRINTABLE VERSION are the same data in different formats, PRINTABLE VERSION is printer friendly. This summary data includes the hydrocarbon concentrations corrected for dry weight of soil and biodegradation and weathering indices.\n\nGRAPHS are graphs.\n \nFIELD MEASUREMENTS show the results of the measurements taken in the field and include PID (ppm), Soil temperature (C), Air temperature (C), Ph and MC (moisture content) (%).\n\nNOTES shows the chemicals added to each trial, and a short summary of the samples.\n\nThe next 21 sheets show the raw GC-FID data for the 00/01 field season, labelled to previously explained method. PRINTABLE (0001) is a summary of the raw GC-FID data.\n\nThe next 3 sheets show the raw GC-FID data for the 01/02 field season, labelled to previously explained method. PRINTABLE (0102) is a summary of the raw GC-FID data.\n\nMPN-NOTES shows lab book references and set up summary for the Most Probable Number (MPN) analysis.\n\nMPN-DETAILS shows the set up details, calculations and results for each MPN analysis.\n\nMPN-RESULTS shows the raw MPN data.\n\nMPN-Calculations show the results from the MPN Calculator.\n\nThe fields in the dataset are:\nRetention Time\nArea\n% Area\nHeight of peak\nAmount\nInt Type\nUnits\nPeak Type\nCodes", "links": [ { diff --git a/datasets/ICRAF_AfSIS_AfrHySRTM.json b/datasets/ICRAF_AfSIS_AfrHySRTM.json index 42a09bc4af..796cb91a03 100644 --- a/datasets/ICRAF_AfSIS_AfrHySRTM.json +++ b/datasets/ICRAF_AfSIS_AfrHySRTM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICRAF_AfSIS_AfrHySRTM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service: Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (filled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flowpaths can also be generated. AfrHySRTM uses the CGIAR-CSI SRTM 90m Version 4 as the source DEM The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The purpose of the dataset is to serve a wide user community by providing a Digital Elevation Model for the continent of Africa that can be used to predict soil properties as well as for a range of other applications, including erosion and landslide risk. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/.", "links": [ { diff --git a/datasets/ICRAF_AfSIS_SCA.json b/datasets/ICRAF_AfSIS_SCA.json index 3455763057..13fbdf25c6 100644 --- a/datasets/ICRAF_AfSIS_SCA.json +++ b/datasets/ICRAF_AfSIS_SCA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICRAF_AfSIS_SCA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) is a 90m raster dataset showing local flow accumulation and flow direction using the formula SCA = A/I, where A is unit contributing area of land upslope of a length of contour I. The specific catchment area contributing to flow at any given location can be used to determine relative saturation and water runoff and, together with other topographic factors, can be used to model erosion and landslides. The digital elevation model used to construct this dataset is AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The specific catchment area is a useful parameter for modeling of runoff, soil erosion and sediment yield.The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/.", "links": [ { diff --git a/datasets/ICRAF_AfSIS_TWI.json b/datasets/ICRAF_AfSIS_TWI.json index e8d4614842..2c3c8d43b5 100644 --- a/datasets/ICRAF_AfSIS_TWI.json +++ b/datasets/ICRAF_AfSIS_TWI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ICRAF_AfSIS_TWI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) is a 90m raster dataset showing zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low. The topographic wetness index, originally developed by Beven and Kirkby in 1979, provides a measure of wetness conditions at the catchment scale. This dataset combines local upslope contributing area and slope using the digital elevation model AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. This index is commonly used in soil landscape modeling and in the analysis of vegetation patterns. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/.", "links": [ { diff --git a/datasets/IDBMG4_5.json b/datasets/IDBMG4_5.json index 9101933235..67a4267346 100644 --- a/datasets/IDBMG4_5.json +++ b/datasets/IDBMG4_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IDBMG4_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a bed topography/bathymetry map of Greenland based on mass conservation, multi-beam data, and other techniques. It also includes surface elevation and ice thickness data, as well as an ice/ocean/land mask.", "links": [ { diff --git a/datasets/IDCSI4_1.json b/datasets/IDCSI4_1.json index 49d1dbf43f..607ddeac63 100644 --- a/datasets/IDCSI4_1.json +++ b/datasets/IDCSI4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IDCSI4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IDHDT4_1.json b/datasets/IDHDT4_1.json index c4a78be380..599cc69f15 100644 --- a/datasets/IDHDT4_1.json +++ b/datasets/IDHDT4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IDHDT4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects.", "links": [ { diff --git a/datasets/IDS_LIS_0.json b/datasets/IDS_LIS_0.json index c89ec7e11a..8d5578c088 100644 --- a/datasets/IDS_LIS_0.json +++ b/datasets/IDS_LIS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IDS_LIS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Integration of new remote sensing tools for characterization of tidal marsh area extent, vegetation communities and inundation regimes, and advanced retrievals of estuarine biological and biogeochemical processes with multi-disciplinary ecological, paleoecological, and socioeconomic datasets, spatial econometric models of population growth, and a novel coupled hydrodynamic-photo-biogeochemical model specifically designed for the marsh-estuarine continuum in the heavily urbanized Long Island Sound.", "links": [ { diff --git a/datasets/IES.json b/datasets/IES.json index 4314c5b5e1..b06ae47a76 100644 --- a/datasets/IES.json +++ b/datasets/IES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Irrigation Equipment Supply Database is a joint initiative of the Water Resources, Development and Management Service of FAO and the International Programme for Technology and Research in Irrigation and Drainage (IPTRID). It has been developed as part of FAO's mandate to provide information on irrigation. Potential beneficiaries of IES are those who need to locate information on irrigation equipment at regional or country level.\n\nIES seeks to establish an up-to-date list of Suppliers/Manufacturers providing irrigation equipment worldwide. National Suppliers/Manufacturers can be displayed by clicking the dark blue countries on the map. Moreover, the website offers a database query facility for identifying Suppliers/Manufactures providing specific irrigation equipment as well as a description of irrigation equipment, a description of standards and links to other related sites.\n\n[Summary provided by the FAO.]", "links": [ { diff --git a/datasets/IGBGM1B_1.json b/datasets/IGBGM1B_1.json index 2840bafdfc..be8a1008e8 100644 --- a/datasets/IGBGM1B_1.json +++ b/datasets/IGBGM1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBGM1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains vertical acceleration values for Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IGBGM2_1.json b/datasets/IGBGM2_1.json index 36f79e1e83..77c58a5f01 100644 --- a/datasets/IGBGM2_1.json +++ b/datasets/IGBGM2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBGM2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains free air anomaly measurements taken over Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IGBP-DIS_565_1.json b/datasets/IGBP-DIS_565_1.json index 48c8831b4b..ab3ea0854e 100644 --- a/datasets/IGBP-DIS_565_1.json +++ b/datasets/IGBP-DIS_565_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBP-DIS_565_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains global data on soil properties, global maps of soil distributions, and the SoilData System developed by the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). These data were originally distributed on CD-ROM, but are provided here as a single zip file. The SoilData System allows users to generate soil information and maps for geographic regions at soil depths and resolutions selected by the user. Derived surfaces of carbon density, nutrient status, water-holding capacity, and heat capacity are provided for modeling and inventory purposes.", "links": [ { diff --git a/datasets/IGBP-DIS_FIRE_SPAIN.json b/datasets/IGBP-DIS_FIRE_SPAIN.json index bec5cafe3a..9117c56043 100644 --- a/datasets/IGBP-DIS_FIRE_SPAIN.json +++ b/datasets/IGBP-DIS_FIRE_SPAIN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBP-DIS_FIRE_SPAIN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fire Product: Active Fire Detection in Eastern Spain was part of\n the International Geosphere-Biosphere Programme Data and Information\n System (IGBP-DIS) Regional Satellite Fire Data Compilation CD-ROM.\n \n Six large scale forest fires which took place in Eastern Spain from\n July 4 through July 8, 1994 have been detected by means of NOAA-11\n Advanced Very High Resolution Radiometer (AVHRR) infrared\n images. Detection was carried out using the difference of the\n brightness temperatures recorded in the channel 3 (middle infrared)\n and channel 4 (thermal infrared), processed by an automatic procedure\n developed in the University of Valladolid, Laboratory of Remote\n Sensing (LATUV). Detection performed along the period allows a\n monitoring of the active focus evolution.", "links": [ { diff --git a/datasets/IGBP-SurfaceProducts_569_1.json b/datasets/IGBP-SurfaceProducts_569_1.json index d7669f438f..593149d89e 100644 --- a/datasets/IGBP-SurfaceProducts_569_1.json +++ b/datasets/IGBP-SurfaceProducts_569_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBP-SurfaceProducts_569_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global data-surfaces pre-generated by SoilData, at a resolution of 5x5 arc-minutes, in ASCII GRID format for ARC INFO, and for the soil depth interval 0-100 cm.", "links": [ { diff --git a/datasets/IGBTH4_1.json b/datasets/IGBTH4_1.json index 8e6871b7cb..9c3925eb3c 100644 --- a/datasets/IGBTH4_1.json +++ b/datasets/IGBTH4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGBTH4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains bathymetry of Arctic fjords and Antarctic ice shelves based on measurements from the Sander Geophysics Airborne Inertially Referenced Gravimeter (AIRGrav) system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IGCMG1B_1.json b/datasets/IGCMG1B_1.json index 16f7c3d8fe..5ae3b7aac4 100644 --- a/datasets/IGCMG1B_1.json +++ b/datasets/IGCMG1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGCMG1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IGCMG2_1.json b/datasets/IGCMG2_1.json index 5fe9b209c8..312c3ad962 100644 --- a/datasets/IGCMG2_1.json +++ b/datasets/IGCMG2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGCMG2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IGGRV1B_1.json b/datasets/IGGRV1B_1.json index dfc4f3a129..342407a8d5 100644 --- a/datasets/IGGRV1B_1.json +++ b/datasets/IGGRV1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGGRV1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Greenland and Antarctica gravity measurements taken from the Sander Geophysics AIRGrav airborne gravity system. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IGLGS1B_1.json b/datasets/IGLGS1B_1.json index 1ac2a3ee58..1a2d68f5be 100644 --- a/datasets/IGLGS1B_1.json +++ b/datasets/IGLGS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGLGS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains gravity measurements taken over Greenland and Antarctica by the Lamont-Doherty Earth Observatory (LDEO) Gravimeter Suite. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IGZLS1B_1.json b/datasets/IGZLS1B_1.json index 5808fca9ce..88bbff471c 100644 --- a/datasets/IGZLS1B_1.json +++ b/datasets/IGZLS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IGZLS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains vertical, cross body, and along body acceleration values for geophysical survey flights in Antarctica using the ZLS Dynamic Gravity Meter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IHIS2684_1.json b/datasets/IHIS2684_1.json index f120b2a068..873457c94f 100644 --- a/datasets/IHIS2684_1.json +++ b/datasets/IHIS2684_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IHIS2684_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains a list of the location of sixteen soil samples taken from the vicinity of the Casey EPH fuel tank on the 13/02/2012 and 20/03/2012. Soil samples were taken for Total Petroleum Hydrocarbon (TPH) analysis and will be submitted to Analytical Services Tasmania for said analysis. The investigation is related to a leak detected from the threaded unions on the Casey EPH fuel tank supply line (refer to IHIS incident report 2684). Samples 100114 - 100120 were taken at selected locations within the recognisable spill area and down-gradient of the site on the 13/02/2012 by Dan Wilkins (Scientific Officer, Terrestrial and Nearshore Ecosystems, Science Branch.) Samples 99319-99332 were taken from a 5 m grid sampling pattern on the 20/03/2012 by Johan Mets (Plant Operator), acting under the direction of Dan Wilkins. Frozen conditions prevented samples being obtained from recommended depths (i.e. under the road base). \n\nFields in the dataset:\nSTD: Sample Tracking Database number (unique identifier)\nEasting: Easting (UTM 49S)\nNorthing: Northing (UTM 49S)\nSample Depth: Depth of sample beneath soil surface (where recorded)\nComment: Comment on location of sample and any observation about hydrocarbon \nSample Date: Date of sample collection in dd/mm/yyyy format\nSampler: Name of sample collector", "links": [ { diff --git a/datasets/IKONOS.ESA.archive_9.0.json b/datasets/IKONOS.ESA.archive_9.0.json index a37caf351e..f32acaca71 100644 --- a/datasets/IKONOS.ESA.archive_9.0.json +++ b/datasets/IKONOS.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IKONOS.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESA maintains an archive of IKONOS Geo Ortho Kit data previously requested through the TPM scheme and acquired between 2000 and 2008, over Europe, North Africa and the Middle East. The imagery products gathered from IKONOS are categorised according to positional accuracy, which is determined by the reliability of an object in the image to be within the specified accuracy of the actual location of the object on the ground. Within each IKONOS-derived product, location error is defined by a circular error at 90% confidence (CE90), which means that locations of objects are represented on the image within the stated accuracy 90% of the time. There are six levels of IKONOS imagery products, determined by the level of positional accuracy: Geo, Standard Ortho, Reference, Pro, Precision and PrecisionPlus. The product provided by ESA to Category-1 users is the Geo Ortho Kit, consisting of IKONOS Black-and-White images with radiometric and geometric corrections (1-metre pixels, CE90=15 metres) bundled with IKONOS multispectral images with absolute radiometry (4-metre pixels, CE90=50 metres). IKONOS collects 1m and 4m Geo Ortho Kit imagery (nominally at nadir 0.82m for panchromatic image, 3.28m for multispectral mode) at an elevation angle between 60 and 90 degrees. To increase the positional accuracy of the final orthorectified imagery, customers should select imagery with IKONOS elevation angle between 72 and 90 degrees. The Geo Ortho Kit is tailored for sophisticated users such as photogrammetrists who want to control the orthorectification process. Geo Ortho Kit images include the camera geometry obtained at the time of image collection. Applying Geo Ortho Kit imagery, customers can produce their own highly accurate orthorectified products by using commercial off the shelf software, digital elevation models (DEMs) and optional ground control. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/IKONOS2/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/IKONOS_MSI_L1B_1.json b/datasets/IKONOS_MSI_L1B_1.json index 5a796581d7..228f257f88 100644 --- a/datasets/IKONOS_MSI_L1B_1.json +++ b/datasets/IKONOS_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IKONOS_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IKONOS Level 1B Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the IKONOS satellite using the Optical Sensor Assembly instrument across the global land surface from October 1999 to March 2015. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The spatial resolution is 3.2m at nadir and the temporal resolution is approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/IKONOS_Pan_L1B_1.json b/datasets/IKONOS_Pan_L1B_1.json index c1e7caae82..fe27d9aedd 100644 --- a/datasets/IKONOS_Pan_L1B_1.json +++ b/datasets/IKONOS_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IKONOS_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IKONOS Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the IKONOS satellite using the Optical Sensor Assembly instrument across the global land surface from October 1999 to March 2015. This data product includes panchromatic imagery with a spatial resolution of 0.82m at nadir and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/ILAKP1B_1.json b/datasets/ILAKP1B_1.json index fb9f0c24d8..177bfc9a7e 100644 --- a/datasets/ILAKP1B_1.json +++ b/datasets/ILAKP1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILAKP1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface profiles of Alaska Glaciers acquired using the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILAKS1B_1.json b/datasets/ILAKS1B_1.json index b49641c67e..e66ca98f06 100644 --- a/datasets/ILAKS1B_1.json +++ b/datasets/ILAKS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILAKS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains scanning laser altimetry data points of Alaskan glaciers and parts of East and West Antarctica acquired by the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/ILATM1B_1.json b/datasets/ILATM1B_1.json index 63c7710d65..e2bd6389c4 100644 --- a/datasets/ILATM1B_1.json +++ b/datasets/ILATM1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILATM1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILATM1B_2.json b/datasets/ILATM1B_2.json index d589cd09bc..662db5282b 100644 --- a/datasets/ILATM1B_2.json +++ b/datasets/ILATM1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILATM1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILATM2_2.json b/datasets/ILATM2_2.json index 93df86ff31..6d7831970e 100644 --- a/datasets/ILATM2_2.json +++ b/datasets/ILATM2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILATM2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILATMGR_1.json b/datasets/ILATMGR_1.json index 289ba5a1cc..fbdbacd582 100644 --- a/datasets/ILATMGR_1.json +++ b/datasets/ILATMGR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILATMGR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports surface grain size estimates of snow and ice using waveform measurements from NASA's Airborne Topographic Mapper (ATM) narrow-swath and wide-swath instrumentation over the Greenland ice sheet and surrounding sea ice.", "links": [ { diff --git a/datasets/ILATMW1B_1.json b/datasets/ILATMW1B_1.json index 6ea3b7eb4f..48d524cda3 100644 --- a/datasets/ILATMW1B_1.json +++ b/datasets/ILATMW1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILATMW1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements with corresponding waveforms of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILNIRW1B_1.json b/datasets/ILNIRW1B_1.json index 2f6637f830..bbfdcbc441 100644 --- a/datasets/ILNIRW1B_1.json +++ b/datasets/ILNIRW1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILNIRW1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocated waveforms of Greenland, Arctic, and Antarctic sea ice measured by the Airborne Topographic Mapper (ATM) near-infrared (NIR) lidar. The data complement, and are intended to be used with, the IceBridge Narrow Swath ATM L1B Elevation and Return Strength with Waveforms data, which are measured at green wavelength. Both of these narrow-swath data sets are closely related to the wide-swath IceBridge ATM L1B Elevation and Return Strength with Waveforms data set. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.", "links": [ { diff --git a/datasets/ILNSA1B_1.json b/datasets/ILNSA1B_1.json index 4bd0c71e6f..2c5b40a3f2 100644 --- a/datasets/ILNSA1B_1.json +++ b/datasets/ILNSA1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILNSA1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) 4CT3 narrow scan instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILNSA1B_2.json b/datasets/ILNSA1B_2.json index 939af1ef3d..7f0069d5ee 100644 --- a/datasets/ILNSA1B_2.json +++ b/datasets/ILNSA1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILNSA1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) narrow-swath instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/ILNSAW1B_1.json b/datasets/ILNSAW1B_1.json index 46d318d4cd..4627741445 100644 --- a/datasets/ILNSAW1B_1.json +++ b/datasets/ILNSAW1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILNSAW1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains spot elevation measurements with corresponding waveforms of Greenland, Arctic, and Antarctic sea ice. The data complement the IceBridge ATM L1B Near-Infrared Waveforms data, which are measured at near-infrared wavelength. Both of these narrow-swath data sets are closely related to the wide-swath IceBridge ATM L1B Elevation and Return Strength with Waveforms data set. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.", "links": [ { diff --git a/datasets/ILSIG1B_1.json b/datasets/ILSIG1B_1.json index 35da7671eb..78473e6437 100644 --- a/datasets/ILSIG1B_1.json +++ b/datasets/ILSIG1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILSIG1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocated photon elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/ILSNP1B_1.json b/datasets/ILSNP1B_1.json index d8daa1ba19..a2f002773b 100644 --- a/datasets/ILSNP1B_1.json +++ b/datasets/ILSNP1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILSNP1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains nadir photon counting data captured over Antarctica using the Sigma Space photon counting lidar. Position and orientation data are included. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/ILSNP4_1.json b/datasets/ILSNP4_1.json index dd2252978b..a822a0a2af 100644 --- a/datasets/ILSNP4_1.json +++ b/datasets/ILSNP4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILSNP4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocated surface elevation measurements captured over Antarctica using the Sigma Space Mapping Photon Counting Lidar and Riegl Laser Altimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/ILUTP1B_1.json b/datasets/ILUTP1B_1.json index 741235f670..0a81b711b2 100644 --- a/datasets/ILUTP1B_1.json +++ b/datasets/ILUTP1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILUTP1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains laser ranges, returned pulses, and deviation for returned pulses in Antarctica and Greenland using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA's Operation IceBridge.", "links": [ { diff --git a/datasets/ILUTP2_1.json b/datasets/ILUTP2_1.json index 103b2f4810..b6c0259d74 100644 --- a/datasets/ILUTP2_1.json +++ b/datasets/ILUTP2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILUTP2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface range values for Antarctica and Greenland derived from measurements captured by the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/ILVGH1B_1.json b/datasets/ILVGH1B_1.json index 5ceedbac3b..5c35d78e7b 100644 --- a/datasets/ILVGH1B_1.json +++ b/datasets/ILVGH1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVGH1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains energy waveform data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/ILVGH2_1.json b/datasets/ILVGH2_1.json index 68f8028369..d5fc5c27dd 100644 --- a/datasets/ILVGH2_1.json +++ b/datasets/ILVGH2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVGH2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/ILVIS0_1.json b/datasets/ILVIS0_1.json index feae2b6410..8d30e47e0c 100644 --- a/datasets/ILVIS0_1.json +++ b/datasets/ILVIS0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVIS0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw Inertial Measurement Unit (IMU), Global Positioning System (GPS), and camera data over Greenland, Antarctica, and Alaska measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns, including the Arctic Radiation - IceBridge Sea and Ice Experiment (ARISE).", "links": [ { diff --git a/datasets/ILVIS1B_2.json b/datasets/ILVIS1B_2.json index 915096e5aa..62213bfcba 100644 --- a/datasets/ILVIS1B_2.json +++ b/datasets/ILVIS1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVIS1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains return energy waveform data measured over Greenland, Alaska, and Antarctica by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/ILVIS2_1.json b/datasets/ILVIS2_1.json index 899e942a6b..743f4bc8c4 100644 --- a/datasets/ILVIS2_1.json +++ b/datasets/ILVIS2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVIS2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation data over Greenland, Alaska, and Antarctica, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/ILVIS2_2.json b/datasets/ILVIS2_2.json index decd90688e..07fd96a08f 100644 --- a/datasets/ILVIS2_2.json +++ b/datasets/ILVIS2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ILVIS2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevation data over parts of Greenland, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IMARPE-Callao_Station_0.json b/datasets/IMARPE-Callao_Station_0.json index a3537a87b8..27ccb295fd 100644 --- a/datasets/IMARPE-Callao_Station_0.json +++ b/datasets/IMARPE-Callao_Station_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMARPE-Callao_Station_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IMARPE-Callao station is located offshore from the port of Callao, Peru at a water depth of approximately 150 m. This station is operated by the Instituto del Mar del Peru with the objective to study the oceanographic variability off the Peruvian coast, algal blooms and relationships with ENSO.", "links": [ { diff --git a/datasets/IMCS31B_2.json b/datasets/IMCS31B_2.json index ccf272b7d6..a117f80736 100644 --- a/datasets/IMCS31B_2.json +++ b/datasets/IMCS31B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMCS31B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains magnetic field readings taken over Antarctica using the Scintrex CS-3 Cesium Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IMECOCAL_0.json b/datasets/IMECOCAL_0.json index fca8315add..3de2dee4fb 100644 --- a/datasets/IMECOCAL_0.json +++ b/datasets/IMECOCAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMECOCAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the IMECOCAL program (Investigaciones Mexicanas de la Corriente de California, translated: Mexican Research of the California Current) from 2002 to 2004.", "links": [ { diff --git a/datasets/IMERG_Precip_Canada_Alaska_2097_1.json b/datasets/IMERG_Precip_Canada_Alaska_2097_1.json index 7306f8cdb6..90776cbfbc 100644 --- a/datasets/IMERG_Precip_Canada_Alaska_2097_1.json +++ b/datasets/IMERG_Precip_Canada_Alaska_2097_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMERG_Precip_Canada_Alaska_2097_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.", "links": [ { diff --git a/datasets/IMFGM1B_1.json b/datasets/IMFGM1B_1.json index db691728a9..65f5c2d112 100644 --- a/datasets/IMFGM1B_1.json +++ b/datasets/IMFGM1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMFGM1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains time-registered Level-1B field readings taken over Antarctica using the Watson-Gyro Fluxgate Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IMS1_HYSI_GEO_1.0.json b/datasets/IMS1_HYSI_GEO_1.0.json index d5c837dee8..8ad0a60f62 100644 --- a/datasets/IMS1_HYSI_GEO_1.0.json +++ b/datasets/IMS1_HYSI_GEO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IMS1_HYSI_GEO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data received from IMS1, HySI which operates in 64 spectral bands in VNIR bands(400-900nm) with 500 meter spatial resolution and swath of 128 kms.", "links": [ { diff --git a/datasets/IN2017_V01_Diatoms_1.json b/datasets/IN2017_V01_Diatoms_1.json index 9f79df9bbe..96acd3c8ab 100644 --- a/datasets/IN2017_V01_Diatoms_1.json +++ b/datasets/IN2017_V01_Diatoms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IN2017_V01_Diatoms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were generated by Raffaella Tolotti (raffaella.tolotti@virgilio.it) thanks to a scholarship founded by the Italian P.N.R.A. \u2018TYTAN Project (PdR 14_00119): \u2018Totten Glacier dYnamics and Southern Ocean circulation impact on deposiTional processes since the mid-lAte CeNozoic\u2019 (Principal Investigator Dr. Donda Federica, Dr. Caburlotto A. - OGS, Trieste) and University of Genova (DISTAV - Prof. Corradi Nicola). \nThese data are based on samples collected during research cruise IN2017_V01 of the RV Investigator, co-chief scientists, Leanne Armand and Phil O\u2019Brien and were collected to provide paleoceanographic and bio/ stratigraphic information on Aurora Basin Antarctic margin evolution.\nThe IN2017-V01post-cruise report is available through open access via the e-document portal through the ANU library.\n\nhttps://openresearch-repository.anu.edu.au/handle/1885/142525\n\nThe document DOI:\n10.4225/13/5acea64c48693\nThe preferred citation are:\n\nL.K. Armand, P.E. O\u2019Brien and On-board Scientific Party. 2018. Interactions of the Totten Glacier with the Southern Ocean through multiple glacial cycles (IN2017-V01): Post-survey report, Research School of Earth Sciences, Australian National University: Canberra, http://dx.doi.org/10.4225/13/5acea64c48693\n\nDonda F., Leitchenkov, Brancolini G., Romeo R., De Santis L., Escutia C., O'Brien P., Armand L., Caburlotto, A., Cotterle, D., 2020. The influence of Totten Glacier on the Late Cenozoic sedimentary record. Antarctic Science, 1 -3; http://doi:10.1017/S0954102020000188\n\nO\u2019Brien, P.E., Post, A.L., Edwards, S., Martin, T., Carburlotto, A., Donda, F., Leitchenkov, G., Romero, R., Duffy, M., Evangelinos, D., Holder, L., Leventer, A., L\u00f3pez-Quir\u00f3s, A., Opdyke, B.N., and Armand, L.K. in press. Continental slope and rise geomorphology seaward of the Totten Glacier, East Antarctica (112\u00b0E-122\u00b0E). Marine Geology.\n\nSamples for diatom analysis were collected on board ship immediately after core recovery. Sub-samples were sent, according to the Australian standard procedures, to the DISTAV sedimentological laboratory in Genoa (Italy) and prepared for the micro-paleontological analysis according to the laboratory\u2019s protocol (imported and tested from Salamanca University lab.; Referring Prof. B\u00e1rcena). Smear-slides and the qualitative-quantitative analyses were performed every 20 cm. Previous onboard smear slides analyses on PC03 highlighted notable variations from the other piston cores, containing some older diatom species. Moreover this core exceptionally did not exhibit a clear cyclicity like the others. It was so assumed to target a condensed sedimentary sequence giving access to older sediments. \nThe further, more in-depth diatom biostratigraphic and quantitative analyses were performed in accordance with the international stratigraphic guide (https://stratigraphy.org/guide/), with the pluri-decennial DSDP and IODP Antarctic diatom biostratigraphic reports and specific papers (see References). \nSample preparation, diatom species identification and counting were those described in Schrader and Gersonde (1978), Barde (1981 - modified) and Bod\u00e9n (1991). \nDiatom analysis was performed with an immersion 1000x LM Reichert Jung-Polyvar microscope (Wien). Whenever possible, almost 300 diatom valves were counted per slide following the counting methodology presented in Schrader and Gersonde (1978). When diatom concentration proved too low or too concentrated, slides with modified concentrations have been prepared to optimize counting and identification while at least one hundred fields-of-view per poor concentration slide have been analyzed. For samples that were too diatom-poor, the over-concentration of material on the slides resulted in limiting resolution and taxonomic identification of the rare and mostly fragmented valves. Where diatom occurrence was rare only major fragments (>50%) or entire valves were counted. \n\nThe file (.xls) contains 2 sheets:\n\nSheet: PC03 diatoms dataset.\nThe absolute diatom valve concentration (ADA= Absolute Valves Abundance) was then calculated following Abrantes et al. (2005), Warnock & Scherer (2014) and ADA in Taylor\u2010Silva & Riesselmann (2018), taking in account initial weights, concentration of the samples and microscope\u2019s characteristics, as the number of valves per gram of dry sediment. Diatoms were identified to species level following Crosta et al. (2005), Armand et al. (2005), Cefarelli et al. (2010) for modern assemblages. Older diatom taxa were identified following Gersonde et B\u00e1rcena, 1998, Witkowski et al., 2014; Bohaty et al., 2011; Gombos, 1985; Gombos, 2007; Gersonde et al., 1990; Barron et al., 2004; Harwood et al., 2001; Harwood etal., 1992. Species were considered extinct when observed stratigraphically higher than extinction boundaries as identified by Cody et al. (2008) but the coexistence or the alternation in the stratigraphic sequence of taxa referring to different biostratigraphic age ranges were considered signs of reworking.\nSheet: PC03 tephra dataset.\nDuring LM microscopic observations some volcanic glass shards were observed first in smear slides and then counted during the activities of microfossils count for diatoms. This allowed to obtain the number of glass shards/g. dry sed. useful to compare with diatom and sediment datasets.\n\nCore location:\nStation_core Longitude Latitude\nA006_PC03 115.043 -64.463\n\nDepth:\nThe core was taken at Site A006 that was chosen into an overbank deposit on the upper western side of a turbidite channel (Minang-a Canyon) (Fig. 39 \u2013 Armand et al., 2017; O\u2019Brien et al., 2020). The setting is at 1862 m depth, shallower respect the other cores. A possible higher energy environment, with a lower sedimentation rate has been first supposed.\n\nTemporal coverage:\nStart date: 2017-01-14 - Stop date: 2018-11-30\n\nReferences:\n\nArmand, L.K., X. Crosta, O. Romero, J. J. Pichon (2005). The biogeography of major diatom taxa in Southern Ocean sediments: 1. Sea ice related species, Paleogeography, Paleoclimatology, Paleoecology, 223, 93-126.\n\nCefarelli, A.O., M. E. Ferrario, G. O. Almandoz, A. G. Atencio, R. Akselman, M. Vernet (2010). Diversity of the diatom genus Fragilariopsis in the Argentine Sea and Antarctic waters: morphology, distribution and abundance, Polar Biology, 33(2), 1463-1484.\n\nCody, R., R. H. Levy, D. M. Harwood, P. M. Sadler (2008). Thinking outside the zone: High-resolution quantitative diatom biochronology for the Antarctic Neogene, Palaeogeography, Palaeoclimatology, Palaeoecology, 260, 92-121; doi:10.1016/j.palaeo.2007.08.020\nCrosta, X., O. Romero, L. K. Armand, J. Pichon (2005). The biogeography of major diatom taxa in Southern Ocean sediments: 2. Open ocean related species, Palaeogeography, Palaeoclimatology, Palaeoecology, 223, 66-92.\nRebesco, M., E. Domack, F. Zgur, C. Lavoie, A. Leventer, S. Brachfeld, V. Willmott, G. Halverson, M. Truffer, T. Scambos, J. Smith, E. Pettit (2014). Boundary condition of grounding lines prior to collapse, Larsen-B Ice Shelf, Antarctica, Science, 345, 1354-1358.\n\nWarnock, J. P., R. P. Scherer (2014). A revised method for determining the absolute abundance of diatoms, J. Paleolimnol.; doi:10.1007/s10933-014-9808-0\nWitkowski, J., Bohaty, S.M., McCartney, K., Harwood, D.M., (2012) . Enhanced siliceous plankton productivity in response to middle Eocene warming at Southern Ocean ODP Sites 748 and 749 Palaeogeog., Palaeoclimat., Palaeoecol., 326\u2013328, 78\u201394; doi:10.1016/j.palaeo.2012.02.006\n\nWitkowski, J., Bohaty, S.M., Edgar, K.M., Harwood, D.M., (2014). Rapid fluctuations in mid-latitude siliceous plankton production during the Middle Eocene Climatic Optimum (ODP Site 1051, Western North Atlantic). Mar. Micropal., 106, 110\u2013129. http://dx.doi.org/10.1016/j.marmicro.2014.01.001\n\nRaffaella Tolotti\nunpublished data", "links": [ { diff --git a/datasets/INC_NCMF.json b/datasets/INC_NCMF.json index 68dde5e74c..3a2048b73b 100644 --- a/datasets/INC_NCMF.json +++ b/datasets/INC_NCMF.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INC_NCMF", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nature Characterization Map of Flanders is a collection of all available\ngeographic information at the regional level that is considered relevant for\nnature conservation. The purpose of the map was to compile a database, making\nit possible to objectively grade the ecological value of a specific location.\nThis grading system is based on three modules?the actual natural value, the\nabiotic system features, and the legal framework.\n\nThe actual natural value is primarily derived from the Biological Valuation\nmap, a vegetation and land use map covering the entire Flemish region.\nAdditional information comes from maps of (international) important wildlife\nareas, biotope rareness, level of habitat fragmentation, and the location of\nvaluable rivers and streams.\n\nThe abiotic system features are used as a tool to integrate larger areas and to\nlocate potentially valuable systems. The main information source is the soil\nmap, from which several other features are derived.\n\nThe third module, the legal and policy framework, is important for establishing\nthe feasibility of any proposed conservation projects. It includes information\non the legal designation of land use and national and international protection\nstatus.\n\nIn the long term, the aim is to compile the information from the three modules\ninto a single score, based on multicriteria analysis. The system should also\nallow for expansion and updating when new information becomes available. The\nmap's primary use is to supply policy makers, planners, and nature conservation\norganizations at the regional and local levels with extensive and objective\ninformation.", "links": [ { diff --git a/datasets/INDOEX_0.json b/datasets/INDOEX_0.json index 34084e8e6e..ee1b162b37 100644 --- a/datasets/INDOEX_0.json +++ b/datasets/INDOEX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INDOEX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the India Ocean Experiment (INDOEX) in 1999.", "links": [ { diff --git a/datasets/INPE_AQUA1_MODIS.json b/datasets/INPE_AQUA1_MODIS.json index 267cda212c..d6b467d917 100644 --- a/datasets/INPE_AQUA1_MODIS.json +++ b/datasets/INPE_AQUA1_MODIS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_AQUA1_MODIS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Imagery from MODIS sensor, abord Aqua platform, held by INPE.", "links": [ { diff --git a/datasets/INPE_CBERS2B_CCD.json b/datasets/INPE_CBERS2B_CCD.json index 5114cf24b9..91ba68be83 100644 --- a/datasets/INPE_CBERS2B_CCD.json +++ b/datasets/INPE_CBERS2B_CCD.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS2B_CCD", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of \u00b1 32 degrees, it is capable of taking stereoscopic images of a certain region. \n\nThe CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 \u00b5m. A complete coverage cycle of the CCD camera takes 26 days.", "links": [ { diff --git a/datasets/INPE_CBERS2B_HRC.json b/datasets/INPE_CBERS2B_HRC.json index efd3af547c..e6d7eb80a8 100644 --- a/datasets/INPE_CBERS2B_HRC.json +++ b/datasets/INPE_CBERS2B_HRC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS2B_HRC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HRC camera operates in a single spectral band which covers visible and near-infrared bands. It is only present in CBERS-2B. It generates images of 27km width and resolution 2.7m, which will allow the observation of surface objects with large detail. Given its 27km swath, five 26 days cycles are necessary for the 113km standard CCD swath to be covered by HRC.", "links": [ { diff --git a/datasets/INPE_CBERS2_CCD.json b/datasets/INPE_CBERS2_CCD.json index a92cdfde45..fca2ea9f4b 100644 --- a/datasets/INPE_CBERS2_CCD.json +++ b/datasets/INPE_CBERS2_CCD.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS2_CCD", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-2 CCD - High Resolution CCD Camera.\n\nThe CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of \u00b1 32 degrees, it is capable of taking stereoscopic images of a certain region. \n\nThe CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 \u00b5m. A complete coverage cycle of the CCD camera takes 26 days.", "links": [ { diff --git a/datasets/INPE_CBERS2_IRM.json b/datasets/INPE_CBERS2_IRM.json index 8176c73110..4b1418aeb6 100644 --- a/datasets/INPE_CBERS2_IRM.json +++ b/datasets/INPE_CBERS2_IRM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS2_IRM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CBERS-2 satellite is designed for global coverage and include cameras that make optical observations and a Data Collection System transponder to gather data on the environment. They are unique systems due to the use of on board cameras which combine features that are specially designed to resolve the broad range of space and time scales involved in our ecosystem.\n\nThe IRMSS operates in 4 spectral bands, thus extending the CBERS spectral coverage up to the thermal infrared range. It images a 120 km swath with the resolution of 80m (160m in the thermal channel). In 26 days one obtains a complete Earth coverage that can be correlated with the images of the CCD camera.", "links": [ { diff --git a/datasets/INPE_CBERS4_AWFI_1.json b/datasets/INPE_CBERS4_AWFI_1.json index 40b6b3b479..754354001f 100644 --- a/datasets/INPE_CBERS4_AWFI_1.json +++ b/datasets/INPE_CBERS4_AWFI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS4_AWFI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WFI - Wide Field Camera camera can make quick revisits to a certain area - usually in less than five days, aiming at support monitoring and surveillance activities. It complements other sensors with more revisit capability (e.g. AVHRR/NOAA or MODIS/Terra and Aqua), sensors with lower revisit capacity such as TM/Landsat, and the other CBERS-4 cameras. \n\nIt has 4 4pectral bands:\n\n0,45-0,52?m (B)\n\n0,52-0,59?m (G)\n\n0,63-0,69?m (R)\n\n0,77-0,89?m (NIR). \n\nThe swath width is 866 km, the spatial resolution is 64 m on nadir and the image data bit rate is 50 Mbit/s.", "links": [ { diff --git a/datasets/INPE_CBERS4_IRS_1.json b/datasets/INPE_CBERS4_IRS_1.json index 71b3e8e372..c9fb229453 100644 --- a/datasets/INPE_CBERS4_IRS_1.json +++ b/datasets/INPE_CBERS4_IRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS4_IRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Infrared Medium Resolution Scanner.\nThis camera is built under China responsibility and it is an upgrade of the Infrared Multispectral Scanner (IRMSS) of the CBERS-1 and 2 satellites. \nIt has 4 spectral bands: \n\nB09: 0,50 - 0,90 ?m\n\nB10: 1,55 - 1,75 ?m\n\nB11: 2,08 - 2,35 ?m\n\nB12: 10,4 - 12,5 ?m\n\nIts spatial resolution is 40 meters in the panchromatic and SWIR (shortwave infrared) bands and 80 meters in the thermal band.\n\nA complete coverage cycle of the panchromatic camera takes 26 days.", "links": [ { diff --git a/datasets/INPE_CBERS4_MUX_1.json b/datasets/INPE_CBERS4_MUX_1.json index 5e029d90df..c972e69045 100644 --- a/datasets/INPE_CBERS4_MUX_1.json +++ b/datasets/INPE_CBERS4_MUX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS4_MUX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4 MUX - Multispectral Camera.\nThis camera is built under Brazilian responsibility. \n\nIt is a multispectral camera with four spectral band covering the wavelength range from blue to near infrared (from 450 nm to 890 nm) with a ground resolution of 20 m and a ground swath width of 120 km\nA complete coverage cycle of the MUX camera takes 26 days.", "links": [ { diff --git a/datasets/INPE_CBERS4_PAN10M_1.json b/datasets/INPE_CBERS4_PAN10M_1.json index 2e5684223e..4d6ac71301 100644 --- a/datasets/INPE_CBERS4_PAN10M_1.json +++ b/datasets/INPE_CBERS4_PAN10M_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CBERS4_PAN10M_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4 Multispectral 10 Meters Camera.\nThis camera is built under China responsibility. \n\nIt is a panchromatic camera with 4 spectral bands:\n\nB02: 0,52 - 0,59 ?m\n\nB03: 0,63 - 0,69 ?m\n\nB04: 0,77 - 0,89 ?m\n\nThe ground resolution is 10 m and the ground swath width is 60 km.\n\nA complete coverage cycle of the panchromatic camera takes 52 days.", "links": [ { diff --git a/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json b/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json index 2f810eb83a..6fe5541ab6 100644 --- a/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json +++ b/datasets/INPE_CPTEC_CLIMATE_BRAZIL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CPTEC_CLIMATE_BRAZIL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly precipitation and temperature maps and\ntheir respective anomalies in relation to the 30-year climatology\n(1961-1990). Data was used from the Instituto Nacional de Meteorologia\n(INMET/BR).\n\nThis dataset can be obtained via World Wide Web from the CPTEC Home Page.\nLink to: http://www.cptec.inpe.br/", "links": [ { diff --git a/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json b/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json index 10858b8448..de34be4937 100644 --- a/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json +++ b/datasets/INPE_CPTEC_CLIMATE_GLOBAL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CPTEC_CLIMATE_GLOBAL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of Global monthly fields of several variables\nand their respective anomalies in relation to the 16 years climatology\n(1979-1995), using reanalysis data from National Center for\nEnvironmental Prediction (NCEP/USA). Variables include Geopotential\nHeight, Streamlines(850hPa, 200hPa), upper level winds(850hPa, 200hPa)\n, sea level temperature, outgoing long wave radiation, and sea level\npressure.\n\nThis dataset can be obtained via World Wide Web from the CPTEC Home Page.\nLink to: http://www.cptec.inpe.br/", "links": [ { diff --git a/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json b/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json index 8e1cbc6884..d378d31728 100644 --- a/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json +++ b/datasets/INPE_CPTEC_GLOBAL_METEOGRAM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CPTEC_GLOBAL_METEOGRAM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forecast model meteograms for 26 locations in South America are\n available from CPTEC (Centro de Previsao de Tempo e Estudos\n Climaticos) in Brazil. Forecast time steps range from the initial\n time out to six days. The user may view forecasts from the most\n recent forecast run back to the previous 36 hours at twelve hour\n steps.\n \n Parameters Forecasted:\n \n Relative Humidity (%)\n Precipitation (mm/h)\n Mean Sea Level Pressure (mb)\n Surface Wind (m/s)\n Surface Temperature (C)\n \n Forecasted meteograms may be obtained via World Wide Web from CPTEC's\n Home Page.\n Link to: \"http://www.cptec.inpe.br/\"", "links": [ { diff --git a/datasets/INPE_CPTEC_IR_SAT.json b/datasets/INPE_CPTEC_IR_SAT.json index 1ea0c63ae3..89e75703fb 100644 --- a/datasets/INPE_CPTEC_IR_SAT.json +++ b/datasets/INPE_CPTEC_IR_SAT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_CPTEC_IR_SAT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOES-8 and Meteosat-5 infrared images of South America are available\nfrom CPTEC (Centro de Previsao de Tempo e Estudos Climaticos) in Brazil.\nOnly the most recent month's images are archived. A new image is\nprovided every three hours. Please read carefully the Disclaimer and\nCopyright information.\n\nAll satellite images and additional information may be obtained via\nthe World Wide Web from the CPTEC Home Page.\nLink to: http://www.cptec.inpe.br/", "links": [ { diff --git a/datasets/INPE_ER_SAR.json b/datasets/INPE_ER_SAR.json index 21c6a29a66..61affb24b6 100644 --- a/datasets/INPE_ER_SAR.json +++ b/datasets/INPE_ER_SAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_ER_SAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INPE's only receiving station for ERS-1 and ERS-2 SAR is located in\n Cuiaba, MT (geographic coordinates approx. 15.5S/56.0W). The SAR tapes\n recorded at Cuiaba are air shipped to the processing and distribution\n center in Cachoeira Paulista, SP, where they are kept. A copy of all\n recorded tapes is sent to the ESA PAF at the DLR facilities in\n Germany.\n \n Early ERS-1 SAR data were acquired primarily for station checkout and\n Principal Investigator service. A small number of passes, 15 seconds\n to 2 minutes long, were acquired from August, 1991 to March, 1992,\n during the Commissioning and Ice phases where the repeat cycle was 3\n days but the ground coverage was sparse. More extensive and regular\n acquisitions began in April, 1992, with the satellite already in the\n so-called Multidisciplinary phase (full ground coverage and 35-day\n repeat cycle).\n \n INPE is allowed to service user requests originated in Brazil\n only. Only digital products are available. Requests for products and\n for image search listings will be handled directly by the processing\n center (listed at the Data Center entry), through contacts by mail,\n phone or fax. No online remote access is available, although plans\n exist to implement it, including the International Directory\n protocols. No firm date is speculated for that, but hopes are that it\n can be made before 1998.\n \n Requests from countries other that Brazil must be routed to the ESA\n licensed regional distributors. Information can be obtained with the\n ESA ERS-1 Order Desk, c/o ESRIN, C.P. 64, I-00044 Frascati, Italy. The\n phone numbers are +39.6.941-80600 (voice) and +39.6.941-80510 (fax).", "links": [ { diff --git a/datasets/INPE_IRS_AWIFS.json b/datasets/INPE_IRS_AWIFS.json index d5743a63d8..5ef4782ab2 100644 --- a/datasets/INPE_IRS_AWIFS.json +++ b/datasets/INPE_IRS_AWIFS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_IRS_AWIFS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AWIFS, aboard IRS \u2013 P6 (RESOURCESAT-I), imagery held by INPE.", "links": [ { diff --git a/datasets/INPE_IRS_LISS3.json b/datasets/INPE_IRS_LISS3.json index 9a6a86e5ee..0586eb0642 100644 --- a/datasets/INPE_IRS_LISS3.json +++ b/datasets/INPE_IRS_LISS3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_IRS_LISS3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISS 3, aboard IRS \u2013 P6 (RESOURCESAT-I), imagery held by INPE.", "links": [ { diff --git a/datasets/INPE_LANDSAT1_MSS.json b/datasets/INPE_LANDSAT1_MSS.json index b1dc0ca486..d9c2c9f913 100644 --- a/datasets/INPE_LANDSAT1_MSS.json +++ b/datasets/INPE_LANDSAT1_MSS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LANDSAT1_MSS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDSAT 1 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", "links": [ { diff --git a/datasets/INPE_LANDSAT2_MSS.json b/datasets/INPE_LANDSAT2_MSS.json index ec1b976d1b..28928c3c32 100644 --- a/datasets/INPE_LANDSAT2_MSS.json +++ b/datasets/INPE_LANDSAT2_MSS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LANDSAT2_MSS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDSAT 2 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", "links": [ { diff --git a/datasets/INPE_LANDSAT3_MSS.json b/datasets/INPE_LANDSAT3_MSS.json index ef0d9c92d2..c8190670bc 100644 --- a/datasets/INPE_LANDSAT3_MSS.json +++ b/datasets/INPE_LANDSAT3_MSS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LANDSAT3_MSS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDSAT 3 MSS imagery held by the National Institute for Space Research (INPE), Brazil.", "links": [ { diff --git a/datasets/INPE_LANDSAT5_TM.json b/datasets/INPE_LANDSAT5_TM.json index ce4a9b3036..829f244e79 100644 --- a/datasets/INPE_LANDSAT5_TM.json +++ b/datasets/INPE_LANDSAT5_TM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LANDSAT5_TM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDSAT 5 TM imagery held by the National Institute for Space Research (INPE), Brazil.", "links": [ { diff --git a/datasets/INPE_LANDSAT7_ETM.json b/datasets/INPE_LANDSAT7_ETM.json index 92ca453828..6e179b7222 100644 --- a/datasets/INPE_LANDSAT7_ETM.json +++ b/datasets/INPE_LANDSAT7_ETM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LANDSAT7_ETM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDSAT 7 ETM+ imagery held by the National Institute for Space Research (INPE), Brazil.", "links": [ { diff --git a/datasets/INPE_LS_MSS.json b/datasets/INPE_LS_MSS.json index e7d8e61b3e..4f5a7238ae 100644 --- a/datasets/INPE_LS_MSS.json +++ b/datasets/INPE_LS_MSS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LS_MSS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INPE's only receiving station for Landsat MSS was located in Cuiaba,\n MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired\n routinely over the entire range from 1973 until 1986, some time after\n TM data began being received. MSS data recordings were then reduced to\n Brazilian territory only. Also, many gaps exist, some of them several\n months long, related mostly to station downtimes caused by lightning.\n \n The MSS tapes recorded at Cuiaba were air shipped to the processing\n and distribution center in Cachoeira Paulista, SP, where they are\n kept. The holdings are estimated to be around 75,000 scenes (or\n 300,000 images, counting individually each of the four spectral\n bands), not all of them processed. The fifth band (thermal infrared)\n available on Landsat 3 is not counted for practical purposes. Very few\n were processed at INPE with disappointing results and it was soon\n dropped as a product.\n \n Demand for MSS products decreased sharply after TM products started\n being distributed. This determined the reduction and eventually the\n discontinuing of MSS recordings in 1987. The original processing\n system, based on 16-bit minicomputers, was dismantled in early\n 1991. An alternative ingestion system is being developed to allow\n limited processing of MSS data by the TM system, with a forecast to be\n ready in late 1998.\n \n Meanwhile, available products are limited to reproduction of existing\n photographic originals (about 150,000 black-and-white and color\n images). No digital products can be delivered, since no copies were\n kept from produced CCTs. Requests for products and for image search\n listings are handled directly by the processing center (listed at the\n Data Center entry), through contacts by mail, phone or fax. No online\n remote access is available to the moment, although plans exist to\n implement it, including the International Directory protocols. No firm\n date is speculated for that, but hopes are that it can be made before\n 1998. Information about costs, delivery time and available formats can\n be requested at the same address.", "links": [ { diff --git a/datasets/INPE_LS_RBV.json b/datasets/INPE_LS_RBV.json index 9210c94e0b..1ae7d1dee1 100644 --- a/datasets/INPE_LS_RBV.json +++ b/datasets/INPE_LS_RBV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LS_RBV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INPE's only receiving station for Landsat RBV was located in Cuiaba,\n MT (geographic coordinates approx. 15.5S/56.0W). Data were acquired\n routinely over the entire range during the lifetime of Landsat 3. This\n means therefore the twin-camera, panchromatic version of the RBV. Some\n gaps exist, some of them several months long, related mostly to\n station downtimes caused by lightning.\n \n The RBV tapes recorded at Cuiaba were air shipped to the processing\n and distribution center in Cachoeira Paulista, SP, where they are\n kept. The holdings (not all of them processed to film) are estimated\n to be around 100,000 images.\n \n Demand for RBV products experienced a brief peak while they were a\n novelty with higher resolution than MSS (30m vs. 80m), but decreased\n quickly as the all-analog processing system allowed no digital\n products and the shading effect could not be effectively corrected,\n yielding poor quality images. Requests practically vanished after the\n Thematic Mapper came into scene, and RBV products were taken off the\n INPE products list in late 1984. The processing system was dismantled\n in early 1991. Some 50,000 photographic originals are still kept, but\n no intention of resuming distribution exists in principle.", "links": [ { diff --git a/datasets/INPE_LS_TM.json b/datasets/INPE_LS_TM.json index 50cb523dc1..cdc364f210 100644 --- a/datasets/INPE_LS_TM.json +++ b/datasets/INPE_LS_TM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_LS_TM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INPE's only receiving station for Landsat TM is located in Cuiaba, MT\n (geographic coordinates approx. 15.5S/56.0W). Data were always\n acquired in a routine fashion, initially over Brazil only, with\n extension to the whole range in 1987. A few gaps exist, some of them\n several months long, related mostly to station downtimes caused by\n lightning.\n \n The TM tapes recorded at Cuiaba are air shipped to the processing and\n distribution center in Cachoeira Paulista, SP, where they are\n kept. The holdings are estimated to be around 70,000 scenes (or\n 500,000 images, counting individually each of the seven spectral\n bands). About 14,000 scenes have been processed to black-and-white or\n color photographic originals and can be reproduced as products quicker\n than unprocessed ones. No copies are kept from delivered digital\n products.\n \n Requests for products and for image search listings are handled\n directly by the processing center (listed at the Data Center entry),\n through contacts by mail, phone or fax. No online remote access is\n available to the moment, although plans exist to implement it,\n including the International Directory protocols. No firm date is\n speculated for that, but hopes are that it can be made before 1998.\n Information about costs, delivery time and available formats can be\n requested at the same address.", "links": [ { diff --git a/datasets/INPE_TOPODATA.json b/datasets/INPE_TOPODATA.json index e9a0a1a974..b762708702 100644 --- a/datasets/INPE_TOPODATA.json +++ b/datasets/INPE_TOPODATA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INPE_TOPODATA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DTM and its local geomorphometric derivations from SRTM (Shuttle Radar Topography Mission) throughout the entire Brazilian territory. The processing, restricted to local geomorphometric derivations, targeted the production of information layers of the primary variables: elevation, slope, aspect, vertical curvature and horizontal curvature, in their full numerical expression and in interval class schemes. It also includes secondary (or combined) variables, layers respective to landforms, watershed delineation and solar illumination.", "links": [ { diff --git a/datasets/INTEXA_AIRMAP_1.json b/datasets/INTEXA_AIRMAP_1.json index 5ef46f2d6b..b0ae2861f7 100644 --- a/datasets/INTEXA_AIRMAP_1.json +++ b/datasets/INTEXA_AIRMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_AIRMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_DC8_AIRCRAFT_1.json b/datasets/INTEXA_DC8_AIRCRAFT_1.json index 0b82992a12..48aab93fb4 100644 --- a/datasets/INTEXA_DC8_AIRCRAFT_1.json +++ b/datasets/INTEXA_DC8_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_DC8_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEXA_DC8_AIRCRAFT is the Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) Aircraft data product. INTEX-A was an integrated atmospheric field experiment performed over North America. The study sought to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases.INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_J31_AIRCRAFT_1.json b/datasets/INTEXA_J31_AIRCRAFT_1.json index 8647c0d867..f9ae9fc591 100644 --- a/datasets/INTEXA_J31_AIRCRAFT_1.json +++ b/datasets/INTEXA_J31_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_J31_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_MERGES_1.json b/datasets/INTEXA_MERGES_1.json index 9d4151ecf4..2d6dab3510 100644 --- a/datasets/INTEXA_MERGES_1.json +++ b/datasets/INTEXA_MERGES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_MERGES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_MODEL_1.json b/datasets/INTEXA_MODEL_1.json index 3ac989f6a9..07b2fc5d5d 100644 --- a/datasets/INTEXA_MODEL_1.json +++ b/datasets/INTEXA_MODEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_MODEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_O3SONDES_1.json b/datasets/INTEXA_O3SONDES_1.json index 331247b694..1f0df772b6 100644 --- a/datasets/INTEXA_O3SONDES_1.json +++ b/datasets/INTEXA_O3SONDES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_O3SONDES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_PROTEUS_AIRCRAFT_1.json b/datasets/INTEXA_PROTEUS_AIRCRAFT_1.json index ea045ea6c5..422a1d31fa 100644 --- a/datasets/INTEXA_PROTEUS_AIRCRAFT_1.json +++ b/datasets/INTEXA_PROTEUS_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_PROTEUS_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_SATELLITE_1.json b/datasets/INTEXA_SATELLITE_1.json index f2e6ae4f4c..b5729c86c5 100644 --- a/datasets/INTEXA_SATELLITE_1.json +++ b/datasets/INTEXA_SATELLITE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_SATELLITE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXA_TRAJECTORY_1.json b/datasets/INTEXA_TRAJECTORY_1.json index f55a1e2b54..721ccddfc3 100644 --- a/datasets/INTEXA_TRAJECTORY_1.json +++ b/datasets/INTEXA_TRAJECTORY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXA_TRAJECTORY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intercontinental Chemical Transport Experiment - North America Phase A (INTEX-A) is an integrated atmospheric field experiment performed over North America. The study seeks to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and their impact on air quality and climate. A particular focus in this study is to quantify and characterize the inflow and outflow of pollution over North America. The main constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. INTEX-NA is part of a larger international ITCT (Intercontinental Transport and Chemical Transformation) initiative. INTEX-NA goals are greatly facilitated and enhanced by a number of concurrent and coordinated national and international field campaigns and satellite observations. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of a hierarchy of models is an important goal of INTEX-NA.", "links": [ { diff --git a/datasets/INTEXB_Be200_AIRCRAFT_1.json b/datasets/INTEXB_Be200_AIRCRAFT_1.json index c0ed65f475..a367d2dd54 100644 --- a/datasets/INTEXB_Be200_AIRCRAFT_1.json +++ b/datasets/INTEXB_Be200_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_Be200_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_C130_AIRCRAFT_1.json b/datasets/INTEXB_C130_AIRCRAFT_1.json index 05da197d55..7e2d876025 100644 --- a/datasets/INTEXB_C130_AIRCRAFT_1.json +++ b/datasets/INTEXB_C130_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_C130_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_Cessna_AIRCRAFT_1.json b/datasets/INTEXB_Cessna_AIRCRAFT_1.json index 0814e640a0..779930b117 100644 --- a/datasets/INTEXB_Cessna_AIRCRAFT_1.json +++ b/datasets/INTEXB_Cessna_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_Cessna_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_DC8_AIRCRAFT_1.json b/datasets/INTEXB_DC8_AIRCRAFT_1.json index 2cbc766c16..528fcf2804 100644 --- a/datasets/INTEXB_DC8_AIRCRAFT_1.json +++ b/datasets/INTEXB_DC8_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_DC8_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_Duchess_AIRCRAFT_1.json b/datasets/INTEXB_Duchess_AIRCRAFT_1.json index 70c0570782..6844f61528 100644 --- a/datasets/INTEXB_Duchess_AIRCRAFT_1.json +++ b/datasets/INTEXB_Duchess_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_Duchess_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_GROUND_1.json b/datasets/INTEXB_GROUND_1.json index 44702bc9e8..386a8017cb 100644 --- a/datasets/INTEXB_GROUND_1.json +++ b/datasets/INTEXB_GROUND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_GROUND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_J31_AIRCRAFT_1.json b/datasets/INTEXB_J31_AIRCRAFT_1.json index a9b6545c78..8d5d464c9b 100644 --- a/datasets/INTEXB_J31_AIRCRAFT_1.json +++ b/datasets/INTEXB_J31_AIRCRAFT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_J31_AIRCRAFT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_MERGES_1.json b/datasets/INTEXB_MERGES_1.json index 1f8eb08da6..44f82c3111 100644 --- a/datasets/INTEXB_MERGES_1.json +++ b/datasets/INTEXB_MERGES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_MERGES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_MODEL_1.json b/datasets/INTEXB_MODEL_1.json index 71c841a5d0..abb8d76385 100644 --- a/datasets/INTEXB_MODEL_1.json +++ b/datasets/INTEXB_MODEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_MODEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_O3SONDES_1.json b/datasets/INTEXB_O3SONDES_1.json index ba50b75ea0..41c12e6e23 100644 --- a/datasets/INTEXB_O3SONDES_1.json +++ b/datasets/INTEXB_O3SONDES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_O3SONDES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_SATELLITE_1.json b/datasets/INTEXB_SATELLITE_1.json index b3cf82b4de..60e8472480 100644 --- a/datasets/INTEXB_SATELLITE_1.json +++ b/datasets/INTEXB_SATELLITE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_SATELLITE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.", "links": [ { diff --git a/datasets/INTEXB_TRAJECTORY_1.json b/datasets/INTEXB_TRAJECTORY_1.json index f950e7f4fa..9ee7ec81df 100644 --- a/datasets/INTEXB_TRAJECTORY_1.json +++ b/datasets/INTEXB_TRAJECTORY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTEXB_TRAJECTORY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to: quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; validate and refine satellite observations of tropospheric composition; and map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks. Data collection for INTEX-B is now complete.", "links": [ { diff --git a/datasets/INTRO_0.json b/datasets/INTRO_0.json index 41c11fc7ae..d362e7cacc 100644 --- a/datasets/INTRO_0.json +++ b/datasets/INTRO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "INTRO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of the project was to study the relationships between intermediate trophic level organisms (predominantly zooplankton and small epipelagic and mesopelagic fish), primary production, and physical processes at oceanic frontal structures.", "links": [ { diff --git a/datasets/IOCAM0_1.json b/datasets/IOCAM0_1.json index 49c27857a9..f2cbab8aca 100644 --- a/datasets/IOCAM0_1.json +++ b/datasets/IOCAM0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IOCAM0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw images and associated aircraft position and attitude data, taken over Antarctica and Greenland by the Continuous Airborne Mapping By Optical Translator (CAMBOT), part of the Airborne Topographic Mapper (ATM) instrument suite. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n\nThe images are provided as JPEG files (.jpg). Ancillary data (e.g., positioning information) are provided as ASCII text files (.csv), which are available as a single zip file named IOCAM0_metadata.zip.", "links": [ { diff --git a/datasets/IOCAM1B_1.json b/datasets/IOCAM1B_1.json index 17764b0edf..054e430666 100644 --- a/datasets/IOCAM1B_1.json +++ b/datasets/IOCAM1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IOCAM1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains images taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) over Antarctica and Greenland.", "links": [ { diff --git a/datasets/IOCAM1B_2.json b/datasets/IOCAM1B_2.json index 597947c42a..3fecd5932d 100644 --- a/datasets/IOCAM1B_2.json +++ b/datasets/IOCAM1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IOCAM1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains high-resolution imagery taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) system over Antarctica and Greenland. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IODCC0_1.json b/datasets/IODCC0_1.json index 5073d14525..4883066d8b 100644 --- a/datasets/IODCC0_1.json +++ b/datasets/IODCC0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IODCC0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains camera calibration reports for IceBridge Digital Mapping System (DMS) missions flown over Antarctica and Greenland.", "links": [ { diff --git a/datasets/IODEM3_1.json b/datasets/IODEM3_1.json index 96090932a5..f465ef1b32 100644 --- a/datasets/IODEM3_1.json +++ b/datasets/IODEM3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IODEM3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set represents a collection of digital elevation models (DEMs) obtained by processing Operation IceBridge DMS stereo images and lidar data using the NASA Ames Stereo Pipeline.", "links": [ { diff --git a/datasets/IODIM3_1.json b/datasets/IODIM3_1.json index da3ec6a187..89ed7d4286 100644 --- a/datasets/IODIM3_1.json +++ b/datasets/IODIM3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IODIM3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set represents a collection of orthorectified images obtained by processing Operation IceBridge DMS stereo images and lidar data using the NASA Ames Stereo Pipeline.", "links": [ { diff --git a/datasets/IODMS1B_1.json b/datasets/IODMS1B_1.json index 1f28a8432a..4d148ed78a 100644 --- a/datasets/IODMS1B_1.json +++ b/datasets/IODMS1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IODMS1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-1B imagery taken from the Digital Mapping System (DMS) over Greenland and Antarctica. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IODMS3_1.json b/datasets/IODMS3_1.json index d1cd1b8501..45a252f307 100644 --- a/datasets/IODMS3_1.json +++ b/datasets/IODMS3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IODMS3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IceBridge DMS L3 Photogrammetric DEM (IODMS3) data set contains gridded digital elevation models and orthorectified images of Greenland and Antarctica derived from the Digital Mapping System (DMS). The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IOFFE_0.json b/datasets/IOFFE_0.json index dc45e79bc5..d2abd0e6c2 100644 --- a/datasets/IOFFE_0.json +++ b/datasets/IOFFE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IOFFE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the Akademik Ioffe Russian vessel along a Atlantic Meridional Transect in 2001 to 2002.", "links": [ { diff --git a/datasets/IOLVIS1A_1.json b/datasets/IOLVIS1A_1.json index 997b9a3c46..6d590947b9 100644 --- a/datasets/IOLVIS1A_1.json +++ b/datasets/IOLVIS1A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IOLVIS1A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geotagged images taken over Greenland and Antarctica by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IPAB_1.json b/datasets/IPAB_1.json index bf10412c1d..3f4beae810 100644 --- a/datasets/IPAB_1.json +++ b/datasets/IPAB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IPAB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The International Programme for Antarctic Buoys (IPAB) is run by the World Climate Research Programme (WCRP). IPAB is a self-sustaining project of the WCRP, and provides a link between institutions with Antarctic and Southern Ocean interests. IPAB was formally established, following a one year pilot phase, at a meeting in Helsinki, Finland in June 1994. IPAB aims to establish and maintain a network of drifting buoys in the Antarctic sea-ice zone, which monitor ice motion, pressure and temperature. In 1997, 16 organisations, representing 11 countries, were involved in the IPAB programme, including: Alfred Wegener Institute, Antarctic CRC, Australian Antarctic Division, British Antarctic Survey, Commonwealth Bureau of Meteorology, INPE -National Institute for Space Research, Institute for Marine Research and University of Helsinki, Hydrographic Department, Maritime Safety Agency, National Ice Center, National Institute of Polar Research, Programma Nazionale di Ricerche in Antardtide, Scott Polar Research Institute, Service Argos, South African Weather Bureau, United Kingdom Meteorological Office, and World Data Center A Glaciology. Tables of data availability, information, experiment details, literature, and data sets are available from the IPAB home page. Links are also available to databases held by other organisations, and links to Arctic and Indian Ocean buoy databases.\n\nThe data are available via several provided URLs. Further information and the data can be obtained from the IPAB home page URL. The data and documentation are also available directly from the NSIDC website. Finally, an older copy of the data are also held locally on the Australian Antarctic Data Centre's servers.\n\nThe documentation held at the NSIDC website provides important information on interpreting the dataset. A static copy of this document is included with the local copy of the dataset held on the Australian Antarctic Data Centre's servers.\n\nData from January 1995 to July 1998 only has been made available on the NSIDC website (and correspondingly on the AADC's servers). More data should be available soon.\n\nThis work was also completed as part of ASAC projects 732, 742 and 2678.\n\nThe fields in this dataset are:\nBuoy Number\nYear\nTime\nLongitude\nLatitude\nARGOS Positional Accuracy\nSea Ice Flag\nAir Pressure\nAir Temperature\nWater Temperature\nVelocity", "links": [ { diff --git a/datasets/IPFLR1B_1.json b/datasets/IPFLR1B_1.json index e68e9a64f8..c720b02c43 100644 --- a/datasets/IPFLR1B_1.json +++ b/datasets/IPFLR1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IPFLR1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains flight reports from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. Flight reports contain information on region, mission, aircraft model, flight data, purpose of flight, and on-board sensors. The flight reports were collected as part of Operation IceBridge funded aircraft survey campaigns.\n\nThe corresponding flight lines can be found in the IceBridge L1B Thinned Flight Lines (IPFLT1B) data set.", "links": [ { diff --git a/datasets/IPFLT1B_1.json b/datasets/IPFLT1B_1.json index 9becbe7959..29cbaacc9a 100644 --- a/datasets/IPFLT1B_1.json +++ b/datasets/IPFLT1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IPFLT1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains simplified, or thinned, flight lines from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. The thinning was performed using a Python library called Shapely. The full resolution flight line data were collected as part of Operation IceBridge funded aircraft survey campaigns. The following input data sets were used to generate this data set:\n\n* IceBridge LVIS POS/AV L1B Corrected Position and Attitude Data, Version 1\n* IceBridge POS/AV L1B Corrected Position and Attitude Data, Version 1\n* IceBridge UAF GPS/IMU L1B Corrected Position and Attitude Data, Version 1\n* IceBridge GPS L1B Time-Tagged Real-Time Position and Attitude Solution, Version 1\n\nThe corresponding flight reports can be found in the IceBridge L1B Flight Reports (IPFLR1B) data set.", "links": [ { diff --git a/datasets/IPS_IONOSONDE_1.json b/datasets/IPS_IONOSONDE_1.json index b702ccbd7e..016e975a38 100644 --- a/datasets/IPS_IONOSONDE_1.json +++ b/datasets/IPS_IONOSONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IPS_IONOSONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Routine high latitude observations made in Antarctica form a climatological baseline of the ionosphere extending back to 1958 at Mawson and Casey, and for almost two decades at Davis and Macquarie Island. The ionosonde data collection program operates so that the ionosphere is sampled sufficiently often that changes can be unambiguously identified. Each data sample is called an ionogram and is the raw data collected by the ionosonde. Ionograms are reduced to a series of Internationally agreed tabulations. This process ensures the long-term integrity of the data set. The tabulated data are: fmin, foE, foF1, foF2, fxI, h'E, h'F, h'F2, M(3000)F2, foEs, fbEs, h'Es, type of Es. The scaling conventions ensure that these scaled values also contain additional information on radiowave scattering in the F region and the effects of absorption and interference using a range of descriptive and qualifying terms with the scaled values. The tabulated data are available from World Data Centers. Data tabulated prior to 1985 is available on the WDC CDROM of ionospheric data.", "links": [ { diff --git a/datasets/IR1HI1B_1.json b/datasets/IR1HI1B_1.json index 151fbe9eba..2b99fa842a 100644 --- a/datasets/IR1HI1B_1.json +++ b/datasets/IR1HI1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IR1HI1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 1 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IR1HI2_1.json b/datasets/IR1HI2_1.json index ba83c31920..718cd589d1 100644 --- a/datasets/IR1HI2_1.json +++ b/datasets/IR1HI2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IR1HI2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IR2HI1B_1.json b/datasets/IR2HI1B_1.json index 852f9f31c4..fdadd29424 100644 --- a/datasets/IR2HI1B_1.json +++ b/datasets/IR2HI1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IR2HI1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 2 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IR2HI2_1.json b/datasets/IR2HI2_1.json index 2838dafc5b..8a4b6d6bdf 100644 --- a/datasets/IR2HI2_1.json +++ b/datasets/IR2HI2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IR2HI2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.", "links": [ { diff --git a/datasets/IRACC1B_2.json b/datasets/IRACC1B_2.json index 252a43ca1b..50e9a66113 100644 --- a/datasets/IRACC1B_2.json +++ b/datasets/IRACC1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRACC1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains radar echograms taken over Greenland and Antarctica using the Center for Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar instrument. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IRARES1B_1.json b/datasets/IRARES1B_1.json index 21128feee6..5529af6adc 100644 --- a/datasets/IRARES1B_1.json +++ b/datasets/IRARES1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRARES1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains radar echograms acquired by the Arizona Radio-Echo Sounder (ARES) over select glaciers in Alaska. The data are provided in HDF5 formatted files, which include important metadata for interpreting the data. Browse images are also available.", "links": [ { diff --git a/datasets/IRISN4RAD_001.json b/datasets/IRISN4RAD_001.json index 23bceb8538..33aed7bdf6 100644 --- a/datasets/IRISN4RAD_001.json +++ b/datasets/IRISN4RAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRISN4RAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-4 Infrared Interferometer Spectrometer (IRIS) Level 1 Radiance Data contain thermal emissions of the Earth's atmosphere at wave numbers between 400 and 1600 cm**-1, with a nominal resolution of 2.8 cm**-1. The data also contain documentation information, reference calibration, average instrument temperatures, and a summary for each orbital pass. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, and each file contains an entire day of measurements. The product contains data from April 9, 1970 (day of year 99) through Jan 31, 1971 (day of year 31).\n\nThe IRIS instrument was designed to provide information on the vertical structure of the atmosphere and on the emissive properties of the earth's surface by measuring the surface and atmospheric radiation in the 6.25 to 25 micrometer range using a modified Michelson interferometer. IRIS viewed along the satellite track direction with a spatial resolution of 94 km at nadir. A Fourier transform was applied to the interferograms to produce thermal emmision spectra of the Earth which could be used to derive vertical profiles of temperature, water vapor, and ozone, as well as other parameters of meteorological interest. The Infrared Interferometer Spectrometer (IRIS) experiment on Nimbus-4 is a follow on experiment to the Nimbus-3 IRIS experiment. The IRIS Principal Investigator was Dr. Rudolf A. Hanel.\n\nThese data were previously archived at NASA NSSDC under the entry ID ESAD-00093 (originally 70-025A-03A).", "links": [ { diff --git a/datasets/IRKUB1B_2.json b/datasets/IRKUB1B_2.json index 285ccef718..4bfca3d991 100644 --- a/datasets/IRKUB1B_2.json +++ b/datasets/IRKUB1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRKUB1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains elevation and surface measurements over Greenland, the Arctic, and Antarctica, as well as flight path charts and echogram images acquired using the Center for Remote Sensing of Ice Sheets (CReSIS) Ku-Band Radar Altimeter.", "links": [ { diff --git a/datasets/IRMCR1B_2.json b/datasets/IRMCR1B_2.json index 940502b5da..d2d36e6532 100644 --- a/datasets/IRMCR1B_2.json +++ b/datasets/IRMCR1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRMCR1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra Multichannel Coherent Radar Depth Sounder (MCoRDS) over land and sea ice in the Arctic and Antarctic.", "links": [ { diff --git a/datasets/IRMCR2_1.json b/datasets/IRMCR2_1.json index a3b29e2fec..20c255315b 100644 --- a/datasets/IRMCR2_1.json +++ b/datasets/IRMCR2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRMCR2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness for Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IRMCR3_2.json b/datasets/IRMCR3_2.json index c07c075528..a4485d35ac 100644 --- a/datasets/IRMCR3_2.json +++ b/datasets/IRMCR3_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRMCR3_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains products from depth sounder measurements over Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of NASA Operation IceBridge funded campaigns. Browse files for this data set are duplicates for the thickness PNG files.", "links": [ { diff --git a/datasets/IRPAR2_1.json b/datasets/IRPAR2_1.json index c7298fa3e8..5b3d5250a1 100644 --- a/datasets/IRPAR2_1.json +++ b/datasets/IRPAR2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRPAR2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains contains Greenland ice thickness measurements acquired using the Pathfinder Advanced Radar Ice Sounder (PARIS).The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IRS-1.archive_5.0.json b/datasets/IRS-1.archive_5.0.json index c58d43eb96..20b80561c7 100644 --- a/datasets/IRS-1.archive_5.0.json +++ b/datasets/IRS-1.archive_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRS-1.archive_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "IRS-1C/1D dataset is composed of products generated by the Indian Remote Sensing (IRS) Satellites 1C/1D PAN sensor. The products, acquired from 1996 to 2004 over Europe, are radiometrically and ortho corrected level 1 black and white images at 5 metre resolution and cover an area of up to 70 x 70 km. Sensor: PAN Type: Panchromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected Ortho corrected (DN) Acquisition in Neustrelitz: 1996 - 2004 5 70 x 70 X X Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/IRS1/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/IRS-1C_1D.Full.archive_5.0.json b/datasets/IRS-1C_1D.Full.archive_5.0.json index 6cc2219cf6..cf33166914 100644 --- a/datasets/IRS-1C_1D.Full.archive_5.0.json +++ b/datasets/IRS-1C_1D.Full.archive_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRS-1C_1D.Full.archive_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following products are available \u2022 PAN: Panchromatic, resolution 5 m, Coverage 70 km x 70 km, radiometrically and ortho (DN) corrected, \u2022 LISS-III: Multi-spectral, resolution 25 m, Coverage 140 km x 140 km, radiometrically and ortho (DN) corrected (ortho delivered without Band 5) \u2022 WiFS: Multi-spectral, resolution 180 m, Coverage 800 km x 800 km, radiometrically and ortho (DN) corrected Sensor: PAN, Type: Panchromatic, Resolution (m): 5, Coverage (km x km): 70 x 70, System or radiometrically corrected, Ortho corrected (DN), Global archive: 1996 \u2013 2007 (IRS-1C) and 1998 \u2013 2009 (IRS-1D) Sensor: LISS-III, Type: Multi-spectral, Resolution (m): 25, Coverage (km x km): 140 x 140, System or radiometrically corrected, Ortho corrected (DN) (without band 5), Global archive: 1996 \u2013 2007 (IRS-1C) and 1998 \u2013 2009 (IRS-1D) Sensor: WiFS, Type: Multi-spectral, Resolution (m): 180, Coverage (km x km): 800 x 800, System or radiometrically corrected, Ortho corrected (DN), Global archive: 1996 \u2013 2007 (IRS-1C) and 1998 \u2013 2009 (IRS-1D) Note: \u2022 Whether system corrected or radiometrically corrected products are available depends on sensor and processing centre \u2022 For PAN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable quality \u2022 For WiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. 'IRS-1C/1D Full archive' collection has worldwide coverage: data can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf).", "links": [ { diff --git a/datasets/IRSNO1B_2.json b/datasets/IRSNO1B_2.json index 4961cb6c7b..9cde82ae20 100644 --- a/datasets/IRSNO1B_2.json +++ b/datasets/IRSNO1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRSNO1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra wide-band snow radar over land and sea ice in the Arctic and Antarctic. In addition, airborne snow measurements were taken during 10 flights over Alaska mountains, ice fields, and glaciers at the end of May 2018 by a compact CReSIS FMCW radar system installed on a Single Otter aircraft. The data were collected as part of Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IRTIT3_2.json b/datasets/IRTIT3_2.json index 84e41c2250..0244d84cde 100644 --- a/datasets/IRTIT3_2.json +++ b/datasets/IRTIT3_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRTIT3_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-3 tomographic ice thickness measurements and ice thickness errors over areas of Greenland and Antarctica. Two of the data files additionally provide bed elevation measurements. The data were derived from measurements taken by the Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Radar Depth Sounder (MCoRDS) instrument and were collected as part of NASA Operation IceBridge funded campaigns.", "links": [ { diff --git a/datasets/IRUAFHF1B_1.json b/datasets/IRUAFHF1B_1.json index 47954596d3..a281a0a039 100644 --- a/datasets/IRUAFHF1B_1.json +++ b/datasets/IRUAFHF1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRUAFHF1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains radar echograms acquired by the University of Alaska Fairbanks High-Frequency Radar Sounder over select glaciers in Alaska. The data are provided in HDF5 formatted files, which include important metadata for interpreting the data. Browse images are also available.", "links": [ { diff --git a/datasets/IRWIS2_1.json b/datasets/IRWIS2_1.json index 60690bc64d..1bd6fa933f 100644 --- a/datasets/IRWIS2_1.json +++ b/datasets/IRWIS2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IRWIS2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains depth sounder measurements of elevation, surface, bottom, and thickness for Alaska taken from the Warm Ice Sounding Explorer (WISE). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.", "links": [ { diff --git a/datasets/IS2ATBABD_1.json b/datasets/IS2ATBABD_1.json index 327f2d6c2b..83c5850ff0 100644 --- a/datasets/IS2ATBABD_1.json +++ b/datasets/IS2ATBABD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2ATBABD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a quality-filtered set of ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 5 (ATL08) observations of relative canopy heights and aboveground biomass density model results for circumpolar boreal forests. The data were collected at 30 m along-track segment lengths for strong beams only during the 2019\u20132021 high northern latitude growing seasons. The ATL08 point observations were clipped to the extent of the boreal forest spatial domain.", "links": [ { diff --git a/datasets/IS2GZANT_1.json b/datasets/IS2GZANT_1.json index 1c12a60895..c494a44e3e 100644 --- a/datasets/IS2GZANT_1.json +++ b/datasets/IS2GZANT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2GZANT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an Antarctic ice shelf grounding zone geolocation product, including the landward limit of ice flexure caused by ocean tidal movement (Point F), the seaward limit of ice flexure (Point H), and the break in surface slope (Point Ib) based on the ATLAS/ICESat-2 ATL06 Land Ice Height data set acquired between March 2019 and September 2020. The grounding zone estimates were derived from automated techniques using ICESat-2 repeat tracks.", "links": [ { diff --git a/datasets/IS2MPDDA_3.json b/datasets/IS2MPDDA_3.json index 3e03abfe6e..0c9227283c 100644 --- a/datasets/IS2MPDDA_3.json +++ b/datasets/IS2MPDDA_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2MPDDA_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides locations and depths of melt ponds in the Multi-Year Arctic Sea Ice Region, calculated from ATLAS/ICESat-2 L2A Global Geolocated Photon Data, Version 5 (ATL03) using an autoadaptive algorithm.", "links": [ { diff --git a/datasets/IS2OLVIS1BCV_1.json b/datasets/IS2OLVIS1BCV_1.json index 8851547c08..a8d3c6b98e 100644 --- a/datasets/IS2OLVIS1BCV_1.json +++ b/datasets/IS2OLVIS1BCV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2OLVIS1BCV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains georeferenced imagery from the NASA Land, Vegetation, and Ice Sensor (LVIS) PhaseOne medium-format camera, which was operated on high-altitude segments of flights during the ICESat-2 2022 Arctic Summer calibration campaign.", "links": [ { diff --git a/datasets/IS2SITDAT4_001.json b/datasets/IS2SITDAT4_001.json index a46903085c..751c8830d5 100644 --- a/datasets/IS2SITDAT4_001.json +++ b/datasets/IS2SITDAT4_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2SITDAT4_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports daily, along-track winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.", "links": [ { diff --git a/datasets/IS2SITMOGR4_3.json b/datasets/IS2SITMOGR4_3.json index 668c0cab0d..f871873d1e 100644 --- a/datasets/IS2SITMOGR4_3.json +++ b/datasets/IS2SITMOGR4_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IS2SITMOGR4_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) Version 6 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.", "links": [ { diff --git a/datasets/ISCCP_B3_NAT_1.json b/datasets/ISCCP_B3_NAT_1.json index 34db923931..03b75c2a67 100644 --- a/datasets/ISCCP_B3_NAT_1.json +++ b/datasets/ISCCP_B3_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_B3_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ISCCP_B3_NAT data is the International Satellite Cloud Climatology Project (ISCCP) Stage B3 Reduced Radiances in Native Format data product. This is the original radiance data, sampled to 30 Km and 3-hour spacing. Data collection for this product is complete and was collected using several instruments on multiple platforms, please see the instrument and platform list of this record for a comprehensive list. The normalization of all radiances to a standard calibration made these data a globally uniform set of measurements that can be used for detailed cloud process studies.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.", "links": [ { diff --git a/datasets/ISCCP_D1_1.json b/datasets/ISCCP_D1_1.json index 148bd14b6b..4cdbfe8913 100644 --- a/datasets/ISCCP_D1_1.json +++ b/datasets/ISCCP_D1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_D1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ISCCP_D1_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Products - Revised Algorithm data set in Hierarchical Data Format. This data set contains 3-hourly, 280 KM equal-area grid data from various polar and geostationary satellites. The Gridded Cloud Product contents are spatial averages of DX quantities and statistical summaries, including properties of cloud types. Satellites are merged into a global grid. Atmosphere and surface properties from TOVS are appended. Data collection for this data set is complete. \r\n\r\nISCCP, the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nStarting in 1983 an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.", "links": [ { diff --git a/datasets/ISCCP_D2_1.json b/datasets/ISCCP_D2_1.json index a2476e6ed3..386f9c3b19 100644 --- a/datasets/ISCCP_D2_1.json +++ b/datasets/ISCCP_D2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_D2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ISCCP_D2 data set contains monthly, 280 KM equal-area grid data from various polar and geostationary satellites. Climatological Summary Product contents contain monthly average of D1 quantities including mean diurnal cycle, distribution and properties of total cloudiness and cloud types.\r\n\r\nThe International Satellite Cloud Climatology Project (ISCCP), the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983): \r\n- To produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived. \r\n- To stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models. \r\n- To promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combines satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determines the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.", "links": [ { diff --git a/datasets/ISCCP_DX_1.json b/datasets/ISCCP_DX_1.json index 24d7aa681d..af7ad4a14e 100644 --- a/datasets/ISCCP_DX_1.json +++ b/datasets/ISCCP_DX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_DX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ISCCP_DX_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage DX Pixel Level Cloud Product - Revised Algorithm in Binary Format data set. It contains 3-hourly, 30 KM satellite image projection data from various polar and geostationary satellites. Pixel Level Cloud Product contents include calibrated radiances, cloud detection results, and cloud and surface properties from radiative analysis. Data collection for this data set is complete.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to:\r\nproduce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle.\r\n\r\nStarting in 1983, an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis was also concerned with improving understanding of clouds in the global hydrological cycle.\r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985, Rossow et al. 1987) with the Tiros Operational Vertical Sounder (TOVS) atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of or absence of clouds in each individual image pixel and retrieved the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.", "links": [ { diff --git a/datasets/ISCCP_ICESNOW_NAT_1.json b/datasets/ISCCP_ICESNOW_NAT_1.json index 721d7e3358..f2be9e6c58 100644 --- a/datasets/ISCCP_ICESNOW_NAT_1.json +++ b/datasets/ISCCP_ICESNOW_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_ICESNOW_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ISCCP_ICESNOW_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) Ice Snow Product in Native Data Format data set. It is a merged product containing separate snow and sea ice data sets. The values given are fractional coverage for 5-day intervals. Data collection for this data set is complete. \r\n\r\nThe data were collected on a global equal-area grid with the cell area equivalent to 1 degree latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 degree longitude) and the South Pole as a cell corner. Ice/Snow Data Set contents included 5-day averages of snow and sea ice fractional coverage. Snow is deduced from visible satellite imagery plus ground data while sea ice was deduced from ship/shore and visible satellite imagery, and microwave measurements. \r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.", "links": [ { diff --git a/datasets/ISCCP_TOVS_NAT_1.json b/datasets/ISCCP_TOVS_NAT_1.json index 17d2c420be..a131d70f4a 100644 --- a/datasets/ISCCP_TOVS_NAT_1.json +++ b/datasets/ISCCP_TOVS_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISCCP_TOVS_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ISCCP_TOVS_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) TIROS Operational Vertical Sounder (TOVS) data set in the Native Data Format. It is a daily, global description of the ozone, temperature, and humidity distributions obtained from the analysis of data from the TOVS system. The TOVS data set contents include atmosphere and surface data including temperature structure, water, and ozone abundances obtained from the TOVS product and supplemented by two climatologies. The ISCCP_TOVS_NAT data set contains information concerning the atmospheric temperature and humidity profiles as well as the ozone column abundance. Data collection for this data set is complete.\r\n\r\nThis data set is composed of 3 types of data files: CLIM MONTHLY, which contains the monthly climatological data obtained from balloon observations; TOVS MONTHLY, which contains the monthly climatological data computed from the daily TOVS values; and TOVS DAILY, which contains the daily composite of the TOVS Sounding Product. The data was collected on a global equal-area grid with the cell area equivalent to 2.5 degrees latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 deg. longitude) and the South Pole as a cell corner. \r\n\r\nThe TOVS system flew on the NOAA Operational Polar Orbiting Satellite series. Measurements from the High Resolution Infrared Radiation Sounder (HIRS/2), the Stratospheric Sounding Unit (SSU), and the Microwave Sounding Unit (MSU) were processed by NOAA to produce the TOVS Sounding Product.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.", "links": [ { diff --git a/datasets/ISERV_1.json b/datasets/ISERV_1.json index 6270572e95..533ae9edd0 100644 --- a/datasets/ISERV_1.json +++ b/datasets/ISERV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISERV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions.", "links": [ { diff --git a/datasets/ISLSCP_919_1.json b/datasets/ISLSCP_919_1.json index 77f317f039..29a458e02d 100644 --- a/datasets/ISLSCP_919_1.json +++ b/datasets/ISLSCP_919_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISLSCP_919_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models. All of the data have been processed to the same global spatial resolution (1 deg. x 1 deg.), using the same land/sea mask and steps have been taken to ensure spatial and temporal continuity of the data. The data sets cover the period 1987-1988 at 1-month time resolution for most of the seasonally varying quantities. For this pre-LBA data set, the ISLSCP I data are provided as global coverages. The companion file illustrations were subset over the LBA study area, from 35-85 deg. W longitude and 20 deg. S to 10 deg. N latitude, as shown in Figure 1.The data files and illustrations are organized into the three groups listed below.1. Hydrology and Soils2. Radiation and Clouds3. VegetationThe data within each of these areas were acquired from a variety of sources including model output, satellites, and ground measurements. The individual data sets were provided in a variety of forms. In some cases, this required the data publication team to regrid and reformat data sets and in others to produce monthly averages from finer resolution data. The specific processing for each data set is detailed in the documentation. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but are now archived individually. ", "links": [ { diff --git a/datasets/ISPOL2004_AAD_BuoyData_1.json b/datasets/ISPOL2004_AAD_BuoyData_1.json index abba02df45..c0137998df 100644 --- a/datasets/ISPOL2004_AAD_BuoyData_1.json +++ b/datasets/ISPOL2004_AAD_BuoyData_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISPOL2004_AAD_BuoyData_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice Station POLarstern [ISPOL] was a multi-national, interdisciplinary study coordinated by the Alfred Wegener Institute for Polar and Marine Research, Germany, involving scientists from different institutes and nations across a range of scientific disciplines. ISPOL had been planned as a 50-day drift station in the Western Weddell Sea. Due to particularly heavy sea-ice conditions, the start of the drifting ice station was delayed, so that the drift interval, originating at -68 degrees 10'N, -54 degrees 46'W, lasted only a total of 35 days (28.11.2004 - 01.01.2005).\n \nData and auxiliary information presented here are on the sea-ice drift and deformation experiment, which was a collaborative research program involving the International Arctic Research Center [IARC] at the University of Alaska Fairbanks, the Australian Antarctic Division [AAD], the Finnish Institute of Marine Research [FIMR] and the Alfred Wegener Institute [AWI]. Buoy contributions came from all four institutions listed above. - This metadata record covers only AAD buoy data from the ISPOL 2004 experiment.\n \nTo estimate the characteristics of the sea-ice drift and dynamics in the Western Weddell Sea a meso-scale array of 26 drifting ice buoys was deployed for about 30 days during late November and December 2004. Sea-ice drift was obtained from the horizontal GPS-derived location measurements, which were made at all buoys but collected at various temporal resolutions and different spatial accuracies. Auxiliary instruments were attached to some of the sea-ice drifters, including temperature probes for air and sea-ice temperatures, and air pressure sensors. Four of the buoys were left in the ice pack after the end of the ISPOL field phase to record the large-scale drift in the region around the ice station from late summer into winter.\n \nSee the metadata record 'Ice Station Polarstern. Aerial photographs over sea ice taken during the ISLOP project' for more information on the ISPOL project. Also, see the URL given below for the ISPOL home page.", "links": [ { diff --git a/datasets/ISSITGR4_1.json b/datasets/ISSITGR4_1.json index 3edf47fa0d..b74aabb484 100644 --- a/datasets/ISSITGR4_1.json +++ b/datasets/ISSITGR4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ISSITGR4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports seasonal gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ICESat/GLAS L3A Sea Ice Freeboard data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) Version 1.1 snow loading.\n\nThis data set is a historical complement to ICESat-2 L4 Monthly Gridded Sea Ice Thickness.", "links": [ { diff --git a/datasets/ITPRN5L1_001.json b/datasets/ITPRN5L1_001.json index 9d6c7e38f2..1fb6ba71f9 100644 --- a/datasets/ITPRN5L1_001.json +++ b/datasets/ITPRN5L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ITPRN5L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ITPRN5L1 is the Nimbus-5 Infrared Temperature Profile Radiometer (ITPR) Level-1 Calibrated Radiances data product which contains radiances at 7 infrared spectral regions (2683.0, 899.0, 747.0, 713.8, 689.5, 668.3, and 507.4 cm-1) in a single binary data file. Four are centered near the 15 micron CO2 band, one interval in the water vapor rotational band near 20 microns and two spectral intervals in the atmospheric window regions near 3.7 and 11 microns. The instrument scan sequence consists of three separate grid matrices, to the right, center and left of nadir. Each matrix consists of 10 scan lines with 14 scenes per scan. Each scan footprint is 32 km wide.\n\nDue to problems with the instrument, data are limited to three time periods from 14 February 1975 to 1 March 1975 covering East Asia, from 10 May 1976 to 4 June 1976 covering the United States and the Gulf of Mexico, and from 1 September 1976 to 30 September 1976 covering southern Australia and New Zealand. The principal investigator for the ITPR experiment was William L. Smith from NOAA.", "links": [ { diff --git a/datasets/IXBMI2AE_2.json b/datasets/IXBMI2AE_2.json index 1c2c188cbf..d6272e7cb5 100644 --- a/datasets/IXBMI2AE_2.json +++ b/datasets/IXBMI2AE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMI2AE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 Aerosol Product containing aerosol optical depth and particle type, with associated atmospheric data for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IXBMI2LS_2.json b/datasets/IXBMI2LS_2.json index fbd9d4f8ee..a90c6d4f8e 100644 --- a/datasets/IXBMI2LS_2.json +++ b/datasets/IXBMI2LS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMI2LS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 Land Surface product containing information on land directional reflectance properties,albedos(spectral & PAR integrated),FPAR,asssociated radiation parameters & terrain-referenced geometric parameters for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IXBMI2ST_2.json b/datasets/IXBMI2ST_2.json index c26f566179..3e6cc11231 100644 --- a/datasets/IXBMI2ST_2.json +++ b/datasets/IXBMI2ST_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMI2ST_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 2 TOA/Cloud Stereo Product containing the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IXBMIB2E_3.json b/datasets/IXBMIB2E_3.json index f18f53f0d2..23fbbffb0b 100644 --- a/datasets/IXBMIB2E_3.json +++ b/datasets/IXBMIB2E_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMIB2E_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Ellipsoid-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IXBMIB2T_3.json b/datasets/IXBMIB2T_3.json index f9404603ea..e849621f42 100644 --- a/datasets/IXBMIB2T_3.json +++ b/datasets/IXBMIB2T_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMIB2T_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Terrain-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IXBMIGEO_2.json b/datasets/IXBMIGEO_2.json index 2fb704d44e..334a68eada 100644 --- a/datasets/IXBMIGEO_2.json +++ b/datasets/IXBMIGEO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IXBMIGEO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid for the INTEXB_2006 theme.", "links": [ { diff --git a/datasets/IZIKO_Fish.json b/datasets/IZIKO_Fish.json index b6451450ad..994eada056 100644 --- a/datasets/IZIKO_Fish.json +++ b/datasets/IZIKO_Fish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IZIKO_Fish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The iziko South African Museum has a comprehensive holdings comprising of\n identified bony and cartilaginous fish, mostly from Cape waters, but extending\n to Angola and Mozambique and the Southern, Indian and Atlantic Oceans. \n \n It currently contains 15048 records of 293 families.", "links": [ { diff --git a/datasets/IZIKO_Marine_Mammals.json b/datasets/IZIKO_Marine_Mammals.json index e923c5cfbe..d03adc5577 100644 --- a/datasets/IZIKO_Marine_Mammals.json +++ b/datasets/IZIKO_Marine_Mammals.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IZIKO_Marine_Mammals", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The iziko South African Museum has a comprehensive collection of cetacean and\n Cape fur seal skeletal material. Skeletal material from other marine mammals is\n also held. Part of this collection is on exhibition in the museum's Whale Well.\n \n It currently contains 14484 records of 46 families.", "links": [ { diff --git a/datasets/IceMargin_79E-108E_1.json b/datasets/IceMargin_79E-108E_1.json index 8a67532ebe..da6d504370 100644 --- a/datasets/IceMargin_79E-108E_1.json +++ b/datasets/IceMargin_79E-108E_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IceMargin_79E-108E_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geographic location of the outer margin of the Antarctic ice cover for the sector between longitudes 79E and 108E, including margins of ice shelves, glaciers, and iceberg tongues. The data set does not in general include the grounding zone at the inland margin of the ice shelves or glaciers.\n\nThe margin was defined by interpretation of an image mosaic generated from Synthetic Aperture Radar data. The image mosaic was built using navigation data accompanying the SAR images to transform the images to a map projection. The image navigation data were adjusted so that overlapping images were registered to one another, the indivual images merged into a mosaic, and the overall process adjusted so that the mosaic was tied to the few ground control points available in this large sector. Two separate mosaics were used to span the whole sector.\n\nThe majority of the SAR data were acquired by the ERS-SAR instruments in August 1996, some ERS data were acquired in August 1993, and one Radarsat scene was acquired in September 1997. The data were pre-processed to produce a mosaic with a 100 m pixel size, and adjusted so that the majority of the coastline positions refer to the August 1996 epoch.\n\nThe location data are internally consistent, and extracted at nominally 200 m intervals. The external position accuracy is generally better than 600 m. The coverage is complete over the whole sector. The coordinate set includes some island/ice rise features. Two very large grounded icebergs are included.\n\nData are in an ascii arc/info export file format as geographic coordinates on the ITRF1996 system and contains attribute information.\n\nERS-SAR data, copyright ESA, 1993, 1996\nRadarsat data, copyright Canadian Space Agency, Agence spatiale canadienne, 1997.\n\nThis work was completed as part of ASAC projects 454, 1125 and 2224 (ASAC_454, ASAC_1125 and ASAC_2224).", "links": [ { diff --git a/datasets/Idaho_field_shrub_data_1503_1.json b/datasets/Idaho_field_shrub_data_1503_1.json index 5094af5074..54fa687347 100644 --- a/datasets/Idaho_field_shrub_data_1503_1.json +++ b/datasets/Idaho_field_shrub_data_1503_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Idaho_field_shrub_data_1503_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the results of the characterization of shrubland vegetation at two study areas in southern Idaho, USA: the Reynolds Creek Experimental Watershed (RCEW) and Hollister. Data were collected in September and October 2014. In each study area, several 10-m x 10-m plots were randomly established that are representative of the local dominant vegetation types. Measurements are reported for both plot and individual shrub attributes. Plot measurements include shrub density and biometric data, percent shrub cover derived from line intercept transects, percent plant species and bare ground cover derived from photo analysis, and average LAI. Measurements for selected individual shrubs include height, width, length, number of stems, and LAI. Leaf samples were collected for determining LAI, specific leaf area (SLA), carbon and nitrogen concentrations, and isotopic nitrogen and carbon.", "links": [ { diff --git a/datasets/Image2006_8.0.json b/datasets/Image2006_8.0.json index 5253081df8..304888405d 100644 --- a/datasets/Image2006_8.0.json +++ b/datasets/Image2006_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Image2006_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Image 2006 collection is a SPOT-4, SPOT-5 and ResourceSat-1 (also known as IRS-P6) cloud free coverage over 38 European countries in 2006 (from February 2005 to November 2007). The Level 1 data provided in this collection originate from the SPOT-4 HRVIR instrument (with 20m spatial resolution), from SPOT-5 HRG (with 10m spatial resolution resampled to 20m) and IRS-P6 LISS III (with 23m spatial resolution), each with four spectral bands. The swath is of about 60 km for the SPOT satellites and 140 km for the IRS-P6 satellite. In addition to the Level 1, the collection provides the same data geometrically corrected towards a European Map Projection with 25m resolution. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2006/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/Image2007_8.0.json b/datasets/Image2007_8.0.json index ee407530a0..afccc5840f 100644 --- a/datasets/Image2007_8.0.json +++ b/datasets/Image2007_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Image2007_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Image 2007 collection is composed by products acquired by Disaster Monitoring Constellation 1st generation (DMC-1) satellites over European countries (plus Turkey) in 2007. The data provided in this collection are 32m multispectral images captured by the DMC SLIM-6 imager sensor, with two processing levels: \u2022 L1R Band registered product derived from the L0R product \u2022 L1T Orthorectified product derived from the L1R product using manually collected GCPs from Landsat ETM+ data and SRTM DEM V31 data Data disseminated come from the following satellites belonging to DMC-1 constellation: \u2022 UK-DMC-1 \u2022 Bejing-1 \u2022 NigeriaSat-1 Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2007/ available on the Third Party Missions Dissemination Service.", "links": [ { diff --git a/datasets/Imnavait_Creek_Veg_Maps_1385_1.json b/datasets/Imnavait_Creek_Veg_Maps_1385_1.json index 6aacd9cb68..0efdae94bb 100644 --- a/datasets/Imnavait_Creek_Veg_Maps_1385_1.json +++ b/datasets/Imnavait_Creek_Veg_Maps_1385_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Imnavait_Creek_Veg_Maps_1385_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.", "links": [ { diff --git a/datasets/Imnavait_Creek_Veg_Plots_1356_1.json b/datasets/Imnavait_Creek_Veg_Plots_1356_1.json index 0ca606cefe..266bd834bf 100644 --- a/datasets/Imnavait_Creek_Veg_Plots_1356_1.json +++ b/datasets/Imnavait_Creek_Veg_Plots_1356_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Imnavait_Creek_Veg_Plots_1356_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental, soil, and vegetation data collected during the periods of August 1984 and August-September 1985 from 84 study plots at the Imnavait Creek research site. Imnavait Creek is located in a shallow basin at the foothills of the central Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 14 plant communities that occur in 19 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Imnavait Creek region and across Alaska.", "links": [ { diff --git a/datasets/InSAR_Prudhoe_Bay_1267_1.json b/datasets/InSAR_Prudhoe_Bay_1267_1.json index 5dfd162a58..162cfcd46f 100644 --- a/datasets/InSAR_Prudhoe_Bay_1267_1.json +++ b/datasets/InSAR_Prudhoe_Bay_1267_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "InSAR_Prudhoe_Bay_1267_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface. This data set includes the mean Remotely Sensed Active Layer Thickness (ReSALT) over years 1992 to 2000 for an area near Prudhoe Bay, Alaska. The data were produced by an Interferometric Synthetic Aperture Radar (InSAR) technique that measures seasonal surface subsidence and infers ALT. ReSALT estimates were validated by comparison with ground-based ALT measurements at multiple sites. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain.These data provide gridded (100-m) estimates of active layer thickness (cm; ALT), seasonal subsidence (cm) and subsidence trend (mm/yr), as well as calculated uncertainty in each of these parameters. This data set was developed in support of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign.The data are presented in one netCDF (*.nc) file. ", "links": [ { diff --git a/datasets/Insitu_Tower_Greenhouse_Gas_1798_1.json b/datasets/Insitu_Tower_Greenhouse_Gas_1798_1.json index 22fd80fd00..55a0b7bcbb 100644 --- a/datasets/Insitu_Tower_Greenhouse_Gas_1798_1.json +++ b/datasets/Insitu_Tower_Greenhouse_Gas_1798_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Insitu_Tower_Greenhouse_Gas_1798_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019. The measurement period varied by tower site. The Picarro analyzers continuously measured total CH4, isotopic ratio of CH4, CO2, CO, and other greenhouse gas concentrations. Not all species were measured at all sites. Complete tower location, elevation, instrument height, and date/time information are also provided. Determination of greenhouse gas fluxes and uncertainty bounds is essential for the evaluation of the effectiveness of mitigation strategies. These L1 data are raw instrument outputs from the Picarro instruments. A Level 2 (L2) product derived from this L1 data is available and generally would be the preferred data for most use cases.", "links": [ { diff --git a/datasets/Interior_Alaska_Subsistence_1725_1.json b/datasets/Interior_Alaska_Subsistence_1725_1.json index 60cbd4e9ca..19f24428de 100644 --- a/datasets/Interior_Alaska_Subsistence_1725_1.json +++ b/datasets/Interior_Alaska_Subsistence_1725_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Interior_Alaska_Subsistence_1725_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment.", "links": [ { diff --git a/datasets/Interpolated_Met_Products_1876_1.json b/datasets/Interpolated_Met_Products_1876_1.json index 24be79530f..6d33824a8d 100644 --- a/datasets/Interpolated_Met_Products_1876_1.json +++ b/datasets/Interpolated_Met_Products_1876_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Interpolated_Met_Products_1876_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides modeled meteorological conditions and tagged-CO tracer concentrations along ATom flight paths derived from the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation products from the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. The GMAO \"GEOS fp\" forward processing system ingests satellite, ground-based, and airborne data, using a sophisticated model along with the data's statistical properties to obtain global three-dimensional data gridded fields at regular time intervals. These data are from the GMAO model output that were fitted to the ATom flight tracks by interpolating the GMAO model output to the horizontal ATom flight tracks for each of the 4 ATom Deployments. The dataset also provides tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. The data products produced are consistent with both the original measurements and the physical laws governing the atmosphere. To provide some meteorological context for the ATom flights, the GEOS5 gridded data are interpolated in space and time to the flight tracks.", "links": [ { diff --git a/datasets/InundationMap_YkFlats_PeaceAth_1901_1.json b/datasets/InundationMap_YkFlats_PeaceAth_1901_1.json index eb2298055d..662f92b397 100644 --- a/datasets/InundationMap_YkFlats_PeaceAth_1901_1.json +++ b/datasets/InundationMap_YkFlats_PeaceAth_1901_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "InundationMap_YkFlats_PeaceAth_1901_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.", "links": [ { diff --git a/datasets/IronEx_0.json b/datasets/IronEx_0.json index 6a5126bf46..1539ac1ab4 100644 --- a/datasets/IronEx_0.json +++ b/datasets/IronEx_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IronEx_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the IronEx (Iron Fertilization Experiment) in the central eastern Pacific Ocean in 1995.", "links": [ { diff --git a/datasets/IsricWiseGrids_546_1.json b/datasets/IsricWiseGrids_546_1.json index f1b49291ea..681013a785 100644 --- a/datasets/IsricWiseGrids_546_1.json +++ b/datasets/IsricWiseGrids_546_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IsricWiseGrids_546_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The World Inventory of Soil Emission Potentials (WISE) database was used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World. These data sets were then used to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30cm depth range); soil organic carbon density (kg C/m**2 for 0-100cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range).", "links": [ { diff --git a/datasets/IsricWise_547_1.json b/datasets/IsricWise_547_1.json index 323bb0ccdf..0a11ec3719 100644 --- a/datasets/IsricWise_547_1.json +++ b/datasets/IsricWise_547_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "IsricWise_547_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ISRIC-WISE International soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme (IGBP) - Data and Information System (DIS). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data.", "links": [ { diff --git a/datasets/J1_CrIS_VIIRS750m_IND_1.json b/datasets/J1_CrIS_VIIRS750m_IND_1.json index 8d42fb4821..22905deb69 100644 --- a/datasets/J1_CrIS_VIIRS750m_IND_1.json +++ b/datasets/J1_CrIS_VIIRS750m_IND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "J1_CrIS_VIIRS750m_IND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " This dataset includes JPSS-1 VIIRS-CrIS collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).\n\nThe short name for this collections is J1_CrIS_VIIRS750m_IND_1\n\n", "links": [ { diff --git a/datasets/JASON-1_JMR_ENH_1.json b/datasets/JASON-1_JMR_ENH_1.json index c3750039d9..66241d5f28 100644 --- a/datasets/JASON-1_JMR_ENH_1.json +++ b/datasets/JASON-1_JMR_ENH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_JMR_ENH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The enhanced Jason-1 Microwave Radiometer (JMR) corrections contains better wet tropospheric path delay corrections along with better land, rain and ice flagging for coastal regions than that found in the Jason-1 Geophysical Data Records (GDR). The enhanced corrections can be used in place of the GDR wet troposphere correction to provide more accurate Sea Surface Height Anomalies for coastal regions.", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPN_E_E.json b/datasets/JASON-1_L2_OST_GPN_E_E.json index 4068a4499b..1618ee8393 100644 --- a/datasets/JASON-1_L2_OST_GPN_E_E.json +++ b/datasets/JASON-1_L2_OST_GPN_E_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPN_E_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Jason-1 Geophysical Data Records (GDR) contain full accuracy altimeter data to measure sea surface height, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height. Sea surface height anomalies calculation and recommended data edit criteria are specified in the Jason-1 GDR User Handbook at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/jason1/open/L2/gdr_netcdf_e/docs/Handbook_Jason-1_v5.1_April2016.pdf", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPN_E_GEODETIC_E.json b/datasets/JASON-1_L2_OST_GPN_E_GEODETIC_E.json index db9a7fa99d..892cf611e7 100644 --- a/datasets/JASON-1_L2_OST_GPN_E_GEODETIC_E.json +++ b/datasets/JASON-1_L2_OST_GPN_E_GEODETIC_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPN_E_GEODETIC_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Jason-1 Geophysical Data Records (GDR) Geodetic Mission contain full accuracy altimeter data, with a high precision orbit, provided approximately 35 days after data collection. The data are sorted into cycles that are approximately 11 days long and contain 280 pass files. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height.", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPR_E_E.json b/datasets/JASON-1_L2_OST_GPR_E_E.json index ef0c717896..00abb1fd7d 100644 --- a/datasets/JASON-1_L2_OST_GPR_E_E.json +++ b/datasets/JASON-1_L2_OST_GPR_E_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPR_E_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR). Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation. Using the various parameter the SSHA can be calculated and are provided in this dataset. The data are in NetCDF format. This dataset only contains the parameters that are directly related to SSHA.", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPR_E_GEODETIC_E.json b/datasets/JASON-1_L2_OST_GPR_E_GEODETIC_E.json index 393431f41c..6dc7475b38 100644 --- a/datasets/JASON-1_L2_OST_GPR_E_GEODETIC_E.json +++ b/datasets/JASON-1_L2_OST_GPR_E_GEODETIC_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPR_E_GEODETIC_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR) Geodetic Mission. Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation. Using the various parameter the SSHA can be calculated and are provided in this dataset. The data are in NetCDF format.", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPS_E_E.json b/datasets/JASON-1_L2_OST_GPS_E_E.json index f1196a8640..634802393a 100644 --- a/datasets/JASON-1_L2_OST_GPS_E_E.json +++ b/datasets/JASON-1_L2_OST_GPS_E_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPS_E_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sensory Geophysical Data Record (SGDR) files contain full accuracy altimeter data, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking. The SGDR is an expert level product, if you do not require the waveforms then the GDR/GPN or GPR will be more suited for your needs.", "links": [ { diff --git a/datasets/JASON-1_L2_OST_GPS_E_GEODETIC_E.json b/datasets/JASON-1_L2_OST_GPS_E_GEODETIC_E.json index ff63af8d45..d13234382b 100644 --- a/datasets/JASON-1_L2_OST_GPS_E_GEODETIC_E.json +++ b/datasets/JASON-1_L2_OST_GPS_E_GEODETIC_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON-1_L2_OST_GPS_E_GEODETIC_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sensory Geophysical Data Record (SGDR) files from the Geodetic Mission contain full accuracy altimeter data, with a high precision orbit. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking. The SGDR is an expert level product, if you do not require the waveforms then the GDR will be more suited for your needs.", "links": [ { diff --git a/datasets/JASON_3_L2_OST_OGDR_GPS_F.json b/datasets/JASON_3_L2_OST_OGDR_GPS_F.json index e34eafbebf..757c46c9a5 100644 --- a/datasets/JASON_3_L2_OST_OGDR_GPS_F.json +++ b/datasets/JASON_3_L2_OST_OGDR_GPS_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_3_L2_OST_OGDR_GPS_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a near real time dataset that provides a GPS based orbit and Sea Surface Height Anomalies (SSHA) from that orbit. It is similar to the Jason-3 Operation Geophysical Data Record (OGDR) that is distributed at NOAA (http://www.nodc.noaa.gov/sog/jason/), but includes the GPS orbit and SSHA as two additional variables. It has a 5 hour time lag due to the time needed to calculate the GPS orbit and SSHA. The GPS orbits have been shown to be more accurate than the DORIS orbits on a near real time scale and therefore produces a more accurate SSHA.", "links": [ { diff --git a/datasets/JASON_3_PD_CORRECTION_F.json b/datasets/JASON_3_PD_CORRECTION_F.json index b9daf40d8f..3116b747ca 100644 --- a/datasets/JASON_3_PD_CORRECTION_F.json +++ b/datasets/JASON_3_PD_CORRECTION_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_3_PD_CORRECTION_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides supplementary wet tropospheric corrections for historical Jason-3 observations (https://www.ncei.noaa.gov/archive/accession/Jason3-xGDR). Recent assessments of the global sea level budget have resulted in increased scrutiny of estimates of global sea level change based on Jason-3. After a careful assessment of the wet tropospheric correction derived from the Advanced Microwave Radiometer (AMR) instrument, it was determined that further improvements to the accuracy of the historical Jason-3 observations could be made. Since this assessment was only completed after Jason-3 data was reprocessed to GDR-F (Geophysical Data Record \u2013 Version F) standards, it was not included in the GDR-F product release. For this reason, this supplementary correction product has been created using the method of Brown et al. (2012) to allow users to correct path delay and sea surface height observations, reducing errors in estimates of global sea level change by 2-3 mm over 8 years.

\r\nThe correction was computed based on comparison of the AMR-observed brightness temperatures with independent satellite observations from the Special Sensor Microwave Imager Sounder (SSMI), F16, F17 and F18, Fundamental Climate Data Records. SSMI data was obtained from the NOAA Climate Data Record (CDR) of SSMIS Microwave Brightness Temperatures, RSS Version 8 (Wentz et al., 2019, https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C01567/html). The method described in Brown et al. (2012) to map SSMI Brightness Temperatures to AMR equivalent brightness temperatures (TBs) was used. Although it was found that it made little difference to the result, a bias was removed between SSMI equivalent AMR TBs and AMR TBs with respect to latitude for all data prior to computing temporal trends. In addition, only rain free, mostly clear data (TB18.7 GHz < 160K) data were considered.

\r\nThe correction is supplied on a pass-by-pass basis in a 4-column text file. See the product documentation for guidance on how to apply it to Jason-3 observations.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1A_ALT_HR_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L1A_ALT_HR_NTC_F08_F08.json index cd4c0573d1..f9bc54334d 100644 --- a/datasets/JASON_CS_S6A_L1A_ALT_HR_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L1A_ALT_HR_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1A_ALT_HR_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L1A high resolution (HR) non-time critical (NTC; 60-day latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1A_ALT_HR_STC_F_F.json b/datasets/JASON_CS_S6A_L1A_ALT_HR_STC_F_F.json index eaed2b5992..71b8d74012 100644 --- a/datasets/JASON_CS_S6A_L1A_ALT_HR_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L1A_ALT_HR_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1A_ALT_HR_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1A high resolution (HR) short time critical (STC; 36-hour latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_ALT_HR_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L1B_ALT_HR_NTC_F08_F08.json index b5c1a02ef6..501ab52081 100644 --- a/datasets/JASON_CS_S6A_L1B_ALT_HR_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L1B_ALT_HR_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_ALT_HR_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L1B high resolution (HR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_ALT_HR_STC_F_F.json b/datasets/JASON_CS_S6A_L1B_ALT_HR_STC_F_F.json index 4c0cc212cf..489db12e52 100644 --- a/datasets/JASON_CS_S6A_L1B_ALT_HR_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L1B_ALT_HR_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_ALT_HR_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1B high resolution (HR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_ALT_LR_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L1B_ALT_LR_NTC_F08_F08.json index 243e47605f..0b554b02c8 100644 --- a/datasets/JASON_CS_S6A_L1B_ALT_LR_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L1B_ALT_LR_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_ALT_LR_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L1B low resolution (LR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_ALT_LR_STC_F_F.json b/datasets/JASON_CS_S6A_L1B_ALT_LR_STC_F_F.json index b2ee85619c..78e1a774b1 100644 --- a/datasets/JASON_CS_S6A_L1B_ALT_LR_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L1B_ALT_LR_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_ALT_LR_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1B low resolution (LR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_GNSS_POD_DAILY_F.json b/datasets/JASON_CS_S6A_L1B_GNSS_POD_DAILY_F.json index 68efb86dc8..882c0df4d5 100644 --- a/datasets/JASON_CS_S6A_L1B_GNSS_POD_DAILY_F.json +++ b/datasets/JASON_CS_S6A_L1B_GNSS_POD_DAILY_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_GNSS_POD_DAILY_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1B daily GNSS-POD tracking data for the Sentinel-6A radar altimetry mission. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_GNSS_POD_HOURLY_F.json b/datasets/JASON_CS_S6A_L1B_GNSS_POD_HOURLY_F.json index 31daa0d251..1ade8d56b2 100644 --- a/datasets/JASON_CS_S6A_L1B_GNSS_POD_HOURLY_F.json +++ b/datasets/JASON_CS_S6A_L1B_GNSS_POD_HOURLY_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_GNSS_POD_HOURLY_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1B hourly GNSS-POD tracking data for the Sentinel-6A radar altimetry mission. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY_F.json b/datasets/JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY_F.json index 0ea5f4d8d3..be70d78737 100644 --- a/datasets/JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY_F.json +++ b/datasets/JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L1B GNSS-RO-POD tracking data for the Sentinel-6A radar altimetry mission. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08_F08.json index e4f815f0df..6559d8d68a 100644 --- a/datasets/JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2P high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour. Environmental and geophysical corrections, significant wave height, and wind-speed information are supplied by the AMR-C. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08_F08.json index 7b4f082ed2..8422c516f2 100644 --- a/datasets/JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2P low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour. Environmental and geophysical corrections, significant wave height, and wind-speed information are supplied by the AMR-C. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F_F.json index 9669377076..da2f981ccb 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NRT product is analogous to the Jason-3 OGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_F08.json index bc006310f9..66bde0f3a6 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED_F08.json index 4267aa6079..29a087bf3f 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F_F.json index 92acfc760c..c376027930 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F_F.json index ff08bba96d..2d2e34bcc8 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NRT product is analogous to the Jason-3 OGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_F08.json index 2ab0182d79..1f72c34ca2 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED_F08.json index b479731df0..48bb32336a 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F_F.json index bf2ccc8539..540da6f99c 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F_F.json index c86bb84451..e1f53f3574 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NRT product is analogous to the Jason-3 OGDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_F08.json index 5f65d5e9a4..79a465294d 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NTC product is analogous to the Jason-3 GDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED_F08.json index 682566cfd2..c6656ff820 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NTC product is analogous to the Jason-3 GDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json index 1d8d2cc949..d17551ab7f 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The STC product is analogous to the Jason-3 IGDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F.json index 7b6c0c72c5..baa86d64c1 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NRT product is analogous to the Jason-3 OGDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json index 4ef82a4143..0825686c87 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NTC product is analogous to the Jason-3 GDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json index 1f9056b19f..f6fe2e165a 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L2 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F_F.json index 4f0feb85a8..4d1aaecffb 100644 --- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F_F.json +++ b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The STC product is analogous to the Jason-3 IGDR product. ", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_AMR_RAD_NRT_F.json b/datasets/JASON_CS_S6A_L2_AMR_RAD_NRT_F.json index d58ec6ae54..fb6ca62484 100644 --- a/datasets/JASON_CS_S6A_L2_AMR_RAD_NRT_F.json +++ b/datasets/JASON_CS_S6A_L2_AMR_RAD_NRT_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_AMR_RAD_NRT_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 near real time (NRT; 3-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NRT product is analogous to the Jason-3 OGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_F08.json index bce2103281..c9abd94426 100644 --- a/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 non-time critical (NTC; 60-day latency) validated geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED_F08.json index 239efe7a84..a1552df4fd 100644 --- a/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED_F08.json +++ b/datasets/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides reprocessed L2 non-time critical (NTC; 60-day latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L2_AMR_RAD_STC_F.json b/datasets/JASON_CS_S6A_L2_AMR_RAD_STC_F.json index c2fc6d2780..58ce6b4f3b 100644 --- a/datasets/JASON_CS_S6A_L2_AMR_RAD_STC_F.json +++ b/datasets/JASON_CS_S6A_L2_AMR_RAD_STC_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L2_AMR_RAD_STC_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L2 short time critical (STC; 36-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A STC product is analogous to the Jason-3 IGDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08.json index 4b8045b34b..6346dc50b0 100644 --- a/datasets/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L3 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08.json index 4cd8053f5b..ee8e9d50d5 100644 --- a/datasets/JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08.json +++ b/datasets/JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Provides L3 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.", "links": [ { diff --git a/datasets/JAXAL2InstChecked_4.0.json b/datasets/JAXAL2InstChecked_4.0.json index eb5cead547..a56843b0d6 100644 --- a/datasets/JAXAL2InstChecked_4.0.json +++ b/datasets/JAXAL2InstChecked_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JAXAL2InstChecked_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection is restricted, and contains the following data products:\r\r\u00b7 Level 2a: Single-Instrument Geophysical Products\r\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena.\r\r\u00b7 Level 2b: Synergistic Geophysical Products\r\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/JAXAL2Products_5.0.json b/datasets/JAXAL2Products_5.0.json index 06f65eb87f..1289c60bbf 100644 --- a/datasets/JAXAL2Products_5.0.json +++ b/datasets/JAXAL2Products_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JAXAL2Products_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection contains the following data products: \r\rLevel 2a: Single-Instrument Geophysical Products \r\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. \r\rLevel 2b: Synergistic Geophysical Products \r\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/JAXAL2Validated_3.0.json b/datasets/JAXAL2Validated_3.0.json index 71f0c6056b..6a2673e3f2 100644 --- a/datasets/JAXAL2Validated_3.0.json +++ b/datasets/JAXAL2Validated_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JAXAL2Validated_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EarthCARE collection contains the following data products: \r\rLevel 2a: Single-Instrument Geophysical Products \r\rThese products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. \r\rLevel 2b: Synergistic Geophysical Products \r\rLevel 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes.", "links": [ { diff --git a/datasets/JCADM_USA_PENGUINS.json b/datasets/JCADM_USA_PENGUINS.json index abfedb6757..474883c802 100644 --- a/datasets/JCADM_USA_PENGUINS.json +++ b/datasets/JCADM_USA_PENGUINS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JCADM_USA_PENGUINS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecology of Adelie Penguins breeding at colonies in SW Ross Sea.", "links": [ { diff --git a/datasets/JERS-1.OPS.SYC_7.0.json b/datasets/JERS-1.OPS.SYC_7.0.json index e3e42e3742..dba2671677 100644 --- a/datasets/JERS-1.OPS.SYC_7.0.json +++ b/datasets/JERS-1.OPS.SYC_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1.OPS.SYC_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The JERS-1 Optical System (OPS) is composed of a Very Near Infrared Radiometer (VNIR) and a Short Wave Infrared Radiometer (SWIR). The instrument has 8 observable spectral bands from visible to short wave infrared. Data acquired by ESA ground stations The JERS-1 OPS products are available in GeoTIFF format. These products are available only for the VNIR sensor. All four bands are corrected. The correction consists in a vertical and horizontal destriping, the radiometry values are expanded from the range [0,63] to the range [0,255]. No geometrical correction is applied on level 1. The pixel size of approximately 18 x 24.2 metres for raw data is newly dimensioned to 18 x 18 metres for System Corrected data using a cubic convolution algorithm. Disclaimer: Cloud coverage for JERS OPS products has not been computed using an algorithm. The cloud cover assignment was performed manually by operators at the acquisition stations. Due to missing attitude information, the Nadir looking band (band 3) and the corresponding forward looking band (band 4) are not well coregistered, resulting in some accuracy limitations. The quality control was not performed systematically for each frame. A subset of the entire JERS Optical dataset was selected and manually checked. As a result of this, users may occasionally encounter issues with some of the individual products.", "links": [ { diff --git a/datasets/JERS-1.SAR.PRI_7.0.json b/datasets/JERS-1.SAR.PRI_7.0.json index 79d5564f76..0c3fd22dc9 100644 --- a/datasets/JERS-1.SAR.PRI_7.0.json +++ b/datasets/JERS-1.SAR.PRI_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1.SAR.PRI_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The JSA_PRI_1P product is comparable to the ESA PRI/IMP images generated for Envisat ASAR and ERS SAR instruments. It is a ground range projected detected image in zero-Doppler SAR coordinates, with a 12.5 metre pixel spacing. It has four overlapping looks in Doppler covering a total bandwidth of 1000Hz, with each look covering a 300Hz bandwidth. Sidelobe reduction is applied to achieve a nominal PSLR of less than -21dB. The image is not geocoded, and terrain distortion (foreshortening and layover) has not been removed. Data acquired by ESA ground stations.", "links": [ { diff --git a/datasets/JERS-1.SAR.SLC_7.0.json b/datasets/JERS-1.SAR.SLC_7.0.json index 19b98e28c4..d431661195 100644 --- a/datasets/JERS-1.SAR.SLC_7.0.json +++ b/datasets/JERS-1.SAR.SLC_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1.SAR.SLC_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The JSA_SLC_1P product is comparable to the ESA SLC/IMS images generated for Envisat ASAR and ERS SAR instruments. It is a slant-range projected complex image in zero-Doppler SAR coordinates. The data is sampled in natural units of time in range and along track, with the range pixel spacing corresponding to the reciprocal of the platform ADC rate and the along track spacing to the reciprocal of the PRF. Data is processed to an unweighted Doppler bandwidth of 1000Hz, without sidelobe reduction. The product is suitable for interferometric, calibration and quality analysis applications. Data acquired by ESA ground stations", "links": [ { diff --git a/datasets/JERS-1_L0_1.json b/datasets/JERS-1_L0_1.json index e7b9d2c419..32e0df05fe 100644 --- a/datasets/JERS-1_L0_1.json +++ b/datasets/JERS-1_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JERS-1 Level 1 Data", "links": [ { diff --git a/datasets/JERS-1_L1_1.json b/datasets/JERS-1_L1_1.json index 378cca9276..e1c01fb8ab 100644 --- a/datasets/JERS-1_L1_1.json +++ b/datasets/JERS-1_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JERS-1 Level 0 Data", "links": [ { diff --git a/datasets/JERS-1_OPS_L2_SWIR_NA.json b/datasets/JERS-1_OPS_L2_SWIR_NA.json index 08e2fb8394..d8d8e952bd 100644 --- a/datasets/JERS-1_OPS_L2_SWIR_NA.json +++ b/datasets/JERS-1_OPS_L2_SWIR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_OPS_L2_SWIR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JSRS-1/OPS L2 Short Wave Infrared Radiometer Data is obtained from the OPS sensor onboard JERS-1 and produced by National Space Development Agency of Japan (NASDA).JERS-1, which mounts OPS is Sun-synchronous sub-recurrent Orbit satellite launched on February 11, 1992. The dataset includes System Corrected Image Product. No radiometric correction is applied at the moment due to the lack of the calibration coefficients. Spatial Resolution is 18m x 24m. Map projection is UTM, SOM and PS. The provided format is CEOS.", "links": [ { diff --git a/datasets/JERS-1_OPS_L2_VNIR_NA.json b/datasets/JERS-1_OPS_L2_VNIR_NA.json index 5a132c360e..5797b9936e 100644 --- a/datasets/JERS-1_OPS_L2_VNIR_NA.json +++ b/datasets/JERS-1_OPS_L2_VNIR_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_OPS_L2_VNIR_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JSRS-1/OPS L2 Visible and Near Infrared Radiometer Data is obtained from the OPS sensor onboard JERS-1 and produced by National Space Development Agency of Japan (NASDA).JERS-1, which mounts OPS is Sun-synchronous sub-recurrent Orbit satellite launched on February 11, 1992. The dataset includes System Corrected Image Product. No radiometric correction is applied at the moment due to the lack of the calibration coefficients. Spatial Resolution is 18m x 24m. Map projection is UTM, SOM and PS. The provided format is CEOS.", "links": [ { diff --git a/datasets/JERS-1_SAR_GRFM_Amazon_Mosaics_1280_2.json b/datasets/JERS-1_SAR_GRFM_Amazon_Mosaics_1280_2.json index 6b65fe4158..89b4c80d49 100644 --- a/datasets/JERS-1_SAR_GRFM_Amazon_Mosaics_1280_2.json +++ b/datasets/JERS-1_SAR_GRFM_Amazon_Mosaics_1280_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_SAR_GRFM_Amazon_Mosaics_1280_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides ~100-m resolution image mosaics of South America acquired during the low flood season between September and December 1995 and during the high flood season between May and July of 1996. The images cover the same areas during both seasons and were obtained from the Japanese Earth Resources Satellite 1 (JERS-1) Synthetic Aperture Radar (SAR) of the National Space Development Agency of Japan (NASDA). The data were mosaicked into 34 tiles for each season, each consisting of about 50 JERS-1 scenes. This data set constitutes the first-ever high-resolution and single season coverage of the entire Amazon River Basin, made possible by the cloud penetrating properties of the radar sensor. The images are from the original JERS-1 SAR Global Rain Forest Mapping Project. This data set contains 66 files in GeoTIFF (.tiff) format. There are 32 files for the low flood season and 34 files for the high flood season. ", "links": [ { diff --git a/datasets/JERS-1_SAR_L0_Data_NA.json b/datasets/JERS-1_SAR_L0_Data_NA.json index e36c3d58b8..be033efa02 100644 --- a/datasets/JERS-1_SAR_L0_Data_NA.json +++ b/datasets/JERS-1_SAR_L0_Data_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_SAR_L0_Data_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JERS-1/SAR L0 SAR Data is obtained from the SAR sensor onboard JERS-1 and produced by National Space Development Agency of Japan (NASDA).JERS-1 which mounts SAR is Sun-synchronous sub-recurrent Orbit satellite launched on February 11, 1992. This dataset includes Unprocessed Signal Data Product Data. Data which has undergone absolutely no correction is recorded. Data required for higher level correction is ALOS recorded. It is same as the unprocessed data of other sensors prepared at the EOC. The provided format is CEOS.", "links": [ { diff --git a/datasets/JERS-1_SAR_L2.1_Data_NA.json b/datasets/JERS-1_SAR_L2.1_Data_NA.json index 139e92b53f..c745f7b8b4 100644 --- a/datasets/JERS-1_SAR_L2.1_Data_NA.json +++ b/datasets/JERS-1_SAR_L2.1_Data_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JERS-1_SAR_L2.1_Data_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "JERS-1/SAR L2.1 SAR Data is obtained from the SAR sensor onboard JERS-1 and produced by National Space Development Agency of Japan (NASDA).JERS-1 is Sun-synchronous sub-recurrent Orbit satellite launched on February 11, 1992, which mounts SAR. This dataset includes Standard Geocoded Image. After range and multi-look azimuth compression are performed, radiometric and geometric corrections are performed according to the map projection. The spacial resolution is 12.5m Map projection is UTM and PS. The provided format is CEOS.", "links": [ { diff --git a/datasets/JGOFS_0.json b/datasets/JGOFS_0.json index 6f52812df4..1b384ca86a 100644 --- a/datasets/JGOFS_0.json +++ b/datasets/JGOFS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Joint Global Ocean Flux Study (JGOFS), spanning 1986 to 1998.", "links": [ { diff --git a/datasets/JGOFS_Arabian_Sea_0.json b/datasets/JGOFS_Arabian_Sea_0.json index f8445064cf..a9aa1f7e6e 100644 --- a/datasets/JGOFS_Arabian_Sea_0.json +++ b/datasets/JGOFS_Arabian_Sea_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_Arabian_Sea_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Joint Global Ocean Flux Study (JGOFS) Arabian Sea measurements from 1994 and 1995.", "links": [ { diff --git a/datasets/JGOFS_BOFS_0.json b/datasets/JGOFS_BOFS_0.json index 61a23451d6..a0cae794fc 100644 --- a/datasets/JGOFS_BOFS_0.json +++ b/datasets/JGOFS_BOFS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_BOFS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Joint Global Ocean Flux Study (JGOFS) measurements taken by Germany, The Netherlands, and the United Kingdom from 1991.", "links": [ { diff --git a/datasets/JGOFS_EQPAC_0.json b/datasets/JGOFS_EQPAC_0.json index 87681a2f1d..8a9f3e8d0a 100644 --- a/datasets/JGOFS_EQPAC_0.json +++ b/datasets/JGOFS_EQPAC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_EQPAC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Joint Global Ocean Flux Study (JGOFS) Central Equatorial Pacific measurements from 1992.", "links": [ { diff --git a/datasets/JGOFS_EQPAC_CYANOBACT_NANOPLANK.json b/datasets/JGOFS_EQPAC_CYANOBACT_NANOPLANK.json index 4f5fdd390e..19ea047447 100644 --- a/datasets/JGOFS_EQPAC_CYANOBACT_NANOPLANK.json +++ b/datasets/JGOFS_EQPAC_CYANOBACT_NANOPLANK.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_EQPAC_CYANOBACT_NANOPLANK", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W\n longitude during 1992.\n \n Four cruises took place: February 3 - March 9, March 19 - April 15,\n August 5 - September 18, and September 24 - October 21. A fifth benthic\n cruise and sediment trap legs were added. During the first cruise\n (TT007), 15 stations were occupied along 140 deg W longitude from\n 12 deg N latitude to 12 deg S latitude. During the second cruise\n (TT008), data were collected at 8 stations along 140 deg W longitude\n from 9 deg S latitude to 9 deg N latitude. During the third cruise\n (TT011), data were collected at 15 stations along 140 deg W from 12 deg N\n latitude to 12 deg S latitude. During the fourth cruise (TT012), data were\n collected at 5 stations along 140 deg W longitude from 17 deg S\n latitude to the equator.\n \n Abundance, biovolume and biomass of cyanobacteria and eukaryotic\n plankton were measured at each station in vertical profiles using the CTD\n rosette water sampler. The cyanobacteria and plankton were enumerated and\n sized using color image analyzed fluorescence microscopy. The following\n parameters were measured:\n abundance of synechococcus-type cyanobacteria\n biovolume of synechococcus-type cyanobacteria\n biomass of synechococcus-type cyanobacteria\n abundance of phototrophic eucaryotic pico- and nanoplankton\n biovolume of phototrophic eucaryotic pico- and nanoplankton\n biomass of phototropic eucaryotic pico- and nanoplankton\n abundance of heterotrophic eucaryotic pico- and nanoplankton\n biovolume of heterotrophic eucaryotic pico- and nanoplankton\n biomass of heterotrophic eucaryotic pico- and nanoplankton\n \n The abundances are in units of cells/liter; the biovolumes are in\n units of cubic micrometers; and the biomasses are in units of\n micrograms of carbon per liter.\n \n The data is public domain and can be retrieved on-line at\n \"http://usjgofs.whoi.edu/jg/dir/jgofs/\"\n \n [The information in this summary was derived from the JGOFS\n World Wide Web pages.]", "links": [ { diff --git a/datasets/JGOFS_EQPAC_DINOFLAG.json b/datasets/JGOFS_EQPAC_DINOFLAG.json index 13d144f39b..e64ee75155 100644 --- a/datasets/JGOFS_EQPAC_DINOFLAG.json +++ b/datasets/JGOFS_EQPAC_DINOFLAG.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_EQPAC_DINOFLAG", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W\n longitude during 1992.\n \n Four cruises took place: February 3 - March 9, March 19 - April 15,\n August 5 - September 18, and September 24 - October 21. A fifth benthic\n cruise and sediment trap legs were added. During the first cruise\n (TT007), 15 stations were occupied along 140 deg W longitude from\n 12 deg N latitude to 12 deg S latitude. During the second cruise\n (TT008), data were collected at 8 stations along 140 deg W longitude\n from 9 deg S latitude to 9 deg N latitude. During the third cruise\n (TT011), data were collected at 15 stations along 140 deg W from 12 deg N\n latitude to 12 deg S latitude. During the fourth cruise (TT012), data were\n collected at 5 stations along 140 deg W longitude from 17 deg S\n latitude to the equator.\n \n Samples were collected at each station in a vertical profile using\n the CTD rosette bottle sampler for the measurement of heterotrophic\n dinoflagellates. Microzooplankton were enumerated by inverted\n microscopy of settled samples. Abundance (cells/ml), biovolume (cubic\n micrometers), and biomass (ugC/l) were measured.\n \n The data is public domain and can be retrieved on-line at\n \"http://usjgofs.whoi.edu/jg/dir/jgofs/\"\n \n [The information in this summary was derived from the JGOFS\n World Wide Web pages.]", "links": [ { diff --git a/datasets/JGOFS_EQPAC_MARINE_SNOW.json b/datasets/JGOFS_EQPAC_MARINE_SNOW.json index 64001ddb39..e8252eb964 100644 --- a/datasets/JGOFS_EQPAC_MARINE_SNOW.json +++ b/datasets/JGOFS_EQPAC_MARINE_SNOW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_EQPAC_MARINE_SNOW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W\n longitude during 1992.\n \n Four cruises took place: February 3 - March 9, March 19 - April 15,\n August 5 - September 18, and September 24 - October 21. A fifth benthic\n cruise and sediment trap legs were added. During the first cruise\n (TT007), 15 stations were occupied along 140 deg W longitude from\n 12 deg N latitude to 12 deg S latitude. During the second cruise\n (TT008), data were collected at 8 stations along 140 deg W longitude\n from 9 deg S latitude to 9 deg N latitude. During the third cruise\n (TT011), data were collected at 15 stations along 140 deg W from 12 deg N\n latitude to 12 deg S latitude. During the fourth cruise (TT012), data were\n collected at 5 stations along 140 deg W longitude from 17 deg S\n latitude to the equator.\n \n On the second cruise, a camera and strobelights were used to\n illuminate aggregate particles. The system was lowered slowly\n 10-20 m/min through the water column on a trawl wire, exposing frames\n at a time interval of 7-20 sec calculated to yield 700-800 frames\n between the surface and the sea floor. Depth was monitored and\n recorded using a pinger and the ship's precision depth recorder.\n The parameter measured was the number of aggregates greater than 0.5 mm.\n \n The data is public domain and can be retrieved on-line at\n \"http://usjgofs.whoi.edu/jg/dir/jgofs/\"\n \n [The information in this summary was taken from the JGOFS\n World Wide Web pages.]", "links": [ { diff --git a/datasets/JGOFS_WOCE_0.json b/datasets/JGOFS_WOCE_0.json index 47833e901d..80c45e008b 100644 --- a/datasets/JGOFS_WOCE_0.json +++ b/datasets/JGOFS_WOCE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JGOFS_WOCE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Joint Global Ocean Flux Study (JGOFS) World Ocean Circulation Experiment measurements from 1991.", "links": [ { diff --git a/datasets/JHUAPL_SRI_Kauai_0.json b/datasets/JHUAPL_SRI_Kauai_0.json index dcbc8c6cc2..c84a6101c1 100644 --- a/datasets/JHUAPL_SRI_Kauai_0.json +++ b/datasets/JHUAPL_SRI_Kauai_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JHUAPL_SRI_Kauai_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the Johns Hopkins University Applied Physics Laboratory (JHUAPL) near the island of Kauai in 1993.", "links": [ { diff --git a/datasets/JPL_RECON_GMSL_1.0.json b/datasets/JPL_RECON_GMSL_1.0.json index 459b705004..12796d80f8 100644 --- a/datasets/JPL_RECON_GMSL_1.0.json +++ b/datasets/JPL_RECON_GMSL_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JPL_RECON_GMSL_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains reconstructed global-mean sea level evolution and the estimated contributing processes over 1900-2018. Reconstructed sea level is based on annual-mean tide-gauge observations and uses the virtual-station method to aggregate the individual observations into a global estimate. The contributing processes consist of thermosteric changes, glacier mass changes, mass changes of the Greenland and Antarctic Ice Sheet, and terrestrial water storage changes. The glacier, ice sheet, and terrestrial water storage are estimated by combining GRACE observations (2003-2018) with long-term estimates from in-situ observations and models. Steric estimates are based on in-situ temperature profiles. The upper- and lower bound represent the 5 and 95 percent confidence level. The numbers are equal to the ones presented in Frederikse et al. The causes of sea-level rise since 1900, Nature, 2020.This dataset was produced by the Heat and Ocean Mass from Gravity ESDR (HOMAGE) project, with funding from MeASUREs-2017. HOMAGE is combining satellite observations to create a set of ESDRs that provide a homogeneous basis for accurate and current quantification of the planetary sea level budget, ocean heat content, and large-scale ocean transport variations.", "links": [ { diff --git a/datasets/JPL_SRTM.json b/datasets/JPL_SRTM.json index 7e2211846f..6bb9f23be6 100644 --- a/datasets/JPL_SRTM.json +++ b/datasets/JPL_SRTM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JPL_SRTM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to \"virtual\" Earth exploration. Currently, the mission's homepage \"http://www.jpl.nasa.gov/srtm\" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at \"http://www2.jpl.nasa.gov/srtm/dataprod.htm\" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, \"http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html\". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated: \n \n- Radar Image \n- Radar Image with Color as Height \n- Radar Image with Color Wrapped Fringes \n-Shaded Relief \n- Perspective View with B/W Radar Image Overlaid \n- Perspective View with Radar Image Overlaid, Color as Height \n- Perspective View of Shaded Relief \n- Perspective View\nwith Landsat or other Image Overlaid \n- Contour Map - B/W with Contour Lines \n- Stereo Pair \n- Anaglypgh \n\nThe SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.\n", "links": [ { diff --git a/datasets/JPL_SRTM_V2_2.json b/datasets/JPL_SRTM_V2_2.json index bfc2c958c4..67664e5e92 100644 --- a/datasets/JPL_SRTM_V2_2.json +++ b/datasets/JPL_SRTM_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JPL_SRTM_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " NASA has released version 2 of the Shuttle Radar Topography Mission digital\n topographic data (also known as the \"finished\" version). Version 2 is the\n result of a substantial editing effort by the National Geospatial Intelligence\n Agency and exhibits well-defined water bodies and coastlines and the absence of\n spikes and wells (single pixel errors), although some areas of missing data\n ('voids') are still present. The Version 2 directory also contains the vector\n coastline mask derived by NGA during the editing, called the SRTM Water Body\n Data (SWBD), in ESRI Shapefile format.\n \n [Summary provided by NASA.]\n", "links": [ { diff --git a/datasets/JWasley-LabBook-Casey-1999-2000_1.json b/datasets/JWasley-LabBook-Casey-1999-2000_1.json index af5f28b97b..58ae623920 100644 --- a/datasets/JWasley-LabBook-Casey-1999-2000_1.json +++ b/datasets/JWasley-LabBook-Casey-1999-2000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "JWasley-LabBook-Casey-1999-2000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scanned laboratory notebook. \n - Notebook owner: Jane Wasley\n - Project: Jane Wasely PhD (ASAC 1087: The influence of water and nutrient availability on bryophyte communities in continental Antarctica)\n - Notebook type: Laboratory (A4 Hardcover)\n\nLocation/s: \n - Casey 1999/2000 season \n - Wollongong 2000\n - Vienna 2000 \n\nDate range: 27/11/1999 to 10/09/2000\n\nThe notebook is scanned as four files: \n - JWasley-LabBook-Casey 1999-2000_P1-39.pdf\n - JWasley-LabBook-Casey 1999-2000_P40-89.pdf\n - JWasley-LabBook-Casey 1999-2000_P90-143.pdf\n - JWasley-LabBook-Casey 1999-2000_P144-275.pdf\n\nPlus three files that were looose pages with the notebook: \n - JWasley-LabBook-Casey 1999-2000_loose pages-sugar mass.pdf\n - JWasley-LabBook-Casey 1999-2000_loose pages-Sabine emails.pdf\n - JWasley-LabBook-Casey 1999-2000_loose pages-phosphorous methods.pdf\n\n\nSome pages were blank and not scanned: \n - 66-69\n - 122-127\n - 167\n - 214-259\n - 262-263\n - 276-277\n\nSome pages had notes that were not data, and were not scanned: \n - 168-173 notes about ASAC proposal\n - 260-261 location of samples in freezers [at Casey?], dated 3/6/2000\n - 264-269 RTA inventory for equipment returning from Casey on V6 1999/2000, dated 15/03/2000\n - 278-279 RTA inventory for equipment returning from Casey on V5 1999/2000, dated 02/02/2000\n - 280-284 misc notes", "links": [ { diff --git a/datasets/K001D_2010_2012_NZ_1.json b/datasets/K001D_2010_2012_NZ_1.json index 3b110c7a31..7d2875a5ff 100644 --- a/datasets/K001D_2010_2012_NZ_1.json +++ b/datasets/K001D_2010_2012_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K001D_2010_2012_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To quantify the distribution, composition and overall flux of aeolian (windblown) sediment that accumulates on Ice shelves and annual sea ice in the SW Ross Sea region and is subsequently released into the water column during melting. The sediment is an important contributor to sea floor sedimentation and is thought to be an important source of the micro-nutrient iron (Fe), triggering vast phytoplankton blooms each spring in the Ross Sea Region. These blooms are major productivity events that contribute large volumes of biogenic sediment to the seafloor and ultimately to the stratigraphic record (e.g. ANDRILL cores). Although the contribution of aeolian sediment has long been considered important, the actual flux of such material, its Fe content and availability to phytoplankton is poorly known. Understanding these modern processes is a key part of interpreting the past record of environmental change in the region.\n\nField work carried out in the 2010/11 season retrieved a network of samples from the surface of the sea ice in Western McMurdo Sound and covers almost all previous geological drill sites (CRP1,2,3; CIROS 1,2; ANDRILL- 2a). 500ml bottles of snow were collected with trace metal clean technique and bags of snow (and dust) from a grid of sites (2.5 and 5km spacing) on the Western side of McMurdo Sound. This unprecedented dataset will for the first time allow us to quantify the flux, size range and provenance of aeolian sediment entering the McMurdo Sound and evaluate its importance as both a direct sediment contributor and also as a source of Fe influencing the regional biogeochemical cycle. \n\nFieldwork carried out in the 2011/12 season strengthened this dataset by resampling keys sites from the 2010/11 survey in Southern McMurdo Sound to investigate inter-annual variability. In addition, a firn core was collected from Windless Bight at the same location as a core recovered in 2006 (Dunbar et al. 2009). Preliminary analysis on this core has shown clear annual layering and promising potential for extracting a record of dust to overlapping with previous cores (Atkins et al. 2011.) The sampling for the season involved collecting bags of snow from sea ice and ice shelf surfaces, short firn cores (up to 5m), aeolian sediment trap samples, geological samples and climate station data (wind speed and dirtection) in Southern McMurdo Sound and Nansen Ice shelf, Terra Nova Bay, Antarctica to quantify aeolian sediment distribution.\n\nThe main focus of the 2011/12 season was in the Terra Nova Bay area. This region has a well-studied polnyna and annual algal bloom. In addition it is renowned for its katabatic airflow. A major limitation for understanding the biogeochemical cycles in the area is the lack of quantitative data on aeolian dust flux. Custom-built sediment traps and a climate station were deployed along the edge of the Nansen Ice Shelf during November to January. In addition, surface snow samples, short firn cores and exposed rocks were sampled in the region to help quantify the dust flux into the Terra Nova Bay polnyna. Preliminary analysis shows that the sediment traps were an effective way of sampling aeolian sediment and dust from snow samples has allowed us to begin mapping the sediment distribution and transport pathways at Terra Nova Bay.", "links": [ { diff --git a/datasets/K009_1971_1972_NZ_2.json b/datasets/K009_1971_1972_NZ_2.json index 9250a8e25f..52d1589a61 100644 --- a/datasets/K009_1971_1972_NZ_2.json +++ b/datasets/K009_1971_1972_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K009_1971_1972_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two weeks were spent in the Wright Valley to survey suitable sites for boreholes to be put down as part of the International Drilling Programme. It was proposed to core the entire thickness of bottom sediments in Lake Vanda to elucidate, among other things, aspects of lake stratigraphy, petrology and hydrology, geothermal gradients in the area and paleoclimates. To locate the best site, a general bathymetric map of the lake and the nature of the bottom surface sediments was conducted. Results of the general reconnaissance are reported in the associated publication including lake depth and lake bottom sediment descriptions. Detailed textural, mineralogical, geochemical and biological investigation of the sediments was conducted.", "links": [ { diff --git a/datasets/K009_1972_1973_NZ_1.json b/datasets/K009_1972_1973_NZ_1.json index dc3dbaa4b2..e7d8b10001 100644 --- a/datasets/K009_1972_1973_NZ_1.json +++ b/datasets/K009_1972_1973_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K009_1972_1973_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A geochemical reconnaisance of the salts in the Victoria Valley was undertaken in the 1972/73 season. A field camp was set up at Lake Vida and the area from Lake Vida to Lake Vaska, Lake Clarke and up the mountains to the north of Lake Vida were surveyed. Samples of salts were collected where they were visible and a number of soils were collected in closed drainage basins and at the edges of small lakes. A total of 2m of sediments 10m above the lake level were described and calcium carbonate 'biscuit' concretions were collected for 14C and/or U-Th dating.", "links": [ { diff --git a/datasets/K009_1975_1976_NZ_2.json b/datasets/K009_1975_1976_NZ_2.json index 10f00af0eb..8349eb49ec 100644 --- a/datasets/K009_1975_1976_NZ_2.json +++ b/datasets/K009_1975_1976_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K009_1975_1976_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A few days were spent in the Miers Valley to collect samples of gypsum for geochemical analyses. Surprisingly carbonate \"biscuit\" similar to that found in the Taylor Valley were found. Thus, we noted the elevations of carbonate and gypsum, also in relation to ancient lake levels and moraines. Samples were subjected to geochemical analyses. Kenyte-like boulders in the terrace sequence had been depositied in a tuffaceious matrix. Apparently the boulders had been deposited on the subaqueous part of the delta at a time of higher lake level.The feldspar crystals in these boulders were dated with K-Ar as well as having the glass in the tuffaceous matrix fission-track dated. With dating, we should be able to tie in the age, form and evolution of the old lake levels, deltas and moraines of the Miers Valley with the Taylor Valley. Further samples were collected the following season for dating the formation of the major landforms, especially the moraines and lake levels in the Miers Valley. The Marshall Valley was visited and a massive gypsum vein was sampled and dated. The Walcott Bay was surveyed but no carbonate was found and the shoreline of Mt Discovery was surveyed for carbonates.", "links": [ { diff --git a/datasets/K009_1979_1980_NZ_1.json b/datasets/K009_1979_1980_NZ_1.json index db8d381fe8..b9d4526ca0 100644 --- a/datasets/K009_1979_1980_NZ_1.json +++ b/datasets/K009_1979_1980_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K009_1979_1980_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three holes were drilled into frozen sediments around Lake Fryxell. The first was 4ft in depth in frozen silts approx 50m NW of the Fryxell Hut. The second hole was 30m east of the first hole and a depth of 16ft. A third hole was drilled approximately 1km east of the second hole to a depth of 46ft. The cores were analysed and the lacustre carbonates within were dated. This was the first time that diamond drilling was used to drill the cores.", "links": [ { diff --git a/datasets/K012_1978_1980_NZ_1.json b/datasets/K012_1978_1980_NZ_1.json index 5befeadc33..5b14eb2265 100644 --- a/datasets/K012_1978_1980_NZ_1.json +++ b/datasets/K012_1978_1980_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K012_1978_1980_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The low temperature adaptations involved in neuromuscular transmission in Antarctica fish was characterized. An exploratory dissection of Pagothenia borchgrevinki revealed that the inferior oblique ocular muscle was well suited for neuromuscular studies. Visual observations of contraction while stimulating the oculomotor was conducted and the interaction of stimulus frequency and temperature on muscle contraction was monitored. Electromyograms were used to record the muscle contraction at different temperatures and to assess the sensitivity of the neuromuscular junction to curare (tubocurarine - HCl). Photographic records of the EMG experiments were analysed. \n\nA sequence of neurophysiological experiments were conducted to further characterize the neuromuscular transmission in fishes including: \na) Determination of optimum stimulation frequency and changes with temperature, \nb) Dose response measurements of acetylcholine and changes with temperature, \nc) Changes of the resting potential with temperature and \nd) recording the spontaneous miniature end-plate potentials (MEPP) and temperature induced changes in MEPP size, frequency and rate of decay. \n\nBrain and cranial nerves were dissected from five species of fish; P. borchgrevinki, Trematomus bernacchii, T. hansoni, Dissostichus mawsoni and Gymnodraco acuticeps, and preserved in methanol-acetic acid-formalin for anatomical, histological studies and lipid analysis. Glycerated muscle preparations of P. borchgrevinki eye muscles were made to analyse the myosin ATP-ase system responsible for the actual force of the contraction.", "links": [ { diff --git a/datasets/K014_1969_1970_NZ_1.json b/datasets/K014_1969_1970_NZ_1.json index 42f34ce98f..dee2d2f565 100644 --- a/datasets/K014_1969_1970_NZ_1.json +++ b/datasets/K014_1969_1970_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1969_1970_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On arrival at Cape Bird it was found that the pack ice had broken early and sampling had to be limited to inshore waters from ice piers with water depths never greater than about 20 feet. Plankton samples were obtained every third day through the summer to provide records of plankton abundance and composition and chlorophyll content of the water. Records were kept of prevailing sea and weather conditions and sea temperatures and conductivity.", "links": [ { diff --git a/datasets/K014_1970_1971_NZ_5.json b/datasets/K014_1970_1971_NZ_5.json index 6cabdb0703..2b08703aea 100644 --- a/datasets/K014_1970_1971_NZ_5.json +++ b/datasets/K014_1970_1971_NZ_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1970_1971_NZ_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A survey of the region from the ice face to McDonald Beach and to a depth of about 300 meters with regard to distribution of sediment types, boundaries of faunal zones and the general bathymetry of the area was completed at Cape Bird. The current pattern around the cape coast was observed and measured and its effect on the local benthic habitat was described.", "links": [ { diff --git a/datasets/K014_1974_1975_NZ_1.json b/datasets/K014_1974_1975_NZ_1.json index 635d314b03..2b1adc6c49 100644 --- a/datasets/K014_1974_1975_NZ_1.json +++ b/datasets/K014_1974_1975_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1974_1975_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observations were made on the behaviour and breeding success of penguins and skuas in areas of the Cape Bird northern colony subject to interference by man. Interferance being taken as the presence of man and/or man made objects. Areas free from interference except for the observers presence were observed as controls. 300 Adelie penguin and 24 McCormick skua nests were checked daily for eggs and chick success.", "links": [ { diff --git a/datasets/K014_1974_1975_NZ_4.json b/datasets/K014_1974_1975_NZ_4.json index 97f1c2a82e..17ef00a81a 100644 --- a/datasets/K014_1974_1975_NZ_4.json +++ b/datasets/K014_1974_1975_NZ_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1974_1975_NZ_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A population census of the three Adelie penguin colonies in the area of Cape Bird was carried out over several seasons since 1965, between November and December each year. These counts were conducted by ground based observations. Simultaneously, aerial photographs were taken by another study. The total number of birds was counted by 2 people using hand counters. The totals needed to be within 1% of each other or they were recounted. The final number for each colony was determined by averaging all the totals for that colony and rounding to the lower number. Occupied nests were counted with the same technique. Initial maps of the three main colonies were drawn from aerial photographs taken in the late 1960's. Copies of original maps were examined in the field and amendments were made to document changes in the colonies over the years and to update the information for future colony counts. Any penguin or McCormicks skua with bands were read while making the annual colonies count (penguin) or search for during the evenings (skua). The nest sites of skuas were mapped and band numbers of skuas using the nests were recorded in some years. A census of the penguin colonies at Cape Royds was conduction in 1959, 1975, 1977, 1979-1988 using the same methods.", "links": [ { diff --git a/datasets/K014_1982_1983_NZ_1.json b/datasets/K014_1982_1983_NZ_1.json index b7effa1479..44b1ecb489 100644 --- a/datasets/K014_1982_1983_NZ_1.json +++ b/datasets/K014_1982_1983_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1982_1983_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In January-February 1983, a four person party spent five weeks at Cape Hallett, Northern Victoria Land, under the auspices of the New Zealand Committee for the International Survey of Antarctic Seabirds (ISAS). The major objectives of this expedition were a census of the Adelie penguin and skua populations and a study of the foods of Adelie penguins. The last penguin census at Cape Hallett prior to this was in 1968. The old Cape Hallett station was abandoned in 1973 and the recovery of the penguin population was checked. All chicks were counted in each colony and their number was compared with counts made in 1961 and aerial photographs from 1982. A skua census was also completed in two separate counts. The feeding ecology of adelie penguins was examined to take the opportunity for making comparisons with results from earlier studies at Cape Hallett. Stomach samples were collected at the creche stage from 76 adult penguins. The penguins were captured as they returned from feeding at sea and stomach contents were sampled using the wet offloading techniqe. The type, abundance and characteristics of the prey species was determine and compared.", "links": [ { diff --git a/datasets/K014_1982_1983_NZ_3.json b/datasets/K014_1982_1983_NZ_3.json index b82983cc34..302a9f3812 100644 --- a/datasets/K014_1982_1983_NZ_3.json +++ b/datasets/K014_1982_1983_NZ_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1982_1983_NZ_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Specially Protected area No.7 is located at the base of Seabee Spit and comprises two major habitat types: a large flat area interrupted by small hummocks and depressions, and adjoining steep scree slopes which form part of the western side of Cape Hallett. In order to provide some up to date information on the current status of the SPA, the distribution of vegetation was surveyed and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man. The adequacy of the present boundaries (1983) was also examined. The algae, mosses and lichens of Cape Hallett were surveyed in two ways: a) A series of photographs was taken to provide overlapping coverage of the SPA and surrounding areas at a small scale. This will allow a sketch map to be made showing broad vegetation distribution patterns, extent of penguin colonies, nature of the topography, occurrence of permanent snow patches and areas of melt water accumulation. b) Three vertical transects were laid across the SPA running west to east over the flat and up the scree slopes. At 5m intervals along each transect the area within a 25 x 25 cm quadrat was examined to provide data on species distribution and cover, the nature of the substrate, slope, aspect, and relative abundance and moisture. The presence/absence of collembola and mites was also recorded as was evidence of the presence of skuas, seals and penguins. A total of 600 quadrats were sampled.", "links": [ { diff --git a/datasets/K014_1999_2000_NZ_1.json b/datasets/K014_1999_2000_NZ_1.json index 8c3241cc5b..7659c429d1 100644 --- a/datasets/K014_1999_2000_NZ_1.json +++ b/datasets/K014_1999_2000_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K014_1999_2000_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The impact of petroleum derivatives derived from fuel drums dumped into McMurdo Sound during the period before environmental management practices were regarded was examined on fish in Winterquarters Bay (McMurdo Sound). Experimental fish were captured from a relatively pristine site (Backdoor Bay, Cape Royds) and transported to Winterquarter Bay (heavily polluted) and Cape Armitage (minimally impacted) where they were held in cages. The fish were sampled from both sites after periods of 2 and 4 weeks and examined for physiological condition. Naturally resident fish were also collected from Backdoor Bay and Winterquarters Bay to provide a second, independent set of data. The physical condition of each fish was noted on gross examination and morphometric data was gathered to provide further information on health status. Internal organs (gills and liver) were then sampled for histopathological and biochemical analysis (measurement of cytochrome P450 content and activity). Bile was also removed from the gall bladder for subsequent analysis of petroleum derivative content by fluorimetry. These methods test for correlations between the amount and activity of cytochrome P450 in exposed fish and the quantity of contaminating petroleum contaminants.", "links": [ { diff --git a/datasets/K017_1967_1968_NZ_2.json b/datasets/K017_1967_1968_NZ_2.json index 8181653281..ea49ec4a92 100644 --- a/datasets/K017_1967_1968_NZ_2.json +++ b/datasets/K017_1967_1968_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K017_1967_1968_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A study of skua territories was conducted by examining siting, establishment and maintenance of territories in two very different conditions including in an area close to the penguins where skuas nest in a tight concentration and in an alpine exposed area of low skua concentration. Direct observations of conflicts and encounters through the summer and the changing position of boundaries was followed in relation to breeding state of the the skua pairs. An independent assessment of a social hierarchy was made to allow investigation of the relation between this hierarchy and territory size, position and breeding success to be concluded. The relation between territory factor and breeding success, especially the survival of the chicks following the displacement of one of the two chicks from the nest that invariable occurs soon after both hatch was also recorded.", "links": [ { diff --git a/datasets/K022_1977_1978_NZ_1.json b/datasets/K022_1977_1978_NZ_1.json index 14d0f5f13d..767e565e0c 100644 --- a/datasets/K022_1977_1978_NZ_1.json +++ b/datasets/K022_1977_1978_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K022_1977_1978_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A variety of research activities on the organisms in the Ross Dependency was undertaken to determine the biological research potential of the organisms. Most work focused on photoreceptors of different invertebrates and fishes. The studies included work on: \n \n a) Glyptonotus antarcticus: The dorsal and ventral eyes of this big isopod were prefixed, postfixed, dehydrated and embedded for transmission electron microscopy (TEM). Additional eyes were prepared for TEM of the inner and outer surfaces. Groups of 4 animals were adapted to 0\u00b0C, 5\u00b0C and 10\u00b0C and their eyes were also prepared for TEM. Another experiment involved painted one eye black and exposing the other to 200 lux for 1 week. Both eyes were analysed with TEM. \n \n b) Orchomenella plebs: Freshly caught amphipods were exposed to bright sunlight for 1, 2 and 3 hours. Their eyes, as well as those of fully dark adapted ones were prepared for TEM. This species can also recover when placed in 10\u00b0C for 7h and then returned to 0\u00b0C water. Eyes of animals adapted to 5\u00b0C and 10\u00b0C and those that had recovered afterwards in 0\u00b0C were prepared for TEM. \n \n c) The compound eyes of approx 100 facets belonging to a tiny (1-2mm) parasitic isopod from fish and invertebrate hosts were prepared for TEM. \n \n d) Retinae of 3 species of fishes (Trematomus bernacchii, Trematomus brochgrevinkii and Dissostichus mawsoni) were fixed for TEM. The eyes of the Trematomus species were prepared for gas-chromatographical analyses of the fatty acid composition. Observations were carried out on the antifreeze behaviour of D. mawsoni aqueous and vitreous humor. \n \n e) The microfauna and flora of Deep Lake and Skua Lake were studied in culture. Numerous drawings of the microorganisms were prepared. \n \n f) A number of organisms were collected for identification including benthic marine organism from under the 3-5m thick sea ice, marine mite species, skua egg shells, moss samples (from the top of Mt Erebus) and bacteria which were attempted to be cultured from snow samples.", "links": [ { diff --git a/datasets/K024_1996_1997_NZ_3.json b/datasets/K024_1996_1997_NZ_3.json index f59e688c41..a3e34dce3b 100644 --- a/datasets/K024_1996_1997_NZ_3.json +++ b/datasets/K024_1996_1997_NZ_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K024_1996_1997_NZ_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The vegetation at Beaufort Island was assessed and a report written to ICAIR including a description of the area, species present, comparison to other Dry Valley vegetation, the merits of the vegetation and recommendations of other features worthy of protection.", "links": [ { diff --git a/datasets/K029_1999_2000_NZ_1.json b/datasets/K029_1999_2000_NZ_1.json index fab8956fff..d6bfcb5401 100644 --- a/datasets/K029_1999_2000_NZ_1.json +++ b/datasets/K029_1999_2000_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K029_1999_2000_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguins (Pygoscelis adeliae) at Cape Royds (11-12 November, 1999) were captured and checked for chewing lice. Emperor penguins (Aptenodytes forsteri) at Cape Crozier (15-16 November, 1999) were captured and checked for lice as well. Two species of chewing lice were found, Austrogonioides antarcticus and A. mawsoni on adelies and emperors respectively. The aim of the project was to obtain specimens of all species of lice (15) parasitising penguins (17) and to use molecular and morphological characters to produce a phylogeny for the lice and to compare the lice phylogeny to the penguin phylogeny. PCR was used to allow sequencing of genetic material from the lice, with the sequencing of two gene regions (12s and Cytochrome Oxidase 1). Lice speciation events were dated using molecular data to differentiate between co-speciation and host switching events.", "links": [ { diff --git a/datasets/K042_1964_1965_NZ_2.json b/datasets/K042_1964_1965_NZ_2.json index 29b682bed8..cfeefb2106 100644 --- a/datasets/K042_1964_1965_NZ_2.json +++ b/datasets/K042_1964_1965_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1964_1965_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A mineralisation survey was conducted in the Koettlitz-Blue Glacier and Taylor Valley region because previous work in these areas mapped Precambrian basement rocks similar to those found in mineralised areas in Australia, South Africa, Canada and Scandinavia. The geological environment in these areas was examined and mineralised boulders in the moraines were investigated. Environments in the area considered most likely to be mineralised are faults, amphibolite-marble-faults contacts, granite-marble contacts (Skarns) and pegmatitie dykes. Very few faults were mapped in the region and none were accessible. Several small faults were examined and found to be barren. Soil samples were collected in the vicinity of faults and examined for copper and zinc. Amphibolite was found to be generally present in minor amounts within metasediments which are mainly marbles but field examination indicated that these were unfavourable for mineralisation. Granite-marble contacts were generally barren, but minor amounts of pyrrhotite and lesser chalcopyrite were found and traces of malachite were present at most localities. Numerous pegmatites were examined but they were invariably small and of a type commonly found in granite but rarely associated with mineralisation. The Koettlitz-Blue Glacier and Taylor Valley region is characterised by a lack of sulphides and must be regarded as generally unfavourable to base metal sulphide mineralisation. No appreciable quantities of industrial minerals were located during the survey, apart from marbles which are abundant and in most cases of apparently high quality. Thirty soil samples were collected in the region and will be analysed for copper and zinc to test the effectiveness of geochemical prospecting in the region.", "links": [ { diff --git a/datasets/K042_1976_1977_NZ_3.json b/datasets/K042_1976_1977_NZ_3.json index 9629e78048..94d9aeae38 100644 --- a/datasets/K042_1976_1977_NZ_3.json +++ b/datasets/K042_1976_1977_NZ_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1976_1977_NZ_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A quantitative survey of the ecology of mosses in the McMurdo Sound region was conducted in the 1976/77 field season. Moss was found around streams below the Rhone, Hughes and Calkin Glaciers in the Taylor Valley, the moraines below the Hobbs Glacier and in the Salmon, Garwood and Towle Valleys, and in the Scott Base, McMurdo Station areas. Other areas searched where moss was not found included Kennar and Beacon Valleys, the area below La Croix Glacier and the side of the Taylor Valley around Lake Conney not near melt streams below alpine glaciers and the Towle Valley. Algae and lichen were recorded from most of the areas visited. Detailed quantitative surveys of moss were done below the Rhone, Calkin and Hughes Glacier and on the delta below the snout of the Hobbs Glacier. Air spore samples were collected daily, fresh algae was collected from Lake Fryxell and Lake Vanda for C14 dating standards and soils were sampled for tests for microorganisms, pH, carbon and nitrogen content.", "links": [ { diff --git a/datasets/K042_1979_1980_NZ_3.json b/datasets/K042_1979_1980_NZ_3.json index e007e77476..03ef0273cf 100644 --- a/datasets/K042_1979_1980_NZ_3.json +++ b/datasets/K042_1979_1980_NZ_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1979_1980_NZ_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A gravity survey of the lower Taylor Valley, from New Harbour to the Suess Glacier was completed in the 1977-1978 field season to tie in with the Dry Valley Drilling Project (DVDP) holes and to trace the bedrock profile as part of the DVDP. In the 1979-1980 season, a gravity survey of the Dry Valleys was designed to compliment sea ice gravity surveys made during the same season and to fill gaps in the existing data measured by Bull (1962, 1964), Smithson (1971), Stern (1978), Hicks (1978) and Hicks and Bennet (1981). A detailed gravity traverse was completed down the Taylor Valley from Northwest Mountain to the sea, with stations at 1 to 3 km intervals. Gravity readings were also made at approximately 10km spacings in the Lower Ferrar and on the Dailey Islands.", "links": [ { diff --git a/datasets/K042_1980_1981_NZ_1.json b/datasets/K042_1980_1981_NZ_1.json index c8694ffc73..051cd692bc 100644 --- a/datasets/K042_1980_1981_NZ_1.json +++ b/datasets/K042_1980_1981_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1980_1981_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A seismic refraction survey was conducted on sea ice near Butter Point to provide data on sediment thickness for possible further drilling and to investigate the cause of a reported gravity anomaly. 12 vertical geophones were spaced at 29.95m intervals, frozen in to holes chipped in the sea ice and covered by 100-200mm of snow. Two reverse lines were shot, using four shot points.", "links": [ { diff --git a/datasets/K042_1982_1983_NZ_2.json b/datasets/K042_1982_1983_NZ_2.json index df725e5d19..857c8eaf87 100644 --- a/datasets/K042_1982_1983_NZ_2.json +++ b/datasets/K042_1982_1983_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1982_1983_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A seismic refraction survey was conducted on sea ice at New Harbour and the Dailey Islands to provide data on sediment thickness for possible further drilling for Cenozoic investigations in the Western Ross Sea. At New Harbour, two seismic lines, each 8.66km long with shot points at each end and at the centre were laid out in the form of a cross. Water depth was measured at each shot site. At the Dailey Islands, sea bottom depth and dip along the seismic line were determined at each spread by stacking sledge hammer blows on the ice. Two 8.66km lines similar to those at New Harbour were laid out in the for of a \"T\". Four extra shot points were incldued on line A because a complex sea bottom was expected near the islands.", "links": [ { diff --git a/datasets/K042_1990_1991_NZ_2.json b/datasets/K042_1990_1991_NZ_2.json index c5b819c099..32a6da0dc0 100644 --- a/datasets/K042_1990_1991_NZ_2.json +++ b/datasets/K042_1990_1991_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K042_1990_1991_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 1:20,000 scale geological map of Allan Hills and acompanying text was competed with the Weller Coal Measures being mapped to member level. Additional geographic control was established using a total station and three GPS sites. Three cairns were established near the head of Manhaul Bay and tied into the GPS network.", "links": [ { diff --git a/datasets/K043_1980_1982_NZ_1.json b/datasets/K043_1980_1982_NZ_1.json index b1ad7c2629..4667002915 100644 --- a/datasets/K043_1980_1982_NZ_1.json +++ b/datasets/K043_1980_1982_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K043_1980_1982_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The paleohydraulic Triassic alluvial plain sequence at the head of the Dry Valleys was studied. The Triassic Beacon Supergroup is divided into five stratigraphic units (The Fleming Member of the Feather Conglomerate and the Members A-D of the Lashly Formation) and all are exposed at Mt Bastion where this study was concerned. A detailed investigation of each unit was conducted to determine the paleohydraulic regimes operating during the Triassic deposition. The character of the river system (sinuosity, channel width, depth, slope, discharge, etc) was determined from features of the sedimentary sequence.", "links": [ { diff --git a/datasets/K043_2006_2007_NZ_1.json b/datasets/K043_2006_2007_NZ_1.json index 4dad7c7993..742d8b6358 100644 --- a/datasets/K043_2006_2007_NZ_1.json +++ b/datasets/K043_2006_2007_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K043_2006_2007_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical, geographic and biological data were linked into a mathmatical model of population dynamics to integrate and explain the changes in biodiversity of phytoplankton, bacteria and cyanobacteria in ice covered marine ecosystems at three coastal Antarctic sites (Terra Nova Bay, Granite Harbour and Cape Evans) over several seasons. Data for the model was collected from each site in different seasons. In this way, the model changes with latitude in the relative contributions from each community as well as changes in species composition and distribution. Over the course of study, repeat samplings at each site in different years will facilitate a build of a series of models that describe the biodiversity and health of microbial populations at each site, to enable a better understanding of their ecosystem function and the pressures they may be under. Satellite imagery of ice distributions, thickness and snow cover, and weather patterns were linked with latitudinal variations in biological data, and models of population structure and dynamics were developed. The data that was incorporated into the model included total biomass, chlorophyll content, rates of productivity, species distributions and abundances of microbial organisms within sea ice and in the water beneath. Where possible, variations in local conditions such as snow cover, ice thickness, surface and under ice irradiance were included.", "links": [ { diff --git a/datasets/K043_2006_2008_NZ_2.json b/datasets/K043_2006_2008_NZ_2.json index 75714cc986..c0f6d09d45 100644 --- a/datasets/K043_2006_2008_NZ_2.json +++ b/datasets/K043_2006_2008_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K043_2006_2008_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three ice cores were drilled in sea ice (2.1 m thick) in the region of Gondwana Station in Terra Nova Bay during the 06-07 season. The cores were stored in black plastic bags and then replaced back within the same hole but in reverse order so that the algae from the bottom of the ice were now at the surface of the ice and the ice at the ice surface were now at the ice water interface at the bottom of the sea ice. An additional three profile cores were also drilled but were replaced back into their original holes in the normal configuration as a control. A further 3 cores were then extracted from the ice and processed for chlorophyll, cell numbers and species composition etc as above. At the end of the deployment period the six cores still in the ice were redrilled and extracted from the ice and samples also taken for chlorophyll, cell numbers and species composition as above. A further 3 cores of undisturbed ice were also taken.", "links": [ { diff --git a/datasets/K048_1992_1993_NZ_1.json b/datasets/K048_1992_1993_NZ_1.json index 382a5c2f3d..1c7d1f6e3a 100644 --- a/datasets/K048_1992_1993_NZ_1.json +++ b/datasets/K048_1992_1993_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K048_1992_1993_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lithospheric xenoliths are a convenient and relatively cost efficient means of gaining an insight into the petrology of the deep earth. As such, they provide important information on lithospheric structure and processes and can be used to gauge thermal regime and possibly , the timing of events. Lithospheric xenoliths were collected in the 1989/90 and 1990/91 season from Marie Byrd Land, West Antarctica, including Mt Waesche, Mt Sidley, Mt Cumming, Mt Hampton and the USAS Escarpment (Mt Aldaz) in the Executive Committee Range and Mt Murphy in the Mount Murphy Volcanic Complex. Further samples were collected in the 1992/93 season from the McMurdo Volcanic Province at a number of localities on and adjacent to Ross Island (Hut Point Peninsula (Half Moon Crater, Sulphur Cones, Turtle Rock) and Cape Bird), Black Island and in the foothills of the Transantarctic Mountains (Foster Crater on the Koettlitz Glacier). The majority of the samples collected in the 1992/93 season supplemented a collection compiled from the 1982/83 and 1984/85 season. The xenoliths vary from texturally variable, spinel lherzolites and dunites representative of upper mantle assemblages to ultramafic Al-augite kaersutite bearing ultramafic rocks and plagioclase bearing ultramafic to mafic granulites thought to represent the transition zone between upper mantle and lower crust.", "links": [ { diff --git a/datasets/K052_1982_1983_NZ_4.json b/datasets/K052_1982_1983_NZ_4.json index 4ec61f64d4..c1c3b01d15 100644 --- a/datasets/K052_1982_1983_NZ_4.json +++ b/datasets/K052_1982_1983_NZ_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K052_1982_1983_NZ_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples were collected from the crater of Mt Erebus. Yeast glucose agar and penicillin and streptomycin was used to culture thermophilic microbes, fungi and actinomycetes. Several thermophilic microbes, fungi and actinomycetes were isolated and established in pure culture.", "links": [ { diff --git a/datasets/K052_1982_1983_NZ_5.json b/datasets/K052_1982_1983_NZ_5.json index c1cf69a0da..8e3d6049c4 100644 --- a/datasets/K052_1982_1983_NZ_5.json +++ b/datasets/K052_1982_1983_NZ_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K052_1982_1983_NZ_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A small perspex frame was placed over bare mineral soil adjacent to the mosses in Keble Valley to examine the effects of humidity, temperature and microclimate on plant establishment. Many green shoots and algae were observed within the frame whilst the control site was bare of vegetation. The area was resurveyed a year later. A six channel temperature probe was used to test the microclimate.", "links": [ { diff --git a/datasets/K054_1988_1989_NZ_1.json b/datasets/K054_1988_1989_NZ_1.json index 673ec8fd72..8903f67582 100644 --- a/datasets/K054_1988_1989_NZ_1.json +++ b/datasets/K054_1988_1989_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K054_1988_1989_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A dive site was selected at Cape Armitage to conduct a marine benthos survey. The water was approximately 25m deep and the bottom was found to be rocky and inhabited by sponges. Four sponge species were grafted in an exercise to test the sponges ability to recognise self from non-self tissue and to examine any immune response. The experiments also allowed for the examination of the genetic relatedness among individuals on the reef. Grafter were made by cutting 1cm3 pieces of tissue from a donor sponge and embedding them in replicate host sponges of the same species at varying distances from the donor. Grafters were left in place for up to one week and were monitored daily. At the completion of the experiment, the graft site was excised from the host and frozen for further analysis.", "links": [ { diff --git a/datasets/K054_1988_1989_NZ_3.json b/datasets/K054_1988_1989_NZ_3.json index 6541967338..bb9318141c 100644 --- a/datasets/K054_1988_1989_NZ_3.json +++ b/datasets/K054_1988_1989_NZ_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K054_1988_1989_NZ_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to determine the grazing pressure of starfish and sea urchin species on the benthic community of a reef at Cape Armitage, a survey was made of these species densities. The survey was stratified by depth. All individuals encountered in five 20m x 1m transects at each depth level were identified and measured. Each animal was examined in order to identify any species. Twelve further 1m x 1m quadrats were examined in detail specifically to look for smaller individuals.", "links": [ { diff --git a/datasets/K057_1999_2000_NZ_2.json b/datasets/K057_1999_2000_NZ_2.json index 87c84ec9b8..00bfab7738 100644 --- a/datasets/K057_1999_2000_NZ_2.json +++ b/datasets/K057_1999_2000_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K057_1999_2000_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Captured Pagothenia borchgrevinki fish were placed into an aquarium and partitioned into tanks as all healthy, all x-cell or a mixture of the two. Lengths and weights of all fish were measured and the degree of infection was determined for all affected fish. Fish were left in this set up for one month. At the end of the month, the death rate of the fish was measured to help determine unknown factors of the disease such as what the disease is, how is it spread, how quickly does it travel along the gills of individual fish, what happens when 100% of a fishes fills become covered with the disease and does the fish recover? Samples of healthy and x-cell affected tissues were collected for analysis.", "links": [ { diff --git a/datasets/K061_1986_1987_NZ_2.json b/datasets/K061_1986_1987_NZ_2.json index 0627e2c2a7..c2ad95e214 100644 --- a/datasets/K061_1986_1987_NZ_2.json +++ b/datasets/K061_1986_1987_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K061_1986_1987_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A detailed study of the Olympus Granite Gneiss with particular emphasis on foliation development and its relationship to deformation of Koettlitz Group metasediments, in an attempt to understand its origin was undertaken with a three stage investigation. Firstly, the Olympus Granite Gneiss in the Bull Pass area was studied and sampled with emphasis on its relation to Dais Granite. Secondly, the Koettlitz Group metasediment was studied and sampled looking in detail at anatectic processes associated with deformation of these rocks, including mapping and measuring sections of both Olympus Granite Gneiss/Koettlitz Group contacts. Thirdly, the 'classic locality' of Dais Granite was studied and this rock-types relationship to highly deformed rocks mapped by earlier workers. Laboratory work included detailed structural analysis at all scales, petrographic studies and geochemical analyses.", "links": [ { diff --git a/datasets/K061_1992_1995_NZ_1.json b/datasets/K061_1992_1995_NZ_1.json index 9552138989..e03b5502a8 100644 --- a/datasets/K061_1992_1995_NZ_1.json +++ b/datasets/K061_1992_1995_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K061_1992_1995_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (Wilson Terrane) was carried out over three field seasons to determine a) if the two groups could be correlatives, b) the nature of their relationship and c) to account for the difference in strain between them. The effect of plutons on regional and local structure of the Wilson Terrane was examined. The Renegar Glacier was mapped in detail and a study of high strain zones between Koettlitz Group and mafic plutonic bodies was assessed. Samples of plutonic mafic rocks were taken to analyse the chemical and mineralogical response of these rocks to high strain. Detailed mapping of the Skelton Group was carried out around the Cocks Glacier from north of Baronick Glacier to Red Dyke to the SW ridge of Mt Cocks. The lithologies were examined and the stratigraphy at three different localities was established on local and regional scales. North of the Renegar Glacier, the Koettlitz group was also examined. Samples, orientated to distinctive lithogies, were collected. The variation in strain was noted, large bodies of orthogneiss was examined structurally and lithologically and sampled for dating. The outcrop of the Skelton Group was mapped on the east ridge of Mt Kemp and structural relation to the neighbouring rocks was determined. The Williams Peak \u2013 Hobbs Peak area was mapped in detail and salmon marble was sampled. The nature of the eastern contact of the Bonney Pluton and the effect of the intrusion of this pluton into the Koettlitz Group was examined. The type section of the Hobbs formation was studied along the east ridge of Hobbs Peak with the degree of strain ascertained. Outcrops and rocks were examined at Radian Ridge, Mount Cocks, Preistly Glacier, Salient Glacier and Substitution Ridge. Field notes and samples were taken along the way to establish the relationships between tectonic and metamorphic sub-areas. Granite, schists, diorite and gabbro were sampled from Panorama Glacier, Marshall Valley, Taylor Valley, Walker Rocks, and Campbell Glacier to propose an indication of the original environment of initial formation of the rocks and provided insight into the processes operating at varying crustal levels during orogenesis. At Mt Dromedary, a sequence was examined for the significant shear zone separating two distinct structural blocks, inferred from pervious mapping. At Teal Island the area was examined and found sediments and rocks which link between the lithologies of the Skelton area. At Mt Huggins a subsidiary ridge was examined finding undeformed metasediments.", "links": [ { diff --git a/datasets/K061_2001_2002_NZ_2.json b/datasets/K061_2001_2002_NZ_2.json index 7baaa2695a..320cbaa53a 100644 --- a/datasets/K061_2001_2002_NZ_2.json +++ b/datasets/K061_2001_2002_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K061_2001_2002_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The contact relationship between volcanic deposits and surrounding country rocks of the Beacon Supergroup are steep over a large area. Beyond the landslide deposits along the contact between Beacon country rock and Mawson volcaniclastic rocks lies the Mawson itself. An area in which the remains of a single vent of the vent complex was well exposed, on both steep and subhorizontal ground surfaces, was mapped in detail with the geometric relationships between different bodies of volcaniclastic rock examined. The characteristics of the processes that cause one body of debris to be apparently shot through the other was investigated. Standard geological mapping techniques, photographs, scaled sketches and rock samples were used to create a 3-dimensional reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption.", "links": [ { diff --git a/datasets/K062_2003_2004_NZ_1.json b/datasets/K062_2003_2004_NZ_1.json index 327f271533..caf986822e 100644 --- a/datasets/K062_2003_2004_NZ_1.json +++ b/datasets/K062_2003_2004_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K062_2003_2004_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "It is suggested that the Ross Orogeny is composed of a wide variety of crustal slices that are exotic to their present location and were accreted to the East Antarctic craton during the lower paleozoic Ross Orogeny. To test this hypothesis, rocks (metasedimentary rocks and granite) were sampled from crustal slices in both the Skelton Glacier and Royal Society Ranges including Renegar Glacier area, lower Radian Ridge, Rucker Ridge, Gloomy Hill, the Radian Glacier area, the upper Skelton Glacier area and Stepaside Spur. Samples were crushed and processed through heavy liquids and magnetic separation to isolate detrital grain of zircon and analysed by LAP-ICP-MS and their ages determined. The provenance, or source, of the detrital zircons can also be assessed from the specific characteristics of the age histogram. This enables (a) ready comparison between individual crustal slices to assess whether they originated in the same place prior to accretion and (b) it allows reconstruction of the terranes at the time of sedimentation and (c) it offers the possibility of determining the likely distance of travel of so called exotic terrances prior to accretion.", "links": [ { diff --git a/datasets/K063_1987_1988_NZ_2.json b/datasets/K063_1987_1988_NZ_2.json index 8aa7f67407..4fd59b0866 100644 --- a/datasets/K063_1987_1988_NZ_2.json +++ b/datasets/K063_1987_1988_NZ_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K063_1987_1988_NZ_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As an index of physiological condition and success of foraging, penguins were weighed early in the season when they were flipper banded and then re-weighed when they returned from their foraging trip. Three groups were compared: a control group that was left undisturbed except for the weighing, the removal group which the first egg from the nest was removed and the penned group where the female were prevented from going to sea for their first foraging trip by being placed in a pen for 4 days. These observations will contribute to the determination of any annual fluctuations in the success of penguin foraging.", "links": [ { diff --git a/datasets/K065_1996_1998_NZ_1.json b/datasets/K065_1996_1998_NZ_1.json index 58f808dd7b..afe45d63c3 100644 --- a/datasets/K065_1996_1998_NZ_1.json +++ b/datasets/K065_1996_1998_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K065_1996_1998_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Animlas can be harmed by artificially introduced chemicals either through the food chain or directly. This study aimed to determine how penguins detoxify chemical pollutants they may be exposed to. Liver samples were collected from Adelie penguins from Cape Bird, Cape Royds and Cape Crozier, both adults (10) and chicks (20). The samples were analysed for liver enzymes with the aim to characterize different P450 enzymes involved in biotransformation and detoxification of chemical pollutants. The aim is to determine the susceptibility of Antarctic penguins to environmental chemicals.", "links": [ { diff --git a/datasets/K081_1983_1986_NZ_1.json b/datasets/K081_1983_1986_NZ_1.json index aff9bc4cf9..a6cc3cad06 100644 --- a/datasets/K081_1983_1986_NZ_1.json +++ b/datasets/K081_1983_1986_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K081_1983_1986_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A three year study of lakes and stream of southern Victoria Land was conducted from 1983-1986. In the first season, the algal composition and physico-chemical characteristics of South Victoria Land Streams was investigated. Four rivers were visited in the coure of the summer, once early in the season before they had begun to flow, and then several weeks later when discharge was near to its annual maximum. An additional 8 streams were examined less intensively in the course of the season. These all include Adams, Whangamata, Onyx, Bird, Salmon, Bartley, Fryxell, Commonwealth, Stream CC1 and CC2, Harrison and Miers. \n\nSpecific studies included \n1) Overwintering algal biomass: naturally freeze-dried algal mats were quantified by transect analysis and by chlorophyll a samples, \n2) Chlorophyll a biomass levels: stream samples were taken from areas with visually maximum biomass at each site during early and late season and assayed by fluorometer or spectrophotometer, \n3) Algal community structure: taxonomic analysis of the stream periphyton, \n4) Algal growth and production: artificial substrates deployed and processed for chlorophyll a analysis, 5) Metabolic responses by Antarctic stream algae: recovery from freeze dry conditions (early season) and nutrient uptake by developed communities, \n6) Dissolved organic carbon: measured from water samples, \n7) Nutrient extraction from stream bed soils: nitrogen and phosphorous released after soaking for 12 hours in glacier melt water, \n8) Stream nutrient levels: chemical analysis of water samples, \n9) Diurnal studies on variability in nutrient concentrations: monitoring stream parameters every three hours for a 26 hour period, \n10) Lake Miers studies: a broad range of limnological measurements made at Lake Miers, possibly the southern most meromictic waterbody no the continent. \n\nIn the second season, studies were further extended on the epilithic algal and bacterial communities of southern Victoria Land streams to follow respiratory and photosynthetic carbon metabolism by communities at two select stream sites. Nitrogen cycling and photosynthetic metabolism in Lake Fryxell and Lake Vanda was also examined. In the third and final season, preliminary analysis of waters on the McMurdo Ice Shelf and the structure and metabolic properties of the stream algal mats, with special reference to temperature, light and nutrient effects and factors controlling nitrogen cycling, and photosynthesis in Dry Valley lakes (Lake Fryxell, Lake Vanda and Lake Miers), with particular attention to the deep chlorophyll maximum was studied.", "links": [ { diff --git a/datasets/K089_2001_2008_NZ_1.json b/datasets/K089_2001_2008_NZ_1.json index 6ef1cc524d..36abbcbb7c 100644 --- a/datasets/K089_2001_2008_NZ_1.json +++ b/datasets/K089_2001_2008_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K089_2001_2008_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data.", "links": [ { diff --git a/datasets/K089_2001_2012_NZ_1.json b/datasets/K089_2001_2012_NZ_1.json index 6482ab9e6c..29646172a9 100644 --- a/datasets/K089_2001_2012_NZ_1.json +++ b/datasets/K089_2001_2012_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K089_2001_2012_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data.\n\nThe tide gauge records data at 5 minute intervals. Annually LINZ (Land Information New Zealand)calibrate the tide gauge over four tide cycles. A geodetic grade GPS receiver is set up on the sea ice near the tide gauge and another is set up on a permanent reference mark ashore. The GPS \u201cobserves\u201d the rise and fall of the tide by measuring the changing height of the sea ice. A hole is drilled through the sea ice to enable the height of the reference point of the GPS receiver above the sea surface to be determined. The relationship of the height of the shore-based reference mark and the zero of the sea level sensor is known. These connections enable the height of the sea surface as determined by the sea level sensor to be compared to the height as determined by the GPS measurements.\n", "links": [ { diff --git a/datasets/K089_2001_2013_NZ_1.json b/datasets/K089_2001_2013_NZ_1.json index 4a5e7caf62..1634c1ba6f 100644 --- a/datasets/K089_2001_2013_NZ_1.json +++ b/datasets/K089_2001_2013_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K089_2001_2013_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data.\n\nThe tide gauge records data at 5 minute intervals. Annually LINZ (Land Information New Zealand)calibrate the tide gauge over four tide cycles. A geodetic grade GPS receiver is set up on the sea ice near the tide gauge and another is set up on a permanent reference mark ashore. The GPS \u201cobserves\u201d the rise and fall of the tide by measuring the changing height of the sea ice. A hole is drilled through the sea ice to enable the height of the reference point of the GPS receiver above the sea surface to be determined. The relationship of the height of the shore-based reference mark and the zero of the sea level sensor is known. These connections enable the height of the sea surface as determined by the sea level sensor to be compared to the height as determined by the GPS measurements.", "links": [ { diff --git a/datasets/K10_SST-NAVO-L4-GLOB-v01_1.0.json b/datasets/K10_SST-NAVO-L4-GLOB-v01_1.0.json index 71ba118565..4b227868ee 100644 --- a/datasets/K10_SST-NAVO-L4-GLOB-v01_1.0.json +++ b/datasets/K10_SST-NAVO-L4-GLOB-v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K10_SST-NAVO-L4-GLOB-v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis dataset produced daily on an operational basis by the Naval Oceanographic Office (NAVO) on a global 0.1x0.1 degree grid. The K10 (NAVO 10-km gridded SST analyzed product) L4 analysis uses SST observations from the following instruments: Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The AVHRR data for this comes from the MetOp-A, MetOp-B, and NOAA-19 satellites; VIIRS data is sourced from the Suomi_NPP satellite; SEVIRI data comes from the Meteosat-8 and -11 satellites. The age (time-lag), reliability, and resolution of the data are used in the weighted average with the analysis tuned to represent SST at a reference depth of 1-meter. Input data from the AVHRR Pathfinder 9km climatology dataset (1985-1999) is used when no new satellite SST retrievals are available after 34 days. Comparing with its predecessor (DOI: https://doi.org/10.5067/GHK10-L4N01 ), this updated dataset has no major changes in Level-4 interpolated K10 algorithm, except for using different satellite instrument data, and updating metadata and file format. The major updates include: (a) updated and enhanced the granule-level metadata information, (b) converted the SST file from GHRSST Data Specification (GDS) v1.0 to v2.0, (c) added the sea_ice_fraction variable to the product, and (d) updated the filename convention to reflect compliance with GDS v2.0.", "links": [ { diff --git a/datasets/K112_1990_1991_NZ_1.json b/datasets/K112_1990_1991_NZ_1.json index 8ed14edf15..5c751ac913 100644 --- a/datasets/K112_1990_1991_NZ_1.json +++ b/datasets/K112_1990_1991_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K112_1990_1991_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DSIRGEO mapping programme in the 1990/91 season was designed to link the area covered in 1989/90 (Convoy Range) with that covered in 1988/89 (Thundergut Sheet). The eventual aim of the programme is to produce a revised geology of Southern Victoria Land at a scale of 1:250,000. All rock types in the area between the central Wright Valley and the Mackay Glacier, from the Miller Glacier to west of the Victoria Valley were mapped at 1:50,000. The resulting St Johns map sheet will also incorporate previous studies. Field work aimed to establish the extent and intrusive relationships of the various granitoid plutons known to exist in the area and relate them to the area mapping in 1988/89 season to the south. The extent and nature of the small areas of Beacon sediments was also covered. Five major rock groups were mapped including Koettlitz Group metasediments and associated orthogneisses, granitoid plutons and related dikes, Beacon Supergroup sediments, Ferrar Group dolerites and surficial glacial and fluvioglacial deposits.", "links": [ { diff --git a/datasets/K122_2004_2005_NZ_4.json b/datasets/K122_2004_2005_NZ_4.json index 4908aaebd2..5191b64b1d 100644 --- a/datasets/K122_2004_2005_NZ_4.json +++ b/datasets/K122_2004_2005_NZ_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K122_2004_2005_NZ_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In conjunction with aerial photographs of the colonies ground truth counts were made since the 1983-1984 season at the Ross Island colonies. The number of occupied nests, nests with eggs, nests with both adults present and total penguins at the colony were censused to be able to check for accuracy of the counts from aerial photographs and to assess the breeding status and condition of the birds for that year. Since 1990, ground counts of chicks at each rookey were conducted in late January to measure breeding success (number of chicks/breeding pair). Approximately 100 chicks were selected randomly at each site and they had their weight and flipper length measured to calculate a chick condition index which is comparable between years and between the rookeries.", "links": [ { diff --git a/datasets/K138_1992_1993_NZ_1.json b/datasets/K138_1992_1993_NZ_1.json index ec58d7e0ba..1ab1c88206 100644 --- a/datasets/K138_1992_1993_NZ_1.json +++ b/datasets/K138_1992_1993_NZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K138_1992_1993_NZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The performance of GPS navigation equipment for possible future deployment on Antarctic resupply flights was investigated. In addition, using Hercules C-130 aircrafts fitted with GPS, VLF propagation studies in the Antarctic region and studies of antipodally propagating VLF signals during flights to Antarctica was investigated. VLF/GPS receivers were installed on the RNZAF resupply aircrafts and recordings were made on all available New Zealand flights to the Antarctic.", "links": [ { diff --git a/datasets/K1VHR_L02_HEM.json b/datasets/K1VHR_L02_HEM.json index 8d2745ab7b..0331347a76 100644 --- a/datasets/K1VHR_L02_HEM.json +++ b/datasets/K1VHR_L02_HEM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L02_HEM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kalpana-1 VHRR Level-2B Precipitation using Hydroestimator Technique in HDF-5 Format", "links": [ { diff --git a/datasets/K1VHR_L02_OLR.json b/datasets/K1VHR_L02_OLR.json index 0a0876c3d8..8a393cfdfa 100644 --- a/datasets/K1VHR_L02_OLR.json +++ b/datasets/K1VHR_L02_OLR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L02_OLR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kalpana-1 VHRR Level-2B Outgoing Longwave Radation (OLR) in HDF-5 Format", "links": [ { diff --git a/datasets/K1VHR_L02_SGP.json b/datasets/K1VHR_L02_SGP.json index 729cdbac1c..82fd8b175c 100644 --- a/datasets/K1VHR_L02_SGP.json +++ b/datasets/K1VHR_L02_SGP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L02_SGP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KALPANA-1 VHRR Level-1C Sector Product (Geocoded, all pixels at same resolution) contains 3 channels data in HDF-5 Format", "links": [ { diff --git a/datasets/K1VHR_L02_SST.json b/datasets/K1VHR_L02_SST.json index 563a9a7222..2da7dc069f 100644 --- a/datasets/K1VHR_L02_SST.json +++ b/datasets/K1VHR_L02_SST.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L02_SST", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kalpana-1 VHRR Level-2B Sea Surface Temperature in HDF-5 Format", "links": [ { diff --git a/datasets/K1VHR_L02_UTH.json b/datasets/K1VHR_L02_UTH.json index 5c902c1c55..c1362233ba 100644 --- a/datasets/K1VHR_L02_UTH.json +++ b/datasets/K1VHR_L02_UTH.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L02_UTH", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KALPANA-1 VHRR Level-2B Upper Tropospheric Humidity (UTH) in HDF-5 Format", "links": [ { diff --git a/datasets/K1VHR_L1B_STD.json b/datasets/K1VHR_L1B_STD.json index 17dc0e649c..71993cec18 100644 --- a/datasets/K1VHR_L1B_STD.json +++ b/datasets/K1VHR_L1B_STD.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "K1VHR_L1B_STD", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KALPANA-1 VHRR Level-1B Standard Product containing 3 channels data in HDF-5 Format", "links": [ { diff --git a/datasets/KADAI-OUKA-SAKURAJIMA-1992.json b/datasets/KADAI-OUKA-SAKURAJIMA-1992.json index 31b8b55a2b..50c54f3620 100644 --- a/datasets/KADAI-OUKA-SAKURAJIMA-1992.json +++ b/datasets/KADAI-OUKA-SAKURAJIMA-1992.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KADAI-OUKA-SAKURAJIMA-1992", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precipitation, pH, SO4 and CL from rainfall were collected during one\nmonth. They were measured by the English standard deposit gauge at\n10-odd points in Kagoshima City since 1987. Measurments were also\ntaken in Kagoshima City and in the Sakurajima area from 1978 to 1986.\n\nSOx in the atmosphere (average value for one month) and NOx\n(24hr) were both measured by the SOx adsorption method (1978-1986).\nThe NOx badge method has also been used since 1987.", "links": [ { diff --git a/datasets/KAIMIMOANA_0.json b/datasets/KAIMIMOANA_0.json index 1e878bd828..a2fd2ed075 100644 --- a/datasets/KAIMIMOANA_0.json +++ b/datasets/KAIMIMOANA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KAIMIMOANA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the NOAA ship, the Kaimimoana between 1999 and 2002.", "links": [ { diff --git a/datasets/KFDBAM_ANU_1.json b/datasets/KFDBAM_ANU_1.json index f3c173c206..653c427a2e 100644 --- a/datasets/KFDBAM_ANU_1.json +++ b/datasets/KFDBAM_ANU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KFDBAM_ANU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Records from 69 sites, covering the whole island.\nSites stratified by topography (2 classes: slopes or drainage lines), altitude (4 classes: 0-100 m, 100-200 m, 200-300 m, 300m+), vegetation type (5 types), aspect (2 classes: E,W), north-south position on island (3 classes: north, middle, south).\nPitfall traps and yellow pan traps opened for 6 weeks (summer).\nHand searches for worms, slugs and snails.\nSpecimens identified to species level.\nWe used the data to construct statistical models of the spatial distribution of species in relation to the above variables.\nIntended as a baseline survey to detect, monitor and predict effects of climate change and local human impacts (e.g. alien species introductions) on biota.\n\nThis work was carried out as part of ASAC project 104 (ASAC_104).\n\nThe fields in this dataset are:\n\nSite\nTopography\nRegion\nAspect\nAltitude\nVegetation Type\nMethod\nNotes\nSpecies\n\nThe detergent column indicates whether a drop of detergent was added to the yellowpans or not. A 1 = yes.", "links": [ { diff --git a/datasets/KILVOLC_FlowerKahn2021_1.json b/datasets/KILVOLC_FlowerKahn2021_1.json index f8425da3b0..9edae4f130 100644 --- a/datasets/KILVOLC_FlowerKahn2021_1.json +++ b/datasets/KILVOLC_FlowerKahn2021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KILVOLC_FlowerKahn2021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KILVOLC_FlowerKahn2021_1 dataset is the MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information version 1 dataset. It comprises MISR-derived output from a comprehensive analysis of Kilauea volcanic eruptions (2000-2018). Data collection for this dataset is complete. The data presented here are analyzed and discussed in the following paper: Flower, V.J.B., and R.A. Kahn, 2021. Twenty years of NASA-EOS multi-sensor satellite observations at K\u012blauea volcano (2000-2019). J. Volc. Geo. Res. (in press).\r\n\r\nThe data is subdivided by date and MISR orbit number. Within each case folder, there are up to 11 files relating to an individual MISR overpass. Files include plume height records (from both the red and blue spectral bands) derived from the MISR INteractive eXplorer (MINX) program, displayed in: map view, downwind profile plot (along with the associated wind vectors retrieved at plume elevation), a histogram of retrieved plume heights and a text file containing the digital plume height values. An additional JPG is included delineating the plume analysis region, start point for assessing downwind distance, and input wind direction used to initialize the MINX retrieval. A final two files are generated from the MISR Research Aerosol (RA) retrieval algorithm (Limbacher, J.A., and R.A. Kahn, 2014. MISR Research-Aerosol-Algorithm: Refinements For Dark Water Retrievals. Atm. Meas. Tech. 7, 1-19, doi:10.5194/amt-7-1-2014). These files include the RA model output in HDF5, and an associated JPG of key derived variables (e.g. Aerosol Optical Depth, Angstrom Exponent, Single Scattering Albedo, Fraction of Non-Spherical components, model uncertainty classifications and example camera views). \r\n\r\nFile numbers per folder vary depending on the retrieval conditions of specific observations. RA plume retrievals are limited when cloud cover was widespread or the solar radiance was insufficient to run the RA. In these cases the RA files are not included in the individual folders. \r\nIn cases where activity was observed from multiple volcanic zones in a single overpass, individual folders containing data relating to a single region, are included, and defined by a qualifier (e.g. '_1').", "links": [ { diff --git a/datasets/KOMPSAT-2.ESA.archive_9.0.json b/datasets/KOMPSAT-2.ESA.archive_9.0.json index 0523aac754..06f89fffe2 100644 --- a/datasets/KOMPSAT-2.ESA.archive_9.0.json +++ b/datasets/KOMPSAT-2.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOMPSAT-2.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kompsat-2 ESA archive collection is composed by bundle (Panchromatic and Multispectral separated images) products from the Multi-Spectral Camera (MSC) onboard KOMPSAT-2 acquired from 2007 to 2014: 1m resolution for PAN, 4m resolution for MS. Spectral Bands: \u2022 Pan: 500 - 900 nm (locate, identify and measure surface features and objects primarily by their physical appearance) \u2022 MS1 (blue): 450 - 520 nm (mapping shallow water, differentiating soil from vegetation) \u2022 MS2 (green): 520 - 600 nm (differentiating vegetation by health) \u2022 MS3 (red): 630 - 690 nm (differentiating vegetation by species) \u2022 MS4 (near-infrared): 760 - 900 nm (mapping vegetation, mapping vegetation vigor/health, Differentiating vegetation by species)", "links": [ { diff --git a/datasets/KOMPSAT-2.json b/datasets/KOMPSAT-2.json index c1a1820943..f3f1294c2b 100644 --- a/datasets/KOMPSAT-2.json +++ b/datasets/KOMPSAT-2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOMPSAT-2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KOMPSAT-2 allows the generation of high resolution images with a GSD of better than 1 m for PAN data and 4 m for MS data with nadir viewing condition at the nominal altitude of 685 km. The MSC has a single PAN spectral band between 500 - 900 nm and 4 MS spectral bands between 450-900 nm. PAN imaging and MS imaging can be operated simultaneously during mission operations. The swath width is greater than or equal to 15 km at the mission altitude for PAN data and MS data. The system is equipped with a solid state recorder to record images not less than 1,000km long at the end of life. The satellite can be rolled up to \u00b130 degrees off-nadir to pre-position the MSC swath. The KOMPSAT-2 can provide across-track stereo images by multiple passes of the satellite using off-nadir pointing capability. The satellite is compatible with daily revisit operation by off-nadir pointing with degraded GSD. Also, the image products according to the requested products quality standard can be made within one (1) day after satellite passes over the KGS.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000001_1.json b/datasets/KOPRI-KPDC-00000001_1.json index ad9722c8bf..d0125eaa02 100644 --- a/datasets/KOPRI-KPDC-00000001_1.json +++ b/datasets/KOPRI-KPDC-00000001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 07 Dec. to 14 Dec. (7 days) in 2007. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) and 7 researcher in the cruise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000002_1.json b/datasets/KOPRI-KPDC-00000002_1.json index afb66a5fc5..0701eaa6db 100644 --- a/datasets/KOPRI-KPDC-00000002_1.json +++ b/datasets/KOPRI-KPDC-00000002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out the fifth year project as step 3 project in the last annual of \u2018The Antarctic Undersea Geological Survey\u2019 was conducted in the northern Fowell Basin of the Weddell Sea. The research period was from 25 Nov. to 9 Dec. (15 days) in 2004. Geophysical research including acquisition of multi-channel seismic data was preceded. According to the results of seismic investigation, the drilling investigation was conducted at the coring point. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) and 12 researcher in the cruise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000003_1.json b/datasets/KOPRI-KPDC-00000003_1.json index 47bdd5c961..b4acf053b9 100644 --- a/datasets/KOPRI-KPDC-00000003_1.json +++ b/datasets/KOPRI-KPDC-00000003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 3 project in year 4 of \u2018The Antarctic Undersea Geological Survey\u2019 was conducted in the Powell Basin (IV region) of the northern Weddell Sea, Antarctica.\nBecause Korea doesn't have an icebreaker for Antarctic research, during the Antarctic site survey period, research ships are secured and conducted through a chartering. The available chartering are limited. It's because the duration of the chartering is concentrated in the summer season like any other country. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) used on lease by NOAA in the United States as in other years. It was used from November to December, just before the NOAA use period.\nThe research period was from 24 Nov. to 9 Dec. (8 days) in 2003. After geophysical research including acquisition of multichannel seismic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 12 researchers from KOPRI, Seoul University etc. participated in the cruise as field investigation personnel.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000004_1.json b/datasets/KOPRI-KPDC-00000004_1.json index 74aa59cfe5..0792c811ea 100644 --- a/datasets/KOPRI-KPDC-00000004_1.json +++ b/datasets/KOPRI-KPDC-00000004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 3 project in year 3 of \u2018The Antarctic Undersea Geological Survey\u2019 was conducted in the Powell Basin(\u2162) of the northern Weddell Sea, Antarctica.\nThe research period was from 16 Dec. to 23 Dec. (8 days) in 2002. \n After geophysical research including acquisition of multi-channel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) and 7 researchers from \u2018Korea Ocean Research and Development Institute\u2019 participated in the cruise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000005_1.json b/datasets/KOPRI-KPDC-00000005_1.json index a6d74f6a70..6861e03288 100644 --- a/datasets/KOPRI-KPDC-00000005_1.json +++ b/datasets/KOPRI-KPDC-00000005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 3 project in year 2 of \u2018The Antarctic Undersea Geological Survey\u2019 was conducted in the Powell Basin of the northern Weddell Sea, Antarctica.\nThe research period was from 15 Dec. to 21 Dec. (7 days) in 2001. \n After geophysical research including acquisition of multichannel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and an out-of-the-way researcher participated in the cruise. We took on lease Russian \"Yuzhmorgeologiya\".", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000006_1.json b/datasets/KOPRI-KPDC-00000006_1.json index 3b8215c381..b6b00403c2 100644 --- a/datasets/KOPRI-KPDC-00000006_1.json +++ b/datasets/KOPRI-KPDC-00000006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rock samples of Prince Albert Mountains, Antarctica collected in 2011-12 austral summer season. The collection includes volcanic rocks (basalt, dolerite, hyaloclasite, and tuff) from Ferrar Supergroup and sedimentary rocks (sandstone, siltstone) from Ferrar Supergroup and Beacon Supergroup. A few plant fossil fragments and fragmentd of coals, most likely from the Beacon Supergroup, are also listed in this entry.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the David Glacier. Information on the stratigraphy of the volcanics and sedimentary succession will be helpful for understanding geological processes and paleoenvironments of the Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000007_1.json b/datasets/KOPRI-KPDC-00000007_1.json index c4fe0cb0f0..7a7841ee88 100644 --- a/datasets/KOPRI-KPDC-00000007_1.json +++ b/datasets/KOPRI-KPDC-00000007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 3 project in year 1 of \u2018The Antarctic Undersea Geological Survey\u2019 was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 3 Dec. to 11 Dec. (9 days) in 2000. After geophysical research including acquisition of seismic data, submarine topography, geomagnatic data was conducted in coring point was decided from combined geophysical data. \nWe took on lease Russian icebreaker \"Yuzhmorgeologiya\" and 13 researcher from \u2018Korea Ocean Research and Development Institute\u2019 including a field winter researcher in the cruise. \nDue to a lot of icebergs and floating ice in the area, the originally planned survey of the side lines is impossible. A survey was conducted on the modified side lines.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000008_1.json b/datasets/KOPRI-KPDC-00000008_1.json index 7ffa75d20b..44aec21212 100644 --- a/datasets/KOPRI-KPDC-00000008_1.json +++ b/datasets/KOPRI-KPDC-00000008_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000008_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the \u2161 region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation.\nAfter seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. \n10 researcher from \u2018Korea Ocean Research and Development Institute\u2019 participated in the field survey. We took on lease Russian icebreaker \"Yuzhmorgeologiya\".", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000009_1.json b/datasets/KOPRI-KPDC-00000009_1.json index d9a043ed81..20bf3a5042 100644 --- a/datasets/KOPRI-KPDC-00000009_1.json +++ b/datasets/KOPRI-KPDC-00000009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 2 project in year 1 of \u2018The Antarctic Undersea Geological Survey\u2019 in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula.\nThe research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. \nWe took on lease Norway R/V 'Polar Duke' and 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 participated as field investigation personnel.\nThe Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 \u2013channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000010_1.json b/datasets/KOPRI-KPDC-00000010_1.json index 0e67ba7dd3..06bb3e6b44 100644 --- a/datasets/KOPRI-KPDC-00000010_1.json +++ b/datasets/KOPRI-KPDC-00000010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 21 days from 2011 July 31 to August 20 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 18 stations.\nTo investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000011_1.json b/datasets/KOPRI-KPDC-00000011_1.json index d9f8cdaa3f..c44623c02f 100644 --- a/datasets/KOPRI-KPDC-00000011_1.json +++ b/datasets/KOPRI-KPDC-00000011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and 3 academic personnel participated in the cruise as field investigation personnel.\nWe took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000012_1.json b/datasets/KOPRI-KPDC-00000012_1.json index 13caa60db0..ff84c31677 100644 --- a/datasets/KOPRI-KPDC-00000012_1.json +++ b/datasets/KOPRI-KPDC-00000012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as in year 2 project of \"Antarctic submarine topography and sediment investigation\", The Field Survey of Antarctica was conducted at the end of 1995 was conducted the multi-channel Seismic Investigation and the drilling Investigation in the eastern part of the Bransfield Strait between the Antarctic Peninsula and the South Shetland Islands and near Sejong Station. We took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker for field investigation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000013_1.json b/datasets/KOPRI-KPDC-00000013_1.json index 0d04f4c397..9236503ba7 100644 --- a/datasets/KOPRI-KPDC-00000013_1.json +++ b/datasets/KOPRI-KPDC-00000013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lead (Pb), cadmium (Cd), copper (Cu) and zinc (Zn) have been measured by electrothermal atomic absorption spectrometry in various sections of the 3623m deep ice core drilled at Vostok, in central East Antarctica. The sections were dated from 240 to 410 kyear BP (Marine Isotopic Stages (MIS) 7.5 to 11.3), which corresponds to the 3rd and 4th glacial interglacial cycles before present. Concentrations are found to have varied greatly during this 170 kyear time period, with high concentration values during the coldest climatic stages such as MIS 8.4 and 10.2 and much lower concentration values during warmer periods, such as the interglacials MIS 7.5, 9.3 and 11.3. Rock and soil dust were the dominant sources for Pb, whatever the period, and for Zn and Cu and possibly Cd during cold climatic stages. The contribution from volcanic emissions was important for Cd during all periods and might have beensignificant for Cu and Zn during warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000014_1.json b/datasets/KOPRI-KPDC-00000014_1.json index 4f0cd2c857..a9bce9a8b9 100644 --- a/datasets/KOPRI-KPDC-00000014_1.json +++ b/datasets/KOPRI-KPDC-00000014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). \n- Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica.\n- Securing data for tectonic settings research in the same region.\n- Obtaining basic data for understanding marine geology and sedimentary layers in the same region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000015_1.json b/datasets/KOPRI-KPDC-00000015_1.json index 8159952733..e26d18dfc7 100644 --- a/datasets/KOPRI-KPDC-00000015_1.json +++ b/datasets/KOPRI-KPDC-00000015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey carried out as part of step 2 project in year 3 of 'The Antarctic Undersea Geological Survey' in 1999 was conducted in the periphery of the continent near Anvers Island in the northwestern part of the Antarctic Peninsula. The research period was from 27 Dec. in 1999 to 3 Jan. in 2000 (8 days). After a geophysical survey was conducted to obtain data such as seismic, submarine topography, gravity, terrestrial magnetism, drilling investigation was conducted in the coring point was decided from combined geophysics data. 13 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and an out-of-the-way researcher participated for field investigation members. We used a 'Onnuri', of 'the Korea Ocean Research Institute' to be used for Antarctic research since 1993.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000016_1.json b/datasets/KOPRI-KPDC-00000016_1.json index d4f0ed6602..6d69aa2f47 100644 --- a/datasets/KOPRI-KPDC-00000016_1.json +++ b/datasets/KOPRI-KPDC-00000016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1998 to 2006. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity \r\nsensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000017_1.json b/datasets/KOPRI-KPDC-00000017_1.json index e9a3880ced..6238354063 100644 --- a/datasets/KOPRI-KPDC-00000017_1.json +++ b/datasets/KOPRI-KPDC-00000017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes sedimentological and geochemical analyses of more than 80 \r\ngravity cores retrieved from Antarctic Peninsula region during the KARP (Korea \r\nAntarctic Research Program) cruise from 1996 to 2006. The cores are generally \r\nshorter than 10 m and represent late Pleistocene and Holocene sedimentation. \r\nThe following data were obtained for all cores: magnetic susceptibility, \r\nX-radiographs, granulometry, total carbon and nitrogen content, and total \r\norganic and inorganic carbon content. Chronology of the cores were determined \r\nby AMS radiocarbon dating method. For selected cores, diatom assemblage, trace \r\nand rare earth element concentration, stable and radiogenic isotope \r\ncompositions were analyzed.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000018_1.json b/datasets/KOPRI-KPDC-00000018_1.json index 21ab91c467..038d635a14 100644 --- a/datasets/KOPRI-KPDC-00000018_1.json +++ b/datasets/KOPRI-KPDC-00000018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Terra Nova Bay collected in 2010. Locality, habitat information for 31 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000019_1.json b/datasets/KOPRI-KPDC-00000019_1.json index 470fbbe8fc..e44825378a 100644 --- a/datasets/KOPRI-KPDC-00000019_1.json +++ b/datasets/KOPRI-KPDC-00000019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Svalbard collected in 2010. Locality, habitat information for 78 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000020_1.json b/datasets/KOPRI-KPDC-00000020_1.json index 7d0382fa3d..c4725436a8 100644 --- a/datasets/KOPRI-KPDC-00000020_1.json +++ b/datasets/KOPRI-KPDC-00000020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Islands collected in 2010. Locality, habitat information for 219 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000021_1.json b/datasets/KOPRI-KPDC-00000021_1.json index cb6fdcb7dd..8f00b7ef7d 100644 --- a/datasets/KOPRI-KPDC-00000021_1.json +++ b/datasets/KOPRI-KPDC-00000021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Cape Burks collected in 2010. Locality, habitat information for 103 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000022_1.json b/datasets/KOPRI-KPDC-00000022_1.json index a8c91455dd..c5144bf46a 100644 --- a/datasets/KOPRI-KPDC-00000022_1.json +++ b/datasets/KOPRI-KPDC-00000022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Svalbard collected in 2009. Locality, habitat information for 59 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000023_1.json b/datasets/KOPRI-KPDC-00000023_1.json index 9f9fe4ac22..8039e1a5fe 100644 --- a/datasets/KOPRI-KPDC-00000023_1.json +++ b/datasets/KOPRI-KPDC-00000023_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000023_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Island collected in 2009. Locality, habitat information for 69 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000024_1.json b/datasets/KOPRI-KPDC-00000024_1.json index 2ae3a81012..aad9ecadc5 100644 --- a/datasets/KOPRI-KPDC-00000024_1.json +++ b/datasets/KOPRI-KPDC-00000024_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000024_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Falkland collected in 2009. Locality, habitat information for 197 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000025_1.json b/datasets/KOPRI-KPDC-00000025_1.json index f17f6c8242..2bcecc51a5 100644 --- a/datasets/KOPRI-KPDC-00000025_1.json +++ b/datasets/KOPRI-KPDC-00000025_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000025_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Punta Arenas collected in 2008. Locality, habitat information for 152 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000026_1.json b/datasets/KOPRI-KPDC-00000026_1.json index 3e9d01d992..a59138854b 100644 --- a/datasets/KOPRI-KPDC-00000026_1.json +++ b/datasets/KOPRI-KPDC-00000026_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000026_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Nepal collected in 2008. Locality, habitat information for 55 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000027_1.json b/datasets/KOPRI-KPDC-00000027_1.json index 33252c68d6..5572a94cbf 100644 --- a/datasets/KOPRI-KPDC-00000027_1.json +++ b/datasets/KOPRI-KPDC-00000027_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000027_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Island collected in 2008. Locality, habitat information for 406 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000028_1.json b/datasets/KOPRI-KPDC-00000028_1.json index a66c5ed419..7d2e81ca9b 100644 --- a/datasets/KOPRI-KPDC-00000028_1.json +++ b/datasets/KOPRI-KPDC-00000028_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000028_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Island collected in 2007. Locality, habitat information for 217 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000029_1.json b/datasets/KOPRI-KPDC-00000029_1.json index 97d5ca2684..27875d7046 100644 --- a/datasets/KOPRI-KPDC-00000029_1.json +++ b/datasets/KOPRI-KPDC-00000029_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000029_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Svalbard collected in 2006. Locality, habitat information for 137 lichen samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000030_2.json b/datasets/KOPRI-KPDC-00000030_2.json index c32cb01fe9..159991406f 100644 --- a/datasets/KOPRI-KPDC-00000030_2.json +++ b/datasets/KOPRI-KPDC-00000030_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000030_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ciliate from King George Island collected in 2011, characterization based on morphology", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000031_1.json b/datasets/KOPRI-KPDC-00000031_1.json index a0a5d61e1b..229f2575e0 100644 --- a/datasets/KOPRI-KPDC-00000031_1.json +++ b/datasets/KOPRI-KPDC-00000031_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000031_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine Invertebrate samples (Crustacea) from King George Island collected in 2011, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000032_2.json b/datasets/KOPRI-KPDC-00000032_2.json index b9d788e4ac..6c7c754c11 100644 --- a/datasets/KOPRI-KPDC-00000032_2.json +++ b/datasets/KOPRI-KPDC-00000032_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000032_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ciliate from King George Island collected in 2011, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000033_2.json b/datasets/KOPRI-KPDC-00000033_2.json index a81617ac27..93a2c471d4 100644 --- a/datasets/KOPRI-KPDC-00000033_2.json +++ b/datasets/KOPRI-KPDC-00000033_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000033_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ciliate from King George Island collected in 2011, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000034_2.json b/datasets/KOPRI-KPDC-00000034_2.json index 443443c35d..860f1d2ad0 100644 --- a/datasets/KOPRI-KPDC-00000034_2.json +++ b/datasets/KOPRI-KPDC-00000034_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000034_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ciliate from King George Island collected in 2011, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000035_4.json b/datasets/KOPRI-KPDC-00000035_4.json index 0b13f386cc..17a83e8e29 100644 --- a/datasets/KOPRI-KPDC-00000035_4.json +++ b/datasets/KOPRI-KPDC-00000035_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000035_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ciliate from King George Island collected in 2011, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000036_1.json b/datasets/KOPRI-KPDC-00000036_1.json index 4c9b2c1bcd..f2a2542cd6 100644 --- a/datasets/KOPRI-KPDC-00000036_1.json +++ b/datasets/KOPRI-KPDC-00000036_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000036_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine Invertebrate samples from Arctic sea collected in 2010, characterization based on morphology and molecular data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000037_1.json b/datasets/KOPRI-KPDC-00000037_1.json index 183ebacb36..157fc8fae8 100644 --- a/datasets/KOPRI-KPDC-00000037_1.json +++ b/datasets/KOPRI-KPDC-00000037_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000037_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microalgae from Arctic Ocean collected in 2011 using the Korea ice breaker, ARAON, Locality, habitat information for marine microalgae samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000038_1.json b/datasets/KOPRI-KPDC-00000038_1.json index 8002a24337..d0b27378c8 100644 --- a/datasets/KOPRI-KPDC-00000038_1.json +++ b/datasets/KOPRI-KPDC-00000038_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000038_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microalgae from King George Island collected in 2011, Locality, habitat information for marine microalgae samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000039_1.json b/datasets/KOPRI-KPDC-00000039_1.json index dad42c1272..f581def4f9 100644 --- a/datasets/KOPRI-KPDC-00000039_1.json +++ b/datasets/KOPRI-KPDC-00000039_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000039_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nine psychrophilic polar diatom species within six genera (Chaetoceros, Fragilaria, Navicula, Nitzschia, Porosira, and Stellarima) were found near King Sejong Station, Maxwell Bay, King George Island, Antarctica and near Dasan Station, Ny-\u00c3\u2026lesund, Svalbard, in the Arctic, in November 1998 and January 2005, respectively.\nWe attempted to access the diversity of psychrophilic polar diatoms cultivated in the culture room of the Korea Polar Research Institute (KOPRI) Culture Collections for Polar Microorganisms (KCCPM) and to establish the phylogenetic relationships among diverse diatoms based on morphological and molecular data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000040_1.json b/datasets/KOPRI-KPDC-00000040_1.json index 09d433ac88..6119a00705 100644 --- a/datasets/KOPRI-KPDC-00000040_1.json +++ b/datasets/KOPRI-KPDC-00000040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diversity and biogeography of representative brown algae, the Desmarestiales and the Laminariales in the Arctic, the Antarctic and their neighbour regions including North Atlantic, southern Chile, Tasmania and South Africa were investigated. We recognized eight desmarestialean and 15 laminarialean entities based on their morphological characteristics.\nThe aim of the current investigation has been to survey on diversity and DNA barcoding of brown algae around Dasan Station in Svalbard (Spitsbergen), the Arctic, and King Sejong Station in King George Island, the Antarctic based on morphology and DNA barcoding.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000041_1.json b/datasets/KOPRI-KPDC-00000041_1.json index 75ce30e68f..496ba82b8a 100644 --- a/datasets/KOPRI-KPDC-00000041_1.json +++ b/datasets/KOPRI-KPDC-00000041_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000041_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Each six sequences were newly determined in this study.\u00a0 Molecular data from over 56 taxa of the Bangiales worldwide including previously published sequences, indicated that monophyly for the genera Bangia and Porphyra is not supported, as in previous molecular studies.\nNuclear SSU rDNA, plastid rbcL and mitochondrial cox1 gene sequences were investigated for the Bangiales from the Antarctica and its adjacent waters.\u00a0", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000042_1.json b/datasets/KOPRI-KPDC-00000042_1.json index 066a39ad98..f5a29d39f8 100644 --- a/datasets/KOPRI-KPDC-00000042_1.json +++ b/datasets/KOPRI-KPDC-00000042_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000042_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Each six sequences were newly determined in this study.\u00a0 Molecular data from over 56 taxa of the Bangiales worldwide including previously published sequences, indicated that monophyly for the genera Bangia and Porphyra is not supported, as in previous molecular studies.\nNuclear SSU rDNA, plastid rbcL and mitochondrial cox1 gene sequences were investigated for the Bangiales from the Antarctica and its adjacent waters.\u00a0", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000043_1.json b/datasets/KOPRI-KPDC-00000043_1.json index 65d79fb8f9..e01e1f1f26 100644 --- a/datasets/KOPRI-KPDC-00000043_1.json +++ b/datasets/KOPRI-KPDC-00000043_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000043_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 3 Nov. to 11 Dec. (9 days) in 2000. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including the acquisition of multichannel seismic, bathymetry, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000044_1.json b/datasets/KOPRI-KPDC-00000044_1.json index b927a933fb..b83dffc156 100644 --- a/datasets/KOPRI-KPDC-00000044_1.json +++ b/datasets/KOPRI-KPDC-00000044_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000044_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 11 researchers participated in the cruise, including acquisition of multichannel seismic and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000045_1.json b/datasets/KOPRI-KPDC-00000045_1.json index 2d996b9b5c..7276367e65 100644 --- a/datasets/KOPRI-KPDC-00000045_1.json +++ b/datasets/KOPRI-KPDC-00000045_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000045_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the Powell Basin (III region) of the northern Weddell Sea. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 7 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000046_1.json b/datasets/KOPRI-KPDC-00000046_1.json index 852367da93..527c8509c0 100644 --- a/datasets/KOPRI-KPDC-00000046_1.json +++ b/datasets/KOPRI-KPDC-00000046_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000046_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the Powell Basin (IV region) of the northern Weddell Sea. The research period was from 24 Nov. to 9 Dec. (15 days) in 2003. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000047_1.json b/datasets/KOPRI-KPDC-00000047_1.json index 12d781ef4a..6ad03427fd 100644 --- a/datasets/KOPRI-KPDC-00000047_1.json +++ b/datasets/KOPRI-KPDC-00000047_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000047_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the Powell Basin (V region) of the northern Weddell Sea. The research period was from 25 Nov. to 9 Dec. (15 days) in 2004. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000048_1.json b/datasets/KOPRI-KPDC-00000048_1.json index 03cc8b8314..170d2356d0 100644 --- a/datasets/KOPRI-KPDC-00000048_1.json +++ b/datasets/KOPRI-KPDC-00000048_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000048_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 7 Dec. in 2008 to Jan. in 2009. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 7 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000049_1.json b/datasets/KOPRI-KPDC-00000049_1.json index 8b660e0cf4..b9131c6c49 100644 --- a/datasets/KOPRI-KPDC-00000049_1.json +++ b/datasets/KOPRI-KPDC-00000049_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000049_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in northern sea area of the south Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) and 12 researcher.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000050_1.json b/datasets/KOPRI-KPDC-00000050_1.json index c8ee594857..3501ccead4 100644 --- a/datasets/KOPRI-KPDC-00000050_1.json +++ b/datasets/KOPRI-KPDC-00000050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 05 Dec. to 12 Dec. (8 days) in 2006. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) and 12 researcher.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000051_1.json b/datasets/KOPRI-KPDC-00000051_1.json index b294cee07a..b166e17fd9 100644 --- a/datasets/KOPRI-KPDC-00000051_1.json +++ b/datasets/KOPRI-KPDC-00000051_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000051_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the first year of study \"The Antarctic Undersea Geological Survey\", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station.\nThe field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995.\n- Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica.\n- Securing data for tectonic settings research in the same region.\n- Obtaining basic data for understanding marine geology and sedimentary layers in the same region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000052_1.json b/datasets/KOPRI-KPDC-00000052_1.json index 05ca62d8c0..f7df1a9562 100644 --- a/datasets/KOPRI-KPDC-00000052_1.json +++ b/datasets/KOPRI-KPDC-00000052_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000052_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the east basin of the Bransfield Strait between the Antarctic peninsula and south Shetland Islands and Maxwell Bay located at Sejong Station was conducted multi-channel seismic investigation and drilling investigation. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) which is marine geology, geophysical survey vessel and Icebreaker for field investigation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000053_1.json b/datasets/KOPRI-KPDC-00000053_1.json index 2e7c1020e0..16061262a1 100644 --- a/datasets/KOPRI-KPDC-00000053_1.json +++ b/datasets/KOPRI-KPDC-00000053_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000053_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and 3 academic personnel participated in the cruise as field investigation personnel.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000054_1.json b/datasets/KOPRI-KPDC-00000054_1.json index 59de73eed9..f411bf8f15 100644 --- a/datasets/KOPRI-KPDC-00000054_1.json +++ b/datasets/KOPRI-KPDC-00000054_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000054_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in 1997 carried out in a continental shelf in the northwestern part of the Antarctic Peninsula. It took 2 days.\nWe took on lease Norway R/V 'Polar Duke' and 11 researchers from \u2018Korea Ocean Research and Development Institute\u2019 participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12-channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000055_1.json b/datasets/KOPRI-KPDC-00000055_1.json index d4f0513148..179e851fc4 100644 --- a/datasets/KOPRI-KPDC-00000055_1.json +++ b/datasets/KOPRI-KPDC-00000055_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000055_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000056_1.json b/datasets/KOPRI-KPDC-00000056_1.json index 473de2be5e..cbdf785519 100644 --- a/datasets/KOPRI-KPDC-00000056_1.json +++ b/datasets/KOPRI-KPDC-00000056_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000056_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the continental margin off the Anvers Island of the northwestern Antarctic Peninsula. The research period was from 25 Nov. in 1999 to 3 Jan. in 2000 (8 days). We took on Korean R/V \"Onnuri\" (KORDI) and 13 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic, SBP, gravity, and magnetometer surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000057_1.json b/datasets/KOPRI-KPDC-00000057_1.json index c5c0e1ad1b..cdb47b74fd 100644 --- a/datasets/KOPRI-KPDC-00000057_1.json +++ b/datasets/KOPRI-KPDC-00000057_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000057_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000058_1.json b/datasets/KOPRI-KPDC-00000058_1.json index e676f224c6..1b6c2ef66e 100644 --- a/datasets/KOPRI-KPDC-00000058_1.json +++ b/datasets/KOPRI-KPDC-00000058_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000058_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 5 Dec. to 12 Dec. (8 days) in 2006. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 8 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples).\n1. Geophysical researches (Multichannel seismic and SBP surveys)\r\n2. Paleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000059_1.json b/datasets/KOPRI-KPDC-00000059_1.json index 24aaad3dcb..fc602edc82 100644 --- a/datasets/KOPRI-KPDC-00000059_1.json +++ b/datasets/KOPRI-KPDC-00000059_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000059_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For USA-Korea collaborative studies we took RV Palmer to get core sediments in 2010. After we obtained X-radiographs, gray scale analysis was conducted from core sediments.\nPaleoceanographic researches (LARISSA program)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000060_1.json b/datasets/KOPRI-KPDC-00000060_1.json index 8e97e17a66..9bff82f03d 100644 --- a/datasets/KOPRI-KPDC-00000060_1.json +++ b/datasets/KOPRI-KPDC-00000060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic survey were conducted in Bransfiedl Strait and off Joinville Island for 2010 K-Polar project. We took RV Araon to obtain gravity core sediments for paleoceanographic studies.\nPaleoceanographic studies", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000061_1.json b/datasets/KOPRI-KPDC-00000061_1.json index 08f736a52b..3f4a430337 100644 --- a/datasets/KOPRI-KPDC-00000061_1.json +++ b/datasets/KOPRI-KPDC-00000061_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000061_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korean Antarctic survey was conducted off Amundsen Sea, West Antarctica. We took RV Araon in 2012 to obtain gravity core sediments for K-Polar Amundsen Sea project.\nPaleoceanographic researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000062_1.json b/datasets/KOPRI-KPDC-00000062_1.json index ab6c6434f4..dd9026913c 100644 --- a/datasets/KOPRI-KPDC-00000062_1.json +++ b/datasets/KOPRI-KPDC-00000062_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000062_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For USA-Korea collaborative studies we took RV Palmer and obtained core sediments in 2012. After that, X-radiography and non-destructive XRF of core sediments were conducted.\nPaleoceanographic researches (LARISSA program)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000063_1.json b/datasets/KOPRI-KPDC-00000063_1.json index dec3f9dbea..2f39f4c719 100644 --- a/datasets/KOPRI-KPDC-00000063_1.json +++ b/datasets/KOPRI-KPDC-00000063_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000063_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1996. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity \r\nsensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000064_1.json b/datasets/KOPRI-KPDC-00000064_1.json index 63410aaeee..db53199749 100644 --- a/datasets/KOPRI-KPDC-00000064_1.json +++ b/datasets/KOPRI-KPDC-00000064_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000064_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1997. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000065_1.json b/datasets/KOPRI-KPDC-00000065_1.json index 1aa635297e..55e0ad689e 100644 --- a/datasets/KOPRI-KPDC-00000065_1.json +++ b/datasets/KOPRI-KPDC-00000065_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000065_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1998. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000066_1.json b/datasets/KOPRI-KPDC-00000066_1.json index 518a93facd..c39f9a0def 100644 --- a/datasets/KOPRI-KPDC-00000066_1.json +++ b/datasets/KOPRI-KPDC-00000066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1999. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000067_1.json b/datasets/KOPRI-KPDC-00000067_1.json index 0f6706ea34..503c21e3ea 100644 --- a/datasets/KOPRI-KPDC-00000067_1.json +++ b/datasets/KOPRI-KPDC-00000067_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000067_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2000. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000068_1.json b/datasets/KOPRI-KPDC-00000068_1.json index 3db12f430d..da964412a3 100644 --- a/datasets/KOPRI-KPDC-00000068_1.json +++ b/datasets/KOPRI-KPDC-00000068_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000068_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2001. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000069_1.json b/datasets/KOPRI-KPDC-00000069_1.json index 466e08752a..7772d1aec6 100644 --- a/datasets/KOPRI-KPDC-00000069_1.json +++ b/datasets/KOPRI-KPDC-00000069_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000069_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2002. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000070_1.json b/datasets/KOPRI-KPDC-00000070_1.json index 1f416ef6b1..486408008e 100644 --- a/datasets/KOPRI-KPDC-00000070_1.json +++ b/datasets/KOPRI-KPDC-00000070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2003. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000071_1.json b/datasets/KOPRI-KPDC-00000071_1.json index 40efe555f2..68a3a5e60f 100644 --- a/datasets/KOPRI-KPDC-00000071_1.json +++ b/datasets/KOPRI-KPDC-00000071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2004. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000072_1.json b/datasets/KOPRI-KPDC-00000072_1.json index 7b92f21c2e..7b6f539d7e 100644 --- a/datasets/KOPRI-KPDC-00000072_1.json +++ b/datasets/KOPRI-KPDC-00000072_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000072_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2005. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000073_1.json b/datasets/KOPRI-KPDC-00000073_1.json index 2c921091b3..22f5a7cf20 100644 --- a/datasets/KOPRI-KPDC-00000073_1.json +++ b/datasets/KOPRI-KPDC-00000073_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000073_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2006. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000074_1.json b/datasets/KOPRI-KPDC-00000074_1.json index b0ee493c42..ab50ce655a 100644 --- a/datasets/KOPRI-KPDC-00000074_1.json +++ b/datasets/KOPRI-KPDC-00000074_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000074_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2007. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000075_1.json b/datasets/KOPRI-KPDC-00000075_1.json index 0b123589d4..d56fef7b6a 100644 --- a/datasets/KOPRI-KPDC-00000075_1.json +++ b/datasets/KOPRI-KPDC-00000075_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000075_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2008. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000076_1.json b/datasets/KOPRI-KPDC-00000076_1.json index b3885dfeed..37e3cd0cae 100644 --- a/datasets/KOPRI-KPDC-00000076_1.json +++ b/datasets/KOPRI-KPDC-00000076_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000076_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2009. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000077_1.json b/datasets/KOPRI-KPDC-00000077_1.json index ab6204e488..0c438b2f01 100644 --- a/datasets/KOPRI-KPDC-00000077_1.json +++ b/datasets/KOPRI-KPDC-00000077_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000077_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2010. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000078_1.json b/datasets/KOPRI-KPDC-00000078_1.json index e421f79296..9cb46bce44 100644 --- a/datasets/KOPRI-KPDC-00000078_1.json +++ b/datasets/KOPRI-KPDC-00000078_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000078_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 2011. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000079_1.json b/datasets/KOPRI-KPDC-00000079_1.json index 283bc2eda4..b98e9d2973 100644 --- a/datasets/KOPRI-KPDC-00000079_1.json +++ b/datasets/KOPRI-KPDC-00000079_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000079_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to grasp the structure of the primary production and phytoplankton communities, chlorophyll-a were measured at 39 stations and phytoplankton communities at 10 stations during the period from August 9 to August 21, 2003 in the Barents Sea. The concentration of total, microphytoplankton, and nano-picophytoplankton chlorophyll-a were higher at middle layer than those of surface and bottom. Leading organims for the primary production were nano- and picophytoplankton. Phytoplankton communities were composed of diatoms, dinoflagellates, cryptophyceae, silicoflagellate and prymnesiophyceae and showed 53 taxa in surface and 27 taxa in bottom. The first dominant species was picophytoplankton in all station and layers, but the second was 2 and/or 3 taxa. Phytoplankton standing crops ranged from minimum 3.73\u00d7105 cells/\u2113 to maximum 2.5\u00d7106 cells/\u2113, showing some resemblance between surface and bottom. As a results, (1) phytoplankton primary production was more active in middle layer than those of surface and bottom, (2) netphytoplankton species were dominated in surface, but phytoplankton standing crops analogous to each other.\n1) To investigate on biodiversity, species composition, standing crops and dominant species of phytoplankton communities in the Barents Sea\r\n2) To study on taxonomic research and indicator species of phytoplankton communities\r\n3) To reveal a relationship between chlorophyll-a and phytoplankton standing crops based on phytoplankton communities", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000080_1.json b/datasets/KOPRI-KPDC-00000080_1.json index 67c555c322..c9f7828592 100644 --- a/datasets/KOPRI-KPDC-00000080_1.json +++ b/datasets/KOPRI-KPDC-00000080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, chlorophyll a and phytoplankton communities were measured at 11 stations from September 17 to 28, 2004 in the Southeastern Barents Sea. The concentrations of total microphytoplankton, and nano-picophytoplankton chlorophyll a were higher at surface than those of lower waters. Dominant phytoplankton were nano-picophytoplankton such as Phaeocystis sp., Dinobryon belgica. Phytoplankton communities were composed of diatoms, dinoflagellates, cryptophyceae, silicoflagellate, prasinophyceae and prymnesiophyceae and showed 23 taxa in surface. Except in station 3, the most abundant species was picophytoplankton in all station, but the second was variable. Phytoplankton Cell abundance ranged from minimum 5.5\u00d7105 cells \u2113-1 to maximum 1.8\u00d7106 cells \u2113-1.\n1) To investigate on biodiversity, species composition, abundance and dominant species of phytoplankton communities in the southeastern Barents Sea\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000081_1.json b/datasets/KOPRI-KPDC-00000081_1.json index 3be5c60da1..bb12aeeb95 100644 --- a/datasets/KOPRI-KPDC-00000081_1.json +++ b/datasets/KOPRI-KPDC-00000081_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000081_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, physical environmental factors and chlorophyll a, this study was carried out at 10 stations on surface from May 23th to June 3rd, 2005 in the Okhotsk Sea.\r\nThe highest water temperature was 2.8\u2103 at station 2 and the lowest water temperature was 0.9\u2103 at station 5. The mean salinity was 33.34 psu. Water temperature and salinity were influenced by sea ice. The highest total Chl a concentration(2.522 Chl a \u338d \u2113-1) was appeared at station 10, the lowest concentration was appeared 0.484 Chl a \u338d \u2113-1 at station 2. Phytoplankton dominant species were nano-picophytoplankton. Phytoplankton communities were composed of 25 taxa representing diatoms, dinoflagellates, cryptophyceae, prasinophyceae, prymnesiophyceae in surface The most abundant species was picophytoplankton in all station except station 10, but the second was variable\nTo investigate on biodiversity, species composition, abundance and dominant species of phytoplankton communities in Okhotsk Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000082_1.json b/datasets/KOPRI-KPDC-00000082_1.json index 1237ffba10..b56efb4dc3 100644 --- a/datasets/KOPRI-KPDC-00000082_1.json +++ b/datasets/KOPRI-KPDC-00000082_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000082_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, physical environmental factors and chlorophyll a, this study was carried out at 14 stations from August 20 to August 25, 2005 in the Kara Sea. Water temperature and salinity were influenced under the Ob River. Concentrations of total microphytoplankton, and nano-picophytoplankton chlorophyll a were higher at surface than those of SCM depth. Phytoplankton dominant species were nano-picophytoplankton such as Cryptomonas sp., Dinobryon belgica. Phytoplankton communities were composed of 25 taxa representing diatoms, dinoflagellates, cryptophyceae, silicoflagellate, prasinophyceae and prymnesiophyceae in surface. The most abundant species was picophytoplankton in all station except station 2, but the second was variable. Phytoplankton standing crops ranged from minimum 8.4\u00d7105cells \u2113-1 to maximum 1.7\u00d7107cells \u2113-1 in surface and 6.2\u00d7105 cells \u2113-1 to maximum 1.7\u00d7107 cells \u2113-1 in SCM depth\n1) To investigate on physical biodiversity, species composition, abundance and dominant species of phytoplankton communities in the Kara Sea\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000083_1.json b/datasets/KOPRI-KPDC-00000083_1.json index b66dc0f683..fe01e4c0cf 100644 --- a/datasets/KOPRI-KPDC-00000083_1.json +++ b/datasets/KOPRI-KPDC-00000083_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000083_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities and chlorophyll a, this study was carried out at 9 stations from June 21 to June 26, 2006 in the Bering Sea. Concentrations of total microphytoplankton and nano-picophytoplankton chlorophyll a were similar in the eastern shelf. But in the Aleutian Basin, concentrations of total microphytoplankton were higher than nano-picophytoplankton chlorophyll a. Chlorophyll maximum depth were approximately 20 meter in the eastern shelf and surface in the Aleutian Basin. Phytoplankton dominant species were Thalassiosira sp., Chaetoceros sp. and nano-picophytoplankton such as Dinobryon belgica. Phytoplankton communities were composed of 48 taxa representing dinoflagellate, diatoms, cryptophyceae, chrysophyceae, dictyochophyceae, prymneosiophyceae. The most abundant species was picophytoplankton in all station except station 4, but the second was variable. Phytoplankton standing crops ranged from minimum 9.8\u00d7105cells \u2113-1 at station 137 to maximum 2.0\u00d7107cells \u2113-1 at station 135 in surface and 8.7\u00d7105 cells \u2113-1 at station 138 to maximum 4.1\u00d7106 cells \u2113-1 at station 151 in SCM depth.\n1) To investigate on species composition, abundance and dominant species of phytoplankton communities in the Bering Sea\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000084_1.json b/datasets/KOPRI-KPDC-00000084_1.json index 8d469a1be6..e9acf0514b 100644 --- a/datasets/KOPRI-KPDC-00000084_1.json +++ b/datasets/KOPRI-KPDC-00000084_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000084_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, this study was carried out at 14 stations from July 24 to August 26, 2007 in the Bering Sea and Chukchi Sea. Phytoplankton communities were composed of 57 taxa representing Dinophyceae, Bacillariophyceae, Chrysophyceae, Dictyochophyceae, rasinophyceae, Prymneosiophyceae and unidentified phytoplankton(< 20\u339b) in the study area. Phytoplankton standing crops ranged from minimum 4.31\u00d7105cells \u2113-1 at station B04 to maximum 3.47\u00d7106cells \u2113-1 at station B04 in the Bering Sea. and 5.43\u00d7105cells \u2113-1 at station C09 to maximum 2.42\u00d7106cells \u2113-1 at station C16 in the Chukchi Sea. The most abundant species was nano-pico sized phytoplankton in almost station, but the second was variable. Phytoplankton dominant species were Thalassiosira sp., Chaetoceros sp. and nano-picophytoplankton such as Dinobryon belgica. In the Bering strait, the diversity was higher than other study area but cell abundance was not enough of a difference.\n1) To investigate on species composition, abundance and dominant species of phytoplankton communities in the Bering Sea and Chukchi Sea\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000085_2.json b/datasets/KOPRI-KPDC-00000085_2.json index 48992dea97..51d909fa01 100644 --- a/datasets/KOPRI-KPDC-00000085_2.json +++ b/datasets/KOPRI-KPDC-00000085_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000085_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, this study was carried out at 37 stations from July 19 to September 5, 2008 in the Bering Sea, Chukchi Sea and Canadian Basin. Phytoplankton communities were composed of 71 taxa representing Dinophyceae, Cryptophyceae, Bacillariophyceae, Chrysophyceae, Dictyochophyceae, rasinophyceae, Prymneosiophyceae and unidentified phytoplankton(< 20\u339b) in the study area. Phytoplankton standing crops ranged from minimum 2.19\u00d7105cells \u2113-1 at station D84 to maximum 8.29\u00d7106cells \u2113-1 at station R09 in the study area. The most abundant species was nano-pico sized phytoplankton in almost station, but the second was variable. Phytoplankton dominant species were Thalassiosira sp., Chaetoceros sp. and nano-picophytoplankton such as Dinobryon belgica and Cryptomonas sp.. There were positive correlations between phytoplankton biomass and physical factors. From western Bering sea to Bering strait the biomass was more higher, but after through the Bering Strait it was more lower along latitude to the arctic.\r\n1) To investigate on species composition, abundance and dominant species of phytoplankton communities in the Bering Sea, Chukchi Sea and Canadian Basin\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000086_2.json b/datasets/KOPRI-KPDC-00000086_2.json index 3714fa30fd..b6af838155 100644 --- a/datasets/KOPRI-KPDC-00000086_2.json +++ b/datasets/KOPRI-KPDC-00000086_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000086_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, this study was carried out at 20 stations from September 2 to 30, 2009 in the Bering strait, Chukchi Sea and Canadian Basin. Phytoplankton communities were composed of 59 taxa representing Dinophyceae, Cryptophyceae, Bacillariophyceae, Chrysophyceae, Dictyochophyceae, rasinophyceae, Prymneosiophyceae and unidentified phytoplankton(< 20\u339b) in the study area. Phytoplankton standing crops ranged from minimum 5.27\u00d7105cells \u2113-1 at station LS3 to maximum 7.82\u00d7106cells \u2113-1 at station BS2 in the study area. The most abundant species was nano-pico sized phytoplankton in almost station, but the second was variable. Phytoplankton dominant species were Thalassiosira sp., Chaetoceros sp. and nano-picophytoplankton such as Dinobryon belgica and Cryptomonas sp.. From Bering strait to Canadian Basin, the biomass was more lower along latitude to the arctic.\r\n1) To investigate on species composition, abundance and dominant species of phytoplankton communities in Chukchi Sea and Canadian Basin\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000087_1.json b/datasets/KOPRI-KPDC-00000087_1.json index 87363872ad..d9409a0302 100644 --- a/datasets/KOPRI-KPDC-00000087_1.json +++ b/datasets/KOPRI-KPDC-00000087_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000087_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, this study was carried out at 18 stations from July 29 to August 20, 2011 in the Chukchi Sea and Sea Ice. Concentrations of total microphytoplankton, and nano-picophytoplankton chlorophyll a were higher at southwest area than northern area in the study area due to Bering shelf Anadyr Water current from Bering strait. In the Melting ponds, phytoplankton communities were composed of 31 taxa representing Bacillariophyceae, Chrysophyceae, Dictyochophyceae, Prasinophyceae and unidentified phytoplankton(< 20\u00e3\u017d\u203a). The most abundant species were Pyramimonas sp. and Thalassiosira sp. except nano-pico sized phytoplankton in Melting pond.\n1) To investigate on species composition, abundance and dominant species of phytoplankton communities in the Chukchi Sea and Sea Ice\r\n2) To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000088_1.json b/datasets/KOPRI-KPDC-00000088_1.json index 79778a1460..46a87465f8 100644 --- a/datasets/KOPRI-KPDC-00000088_1.json +++ b/datasets/KOPRI-KPDC-00000088_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000088_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplanktons were surveyed from 5 to 15 August 2002 at nine stations in King's Bay, Svalbard. Zooplankton community consisted of six taxa including eight species of copepods, hydrozoans, chaetognaths, polychaetes, appedicularians, and Ophiopluteus larvae. Copepods were dominant group and showed highest value in the frequency of abundance with 89.8% of total zooplankton. Among the zooplanktons, copepods, Pseudocalanus minutus, Pseudocalanus acuspes, Calanus glacialis, Calanus fimmarchicus, Oithona similis, Oithona atlantica, Oncaea sp. and Microsetella sp. were identified. Mean abundance was 2,348 inds./m3 ranging from 835 indv./m3 at st.4 to 6,232 indv./m3at st. 2. during the study periods. Zooplankton abundances were affected by the fluctuations of temperature and salinity. Abundances were fewer in the inner bay area near from glacier than in the open ocean mainly due to the copepod abundances. Generally zooplankton abundances were higher in high temperature and high salinity area than in low temperature and low salinity glacial coastal area. A cyclopoid copepod, Oithona similis was dominant in the surveyed area\nTo monitoring on zooplankton communities in Kongsfjorden", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000089_1.json b/datasets/KOPRI-KPDC-00000089_1.json index 4122bc979f..1b0b5b00b8 100644 --- a/datasets/KOPRI-KPDC-00000089_1.json +++ b/datasets/KOPRI-KPDC-00000089_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000089_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To recognize glaciomarine characteristics and distribution of phytoplankton at Kongsfjorden, Svalbard Islands, Norway, water column characteristics (temperature, salinity, and Chl a concentration) was measured using YSI 6920 CTD and water samples at the surface was obtained in summer. Due to glacier melting and fluvial fresh water, at the upper surface (3.0 \u00e2\u201e\u0192 was occupied below 30 m with fresh upper surface water. In particular, the distribution of Chl a shows patch tendency irrelevant to the current\n1) To monitoring on phytoplankton communities in Kongsfjorden \r\n2) To monitoring on environmental factors in Kongsfjorden", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000090_1.json b/datasets/KOPRI-KPDC-00000090_1.json index 12d0adb090..2a872341b5 100644 --- a/datasets/KOPRI-KPDC-00000090_1.json +++ b/datasets/KOPRI-KPDC-00000090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, chlorophyll a and phytoplankton communities were measured at 15 stations from October 9 to 11, 2005 at Kongsfjorden, Svalbard Islands, Norway. The concentrations of nano-picophytoplankton chlorophyll a were higher than those of microphytoplankton. Dominant phytoplankton were nano-picophytoplankton such as Phaeocystis sp., Pyramimonas sp.. Phytoplankton communities were composed of diatoms, dinoflagellates, cryptophyceae, prasinophyceae and prymnesiophyceae and showed 17 taxa in surface. The most abundant species was picophytoplankton in all station, but the second was variable. Phytoplankton Cell abundance ranged from minimum 1.9\u00d7106cells \u2113-1 to maximum 7.2\u00d7106cells \u2113-1. As a result, Phytoplankton might be controlled by physical factors such as Norwegian Atlantic Current at the study area.\nRecognizing the distribution and diversity of phytoplankton in Arctic fjords", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000091_1.json b/datasets/KOPRI-KPDC-00000091_1.json index 60d0977b4f..7539336c96 100644 --- a/datasets/KOPRI-KPDC-00000091_1.json +++ b/datasets/KOPRI-KPDC-00000091_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000091_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, chlorophyll a and phytoplankton communities were measured at 4 stations from August 7 to 11, 2006 at Kongsfjorden, Svalbard Islands, Norway. Mean water temperature was 5.15\u00c2\u00b0C and salinity was 30.02psu on surface. Pico and nano sized phytoplankton were dominate in the study area. As a result, Phytoplankton might be controlled by physical factors such as Norwegian Atlantic Current at the study area.\nRecognizing the distribution and diversity of phytoplankton in Arctic fjords", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000092_1.json b/datasets/KOPRI-KPDC-00000092_1.json index e8de0ae9df..19416d0ccb 100644 --- a/datasets/KOPRI-KPDC-00000092_1.json +++ b/datasets/KOPRI-KPDC-00000092_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000092_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, chlorophyll a and phytoplankton communities were measured at 21 stations from June 12 to 17, 2007 at Kongsfjorden, Svalbard Islands, Norway. Mean water temperature was 4.3\u00c2\u00b0C and salinity was 32.9 psu on surface. Pico and nano sized phytoplankton were dominate in the study area.\nRecognizing the distribution and diversity of phytoplankton in Arctic fjords", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000093_1.json b/datasets/KOPRI-KPDC-00000093_1.json index 73538717d9..8f76413ecf 100644 --- a/datasets/KOPRI-KPDC-00000093_1.json +++ b/datasets/KOPRI-KPDC-00000093_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000093_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000094_1.json b/datasets/KOPRI-KPDC-00000094_1.json index 13c38c16b2..1891a49707 100644 --- a/datasets/KOPRI-KPDC-00000094_1.json +++ b/datasets/KOPRI-KPDC-00000094_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000094_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000095_1.json b/datasets/KOPRI-KPDC-00000095_1.json index da751c179a..fee7da180b 100644 --- a/datasets/KOPRI-KPDC-00000095_1.json +++ b/datasets/KOPRI-KPDC-00000095_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000095_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000096_1.json b/datasets/KOPRI-KPDC-00000096_1.json index bd1373b6da..f0b2d4c6c8 100644 --- a/datasets/KOPRI-KPDC-00000096_1.json +++ b/datasets/KOPRI-KPDC-00000096_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000096_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000097_1.json b/datasets/KOPRI-KPDC-00000097_1.json index 98be05fc49..eee2528699 100644 --- a/datasets/KOPRI-KPDC-00000097_1.json +++ b/datasets/KOPRI-KPDC-00000097_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000097_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000098_1.json b/datasets/KOPRI-KPDC-00000098_1.json index 3244a39e8f..85fe977db6 100644 --- a/datasets/KOPRI-KPDC-00000098_1.json +++ b/datasets/KOPRI-KPDC-00000098_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000098_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000099_1.json b/datasets/KOPRI-KPDC-00000099_1.json index 877e60f89c..7e8eb560ce 100644 --- a/datasets/KOPRI-KPDC-00000099_1.json +++ b/datasets/KOPRI-KPDC-00000099_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000099_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000100_1.json b/datasets/KOPRI-KPDC-00000100_1.json index de962c20be..8f5d673097 100644 --- a/datasets/KOPRI-KPDC-00000100_1.json +++ b/datasets/KOPRI-KPDC-00000100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000101_1.json b/datasets/KOPRI-KPDC-00000101_1.json index ffacf920f5..185b5234a2 100644 --- a/datasets/KOPRI-KPDC-00000101_1.json +++ b/datasets/KOPRI-KPDC-00000101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000102_1.json b/datasets/KOPRI-KPDC-00000102_1.json index c8b53cc86e..56079d2883 100644 --- a/datasets/KOPRI-KPDC-00000102_1.json +++ b/datasets/KOPRI-KPDC-00000102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000103_1.json b/datasets/KOPRI-KPDC-00000103_1.json index e2d98a9f84..e17adf0f94 100644 --- a/datasets/KOPRI-KPDC-00000103_1.json +++ b/datasets/KOPRI-KPDC-00000103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000104_1.json b/datasets/KOPRI-KPDC-00000104_1.json index 8f0ac53d1b..d9d78a1d95 100644 --- a/datasets/KOPRI-KPDC-00000104_1.json +++ b/datasets/KOPRI-KPDC-00000104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000105_1.json b/datasets/KOPRI-KPDC-00000105_1.json index cd0340bcb9..5686c787ed 100644 --- a/datasets/KOPRI-KPDC-00000105_1.json +++ b/datasets/KOPRI-KPDC-00000105_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000105_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000106_1.json b/datasets/KOPRI-KPDC-00000106_1.json index c60267299b..c9f3d70dec 100644 --- a/datasets/KOPRI-KPDC-00000106_1.json +++ b/datasets/KOPRI-KPDC-00000106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000107_1.json b/datasets/KOPRI-KPDC-00000107_1.json index 00804adc48..c4304226fb 100644 --- a/datasets/KOPRI-KPDC-00000107_1.json +++ b/datasets/KOPRI-KPDC-00000107_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000107_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000108_1.json b/datasets/KOPRI-KPDC-00000108_1.json index ca9c3afe9d..61102aa4ff 100644 --- a/datasets/KOPRI-KPDC-00000108_1.json +++ b/datasets/KOPRI-KPDC-00000108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000109_1.json b/datasets/KOPRI-KPDC-00000109_1.json index 4edc63a440..e26bdfefc5 100644 --- a/datasets/KOPRI-KPDC-00000109_1.json +++ b/datasets/KOPRI-KPDC-00000109_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000109_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000110_1.json b/datasets/KOPRI-KPDC-00000110_1.json index c4c3e914f9..aff9b5a841 100644 --- a/datasets/KOPRI-KPDC-00000110_1.json +++ b/datasets/KOPRI-KPDC-00000110_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000110_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000111_1.json b/datasets/KOPRI-KPDC-00000111_1.json index 23134c9f0f..e60a3c435a 100644 --- a/datasets/KOPRI-KPDC-00000111_1.json +++ b/datasets/KOPRI-KPDC-00000111_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000111_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000112_1.json b/datasets/KOPRI-KPDC-00000112_1.json index ca6137ca6c..0a19561753 100644 --- a/datasets/KOPRI-KPDC-00000112_1.json +++ b/datasets/KOPRI-KPDC-00000112_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000112_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000113_1.json b/datasets/KOPRI-KPDC-00000113_1.json index 7d8e295c45..09eda6eadb 100644 --- a/datasets/KOPRI-KPDC-00000113_1.json +++ b/datasets/KOPRI-KPDC-00000113_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000113_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000114_1.json b/datasets/KOPRI-KPDC-00000114_1.json index 6539231d48..0a398c5483 100644 --- a/datasets/KOPRI-KPDC-00000114_1.json +++ b/datasets/KOPRI-KPDC-00000114_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000114_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000115_1.json b/datasets/KOPRI-KPDC-00000115_1.json index e4af52bd21..7292698f98 100644 --- a/datasets/KOPRI-KPDC-00000115_1.json +++ b/datasets/KOPRI-KPDC-00000115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000116_1.json b/datasets/KOPRI-KPDC-00000116_1.json index 500a2a5ad4..2df051ff45 100644 --- a/datasets/KOPRI-KPDC-00000116_1.json +++ b/datasets/KOPRI-KPDC-00000116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000117_1.json b/datasets/KOPRI-KPDC-00000117_1.json index e520924772..80fc5f255b 100644 --- a/datasets/KOPRI-KPDC-00000117_1.json +++ b/datasets/KOPRI-KPDC-00000117_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000117_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000118_1.json b/datasets/KOPRI-KPDC-00000118_1.json index 58a1e448df..ec82871686 100644 --- a/datasets/KOPRI-KPDC-00000118_1.json +++ b/datasets/KOPRI-KPDC-00000118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000119_1.json b/datasets/KOPRI-KPDC-00000119_1.json index 9b95ec1a95..5aa92a5657 100644 --- a/datasets/KOPRI-KPDC-00000119_1.json +++ b/datasets/KOPRI-KPDC-00000119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000120_1.json b/datasets/KOPRI-KPDC-00000120_1.json index 0bd57d4432..2150467e44 100644 --- a/datasets/KOPRI-KPDC-00000120_1.json +++ b/datasets/KOPRI-KPDC-00000120_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000120_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000121_1.json b/datasets/KOPRI-KPDC-00000121_1.json index 5f38937a9c..0d4c4abd38 100644 --- a/datasets/KOPRI-KPDC-00000121_1.json +++ b/datasets/KOPRI-KPDC-00000121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000122_1.json b/datasets/KOPRI-KPDC-00000122_1.json index 3e92096f17..3bf42e0149 100644 --- a/datasets/KOPRI-KPDC-00000122_1.json +++ b/datasets/KOPRI-KPDC-00000122_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000122_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000123_1.json b/datasets/KOPRI-KPDC-00000123_1.json index 30ccd4d43f..54bc6304f8 100644 --- a/datasets/KOPRI-KPDC-00000123_1.json +++ b/datasets/KOPRI-KPDC-00000123_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000123_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000124_1.json b/datasets/KOPRI-KPDC-00000124_1.json index 2c5e073bfe..24beab97d4 100644 --- a/datasets/KOPRI-KPDC-00000124_1.json +++ b/datasets/KOPRI-KPDC-00000124_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000124_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000125_1.json b/datasets/KOPRI-KPDC-00000125_1.json index f581f02b83..0bab6f8a9a 100644 --- a/datasets/KOPRI-KPDC-00000125_1.json +++ b/datasets/KOPRI-KPDC-00000125_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000125_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000126_1.json b/datasets/KOPRI-KPDC-00000126_1.json index db59e9436e..ccb551fc70 100644 --- a/datasets/KOPRI-KPDC-00000126_1.json +++ b/datasets/KOPRI-KPDC-00000126_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000126_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000127_1.json b/datasets/KOPRI-KPDC-00000127_1.json index 4aeadea84e..5cd71ff557 100644 --- a/datasets/KOPRI-KPDC-00000127_1.json +++ b/datasets/KOPRI-KPDC-00000127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000128_1.json b/datasets/KOPRI-KPDC-00000128_1.json index 518860cd4b..6c9fda331e 100644 --- a/datasets/KOPRI-KPDC-00000128_1.json +++ b/datasets/KOPRI-KPDC-00000128_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000128_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000129_1.json b/datasets/KOPRI-KPDC-00000129_1.json index 3fe9d0d515..ec2b1be24a 100644 --- a/datasets/KOPRI-KPDC-00000129_1.json +++ b/datasets/KOPRI-KPDC-00000129_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000129_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000130_1.json b/datasets/KOPRI-KPDC-00000130_1.json index 8694e7d799..04b6cf3259 100644 --- a/datasets/KOPRI-KPDC-00000130_1.json +++ b/datasets/KOPRI-KPDC-00000130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000131_1.json b/datasets/KOPRI-KPDC-00000131_1.json index 41387c0d5b..0cff5cba9f 100644 --- a/datasets/KOPRI-KPDC-00000131_1.json +++ b/datasets/KOPRI-KPDC-00000131_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000131_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000132_1.json b/datasets/KOPRI-KPDC-00000132_1.json index 58553344d6..444c7ac13f 100644 --- a/datasets/KOPRI-KPDC-00000132_1.json +++ b/datasets/KOPRI-KPDC-00000132_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000132_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer from EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz\nThe Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000133_1.json b/datasets/KOPRI-KPDC-00000133_1.json index ea2e129bb9..af96c0cb74 100644 --- a/datasets/KOPRI-KPDC-00000133_1.json +++ b/datasets/KOPRI-KPDC-00000133_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000133_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geo-stationary Ocean Color Imager (GOCI) is completing development to provide a monitoring of ocean color at the Korean Peninsula from a geo-stationary platform. GOCI will be carried by the Communication, Ocean, and Meteorological Satellite (COMS) of Korea.\nThe GOCI is designed to provide multi-spectral data to detect, monitor, quantify, and predict short-term changes of coastal ocean environment for marine science research and application purpose.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000134_1.json b/datasets/KOPRI-KPDC-00000134_1.json index a259056c8f..7f6857c663 100644 --- a/datasets/KOPRI-KPDC-00000134_1.json +++ b/datasets/KOPRI-KPDC-00000134_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000134_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Color and Temperature Scanner (OCTS) is an optical radiometer to achieve highly sensitive spectral measurement with 12 bands covering visible and thermal infrared region. In the visible and near-infrared bands, the ocean conditions are observed by taking advantage of spectral reflectance of the dissolved substances in the water and phytoplankton.\nOCTS mainly serves as an observation sensor of the ocean conditions, including chlorophyll and dissolved substances in the water, temperature profile and cloud formation processes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000135_1.json b/datasets/KOPRI-KPDC-00000135_1.json index 81cba64293..6c1b159054 100644 --- a/datasets/KOPRI-KPDC-00000135_1.json +++ b/datasets/KOPRI-KPDC-00000135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Color and Temperature Scanner (OCTS) is an optical radiometer to achieve highly sensitive spectral measurement with 12 bands covering visible and thermal infrared region. In the visible and near-infrared bands, the ocean conditions are observed by taking advantage of spectral reflectance of the dissolved substances in the water and phytoplankton.\nOCTS mainly serves as an observation sensor of the ocean conditions, including chlorophyll and dissolved substances in the water, temperature profile and cloud formation processes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000136_1.json b/datasets/KOPRI-KPDC-00000136_1.json index 0e9ef50aaf..91e1efdb10 100644 --- a/datasets/KOPRI-KPDC-00000136_1.json +++ b/datasets/KOPRI-KPDC-00000136_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000136_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Color and Temperature Scanner (OCTS) is an optical radiometer to achieve highly sensitive spectral measurement with 12 bands covering visible and thermal infrared region. In the visible and near-infrared bands, the ocean conditions are observed by taking advantage of spectral reflectance of the dissolved substances in the water and phytoplankton.\nOCTS mainly serves as an observation sensor of the ocean conditions, including chlorophyll and dissolved substances in the water, temperature profile and cloud formation processes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000137_1.json b/datasets/KOPRI-KPDC-00000137_1.json index 6d8edab5cb..cd863ebdc7 100644 --- a/datasets/KOPRI-KPDC-00000137_1.json +++ b/datasets/KOPRI-KPDC-00000137_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000137_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS forms part of the core instrument payload of ESA's environmental research satellite ENVISAT-1. The demands of the European scientific community for a global environmental monitoring system, whose technical characteristics enable the extraction of quantitative information from ocean color data, as well as for documentation of the state and evolution of the atmosphere and land surfaces, led to the conception of MERIS.\nThe oceanographic mission is radiometrically the most demanding in terms of low radiance levels and their associated high signal-to noise ratios. Therefore, the instrument must be capable of detecting the low levels of radiation emerging from the ocean (linked to the water constituents by the processes of absorption and scattering). The characteristics of MERIS are also of great value for the retrieval of information on land surfaces, in particular that of global biomass.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000138_1.json b/datasets/KOPRI-KPDC-00000138_1.json index c1a5de3938..950e50599c 100644 --- a/datasets/KOPRI-KPDC-00000138_1.json +++ b/datasets/KOPRI-KPDC-00000138_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000138_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS forms part of the core instrument payload of ESA's environmental research satellite ENVISAT-1. The demands of the European scientific community for a global environmental monitoring system, whose technical characteristics enable the extraction of quantitative information from ocean color data, as well as for documentation of the state and evolution of the atmosphere and land surfaces, led to the conception of MERIS.\nThe oceanographic mission is radiometrically the most demanding in terms of low radiance levels and their associated high signal-to noise ratios. Therefore, the instrument must be capable of detecting the low levels of radiation emerging from the ocean (linked to the water constituents by the processes of absorption and scattering). The characteristics of MERIS are also of great value for the retrieval of information on land surfaces, in particular that of global biomass.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000139_1.json b/datasets/KOPRI-KPDC-00000139_1.json index 345f2fd6e2..6542ce6134 100644 --- a/datasets/KOPRI-KPDC-00000139_1.json +++ b/datasets/KOPRI-KPDC-00000139_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000139_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS forms part of the core instrument payload of ESA's environmental research satellite ENVISAT-1. The demands of the European scientific community for a global environmental monitoring system, whose technical characteristics enable the extraction of quantitative information from ocean color data, as well as for documentation of the state and evolution of the atmosphere and land surfaces, led to the conception of MERIS.\nThe oceanographic mission is radiometrically the most demanding in terms of low radiance levels and their associated high signal-to noise ratios. Therefore, the instrument must be capable of detecting the low levels of radiation emerging from the ocean (linked to the water constituents by the processes of absorption and scattering). The characteristics of MERIS are also of great value for the retrieval of information on land surfaces, in particular that of global biomass.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000140_1.json b/datasets/KOPRI-KPDC-00000140_1.json index 0ce006287d..020b6c3784 100644 --- a/datasets/KOPRI-KPDC-00000140_1.json +++ b/datasets/KOPRI-KPDC-00000140_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000140_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000141_1.json b/datasets/KOPRI-KPDC-00000141_1.json index 78ff748828..f09dfee6d1 100644 --- a/datasets/KOPRI-KPDC-00000141_1.json +++ b/datasets/KOPRI-KPDC-00000141_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000141_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000142_1.json b/datasets/KOPRI-KPDC-00000142_1.json index d99c819b93..ec61ac0e01 100644 --- a/datasets/KOPRI-KPDC-00000142_1.json +++ b/datasets/KOPRI-KPDC-00000142_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000142_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000143_1.json b/datasets/KOPRI-KPDC-00000143_1.json index e7bca0cb3e..4ca846cb11 100644 --- a/datasets/KOPRI-KPDC-00000143_1.json +++ b/datasets/KOPRI-KPDC-00000143_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000143_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000144_1.json b/datasets/KOPRI-KPDC-00000144_1.json index 0a91f78482..1b1c0efca8 100644 --- a/datasets/KOPRI-KPDC-00000144_1.json +++ b/datasets/KOPRI-KPDC-00000144_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000144_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000145_1.json b/datasets/KOPRI-KPDC-00000145_1.json index f56e97799a..83afdc0cfa 100644 --- a/datasets/KOPRI-KPDC-00000145_1.json +++ b/datasets/KOPRI-KPDC-00000145_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000145_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000146_1.json b/datasets/KOPRI-KPDC-00000146_1.json index 256cddbb4f..61e11d06d5 100644 --- a/datasets/KOPRI-KPDC-00000146_1.json +++ b/datasets/KOPRI-KPDC-00000146_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000146_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000147_1.json b/datasets/KOPRI-KPDC-00000147_1.json index 13f276a296..ab0923b11b 100644 --- a/datasets/KOPRI-KPDC-00000147_1.json +++ b/datasets/KOPRI-KPDC-00000147_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000147_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000148_1.json b/datasets/KOPRI-KPDC-00000148_1.json index 378737b46c..542777b6cf 100644 --- a/datasets/KOPRI-KPDC-00000148_1.json +++ b/datasets/KOPRI-KPDC-00000148_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000148_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft.\nCZCS attempted to discriminate between organic and inorganic materials in the water, determine the quantity of material and discriminate between different organic particulate types.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000149_1.json b/datasets/KOPRI-KPDC-00000149_1.json index 3eab997d3e..ffd7750c72 100644 --- a/datasets/KOPRI-KPDC-00000149_1.json +++ b/datasets/KOPRI-KPDC-00000149_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000149_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000150_1.json b/datasets/KOPRI-KPDC-00000150_1.json index 0ae9c47d0a..9eef885f5f 100644 --- a/datasets/KOPRI-KPDC-00000150_1.json +++ b/datasets/KOPRI-KPDC-00000150_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000150_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000151_1.json b/datasets/KOPRI-KPDC-00000151_1.json index e995e38b90..05e8a2fae5 100644 --- a/datasets/KOPRI-KPDC-00000151_1.json +++ b/datasets/KOPRI-KPDC-00000151_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000151_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000152_1.json b/datasets/KOPRI-KPDC-00000152_1.json index 0c928558bc..94b87247e3 100644 --- a/datasets/KOPRI-KPDC-00000152_1.json +++ b/datasets/KOPRI-KPDC-00000152_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000152_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000153_1.json b/datasets/KOPRI-KPDC-00000153_1.json index 38e3f9a47c..772624bb2b 100644 --- a/datasets/KOPRI-KPDC-00000153_1.json +++ b/datasets/KOPRI-KPDC-00000153_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000153_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000154_1.json b/datasets/KOPRI-KPDC-00000154_1.json index 1b1b2687f4..2cf1b7ccd3 100644 --- a/datasets/KOPRI-KPDC-00000154_1.json +++ b/datasets/KOPRI-KPDC-00000154_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000154_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000155_1.json b/datasets/KOPRI-KPDC-00000155_1.json index 71dbce35ae..2501028bff 100644 --- a/datasets/KOPRI-KPDC-00000155_1.json +++ b/datasets/KOPRI-KPDC-00000155_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000155_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000156_1.json b/datasets/KOPRI-KPDC-00000156_1.json index 82e63bd8ae..9e8af1dbca 100644 --- a/datasets/KOPRI-KPDC-00000156_1.json +++ b/datasets/KOPRI-KPDC-00000156_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000156_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000157_1.json b/datasets/KOPRI-KPDC-00000157_1.json index d2beca9144..a9689eceae 100644 --- a/datasets/KOPRI-KPDC-00000157_1.json +++ b/datasets/KOPRI-KPDC-00000157_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000157_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000158_1.json b/datasets/KOPRI-KPDC-00000158_1.json index 5239b76a15..b11812d679 100644 --- a/datasets/KOPRI-KPDC-00000158_1.json +++ b/datasets/KOPRI-KPDC-00000158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000159_1.json b/datasets/KOPRI-KPDC-00000159_1.json index b3622e69d9..abceab5c81 100644 --- a/datasets/KOPRI-KPDC-00000159_1.json +++ b/datasets/KOPRI-KPDC-00000159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000160_1.json b/datasets/KOPRI-KPDC-00000160_1.json index 396f498d88..ba5658508e 100644 --- a/datasets/KOPRI-KPDC-00000160_1.json +++ b/datasets/KOPRI-KPDC-00000160_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000160_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000161_1.json b/datasets/KOPRI-KPDC-00000161_1.json index 0818941e88..a5bea5fe3c 100644 --- a/datasets/KOPRI-KPDC-00000161_1.json +++ b/datasets/KOPRI-KPDC-00000161_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000161_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000162_1.json b/datasets/KOPRI-KPDC-00000162_1.json index f04ba443ba..3b4d9f27b4 100644 --- a/datasets/KOPRI-KPDC-00000162_1.json +++ b/datasets/KOPRI-KPDC-00000162_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000162_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS is designed to look at our planet from space to better understand it as a system in both behavior and evolution.\nThe purpose of SeaWiFS is to provide quantitative data on global ocean bio-optical properties to the Earth science community.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000163_1.json b/datasets/KOPRI-KPDC-00000163_1.json index 9ae5d94355..ee69b33c8f 100644 --- a/datasets/KOPRI-KPDC-00000163_1.json +++ b/datasets/KOPRI-KPDC-00000163_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000163_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000164_1.json b/datasets/KOPRI-KPDC-00000164_1.json index 51ab1d00e5..4354175d40 100644 --- a/datasets/KOPRI-KPDC-00000164_1.json +++ b/datasets/KOPRI-KPDC-00000164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000165_1.json b/datasets/KOPRI-KPDC-00000165_1.json index f82da022b4..a5cd2715c2 100644 --- a/datasets/KOPRI-KPDC-00000165_1.json +++ b/datasets/KOPRI-KPDC-00000165_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000165_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000166_1.json b/datasets/KOPRI-KPDC-00000166_1.json index 10afb7e936..bb5f04c839 100644 --- a/datasets/KOPRI-KPDC-00000166_1.json +++ b/datasets/KOPRI-KPDC-00000166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000167_1.json b/datasets/KOPRI-KPDC-00000167_1.json index 6d05b01a50..ff31645786 100644 --- a/datasets/KOPRI-KPDC-00000167_1.json +++ b/datasets/KOPRI-KPDC-00000167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000168_1.json b/datasets/KOPRI-KPDC-00000168_1.json index 1dbf15f82d..feca29bb03 100644 --- a/datasets/KOPRI-KPDC-00000168_1.json +++ b/datasets/KOPRI-KPDC-00000168_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000168_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000169_1.json b/datasets/KOPRI-KPDC-00000169_1.json index c0a5fc283e..c2f6606ee4 100644 --- a/datasets/KOPRI-KPDC-00000169_1.json +++ b/datasets/KOPRI-KPDC-00000169_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000169_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000170_1.json b/datasets/KOPRI-KPDC-00000170_1.json index 080cd7ce63..e9c97e8eb3 100644 --- a/datasets/KOPRI-KPDC-00000170_1.json +++ b/datasets/KOPRI-KPDC-00000170_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000170_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000171_1.json b/datasets/KOPRI-KPDC-00000171_1.json index 0ccb1d762b..cde1e26b25 100644 --- a/datasets/KOPRI-KPDC-00000171_1.json +++ b/datasets/KOPRI-KPDC-00000171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000172_1.json b/datasets/KOPRI-KPDC-00000172_1.json index 4cf8bf17c8..934da3000a 100644 --- a/datasets/KOPRI-KPDC-00000172_1.json +++ b/datasets/KOPRI-KPDC-00000172_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000172_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000173_1.json b/datasets/KOPRI-KPDC-00000173_1.json index 6883ba3ac3..ba781bdb47 100644 --- a/datasets/KOPRI-KPDC-00000173_1.json +++ b/datasets/KOPRI-KPDC-00000173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000174_1.json b/datasets/KOPRI-KPDC-00000174_1.json index 75a39616a3..ed4c7a6be8 100644 --- a/datasets/KOPRI-KPDC-00000174_1.json +++ b/datasets/KOPRI-KPDC-00000174_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000174_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic, Arctic, Korea Peninsula. The first MODIS instrument was launched on board the Terra satellite in December 1999\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000175_2.json b/datasets/KOPRI-KPDC-00000175_2.json index 5cefbcd507..d3711bce46 100644 --- a/datasets/KOPRI-KPDC-00000175_2.json +++ b/datasets/KOPRI-KPDC-00000175_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000175_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oshoro-Maru cruise in 2007 provided an important opportunity to compare the 4 areas with different environmental conditions from the southwestern Bering Sea to the Northern Chukchi Sea. Carbon/nitrogen uptake rates and nutrient measurements were obtained at 17 stations. In addition, 3 different UV light were measured at every 1hour at 4 stations. In the southern Bering Sea, there was a strong stratification at 20 m water depth, which is a characteristic water structure during the summer period. In the Chukchi Sea, there was a thermal stratification existed at 10 m depth after the Bering Strait. But, the salinity structure in the central Chukchi Sea showed some mixing from surface to bottom. In general, inorganic nutrients were not totally depleted in the euphotic layers, which might indicate the nutrients are not limiting to the phytoplankton production in the sampling areas for this time of 2007. Integrated chlorophyll-a in the water column were generally low (< 50 mg chl-a m-2) in the study areas, except C05 and C06 (> 500 mg chl-a m-2). The mean hourly carbon uptake rates were 13.0 \u00b1 3.1 mg C m-2 h-1 and 10.2 \u00b1 4.9 mg C m-2 h-1) respectively for the southern Bering Sea (B14, B21, B04, B10) and northern Bering Sea (B26, B40, B42). The mean rate in the central Chukchi Sea (RC03 and C04) was 108.3 mg C m-2 h-1. If the photic hour is 15 hours a day, then the daily carbon uptake rate was 1.63 g C m-2 d-1, which is comparable to the recent study although the chlorophyll-a concentration was so high (> 500 mg chl-a m-2) in this year. In comparison, the mean hourly nitrogen uptake rate was 45.2 mg N m-2 h-1 in the central Chukchi Sea which was higher than 22.6 mg N m-2 h-1 in Lee et al. (2007), but lower than 85.5 mg N m-2 h-1 in Hansell and Goering (1990). In the northern Bering Sea, the rate was 12.6 mg N m-2 h-1 which was rather higher than those from other study regions, but the ammonia uptake rate here except the station B26 was much higher than the nitrate uptake rate based on f-ratio (0.15). This is indicating that ammonium is an important nitrogen source in this region. In the central Chukchi Sea, the f-ratio was 0.68 which suggests nitrate was about 70 % of total nitrogen source for the phytoplankton production.\r\n1) To investigate on water temperature and salinity distribution and major inorganic nutrients concentration in the study area\r\n2) To study on primary productivity of phytoplankton and relationship with environmental factors in Bering and Chukchi Sea\r\n3) To study on productivity of ammonia and nitrogen in Bering and Chukchi Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000176_2.json b/datasets/KOPRI-KPDC-00000176_2.json index f74ae33ecb..724bd65460 100644 --- a/datasets/KOPRI-KPDC-00000176_2.json +++ b/datasets/KOPRI-KPDC-00000176_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000176_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a Chinese IPY event, the 3rd Chinese National Arctic Research Expedition (CHINARE) was conducted from the Chukchi Sea to the central Arctic Ocean from late July to early September in 2008. During the period, the primary productivity experiments were measured at 12 stations in the Chukchi Sea and 12 stations in the central Arctic Ocean, using a 13C-15N dual isotope tracer. In addition, we measured ice algae productivities in the melting ponds from 5 different ice stations. The temperature and salinity at surface were 4-6 \u2103 and 30 in the Chukchi Sea and -1-3 \u2103 and 25-27 in the central Arctic Ocean. The primary productivity of phytoplankton was higher in the Chukchi Sea than in Canada Basin during the cruise period. The nitrogen up take rates in the Chukchi Sea were lower in 2008 than in 2007. Based on high f-ratios in the central Arctic sea, nitrate uptake rates compared to ammonium uptake rates were relatively higher than those from other studies. The productivity of the algae in melting ponds ranged from 0.01 to 0.34 mg C m-3 h-1(average\u00b1 S.D.= 0.09\u00b10.11 mg C m-3 h-1) which were some what higher than those of phytoplankton(average\u00b1 S.D.= 0.07\u00b10.06 mg C m-3 h-1) at surface water over 80\u00b0N in the Arctic Ocean. From the careful examination on micro algae communities within or under sea ice at the refrozen surface of the melting ponds, they are believed to an important food source for the zooplanktons such as copepods and amphipods as well as Arctic cods before along winter period. However, current and on going climate changes such as a sea ice decrease are expected to impact largely on this unique habitat in the melting ponds.\r\n1) Primary production measurement by phytoplankton using a 13C-15N dual isotope tracer\r\n2) Developmental stages of melting ponds and ice algal productivity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000177_2.json b/datasets/KOPRI-KPDC-00000177_2.json index 48acd06c63..fcecfb7cb9 100644 --- a/datasets/KOPRI-KPDC-00000177_2.json +++ b/datasets/KOPRI-KPDC-00000177_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000177_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "It is very important to study the Chukchi Sea and Arctic Ocean in order to understand the global marine ecosystems responding to the current climate changes, but there have been not much study because of the difficulty in logistics. The RUSALCA cruise in 2009 provided very important opportunities to research marine environments and ecosystems in the Russian and US sides of the Chukchi Sea. The main objectives were to measure primary productivity of phytoplankton and understand which controlling factors are important for the phytoplankton growth in the Chukchi Sea. The light intensity at the highest peak in a day ranged from 200 \u03bcE m-2 s-1 to 1200 \u03bcE m-2 s-1 at around 4 pm depending on weather at that time when they were measured. Prochlorococcus and Synechococcus found first in the Chukchi Sea contributed about 30% in the cell abundance of small phytoplankton community (20 \u03bcm). Integrated chlorophyll-a concentrations were relatively low (< 100 mg chl-a m-2) in the Chukchi Sea this year. The average of integrated chlorophyll-a concentrations was 57.7 mg chl-a m-2 (\u00b1 37.8 mg chl-a m-2), which was 3 times lower than that (155.6 mg chl-a m-2) in 2004 (Lee et al. 2007). In consistent, the average of the carbon production rate was 17.03 mg C m-3 h-1, which was also 2 fold lower than that in 2004 (Lee et al. 2007). In this study, we found that light intensity was an main factor controlling phytoplankton growth in the Chukchi Sea rather than major nutrient concentrations such as nitrate and ammonium.\r\n1) Primary production measurement by phytoplankton using a 13C-15N dual isotope tracer\r\n2) Developmental stages of melting ponds and ice algal productivity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000178_2.json b/datasets/KOPRI-KPDC-00000178_2.json index 6868e0bd95..6fae6bc9ea 100644 --- a/datasets/KOPRI-KPDC-00000178_2.json +++ b/datasets/KOPRI-KPDC-00000178_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000178_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Ocean Ice Study (JOIS) was conducted in the Canada Basin from mid September to mid October in 2009. During the period, the primary productivity of phytoplankton was measured at six different light depths of 11 stations in the Canada Basin, using a 13C-15N dual isotope tracer. In addition, we identified the effects of light and nutrient enrichments on the primary production of phytoplankton in the chlorophyll a maximum layer. The temperature and salinity at surface were -2~ 1\u2103 and 24-27, respectively in the Canada Basin. In contrast, the very salty water (>31) was existed below 60 m water depth and the strong stratification was developed at the depth. In general, the NO3- concentration was depleted from surface to 60 m over the North of 72\u00b0. The primary productivity of phytoplankton was somewhat lower in the Canada Basin during the cruise period in 2009 compared to other regions in the Arctic Ocean. The averaged hourly primary production rate vertically integrated from 100% to 1% light depth was 1 mg C m-2 h-1 from this cruise. The production rates were not significant different depending on different sea ice concentrations (50%). The light and nutrient enrichments induced higher primary productivity of the phytoplankton in the chlorophyll a maximum layer. The increases of primary productivity at the stations with 50% ice cover. This is probably because the phytoplankton at the stations with the higher ice cover was more shade-adapted and thus slower response on the light enrichments.\r\n1) Quantification of Primary production of phytoplankton and Physical \u2022 Chemical environmental conditions in the Canada Basin\r\n2) The effect of light and nutrient on the primary production of phytoplankton in the Chl a maximum layer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000179_2.json b/datasets/KOPRI-KPDC-00000179_2.json index 42d872b9dc..ad3973f7bd 100644 --- a/datasets/KOPRI-KPDC-00000179_2.json +++ b/datasets/KOPRI-KPDC-00000179_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000179_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study the nutrients and phytoplankton pigment distribution in n the Chukchi and Canada Basin, samples were collected in 38 stations. Stations were chosen based on physio-chemical characteristics of the water environment. We investigate the spatial and temporal variations of the micro nutrients nitrate+nitrite, silicate, ammonium, and phosphate in the study area. Dissolved inorganic nitrate+nitrite concentrations remained low throughout the most region and some stations concentrations were found to be just above the level of detection. Dissolved inorganic phosphate and silicate shows maximum concentration in 200~300m depths. In general there was an excellent agreement between chlorophyll a, phytoplankton accessory pigment distribution, and nutrients distribution. The phytoplankton community structure assumed by phytoplankton pigment analysis show that the distinct succession of dominant phytoplankton group within the study area might related to nutrients availabilities.\r\n1) Phytoplankton community structure study using phytoplankton pigment analysis\r\n2) The effect of physical environments on the nutrients and phytoplankton pigment distribution", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000180_2.json b/datasets/KOPRI-KPDC-00000180_2.json index 40fc164e6c..2f1ba89919 100644 --- a/datasets/KOPRI-KPDC-00000180_2.json +++ b/datasets/KOPRI-KPDC-00000180_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000180_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first Arctic cruise of ARAON was conducted in the Chukchi Sea and Canada Basin from mid July to mid August in 2010. During the period, the carbon and nitrogen production rates of phytoplankton were measured at six different light depths of 19 stations in the Chukchi Sea and Canada Basin, using a 13C-15N dual isotope tracer. In addition, we identified the effect of light enrichments on the carbon and nitrogen production rates of phytoplankton in the chlorophyll a maximum layer. The averaged hourly carbon and nitrogen production rates vertically integrated from 100% to 1% light depth were 1.27 mg C m-2 h-1 (S.D.= \u00b1 1.13 mg C m-3 h-1) and 4.20 mg N m-3 h-1 (S.D.= \u00b1 4.58 mg N m-3 h-1) from this cruise. The nitrogen production rate of phytoplankton was somewhat higher than the carbon production rate. The small phytoplankton (0.7~5 \u00b5m) were dominated with value of over 50% in the study area. The light enrichment induced higher carbon and nitrogen production rates of the phytoplankton in the chlorophyll a maximum layer. The productivity increasing under higher light levels indicates that the growth of phytoplankton in this layer was light-limited during the cruise period in 2010.\r\nPrimary production measurement by phytoplankton using a 13C-15N dual isotope tracer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000181_1.json b/datasets/KOPRI-KPDC-00000181_1.json index 7d67117f8c..8fd4b42466 100644 --- a/datasets/KOPRI-KPDC-00000181_1.json +++ b/datasets/KOPRI-KPDC-00000181_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000181_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of the present study was to understand the feeding ecology of copepods in the upper water layers of the western Arctic Ocean. We investigated the trophic ecology of copepods collected at the sea ice water interface and from the water column. The objective was to understand the feeding ecology of copepods that dominate the plankton in the ARCTIC at different spatial (horizontal and vertical distribution) and temporal scales during the summer season.\r\n\tFeeding of Arctic zooplanktonic copepods was investigated by the analysis of gut contents using microscopy (LM and SEM - at Sangmyung University) and the analysis of chlorophyll a gut contents (at the NTOU in Keelung, Taiwan). Furthermore, were feeding experiments done with algal cultures from the Arctic and other invertebrates. Field sampling and laboratory analysis and experiments provide an integrated approach to the ecology and evolution of zooplankton with particular emphasis on the Copepoda. Several microscopic techniques (LM, TEM, SEM) are used for the analysis of their trophic biology and ecology. Measurements of the gut pigment contents of as many as possible copepod species allowed to differentiate between different feeding guilts. The gut pigment contents of copepod species was correlated with environmental parameters such as chlorophyll a concentration of ambient waters, seawater temperature and illumination (time of day/ season).\r\n\tWe measured the gut pigment contents for 21 copepod species by the gut fluorescence method. The gut chlorophyll a values of most small size copepod (< 1 mm) were lower than 0.85 ng Chl a individual-1. The highest gut pigment content was recorded in Metridia longa (7.31 ng Chl a individual-1). The gut pigment contents of 21 copepod species (including 27 samples and 987 individuals) estimated here represents a negative function of seawater temperature (Pearson correlation, r = -0.292, p = 0.014) and was positively correlated with the chlorophyll a concentration of ambient waters (Pearson correlation, r = 0.243, p = 0.043). Mean gut pigment content, ingestion and clearance rates (from 27 samples and 684 individuals) shows that larger copepods (> 2 mm) had significantly higher values than medium sized copepods (1-2 mm) and smaller sized copepods. The present study confirms that copepods obtained from the western Arctic were opportunistic feeders and the feeding on phytoplankton varied with different sized copepod groups. Particular items of gut content and gut pigment content demonstrated that different sized copepods preferred different food sources and belonged to different feeding guilts.\n1) The analysis of gut contents using microscopy (LM and SEM - at Sangmyung University) and the analysis of chlorophyll a gut contents\r\n2) Feeding experiments with algal cultures from the Arctic and other invertebrates\r\n3) To the ecology and evolution of zooplankton with particular emphasis on the Copepoda\r\n4) The analysis of their trophic biology and ecology by microscopic techniques (LM, TEM, SEM)\r\n\uf06c\tStatistical analysis(correlation) between the gut pigment contents of copepod species and environmental parameters such as chlorophyll a concentration of ambient waters, seawater temperature and illumination (time of day/ season)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000182_1.json b/datasets/KOPRI-KPDC-00000182_1.json index 175831b572..73d7a82c49 100644 --- a/datasets/KOPRI-KPDC-00000182_1.json +++ b/datasets/KOPRI-KPDC-00000182_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000182_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic events including earthquake, ice quake, and volcanic activities, etc.\nmonitoring ice quake volcanic activity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000183_1.json b/datasets/KOPRI-KPDC-00000183_1.json index fb3e99b052..d77db574d3 100644 --- a/datasets/KOPRI-KPDC-00000183_1.json +++ b/datasets/KOPRI-KPDC-00000183_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000183_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000184_1.json b/datasets/KOPRI-KPDC-00000184_1.json index e4d208c23b..c561ee4e7b 100644 --- a/datasets/KOPRI-KPDC-00000184_1.json +++ b/datasets/KOPRI-KPDC-00000184_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000184_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000185_1.json b/datasets/KOPRI-KPDC-00000185_1.json index 9052a5f22e..d9e4d5225a 100644 --- a/datasets/KOPRI-KPDC-00000185_1.json +++ b/datasets/KOPRI-KPDC-00000185_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000185_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000186_1.json b/datasets/KOPRI-KPDC-00000186_1.json index 8b5e1f355b..29e979f1c9 100644 --- a/datasets/KOPRI-KPDC-00000186_1.json +++ b/datasets/KOPRI-KPDC-00000186_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000186_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000187_1.json b/datasets/KOPRI-KPDC-00000187_1.json index 225ca4c3d9..ae79141636 100644 --- a/datasets/KOPRI-KPDC-00000187_1.json +++ b/datasets/KOPRI-KPDC-00000187_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000187_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000188_1.json b/datasets/KOPRI-KPDC-00000188_1.json index 965efd028a..7159e283d3 100644 --- a/datasets/KOPRI-KPDC-00000188_1.json +++ b/datasets/KOPRI-KPDC-00000188_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000188_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000189_1.json b/datasets/KOPRI-KPDC-00000189_1.json index 4084017a6a..f56cebcda3 100644 --- a/datasets/KOPRI-KPDC-00000189_1.json +++ b/datasets/KOPRI-KPDC-00000189_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000189_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000190_1.json b/datasets/KOPRI-KPDC-00000190_1.json index d580190255..cf80f3f9d5 100644 --- a/datasets/KOPRI-KPDC-00000190_1.json +++ b/datasets/KOPRI-KPDC-00000190_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000190_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "observing continuous seismic data\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000191_1.json b/datasets/KOPRI-KPDC-00000191_1.json index 40a24eba9d..0578e6ffe2 100644 --- a/datasets/KOPRI-KPDC-00000191_1.json +++ b/datasets/KOPRI-KPDC-00000191_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000191_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We reconstruct the longest ice core records of the past atmospheric environmental changes by extending the previous records back to 800 kyr BP from EPICA(European Project for Ice Coring in Antarctica (EPICA) Dome C deep ice core in East Antarctica, covering the past 800,000 years.\n- Determination of various trace elements derived from crustal dust, volcanic emissions and sea-salt spray using ICP-SF-MS\r\n- Determination of Rare earth elements using ICP-SF-MS", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000192_1.json b/datasets/KOPRI-KPDC-00000192_1.json index e8d61a6e63..91dfd0ef15 100644 --- a/datasets/KOPRI-KPDC-00000192_1.json +++ b/datasets/KOPRI-KPDC-00000192_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000192_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000193_1.json b/datasets/KOPRI-KPDC-00000193_1.json index ca3a9c927f..923529e0b0 100644 --- a/datasets/KOPRI-KPDC-00000193_1.json +++ b/datasets/KOPRI-KPDC-00000193_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000193_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000194_1.json b/datasets/KOPRI-KPDC-00000194_1.json index 85c5f4f786..7552065e3a 100644 --- a/datasets/KOPRI-KPDC-00000194_1.json +++ b/datasets/KOPRI-KPDC-00000194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000195_1.json b/datasets/KOPRI-KPDC-00000195_1.json index d84f9a0422..dd2eb3acf1 100644 --- a/datasets/KOPRI-KPDC-00000195_1.json +++ b/datasets/KOPRI-KPDC-00000195_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000195_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000196_1.json b/datasets/KOPRI-KPDC-00000196_1.json index a14662935d..b72505e171 100644 --- a/datasets/KOPRI-KPDC-00000196_1.json +++ b/datasets/KOPRI-KPDC-00000196_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000196_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000197_1.json b/datasets/KOPRI-KPDC-00000197_1.json index f61638c0d3..7110700a61 100644 --- a/datasets/KOPRI-KPDC-00000197_1.json +++ b/datasets/KOPRI-KPDC-00000197_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000197_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000198_1.json b/datasets/KOPRI-KPDC-00000198_1.json index 6845d2cd8d..f45f69e63c 100644 --- a/datasets/KOPRI-KPDC-00000198_1.json +++ b/datasets/KOPRI-KPDC-00000198_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000198_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000199_1.json b/datasets/KOPRI-KPDC-00000199_1.json index 89f7d73b88..8d26f759f9 100644 --- a/datasets/KOPRI-KPDC-00000199_1.json +++ b/datasets/KOPRI-KPDC-00000199_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000199_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000200_1.json b/datasets/KOPRI-KPDC-00000200_1.json index 70d1d1c720..8c392a299f 100644 --- a/datasets/KOPRI-KPDC-00000200_1.json +++ b/datasets/KOPRI-KPDC-00000200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000201_1.json b/datasets/KOPRI-KPDC-00000201_1.json index 7b9475c121..053289062c 100644 --- a/datasets/KOPRI-KPDC-00000201_1.json +++ b/datasets/KOPRI-KPDC-00000201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000202_1.json b/datasets/KOPRI-KPDC-00000202_1.json index 5adee84b58..3f0dc402fa 100644 --- a/datasets/KOPRI-KPDC-00000202_1.json +++ b/datasets/KOPRI-KPDC-00000202_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000202_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000203_1.json b/datasets/KOPRI-KPDC-00000203_1.json index 9aad70518b..cdb6ebf227 100644 --- a/datasets/KOPRI-KPDC-00000203_1.json +++ b/datasets/KOPRI-KPDC-00000203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000204_1.json b/datasets/KOPRI-KPDC-00000204_1.json index bbd7ce2138..86e5e1f9ec 100644 --- a/datasets/KOPRI-KPDC-00000204_1.json +++ b/datasets/KOPRI-KPDC-00000204_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000204_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000205_1.json b/datasets/KOPRI-KPDC-00000205_1.json index cded63d6a8..4708b6eee7 100644 --- a/datasets/KOPRI-KPDC-00000205_1.json +++ b/datasets/KOPRI-KPDC-00000205_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000205_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000206_1.json b/datasets/KOPRI-KPDC-00000206_1.json index 16954fedd1..2612408f99 100644 --- a/datasets/KOPRI-KPDC-00000206_1.json +++ b/datasets/KOPRI-KPDC-00000206_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000206_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000207_1.json b/datasets/KOPRI-KPDC-00000207_1.json index 94b5f4826e..c3c639ef71 100644 --- a/datasets/KOPRI-KPDC-00000207_1.json +++ b/datasets/KOPRI-KPDC-00000207_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000207_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000208_1.json b/datasets/KOPRI-KPDC-00000208_1.json index fe54959cf8..762e86c0ff 100644 --- a/datasets/KOPRI-KPDC-00000208_1.json +++ b/datasets/KOPRI-KPDC-00000208_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000208_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000209_1.json b/datasets/KOPRI-KPDC-00000209_1.json index 45dab85ec7..e277c45e76 100644 --- a/datasets/KOPRI-KPDC-00000209_1.json +++ b/datasets/KOPRI-KPDC-00000209_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000209_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000210_1.json b/datasets/KOPRI-KPDC-00000210_1.json index 12ad4ebc2a..19eb7cd606 100644 --- a/datasets/KOPRI-KPDC-00000210_1.json +++ b/datasets/KOPRI-KPDC-00000210_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000210_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000211_1.json b/datasets/KOPRI-KPDC-00000211_1.json index f233fbea8e..757a0a1ba9 100644 --- a/datasets/KOPRI-KPDC-00000211_1.json +++ b/datasets/KOPRI-KPDC-00000211_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000211_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000212_1.json b/datasets/KOPRI-KPDC-00000212_1.json index c7601a1f55..239e950d71 100644 --- a/datasets/KOPRI-KPDC-00000212_1.json +++ b/datasets/KOPRI-KPDC-00000212_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000212_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000213_1.json b/datasets/KOPRI-KPDC-00000213_1.json index 3c68db10bc..5dc20d3472 100644 --- a/datasets/KOPRI-KPDC-00000213_1.json +++ b/datasets/KOPRI-KPDC-00000213_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000213_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000214_1.json b/datasets/KOPRI-KPDC-00000214_1.json index c87dd03573..59edb9cb53 100644 --- a/datasets/KOPRI-KPDC-00000214_1.json +++ b/datasets/KOPRI-KPDC-00000214_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000214_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000215_1.json b/datasets/KOPRI-KPDC-00000215_1.json index 589c1b1fc2..43945089fd 100644 --- a/datasets/KOPRI-KPDC-00000215_1.json +++ b/datasets/KOPRI-KPDC-00000215_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000215_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000216_1.json b/datasets/KOPRI-KPDC-00000216_1.json index 029f8e1c24..43df9981f6 100644 --- a/datasets/KOPRI-KPDC-00000216_1.json +++ b/datasets/KOPRI-KPDC-00000216_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000216_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000217_1.json b/datasets/KOPRI-KPDC-00000217_1.json index 28c8dd28d6..f4863d6570 100644 --- a/datasets/KOPRI-KPDC-00000217_1.json +++ b/datasets/KOPRI-KPDC-00000217_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000217_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000218_1.json b/datasets/KOPRI-KPDC-00000218_1.json index cf223d95ea..32f662ceb0 100644 --- a/datasets/KOPRI-KPDC-00000218_1.json +++ b/datasets/KOPRI-KPDC-00000218_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000218_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000219_1.json b/datasets/KOPRI-KPDC-00000219_1.json index f6d074c31a..3b9deb1e01 100644 --- a/datasets/KOPRI-KPDC-00000219_1.json +++ b/datasets/KOPRI-KPDC-00000219_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000219_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000220_1.json b/datasets/KOPRI-KPDC-00000220_1.json index eb8f76c67f..19b16b48a0 100644 --- a/datasets/KOPRI-KPDC-00000220_1.json +++ b/datasets/KOPRI-KPDC-00000220_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000220_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000221_1.json b/datasets/KOPRI-KPDC-00000221_1.json index daebeccd9f..885ddbd4b1 100644 --- a/datasets/KOPRI-KPDC-00000221_1.json +++ b/datasets/KOPRI-KPDC-00000221_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000221_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000222_1.json b/datasets/KOPRI-KPDC-00000222_1.json index 013f435782..0d5da16739 100644 --- a/datasets/KOPRI-KPDC-00000222_1.json +++ b/datasets/KOPRI-KPDC-00000222_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000222_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000223_1.json b/datasets/KOPRI-KPDC-00000223_1.json index e9961c7c9b..ad7dbdbe37 100644 --- a/datasets/KOPRI-KPDC-00000223_1.json +++ b/datasets/KOPRI-KPDC-00000223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000224_1.json b/datasets/KOPRI-KPDC-00000224_1.json index eb81d06184..630b426146 100644 --- a/datasets/KOPRI-KPDC-00000224_1.json +++ b/datasets/KOPRI-KPDC-00000224_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000224_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000225_1.json b/datasets/KOPRI-KPDC-00000225_1.json index 50c3d39a80..3e3e7b20ee 100644 --- a/datasets/KOPRI-KPDC-00000225_1.json +++ b/datasets/KOPRI-KPDC-00000225_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000225_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000226_1.json b/datasets/KOPRI-KPDC-00000226_1.json index 6c15f5cb7a..633821381b 100644 --- a/datasets/KOPRI-KPDC-00000226_1.json +++ b/datasets/KOPRI-KPDC-00000226_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000226_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The collapse of the Larsen A Ice Shelve at the eastern coast of the Antarctic Peninsula occurred in 1995. However, no information is available on the spatial distributions of abundances and compositions of viruses and bacteria in the Larsen A area. During the NBP cruise from March 11 to April 19 in 2012, we collected seawater samples for microbial ecology at 7 stations in the study area. For the first time, we will provide the data on the distributions and compositions for marine microbes in the Lasen A area.\nTo investigate distributions and diversities of viruses and bacteria in Larsen A in the Weddell Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000227_1.json b/datasets/KOPRI-KPDC-00000227_1.json index 74fa9798c8..bd187eaa94 100644 --- a/datasets/KOPRI-KPDC-00000227_1.json +++ b/datasets/KOPRI-KPDC-00000227_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000227_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three different kinds of soil humic substances were extracted from soil and plant debris samples in cold environments of Alaska tundra region. A total of 143 cold-adapted bacterial strains having an ability to degrade or bioconvert humic substances were isolated from the samples. The isolates were identified through the analysis of their 16S rRNA genes and the bacterial diversity was analyzed to be simple.\nThe objective is to isolate bacterial strains able to degrade humic substances from cold environments in the Arctic region and to analyze their microbial diversity. Also, a functional genomic study on the microbial degradative pathway(s) for soil humic substances is an another main purpose.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000228_1.json b/datasets/KOPRI-KPDC-00000228_1.json index 5fcd9e251f..3776741577 100644 --- a/datasets/KOPRI-KPDC-00000228_1.json +++ b/datasets/KOPRI-KPDC-00000228_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000228_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000229_2.json b/datasets/KOPRI-KPDC-00000229_2.json index 3b590ff3cf..c117b23f23 100644 --- a/datasets/KOPRI-KPDC-00000229_2.json +++ b/datasets/KOPRI-KPDC-00000229_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000229_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "O2/Ar in seawater, pumped from the intake at 7 m below sea level, was measured using an equilibrator inlet mass spectrometer. The mass spectrometer measured a series of dissolved gases including O2 and Ar every 10 seconds. The data record ion currents of those gases and total pressure in the mass spectrometer.\nNet community production (NCP), defined as the difference between autotrophic photosynthesis and (autrophicand heterotrophic) respiration, produces O2 proportional to the amount of net carbon. By measuring chemically and biologically inert Ar together with O2, it is possible to remove O2 variation by physical processes (e.g., air temperature and pressure change and mixing of water masses) and deduce O2 variation by biological processes. To determine the net community (oxygen) production underway, we measured continuous O2/ Ar measurement system using an equilibrator inlet mass spectrometer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000230_1.json b/datasets/KOPRI-KPDC-00000230_1.json index c13f4da11c..9614918810 100644 --- a/datasets/KOPRI-KPDC-00000230_1.json +++ b/datasets/KOPRI-KPDC-00000230_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000230_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Psychrophilic Arctic yeast Leucosporidium sp. produces a glycosylated ice-binding protein (LeIBP) with a molecular mass of approximately 25 kDa, which can lower the freezing point below the melting point once it binds to ice. LeIBP exhibits low amino acid sequence similarity to other antifreeze proteins with known protein structures. Recently, we developed an expression system allowing high-level production and efficient purification of recombinant pLeIBP. Furthermore, crystallization and preliminary X-ray crystallographic analysis of the ice-binding protein were performed.\nTo investigate the antifreeze mechanism of LeIBP, we have carried out structural studies. As the first step toward its structural elucidation, we report the results of preliminary X-ray crystallographic experiments with LeIBP.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000231_1.json b/datasets/KOPRI-KPDC-00000231_1.json index f534220f22..271616c296 100644 --- a/datasets/KOPRI-KPDC-00000231_1.json +++ b/datasets/KOPRI-KPDC-00000231_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000231_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During December, 2010, KOPRI conducted seismic survey in the around Antarctic peninsila. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000232_1.json b/datasets/KOPRI-KPDC-00000232_1.json index 21a35028d9..23994bfca0 100644 --- a/datasets/KOPRI-KPDC-00000232_1.json +++ b/datasets/KOPRI-KPDC-00000232_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000232_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 21 days from 2011 July 31 to August 20 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP.\nTo investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000233_1.json b/datasets/KOPRI-KPDC-00000233_1.json index 2edd808a49..1b34e85948 100644 --- a/datasets/KOPRI-KPDC-00000233_1.json +++ b/datasets/KOPRI-KPDC-00000233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 21 days from 2011 July 31 to August 20 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP.\nTo investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000234_1.json b/datasets/KOPRI-KPDC-00000234_1.json index ac1170cbd2..ad902d91d3 100644 --- a/datasets/KOPRI-KPDC-00000234_1.json +++ b/datasets/KOPRI-KPDC-00000234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To measure the vertical profiles of temperature and salinity in the Chukchi Borderland/Mendeleev Ridge, an intensive oceanographic survey was conducted during 21 days from 2011 July 31 to August 20 by IBRV ARAON and to increase the spatial resolution for temperature and salinity, XCTD probes were used at 33 stations between regular hydrographic stations.\nTo investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000235_1.json b/datasets/KOPRI-KPDC-00000235_1.json index 1aa9c0fd64..fbd35d3725 100644 --- a/datasets/KOPRI-KPDC-00000235_1.json +++ b/datasets/KOPRI-KPDC-00000235_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000235_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. The vertical temperature, salinity and depth were obtained at 52 stations using CTD and Rosette water sampler.\nIn order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2012.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000236_1.json b/datasets/KOPRI-KPDC-00000236_1.json index a1b70e484d..f405dc654f 100644 --- a/datasets/KOPRI-KPDC-00000236_1.json +++ b/datasets/KOPRI-KPDC-00000236_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000236_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. A lowered acoustic Doppler current profiler (LADCP) was attached to the CTD frame to measure the full profile of current velocities.\nIn order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2012.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000237_1.json b/datasets/KOPRI-KPDC-00000237_1.json index c7253a00f6..845a82b032 100644 --- a/datasets/KOPRI-KPDC-00000237_1.json +++ b/datasets/KOPRI-KPDC-00000237_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000237_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. In order to produce a record of water current velocities for a range of depths was used by ADCP. On the cruise track, the vessel-mounted ADCP was continuously conducted.\nIn order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion.A total of the oceanographic investigation was conducted using ship (ARAON) in 2012.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000238_1.json b/datasets/KOPRI-KPDC-00000238_1.json index e63b0af091..e2ab02e500 100644 --- a/datasets/KOPRI-KPDC-00000238_1.json +++ b/datasets/KOPRI-KPDC-00000238_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000238_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. The vertical temperature and depth were obtained at 25 stations using XBT.\nIn order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2012.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000239_1.json b/datasets/KOPRI-KPDC-00000239_1.json index b50a7ddf63..206d3686fb 100644 --- a/datasets/KOPRI-KPDC-00000239_1.json +++ b/datasets/KOPRI-KPDC-00000239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton Calanus glacialis were collected in the Arctic marine around Dasan station in 2006. We sequenced about 28,000 EST clones using GS 20 (Genome Sequencer 20).\nThe aim of the ESTs collection from the Arctic zooplankton is to study phenomena of life of the Arctic marine organisms. The ESTs of Calanus glacialis can be used to analyze the functions and the expression of interesting genes at the molecular level.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000240_1.json b/datasets/KOPRI-KPDC-00000240_1.json index 4ce3afa567..20d01a71fc 100644 --- a/datasets/KOPRI-KPDC-00000240_1.json +++ b/datasets/KOPRI-KPDC-00000240_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000240_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genomics is high-profile science, impacting on all areas of biology, especially functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein\u00e2\u20ac\u201cprotein interactions for attempting to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products.\nThe Antarctic genomics project is their genome-wide approach to these questions for various Antarctic biota, such as fishes, amphipodas, plants, lichens and microorganisms involving high-throughput methods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000241_1.json b/datasets/KOPRI-KPDC-00000241_1.json index e48b700d58..e663dfa8b7 100644 --- a/datasets/KOPRI-KPDC-00000241_1.json +++ b/datasets/KOPRI-KPDC-00000241_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000241_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genomics is high-profile science, impacting on all areas of biology, especially functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein\u00e2\u20ac\u201cprotein interactions for attempting to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products.\nThe Antarctic genomics project is their genome-wide approach to these questions for various Antarctic biota, such as fishes, amphipodas, plants, lichens and microorganisms involving high-throughput methods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000242_1.json b/datasets/KOPRI-KPDC-00000242_1.json index 77a01fb355..4026ecd10e 100644 --- a/datasets/KOPRI-KPDC-00000242_1.json +++ b/datasets/KOPRI-KPDC-00000242_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000242_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genomics is high-profile science, impacting on all areas of biology, especially functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein\u00e2\u20ac\u201cprotein interactions for attempting to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products.\nThe Antarctic genomics project is their genome-wide approach to these questions for various Antarctic biota, such as fishes, amphipodas, plants, lichens and microorganisms involving high-throughput methods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000243_1.json b/datasets/KOPRI-KPDC-00000243_1.json index 28543c4173..0492995f7e 100644 --- a/datasets/KOPRI-KPDC-00000243_1.json +++ b/datasets/KOPRI-KPDC-00000243_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000243_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from Barton Peninsular collected in 2010-2011\nmicrobial diversity survey in soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000244_1.json b/datasets/KOPRI-KPDC-00000244_1.json index d18fed921e..08a1836a7a 100644 --- a/datasets/KOPRI-KPDC-00000244_1.json +++ b/datasets/KOPRI-KPDC-00000244_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000244_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from Barton Peninsular collected in 2011-12\nmicrobial diversity survey in soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000245_1.json b/datasets/KOPRI-KPDC-00000245_1.json index 033f6d4b87..6eda8a071b 100644 --- a/datasets/KOPRI-KPDC-00000245_1.json +++ b/datasets/KOPRI-KPDC-00000245_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000245_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Freshwater, biofilm and sediment from Barton Peninsular collected in 2012\nmicrobial diversity survey in freshwater ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000246_1.json b/datasets/KOPRI-KPDC-00000246_1.json index ae457ce767..505b636aa5 100644 --- a/datasets/KOPRI-KPDC-00000246_1.json +++ b/datasets/KOPRI-KPDC-00000246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples near Terra Nova Bay in 2011\nmicrobial diversity survey in soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000247_1.json b/datasets/KOPRI-KPDC-00000247_1.json index 5c6ffb9025..d26498a77d 100644 --- a/datasets/KOPRI-KPDC-00000247_1.json +++ b/datasets/KOPRI-KPDC-00000247_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000247_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples near Terra Nova Bay in 2012\nmicrobial diversity survey in soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000248_1.json b/datasets/KOPRI-KPDC-00000248_1.json index a9fd49f5b4..898127b9cb 100644 --- a/datasets/KOPRI-KPDC-00000248_1.json +++ b/datasets/KOPRI-KPDC-00000248_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000248_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples in Alaska in 2010\nmicrobial diversity survey in permafrost soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000249_1.json b/datasets/KOPRI-KPDC-00000249_1.json index 0ee831e87d..7836e3f779 100644 --- a/datasets/KOPRI-KPDC-00000249_1.json +++ b/datasets/KOPRI-KPDC-00000249_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000249_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples in Alaska in 2011\nmicrobial diversity survey in permafrost soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000250_1.json b/datasets/KOPRI-KPDC-00000250_1.json index a611f20e85..1b4734ae2f 100644 --- a/datasets/KOPRI-KPDC-00000250_1.json +++ b/datasets/KOPRI-KPDC-00000250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples near Cambridge Bay of Canada in 2012\nmicrobial diversity survey in permafrost soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000251_1.json b/datasets/KOPRI-KPDC-00000251_1.json index 414bdc7a51..4b6a5d213a 100644 --- a/datasets/KOPRI-KPDC-00000251_1.json +++ b/datasets/KOPRI-KPDC-00000251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Eddy covariance data obtained at a permafrost site of Council, Alaska, USA\nTo measure atmosphere-permafrost exchanges of momentum, heat, moisture, and carbon", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000252_1.json b/datasets/KOPRI-KPDC-00000252_1.json index 2c9f97d69f..3f498513a9 100644 --- a/datasets/KOPRI-KPDC-00000252_1.json +++ b/datasets/KOPRI-KPDC-00000252_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000252_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to cal/val of ocean color satellite data during the cruise (From Korea to Arctic), the in-situ research was conducted from 2012 July 9 to September 22 by ARAON. The Sea surface reflectance was obtained using HPRO\u00e2\u2026\u00a1 and HSAS.\nTo improve ocean color satellite data accuracy, we try to get bio-optical data. To calculate Remote sensing reflectance, we need to Lu (downward irradiance) and Ed (upwelling radiance). For the measuring apparent optical properties, we deployed hyper-spectro-radiometer until euphotic depth having 1% light intensity from sea surface. At the same time, we observed above water reflectance by using a Above water spectro-radiometer every 15 minutes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000253_1.json b/datasets/KOPRI-KPDC-00000253_1.json index aad7f143f7..debd5af1a7 100644 --- a/datasets/KOPRI-KPDC-00000253_1.json +++ b/datasets/KOPRI-KPDC-00000253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam data of Chukchi sea in Arctic ocean\nSwath bathymetry and high-resolution reflection data (~3.5KHz) were collected during the ARA03B cruise. On the sea floating ices were not densely distributed and wind and waves were mild and calm. Because of the relatively good sea condition, we could be able to acquire geophysical data with high signal-to-noise ratio. When the ship was ramming to find a thick multi-year ice for an ice station, however, very noisy data were acquired due to the interference to the transducers by crashed ices.\r\nThe survey tracks the western part of the Arctic Ocean ranging from Mendeleev Ridge to the Northwind ridge (Fig. 1). Most of the Arctic Ocean is poorly surveyed(Kristoffersen and Mikkelsen, 2003) so that new findings were expected through this cruise. The aim of the geophysical survey is to reveal the subsurface feature and sedimentary structures related to the climate change and geological evolution. For this aim, we focused geophysical survey on the following topics. The topics are:\r\n- to map subsurface structure on the unknown area\r\n- to characterize ice sheet lineation\r\n- to survey the distribution of pockmarks", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000254_1.json b/datasets/KOPRI-KPDC-00000254_1.json index 8bc9bffa62..6c761176ec 100644 --- a/datasets/KOPRI-KPDC-00000254_1.json +++ b/datasets/KOPRI-KPDC-00000254_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000254_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profiler data of Chukchi sea in Arctic ocean\nThe SBP120 Sub-bottom profiler installed on ARAON in 2008 is an optional extension to the highly acclaimed EM122 Multibeam echo sounder. The receive transducer array shared with the EM122 is wideband. By adding a separate low frequency transmit transducer and electronic cabinets and operator stations, the SBP 120 has a capability of the sub-bottom profiling. The system beam width is 12 degrees with 24 transducers.\r\nDuring the survey the SBP120 is synchronized by the Synchronize Unit which controls the triggering timing to reduce interference between acoustic equipment such as EM122 and ADCP. It has a much narrower beam width than a conventional sub bottom profiler with correspondingly lesser smearing. It thus provides deeper penetration into the bottom, and higher angular resolution. The frequency ranges from 2.5 to 7kHz. Its beam is electronically stabilized for roll and pitch. It can also be steered to take into account the bottom slope. The ping rate is synchronized to that of the EM122 if both are running simultaneously. \r\nThe topics are:\r\n- to examine sedimentary structure below the coring sites\r\n- to investigate shallow sedimentary structure\r\n- to reveal sedimentary structures related to ice ages.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000255_1.json b/datasets/KOPRI-KPDC-00000255_1.json index b5d7d208d8..537105ffad 100644 --- a/datasets/KOPRI-KPDC-00000255_1.json +++ b/datasets/KOPRI-KPDC-00000255_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000255_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On board turbulent fluxes of CO2, CH4 and energy were measured during the cruise in the Chukchi Borderland/Mendeleev Ridge in boreal summer of 2012. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and closed-path cavity ring-down spectrometer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz.\nTurbulent flux measurements are used to 1) better understand the air-sea energy exchanges and 2) evaluate how much the Chukchi sea absorbs or emits green house gases such as CO2 and CH4 in the Chukchi sea, the Arctic in summer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000256_1.json b/datasets/KOPRI-KPDC-00000256_1.json index 548b7cb787..e9bddcefde 100644 --- a/datasets/KOPRI-KPDC-00000256_1.json +++ b/datasets/KOPRI-KPDC-00000256_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000256_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An dural polarization LIDAR was used to measure the aerosol back-scattering intensity and its depolarization ratio with altitude up to 10 km over the Chukchi sea.\nLIDAR data are used to characterize aerosol profile and its shape over the Chukchi sea and evaluate the variation in the atmospheric boundary layer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000257_1.json b/datasets/KOPRI-KPDC-00000257_1.json index 62830b579d..69a4a91724 100644 --- a/datasets/KOPRI-KPDC-00000257_1.json +++ b/datasets/KOPRI-KPDC-00000257_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000257_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of wind and four radiative components were made to provide basic meteorological information under which the research cruise using a IBRV, ARAON was made in the Chukchi Borderland/Mendeleev Ridge in boreal summer of 2012.\nThey are used to provide basic data for research cruise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000258_1.json b/datasets/KOPRI-KPDC-00000258_1.json index ef06a4b0c9..5f5ace0f31 100644 --- a/datasets/KOPRI-KPDC-00000258_1.json +++ b/datasets/KOPRI-KPDC-00000258_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000258_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of meteorological data such as air temperature were made to provide basic meteorological information under which the research cruise using a IBRV, ARAON was made in the Chukchi Borderland/Mendeleev Ridge in boreal summer of 2012.\nThey are used to provide basic data for research cruise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000259_1.json b/datasets/KOPRI-KPDC-00000259_1.json index eb788b80bb..43145fe55f 100644 --- a/datasets/KOPRI-KPDC-00000259_1.json +++ b/datasets/KOPRI-KPDC-00000259_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000259_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 35 days from 2012 August 4 to September 7 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 102 areas.\nTo investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000260_1.json b/datasets/KOPRI-KPDC-00000260_1.json index c04f1598b1..fa7c5fd343 100644 --- a/datasets/KOPRI-KPDC-00000260_1.json +++ b/datasets/KOPRI-KPDC-00000260_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000260_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 35 days from 2012 August 4 to September 7 by IBRV ARAON to measure the spatial and temporal variation of seawater circulation in the Chukchi Borderland/Mendeleev Ridge. The circulation patten of seawater was observed using LADCP.\nTo investigate pathways of the Pacific origin Summer Water (PSW) and understand the relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000261_2.json b/datasets/KOPRI-KPDC-00000261_2.json index 70db85f83c..bb1ebfd99e 100644 --- a/datasets/KOPRI-KPDC-00000261_2.json +++ b/datasets/KOPRI-KPDC-00000261_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000261_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To measure the vertical profiles of temperature and salinity in the Chukchi Borderland/Mendeleev Ridge, an intensive oceanographic survey was conducted during 34 days from 2012 August 4 to September 7 by IBRV ARAON and to increase the spatial resolution for temperature and salinity, XCTD probes were used at 48 stations between regular hydrographic stations.\r\nTo investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000262_1.json b/datasets/KOPRI-KPDC-00000262_1.json index ea7cdc5285..469f7ee24b 100644 --- a/datasets/KOPRI-KPDC-00000262_1.json +++ b/datasets/KOPRI-KPDC-00000262_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000262_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fifteen soil core (around one meter) samples were collected in the permafrost region of Council, Alaska in 2012. We took three cores for replication from five sampling sites. Three of them had different soil physical properties that based on results of Geophysical research in 2011. The rest of sites have different soil textural properties, such as mainly organic-rich and mineral soil.\nTo investigate the difference of microbial community structures and their metabolism linving in permafrost soils which have different physical and chemical properties.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000263_2.json b/datasets/KOPRI-KPDC-00000263_2.json index 7226691be1..2435baa8d6 100644 --- a/datasets/KOPRI-KPDC-00000263_2.json +++ b/datasets/KOPRI-KPDC-00000263_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000263_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2011\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000264_1.json b/datasets/KOPRI-KPDC-00000264_1.json index f01095f477..2d6f50f032 100644 --- a/datasets/KOPRI-KPDC-00000264_1.json +++ b/datasets/KOPRI-KPDC-00000264_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000264_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During February and March, 2012, KOPRI conducted marine survey in the Amundsen Sea, Antarctica. During the cruise, we collected multibeam data\nBecause seafloor mapping is not the major purpose of the survey, tracks were determined to connect other stationary observation and sampling such as sediemint coring, CTD casting and so on. However, there are many areas are not surveyed yet in the Amundsen Sea, the acquired multibeam data will be utilized to fill the gap in the seafloor feature.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000265_1.json b/datasets/KOPRI-KPDC-00000265_1.json index e8a3b20934..6c02ac8f9d 100644 --- a/datasets/KOPRI-KPDC-00000265_1.json +++ b/datasets/KOPRI-KPDC-00000265_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000265_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During August, 2012, KOPRI conducted marine survey in the Chukchi sea, Arctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic research works.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000266_1.json b/datasets/KOPRI-KPDC-00000266_1.json index a3321c9f4c..95a3e841d9 100644 --- a/datasets/KOPRI-KPDC-00000266_1.json +++ b/datasets/KOPRI-KPDC-00000266_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000266_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to investigate the structure of phytoplankton communities, this study was carried out at 32 stations, 7 \u00e2\u20ac\u201c 8 depths from August 4 to September 6, 2012 in the Chukchi Sea and Melting Ponds on the Sea Ice.\n- To investigate on species composition, abundance and dominant species of phytoplankton communities in the Chukchi Sea and Sea Ice\r\n- To study on taxonomic research and dominant species of phytoplankton communities for investigate on indicator species", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000267_1.json b/datasets/KOPRI-KPDC-00000267_1.json index 488c2f0651..5b697106bb 100644 --- a/datasets/KOPRI-KPDC-00000267_1.json +++ b/datasets/KOPRI-KPDC-00000267_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000267_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerosol scattering coefficients for three different wavelengths (\u00ce\u00bb=450, 550, and 700nm) are measured almost continuously by a nephelometer in the Arctic ocean.\nTo determine the optical properties of aerosols in the Arctic ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000268_1.json b/datasets/KOPRI-KPDC-00000268_1.json index e19a8d0d10..4018656b9b 100644 --- a/datasets/KOPRI-KPDC-00000268_1.json +++ b/datasets/KOPRI-KPDC-00000268_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000268_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll-a concentration is investigated in the Amundsen Sea of Southern Ocean from December 2010 to January 2011. This data includes investigator and locality for chlorophyll-a concentration\nChlorophyll-a concentration in Antarctic Amundsen Sea 2010/2011", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000269_1.json b/datasets/KOPRI-KPDC-00000269_1.json index 298a866a96..9cb5c119e7 100644 --- a/datasets/KOPRI-KPDC-00000269_1.json +++ b/datasets/KOPRI-KPDC-00000269_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000269_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll-a concentration is investigated in the Amundsen Sea of Southern Ocean from January to March 2012. This data includes investigator and locality for chlorophyll-a concentration\nChlorophyll-a concentration in Antarctic Amundsen Sea 2012", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000270_1.json b/datasets/KOPRI-KPDC-00000270_1.json index 40edf53b70..a49c87065b 100644 --- a/datasets/KOPRI-KPDC-00000270_1.json +++ b/datasets/KOPRI-KPDC-00000270_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000270_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marin protozoa are collected in the Chuckchi Sea of central Arctic Sea from 1 August to 10 September 2012. This data includes collector, locality and abundance for marine protozoa\nAbundance and community structure analysis of marine protozoa in Arctic Chuckchi Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000271_1.json b/datasets/KOPRI-KPDC-00000271_1.json index 31414db1a2..01d6c93a11 100644 --- a/datasets/KOPRI-KPDC-00000271_1.json +++ b/datasets/KOPRI-KPDC-00000271_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000271_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The research area involves the glacier-retreat region in Vestre Lovenbreen and Midtre Lovenbreen which are located in Kongsfijorden in Svalvard, Norway. We collected our soil samples along a transect in each glacier. Additionally, we investigated near Climate Change Tower(CCT) established by the Italian research team and collected some soil samples. We are analyzing soil properties and microorganism community structure.\nDue to the climate change, glaciers have been retreated, and soil underneath glacier is getting exposed greatly. These changes affect the ecosystems of these region. Although many studies have been done in the field of vegetation succession along the glacier chronosequence, little is known about microbial community structure and characteristics of soil carbon in glacier forelands. This research mainly focused on how glacier retreat influences the community structure of microorganism and soil organic carbon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000272_1.json b/datasets/KOPRI-KPDC-00000272_1.json index 1ad80978a2..4e5da5dd1b 100644 --- a/datasets/KOPRI-KPDC-00000272_1.json +++ b/datasets/KOPRI-KPDC-00000272_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000272_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are two long-term monitoring plots which have different dominated plant species (Cassiope tetragona and Salix arctica) in Zackenberg. Each plot has five climate manipulation treatment with five replication. In 2011, we collected 25 soil cores (15 cm) in Cassiope tetragona plot to analyze soil microbial community structure and soil organic carbon quality.\nIncreased temperature and the amount of cloud cover are expected in Zackenberg, Greenland in the future. Professor Anders Michelsen at the University of Copenhagen set up long-term monitoring plots to investigate effects these environmental changes on ecosystem of Zackenberg. We are studying on changes in microbes and soil properties in response to increasing summer temperature, decreasing solar radiation and changing growing periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000273_1.json b/datasets/KOPRI-KPDC-00000273_1.json index 7a3d3d100f..ef272fdd61 100644 --- a/datasets/KOPRI-KPDC-00000273_1.json +++ b/datasets/KOPRI-KPDC-00000273_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000273_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are two long-term monitoring plots which have different dominated plant species (Cassiope tetragona and Salix arctica). Each plot has five climate manipulation treatments with five replication. In 2012, we collected 75 soil cores (15 cm depth) in Salix arctica plots to analyze soil microbial community structure and soil organic carbon quality.\nIncreased temperature and the amount of cloud cover are expected in Zackenberg, Greenland in the future. Professor Anders Michelsen at the University of Copenhagen set up long-term monitoring plots to investigate effects these environmental changes on ecosystem of Zackenberg. We are studying on changes in microbes and soil properties in response to increasing summer temperature, decreasing solar radiation and changing growing periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000274_1.json b/datasets/KOPRI-KPDC-00000274_1.json index df23ce954c..3dc67d4d31 100644 --- a/datasets/KOPRI-KPDC-00000274_1.json +++ b/datasets/KOPRI-KPDC-00000274_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000274_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The research area involves the glacier-retreat regions in Vestre Lovenbreen, Midtre Lovenbreen and Austre Lovenbreen, which are located in Kongsfijorden in Svalvard, Norway. We collected our soil samples along the transect in each glacier. The analysis of soil properties and microorganism community structure are underway.\nDue to the climate change, glaciers have been retreated, and soil underneath glacier is getting exposed greatly. These changes affect the ecosystems of these region. Although many studies have been done in the field of vegetation succession along the glacier chronosequence, little is known about microbial community structure and characteristics of soil carbon in glacier forelands. This research mainly focused on how glacier retreat influences the community structure of microorganism and soil organic carbon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000275_1.json b/datasets/KOPRI-KPDC-00000275_1.json index a97b94b1be..fca8a5ff7d 100644 --- a/datasets/KOPRI-KPDC-00000275_1.json +++ b/datasets/KOPRI-KPDC-00000275_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000275_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During March, 2011, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000276_1.json b/datasets/KOPRI-KPDC-00000276_1.json index 721eca48cf..a82331f456 100644 --- a/datasets/KOPRI-KPDC-00000276_1.json +++ b/datasets/KOPRI-KPDC-00000276_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000276_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During December, 2011, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000277_1.json b/datasets/KOPRI-KPDC-00000277_1.json index 1415be3e8c..6b33aea16c 100644 --- a/datasets/KOPRI-KPDC-00000277_1.json +++ b/datasets/KOPRI-KPDC-00000277_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000277_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korea Seismic Line 2010 that are multi-channel seismic data were collected during the 2010-11 austral summer with IBRV Araon in the South Shetland Continental Margin, Antarctica.\nPurpose of this survey is to investigate the characteristics and distribution of BSR and to analysis geological structure using the multi-channel seismic data from Drake Passage and Bransfield Strait.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000278_1.json b/datasets/KOPRI-KPDC-00000278_1.json index 93e595572e..dc26e5262c 100644 --- a/datasets/KOPRI-KPDC-00000278_1.json +++ b/datasets/KOPRI-KPDC-00000278_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000278_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerosol scattering coefficients for three different wavelengths (\u00ce\u00bb=450, 550, and 700nm) are measured almost continuously by a nephelometer in the Antarctic ocean.\nTo determine the optical properties of aerosols in the Antarctic ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000279_1.json b/datasets/KOPRI-KPDC-00000279_1.json index 2c165bfdff..f21a929a17 100644 --- a/datasets/KOPRI-KPDC-00000279_1.json +++ b/datasets/KOPRI-KPDC-00000279_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000279_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter for CPC3772 and >2.5nm for CPC3776.\nTo study aerosol formation and growth in Arctic-Antarctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000280_1.json b/datasets/KOPRI-KPDC-00000280_1.json index 6bcbad5cfd..fe22c4f9fd 100644 --- a/datasets/KOPRI-KPDC-00000280_1.json +++ b/datasets/KOPRI-KPDC-00000280_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000280_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter for CPC3772 and > 2.5nm for CPC3776.\nTo study aerosol formation and growth in Antarctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000281_1.json b/datasets/KOPRI-KPDC-00000281_1.json index 229bd60edf..fffeccfdb0 100644 --- a/datasets/KOPRI-KPDC-00000281_1.json +++ b/datasets/KOPRI-KPDC-00000281_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000281_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Black carbon (BC) concentrations were measured to investigate the filter spot loading effect in raw BC data at 5-minute time-based resolution using a Dual-wavelength(BC 880nm, UV 370nm).\nMeasurement of optically-absorbing Black Carbon particles in Arctic-Antarctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000282_1.json b/datasets/KOPRI-KPDC-00000282_1.json index ab500bc8ac..92ea350e2d 100644 --- a/datasets/KOPRI-KPDC-00000282_1.json +++ b/datasets/KOPRI-KPDC-00000282_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000282_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Black carbon (BC) concentrations were measured to investigate the filter spot loading effect in raw BC data at 5-minute time-based resolution using a Dual-wavelength(BC 880nm, UV 370nm).\nMeasurement of optically-absorbing Black Carbon particles in Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000283_1.json b/datasets/KOPRI-KPDC-00000283_1.json index d5caa1596a..1e9d9b9e4b 100644 --- a/datasets/KOPRI-KPDC-00000283_1.json +++ b/datasets/KOPRI-KPDC-00000283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korea Seismic Line 2009, multi-channel seismic data, were collected during the 2009-2010 austral summer with RV JCR in the South Shetland Continental margin.\nThe major purpose of this survey is to investigate detailed features of distribution and characteristics of gas hydrates buried in the South Shetland continental margin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000284_1.json b/datasets/KOPRI-KPDC-00000284_1.json index 4656f472d5..2a0237109e 100644 --- a/datasets/KOPRI-KPDC-00000284_1.json +++ b/datasets/KOPRI-KPDC-00000284_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000284_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korea Seismic Line 2008, multi-channel seismic data, were collected during the 2008-2009 austral summer with RV Yuzhmorgeologiya in the South Shetland Continental margin.\nThe major purpose of this survey is to investigate detailed features of distribution and characteristics of gas hydrates buried in the South Shetland continental margin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000285_1.json b/datasets/KOPRI-KPDC-00000285_1.json index f42222446e..465324a4fe 100644 --- a/datasets/KOPRI-KPDC-00000285_1.json +++ b/datasets/KOPRI-KPDC-00000285_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000285_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric carbon monoxide in the marine boundary layer was monitored from July 14 to September 24 by the LGR CO/N2O analyzer along the cruise track of R/V Araon in the Northwestern Pacific and the Arctic Ocean. The air inlet to the instrument wa located at 29 m asl and the CO was measured at 0.1 Hz.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000286_1.json b/datasets/KOPRI-KPDC-00000286_1.json index e24b5128fa..491c8e51c7 100644 --- a/datasets/KOPRI-KPDC-00000286_1.json +++ b/datasets/KOPRI-KPDC-00000286_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000286_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric carbon monoxide in marine boundary laryer\nSince atmospheric carbon monoxide plays a key role in tropospheric chemistry, it is essential to understand chemical processes in marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000287_1.json b/datasets/KOPRI-KPDC-00000287_1.json index 940e05b5b8..2319f0c6fd 100644 --- a/datasets/KOPRI-KPDC-00000287_1.json +++ b/datasets/KOPRI-KPDC-00000287_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000287_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric carbon monoxide in marine boundary laryer\nSince atmospheric carbon monoxide plays a key role in tropospheric chemistry, it is essential to understand chemical processes in marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000288_1.json b/datasets/KOPRI-KPDC-00000288_1.json index 44b82e9385..a2e4e0ecac 100644 --- a/datasets/KOPRI-KPDC-00000288_1.json +++ b/datasets/KOPRI-KPDC-00000288_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000288_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000289_1.json b/datasets/KOPRI-KPDC-00000289_1.json index cc466cbc1d..cf48dd0eef 100644 --- a/datasets/KOPRI-KPDC-00000289_1.json +++ b/datasets/KOPRI-KPDC-00000289_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000289_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx (NO+NO2) predominantly comes from pollution driven by human-activities and biomass burning, and plays an critical precursor of O3 formation in the troposphere. Satellite observations show high concentration in the east Asian countries, in particular near China, Korea, and Japan. We investigated the impact of such human activities to the clean marine boundary layer by measuring NOx concentration from Korean peninsular to Alaska, U.S.A, along the west boundary of the North Pacific. Further sailing to the Chukchi Sea, we had opportunity to monitor NOx concentration along the cruise track of R/V Araon for one month.\nTo investigate the air quality and influence of pollution driven by human activities in the east Asian countries and to monitor NOx concentration in the marine boundary layer over the North Pacific and the Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000290_1.json b/datasets/KOPRI-KPDC-00000290_1.json index c103f6b88e..782df36a6b 100644 --- a/datasets/KOPRI-KPDC-00000290_1.json +++ b/datasets/KOPRI-KPDC-00000290_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000290_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000291_1.json b/datasets/KOPRI-KPDC-00000291_1.json index 2195200272..63ae19fcea 100644 --- a/datasets/KOPRI-KPDC-00000291_1.json +++ b/datasets/KOPRI-KPDC-00000291_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000291_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric volatile organic compounds in the marine boundary layer was monitored from July 14 to September 24 by the PTR-TOF-MS (Proton Transfer Reaction Time of Flight Mass Spectrometer) along the cruise track of R/V Araon from Incheon to Incheon carrying out a series expeditions in the Northwestern Pacific and the Arctic Ocean. The air inlet to the instrument was located at 29 m asl and the species were measured at tens minutes.\nAtmospheric VOCs take part in tropospheric chemistry in particular related to the oxidation chemical processes in the marine boundary layer. This oxidation chemistry produces aerosol precursors and oxidative radicals which can further involve in the chemistry, which may lead to the control of atmospheric oxidation capacity and radiation budget. To understand these processes we measured VOCs in the marine boundary layer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000292_1.json b/datasets/KOPRI-KPDC-00000292_1.json index e605c46990..67cc8f0e7f 100644 --- a/datasets/KOPRI-KPDC-00000292_1.json +++ b/datasets/KOPRI-KPDC-00000292_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000292_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2012 Amundsen Sea cruise, phytoplankton physiological parameters were measured by Fluorescence Induction and Relaxation (FIRe) system.\nTo investigate the impact of physico-chemical conditions (especially iron limitation) on phytoplankton photosynthesis, the photosynthetic characteristics of phytoplankton were measured", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000293_1.json b/datasets/KOPRI-KPDC-00000293_1.json index 6ae8a18094..2feb14e4a8 100644 --- a/datasets/KOPRI-KPDC-00000293_1.json +++ b/datasets/KOPRI-KPDC-00000293_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000293_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous data of a broadband seismometer installed in King Sejong Base for the period of 2012/1/1~2012/12/31.\nmonitoring ice quake earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000294_1.json b/datasets/KOPRI-KPDC-00000294_1.json index 36cec078c1..9e134806c4 100644 --- a/datasets/KOPRI-KPDC-00000294_1.json +++ b/datasets/KOPRI-KPDC-00000294_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000294_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract (English): We installed a new seismic station on the top of Mt. Melbourne (MtM) and moved TNB station to the better site in January 2012. This data set includes continuous seismic data observed on the Korea Polar Seismic Network at TNB (KPSN@TNB) consisting of 4 sets of Taurus +Trillium Compact and 1 set of Q330 + Trillium 240.\nPurpose (English): KPSN@TNB is for monitoring seismic activities cause by glacial movements, ice melting, and vocanic activities, etc. These data set will be utilized to assess seismic hazard for the Jang-Bogo station and model seismic velocity structure of crust and upper mantle beneath the Terra Nova Bay.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000295_1.json b/datasets/KOPRI-KPDC-00000295_1.json index 1c45701338..65634d4d92 100644 --- a/datasets/KOPRI-KPDC-00000295_1.json +++ b/datasets/KOPRI-KPDC-00000295_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000295_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snowmelt from seasonal snow covers can be significant in many environments of northern and alpine areas. Water flowand chemical transport resulting from snowmelt have been studied for an understanding of contributions to watershedsor catchments. A Mobile-Immobile water Model (MIM) was developed to describe the movement of ionic tracers througha snowpack by Lee et al. (2008a) and Lee et al. (2008b). To validate the model used in the studies, mass balance cal-culations of the model were conducted and comparisons were made between model results and analytical solutions in thiswork. Mass balance was calculated based on the fact that change in total mass within a snowpack with time is equal tosum of any change in the flux of water or ionic tracers into and out of the snowpack. Calculations of both water and ionicmass show almost perfect agreement between changes of two water and solute mass fluxes. Comparisons between modelresults and analytical solutions including wave velocity and effective saturation show almost perfect agreement.\nValidations of the model used in Lee et al. (2008, Water Resources Research) using mass balance calculations", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000296_1.json b/datasets/KOPRI-KPDC-00000296_1.json index 0e5d9ee55c..68c8be4f21 100644 --- a/datasets/KOPRI-KPDC-00000296_1.json +++ b/datasets/KOPRI-KPDC-00000296_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000296_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and isotopic variations of snowmelt provide important clues for understanding snowmelt processes and the timing and contribution of snowmelt to catchment or watershed in spring. The newly developed model includes a hydraulic exchange between mobile and immobile water (\u00cf\u2030), and isotopic exchanges between both mobile water and ice (f1) and immobile water and ice (f2). Since the new model is based on the mobile-immobile water conceptualization, which is widely used for describing chemical tracer transport in snow, it allows simultaneous calculations of chemical as well as isotopic variations in snowpack discharge. We compare the model results with a study of solute transport and isotopic evolution of snowmelt in snow, using artificial rain-on-snow experiments with conservative anion (Br-). These observations are used to test the newly developed model and to better understand physical processes in a seasonal snowpack where our model simulates the chemical and isotopic variations.\nTo describe both chemical and isotopic transport of snowmelt", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000297_1.json b/datasets/KOPRI-KPDC-00000297_1.json index bab51116e4..b74aec5080 100644 --- a/datasets/KOPRI-KPDC-00000297_1.json +++ b/datasets/KOPRI-KPDC-00000297_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000297_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding snowmelt movement to the watershed is crucial for both climate change and hydrological studies because the snowmelt is a significant component of groundwater and surface runoff in temperature area. In this work, a new energy balance budget algorithm has been developed for melting snow from a snowpack at the Central Sierra Snow Laboratory (CSSL) in California, US. Using two sets of experiments, artificial rain-on-snow experiments and observations of diel variations, carried out in the winter of 2002 and 2003, we investigate how to calculate the amount of snowmelt from the snowpack using radiation energy and air temperature. To address the effect of air temperature, we calculate the integrated daily solar radiation energy input, and the integrated discharge of snowmelt under the snowpack and the energy required to generate such an amount of meltwater. The difference between the two is the excess (or deficit) energy input and we compare this energy to the average daily temperature. The resulting empirical relationship is used to calculate the instantaneous snowmelt rate in the model used by Lee et al. (2008a; 2010), in addition to the net-short radiation. If for a given 10 minute interval, the energy obtained by the melt calculation is negative, then no melt is generated. The input energy from the sun is considered to be used to increase the temperature of the snowpack. Positive energy is used for melting snow for the 10-minute interval. Using this energy budget algorithm, we optimize the intrinsic permeability of the snowpack for the two sets of experiments using one-dimensional water percolation model, which are 52.5\u00d710-10 m2 and 75\u00d710-10 m2 for the artificial rain-on-snow experiments and observations of diel variation, respectively.\nA new energy balance budget algorithm developed for melting snow from a snowpack", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000298_1.json b/datasets/KOPRI-KPDC-00000298_1.json index bdca2a583f..dfc202fd79 100644 --- a/datasets/KOPRI-KPDC-00000298_1.json +++ b/datasets/KOPRI-KPDC-00000298_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000298_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "With the aim of global environmental monitoring we carried out GPR (Ground Penetrating Radar) surveys\r\nat the Livingstion Island in Antarctica. Research area is near the Mt. Charra (340 m) in Livingston\r\nIsland which is located 80 km to the southwest of the King Sejong Station. We have collected 5 lines\r\nof GPR data. Two kinds of survey, CMP (Common Midpoint) surveys and common offset profiles, were\r\nperformed. We classified the glacier into the three layers using electromagnetic velocity of the ice\r\nand reflection characteristic. The depth of glacier reached about 80\u00e2\u02c6\u00bc110 m.\nSome reflectors showed the evidence of the water filled englacial drainage and volcanic ash-layers.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000299_1.json b/datasets/KOPRI-KPDC-00000299_1.json index f8c258bdbf..ec70b63e0a 100644 --- a/datasets/KOPRI-KPDC-00000299_1.json +++ b/datasets/KOPRI-KPDC-00000299_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000299_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glacier changes in Marian Cove, King George Island (KGI) were investigated on the basis of observed and modeled data. Air temperature observations for the past 51 years recorded at the Russian Bellingshausen Station (BS) and aerial photographs provided adequate proofs of glacier retreat and regional warming.\nOne-dimensional numerical simulations with yearly variant mass balance were performed to evaluate the glacier advance and retreat history. Then the models were validated by observed data. The results indicate that the mass balance of 0.6 m a-1 is a threshold point determining glacier advance or retreat in Marian Cove. The glacier changes are mainly affected by the ice thickness at the terminus and the local mass balance. The mass balance also affects glacier displacement in subsequent years. Marian Cove is probably ice-free by 2060 under condition of present warming trend.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000300_1.json b/datasets/KOPRI-KPDC-00000300_1.json index 11506957df..adc7339752 100644 --- a/datasets/KOPRI-KPDC-00000300_1.json +++ b/datasets/KOPRI-KPDC-00000300_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000300_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To determine subglacial topography and internal features of the Fourcade Glacier on King George Island in\r\nAntarctica, helicopter-borne and ground-towed ground-penetrating radar (GPR) data were recorded along four profiles in\r\nNovember 2006. Signature deconvolution, f-k migration velocity analysis, and finite-difference depth migration applied to the\r\nmixed-phase, single-channel, ground-towed data, were effective in increasing vertical resolution, obtaining the velocity\r\nfunction, and yielding clear depth images, respectively. For the helicopter-borne GPR, migration velocities were obtained as\r\nroot-mean-squared velocities in a two-layer model of air and ice. The radar sections show rugged subglacial topography,\r\nenglacial sliding surfaces, and localised scattering noise. The maximum depth to the basement is over 79m in the subglacial\r\nvalley adjacent to the south-eastern slope of the divide ridge between Fourcade and Moczydlowski Glaciers.\nIn the groundtowed\r\nprofile,weinterpret a complicated conduit above possible basal water and other isolated cavities, which are a few metres\r\nwide. Near the terminus, the GPR profiles image sliding surfaces, fractures, and faults that will contribute to the tidewater\r\ncalving mechanism forming icebergs in Potter Cove.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000301_1.json b/datasets/KOPRI-KPDC-00000301_1.json index ca3bc409cf..93acc04ebc 100644 --- a/datasets/KOPRI-KPDC-00000301_1.json +++ b/datasets/KOPRI-KPDC-00000301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To determine P- and S-wave velocities, elastic properties and subglacial topography of the polythermal Fourcade Glacier, surface seismic and radar surveys were conducted along a 470-m profile in November 2006. P- and S-wave velocity structures were determined by travel-time tomography and inversion of Rayleigh wave dispersion curves, respectively. The average P- and S-wave velocities of ice are 3466 and 1839 m s-1, respectively. Radar velocities were obtained by migration velocity analysis of 112 diffraction events. An estimate of 920 kg m-3 for the bulk density of wet ice corresponds to water contents of 5.1 and 3.2%, which were derived from the average P-wave and radar velocities, respectively.\nUsing this density and the average P- and S-wave velocities, we estimate that the corresponding incompressibility and rigidity of the ice are 6.925 and 3.119 GPa, respectively. Synergistic interpretation of the radar profile and P- and S-wave velocities indicates the presence of a fracture zone above a subglacial high. Here, the P- and S-wave velocities are approximately 5 and 3% less than in the ice above a subglacial valley, respectively. The S-wave velocities indicate that warmer and less rigid ice underlies 10\u00e2\u20ac\u201c15 m of colder ice near the surface of the glacier. Such layering is characteristic of polythermal glaciers. As a relatively simple non-invasive approach, integration of P-wave tomography, Rayleigh wave inversion and ground-towed radar is effective for various glaciological studies, including the elastic properties of englacial and subglacial materials, cold/warm ice interfaces, topography of a glacier bed and location of fracture zones.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000302_2.json b/datasets/KOPRI-KPDC-00000302_2.json index ad161a14b0..bf97e35fca 100644 --- a/datasets/KOPRI-KPDC-00000302_2.json +++ b/datasets/KOPRI-KPDC-00000302_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000302_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2012\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000303_1.json b/datasets/KOPRI-KPDC-00000303_1.json index b83826ca0b..876d7ad98d 100644 --- a/datasets/KOPRI-KPDC-00000303_1.json +++ b/datasets/KOPRI-KPDC-00000303_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000303_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The distribution of small fractures and water content of the Fourcade glacier on King George Island, Antarctica, was investi- gated in November 2006 and December 2007 by two ground-based (470- and 490-m-long profiles) and one helicopter-borne (470-m-long profile) ground-penetrating radar (GPR) surveys using 50-, 100-, and 500-MHz antennas. Radar images in the pre-migrated GPR sections are characterized by a smooth ice surface and irregular bed topography, numerous diffraction hy- perbolas in the ice and at the glacier bed, strong scattering noise, and near-surface folded layers. Scattering noise above a mound in the center of the profiles is associated with an area of dense fractures extending down from the ice surface that has relatively low reflection strength. Near the northeast ends of the profiles where few englacial fractures occur, scattering noise may result from the presence of warmer ice. A water-filled conduit and an air-filled cavity are interpreted as the source of two distinct hyperbolas in sub-glacial valleys based on the polarity of the reflections.\nThrough migration velocity analysis on 106 hyperbolas, radar velocities were obtained for the 100-MHz ground-based profile. Using the velocities and Paren\u2019s mixture formula, we calculated the water content of the ice to have been in the range of 0.00\u20130.09. High water content occurs near the glacier margin, in sub-glacial valleys, and in zones of scattering noise.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000304_1.json b/datasets/KOPRI-KPDC-00000304_1.json index d33f76ba0b..ee097f98e3 100644 --- a/datasets/KOPRI-KPDC-00000304_1.json +++ b/datasets/KOPRI-KPDC-00000304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000305_1.json b/datasets/KOPRI-KPDC-00000305_1.json index 04dab9bb0c..df9d72d3f2 100644 --- a/datasets/KOPRI-KPDC-00000305_1.json +++ b/datasets/KOPRI-KPDC-00000305_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000305_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000306_1.json b/datasets/KOPRI-KPDC-00000306_1.json index d497a44302..6661435d41 100644 --- a/datasets/KOPRI-KPDC-00000306_1.json +++ b/datasets/KOPRI-KPDC-00000306_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000306_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000307_1.json b/datasets/KOPRI-KPDC-00000307_1.json index 611ac2f739..ade86703ad 100644 --- a/datasets/KOPRI-KPDC-00000307_1.json +++ b/datasets/KOPRI-KPDC-00000307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000308_1.json b/datasets/KOPRI-KPDC-00000308_1.json index 09e783734d..4ed7276972 100644 --- a/datasets/KOPRI-KPDC-00000308_1.json +++ b/datasets/KOPRI-KPDC-00000308_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000308_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000309_1.json b/datasets/KOPRI-KPDC-00000309_1.json index e603378c05..483af28f12 100644 --- a/datasets/KOPRI-KPDC-00000309_1.json +++ b/datasets/KOPRI-KPDC-00000309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured to December in 2003 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand\r\n1) the air-ocean-land-sea ice energy exchanges and\r\n2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000310_1.json b/datasets/KOPRI-KPDC-00000310_1.json index e779f8afca..037d94b045 100644 --- a/datasets/KOPRI-KPDC-00000310_1.json +++ b/datasets/KOPRI-KPDC-00000310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2004 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000311_1.json b/datasets/KOPRI-KPDC-00000311_1.json index c953a70cc0..a2dec4021f 100644 --- a/datasets/KOPRI-KPDC-00000311_1.json +++ b/datasets/KOPRI-KPDC-00000311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2005 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000312_1.json b/datasets/KOPRI-KPDC-00000312_1.json index 10f90151cc..455d47e547 100644 --- a/datasets/KOPRI-KPDC-00000312_1.json +++ b/datasets/KOPRI-KPDC-00000312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2006 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000313_1.json b/datasets/KOPRI-KPDC-00000313_1.json index 5f5687cd87..4fc5fc7939 100644 --- a/datasets/KOPRI-KPDC-00000313_1.json +++ b/datasets/KOPRI-KPDC-00000313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2007 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000314_1.json b/datasets/KOPRI-KPDC-00000314_1.json index ccc3438010..3f456d6000 100644 --- a/datasets/KOPRI-KPDC-00000314_1.json +++ b/datasets/KOPRI-KPDC-00000314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2008 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000315_1.json b/datasets/KOPRI-KPDC-00000315_1.json index 753cbc788f..c09146427d 100644 --- a/datasets/KOPRI-KPDC-00000315_1.json +++ b/datasets/KOPRI-KPDC-00000315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2009 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000316_1.json b/datasets/KOPRI-KPDC-00000316_1.json index cdc29e7d14..df7b7e585b 100644 --- a/datasets/KOPRI-KPDC-00000316_1.json +++ b/datasets/KOPRI-KPDC-00000316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2010 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000317_1.json b/datasets/KOPRI-KPDC-00000317_1.json index 5d0549252e..017063e57d 100644 --- a/datasets/KOPRI-KPDC-00000317_1.json +++ b/datasets/KOPRI-KPDC-00000317_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000317_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2011 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000318_1.json b/datasets/KOPRI-KPDC-00000318_1.json index 2fc0d80e14..8ba37a946e 100644 --- a/datasets/KOPRI-KPDC-00000318_1.json +++ b/datasets/KOPRI-KPDC-00000318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2012 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000319_1.json b/datasets/KOPRI-KPDC-00000319_1.json index 7e16f79647..353ceca493 100644 --- a/datasets/KOPRI-KPDC-00000319_1.json +++ b/datasets/KOPRI-KPDC-00000319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During January to February, 2013, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000320_1.json b/datasets/KOPRI-KPDC-00000320_1.json index 5780403b5a..f4fd52b868 100644 --- a/datasets/KOPRI-KPDC-00000320_1.json +++ b/datasets/KOPRI-KPDC-00000320_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000320_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Korea Seismic Line 2012, multi-channel seismic data, were collected during the 2012-2013 austral summer with RV Araon in the Continental margin of Ross Sea.\nThe major purpose of this survey is to investigate stratigraphy and sedimentary structure of the continental slope of Ross Sea, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000321_2.json b/datasets/KOPRI-KPDC-00000321_2.json index be3d8d0a36..8459e97476 100644 --- a/datasets/KOPRI-KPDC-00000321_2.json +++ b/datasets/KOPRI-KPDC-00000321_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000321_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. The vertical temperature, salinity and depth were obtained at 41 stations using CTD and Rosette water sampler.\r\nIn order to identify the temporal and spatial distribution of CDW on the Ross shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2013.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000322_1.json b/datasets/KOPRI-KPDC-00000322_1.json index f18aadc334..88a85906b0 100644 --- a/datasets/KOPRI-KPDC-00000322_1.json +++ b/datasets/KOPRI-KPDC-00000322_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000322_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. A lowered acoustic Doppler current profiler (LADCP) was attached to the CTD frame to measure the full profile of current velocities.\nIn order to identify the temporal and spatial distribution of CDW on the Ross Sea shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2013.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000323_1.json b/datasets/KOPRI-KPDC-00000323_1.json index 829bfd05e5..c51676ec1b 100644 --- a/datasets/KOPRI-KPDC-00000323_1.json +++ b/datasets/KOPRI-KPDC-00000323_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000323_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "- Low-Z particle EPMA measurements were carried out on a JEOL JSM-6390 SEM equipped with an Oxford Link SATW ultrathin window energy-dispersive X-ray (EDX) detector. X-ray spectra were recorded under the control of INCA software (Oxford). An accelerating voltage of 10 kV, beam current of 0.5 nA, and a typical measuring time of 15 s were employed. \r\n\r\n- ATR-FT-IR imaging measurements were performed using a Perkin-Elmer Spectrum 100 FT-IR spectrometer interfaced to a Spectrum Spotlight 400 FT-IR microscope. For ATR imaging, an ATR accessory employing a germanium hemispherical internal reflection element (IRE) crystal with a diameter of 600 \u00ce\u00bcm was used.\nA new single particle analytical methodology that combines low-Z particle EPMA and ATR-FT-IR imaging technique will be developed to obtain the full description for the micro-physicochemical properties of the same individual Antarctic aerosol particles.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000324_1.json b/datasets/KOPRI-KPDC-00000324_1.json index 776e493d0d..36172b6677 100644 --- a/datasets/KOPRI-KPDC-00000324_1.json +++ b/datasets/KOPRI-KPDC-00000324_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000324_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "- Utilization GRIMs model to investigate impacts of the snow cover changes on variation of Siberian High intensity and thawing permafrost\n- Understanding the climate change mechanism of polar regions through an examination of the Arctic permafrost\r\n- Numerical simulation and future prediction for the permafrost environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000325_1.json b/datasets/KOPRI-KPDC-00000325_1.json index 9bda549fc2..5986c3fffa 100644 --- a/datasets/KOPRI-KPDC-00000325_1.json +++ b/datasets/KOPRI-KPDC-00000325_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000325_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data is produced by using National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3). Files are in netcdf format and self-explanatory\r\n. It includes atmospheric variables, such as zonal wind, meridional wind, temperature, and geopotential height, related the change in tropical sea surface temperature magnitudes.\nThis data is used for analyzing the change in southern annular mode (SAM) to the change in the tropical El-Nino Southern Oscillation (ENSO) magnitude, and for comparing with reanalysis data for austral summer season (December-January-February).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000326_1.json b/datasets/KOPRI-KPDC-00000326_1.json index db35d1702d..cba6f3d80e 100644 --- a/datasets/KOPRI-KPDC-00000326_1.json +++ b/datasets/KOPRI-KPDC-00000326_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000326_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigatio of the feedback between vegetation and climate in permafrost regions\nUnderstanding the climate change mechanism of polar regions through an examination of the Arctic permafrost", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000327_2.json b/datasets/KOPRI-KPDC-00000327_2.json index 9c188231e3..cfc9021f29 100644 --- a/datasets/KOPRI-KPDC-00000327_2.json +++ b/datasets/KOPRI-KPDC-00000327_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000327_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial diversity based on 454-pyrosequencing in freshwater samples from McMurdo Dry Valleys, Victoria Land, collected in 2012-2013\r\nMicrobial diversity survey in freshwater lakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000328_1.json b/datasets/KOPRI-KPDC-00000328_1.json index b72d350fcb..b46d2d428f 100644 --- a/datasets/KOPRI-KPDC-00000328_1.json +++ b/datasets/KOPRI-KPDC-00000328_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000328_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rock samples of Northern Victoria Land (NVL), Antarctica collected in 2012-13 austral summer season. The collection includes sedimentary rocks (sandstone, limestone, conglomerate, and so on) of the Lower Paleozoic Bowers and Beacon supergroups, metamorphic rocks of the Wilson Terrane, and volcanic rocks of the McMurdo Volcanics.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information on stratigraphy, metamorphism, and volcanism will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000329_1.json b/datasets/KOPRI-KPDC-00000329_1.json index 53f5b21ac0..9200b0714e 100644 --- a/datasets/KOPRI-KPDC-00000329_1.json +++ b/datasets/KOPRI-KPDC-00000329_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000329_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the fossil specimens of Northern Victoria Land (NVL), Antarctica collected in 2012-13 austral summer season. The collection includes trilobites and brachipods of the Lower Paleozoic Bowers Supergroup and plant fossils of the Beacon Supergroup.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information from the fossils will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000330_1.json b/datasets/KOPRI-KPDC-00000330_1.json index cf7f5974d9..9b97224634 100644 --- a/datasets/KOPRI-KPDC-00000330_1.json +++ b/datasets/KOPRI-KPDC-00000330_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000330_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 377 samples for total alkalinity (TA) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from King George Island to Christchurch. 141 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000331_1.json b/datasets/KOPRI-KPDC-00000331_1.json index 9c092fa6f3..46f3d6d2f8 100644 --- a/datasets/KOPRI-KPDC-00000331_1.json +++ b/datasets/KOPRI-KPDC-00000331_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000331_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for total alkalinity (TA) were collected from Niskin bottle on board. In addition to these investigation, to understand the distribution of the various component of carbonic system in the surface seawaters, underway observation of CO2 parameters was carried out along the cruise track. 271 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000332_1.json b/datasets/KOPRI-KPDC-00000332_1.json index d29d9f3d93..f5dbc840ac 100644 --- a/datasets/KOPRI-KPDC-00000332_1.json +++ b/datasets/KOPRI-KPDC-00000332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains optical backscatter and oxidation-reduction potential signals along with conventional temperature, salinity, and pressure data, collected during tow-yo survey over a mid-ocean ridge.\nBecause hydrothermal plumes typically carry turbid and/or reduced compounds (H2S, Fe2+), it is proven that optical backscatter and oxidation-reduction potentials sensors are very effective in hydrothermal plume detection. These sensors, attached to a CTD, can be towed along/cross a mid-ocean ridge to map the distribution of hydrothermal plumes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000333_1.json b/datasets/KOPRI-KPDC-00000333_1.json index e7f8668b22..5dc2cc60c4 100644 --- a/datasets/KOPRI-KPDC-00000333_1.json +++ b/datasets/KOPRI-KPDC-00000333_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000333_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Southern Ocean, hydrographic survey using R/V Polarstern was carried out from November 26, 2009 to January 20, 2010. At 28 stations, 325 samples for total alkalinity were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from Punta Arenas, Chile, to Wellington, New Zealand. 576 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000334_1.json b/datasets/KOPRI-KPDC-00000334_1.json index e9fadc70fc..067ddde6f9 100644 --- a/datasets/KOPRI-KPDC-00000334_1.json +++ b/datasets/KOPRI-KPDC-00000334_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000334_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of rapid retreat of Arctic sea ice on distribution of the various of the carbonic system, hydrographic survey was carried out by the IBRV Araon from July 17 to August 12 in Chuckchi Borderland and western Canada Basin. At each hydrographic station, 244 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000335_1.json b/datasets/KOPRI-KPDC-00000335_1.json index fdf961dd9d..2f35f74dac 100644 --- a/datasets/KOPRI-KPDC-00000335_1.json +++ b/datasets/KOPRI-KPDC-00000335_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000335_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During April to May, 2013, KOPRI conducted marine survey in the Weddell sea, Western Antarctic peninsula. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000336_1.json b/datasets/KOPRI-KPDC-00000336_1.json index 2885eec4e0..46c17fb418 100644 --- a/datasets/KOPRI-KPDC-00000336_1.json +++ b/datasets/KOPRI-KPDC-00000336_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000336_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from 2010 December 20 to 2011 January 22 by the GC 7890A along the cruise track of R/V Araon from the King Sejong Station to Christchurch(New Zealand) carrying out a series of expeditions in The Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000337_1.json b/datasets/KOPRI-KPDC-00000337_1.json index 4367f1118d..284bcd4eeb 100644 --- a/datasets/KOPRI-KPDC-00000337_1.json +++ b/datasets/KOPRI-KPDC-00000337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from January 22 to March 11 by the GC 7890A along the cruise track of R/V Araon from Christchurch(New Zealand) to Christchurch carrying out a series of expeditions in The Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000338_1.json b/datasets/KOPRI-KPDC-00000338_1.json index 3862ead3e7..6313d90803 100644 --- a/datasets/KOPRI-KPDC-00000338_1.json +++ b/datasets/KOPRI-KPDC-00000338_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000338_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from January 26 to February 28 by the GC 7890A along the cruise track of R/V Araon from Christchurch(New Zealand) to the McMurdo Station(Ross Island) carrying out a series of expeditions in The Ross Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000339_1.json b/datasets/KOPRI-KPDC-00000339_1.json index c95dbe5050..fca38d1df9 100644 --- a/datasets/KOPRI-KPDC-00000339_1.json +++ b/datasets/KOPRI-KPDC-00000339_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000339_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from November 26, 2010, to January 22, 2011 by the GC 7890A along the cruise track of R/V Polarstern from Punta Arenas, Chile, to Wellington, New Zealand, carrying out the expedition, ANTXXVI/2, in the Southern Ocean. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000340_1.json b/datasets/KOPRI-KPDC-00000340_1.json index e4fac8c66c..1092e759c3 100644 --- a/datasets/KOPRI-KPDC-00000340_1.json +++ b/datasets/KOPRI-KPDC-00000340_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000340_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from July 17 to August 12 by the GC 7890A along the cruise track of R/V Araon from Nome(Alaska) to Nome carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000341_1.json b/datasets/KOPRI-KPDC-00000341_1.json index e687750506..1fb90b8668 100644 --- a/datasets/KOPRI-KPDC-00000341_1.json +++ b/datasets/KOPRI-KPDC-00000341_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000341_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer were monitored from July 14 to July 29 by the GC 7890A along the cruise track of R/V Araon from Incheon to Nome(Alaska) carrying out a series of expeditions in The Northwest Pacific. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000342_1.json b/datasets/KOPRI-KPDC-00000342_1.json index 20d9c6c42f..da03edc300 100644 --- a/datasets/KOPRI-KPDC-00000342_1.json +++ b/datasets/KOPRI-KPDC-00000342_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000342_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic methane in the marine boundary layer was monitored from August 1 to September 10 by the GC 7890A along the cruise track of R/V Araon from Nome(Alaska) to Nome carrying out a series of expeditions in The Arctic Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CH4 was measured every 40 minutes.\nAs atmospheric methane is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000343_1.json b/datasets/KOPRI-KPDC-00000343_1.json index fa713d1321..7999612208 100644 --- a/datasets/KOPRI-KPDC-00000343_1.json +++ b/datasets/KOPRI-KPDC-00000343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from October 10 to October 29 by the RGA along the cruise track of R/V Araon from Incheon to Christchurch(New Zealand) carrying out a series of expeditions in the Western Equatorial Pacific. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000344_1.json b/datasets/KOPRI-KPDC-00000344_1.json index a250a89257..54db608142 100644 --- a/datasets/KOPRI-KPDC-00000344_1.json +++ b/datasets/KOPRI-KPDC-00000344_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000344_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from November 1 to November 30 by RGA along the cruise track of R/V Araon from Christchurch(New Zealand) to the King Sejong station carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000345_1.json b/datasets/KOPRI-KPDC-00000345_1.json index 852d88eb47..9458f494db 100644 --- a/datasets/KOPRI-KPDC-00000345_1.json +++ b/datasets/KOPRI-KPDC-00000345_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000345_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from 2010 December 20 to 2011 January 22 by RGA along the cruise track of R/V Araon from the King Sejong Station to Christchurch(New Zealand) carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000346_1.json b/datasets/KOPRI-KPDC-00000346_1.json index a11bd9f979..7ebbec006d 100644 --- a/datasets/KOPRI-KPDC-00000346_1.json +++ b/datasets/KOPRI-KPDC-00000346_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000346_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from February 25 to March 12 by RGA along the cruise track of R/V Araon from Christchurch(New Zealand) to Christchurch carrying out a series of expeditions in the Antarctic Ridge. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000347_1.json b/datasets/KOPRI-KPDC-00000347_1.json index 4b8d3abf02..74f0ead0ae 100644 --- a/datasets/KOPRI-KPDC-00000347_1.json +++ b/datasets/KOPRI-KPDC-00000347_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000347_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from October 4 to November 15 by LGR, RGA and AL5002 along the cruise track of R/V Araon from Incheon to the King Sejong Station carrying out a series of expeditions in the Pacific. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000348_1.json b/datasets/KOPRI-KPDC-00000348_1.json index a7c232359c..6c461f189a 100644 --- a/datasets/KOPRI-KPDC-00000348_1.json +++ b/datasets/KOPRI-KPDC-00000348_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000348_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from November 21 to December 15 by LGR and RGA along the cruise track of R/V Araon from the King Sejong Station to Christchurch(New Zealand) carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000349_1.json b/datasets/KOPRI-KPDC-00000349_1.json index 8e26963fc9..d71f88c3ae 100644 --- a/datasets/KOPRI-KPDC-00000349_1.json +++ b/datasets/KOPRI-KPDC-00000349_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000349_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from January 22 to March 11 by LGR and RGA along the cruise track of R/V Araon from Christchurch(New Zealand) to Christchurch carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000350_1.json b/datasets/KOPRI-KPDC-00000350_1.json index b0daa049a0..516504c6fd 100644 --- a/datasets/KOPRI-KPDC-00000350_1.json +++ b/datasets/KOPRI-KPDC-00000350_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000350_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from January 26 to February 28 by LGR and RGA along the cruise track of R/V Araon from Christchurch(New Zealand) to the McMurdo Station(Ross Island) carrying out a series of expeditions in the Ross Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000351_1.json b/datasets/KOPRI-KPDC-00000351_1.json index a0991794c5..025d8f30ab 100644 --- a/datasets/KOPRI-KPDC-00000351_1.json +++ b/datasets/KOPRI-KPDC-00000351_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000351_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric carbon monoxide in the marine boundary layer was monitored from March 1 to March 19 by LGR along the cruise track of R/V Araon from the McMurdo Station(Ross Island) to Christchurch(New zealand) carrying out a series of expeditions near Teranova bay-the Jang Bogo Station. The air inlet to the instrument was located at 29 m asl and CO was measured every 10 seconds.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000352_1.json b/datasets/KOPRI-KPDC-00000352_1.json index fb3ad76ce5..58abf9cd1e 100644 --- a/datasets/KOPRI-KPDC-00000352_1.json +++ b/datasets/KOPRI-KPDC-00000352_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000352_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric carbon monoxide in the marine boundary layer was monitored from May 13 to June 20 by LGR along the cruise track of R/V Araon from Punta Arenas(Chile) to Incheon carrying out a series of expeditions in the Pacific. The air inlet to the instrument was located at 29 m asl and CO was measured every 10 seconds.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000353_1.json b/datasets/KOPRI-KPDC-00000353_1.json index 641f2a4bc4..341349c915 100644 --- a/datasets/KOPRI-KPDC-00000353_1.json +++ b/datasets/KOPRI-KPDC-00000353_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000353_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from November 26, 2010, to January 22, 2011, using a RGA gas chromatograph along the cruise track of R/V Polarstern from Punta Arenas, Chile, to Wellington, New Zealand, carrying out the expedition, ANTXXVI/2, in the Southern Ocean. The air inlet to the instrument was located at ~30 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000354_1.json b/datasets/KOPRI-KPDC-00000354_1.json index 2514777464..d6bac36e50 100644 --- a/datasets/KOPRI-KPDC-00000354_1.json +++ b/datasets/KOPRI-KPDC-00000354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon monoxide in the marine boundary layer was monitored from July 14 to July 29 by LGR and RGA along the cruise track of R/V Araon from Incheon to Nome(Alaska) carrying out a series of expeditions in the Northwest Pacific. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. CO was measured every 45 minutes\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000355_1.json b/datasets/KOPRI-KPDC-00000355_1.json index 926ef1f4e7..fdb956a478 100644 --- a/datasets/KOPRI-KPDC-00000355_1.json +++ b/datasets/KOPRI-KPDC-00000355_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000355_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric carbon monoxide in the marine boundary layer was monitored from August 1 to September 10 by LGR along the cruise track of R/V Araon from Nome(Alaska) to Nome carrying out a series of expeditions in the Arctic Ocean. The air inlet to the instrument was located at 29 m asl and CO was measured every 10 seconds.\nAtmospheric carbon monoxide plays a key role in tropospheric chemistry in particular related to the oxidation capacity and to understanding the chemical processes in the marine boundary layer. Also underway continuous measurement of CO enables us to estimate marine emission of CO in global scale.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000356_1.json b/datasets/KOPRI-KPDC-00000356_1.json index de0ca0716a..73855109b9 100644 --- a/datasets/KOPRI-KPDC-00000356_1.json +++ b/datasets/KOPRI-KPDC-00000356_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000356_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from October 10 to October 29 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to Christchurch, New Zealand, carrying out a series of expeditions in the West Equator Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000357_1.json b/datasets/KOPRI-KPDC-00000357_1.json index 11ef06b342..70ef9ce753 100644 --- a/datasets/KOPRI-KPDC-00000357_1.json +++ b/datasets/KOPRI-KPDC-00000357_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000357_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from November 1 to November 30 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Christchurch, New Zealand, to King Sejong Station carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000358_1.json b/datasets/KOPRI-KPDC-00000358_1.json index 96d9234e1a..4db646873a 100644 --- a/datasets/KOPRI-KPDC-00000358_1.json +++ b/datasets/KOPRI-KPDC-00000358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from December 5 to December 19 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from King Sejong Station to King Sejong Station carrying out a series of expeditions in the Weddell Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000359_1.json b/datasets/KOPRI-KPDC-00000359_1.json index f7ddab5cc9..d5e1c8ec6a 100644 --- a/datasets/KOPRI-KPDC-00000359_1.json +++ b/datasets/KOPRI-KPDC-00000359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from December 20 to January 22 by the Gas Chromatography and the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from King Sejong Station to Christchurch, New Zealand, carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000360_1.json b/datasets/KOPRI-KPDC-00000360_1.json index 197bcf806c..217012bea6 100644 --- a/datasets/KOPRI-KPDC-00000360_1.json +++ b/datasets/KOPRI-KPDC-00000360_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000360_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from October 4 to November 15 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to King Sejong Station carrying out a series of expeditions in the Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000361_1.json b/datasets/KOPRI-KPDC-00000361_1.json index 9104256a39..c6da0d971b 100644 --- a/datasets/KOPRI-KPDC-00000361_1.json +++ b/datasets/KOPRI-KPDC-00000361_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000361_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from January 22 to March 11 by the Gas Chromatography and the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Christchurch, New Zealand, to Christchurch, New Zealand, carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000362_1.json b/datasets/KOPRI-KPDC-00000362_1.json index 4d03a655bf..c1359432f5 100644 --- a/datasets/KOPRI-KPDC-00000362_1.json +++ b/datasets/KOPRI-KPDC-00000362_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000362_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from January 26 to February 28 by the Gas Chromatography and the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Christchurch, New Zealand, to McMurdo Station carrying out a series of expeditions in the Ross Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000363_1.json b/datasets/KOPRI-KPDC-00000363_1.json index f29a65ae14..dfdcb52eba 100644 --- a/datasets/KOPRI-KPDC-00000363_1.json +++ b/datasets/KOPRI-KPDC-00000363_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000363_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from March 1 to March 19 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from McMurdo Station to Christchurch, New Zealand, carrying out a series of expeditions in the Terra Nova Bay - Jang Bogo Station. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000364_1.json b/datasets/KOPRI-KPDC-00000364_1.json index 3e13e94505..9ecbe862f8 100644 --- a/datasets/KOPRI-KPDC-00000364_1.json +++ b/datasets/KOPRI-KPDC-00000364_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000364_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from November 26 to January 20 by the Gas Chromatography and the Non-Dispersive Infrared Analyser along the cruise track of R/V Polarstern from Punta Arenas, Chile, to Wellington, New Zealand, carrying out the expedition in the Southern Ocean. The air inlet to the instrument was located at ca. 30 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000365_2.json b/datasets/KOPRI-KPDC-00000365_2.json index c72675c5ae..641c834f58 100644 --- a/datasets/KOPRI-KPDC-00000365_2.json +++ b/datasets/KOPRI-KPDC-00000365_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000365_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 1 to July 13 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwest Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000366_1.json b/datasets/KOPRI-KPDC-00000366_1.json index efb9b32de7..c612a53058 100644 --- a/datasets/KOPRI-KPDC-00000366_1.json +++ b/datasets/KOPRI-KPDC-00000366_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000366_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from july 15 to July 28 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwest Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000367_1.json b/datasets/KOPRI-KPDC-00000367_1.json index eb64e27ea7..f024406bc7 100644 --- a/datasets/KOPRI-KPDC-00000367_1.json +++ b/datasets/KOPRI-KPDC-00000367_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000367_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 30 to August 19 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Nome, Alaska, carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000368_1.json b/datasets/KOPRI-KPDC-00000368_1.json index 0d73ace835..db69aceb2c 100644 --- a/datasets/KOPRI-KPDC-00000368_1.json +++ b/datasets/KOPRI-KPDC-00000368_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000368_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 14 to July 29 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwest Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000369_1.json b/datasets/KOPRI-KPDC-00000369_1.json index 3e044d5604..0f5d4b93e9 100644 --- a/datasets/KOPRI-KPDC-00000369_1.json +++ b/datasets/KOPRI-KPDC-00000369_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000369_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from August 1 to September 10 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Nome, Alaska, carrying out a series of expeditions in the Arctic Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000370_1.json b/datasets/KOPRI-KPDC-00000370_1.json index 7177f25cff..bdd09482b5 100644 --- a/datasets/KOPRI-KPDC-00000370_1.json +++ b/datasets/KOPRI-KPDC-00000370_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000370_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 377 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. And 374 samples for seawater pH were drawn to 250 ml polypropylene bottle. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from King George Island to Christchurch. 141 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000371_1.json b/datasets/KOPRI-KPDC-00000371_1.json index 0f9ef32a4c..083dbc04eb 100644 --- a/datasets/KOPRI-KPDC-00000371_1.json +++ b/datasets/KOPRI-KPDC-00000371_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000371_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle on board. In addition to these investigation, to understand the distribution of the various component of carbonic system in the surface seawaters, underway observation of CO2 parameters was carried out along the cruise track. 271 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000372_1.json b/datasets/KOPRI-KPDC-00000372_1.json index 11f882d308..6a998a2386 100644 --- a/datasets/KOPRI-KPDC-00000372_1.json +++ b/datasets/KOPRI-KPDC-00000372_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000372_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Southern Ocean, hydrographic survey using R/V Polarstern was carried out from November 26, 2009 to January 20, 2010. At 28 stations, 325 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from Punta Arenas, Chile, to Wellington, New Zealand. 576 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000373_1.json b/datasets/KOPRI-KPDC-00000373_1.json index daac355e72..ce06cc9c9c 100644 --- a/datasets/KOPRI-KPDC-00000373_1.json +++ b/datasets/KOPRI-KPDC-00000373_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000373_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of rapid retreat of Arctic sea ice on distribution of the various of the carbonic system, hydrographic survey was carried out by the IBRV Araon from July 17 to August 12 in Chuckchi Borderland and western Canada Basin. At each hydrographic station, 244 samples for dissolved inorganic carbon (DIC) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000374_1.json b/datasets/KOPRI-KPDC-00000374_1.json index 55784bb647..a752afb171 100644 --- a/datasets/KOPRI-KPDC-00000374_1.json +++ b/datasets/KOPRI-KPDC-00000374_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000374_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effects on the processes controlling inorganic CO2 system during the summer ice-free condition in the Arctic Ocean, an intensive oceanographic survey using the IBRV Araon from July 30 to August 19 was performed in Mendeleyev Ridge, East Siberian Sea, and Chuckchi Borderland. At each site, 259 samples for dissolved inorganic carbon (DIC) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000375_1.json b/datasets/KOPRI-KPDC-00000375_1.json index 7b8e6d11f9..8916c96855 100644 --- a/datasets/KOPRI-KPDC-00000375_1.json +++ b/datasets/KOPRI-KPDC-00000375_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000375_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 SHIp borne Pole-to-Pole Observations program, the IBRV Araon occupied 12 hydrographic stations in the Northwestern Pacific from July 14 to July 29. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 225 samples for dissolved inorganic carbon (DIC) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the North Pacific Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000376_1.json b/datasets/KOPRI-KPDC-00000376_1.json index 5408098d3f..ef19596522 100644 --- a/datasets/KOPRI-KPDC-00000376_1.json +++ b/datasets/KOPRI-KPDC-00000376_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000376_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 Korea-Polar Ocean Rapid Transition (K-PORT) program, the IBRV Araon occupied 44 hydrographic stations in the Chukchi Borderland and Medeleev Ridge of the Arctic Ocean from August 1 to September 10. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 354 samples for dissolved inorganic carbon (DIC) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000377_1.json b/datasets/KOPRI-KPDC-00000377_1.json index d966c12db8..b97334daef 100644 --- a/datasets/KOPRI-KPDC-00000377_1.json +++ b/datasets/KOPRI-KPDC-00000377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from October 10 to 29 by the RGA3 along the cruise track of R/V Araon from Incheon to Christchurch carrying out a series of expeditions in The West Equatorial Pacific. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000378_1.json b/datasets/KOPRI-KPDC-00000378_1.json index 1ba0560872..4db9390f6b 100644 --- a/datasets/KOPRI-KPDC-00000378_1.json +++ b/datasets/KOPRI-KPDC-00000378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from November 1 to 30 by the RGA3 along the cruise track of R/V Araon from Christchurch to King Sejong carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000379_1.json b/datasets/KOPRI-KPDC-00000379_1.json index adc22de21e..ed2c40e8d8 100644 --- a/datasets/KOPRI-KPDC-00000379_1.json +++ b/datasets/KOPRI-KPDC-00000379_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000379_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from December 20, 2011 to January 22, 2012 by the RGA3 along the cruise track of R/V Araon from King Sejong to Christchurch carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000380_1.json b/datasets/KOPRI-KPDC-00000380_1.json index e4161f78a4..3eb9804440 100644 --- a/datasets/KOPRI-KPDC-00000380_1.json +++ b/datasets/KOPRI-KPDC-00000380_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000380_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from February 25 to March 12 by the RGA3 along the cruise track of R/V Araon from Christchurchto to Christchurch carrying out a series of expeditions in the Antarctic Ridge. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000381_1.json b/datasets/KOPRI-KPDC-00000381_1.json index c4802a1aab..2d41a3d228 100644 --- a/datasets/KOPRI-KPDC-00000381_1.json +++ b/datasets/KOPRI-KPDC-00000381_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000381_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from October 4 to November 15 by the RGA3 along the cruise track of R/V Araon from Incheon to King Sejong carrying out a series of expeditions in the Pacific Transect. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000382_1.json b/datasets/KOPRI-KPDC-00000382_1.json index d705cd675a..cadf33c129 100644 --- a/datasets/KOPRI-KPDC-00000382_1.json +++ b/datasets/KOPRI-KPDC-00000382_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000382_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from November 21 to December 15 by the RGA3 along the cruise track of R/V Araon from King Sejong to Christchurch carrying out a series of expeditions in The Southern Ocean. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000383_1.json b/datasets/KOPRI-KPDC-00000383_1.json index 719f8d6f32..3b4ba0e814 100644 --- a/datasets/KOPRI-KPDC-00000383_1.json +++ b/datasets/KOPRI-KPDC-00000383_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000383_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from January 22 to March 11 by the RGA3 along the cruise track of R/V Araon from Christchurch to Christchurch\t carrying out a series of expeditions in the Ross Sea The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000384_1.json b/datasets/KOPRI-KPDC-00000384_1.json index 754e2ba88e..7f2e309803 100644 --- a/datasets/KOPRI-KPDC-00000384_1.json +++ b/datasets/KOPRI-KPDC-00000384_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000384_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from January 26 to February 28 by the RGA3 along the cruise track of R/V Araon from Christchurch to McMurdo carrying out a series of expeditions in the Ross Sea. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000385_1.json b/datasets/KOPRI-KPDC-00000385_1.json index 95a4cc308a..28c3743a6f 100644 --- a/datasets/KOPRI-KPDC-00000385_1.json +++ b/datasets/KOPRI-KPDC-00000385_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000385_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from November 26, 2010, to January 22, 2011, using a RGA gas chromatograph along the cruise track of R/V Polarstern from Punta Arenas, Chile, to Wellington, New Zealand, carrying out the expedition, ANTXXVI/2, in the Southern Ocean. The air inlet to the instrument was located at ~30 m asl and the sea water inlet was located at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000386_1.json b/datasets/KOPRI-KPDC-00000386_1.json index 4b6e9f8f31..6bf817d6b1 100644 --- a/datasets/KOPRI-KPDC-00000386_1.json +++ b/datasets/KOPRI-KPDC-00000386_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000386_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic molecular hydrogen in the marine boundary layer was monitored from July 14 to 29 by the RGA3 along the cruise track of R/V Araon from Incheon to Nome (Alask) carrying out a series expeditions in the Northwestern Pacific transect. The air inlet to the instrument was located at 29 m the foremast and seawater inlet at 7m depth. Both air and water samples were measured once per 45 minutes.\nAtmospheric molecular hydrogen goes up to the stratosphere easyly, and oxidized to H2O. Therefore it plays a key role in the stratospheric ozone chemistry and affects the global climate. Also its measurements in the ocean is very important because the production and removal mechanism in the ocean that is one of the sources of H2 has not been understood yet.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000387_1.json b/datasets/KOPRI-KPDC-00000387_1.json index 78c612f1e6..1b96039703 100644 --- a/datasets/KOPRI-KPDC-00000387_1.json +++ b/datasets/KOPRI-KPDC-00000387_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000387_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from October 4 to November 15 by the Hg GEM analyzer along the cruise track of R/V Araon from Incheon to King Sejong Station carrying out a series expeditions in the Pacific Transect.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000388_1.json b/datasets/KOPRI-KPDC-00000388_1.json index e5f968cd2b..c45343168e 100644 --- a/datasets/KOPRI-KPDC-00000388_1.json +++ b/datasets/KOPRI-KPDC-00000388_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000388_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from November 21 to December 15 by the Hg GEM analyzer along the cruise track of R/V Araon from King Sejong Station to Christchurch carrying out a series expeditions in the Southern Ocean.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000389_1.json b/datasets/KOPRI-KPDC-00000389_1.json index d846eaeebd..7cf7862d06 100644 --- a/datasets/KOPRI-KPDC-00000389_1.json +++ b/datasets/KOPRI-KPDC-00000389_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000389_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from January 22 to March 11 by the Hg GEM analyzer along the cruise track of R/V Araon from Christchurch to Christchurch carrying out a series expeditions in the Amundsen Sea.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000390_1.json b/datasets/KOPRI-KPDC-00000390_1.json index 51cc58ea50..a9294bc3ef 100644 --- a/datasets/KOPRI-KPDC-00000390_1.json +++ b/datasets/KOPRI-KPDC-00000390_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000390_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from January 26 to February 28 by the Hg GEM, RGM, PBM analyzer along the cruise track of R/V Araon from Christchurch to Mcmurdo Station(Ross Sea) carrying out a series expeditions in the Ross Sea.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000391_1.json b/datasets/KOPRI-KPDC-00000391_1.json index 9e9ff52c95..4265aca5fc 100644 --- a/datasets/KOPRI-KPDC-00000391_1.json +++ b/datasets/KOPRI-KPDC-00000391_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000391_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from March 1 to March 19 by the Hg GEM, RGM, PBM analyzer along the cruise track of R/V Araon from Terranova Bay to Jang Bogo Antarctic Research Station carrying out a series expeditions in the TNB-JBS.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000392_1.json b/datasets/KOPRI-KPDC-00000392_1.json index 974d9e64bc..300111b311 100644 --- a/datasets/KOPRI-KPDC-00000392_1.json +++ b/datasets/KOPRI-KPDC-00000392_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000392_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from August 01 to September 10 by the Hg GEM, RGM, PBM analyzer along the cruise track of R/V Araon from Nome(Alaska) to Nome(Alaska) carrying out a series expeditions in the Arctic sea.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000393_1.json b/datasets/KOPRI-KPDC-00000393_1.json index ee05a2e8f2..d0985ba16e 100644 --- a/datasets/KOPRI-KPDC-00000393_1.json +++ b/datasets/KOPRI-KPDC-00000393_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000393_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric Hg in the marine boundary layer was monitored from September 12 to September 24 by the Hg GEM analyzer along the cruise track of R/V Araon from Nome(Alaska) to Incheon carrying out a series expeditions in the Northwestern Pacific Transect.\nMercury (Hg) is a toxic pollutant. Its bioaccumulation causes serious health problem. In the atmosphere, mercury typically exists in gaseous elemental mercury (GEM), reactive gaseous species (RGM) and particulate bounded mercury (PBM). Since more than 95% of mercury exists in GEM, significant amount of Hg is transported long-range. In spite of the importance of mercury in the atmosphere, the role of ocean in the global budget of Hg has not been clearly studied yet and its measurement over the marine boundary layer will enhance the OH-related chemistry.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000394_1.json b/datasets/KOPRI-KPDC-00000394_1.json index d6ed46ad1f..1fc44e877a 100644 --- a/datasets/KOPRI-KPDC-00000394_1.json +++ b/datasets/KOPRI-KPDC-00000394_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000394_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from 2010 December 20 to 2011 January 22 by the GC 7890A along the cruise track of R/V Araon from the King Sejong Station to Christchurch (New Zealand) carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000395_1.json b/datasets/KOPRI-KPDC-00000395_1.json index 0abbfa21a4..9ee1bdcc15 100644 --- a/datasets/KOPRI-KPDC-00000395_1.json +++ b/datasets/KOPRI-KPDC-00000395_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000395_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from January 26 to February 28 by the GC 7890A along the cruise track of R/V Araon from Christchurch(New Zealand) to the McMurdo Station(Ross Island) carrying out a series of expeditions in The Ross Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000396_1.json b/datasets/KOPRI-KPDC-00000396_1.json index c53359aabc..325043bfa0 100644 --- a/datasets/KOPRI-KPDC-00000396_1.json +++ b/datasets/KOPRI-KPDC-00000396_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000396_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from November 26, 2010, to January 22, 2011 by the GC 7890A along the cruise track of R/V Polarstern from Punta Arenas, Chile, to Wellington, New Zealand, carrying out the expedition, ANTXXVI/2, in the Southern Ocean. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000397_1.json b/datasets/KOPRI-KPDC-00000397_1.json index 0b9d98dc9c..d4df7f58b3 100644 --- a/datasets/KOPRI-KPDC-00000397_1.json +++ b/datasets/KOPRI-KPDC-00000397_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000397_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from July 17 to August 12 by the GC 7890A along the cruise track of R/V Araon from Nome(Alaska) to Nome carrying out a series of expeditions in The Chuckchi Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000398_1.json b/datasets/KOPRI-KPDC-00000398_1.json index 8306ed5eb0..dcc0bcc8bd 100644 --- a/datasets/KOPRI-KPDC-00000398_1.json +++ b/datasets/KOPRI-KPDC-00000398_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000398_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from August 1 to September 10 by the GC 7890A along the cruise track of R/V Araon from Nome(Alaska) to Nome carrying out a series of expeditions in The Arctic Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000399_1.json b/datasets/KOPRI-KPDC-00000399_1.json index e5d0efc5ca..dc8dd33b66 100644 --- a/datasets/KOPRI-KPDC-00000399_1.json +++ b/datasets/KOPRI-KPDC-00000399_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000399_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx in the marine boundary layer was monitored from January 26 to February 28 by the CraNOx\u2161 NOx analyzer along the cruise track of R/V Araon from Christchurch to Mcmurdo Station(Ross Sea) carrying out a series expeditions in the Ross Sea.\nAtmospheric NOx is an important molecule in the atmospheric chemical aspects, because it is involved in the tropospheric ozone chemistry. Also continuous measurements of it enable us to estimate the impacts of NOx emitted mainly by human activities on the relative unpolluted air.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000400_1.json b/datasets/KOPRI-KPDC-00000400_1.json index d15b786930..811dd60e47 100644 --- a/datasets/KOPRI-KPDC-00000400_1.json +++ b/datasets/KOPRI-KPDC-00000400_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000400_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx in the marine boundary layer was monitored from March 01 to March 19 by the CraNOx\u2161 NOx analyzer along the cruise track of R/V Araon from Terranova Bay to Jang Bogo Antarctic Research Station carrying out a series expeditions in the Antarctic Sea.\nAtmospheric NOx is an important molecule in the atmospheric chemical aspects, because it is involved in the tropospheric ozone chemistry. Also continuous measurements of it enable us to estimate the impacts of NOx emitted mainly by human activities on the relative unpolluted air.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000401_2.json b/datasets/KOPRI-KPDC-00000401_2.json index 92dbbb0bca..a218417930 100644 --- a/datasets/KOPRI-KPDC-00000401_2.json +++ b/datasets/KOPRI-KPDC-00000401_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000401_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx in the marine boundary layer was monitored from July 14 to July 29 by the CraNOx\u00e2\u2026\u00a1 NOx analyzer along the cruise track of R/V Araon from Incheon to Nome(Alaska) carrying out a series expeditions in the Northwestern Pacific.\nAtmospheric NOx is an important molecule in the atmospheric chemical aspects, because it is involved in the tropospheric ozone chemistry. Also continuous measurements of it enable us to estimate the impacts of NOx emitted mainly by human activities on the relative unpolluted air.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000402_1.json b/datasets/KOPRI-KPDC-00000402_1.json index bcc987769a..ff30f95e64 100644 --- a/datasets/KOPRI-KPDC-00000402_1.json +++ b/datasets/KOPRI-KPDC-00000402_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000402_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx in the marine boundary layer was monitored from August 01 to September 10 by the CraNOx\u00e2\u2026\u00a1 NOx analyzer along the cruise track of R/V Araon from Nome(Alaska) to Nome(Alaska) carrying out a series expeditions in the Arctic Ocean.\nAtmospheric NOx is an important molecule in the atmospheric chemical aspects, because it is involved in the tropospheric ozone chemistry. Also continuous measurements of it enable us to estimate the impacts of NOx emitted mainly by human activities on the relative unpolluted air.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000403_2.json b/datasets/KOPRI-KPDC-00000403_2.json index 138cabb98c..559f67dd97 100644 --- a/datasets/KOPRI-KPDC-00000403_2.json +++ b/datasets/KOPRI-KPDC-00000403_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000403_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric NOx in the marine boundary layer was monitored from September 12 to September 24 by the CraNOx\u00e2\u2026\u00a1 NOx analyzer along the cruise track of R/V Araon from Nome(Alaska) to Incheon carrying out a series expeditions in the Northwestern Pacific.\nAtmospheric NOx is an important molecule in the atmospheric chemical aspects, because it is involved in the tropospheric ozone chemistry. Also continuous measurements of it enable us to estimate the impacts of NOx emitted mainly by human activities on the relative unpolluted air.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000404_1.json b/datasets/KOPRI-KPDC-00000404_1.json index e1698bb53a..8dea69cba3 100644 --- a/datasets/KOPRI-KPDC-00000404_1.json +++ b/datasets/KOPRI-KPDC-00000404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from October 10 to October 29 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Incheon, to Christchurch, carrying out a series of expeditions in the West Equatorial Pacific Transect. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000405_1.json b/datasets/KOPRI-KPDC-00000405_1.json index 150a63aa01..7ba0e648a6 100644 --- a/datasets/KOPRI-KPDC-00000405_1.json +++ b/datasets/KOPRI-KPDC-00000405_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000405_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from November 1 to November 30 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch, to King sejong Sstation, carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000406_1.json b/datasets/KOPRI-KPDC-00000406_1.json index 9602df2231..86e2555962 100644 --- a/datasets/KOPRI-KPDC-00000406_1.json +++ b/datasets/KOPRI-KPDC-00000406_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000406_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from December 5 to December 19 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from King sejong Station to King sejong Station, carrying out a series of expeditions in the Weddell Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000407_1.json b/datasets/KOPRI-KPDC-00000407_1.json index 71c74b15d8..2302484e05 100644 --- a/datasets/KOPRI-KPDC-00000407_1.json +++ b/datasets/KOPRI-KPDC-00000407_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000407_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from December 20 to January 22 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from King sejong Station to Christchurch, carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000408_1.json b/datasets/KOPRI-KPDC-00000408_1.json index fe8c6bed96..840297e8b1 100644 --- a/datasets/KOPRI-KPDC-00000408_1.json +++ b/datasets/KOPRI-KPDC-00000408_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000408_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from December 20 to January 22 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Christchurch, carrying out a series of expeditions in the Ross Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000409_1.json b/datasets/KOPRI-KPDC-00000409_1.json index 958935d761..45ef2c7ea3 100644 --- a/datasets/KOPRI-KPDC-00000409_1.json +++ b/datasets/KOPRI-KPDC-00000409_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000409_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from February 25 to March 12 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Christchurch, carrying out a series of expeditions in the Antarctic Ridge. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000410_1.json b/datasets/KOPRI-KPDC-00000410_1.json index b06de2fcae..ec8022fe05 100644 --- a/datasets/KOPRI-KPDC-00000410_1.json +++ b/datasets/KOPRI-KPDC-00000410_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000410_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from Narch 16 to May 10 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Incheon, carrying out a series of expeditions in the Equatoreal Ridge. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000411_1.json b/datasets/KOPRI-KPDC-00000411_1.json index 22e2c30f89..fd80c5e39a 100644 --- a/datasets/KOPRI-KPDC-00000411_1.json +++ b/datasets/KOPRI-KPDC-00000411_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000411_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from October 4 to November 15 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Incheon, to King sejong Station, carrying out a series of expeditions in the Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000412_1.json b/datasets/KOPRI-KPDC-00000412_1.json index be31179bd9..a33e237114 100644 --- a/datasets/KOPRI-KPDC-00000412_1.json +++ b/datasets/KOPRI-KPDC-00000412_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000412_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from November 21 to December 15 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from King sejong Station to Christchurch, carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000413_1.json b/datasets/KOPRI-KPDC-00000413_1.json index fcb654a8cd..678c8fdead 100644 --- a/datasets/KOPRI-KPDC-00000413_1.json +++ b/datasets/KOPRI-KPDC-00000413_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000413_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from January 22 to March 11 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Christchurch, carrying out a series of expeditions in the Amundsen Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000414_1.json b/datasets/KOPRI-KPDC-00000414_1.json index 09ce44375c..10261f99e6 100644 --- a/datasets/KOPRI-KPDC-00000414_1.json +++ b/datasets/KOPRI-KPDC-00000414_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000414_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from March 14 to March 31 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Incheon, carrying out a series of expeditions in the Western Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000415_1.json b/datasets/KOPRI-KPDC-00000415_1.json index 8cf36a2959..51d0f8c2bc 100644 --- a/datasets/KOPRI-KPDC-00000415_1.json +++ b/datasets/KOPRI-KPDC-00000415_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000415_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from January 26 to February 28 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to McMurdo Station(Ross Island), carrying out a series of expeditions in the Ross Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000417_1.json b/datasets/KOPRI-KPDC-00000417_1.json index b89652fb73..a09e3c76da 100644 --- a/datasets/KOPRI-KPDC-00000417_1.json +++ b/datasets/KOPRI-KPDC-00000417_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000417_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from March 23 to April 8 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Christchurch to Punta arenas, Chile, carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000418_1.json b/datasets/KOPRI-KPDC-00000418_1.json index f0dab5b3e5..12e55a0cc9 100644 --- a/datasets/KOPRI-KPDC-00000418_1.json +++ b/datasets/KOPRI-KPDC-00000418_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000418_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from April 16 to May 10 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Punta arenas, Chile to Punta arenas, Chile, carrying out a series of expeditions in the Weddell Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000419_1.json b/datasets/KOPRI-KPDC-00000419_1.json index 0252329a74..73fbb498d1 100644 --- a/datasets/KOPRI-KPDC-00000419_1.json +++ b/datasets/KOPRI-KPDC-00000419_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000419_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from May 13 to June 20 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Punta arenas, Chile to Incheon, carrying out a series of expeditions in the Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000420_1.json b/datasets/KOPRI-KPDC-00000420_1.json index 87e1cacff1..d694caa03d 100644 --- a/datasets/KOPRI-KPDC-00000420_1.json +++ b/datasets/KOPRI-KPDC-00000420_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000420_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from July 01 to July 13 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwestern Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000421_1.json b/datasets/KOPRI-KPDC-00000421_1.json index bac91b941b..f1ffca5e9b 100644 --- a/datasets/KOPRI-KPDC-00000421_1.json +++ b/datasets/KOPRI-KPDC-00000421_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000421_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from July 17 to August 12 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Nome, Alaska, carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000422_1.json b/datasets/KOPRI-KPDC-00000422_1.json index c7c6b714ae..5c584e147b 100644 --- a/datasets/KOPRI-KPDC-00000422_1.json +++ b/datasets/KOPRI-KPDC-00000422_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000422_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from August 16 to August 30 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Incheon, carrying out a series of expeditions in the Northwestern Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000425_2.json b/datasets/KOPRI-KPDC-00000425_2.json index 4c0e877d92..6f763b4af3 100644 --- a/datasets/KOPRI-KPDC-00000425_2.json +++ b/datasets/KOPRI-KPDC-00000425_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000425_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from July 01 to July 13 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwestern Pacific Oceans. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000426_1.json b/datasets/KOPRI-KPDC-00000426_1.json index 0c8d45a788..6d8a27938a 100644 --- a/datasets/KOPRI-KPDC-00000426_1.json +++ b/datasets/KOPRI-KPDC-00000426_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000426_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from July 30 to August 19 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Nome, Alaska, carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000427_2.json b/datasets/KOPRI-KPDC-00000427_2.json index e600377b04..784dfeb380 100644 --- a/datasets/KOPRI-KPDC-00000427_2.json +++ b/datasets/KOPRI-KPDC-00000427_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000427_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from August 21 to september 3 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Incheon, carrying out a series of expeditions in the Northwestern Pacific Oceans. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000428_2.json b/datasets/KOPRI-KPDC-00000428_2.json index c0e3fcf523..2a93d04c6c 100644 --- a/datasets/KOPRI-KPDC-00000428_2.json +++ b/datasets/KOPRI-KPDC-00000428_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000428_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from July 14 to July 29 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwestern Pacific. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000429_1.json b/datasets/KOPRI-KPDC-00000429_1.json index 3e0ab83c9b..0f3462a724 100644 --- a/datasets/KOPRI-KPDC-00000429_1.json +++ b/datasets/KOPRI-KPDC-00000429_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000429_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from August 1 to september 10 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Nome, Alaska, carrying out a series of expeditions in the Arctic Ocean. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000430_2.json b/datasets/KOPRI-KPDC-00000430_2.json index 4d1f314dee..ac302114d4 100644 --- a/datasets/KOPRI-KPDC-00000430_2.json +++ b/datasets/KOPRI-KPDC-00000430_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000430_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring atmospheric ozone in the marine boundary layer was monitored from September 12 to September 24 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Incheon, carrying out a series of expeditions in the Northwestern Pacific Oceans. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz.\nTropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000431_1.json b/datasets/KOPRI-KPDC-00000431_1.json index 3e2b1dff7a..7f754a1cb8 100644 --- a/datasets/KOPRI-KPDC-00000431_1.json +++ b/datasets/KOPRI-KPDC-00000431_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000431_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 374 samples for pH were collected from Niskin bottle to 250 ml polypropylene bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000432_1.json b/datasets/KOPRI-KPDC-00000432_1.json index b1b2fe4eeb..3501d16ccf 100644 --- a/datasets/KOPRI-KPDC-00000432_1.json +++ b/datasets/KOPRI-KPDC-00000432_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000432_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for seawater pH were collected from Niskin bottle on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000433_1.json b/datasets/KOPRI-KPDC-00000433_1.json index 7c21dfc152..f42ff095ec 100644 --- a/datasets/KOPRI-KPDC-00000433_1.json +++ b/datasets/KOPRI-KPDC-00000433_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000433_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 Korea-Polar Ocean Rapid Transition (K-PORT) program, the IBRV Araon occupied 44 hydrographic stations in the Chukchi Borderland and Medeleev Ridge of the Arctic Ocean from August 1 to September 10. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 81 samples for pH analysis were taken from Niskin bottle to 100 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000434_1.json b/datasets/KOPRI-KPDC-00000434_1.json index f54fd634f7..ee092f866c 100644 --- a/datasets/KOPRI-KPDC-00000434_1.json +++ b/datasets/KOPRI-KPDC-00000434_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000434_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effects on the processes controlling inorganic CO2 system during the summer ice-free condition in the Arctic Ocean, an intensive oceanographic survey using the IBRV Araon from July 30 to August 19 was performed in Mendeleyev Ridge, East Siberian Sea, and Chuckchi Borderland. At each site, 259 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000435_1.json b/datasets/KOPRI-KPDC-00000435_1.json index 76aee108f8..9a12eba751 100644 --- a/datasets/KOPRI-KPDC-00000435_1.json +++ b/datasets/KOPRI-KPDC-00000435_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000435_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 SHIp borne Pole-to-Pole Observations program, the IBRV Araon occupied 12 hydrographic stations in the Northwestern Pacific from July 14 to July 29. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 225 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the North Pacific Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000436_1.json b/datasets/KOPRI-KPDC-00000436_1.json index 4b6d45038d..bee70014ad 100644 --- a/datasets/KOPRI-KPDC-00000436_1.json +++ b/datasets/KOPRI-KPDC-00000436_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000436_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 Korea-Polar Ocean Rapid Transition (K-PORT) program, the IBRV Araon occupied 44 hydrographic stations in the Chukchi Borderland and Medeleev Ridge of the Arctic Ocean from August 1 to September 10. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 354 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000437_1.json b/datasets/KOPRI-KPDC-00000437_1.json index e89e0cd061..39eee27989 100644 --- a/datasets/KOPRI-KPDC-00000437_1.json +++ b/datasets/KOPRI-KPDC-00000437_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000437_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 17 to August 12 by the Gas Chromatography and the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Nome, Alaska, carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000438_3.json b/datasets/KOPRI-KPDC-00000438_3.json index fde1de682e..3be268b73c 100644 --- a/datasets/KOPRI-KPDC-00000438_3.json +++ b/datasets/KOPRI-KPDC-00000438_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000438_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from August 16 to August 30 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Incheon carrying out a series of expeditions in the Northwest Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000439_1.json b/datasets/KOPRI-KPDC-00000439_1.json index cf16a11592..03710c46d7 100644 --- a/datasets/KOPRI-KPDC-00000439_1.json +++ b/datasets/KOPRI-KPDC-00000439_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000439_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic nitrous oxide in the marine boundary layer was monitored from July 14 to July 29 by the GC 7890A along the cruise track of R/V Araon from Oncheon to Nome(Alaska) carrying out a series of expeditions in The Northwest Pacific. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth. N2O was measured every 40 minutes.\nAs atmospheric nitrous oxide is one of the major green house gases, it is important to understand the degree of influence of it. Although oceanic source doesn't contribute highly, it is required to monitor air-sea flux continuously because the area of the Ocean is large and the flux changes easily according to the chemical and biological conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000440_1.json b/datasets/KOPRI-KPDC-00000440_1.json index 9c8c6e80c6..7235886139 100644 --- a/datasets/KOPRI-KPDC-00000440_1.json +++ b/datasets/KOPRI-KPDC-00000440_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000440_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples around Terra Nova Bay in 2013\nMicrobial diversity survey in soil ecosystems", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000441_1.json b/datasets/KOPRI-KPDC-00000441_1.json index 2f155827ff..fe8ec7805d 100644 --- a/datasets/KOPRI-KPDC-00000441_1.json +++ b/datasets/KOPRI-KPDC-00000441_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000441_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a part of the 2012 SHIp borne Pole-to-Pole Observations program, the IBRV Araon occupied 12 hydrographic stations in the Northwestern Pacific from July 14 to July 29. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 225 samples for pH analysis were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board.\nAccurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the North Pacific Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000442_1.json b/datasets/KOPRI-KPDC-00000442_1.json index 858e17cfaa..4cdd1d5ff1 100644 --- a/datasets/KOPRI-KPDC-00000442_1.json +++ b/datasets/KOPRI-KPDC-00000442_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000442_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from November 21 to December 15 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from King Sejong Station to Christchurch, New Zealand, carrying out a series of expeditions in the Southern Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth.\nUnderway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea\u2013air CO2 flux and its time\u2013space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000443_1.json b/datasets/KOPRI-KPDC-00000443_1.json index 9586bec015..5baaf481c7 100644 --- a/datasets/KOPRI-KPDC-00000443_1.json +++ b/datasets/KOPRI-KPDC-00000443_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000443_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We successfully expressed and crystallized the dddC gene (Uniprot code G5CZI2) product from the Gram-negative marine bacterium Oceanimonas doudoroffii. The DddC is a methylmalonate-semialdehyde dehydrogenase (OdMMSDH) enzyme that is involved in dimethyl sulfonio propionate (DMSP) catabolism. DMSP is produced by marine phytoplankton and macroalgae, and is enzymatically metabolized into dimethylsulfide (DMS) or 3-methiolpropionate. DMS is a major source of sulfur gases in marine environments and induces cloud nuclei condensation. As a result of DMSP catabolism, DMS plays an important role in the global sulfur cycle and climate change. In order to understand the structural details of OdMMSDH, the recombinant protein was over-expressed in Escherichia coli and successfully crystallized in 21% (w/v) PEG 3350 and 0.2 M potassium sodium tartrate, pH 7.5. Furthermore, a complete native diffraction data set was collected up to 2.9 \u00c5 resolution and processed in the P21212 space group with unit-cell parameters a = 156.7, b = 160.3, and c = 238.9 \u00c5. Phase information was obtained by molecular replacement, and structure refinement and model building are in progress.\nTo better understand the enzymatic mechanisms of MMSDH from O. doudoroffii (OdMMSDH), we have performed biochemical and structural studies. As a first step towards its structural characterization, we here report the over-expression, purification, crystallization, and preliminary X-ray diffraction analysis of OdMMSDH.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000444_1.json b/datasets/KOPRI-KPDC-00000444_1.json index 6761e3e0d2..3df7f65507 100644 --- a/datasets/KOPRI-KPDC-00000444_1.json +++ b/datasets/KOPRI-KPDC-00000444_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000444_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Arctic Limacina helicina at pH 8.3\nTo analyze EST of the Arctic pteropod, Limacina helicina at pH 8.3, which is normal natural condition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000445_1.json b/datasets/KOPRI-KPDC-00000445_1.json index 2196d70437..b709dd1f88 100644 --- a/datasets/KOPRI-KPDC-00000445_1.json +++ b/datasets/KOPRI-KPDC-00000445_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000445_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The psychrophilic organism Colwellia psychrerythraea strain 34H has been demonstrated that high concentrated production of polysaccharide substrates at low temperature is a mechanism of adaptation in cold environment. The Sedoheptulose 7-phosphate isomerase(GmhA) is an essential enzyme involved the biosynthesis of polysaccharide. The crystal structure of a CpGmhA from Colwellia psychrerythraea strain 34H has been determined to up to a 2.8\u00c3\u2026 resolution. The CpGmhA structure reported here provides further insights into the structural correlation between activity and structure.\nTo investigate psychrophilic enzymes molecular basis of cold adaptation of Colwellia psychrerythraea 34H, we have cloned and over-expressed the methylmalonate-semialdehyde dehydrogenase and determined the structure by using X-ray crystallographic experiments", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000446_2.json b/datasets/KOPRI-KPDC-00000446_2.json index 8421147ded..229d030f5e 100644 --- a/datasets/KOPRI-KPDC-00000446_2.json +++ b/datasets/KOPRI-KPDC-00000446_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000446_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine benthic invertebrates were investigated by SCUBA diving in nearshore coastal waters in Maxwell Bay around King Sejong Station in the four consecutive austral summers (2009/2010, 2010/2011, 2011/2012, 2012/2013). Over 700 specimens were collected: 391 preserved in 70% ethanol for taxonomic studies, 284 in 100% ethanol for DNA analysis and 54 kept frozen for other studies.\nTaxonomic and ecological studies on Antarctic Marine Benthic Invertebrates.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000447_1.json b/datasets/KOPRI-KPDC-00000447_1.json index 62fcb3ae00..a4b77eaeb1 100644 --- a/datasets/KOPRI-KPDC-00000447_1.json +++ b/datasets/KOPRI-KPDC-00000447_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000447_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice growth in a cold environment is fatal for polar organisms, not only because of the physical destruction of inner cell organelles but also because of the resulting chemical damage owing to processes such as osmotic shock. The properties of ice-binding proteins (IBPs), which include antifreeze proteins (AFPs), have been characterized and IBPs exhibit the ability to inhibit ice growth by binding to specific ice planes and lowering the freezing point. An ice-binding protein (FfIBP) from the Gram-negative bacterium Flavobacterium frigoris PS1, which was isolated from the Antarctic, has recently been overexpressed. Interestingly, the thermal hysteresis activity of FfIBP was approximately 2.5 K at 50 mM, which is ten times higher than that of the moderately active IBP from Arctic yeast (LeIBP). Although FfIBP closely resembles LeIBP in its amino-acid sequence, the antifreeze activity of FfIBP appears to be much greater than that of LeIBP. In an effort to understand the reason for this difference, an attempt was made to solve the crystal structure of FfIBP. Here, the crystallization and X-ray diffraction data of FfIBP are reported. FfIBP was crystallized using the hanging-drop vapour-diffusion method with 0.1M sodium acetate pH 4.4 and 3M sodium chloride as precipitant. A complete diffraction data set was collected to a resolution of 2.9 A\u02da . The crystal belonged to space group P4122, with unit-cell parameters a = b = 69.4, c = 178.2 A\u02da . The asymmetric unit contained one monomer.\nTo investigate the structure and antifreeze mechanism of FfIBP derived from Flavobacterium frigoris PS1, we have carried out structural determination by using X-ray crystallographic experiments", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000448_1.json b/datasets/KOPRI-KPDC-00000448_1.json index 7b4261dab6..fde51bc81e 100644 --- a/datasets/KOPRI-KPDC-00000448_1.json +++ b/datasets/KOPRI-KPDC-00000448_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000448_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Arctic Limacina helicina at pH 7-8\nTo analyze EST of the Arctic pteropod, Limacina helicina at pH 7-8, which is moderate acidic condition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000449_1.json b/datasets/KOPRI-KPDC-00000449_1.json index 8ab20e8f42..4bd7dd2cf7 100644 --- a/datasets/KOPRI-KPDC-00000449_1.json +++ b/datasets/KOPRI-KPDC-00000449_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000449_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Arctic Limacina helicina at pH 6-7\nTo analyze EST of the Arctic pteropod, Limacina helicina at pH 6-7, which is strong acidic condition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000450_1.json b/datasets/KOPRI-KPDC-00000450_1.json index 502853780b..413caa9531 100644 --- a/datasets/KOPRI-KPDC-00000450_1.json +++ b/datasets/KOPRI-KPDC-00000450_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000450_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Arctic Calanus glacialis\nTo analyze EST of the Arctic copepod Calanus glacialis", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000451_1.json b/datasets/KOPRI-KPDC-00000451_1.json index b9397b37ce..e71b0a9918 100644 --- a/datasets/KOPRI-KPDC-00000451_1.json +++ b/datasets/KOPRI-KPDC-00000451_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000451_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Arctic Tisbe sp. acclimated to Lab condition\nTo analyze EST of the Arctic copepod Tisbe sp. acclimated to Lab condition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000452_1.json b/datasets/KOPRI-KPDC-00000452_1.json index 5c99abd66d..bc8ef6c2b3 100644 --- a/datasets/KOPRI-KPDC-00000452_1.json +++ b/datasets/KOPRI-KPDC-00000452_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000452_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EST analysis of the Antarctic Tisbe sp. in natural condition\nTo analyze EST of the Antarctic copepod Tisbe sp. in natural condition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000453_1.json b/datasets/KOPRI-KPDC-00000453_1.json index e9ccedb67e..43fbe7afa6 100644 --- a/datasets/KOPRI-KPDC-00000453_1.json +++ b/datasets/KOPRI-KPDC-00000453_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000453_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genome analysis of the Antarctic Pseudomonas pelagia CL-AP6\nTo analyze draft genome sequence of Pseudomonas gelagia CL-AP6, an aerobic bacterium isolated from a culture of the Antarctic green algae Pyramimonas gelidicola", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000454_1.json b/datasets/KOPRI-KPDC-00000454_1.json index 0edad529c6..5e38e8f3f3 100644 --- a/datasets/KOPRI-KPDC-00000454_1.json +++ b/datasets/KOPRI-KPDC-00000454_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000454_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genome analysis of an Arctic psychrophilic bacterium Moritella dasanensis ArB 0140\nTo analyze draft genome of an Arctic psychrophilic bacterium Moritella dasanensis ArB 0140", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000455_1.json b/datasets/KOPRI-KPDC-00000455_1.json index bc246086b0..60f04610a2 100644 --- a/datasets/KOPRI-KPDC-00000455_1.json +++ b/datasets/KOPRI-KPDC-00000455_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000455_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genome analysis of Antarctic psychrophilic bacterium Paenisporosarcina sp. TG-20\nTo analyze draft genome of an Antarctic psychrophilic bacterium Paenisporosarcina sp. TG-20", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000456_1.json b/datasets/KOPRI-KPDC-00000456_1.json index a8221f9e96..b2bd9e8565 100644 --- a/datasets/KOPRI-KPDC-00000456_1.json +++ b/datasets/KOPRI-KPDC-00000456_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000456_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three different kinds of soil humic substances were extracted from soil and plant debris samples from surrounding area of the King Sejong Station. A total of 53 cold-adapted bacterial strains having an ability to degrade or bioconvert humic substances were isolated from the samples. The isolates were identified through the analysis of their 16S rRNA genes and the bacterial diversity was analyzed to be simple.\nThe objective is to isolate bacterial strains able to degrade humic substances from cold environments in the Arctic and Antarctic regions and to analyze their microbial diversity. Also, a functional genomic study on the microbial degradative pathway(s) for soil humic substances is an another main purpose.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000457_1.json b/datasets/KOPRI-KPDC-00000457_1.json index 12d183e96a..9fb48302f7 100644 --- a/datasets/KOPRI-KPDC-00000457_1.json +++ b/datasets/KOPRI-KPDC-00000457_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000457_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 20 cold-adapted bacterial strains having an ability to degrade or bioconvert humic substances were isolated from surrounding area of the Dasan Station. The isolates were identified through the analysis of their 16S rRNA genes and the bacterial diversity was analyzed to be simple.\nThe objective is to isolate bacterial strains able to degrade humic substances from cold environments in the Arctic and Antarctic regions and to analyze their microbial diversity. Also, a functional genomic study on the microbial degradative pathway(s) for soil humic substances is an another main purpose.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000458_1.json b/datasets/KOPRI-KPDC-00000458_1.json index 47780e9cff..0c72961a69 100644 --- a/datasets/KOPRI-KPDC-00000458_1.json +++ b/datasets/KOPRI-KPDC-00000458_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000458_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted survey at 14 localities in shallow sublittoral zone. A total of 22 amphipod species, belonging to 12 families, were identified. Of these six species were new for the whole Maxwell Bay. Our findings increase the amphipod fauna of Maxwell Bay from the previous 55 species, to 61 amphipods.\nWe present the first preliminary account of amphipods from Marian Cove, a part of Maxwell Bay, near King Sejong Station, King George Island, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000459_1.json b/datasets/KOPRI-KPDC-00000459_1.json index b9c4a3b158..bcb4c94408 100644 --- a/datasets/KOPRI-KPDC-00000459_1.json +++ b/datasets/KOPRI-KPDC-00000459_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000459_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the long-term monitoring projects on Antarctic terrestrial vegetation in relation\r\nto global climate change, a bryophyta floristical survey was conducted around the Korean\r\nAntarctic Station (King Sejong Station), which is located on Barton Peninsula, King George\r\nIsland, from 31 Dec. 2012 to 17 Feb. 2013. Three hundred and sixteenth bryophyta specimens were collected and nineteenth bryophyta species in twelve genera were identified by morphological characteristics.\nAs part of the long-term monitoring projects on Antarctic terrestrial vegetation in relation\r\nto global climate change, a bryophyta floristical survey was conducted around the Korean\r\nAntarctic Station (King Sejong Station), which is located on Barton Peninsula, King George\r\nIsland.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000460_1.json b/datasets/KOPRI-KPDC-00000460_1.json index 183b9f8307..f228e94c6e 100644 --- a/datasets/KOPRI-KPDC-00000460_1.json +++ b/datasets/KOPRI-KPDC-00000460_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000460_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine algae, total 345 specimens and DNA samples were collected in the intertidal and subtidal zones for the studies on biodiversity and changing ecosystems in King George Islands, Antarctica. The biodiversity and genetic informations in the antarctic marine algae were obtained by morphological and molecular analysis, and the phylogenetic relationships will be discussed.\nTo obtained the biodiversity of marine algae in the Antarctic", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000461_1.json b/datasets/KOPRI-KPDC-00000461_1.json index 8b45375a2d..e4e25f8da2 100644 --- a/datasets/KOPRI-KPDC-00000461_1.json +++ b/datasets/KOPRI-KPDC-00000461_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000461_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annual variation of phytoplankton at the Marian Cove, King George Island, Antarctica, 2012", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000462_1.json b/datasets/KOPRI-KPDC-00000462_1.json index d4c96c3440..57f9e3841a 100644 --- a/datasets/KOPRI-KPDC-00000462_1.json +++ b/datasets/KOPRI-KPDC-00000462_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000462_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microalgae from Antarctic Ocean collected in 2012 using the habitat information for marine microalgae samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000463_1.json b/datasets/KOPRI-KPDC-00000463_1.json index 3d2c5ea2d9..44792691dd 100644 --- a/datasets/KOPRI-KPDC-00000463_1.json +++ b/datasets/KOPRI-KPDC-00000463_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000463_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On board turbulent fluxes of CO2, CH4 and energy were measured during the cruise in the Chukchi Borderland/Mendeleev Ridge/Beaufort Sea in boreal summer of 2013. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and closed-path cavity ring-down spectrometer was used for the measurement. Motion sensor was added to the flux system to correct the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz.\nTurbulent flux measurements are used to 1) better understand the air-sea energy exchanges and 2) evaluate how much the Chukchi sea absorbs or emits green house gases such as CO2 and CH4 in the Chukchi sea, the Arctic in summer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000464_1.json b/datasets/KOPRI-KPDC-00000464_1.json index e7771abd08..3516d39e50 100644 --- a/datasets/KOPRI-KPDC-00000464_1.json +++ b/datasets/KOPRI-KPDC-00000464_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000464_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 19 days from 2013 September 7 to September 27 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 16 stations.\nTo investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000465_1.json b/datasets/KOPRI-KPDC-00000465_1.json index 38f98eda1b..6b701c3bfe 100644 --- a/datasets/KOPRI-KPDC-00000465_1.json +++ b/datasets/KOPRI-KPDC-00000465_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000465_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An intensive oceanographic survey was conducted during 15 days from 2013 August 21 to September 4 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP.\nTo investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000466_1.json b/datasets/KOPRI-KPDC-00000466_1.json index 4f31a6ae3d..d2ae1fee9a 100644 --- a/datasets/KOPRI-KPDC-00000466_1.json +++ b/datasets/KOPRI-KPDC-00000466_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000466_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The complete mitochondrial genome sequence (15,502 nt) of Lepas australis (Crustacea, Maxillopoda, Cirripedia) was determinated. L. australis was collected from Marian Cove near King Sejong station in Antarctica. It consists of the usual 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region (444 nt). To analyze the mitogenome of the cirriped, we obtained the sequences of CO1, 12S, 16S, CO3 and Cytb using universal primers newly designed in our group and then, amplified the complete mitogenome of using long-PCR by specific primers and genome walking techniques.\nMitochondrial genomes contain the most informative sequences and gene arrangement for deeper phylogenetic analyses and they reflect evolutionary relationships and biogeography in the metazoans. We analyzed mitochondrial genome of the Antarctic cirriped L. australis.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000467_1.json b/datasets/KOPRI-KPDC-00000467_1.json index 655025ba03..7b2dc778c3 100644 --- a/datasets/KOPRI-KPDC-00000467_1.json +++ b/datasets/KOPRI-KPDC-00000467_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000467_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous data of a broadband seismometer installed in David Glacier, Antarctica for the period of 2012/1/28~2012/11/20.\nmonitoring icequakes and earthquakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000468_1.json b/datasets/KOPRI-KPDC-00000468_1.json index 9ab6781498..c07f5de75d 100644 --- a/datasets/KOPRI-KPDC-00000468_1.json +++ b/datasets/KOPRI-KPDC-00000468_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000468_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface temperature observed at coast of King Sejong Station, Antarctica in 2013. Infrared sensor (Apogee) was used to measure SST. During sea-ice period, measured temperature represent sea-ice surface temperature not SST. Data interval has been obtained continuously at 30-minute interval.\nSurface temperature plays critical role in determining air-sea-seaice heat flux. SST (or Sea-ice surface temperature) is used to interpret measured turbulent heat flux.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000469_1.json b/datasets/KOPRI-KPDC-00000469_1.json index a320335797..42e20a7402 100644 --- a/datasets/KOPRI-KPDC-00000469_1.json +++ b/datasets/KOPRI-KPDC-00000469_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000469_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The rapid melting of glaciers as well as the loss of sea ice in the Amundsen Sea makes it an ideal environmental setting for the investigation of the impacts of climate change in the Antarctic on the distribution and production of mesozooplankton. \r\n Mesozooplankton samples were collected with a Bongo net (mesh apertures 330 and 505 lm) at 15 selected stations. The net was towed twice vertically or obliquely within the upper 200 m of the water column. Tow speed and duration were about 1.5\u20132 knots and 15\u201320 min, respectively.\nThe primary objectives were to describe the mesozooplankton community and to investigate the linkages between major environmental factors and the mesozooplankton community in the Amundsen Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000470_1.json b/datasets/KOPRI-KPDC-00000470_1.json index f09536f3cc..175100d116 100644 --- a/datasets/KOPRI-KPDC-00000470_1.json +++ b/datasets/KOPRI-KPDC-00000470_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000470_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. During the 2012 Amundsen Sea cruise, a total of 2 moorings were successfully recovered.\nIn order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2012.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000471_2.json b/datasets/KOPRI-KPDC-00000471_2.json index 86bd55c622..09bf647fbb 100644 --- a/datasets/KOPRI-KPDC-00000471_2.json +++ b/datasets/KOPRI-KPDC-00000471_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000471_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seawaters in 14 water columns were collected during February and March 2012, and analyzed for total and dissolved 234Th, and particulate organic carbon and biogenic silica. 234Th activities were analyzed using a gas-flow proportional \u03b2-spectrometer manufactured by Ris\u00f8 National Laboratories (Roskilde, Denmark) following methods described in Buesseler et al. (2001).\nThe export fluxes of particulate organic carbon (POC) play an important role in the transfer of carbon between the atmosphere and the ocean. Accurate estimates of POC export fluxes are critical for constraining models of the global carbon cycle. Over the past few decades, the radioisotope pair 238U and 234Th has been increasingly used to estimate POC export fluxes from the euphotic zone. This method is based on the uptake of 234Th onto biogenic particles in the euphotic zone and the subsequent sinking of particles into deep water. The POC export flux is determined by multiplying the depth-integrated 234Th sinking flux by the POC/234Th ratio on sinking particles. This study aims to estimate the POC export fluxes in the Amundsen Sea using 234Th/238U disequilibrium method.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000472_1.json b/datasets/KOPRI-KPDC-00000472_1.json index 79a4c49bb0..22c5566be4 100644 --- a/datasets/KOPRI-KPDC-00000472_1.json +++ b/datasets/KOPRI-KPDC-00000472_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000472_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine geology program is conducted during the 4th ARAON Arctic Expedition in 2013. Geological stations were selected based on the study objectives, and their locations were specified using multi-beam bathymetric mapping and sub-bottom profiles. Coring was carried out using several devices. To retrieve the sediment cores at selected geological and oceanographic stations we used different coring gears such as box corer, multiple corers and gravity corer. A box corer (BOX) (50x50x60cm) and a multiple corer (MUC) with 8 tubes were used to obtain surface sediments. For relatively long sediment cores, we used a gravity corer (GC) with 3 or 6-m long barrel. Once retrieved on deck, gravity cores were cut up in lengths of 1.5m and labeled.\nOverall goal of marine geology for the 4th ARAON Arctic cruise is to take new and undisturbed sediment cores from the selected research target areas including the East Siberian-Chukchi Sea and Beaufort Sea in the western Arctic Ocean. To achieve the study objectives we employed the following geological/geophysical methods: 1) coring seafloor sediment with a gravity corer for sediment composition and stratigraphy (up to ~5 m deep), 2) coring with a multiple corer/box corer for modern/recent seafloor processes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000473_1.json b/datasets/KOPRI-KPDC-00000473_1.json index a9dd0f51ed..d5ae78d8a7 100644 --- a/datasets/KOPRI-KPDC-00000473_1.json +++ b/datasets/KOPRI-KPDC-00000473_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000473_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted Araon-based expedition on the western and eastern Antarctic Peninsula and build up ice-shelf monitoring system. Main coring equipment is gravity corer and after half-cut of sediment cores we measured MS, XRF from ITRAX core scanner on cruise. During the expedition, we have some chance to take new geophysical information at Bigo & Leroux bays and off Larsen C ice shelf.\n(1) to establish an monitoring system for ice shelf movements\r\n(2) to reconstruct the environmental changes caused by past climatic changes in the ice shelf area (West Antarctica).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000474_1.json b/datasets/KOPRI-KPDC-00000474_1.json index 3ef315831d..b6ddb9fcca 100644 --- a/datasets/KOPRI-KPDC-00000474_1.json +++ b/datasets/KOPRI-KPDC-00000474_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000474_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were executed by incubating phytoplankton in the incubators on the deck for 3-4 hours after stable isotopes (13C, 15NO3, and 15NH4) as tracers were inoculated into each bottle. Total 18 productivity experiments were completed during this cruise. At every CTD station, the productivity samples were collected by CTD rosette water samplers at 6 different light depths (100, 30, 12, 5 and 1%).\nTo understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem through studying ecological and physiological for phytoplankton in the Amundsen Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000475_1.json b/datasets/KOPRI-KPDC-00000475_1.json index b1c8aa1ead..f7e6ca6fa7 100644 --- a/datasets/KOPRI-KPDC-00000475_1.json +++ b/datasets/KOPRI-KPDC-00000475_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000475_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On board turbulent fluxes of CO2 and energy were measured during the cruise in the Chukchi Borderland in boreal summer of 2010. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo better understanding the role of Chukchi sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000476_1.json b/datasets/KOPRI-KPDC-00000476_1.json index bf954039f0..4ffc0fd617 100644 --- a/datasets/KOPRI-KPDC-00000476_1.json +++ b/datasets/KOPRI-KPDC-00000476_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000476_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On board turbulent fluxes of CO2 and energy were measured during the cruise in the Amundsen Sea in summer of 2011. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo better understanding the role of Amundsen sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000477_2.json b/datasets/KOPRI-KPDC-00000477_2.json index d46ea41dd0..65831d6124 100644 --- a/datasets/KOPRI-KPDC-00000477_2.json +++ b/datasets/KOPRI-KPDC-00000477_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000477_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "O2/Ar in seawater, pumped from the intake at 7 m below sea level, was measured using an equilibrator inlet mass spectrometer. The mass spectrometer measured a series of dissolved gases including O2 and Ar every 10 seconds. The data contain ion currents of those gases and total pressure in the mass spectrometer.\nNet community production (NCP), defined as the difference between autotrophic photosynthesis and (autrophic and heterotrophic) respiration, produces O2 proportional to the amount of net carbon. By measuring chemically and biologically inert Ar together with O2, it is possible to isolate O2 variation by physical processes (e.g., air temperature and pressure change and mixing of water masses) and deduce O2 variation by biological processes. To determine the net community (oxygen) production underway, we measured continuous O2/ Ar measurement system using an equilibrator inlet mass spectrometer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000478_1.json b/datasets/KOPRI-KPDC-00000478_1.json index 676fe7c72f..a2fe83a06b 100644 --- a/datasets/KOPRI-KPDC-00000478_1.json +++ b/datasets/KOPRI-KPDC-00000478_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000478_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic survey was conducted to understand the variability of krill distribution\r\nalong two representative ice shelves in the Amundsen Sea: Dotson ice shelf\r\nand Getz ice shelf.Acoustic data were collected from surface to\r\n500-m depths using a scientific echo sounder (EK60, Simrad) configured with\r\ndown-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon.\n- To identify the horizontal and vertical distribution of krill from Dotson ice shelf to Getz ice shelf.\r\n- To reveal the main forcing that affects the variability of krill distribution,", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000479_1.json b/datasets/KOPRI-KPDC-00000479_1.json index a9a41fde22..df989dcd19 100644 --- a/datasets/KOPRI-KPDC-00000479_1.json +++ b/datasets/KOPRI-KPDC-00000479_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000479_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic survey was conducted to observe the distribution and density of zooplankton in the Chukchi sea of Arctic from July to August in 2014. Acoustic data were collected from surface to 400 m using split-beam transducers of 38, 120 and 200 kHZ (Simard EK60 scientific echosounder) during the survey.\nObservation of the spatial distribution (horizontal and vertical) and density of zooplankton using the acoustic system to understand their variability around Chukchi sea, Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000480_1.json b/datasets/KOPRI-KPDC-00000480_1.json index 34d8c03ea3..cfdd971177 100644 --- a/datasets/KOPRI-KPDC-00000480_1.json +++ b/datasets/KOPRI-KPDC-00000480_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000480_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2014 Amundsen cruise, seawater samples for dissolved oragnic carbon and nitrogen analyses were collected over the Amundsen Sea, Antarctica.\nTo investigate the distributions of dissolved organic carbon and nitrogen and estimate flux of these compounds in the Amundsen Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000481_1.json b/datasets/KOPRI-KPDC-00000481_1.json index ad2af3dac2..6cc0cf2094 100644 --- a/datasets/KOPRI-KPDC-00000481_1.json +++ b/datasets/KOPRI-KPDC-00000481_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000481_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine microbes including bacteria and viruses are the most abundant organisms on the planet and play vital roles in the biogeochemical cycle in marine environments. Marine microbes also exist in diverse environments extending from equatorial to polar seas. In this cruise, abundances of pelagic bacteria and viruses were investigated at 18 stations in the Amundsen Sea, 2013/14.\nTo understand the relationships between microbial abundances and environmental variables in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000482_1.json b/datasets/KOPRI-KPDC-00000482_1.json index 20fd50d9f3..fd25e92bfe 100644 --- a/datasets/KOPRI-KPDC-00000482_1.json +++ b/datasets/KOPRI-KPDC-00000482_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000482_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The rapid melting of glaciers as well as the loss of sea ice in the Amundsen Sea makes it an ideal environmental setting for the investigation of the impacts of climate change in the Antarctic on the distribution and production of mesozooplankton. \r\n Mesozooplankton samples were collected with a Bongo net (mesh apertures 330 and 505 lm) at 15 selected stations. The net was towed twice vertically or obliquely within the upper 200 m of the water column. Tow speed and duration were about 1.5\u20132 knots and 15\u201320 min, respectively.\nThe primary objectives were to describe the mesozooplankton community and to investigate the linkages between major environmental factors and the mesozooplankton community in the Amundsen Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000483_1.json b/datasets/KOPRI-KPDC-00000483_1.json index a61ed23630..f30ce93cbc 100644 --- a/datasets/KOPRI-KPDC-00000483_1.json +++ b/datasets/KOPRI-KPDC-00000483_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000483_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll-a concentration is investigated in the Amundsen Sea of Southern Ocean from January to Feburary 2014. This data includes investigator and locality for chlorophyll-a concentration\nChlorophyll-a concentration in Antarctic Amundsen Sea 2014", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000484_1.json b/datasets/KOPRI-KPDC-00000484_1.json index 2079ee0d24..4166e0d62e 100644 --- a/datasets/KOPRI-KPDC-00000484_1.json +++ b/datasets/KOPRI-KPDC-00000484_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000484_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Breeding records of kelp gulls in areas newly exposed by glacier retreat on King George Island, Antarctica\nAnalysis of nest distribution pattern of kelp gulls in newly exposed areas after glacial retreating", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000485_2.json b/datasets/KOPRI-KPDC-00000485_2.json index b7e7380441..7158fd98f1 100644 --- a/datasets/KOPRI-KPDC-00000485_2.json +++ b/datasets/KOPRI-KPDC-00000485_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000485_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000486_2.json b/datasets/KOPRI-KPDC-00000486_2.json index 4e644b5b58..9ba581a09a 100644 --- a/datasets/KOPRI-KPDC-00000486_2.json +++ b/datasets/KOPRI-KPDC-00000486_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000486_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2014. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000487_2.json b/datasets/KOPRI-KPDC-00000487_2.json index 499e35ab6d..7ca5bf75ed 100644 --- a/datasets/KOPRI-KPDC-00000487_2.json +++ b/datasets/KOPRI-KPDC-00000487_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000487_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from 45 sites in the glacier foreland and 9 sites from outside of glacier foreland of Midtre Lovenbreen were collected in 2014 summer. We will be able to understand soil development especially soil organic carbon accumulation in regards to glacier retreat periods and microtopography.\r\nSoil samples to analyze soil organic carbon in a glacier foreland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000488_1.json b/datasets/KOPRI-KPDC-00000488_1.json index 388ecbb1f4..c8ab8812fe 100644 --- a/datasets/KOPRI-KPDC-00000488_1.json +++ b/datasets/KOPRI-KPDC-00000488_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000488_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2014 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000489_1.json b/datasets/KOPRI-KPDC-00000489_1.json index ee7ef28ff1..7f2ec8b2d7 100644 --- a/datasets/KOPRI-KPDC-00000489_1.json +++ b/datasets/KOPRI-KPDC-00000489_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000489_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface temperature observed at coast of King Sejong Station, Antarctica in 2014. Infrared sensor (Apogee) was used to measure SST. During sea-ice period, measured temperature represent sea-ice surface temperature not SST. Data interval has been obtained continuously at 30-minute interval.\nSurface temperature plays critical role in determining air-sea-seaice heat flux. SST (or Sea-ice surface temperature) is used to interpret measured turbulent heat flux.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000490_1.json b/datasets/KOPRI-KPDC-00000490_1.json index d3ea456578..b5c330d942 100644 --- a/datasets/KOPRI-KPDC-00000490_1.json +++ b/datasets/KOPRI-KPDC-00000490_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000490_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on horizontal global radiation and its analysis of data measured by Eppley Precision Pyranometer at the King Sejong Station in the Antarctic, 2014\nTrend analysis and measurement of horizontal global radiation at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000491_1.json b/datasets/KOPRI-KPDC-00000491_1.json index bd8b661a6f..7d216ec1a2 100644 --- a/datasets/KOPRI-KPDC-00000491_1.json +++ b/datasets/KOPRI-KPDC-00000491_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000491_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation has been carried out at the Jang Bogo Station since 2014. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000492_1.json b/datasets/KOPRI-KPDC-00000492_1.json index af8e168066..1506e2fe7c 100644 --- a/datasets/KOPRI-KPDC-00000492_1.json +++ b/datasets/KOPRI-KPDC-00000492_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000492_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly relative humidity data from Barton Peninsular collected in 2013\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000493_1.json b/datasets/KOPRI-KPDC-00000493_1.json index f49ba893b6..fb16ae8cf8 100644 --- a/datasets/KOPRI-KPDC-00000493_1.json +++ b/datasets/KOPRI-KPDC-00000493_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000493_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arctic sample", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000494_1.json b/datasets/KOPRI-KPDC-00000494_1.json index 51443f8f4f..97f8b47602 100644 --- a/datasets/KOPRI-KPDC-00000494_1.json +++ b/datasets/KOPRI-KPDC-00000494_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000494_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rock samples of Northern Victoria Land (NVL), Antarctica collected in 2013-14 austral summer season. The collection includes sedimentary rocks (sandstone, limestone, conglomerate, and so on) of the Lower Paleozoic Bowers and Beacon supergroups, metamorphic rocks of the Wilson Terrane, and volcanic rocks of the McMurdo Volcanics.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information on stratigraphy, metamorphism, and volcanism will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000495_1.json b/datasets/KOPRI-KPDC-00000495_1.json index 14e55c046b..acf8e7f518 100644 --- a/datasets/KOPRI-KPDC-00000495_1.json +++ b/datasets/KOPRI-KPDC-00000495_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000495_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted Araon-based expedition on the Ross Sea area near the Jang Bogo Station and the Drygalski ice tongue, East Antarctica. We obtained sediment cores using \r\ngravity corer and box corer. After cruise, sediment cores are half-cut and we measured MS and water content on laboratory. X-ray images are also obtained from sediment cores.\nto reconstruct the environmental changes caused by past climatic changes in the Ross sea area near Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000496_1.json b/datasets/KOPRI-KPDC-00000496_1.json index 9f25da028e..6d07aa7b23 100644 --- a/datasets/KOPRI-KPDC-00000496_1.json +++ b/datasets/KOPRI-KPDC-00000496_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000496_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were executed by incubating phytoplankton in the incubators on the deck for 4 hours after stable isotopes (13C, 15NO3, and 15NH4) as tracers were inoculated into each bottle. Total 3 productivity experiments were completed during this cruise. At every CTD station, the productivity samples were collected by CTD rosette water samplers at 6 different light depths (100, 50, 30, 12, 5 and 1%).\nTo understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem through studying ecological and physiological for phytoplankton in the Amundsen Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000497_1.json b/datasets/KOPRI-KPDC-00000497_1.json index 5991eb228d..e4414fdbaa 100644 --- a/datasets/KOPRI-KPDC-00000497_1.json +++ b/datasets/KOPRI-KPDC-00000497_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000497_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the fossil specimens of Northern Victoria Land (NVL), Antarctica collected in 2013-14 austral summer season. The collection includes trilobites and brachipods of the Lower Paleozoic Bowers Supergroup and conchostracan and plant fossils of the Beacon Supergroup.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information from the fossils will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000498_1.json b/datasets/KOPRI-KPDC-00000498_1.json index d05219cf6f..ac27bc7a7c 100644 --- a/datasets/KOPRI-KPDC-00000498_1.json +++ b/datasets/KOPRI-KPDC-00000498_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000498_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rocks and fossils of Arctic Svalbard acquired during reconnaissance visits to Kapp Starostin, Kapp Gnaloden, and De Geerbukta. It includes rock samples from the type section of Kapp Starostin Formation, Precambrian oolites and stromatolites, and Cambrian limestones. It is about 100 kg in total weight.\nThese pilot samples will be examined and contribute select localities of future study for the remote areas of the Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000499_1.json b/datasets/KOPRI-KPDC-00000499_1.json index d2832531f8..ae3cfbb1ea 100644 --- a/datasets/KOPRI-KPDC-00000499_1.json +++ b/datasets/KOPRI-KPDC-00000499_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000499_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the late Paleozoic rocks and fossils from Broggerhalvoya. It includes rock samples (sandstone, limestone) and fossils (coral, palaeoaplysina, chaetitids, fusulinids, brachiopods, silicified faunas) from Broggertinden, Scheteligfjellet, Wordiekammen formations. It is about 500 kg in total weight.\nThese samples are for revision and establishment of the late Paleozoic stratigraphy of the Broggerhalvoya area. The rock samples will provide microfacies and isotope data. The fossil data will provide time framework to the lithostratigraphic succession and will allow reconstruction of the paleoecology of the research area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000500_1.json b/datasets/KOPRI-KPDC-00000500_1.json index d13b520cd3..8f2f5992b3 100644 --- a/datasets/KOPRI-KPDC-00000500_1.json +++ b/datasets/KOPRI-KPDC-00000500_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000500_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the Precambrian basement rocks and late Paleozoic rocks and fossils from Broggerhalvoya. It includes rock samples (metamorphic rocks, sandstone, limestone) and fossils (coral, brachiopods, bryozoans) from basement rocks, Broggertinden, Scheteligfjellet, Wordiekammen, Kapp Starostin formations.\nThese samples are for revision and establishment of the late Paleozoic stratigraphy of the Broggerhalvoya area. Basement rocks will provide source rock lithology of the late Paleozoic siliciclastic sediments. The sedimentary rock samples will provide microfacies and isotope data. The fossil data will provide time framework to the lithostratigraphic succession and will allow reconstruction of the paleoecology of the research area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000501_1.json b/datasets/KOPRI-KPDC-00000501_1.json index 3954949890..0de2e9111e 100644 --- a/datasets/KOPRI-KPDC-00000501_1.json +++ b/datasets/KOPRI-KPDC-00000501_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000501_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the Precambrian basement rocks and late Paleozoic rocks and fossils from Broggerhalvoya. It includes rock samples (metamorphic rocks, sandstone, limestone) and fossils (coral, brachiopods, bryozoans) from basement rocks, Broggertinden, Scheteligfjellet, Wordiekammen, Kapp Starostin formations.\nThese samples are for revision and establishment of the late Paleozoic stratigraphy of the Broggerhalvoya area. Basement rocks will provide source rock lithology of the late Paleozoic siliciclastic sediments. The sedimentary rock samples will provide microfacies and isotope data. The fossil data will provide time framework to the lithostratigraphic succession and will allow reconstruction of the paleoecology of the research area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000502_1.json b/datasets/KOPRI-KPDC-00000502_1.json index 525ab82d1e..96b3845263 100644 --- a/datasets/KOPRI-KPDC-00000502_1.json +++ b/datasets/KOPRI-KPDC-00000502_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000502_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km\r\naltitude obtained from the meteor observations at King Sejong Station, Antarctica.\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000503_1.json b/datasets/KOPRI-KPDC-00000503_1.json index c60e9750b0..80677c51bd 100644 --- a/datasets/KOPRI-KPDC-00000503_1.json +++ b/datasets/KOPRI-KPDC-00000503_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000503_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial diversity and physiological characteristic in rock samples of Victoria land in Antarctica using culture-dependent method\nCollecting rock samples to analysis biodiversity using culture-dependent method", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000504_1.json b/datasets/KOPRI-KPDC-00000504_1.json index ef1b8fab8a..7158d3d975 100644 --- a/datasets/KOPRI-KPDC-00000504_1.json +++ b/datasets/KOPRI-KPDC-00000504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000505_1.json b/datasets/KOPRI-KPDC-00000505_1.json index ad9fb08800..94e69a2e4e 100644 --- a/datasets/KOPRI-KPDC-00000505_1.json +++ b/datasets/KOPRI-KPDC-00000505_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000505_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000506_1.json b/datasets/KOPRI-KPDC-00000506_1.json index ca69149b81..c6d31b5f20 100644 --- a/datasets/KOPRI-KPDC-00000506_1.json +++ b/datasets/KOPRI-KPDC-00000506_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000506_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted Araon-based expedition on the Ross Sea area near the Jang Bogo Station and the Drygalski ice tongue, East Antarctica. We obtained sediment cores using gravity corer and box corer. After cruise, sediment cores are half-cut and we measured MS and water content on laboratory. X-ray images are also obtained from sediment cores.\nto reconstruct the environmental changes caused by past climatic changes in the Ross sea area near Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000507_1.json b/datasets/KOPRI-KPDC-00000507_1.json index a616803b9f..1ed4e6efbc 100644 --- a/datasets/KOPRI-KPDC-00000507_1.json +++ b/datasets/KOPRI-KPDC-00000507_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000507_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000508_1.json b/datasets/KOPRI-KPDC-00000508_1.json index 51aeaa35b8..4eca51973d 100644 --- a/datasets/KOPRI-KPDC-00000508_1.json +++ b/datasets/KOPRI-KPDC-00000508_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000508_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica.\nStudy of the atmospheric wave activities in the southern high-latitude MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000509_1.json b/datasets/KOPRI-KPDC-00000509_1.json index 2821b3e817..594d6f2cd9 100644 --- a/datasets/KOPRI-KPDC-00000509_1.json +++ b/datasets/KOPRI-KPDC-00000509_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000509_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted Araon-based expedition on the Ross Sea area near the Jang Bogo Station and the Drygalski ice tongue, East Antarctica. We obtained sediment cores using gravity corer and box corer. After cruise, sediment cores are half-cut and we measured MS and water content on laboratory. X-ray images are also obtained from sediment cores.\nto reconstruct the environmental changes caused by past climatic changes in the Ross sea area near Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000510_1.json b/datasets/KOPRI-KPDC-00000510_1.json index 0a3feb9290..a8e42354b6 100644 --- a/datasets/KOPRI-KPDC-00000510_1.json +++ b/datasets/KOPRI-KPDC-00000510_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000510_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted Araon-based expedition on the Ross Sea area near the Jang Bogo Station and the Drygalski ice tongue, East Antarctica. We obtained sediment cores using gravity corer and box corer. After cruise, sediment cores are half-cut and we measured MS and water content on laboratory. X-ray images are also obtained from sediment cores.\nto reconstruct the environmental changes caused by past climatic changes in the Ross sea area near Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000511_1.json b/datasets/KOPRI-KPDC-00000511_1.json index 16022cf7ce..9c693425ab 100644 --- a/datasets/KOPRI-KPDC-00000511_1.json +++ b/datasets/KOPRI-KPDC-00000511_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000511_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soiland water samples from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000512_3.json b/datasets/KOPRI-KPDC-00000512_3.json index ce1fd1caad..f60b82954a 100644 --- a/datasets/KOPRI-KPDC-00000512_3.json +++ b/datasets/KOPRI-KPDC-00000512_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000512_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seawaters in 10 water columns were collected during January 2014, and analyzed for total and dissolved 234Th, and particulate organic carbon and biogenic silica. 234Th activities were analyzed using a gas-flow proportional \u03b2-spectrometer manufactured by Ris\u00f8 National Laboratories (Roskilde, Denmark) following methods described in Buesseler et al. (2001).\nThe export fluxes of particulate organic carbon (POC) play an important role in the transfer of carbon between the atmosphere and the ocean. Accurate estimates of POC export fluxes are critical for constraining models of the global carbon cycle. Over the past few decades, the radioisotope pair 238U and 234Th has been increasingly used to estimate POC export fluxes from the euphotic zone. This method is based on the uptake of 234Th onto biogenic particles in the euphotic zone and the subsequent sinking of particles into deep water. The POC export flux is determined by multiplying the depth-integrated 234Th sinking flux by the POC/234Th ratio on sinking particles. This study aims to estimate the POC export fluxes in the Amundsen Sea using 234Th/238U disequilibrium method.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000513_2.json b/datasets/KOPRI-KPDC-00000513_2.json index a9c134344c..37361813b9 100644 --- a/datasets/KOPRI-KPDC-00000513_2.json +++ b/datasets/KOPRI-KPDC-00000513_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000513_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "O2/Ar in seawater, pumped from the intake at 7 m below sea level, was measured using an equilibrator inlet mass spectrometer. The mass spectrometer measured a series of dissolved gases including O2 and Ar every 10 seconds. The data contain ion currents of those gases and total pressure in the mass spectrometer.\nNet community production (NCP), defined as the difference between autotrophic photosynthesis and (autrophic and heterotrophic) respiration, produces O2 proportional to the amount of net carbon. By measuring chemically and biologically inert Ar together with O2, it is possible to isolate O2 variation by physical processes (e.g., air temperature and pressure change and mixing of water masses) and deduce O2 variation by biological processes. To determine the net community (oxygen) production underway, we measured continuous O2/ Ar measurement system using an equilibrator inlet mass spectrometer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000514_1.json b/datasets/KOPRI-KPDC-00000514_1.json index f6102fd52b..ec159e6bf0 100644 --- a/datasets/KOPRI-KPDC-00000514_1.json +++ b/datasets/KOPRI-KPDC-00000514_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000514_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract (English): During the 2014 Amundsen Sea cruise, phytoplankton physiological parameters were measured by Fluorescence Induction and Relaxation (FIRe) system.\nTo investigate the impact of physico-chemical conditions (especially iron limitation) on phytoplankton photosynthesis, the photosynthetic characteristics of phytoplankton were measured", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000515_1.json b/datasets/KOPRI-KPDC-00000515_1.json index 17d4129042..57a8b4d285 100644 --- a/datasets/KOPRI-KPDC-00000515_1.json +++ b/datasets/KOPRI-KPDC-00000515_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000515_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica.\nStudy of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000516_1.json b/datasets/KOPRI-KPDC-00000516_1.json index 35628c8f07..487a8362a5 100644 --- a/datasets/KOPRI-KPDC-00000516_1.json +++ b/datasets/KOPRI-KPDC-00000516_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000516_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-channel seismic data were collected during the 2013 ARA04C cruise with R/V Araon in the Beaufort Sea, Arctic Ocean\nThe aim of this survey is to investigate stratigraphy, permafrost, gas-hydrate and deep crustal structure in continental shelf and slope of the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000517_1.json b/datasets/KOPRI-KPDC-00000517_1.json index 5c264eb5b0..4f8933ce61 100644 --- a/datasets/KOPRI-KPDC-00000517_1.json +++ b/datasets/KOPRI-KPDC-00000517_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000517_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-channel seismic data were collected during the 2014 ARA05C cruise with R/V Araon in the Beaufort Sea, Arctic Ocean\nThe aim of this survey is to investigate stratigraphy, permafrost, gas-hydrate and deep crustal structure in continental shelf and slope of the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000518_1.json b/datasets/KOPRI-KPDC-00000518_1.json index 87b040300f..1be072237a 100644 --- a/datasets/KOPRI-KPDC-00000518_1.json +++ b/datasets/KOPRI-KPDC-00000518_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000518_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Longcore drilling for exploration of the Ross Sea in Antarctica in 2015", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000519_1.json b/datasets/KOPRI-KPDC-00000519_1.json index 98e101e578..abc95e4658 100644 --- a/datasets/KOPRI-KPDC-00000519_1.json +++ b/datasets/KOPRI-KPDC-00000519_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000519_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Longcore drilling for exploration of the Ross Sea in Antarctica in 2015", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000520_1.json b/datasets/KOPRI-KPDC-00000520_1.json index 3d02827f1e..e28ddf6057 100644 --- a/datasets/KOPRI-KPDC-00000520_1.json +++ b/datasets/KOPRI-KPDC-00000520_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000520_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Longcore drilling for exploration of the Ross Sea in Antarctica in 2015", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000521_1.json b/datasets/KOPRI-KPDC-00000521_1.json index 1cb9a26256..79953d57e7 100644 --- a/datasets/KOPRI-KPDC-00000521_1.json +++ b/datasets/KOPRI-KPDC-00000521_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000521_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Longcore drilling for exploration of the Ross Sea in Antarctica in 2015", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000522_1.json b/datasets/KOPRI-KPDC-00000522_1.json index b1a9ac3507..ae8d9af0d4 100644 --- a/datasets/KOPRI-KPDC-00000522_1.json +++ b/datasets/KOPRI-KPDC-00000522_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000522_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil and water samples from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000523_2.json b/datasets/KOPRI-KPDC-00000523_2.json index 93c2db7104..90c14cae98 100644 --- a/datasets/KOPRI-KPDC-00000523_2.json +++ b/datasets/KOPRI-KPDC-00000523_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000523_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 7 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition.\nThe overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000524_2.json b/datasets/KOPRI-KPDC-00000524_2.json index ed90825384..b88da361b1 100644 --- a/datasets/KOPRI-KPDC-00000524_2.json +++ b/datasets/KOPRI-KPDC-00000524_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000524_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 6 geological stations were chosen to obtain multi core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistry, biogenic opal contents, microfossils and biomarkers will be performed after this expedition.\nThe overall objective of coring using the multi core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000525_2.json b/datasets/KOPRI-KPDC-00000525_2.json index b75395030b..023a3220ad 100644 --- a/datasets/KOPRI-KPDC-00000525_2.json +++ b/datasets/KOPRI-KPDC-00000525_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000525_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 3 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition.\nThe overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000526_2.json b/datasets/KOPRI-KPDC-00000526_2.json index d18369275d..050bbfbf9a 100644 --- a/datasets/KOPRI-KPDC-00000526_2.json +++ b/datasets/KOPRI-KPDC-00000526_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000526_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of 4 geological stations were chosen to obtain Jumbo piston core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition.\nThe overall objective of coring using the Jumbo Piston Corer during cruise ARA06C was to obtain longer records of sediments to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000527_1.json b/datasets/KOPRI-KPDC-00000527_1.json index 0de0322799..0dea6d3843 100644 --- a/datasets/KOPRI-KPDC-00000527_1.json +++ b/datasets/KOPRI-KPDC-00000527_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000527_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shallow ice cores drilled from the Styx glacier about 85 km north of the Jang Bogo station in the 2014-2015 summer season, and a 210.5 m long ice core was taken. The age at the bottom of the ice core was estimated to be 1.36 ka based on the depth-density profile and on the temperature at 15 m depth.\nReconstruction of past climate and environmental change such as Ross sea ice extent and greenhouse gases", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000528_1.json b/datasets/KOPRI-KPDC-00000528_1.json index 682ed5e84e..2a08e586c0 100644 --- a/datasets/KOPRI-KPDC-00000528_1.json +++ b/datasets/KOPRI-KPDC-00000528_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000528_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The fatty acid-binding proteins (FABPs) are involved in transporting hydrophobic fatty acids between various aqueous compartments of the cell by direct binding of ligands inside their \u03b2-barrel cavities. Here, we report the crystal structures of ligand-unbound pFABP4, linoleate-bound pFABP4 and palmitate-bound pFABP5 from the gentoo penguin (Pygoscelis papua) at 2.1, 2.2, and 2.3 \u00c5 resolutions, respectively. The pFABP4 and pFABP5 proteins comprise a canonical \u03b2-barrel structure with two short \u03b1-helices forming a cap region and fatty acid ligand binds in the hydrophobic cavity inside the \u03b2-barrel structure. The two linoleate-bound pFABP4 and palmitate-bound pFABP5 structures shows a different ligand-binding mode and a unique ligand-binding pocket caused by several sequence differences (A76/L78, T30/M32, underlining used to indicate pFABP4 residues). Structural comparison also shows a significantly different conformation change in the \u03b23-\u03b24 loop region (residues 57-62) of pFABP5 as well as flipped Phe60 residue (the corresponding residue in pFABP4 is Phe58). Moreover, a ligand-binding study using fluorophore displacement assays indicated that pFABP4 has a relatively strong affinity to linoleate compared with pFABP5. In contrast, pFABP5 clearly exhibits higher affinity for the palmitate compared with pFABP4. Conclusively, our high-resolution structures and ligand-binding study provide useful insights into the ligand-binding preferences of pFABPs based on key protein-ligand interactions.\nTo investigate mechanism of fatty acid transfer, we have carried out structural studies. As the first step toward its structural elucidation, we report the results of preliminary X-ray crystallographic experiments with pFABP4, pFABP4-Linoleate and pFABP5-Palmitate.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000529_1.json b/datasets/KOPRI-KPDC-00000529_1.json index 2d8899ea30..ebc1745380 100644 --- a/datasets/KOPRI-KPDC-00000529_1.json +++ b/datasets/KOPRI-KPDC-00000529_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000529_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ubiX gene of Colwellia psychrerythraea strain 34H encodes a 3-octaprenyl-4-hydroxybenzoate carboxylase (CpsUbiX, UniProtKB code: Q489U8) that is involved in the third step of the ubiquinone biosynthesis pathway and uses flavin mononucleotide (FMN) as a cofactor. Here, we report the crystal structures of two forms of CpsUbiX: an FMN-bound wild type form and an FMN-unbound V47S mutant form. CpsUbiX is a dodecameric enzyme, and each monomer possesses a typical Rossmann-fold structure. However, to our knowledge, the architecture of the FMN-binding domain formed by three neighboring subunits described here is novel and unique to UbiX. The highly conserved Gly15, Ser41, Val47, and Tyr171 residues play important roles in FMN binding. Structural comparison of the FMN-bound wild type form with the FMN-free form revealed a significant conformational difference in the C-terminal loop region (comprising residues 170\u2013177 and 195\u2013206). Subsequent computational modeling and liposome binding assay both suggested that the conformational change observed in the C-terminal loops upon FMN binding plays an important role in substrate binding. The crystal structures presented in this work provide structural framework and insights into the catalytic mechanism of CpsUbiX.\nTo investigate FMN binding mechanism, we have carried out structural studies. As the first step toward its structural elucidation, we report the results of preliminary X-ray crystallographic experiments with CpsUbiX with or without (V47S) cofactor FMN.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000530_1.json b/datasets/KOPRI-KPDC-00000530_1.json index 27a72fe658..fbf66e49d2 100644 --- a/datasets/KOPRI-KPDC-00000530_1.json +++ b/datasets/KOPRI-KPDC-00000530_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000530_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2014", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000531_2.json b/datasets/KOPRI-KPDC-00000531_2.json index 521aea20ac..56ed11d309 100644 --- a/datasets/KOPRI-KPDC-00000531_2.json +++ b/datasets/KOPRI-KPDC-00000531_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000531_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Islands collected in 2015. Locality, habitat information for Lichen/Moss/Plant samples.\ninvestigation microbial diversity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000532_2.json b/datasets/KOPRI-KPDC-00000532_2.json index 65f20e6302..918b30db51 100644 --- a/datasets/KOPRI-KPDC-00000532_2.json +++ b/datasets/KOPRI-KPDC-00000532_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000532_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Islands collected in 2015. Locality, habitat information for Lichen/Moss/Plant samples.\ninvestigation microbial diversity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000533_1.json b/datasets/KOPRI-KPDC-00000533_1.json index f0ca6c253f..ec5e075160 100644 --- a/datasets/KOPRI-KPDC-00000533_1.json +++ b/datasets/KOPRI-KPDC-00000533_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000533_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four radiative components at the surface have been measured from April 2014 at Antarctic Jang Bogo station using a net radiometer at a height of 10.5 m. Four radiative components are sampled every minute and half-hourly averaged data are recorded on a data logger.\nQuantification of the surface radiative budget at Antarctic Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000534_1.json b/datasets/KOPRI-KPDC-00000534_1.json index 61cf6f54c2..930f4b6391 100644 --- a/datasets/KOPRI-KPDC-00000534_1.json +++ b/datasets/KOPRI-KPDC-00000534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Islands collected in 2015. Locality, habitat information for Lichen/Moss/Plant samples.\ninvestigation microbial diversity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000535_1.json b/datasets/KOPRI-KPDC-00000535_1.json index 37da74e7db..1cc08f6f6a 100644 --- a/datasets/KOPRI-KPDC-00000535_1.json +++ b/datasets/KOPRI-KPDC-00000535_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000535_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat and water vapor have been measured from December 2014 at Antarctica Jang Bogo station. Eddy covariance system, consisting of two 3-D sonic anemometer and fast response hygrometer are used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to understand the atmosphere-land energy exchanges over permafrost, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000536_1.json b/datasets/KOPRI-KPDC-00000536_1.json index 2a3f22157e..4edacabdf3 100644 --- a/datasets/KOPRI-KPDC-00000536_1.json +++ b/datasets/KOPRI-KPDC-00000536_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000536_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Lindsey Island was carried out from February in 2008. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Antarctic region. Primary climate factors including solar radiation wind speed and direction, air temperature, pressure, relative humidity and snow depth has been monitored using automatic weather monitoring system at Lindsey Island. One and Two-hourly averaged data are stored at a data logger and an Argos Satellite transmitter is used to transmit two-hourly averaged data. The objectives of this monitoring are to record the past and current climate change through continuous operation of AWS, and to understand characteristics of meteorological phenomena at Lindsey Island.\nMonitoring on meteorology at Lindsay Island", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000537_1.json b/datasets/KOPRI-KPDC-00000537_1.json index 6f9c74a39b..00f6ef937c 100644 --- a/datasets/KOPRI-KPDC-00000537_1.json +++ b/datasets/KOPRI-KPDC-00000537_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000537_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microclimate data from King George Islands collected in 2015.\nInvestigate relationship between biota", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000538_2.json b/datasets/KOPRI-KPDC-00000538_2.json index c50692de00..b5818b327f 100644 --- a/datasets/KOPRI-KPDC-00000538_2.json +++ b/datasets/KOPRI-KPDC-00000538_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000538_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2015. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000539_2.json b/datasets/KOPRI-KPDC-00000539_2.json index 34bcc6da40..9d85ff5c89 100644 --- a/datasets/KOPRI-KPDC-00000539_2.json +++ b/datasets/KOPRI-KPDC-00000539_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000539_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000540_1.json b/datasets/KOPRI-KPDC-00000540_1.json index 865d953849..a5e28ae838 100644 --- a/datasets/KOPRI-KPDC-00000540_1.json +++ b/datasets/KOPRI-KPDC-00000540_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000540_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2015 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000541_1.json b/datasets/KOPRI-KPDC-00000541_1.json index 6256f199de..8ee1318299 100644 --- a/datasets/KOPRI-KPDC-00000541_1.json +++ b/datasets/KOPRI-KPDC-00000541_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000541_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface temperature observed at coast of King Sejong Station, Antarctica in 2015. Infrared sensor (Apogee) was used to measure SST. During sea-ice period, measured temperature represent sea-ice surface temperature not SST. Data interval has been obtained continuously at 30-minute interval.\nSurface temperature plays critical role in determining air-sea-seaice heat flux. SST (or Sea-ice surface temperature) is used to interpret measured turbulent heat flux.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000542_1.json b/datasets/KOPRI-KPDC-00000542_1.json index 0132f63247..4ef4bf26d4 100644 --- a/datasets/KOPRI-KPDC-00000542_1.json +++ b/datasets/KOPRI-KPDC-00000542_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000542_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on horizontal global radiation and its analysis of data measured by Eppley Precision Pyranometer at the King Sejong Station in the Antarctic, 2015\nTrend analysis and measurement of horizontal global radiation at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000543_1.json b/datasets/KOPRI-KPDC-00000543_1.json index ce299cc56f..c78248420a 100644 --- a/datasets/KOPRI-KPDC-00000543_1.json +++ b/datasets/KOPRI-KPDC-00000543_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000543_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2015. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000544_1.json b/datasets/KOPRI-KPDC-00000544_1.json index 892257a716..375bf4f0ea 100644 --- a/datasets/KOPRI-KPDC-00000544_1.json +++ b/datasets/KOPRI-KPDC-00000544_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000544_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors The temporal influences of environmental factors on marine phytoplankton community were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Marian Cove in Antarctica for the environmental monitoring in surface sea water", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000545_1.json b/datasets/KOPRI-KPDC-00000545_1.json index 3e70b6a18b..754ba1655f 100644 --- a/datasets/KOPRI-KPDC-00000545_1.json +++ b/datasets/KOPRI-KPDC-00000545_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000545_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the environmental monitoring in surface sea water", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000546_1.json b/datasets/KOPRI-KPDC-00000546_1.json index 22604b44d1..fcaf1d84ba 100644 --- a/datasets/KOPRI-KPDC-00000546_1.json +++ b/datasets/KOPRI-KPDC-00000546_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000546_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the Precambrian basement rocks and late Paleozoic rocks and fossils from Broggerhalvoya. It includes rock samples (metamorphic rocks, sandstone, limestone) and fossils (coral, brachiopods, bryozoans) from basement rocks, Broggertinden, Scheteligfjellet, Wordiekammen, Kapp Starostin formations.\nThese samples are for revision and establishment of the late Paleozoic stratigraphy of the Broggerhalvoya area. The rock samples will provide microfacies and isotope data. The fossil data will provide time framework to the lithostratigraphic succession and will allow reconstruction of the paleoecology of the research area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000547_1.json b/datasets/KOPRI-KPDC-00000547_1.json index df22d90005..b49ee255b1 100644 --- a/datasets/KOPRI-KPDC-00000547_1.json +++ b/datasets/KOPRI-KPDC-00000547_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000547_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the late Paleozoic rocks and fossils from Broggerhalvoya. It includes rock samples (sandstone, limestone) and fossils (coral, palaeoaplysina, chaetitids, fusulinids, brachiopods, silicified faunas) from Broggertinden, Scheteligfjellet, Wordiekammen formations. It is about 500 kg in total weight.\nThese samples are for revision and establishment of the late Paleozoic stratigraphy of the Broggerhalvoya area. The rock samples will provide microfacies and isotope data. The fossil data will provide time framework to the lithostratigraphic succession and will allow reconstruction of the paleoecology of the research area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000548_1.json b/datasets/KOPRI-KPDC-00000548_1.json index 5c4ad5f8e4..0ca50620af 100644 --- a/datasets/KOPRI-KPDC-00000548_1.json +++ b/datasets/KOPRI-KPDC-00000548_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000548_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four ciliates discovered from King George Island in 2013-14\nIdentification based on morphology and molecular data (Metaurostylopsis antarctica, Neokeronopsis asiatica, Urosomoida sejongensis, Gonostomum strenuum)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000549_1.json b/datasets/KOPRI-KPDC-00000549_1.json index b87a2391d8..6f88b09317 100644 --- a/datasets/KOPRI-KPDC-00000549_1.json +++ b/datasets/KOPRI-KPDC-00000549_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000549_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four soil ciliates discovered from King George Island in 2014-15\nIdentification based on morphology and molecular data (Anteholostica rectangula, Pseudonotohymena antarctica, Keronopsis sp., Paraholosticha muscicola)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000550_1.json b/datasets/KOPRI-KPDC-00000550_1.json index 473d07ff69..fde480a614 100644 --- a/datasets/KOPRI-KPDC-00000550_1.json +++ b/datasets/KOPRI-KPDC-00000550_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000550_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Comparison of biota from Subantarctic and Antarctic (Barton Peninsular, South Shetland Islands and Navarino Island in Chile)\nInterrelation of habual difference and species composition between Subantarctic and Antarctic regions", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000551_1.json b/datasets/KOPRI-KPDC-00000551_1.json index 4acd55a5d5..2f47d591a2 100644 --- a/datasets/KOPRI-KPDC-00000551_1.json +++ b/datasets/KOPRI-KPDC-00000551_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000551_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Comparison of 16S rRNA gene sequences showed that strain PAMC 80007 was most closely related to Domibacillus genus. Then, whole genome sequencing of the strains belong to Domibacillus and PAMC 80007 was performed. Genomic information comparison result showed that the strain PAMC 80007 belonged to the genus Domibacillus. Therefore, strain PAMC 80007 represents a novel species of the genus Domibacillus, for which the name Domibacillus tundrae sp. nov. is proposed.\nGenomic information comparison of strains belonged to the genus Domibacillus for identification of strain PAMC 80007", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000552_1.json b/datasets/KOPRI-KPDC-00000552_1.json index 9e1a2a7095..9159c4226f 100644 --- a/datasets/KOPRI-KPDC-00000552_1.json +++ b/datasets/KOPRI-KPDC-00000552_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000552_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ozone sonde observation is made from August to November. The observation is made once or twice a week around 00 UTC of the day. Data of ozone concentration and total ozone with altitude is sampled every two-second and recorded on a digital file. At the same time, pressure, temperature, relative humidity, wind speed and wind direction with altitude are sampled every two-second and recorded on a separate digital file.\nMonitoring of the variation in stratospheric ozone concentration with altitude and total ozone over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000553_1.json b/datasets/KOPRI-KPDC-00000553_1.json index 156183b756..78003a68ae 100644 --- a/datasets/KOPRI-KPDC-00000553_1.json +++ b/datasets/KOPRI-KPDC-00000553_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000553_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2/CH4 analyzer is used to measure atmspheric CO2 and CH4 concentration. Sampled air is dried using automatic dehumidifying system consisting of two refregerator type unit switchwing every 12 hour. Data are sampled every second and recored.\nMonitoring of Atmospheric CO2 and CH4 Concetration at Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000554_1.json b/datasets/KOPRI-KPDC-00000554_1.json index d24c392406..366098e5e3 100644 --- a/datasets/KOPRI-KPDC-00000554_1.json +++ b/datasets/KOPRI-KPDC-00000554_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000554_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pyranometer, Pyrgeometer, Total Ultraviolet, UV-A and UV-B are operated year round continously. Downward solar radiation, atmospheric longwave radiation, total ultraviolet radiation, UV-A and UV-B are sampled every second and ten-minute averaged data are recorded on a data logger.\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000555_1.json b/datasets/KOPRI-KPDC-00000555_1.json index 69a526413c..b0e6cf5020 100644 --- a/datasets/KOPRI-KPDC-00000555_1.json +++ b/datasets/KOPRI-KPDC-00000555_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000555_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Synotpci meteorological data are obtained from automatic synotpci observation system (WMO Index No. 89859).. The heights of wind sensor, temperature/humidity probe, barometer and visibility sensor are 10, 1.8, 1.3 m and 1.6 m, respectively. Wind data is sampled every three-second and the other data (temperature/relative humidity, pressureare and visibility) sampled once a minute. Ten-minute averaged data are stored at a data logger.\nSynoptic meteorological observation at Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000556_1.json b/datasets/KOPRI-KPDC-00000556_1.json index 4d8b05a927..555816ccc8 100644 --- a/datasets/KOPRI-KPDC-00000556_1.json +++ b/datasets/KOPRI-KPDC-00000556_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000556_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Halomonas bacteria are known as halophiles that have been isolated from diverse marine and hypersaline environments including in polar regions. In the culture center of Polar and Alpine Microbial Collection (PAMC), many strains of polar Halomonas spp. are deposited and are subject to genome analysis for understanding apdaptions of the microorganisms against cold and saline environments. In this study, draft genomes of the three type strains of Halomonas spp. (H. caseinilytica JCM 14802T, H. halodurans DSM 5160T and H. sinaiensis DSM 18067T) were obtained for comparative genomics among polar and non-polar Halomonas bacteria.\nTo establish a database of genomes for halophile Halomonas bacteria.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000557_1.json b/datasets/KOPRI-KPDC-00000557_1.json index fe2db45aa5..5e2954ea49 100644 --- a/datasets/KOPRI-KPDC-00000557_1.json +++ b/datasets/KOPRI-KPDC-00000557_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000557_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "RNAseq data from Sanionia uncinata under the different environments in King George Island\nIdentification of effects of water gradient on transcripotme of Sanionia uncinata in natural habitats.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000558_1.json b/datasets/KOPRI-KPDC-00000558_1.json index 5ec20bd3e3..b1c80b8c18 100644 --- a/datasets/KOPRI-KPDC-00000558_1.json +++ b/datasets/KOPRI-KPDC-00000558_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000558_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To measure the vertical profiles of temperature and salinity in the ACC (Antarctic circumpolar current), an intensive oceanographic survey was conducted during 7 days from 2015 Janurary 3 to 9 by IB/RV ARAON and to increase the spatial resolution for temperature and salinity, XCTD probes were used at 28 stations between regular hydrographic stations.\nTo investigate the variability in spatial and temporal distribution of water temperature and salinity in the ACC (Antarctic circumpolar current).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000559_1.json b/datasets/KOPRI-KPDC-00000559_1.json index bb1dd79ecc..70bafbb5a3 100644 --- a/datasets/KOPRI-KPDC-00000559_1.json +++ b/datasets/KOPRI-KPDC-00000559_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000559_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2015 Antarctic cruise, phytoplankton physiological parameters were measured by Fluorescence Induction and Relaxation (FIRe) system.\nTo investigate the impact of physico-chemical conditions (especially nutritional limitation) on phytoplankton photosynthesis, the photosynthetic characteristics of phytoplankton were measured", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000560_1.json b/datasets/KOPRI-KPDC-00000560_1.json index 43491cf8ae..5828d8cd3d 100644 --- a/datasets/KOPRI-KPDC-00000560_1.json +++ b/datasets/KOPRI-KPDC-00000560_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000560_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000561_1.json b/datasets/KOPRI-KPDC-00000561_1.json index 40a775288e..cb53e9fd75 100644 --- a/datasets/KOPRI-KPDC-00000561_1.json +++ b/datasets/KOPRI-KPDC-00000561_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000561_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000562_1.json b/datasets/KOPRI-KPDC-00000562_1.json index bf494fd484..d902657adc 100644 --- a/datasets/KOPRI-KPDC-00000562_1.json +++ b/datasets/KOPRI-KPDC-00000562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000563_1.json b/datasets/KOPRI-KPDC-00000563_1.json index 713aa014be..9baec82ea5 100644 --- a/datasets/KOPRI-KPDC-00000563_1.json +++ b/datasets/KOPRI-KPDC-00000563_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000563_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region.\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000564_1.json b/datasets/KOPRI-KPDC-00000564_1.json index 9312a98375..93634ce006 100644 --- a/datasets/KOPRI-KPDC-00000564_1.json +++ b/datasets/KOPRI-KPDC-00000564_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000564_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region\nLong-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000565_1.json b/datasets/KOPRI-KPDC-00000565_1.json index 1a99a31e97..98b9b13c21 100644 --- a/datasets/KOPRI-KPDC-00000565_1.json +++ b/datasets/KOPRI-KPDC-00000565_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000565_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000566_1.json b/datasets/KOPRI-KPDC-00000566_1.json index 3fb75beef6..602005f062 100644 --- a/datasets/KOPRI-KPDC-00000566_1.json +++ b/datasets/KOPRI-KPDC-00000566_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000566_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region\nLong-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000567_1.json b/datasets/KOPRI-KPDC-00000567_1.json index bb7be233e1..5f89c37319 100644 --- a/datasets/KOPRI-KPDC-00000567_1.json +++ b/datasets/KOPRI-KPDC-00000567_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000567_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica\nLong-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000568_1.json b/datasets/KOPRI-KPDC-00000568_1.json index 23df394cb2..bbe44a0a62 100644 --- a/datasets/KOPRI-KPDC-00000568_1.json +++ b/datasets/KOPRI-KPDC-00000568_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000568_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000569_1.json b/datasets/KOPRI-KPDC-00000569_1.json index 3d1bc736c1..f4cd156326 100644 --- a/datasets/KOPRI-KPDC-00000569_1.json +++ b/datasets/KOPRI-KPDC-00000569_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000569_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000570_1.json b/datasets/KOPRI-KPDC-00000570_1.json index adff8704a6..4c6b9c2631 100644 --- a/datasets/KOPRI-KPDC-00000570_1.json +++ b/datasets/KOPRI-KPDC-00000570_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000570_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at Jang Bogo Station, Antarctica\nStudy of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000571_1.json b/datasets/KOPRI-KPDC-00000571_1.json index 6e11eabd16..36071eca20 100644 --- a/datasets/KOPRI-KPDC-00000571_1.json +++ b/datasets/KOPRI-KPDC-00000571_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000571_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica\nStudy of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000572_1.json b/datasets/KOPRI-KPDC-00000572_1.json index 01105cfe19..0fc03a371a 100644 --- a/datasets/KOPRI-KPDC-00000572_1.json +++ b/datasets/KOPRI-KPDC-00000572_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000572_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica\nComprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000573_1.json b/datasets/KOPRI-KPDC-00000573_1.json index 50d39fac55..bad100e132 100644 --- a/datasets/KOPRI-KPDC-00000573_1.json +++ b/datasets/KOPRI-KPDC-00000573_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000573_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total electron content in the ionosphere at KSS station, Antarctica\nStudy of the statistical characteristics of ionosphere in southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000574_1.json b/datasets/KOPRI-KPDC-00000574_1.json index 1fd2ae5d4d..a153baa0fe 100644 --- a/datasets/KOPRI-KPDC-00000574_1.json +++ b/datasets/KOPRI-KPDC-00000574_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000574_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total electron content in the ionosphere over Kiruna, Sweden\nStudy of the statistical characteristics of ionosphere in northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000575_1.json b/datasets/KOPRI-KPDC-00000575_1.json index 1405ba53dc..f9531fd726 100644 --- a/datasets/KOPRI-KPDC-00000575_1.json +++ b/datasets/KOPRI-KPDC-00000575_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000575_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heat flow measurements in the Adare Trough, Antarctica\nInvestigation to the thermal structure of the Adare Trough, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000576_1.json b/datasets/KOPRI-KPDC-00000576_1.json index 6d71e4b951..9b52764d44 100644 --- a/datasets/KOPRI-KPDC-00000576_1.json +++ b/datasets/KOPRI-KPDC-00000576_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000576_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity coring for paleomagnetic research in the Adare Trough\nInvestigation to paleomagnetism in the Adare Trough", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000577_1.json b/datasets/KOPRI-KPDC-00000577_1.json index 646b510c05..f4f6420f07 100644 --- a/datasets/KOPRI-KPDC-00000577_1.json +++ b/datasets/KOPRI-KPDC-00000577_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000577_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI conducted scientific expedition on the Ross Sea, Antarctic. In this cruse, we collected the bathymetric data (Multi-beam and SBP) during 2 months (Jan ~ Feb, 2015) to investigate the sub-bottom geological and oceanograpic structures.\nBathymetric data was collected using Ice-breaker RV Araon to investigate the geologic and oceanographical information on the Antarctic area. Collected bathymetric data is utilized as reference information to determine the sediment coring site and understanding for submarine geological environment.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000578_1.json b/datasets/KOPRI-KPDC-00000578_1.json index 1f990b3225..da503e88ed 100644 --- a/datasets/KOPRI-KPDC-00000578_1.json +++ b/datasets/KOPRI-KPDC-00000578_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000578_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000579_1.json b/datasets/KOPRI-KPDC-00000579_1.json index d84c5330d0..78db03e198 100644 --- a/datasets/KOPRI-KPDC-00000579_1.json +++ b/datasets/KOPRI-KPDC-00000579_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000579_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000580_2.json b/datasets/KOPRI-KPDC-00000580_2.json index e77de93349..bd8f47cfd5 100644 --- a/datasets/KOPRI-KPDC-00000580_2.json +++ b/datasets/KOPRI-KPDC-00000580_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000580_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000581_2.json b/datasets/KOPRI-KPDC-00000581_2.json index da454a4d2a..f2b0059f6b 100644 --- a/datasets/KOPRI-KPDC-00000581_2.json +++ b/datasets/KOPRI-KPDC-00000581_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000581_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000582_1.json b/datasets/KOPRI-KPDC-00000582_1.json index 8c5c25b935..3308e8eb36 100644 --- a/datasets/KOPRI-KPDC-00000582_1.json +++ b/datasets/KOPRI-KPDC-00000582_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000582_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000583_1.json b/datasets/KOPRI-KPDC-00000583_1.json index fce7149e5f..2d5f6f74d8 100644 --- a/datasets/KOPRI-KPDC-00000583_1.json +++ b/datasets/KOPRI-KPDC-00000583_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000583_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000584_1.json b/datasets/KOPRI-KPDC-00000584_1.json index 95df475f33..a177d9b79c 100644 --- a/datasets/KOPRI-KPDC-00000584_1.json +++ b/datasets/KOPRI-KPDC-00000584_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000584_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000585_1.json b/datasets/KOPRI-KPDC-00000585_1.json index ce75f060e0..65ae50f54e 100644 --- a/datasets/KOPRI-KPDC-00000585_1.json +++ b/datasets/KOPRI-KPDC-00000585_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000585_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil volumetric moisture content and temperature for 5 cm depth from climate manipulation (combination of warming and precipitation) plots\nTo monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000586_1.json b/datasets/KOPRI-KPDC-00000586_1.json index 99a1eed547..2eabadfad4 100644 --- a/datasets/KOPRI-KPDC-00000586_1.json +++ b/datasets/KOPRI-KPDC-00000586_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000586_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nine permafrost core samples were collected in Council, Alaska. Three sampling sites were determined by soil resistivity test, and three replicates were collected in each site. Soil core was about 1.1 \u00e2\u20ac\u201c 1.5 m in length. Soil microbial community and physical and chemical properties will be analyzed.\nTo investigate the differences of microbial community structure and soil physical and chemical properties 1) between active and permafrost layers and 2) among soils showing different resistivity.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000587_1.json b/datasets/KOPRI-KPDC-00000587_1.json index f359a3dc56..179c9ca88c 100644 --- a/datasets/KOPRI-KPDC-00000587_1.json +++ b/datasets/KOPRI-KPDC-00000587_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000587_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2014 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000588_1.json b/datasets/KOPRI-KPDC-00000588_1.json index 613e0563b6..5aa04e61e0 100644 --- a/datasets/KOPRI-KPDC-00000588_1.json +++ b/datasets/KOPRI-KPDC-00000588_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000588_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-frequency methane concentration was measured in July 2014 at Council, Alaska. Along with atmospheric turbulence data from 3-D sonic anemometer, methane flux was obtained at 30-minute interval.\nTo monitor and understand methane flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000589_1.json b/datasets/KOPRI-KPDC-00000589_1.json index d180b9fd6f..b8e3aa8544 100644 --- a/datasets/KOPRI-KPDC-00000589_1.json +++ b/datasets/KOPRI-KPDC-00000589_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000589_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation)\nTo monitor the changes in micro-climate properties in air by increasing temperature and precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000590_1.json b/datasets/KOPRI-KPDC-00000590_1.json index d2b5a23741..1357001bc6 100644 --- a/datasets/KOPRI-KPDC-00000590_1.json +++ b/datasets/KOPRI-KPDC-00000590_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000590_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from climate manipulation plots after one year of warming and increasing precipitation\nTo determine the effects of climate change on soil properties and microbial diversity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000591_1.json b/datasets/KOPRI-KPDC-00000591_1.json index bada7bda92..a225c5b756 100644 --- a/datasets/KOPRI-KPDC-00000591_1.json +++ b/datasets/KOPRI-KPDC-00000591_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000591_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from climate manipulation plots after three years of warming and increasing precipitation\nTo determine the effects of climate change on soil properties and microbial structure and function", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000592_1.json b/datasets/KOPRI-KPDC-00000592_1.json index ae1faffb5e..e118efebf6 100644 --- a/datasets/KOPRI-KPDC-00000592_1.json +++ b/datasets/KOPRI-KPDC-00000592_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000592_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation in 2013\nTo monitor the changes in micro-climate properties in air by increasing temperature and precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000593_1.json b/datasets/KOPRI-KPDC-00000593_1.json index a5cf55258c..8865fd1579 100644 --- a/datasets/KOPRI-KPDC-00000593_1.json +++ b/datasets/KOPRI-KPDC-00000593_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000593_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) in 2014\nTo monitor the changes in micro-climate properties in air by increasing temperature and precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000594_1.json b/datasets/KOPRI-KPDC-00000594_1.json index 6f14a9e5ea..b834c1e120 100644 --- a/datasets/KOPRI-KPDC-00000594_1.json +++ b/datasets/KOPRI-KPDC-00000594_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000594_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil volumetric moisture content and temperature for 5 cm depth from climate manipulation (combination of warming and precipitation) plots in 2013\nTo monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000595_1.json b/datasets/KOPRI-KPDC-00000595_1.json index 9d4d202072..be84e611f7 100644 --- a/datasets/KOPRI-KPDC-00000595_1.json +++ b/datasets/KOPRI-KPDC-00000595_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000595_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture and temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2014", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000596_1.json b/datasets/KOPRI-KPDC-00000596_1.json index b91d176dfa..68c74b7439 100644 --- a/datasets/KOPRI-KPDC-00000596_1.json +++ b/datasets/KOPRI-KPDC-00000596_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000596_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the fossil specimens of Northern Victoria Land (NVL), Antarctica collected in 2014-15 austral summer season. The collection includes trilobites of the Lower Paleozoic Bowers Supergroup and plant fossils of the Beacon Supergroup.\nInformation from the fossils will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000597_1.json b/datasets/KOPRI-KPDC-00000597_1.json index fbe6a20980..e34ae793e8 100644 --- a/datasets/KOPRI-KPDC-00000597_1.json +++ b/datasets/KOPRI-KPDC-00000597_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000597_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rock samples of Northern Victoria Land (NVL), Antarctica collected in 2014-15 austral summer season. The collection includes sedimentary rocks (sandstone, limestone, conglomerate, and so on) of the Lower Paleozoic Bowers and Beacon supergroups, metamorphic rocks of the Wilson Terrane, and volcanic rocks of the McMurdo Volcanics.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information on stratigraphy, metamorphism, and volcanism will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000598_2.json b/datasets/KOPRI-KPDC-00000598_2.json index 41c7581d69..5f619823e9 100644 --- a/datasets/KOPRI-KPDC-00000598_2.json +++ b/datasets/KOPRI-KPDC-00000598_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000598_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collecting long-term seismic observation to study tectonics, volcanic activities, in addition to the movement of glaciers around the David Glacier and Terra Nova Bay, Antarctica\r\nTo understand the interaction between lithospere and cryosphere in the Northern Victoria Land, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000599_2.json b/datasets/KOPRI-KPDC-00000599_2.json index 91c3b2939b..f042d46c27 100644 --- a/datasets/KOPRI-KPDC-00000599_2.json +++ b/datasets/KOPRI-KPDC-00000599_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000599_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Superconducting gravimeter data at Jang Bogo Station in the installation stage\r\nInvestigation to earth tide, polar motion, cryospheric mass balance", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000600_2.json b/datasets/KOPRI-KPDC-00000600_2.json index df2cf452df..800256cdcd 100644 --- a/datasets/KOPRI-KPDC-00000600_2.json +++ b/datasets/KOPRI-KPDC-00000600_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000600_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round records of remotely operating GPS system\nInvestigation to the behavior of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000601_2.json b/datasets/KOPRI-KPDC-00000601_2.json index 97c08dffb2..7119ecd0c1 100644 --- a/datasets/KOPRI-KPDC-00000601_2.json +++ b/datasets/KOPRI-KPDC-00000601_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000601_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Ross Sea during the ARAON cruise in December 2015. Depth profiles of temperature and salinity were collected at 22 stations using CTD.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Ross Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000602_2.json b/datasets/KOPRI-KPDC-00000602_2.json index c27e3b76c9..89e7a6ebeb 100644 --- a/datasets/KOPRI-KPDC-00000602_2.json +++ b/datasets/KOPRI-KPDC-00000602_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000602_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round records of remotely operating GPS, weather sensor, and digital camera\nInvestigation to the behavior of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000603_1.json b/datasets/KOPRI-KPDC-00000603_1.json index a3ce8fbed6..79969f0786 100644 --- a/datasets/KOPRI-KPDC-00000603_1.json +++ b/datasets/KOPRI-KPDC-00000603_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000603_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of glacier thickness in Cambel glacier, Victoria land, Antarctica\nInvestigation to glacier thickness in Cambel glacier for the monitoring by environment / time change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000604_1.json b/datasets/KOPRI-KPDC-00000604_1.json index ff06fe6aa6..a8b1638e85 100644 --- a/datasets/KOPRI-KPDC-00000604_1.json +++ b/datasets/KOPRI-KPDC-00000604_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000604_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of glacier thickness in Hercules Neve, Victoria land, Antarctica\nInvestigation to glacier thickness in Hercules Neve for the monitoring by environment / time change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000605_1.json b/datasets/KOPRI-KPDC-00000605_1.json index 1e5edcfd25..96b6384183 100644 --- a/datasets/KOPRI-KPDC-00000605_1.json +++ b/datasets/KOPRI-KPDC-00000605_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000605_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pre-investigation of Korean route from Victoria land, Antarctica\nPre-investigation of safety to Korean route in Victoria land, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000606_1.json b/datasets/KOPRI-KPDC-00000606_1.json index 8596f17a0d..f67faa3d49 100644 --- a/datasets/KOPRI-KPDC-00000606_1.json +++ b/datasets/KOPRI-KPDC-00000606_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000606_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000607_1.json b/datasets/KOPRI-KPDC-00000607_1.json index 9d54ab1f09..47f88eb978 100644 --- a/datasets/KOPRI-KPDC-00000607_1.json +++ b/datasets/KOPRI-KPDC-00000607_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000607_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000608_1.json b/datasets/KOPRI-KPDC-00000608_1.json index ccd0ee444c..54763a1491 100644 --- a/datasets/KOPRI-KPDC-00000608_1.json +++ b/datasets/KOPRI-KPDC-00000608_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000608_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000609_1.json b/datasets/KOPRI-KPDC-00000609_1.json index baa3e5b209..518bb85a9d 100644 --- a/datasets/KOPRI-KPDC-00000609_1.json +++ b/datasets/KOPRI-KPDC-00000609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000610_1.json b/datasets/KOPRI-KPDC-00000610_1.json index 67421e68a7..f3498bd562 100644 --- a/datasets/KOPRI-KPDC-00000610_1.json +++ b/datasets/KOPRI-KPDC-00000610_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000610_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000611_1.json b/datasets/KOPRI-KPDC-00000611_1.json index f8fa245a22..d9cd3a162d 100644 --- a/datasets/KOPRI-KPDC-00000611_1.json +++ b/datasets/KOPRI-KPDC-00000611_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000611_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000612_1.json b/datasets/KOPRI-KPDC-00000612_1.json index 876135eca1..6d23bc9b84 100644 --- a/datasets/KOPRI-KPDC-00000612_1.json +++ b/datasets/KOPRI-KPDC-00000612_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000612_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000613_1.json b/datasets/KOPRI-KPDC-00000613_1.json index 1b5643f6f5..6b62515dfd 100644 --- a/datasets/KOPRI-KPDC-00000613_1.json +++ b/datasets/KOPRI-KPDC-00000613_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000613_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000614_1.json b/datasets/KOPRI-KPDC-00000614_1.json index fa0016a4c8..7601e61967 100644 --- a/datasets/KOPRI-KPDC-00000614_1.json +++ b/datasets/KOPRI-KPDC-00000614_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000614_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000615_1.json b/datasets/KOPRI-KPDC-00000615_1.json index 8770bfba2f..06c76b234c 100644 --- a/datasets/KOPRI-KPDC-00000615_1.json +++ b/datasets/KOPRI-KPDC-00000615_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000615_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Midtre lov\u00e9nbreen foreland is a glacier retreating region with low organic carbon content. Since soil organic matter (SOM) is a mixture of materials showing various turnover time. Therefore, density-size based SOM fractionation and pyrolysis-Gas Chromatography/Mass Spectrometry (py-GC/MS) were used to understand SOM characteristics. Firstly, SOM was separated soil into the free light fraction (FLF) and the heavy fraction (HF). Secondly, the HF was further separated as the sand-size fraction and silt and clay-size fraction based on size. Before analyzing molecular compositions of SOM fractions, the sand-sized and silt and clay sized fractions were treated with hydrofluoric acid to increase carbon concentration by removing mineral particles. Molecular compositions of each fraction were analyzed by py-GC/MS. Then, we used a multivariate statistical analysis (sparse PCA) to compare the different soil organic carbon characteristics.\nTo establish methods to separate soil organic matter fraction in the Arctic region with low soil organic carbon content and to analyze the molecular characteristics using pyrolysis-GC/MS", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000616_1.json b/datasets/KOPRI-KPDC-00000616_1.json index 7d0c374225..b699799ed8 100644 --- a/datasets/KOPRI-KPDC-00000616_1.json +++ b/datasets/KOPRI-KPDC-00000616_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000616_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The potential risk from direct sewage discharge to the Antarctic marine environment is highly significant because it contains chemical substances, persistent organic materials, bacterial and viral agents. To investigate the impact of sewage from the station to near coastal of Jang Bogo station, we completed the installation of the observing system for marine benthic animals during the summer activity in 2015/2016 following last year. As benthic organisms are sensitive to habitual disturbance and affected by the domestic wastes and sediments, these ecological characteristics allow them to be an important factor to measure sea area environment. We installed a transect line C starting from sewage outlet and two lines B and C for control sites at 10m apart from C. Then, we took the pictures from four quadrats (50cm x 50cm) at intervals of 10m along lines, recorded the video and collected the representative benthic species. Also, seawater running from sewage outlet and marine sediment sunk on sea bottom were collected in order to measure the possible pollutants in them.\nThis activity aims to investigate the impact of sewage disposal on benthic communities in nearshore of Jang Bogo Station and to discuss the available treatment options.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000617_1.json b/datasets/KOPRI-KPDC-00000617_1.json index 8af7b469ee..b7b3bba2df 100644 --- a/datasets/KOPRI-KPDC-00000617_1.json +++ b/datasets/KOPRI-KPDC-00000617_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000617_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Jang Bogo station.\nMonitoring of Black Carbon concentration over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000618_1.json b/datasets/KOPRI-KPDC-00000618_1.json index 9d42703736..dfaeb4416b 100644 --- a/datasets/KOPRI-KPDC-00000618_1.json +++ b/datasets/KOPRI-KPDC-00000618_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000618_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil and fresh/sea water samples from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000619_1.json b/datasets/KOPRI-KPDC-00000619_1.json index 81c0f5567b..10ac2e9052 100644 --- a/datasets/KOPRI-KPDC-00000619_1.json +++ b/datasets/KOPRI-KPDC-00000619_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000619_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microclimate data from King George Islands collected in 2016.\nInvestigate relationship between biota", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000620_1.json b/datasets/KOPRI-KPDC-00000620_1.json index f6aed20874..343245d4f4 100644 --- a/datasets/KOPRI-KPDC-00000620_1.json +++ b/datasets/KOPRI-KPDC-00000620_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000620_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000621_1.json b/datasets/KOPRI-KPDC-00000621_1.json index 41d9123708..b3689bc3d4 100644 --- a/datasets/KOPRI-KPDC-00000621_1.json +++ b/datasets/KOPRI-KPDC-00000621_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000621_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil and water samples of the Antarctic Jang Bogo Station from Terra Nova Bay in Antarctica\nInvestigation to the terrestrial biodiversity in Terra Nova Bay for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000622_1.json b/datasets/KOPRI-KPDC-00000622_1.json index 474a682d01..8cf750c0f2 100644 --- a/datasets/KOPRI-KPDC-00000622_1.json +++ b/datasets/KOPRI-KPDC-00000622_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000622_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Identification of ciliate biota and environmental data of habitats from Antarctica (Barton Peninsular)\nIdentification of the relationship between biotic sample and abiotic data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000623_1.json b/datasets/KOPRI-KPDC-00000623_1.json index 727ead7c1c..94582f138d 100644 --- a/datasets/KOPRI-KPDC-00000623_1.json +++ b/datasets/KOPRI-KPDC-00000623_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000623_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000624_1.json b/datasets/KOPRI-KPDC-00000624_1.json index 0fc0bc1c7c..e74e52f30e 100644 --- a/datasets/KOPRI-KPDC-00000624_1.json +++ b/datasets/KOPRI-KPDC-00000624_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000624_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Barton Peninsular collected in 2016\nEcophysiological study of lichen", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000625_2.json b/datasets/KOPRI-KPDC-00000625_2.json index d50d7df0e9..91597ab1b7 100644 --- a/datasets/KOPRI-KPDC-00000625_2.json +++ b/datasets/KOPRI-KPDC-00000625_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000625_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2016. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000626_1.json b/datasets/KOPRI-KPDC-00000626_1.json index 2d945e7916..b8b85acd35 100644 --- a/datasets/KOPRI-KPDC-00000626_1.json +++ b/datasets/KOPRI-KPDC-00000626_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000626_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000627_1.json b/datasets/KOPRI-KPDC-00000627_1.json index 4f9f0ef31c..1669a307dc 100644 --- a/datasets/KOPRI-KPDC-00000627_1.json +++ b/datasets/KOPRI-KPDC-00000627_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000627_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hyperspectrul images from Barton Peninsular in King George Island in 2016\nLong-Term Ecological Researches on King George Island to Predict Ecosystem Responses to Climate Change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000628_1.json b/datasets/KOPRI-KPDC-00000628_1.json index 045cb4bd21..34f780b828 100644 --- a/datasets/KOPRI-KPDC-00000628_1.json +++ b/datasets/KOPRI-KPDC-00000628_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000628_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NIR and RGB images from Barton Peninsular in King George Island in 2016\nLong-Term Ecological Researches on King George Island to Predict Ecosystem Responses to Climate Change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000629_1.json b/datasets/KOPRI-KPDC-00000629_1.json index c01df6e7cb..792143c964 100644 --- a/datasets/KOPRI-KPDC-00000629_1.json +++ b/datasets/KOPRI-KPDC-00000629_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000629_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dissolved organic carbon and nitrogen data in the Amundsen Sea in 2016", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000630_1.json b/datasets/KOPRI-KPDC-00000630_1.json index 163c619f8a..bd85678662 100644 --- a/datasets/KOPRI-KPDC-00000630_1.json +++ b/datasets/KOPRI-KPDC-00000630_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000630_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic survey was conducted to understand the variability of zooplankton distribution around Dotson ice shelf in the Amundsen Sea. Acoustic data were collected from surface to 500-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon\nTo identify the horizontal and vertical distribution of zooplankton around Dotson ice shelf.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000631_1.json b/datasets/KOPRI-KPDC-00000631_1.json index d2cac224d0..53a8889961 100644 --- a/datasets/KOPRI-KPDC-00000631_1.json +++ b/datasets/KOPRI-KPDC-00000631_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000631_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As benthic organisms are sensitive to habitual disturbance and affected by the domestic wastes and sediments, these ecological characteristics allow them to be an important factor to measure sea area environment. We selected two monitoring sites and installed two permanent 30m line transects per a site. Then, we obtained the pictures from four quadrats(50cm x 50 cm) at intervals of 10m along lines, recorded the video and collected the representative benthic species. Also, seawater running from sewage outlet and marine sediment sunk on sea bottom were collected in order to measure the possible pollutants in them.\nAn investigation of the effect of sewage caused by human activities on benthic communities", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000632_2.json b/datasets/KOPRI-KPDC-00000632_2.json index d501f5f3e8..11a79cbc1e 100644 --- a/datasets/KOPRI-KPDC-00000632_2.json +++ b/datasets/KOPRI-KPDC-00000632_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000632_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring on nest distribution and breeding indicators of seabirds around the King Sejong Station during the 2015/16 austral summer season\nUnderstanding of the fluctuation in the nest distribution with long-term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000633_1.json b/datasets/KOPRI-KPDC-00000633_1.json index 04e8535570..38f7a34930 100644 --- a/datasets/KOPRI-KPDC-00000633_1.json +++ b/datasets/KOPRI-KPDC-00000633_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000633_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A set of images with two digital cameras for 3D image building and one NIR camera for vegetation distribution mapping.\nto build a stitched image with 3D structure and vegetation mapping", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000634_2.json b/datasets/KOPRI-KPDC-00000634_2.json index 0fe921d4b9..3be3b15235 100644 --- a/datasets/KOPRI-KPDC-00000634_2.json +++ b/datasets/KOPRI-KPDC-00000634_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000634_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2015/2016 expedition. During the 2016 Amundsen Sea cruise (ANA06B) by IBRV Araon, a total of 81 CTD stations were visited.\r\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000635_2.json b/datasets/KOPRI-KPDC-00000635_2.json index ea5b74bcfa..be8c1a243a 100644 --- a/datasets/KOPRI-KPDC-00000635_2.json +++ b/datasets/KOPRI-KPDC-00000635_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000635_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2015/2016 expedition. During the 2016 Amundsen Sea cruise (ANA06B) by IBRV Araon, a total of 81 CTD stations were visited.\r\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000636_1.json b/datasets/KOPRI-KPDC-00000636_1.json index 0e5813c533..d0ed926c81 100644 --- a/datasets/KOPRI-KPDC-00000636_1.json +++ b/datasets/KOPRI-KPDC-00000636_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000636_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2015/2016 expedition. During the 2016 Amundsen Sea cruise (ANA06B) by IBRV Araon.\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000637_1.json b/datasets/KOPRI-KPDC-00000637_1.json index 66e50d357b..5e180e76ee 100644 --- a/datasets/KOPRI-KPDC-00000637_1.json +++ b/datasets/KOPRI-KPDC-00000637_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000637_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow samples were collected from the wall of a 1.6 m snow pit at Styx Glacier plateau in Victoria Land, Antarctica, during 2014/2015 austral summer season.\u00c2\u00a0 Here we present the data record for trace elements from the snow samples.\nThe study of environmental change in Victoria Land, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000638_1.json b/datasets/KOPRI-KPDC-00000638_1.json index 8e17250c56..4f422c3b62 100644 --- a/datasets/KOPRI-KPDC-00000638_1.json +++ b/datasets/KOPRI-KPDC-00000638_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000638_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) was installed in mid-October 2016 at Ny-Alesund where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 3km typically on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field can be obtained using PPI and RHI modes, respectively.\nTo understand interaction between Arctic cloud and boundary layer wind", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000639_2.json b/datasets/KOPRI-KPDC-00000639_2.json index a6543f13a6..057339c894 100644 --- a/datasets/KOPRI-KPDC-00000639_2.json +++ b/datasets/KOPRI-KPDC-00000639_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000639_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000640_1.json b/datasets/KOPRI-KPDC-00000640_1.json index d233770c9c..821d6ecc94 100644 --- a/datasets/KOPRI-KPDC-00000640_1.json +++ b/datasets/KOPRI-KPDC-00000640_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000640_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2016 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000641_1.json b/datasets/KOPRI-KPDC-00000641_1.json index 716d326bcf..7b18ad65c0 100644 --- a/datasets/KOPRI-KPDC-00000641_1.json +++ b/datasets/KOPRI-KPDC-00000641_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000641_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on horizontal global radiation and its analysis of data measured by Eppley Precision Pyranometer at the King Sejong Station in the Antarctic, 2016\nTrend analysis and measurement of horizontal global radiation at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000642_1.json b/datasets/KOPRI-KPDC-00000642_1.json index 478ccf1a60..24e5d5be6e 100644 --- a/datasets/KOPRI-KPDC-00000642_1.json +++ b/datasets/KOPRI-KPDC-00000642_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000642_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface temperature observed at coast of King Sejong Station, Antarctica in 2016. Infrared sensor (Apogee) was used to measure SST. During sea-ice period, measured temperature represent sea-ice surface temperature not SST. Data interval has been obtained continuously at 30-minute interval.\nSurface temperature plays critical role in determining air-sea-seaice heat flux. SST (or Sea-ice surface temperature) is used to interpret measured turbulent heat flux.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000643_1.json b/datasets/KOPRI-KPDC-00000643_1.json index 57237e1913..8c5de87a20 100644 --- a/datasets/KOPRI-KPDC-00000643_1.json +++ b/datasets/KOPRI-KPDC-00000643_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000643_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2016. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor climate variation at the Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000644_1.json b/datasets/KOPRI-KPDC-00000644_1.json index 926d9a3118..e86ea9960c 100644 --- a/datasets/KOPRI-KPDC-00000644_1.json +++ b/datasets/KOPRI-KPDC-00000644_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000644_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015/2016 Ross Sea core ,Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000645_1.json b/datasets/KOPRI-KPDC-00000645_1.json index 86166dd8da..9c6e8c683c 100644 --- a/datasets/KOPRI-KPDC-00000645_1.json +++ b/datasets/KOPRI-KPDC-00000645_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000645_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015/2016 Ross Sea core ,Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000646_1.json b/datasets/KOPRI-KPDC-00000646_1.json index f3b088dfc5..c3dc2e4e31 100644 --- a/datasets/KOPRI-KPDC-00000646_1.json +++ b/datasets/KOPRI-KPDC-00000646_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000646_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015/2016 Ross Sea core ,Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000647_1.json b/datasets/KOPRI-KPDC-00000647_1.json index d1ecd55a7a..cb6e3b578c 100644 --- a/datasets/KOPRI-KPDC-00000647_1.json +++ b/datasets/KOPRI-KPDC-00000647_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000647_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015/2016 Ross Sea core ,Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000648_3.json b/datasets/KOPRI-KPDC-00000648_3.json index fbf1f771ae..67bf74eb4e 100644 --- a/datasets/KOPRI-KPDC-00000648_3.json +++ b/datasets/KOPRI-KPDC-00000648_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000648_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015/2016 Ross Sea core ,Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000649_1.json b/datasets/KOPRI-KPDC-00000649_1.json index 36d7a63c37..3a0a143225 100644 --- a/datasets/KOPRI-KPDC-00000649_1.json +++ b/datasets/KOPRI-KPDC-00000649_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000649_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The phytoplantkon biomass (chl-a) was investigated in the Amundsen Sea, Antarctica from January to February 2016. This data includes investigator and locality for chlorophyll-a concentration.\nThe investigation of chlorophyll-a concentration in the Amundsen Sea, Antarctica 2016.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000650_1.json b/datasets/KOPRI-KPDC-00000650_1.json index 19659304a6..ccc7a43496 100644 --- a/datasets/KOPRI-KPDC-00000650_1.json +++ b/datasets/KOPRI-KPDC-00000650_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000650_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temporary depth-age relationship of a shallow ice core drilled at Styx glacial, Antarctica in 2014-2015 based on the Herron-Langway firn densification model.\nTo provide fundamental information on the ice core.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000651_1.json b/datasets/KOPRI-KPDC-00000651_1.json index edbb499ba1..b85768615c 100644 --- a/datasets/KOPRI-KPDC-00000651_1.json +++ b/datasets/KOPRI-KPDC-00000651_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000651_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temporary depth-age relationship of a shallow ice core drilled at GV7, East Antarctica in 2013-2014, based on the water isotope ratio and electrical conductivity.\nTo provide fundamental information on the ice core.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000652_1.json b/datasets/KOPRI-KPDC-00000652_1.json index c06ae5a716..215ed0ef71 100644 --- a/datasets/KOPRI-KPDC-00000652_1.json +++ b/datasets/KOPRI-KPDC-00000652_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000652_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Transcriptome data of Field vs. Chamber samples of Colobanthus quitensis\nIdentification of environmental stress-responsive genes of C.quitensis", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000653_1.json b/datasets/KOPRI-KPDC-00000653_1.json index 4f506dea16..973b08f354 100644 --- a/datasets/KOPRI-KPDC-00000653_1.json +++ b/datasets/KOPRI-KPDC-00000653_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000653_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stable water isotope composition of the shallow ice core drilled at the Styx glacier in 2014-2015. The current version contains data for the upper 27 m with a depth resoultion of 22 mm.\nDating the ice core/Paleoclimate research", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000654_1.json b/datasets/KOPRI-KPDC-00000654_1.json index b7ea409c11..cc93ae00b1 100644 --- a/datasets/KOPRI-KPDC-00000654_1.json +++ b/datasets/KOPRI-KPDC-00000654_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000654_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stable water isotope composition of the shallow ice core drilled at the GV7 site in 2013-2014.\nDating the ice core/Paleoclimate research", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000655_1.json b/datasets/KOPRI-KPDC-00000655_1.json index 7e3c72431e..8259deb4fe 100644 --- a/datasets/KOPRI-KPDC-00000655_1.json +++ b/datasets/KOPRI-KPDC-00000655_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000655_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000656_1.json b/datasets/KOPRI-KPDC-00000656_1.json index 6eeb66f40f..eb2d1ec6ad 100644 --- a/datasets/KOPRI-KPDC-00000656_1.json +++ b/datasets/KOPRI-KPDC-00000656_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000656_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Transcriptome profiling of arctic Chlamydomonas sp. under low temperature\nIdentification of environmental response genes using transcriptome profiling of arctic Chlamydomonas sp. under low temperature", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000657_1.json b/datasets/KOPRI-KPDC-00000657_1.json index 2798afe2f9..985da1071f 100644 --- a/datasets/KOPRI-KPDC-00000657_1.json +++ b/datasets/KOPRI-KPDC-00000657_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000657_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Transcriptome profiling of arctic Chloromonas sp. under low temperature\nIdentification of low temperature response genes of arctic Chloromonas sp.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000658_1.json b/datasets/KOPRI-KPDC-00000658_1.json index 77ca76d959..455dda86b6 100644 --- a/datasets/KOPRI-KPDC-00000658_1.json +++ b/datasets/KOPRI-KPDC-00000658_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000658_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heat flow measurements in the Adare Trough, Antarctica\nInvestigation to the thermal structure of the Adare Trough, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000659_1.json b/datasets/KOPRI-KPDC-00000659_1.json index 7e4c751964..6f157bd8fa 100644 --- a/datasets/KOPRI-KPDC-00000659_1.json +++ b/datasets/KOPRI-KPDC-00000659_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000659_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During March, 2016, KOPRI conducted marine survey in the Ross sea, Antarctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000660_1.json b/datasets/KOPRI-KPDC-00000660_1.json index 4c00dd35fe..85c1d30d2e 100644 --- a/datasets/KOPRI-KPDC-00000660_1.json +++ b/datasets/KOPRI-KPDC-00000660_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000660_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrophone data near the Balleny Islands, Antarctica\nInvestigation for a tectonic activity of the West Antarctic Rift System", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000661_1.json b/datasets/KOPRI-KPDC-00000661_1.json index 761d83fd5e..c22b28b7d4 100644 --- a/datasets/KOPRI-KPDC-00000661_1.json +++ b/datasets/KOPRI-KPDC-00000661_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000661_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profile data in the Ross Sea, Antarctica\nInvestigation for sedimentary structure at a shallow depth", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000662_1.json b/datasets/KOPRI-KPDC-00000662_1.json index d80371fadf..c04c5955d8 100644 --- a/datasets/KOPRI-KPDC-00000662_1.json +++ b/datasets/KOPRI-KPDC-00000662_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000662_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CTD data in the Adare Trough\nMeasurement for sea water temperature", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000663_2.json b/datasets/KOPRI-KPDC-00000663_2.json index 578cf694b1..aede6402e9 100644 --- a/datasets/KOPRI-KPDC-00000663_2.json +++ b/datasets/KOPRI-KPDC-00000663_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000663_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of ionic species in the upper section (~0-15m) of shallow ice core from GV7 site in Antarctica\nReconstruction of ionic species to indicate paleo atmospheric environment/climate change of Northern Victoria Land, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000664_1.json b/datasets/KOPRI-KPDC-00000664_1.json index 0dbaa611c4..68a7f1189d 100644 --- a/datasets/KOPRI-KPDC-00000664_1.json +++ b/datasets/KOPRI-KPDC-00000664_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000664_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of ionic species in snowpit samples from Styx glacier in Antarctica\nDetermination of ionic species from aerosols and gaseous species deposited from atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000665_1.json b/datasets/KOPRI-KPDC-00000665_1.json index 92736a181d..ad6ac95514 100644 --- a/datasets/KOPRI-KPDC-00000665_1.json +++ b/datasets/KOPRI-KPDC-00000665_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000665_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of Gravity and box core samples in Svalbard Fjorden for reconstrcution of the Paleoenvironment and clamate changes\nReconstruction of Holocene paleoenvironmental changes in Svalbard Fjorden.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000666_1.json b/datasets/KOPRI-KPDC-00000666_1.json index a67b97f045..389b37c9f5 100644 --- a/datasets/KOPRI-KPDC-00000666_1.json +++ b/datasets/KOPRI-KPDC-00000666_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000666_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shotgun metagenome data of soils collected from the foreland of Midtre Lovenbreen, Svalbard\nReveal taxonomic and functional dynamics of soil micrboes in the glacier foreland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000667_1.json b/datasets/KOPRI-KPDC-00000667_1.json index ef55de7976..c7fe3a46bc 100644 --- a/datasets/KOPRI-KPDC-00000667_1.json +++ b/datasets/KOPRI-KPDC-00000667_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000667_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000668_1.json b/datasets/KOPRI-KPDC-00000668_1.json index a2b7991818..53a519fc0f 100644 --- a/datasets/KOPRI-KPDC-00000668_1.json +++ b/datasets/KOPRI-KPDC-00000668_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000668_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil samples from glacier forelands in Midtre and Austre Lov\u00e9nbreen and Bloomstrandbreen were collected in 2016 summer. A line transect method was applied, and three transects covering the whole foreland (8-10 sites in each transact) were selected. We will try to understand soil development and microbial succession processes. These samples will also be used to validate the SOC accumulation model established from 2014 sampling.\nTo understand SOC development and microbial succession in the glacier foreland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000669_1.json b/datasets/KOPRI-KPDC-00000669_1.json index 4ec1374727..799ca1263b 100644 --- a/datasets/KOPRI-KPDC-00000669_1.json +++ b/datasets/KOPRI-KPDC-00000669_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000669_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-Channel seismic data were collected during the 2016 ARA07C cruise in the East Siberian Sea, Arctic Ocean\nInvestigation of submarine resource environment and seabed methane release in the East Siberian Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000670_1.json b/datasets/KOPRI-KPDC-00000670_1.json index 325cbf7b5a..34383d3379 100644 --- a/datasets/KOPRI-KPDC-00000670_1.json +++ b/datasets/KOPRI-KPDC-00000670_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000670_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nano-SMPS data were collected at the Zeppelin station in 2016\nAn investigation of nanoparticle formation, aerosols\u00e2\u20ac\u2122 sources, formation mechanisms, transport pathways, and their effects on climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000671_1.json b/datasets/KOPRI-KPDC-00000671_1.json index 77a7ca63fc..d638f6e27b 100644 --- a/datasets/KOPRI-KPDC-00000671_1.json +++ b/datasets/KOPRI-KPDC-00000671_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000671_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological data are obtained from the Weather Research and Forecasting (WRF) v3.4.1 model in conjunction with NCEP reanalyzed data during the 4 months from May to August 2008. The data are provides on an hourly basis. The WRF domain covers the areas of Northern Hemisphere with 54x54 km^2 horizontal resolution.\nMeteorological input data for 3D- chemistry and transport model modeling", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000672_1.json b/datasets/KOPRI-KPDC-00000672_1.json index 9bd97ec93e..b9ebc21ff8 100644 --- a/datasets/KOPRI-KPDC-00000672_1.json +++ b/datasets/KOPRI-KPDC-00000672_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000672_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Activities of Hydrolases (beta-glucosidase, cellobiase, N-acetyl-glucosaminidase, and aminopeptidase) and phenol oxidase in soil under warming and precipitation increase\r\nAncillary data including dissolved organic carbon (DOC) content, specific UV absorbance (SUVA), and carbon stable isotope ratio of plant leaves\nTo determine the effects of climate change on soil enzyme activities that is related to decomposition", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000673_1.json b/datasets/KOPRI-KPDC-00000673_1.json index 92a303dc64..5180b2af31 100644 --- a/datasets/KOPRI-KPDC-00000673_1.json +++ b/datasets/KOPRI-KPDC-00000673_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000673_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snapshot of CO2 and CH4 fluxes between soil and atmosphere under warming and precipitation increase\r\nAbundance of methanogen and methanotroph in soil under warming and precipitation increase\nTo determine the effects of climate change on GHGs flux", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000674_1.json b/datasets/KOPRI-KPDC-00000674_1.json index e8a4dcb1e5..0389b2037e 100644 --- a/datasets/KOPRI-KPDC-00000674_1.json +++ b/datasets/KOPRI-KPDC-00000674_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000674_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected.\nTo monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000675_1.json b/datasets/KOPRI-KPDC-00000675_1.json index 3497e696e2..e8895dc2c5 100644 --- a/datasets/KOPRI-KPDC-00000675_1.json +++ b/datasets/KOPRI-KPDC-00000675_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000675_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected.\nTo monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000676_1.json b/datasets/KOPRI-KPDC-00000676_1.json index 8e51b36be3..34e1630623 100644 --- a/datasets/KOPRI-KPDC-00000676_1.json +++ b/datasets/KOPRI-KPDC-00000676_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000676_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bulk and core samples from four sites of tussock and inter-tussock areas were collected in August. 2016. In the active layer, soil pits were made and bulk samples were collected from the face of opened pits. After describing soil profiles in the active layer, soil cores were acquired by SIPRI corer. In most sampling points, about 2-m deep soil samples were collected.\nTo conduct laboratory soil incubation study", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000677_1.json b/datasets/KOPRI-KPDC-00000677_1.json index 6f1a2cc3af..fd90ad48a3 100644 --- a/datasets/KOPRI-KPDC-00000677_1.json +++ b/datasets/KOPRI-KPDC-00000677_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000677_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from late April to September 2016 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000678_1.json b/datasets/KOPRI-KPDC-00000678_1.json index 712b8dfe85..8c2c020b11 100644 --- a/datasets/KOPRI-KPDC-00000678_1.json +++ b/datasets/KOPRI-KPDC-00000678_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000678_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2015 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000680_1.json b/datasets/KOPRI-KPDC-00000680_1.json index 95eea413af..d1d80d5631 100644 --- a/datasets/KOPRI-KPDC-00000680_1.json +++ b/datasets/KOPRI-KPDC-00000680_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000680_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-frequency methane concentration was measured in 2016 at Cambridge Bay, Canada. Along with atmospheric turbulence data from 3-D sonic anemometer, methane flux was obtained at 30-minute interval.\nTo monitor and understand methane flux over Cambridge Bay region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000682_1.json b/datasets/KOPRI-KPDC-00000682_1.json index c55337f9bd..6701446388 100644 --- a/datasets/KOPRI-KPDC-00000682_1.json +++ b/datasets/KOPRI-KPDC-00000682_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000682_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2010 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000683_1.json b/datasets/KOPRI-KPDC-00000683_1.json index e0d5a1b14b..7bb746423e 100644 --- a/datasets/KOPRI-KPDC-00000683_1.json +++ b/datasets/KOPRI-KPDC-00000683_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000683_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2011 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000684_1.json b/datasets/KOPRI-KPDC-00000684_1.json index 3a4e96795c..7dde02017d 100644 --- a/datasets/KOPRI-KPDC-00000684_1.json +++ b/datasets/KOPRI-KPDC-00000684_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000684_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2012 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000685_1.json b/datasets/KOPRI-KPDC-00000685_1.json index e0830cd17c..df1633019b 100644 --- a/datasets/KOPRI-KPDC-00000685_1.json +++ b/datasets/KOPRI-KPDC-00000685_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000685_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2014 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000686_1.json b/datasets/KOPRI-KPDC-00000686_1.json index 3b18a5113b..c22f3f77a8 100644 --- a/datasets/KOPRI-KPDC-00000686_1.json +++ b/datasets/KOPRI-KPDC-00000686_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000686_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2015 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000687_1.json b/datasets/KOPRI-KPDC-00000687_1.json index ca5278e01d..3208760e71 100644 --- a/datasets/KOPRI-KPDC-00000687_1.json +++ b/datasets/KOPRI-KPDC-00000687_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000687_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2016 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000688_1.json b/datasets/KOPRI-KPDC-00000688_1.json index 8cf6e834e2..4da4c8095a 100644 --- a/datasets/KOPRI-KPDC-00000688_1.json +++ b/datasets/KOPRI-KPDC-00000688_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000688_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 efflux at 81 grids (9*9) of 5-m interval were measured manually using dark chamber during summertime in 2011 at Council, Alaska. Dark chamber blocks sunlight so that respiration from soil and vegetation can be measured. Data of whole 81 grids was obtained once a month from July to September.\nTo monitor and understand spatial variation of CO2 efflux at Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000689_1.json b/datasets/KOPRI-KPDC-00000689_1.json index 9dc5a5a54d..82473ede11 100644 --- a/datasets/KOPRI-KPDC-00000689_1.json +++ b/datasets/KOPRI-KPDC-00000689_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000689_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 efflux at 81 grids (9*9) of 5-m interval were measured manually using dark chamber during summertime in 2012 at Council, Alaska. Dark chamber blocks sunlight so that respiration from soil and vegetation can be measured. Data of whole 81 grids was obtained once a month from July to September.\nTo monitor and understand spatial variation of CO2 efflux at Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000690_1.json b/datasets/KOPRI-KPDC-00000690_1.json index 4a8b4b2ce1..ebdd518a2d 100644 --- a/datasets/KOPRI-KPDC-00000690_1.json +++ b/datasets/KOPRI-KPDC-00000690_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000690_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 efflux at 81 grids (9*9) of 5-m interval were measured manually using dark chamber during summertime in 2013 at Council, Alaska. Dark chamber blocks sunlight so that respiration from soil and vegetation can be measured. Data of whole 81 grids was obtained once a month from July to September.\nTo monitor and understand spatial variation of CO2 efflux at Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000691_1.json b/datasets/KOPRI-KPDC-00000691_1.json index ed69ed3ab2..3960ae6c37 100644 --- a/datasets/KOPRI-KPDC-00000691_1.json +++ b/datasets/KOPRI-KPDC-00000691_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000691_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 efflux at 81 grids (9*9) of 5-m interval were measured manually using dark chamber during summertime in 2014 at Council, Alaska. Dark chamber blocks sunlight so that respiration from soil and vegetation can be measured. Data of whole 81 grids was obtained once a month from July to September.\nTo monitor and understand spatial variation of CO2 efflux at Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000692_1.json b/datasets/KOPRI-KPDC-00000692_1.json index 8a9f56000a..491a847d19 100644 --- a/datasets/KOPRI-KPDC-00000692_1.json +++ b/datasets/KOPRI-KPDC-00000692_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000692_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The research area involves the deglaciated region in Vestre Lovenbreen and Midtre Lovenbreen which are located in Kongsfijorden in Svalvard, Norway. We collected our soil samples along a transect in each glacier. We are analyzing soil properties and microorganism community structure.\nDue to the climate change, glaciers have been retreated, and soil underneath glacier is getting exposed greatly. These changes affect the ecosystems of these region. Although many studies have been done in the field of vegetation succession along the glacier chronosequence, little is known about microbial community structure and characteristics of soil carbon in glacier forelands. This research mainly focused on how glacier retreat influences the community structure of microorganism and soil organic carbon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000693_1.json b/datasets/KOPRI-KPDC-00000693_1.json index 1c2ea85811..84098fc2f3 100644 --- a/datasets/KOPRI-KPDC-00000693_1.json +++ b/datasets/KOPRI-KPDC-00000693_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000693_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2011, we collected 70 soil samples with 25-m intervals between sampling\r\npoints from 0\u201310 cm to 10\u201320 cm depths in Council, Alaska and bacterial 16S pyroseuqencing was performed.\nTo investigate the extent to which soil properties structure bacterial communities in subarctic tundra in Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000694_2.json b/datasets/KOPRI-KPDC-00000694_2.json index 4911e09656..4095360754 100644 --- a/datasets/KOPRI-KPDC-00000694_2.json +++ b/datasets/KOPRI-KPDC-00000694_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000694_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Cambridge bay station.\nMonitoring of Black Carbon concentration over Cambridge bay station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000695_1.json b/datasets/KOPRI-KPDC-00000695_1.json index b993d9bae4..9b920d33ff 100644 --- a/datasets/KOPRI-KPDC-00000695_1.json +++ b/datasets/KOPRI-KPDC-00000695_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000695_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000696_1.json b/datasets/KOPRI-KPDC-00000696_1.json index 0bad68ac3b..9be312b8b5 100644 --- a/datasets/KOPRI-KPDC-00000696_1.json +++ b/datasets/KOPRI-KPDC-00000696_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000696_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Psychrophilic bacteria are considered as source of cold-active enzymes that can be used in industrial applications. The Arctic bacterium Colwellia hornerae PAMC20917 strain has been isolated from offshore sediment near Ny-\u00c5lesund, Svalbard. The optimal growth temperature of the strain was 10\u00b0C on marine agar. The complete genome of PAMC20917 may be a good source of cold enzymes and provide basic information for the wider exploitation of cold-active and thermolabile industrial enzymes.\nThe complete genome of PAMC20917 may be a good source of cold enzymes and provide basic information for the wider exploitation of cold-active and thermolabile industrial enzymes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000697_1.json b/datasets/KOPRI-KPDC-00000697_1.json index 613fa2c8ca..5f7aed20a7 100644 --- a/datasets/KOPRI-KPDC-00000697_1.json +++ b/datasets/KOPRI-KPDC-00000697_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000697_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed under Cassiope from climate manipulation plots in 2011.\nTo understand changes in soil properties in response to climate change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000698_1.json b/datasets/KOPRI-KPDC-00000698_1.json index 9e121f52cb..8af9824c52 100644 --- a/datasets/KOPRI-KPDC-00000698_1.json +++ b/datasets/KOPRI-KPDC-00000698_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000698_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed under Salix from climate manipulation plots in 2012.\nTo understand changes in soil properties in response to climate change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000699_1.json b/datasets/KOPRI-KPDC-00000699_1.json index d4b0788a5d..2156fcc732 100644 --- a/datasets/KOPRI-KPDC-00000699_1.json +++ b/datasets/KOPRI-KPDC-00000699_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000699_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed in the glacier foreland of Midtre Lovenbreen in 2011\nTo understand soil development processes after glacier retreat", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000700_1.json b/datasets/KOPRI-KPDC-00000700_1.json index 9e055d8847..6311b2b7c3 100644 --- a/datasets/KOPRI-KPDC-00000700_1.json +++ b/datasets/KOPRI-KPDC-00000700_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000700_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed in the glacier foreland of Austre Lovenbreen in 2012\nTo understand soil development processes after glacier retreat", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000701_1.json b/datasets/KOPRI-KPDC-00000701_1.json index 0e1f4650d2..52d1e56dc9 100644 --- a/datasets/KOPRI-KPDC-00000701_1.json +++ b/datasets/KOPRI-KPDC-00000701_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000701_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface soils from 36 points in Council, AK were collected in 2011. Sampling depths were 0-10 and 10-20 cm. Physical and chemical properties of soil were analyzed.\nTo understand the relationship between microbial community structure and soil physical and chemical properties.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000702_1.json b/datasets/KOPRI-KPDC-00000702_1.json index 11ee3744bd..7ee021e3ed 100644 --- a/datasets/KOPRI-KPDC-00000702_1.json +++ b/datasets/KOPRI-KPDC-00000702_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000702_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed from three sampling points based on results of Geophysical research in 2011.\nTo investigate the relationship between microbial community structure and soil physical and chemical properties.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000703_1.json b/datasets/KOPRI-KPDC-00000703_1.json index fac014f126..3a9eedfb6d 100644 --- a/datasets/KOPRI-KPDC-00000703_1.json +++ b/datasets/KOPRI-KPDC-00000703_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000703_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil physical and chemical properties were analyzed from nine core samples collected in 2014.\nTo investigate the relationship between microbial community structure and soil physical and chemical properties.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000704_1.json b/datasets/KOPRI-KPDC-00000704_1.json index dbb2fffa83..704144e860 100644 --- a/datasets/KOPRI-KPDC-00000704_1.json +++ b/datasets/KOPRI-KPDC-00000704_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000704_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical and chemical properties of soil which were collected in 2012 (before climate manipulation) were analyzed. Soils from 0-5 and 5-10 cm depths were sampled.\nTo monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000705_1.json b/datasets/KOPRI-KPDC-00000705_1.json index 3487220638..699cfaaeb3 100644 --- a/datasets/KOPRI-KPDC-00000705_1.json +++ b/datasets/KOPRI-KPDC-00000705_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000705_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical and chemical properties of soil which were collected in 2013 after one year of climate manipulation were analyzed. Soils from 0-5 and 5-10 cm depths were sampled.\nTo monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000706_1.json b/datasets/KOPRI-KPDC-00000706_1.json index 87f0b2ae57..a049daec31 100644 --- a/datasets/KOPRI-KPDC-00000706_1.json +++ b/datasets/KOPRI-KPDC-00000706_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000706_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical and chemical properties of soil which were collected in 2015 after three years of climate manipulation were analyzed. Soils from organic and mineral layers were sampled.\nTo monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000707_3.json b/datasets/KOPRI-KPDC-00000707_3.json index bd1944e3b5..23ee764b42 100644 --- a/datasets/KOPRI-KPDC-00000707_3.json +++ b/datasets/KOPRI-KPDC-00000707_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000707_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3D floorplan for CAD of Jang Bogo Station\nTo use for Numerical Weather Prediction Model", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000708_1.json b/datasets/KOPRI-KPDC-00000708_1.json index ba1740fcc8..aaa9b10779 100644 --- a/datasets/KOPRI-KPDC-00000708_1.json +++ b/datasets/KOPRI-KPDC-00000708_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000708_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PaMBF1c (Multiprotein-bridging factor 1c-like) gene considered as an abiotic stimulus related genes from an Antarctic moss Polytrichastrum alpinum\nInvestigation of molecular adaptation mechanism of the Antarcic moss to Antarctic environment", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000709_1.json b/datasets/KOPRI-KPDC-00000709_1.json index cc652d6b65..243bdc49e4 100644 --- a/datasets/KOPRI-KPDC-00000709_1.json +++ b/datasets/KOPRI-KPDC-00000709_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000709_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS, camera, and weather (air temperature, humidity, pressure, wind speed, wind direction) measurements from the AMIGOS systems in the Drygalski Ice Tongue\nMonitoring the movement and environmental change of Drygalski Ice Tongue", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000710_1.json b/datasets/KOPRI-KPDC-00000710_1.json index 331f2c81a7..e0d01d4535 100644 --- a/datasets/KOPRI-KPDC-00000710_1.json +++ b/datasets/KOPRI-KPDC-00000710_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000710_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We had very remarkable results from the CHAOS-1 (2003) and CHAOS-2 (2005) project; lots of gas flares in the water column, many gas venting structures on the seafloor, gas hydrate samples including massive gas hydrate chunk (about 45 cm thick) near the seafloor, and gas hydrate-related structures in deep sub-bottom depth. These results encourage us to continue and expend the CHAOS project.\r\nSince the previous expedition focused on the relatively small area where gas hydrate-related phenomena has been known to be active, the basic aim of the CHAOS-III expedition is to improve understanding on gas hydrate-related phenomena in the Sea of Okhotsk in terms of multidisciplinary areas including geology, chemistry, oceanology and biology.\n1. Detection of new gas hydrate-related structures including gas flares and gas venting structures.\r\n2. Definition of the boundaries of the gas hydrate province\r\n3. Mapping of the seafloor expressions related with gas hydrates and gas seepages using side-scan sonar.\r\n4. Recognition of size, shape, and morphology of gas seepages on the seafloor.\r\n5. High-resolution seismic investigation to examine inner structures and the gas hydrate stability condition in gas hydrate-baring sediments in detail.\r\n6. Detection of gas flares in the water column emitted from gas seepages.\r\n7. Study on hydrated water and dissociated gas sample\r\n8. Chemistry of gas, gas hydrate, hydrate-forming fluids and carbonates including isotopic analysis.\r\n9. Determination of methane concentration in the water column.\r\n10. Underway survey to understand distribution of methane and dioxide in surface water and its controlling factor.\r\n11. Detailed investigation of marine sedimentological environment in the gas hydrate area \r\n12. Mechanism of formation-dissociation for gas-hydrates.\r\n13. Interrelation of methane fluxes and mercury\r\n14. Organic geochemical information related to the origin and composition of sedimentary organic matter.\r\n15. Identification of biomarkers of microorganisms associated methane cycle.\r\n16. Understanding of the composition of microbial community in gas hydrate environment", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000711_1.json b/datasets/KOPRI-KPDC-00000711_1.json index 968c57dbce..128a0f4658 100644 --- a/datasets/KOPRI-KPDC-00000711_1.json +++ b/datasets/KOPRI-KPDC-00000711_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000711_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "10 meters ice core which was drilled in Styx glacier around Jang Bogo Station from 2nd Jan to 7th Jan in 2012\nTo study the weather change and understand the interaction between the inland and the sea around Jang Bogo Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000712_1.json b/datasets/KOPRI-KPDC-00000712_1.json index 8dc3e34fc8..d91653c4a8 100644 --- a/datasets/KOPRI-KPDC-00000712_1.json +++ b/datasets/KOPRI-KPDC-00000712_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000712_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 40 m ice core was drilled on the Tsambagarav glacier in the Mongolian Altai in 2008. After pretreatment of the ice core at the KOPRI lab including ice melting, the ice melt samples were analyzed for water isotopes (\u03b418O and \u03b4D) using Wavelength-Scanned Cavity Ring Down Spectroscopy (Picarro, Canada).\nStable water isotope composition is used as fundamental data for depth-to-age conversion in an ice core. It can also provide valuable information on water vapor source for the sampling site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000713_1.json b/datasets/KOPRI-KPDC-00000713_1.json index bd44331a45..ca3d5a6f33 100644 --- a/datasets/KOPRI-KPDC-00000713_1.json +++ b/datasets/KOPRI-KPDC-00000713_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000713_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 40 m ice core was drilled on the Tsambagarav glacier in the Mongolian Altai in 2008. After pretreatment of the ice core at the KOPRI lab including ice melting, the melted ice core samples were analyzed for major ion (cation and anion) composition using an DIONEX ion chromatograph.\nVariation in the major ion composition with depth in the ice core can indicate environmental and/or meteorological change with time governing the production, transport and deposition of airborne aerosols. Therefore, the data is useful for reconstructing the regional change through time.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000714_2.json b/datasets/KOPRI-KPDC-00000714_2.json index de84858e91..a69463f474 100644 --- a/datasets/KOPRI-KPDC-00000714_2.json +++ b/datasets/KOPRI-KPDC-00000714_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000714_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the circumpolar deep water (CDW) and associated rapid melting of glaciers in the Amundsen Shelf, three institutes (KOPRI, UGOT, and RU) from Korea, Sweden, and US have launched an international collaboration program. During the 2014 Amundsen Sea cruise (ANA04B) by IBRV Araon, a total of 35 CTD stations were visited on the shelf troughs and near the ice shelf front as well as the polynya.\nThe overall purpose in the field of physical oceanography are: (1) identify the temporal and spatial variation of CDW in Amundsen; (2) estimation of heat transport by CDW intrusion to understand the effect of CDW on the melting of ice shelves.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000715_1.json b/datasets/KOPRI-KPDC-00000715_1.json index fe80ca53d3..af618513e5 100644 --- a/datasets/KOPRI-KPDC-00000715_1.json +++ b/datasets/KOPRI-KPDC-00000715_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000715_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the circumpolar deep water (CDW) and associated rapid melting of glaciers in the Amundsen Shelf, three institutes (KOPRI, UGOT, and RU) from Korea, Sweden, and US have launched an international collaboration program. During the 2014 Amundsen Sea cruise (ANA04B) by IBRV Araon, a total of 35 CTD stations were visited on the shelf troughs and near the ice shelf front as well as the polynya. A lowered acoustic Doppler current profiler (LADCP, RDI, 300 kHz) was attached to the CTD frame to measure the full profile of current velocities.\nThe overall purpose in the field of physical oceanography are: (1) identify the temporal and spatial variation of CDW in Amundsen; (2) estimation of heat transport by CDW intrusion to understand the effect of CDW on the melting of ice shelves.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000716_1.json b/datasets/KOPRI-KPDC-00000716_1.json index b3dab42127..5123b334cc 100644 --- a/datasets/KOPRI-KPDC-00000716_1.json +++ b/datasets/KOPRI-KPDC-00000716_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000716_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2014 Amundsen Sea cruise (ANA04B) by IBRV Araon, a total of 35 CTD stations were visited, 3 moorings were successfully recovered on the shelf troughs and near the ice shelf front as well as the polynya.\nMonitor the circumpolar deep water (CDW) and associated rapid melting of glaciers in the Amundsen Shelf.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000717_1.json b/datasets/KOPRI-KPDC-00000717_1.json index 1e31c45e94..8979cc38e4 100644 --- a/datasets/KOPRI-KPDC-00000717_1.json +++ b/datasets/KOPRI-KPDC-00000717_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000717_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected sediment samples by using multi and box core. Two sub core was sliced into 1 cm interval and stored frozen.\nSediment sample will be used for geochemistry analysis of organic mattr", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000718_1.json b/datasets/KOPRI-KPDC-00000718_1.json index 05a3cad600..1d21f536a2 100644 --- a/datasets/KOPRI-KPDC-00000718_1.json +++ b/datasets/KOPRI-KPDC-00000718_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000718_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "According to dissolved inorganic carbon(DIC) sampling protocol, collect 500ml seawater sample and poison it with HgCl2. \n It keeps at room temperature. K-Polar Amundsen Sea water mass circulation research", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000719_1.json b/datasets/KOPRI-KPDC-00000719_1.json index e00591f3a2..eea7499c55 100644 --- a/datasets/KOPRI-KPDC-00000719_1.json +++ b/datasets/KOPRI-KPDC-00000719_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000719_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "According to dissolved organic carbon(DOC) sampling protocol, collect 1.000ml seawater sample and \nkeep it in a freezer for understanding organic carbon cycle.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000720_1.json b/datasets/KOPRI-KPDC-00000720_1.json index 5d73fbe941..8f5fd7c72e 100644 --- a/datasets/KOPRI-KPDC-00000720_1.json +++ b/datasets/KOPRI-KPDC-00000720_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000720_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogeochemical data of seawater and sediment", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000721_1.json b/datasets/KOPRI-KPDC-00000721_1.json index 3b3ddec597..c28e766756 100644 --- a/datasets/KOPRI-KPDC-00000721_1.json +++ b/datasets/KOPRI-KPDC-00000721_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000721_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Barton Peninsular collected in 2014. Locality, habitat information for 1286 lichen samples\nInvestigation to diversity, morphology, phylogeography and ecophysiology in lichen", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000722_1.json b/datasets/KOPRI-KPDC-00000722_1.json index 92431cd985..bc175fe0ca 100644 --- a/datasets/KOPRI-KPDC-00000722_1.json +++ b/datasets/KOPRI-KPDC-00000722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from Chile collected in 2014. Locality, habitat information for 165 lichen samples\nInvestigation to diversity, morphology and phylogeography in lichen", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000723_1.json b/datasets/KOPRI-KPDC-00000723_1.json index b0abee9ace..a2caf7a5a1 100644 --- a/datasets/KOPRI-KPDC-00000723_1.json +++ b/datasets/KOPRI-KPDC-00000723_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000723_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly air temperature data from Barton Peninsular collected in 2012\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000724_1.json b/datasets/KOPRI-KPDC-00000724_1.json index 49dd79479a..410f8e5fc9 100644 --- a/datasets/KOPRI-KPDC-00000724_1.json +++ b/datasets/KOPRI-KPDC-00000724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000725_1.json b/datasets/KOPRI-KPDC-00000725_1.json index 8d0453a8e3..7b51839938 100644 --- a/datasets/KOPRI-KPDC-00000725_1.json +++ b/datasets/KOPRI-KPDC-00000725_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000725_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 3 m snow pit was collected at GV7 (Antarctica) in the 2013-2014 summer season. Its water isotope composition (dD, d18O) was determined using cavity ringdown spectroscopy (PICARRO).\nTo detect annual (seasonal) layering of snowpack.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000726_1.json b/datasets/KOPRI-KPDC-00000726_1.json index bc03a3e0da..434b1eebf7 100644 --- a/datasets/KOPRI-KPDC-00000726_1.json +++ b/datasets/KOPRI-KPDC-00000726_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000726_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We obtained ice cores after participating the North Greenland Eemian Ice Drilling program.\nWe reconstruct the high-resolution ice record of a shift of mineral dust sources in response to climate transition between the Last Glacial Maximum(~25,000 yr BP) and Holocene(8,000 yr BP) by analyzing trace elements including rare earth elements from a Greenland NEEM ice core.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000727_1.json b/datasets/KOPRI-KPDC-00000727_1.json index b17232af94..8dd4da75e4 100644 --- a/datasets/KOPRI-KPDC-00000727_1.json +++ b/datasets/KOPRI-KPDC-00000727_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000727_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARA05C BC", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000728_2.json b/datasets/KOPRI-KPDC-00000728_2.json index 26a694310b..d37a53ef69 100644 --- a/datasets/KOPRI-KPDC-00000728_2.json +++ b/datasets/KOPRI-KPDC-00000728_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000728_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The major ion species were analyzed in the surface snow with depth of 3 m sampled at GV7 site of George V land of East Antarctica.\r\nThe ion species were anlyzed in order to estimate the relationship between age and depth on surface snow sample and reconstrcut the snow chemistry in the past", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000729_2.json b/datasets/KOPRI-KPDC-00000729_2.json index 76b149a187..7064c06b10 100644 --- a/datasets/KOPRI-KPDC-00000729_2.json +++ b/datasets/KOPRI-KPDC-00000729_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000729_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The visual stratigrapgy was investigated from GV7 shallow ice core sampled at Gerorge V land 7 site with depth of roughly 80m.\r\nThe visual stratigraphy (VS) is the most basic information to reconstruct proxy record such as the impurities an ice core science", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000730_1.json b/datasets/KOPRI-KPDC-00000730_1.json index 83ad99d61d..c982dab2b0 100644 --- a/datasets/KOPRI-KPDC-00000730_1.json +++ b/datasets/KOPRI-KPDC-00000730_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000730_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polarstern 87", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000731_1.json b/datasets/KOPRI-KPDC-00000731_1.json index d321d5f414..5efc0895a7 100644 --- a/datasets/KOPRI-KPDC-00000731_1.json +++ b/datasets/KOPRI-KPDC-00000731_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000731_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARA02B BC", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000732_1.json b/datasets/KOPRI-KPDC-00000732_1.json index dead06154f..22a61dc49d 100644 --- a/datasets/KOPRI-KPDC-00000732_1.json +++ b/datasets/KOPRI-KPDC-00000732_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000732_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the Quaternary igneous rocks from Sverrefjell, NW Spitsbergen. It includes volcanic lava and mantle xenolith rock samples. It is about 100 kg in total weight.\nThese samples are to understand the origin and evolution of the continental mantle beneath Spitsbergen (formerly a part of northern Laurentia) and globally. The rock samples will provide whole rock and mineral geochemical data as well as isotope data. This work will help to establish radiogenic isotope signatures of the peridotite mantle beneath Spitsbergen.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000733_1.json b/datasets/KOPRI-KPDC-00000733_1.json index cc3eb8dfff..e7085112cc 100644 --- a/datasets/KOPRI-KPDC-00000733_1.json +++ b/datasets/KOPRI-KPDC-00000733_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000733_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring chromaticity, section of core was measured by using spectrophotometa. Measure the chromaticity of the core and use it to understand the sedimentary environment", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000734_1.json b/datasets/KOPRI-KPDC-00000734_1.json index 0fa9edb800..4ad7f6c382 100644 --- a/datasets/KOPRI-KPDC-00000734_1.json +++ b/datasets/KOPRI-KPDC-00000734_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000734_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collect column sediments using acrylic plates 1cm long and 30cm deep, work to see clearly sedimentary environment of \nX-rayed data core.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000735_1.json b/datasets/KOPRI-KPDC-00000735_1.json index 5d05f048d1..43ce3faaf2 100644 --- a/datasets/KOPRI-KPDC-00000735_1.json +++ b/datasets/KOPRI-KPDC-00000735_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000735_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARA05C Cruise report \r\n\r\n\r\nSummary\r\n\r\nY. K. Jin, R. Gwiazda, and S. Dallimore\r\n\r\n\r\nResearch activities conducted and preliminary findings\r\n\r\nThe Expedition ARA05C was a highly multidisciplinary undertaking in the Beaufort Sea, carried out in an international collaboration between the Korea Polar Research Institute (KOPRI Korea), the Geological Survey of Canada (GSC), the Monterey Bay Aquarium Research Institute (MBARI, USA), the Department of Fisheries and Ocean (DFO, Canada) and Bremen University (BARUM, Germany). During the ARA05C expedition in the Beaufort Sea (Figures S1 and S2), on the IBRV Araon from August 30 to September 19 2014, multiple research activities were undertaken to study geological processes related to the degrading permafrost, fluid flow and degassing and associated geohazards, the seismostratigraphy of the Beaufort shelf and slope region, as well as physical and chemical oceanography studies of the Arctic Ocean, coupled with continuous atmospheric monitoring studies. The expedition focused on two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014, and the Mackenzie Trough area from September 11 to September 15, 2014.\r\n \r\nFigure S1. Overview map of the ship track and stations of expedition ARA05C. The expedition was split into two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014 and the Mackenzie Trough area from September 11 to September 15, 2014.\r\n \r\nFigure S2. Details of the ship track and stations for Expedition ARA05C \r\n\r\nFigure S3. Map showing seismic survey lines. \r\n\r\nMulti-channel seismic data were collected in support of drilling proposals, in particular IODP pre-proposal #806 (Dallimore et al., 2012) and #753 (O\u2019Regan, 2010), and to verify the distribution and internal structures of offshore permafrost occurrences (Figure S3). The multi-channel seismic data were acquired on the outer continental shelf and slope of the Canadian Beaufort Sea, totaling 20 lines with ~1,000 line-kilometers and ~20,000 shot gathers from September 1 to September 13, 2014 (see Chapter 3 for more details). The multichannel seismic data will be processed post-expedition at KOPRI and at GSC. The seismic and OBS data obtained in the 2013 and 2014 Araon cruises will allow us: 1) to investigate the permafrost signature in the shelf area through detailed velocity analyses, and identify and map zones of high-velocity sediments which would be indicative of the presence of ice along the four seismic main lines crossing the OBS stations, and 2) to conduct detailed analysis of the deep structures of the mud volcanoes (fluid expulsion structures) in the slope area. \r\nContinuous sub-bottom profiler (SBP) and multibeam data were collected along all ship tracks for detailed subsurface imaging of sediment structures and permafrost, as well as for core-site location verification (see Chapter 5 and 6 for more details). During expedition ARA05C, more than 3,000 line-kilometers of SBP data were collected, co-located with multibeam and backscatter data. These data are an essential part of the study of the sub-seafloor permafrost distribution, and they will provide further insights into sediment dynamics in areas underlaid by permafrost, and at critical boundaries, especially at the shelf edge region. In the shelf, the occurrence of mounds and pingo-like features (PLFs) result in a characteristically rugged landscape with lots of mounds, knolls and PLFs piercing through otherwise laminated sediments. Multibeam and backscatter data were collected along all ship tracks, adding to the database of existing information gathered through previous expeditions in the study region.\r\nHeat flow measurements were undertaken at a total of 5 stations and thermal conductivity measurements were also carried out in 5 gravity cores to study the distribution of sub-seafloor permafrost and the thermal structure of fluid expulsion features, as well as the heat flow regime of slope background areas (see Chapter 7 for details). A very important finding was the observation that seafloor temperatures at the mud volcano in 740 m water depth are much higher than those measured in all other stations.\r\nGeological sampling using gravity coring and box-coring was performed at strategic sites supporting ongoing international research linked to IODP pre-proposals #753 (O'Regan et al., 2010) and #806 (Dallimore et al., 2012), and at sites of regional interest to define key seismo-stratigraphic horizons critical for the understanding of geohazards in the region (see Chapter 8 for details). In total, 10 gravity cores at 9 sites and 22 box-cores were taken (Figure S1). Most sediment analyses on the recovered cores will be performed post- expedition at various labs in KOPRI, MBARI, and in laboratories of other University-based collaborators in Korea. Onboard, sub-samples were taken from all gravity cores. On selected cores from the Canadian Beaufort study region pore-waters were extracted using rhizones, after logging of physical properties. These samples will be analyzed post-expedition by research collaborators at MBARI.\r\nThe coring program undertaken augments and complements the database of gravity, piston and vibra-cores collected by the CCGS S.W. Laurier and the IBRV Araon in previous years expeditions in the Beaufort Sea. One of the highlights of this expedition was the first documentation and collection of gas-hydrates from the mud volcano at 740 m water depth. Another important finding was the first documented presence of freshwater ice in the Cyan unit. This unit underlies most of the upper seismo-stratigraphic units of Holocene and late-Glacial age in the Beaufort Sea shelf and slope in the eastern margin of the Mackenzie Trough. In sub-bottom profiles it displays a plastic behavior, with upwards flowing structures that pierce through the overlying units, but reach the seafloor only on a few limited locations. The successful targeting via gravity coring of the small exposure of this unit and the collection of sediments and ice from it was only possible due to the dynamic positioning capabilities of the Araon, which allows it to position the gravity corer within a few meters of the desired target. \r\nWater sampling and Conductivity-Temperature-Depth (CTD) profiling was undertaken at most core sites to study the physical and chemical properties of seawater (Figure S1). These station-measurements were complemented by continuous water-properties and atmospheric measurements when the Araon was underway. Seawater samples will be analyzed for DIC/TA, nutrients, DOC, and POC post- expedition at KOPRI. Accurate measurements of the pH of seawater, and the underway continuous stream of measurements of seawater and atmospheric pCO2, CH4, and N2O, required a variety of seawater/air physical properties to be considered in the calculation. Methane was also measured with a methane sensor attached to the CTD tool and at the mud volcanoes in 290 m, 420 m, and 740 m water depths. The methane plumes emanating from these volcanoes were also acoustically imaged with the echo sounder systems onboard the IBRV Araon. Further details on the water sampling and atmospheric measurements are given in Chapter 10 and 11.\r\n\r\nReferences\r\n\r\nDallimore, S.R., Paull, C.K., Collett, T.S., Jin, Y.K., Mienert, J., Mangelsdorf, K., Riedel, M., 2012. Drilling to investigate methane release and geologic processes associated with warming permafrost and gas hydrate deposits beneath the Beaufort Sea Shelf. IODP Pre-Proposal 806, available online at http://iodp.org/\r\nO\u2019Regan, M., de Vernal, A., Hill, P., Hillaire-Marcel, C., Jakobsson, M., Moran, K., Rochon, A., St-Onge, G., 2010. Late quaternary paleoceanography and glacial dynamics in the Beaufort Sea, IODP pre-proposal #753, available online at http://iodp.org/.\nDuring the Expedition ARA05C, multiple research activities were undertaken to study geological processes related to the degrading permafrost, fluid flow and degassing and associated geohazards, the seismostratigraphy of the Beaufort shelf and slope region, as well as physical and chemical oceanography studies of the Arctic Ocean, coupled with continuous atmospheric monitoring studies. The expedition focused on two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014, and the Mackenzie Trough area from September 11 to September 15, 2014.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000736_1.json b/datasets/KOPRI-KPDC-00000736_1.json index 9820577c61..8c2b9d63b8 100644 --- a/datasets/KOPRI-KPDC-00000736_1.json +++ b/datasets/KOPRI-KPDC-00000736_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000736_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2014 Amundsen Sea cruise, Community respiration were measured decreasing oxygen concentration by time.\nThe objective of this study is to investigate the fate of organic carbon produced by primary producer in the water column.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000737_1.json b/datasets/KOPRI-KPDC-00000737_1.json index 22ff38bf73..f33a860033 100644 --- a/datasets/KOPRI-KPDC-00000737_1.json +++ b/datasets/KOPRI-KPDC-00000737_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000737_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000738_1.json b/datasets/KOPRI-KPDC-00000738_1.json index 15a7424cdb..ac864f875c 100644 --- a/datasets/KOPRI-KPDC-00000738_1.json +++ b/datasets/KOPRI-KPDC-00000738_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000738_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2013 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000739_1.json b/datasets/KOPRI-KPDC-00000739_1.json index f4fa5a5c2a..3bf6b856b4 100644 --- a/datasets/KOPRI-KPDC-00000739_1.json +++ b/datasets/KOPRI-KPDC-00000739_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000739_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2014 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000740_1.json b/datasets/KOPRI-KPDC-00000740_1.json index be14db0a83..fd04264986 100644 --- a/datasets/KOPRI-KPDC-00000740_1.json +++ b/datasets/KOPRI-KPDC-00000740_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000740_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2015 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000741_1.json b/datasets/KOPRI-KPDC-00000741_1.json index 98f93632bd..c5bfaa3883 100644 --- a/datasets/KOPRI-KPDC-00000741_1.json +++ b/datasets/KOPRI-KPDC-00000741_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000741_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2012 at Cambridge Bay site, Canada. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at Cambridge Bay Site", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000742_1.json b/datasets/KOPRI-KPDC-00000742_1.json index bc73f24516..86efc9ec60 100644 --- a/datasets/KOPRI-KPDC-00000742_1.json +++ b/datasets/KOPRI-KPDC-00000742_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000742_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2013 at Cambridge Bay site, Canada. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at Cambridge Bay Site", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000743_1.json b/datasets/KOPRI-KPDC-00000743_1.json index 93a3a112df..0666024024 100644 --- a/datasets/KOPRI-KPDC-00000743_1.json +++ b/datasets/KOPRI-KPDC-00000743_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000743_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2014 at Cambridge Bay site, Canada. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at Cambridge Bay Site", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000744_1.json b/datasets/KOPRI-KPDC-00000744_1.json index 60f5f28359..12c2984174 100644 --- a/datasets/KOPRI-KPDC-00000744_1.json +++ b/datasets/KOPRI-KPDC-00000744_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000744_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2015 at Cambridge Bay site, Canada. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at Cambridge Bay Site", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000745_1.json b/datasets/KOPRI-KPDC-00000745_1.json index 759c848db3..7e84c3662f 100644 --- a/datasets/KOPRI-KPDC-00000745_1.json +++ b/datasets/KOPRI-KPDC-00000745_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000745_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the rock samples of Northern Victoria Land (NVL), Antarctica collected in 2015-16 austral summer season. The collection includes sedimentary rocks (sandstone, limestone, conglomerate, and so on) of the Lower Paleozoic Bowers and Beacon supergroups, metamorphic rocks of the Wilson Terrane, and volcanic rocks of the McMurdo Volcanics. Many of the rock samples this season, especially, are from the Helliwell Hills camp.\nThe samples were collected in order to understand the lithologic characters of basement rocks underneath the glaciers. Information on stratigraphy, metamorphism, and volcanism will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000746_1.json b/datasets/KOPRI-KPDC-00000746_1.json index ed0dca2827..c811f77429 100644 --- a/datasets/KOPRI-KPDC-00000746_1.json +++ b/datasets/KOPRI-KPDC-00000746_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000746_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry is for the fossil specimens of Northern Victoria Land (NVL), Antarctica collected in 2015-16 austral summer season. The collection includes trilobites of the Lower Paleozoic Bowers Supergroup and plant fossils of the Beacon Supergroup. Many of the samples this season are from the vicinity of the Helliwell Hills camp.\nInformation from the fossils will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000747_2.json b/datasets/KOPRI-KPDC-00000747_2.json index a30618bb93..db34036d53 100644 --- a/datasets/KOPRI-KPDC-00000747_2.json +++ b/datasets/KOPRI-KPDC-00000747_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000747_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round or short-term records of remotely operating GPS system\nInvestigation to the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000748_2.json b/datasets/KOPRI-KPDC-00000748_2.json index 8975176f77..b13aee9afd 100644 --- a/datasets/KOPRI-KPDC-00000748_2.json +++ b/datasets/KOPRI-KPDC-00000748_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000748_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round records of remotely operating weather sensor and digital camera\nInvestigation to the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000749_2.json b/datasets/KOPRI-KPDC-00000749_2.json index 04d4bc3059..7e97f4bb7d 100644 --- a/datasets/KOPRI-KPDC-00000749_2.json +++ b/datasets/KOPRI-KPDC-00000749_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000749_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed to the North of the Drygalski Ice Tongue on 14 December 2015 as a part of the ANA06A research cruise, and it was recovered on 8 Feburary 2017\nTo monitor physical properties(Temperature, Salinity, Current) of ocean water in the north of the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000750_3.json b/datasets/KOPRI-KPDC-00000750_3.json index a7b9f601dd..a814991ccb 100644 --- a/datasets/KOPRI-KPDC-00000750_3.json +++ b/datasets/KOPRI-KPDC-00000750_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000750_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NOAA, three Autonomous Underwater Hydrophones(AUH) were deployed in Southern Terra Nova Bay on 10 December 2015 as a part of the ANA06A research cruise to monitor icequakes, tectonic activities, and ocean ambient noise. The three AUHs were recovered on 9 February 2017", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000751_1.json b/datasets/KOPRI-KPDC-00000751_1.json index e41ec6d512..197b911123 100644 --- a/datasets/KOPRI-KPDC-00000751_1.json +++ b/datasets/KOPRI-KPDC-00000751_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000751_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 and CH4 had been measured during summertime in 2016 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000752_1.json b/datasets/KOPRI-KPDC-00000752_1.json index 710ca05f14..4df81bdc44 100644 --- a/datasets/KOPRI-KPDC-00000752_1.json +++ b/datasets/KOPRI-KPDC-00000752_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000752_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A vegetation index NDVI was measured during growing season at the Council site, 70-miles northeast from the Nome, Alaska.\r\nThe sensor was developed by Seoul National University (Prof. Young-Ryul Ryu) and provided for in-situ installation.\r\nThe sensor is composed of one pair of upward/downward looking LEDs to obtain reflectivity in each bandwidth.\nTo monitor high-temporal variation of vegetaion activity at permafrost region, west Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000753_1.json b/datasets/KOPRI-KPDC-00000753_1.json index ea7da5ade9..2bc0ae9803 100644 --- a/datasets/KOPRI-KPDC-00000753_1.json +++ b/datasets/KOPRI-KPDC-00000753_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000753_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the weather data at the Council site, 70-mile northeast from Nome, Alaska in 2015.\r\nA research team of Univ. of Alaska, Fairbanks is operating an AWS(automatic weather station) at the the site since 1999.\r\nAir temperature and humidity measured at 1 m and 3m above ground level.\r\nWind speed and direction was measured at 3m agl.\r\nPrecipitation is also being measured.\r\nThe data was obtained from Mr. Bob Busey of UAF.\nTo understand hydrological characteristics at discontinous permafrost region in western Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000754_1.json b/datasets/KOPRI-KPDC-00000754_1.json index f8296c8cbf..c46f0ae8b4 100644 --- a/datasets/KOPRI-KPDC-00000754_1.json +++ b/datasets/KOPRI-KPDC-00000754_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000754_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2017 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTurbulent flux measurements are used to better understand 1) the air-ocean-sea ice energy exchanges and 2) water and carbon dioxide gases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000755_1.json b/datasets/KOPRI-KPDC-00000755_1.json index b760878f0e..c5bd5ce734 100644 --- a/datasets/KOPRI-KPDC-00000755_1.json +++ b/datasets/KOPRI-KPDC-00000755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on horizontal global radiation and its analysis of data measured at the King Sejong Station in the Antarctic, 2017\nTrend analysis and measurement of horizontal global radiation at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000756_1.json b/datasets/KOPRI-KPDC-00000756_1.json index 8e56a470b7..786b8b995d 100644 --- a/datasets/KOPRI-KPDC-00000756_1.json +++ b/datasets/KOPRI-KPDC-00000756_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000756_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2010/2011 Weddell Sea core ,Antarctica_ITRAX data\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000757_1.json b/datasets/KOPRI-KPDC-00000757_1.json index cf5bd160e7..daa208b318 100644 --- a/datasets/KOPRI-KPDC-00000757_1.json +++ b/datasets/KOPRI-KPDC-00000757_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000757_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Several soil physical and chemical properties (moisture content, bulk density, C and N content, etc.) were analyzed from soil samples acquired in tussock and inter-tussock areas in August. 2016.\nTo use for the basic information in the laboratory incubation study and to understand the site characteristics", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000758_1.json b/datasets/KOPRI-KPDC-00000758_1.json index f87749c264..c5d1d61a21 100644 --- a/datasets/KOPRI-KPDC-00000758_1.json +++ b/datasets/KOPRI-KPDC-00000758_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000758_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Isoaspartyl dipeptidase (IadA) is an enzyme that catalyzes the hydrolysis of an isoaspartyl dipeptide-like moiety, which can be inappropriately formed in proteins, between the \u03b2-carboxyl group side chain of Asp and the amino group of the following amino acid. Here, we have determined the structures of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea, both ligand-free and that complexed with \u03b2-isoaspartyl lysine, at 1.85-\u00c5 and 2.33-\u00c5 resolution, respectively. In both structures, CpsIadA formed an octamer with two Zn ions in the active site. A structural comparison with Escherichia coli isoaspartyl dipeptidase (EcoIadA) revealed a major difference in the structure of the active site. For metal ion coordination, CpsIadA has a Glu166 residue in the active site, whereas EcoIadA has a post-translationally carbamylated-lysine 162 residue. Site-directed mutagenesis studies confirmed that the Glu166 residue is critical for CpsIadA enzymatic activity. This residue substitution from lysine to glutamate induces the protrusion of the \u03b212-\u03b18 loop into the active site to compensate for the loss of length of the side chain. In addition, the \u03b13-\u03b29 loop of CpsIadA adopts a different conformation compared to EcoIadA, which induces a change in the structure of the substrate-binding pocket. Despite CpsIadA having a different active-site residue composition and substrate-binding pocket, there is only a slight difference in CpsIadA substrate specificity compared with EcoIadA. Comparative sequence analysis classified IadA-containing bacteria and archaea into two groups based on the active-site residue composition, with Type I IadAs having a glutamate residue and Type II IadAs having a carbamylated-lysine residue. CpsIadA has maximal activity at pH 8\u00b18.5 and 45\u00caC, and was completely inactivated at 60\u00caC. Despite being isolated from a psychrophilic bacteria, CpsIadA is thermostable probably owing to its octameric structure. This is the first conclusive description of the structure and properties of a Type I IadA.\nTo determine the structures of an isoaspartyl dipeptidase IadA from a psychrophilic bacterium Colwellia psychrerythraea strain 34H (CpsIadA) in both the ligand-free form and that complexed with \u03b2-isoaspartyl lysine", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000759_1.json b/datasets/KOPRI-KPDC-00000759_1.json index 0a847ac8e8..c4555750cc 100644 --- a/datasets/KOPRI-KPDC-00000759_1.json +++ b/datasets/KOPRI-KPDC-00000759_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000759_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A novel microbial esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7, was identified and characterized. To our knowledge, this is the first report describing structural analysis and biochemical characterization of an esterase isolated from the genus Exiguobacterium. Crystal structure of EaEST, determined at a resolution of 1.9 \u00c5, showed that the enzyme has a canonical \u03b1/\u03b2 hydrolase fold with an \u03b1-helical cap domain and a catalytic triad consisting of Ser96, Asp220, and His248. Interestingly, the active site of the structure of EaEST is occupied by a peracetate molecule, which is the product of perhydrolysis of acetate. This result suggests that EaEST may have perhydrolase activity. The activity assay showed that EaEST has significant perhydrolase and esterase activity with respect to short-chain p-nitrophenyl esters (\u2264C8), naphthyl derivatives, phenyl acetate, and glyceryl tributyrate. However, the S96A single mutant had low esterase and perhydrolase activity. Moreover, the L30A mutant showed low levels of protein expression and solubility as well as preference for different substrates. On conducting an enantioselectivity analysis using R- and S-methyl-3-hydroxy-2-methylpropionate, a preference for R-enantiomers was observed. Surprisingly, immobilized EaEST was found to not only retain 200% of its initial activity after incubation for 1 h at 80\u00b0C, but also retained more than 60% of its initial activity after 20 cycles of reutilization. This research will serve as basis for future engineering of this esterase for biotechnological and industrial applications.\nOur goal was to identify a novel cold-active esterase from a polar microorganism. We identified and characterized a novel esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7. Further structural and functional analysis indicated that EaEST had dual activity of a perhydrolase and an esterase. It is known that perhydrolysis is a side activity of esterases and it may be useful in industrial and organic synthesis. Moreover, the peracetate-bound EaEST structure reported in our study provides the first snapshot of the peracetate binding mode, and a comparison of the structure of EaEST with that of PfEST (PDB code 3HI4) reveals a comprehensive structural basis for the conformational changes of this enzyme induced by binding of different substrates.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000760_1.json b/datasets/KOPRI-KPDC-00000760_1.json index a1895ec84a..2a6eae11bf 100644 --- a/datasets/KOPRI-KPDC-00000760_1.json +++ b/datasets/KOPRI-KPDC-00000760_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000760_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "David glacier area ice surface / bed elevation\nice surface / bed elevation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000761_1.json b/datasets/KOPRI-KPDC-00000761_1.json index 0cfad2fe50..f2ba09022f 100644 --- a/datasets/KOPRI-KPDC-00000761_1.json +++ b/datasets/KOPRI-KPDC-00000761_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000761_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Identification of ciliate diversity from Korea and Antarctica (Barton Peninsular)\nComparison of both data to know the specific ciliate in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000762_1.json b/datasets/KOPRI-KPDC-00000762_1.json index 66173421f9..ba91639dad 100644 --- a/datasets/KOPRI-KPDC-00000762_1.json +++ b/datasets/KOPRI-KPDC-00000762_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000762_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first high resolution records of atmospherc trace metals for 1711~1969 were recovered from Greenland NEEM shallow ice core together with ions records. These records reveal increases in various atmospheric metals since the Industrial Revolution. Also, the comparion between these records and those from other Greenland ice cores represents regional differences in anthropogenic contributions.\nResearches for changes in atmospheric trace element over Greenland after the Industrial Revolution and contributions from natural/anthropogenic sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000763_1.json b/datasets/KOPRI-KPDC-00000763_1.json index 69dc35c4fa..f0d704804f 100644 --- a/datasets/KOPRI-KPDC-00000763_1.json +++ b/datasets/KOPRI-KPDC-00000763_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000763_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPS2 is termed as cell-protection substances 2 capable of protection of the cells and lowering freezing points below melting points. Antarctic freshwater green microalga, Chloromonas sp. was reported to produce and secrete CPS2.\nCPS2 genes will be utilized to protect the skin and tissue cells by applying any valuable products.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000764_1.json b/datasets/KOPRI-KPDC-00000764_1.json index 40a25365fb..a50972383e 100644 --- a/datasets/KOPRI-KPDC-00000764_1.json +++ b/datasets/KOPRI-KPDC-00000764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fatty acid content of polar microalgae and mesophilic microalga\nComparison and analysis of fatty acid content of both microalagae", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000765_2.json b/datasets/KOPRI-KPDC-00000765_2.json index 76d42ec0b1..61c68557e2 100644 --- a/datasets/KOPRI-KPDC-00000765_2.json +++ b/datasets/KOPRI-KPDC-00000765_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000765_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2017. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000766_1.json b/datasets/KOPRI-KPDC-00000766_1.json index 93338d0e34..80a4e11985 100644 --- a/datasets/KOPRI-KPDC-00000766_1.json +++ b/datasets/KOPRI-KPDC-00000766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000767_1.json b/datasets/KOPRI-KPDC-00000767_1.json index 6ab8cf66d5..2dd7983ded 100644 --- a/datasets/KOPRI-KPDC-00000767_1.json +++ b/datasets/KOPRI-KPDC-00000767_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000767_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016\nMicro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000768_1.json b/datasets/KOPRI-KPDC-00000768_1.json index 610cdd0b29..887aa6da70 100644 --- a/datasets/KOPRI-KPDC-00000768_1.json +++ b/datasets/KOPRI-KPDC-00000768_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000768_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of Rn gas at KSG, Antarctica\nInvestigation of air mass path moving to the KSG, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000769_1.json b/datasets/KOPRI-KPDC-00000769_1.json index 173953fb21..dec2743095 100644 --- a/datasets/KOPRI-KPDC-00000769_1.json +++ b/datasets/KOPRI-KPDC-00000769_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000769_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric wind climatology at 850 hPa from the preindustrial simulation, Last Glacial Maximum simulation, LGM-SST simulation, LGM-SEAICE simulation, and LGM-topography simulation.\nTo examine the responses of SH westerly winds to LGM boundary conditions using the state-of-the-art numerical model. To evaluate which boundary conditions are more important in the position and strength of SH westerly winds.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000770_1.json b/datasets/KOPRI-KPDC-00000770_1.json index 03a28af941..6b489c6d6f 100644 --- a/datasets/KOPRI-KPDC-00000770_1.json +++ b/datasets/KOPRI-KPDC-00000770_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000770_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter\nMonitoring of Aerosol Number Concentration (>10nm) from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000771_1.json b/datasets/KOPRI-KPDC-00000771_1.json index 964aaee571..c8ee99d6b1 100644 --- a/datasets/KOPRI-KPDC-00000771_1.json +++ b/datasets/KOPRI-KPDC-00000771_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000771_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Italian Seismic Line 2017, single channel seismic data, were collected during the 2016-2017 austral summer with the RV OGS Explora in the Ross Sea continental margin, Antarctica\nThe major purpose of this survey is to investigate stratigraphy and sedimentary structure of the Ross Sea continental margin, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000772_1.json b/datasets/KOPRI-KPDC-00000772_1.json index 2510859980..9e65b44f7d 100644 --- a/datasets/KOPRI-KPDC-00000772_1.json +++ b/datasets/KOPRI-KPDC-00000772_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000772_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Survey of marine benthic invertebrate biota by diving around King Sejong Station\nDiversity of marine benthic invertebrates", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000773_2.json b/datasets/KOPRI-KPDC-00000773_2.json index 3717e79b40..1e2282f622 100644 --- a/datasets/KOPRI-KPDC-00000773_2.json +++ b/datasets/KOPRI-KPDC-00000773_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000773_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Identification of ciliate diversity from Korea and Antarctica (Jang Bogo Station)\r\nComparison of both data to know the specific ciliate in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000774_1.json b/datasets/KOPRI-KPDC-00000774_1.json index 44c63492dc..fd659f7c41 100644 --- a/datasets/KOPRI-KPDC-00000774_1.json +++ b/datasets/KOPRI-KPDC-00000774_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000774_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-Channel seismic data were collected during the 2016-2017 ANA07C cruise in the Ross Sea, Antarctic Ocean\nThe major purpose of this survey is to investigate stratography and the structure of sediments across the Terror Rift, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000775_1.json b/datasets/KOPRI-KPDC-00000775_1.json index 9c8b71c316..06230c0005 100644 --- a/datasets/KOPRI-KPDC-00000775_1.json +++ b/datasets/KOPRI-KPDC-00000775_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000775_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016.\nMonitoring of aerosol size distribution from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000776_1.json b/datasets/KOPRI-KPDC-00000776_1.json index fd93723e92..f9fc4c4560 100644 --- a/datasets/KOPRI-KPDC-00000776_1.json +++ b/datasets/KOPRI-KPDC-00000776_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000776_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at BearPeninsula DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Antarctic region. Primary climate factors including solar radiation wind speed and direction, air temperature, pressure and relative humidity has been monitored using automatic weather monitoring system at Bear Peninsula. One hourly averaged data are stored at a data logger and an Argos Satellite transmitter is used to transmit daily data. The objectives of this monitoring are to record the past and current climate change through continuous operation of AWS, and to understand characteristics of meteorological phenomena at Bear Peninsula.\nMonitoring on meteorology at Bear Peninsula.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000777_2.json b/datasets/KOPRI-KPDC-00000777_2.json index 93ef25c27e..78a5317a0b 100644 --- a/datasets/KOPRI-KPDC-00000777_2.json +++ b/datasets/KOPRI-KPDC-00000777_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000777_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 600 kg of fossils were collected during 2016 expedition.\r\nThe Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000778_1.json b/datasets/KOPRI-KPDC-00000778_1.json index ee67a1b018..8ca84c44a2 100644 --- a/datasets/KOPRI-KPDC-00000778_1.json +++ b/datasets/KOPRI-KPDC-00000778_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000778_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GV7_S2_dust data\nMS4_GV7 S22 dust data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000779_1.json b/datasets/KOPRI-KPDC-00000779_1.json index 149816b130..7919342061 100644 --- a/datasets/KOPRI-KPDC-00000779_1.json +++ b/datasets/KOPRI-KPDC-00000779_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000779_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine algal flora, phylogenetic relationships and subtidal distribution has been investigated in Maxwell Bay, King George Island, Antarctica. Specimens of Chlorophyta, Chrysophyta, Phaeophyta and Rhodophyta.\nWe have tried to investigate marine algal diversity, phylogeny and biogeography in Maxwell Bay, King George Island, Antarctica and to evaluate Antarctic coastal marine ecosystem responses caused by climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000780_2.json b/datasets/KOPRI-KPDC-00000780_2.json index eacef18188..1ab78eb68c 100644 --- a/datasets/KOPRI-KPDC-00000780_2.json +++ b/datasets/KOPRI-KPDC-00000780_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000780_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Survey of marine benthic invertebrate biota by diving around Jang Bogo Station \r\nDiversity of marine benthic invertebrates", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000781_1.json b/datasets/KOPRI-KPDC-00000781_1.json index ace8852d90..ca6f4580b7 100644 --- a/datasets/KOPRI-KPDC-00000781_1.json +++ b/datasets/KOPRI-KPDC-00000781_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000781_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Bellinghausen Sea core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000782_1.json b/datasets/KOPRI-KPDC-00000782_1.json index d21e4f228a..173fe97c7d 100644 --- a/datasets/KOPRI-KPDC-00000782_1.json +++ b/datasets/KOPRI-KPDC-00000782_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000782_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Bellinghausen Sea core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000783_2.json b/datasets/KOPRI-KPDC-00000783_2.json index 02e5a6312d..aaf4c91f1b 100644 --- a/datasets/KOPRI-KPDC-00000783_2.json +++ b/datasets/KOPRI-KPDC-00000783_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000783_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stable water isotope composition of the shallow ice core drilled at the Styx glacier in 2014-2015. The current version contains data for the upper 80 m with a depth resolution of 22 mm.\r\nDating the ice core/Paleoclimate research", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000784_2.json b/datasets/KOPRI-KPDC-00000784_2.json index 1ac500670d..65c31ba966 100644 --- a/datasets/KOPRI-KPDC-00000784_2.json +++ b/datasets/KOPRI-KPDC-00000784_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000784_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2017 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 1000 kg of fossils were collected during 2016 expedition.\r\nThe Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000785_1.json b/datasets/KOPRI-KPDC-00000785_1.json index 846d2dedfd..9c035818ab 100644 --- a/datasets/KOPRI-KPDC-00000785_1.json +++ b/datasets/KOPRI-KPDC-00000785_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000785_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Beillinghausen Sea core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000786_1.json b/datasets/KOPRI-KPDC-00000786_1.json index 09f29d3b72..026c73e08e 100644 --- a/datasets/KOPRI-KPDC-00000786_1.json +++ b/datasets/KOPRI-KPDC-00000786_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000786_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Bellinghausen Sea core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000787_1.json b/datasets/KOPRI-KPDC-00000787_1.json index 7be171844b..c799ad8223 100644 --- a/datasets/KOPRI-KPDC-00000787_1.json +++ b/datasets/KOPRI-KPDC-00000787_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000787_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Bellinghausen Sea Long core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000788_1.json b/datasets/KOPRI-KPDC-00000788_1.json index 44ad0bcb4f..7a908918ba 100644 --- a/datasets/KOPRI-KPDC-00000788_1.json +++ b/datasets/KOPRI-KPDC-00000788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) has been in normal operation since April 2017 near Climate Change Tower of Ny-Alesund where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 3km typically on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field can be obtained using PPI and RHI modes, respectively.\nTo understand interaction between Arctic cloud and boundary layer wind", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000789_2.json b/datasets/KOPRI-KPDC-00000789_2.json index dab67b404a..6d58e457b8 100644 --- a/datasets/KOPRI-KPDC-00000789_2.json +++ b/datasets/KOPRI-KPDC-00000789_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000789_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of ionic species in the section of ~15-78m depth of shallow ice core from GV7 site in Antarctica\r\nReconstruction of ionic species to indicate paleo atmospheric environment/climate change of Northern Victoria Land, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000790_3.json b/datasets/KOPRI-KPDC-00000790_3.json index a9558cf725..a6bce8b55d 100644 --- a/datasets/KOPRI-KPDC-00000790_3.json +++ b/datasets/KOPRI-KPDC-00000790_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000790_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of ionic species in the upper section of firn core from Styx glacier in Antarctica\nDetermination of ionic species in the upper section of firn core from Styx glacier in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000791_1.json b/datasets/KOPRI-KPDC-00000791_1.json index 2576c0f8e2..619248ce18 100644 --- a/datasets/KOPRI-KPDC-00000791_1.json +++ b/datasets/KOPRI-KPDC-00000791_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000791_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Island collected in 2016 and 2017\nEcophysiological study of lichen", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000792_3.json b/datasets/KOPRI-KPDC-00000792_3.json index 0580d99831..318f68349e 100644 --- a/datasets/KOPRI-KPDC-00000792_3.json +++ b/datasets/KOPRI-KPDC-00000792_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000792_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2016\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000793_2.json b/datasets/KOPRI-KPDC-00000793_2.json index c217160ac3..3a675cfbc5 100644 --- a/datasets/KOPRI-KPDC-00000793_2.json +++ b/datasets/KOPRI-KPDC-00000793_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000793_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Dasan Station, Arctic\nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000794_3.json b/datasets/KOPRI-KPDC-00000794_3.json index 91a7f701b2..1b6f26213d 100644 --- a/datasets/KOPRI-KPDC-00000794_3.json +++ b/datasets/KOPRI-KPDC-00000794_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000794_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Dasan Station, Arctic\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000795_2.json b/datasets/KOPRI-KPDC-00000795_2.json index 2540745640..83f834573f 100644 --- a/datasets/KOPRI-KPDC-00000795_2.json +++ b/datasets/KOPRI-KPDC-00000795_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000795_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctic\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000796_3.json b/datasets/KOPRI-KPDC-00000796_3.json index 8bddaaaf05..eca380a01c 100644 --- a/datasets/KOPRI-KPDC-00000796_3.json +++ b/datasets/KOPRI-KPDC-00000796_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000796_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Jang Bogo Station, Antarctica\nStudy of the atmosphere wave activities in the upper atmosphere in the southern/northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000797_3.json b/datasets/KOPRI-KPDC-00000797_3.json index f9006ddb27..b6432f0d1a 100644 --- a/datasets/KOPRI-KPDC-00000797_3.json +++ b/datasets/KOPRI-KPDC-00000797_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000797_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Electron density profile, plasma drift velocity, and ionospheric tilt information measured from VIPIR (ionosonde) at Jang Bogo Station, Antarctica\nStudy of the ionospheric characteristics in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000798_1.json b/datasets/KOPRI-KPDC-00000798_1.json index 70e39af8db..8692bce6f0 100644 --- a/datasets/KOPRI-KPDC-00000798_1.json +++ b/datasets/KOPRI-KPDC-00000798_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000798_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The rocks and basaltic glasses from Australian-Antarctic Ridge(AAR) are collected by dredge and rock corer on ARAON in 16-17 season.\nWe collected the rocks to understand the mantle magmatism in on-axis Mid Ocean ridge and off-axis seamounts from Australian-Antarctic Ridge(AAR)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000799_1.json b/datasets/KOPRI-KPDC-00000799_1.json index bb02328d8d..3597e80a6f 100644 --- a/datasets/KOPRI-KPDC-00000799_1.json +++ b/datasets/KOPRI-KPDC-00000799_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000799_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI conducted scientific expedition on the KOPRIdge, Antarctic. In this cruise, we collected the bathymetric data during 14 days (Dec ~ Jan, 2016/17) to investigate the oceanograpic structures.\nBathymetric data was collected using Ice-breaker RV Araon to investigate the geologic and oceanographical information on the Antarctic area. Collected bathymetric data is utilized as reference information to determine the sediment coring site and understanding for submarine geological environment.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000800_1.json b/datasets/KOPRI-KPDC-00000800_1.json index 7ba037492d..e18c49924d 100644 --- a/datasets/KOPRI-KPDC-00000800_1.json +++ b/datasets/KOPRI-KPDC-00000800_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000800_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI conducted scientific expedition on the Ross Sea, Antarctic. In this cruise, we collected the bathymetric data during 20 days (Jan ~ Feb, 2017) to investigate the oceanograpic structures.\nBathymetric data was collected using Ice-breaker RV Araon to investigate the geologic and oceanographical information on the Antarctic area. Collected bathymetric data is utilized as reference information to determine the sediment coring site and understanding for submarine geological environment.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000801_3.json b/datasets/KOPRI-KPDC-00000801_3.json index 63ec4db4cd..ef22efd348 100644 --- a/datasets/KOPRI-KPDC-00000801_3.json +++ b/datasets/KOPRI-KPDC-00000801_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000801_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station, Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000803_3.json b/datasets/KOPRI-KPDC-00000803_3.json index 2c31ca8d9b..7a31e2c6ea 100644 --- a/datasets/KOPRI-KPDC-00000803_3.json +++ b/datasets/KOPRI-KPDC-00000803_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000803_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Sweden \nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000804_4.json b/datasets/KOPRI-KPDC-00000804_4.json index 7933f7f533..3647118d0e 100644 --- a/datasets/KOPRI-KPDC-00000804_4.json +++ b/datasets/KOPRI-KPDC-00000804_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000804_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden\nStudy of the atmospheric wave activities in the upper atmosphere in northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000805_3.json b/datasets/KOPRI-KPDC-00000805_3.json index 776b0488a1..c3efb9e4e8 100644 --- a/datasets/KOPRI-KPDC-00000805_3.json +++ b/datasets/KOPRI-KPDC-00000805_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000805_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000806_4.json b/datasets/KOPRI-KPDC-00000806_4.json index 0d2970ee19..978ecb1979 100644 --- a/datasets/KOPRI-KPDC-00000806_4.json +++ b/datasets/KOPRI-KPDC-00000806_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000806_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000807_3.json b/datasets/KOPRI-KPDC-00000807_3.json index 5ef0e1076e..ae1480560f 100644 --- a/datasets/KOPRI-KPDC-00000807_3.json +++ b/datasets/KOPRI-KPDC-00000807_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000807_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Spectral Airglow Temperature Imager (SATI) at King Sejong Station\nStudy of atmospheric wave activities and temperature variations in mesosphere and lower thermosphere (MLT) at southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000808_3.json b/datasets/KOPRI-KPDC-00000808_3.json index d5ac1eedbe..71cc0c90fa 100644 --- a/datasets/KOPRI-KPDC-00000808_3.json +++ b/datasets/KOPRI-KPDC-00000808_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000808_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station, Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000809_2.json b/datasets/KOPRI-KPDC-00000809_2.json index 297228b713..ec39ccc565 100644 --- a/datasets/KOPRI-KPDC-00000809_2.json +++ b/datasets/KOPRI-KPDC-00000809_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000809_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cosmic ray origin neutron count measured from neutron monitor at Jang Bogo Station, Antarctica\nStudy of the variation of neutron count in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000810_3.json b/datasets/KOPRI-KPDC-00000810_3.json index 3edb711a99..92c3d36a90 100644 --- a/datasets/KOPRI-KPDC-00000810_3.json +++ b/datasets/KOPRI-KPDC-00000810_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000810_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Dasan station, Arctic \nStudy of the atmospheric wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000811_2.json b/datasets/KOPRI-KPDC-00000811_2.json index 4a972870c4..3531c0811b 100644 --- a/datasets/KOPRI-KPDC-00000811_2.json +++ b/datasets/KOPRI-KPDC-00000811_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000811_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Dasan Station, Arctic\nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000812_2.json b/datasets/KOPRI-KPDC-00000812_2.json index 86f4dc8cc8..137b36c9cd 100644 --- a/datasets/KOPRI-KPDC-00000812_2.json +++ b/datasets/KOPRI-KPDC-00000812_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000812_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctic\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000813_2.json b/datasets/KOPRI-KPDC-00000813_2.json index 89d179aba2..76124f231f 100644 --- a/datasets/KOPRI-KPDC-00000813_2.json +++ b/datasets/KOPRI-KPDC-00000813_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000813_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer (SCM) at Jang Bogo Station, antarctica\nStudy of the activity of ultra low frequency (ULF) wave in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000814_2.json b/datasets/KOPRI-KPDC-00000814_2.json index d9b28b777d..a3e254b6e6 100644 --- a/datasets/KOPRI-KPDC-00000814_2.json +++ b/datasets/KOPRI-KPDC-00000814_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000814_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at JBS, Antarctica\nStudy of the aurora (proton) characteristics in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000815_1.json b/datasets/KOPRI-KPDC-00000815_1.json index 3ebf3e7555..5aeeba1699 100644 --- a/datasets/KOPRI-KPDC-00000815_1.json +++ b/datasets/KOPRI-KPDC-00000815_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000815_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amino acid and DNA sequences for the production of metabolites in Antarctic copepod T. kingsejongensis\nGenetic information to understand mechanism of useful metabolites", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000816_2.json b/datasets/KOPRI-KPDC-00000816_2.json index 2306f9535c..c955e5d7a5 100644 --- a/datasets/KOPRI-KPDC-00000816_2.json +++ b/datasets/KOPRI-KPDC-00000816_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000816_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory, Longyearbyen, Norway\nStudy of the aurora characteristics in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000817_3.json b/datasets/KOPRI-KPDC-00000817_3.json index 60ef907f7c..4d09934214 100644 --- a/datasets/KOPRI-KPDC-00000817_3.json +++ b/datasets/KOPRI-KPDC-00000817_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000817_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Jang Bogo Station (JBS), Antarctica\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000818_2.json b/datasets/KOPRI-KPDC-00000818_2.json index 900a0fd21c..2321a76c17 100644 --- a/datasets/KOPRI-KPDC-00000818_2.json +++ b/datasets/KOPRI-KPDC-00000818_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000818_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cosmic ray origin neutron count measured from neutron monitor at Jang Bogo Station, Antarctica\nStudy of the variation of neutron count in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000819_2.json b/datasets/KOPRI-KPDC-00000819_2.json index 3d37f6b739..d4bb647dcd 100644 --- a/datasets/KOPRI-KPDC-00000819_2.json +++ b/datasets/KOPRI-KPDC-00000819_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000819_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station, Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000820_1.json b/datasets/KOPRI-KPDC-00000820_1.json index ede97cc201..057c949991 100644 --- a/datasets/KOPRI-KPDC-00000820_1.json +++ b/datasets/KOPRI-KPDC-00000820_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000820_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000821_2.json b/datasets/KOPRI-KPDC-00000821_2.json index c54a364f7c..e804546f27 100644 --- a/datasets/KOPRI-KPDC-00000821_2.json +++ b/datasets/KOPRI-KPDC-00000821_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000821_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Electron density profile, plasma drift velocity, and ionospheric tilt information measured from VIPIR (ionosonde) at Jang Bogo Station, Antarctica\nStudy of the ionospheric characteristics in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000822_2.json b/datasets/KOPRI-KPDC-00000822_2.json index 15009851e4..cf085f57f0 100644 --- a/datasets/KOPRI-KPDC-00000822_2.json +++ b/datasets/KOPRI-KPDC-00000822_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000822_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000823_4.json b/datasets/KOPRI-KPDC-00000823_4.json index 3a0e65c9e7..48ad05d942 100644 --- a/datasets/KOPRI-KPDC-00000823_4.json +++ b/datasets/KOPRI-KPDC-00000823_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000823_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000824_2.json b/datasets/KOPRI-KPDC-00000824_2.json index 5762ded7a7..ffbae62432 100644 --- a/datasets/KOPRI-KPDC-00000824_2.json +++ b/datasets/KOPRI-KPDC-00000824_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000824_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Spectral Airglow Temperature Imager (SATI) at King Sejong Station\nStudy of atmospheric wave activities and temperature variations in mesosphere and lower thermosphere (MLT) at southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000825_2.json b/datasets/KOPRI-KPDC-00000825_2.json index d6dbd7274c..3912ea2659 100644 --- a/datasets/KOPRI-KPDC-00000825_2.json +++ b/datasets/KOPRI-KPDC-00000825_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000825_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station, Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000826_3.json b/datasets/KOPRI-KPDC-00000826_3.json index b68de3e3e9..0c645783d7 100644 --- a/datasets/KOPRI-KPDC-00000826_3.json +++ b/datasets/KOPRI-KPDC-00000826_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000826_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Kiruna, Sweden\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000827_2.json b/datasets/KOPRI-KPDC-00000827_2.json index c97b58c227..c115bd58dd 100644 --- a/datasets/KOPRI-KPDC-00000827_2.json +++ b/datasets/KOPRI-KPDC-00000827_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000827_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Sweden \nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000828_2.json b/datasets/KOPRI-KPDC-00000828_2.json index ee3ef51525..d3b92f6bfa 100644 --- a/datasets/KOPRI-KPDC-00000828_2.json +++ b/datasets/KOPRI-KPDC-00000828_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000828_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000829_1.json b/datasets/KOPRI-KPDC-00000829_1.json index 88fbe0ff5c..da046d166b 100644 --- a/datasets/KOPRI-KPDC-00000829_1.json +++ b/datasets/KOPRI-KPDC-00000829_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000829_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A list of metabolites derived from Antarctic fungi and lichens was produced. It can be used to find new substances.\nTo develop new natural medicine", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000830_4.json b/datasets/KOPRI-KPDC-00000830_4.json index 88f08f9712..a1b3518f7f 100644 --- a/datasets/KOPRI-KPDC-00000830_4.json +++ b/datasets/KOPRI-KPDC-00000830_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000830_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NOAA, 5 Autonomous Underwater Hydrophones(AUH) were deployed in the Balleny Islands region, Antarctica, in Jan 2015 to monitor icequakes, tectonic activities, and ocean ambient noise. The 5 AUHs were recovered in Mar 2016, and 3 of the 5 AUHs successfully recorded continuous data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000831_1.json b/datasets/KOPRI-KPDC-00000831_1.json index c59c80ce6c..ea7cb463d2 100644 --- a/datasets/KOPRI-KPDC-00000831_1.json +++ b/datasets/KOPRI-KPDC-00000831_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000831_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lists of extracts derived from Antarctic lichens and fungi were made. Many extracts can be used in natural product research.\nTo provide samples for finding a bioactive substance", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000832_3.json b/datasets/KOPRI-KPDC-00000832_3.json index 9278841260..d63ba5ee27 100644 --- a/datasets/KOPRI-KPDC-00000832_3.json +++ b/datasets/KOPRI-KPDC-00000832_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000832_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the chlorophyll a concentration was investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica, 2017.\nInvestigation to marine phytoplankton biomass(chl-a) in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000833_3.json b/datasets/KOPRI-KPDC-00000833_3.json index 0a720d8a52..a02ef46d84 100644 --- a/datasets/KOPRI-KPDC-00000833_3.json +++ b/datasets/KOPRI-KPDC-00000833_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000833_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the phytoplankton abundance was investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica, 2017.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000834_2.json b/datasets/KOPRI-KPDC-00000834_2.json index 4a8f78a257..4e1fcea0b2 100644 --- a/datasets/KOPRI-KPDC-00000834_2.json +++ b/datasets/KOPRI-KPDC-00000834_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000834_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000835_2.json b/datasets/KOPRI-KPDC-00000835_2.json index 95a65a12a7..268fd40038 100644 --- a/datasets/KOPRI-KPDC-00000835_2.json +++ b/datasets/KOPRI-KPDC-00000835_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000835_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000836_2.json b/datasets/KOPRI-KPDC-00000836_2.json index fbe9001a8c..28774e134c 100644 --- a/datasets/KOPRI-KPDC-00000836_2.json +++ b/datasets/KOPRI-KPDC-00000836_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000836_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000837_2.json b/datasets/KOPRI-KPDC-00000837_2.json index f214befb52..67c04b1fe7 100644 --- a/datasets/KOPRI-KPDC-00000837_2.json +++ b/datasets/KOPRI-KPDC-00000837_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000837_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000838_2.json b/datasets/KOPRI-KPDC-00000838_2.json index 6543c5afd3..63f783a962 100644 --- a/datasets/KOPRI-KPDC-00000838_2.json +++ b/datasets/KOPRI-KPDC-00000838_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000838_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000839_2.json b/datasets/KOPRI-KPDC-00000839_2.json index 589da757b0..e4f2a27b59 100644 --- a/datasets/KOPRI-KPDC-00000839_2.json +++ b/datasets/KOPRI-KPDC-00000839_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000839_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000840_2.json b/datasets/KOPRI-KPDC-00000840_2.json index 2f66fb1f7f..0f7cc0cb6f 100644 --- a/datasets/KOPRI-KPDC-00000840_2.json +++ b/datasets/KOPRI-KPDC-00000840_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000840_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000841_4.json b/datasets/KOPRI-KPDC-00000841_4.json index 099b41b9ee..84559567d9 100644 --- a/datasets/KOPRI-KPDC-00000841_4.json +++ b/datasets/KOPRI-KPDC-00000841_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000841_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans. \nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000842_1.json b/datasets/KOPRI-KPDC-00000842_1.json index b55c12c59e..8707b11d91 100644 --- a/datasets/KOPRI-KPDC-00000842_1.json +++ b/datasets/KOPRI-KPDC-00000842_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000842_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000843_1.json b/datasets/KOPRI-KPDC-00000843_1.json index 7458796ac1..3eba08e13d 100644 --- a/datasets/KOPRI-KPDC-00000843_1.json +++ b/datasets/KOPRI-KPDC-00000843_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000843_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000844_1.json b/datasets/KOPRI-KPDC-00000844_1.json index 9b87324ca0..b8faadb714 100644 --- a/datasets/KOPRI-KPDC-00000844_1.json +++ b/datasets/KOPRI-KPDC-00000844_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000844_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000845_1.json b/datasets/KOPRI-KPDC-00000845_1.json index 6ba20bfe2f..498ff4e152 100644 --- a/datasets/KOPRI-KPDC-00000845_1.json +++ b/datasets/KOPRI-KPDC-00000845_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000845_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000846_1.json b/datasets/KOPRI-KPDC-00000846_1.json index 143289ad27..655f738d0f 100644 --- a/datasets/KOPRI-KPDC-00000846_1.json +++ b/datasets/KOPRI-KPDC-00000846_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000846_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000847_1.json b/datasets/KOPRI-KPDC-00000847_1.json index 5809a2e15d..78bf464107 100644 --- a/datasets/KOPRI-KPDC-00000847_1.json +++ b/datasets/KOPRI-KPDC-00000847_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000847_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000848_1.json b/datasets/KOPRI-KPDC-00000848_1.json index 5d54db84f4..a90b9058d3 100644 --- a/datasets/KOPRI-KPDC-00000848_1.json +++ b/datasets/KOPRI-KPDC-00000848_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000848_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000849_1.json b/datasets/KOPRI-KPDC-00000849_1.json index 28425c3f7c..c47c4e4ade 100644 --- a/datasets/KOPRI-KPDC-00000849_1.json +++ b/datasets/KOPRI-KPDC-00000849_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000849_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000850_1.json b/datasets/KOPRI-KPDC-00000850_1.json index 9d953df2d6..ee7a9324f3 100644 --- a/datasets/KOPRI-KPDC-00000850_1.json +++ b/datasets/KOPRI-KPDC-00000850_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000850_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000851_1.json b/datasets/KOPRI-KPDC-00000851_1.json index 4516418ce5..30f31d9d78 100644 --- a/datasets/KOPRI-KPDC-00000851_1.json +++ b/datasets/KOPRI-KPDC-00000851_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000851_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000852_1.json b/datasets/KOPRI-KPDC-00000852_1.json index 93e769bb4b..b3ef6bd328 100644 --- a/datasets/KOPRI-KPDC-00000852_1.json +++ b/datasets/KOPRI-KPDC-00000852_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000852_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000853_1.json b/datasets/KOPRI-KPDC-00000853_1.json index d27ef19ee3..e5e0ebf81e 100644 --- a/datasets/KOPRI-KPDC-00000853_1.json +++ b/datasets/KOPRI-KPDC-00000853_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000853_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000854_1.json b/datasets/KOPRI-KPDC-00000854_1.json index 827f49329a..8ae8243815 100644 --- a/datasets/KOPRI-KPDC-00000854_1.json +++ b/datasets/KOPRI-KPDC-00000854_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000854_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000855_1.json b/datasets/KOPRI-KPDC-00000855_1.json index 54b6b835af..559e44d53d 100644 --- a/datasets/KOPRI-KPDC-00000855_1.json +++ b/datasets/KOPRI-KPDC-00000855_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000855_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000856_1.json b/datasets/KOPRI-KPDC-00000856_1.json index 161b7f61d6..6e80fe4c25 100644 --- a/datasets/KOPRI-KPDC-00000856_1.json +++ b/datasets/KOPRI-KPDC-00000856_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000856_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000857_1.json b/datasets/KOPRI-KPDC-00000857_1.json index 277de26731..aa25d7faf0 100644 --- a/datasets/KOPRI-KPDC-00000857_1.json +++ b/datasets/KOPRI-KPDC-00000857_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000857_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000858_1.json b/datasets/KOPRI-KPDC-00000858_1.json index af0701b28b..78664f98de 100644 --- a/datasets/KOPRI-KPDC-00000858_1.json +++ b/datasets/KOPRI-KPDC-00000858_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000858_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quantitative oxygen isotope images (isotopographs) of refractory minerals in a Ca-Al-rich inclusion (CAI) from Allende were obtained with the isotope microscope system at Hokkaido University, Japan\nHigh precision and high spatial resolution oxygen isotopographs of CAI minerals can constrain oxygen isotope exchange and diffusion during cooling.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000859_1.json b/datasets/KOPRI-KPDC-00000859_1.json index 9a1b8f4897..f494799d45 100644 --- a/datasets/KOPRI-KPDC-00000859_1.json +++ b/datasets/KOPRI-KPDC-00000859_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000859_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During February, 2013, KOPRI conducted marine survey in the Ross sea, Antarctic ocean. During the cruise, we collected multibeam data.\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000860_2.json b/datasets/KOPRI-KPDC-00000860_2.json index daf7003208..e168eebdb7 100644 --- a/datasets/KOPRI-KPDC-00000860_2.json +++ b/datasets/KOPRI-KPDC-00000860_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000860_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Cambridge bay station.\nMonitoring of Black Carbon concentration over Cambridge bay station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000861_1.json b/datasets/KOPRI-KPDC-00000861_1.json index 70b2b0e19c..e9c74ebf7f 100644 --- a/datasets/KOPRI-KPDC-00000861_1.json +++ b/datasets/KOPRI-KPDC-00000861_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000861_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly chemical data are obtained from CMAQ v5.1 model simulations during the 4 months from May to Aug. 2008. MACCity, MEGAN-MACC, and GFED3 emission inventories are applied to the simulation for anthropogenic, biogenic, and biomass burning sources, respectively. The CMAQ domain covers the areas of the Arctic (D1 domain) and Svalbard (D2 domain) with 18km x 18km and 6km x 6km horizontal resolutions, respectively.\nTo investigate spatial and temporal patterns of particulate matters (PM10) and photochemical species (O3, NOx, and PAN).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000862_1.json b/datasets/KOPRI-KPDC-00000862_1.json index daea8c16fc..e8b2c2aed5 100644 --- a/datasets/KOPRI-KPDC-00000862_1.json +++ b/datasets/KOPRI-KPDC-00000862_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000862_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2016 at Cambridge Bay site, Canada. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at Cambridge Bay Site", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000864_1.json b/datasets/KOPRI-KPDC-00000864_1.json index be0daade0e..9997fe7c5c 100644 --- a/datasets/KOPRI-KPDC-00000864_1.json +++ b/datasets/KOPRI-KPDC-00000864_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000864_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2017 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000865_1.json b/datasets/KOPRI-KPDC-00000865_1.json index 2a268ced91..a25bf4ffe0 100644 --- a/datasets/KOPRI-KPDC-00000865_1.json +++ b/datasets/KOPRI-KPDC-00000865_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000865_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2016 at NORD. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000866_1.json b/datasets/KOPRI-KPDC-00000866_1.json index 4cb272d903..80d082fb11 100644 --- a/datasets/KOPRI-KPDC-00000866_1.json +++ b/datasets/KOPRI-KPDC-00000866_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000866_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2017 at NORD. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000867_1.json b/datasets/KOPRI-KPDC-00000867_1.json index 7c8f0a0dd5..1eb7ac940e 100644 --- a/datasets/KOPRI-KPDC-00000867_1.json +++ b/datasets/KOPRI-KPDC-00000867_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000867_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Alaska permafrost DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Arctic region. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena atAlaska permafrost .\nMonitoring on meteorology at Alaska permafrost", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000868_1.json b/datasets/KOPRI-KPDC-00000868_1.json index 9f23c7d742..6fe8dcc0b4 100644 --- a/datasets/KOPRI-KPDC-00000868_1.json +++ b/datasets/KOPRI-KPDC-00000868_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000868_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Alaska permafrost DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Arctic region. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena atAlaska permafrost .\nMonitoring on meteorology at Alaska permafrost", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000869_1.json b/datasets/KOPRI-KPDC-00000869_1.json index 7cc3cc4c8b..2a574c946b 100644 --- a/datasets/KOPRI-KPDC-00000869_1.json +++ b/datasets/KOPRI-KPDC-00000869_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000869_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000870_1.json b/datasets/KOPRI-KPDC-00000870_1.json index b7e9c72261..4de308866b 100644 --- a/datasets/KOPRI-KPDC-00000870_1.json +++ b/datasets/KOPRI-KPDC-00000870_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000870_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000871_1.json b/datasets/KOPRI-KPDC-00000871_1.json index 7fe31e007f..06a524e5f4 100644 --- a/datasets/KOPRI-KPDC-00000871_1.json +++ b/datasets/KOPRI-KPDC-00000871_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000871_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000872_1.json b/datasets/KOPRI-KPDC-00000872_1.json index 0e43ac5b4c..54f835962e 100644 --- a/datasets/KOPRI-KPDC-00000872_1.json +++ b/datasets/KOPRI-KPDC-00000872_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000872_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000873_1.json b/datasets/KOPRI-KPDC-00000873_1.json index 120e6c3092..3fb027b263 100644 --- a/datasets/KOPRI-KPDC-00000873_1.json +++ b/datasets/KOPRI-KPDC-00000873_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000873_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000874_1.json b/datasets/KOPRI-KPDC-00000874_1.json index ff611f6016..d4b582c685 100644 --- a/datasets/KOPRI-KPDC-00000874_1.json +++ b/datasets/KOPRI-KPDC-00000874_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000874_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\nMonitoring on meteorology at Cambridge Bay site in Canada", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000875_1.json b/datasets/KOPRI-KPDC-00000875_1.json index 11673538de..8ace45e74c 100644 --- a/datasets/KOPRI-KPDC-00000875_1.json +++ b/datasets/KOPRI-KPDC-00000875_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000875_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2016 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000876_1.json b/datasets/KOPRI-KPDC-00000876_1.json index e7840c26d5..88ddaee2b7 100644 --- a/datasets/KOPRI-KPDC-00000876_1.json +++ b/datasets/KOPRI-KPDC-00000876_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000876_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2017 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000877_1.json b/datasets/KOPRI-KPDC-00000877_1.json index 13e453b0af..4712c1da49 100644 --- a/datasets/KOPRI-KPDC-00000877_1.json +++ b/datasets/KOPRI-KPDC-00000877_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000877_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000878_1.json b/datasets/KOPRI-KPDC-00000878_1.json index c340151e7d..27d6acb20b 100644 --- a/datasets/KOPRI-KPDC-00000878_1.json +++ b/datasets/KOPRI-KPDC-00000878_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000878_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A vegetation index NDVI was measured during growing season at the Council site, 70-miles northeast from the Nome, Alaska.\r\n The sensor was developed by Seoul National University (Prof. Young-Ryul Ryu) and provided for in-situ installation.\r\n The sensor is composed of one pair of upward/downward looking LEDs to obtain reflectivity in each bandwidth. \r\nWe can calculate NDVI (normalized difference vegetation index) using this sensor to monitor vegetation activity.\nTo monitor high-temporal variation of vegetaion activity at permafrost region, west Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000879_1.json b/datasets/KOPRI-KPDC-00000879_1.json index 521eb7936a..95fb342ce1 100644 --- a/datasets/KOPRI-KPDC-00000879_1.json +++ b/datasets/KOPRI-KPDC-00000879_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000879_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected.\nTo monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000880_1.json b/datasets/KOPRI-KPDC-00000880_1.json index dce02a641b..79dccbd629 100644 --- a/datasets/KOPRI-KPDC-00000880_1.json +++ b/datasets/KOPRI-KPDC-00000880_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000880_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected.\nTo monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000881_1.json b/datasets/KOPRI-KPDC-00000881_1.json index ce459efdd0..9752e51099 100644 --- a/datasets/KOPRI-KPDC-00000881_1.json +++ b/datasets/KOPRI-KPDC-00000881_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000881_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2017 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000882_1.json b/datasets/KOPRI-KPDC-00000882_1.json index d2fc692b24..59dae337d2 100644 --- a/datasets/KOPRI-KPDC-00000882_1.json +++ b/datasets/KOPRI-KPDC-00000882_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000882_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every two-second. The minimum observation height is over 20 km.\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000883_1.json b/datasets/KOPRI-KPDC-00000883_1.json index 7d7d6da390..edfb0e47b6 100644 --- a/datasets/KOPRI-KPDC-00000883_1.json +++ b/datasets/KOPRI-KPDC-00000883_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000883_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every two-second. The minimum observation height is over 20 km.\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000884_1.json b/datasets/KOPRI-KPDC-00000884_1.json index 37c81fc15c..f25d2f80f5 100644 --- a/datasets/KOPRI-KPDC-00000884_1.json +++ b/datasets/KOPRI-KPDC-00000884_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000884_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every two-second. The minimum observation height is over 20 km.\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000885_1.json b/datasets/KOPRI-KPDC-00000885_1.json index c4b49c012d..66817e675c 100644 --- a/datasets/KOPRI-KPDC-00000885_1.json +++ b/datasets/KOPRI-KPDC-00000885_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000885_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Macromolecular compositions (carbohydrates, proteins, and lipids) of particulate organic matter (POM) are crucial as a basic marine food quality. To date, however, one investigation (Kim et al., 2016) has been carried out in the Amundsen Sea which is one of the fastest warming locations in the Southern Ocean. Water samples for macromolecular compositions were obtained at selected 7 stations in the Amundsen Sea Polynya (AP) during the austral summer in 2014 to investigate vertical characteristics of POM.\n(1) to investigate the macromolecular compositions (proteins, lipids, and carbohydrates) and the fate of POM between in the photic and aphotic layers and (2) estimate physiological status and nutritional condition of phytoplankton as a major source of POM in the AP.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000886_1.json b/datasets/KOPRI-KPDC-00000886_1.json index c963c39ec8..096c4f9098 100644 --- a/datasets/KOPRI-KPDC-00000886_1.json +++ b/datasets/KOPRI-KPDC-00000886_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000886_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pyranometer, Pyrgeometer, Total Ultraviolet, UV-A and UV-B are operated year round continously. Downward solar radiation, atmospheric longwave radiation, total ultraviolet radiation, UV-A and UV-B are sampled every second and ten-minute averaged data are recorded on a data logger\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000887_1.json b/datasets/KOPRI-KPDC-00000887_1.json index 65a260956f..9f1a55e166 100644 --- a/datasets/KOPRI-KPDC-00000887_1.json +++ b/datasets/KOPRI-KPDC-00000887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar lows are intense mesoscale cyclones that mainly occur over the sea in polar regions. Owing to their small spatial scale of a diameter less than 1000km, simulating polar lows is a challenging task. At King Sejong station in West Antartica, polar lows are often observed. Despite the recent significant climatic changes observed over West Antarctica, adequate validation of regional simulations of extreme weather events such as polar lows are rare for this region. To address this gap, simulation results from a recent version of the Polar Weather Research and Forecasting model (Polar WRF) covering Antartic Peninsula at a high horizontal resolution of 3 km are validated against near-surface meteorological observations. We selected a case of high wind speed event on 7 January 2013 recorded at Automatic Meteorological Observation Station (AMOS) in King Sejong station, Antarctica. It is revealed by in situ observations, numerical weather prediction, and reanalysis fields that the synoptic and mesoscale environment of the strong wind event was due to the passage of a strong mesoscale polar low of center pressure 950hPa. Verifying model results from 3km grid resolution simulation against AMOS observation showed that high skill in simulating wind speed and surface pressure with a bias of -1.1m/s and -1.2hPa, respectively. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation of Antartic weather systems and the near-surface meteorological instruments installed in King Sejong station can provide invaluable data for polar low studies over West Antartica.\nA Numerical Simulation Study of Blizzard caused by Polar Low at King Sejong Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000888_1.json b/datasets/KOPRI-KPDC-00000888_1.json index 41b002a614..1b9556f2e4 100644 --- a/datasets/KOPRI-KPDC-00000888_1.json +++ b/datasets/KOPRI-KPDC-00000888_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000888_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Jang Bogo Station is located in Terra Nova Bay over the East Antarctica, which is often affected by individual storms moving along nearby storm tracks and a katabatic flow from the continental interior towards the coast. A numerical simulation for two strong wind events of maximum instantaneous wind speed (41.17 m/s) and daily mean wind speed (23.92 m/s) at Jangbogo station are conducted using the polar-optimized version of Weather Research and Forecasting model (Polar WRF). Verifying model results from 3 km grid resolution simulation against AWS observation at Jangbogo station, the case of maximum instantaneous wind speed is relatively simulated well with high skill in wind with a bias of -3.3 m/s and standard deviation of 5.4 m/s. The case of maximum daily mean wind speed showed comparatively lower accuracy for the simulation of wind speed with a bias of -7.0 m/s and standard deviation of 8.6 m/s. From the analysis, it is revealed that the each case has different origins for strong wind. The highest maximum instantaneous wind case is caused by the approach of the strong synoptic low pressure system moving toward Terra Nova Bay from North and the other daily wind maximum speed case is mainly caused by the katabatic flow from the interiors of Terra Nova Bay towards the coast. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation and investigation of high wind events at Jangbogo station. However, additional efforts in utilizing the high resolution terrain is required to reduce the simulation error of high wind mainly caused by katabatic flow, which is received a lot of influence of the surrounding terrain.\nA Numerical Simulation Study of Strong Wind Events at Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000889_1.json b/datasets/KOPRI-KPDC-00000889_1.json index ed1012fc9d..763d79b431 100644 --- a/datasets/KOPRI-KPDC-00000889_1.json +++ b/datasets/KOPRI-KPDC-00000889_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000889_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-Channel seismic data were collected during the 2017 ARA08C cruise in the Beaufort Sea, Arctic Ocean\nInvestigation of submarine resource environment and seabed methane release in the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000890_1.json b/datasets/KOPRI-KPDC-00000890_1.json index f6ede4cafe..656156e475 100644 --- a/datasets/KOPRI-KPDC-00000890_1.json +++ b/datasets/KOPRI-KPDC-00000890_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000890_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profiler data were collected during the 2017 ARA08C cruise in the Beaufort Sea, Arctic Ocean\nInvestigation of submarine resource environment and seabed methane release in the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000891_1.json b/datasets/KOPRI-KPDC-00000891_1.json index d0530252c2..3d62e657e3 100644 --- a/datasets/KOPRI-KPDC-00000891_1.json +++ b/datasets/KOPRI-KPDC-00000891_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000891_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam data were collected during the 2017 ARA08C cruise in the Beaufort Sea, Arctic Ocean\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000892_1.json b/datasets/KOPRI-KPDC-00000892_1.json index 7da2be3837..4d15a4bc8c 100644 --- a/datasets/KOPRI-KPDC-00000892_1.json +++ b/datasets/KOPRI-KPDC-00000892_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000892_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heat flow measurements in the Beaufort Sea, Arctic Ocean\nInvestigation to the thermal structure in the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000893_1.json b/datasets/KOPRI-KPDC-00000893_1.json index 46cdcac887..6d685a44b1 100644 --- a/datasets/KOPRI-KPDC-00000893_1.json +++ b/datasets/KOPRI-KPDC-00000893_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000893_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To measure the vertical profiles of temperature and salinity in the Beaufort Sea, Arctic Ocean\nTo investigate the variability in spatial and temporal distribution of water temperature and salinity in the Beaufort Sea, Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000894_1.json b/datasets/KOPRI-KPDC-00000894_1.json index 0b603f5651..64b4d81881 100644 --- a/datasets/KOPRI-KPDC-00000894_1.json +++ b/datasets/KOPRI-KPDC-00000894_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000894_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metagenomics aims to understand microbial metabolic potential of a given environment by sequencing every genetic information of microbes. In reality, the DNA sequencing data is too huge to analyze. So, it is necessary to develop user-friendly bioinformatics tool. Here, we developed a large pieces of the tools people need and deposit the programming source codes to the KOPRI data center.\nProgramming source codes will help biologists easily analyze the large-scale DNA sequence data obtained from metagenomic samples.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000895_2.json b/datasets/KOPRI-KPDC-00000895_2.json index 3b9dc49925..66e91d3bc8 100644 --- a/datasets/KOPRI-KPDC-00000895_2.json +++ b/datasets/KOPRI-KPDC-00000895_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000895_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in March, 2018. CTD profiles were collected at 42 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000896_2.json b/datasets/KOPRI-KPDC-00000896_2.json index 21cf525da7..28b8b2063d 100644 --- a/datasets/KOPRI-KPDC-00000896_2.json +++ b/datasets/KOPRI-KPDC-00000896_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000896_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in March, 2018. LADCP profiles were collected at 42 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000897_1.json b/datasets/KOPRI-KPDC-00000897_1.json index 09f3edaf16..a2070be892 100644 --- a/datasets/KOPRI-KPDC-00000897_1.json +++ b/datasets/KOPRI-KPDC-00000897_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000897_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed to the South of the Drygalski Ice Tongue on 12 February 2017 as a part of the ANA07C research cruise, and it was recovered on 7 March 2018\nTo monitor physical properties(Temperature, Salinity, Current) of ocean water in the south of the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000898_2.json b/datasets/KOPRI-KPDC-00000898_2.json index 963825333e..24401df080 100644 --- a/datasets/KOPRI-KPDC-00000898_2.json +++ b/datasets/KOPRI-KPDC-00000898_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000898_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed to the North of the Drygalski Ice Tongue on 9 February 2017 as a part of the ANA07C research cruise, and it was recovered on 5 March 2018\nTo monitor physical properties(Temperature, Salinity, Current) of ocean water in the north of the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000899_5.json b/datasets/KOPRI-KPDC-00000899_5.json index 943efddcb1..914d04708c 100644 --- a/datasets/KOPRI-KPDC-00000899_5.json +++ b/datasets/KOPRI-KPDC-00000899_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000899_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NOAA, 6 Autonomous Underwater Hydrophones(AUH) were deployed in Southern Terra Nova Bay during ANA07C (Feb 2017) research cruise to monitor icequakes, tectonic activities, and ocean ambient noise. 5 AUHs were recovered during ANA08C(Mar 2018) research cruise, and an AUH was recovered in Jan 2019", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000900_1.json b/datasets/KOPRI-KPDC-00000900_1.json index 794ad4a722..5c6f523b57 100644 --- a/datasets/KOPRI-KPDC-00000900_1.json +++ b/datasets/KOPRI-KPDC-00000900_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000900_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution mapping of breeding site by aerial photographing\nInvestigation of nest space distribution", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000901_3.json b/datasets/KOPRI-KPDC-00000901_3.json index 0d897a1712..45193da7f1 100644 --- a/datasets/KOPRI-KPDC-00000901_3.json +++ b/datasets/KOPRI-KPDC-00000901_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000901_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round remotely operating GPS system\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000902_3.json b/datasets/KOPRI-KPDC-00000902_3.json index 621cdb8561..258f138946 100644 --- a/datasets/KOPRI-KPDC-00000902_3.json +++ b/datasets/KOPRI-KPDC-00000902_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000902_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round records of remotely operating weather station and digital camera\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000903_1.json b/datasets/KOPRI-KPDC-00000903_1.json index e0f163268d..e20c40d392 100644 --- a/datasets/KOPRI-KPDC-00000903_1.json +++ b/datasets/KOPRI-KPDC-00000903_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000903_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ApRES (Automated phase-sensitive Radar Echo Sounding) data to detect the change of ice thickness and basal melt\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000904_2.json b/datasets/KOPRI-KPDC-00000904_2.json index bf13bbd586..0c189d7963 100644 --- a/datasets/KOPRI-KPDC-00000904_2.json +++ b/datasets/KOPRI-KPDC-00000904_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000904_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Bottom Seismometer was deployed around a Terranova Bay in 05 February 2017(Cruise ANA07C) and recovered and take the data in 09 March 2018(Cruise ANA08C). \r\n(The location name is changed originally KPOBS03 to newly OBS181)\nMonitoring of tectonic activities and icequakes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000905_1.json b/datasets/KOPRI-KPDC-00000905_1.json index 64590eb90c..9e6f4163c9 100644 --- a/datasets/KOPRI-KPDC-00000905_1.json +++ b/datasets/KOPRI-KPDC-00000905_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000905_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with LDEO, an oceanographic mooring was deployed in front of Nansen Ice Shelf on 6 February 2017 as a part of the ANA07C research cruise, and it was recovered on 5 March 2018\nTo monitor the generation of the High Salinity Shelf Water (HSSW) in Terra Nova Bay", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000906_2.json b/datasets/KOPRI-KPDC-00000906_2.json index 654f3c1ad3..00ed9c60b1 100644 --- a/datasets/KOPRI-KPDC-00000906_2.json +++ b/datasets/KOPRI-KPDC-00000906_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000906_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed close to the bottom depth near the Drygalski Ice Tongue on 5 February 2017 as a part of the ANA07C research cruise, and it was recovered on 5 March 2018\nTo monitor physical properties(Temperature, Salinity, Current) of deep water near the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000907_1.json b/datasets/KOPRI-KPDC-00000907_1.json index 7307a75027..9dd5a90bc5 100644 --- a/datasets/KOPRI-KPDC-00000907_1.json +++ b/datasets/KOPRI-KPDC-00000907_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000907_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2017/2018 expedition. During the 2018 Amundsen Sea cruise (ANA08B) by IBRV Araon, a total of 53 CTD stations were visited.\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000908_1.json b/datasets/KOPRI-KPDC-00000908_1.json index ece09187c0..eda8ab30a2 100644 --- a/datasets/KOPRI-KPDC-00000908_1.json +++ b/datasets/KOPRI-KPDC-00000908_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000908_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2017/2018 expedition. During the 2018 Amundsen Sea cruise (ANA08B) by IBRV Araon, a total of 53 CTD/LADCP stations were visited.\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000909_1.json b/datasets/KOPRI-KPDC-00000909_1.json index ed610a5111..4e0c3f772f 100644 --- a/datasets/KOPRI-KPDC-00000909_1.json +++ b/datasets/KOPRI-KPDC-00000909_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000909_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2017/2018 expedition. During the 2018 Amundsen Sea cruise (ANA08B) by IBRV Araon.\nIdentify the temporal and spatial variation of Circumpolar Deep Water (CDW) in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000910_2.json b/datasets/KOPRI-KPDC-00000910_2.json index f4a2e63e1e..8b0dc32b01 100644 --- a/datasets/KOPRI-KPDC-00000910_2.json +++ b/datasets/KOPRI-KPDC-00000910_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000910_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove, an extensive oceanographic survey was conducted on the 2018 expedition. During the 2018 ARAON cruise (ANA08D), a total of 31 CTD stations were visited.\r\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000911_2.json b/datasets/KOPRI-KPDC-00000911_2.json index db182848fb..88d65e04db 100644 --- a/datasets/KOPRI-KPDC-00000911_2.json +++ b/datasets/KOPRI-KPDC-00000911_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000911_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove, an extensive oceanographic survey was conducted on the 2018 expedition. During the 2018 ARAON cruise (ANA08D), a total of 31 CTD/LADCP stations were visited.\r\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000912_1.json b/datasets/KOPRI-KPDC-00000912_1.json index b7b45a7fee..f5a42c2919 100644 --- a/datasets/KOPRI-KPDC-00000912_1.json +++ b/datasets/KOPRI-KPDC-00000912_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000912_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of soil temperature and moisture\nMonitoring of soil temperature and moisture for regulating soil CO2 efflux in terrestrial ecosystems in Alaska", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000913_1.json b/datasets/KOPRI-KPDC-00000913_1.json index da601f83aa..4d621a656d 100644 --- a/datasets/KOPRI-KPDC-00000913_1.json +++ b/datasets/KOPRI-KPDC-00000913_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000913_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Map for about 2km x 2km around the Jang Bogo Station, Antarctica\nTopographic survey around the base for research", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000914_1.json b/datasets/KOPRI-KPDC-00000914_1.json index d5b52a5ccf..01d3dfa8ea 100644 --- a/datasets/KOPRI-KPDC-00000914_1.json +++ b/datasets/KOPRI-KPDC-00000914_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000914_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the long-term monitoring projects on Antarctic terrestrial vegetation in relation\r\nto global climate change, a bryophyta floristical survey was conducted around the Korean\r\nAntarctic Station (King Sejong Station), which is located on Barton Peninsula, King George\r\nIsland, from 31 Dec. 2012 to 17 Feb. 2013. Three hundred and sixteenth bryophyta specimens were collected and nineteenth bryophyta species in twelve genera were identified by morphological characteristics.\nAs part of the long-term monitoring projects on Antarctic terrestrial vegetation in relation\r\nto global climate change, a bryophyta floristical survey was conducted around the Korean\r\nAntarctic Station (King Sejong Station), which is located on Barton Peninsula, King George\r\nIsland.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000915_1.json b/datasets/KOPRI-KPDC-00000915_1.json index 0f1b92bb1b..525b8c6f7a 100644 --- a/datasets/KOPRI-KPDC-00000915_1.json +++ b/datasets/KOPRI-KPDC-00000915_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000915_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data of public relations (Documentary production, Media report)\nPromoting the conservation of the Antarctic marine ecosystem", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000916_1.json b/datasets/KOPRI-KPDC-00000916_1.json index d5f70e533e..194edaebdd 100644 --- a/datasets/KOPRI-KPDC-00000916_1.json +++ b/datasets/KOPRI-KPDC-00000916_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000916_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Domestic scientific research Roadmap\nAgenda analysis for ecosystem conservation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000917_1.json b/datasets/KOPRI-KPDC-00000917_1.json index 95b6b8bbbe..859b5293cc 100644 --- a/datasets/KOPRI-KPDC-00000917_1.json +++ b/datasets/KOPRI-KPDC-00000917_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000917_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WG-EMM(Working Group on Ecosystem Monitoring and Management) final report\nCCAMLR (Convention on the Conservation of Antarctic Marine Living Resources) agenda analysis", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000918_1.json b/datasets/KOPRI-KPDC-00000918_1.json index 3d693e6816..34c9354428 100644 --- a/datasets/KOPRI-KPDC-00000918_1.json +++ b/datasets/KOPRI-KPDC-00000918_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000918_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Installation and operation of AMIGOS\nMeteorological observation of breeding site in the Adelie Penguin", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000919_1.json b/datasets/KOPRI-KPDC-00000919_1.json index 4f935c9063..a2aa088de3 100644 --- a/datasets/KOPRI-KPDC-00000919_1.json +++ b/datasets/KOPRI-KPDC-00000919_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000919_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample collection of chlorophyll a (13 spots)\nLong-term monitoring of environmental change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000920_1.json b/datasets/KOPRI-KPDC-00000920_1.json index aa73a80190..38fa4ba2f5 100644 --- a/datasets/KOPRI-KPDC-00000920_1.json +++ b/datasets/KOPRI-KPDC-00000920_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000920_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Images of Polynyas forming process\nStudy on primary production of Polynya", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000921_1.json b/datasets/KOPRI-KPDC-00000921_1.json index 0313de0f42..f34076c08e 100644 --- a/datasets/KOPRI-KPDC-00000921_1.json +++ b/datasets/KOPRI-KPDC-00000921_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000921_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution map of Pinnipedia\nMonitoring of breeding population", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000922_1.json b/datasets/KOPRI-KPDC-00000922_1.json index 424e25780a..076f73ee8a 100644 --- a/datasets/KOPRI-KPDC-00000922_1.json +++ b/datasets/KOPRI-KPDC-00000922_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000922_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data acquisition of bio-logger (Radius of action, Dive depth)\nAnalysis of feeding place use in the Adelie Penguin", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000923_1.json b/datasets/KOPRI-KPDC-00000923_1.json index aee96c8ff9..6738cd4e01 100644 --- a/datasets/KOPRI-KPDC-00000923_1.json +++ b/datasets/KOPRI-KPDC-00000923_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000923_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurement of Breeding success in the Adelie Penguin\nRegister data with CEMP", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000924_1.json b/datasets/KOPRI-KPDC-00000924_1.json index 19b849d47a..1612472644 100644 --- a/datasets/KOPRI-KPDC-00000924_1.json +++ b/datasets/KOPRI-KPDC-00000924_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000924_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Building of field survey camp\nLong-term ecological monitoring of the Adelie penguin", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000925_1.json b/datasets/KOPRI-KPDC-00000925_1.json index 4dabeb3bf1..2d215f6ffb 100644 --- a/datasets/KOPRI-KPDC-00000925_1.json +++ b/datasets/KOPRI-KPDC-00000925_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000925_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quantitative analysis of zooplankton\nUnderstanding of community structure", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000926_1.json b/datasets/KOPRI-KPDC-00000926_1.json index 9036e325f4..f233460d51 100644 --- a/datasets/KOPRI-KPDC-00000926_1.json +++ b/datasets/KOPRI-KPDC-00000926_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000926_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample collection of zooplankton and phytoplankton (17 spots)\nA Characteristic study of plankton in the marine ecosystem", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000927_2.json b/datasets/KOPRI-KPDC-00000927_2.json index 13b9739af2..abdfe93587 100644 --- a/datasets/KOPRI-KPDC-00000927_2.json +++ b/datasets/KOPRI-KPDC-00000927_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000927_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000928_2.json b/datasets/KOPRI-KPDC-00000928_2.json index f82c7006e4..c9e486ca4a 100644 --- a/datasets/KOPRI-KPDC-00000928_2.json +++ b/datasets/KOPRI-KPDC-00000928_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000928_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000929_2.json b/datasets/KOPRI-KPDC-00000929_2.json index 32c880963d..a630697b04 100644 --- a/datasets/KOPRI-KPDC-00000929_2.json +++ b/datasets/KOPRI-KPDC-00000929_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000929_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000930_2.json b/datasets/KOPRI-KPDC-00000930_2.json index 2c62276e06..5f18dc13f6 100644 --- a/datasets/KOPRI-KPDC-00000930_2.json +++ b/datasets/KOPRI-KPDC-00000930_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000930_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000931_2.json b/datasets/KOPRI-KPDC-00000931_2.json index f7e62d70a3..b6b1444cd3 100644 --- a/datasets/KOPRI-KPDC-00000931_2.json +++ b/datasets/KOPRI-KPDC-00000931_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000931_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000932_2.json b/datasets/KOPRI-KPDC-00000932_2.json index 0fc7c771fa..8cbbabc272 100644 --- a/datasets/KOPRI-KPDC-00000932_2.json +++ b/datasets/KOPRI-KPDC-00000932_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000932_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000933_2.json b/datasets/KOPRI-KPDC-00000933_2.json index 980e0fc52e..d59d5aae5a 100644 --- a/datasets/KOPRI-KPDC-00000933_2.json +++ b/datasets/KOPRI-KPDC-00000933_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000933_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000934_2.json b/datasets/KOPRI-KPDC-00000934_2.json index b28d2ba77a..d98d1a3deb 100644 --- a/datasets/KOPRI-KPDC-00000934_2.json +++ b/datasets/KOPRI-KPDC-00000934_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000934_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000935_2.json b/datasets/KOPRI-KPDC-00000935_2.json index 45978e87ff..4c3ba4593f 100644 --- a/datasets/KOPRI-KPDC-00000935_2.json +++ b/datasets/KOPRI-KPDC-00000935_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000935_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000936_2.json b/datasets/KOPRI-KPDC-00000936_2.json index 55fda65ed3..103e87da50 100644 --- a/datasets/KOPRI-KPDC-00000936_2.json +++ b/datasets/KOPRI-KPDC-00000936_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000936_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000937_2.json b/datasets/KOPRI-KPDC-00000937_2.json index 6ba42469cc..85afd11c43 100644 --- a/datasets/KOPRI-KPDC-00000937_2.json +++ b/datasets/KOPRI-KPDC-00000937_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000937_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000938_2.json b/datasets/KOPRI-KPDC-00000938_2.json index cb6da7b9f0..2e7b7d59ef 100644 --- a/datasets/KOPRI-KPDC-00000938_2.json +++ b/datasets/KOPRI-KPDC-00000938_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000938_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000939_1.json b/datasets/KOPRI-KPDC-00000939_1.json index d7fbf84f49..0e46c24c42 100644 --- a/datasets/KOPRI-KPDC-00000939_1.json +++ b/datasets/KOPRI-KPDC-00000939_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000939_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000940_1.json b/datasets/KOPRI-KPDC-00000940_1.json index a7d8698223..9b9a80924b 100644 --- a/datasets/KOPRI-KPDC-00000940_1.json +++ b/datasets/KOPRI-KPDC-00000940_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000940_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Korea. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000941_1.json b/datasets/KOPRI-KPDC-00000941_1.json index fa8ea00924..9aa3d0a22e 100644 --- a/datasets/KOPRI-KPDC-00000941_1.json +++ b/datasets/KOPRI-KPDC-00000941_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000941_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000942_1.json b/datasets/KOPRI-KPDC-00000942_1.json index 7d7681d19a..72b854585c 100644 --- a/datasets/KOPRI-KPDC-00000942_1.json +++ b/datasets/KOPRI-KPDC-00000942_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000942_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000943_1.json b/datasets/KOPRI-KPDC-00000943_1.json index 2f9251dad0..3aaee70533 100644 --- a/datasets/KOPRI-KPDC-00000943_1.json +++ b/datasets/KOPRI-KPDC-00000943_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000943_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000944_1.json b/datasets/KOPRI-KPDC-00000944_1.json index 9635884584..1dc3515a4a 100644 --- a/datasets/KOPRI-KPDC-00000944_1.json +++ b/datasets/KOPRI-KPDC-00000944_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000944_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000945_1.json b/datasets/KOPRI-KPDC-00000945_1.json index 82c05b3204..1982d3520e 100644 --- a/datasets/KOPRI-KPDC-00000945_1.json +++ b/datasets/KOPRI-KPDC-00000945_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000945_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000946_1.json b/datasets/KOPRI-KPDC-00000946_1.json index 84c7a1e842..12f3ef5992 100644 --- a/datasets/KOPRI-KPDC-00000946_1.json +++ b/datasets/KOPRI-KPDC-00000946_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000946_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic.\nTo derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000947_1.json b/datasets/KOPRI-KPDC-00000947_1.json index 07a9d5bfa6..e68beb953e 100644 --- a/datasets/KOPRI-KPDC-00000947_1.json +++ b/datasets/KOPRI-KPDC-00000947_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000947_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic.\nTo derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000948_1.json b/datasets/KOPRI-KPDC-00000948_1.json index 9cc15e1aa7..bc0f12ff54 100644 --- a/datasets/KOPRI-KPDC-00000948_1.json +++ b/datasets/KOPRI-KPDC-00000948_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000948_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data around the Jang Bogo Station in Antarctic.\nTo derive products including vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans around the Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000949_1.json b/datasets/KOPRI-KPDC-00000949_1.json index 2ee98679c4..8fee243df0 100644 --- a/datasets/KOPRI-KPDC-00000949_1.json +++ b/datasets/KOPRI-KPDC-00000949_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000949_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERSI is a scanner carried aboard the third FengYun (FY-3) series of meteorological satellites launched by China and obtained data around the Jang Bogo Station in Antarctic.\nTo derive products including cloud, vegetation, snow and ice, ocean color around the Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000952_1.json b/datasets/KOPRI-KPDC-00000952_1.json index e7496ff451..fede6f3c9d 100644 --- a/datasets/KOPRI-KPDC-00000952_1.json +++ b/datasets/KOPRI-KPDC-00000952_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000952_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000953_1.json b/datasets/KOPRI-KPDC-00000953_1.json index 14fada1a33..0dbba7d43b 100644 --- a/datasets/KOPRI-KPDC-00000953_1.json +++ b/datasets/KOPRI-KPDC-00000953_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000953_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000954_1.json b/datasets/KOPRI-KPDC-00000954_1.json index 75d0a3a772..c8889c6113 100644 --- a/datasets/KOPRI-KPDC-00000954_1.json +++ b/datasets/KOPRI-KPDC-00000954_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000954_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000955_1.json b/datasets/KOPRI-KPDC-00000955_1.json index 1c8d07ddb7..a1b7e5475e 100644 --- a/datasets/KOPRI-KPDC-00000955_1.json +++ b/datasets/KOPRI-KPDC-00000955_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000955_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000956_1.json b/datasets/KOPRI-KPDC-00000956_1.json index 95348b6f31..4bb2cf21b2 100644 --- a/datasets/KOPRI-KPDC-00000956_1.json +++ b/datasets/KOPRI-KPDC-00000956_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000956_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002.\nDerive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000957_1.json b/datasets/KOPRI-KPDC-00000957_1.json index 58fe77d83c..60162968ea 100644 --- a/datasets/KOPRI-KPDC-00000957_1.json +++ b/datasets/KOPRI-KPDC-00000957_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000957_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Members of candidate phylum OP9 are found in geothermal systems, petroleum reservoirs, anaerobic digesters, wastewater treatment facilities, and marine sediments.\r\nIn this study, 8 single-cell genomes (SCGs) of OP9, which was predominant composing 26.7% in the marine sediments of the Ross Sea, Antarctica was obtained through single-cell sequencing. Eight SCGs showed high 16S rRNA gene identity (>99.3%) each other while they had low 16S rRNA gene similarities (\n-Yet, little information regarding their metabolic capabilities and ecological role within such habitats is currently available due to the lack of cultured isolates.\r\n-As a first study on the OP9 genome from Antarctic marine sediment, more detailed analyses will provide the glimpse into the lifestyle of a member of widely distributed, yet poorly understood bacterial candidate division OP9.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000958_1.json b/datasets/KOPRI-KPDC-00000958_1.json index 6152dcb8a3..dd6a25f519 100644 --- a/datasets/KOPRI-KPDC-00000958_1.json +++ b/datasets/KOPRI-KPDC-00000958_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000958_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A sediment sample collected from the coast of Svalbard, Arctic (78\u00b0 55' N, 11\u00b0 53' E). The sediment sample was suspended in 20 % glycerol and preserved at -80\uf0b0C until use. Strain PAMC 20958T was isolated by using standard dilution plating method on ZoBell agar containing 15 g bacto agar, 5 g bacto peptone, 1 g yeast extract, and 0.1 g ferric citrate in 1 L of 0.2-\u03bcm filtered seawater and incubating the plate at 10 \uf0b0C for 12 days. After determination of the optimum growth temperature, strain PAMC 20958T was maintained routinely on Marine agar (MA; BD Difco) or in marine broth (MB; BD Difco) at 20 \u00b0C and preserved as glycerol suspensions (20 % in distilled water, v/v) at -80 \u00b0C. Reference strain H. namhaensis KCTC 32362T was obtained from the KCTC (Korean Collection for Type Cultures, Korea) and was maintained routinely on MA at 20 \u00b0C.\r\n\r\nGenome relatedness between PAMC 20958T and H. namhaensis KCTC 32362T was investigated by whole genome sequencing.\n-Analysis of Bacterial Community in coast of Svalbard, Arctic.\r\n-Basic data acquisition of PAMC(Polar and Alpine Microbial Collection).\r\n-Academic report of novel strain.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000959_1.json b/datasets/KOPRI-KPDC-00000959_1.json index 0b552623a8..37b56797de 100644 --- a/datasets/KOPRI-KPDC-00000959_1.json +++ b/datasets/KOPRI-KPDC-00000959_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000959_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A terrestrial soil sample was collected from King George Island, Antarctica (62\u00b012.37\u2019S, 58\u00b0 47.40\u2019W) on February 12, 2011. The terrestrial soil sample was preserved at -80\uf0b0C until the use. For cultivation, a serially diluted aliquot (100 \u00b5l) of the sample in 0.85% NaCl (w/v) was spread on R2A (BD Difco) plates and incubated at 15\uf0b0C for 11 days. Pure cultures of the bacterial isolates obtained were deposited to Polar and Alpine Microbial Collection (PAMC, Lee et al., 2012) and preserved as glycerol suspensions (20% in distilled water, v/v) at -80 \uf0b0C. In this study, one of these strains, PAMC 27389T, was routinely cultured on 0.5 % TYS at 15\uf0b0C after the determination of optimal temperature for growth. For comparing with strain PAMC 27389T, four type strains of Pseudorhodobacter species, P. wandonensis KCTC 23672T, P. antarcticus KCTC 23700T, P. aquimaris KCTC 23043T, and P. ferrugineus LMG 22047T were purchased from Korean Collection of Type Cultures (KCTC) and Laboratory of Microbiology Gent Bacteria Collection (LMG) and used as reference strains following cultivation under comparable conditions as PAMC 27389T.\r\n\r\nGenome relatedness was investigated by whole genome sequencing of strain PAMC 27389T and four type strains of species of genus Pseudorhodobacter, P. wandonensis KCTC 23672T, P. antarcticus KCTC 23700T, P. ferrugineus LMG 22047T, and P. aquimaris KCTC 23043T.\n-Analysis of Bacterial Community in King George Island, Antarctica.\r\n-Basic data acquisition of PAMC(Polar and Alpine Microbial Collection).\r\n-Academic report of novel strain.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000960_2.json b/datasets/KOPRI-KPDC-00000960_2.json index e01cded9ed..b7addde90d 100644 --- a/datasets/KOPRI-KPDC-00000960_2.json +++ b/datasets/KOPRI-KPDC-00000960_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000960_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2013. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000961_1.json b/datasets/KOPRI-KPDC-00000961_1.json index b35fc557f2..f5c687ac8d 100644 --- a/datasets/KOPRI-KPDC-00000961_1.json +++ b/datasets/KOPRI-KPDC-00000961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long cores from Antarctic Bransfield (AP18-LC08).\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000962_1.json b/datasets/KOPRI-KPDC-00000962_1.json index 9a95d0fc4b..55961d1b93 100644 --- a/datasets/KOPRI-KPDC-00000962_1.json +++ b/datasets/KOPRI-KPDC-00000962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Weddell Sea Core, Antarctica\r\nMarine Sediment (Long core sample 33m)\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000963_1.json b/datasets/KOPRI-KPDC-00000963_1.json index 46ed08cb22..83b5583d54 100644 --- a/datasets/KOPRI-KPDC-00000963_1.json +++ b/datasets/KOPRI-KPDC-00000963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Bransfield core ,Antarctica. Box Core Sample\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000964_1.json b/datasets/KOPRI-KPDC-00000964_1.json index 32a207ee6e..2f45f4685b 100644 --- a/datasets/KOPRI-KPDC-00000964_1.json +++ b/datasets/KOPRI-KPDC-00000964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Bransfield Box core, Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000965_1.json b/datasets/KOPRI-KPDC-00000965_1.json index 7635089af7..65fd57a82e 100644 --- a/datasets/KOPRI-KPDC-00000965_1.json +++ b/datasets/KOPRI-KPDC-00000965_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000965_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Bransfield core, Antarctica\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000966_1.json b/datasets/KOPRI-KPDC-00000966_1.json index dcc300c57e..d8d4b43f18 100644 --- a/datasets/KOPRI-KPDC-00000966_1.json +++ b/datasets/KOPRI-KPDC-00000966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Bransfield core, Antarctic\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000967_1.json b/datasets/KOPRI-KPDC-00000967_1.json index 5418c814b4..1b537cdaff 100644 --- a/datasets/KOPRI-KPDC-00000967_1.json +++ b/datasets/KOPRI-KPDC-00000967_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000967_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Joinville Island Multiple core , Antarctic\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000968_2.json b/datasets/KOPRI-KPDC-00000968_2.json index 7c27ba729e..2cc8a590d4 100644 --- a/datasets/KOPRI-KPDC-00000968_2.json +++ b/datasets/KOPRI-KPDC-00000968_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000968_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2010\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000969_2.json b/datasets/KOPRI-KPDC-00000969_2.json index 7d64c9b400..45b3ccd00d 100644 --- a/datasets/KOPRI-KPDC-00000969_2.json +++ b/datasets/KOPRI-KPDC-00000969_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000969_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2009\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000970_2.json b/datasets/KOPRI-KPDC-00000970_2.json index f67bb538e1..515bb8675b 100644 --- a/datasets/KOPRI-KPDC-00000970_2.json +++ b/datasets/KOPRI-KPDC-00000970_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000970_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2008\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000971_1.json b/datasets/KOPRI-KPDC-00000971_1.json index 86fa82df66..9c4f6c2c98 100644 --- a/datasets/KOPRI-KPDC-00000971_1.json +++ b/datasets/KOPRI-KPDC-00000971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Maxwell Bay core, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000972_1.json b/datasets/KOPRI-KPDC-00000972_1.json index ced16a1cb8..6e63af149a 100644 --- a/datasets/KOPRI-KPDC-00000972_1.json +++ b/datasets/KOPRI-KPDC-00000972_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000972_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2017/2018 Marian Cove core, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000973_2.json b/datasets/KOPRI-KPDC-00000973_2.json index e34ec8bcba..ca6caac2af 100644 --- a/datasets/KOPRI-KPDC-00000973_2.json +++ b/datasets/KOPRI-KPDC-00000973_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000973_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2007\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000974_2.json b/datasets/KOPRI-KPDC-00000974_2.json index 0c5cb04449..a2f5ee5eb3 100644 --- a/datasets/KOPRI-KPDC-00000974_2.json +++ b/datasets/KOPRI-KPDC-00000974_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000974_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2006\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000975_2.json b/datasets/KOPRI-KPDC-00000975_2.json index 5b3da3fba7..d7145e5213 100644 --- a/datasets/KOPRI-KPDC-00000975_2.json +++ b/datasets/KOPRI-KPDC-00000975_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000975_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2005\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000976_2.json b/datasets/KOPRI-KPDC-00000976_2.json index 827454690d..d1123fea11 100644 --- a/datasets/KOPRI-KPDC-00000976_2.json +++ b/datasets/KOPRI-KPDC-00000976_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000976_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2004\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000977_2.json b/datasets/KOPRI-KPDC-00000977_2.json index d23ee332b8..ba201fe535 100644 --- a/datasets/KOPRI-KPDC-00000977_2.json +++ b/datasets/KOPRI-KPDC-00000977_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000977_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2003\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000978_2.json b/datasets/KOPRI-KPDC-00000978_2.json index 12c4758f6e..84d56d2918 100644 --- a/datasets/KOPRI-KPDC-00000978_2.json +++ b/datasets/KOPRI-KPDC-00000978_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000978_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2002\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000979_2.json b/datasets/KOPRI-KPDC-00000979_2.json index 74a428ff29..0993a53fee 100644 --- a/datasets/KOPRI-KPDC-00000979_2.json +++ b/datasets/KOPRI-KPDC-00000979_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000979_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2001\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000980_2.json b/datasets/KOPRI-KPDC-00000980_2.json index 0399db908e..1ad478746a 100644 --- a/datasets/KOPRI-KPDC-00000980_2.json +++ b/datasets/KOPRI-KPDC-00000980_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000980_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2000\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000981_2.json b/datasets/KOPRI-KPDC-00000981_2.json index d120b9e698..e50660ee9d 100644 --- a/datasets/KOPRI-KPDC-00000981_2.json +++ b/datasets/KOPRI-KPDC-00000981_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000981_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1999\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000982_2.json b/datasets/KOPRI-KPDC-00000982_2.json index 27b23cf33a..04d0cdc225 100644 --- a/datasets/KOPRI-KPDC-00000982_2.json +++ b/datasets/KOPRI-KPDC-00000982_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000982_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1998\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000983_2.json b/datasets/KOPRI-KPDC-00000983_2.json index 8d15b2cf12..ba30edfe87 100644 --- a/datasets/KOPRI-KPDC-00000983_2.json +++ b/datasets/KOPRI-KPDC-00000983_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000983_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1997\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000984_2.json b/datasets/KOPRI-KPDC-00000984_2.json index cd1b76832a..3689adb243 100644 --- a/datasets/KOPRI-KPDC-00000984_2.json +++ b/datasets/KOPRI-KPDC-00000984_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000984_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1996\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000985_2.json b/datasets/KOPRI-KPDC-00000985_2.json index 36fb487ccd..168d5d8d7c 100644 --- a/datasets/KOPRI-KPDC-00000985_2.json +++ b/datasets/KOPRI-KPDC-00000985_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000985_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1995\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000986_2.json b/datasets/KOPRI-KPDC-00000986_2.json index c78d8d90f5..d43538bd2c 100644 --- a/datasets/KOPRI-KPDC-00000986_2.json +++ b/datasets/KOPRI-KPDC-00000986_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000986_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1994\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000987_2.json b/datasets/KOPRI-KPDC-00000987_2.json index 98884844ef..4d328e2a80 100644 --- a/datasets/KOPRI-KPDC-00000987_2.json +++ b/datasets/KOPRI-KPDC-00000987_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000987_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1993\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000988_2.json b/datasets/KOPRI-KPDC-00000988_2.json index 41d28b0a66..576cb81d33 100644 --- a/datasets/KOPRI-KPDC-00000988_2.json +++ b/datasets/KOPRI-KPDC-00000988_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000988_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1992\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000989_2.json b/datasets/KOPRI-KPDC-00000989_2.json index c0d3a66bbe..6231a28dcb 100644 --- a/datasets/KOPRI-KPDC-00000989_2.json +++ b/datasets/KOPRI-KPDC-00000989_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000989_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1991\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000990_3.json b/datasets/KOPRI-KPDC-00000990_3.json index 60b8a1a5df..768cc47759 100644 --- a/datasets/KOPRI-KPDC-00000990_3.json +++ b/datasets/KOPRI-KPDC-00000990_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000990_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1990\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000991_3.json b/datasets/KOPRI-KPDC-00000991_3.json index 638f291a5a..32b67d7bb4 100644 --- a/datasets/KOPRI-KPDC-00000991_3.json +++ b/datasets/KOPRI-KPDC-00000991_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000991_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1989\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000992_4.json b/datasets/KOPRI-KPDC-00000992_4.json index 59ff38d85b..9371914d56 100644 --- a/datasets/KOPRI-KPDC-00000992_4.json +++ b/datasets/KOPRI-KPDC-00000992_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000992_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation at King Sejong station was carried out from February in 1988. Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 1988\nAnnual meteorological observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000993_1.json b/datasets/KOPRI-KPDC-00000993_1.json index 88df6940a4..c920453f47 100644 --- a/datasets/KOPRI-KPDC-00000993_1.json +++ b/datasets/KOPRI-KPDC-00000993_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000993_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2018 Weddell Sea Ice, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000994_1.json b/datasets/KOPRI-KPDC-00000994_1.json index 2865fca3b9..d19960b8dd 100644 --- a/datasets/KOPRI-KPDC-00000994_1.json +++ b/datasets/KOPRI-KPDC-00000994_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000994_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2018 Weddell Sea Ice, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000995_1.json b/datasets/KOPRI-KPDC-00000995_1.json index 8b036d7069..6007465bfb 100644 --- a/datasets/KOPRI-KPDC-00000995_1.json +++ b/datasets/KOPRI-KPDC-00000995_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000995_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2018 SW of James Ross Island Sea Ice, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000996_1.json b/datasets/KOPRI-KPDC-00000996_1.json index f0e1ac8539..2c0cfa56fe 100644 --- a/datasets/KOPRI-KPDC-00000996_1.json +++ b/datasets/KOPRI-KPDC-00000996_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000996_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2018 W of James Ross Island Sea Ice, Antarctic\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000997_1.json b/datasets/KOPRI-KPDC-00000997_1.json index 11d97f32ad..10d64c8cdb 100644 --- a/datasets/KOPRI-KPDC-00000997_1.json +++ b/datasets/KOPRI-KPDC-00000997_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000997_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Identification of growth rate of ciliates from Barton Peninsular, South Shetland Islands in Antarctica\nTo show the growth rate of ciliates based on temperature in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000998_2.json b/datasets/KOPRI-KPDC-00000998_2.json index c8cf6b12c2..8d84eecff4 100644 --- a/datasets/KOPRI-KPDC-00000998_2.json +++ b/datasets/KOPRI-KPDC-00000998_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000998_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine magnetic data were collected during the ANA08C Expedition in the 2017-2018 austral summer in the Ross Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00000999_2.json b/datasets/KOPRI-KPDC-00000999_2.json index c76ed76952..5811dec0e9 100644 --- a/datasets/KOPRI-KPDC-00000999_2.json +++ b/datasets/KOPRI-KPDC-00000999_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00000999_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001000_2.json b/datasets/KOPRI-KPDC-00001000_2.json index 4dc021e16e..c48c540841 100644 --- a/datasets/KOPRI-KPDC-00001000_2.json +++ b/datasets/KOPRI-KPDC-00001000_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001000_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profile (SBP) data were collected during the ANA08C Expedition in the Ross Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001001_1.json b/datasets/KOPRI-KPDC-00001001_1.json index ba0b501447..77086763a8 100644 --- a/datasets/KOPRI-KPDC-00001001_1.json +++ b/datasets/KOPRI-KPDC-00001001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Despite the importance, the molecular responses of S. uncinata related to the decrease in water availability in the long-term future have not yet been identified. To explain physiological and molecular change induced by dehydration, we performed de novo transcriptome assembly. Using the short-read assembly program, 32,100 unigenes were assembled with an N50 of 1,296 bp.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001002_1.json b/datasets/KOPRI-KPDC-00001002_1.json index 6655ebe9df..a5160eb734 100644 --- a/datasets/KOPRI-KPDC-00001002_1.json +++ b/datasets/KOPRI-KPDC-00001002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Greenland EastGRIP 2017 snow pit trace metals\nInvestigation of seasonal changes in atmospheric trace metals over northeastern Greenland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001003_1.json b/datasets/KOPRI-KPDC-00001003_1.json index e7d0d1dde8..b3886ece87 100644 --- a/datasets/KOPRI-KPDC-00001003_1.json +++ b/datasets/KOPRI-KPDC-00001003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Selenium, one of candidates for sea ice proxy, recovered from Antarctic GV7 snow pit samples\nInvestigation of the relation between selenium in snow and sea ice changes", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001004_1.json b/datasets/KOPRI-KPDC-00001004_1.json index 7de4411844..589b34bc01 100644 --- a/datasets/KOPRI-KPDC-00001004_1.json +++ b/datasets/KOPRI-KPDC-00001004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stable water isotope composition of a 210m ice core drilled on Styx Galcier, Northern Vicvoria Land, East Antarctica in the 2014-5 summer season, for the depth interval of 0-170 m with <25 mm resolution. The dataset will be updated in a following version.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001005_1.json b/datasets/KOPRI-KPDC-00001005_1.json index 60842b4421..5f250bef3f 100644 --- a/datasets/KOPRI-KPDC-00001005_1.json +++ b/datasets/KOPRI-KPDC-00001005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major\r\nanthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1\r\nlevels of artificial Pu, released predominantly by atmospheric nuclear weapons tests. We\r\nmeasured 351 samples which collected a 78 m-depth fire core at the site of GV7 (S 70\u00b041\r\n\u00b417.1\", E 158\u00b051\u00b448.9\", 1950 m a.s.l.), Northern Victoria Land, East Antarctica. To determine\r\nthe Pu concentration in the samples, we used an inductively coupled plasma sector field\r\nmass spectrometry coupled with an Apex high-efficiency sample introduction system, which\r\nhas the advantages of small sample consumption and simple sample preparation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001006_1.json b/datasets/KOPRI-KPDC-00001006_1.json index eebb14f4d7..1a1f1ed61c 100644 --- a/datasets/KOPRI-KPDC-00001006_1.json +++ b/datasets/KOPRI-KPDC-00001006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major\r\nanthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1\r\nlevels of artificial Pu, released predominantly by atmospheric nuclear weapons tests.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001007_1.json b/datasets/KOPRI-KPDC-00001007_1.json index 46a85afbc5..3703368fff 100644 --- a/datasets/KOPRI-KPDC-00001007_1.json +++ b/datasets/KOPRI-KPDC-00001007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Deschampsia antarctica is an Antarctic hairgrass that grows on the west coast of the Antarctic peninsula. In this report, we have identified and characterized DaGolS2, that is a member of the galactinol synthase group 2. To investigate its possible cellular role in cold tolerance, a transgenic rice system was employed. DaGolS2-overexpressing transgenic rice plants (Ubi:DaGolS2) exhibited markedly increased tolerance to cold and drought stress compared to wild-type plants without growth defects; however, overexpression of DaGolS2 exerted little effect on tolerance to salt stress. These results suggest that overexpression of DaGolS2 directly and indirectly confers enhanced tolerance to cold and drought stresses.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001008_2.json b/datasets/KOPRI-KPDC-00001008_2.json index 72bfa16ef6..e0786db84d 100644 --- a/datasets/KOPRI-KPDC-00001008_2.json +++ b/datasets/KOPRI-KPDC-00001008_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001008_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition.\r\nThe Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001009_2.json b/datasets/KOPRI-KPDC-00001009_2.json index 388a1b9b5a..c1eab606df 100644 --- a/datasets/KOPRI-KPDC-00001009_2.json +++ b/datasets/KOPRI-KPDC-00001009_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001009_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001010_2.json b/datasets/KOPRI-KPDC-00001010_2.json index 3cdcb830f2..6348de6e25 100644 --- a/datasets/KOPRI-KPDC-00001010_2.json +++ b/datasets/KOPRI-KPDC-00001010_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001010_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001011_2.json b/datasets/KOPRI-KPDC-00001011_2.json index cd18e2b231..99ca4f7375 100644 --- a/datasets/KOPRI-KPDC-00001011_2.json +++ b/datasets/KOPRI-KPDC-00001011_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001011_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001012_1.json b/datasets/KOPRI-KPDC-00001012_1.json index 7ec550c1b5..2cee19ff17 100644 --- a/datasets/KOPRI-KPDC-00001012_1.json +++ b/datasets/KOPRI-KPDC-00001012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dihydrodipicolinate reductase (DHDPR) is a key enzyme in the diaminopimelate- and lysine-synthesis pathways that reduces DHDP to tetrahydrodipicolinate. Although DHDPR uses both NADPH and NADH as a cofactor, the structural basis for cofactor specificity and preference remains unclear. Here, we report that Paenisporosarcina sp. TG-14 DHDPR has a strong preference for NADPH over NADH, as determined by isothermal titration calorimetry and enzymatic activity assays. We determined the crystal structures of PaDHDPR alone, with its competitive inhibitor (dipicolinate), and the ternary complex of the enzyme with dipicolinate and NADPH, with results showing that only the ternary complex had a fully closed conformation and suggesting that binding of both substrate and nucleotide cofactor is required for enzymatic activity. Moreover, NADPH binding induced local conformational changes in the N-terminal long loop (residues 34\uff1f59) of PaDHDPR, as the His35 and Lys36 residues in this loop interacted with the 2\u2032-phosphate group of NADPH, possibly accounting for the strong preference of PaDHDPR for NADPH. Mutation of these residues revealed reduced NADPH binding and enzymatic activity, confirming their importance in NADPH binding. These findings provide insight into the mechanism of action and cofactor selectivity of this important bacterial enzyme.\nThis manuscript describes structural and functional studies of dihydrodipicolinate reductase (DHDPR) from the psychrophilic species, Paenisporosarcina sp. TG-14. The manuscript reports high resolution crystal structures of PaDHDPR in the unliganded, DPA bound, and NADPH + DPA bounds states; together with enzyme kinetics and ITC studies of wild-type and mutant forms of the enzyme. The study is generally well designed, the manuscript well written, and the main conclusions supported by high quality data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001013_1.json b/datasets/KOPRI-KPDC-00001013_1.json index c11939ce2a..9878ef58ba 100644 --- a/datasets/KOPRI-KPDC-00001013_1.json +++ b/datasets/KOPRI-KPDC-00001013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cold-active acetyl xylan esterases allow for reduced bioreactor heating costs in bioenergy production. Here, we isolated and characterized a cold-active acetyl xylan esterase (PbAcE) from the psychrophilic soil microbe Paenibacillus sp. R4. The enzyme reversibly hydrolyzes glucose penta-acetate and xylan acetate, alternatively producing acetyl xylan from xylan, and it shows higher activity at 4\u00b0C than at 25\u00b0C. We solved the crystal structure of PbAcE at 2.1-\u00c5 resolution to investigate its active site and the reason for its low-temperature activity. Structural analysis showed that PbAcE forms a hexamer with a central substrate binding tunnel, and the inter-subunit interactions are relatively weak compared with those of its mesophilic and thermophilic homologs. PbAcE also has a shorter loop and different residue composition in the \u03b24\u2013\u03b13 and \u03b25\u2013\u03b14 regions near the substrate binding site. Flexible subunit movements and different active site loop conformations may enable the strong low-temperature activity and broad substrate specificity of PbAcE. In addition, PbAcE was found to have strong activity against antibiotic compound substrates, such as cefotaxime and 7-amino cephalosporanic acid (7-ACA). In conclusion, the PbAcE structure and our biochemical results provide the first example of a cold-active acetyl xylan esterase and a starting template for structure-based protein engineering.\nThis manuscript describes the thorough characterization of a new member of the C7 family of carbohydrate esters, specifically the acetyl xylan esterase (AXE) from Paenibacillus sp (PbAcE). This is the first characterization of a cold-active AXE and the enzyme has a number of potential industrial applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001014_2.json b/datasets/KOPRI-KPDC-00001014_2.json index 6cdfb95bb3..a96f2d07b1 100644 --- a/datasets/KOPRI-KPDC-00001014_2.json +++ b/datasets/KOPRI-KPDC-00001014_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001014_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genome sequences of eight screened bacteria from Alaskan soil cores.\r\nMicroorganisms from Alaskan soil cores were screened on media containing cellulose, xylan, and chitin.\r\nEight bacteria showing cellulase, esterase, and xylanase activity were identified and sequenced using nanopore technology.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001015_2.json b/datasets/KOPRI-KPDC-00001015_2.json index f9c410608c..a466d4c09e 100644 --- a/datasets/KOPRI-KPDC-00001015_2.json +++ b/datasets/KOPRI-KPDC-00001015_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001015_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001016_2.json b/datasets/KOPRI-KPDC-00001016_2.json index dd21c433aa..487e7b04de 100644 --- a/datasets/KOPRI-KPDC-00001016_2.json +++ b/datasets/KOPRI-KPDC-00001016_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001016_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001017_2.json b/datasets/KOPRI-KPDC-00001017_2.json index 1d17b53c2e..4b4b2cf2a6 100644 --- a/datasets/KOPRI-KPDC-00001017_2.json +++ b/datasets/KOPRI-KPDC-00001017_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001017_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001018_2.json b/datasets/KOPRI-KPDC-00001018_2.json index 933fb8881b..25aff608d1 100644 --- a/datasets/KOPRI-KPDC-00001018_2.json +++ b/datasets/KOPRI-KPDC-00001018_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001018_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001019_2.json b/datasets/KOPRI-KPDC-00001019_2.json index d74634cbd3..0030f9a803 100644 --- a/datasets/KOPRI-KPDC-00001019_2.json +++ b/datasets/KOPRI-KPDC-00001019_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001019_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001020_2.json b/datasets/KOPRI-KPDC-00001020_2.json index 2370fd617c..9df8ade097 100644 --- a/datasets/KOPRI-KPDC-00001020_2.json +++ b/datasets/KOPRI-KPDC-00001020_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001020_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001021_2.json b/datasets/KOPRI-KPDC-00001021_2.json index 2d17489cfa..10235483e7 100644 --- a/datasets/KOPRI-KPDC-00001021_2.json +++ b/datasets/KOPRI-KPDC-00001021_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001021_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001022_2.json b/datasets/KOPRI-KPDC-00001022_2.json index dfe4db13e2..0fdad21951 100644 --- a/datasets/KOPRI-KPDC-00001022_2.json +++ b/datasets/KOPRI-KPDC-00001022_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001022_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.\nThis product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001023_1.json b/datasets/KOPRI-KPDC-00001023_1.json index 19806dd188..8c31808433 100644 --- a/datasets/KOPRI-KPDC-00001023_1.json +++ b/datasets/KOPRI-KPDC-00001023_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001023_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inflammation mediated by the innate immune system is an organism\u2019s protective mechanism against\r\ninfectious environmental risk factors. It is also a driver of the pathogeneses of various human\r\ndiseases, including cancer development and progression. Microalgae are increasingly being focused\r\non as sources of bioactive molecules with therapeutic potential against various diseases.\r\nFurthermore, the antioxidant, anti-inflammatory, and anticancer potentials of microalgae and their\r\nsecondary metabolites have been widely reported. However, the underlying mechanisms remain to\r\nbe elucidated. Therefore, in this study, we investigated the molecular mechanisms underlying the\r\nanti-inflammatory and anticancer activities of the ethanol extract of the Antarctic freshwater\r\nmicroalga Micractinium sp. (ETMI) by several in vitro assays using RAW 264.7 macrophages and\r\nHCT116 human colon cancer cells. ETMI exerted its anti-inflammatory activity by modulating the\r\nmain inflammatory indicators such as cyclooxygenase (COX)-2, interleukin (IL)-6, inducible nitric\r\noxide synthase (iNOS), tumor necrosis factor (TNF)-\u03b1, and nitric oxide (NO) in a dose-dependent\r\nmanner. In addition, ETMI exerted cytotoxic activity against HCT116 cells in a dose-dependent\r\nmanner, leading to significantly reduced cancer cell proliferation. Further, it induced cell cycle arrest\r\nin the G1 phase through the regulation of hallmark genes of the G1/S phase transition, including\r\nCDKN1A, and cyclin-dependent kinase 4 and 6 (CDK4 and CDK6, respectively). At the transcriptional\r\nlevel, the expression of CDKN1A gradually increased in response to ETMI treatment while that of\r\nCDK4 and CDK6 decreased. Taken together, our findings suggest that the anti-inflammatory and\r\nanticancer activities of the Antarctic freshwater microalga, Micractinium sp., and ETMI may provide a\r\nnew clue for understanding the molecular link between inflammation and cancer and that ETMI may\r\nbe a potential anticancer agent for targeted therapy of colorectal cancer.\nThe main purpose of this study was to assess the anti-inflammatory and cytotoxic effects of the ethanol extract from the polar microalga Micractinium sp. (ETMI) on the human colon cancer cell line, HCT116.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001024_1.json b/datasets/KOPRI-KPDC-00001024_1.json index 80184105d4..477d28ee2e 100644 --- a/datasets/KOPRI-KPDC-00001024_1.json +++ b/datasets/KOPRI-KPDC-00001024_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001024_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPS1m is termed as 'cell-protection substances 1 mutant' capable of protection of the cells and lowering freezing points below melting points. Antarctic marine diatom, Chaetoceros neogracile was reported to produce and secrete CPS1m.\nCPS1m genes will be utilized to protect the skin and tissue cells by applying any valuable products.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001025_1.json b/datasets/KOPRI-KPDC-00001025_1.json index fbf5bd1baa..fc1e0384af 100644 --- a/datasets/KOPRI-KPDC-00001025_1.json +++ b/datasets/KOPRI-KPDC-00001025_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001025_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 2018 Amundsen cruise, seawater samples for dissolved organic carbon and nitrogen analyses were collected in the Amundsen Sea, Antarctica.\nTo investigate the distributions of dissolved organic carbon and nitrogen, and estimate flux of these compounds in the Amundsen Sea", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001026_1.json b/datasets/KOPRI-KPDC-00001026_1.json index f348018ad4..5961260956 100644 --- a/datasets/KOPRI-KPDC-00001026_1.json +++ b/datasets/KOPRI-KPDC-00001026_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001026_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2015 Ross Sea core ,Antarctica\r\nRS15-GC41_Density\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001027_1.json b/datasets/KOPRI-KPDC-00001027_1.json index 5203c5138e..c50ec00aa4 100644 --- a/datasets/KOPRI-KPDC-00001027_1.json +++ b/datasets/KOPRI-KPDC-00001027_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001027_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2013 Weddell Sea, Antarctica\r\nWAP13-GC47_Opal Silicate Analysis\nClimate change observation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001028_2.json b/datasets/KOPRI-KPDC-00001028_2.json index e153ec7cab..b5ad22b23d 100644 --- a/datasets/KOPRI-KPDC-00001028_2.json +++ b/datasets/KOPRI-KPDC-00001028_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001028_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001029_1.json b/datasets/KOPRI-KPDC-00001029_1.json index 1f740a0005..e06fede389 100644 --- a/datasets/KOPRI-KPDC-00001029_1.json +++ b/datasets/KOPRI-KPDC-00001029_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001029_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Evaluation of skin protection efficacy of KSF0031 and compounds\nThe effect of KSF0031 derived material on skin cell protection and its mechanism of action", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001030_1.json b/datasets/KOPRI-KPDC-00001030_1.json index 082fa27f4c..e2a769681c 100644 --- a/datasets/KOPRI-KPDC-00001030_1.json +++ b/datasets/KOPRI-KPDC-00001030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001031_3.json b/datasets/KOPRI-KPDC-00001031_3.json index ed552e34a0..e275e7e36c 100644 --- a/datasets/KOPRI-KPDC-00001031_3.json +++ b/datasets/KOPRI-KPDC-00001031_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001031_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001032_2.json b/datasets/KOPRI-KPDC-00001032_2.json index 51357978bb..9692a01f7b 100644 --- a/datasets/KOPRI-KPDC-00001032_2.json +++ b/datasets/KOPRI-KPDC-00001032_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001032_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001033_1.json b/datasets/KOPRI-KPDC-00001033_1.json index 5c33409037..7d602d17f1 100644 --- a/datasets/KOPRI-KPDC-00001033_1.json +++ b/datasets/KOPRI-KPDC-00001033_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001033_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of Rn gas at JBS, Antarctica\nInvestigation of air mass path moving to the JBS, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001034_1.json b/datasets/KOPRI-KPDC-00001034_1.json index be6357b4d5..99e60ef50e 100644 --- a/datasets/KOPRI-KPDC-00001034_1.json +++ b/datasets/KOPRI-KPDC-00001034_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001034_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of Rn gas at KSG, Antarctica\nInvestigation of air mass path moving to the KSG, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001035_2.json b/datasets/KOPRI-KPDC-00001035_2.json index de02115140..e0df27098b 100644 --- a/datasets/KOPRI-KPDC-00001035_2.json +++ b/datasets/KOPRI-KPDC-00001035_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001035_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of ionic species in the upper section of firn core from Styx glacier in Antarctica\r\nDetermination of ionic species in the upper section of firn core from Styx glacier in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001036_2.json b/datasets/KOPRI-KPDC-00001036_2.json index 259ed04df8..4dbbe99e1e 100644 --- a/datasets/KOPRI-KPDC-00001036_2.json +++ b/datasets/KOPRI-KPDC-00001036_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001036_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001037_2.json b/datasets/KOPRI-KPDC-00001037_2.json index 36fad4e858..c3aaa1876d 100644 --- a/datasets/KOPRI-KPDC-00001037_2.json +++ b/datasets/KOPRI-KPDC-00001037_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001037_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001038_2.json b/datasets/KOPRI-KPDC-00001038_2.json index a4386c435d..3e9c707837 100644 --- a/datasets/KOPRI-KPDC-00001038_2.json +++ b/datasets/KOPRI-KPDC-00001038_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001038_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001039_2.json b/datasets/KOPRI-KPDC-00001039_2.json index 35844e226c..ef7ba5b726 100644 --- a/datasets/KOPRI-KPDC-00001039_2.json +++ b/datasets/KOPRI-KPDC-00001039_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001039_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001040_2.json b/datasets/KOPRI-KPDC-00001040_2.json index 8e41ad0b44..449164ccb5 100644 --- a/datasets/KOPRI-KPDC-00001040_2.json +++ b/datasets/KOPRI-KPDC-00001040_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001040_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001041_3.json b/datasets/KOPRI-KPDC-00001041_3.json index c34f8f8c3c..3136f487ce 100644 --- a/datasets/KOPRI-KPDC-00001041_3.json +++ b/datasets/KOPRI-KPDC-00001041_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001041_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) number concentration at King Sejong Station\nInstruments : CCN-100(TSI, USA)\nTime resolution : 1Hz", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001042_2.json b/datasets/KOPRI-KPDC-00001042_2.json index 9c6bfd896c..3a34f7a7ee 100644 --- a/datasets/KOPRI-KPDC-00001042_2.json +++ b/datasets/KOPRI-KPDC-00001042_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001042_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001043_2.json b/datasets/KOPRI-KPDC-00001043_2.json index 6695796bf3..cf6f2a47de 100644 --- a/datasets/KOPRI-KPDC-00001043_2.json +++ b/datasets/KOPRI-KPDC-00001043_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001043_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001044_2.json b/datasets/KOPRI-KPDC-00001044_2.json index 8011de2264..ea232546fb 100644 --- a/datasets/KOPRI-KPDC-00001044_2.json +++ b/datasets/KOPRI-KPDC-00001044_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001044_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001045_2.json b/datasets/KOPRI-KPDC-00001045_2.json index edf31209dc..cac9ebd117 100644 --- a/datasets/KOPRI-KPDC-00001045_2.json +++ b/datasets/KOPRI-KPDC-00001045_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001045_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001046_2.json b/datasets/KOPRI-KPDC-00001046_2.json index 171906d104..0e71fe8d8e 100644 --- a/datasets/KOPRI-KPDC-00001046_2.json +++ b/datasets/KOPRI-KPDC-00001046_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001046_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001047_2.json b/datasets/KOPRI-KPDC-00001047_2.json index 857a0d7a0b..d26047958e 100644 --- a/datasets/KOPRI-KPDC-00001047_2.json +++ b/datasets/KOPRI-KPDC-00001047_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001047_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001048_2.json b/datasets/KOPRI-KPDC-00001048_2.json index 231e435f8a..00b13809cd 100644 --- a/datasets/KOPRI-KPDC-00001048_2.json +++ b/datasets/KOPRI-KPDC-00001048_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001048_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001049_1.json b/datasets/KOPRI-KPDC-00001049_1.json index 1bc43db388..d99d00c90b 100644 --- a/datasets/KOPRI-KPDC-00001049_1.json +++ b/datasets/KOPRI-KPDC-00001049_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001049_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The phytoplantkon biomass (chl-a) was investigated in the Amundsen Sea, Antarctica from January to February 2018. This data includes investigator and locality for chlorophyll-a concentration.\nThe investigation of chlorophyll-a concentration in the Amundsen Sea, Antarctica 2018.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001050_1.json b/datasets/KOPRI-KPDC-00001050_1.json index 89d7192ef1..20a779e8cb 100644 --- a/datasets/KOPRI-KPDC-00001050_1.json +++ b/datasets/KOPRI-KPDC-00001050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric DMS mixing ratio measured at King Sejong Station in December 2017 by using custom-made trapping and desorption system equipped with pulsed flame photometric detector.\nMonitoring of atmospheric DMS mixing ration at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001051_1.json b/datasets/KOPRI-KPDC-00001051_1.json index 7b07cc6c33..6f3b3c615d 100644 --- a/datasets/KOPRI-KPDC-00001051_1.json +++ b/datasets/KOPRI-KPDC-00001051_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001051_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric DMS mixing ratio measured at King Sejong Station in January 2018 by using custom-made trapping and desorption system equipped with pulsed flame photometric detector.\nMonitoring of atmospheric DMS mixing ration at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001052_2.json b/datasets/KOPRI-KPDC-00001052_2.json index a70c383fbc..de6640d047 100644 --- a/datasets/KOPRI-KPDC-00001052_2.json +++ b/datasets/KOPRI-KPDC-00001052_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001052_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the ANA08B western Antarctic expedition, sediment coring was performed with a box corer consisting of a headstand weighing ca. 350 kg attached to the top of a rectangular steel tube (30\u00d740\u00d760 cm) at 4 stations. The coring tool was deployed from the A frame in the stern using the Deep Sea Traction (DST) winch with spectra rope. Once on deck the top centimeter of sediments captured in the steel tube was skimmed for palynological and organic geochemical determinations. The sediment in the box core was subsampled into a cylindrical push core and the rest of the material left in the box was examined for megafauna presence and then discarded.\r\nA sediment coring program was conducted during the ANA08B cruise to meet several scientific objectives: i) to obtain integrated sedimentary records of fossilized micro-flora to provide spatio-temporal distributions of planktonic population of the Amundsen Sea area, and ii) to attain data on past changes in chemical compositions of seafloor sediments assessing natural instabilities in biogeochemical processes in this region. Main purpose of this sediment coring processes is to retrieve new and undisturbed sediments from the selected research target areas using a box coring device.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001053_1.json b/datasets/KOPRI-KPDC-00001053_1.json index eb25ded1e1..601ebedf6f 100644 --- a/datasets/KOPRI-KPDC-00001053_1.json +++ b/datasets/KOPRI-KPDC-00001053_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001053_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in waters of Amundsen Sea in Antarctica, the community of phytoplankton. The temporal influences of environmental factors on marine phytoplankton community were investigated in Amundsen Sea in Antarctica.\nInvestigation of marine phytoplankton abundance in the waters around the Amundsen Sea in Antarctica for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001054_2.json b/datasets/KOPRI-KPDC-00001054_2.json index 400638f6c6..66e3e023f9 100644 --- a/datasets/KOPRI-KPDC-00001054_2.json +++ b/datasets/KOPRI-KPDC-00001054_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001054_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We deployed four autonomous phase-sensitive radio echo sounders (ApRES) on the Getz Ice Shelf (GIS) to measure the ice-shelf basal melt rates and horizontal moving speed of the ice shelf. The four sites are located on the west side of GIS: GW1 near the Siple Island and GW2~GW4 to the southwest from GW1. The melt rates were calculated from the ice-shelf thinning rates, offset by the strain rate through the ice column.\nApRES observation is to measure the ice-shelf basal melt rates and horizontal moving speed of the ice shelf.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001055_1.json b/datasets/KOPRI-KPDC-00001055_1.json index b123227072..982208a689 100644 --- a/datasets/KOPRI-KPDC-00001055_1.json +++ b/datasets/KOPRI-KPDC-00001055_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001055_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variable fluorescence was measured with a new miniaturized Fluorescence Induction and Relaxation System during the Amundsen 2018 cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001056_1.json b/datasets/KOPRI-KPDC-00001056_1.json index 65d9a19823..97b9abd585 100644 --- a/datasets/KOPRI-KPDC-00001056_1.json +++ b/datasets/KOPRI-KPDC-00001056_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001056_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amino acid and DNA sequences for the production of metabolites in Antarctic copepod T. kingsejongensis\nGenetic information to understand mechanism of useful metabolites", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001057_1.json b/datasets/KOPRI-KPDC-00001057_1.json index 94e81c82ed..675ab8cfb9 100644 --- a/datasets/KOPRI-KPDC-00001057_1.json +++ b/datasets/KOPRI-KPDC-00001057_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001057_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of extracts derived from Antarctic lichens and fungi were made. Many extracts can be used in natural product research.\nTo provide samples for finding bioactive substances", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001058_1.json b/datasets/KOPRI-KPDC-00001058_1.json index 7ee722312c..8033d10b26 100644 --- a/datasets/KOPRI-KPDC-00001058_1.json +++ b/datasets/KOPRI-KPDC-00001058_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001058_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A list of metabolites derived from Antarctic microorganisms and lichens was produced. It can be used to find new substances.\nTo develop new natural medicine", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001059_2.json b/datasets/KOPRI-KPDC-00001059_2.json index b5a5ed31ff..220e0ea060 100644 --- a/datasets/KOPRI-KPDC-00001059_2.json +++ b/datasets/KOPRI-KPDC-00001059_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001059_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the phytoplankton abundance was investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica, 2016.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001060_3.json b/datasets/KOPRI-KPDC-00001060_3.json index 2a544c51ef..0cf8757ce0 100644 --- a/datasets/KOPRI-KPDC-00001060_3.json +++ b/datasets/KOPRI-KPDC-00001060_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001060_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed to the North of the Drygalski Ice Tongue on 12 December 2014 as a part of the ANA05A research cruise, and it was recovered on 10 December 2015\nTo monitor physical properties(Temperature, Salinity, Current) of ocean water in the north of the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001061_2.json b/datasets/KOPRI-KPDC-00001061_2.json index 1a8ac6ac36..36325f339e 100644 --- a/datasets/KOPRI-KPDC-00001061_2.json +++ b/datasets/KOPRI-KPDC-00001061_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001061_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in December, 2015. LADCP profiles were collected at 22 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001062_2.json b/datasets/KOPRI-KPDC-00001062_2.json index 24eae34df6..47ff72d9d8 100644 --- a/datasets/KOPRI-KPDC-00001062_2.json +++ b/datasets/KOPRI-KPDC-00001062_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001062_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in December, 2014. CTD profiles were collected at 23 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001063_2.json b/datasets/KOPRI-KPDC-00001063_2.json index cd126d26df..329405e9c7 100644 --- a/datasets/KOPRI-KPDC-00001063_2.json +++ b/datasets/KOPRI-KPDC-00001063_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001063_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in January, 2017. CTD profiles were collected at 53 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001064_2.json b/datasets/KOPRI-KPDC-00001064_2.json index 63c8a66400..747f1616cb 100644 --- a/datasets/KOPRI-KPDC-00001064_2.json +++ b/datasets/KOPRI-KPDC-00001064_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001064_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in December, 2014. LADCP profiles were collected at 23 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001065_2.json b/datasets/KOPRI-KPDC-00001065_2.json index d5a053bc41..a8338b8ef2 100644 --- a/datasets/KOPRI-KPDC-00001065_2.json +++ b/datasets/KOPRI-KPDC-00001065_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001065_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in January, 2017. LADCP profiles were collected at 53 stations.\nTo investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001066_1.json b/datasets/KOPRI-KPDC-00001066_1.json index 57ff7c0ddf..5a284bcd67 100644 --- a/datasets/KOPRI-KPDC-00001066_1.json +++ b/datasets/KOPRI-KPDC-00001066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of Marin macroinverterbrates around Jang Bogo Station (2017/18) in Antarctica\nTo establish the inventory of Antarctic marine inverterbrates", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001067_2.json b/datasets/KOPRI-KPDC-00001067_2.json index 559d055ef9..e86202dc90 100644 --- a/datasets/KOPRI-KPDC-00001067_2.json +++ b/datasets/KOPRI-KPDC-00001067_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001067_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the King Sejong Station in 2018. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001068_1.json b/datasets/KOPRI-KPDC-00001068_1.json index 8838a3086d..4c1d7395b2 100644 --- a/datasets/KOPRI-KPDC-00001068_1.json +++ b/datasets/KOPRI-KPDC-00001068_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001068_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal global radiation data measured at the King Sejong Station, King George Islands, Antarctica in 2018\nMonitoring of solar energy at the King Sejong Station and analysis of climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001069_1.json b/datasets/KOPRI-KPDC-00001069_1.json index 1ce15f2f17..f2aa2db8cf 100644 --- a/datasets/KOPRI-KPDC-00001069_1.json +++ b/datasets/KOPRI-KPDC-00001069_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001069_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2018 at a coastal region of the King Sejong Station. Eddy co-variance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTo understand air-ocean-sea-ice interactions in terms of momentum/energy/H2O/CO2 at the coastal Antarctic region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001070_1.json b/datasets/KOPRI-KPDC-00001070_1.json index 10a9a9979c..18f1967b17 100644 --- a/datasets/KOPRI-KPDC-00001070_1.json +++ b/datasets/KOPRI-KPDC-00001070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper air observation has been made once a week at 12 UTC Wednesday from March to mid-November by manual launch of radiosonde. Data consist of pressure, temperature, relative humidity, wind speed and wind direction every two-second up to normally about 20 km.\r\nFrom Nov 16th to end of December, the sounding was carried out 12 UTC everyday as contribution to YOPP-SH special observation.\n- Analysis of upper air structure over the King Sejong Station, Antarctica\r\n- Improvement of weather analysis in Antarctic regions by providing additional upper air data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001071_1.json b/datasets/KOPRI-KPDC-00001071_1.json index 3d569baaf0..dbf4e67fde 100644 --- a/datasets/KOPRI-KPDC-00001071_1.json +++ b/datasets/KOPRI-KPDC-00001071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) has been in operation since April 2017 near Climate Change Tower of Ny-Alesund, Svalbard where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 1.5 km on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field are obtained using PPI and RHI modes, respectively.\nTo understand Arctic boundary layer(BL) structure and interaction between cloud-BL in the Arctic", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001072_1.json b/datasets/KOPRI-KPDC-00001072_1.json index be78df9c99..9fe94c59e9 100644 --- a/datasets/KOPRI-KPDC-00001072_1.json +++ b/datasets/KOPRI-KPDC-00001072_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001072_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A forward scatter cloud droplet particle counter CDP-2 (DMT, USA) has been in operation since September 2017 at the Zeppelin Observatory, Ny-Alesund . CDP2 produces information of number concentration and size of cloud droplet when cloud hits the Zeppelin Mt.\n- To understand micro-physical characteristics of Arctic cloud and its temporal variation\r\n- To understand the various effects of Arctic cloud in Arctic climate system", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001073_2.json b/datasets/KOPRI-KPDC-00001073_2.json index fa4977c0ed..645f78df18 100644 --- a/datasets/KOPRI-KPDC-00001073_2.json +++ b/datasets/KOPRI-KPDC-00001073_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001073_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, ocean pCO2 and its relevant physical, chemical, biological parameters start monitoring in 2015. These include atmospheric CO2 concentration, ocean pCO2, seawater temperature, salinity, dissolved oxygen, pH, chlorophyll-a, CDOM, and turbidity.\nTo investigate the trend of ocean acidification in the coastal waters of the Terra Nova Bay", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001074_6.json b/datasets/KOPRI-KPDC-00001074_6.json index 9d89d19c24..6fb5c39273 100644 --- a/datasets/KOPRI-KPDC-00001074_6.json +++ b/datasets/KOPRI-KPDC-00001074_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001074_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic fauna specimens collected by SCUBA at the depth of approximately 30 m in Marian Cove, Antarctica from Dec. 2017 to Feb. 2018.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001075_6.json b/datasets/KOPRI-KPDC-00001075_6.json index dd55f98260..16c3447778 100644 --- a/datasets/KOPRI-KPDC-00001075_6.json +++ b/datasets/KOPRI-KPDC-00001075_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001075_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic fauna specimens collected by agassiz trawl at the dept 67 to 1570 in King George Island, Antarctica from Apr. to May 2018", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001076_1.json b/datasets/KOPRI-KPDC-00001076_1.json index 5f405d7b58..904de5ebe7 100644 --- a/datasets/KOPRI-KPDC-00001076_1.json +++ b/datasets/KOPRI-KPDC-00001076_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001076_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pyranometer, Pyrgeometer, Total Ultraviolet, UV-A and UV-B are operated year round continously. Downward solar radiation, atmospheric longwave radiation, total ultraviolet radiation, UV-A and UV-B are sampled every second and ten-minute averaged data are recorded on a data logger\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001077_1.json b/datasets/KOPRI-KPDC-00001077_1.json index e08d838fa7..46c72dd5ef 100644 --- a/datasets/KOPRI-KPDC-00001077_1.json +++ b/datasets/KOPRI-KPDC-00001077_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001077_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 and CH4 concentration measurement started using a Cavity Ring Down Spectroscopy(CRDS) at the Antarctic Jang Bogo Station in 2015. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 and CH4 concentration at the Jang Bogo Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001078_2.json b/datasets/KOPRI-KPDC-00001078_2.json index e415e86452..83f171bfc4 100644 --- a/datasets/KOPRI-KPDC-00001078_2.json +++ b/datasets/KOPRI-KPDC-00001078_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001078_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001079_1.json b/datasets/KOPRI-KPDC-00001079_1.json index 33d8903f6e..0e375586a6 100644 --- a/datasets/KOPRI-KPDC-00001079_1.json +++ b/datasets/KOPRI-KPDC-00001079_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001079_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON gravity meter data in Arctic cruise\nARAON gravity meter data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001080_1.json b/datasets/KOPRI-KPDC-00001080_1.json index 71bfa50a6f..ed0a2c1797 100644 --- a/datasets/KOPRI-KPDC-00001080_1.json +++ b/datasets/KOPRI-KPDC-00001080_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001080_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ARAON wind sensor data in Arctic cruise\r\nField Information :\r\nUTC Date,UTC Time,Latitude,N/S,Longitude,E/W,[MWV] Wind angle,[MWV] R/T,[MWV] Wind speed,[MWV] Wind speed units,[MWV] Status\nARAON wind sensor data in Arctic cruise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001081_1.json b/datasets/KOPRI-KPDC-00001081_1.json index cd695313d4..523bd3615d 100644 --- a/datasets/KOPRI-KPDC-00001081_1.json +++ b/datasets/KOPRI-KPDC-00001081_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001081_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanic Nitrous oxdie(N2O) flux at Marian Cove in January 2018 estimated by using sea surface N2O concentration and wind speed.\nMonitoring air-sea gas exchange of N2O at Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001082_1.json b/datasets/KOPRI-KPDC-00001082_1.json index 8e19619fe9..b77a4446f2 100644 --- a/datasets/KOPRI-KPDC-00001082_1.json +++ b/datasets/KOPRI-KPDC-00001082_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001082_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanic Nitrous oxdie(N2O) flux at Marian Cove in February 2017 estimated by using sea surface N2O concentration and wind speed.\nMonitoring air-sea gas exchange of N2O at Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001083_1.json b/datasets/KOPRI-KPDC-00001083_1.json index 74f7957bb5..f590ea4f12 100644 --- a/datasets/KOPRI-KPDC-00001083_1.json +++ b/datasets/KOPRI-KPDC-00001083_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001083_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanic emission of the trace gas dimethyl sulfide (DMS) is the major source of reduced sulfur into the marine boundary layer, influencing atmospheric chemistry (von Glasow et al. 2004) and contributing to the radiative properties of oceanic clouds (Ayers et al 1991, Charlson et al. 1987, Korhonen et al 2008). DMS is an enzymatic breakdown product of dimethylsulfoniopropionate (DMSP) synthesised by phytoplankton. Both DMS and DMSP also contribute significant proportions of the carbon and sulphur flux through microbial foodwebs (Simo et al. 2004) and may play important roles as infochemicals, influencing predator prey interactions (Wolfe et al. 1997). \r\nWe observed the horizonal and vertical (upper 100 m) distributions of DMS in the Amundsen Sea using a membrane inlet mass spectometer (MIMS).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001084_1.json b/datasets/KOPRI-KPDC-00001084_1.json index fbe4b11d33..aaf553f648 100644 --- a/datasets/KOPRI-KPDC-00001084_1.json +++ b/datasets/KOPRI-KPDC-00001084_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001084_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coastal polynyas in the Amundsen Sea are known for high primary production in austral summer (Arrigo et al., 2012). Rapid environmental changes in the Amundsen Sea poses questions such as how the biological and chemical systems respond to the environmental changes. During the cruise, we measured net community production (NCP), defined as the difference between autotrophic photosynthesis and (autotrophic and heterotrophic) respira-tion, as a measure of biological pump. By measuring chemically and biologically inert Ar to-gether with O2, it is possible to remove O2 variation by physical processes (e.g., air tempera-ture and pressure change and mixing of water masses) and deduce O2 variation by biological processes (Craig and Hayward, 1987). \r\nIn order to investigate the spatial and temporal variations of NCP in the surface wa-ters of the polynyas and the proposed correlation between them, we observed the horizontal and vertical (upper 100 m) distributions of \u00ce\u201dO2/Ar, a proxy of NCP, in the Amundsen Sea using a membrane inlet mass spectrometer (MIMS) (Tortell, 2005).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001085_1.json b/datasets/KOPRI-KPDC-00001085_1.json index 5266996fbe..5b9e3d2cac 100644 --- a/datasets/KOPRI-KPDC-00001085_1.json +++ b/datasets/KOPRI-KPDC-00001085_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001085_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Nitrous oxdie(N2O) at Marian Cove in February 2017 measured by Cavity ring-down spectrometer(CRDS)\nMonitoring of N2O concentration at Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001086_1.json b/datasets/KOPRI-KPDC-00001086_1.json index 39f5575ef9..1f86f2ab8c 100644 --- a/datasets/KOPRI-KPDC-00001086_1.json +++ b/datasets/KOPRI-KPDC-00001086_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001086_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Nitrous oxdie(N2O) at Marian Cove in January 2018 measured by Cavity ring-down spectrometer(CRDS)\nMonitoring of N2O concentration at Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001087_2.json b/datasets/KOPRI-KPDC-00001087_2.json index c08c0c73c2..18db6d4bbf 100644 --- a/datasets/KOPRI-KPDC-00001087_2.json +++ b/datasets/KOPRI-KPDC-00001087_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001087_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every a second. The minimum observation height is over 20 km.\r\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001088_1.json b/datasets/KOPRI-KPDC-00001088_1.json index d94c2af0d7..e3f2d70030 100644 --- a/datasets/KOPRI-KPDC-00001088_1.json +++ b/datasets/KOPRI-KPDC-00001088_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001088_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Molecular complex of organic aerosol particles collected at Arctic Dasan station analyzed by Ultra high resolution mass spectrometry (15T-FT-ICR MS)\nIdentifying molecular complex of organic aerosol collected at Arctic Dasan station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001089_1.json b/datasets/KOPRI-KPDC-00001089_1.json index 1f2c471a15..f31106b769 100644 --- a/datasets/KOPRI-KPDC-00001089_1.json +++ b/datasets/KOPRI-KPDC-00001089_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001089_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Molecular complex of organofulfates collected at Arctic Dasan station analyzed by Ultra high resolution mass spectrometry (15T-FT-ICR MS)\nIdentifying molecular complex of organic aerosol collected at Arctic Dasan station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001090_1.json b/datasets/KOPRI-KPDC-00001090_1.json index d355534251..8ad38ec761 100644 --- a/datasets/KOPRI-KPDC-00001090_1.json +++ b/datasets/KOPRI-KPDC-00001090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR4 are operated year round continuously. Net radiation is sampled every second and 30-minutes\r\n averaged data are recorded on a data logger\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001091_1.json b/datasets/KOPRI-KPDC-00001091_1.json index 29dc6961b3..f358f25ef2 100644 --- a/datasets/KOPRI-KPDC-00001091_1.json +++ b/datasets/KOPRI-KPDC-00001091_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001091_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR4 are operated year round continuously. Net radiation is sampled every second and 30-minutes\r\n averaged data are recorded on a data logger\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001092_1.json b/datasets/KOPRI-KPDC-00001092_1.json index b491c17cda..120d1dc687 100644 --- a/datasets/KOPRI-KPDC-00001092_1.json +++ b/datasets/KOPRI-KPDC-00001092_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001092_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Kongsfjorden is fjord of the west Spitsbergen and located in the Svalbard archipelago. Chlorophyll a samples were collected from the Kongsfjorden from April and Jun, 2018 onboard the Teisten.\nThe purpose of this study were (i) to investigate spatial variation in phytoplankton size structure based on chlorophyll a data (ii) to estimate contribution of pico-phytoplankton to total biomass during April and Jun in the Kongsfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001093_2.json b/datasets/KOPRI-KPDC-00001093_2.json index a5d5df3ac8..58b36931de 100644 --- a/datasets/KOPRI-KPDC-00001093_2.json +++ b/datasets/KOPRI-KPDC-00001093_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001093_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001094_2.json b/datasets/KOPRI-KPDC-00001094_2.json index 3622dd4014..68e708f3e8 100644 --- a/datasets/KOPRI-KPDC-00001094_2.json +++ b/datasets/KOPRI-KPDC-00001094_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001094_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001095_2.json b/datasets/KOPRI-KPDC-00001095_2.json index 3d62c87563..a006e9f2f8 100644 --- a/datasets/KOPRI-KPDC-00001095_2.json +++ b/datasets/KOPRI-KPDC-00001095_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001095_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001096_2.json b/datasets/KOPRI-KPDC-00001096_2.json index b78735f95c..cc4eed3e96 100644 --- a/datasets/KOPRI-KPDC-00001096_2.json +++ b/datasets/KOPRI-KPDC-00001096_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001096_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001097_2.json b/datasets/KOPRI-KPDC-00001097_2.json index 02922caf45..941c048f57 100644 --- a/datasets/KOPRI-KPDC-00001097_2.json +++ b/datasets/KOPRI-KPDC-00001097_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001097_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001098_2.json b/datasets/KOPRI-KPDC-00001098_2.json index 34205ddc7d..fccd0c3a22 100644 --- a/datasets/KOPRI-KPDC-00001098_2.json +++ b/datasets/KOPRI-KPDC-00001098_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001098_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We have investigated marine algal diversity and distribution in Maxwell Bay, King George Island in 2017-2018 season.\r\nThe main goal is to address how marine algal biodiversity and and subtidal distribution in Maxwell Bay, King George Island responds to climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001099_5.json b/datasets/KOPRI-KPDC-00001099_5.json index e845181736..55cec2fb56 100644 --- a/datasets/KOPRI-KPDC-00001099_5.json +++ b/datasets/KOPRI-KPDC-00001099_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001099_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001100_3.json b/datasets/KOPRI-KPDC-00001100_3.json index 571cc3c83c..1ba912d5b8 100644 --- a/datasets/KOPRI-KPDC-00001100_3.json +++ b/datasets/KOPRI-KPDC-00001100_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001100_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station (KSS), Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001101_5.json b/datasets/KOPRI-KPDC-00001101_5.json index ef9293e1a7..3f7e7fb47d 100644 --- a/datasets/KOPRI-KPDC-00001101_5.json +++ b/datasets/KOPRI-KPDC-00001101_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001101_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at KSS station, Antarctica\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001102_3.json b/datasets/KOPRI-KPDC-00001102_3.json index 11e18abde3..ab97c2d4a4 100644 --- a/datasets/KOPRI-KPDC-00001102_3.json +++ b/datasets/KOPRI-KPDC-00001102_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001102_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001103_3.json b/datasets/KOPRI-KPDC-00001103_3.json index 47e8929681..82faa3fa84 100644 --- a/datasets/KOPRI-KPDC-00001103_3.json +++ b/datasets/KOPRI-KPDC-00001103_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001103_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001104_3.json b/datasets/KOPRI-KPDC-00001104_3.json index c08d9e8202..4bded8c9a2 100644 --- a/datasets/KOPRI-KPDC-00001104_3.json +++ b/datasets/KOPRI-KPDC-00001104_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001104_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Electron density profile, plasma drift velocity, and ionospheric tilt information measured from VIPIR (ionosonde) at Jang Bogo Station, Antarctica\nStudy of the ionospheric characteristics in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001105_4.json b/datasets/KOPRI-KPDC-00001105_4.json index d48af35482..0dafd7c52c 100644 --- a/datasets/KOPRI-KPDC-00001105_4.json +++ b/datasets/KOPRI-KPDC-00001105_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001105_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Jang Bogo Station (JBS), Antarctica\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001106_3.json b/datasets/KOPRI-KPDC-00001106_3.json index 04e5b9771a..d7d84358f7 100644 --- a/datasets/KOPRI-KPDC-00001106_3.json +++ b/datasets/KOPRI-KPDC-00001106_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001106_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cosmic ray origin neutron count measured from neutron monitor at Jang Bogo Station, Antarctica\nStudy of the variation of neutron count in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001107_4.json b/datasets/KOPRI-KPDC-00001107_4.json index d187eb693d..bd07128269 100644 --- a/datasets/KOPRI-KPDC-00001107_4.json +++ b/datasets/KOPRI-KPDC-00001107_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001107_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station, Antarctica\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001108_4.json b/datasets/KOPRI-KPDC-00001108_4.json index ce4fe7d5f7..7a01750daf 100644 --- a/datasets/KOPRI-KPDC-00001108_4.json +++ b/datasets/KOPRI-KPDC-00001108_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001108_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at Jang Bogo Station, Antarctica\nStudy of the aurora characteristics in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001109_4.json b/datasets/KOPRI-KPDC-00001109_4.json index ebe005f420..ea6490e22c 100644 --- a/datasets/KOPRI-KPDC-00001109_4.json +++ b/datasets/KOPRI-KPDC-00001109_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001109_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer (SCM) at Jang Bogo Station, antarctica\nStudy of the activity of ultra low frequency (ULF) wave in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001110_4.json b/datasets/KOPRI-KPDC-00001110_4.json index 5709766f31..d0acae4fe3 100644 --- a/datasets/KOPRI-KPDC-00001110_4.json +++ b/datasets/KOPRI-KPDC-00001110_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001110_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Febry-Perot interferometer (FPI) at Dasan station, Arctic\nStudy of the atmosphere wave activities in the upper atmosphere in the southern/northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001111_4.json b/datasets/KOPRI-KPDC-00001111_4.json index dd7357d36b..c2eabc8359 100644 --- a/datasets/KOPRI-KPDC-00001111_4.json +++ b/datasets/KOPRI-KPDC-00001111_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001111_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctica\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001112_4.json b/datasets/KOPRI-KPDC-00001112_4.json index 463581fcba..b5bc56d2ae 100644 --- a/datasets/KOPRI-KPDC-00001112_4.json +++ b/datasets/KOPRI-KPDC-00001112_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001112_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway\nStudy of the aurora (proton) characteristics in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001113_3.json b/datasets/KOPRI-KPDC-00001113_3.json index c1164b3faa..6851544486 100644 --- a/datasets/KOPRI-KPDC-00001113_3.json +++ b/datasets/KOPRI-KPDC-00001113_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001113_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Sweden\nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001114_4.json b/datasets/KOPRI-KPDC-00001114_4.json index 955cf38dc7..3fae41ed80 100644 --- a/datasets/KOPRI-KPDC-00001114_4.json +++ b/datasets/KOPRI-KPDC-00001114_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001114_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Kiruna, Sweden\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001115_2.json b/datasets/KOPRI-KPDC-00001115_2.json index 057e8d039e..e2f1541661 100644 --- a/datasets/KOPRI-KPDC-00001115_2.json +++ b/datasets/KOPRI-KPDC-00001115_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001115_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total electron content in the ionosphere over Kiruna, Sweden\nStudy of the statistical characteristics of ionosphere in northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001116_1.json b/datasets/KOPRI-KPDC-00001116_1.json index 826f621a3f..495a6a82a8 100644 --- a/datasets/KOPRI-KPDC-00001116_1.json +++ b/datasets/KOPRI-KPDC-00001116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in waters of Kongsfjorden in Svalbard, the community of phytoplankton. The spatial influences of environmental factors on marine phytoplankton community were investigated in Kongfjorden in Svalbard.\nInvestigation of marine phytoplankton abundance in the waters around the Kongsfjorden in Svalbard for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001117_1.json b/datasets/KOPRI-KPDC-00001117_1.json index b26b777def..0136549299 100644 --- a/datasets/KOPRI-KPDC-00001117_1.json +++ b/datasets/KOPRI-KPDC-00001117_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001117_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in surface waters of Kongsfjorden in Svalbard, the community of phytoplankton. The spatial influences of environmental factors on marine phytoplankton community were investigated in Kongfjorden in Svalbard.\nInvestigation of marine phytoplankton abundance in the surface waters around the Kongsfjorden in Svalbard for the monitoring by environmental change in the surface water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001118_1.json b/datasets/KOPRI-KPDC-00001118_1.json index e84bc4763a..b16fdfc414 100644 --- a/datasets/KOPRI-KPDC-00001118_1.json +++ b/datasets/KOPRI-KPDC-00001118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in waters of Kongsfjorden in Svalbard, the community of phytoplankton. The spatial influences of environmental factors on marine phytoplankton community were investigated in Kongfjorden in Svalbard.\nInvestigation of marine phytoplankton abundance in the waters around the Kongsfjorden in Svalbard for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001119_1.json b/datasets/KOPRI-KPDC-00001119_1.json index 81a6e5a61e..7c150ff225 100644 --- a/datasets/KOPRI-KPDC-00001119_1.json +++ b/datasets/KOPRI-KPDC-00001119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in waters of Kongsfjorden in Svalbard, the community of phytoplankton. The spatial influences of environmental factors on marine phytoplankton community were investigated in Kongfjorden in Svalbard.\nInvestigation of marine phytoplankton abundance in the waters around the Kongsfjorden in Svalbard for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001120_1.json b/datasets/KOPRI-KPDC-00001120_1.json index e1c93a6561..dd03335e64 100644 --- a/datasets/KOPRI-KPDC-00001120_1.json +++ b/datasets/KOPRI-KPDC-00001120_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001120_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the May, 2017.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001121_1.json b/datasets/KOPRI-KPDC-00001121_1.json index 5670a9f369..d41fe121ad 100644 --- a/datasets/KOPRI-KPDC-00001121_1.json +++ b/datasets/KOPRI-KPDC-00001121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the October, 2017.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001122_1.json b/datasets/KOPRI-KPDC-00001122_1.json index 74c8a6b4ab..345910dfe5 100644 --- a/datasets/KOPRI-KPDC-00001122_1.json +++ b/datasets/KOPRI-KPDC-00001122_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001122_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the April, 2018.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001123_1.json b/datasets/KOPRI-KPDC-00001123_1.json index 1b1ee78f38..9c0a8b1670 100644 --- a/datasets/KOPRI-KPDC-00001123_1.json +++ b/datasets/KOPRI-KPDC-00001123_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001123_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the June, 2018.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001124_4.json b/datasets/KOPRI-KPDC-00001124_4.json index d2451a9c03..ff135cbdb2 100644 --- a/datasets/KOPRI-KPDC-00001124_4.json +++ b/datasets/KOPRI-KPDC-00001124_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001124_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica\nStudy of the aurora characteristics in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001125_4.json b/datasets/KOPRI-KPDC-00001125_4.json index fd5cf9ee30..9096473864 100644 --- a/datasets/KOPRI-KPDC-00001125_4.json +++ b/datasets/KOPRI-KPDC-00001125_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001125_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001126_5.json b/datasets/KOPRI-KPDC-00001126_5.json index 118f8b25f3..43e118a055 100644 --- a/datasets/KOPRI-KPDC-00001126_5.json +++ b/datasets/KOPRI-KPDC-00001126_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001126_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001127_3.json b/datasets/KOPRI-KPDC-00001127_3.json index 437e8d321e..4502a1985f 100644 --- a/datasets/KOPRI-KPDC-00001127_3.json +++ b/datasets/KOPRI-KPDC-00001127_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001127_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001128_1.json b/datasets/KOPRI-KPDC-00001128_1.json index 1e605ff192..074ad24400 100644 --- a/datasets/KOPRI-KPDC-00001128_1.json +++ b/datasets/KOPRI-KPDC-00001128_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001128_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06 ~ 2018. 06) were collected.\nTo monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001129_1.json b/datasets/KOPRI-KPDC-00001129_1.json index e58bdd89e8..1e179e849a 100644 --- a/datasets/KOPRI-KPDC-00001129_1.json +++ b/datasets/KOPRI-KPDC-00001129_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001129_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected\nTo monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001130_1.json b/datasets/KOPRI-KPDC-00001130_1.json index fe6d1054ee..7a3a78036a 100644 --- a/datasets/KOPRI-KPDC-00001130_1.json +++ b/datasets/KOPRI-KPDC-00001130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Custum-made DMS analyzer was installed at the Storhofdi observatory, Iceland, and monitored the atmospheric DMS mixing ratio in 2017-208.\nAnalyzing in-situ DMs mixing ratio Storhofdi, Iceland.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001131_1.json b/datasets/KOPRI-KPDC-00001131_1.json index 11432c726b..84d503947f 100644 --- a/datasets/KOPRI-KPDC-00001131_1.json +++ b/datasets/KOPRI-KPDC-00001131_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001131_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001132_1.json b/datasets/KOPRI-KPDC-00001132_1.json index b412a2a622..bb39ecac6a 100644 --- a/datasets/KOPRI-KPDC-00001132_1.json +++ b/datasets/KOPRI-KPDC-00001132_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001132_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2017 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001133_1.json b/datasets/KOPRI-KPDC-00001133_1.json index 9c2befde47..6be329e0da 100644 --- a/datasets/KOPRI-KPDC-00001133_1.json +++ b/datasets/KOPRI-KPDC-00001133_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001133_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2017 at Nord, Greenland. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR5000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001134_2.json b/datasets/KOPRI-KPDC-00001134_2.json index ac73d06f80..ae647aac7e 100644 --- a/datasets/KOPRI-KPDC-00001134_2.json +++ b/datasets/KOPRI-KPDC-00001134_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001134_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001135_2.json b/datasets/KOPRI-KPDC-00001135_2.json index cbb74a201e..32a1596add 100644 --- a/datasets/KOPRI-KPDC-00001135_2.json +++ b/datasets/KOPRI-KPDC-00001135_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001135_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2/Soil temperature profile had been measured during summertime in 2018 at Council, Alaska.\nTo monitor and understand CO2 emission and soil temperature change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001136_6.json b/datasets/KOPRI-KPDC-00001136_6.json index 986b900a40..421b838613 100644 --- a/datasets/KOPRI-KPDC-00001136_6.json +++ b/datasets/KOPRI-KPDC-00001136_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001136_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMPS(nano and normal) measures the Concentration for each diameter Data on ARAON Arctic Cruise, 2017", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001137_4.json b/datasets/KOPRI-KPDC-00001137_4.json index c4ebe23bbe..d3da55cb08 100644 --- a/datasets/KOPRI-KPDC-00001137_4.json +++ b/datasets/KOPRI-KPDC-00001137_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001137_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of Condensation Particle Counter (CPC3776 and CPC3772) data on the ice-breaker(ARAON) in arctic ocean regions, 2017", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001138_5.json b/datasets/KOPRI-KPDC-00001138_5.json index 3c15d5fe7c..0adeeccfa9 100644 --- a/datasets/KOPRI-KPDC-00001138_5.json +++ b/datasets/KOPRI-KPDC-00001138_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001138_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OPC measures the concentration of aerosol for each diameter", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001139_1.json b/datasets/KOPRI-KPDC-00001139_1.json index 3e8c0d28b4..5f6db050bf 100644 --- a/datasets/KOPRI-KPDC-00001139_1.json +++ b/datasets/KOPRI-KPDC-00001139_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001139_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scattering coefficeint of Nephelometer on ARAON, Arctic ocean regions, 2017", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001140_4.json b/datasets/KOPRI-KPDC-00001140_4.json index 8f916963e1..045aeea4f6 100644 --- a/datasets/KOPRI-KPDC-00001140_4.json +++ b/datasets/KOPRI-KPDC-00001140_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001140_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of Black Carbon", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001141_5.json b/datasets/KOPRI-KPDC-00001141_5.json index e14bd4801e..126edd36e5 100644 --- a/datasets/KOPRI-KPDC-00001141_5.json +++ b/datasets/KOPRI-KPDC-00001141_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001141_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001142_1.json b/datasets/KOPRI-KPDC-00001142_1.json index bbf4edcd58..2585132f70 100644 --- a/datasets/KOPRI-KPDC-00001142_1.json +++ b/datasets/KOPRI-KPDC-00001142_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001142_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface ocean methane was monitored from August 31 to September 19 using a CRDS CH4/CO2 analyzing system along the cruise track of R/V Araon from Barrow, U.S.A., to Nome, U.S.A., while conducting an expedition in The Chukchi Sea and East Siberian Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth.\nTo investigate ocean emissions of CH4 and CO2 in the Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001143_1.json b/datasets/KOPRI-KPDC-00001143_1.json index 341f5c269d..f3feb0777a 100644 --- a/datasets/KOPRI-KPDC-00001143_1.json +++ b/datasets/KOPRI-KPDC-00001143_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001143_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric and marine CO2 and CH4 in the marine boundary layer were monitored from August 31 to September 19 by the GC 7890A along the cruise track of R/V Araon from Barrow, U.S.A., to Nome, U.S.A., carrying out an expedition in The Chukchi Sea and East Siberian Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth.\nTo investigate ocean emissions of CO2 and CH4 in the Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001144_1.json b/datasets/KOPRI-KPDC-00001144_1.json index bfd9b2a790..f58defe14b 100644 --- a/datasets/KOPRI-KPDC-00001144_1.json +++ b/datasets/KOPRI-KPDC-00001144_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001144_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface ocean pCO2 was monitored from August 31 to September 19 using an NDIR pCO2 analyzing system along the cruise track of R/V Araon from Barrow, U.S.A., to Nome, U.S.A., while conducting an expedition in The Chukchi Sea and East Siberian Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet was located at 7m depth.\nTo investigate ocean emissions of CO2 in the Arctic Ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001145_2.json b/datasets/KOPRI-KPDC-00001145_2.json index 7bada15a78..88f0ccafaf 100644 --- a/datasets/KOPRI-KPDC-00001145_2.json +++ b/datasets/KOPRI-KPDC-00001145_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001145_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heat flow measurements in Chukchi Plateau and East Siberian shelf areas on Arctic ocean\nInvestigation to the thermal structure in Chukchi Plateau and East Siberian shelf areas on Arctic ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001146_1.json b/datasets/KOPRI-KPDC-00001146_1.json index 1ee69e726e..39342955a6 100644 --- a/datasets/KOPRI-KPDC-00001146_1.json +++ b/datasets/KOPRI-KPDC-00001146_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001146_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam data were collected during the 2018 ARA09C cruise in Chukchi Plateau and East Siberian shelf areas on Arctic ocean\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001147_1.json b/datasets/KOPRI-KPDC-00001147_1.json index 0a7644e91b..b38d76dbca 100644 --- a/datasets/KOPRI-KPDC-00001147_1.json +++ b/datasets/KOPRI-KPDC-00001147_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001147_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profiler data were collected during the 2018 ARA09C cruise in Chukchi Plateau and East Siberian shelf areas on Arctic ocean\nInvestigation of submarine resource environment and seabed methane release in Chukchi Plateau and East Siberian shelf areas on Arctic ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001148_1.json b/datasets/KOPRI-KPDC-00001148_1.json index a3da7b98e3..05a95aae2d 100644 --- a/datasets/KOPRI-KPDC-00001148_1.json +++ b/datasets/KOPRI-KPDC-00001148_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001148_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seismic sparker data were collected during the 2018 ARA09C cruise in Chukchi Plateau and East Siberian shelf areas on Arctic ocean\nInvestigation of submarine resource environment and seabed methane release in Chukchi Plateau and East Siberian shelf areas on Arctic ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001149_1.json b/datasets/KOPRI-KPDC-00001149_1.json index 836fdef72f..055ae5fe1a 100644 --- a/datasets/KOPRI-KPDC-00001149_1.json +++ b/datasets/KOPRI-KPDC-00001149_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001149_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea-ice concentration for the period from 6k BP to 0 BP simulated by two global climate models - CESM and LOVECLIM.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001150_1.json b/datasets/KOPRI-KPDC-00001150_1.json index a4b32aeaf0..6cffa7bc72 100644 --- a/datasets/KOPRI-KPDC-00001150_1.json +++ b/datasets/KOPRI-KPDC-00001150_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001150_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2017. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor climate variation at Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001151_1.json b/datasets/KOPRI-KPDC-00001151_1.json index 9f1eec7700..89b490c3aa 100644 --- a/datasets/KOPRI-KPDC-00001151_1.json +++ b/datasets/KOPRI-KPDC-00001151_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001151_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2018. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomema and to monitor climate variation at Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001152_1.json b/datasets/KOPRI-KPDC-00001152_1.json index c65a29702b..578a27fe63 100644 --- a/datasets/KOPRI-KPDC-00001152_1.json +++ b/datasets/KOPRI-KPDC-00001152_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001152_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2017. Observational elements are composed of wind, air temperature, relative humidity profiles, and radiations. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as every ten-minute averaged data.\nTo understand weather phenomema and to monitor climate variation at the Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001153_2.json b/datasets/KOPRI-KPDC-00001153_2.json index 763561c80a..93d408307f 100644 --- a/datasets/KOPRI-KPDC-00001153_2.json +++ b/datasets/KOPRI-KPDC-00001153_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001153_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2018. Observational elements are composed of wind, air temperature, relative humidity profiles, and radiations. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as every ten-minute averaged data.\nTo understand weather phenomema and to monitor climate variation at the Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001154_2.json b/datasets/KOPRI-KPDC-00001154_2.json index c7fdf21c8b..14d0ea1011 100644 --- a/datasets/KOPRI-KPDC-00001154_2.json +++ b/datasets/KOPRI-KPDC-00001154_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001154_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to model the distribution and physiological response of Antarctic hairgrass, we obtained 2,127 data points (Po, average 118.7) of distributions and physiological response observations in the vicinity of Sejong Station, King George Island, Antarctica in 2017. In addition, we obtained 2,127 data points for this species. With these data, the prediction accuracy of the model acquired in 2018 was 83.3%.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001155_2.json b/datasets/KOPRI-KPDC-00001155_2.json index 40c9398526..da7fe0cca7 100644 --- a/datasets/KOPRI-KPDC-00001155_2.json +++ b/datasets/KOPRI-KPDC-00001155_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001155_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to model the distribution and physiological response of Antarctic pearlwort, we obtained 1,150 data points (Po, average 96.7) of distributions and physiological response observations in the vicinity of Sejong Station, King George Island, Antarctica in 2016. In addition, we obtained 1,150 data points for this species. With these data, the prediction accuracy of the model acquired in 2017 was 78.84%.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001156_4.json b/datasets/KOPRI-KPDC-00001156_4.json index 32aed8660f..43bcb3b41c 100644 --- a/datasets/KOPRI-KPDC-00001156_4.json +++ b/datasets/KOPRI-KPDC-00001156_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001156_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at KSS station, Antarctica\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001157_3.json b/datasets/KOPRI-KPDC-00001157_3.json index 1fb7960ae2..2038d5266b 100644 --- a/datasets/KOPRI-KPDC-00001157_3.json +++ b/datasets/KOPRI-KPDC-00001157_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001157_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at Jang Bogo Station, Antarctica\nStudy of the atmospheric wave activities in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001158_1.json b/datasets/KOPRI-KPDC-00001158_1.json index f7f3be9a66..36fb17a876 100644 --- a/datasets/KOPRI-KPDC-00001158_1.json +++ b/datasets/KOPRI-KPDC-00001158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second.\nMonitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001159_1.json b/datasets/KOPRI-KPDC-00001159_1.json index f4531798b4..d5b1161c99 100644 --- a/datasets/KOPRI-KPDC-00001159_1.json +++ b/datasets/KOPRI-KPDC-00001159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Brewer Ozone spectroscopy (BREWER) accurately measures the amount of light from a certain wavelength (286.5 nm to 363 nm) that absorbs ozone and is a total of ozone.\nMonitoring of changes in meteorological variables (O3) at Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001160_2.json b/datasets/KOPRI-KPDC-00001160_2.json index fc55fce2a3..a382ba37dd 100644 --- a/datasets/KOPRI-KPDC-00001160_2.json +++ b/datasets/KOPRI-KPDC-00001160_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001160_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper air observation is made once a day at 1800UTC during YOPP-SH (from 16 NOV. 2018 and 11 FEB 2019) by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every a second. The minimum observation height is over 20 km.\nMonitoring of changes in meteorological variables with altitude over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001161_3.json b/datasets/KOPRI-KPDC-00001161_3.json index f0c226ccd9..8c470c4730 100644 --- a/datasets/KOPRI-KPDC-00001161_3.json +++ b/datasets/KOPRI-KPDC-00001161_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001161_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous broadband seismic data recorded on Korea Polar Seismic network\nTo monitor the activites of Mt. Melbourne and glacial movements", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001162_1.json b/datasets/KOPRI-KPDC-00001162_1.json index 8c7e511f08..0574f40438 100644 --- a/datasets/KOPRI-KPDC-00001162_1.json +++ b/datasets/KOPRI-KPDC-00001162_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001162_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous broadband seismic data recorded on Korea Polar Seismic Network\nTo monitor the activites of Mt. Melbourne and glacial movements", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001164_1.json b/datasets/KOPRI-KPDC-00001164_1.json index 8f4f248353..cc346b2862 100644 --- a/datasets/KOPRI-KPDC-00001164_1.json +++ b/datasets/KOPRI-KPDC-00001164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in January, 2019. LADCP profiles were collected at 61 stations. To investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001165_1.json b/datasets/KOPRI-KPDC-00001165_1.json index 867bdfe870..982b9eaf74 100644 --- a/datasets/KOPRI-KPDC-00001165_1.json +++ b/datasets/KOPRI-KPDC-00001165_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001165_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in January, 2019. CTD profiles were collected at 64 stations. To investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001166_1.json b/datasets/KOPRI-KPDC-00001166_1.json index 3fc256f96b..c76a6d37e5 100644 --- a/datasets/KOPRI-KPDC-00001166_1.json +++ b/datasets/KOPRI-KPDC-00001166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed to the North of the Drygalski Ice Tongue on 3 March 2018 as a part of the ANA08C research cruise, and it was recovered on 4 January 2019 To monitor physical properties(Temperature, Salinity, Current) of ocean water in the north of the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001167_1.json b/datasets/KOPRI-KPDC-00001167_1.json index f56aefb463..1b8f59b3d3 100644 --- a/datasets/KOPRI-KPDC-00001167_1.json +++ b/datasets/KOPRI-KPDC-00001167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA, an oceanographic mooring was deployed close to the bottom depth near the Drygalski Ice Tongue on 9 March 2018 as a part of the ANA08C research cruise, and it was recovered on 3 January 2019 To monitor physical properties(Temperature, Salinity, Current) of deep water near the Drygalski ice tongue.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001168_1.json b/datasets/KOPRI-KPDC-00001168_1.json index 7ab88082fc..f738381523 100644 --- a/datasets/KOPRI-KPDC-00001168_1.json +++ b/datasets/KOPRI-KPDC-00001168_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001168_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NIWA and LDEO, an oceanographic mooring was deployed close to the bottom depth in the Drygalski Basin (deep trough) on 6 March 2018 as a part of the ANA08C research cruise, and it was recovered on 5 January 2019 to monitor physical properties(Temperature, Salinity, Current) of deep water in the Drygalski Basin. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001169_5.json b/datasets/KOPRI-KPDC-00001169_5.json index 8f9c45c070..d07266718e 100644 --- a/datasets/KOPRI-KPDC-00001169_5.json +++ b/datasets/KOPRI-KPDC-00001169_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001169_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the activites of Mt. Melbourne and glacial movements", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001171_4.json b/datasets/KOPRI-KPDC-00001171_4.json index e722112d62..98b8ed9a4d 100644 --- a/datasets/KOPRI-KPDC-00001171_4.json +++ b/datasets/KOPRI-KPDC-00001171_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001171_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborating with NOAA, six Autonomous Underwater Hydrophones(AUH) were deployed in Southern Terra Nova Bay in March 2018 as a part of the ANA08C research cruise to monitor icequakes, tectonic activities, and ocean ambient noise. The five AUHs were recovered in January 2019; four of the five AUHs successfully recorded continuous data, but one failed to record data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001172_2.json b/datasets/KOPRI-KPDC-00001172_2.json index dcb80849ca..165f30eebd 100644 --- a/datasets/KOPRI-KPDC-00001172_2.json +++ b/datasets/KOPRI-KPDC-00001172_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001172_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Bottom Seismometer was deployed in front of Ross Ice shelf, Antarctica, to study dynamic Response of the Ross Ice Shelf to Wave-induced Vibrations. \n\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001173_5.json b/datasets/KOPRI-KPDC-00001173_5.json index c171c3883a..8e4dffb6f8 100644 --- a/datasets/KOPRI-KPDC-00001173_5.json +++ b/datasets/KOPRI-KPDC-00001173_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001173_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year-round records of temperature, humidity, pressure at the GPS stations around the Jang Bogo Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001174_3.json b/datasets/KOPRI-KPDC-00001174_3.json index fadc64eee0..80564a80a1 100644 --- a/datasets/KOPRI-KPDC-00001174_3.json +++ b/datasets/KOPRI-KPDC-00001174_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001174_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Remotely operating weather station and digital camera\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001175_4.json b/datasets/KOPRI-KPDC-00001175_4.json index 1ec9cefef3..173a09e5d2 100644 --- a/datasets/KOPRI-KPDC-00001175_4.json +++ b/datasets/KOPRI-KPDC-00001175_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001175_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Remotely operating GPS system\r\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001176_4.json b/datasets/KOPRI-KPDC-00001176_4.json index a8ba7c0038..dc5e9bdddf 100644 --- a/datasets/KOPRI-KPDC-00001176_4.json +++ b/datasets/KOPRI-KPDC-00001176_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001176_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators on the deck for 3-4 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. Productivity experiments were completed during three cruises. The samples for productivity were collected by CTD rosette water samplers at 6 different light depths (100, 50, 30, 12, 5 and 1%). To understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem, productivity experiments were executed in the Amundsen Sea, Antarctica.\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001177_3.json b/datasets/KOPRI-KPDC-00001177_3.json index d5199da239..4a4a84fea4 100644 --- a/datasets/KOPRI-KPDC-00001177_3.json +++ b/datasets/KOPRI-KPDC-00001177_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001177_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "David glacier area ice surface / bed elevation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001178_2.json b/datasets/KOPRI-KPDC-00001178_2.json index 903d45fee6..040a142fd4 100644 --- a/datasets/KOPRI-KPDC-00001178_2.json +++ b/datasets/KOPRI-KPDC-00001178_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001178_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators for 4-5 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. The purposes of this study were to estimate the carbon and nitrogen uptake rates of pico-phytoplanktontwo survey periods (2017 and 2018) in Kongsfjorden, Svalbard. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001179_2.json b/datasets/KOPRI-KPDC-00001179_2.json index 07e40f97e9..06833ed901 100644 --- a/datasets/KOPRI-KPDC-00001179_2.json +++ b/datasets/KOPRI-KPDC-00001179_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001179_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during April in Kongsfjorden, Svalbard. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001180_2.json b/datasets/KOPRI-KPDC-00001180_2.json index 546d68a9df..41f0f060c8 100644 --- a/datasets/KOPRI-KPDC-00001180_2.json +++ b/datasets/KOPRI-KPDC-00001180_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001180_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during the spring period in Kongsfjorden, Svalbard. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001181_2.json b/datasets/KOPRI-KPDC-00001181_2.json index 066f790970..4ea0804c3f 100644 --- a/datasets/KOPRI-KPDC-00001181_2.json +++ b/datasets/KOPRI-KPDC-00001181_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001181_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Kongsfjorden is fjord of the west Spitsbergen and located in the Svalbard archipelago. Chlorophyll a samples were collected from the Kongsfjorden from July, 2019 onboard the Teisten. The purpose of this study were (i) to investigate spatial variation in phytoplankton size structure based on chlorophyll a data (ii) to estimate contribution of pico-phytoplankton to total biomass during July in the Kongsfjorden, Svalbard.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001182_3.json b/datasets/KOPRI-KPDC-00001182_3.json index 0db186321b..19b5fe6a96 100644 --- a/datasets/KOPRI-KPDC-00001182_3.json +++ b/datasets/KOPRI-KPDC-00001182_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001182_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001183_2.json b/datasets/KOPRI-KPDC-00001183_2.json index eaf7350225..eb90c967c0 100644 --- a/datasets/KOPRI-KPDC-00001183_2.json +++ b/datasets/KOPRI-KPDC-00001183_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001183_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001184_2.json b/datasets/KOPRI-KPDC-00001184_2.json index 686e013502..af30e6a4e0 100644 --- a/datasets/KOPRI-KPDC-00001184_2.json +++ b/datasets/KOPRI-KPDC-00001184_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001184_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001185_4.json b/datasets/KOPRI-KPDC-00001185_4.json index 5dcc48b9af..0c36a7aa85 100644 --- a/datasets/KOPRI-KPDC-00001185_4.json +++ b/datasets/KOPRI-KPDC-00001185_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001185_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001186_1.json b/datasets/KOPRI-KPDC-00001186_1.json index 663207f3f7..893a61d4df 100644 --- a/datasets/KOPRI-KPDC-00001186_1.json +++ b/datasets/KOPRI-KPDC-00001186_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001186_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001187_1.json b/datasets/KOPRI-KPDC-00001187_1.json index e95b6efefd..20372ea40e 100644 --- a/datasets/KOPRI-KPDC-00001187_1.json +++ b/datasets/KOPRI-KPDC-00001187_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001187_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001188_1.json b/datasets/KOPRI-KPDC-00001188_1.json index 1eaefb2417..80ae57b686 100644 --- a/datasets/KOPRI-KPDC-00001188_1.json +++ b/datasets/KOPRI-KPDC-00001188_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001188_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001189_1.json b/datasets/KOPRI-KPDC-00001189_1.json index c876bfbf29..103a3d67e3 100644 --- a/datasets/KOPRI-KPDC-00001189_1.json +++ b/datasets/KOPRI-KPDC-00001189_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001189_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001190_1.json b/datasets/KOPRI-KPDC-00001190_1.json index 4dc4c0f047..319f0f93ea 100644 --- a/datasets/KOPRI-KPDC-00001190_1.json +++ b/datasets/KOPRI-KPDC-00001190_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001190_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001191_2.json b/datasets/KOPRI-KPDC-00001191_2.json index 6b89fcf073..d5d73c7e40 100644 --- a/datasets/KOPRI-KPDC-00001191_2.json +++ b/datasets/KOPRI-KPDC-00001191_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001191_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001192_4.json b/datasets/KOPRI-KPDC-00001192_4.json index ea0bfa3c5c..ed11d37f4e 100644 --- a/datasets/KOPRI-KPDC-00001192_4.json +++ b/datasets/KOPRI-KPDC-00001192_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001192_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001193_1.json b/datasets/KOPRI-KPDC-00001193_1.json index 5fdcf7be43..6f7a4a7c63 100644 --- a/datasets/KOPRI-KPDC-00001193_1.json +++ b/datasets/KOPRI-KPDC-00001193_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001193_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001194_1.json b/datasets/KOPRI-KPDC-00001194_1.json index 09ec9ad46e..e0d6da8a05 100644 --- a/datasets/KOPRI-KPDC-00001194_1.json +++ b/datasets/KOPRI-KPDC-00001194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal variation of surface water mass in the Marian Cove, oceanographic observation were conducted from 2011 to 2019. the observation station is located near the King Sejong station and the data include sea surface Conductivity, Temperature, and Fluorometer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001195_1.json b/datasets/KOPRI-KPDC-00001195_1.json index 6ee27b098d..be9cdcb484 100644 --- a/datasets/KOPRI-KPDC-00001195_1.json +++ b/datasets/KOPRI-KPDC-00001195_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001195_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Marian Cove, an oceanographic surveys were conducted from 2011 to 2019. the data include Conductivity, Temperature, Depth, and Fluorometer.\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001196_1.json b/datasets/KOPRI-KPDC-00001196_1.json index 92b83758d8..90b05dfffd 100644 --- a/datasets/KOPRI-KPDC-00001196_1.json +++ b/datasets/KOPRI-KPDC-00001196_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001196_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove, the mooring was installed.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001197_2.json b/datasets/KOPRI-KPDC-00001197_2.json index 10ea39d5c1..40ef250828 100644 --- a/datasets/KOPRI-KPDC-00001197_2.json +++ b/datasets/KOPRI-KPDC-00001197_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001197_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We deployed four autonomous phase-sensitive radio echo sounders (ApRES) on the Getz Ice Shelf (GIS) to measure the ice-shelf basal melt rates and horizontal moving speed of the ice shelf. Two sites are located on the eastern side of GIS: GE1 and GE3. The melt rates were calculated from the ice-shelf thinning rates, offset by the strain rate through the ice column. ApRES observation is to measure the ice-shelf basal melt rates and horizontal moving speed of the ice shelf.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001198_1.json b/datasets/KOPRI-KPDC-00001198_1.json index ac37634494..0542cadfe0 100644 --- a/datasets/KOPRI-KPDC-00001198_1.json +++ b/datasets/KOPRI-KPDC-00001198_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001198_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A forward scatter cloud droplet particle counter CDP-2 (DMT, USA) has been in operation since September 2017 at the Zeppelin Observatory, Ny-Alesund . CDP2 produces information of number concentration and size of cloud droplet when cloud hits the Zeppelin Mt.\n- To understand micro-physical characteristics of Arctic cloud and its temporal variation\n- To understand the various effects of Arctic cloud in Arctic climate system", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001199_1.json b/datasets/KOPRI-KPDC-00001199_1.json index c1399eec59..e64c3aa5d4 100644 --- a/datasets/KOPRI-KPDC-00001199_1.json +++ b/datasets/KOPRI-KPDC-00001199_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001199_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) has been in operation since April 2017 near Climate Change Tower of Ny-Alesund, Svalbard where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 1.5 km on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field are obtained using PPI and RHI modes, respectively.\nTo understand Arctic boundary layer(BL) structure and interaction between cloud-BL in the Arctic", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001200_1.json b/datasets/KOPRI-KPDC-00001200_1.json index 564482cea7..7d7c90ccc1 100644 --- a/datasets/KOPRI-KPDC-00001200_1.json +++ b/datasets/KOPRI-KPDC-00001200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal global radiation data (HGRD) measured at the King Sejong Station, King George Islands, Antarctica in 2019. HGRD is included in the meteological data of the KSJ and full-year data will be uploaded after December.\nMonitoring of solar energy at the King Sejong Station and analysis of climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001201_1.json b/datasets/KOPRI-KPDC-00001201_1.json index 3ccf223191..3047abba19 100644 --- a/datasets/KOPRI-KPDC-00001201_1.json +++ b/datasets/KOPRI-KPDC-00001201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2019 at a coastal region of the King Sejong Station. Eddy co-variance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTo understand air-ocean-sea-ice interactions in terms of momentum/energy/H2O/CO2 at the coastal Antarctic region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001202_1.json b/datasets/KOPRI-KPDC-00001202_1.json index 4acdb288b3..aa658283ff 100644 --- a/datasets/KOPRI-KPDC-00001202_1.json +++ b/datasets/KOPRI-KPDC-00001202_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001202_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation has been carried out at the King Sejong Station in 2019. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001203_1.json b/datasets/KOPRI-KPDC-00001203_1.json index 1ff21bcd5c..7392e771a7 100644 --- a/datasets/KOPRI-KPDC-00001203_1.json +++ b/datasets/KOPRI-KPDC-00001203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Upper air observation has been made once a week at 12 UTC Wednesday from January to mid-February by manual launch of radiosonde. Data consist of pressure, temperature, relative humidity, wind speed and wind direction every two-second up to normally about 20 km.\nThe sounding was carried out 12 UTC everyday as contribution to YOPP-SH special observation.\n- Analysis of upper air structure over the King Sejong Station, Antarctica\n- Improvement of weather analysis in Antarctic regions by providing additional upper air data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001204_4.json b/datasets/KOPRI-KPDC-00001204_4.json index 92d0c21f88..7d8a412d20 100644 --- a/datasets/KOPRI-KPDC-00001204_4.json +++ b/datasets/KOPRI-KPDC-00001204_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001204_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\u25cb Clarifying origin and characteristics of Terror Rift using geophysical investigation of mantle structure\n\u25cb Investigation of tectonics in the vicinity of Terror Rift and Victoria Land Basin\n - Tectonics and sedimentary variations from the evolution of Terror Rift\n - Neotectonic activities and Cenozoic evolution of West Antarctic Rift System\n\n\u25cb Manufacture by K.U.M of Germany(OBS System)\n - Seismometer : from 4.5Hz(normal), 14Hz, 30Hz to some 100Hz, low noise even with current\n - Releaser : ORE Offshore, EdgeTech, EG&G, Benthos, Oceano, and MORS\n - Hydrophone : -194dB, 0.01Hz to 8kHz, 6000 meters\n - Data Logger : 142 dB Signal-Noise_Ratio, 32bit@250sps, 50-4000Hz samplerate, Up to 2TB Memory\n - Frame : titanium, flexible, 6000m", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001210_1.json b/datasets/KOPRI-KPDC-00001210_1.json index 6e3f220b39..c12be96c85 100644 --- a/datasets/KOPRI-KPDC-00001210_1.json +++ b/datasets/KOPRI-KPDC-00001210_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001210_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cells regulate their intracellular mRNA levels by using specific ribonucleases. Oligoribonuclease (ORN) is a 3 -5 exoribonuclease for small RNA molecules, important in RNA degradation and re-utilisation. However, there is no structural information on the ligand-binding form of ORNs. In this study, the crystal structures of oligoribonuclease from Colwellia psychrerythraea strain 34H (CpsORN) were determined in four different forms: unliganded-structure, thymidine 5 -monophosphate p-nitrophenyl ester (pNP-TMP)-bound, two separated uridine-bound, and two linked uridine (U-U)-bound forms. The crystal structures show that CpsORN is a tight dimer, with two separated active sites and one divalent metal cation ion in each active site. These structures represent several snapshots of the enzymatic reaction process, which allowed us to predict a possible one-metal-dependent reaction mechanism for CpsORN. Moreover, the biochemical data support our suggested mechanism and identified the key residues responsible for enzymatic catalysis of CpsORN.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001211_1.json b/datasets/KOPRI-KPDC-00001211_1.json index 3b71297815..43394591d3 100644 --- a/datasets/KOPRI-KPDC-00001211_1.json +++ b/datasets/KOPRI-KPDC-00001211_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001211_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A novel cold-active S-formylglutathione hydrolase (SfSFGH) from Shewanella frigidimarina, composed of 279 amino acids with a molecular mass of ~31.0 kDa was identified, expressed, and characterized. Sequence analysis of SfSFGH revealed a conserved pentapeptide of G-X-S-X-G that is found in various lipolytic enzymes along with a putative catalytic triad of Ser148-Asp224-His257. Activity analysis showed that SfSFGH was active towards short-chain esters, such as p-nitrophenyl acetate, butyrate, hexanoate, and octanoate. The optimum pH for enzyme activity was slightly alkaline (pH 8.0). To investigate the active site configuration of SfSFGH, we determined the crystal structure of SfSFGH at 2.32 ? resolution. Structural analysis showed that a Trp182 residue is located in the active site entrance, allowing it to act as a gatekeeper residue to control substrate binding in SfSFGH. Mutation of Trp182 to Ala allowed SfSFGH to accommodate a longer chain of substrates. It is thought that the W182A mutation may increase the substrate-binding pocket and decrease the steric effect for larger substrates in SfSFGH. Consequently, the W182A mutant has broader substrate specificity compared to wild-type SfSFGH. Moreover, SfSFGH displayed more than 50% of its initial activity in the presence of various chemicals, including 30% EtOH, 1% Triton X-100, 1% SDS, and 5 M urea. Taken together, this study provides useful structure-function data of a SFGH family member and may inform protein engineering strategies for industrial applications of SfSFGH.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001212_1.json b/datasets/KOPRI-KPDC-00001212_1.json index 7903897e0a..14db9adaab 100644 --- a/datasets/KOPRI-KPDC-00001212_1.json +++ b/datasets/KOPRI-KPDC-00001212_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001212_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In cold and harsh environments such as glaciers and sediments in ice cores, microbes can survive by forming spores. Spores are composed of a thick coat protein, which protects against external factors such as heat-shock, high salinity, and nutrient deficiency. GerE is a key transcription factor involved in spore coat protein expression in the mother cell during sporulation. GerE regulates transcription during the late sporulation stage by directly binding to the promoter of cotB gene. Here, we report the crystal structure of PaGerE at 2.09 ? resolution from Paenisporosarcina sp. TG-14, which was isolated from the Taylor glacier. The PaGerE structure is composed of four \u03b1-helices and adopts a helix-turn-helix architecture with 68 amino acid residues. Based on our DNA binding analysis, the PaGerE binds to the promoter region of CotB to affect protein expression. Additionally, our structural comparison studies suggest that DNA binding by PaGerE causes a conformational change in the \u03b14-helix region, which may strongly induce dimerization of PaGerE.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001213_4.json b/datasets/KOPRI-KPDC-00001213_4.json index 6fa4bf1532..825904ba06 100644 --- a/datasets/KOPRI-KPDC-00001213_4.json +++ b/datasets/KOPRI-KPDC-00001213_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001213_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1) Abstract (English) Atmospheric DMS mixing ratio measured at King Sejong Station in 2019 (from 1 Jan to 4 April) by using custom-made trapping and desorption system equipped with pulsed flame photometric detector. 2) Purpose (English) Monitoring of atmospheric DMS mixing ration at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001214_4.json b/datasets/KOPRI-KPDC-00001214_4.json index 49753c56e5..80052d632a 100644 --- a/datasets/KOPRI-KPDC-00001214_4.json +++ b/datasets/KOPRI-KPDC-00001214_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001214_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter\nMonitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001215_3.json b/datasets/KOPRI-KPDC-00001215_3.json index 764a4f8b64..8049b35d44 100644 --- a/datasets/KOPRI-KPDC-00001215_3.json +++ b/datasets/KOPRI-KPDC-00001215_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001215_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001216_3.json b/datasets/KOPRI-KPDC-00001216_3.json index b9e8aeaf38..d8c2ef968d 100644 --- a/datasets/KOPRI-KPDC-00001216_3.json +++ b/datasets/KOPRI-KPDC-00001216_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001216_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001217_3.json b/datasets/KOPRI-KPDC-00001217_3.json index d0de0f3826..3fc3b16dd4 100644 --- a/datasets/KOPRI-KPDC-00001217_3.json +++ b/datasets/KOPRI-KPDC-00001217_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001217_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\r\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001218_3.json b/datasets/KOPRI-KPDC-00001218_3.json index f5832a1e6d..62050c66e3 100644 --- a/datasets/KOPRI-KPDC-00001218_3.json +++ b/datasets/KOPRI-KPDC-00001218_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001218_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter\nMonitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001219_3.json b/datasets/KOPRI-KPDC-00001219_3.json index 244a4d5a5d..49ebafa9d7 100644 --- a/datasets/KOPRI-KPDC-00001219_3.json +++ b/datasets/KOPRI-KPDC-00001219_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001219_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter\nMonitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001220_2.json b/datasets/KOPRI-KPDC-00001220_2.json index 814b32d6fb..104c19aea8 100644 --- a/datasets/KOPRI-KPDC-00001220_2.json +++ b/datasets/KOPRI-KPDC-00001220_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001220_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016.\nMonitoring of aerosol size distribution from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001221_3.json b/datasets/KOPRI-KPDC-00001221_3.json index 52368a661b..f4129b838a 100644 --- a/datasets/KOPRI-KPDC-00001221_3.json +++ b/datasets/KOPRI-KPDC-00001221_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001221_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectrum intensity for gaseous halogen compounds measured at King Sejong Station in 2018-2019 (from 9 Dec 2018 to 12 June 2019) by using Multi-Axis Differential Optic Absorption Spectroscopy (Max-DOAS)\nMonitoring of atmospheric halogen compounds at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001222_2.json b/datasets/KOPRI-KPDC-00001222_2.json index 33105de218..92c14912ee 100644 --- a/datasets/KOPRI-KPDC-00001222_2.json +++ b/datasets/KOPRI-KPDC-00001222_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001222_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meltwater samples were obtained in Barton Peninsula to investigate ice chemical reactions in polar region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001223_2.json b/datasets/KOPRI-KPDC-00001223_2.json index c467b8a93c..9f93f0d9d3 100644 --- a/datasets/KOPRI-KPDC-00001223_2.json +++ b/datasets/KOPRI-KPDC-00001223_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001223_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meltwater was sampled in Barton Peninsula to investigate ice chemical reactions in polar region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001224_2.json b/datasets/KOPRI-KPDC-00001224_2.json index 1fb988210c..6b3c6e36b3 100644 --- a/datasets/KOPRI-KPDC-00001224_2.json +++ b/datasets/KOPRI-KPDC-00001224_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001224_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seismic sparker data were collected during the 2018-2019 ANA09B cruise in Ross sea. \n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001229_2.json b/datasets/KOPRI-KPDC-00001229_2.json index 75997aa25b..9465678522 100644 --- a/datasets/KOPRI-KPDC-00001229_2.json +++ b/datasets/KOPRI-KPDC-00001229_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001229_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pondwater sample was obtained in Barton Peninsula to investigate ice chemical reactions in polar region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001230_2.json b/datasets/KOPRI-KPDC-00001230_2.json index 4f08b51e2c..376cc553d0 100644 --- a/datasets/KOPRI-KPDC-00001230_2.json +++ b/datasets/KOPRI-KPDC-00001230_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001230_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pondwater sample was obtained in Barton Peninsula to investigate ice chemical reactions in polar region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001231_2.json b/datasets/KOPRI-KPDC-00001231_2.json index 7406303ff2..42239fc20b 100644 --- a/datasets/KOPRI-KPDC-00001231_2.json +++ b/datasets/KOPRI-KPDC-00001231_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001231_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fresh snow was sampled after falling off in Barton Peninsula to investigate ice chemical reactions in polar region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001232_1.json b/datasets/KOPRI-KPDC-00001232_1.json index 67672b8612..7903bfef16 100644 --- a/datasets/KOPRI-KPDC-00001232_1.json +++ b/datasets/KOPRI-KPDC-00001232_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001232_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We have investigated marine algal diversity and distribution in Maxwell Bay, King George Island in 2017-2019 season.\nThe main goal is to address how marine algal biodiversity and subtidal distribution in Maxwell Bay, King George Island responds to climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001233_1.json b/datasets/KOPRI-KPDC-00001233_1.json index ca5721d36c..b8f325244e 100644 --- a/datasets/KOPRI-KPDC-00001233_1.json +++ b/datasets/KOPRI-KPDC-00001233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2019. Observational elements are composed of wind, air temperature, relative humidity profiles, and radiations. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as every ten-minute averaged data.\nTo understand weather phenomena and to monitor climate variation at the Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001234_1.json b/datasets/KOPRI-KPDC-00001234_1.json index 7894a5ab89..15191f6e7b 100644 --- a/datasets/KOPRI-KPDC-00001234_1.json +++ b/datasets/KOPRI-KPDC-00001234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2019. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variation at Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001235_1.json b/datasets/KOPRI-KPDC-00001235_1.json index f10047ef2d..81030f5a11 100644 --- a/datasets/KOPRI-KPDC-00001235_1.json +++ b/datasets/KOPRI-KPDC-00001235_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001235_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Methane(CH4) at Konsfjorden in July 2019 by using Cavity ring-down spectrometer(CRDS) ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001236_2.json b/datasets/KOPRI-KPDC-00001236_2.json index 9cf5eba145..479654b9ab 100644 --- a/datasets/KOPRI-KPDC-00001236_2.json +++ b/datasets/KOPRI-KPDC-00001236_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001236_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanic Nitrous oxdie(N2O) flux aat Konsfjorden in July 2019 by calculation using N2O concentration and wind speed.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001237_1.json b/datasets/KOPRI-KPDC-00001237_1.json index 5b86f47032..76a332a8b4 100644 --- a/datasets/KOPRI-KPDC-00001237_1.json +++ b/datasets/KOPRI-KPDC-00001237_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001237_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Nitrous oxdie(N2O) at Konsfjorden in July 2019 by using Cavity ring-down spectrometer(CRDS) ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001238_1.json b/datasets/KOPRI-KPDC-00001238_1.json index 6219fd1a03..1d50bf458c 100644 --- a/datasets/KOPRI-KPDC-00001238_1.json +++ b/datasets/KOPRI-KPDC-00001238_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001238_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Methane(CH4) at Marian Cove in January to February 2019 by using Cavity ring-down spectrometer(CRDS) ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001239_1.json b/datasets/KOPRI-KPDC-00001239_1.json index 0a7f2a6297..980d6753d9 100644 --- a/datasets/KOPRI-KPDC-00001239_1.json +++ b/datasets/KOPRI-KPDC-00001239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of oceanic Nitrous oxdie(N2O) at Marian Cove in January to February 2019 by using Cavity ring-down spectrometer(CRDS) ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001240_3.json b/datasets/KOPRI-KPDC-00001240_3.json index 4ed577fc92..9dad0f844a 100644 --- a/datasets/KOPRI-KPDC-00001240_3.json +++ b/datasets/KOPRI-KPDC-00001240_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001240_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanic Nitrous oxdie(N2O) flux at Marian Cove in January to February 2019 by calculation using N2O concentration and wind speed.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001241_10.json b/datasets/KOPRI-KPDC-00001241_10.json index ff20069244..18b04c4993 100644 --- a/datasets/KOPRI-KPDC-00001241_10.json +++ b/datasets/KOPRI-KPDC-00001241_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001241_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of marine phytoplankton abundance in the waters around the Amundsen Sea in Antarctica for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001242_2.json b/datasets/KOPRI-KPDC-00001242_2.json index 749df04abe..5a7bed20ab 100644 --- a/datasets/KOPRI-KPDC-00001242_2.json +++ b/datasets/KOPRI-KPDC-00001242_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001242_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001243_3.json b/datasets/KOPRI-KPDC-00001243_3.json index a7d6573917..27cacb4b86 100644 --- a/datasets/KOPRI-KPDC-00001243_3.json +++ b/datasets/KOPRI-KPDC-00001243_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001243_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used.\n\nThe SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001244_1.json b/datasets/KOPRI-KPDC-00001244_1.json index 152c2df1e0..3ccc28ae32 100644 --- a/datasets/KOPRI-KPDC-00001244_1.json +++ b/datasets/KOPRI-KPDC-00001244_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001244_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001245_1.json b/datasets/KOPRI-KPDC-00001245_1.json index a90567b571..d2971a3dc6 100644 --- a/datasets/KOPRI-KPDC-00001245_1.json +++ b/datasets/KOPRI-KPDC-00001245_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001245_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001246_1.json b/datasets/KOPRI-KPDC-00001246_1.json index 487392bd39..8f960d6e78 100644 --- a/datasets/KOPRI-KPDC-00001246_1.json +++ b/datasets/KOPRI-KPDC-00001246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001247_1.json b/datasets/KOPRI-KPDC-00001247_1.json index a6e49222be..285ef6c8a7 100644 --- a/datasets/KOPRI-KPDC-00001247_1.json +++ b/datasets/KOPRI-KPDC-00001247_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001247_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001248_2.json b/datasets/KOPRI-KPDC-00001248_2.json index 796709bb7a..5d47867616 100644 --- a/datasets/KOPRI-KPDC-00001248_2.json +++ b/datasets/KOPRI-KPDC-00001248_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001248_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001249_1.json b/datasets/KOPRI-KPDC-00001249_1.json index cfda6450ae..5b1f89382e 100644 --- a/datasets/KOPRI-KPDC-00001249_1.json +++ b/datasets/KOPRI-KPDC-00001249_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001249_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001250_1.json b/datasets/KOPRI-KPDC-00001250_1.json index 760de9882d..f4251ca07f 100644 --- a/datasets/KOPRI-KPDC-00001250_1.json +++ b/datasets/KOPRI-KPDC-00001250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These sea ice concentration data are retrieved with the ARTIST Sea Ice (ASI) algorithm (Spreen et al., 2008) which is applied to microwave radiometer data of the sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua, and AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1. The ASI algorithm using AMSR-E data was first implemented at IUP in 2002 and has been continuously producing sea ice concentration data since then. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 were reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the following sections.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001251_1.json b/datasets/KOPRI-KPDC-00001251_1.json index 46a545e00c..627c8e7e69 100644 --- a/datasets/KOPRI-KPDC-00001251_1.json +++ b/datasets/KOPRI-KPDC-00001251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, which was successfully launched on October 28, 2011. The VIIRS nadir door was opened on November 21, 2011, which enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the AVHRR on NOAA and MODIS on Terra and Aqua satellites. The VIIRS empowers operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for more than twenty environmental data records including clouds, sea surface temperature, ocean color, polar wind, vegetation fraction, aerosol, fire, snow and ice, vegetation, , and other applications.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001252_1.json b/datasets/KOPRI-KPDC-00001252_1.json index 94f853bf1f..91629defab 100644 --- a/datasets/KOPRI-KPDC-00001252_1.json +++ b/datasets/KOPRI-KPDC-00001252_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001252_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 5 August 2019 to 17 September 2019 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals (00, 06, 12, 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001253_1.json b/datasets/KOPRI-KPDC-00001253_1.json index 739df1c2fd..b0d3191bf7 100644 --- a/datasets/KOPRI-KPDC-00001253_1.json +++ b/datasets/KOPRI-KPDC-00001253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Macromolecular compositions (carbohydrates, proteins, and lipids) of particulate organic matter (POM) are crucial as a basic marine food quality. To date, however, a little investigation has been carried out in the Amundsen Sea which is one of the fastest warming locations in the Southern Ocean. Water samples for macromolecular compositions were obtained at selected 19 stations in the Amundsen Sea Polynya (AP) and Amundsen Sea non-polynya (ANP) during the austral summer in 2016 to investigate vertical characteristics of POM.\n(1) to investigate the macromolecular compositions (proteins, lipids, and carbohydrates) of size-fractionated POM in the photic layer and (2) estimate physiological status and nutritional condition of phytoplankton as a major source of POM in the Amundsen Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001254_2.json b/datasets/KOPRI-KPDC-00001254_2.json index c9bc7a7abc..9570c2d010 100644 --- a/datasets/KOPRI-KPDC-00001254_2.json +++ b/datasets/KOPRI-KPDC-00001254_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001254_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "list of marine benthos by diving around Jang Bogo Station (2018/19) in Antarctica\r\nTo establish the inventory of Antarctic marine benthos", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001255_3.json b/datasets/KOPRI-KPDC-00001255_3.json index e6a6a188f6..7048fee86f 100644 --- a/datasets/KOPRI-KPDC-00001255_3.json +++ b/datasets/KOPRI-KPDC-00001255_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001255_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oxygen isotope data collected in the Amundsen Sea during the austral summer of 2018", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001256_2.json b/datasets/KOPRI-KPDC-00001256_2.json index 0f04bad331..531097edbe 100644 --- a/datasets/KOPRI-KPDC-00001256_2.json +++ b/datasets/KOPRI-KPDC-00001256_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001256_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amino acid and DNA sequences for the production of metabolites in Antarctic copepod T. kingsejongensis \r\nGenetic information to understand mechanism of useful metabolites", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001257_2.json b/datasets/KOPRI-KPDC-00001257_2.json index ce639fc2f4..e04ed05189 100644 --- a/datasets/KOPRI-KPDC-00001257_2.json +++ b/datasets/KOPRI-KPDC-00001257_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001257_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of extracts derived from Antarctic lichens and fungi were made. Many extracts can be used in natural product research. \r\nTo provide samples for finding bioactive substances", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001258_2.json b/datasets/KOPRI-KPDC-00001258_2.json index 44d800630b..a89db2febb 100644 --- a/datasets/KOPRI-KPDC-00001258_2.json +++ b/datasets/KOPRI-KPDC-00001258_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001258_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A list of metabolites derived from Antarctic microorganisms and lichens was produced. It can be used to find new substances. \r\nTo develop new natural medicine", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001260_1.json b/datasets/KOPRI-KPDC-00001260_1.json index 26b0ae958a..47eee362c5 100644 --- a/datasets/KOPRI-KPDC-00001260_1.json +++ b/datasets/KOPRI-KPDC-00001260_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001260_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " \"Guidelines for the Evaluation of Skin Wrinkles Improvement Effect of Eicoglycerol (EG)\" commissioned by Korea Institute of Oriental Medicine, Tested in accordance with the Guidelines for the Application of Cosmetic Human Body [Guide for Food and Drug Safety] and Guidelines for the Evaluation of Effectiveness of Functional Cosmetics (MFDS. 2005.07)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001261_1.json b/datasets/KOPRI-KPDC-00001261_1.json index efd1e55eb9..548cb6cf33 100644 --- a/datasets/KOPRI-KPDC-00001261_1.json +++ b/datasets/KOPRI-KPDC-00001261_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001261_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of marine phytoplankton abundance in the waters around the Kongsfjorden in Svalbard for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001262_4.json b/datasets/KOPRI-KPDC-00001262_4.json index 6d1d9e8fce..201a2ed479 100644 --- a/datasets/KOPRI-KPDC-00001262_4.json +++ b/datasets/KOPRI-KPDC-00001262_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001262_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001263_3.json b/datasets/KOPRI-KPDC-00001263_3.json index 5a1d065703..d7953356f9 100644 --- a/datasets/KOPRI-KPDC-00001263_3.json +++ b/datasets/KOPRI-KPDC-00001263_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001263_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001264_4.json b/datasets/KOPRI-KPDC-00001264_4.json index 7ea3be2f9c..af07e6bb74 100644 --- a/datasets/KOPRI-KPDC-00001264_4.json +++ b/datasets/KOPRI-KPDC-00001264_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001264_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna,\nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001265_3.json b/datasets/KOPRI-KPDC-00001265_3.json index cc153366f8..b0717be414 100644 --- a/datasets/KOPRI-KPDC-00001265_3.json +++ b/datasets/KOPRI-KPDC-00001265_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001265_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen\nStudy of the aurora characteristics in thenorthern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001266_4.json b/datasets/KOPRI-KPDC-00001266_4.json index d929d3bcc6..ddaf4cfe53 100644 --- a/datasets/KOPRI-KPDC-00001266_4.json +++ b/datasets/KOPRI-KPDC-00001266_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001266_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001267_3.json b/datasets/KOPRI-KPDC-00001267_3.json index 46a027caac..3bb763e0ce 100644 --- a/datasets/KOPRI-KPDC-00001267_3.json +++ b/datasets/KOPRI-KPDC-00001267_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001267_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Dasan station, Arctic region\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001268_2.json b/datasets/KOPRI-KPDC-00001268_2.json index a797d296d3..704532fa41 100644 --- a/datasets/KOPRI-KPDC-00001268_2.json +++ b/datasets/KOPRI-KPDC-00001268_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001268_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The value of geomagnetic field intensity observed at Jang Bogo Station, Antarctica\nTo investigate the interaction between ionosphere and geomagnetic disturbances", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001269_3.json b/datasets/KOPRI-KPDC-00001269_3.json index a0146dad54..8c2b19d7c4 100644 --- a/datasets/KOPRI-KPDC-00001269_3.json +++ b/datasets/KOPRI-KPDC-00001269_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001269_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora-ASC (All Sky Camera) observes the aurora in visible range over Jang Bogo Station, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001270_3.json b/datasets/KOPRI-KPDC-00001270_3.json index a68cf85cf9..7d1369ae18 100644 --- a/datasets/KOPRI-KPDC-00001270_3.json +++ b/datasets/KOPRI-KPDC-00001270_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001270_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Proton aurora images near polar cap region observed at JBS, Antarctica\r\nTo study the statistical characteristics of proton aurora in southern polar cap region and the interaction between magnetospheric disturbance and ionosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001271_2.json b/datasets/KOPRI-KPDC-00001271_2.json index 410c0d1e09..2a0dfc7d57 100644 --- a/datasets/KOPRI-KPDC-00001271_2.json +++ b/datasets/KOPRI-KPDC-00001271_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001271_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total electron content in the ionosphere at JBS station, Antarctica\nStudy of the statistical characteristics of ionosphere in southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001272_2.json b/datasets/KOPRI-KPDC-00001272_2.json index dbadafaf1f..f26cfa64af 100644 --- a/datasets/KOPRI-KPDC-00001272_2.json +++ b/datasets/KOPRI-KPDC-00001272_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001272_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Neutron Monitor observes the neutron flux incoming from space to earth's atmosphere over JBS, Antarctica.\nTo study the variation of neutron flux with the strength of solar activity and the relationship between neutron flux and atmospheric constituents.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001273_2.json b/datasets/KOPRI-KPDC-00001273_2.json index dbbc8c0825..0e26502547 100644 --- a/datasets/KOPRI-KPDC-00001273_2.json +++ b/datasets/KOPRI-KPDC-00001273_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001273_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica\nStudy of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001274_2.json b/datasets/KOPRI-KPDC-00001274_2.json index 1e33c7a7ba..7df1e4d1f8 100644 --- a/datasets/KOPRI-KPDC-00001274_2.json +++ b/datasets/KOPRI-KPDC-00001274_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001274_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica\nComprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001275_3.json b/datasets/KOPRI-KPDC-00001275_3.json index f60bd4f52a..6f5ee4b39b 100644 --- a/datasets/KOPRI-KPDC-00001275_3.json +++ b/datasets/KOPRI-KPDC-00001275_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001275_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001276_3.json b/datasets/KOPRI-KPDC-00001276_3.json index c92408e1cc..a4ae961420 100644 --- a/datasets/KOPRI-KPDC-00001276_3.json +++ b/datasets/KOPRI-KPDC-00001276_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001276_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001277_3.json b/datasets/KOPRI-KPDC-00001277_3.json index 57c69914a0..9c022f80be 100644 --- a/datasets/KOPRI-KPDC-00001277_3.json +++ b/datasets/KOPRI-KPDC-00001277_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001277_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001278_4.json b/datasets/KOPRI-KPDC-00001278_4.json index f5740aaa51..c6c3e264d5 100644 --- a/datasets/KOPRI-KPDC-00001278_4.json +++ b/datasets/KOPRI-KPDC-00001278_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001278_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001279_1.json b/datasets/KOPRI-KPDC-00001279_1.json index d8c4824c81..a5af8e326b 100644 --- a/datasets/KOPRI-KPDC-00001279_1.json +++ b/datasets/KOPRI-KPDC-00001279_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001279_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to comprehensively understand the Baton Peninsula terrestrial ecosystem where King Sejong Antarctic Research Station is located, multidisciplinary observations were conducted from 2017 to 2019 through the research project \"Modeling biological responses of terrestrial organisms to changing environments on King George Island\". For this purpose, we performed the continuous observation of meteorological elements such as soil moisture, temperature, and quantity of light, the reaction of vegetation with photosynthesis, and carbon dioxide fluxes. Through a massive analysis of these observation data, a comprehensive relational map was prepared to identify the effects and quantitative relationships of various environmental factors on the physiological responses of Baton Peninsula organisms.\r\nContinuous observation data obtained during this process were 151,020 points for soil moisture and light volume, 453,060 points for temperature, 54,234 points for photosynthesis, and 9,524 points for carbon dioxide flux.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001280_2.json b/datasets/KOPRI-KPDC-00001280_2.json index c63a5e7699..571f9db095 100644 --- a/datasets/KOPRI-KPDC-00001280_2.json +++ b/datasets/KOPRI-KPDC-00001280_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001280_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at Jang Bogo Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001281_2.json b/datasets/KOPRI-KPDC-00001281_2.json index e4625f0818..ac825fc123 100644 --- a/datasets/KOPRI-KPDC-00001281_2.json +++ b/datasets/KOPRI-KPDC-00001281_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001281_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inclination/declination and total intensity of the Earth's magnetic field measured from dIdD at JBS station, Antarctica\nStudy of the Earth's magnetic field over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001282_2.json b/datasets/KOPRI-KPDC-00001282_2.json index 2506af4562..7fb7f414c9 100644 --- a/datasets/KOPRI-KPDC-00001282_2.json +++ b/datasets/KOPRI-KPDC-00001282_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001282_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at Jang Bogo Station, Antarctica\nStudy of the atmospheric wave activities in the southern high-latitude MLT region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001283_3.json b/datasets/KOPRI-KPDC-00001283_3.json index 91b109e82a..984bc42db9 100644 --- a/datasets/KOPRI-KPDC-00001283_3.json +++ b/datasets/KOPRI-KPDC-00001283_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001283_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer at King Sejong Station.\nStudy of the activity of ultra low frequency (ULF) wave in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001284_1.json b/datasets/KOPRI-KPDC-00001284_1.json index 0fe382284f..5b796441e7 100644 --- a/datasets/KOPRI-KPDC-00001284_1.json +++ b/datasets/KOPRI-KPDC-00001284_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001284_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate ice chemical reactions in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001285_1.json b/datasets/KOPRI-KPDC-00001285_1.json index 64df91a726..9cd66d3ab0 100644 --- a/datasets/KOPRI-KPDC-00001285_1.json +++ b/datasets/KOPRI-KPDC-00001285_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001285_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate ice chemical reactions in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001286_2.json b/datasets/KOPRI-KPDC-00001286_2.json index 4a442ee259..118f1d9b01 100644 --- a/datasets/KOPRI-KPDC-00001286_2.json +++ b/datasets/KOPRI-KPDC-00001286_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001286_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate ice chemical reactions in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001287_1.json b/datasets/KOPRI-KPDC-00001287_1.json index bb00b56120..31ca9cc3ab 100644 --- a/datasets/KOPRI-KPDC-00001287_1.json +++ b/datasets/KOPRI-KPDC-00001287_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001287_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate ice chemical reactions in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001288_2.json b/datasets/KOPRI-KPDC-00001288_2.json index 904fcc3d6b..e575f22f56 100644 --- a/datasets/KOPRI-KPDC-00001288_2.json +++ b/datasets/KOPRI-KPDC-00001288_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001288_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate ice chemical reactions in Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001290_6.json b/datasets/KOPRI-KPDC-00001290_6.json index dfc146861f..fda1b382ff 100644 --- a/datasets/KOPRI-KPDC-00001290_6.json +++ b/datasets/KOPRI-KPDC-00001290_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001290_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Results of atmospheric ionic analysis using High volume air sampler installed at the Storhofdi observatory, Iceland in 2017.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001291_1.json b/datasets/KOPRI-KPDC-00001291_1.json index 5cb31a22c4..eb0b4ff999 100644 --- a/datasets/KOPRI-KPDC-00001291_1.json +++ b/datasets/KOPRI-KPDC-00001291_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001291_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001292_1.json b/datasets/KOPRI-KPDC-00001292_1.json index a1496d912a..00eb98ca82 100644 --- a/datasets/KOPRI-KPDC-00001292_1.json +++ b/datasets/KOPRI-KPDC-00001292_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001292_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microorganisms from Alaskan soil cores were screened on media containing cellulose, xylan, and chitin.\nPaenibacillus sp R4 showing cellulase, esterase, and xylanase activity were identified and sequenced using nanopore technology.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001293_1.json b/datasets/KOPRI-KPDC-00001293_1.json index 68d4f8d22f..c704f0c7b5 100644 --- a/datasets/KOPRI-KPDC-00001293_1.json +++ b/datasets/KOPRI-KPDC-00001293_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001293_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Paenibacillus sp C1 showed cellulase and xylanase activity in activity screening from the Alaska soil sample. To identify gene coding cellulase and xylanase, genome sequence was performed.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001294_2.json b/datasets/KOPRI-KPDC-00001294_2.json index 87c7435a42..5203c3c23e 100644 --- a/datasets/KOPRI-KPDC-00001294_2.json +++ b/datasets/KOPRI-KPDC-00001294_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001294_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001295_1.json b/datasets/KOPRI-KPDC-00001295_1.json index 6494c2aba1..417dbec15c 100644 --- a/datasets/KOPRI-KPDC-00001295_1.json +++ b/datasets/KOPRI-KPDC-00001295_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001295_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001296_1.json b/datasets/KOPRI-KPDC-00001296_1.json index 4d899e061f..bb443232a1 100644 --- a/datasets/KOPRI-KPDC-00001296_1.json +++ b/datasets/KOPRI-KPDC-00001296_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001296_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001297_1.json b/datasets/KOPRI-KPDC-00001297_1.json index 60f08de956..0b16b96b1f 100644 --- a/datasets/KOPRI-KPDC-00001297_1.json +++ b/datasets/KOPRI-KPDC-00001297_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001297_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001298_1.json b/datasets/KOPRI-KPDC-00001298_1.json index baaed277c5..f0fad0c142 100644 --- a/datasets/KOPRI-KPDC-00001298_1.json +++ b/datasets/KOPRI-KPDC-00001298_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001298_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001299_1.json b/datasets/KOPRI-KPDC-00001299_1.json index b089859c1c..7ccb7933c8 100644 --- a/datasets/KOPRI-KPDC-00001299_1.json +++ b/datasets/KOPRI-KPDC-00001299_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001299_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001300_1.json b/datasets/KOPRI-KPDC-00001300_1.json index d416cfe35d..547c1a622a 100644 --- a/datasets/KOPRI-KPDC-00001300_1.json +++ b/datasets/KOPRI-KPDC-00001300_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001300_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001301_1.json b/datasets/KOPRI-KPDC-00001301_1.json index a24fc267a1..623f42427e 100644 --- a/datasets/KOPRI-KPDC-00001301_1.json +++ b/datasets/KOPRI-KPDC-00001301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001302_1.json b/datasets/KOPRI-KPDC-00001302_1.json index 348c6022d9..9b78ab5cdd 100644 --- a/datasets/KOPRI-KPDC-00001302_1.json +++ b/datasets/KOPRI-KPDC-00001302_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001302_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001303_1.json b/datasets/KOPRI-KPDC-00001303_1.json index 6c6772dd6f..5bf416dc5c 100644 --- a/datasets/KOPRI-KPDC-00001303_1.json +++ b/datasets/KOPRI-KPDC-00001303_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001303_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001304_1.json b/datasets/KOPRI-KPDC-00001304_1.json index daf4a90006..493f932fef 100644 --- a/datasets/KOPRI-KPDC-00001304_1.json +++ b/datasets/KOPRI-KPDC-00001304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001305_1.json b/datasets/KOPRI-KPDC-00001305_1.json index 0e3d3016ff..7a61de2063 100644 --- a/datasets/KOPRI-KPDC-00001305_1.json +++ b/datasets/KOPRI-KPDC-00001305_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001305_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001306_1.json b/datasets/KOPRI-KPDC-00001306_1.json index 6ce928fd13..2912c1b30a 100644 --- a/datasets/KOPRI-KPDC-00001306_1.json +++ b/datasets/KOPRI-KPDC-00001306_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001306_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001307_1.json b/datasets/KOPRI-KPDC-00001307_1.json index 18ba4e96c5..ab79472210 100644 --- a/datasets/KOPRI-KPDC-00001307_1.json +++ b/datasets/KOPRI-KPDC-00001307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001308_1.json b/datasets/KOPRI-KPDC-00001308_1.json index 2312c43b11..eb0c3bd21e 100644 --- a/datasets/KOPRI-KPDC-00001308_1.json +++ b/datasets/KOPRI-KPDC-00001308_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001308_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001309_1.json b/datasets/KOPRI-KPDC-00001309_1.json index 7039231367..f1d6ffadc5 100644 --- a/datasets/KOPRI-KPDC-00001309_1.json +++ b/datasets/KOPRI-KPDC-00001309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001310_1.json b/datasets/KOPRI-KPDC-00001310_1.json index 082b0ebdfb..acabe38146 100644 --- a/datasets/KOPRI-KPDC-00001310_1.json +++ b/datasets/KOPRI-KPDC-00001310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001311_1.json b/datasets/KOPRI-KPDC-00001311_1.json index aac60e1089..ef7b94bc72 100644 --- a/datasets/KOPRI-KPDC-00001311_1.json +++ b/datasets/KOPRI-KPDC-00001311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001312_1.json b/datasets/KOPRI-KPDC-00001312_1.json index 527c8516ef..ab80b4dc5a 100644 --- a/datasets/KOPRI-KPDC-00001312_1.json +++ b/datasets/KOPRI-KPDC-00001312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001313_2.json b/datasets/KOPRI-KPDC-00001313_2.json index a4bc44f498..b715234662 100644 --- a/datasets/KOPRI-KPDC-00001313_2.json +++ b/datasets/KOPRI-KPDC-00001313_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001313_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001314_1.json b/datasets/KOPRI-KPDC-00001314_1.json index e934e16cd5..b2be90e72b 100644 --- a/datasets/KOPRI-KPDC-00001314_1.json +++ b/datasets/KOPRI-KPDC-00001314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001315_1.json b/datasets/KOPRI-KPDC-00001315_1.json index 2d57e8c049..ebaa68b024 100644 --- a/datasets/KOPRI-KPDC-00001315_1.json +++ b/datasets/KOPRI-KPDC-00001315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001316_1.json b/datasets/KOPRI-KPDC-00001316_1.json index 9937512223..4141697634 100644 --- a/datasets/KOPRI-KPDC-00001316_1.json +++ b/datasets/KOPRI-KPDC-00001316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001317_1.json b/datasets/KOPRI-KPDC-00001317_1.json index 06a69e29cf..a657a9c670 100644 --- a/datasets/KOPRI-KPDC-00001317_1.json +++ b/datasets/KOPRI-KPDC-00001317_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001317_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001318_1.json b/datasets/KOPRI-KPDC-00001318_1.json index 755f550340..505669a4d6 100644 --- a/datasets/KOPRI-KPDC-00001318_1.json +++ b/datasets/KOPRI-KPDC-00001318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001319_1.json b/datasets/KOPRI-KPDC-00001319_1.json index fd95823167..7d2d088aa8 100644 --- a/datasets/KOPRI-KPDC-00001319_1.json +++ b/datasets/KOPRI-KPDC-00001319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001320_1.json b/datasets/KOPRI-KPDC-00001320_1.json index 5de3469e74..7dca2f1b92 100644 --- a/datasets/KOPRI-KPDC-00001320_1.json +++ b/datasets/KOPRI-KPDC-00001320_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001320_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001321_1.json b/datasets/KOPRI-KPDC-00001321_1.json index cc0926904a..1a0e2cabd4 100644 --- a/datasets/KOPRI-KPDC-00001321_1.json +++ b/datasets/KOPRI-KPDC-00001321_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001321_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001322_2.json b/datasets/KOPRI-KPDC-00001322_2.json index 911bb511e8..08493fd519 100644 --- a/datasets/KOPRI-KPDC-00001322_2.json +++ b/datasets/KOPRI-KPDC-00001322_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001322_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001323_2.json b/datasets/KOPRI-KPDC-00001323_2.json index 8203d56af9..5018a34d8b 100644 --- a/datasets/KOPRI-KPDC-00001323_2.json +++ b/datasets/KOPRI-KPDC-00001323_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001323_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001324_2.json b/datasets/KOPRI-KPDC-00001324_2.json index 2b2a9a0c1b..f5ba0b0d91 100644 --- a/datasets/KOPRI-KPDC-00001324_2.json +++ b/datasets/KOPRI-KPDC-00001324_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001324_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001325_2.json b/datasets/KOPRI-KPDC-00001325_2.json index 459c0261df..b616eb1ea4 100644 --- a/datasets/KOPRI-KPDC-00001325_2.json +++ b/datasets/KOPRI-KPDC-00001325_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001325_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001326_2.json b/datasets/KOPRI-KPDC-00001326_2.json index 10f41f0a17..641e722682 100644 --- a/datasets/KOPRI-KPDC-00001326_2.json +++ b/datasets/KOPRI-KPDC-00001326_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001326_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001327_2.json b/datasets/KOPRI-KPDC-00001327_2.json index fa1b8d06bd..cab8f6b902 100644 --- a/datasets/KOPRI-KPDC-00001327_2.json +++ b/datasets/KOPRI-KPDC-00001327_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001327_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001328_2.json b/datasets/KOPRI-KPDC-00001328_2.json index 454cceb1e6..5af79b56d6 100644 --- a/datasets/KOPRI-KPDC-00001328_2.json +++ b/datasets/KOPRI-KPDC-00001328_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001328_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001329_2.json b/datasets/KOPRI-KPDC-00001329_2.json index 09a64402a8..2e20cd6e90 100644 --- a/datasets/KOPRI-KPDC-00001329_2.json +++ b/datasets/KOPRI-KPDC-00001329_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001329_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001330_2.json b/datasets/KOPRI-KPDC-00001330_2.json index 0bde7de80f..c5b641b3a9 100644 --- a/datasets/KOPRI-KPDC-00001330_2.json +++ b/datasets/KOPRI-KPDC-00001330_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001330_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001331_2.json b/datasets/KOPRI-KPDC-00001331_2.json index 2a45b5d30e..135efdca7d 100644 --- a/datasets/KOPRI-KPDC-00001331_2.json +++ b/datasets/KOPRI-KPDC-00001331_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001331_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001332_1.json b/datasets/KOPRI-KPDC-00001332_1.json index e8cd3f7b03..29a26b246d 100644 --- a/datasets/KOPRI-KPDC-00001332_1.json +++ b/datasets/KOPRI-KPDC-00001332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001333_1.json b/datasets/KOPRI-KPDC-00001333_1.json index 1e5c0a0ffe..f64869f735 100644 --- a/datasets/KOPRI-KPDC-00001333_1.json +++ b/datasets/KOPRI-KPDC-00001333_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001333_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001334_1.json b/datasets/KOPRI-KPDC-00001334_1.json index 1a0d33b716..97e17e31c6 100644 --- a/datasets/KOPRI-KPDC-00001334_1.json +++ b/datasets/KOPRI-KPDC-00001334_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001334_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001335_1.json b/datasets/KOPRI-KPDC-00001335_1.json index 8f784cb1e1..b2c23150c9 100644 --- a/datasets/KOPRI-KPDC-00001335_1.json +++ b/datasets/KOPRI-KPDC-00001335_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001335_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001336_2.json b/datasets/KOPRI-KPDC-00001336_2.json index d429462e4c..941fcdbd8c 100644 --- a/datasets/KOPRI-KPDC-00001336_2.json +++ b/datasets/KOPRI-KPDC-00001336_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001336_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001337_1.json b/datasets/KOPRI-KPDC-00001337_1.json index 40f36369f6..db1e71c7d9 100644 --- a/datasets/KOPRI-KPDC-00001337_1.json +++ b/datasets/KOPRI-KPDC-00001337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001338_1.json b/datasets/KOPRI-KPDC-00001338_1.json index ce9cb3a262..8d3f67859d 100644 --- a/datasets/KOPRI-KPDC-00001338_1.json +++ b/datasets/KOPRI-KPDC-00001338_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001338_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001339_1.json b/datasets/KOPRI-KPDC-00001339_1.json index eb92146d61..59b865da78 100644 --- a/datasets/KOPRI-KPDC-00001339_1.json +++ b/datasets/KOPRI-KPDC-00001339_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001339_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001340_1.json b/datasets/KOPRI-KPDC-00001340_1.json index 05c68baf46..913a6535a3 100644 --- a/datasets/KOPRI-KPDC-00001340_1.json +++ b/datasets/KOPRI-KPDC-00001340_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001340_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001341_2.json b/datasets/KOPRI-KPDC-00001341_2.json index 4b43fd937a..1933a5db64 100644 --- a/datasets/KOPRI-KPDC-00001341_2.json +++ b/datasets/KOPRI-KPDC-00001341_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001341_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset is a set of namelist files for WRF and COSP runs for reproduction of the results in \"Simulations of winter Arctic clouds and associated radiation fluxes using different cloud microphysics schemes in the Polar WRF: Comparisons with CloudSat, CALIPSO, and CERES\"", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001342_1.json b/datasets/KOPRI-KPDC-00001342_1.json index 15e00d1e2d..b0b3468c65 100644 --- a/datasets/KOPRI-KPDC-00001342_1.json +++ b/datasets/KOPRI-KPDC-00001342_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001342_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001343_1.json b/datasets/KOPRI-KPDC-00001343_1.json index 7e802cd4b6..31586249b8 100644 --- a/datasets/KOPRI-KPDC-00001343_1.json +++ b/datasets/KOPRI-KPDC-00001343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001344_1.json b/datasets/KOPRI-KPDC-00001344_1.json index e2d15cf50f..df0cf3be00 100644 --- a/datasets/KOPRI-KPDC-00001344_1.json +++ b/datasets/KOPRI-KPDC-00001344_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001344_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001345_1.json b/datasets/KOPRI-KPDC-00001345_1.json index 24879a853f..add3276c28 100644 --- a/datasets/KOPRI-KPDC-00001345_1.json +++ b/datasets/KOPRI-KPDC-00001345_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001345_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001346_1.json b/datasets/KOPRI-KPDC-00001346_1.json index 720c9ec8e8..ba0e118268 100644 --- a/datasets/KOPRI-KPDC-00001346_1.json +++ b/datasets/KOPRI-KPDC-00001346_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001346_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001347_1.json b/datasets/KOPRI-KPDC-00001347_1.json index e0aeadd2ef..b9903902df 100644 --- a/datasets/KOPRI-KPDC-00001347_1.json +++ b/datasets/KOPRI-KPDC-00001347_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001347_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001348_1.json b/datasets/KOPRI-KPDC-00001348_1.json index 60aabb11ee..1a775ab513 100644 --- a/datasets/KOPRI-KPDC-00001348_1.json +++ b/datasets/KOPRI-KPDC-00001348_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001348_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001349_1.json b/datasets/KOPRI-KPDC-00001349_1.json index 6ed2998f85..fb29aa37f1 100644 --- a/datasets/KOPRI-KPDC-00001349_1.json +++ b/datasets/KOPRI-KPDC-00001349_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001349_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001350_1.json b/datasets/KOPRI-KPDC-00001350_1.json index 745c6cc73d..fac8e5eb1f 100644 --- a/datasets/KOPRI-KPDC-00001350_1.json +++ b/datasets/KOPRI-KPDC-00001350_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001350_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001351_1.json b/datasets/KOPRI-KPDC-00001351_1.json index 0f017d08fc..63e99bf6b3 100644 --- a/datasets/KOPRI-KPDC-00001351_1.json +++ b/datasets/KOPRI-KPDC-00001351_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001351_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001352_1.json b/datasets/KOPRI-KPDC-00001352_1.json index 539b019ae1..5575a2ffec 100644 --- a/datasets/KOPRI-KPDC-00001352_1.json +++ b/datasets/KOPRI-KPDC-00001352_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001352_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001353_1.json b/datasets/KOPRI-KPDC-00001353_1.json index 639ce39f7a..8cc89bc358 100644 --- a/datasets/KOPRI-KPDC-00001353_1.json +++ b/datasets/KOPRI-KPDC-00001353_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001353_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001354_1.json b/datasets/KOPRI-KPDC-00001354_1.json index 337b839739..eabbcd76fd 100644 --- a/datasets/KOPRI-KPDC-00001354_1.json +++ b/datasets/KOPRI-KPDC-00001354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001355_1.json b/datasets/KOPRI-KPDC-00001355_1.json index 996abcca4e..3623f92a7f 100644 --- a/datasets/KOPRI-KPDC-00001355_1.json +++ b/datasets/KOPRI-KPDC-00001355_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001355_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001356_1.json b/datasets/KOPRI-KPDC-00001356_1.json index 9c97190f01..5ce12ddf03 100644 --- a/datasets/KOPRI-KPDC-00001356_1.json +++ b/datasets/KOPRI-KPDC-00001356_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001356_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001357_1.json b/datasets/KOPRI-KPDC-00001357_1.json index 2a77a19105..a3ea9cd0e0 100644 --- a/datasets/KOPRI-KPDC-00001357_1.json +++ b/datasets/KOPRI-KPDC-00001357_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001357_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001358_1.json b/datasets/KOPRI-KPDC-00001358_1.json index 0ce6296402..02dcec6b93 100644 --- a/datasets/KOPRI-KPDC-00001358_1.json +++ b/datasets/KOPRI-KPDC-00001358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001359_1.json b/datasets/KOPRI-KPDC-00001359_1.json index d60400298b..6cccb3e866 100644 --- a/datasets/KOPRI-KPDC-00001359_1.json +++ b/datasets/KOPRI-KPDC-00001359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001360_1.json b/datasets/KOPRI-KPDC-00001360_1.json index b336ac57df..762acd0c9d 100644 --- a/datasets/KOPRI-KPDC-00001360_1.json +++ b/datasets/KOPRI-KPDC-00001360_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001360_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001361_1.json b/datasets/KOPRI-KPDC-00001361_1.json index f39c86ac5f..86e401e34d 100644 --- a/datasets/KOPRI-KPDC-00001361_1.json +++ b/datasets/KOPRI-KPDC-00001361_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001361_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001362_1.json b/datasets/KOPRI-KPDC-00001362_1.json index 8f768e00e5..bc82bb837c 100644 --- a/datasets/KOPRI-KPDC-00001362_1.json +++ b/datasets/KOPRI-KPDC-00001362_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001362_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001363_1.json b/datasets/KOPRI-KPDC-00001363_1.json index 0b47ea36a7..5cd9d4e28a 100644 --- a/datasets/KOPRI-KPDC-00001363_1.json +++ b/datasets/KOPRI-KPDC-00001363_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001363_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001364_1.json b/datasets/KOPRI-KPDC-00001364_1.json index 48ba0b4f69..94d7256810 100644 --- a/datasets/KOPRI-KPDC-00001364_1.json +++ b/datasets/KOPRI-KPDC-00001364_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001364_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001365_1.json b/datasets/KOPRI-KPDC-00001365_1.json index f6cafe7043..a89c3b04bc 100644 --- a/datasets/KOPRI-KPDC-00001365_1.json +++ b/datasets/KOPRI-KPDC-00001365_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001365_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001366_1.json b/datasets/KOPRI-KPDC-00001366_1.json index 5bad143e95..53b00f498c 100644 --- a/datasets/KOPRI-KPDC-00001366_1.json +++ b/datasets/KOPRI-KPDC-00001366_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001366_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001367_1.json b/datasets/KOPRI-KPDC-00001367_1.json index ee3054c74e..54da033f38 100644 --- a/datasets/KOPRI-KPDC-00001367_1.json +++ b/datasets/KOPRI-KPDC-00001367_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001367_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001368_1.json b/datasets/KOPRI-KPDC-00001368_1.json index ba9fcd7c74..2e936e0add 100644 --- a/datasets/KOPRI-KPDC-00001368_1.json +++ b/datasets/KOPRI-KPDC-00001368_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001368_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001369_1.json b/datasets/KOPRI-KPDC-00001369_1.json index 52bccad1b5..9493e404c8 100644 --- a/datasets/KOPRI-KPDC-00001369_1.json +++ b/datasets/KOPRI-KPDC-00001369_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001369_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001370_1.json b/datasets/KOPRI-KPDC-00001370_1.json index 9059f1b37c..8ad806e91b 100644 --- a/datasets/KOPRI-KPDC-00001370_1.json +++ b/datasets/KOPRI-KPDC-00001370_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001370_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001371_1.json b/datasets/KOPRI-KPDC-00001371_1.json index d9e049941d..7f2e9fafa1 100644 --- a/datasets/KOPRI-KPDC-00001371_1.json +++ b/datasets/KOPRI-KPDC-00001371_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001371_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001372_1.json b/datasets/KOPRI-KPDC-00001372_1.json index 74795e9f5e..0f6ed6e94d 100644 --- a/datasets/KOPRI-KPDC-00001372_1.json +++ b/datasets/KOPRI-KPDC-00001372_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001372_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001373_1.json b/datasets/KOPRI-KPDC-00001373_1.json index b38312ac2d..8ab21e785e 100644 --- a/datasets/KOPRI-KPDC-00001373_1.json +++ b/datasets/KOPRI-KPDC-00001373_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001373_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001374_1.json b/datasets/KOPRI-KPDC-00001374_1.json index f5c828cda7..f3b91c706b 100644 --- a/datasets/KOPRI-KPDC-00001374_1.json +++ b/datasets/KOPRI-KPDC-00001374_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001374_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001375_1.json b/datasets/KOPRI-KPDC-00001375_1.json index 3e86730ca2..2ece0c37e1 100644 --- a/datasets/KOPRI-KPDC-00001375_1.json +++ b/datasets/KOPRI-KPDC-00001375_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001375_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001376_1.json b/datasets/KOPRI-KPDC-00001376_1.json index 26a81fb516..e3a23a20a7 100644 --- a/datasets/KOPRI-KPDC-00001376_1.json +++ b/datasets/KOPRI-KPDC-00001376_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001376_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001377_1.json b/datasets/KOPRI-KPDC-00001377_1.json index 784b5eb1c8..fea4e5a863 100644 --- a/datasets/KOPRI-KPDC-00001377_1.json +++ b/datasets/KOPRI-KPDC-00001377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001378_1.json b/datasets/KOPRI-KPDC-00001378_1.json index 7d23d73f69..9533a9d116 100644 --- a/datasets/KOPRI-KPDC-00001378_1.json +++ b/datasets/KOPRI-KPDC-00001378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001379_1.json b/datasets/KOPRI-KPDC-00001379_1.json index 46024bdc13..cb72266cb1 100644 --- a/datasets/KOPRI-KPDC-00001379_1.json +++ b/datasets/KOPRI-KPDC-00001379_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001379_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001380_1.json b/datasets/KOPRI-KPDC-00001380_1.json index bdfc524da0..d0437289c3 100644 --- a/datasets/KOPRI-KPDC-00001380_1.json +++ b/datasets/KOPRI-KPDC-00001380_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001380_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001381_1.json b/datasets/KOPRI-KPDC-00001381_1.json index e43f421bce..0299bb7f40 100644 --- a/datasets/KOPRI-KPDC-00001381_1.json +++ b/datasets/KOPRI-KPDC-00001381_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001381_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001382_1.json b/datasets/KOPRI-KPDC-00001382_1.json index 715016acff..c85486ee98 100644 --- a/datasets/KOPRI-KPDC-00001382_1.json +++ b/datasets/KOPRI-KPDC-00001382_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001382_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001383_1.json b/datasets/KOPRI-KPDC-00001383_1.json index 52c097dcce..03a85d6473 100644 --- a/datasets/KOPRI-KPDC-00001383_1.json +++ b/datasets/KOPRI-KPDC-00001383_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001383_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001384_1.json b/datasets/KOPRI-KPDC-00001384_1.json index c1647bd956..ecbe63c81c 100644 --- a/datasets/KOPRI-KPDC-00001384_1.json +++ b/datasets/KOPRI-KPDC-00001384_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001384_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001385_1.json b/datasets/KOPRI-KPDC-00001385_1.json index 6a395236da..89469a5213 100644 --- a/datasets/KOPRI-KPDC-00001385_1.json +++ b/datasets/KOPRI-KPDC-00001385_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001385_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001386_1.json b/datasets/KOPRI-KPDC-00001386_1.json index 02982c64c6..fa7141457a 100644 --- a/datasets/KOPRI-KPDC-00001386_1.json +++ b/datasets/KOPRI-KPDC-00001386_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001386_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001387_1.json b/datasets/KOPRI-KPDC-00001387_1.json index bbb3cdd9cc..f7f2b3adbd 100644 --- a/datasets/KOPRI-KPDC-00001387_1.json +++ b/datasets/KOPRI-KPDC-00001387_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001387_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001388_1.json b/datasets/KOPRI-KPDC-00001388_1.json index f9dd2ef2a0..cd54900a54 100644 --- a/datasets/KOPRI-KPDC-00001388_1.json +++ b/datasets/KOPRI-KPDC-00001388_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001388_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001389_1.json b/datasets/KOPRI-KPDC-00001389_1.json index b51b2bb356..9f41c1840e 100644 --- a/datasets/KOPRI-KPDC-00001389_1.json +++ b/datasets/KOPRI-KPDC-00001389_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001389_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001390_1.json b/datasets/KOPRI-KPDC-00001390_1.json index 99d4dacd38..beb707e8c4 100644 --- a/datasets/KOPRI-KPDC-00001390_1.json +++ b/datasets/KOPRI-KPDC-00001390_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001390_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001391_1.json b/datasets/KOPRI-KPDC-00001391_1.json index b37be97724..283a9ca2fe 100644 --- a/datasets/KOPRI-KPDC-00001391_1.json +++ b/datasets/KOPRI-KPDC-00001391_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001391_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001392_1.json b/datasets/KOPRI-KPDC-00001392_1.json index 75b641d022..68f9854327 100644 --- a/datasets/KOPRI-KPDC-00001392_1.json +++ b/datasets/KOPRI-KPDC-00001392_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001392_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001393_1.json b/datasets/KOPRI-KPDC-00001393_1.json index 20fa9b263a..80f66bb29c 100644 --- a/datasets/KOPRI-KPDC-00001393_1.json +++ b/datasets/KOPRI-KPDC-00001393_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001393_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001394_1.json b/datasets/KOPRI-KPDC-00001394_1.json index 335ead1eac..3d8c55725b 100644 --- a/datasets/KOPRI-KPDC-00001394_1.json +++ b/datasets/KOPRI-KPDC-00001394_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001394_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001395_1.json b/datasets/KOPRI-KPDC-00001395_1.json index 56d9d26d85..7ac0a0ba4e 100644 --- a/datasets/KOPRI-KPDC-00001395_1.json +++ b/datasets/KOPRI-KPDC-00001395_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001395_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001396_4.json b/datasets/KOPRI-KPDC-00001396_4.json index aea40b3e30..6f8f872599 100644 --- a/datasets/KOPRI-KPDC-00001396_4.json +++ b/datasets/KOPRI-KPDC-00001396_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001396_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001397_1.json b/datasets/KOPRI-KPDC-00001397_1.json index b2f5e3d988..80d1aa1372 100644 --- a/datasets/KOPRI-KPDC-00001397_1.json +++ b/datasets/KOPRI-KPDC-00001397_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001397_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001398_1.json b/datasets/KOPRI-KPDC-00001398_1.json index dfb7b0561f..b62e5efd02 100644 --- a/datasets/KOPRI-KPDC-00001398_1.json +++ b/datasets/KOPRI-KPDC-00001398_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001398_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001399_1.json b/datasets/KOPRI-KPDC-00001399_1.json index c69bd753a2..ca8cdb23fb 100644 --- a/datasets/KOPRI-KPDC-00001399_1.json +++ b/datasets/KOPRI-KPDC-00001399_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001399_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001400_1.json b/datasets/KOPRI-KPDC-00001400_1.json index 4182032ca5..92601f38dd 100644 --- a/datasets/KOPRI-KPDC-00001400_1.json +++ b/datasets/KOPRI-KPDC-00001400_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001400_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001401_1.json b/datasets/KOPRI-KPDC-00001401_1.json index 0cc45f4294..d19468fc7e 100644 --- a/datasets/KOPRI-KPDC-00001401_1.json +++ b/datasets/KOPRI-KPDC-00001401_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001401_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001402_1.json b/datasets/KOPRI-KPDC-00001402_1.json index ec8ea569c1..79e54edb6c 100644 --- a/datasets/KOPRI-KPDC-00001402_1.json +++ b/datasets/KOPRI-KPDC-00001402_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001402_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001403_1.json b/datasets/KOPRI-KPDC-00001403_1.json index 02729d02af..29e1455843 100644 --- a/datasets/KOPRI-KPDC-00001403_1.json +++ b/datasets/KOPRI-KPDC-00001403_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001403_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001404_1.json b/datasets/KOPRI-KPDC-00001404_1.json index 37fe0d7250..14d194b9ea 100644 --- a/datasets/KOPRI-KPDC-00001404_1.json +++ b/datasets/KOPRI-KPDC-00001404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001405_1.json b/datasets/KOPRI-KPDC-00001405_1.json index 9d0589d302..faa5c458f8 100644 --- a/datasets/KOPRI-KPDC-00001405_1.json +++ b/datasets/KOPRI-KPDC-00001405_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001405_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001406_1.json b/datasets/KOPRI-KPDC-00001406_1.json index d145d5acc1..6c36e0eb1f 100644 --- a/datasets/KOPRI-KPDC-00001406_1.json +++ b/datasets/KOPRI-KPDC-00001406_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001406_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Cambridge bay station in 2018.09-2019.06", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001407_2.json b/datasets/KOPRI-KPDC-00001407_2.json index c4376c33a0..c4fa258896 100644 --- a/datasets/KOPRI-KPDC-00001407_2.json +++ b/datasets/KOPRI-KPDC-00001407_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001407_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam data were collected during the 2019 ARA10C cruise in Chukchi Plateau and East Siberian shelf areas on Arctic ocean\nAn accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001408_4.json b/datasets/KOPRI-KPDC-00001408_4.json index d268d54583..f58aee835a 100644 --- a/datasets/KOPRI-KPDC-00001408_4.json +++ b/datasets/KOPRI-KPDC-00001408_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001408_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profiler(Kongsberg SBP27) data were collected during the 2019 ARA10C cruise in the Chukchi Sea, Arctic Ocean\nInvestigation of submarine resource environment and seabed methane release in the Chukchi Sea, Arctic Ocean\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001409_5.json b/datasets/KOPRI-KPDC-00001409_5.json index 85f9e037c2..597a1ef48b 100644 --- a/datasets/KOPRI-KPDC-00001409_5.json +++ b/datasets/KOPRI-KPDC-00001409_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001409_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-Channel seismic data were collected during the 2019 ARA10C cruise in the Chukchi Sea, Arctic Ocean\nInvestigation of submarine resource environment and seabed methane release in the Chukchi rise", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001410_1.json b/datasets/KOPRI-KPDC-00001410_1.json index 94a187b36b..3f2b54044c 100644 --- a/datasets/KOPRI-KPDC-00001410_1.json +++ b/datasets/KOPRI-KPDC-00001410_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001410_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation, micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18.~2019.06.30) were collected.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001411_1.json b/datasets/KOPRI-KPDC-00001411_1.json index b043638c50..6bac19d672 100644 --- a/datasets/KOPRI-KPDC-00001411_1.json +++ b/datasets/KOPRI-KPDC-00001411_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001411_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2/Soil temperature profile had been measured during summertime in 2019 at Council, Alaska.\nTo monitor and understand CO2 emission and soil temperature change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001412_1.json b/datasets/KOPRI-KPDC-00001412_1.json index c8460e2a1b..f54b8ef5a9 100644 --- a/datasets/KOPRI-KPDC-00001412_1.json +++ b/datasets/KOPRI-KPDC-00001412_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001412_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation, micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18~2019.06.30) were collected\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001413_2.json b/datasets/KOPRI-KPDC-00001413_2.json index cce7e102c5..067043bbb3 100644 --- a/datasets/KOPRI-KPDC-00001413_2.json +++ b/datasets/KOPRI-KPDC-00001413_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001413_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001414_1.json b/datasets/KOPRI-KPDC-00001414_1.json index ecba4ea7f2..c0b0b80142 100644 --- a/datasets/KOPRI-KPDC-00001414_1.json +++ b/datasets/KOPRI-KPDC-00001414_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001414_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001415_1.json b/datasets/KOPRI-KPDC-00001415_1.json index b676967659..6a4ffcb36f 100644 --- a/datasets/KOPRI-KPDC-00001415_1.json +++ b/datasets/KOPRI-KPDC-00001415_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001415_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Nord, Greenland. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR5000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001416_1.json b/datasets/KOPRI-KPDC-00001416_1.json index de4cbd0fd3..a268857ffc 100644 --- a/datasets/KOPRI-KPDC-00001416_1.json +++ b/datasets/KOPRI-KPDC-00001416_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001416_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2019 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001417_1.json b/datasets/KOPRI-KPDC-00001417_1.json index 24ac8eaccc..be52013db2 100644 --- a/datasets/KOPRI-KPDC-00001417_1.json +++ b/datasets/KOPRI-KPDC-00001417_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001417_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Baranova, Russia. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR5000 logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001418_1.json b/datasets/KOPRI-KPDC-00001418_1.json index 26fcbb1933..b7af972e3c 100644 --- a/datasets/KOPRI-KPDC-00001418_1.json +++ b/datasets/KOPRI-KPDC-00001418_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001418_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2019 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz.\nTo monitor and understand energy/water/green-house-gas flux at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001420_2.json b/datasets/KOPRI-KPDC-00001420_2.json index 1afc68ff6e..6a7abc2c6b 100644 --- a/datasets/KOPRI-KPDC-00001420_2.json +++ b/datasets/KOPRI-KPDC-00001420_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001420_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heat flow measurements in Chukchi Plateau and East Siberian shelf areas on Arctic ocean Investigation to the thermal structure in Chukchi Plateau and East Siberian shelf areas on Arctic ocean", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001421_1.json b/datasets/KOPRI-KPDC-00001421_1.json index 668b417fd0..af993cf539 100644 --- a/datasets/KOPRI-KPDC-00001421_1.json +++ b/datasets/KOPRI-KPDC-00001421_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001421_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). To understand the behavior of gas hydrate in the sediment and to estimate the CH4 fluxes from the sediment through the water column to the atmosphere, we obtained data on water temperature, salinity, density and fluorescence in the water column.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001422_2.json b/datasets/KOPRI-KPDC-00001422_2.json index a23dfe21a6..b9cce72a5f 100644 --- a/datasets/KOPRI-KPDC-00001422_2.json +++ b/datasets/KOPRI-KPDC-00001422_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001422_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). We study to estimate the CH4 fluxes on the interface of air and seawater. The CH4 in the ambient air and the surface water were quantitatively measured along the ship track.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001423_2.json b/datasets/KOPRI-KPDC-00001423_2.json index fba61bb455..71d0354fda 100644 --- a/datasets/KOPRI-KPDC-00001423_2.json +++ b/datasets/KOPRI-KPDC-00001423_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001423_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001424_1.json b/datasets/KOPRI-KPDC-00001424_1.json index 6106ad6470..cc23cbbbb4 100644 --- a/datasets/KOPRI-KPDC-00001424_1.json +++ b/datasets/KOPRI-KPDC-00001424_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001424_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected the manganese nodule by dredge to study the distribution of manganese nodule in the East siberian sea, Arctic Ocean.\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001425_1.json b/datasets/KOPRI-KPDC-00001425_1.json index 58bf0ef5de..cbe46384b6 100644 --- a/datasets/KOPRI-KPDC-00001425_1.json +++ b/datasets/KOPRI-KPDC-00001425_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001425_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 6 August 2016 to 8 September 2016 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001426_1.json b/datasets/KOPRI-KPDC-00001426_1.json index c5871912c2..f32377bbd9 100644 --- a/datasets/KOPRI-KPDC-00001426_1.json +++ b/datasets/KOPRI-KPDC-00001426_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001426_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 7 August 2017 to 13 September 2017 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001427_1.json b/datasets/KOPRI-KPDC-00001427_1.json index acc609694a..ca2ca3c5d4 100644 --- a/datasets/KOPRI-KPDC-00001427_1.json +++ b/datasets/KOPRI-KPDC-00001427_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001427_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 5 August 2018 to 18 September 2018 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001428_2.json b/datasets/KOPRI-KPDC-00001428_2.json index e8464934c8..bba6ef0956 100644 --- a/datasets/KOPRI-KPDC-00001428_2.json +++ b/datasets/KOPRI-KPDC-00001428_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001428_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001429_2.json b/datasets/KOPRI-KPDC-00001429_2.json index dd1ee011f3..251c73b5c7 100644 --- a/datasets/KOPRI-KPDC-00001429_2.json +++ b/datasets/KOPRI-KPDC-00001429_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001429_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001430_1.json b/datasets/KOPRI-KPDC-00001430_1.json index d390cd7043..6f01f25724 100644 --- a/datasets/KOPRI-KPDC-00001430_1.json +++ b/datasets/KOPRI-KPDC-00001430_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001430_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to study for the effect of sea ice alkalinity on the summertime CO2 absorption capacity of the East Siberian Sea. \nThis study was performed in August 2017 using IBRV ARAON. Two ice camps were conducted for sampling sea ice cores. The first ice camp (IC1) was conducted on 13 August and located at 77\u00b035.8552\u2019N, 179\u00b019.4508\u2019E. The second ice camp (IC2) was conducted on 16 August and located at 75\u00b022.0475\u2019N, 176\u00b014.0973\u2019E.\n\n\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001431_2.json b/datasets/KOPRI-KPDC-00001431_2.json index 89bc7064fc..9055337d46 100644 --- a/datasets/KOPRI-KPDC-00001431_2.json +++ b/datasets/KOPRI-KPDC-00001431_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001431_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To evaluate oceanic contributions on instability of the Thwaites Glacier, an investigation was conducted in front of the Thwaites Glacier, Amundsen Sea during the ARAON cruise in February, 2020. CTD profiles were collected at 67 stations (89 casts). CTD casts were mainly conducted in the trough region (> 1,000 m) to check Circumpolar Deep Water (CDW) pathway and its properties. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001432_2.json b/datasets/KOPRI-KPDC-00001432_2.json index 2884fb1060..cc5ff5af0b 100644 --- a/datasets/KOPRI-KPDC-00001432_2.json +++ b/datasets/KOPRI-KPDC-00001432_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001432_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To evaluate oceanic contributions on instability of the Thwaites Glacier, an investigation was conducted in front of the Thwaites Glacier, Amundsen Sea during the ARAON cruise in February, 2020. LADCP (Lowered ADCP, ADCP attached to the CTD frame) data were collected at 67 stations (89 casts). The casts were mainly conducted in the trough region (> 1,000 m) to check Circumpolar Deep Water (CDW) pathway and its properties. ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001433_5.json b/datasets/KOPRI-KPDC-00001433_5.json index e2d6678a2f..5fa182f7b0 100644 --- a/datasets/KOPRI-KPDC-00001433_5.json +++ b/datasets/KOPRI-KPDC-00001433_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001433_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the activites of Mt. Melbourne and glacial movements\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001434_2.json b/datasets/KOPRI-KPDC-00001434_2.json index 2ae7648163..7287862692 100644 --- a/datasets/KOPRI-KPDC-00001434_2.json +++ b/datasets/KOPRI-KPDC-00001434_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001434_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in March, 2020. LADCP profiles were collected at 27 stations. To investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001435_2.json b/datasets/KOPRI-KPDC-00001435_2.json index 24cd86c953..bf858b4412 100644 --- a/datasets/KOPRI-KPDC-00001435_2.json +++ b/datasets/KOPRI-KPDC-00001435_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001435_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in March, 2020. CTD profiles were collected at 27 stations. To investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001436_3.json b/datasets/KOPRI-KPDC-00001436_3.json index 2ccc0a5f6e..1ebf2ea350 100644 --- a/datasets/KOPRI-KPDC-00001436_3.json +++ b/datasets/KOPRI-KPDC-00001436_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001436_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Remotely operating GPS system\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001437_11.json b/datasets/KOPRI-KPDC-00001437_11.json index 98d3110202..5c875dae5a 100644 --- a/datasets/KOPRI-KPDC-00001437_11.json +++ b/datasets/KOPRI-KPDC-00001437_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001437_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We provide two hyperspectral datasets from GNSS/INS-assisted co-aligned pushbroom hyperspectral scanners on a drone. The hyperspectral datasets consist of raw/post-processed hyperspectral data, raw/post-processed GNSS/IMU data, and digital surface models, and were radiometrically and geometrically evaluated. These datasets are expected to aid the improvement of UAV-based hyperspectral data processing and analysis algorithms.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001438_1.json b/datasets/KOPRI-KPDC-00001438_1.json index 8afa8bf214..63b808c7e9 100644 --- a/datasets/KOPRI-KPDC-00001438_1.json +++ b/datasets/KOPRI-KPDC-00001438_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001438_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ApRES (Automated phase-sensitive Radar Echo Sounding) data to detect the change of ice thickness and basal melt\nInvestigation of the behaviour of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001439_2.json b/datasets/KOPRI-KPDC-00001439_2.json index 89121299ab..baf15f772f 100644 --- a/datasets/KOPRI-KPDC-00001439_2.json +++ b/datasets/KOPRI-KPDC-00001439_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001439_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice penetrating radar data to map the ice thickness and bedrock elevation of ice sheet", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001440_1.json b/datasets/KOPRI-KPDC-00001440_1.json index 61a80036f8..f017de4655 100644 --- a/datasets/KOPRI-KPDC-00001440_1.json +++ b/datasets/KOPRI-KPDC-00001440_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001440_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2019/2020 expedition. During the 2020 Amundsen Sea cruise (ANA10B) by IBRV Araon, we recover 4 mooring systems.\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001441_1.json b/datasets/KOPRI-KPDC-00001441_1.json index cd91556efa..03fde49170 100644 --- a/datasets/KOPRI-KPDC-00001441_1.json +++ b/datasets/KOPRI-KPDC-00001441_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001441_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2019/2020 expedition. During the 2020 Amundsen Sea cruise (ANA10B) by IBRV Araon, a total of 29 CTD stations were visited.\n", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001442_1.json b/datasets/KOPRI-KPDC-00001442_1.json index d848a52539..db6550639c 100644 --- a/datasets/KOPRI-KPDC-00001442_1.json +++ b/datasets/KOPRI-KPDC-00001442_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001442_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of circumpolar deep water (CDW) and its effect on the rapid melting of glaciers in the Amundsen Sea, an extensive oceanographic survey was conducted on the 2019/2020 expedition. During the 2020 Amundsen Sea cruise (ANA10B) by IBRV Araon, a total of 29 CTD/LADCP stations were visited.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001443_1.json b/datasets/KOPRI-KPDC-00001443_1.json index 8adb280d44..2fe80a1fa7 100644 --- a/datasets/KOPRI-KPDC-00001443_1.json +++ b/datasets/KOPRI-KPDC-00001443_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001443_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001444_1.json b/datasets/KOPRI-KPDC-00001444_1.json index 9c5151498f..b85bd8f6b3 100644 --- a/datasets/KOPRI-KPDC-00001444_1.json +++ b/datasets/KOPRI-KPDC-00001444_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001444_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001445_1.json b/datasets/KOPRI-KPDC-00001445_1.json index 77a1c7e18a..8bfc4cf849 100644 --- a/datasets/KOPRI-KPDC-00001445_1.json +++ b/datasets/KOPRI-KPDC-00001445_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001445_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001446_1.json b/datasets/KOPRI-KPDC-00001446_1.json index d7c23e0e87..e37cf486db 100644 --- a/datasets/KOPRI-KPDC-00001446_1.json +++ b/datasets/KOPRI-KPDC-00001446_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001446_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001447_1.json b/datasets/KOPRI-KPDC-00001447_1.json index abd3296425..11643ff3fe 100644 --- a/datasets/KOPRI-KPDC-00001447_1.json +++ b/datasets/KOPRI-KPDC-00001447_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001447_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001448_1.json b/datasets/KOPRI-KPDC-00001448_1.json index 1a9bea93af..8d2f84a56f 100644 --- a/datasets/KOPRI-KPDC-00001448_1.json +++ b/datasets/KOPRI-KPDC-00001448_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001448_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001449_1.json b/datasets/KOPRI-KPDC-00001449_1.json index 6bde91abc6..7fc854bb95 100644 --- a/datasets/KOPRI-KPDC-00001449_1.json +++ b/datasets/KOPRI-KPDC-00001449_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001449_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001450_3.json b/datasets/KOPRI-KPDC-00001450_3.json index 50d5e80b53..f636186fed 100644 --- a/datasets/KOPRI-KPDC-00001450_3.json +++ b/datasets/KOPRI-KPDC-00001450_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001450_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of monitoring of the discharge water quality from Jang Bogo Station is to manage contamination source and equipment for mitigation of environmental pollution cuased by the station operation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001451_2.json b/datasets/KOPRI-KPDC-00001451_2.json index 37d9a575c1..c37d71e313 100644 --- a/datasets/KOPRI-KPDC-00001451_2.json +++ b/datasets/KOPRI-KPDC-00001451_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001451_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1) Abstract (English) Atmospheric DMS mixing ratio measured at King Sejong Station in 2019-20 (from 8 Nov 2019 to 15 Feb 2020) by using custom-made trapping and desorption system equipped with pulsed flame photometric detector. 2) Purpose (English) Monitoring of atmospheric DMS mixing ration at King Sejong Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001452_6.json b/datasets/KOPRI-KPDC-00001452_6.json index 8330716837..51549aaa02 100644 --- a/datasets/KOPRI-KPDC-00001452_6.json +++ b/datasets/KOPRI-KPDC-00001452_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001452_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NDI values and estimated phosphate concentrations from suspended particulate matter (SPM), surface sediments, and sinking particles in the East Sea. Annual mean phosphate concentrations were obtained from the World Ocean Atlas 13.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001453_3.json b/datasets/KOPRI-KPDC-00001453_3.json index f06e016aac..1bd1e8c31e 100644 --- a/datasets/KOPRI-KPDC-00001453_3.json +++ b/datasets/KOPRI-KPDC-00001453_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001453_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fluxes of individual LCDs and NDI values obtained from sinking particles at 1000 m and 2300 m water depth in the East Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001454_1.json b/datasets/KOPRI-KPDC-00001454_1.json index d87b865e4f..824fd8f292 100644 --- a/datasets/KOPRI-KPDC-00001454_1.json +++ b/datasets/KOPRI-KPDC-00001454_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001454_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Compiled data of individual long-chain diols, environmeltal factors and nutrient diol index (NDI) from suspenden particulate matter and surface sediments", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001455_1.json b/datasets/KOPRI-KPDC-00001455_1.json index 01e29e98e1..ce73f4715f 100644 --- a/datasets/KOPRI-KPDC-00001455_1.json +++ b/datasets/KOPRI-KPDC-00001455_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001455_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data is phytoplankton photopohysiological properties in the Arctic Ocean. and the data were acquired to confirm the photosynthetic status of phytoplankton in the summer in the Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001456_1.json b/datasets/KOPRI-KPDC-00001456_1.json index 3519c2c5be..9175d926b4 100644 --- a/datasets/KOPRI-KPDC-00001456_1.json +++ b/datasets/KOPRI-KPDC-00001456_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001456_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The post-processed upper-ocean CTD data (0-100 m) for the period of 2011-2016 obtained from IBRV Araon over the Chukchi-East Siberian sea sector of the Arctic Ocean (Area: 75.5N~80N, 172E~160W)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001457_2.json b/datasets/KOPRI-KPDC-00001457_2.json index e7eef8c2a6..5883a334c8 100644 --- a/datasets/KOPRI-KPDC-00001457_2.json +++ b/datasets/KOPRI-KPDC-00001457_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001457_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data includes the 10-min averaged ship-borne meteorological observations at the foremast of IBRV Araon during the summer of 2016 and covers the period of two Arctic cruises (ARA07B and ARA07C). The missing value is -99999.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001458_2.json b/datasets/KOPRI-KPDC-00001458_2.json index b39e454ecc..e644dde772 100644 --- a/datasets/KOPRI-KPDC-00001458_2.json +++ b/datasets/KOPRI-KPDC-00001458_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001458_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data includes the hourly GPS locations and 2-hourly surface temperatures of the drifting sea ice floe where the Arctic sea ice camp was carried out in 2016 (ARA07B), which was obtained by the sea ice mass balance buoy deployed on 15 August. Originally the buoy measured the 5-m depth vertical temperature profile at 2.5 cm intervals from near-surface air to upper-ocean underneath sea ice. From the temperature profile, the surface temperature was retrieved by picking up the two neighboring thermistor chips just below the air-snow/ice interface and averaging them. The period is limited from the deployment to 19 August when the IBRV Araon sailed the Chukchi Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001460_1.json b/datasets/KOPRI-KPDC-00001460_1.json index c925b9badd..7205aced71 100644 --- a/datasets/KOPRI-KPDC-00001460_1.json +++ b/datasets/KOPRI-KPDC-00001460_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001460_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical and chemical dataset obtained in the northern Chukchi Sea during the ARA06B (2015), ARA07B (2016), and ARA08B (2017) cruises.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001461_1.json b/datasets/KOPRI-KPDC-00001461_1.json index ebedb5cc16..20129c42fb 100644 --- a/datasets/KOPRI-KPDC-00001461_1.json +++ b/datasets/KOPRI-KPDC-00001461_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001461_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "If the recent global warming trend observed on the Antarctic Peninsula persists over the long term, increasing aridity is expected, due to the loss of glaciers. However, the molecular mechanisms of Antarctic moss that allow survival in this harsh and dynamic region have yet to be investigated. Thus, we assembled a draft genome of Sanionia uncinata. A high-quality genome assembly of Sanionia uncinata will facilitate genomic, transcriptomic, and metabolomic analyses of the quality traits that make the moss species one of the world\u00e2\u20ac\u2122s strongest plant.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001462_1.json b/datasets/KOPRI-KPDC-00001462_1.json index f1eb432c91..6c6cf13297 100644 --- a/datasets/KOPRI-KPDC-00001462_1.json +++ b/datasets/KOPRI-KPDC-00001462_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001462_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001463_1.json b/datasets/KOPRI-KPDC-00001463_1.json index a0f6265d1e..1f994bc07e 100644 --- a/datasets/KOPRI-KPDC-00001463_1.json +++ b/datasets/KOPRI-KPDC-00001463_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001463_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001464_1.json b/datasets/KOPRI-KPDC-00001464_1.json index 935a00b73a..abb332d96a 100644 --- a/datasets/KOPRI-KPDC-00001464_1.json +++ b/datasets/KOPRI-KPDC-00001464_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001464_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001465_1.json b/datasets/KOPRI-KPDC-00001465_1.json index 7da66f3f65..d77ff2ac32 100644 --- a/datasets/KOPRI-KPDC-00001465_1.json +++ b/datasets/KOPRI-KPDC-00001465_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001465_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001466_1.json b/datasets/KOPRI-KPDC-00001466_1.json index 959e0935b1..c21a9f831d 100644 --- a/datasets/KOPRI-KPDC-00001466_1.json +++ b/datasets/KOPRI-KPDC-00001466_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001466_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001467_2.json b/datasets/KOPRI-KPDC-00001467_2.json index fcc1dbd354..c4b2e2beef 100644 --- a/datasets/KOPRI-KPDC-00001467_2.json +++ b/datasets/KOPRI-KPDC-00001467_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001467_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea ice type classification of Sentinel-1 SAR EW mode dual polarization (HH/HV) images. \nProcessing details, expected accuracy, and potential error sources are described in the following article: https://doi.org/10.5194/tc-14-2629-2020\nFor each date, there are four images: Radar backscattering intensity in HH-polarization, radar backscattering intensity in HV-polarization, S-1 retrieved ice type map, and reprojected OSI SAF OSI-403-c ice type map.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001468_2.json b/datasets/KOPRI-KPDC-00001468_2.json index f44a2856b8..cde5393b08 100644 --- a/datasets/KOPRI-KPDC-00001468_2.json +++ b/datasets/KOPRI-KPDC-00001468_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001468_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea ice drift vectors retrieved from Sentinel-1 SAR EW mode images. \nProcessing details, expected accuracy, and potential error sources are described in the following article: \nhttps://doi.org/10.3390/rs9030258\nFor each date, there are two kinds of vectors: black colored vectors from Sentinel-1 and red colored vectors from NSIDC Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001470_3.json b/datasets/KOPRI-KPDC-00001470_3.json index 93e34ed178..a26668b02e 100644 --- a/datasets/KOPRI-KPDC-00001470_3.json +++ b/datasets/KOPRI-KPDC-00001470_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001470_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of a multispectral remote sensing image and digital surface model over Council, Alaska. Images were acquired from the Pleiades satellite at 50cm spatial resolution. This data is used for change detection and monitoring of the permafrost area.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001471_1.json b/datasets/KOPRI-KPDC-00001471_1.json index 6786b4ea2c..51536ef99d 100644 --- a/datasets/KOPRI-KPDC-00001471_1.json +++ b/datasets/KOPRI-KPDC-00001471_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001471_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001472_1.json b/datasets/KOPRI-KPDC-00001472_1.json index 98fff78e21..3decd5f34f 100644 --- a/datasets/KOPRI-KPDC-00001472_1.json +++ b/datasets/KOPRI-KPDC-00001472_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001472_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001473_1.json b/datasets/KOPRI-KPDC-00001473_1.json index 40af4ec379..9522debde0 100644 --- a/datasets/KOPRI-KPDC-00001473_1.json +++ b/datasets/KOPRI-KPDC-00001473_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001473_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001474_1.json b/datasets/KOPRI-KPDC-00001474_1.json index 81f6d5e6f2..9e0a1a04c6 100644 --- a/datasets/KOPRI-KPDC-00001474_1.json +++ b/datasets/KOPRI-KPDC-00001474_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001474_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001475_1.json b/datasets/KOPRI-KPDC-00001475_1.json index 97e1a70218..f5d16310df 100644 --- a/datasets/KOPRI-KPDC-00001475_1.json +++ b/datasets/KOPRI-KPDC-00001475_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001475_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001476_1.json b/datasets/KOPRI-KPDC-00001476_1.json index 391baba22c..7aefd87be4 100644 --- a/datasets/KOPRI-KPDC-00001476_1.json +++ b/datasets/KOPRI-KPDC-00001476_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001476_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Brewer Ozone spectroscopy (BREWER) accurately measures the amount of light from a certain wavelength (286.5 nm to 363 nm) that absorbs ozone and is a total of ozone.\nMonitoring of changes in meteorological variables (O3) at Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001477_1.json b/datasets/KOPRI-KPDC-00001477_1.json index 094d3f6d65..3b9a2ed626 100644 --- a/datasets/KOPRI-KPDC-00001477_1.json +++ b/datasets/KOPRI-KPDC-00001477_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001477_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Brewer spectrophotometer (BREWER, Model: MKIV) accurately measures the amount of light from a certain wavelength (286.5 nm to 363 nm) that absorbs ozone and determines Total Column Ozone at King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001478_1.json b/datasets/KOPRI-KPDC-00001478_1.json index cbf6b978d7..42b3817183 100644 --- a/datasets/KOPRI-KPDC-00001478_1.json +++ b/datasets/KOPRI-KPDC-00001478_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001478_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averaged ozone mixing ratio data in the Antarctic for pressure heights: 5.6 hPa , 10.0 hPa , 31.6 hPa , 46.4 hPa , 68.1 hPa from Microwave Limb Sounder (MLS) on the NASA's EOS Aura satellite.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001479_1.json b/datasets/KOPRI-KPDC-00001479_1.json index 363f418f71..c2e99a6f65 100644 --- a/datasets/KOPRI-KPDC-00001479_1.json +++ b/datasets/KOPRI-KPDC-00001479_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001479_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averaged total ozone column (unit: DU) data in the Antarctic from satellite: OMI/Aura TOMS-Like Ozone, 2004 - 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001480_1.json b/datasets/KOPRI-KPDC-00001480_1.json index dba6a83df2..5d04223d37 100644 --- a/datasets/KOPRI-KPDC-00001480_1.json +++ b/datasets/KOPRI-KPDC-00001480_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001480_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averaged total ozone column (unit: DU) data at Jang Bogo Station and King Sejong Station from satellite: OMI/Aura TOMS-Like Ozone, 2004 - 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001481_1.json b/datasets/KOPRI-KPDC-00001481_1.json index c2c9b1f1b0..af91ea00c7 100644 --- a/datasets/KOPRI-KPDC-00001481_1.json +++ b/datasets/KOPRI-KPDC-00001481_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001481_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averaged ozone mixing ratio data (unit: ppm) at the Jang Bogo Station and King Sejong Station for pressure heights: 5.6 hPa , 10.0 hPa , 31.6 hPa , 46.4 hPa , 68.1 hPa from Microwave Limb Sounder (MLS) on the NASA's EOS Aura satellite in 2004 - 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001482_2.json b/datasets/KOPRI-KPDC-00001482_2.json index 67dfe3f947..29f4622908 100644 --- a/datasets/KOPRI-KPDC-00001482_2.json +++ b/datasets/KOPRI-KPDC-00001482_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001482_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\r\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001483_3.json b/datasets/KOPRI-KPDC-00001483_3.json index 95c1eb0f39..51c3f60e91 100644 --- a/datasets/KOPRI-KPDC-00001483_3.json +++ b/datasets/KOPRI-KPDC-00001483_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001483_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001484_2.json b/datasets/KOPRI-KPDC-00001484_2.json index b5a439c9c2..232be99c3a 100644 --- a/datasets/KOPRI-KPDC-00001484_2.json +++ b/datasets/KOPRI-KPDC-00001484_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001484_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The multichannel seismic (MCS) survey was conducted on the outer continental shelf and slope of the Beaufort Sea, the Arctic Ocean from 2nd to 13th September 2014, and the main objective of the survey is to investigate sedimentary stratigraphy, locations of permafrost. Presented inverted P-wave velocity models were calculated by the Laplace domain full-waveform inversion method from the acquired seismic dataset. It uses to image the subsea permafrost on the continental shelf of the Canadian Beaufort Sea by interpretation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001485_1.json b/datasets/KOPRI-KPDC-00001485_1.json index f32401b45c..d2560e544f 100644 --- a/datasets/KOPRI-KPDC-00001485_1.json +++ b/datasets/KOPRI-KPDC-00001485_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001485_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001486_1.json b/datasets/KOPRI-KPDC-00001486_1.json index 234129bf21..d3f752a602 100644 --- a/datasets/KOPRI-KPDC-00001486_1.json +++ b/datasets/KOPRI-KPDC-00001486_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001486_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\r\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001487_1.json b/datasets/KOPRI-KPDC-00001487_1.json index 033a861754..d98d94633e 100644 --- a/datasets/KOPRI-KPDC-00001487_1.json +++ b/datasets/KOPRI-KPDC-00001487_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001487_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS.\nContinuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001488_2.json b/datasets/KOPRI-KPDC-00001488_2.json index 2c3c1fc5c0..27e140eb1a 100644 --- a/datasets/KOPRI-KPDC-00001488_2.json +++ b/datasets/KOPRI-KPDC-00001488_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001488_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001489_2.json b/datasets/KOPRI-KPDC-00001489_2.json index 76c5d744f8..b0eb93f7dc 100644 --- a/datasets/KOPRI-KPDC-00001489_2.json +++ b/datasets/KOPRI-KPDC-00001489_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001489_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer at King Sejong Station.\nStudy of the activity of ultra low frequency (ULF) wave in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001490_2.json b/datasets/KOPRI-KPDC-00001490_2.json index 19672e5a4e..f11e7a30e8 100644 --- a/datasets/KOPRI-KPDC-00001490_2.json +++ b/datasets/KOPRI-KPDC-00001490_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001490_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, \nStudy of the long-term trend of mesospheric temperature in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001491_2.json b/datasets/KOPRI-KPDC-00001491_2.json index 24504acf2b..dff1101f99 100644 --- a/datasets/KOPRI-KPDC-00001491_2.json +++ b/datasets/KOPRI-KPDC-00001491_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001491_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001492_2.json b/datasets/KOPRI-KPDC-00001492_2.json index 456dbebdf8..4bf2125e52 100644 --- a/datasets/KOPRI-KPDC-00001492_2.json +++ b/datasets/KOPRI-KPDC-00001492_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001492_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Dasan station, Arctic region\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001493_2.json b/datasets/KOPRI-KPDC-00001493_2.json index c3faefa839..aef8b8acfb 100644 --- a/datasets/KOPRI-KPDC-00001493_2.json +++ b/datasets/KOPRI-KPDC-00001493_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001493_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km, and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001494_2.json b/datasets/KOPRI-KPDC-00001494_2.json index 1b8bac95df..495b49bdc0 100644 --- a/datasets/KOPRI-KPDC-00001494_2.json +++ b/datasets/KOPRI-KPDC-00001494_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001494_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001495_3.json b/datasets/KOPRI-KPDC-00001495_3.json index 9092154dd5..3bc67b0706 100644 --- a/datasets/KOPRI-KPDC-00001495_3.json +++ b/datasets/KOPRI-KPDC-00001495_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001495_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The metamorphic P-T condition of the Dessent Ridge (Mountaineer Range) amphibolite (SB171119-3B) was calculated in order to investigate the history of tectonic evolution in northern Victoria Land, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001496_3.json b/datasets/KOPRI-KPDC-00001496_3.json index 73e8ac1c91..b1ce5863fb 100644 --- a/datasets/KOPRI-KPDC-00001496_3.json +++ b/datasets/KOPRI-KPDC-00001496_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001496_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SHRIMP U-Pb age of the Mt. Murchison (Mountaineer Range) gneiss was measured in order to examine the history of tectonic evolution in northern Victoria Land, Antarctica. The metamorphic and detrital ages of the migmatitic gneiss SB171122-3 (four different parts) were obtained.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001497_2.json b/datasets/KOPRI-KPDC-00001497_2.json index c3d8a17b1f..aff62a163d 100644 --- a/datasets/KOPRI-KPDC-00001497_2.json +++ b/datasets/KOPRI-KPDC-00001497_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001497_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lichen samples from King George Island collected in 2020\nEcophysiological study of lichen", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001498_2.json b/datasets/KOPRI-KPDC-00001498_2.json index 199c86d065..03970f1158 100644 --- a/datasets/KOPRI-KPDC-00001498_2.json +++ b/datasets/KOPRI-KPDC-00001498_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001498_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2019\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001501_2.json b/datasets/KOPRI-KPDC-00001501_2.json index 78bfb9cd44..85d2fea67a 100644 --- a/datasets/KOPRI-KPDC-00001501_2.json +++ b/datasets/KOPRI-KPDC-00001501_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001501_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001502_4.json b/datasets/KOPRI-KPDC-00001502_4.json index 4ec4f4ee8e..6fc8c57ee2 100644 --- a/datasets/KOPRI-KPDC-00001502_4.json +++ b/datasets/KOPRI-KPDC-00001502_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001502_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physicochemical data (pH, EC, TC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Barton and Weaver Peninsula in King George Island at 2019", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001503_4.json b/datasets/KOPRI-KPDC-00001503_4.json index 7cb7afd174..55dedc112c 100644 --- a/datasets/KOPRI-KPDC-00001503_4.json +++ b/datasets/KOPRI-KPDC-00001503_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001503_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were obtained to examine fungal community structure and reveal the correlation between soil physicochemical factors and soil fungal composition in glacial foreland of the Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001504_1.json b/datasets/KOPRI-KPDC-00001504_1.json index 60ed527799..47f6bd180c 100644 --- a/datasets/KOPRI-KPDC-00001504_1.json +++ b/datasets/KOPRI-KPDC-00001504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of microbial community structure and diversity in soil and freshwater samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica\nInvestigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001505_5.json b/datasets/KOPRI-KPDC-00001505_5.json index 2a342c93bc..4d52b6a1cc 100644 --- a/datasets/KOPRI-KPDC-00001505_5.json +++ b/datasets/KOPRI-KPDC-00001505_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001505_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001506_6.json b/datasets/KOPRI-KPDC-00001506_6.json index 56d7f903f3..28d6f9e3ac 100644 --- a/datasets/KOPRI-KPDC-00001506_6.json +++ b/datasets/KOPRI-KPDC-00001506_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001506_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001507_6.json b/datasets/KOPRI-KPDC-00001507_6.json index d8e9b3f44e..5bfd7a793e 100644 --- a/datasets/KOPRI-KPDC-00001507_6.json +++ b/datasets/KOPRI-KPDC-00001507_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001507_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001508_4.json b/datasets/KOPRI-KPDC-00001508_4.json index 2faa77b6a8..86a7d32daf 100644 --- a/datasets/KOPRI-KPDC-00001508_4.json +++ b/datasets/KOPRI-KPDC-00001508_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001508_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen\nStudy of the aurora characteristics in thenorthern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001509_1.json b/datasets/KOPRI-KPDC-00001509_1.json index 3244abd691..0f98e4940f 100644 --- a/datasets/KOPRI-KPDC-00001509_1.json +++ b/datasets/KOPRI-KPDC-00001509_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001509_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001510_2.json b/datasets/KOPRI-KPDC-00001510_2.json index 9c87e9f065..5aaf74c484 100644 --- a/datasets/KOPRI-KPDC-00001510_2.json +++ b/datasets/KOPRI-KPDC-00001510_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001510_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow cover on the Barton Peninsula, Antarctica extracted from time-series Landsat satellite data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001511_3.json b/datasets/KOPRI-KPDC-00001511_3.json index 9a222501a5..d1419cf179 100644 --- a/datasets/KOPRI-KPDC-00001511_3.json +++ b/datasets/KOPRI-KPDC-00001511_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001511_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were obtained to examine bacterial community structure and reveal the correlation between soil physicochemical factors and soil bacterial composition in glacial foreland of the Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001512_2.json b/datasets/KOPRI-KPDC-00001512_2.json index 4cc6834b49..619d13cee6 100644 --- a/datasets/KOPRI-KPDC-00001512_2.json +++ b/datasets/KOPRI-KPDC-00001512_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001512_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOAL\n\u25cb Development of Korean route and infrastructure such as research camp to approach the Antarctic inland \n\u25cb Establishment of support system for the Antarctic inland researches\n\nRESEARCH CONTENTS\n\u25cb A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches\n\u25cb Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001513_2.json b/datasets/KOPRI-KPDC-00001513_2.json index 6570974af4..eb2b49757b 100644 --- a/datasets/KOPRI-KPDC-00001513_2.json +++ b/datasets/KOPRI-KPDC-00001513_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001513_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "- Various soil physical and chemical properties are interacting with environment and soil microorganisms.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001514_3.json b/datasets/KOPRI-KPDC-00001514_3.json index 5e03e67855..bdb6f1466c 100644 --- a/datasets/KOPRI-KPDC-00001514_3.json +++ b/datasets/KOPRI-KPDC-00001514_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001514_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, ocean pCO2 and its relevant physical, chemical, biological parameters start monitoring in 2020. These include atmospheric CO2 concentration, ocean pCO2, seawater temperature, salinity, dissolved oxygen, pH, chlorophyll-a, CDOM, and, turbidity.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001515_2.json b/datasets/KOPRI-KPDC-00001515_2.json index 58855d670b..ce3e697a32 100644 --- a/datasets/KOPRI-KPDC-00001515_2.json +++ b/datasets/KOPRI-KPDC-00001515_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001515_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, nutrients were measured using a QuAAtro auto analyzer (Seal Analytical, Germany) in 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001516_2.json b/datasets/KOPRI-KPDC-00001516_2.json index 6ca45460ef..d6d6e43930 100644 --- a/datasets/KOPRI-KPDC-00001516_2.json +++ b/datasets/KOPRI-KPDC-00001516_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001516_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, dissolved inorganic carbon and total alkalinity were measured using VINDTA 3C (Total alkalinity and dissolved inorganic carbon in seawater) in 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001517_2.json b/datasets/KOPRI-KPDC-00001517_2.json index 0ff384e9fa..1e439c0b06 100644 --- a/datasets/KOPRI-KPDC-00001517_2.json +++ b/datasets/KOPRI-KPDC-00001517_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001517_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitering of bacterial community changes in maritime Antarctic tundra soils from late Summer to early Winter", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001518_3.json b/datasets/KOPRI-KPDC-00001518_3.json index 704bd9461e..971f7a252c 100644 --- a/datasets/KOPRI-KPDC-00001518_3.json +++ b/datasets/KOPRI-KPDC-00001518_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001518_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "- Metagenome and Metatranscriptome data from Alaska active layer soil and permafrost.\n- Metagenome data from the environment contains the genetic information of Virus, Bactetia, Fungi, and eukaryotic organisms.\n- Metatranscriptome data from the environment contains genetic and gene expression information of Virus, Bactetia, Fungi, and eukaryotic organisms.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001519_2.json b/datasets/KOPRI-KPDC-00001519_2.json index 17feda155f..733e40131c 100644 --- a/datasets/KOPRI-KPDC-00001519_2.json +++ b/datasets/KOPRI-KPDC-00001519_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001519_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-1A is a metamorphic greenstone (garnet-bearing) in the Mountaineer Range. This sample is considered to be a meta-igneous rock corresponding to the Glasgow volcanics (Sledgers Group) of the Bowers Terrane.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001520_2.json b/datasets/KOPRI-KPDC-00001520_2.json index 92018b6dee..9b65de33d4 100644 --- a/datasets/KOPRI-KPDC-00001520_2.json +++ b/datasets/KOPRI-KPDC-00001520_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001520_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-4C is a granite dyke intruding hornblende gneiss (SB171122-4A and 4B) of Mt. Murchison. This granite could be a post-Ross igneous rock.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001521_2.json b/datasets/KOPRI-KPDC-00001521_2.json index 44b48333ef..7c27002f51 100644 --- a/datasets/KOPRI-KPDC-00001521_2.json +++ b/datasets/KOPRI-KPDC-00001521_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001521_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-4A is a hornblende gneiss (highly altered) in Mt. Murchison. This sample could be a Ross metamorphic rock intruded by a post-Ross orogenic granite (SB171122-4C).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001522_2.json b/datasets/KOPRI-KPDC-00001522_2.json index 1106e0c9b5..20aa34e677 100644 --- a/datasets/KOPRI-KPDC-00001522_2.json +++ b/datasets/KOPRI-KPDC-00001522_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001522_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-4A is a hornblende gneiss in Mt. Murchison. This sample could be a Ross metamorphic rock and is intruded by a post-Ross orogenic granite (SB171122-4C).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001523_2.json b/datasets/KOPRI-KPDC-00001523_2.json index 8e40d6992d..3834226e16 100644 --- a/datasets/KOPRI-KPDC-00001523_2.json +++ b/datasets/KOPRI-KPDC-00001523_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001523_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-3C is a coarse-grained granitic rock (metamorphic melt) within migmatitic gneiss of Mt. Murchison. Since metamorphic age of other three samples in the same outcrop is confirmed as 498.3 \u00c2\u00b1 3.4 Ma, formation age of this sample could be synchronous with the metamorphic (migmatization) age.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001524_2.json b/datasets/KOPRI-KPDC-00001524_2.json index a9e5fa9266..4a23671bf8 100644 --- a/datasets/KOPRI-KPDC-00001524_2.json +++ b/datasets/KOPRI-KPDC-00001524_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001524_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-3B is a folded migmatitic gneiss in Mt. Murchison. Metamorphic age (SHRIMP U-Pb zircon) of this sample is confirmed as 503 \u00b1 26 Ma and 502 \u00b1 9 Ma. A combined age with other sample's data within same outcrop yields a metamorphic age of 498.3 \u00b1 3.4 Ma. SHRIMP U-Pb titanite age 467 \u00b1 6 Ma of this sample indicates its metamorphic cooling age.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001525_2.json b/datasets/KOPRI-KPDC-00001525_2.json index 932da8e1c9..78a3ad60c9 100644 --- a/datasets/KOPRI-KPDC-00001525_2.json +++ b/datasets/KOPRI-KPDC-00001525_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001525_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-3A is a fine-grained granitic rock within migmatitic gneiss of Mt. Murchison. Since the formation age (SHRIMP U-Pb zircon) of this sample is confirmed as 495 \u00b1 4 Ma, metamorphic (migmatization) age of the Mt. Murchison migmatitic gneiss could be synchronous with this age. Combined three samples' data within same outcrop yield a metamorphic age of 498.3 \u00b1 3.4 Ma.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001526_2.json b/datasets/KOPRI-KPDC-00001526_2.json index 0574ca0c06..af4d25fd24 100644 --- a/datasets/KOPRI-KPDC-00001526_2.json +++ b/datasets/KOPRI-KPDC-00001526_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001526_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-2 is a metamorphic greenstone in the Mountaineer Range. This sample is considered to be a meta-igneous rock corresponding to the Sledgers Group of the Bowers Terrane.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001527_2.json b/datasets/KOPRI-KPDC-00001527_2.json index 91b312c146..c1ccf225ea 100644 --- a/datasets/KOPRI-KPDC-00001527_2.json +++ b/datasets/KOPRI-KPDC-00001527_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001527_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-1H is a calc-silicate rock (hosting garnet and clinopyroxene) in the Mountaineer Range. This sample is considered to be a metasedimentary rock comprising the Glasgow volcanics (Sledgers Group) of the Bowers Terrane.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001528_2.json b/datasets/KOPRI-KPDC-00001528_2.json index 01d328e11a..f272fbefa2 100644 --- a/datasets/KOPRI-KPDC-00001528_2.json +++ b/datasets/KOPRI-KPDC-00001528_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001528_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sample SB171122-1G is a greenschist (garnet-bearing) in the Mountaineer Range. This sample is considered to be a meta-igneous rock corresponding to the Glasgow volcanics (Sledgers Group) of the Bowers Terrane.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001529_5.json b/datasets/KOPRI-KPDC-00001529_5.json index cc1e296a10..a2126ef0f9 100644 --- a/datasets/KOPRI-KPDC-00001529_5.json +++ b/datasets/KOPRI-KPDC-00001529_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001529_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001530_5.json b/datasets/KOPRI-KPDC-00001530_5.json index 728ab451f8..0a8e3722a3 100644 --- a/datasets/KOPRI-KPDC-00001530_5.json +++ b/datasets/KOPRI-KPDC-00001530_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001530_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001531_2.json b/datasets/KOPRI-KPDC-00001531_2.json index 9475f4cc9a..53273daf61 100644 --- a/datasets/KOPRI-KPDC-00001531_2.json +++ b/datasets/KOPRI-KPDC-00001531_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001531_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from FPI instrument at JBS station, Antarctica\nStudy of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001532_2.json b/datasets/KOPRI-KPDC-00001532_2.json index b8d4416551..01b72eb335 100644 --- a/datasets/KOPRI-KPDC-00001532_2.json +++ b/datasets/KOPRI-KPDC-00001532_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001532_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The value of geomagnetic field intensity observed at Jang Bogo Station, Antarctica\nTo investigate the interaction between ionosphere and geomagnetic disturbances", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001533_2.json b/datasets/KOPRI-KPDC-00001533_2.json index 2373b923a3..34ba9e6a9a 100644 --- a/datasets/KOPRI-KPDC-00001533_2.json +++ b/datasets/KOPRI-KPDC-00001533_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001533_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The value of geomagnetic field intensity observed at KSS, Antarctica\nTo investigate the interaction between ionosphere and geomagnetic disturbances", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001534_2.json b/datasets/KOPRI-KPDC-00001534_2.json index a8202f7a67..119142b50b 100644 --- a/datasets/KOPRI-KPDC-00001534_2.json +++ b/datasets/KOPRI-KPDC-00001534_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001534_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total electron content in the ionosphere at JBS station, Antarctica\nStudy of the statistical characteristics of ionosphere in southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001535_2.json b/datasets/KOPRI-KPDC-00001535_2.json index b59428d881..836046773f 100644 --- a/datasets/KOPRI-KPDC-00001535_2.json +++ b/datasets/KOPRI-KPDC-00001535_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001535_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For recording heavy machine operation and fuel consumption during 2019/20 Korean Route Traverse period.\nData consist of eight sheets(six Pisten Bullys and two Challenger)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001536_2.json b/datasets/KOPRI-KPDC-00001536_2.json index 6a67a313cc..8342d68b56 100644 --- a/datasets/KOPRI-KPDC-00001536_2.json +++ b/datasets/KOPRI-KPDC-00001536_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001536_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Neutron Monitor observes the neutron flux incoming from space to earth's atmosphere over JBS, Antarctica.\nTo study the variation of neutron flux with the strength of solar activity and the relationship between neutron flux and atmospheric constituents.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001537_3.json b/datasets/KOPRI-KPDC-00001537_3.json index 28fba6cf9b..95b4bac177 100644 --- a/datasets/KOPRI-KPDC-00001537_3.json +++ b/datasets/KOPRI-KPDC-00001537_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001537_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Environmental evaluation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001538_1.json b/datasets/KOPRI-KPDC-00001538_1.json index 229adf175a..a4e7432efc 100644 --- a/datasets/KOPRI-KPDC-00001538_1.json +++ b/datasets/KOPRI-KPDC-00001538_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001538_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor ocean environment data (Temperature, Salinity, Chlorophyll a) of ocean water on the coast of the Jang Bogo Station, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001539_1.json b/datasets/KOPRI-KPDC-00001539_1.json index 62227ecaa7..986692abcc 100644 --- a/datasets/KOPRI-KPDC-00001539_1.json +++ b/datasets/KOPRI-KPDC-00001539_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001539_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation has been carried out at the King Sejong Station in 2020. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001540_2.json b/datasets/KOPRI-KPDC-00001540_2.json index 94e8b5cc1d..caf4bd5008 100644 --- a/datasets/KOPRI-KPDC-00001540_2.json +++ b/datasets/KOPRI-KPDC-00001540_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001540_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Marian Cove, an oceanographic surveys were conducted from 2019 to 2020. the data include Conductivity, Temperature, Depth, and Fluorometer.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001541_3.json b/datasets/KOPRI-KPDC-00001541_3.json index 51fc50c6f1..8b56f5bb1d 100644 --- a/datasets/KOPRI-KPDC-00001541_3.json +++ b/datasets/KOPRI-KPDC-00001541_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001541_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove, the mooring was installed.\nTo investigate the temporal and spatial variation of water mass and ocean circulation in the Maxwell Bay and Marian Cove", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001542_2.json b/datasets/KOPRI-KPDC-00001542_2.json index b8fdf29ac8..023d21085a 100644 --- a/datasets/KOPRI-KPDC-00001542_2.json +++ b/datasets/KOPRI-KPDC-00001542_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001542_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora-ASC (All Sky Camera) observes the aurora in visible range over Jang Bogo Station, Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001544_1.json b/datasets/KOPRI-KPDC-00001544_1.json index 019e441c49..87a07bfec8 100644 --- a/datasets/KOPRI-KPDC-00001544_1.json +++ b/datasets/KOPRI-KPDC-00001544_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001544_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The high-quality genomic information on M. leonina will be essential for further understanding of adaptive metabolism upon repeated breath-hold dives and the exploration of molecular mechanisms contributing to its unique biochemical and physiological characteristics.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001545_2.json b/datasets/KOPRI-KPDC-00001545_2.json index e64cb046a4..279874d62b 100644 --- a/datasets/KOPRI-KPDC-00001545_2.json +++ b/datasets/KOPRI-KPDC-00001545_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001545_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The complete T. quadricornis mitochondrion was sequenced by high-throughput Illumina HiSeq platform. This complete mitochondrial DNA information of T. quadricornis will provide an essential genomic resource to elucidate the phylogenetic relationship and evolutionary history of the family Cottidae.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001546_1.json b/datasets/KOPRI-KPDC-00001546_1.json index 39d5ad0e61..14a0a2b835 100644 --- a/datasets/KOPRI-KPDC-00001546_1.json +++ b/datasets/KOPRI-KPDC-00001546_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001546_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data consists of fifteen most strongest windy days during 1988-2019. Each time series contains 3-day-long hourly data of wind, air temperature, humidity, sea level pressure, solar radiation in local time (UTC-4).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001547_1.json b/datasets/KOPRI-KPDC-00001547_1.json index 6695f96af3..bb1d194563 100644 --- a/datasets/KOPRI-KPDC-00001547_1.json +++ b/datasets/KOPRI-KPDC-00001547_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001547_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal global radiation data (HGRD) measured at the King Sejong Station, King George Islands, Antarctica in 2020. HGRD is included in the meteological data of the KSJ and full-year data will be uploaded after December.\nMonitoring of solar energy at the King Sejong Station and analysis of climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001548_1.json b/datasets/KOPRI-KPDC-00001548_1.json index 9304097a16..2f15aba50c 100644 --- a/datasets/KOPRI-KPDC-00001548_1.json +++ b/datasets/KOPRI-KPDC-00001548_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001548_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2020 at a coastal location of the King Sejong Station. Eddy co-variance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTo understand air-ocean-sea-ice interactions in terms of momentum/energy/H2O/CO2 at the coastal Antarctic region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001549_3.json b/datasets/KOPRI-KPDC-00001549_3.json index b436e0080b..594346e72a 100644 --- a/datasets/KOPRI-KPDC-00001549_3.json +++ b/datasets/KOPRI-KPDC-00001549_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001549_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001550_1.json b/datasets/KOPRI-KPDC-00001550_1.json index 95aedc903b..8ad72ab6de 100644 --- a/datasets/KOPRI-KPDC-00001550_1.json +++ b/datasets/KOPRI-KPDC-00001550_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001550_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Size distribution of primary aerosol particles generated from seawater sampled at King Sejong Station in 2019 (September 28 2019)\r\nPurpose : Understanding effects of organic matters in seawater on Antarctic primary aerosol properties", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001551_2.json b/datasets/KOPRI-KPDC-00001551_2.json index f1cae1949f..5c9a654e0b 100644 --- a/datasets/KOPRI-KPDC-00001551_2.json +++ b/datasets/KOPRI-KPDC-00001551_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001551_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of marine phytoplankton abundance in the waters around the King sejong station (Marian Cove, Maxwell bay) in Antarctica for the monitoring by environmental change in the sea water.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001555_2.json b/datasets/KOPRI-KPDC-00001555_2.json index c4d1e2e339..677fbbaf10 100644 --- a/datasets/KOPRI-KPDC-00001555_2.json +++ b/datasets/KOPRI-KPDC-00001555_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001555_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to constrain the (re)crystallization time of basement rocks in the Ross orogen, Antarctica, zircon U-Pb age was measured using SHRIMP-IIe.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001556_2.json b/datasets/KOPRI-KPDC-00001556_2.json index 2ce6069feb..0060d77fe3 100644 --- a/datasets/KOPRI-KPDC-00001556_2.json +++ b/datasets/KOPRI-KPDC-00001556_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001556_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to constrain the (re)crystallization time of basement rocks in the Ross orogen, Antarctica, zircon U-Pb age was measured using SHRIMP-IIe.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001559_3.json b/datasets/KOPRI-KPDC-00001559_3.json index 63ee7ed0b0..2ec1b48bc9 100644 --- a/datasets/KOPRI-KPDC-00001559_3.json +++ b/datasets/KOPRI-KPDC-00001559_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001559_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution melt pond images for investigation of detailed melt pond structure", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001560_4.json b/datasets/KOPRI-KPDC-00001560_4.json index 04ec66640e..c424cc2994 100644 --- a/datasets/KOPRI-KPDC-00001560_4.json +++ b/datasets/KOPRI-KPDC-00001560_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001560_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected to study behavioral ecology on phocid seals in polar ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001561_2.json b/datasets/KOPRI-KPDC-00001561_2.json index 553d745609..c2604c7ff6 100644 --- a/datasets/KOPRI-KPDC-00001561_2.json +++ b/datasets/KOPRI-KPDC-00001561_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001561_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of extracts derived from Arctic plants were made. Many extracts can be used in natural product research to provide samples for finding bioactive substances.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001562_2.json b/datasets/KOPRI-KPDC-00001562_2.json index 13ccd7101a..18334f9ede 100644 --- a/datasets/KOPRI-KPDC-00001562_2.json +++ b/datasets/KOPRI-KPDC-00001562_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001562_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To prospect the community responses of Antarctic Peninsular vegetations with the environmental changes, the photosynthetic efficiency of the representative plant species was measured under the different environmental conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001563_1.json b/datasets/KOPRI-KPDC-00001563_1.json index dabf84e817..8a6a722345 100644 --- a/datasets/KOPRI-KPDC-00001563_1.json +++ b/datasets/KOPRI-KPDC-00001563_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001563_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The phytoplantkon biomass (chl-a) was investigated in the Amundsen Sea, Antarctica from January to February 2020. \nThis data includes investigator and locality for chlorophyll-a concentration.\nThe investigation of chlorophyll-a concentration in the Amundsen Sea, Antarctica 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001564_4.json b/datasets/KOPRI-KPDC-00001564_4.json index c1db62b5b5..dad56871b4 100644 --- a/datasets/KOPRI-KPDC-00001564_4.json +++ b/datasets/KOPRI-KPDC-00001564_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001564_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition.\nThe Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001565_2.json b/datasets/KOPRI-KPDC-00001565_2.json index a4619e689c..a6c84f3309 100644 --- a/datasets/KOPRI-KPDC-00001565_2.json +++ b/datasets/KOPRI-KPDC-00001565_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001565_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected pondwater sample from Weaver Peninsula in Antarctica to investigate the chemical reactions in ice.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001566_2.json b/datasets/KOPRI-KPDC-00001566_2.json index f8646e560e..2048205e58 100644 --- a/datasets/KOPRI-KPDC-00001566_2.json +++ b/datasets/KOPRI-KPDC-00001566_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001566_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected fresh snow sample from Weaver Peninsula in Antarctica to investigate the chemical reactions in ice.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001567_1.json b/datasets/KOPRI-KPDC-00001567_1.json index 6fc2dc75e7..a96e6da5b9 100644 --- a/datasets/KOPRI-KPDC-00001567_1.json +++ b/datasets/KOPRI-KPDC-00001567_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001567_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001568_1.json b/datasets/KOPRI-KPDC-00001568_1.json index d299a3a726..ada2bafe7d 100644 --- a/datasets/KOPRI-KPDC-00001568_1.json +++ b/datasets/KOPRI-KPDC-00001568_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001568_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001569_1.json b/datasets/KOPRI-KPDC-00001569_1.json index 9a2e6de17d..d7c3831c45 100644 --- a/datasets/KOPRI-KPDC-00001569_1.json +++ b/datasets/KOPRI-KPDC-00001569_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001569_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001570_1.json b/datasets/KOPRI-KPDC-00001570_1.json index 3e1d8567f7..db75b53ed5 100644 --- a/datasets/KOPRI-KPDC-00001570_1.json +++ b/datasets/KOPRI-KPDC-00001570_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001570_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001571_1.json b/datasets/KOPRI-KPDC-00001571_1.json index f95761c568..e3dd563d8d 100644 --- a/datasets/KOPRI-KPDC-00001571_1.json +++ b/datasets/KOPRI-KPDC-00001571_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001571_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001572_1.json b/datasets/KOPRI-KPDC-00001572_1.json index 04b287bb00..bf8dc66853 100644 --- a/datasets/KOPRI-KPDC-00001572_1.json +++ b/datasets/KOPRI-KPDC-00001572_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001572_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001573_2.json b/datasets/KOPRI-KPDC-00001573_2.json index a3eae1d078..343838ed18 100644 --- a/datasets/KOPRI-KPDC-00001573_2.json +++ b/datasets/KOPRI-KPDC-00001573_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001573_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001574_2.json b/datasets/KOPRI-KPDC-00001574_2.json index 1d9008ef8b..42e0cb18f8 100644 --- a/datasets/KOPRI-KPDC-00001574_2.json +++ b/datasets/KOPRI-KPDC-00001574_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001574_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001575_2.json b/datasets/KOPRI-KPDC-00001575_2.json index 4e9897153c..e9801dd9f9 100644 --- a/datasets/KOPRI-KPDC-00001575_2.json +++ b/datasets/KOPRI-KPDC-00001575_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001575_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract : Excitation-emission matrixes (EEM) of Antarctic seawater samples measured using a fluorescence spectrometer Purpose : Understanding optical properties of organic matters in seawater to predict their sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001576_2.json b/datasets/KOPRI-KPDC-00001576_2.json index 759b1aff61..9a60e9b925 100644 --- a/datasets/KOPRI-KPDC-00001576_2.json +++ b/datasets/KOPRI-KPDC-00001576_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001576_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Jang Bogo station.\nMonitoring of Black Carbon concentration over Jang Bogo station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001578_3.json b/datasets/KOPRI-KPDC-00001578_3.json index e640996e32..777953a4a0 100644 --- a/datasets/KOPRI-KPDC-00001578_3.json +++ b/datasets/KOPRI-KPDC-00001578_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001578_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected to study behavioral ecology on phocid seals in polar ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001579_1.json b/datasets/KOPRI-KPDC-00001579_1.json index 21f0152252..f8c394b74e 100644 --- a/datasets/KOPRI-KPDC-00001579_1.json +++ b/datasets/KOPRI-KPDC-00001579_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001579_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric aerosol number concentration at King Sejong Station collected in 2019.Nov. - 2020.Oct. by CPC3776 and CPC3772\n\nInstruments : CPC3776 and CPC3772, TSI company\nCPC : Condensation Particle Counter", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001580_1.json b/datasets/KOPRI-KPDC-00001580_1.json index 2f5770390d..4060aed7c4 100644 --- a/datasets/KOPRI-KPDC-00001580_1.json +++ b/datasets/KOPRI-KPDC-00001580_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001580_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001581_1.json b/datasets/KOPRI-KPDC-00001581_1.json index 1336a1150b..e7230c1828 100644 --- a/datasets/KOPRI-KPDC-00001581_1.json +++ b/datasets/KOPRI-KPDC-00001581_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001581_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMPS measures the concentration for each diameter\n\nSize distribution of aerosol particles at King Sejong Station 2019.Nov. - 2020.Oct.\n\nUnderstanding growth of aerosol particles(2.5 - 310.6 nm) at KSJ", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001582_2.json b/datasets/KOPRI-KPDC-00001582_2.json index a2745e79b9..55d4ef5447 100644 --- a/datasets/KOPRI-KPDC-00001582_2.json +++ b/datasets/KOPRI-KPDC-00001582_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001582_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected meltwater samples from Marian Cove in Antarctica to investigate the chemical reactions in ice.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001583_2.json b/datasets/KOPRI-KPDC-00001583_2.json index 1c1a033b3f..9fba484edc 100644 --- a/datasets/KOPRI-KPDC-00001583_2.json +++ b/datasets/KOPRI-KPDC-00001583_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001583_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected meltwater samples from Potter Cove in Antarctica to investigate the chemical reactions in ice.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001584_3.json b/datasets/KOPRI-KPDC-00001584_3.json index ed63c6a7c1..ffcf2d65c6 100644 --- a/datasets/KOPRI-KPDC-00001584_3.json +++ b/datasets/KOPRI-KPDC-00001584_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001584_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "XRF analysis of granite powder used in chemical weathering experiment in ice", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001585_3.json b/datasets/KOPRI-KPDC-00001585_3.json index 4ddeb46b70..99cae7caab 100644 --- a/datasets/KOPRI-KPDC-00001585_3.json +++ b/datasets/KOPRI-KPDC-00001585_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001585_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global atmospheric ensemble reanalysis dataset for atmospheric research\nThe dataset is produced from CAM-LETKF global atmospheric analysis-forecast system. Analysis-forecast cycles are run with 10 ensemble members and 2-degree horizontal resolution. Only conventional observations are assimilated using LETKF. This dataset can be used for a variety of atmospheric researches.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001586_4.json b/datasets/KOPRI-KPDC-00001586_4.json index 5d85010e1f..1e9c7af56a 100644 --- a/datasets/KOPRI-KPDC-00001586_4.json +++ b/datasets/KOPRI-KPDC-00001586_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001586_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the spatial distributions of dissolved organic carbon in Marian cove and Maxwell bay in January 2020", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001587_2.json b/datasets/KOPRI-KPDC-00001587_2.json index 76cb985a4b..8dc6609047 100644 --- a/datasets/KOPRI-KPDC-00001587_2.json +++ b/datasets/KOPRI-KPDC-00001587_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001587_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A list of metabolites derived from Arctic plants was produced. It can be used to find new substances to develop new natural medicines.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001588_2.json b/datasets/KOPRI-KPDC-00001588_2.json index f7b651fbf7..c3bd518164 100644 --- a/datasets/KOPRI-KPDC-00001588_2.json +++ b/datasets/KOPRI-KPDC-00001588_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001588_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace elements in tourmaline plateau snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001589_2.json b/datasets/KOPRI-KPDC-00001589_2.json index d233bd5ec6..e862967814 100644 --- a/datasets/KOPRI-KPDC-00001589_2.json +++ b/datasets/KOPRI-KPDC-00001589_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001589_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Identification of atmospheric transport characteristics", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001590_2.json b/datasets/KOPRI-KPDC-00001590_2.json index 4316395496..cbf461ba6a 100644 --- a/datasets/KOPRI-KPDC-00001590_2.json +++ b/datasets/KOPRI-KPDC-00001590_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001590_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of air mass transport path", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001591_2.json b/datasets/KOPRI-KPDC-00001591_2.json index f68bd5fd60..e7d83e8123 100644 --- a/datasets/KOPRI-KPDC-00001591_2.json +++ b/datasets/KOPRI-KPDC-00001591_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001591_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of paleo atmospheric and oceanic environment using ice cores from Northern Victoria Land", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001592_2.json b/datasets/KOPRI-KPDC-00001592_2.json index 0a43caf6ea..4d6123197c 100644 --- a/datasets/KOPRI-KPDC-00001592_2.json +++ b/datasets/KOPRI-KPDC-00001592_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001592_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amino acid and DNA sequences for the production of metabolites in Antarctic copepod. \nGenetic information to understand mechanism of useful metabolites.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001594_4.json b/datasets/KOPRI-KPDC-00001594_4.json index c6e3671830..dca17a4df8 100644 --- a/datasets/KOPRI-KPDC-00001594_4.json +++ b/datasets/KOPRI-KPDC-00001594_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001594_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001595_1.json b/datasets/KOPRI-KPDC-00001595_1.json index 94ea0b63cd..047d0ebc21 100644 --- a/datasets/KOPRI-KPDC-00001595_1.json +++ b/datasets/KOPRI-KPDC-00001595_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001595_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) has been operated near Climate Change Tower of Ny-Alesund, Svalbard where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 1.5 km on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field are obtained using PPI and RHI modes, respectively.\nTo understand Arctic boundary layer(BL) structure and interaction between cloud-BL in the Arctic", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001596_1.json b/datasets/KOPRI-KPDC-00001596_1.json index 72c54e88a0..3c92705d20 100644 --- a/datasets/KOPRI-KPDC-00001596_1.json +++ b/datasets/KOPRI-KPDC-00001596_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001596_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud droplet probes CDP-2 and BCPD (DMT, USA) have been operated on the roof of the Zeppelin Observatory, Ny-Alesund . The probes produces information on cloud droplet size and number: number concentration, size, liquid water content, polarization ratio(BCPD) in range of 2-50 micrometer, while cloud hits the Zeppelin Mountain.\n- To understand micro-physical characteristics of Arctic cloud and its temporal variation\n- To understand the various effects of Arctic cloud in Arctic climate system", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001597_2.json b/datasets/KOPRI-KPDC-00001597_2.json index e1bde07c2b..c648350809 100644 --- a/datasets/KOPRI-KPDC-00001597_2.json +++ b/datasets/KOPRI-KPDC-00001597_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001597_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fied spectra of 17 representative vegetation species in the Barton Peninsula. The species in this spectral library includes Andreaea, Chorisodontium, Dead moss, Polytrichastrum, Polytrichum, Sanionia, Cladonia, Himantormia, Ochrolechia, Placopsis, Psoroma, Sphaerophorus, Stereocaulon, Usnea, Colobanthus, Deschampsia, Prasiola. The spectra obtained from 400 to 2500nm with 1nm spectral resolution using ASD Fieldspec 4. This spectral library can be used to classify and decompose multispectral or hyperspectral remote sensing data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001598_3.json b/datasets/KOPRI-KPDC-00001598_3.json index a3bd4936d9..6cdf478695 100644 --- a/datasets/KOPRI-KPDC-00001598_3.json +++ b/datasets/KOPRI-KPDC-00001598_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001598_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two populations of Deschampsia antarctica, living in Barton Peninsula on King George Island and Lagotellerie Island on Antarctic Peninsula, exhibited different photosynthetic responses depending on locations. NGS sequences were generated for RNAseq analysis to compare transcriptome responsible for their physiological differences,", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001599_1.json b/datasets/KOPRI-KPDC-00001599_1.json index 77addd2d10..a47b2c1fd4 100644 --- a/datasets/KOPRI-KPDC-00001599_1.json +++ b/datasets/KOPRI-KPDC-00001599_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001599_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acquisition of antarctic macroalgal specimens", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001602_2.json b/datasets/KOPRI-KPDC-00001602_2.json index e32e5acf65..080548879f 100644 --- a/datasets/KOPRI-KPDC-00001602_2.json +++ b/datasets/KOPRI-KPDC-00001602_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001602_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine magnetic data can contribute to investigate the formation time and plate tectonic evolution of the oceanic lithosphere in the large-scaled transform-fault zone between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001603_2.json b/datasets/KOPRI-KPDC-00001603_2.json index e0ce637867..f8e46006f3 100644 --- a/datasets/KOPRI-KPDC-00001603_2.json +++ b/datasets/KOPRI-KPDC-00001603_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001603_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "By measuring the photosynthetic efficiency according to relative humidity change of Sanionia uncinata, an Antarctic dominant species, a simulation experiment was performed with bioreaction data to develop a predictive model for vegetation change due to future climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001604_2.json b/datasets/KOPRI-KPDC-00001604_2.json index 72084bd8a0..6a9d970a07 100644 --- a/datasets/KOPRI-KPDC-00001604_2.json +++ b/datasets/KOPRI-KPDC-00001604_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001604_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "By measuring the photosynthetic efficiency according to temperature change of Sanionia uncinata, an Antarctic dominant species, a simulation experiment was performed with bioreaction data to develop a predictive model for vegetation change due to future climate change.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001605_2.json b/datasets/KOPRI-KPDC-00001605_2.json index 893315df90..367960f0bb 100644 --- a/datasets/KOPRI-KPDC-00001605_2.json +++ b/datasets/KOPRI-KPDC-00001605_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001605_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2016/2017 Bellinghausen Sea core, Antarctica\nClimate change observation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001606_3.json b/datasets/KOPRI-KPDC-00001606_3.json index 462f696db9..b35bcdb16e 100644 --- a/datasets/KOPRI-KPDC-00001606_3.json +++ b/datasets/KOPRI-KPDC-00001606_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001606_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EGRIP snow pit particle data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001607_2.json b/datasets/KOPRI-KPDC-00001607_2.json index 193bad44f9..d79e999103 100644 --- a/datasets/KOPRI-KPDC-00001607_2.json +++ b/datasets/KOPRI-KPDC-00001607_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001607_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data include 1) country-based regionally classified daily PM10 concentrations in South Korea/China and 2) daily cosine similarities derived by projecting the anomalous large-scale pattern (domain: 20-80N, 30-180E) for each day during the high PM10 events (> 80 ug/m3) in South Korea to the characteristic anomalous large-scale pattern of all historical high PM10 cases in South Korea. The cosine similarities were respectively derived for geopotential heights (500 and 1000 hPa), air temperature (850 hPa), relative humidity (850 hPa), horizontal wind speed (1000 hPa), and pressure vertical velocity (1000 hPa). The number of daily data per file is 5113 for the PM10 concentration and 2873 for the cosine similarity.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001608_2.json b/datasets/KOPRI-KPDC-00001608_2.json index 633c198898..0b1c887687 100644 --- a/datasets/KOPRI-KPDC-00001608_2.json +++ b/datasets/KOPRI-KPDC-00001608_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001608_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the potential functions of bacteria within the lichens in Barton Peninsula in King George Island, we analyzed the composition of lichen-associated bacterial communities across the lichens of 57 specimens of the Aspicilia sp., Cetraria aculeata, Cladonia borealis, Pseudephebe pubescens, Psoroma sp., Sphaerophorus globosus using Illumina sequencing of 16S rRNA gene.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001612_5.json b/datasets/KOPRI-KPDC-00001612_5.json index 63bff2e0eb..4fedbee5a5 100644 --- a/datasets/KOPRI-KPDC-00001612_5.json +++ b/datasets/KOPRI-KPDC-00001612_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001612_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil metagenome data was obtained to study microbial dynamics and the presence of potentially harmful microbes in the foreland of Styggedalsbreen glacier.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001614_2.json b/datasets/KOPRI-KPDC-00001614_2.json index f8f342c24c..3fcd5bf6fa 100644 --- a/datasets/KOPRI-KPDC-00001614_2.json +++ b/datasets/KOPRI-KPDC-00001614_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001614_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placenta and hair samples were collected to study breeding ecology of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001615_2.json b/datasets/KOPRI-KPDC-00001615_2.json index f3293f3eaa..050cc0218c 100644 --- a/datasets/KOPRI-KPDC-00001615_2.json +++ b/datasets/KOPRI-KPDC-00001615_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001615_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Chinstrap penguin and Gentoo penguin at Ardley Island and Narebski Point in December 2017 and January 2018. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001616_2.json b/datasets/KOPRI-KPDC-00001616_2.json index 8ccb5a067e..5aa7a81b8c 100644 --- a/datasets/KOPRI-KPDC-00001616_2.json +++ b/datasets/KOPRI-KPDC-00001616_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001616_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica\nComprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001617_3.json b/datasets/KOPRI-KPDC-00001617_3.json index fa0a4b2a46..64f7592206 100644 --- a/datasets/KOPRI-KPDC-00001617_3.json +++ b/datasets/KOPRI-KPDC-00001617_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001617_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To record underwater sound in the East Siberian Sea. \nTo identify the variability of the sound pressure level and its sources", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001618_2.json b/datasets/KOPRI-KPDC-00001618_2.json index 5a4470e24a..8df119c93d 100644 --- a/datasets/KOPRI-KPDC-00001618_2.json +++ b/datasets/KOPRI-KPDC-00001618_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001618_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001619_2.json b/datasets/KOPRI-KPDC-00001619_2.json index 84209d4180..09c406b314 100644 --- a/datasets/KOPRI-KPDC-00001619_2.json +++ b/datasets/KOPRI-KPDC-00001619_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001619_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001620_2.json b/datasets/KOPRI-KPDC-00001620_2.json index 63c919d164..c408040e39 100644 --- a/datasets/KOPRI-KPDC-00001620_2.json +++ b/datasets/KOPRI-KPDC-00001620_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001620_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001621_2.json b/datasets/KOPRI-KPDC-00001621_2.json index 63cd4ea177..bb7e8421a1 100644 --- a/datasets/KOPRI-KPDC-00001621_2.json +++ b/datasets/KOPRI-KPDC-00001621_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001621_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001622_2.json b/datasets/KOPRI-KPDC-00001622_2.json index edc6a18d2a..fa5bddec25 100644 --- a/datasets/KOPRI-KPDC-00001622_2.json +++ b/datasets/KOPRI-KPDC-00001622_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001622_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001623_2.json b/datasets/KOPRI-KPDC-00001623_2.json index 6c5dc9d563..79c4f2077c 100644 --- a/datasets/KOPRI-KPDC-00001623_2.json +++ b/datasets/KOPRI-KPDC-00001623_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001623_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of the seafloor topography and magnetic field of the seamounts", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001625_2.json b/datasets/KOPRI-KPDC-00001625_2.json index c871c9e63f..3e32d5eb57 100644 --- a/datasets/KOPRI-KPDC-00001625_2.json +++ b/datasets/KOPRI-KPDC-00001625_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001625_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The subbottom profiler (SBP) and multibeam echo sounder (MBES) data were densely collected in the Chukchi Rise during the IBRV Araon Arctic Expeditions from 2012 to 2019. These data were used for a high-resolution seismostratigraphic and morphobathymetric analysis of the seabed and seafloor on the northwestern Chukchi margin. The sediment thickness were produced by mapping the major seismostratigraphic unit boundaries using the SBP data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001626_2.json b/datasets/KOPRI-KPDC-00001626_2.json index 3be277b335..ff4db2255a 100644 --- a/datasets/KOPRI-KPDC-00001626_2.json +++ b/datasets/KOPRI-KPDC-00001626_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001626_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "(Purpose) Characterization of soil organic matter (Conditions) Dried soil, cannot be shared with other institutes because of the imported soil management rule", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001627_1.json b/datasets/KOPRI-KPDC-00001627_1.json index e7c9423190..597dad785e 100644 --- a/datasets/KOPRI-KPDC-00001627_1.json +++ b/datasets/KOPRI-KPDC-00001627_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001627_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The phytoplantkon species images were acquired in the west Antarctica using the Imaging FlowCytobot in January 2020.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001628_3.json b/datasets/KOPRI-KPDC-00001628_3.json index f9f6fe164d..b2d9b75ac1 100644 --- a/datasets/KOPRI-KPDC-00001628_3.json +++ b/datasets/KOPRI-KPDC-00001628_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001628_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "10-day weather forecast data (including wind, temperature, and humidity) over the Arctic region based on the Weather Research and Forecasting (WRF) model and its data assimilation system (WRFDA)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001629_1.json b/datasets/KOPRI-KPDC-00001629_1.json index 77f2a7f6ed..9e56cbf761 100644 --- a/datasets/KOPRI-KPDC-00001629_1.json +++ b/datasets/KOPRI-KPDC-00001629_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001629_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Chinstrap penguin and Gentoo penguin at Narebski Point from December 2006 to January 2020. In sheet1 and sheet2, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001630_1.json b/datasets/KOPRI-KPDC-00001630_1.json index 87a796bcf4..4a5621721d 100644 --- a/datasets/KOPRI-KPDC-00001630_1.json +++ b/datasets/KOPRI-KPDC-00001630_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001630_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Ad\u00e9lie penguin at Inexpressible Island on December 2018. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001631_2.json b/datasets/KOPRI-KPDC-00001631_2.json index 3cc8300985..acce3e7dd4 100644 --- a/datasets/KOPRI-KPDC-00001631_2.json +++ b/datasets/KOPRI-KPDC-00001631_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001631_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Ad\u00e9lie penguin at Ad\u00e9lie Cove from December 2018 to January 2019. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001632_1.json b/datasets/KOPRI-KPDC-00001632_1.json index 1fc90c7063..e65a905f55 100644 --- a/datasets/KOPRI-KPDC-00001632_1.json +++ b/datasets/KOPRI-KPDC-00001632_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001632_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001633_1.json b/datasets/KOPRI-KPDC-00001633_1.json index 3549eb9f22..5eb980ae39 100644 --- a/datasets/KOPRI-KPDC-00001633_1.json +++ b/datasets/KOPRI-KPDC-00001633_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001633_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is dissolved noble gases obtained during ANA01C cruise. The dataset also contain potential temperature, salinity and dissolved oxygen obtained by CTD rosette system. The dataset constituted 5 station along the Dotson Trough, Amundsen Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001634_2.json b/datasets/KOPRI-KPDC-00001634_2.json index 2068b50034..7c9eeb8a01 100644 --- a/datasets/KOPRI-KPDC-00001634_2.json +++ b/datasets/KOPRI-KPDC-00001634_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001634_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are the Lowered Acoustic Doppler Current Profiler (LADCP) data obtained from R/V Icebreaker ARAON in August 2016. The dataset contains LADCP data from surface to 100 m depth (5-m interval) at 4 CTD stations (Sts. 23, 24, 29, and 30) aiming at measuring instantaneous current profiles.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001635_2.json b/datasets/KOPRI-KPDC-00001635_2.json index 8c65269638..ff0e5c8d6b 100644 --- a/datasets/KOPRI-KPDC-00001635_2.json +++ b/datasets/KOPRI-KPDC-00001635_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001635_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation was carried out at the Jang Bogo Station in 2020. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variation at Jang Bogo Station, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001636_1.json b/datasets/KOPRI-KPDC-00001636_1.json index f425f085fd..4b13b1f86d 100644 --- a/datasets/KOPRI-KPDC-00001636_1.json +++ b/datasets/KOPRI-KPDC-00001636_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001636_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To identify migratory route of Arctic migratory Korea wintering bird (White-fronted goose) by attaching gps location tracker and collecting individual metadata", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001637_2.json b/datasets/KOPRI-KPDC-00001637_2.json index 638fb94de8..06d7d421d2 100644 --- a/datasets/KOPRI-KPDC-00001637_2.json +++ b/datasets/KOPRI-KPDC-00001637_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001637_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data includes the 10-min averaged ship-borne meteorological observations from the IBRV Araon during Aug 10-21, 2016. The wind speed values were converted to the true values from the relative winds obtained at the radarmast (33m ASL) of the ship. The air temperature, pressure, and relative humidity were obtained at the foremast.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001638_2.json b/datasets/KOPRI-KPDC-00001638_2.json index 7d6ff98875..7922d87552 100644 --- a/datasets/KOPRI-KPDC-00001638_2.json +++ b/datasets/KOPRI-KPDC-00001638_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001638_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001639_2.json b/datasets/KOPRI-KPDC-00001639_2.json index 90ba58183b..36970d09e8 100644 --- a/datasets/KOPRI-KPDC-00001639_2.json +++ b/datasets/KOPRI-KPDC-00001639_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001639_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001640_2.json b/datasets/KOPRI-KPDC-00001640_2.json index 26189adb09..44848ada58 100644 --- a/datasets/KOPRI-KPDC-00001640_2.json +++ b/datasets/KOPRI-KPDC-00001640_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001640_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station.\nMonitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001641_2.json b/datasets/KOPRI-KPDC-00001641_2.json index 464858b3c0..5300e3a757 100644 --- a/datasets/KOPRI-KPDC-00001641_2.json +++ b/datasets/KOPRI-KPDC-00001641_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001641_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Cambridge bay station in 2019.07-2020.10", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001642_2.json b/datasets/KOPRI-KPDC-00001642_2.json index 169e9f58c8..aca3e78d1f 100644 --- a/datasets/KOPRI-KPDC-00001642_2.json +++ b/datasets/KOPRI-KPDC-00001642_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001642_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001643_2.json b/datasets/KOPRI-KPDC-00001643_2.json index f248eb2f84..26fce4bc74 100644 --- a/datasets/KOPRI-KPDC-00001643_2.json +++ b/datasets/KOPRI-KPDC-00001643_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001643_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001644_2.json b/datasets/KOPRI-KPDC-00001644_2.json index 28a7b8c92a..b7f401ec65 100644 --- a/datasets/KOPRI-KPDC-00001644_2.json +++ b/datasets/KOPRI-KPDC-00001644_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001644_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amniotic fluid and blood samples were collected to study breeding ecology and adaptation of Weddell seals in Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001645_2.json b/datasets/KOPRI-KPDC-00001645_2.json index c61f4be3d4..7983696216 100644 --- a/datasets/KOPRI-KPDC-00001645_2.json +++ b/datasets/KOPRI-KPDC-00001645_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001645_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placenta samples were collected to study breeding ecology and adaptation of Weddell seals in Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001646_2.json b/datasets/KOPRI-KPDC-00001646_2.json index 0c6262689c..90a58dcb70 100644 --- a/datasets/KOPRI-KPDC-00001646_2.json +++ b/datasets/KOPRI-KPDC-00001646_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001646_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rock Physical Properties Changing Data from Freeze Thaw weathering at Dasan station region in 2020", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001647_2.json b/datasets/KOPRI-KPDC-00001647_2.json index aec700b63e..ce1b687e24 100644 --- a/datasets/KOPRI-KPDC-00001647_2.json +++ b/datasets/KOPRI-KPDC-00001647_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001647_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2019 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval.\nTo monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001648_2.json b/datasets/KOPRI-KPDC-00001648_2.json index 1961bce760..4eacceb78c 100644 --- a/datasets/KOPRI-KPDC-00001648_2.json +++ b/datasets/KOPRI-KPDC-00001648_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001648_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the weather data at the Council site, 70-mile northeast from Nome, Alaska in 2019.\nA WXT520 sensor operated by KOPRI-team was used to obtain meteorological variables at the site since 2017 summer.\nThe sensor is located 3-m above ground level and measures wind speed and direction, air temperature and humidity, pressure.\nThe data is to understand environmental variation at a Western Alaska tundra region.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001649_2.json b/datasets/KOPRI-KPDC-00001649_2.json index 38b7a4f281..d57cfc6f31 100644 --- a/datasets/KOPRI-KPDC-00001649_2.json +++ b/datasets/KOPRI-KPDC-00001649_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001649_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the weather data at the Council site, 70-mile northeast from Nome, Alaska in 2018.\nA research team of Univ. of Alaska, Fairbanks is operating an AWS(automatic weather station) at the the site since 1999.\nAir temperature and humidity measured at 1 m and 3m above ground level.\nWind speed and direction was measured at 3m agl.\nPrecipitation is also being measured.\nThe data was obtained from Mr. Bob Busey of UAF.\nTo understand hydrological characteristics at discontinous permafrost region in western Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001650_1.json b/datasets/KOPRI-KPDC-00001650_1.json index 414921c3eb..511035e13d 100644 --- a/datasets/KOPRI-KPDC-00001650_1.json +++ b/datasets/KOPRI-KPDC-00001650_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001650_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the weather data at the Council site, 70-mile northeast from Nome, Alaska in 2017.\nA research team of Univ. of Alaska, Fairbanks is operating an AWS(automatic weather station) at the the site since 1999.\nAir temperature and humidity measured at 1 m and 3m above ground level.\nWind speed and direction was measured at 3m agl.\nPrecipitation is also being measured.\nThe data was obtained from Mr. Bob Busey of UAF.\nTo understand hydrological characteristics at discontinous permafrost region in western Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001651_2.json b/datasets/KOPRI-KPDC-00001651_2.json index e766b96ef5..bafcb5fb38 100644 --- a/datasets/KOPRI-KPDC-00001651_2.json +++ b/datasets/KOPRI-KPDC-00001651_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001651_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the weather data at the Council site, 70-mile northeast from Nome, Alaska in 2016.\nA research team of Univ. of Alaska, Fairbanks is operating an AWS(automatic weather station) at the the site since 1999.\nAir temperature and humidity measured at 1 m and 3m above ground level.\nWind speed and direction was measured at 3m agl.\nPrecipitation is also being measured.\nThe data was obtained from Mr. Bob Busey of UAF.\nTo understand hydrological characteristics at discontinous permafrost region in western Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001652_1.json b/datasets/KOPRI-KPDC-00001652_1.json index 892bc095f3..078f07549e 100644 --- a/datasets/KOPRI-KPDC-00001652_1.json +++ b/datasets/KOPRI-KPDC-00001652_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001652_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica.\nInvestigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001653_2.json b/datasets/KOPRI-KPDC-00001653_2.json index c9960abd0c..298909afde 100644 --- a/datasets/KOPRI-KPDC-00001653_2.json +++ b/datasets/KOPRI-KPDC-00001653_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001653_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001654_2.json b/datasets/KOPRI-KPDC-00001654_2.json index cc312c68b4..9bb081649b 100644 --- a/datasets/KOPRI-KPDC-00001654_2.json +++ b/datasets/KOPRI-KPDC-00001654_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001654_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fur samples of spotted seal (Phoca largha) were collected in Baekryeong island.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001655_1.json b/datasets/KOPRI-KPDC-00001655_1.json index def1868fc8..1bf74a5fcb 100644 --- a/datasets/KOPRI-KPDC-00001655_1.json +++ b/datasets/KOPRI-KPDC-00001655_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001655_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the influence of glacier and/or sea-ice melt water on marine environments, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in December, 2020. CTD profiles were collected 56 Datas at 55 stations. To investigate the effects of glacier and/or sea-ice melt water on marine environments in the Terra Nova Bay polynya, Ross Sea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001656_2.json b/datasets/KOPRI-KPDC-00001656_2.json index 6920365dd9..443e0e81f1 100644 --- a/datasets/KOPRI-KPDC-00001656_2.json +++ b/datasets/KOPRI-KPDC-00001656_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001656_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To evaluate oceanic contributions on instability of the Thwaites Glacier, an investigation was conducted in the Terra Nova Bay, Ross Sea during the ARAON cruise in December, 2020. LADCP (Lowered ADCP, ADCP attached to the CTD frame) data were collected at 55 stations (56 casts). The casts were mainly conducted in the trough region (> 1,000 m) to check Circumpolar Deep Water (CDW) pathway and its properties.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001657_4.json b/datasets/KOPRI-KPDC-00001657_4.json index 757a05f40b..3ba82438c8 100644 --- a/datasets/KOPRI-KPDC-00001657_4.json +++ b/datasets/KOPRI-KPDC-00001657_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001657_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the aerosolization process of DMS-induced particle, comprehensive in-situ atmospheric measurement (e.g., atmospheric DMS and aerosol properties) was conducted at King Sejong station during the productive summer period in 2018-2020. The atmospheric DMS mixing ratios were observed by using a custom-made trapping and desorption system equipped with pulsed flame photometric detector. The aerosol size distributions were measured by regular-SMPS (Scanning Mobility Particle Sizer). PM2.5 samples were collected by using a high volume sampler, and than the concentration of major ions (e.g., non-sea salt sulfate, methanesulfonic acid, and sodium) in PM2.5 particles were detected by ion chromatography.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001658_4.json b/datasets/KOPRI-KPDC-00001658_4.json index f0ca6ef2bc..5b3b0662a3 100644 --- a/datasets/KOPRI-KPDC-00001658_4.json +++ b/datasets/KOPRI-KPDC-00001658_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001658_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. 24 weddell seals and 3 crabeater seals were captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001659_1.json b/datasets/KOPRI-KPDC-00001659_1.json index d437ead12a..7ac5b08ca6 100644 --- a/datasets/KOPRI-KPDC-00001659_1.json +++ b/datasets/KOPRI-KPDC-00001659_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001659_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physical, chemical and biological dataset obtained in the northern Chukchi Sea during the ARA09B cruise in the summer of 2018.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001660_2.json b/datasets/KOPRI-KPDC-00001660_2.json index 0dd3912526..aaf7c5e20d 100644 --- a/datasets/KOPRI-KPDC-00001660_2.json +++ b/datasets/KOPRI-KPDC-00001660_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001660_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second.\nMonitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001661_1.json b/datasets/KOPRI-KPDC-00001661_1.json index a4a7f8d84f..7db7f230c8 100644 --- a/datasets/KOPRI-KPDC-00001661_1.json +++ b/datasets/KOPRI-KPDC-00001661_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001661_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001662_1.json b/datasets/KOPRI-KPDC-00001662_1.json index dbcea5e61a..e6aea75096 100644 --- a/datasets/KOPRI-KPDC-00001662_1.json +++ b/datasets/KOPRI-KPDC-00001662_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001662_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001663_1.json b/datasets/KOPRI-KPDC-00001663_1.json index d5177e7b96..bb3c059804 100644 --- a/datasets/KOPRI-KPDC-00001663_1.json +++ b/datasets/KOPRI-KPDC-00001663_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001663_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001664_1.json b/datasets/KOPRI-KPDC-00001664_1.json index b9ad722df6..0a0cc7a1a5 100644 --- a/datasets/KOPRI-KPDC-00001664_1.json +++ b/datasets/KOPRI-KPDC-00001664_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001664_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001665_1.json b/datasets/KOPRI-KPDC-00001665_1.json index 2755701036..5a52a65803 100644 --- a/datasets/KOPRI-KPDC-00001665_1.json +++ b/datasets/KOPRI-KPDC-00001665_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001665_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001666_2.json b/datasets/KOPRI-KPDC-00001666_2.json index 0e1068c063..25858c316c 100644 --- a/datasets/KOPRI-KPDC-00001666_2.json +++ b/datasets/KOPRI-KPDC-00001666_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001666_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001667_2.json b/datasets/KOPRI-KPDC-00001667_2.json index 204c231fa1..03238d62d5 100644 --- a/datasets/KOPRI-KPDC-00001667_2.json +++ b/datasets/KOPRI-KPDC-00001667_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001667_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second.\nMonitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001668_2.json b/datasets/KOPRI-KPDC-00001668_2.json index edcec33efe..659cf93039 100644 --- a/datasets/KOPRI-KPDC-00001668_2.json +++ b/datasets/KOPRI-KPDC-00001668_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001668_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second.\nMonitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001669_2.json b/datasets/KOPRI-KPDC-00001669_2.json index cf886385c8..51b06ace43 100644 --- a/datasets/KOPRI-KPDC-00001669_2.json +++ b/datasets/KOPRI-KPDC-00001669_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001669_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second.\nMonitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001671_3.json b/datasets/KOPRI-KPDC-00001671_3.json index 9429332105..6ac27b6db5 100644 --- a/datasets/KOPRI-KPDC-00001671_3.json +++ b/datasets/KOPRI-KPDC-00001671_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001671_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001672_3.json b/datasets/KOPRI-KPDC-00001672_3.json index 63b853450a..c73b95a942 100644 --- a/datasets/KOPRI-KPDC-00001672_3.json +++ b/datasets/KOPRI-KPDC-00001672_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001672_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001673_2.json b/datasets/KOPRI-KPDC-00001673_2.json index fc5c08ab8b..4895ab834a 100644 --- a/datasets/KOPRI-KPDC-00001673_2.json +++ b/datasets/KOPRI-KPDC-00001673_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001673_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 2020/2021 summer season, due to sea ice, we obtained high resolution bathymetric data and marine magnetic data for only one short spreading-segment in \u201clarge-scaled spreading and fracture zones (or leaky transform faults)\u201d located between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR). It is expected that it will be able to contribute to the investigations for the tectonic evolution of the Antarctica related to the Australian-Pacific-Antarctic plates and the evolution of the Zealandia-Antarctic mantle, through the bathymetric and magnetic data that will be accumulated in the future.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001674_4.json b/datasets/KOPRI-KPDC-00001674_4.json index 513c1f1cdf..2a12c107f7 100644 --- a/datasets/KOPRI-KPDC-00001674_4.json +++ b/datasets/KOPRI-KPDC-00001674_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001674_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Attached is a namelist for polar region optimized version of WRF model. It was used for the study \"Short-term Atmospheric Response to Recent Arctic Sea Ice Loss\" (submitted to Geophysical Research Letters).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001675_2.json b/datasets/KOPRI-KPDC-00001675_2.json index 37c0d1153d..23bda1c2c6 100644 --- a/datasets/KOPRI-KPDC-00001675_2.json +++ b/datasets/KOPRI-KPDC-00001675_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001675_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flux of individual lipid biomarkers in the KAMS1 and KAMS2 which collected from August 2017 to August 2018.\nTable 1 contained TMF, POC, SIC and Chla data set. \nTable 2 and 3 contained individual lipid biomarkers data in the KAMS1 and KAMS2, respectively.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001676_2.json b/datasets/KOPRI-KPDC-00001676_2.json index e698d1968d..5c8ba6a811 100644 --- a/datasets/KOPRI-KPDC-00001676_2.json +++ b/datasets/KOPRI-KPDC-00001676_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001676_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amino acid and DNA sequences for the production of metabolites in Antarctic copepod.\nGenetic information to understand mechanism of useful metabolites.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001677_3.json b/datasets/KOPRI-KPDC-00001677_3.json index 01d72114fd..1dc1caf84b 100644 --- a/datasets/KOPRI-KPDC-00001677_3.json +++ b/datasets/KOPRI-KPDC-00001677_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001677_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seafloor bathymetry and magnetic anomalies for the seamounts around the KR1", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001678_5.json b/datasets/KOPRI-KPDC-00001678_5.json index 26d66a65f7..a28505d746 100644 --- a/datasets/KOPRI-KPDC-00001678_5.json +++ b/datasets/KOPRI-KPDC-00001678_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001678_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80-100km) and temperature (~90km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the mesosphere and lower-thermosphere (MLT) in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001679_1.json b/datasets/KOPRI-KPDC-00001679_1.json index a83c9371eb..17ca587e2a 100644 --- a/datasets/KOPRI-KPDC-00001679_1.json +++ b/datasets/KOPRI-KPDC-00001679_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001679_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Here, we report the complete mitochondrial genome of the Arctic fairy shrimp, Branchinecta paludosa O.F. M\u00c3\u00bcller 1788 (Anostraca: Branchinectidae), which inhabits the northernmost Arctic ponds and lakes. A complete 16,059 bp mitochondrion of B. paludosa was sequenced and assembled with the Illumina next generation sequencing platform. The B. paludosa mitogenome contained 37 genes that are commonly observed in most metazoans and the gene arrangement was conserved within the mitogenomes of Anostraca. The B. paludosa mitogenome will be useful for understanding the geographical distribution and phylogenetic relationship of Anostraca mitogenomes.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001680_2.json b/datasets/KOPRI-KPDC-00001680_2.json index 46e745e747..201361d48e 100644 --- a/datasets/KOPRI-KPDC-00001680_2.json +++ b/datasets/KOPRI-KPDC-00001680_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001680_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the \"Carbon cycle change and ecosystem response under the Southern Ocean warming\" research project, the production and fate of organic carbon and nitrogen from the Marian Cove and Maxwell Bay were investigated during summer (20-27 January) cruises in 2021. Spatial observations of suspended particulate organic carbon (POC) and nitrogen (PON) suggest that there was a large accumulation of carbon (C) and nitrogen (N) in Maxwell bay those of Marian Cove, due to relatively high phytoplankton productivity. Vertical distribution in the molar carbon:nitrogen (C:N) ratio of the suspended particulate organic matter (POM) pool reflect a change in the quality of the organic material and presumably being exported to the sediment. Considerable high particulate C:N ratios (>18) at surface water in inner Cove indicated transported from glarier and/or land-base organic matter into the Cove.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001681_2.json b/datasets/KOPRI-KPDC-00001681_2.json index 1b33945d55..5eaa21c941 100644 --- a/datasets/KOPRI-KPDC-00001681_2.json +++ b/datasets/KOPRI-KPDC-00001681_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001681_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate the distribution of major inorganic nutrients, samples were collected during summer (20-27 January) cruise in 2021 (ANA11B). Nitrate+nitrite, silicate, ammonium, and phosphate were measured at each 5 stations of the Marian Cove and the Maxwell Bay.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001682_4.json b/datasets/KOPRI-KPDC-00001682_4.json index 8c02a38cba..79090ee8b9 100644 --- a/datasets/KOPRI-KPDC-00001682_4.json +++ b/datasets/KOPRI-KPDC-00001682_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001682_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001683_2.json b/datasets/KOPRI-KPDC-00001683_2.json index 3d74ce3890..ddc455be8e 100644 --- a/datasets/KOPRI-KPDC-00001683_2.json +++ b/datasets/KOPRI-KPDC-00001683_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001683_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples for dissolved organic carbon and nitrogen were collected during the summer cruise in 2021 (ANA11B) at each 5 stations of the Marian Cove and the Maxwell Bay. Chromophoric dissolved organic matter (CDOM) was also measured at all stations.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001684_2.json b/datasets/KOPRI-KPDC-00001684_2.json index c11ce70057..5dd4a2a675 100644 --- a/datasets/KOPRI-KPDC-00001684_2.json +++ b/datasets/KOPRI-KPDC-00001684_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001684_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001685_2.json b/datasets/KOPRI-KPDC-00001685_2.json index 7e6600a82e..2275b6c336 100644 --- a/datasets/KOPRI-KPDC-00001685_2.json +++ b/datasets/KOPRI-KPDC-00001685_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001685_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001686_2.json b/datasets/KOPRI-KPDC-00001686_2.json index a7faf2408d..abf2b0adf4 100644 --- a/datasets/KOPRI-KPDC-00001686_2.json +++ b/datasets/KOPRI-KPDC-00001686_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001686_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001687_2.json b/datasets/KOPRI-KPDC-00001687_2.json index c665077967..5f41f75745 100644 --- a/datasets/KOPRI-KPDC-00001687_2.json +++ b/datasets/KOPRI-KPDC-00001687_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001687_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001688_2.json b/datasets/KOPRI-KPDC-00001688_2.json index 67d940179c..753200cab4 100644 --- a/datasets/KOPRI-KPDC-00001688_2.json +++ b/datasets/KOPRI-KPDC-00001688_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001688_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001689_4.json b/datasets/KOPRI-KPDC-00001689_4.json index c3375a29c2..0cad192d70 100644 --- a/datasets/KOPRI-KPDC-00001689_4.json +++ b/datasets/KOPRI-KPDC-00001689_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001689_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001690_2.json b/datasets/KOPRI-KPDC-00001690_2.json index b07513afbf..5e100b4ca1 100644 --- a/datasets/KOPRI-KPDC-00001690_2.json +++ b/datasets/KOPRI-KPDC-00001690_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001690_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001691_2.json b/datasets/KOPRI-KPDC-00001691_2.json index 1e53232d33..6933c5c18d 100644 --- a/datasets/KOPRI-KPDC-00001691_2.json +++ b/datasets/KOPRI-KPDC-00001691_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001691_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of organic aerosol collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001692_2.json b/datasets/KOPRI-KPDC-00001692_2.json index eb22006ae1..d101d6c6bd 100644 --- a/datasets/KOPRI-KPDC-00001692_2.json +++ b/datasets/KOPRI-KPDC-00001692_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001692_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To Identify molecular complex of melted snow collected at King Sejong Station by Ultra high resolution mass spectrometry (Orbitrap MS)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001693_1.json b/datasets/KOPRI-KPDC-00001693_1.json index 6588da2e1f..222b823504 100644 --- a/datasets/KOPRI-KPDC-00001693_1.json +++ b/datasets/KOPRI-KPDC-00001693_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001693_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica.\nInvestigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001694_1.json b/datasets/KOPRI-KPDC-00001694_1.json index 9cdbb555e7..b224098b6c 100644 --- a/datasets/KOPRI-KPDC-00001694_1.json +++ b/datasets/KOPRI-KPDC-00001694_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001694_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The phytoplantkon biomass (chl-a) was investigated in the Little America Basin of the west Antarctica in December 2020. \nThis data includes investigator and locality for chlorophyll-a concentration.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001695_1.json b/datasets/KOPRI-KPDC-00001695_1.json index df8eb6fe33..dbae4d8e05 100644 --- a/datasets/KOPRI-KPDC-00001695_1.json +++ b/datasets/KOPRI-KPDC-00001695_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001695_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Condesnation Particle Counter(CPC 3776 and 3772, TSI) measures the number of aerosol partile Monitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001696_2.json b/datasets/KOPRI-KPDC-00001696_2.json index fdcd6f529e..7a867bd5c6 100644 --- a/datasets/KOPRI-KPDC-00001696_2.json +++ b/datasets/KOPRI-KPDC-00001696_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001696_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aethalometer(AE33) measures the Black Carbon concentration in atmosphere", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001697_2.json b/datasets/KOPRI-KPDC-00001697_2.json index b9a4b55183..5a184b6b80 100644 --- a/datasets/KOPRI-KPDC-00001697_2.json +++ b/datasets/KOPRI-KPDC-00001697_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001697_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN\nMonitoring of Aerosol CCN from King Sejong Station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001698_2.json b/datasets/KOPRI-KPDC-00001698_2.json index eb9a7459a7..a69f54b7ce 100644 --- a/datasets/KOPRI-KPDC-00001698_2.json +++ b/datasets/KOPRI-KPDC-00001698_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001698_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of Condensation Particle Counter (CPC3776 and CPC3772) data on the ice-breaker(ARAON) in Antarctic ocean regions, 2021", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001699_1.json b/datasets/KOPRI-KPDC-00001699_1.json index ba560ea291..161f7cc44b 100644 --- a/datasets/KOPRI-KPDC-00001699_1.json +++ b/datasets/KOPRI-KPDC-00001699_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001699_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001700_1.json b/datasets/KOPRI-KPDC-00001700_1.json index 39f4495b80..583bfe8f0e 100644 --- a/datasets/KOPRI-KPDC-00001700_1.json +++ b/datasets/KOPRI-KPDC-00001700_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001700_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001701_1.json b/datasets/KOPRI-KPDC-00001701_1.json index 90cf03c809..98506cd67e 100644 --- a/datasets/KOPRI-KPDC-00001701_1.json +++ b/datasets/KOPRI-KPDC-00001701_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001701_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001702_1.json b/datasets/KOPRI-KPDC-00001702_1.json index 0e75de15ef..de95952867 100644 --- a/datasets/KOPRI-KPDC-00001702_1.json +++ b/datasets/KOPRI-KPDC-00001702_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001702_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001703_1.json b/datasets/KOPRI-KPDC-00001703_1.json index f4c8905aac..045c7d5dba 100644 --- a/datasets/KOPRI-KPDC-00001703_1.json +++ b/datasets/KOPRI-KPDC-00001703_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001703_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001704_1.json b/datasets/KOPRI-KPDC-00001704_1.json index 8dd7f9143e..62d7d7b97d 100644 --- a/datasets/KOPRI-KPDC-00001704_1.json +++ b/datasets/KOPRI-KPDC-00001704_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001704_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001705_1.json b/datasets/KOPRI-KPDC-00001705_1.json index e5f7904a4e..03126a7dbb 100644 --- a/datasets/KOPRI-KPDC-00001705_1.json +++ b/datasets/KOPRI-KPDC-00001705_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001705_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Araon is an ice-breaker research vessel for scientific research in Arctic and Southern Oceans.\nThis is one of data collected by DaDiS, the primary data management system for conducting research and observations at Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001706_2.json b/datasets/KOPRI-KPDC-00001706_2.json index 91d3f2398e..7f11b3503d 100644 --- a/datasets/KOPRI-KPDC-00001706_2.json +++ b/datasets/KOPRI-KPDC-00001706_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001706_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand an effects of organic matter in seawater on primary aerosol production using a bubble bursting chamber system", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001707_2.json b/datasets/KOPRI-KPDC-00001707_2.json index 5fe1b0e862..8765fa6586 100644 --- a/datasets/KOPRI-KPDC-00001707_2.json +++ b/datasets/KOPRI-KPDC-00001707_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001707_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concentration of individual sterols and n-alkanes in the seawater of the Kongsfjorden.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001708_1.json b/datasets/KOPRI-KPDC-00001708_1.json index c27d9fb594..0ea23b1b0c 100644 --- a/datasets/KOPRI-KPDC-00001708_1.json +++ b/datasets/KOPRI-KPDC-00001708_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001708_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica. Investigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001709_2.json b/datasets/KOPRI-KPDC-00001709_2.json index 3f1d587135..53f7ad86d3 100644 --- a/datasets/KOPRI-KPDC-00001709_2.json +++ b/datasets/KOPRI-KPDC-00001709_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001709_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001710_2.json b/datasets/KOPRI-KPDC-00001710_2.json index 56efe870ce..10ec5f7b25 100644 --- a/datasets/KOPRI-KPDC-00001710_2.json +++ b/datasets/KOPRI-KPDC-00001710_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001710_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001711_2.json b/datasets/KOPRI-KPDC-00001711_2.json index 5327beb249..b2e96095c7 100644 --- a/datasets/KOPRI-KPDC-00001711_2.json +++ b/datasets/KOPRI-KPDC-00001711_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001711_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001712_2.json b/datasets/KOPRI-KPDC-00001712_2.json index e38fd0ce3a..281bcef591 100644 --- a/datasets/KOPRI-KPDC-00001712_2.json +++ b/datasets/KOPRI-KPDC-00001712_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001712_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-G5-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001713_2.json b/datasets/KOPRI-KPDC-00001713_2.json index 32e01a5238..8d01a7a545 100644 --- a/datasets/KOPRI-KPDC-00001713_2.json +++ b/datasets/KOPRI-KPDC-00001713_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001713_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-352-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001718_1.json b/datasets/KOPRI-KPDC-00001718_1.json index bbb64d8685..df3f90aa0e 100644 --- a/datasets/KOPRI-KPDC-00001718_1.json +++ b/datasets/KOPRI-KPDC-00001718_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001718_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001719_2.json b/datasets/KOPRI-KPDC-00001719_2.json index f5299c7eb4..8351c06482 100644 --- a/datasets/KOPRI-KPDC-00001719_2.json +++ b/datasets/KOPRI-KPDC-00001719_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001719_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001720_2.json b/datasets/KOPRI-KPDC-00001720_2.json index 0d142288e5..efec043d0d 100644 --- a/datasets/KOPRI-KPDC-00001720_2.json +++ b/datasets/KOPRI-KPDC-00001720_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001720_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001721_1.json b/datasets/KOPRI-KPDC-00001721_1.json index 8e3247cbf1..0f212e61e2 100644 --- a/datasets/KOPRI-KPDC-00001721_1.json +++ b/datasets/KOPRI-KPDC-00001721_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001721_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001722_1.json b/datasets/KOPRI-KPDC-00001722_1.json index 335e0ab52b..e60bf8ae93 100644 --- a/datasets/KOPRI-KPDC-00001722_1.json +++ b/datasets/KOPRI-KPDC-00001722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001723_1.json b/datasets/KOPRI-KPDC-00001723_1.json index e2b2288679..16b5d0b281 100644 --- a/datasets/KOPRI-KPDC-00001723_1.json +++ b/datasets/KOPRI-KPDC-00001723_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001723_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001724_1.json b/datasets/KOPRI-KPDC-00001724_1.json index da1a8a351b..ce0b9cbe24 100644 --- a/datasets/KOPRI-KPDC-00001724_1.json +++ b/datasets/KOPRI-KPDC-00001724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001725_1.json b/datasets/KOPRI-KPDC-00001725_1.json index 554bee364c..2b46ec0404 100644 --- a/datasets/KOPRI-KPDC-00001725_1.json +++ b/datasets/KOPRI-KPDC-00001725_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001725_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001726_1.json b/datasets/KOPRI-KPDC-00001726_1.json index 62b53a77ee..3aaab75070 100644 --- a/datasets/KOPRI-KPDC-00001726_1.json +++ b/datasets/KOPRI-KPDC-00001726_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001726_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001727_1.json b/datasets/KOPRI-KPDC-00001727_1.json index 63f0c3229d..2d2e66bce5 100644 --- a/datasets/KOPRI-KPDC-00001727_1.json +++ b/datasets/KOPRI-KPDC-00001727_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001727_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001728_1.json b/datasets/KOPRI-KPDC-00001728_1.json index 2c5238d42e..aa18486bb0 100644 --- a/datasets/KOPRI-KPDC-00001728_1.json +++ b/datasets/KOPRI-KPDC-00001728_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001728_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001729_1.json b/datasets/KOPRI-KPDC-00001729_1.json index 0157fe5b34..48422584c3 100644 --- a/datasets/KOPRI-KPDC-00001729_1.json +++ b/datasets/KOPRI-KPDC-00001729_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001729_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001730_1.json b/datasets/KOPRI-KPDC-00001730_1.json index 890534cff7..c092512473 100644 --- a/datasets/KOPRI-KPDC-00001730_1.json +++ b/datasets/KOPRI-KPDC-00001730_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001730_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001731_1.json b/datasets/KOPRI-KPDC-00001731_1.json index 25685fd4b4..b5ce4defaa 100644 --- a/datasets/KOPRI-KPDC-00001731_1.json +++ b/datasets/KOPRI-KPDC-00001731_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001731_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001732_1.json b/datasets/KOPRI-KPDC-00001732_1.json index 1ab217edef..9deae67863 100644 --- a/datasets/KOPRI-KPDC-00001732_1.json +++ b/datasets/KOPRI-KPDC-00001732_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001732_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-329-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001733_2.json b/datasets/KOPRI-KPDC-00001733_2.json index ab4bda820a..4e774571fd 100644 --- a/datasets/KOPRI-KPDC-00001733_2.json +++ b/datasets/KOPRI-KPDC-00001733_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001733_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-G4-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001734_2.json b/datasets/KOPRI-KPDC-00001734_2.json index 568c1a0dec..10d43f194d 100644 --- a/datasets/KOPRI-KPDC-00001734_2.json +++ b/datasets/KOPRI-KPDC-00001734_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001734_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-G1-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001735_2.json b/datasets/KOPRI-KPDC-00001735_2.json index c063998b3d..028ff80daa 100644 --- a/datasets/KOPRI-KPDC-00001735_2.json +++ b/datasets/KOPRI-KPDC-00001735_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001735_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-378-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001736_2.json b/datasets/KOPRI-KPDC-00001736_2.json index d845150f10..093c5bddd7 100644 --- a/datasets/KOPRI-KPDC-00001736_2.json +++ b/datasets/KOPRI-KPDC-00001736_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001736_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-375-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001737_2.json b/datasets/KOPRI-KPDC-00001737_2.json index 1472a287a2..a7c30c0d95 100644 --- a/datasets/KOPRI-KPDC-00001737_2.json +++ b/datasets/KOPRI-KPDC-00001737_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001737_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-369-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001738_2.json b/datasets/KOPRI-KPDC-00001738_2.json index c8c19223a4..36e390ceb5 100644 --- a/datasets/KOPRI-KPDC-00001738_2.json +++ b/datasets/KOPRI-KPDC-00001738_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001738_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-354-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001739_2.json b/datasets/KOPRI-KPDC-00001739_2.json index edb58311ec..b731ccd17c 100644 --- a/datasets/KOPRI-KPDC-00001739_2.json +++ b/datasets/KOPRI-KPDC-00001739_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001739_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-353-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001740_2.json b/datasets/KOPRI-KPDC-00001740_2.json index 26d8d069f1..6b0e927ade 100644 --- a/datasets/KOPRI-KPDC-00001740_2.json +++ b/datasets/KOPRI-KPDC-00001740_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001740_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-350-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001741_2.json b/datasets/KOPRI-KPDC-00001741_2.json index 61ac620850..acb1eee64c 100644 --- a/datasets/KOPRI-KPDC-00001741_2.json +++ b/datasets/KOPRI-KPDC-00001741_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001741_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-351-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001742_1.json b/datasets/KOPRI-KPDC-00001742_1.json index 6442f2c416..68e0993405 100644 --- a/datasets/KOPRI-KPDC-00001742_1.json +++ b/datasets/KOPRI-KPDC-00001742_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001742_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-349-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001743_1.json b/datasets/KOPRI-KPDC-00001743_1.json index edecce818a..697624cc7a 100644 --- a/datasets/KOPRI-KPDC-00001743_1.json +++ b/datasets/KOPRI-KPDC-00001743_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001743_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-348-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001744_2.json b/datasets/KOPRI-KPDC-00001744_2.json index dca59365bc..bd6fb1585e 100644 --- a/datasets/KOPRI-KPDC-00001744_2.json +++ b/datasets/KOPRI-KPDC-00001744_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001744_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-347-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001745_2.json b/datasets/KOPRI-KPDC-00001745_2.json index f4d990e6b0..cadc1ced86 100644 --- a/datasets/KOPRI-KPDC-00001745_2.json +++ b/datasets/KOPRI-KPDC-00001745_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001745_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-343-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001746_2.json b/datasets/KOPRI-KPDC-00001746_2.json index f14ac775ea..162856d7ae 100644 --- a/datasets/KOPRI-KPDC-00001746_2.json +++ b/datasets/KOPRI-KPDC-00001746_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001746_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-344-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001747_2.json b/datasets/KOPRI-KPDC-00001747_2.json index c41e6c0fed..f7d1751c7d 100644 --- a/datasets/KOPRI-KPDC-00001747_2.json +++ b/datasets/KOPRI-KPDC-00001747_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001747_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-345-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001748_1.json b/datasets/KOPRI-KPDC-00001748_1.json index 7d9d43efe0..d9fbfacb9d 100644 --- a/datasets/KOPRI-KPDC-00001748_1.json +++ b/datasets/KOPRI-KPDC-00001748_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001748_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-337-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001749_2.json b/datasets/KOPRI-KPDC-00001749_2.json index 72d1051764..5dfc81ac1e 100644 --- a/datasets/KOPRI-KPDC-00001749_2.json +++ b/datasets/KOPRI-KPDC-00001749_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001749_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-346-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001750_1.json b/datasets/KOPRI-KPDC-00001750_1.json index ddfd924716..5e4c10ba35 100644 --- a/datasets/KOPRI-KPDC-00001750_1.json +++ b/datasets/KOPRI-KPDC-00001750_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001750_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-335-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001751_2.json b/datasets/KOPRI-KPDC-00001751_2.json index 88ad91b0a4..6795eeee65 100644 --- a/datasets/KOPRI-KPDC-00001751_2.json +++ b/datasets/KOPRI-KPDC-00001751_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001751_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-334-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001752_1.json b/datasets/KOPRI-KPDC-00001752_1.json index 0a9380b01e..fc464a538e 100644 --- a/datasets/KOPRI-KPDC-00001752_1.json +++ b/datasets/KOPRI-KPDC-00001752_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001752_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-333-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001753_1.json b/datasets/KOPRI-KPDC-00001753_1.json index 6e30b6442a..b055828c8d 100644 --- a/datasets/KOPRI-KPDC-00001753_1.json +++ b/datasets/KOPRI-KPDC-00001753_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001753_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These CTD profiles have obtained from Seal-tagging bio-loggers which record CTD and transmit via Argos satellite systems. Weddell seal (wd16-330-20) was captured and attached bio-loggers in Feb, 2021 near Jangbogo station.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001754_1.json b/datasets/KOPRI-KPDC-00001754_1.json index 46d0a60fc8..0550b4bcb9 100644 --- a/datasets/KOPRI-KPDC-00001754_1.json +++ b/datasets/KOPRI-KPDC-00001754_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001754_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001755_1.json b/datasets/KOPRI-KPDC-00001755_1.json index 1032caec11..b661febf3e 100644 --- a/datasets/KOPRI-KPDC-00001755_1.json +++ b/datasets/KOPRI-KPDC-00001755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001756_1.json b/datasets/KOPRI-KPDC-00001756_1.json index a046597a8b..a817456685 100644 --- a/datasets/KOPRI-KPDC-00001756_1.json +++ b/datasets/KOPRI-KPDC-00001756_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001756_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fecal samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001757_1.json b/datasets/KOPRI-KPDC-00001757_1.json index 9d8d8f2cd7..1c46a6766f 100644 --- a/datasets/KOPRI-KPDC-00001757_1.json +++ b/datasets/KOPRI-KPDC-00001757_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001757_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001758_1.json b/datasets/KOPRI-KPDC-00001758_1.json index 77fed8068a..81b5e6c20d 100644 --- a/datasets/KOPRI-KPDC-00001758_1.json +++ b/datasets/KOPRI-KPDC-00001758_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001758_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001759_1.json b/datasets/KOPRI-KPDC-00001759_1.json index 431b665a16..579af812fa 100644 --- a/datasets/KOPRI-KPDC-00001759_1.json +++ b/datasets/KOPRI-KPDC-00001759_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001759_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001760_1.json b/datasets/KOPRI-KPDC-00001760_1.json index b51a2f5433..79a5654ca8 100644 --- a/datasets/KOPRI-KPDC-00001760_1.json +++ b/datasets/KOPRI-KPDC-00001760_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001760_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blood samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001761_1.json b/datasets/KOPRI-KPDC-00001761_1.json index 885090d1a5..3a39b8aa4d 100644 --- a/datasets/KOPRI-KPDC-00001761_1.json +++ b/datasets/KOPRI-KPDC-00001761_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001761_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amniotic fluid samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001762_1.json b/datasets/KOPRI-KPDC-00001762_1.json index 1641abd098..5d04e3203e 100644 --- a/datasets/KOPRI-KPDC-00001762_1.json +++ b/datasets/KOPRI-KPDC-00001762_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001762_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amniotic fluid samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001763_1.json b/datasets/KOPRI-KPDC-00001763_1.json index 47208cbb0d..7d364cb5a2 100644 --- a/datasets/KOPRI-KPDC-00001763_1.json +++ b/datasets/KOPRI-KPDC-00001763_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001763_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placental samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001764_1.json b/datasets/KOPRI-KPDC-00001764_1.json index d061420732..4bd648c58d 100644 --- a/datasets/KOPRI-KPDC-00001764_1.json +++ b/datasets/KOPRI-KPDC-00001764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placental samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001765_1.json b/datasets/KOPRI-KPDC-00001765_1.json index f913d90add..cef93690be 100644 --- a/datasets/KOPRI-KPDC-00001765_1.json +++ b/datasets/KOPRI-KPDC-00001765_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001765_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001766_1.json b/datasets/KOPRI-KPDC-00001766_1.json index a375de5c97..5cc6b0fb09 100644 --- a/datasets/KOPRI-KPDC-00001766_1.json +++ b/datasets/KOPRI-KPDC-00001766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001767_1.json b/datasets/KOPRI-KPDC-00001767_1.json index effbe78129..88f433b486 100644 --- a/datasets/KOPRI-KPDC-00001767_1.json +++ b/datasets/KOPRI-KPDC-00001767_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001767_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001768_1.json b/datasets/KOPRI-KPDC-00001768_1.json index 49a867e294..3fd7dd3e36 100644 --- a/datasets/KOPRI-KPDC-00001768_1.json +++ b/datasets/KOPRI-KPDC-00001768_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001768_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001769_1.json b/datasets/KOPRI-KPDC-00001769_1.json index a220accb77..4ca7a4db4b 100644 --- a/datasets/KOPRI-KPDC-00001769_1.json +++ b/datasets/KOPRI-KPDC-00001769_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001769_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001770_1.json b/datasets/KOPRI-KPDC-00001770_1.json index 5ac837ca86..2c98915719 100644 --- a/datasets/KOPRI-KPDC-00001770_1.json +++ b/datasets/KOPRI-KPDC-00001770_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001770_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001771_1.json b/datasets/KOPRI-KPDC-00001771_1.json index 51f6b9f1d2..9eaf8442fe 100644 --- a/datasets/KOPRI-KPDC-00001771_1.json +++ b/datasets/KOPRI-KPDC-00001771_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001771_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001772_1.json b/datasets/KOPRI-KPDC-00001772_1.json index 398e7976e0..58b35dfbbb 100644 --- a/datasets/KOPRI-KPDC-00001772_1.json +++ b/datasets/KOPRI-KPDC-00001772_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001772_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001773_2.json b/datasets/KOPRI-KPDC-00001773_2.json index 67105d891a..634513bbbf 100644 --- a/datasets/KOPRI-KPDC-00001773_2.json +++ b/datasets/KOPRI-KPDC-00001773_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001773_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CYP19A1 genes are thought to be crucial to live in marine environments for cetacean. We extracted CYPA1 genes from public sequence data.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001774_1.json b/datasets/KOPRI-KPDC-00001774_1.json index 09a9d93a8c..8b560f8afd 100644 --- a/datasets/KOPRI-KPDC-00001774_1.json +++ b/datasets/KOPRI-KPDC-00001774_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001774_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001776_4.json b/datasets/KOPRI-KPDC-00001776_4.json index 480d027c00..1ff7bebace 100644 --- a/datasets/KOPRI-KPDC-00001776_4.json +++ b/datasets/KOPRI-KPDC-00001776_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001776_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate variations of water masses in the Chukchi Borderland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001777_2.json b/datasets/KOPRI-KPDC-00001777_2.json index 84c434ea14..2238aeded1 100644 --- a/datasets/KOPRI-KPDC-00001777_2.json +++ b/datasets/KOPRI-KPDC-00001777_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001777_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Physicochemical data (pH, EC, TC, SOC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Ardley and King George Island at 2019", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001778_2.json b/datasets/KOPRI-KPDC-00001778_2.json index fa5d1ba674..5b97190a5d 100644 --- a/datasets/KOPRI-KPDC-00001778_2.json +++ b/datasets/KOPRI-KPDC-00001778_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001778_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOAL\n\u25cb Development of Korean route and infrastructure such as research camp to approach the Antarctic inland\n\u25cb Establishment of support system for the Antarctic inland researches\n\nRESEARCH CONTENTS\n\u25cb A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches\n\u25cb Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001779_3.json b/datasets/KOPRI-KPDC-00001779_3.json index 2d0e6d033a..5a7bf19f7e 100644 --- a/datasets/KOPRI-KPDC-00001779_3.json +++ b/datasets/KOPRI-KPDC-00001779_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001779_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Loopseq long sequencing read data amplified 16S-18S, 18S-ITS region through synthetic long-read (SLR) sequencing technology to identify microbial species in glacial forelands of the Antarctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001780_7.json b/datasets/KOPRI-KPDC-00001780_7.json index bc6811d64c..73629824bb 100644 --- a/datasets/KOPRI-KPDC-00001780_7.json +++ b/datasets/KOPRI-KPDC-00001780_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001780_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since last year, the frequency of earthquakes has increased in the vicinity of Orca seamount in the Bransfield Strait. Accordingly, in order to confirm the change of the submarine topography due to the earthquake, a side line was set in the epicenter where earthquakes mainly occur and the area covering the Orca seamount, and multi-beam survey was conducted.\nThe survey area shows a distribution of water depth of -300 to -2000m. The observation results that have been post-processed will be used as basic data to analyze geological and geophysical characteristics of the region in the future.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001781_5.json b/datasets/KOPRI-KPDC-00001781_5.json index 8972949b47..3143659d47 100644 --- a/datasets/KOPRI-KPDC-00001781_5.json +++ b/datasets/KOPRI-KPDC-00001781_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001781_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the activites of Mt. Melbourne and glacial movements", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001782_1.json b/datasets/KOPRI-KPDC-00001782_1.json index dc46a9e902..0e34820750 100644 --- a/datasets/KOPRI-KPDC-00001782_1.json +++ b/datasets/KOPRI-KPDC-00001782_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001782_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ion Chromatography analysis was conducted to investigate redox chemical reactions in ice", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001783_1.json b/datasets/KOPRI-KPDC-00001783_1.json index 798341a037..f73837e126 100644 --- a/datasets/KOPRI-KPDC-00001783_1.json +++ b/datasets/KOPRI-KPDC-00001783_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001783_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ICP-OES analysis was conducted to investigate mineral formation in ice", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001784_1.json b/datasets/KOPRI-KPDC-00001784_1.json index 06400ecffa..3ca359c5d0 100644 --- a/datasets/KOPRI-KPDC-00001784_1.json +++ b/datasets/KOPRI-KPDC-00001784_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001784_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The listed compounds are candidates of 4-chlorophenol decomposition by-products in the chloride mediated peroxymonosulfate activation system which operated in -20 celsius degree. Those candidates are proposed by Compound Discoverer 3.1 with m/z Cloud and ChemSpider databases.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001785_1.json b/datasets/KOPRI-KPDC-00001785_1.json index 895ad7d2b2..2212f4ba57 100644 --- a/datasets/KOPRI-KPDC-00001785_1.json +++ b/datasets/KOPRI-KPDC-00001785_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001785_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Obtained by in-situ Cryo-Raman microscope image combined with temperature controlled stage.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001786_1.json b/datasets/KOPRI-KPDC-00001786_1.json index a5dc954321..3e03423450 100644 --- a/datasets/KOPRI-KPDC-00001786_1.json +++ b/datasets/KOPRI-KPDC-00001786_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001786_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cryo-Raman chemical mapping image of peroxymonosulfate concentrated in the ice grain boundaries. (Relative signal intensity : ordered by rainbow scale)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001787_1.json b/datasets/KOPRI-KPDC-00001787_1.json index 1a5739c020..2537f3000d 100644 --- a/datasets/KOPRI-KPDC-00001787_1.json +++ b/datasets/KOPRI-KPDC-00001787_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001787_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cryo-Raman microscope image of ice grain boundaries in the present of cryoprotectant to measure the physical properties of ice gain boundaries and ice surfaces.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001788_1.json b/datasets/KOPRI-KPDC-00001788_1.json index 3bb6e82803..57388d040a 100644 --- a/datasets/KOPRI-KPDC-00001788_1.json +++ b/datasets/KOPRI-KPDC-00001788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Genome information of pathogenic fungi isolated from diseased plants identified in KGI, Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001789_3.json b/datasets/KOPRI-KPDC-00001789_3.json index 38d3556c25..6b9f6a8707 100644 --- a/datasets/KOPRI-KPDC-00001789_3.json +++ b/datasets/KOPRI-KPDC-00001789_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001789_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Viral genome contig sequence data from Chersky (Russia) permafrost metagenome", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001790_2.json b/datasets/KOPRI-KPDC-00001790_2.json index 5678ff7fd7..6283a7bcf6 100644 --- a/datasets/KOPRI-KPDC-00001790_2.json +++ b/datasets/KOPRI-KPDC-00001790_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001790_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SHRIMP zircon U-Pb ages for the Gerlache Inlet Shear Zone paragneisses were measured in order to examine the tectonic history of the Terra Nova Intrusive Complex in northern Victoria Land, Antarctica. Detrital and metamorphic (530-490 Ma) ages of the mylonitic paragneiss (SB171127-1D) were obtained.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001791_2.json b/datasets/KOPRI-KPDC-00001791_2.json index da1665f8d8..b9c0adb5d5 100644 --- a/datasets/KOPRI-KPDC-00001791_2.json +++ b/datasets/KOPRI-KPDC-00001791_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001791_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SHRIMP zircon U-Pb ages of the Browning intrusive Unit were measured in order to examine the tectonic history of the Terra Nova Intrusive Complex in northern Victoria Land, Antarctica. The igneous age (501.5 \u00c2\u00b1 3.6 Ma) of theBrowning foliated biotite granite (J-58) was obtained.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001792_2.json b/datasets/KOPRI-KPDC-00001792_2.json index 1d1e2d0480..f6c8075ee7 100644 --- a/datasets/KOPRI-KPDC-00001792_2.json +++ b/datasets/KOPRI-KPDC-00001792_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001792_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SHRIMP zircon U-Pb ages of the Browning intrusive Unit were measured in order to examine the tectonic history of the Terra Nova Intrusive Complex in northern Victoria Land, Antarctica. The igneous age (503.8 \u00c2\u00b1 5.7 Ma) of the Browning foliated leucogranite (SB171116-6C) was obtained.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001793_2.json b/datasets/KOPRI-KPDC-00001793_2.json index 47c18ce927..3c643d750a 100644 --- a/datasets/KOPRI-KPDC-00001793_2.json +++ b/datasets/KOPRI-KPDC-00001793_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001793_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SHRIMP zircon U-Pb age of the Abbott intrusive Unit was measured in order to examine the tectonic history of the Terra Nova Intrusive Complex in northern Victoria Land, Antarctica. The igneous age (479.0 \u00c2\u00b1 4.3 Ma) of the Abbott alkali feldspar granite (SB171116-1) was obtained.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001794_2.json b/datasets/KOPRI-KPDC-00001794_2.json index e1749ae525..1e3cafe4cb 100644 --- a/datasets/KOPRI-KPDC-00001794_2.json +++ b/datasets/KOPRI-KPDC-00001794_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001794_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 18 July 2021 to 12 September 2021 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at two times daily intervals(00 and 12UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001795_2.json b/datasets/KOPRI-KPDC-00001795_2.json index 80c6c88520..fa62ade3a5 100644 --- a/datasets/KOPRI-KPDC-00001795_2.json +++ b/datasets/KOPRI-KPDC-00001795_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001795_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The radiosonde balloon sounding observations were performed from 8 July 2021 to 14 July 2021 to obtain the Bering Sea high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001796_2.json b/datasets/KOPRI-KPDC-00001796_2.json index 32b8ee73c5..def386666e 100644 --- a/datasets/KOPRI-KPDC-00001796_2.json +++ b/datasets/KOPRI-KPDC-00001796_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001796_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate miRNA profiling of antartic moss Sanionia uncinata during seasonal changes\nUsing field samples and lab cultre samples", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001797_2.json b/datasets/KOPRI-KPDC-00001797_2.json index dd149d949d..91f2d6d5c6 100644 --- a/datasets/KOPRI-KPDC-00001797_2.json +++ b/datasets/KOPRI-KPDC-00001797_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001797_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Age measurement of Antarctic scallops by shell height", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001798_2.json b/datasets/KOPRI-KPDC-00001798_2.json index 63247f5b92..bb23648c49 100644 --- a/datasets/KOPRI-KPDC-00001798_2.json +++ b/datasets/KOPRI-KPDC-00001798_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001798_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extraction of fast ice area using satellite data", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001800_2.json b/datasets/KOPRI-KPDC-00001800_2.json index ebd0aea489..7fe27d7847 100644 --- a/datasets/KOPRI-KPDC-00001800_2.json +++ b/datasets/KOPRI-KPDC-00001800_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001800_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Species list and coverage of benthic animals in Ross Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001801_2.json b/datasets/KOPRI-KPDC-00001801_2.json index ed8c614383..5b6a85b6b0 100644 --- a/datasets/KOPRI-KPDC-00001801_2.json +++ b/datasets/KOPRI-KPDC-00001801_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001801_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biodiversity analysis of benthic animals in Ross Sea, Antarctica", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001804_2.json b/datasets/KOPRI-KPDC-00001804_2.json index 09a0a32820..cf8afd3489 100644 --- a/datasets/KOPRI-KPDC-00001804_2.json +++ b/datasets/KOPRI-KPDC-00001804_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001804_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020\nLong term monitoring", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001809_2.json b/datasets/KOPRI-KPDC-00001809_2.json index 2356c59c63..8c8567a404 100644 --- a/datasets/KOPRI-KPDC-00001809_2.json +++ b/datasets/KOPRI-KPDC-00001809_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001809_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate climate factors which regulate life cycle of antartic moss Sanionia uncinata\nLab culter Sanionia uncinata were treated with the condition that mimic the climate condition of King George Isaland", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001810_2.json b/datasets/KOPRI-KPDC-00001810_2.json index 778d1e8703..5c9411544f 100644 --- a/datasets/KOPRI-KPDC-00001810_2.json +++ b/datasets/KOPRI-KPDC-00001810_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001810_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Developing a seasonal prediction system with atmosphere global climate model CAM6, monthly surface air temperature data was generated from the 2000-2019 wintertime hindcast simulation.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001811_3.json b/datasets/KOPRI-KPDC-00001811_3.json index 2e13c1b67e..7298c00338 100644 --- a/datasets/KOPRI-KPDC-00001811_3.json +++ b/datasets/KOPRI-KPDC-00001811_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001811_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral wind (80 \u00e2\u20ac\u201c 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica\nStudy of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001812_3.json b/datasets/KOPRI-KPDC-00001812_3.json index bb2b5f818c..16e6b1de56 100644 --- a/datasets/KOPRI-KPDC-00001812_3.json +++ b/datasets/KOPRI-KPDC-00001812_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001812_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001813_2.json b/datasets/KOPRI-KPDC-00001813_2.json index 110d6511da..e7a5588555 100644 --- a/datasets/KOPRI-KPDC-00001813_2.json +++ b/datasets/KOPRI-KPDC-00001813_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001813_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 87km, 97km and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station\nStudy of the atmosphere wave activities in the upper atmosphere in the southern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001814_2.json b/datasets/KOPRI-KPDC-00001814_2.json index f29e694574..23b2ebd735 100644 --- a/datasets/KOPRI-KPDC-00001814_2.json +++ b/datasets/KOPRI-KPDC-00001814_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001814_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station\nStudy of the ionospheric irregularity in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001815_2.json b/datasets/KOPRI-KPDC-00001815_2.json index ba08c9ad3f..71f6091290 100644 --- a/datasets/KOPRI-KPDC-00001815_2.json +++ b/datasets/KOPRI-KPDC-00001815_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001815_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden\nStudy of the ionospheric irregularity in the northern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001816_3.json b/datasets/KOPRI-KPDC-00001816_3.json index 4b0e308058..f11841a82c 100644 --- a/datasets/KOPRI-KPDC-00001816_3.json +++ b/datasets/KOPRI-KPDC-00001816_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001816_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer at King Sejong Station. \nStudy of the activity of ultra low frequency (ULF) wave in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001817_2.json b/datasets/KOPRI-KPDC-00001817_2.json index 4f1f900c58..2aa73189fc 100644 --- a/datasets/KOPRI-KPDC-00001817_2.json +++ b/datasets/KOPRI-KPDC-00001817_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001817_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica\nStudy of the atmospheric wave activities in the southern high latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001818_2.json b/datasets/KOPRI-KPDC-00001818_2.json index 6b2c734163..4a47cf2f58 100644 --- a/datasets/KOPRI-KPDC-00001818_2.json +++ b/datasets/KOPRI-KPDC-00001818_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001818_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden\nStudy of the atmosphere wave activities in the upper atmosphere in the northern high-latitude", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001819_2.json b/datasets/KOPRI-KPDC-00001819_2.json index cbaa43dd16..aaf865cddb 100644 --- a/datasets/KOPRI-KPDC-00001819_2.json +++ b/datasets/KOPRI-KPDC-00001819_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001819_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metagenomic Analysis of Bacterial Communities in Colobanthus quitensis in KGI, Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001820_2.json b/datasets/KOPRI-KPDC-00001820_2.json index 342a0ab8d3..ba24f4baea 100644 --- a/datasets/KOPRI-KPDC-00001820_2.json +++ b/datasets/KOPRI-KPDC-00001820_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001820_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metagenomic Analysis of Fungal Communities in Colobanthus quitensis in KGI, Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001821_2.json b/datasets/KOPRI-KPDC-00001821_2.json index f64b8dce3d..bd04501210 100644 --- a/datasets/KOPRI-KPDC-00001821_2.json +++ b/datasets/KOPRI-KPDC-00001821_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001821_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nucleotide sequence of terpene synthase genes and GC analysis data of terpenoid in Antarctic moss, Sanionia uncinata", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001822_2.json b/datasets/KOPRI-KPDC-00001822_2.json index 5ccfbb8f60..fc9b6e01ba 100644 --- a/datasets/KOPRI-KPDC-00001822_2.json +++ b/datasets/KOPRI-KPDC-00001822_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001822_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The seismic survey was conducted on the D2 subglacial lake, David Glacier from 20th November to 5th December 2019, and the main objective of the survey is to investigate the subglacial lake.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001823_1.json b/datasets/KOPRI-KPDC-00001823_1.json index f5a809f2f6..9c332c66a5 100644 --- a/datasets/KOPRI-KPDC-00001823_1.json +++ b/datasets/KOPRI-KPDC-00001823_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001823_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data consists of fifteen most coldest days during 1988-2019. Each time series contains 3-day-long hourly data of wind, air temperature, humidity, sea level pressure, solar radiation in local time (UTC-4).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001824_1.json b/datasets/KOPRI-KPDC-00001824_1.json index b53fb47d3c..c0651ca033 100644 --- a/datasets/KOPRI-KPDC-00001824_1.json +++ b/datasets/KOPRI-KPDC-00001824_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001824_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal global radiation data (HGRD) measured at the King Sejong Station, King George Islands, Antarctica in 2021. HGRD is included in the meteorological data of the KSJ and full-year data will be uploaded after December.\nMonitoring of solar energy at the King Sejong Station and analysis of climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001825_1.json b/datasets/KOPRI-KPDC-00001825_1.json index 0bb2f9f324..6e080352e5 100644 --- a/datasets/KOPRI-KPDC-00001825_1.json +++ b/datasets/KOPRI-KPDC-00001825_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001825_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2021 at a coastal location of the King Sejong Station. Eddy co-variance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz.\nTo understand air-ocean-sea-ice interactions in terms of momentum/energy/H2O/CO2 at the coastal Antarctic region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001826_1.json b/datasets/KOPRI-KPDC-00001826_1.json index 8183be07c5..b2d4b9c6a1 100644 --- a/datasets/KOPRI-KPDC-00001826_1.json +++ b/datasets/KOPRI-KPDC-00001826_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001826_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological observation has been carried out at the King Sejong Station in 2021. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report.\nTo understand weather phenomena and to monitor climate variability at Antarctic Peninsula", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001827_3.json b/datasets/KOPRI-KPDC-00001827_3.json index 6f2be9690f..9b4d49ee9b 100644 --- a/datasets/KOPRI-KPDC-00001827_3.json +++ b/datasets/KOPRI-KPDC-00001827_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001827_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global atmospheric ensemble reanalysis dataset for atmospheric research\nThe dataset is produced from CAM6-DART global atmospheric analysis-forecast system. Analysis-forecast cycles are run with 20 ensemble members and 2-degree horizontal resolution. Conventional, GPS RO, and AMSU-A radiance observations are assimilated using EAKF. This dataset can be used for a variety of atmospheric researches.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001828_1.json b/datasets/KOPRI-KPDC-00001828_1.json index e7bb46f1d3..0bfde7b6dd 100644 --- a/datasets/KOPRI-KPDC-00001828_1.json +++ b/datasets/KOPRI-KPDC-00001828_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001828_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitering of bacterial community changes during humic substances-degradation in Antarctic tundra soils", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001829_1.json b/datasets/KOPRI-KPDC-00001829_1.json index 2e515eedfa..3b29637e8f 100644 --- a/datasets/KOPRI-KPDC-00001829_1.json +++ b/datasets/KOPRI-KPDC-00001829_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001829_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitering of bacterial community changes during humic substances-degradation in Alaska tundra soils", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001831_2.json b/datasets/KOPRI-KPDC-00001831_2.json index a4cf6117e6..f9beb2d238 100644 --- a/datasets/KOPRI-KPDC-00001831_2.json +++ b/datasets/KOPRI-KPDC-00001831_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001831_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quadrat (50x50 cm) image data for monitoring of benthic megafauna response to climate change", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001832_2.json b/datasets/KOPRI-KPDC-00001832_2.json index 8a09f7dcbc..bae31bfcfe 100644 --- a/datasets/KOPRI-KPDC-00001832_2.json +++ b/datasets/KOPRI-KPDC-00001832_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001832_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of Antarctic macroalgal specimen registrated in KOPRI Virtual Herbarium (KVH; https://kvh.kopri.re.kr/)\nKOPRI-AL01371~AL01550 (177 samples)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001833_2.json b/datasets/KOPRI-KPDC-00001833_2.json index fbc599cec1..72577d6298 100644 --- a/datasets/KOPRI-KPDC-00001833_2.json +++ b/datasets/KOPRI-KPDC-00001833_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001833_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains DNA extraction and sexing results from Arctic migratory Korea wintering bird (White-fronted goose) feather and feces, sampled at Gimpo-si, Republic of Korea.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001834_1.json b/datasets/KOPRI-KPDC-00001834_1.json index c49eed28f7..e9e3b1c09f 100644 --- a/datasets/KOPRI-KPDC-00001834_1.json +++ b/datasets/KOPRI-KPDC-00001834_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001834_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud droplet probe CDP-2 (DMT, USA) has been operated on the roof of the Zeppelin Observatory, Ny-Alesund . The probes produces information on cloud droplet size and number: number concentration, size, liquid water content in range of 2-50 micrometer, while cloud hits the Zeppelin Mountain.\n- To understand micro-physical characteristics of Arctic cloud and its temporal variation\n- To understand the various effects of Arctic cloud in Arctic climate system", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001835_2.json b/datasets/KOPRI-KPDC-00001835_2.json index d847d11615..423d719cfe 100644 --- a/datasets/KOPRI-KPDC-00001835_2.json +++ b/datasets/KOPRI-KPDC-00001835_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001835_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001836_2.json b/datasets/KOPRI-KPDC-00001836_2.json index 2145c9a219..9df3410582 100644 --- a/datasets/KOPRI-KPDC-00001836_2.json +++ b/datasets/KOPRI-KPDC-00001836_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001836_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001837_1.json b/datasets/KOPRI-KPDC-00001837_1.json index 397216d318..52860f2c3d 100644 --- a/datasets/KOPRI-KPDC-00001837_1.json +++ b/datasets/KOPRI-KPDC-00001837_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001837_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Doppler wind lidar(DWL) has been operated near Climate Change Tower of Ny-Alesund, Svalbard where Arctic DASAN station is located. DWL is acquiring vertical profile of wind up to 1.5 km on continuous basis. In addition to vertical observation mode, horizontal and vertical cross-section of wind field are obtained using PPI and RHI modes, respectively.\nTo understand Arctic boundary layer(BL) structure and interaction between cloud-BL in the Arctic", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001838_1.json b/datasets/KOPRI-KPDC-00001838_1.json index cd0b843051..2b5d1f86e2 100644 --- a/datasets/KOPRI-KPDC-00001838_1.json +++ b/datasets/KOPRI-KPDC-00001838_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001838_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstruction of Antarctic ice sheet and ocean history for the past two million years using sediment records", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001839_3.json b/datasets/KOPRI-KPDC-00001839_3.json index 75012c901d..9a8f8c03ad 100644 --- a/datasets/KOPRI-KPDC-00001839_3.json +++ b/datasets/KOPRI-KPDC-00001839_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001839_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001840_2.json b/datasets/KOPRI-KPDC-00001840_2.json index a56ad979c3..6189fe8ae9 100644 --- a/datasets/KOPRI-KPDC-00001840_2.json +++ b/datasets/KOPRI-KPDC-00001840_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001840_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001841_2.json b/datasets/KOPRI-KPDC-00001841_2.json index 1822093285..57bc61b93b 100644 --- a/datasets/KOPRI-KPDC-00001841_2.json +++ b/datasets/KOPRI-KPDC-00001841_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001841_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001842_2.json b/datasets/KOPRI-KPDC-00001842_2.json index db55954213..351f00d266 100644 --- a/datasets/KOPRI-KPDC-00001842_2.json +++ b/datasets/KOPRI-KPDC-00001842_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001842_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice sheet retreat and ocean circulation in West Antarctica during the past warm periods", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001843_1.json b/datasets/KOPRI-KPDC-00001843_1.json index ec8ff82f81..85943d9d31 100644 --- a/datasets/KOPRI-KPDC-00001843_1.json +++ b/datasets/KOPRI-KPDC-00001843_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001843_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Environmental evaluation", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001844_2.json b/datasets/KOPRI-KPDC-00001844_2.json index cf5d9a39af..93b7d464d0 100644 --- a/datasets/KOPRI-KPDC-00001844_2.json +++ b/datasets/KOPRI-KPDC-00001844_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001844_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Objectives;\n1, Investigation of atmospheric transport path to Antarctica\n2, Evaluation of GCM model performance", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001845_2.json b/datasets/KOPRI-KPDC-00001845_2.json index 9f3f663a74..3d25a0fb9d 100644 --- a/datasets/KOPRI-KPDC-00001845_2.json +++ b/datasets/KOPRI-KPDC-00001845_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001845_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Objectives;\n1, Investigation of atmospheric transport path to Antarctica\n2, Evaluation of GCM model performance", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001846_2.json b/datasets/KOPRI-KPDC-00001846_2.json index 0028e61565..e8d526b059 100644 --- a/datasets/KOPRI-KPDC-00001846_2.json +++ b/datasets/KOPRI-KPDC-00001846_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001846_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Objectives\n1, Reconstruction of environmental and climatic parameters in the past\n\nContents\n1, depth: 42-62m\n2, species: MSA, SO42-, NO3-, Na+", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001847_2.json b/datasets/KOPRI-KPDC-00001847_2.json index 66d3a4c2b3..368fbe20a6 100644 --- a/datasets/KOPRI-KPDC-00001847_2.json +++ b/datasets/KOPRI-KPDC-00001847_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001847_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace elements in GV7 snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001848_2.json b/datasets/KOPRI-KPDC-00001848_2.json index 505fda3cfd..f0438c19f9 100644 --- a/datasets/KOPRI-KPDC-00001848_2.json +++ b/datasets/KOPRI-KPDC-00001848_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001848_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace elements in Hercules Neve snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001850_3.json b/datasets/KOPRI-KPDC-00001850_3.json index 1084f137a7..16e63bac1a 100644 --- a/datasets/KOPRI-KPDC-00001850_3.json +++ b/datasets/KOPRI-KPDC-00001850_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001850_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, ocean pCO2 and its relevant physical, chemical, biological parameters start monitoring in 2020. These include atmospheric CO2 concentration, ocean pCO2, seawater temperature, salinity, dissolved oxygen, pH, chlorophyll-a, CDOM, and, turbidity.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001851_2.json b/datasets/KOPRI-KPDC-00001851_2.json index d400af68d3..a19cb6b245 100644 --- a/datasets/KOPRI-KPDC-00001851_2.json +++ b/datasets/KOPRI-KPDC-00001851_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001851_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001852_2.json b/datasets/KOPRI-KPDC-00001852_2.json index 28a411d21c..7846b7f51f 100644 --- a/datasets/KOPRI-KPDC-00001852_2.json +++ b/datasets/KOPRI-KPDC-00001852_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001852_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) Jang Bogo Station, Antarctica\nStudy of the atmospheric wave activities in the upper atmosphere in the southern high-latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001853_2.json b/datasets/KOPRI-KPDC-00001853_2.json index ed8e11f4bc..9e7721a8ed 100644 --- a/datasets/KOPRI-KPDC-00001853_2.json +++ b/datasets/KOPRI-KPDC-00001853_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001853_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Electron density profile, plasma drift velocity, and ionospheric tile information measured from VIPIR (ionosonde) at Jang Bogo Station.\nStudy of the ionospheric characteristics in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001854_2.json b/datasets/KOPRI-KPDC-00001854_2.json index de7ebccb3f..957ae453be 100644 --- a/datasets/KOPRI-KPDC-00001854_2.json +++ b/datasets/KOPRI-KPDC-00001854_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001854_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cosmic ray origin neutron count from space measured from neutron monitor at Jang Bogo Station, Antarctica. Study of the variation of neutron count in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001855_2.json b/datasets/KOPRI-KPDC-00001855_2.json index 24d60b2664..b45736490c 100644 --- a/datasets/KOPRI-KPDC-00001855_2.json +++ b/datasets/KOPRI-KPDC-00001855_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001855_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station. Study of the ionospheric irregularity in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001856_2.json b/datasets/KOPRI-KPDC-00001856_2.json index c2a8df65f5..f76d490cf7 100644 --- a/datasets/KOPRI-KPDC-00001856_2.json +++ b/datasets/KOPRI-KPDC-00001856_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001856_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Variation of geomagnetic field measured from search-coil magnetometer at Jang Bogo Station. Study of the activity of ultra low frequency (ULF) wave in the southern high latitude.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001859_2.json b/datasets/KOPRI-KPDC-00001859_2.json index 25f463af21..fd57bbba10 100644 --- a/datasets/KOPRI-KPDC-00001859_2.json +++ b/datasets/KOPRI-KPDC-00001859_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001859_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic lichen, Stereocaulon, is usually found in the dried areas near King Sejong Station, Barton Peninsula. Such a dry condition is likely to inhibit the photosynthetic performance of photobionts in Stereocaulon. To understand how this lichen responds to drier conditions, we set up the automatic recording system storing data of photosynthetic parameters, weight, humidity under controlled dehydration (RH 15-20%) and rehydration (over RH 95%) conditions in the KOPRI. We report the F, Fm' and Y(II) values of three individual lichens under 24hr dehydration and next 24hr rehydration conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001860_2.json b/datasets/KOPRI-KPDC-00001860_2.json index e565c11e2a..44bc76c608 100644 --- a/datasets/KOPRI-KPDC-00001860_2.json +++ b/datasets/KOPRI-KPDC-00001860_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001860_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic lichen, Usnea, is usually found in the dried areas near King Sejong Station, Barton Peninsula. Such a dry condition is likely to inhibit the photosynthetic performance of photobionts in Usnea. To understand how this lichen responds to drier conditions, we set up the automatic recording system storing data of photosynthetic parameters, weight, humidity under controlled dehydration (RH 15-20%) and rehydration (over RH 95%) conditions in the KOPRI. We report the F, Fm' and Y(II) values of three individual lichens under 24hr dehydration and next 24hr rehydration conditions.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001861_1.json b/datasets/KOPRI-KPDC-00001861_1.json index d3ab7abd05..e25ed3a71e 100644 --- a/datasets/KOPRI-KPDC-00001861_1.json +++ b/datasets/KOPRI-KPDC-00001861_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001861_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace elements and Pb isotopes in Greenland ice core (NEEM ice) for tracing dust source", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001862_1.json b/datasets/KOPRI-KPDC-00001862_1.json index 8ca4540b6c..4c6999fec1 100644 --- a/datasets/KOPRI-KPDC-00001862_1.json +++ b/datasets/KOPRI-KPDC-00001862_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001862_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placental samples were collected to study breeding ecology and adaptation from wild weddell seal in Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001863_1.json b/datasets/KOPRI-KPDC-00001863_1.json index c3e41c8080..802c852afe 100644 --- a/datasets/KOPRI-KPDC-00001863_1.json +++ b/datasets/KOPRI-KPDC-00001863_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001863_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placental samples were collected to study breeding ecology and adaptation from wild weddell seal in Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001864_1.json b/datasets/KOPRI-KPDC-00001864_1.json index 730a444ad0..d49da6f84a 100644 --- a/datasets/KOPRI-KPDC-00001864_1.json +++ b/datasets/KOPRI-KPDC-00001864_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001864_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Placental samples were collected to study breeding ecology and adaptation from wild weddell seal in Antarctica.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001865_1.json b/datasets/KOPRI-KPDC-00001865_1.json index 3dde5ae1a9..8166f0062f 100644 --- a/datasets/KOPRI-KPDC-00001865_1.json +++ b/datasets/KOPRI-KPDC-00001865_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001865_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract: Nitrogen oxide (NOx) concentrations at the surface of Jang Bogo was measured every minutes in 2020. The data were stored in a storage module at the base and transferred them to the institute in regular basis.\n\nPurpose: To monitor the influence of human activities at the station to the Antarctic atmospheric environment\n\nInstrument: NOx analyzer-Model 42i trace level chemiluminescence NO-NO2-NOx analyzer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001866_1.json b/datasets/KOPRI-KPDC-00001866_1.json index d07dc8c4d9..aa56cb7261 100644 --- a/datasets/KOPRI-KPDC-00001866_1.json +++ b/datasets/KOPRI-KPDC-00001866_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001866_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract: Sulfur dioxide (SO2) concentration at Jang Bogo was measured every minutes in 2020. The data were stored in a storage module at the base and transferred them to the institute in regular basis.\n\nPurpose: To monitor the influence of human activities at the base to the Antarctic atmospheric environment.\n\nInstrument: SO2 analyzer-Model 43i trace level pulsed fluorescence SO2 analyzer", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001867_1.json b/datasets/KOPRI-KPDC-00001867_1.json index 9f904b6399..0ee88091b0 100644 --- a/datasets/KOPRI-KPDC-00001867_1.json +++ b/datasets/KOPRI-KPDC-00001867_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001867_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "(Purpose) Characterization of soil organic matter and inorganic nitrogen (Conditions) Dried soil, cannot be shared with other institutes because of the imported soil management rule", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001869_1.json b/datasets/KOPRI-KPDC-00001869_1.json index 1596c24a27..7e90a8375e 100644 --- a/datasets/KOPRI-KPDC-00001869_1.json +++ b/datasets/KOPRI-KPDC-00001869_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001869_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Molecular characteristics of Arctic aerosols at Gruvebadet observatory, Svalbard in 2017", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001870_1.json b/datasets/KOPRI-KPDC-00001870_1.json index d2b3211f05..bf8202327e 100644 --- a/datasets/KOPRI-KPDC-00001870_1.json +++ b/datasets/KOPRI-KPDC-00001870_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001870_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacterial and archaeal genome sequence data from anaerobically incubated permafrost soil (Alaska)\nMetagenome-assembled genomes (MAGs)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001871_2.json b/datasets/KOPRI-KPDC-00001871_2.json index ba819ec4a2..c9f93a5123 100644 --- a/datasets/KOPRI-KPDC-00001871_2.json +++ b/datasets/KOPRI-KPDC-00001871_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001871_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sparker single-channel seismic data were collected during the 2021 ARA12C cruise in the East siberian sea, Arctic Ocean. The major purposes of ARA12C sparker single channel seismic survey were to examine the subsurface stratigraphy and geological structures, and to detect gas/fluid seepage structures at the East Siberian Shelf.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001872_1.json b/datasets/KOPRI-KPDC-00001872_1.json index 9a00e7d4a3..fc22783ea1 100644 --- a/datasets/KOPRI-KPDC-00001872_1.json +++ b/datasets/KOPRI-KPDC-00001872_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001872_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the meteorological data of the Council site, 70-mile northeast from Nome, Alaska in 2021.\nUnlike previous years when an AWS sensor was used, ERA5 reanalysis data was downscaled for the location because there was problem of the AWS datalogger which was caused by absence of maintenance for longtime due to COVID19 situation since 2020.\nThe meteorological data consists of air temperature, relative humidity, atmospheric pressure, downward solar radiation, and wind at 30-minute interval.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001873_1.json b/datasets/KOPRI-KPDC-00001873_1.json index 1784555bd0..ac58edca2a 100644 --- a/datasets/KOPRI-KPDC-00001873_1.json +++ b/datasets/KOPRI-KPDC-00001873_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001873_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment cores during ARA12C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001874_1.json b/datasets/KOPRI-KPDC-00001874_1.json index 87e271a0fb..82aaaafff4 100644 --- a/datasets/KOPRI-KPDC-00001874_1.json +++ b/datasets/KOPRI-KPDC-00001874_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001874_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected the manganese nodule by dredge to study the distribution of manganese nodule in Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001875_2.json b/datasets/KOPRI-KPDC-00001875_2.json index a1597b9fcf..8da2135b2f 100644 --- a/datasets/KOPRI-KPDC-00001875_2.json +++ b/datasets/KOPRI-KPDC-00001875_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001875_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multibeam data were collected during the 2021 ARA12C cruise in Chukchi Plateau and East Siberian shelf areas on Arctic ocean An accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001876_2.json b/datasets/KOPRI-KPDC-00001876_2.json index e44a6162d3..1bd24043ce 100644 --- a/datasets/KOPRI-KPDC-00001876_2.json +++ b/datasets/KOPRI-KPDC-00001876_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001876_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sub-bottom profiler data were collected during the 2021 ARA12C cruise in the Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001877_1.json b/datasets/KOPRI-KPDC-00001877_1.json index 785b3034da..b22a33c532 100644 --- a/datasets/KOPRI-KPDC-00001877_1.json +++ b/datasets/KOPRI-KPDC-00001877_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001877_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the changes in climate properties in soil by increasing snow depth by snow fence, micro-climate data (soil volumetric content for 5 and 20 cm depth, and temperature for 5, 10 and 20 cm depth) for 2 year (2019.06.24.~2021.09.19) were collected.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001878_1.json b/datasets/KOPRI-KPDC-00001878_1.json index 1a0859ed59..572e8df00f 100644 --- a/datasets/KOPRI-KPDC-00001878_1.json +++ b/datasets/KOPRI-KPDC-00001878_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001878_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation, micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 2 year (2019.06.01~2021.09.18) were collected", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001879_1.json b/datasets/KOPRI-KPDC-00001879_1.json index 053c0f96a9..895e8a72f0 100644 --- a/datasets/KOPRI-KPDC-00001879_1.json +++ b/datasets/KOPRI-KPDC-00001879_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001879_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation, micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 2 year (2019.06.01.~2021.09.20) were collected.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001880_2.json b/datasets/KOPRI-KPDC-00001880_2.json index f4c0084a86..d96b35ef26 100644 --- a/datasets/KOPRI-KPDC-00001880_2.json +++ b/datasets/KOPRI-KPDC-00001880_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001880_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the meteorological data of Nord in 2021.\nUnlike previous years when an AWS sensor was used, ERA5 reanalysis data was downscaled for the location because there was problem of the AWS datalogger which was caused by absence of maintenance for longtime due to COVID19 situation since 2020.\nThe meteorological data consists of air temperature, relative humidity, atmospheric pressure, downward solar radiation, and wind at 1-hour interval.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001881_1.json b/datasets/KOPRI-KPDC-00001881_1.json index 343c5adf68..fa9bbce047 100644 --- a/datasets/KOPRI-KPDC-00001881_1.json +++ b/datasets/KOPRI-KPDC-00001881_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001881_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at DASAN Station data. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at DASAN Station", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001882_1.json b/datasets/KOPRI-KPDC-00001882_1.json index 82e2dbd5f7..fa2079e7e8 100644 --- a/datasets/KOPRI-KPDC-00001882_1.json +++ b/datasets/KOPRI-KPDC-00001882_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001882_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 profile had been measured during summertime in 2021 at Council, Alaska.\nTo monitor and understand CO2 change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001883_1.json b/datasets/KOPRI-KPDC-00001883_1.json index 2783d208da..912990930c 100644 --- a/datasets/KOPRI-KPDC-00001883_1.json +++ b/datasets/KOPRI-KPDC-00001883_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001883_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil volumetric water content profile had been measured during summertime in 2021 at Council, Alaska.\nTo monitor and understand soil volumetric water content change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001884_1.json b/datasets/KOPRI-KPDC-00001884_1.json index 32b5d4059d..5816bc48ac 100644 --- a/datasets/KOPRI-KPDC-00001884_1.json +++ b/datasets/KOPRI-KPDC-00001884_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001884_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CH4 profile had been measured during summertime in 2021 at Council, Alaska.\nTo monitor and understand CH4 change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001885_1.json b/datasets/KOPRI-KPDC-00001885_1.json index 046e5bcdc6..859793dcba 100644 --- a/datasets/KOPRI-KPDC-00001885_1.json +++ b/datasets/KOPRI-KPDC-00001885_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001885_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil temperature profile had been measured during summertime in 2021 at Council, Alaska.\nTo monitor and understand soil temperature change over permafrost region", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001886_1.json b/datasets/KOPRI-KPDC-00001886_1.json index ce7d74dbcf..81bf023c04 100644 --- a/datasets/KOPRI-KPDC-00001886_1.json +++ b/datasets/KOPRI-KPDC-00001886_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001886_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily soil temperature data at the manual chamber site, operated by Dr. Yongwon Kim of UAF\nThe data was recovered in September 2021, covers from 2019/09/19 to 2020/12/14.\nSoil temperature data are obtained at four depths: 2, 5, 10, 20 cm.\nMain reason of the monitoring is because soil temperature and moisture regulate soil CO2 efflux in terrestrial ecosystems in Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001887_1.json b/datasets/KOPRI-KPDC-00001887_1.json index 908b1902e0..9d6757d207 100644 --- a/datasets/KOPRI-KPDC-00001887_1.json +++ b/datasets/KOPRI-KPDC-00001887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily soil temperature and moisture of the burned and unburned plots of the Kougarok site, Alaska.\nThe data was recovered in September 2021 by Dr. Yongwon Kim of UAF and covers from 2019/09/23 to 2021/09/07.\nSoil temperatures are obtained for four depths: 5, 10, 20, 50cm and soil moisture data are obtained for two depths: 10 and 30 cm.\nBurned plot is located upper side of the road and un-burned plot is located lower side of the road.\nMonitoring of soil temperature and moisture is to understand regulating factors of soil CO2 efflux in terrestrial ecosystems in Alaska.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001888_1.json b/datasets/KOPRI-KPDC-00001888_1.json index c29c6f8d7a..528a7bbc63 100644 --- a/datasets/KOPRI-KPDC-00001888_1.json +++ b/datasets/KOPRI-KPDC-00001888_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001888_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological observation at Cambridge Bay site in Canada DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change. half hourly averaged data are stored at a data logger. The objectives of this monitoring are to understand characteristics of meteorological phenomena at Cambridge Bay site in Canada.\n(No data from June 12 to September 21, 2021 due to on-site power off.)", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001889_1.json b/datasets/KOPRI-KPDC-00001889_1.json index 105730b576..67617160ba 100644 --- a/datasets/KOPRI-KPDC-00001889_1.json +++ b/datasets/KOPRI-KPDC-00001889_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001889_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the meteorological data of Baranova, Russia in 2021.\nUnlike previous years when an AWS sensor was used, ERA5 reanalysis data was downscaled for the location because there was problem of the AWS datalogger which was caused by absence of maintenance for longtime due to COVID19 situation since 2020.\nThe meteorological data consists of air temperature, relative humidity, atmospheric pressure, downward solar radiation, and wind at 1-hour interval.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001890_1.json b/datasets/KOPRI-KPDC-00001890_1.json index cb0c383c03..3d07be15f1 100644 --- a/datasets/KOPRI-KPDC-00001890_1.json +++ b/datasets/KOPRI-KPDC-00001890_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001890_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A GIS data sets of the ARAON Arctic seismic survey track lines conducted in 2013-2021.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001893_1.json b/datasets/KOPRI-KPDC-00001893_1.json index 35f84200e4..22cf86e3a4 100644 --- a/datasets/KOPRI-KPDC-00001893_1.json +++ b/datasets/KOPRI-KPDC-00001893_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001893_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The vertical profiles of physico-chemical and microbiological parameters in the Amundsen Sea from January 14 to February 16, 2016.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001896_1.json b/datasets/KOPRI-KPDC-00001896_1.json index 03042f388c..ab46f091df 100644 --- a/datasets/KOPRI-KPDC-00001896_1.json +++ b/datasets/KOPRI-KPDC-00001896_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001896_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To understand the behavior of gas hydrate in the sediment and to estimate the CH4 fluxes from the sediment through the water column to the atmosphere, we obtained data on water temperature, salinity, density and fluorescence in the water column.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001897_1.json b/datasets/KOPRI-KPDC-00001897_1.json index ccedc82198..8869f74a91 100644 --- a/datasets/KOPRI-KPDC-00001897_1.json +++ b/datasets/KOPRI-KPDC-00001897_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001897_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 2021/2022 summer season, we obtained high resolution bathymetric data located between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR). It is expected that it will be able to contribute to the investigations for the tectonic evolution of the Antarctica related to the Australian-Pacific-Antarctic plates and the evolution of the Zealandia-Antarctic mantle, through the bathymetric and magnetic data that will be accumulated in the future.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001899_1.json b/datasets/KOPRI-KPDC-00001899_1.json index 7fc0f1d2c9..b3ab9b3842 100644 --- a/datasets/KOPRI-KPDC-00001899_1.json +++ b/datasets/KOPRI-KPDC-00001899_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001899_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring of changes in tide, salinity and temperature of seawater", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001900_1.json b/datasets/KOPRI-KPDC-00001900_1.json index b66f274332..53abb51140 100644 --- a/datasets/KOPRI-KPDC-00001900_1.json +++ b/datasets/KOPRI-KPDC-00001900_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001900_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Magnetic susceptibility, total organic carbon, total nitrogen, C/N ratio, biogenic opal, CaCO3, nitrogen isotope of acid treated samples, grain size analysis data of GC05-DP02 covering the last 600 kyrs.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001902_1.json b/datasets/KOPRI-KPDC-00001902_1.json index 50d2380542..9e0f7b84fa 100644 --- a/datasets/KOPRI-KPDC-00001902_1.json +++ b/datasets/KOPRI-KPDC-00001902_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001902_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data were collected and processed to monitor the vertical dynamics of zooplankton and micro nekton in the Arctic Ocean.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001904_1.json b/datasets/KOPRI-KPDC-00001904_1.json index 0dcb4515d5..63300e1e34 100644 --- a/datasets/KOPRI-KPDC-00001904_1.json +++ b/datasets/KOPRI-KPDC-00001904_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001904_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To establish a reconstruction technique for past sea ice changes based on pure domestic technology.\nAcquisition of lipid biomarkers (HBIs, sterols) from surface sediments in the Western Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001905_1.json b/datasets/KOPRI-KPDC-00001905_1.json index 19580a74e9..fbafe34547 100644 --- a/datasets/KOPRI-KPDC-00001905_1.json +++ b/datasets/KOPRI-KPDC-00001905_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001905_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To identify past sea ice changes based on lipid biomarkers of a sediment core (ARA06C-01JPC) covering the Holocene in the Western Arctic.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001906_1.json b/datasets/KOPRI-KPDC-00001906_1.json index fe1b9a9e92..2eba3a216a 100644 --- a/datasets/KOPRI-KPDC-00001906_1.json +++ b/datasets/KOPRI-KPDC-00001906_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001906_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica. Investigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001907_1.json b/datasets/KOPRI-KPDC-00001907_1.json index b3535d1164..77d522120b 100644 --- a/datasets/KOPRI-KPDC-00001907_1.json +++ b/datasets/KOPRI-KPDC-00001907_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001907_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica. Investigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001908_1.json b/datasets/KOPRI-KPDC-00001908_1.json index bb2a443e90..0acc91189b 100644 --- a/datasets/KOPRI-KPDC-00001908_1.json +++ b/datasets/KOPRI-KPDC-00001908_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001908_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic krill and ice krill samples were collected in the western Ross Sea during the ARAON cruise in 2018-2019 (ANA09B). Chick carcasses of Adelie and Emperor penguins were collected at Cape Hallett, Inexpressible Island, Cape Washington, and Coulman Island. Pretreated samples were used for carbon stable isotope analysis, and untreated samples were used for nitrogen stable isotope. The stable isotope ratios of carbon and nitrogen were determined using an isotope ratio mass spectrometer coupled with and elemental analyzer (EA-IRMS).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001909_1.json b/datasets/KOPRI-KPDC-00001909_1.json index 2cb36e5470..cbd0458b47 100644 --- a/datasets/KOPRI-KPDC-00001909_1.json +++ b/datasets/KOPRI-KPDC-00001909_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001909_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The standard ocean color-based chlorophyll-a concentration product has numerous gaps due to various reasons such as cloud and fog. This chlorophyll-a concentration dataset is fully reconstructed using the machine learning technique (Random Forest) and covers off the Cape Hallett.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001910_1.json b/datasets/KOPRI-KPDC-00001910_1.json index 3f22388e65..4dd6263e98 100644 --- a/datasets/KOPRI-KPDC-00001910_1.json +++ b/datasets/KOPRI-KPDC-00001910_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001910_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic data were collected to understand the variability of zooplankton/krill/fish distribution in the Ross Sea marine protected area. Data were collected from surface to 1000-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001911_1.json b/datasets/KOPRI-KPDC-00001911_1.json index ccfd0ea612..df57bf6689 100644 --- a/datasets/KOPRI-KPDC-00001911_1.json +++ b/datasets/KOPRI-KPDC-00001911_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001911_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic data were collected to understand the variability of zooplankton/krill/fish distribution in the Ross Sea marine protected area. Data were collected from surface to 1000-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001912_1.json b/datasets/KOPRI-KPDC-00001912_1.json index d7a8dddf68..15ff325ff1 100644 --- a/datasets/KOPRI-KPDC-00001912_1.json +++ b/datasets/KOPRI-KPDC-00001912_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001912_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic data were collected to understand the variability of zooplankton/krill/fish distribution in the Ross Sea marine protected area. Data were collected from surface to 1000-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001913_1.json b/datasets/KOPRI-KPDC-00001913_1.json index 12f72db036..d79a6999a8 100644 --- a/datasets/KOPRI-KPDC-00001913_1.json +++ b/datasets/KOPRI-KPDC-00001913_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001913_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic data were collected to understand the variability of zooplankton/krill/fish distribution in the Ross Sea marine protected area. Data were collected from surface to 1000-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001914_1.json b/datasets/KOPRI-KPDC-00001914_1.json index 77634464f3..3aa3317010 100644 --- a/datasets/KOPRI-KPDC-00001914_1.json +++ b/datasets/KOPRI-KPDC-00001914_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001914_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI should submit to CCAMLR Secretariat monitoring data using the CEMP form for inclusion in the CEMP database annually. To determine interannual trends in the population size of Adelie penguins at Cape Hallett, monitoring data was collected according to the CEMP standard methods(A3B).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001915_1.json b/datasets/KOPRI-KPDC-00001915_1.json index 0033e1f5ab..80ab13a4f4 100644 --- a/datasets/KOPRI-KPDC-00001915_1.json +++ b/datasets/KOPRI-KPDC-00001915_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001915_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI should submit to CCAMLR Secretariat monitoring data using the CEMP form for inclusion in the CEMP database annually. To determine interannual trends in the population size of Adelie penguins at Cape Hallett, monitoring data was collected according to the CEMP standard methods(A3B).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001916_1.json b/datasets/KOPRI-KPDC-00001916_1.json index 78c9810999..efc94db40e 100644 --- a/datasets/KOPRI-KPDC-00001916_1.json +++ b/datasets/KOPRI-KPDC-00001916_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001916_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KOPRI should submit to CCAMLR Secretariat monitoring data using the CEMP form for inclusion in the CEMP database annually. To determine interannual trends in the population size of Adelie penguins at Cape Hallett, monitoring data was collected according to the CEMP standard methods(A3B).", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001917_1.json b/datasets/KOPRI-KPDC-00001917_1.json index 08c6bfdc02..5e12546fd3 100644 --- a/datasets/KOPRI-KPDC-00001917_1.json +++ b/datasets/KOPRI-KPDC-00001917_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001917_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are three emperor penguin breeding colonies located along the coast of Northern Victoria Land, Ross Sea. KOPRI conducted a population monitoring survey on three of emperor penguin colonies, Cape Washington (ASPA No. 173), Coulman Island and Cape Roget. The colony of Cape Roget was surveyed by researchers for the first time in November 2021. The emperor penguin chick counting on Cape Washington was conducted seven times during austral summer seasons from 2014 to 2021. And we surveyed colony of Coulman Island, one of the largest colonies in Antarctica, four times from 2017 to 2021. We visited on the ground and counted chicks by researchers in November 2014 and December 2015, while we used aerial photograph to determine the colony size of emperor penguins from 2016. We could not visit the colonies of Cape Washington and Coulman Island in 2020, because field activities were cancelled due to the pandemic situation of covid-19 worldwide.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001918_1.json b/datasets/KOPRI-KPDC-00001918_1.json index c5e8ebf5d3..25541ebde9 100644 --- a/datasets/KOPRI-KPDC-00001918_1.json +++ b/datasets/KOPRI-KPDC-00001918_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001918_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of foraging range of Adelie penguins breeding at Cape Hallett using animal-borne GPS loggers.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001919_1.json b/datasets/KOPRI-KPDC-00001919_1.json index 95ed3c7439..65d8db3f60 100644 --- a/datasets/KOPRI-KPDC-00001919_1.json +++ b/datasets/KOPRI-KPDC-00001919_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001919_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of foraging range of Adelie penguins breeding at Cape Hallett using animal-borne GPS loggers.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001920_1.json b/datasets/KOPRI-KPDC-00001920_1.json index c010ab0ba3..aedbf42d2f 100644 --- a/datasets/KOPRI-KPDC-00001920_1.json +++ b/datasets/KOPRI-KPDC-00001920_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001920_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of foraging range of Adelie penguins breeding at Cape Hallett using animal-borne GPS loggers.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001921_1.json b/datasets/KOPRI-KPDC-00001921_1.json index adee4d7357..43d8f9460d 100644 --- a/datasets/KOPRI-KPDC-00001921_1.json +++ b/datasets/KOPRI-KPDC-00001921_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001921_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of foraging range of Adelie penguins breeding at Terra Nova Bay using animal-borne GPS loggers.", "links": [ { diff --git a/datasets/KOPRI-KPDC-00001922_1.json b/datasets/KOPRI-KPDC-00001922_1.json index ad46d5f9ed..e3730bdebb 100644 --- a/datasets/KOPRI-KPDC-00001922_1.json +++ b/datasets/KOPRI-KPDC-00001922_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KOPRI-KPDC-00001922_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Investigation of foraging range of Adelie penguins breeding on Inexpressible Island using animal-borne GPS loggers.", "links": [ { diff --git a/datasets/KORUSAQ_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/KORUSAQ_Aerosol_AircraftInSitu_DC8_Data_1.json index 3ca2bb92ac..ab6747ad56 100644 --- a/datasets/KORUSAQ_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/KORUSAQ_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSQ_Aerosol_AircraftInSitu_DC8_Data are in-situ aerosol measurements conducted onboard the DC-8 aircraft during the KORUS-AQ field campaign. This product features data collected from a variety of in-situ instrumentation, including the AMS, APS, CPC, SMPS, PSAP, Nephelometers, and 4STAR along with other aerosol instrumentation. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data_1.json b/datasets/KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data_1.json index de0c3ef26d..076bfd9e07 100644 --- a/datasets/KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data_1.json +++ b/datasets/KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_AircraftRemoteSensing_DIAL_DC8_Data features remotely sensed data collected by the Differential Absorption Lidar (DIAL) onboard the DC-8 aircraft during the KORUS-AQ field campaign. Ozone and various lidar properties are measurements featured in this collection. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data_1.json b/datasets/KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data_1.json index 3f74caabb9..149682ed97 100644 --- a/datasets/KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data_1.json +++ b/datasets/KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data are remotely sensed data collected by the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument onboard the B200 aircraft during the KORUS-AQ field campaign. NO2 and HCHO trace gas slant column data are featured in this collection. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Analysis_Data_1.json b/datasets/KORUSAQ_Analysis_Data_1.json index 377b87175c..8bbf2b8d53 100644 --- a/datasets/KORUSAQ_Analysis_Data_1.json +++ b/datasets/KORUSAQ_Analysis_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Analysis_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Analysis_Data are supplementary ancillary analysis files collected during the KORUS-AQ field campaign. This collection includes plume flags, co/co2 ratios, AMS analysis, and DIAL mixed layer heights. \r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/KORUSAQ_Cloud_AircraftInSitu_DC8_Data_1.json index 7e0569407c..265abe478d 100644 --- a/datasets/KORUSAQ_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/KORUSAQ_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Cloud_AircraftInSitu_DC8_Data are in-situ cloud measurements collected onboard the DC-8 aircraft during the KORUS-AQ field campaign. This product features cloud flag data. Data collection for this product is complete. \r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Ground_EPA_Data_1.json b/datasets/KORUSAQ_Ground_EPA_Data_1.json index 359c96e2e5..f6f80f305c 100644 --- a/datasets/KORUSAQ_Ground_EPA_Data_1.json +++ b/datasets/KORUSAQ_Ground_EPA_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Ground_EPA_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Ground_EPA_Data are the Environmental Protection Agency (EPA) data collected at various ground sites as part of the KORUS-AQ field campaign. Contained in this dataset are measurements collected by the Teledyne CAPS analyzer, 2B Technologies Ozone Analyzer, Aerodyne QCL, and ceilometer. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Ground_NASA_Data_1.json b/datasets/KORUSAQ_Ground_NASA_Data_1.json index cf7d0c9180..a8a9bc7594 100644 --- a/datasets/KORUSAQ_Ground_NASA_Data_1.json +++ b/datasets/KORUSAQ_Ground_NASA_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Ground_NASA_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Ground_NASA_Data are ground site measurements collected by NASA instrumentation at the NIER-Taehwa ground site during the KORUS-AQ field campaign. This product features data collected by TILDAS and DIAL. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Merge_Data_1.json b/datasets/KORUSAQ_Merge_Data_1.json index 3a60f87af4..183dd00cd1 100644 --- a/datasets/KORUSAQ_Merge_Data_1.json +++ b/datasets/KORUSAQ_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Merge_Data are pre-generated merge data files combining various products collected during the KORUS-AQ field campaign. This collection features pre-generated merge files for the DC-8 aircraft. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_MetNav_AircraftInSitu_B200_Data_1.json b/datasets/KORUSAQ_MetNav_AircraftInSitu_B200_Data_1.json index 282b390201..3e6a402f2f 100644 --- a/datasets/KORUSAQ_MetNav_AircraftInSitu_B200_Data_1.json +++ b/datasets/KORUSAQ_MetNav_AircraftInSitu_B200_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_MetNav_AircraftInSitu_B200_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_MetNav_AircraftInSitu_B200_Data are in-situ meteorological and navigational data collected onboard the B200 aircraft during the KORUS-AQ field campaign. This dataset contains the navigational data for the B200 aircraft. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/KORUSAQ_MetNav_AircraftInSitu_DC8_Data_1.json index f5fdd55f76..5bceb27ca3 100644 --- a/datasets/KORUSAQ_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/KORUSAQ_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_MetNav_AircraftInSitu_DC8_Data are in-situ meteorological and navigational data collected onboard the DC-8 aircraft during KORUS-AQ. This data product features navigational data for the DC-8 aircraft, along with measurements conducted by the DLH and CLH2. Data collection for this product is complete. \r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Miscellaneous_Data_1.json b/datasets/KORUSAQ_Miscellaneous_Data_1.json index 3d717ff238..b1741bef93 100644 --- a/datasets/KORUSAQ_Miscellaneous_Data_1.json +++ b/datasets/KORUSAQ_Miscellaneous_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Miscellaneous_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Miscellaneous_Data are miscellaneous ancillary files collected during the KORUS-AQ field campaign. This product includes data collected onboard the UMD Cessna Aircraft. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Model_Data_1.json b/datasets/KORUSAQ_Model_Data_1.json index 8f97ba49c2..51a17d9351 100644 --- a/datasets/KORUSAQ_Model_Data_1.json +++ b/datasets/KORUSAQ_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Model_Data features ancillary model data products for the KORUS-AQ field campaign. This product features output from the WRF model, CAM-chem, model inter-comparisons, and GEOS-chem models. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Pandora_Data_1.json b/datasets/KORUSAQ_Pandora_Data_1.json index c5d58671fb..d1f75ca48c 100644 --- a/datasets/KORUSAQ_Pandora_Data_1.json +++ b/datasets/KORUSAQ_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Ground_Pandora_Data contains all of the Pandora instrumentation data collected during the KORUS-AQ field study. Contained in this dataset are column measurements of NO2, O3, and HCHO. Pandoras were situated at various ground sites across the study area, including, NIER-Taehwa, NIER-Olympic Park, NIER-Gwangju, NIER-Anmyeon, Busan, Yonsei University, Songchon, and Yeoju. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_RVJangMokShip_Data_1.json b/datasets/KORUSAQ_RVJangMokShip_Data_1.json index 536d9202a4..7e0ee57b08 100644 --- a/datasets/KORUSAQ_RVJangMokShip_Data_1.json +++ b/datasets/KORUSAQ_RVJangMokShip_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_RVJangMokShip_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_RVJangMokShip_Data features data collected onboard the Research Vessel JangMok during the KORUS-AQ field campaign. This product features trace gas and meteorological and navigational data. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_RVOnnuriShip_Data_1.json b/datasets/KORUSAQ_RVOnnuriShip_Data_1.json index f955d9aab9..63e7813955 100644 --- a/datasets/KORUSAQ_RVOnnuriShip_Data_1.json +++ b/datasets/KORUSAQ_RVOnnuriShip_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_RVOnnuriShip_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_RVOnnuriShip_Data features data collected onboard the Research Vessel Onnuri during the KORUS-AQ field campaign. This product features trace gas data and absorption coefficient spectra. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Sondes_Data_1.json b/datasets/KORUSAQ_Sondes_Data_1.json index 3b096b4ae8..220cf2820c 100644 --- a/datasets/KORUSAQ_Sondes_Data_1.json +++ b/datasets/KORUSAQ_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Sondes_Data features data collected via ozonesonde launches at Olympic Park and Taehwa during the KORUS-AQ field campaign. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/KORUSAQ_TraceGas_AircraftInSitu_DC8_Data_1.json index 090b9568a6..7964b8f581 100644 --- a/datasets/KORUSAQ_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/KORUSAQ_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_TraceGas_AircraftInSitu_DC8_Data are in-situ trace gas data collected onboard the DC-8 aircraft during the KORUS-AQ field campaign. Data were collected using a variety of instrumentation, including 4STAR, DACOM, PTR-ToF-MS, CIT-ToF-CIMS, TD-LIF, and ATHOS. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data_1.json b/datasets/KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data_1.json index 305fa56acf..5cb19cbfaf 100644 --- a/datasets/KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data_1.json +++ b/datasets/KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_TraceGas_AircraftInSitu_HanseoKingAir_Data are in-situ trace gas measurements collected onboard the Hanseo King Air aircraft during the KORUS-AQ field campaign. This collection features trace gas data including, O3, NO2, CH2O, SO2, CO, CH4, H2O, and CO2. Data collection for this product is complete. \r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_Trajectory_Data_1.json b/datasets/KORUSAQ_Trajectory_Data_1.json index 6a3c512f98..864421058c 100644 --- a/datasets/KORUSAQ_Trajectory_Data_1.json +++ b/datasets/KORUSAQ_Trajectory_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_Trajectory_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_Trajectory_Data are FLEXPART backtrajectory products for the DC-8 and Hanseo King Air aircrafts (flight tracks), R/V Onnuri ship tracks and ground stations as part of the KORUS-AQ field campaign. Data collection for this product is complete. \r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUSAQ_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/KORUSAQ_jValue_AircraftInSitu_DC8_Data_1.json index cb23e346fe..5b0a4136d7 100644 --- a/datasets/KORUSAQ_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/KORUSAQ_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUSAQ_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "KORUSAQ_jValue_AircraftInSitu_DC8_Data are in-situ j-value (photolysis rate) measurements collected onboard the DC-8 aircraft during the KORUS-AQ field campaign. Photolysis rates were calculated from NCAR CAFS. Data collection for this product is complete.\r\n\r\nThe KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea\u2019s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.\r\n\r\nSurface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.\r\n\r\nThe major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.", "links": [ { diff --git a/datasets/KORUS_0.json b/datasets/KORUS_0.json index 831ff52ab6..c6e77e3eff 100644 --- a/datasets/KORUS_0.json +++ b/datasets/KORUS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KORUS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KORUS-OC (Korea-United States Ocean Color) expedition was a venture among scientist from the Korean Institute of Ocean Science and Technology (KIOST), NASA, and other institutions to study the daily changes of the seas surrounding South Korea.", "links": [ { diff --git a/datasets/KROCK_Ocean_1.json b/datasets/KROCK_Ocean_1.json index f7accf8efd..600767c0d0 100644 --- a/datasets/KROCK_Ocean_1.json +++ b/datasets/KROCK_Ocean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KROCK_Ocean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains CTD (conductivity, temperature, depth) data obtained from the Krill and Rock (KROCK) 92/93 cruise of the Aurora Australis, during Jan - Mar 1993. 62 CTD casts were taken in the Prydz Bay region, as a supplement to the krill and geology research program. Casts were made about 200 m except for one off the shelf. This dataset is a subset of the whole cruise data.\n\nThe fields in this dataset are:\nPressure\nTemperature\nSigma-T\nSalinity\nGeopotential Anomaly\nSpecific volume Anomaly\nsamples\ndeviation\nconduction", "links": [ { diff --git a/datasets/KV1_MSS_0.1.json b/datasets/KV1_MSS_0.1.json index 5843056aad..079c0d1af5 100644 --- a/datasets/KV1_MSS_0.1.json +++ b/datasets/KV1_MSS_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KV1_MSS_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MSS multispectral images from Kanopus-V\n\nMultiband surveying sensor from ?Kanopus-V? satellite that has circular sun synchronous orbit. The sensor is designed for monitoring man-caused and natural-caused emergency situations. The sensor provides earth surface images in 4 spectral bands (blue ? 460-520 nm, green ? 510-600 nm, red ? 630-690 nm, near infrared 750-840 nm). Nadir spatial resolution is 12 m. Swath with of the system is 20 km. The system has the ability to point the sensor to 40\ufffd from nadir in either side, which enables swath view of 920 km. The revisit frequency depends on latitude and can vary from 3 to 16 days. Obtained data can be used for tackling various problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring.", "links": [ { diff --git a/datasets/KYOTO_GREENHOUSEGASES.json b/datasets/KYOTO_GREENHOUSEGASES.json index ad9894592e..bb9f6376b1 100644 --- a/datasets/KYOTO_GREENHOUSEGASES.json +++ b/datasets/KYOTO_GREENHOUSEGASES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KYOTO_GREENHOUSEGASES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This set of interactive graphics was produced in preparation for the\nseventh Conference of the Parties (COP-7) to the United Nations\nFramework Convention on Climate Change (UNFCCC) held in The\nNetherlands in October-November 2001. They are based on several UNFCCC\nSecretariat documents compiling data from submissions by Annex I\ncountries; these include First and Second National Communications, as\nwell as annual national inventory data. Additional sources include\nupdated reports from individual countries; exceptions are noted on the\ngraphs.\n\nThe graphs feature actual (1990-2000) and projected (2005, 2010)\nemissions of the six greenhouse gases: carbon dioxide (CO2), methane\n(CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs),\nperfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The emissions\nare aggregated and represented as CO2 equivalents in million tonnes\n(1012); please note that in UNFCCC documents, emissions are measured\nin gigagrams (10**9).\n\nFor more information on the derivation of GHG emissions statistics,\nsee:\n\"http://www.grida.no/db/maps/collection/climate6/about.htm\"", "links": [ { diff --git a/datasets/Kennebec_0.json b/datasets/Kennebec_0.json index 91da889606..244da6dc7f 100644 --- a/datasets/Kennebec_0.json +++ b/datasets/Kennebec_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kennebec_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Maine between 2005 and 2007.", "links": [ { diff --git a/datasets/Kerg_Heard_Plateau_Under_Sea_Geomorphology_1.json b/datasets/Kerg_Heard_Plateau_Under_Sea_Geomorphology_1.json index 2e3f691f8b..41da374289 100644 --- a/datasets/Kerg_Heard_Plateau_Under_Sea_Geomorphology_1.json +++ b/datasets/Kerg_Heard_Plateau_Under_Sea_Geomorphology_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kerg_Heard_Plateau_Under_Sea_Geomorphology_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geomorphology was digitised using contours derived from the DEM created by Dr. R. Beaman from James Cook University for Geoscience Australia.\n\nThe data, the metadata record and the report related to the creation of that DEM are available on the Geoscience Australia website:\nName of data set: Kerguelen Plateau Bathymetric Grid 2010\nCatalogue number: 71552\nhttps://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search?node=srv#/metadata/a05f7893-007f-7506-e044-00144fdd4fa6\n\nDigitising:\nIt must be stressed that neither seismic data, sea floor sediments, nor sea floor biota were used to determine the sea floor geomorphology. The description on how the geomorphology was derived is described in the attached report.\n\nThe features described as slopes from the 900m to 1300m isobaths and from the 1300m to 2500m isobaths were identified for fisheries purposes and not geomorphology purposes. A geomorphologist may combine these slopes into a single feature.\n\nSome of the larger shallow features identified as banks may more properly be identified as plateaus. It would require a more in depth analysis of the DEM, slopes and sediments to accurately identify the feature as a bank or plateau.", "links": [ { diff --git a/datasets/Kerguelen_emapex_1.json b/datasets/Kerguelen_emapex_1.json index e27d91d3e1..051bece14c 100644 --- a/datasets/Kerguelen_emapex_1.json +++ b/datasets/Kerguelen_emapex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kerguelen_emapex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected by 8 EM-APEX profiling floats, which are a sophisticated version of the standard Argo float. They measure temperature, salinity and pressure, as for standard Argo. They also use electromagnetic techniques to measure horizontal velocity. The floats were deployed across the northern Kerguelen Platueau in November 2008, and drifted eastward with the Antarctic Circumpolar Current as they profiled between the surface and 1600 dbar. They transmitted data through the Iridium satellite system and continued to profile eastward until their batteries failed. The range of latitudes covered is approx. 40S-50S, and longitudes 65E-90E. Although most of the data is in the longitude band 65E-78E. The temporal range of the data is Nov 2008 to approx. Sep 2009.\n \nThe file \"emapex_final.mat\" contains the quality-controlled and calibrated data from 8 EM-APEX profiling floats deployed across the northern Kerguelen Plateau during the Southern Ocean Finestructure (SOFine) experiment aboard the U.K. RRS James Cook, Cruise 29, 1st Nov-22nd Dec 2008, Cape Town to Cape Town.\n \nFunding for the EM-APEX component of the experiment was from the Australian Research Council Discovery Project DP0877098 (N. Bindoff, H. Phillips and S. Rintoul). The Australian Antarctic Division provided subantarctic clothing for Bindoff and Phillips under AAS project #3002 (H. Phillips and N. Bindoff). AAS project #3228 (N. Bindoff and H. Phillips) provided $27,000 for salary support for a research assistant to work on analysis of the data and publication of a manuscript. Significant in-kind support was provided by CSIRO Marine and Atmospheric Research for the EM-APEX component.\n \nDetails of the shipboard operations and deployment of the EM-APEX floats can be found in the document \"emapex_deployment_report.pdf\". The complete voyage report is available from h.e.phillips@utas.edu.au. It may be cited as\n \nNaveira Garabato, A.; Bindoff, N.; Phillips, H.; Polzin, K.; Sloyan, B.; Stevens, D. and Waterman, S. RRS James Cook Cruise 29, 01 Nov - 22 Dec 2008. SOFine Cruise Report: Southern Ocean Finestructure National Oceanography Centre, Southampton, 2009\n \nSee the download file for more information, which contains a data report and a data description file as well as the data.", "links": [ { diff --git a/datasets/Kieber_Photochemistry_0.json b/datasets/Kieber_Photochemistry_0.json index fa640489d0..86f8673738 100644 --- a/datasets/Kieber_Photochemistry_0.json +++ b/datasets/Kieber_Photochemistry_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kieber_Photochemistry_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the mid-Atlantic and New England coastal regions.", "links": [ { diff --git a/datasets/KingSejong_0.json b/datasets/KingSejong_0.json index 98be1021ad..d958db1c0b 100644 --- a/datasets/KingSejong_0.json +++ b/datasets/KingSejong_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "KingSejong_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off King George Island near Antarctica between 2000 and 2001.", "links": [ { diff --git a/datasets/King_Penguins_at_GG_1.json b/datasets/King_Penguins_at_GG_1.json index c986ca0247..ede740b735 100644 --- a/datasets/King_Penguins_at_GG_1.json +++ b/datasets/King_Penguins_at_GG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "King_Penguins_at_GG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground counts of King Penguin eggs, chicks, fledglings and adults at Gadget Gully on Macquarie Island (1993-2008 incomplete).\n\nCounts were obtained in the field by observers at Gadget gully.\n\nThe data were also used in an online publication - the abstract is copied below:\n\nDuring the late 19th and early 20th centuries, when blubber oil fuelled house lamps, the king penguin population at Macquarie Island was reduced from two very large (perhaps hundreds of thousands of birds) colonies to about 3000 birds. One colony, located on the isthmus when the island was discovered in 1810, was extinct by 1894 and it took about 100 years for king penguins to re-establish a viable breeding population there. Here we document this recovery. The first eggs laid at Gadget Gully on the isthmus were recorded in late February 1995 but in subsequent years egg laying took place earlier between November and February (this temporal discontinuity is a consequence of king penguin breeding behaviour). The first chick was hatched in April 1995 but the first fledgling was not raised until the following breeding season in October 1996. The colony increased on average 66% per annum in the five years between 1995 and 2000. King penguins appear resilient to catastrophic population reductions, and as the island's population increases, it is likely that other previously abandoned breeding sites will be reoccupied.", "links": [ { diff --git a/datasets/King_Rim_Fire_Analysis_1288_1.json b/datasets/King_Rim_Fire_Analysis_1288_1.json index ce599f09e1..42d1ecfd5a 100644 --- a/datasets/King_Rim_Fire_Analysis_1288_1.json +++ b/datasets/King_Rim_Fire_Analysis_1288_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "King_Rim_Fire_Analysis_1288_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.", "links": [ { diff --git a/datasets/Krill_Technical_Reports_1.json b/datasets/Krill_Technical_Reports_1.json index ccd1cab111..e91b9d2a19 100644 --- a/datasets/Krill_Technical_Reports_1.json +++ b/datasets/Krill_Technical_Reports_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Krill_Technical_Reports_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Krill Ecology - Technical Reports and Systems Guides\n\nA series of documents detailing work completed and methods used at the Krill Aquarium located at the Australian Antarctic Division.\n\nTechnical Report # Title and Author\n\nTechnical Report 1. 26th January 1994. DAPI Epiflourescence Technique. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 2. 5th March 1995. Bag Culture - Cell Growth Count Protocol. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 3. 12th January 1996. Chemical 'Spiking' of Krill Aquarium Bio-filter T12. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 4. 24th June 1996. Cold Temperature Algal Bag Culture Methodology. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 5. 16th April 1997. Algal Bag Culture - Harvesting Method. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 6. 26th October 1999. Aquarium System Bulk Seawater Collection and Storage. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 7. 11th October 1999. Sodium Hypochlorite Treatment of Algal Bag Culture Filtration Unit. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 8. 18th October 1999. Feeding Krill - Algal Strains, Feeding Rate and Nutritional Values. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 9. 22nd November 1999. Krill Biology Section - Parental Algal Culture Maintenance. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 10. 10th April 2000. Krill Group Databases and Maintaining Daily Data Records. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 11. 11th May 2000. Making Up and Use of Iodine Solution as an Indicator of the Presence of Chlorine in Freshwater. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 12. 1st June 2000. Testing for Harmful Ammonia (NH3) in Aquarium Sea Water. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 13. 12th June 2000. Digitron Digilog 2088T Digital Temperature Logger/Gauge - Operating Instructions and Down-Loading Logged Data Guide. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 14. 27th June 2000. Krill Biology - Marine Science Support Shed Gear Storage. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 15. 15th October 2000. Making up of fe Growth Media Stock Solutions for Parental and Algal Bag Culture Production. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 16. 15th January 2001. Algal Bag Culture - Growth Rate Analysis. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 17. 19th July 2004. Protective Epoxy Coating of Onga Seawater Collection Fire Pump. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 18. 27th October 2004. New Krill Aquarium - Bulk Seawater Collection and Storage Logistics. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 19. 11th March 2005. New Krill Aquarium - Algal Bag Culture Filtration System. Author: P. M. Cramp. Australian Antarctic Division. \n\nTechnical Report 20. 6th April 2005. New Culture Cabinet Bag to Bag Inoculation Procedure. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 21. 17th June 2005. Agar Bacterial Plate Testing for Krill Algal Culture Stocks. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 22. 29th July 2004. New Algal Culture Cabinet - Bag Culture Setup Methodology. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 23. 24th May 2005. Protocol for Sterilization of Bag Culture Air Supply System. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 24. 30th May 2005. 200 litre tank Algal Batch Culture Setup. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechnical Report 25. 22nd June 2005. Making Up and Shaping Plastic Bags for Algal Culture. Author: P. M. Cramp. Australian Antarctic Division.\n\nTechincal Report 26. 19th December 2005. New Krill Aquarium - Algal Strains, Feeding Rates and Nutritional Values. Author: P. M. Cramp. Australian Antarctic Division.", "links": [ { diff --git a/datasets/Krill_growth_rates_1.json b/datasets/Krill_growth_rates_1.json index c7b1fd0703..472ec673bd 100644 --- a/datasets/Krill_growth_rates_1.json +++ b/datasets/Krill_growth_rates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Krill_growth_rates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata record for data from ASAC Project 2337 See the link below for public details on this project.\n \n---- Public Summary from Project ----\nThe experimental krill research program is focused on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the program will concentrate on studies into schooling, growth and ageing of krill.\n \nFrom the abstracts of some of the referenced papers:\n \nNucleic acid contents of tissue were determined from field-caught Antarctic krill to determine whether they could be used as an alternative estimator of individual growth rates which can currently only be obtained by labour intensive on-board incubations. Krill from contrasting growth regimes from early and late summer exhibited differences in RNA-based indices. There was a significant correlation between the independently measured individual growth rates and the RNA-based indices. There was a significant correlation between the independently measured individual growth rates and the RNA:DNA ratio and also the RNA concentration of krill tissue, although the strength of the relationship was only modest. DNA concentration, on average, was relatively constant, irrespective of the growth rates. The moult stage did not appear to have a significant effect on the nucleic acid contents of tissue. Overall, the amount of both nucleic acids varied considerably between individuals. Nucleic acid-based indicators may provide information concerning the recent growth and nutritional status of krill and further experimentation under controlled conditions is warranted. The are, however, reasonably costly and time-consuming measurements.\n \nGrowth rates of Antarctic krill Euphausia superba Dana in the Indian Ocean sector of the Southern Ocean were measured in 4 summers. Growth rate was measured using an 'instantaneous growth rate' technique which involved measuring the mean change in length if the uropods at moulting. In the first 4 days following collection mean growth rates ranged from 0.35 to 7.34% per moult in adults and 2.42 to 9.05% in juveniles. Mean growth rates of adult and juvenile krill differed between areas and between the different years of the investigation. When food was restricted under experimental conditions, individual krill began to shrink immediately and mean population growth rates decreased gradually, becoming negative after as little as 7 days. Populations of krill which exhibited initial growth rates began to shrink later than those which had initially been growing more slowly.\n \nData were collected on growth rates of krill.\n \nThese data were collected as part of ASAC projects 34, 1074, 2220 and 2337.\n \nASAC_34 - Ecophysiology of Antarctic Krill 'Euphausia superba'\nASAC_1074 - Seasonal growth in krill\nASAC_2220 - Collection of live Antarctic krill\nASAC_2337 - Experimental studies into growth and ageing of krill\n\nThe fields in this dataset are:\n\nField season (eg FS9596 = Field Season 1995-1996) Area (eg Indian Ocean) Cruise Month Date Latitude Longitude Total Number of Krill Dead Krill Moulted Krill Experiment ID Station ID Sample ID Sex Growth (IGR%) (% growth at time of moulting) Uropod Size (mm) Days after capture (when moulted) Standard length", "links": [ { diff --git a/datasets/Kuparuk_Veg_Maps_1378_1.json b/datasets/Kuparuk_Veg_Maps_1378_1.json index 3e977c0bd5..549a17e822 100644 --- a/datasets/Kuparuk_Veg_Maps_1378_1.json +++ b/datasets/Kuparuk_Veg_Maps_1378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kuparuk_Veg_Maps_1378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31.", "links": [ { diff --git a/datasets/Kuroshio_Area_0.json b/datasets/Kuroshio_Area_0.json index 343c57c873..82640c12a2 100644 --- a/datasets/Kuroshio_Area_0.json +++ b/datasets/Kuroshio_Area_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kuroshio_Area_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements in the Kuroshio, western boundary current in the North Pacific Ocean, from 1997.", "links": [ { diff --git a/datasets/Kyle-Ferrar_Igneous_Province.json b/datasets/Kyle-Ferrar_Igneous_Province.json index bd3cdc585e..ff4cc2a3cb 100644 --- a/datasets/Kyle-Ferrar_Igneous_Province.json +++ b/datasets/Kyle-Ferrar_Igneous_Province.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Kyle-Ferrar_Igneous_Province", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plagioclase mineral separates from basaltic extrusive (lavas) and\n instrusive (dolerite and gabbro) samples from the Dronning Maud Land\n area of Antarctica were dated by the incremental heating 40Ar/39Ar\n method. 32 individual samples were dated with 11 samples having\n duplicate analyses.", "links": [ { diff --git a/datasets/L1B_Wind_Products_3.0.json b/datasets/L1B_Wind_Products_3.0.json index e452dc2b9f..85366dc673 100644 --- a/datasets/L1B_Wind_Products_3.0.json +++ b/datasets/L1B_Wind_Products_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L1B_Wind_Products_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition.", "links": [ { diff --git a/datasets/L2B_Wind_Products_3.0.json b/datasets/L2B_Wind_Products_3.0.json index 5a96f16b92..e9c7215bc4 100644 --- a/datasets/L2B_Wind_Products_3.0.json +++ b/datasets/L2B_Wind_Products_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L2B_Wind_Products_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition.", "links": [ { diff --git a/datasets/L2C_Wind_products_5.0.json b/datasets/L2C_Wind_products_5.0.json index a7ed58368e..8f8ff05586 100644 --- a/datasets/L2C_Wind_products_5.0.json +++ b/datasets/L2C_Wind_products_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L2C_Wind_products_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds.", "links": [ { diff --git a/datasets/L2SW_Open_3.0.json b/datasets/L2SW_Open_3.0.json index fb29b6f622..4fe6fa0324 100644 --- a/datasets/L2SW_Open_3.0.json +++ b/datasets/L2SW_Open_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L2SW_Open_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMOS retrieved surface wind speed gridded maps (with a spatial sampling of 1/4 x 1/4 degrees) are available in NetCDF format.\r\rEach product contains parts of ascending and descending orbits and it is generated by Ifremer, starting from the SMOS L1B data products, in Near Real Time i.e. within 4 to 6 hours from sensing time.\r\rBefore using this dataset, please check the read-me-first note available in the Resources section below.", "links": [ { diff --git a/datasets/L3SW_Open_4.0.json b/datasets/L3SW_Open_4.0.json index cc1d647292..3d331f2c6e 100644 --- a/datasets/L3SW_Open_4.0.json +++ b/datasets/L3SW_Open_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3SW_Open_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMOS L3WS products are daily composite maps of the collected SMOS L2 swath wind products for a specific day, provided with the same grid than the Level 2 wind data (SMOS L2WS NRT) but separated into ascending and descending passes.\r\rThis product is available the day after sensing from Ifremer, in NetCDF format.\r\rBefore using this dataset, please check the read-me-first note available in the Resources section below.", "links": [ { diff --git a/datasets/L3S_LEO_AM-STAR-v2.80_2.80.json b/datasets/L3S_LEO_AM-STAR-v2.80_2.80.json index 5772809876..65aed9d052 100644 --- a/datasets/L3S_LEO_AM-STAR-v2.80_2.80.json +++ b/datasets/L3S_LEO_AM-STAR-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3S_LEO_AM-STAR-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format.\r\nThe L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs.", "links": [ { diff --git a/datasets/L3S_LEO_DY-STAR-v2.81_2.81.json b/datasets/L3S_LEO_DY-STAR-v2.81_2.81.json index 0fd39dc090..e565210f05 100644 --- a/datasets/L3S_LEO_DY-STAR-v2.81_2.81.json +++ b/datasets/L3S_LEO_DY-STAR-v2.81_2.81.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3S_LEO_DY-STAR-v2.81_2.81", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3S_LEO_DY-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from multiple instruments, including the VIIRS onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites, AVHRR onboard Metop-A, B , C satellites and MODIS onboard the Terra and Aqua satellites. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product.

\r\n \r\nThe L3S-DY-SST combines the both L3S-LEO-PM/AM SSTs into a single daily product. It covers from 2000-02-24 to present and is reported in one file per 24h interval. Data are in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 succeeds the v2.80 dataset (not available from the PO.DAAC) with the following improvements: (1) The L3S-LEO-PM input was updated from v2.80 to v2.81; and (2) ACSPO Terra MODIS SST is included from 2000-02-24 to 2021-12-31. The inclusion of Terra extends the availability of L3S-LEO-DY back to 2000-02-24 (from 2006-12-01 in v2.80). The SST diurnal warming effects from different daily observation times across the series of instruments have been corrected and are described in the publications by Jonasson et al., 2022

\r\n\r\nThe Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam) ", "links": [ { diff --git a/datasets/L3S_LEO_PM-STAR-v2.81_2.81.json b/datasets/L3S_LEO_PM-STAR-v2.81_2.81.json index 41bfcef8cd..a60d410b73 100644 --- a/datasets/L3S_LEO_PM-STAR-v2.81_2.81.json +++ b/datasets/L3S_LEO_PM-STAR-v2.81_2.81.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3S_LEO_PM-STAR-v2.81_2.81", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO-PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product ( https://doi.org/10.5067/GHLDY-3S281 ).

\r\n\r\nThis PM SST product is derived by collating individual satellite ACSPO L3U data ( https://doi.org/10.5067/GHVRS-3UO61, https://doi.org/10.5067/GHV20-3UO61 and https://doi.org/10.5067/GHN21-3U280 ). It covers from 2002-07-04 to present and is reported in 2 files daily, day and night, at 1:30am/pm local time. The SST is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 is updated from the previous v2.80 ( https://doi.org/10.5067/GHLPM-3SS28 ): (1) v2.81 includes 3 VIIRSs (NPP, N20, and N21 from 2023-03-19 - on); (2) Aqua MODIS SST included from 2002-07-04 to 2022-12-31; (3) Time series in v2.81 extended back to 2002-07-04 (from 2012-02-01 in v2.80); (4) recently uncovered VIIRS daytime SST drifts in NPP and N20 SSTs of approximately -0.1 K/decade mitigated.

\r\n\r\nThe Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam) ", "links": [ { diff --git a/datasets/L3_FT_Open_6.0.json b/datasets/L3_FT_Open_6.0.json index d5de0eacbd..8c3843e5d3 100644 --- a/datasets/L3_FT_Open_6.0.json +++ b/datasets/L3_FT_Open_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3_FT_Open_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMOS Level 3 Freeze and Thaw (F/T) product provides daily information on the soil state in the Northern Hemisphere based on SMOS observations and associated ancillary data. Daily products, in NetCDF format, are generated by the Finnish Meteorological Institute (FMI) and are available from 2010 onwards. The processing algorithm makes use of gridded Level 3 brightness temperatures provided by CATDS (https://www.catds.fr). The data is provided in the Equal-Area Scalable Earth Grid (EASE2-Grid), at 25 km x 25 km resolution. For an optimal exploitation of this dataset, please refer to the Resources section below to access Product Specifications, read-me-first notes, etc.", "links": [ { diff --git a/datasets/L3_SIT_Open_6.0.json b/datasets/L3_SIT_Open_6.0.json index 621fc4f728..60884e7f2f 100644 --- a/datasets/L3_SIT_Open_6.0.json +++ b/datasets/L3_SIT_Open_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L3_SIT_Open_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMOS Level 3 Sea Ice Thickness product, in NetCDF format, provides daily estimations of SMOS-retrieved sea ice thickness (and its uncertainty) at the edge of the Arctic Ocean during the October-April (winter) season, from year 2010 onwards. The sea ice thickness is retrieved from the SMOS L1C product, up to a depth of approximately 0.5-1 m, depending on the ice temperature and salinity. Daily maps, projected on polar stereographic grid of 12.5 km, are generated by the Alfred Wegener Institut (AWI). This product is complementary with sea ice thickness measurements from ESA's CryoSat and Copernicus Sentinel-3 missions.", "links": [ { diff --git a/datasets/L4WR_Open_3.0.json b/datasets/L4WR_Open_3.0.json index 8091338ed3..7e1723ae25 100644 --- a/datasets/L4WR_Open_3.0.json +++ b/datasets/L4WR_Open_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L4WR_Open_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMOS WRF product is available in Near Real Time to support tropical cyclones (TC) forecasts. \rIt is generated within 4 to 6 hours from sensing from the SMOS L2 swath wind speed products, in the so-called "Fix (F-deck)" format compatible with the US Navy's ATCF (Automated Tropical Cyclone Forecasting) System.\r\rThe SMOS WRF "fixes" to the best-track forecasts contain: the SMOS 10-min maximum-sustained winds (in knots) and wind radii (in nautical miles) for the 34 kt (17 m/s), 50 kt (25 m/s) and 64 kt (33 m/s) winds per geographical storm quadrants, and for each SMOS pass intercepting a TC in all the active ocean basins.", "links": [ { diff --git a/datasets/L4_SIT_Open_5.0.json b/datasets/L4_SIT_Open_5.0.json index be463bcf1d..4c408c2577 100644 --- a/datasets/L4_SIT_Open_5.0.json +++ b/datasets/L4_SIT_Open_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L4_SIT_Open_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMOS-CryoSat merged Sea Ice Thickness Level 4 product, in NetCDF format, is based on estimates from both the MIRAS and the SIRAL instruments, with a significant reduction in the relative uncertainty for the thickness of the thin ice. A weekly averaged product is generated every day by the Alfred Wegener Institut (AWI), by merging the weekly AWI CryoSat-2 sea ice product and the daily SMOS sea ice thickness retrieval. All grids are projected onto the 25 km EASE2 Grid, based on a polar aspect spherical Lambert azimuthal equal-area projection. The grid dimension is 5400 x 5400 km, equal to a 432 x 432 grid centered on the geographic Pole. Coverage is limited to the October-April (winter) period for the Northern Hemisphere, due to the melting season, from year 2010 onwards.", "links": [ { diff --git a/datasets/L7PAN128112_141101_R_1.json b/datasets/L7PAN128112_141101_R_1.json index b4679357f0..b9a118e48c 100644 --- a/datasets/L7PAN128112_141101_R_1.json +++ b/datasets/L7PAN128112_141101_R_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L7PAN128112_141101_R_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Georeferenced Landsat 7 image of the Prince Charles Mountains and Lambert Glacier. The image was captured on the 14th of November, 2001.", "links": [ { diff --git a/datasets/L7_ETM_SLC_OFF.json b/datasets/L7_ETM_SLC_OFF.json index bb4dd1f014..5c5c522936 100644 --- a/datasets/L7_ETM_SLC_OFF.json +++ b/datasets/L7_ETM_SLC_OFF.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "L7_ETM_SLC_OFF", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge.\n", "links": [ { diff --git a/datasets/LAB97_0.json b/datasets/LAB97_0.json index 310ffd99bc..5765d77710 100644 --- a/datasets/LAB97_0.json +++ b/datasets/LAB97_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAB97_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bio-optical validation observations were made on the CCGS Hudson in spring from 9 May to 11 June 1997 in the Labrador Sea. Stations were occupied along several sections between Labrador and Greenland with some locations revisited more than once during a cruise. The most heavily sampled SW-NE section from Hamilton Bank on the Labrador Shelf to Cape Desolation on the Greenland Shelf is the AR7 line of the World Ocean Circulation Experiment.", "links": [ { diff --git a/datasets/LACHYSIS.json b/datasets/LACHYSIS.json index 902258890b..6493430e08 100644 --- a/datasets/LACHYSIS.json +++ b/datasets/LACHYSIS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LACHYSIS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information System on Hydrology and Water resources in Latin America\nand the Caribbean countries.", "links": [ { diff --git a/datasets/LADSII_hydrographic_survey_1.json b/datasets/LADSII_hydrographic_survey_1.json index 51d3253668..b72ba023b9 100644 --- a/datasets/LADSII_hydrographic_survey_1.json +++ b/datasets/LADSII_hydrographic_survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LADSII_hydrographic_survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RAN Australian Hydrographic Service conducted an airborne hydrographic survey LADSII at Macquarie Island, February to March 1999. The areas surveyed included the northern coast between Handspike Point and Garden Bay and an area in the vicinity of Judge and Clerk Islets north of Macquarie Island.\nThe survey dataset, which includes metadata, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record.\nThe survey was lead by M.J.Sinclair.\n\nThese data are not suitable for navigation.", "links": [ { diff --git a/datasets/LAI_Africa_2325_1.json b/datasets/LAI_Africa_2325_1.json index b13c74b37b..9a9eaa92de 100644 --- a/datasets/LAI_Africa_2325_1.json +++ b/datasets/LAI_Africa_2325_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAI_Africa_2325_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size. Data from the MCD15A2H product were processed further to generate three layers including: a smoothed and gap filled LAI layer referred to as aggregate leaf area index and two additional layers processed to separate woody LAI (tree and shrubs) and herbaceous LAI (grass and forbs). The data include 31 MODIS 10-degree tiles and cover 2002 to 2022. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/LAI_Canada_816_1.json b/datasets/LAI_Canada_816_1.json index c4a4b5f7fc..64cf119fc0 100644 --- a/datasets/LAI_Canada_816_1.json +++ b/datasets/LAI_Canada_816_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAI_Canada_816_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).", "links": [ { diff --git a/datasets/LAI_VALERI_Canada_829_1.json b/datasets/LAI_VALERI_Canada_829_1.json index 1fe085dd1f..7ef5cf7af4 100644 --- a/datasets/LAI_VALERI_Canada_829_1.json +++ b/datasets/LAI_VALERI_Canada_829_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAI_VALERI_Canada_829_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provide local LAI maps for the Larose (Ontario) site in Canada. These derived maps may also be useful for validating other LAI maps over this same site given that the area is protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The dataset may also be useful for monitoring changes in the land surface. A complete description of producing the maps for the Larose site and the ground measurement campaign is provided in the companion document Larose2003FTReport.pdf.", "links": [ { diff --git a/datasets/LAI_Woody_Plants_1231_1.json b/datasets/LAI_Woody_Plants_1231_1.json index f85d609e3a..e37b32589b 100644 --- a/datasets/LAI_Woody_Plants_1231_1.json +++ b/datasets/LAI_Woody_Plants_1231_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAI_Woody_Plants_1231_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources.", "links": [ { diff --git a/datasets/LAI_surfaces_747_1.json b/datasets/LAI_surfaces_747_1.json index d0868ca185..c5d77d2c68 100644 --- a/datasets/LAI_surfaces_747_1.json +++ b/datasets/LAI_surfaces_747_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAI_surfaces_747_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. BigFoot was funded by NASA's Terrestrial Ecology Program.", "links": [ { diff --git a/datasets/LAMONT_ATL_0.json b/datasets/LAMONT_ATL_0.json index f29df8a379..aea771d08b 100644 --- a/datasets/LAMONT_ATL_0.json +++ b/datasets/LAMONT_ATL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAMONT_ATL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the South Atlantic Ocean (ATL) made by researchers at Columbia Universitys Lamont-Doherty Earth Observatory (LDEO).", "links": [ { diff --git a/datasets/LAMONT_GOM_0.json b/datasets/LAMONT_GOM_0.json index 49cd55092a..087a89a20e 100644 --- a/datasets/LAMONT_GOM_0.json +++ b/datasets/LAMONT_GOM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAMONT_GOM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Gulf of Mexico (GOM) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO).", "links": [ { diff --git a/datasets/LAMONT_SAB_0.json b/datasets/LAMONT_SAB_0.json index 51450a6241..c0b6af67a3 100644 --- a/datasets/LAMONT_SAB_0.json +++ b/datasets/LAMONT_SAB_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAMONT_SAB_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the South Atlantic Bight (SAB) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO).", "links": [ { diff --git a/datasets/LAMONT_SCS_0.json b/datasets/LAMONT_SCS_0.json index 781e857bec..afb95aeef1 100644 --- a/datasets/LAMONT_SCS_0.json +++ b/datasets/LAMONT_SCS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LAMONT_SCS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the South China Sea (SCS) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO).", "links": [ { diff --git a/datasets/LANDFIRE.json b/datasets/LANDFIRE.json index bc2bc03121..bb44802386 100644 --- a/datasets/LANDFIRE.json +++ b/datasets/LANDFIRE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDFIRE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LANDFIRE National products comprise a set of 20+ digital maps of vegetation composition and structure; wildland fuel (crown and surface); and current departure from simulated historical vegetation conditions. LANDFIRE National procedures integrate relational databases, remote sensing, systems ecology, gradient modeling, and landscape simulation to create consistent and comprehensive products that are standardized across the entire United States. LANDFIRE will deliver national products on an incremental basis through FY 2009. LANDFIRE national data layers can be obtained through The National Map. ", "links": [ { diff --git a/datasets/LANDMET_1.json b/datasets/LANDMET_1.json index a6714214e3..6c853f310e 100644 --- a/datasets/LANDMET_1.json +++ b/datasets/LANDMET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDMET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is a multi-variate data compilation that reconciles the variation scales of these multiple measurements from varies resources, merges and maps them into a comprehensive description of the near-surface atmospheric properties together with the land surface property variations on diurnal-to-decadal time scales. Many of these data products, especially those based on surface measurements, are spatially and/or temporally sparse or incomplete in coverage, so procedures were developed to fill missing values. \nThe data product is comprised of a sequence of daily global files, where quantities are mapped into 1.0-degree equivalent equal-area grid, with time sampling is reported at daily or 3-hourly intervals. The time period overlap among the products covers 10 years from 1998 to 2007.", "links": [ { diff --git a/datasets/LANDMET_ANC_SM_1.json b/datasets/LANDMET_ANC_SM_1.json index 9b6f8bfe60..c60d313611 100644 --- a/datasets/LANDMET_ANC_SM_1.json +++ b/datasets/LANDMET_ANC_SM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDMET_ANC_SM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary climatology soil porosity and wetlands coverage information were derived from the daily GEWEX fusion of satellite active and passive microwave measurements. These quantities are re-mapped to a 1.0 degree equal-area grid from the original 0.25 degree equal-angle map grid.", "links": [ { diff --git a/datasets/LANDMET_ANC_ST_1.json b/datasets/LANDMET_ANC_ST_1.json index d4c93d52cd..b94990415c 100644 --- a/datasets/LANDMET_ANC_ST_1.json +++ b/datasets/LANDMET_ANC_ST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDMET_ANC_ST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an ancillary product containing the land surface type information includes, for each map grid cell, the type and coverage fractions of the top three surface types present in the cell. The sum of these three fractions may not equal the land fraction if more types are present (usually a small difference). The product is a climatology data, no change in time, with spatial grid cell on equal-area mapping at 1.0-degree-equivalent.", "links": [ { diff --git a/datasets/LANDMET_ANC_TEIME_1.json b/datasets/LANDMET_ANC_TEIME_1.json index 83caf22484..11c18baf37 100644 --- a/datasets/LANDMET_ANC_TEIME_1.json +++ b/datasets/LANDMET_ANC_TEIME_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDMET_ANC_TEIME_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an ancillary product, climatology monthly mean and standard deviation, containing a number of emissivity data. They are broadband thermal IR emissivity from the ISCCP FD radiative fluxes product the emissivity at 10.5 microns from the ISCCP IREMISS product, and the microwave emissivities at four frequencies and two polarizations from the combined analysis of SSM/I and window IR based on ISCCP DX product. The data has spatial grid cell on equal-area mapping at 1.0-degree-equivalent.", "links": [ { diff --git a/datasets/LANDMET_ANC_TESA_1.json b/datasets/LANDMET_ANC_TESA_1.json index b56ea9f3e8..af7e2072f3 100644 --- a/datasets/LANDMET_ANC_TESA_1.json +++ b/datasets/LANDMET_ANC_TESA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDMET_ANC_TESA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an ancillary product containing the total effective surface albedo at solar wavelengths originated from the ISCCP FD radiative fluxes product, and the spectral albedos from the MODIS black-sky products. This is a monthly climatology data, with spatial grid cell on equal-area mapping at 1.0-degree-equivalent.", "links": [ { diff --git a/datasets/LANDSAT-16D-1_NA.json b/datasets/LANDSAT-16D-1_NA.json index e2cb1e71ea..06ea48424a 100644 --- a/datasets/LANDSAT-16D-1_NA.json +++ b/datasets/LANDSAT-16D-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT-16D-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Observation Data Cube generated from Landsat Level-2 product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 30 meters of spatial resolution, reprojected and cropped to BDC_MD grid Version 2 (BDC_MD V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.", "links": [ { diff --git a/datasets/LANDSAT.ETM.GTC_8.0.json b/datasets/LANDSAT.ETM.GTC_8.0.json index 2904a7cd12..759d966b59 100644 --- a/datasets/LANDSAT.ETM.GTC_8.0.json +++ b/datasets/LANDSAT.ETM.GTC_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT.ETM.GTC_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all the Landsat 7 Enhanced Thematic Mapper high-quality ortho-rectified L1T dataset (or L1Gt where not enough GCPs are available) over Kiruna, Maspalomas, Matera and Neustrelitz visibility masks. The Landsat 7 ETM+ scenes typically covers 185 x 170 km. A standard full scene is nominally centred on the intersection between a Path and Row (the actual image centre can vary by up to 100m). Each band requires 50MB (uncompressed), and Band 8 requires 200MB (panchromatic band with resolution of 15m opposed to 30m).", "links": [ { diff --git a/datasets/LANDSAT.TM.GTC_9.0.json b/datasets/LANDSAT.TM.GTC_9.0.json index 67ceadf54f..3bc64b328b 100644 --- a/datasets/LANDSAT.TM.GTC_9.0.json +++ b/datasets/LANDSAT.TM.GTC_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT.TM.GTC_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all the Landsat 5 Thematic Mapper high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Matera, Kiruna and Maspalomas visibility masks, as well as campaign data over Malindi, Bishkek, Chetumal, Libreville and O'Higgins. The acquired Landsat TM scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 100m). A full image is composed of 6920 pixels x 5760 lines and each band requires 40 Mbytes of storage space (uncompressed) at 30m spatial resolution in the VIS, NIR and SWIR as well as 120m in the TIR spectral range.", "links": [ { diff --git a/datasets/LANDSAT_FISHER_FEATURES_1.json b/datasets/LANDSAT_FISHER_FEATURES_1.json index df913c5238..c087297f84 100644 --- a/datasets/LANDSAT_FISHER_FEATURES_1.json +++ b/datasets/LANDSAT_FISHER_FEATURES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT_FISHER_FEATURES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fisher Massif Features Mapped from Mosaiced Pan Sharpened Landsat 7 Imagery.\n\nFEATURE MAPPING\nAn unsupervised classification was run on the final image to create an image with 12 distinct grey scale values. An automated feature extraction process was then performed in ERDAS to automatically select and extract areas of Rock and Snow. These areas were then compared with the true colour image mosaic and the boundaries were manually adjusted where necessary. All other feature types were mapped in ESRI's ArcGIS by manually tracing along and around features using a stream digitising technique. Relevant linear features were then polygonised.\n\nThe accuracy of the mapping was within +/- 30m for 95% of mapped features. This is a relative accuracy as there were no control points available to provide an absolute image orientation.\n\nThe datasets were converted to double precision ArcInfo Coverages in UTM Zone 42. ESRI?s ArcGIS Desktop and Workstation were used to process the vector data.\n\nThe Rock and Snow features which had been automatically extracted from the image were generalised using a distance of 40m with the bend simplify option of remove redundant vertices. The lines were then splined with a grain tolerance of 20m to smooth them.\n\nThe line feature coverages were cleaned with a tolerance of 0.1m. ArcEdit was then used to tidy the line work in the resultant coverage. Arcs were extended where required and overshoots were deleted. The resultant coverages were built for line and polygon topology.\n\nThe polygons were attributed using the Landsat image as a backdrop. The features were then extracted into separate feature coverages. The data were attributed according to the AADC Feature Type Catalogue. The individual feature coverages were built and checked for errors. A further visual check was then performed to check the features corresponded to the image.\n\nThe absolute accuracy of the features mapped is +/-280m, with a relative accuracy of +/-30m.\n\nThe individual feature coverages were projected to Geographicals (WGS84).\n\nAfter discussions with Mike Verrier, from the AAD on 30 April 2003, it was decided that ridgelines would only be picked up where there were major variations in the surface and not where there was a small hollow in which snow was settling.", "links": [ { diff --git a/datasets/LANDSAT_FISHER_MOSIMAGE_1.json b/datasets/LANDSAT_FISHER_MOSIMAGE_1.json index f5bde1edf9..11dbe63161 100644 --- a/datasets/LANDSAT_FISHER_MOSIMAGE_1.json +++ b/datasets/LANDSAT_FISHER_MOSIMAGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT_FISHER_MOSIMAGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mosaiced Pan Sharpened Landsat 7 Image of Fisher Massif.\n\nThe orientation parameters were derived from the Landsat 7 image header information to reference the images. The accuracy achieved from the data using the orientation parameters within the header information is within 250m. Multi spectral Landsat 7 images have a pixel resolution of 30m while the pan image has a resolution of 15m. These images were combined to produce a higher resolution colour image known as a pan sharpened image. To produce the pan sharpened image the following parameters were used: Method - Brovey Transform, Resampling Technique - Cubic Convolution, Data Type - Unsigned 8 bit. The pan sharpened image's histograms were then edited to reduce the overall contrast and to reveal feature detail in areas of deep shadow. The resulting images were mosaiced together. The pixel resolution of the mosaiced image is 15m. The image is in UTM Zone 42 (WGS84).\n\nSee the quality field for a more detailed explanation.", "links": [ { diff --git a/datasets/LANDSAT_SURFACE_REFLECTANCE_L4-5_TM.json b/datasets/LANDSAT_SURFACE_REFLECTANCE_L4-5_TM.json index 0dc498eee0..f430cea5e5 100644 --- a/datasets/LANDSAT_SURFACE_REFLECTANCE_L4-5_TM.json +++ b/datasets/LANDSAT_SURFACE_REFLECTANCE_L4-5_TM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT_SURFACE_REFLECTANCE_L4-5_TM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change but shoulder the burden of post-production processing to create applications-ready data sets.", "links": [ { diff --git a/datasets/LANDSAT_SURFACE_REFLECTANCE_L7_ETM.json b/datasets/LANDSAT_SURFACE_REFLECTANCE_L7_ETM.json index ed85e03fc3..92e4cd1953 100644 --- a/datasets/LANDSAT_SURFACE_REFLECTANCE_L7_ETM.json +++ b/datasets/LANDSAT_SURFACE_REFLECTANCE_L7_ETM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT_SURFACE_REFLECTANCE_L7_ETM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change but shoulder the burden of post-production processing to create applications-ready data sets.", "links": [ { diff --git a/datasets/LANDSAT_SURFACE_REFLECTANCE_L8_OLI_TIRS.json b/datasets/LANDSAT_SURFACE_REFLECTANCE_L8_OLI_TIRS.json index d019b174b4..9e92f0c843 100644 --- a/datasets/LANDSAT_SURFACE_REFLECTANCE_L8_OLI_TIRS.json +++ b/datasets/LANDSAT_SURFACE_REFLECTANCE_L8_OLI_TIRS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LANDSAT_SURFACE_REFLECTANCE_L8_OLI_TIRS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change but shoulder the burden of post-production processing to create applications-ready data sets.", "links": [ { diff --git a/datasets/LASE_AFWEX_1.json b/datasets/LASE_AFWEX_1.json index e87769231a..46ca888681 100644 --- a/datasets/LASE_AFWEX_1.json +++ b/datasets/LASE_AFWEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_AFWEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LASE_AFWEX data are Lidar Atmospheric Sensing Experiment water vapor and aerosol data measurements taken during ARM-FIRE (Atmospheric Radiation Measurement - First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment Water Vapor Experiment (AFWEX) Lidar Atmospheric Sensing Experiment (LASE) is an airborne autonomous DIfferential Absorption Lidar (DIAL) system developed to measure water vapor, aerosol, and cloud profiles. These measurements can be used in various atmospheric investigations, including studies of air mass modification, latent heat flux, the water vapor component of the hydrologic cycle, and atmospheric transport using water vapor as a tracer of atmospheric motions. The simultaneous measurement of aerosol and cloud distributions can provide important information on atmospheric structure and transport, and many meteorological parameters can also be inferred from these data.The LASE ARM-FIRE Water Vapor Experiment (AFWEX) field experiment was conducted from November 27 - December 15, 2000 at the ARM Southern Great Plains Cloud and Radiation Testbed (CART) Site site in Lamont, Oklahoma. The goals of the mission were to characterize and improve the accuracy of water vapor measurements under a wide variety of conditions. LASE airborne lidar produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude (6-8 km) down to the surface. AFWEX consisted of both airborne and ground-based instruments. The main result of AFWEX was to demonstrate that, with careful analysis, a core group of 5 instruments was accurate at the 5% level for the profile of water vapor.", "links": [ { diff --git a/datasets/LASE_CAMEX3_1.json b/datasets/LASE_CAMEX3_1.json index da937ffaf3..a9f29f5e69 100644 --- a/datasets/LASE_CAMEX3_1.json +++ b/datasets/LASE_CAMEX3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_CAMEX3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LASE_CAMEX3 data are Lidar Atmospheric Sensing Experiment water vapor and aerosol data measurements taken during the 3rd Convection and Moisture Experiment (CAMEX3).LASE (Lidar Atmospheric Sensing Experiment) is an airborne autonomous DIAL system developed to measure water vapor and aerosol profiles. The Convection And Moisture EXperiment (CAMEX-3) campaign was based at Patrick Air Force Base, Florida from 6 August - 23 September, 1998. CAMEX-3 successfully studied Hurricanes Bonnie, Danielle, Earl and Georges. CAMEX-3 collected data for research in tropical cyclone development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation.The CAMEX-3 study yields high spatial and temporal information of hurricane structure, dynamics, and motion. The LASE instrument's purpose in this experiment is to characterize the hurricane environment using water vapor and aerosol measurements for use as input to models and assimilation schemes and to fill in sonde data voids.", "links": [ { diff --git a/datasets/LASE_SGP97_1.json b/datasets/LASE_SGP97_1.json index a601293c47..978f814cc2 100644 --- a/datasets/LASE_SGP97_1.json +++ b/datasets/LASE_SGP97_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_SGP97_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LASE Southern Great Plains (SGP97) field experiment was conducted in Oklahoma during June-July 1997. SGP97 was a NASA EOS Interdisciplinary Science Investigation to validate soil moisture retrieval algorithms at satellite temporal and spatial scales using remote sensing moisture measurements from aircraft and in situ soil measurements. One of the major objectives of SGP97 was the study of the impact of soil moisture on the atmospheric boundary layer (ABL) development. To aid convective boundary layer (CBL) studies, LASE was deployed on the NASA P-3B aircraft along with other instruments. LASE (Lidar Atmospheric Sensing Experiment) airborne lidar produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude (6-8 km) down to the surface. Such profiles show the vertical context in which the SGP97 in situ and radiometric measurements are made, thus supporting the vertical extension of the in situ measurements and detecting any unsampled layers or inhomogeneities, which would impact the surface and airborne measurements.", "links": [ { diff --git a/datasets/LASE_SOLVE_1.json b/datasets/LASE_SOLVE_1.json index 1848fb08ef..1aff2eff2a 100644 --- a/datasets/LASE_SOLVE_1.json +++ b/datasets/LASE_SOLVE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_SOLVE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LASE_SOLVE is the Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the SAGE III Ozone Loss and Validation Experiment (SOLVE) data product. Data collection for this data set is complete.\r\n\r\nThe LASE SOLVE field experiment was conducted in the Arctic during November 1999 to March 2000 with the scientists based above the Arctic Circle at the airport in Kiruna, Sweden. Measurements of stratospheric composition over the Arctic were made using a large suite of instruments aboard several European aircraft, as well as on NASA's DC-8 and ER-2 aircraft. Additionally, balloons and ground-based instruments also took atmospheric readings and scientists gathered ozone-related data to use in validating measurements by the SAGE III instrument aboard the Russian Meteor-3 satellite. \r\n\r\nLASE airborne lidar produced measurements of aerosols and water vapor vertical profiles from the aircraft altitude (6-8 km) down to the surface. SOLVE was a measurement campaign designed to examine the processes which control polar to mid-latitude stratospheric ozone levels. The goal of SOLVE was for its results to expand the understanding polar ozone processes to provide greater confidence in ozone monitoring capabilities.", "links": [ { diff --git a/datasets/LASE_TARFOX_1.json b/datasets/LASE_TARFOX_1.json index 4fb94e25fa..d716940452 100644 --- a/datasets/LASE_TARFOX_1.json +++ b/datasets/LASE_TARFOX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_TARFOX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lidar Atmospheric Sensing Experiment (LASE) Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) data set was collected over the Western Atlantic Ocean in July 1996. The overall goal of TARFOX was to reduce uncertainties in the effects of aerosols on climate by determining the direct radiative impacts, as well as the chemical, physical, and optical properties, of the aerosols carried over the western Atlantic Ocean from the United States. LASE is an airborne autonomous DIAL system which produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude down to the surface. Such profiles show the vertical context in which the TARFOX in situ and radiometric measurements are made, thus supporting the vertical extension of the in situ measurements and detecting any unsampled layers or inhomogeneities, which would impact the airborne and satellite radiative flux measurements. Note that the LASE_TARFOX data set is also available under the TARFOX project as the TARFOX_LASE data set. The data files included in these two data sets are identical.", "links": [ { diff --git a/datasets/LASE_VALIDATION_1.json b/datasets/LASE_VALIDATION_1.json index f8058642d8..ed81a1e6b5 100644 --- a/datasets/LASE_VALIDATION_1.json +++ b/datasets/LASE_VALIDATION_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LASE_VALIDATION_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An extensive validation experiment was conducted in September 1995 from Wallops Island, Virginia, to evaluate the performance of the LASE (Lidar Atmospheric Sensing Experiment) system for the measurement of water vapor profiles under a wide range of atmospheric and solar background conditions. During this experiment, the LASE system was flown on a high-altitude (ER-2) aircraft on ten missions for a total of 60 hours. LASE measurements of tropospheric water vapor were compared with in situ measurements from balloons and aircraft that were flown under the ER-2 and with remote measurements from the ground and from aircraft. A high-altitude aircraft (Lear Jet) was equipped with two in situ hygrometers, and a medium to low altitude aircraft (C-130) had onboard the NASA Langley airborne water vapor DIAL system and two in situ hygrometers. Several radiosondes were launched during each LASE flight, and some of these sondes were part of a concurrent international radiosonde intercomparison campaign sponsored by the World Meteorological Organization. The NASA Goddard Scanning Raman lidar also provided nighttime water vapor profile measurements from the ground. During this field experiment, LASE was also used in a number of atmospheric case studies including measurements of Hurricane Luis, a coastal sea breeze development, a strong cold front, an upper level front, and cirrus clouds.", "links": [ { diff --git a/datasets/LATTE_0.json b/datasets/LATTE_0.json index 537b64cf2c..b4858a0d89 100644 --- a/datasets/LATTE_0.json +++ b/datasets/LATTE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LATTE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the New Jersey coast under the Lagrangian Transport and Transformation Experiment (LaTTE) in 2004 and 2005.", "links": [ { diff --git a/datasets/LB02_0.json b/datasets/LB02_0.json index ccdebed942..74309caf9f 100644 --- a/datasets/LB02_0.json +++ b/datasets/LB02_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LB02_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the research vessel Lady Basten in 2002 off the northeast Australia coast.", "links": [ { diff --git a/datasets/LC01_Boundaries_Ecuador_1057_1.json b/datasets/LC01_Boundaries_Ecuador_1057_1.json index 136537e381..907d7fa14e 100644 --- a/datasets/LC01_Boundaries_Ecuador_1057_1.json +++ b/datasets/LC01_Boundaries_Ecuador_1057_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Boundaries_Ecuador_1057_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the national and provincial boundaries of Ecuador as well as the boundaries of two national parks: the Cuyabeno Wildlife Reserve and the Yasuni National Park. There are four data files in ESRI ARCGIS Shapefile format within this data set. Each shape file has been compressed into a single compressed file (*.zip).", "links": [ { diff --git a/datasets/LC01_Cities_Communities_Roads_1058_1.json b/datasets/LC01_Cities_Communities_Roads_1058_1.json index a2a6048e0c..c5dcc017a5 100644 --- a/datasets/LC01_Cities_Communities_Roads_1058_1.json +++ b/datasets/LC01_Cities_Communities_Roads_1058_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Cities_Communities_Roads_1058_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the boundaries of the four major cities in the Northern Ecuadorian Amazon, the locations of primary communities in the colonist settlement area, and the locations of the road network, circa 2002. This area in northeastern Ecuador, know as the northern Oriente of Ecuador, borders the Andes Mountains and contains the headwaters of the Amazon River.The road network was originally digitized from 1:50,000 scale topographic maps from 1990. The surface attributes for the majority of the roads have been updated based on later remote sensing and field observations from 1999 and 2002. There are three compressed (*.zip) files with this data set.", "links": [ { diff --git a/datasets/LC01_Households_NEC_1052_1.json b/datasets/LC01_Households_NEC_1052_1.json index 278325ce0b..6b7d13bf66 100644 --- a/datasets/LC01_Households_NEC_1052_1.json +++ b/datasets/LC01_Households_NEC_1052_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Households_NEC_1052_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports summary statistics from socioeconomic and demographic surveys administered to the male and female heads of household on 767 farm plots. The surveys were performed in the provinces of Sucumbios and Napo/Orellana, in the northern Ecuadorian Amazon colonist settlements (Oriente) in 1999 (Pan and Bilsborrow, 2005). In addition, perception of, and opinions about local climate, soil quality, and environmental contamination were assessed for both the male and female heads of household. There are two comma-delimited (csv) ASCII data files. One file provides summary data from male respondents; the other data file provides summary responses from the female household survey (generally the spousal respondent). The original questionnaire forms are included as companion files (PDF format).", "links": [ { diff --git a/datasets/LC01_Hydrography_Edaphology_NEC_1059_1.json b/datasets/LC01_Hydrography_Edaphology_NEC_1059_1.json index 39488e37f6..d5f1c5598b 100644 --- a/datasets/LC01_Hydrography_Edaphology_NEC_1059_1.json +++ b/datasets/LC01_Hydrography_Edaphology_NEC_1059_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Hydrography_Edaphology_NEC_1059_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides map images of hydrographic, morphologic, and edaphic features for the northern Amazon Basin in eastern Ecuador. The hydrographic data are available at two scales based on the 1:50,000 and 1:250,000-scale topographic source maps that were generated in 1990 and 1993, respectively. Morphological and edaphological data were digitized from a 1:500,000 map published in 1983. There are 3 compressed (*.zip) data files with this data set.", "links": [ { diff --git a/datasets/LC01_LULC_Classes_Ecuador_ISA_1084_1.json b/datasets/LC01_LULC_Classes_Ecuador_ISA_1084_1.json index 9517ac11a3..9ed9b07a1f 100644 --- a/datasets/LC01_LULC_Classes_Ecuador_ISA_1084_1.json +++ b/datasets/LC01_LULC_Classes_Ecuador_ISA_1084_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_LULC_Classes_Ecuador_ISA_1084_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area. These areas are in the Northern Ecuadorian Amazon, in the area known as the northern Oriente of Ecuador. The resolution of the data is 30 meters. There are 12 image files (.tif) with this data set. ", "links": [ { diff --git a/datasets/LC01_Landsat_1187_1.json b/datasets/LC01_Landsat_1187_1.json index 3d18665735..ca732dd98f 100644 --- a/datasets/LC01_Landsat_1187_1.json +++ b/datasets/LC01_Landsat_1187_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Landsat_1187_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a time series of early Landsat-4 MSS satellite imagery as well as Landsat-5 TM and Landsat-7 ETM+ satellite imagery of the northern Ecuadorian Amazon. Some of the TM and ETM images have been georectified to UTM Zone 18 South, WGS84 Datum. Not all of the images have been georectified. ", "links": [ { diff --git a/datasets/LC01_SRTM_DEM_90m_NEC_1083_1.json b/datasets/LC01_SRTM_DEM_90m_NEC_1083_1.json index 75466daf1d..f1fa6096c8 100644 --- a/datasets/LC01_SRTM_DEM_90m_NEC_1083_1.json +++ b/datasets/LC01_SRTM_DEM_90m_NEC_1083_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_SRTM_DEM_90m_NEC_1083_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides 90-meter resolution Digital Elevation Model data used in the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects. The topographic data were derived from Shuttle Radar Topography Mission (SRTM) C-band and X-band interferometric synthetic aperture radars (IFSARs) data that were acquired over 80% of Earth's land mass in February 2000. This data set includes one image in GeoTiff format that is a subset for the Northern Ecuadorian Amazon region.", "links": [ { diff --git a/datasets/LC01_Topography_Ecuador_ISA_1082_1.json b/datasets/LC01_Topography_Ecuador_ISA_1082_1.json index a0b63d9293..a02d9d22d9 100644 --- a/datasets/LC01_Topography_Ecuador_ISA_1082_1.json +++ b/datasets/LC01_Topography_Ecuador_ISA_1082_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC01_Topography_Ecuador_ISA_1082_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains topographic/geomorphological data associated with the four Intensive Study Areas (ISAs) in the Northern Ecuadorian Amazon (northern Oriente) that are part of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects study. Study area boundaries were developed directly from 1:50,000 topographical maps. Point elevation features and 20-meter elevation contours were digitized from these same maps. Digital elevation models (DEMs) were derived from these elevation data and, in turn, terrain aspect and terrain slope were derived from the digital elevation models. Only boundary data were provided for the southwestern ISA. These data are provided in ESRI shapefile format and GeoTiff. There are six compressed (*.zip) data files with this data set.", "links": [ { diff --git a/datasets/LC02_Forest_Flammability_Acre_1089_1.json b/datasets/LC02_Forest_Flammability_Acre_1089_1.json index ab956a3878..a558a02ab3 100644 --- a/datasets/LC02_Forest_Flammability_Acre_1089_1.json +++ b/datasets/LC02_Forest_Flammability_Acre_1089_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_Forest_Flammability_Acre_1089_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of controlled burns conducted to assess the flammability of mature forests on the Catuaba Experimental Farm of the Federal University of Acre - Rio Branco, Acre, Brazil. Controlled burns were conducted in 1998, and the rate of fire spread was calculated based on the duration of the fire and the measured extent of the burned area. Environmental variables measured included type of forest, canopy openness, leaf area index, number of days without rainfall, precipitation, height of litter, litter humidity, brushwood humidity, amount of water in the ground, air temperature, and relative humidity. Results from 50 fires set in 1998 are reported. There is one comma-delimited data file with this data set.These data are part of a larger study reported in the thesis by Elsa Renee Huamon Mendoza, Susceptibility of primary forest to fire in 1998 and 1999: A case study in Acre, south-eastern Amazonia, Brazil. The thesis, in Portuguese, is included as a companion file with this data set.", "links": [ { diff --git a/datasets/LC02_GOES8_Hotpixel_Acre_1092_1.json b/datasets/LC02_GOES8_Hotpixel_Acre_1092_1.json index 60640beaae..04fe2608e3 100644 --- a/datasets/LC02_GOES8_Hotpixel_Acre_1092_1.json +++ b/datasets/LC02_GOES8_Hotpixel_Acre_1092_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_GOES8_Hotpixel_Acre_1092_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides hot pixel data, as an indicator of fires that were detected by the GOES-8 satellite for the state of Acre, Brazil. Image data were collected for extended periods over the course of 3 years (1998, 2000 and 2001). Data were filtered to select only pixels identified and processed by the GOES-8 Automated Biomass Burning Algorithm (ABBA), where estimates of sub-pixel fire characteristics including size and temperature were able to be determined. There are three comma-delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/LC02_MAP_Fire_Indicators_1044_1.json b/datasets/LC02_MAP_Fire_Indicators_1044_1.json index 8af7f7d294..9de82995ca 100644 --- a/datasets/LC02_MAP_Fire_Indicators_1044_1.json +++ b/datasets/LC02_MAP_Fire_Indicators_1044_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_MAP_Fire_Indicators_1044_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides hot pixel data, as an indicator of fires, that were detected by various satellites in the tri-national MAP region (Madre de Dios-Peru, Acre-Brazil, and Pando-Bolivia) in 2003, 2004, 2005, and 2006. Data from the following satellites/sensors were compiled: NOAA-12, NOAA-14, NOAA-15, and NOAA-16, which transports the AVHRR sensor; GOES-8 and GOES- 12, which transports the GOES Imager; and AQUA and TERRA, both which transport the MODIS sensor. These data were made available by the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC) of the Instituto Nacional de Pesquisas Espaciais (INPE) via the internet (http://sigma.cptec.inpe.br/queimadas/). This data set contains 12 comma-delimited ASCII data files.Hot pixel data from satellites can be used as an indicator of fires and for the understanding of fire frequency in remote areas. The publication by Vasconcelos and Brown, 2007, which has been included as a companion file, describes the application of these data in the MAP region. In addition to the the hot pixel data, each observation has a derived vegetation type, susceptibility to fire, recent and past precipitation amounts, and a calculated fire risk value. These data are described in the Fire Risk Factor companion file, by Alberto W. Setzer and Raffi A. Sismanoglu, Version 5, February 2006. ", "links": [ { diff --git a/datasets/LC02_Meteorology_Acre_1091_1.json b/datasets/LC02_Meteorology_Acre_1091_1.json index 8f24c9abfd..489af9027b 100644 --- a/datasets/LC02_Meteorology_Acre_1091_1.json +++ b/datasets/LC02_Meteorology_Acre_1091_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_Meteorology_Acre_1091_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides meteorological measurements collected from 3 different meteorological stations within a radius of 8 km in Rio Branco, Acre Brazil, for the periods of June of 1970 to 1974, December of 1974 to 1980, and May of 1980 thru May 31, 2001. Daily average values for rainfall, relative humidity, evapotranspiration, maximum and minimum temperature, pressure, wind direction and speed, solar radiation, and cloud cover are reported. There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/LC02_PermPlot_Acre_1237_1.json b/datasets/LC02_PermPlot_Acre_1237_1.json index 9321d65c4a..3f21cb3562 100644 --- a/datasets/LC02_PermPlot_Acre_1237_1.json +++ b/datasets/LC02_PermPlot_Acre_1237_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_PermPlot_Acre_1237_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides diameter at breast height (DBH) measurements for 1,063 trees located at the Catuaba Experimental Farm, and 812 trees located in the Humaita Forest Reserve. Both sites are in the state of Acre, southwest Amazonia, Brazil. Measurements were made on individuals with DBH between 10 and 35 cm and individuals with DBH > 35 cm. The Catuaba Experimental Farm is part of a forest fragment of approximately 800 ha. The Humaita Forest Reserve is located in a 1,500-ha forest band with dominant bamboo characteristic. Ten-ha areas were inventoried at both sites. There is one data file in comma-delimited (.csv) format with this data set. There is also one companion data file with supplemental Catuaba site tree height and biomass data.", "links": [ { diff --git a/datasets/LC02_Streams_Acre_1243_1.json b/datasets/LC02_Streams_Acre_1243_1.json index 4b787d2154..f1ab0b91bf 100644 --- a/datasets/LC02_Streams_Acre_1243_1.json +++ b/datasets/LC02_Streams_Acre_1243_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_Streams_Acre_1243_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides coordinates for points at the mouth of tributaries of the Acre River in the Tri-national River Basin in South America. Three Global Positioning System (GPS) readings were made at the outlet of each tributary and the average of the three readings is reported. The Tri-national River Basin is located in the tri-national frontier region of Madre de Dios, Peru, Acre, Brazil, and Pando, Bolivia (known as the MAP region). The MAP region is approximately 300,000 km2. The Acre River flows through Brazil, Bolivia, and Peru. Data on the basin drainage network from the Digital Elevation Model (DEM) Shuttle Radar Topography Mission (SRTM) was obtained as a source of information for the border areas. The GPS readings were part of an assessment of the reliability of the DEM/SRTM drainage network data (Maldonado and Brown, 2003). There is one data file in comma-delimited (.csv) format and one compamion file (.pdf) with this data set. DATA QUALITY STATEMENT: This data set provides GPS coordinates only and is not associated with any additional measurements. There is no associated research documentation. ", "links": [ { diff --git a/datasets/LC02_Water_Table_Acre_1062_1.json b/datasets/LC02_Water_Table_Acre_1062_1.json index 2aa99e7658..ea93b62832 100644 --- a/datasets/LC02_Water_Table_Acre_1062_1.json +++ b/datasets/LC02_Water_Table_Acre_1062_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC02_Water_Table_Acre_1062_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports bi-weekly or monthly depth-to-water measurements for three wells located in a ~1,500 ha forest fragment on the Catuaba Experimental Farm, which is the property of the Federal University of Acre, Brazil. Data were collected between February 1999 and December 2004. There is one comma-delimited ASCII data file with this data set.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The depth-to-water measurements for the three wells lack ground surface elevation reference points, therefore, the groundwater table elevation for the site cannot be determined. The depth-to-water measurements are of limited use unless paired with other site data for precipitation, tree growth, etc.", "links": [ { diff --git a/datasets/LC03_Hypsography_DEM_1094_1.json b/datasets/LC03_Hypsography_DEM_1094_1.json index f469fd2dca..be59d185a3 100644 --- a/datasets/LC03_Hypsography_DEM_1094_1.json +++ b/datasets/LC03_Hypsography_DEM_1094_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC03_Hypsography_DEM_1094_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed *.zip files containing ArcInfo shape files and 3 GeoTIFFS.This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978.The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS:The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data. - Any use of the derived data is not recommended because the results have not been validated.- However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.", "links": [ { diff --git a/datasets/LC03_SAR_LC_Biomass_1093_1.json b/datasets/LC03_SAR_LC_Biomass_1093_1.json index 2f541dd969..47f68a2c29 100644 --- a/datasets/LC03_SAR_LC_Biomass_1093_1.json +++ b/datasets/LC03_SAR_LC_Biomass_1093_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC03_SAR_LC_Biomass_1093_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three related land cover products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include (1) orthorectified JERS-1 and RadarSat images, (2) land cover classifications derived from the SAR data, and (3) biomass estimates in tons per hectare based on the land cover classification. There are 12 image files (.tif) with this data set.Orthorectified JERS-1 and RadarSat images are provided as GeoTIFF images - one file for each study area.For the Manaus and Tapajos sites: The images are orthorectified at 12.5-meter resolution and then re-sampled at 25-meter resolution.For the Rondonia and Rio Branco sites: The images from 1978 are orthorectified at 25-meter resolution and then re-sampled at 90-meter resolution. Each GeoTIFF file contains 3 image channels: - 2 L-band JERS-1 data in Fall and Spring seasons and - 1 C-band RadarSat data.Land cover classifications are based on two JERS-1 images and one RadarSat image and provided as GeoTIFFs - one file for each study area. Four major land cover classes are distinguished: (1) Flat surface; (2) Regrowth area; (3) Short vegetation; and (4) Tall vegetation. The biomass estimates in tons per hectare are based on the land cover classification results and are reported in one GeoTIFF file for each study area.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.KNOWN PROBLEMS: The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data.Any use of the derived data is not recommended because the results have not been validated. However, the DEM and vectors (related data set), and orthorectified SAR data can be used if the user understands how these were produced and accepts the limitations. ", "links": [ { diff --git a/datasets/LC04_IBIS_Model_1139_1.json b/datasets/LC04_IBIS_Model_1139_1.json index 720b5f24e9..ac4fdc8fb0 100644 --- a/datasets/LC04_IBIS_Model_1139_1.json +++ b/datasets/LC04_IBIS_Model_1139_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC04_IBIS_Model_1139_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The provided data were generated by the Integrated BIosphere Simulator (IBIS) terrestrial ecosystem model using data from the East Anglia Climate Research Unit climate record for the years 1921-1998. Data are included for the annual net ecosystem exchange of the surface, microbial respiration, root respiration, total soil respiration, soil moisture, leaf area index, drainage, and surface and subsurface runoff, for the entire Amazon and Tocantins basins. The data files are provided in netCDF format and standard ESRI ARCGIS ARC/INFO ASCIIGRID format. The netCDF files consist of either annual or monthly means from 1921 to 1998. The ASCII files are available only for the annual mean files.", "links": [ { diff --git a/datasets/LC04_Land_Use_5min_906_1.json b/datasets/LC04_Land_Use_5min_906_1.json index 74ade9d9e8..f5f2f4c98f 100644 --- a/datasets/LC04_Land_Use_5min_906_1.json +++ b/datasets/LC04_Land_Use_5min_906_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC04_Land_Use_5min_906_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 5-minute land use maps for agricultural activity in Amazonia. The data set was produced by the statistical fusion of agricultural census data from Brazil,Columbia, Bolivia, and Peru with the land cover data product from the Global Land Cover Facility. These land use maps indicate the estimated total amount of cropland and pasture (natural and planted) for the Amazon and Tocantins River basins in 1995 and 1980 and are suitable for use in models or other similar purposes. Data are provided in the netCDF format and the ARC/INFO GRID ASCII format.The 1995 data were generated from a fusion of agricultural census data and a satellite classification, and are described in Cardille, Foley, and Costa (2002). The fusion technique merges agricultural census data from Brazil, Columbia, Peru, and Bolivia with land cover data from the University of Maryland Global Land Cover Facility 1-km classification. This technique was used to derive an estimate of the mid-1990s total agriculture surface for the region, which was then apportioned according to agriculture census data into cultivated area, natural pasture, and planted pasture.The 1980 maps, including only the Brazilian portion of the Amazon/Tocantins river drainage basins, were created by scaling the mid-1990s snapshots backward in time using the relative increase or decrease in agriculture, as derived from mid-1980s census data and United Nations Food and Agriculture Organization (FAO) data (Cardille and Foley, 2003).", "links": [ { diff --git a/datasets/LC04_Macrohydrology_1048_1.json b/datasets/LC04_Macrohydrology_1048_1.json index fc328d861e..063214ab5b 100644 --- a/datasets/LC04_Macrohydrology_1048_1.json +++ b/datasets/LC04_Macrohydrology_1048_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC04_Macrohydrology_1048_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides continental-scale hydrological river flow routing parameter data for the Amazon and Tocantins River basins at 5 minute (~9 km) resolution (Costa et al., 2002). The data set includes four geospatial data files (in standard ESRI Arc/Info ASCII Grid format): (1) the river network (flow direction); (2) sinuosity of each of the main rivers, measured at 111 river sections in the basins; (3) depth to the water table; and (4) transmissivity of the aquifer. The latter two parameters were derived from measurements taken at 81 wells located throughout the basins. There is also a compressed file (*.zip) which contains the time series of monthly mean river discharge and long-term climatology (monthly mean) for the period of record at each of 122 fluviometric stations located throughout the basin. These files are provided in ASCII common-separated (.csv) format. Also included in this data set are two data files in *.csv format; one containing river discharge station location and drainage area information and one containing original well data.", "links": [ { diff --git a/datasets/LC04_THMB-HYDRA_Model_1138_1.json b/datasets/LC04_THMB-HYDRA_Model_1138_1.json index f5eb8a3cc3..b204ef9328 100644 --- a/datasets/LC04_THMB-HYDRA_Model_1138_1.json +++ b/datasets/LC04_THMB-HYDRA_Model_1138_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC04_THMB-HYDRA_Model_1138_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The model output data provided were generated by the THMB 1.2 (Terrestrial Hydrology Model with Biogeochemistry) model which simulates the flow of water through groundwater systems, rivers, lakes and wetlands. The model operates at a 5-minute latitude-by-longitude grid with a 1-hour time step and requires as boundary conditions: topography, evaporation from water surfaces, surface runoff, base flow, and precipitation. Data are included for the mean monthly simulated water height above flood stage, mean monthly simulated river discharge, and mean monthly inundated area for the period 1939-1998 for the entire Amazon and Tocantins River basins. There are three netCDF files (.nc) with this data set.", "links": [ { diff --git a/datasets/LC05_BDFF_Biomass_Soils_1040_1.json b/datasets/LC05_BDFF_Biomass_Soils_1040_1.json index 876f79ff8c..cfef7baf38 100644 --- a/datasets/LC05_BDFF_Biomass_Soils_1040_1.json +++ b/datasets/LC05_BDFF_Biomass_Soils_1040_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC05_BDFF_Biomass_Soils_1040_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports (1) total aboveground dry biomass based on detailed estimates of all live and dead plant material, (2) results from repeated surveys of aboveground biomass allowing the calculation of above-ground productivity, and (3) soil chemical and physical characteristics for 50 1-ha plots of undisturbed and fragmented central Amazonian rainforest within the Biological Dynamics of Forest Fragments Project (BDFFP) study area. The reported data are plot-level summaries based on plant and soil samples and measurements obtained over the 1997 to 2001 timeframe. The BDFFP study area is an experimentally fragmented landscape spanning 1,000 km2 located 70-90 km north of Manaus, Amazonas, Brazil. For additional information about the BDFFP and research conducted at the site, please visit their web site at http://pdbff.inpa.gov.br/index.html.There are six comma-separated ASCII data files with this data set.", "links": [ { diff --git a/datasets/LC07_Airborne_Rasters_1274_1.json b/datasets/LC07_Airborne_Rasters_1274_1.json index 6399acb8df..6f16ee89bb 100644 --- a/datasets/LC07_Airborne_Rasters_1274_1.json +++ b/datasets/LC07_Airborne_Rasters_1274_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Airborne_Rasters_1274_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes high-resolution geocoded mosaics derived from the Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere (LBA) Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover in the Amazon Basin conducted by the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite). Digital camcorder systems were installed in a Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1-m resolution for wetlands, forests, savannas, and human-impacted areas. Geocoded mosaics were generated by processing the aerial videography into GeoTIFF format, maximizing its usefulness for environmental monitoring applications. Other applications of the VOAM99 videography include acquisition of ground control points for image geolocation, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling a high-resolution validation data set for land cover mapping in remote, cloud-covered regions. ", "links": [ { diff --git a/datasets/LC07_Airborne_Videography_1272_1.json b/datasets/LC07_Airborne_Videography_1272_1.json index 7b7d57854e..0073c5c32b 100644 --- a/datasets/LC07_Airborne_Videography_1272_1.json +++ b/datasets/LC07_Airborne_Videography_1272_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Airborne_Videography_1272_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents georeferenced digital video files from Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover with the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite) mosaics of the Amazon basin. Digital camcorder systems were installed in a Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1-m (wide-angle format) and 10-cm (zoom format) for wetlands, forests, savannas, and human-impacted areas. Other applications of the VOAM videography include acquisition of ground control points for image geolocation, creation of a high-resolution geocoded mosaic of a forest study area, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling a high-resolution validation data set for land cover mapping in remote, cloud-covered regions.", "links": [ { diff --git a/datasets/LC07_Amazon_Wetlands_1284_2.json b/datasets/LC07_Amazon_Wetlands_1284_2.json index f6e70a3d77..ac69d43529 100644 --- a/datasets/LC07_Amazon_Wetlands_1284_2.json +++ b/datasets/LC07_Amazon_Wetlands_1284_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Amazon_Wetlands_1284_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a map of wetland extent, vegetation type, and dual-season flooding state of the entire lowland Amazon basin. As described in Hess et al. (2015), the classified image was derived from the Global Rain Forest Mapping Project (GRFM) Amazon mosaics (Rosenqvist et al 2000; Siqueira et al. 2002) acquired during Oct.-Nov. 1995 and May-June 1996, corresponding to the low-flood and high-flood seasons for much of the central Amazon. Hess et al. (2003) mapped wetland extent, vegetative cover, and flooding state for an 18 degree \u00d7 8 degree portion of the central Amazon using the dual-season GRFM mosaics. This study extends the previous wetlands mapping to report the first validated estimate of wetland extent, cover, and flooding for the lowland Amazon basin. A wetlands mask was created by segmentation of the mosaics and clustering of the resulting polygons; a rules set was then applied to classify wetland areas into five land cover classes and two flooding classes using dual-season backscattering values. The mapped wetland area of 8.4 \u00d7 105 km2 is equivalent to 14 % of the total basin area (5.83 \u00d7 106 km2) and 17% of the lowland basin (5.06 \u00d7 106 km2). The mapped flooding extent is representative of average high and low-flood conditions for latitudes north of 6 degrees S; flooding conditions were less well captured for the southern part of the basin. The wetlands map is provided in GeoTIFF format using two coordinate systems: unprojected (Geographic) with pixel size of 3 arcseconds, and Albers Conical Equal Area with pixel size of 100 m.", "links": [ { diff --git a/datasets/LC07_Bathymetry_Curuai_999_1.json b/datasets/LC07_Bathymetry_Curuai_999_1.json index aa53ec1443..2dd90aa10c 100644 --- a/datasets/LC07_Bathymetry_Curuai_999_1.json +++ b/datasets/LC07_Bathymetry_Curuai_999_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Bathymetry_Curuai_999_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The bathymetry data provided represent a continuous surface of interpolated point measurements of depth values of Lago Curuai, an Amazon River floodplain lake, upstream from Santarem, Para, Brazil, from measurements made in June of 2004. The first product contains the actual depth values (in meters) of the interpolated continuous surface saved as real numbers in both ENVI and GeoTIFF formats. Also available is a color scaled depth GeoTIFF image which has an embedded color scale bar. This secondary file is meant only for viewing but has the unique advantage of being a GeoTIFF file. Therefore, this map can be a background image with other projected files of interest in the area. Data provided in this data set were used to develop a methodology for processing and applying high resolution bathymetric data acquired with a Lowrance-480M ecosounder in the Amazon floodplain. This research was supported by the addition of Landast/TM images for planning and executing the survey. 4600 km of transects were processed semi-automatically and integrated into a georeferenced database. A digital elevation model with 15 m horizontal resolution and 1 cm vertical resolution was generated for the floodplain. The changes in inundated area and volume of water on the floodplain were estimated. Regression models were constructed to predict flood area and water stored volume from water level. The results of this research show that water level and flooded area mapped from images are good enough for estimating water stored volume in the Lago Grande de Curuai (Barbosa et al., 2006).", "links": [ { diff --git a/datasets/LC07_Biomass_LGrande_1127_1.json b/datasets/LC07_Biomass_LGrande_1127_1.json index 92fbc24bcc..f45e106b48 100644 --- a/datasets/LC07_Biomass_LGrande_1127_1.json +++ b/datasets/LC07_Biomass_LGrande_1127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Biomass_LGrande_1127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports measurements of aquatic macrophyte biomass, phenology, leaf characteristics, and length and diameter of stems of both submerged and unsubmerged macrophytes. Data were collected from sites in the Monte Alegre Lake region on the eastern Amazon River floodplain in Para, Brazil. Ten field surveys were made at approximately monthly intervals from December 2003 to November 2004. There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/LC07_Curuai_chl_1134_1.json b/datasets/LC07_Curuai_chl_1134_1.json index 939d074b01..aff804f13e 100644 --- a/datasets/LC07_Curuai_chl_1134_1.json +++ b/datasets/LC07_Curuai_chl_1134_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Curuai_chl_1134_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports (1) concentrations of total, organic, and inorganic suspended solids; dissolved inorganic, and organic carbon; chlorophyll-a and (2) measurements of turbidity, ph, temperature, transparency, conductivity, and calculated carbon dioxide (CO2) in water samples collected from Lago Curuai (Lake Curuai), in the floodplain of the Amazon River south of Obidos, Para, Brazil. Approximately 70 stations were sampled during four phases of the hydrological cycle: receding (September 2003), low (November 2003), rising (February 2004), and high water (June 2004). There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/LC07_Lake_Chlorophyll_MODIS_1000_1.json b/datasets/LC07_Lake_Chlorophyll_MODIS_1000_1.json index f7b45d20b2..baa756ac2f 100644 --- a/datasets/LC07_Lake_Chlorophyll_MODIS_1000_1.json +++ b/datasets/LC07_Lake_Chlorophyll_MODIS_1000_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Lake_Chlorophyll_MODIS_1000_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains chlorophyll concentration maps of the Amazon River floodplain region from Parintins (Amazonas) to Almeirim (Para). These chlorophyll fraction maps were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09) for 19 months from April 2002 to December 2003. The study was conducted in a floodplain reach upstream from Santarem, Para, in order to assess seasonal changes in phytoplanktonic chlorophyll-a distributions in the floodplain Lake Curuai. MODIS reflectance data were acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). Chlorophyll maps were derived and used to compute the weighted average of chlorophyll concentration from MODIS images in the region. Field measurements of suspended inorganic matter and chlorophyll-a in Lake Curuai were made almost concurrently with satellite overpasses (Barbosa, 2005). The images and the estimated chlorophyll concentrations were compared to measured chlorophyll concentrations at control points for different hydrological states. This data set may be applied to better understand the seasonal dynamics of primary production of the Amazon floodplains. The maps of chlorophyll-a concentration may be used to model spatial and temporal variations of primary production in this region.The monthly chlorophyll-a maps are provided as GeoTIFF files. There are two formats: (1) color-mapped pixels and (2) pixels as chlorophyll-a concentrations. These latter images are not intended for browsing. These images have pixel values that are the chlorophyll-a concentration in mg/m3 and need to be download and opened in GIS software.", "links": [ { diff --git a/datasets/LC07_Lake_Nutrient_Sediments_1050_1.json b/datasets/LC07_Lake_Nutrient_Sediments_1050_1.json index 23dc5e99eb..64026be0ab 100644 --- a/datasets/LC07_Lake_Nutrient_Sediments_1050_1.json +++ b/datasets/LC07_Lake_Nutrient_Sediments_1050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Lake_Nutrient_Sediments_1050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports lake sediment texture and porosity, carbon (C), nitrogen (N), and phosphorus (P) content of surficial sediments, 210Pb-derived nutrient accumulation rates in sediments, and burial rates of C, N, and P in sediments at eleven locations in Lake Calado, Amazonas, Brazil. Field samples were collected between February 1982 and August 1984. There are eight comma-delimited ASCII data files with this data set. ", "links": [ { diff --git a/datasets/LC07_Monthly_Inundated_Areas_1049_1.json b/datasets/LC07_Monthly_Inundated_Areas_1049_1.json index d89862d438..fb49e0c116 100644 --- a/datasets/LC07_Monthly_Inundated_Areas_1049_1.json +++ b/datasets/LC07_Monthly_Inundated_Areas_1049_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Monthly_Inundated_Areas_1049_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports monthly mean inundation areas (square kilometers) for four cover classes of Central Amazon wetlands habitat: Open water (OW), river channel (RC) class, macrophyte (MA) class, and a flooded forest (FF) class, which also incorporates a flooded shrub class. The full study area was a 1.77 million km2 quadrant covering the Central Amazon Basin. Inundation was also calculated from three subsets of this area: (1) covering only the Amazon/Solimoes River mainstem and (2) the Eastern and (3) the Western halves of this mainstem area. There is one comma-delimited ASCII data file in this data set.", "links": [ { diff --git a/datasets/LC07_Reservoir_GHG_1143_1.json b/datasets/LC07_Reservoir_GHG_1143_1.json index c13de5e6d3..ed4ff9ce35 100644 --- a/datasets/LC07_Reservoir_GHG_1143_1.json +++ b/datasets/LC07_Reservoir_GHG_1143_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Reservoir_GHG_1143_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides flux measurements of methane (CH4) and carbon dioxide (CO2) from surface waters to the atmosphere. It also provides CH4, CO2, and oxygen (O2) concentrations of surface water, and concentrations measured at several depths of the Balbina Reservoir in the central Amazon Basin, Amazonas, Brazil. The Balbina Reservoir was formed by impounding the Uatuma River in 1987. Reservoir surface water samples, bottom water samples, and gas samples from static flux enclosures were collected at 10 to 14 sites at monthly intervals between April and November of 2005, and 6 times in February, 2006. Water samples to determine the vertical profiles of temperature, dissolved O2, CH4, and CO2 were collected during the rainy and dry seasons immediately above the dam between September 2004 and February 2006. Water samples were collected downstream from the dam from July 2004 - November 2005 for analysis of CH4 and CO2 concentrations.There are three comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/LC07_Reservoir_Methane_Emissions_1047_1.json b/datasets/LC07_Reservoir_Methane_Emissions_1047_1.json index 7fa8b1ebe9..1ffb5d9393 100644 --- a/datasets/LC07_Reservoir_Methane_Emissions_1047_1.json +++ b/datasets/LC07_Reservoir_Methane_Emissions_1047_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Reservoir_Methane_Emissions_1047_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports methane (CH4) fluxes at the water-air interface and concentrations and isotopic signals of CH4 in the bubbles stirred up from the sediment in Tucurui and Samuel reservoirs in 2000 and 2001. Tucurui (deep) reservoir is located near Belem city in the Tocantins-Araguaia basin in the eastern Amazon. Samuel (shallow) reservoir is situated near Porto Velho city in the Jamari River, a tributary of the Madeira River in the western Amazon. Field samples were collected between June 2000 and September 2001. There are two comma-delimited ASCII data files in this data set. This study was carried out to identify differences in methane cycling between deep and shallow reservoirs (Lima, 2005). Isotopic and concentration analyses of methane in bubbles, dissolved in the water column, and emitted to the atmosphere demonstrate that water depth is critical regarding methane emissions from hydroreservoirs in the Amazon. Methanotrophic activities are greater in Tucurui (deep) while light isotopic methane is directly released from Samuel (shallow). Therefore, the methanotrophic layer of the deep reservoir is more efficient in oxidizing methane before reaching the atmosphere, since the quantity of methane in the sediments of the reservoirs were equivalent.", "links": [ { diff --git a/datasets/LC07_SMMR_Inundated_Area_1051_1.json b/datasets/LC07_SMMR_Inundated_Area_1051_1.json index f0b78fc3be..4d62aaa972 100644 --- a/datasets/LC07_SMMR_Inundated_Area_1051_1.json +++ b/datasets/LC07_SMMR_Inundated_Area_1051_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_SMMR_Inundated_Area_1051_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the monthly record of inundated area, in square km, for six floodplain and open water regions in South America. The following floodplains were analyzed: (1) mainstem Amazon River floodplain in Brazil; (2) Llanos de Mojos (Beni and Mamore rivers) in Bolivia; (3) Bananal Island (Araguaia River) in Brazil; (4) Roraima savannas (Branco and Rupununi rivers) in Brazil and Guyana; (5) Llanos del Orinoco (Apure and Meta rivers) in Venezuela and Colombia; and (6) Pantanal wetland (Paraguay River) in Brazil. Flooded area was estimated at monthly intervals from December 1978 through August 1987 for the Amazon mainstem region and from January 1979 through August 1987 for the other five regions. Inundated area was determined from SMMR (Scanning Multichannel Microwave Radiometer) passive microwave data. Area estimates include permanent open water as well as land subject to seasonal inundation. This data set contains five data files: two comma-delimited (.csv) ASCII data files providing the monthly inundation area values for six floodplain and open water regions in South America; a compressed (.zip) file providing seventeen ESRI Shape files for the region bounding polygons; and two .csv files providing information about the region bounding polygons and latitude/longitude verticies.", "links": [ { diff --git a/datasets/LC07_Spectroradiometry_1144_1.json b/datasets/LC07_Spectroradiometry_1144_1.json index 1b383db6c3..9d82522d6a 100644 --- a/datasets/LC07_Spectroradiometry_1144_1.json +++ b/datasets/LC07_Spectroradiometry_1144_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Spectroradiometry_1144_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes bidirectional reflectance (BDR) spectra and water-quality data of floodplain lakes of the Solimoes and Negro Rivers in the central Amazon basin, Amazonas, Brazil. Samples and measurements were collected during July 2000 to August 2000. Bidirectional reflectance factors were recorded, at 3 nm intervals from 400 to 900 nm, concurrently with in situ measurements of water temperature and Secchi depth, and collection of samples for analysis of optically active components including total suspended solids, chlorophyll, and dissolved organic carbon (DOC).The lakes sampled were in the low-lying varzea of the Solimoes River (\"varzea\" is the local name for the floodplain formed by the overflow of white-water rivers) and igapo (\"igapo\" is the local name for the floodplain formed by the overflow of black-water rivers) of the Negro River.There are two comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/LC07_Wetlands_fluxes_1209_1.json b/datasets/LC07_Wetlands_fluxes_1209_1.json index 431f4dacfe..cb46554011 100644 --- a/datasets/LC07_Wetlands_fluxes_1209_1.json +++ b/datasets/LC07_Wetlands_fluxes_1209_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC07_Wetlands_fluxes_1209_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of daily and monthly carbon dioxide (CO2) and methane (CH4) diffusive and ebullitive flux for dry and flooded areas from two study sites, Cuini and Itu, in the interfluvial wetlands of the upper Negro River basin, Brazil. CO2 (ebullitive and diffusive) and CH4 diffusive flux measurements were made one day each month from February 2005 through January 2006 in both permanently and seasonally flooded areas. For the remaining days of each month, fluxes were calculated as the mean of the two measurements bracketing that time period, times the area flooded each day. Total site area, dry area, and seasonally varying flooded area estimates for the two wetlands were determined through analysis of synthetic aperture radar data from Radarsat images. From these estimates, the total flux of CO2 and CH4 for the sites was calculated. Values for CH4 ebullitive flux were determined from a constant for each area based on whether the water was rising or falling and the area flooded. Hydrologic measurements were taken from April 2004 through January, 2006.There are three comma-separated (.csv) data files with this data set.", "links": [ { diff --git a/datasets/LC08_EOS_Maps_1155_1.json b/datasets/LC08_EOS_Maps_1155_1.json index 91c86a006e..aadfabf9d5 100644 --- a/datasets/LC08_EOS_Maps_1155_1.json +++ b/datasets/LC08_EOS_Maps_1155_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC08_EOS_Maps_1155_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides (1) soil maps for Brazil that are digital versions of the MAPA DE SOLOS DO BRASIL (EMBRAPA, 1981) classified at three levels of detail, 19-class, 70-class and 249-class; (2) vegetation maps for Brazil that are digital versions of the MAPA DE VEGETACAO DO BRASIL (IBGE, 1988) classified at three levels of detail, 13-class, 59-class, and an overprint (combination) class; and (3) a land cover map for all of South America that was derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991 (Stone et al., 1994).The seven soil, vegetation, and general land cover classification maps are provided as GeoTIFF files (*.tif) files. There are also three companion files (.pdf), one each, for the soil, vegetation, and land cover maps, with information on map units, class values, codes, and descriptions.", "links": [ { diff --git a/datasets/LC08_Ecosystem_Demography_Model_1102_1.json b/datasets/LC08_Ecosystem_Demography_Model_1102_1.json index 3bbb7001b6..37d56b81af 100644 --- a/datasets/LC08_Ecosystem_Demography_Model_1102_1.json +++ b/datasets/LC08_Ecosystem_Demography_Model_1102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC08_Ecosystem_Demography_Model_1102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass (kg C/m2), and potential average soil carbon (kg C/m2) for the Brazilian Amazon at 1 degree resolution. Ecosystem Demography Model predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP, and evapotranspiration) from climate, soil, and land-use inputs. Estimates for the Brazilian Amazon include the effects of natural disturbances such as windthrow and fire, but do not include the effects of human land use. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988 and averaged into a single average year (Moorcroft et al., 2001).The data are provided for the three estimates in both ASCII text and in NetCDF formatted files. ", "links": [ { diff --git a/datasets/LC08_Fire_Observations_1095_1.json b/datasets/LC08_Fire_Observations_1095_1.json index 80ab47d461..7c10ef0416 100644 --- a/datasets/LC08_Fire_Observations_1095_1.json +++ b/datasets/LC08_Fire_Observations_1095_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC08_Fire_Observations_1095_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports observations of fires in the vicinity of Maraba, Para, Brazil, from November 3-5th, 2001, and in Mato Grosso, Brazil, between Cuiaba and Alta Floresta, for July 12-15th, 2002. These ground-based data were collected by visual inspection from roads primarily during daylight hours. Data include fire position and time, estimates of fire size, and type of vegetation burned. There is one comma-delimited ASCII file with this data set.", "links": [ { diff --git a/datasets/LC09_GIS_Study_Areas_986_1.json b/datasets/LC09_GIS_Study_Areas_986_1.json index ab88b21cad..0e4fde4983 100644 --- a/datasets/LC09_GIS_Study_Areas_986_1.json +++ b/datasets/LC09_GIS_Study_Areas_986_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_GIS_Study_Areas_986_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes 16 zipped archives of shapefiles of cities, rivers and streams, roads, and study area boundaries of several Amazonian study sites: Altamira, Santarem, Bragantina, and Ponta de Pedras, in the state of Para, and 1 site at Machadinho D'Oeste, in the state of Rondonia. Data from Brazil were digitized from Instituto Nacional de Colonizacao e Reforma Agraria (INCRA) maps and other data from Instituto Brasileiro de Geografia e Estatistica (IBGE). These products were prepared in the 2000-2004 time period. The data of creation for the source material is unknown.", "links": [ { diff --git a/datasets/LC09_Landsat_987_1.json b/datasets/LC09_Landsat_987_1.json index b40edce1e5..ea8ed4abc1 100644 --- a/datasets/LC09_Landsat_987_1.json +++ b/datasets/LC09_Landsat_987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_Landsat_987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes 15 zipped archives of rectified .tif format Landsat 5 TM and Landsat 7 ETM+ scenes from near the study sites of Altamira, Santarem, Ponta de Pedras, and Bragantina in the state of Para, Brazil and Machadinho D'Oeste in Rondonia, Brazil. Dates represent the most cloud-free image retrievals from 1985-2004 and are therefore not continuous. These images may be useful to evaluate potential environmental impacts resulting from the establishment of colonization projects in the Amazon. ", "links": [ { diff --git a/datasets/LC09_Precipitation_940_1.json b/datasets/LC09_Precipitation_940_1.json index e637bfb9b7..eaf87166f4 100644 --- a/datasets/LC09_Precipitation_940_1.json +++ b/datasets/LC09_Precipitation_940_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_Precipitation_940_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports daily total precipitation data retrieved from Brazilian National Institute of Meteorology (INMET) network for three stations near two Amazonian research sites: Altamira, and Santarem, from 1961-1998.Daily precipitation totals are provided in one comma-separated ASCII file for three stations in Para, including two sites in Altamira: Altamira City and on the Transamazon Highway at Km 100 near Medicilandia (operated by EMBRAPA); and, one site in Santarem: Taperinha. Data availability varies by station (sublocation): Altamira City from 1961-1990, Transamazon Km 100 from 1982-1998, and Taperinha from 1983-1992.", "links": [ { diff --git a/datasets/LC09_Soil_Composition_938_1.json b/datasets/LC09_Soil_Composition_938_1.json index 586b8d66f8..45dda65127 100644 --- a/datasets/LC09_Soil_Composition_938_1.json +++ b/datasets/LC09_Soil_Composition_938_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_Soil_Composition_938_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports basic soil structure and composition information for five Amazonian research sites: Altamira, Bragantina, Tome-Acu, and Ponta de Pedras, all four in the state of Para, Brazil; and one site in Yapu, Colombia. Soil characteristics reported for all five study sites include cation information (e.g., H, Al, Mg, K, Na, S), percent of soil C, N, and organic matter, soil texture/composition and color, pH, and land use history. Soil bulk density and tons of carbon/ha are reported for only three of the study sites: Altamira, Bragantina, and Tome-Acu. All of the data are provided in one comma-separated data file.The five study areas represent characteristic differences in soil fertility and a range of land uses typical of the Amazon region. One of these areas, Altamira, is characterized by above average pH, nutrients, and texture. The other four areas are more typical of the 75 percent of the Amazon that is characterized by Oxisols and Ultisols, with well-drained but low pH and low levels of nutrients. Ponta de Pedras in Marajo Island, located in the estuary, is composed of upland Oxisols and floodplain alluvial soils. Igarape-Acu in the Bragantina region is characterized by both nutrient-poor Spodosols and Oxisols. Tome-Acu, south of Igarape-Acu, represents a mosaic of Oxisols and Ultisols. Yapu, in the Colombian Vaupes, is composed of patches of Spodosols and Oxisols. Three of the areas are colonization regions at various degrees of development: Altamira is a colonization front that opened up in 1971, whereas Tome-Acu was settled by a Japanese population in the 1930s, and Bragantina was settled in the early part of the twentieth century. Marajo (Ponta de Pedras) is the home of caboclos, whereas Yapu is home to Tukanoan Native American populations. In these study areas slash-and-burn cultivation as well as plantation agriculture and mechanized agriculture are employed. Length of fallows vary in these communities. The two indigenous areas leave their land in longer fallow than do the three colonization areas, and the proportion of land prepared from secondary forests increases with length of settlement as the stock of mature forest declines over time.", "links": [ { diff --git a/datasets/LC09_Transition_Matricies_1098_1.json b/datasets/LC09_Transition_Matricies_1098_1.json index 196243d94d..f5546553be 100644 --- a/datasets/LC09_Transition_Matricies_1098_1.json +++ b/datasets/LC09_Transition_Matricies_1098_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_Transition_Matricies_1098_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change. Dates of acquired images represent the most cloud-free image retrievals from 1970-2001 for each site and are therefore not continuous. There are 3 GeoTIFF files (.tif) with this data set.", "links": [ { diff --git a/datasets/LC09_Vegetation_Composition_939_1.json b/datasets/LC09_Vegetation_Composition_939_1.json index 437893a4a8..7d3931679f 100644 --- a/datasets/LC09_Vegetation_Composition_939_1.json +++ b/datasets/LC09_Vegetation_Composition_939_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC09_Vegetation_Composition_939_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files with vegetation data for five Amazonian sites: Altamira, Bragantina, Tome-Acu, and Ponta de Pedras, all in the state of Para, and Yapu, Colombia. One file describes vegetation composition and structure (basal area, biomass, species composition) with different land use histories for all five study sites; the second file describes more specific information about individual plant characteristics (family/species names, DBH, stem and total plant height) within each plot.", "links": [ { diff --git a/datasets/LC10_Landsat_ETM_846_1.json b/datasets/LC10_Landsat_ETM_846_1.json index 33b6407526..76492fdff3 100644 --- a/datasets/LC10_Landsat_ETM_846_1.json +++ b/datasets/LC10_Landsat_ETM_846_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC10_Landsat_ETM_846_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes orthorectified Landsat ETM+ scenes across the Legal Amazon region. At least one scene is provided for each spatial tile, representing the most cloud-free retrievals from mid-1999 through late 2001 (Fig. 1). Dates are therefore not continuous but include scenes from July 8, 1999 to November 13, 2001. Data have been atmospherically corrected and orthorectified. The individual images should be highly useful as they include very little cloud cover, but they should not be mosaicked together since retrieval dates vary.Data files (and format) included for each scene are: six multispectral bands (tif), two thermal bands (tif), one panchromatic band (tif), two preview files (jpg), and one metadata file (txt). The individual Geotiff files have been g-zipped and subsequently all of the files for a scene have been g-zipped together for ordering convenience.", "links": [ { diff --git a/datasets/LC10_Landsat_TM_852_1.json b/datasets/LC10_Landsat_TM_852_1.json index c4c6ca2e9e..79ee3da2ff 100644 --- a/datasets/LC10_Landsat_TM_852_1.json +++ b/datasets/LC10_Landsat_TM_852_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC10_Landsat_TM_852_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes Landsat TM scenes from across the Legal Amazon region. A single image is provided for each spatial tile, representing the most cloud-free retrieval from 9/21/86 to 9/17/94. All files are in a single directory, including one band-sequential (bsq) file and one database (ddr) file for each scene.", "links": [ { diff --git a/datasets/LC13_GIS_Cauaxi_890_1.json b/datasets/LC13_GIS_Cauaxi_890_1.json index 03c0164735..311998a4d9 100644 --- a/datasets/LC13_GIS_Cauaxi_890_1.json +++ b/datasets/LC13_GIS_Cauaxi_890_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC13_GIS_Cauaxi_890_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains GIS coverage constructed from measurements taken of four logged areas in Cauaxi, Para, Brazil. Logged areas were selectively harvested either using conventional logging techniques or using reduced-impact logging (RIL) techniques (Pereira et al., 2002). Two areas were harvested with each technique in 1996 and two additional areas were harvested in 1998. Coverage includes log decks (patios), roads, skids trails, tree-fall locations, and logging area (block) boundary. These field surveys were conducted as part of an investigation of canopy damage following selective logging by two different techniques (Asner et al., 2004).Coverage is in ArcInfo interchange format (*.e00). The geographic projection of the data is UTM zone 22, South WGS84 datum.", "links": [ { diff --git a/datasets/LC13_GIS_Juruena_888_1.json b/datasets/LC13_GIS_Juruena_888_1.json index 29c5a78619..deb05fe276 100644 --- a/datasets/LC13_GIS_Juruena_888_1.json +++ b/datasets/LC13_GIS_Juruena_888_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC13_GIS_Juruena_888_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains GIS coverages constructed from measurements taken of logged areas in Juruena, Mato Grosso. Classes include roads, skids trails, tree crowns, tree-fall locations, and block boundary (Asner et al., 2004). Coverages are in ArcInfo SHAPE format (*.shp), UTM projection, using the WGS84 datum.", "links": [ { diff --git a/datasets/LC13_GIS_Tapajos_893_1.json b/datasets/LC13_GIS_Tapajos_893_1.json index c6efe55d98..01500b5987 100644 --- a/datasets/LC13_GIS_Tapajos_893_1.json +++ b/datasets/LC13_GIS_Tapajos_893_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC13_GIS_Tapajos_893_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains GIS coverages constructed from measurements taken of logged areas in the Tapajos National Forest region of Para, Brazil in 1996 and 1998 (Asner et al., 2004). Coverages include log decks (patios), roads, skids trails, tree-fall locations, and block boundary. They are in ArcInfo SHAPE format (*.shp). The Tapajos data are in latitude/longitude using the WGS84 datum.", "links": [ { diff --git a/datasets/LC14_Aboveground_Prod_1196_1.json b/datasets/LC14_Aboveground_Prod_1196_1.json index 31c57febe0..576d94a155 100644 --- a/datasets/LC14_Aboveground_Prod_1196_1.json +++ b/datasets/LC14_Aboveground_Prod_1196_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC14_Aboveground_Prod_1196_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports forest biophysical measurements from a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil from 1998 to 2006. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002). Data are reported for stem inventory, tree diameter at breast height (DBH) and height, dendrometer measurements of tree diameter growth increments, canopy density, leaf area index (LAI), and coarse and fine litter mass.The measurements were made monthly from September 28, 1998 through November 10, 2006. There are six comma-delimited data files (.csv), one text file (.txt), and two companion files with this data set. ", "links": [ { diff --git a/datasets/LC14_Amazon_Scenarios_1153_1.json b/datasets/LC14_Amazon_Scenarios_1153_1.json index 3d3d2f381e..89b962208d 100644 --- a/datasets/LC14_Amazon_Scenarios_1153_1.json +++ b/datasets/LC14_Amazon_Scenarios_1153_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC14_Amazon_Scenarios_1153_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of the two modeled scenarios for future patterns of deforestation across the Amazon Basin from 2002 to 2050. This larger defined Amazon Basin (PanAmazon area) includes the Amazon River watershed, the Legal Amazon in Brazil, and the Guiana region. The model SimAmazonia was used to simulate monthly deforestation in the Amazon Basin from 2002 to 2050 for two scenarios: (1) a \"Business-as-Usual\" scenario, which considered the deforestation trends across the basin and projected the rates by using historical images and their variations from 1997 to 2002 and then added to that the effect of paving a set of major roads, and (2) a \"Governance\" scenario, that also considered the current deforestation trends, but assumed a 50% limit imposed for deforested land within each basin's subregion, and that existing and proposed Protected Areas (PAs), play a decisive role in limiting deforestation as well (Soares et al., 2006).The provided data products include one GeoTiff (*.tif) for each year (2002 to 2050) for both model scenarios for a total of 98 files. The files have been compressed in two *.zip files, one for each model scenario. There is also one comma-delimited file that contains the model input data derived from satellite deforestation maps. ", "links": [ { diff --git a/datasets/LC14_REE_SLA_1211_1.json b/datasets/LC14_REE_SLA_1211_1.json index 38d9808d4c..f81a43cbdb 100644 --- a/datasets/LC14_REE_SLA_1211_1.json +++ b/datasets/LC14_REE_SLA_1211_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC14_REE_SLA_1211_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of specific leaf area and monthly phenological observations for selected tree and vine species at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site. Phenological observations were made from 2001-2004 in rainfall exclusion and control plots. In total, 3,224 leaves were observed across 223 individuals and 56 species. The phenological observations included the month and year when a given leaf was first observed fully expanded and last observed alive. Starting in July 2004 and continuing through January 2006, leaves that had been followed in the phenology study were sampled and leaf area and mass were determined and the specific leaf area was calculated.There are two comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/LC14_RISQUE_1147_1.json b/datasets/LC14_RISQUE_1147_1.json index 684ce074d0..653bcbd55b 100644 --- a/datasets/LC14_RISQUE_1147_1.json +++ b/datasets/LC14_RISQUE_1147_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC14_RISQUE_1147_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A simple GIS soil-water balance model for the Amazon Basin, called RisQue (Risco de Queimadasa -- Fire Risk), was used to conduct an analysis of spatial and temporal patterns of drought in moist tropical forests and the complex relationships between patterns of drought and forest fire regimes from 1995 through 2001. The provided data products are the model output estimates of maximum plant-available soil water (PAWmax) at 10 m depth at 8 km resolution and model data inputs of monthly precipitation and evapotranspiration. RisQue estimates PAWmax at 10 m depth starting with a map of PAWmax (1-2 m depth) developed using 1,565 RADAMBRASIL soil texture profiles and empirical relationships between soil texture and critical soil water parameters and then interpolated to 8 km resolution. In RisQue, plant-available soil water (PAW) is depleted by monthly evapotranspiration estimated using the Penman Monteith equation and satellite-derived radiation and recharged by monthly precipitation.There are three data files with this data set, two *.zip, and one GeoTIFF image (.tif). The *.zip files expand to 83 *.asc files of evapotranspiration and 89 *.asc files for precipitation data. The image (.tif) is a map of maximum percent available water at 10 m depth. All the files in this data set are in standard arc/info asciigrid format at 8 km resolution.", "links": [ { diff --git a/datasets/LC14_Surface_Roots_Phenology_1268_1.json b/datasets/LC14_Surface_Roots_Phenology_1268_1.json index 1e656728c7..79aec78909 100644 --- a/datasets/LC14_Surface_Roots_Phenology_1268_1.json +++ b/datasets/LC14_Surface_Roots_Phenology_1268_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC14_Surface_Roots_Phenology_1268_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains biomass estimates for coarse roots measured on the forest floor and measurements of fine root growth down to 2-m depth at the km 67 Rainfall Exclusion Experiment site, Tapajos National Forest, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site. All surface roots intersected along three 1000-m long x 1-m wide transects were identified to species, measured, and biomass calculated. The collections were made on January 26, 2001 during the experimental rain exclusion period.The fine root growth was measured from 0.5-m to 2-m depth with a rhizotron. The rhizotron tubes were inserted into deep soil pits in the control and treatment plots. Average root growth measurements are provided by depth interval on a monthly basis from July 25, 2000 to December 14, 2003. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: There are discrepancies with the documentation, collection dates reported and collection method for fine roots utilizing rhizotrons. ", "links": [ { diff --git a/datasets/LC15_AGLB_Distribution_Map_908_1.json b/datasets/LC15_AGLB_Distribution_Map_908_1.json index a0ee75fd10..4fa0a7c80b 100644 --- a/datasets/LC15_AGLB_Distribution_Map_908_1.json +++ b/datasets/LC15_AGLB_Distribution_Map_908_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_AGLB_Distribution_Map_908_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a single raster image containing the spatial distribution of aboveground live forest biomass of the Amazon basin. This product was derived using a methodology based on a combination of land cover map, remote sensing derived metrics, and more than 500 forest plots distributed over the basin (Saatchi, et al., 2007).The distributed map was produced in ENVI, in Tiff format and it contains forest biomass divided among 11 classes at 1 km spatial resolution with reasonable accuracy (better than 70%). Remote sensing and ground data used in this product were collected from 1990-2000. The Biomass map represents average biomass distribution over the Amazon basin over this period and was used to estimate the total carbon stock of the basin, including the dead and belowground biomass.", "links": [ { diff --git a/datasets/LC15_GRFM_JERS1_Mosaic_1024_1.json b/datasets/LC15_GRFM_JERS1_Mosaic_1024_1.json index 46b1a05940..875c192203 100644 --- a/datasets/LC15_GRFM_JERS1_Mosaic_1024_1.json +++ b/datasets/LC15_GRFM_JERS1_Mosaic_1024_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_GRFM_JERS1_Mosaic_1024_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two image mosaics of L-band radar backscatter and two image mosaics of first order texture. The two backscatter images are mosaics of L-band Radar Backscatter at Horizontal-Horizontal (HH) Polarization created from 1,500 images collected by the Japanese Earth Resources Satellite-1 (JERS-1) Synthetic Aperture Radar (SAR) over the Amazon River Basin as part of the Global Rainforest Mapping Project (GRMP). These backscatter image mosaics were developed using data collected over 62 days from August to November of 1995 for the peak of the dry season and for 62 days from May to June of 1996 during the peak of the wet season. The two image mosaics are at 3 arc-sec resolution. Data provided under this project are resampled images at 30 arc-sec resolution (or about 1 km resolution). For each radar backscatter image, first order texture statistical information was derived and is distributed along with the image mosaic.This data set contains four images each in both geotiff and ENVI formats, provided in eight zip files. The four files in ENVI file format contain o_envi? in their file name and when extrapolated contain an envi image (*_envi.dat) and an envi image header file (_envi.hdr). The four files in geotiff format contain o_geotiff? in their file name and when extrapolated contain *.tif and *.tfw file pairs. See Section 2 for more information about the characteristics of these data files.", "links": [ { diff --git a/datasets/LC15_MODIS_TreeCover_1035_1.json b/datasets/LC15_MODIS_TreeCover_1035_1.json index 4ea8117978..58b501a4a2 100644 --- a/datasets/LC15_MODIS_TreeCover_1035_1.json +++ b/datasets/LC15_MODIS_TreeCover_1035_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_MODIS_TreeCover_1035_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as \"forest\" or \"grassland\" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin. These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/). ", "links": [ { diff --git a/datasets/LC15_Roughness_Map_1182_1.json b/datasets/LC15_Roughness_Map_1182_1.json index 8dcc684223..d72117cff9 100644 --- a/datasets/LC15_Roughness_Map_1182_1.json +++ b/datasets/LC15_Roughness_Map_1182_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_Roughness_Map_1182_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides physical roughness maps of vegetation canopies in the Amazon Basin. The images are estimates of aerodynamic roughness length (Z0) and zero plane displacement height (D0) at 1-km spatial resolution. The aerodynamic roughness length (Z0) is an important parameter to determine the vertical gradients of mean wind speed and the conditions for momentum transfer over a vegetated or bare rough surface.The maps were produced from a multivariate regression model algorithm developed from field-measured vegetation structure and remote-sensing data. The data input sources included Shuttle Radar Topography Mission (SRTM) (Saatchi, 2013), JERS-1, MODIS, and field data from vegetation biomass plots over the Amazon basin, as well as tower-based wind profile measurements, and roughness parameters from LBA tower sites. There are two GeoTIFF (.tif) files with this data set. ", "links": [ { diff --git a/datasets/LC15_SPOT_Metrics_1239_1.json b/datasets/LC15_SPOT_Metrics_1239_1.json index 834e885287..69481d63f2 100644 --- a/datasets/LC15_SPOT_Metrics_1239_1.json +++ b/datasets/LC15_SPOT_Metrics_1239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_SPOT_Metrics_1239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Normalized Difference Vegetation Index (NDVI) composite images of the Amazon Basin for the years 1999-2000 at approximately1-km spatial resolution. The images were from the VEGETATION 1 sensor, aboard the SPOT 4 satellite.Ten day composite images were reprocessed through several filters for cloud removal. Monthly NDVI data were used to create five metrics: maximum NDVI, minimum of 6 greenest months, range of NDVI between min and max, mean NDVI dry months, and mean NDVI wet months. There are five GeoTIFF (.tif) files with this data set.", "links": [ { diff --git a/datasets/LC15_SRTM_Topography_1181_1.1.json b/datasets/LC15_SRTM_Topography_1181_1.1.json index ab878febeb..c6307bafb3 100644 --- a/datasets/LC15_SRTM_Topography_1181_1.1.json +++ b/datasets/LC15_SRTM_Topography_1181_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC15_SRTM_Topography_1181_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a subset of the SRTM30 Digital Elevation Model (DEM) elevation and standard deviation data for the Amazon Basin. SRTM30 is a near-global digital elevation model (DEM) comprising a combination of data from the Shuttle Radar Topography Mission (SRTM), flown in February, 2000, and the earlier U.S. Geological Survey's GTOPO30 data set. The SRTM30 resolution is 30 arc-sec or about 1 km. In processing the SRTM data, to combine with GTOPO30, the data were resampled from 3 arc-sec to 30 arc-sec. Provided here are the mean elevation and the standard deviation (STD) of the data points used in the averaging. The STD is thus an indication of topographic roughness useful in some applications.", "links": [ { diff --git a/datasets/LC18_Hyperion_889_1.json b/datasets/LC18_Hyperion_889_1.json index 177f190b08..4d665e24d0 100644 --- a/datasets/LC18_Hyperion_889_1.json +++ b/datasets/LC18_Hyperion_889_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC18_Hyperion_889_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This image was collected by the Hyperion sensor on 10-July-2004 at 13:16:16 GMT. It was calibrated to apparent surface reflectance using the ACORN atmospheric model.The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines) (http://eo1.usgs.gov/sensors.php). Through these large number of spectral bands, complex land ecosystems can be imaged and accurately classified.Data from the EO-1 Hyperion imaging spectrometer may greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin using four biogeophysical indicators not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes.", "links": [ { diff --git a/datasets/LC19_Field_2002_1261_1.json b/datasets/LC19_Field_2002_1261_1.json index 1fa5459ab9..fbd978a0ff 100644 --- a/datasets/LC19_Field_2002_1261_1.json +++ b/datasets/LC19_Field_2002_1261_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC19_Field_2002_1261_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for soil physical and chemical properties, rooting depth and weight, leaf area index (LAI), plant area index (PAI), biomass, fraction of photosynthetically active radiation (fPAR), and ground-based reflectance measurements of soil and litter samples. The samples were collected from 23 areas within the Brazilian research sites of the Brasilia National Park (BNP) and Aguas Emnendadas Ecological Station (AE), Brasilia; Cangacu Research Center, Tocantins; and Tapajos National Forest, Para.The research areas were in the most intensely stressed areas in Brazil, with rapid and aggressive land use conversions in forested and cerrado-transition areas. These field measurements were conducted from June to July 2002. There are 61 comma-delimited (.csv) data files with this data set.", "links": [ { diff --git a/datasets/LC21_Foliar_Nutrients_1234_1.json b/datasets/LC21_Foliar_Nutrients_1234_1.json index b7588898b5..a97c193704 100644 --- a/datasets/LC21_Foliar_Nutrients_1234_1.json +++ b/datasets/LC21_Foliar_Nutrients_1234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC21_Foliar_Nutrients_1234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for foliar nutrients from logging blocks in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), and potassium (K) concentrations. In March 2003 foliar samples were collected from the cover types remaining after selective logging in 2002: forest, tree-fall gaps, skids, roads, and deck areas. Fresh foliage was also collected in March 2003, from 192 upper canopy species at an intact forest site 17 km from the logging area. There are two data files with this data set.", "links": [ { diff --git a/datasets/LC21_Fractional_Cover_1152_1.json b/datasets/LC21_Fractional_Cover_1152_1.json index 9c1868b56e..2dd3b0281b 100644 --- a/datasets/LC21_Fractional_Cover_1152_1.json +++ b/datasets/LC21_Fractional_Cover_1152_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC21_Fractional_Cover_1152_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, derived classified land cover products, and cloud-water masks for selected Brazilian states (Acre, Amapa, Amazonas, Maranhao, Mato Grosso, Para, Rondonia, and Roraima) for the years 1999-2002. The Landsat ETM+ images were processed to derive fractional land cover types (photosynthetic vegetation [PV], non-photosynthetic vegetation [NPV], and bare substrate) by application of the Carnegie Landsat Analysis System (CLAS) methodology (Asner et al., 2005). CLAS utilizes a quantitative determination of fractional land cover at the subpixel scale (e.g., within each Landsat 30 x 30 m pixel). The resulting images display estimates of subpixel land cover fraction values including free of clouds, cloud shadows, and water. There are 584 *.zip files in this data set which when expanded, contain a total of 1,717 (*.tif) images files (GeoTiff Standard format).", "links": [ { diff --git a/datasets/LC21_Selective_Logging_1172_1.json b/datasets/LC21_Selective_Logging_1172_1.json index 4374bc8471..d92fb891d7 100644 --- a/datasets/LC21_Selective_Logging_1172_1.json +++ b/datasets/LC21_Selective_Logging_1172_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC21_Selective_Logging_1172_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of analyses of Landsat Enhanced Thematic Mapper Plus (ETM+) images for selective logging activity in the Brazilian states of Para, Mato Gross, Rondonia, Roraima, and Acre over the years 1999 through 2001. Images were analyzed using the Carnegie Landsat Analysis System (CLAS) to detect and to quantify the amount of damage due to selective logging in the major timber-production states of the Brazilian Amazon. This approach provided automated image analysis using atmospheric modeling for detection of forest canopy openings, surface debris, and bare soil exposed by forest disturbances; and pattern-recognition techniques. CLAS provides detailed measurements of forest-canopy damage at a spatial resolution of 30 x 30m. Fifteen GeoTiff format files are included -- one for each of the three years from 1999-2001 for each of the five states. Each GeoTiff is a single band image where each pixel represents if logging activity was or was not detected. A zero (0) value indicates that no logging was detected, while a value of one (1) indicates that damage from logging was detected. The 15 GeoTiff (*.tif) files have been compressed into one *.zip file.", "links": [ { diff --git a/datasets/LC21_Soil_Characteristics_1236_1.json b/datasets/LC21_Soil_Characteristics_1236_1.json index bc86d83808..a670740309 100644 --- a/datasets/LC21_Soil_Characteristics_1236_1.json +++ b/datasets/LC21_Soil_Characteristics_1236_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC21_Soil_Characteristics_1236_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements for soil nutrients from areas that were selectively logged and from control areas in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), aluminum (Al), iron (Fe), silicon (Si), carbon 13, nitrogen 15, and potassium (K) concentrations. In addition, data are included for Phosphorus fractionation which was performed on a subset of the soils, and soil bulk density measurements. The samples were from clay-dominated (oxisols) soils.", "links": [ { diff --git a/datasets/LC22_MODIS_Field_Val_2004_1262_1.json b/datasets/LC22_MODIS_Field_Val_2004_1262_1.json index a8b0050210..68c2e4ecd5 100644 --- a/datasets/LC22_MODIS_Field_Val_2004_1262_1.json +++ b/datasets/LC22_MODIS_Field_Val_2004_1262_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC22_MODIS_Field_Val_2004_1262_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period August 2003 to July 2004. The field observations were conducted in the forested areas between Nova Mutum and Sinop, MT. These data were part of a study to validate Moderate Resolution Imaging Spectroradiometer (MODIS) data at 250-m resolution for the detection of deforested areas.There are 16 data files with this data set. This includes 10 shapefile (.shp) and six comma-separated files (.csv).", "links": [ { diff --git a/datasets/LC22_MODIS_Field_Val_2005_1260_1.json b/datasets/LC22_MODIS_Field_Val_2005_1260_1.json index df0c0cee7a..31178230e6 100644 --- a/datasets/LC22_MODIS_Field_Val_2005_1260_1.json +++ b/datasets/LC22_MODIS_Field_Val_2005_1260_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC22_MODIS_Field_Val_2005_1260_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period March 17-24,2005. Fieldwork was conducted in the regions surrounding Sinop, Mato Grosso, with specific emphasis on large clearings occurring in the Xingu Basin. The field campaign was designed to validate preliminary MODIS deforestation products designed to detect deforestation during the wet season. There are five data files with this data set: four shapefiles (.shp) and one comma-separated file (.csv).", "links": [ { diff --git a/datasets/LC22_MODIS_Phenology_Mato_Grosso_1185_1.json b/datasets/LC22_MODIS_Phenology_Mato_Grosso_1185_1.json index a96634f3f7..f7e9fd5f69 100644 --- a/datasets/LC22_MODIS_Phenology_Mato_Grosso_1185_1.json +++ b/datasets/LC22_MODIS_Phenology_Mato_Grosso_1185_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC22_MODIS_Phenology_Mato_Grosso_1185_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, LBA-ECO LC-22 Land Cover from MODIS Vegetation Indices, Mato Grosso, Brazil, provides land cover classifications for Mato Grosso, Brazil, for the years 2000-2001 and 2003-2004. The classifications were derived from annual vegetation phenology information from a time series of Collection 4, 16-day MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI) vegetation data, at 250-m resolution. A decision tree classifier was trained using field observations and Landsat TM data of land cover from 2003-2004 to identify seven land-cover classes. The classifier was applied to the 2000-2001 and 2003-2004 MODIS ENVI and EVI data. There are two GeoTIFF (.tif) files with this data set.", "links": [ { diff --git a/datasets/LC22_MODIS_VCF_Tree_Cover_1112_1.json b/datasets/LC22_MODIS_VCF_Tree_Cover_1112_1.json index e7bd870817..0dee6f8b03 100644 --- a/datasets/LC22_MODIS_VCF_Tree_Cover_1112_1.json +++ b/datasets/LC22_MODIS_VCF_Tree_Cover_1112_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC22_MODIS_VCF_Tree_Cover_1112_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains proportional estimates for the vegetative cover types of tree cover, herbaceous vegetation, and bare ground over South America for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of 500-m MOD09A1 Surface Reflectance 8-day minimum blue reflectance composites were used as input data. To reduce the presence of cloud shadows, The data were converted to 40-day composites using a second darkest albedo (sum of blue, green, and red bands), and the Vegetation Continuous Fields (VCF) algorithmn was utilized (Hansen et al., 2002). The VCF shows how much of a land cover such as forest or grassland exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated. There are three images provided in GeoTIFF format.", "links": [ { diff --git a/datasets/LC22_Post_Deforestation_LULC_1099_1.json b/datasets/LC22_Post_Deforestation_LULC_1099_1.json index df69b0c458..83444a2ed6 100644 --- a/datasets/LC22_Post_Deforestation_LULC_1099_1.json +++ b/datasets/LC22_Post_Deforestation_LULC_1099_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC22_Post_Deforestation_LULC_1099_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides (1) areal estimates of deforestation events (>25 ha) that were identified from 2001-2004 in Mato Grosso by the Brazilian Institute for Space Research (INPE) as part of the Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) and (2) the classification of the post-deforestation land use as either cropland, cattle pasture, or not in production (deforested areas that were never fully cleared or returned immediately to secondary forest) in the years after the large deforestation events from 2002-2005. Data are provided in ESRI shapefile format. There are five compressed (*.zip) data files with this data set. Each shapefile represents one year of post-deforestation land use. Land use in the years following deforestation was estimated using annual time series of MODIS NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index). Metrics of vegetation phenology derived annual time series of MODIS NDVI and EVI data were analyzed using a decision-tree classifier to characterize the major cover type in each area of new deforestation. Post-deforestation land use for each large deforestation event was classified based on the classification of MODIS phenology metrics for all years following deforestation during 2002-2005. ", "links": [ { diff --git a/datasets/LC23_MODIS_ASTER_Fire_Comparisons_839_1.json b/datasets/LC23_MODIS_ASTER_Fire_Comparisons_839_1.json index 7cd17e8ab3..d375b8c59a 100644 --- a/datasets/LC23_MODIS_ASTER_Fire_Comparisons_839_1.json +++ b/datasets/LC23_MODIS_ASTER_Fire_Comparisons_839_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC23_MODIS_ASTER_Fire_Comparisons_839_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations. The two algorithms are NASA's operational Earth Observing System MODIS fire detection product and Brazil's National Institute for Space Research (INPE) algorithm. These data are the ASCII files used in the logistic regression and error matrices presented in the paper. ", "links": [ { diff --git a/datasets/LC23_Vegetation_Fire_Dynamics_843_1.json b/datasets/LC23_Vegetation_Fire_Dynamics_843_1.json index 787e647eee..0c5d1feb3f 100644 --- a/datasets/LC23_Vegetation_Fire_Dynamics_843_1.json +++ b/datasets/LC23_Vegetation_Fire_Dynamics_843_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC23_Vegetation_Fire_Dynamics_843_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite fire detection was determined from two sensors, the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001- 2003 to characterize fire activity in Brazil, giving special emphasis to the Amazon region. Active fire data for AVHRR/NOAA-12 was produced using a fixed threshold fire detection technique based on the algorithm developed by the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC/INPE) (Setzer and Pereira, 1991; Setzer et al., 1994; Setzer and Malingreau, 1996). Active fire data for MODIS/Terra and MODIS/Aqua was produced using a contextual fire detection technique based on NASA-University of Maryland algorithm (Justice et al., 2003; Giglio et al.2003).Resulting fire counts were compared for major biomes of Brazil (Figure 1), the nine states of the Legal Amazon (e.g., Tocantins, Figure 2), and two important road corridors in the Amazon region (Figure 3). In evaluating the daily fire counts, there is a dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics (Schroeder et al., 2005). The data provided are the coordinates of daily active vegetation fires in Brazil for 2001 through 2003 at 1km resolution for both AVHRR and MODIS sensors. Data are provided in both Arcview (shape file format) and ASCII comma separated file formats. Vector files for the major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region are also included.", "links": [ { diff --git a/datasets/LC23_Vegetation_Fires_2003_887_1.json b/datasets/LC23_Vegetation_Fires_2003_887_1.json index 5b4d98d881..54400ad889 100644 --- a/datasets/LC23_Vegetation_Fires_2003_887_1.json +++ b/datasets/LC23_Vegetation_Fires_2003_887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC23_Vegetation_Fires_2003_887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ASTER sensor Level-1B satellite imagery over controlled burns in the State of Roraima in Northern Brazil on January 19 and 28, 2003, plus simultaneously collected soil and near-surface air temperature profiles on January 28th. The ASTER imagery is provided in 14 zipped files containing HDF-EOS files (*.hdf and *.met file pairs), while the sample-based temperature profiles, one for the air the other for the ground, are provided as comma separated ASCII files.", "links": [ { diff --git a/datasets/LC24_Basin_Scale_Hot_Pixels_2001_882_1.json b/datasets/LC24_Basin_Scale_Hot_Pixels_2001_882_1.json index c7181eab4b..e8f827ee10 100644 --- a/datasets/LC24_Basin_Scale_Hot_Pixels_2001_882_1.json +++ b/datasets/LC24_Basin_Scale_Hot_Pixels_2001_882_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_Basin_Scale_Hot_Pixels_2001_882_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the number of hot spots detected across the legal Amazon Basin at 5- km resolution by the AVHRR (Advanced Very High Resolution Radiometer) on NOAA 12, 14, 15, 16, 17, and 18 satellites for the entirety of 2001 (January 1 - December 31). Only hot spots detected at night are included. This data is useful for modeling fire events and evaluating human impacts on the Amazon Basin using fire as an indicator of anthropogenic disturbance (Arima et al., 2007).", "links": [ { diff --git a/datasets/LC24_Cadastral_Property_Map_Para_1042_1.json b/datasets/LC24_Cadastral_Property_Map_Para_1042_1.json index f20e50d240..8fe29cccfd 100644 --- a/datasets/LC24_Cadastral_Property_Map_Para_1042_1.json +++ b/datasets/LC24_Cadastral_Property_Map_Para_1042_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_Cadastral_Property_Map_Para_1042_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a shapefile of a digitized map of the land parcel information of the original properties of the Uruara colonization site, Para, Brazil, acquired from the Instituto de Colonizacao e Reforma Agraria, or the Colonization and Agrarian Reform Institute (INCRA). The Uruara settlement geometry was initially designed by INCRA, and consists of mostly 100 hectare lots (400 x 2500 meters, and 500 x 2000 meters), running north and south of the Trans-Amazon Highway, as a fine network of small, narrow rectangles. The other parcels in the landscape are the so-called glebas that range up to 3,000 hectares. The map was in the form of a paper map without a projection (a spherical geographic coordinate system) in the South American 1969 datum (SAD 1969). This paper map was digitized in Environmental Science Research Institute (ESRI) ArcInfo 8.1 using a digitizing table, and the digital cadastral data were geo-referenced and projected to match the Universal Transverse Mercator projection (Zone 22 South, World Geodetic System 1984 datum) of Landsat imagery (Landsat.org). There is one compressed (*.zip) file with this data set.", "links": [ { diff --git a/datasets/LC24_ETM_Deforestation_Map_Para_1999_1054_1.json b/datasets/LC24_ETM_Deforestation_Map_Para_1999_1054_1.json index 5fd6c077b3..201eea28af 100644 --- a/datasets/LC24_ETM_Deforestation_Map_Para_1999_1054_1.json +++ b/datasets/LC24_ETM_Deforestation_Map_Para_1999_1054_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_ETM_Deforestation_Map_Para_1999_1054_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This image may be overlain with the cadastral property map of the same area (see related data set LBA-ECO LC-24 Cadastral Property Map of Uruara, Para, Brazil: ca.1975). This data sets contains a single geotiff image distributed as deforested_large.zip.", "links": [ { diff --git a/datasets/LC24_Historical_Roads_Amazon_1043_1.json b/datasets/LC24_Historical_Roads_Amazon_1043_1.json index 90bf8eb333..361046254d 100644 --- a/datasets/LC24_Historical_Roads_Amazon_1043_1.json +++ b/datasets/LC24_Historical_Roads_Amazon_1043_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_Historical_Roads_Amazon_1043_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ESRI shapefiles of historical roads (basin-wide federal and state roads) in nine Brazilian states for the Legal Amazon: Amazonas, Para, Acre, Rondonia, Roraima, Tocantins, Amapa, Matto Grosso, and Maranhao. There are 48 compressed (*.zip) files for the years 1968, 1975, 1981, 1985, 1987, and 1993 in GCS South American 1969 projection. ", "links": [ { diff --git a/datasets/LC24_Land_Cover_Southern_Para_1055_1.json b/datasets/LC24_Land_Cover_Southern_Para_1055_1.json index 3e573004e4..66eeccc9b4 100644 --- a/datasets/LC24_Land_Cover_Southern_Para_1055_1.json +++ b/datasets/LC24_Land_Cover_Southern_Para_1055_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_Land_Cover_Southern_Para_1055_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a five-class land cover for Southern Para for the years 1984 (Landsat MSS), 1988 (Landsat TM), 1996, and 2003 (Landsat ETM+). The final classification shows five classes derived using visual comparison (Water, Clouds/Shadow, Forest, Not Forest, Background). These data were used in 2007 to illustrate the nature of deforestation in Southern Para, Brazil over the past twenty years (Simmons et al. 2007). There are four annual GeoTIFF files distributed with this data set. Each GeoTIFF file and accompanying *.tfw file have been compressed into a single *.zip file. ", "links": [ { diff --git a/datasets/LC24_Land_Cover_Uruara_Para_1053_1.json b/datasets/LC24_Land_Cover_Uruara_Para_1053_1.json index 1601cdad0a..311a3014ad 100644 --- a/datasets/LC24_Land_Cover_Uruara_Para_1053_1.json +++ b/datasets/LC24_Land_Cover_Uruara_Para_1053_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_Land_Cover_Uruara_Para_1053_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides course land cover classifications derived from Landsat TM images for 1986, 1988, and 1991 for the area surrounding the municipality of Uruara, Para, Brazil. Five land cover classes (Water, Clouds/Shadow, Forest, Not Forest, and Background) were derived (Aldrich et al. 2006). The Land Cover is in a compressed (*.zip) GeoTIFF file for each year.", "links": [ { diff --git a/datasets/LC24_MODIS_Forest_Cover_500-m_1056_2.json b/datasets/LC24_MODIS_Forest_Cover_500-m_1056_2.json index 7f47d171ab..49e6e67d83 100644 --- a/datasets/LC24_MODIS_Forest_Cover_500-m_1056_2.json +++ b/datasets/LC24_MODIS_Forest_Cover_500-m_1056_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC24_MODIS_Forest_Cover_500-m_1056_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, LBA-ECO LC-24 Forest Cover Map from MODIS, 500-m, South America: 2001, contains forest cover information for 2001 for all of South America. The data were collected by the MODerate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Earth Observing System, TERRA (AM-1) satellite platform and released by the MODIS science team as an image showing percent canopy cover. This information was then reclassified so that all pixels with a percent canopy cover greater than 40% (40% after the 1973 UNESCO standard) were classified as forest (a value of 1), and all other pixels were classified as non-forest (a value of 2). Water features were given a value of 3. This data has a pixel resolution of 500 meters and is unprojected with the WGS-1984 datum (Hansen et al. 2006). There is one GeoTIFF data file for this data set.", "links": [ { diff --git a/datasets/LC31_AMZ_Historical_LU_1170_1.json b/datasets/LC31_AMZ_Historical_LU_1170_1.json index 06af3c7604..c6bb15d268 100644 --- a/datasets/LC31_AMZ_Historical_LU_1170_1.json +++ b/datasets/LC31_AMZ_Historical_LU_1170_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC31_AMZ_Historical_LU_1170_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides annual spatial patterns of cropland, natural pasture, and planted pasture land uses across Amazonia for the period 1940/1950-1995. Two series of 5-minute grid cell historical maps were generated starting from land use classification products for 1995. Annual data are the fraction of natural pasture, planted pasture, and cropland in each 5-min grid cell. The annual maps are provided in two NetCDF (.nc) format file at 5-minute resolution. The AMZ-C.nc file covers the Brazilian portion of Amazon and Tocantins Rivers basins, and is based on the 1995 land use classification of Cardille et al. (2002), generated through the fusion of remote sensing (AVHRR) and agricultural census data. The second file, AMZ-R.nc, covers the entire Legal Amazon region and adjacent areas and is based on the 1995 land use classification by Ramankutty et al. (2008). The land use classification was generated by the fusion of satellite imagery (MODIS and VEGETATION-SPOT) and data from the agricultural census. A historical land-use reconstruction algorithm was used to generate the annual spatial patterns (based on work from Ramunkutty and Foley, 1999).", "links": [ { diff --git a/datasets/LC31_SITE_1173_1.json b/datasets/LC31_SITE_1173_1.json index c933dac746..c9c101ee26 100644 --- a/datasets/LC31_SITE_1173_1.json +++ b/datasets/LC31_SITE_1173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC31_SITE_1173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product provides the Fortran source code and input data for the Simple Tropical Ecosystem Model (SITE). SITE is a simplified point model of vegetation dynamics that uses an integration interval of one hour to estimate the fluxes of CO2, water, and energy. Model forcing data are hourly meteorological parameters. SITE is a simplified model of vegetation dynamics for tropical ecosystems developed by Santos and Costa (2004).Model input data measurements of temperature, wind velocity, precipitation, latent heat, sensible heat, downward incident solar flux, and downward incident infrared flux were collected at the km 67 Tapajos National Forest site, Para, Brazil, from 2002 to 2003.SITE is structured with a canopy layer and two soil layers, and incorporates the following processes:*infrared radiation balance in the canopy and balance of solar radiation*aerodynamic processes*plant physiology*transpiration*balance of water intercepted by the canopy*transport of mass and energy fluxes*soil heat flux and soil moisture*carbon balance There are five files provided with this data set: the Fortran source code (version 1.1-0d), one file for the main program that declares variables and input parameters, one file that initializes vegetation parameters, one file used to compile the SITE model, and the km 67 input data file in comma-delimited (.csv) format. The four SITE files are provided in the compressed file SITE_Model.zip. One companion file is also provided that describes the collection and processing of the meteorological and flux measurements at the km 67 Tapajos National Forest site and the use of the data to calibrate SITE. ", "links": [ { diff --git a/datasets/LC35_GOES_WF_ABBA_1180_1.json b/datasets/LC35_GOES_WF_ABBA_1180_1.json index 357d1c646c..57145ca784 100644 --- a/datasets/LC35_GOES_WF_ABBA_1180_1.json +++ b/datasets/LC35_GOES_WF_ABBA_1180_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC35_GOES_WF_ABBA_1180_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is an active fire detection product resulting from the application of The Wildfire Automated Biomass Burning Algorithm (WF_ABBA) to Geostationary Environmental Operational Satellite (GOES) imager data for all of South America from 2000 through 2005. GOES imager data are available at 30 minute intervals with a nominal 4 x 4-km resolution. The data provided are the latitude/longitude, brightness temperature, estimates of sub-pixel fire size and temperature, Global Land Cover Characterization (GLCC) ecosystem type, and a pixel-fire flag (0-5, information regarding the probability of a fire or processing characteristics) for each active fire detected by WF_ABBA for a 30 minute imager interval. Spatial area coverage data files are provided as a complement to individual fire detection data files because the area of the latter varied according to the GOES imager scan mode in use. Versions 5.9 and 6.0 WF_ABBA data are provided. Differences between the two versions are assumed to be small though (typically less than 10%). An in-line temporal filter has been added to the algorithm to screen out false alarms associated with noise in the imagery and cloud edge issues in version 6.0. This is especially important for screening false alarms due to reflection off clouds at extreme view angles and at sunrise and sunset.There are nine compressed (*.zip) files with this data set which expand to the filtered ASCII text data files (.filt), and seven coverage files text (.txt).", "links": [ { diff --git a/datasets/LC35_Landsat7_Fire_Masks_1071_1.json b/datasets/LC35_Landsat7_Fire_Masks_1071_1.json index 7864aa0e24..8932aeb78b 100644 --- a/datasets/LC35_Landsat7_Fire_Masks_1071_1.json +++ b/datasets/LC35_Landsat7_Fire_Masks_1071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC35_Landsat7_Fire_Masks_1071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides active fire detection images and associated summary information derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images for various locations in Brazilian Amazonia during 2001-2003. There are two image types: (1) GeoTiff images (masks) of active fire pixels, and (2) GeoTiff images (masks) of clustered active fire pixels where a distinct cluster identification number has been assigned to each individual group of contiguous active fire pixels. There are 122 GeoTiff format files of each type of fire mask; a total of 244 images. The spatial resolution of the fire mask images is 30 meters. ETM+ images were selected based on data quality, availability, as well as on the occurrence of vegetation fires.In addition to the two image types, there are also two types of fire pixel summary information provided in text files: (1) one file of active fire pixel summary information derived from the active fire pixel images, and (2) 122 files of clustered active fire pixel information derived from individual clustered fire pixel masks, each of which correspond to a clustered image.", "links": [ { diff --git a/datasets/LC39_DECAF_Model_1190_1.json b/datasets/LC39_DECAF_Model_1190_1.json index 7b3623dcee..6617768923 100644 --- a/datasets/LC39_DECAF_Model_1190_1.json +++ b/datasets/LC39_DECAF_Model_1190_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC39_DECAF_Model_1190_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains modeled estimates of carbon flux, biomass, and annual burning emissions across the Brazilian state of Mato Grosso from 2000-2006. The model, DEforestation CArbon Flux (DECAF), was used to provide annual carbon fluxes from large deforestation events (>25 ha) based on post-deforestation land use, and the frequency and duration of active fires during the deforestation process. Carbon fluxes associated with the conversion of Cerrado to mechanized crop production, fires in Cerrado, and managed pasture cover types were also estimated. Model data outputs provided include: * Estimated aboveground live biomass from DECAF in 2000 and 2004.* Annual biomass burning emissions estimates for 2001-2005 from low, middle, and high emissions scenarios with DECAF. There are 15 GeoTIFF files for annual emissions which represent the carbon emissions per pixel in grams of carbon per m2 (g C m-2). Model data inputs provided include: * Annual burn trajectories for 2001 - 2005, including deforestation, Cerrado land cover conversion, and fires in pasture and Cerrado ecosystems unrelated to agricultural expansion. These data were assembled from three sources: MODIS 500-m burned area maps, annual deforestation based on data from the INPE PRODES program, and the conversion of Cerrado savannah/woodland to cropland estimated from land cover information from MODIS phenology metrics.* Annual land cover data 2001-2004 for the portion of Mato Grosso covered by MODIS phenology metrics, tile h12v10, updated based on annual land cover changes in Amazon forest and Cerrado cover types.* Monthly Normalized Difference Vegetation Index (NDVI) for MODIS tile h12v10 from 10/2000 - 09/2006, based on cloud and gap-filled 16-day NDVI data from MODIS Collection 4 16-day NDVI composites MOD13 product (Huete et al., 2002).There are six compressed (*.gz) files with this data set.", "links": [ { diff --git a/datasets/LC39_MODIS_Fire_SA_1186_1.json b/datasets/LC39_MODIS_Fire_SA_1186_1.json index c072ef147c..a1d47c7aac 100644 --- a/datasets/LC39_MODIS_Fire_SA_1186_1.json +++ b/datasets/LC39_MODIS_Fire_SA_1186_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LC39_MODIS_Fire_SA_1186_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections. The analysis considered a high-confidence subset of all MODIS fire detections to reduce the influence of false fire detections over small forest clearings in Amazonia (Schroeder et al., 2008). The number of unique days on which the active fire detections were recorded within a 1 km radius was estimated from the subset of active fire detections and the ArcGIS neighborhood variety algorithm. There are 14 data files with this data set: 7 GeoTIFF (.tif) files of fire frequency at MODIS 250 m resolution, where each grid cell value represents the number of days in that year on which active fires were detected, and 7 shape files of active fire locations for the years 2001-2007.", "links": [ { diff --git a/datasets/LD2012-d18O-Native-age_1.json b/datasets/LD2012-d18O-Native-age_1.json index c802d43fcb..1736b9745b 100644 --- a/datasets/LD2012-d18O-Native-age_1.json +++ b/datasets/LD2012-d18O-Native-age_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LD2012-d18O-Native-age_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LD2012-d18O-Native-age record is the annual mean water isotope (d18O) record for the \"DSS\" (Dome Summit South) Law Dome ice core with extensions (e.g. As described in van Ommen et al., Nature Geoscience, 2010) from overlapping ice cores which are dated by comparing multiple chemical species as well as water isotopes. LD2012-d18O-Native-age record spans 2007 A.D. to 174 A.D. The d18O measurements were completed using Isotope Ratio Mass Spectrometers.\n\nThis work was done as part of AAS 757 and AAS 4061.", "links": [ { diff --git a/datasets/LDEO_INDICES_INDIA.json b/datasets/LDEO_INDICES_INDIA.json index 371219d687..974a2822ac 100644 --- a/datasets/LDEO_INDICES_INDIA.json +++ b/datasets/LDEO_INDICES_INDIA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LDEO_INDICES_INDIA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An all-India summer monsoon rainfall series for the instrumental\nperiod of 1844-1991 has been constructed using a progressively\nincreasing station density to 1870, and one that is fixed thereafter\nat a uniformly distributed 36 stations. The statistical scheme\naccounts for the increasing variance contributed to the all-India\nseries by the increasing number of stations during the period\n1844-1870. An interesting outcome of this study is that a reliable\nestimate of summer monsoon rainfall over India can be obtained using\nonly 36 observations.", "links": [ { diff --git a/datasets/LEOLSTCMG30_001.json b/datasets/LEOLSTCMG30_001.json index b6487e03db..169708786a 100644 --- a/datasets/LEOLSTCMG30_001.json +++ b/datasets/LEOLSTCMG30_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LEOLSTCMG30_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 1 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present. The MEaSUREs LEOLST product is generated by regridding the monthly LST CMG products from MODIS (MYD21C3.061) and VIIRS (VNP21C3.002). \r\n\r\nThe product will be available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format.\r\n", "links": [ { diff --git a/datasets/LEOLSTCMG30_002.json b/datasets/LEOLSTCMG30_002.json index 045fa25624..b5992a0331 100644 --- a/datasets/LEOLSTCMG30_002.json +++ b/datasets/LEOLSTCMG30_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LEOLSTCMG30_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 2 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present.The MEaSUREs LEOLST product is generated by regridding the monthly CMG products from Aqua MODIS (MYD21C3) and VIIRS (VNP21C3 and VJ121). The product is available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format. ", "links": [ { diff --git a/datasets/LEO_0.json b/datasets/LEO_0.json index f8a9313405..015f74d24b 100644 --- a/datasets/LEO_0.json +++ b/datasets/LEO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LEO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the LEO station off the Atlantic Coast of New Jersey in 2001.", "links": [ { diff --git a/datasets/LEVEL_1C__3_5.0.json b/datasets/LEVEL_1C__3_5.0.json index 0a5b0fdfff..c5c2cbf6e3 100644 --- a/datasets/LEVEL_1C__3_5.0.json +++ b/datasets/LEVEL_1C__3_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LEVEL_1C__3_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Proba-V VEGETATION Raw products (Level 1C/P) and synthesis products (Level 3, S1 = daily, S5 = 5 days, S10 = decade) ensure coverage of all significant landmasses worldwide with, in the case of a 10-day synthesis product, a minimum effect of cloud cover, resulting from selection of cloud-free acquisitions during the 10-day period. It ensures a daily coverage between Lat. 35\u00b0N and 75\u00b0N, and between 35\u00b0S and 56\u00b0S, and a full coverage every two days at equator. The VEGETATION instrument is pre-programmed with an indefinite repeated sequence of acquisitions. This nominal acquisition scenario allows a continuous series of identical products to be generated, aiming to map land cover and vegetation growth across the entire planet every two days.Products overview \u2022 Projection: Plate carr\u00e9e projection \u2022 Spectral bands: All 4 + NDVI \u2022 Format: HDF5 & GeoTiFF The Proba-V VEGETATION Level 3 synthesis products are divided into so called granules, each measuring 10 degrees x 10 degrees, each granule being delivered as a single file. Level 3 products are: - Syntesys S1, with resolution 100m (TOA, TOC and TOC NDVI reflectance), 333m (TOA and TOC reflectance) and 1km (TOA and TOC reflectance) - Syntesys S5, with resolution 100m (TOA, TOC and TOC NDVI reflectance) - Syntesys S10, with resolution 333m (TOC and TOC NDVI reflectance) and 1km (TOC and TOC NDVI reflectance)", "links": [ { diff --git a/datasets/LF_Bibliography_1.json b/datasets/LF_Bibliography_1.json index 17012af0b8..50fb1fcaea 100644 --- a/datasets/LF_Bibliography_1.json +++ b/datasets/LF_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LF_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The bibliography covers a wealth of published, 'grey', and unpublished literature addressing the effects of longline fishing on seabird mortality. The scope is global, but with a special emphasis on the Southern Ocean.\n\nInformation on longline methodology is included and attention is given to materials that cover the various mitigation methods in use, tested or proposed. Further, information on the relevant aspects of the ecology of affected seabird species is covered, especially that dealing with mortality levels, at-sea distributions and population and conservation biology.\n\nData sources covered include the scientific literature, popular publications, newspaper articles, videos, brochures, maps and posters, as well as government, NGO and IGO reports.", "links": [ { diff --git a/datasets/LGB_10m_traverse_1.json b/datasets/LGB_10m_traverse_1.json index cabdffb06e..fe22c87b6d 100644 --- a/datasets/LGB_10m_traverse_1.json +++ b/datasets/LGB_10m_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGB_10m_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station.\n\n10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel).\n\nSummary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'.\n\nThis work was completed as part of ASAC projects 3 and 2216.\n\nSome of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided).\n\nThe fields in this dataset are:\n\nLatitutde\nLongitude\nHeight\nCane\nDistance\nElevation\nDensity\nMass Accumulation\nYear\nDelta Oxygen-18\nGrain Size\nIce Crusts\nDepth Hoar", "links": [ { diff --git a/datasets/LGB_Del_traverse_1.json b/datasets/LGB_Del_traverse_1.json index 41a31a0476..a30aff80ec 100644 --- a/datasets/LGB_Del_traverse_1.json +++ b/datasets/LGB_Del_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGB_Del_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Several shallow depth ice cores (15-60 m) were drilled at selected sites along 2014 km of the main traverse track from LGB00 (68.6543 S, 61.1201 E) near Mawson Station to LGB72 (69.9209 S,76.4933 E) near Davis Station, and at selected sites along a western traverse line from LGB00 toward Enderby Land. Surface cores (2 m) were collected at 30 km intervals along the entire route from LGB00-LGB72. \n\nIce cores have been kept in cool storage at a local cold room storage facility. Isotope data from the cores have been saved in various spreadsheet files (mainly MS Excel).\n\nInitial summary data can be obtained from CRC Research Note No.09 'Surface mass balance and snow surface properties from the Lambert Glacier Basin Traverses 1990-94'.\n\nThis work was completed as part of ASAC projects 3 and 2216.\n\nSome of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided).\n\nThe fields in this dataset are:\n\nLatitutde\nLongitude\nHeight\nCane\nDistance\nElevation\nDensity\nMass Accumulation\nYear\nDelta Oxygen-18\nGrain Size\nIce Crusts\nDepth Hoar", "links": [ { diff --git a/datasets/LGB_Gra_traverse_1.json b/datasets/LGB_Gra_traverse_1.json index bfca32ced6..97a106ac8b 100644 --- a/datasets/LGB_Gra_traverse_1.json +++ b/datasets/LGB_Gra_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGB_Gra_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. LaCoste and Romberg gravity meters were used to record measurements of the Earth's gravity field approximately every 2 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. Gravity readings were also obtained at 5 km intervals along a 516 km upper western offset track (50 km parallel upslope from main route) from LGBUW485 (68.6458 S, 60.0272 E) to LGBUW000 (72.6508 S, 55.9275 E).\n\nRaw data were stored as meter readings in field notebooks, transferred manually to spreadsheet files (MS Excel). Processed data were stored in spreadsheet files (MS Excel). The data available at the url below are stored in various formats.\n\nSummary data (2 km spatial resolution) can be obtained from CRC Research Note No.27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1990-95'.\n\nDocuments providing archive details of the logbooks are available for download from the provided URL.\n\nThis work was completed as part of ASAC projects 3 and 2216.\n\nLogbook(s):\n- Gravity Meter Log 89/90\n- LGBT Gravity #2 1992-93\n- Glaciology Gravity Readings LGBT 1990-91", "links": [ { diff --git a/datasets/LGB_Ht_traverse_1.json b/datasets/LGB_Ht_traverse_1.json index 5f705baf2c..4134e4ef26 100644 --- a/datasets/LGB_Ht_traverse_1.json +++ b/datasets/LGB_Ht_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGB_Ht_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ANARE Lambert Glacier Basin (LGB) series of oversnow traverses were conducted during the period 1989-95. Field operations were carried out along the proximity of the 2500 m elevation contour around the interior basin between Mawson and Davis stations. The main traverse route covered some 2014 km of track from LGB00 at 68.6543 S, 61.1201 E, and LGB72 at 69.9209 S, 76.4933 E. An offset route (50 km upslope) parallels the main traverse track around the western half of the basin. \n\nRaw data were stored in binary files containing pressure, temperature, navigational position and a variety of other parameters at an approximately 10 m spacing associated with each 2 km long section of track. Processed data were stored as 2 km averaged ice sheet surface elevation spreadsheet files (MS Excel). The data available at the url below are stored in various formats.\n\nSummary data (2 km spatial average) can be obtained from CRC Research Note No. 27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1989-95'.\n\nThis work was completed as part of ASAC projects 3 and 2216.", "links": [ { diff --git a/datasets/LGB_Vel_traverse_1.json b/datasets/LGB_Vel_traverse_1.json index 165f1bc1a0..d390697b46 100644 --- a/datasets/LGB_Vel_traverse_1.json +++ b/datasets/LGB_Vel_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGB_Vel_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ANARE Lambert Glacier Basin (LGB) series of oversnow traverses were conducted during the period 1989-95. Field operations were carried out along the proximity of the 2500 m elevation contour around the interior basin between Mawson and Davis stations. The main traverse route covered some 2014 km of track from LGB00 at 68.6543 S, 61.1201 E, and LGB72 at 69.9209 S, 76.4933 E. Ice sheet surface velocities were obtained for 73 sites known as Ice Movement Stations (IMS), spaced approximately 30 km apart between LGB00 and LGB72.\n\nRaw data were recorded in Wild-Leitz (WM102) or Leica-Wild (200-series) proprietary mode including data, observation, almanac and ephemeris files. Processed data were stored in proprietary software output modes and has been written to standard spreadsheet (MS Excel) files for sharing with downstream processing programs. The data available at the url below are stored in various formats.\n\nSummary data (2 km spatial average) can be obtained from CRC Research Note No. 23, 'Ice Sheet Surface Velocities along the Lambert Basin Traverse Route'.\n\nDocuments providing archive details of the logbooks are available for download from the provided URL.\n\nThis work was completed as part of ASAC projects 3 and 2216.", "links": [ { diff --git a/datasets/LGP_2.json b/datasets/LGP_2.json index f99bf0964f..1f0f4f728c 100644 --- a/datasets/LGP_2.json +++ b/datasets/LGP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record relates to the Australian component of the Latitudinal Gradient Project. The LGP is largely a New Zealand, US and Italian venture, but a small contribution has been made by Australian scientists.\n\nThe Australian component of this work was completed as part of ASAC projects 2361 and 2682 (ASAC_2361, and ASAC_2682).\n\nData from this project were entered into the herbarium access database, which has been linked to this record.\n\nThe list below contains details of where and when samples were collected, and also the type of sample and the method of sampling.\n\nCape Hallett and vicinity (2000, 2004): Biodiversity assessment of terrestrial plants (mosses, lichens); Invertebrate collections (mites, Collembola); plant ecology and community analysis; photosynthetic physiology of mosses and lichens; molecular genetics of mosses and lichens. Random sampling for biodiversity studies; point quadrats, releves for vegetation analysis, field laboratory experiments for physiological studies.\n\nDry Valleys: Taylor Valley (1989, 1996), Garwood Valley (2001), Granite Harbour (1989; 1994, 1996) - plant ecology; plant physiology; biodiversity; invertebrate collections; molecular genetics of mosses. Random sampling for biodiversity studies; point quadrats, releves for vegetation analysis, field laboratory experiments for physiological studies.\n\nBeaufort Island (1996) - plant biodiversity; molecular genetics of mosses. Random sampling for biodiversity studies; point quadrats, releves for vegetation analysis, laboratory studies for molecular genetics.\n\nDarwin Glacier (1994): plant biodiversity; molecular genetics of invertebrates and mosses (random sampling for biodiversity; laboratory studies of invertebrate and moss molecular genetics).\n \nProject objectives:\n1. Investigate the distribution of bryophytes and lichens in continental Antarctica\n1a). to test the null hypothesis that species diversity does not change significantly with latitude;\n1b). to explore the relationships between species and key environmental attributes including latitude, distance from the coast, temperature, substrate, snow cover, age of ice-free substrate.\n\n2. To continue to participate in the Ross Sea Sector Latitudinal Gradient Project and develop an Australian corollary in the Prince Charles Mountains, involving international collaborators, incorporating the first two objectives of this project.\n\n3. To develop an international collaborative biodiversity and ecophysiological program in the Prince Charles Mountains that will provide a parallel N-S latitude gradient study to mirror the LGP program in the Ross Sea region as part of the present RISCC cooperative program (to be superseded by the EBA (Evolution and Biodiversity of Antarctica) program) to address the above objectives. \n\nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nContinuing identification of moss and lichen samples previously collected from Cape Hallett, Granite Harbour and Darwin Glacier region. Lecidea s.l. lichens currently being studied in Austria by PhD student. Field work in Dry Valleys significantly curtailed by adverse weather.\nField work planned for Darwin Glacier region and McMurdo Dry Valleys, particularly Taylor Valley and Granite Harbour region was severely curtailed due to adverse weather, helicopter diversions due to a Medical Evacuation, and other logistic constraints. 10 days of field time were lost. Limitations on field travel in Darwin Glacier region restricted the field work to a biologically depauperate region.\nThe Prince Charles Mountains N-S transect, the only continental transect possibility for comparison with the Ross Sea area, unfortunately appears to have been abandoned through lack of logistic support. \n\nTaken from the 2009-2010 Progress Report:\nIdentification of samples collected from AAT and Ross Sea Region continued during the year, interrupted significantly by the packing of the collection and transfer of specimens to the Tasmanian Herbarium. Work is now proceeding at the Herbarium with sorting, databasing and incorporation of packets into the Herbarium collection. The merging of the collection provides long-term security of curation and significantly boosts the cryptogam collections (35000 numbers) of the Tasmanian Herbarium.", "links": [ { diff --git a/datasets/LGRIP30_001.json b/datasets/LGRIP30_001.json index 56aed3bd7f..9a1c794fdf 100644 --- a/datasets/LGRIP30_001.json +++ b/datasets/LGRIP30_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world\u2019s agricultural lands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.\r\n\r\nEach LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also available. \r\n", "links": [ { diff --git a/datasets/LGRIP30_L1_IRRI_002.json b/datasets/LGRIP30_L1_IRRI_002.json index 668dea592c..e9a35fcc45 100644 --- a/datasets/LGRIP30_L1_IRRI_002.json +++ b/datasets/LGRIP30_L1_IRRI_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_L1_IRRI_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-Derived Global Irrigated-Cropland Product Level 1 2020 (LGRIP30_L1_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L1_IRRI V2 maps agricultural lands by dividing them into 32 irrigated cropland types and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. \n\nEach LGRIP30 L1 V2 Irrigated 30 m resolution GeoTIFF file contains a layer that identifies areas of irrigated cropland (cropland that had at least one irrigation during the crop growing period) divided into 32 types, non-irrigated land (rainfed cropland and areas not classified as cropland), and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. \n\nCurrently, LGRIP30 V2 products contain data only for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "links": [ { diff --git a/datasets/LGRIP30_L1_RAIN_002.json b/datasets/LGRIP30_L1_RAIN_002.json index 1b1256247f..0cbe555b43 100644 --- a/datasets/LGRIP30_L1_RAIN_002.json +++ b/datasets/LGRIP30_L1_RAIN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_L1_RAIN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-Derived Global Rainfed-Cropland Product Level 1 2020 (LGRIP30_L1_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP30_L1_RAIN V2 maps agricultural lands by dividing them into 24 types of rainfed croplands and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. \n\nEach LGRIP L1 Rainfed 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering) divided into 24 types, non-rainfed land (irrigated croplands and areas not classified as cropland), and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. \n\nCurrently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "links": [ { diff --git a/datasets/LGRIP30_L2_IRRI_002.json b/datasets/LGRIP30_L2_IRRI_002.json index 2fb8f168d9..7d4ec2c3af 100644 --- a/datasets/LGRIP30_L2_IRRI_002.json +++ b/datasets/LGRIP30_L2_IRRI_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_L2_IRRI_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-Derived Global Irrigated-Cropland Product Level 2 2020 (LGRIP30_L2_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_IRRI V2 maps agricultural lands by dividing them into irrigated single crop, double crop, and continuous croplands, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. \n\nEach LGRIP L2 Irrigated 30 m resolution GeoTIFF file contains a layer that identifies areas of irrigated cropland (cropland that had at least one irrigation during the crop growing period) divided into single, double, and continuous crop classifications, non-irrigated land (rainfed cropland and areas not classified as cropland), and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. \n\nCurrently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "links": [ { diff --git a/datasets/LGRIP30_L2_RAIN_002.json b/datasets/LGRIP30_L2_RAIN_002.json index 1415805d86..0dbfde31fc 100644 --- a/datasets/LGRIP30_L2_RAIN_002.json +++ b/datasets/LGRIP30_L2_RAIN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_L2_RAIN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-Derived Global Rainfed-Cropland Product Level 2 2020 (LGRIP30_L2_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_RAIN V2 maps agricultural lands by dividing them into rainfed single croplands and rainfed single croplands mixed with natural vegetation, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. \n\nEach LGRIP L2 Rainfed 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering) divided into single crop and single crop that is mixed with natural vegetation, non-rainfed land (irrigated croplands and areas not classified as cropland), and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. \n\nCurrently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "links": [ { diff --git a/datasets/LGRIP30_L3_002.json b/datasets/LGRIP30_L3_002.json index c9896af200..780c3af6f8 100644 --- a/datasets/LGRIP30_L3_002.json +++ b/datasets/LGRIP30_L3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LGRIP30_L3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. \n\nEach LGRIP30 L3 V2 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. \n\nCurrently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.", "links": [ { diff --git a/datasets/LIDA.json b/datasets/LIDA.json index 589b19f943..f3c5766f01 100644 --- a/datasets/LIDA.json +++ b/datasets/LIDA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIDA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FISAT home page on the WWW is http://www.laser.inpe.br/fisat/ .\n \n This set contains data obtained at the location of Sao Jose dos Campos\n (23 degrees S, 45 degrees W), only.\n \n >From 1972 to 1981 only night-time data of the Lidar backscatter return\n at 589.0 nm are available. The periodicity of the data is\n irregular. Generally short-duration measurements (less than 2 hours)\n are available at about one measurerent per week. Long-duration data\n covering most of the night are available in a few campaigns. Data are\n also given, in processed form, providing aerosol backscatter ratio\n from 15 to 30 km altitude and sodium density from 75 to 105 km\n altitude.\n \n >From 1981 to 1993, campaigns of sodium measurements taken during the\n day, including several diurnal cycles are also available.\n \n >From 1983 to the present day a new powerful laser at 593.0 nm provides\n the Rayleigh scatter profiles giving the atmospheric density and\n temperatures from 35 to nearly 70 km altitude. Data are currently\n obtained, approximately, on a weekly basis.", "links": [ { diff --git a/datasets/LIDAR_0.json b/datasets/LIDAR_0.json index be08eca051..0762dd8f5c 100644 --- a/datasets/LIDAR_0.json +++ b/datasets/LIDAR_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIDAR_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pigment measurements from 1989 and 1990 in the Gulf of St Lawrence.", "links": [ { diff --git a/datasets/LIDAR_FOREST_CANOPY_HEIGHTS_1271_1.json b/datasets/LIDAR_FOREST_CANOPY_HEIGHTS_1271_1.json index 79eb6130c6..238f38daa9 100644 --- a/datasets/LIDAR_FOREST_CANOPY_HEIGHTS_1271_1.json +++ b/datasets/LIDAR_FOREST_CANOPY_HEIGHTS_1271_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIDAR_FOREST_CANOPY_HEIGHTS_1271_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute.Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.", "links": [ { diff --git a/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json b/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json index 3f39bb8b0c..7e21ca11cb 100644 --- a/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json +++ b/datasets/LIFE_ECO_NBS_SIER_VEG_FUEL1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIFE_ECO_NBS_SIER_VEG_FUEL1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The fuel inventory data involves 144 var. radius plots measured for vegetative\ncover and structure by species and fuel loading. Standing and downed fuel is\nestimated by size, class and type. Wood and leaf litter fall data are\ncollected annually. This dataset was collected in Yosemite National Park road\ncorridors between 1200 and 2400 meters.\n\nThis dataset is part of the U.S. Geological Survey, Biological Resources\nDivision, Global Change Program.", "links": [ { diff --git a/datasets/LIMSN7L1PROFILER_001.json b/datasets/LIMSN7L1PROFILER_001.json index 35c715b02a..65917aa3b0 100644 --- a/datasets/LIMSN7L1PROFILER_001.json +++ b/datasets/LIMSN7L1PROFILER_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIMSN7L1PROFILER_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LIMSN7L1PROFILER is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Profiles of Radiance Data product and contains selected daily vertical profiles across the earth\u2019s atmospheric limb derived from the LIMS Level-1 Radiance Archival Tape (RAT) data product. Measurements are obtained, as a function of tangent height (or scan angle), once every 12 seconds in each of the six spectral bands (two 15-micrometer CO2 bands (narrow and wide), an 11.3-micrometer HNO3 band, a 9.6-micrometer O3 band, a 6.9-micrometer H2O band, and a 6.2-micrometer NO2 band) from the highest pressure level to the lowest in steps of 0.1 km\n\nEach file contains one days worth of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR.\n\nThis product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B).", "links": [ { diff --git a/datasets/LIMSN7L1RAT_001.json b/datasets/LIMSN7L1RAT_001.json index cd0f948240..4850dcb266 100644 --- a/datasets/LIMSN7L1RAT_001.json +++ b/datasets/LIMSN7L1RAT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIMSN7L1RAT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LIMSN7L1RAT is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Radiance Data product. It contains calibrated, earth-located radiances, as well as housekeeping information, instrument status, and data quality information. Radiances of the Earth limb were measured both day and night in six spectral bands (6.2, 6.3, 9.6, 11.3, and two at 15 micrometers). Though calibrated, the radiances are not corrected for instrument effects such as field-of-view, electronic delay, and spacecraft motion.\n\nEach file contains one orbit of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR.\n\nThis product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B).", "links": [ { diff --git a/datasets/LIMSN7L2_006.json b/datasets/LIMSN7L2_006.json index 94ad958f7d..6cdecb1b2b 100644 --- a/datasets/LIMSN7L2_006.json +++ b/datasets/LIMSN7L2_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIMSN7L2_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-2 data product consists of daily, geolocated, vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). Version 6 LIMS data have improved spatial resolution in both the vertical and along the orbital track, as well as improved accuracy and precision of measured geophysical parameters.\n\nThe data files are in an ASCII text format and each data file is accompanied by three data screening files. The LIMS instrument was launched on the Nimbus-7 satellite and was operational from 25 October 1978 until May 28, 1979.\n\nThese data supersede the previous version 5 product, known as the LIMS Inverted Profile Archival Tape (LAIPAT).", "links": [ { diff --git a/datasets/LIMSN7L3_006.json b/datasets/LIMSN7L3_006.json index bf224be106..2c23c50eba 100644 --- a/datasets/LIMSN7L3_006.json +++ b/datasets/LIMSN7L3_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIMSN7L3_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-3 data product consists of daily, 2 degree zonal Fourier coefficients, of vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). The data are on 28 pressure levels, equally spaced logarithmically, between 316 hPa and 0.01 hPa. Version 6 LIMS data have improved accuracy and precision of measured geophysical parameters.\n\nThe data files are in an ASCII text format compressed using gzip. The LIMS instrument was launched on the Nimbus-7 satellite and was operational from 25 October 1978 until May 28, 1979.\n\nThese data supercede the previous version 5 products, known as the LIMS Map Archival Tape (LAMAT) and the LIMS Seasonal Map Archival Tape (LASMAT).", "links": [ { diff --git a/datasets/LINKAGES_1166_1.json b/datasets/LINKAGES_1166_1.json index 51cd0f25a3..706a550521 100644 --- a/datasets/LINKAGES_1166_1.json +++ b/datasets/LINKAGES_1166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LINKAGES_1166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product contains the source codes for version 1 of the individual-based forest ecosystem biogeochemistry model LINKAGES and two subsequent versions as well as example input and output data. LINKAGES predicts long-term structure and dynamics of forest ecosystems as constrained by nitrogen availability, climate, and soil moisture. Model simulations compare favorably to field data from different geographic areas worldwide. LINKAGES, written in FORTRAN and provided in ASCII format, simulates birth, growth, and death of all trees greater than 1.43-cm dbh. Litter fall and decomposition are also simulated. Sunlight is the driving variable. Growing season degree days, soil water availability, and AET are calculated from precipitation, temperature, soil field moisture capacity, and wilting point. Decomposition and soil N availability are calculated from organic matter quantity and carbon chemistry, evapotranspiration, and degree of canopy closure. Light availability to each tree is a function of leaf biomass of taller trees. Degree days and availabilities of light and water constrain species reproduction. These variables plus soil N constrain tree growth and carbon accumulation in biomass. Tree death probability increases with age and slow growth. Leaf, root, and woody litter are returned to the soil at the end of each year to decay the following year. Climatic and forest data for eastern North America and New South Wales are provided as example model inputs. Modelers may use their own site data within any version of LINKAGES. Example model output is also provided.", "links": [ { diff --git a/datasets/LISCO_mooring_0.json b/datasets/LISCO_mooring_0.json index 80d0c3dc54..20b1914f79 100644 --- a/datasets/LISCO_mooring_0.json +++ b/datasets/LISCO_mooring_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISCO_mooring_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long Island Sound Coastal Observational platform (LISCO) near Northport, New York, has both multispectral and hyperspectral radiometers for ocean color measurements to support satellite data validation.", "links": [ { diff --git a/datasets/LISTOS_AircraftInSitu_StonyBrookAircraft_Data_1.json b/datasets/LISTOS_AircraftInSitu_StonyBrookAircraft_Data_1.json index f75916e858..23c2235c37 100644 --- a/datasets/LISTOS_AircraftInSitu_StonyBrookAircraft_Data_1.json +++ b/datasets/LISTOS_AircraftInSitu_StonyBrookAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_AircraftInSitu_StonyBrookAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_AircraftInSitu_StonyBrookAircraft_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) in-situ data collected onboard the Stony Brook Aircraft during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_AircraftInSitu_UMDAircraft_Data_1.json b/datasets/LISTOS_AircraftInSitu_UMDAircraft_Data_1.json index e329ceabc4..39fdcabb6a 100644 --- a/datasets/LISTOS_AircraftInSitu_UMDAircraft_Data_1.json +++ b/datasets/LISTOS_AircraftInSitu_UMDAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_AircraftInSitu_UMDAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_AircraftInSitu_UMDAircraft_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) in-situ data collected onboard the University of Maryland Cessna Aircraft during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_AircraftRemoteSensing_NASAAircraft_Data_1.json b/datasets/LISTOS_AircraftRemoteSensing_NASAAircraft_Data_1.json index 8e7b4d5297..d301001a0b 100644 --- a/datasets/LISTOS_AircraftRemoteSensing_NASAAircraft_Data_1.json +++ b/datasets/LISTOS_AircraftRemoteSensing_NASAAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_AircraftRemoteSensing_NASAAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_AircraftRemoteSensing_NASAAircraft_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) remote sensing data collected onboard the NASA aircraft during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_Bayonne_Data_1.json b/datasets/LISTOS_Ground_Bayonne_Data_1.json index 309a8d2123..af0474bd29 100644 --- a/datasets/LISTOS_Ground_Bayonne_Data_1.json +++ b/datasets/LISTOS_Ground_Bayonne_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_Bayonne_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_Bayonne_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Bayonne ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_BronxPfizer_Data_1.json b/datasets/LISTOS_Ground_BronxPfizer_Data_1.json index eb02a0644d..7b760f916b 100644 --- a/datasets/LISTOS_Ground_BronxPfizer_Data_1.json +++ b/datasets/LISTOS_Ground_BronxPfizer_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_BronxPfizer_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_BronxPfizer_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Bronx Pfizer ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_CCNY_Data_1.json b/datasets/LISTOS_Ground_CCNY_Data_1.json index 30dcf8d2a9..005aa0c0ad 100644 --- a/datasets/LISTOS_Ground_CCNY_Data_1.json +++ b/datasets/LISTOS_Ground_CCNY_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_CCNY_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_CCNY_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the CCNY ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_FlaxPond_Data_1.json b/datasets/LISTOS_Ground_FlaxPond_Data_1.json index e05dc36658..db64483c13 100644 --- a/datasets/LISTOS_Ground_FlaxPond_Data_1.json +++ b/datasets/LISTOS_Ground_FlaxPond_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_FlaxPond_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_FlaxPond_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Flax Pond ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_Hammonasset_Data_1.json b/datasets/LISTOS_Ground_Hammonasset_Data_1.json index 5ea412c323..54184b5514 100644 --- a/datasets/LISTOS_Ground_Hammonasset_Data_1.json +++ b/datasets/LISTOS_Ground_Hammonasset_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_Hammonasset_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_Hammonasset_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Hammonasset ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_NewHaven_Data_1.json b/datasets/LISTOS_Ground_NewHaven_Data_1.json index d6a9febe1a..f3fe4e63dd 100644 --- a/datasets/LISTOS_Ground_NewHaven_Data_1.json +++ b/datasets/LISTOS_Ground_NewHaven_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_NewHaven_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_NewHaven_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the New Haven ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_Other_Data_1.json b/datasets/LISTOS_Ground_Other_Data_1.json index f0c166013b..2c0798015f 100644 --- a/datasets/LISTOS_Ground_Other_Data_1.json +++ b/datasets/LISTOS_Ground_Other_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_Other_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_Other_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at a collection of ground sites during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO. LISTOS_Ground_Other_Data are data collected at other/miscellaneous ground sites during the LISTOS campaign.", "links": [ { diff --git a/datasets/LISTOS_Ground_OuterIsland_Data_1.json b/datasets/LISTOS_Ground_OuterIsland_Data_1.json index 940e3c5ebe..abe9f89cd8 100644 --- a/datasets/LISTOS_Ground_OuterIsland_Data_1.json +++ b/datasets/LISTOS_Ground_OuterIsland_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_OuterIsland_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_OuterIsland_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Outer Island ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation, and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of the Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). \r\n\r\nLISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_QueensCollege_Data_1.json b/datasets/LISTOS_Ground_QueensCollege_Data_1.json index a22333cce5..106a5224fe 100644 --- a/datasets/LISTOS_Ground_QueensCollege_Data_1.json +++ b/datasets/LISTOS_Ground_QueensCollege_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_QueensCollege_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_QueensCollege_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Queens College ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_Rutgers_Data_1.json b/datasets/LISTOS_Ground_Rutgers_Data_1.json index 6478fcce93..611bad7125 100644 --- a/datasets/LISTOS_Ground_Rutgers_Data_1.json +++ b/datasets/LISTOS_Ground_Rutgers_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_Rutgers_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_Rutgers_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) Rutgers ground site data collected during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_Westport_Data_1.json b/datasets/LISTOS_Ground_Westport_Data_1.json index 789bce1548..50924a8ab9 100644 --- a/datasets/LISTOS_Ground_Westport_Data_1.json +++ b/datasets/LISTOS_Ground_Westport_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_Westport_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_Westport_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) Wesport ground site data collected during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_Ground_YaleCoastal_Data_1.json b/datasets/LISTOS_Ground_YaleCoastal_Data_1.json index c8d12ff1e4..aef0103205 100644 --- a/datasets/LISTOS_Ground_YaleCoastal_Data_1.json +++ b/datasets/LISTOS_Ground_YaleCoastal_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_Ground_YaleCoastal_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_Ground_YaleCoastal_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Yale Coastal ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data_1.json b/datasets/LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data_1.json index acba211492..f72fec8048 100644 --- a/datasets/LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data_1.json +++ b/datasets/LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_MetNav_AircraftInSitu_NASAAircraft_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) in-situ meteorological and navigational data collected onboard the NASA aircraft during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LISTOS_SurfaceMobile_InSitu_Data_1.json b/datasets/LISTOS_SurfaceMobile_InSitu_Data_1.json index 264a48bb76..3617337703 100644 --- a/datasets/LISTOS_SurfaceMobile_InSitu_Data_1.json +++ b/datasets/LISTOS_SurfaceMobile_InSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LISTOS_SurfaceMobile_InSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LISTOS_SurfaceMobile_InSitu_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) surface mobile data collected via mobile platforms during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. This product features data collected by the Connecticut Department of Energy and Environmental Protection (CT DEEP) special purpose mobile monitor located on the Park City ferry on Long Island Sound and other mobile platforms. Data collection is complete.\r\n\r\nThe New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.", "links": [ { diff --git a/datasets/LIS_0.json b/datasets/LIS_0.json index f22e2a5130..76b70c6ac1 100644 --- a/datasets/LIS_0.json +++ b/datasets/LIS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LIS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Long Island, New York between 2004 and 2009.", "links": [ { diff --git a/datasets/LITE_L1_1.json b/datasets/LITE_L1_1.json index d574dfe75a..53a1ac82aa 100644 --- a/datasets/LITE_L1_1.json +++ b/datasets/LITE_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LITE_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LITE_L1 data are LIDAR Vertical profile data along the orbital flight path of STS-64.Lidar In-Space Technology Experiment (LITE) used a three-wavelength (355 nm, 532 nm and 1064 nm) backscatter lidar which flew on the space shuttle Discovery as part of the STS-64 mission between September 9 and September 20, 1994. The LITE instrument was designed with the capability to make measurements of clouds, aerosols in the stratosphere and troposphere, the height of the planetary boundary layer, and atmospheric temperature and density in the stratosphere between 25 km and 40 km altitude. Additionally, limited measurements of the surface return strength over both land and ocean were collected to explore retrievals of surface properties.The LITE data were transmitted real time the by Ku-band system through TDRSS downlink to the LITE operations center at JSC. There was a gap in the high-rate coverage between 60 E and 85 E due to the zone of exclusion, where neither TDRSS satellite was in view. Additional random gaps in the data occurred due to telemetry dropouts during data transmission.The LITE L1 data product was formed by processing and reformatting the LITE high-rate telemetry data. The LITE L1 processing steps included:Correcting the profiles for instrument artifacts. Subtracting the DC offset from each lidar profile. Interpolating lidar profiles to a geolocated, common altitude grid, which extends from -4.985 to 40.0 km with a 15 m vertical resolution. Determining the LITE system calibration constants for the 355 nm and 532 nm wavelength profiles.Merged with the LITE L1 lidar profiles are: Identification Parameters, Time Parameters, Location Parameters, Operation Mode Parameters, Validity Flags, Measurement Location Descriptions, Temperature and Pressure Profiles Derived from NMC Data, Instrument Status Information.The archived files are concatenations of about 1000 (depending on data gaps) sets of headers and profiles. Read software programs written in C or IDL are available.", "links": [ { diff --git a/datasets/LMER-TIES_0.json b/datasets/LMER-TIES_0.json index 322540778d..4765f77cd0 100644 --- a/datasets/LMER-TIES_0.json +++ b/datasets/LMER-TIES_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMER-TIES_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken under the Chesapeake Bay Land Margin Ecosystem Research (LMER): Trophic Interactions in Estuarine Systems (TIES) between 1993 and 2001.", "links": [ { diff --git a/datasets/LMOS_AircraftInSitu_ScientificAviation_Data_1.json b/datasets/LMOS_AircraftInSitu_ScientificAviation_Data_1.json index 278b025ebf..8ddc8c56dc 100644 --- a/datasets/LMOS_AircraftInSitu_ScientificAviation_Data_1.json +++ b/datasets/LMOS_AircraftInSitu_ScientificAviation_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_AircraftInSitu_ScientificAviation_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_AircraftInSitu_ScientificAviation_Data_1 is the Lake Michigan Ozone Study (LMOS) in-situ data collected onboard the Scientific Aviation aircraft during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_AircraftRemoteSensing_UC12_Data_1.json b/datasets/LMOS_AircraftRemoteSensing_UC12_Data_1.json index 8679f4d423..c6208399d5 100644 --- a/datasets/LMOS_AircraftRemoteSensing_UC12_Data_1.json +++ b/datasets/LMOS_AircraftRemoteSensing_UC12_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_AircraftRemoteSensing_UC12_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_AircraftRemoteSensing_UC12_Data_1 is the Lake Michigan Ozone Study (LMOS) remote sensing data collected onboard the NASA UC-12 aircraft during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_Grafton_Data_1.json b/datasets/LMOS_Ground_Grafton_Data_1.json index 36cb6fa60e..e678f5f9b8 100644 --- a/datasets/LMOS_Ground_Grafton_Data_1.json +++ b/datasets/LMOS_Ground_Grafton_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_Grafton_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_Grafton_Data_1 is the Lake Michigan Ozone Study (LMOS) Grafton ground site data collected during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_IEPA_Data_1.json b/datasets/LMOS_Ground_IEPA_Data_1.json index 25526488ec..bcca9a7f47 100644 --- a/datasets/LMOS_Ground_IEPA_Data_1.json +++ b/datasets/LMOS_Ground_IEPA_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_IEPA_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_IEPA_Data_1 is the Lake Michigan Ozone Study (LMOS) ground site data collected at the Illinois EPA (IEPA) ground site during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_Milwaukee_Data_1.json b/datasets/LMOS_Ground_Milwaukee_Data_1.json index c448def840..d5f079d379 100644 --- a/datasets/LMOS_Ground_Milwaukee_Data_1.json +++ b/datasets/LMOS_Ground_Milwaukee_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_Milwaukee_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_Milwaukee_Data_1 is the Lake Michigan Ozone Study (LMOS) Milwaukee ground site data collected during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_SchillerPark_Data_1.json b/datasets/LMOS_Ground_SchillerPark_Data_1.json index 00d1cecbe8..662884043c 100644 --- a/datasets/LMOS_Ground_SchillerPark_Data_1.json +++ b/datasets/LMOS_Ground_SchillerPark_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_SchillerPark_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_SchillerPark_Data_1 is the Lake Michigan Ozone Study (LMOS) data collected at the Schiller Park ground site during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_Sheboygan_Data_1.json b/datasets/LMOS_Ground_Sheboygan_Data_1.json index 3dde34ee39..03dba65cd8 100644 --- a/datasets/LMOS_Ground_Sheboygan_Data_1.json +++ b/datasets/LMOS_Ground_Sheboygan_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_Sheboygan_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_Sheboygan_Data_1 is the Lake Michigan Ozone Study (LMOS) is Sheboygan ground site data collected during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_WDNRRoutine_Data_1.json b/datasets/LMOS_Ground_WDNRRoutine_Data_1.json index baa4d15b8e..8e52efd48c 100644 --- a/datasets/LMOS_Ground_WDNRRoutine_Data_1.json +++ b/datasets/LMOS_Ground_WDNRRoutine_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_WDNRRoutine_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_WDNRRoutine_Data_1 is the Lake Michigan Ozone Study (LMOS) ground site data collected at the Wisconsin Department of Natural Resources (WDNR) Routine ground site during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection for this product is complete.\r\rElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers of the shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Ground_Zion_Data_1.json b/datasets/LMOS_Ground_Zion_Data_1.json index b65887ffcb..5b698faf54 100644 --- a/datasets/LMOS_Ground_Zion_Data_1.json +++ b/datasets/LMOS_Ground_Zion_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Ground_Zion_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Ground_Zion_Data_1 is the Lake Michigan Ozone Study (LMOS) data collected at the Zion ground site during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\rElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_MetNav_AircraftInSitu_UC12_Data_1.json b/datasets/LMOS_MetNav_AircraftInSitu_UC12_Data_1.json index e0ebe67572..d63f6b7454 100644 --- a/datasets/LMOS_MetNav_AircraftInSitu_UC12_Data_1.json +++ b/datasets/LMOS_MetNav_AircraftInSitu_UC12_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_MetNav_AircraftInSitu_UC12_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_MetNav_AircraftInSitu_UC12_Data_1 is the Lake Michigan Ozone Study (LMOS) in-situ meteorological and navigational data collected onboard the NASA UC-12 aircraft during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_Miscellaneous_Data_1.json b/datasets/LMOS_Miscellaneous_Data_1.json index 33905ae414..6578dbc86a 100644 --- a/datasets/LMOS_Miscellaneous_Data_1.json +++ b/datasets/LMOS_Miscellaneous_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_Miscellaneous_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_Miscellaneous_Data is the supplementary and ancillary data to support the Lake Michigan Ozone Study (LMOS). This data product currently features supplementary satellite data. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_TraceGas_ShipInSitu_Data_1.json b/datasets/LMOS_TraceGas_ShipInSitu_Data_1.json index 17a14c6a3e..9a7cd49a0b 100644 --- a/datasets/LMOS_TraceGas_ShipInSitu_Data_1.json +++ b/datasets/LMOS_TraceGas_ShipInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_TraceGas_ShipInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_TraceGas_ShipInSitu_Data_1 is the Lake Michigan Ozone Study (LMOS) in-situ trace gas data collected onboard the NOAA Research Vessel during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers of the shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1.json b/datasets/LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1.json index aa1bdbd65f..89bbf8338e 100644 --- a/datasets/LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1.json +++ b/datasets/LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1 is the Lake Michigan Ozone Study (LMOS) trace gas surface mobile data collected via the Environmental Protection Agency (EPA) GMAP mobile platform during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1.json b/datasets/LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1.json index 4e5ee1aa9e..82088d333e 100644 --- a/datasets/LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1.json +++ b/datasets/LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMOS_TraceGas_SurfaceMobile_UWEC-Auto_Data_1 is the Lake Michigan Ozone Study (LMOS) trace gas surface mobile data collected onboard the University of Wisconsin-Eau Claire (UWEC) surface mobile platform during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection for this product is complete.\r\n\r\nElevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime \u201clake breeze\u201d airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.", "links": [ { diff --git a/datasets/LOBO_timeseries_0.json b/datasets/LOBO_timeseries_0.json index 68bada62df..f854cc3451 100644 --- a/datasets/LOBO_timeseries_0.json +++ b/datasets/LOBO_timeseries_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LOBO_timeseries_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Dartmouth, Nova Scotia in 2009.", "links": [ { diff --git a/datasets/LOCSS_L1_V1_1.0.json b/datasets/LOCSS_L1_V1_1.0.json index f88a12cfe8..767748f793 100644 --- a/datasets/LOCSS_L1_V1_1.0.json +++ b/datasets/LOCSS_L1_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LOCSS_L1_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings. LOCSS project aims to collaborate with local citizens to monitor small and medium sized lakes (i.e., lakes with an average surface area less than 100 km2). At each location, a lake gauge is installed and provided with a cellphone number. Local citizens read the water level at each lake gauge and sent it in a text message. Data can also be manually collected and uploaded later from the website in remote places where cellphone signal is challenged. The readings are specified in cm, m, or ft, according to the local unit system. This version of the dataset has lakes located in seven (7) countries: Bangladesh, India, Canada, the United States, Pakistan, and Nepal. This product consists of two files in comma-separated values (csv) format : 1) the list of gauges whose attributes include gauge coordinates, installation dates, the height of the gauge, reading units, city, time zone, and installation notes; 2) list of readings by each gauge specified in the local time. To discover more details about LOCSS, please visit https://www.locss.org/.", "links": [ { diff --git a/datasets/LOM_2.json b/datasets/LOM_2.json index 0cc47d7f3b..4424896b6a 100644 --- a/datasets/LOM_2.json +++ b/datasets/LOM_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LOM_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Operations Area map was produced in 1994 by the Australian Surveying and Land Information Group for the Australian Antarctic Division's Glaciology traverse team. This GIS dataset was used to create the map.\nA link to the entry for the map in the SCAR Map Catalogue is included in this metadata record.", "links": [ { diff --git a/datasets/LPJ-WHyMe_v1-3-1_1150_1.json b/datasets/LPJ-WHyMe_v1-3-1_1150_1.json index ea1d521542..c24e332264 100644 --- a/datasets/LPJ-WHyMe_v1-3-1_1150_1.json +++ b/datasets/LPJ-WHyMe_v1-3-1_1150_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ-WHyMe_v1-3-1_1150_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions.The model processes can be simulated on an area-averaged 0.5 or 1.0 degree grid cell basis at global, regional, or site scales and on a daily, monthly, or annual time step as appropriate. Input driver data are monthly mean air temperature, total precipitation, percentage of full sunshine, annual atmospheric CO2 concentration, and soil texture class. The simulation for each grid cell begins from \"bare ground\", requiring a \"spin up\" (under non-transient climate) of ca. 1,000 years to develop equilibrium vegetation, carbon, and soil structure. Model simulations compare favorably, with some exceptions, to field observations collected from peatland sites (e.g., Degero, Sweden; Lakkasuo, Finland; BOREAS Northern Study Area, Canada; and others) and non-peatland sites (e.g., Point Barrow, Alaska, and Spasskaya, Siberia). LPJ-WHyMe is a further development of LPJ-WHy, which dealt with the introduction of permafrost and peatlands into LPJ. Implementing peatlands in LPJ required the addition of two new plant functional types (PFTs) (flood tolerant C3 graminoids and Sphagnum mosses) to the already existing ten PFTs, the introduction of inundation stress for non-peatland PFTs, a slow-down in decomposition under inundation, and the addition of a root exudates pool. LPJ-WHyMe v1.3.1 adds a methane model subroutine. This model product has one compressed data file (*.zip) and seven companion files.", "links": [ { diff --git a/datasets/LPJ_EOSIM_L2_DCH4E_001.json b/datasets/LPJ_EOSIM_L2_DCH4E_001.json index 44824a9f91..1e57a08835 100644 --- a/datasets/LPJ_EOSIM_L2_DCH4E_001.json +++ b/datasets/LPJ_EOSIM_L2_DCH4E_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_EOSIM_L2_DCH4E_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and help with strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots.\r\n\r\nThe LPJ-EOSIM Level 2 Global Simulated Daily Wetland Methane Flux (LPJ_EOSIM_L2_DCH4E) Version 1 data product provides simulated daily wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The daily data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values.\r\n\r\nDue to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated daily CH4 flux data products, low latency (LPJ_EOSIM_L2_DCH4E_LL) and high latency (LPJ_EOSIM_L2_DCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration\u2019s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year\u2019s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please see Section 2.0.1 of the User Guide for a more detailed explanation of CO2 estimate inputs and timing for scheduled updates to the collections.", "links": [ { diff --git a/datasets/LPJ_EOSIM_L2_DCH4E_LL_001.json b/datasets/LPJ_EOSIM_L2_DCH4E_LL_001.json index 0b617cd4ba..a70c727962 100644 --- a/datasets/LPJ_EOSIM_L2_DCH4E_LL_001.json +++ b/datasets/LPJ_EOSIM_L2_DCH4E_LL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_EOSIM_L2_DCH4E_LL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and help with strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots.\r\n\r\nThe LPJ-EOSIM Level 2 Global Simulated Daily Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_DCH4E_LL) Version 1 data product provides simulated daily wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The daily data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values.\r\n\r\nDue to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated daily CH4 flux data products, low latency (LPJ_EOSIM_L2_DCH4E_LL) and high latency (LPJ_EOSIM_L2_DCH4E) collections are available. Low latency data are delivered on a two-month cadence throughout the year. Granules will also be updated as new CO2 input data become available. Please see Section 2.0.1 of the User Guide for a more detailed explanation of CO2 estimate inputs and timing for scheduled updates to the collections.", "links": [ { diff --git a/datasets/LPJ_EOSIM_L2_MCH4E_001.json b/datasets/LPJ_EOSIM_L2_MCH4E_001.json index c3c2497c29..d295a3b5d7 100644 --- a/datasets/LPJ_EOSIM_L2_MCH4E_001.json +++ b/datasets/LPJ_EOSIM_L2_MCH4E_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_EOSIM_L2_MCH4E_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots.\r\n\r\nThe LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux (LPJ_EOSIM_L2_MCH4E) Version 1 data product provides simulated monthly wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux (LPJ_EOSIM_L2_DCH4E) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data.\r\n\r\nDue to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency (LPJ_EOSIM_L2_MCH4E_LL) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration\u2019s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year\u2019s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please see Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections.", "links": [ { diff --git a/datasets/LPJ_EOSIM_L2_MCH4E_LL_001.json b/datasets/LPJ_EOSIM_L2_MCH4E_LL_001.json index bd05b07916..5d026f59b3 100644 --- a/datasets/LPJ_EOSIM_L2_MCH4E_LL_001.json +++ b/datasets/LPJ_EOSIM_L2_MCH4E_LL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_EOSIM_L2_MCH4E_LL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback, and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots.\r\n\r\nThe LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_MCH4E_LL) Version 1 data product provides simulated monthly wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_DCH4E_LL) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data.\r\n\r\nDue to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency (LPJ_EOSIM_L2_MCH4E_LL) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. Low latency data are delivered on a two-month cadence throughout the year. Granules will also be updated as new CO2 input data become available. Please see Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections.\r\n", "links": [ { diff --git a/datasets/LPJ_L2_SSREF_001.json b/datasets/LPJ_L2_SSREF_001.json index b85e7f0b5c..13a0d89c61 100644 --- a/datasets/LPJ_L2_SSREF_001.json +++ b/datasets/LPJ_L2_SSREF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_L2_SSREF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LPJ-PROSAIL simulated data products are produced through the coupling of the Lund-Potsdam-Jena dynamic global vegetation model (LPJ) and PROSAIL, a radiative transfer model. The simulated imaging spectroscopy data were produced to aid in the development of workflows, algorithm testing, and other activities during the lead up to future global spaceborne imaging spectroscopy missions such as NASA\u2019s Surface Biology and Geology (SBG). The LPJ-PROSAIL Level 2 Global Simulated Dynamic Surface Reflectance (LPJ_L2_SSREF) Version 1 data product provides simulated dynamic surface reflectance data in five Network Common Data Format 4 (netCDF4) files, each containing a different reflectance stream at a spatial resolution of 0.5 degrees (~50 kilometers): bidirectional (BDR), bi-hemispherical (BHR), hemispherical-directional (HDR), directional-hemispherical (DHR), and directional (DR). Each reflectance file within a granule contains simulated surface reflectance measurements of 211 bands with 10 nanometer (nm) spectral resolution across a spectral range of 400 to 2500 nm for the entire globe. The data are presented with four dimensions: latitude, longitude, bands (wavelength), and time. Each netCDF4 file holds a one-dimensional list for each of the four dimensions containing the values that are associated with those dimensions. LPJ_L2_SSREF Version 1 is composed of one granule containing data for the year 2020 with monthly time increments. \r\nData Usage Warning - Due to the simulated nature of these data, they should not be used for any real-world scientific analyses or conclusions. These data are meant to be used in development of workflows, algorithms, and other instances where large imaging spectroscopy datasets are needed for testing. These data are not intended for scientific use. ", "links": [ { diff --git a/datasets/LPJ_L2_SSREF_002.json b/datasets/LPJ_L2_SSREF_002.json index 66f13b29ac..5288ad7cb1 100644 --- a/datasets/LPJ_L2_SSREF_002.json +++ b/datasets/LPJ_L2_SSREF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPJ_L2_SSREF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LPJ-PROSAIL simulated data products are produced through the coupling of Lund-Potsdam-Jena dynamic global vegetation model (LPJ) and PROSAIL, a radiative transfer model. The simulated imaging spectroscopy data were produced to aid in the development of workflows, algorithm testing, and other activities during the lead up to future global spaceborne imaging spectroscopy missions such as NASA\u2019s Surface Biology and Geology (SBG).\r\nThe LPJ-PROSAIL Level 2 Global Simulated Dynamic Surface Reflectance (LPJ_L2_SSREF) Version 2 data product provides simulated dynamic surface reflectance data in five Network Common Data Format Version 4 (netCDF-4) files, each containing a different reflectance stream at a spatial resolution of 0.5 degrees (~50 kilometers): bidirectional (BDR), bi-hemispherical (BHR), hemispherical-directional (HDR), directional-hemispherical (DHR), and directional (DR). Each reflectance file within a granule contains simulated surface reflectance measurements of 211 bands with 10 nanometer (nm) spectral resolution across a spectral range of 400 to 2500 nm for the entire globe. The data are presented with four dimensions: latitude, longitude, bands (wavelength), and time. Each netCDF-4 file holds a one-dimensional list for each of the four dimensions containing the values that are associated with those dimensions. LPJ_L2_SSREF Version 2 is composed of data for the years 2000 to 2022 with monthly time increments. \r\nData Usage Warning - Due to the simulated nature of these data, they should not be used for any real-world scientific analyses or conclusions. These data are meant to be used in development of workflows, algorithms, and other instances where large imaging spectroscopy datasets are needed for testing. These data are not intended for scientific use.", "links": [ { diff --git a/datasets/LPRM_AMSR2_A_SOILM3_001.json b/datasets/LPRM_AMSR2_A_SOILM3_001.json index 40d169efa2..31a2a136fc 100644 --- a/datasets/LPRM_AMSR2_A_SOILM3_001.json +++ b/datasets/LPRM_AMSR2_A_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_A_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Level 2 product, LPRM_AMSR2_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_AMSR2_DS_A_SOILM3_001.json b/datasets/LPRM_AMSR2_DS_A_SOILM3_001.json index 79153de01c..78e48f9248 100644 --- a/datasets/LPRM_AMSR2_DS_A_SOILM3_001.json +++ b/datasets/LPRM_AMSR2_DS_A_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_DS_A_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Downscaled Level 2 product, LPRM_AMSR2_DS_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_AMSR2_DS_D_SOILM3_001.json b/datasets/LPRM_AMSR2_DS_D_SOILM3_001.json index cf4e1a5249..61a8b7318b 100644 --- a/datasets/LPRM_AMSR2_DS_D_SOILM3_001.json +++ b/datasets/LPRM_AMSR2_DS_D_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_DS_D_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Downscaled Level 2 product, LPRM_AMSR2_DS_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_AMSR2_DS_SOILM2_001.json b/datasets/LPRM_AMSR2_DS_SOILM2_001.json index 16ee59bfcf..4b0aa39768 100644 --- a/datasets/LPRM_AMSR2_DS_SOILM2_001.json +++ b/datasets/LPRM_AMSR2_DS_SOILM2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_DS_SOILM2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 downscaled surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The spatial resolution of the data is based on a resampling of the nominally 46 and 31 km resolutions, respectively, of AMSR2's C and X bands (6.9/7.3 and 10.7 GHz, respectively) to 25 km by 25 km and then a downscaling, using the smoothing filter-based intensity modulation (SFIM) technique, to 10 km by 10 km grids.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, archived at JAXA.", "links": [ { diff --git a/datasets/LPRM_AMSR2_D_SOILM3_001.json b/datasets/LPRM_AMSR2_D_SOILM3_001.json index cbd0602d74..75354e4868 100644 --- a/datasets/LPRM_AMSR2_D_SOILM3_001.json +++ b/datasets/LPRM_AMSR2_D_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_D_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Level 2 product, LPRM_AMSR2_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_AMSR2_SOILM2_001.json b/datasets/LPRM_AMSR2_SOILM2_001.json index 10d100fe7d..949b76f9e0 100644 --- a/datasets/LPRM_AMSR2_SOILM2_001.json +++ b/datasets/LPRM_AMSR2_SOILM2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSR2_SOILM2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR2/GCOM-W1 surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present, at a spatial resolution (nominally 46 and 31 km, respectively) of AMSR2's C and X bands (6.9/7.3 and 10.7 GHz, respectively).\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, archived at JAXA.", "links": [ { diff --git a/datasets/LPRM_AMSRE_A_SOILM3_002.json b/datasets/LPRM_AMSRE_A_SOILM3_002.json index 203b20bd31..403b73653a 100644 --- a/datasets/LPRM_AMSRE_A_SOILM3_002.json +++ b/datasets/LPRM_AMSRE_A_SOILM3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSRE_A_SOILM3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna).\n \nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).", "links": [ { diff --git a/datasets/LPRM_AMSRE_D_SOILM3_002.json b/datasets/LPRM_AMSRE_D_SOILM3_002.json index 65bfa40d55..a01f367816 100644 --- a/datasets/LPRM_AMSRE_D_SOILM3_002.json +++ b/datasets/LPRM_AMSRE_D_SOILM3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSRE_D_SOILM3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna).\n \nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).", "links": [ { diff --git a/datasets/LPRM_AMSRE_SOILM2_002.json b/datasets/LPRM_AMSRE_SOILM2_002.json index 8f15de2a61..1b386d8574 100644 --- a/datasets/LPRM_AMSRE_SOILM2_002.json +++ b/datasets/LPRM_AMSRE_SOILM2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_AMSRE_SOILM2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR-E/Aqua surface soil moisture (LPRM) L2B V002 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna), at the spatial resolution (nominally 56 and 38 km, respectively) of AMSR-E's C and X bands (6.9 and 10.7 GHz, respectively).\n \nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, archived at the National Snow and Ice Data Center (NSIDC).", "links": [ { diff --git a/datasets/LPRM_TMI_DY_SOILM3_001.json b/datasets/LPRM_TMI_DY_SOILM3_001.json index a3ef304283..580e4fe141 100644 --- a/datasets/LPRM_TMI_DY_SOILM3_001.json +++ b/datasets/LPRM_TMI_DY_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_TMI_DY_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one daytime and one nighttime, archived as two different products. This document is for the daytime product. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere).\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the TMI Brightness Temperatures (1B-11) product, daytime passes, as processed using LPRM (i.e., LPRM/TMI/TRMM Level 2 product, LPRM_TMI_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_TMI_NT_SOILM3_001.json b/datasets/LPRM_TMI_NT_SOILM3_001.json index a1d6eeed44..cc654eb2a4 100644 --- a/datasets/LPRM_TMI_NT_SOILM3_001.json +++ b/datasets/LPRM_TMI_NT_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_TMI_NT_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one daytime and one nighttime, archived as two different products. This document is for the nighttime product. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere).\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the TMI Brightness Temperatures (1B-11) product, nighttime passes, as processed using LPRM (i.e., LPRM/TMI/TRMM Level 2 product, LPRM_TMI_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_TMI_SOILM2_001.json b/datasets/LPRM_TMI_SOILM2_001.json index 9369cb4626..8af0068f16 100644 --- a/datasets/LPRM_TMI_SOILM2_001.json +++ b/datasets/LPRM_TMI_SOILM2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_TMI_SOILM2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMI/TRMM surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere), at the spatial resolution (nominally 45 km) of TMI's X band (10.7 GHz).\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n\nInput data are from the TMI Brightness Temperatures (1B-11) product, archived at the Goddard Earth Sciences Data and Information Services Center (GES DISC).", "links": [ { diff --git a/datasets/LPRM_WINDSAT_DY_SOILM3_001.json b/datasets/LPRM_WINDSAT_DY_SOILM3_001.json index f2b0a8167f..0ff99896cd 100644 --- a/datasets/LPRM_WINDSAT_DY_SOILM3_001.json +++ b/datasets/LPRM_WINDSAT_DY_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_WINDSAT_DY_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from February 2003 to July 2012.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the WindSat brightness temperatures (sdrLowRes) product, daytime passes, as processed using LPRM (i.e., LPRM/WindSat/Coriolis L2 product, LPRM_WINDSAT_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_WINDSAT_NT_SOILM3_001.json b/datasets/LPRM_WINDSAT_NT_SOILM3_001.json index 2375184667..55ab5750a2 100644 --- a/datasets/LPRM_WINDSAT_NT_SOILM3_001.json +++ b/datasets/LPRM_WINDSAT_NT_SOILM3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_WINDSAT_NT_SOILM3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from February 2003 to July 2012.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the WindSat brightness temperatures (sdrLowRes) product, nighttime passes, as processed using LPRM (i.e., LPRM/WindSat/Coriolis L2 product, LPRM_WINDSAT_SOILM2_V001).", "links": [ { diff --git a/datasets/LPRM_WINDSAT_SOILM2_001.json b/datasets/LPRM_WINDSAT_SOILM2_001.json index c0e6493397..8eedc348b4 100644 --- a/datasets/LPRM_WINDSAT_SOILM2_001.json +++ b/datasets/LPRM_WINDSAT_SOILM2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LPRM_WINDSAT_SOILM2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WindSat/Coriolis surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from February 2003 to July 2012.\n\nThe LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.\n \nInput data are from the WindSat brightness temperatures (sdrLowRes) product, archived at the Goddard Earth Sciences Data and Information Services Center (GES DISC).", "links": [ { diff --git a/datasets/LRIRN6L2IPAT_001.json b/datasets/LRIRN6L2IPAT_001.json index ce6ae25d01..7018acd831 100644 --- a/datasets/LRIRN6L2IPAT_001.json +++ b/datasets/LRIRN6L2IPAT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LRIRN6L2IPAT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LRIRN6L2IPAT is the Nimbus-6 Limb Radiance Inversion Radiometer (LRIR) Level 2 Inverted Profiles of Temperature and Ozone data product. The product contains daily profiles of temperature and ozone concentration profiles that were inverted from radiances measured in four spectral regions: two in the 15 micron carbon dioxide band; one in the 9.7 micron ozone band; and one located in the rotational water vapor band (23 to 27 microns). The calibrated radiances are also included in this product. There are a maximum of 13 orbits per day each with up to 115 profiles per orbit.\n\nLRIR is a limb profiler with spatial coverage from latitude -64 to +84 degrees. Vertical profiles are provided at 17 standard pressure levels (from 100 to 0.1 mbar, i.e., from 15 to 64 km) with about 1.5 km vertical resolution. The instrument operated successfully and data are available from 20 June 1975 to 6 January 1976. After this, the detector temperature began to rise rapidly, and the instrument was turned off. The principal investigator for the LRIR experiment was Dr. John Gille from NCAR.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00037 (old ID 75-052A-04A).", "links": [ { diff --git a/datasets/LSATUSERV.json b/datasets/LSATUSERV.json index a5f1ba4021..306f8f1766 100644 --- a/datasets/LSATUSERV.json +++ b/datasets/LSATUSERV.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSATUSERV", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Brazilian receiving station for Landsat data is located in Cuiaba,\n state of Mato Grosso, Midwest region: 56 degrees 5 minutes 30 seconds\n West longitude, and 15 degrees 32 minutes 30 seconds South\n latitude. The Cuiaba station started to operate in May 1973. MSS data\n were acquired on a routine basis over Brazil and by special request\n over the other countries. RBV images were acquired during the Landsat\n 3 years. Thematic mapper data have been recorded over Brazil since\n February 1984. By October 1987, the Cuiaba station stopped recording\n MSS data and began to acquire Thematic Mapper images on routine\n fashion over the whole range of the antenna. MSS products are for\n while available just through the reproduction of existing photographic\n originals (about 150.000 B/W and color images). A new processing\n system was designed and began running digital frames in October\n 1995. Thematic Mapper images are available and can be ordered both in\n print and digital formats.", "links": [ { diff --git a/datasets/LSC_Aeromonas_salmonicida.json b/datasets/LSC_Aeromonas_salmonicida.json index 2e84ff4edb..94c5ce15fa 100644 --- a/datasets/LSC_Aeromonas_salmonicida.json +++ b/datasets/LSC_Aeromonas_salmonicida.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_Aeromonas_salmonicida", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Furunculosis is a significant cause of disease and mortality to hatchery reared\nand wild populations of salmonid fishes, particularly the salmon species. The\ndisease is caused by Aeromonas salmonicida, a Gram nagative bacterium that is\nhighly virulent and is readily transmitted horozontally. An asymptomatic form\nof the disease occurs, including those individuals that survive epizootics, and\nthese fish can serve as a source of infection in subsequent outbreaks. The\ndisease is treated by antimicrobial therapy. Romet, a potentiated sulfonimide,\nwas approved for use by the FDA in 1986 and is one of only three agents\napproved. Since approval of Romet, resistant strains of A. salmonicida have\nemerged and this removes treatment with Romet as an alternative. Recent work\nhas described an R-plasmid mediated resistance in many of the resistant A.\nsalmonicida strains.", "links": [ { diff --git a/datasets/LSC_Aeromonasinsalmon.json b/datasets/LSC_Aeromonasinsalmon.json index 88eb5a2b5d..50af99a39c 100644 --- a/datasets/LSC_Aeromonasinsalmon.json +++ b/datasets/LSC_Aeromonasinsalmon.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_Aeromonasinsalmon", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the early 1900s, several researchers cultured Aeromonas salmonicida,\ncause of furunculosis disease, from the kidneys and intestines of apparently\nhealthy trout. Home (1928) speculated that asymptomatic carriers become\nreservoirs of infection. Hence, a need was perceived for bacteriological\nexaminations to be conducted even before asymptoimatic fish were stocked.\nDuring the devastating outbreaks of furunculosis that occurred in Great Britain\nin the 1920-30s, Mackie et al. (1933) noted that epizootics in natural waters\ncorrelated with the stocking of fish originating from infected farms. They also\nnoted that the kidney was the usual site of harborage of the pathogen but\nculture often provided inadequate detection. More recent studies continue to\nemphasize the importance of asymptomatic, carrier fish as reservoirs of\ninfection and spread of the disease within the natural environment (Jarp et al.\n1993, Johnsen and Jensen 1994).", "links": [ { diff --git a/datasets/LSC_AtlanticSalmon.json b/datasets/LSC_AtlanticSalmon.json index 16ba274a8c..3ddfc93804 100644 --- a/datasets/LSC_AtlanticSalmon.json +++ b/datasets/LSC_AtlanticSalmon.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_AtlanticSalmon", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The eastern coastal rivers of North America have historically supported\nanadromous populations of Atlantic salmon (Salmo salar). Numbers of these\nanimals have declined due to overfishing and loss of habitat, and population\nnumbers have been supplemented by stocking efforts that span at least the last\nhundred years. Often, these stockings used fish of diverse origins. This is\nexemplified by the fact that several Maine rivers were stocked with Canadian\nfish from at least two locations. Because of this stocking history, it is not\nknown if significant remnants of native Atlantic salmon stocks exist in the\ncoastal rivers of Maine. Atlantic salmon in five Maine rivers were designated\nas category 2 candidates for listing under the Endangered Species Act in 1991\nin response to the precipitous decline in population numbers. In October 1993,\nall anadromous U.S. Atlantic salmon were included in a petition to the U.S.\nFish and Wildlife Service (FWS) for a Rule to List the species under the\nEndangered Species Act.\n\nInformation was obtained from http://www.lsc.usgs.gov", "links": [ { diff --git a/datasets/LSC_Flavobacteriumpsychrophilum.json b/datasets/LSC_Flavobacteriumpsychrophilum.json index 385a5facc9..d17b9a49d4 100644 --- a/datasets/LSC_Flavobacteriumpsychrophilum.json +++ b/datasets/LSC_Flavobacteriumpsychrophilum.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_Flavobacteriumpsychrophilum", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The US Fish and Wildlife Service along with the Washington Department of\nFisheries and Wildlife work in cooperation on the Pacific salmon restoration\neffort. For a number of reasons, population numbers of some Pacific salmon\nspecies have declined dramatically over recent years and a large part of the\nrestoration effort encompasses hatchery propagation of progeny from returning\nfish for subsequent planting into their natural waters. With hatchery rearing\noperations fish are maintained in high densities and this situation lends\nitself to disease problems. One such disease is bacterial cold water disease,\ncaused by the Gram negative bacterium Flavobacterium psychrophilum. Source of\nthe infection is the ubiquitous nature of the pathogen and because of the\nverticle transmissability. Only recently has the biochemical and taxonomic\nposition of F. psychrophilum been elucidated more accurately. \n\nInformation was obtained from http://www.lsc.usgs.gov", "links": [ { diff --git a/datasets/LSC_biomarkers.json b/datasets/LSC_biomarkers.json index 6f0a17b849..36c0c62f2d 100644 --- a/datasets/LSC_biomarkers.json +++ b/datasets/LSC_biomarkers.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_biomarkers", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study is part of a larger project entifled Biomonitoring of Environmental\nStatus and Trends (BEST) Program: Testing and Implementation of Selected\nAquatic Ecosystem Indicators in the Mississippi River System, 1995. This pilot\nproject includes assessment of a variety of biomarkers of which we are\nresponsible for the histologic and immunologic markers. During the period in\nwhich concentrations of persistent contaminants were declining, the use of and\nconcerns about other chemicals, especially those that do not accumulate in\nbiota, increased. At least part of this concern stemmed from increasingly\nfrequent reports of fish kills and avi an wildlife mortality incidents related\nto the use of soft pesticides-highly toxic, but short-lived organophosphate and\ncarbamate insecticides that do not accumulate (e.g.; Glaser 1995). Herbicides\nare also now widely distributed in surface and ground waters of agricultural\nareas. Information was obtained from http://www.lsc.usgs.gov/", "links": [ { diff --git a/datasets/LSC_immunereprohistologic.json b/datasets/LSC_immunereprohistologic.json index b442c0804a..3127650bc3 100644 --- a/datasets/LSC_immunereprohistologic.json +++ b/datasets/LSC_immunereprohistologic.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSC_immunereprohistologic", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study is part of a larger project entitled \"Contaminants and Biomarkers in\nFish in the Columbia River and Rio Grande Basins, 1997\" ( Mid-Continent\nEcological Science Center) This project is part of the Biomonitoring of\nEnvironmental Status and Trends (BEST) program. The BEST program incorporates\nboth analytical chemistry arid a suite of biological responses to describe and\ntrack contaminant exposure and effects. Our part of this program is to measure\nand evaluate selected histologic, immunological and reproductive biomarkers.\nOur objectives are: to document the presence of selected histologic lesions\nwhich have been validated or widely accepted as indicators of contaminant\nexposure; to determine if there is evidence of immunosuppression using immune\nsystem biomarkers; evaluate gonad histology utilizing new potential biomarkers;\ndetermine if changes in gonad histology correlate with circulating vitellogenin\nlevels; determine if these findings correlate with contaminant presence or\nconcentration. Information was obtained from http://www.lsc.usgs.gov", "links": [ { diff --git a/datasets/LSM_807_1.json b/datasets/LSM_807_1.json index 8814cb531a..551dbd8007 100644 --- a/datasets/LSM_807_1.json +++ b/datasets/LSM_807_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LSM_807_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate. Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide, the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files.", "links": [ { diff --git a/datasets/LS_TM_ARC.json b/datasets/LS_TM_ARC.json index 48f75e37d3..7b0207c525 100644 --- a/datasets/LS_TM_ARC.json +++ b/datasets/LS_TM_ARC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LS_TM_ARC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat 5 was launched on March 1, 1984, carrying a seven-band TM sensor, and\nstill operates properly at present. The satellite takes a sun-synchronized\norbit with 705km altitude and 98.22 deg. inclination.\n\nA TM scene covers 185km by 170km earth surface approximately, with 30m ground\nresolution for band 1,2,3,4,5,7 and 120m for band6. For a particular place, the\nrevisit cycle of the satellite is 16 days.\n\nChaina Remote Sensing Satellite Ground Station(CRSGS) was inaugurated and\nbecome operational in Dec. 1986. Up to now it is the most important source of\nremote sensing satellite data in China for earth resouce exploration and\nenvironment monitoring.\n\nCRSGS has provided a large amount of satellite remote sensing products to more\nthan 400 users, domestic and abroad. Applications of TM images have resulted\nin great economic and social benefits in a wide range of areas of national\neconomy: resource survey and utilization, environment monitoring, geographic\ncartography, minerarl exploration, disaster detecting and assessing, etc.\n\nTM data received by CRSGS since 1986 have been archived. Through a Catalogue\nArchive and Browse System(CABS), users can retrieve useful information about\ndata of interests. A image(or a group of images) could be searched according\nto date, location(latitude-longitude or path-row), and quality, etc. Text\ncatalogue is available for all TM data in the archival. In addition to text\ncontents, sub-sampled browse images are available for data acquired after\nApr.,1994.\n\nThe major products of CRSGS are TM data on CCTs, floppy disks and imagery on\nfilms or papaer prints. Products fall into two categories with respect to\nprocessing methods.\n1. Standard processing\nincludes systematic correction, precision correction, and geocoding, etc.\n2.Special product(user dependent)\nincludes multi-scene mosaicking, image classification, user defined annotation\nor administrative boundary adding, special juts enhancement, etc.", "links": [ { diff --git a/datasets/LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001.json b/datasets/LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001.json index 4a1ed8b3c2..a07ef796f4 100644 --- a/datasets/LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001.json +++ b/datasets/LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75\u00b005\u203259\u2033S 123\u00b019\u203256\u2033E at an elevation of 3233 m.\n\nThe data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019.\n\nThe SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles.", "links": [ { diff --git a/datasets/LTER_0.json b/datasets/LTER_0.json index 188284ea71..08abfc40c8 100644 --- a/datasets/LTER_0.json +++ b/datasets/LTER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LTER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Long Term Ecological Research Network (LTER) between 1981 and 1999.", "links": [ { diff --git a/datasets/LUH2_GCB2019_1851_1.json b/datasets/LUH2_GCB2019_1851_1.json index cbace35d39..229dc6685e 100644 --- a/datasets/LUH2_GCB2019_1851_1.json +++ b/datasets/LUH2_GCB2019_1851_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LUH2_GCB2019_1851_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.", "links": [ { diff --git a/datasets/LULC_Nigeria_Ethiopia_SAfrica_2367_1.json b/datasets/LULC_Nigeria_Ethiopia_SAfrica_2367_1.json index ff8f650d1a..361e28c940 100644 --- a/datasets/LULC_Nigeria_Ethiopia_SAfrica_2367_1.json +++ b/datasets/LULC_Nigeria_Ethiopia_SAfrica_2367_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LULC_Nigeria_Ethiopia_SAfrica_2367_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.", "links": [ { diff --git a/datasets/LVISC1B_1.json b/datasets/LVISC1B_1.json index 3a35a504e9..d3ebf4089e 100644 --- a/datasets/LVISC1B_1.json +++ b/datasets/LVISC1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LVISC1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-1B geolocated return energy waveforms collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.", "links": [ { diff --git a/datasets/LVISC2_1.json b/datasets/LVISC2_1.json index 301e7d36a8..8caf4c688b 100644 --- a/datasets/LVISC2_1.json +++ b/datasets/LVISC2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LVISC2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-2 geolocated surface elevation and canopy height measurements collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.", "links": [ { diff --git a/datasets/LVISF1B_1.json b/datasets/LVISF1B_1.json index 780f9c472f..1bb47544cd 100644 --- a/datasets/LVISF1B_1.json +++ b/datasets/LVISF1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LVISF1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-1B geolocated return energy waveforms collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.", "links": [ { diff --git a/datasets/LVISF2_1.json b/datasets/LVISF2_1.json index 9f92286848..a1817edbf3 100644 --- a/datasets/LVISF2_1.json +++ b/datasets/LVISF2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LVISF2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Level-2 geolocated surface elevation and canopy height measurements collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.", "links": [ { diff --git a/datasets/LWAD01-2_0.json b/datasets/LWAD01-2_0.json index 27496ca395..86d9c2069e 100644 --- a/datasets/LWAD01-2_0.json +++ b/datasets/LWAD01-2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LWAD01-2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken under the Office of Naval Reaserch (ONR) Littoral Warfare Advanced Development (LWAD) Program along the East China Sea during 2001", "links": [ { diff --git a/datasets/LaSelva_Biomass_1215_1.json b/datasets/LaSelva_Biomass_1215_1.json index d47ea01e43..0401199513 100644 --- a/datasets/LaSelva_Biomass_1215_1.json +++ b/datasets/LaSelva_Biomass_1215_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LaSelva_Biomass_1215_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest. The diameter and height data were used to calculate aboveground biomass for each of the 30 plots. The crown measurements were used to estimate a vertical profile for each plot, showing the vegetation volume in 1 meter increments from the ground to the top of the canopy.There are three comma-delimited data files and two shapefiles with this data set. The files contain the measurements and calculated biomass for the individual stems as well as the summary data at the plot level.", "links": [ { diff --git a/datasets/LaSelva_Land_Use_1312_1.json b/datasets/LaSelva_Land_Use_1312_1.json index 06131e389d..d9cc322eb2 100644 --- a/datasets/LaSelva_Land_Use_1312_1.json +++ b/datasets/LaSelva_Land_Use_1312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LaSelva_Land_Use_1312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains land-use, canopy height, and aboveground carbon estimates derived from LiDAR data collected at La Selva Biological Station in Costa Rica in March 1998 and March 2005. The data are provided as GeoTIFFs (*.tif) of 100-m (1-ha) resolution. A look-up table is provided that relates modeled changes in height to changes in stand characteristics (including age and carbon content). The data were used to test the accuracy and scale-dependency of high-resolution predictions of vegetation dynamics and carbon flux by the Ecosystem Demography (ED). The ED model is an individual-based terrestrial ecosystem model that predicts both ecosystem structure and corresponding ecosystem fluxes from climate, soil, and land-use inputs.", "links": [ { diff --git a/datasets/Lab2000_0.json b/datasets/Lab2000_0.json index 64cba45a0d..a6413090f9 100644 --- a/datasets/Lab2000_0.json +++ b/datasets/Lab2000_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Lab2000_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Labrador Sea during 2000.", "links": [ { diff --git a/datasets/Lab96_0.json b/datasets/Lab96_0.json index d3b154b3aa..e99cc78b0c 100644 --- a/datasets/Lab96_0.json +++ b/datasets/Lab96_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Lab96_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Labrador Sea during 1996.", "links": [ { diff --git a/datasets/LakeBathymetry_Model_NSlope_AK_2243_1.json b/datasets/LakeBathymetry_Model_NSlope_AK_2243_1.json index 2c01bd3dbb..41fbd4ffff 100644 --- a/datasets/LakeBathymetry_Model_NSlope_AK_2243_1.json +++ b/datasets/LakeBathymetry_Model_NSlope_AK_2243_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LakeBathymetry_Model_NSlope_AK_2243_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides lake bathymetry maps derived from Landsat surface reflectance products for a portion of the North Slope area of Alaska. A random forest regression algorithm was used to generate depths for each point identified as being part of a lake, creating depth prediction files for each Landsat scene available for the study period: 2016-07-01 to 2018-08-31. These products are fitted to the ABoVE standard projection and reference grid to make them easily scalable and geometrically compatible with other products in the ABoVE study domain. The data are provided in cloud-optimized GeoTIFF (COG) format.", "links": [ { diff --git a/datasets/LakeSuperior_0.json b/datasets/LakeSuperior_0.json index 386ba5eda9..03def87236 100644 --- a/datasets/LakeSuperior_0.json +++ b/datasets/LakeSuperior_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LakeSuperior_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Lake Superior by researchers at the University of Rhode Island.", "links": [ { diff --git a/datasets/Lake_MI_2012_WaterQual_0.json b/datasets/Lake_MI_2012_WaterQual_0.json index 42a150f214..15681e9570 100644 --- a/datasets/Lake_MI_2012_WaterQual_0.json +++ b/datasets/Lake_MI_2012_WaterQual_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Lake_MI_2012_WaterQual_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in Lake Michigan and Green Bay in 2012 as part of a water quality monitoring program.", "links": [ { diff --git a/datasets/Lake_Wetland_Classes_UAVSAR_1883_1.json b/datasets/Lake_Wetland_Classes_UAVSAR_1883_1.json index d1f055c8e2..fd1954012b 100644 --- a/datasets/Lake_Wetland_Classes_UAVSAR_1883_1.json +++ b/datasets/Lake_Wetland_Classes_UAVSAR_1883_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Lake_Wetland_Classes_UAVSAR_1883_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions.", "links": [ { diff --git a/datasets/LandCoverNet Africa_1.json b/datasets/LandCoverNet Africa_1.json index d35abe08c1..d95277648e 100644 --- a/datasets/LandCoverNet Africa_1.json +++ b/datasets/LandCoverNet Africa_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet Africa_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Africa contains data across Africa, which accounts for ~1/5 of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.\n
There are a total of 1980 image chips of 256 x 256 pixels in LandCoverNet Africa V1.0 spanning 66 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):\n* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution\n* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution\n* Landsat-8 surface reflectance product from Collection 2 Level-2\n
Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/LandCoverNet Asia_1.json b/datasets/LandCoverNet Asia_1.json index 1b1e2ef83d..8f5a24661b 100644 --- a/datasets/LandCoverNet Asia_1.json +++ b/datasets/LandCoverNet Asia_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet Asia_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/LandCoverNet Australia_1.json b/datasets/LandCoverNet Australia_1.json index a94d7aaea3..09ca4ca8c0 100644 --- a/datasets/LandCoverNet Australia_1.json +++ b/datasets/LandCoverNet Australia_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet Australia_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Australia contains data across Australia, which accounts for ~7% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 600 image chips of 256 x 256 pixels in LandCoverNet Australia V1.0 spanning 20 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/LandCoverNet Europe_1.json b/datasets/LandCoverNet Europe_1.json index 03484ba1a7..eab0ee1b6c 100644 --- a/datasets/LandCoverNet Europe_1.json +++ b/datasets/LandCoverNet Europe_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet Europe_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Europe contains data across Europe, which accounts for ~9.5% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 840 image chips of 256 x 256 pixels in LandCoverNet Europe V1.0 spanning 28 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/LandCoverNet North America_1.json b/datasets/LandCoverNet North America_1.json index 5fc32a6d36..5a7ee7230d 100644 --- a/datasets/LandCoverNet North America_1.json +++ b/datasets/LandCoverNet North America_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet North America_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.

There are a total of 1561 image chips of 256 x 256 pixels in LandCoverNet North America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/LandCoverNet South America_1.json b/datasets/LandCoverNet South America_1.json index 4f4018cdd3..87324b4b3f 100644 --- a/datasets/LandCoverNet South America_1.json +++ b/datasets/LandCoverNet South America_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandCoverNet South America_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet South America contains data across South America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.\n
There are a total of 1200 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):\n* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution\n* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution\n* Landsat-8 surface reflectance product from Collection 2 Level-2\n
Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).", "links": [ { diff --git a/datasets/Land_Cover_surfaces_748_1.json b/datasets/Land_Cover_surfaces_748_1.json index e793cb064e..343b8ca585 100644 --- a/datasets/Land_Cover_surfaces_748_1.json +++ b/datasets/Land_Cover_surfaces_748_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Land_Cover_surfaces_748_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. BigFoot was funded by NASA's Terrestrial Ecology Program.", "links": [ { diff --git a/datasets/Land_Use_Harmonization_V1_1248_1.json b/datasets/Land_Use_Harmonization_V1_1248_1.json index 22e9c2f7d4..62ac1cd870 100644 --- a/datasets/Land_Use_Harmonization_V1_1248_1.json +++ b/datasets/Land_Use_Harmonization_V1_1248_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Land_Use_Harmonization_V1_1248_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data represent fractional land use and land cover patterns annually for the years 1500 - 2100 for the globe at 0.5-degree (~50-km) spatial resolution. Land use categories of cropland, pasture, primary land, secondary (recovering) land, and urban land, and underlying annual land-use transitions, are included. Annual data on age and biomass density of secondary land, as well as annual wood harvest, are included for each grid cell. Historical land cover data for the years 1500 - 2005 are based on HYDE 3.1 and future land cover projections for the period 2006 - 2100 came from four Integrated Assessment Model (IAM) scenarios which reach different levels of radiative forcing by year 2100: MESSAGE (8.5 W/m2), AIM (6 W/m2), GCAM (4.5 W/m2), and IMAGE (2.6 W/m2). A key feature of these data is that historical reconstructions of land use were harmonized (computationally adjusted to minimize differences at the transition period) with modeled future scenarios, allowing for a seamless examination of historical and future land use. The output data present a single consistent, spatially gridded set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. For additional information about the algorithms, inputs, and options used in creating the land use transitions data, please refer to Hurtt et al. (2006) and Hurtt et al. (2011).Data are presented as a series of twenty (20) different data products representing different past and future model scenarios. There are a total of 560 NetCDF v4 files (*.nc4), one for each combination of data product and land use variable.", "links": [ { diff --git a/datasets/Land_Use_Harmonization_V2_1721_1.json b/datasets/Land_Use_Harmonization_V2_1721_1.json index 1a471540bc..78bc6b2c15 100644 --- a/datasets/Land_Use_Harmonization_V2_1721_1.json +++ b/datasets/Land_Use_Harmonization_V2_1721_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Land_Use_Harmonization_V2_1721_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 0.25-degree gridded, global, annual estimates of fractional land use and land cover patterns for the period 2015-2100, designed to support the ISIMIP2b effort to assess the impacts of 1.5 Deg Celcius global warming. Land use types, land use transitions, and cropland estimates of area fraction are provided and include detailed separation of primary and secondary natural vegetation into forest and non-forest sub-types, pasture into managed pasture and rangeland, and cropland into multiple crop functional types; all transitions between land use states per grid cell per year, including crop rotations, shifting cultivation, and wood harvest; and agriculture management including irrigation, synthetic nitrogen fertilizer, and biofuel management. The LUH2-ISIMIP2b datasets were derived using Land Use Harmonization 2 (LUH2) methodology and are based on land-use scenarios provided by the REMIND-MAgPIE Integrated Assessment Model using an SSP2 storyline along with RCP2.6 and RCP6.0 emissions scenarios. In contrast to the standard SSP scenarios, these land use changes additionally account for climate and atmospheric CO2 fertilization effects on the underlying patterns of potential crop yields, water availability, and terrestrial carbon content. This is achieved by using the LPJmL (Lund-Potsdam-Jena managed land) model forced with atmospheric CO2 concentrations and patterns of climate change generated from 4 different climate models (GFDL, HADGEM, IPSL, and MIROC) consistent with the 2 different RCP scenarios, resulting in a set of 8 different LUH2-ISIMIP2b datasets.", "links": [ { diff --git a/datasets/Landcover_Colombian_Amazon_1783_1.json b/datasets/Landcover_Colombian_Amazon_1783_1.json index 14f0ab47f0..41309b5116 100644 --- a/datasets/Landcover_Colombian_Amazon_1783_1.json +++ b/datasets/Landcover_Colombian_Amazon_1783_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landcover_Colombian_Amazon_1783_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data. Annual maps of land cover were created for each Landsat scene and then post-processed and mosaicked. Land cover types include unclassified, forest, natural grasslands, urban, pastures, secondary forest, water, or highly reflective surfaces. The training data are not included with this dataset.", "links": [ { diff --git a/datasets/Landsat5TMEuropeandNorthAfricaCoverage198485_5.0.json b/datasets/Landsat5TMEuropeandNorthAfricaCoverage198485_5.0.json index 9d3b2558ef..d168dca4d4 100644 --- a/datasets/Landsat5TMEuropeandNorthAfricaCoverage198485_5.0.json +++ b/datasets/Landsat5TMEuropeandNorthAfricaCoverage198485_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat5TMEuropeandNorthAfricaCoverage198485_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from April 1984 to December 1985. The available data products have a cloud cover percentage of less than 20%.\rThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "links": [ { diff --git a/datasets/Landsat5TMEuropeandNorthAfricaCoverage198689_4.0.json b/datasets/Landsat5TMEuropeandNorthAfricaCoverage198689_4.0.json index ae5e997f25..ab0cb73d4a 100644 --- a/datasets/Landsat5TMEuropeandNorthAfricaCoverage198689_4.0.json +++ b/datasets/Landsat5TMEuropeandNorthAfricaCoverage198689_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat5TMEuropeandNorthAfricaCoverage198689_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from January 1986 to November 1989. The available data products have a cloud cover percentage of less than 20%.\rThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "links": [ { diff --git a/datasets/Landsat5TMEuropeandNorthAfricaCoverage199598_4.0.json b/datasets/Landsat5TMEuropeandNorthAfricaCoverage199598_4.0.json index 9bb27dd97d..f96629a41b 100644 --- a/datasets/Landsat5TMEuropeandNorthAfricaCoverage199598_4.0.json +++ b/datasets/Landsat5TMEuropeandNorthAfricaCoverage199598_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat5TMEuropeandNorthAfricaCoverage199598_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from January 1995 to December 1998. The available data products have a cloud cover percentage of less than 20%.\rThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "links": [ { diff --git a/datasets/Landsat8.Collection2.European.Coverage_8.0.json b/datasets/Landsat8.Collection2.European.Coverage_8.0.json index 278ab8f891..06fe9ebadb 100644 --- a/datasets/Landsat8.Collection2.European.Coverage_8.0.json +++ b/datasets/Landsat8.Collection2.European.Coverage_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat8.Collection2.European.Coverage_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the European Coverage of Landsat 8 Collection 2 data, both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection 2 is the result of reprocessing effort on the archive and on fresh products with significant improvement with respect to Collection 1 on data quality, obtained by means of advancements in data processing, algorithm development. The primary characteristic is a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image, GRI) but includes also updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format. Landsat 8 level 1 products combine data from the 2 Landsat instruments, OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: \u2022 L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. \u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products \u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). \u2022 Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of \u2266 12-meter radial root mean square error (RMSE). \u2022 Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \u2022 L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. \u2022 L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). For the Level 2 data, only LC combined products are generated", "links": [ { diff --git a/datasets/Landsat8_Sentinel2_Phenocam_2248_1.json b/datasets/Landsat8_Sentinel2_Phenocam_2248_1.json index 2f1da75c10..034e6a8b80 100644 --- a/datasets/Landsat8_Sentinel2_Phenocam_2248_1.json +++ b/datasets/Landsat8_Sentinel2_Phenocam_2248_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat8_Sentinel2_Phenocam_2248_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a reference of land surface phenology (LSP) at 30-m pixels for 78 regions of 10 x 10 km2 across a wide range of ecological and climatic regions in North America during 2019 and 2020. The data were derived by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near- surface PhenoCam time series (hereafter called HP-LSP). The HP-LSP dataset consists of two parts: (1) the 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series and (2) four key phenological transition dates that are greenup onset, maturity onset, senescence onset, and dormancy onset (accuracy less than or equal to five days). The PhenoCam network offers near-surface observations via the RGB (Red, Green, and Blue) imagery every 30 minutes. Each RGB imagery enables us to calculate as many as 100 Green Chromatic Coordinate (GCC) for generating a collection of localized vegetation dynamics. The HLS EVI2 time series with frequent gaps was fused with the most comparable PhenoCam GCC temporal shape selected from the GCC collection using the Spatiotemporal Shape Matching Model (SSMM) to create the synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to establish the physically-based hybrid piecewise logistic model (HPLM) for detecting phenological transition dates (phenometrics).", "links": [ { diff --git a/datasets/LandsatETMCloudFree_9.0.json b/datasets/LandsatETMCloudFree_9.0.json index 457fc1dfd6..aee97f5a49 100644 --- a/datasets/LandsatETMCloudFree_9.0.json +++ b/datasets/LandsatETMCloudFree_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandsatETMCloudFree_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the cloud-free products from Landsat 7 Enhanced Thematic Mapper collection acquired over Europe, North Africa and middle East; for each scene only one product is selected, with the minimal cloud coverage. The Landsat 7 ETM+ scenes typically cover 185 x 170 km. A standard full scene is nominally centred on the intersection between a Path and Row (the actual image centre can vary by up to 100m). The data are system corrected.", "links": [ { diff --git a/datasets/LandsatTMCloudFree_10.0.json b/datasets/LandsatTMCloudFree_10.0.json index 4f2da893d5..274770b3cd 100644 --- a/datasets/LandsatTMCloudFree_10.0.json +++ b/datasets/LandsatTMCloudFree_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LandsatTMCloudFree_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the cloud-free products from Landsat 5 Thematic Mapper collection acquired over Europe, North Africa and middle East; for each scene only one product is selected, with the minimal cloud coverage. The acquired Landsat TM scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 100 m). The data are system corrected.", "links": [ { diff --git a/datasets/Landsat_8.json b/datasets/Landsat_8.json index 8a4efc136d..1d0273b27c 100644 --- a/datasets/Landsat_8.json +++ b/datasets/Landsat_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are onboard the Landsat 8 satellite, have acquired images of the Earth since February 2013. The sensors collect images of the Earth with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).\r\n\r\nLandsat 8 image data files consist of 11 spectral bands with a spatial resolution of 30 meters for bands 1-7 and bands 9-11; 15-meters for the panchromatic band 8. Delivered Landsat 8 Level-1 data typically include both OLI and TIRS data files; however, there may be OLI-only and/or TIRS-only scenes in the USGS archive. A Quality Assurance (QA.tif) band\u00a0is also included. This file provides bit information regarding conditions that may affect the accuracy and usability of a given pixel \u2013 clouds, water or snow, for example.", "links": [ { diff --git a/datasets/Landsat_MSS_ESA_Archive_9.0.json b/datasets/Landsat_MSS_ESA_Archive_9.0.json index fb2081e1ec..9d1fdadc4a 100644 --- a/datasets/Landsat_MSS_ESA_Archive_9.0.json +++ b/datasets/Landsat_MSS_ESA_Archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat_MSS_ESA_Archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all the Landsat 1 to Landsat 5 Multi Spectral Scanner (MSS) high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Kiruna (active from April to September only) and Maspalomas (on campaign basis) visibility masks. The acquired Landsat MSS scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 200m). The altitude changed from 917 Km to 705 km and therefore two World Reference Systems (WRS) were. A full image is composed of 3460 pixels x 2880 lines with a pixel size of 60m. Level 1 Geometrically and terrain corrected GTC products (L1T) are available: it is the most accurate level of processing as it incorporates Ground Control Points (GCPs) and a Digital Elevation Model (DEM) to provide systematic geometric and topographic accuracy, with geodetic accuracy dependent on the number, spatial distribution and accuracy of the GCPs over the scene extent, and the resolution of the DEM used.", "links": [ { diff --git a/datasets/Landsat_RBV_8.0.json b/datasets/Landsat_RBV_8.0.json index 2f2bba1d60..6cc4b415e2 100644 --- a/datasets/Landsat_RBV_8.0.json +++ b/datasets/Landsat_RBV_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Landsat_RBV_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Landsat 3 Return Beam Vidicon (RBV) products, acquired by ESA by the Fucino ground station over its visibility mask. The data (673 scenes) are the result of the digitalization of the original 70 millimetre (mm) black and white film rolls.\rThe RBV instrument was mounted on board the Landsat 1 to 3 satellites between 1972 and 1983, with 80 meter resolution. Three independent co-aligned television cameras, one for each spectral band (band 1: blue-green, band 2: yellow-red, band 3: NIR), constituted this instrument. \rThe RBV system was redesigned for Landsat 3 to use two cameras operating in one broad spectral band (green to near-infrared; 0.505\u20130.750 \u00b5m), mounted side-by-side, with panchromatic spectral response and higher spatial resolution than on Landsat-1 and Landsat-2. Each of the cameras produced a swath of about 90 km (for a total swath of 180 km), with a spatial resolution of 40 m.", "links": [ { diff --git a/datasets/Large_River_DOC_Export_0.json b/datasets/Large_River_DOC_Export_0.json index 8669ac9e45..18e4cef1c1 100644 --- a/datasets/Large_River_DOC_Export_0.json +++ b/datasets/Large_River_DOC_Export_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Large_River_DOC_Export_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken as a part of a project to quanitfy and assess the export of dissolved organic carbon by large rivers.", "links": [ { diff --git a/datasets/Last_Day_Spring_Snow_1528_1.json b/datasets/Last_Day_Spring_Snow_1528_1.json index d67669c8d2..5ce5212c17 100644 --- a/datasets/Last_Day_Spring_Snow_1528_1.json +++ b/datasets/Last_Day_Spring_Snow_1528_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Last_Day_Spring_Snow_1528_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as \"Snow\" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2).", "links": [ { diff --git a/datasets/Leaf_Carbon_Nutrients_1106_1.json b/datasets/Leaf_Carbon_Nutrients_1106_1.json index e17a2b991c..c85181df40 100644 --- a/datasets/Leaf_Carbon_Nutrients_1106_1.json +++ b/datasets/Leaf_Carbon_Nutrients_1106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Leaf_Carbon_Nutrients_1106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/Leaf_Photosynthesis_Traits_1224_1.json b/datasets/Leaf_Photosynthesis_Traits_1224_1.json index 2b9f041d2c..c5e587a3b6 100644 --- a/datasets/Leaf_Photosynthesis_Traits_1224_1.json +++ b/datasets/Leaf_Photosynthesis_Traits_1224_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Leaf_Photosynthesis_Traits_1224_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file.", "links": [ { diff --git a/datasets/Level_2A_aerosol_cloud_optical_products_3.0.json b/datasets/Level_2A_aerosol_cloud_optical_products_3.0.json index de66470aba..1c1034fc06 100644 --- a/datasets/Level_2A_aerosol_cloud_optical_products_3.0.json +++ b/datasets/Level_2A_aerosol_cloud_optical_products_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Level_2A_aerosol_cloud_optical_products_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes).", "links": [ { diff --git a/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json b/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json index bb05dcf715..86f1e583f7 100644 --- a/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json +++ b/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LiDAR_Forest_Inventory_Brazil_1644_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.", "links": [ { diff --git a/datasets/LiDAR_Tundra_Forest_AK_1782_1.json b/datasets/LiDAR_Tundra_Forest_AK_1782_1.json index f8053b70c5..8e1a774ea3 100644 --- a/datasets/LiDAR_Tundra_Forest_AK_1782_1.json +++ b/datasets/LiDAR_Tundra_Forest_AK_1782_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LiDAR_Tundra_Forest_AK_1782_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution.", "links": [ { diff --git a/datasets/LiDAR_Veg_Ht_Idaho_1532_1.json b/datasets/LiDAR_Veg_Ht_Idaho_1532_1.json index 45878ec14b..8a5bca24b5 100644 --- a/datasets/LiDAR_Veg_Ht_Idaho_1532_1.json +++ b/datasets/LiDAR_Veg_Ht_Idaho_1532_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "LiDAR_Veg_Ht_Idaho_1532_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site.", "links": [ { diff --git a/datasets/Lidar_Bibliography_1.json b/datasets/Lidar_Bibliography_1.json index 1d87b5ad0d..04710a9999 100644 --- a/datasets/Lidar_Bibliography_1.json +++ b/datasets/Lidar_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Lidar_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program).\n\nAt the 4th of June, 2007, the bibliography contained 996 references.\n\nThe bibliography can also be searched via the scientific bibliographies database available at the URL given below.\n\nThe fields in this dataset are:\nyear\nauthor\ntitle\njournal", "links": [ { diff --git a/datasets/Light_Tipping_Points_1.json b/datasets/Light_Tipping_Points_1.json index 83f2fb0456..6084bb28da 100644 --- a/datasets/Light_Tipping_Points_1.json +++ b/datasets/Light_Tipping_Points_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Light_Tipping_Points_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Some ecosystems can undergo abrupt transformation in response to relatively small environmental change. Identifying imminent \"tipping points\" is crucial for biodiversity conservation, particularly in the face of climate change. Here we describe a tipping point mechanism likely to induce widespread regime shifts in polar ecosystems. Seasonal snow and ice cover periodically block sunlight reaching polar ecosystems, but the effect of this on annual light depends critically on the timing of cover within the annual solar cycle. At high latitudes sunlight is strongly seasonal, and ice-free days around the summer solstice receive orders of magnitude more light than those in winter. Early melt that brings the date of ice-loss closer to midsummer will cause an exponential increase in the amount of sunlight reaching some areas per year. This is likely to drive ecological tipping points in which primary producers (plants and algae) flourish and out-compete dark-adapted communities. We demonstrate this principle on Antarctic shallow seabed ecosystems, which our data suggest are sensitive to small changes in the timing of sea-ice loss. Algae respond to light thresholds that are easily exceeded by a slight reduction in sea-ice duration. Earlier sea-ice loss is likely to cause extensive regime-shifts in which endemic shallow-water invertebrate communities are replaced by algae, reducing coastal biodiversity and fundamentally changing ecosystem functioning. Modeling shows that recent changes in ice and snow cover have already transformed annual light budgets in large areas of the Arctic and Antarctic, and both aquatic and terrestrial ecosystems are likely to experience further significant change in light. The interaction between ice loss and solar irradiance renders polar ecosystems acutely vulnerable to abrupt ecosystem change, as light-driven tipping points are readily breached by relatively slight shifts in the timing of snow and ice loss.\n\nThis archive contains data and statistical code for the article:\nGraeme F. Clark, Jonathan S. Stark, Emma L. Johnston, John W. Runcie, Paul M. Goldsworthy, Ben Raymond and Martin J. Riddle (2013) Light-driven tipping points in polar ecosystems. Global Change Biology\nData and code are organised into folders according to figures in the article. See the article for a full description of methods. Statistical code was written in R v. 2.15.0. In data files, rows are samples and columns are variables. Details for numerical variables in each data file are listed below. Figures 7 and 8 were made in MATLAB and code is not provided.\n\nFigure 1: rad_data.csv\nSolar irradiance data derived from: \nSuri M, Hofierja J (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS 8: 175-190.\n\nFigure 2: Fig. 2c.1.csv\nLight: Measured light at the seabed per day (mol photons m-2 d-1).\n\nFigure 2: Fig. 2c.2.csv\nLight: Measured light at the seabed per day (mol photons m-2 d-1).\nLight.mod.p: Light at the seabed per day (mol photons m-2 d-1) predicted from modeled seasonal variation.\n\nFigure 2: Fig. 2d.csv\nLight: Measured light at the seabed per day (mol photons m-2 d-1).\n\nFigure 3: Fig. 3a.csv\nIrradiance: Mean irradiance (micro mol photons m-2 s-1).\nP/R: Productivity/respiration ratios (micro mol photons O2-1 gFW-1 h-1).\n\nFigure 3: Fig. 3b.csv\nLight: Mean irradiance (micro mol photons m-2 s-1) in experimental treatments.\nGrowth: Thallus growth (mm) of Palmaria decipiens under experimental treatments.\n\nFigure 3: Fig. 3c.csv\nDes, Him, Irr, Pal: Ice-free days required for minimum annual light budget\n\nFigure 3: Fig. 3c.bars.csv\nProp: relative cover (sums to 1 per site) of algae and invertebrates, excluding Inversiula nutrix and Spirorbis nordenskjoldi.\n\nFigure 4: Fig. 4.csv\nTime: months after deployment\nLength: length of thalli (mm)\n\nFigure 5: Fig. 5c and d.csv\nAxis 1 and Axis 1: Values from first two axes of principal coordinate analysis\nIceCover: proportion of days that each site is free of sea-ice per year.\nBeta: Beta-diversity. Calculated as Jaccard similarity between the most ice-covered site (OB1) and each other site.\n\nFigure 5: Fig. 5e and f.csv\nIceCover: proportion of days that each site is free of sea-ice per year.\nValue: number of species per boulder (for Metric=Diversity), or percent cover per boulder (for Metric=Cover).\n\nFigure 6: Fig. 6a.csv\n Sites.lost: number of sites removed from dataset due to sea-ice loss.\n Ice: maximum ice-free days within the region (d yr-1).\n S: Total species richness across each subset of sites.\n Effort: relative sampling effort (number of sites sampled).", "links": [ { diff --git a/datasets/Line_P_0.json b/datasets/Line_P_0.json index 94f5722d97..55c790e749 100644 --- a/datasets/Line_P_0.json +++ b/datasets/Line_P_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Line_P_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Line P is an oceanic transect of 26 periodically sampled stations running from southern Vancouver Island to Ocean Station Papa, situated at 50N and 145W.", "links": [ { diff --git a/datasets/Long_Fjord_Depth_Measurements_2007_1.json b/datasets/Long_Fjord_Depth_Measurements_2007_1.json index e152df8906..bc29a9e352 100644 --- a/datasets/Long_Fjord_Depth_Measurements_2007_1.json +++ b/datasets/Long_Fjord_Depth_Measurements_2007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Long_Fjord_Depth_Measurements_2007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water depth measurements were taken in Long Fjord during early winter in 2007. The measurements were collected by Graham Cook, station leader at Davis Station in the Australian Antarctic Territory. The measurements were made by dropping a weighted line off the back of a quad bike, after drilling a hole through the sea ice. Measurements were made approximately every 100 metres.\n\nThe download file contains a csv spreadsheet which lists each waypoint, plus the corresponding water depth and any comments. The text file contains the waypoint information collected by the Garmin GPS unit. Data in the text file are comma separated and are interpreted as follows:\n\nWP,D,001 (waypoint) , -68.51341000, 78.06903000,(Latitude and Longitude) 05/25/2007, 10:25:35, (Date and time Downloaded to Computer) 24-MAY-07 11:40:42 (Date and time of reading). Time is in local time.\n\nVegetation was found on the weight that we used when we first started at the seaward end of the Fjord and then again in shallow water between Brookes Hut and a small island 800 or 900 metres out from Brookes. The weight is quite smooth and does not pick up a lot.\n\nThe reference given below provides some further information about previously collected bathymetry data in Long Fjord. Furthermore, also see the metadata records:\n\n'Bathymetric data of Long and Tryne Fjords at Vestfold Hills, Antarctica, collected in December 1999 [VH_bathy_99]'\n\n'Interpolated bathymetry of Long and Tryne Fjords, Vestfold Hills, Antarctica [long_tryne_bathy]'\n\nThe fields in this dataset are:\nWaypoint\nLatitude\nLongitude\nWater Depth\nDate\nTime", "links": [ { diff --git a/datasets/M1_AVH02C1_6.json b/datasets/M1_AVH02C1_6.json index 0f31ab6532..abd698ae6b 100644 --- a/datasets/M1_AVH02C1_6.json +++ b/datasets/M1_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M1_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\nThe METOP-B AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05 Deg CMG, short-name M1_AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The M1_AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/M1_AVH09C1_6.json b/datasets/M1_AVH09C1_6.json index 91b3dc647c..8b476131df 100644 --- a/datasets/M1_AVH09C1_6.json +++ b/datasets/M1_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M1_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe METOP-B AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg CMG, short-name M1_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The M1_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/M1_AVH13C1_6.json b/datasets/M1_AVH13C1_6.json index c0955fe3e5..9ad34978fe 100644 --- a/datasets/M1_AVH13C1_6.json +++ b/datasets/M1_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M1_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/M2C0NXASM_5.12.4.json b/datasets/M2C0NXASM_5.12.4.json index 9a30f105b7..0973bc939c 100644 --- a/datasets/M2C0NXASM_5.12.4.json +++ b/datasets/M2C0NXASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2C0NXASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2C0NXASM (or const_2d_asm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters, such as the fraction of lake, land, and ocean within a model grid cell. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. \n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2C0NXCTM_5.12.4.json b/datasets/M2C0NXCTM_5.12.4.json index 2a3ba38301..fa5d3c2a66 100644 --- a/datasets/M2C0NXCTM_5.12.4.json +++ b/datasets/M2C0NXCTM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2C0NXCTM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2C0NXCTM (or const_2d_ctm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters for usage by the chemistry transport model (CTM), such as the fraction of lake, land, ice, or ocean within a model grid cell. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. \n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2C0NXLND_5.12.4.json b/datasets/M2C0NXLND_5.12.4.json index 50c11ad871..15d883f821 100644 --- a/datasets/M2C0NXLND_5.12.4.json +++ b/datasets/M2C0NXLND_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2C0NXLND_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2C0NXLND (or const_2d_lnd_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant land surface parameters, such as thickness of the predefined soil layers, soil porosity, and soil wilting point. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. \n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I1NXASM_5.12.4.json b/datasets/M2I1NXASM_5.12.4.json index fc27b302f4..3d5a306762 100644 --- a/datasets/M2I1NXASM_5.12.4.json +++ b/datasets/M2I1NXASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I1NXASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I1NXASM (or inst1_2d_asm_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, \u2026 , 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I1NXINT_5.12.4.json b/datasets/M2I1NXINT_5.12.4.json index 6c078d3897..b1d9a1d827 100644 --- a/datasets/M2I1NXINT_5.12.4.json +++ b/datasets/M2I1NXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I1NXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I1NXINT (or inst1_2d_int_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, \u2026 , 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I1NXLFO_5.12.4.json b/datasets/M2I1NXLFO_5.12.4.json index 20514de03c..0a5d384b01 100644 --- a/datasets/M2I1NXLFO_5.12.4.json +++ b/datasets/M2I1NXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I1NXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I1NXLFO (or inst1_2d_lfo_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, \u2026 , 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NPASM_5.12.4.json b/datasets/M2I3NPASM_5.12.4.json index c3aa23059d..eb90283ef5 100644 --- a/datasets/M2I3NPASM_5.12.4.json +++ b/datasets/M2I3NPASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NPASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NVAER_5.12.4.json b/datasets/M2I3NVAER_5.12.4.json index fae9281833..c4c24c3f2b 100644 --- a/datasets/M2I3NVAER_5.12.4.json +++ b/datasets/M2I3NVAER_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NVAER_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NVASM_5.12.4.json b/datasets/M2I3NVASM_5.12.4.json index 37f68d8695..94e07efbb1 100644 --- a/datasets/M2I3NVASM_5.12.4.json +++ b/datasets/M2I3NVASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NVASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NVCHM_5.12.4.json b/datasets/M2I3NVCHM_5.12.4.json index 972c93a2dc..e49ae185d5 100644 --- a/datasets/M2I3NVCHM_5.12.4.json +++ b/datasets/M2I3NVCHM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NVCHM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NVCHM (or inst3_3d_chm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of carbon monoxide and ozone mixing ratio at 72 model layers. The data is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NVGAS_5.12.4.json b/datasets/M2I3NVGAS_5.12.4.json index 4d120a7589..20938debe2 100644 --- a/datasets/M2I3NVGAS_5.12.4.json +++ b/datasets/M2I3NVGAS_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NVGAS_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NVGAS (or inst3_3d_gas_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of aerosol mixing ratio analysis increments at 72 model layers, such as mixing ratio analysis increments of black carbon, dust, organic carbon, sea salt, and sulfate. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I3NXGAS_5.12.4.json b/datasets/M2I3NXGAS_5.12.4.json index 764ba1e6a3..e959c7bd0e 100644 --- a/datasets/M2I3NXGAS_5.12.4.json +++ b/datasets/M2I3NXGAS_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I3NXGAS_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I3NXGAS (or inst3_3d_gas_Nx) is an instantaneous 2-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I6NPANA_5.12.4.json b/datasets/M2I6NPANA_5.12.4.json index bfe27664c8..3d1b3391f8 100644 --- a/datasets/M2I6NPANA_5.12.4.json +++ b/datasets/M2I6NPANA_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I6NPANA_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I6NPANA (or inst6_3d_ana_Np) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. The data field is available every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, \u2026 , 18:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2I6NVANA_5.12.4.json b/datasets/M2I6NVANA_5.12.4.json index 1df1a139f9..c7d9de0fbf 100644 --- a/datasets/M2I6NVANA_5.12.4.json +++ b/datasets/M2I6NVANA_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2I6NVANA_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2I6NVANA (or inst6_3d_ana_Nv) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analized meteorological fields at 72 model layers, such as temperature, wind components,specific humidity, and layer pressure thickness. The data field is available every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, \u2026 , 18:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNPANA_5.12.4.json b/datasets/M2IMNPANA_5.12.4.json index fccb33b073..46eaa9ebf1 100644 --- a/datasets/M2IMNPANA_5.12.4.json +++ b/datasets/M2IMNPANA_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNPANA_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNPANA (or instM_3d_ana_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes certain quadratic information (such as the variance and covariance of certain parameters). \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNPASM_5.12.4.json b/datasets/M2IMNPASM_5.12.4.json index 8fc360c27f..bfe3dc2058 100644 --- a/datasets/M2IMNPASM_5.12.4.json +++ b/datasets/M2IMNPASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNPASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNPASM (or instM_3d_asm_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes certain quadratic information (such as the variance and covariance of certain parameters).\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNXASM_5.12.4.json b/datasets/M2IMNXASM_5.12.4.json index 03e517505b..991961830e 100644 --- a/datasets/M2IMNXASM_5.12.4.json +++ b/datasets/M2IMNXASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNXASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNXASM (or instM_2d_asm_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNXGAS_5.12.4.json b/datasets/M2IMNXGAS_5.12.4.json index 8e4f767349..e509fc7356 100644 --- a/datasets/M2IMNXGAS_5.12.4.json +++ b/datasets/M2IMNXGAS_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNXGAS_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNXGAS (or instM_3d_gas_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. The collection also includes the variance of parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNXINT_5.12.4.json b/datasets/M2IMNXINT_5.12.4.json index 261ccef615..ad241b613a 100644 --- a/datasets/M2IMNXINT_5.12.4.json +++ b/datasets/M2IMNXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNXINT (or instM_2d_int_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). The collection also includes variance of certain variables.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IMNXLFO_5.12.4.json b/datasets/M2IMNXLFO_5.12.4.json index 792e259473..caf2c10b3e 100644 --- a/datasets/M2IMNXLFO_5.12.4.json +++ b/datasets/M2IMNXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IMNXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IMNXLFO (or instM_2d_lfo_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNPANA_5.12.4.json b/datasets/M2IUNPANA_5.12.4.json index f541185dce..fcbaf37cc1 100644 --- a/datasets/M2IUNPANA_5.12.4.json +++ b/datasets/M2IUNPANA_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNPANA_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNPANA (or instU_3d_ana_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. It is the monthly mean of data fields every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, \u2026 , 18:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNPASM_5.12.4.json b/datasets/M2IUNPASM_5.12.4.json index 7a40096cd3..ab006468b0 100644 --- a/datasets/M2IUNPASM_5.12.4.json +++ b/datasets/M2IUNPASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNPASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNPASM (or instU_3d_asm_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data collection is the monthly mean of data fields every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNXASM_5.12.4.json b/datasets/M2IUNXASM_5.12.4.json index fd7fa03b22..bc4639233b 100644 --- a/datasets/M2IUNXASM_5.12.4.json +++ b/datasets/M2IUNXASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNXASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNXASM (or instU_2d_asm_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The data consists of the monthly mean of the data field at each hour of a day, e.g., 00:00, 01:00, \u2026, 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNXGAS_5.12.4.json b/datasets/M2IUNXGAS_5.12.4.json index 51bce9295a..862c947a99 100644 --- a/datasets/M2IUNXGAS_5.12.4.json +++ b/datasets/M2IUNXGAS_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNXGAS_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNXGAS (or instU_3d_gas_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. It consists of the monthly mean of the data fields every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, \u2026 , 21:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNXINT_5.12.4.json b/datasets/M2IUNXINT_5.12.4.json index ec8ecdb843..e7689e2b80 100644 --- a/datasets/M2IUNXINT_5.12.4.json +++ b/datasets/M2IUNXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNXINT (or instU_2d_int_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). It consists of the monthly mean of the data fields on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, \u2026 , 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2IUNXLFO_5.12.4.json b/datasets/M2IUNXLFO_5.12.4.json index 28b0217ea6..ae7c69a362 100644 --- a/datasets/M2IUNXLFO_5.12.4.json +++ b/datasets/M2IUNXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2IUNXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2IUNXLFO (or instU_2d_lfo_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. It consists of the monthly mean of the data fields on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, \u2026 , 23:00 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SDNXSLV_5.12.4.json b/datasets/M2SDNXSLV_5.12.4.json index 89da79d54f..b82c2c97cc 100644 --- a/datasets/M2SDNXSLV_5.12.4.json +++ b/datasets/M2SDNXSLV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SDNXSLV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SDNXSLV (or statD_2d_slv_Nx) is a 2-dimensional daily data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SMNXEDI_1.json b/datasets/M2SMNXEDI_1.json index 2161793893..0f6dcf5a62 100644 --- a/datasets/M2SMNXEDI_1.json +++ b/datasets/M2SMNXEDI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SMNXEDI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V1, the original version of this extreme detection indices data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022. In contrast, V2, the second version, is calculated based on a 30-year climatology (1991-2020), covering the period from January 1980 to the present. \n\nThis collection consists of indices used to identify or characterize extreme weather events associated with temperature, such as heatwaves and cold spells (e.g., their frequency, duration, and intensity), as well as events associated with precipitation, such as dry days and wet days (e.g., their frequency, duration, and intensity).\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SMNXEDI_2.json b/datasets/M2SMNXEDI_2.json index b00991c440..43f1006cc7 100644 --- a/datasets/M2SMNXEDI_2.json +++ b/datasets/M2SMNXEDI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SMNXEDI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V2 of this extreme detection indices data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present. In contrast, V1, the original version, is computed based on an earlier 30-year climatology (1981-2010). \n\nThis collection consists of indices used to identify or characterize extreme weather events associated with temperature, such as heatwaves and cold spells (e.g., their frequency, duration, and intensity), as well as events associated with precipitation, such as dry days and wet days (e.g., their frequency, duration, and intensity).\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SMNXPCT_1.json b/datasets/M2SMNXPCT_1.json index 1348b10438..9546b3ef29 100644 --- a/datasets/M2SMNXPCT_1.json +++ b/datasets/M2SMNXPCT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SMNXPCT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V1, the original version of this percentile data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022. In contrast, V2, the second version, is calculated based on a 30-year climatology (1991-2020), covering the period from January 1980 to the present. \n\nThis collection consists of percentiles used to identify or characterize extreme weather events associated with temperature (maximum, mean, and minimum 2-m air temperature), as well as with precipitation (total precipitation).\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SMNXPCT_2.json b/datasets/M2SMNXPCT_2.json index 1576cde679..69de8071d0 100644 --- a/datasets/M2SMNXPCT_2.json +++ b/datasets/M2SMNXPCT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SMNXPCT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this percentile data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present. In contrast, V1, the original version, is computed based on an earlier 30-year climatology (1981-2010). \n\nThis collection consists of percentiles used to identify or characterize extreme weather events associated with temperature (maximum, mean, and minimum 2-m air temperature), as well as with precipitation (total precipitation).\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2SMNXSLV_5.12.4.json b/datasets/M2SMNXSLV_5.12.4.json index 4297bbd5ac..e54412464f 100644 --- a/datasets/M2SMNXSLV_5.12.4.json +++ b/datasets/M2SMNXSLV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2SMNXSLV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2SMNXSLV (or statM_2d_slv_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of monthly mean of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. The collection also includes the variance of parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXADG_5.12.4.json b/datasets/M2T1NXADG_5.12.4.json index e84f39c90e..d8b8db4756 100644 --- a/datasets/M2T1NXADG_5.12.4.json +++ b/datasets/M2T1NXADG_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXADG_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXADG (or tavg1_2d_adg_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. The data fields are time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXAER_5.12.4.json b/datasets/M2T1NXAER_5.12.4.json index 63a783a423..1109303493 100644 --- a/datasets/M2T1NXAER_5.12.4.json +++ b/datasets/M2T1NXAER_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXAER_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXAER (or tavg1_2d_aer_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXCHM_5.12.4.json b/datasets/M2T1NXCHM_5.12.4.json index 11d652ad68..9a7d65f943 100644 --- a/datasets/M2T1NXCHM_5.12.4.json +++ b/datasets/M2T1NXCHM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXCHM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXCHM (or tavg1_2d_chm_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXCSP_5.12.4.json b/datasets/M2T1NXCSP_5.12.4.json index 84197ae661..9f385b80f4 100644 --- a/datasets/M2T1NXCSP_5.12.4.json +++ b/datasets/M2T1NXCSP_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXCSP_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXCSP (or tavg1_2d_csp_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXFLX_5.12.4.json b/datasets/M2T1NXFLX_5.12.4.json index f2089d8272..d76eb35667 100644 --- a/datasets/M2T1NXFLX_5.12.4.json +++ b/datasets/M2T1NXFLX_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXFLX_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXFLX (or tavg1_2d_flx_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The \u201csurface\u201d in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXINT_5.12.4.json b/datasets/M2T1NXINT_5.12.4.json index 0bf47e5cb4..173cabd3f0 100644 --- a/datasets/M2T1NXINT_5.12.4.json +++ b/datasets/M2T1NXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXINT (or tavg1_2d_int_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically Integrated diagnostics of water and energy, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXLFO_5.12.4.json b/datasets/M2T1NXLFO_5.12.4.json index 6afb6b3965..b7356f4668 100644 --- a/datasets/M2T1NXLFO_5.12.4.json +++ b/datasets/M2T1NXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXLFO (or tavg1_2d_lfo_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXLND_5.12.4.json b/datasets/M2T1NXLND_5.12.4.json index cbf33cb481..e05384f15b 100644 --- a/datasets/M2T1NXLND_5.12.4.json +++ b/datasets/M2T1NXLND_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXLND_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXOCN_5.12.4.json b/datasets/M2T1NXOCN_5.12.4.json index 08ca2c5285..4c793ace11 100644 --- a/datasets/M2T1NXOCN_5.12.4.json +++ b/datasets/M2T1NXOCN_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXOCN_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXOCN (or tavg1_2d_ocn_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXRAD_5.12.4.json b/datasets/M2T1NXRAD_5.12.4.json index 108c398d1c..7c25a0f10e 100644 --- a/datasets/M2T1NXRAD_5.12.4.json +++ b/datasets/M2T1NXRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXRAD (or tavg1_2d_rad_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T1NXSLV_5.12.4.json b/datasets/M2T1NXSLV_5.12.4.json index 6e3e028411..e199b50277 100644 --- a/datasets/M2T1NXSLV_5.12.4.json +++ b/datasets/M2T1NXSLV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T1NXSLV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NEMST_5.12.4.json b/datasets/M2T3NEMST_5.12.4.json index e723791ff1..88ff721618 100644 --- a/datasets/M2T3NEMST_5.12.4.json +++ b/datasets/M2T3NEMST_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NEMST_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NEMST (or tavg3_3d_mst_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moisture processes diagnostics at the 73 model layer edges. The parameters include cumulative mass flux, 3D flux of liquid (or ice) convective (or nonconvective) precipitation, and model layer edge pressure. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NENAV_5.12.4.json b/datasets/M2T3NENAV_5.12.4.json index efa85ace50..d4566dac85 100644 --- a/datasets/M2T3NENAV_5.12.4.json +++ b/datasets/M2T3NENAV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NENAV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NENAV (or tavg3_3d_nav_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertical coordinates of the 73 model layer edges. The parameters include edge pressure and edge heights. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NETRB_5.12.4.json b/datasets/M2T3NETRB_5.12.4.json index 0f9e928cfc..64a202ebe5 100644 --- a/datasets/M2T3NETRB_5.12.4.json +++ b/datasets/M2T3NETRB_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NETRB_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NETRB (or tavg3_3d_trb_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated turbulence diagnostics at the 73 model layer edges. The parameters include total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, Richardson number from Louis, and more. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPCLD_5.12.4.json b/datasets/M2T3NPCLD_5.12.4.json index b9461e08ec..c7dacd453a 100644 --- a/datasets/M2T3NPCLD_5.12.4.json +++ b/datasets/M2T3NPCLD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPCLD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPCLD (or tavg3_3d_cld_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on the 42 pressure levels, such as updraft areal fraction, cloud fraction for radiation , in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPMST_5.12.4.json b/datasets/M2T3NPMST_5.12.4.json index 2b198c908d..5787c46026 100644 --- a/datasets/M2T3NPMST_5.12.4.json +++ b/datasets/M2T3NPMST_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPMST_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPMST (or tavg3_3d_mst_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPODT_5.12.4.json b/datasets/M2T3NPODT_5.12.4.json index 3ff969b5f0..61cef7ef4d 100644 --- a/datasets/M2T3NPODT_5.12.4.json +++ b/datasets/M2T3NPODT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPODT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPODT (or tavg3_3d_odt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPQDT_5.12.4.json b/datasets/M2T3NPQDT_5.12.4.json index 65a932efc9..5aab92eba3 100644 --- a/datasets/M2T3NPQDT_5.12.4.json +++ b/datasets/M2T3NPQDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPQDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPQDT (or tavg3_3d_qdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPRAD_5.12.4.json b/datasets/M2T3NPRAD_5.12.4.json index 64961b46f3..7c953a5bcc 100644 --- a/datasets/M2T3NPRAD_5.12.4.json +++ b/datasets/M2T3NPRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPRAD (or tavg3_3d_rad_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPTDT_5.12.4.json b/datasets/M2T3NPTDT_5.12.4.json index e80e4b40f5..4b065cce7b 100644 --- a/datasets/M2T3NPTDT_5.12.4.json +++ b/datasets/M2T3NPTDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPTDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPTDT (or tavg3_3d_tdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPTRB_5.12.4.json b/datasets/M2T3NPTRB_5.12.4.json index 181fb5ace9..e174dd8820 100644 --- a/datasets/M2T3NPTRB_5.12.4.json +++ b/datasets/M2T3NPTRB_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPTRB_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPTRB (or tavg3_3d_trb_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NPUDT_5.12.4.json b/datasets/M2T3NPUDT_5.12.4.json index 4b8071d3cd..35d11b0bfe 100644 --- a/datasets/M2T3NPUDT_5.12.4.json +++ b/datasets/M2T3NPUDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NPUDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NPUDT (or tavg3_3d_udt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NVASM_5.12.4.json b/datasets/M2T3NVASM_5.12.4.json index 98b339f5ca..68f758d5db 100644 --- a/datasets/M2T3NVASM_5.12.4.json +++ b/datasets/M2T3NVASM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NVASM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NVASM (or tavg3_3d_asm_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as air temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NVCLD_5.12.4.json b/datasets/M2T3NVCLD_5.12.4.json index cde943f7c1..e6d0a78ea8 100644 --- a/datasets/M2T3NVCLD_5.12.4.json +++ b/datasets/M2T3NVCLD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NVCLD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NVCLD (or tavg3_3d_cld_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics at 72 model layers, such as cloud fraction for radiation, pressure thickness, in cloud cloud ice (or liquid) for radiation, and relative humidity. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NVMST_5.12.4.json b/datasets/M2T3NVMST_5.12.4.json index ef589af8a4..382784694e 100644 --- a/datasets/M2T3NVMST_5.12.4.json +++ b/datasets/M2T3NVMST_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NVMST_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NVMST (or tavg3_3d_mst_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated moist processes diagnostics at 72 model layers, such as convective rainwater source, large scale rainwater source, evap subl of convective precipitation, and evap subl of non convective precipitation. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NVRAD_5.12.4.json b/datasets/M2T3NVRAD_5.12.4.json index bdb428b39c..2c3116f404 100644 --- a/datasets/M2T3NVRAD_5.12.4.json +++ b/datasets/M2T3NVRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NVRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NVRAD (or tavg3_3d_rad_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated radiation diagnostics at 72 model layers, such as air temperature tendency due to longwave and air temperature tendency due to shortwave. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2T3NXGLC_5.12.4.json b/datasets/M2T3NXGLC_5.12.4.json index 99b33682cf..6b5e5fa97a 100644 --- a/datasets/M2T3NXGLC_5.12.4.json +++ b/datasets/M2T3NXGLC_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2T3NXGLC_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2T3NXGLC (or tavg3_2d_glc_Nx) is a 2-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TCNPLTM_1.json b/datasets/M2TCNPLTM_1.json index 11759cdbdf..d2617cb856 100644 --- a/datasets/M2TCNPLTM_1.json +++ b/datasets/M2TCNPLTM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TCNPLTM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TCNPLTM (or tavgC_3d_ltm_Np) is a 3-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010.\n\nThis collection consists of meteorological diagnostics at 12 vertical pressure levels (e.g.,850 hPa, 500hPa, and 200 hPa), such as air temperature, wind components, and both relative and specific humidity. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TCNPLTM_2.json b/datasets/M2TCNPLTM_2.json index cf2f45ff2a..5e9316e78f 100644 --- a/datasets/M2TCNPLTM_2.json +++ b/datasets/M2TCNPLTM_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TCNPLTM_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TCNPLTM (or tavgC_3d_ltm_Np) is a 3-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010.\n\nThis collection consists of meteorological diagnostics at 12 vertical pressure levels (e.g.,850 hPa, 500hPa, and 200 hPa), such as air temperature, wind components, and both relative and specific humidity. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TCNXLTM_1.json b/datasets/M2TCNXLTM_1.json index 6dcf6179e2..0b0941db68 100644 --- a/datasets/M2TCNXLTM_1.json +++ b/datasets/M2TCNXLTM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TCNXLTM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TCNXLTM (or tavgC_2d_ltm_Nx) is a 2-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010.\n\nThis collection consists of meteorological diagnostics, such as air temperature (maximum, mean, and minimum at 2-meter), wind components at different vertical levels (2-meter, 10-meter, 50-meter, 850 hPa, 500hPa, and 250 hPa), sea level pressure, surface pressure, and total precipitation, evaporation, and total precipitable water vapor. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TCNXLTM_2.json b/datasets/M2TCNXLTM_2.json index b4fc1b03ef..0e601f0a77 100644 --- a/datasets/M2TCNXLTM_2.json +++ b/datasets/M2TCNXLTM_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TCNXLTM_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TCNXLTM (or tavgC_2d_ltm_Nx) is a 2-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010.\n\nThis collection consists of meteorological diagnostics, such as air temperature (maximum, mean, and minimum at 2-meter), wind components at different vertical levels (2-meter, 10-meter, 50-meter, 850 hPa, 500hPa, and 250 hPa), sea level pressure, surface pressure, and total precipitation, evaporation, and total precipitable water vapor. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.\n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d, linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original filename.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read the \"MERRA-2 File Specification Document'', \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPCLD_5.12.4.json b/datasets/M2TMNPCLD_5.12.4.json index 0f103cae9e..04fa78e062 100644 --- a/datasets/M2TMNPCLD_5.12.4.json +++ b/datasets/M2TMNPCLD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPCLD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPCLD (or tavgM_3d_cld_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPMST_5.12.4.json b/datasets/M2TMNPMST_5.12.4.json index 5274dfb413..a6ad86d60a 100644 --- a/datasets/M2TMNPMST_5.12.4.json +++ b/datasets/M2TMNPMST_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPMST_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPMST (or tavgM_3d_mst_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPODT_5.12.4.json b/datasets/M2TMNPODT_5.12.4.json index 41ce6c032a..86cf4e27e0 100644 --- a/datasets/M2TMNPODT_5.12.4.json +++ b/datasets/M2TMNPODT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPODT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPODT (or tavgM_3d_odt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPQDT_5.12.4.json b/datasets/M2TMNPQDT_5.12.4.json index 5d669d7f3b..82a2c822d1 100644 --- a/datasets/M2TMNPQDT_5.12.4.json +++ b/datasets/M2TMNPQDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPQDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPQDT (or tavgM_3d_qdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPRAD_5.12.4.json b/datasets/M2TMNPRAD_5.12.4.json index d4796b01bf..074fddc3d1 100644 --- a/datasets/M2TMNPRAD_5.12.4.json +++ b/datasets/M2TMNPRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPRAD (or tavgM_3d_rad_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPTDT_5.12.4.json b/datasets/M2TMNPTDT_5.12.4.json index 952b742b32..323f6fea16 100644 --- a/datasets/M2TMNPTDT_5.12.4.json +++ b/datasets/M2TMNPTDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPTDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPTDT (or tavgM_3d_tdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPTRB_5.12.4.json b/datasets/M2TMNPTRB_5.12.4.json index f391d734b1..c81c1e662d 100644 --- a/datasets/M2TMNPTRB_5.12.4.json +++ b/datasets/M2TMNPTRB_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPTRB_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPTRB (or tavgM_3d_trb_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNPUDT_5.12.4.json b/datasets/M2TMNPUDT_5.12.4.json index af7af77e6f..c97f0beed9 100644 --- a/datasets/M2TMNPUDT_5.12.4.json +++ b/datasets/M2TMNPUDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNPUDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNPUDT (or tavgM_3d_udt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXADG_5.12.4.json b/datasets/M2TMNXADG_5.12.4.json index 94acaef4a9..b26019b2f7 100644 --- a/datasets/M2TMNXADG_5.12.4.json +++ b/datasets/M2TMNXADG_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXADG_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXADG (or tavgM_2d_adg_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXAER_5.12.4.json b/datasets/M2TMNXAER_5.12.4.json index 5168f01f86..7c07c5859d 100644 --- a/datasets/M2TMNXAER_5.12.4.json +++ b/datasets/M2TMNXAER_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXAER_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXAER (or tavgM_2d_aer_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXCHM_5.12.4.json b/datasets/M2TMNXCHM_5.12.4.json index 320b6c15ef..543621f87d 100644 --- a/datasets/M2TMNXCHM_5.12.4.json +++ b/datasets/M2TMNXCHM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXCHM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXCHM (or tavgM_2d_chm_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXCSP_5.12.4.json b/datasets/M2TMNXCSP_5.12.4.json index 80aaed39d0..42b845930c 100644 --- a/datasets/M2TMNXCSP_5.12.4.json +++ b/datasets/M2TMNXCSP_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXCSP_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXCSP (or tavgM_2d_csp_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXFLX_5.12.4.json b/datasets/M2TMNXFLX_5.12.4.json index 80677ac93a..3c8e5b9030 100644 --- a/datasets/M2TMNXFLX_5.12.4.json +++ b/datasets/M2TMNXFLX_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXFLX_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXFLX (or tavgM_2d_flx_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The \u201csurface\u201d in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXGLC_5.12.4.json b/datasets/M2TMNXGLC_5.12.4.json index 482a406bc8..97e77307e8 100644 --- a/datasets/M2TMNXGLC_5.12.4.json +++ b/datasets/M2TMNXGLC_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXGLC_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXGLC (or tavgM_2d_glc_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXINT_5.12.4.json b/datasets/M2TMNXINT_5.12.4.json index fabaa8dd42..a192921ceb 100644 --- a/datasets/M2TMNXINT_5.12.4.json +++ b/datasets/M2TMNXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXINT (or tavgM_2d_int_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXLFO_5.12.4.json b/datasets/M2TMNXLFO_5.12.4.json index 5d65054bf4..826b1d3571 100644 --- a/datasets/M2TMNXLFO_5.12.4.json +++ b/datasets/M2TMNXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXLFO (or tavgM_2d_lfo_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXLND_5.12.4.json b/datasets/M2TMNXLND_5.12.4.json index 923a8b338b..0f37c7df95 100644 --- a/datasets/M2TMNXLND_5.12.4.json +++ b/datasets/M2TMNXLND_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXLND_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXLND (or tavgM_2d_lnd_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. The collection also includes variance of certain parameters. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXOCN_5.12.4.json b/datasets/M2TMNXOCN_5.12.4.json index 3558f01f7b..16b86c51be 100644 --- a/datasets/M2TMNXOCN_5.12.4.json +++ b/datasets/M2TMNXOCN_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXOCN_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXOCN (or tavgM_2d_ocn_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXRAD_5.12.4.json b/datasets/M2TMNXRAD_5.12.4.json index d83084cd20..52c6ec3de4 100644 --- a/datasets/M2TMNXRAD_5.12.4.json +++ b/datasets/M2TMNXRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXRAD (or tavgM_2d_rad_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TMNXSLV_5.12.4.json b/datasets/M2TMNXSLV_5.12.4.json index 9635faf294..116f3916bf 100644 --- a/datasets/M2TMNXSLV_5.12.4.json +++ b/datasets/M2TMNXSLV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TMNXSLV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TMNXSLV (or tavgM_2d_slv_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The collection also includes variance of certain parameters.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPCLD_5.12.4.json b/datasets/M2TUNPCLD_5.12.4.json index 5c62753018..5a34358d65 100644 --- a/datasets/M2TUNPCLD_5.12.4.json +++ b/datasets/M2TUNPCLD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPCLD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPCLD (or tavgU_3d_cld_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPMST_5.12.4.json b/datasets/M2TUNPMST_5.12.4.json index 83bf3b731a..90903c8b4b 100644 --- a/datasets/M2TUNPMST_5.12.4.json +++ b/datasets/M2TUNPMST_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPMST_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPMST (or tavgU_3d_mst_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPODT_5.12.4.json b/datasets/M2TUNPODT_5.12.4.json index 0cdc37eb52..2a46fb81ab 100644 --- a/datasets/M2TUNPODT_5.12.4.json +++ b/datasets/M2TUNPODT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPODT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPODT (or tavgU_3d_odt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPQDT_5.12.4.json b/datasets/M2TUNPQDT_5.12.4.json index 76ac7d33bd..c9cfd2add4 100644 --- a/datasets/M2TUNPQDT_5.12.4.json +++ b/datasets/M2TUNPQDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPQDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPQDT (or tavgU_3d_qdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPRAD_5.12.4.json b/datasets/M2TUNPRAD_5.12.4.json index 76546f03d9..b4e91bdfd1 100644 --- a/datasets/M2TUNPRAD_5.12.4.json +++ b/datasets/M2TUNPRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPRAD (or tavgU_3d_rad_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour that is time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPTDT_5.12.4.json b/datasets/M2TUNPTDT_5.12.4.json index 0806f8d24e..6fb0a79233 100644 --- a/datasets/M2TUNPTDT_5.12.4.json +++ b/datasets/M2TUNPTDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPTDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPTDT (or tavgU_3d_tdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPTRB_5.12.4.json b/datasets/M2TUNPTRB_5.12.4.json index e29d78c572..e4fa30b937 100644 --- a/datasets/M2TUNPTRB_5.12.4.json +++ b/datasets/M2TUNPTRB_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPTRB_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPTRB (or tavgU_3d_trb_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNPUDT_5.12.4.json b/datasets/M2TUNPUDT_5.12.4.json index 8bd1c2bf44..fb0e7e7638 100644 --- a/datasets/M2TUNPUDT_5.12.4.json +++ b/datasets/M2TUNPUDT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNPUDT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNPUDT (or tavgU_3d_udt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXADG_5.12.4.json b/datasets/M2TUNXADG_5.12.4.json index 93830b0f5a..dff16f7d3f 100644 --- a/datasets/M2TUNXADG_5.12.4.json +++ b/datasets/M2TUNXADG_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXADG_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXADG (or tavgU_2d_adg_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. This data collection is the monthly mean of data fields for each hour and is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXAER_5.12.4.json b/datasets/M2TUNXAER_5.12.4.json index 48247e474d..3e839899a6 100644 --- a/datasets/M2TUNXAER_5.12.4.json +++ b/datasets/M2TUNXAER_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXAER_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXAER (or tavgU_2d_aer_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. This data collection is the monthly mean of data fields for each hour and is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXCHM_5.12.4.json b/datasets/M2TUNXCHM_5.12.4.json index 0d13740fad..1c5c7a176a 100644 --- a/datasets/M2TUNXCHM_5.12.4.json +++ b/datasets/M2TUNXCHM_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXCHM_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXCHM (or tavgU_2d_chm_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXCSP_5.12.4.json b/datasets/M2TUNXCSP_5.12.4.json index ce9b05e0be..85f49fb872 100644 --- a/datasets/M2TUNXCSP_5.12.4.json +++ b/datasets/M2TUNXCSP_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXCSP_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXCSP (or tavgU_2d_csp_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXFLX_5.12.4.json b/datasets/M2TUNXFLX_5.12.4.json index 03bc41d718..d9d35eb8c8 100644 --- a/datasets/M2TUNXFLX_5.12.4.json +++ b/datasets/M2TUNXFLX_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXFLX_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXFLX (or tavgU_2d_flx_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The \u201csurface\u201d in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXGLC_5.12.4.json b/datasets/M2TUNXGLC_5.12.4.json index eab5c71401..be2c9ac144 100644 --- a/datasets/M2TUNXGLC_5.12.4.json +++ b/datasets/M2TUNXGLC_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXGLC_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXGLC (or tavgU_2d_glc_Nx) is a 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, \u2026 , 22:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXINT_5.12.4.json b/datasets/M2TUNXINT_5.12.4.json index 8863749e27..7464b3d653 100644 --- a/datasets/M2TUNXINT_5.12.4.json +++ b/datasets/M2TUNXINT_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXINT_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXINT (or tavgU_2d_int_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXLFO_5.12.4.json b/datasets/M2TUNXLFO_5.12.4.json index 8627444ea4..3bf3e4191e 100644 --- a/datasets/M2TUNXLFO_5.12.4.json +++ b/datasets/M2TUNXLFO_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXLFO_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXLFO (or tavgU_2d_lfo_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXLND_5.12.4.json b/datasets/M2TUNXLND_5.12.4.json index 892bc5e7ae..e02e8f1f0e 100644 --- a/datasets/M2TUNXLND_5.12.4.json +++ b/datasets/M2TUNXLND_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXLND_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXLND (or tavgU_2d_lnd_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXOCN_5.12.4.json b/datasets/M2TUNXOCN_5.12.4.json index a3f4c5929f..8df2a02fba 100644 --- a/datasets/M2TUNXOCN_5.12.4.json +++ b/datasets/M2TUNXOCN_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXOCN_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXOCN (or tavgU_2d_ocn_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXRAD_5.12.4.json b/datasets/M2TUNXRAD_5.12.4.json index f10f68ea01..8795389b8d 100644 --- a/datasets/M2TUNXRAD_5.12.4.json +++ b/datasets/M2TUNXRAD_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXRAD_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXRAD (or tavgU_2d_rad_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2TUNXSLV_5.12.4.json b/datasets/M2TUNXSLV_5.12.4.json index 5a18b42302..66ab8b14be 100644 --- a/datasets/M2TUNXSLV_5.12.4.json +++ b/datasets/M2TUNXSLV_5.12.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2TUNXSLV_5.12.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2TUNXSLV (or tavgU_2d_slv_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850 hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, \u2026 , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check \u201cRecords of MERRA-2 Data Reprocessing and Service Changes\u201d linked from the \u201cDocumentation\u201d tab on this page. Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/M2_TMAX_PM25_1.json b/datasets/M2_TMAX_PM25_1.json index bfda2baa07..f68d9bba0a 100644 --- a/datasets/M2_TMAX_PM25_1.json +++ b/datasets/M2_TMAX_PM25_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "M2_TMAX_PM25_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "M2_TMAX_PM25 is a value-added product derived from the MERRA-2 aerosol monthly product M2TMNXAER_5.12.4 (or tavgM_2d_aer_Nx). The surface concentration of fine particulate matter (PM2.5) is calculated as the sum of individual aerosol components (organic carbon, black carbon, sulfate, sea salt, and dust) (Buchard et al., 2017) and is recast from the native MERRA-2 model grid. This data collection includes separate files for country-level (and territories) PM2.5 concentrations with and without population weighting applied. \n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\", \u201cMERRA-2 Data Access \u2013 Quick Start Guide\u201d, and FAQs linked from the \u201dDocumentation\u201d tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).", "links": [ { diff --git a/datasets/MAA_0.json b/datasets/MAA_0.json index 2be11366fd..aa2756734c 100644 --- a/datasets/MAA_0.json +++ b/datasets/MAA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken along the Massachusetts and Maine coastal regions in 2007.", "links": [ { diff --git a/datasets/MAC021S0_002.json b/datasets/MAC021S0_002.json index 308dbec0d5..4cdd32da8d 100644 --- a/datasets/MAC021S0_002.json +++ b/datasets/MAC021S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC021S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 10 km cross-track. Thus, MAC021S0 cross-track width is 11 pixels for radiances. Geolocations in the original product, however, are subsampled at 5-km, and thus the cross-track width of the subset geolocations is 3 pixels. Along-track, all MODIS pixels from the original product are preserved. \n \nIn the standard product, the MODIS Level 1B data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of electromagentic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for the solar reflective bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data.\n \nVisible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\n \n(The shortname for this product is MAC021S0).", "links": [ { diff --git a/datasets/MAC021S1_002.json b/datasets/MAC021S1_002.json index 1027ca4eaa..b192d3bf35 100644 --- a/datasets/MAC021S1_002.json +++ b/datasets/MAC021S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC021S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAC021S1 cross-track width is 201 pixels for radiances. Geolocations in the original product, however, are subsampled at 5-km, and thus the cross-track width of the subset geolocations is 41 pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS Level 1B data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of electromagentic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for the solar reflective bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data.\n \nVisible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\n \n \n(The shortname for this product is MAC021S1).", "links": [ { diff --git a/datasets/MAC02QS0_002.json b/datasets/MAC02QS0_002.json index 51342e850e..7830ea201a 100644 --- a/datasets/MAC02QS0_002.json +++ b/datasets/MAC02QS0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC02QS0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 10 km cross-track. Thus, MAC02QS0 cross-track width is 44 pixels for radiances. Geolocations, however, are 1-km at best, and thus the cross-track width for geolocations is 11 pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the 250 meter MODIS Level 1B data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for these solar reflective bands through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. \n \nChannel locations for the MODIS 250 meter data are as follows: \nBand Center Wavelength (um) Primary Use \n-------------------------- ----------- \n1 0.620 - 0.670 Land/Cloud Boundaries \n2 0.841 - 0.876 Land/Cloud Boundaries \n \n \n(The shortname for this product is MAC02QS0).", "links": [ { diff --git a/datasets/MAC02QS1_002.json b/datasets/MAC02QS1_002.json index b376b0caab..d5219a139d 100644 --- a/datasets/MAC02QS1_002.json +++ b/datasets/MAC02QS1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC02QS1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAC02QS1 cross-track width is 804 pixels for radiances. Geolocations, however, are 1-km at best, and thus the cross-track width for geolocations is 201 pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the 250 meter MODIS Level 1B data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for these solar reflective bands through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data.\n \nChannel locations for the MODIS 250 meter data are as follows: \nBand Center Wavelength (um) Primary Use \n-------------------------- ----------- \n1 0.620 - 0.670 Land/Cloud Boundaries \n2 0.841 - 0.876 Land/Cloud Boundaries \n \n \n(The shortname for this product is MAC02QS1).", "links": [ { diff --git a/datasets/MAC03S0_002.json b/datasets/MAC03S0_002.json index d6652d2677..a45f336a67 100644 --- a/datasets/MAC03S0_002.json +++ b/datasets/MAC03S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC03S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 10 km cross-track. Thus, MAC03S0 cross-track width is 11 pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. \n \n(The shortname for this product is MAC03S0).", "links": [ { diff --git a/datasets/MAC03S1_002.json b/datasets/MAC03S1_002.json index fdd0052749..b3d9a2bc6b 100644 --- a/datasets/MAC03S1_002.json +++ b/datasets/MAC03S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC03S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAC03S1 cross-track width is 201 pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. \n \n \n(The shortname for this product is MAC03S1).", "links": [ { diff --git a/datasets/MAC04S0_002.json b/datasets/MAC04S0_002.json index de418f0f16..33baed6e36 100644 --- a/datasets/MAC04S0_002.json +++ b/datasets/MAC04S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC04S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track.However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S0 cross-track width is 2 pixels, the closest on either side of CloudSat track, and the resultant cross-track swath width is about 20 km.Along-track, all MODIS pixels from the original product are preserved.\n \nIn the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land.\n \n \n(The shortname for this product is MAC04S0).", "links": [ { diff --git a/datasets/MAC04S1_002.json b/datasets/MAC04S1_002.json index 571d95287d..21672568ec 100644 --- a/datasets/MAC04S1_002.json +++ b/datasets/MAC04S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC04S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S1 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land.\n \n \n(The shortname for this product is MAC04S1).", "links": [ { diff --git a/datasets/MAC05S0_002.json b/datasets/MAC05S0_002.json index e893754b97..a2fa1bb6e6 100644 --- a/datasets/MAC05S0_002.json +++ b/datasets/MAC05S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC05S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track. However, the original MYD05_L2 has data of 5- and 1-km pixels. Thus, MAC05S0 cross-track width is 3- and 11-pixels, depending on the parameter, and the resultant swath is about 15- and 10-km, correspondingly. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS level-2 atmospheric precipitable water product consists of total atmospheric column water vapor amounts (and ancillary parameters) over clear land areas of the globe, over extended clear oceanic areas with the Sun glint, and above clouds over both land and ocean. These estimates are based on a near-infrared algorithm using only daytime measurements with solar zenith angle less than 72 degrees. The retrieval algorithm relies on observations of water vapor attenuation of near-infrared solar radiation reflected by surfaces and clouds. The product is produced only over areas that have reflective surfaces in the near-infrared. The infrared-derived precipitable water vapor generated for both daytime & nighttime conditions as one component of another MODIS product (MYD07) is also provided as a part of this product. \n \n \n(The shortname for this product is MAC05S0).", "links": [ { diff --git a/datasets/MAC05S1_002.json b/datasets/MAC05S1_002.json index 2de86782f4..3be7ef6bcd 100644 --- a/datasets/MAC05S1_002.json +++ b/datasets/MAC05S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC05S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD05_L2 has data of 5- and 1-km pixels. Thus, MAC05S1 cross-track width is 41- and 201-pixels, depending on the parameter. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS level-2 atmospheric precipitable water product consists of total atmospheric column water vapor amounts (and ancillary parameters) over clear land areas of the globe, over extended clear oceanic areas with the Sun glint, and above clouds over both land and ocean. These estimates are based on a near-infrared algorithm using only daytime measurements with solar zenith angle less than 72 degrees. The retrieval algorithm relies on observations of water vapor attenuation of near-infrared solar radiation reflected by surfaces and clouds. The product is produced only over areas that have reflective surfaces in the near-infrared. The infrared-derived precipitable water vapor generated for both daytime & nighttime conditions as one component of another MODIS product (MYD07) is also provided as a part of this product. \n \n \n(The shortname for this product is MAC05S1).", "links": [ { diff --git a/datasets/MAC06S0_002.json b/datasets/MAC06S0_002.json index 72b1016ae0..36e7b0dbc9 100644 --- a/datasets/MAC06S0_002.json +++ b/datasets/MAC06S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC06S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track. However, the original MYD06_L2 has data of 5- and 1-km pixels. Thus, MAC06S0 cross-track width is 3- and 11-pixels, depending on the parameter, and the resultant swath is about 15- and 10-km, correspondingly. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). \n\nThe shortname for this level-2 MODIS cloud product is MAC06S0.\n\nYD06_L2 and the principal investigators for this product are MODIS scientists Dr. Bo-Cai Gao (gao@rsd.nrl.navy.mil) for cirrus cloud detection; Dr. Paul Menzel (paulm@ssec.wisc.edu) for cloud top properties; and Dr. Michael King(king@climate.gsfc.nasa.gov) for cloud optical properties. \n \n \n(The shortname for this product is MAC06S0).", "links": [ { diff --git a/datasets/MAC06S1_002.json b/datasets/MAC06S1_002.json index 0de3283df1..39939181a3 100644 --- a/datasets/MAC06S1_002.json +++ b/datasets/MAC06S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC06S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD06_L2 has data of 5- and 1-km pixels. Thus, MAC06S1 cross-track width is 41- and 201-pixels, depending on the parameter.Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). \n\n\n(The shortname for this product is MAC06S1).", "links": [ { diff --git a/datasets/MAC07S0_002.json b/datasets/MAC07S0_002.json index 3164011df7..51a8d8b826 100644 --- a/datasets/MAC07S0_002.json +++ b/datasets/MAC07S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC07S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track. However, the original MYD07_L2 has data of 5-km pixels. Thus, MAC07S0 cross-track width is 3-pixels, and the resultant swath is about 15-km, correspondingly. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the level-2 MODIS Temperature and Water Vapor Profile Product MYD07_L2 consists of 30 gridded parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 FOVs are cloud free. \n \n \n(The shortname for this product is MAC07S0).", "links": [ { diff --git a/datasets/MAC07S1_002.json b/datasets/MAC07S1_002.json index 1b057c7300..58fc9f084c 100644 --- a/datasets/MAC07S1_002.json +++ b/datasets/MAC07S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC07S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD07_L2 has data of 5-km pixels. Thus, MAC07S1 cross-track width is 41-pixels. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the level-2 MODIS Temperature and Water Vapor Profile Product MYD07_L2 consists of 30 gridded parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 FOVs are cloud free. \n \n \n(The shortname for this product is MAC07S1).", "links": [ { diff --git a/datasets/MAC35S0_002.json b/datasets/MAC35S0_002.json index 0c8d1bdc6b..1d64b77dbd 100644 --- a/datasets/MAC35S0_002.json +++ b/datasets/MAC35S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC35S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track. Geolocations in the original product, however, are subsampled at 5-km, and thus the crosss-track width of the subset geolocations is 3 pixels. The subset Cloud Mask has 11 pixels across-track. However, some of the Cloud Mask information is at bit level, thus allowing storing actually 250-m information in seemingly 1-km pixels. This is achieved by reserving 2 bytes (the last two out of six) of every 1-km pixel as 16 yes/no-cloud bits. Each one of these 16 bits flags a corresponding 250-m tile inside the 1-km pixel. Their state is described in the local attributes to the Cloud_Mask HDF data set, and accordingly must be interprated as 0=YES, 1=NO. Thus the effective cross-track width, for these two bytes only, is 44 tiles of 250-m denomination.\n \nAlong-track, all MODIS pixels from the original product are preserved. \n \nIn the standard product, the MODIS level-2 cloud mask product is a global product generated for both daytime & nighttime conditions at 1-km spatial resolution (at nadir) and for daytime at 250-m resolution. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel (paulm@ssec.wisc.edu).\n \n \n(The shortname for this product is MAC35S0).", "links": [ { diff --git a/datasets/MAC35S1_002.json b/datasets/MAC35S1_002.json index 982c3d33f4..536e6b8cbd 100644 --- a/datasets/MAC35S1_002.json +++ b/datasets/MAC35S1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAC35S1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. Geolocations in the original product, however, are subsampled at 5-km, and thus the crosss-track width of the subset geolocations is 41 pixels. The subset Cloud Mask has 201 pixels across-track. However, some of the Cloud Mask information is at bit level, thus allowing storing actually 250-m information in seemingly 1-km pixels. This is achieved by reserving 2 bytes (the last two out of six) of every 1-km pixel as 16 yes/no-cloud bits. Each one of these 16 bits flags a corresponding 250-m tile inside the 1-km pixel. Their state is described in the local attributes to the Cloud_Mask HDF data set, and accordingly must be interprated as 0=YES, 1=NO. Thus the effective cross-track width, for these two bytes only, is 804 tiles of 250-m denomination. \n \nAlong-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS level-2 cloud mask product is a global product generated for both daytime & nighttime conditions at 1-km spatial resolution (at nadir) and for daytime at 250-m resolution. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel (paulm@ssec.wisc.edu).\n \n \n(The shortname for this product is MAC35S1).", "links": [ { diff --git a/datasets/MACLWP_diurnal_1.json b/datasets/MACLWP_diurnal_1.json index 6d48c665eb..1e2fae9be5 100644 --- a/datasets/MACLWP_diurnal_1.json +++ b/datasets/MACLWP_diurnal_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACLWP_diurnal_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).", "links": [ { diff --git a/datasets/MACLWP_mean_1.json b/datasets/MACLWP_mean_1.json index 803a09c251..d5aa52c890 100644 --- a/datasets/MACLWP_mean_1.json +++ b/datasets/MACLWP_mean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACLWP_mean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).", "links": [ { diff --git a/datasets/MACPEX_Aerosol_AircraftInSitu_WB57_Data_1.json b/datasets/MACPEX_Aerosol_AircraftInSitu_WB57_Data_1.json index 82e417bc43..f4447af19a 100644 --- a/datasets/MACPEX_Aerosol_AircraftInSitu_WB57_Data_1.json +++ b/datasets/MACPEX_Aerosol_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_Aerosol_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_Aerosol_AircraftInSitu_WB57_Data is the in-situ aerosol data collected during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data was collected by the Electron Microscope Ice Residual Impactor (EMIRI), Particle Analysis by Laser Mass Spectroscopy (PALMS), Single Particle Soot Photometer (SP2), Focused Cavity Aerosol Spectrometer (FCAS), FCAS II, and the Nuclei-Mode Aerosol Size Spectrometer II (NMASS II). Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_Cloud_AircraftInSitu_WB57_Data_1.json b/datasets/MACPEX_Cloud_AircraftInSitu_WB57_Data_1.json index 31d52cdfd0..71e983d285 100644 --- a/datasets/MACPEX_Cloud_AircraftInSitu_WB57_Data_1.json +++ b/datasets/MACPEX_Cloud_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_Cloud_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_Cloud_AircraftInSitu_WB57_Data is the in-situ cloud data collection during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data was collected by the Small Ice Detector (SID), Video Ice Particle Sampler (VIPS), High Volume Precipitation Spectrometer (HVPS), and the 2D-S Stereo Probe (2DS). Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_MetNav_AircraftInSitu_WB57_Data_1.json b/datasets/MACPEX_MetNav_AircraftInSitu_WB57_Data_1.json index 76c62f9d5e..bbb396c3a5 100644 --- a/datasets/MACPEX_MetNav_AircraftInSitu_WB57_Data_1.json +++ b/datasets/MACPEX_MetNav_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_MetNav_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_MetNav_AircraftInSitu_WB57_Data is the in-situ meteorology and navigational data collection during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data from the Meteorological Measurement System (MMS) is featured in this collection. Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_Satellite_Data_1.json b/datasets/MACPEX_Satellite_Data_1.json index 37cb568457..e0ca6e1d4f 100644 --- a/datasets/MACPEX_Satellite_Data_1.json +++ b/datasets/MACPEX_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_Satellite_AircraftInSitu_WB57_Data is the supplementary satellite ancillary data collection during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Cloud properties were retrieved by the GOES-13 satellite using the Visible Infrared Solar-Infrared Split Window Technique (VISST). Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_Sondes_Data_1.json b/datasets/MACPEX_Sondes_Data_1.json index 6e75a54f42..8880ff1157 100644 --- a/datasets/MACPEX_Sondes_Data_1.json +++ b/datasets/MACPEX_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_Sondes_Data is the balloonsonde and ozonesonde data collected during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data were collected by the balloon borne frost point hygrometer (balloon FPH) and ozonesondes. Data collection for this product is complete.\r\n\r\nThe Mid-latitude Airborne Cirrus Properties Experiment (MACPEX) was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_TraceGas_AircraftInSitu_WB57_Data_1.json b/datasets/MACPEX_TraceGas_AircraftInSitu_WB57_Data_1.json index 51e184a399..5645631d15 100644 --- a/datasets/MACPEX_TraceGas_AircraftInSitu_WB57_Data_1.json +++ b/datasets/MACPEX_TraceGas_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_TraceGas_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_TraceGas_AircraftInSitu_WB57_Data is the in-situ trace gas data collection during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data was collected by the NOAA O3 Photometer (NOAA O3), the NOAA UAS O3 Photometer (UASO3), and the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACPEX_Water_AircraftInSitu_WB57_Data_1.json b/datasets/MACPEX_Water_AircraftInSitu_WB57_Data_1.json index dfdc390dc9..c2bdafebe3 100644 --- a/datasets/MACPEX_Water_AircraftInSitu_WB57_Data_1.json +++ b/datasets/MACPEX_Water_AircraftInSitu_WB57_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACPEX_Water_AircraftInSitu_WB57_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MACPEX_Water_AircraftInSitu_WB57_Data is the in-situ water data collection during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX). Data was collected by the Harvard Water Vapor (HWV), Closed-path Laser Hygrometer (CLH), Diode Laser Hygrometer (DLH), JPL Laser Hygrometer (JLH), Unmanned Aerial System Laser Hygrometer (ULH), Fast In-situ Stratospheric Hygrometer (FISH), NOAA Chemical Ionization Mass Spectrometer (CIMS), and the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Data collection for this product is complete.\r\n\r\nThe MACPEX mission was an airborne field campaign that deployed from March 18th to April 26th, 2011. MACPEX sought to investigate cirrus cloud properties and the processes that affect their impact on radiation. The campaign conducted science flights using the NASA WB-57 aircraft based out of Ellington Airfield, Texas. Science flights were focused on the central North America vicinity, with an emphasis over the Southern Great Plains atmospheric observatory (established by the Department of Energy\u2019s (DoE) Atmospheric Radiation Measurement (ARM) user facility) site in Oklahoma. MACPEX was a joint effort between NASA, the NOAA Earth System Research Laboratory (ESRL), the National Center for Atmospheric Research (NCAR), and several U.S. universities.\r\n\r\nThe WB-57 contained a comprehensive instrument payload for detailed in-situ measurements that were targeted to answer MACPEX\u2019s four major science questions. The first science question that MACPEX explored was how prevalent the smaller crystals are in cirrus clouds, and how important they are for extinction, radiative forcing, and radiative heating. MACPEX also sought to understand how cirrus microphysical properties (particle size distribution, ice crystal habit, extinction, ice water content) are related to the dynamical forcing driving cloud formation. Researchers also investigated how cirrus microphysical properties are related to aerosol loading and composition, including the abundance of heterogeneous ice nuclei. Lastly, this campaign examined how cirrus microphysical properties evolve through the lifecycles of the clouds, and the role radiatively driven dynamical motions play.\r\n\r\nIn addition to the in-situ measurements, four flights were coordinated to validate the NASA EOS/A-Train satellite observations. NOAA also launched balloon sondes and ozonesondes, which were used to acquire data about the frost point and water vapor in the atmosphere. The balloon sondes and ozonesondes also acquired pressure, temperature, and humidity data, as well as measurements regarding the ozone in the atmosphere.", "links": [ { diff --git a/datasets/MACQ_NO2_4.json b/datasets/MACQ_NO2_4.json index 6b0b3a2a14..89084be35d 100644 --- a/datasets/MACQ_NO2_4.json +++ b/datasets/MACQ_NO2_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACQ_NO2_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Slant column densities of stratospheric nitrogen dioxide (NO2) determined from spectroscopic measurements of zenith scattered twilight at wavelengths from 430-485 nanometres.\n\nThese data are provided by the NDACC (Network for the Detection of Atmospheric Composition Change) on an annual basis and then stored at the AADC. Only data that are two years old are made available.\n\nFurther information about the dataset is available from the URL given below.\n\nThis work was performed as part of ASAC project 2244, but this project has since been replaced by AAS project 4193, \"Long-term measurements of atmospheric nitrogen dioxide at Macquarie Island\".\n\nThe fields in this dataset are:\nNO2 slant column density (molecules/cm**2)\nError in NO2 slant column density (molecules/cm**2)\nNO2 Air Mass Factor (AMF)\nNO2 vertical column density (molecules/cm**2)\nYear of Observation (UT)\nMonth (UT)\nDay of month (UT)\nHour (UT)\nMinutes (UT)\nLatitude of observation site; decimal degrees\nLongitude of observation site; decimal degrees\nElevation of site; meters\nSolar zenith angle at time of observation; decimal degrees\n \nTaken from the 2008-2009 Progress Report:\nPublic summary of the season progress:\nGround-based measurements of nitrogen dioxide (NO2), one of the key trace gases in the atmosphere, have been made at Macquarie Island since 1996 and span now more than 12 years. These long-term observations made at Macquarie Island help us to bridge the gap between measurements made in the Antarctic and at mid-latitudes. The observations during the 2008/2009 season went smoothly and we have no problems to report.", "links": [ { diff --git a/datasets/MACTWP_mean_1.json b/datasets/MACTWP_mean_1.json index 72e3e59734..907b05994c 100644 --- a/datasets/MACTWP_mean_1.json +++ b/datasets/MACTWP_mean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MACTWP_mean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).", "links": [ { diff --git a/datasets/MAIA_ANC_SURFACEMONITOR_PM_2.5_SPECIES_C01.json b/datasets/MAIA_ANC_SURFACEMONITOR_PM_2.5_SPECIES_C01.json index 071d90cb0c..48a5fc561f 100644 --- a/datasets/MAIA_ANC_SURFACEMONITOR_PM_2.5_SPECIES_C01.json +++ b/datasets/MAIA_ANC_SURFACEMONITOR_PM_2.5_SPECIES_C01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAIA_ANC_SURFACEMONITOR_PM_2.5_SPECIES_C01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MAIA Surface Monitor Stage 0 files are an ancillary dataset containing processed particulate matter (PM) measurements collected from a global in-situ surface monitoring network. The files are generated by the MAIA surface monitoring subsystem software at NASA\u2019s Atmospheric Science Data Center (ASDC).", "links": [ { diff --git a/datasets/MAIA_ANC_SURFACEMONITOR_PM_TOTAL_C01.json b/datasets/MAIA_ANC_SURFACEMONITOR_PM_TOTAL_C01.json index 573fcfb484..3e873d9cc7 100644 --- a/datasets/MAIA_ANC_SURFACEMONITOR_PM_TOTAL_C01.json +++ b/datasets/MAIA_ANC_SURFACEMONITOR_PM_TOTAL_C01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAIA_ANC_SURFACEMONITOR_PM_TOTAL_C01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MAIA Surface Monitor Stage 0 files are an ancillary dataset containing processed particulate matter (PM) measurements collected from a global in-situ surface monitoring network. The files are generated by the MAIA surface monitoring subsystem software at NASA\u2019s Atmospheric Science Data Center (ASDC).", "links": [ { diff --git a/datasets/MALINA_0.json b/datasets/MALINA_0.json index 3553101c99..12c977d82a 100644 --- a/datasets/MALINA_0.json +++ b/datasets/MALINA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MALINA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MALINA oceanographic campaign was conducted during summer 2009 to investigate the carbon stocks and the processes controlling the carbon fluxes in the Mackenzie River estuary and the Beaufort Sea. During the campaign, an extensive suite of physical, chemical and biological variables were measured across seven shelf\u2013basin transects (south\u2013north) to capture the meridional gradient between the estuary and the open ocean. Key variables such as temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured onboard the Canadian research icebreaker CCGS Amundsen and from a barge in shallow coastal areas or for sampling within broken ice fields. Massicotte et al., 2021 (https://doi.org/10.17882/75345)", "links": [ { diff --git a/datasets/MAM03S0_002.json b/datasets/MAM03S0_002.json index 7a71a12826..120e8bc434 100644 --- a/datasets/MAM03S0_002.json +++ b/datasets/MAM03S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM03S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along the Microwave Limb Sounder (MLS) field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAM03S0 cross-track width is 201 pixels. Along-track, all MODIS pixels from the original product are preserved. \n \nIn the standard product, geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. \n\n \n(The shortname for this product is MAM03S0).", "links": [ { diff --git a/datasets/MAM04S0_002.json b/datasets/MAM04S0_002.json index a8c09b95f0..86fd14fd94 100644 --- a/datasets/MAM04S0_002.json +++ b/datasets/MAM04S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM04S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAM04S0 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land.\n \n \n(The shortname for this product is MAM04S0).", "links": [ { diff --git a/datasets/MAM05S0_002.json b/datasets/MAM05S0_002.json index fb61ceff4b..7c8631f66b 100644 --- a/datasets/MAM05S0_002.json +++ b/datasets/MAM05S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM05S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD05_L2 has data of 5- and 1-km pixels. Thus, MAM05S0 cross-track width is 41- and 201-pixels, depending on the parameter. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the MODIS level-2 atmospheric precipitable water product consists of total atmospheric column water vapor amounts (and ancillary parameters) over clear land areas of the globe, over extended clear oceanic areas with the Sun glint, and above clouds over both land and ocean. These estimates are based on a near-infrared algorithm using only daytime measurements with solar zenith angle less than 72 degrees. The retrieval algorithm relies on observations of water vapor attenuation of near-infrared solar radiation reflected by surfaces and clouds. The product is produced only over areas that have reflective surfaces in the near-infrared. The infrared-derived precipitable water vapor generated for both daytime & nighttime conditions as one component of another MODIS product (MYD07) is also provided as a part of this product. \n \n \n(The shortname for this product is MAM05S0).", "links": [ { diff --git a/datasets/MAM06S0_002.json b/datasets/MAM06S0_002.json index 13e0c814c6..26f9929909 100644 --- a/datasets/MAM06S0_002.json +++ b/datasets/MAM06S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM06S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD06_L2 has data of 5- and 1-km pixels. Thus, MAM06S0 cross-track width is 41- and 201-pixels, depending on the parameter. Along-track, all MODIS pixels from the original product are preserved.\n \nIn the standard product, the level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow).\n \n(The shortname for this product is MAM06S0).", "links": [ { diff --git a/datasets/MAM07S0_002.json b/datasets/MAM07S0_002.json index 5bdafde064..080e455065 100644 --- a/datasets/MAM07S0_002.json +++ b/datasets/MAM07S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM07S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD07_L2 has data of 5-km pixels. Thus, MAM07S0 cross-track width is 41-pixels. Along-track, all MODIS pixels from the original product are preserved.\n\nIn the standard product, the level-2 MODIS Temperature and Water Vapor Profile Product MYD07_L2 consists of 30 gridded parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 FOVs are cloud free. \n \n \n (The shortname for this product is MAM07S0).", "links": [ { diff --git a/datasets/MAM35S0_002.json b/datasets/MAM35S0_002.json index 69aeb7723b..5563a42f88 100644 --- a/datasets/MAM35S0_002.json +++ b/datasets/MAM35S0_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAM35S0_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. Geolocations in the original product, however, are subsampled at 5-km, and thus the crosss-track width of the subset geolocations is 41 pixels. The subset Cloud Mask has 201 pixels across-track. However, some of the Cloud Mask information is at bit level, thus allowing storing actually 250-m information in seemingly 1-km pixels. This is achieved by reserving 2 bytes (the last two out of six) of every 1-km pixel as 16 yes/no-cloud bits. Each one of these 16 bits flags a corresponding 250-m tile inside the 1-km pixel. Their state is described in the local attributes to the Cloud_Mask HDF data set, and accordingly must be interprated as 0=YES, 1=NO. Thus the effective cross-track width, for these two bytes only, is 804 tiles of 250-m denomination.\n \nAlong-track, all MODIS pixels from the original product are preserved. \n \nIn the standard product, the MODIS level-2 cloud mask product is a global product generated for both daytime & nighttime conditions at 1-km spatial resolution (at nadir) and for daytime at 250-m resolution. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel (paulm@ssec.wisc.edu).\n \n \n (The shortname for this product is MAM35S0).", "links": [ { diff --git a/datasets/MANTRA_PIRANA_0.json b/datasets/MANTRA_PIRANA_0.json index 538f4979fd..b93bfd8fe1 100644 --- a/datasets/MANTRA_PIRANA_0.json +++ b/datasets/MANTRA_PIRANA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MANTRA_PIRANA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made during the MANTRA and PIRANA repeat cruises between 2000 and 2003.", "links": [ { diff --git a/datasets/MAPSS_853_1.json b/datasets/MAPSS_853_1.json index 58c125b5f3..aadead78c2 100644 --- a/datasets/MAPSS_853_1.json +++ b/datasets/MAPSS_853_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPSS_853_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPSS (Mapped Atmosphere-Plant-Soil System) is a landscape to global vegetation distribution model that was developed to simulate the potential biosphere impacts and biosphere-atmosphere feedbacks from climatic change. Model output from MAPSS has been used extensively in the Intergovernmental Panel on Climate Change's (IPCC) regional and global assessments of climate change impacts on vegetation and in several other projects.", "links": [ { diff --git a/datasets/MAPS_OSTA3_CO5X5_HDF_1.json b/datasets/MAPS_OSTA3_CO5X5_HDF_1.json index 929afb2f6c..a7eec169f9 100644 --- a/datasets/MAPS_OSTA3_CO5X5_HDF_1.json +++ b/datasets/MAPS_OSTA3_CO5X5_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_OSTA3_CO5X5_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into & three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. The data that are available from MAPS OSTA3 include a 5 by 5 degree gridded box (MAPS_OSTA3_5X5_HDF) and a second by second data product (MAPS_OSTA3_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MAPS_OSTA3_COSEC_HDF_1.json b/datasets/MAPS_OSTA3_COSEC_HDF_1.json index cba334bd3d..95d0c4e263 100644 --- a/datasets/MAPS_OSTA3_COSEC_HDF_1.json +++ b/datasets/MAPS_OSTA3_COSEC_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_OSTA3_COSEC_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.The data that are available from MAPS OSTA3 include a 5 by 5 degree gridded box (MAPS_OSTA3_5X5_HDF) and a second by second data product (MAPS_OSTA3_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MAPS_SRL1_CO5X5_HDF_1.json b/datasets/MAPS_SRL1_CO5X5_HDF_1.json index eecac9452f..592c52dbfa 100644 --- a/datasets/MAPS_SRL1_CO5X5_HDF_1.json +++ b/datasets/MAPS_SRL1_CO5X5_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_SRL1_CO5X5_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.Instrument The MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.SRL-1 Mission GoalsThe MAPS SRL-1 mission took place during Northern Hemisphere Spring when global biomass burning does not typically occur. Some burning may occur for the purpose of clearing the damaged and felled trees in the forests of North America after the rather severe winter. The goals of the MAPS SRL-1 mission are to provide a validated, near-global atlas of the distribution of tropospheric Carbon Monoxide during the mission, and to assess the health status of the MAPS instrument as the mission progresses. SL1 SummaryHigh concentrations of carbon monoxide over the Northern Hemisphere can be seen in measurements made by the Measurement of Air Pollution from Space(MAPS) instrument. These April 1994 measurements, made from the Space Shuttle Endeavour(STS-59), show large sources of air pollution in the lower atmosphere (2 to 10 miles above the surface) over the industrialized Northern Hemisphere.The data that are available from MAPS SRL1 include a 5 by 5 degree gridded box (MAPS_SRL1_5X5_HDF) and a second by second data product (MAPS_SRL1_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MAPS_SRL1_COSEC_HDF_1.json b/datasets/MAPS_SRL1_COSEC_HDF_1.json index 37a6e00400..791fdb1113 100644 --- a/datasets/MAPS_SRL1_COSEC_HDF_1.json +++ b/datasets/MAPS_SRL1_COSEC_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_SRL1_COSEC_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs, and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.SRL-1 Mission GoalsThe MAPS SRL-1 mission took place during Northern Hemisphere Spring when global biomass burning does not typically occur. Some burning may occur for the purpose of clearing the damaged and felled trees in the forests of North America after the rather severe winter. The goals of the MAPS SRL-1 mission are to provide a validated, near-global atlas of the distribution of tropospheric Carbon Monoxide during the mission, and to assess the health status of the MAPS instrument as the mission progresses.SL1 Summary High concentrations of carbon monoxide over the Northern Hemisphere can be seen in measurements made by the Measurement of Air Pollution from Space (MAPS) instrument. These April 1994 measurements, made from the Space Shuttle Endeavour (STS-59), show large sources of air pollution in the lower atmosphere (2 to 10 miles above the surface) over the industrialized Northern Hemisphere.The data that are available from MAPS SRL1 include a 5 by 5 degree gridded box (MAPS_SRL1_5X5_HDF) and a second by second data product (MAPS_SRL1_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MAPS_SRL2_CO5X5_HDF_1.json b/datasets/MAPS_SRL2_CO5X5_HDF_1.json index ee6608b58f..a3b2416f6f 100644 --- a/datasets/MAPS_SRL2_CO5X5_HDF_1.json +++ b/datasets/MAPS_SRL2_CO5X5_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_SRL2_CO5X5_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere. The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry. InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. SRL2 GoalsThe MAPS SRL-2 mission took place during the Northern Hemisphere summer when global biomass burning is nearing its maximum. The southern hemispheric burning of savanna and agricultural grasslands can be extensive in central and southern South America and in nearly all of Africa, south of the equator. The tundra regions of the northern boreal zone also are approaching the peak burning season. Other regions may experience scattered fire events as a result of lightning strikes during severe thunderstorms. The primary goal of the MAPS experiment on SRL-2 is to provide a near global survey of the distribution of tropospheric carbon monoxide during northern hemisphere summer. The secondary goal is to determine how the global distribution of carbon monoxide changes over the course of the mission.SL2 SummaryThe high values of carbon monoxide are associated with extensive areas of smoke and haze that have been observed by the Endeavour (STS-68) flight crew. The smoke results from fires that are burning in the continental regions. The carbon monoxide is carried by tropical thunderstorms to the altitudes (2 to 10 miles above the surface) at which it is measured by the MAPS instrument. The data that are available from MAPS SRL2 include a 5 by 5 degree gridded box (MAPS_SRL2_5X5_HDF) and a second by second data product (MAPS_SRL2_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MAPS_SRL2_COSEC_HDF_1.json b/datasets/MAPS_SRL2_COSEC_HDF_1.json index aaa1b17c8a..e20f89dce7 100644 --- a/datasets/MAPS_SRL2_COSEC_HDF_1.json +++ b/datasets/MAPS_SRL2_COSEC_HDF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAPS_SRL2_COSEC_HDF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. SRL2 GoalsThe MAPS SRL-2 mission took place during the Northern Hemisphere summer when global biomass burning is nearing its maximum. The southern hemispheric burning of savanna and agricultural grasslands can be extensive in central and southern South America and in nearly all of Africa, south of the equator. The tundra regions of the northern boreal zone also are approaching the peak burning season. Other regions may experience scattered fire events as a result of lightning strikes during severe thunderstorms. The primary goal of the MAPS experiment on SRL-2 is to provide a near global survey of the distribution of tropospheric carbon monoxide during northern hemisphere summer. The secondary goal is to determine how the global distribution of carbon monoxide changes over the course of the mission.SL2 SummaryThe high values of carbon monoxide are associated with extensive areas of smoke and haze that have been observed by the Endeavour (STS-68) flight crew. The smoke results from fires that are burning in the continental regions. The carbon monoxide is carried by tropical thunderstorms to the altitudes (2 to 10 miles above the surface) at which it is measured by the MAPS instrument.The data that are available from MAPS SRL2 include a 5 by 5 degree gridded box (MAPS_SRL2_5X5_HDF) and a second by second data product (MAPS_SRL2_COSEC_HDF). These data sets are available from the Langley DAAC.", "links": [ { diff --git a/datasets/MASL1B_1.json b/datasets/MASL1B_1.json index 3615e3e4c7..ceb1096862 100644 --- a/datasets/MASL1B_1.json +++ b/datasets/MASL1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASL1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) sensor was developed for NASA's high-altitude ER-2 research aircraft by Daedalus Enterprises, Inc., in support of the MODIS remote sensing algorithm development. The overall goal was to modify the spectral coverage and gains of the MAS to emulate as many of the MODIS spectral channels as possible. With its much higher spatial resolution (50 m vs. 250-1000 m for MODIS), MAS is able to provide unique information on the small-scale distribution of various geophysical parameters. The MAS instrument has been deployed on multiple platforms for many field campaigns since its first mission in 1991, as the prototype Wildfire Spectrometer.\r\n\r\nFor more information and for a list of MAS campaign flights visit ladsweb at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/", "links": [ { diff --git a/datasets/MASL2CLD_1.json b/datasets/MASL2CLD_1.json index 4a7d0422f8..f35db73dcc 100644 --- a/datasets/MASL2CLD_1.json +++ b/datasets/MASL2CLD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASL2CLD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Airborne Simulator (MAS) Level-2 Cloud Data product (MASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances. Multispectral images of the reflectance and brightness temperature at 10 wavelengths between 0.66 and 13.98nm were used to derive the probability of clear sky (or cloud), cloud thermodynamic phase, and the optical thickness and effective radius of liquid water and ice clouds.\r\n\r\nMASL2CLD product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file.\r\n\r\nFor more information and for a list of MAS campaign flights visit ladsweb at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/", "links": [ { diff --git a/datasets/MASS_BAY_0.json b/datasets/MASS_BAY_0.json index f19d17e44f..8317c26898 100644 --- a/datasets/MASS_BAY_0.json +++ b/datasets/MASS_BAY_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASS_BAY_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Massachusetts Bay and the surrounding area from 2002 to 2005.", "links": [ { diff --git a/datasets/MASTER_Ames_August_2003_2045_1.json b/datasets/MASTER_Ames_August_2003_2045_1.json index 99fa9d08a7..1029cc9d62 100644 --- a/datasets/MASTER_Ames_August_2003_2045_1.json +++ b/datasets/MASTER_Ames_August_2003_2045_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Ames_August_2003_2045_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a NASA WB-57 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2003-08-27. This deployment was an instrument validation flight. Imagery was collected over the Cascade Mountains and Lake Tahoe. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 25-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight path, spectral band information, instrument configuration, ancillary notes, and summary information for the flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_B200_Fall_1999_2099_1.json b/datasets/MASTER_B200_Fall_1999_2099_1.json index 0cb78ea93a..5c70ddade2 100644 --- a/datasets/MASTER_B200_Fall_1999_2099_1.json +++ b/datasets/MASTER_B200_Fall_1999_2099_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_B200_Fall_1999_2099_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, Washington, Colorado, and Texas, U.S., on 1999-09-13 to 1999-10-06. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Baja_Mexico_1999_2102_1.json b/datasets/MASTER_Baja_Mexico_1999_2102_1.json index 5e198886cc..0dec425222 100644 --- a/datasets/MASTER_Baja_Mexico_1999_2102_1.json +++ b/datasets/MASTER_Baja_Mexico_1999_2102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Baja_Mexico_1999_2102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 7 flights aboard a DOE B-200 aircraft over Baja California, Mexico, and Nevada, U.S., on 1999-04-23 to 1999-05-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_CARTA_2003_2054_1.1.json b/datasets/MASTER_CARTA_2003_2054_1.1.json index 5a64d69646..03bacdc28b 100644 --- a/datasets/MASTER_CARTA_2003_2054_1.1.json +++ b/datasets/MASTER_CARTA_2003_2054_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_CARTA_2003_2054_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 14 flights aboard a NASA WB-57 aircraft over Texas, U.S., Gulf of Mexico, and Costa Rica on 2003-03-06 to 2003-03-29. The CARTA-2003 project was a collaborative effort between the Centro Nacional de Alta Tecnologia (CENAT) of Costa Rica and NASA. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file", "links": [ { diff --git a/datasets/MASTER_CARTA_2005_2034_1.json b/datasets/MASTER_CARTA_2005_2034_1.json index 85ab7db4c2..a7b7109b05 100644 --- a/datasets/MASTER_CARTA_2005_2034_1.json +++ b/datasets/MASTER_CARTA_2005_2034_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_CARTA_2005_2034_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 23 flights aboard a NASA WB-57 aircraft over Costa Rica on 2005-03-01 to 2005-04-06. The CARTA-2005 project was a collaborative effort between the Centro Nacional de Alta Tecnologia (CENAT) of Costa Rica and NASA. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_CLASIC_2007_2019_1.json b/datasets/MASTER_CLASIC_2007_2019_1.json index 2dac97d79a..d974a7c90b 100644 --- a/datasets/MASTER_CLASIC_2007_2019_1.json +++ b/datasets/MASTER_CLASIC_2007_2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_CLASIC_2007_2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Arizona, New Mexico, Texas, and Oklahoma, U.S., from 2007-09-20 to 2007-09-21. This data collection supported the Cloud And Land Surface Interaction Campaign (CLASIC), a cross-disciplinary interagency research effort to study cumulus convection as an important component in the atmospheric radiation budget and hydrologic cycle of the Southern Great Plains (SGP). Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_April_2004_2043_1.json b/datasets/MASTER_DFRC_April_2004_2043_1.json index a74b857dd3..9f017b90e8 100644 --- a/datasets/MASTER_DFRC_April_2004_2043_1.json +++ b/datasets/MASTER_DFRC_April_2004_2043_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_April_2004_2043_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a NASA ER-2 aircraft over western U.S. and Pacific Ocean from 2004-04-01 to 2004-04-13. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_August_2003_2046_1.json b/datasets/MASTER_DFRC_August_2003_2046_1.json index a5a70bf98b..51880dc06e 100644 --- a/datasets/MASTER_DFRC_August_2003_2046_1.json +++ b/datasets/MASTER_DFRC_August_2003_2046_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_August_2003_2046_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over California, U.S., on 2003-08-05 to 2003-08-11. The objective of this deployment was farmland mapping. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_Fall_2005_2030_1.json b/datasets/MASTER_DFRC_Fall_2005_2030_1.json index f7d47ac947..c646acf84c 100644 --- a/datasets/MASTER_DFRC_Fall_2005_2030_1.json +++ b/datasets/MASTER_DFRC_Fall_2005_2030_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_Fall_2005_2030_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2005-10-19 to 2005-12-09. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_July-Aug_2001_V2_2144_2.json b/datasets/MASTER_DFRC_July-Aug_2001_V2_2144_2.json index f80fe65170..368c00d060 100644 --- a/datasets/MASTER_DFRC_July-Aug_2001_V2_2144_2.json +++ b/datasets/MASTER_DFRC_July-Aug_2001_V2_2144_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_July-Aug_2001_V2_2144_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California, Nevada, Oregon, Washington, U.S., and British Columbia, Canada, from 2001-07-20 to 2001-08-18. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_July_2004_2039_1.json b/datasets/MASTER_DFRC_July_2004_2039_1.json index fa760061f7..b00457c563 100644 --- a/datasets/MASTER_DFRC_July_2004_2039_1.json +++ b/datasets/MASTER_DFRC_July_2004_2039_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_July_2004_2039_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, U.S., from 2004-07-20 to 2004-07-21. The primary objective of this deployment was mapping of farmland and sites of recent wildfires. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_June_1999_2100_1.json b/datasets/MASTER_DFRC_June_1999_2100_1.json index c206b85dd2..9030be14c3 100644 --- a/datasets/MASTER_DFRC_June_1999_2100_1.json +++ b/datasets/MASTER_DFRC_June_1999_2100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_June_1999_2100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DC-8 aircraft and one flight on a NASA ER-2 aircraft over California, Nevada, and eastern Pacific Ocean on 1999-06-18 to 1999-06-30. The objectives of this deployment included validation and cross-calibration of the instrument on the two airborne platforms. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at spatial resolution of 7 to 50 meters. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_June_2011_1976_1.json b/datasets/MASTER_DFRC_June_2011_1976_1.json index 0d4cf774da..2c9f30bcc3 100644 --- a/datasets/MASTER_DFRC_June_2011_1976_1.json +++ b/datasets/MASTER_DFRC_June_2011_1976_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_June_2011_1976_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 6 flights aboard a NASA ER-2 aircraft over southwestern U.S. and northern Mexico, from 2011-06-08 to 2011-06-20. The purposes of these flights include collecting data for wildfire mapping, airborne science initiatives, and calibration data for AVIRIS. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_May-June_2008_2011_1.json b/datasets/MASTER_DFRC_May-June_2008_2011_1.json index ecf84592f0..e29fb74dd8 100644 --- a/datasets/MASTER_DFRC_May-June_2008_2011_1.json +++ b/datasets/MASTER_DFRC_May-June_2008_2011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_May-June_2008_2011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2008-05-29 to 2008-06-19. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_May_2011_1985_1.json b/datasets/MASTER_DFRC_May_2011_1985_1.json index 0613f6219b..936122376e 100644 --- a/datasets/MASTER_DFRC_May_2011_1985_1.json +++ b/datasets/MASTER_DFRC_May_2011_1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_May_2011_1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a NASA ER-2 aircraft over southwestern U.S., from 2011-05-15 to 2011-05-23. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_November_2011_1973_1.json b/datasets/MASTER_DFRC_November_2011_1973_1.json index 55fb322887..3b4f99ba4f 100644 --- a/datasets/MASTER_DFRC_November_2011_1973_1.json +++ b/datasets/MASTER_DFRC_November_2011_1973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_November_2011_1973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over southwestern U.S. from 2011-11-02 to 2011-11-16. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. The L1B file formats are HDF-4 and KMZ. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_October_2003_2044_1.json b/datasets/MASTER_DFRC_October_2003_2044_1.json index c46906757c..2bde0e516b 100644 --- a/datasets/MASTER_DFRC_October_2003_2044_1.json +++ b/datasets/MASTER_DFRC_October_2003_2044_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_October_2003_2044_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over western U.S. and Pacific Ocean on 2003-10-03 to 2003-11-01. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_RSL_August_2002_2066_1.json b/datasets/MASTER_DFRC_RSL_August_2002_2066_1.json index 3a5453ea53..f33c5666d3 100644 --- a/datasets/MASTER_DFRC_RSL_August_2002_2066_1.json +++ b/datasets/MASTER_DFRC_RSL_August_2002_2066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_RSL_August_2002_2066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 and two flights on a DOE B-200 aircraft over California and Nevada U.S. from 2002-08-09 to 2002-08-20. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at spatial resolution of 10 to 50 m. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_September_2002_2064_1.json b/datasets/MASTER_DFRC_September_2002_2064_1.json index 1e24bc2456..cb6caaff6e 100644 --- a/datasets/MASTER_DFRC_September_2002_2064_1.json +++ b/datasets/MASTER_DFRC_September_2002_2064_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_September_2002_2064_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a NASA ER-2 aircraft over southwestern U.S. from 2002-09-10 to 2002-09-26. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_DFRC_September_2006_2022_1.json b/datasets/MASTER_DFRC_September_2006_2022_1.json index 136014921b..597720a817 100644 --- a/datasets/MASTER_DFRC_September_2006_2022_1.json +++ b/datasets/MASTER_DFRC_September_2006_2022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_DFRC_September_2006_2022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Nevada, Wyoming, Utah, Colorado, Nebraska, South Dakota, Wisconsin, and Minnesota, U.S., from 2006-09-19 to 2006-10-13. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_FIREX_AQ_JulySept_2019_1941_1.2.json b/datasets/MASTER_FIREX_AQ_JulySept_2019_1941_1.2.json index ec0e7fcf31..bdae3ec1b7 100644 --- a/datasets/MASTER_FIREX_AQ_JulySept_2019_1941_1.2.json +++ b/datasets/MASTER_FIREX_AQ_JulySept_2019_1941_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_FIREX_AQ_JulySept_2019_1941_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) program during 21 flights aboard a NASA DC-8 aircraft over the central and western U.S. from 2019-07-22 to 2019-09-03. The purpose of these flights was to measure emissions and to characterize the aerosols in the smoke plume above and downwind of the fire, and to determine the overall spatial extent of wildfires and prescribed fires. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_FireSense_2023_2330_1.json b/datasets/MASTER_FireSense_2023_2330_1.json index 9693f1e7c2..3a76af51fe 100644 --- a/datasets/MASTER_FireSense_2023_2330_1.json +++ b/datasets/MASTER_FireSense_2023_2330_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_FireSense_2023_2330_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the FireSense project during 11 flights aboard a NASA B200 aircraft over California, Nevada, Utah, and Arizona, U.S., 2023-10-16 to 2023-10-26. The FireSense project is focused on delivering NASA's unique Earth science and technological capabilities to operational agencies, striving towards measurable improvement in US wildland fire management. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Flightline_Locator_2151_1.0.json b/datasets/MASTER_Flightline_Locator_2151_1.0.json index 29ec03089e..79f0ef1240 100644 --- a/datasets/MASTER_Flightline_Locator_2151_1.0.json +++ b/datasets/MASTER_Flightline_Locator_2151_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Flightline_Locator_2151_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for each year allow users to visualize flight line locations using GIS software.", "links": [ { diff --git a/datasets/MASTER_GEMx_Spring_2024_2370_1.json b/datasets/MASTER_GEMx_Spring_2024_2370_1.json index 2ef9050a5e..ec06a05e0d 100644 --- a/datasets/MASTER_GEMx_Spring_2024_2370_1.json +++ b/datasets/MASTER_GEMx_Spring_2024_2370_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_GEMx_Spring_2024_2370_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 26 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2024-04-02 to 2024-06-24. The Geological Earth Mapping Experiment (GEMx) research project used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Hyperspectral Thermal Emission Spectrometer (HyTES), and MODIS/ASTER Airborne Simulator (MASTER) instruments to collect the measurements over the country's arid and semi-arid regions, including parts of California, Nevada, Arizona, and New Mexico, to map portions of southwest US for critical minerals. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file. Level 2 products from these GEMx flights will be added to this dataset when they become available.", "links": [ { diff --git a/datasets/MASTER_GEMx_Summer_2023_2319_1.json b/datasets/MASTER_GEMx_Summer_2023_2319_1.json index 0f50328787..6091cb5323 100644 --- a/datasets/MASTER_GEMx_Summer_2023_2319_1.json +++ b/datasets/MASTER_GEMx_Summer_2023_2319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_GEMx_Summer_2023_2319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 13 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2023-04-25 to 2023-09-26. The Geological Earth Mapping Experiment (GEMx) research project used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Hyperspectral Thermal Emission Spectrometer (HyTES), and MODIS/ASTER Airborne Simulator (MASTER) instruments to collect the measurements over the country's arid and semi-arid regions, including parts of California, Nevada, Arizona, and New Mexico, to map portions of southwest US for critical minerals. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Hawaii_October_2001_V2_2142_2.json b/datasets/MASTER_Hawaii_October_2001_V2_2142_2.json index 2e752d235c..37ebef0cfa 100644 --- a/datasets/MASTER_Hawaii_October_2001_V2_2142_2.json +++ b/datasets/MASTER_Hawaii_October_2001_V2_2142_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Hawaii_October_2001_V2_2142_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 19 flights aboard a NASA ER-2 aircraft over Hawaii, eastern Pacific Ocean, and western U.S. from 2001-10-14 to 2001-11-14. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Houston_2010_2126_1.json b/datasets/MASTER_Houston_2010_2126_1.json index 2bea106145..61fb53a385 100644 --- a/datasets/MASTER_Houston_2010_2126_1.json +++ b/datasets/MASTER_Houston_2010_2126_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Houston_2010_2126_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected during 9 flights aboard a NASA ER-2 aircraft over the Gulf of Mexico and portions of California, Colorado, Arizona, Utah, Idaho, New Mexico, Texas, Arkansas, Illinois, Wisconsin, Michigan, Louisiana, Mississippi, and Florida from 2010-07-31 to 2010-09-01. A primary purpose of this deployment was to collect imagery related to the Deepwater Horizon-BP Oil Spill that occurred in late April 2010 in the Gulf of Mexico. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Houston_2011_1972_1.json b/datasets/MASTER_Houston_2011_1972_1.json index df7faea978..65e3df261c 100644 --- a/datasets/MASTER_Houston_2011_1972_1.json +++ b/datasets/MASTER_Houston_2011_1972_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Houston_2011_1972_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 16 flights aboard a NASA ER-2 aircraft over portions of California, Colorado, Wisconsin, Michigan, Louisiana, Mississippi, the central Mississippi River basin, and the Gulf of Mexico from 2011-07-19 to 2011-08-18. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_EarlyS2013_V2_2146_2.json b/datasets/MASTER_HyspIRI_EarlyS2013_V2_2146_2.json index 7108c96523..5f9353c2b5 100644 --- a/datasets/MASTER_HyspIRI_EarlyS2013_V2_2146_2.json +++ b/datasets/MASTER_HyspIRI_EarlyS2013_V2_2146_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_EarlyS2013_V2_2146_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-03-26 to 2013-04-19. An additional purpose of this campaign was an underpass of Landsat 8. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_EarlySpring2014_1965_1.json b/datasets/MASTER_HyspIRI_EarlySpring2014_1965_1.json index 14931d6ec6..dfb1c3a17a 100644 --- a/datasets/MASTER_HyspIRI_EarlySpring2014_1965_1.json +++ b/datasets/MASTER_HyspIRI_EarlySpring2014_1965_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_EarlySpring2014_1965_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 10 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-03-31 to 2014-05-07. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Fall_2013_1966_1.json b/datasets/MASTER_HyspIRI_Fall_2013_1966_1.json index 657d7539a5..4878741b0b 100644 --- a/datasets/MASTER_HyspIRI_Fall_2013_1966_1.json +++ b/datasets/MASTER_HyspIRI_Fall_2013_1966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Fall_2013_1966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-09-13 to 2013-12-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Fall_2014_1962_1.json b/datasets/MASTER_HyspIRI_Fall_2014_1962_1.json index 660996fe36..e4ad597a66 100644 --- a/datasets/MASTER_HyspIRI_Fall_2014_1962_1.json +++ b/datasets/MASTER_HyspIRI_Fall_2014_1962_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Fall_2014_1962_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-09-19 to 2014-11-24. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Fall_2015_1956_1.json b/datasets/MASTER_HyspIRI_Fall_2015_1956_1.json index d362d79fa4..c9c43f9264 100644 --- a/datasets/MASTER_HyspIRI_Fall_2015_1956_1.json +++ b/datasets/MASTER_HyspIRI_Fall_2015_1956_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Fall_2015_1956_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-08-24 to 2015-10-26. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Hawaii_2017_1951_1.1.json b/datasets/MASTER_HyspIRI_Hawaii_2017_1951_1.1.json index 9c1535f937..ac175766eb 100644 --- a/datasets/MASTER_HyspIRI_Hawaii_2017_1951_1.1.json +++ b/datasets/MASTER_HyspIRI_Hawaii_2017_1951_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Hawaii_2017_1951_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 18 flights aboard a NASA ER-2 aircraft over Hawaii, California and Nevada, U.S., from 2016-12-14 to 2017-03-03. This deployment includes imagery of Hawaii's volcanoes. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Hawaii_2018_1945_1.json b/datasets/MASTER_HyspIRI_Hawaii_2018_1945_1.json index 17dede56b5..2bcf3e806c 100644 --- a/datasets/MASTER_HyspIRI_Hawaii_2018_1945_1.json +++ b/datasets/MASTER_HyspIRI_Hawaii_2018_1945_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Hawaii_2018_1945_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over Hawaii and southern California, U.S., from 2018-01-11 to 2018-02-20. This campaign includes imagery of Hawaii's volcanoes. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_LateSpring_2013_1968_1.json b/datasets/MASTER_HyspIRI_LateSpring_2013_1968_1.json index 0334b28a0c..7a10a02a0f 100644 --- a/datasets/MASTER_HyspIRI_LateSpring_2013_1968_1.json +++ b/datasets/MASTER_HyspIRI_LateSpring_2013_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_LateSpring_2013_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 7 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-05-02 to 2013-06-26. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_LateSpring_2014_1963_1.json b/datasets/MASTER_HyspIRI_LateSpring_2014_1963_1.json index d65f25dc7c..f016b983c7 100644 --- a/datasets/MASTER_HyspIRI_LateSpring_2014_1963_1.json +++ b/datasets/MASTER_HyspIRI_LateSpring_2014_1963_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_LateSpring_2014_1963_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-05-28 to 2014-06-13. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Spring_2015_1957_1.1.json b/datasets/MASTER_HyspIRI_Spring_2015_1957_1.1.json index 3fea378717..de138a77b4 100644 --- a/datasets/MASTER_HyspIRI_Spring_2015_1957_1.1.json +++ b/datasets/MASTER_HyspIRI_Spring_2015_1957_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Spring_2015_1957_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-04-16 to 2015-05-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Summer_2014_1964_1.json b/datasets/MASTER_HyspIRI_Summer_2014_1964_1.json index cbf3d043c8..d48e8eddf7 100644 --- a/datasets/MASTER_HyspIRI_Summer_2014_1964_1.json +++ b/datasets/MASTER_HyspIRI_Summer_2014_1964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Summer_2014_1964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California, U.S., from 2014-08-18 to 2014-08-29. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Summer_2015_1959_1.json b/datasets/MASTER_HyspIRI_Summer_2015_1959_1.json index 1b2c2cfb70..60908ac941 100644 --- a/datasets/MASTER_HyspIRI_Summer_2015_1959_1.json +++ b/datasets/MASTER_HyspIRI_Summer_2015_1959_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Summer_2015_1959_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-05-28 to 2015-06-11. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Summer_2016_1913_1.2.json b/datasets/MASTER_HyspIRI_Summer_2016_1913_1.2.json index c698cf8d83..b02de08091 100644 --- a/datasets/MASTER_HyspIRI_Summer_2016_1913_1.2.json +++ b/datasets/MASTER_HyspIRI_Summer_2016_1913_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Summer_2016_1913_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2016-06-09 to 2016-06-21. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Summer_2017_1950_1.json b/datasets/MASTER_HyspIRI_Summer_2017_1950_1.json index 717c90b23d..fa2d297dd4 100644 --- a/datasets/MASTER_HyspIRI_Summer_2017_1950_1.json +++ b/datasets/MASTER_HyspIRI_Summer_2017_1950_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Summer_2017_1950_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 9 flights aboard a NASA ER-2 aircraft over southern California and western Nevada, U.S., from 2017-06-07 to 2017-06-28. Two flights on 2017-06-26 and 2017-06-28 were flown jointly for the Student Airborne Research Program (SARP). SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_HyspIRI_Summer_2018_1942_1.json b/datasets/MASTER_HyspIRI_Summer_2018_1942_1.json index 3415f5f276..d34462a83b 100644 --- a/datasets/MASTER_HyspIRI_Summer_2018_1942_1.json +++ b/datasets/MASTER_HyspIRI_Summer_2018_1942_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_HyspIRI_Summer_2018_1942_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 15 flights aboard a NASA ER-2 aircraft over California, Arizona, Oregon, and Nevada, U.S., from 2018-06-19 to 2018-09-06. Two flights on 2018-06-20 and 2018-06-27 were flown jointly with Student Airborne Research Program (SARP). SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_PacificRim_2000_2093_1.json b/datasets/MASTER_PacificRim_2000_2093_1.json index eb56614152..bec5e154cd 100644 --- a/datasets/MASTER_PacificRim_2000_2093_1.json +++ b/datasets/MASTER_PacificRim_2000_2093_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_PacificRim_2000_2093_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 46 flights aboard a NASA DC-8 aircraft over sites encompassing the Pacific Rim, including Alaska, California, Hawaii, islands of the south and western Pacific Ocean, New Zealand, Australia, Polynesia, southeast Asia, South Korea, and Japan. Flights took place on 2000-07-21 to 2000-10-23. The Pacific Rim 2000 (PacRim II) Campaign gathered geographic and atmospheric data for coastal analysis, oceanography, forestry, geology, hydrology and archaeology of various regions using data from the Airborne Synthetic Aperture Radar (AirSAR) and MODIS/ASTER Airborne Simulator (MASTER) instruments. This was the first campaign to operate both the AIRSAR and MASTER instruments simultaneously, providing scientists with additional insight on how topography affects the vegetation and land surface temperature as seen in the MASTER data. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 25-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL-DFRC_October_2008_2014_1.json b/datasets/MASTER_RSL-DFRC_October_2008_2014_1.json index 439eb9e09c..42ce8d0cd5 100644 --- a/datasets/MASTER_RSL-DFRC_October_2008_2014_1.json +++ b/datasets/MASTER_RSL-DFRC_October_2008_2014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL-DFRC_October_2008_2014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 and a NASA ER-2 aircraft over California and New Mexico, U.S., 2008-10-20 to 2008-10-29. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSLPhoenix_2011_1975_1.1.json b/datasets/MASTER_RSLPhoenix_2011_1975_1.1.json index 52f1b78b49..732de7b3d0 100644 --- a/datasets/MASTER_RSLPhoenix_2011_1975_1.1.json +++ b/datasets/MASTER_RSLPhoenix_2011_1975_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSLPhoenix_2011_1975_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a B-200 aircraft over Phoenix, Arizona, and Lake Mead, Nevada from 2011-07-11 to 2011-07-16 as part of a study on urban heat islands. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_April_2003_2053_1.json b/datasets/MASTER_RSL_April_2003_2053_1.json index 2459913342..33127b40e9 100644 --- a/datasets/MASTER_RSL_April_2003_2053_1.json +++ b/datasets/MASTER_RSL_April_2003_2053_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_April_2003_2053_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Phoenix, Arizona, and the Jornada Experimental Range (JORNEX) in New Mexico, U.S., on 2003-04-28 to 2003-05-02. These data were used to evaluate the\u00c3\u0083\u00c2\u0083\u00c3\u0082\u00c2\u0082\u00c3\u0083\u00c2\u0082\u00c3\u0082\u00c2\u00a0Temperature Emissivity Separation (TES) algorithm for extracting land surface temperature and emissivity data from thermal infrared data from ASTER. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_April_2008_2016_1.json b/datasets/MASTER_RSL_April_2008_2016_1.json index 25d71a20a1..0cbfea82b7 100644 --- a/datasets/MASTER_RSL_April_2008_2016_1.json +++ b/datasets/MASTER_RSL_April_2008_2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_April_2008_2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 aircraft over California, U.S., 2008-04-14 to 2008-04-26. The locations sampled include areas affected by wildfires in 2007. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_August_2001_V2_2143_2.json b/datasets/MASTER_RSL_August_2001_V2_2143_2.json index 4a55028057..615eb2efbf 100644 --- a/datasets/MASTER_RSL_August_2001_V2_2143_2.json +++ b/datasets/MASTER_RSL_August_2001_V2_2143_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_August_2001_V2_2143_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a DOE B-200 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2001-08-22 to 2001-08-31. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_August_2004_2036_1.json b/datasets/MASTER_RSL_August_2004_2036_1.json index 05ff52ffc3..d4040b6f8c 100644 --- a/datasets/MASTER_RSL_August_2004_2036_1.json +++ b/datasets/MASTER_RSL_August_2004_2036_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_August_2004_2036_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2004-08-18 to 2004-08-29. Objectives of this deployment included mapping geological faults in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_August_2006_2027_1.json b/datasets/MASTER_RSL_August_2006_2027_1.json index 4be6edc903..a5695ff87c 100644 --- a/datasets/MASTER_RSL_August_2006_2027_1.json +++ b/datasets/MASTER_RSL_August_2006_2027_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_August_2006_2027_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over Nevada, California and Colorado, U.S., from 2006-08-21 to 2006-09-06. This data collection focused on mapping geological faults. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_August_2007_2020_1.json b/datasets/MASTER_RSL_August_2007_2020_1.json index 55765ec70a..ee3a945f67 100644 --- a/datasets/MASTER_RSL_August_2007_2020_1.json +++ b/datasets/MASTER_RSL_August_2007_2020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_August_2007_2020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over Nevada and California, U.S., from 2007-08-30 to 2007-09-02. This data collection focused on mapping earthquake faults in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_August_2008_2010_1.json b/datasets/MASTER_RSL_August_2008_2010_1.json index 3442edab5d..b52244d0a0 100644 --- a/datasets/MASTER_RSL_August_2008_2010_1.json +++ b/datasets/MASTER_RSL_August_2008_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_August_2008_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B200 aircraft over California, U.S., from 2008-08-20 to 2008-08-27. Objectives included mapping for California Fire-Burn Area Emergency Response (BAER). This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_December_1998_2104_1.json b/datasets/MASTER_RSL_December_1998_2104_1.json index ef2ec02042..d65abf528c 100644 --- a/datasets/MASTER_RSL_December_1998_2104_1.json +++ b/datasets/MASTER_RSL_December_1998_2104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_December_1998_2104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 1998-12-02. A primary objective of this deployment was instrument validation. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_January_1999_2103_1.json b/datasets/MASTER_RSL_January_1999_2103_1.json index 3bd3d4c4ec..8103b7594c 100644 --- a/datasets/MASTER_RSL_January_1999_2103_1.json +++ b/datasets/MASTER_RSL_January_1999_2103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_January_1999_2103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, U.S., on 1999-01-17. A primary objective of this deployment was instrument validation. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_July_2003_2047_1.json b/datasets/MASTER_RSL_July_2003_2047_1.json index 6e30b962e0..eccc7a6f0f 100644 --- a/datasets/MASTER_RSL_July_2003_2047_1.json +++ b/datasets/MASTER_RSL_July_2003_2047_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_July_2003_2047_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Nevada, U.S., on 2003-07-14 to 2003-07-22. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_July_2004_2038_1.json b/datasets/MASTER_RSL_July_2004_2038_1.json index fda9fbca59..f4b592f8c3 100644 --- a/datasets/MASTER_RSL_July_2004_2038_1.json +++ b/datasets/MASTER_RSL_July_2004_2038_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_July_2004_2038_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one functional check flight aboard a DOE B-200 aircraft over Nevada, U.S., on 2004-07-29. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_June_1999_2098_1.json b/datasets/MASTER_RSL_June_1999_2098_1.json index 207ebb1890..b7e7cf791c 100644 --- a/datasets/MASTER_RSL_June_1999_2098_1.json +++ b/datasets/MASTER_RSL_June_1999_2098_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_June_1999_2098_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 10 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, and Texas, U.S., on 1999-05-28 to 1999-06-10. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_June_2000_2096_1.1.json b/datasets/MASTER_RSL_June_2000_2096_1.1.json index a21df0f296..1a40ed4862 100644 --- a/datasets/MASTER_RSL_June_2000_2096_1.1.json +++ b/datasets/MASTER_RSL_June_2000_2096_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_June_2000_2096_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 12 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, and New Mexico, U.S., on 2000-06-01 to 2000-06-17. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_June_2001_2090_1.json b/datasets/MASTER_RSL_June_2001_2090_1.json index b5516d7f96..b527a0ee5f 100644 --- a/datasets/MASTER_RSL_June_2001_2090_1.json +++ b/datasets/MASTER_RSL_June_2001_2090_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_June_2001_2090_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2001-06-06 to 2001-06-16. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_June_2002_2067_1.json b/datasets/MASTER_RSL_June_2002_2067_1.json index 13d3d94536..32089750b4 100644 --- a/datasets/MASTER_RSL_June_2002_2067_1.json +++ b/datasets/MASTER_RSL_June_2002_2067_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_June_2002_2067_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, Nevada and Utah, U.S., on 2002-06-07 to 2002-06-18. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_June_2004_2042_1.json b/datasets/MASTER_RSL_June_2004_2042_1.json index f847b7ce18..162027727b 100644 --- a/datasets/MASTER_RSL_June_2004_2042_1.json +++ b/datasets/MASTER_RSL_June_2004_2042_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_June_2004_2042_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Colorado and Utah, U.S., on 2004-07-01. Objectives of this deployment included mapping geological substrates and their mineral content. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_March_2000_2094_1.json b/datasets/MASTER_RSL_March_2000_2094_1.json index 11b1d2193c..0e3af6c5cd 100644 --- a/datasets/MASTER_RSL_March_2000_2094_1.json +++ b/datasets/MASTER_RSL_March_2000_2094_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_March_2000_2094_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2000-03-10 to 2000-03-14. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_March_2002_2068_1.json b/datasets/MASTER_RSL_March_2002_2068_1.json index a06cf4a09f..3257dd757d 100644 --- a/datasets/MASTER_RSL_March_2002_2068_1.json +++ b/datasets/MASTER_RSL_March_2002_2068_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_March_2002_2068_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2002-03-08 to 2002-04-07. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_May_1999_2101_1.json b/datasets/MASTER_RSL_May_1999_2101_1.json index 6e1a0e2561..8a4f32f1d6 100644 --- a/datasets/MASTER_RSL_May_1999_2101_1.json +++ b/datasets/MASTER_RSL_May_1999_2101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_May_1999_2101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, Nevada, and Arizona, U.S., on 1999-05-11. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_May_2001_V2_2145_2.json b/datasets/MASTER_RSL_May_2001_V2_2145_2.json index 726c493b6a..3c661fa16c 100644 --- a/datasets/MASTER_RSL_May_2001_V2_2145_2.json +++ b/datasets/MASTER_RSL_May_2001_V2_2145_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_May_2001_V2_2145_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over California and New Mexico, U.S., on 2001-05-11 to 2001-05-12. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_May_2002_V2_2148_2.json b/datasets/MASTER_RSL_May_2002_V2_2148_2.json index 87d62b7797..17d530f788 100644 --- a/datasets/MASTER_RSL_May_2002_V2_2148_2.json +++ b/datasets/MASTER_RSL_May_2002_V2_2148_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_May_2002_V2_2148_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, New Mexico, and Nevada, U.S., on 2002-05-14 to 2002-05-24. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_May_2004_2041_1.json b/datasets/MASTER_RSL_May_2004_2041_1.json index 6f535f0297..0a6391415a 100644 --- a/datasets/MASTER_RSL_May_2004_2041_1.json +++ b/datasets/MASTER_RSL_May_2004_2041_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_May_2004_2041_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over New Mexico, California, Utah, and Colorado, U.S., on 2004-05-20 to 2004-05-25. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 15-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_May_2006_2029_1.json b/datasets/MASTER_RSL_May_2006_2029_1.json index cf6d798147..bb404b72e6 100644 --- a/datasets/MASTER_RSL_May_2006_2029_1.json +++ b/datasets/MASTER_RSL_May_2006_2029_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_May_2006_2029_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over Nevada, U.S., from 2006-05-26 to 2006-06-01. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_November_2007_2015_1.json b/datasets/MASTER_RSL_November_2007_2015_1.json index cf021afa90..dd5a4af809 100644 --- a/datasets/MASTER_RSL_November_2007_2015_1.json +++ b/datasets/MASTER_RSL_November_2007_2015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_November_2007_2015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, U.S., from 2007-11-05 to 2007-11-15. This data collection focused on mapping area affected by wildfires in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_Oct_2002_V2_2147_2.json b/datasets/MASTER_RSL_Oct_2002_V2_2147_2.json index 0bb160dbe6..c213cdf0e1 100644 --- a/datasets/MASTER_RSL_Oct_2002_V2_2147_2.json +++ b/datasets/MASTER_RSL_Oct_2002_V2_2147_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_Oct_2002_V2_2147_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during nine flights aboard a DOE B-200 aircraft over Arizona, California, Nevada and New Mexico, U.S., on 2002-10-01 to 2002-10-08. Flights included coverage of the Jornada Experimental Range (JORNEX) in New Mexico. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 15-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_October_2003_2048_1.json b/datasets/MASTER_RSL_October_2003_2048_1.json index 77ccac61ff..f757b2eab0 100644 --- a/datasets/MASTER_RSL_October_2003_2048_1.json +++ b/datasets/MASTER_RSL_October_2003_2048_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_October_2003_2048_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2003-10-05 to 2003-10-12. An objective of this deployment was geological fault mapping. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_October_2005_2031_1.json b/datasets/MASTER_RSL_October_2005_2031_1.json index d6e6466b6f..b5a4498424 100644 --- a/datasets/MASTER_RSL_October_2005_2031_1.json +++ b/datasets/MASTER_RSL_October_2005_2031_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_October_2005_2031_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over Catalina Island, California, U.S., on 2005-10-31. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 7-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_October_2007_2013_1.json b/datasets/MASTER_RSL_October_2007_2013_1.json index 1d9d0a68ee..50f2e46437 100644 --- a/datasets/MASTER_RSL_October_2007_2013_1.json +++ b/datasets/MASTER_RSL_October_2007_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_October_2007_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B-200 aircraft over Nevada, Arizona, and New Mexico, U.S., from 2007-10-01 to 2007-10-04. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_October_2010_2127_1.json b/datasets/MASTER_RSL_October_2010_2127_1.json index 8674c80b7c..e2c713452a 100644 --- a/datasets/MASTER_RSL_October_2010_2127_1.json +++ b/datasets/MASTER_RSL_October_2010_2127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_October_2010_2127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, Arizona, and New Mexico, U.S., from 2010-10-04 to 2010-10-13. Objectives included mapping for California Fire-Burn Area Emergency Response (BAER) and Jornada Experimental Range in southern New Mexico (JORNEX). This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_RSL_September_2005_2037_1.json b/datasets/MASTER_RSL_September_2005_2037_1.json index 4da22a74c9..af6e115f11 100644 --- a/datasets/MASTER_RSL_September_2005_2037_1.json +++ b/datasets/MASTER_RSL_September_2005_2037_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_RSL_September_2005_2037_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Arizona, California, and Nevada, U.S., on 2005-09-27 to 2005-09-29. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2009_1990_1.json b/datasets/MASTER_SARP_2009_1990_1.json index b44814fd27..a325637eae 100644 --- a/datasets/MASTER_SARP_2009_1990_1.json +++ b/datasets/MASTER_SARP_2009_1990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2009_1990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2009-07-22 to 2009-07-24 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2009 deployment included two flights with 11 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight.", "links": [ { diff --git a/datasets/MASTER_SARP_2010_1989_1.json b/datasets/MASTER_SARP_2010_1989_1.json index da8bbb782e..3419848fcb 100644 --- a/datasets/MASTER_SARP_2010_1989_1.json +++ b/datasets/MASTER_SARP_2010_1989_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2010_1989_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2010-06-28 to 2010-07-01 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2010 deployment included three flights with 21 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2011_1974_1.json b/datasets/MASTER_SARP_2011_1974_1.json index 22668bc2ac..6b0d3f1c1c 100644 --- a/datasets/MASTER_SARP_2011_1974_1.json +++ b/datasets/MASTER_SARP_2011_1974_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2011_1974_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2011-06-27 to 2011-06-30 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2011 deployment included five flights with 23 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2012_1983_1.json b/datasets/MASTER_SARP_2012_1983_1.json index 20a8b24df0..66a1e8c3d0 100644 --- a/datasets/MASTER_SARP_2012_1983_1.json +++ b/datasets/MASTER_SARP_2012_1983_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2012_1983_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2012-06-25 to 2012-06-27 over southern California, U.S., in a Lockheed P-3B Orion aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2012 deployment included five flights with 19 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2013_1967_1.json b/datasets/MASTER_SARP_2013_1967_1.json index a7a0cffadf..3af9596784 100644 --- a/datasets/MASTER_SARP_2013_1967_1.json +++ b/datasets/MASTER_SARP_2013_1967_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2013_1967_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2013-06-17 to 2013-06-19 over southern California, U.S., in a NASA DC-8 aircraft. The SARP 2013 deployment included four flights with 21 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2014_1961_1.json b/datasets/MASTER_SARP_2014_1961_1.json index daf5dd8b69..62ccc2f576 100644 --- a/datasets/MASTER_SARP_2014_1961_1.json +++ b/datasets/MASTER_SARP_2014_1961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2014_1961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2014-06-23 to 2014-06-25 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2014 deployment included three flights with 17 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution, and the L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2015_1958_1.json b/datasets/MASTER_SARP_2015_1958_1.json index 3f8842e071..bd5dc878c7 100644 --- a/datasets/MASTER_SARP_2015_1958_1.json +++ b/datasets/MASTER_SARP_2015_1958_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2015_1958_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The raw spectral data were collected from flights flown on 2015-06-23 to 2015-06-24 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2015 deployment included three flights with 25 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution, and the L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SARP_2016_1912_1.2.json b/datasets/MASTER_SARP_2016_1912_1.2.json index d33f7b1b67..7488380a6e 100644 --- a/datasets/MASTER_SARP_2016_1912_1.2.json +++ b/datasets/MASTER_SARP_2016_1912_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SARP_2016_1912_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2016-06-17 in a NASA ER-2 aircraft over Santa Barbara, California. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2016 deployment included one flight with 5 flight tracks. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Sky_2003_2052_1.json b/datasets/MASTER_Sky_2003_2052_1.json index 189a963f4b..2d59763212 100644 --- a/datasets/MASTER_Sky_2003_2052_1.json +++ b/datasets/MASTER_Sky_2003_2052_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Sky_2003_2052_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a Cessna Caravan aircraft over California and Nevada, U.S., on 2003-05-31. The purpose of this deployment was a functional check flight. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for the flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Sky_2004_2035_1.json b/datasets/MASTER_Sky_2004_2035_1.json index cbbfc3ef74..d2d7d42ce2 100644 --- a/datasets/MASTER_Sky_2004_2035_1.json +++ b/datasets/MASTER_Sky_2004_2035_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Sky_2004_2035_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a Cessna Caravan aircraft over California, Oregon, Washington, and Colorado, U.S., from 2004-09-15 to 2004-10-14. A focus of this deployment involved mapping volcanic landforms. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_Sky_2006_2026_1.json b/datasets/MASTER_Sky_2006_2026_1.json index 85357ede01..1aaf721cd7 100644 --- a/datasets/MASTER_Sky_2006_2026_1.json +++ b/datasets/MASTER_Sky_2006_2026_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_Sky_2006_2026_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a Cessna Caravan aircraft over Oregon and Wyoming, U.S., from 2006-08-01 to 2006-08-03. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SuomiNPP_2013_1969_1.json b/datasets/MASTER_SuomiNPP_2013_1969_1.json index e59b3027e0..f54d44928c 100644 --- a/datasets/MASTER_SuomiNPP_2013_1969_1.json +++ b/datasets/MASTER_SuomiNPP_2013_1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SuomiNPP_2013_1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California, Texas, and Oklahoma, U.S.; Baja California, Mexico; and eastern Pacific Ocean from 2013-05-07 to 2013-06-01. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_SuomiNPP_2015_1970_1.json b/datasets/MASTER_SuomiNPP_2015_1970_1.json index c6810f4860..c2ee58ae8a 100644 --- a/datasets/MASTER_SuomiNPP_2015_1970_1.json +++ b/datasets/MASTER_SuomiNPP_2015_1970_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_SuomiNPP_2015_1970_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 10 flights aboard a NASA ER-2 aircraft over Greenland, portions of the conterminous U.S., and Canada from 2015-02-23 to 2015-03-31. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_TC4_2007_2021_1.1.json b/datasets/MASTER_TC4_2007_2021_1.1.json index 1fa7d8e6b7..259a9df2fd 100644 --- a/datasets/MASTER_TC4_2007_2021_1.1.json +++ b/datasets/MASTER_TC4_2007_2021_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_TC4_2007_2021_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over California, Nevada, Central America, and eastern Pacific Ocean from 2007-07-29 to 2007-08-18. This deployment supported the Tropical Composition, Cloud and Climate Coupling Campaign (TC4), which investigated the atmospheric structure, properties, and processes in the Eastern Pacific Tropics. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_WDTS_Fall_2022_2141_1.json b/datasets/MASTER_WDTS_Fall_2022_2141_1.json index 4138b0254b..ae1ffdd0c0 100644 --- a/datasets/MASTER_WDTS_Fall_2022_2141_1.json +++ b/datasets/MASTER_WDTS_Fall_2022_2141_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_WDTS_Fall_2022_2141_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during five flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2022-09-02 to 2022-09-09. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_WDTS_SeptOct_2020_1940_1.2.json b/datasets/MASTER_WDTS_SeptOct_2020_1940_1.2.json index dbf38b143b..f4fd780d27 100644 --- a/datasets/MASTER_WDTS_SeptOct_2020_1940_1.2.json +++ b/datasets/MASTER_WDTS_SeptOct_2020_1940_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_WDTS_SeptOct_2020_1940_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) program during nine flights aboard a NASA ER-2 aircraft over selected areas of California, U.S, from 2020-09-17 to 2020-10-15. The WDTS program will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each deployment, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_WDTS_Spring_2021_1953_1.1.json b/datasets/MASTER_WDTS_Spring_2021_1953_1.1.json index f8171c9d3e..1e08c1759d 100644 --- a/datasets/MASTER_WDTS_Spring_2021_1953_1.1.json +++ b/datasets/MASTER_WDTS_Spring_2021_1953_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_WDTS_Spring_2021_1953_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during nine flights aboard a NASA ER-2 aircraft over selected areas of California and Nevada, U.S., from 2021-02-09 to 2021-04-02. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_WDTS_Spring_2023_2252_1.json b/datasets/MASTER_WDTS_Spring_2023_2252_1.json index 0be1c801ef..f397dff615 100644 --- a/datasets/MASTER_WDTS_Spring_2023_2252_1.json +++ b/datasets/MASTER_WDTS_Spring_2023_2252_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_WDTS_Spring_2023_2252_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2023-03-31 to 2023-05-02. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MASTER_WDTS_Spring_2024_2383_1.json b/datasets/MASTER_WDTS_Spring_2024_2383_1.json index bcb130a7f4..7af29da5e1 100644 --- a/datasets/MASTER_WDTS_Spring_2024_2383_1.json +++ b/datasets/MASTER_WDTS_Spring_2024_2383_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MASTER_WDTS_Spring_2024_2383_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2024-06-04 to 2024-06-28. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.", "links": [ { diff --git a/datasets/MAS_832_2.json b/datasets/MAS_832_2.json index 133c4b7bac..b23d78e1ff 100644 --- a/datasets/MAS_832_2.json +++ b/datasets/MAS_832_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MAS_832_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) multispectral data collected during the SAFARI 2000 project. The flights were undertaken over Southern Africa by the NASA ER-2 aircraft during August and September, 2000.", "links": [ { diff --git a/datasets/MB2LME_002.json b/datasets/MB2LME_002.json index e2feb6b6f6..55eaf024ca 100644 --- a/datasets/MB2LME_002.json +++ b/datasets/MB2LME_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MB2LME_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Local Mode Ellipsoid Radiance Data V002 contains the ellipsoid projected TOA parameters for the single local mode scene, resampled to WGS84 ellipsoid.", "links": [ { diff --git a/datasets/MB2LMT_2.json b/datasets/MB2LMT_2.json index 4a10899ee5..46e5b8b6ea 100644 --- a/datasets/MB2LMT_2.json +++ b/datasets/MB2LMT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MB2LMT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MB2LMT_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Local Mode Terrain Radiance Data Version 2 product. It contains the terrain-projected Top-of-Atmosphere (TOA) radiance for the single local mode scene, resampled at the surface and topographically corrected. \r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MBON_0.json b/datasets/MBON_0.json index 88607a769b..34c3e8d5c3 100644 --- a/datasets/MBON_0.json +++ b/datasets/MBON_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MBON_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Marine Biodiversity Observation Network (MBON) is a growing global initiative composed of regional networks of scientists, resource managers, and end-users working to integrate data from existing long-term programs to improve our understanding of changes and connections between marine biodiversity and ecosystem functions.", "links": [ { diff --git a/datasets/MCD06COSP_D3_MODIS_6.2.json b/datasets/MCD06COSP_D3_MODIS_6.2.json index 714a4d8714..4e3cc61ff3 100644 --- a/datasets/MCD06COSP_D3_MODIS_6.2.json +++ b/datasets/MCD06COSP_D3_MODIS_6.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD06COSP_D3_MODIS_6.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The combined MODIS (Aqua/Terra) Cloud Properties Level 3 daily, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product\u2019s short-name. The \u201cCOSP\u201d acronym in its short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. This product is an aggregation of combined MODIS Level-2 inputs from both the Terra and Aqua incarnations (MOD35/MOD06 and MYD35/MYD06, respectively), and employs an aggregation methodology consistent with the MOD08 and MYD08 products. Provided in netCDF4 format, it contains 23 aggregated science data sets (SDS/parameters).\r\n\r\nThe Collection 6.2 (C6.2) is an improved version from the previous version (C6.1) because a number of bugs detected in Collection 6.1 are fixed. ", "links": [ { diff --git a/datasets/MCD06COSP_M3_MODIS_6.2.json b/datasets/MCD06COSP_M3_MODIS_6.2.json index 4c4cde869c..9bd0348872 100644 --- a/datasets/MCD06COSP_M3_MODIS_6.2.json +++ b/datasets/MCD06COSP_M3_MODIS_6.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD06COSP_M3_MODIS_6.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The combined MODIS (Aqua/Terra) Cloud Properties Level 3 monthly, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product\u2019s short-name. The \u201cCOSP\u201d acronym in its short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. The L3 monthly product is derived by aggregating the daily-produced Aqua+Terra/MODIS D3 Cloud Properties product (MCD06COSP_D3_MODIS). Provided in netCDF4 format, it contains 23 aggregated science data sets (SDS/parameters).\r\n\r\n The Collection 6.2 (C6.2) is an improved version from the previous version (C6.1) because a number of bugs detected in Collection 6.1 are fixed.", "links": [ { diff --git a/datasets/MCD12C1_061.json b/datasets/MCD12C1_061.json index 06d4c9740d..39f750e45b 100644 --- a/datasets/MCD12C1_061.json +++ b/datasets/MCD12C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD12C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 data product provides a spatially aggregated and reprojected version of the tiled MCD12Q1 Version 6.1 (https://doi.org/10.5067/MODIS/MCD12Q1.061) data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe from 2001 to 2021. Additionally, sub-pixel proportions of each land cover class in each 0.05 degree pixel is provided along with the aggregated quality assessment information for each of the three land classification schemes. \n\nProvided in each MCD12C1 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3 Assessment, and Majority Land Cover Type 1-3 Percent.\n\nValidation at stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS land cover products.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The MCD12C1 Version 6.1 product has a minor fix to UMD Land Cover Class.", "links": [ { diff --git a/datasets/MCD12Q1_061.json b/datasets/MCD12Q1_061.json index d2714a831c..b8db3f97bc 100644 --- a/datasets/MCD12Q1_061.json +++ b/datasets/MCD12Q1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD12Q1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals (2001-2022). The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology.\r\n \r\nLayers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6.1 Hierarchical Data Format 4 (HDF4) file.\r\n\r\nValidation at stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS land cover products.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The MCD12Q1 Version 6.1 product has a minor fix to UMD Land Cover Class.", "links": [ { diff --git a/datasets/MCD12Q2_061.json b/datasets/MCD12Q2_061.json index 220db1f227..518976ce0b 100644 --- a/datasets/MCD12Q2_061.json +++ b/datasets/MCD12Q2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD12Q2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals from 2001 to 2021. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes.\n\nProvided in each MCD12Q2 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. SDS layers may be multi-dimensional with up to two valid vegetation cycles. \n\nFor areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year.\n\nValidation accuracy (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) is currently still being evaluated, and a validation statement for this product will be forthcoming once the evaluation is complete.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The MCD12Q2 Version 6.1 product has an improved approach to snow filtering.\n\n\n", "links": [ { diff --git a/datasets/MCD14DL_6.1NRT.json b/datasets/MCD14DL_6.1NRT.json index 7961441686..e117e48302 100644 --- a/datasets/MCD14DL_6.1NRT.json +++ b/datasets/MCD14DL_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD14DL_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Terra Thermal Anomalies/Fire locations 1km FIRMS Near Real-Time (NRT) - Collection 61 processed by NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) Fire Information for Resource Management System (FIRMS), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified.\r\n\r\nMCD14DL are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.\r\n\r\nCollection 61 data replaced Collection 6 (DOI:10.5067/FIRMS/MODIS/MCD14DL.NRT.006) in April 2021. The C61 processing does not contain any updates to the science algorithm; changes were made to improve the calibration approach in the generation of the Terra and Aqua MODIS Level 1B products.", "links": [ { diff --git a/datasets/MCD15A2H_061.json b/datasets/MCD15A2H_061.json index 003beb2cb8..99f6fa1f20 100644 --- a/datasets/MCD15A2H_061.json +++ b/datasets/MCD15A2H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD15A2H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA\u2019s Terra and Aqua satellites from within the 8-day period.\r\n\r\nLAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.\r\n\r\nThe LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.\r\n\r\nImprovements/Changes from Previous Versions \r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD15A3H_061.json b/datasets/MCD15A3H_061.json index 5785777a2f..ebc75cbc74 100644 --- a/datasets/MCD15A3H_061.json +++ b/datasets/MCD15A3H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD15A3H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA\u2019s Terra and Aqua satellites from within the 4-day period.\n\nLAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.\n\nThe LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD18A1_062.json b/datasets/MCD18A1_062.json index 813e7276f8..ae7e48a6c8 100644 --- a/datasets/MCD18A1_062.json +++ b/datasets/MCD18A1_062.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD18A1_062", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD18A1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)).\r\n\r\nProvided in the MOD18A1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer.\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Radiation products. Further details regarding MODIS land product validation for the MCD18 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MCD18). \r\n\r\nThe Version 6.2 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: 1) MODIS shortwave infrared bands are included in the retrieval algorithm, which significantly reduces estimation uncertainties in cloud- and snow-covered pixels. \r\n2) An improved climatology of surface reflectance was produced and used in the retrieval algorithm.\r\n\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MCD18A2_062.json b/datasets/MCD18A2_062.json index ede0022fbe..d58cc4f9a1 100644 --- a/datasets/MCD18A2_062.json +++ b/datasets/MCD18A2_062.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD18A2_062", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD18A2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)).\r\n\r\nProvided in the MOD18A2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer.\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Radiation products. Further details regarding MODIS land product validation for the MCD18 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MCD18). \r\n\r\nThe Version 6.2 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: 1) MODIS shortwave infrared bands are included in the retrieval algorithm, which significantly reduces estimation uncertainties in cloud- and snow-covered pixels. \r\n2) An improved climatology of surface reflectance was produced and used in the retrieval algorithm.\r\n\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MCD18C1_062.json b/datasets/MCD18C1_062.json index f20af3e563..6229fc7313 100644 --- a/datasets/MCD18C1_062.json +++ b/datasets/MCD18C1_062.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD18C1_062", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD18C1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG) with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)).\r\n\r\nProvided in the MOD18C1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer.\r\n\r\nValidation at stage 1 ( https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Radiation products. Further details regarding MODIS land product validation for the MCD18 data products are available from the MODIS Land Team Validation site ( https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MCD18). \r\n\r\nThe Version 6.2 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: 1) MODIS shortwave infrared bands are included in the retrieval algorithm, which significantly reduces estimation uncertainties in cloud- and snow-covered pixels. \r\n2) An improved climatology of surface reflectance was produced and used in the retrieval algorithm.\r\n\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MCD18C2_062.json b/datasets/MCD18C2_062.json index a9b4b2b4e2..4e5cdffb0c 100644 --- a/datasets/MCD18C2_062.json +++ b/datasets/MCD18C2_062.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD18C2_062", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD18C2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 0.05 degree (5,600 meters at the equator) resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)).\r\n\r\nProvided in the MOD18C2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer.\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Radiation products. Further details regarding MODIS land product validation for the MCD18 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MCD18). \r\n\r\nThe Version 6.2 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: 1) MODIS shortwave infrared bands are included in the retrieval algorithm, which significantly reduces estimation uncertainties in cloud- and snow-covered pixels. \r\n2) An improved climatology of surface reflectance was produced and used in the retrieval algorithm.\r\n\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MCD19A1CMGL_061.json b/datasets/MCD19A1CMGL_061.json index e2865263f0..6d08c66e16 100644 --- a/datasets/MCD19A1CMGL_061.json +++ b/datasets/MCD19A1CMGL_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A1CMGL_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A1CMGL Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 1-7) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGL product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45\u00b0 and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm.\r\n\r\nThe MCD19A1CMGL MAIAC Surface Reflectance product for land bands includes 25 Science Dataset (SDS) layers: BRF for bands 1-7, BRF normalized to a fixed geometry for bands 1-7, NBAR for bands 1-7, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included.\r\n", "links": [ { diff --git a/datasets/MCD19A1CMGO_061.json b/datasets/MCD19A1CMGO_061.json index 86fb3e93dd..59f4314a99 100644 --- a/datasets/MCD19A1CMGO_061.json +++ b/datasets/MCD19A1CMGO_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A1CMGO_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A1CMGO Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 8-12) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGO product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45\u00b0 and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm.\r\n\r\nThe MCD19A1CMGO MAIAC Surface Reflectance product for ocean bands includes 19 Science Dataset (SDS) layers: BRF for bands 8-12, BRF normalized to a fixed geometry for bands 8-12, NBAR for bands 8-12, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included.", "links": [ { diff --git a/datasets/MCD19A1N_6.1NRT.json b/datasets/MCD19A1N_6.1NRT.json index 06f772822e..39dddf4c29 100644 --- a/datasets/MCD19A1N_6.1NRT.json +++ b/datasets/MCD19A1N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A1N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) Combined Terra and Aqua Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product (MCD19A1N) produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1N product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption.\r\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD19A1_061.json b/datasets/MCD19A1_061.json index 5225c90757..e83e76766b 100644 --- a/datasets/MCD19A1_061.json +++ b/datasets/MCD19A1_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MCD19A1_061", - "stac_version": "1.0.0", - "description": "The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption.\n\nThe MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits.\n\nEach SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The MCD19 Version 6.1 products have added 250 m resolution bands.\n* The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product.\n* MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. \n* Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D.\n* There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.\n", + "stac_version": "1.1.0", + "description": "The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption.\r\n\r\nThe MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits.\r\n\r\nEach SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer.\r\n\r\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The MCD19 Version 6.1 products have added 250 m resolution bands.\r\n* The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product.\r\n* MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. \r\n* Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D.\r\n* There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.\r\n", "links": [ { "rel": "license", @@ -112,29 +112,21 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L2G_BidirectionalReflectance_Bands143.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A1.A2002012.h11v10.061.2022183143141.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Combined_MAIAC_L2G_BidirectionalReflectance_Bands143.jpg", + "title": "Download BROWSE.MCD19A1.A2002012.h11v10.061.2022183143141.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MCD19A1.061/MCD19A1.A2024202.h12v10.061.2024203154723/BROWSE.MCD19A1.A2024202.h12v10.061.2024203154723.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A1.A2002012.h11v10.061.2022183143141.1.jpg", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L2G_BidirectionalReflectance_Bands143.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "gov/MOTA/MCD19A1": { "href": "https://e4ftl01.cr.usgs.gov/MOTA/MCD19A1.061/", "title": "Direct Download [0]", @@ -144,45 +136,25 @@ ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search/granules?p=C2484086031-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C1620263521-LPDAAC_ECS", "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MCD19A1.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MCD19A1_061": { - "href": "s3://lp-prod-protected/MCD19A1.061", - "title": "lp_prod_protected_MCD19A1_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MCD19A1_061": { - "href": "s3://lp-prod-public/MCD19A1.061", - "title": "lp_prod_public_MCD19A1_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and download products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MCD19A1.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MCD19A2CMG_061.json b/datasets/MCD19A2CMG_061.json index 8e44515522..f212315642 100644 --- a/datasets/MCD19A2CMG_061.json +++ b/datasets/MCD19A2CMG_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A2CMG_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products (MCD19A1CMGL (https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and MCD19A1CMGO (https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)). \r\n\r\nThe MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 \u00b5m, green band AOD at 0.55 \u00b5m, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 \u00b5m created using a composite of all available orbits. ", "links": [ { diff --git a/datasets/MCD19A2N_6.1NRT.json b/datasets/MCD19A2N_6.1NRT.json index 9263c8ef7d..744e1f735f 100644 --- a/datasets/MCD19A2N_6.1NRT.json +++ b/datasets/MCD19A2N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A2N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Near Real Time (NRT) Combined Terra and Aqua Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth gridded Level 2 product (MCD19A2N) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A2N product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1N product. \r\n\r\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD19A2_006.json b/datasets/MCD19A2_006.json index 8bae83d9b0..7b924facd8 100644 --- a/datasets/MCD19A2_006.json +++ b/datasets/MCD19A2_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A2_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD19A2 Version 6.1 data product (https://doi.org/10.5067/MODIS/MCD19A2.061).\n\nThe MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product.\n\nThe MCD19A2 AOD data product contains the following Science Dataset (SDS) variables: blue band AOD at 0.47 micron, green band AOD at 0.55 micron, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1 km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5 km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 micron created using a composite of all available orbits.\n\nEach SDS variable within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS variable.\n\nImprovements/Changes from Previous Versions\n\n* New product for MODIS Version 6.\n", "links": [ { diff --git a/datasets/MCD19A2_061.json b/datasets/MCD19A2_061.json index bcdac89109..c9fb0952bb 100644 --- a/datasets/MCD19A2_061.json +++ b/datasets/MCD19A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A2 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product.\r\n\r\nThe MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 \u00b5m, green band AOD at 0.55 \u00b5m, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 \u00b5m created using a composite of all available orbits.\r\n\r\nEach SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer.\r\n\r\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the AOD SDS layers. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The MCD19 Version 6.1 products have added 250 m resolution bands.\r\n* The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product.\r\n* MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. \r\n* Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D.\r\n* There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.", "links": [ { @@ -112,25 +112,17 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L2G_AerosolOpticalDepth.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A2.A2002053.h12v12.061.2022184021412.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Combined_MAIAC_L2G_AerosolOpticalDepth.jpg", + "title": "Download BROWSE.MCD19A2.A2002053.h12v12.061.2022184021412.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MCD19A2.061/MCD19A2.A2024202.h21v11.061.2024203154831/BROWSE.MCD19A2.A2024202.h21v11.061.2024203154831.1.jpg", - "title": "Thumbnail [0]", - "description": "Browse image for Earthdata Search.", - "roles": [ - "thumbnail" - ] - }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L2G_AerosolOpticalDepth.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A2.A2002053.h12v12.061.2022184021412.1.jpg", + "title": "Thumbnail", + "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] @@ -144,45 +136,25 @@ ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search/granules?p=C2324689816-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C1620263538-LPDAAC_ECS", "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MCD19A2.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc. ", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MCD19A2_061": { - "href": "s3://lp-prod-protected/MCD19A2.061", - "title": "lp_prod_protected_MCD19A2_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MCD19A2_061": { - "href": "s3://lp-prod-public/MCD19A2.061", - "title": "lp_prod_public_MCD19A2_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and download products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MCD19A2.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MCD19A3CMG_061.json b/datasets/MCD19A3CMG_061.json index c0405efe15..16299f2d88 100644 --- a/datasets/MCD19A3CMG_061.json +++ b/datasets/MCD19A3CMG_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A3CMG_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption. \r\n\r\nThe MCD19A3CMG Vegetation Index data product contains the following Science Dataset (SDS) layers: NDVI, NDVI normalized to a fixed geometry of solar zenith angle at 45\u00b0 and nadir view, gap-filled NDVI, EVI, and EVI normalized to a fixed geometry of solar zenith angle at 45\u00b0 and nadir view. A low-resolution browse image is also included showing NDVI created using a composite of all available orbits. ", "links": [ { diff --git a/datasets/MCD19A3DN_6.1NRT.json b/datasets/MCD19A3DN_6.1NRT.json index 2398683920..d2fdd7294a 100644 --- a/datasets/MCD19A3DN_6.1NRT.json +++ b/datasets/MCD19A3DN_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD19A3DN_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) Combined Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product (MCD19A3DN) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A3DN product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions.\r\n\r\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ", "links": [ { diff --git a/datasets/MCD19A3D_061.json b/datasets/MCD19A3D_061.json index 010abdbd88..dce39c0141 100644 --- a/datasets/MCD19A3D_061.json +++ b/datasets/MCD19A3D_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MCD19A3D_061", - "stac_version": "1.0.0", - "description": "The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions.\n\nWhen snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC\u2019s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer.\n\nOver snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands.\n\nThe MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. \n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The MCD19 Version 6.1 products have added 250 m resolution bands.\n* The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product.\n* MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. \n* Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D.\n* There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.", + "stac_version": "1.1.0", + "description": "The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions.\r\n\r\nWhen snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC\u2019s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer.\r\n\r\nOver snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands.\r\n\r\nThe MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. \r\n\r\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The MCD19 Version 6.1 products have added 250 m resolution bands.\r\n* The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product.\r\n* MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. \r\n* Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D.\r\n* There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.", "links": [ { "rel": "license", @@ -122,29 +122,21 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L3_IsotropicKernelParameters.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A3D.A2002059.h18v07.061.2022184040430.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Combined_MAIAC_L3_IsotropicKernelParameters.jpg", + "title": "Download BROWSE.MCD19A3D.A2002059.h18v07.061.2022184040430.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MCD19A3D.061/MCD19A3D.A2024202.h08v06.061.2024203154611/BROWSE.MCD19A3D.A2024202.h08v06.061.2024203154611.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2022.07.06/BROWSE.MCD19A3D.A2002059.h18v07.061.2022184040430.1.jpg", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Combined_MAIAC_L3_IsotropicKernelParameters.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "gov/MOTA/MCD19A3D": { "href": "https://e4ftl01.cr.usgs.gov/MOTA/MCD19A3D.061/", "title": "Direct Download [0]", @@ -154,45 +146,25 @@ ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search/granules?p=C2484086411-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C2206959985-LPDAAC_ECS", "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MCD19A3D.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MCD19A3D_061": { - "href": "s3://lp-prod-protected/MCD19A3D.061", - "title": "lp_prod_protected_MCD19A3D_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MCD19A3D_061": { - "href": "s3://lp-prod-public/MCD19A3D.061", - "title": "lp_prod_public_MCD19A3D_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and download products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MCD19A3D.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MCD43A1N_6.1NRT.json b/datasets/MCD43A1N_6.1NRT.json index fad10a543d..eaead46e8b 100644 --- a/datasets/MCD43A1N_6.1NRT.json +++ b/datasets/MCD43A1N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A1N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) MCD43A1N, MODIS Combined Aqua and Terra Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters is produced daily using 16 days of Terra and Aqua MODIS data. This global gridded tiled product provides model parameters/coefficients (isotropic, volume and surface) for characterizing the BRDF of each pixel at 500m resolution in the sinusoidal map projection. BRDF at each pixel for the current day is derived by inverting all available good quality corrected surface reflectance observations acquired by Terra and Aqua MODIS from the 16-day period ending with the current data day. The daily observation are weighed as a function of quality, observation coverage and temporal distance from the current data date. Model parameters are stored as 3D datasets for each of the 7 land bands, visible, near-infrared and shortwave bands along with corresponding mandatory QA flags.\r\nThere is a significant change in the science algorithm of the Collection 61 (C61) NRT BRDF/Albedo products and, therefore significant differences/discontinuities between the C6 and C61 products. C61 algorithm changes are intended to minimize the differences between the NRT and Standard BRDF. The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day.\r\n\r\nAdditional information at MODIS Land Science Team website at https://modis-land.gsfc.nasa.gov/brdf.html", "links": [ { diff --git a/datasets/MCD43A1_061.json b/datasets/MCD43A1_061.json index 7ffbf7c10b..f28360de13 100644 --- a/datasets/MCD43A1_061.json +++ b/datasets/MCD43A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43A3)(https://doi.org/10.5067/MODIS/MCD43A3.061) and BRDF (MCD43A4) (https://doi.org/10.5067/MODIS/MCD43A4.061) products.\r\n\r\nThe MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the quality layers for each of the 10 bands. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43A2N_6.1NRT.json b/datasets/MCD43A2N_6.1NRT.json index 258a2760ef..8d3179c0ea 100644 --- a/datasets/MCD43A2N_6.1NRT.json +++ b/datasets/MCD43A2N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A2N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) Combined Aqua and Terra Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality, MCD43A2N is a L3 daily 16-day composite global gridded tiled product that provides full set of quality control flags for use in determining the quality of the retrievals at pixel level in the daily L3 BRDF/Albedo suite of products: BRDF/Albedo Model Parameters (MCD43A1N), Albedo (MCD43A3N) and the NBAR (MCD43A4N).\r\n\r\nThere is a significant change in the science algorithm of the Collection 61 (C61) NRT BRDF/Albedo products and, therefore significant differences/discontinuities between the C6 and C61 products. C61 algorithm changes are intended to minimize the differences between the NRT and Standard BRDF. The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day.\r\n\r\nAdditional information from MODIS Land Science Team at https://modis-land.gsfc.nasa.gov/brdf.html", "links": [ { diff --git a/datasets/MCD43A2_061.json b/datasets/MCD43A2_061.json index 6481bf411d..10cab941ab 100644 --- a/datasets/MCD43A2_061.json +++ b/datasets/MCD43A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16-day retrieval period which is reflected in the Julian date in the file name. MCD43A2 contains the quality information for the corresponding MCD43A3 (https://doi.org/10.5067/MODIS/MCD43A3.061) Albedo and MCD43A4 (https://doi.org/10.5067/MODIS/MCD43A4.061) Nadir BRDF-Adjusted Reflectance (NBAR) products. \r\n\r\nThe MCD43A2 contains BRDF/Albedo band quality (inversion information) and days of valid observation within the 16-day period for MODIS bands 1 through 7 along with land water type, snow BRDF albedo, local solar noon, platform, and BRDF/Albedo uncertainty.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB\r\n", "links": [ { diff --git a/datasets/MCD43A3N_6.1NRT.json b/datasets/MCD43A3N_6.1NRT.json index 35bfcd046b..90e1736778 100644 --- a/datasets/MCD43A3N_6.1NRT.json +++ b/datasets/MCD43A3N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A3N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) Combined Aqua and Terra Albedo, MCD43A3N is the daily L3 16-day composite global gridded tiled product that provides the black-sky albedo and the white-sky albedo at local solar noon for each of the 7 land bands, visible, near infra-red and the short wave bands. The albedo is retrieved for the current data day as identified in the product granule id using the BRDF/Albedo model parameters provided in the daily MCD43A1N product. The product also contains the mandatory QA dataset for each of the spectral albedo dataset in the file.\r\n\r\n The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day.\r\n\r\nAdditional information from MODIS Land Science Team website at:\r\nhttps://modis-land.gsfc.nasa.gov/brdf.html", "links": [ { diff --git a/datasets/MCD43A3_061.json b/datasets/MCD43A3_061.json index fbb4bd770a..d72eb4caa5 100644 --- a/datasets/MCD43A3_061.json +++ b/datasets/MCD43A3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.\r\n\r\nThe MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the quality layers for each of the 10 bands. \r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43A4N_6.1NRT.json b/datasets/MCD43A4N_6.1NRT.json index 7b4e959815..a8708b6450 100644 --- a/datasets/MCD43A4N_6.1NRT.json +++ b/datasets/MCD43A4N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A4N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Near Real Time (NRT) Combined Aqua and Terra Nadir BRDF-Adjusted Reflectance, MCD43A4N, Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. This product in sinusoidal map projection that provides view angle corrected Nadir BRDF-Adjusted Reflectances (NBAR) at local solar noon for the 7 land bands, along with corresponding mandatory QA dataset.\r\n\r\nThere is a significant change in the science algorithm of the Collection 61 (C61) NRT BRDF/Albedo products and, therefore significant differences/discontinuities between the C6 and C61 products. C61 algorithm changes are intended to minimize the differences between the NRT and Standard BRDF. The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day.\r\n\r\nAdditional information from MODIS Land Science Team at:\r\nhttps://modis-land.gsfc.nasa.gov/brdf.html", "links": [ { diff --git a/datasets/MCD43A4_061.json b/datasets/MCD43A4_061.json index a77d88a36c..082ef0d3d5 100644 --- a/datasets/MCD43A4_061.json +++ b/datasets/MCD43A4_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43A4_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.\r\n\r\nThe MCD43A4 provides NBAR and quality layers for MODIS bands 1 through 7.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43C1_061.json b/datasets/MCD43C1_061.json index 547cbdd587..bade17ae32 100644 --- a/datasets/MCD43C1_061.json +++ b/datasets/MCD43C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. \r\n\r\nMCD43C1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43C3 (https://doi.org/10.5067/MODIS/MCD43C3.061)) and BRDF (MCD43C4 (https://doi.org/10.5067/MODIS/MCD43C4.061)) products. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n", "links": [ { diff --git a/datasets/MCD43C2_061.json b/datasets/MCD43C2_061.json index e44c999e72..6602a2711d 100644 --- a/datasets/MCD43C2_061.json +++ b/datasets/MCD43C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. \r\n\r\nMCD43C2 provides the three model weighting parameters (isotropic, volumetric, and geometric) computed from snow-free retrievals. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, and uncertainty.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n", "links": [ { diff --git a/datasets/MCD43C3_061.json b/datasets/MCD43C3_061.json index ca2a35d623..551c612f7f 100644 --- a/datasets/MCD43C3_061.json +++ b/datasets/MCD43C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C3 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. \r\n\r\nMCD43C3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) at local solar noon. Black-sky albedo and white-sky albedo values are available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 20 albedo layers are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43C4_061.json b/datasets/MCD43C4_061.json index 653d14d003..661322437f 100644 --- a/datasets/MCD43C4_061.json +++ b/datasets/MCD43C4_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43C4_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. \r\n\r\nMCD43C4 removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. These NBAR values are calculated from MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061). The product includes separate NBAR layers for MODIS spectral bands 1 through 7 as well as ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MCD43D01_061.json b/datasets/MCD43D01_061.json index 060772c1f1..3ead4e90f2 100644 --- a/datasets/MCD43D01_061.json +++ b/datasets/MCD43D01_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D01_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D01 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D01 is the Bidirectional Reflectance Distribution Function isotropic parameter for MODIS band 1. This isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the Albedo and BRDF value for MODIS band 1. ", "links": [ { diff --git a/datasets/MCD43D02_061.json b/datasets/MCD43D02_061.json index 1329530d1f..d887b090f8 100644 --- a/datasets/MCD43D02_061.json +++ b/datasets/MCD43D02_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D02_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D02 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D02 is the Bidirectional Reflectance Distribution Function volumetric parameter for MODIS band 1. This volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the Albedo and BRDF value for MODIS band 1. ", "links": [ { diff --git a/datasets/MCD43D03_061.json b/datasets/MCD43D03_061.json index 463b8e3ef7..135a1f6d6e 100644 --- a/datasets/MCD43D03_061.json +++ b/datasets/MCD43D03_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D03_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D03 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D03 is the Bidirectional Reflectance Distribution Function geometric parameter for MODIS band 1. This geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the Albedo and BRDF value for MODIS band 1. ", "links": [ { diff --git a/datasets/MCD43D04_061.json b/datasets/MCD43D04_061.json index 3d04201cc2..8b28cf1358 100644 --- a/datasets/MCD43D04_061.json +++ b/datasets/MCD43D04_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D04_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D04 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D04 is the Bidirectional Reflectance Distribution Function isotropic parameter for MODIS band 2. This isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the Albedo and BRDF value for MODIS band 2. ", "links": [ { diff --git a/datasets/MCD43D05_061.json b/datasets/MCD43D05_061.json index d116805171..0a1186b079 100644 --- a/datasets/MCD43D05_061.json +++ b/datasets/MCD43D05_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D05_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D05 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D05 is the Bidirectional Reflectance Distribution Function volumetric parameter for MODIS band 2. This volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the Albedo and BRDF value for MODIS band 2. ", "links": [ { diff --git a/datasets/MCD43D06_061.json b/datasets/MCD43D06_061.json index c459913d57..80ee69568b 100644 --- a/datasets/MCD43D06_061.json +++ b/datasets/MCD43D06_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D06_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D06 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D06 is the Bidirectional Reflectance Distribution Function geometric parameter for MODIS band 2. This geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the Albedo and BRDF value for MODIS band 2. ", "links": [ { diff --git a/datasets/MCD43D07_061.json b/datasets/MCD43D07_061.json index a00f7a2747..7943ed6207 100644 --- a/datasets/MCD43D07_061.json +++ b/datasets/MCD43D07_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D07_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D07 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D07 is the BRDF isotropic parameter for MODIS band 3. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D08_061.json b/datasets/MCD43D08_061.json index c26433b874..a60db3aad8 100644 --- a/datasets/MCD43D08_061.json +++ b/datasets/MCD43D08_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D08_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D08 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \n\nMCD43D08 is the BRDF volumetric parameter for MODIS band 3. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. \n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MCD43D09_061.json b/datasets/MCD43D09_061.json index 9f349be548..cb6d3ecbb1 100644 --- a/datasets/MCD43D09_061.json +++ b/datasets/MCD43D09_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D09_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D09 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \n\nMCD43D09 is the BRDF geometric parameter for MODIS band 3. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. \n\n**Improvements/Changes from Previous Versions**\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D10_061.json b/datasets/MCD43D10_061.json index 81e250dbe0..6a1a49685a 100644 --- a/datasets/MCD43D10_061.json +++ b/datasets/MCD43D10_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D10_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D10 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D10 is the BRDF isotropic parameter for MODIS band 4. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D11_061.json b/datasets/MCD43D11_061.json index 9c4c31b686..1415585683 100644 --- a/datasets/MCD43D11_061.json +++ b/datasets/MCD43D11_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D11_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D11 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D11 is the BRDF volumetric parameter for MODIS band 4. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D12_061.json b/datasets/MCD43D12_061.json index f68646da9c..de43bfc691 100644 --- a/datasets/MCD43D12_061.json +++ b/datasets/MCD43D12_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D12_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D12 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D12 is the BRDF geometric parameter for MODIS band 4. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D13_061.json b/datasets/MCD43D13_061.json index 9df5dfea39..072d5a4e9c 100644 --- a/datasets/MCD43D13_061.json +++ b/datasets/MCD43D13_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D13_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D13 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D13 is the BRDF isotropic parameter for MODIS band 5. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D14_061.json b/datasets/MCD43D14_061.json index 98b9e87a4e..ca7135b3ce 100644 --- a/datasets/MCD43D14_061.json +++ b/datasets/MCD43D14_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D14_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D14 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D14 is the BRDF volumetric parameter for MODIS band 5. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D15_061.json b/datasets/MCD43D15_061.json index 716b62d5df..da99e59f57 100644 --- a/datasets/MCD43D15_061.json +++ b/datasets/MCD43D15_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D15_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D15 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D15 is the BRDF geometric parameter for MODIS band 5. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D16_061.json b/datasets/MCD43D16_061.json index 6c1733be02..67c358c764 100644 --- a/datasets/MCD43D16_061.json +++ b/datasets/MCD43D16_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D16_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D16 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D16 is the BRDF isotropic parameter for MODIS band 6. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D17_061.json b/datasets/MCD43D17_061.json index 68dde2dcd1..c3da8f4041 100644 --- a/datasets/MCD43D17_061.json +++ b/datasets/MCD43D17_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D17_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D17 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectrometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D17 is the BRDF volumetric parameter for MODIS band 6. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D18_061.json b/datasets/MCD43D18_061.json index d673776bb6..62d7fa44ae 100644 --- a/datasets/MCD43D18_061.json +++ b/datasets/MCD43D18_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D18_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D18 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D18 is the BRDF geometric parameter for MODIS band 6. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D19_061.json b/datasets/MCD43D19_061.json index b0c248e4c2..342f8eaf2c 100644 --- a/datasets/MCD43D19_061.json +++ b/datasets/MCD43D19_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D19_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D19 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D19 is the BRDF isotropic parameter for MODIS band 7. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D20_061.json b/datasets/MCD43D20_061.json index 5c6e350c27..986c870b0a 100644 --- a/datasets/MCD43D20_061.json +++ b/datasets/MCD43D20_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D20_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D20 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D20 is the BRDF volumetric parameter for MODIS band 7. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions \r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D21_061.json b/datasets/MCD43D21_061.json index 0d1abad196..638f073598 100644 --- a/datasets/MCD43D21_061.json +++ b/datasets/MCD43D21_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D21_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D21 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D21 is the BRDF geometric parameter for MODIS band 7. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D22_061.json b/datasets/MCD43D22_061.json index 3d94c738ff..e7be636109 100644 --- a/datasets/MCD43D22_061.json +++ b/datasets/MCD43D22_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D22_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D22 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D22 is the BRDF isotropic parameter for the MODIS visible broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D23_061.json b/datasets/MCD43D23_061.json index 8da2a70df9..a20b1ea911 100644 --- a/datasets/MCD43D23_061.json +++ b/datasets/MCD43D23_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D23_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D23 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D23 is the BRDF volumetric parameter for the MODIS visible broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values ffor the MODIS visible broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D24_061.json b/datasets/MCD43D24_061.json index acd3ed9968..ec3df87d18 100644 --- a/datasets/MCD43D24_061.json +++ b/datasets/MCD43D24_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D24_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D24 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D24 is the BRDF geometric parameter for the MODIS visible broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D25_061.json b/datasets/MCD43D25_061.json index fcca74e848..44f1dc6125 100644 --- a/datasets/MCD43D25_061.json +++ b/datasets/MCD43D25_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D25_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D25 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D25 is the BRDF isotropic parameter for the MODIS NIR broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D26_061.json b/datasets/MCD43D26_061.json index 8d07f09e4a..188aded8cc 100644 --- a/datasets/MCD43D26_061.json +++ b/datasets/MCD43D26_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D26_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D26 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D26 is the BRDF volumetric parameter for the MODIS NIR broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D27_061.json b/datasets/MCD43D27_061.json index d7e6d902b0..ed9467fbc0 100644 --- a/datasets/MCD43D27_061.json +++ b/datasets/MCD43D27_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D27_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D27 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D27 is the BRDF geometric parameter for the MODIS NIR broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D28_061.json b/datasets/MCD43D28_061.json index c9877e7d4b..1c093b8ef0 100644 --- a/datasets/MCD43D28_061.json +++ b/datasets/MCD43D28_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D28_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D28 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D28 is the BRDF isotropic parameter for the MODIS shortwave broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D29_061.json b/datasets/MCD43D29_061.json index 7a82d7ce2c..be1a66e4e8 100644 --- a/datasets/MCD43D29_061.json +++ b/datasets/MCD43D29_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D29_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D29 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands incluided in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D29 is the BRDF volumetric parameter for the MODIS shortwave broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D30_061.json b/datasets/MCD43D30_061.json index 4e192e3785..2707d2ee9c 100644 --- a/datasets/MCD43D30_061.json +++ b/datasets/MCD43D30_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D30_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D30 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D30 is the BRDF geometric parameterfor the MODIS shortwave broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D31_061.json b/datasets/MCD43D31_061.json index 881e559b5f..7cea66778a 100644 --- a/datasets/MCD43D31_061.json +++ b/datasets/MCD43D31_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D31_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D31 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA BRDF Quality dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. MCD43D31 provides BRDF/Albedo quality information for the MCD43D products. \r\n\r\nMCD43D31 consists of a BRDF quality layer representing the overall quality of each pixel along with an individual BRDF/Albedo quality layer for MODIS spectral bands 1 through 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D32_061.json b/datasets/MCD43D32_061.json index 242ce0debe..d7d1858f42 100644 --- a/datasets/MCD43D32_061.json +++ b/datasets/MCD43D32_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D32_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D32 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Local Solar Noon dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D32 provides local solar noon information for the MCD43D products. \r\n\r\nThe MCD43D32 layer contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D33_061.json b/datasets/MCD43D33_061.json index a1cea06e53..520fa485dc 100644 --- a/datasets/MCD43D33_061.json +++ b/datasets/MCD43D33_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D33_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D33 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 1 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D33 provides MODIS band 1 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D33 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 1.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D34_061.json b/datasets/MCD43D34_061.json index 40f3eb7664..bdf2827f9b 100644 --- a/datasets/MCD43D34_061.json +++ b/datasets/MCD43D34_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D34_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D34 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 2 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D34 provides MODIS band 2 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D34 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 2. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D35_061.json b/datasets/MCD43D35_061.json index f628f6c29d..7df58efaa6 100644 --- a/datasets/MCD43D35_061.json +++ b/datasets/MCD43D35_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D35_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D35 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 3 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D35 provides MODIS band 3 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D35 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 3. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D36_061.json b/datasets/MCD43D36_061.json index 257c5fc104..16c5ee8212 100644 --- a/datasets/MCD43D36_061.json +++ b/datasets/MCD43D36_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D36_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D36 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 4 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D36 provides MODIS band 4 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D36 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 4.\r\n\r\nImprovements/Changes from Previous Versions \r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D37_061.json b/datasets/MCD43D37_061.json index c67ec45752..7ac61e1320 100644 --- a/datasets/MCD43D37_061.json +++ b/datasets/MCD43D37_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D37_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D37 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D37 provides MODIS band 5 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D37 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D38_061.json b/datasets/MCD43D38_061.json index d8bbd13c1b..fcd53276f5 100644 --- a/datasets/MCD43D38_061.json +++ b/datasets/MCD43D38_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D38_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D38 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 6 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D38 provides MODIS band 6 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D38 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D39_061.json b/datasets/MCD43D39_061.json index 8f9e81aafd..44906352f7 100644 --- a/datasets/MCD43D39_061.json +++ b/datasets/MCD43D39_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D39_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D39 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 7 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, MCD43D product contains just one data layer. MCD43D39 provides MODIS band 7 valid observation quality information for the MCD43D products. \r\n\r\nMCD43D39 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D40_061.json b/datasets/MCD43D40_061.json index 6993bcff0e..00082f9e69 100644 --- a/datasets/MCD43D40_061.json +++ b/datasets/MCD43D40_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D40_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D40 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Snow Status dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D project contains just one data layer. MCD43D40 provides snow cover information for the MCD43D products. \r\n\r\nMCD43D40 contains the snow status quality layer which identifies each pixel as either \u201cSnow-free Albedo Retrieved\u201d or \u201cSnow Albedo Retrieved\u201d for the acquisition period. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D41_061.json b/datasets/MCD43D41_061.json index 221238ea8e..f6b1442cea 100644 --- a/datasets/MCD43D41_061.json +++ b/datasets/MCD43D41_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D41_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D41 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Uncertainty dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D41 provides BRDF inversion information for the MCD43D products. \r\n\r\nMCD43D41 layer contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D42_061.json b/datasets/MCD43D42_061.json index 9b427589be..130ff3d80a 100644 --- a/datasets/MCD43D42_061.json +++ b/datasets/MCD43D42_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D42_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D42 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D42 is the black-sky albedo for MODIS band 1. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D43_061.json b/datasets/MCD43D43_061.json index f3921f6a7d..a313b7e448 100644 --- a/datasets/MCD43D43_061.json +++ b/datasets/MCD43D43_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D43_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D43 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D43 is the black-sky albedo for MODIS band 2. \r\n\r\nImprovements/Changes from Previous Versions \r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D44_061.json b/datasets/MCD43D44_061.json index ac84621bf9..9d0aee46a9 100644 --- a/datasets/MCD43D44_061.json +++ b/datasets/MCD43D44_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D44_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D44 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D44 is the black-sky albedo for MODIS band 3. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D45_061.json b/datasets/MCD43D45_061.json index 9427dfc595..dbbd1dc564 100644 --- a/datasets/MCD43D45_061.json +++ b/datasets/MCD43D45_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D45_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D45 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D45 is the black-sky albedo for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D46_061.json b/datasets/MCD43D46_061.json index f4f17fdcfb..b7fd599ba9 100644 --- a/datasets/MCD43D46_061.json +++ b/datasets/MCD43D46_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D46_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D46 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D46 is the black-sky albedo for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D47_061.json b/datasets/MCD43D47_061.json index ea2f58be1a..3b0590b2ab 100644 --- a/datasets/MCD43D47_061.json +++ b/datasets/MCD43D47_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D47_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D47 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D47 is the black-sky albedo for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D48_061.json b/datasets/MCD43D48_061.json index 8054561f21..5cf2614509 100644 --- a/datasets/MCD43D48_061.json +++ b/datasets/MCD43D48_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D48_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D48 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D48 is the black-sky albedo for MODIS band 7.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D49_061.json b/datasets/MCD43D49_061.json index d2331eecc6..930ea4defb 100644 --- a/datasets/MCD43D49_061.json +++ b/datasets/MCD43D49_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D49_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D49 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D49 is the black-sky albedo for the MODIS visible broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D50_061.json b/datasets/MCD43D50_061.json index f37a30a972..04718ecbdb 100644 --- a/datasets/MCD43D50_061.json +++ b/datasets/MCD43D50_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D50_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D50 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D50 is the black-sky albedo for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D51_061.json b/datasets/MCD43D51_061.json index c1db6916e4..a213096c1c 100644 --- a/datasets/MCD43D51_061.json +++ b/datasets/MCD43D51_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D51_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D51 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D51 is the black-sky albedo for the MODIS shortwave broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D52_061.json b/datasets/MCD43D52_061.json index 197fbea56e..524df466a8 100644 --- a/datasets/MCD43D52_061.json +++ b/datasets/MCD43D52_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D52_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D52 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D52 is the white-sky albedo for MODIS band 1. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D53_061.json b/datasets/MCD43D53_061.json index 336348dfac..0d024c4e54 100644 --- a/datasets/MCD43D53_061.json +++ b/datasets/MCD43D53_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D53_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D53 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D53 is the white-sky albedo for MODIS band 2. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D54_061.json b/datasets/MCD43D54_061.json index 10a0000c73..1963bf15a2 100644 --- a/datasets/MCD43D54_061.json +++ b/datasets/MCD43D54_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D54_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D54 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D54 is the white-sky albedo for MODIS band 3. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n", "links": [ { diff --git a/datasets/MCD43D55_061.json b/datasets/MCD43D55_061.json index 148f66ebeb..4021f8b063 100644 --- a/datasets/MCD43D55_061.json +++ b/datasets/MCD43D55_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D55_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D55 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D55 is the white-sky albedo for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D56_061.json b/datasets/MCD43D56_061.json index 73fd8a7adc..5f19746f26 100644 --- a/datasets/MCD43D56_061.json +++ b/datasets/MCD43D56_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D56_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D56 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D56 is the white-sky albedo for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D57_061.json b/datasets/MCD43D57_061.json index ad4a07091d..9cb47a949f 100644 --- a/datasets/MCD43D57_061.json +++ b/datasets/MCD43D57_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D57_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D57 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter(m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D57 is the white-sky albedo for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D58_061.json b/datasets/MCD43D58_061.json index 53d8fff729..bba690f033 100644 --- a/datasets/MCD43D58_061.json +++ b/datasets/MCD43D58_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D58_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D58 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D58 is the white-sky albedo for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n", "links": [ { diff --git a/datasets/MCD43D59_061.json b/datasets/MCD43D59_061.json index d4d5889ce9..ff522fb33f 100644 --- a/datasets/MCD43D59_061.json +++ b/datasets/MCD43D59_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D59_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D59 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D59 is the white-sky albedo for the MODIS visible broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D60_061.json b/datasets/MCD43D60_061.json index c22a7db6f1..0554799a04 100644 --- a/datasets/MCD43D60_061.json +++ b/datasets/MCD43D60_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D60_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D60 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D60 is the white-sky albedo for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D61_061.json b/datasets/MCD43D61_061.json index 5c6bc8aafb..4a02dfafb4 100644 --- a/datasets/MCD43D61_061.json +++ b/datasets/MCD43D61_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D61_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D61 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. \r\n\r\nMCD43D61 is the white-sky albedo for the MODIS shortwave broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D62_061.json b/datasets/MCD43D62_061.json index bedbe4ed4a..77310883e2 100644 --- a/datasets/MCD43D62_061.json +++ b/datasets/MCD43D62_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D62_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D62 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D62 is the NBAR for MODIS band 1. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D63_061.json b/datasets/MCD43D63_061.json index 59704c7c32..0dae000669 100644 --- a/datasets/MCD43D63_061.json +++ b/datasets/MCD43D63_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D63_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D63 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D63 is the NBAR for MODIS band 2. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D64_061.json b/datasets/MCD43D64_061.json index 9b9d1b1266..db412c26a8 100644 --- a/datasets/MCD43D64_061.json +++ b/datasets/MCD43D64_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D64_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D64 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D64 is the NBAR for MODIS band 3. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D65_061.json b/datasets/MCD43D65_061.json index 432975cf0d..cbd147e586 100644 --- a/datasets/MCD43D65_061.json +++ b/datasets/MCD43D65_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D65_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D65 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D65 is the NBAR for MODIS band 4. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n\r\n", "links": [ { diff --git a/datasets/MCD43D66_061.json b/datasets/MCD43D66_061.json index a5be54a9d1..4b3aa85041 100644 --- a/datasets/MCD43D66_061.json +++ b/datasets/MCD43D66_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D66_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D66 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D66 is the NBAR for MODIS band 5. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43D67_061.json b/datasets/MCD43D67_061.json index f82ab5791a..db461d7be9 100644 --- a/datasets/MCD43D67_061.json +++ b/datasets/MCD43D67_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D67_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D67 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D67 is the NBAR for MODIS band 6. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCD43D68_061.json b/datasets/MCD43D68_061.json index 7a46f6122e..6ea68ba654 100644 --- a/datasets/MCD43D68_061.json +++ b/datasets/MCD43D68_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43D68_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MCD43D68 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D68 is the NBAR for MODIS band 7. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MCD43GF_006.json b/datasets/MCD43GF_006.json index 4897a6c122..31640df276 100644 --- a/datasets/MCD43GF_006.json +++ b/datasets/MCD43GF_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43GF_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. \n \nThe MCD43GF product includes 67 variables containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data variable is distributed as a separate HDF file. Users are encouraged to download the quality variable for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data.\n\nThe MCD43 product is not recommended for solar zenith angles beyond 70 degrees.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.\n\nImprovements/Changes from Previous Versions\n\n* Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period.\n* MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period.\n* Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day.\n* The MCD43 products use L2G-lite surface reflectance as input.\n* When there are insufficient high quality reflectances, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel.\n* CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid for MCD43C as opposed to aggregating from the 500 m albedo.\n\nImportant Quality Information\n\nThe incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.\n\n", "links": [ { diff --git a/datasets/MCD43GF_061.json b/datasets/MCD43GF_061.json index cc2ef35125..c21aab609c 100644 --- a/datasets/MCD43GF_061.json +++ b/datasets/MCD43GF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD43GF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6.1 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6.1 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the [MCD43D41](https://doi.org/10.5067/MODIS/MCD43D41.061) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. \n \nThe MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data.\n\nThe MCD43 product is not recommended for solar zenith angles beyond 70 degrees.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43gf-cmg-gap-filled-snow-free-products/).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* In Version 6.1 reprocessing, the QA values for the MCD43GF product will change to reflect the band 5 and 6 dead detector issues.\n\n\n", "links": [ { diff --git a/datasets/MCD64A1_061.json b/datasets/MCD64A1_061.json index e709602c19..5ebc50186b 100644 --- a/datasets/MCD64A1_061.json +++ b/datasets/MCD64A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCD64A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. \r\n\r\nThe data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year. \r\n\r\nValidation at stage 3 ( https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Burned Area product. Further details regarding MODIS land product validation for the MCD64A1 data product is available from the MODIS Land Team Validation site ( https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD64).\r\n \r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MCDAODHD_6.1NRT.json b/datasets/MCDAODHD_6.1NRT.json index 5b43a1283b..1f795c875b 100644 --- a/datasets/MCDAODHD_6.1NRT.json +++ b/datasets/MCDAODHD_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDAODHD_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS was launched aboard the Terra satellite on December 18, 1999 (10:30 am equator crossing time) as part of NASA's Earth Observing System (EOS) mission. MODIS with its 2330 km viewing swath width provides almost daily global coverage. It acquires data in 36 high spectral resolution bands between 0.415 to 14.235 micron with spatial resolutions of 250m(2 bands), 500m(5 bands),and 1000m (29 bands). MODIS sensor counts, calibrated radiances, geolocation products and all derived geophysical atmospheric and ocean products are archived at various DAACs and has been made available to public since April 2000.\n\nThe shortname for this level-3 MODIS aerosol product is MCDAODHD. The Naval Research Laboratory and the University of North Dakota developed this value-added aerosol optical depth dataset based on MODIS Level 2 aerosol products. MCDAODHD is a gridded product and is specifically designed for quantitative applications including data assimilation and model validation. It is available through LANCE-MODIS. It offers several enhancements over the MODIS Level 2 data on which it is based. These enhancements include stringent filtering to reduce outliers, eliminate cloud contamination, and exclude conditions where aerosol detection is likely to be inaccurate; reduction of systematic biases over land and ocean by empirical corrections; reduction of random variation in AOD values by spatial averaging; quantitative estimation of uncertainty for each AOD data point.\n\nThe MxDAODHD granules are produced every six hours, and time-stamped 00:00, 06:00, 12:00, and 18:00 (all times UTC). Each granule includes MODIS observations from +/-3 hours from the timestamp (e.g. 12:00 product includes MODIS data from 09:00-15:00 UTC). Production is initiated as soon as the Level 2 inputs become available in the LANCE system.\n\nSee the LANCE-MODIS page for more dataset information: \n\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/modis-nrt", "links": [ { diff --git a/datasets/MCDONALD_QUICKBIRD_GIS_1.json b/datasets/MCDONALD_QUICKBIRD_GIS_1.json index 523d83451f..32d35e7c6e 100644 --- a/datasets/MCDONALD_QUICKBIRD_GIS_1.json +++ b/datasets/MCDONALD_QUICKBIRD_GIS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDONALD_QUICKBIRD_GIS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastline, ridgelines and areas of bare rock of McDonald Islands were digitised from Quickbird satellite imagery acquired 9 April 2003.", "links": [ { diff --git a/datasets/MCDWD_L3_F1C_NRT_6.1.json b/datasets/MCDWD_L3_F1C_NRT_6.1.json index 84ed8c5305..15b22da7d1 100644 --- a/datasets/MCDWD_L3_F1C_NRT_6.1.json +++ b/datasets/MCDWD_L3_F1C_NRT_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDWD_L3_F1C_NRT_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 1-day CS GeoTIFF Product (MCDWD_L3_F1C_NRT) (beta) provides daily maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F1C_NRT is 1-day CS product that has cloud shadow masks applied to the water detections, to help remove cloud-shadow false positives. \r\nThe beta version of the product will be updated. For more information, visit product page at:\r\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt", "links": [ { diff --git a/datasets/MCDWD_L3_F1_NRT_6.1.json b/datasets/MCDWD_L3_F1_NRT_6.1.json index ee6fcbc98e..06d1327744 100644 --- a/datasets/MCDWD_L3_F1_NRT_6.1.json +++ b/datasets/MCDWD_L3_F1_NRT_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDWD_L3_F1_NRT_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 1-day GeoTIFF (MCDWD_L3_F1_NRT) (beta) provides daily maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F1_NRT is a 1-day product. \r\nThe beta version of the product will be updated. For more information, visit product page at:\r\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt", "links": [ { diff --git a/datasets/MCDWD_L3_F2_NRT_6.1.json b/datasets/MCDWD_L3_F2_NRT_6.1.json index 7b538e0030..e662b24782 100644 --- a/datasets/MCDWD_L3_F2_NRT_6.1.json +++ b/datasets/MCDWD_L3_F2_NRT_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDWD_L3_F2_NRT_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 2-day GeoTIFF Product (MCDWD_L3_F2_NRT) (beta) provides maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F2_NRT is 2-day product which is generated from current and previous day\u2019s data.\r\nThe beta version of the product will be updated. For more information, visit product page at:\r\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt", "links": [ { diff --git a/datasets/MCDWD_L3_F3_NRT_6.1.json b/datasets/MCDWD_L3_F3_NRT_6.1.json index 5f4a8bc4e4..ff4c3d88e3 100644 --- a/datasets/MCDWD_L3_F3_NRT_6.1.json +++ b/datasets/MCDWD_L3_F3_NRT_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDWD_L3_F3_NRT_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 2-day GeoTIFF Product (MCDWD_L3_F2_NRT) (beta) provides maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F3_NRT is 3-day product which is generated from current and previous two day\u2019s data.\r\nThe beta version of the product will be updated. For more information, visit product page at:\r\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt", "links": [ { diff --git a/datasets/MCDWD_L3_NRT_6.1.json b/datasets/MCDWD_L3_NRT_6.1.json index 2628750bec..e7ae7e02a4 100644 --- a/datasets/MCDWD_L3_NRT_6.1.json +++ b/datasets/MCDWD_L3_NRT_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCDWD_L3_NRT_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m Global Flood Product (MCDWD_L3_NRT) (beta) provides daily maps of flooding globally. The product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The beta version of the product will be updated.\r\nFor more information, visit product page at:\r\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt", "links": [ { diff --git a/datasets/MCR_LTER_0.json b/datasets/MCR_LTER_0.json index 4e1a98827c..07ff003d17 100644 --- a/datasets/MCR_LTER_0.json +++ b/datasets/MCR_LTER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MCR_LTER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality measurements taken near the island of Moorea, French Polynesia, as part of the Moorea Coral Reef Long-Term Ecological Research site (MCR LTER).", "links": [ { diff --git a/datasets/MELVILLE_0.json b/datasets/MELVILLE_0.json index a0ccfdee86..42b2992acd 100644 --- a/datasets/MELVILLE_0.json +++ b/datasets/MELVILLE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MELVILLE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the eastern Pacific Ocean off the coast of Baja California in 1999.", "links": [ { diff --git a/datasets/MER.RR__1P_5.0.json b/datasets/MER.RR__1P_5.0.json index dc6a9a89d7..30deb1db5e 100644 --- a/datasets/MER.RR__1P_5.0.json +++ b/datasets/MER.RR__1P_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MER.RR__1P_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MERIS Level 1 Reduced Resolution (RR) product contains the Top of Atmosphere (TOA) upwelling spectral radiance measures at reduced resolution. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS RR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 measurements data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values "Band wavelength" and "Bandwidth" provided in the Manifest file of the Level 1 products are the averaged band-pass of each pixel over the instrument field of view. The Auxiliary data used are listed in the Manifest file associated to each product. MERIS was operating continuously on the day side of the Envisat orbit (descending track). RR data was acquired over 43.5 minutes in each orbit, i.e. 80% of the descending track.", "links": [ { diff --git a/datasets/MER.RR__2P_8.0.json b/datasets/MER.RR__2P_8.0.json index c601f886d5..5e61808c81 100644 --- a/datasets/MER.RR__2P_8.0.json +++ b/datasets/MER.RR__2P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MER.RR__2P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS RR Level 2 is a Reduced Resolution (RR) Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS RR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file, which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing mainly: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters. The Auxiliary data used are listed in the Manifest file associated to each product. MERIS was operating continuously on the day side of the Envisat orbit (descending track). RR data was acquired over 43.5 minutes in each orbit, i.e. 80% of the descending track.", "links": [ { diff --git a/datasets/MERCHANT_SHIP_0.json b/datasets/MERCHANT_SHIP_0.json index f2cd105b47..a5bc0e5ce3 100644 --- a/datasets/MERCHANT_SHIP_0.json +++ b/datasets/MERCHANT_SHIP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERCHANT_SHIP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from merchant ships taken in Micronesia and the Southern Ocean between 1997 and 2000.", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3b_CYANTC_5.0.json b/datasets/MERGED_S3_OLCI_L3b_CYANTC_5.0.json index c4943cd4dc..97d5fc876a 100644 --- a/datasets/MERGED_S3_OLCI_L3b_CYANTC_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3b_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3b_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. The sensor spatial resolution is 300m. The CONUS images use a 50m land mask, while the Alaska product uses a less refined 500m land mask. The temporal resolution depends on the sensor and date with best coverage since 2018, as images utilize sensors on two Sentinel-3 satellites. ", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3b_CYANTC_NRT_5.0.json b/datasets/MERGED_S3_OLCI_L3b_CYANTC_NRT_5.0.json index 3db271e289..b52a5ce0a3 100644 --- a/datasets/MERGED_S3_OLCI_L3b_CYANTC_NRT_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3b_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3b_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3b_CYAN_5.0.json b/datasets/MERGED_S3_OLCI_L3b_CYAN_5.0.json index fc123cf0f4..ddd00deeda 100644 --- a/datasets/MERGED_S3_OLCI_L3b_CYAN_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3b_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3b_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. The sensor spatial resolution is 300m. The CONUS images use a 50m land mask, while the Alaska product uses a less refined 500m land mask. The temporal resolution depends on the sensor and date with best coverage since 2018, as images utilize sensors on two Sentinel-3 satellites. ", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3b_CYAN_NRT_5.0.json b/datasets/MERGED_S3_OLCI_L3b_CYAN_NRT_5.0.json index 3b76ac2143..c5d885bab1 100644 --- a/datasets/MERGED_S3_OLCI_L3b_CYAN_NRT_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3b_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3b_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3b_ILW_4.json b/datasets/MERGED_S3_OLCI_L3b_ILW_4.json index d27f3c6d16..ae258a8936 100644 --- a/datasets/MERGED_S3_OLCI_L3b_ILW_4.json +++ b/datasets/MERGED_S3_OLCI_L3b_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3b_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment.The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW is a times series containing 10 years of MERIS (2002-2012) and OLCI from both Sentinel-3a (2016-present) and Sentinel-3b (2018-present). ", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3m_CYANTC_5.0.json b/datasets/MERGED_S3_OLCI_L3m_CYANTC_5.0.json index 96426be45f..aeae44ee43 100644 --- a/datasets/MERGED_S3_OLCI_L3m_CYANTC_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3m_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3m_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. The sensor spatial resolution is 300m. The CONUS images use a 50m land mask, while the Alaska product uses a less refined 500m land mask. The temporal resolution depends on the sensor and date with best coverage since 2018, as images utilize sensors on two Sentinel-3 satellites. ", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3m_CYANTC_NRT_5.0.json b/datasets/MERGED_S3_OLCI_L3m_CYANTC_NRT_5.0.json index 3d86490e79..8de74d1e3c 100644 --- a/datasets/MERGED_S3_OLCI_L3m_CYANTC_NRT_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3m_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3m_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3m_CYAN_5.0.json b/datasets/MERGED_S3_OLCI_L3m_CYAN_5.0.json index 04350e361a..b08b513bfd 100644 --- a/datasets/MERGED_S3_OLCI_L3m_CYAN_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3m_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3m_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. The sensor spatial resolution is 300m. The CONUS images use a 50m land mask, while the Alaska product uses a less refined 500m land mask. The temporal resolution depends on the sensor and date with best coverage since 2018, as images utilize sensors on two Sentinel-3 satellites. ", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3m_CYAN_NRT_5.0.json b/datasets/MERGED_S3_OLCI_L3m_CYAN_NRT_5.0.json index 8beb40ef60..fd511de8b1 100644 --- a/datasets/MERGED_S3_OLCI_L3m_CYAN_NRT_5.0.json +++ b/datasets/MERGED_S3_OLCI_L3m_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3m_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MERGED_S3_OLCI_L3m_ILW_4.json b/datasets/MERGED_S3_OLCI_L3m_ILW_4.json index be85949a92..d7d7389453 100644 --- a/datasets/MERGED_S3_OLCI_L3m_ILW_4.json +++ b/datasets/MERGED_S3_OLCI_L3m_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_S3_OLCI_L3m_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment.The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW is a times series containing 10 years of MERIS (2002-2012) and OLCI from both Sentinel-3a (2016-present) and Sentinel-3b (2018-present). ", "links": [ { diff --git a/datasets/MERGED_TP_J1_OSTM_OST_ALL_V52_5.2.json b/datasets/MERGED_TP_J1_OSTM_OST_ALL_V52_5.2.json index a5976c122b..76cf01e739 100644 --- a/datasets/MERGED_TP_J1_OSTM_OST_ALL_V52_5.2.json +++ b/datasets/MERGED_TP_J1_OSTM_OST_ALL_V52_5.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_TP_J1_OSTM_OST_ALL_V52_5.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA\u2019s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).
\r\nGeophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User\u2019s Handbook (https://doi.org/10.5067/ALTUG-TJ152).
\r\nPlease note that this collection is the same data as https://doi.org/10.5067/ALTCY-TJA52 but with all cycles included in one netCDF file.", "links": [ { diff --git a/datasets/MERGED_TP_J1_OSTM_OST_CYCLES_V52_5.2.json b/datasets/MERGED_TP_J1_OSTM_OST_CYCLES_V52_5.2.json index 6a42fcfd44..45834e518c 100644 --- a/datasets/MERGED_TP_J1_OSTM_OST_CYCLES_V52_5.2.json +++ b/datasets/MERGED_TP_J1_OSTM_OST_CYCLES_V52_5.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_TP_J1_OSTM_OST_CYCLES_V52_5.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) for 10-day cycles from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA\u2019s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).
\r\nGeophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User\u2019s Handbook (https://doi.org/10.5067/ALTUG-TJ152).
\r\nPlease note that this collection contains the same data as https://doi.org/10.5067/ALTTS-TJA52, re-organized into one netCDF file per cycle for convenience.", "links": [ { diff --git a/datasets/MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2.json b/datasets/MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2.json index 3c75c82b4a..f10ea39ff5 100644 --- a/datasets/MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2.json +++ b/datasets/MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.2. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 5.1 and version 5.2 releases are described in detail in the user handbook.", "links": [ { diff --git a/datasets/MERIS_L1_FRS_4.json b/datasets/MERIS_L1_FRS_4.json index f4e45e3349..dd3c587f0e 100644 --- a/datasets/MERIS_L1_FRS_4.json +++ b/datasets/MERIS_L1_FRS_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L1_FRS_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration.", "links": [ { diff --git a/datasets/MERIS_L1_RR_4.json b/datasets/MERIS_L1_RR_4.json index de5091d292..0d5b2d4ca9 100644 --- a/datasets/MERIS_L1_RR_4.json +++ b/datasets/MERIS_L1_RR_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L1_RR_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration.", "links": [ { diff --git a/datasets/MERIS_L2_FRS_IOP_2022.0.json b/datasets/MERIS_L2_FRS_IOP_2022.0.json index 994fd2ea2e..40cce03af8 100644 --- a/datasets/MERIS_L2_FRS_IOP_2022.0.json +++ b/datasets/MERIS_L2_FRS_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L2_FRS_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L2_FRS_OC_2022.0.json b/datasets/MERIS_L2_FRS_OC_2022.0.json index 971e46efa8..cd615e9c89 100644 --- a/datasets/MERIS_L2_FRS_OC_2022.0.json +++ b/datasets/MERIS_L2_FRS_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L2_FRS_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L2_ILW_4.json b/datasets/MERIS_L2_ILW_4.json index 49f0be03cf..cbaa767c19 100644 --- a/datasets/MERIS_L2_ILW_4.json +++ b/datasets/MERIS_L2_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L2_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site. ", "links": [ { diff --git a/datasets/MERIS_L2_RR_IOP_2022.0.json b/datasets/MERIS_L2_RR_IOP_2022.0.json index 3d1038229c..091a11032a 100644 --- a/datasets/MERIS_L2_RR_IOP_2022.0.json +++ b/datasets/MERIS_L2_RR_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L2_RR_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L2_RR_OC_2022.0.json b/datasets/MERIS_L2_RR_OC_2022.0.json index 22e5e4c5ca..4f8bafab3a 100644 --- a/datasets/MERIS_L2_RR_OC_2022.0.json +++ b/datasets/MERIS_L2_RR_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L2_RR_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_CHL_2022.0.json b/datasets/MERIS_L3b_CHL_2022.0.json index 5bd94814a4..0af742f74a 100644 --- a/datasets/MERIS_L3b_CHL_2022.0.json +++ b/datasets/MERIS_L3b_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_CYANTC_5.0.json b/datasets/MERIS_L3b_CYANTC_5.0.json index 72c1ec4f95..07d2a45853 100644 --- a/datasets/MERIS_L3b_CYANTC_5.0.json +++ b/datasets/MERIS_L3b_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/MERIS_L3b_CYAN_5.0.json b/datasets/MERIS_L3b_CYAN_5.0.json index c9a5910aec..3a245dc110 100644 --- a/datasets/MERIS_L3b_CYAN_5.0.json +++ b/datasets/MERIS_L3b_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/MERIS_L3b_GSM_2022.0.json b/datasets/MERIS_L3b_GSM_2022.0.json index 18d8d00dca..53d5910ed9 100644 --- a/datasets/MERIS_L3b_GSM_2022.0.json +++ b/datasets/MERIS_L3b_GSM_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_GSM_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_ILW_4.json b/datasets/MERIS_L3b_ILW_4.json index f09d6e8308..6f6773d5ad 100644 --- a/datasets/MERIS_L3b_ILW_4.json +++ b/datasets/MERIS_L3b_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/MERIS_L3b_IOP_2022.0.json b/datasets/MERIS_L3b_IOP_2022.0.json index 99a7cf9d67..de287d3b8c 100644 --- a/datasets/MERIS_L3b_IOP_2022.0.json +++ b/datasets/MERIS_L3b_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_KD_2022.0.json b/datasets/MERIS_L3b_KD_2022.0.json index 915fe89d97..6db51c8c09 100644 --- a/datasets/MERIS_L3b_KD_2022.0.json +++ b/datasets/MERIS_L3b_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_PAR_2022.0.json b/datasets/MERIS_L3b_PAR_2022.0.json index c736f23ce6..2b6f892ba6 100644 --- a/datasets/MERIS_L3b_PAR_2022.0.json +++ b/datasets/MERIS_L3b_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_PIC_2022.0.json b/datasets/MERIS_L3b_PIC_2022.0.json index ee4c3bb1cb..19c782e136 100644 --- a/datasets/MERIS_L3b_PIC_2022.0.json +++ b/datasets/MERIS_L3b_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_POC_2022.0.json b/datasets/MERIS_L3b_POC_2022.0.json index 4c34d3f20b..1a4c143cac 100644 --- a/datasets/MERIS_L3b_POC_2022.0.json +++ b/datasets/MERIS_L3b_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3b_RRS_2022.0.json b/datasets/MERIS_L3b_RRS_2022.0.json index 574c13d72e..4032b6ef53 100644 --- a/datasets/MERIS_L3b_RRS_2022.0.json +++ b/datasets/MERIS_L3b_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3b_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_CHL_2022.0.json b/datasets/MERIS_L3m_CHL_2022.0.json index d687564140..7d6e0c5b32 100644 --- a/datasets/MERIS_L3m_CHL_2022.0.json +++ b/datasets/MERIS_L3m_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_CYANTC_5.0.json b/datasets/MERIS_L3m_CYANTC_5.0.json index c753fca3b5..ff92c4f145 100644 --- a/datasets/MERIS_L3m_CYANTC_5.0.json +++ b/datasets/MERIS_L3m_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/MERIS_L3m_CYAN_5.0.json b/datasets/MERIS_L3m_CYAN_5.0.json index 4b6861a065..ffce1cb9ba 100644 --- a/datasets/MERIS_L3m_CYAN_5.0.json +++ b/datasets/MERIS_L3m_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/MERIS_L3m_GSM_2022.0.json b/datasets/MERIS_L3m_GSM_2022.0.json index 36ab29b760..d9b37f6ba2 100644 --- a/datasets/MERIS_L3m_GSM_2022.0.json +++ b/datasets/MERIS_L3m_GSM_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_GSM_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_ILW_4.json b/datasets/MERIS_L3m_ILW_4.json index f19f93fabb..0c1ab54205 100644 --- a/datasets/MERIS_L3m_ILW_4.json +++ b/datasets/MERIS_L3m_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/MERIS_L3m_IOP_2022.0.json b/datasets/MERIS_L3m_IOP_2022.0.json index 4ad970a427..e22d25bd15 100644 --- a/datasets/MERIS_L3m_IOP_2022.0.json +++ b/datasets/MERIS_L3m_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_KD_2022.0.json b/datasets/MERIS_L3m_KD_2022.0.json index e22f0920d2..5e27fa4ccb 100644 --- a/datasets/MERIS_L3m_KD_2022.0.json +++ b/datasets/MERIS_L3m_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_PAR_2022.0.json b/datasets/MERIS_L3m_PAR_2022.0.json index bace888abf..fa16f3e273 100644 --- a/datasets/MERIS_L3m_PAR_2022.0.json +++ b/datasets/MERIS_L3m_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_PIC_2022.0.json b/datasets/MERIS_L3m_PIC_2022.0.json index cb29ceff00..3ae50e1235 100644 --- a/datasets/MERIS_L3m_PIC_2022.0.json +++ b/datasets/MERIS_L3m_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_POC_2022.0.json b/datasets/MERIS_L3m_POC_2022.0.json index 4d5c857772..9c075a2af0 100644 --- a/datasets/MERIS_L3m_POC_2022.0.json +++ b/datasets/MERIS_L3m_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERIS_L3m_RRS_2022.0.json b/datasets/MERIS_L3m_RRS_2022.0.json index f9edeed1b1..fb101d2da2 100644 --- a/datasets/MERIS_L3m_RRS_2022.0.json +++ b/datasets/MERIS_L3m_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERIS_L3m_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. ", "links": [ { diff --git a/datasets/MERRA2_CNN_HAQAST_PM25_1.json b/datasets/MERRA2_CNN_HAQAST_PM25_1.json index 0218085651..c1ffcdb13a 100644 --- a/datasets/MERRA2_CNN_HAQAST_PM25_1.json +++ b/datasets/MERRA2_CNN_HAQAST_PM25_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MERRA2_CNN_HAQAST_PM25_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method and is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST).\n\nThe dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in \u00b5g/m\u00b3. QFLAG denotes the quality of data at each grid point, where 4 indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis.\n\n", "links": [ { diff --git a/datasets/MER_FRS_1P_8.0.json b/datasets/MER_FRS_1P_8.0.json index ba62aecaf4..9f2f6692f9 100644 --- a/datasets/MER_FRS_1P_8.0.json +++ b/datasets/MER_FRS_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MER_FRS_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MERIS Level 1 Full Resolution (FR) product contains the Top of Atmosphere (TOA) upwelling spectral radiance measures. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS FR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The 15 sun spectral flux values provided in the instrument data file of the Level 1 products are the in-band reference irradiances adjusted for the Earth-sun distance at the time of measurement. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values "Band wavelength" and "Bandwidth" provided in the Manifest file of the Level 1b products are the averaged band-pass of each pixel over the instrument field of view. Auxiliary data are also listed in the Manifest file associated to each product. The Level 1 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0 (for a maximum of 20 minutes). Thus the estimated size of the Level 1 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage.", "links": [ { diff --git a/datasets/MER_FRS_2P_8.0.json b/datasets/MER_FRS_2P_8.0.json index 34c7f14a2a..441c6423c7 100644 --- a/datasets/MER_FRS_2P_8.0.json +++ b/datasets/MER_FRS_2P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MER_FRS_2P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MERIS FR Level 2 is a Full-Resolution Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS FR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters and annotation. The Auxiliary data used are listed in the Manifest file associated to each product. The Level 2 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0/Level 1. Thus the estimated size of the Level 2 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage.", "links": [ { diff --git a/datasets/MESSR_MOS-1_L2_Data_NA.json b/datasets/MESSR_MOS-1_L2_Data_NA.json index 06afded629..e5affcaaf7 100644 --- a/datasets/MESSR_MOS-1_L2_Data_NA.json +++ b/datasets/MESSR_MOS-1_L2_Data_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MESSR_MOS-1_L2_Data_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MESSR/MOS-1 L2 Data is obtained from the MESSR sensor onboard MOS-1, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1, Japan's first marine observation satellite, is Sun-synchronous sub-recurrent Orbit satellite launched on February 19, 1987 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projection is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m.", "links": [ { diff --git a/datasets/MESSR_MOS-1b_L2_Data_NA.json b/datasets/MESSR_MOS-1b_L2_Data_NA.json index 726002c918..20ae36df75 100644 --- a/datasets/MESSR_MOS-1b_L2_Data_NA.json +++ b/datasets/MESSR_MOS-1b_L2_Data_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MESSR_MOS-1b_L2_Data_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MESSR/MOS-1b L2 Data is obtained from the MESSR sensor onboard MOS-1b, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1b which has the same functions as MOS-1 is Sun-synchronous sub-recurrent Orbit satellite launched on February 7, 1990 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projction is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m.", "links": [ { diff --git a/datasets/MFLL_CO2_Weighting_Functions_1891_1.json b/datasets/MFLL_CO2_Weighting_Functions_1891_1.json index eef336be9a..2432c4c8a6 100644 --- a/datasets/MFLL_CO2_Weighting_Functions_1891_1.json +++ b/datasets/MFLL_CO2_Weighting_Functions_1891_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MFLL_CO2_Weighting_Functions_1891_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction.", "links": [ { diff --git a/datasets/MFLL_XCO2_Range_10Hz_1892_1.json b/datasets/MFLL_XCO2_Range_10Hz_1892_1.json index c3efe926fa..03ce8bf0d0 100644 --- a/datasets/MFLL_XCO2_Range_10Hz_1892_1.json +++ b/datasets/MFLL_XCO2_Range_10Hz_1892_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MFLL_XCO2_Range_10Hz_1892_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude.", "links": [ { diff --git a/datasets/MI03_resp_nutrients_GC1_1.json b/datasets/MI03_resp_nutrients_GC1_1.json index 3493a6f51c..4f21f85fa4 100644 --- a/datasets/MI03_resp_nutrients_GC1_1.json +++ b/datasets/MI03_resp_nutrients_GC1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI03_resp_nutrients_GC1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field samples were collected from the Main Power House at Macquarie Island - coordinates.... The soil sample used for the respirometer trial was made up as a composite of 8 cores, namely: MPH1, MPH3, MPH4, MPH5, MPH7, MPH8 and MPH9. Each core was analysed for petroleum hydrocarbons (PHCs) at 0.05 m intervals. Intervals containing between 2500 and 5000 mg/kg PHC were then combined into a bulked sample used in the respirometer test. The sample was homogenised by placing all the soil (4.5 kg) into a large mixing bowl and stirring with a flat stirrer.\n\nThe respirometer experiment was conducted by Jim Walworth and Andrew Pond at the University of Arizona. The objective was to optimise the nutrient status for microbial degradation of PHC's.\n\nThe respirometer used was an N-Con closed system, with 24 flasks. There were 5 treatments and a control, each run in quadriplate. The control was unammended while treatments were 125, 250, 375, 500, and 625 mg nitrogen/kg of soil (on a dry soil weight basis).\n\nSee:\n\nSheet 'Sample details' for sample barcode, user ID and sample mass summary.\nSheet 'GC-FID Data', cells A1-A18 = sample ID, GC injection file and processing notes\nSheet 'GC-FID Data', Rows 10 and 11 contain TPH estimates and estimated standard uncertainty for the TPH value\nSheet 'GC-FID Data', cells A21-A125 = compounds or GC elution windows measured\nSheet 'GC-FID Data', cells B21-B56 = compound [CAS numbers]\nSheet 'GC-FID Data', cells C21-AL125 = GC-FID area responses\nSheet 'GC-FID Data', cells C128-AL232 = Estimated standard uncertainties for all GC-FID area responses (from blank drifts,local signal/noise etc)\n\nChemical analysis details........Sample Extraction\nA 0.5mL volume of internal standard solution containing a mixture of compounds (cyclo-octane at c.1000mg/L, d8-naphthalene at 100mg/L, p-terphenyl at 100 mg/L and 1-bromoeicosane at 1000mg/L) dissolved in hexane, was pipetted onto the soil with a calibrated positive displacement pipette. This was followed by the addition of 10mL of hexane and 10mL of water. The vials were then tumbled end over end (50rpm) overnight and centrifuged at 1500 rpm. 1.8mL of the clear hexane layer was transferred by Pasteur pipette into a 2mL vial for Gas Chromatography Flame Ionisation Detector (GC-FID) analysis\n\nChemical analysis details........GC-FID parameters\n\nThe download file also includes a paper produced from this data.\n\nThis work was completed as part of ASAC project 1163 (ASAC_1163).", "links": [ { diff --git a/datasets/MI08_soil_properties_1.json b/datasets/MI08_soil_properties_1.json index b5f8049041..90fabf98a6 100644 --- a/datasets/MI08_soil_properties_1.json +++ b/datasets/MI08_soil_properties_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI08_soil_properties_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected on Macquarie Island from three sites: the main powerhouse, the fuel farm and a reference site on the isthmus by the Bioremediation Project team in January 2008. Soil characteristics including conductivity, pH, total petroleum hydrocarbons, total carbon, nitrate, nitrite, ammonium, fluoride, bromide, chloride, sulphate and phosphate were measured.\n\nThe data consists of two files, the rtf file contains the methods used and the csv file contains the soil characteristics. Samples are identified by a barcode which is the barcode number assigned by the Bioremediation Project Sample Tracking Database.\n\nThis work was carried out as part of AAS project 1163.", "links": [ { diff --git a/datasets/MI1AC_2.json b/datasets/MI1AC_2.json index 38269eb7e5..ec8fe5f5c0 100644 --- a/datasets/MI1AC_2.json +++ b/datasets/MI1AC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1AC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI1AC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A Calibration data in DN. The data numbers have been commuted from 12-bit to 16-bit, byte-aligned half-word version 2. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n", "links": [ { diff --git a/datasets/MI1AOBC_2.json b/datasets/MI1AOBC_2.json index 7e0ba767b3..a810d11f4a 100644 --- a/datasets/MI1AOBC_2.json +++ b/datasets/MI1AOBC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1AOBC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI1AOBC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) OBC Data version 2. This file contains the output for the Level 1A On-board Calibrator Data and it provides the radiometry from PIN and HQE diodes and goniometer mechanism readings collected during calibration mode operations near the north and south poles and over the dark side of the Earth (or during science mode operations over the sunlit side of the Earth). The diode radiometry acquired during north and south pole calibration sequences will be used to determine brightness and reflective characteristics of a MISR diffuser panel as observed by each of the nine MISR cameras. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MI1B1_002.json b/datasets/MI1B1_002.json index f64e72488e..d8ad35aeef 100644 --- a/datasets/MI1B1_002.json +++ b/datasets/MI1B1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI1B1_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling and spectral radiances for all MISR channels. Each value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances, using coefficients derived with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels, deployed monthly and reflect sunlight into cameras. The OBC detector standards then measure this reflected light to provide the calibration. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. Data collection for this product is ongoing.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI1B2E_003.json b/datasets/MI1B2E_003.json index 04ec1f1f7f..e84fbf143d 100644 --- a/datasets/MI1B2E_003.json +++ b/datasets/MI1B2E_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2E_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Data V003 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/MI1B2E_004.json b/datasets/MI1B2E_004.json index 2d950cf7b0..3b8c426ceb 100644 --- a/datasets/MI1B2E_004.json +++ b/datasets/MI1B2E_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2E_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI1B2T_003.json b/datasets/MI1B2T_003.json index 143007a480..079aa5f121 100644 --- a/datasets/MI1B2T_003.json +++ b/datasets/MI1B2T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Data V003 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/MI1B2T_004.json b/datasets/MI1B2T_004.json index 53bfc7ba36..a81db7ffe6 100644 --- a/datasets/MI1B2T_004.json +++ b/datasets/MI1B2T_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2T_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI1B2T_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data Version 4 product. It contains Terrain-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI1B2_ELLIPSOID_NRT_001.json b/datasets/MI1B2_ELLIPSOID_NRT_001.json index 7190371e0f..31177d30d0 100644 --- a/datasets/MI1B2_ELLIPSOID_NRT_001.json +++ b/datasets/MI1B2_ELLIPSOID_NRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2_ELLIPSOID_NRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Ellipsoid-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files.", "links": [ { diff --git a/datasets/MI1B2_TERRAIN_NRT_001.json b/datasets/MI1B2_TERRAIN_NRT_001.json index af302b3cf3..6dd8318dba 100644 --- a/datasets/MI1B2_TERRAIN_NRT_001.json +++ b/datasets/MI1B2_TERRAIN_NRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI1B2_TERRAIN_NRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Terrain-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files.", "links": [ { diff --git a/datasets/MI2010_11_Alien-plant-survey_JDS_1.json b/datasets/MI2010_11_Alien-plant-survey_JDS_1.json index a1265aa876..5c17857a8c 100644 --- a/datasets/MI2010_11_Alien-plant-survey_JDS_1.json +++ b/datasets/MI2010_11_Alien-plant-survey_JDS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI2010_11_Alien-plant-survey_JDS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are location and abundance data of alien plants found during a systematic survey of Macquarie Island. It relates to three species Poa annua, Cerastium fontanum and Stellaria media. It is essentially a repeat of the Copson 1977 survey.\n\nThis work has been completed as part of ASAC (AAS) project 2904, \"Aliens in Antarctica\" (ASAC_2904).", "links": [ { diff --git a/datasets/MI2AS_AEROSOL_NRT_001.json b/datasets/MI2AS_AEROSOL_NRT_001.json index 330fefaa73..d0461df7d5 100644 --- a/datasets/MI2AS_AEROSOL_NRT_001.json +++ b/datasets/MI2AS_AEROSOL_NRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI2AS_AEROSOL_NRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 Aerosol Product. It contains Aerosol optical depth and particle type, with associated atmospheric data. It is used for MISR Near Real Time processing, and is derived from session-based Level 1 input files.", "links": [ { diff --git a/datasets/MI2TC_CMV_BFR_NRT_001.json b/datasets/MI2TC_CMV_BFR_NRT_001.json index f876993bf2..a60e28a257 100644 --- a/datasets/MI2TC_CMV_BFR_NRT_001.json +++ b/datasets/MI2TC_CMV_BFR_NRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI2TC_CMV_BFR_NRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MISR Level 2 Cloud Motion Vector Product containing height-resolved cloud motion vectors with associated data in BUFR format. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files.", "links": [ { diff --git a/datasets/MI2TC_CMV_HDF_NRT_001.json b/datasets/MI2TC_CMV_HDF_NRT_001.json index a9be3022d0..4e2b861850 100644 --- a/datasets/MI2TC_CMV_HDF_NRT_001.json +++ b/datasets/MI2TC_CMV_HDF_NRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI2TC_CMV_HDF_NRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the MISR Level 2 Cloud Motion Vector Product containing height-resolved cloud motion vectors with associated data. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files.", "links": [ { diff --git a/datasets/MI3DAEF_002.json b/datasets/MI3DAEF_002.json index e62fcf8956..2d089f8270 100644 --- a/datasets/MI3DAEF_002.json +++ b/datasets/MI3DAEF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DAEF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Aerosol Product covering a day", "links": [ { diff --git a/datasets/MI3DAENF_002.json b/datasets/MI3DAENF_002.json index 286892cb39..43e5645347 100644 --- a/datasets/MI3DAENF_002.json +++ b/datasets/MI3DAENF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DAENF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "It contains a statistical summary of column aerosol \n 555 nanometer optical depth, and a monthly aerosol \n compositional type frequency histogram. Data collection \n for this product is ongoing. This data product is a \n global summary of the Level 2 aerosol parameters of \n interest averaged over a day and reported on a \n geographic grid, with a resolution of 0.5 degree by 0.5 \n degree. \n \n FIRSTLOOK processing uses the new time dependence of \n the Atmospheric and Surface Climatology (TASC) from the \n same month/previous year. The TASC data set now \n contains snow ice and ocean surface wind speed values \n that are updated on a monthly basis. Therefore, these \n data sets cannot be generated until the end of the \n month. Products generated are distinguished by the \n presence of FIRST LOOK in the file names. The MISR \n instrument consists of nine pushbroom cameras which \n measure radiance in four spectral bands. Global \n coverage is achieved in nine days. The cameras are \n arranged with one camera pointing toward the nadir, \n four cameras pointing forward, and four cameras \n pointing aftward. It takes seven minutes for all nine \n cameras to view the same surface location. The view \n angles relative to the surface reference ellipsoid are \n 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral \n band shapes are nominally Gaussian, centered at 443, \n 555, 670, and 865 nm. \n \n MISR itself is an instrument designed to view Earth \n with cameras pointed in 9 different directions. As the \n \"instrument flies overhead, each piece of Earth s.\"\n the surface below is successively imaged by all nine cameras \n in each of 4 wavelengths (blue, green, red, and \n near-infrared). The goal of MISR is to improve our \n understanding of the effects of sunlight on Earth and to distinguish different types of clouds, \n particles, and surfaces. Specifically, MISR monitors the \n monthly, seasonal, and long-term trends in three areas: \n 1) amount and type of atmospheric particles (aerosols), \n including those formed by natural sources and by human \n activities; 2) amounts, types, and heights of clouds, \n and 3) distribution of land surface cover, including \n vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DAER_2.json b/datasets/MI3DAER_2.json index 5375a0739d..a5a8ca76f3 100644 --- a/datasets/MI3DAER_2.json +++ b/datasets/MI3DAER_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DAER_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DAER_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a day version 2. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MI3DALF_002.json b/datasets/MI3DALF_002.json index 41751a8e46..4daa595d94 100644 --- a/datasets/MI3DALF_002.json +++ b/datasets/MI3DALF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DALF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DALF_002 is the Multiangle Imaging SpectroRadiometer (MISR) Level 3 FIRST LOOK Component Global Albedo product covering a day version 2. It is intended to be used starting with MISR Release version 4.2, a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid. It has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. \nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snowice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. The presence of FIRST LOOK in the file names distinguishes the products generated. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the \"instrument flies overhead, each piece of Earth's \"surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DALNF_002.json b/datasets/MI3DALNF_002.json index eccdfecb1d..b04bd1b107 100644 --- a/datasets/MI3DALNF_002.json +++ b/datasets/MI3DALNF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DALNF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DALNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Albedo product in netCDF format covering a day version 2. It is a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\nMISR Level 3 FIRSTLOOK Component Global Albedo is a publicly available product in netCDF format that covers a day.", "links": [ { diff --git a/datasets/MI3DCDF_002.json b/datasets/MI3DCDF_002.json index 7c3fcecb9d..b58a40ad9f 100644 --- a/datasets/MI3DCDF_002.json +++ b/datasets/MI3DCDF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DCDF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a day.\nMI3DCDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Cloud Product covering a day version 2. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5 degrees resolution. Data collection for this product is ongoing. \\r\\n\\r\\nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DCDNF_002.json b/datasets/MI3DCDNF_002.json index 1913135184..268660bfb4 100644 --- a/datasets/MI3DCDNF_002.json +++ b/datasets/MI3DCDNF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DCDNF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DCDNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Cloud public Product in netCDF covering a day version 2. It contains the public MISR Level 3 FIRSTLOOK Global Cloud public product in netCDF format covering a day. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a day and reported on a geographic grid. It has multiple data layers, with varying temporal resolutions of 0.5 degrees by 0.5 degrees and granules of 2.5 degrees by 2.5 degrees. Data collection for this product is ongoing.\nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is an instrument designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n\nThis file contains the public MISR Level 3 FIRSTLOOK Global Cloud public Product in netCDF format covering a day.", "links": [ { diff --git a/datasets/MI3DCLDN_2.json b/datasets/MI3DCLDN_2.json index 503556b04c..205b9f14a2 100644 --- a/datasets/MI3DCLDN_2.json +++ b/datasets/MI3DCLDN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DCLDN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DCLDN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a day version 2. It contains the public MISR Level 3 Global Cloud Product in netCDF format covering a day and is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 0.5 degree by 0.5 degree as well as 2.5 degree by 2.5 degree. Data collection for this product is ongoing. \r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DLSF_002.json b/datasets/MI3DLSF_002.json index 137ec3fb31..4ebb45b699 100644 --- a/datasets/MI3DLSF_002.json +++ b/datasets/MI3DLSF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DLSF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Land Product covering a day", "links": [ { diff --git a/datasets/MI3DLSNF_002.json b/datasets/MI3DLSNF_002.json index 3ec181a076..9623fd3b29 100644 --- a/datasets/MI3DLSNF_002.json +++ b/datasets/MI3DLSNF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DLSNF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Land product in netCDF format covering a day.\nMI3DLSNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a day version 2 data product. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI).\nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DLSR_2.json b/datasets/MI3DLSR_2.json index 93a7c3420c..cd795b454f 100644 --- a/datasets/MI3DLSR_2.json +++ b/datasets/MI3DLSR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DLSR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a dayversion 2. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MI3DRDF_002.json b/datasets/MI3DRDF_002.json index 10959b5a66..43a5c90bdf 100644 --- a/datasets/MI3DRDF_002.json +++ b/datasets/MI3DRDF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DRDF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a day.\nMI3DRDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Radiance Product covering a day version 2 data product. It is a global summary of the Level 1 and Level 2 radiance parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. \nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3DRDR_2.json b/datasets/MI3DRDR_2.json index 4ed46fa306..c9c185b036 100644 --- a/datasets/MI3DRDR_2.json +++ b/datasets/MI3DRDR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3DRDR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3DRDR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Regional public Product covering a day version 2. It contains a global summary of the Level 1 and Level 2 radiance parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MI3MAEF_002.json b/datasets/MI3MAEF_002.json index 6957655c1c..11a54bcea6 100644 --- a/datasets/MI3MAEF_002.json +++ b/datasets/MI3MAEF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MAEF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Aerosol Product covering a month", "links": [ { diff --git a/datasets/MI3MAENF_002.json b/datasets/MI3MAENF_002.json index 2118800472..f0b5350e78 100644 --- a/datasets/MI3MAENF_002.json +++ b/datasets/MI3MAENF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MAENF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Aerosol product in netCDF format covering a month", "links": [ { diff --git a/datasets/MI3MAER_2.json b/datasets/MI3MAER_2.json index f86aa65bd0..69689edc70 100644 --- a/datasets/MI3MAER_2.json +++ b/datasets/MI3MAER_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MAER_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3MAER_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a month version 2. It contains a monthly statistical summary of aerosol optical depth (AOD) and single scattering albedo (SSA) model parameters. Data collection for this product was complete in August 2007.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3MALF_002.json b/datasets/MI3MALF_002.json index 721bc48307..6dd39d0ac0 100644 --- a/datasets/MI3MALF_002.json +++ b/datasets/MI3MALF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MALF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 FIRSTLOOK Component Global Albedo publicly available product covering a month to be used starting with MISR Release V4.2.", "links": [ { diff --git a/datasets/MI3MALNF_002.json b/datasets/MI3MALNF_002.json index 3ee8a8f51d..66e1ea573c 100644 --- a/datasets/MI3MALNF_002.json +++ b/datasets/MI3MALNF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MALNF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 FIRSTLOOK Component Global Albedo publicly available product in netCDF format covering a month.", "links": [ { diff --git a/datasets/MI3MCDF_002.json b/datasets/MI3MCDF_002.json index 18c7ba5041..c19278e45b 100644 --- a/datasets/MI3MCDF_002.json +++ b/datasets/MI3MCDF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MCDF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a month", "links": [ { diff --git a/datasets/MI3MCDNF_002.json b/datasets/MI3MCDNF_002.json index 30f1e8a9a7..0c11f75320 100644 --- a/datasets/MI3MCDNF_002.json +++ b/datasets/MI3MCDNF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MCDNF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Global Cloud public Product in netCDF format covering a month", "links": [ { diff --git a/datasets/MI3MCLDN_002.json b/datasets/MI3MCLDN_002.json index 008625b091..0e3635f73b 100644 --- a/datasets/MI3MCLDN_002.json +++ b/datasets/MI3MCLDN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MCLDN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a month", "links": [ { diff --git a/datasets/MI3MCMVN_002.json b/datasets/MI3MCMVN_002.json index 9374b8ca06..619241c37b 100644 --- a/datasets/MI3MCMVN_002.json +++ b/datasets/MI3MCMVN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MCMVN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Cloud Motion Vector monthly Product in netCDF format", "links": [ { diff --git a/datasets/MI3MLSF_002.json b/datasets/MI3MLSF_002.json index 9f960b9640..c36665ecde 100644 --- a/datasets/MI3MLSF_002.json +++ b/datasets/MI3MLSF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MLSF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Land Product covering a month", "links": [ { diff --git a/datasets/MI3MLSNF_2.json b/datasets/MI3MLSNF_2.json index dd4bb683e9..27d689bd1e 100644 --- a/datasets/MI3MLSNF_2.json +++ b/datasets/MI3MLSNF_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MLSNF_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3MLSNF_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI).\r\rFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3MLSR_2.json b/datasets/MI3MLSR_2.json index 051365abbf..130622ac7d 100644 --- a/datasets/MI3MLSR_2.json +++ b/datasets/MI3MLSR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MLSR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3MLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a month version 2. It contains a daily statistical summary of average directional hemispherical reflectance (DHR), DHR for photosynthetically active spectral region (DHR-PAR), fractional absorbed photosynthetically active radiation (FPAR), leaf area index (LAI), and normalized difference vegetation index (NDVI) model parameters. Data collection for this product was complete in August 2007.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MI3MRDF_002.json b/datasets/MI3MRDF_002.json index 8e42684919..9c832351e3 100644 --- a/datasets/MI3MRDF_002.json +++ b/datasets/MI3MRDF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MRDF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a month", "links": [ { diff --git a/datasets/MI3MRDR_002.json b/datasets/MI3MRDR_002.json index ed5d7f01d9..a9790cc68f 100644 --- a/datasets/MI3MRDR_002.json +++ b/datasets/MI3MRDR_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3MRDR_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Radiance Regional public Product covering a month", "links": [ { diff --git a/datasets/MI3QCLDN_002.json b/datasets/MI3QCLDN_002.json index 913f960caa..89051d6893 100644 --- a/datasets/MI3QCLDN_002.json +++ b/datasets/MI3QCLDN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3QCLDN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MI3QCMVN_002.json b/datasets/MI3QCMVN_002.json index ea6cabbaab..1b33e04892 100644 --- a/datasets/MI3QCMVN_002.json +++ b/datasets/MI3QCMVN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3QCMVN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Cloud Motion Vector quarterly Product in netCDF format", "links": [ { diff --git a/datasets/MI3YCLDN_002.json b/datasets/MI3YCLDN_002.json index 9284e8dfa9..46330476e6 100644 --- a/datasets/MI3YCLDN_002.json +++ b/datasets/MI3YCLDN_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3YCLDN_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a year", "links": [ { diff --git a/datasets/MI3YCMVN_2.json b/datasets/MI3YCMVN_2.json index e8b42ed21b..695a1bedea 100644 --- a/datasets/MI3YCMVN_2.json +++ b/datasets/MI3YCMVN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI3YCMVN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MI3YCMVN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector yearly Product in netCDF format version 2. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene. Estimates of cloud motion are a valuable proxy observation of the horizontal atmospheric wind field at the retrieved altitude of the cloud. Data collection for this product is ongoing.\r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIANACP_1.json b/datasets/MIANACP_1.json index babb51587c..06212214ed 100644 --- a/datasets/MIANACP_1.json +++ b/datasets/MIANACP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIANACP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based, 2) mixtures of pure aerosol to be compared with MISR observations, and 3) the likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium, and large; dirty and clean; spherical and nonspherical [Kahn et al., 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MIANCAGP_1.json b/datasets/MIANCAGP_1.json index 4b977ee93f..d0fe0423a3 100644 --- a/datasets/MIANCAGP_1.json +++ b/datasets/MIANCAGP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIANCAGP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms that generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MIANCARP_2.json b/datasets/MIANCARP_2.json index eeb27aa93f..9994a1933c 100644 --- a/datasets/MIANCARP_2.json +++ b/datasets/MIANCARP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIANCARP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MIANTASC_002.json b/datasets/MIANTASC_002.json index 151567d435..c761fdb352 100644 --- a/datasets/MIANTASC_002.json +++ b/datasets/MIANTASC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIANTASC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Terrestrial Atmosphere and Surface Climatology used in Level 2 Processing. It is produced by the MISR SCF and shipped to the DAAC for generating MISR Level 2 products.", "links": [ { diff --git a/datasets/MIB1LM_002.json b/datasets/MIB1LM_002.json index bc31b5e34d..7b3d81a1a5 100644 --- a/datasets/MIB1LM_002.json +++ b/datasets/MIB1LM_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIB1LM_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Local Mode Level 1B1 Product containing the DNs radiometrically scaled to radiances with no geometric resampling", "links": [ { diff --git a/datasets/MIB2GEOP_002.json b/datasets/MIB2GEOP_002.json index 25d0ae2c27..9f51553370 100644 --- a/datasets/MIB2GEOP_002.json +++ b/datasets/MIB2GEOP_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIB2GEOP_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters V002 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid", "links": [ { diff --git a/datasets/MIB2GEOP_003.json b/datasets/MIB2GEOP_003.json index c6caa38316..45f96ff040 100644 --- a/datasets/MIB2GEOP_003.json +++ b/datasets/MIB2GEOP_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIB2GEOP_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIB2GEOP_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters Version 3 product. It contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid. Data collection for this product is ongoing. The distribution format of this product is NetCDF-4 which is a migration from the previous version's format of HDF-EOS2.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MICASA_FLUX_3H_1.json b/datasets/MICASA_FLUX_3H_1.json index 84ab398a00..03ee58a6ff 100644 --- a/datasets/MICASA_FLUX_3H_1.json +++ b/datasets/MICASA_FLUX_3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MICASA_FLUX_3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately. ", "links": [ { diff --git a/datasets/MICASA_FLUX_D_1.json b/datasets/MICASA_FLUX_D_1.json index da374cb1a6..458b50962a 100644 --- a/datasets/MICASA_FLUX_D_1.json +++ b/datasets/MICASA_FLUX_D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MICASA_FLUX_D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately. ", "links": [ { diff --git a/datasets/MICASA_FLUX_M_1.json b/datasets/MICASA_FLUX_M_1.json index bd26de2a5c..7196cf34ea 100644 --- a/datasets/MICASA_FLUX_M_1.json +++ b/datasets/MICASA_FLUX_M_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MICASA_FLUX_M_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately. ", "links": [ { diff --git a/datasets/MICRONESIAN_0.json b/datasets/MICRONESIAN_0.json index c54c9fd666..525f638c58 100644 --- a/datasets/MICRONESIAN_0.json +++ b/datasets/MICRONESIAN_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MICRONESIAN_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made primarily in Micronesia, but stretching across the Pacific Ocean to the Hawaiian Islands from 1998 to 1999.", "links": [ { diff --git a/datasets/MIL1A_2.json b/datasets/MIL1A_2.json index 63d52959cd..bb004532f6 100644 --- a/datasets/MIL1A_2.json +++ b/datasets/MIL1A_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL1A_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL1A_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A CCD Science data, all cameras version 2. It is the Reformatted Annotated Level 1A product for the CCD science data. The data numbers (DN) have been commuted from 12-bit numbers to 16-bit byte aligned half-words. The MISR CCD Science Instrument Data acquired from all nine of the MISR cameras for each of the four bands represent the raw MISR input data staged for MISR Science Instrument Data processing. There are nine file granules of this type, one corresponding to each of the nine MISR cameras. Each file granule contains four entire swaths of data, one swath for each of the four MISR bands associated with each MISR camera. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MIL2ASAE_002.json b/datasets/MIL2ASAE_002.json index e2bfafa4c8..3babb51fc0 100644 --- a/datasets/MIL2ASAE_002.json +++ b/datasets/MIL2ASAE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASAE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Aerosol parameters V002 contains Aerosol optical depth and particle type, with associated atmospheric data.", "links": [ { diff --git a/datasets/MIL2ASAE_3.json b/datasets/MIL2ASAE_3.json index 936980f9f8..9c4dc735a4 100644 --- a/datasets/MIL2ASAE_3.json +++ b/datasets/MIL2ASAE_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASAE_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL2ASAE_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol parameters Version 3 product. It contains information on retrieved aerosol column amount, aerosol particle properties, and ancillary information based on Level 1B2 geolocated radiances observed by MISR. Data collection for this product is ongoing. \r\n\r\nAs the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL2ASAF_002.json b/datasets/MIL2ASAF_002.json index c06f4c8c9b..9cb4b7889a 100644 --- a/datasets/MIL2ASAF_002.json +++ b/datasets/MIL2ASAF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASAF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 FIRSTLOOK Aerosol Product. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period.\nMIL2ASAF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Aerosol parameters version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period. Data collection for this product is ongoing.\nMulti-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol data products contain various information on the Earth's atmosphere. The aerosol data include tropospheric aerosol optical depth on 17. 6-km centers archived with a compositional model identifier and retrieval residuals, ancillary data including relative humidity, ozone optical depth, stratospheric aerosol optical depth, and retrieval flags. MISR multi-angle imagery will be used to monitor global and regional trends radiatively significant to optical properties (optical depth, single scattering albedo, and size distribution) and amounts (mass loading) of natural and anthropogenic aerosols, including those arising from industrial and volcanic emissions, slash-and-burn agriculture, and desertification. Coupled with MISR's determinations of top-of-atmosphere and surface albedos, these data will measure the global aerosol forcing of the shortwave planetary radiation budget. \nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL2ASLF_002.json b/datasets/MIL2ASLF_002.json index 2fcd6b8217..58ba6f4426 100644 --- a/datasets/MIL2ASLF_002.json +++ b/datasets/MIL2ASLF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASLF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 2 FIRSTLOOK Land Surface product contains directional reflectance properties, albedo(spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period.\nMIL2ASLF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Surface parameters version 2. It contains directional reflectance properties, albedo (spectral and photosynthetically active radiation (PAR) integrated), a fraction of photosynthetically active radiation absorbed by vegetation (FPAR), radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI).\nMulti-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol data products contain information on the Earth's atmosphere. The aerosol data include tropospheric aerosol optical depth on 17. 6-km centers archived with a compositional model identifier and retrieval residuals, ancillary data including relative humidity, ozone optical depth, stratospheric aerosol optical depth, and retrieval flags. MISR multi-angle imagery will be used to monitor global and regional trends radiatively significant to optical properties (optical depth, single scattering albedo, and size distribution) and amounts (mass loading) of natural and anthropogenic aerosols, including those arising from industrial and volcanic emissions, slash-and-burn agriculture, and desertification. Coupled with MISR's determinations of top-of-atmosphere and surface albedos, these data will measure the global aerosol forcing of the shortwave planetary radiation budget. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL2ASLS_2.json b/datasets/MIL2ASLS_2.json index 93baac94f8..ed9c372855 100644 --- a/datasets/MIL2ASLS_2.json +++ b/datasets/MIL2ASLS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASLS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL2ASLS_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 2 data product. It contains a variety of information on the Earth's surface; such ashemispherical directional reflectance factor (HDRF), bihemispherical reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosysenthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles. A surface retrieval is conducted on regions for which valid land aerosol retrieval exists. The retrieval is performed using the corrected equivalent reflectances, retrieved aerosol parameters, and auxiliary information from the Simulated MISR Ancillary Radiative Transfer (SMART) dataset. The spectral and Photosynthetically Active spectral Region (PAR)-integrated BHR and DHR are retrieved, along with the spectral land HDRF and BRF and BRF model parameters, for all valid land and inland water subregions. Subregion surface classification and leaf area index (LAI) and regional FPAR are also determined. Subregion variability is also calculated for land regions. Data collection for this product was completed in June 2017.\r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL2ASLS_3.json b/datasets/MIL2ASLS_3.json index aa8c3ef34d..67fd2b68b5 100644 --- a/datasets/MIL2ASLS_3.json +++ b/datasets/MIL2ASLS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2ASLS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL2ASLS_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 3 data product. It contains a variety of information on the Earth's surface; such ashemispherical directional reflectance factor (HDRF), bihemispherical reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosysenthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles. A surface retrieval is conducted on regions for which valid land aerosol retrieval exists. The retrieval is performed using the corrected equivalent reflectances, retrieved aerosol parameters, and auxiliary information from the Simulated MISR Ancillary Radiative Transfer (SMART) dataset. The spectral and Photosynthetically Active spectral Region (PAR)-integrated BHR and DHR are retrieved, along with the spectral land HDRF and BRF and BRF model parameters, for all valid land and inland water subregions. Subregion surface classification and leaf area index (LAI) and regional FPAR are also determined. Subregion variability is also calculated for land regions. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI).\r\n\r\nThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL2TCAF_001.json b/datasets/MIL2TCAF_001.json index 2eef06ca9b..7113b69e7b 100644 --- a/datasets/MIL2TCAF_001.json +++ b/datasets/MIL2TCAF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCAF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 FIRSTLOOK TOA/Cloud Albedo parameters V001 contains local, restrictive, and expansive albedo, with associated data, produced using ancillary inputs from the previous time period.", "links": [ { diff --git a/datasets/MIL2TCAL_002.json b/datasets/MIL2TCAL_002.json index 2eacfcf92c..91c268be1e 100644 --- a/datasets/MIL2TCAL_002.json +++ b/datasets/MIL2TCAL_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCAL_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Albedo parameters V002 contains local, restrictive, and expansive albedo with associated data.\n", "links": [ { diff --git a/datasets/MIL2TCCF_001.json b/datasets/MIL2TCCF_001.json index 9883c4c4b4..d974716d34 100644 --- a/datasets/MIL2TCCF_001.json +++ b/datasets/MIL2TCCF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCCF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 FIRSTLOOK TOA/Cloud Classifier parameters V001 contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine classifiers, produced using ancillary inputs and Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period.", "links": [ { diff --git a/datasets/MIL2TCCF_002.json b/datasets/MIL2TCCF_002.json index d49d55d8f5..c3cbfc0cf6 100644 --- a/datasets/MIL2TCCF_002.json +++ b/datasets/MIL2TCCF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCCF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 FIRSTLOOK TOA/Cloud Classifier parameters V002 contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine classifiers, produced using ancillary inputs and Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period.", "links": [ { diff --git a/datasets/MIL2TCCL_003.json b/datasets/MIL2TCCL_003.json index 4baa84fed1..7b8df254bb 100644 --- a/datasets/MIL2TCCL_003.json +++ b/datasets/MIL2TCCL_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCCL_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Classifier parameters V003 contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.", "links": [ { diff --git a/datasets/MIL2TCSF_001.json b/datasets/MIL2TCSF_001.json index 2157d45fd9..9c38ee3ad0 100644 --- a/datasets/MIL2TCSF_001.json +++ b/datasets/MIL2TCSF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCSF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 FIRSTLOOK TOA/Cloud Stereo parameters V001 contains the stereoscopically-derived winds, heights and cloud mask along with associated data, produced using ancillary inputs of Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period.", "links": [ { diff --git a/datasets/MIL2TCSP_001.json b/datasets/MIL2TCSP_001.json index 3971cb5005..67fb24f099 100644 --- a/datasets/MIL2TCSP_001.json +++ b/datasets/MIL2TCSP_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCSP_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Height and Motion parameters V001 contains the Stereo Heights, Stereoscopically Derived Cloud Mask (SDCM), and Cloud Motion Vectors with associated data.", "links": [ { diff --git a/datasets/MIL2TCSP_002.json b/datasets/MIL2TCSP_002.json index 4c6c070c11..eb40927750 100644 --- a/datasets/MIL2TCSP_002.json +++ b/datasets/MIL2TCSP_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCSP_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Height and Motion parameters V002 contains the Stereo Heights, Stereoscopically Derived Cloud Mask (SDCM), and Cloud Motion Vectors with associated data.", "links": [ { diff --git a/datasets/MIL2TCST_002.json b/datasets/MIL2TCST_002.json index 101ebf1bd6..ce3f648b2c 100644 --- a/datasets/MIL2TCST_002.json +++ b/datasets/MIL2TCST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL2TCST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Stereo parameters V002 contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data.", "links": [ { diff --git a/datasets/MIL3DAEN_004.json b/datasets/MIL3DAEN_004.json index 7ae58e98ad..920a4bbfc0 100644 --- a/datasets/MIL3DAEN_004.json +++ b/datasets/MIL3DAEN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DAEN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a day", "links": [ { diff --git a/datasets/MIL3DAE_4.json b/datasets/MIL3DAE_4.json index 2f5edafa7c..3b45f14949 100644 --- a/datasets/MIL3DAE_4.json +++ b/datasets/MIL3DAE_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DAE_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3DAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product was completed in June of 2017.\n\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n\nMISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3DALN_006.json b/datasets/MIL3DALN_006.json index b64ca0c880..014cce5ae3 100644 --- a/datasets/MIL3DALN_006.json +++ b/datasets/MIL3DALN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DALN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo is a publicly available product in netCDF format covering a day.\nMIL3DALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a day version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3DAL_006.json b/datasets/MIL3DAL_006.json index 0ccee674a0..314be0737c 100644 --- a/datasets/MIL3DAL_006.json +++ b/datasets/MIL3DAL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DAL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product covering a day to be used starting with MISR Release V3.2.", "links": [ { diff --git a/datasets/MIL3DCFA_001.json b/datasets/MIL3DCFA_001.json index 1ef487b0c7..59ff655060 100644 --- a/datasets/MIL3DCFA_001.json +++ b/datasets/MIL3DCFA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DCFA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Cloud Fraction by Altitude Product covering a day", "links": [ { diff --git a/datasets/MIL3DCLD_002.json b/datasets/MIL3DCLD_002.json index 5b0c318ef6..fceade495b 100644 --- a/datasets/MIL3DCLD_002.json +++ b/datasets/MIL3DCLD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DCLD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Component Global Cloud Product covering a day", "links": [ { diff --git a/datasets/MIL3DCOD_001.json b/datasets/MIL3DCOD_001.json index d4bdafdbeb..04da0c97c4 100644 --- a/datasets/MIL3DCOD_001.json +++ b/datasets/MIL3DCOD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DCOD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. This file contains the public MISR Level 3 Cloud Top Height-Optical Depth Product covering a day.", "links": [ { diff --git a/datasets/MIL3DLSN_004.json b/datasets/MIL3DLSN_004.json index ad24619791..c0da1f594a 100644 --- a/datasets/MIL3DLSN_004.json +++ b/datasets/MIL3DLSN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DLSN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land product in netCDF format covering a day", "links": [ { diff --git a/datasets/MIL3DLS_004.json b/datasets/MIL3DLS_004.json index 94475be334..ea96753a77 100644 --- a/datasets/MIL3DLS_004.json +++ b/datasets/MIL3DLS_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DLS_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land Product covering a day", "links": [ { diff --git a/datasets/MIL3DRD_004.json b/datasets/MIL3DRD_004.json index c089dfa679..b1077dbdb3 100644 --- a/datasets/MIL3DRD_004.json +++ b/datasets/MIL3DRD_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3DRD_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Radiance Product covering a day", "links": [ { diff --git a/datasets/MIL3MAEN_004.json b/datasets/MIL3MAEN_004.json index fd31c2abad..bac5bbc129 100644 --- a/datasets/MIL3MAEN_004.json +++ b/datasets/MIL3MAEN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MAEN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a month", "links": [ { diff --git a/datasets/MIL3MAE_4.json b/datasets/MIL3MAE_4.json index b818c8cbc3..7f02102ff1 100644 --- a/datasets/MIL3MAE_4.json +++ b/datasets/MIL3MAE_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MAE_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3MAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a month version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The collection for this product was completed in May of 2017.\n\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n\nMISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3MALN_006.json b/datasets/MIL3MALN_006.json index 62f6996750..69a4457fc0 100644 --- a/datasets/MIL3MALN_006.json +++ b/datasets/MIL3MALN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MALN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product in netCDF format covering a month.", "links": [ { diff --git a/datasets/MIL3MAL_006.json b/datasets/MIL3MAL_006.json index f4a5a9db76..2a96dad4b2 100644 --- a/datasets/MIL3MAL_006.json +++ b/datasets/MIL3MAL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MAL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo is a publicly available product covering a month, to be used starting with MISR Release V3.2.\nMIL3MAL_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a month version 6. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a month and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree, and 5 degrees by 5 degrees. Data collection for this product is ongoing.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3MCFA_1.json b/datasets/MIL3MCFA_1.json index aff2a0204a..951c62674a 100644 --- a/datasets/MIL3MCFA_1.json +++ b/datasets/MIL3MCFA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MCFA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3MCFA_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a month version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degree by 0.5 degree and a vertical resolution of 500m. For each height bin, the frequency of cloud occurrence of a region over a time period is represented by the temporal mean of the spatial coverage of cloud tops. The spatial coverage of clouds is referred to as cloud fraction, which is defined as the ratio of the number of cloudy pixels to the total number of cloudy and cloud-free pixels observed by the instrument. Clouds are assigned to height bins based on their top height as retrieved by the MISR stereoscopic technique. Data collection for this product is complete.\r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3MCLD_002.json b/datasets/MIL3MCLD_002.json index f51e776def..b8272f738d 100644 --- a/datasets/MIL3MCLD_002.json +++ b/datasets/MIL3MCLD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MCLD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Component Global Cloud Product covering a month.\nMIL3MCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a month version 2. It is a global summary of relevant Level 1 and Level 2 cloud parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3MCOD_001.json b/datasets/MIL3MCOD_001.json index af0a4d1a89..0b173904f4 100644 --- a/datasets/MIL3MCOD_001.json +++ b/datasets/MIL3MCOD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MCOD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. This file contains the public MISR Level 3 Cloud Top Height-Optical Depth Product covering a month.", "links": [ { diff --git a/datasets/MIL3MJTA_2.json b/datasets/MIL3MJTA_2.json index d597a91bbd..4dc5530fdd 100644 --- a/datasets/MIL3MJTA_2.json +++ b/datasets/MIL3MJTA_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MJTA_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3MJTA_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Joint Aerosol monthly product version 2 data product. It contains global statistical summaries of MISR Level 2 aerosol optical depth, on a 5 degree geographic grid. Within each grid cell, optical depth is summarized by a set of representative vectors, each representing a cluster of similar Level 2 aerosol optical depth retrievals. Data is summarized monthly. Data collection for this product is ongoing.\r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument fly\u2019s overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3MLSN_004.json b/datasets/MIL3MLSN_004.json index c4c8af33cb..18f2774049 100644 --- a/datasets/MIL3MLSN_004.json +++ b/datasets/MIL3MLSN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MLSN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land product in netCDF format covering a month", "links": [ { diff --git a/datasets/MIL3MLS_004.json b/datasets/MIL3MLS_004.json index 3bc53dac7e..44655d3364 100644 --- a/datasets/MIL3MLS_004.json +++ b/datasets/MIL3MLS_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MLS_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land Product covering a month", "links": [ { diff --git a/datasets/MIL3MRD_005.json b/datasets/MIL3MRD_005.json index 0419754072..be074d6185 100644 --- a/datasets/MIL3MRD_005.json +++ b/datasets/MIL3MRD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3MRD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Radiance Product covering a month", "links": [ { diff --git a/datasets/MIL3QAEN_004.json b/datasets/MIL3QAEN_004.json index e283e65e19..c7002f5063 100644 --- a/datasets/MIL3QAEN_004.json +++ b/datasets/MIL3QAEN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QAEN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3QAE_004.json b/datasets/MIL3QAE_004.json index d22cc7d317..515cb79e40 100644 --- a/datasets/MIL3QAE_004.json +++ b/datasets/MIL3QAE_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QAE_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol Product covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3QALN_006.json b/datasets/MIL3QALN_006.json index 0ffd60140d..94223338e7 100644 --- a/datasets/MIL3QALN_006.json +++ b/datasets/MIL3QALN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QALN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product in netCDF format covering a quarter (seasonal).", "links": [ { diff --git a/datasets/MIL3QAL_006.json b/datasets/MIL3QAL_006.json index 7ea810bf9b..d42174abd9 100644 --- a/datasets/MIL3QAL_006.json +++ b/datasets/MIL3QAL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QAL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product covering a quarter (seasonal) to be used starting with MISR Release V3.2.", "links": [ { diff --git a/datasets/MIL3QCFA_001.json b/datasets/MIL3QCFA_001.json index e4b0d7ed34..a834795e34 100644 --- a/datasets/MIL3QCFA_001.json +++ b/datasets/MIL3QCFA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QCFA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Cloud Fraction by Altitude Product covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3QCLD_002.json b/datasets/MIL3QCLD_002.json index 4e011f6191..86b56e2b46 100644 --- a/datasets/MIL3QCLD_002.json +++ b/datasets/MIL3QCLD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QCLD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Component Global Cloud Product covering a quarter (seasonal).\nMIL3QCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a quarter (seasonal) version 2 data product. It is a global summary of relevant Level 1 and Level 2 cloud parameters, averaged over a quarter (season) and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing.\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3QCOD_001.json b/datasets/MIL3QCOD_001.json index f177f3912b..61aa0b347c 100644 --- a/datasets/MIL3QCOD_001.json +++ b/datasets/MIL3QCOD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QCOD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. This file contains the public MISR Level 3 CloudTopHeight-OpticalDepth Product covering a quarter (seasonal).", "links": [ { diff --git a/datasets/MIL3QLSN_004.json b/datasets/MIL3QLSN_004.json index d73dab0824..f466e26dfe 100644 --- a/datasets/MIL3QLSN_004.json +++ b/datasets/MIL3QLSN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QLSN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land product in netCDF format covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3QLS_004.json b/datasets/MIL3QLS_004.json index e77015d1eb..cdfe07c8e8 100644 --- a/datasets/MIL3QLS_004.json +++ b/datasets/MIL3QLS_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QLS_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land Product covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3QRD_005.json b/datasets/MIL3QRD_005.json index e3a1f62de8..facb33d0db 100644 --- a/datasets/MIL3QRD_005.json +++ b/datasets/MIL3QRD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3QRD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Radiance Product covering a quarter (seasonal)", "links": [ { diff --git a/datasets/MIL3YAEN_004.json b/datasets/MIL3YAEN_004.json index 886a3194ee..00deac657b 100644 --- a/datasets/MIL3YAEN_004.json +++ b/datasets/MIL3YAEN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YAEN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a year", "links": [ { diff --git a/datasets/MIL3YAE_004.json b/datasets/MIL3YAE_004.json index c4860755df..82b97eb412 100644 --- a/datasets/MIL3YAE_004.json +++ b/datasets/MIL3YAE_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YAE_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Aerosol Product covering a year", "links": [ { diff --git a/datasets/MIL3YALN_006.json b/datasets/MIL3YALN_006.json index 37f2318bcc..e2566e7e5c 100644 --- a/datasets/MIL3YALN_006.json +++ b/datasets/MIL3YALN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YALN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product in netCDF format covering a year.", "links": [ { diff --git a/datasets/MIL3YAL_006.json b/datasets/MIL3YAL_006.json index 370bad585c..bf7fbe39cd 100644 --- a/datasets/MIL3YAL_006.json +++ b/datasets/MIL3YAL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YAL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR Level 3 Component Global Albedo publicly available product covering a year to be used starting with MISR Release V3.2.", "links": [ { diff --git a/datasets/MIL3YCFA_001.json b/datasets/MIL3YCFA_001.json index 3220838478..d6451ea5a3 100644 --- a/datasets/MIL3YCFA_001.json +++ b/datasets/MIL3YCFA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YCFA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Cloud Fraction by Altitude Product covering a year", "links": [ { diff --git a/datasets/MIL3YCLD_002.json b/datasets/MIL3YCLD_002.json index e5edf4e7e4..d3f801ec51 100644 --- a/datasets/MIL3YCLD_002.json +++ b/datasets/MIL3YCLD_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YCLD_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the public MISR Level 3 Component Global Cloud Product covering a year", "links": [ { diff --git a/datasets/MIL3YCOD_1.json b/datasets/MIL3YCOD_1.json index d6e651ffd3..aad3ebe924 100644 --- a/datasets/MIL3YCOD_1.json +++ b/datasets/MIL3YCOD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YCOD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3YCOD_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Top Height-Optical Depth Product covering a year version 1. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3YLSN_004.json b/datasets/MIL3YLSN_004.json index ef6de5a9e9..6438b72f3f 100644 --- a/datasets/MIL3YLSN_004.json +++ b/datasets/MIL3YLSN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YLSN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Land product in netCDF format covering a year. \nMIL3YLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a year version 4. It contains a yearly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a day and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. This collection contains Leaf Area Index (LAI).\nThe MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\nMISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\"\n", "links": [ { diff --git a/datasets/MIL3YLS_4.json b/datasets/MIL3YLS_4.json index 3289b217d5..af37ac00ca 100644 --- a/datasets/MIL3YLS_4.json +++ b/datasets/MIL3YLS_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YLS_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MIL3YLS_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a year version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters, classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a year and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product was completed in November of 2016. This collection contains Leaf Area Index (LAI).\r\rThe MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\r\rMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.", "links": [ { diff --git a/datasets/MIL3YRD_005.json b/datasets/MIL3YRD_005.json index 50e48b0e3a..fd2559eb29 100644 --- a/datasets/MIL3YRD_005.json +++ b/datasets/MIL3YRD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIL3YRD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the MISR Level 3 Component Global Radiance Product covering a year", "links": [ { diff --git a/datasets/MIPOT_0.json b/datasets/MIPOT_0.json index b6c726b268..8517301383 100644 --- a/datasets/MIPOT_0.json +++ b/datasets/MIPOT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIPOT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made during Mediterranean, Indian and Pacific Ocean Transect (MIPOT) cruises in 2001.", "links": [ { diff --git a/datasets/MIRAI_0.json b/datasets/MIRAI_0.json index 074232f2db..2d4e17923e 100644 --- a/datasets/MIRAI_0.json +++ b/datasets/MIRAI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIRAI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the MIRAI research vessel in the JAMSTEC fleet between 2000 and 2003.", "links": [ { diff --git a/datasets/MIRCCMF_001.json b/datasets/MIRCCMF_001.json index c773641664..dcfb9fcdd0 100644 --- a/datasets/MIRCCMF_001.json +++ b/datasets/MIRCCMF_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIRCCMF_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V001 contains the FIRSTLOOK Radiometric camera-by-camera Cloud Mask (RCCM) dataset produced using ancillary inputs Radiometric Camera-by-camera Cloud mask Threshold (RCCT) from the previous time period. It is used to determine whether a scene is clear, cloudy or dusty (over ocean).", "links": [ { diff --git a/datasets/MIRCCMF_002.json b/datasets/MIRCCMF_002.json index 80f441f2bc..6082a2fcb4 100644 --- a/datasets/MIRCCMF_002.json +++ b/datasets/MIRCCMF_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIRCCMF_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the FIRSTLOOK Radiometric Camera-by-camera Cloud Mask (RCCM) product. It contains initial estimated classifications of pixels/regions as clear or cloudy. It also has masks for the presence of glitter or dust. The FIRSTLOOK RCCM product is superceded by the final RCCM product following seasonal calibration.", "links": [ { diff --git a/datasets/MIRCCM_004.json b/datasets/MIRCCM_004.json index c1315ea28b..8ac32ed859 100644 --- a/datasets/MIRCCM_004.json +++ b/datasets/MIRCCM_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIRCCM_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR radiometric camera-by-camera Cloud Mask V004 contains the Radiometric camera-by-camera Cloud Mask dataset. It is used to determine whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over ocean. This version of the ESDT is used by MISR PGE 13.", "links": [ { diff --git a/datasets/MIRC_0.json b/datasets/MIRC_0.json index d5dbd88919..06a6f6f43f 100644 --- a/datasets/MIRC_0.json +++ b/datasets/MIRC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIRC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken by the Marine Information Research Center (MIRC), a division of the Japan Hydrographic Association.", "links": [ { diff --git a/datasets/MISBR_005.json b/datasets/MISBR_005.json index 6473014dff..8ee58f1e6e 100644 --- a/datasets/MISBR_005.json +++ b/datasets/MISBR_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MISBR_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the browse data associated with a particular granule.\nMISBR_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Browse data version 5. It consists of Ellipsoid color images obtained by each camera resampled to 2. 2 km resolution. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/MISC_Apex_Floats_0.json b/datasets/MISC_Apex_Floats_0.json index 23ddd7db7d..a7b595cd6d 100644 --- a/datasets/MISC_Apex_Floats_0.json +++ b/datasets/MISC_Apex_Floats_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MISC_Apex_Floats_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the North Atlantic ocean by miscellaneous APEX floats between 2004 and 2007.", "links": [ { diff --git a/datasets/MISR_885_1.json b/datasets/MISR_885_1.json index bf687f77f8..20bb4a4e67 100644 --- a/datasets/MISR_885_1.json +++ b/datasets/MISR_885_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MISR_885_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR (Multi-angle Imaging SpectroRadiometer) views the sunlit Earth simultaneously at nine widely spaced and collects global images with high spatial detail in four colors at every angle. These images are carefully calibrated to provide accurate measures of the brightness, contrast, and color of reflected sunlight. The change in reflection at different view angles affords the means to distinguish different types of atmospheric particles (aerosols), cloud forms, and land surface covers. Combined with stereoscopic techniques, this enables construction of 3-dimensional models and more accurate estimates of the total amount of sunlight reflected by Earth's diverse environments.MISR was built for NASA by the Jet Propulsion Laboratory. It is part of NASA's Terra spacecraft, launched into a polar orbit around the Earth on December 18, 1999.The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological, and man-made sources such as biomass burning. The area of study and MISR path numbers include Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. These MISR data cover the period August 12 through September 28, 2000.", "links": [ { diff --git a/datasets/MISR_AEROSOL_CLIM_1.json b/datasets/MISR_AEROSOL_CLIM_1.json index 934e942e66..a573b4b75a 100644 --- a/datasets/MISR_AEROSOL_CLIM_1.json +++ b/datasets/MISR_AEROSOL_CLIM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MISR_AEROSOL_CLIM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MISR monthly, global 1 x 1 deg grid 'Clim-Likely' aerosol climatology, derived from 'typical-year' aerosol transport model results available in 1999.", "links": [ { diff --git a/datasets/MISR_Forest_AGB_SW_US_1978_1.json b/datasets/MISR_Forest_AGB_SW_US_1978_1.json index 85f10f86ba..e9c3576b21 100644 --- a/datasets/MISR_Forest_AGB_SW_US_1978_1.json +++ b/datasets/MISR_Forest_AGB_SW_US_1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MISR_Forest_AGB_SW_US_1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of forest aboveground biomass (AGB; in Mg ha-1) at a resolution of 250 m for the southwestern United States over the time period 2000-2021. The AGB estimates were derived from the Jet Propulsion Laboratory Multiangle Imaging Spectro-Radiometer (MISR) Level 1B2 Terrain radiance data and a multi-angle approach that exploits the relationship between forest AGB and a suite of red band reflectance values modeled at viewing angles with respect to the direction of illumination. The year 2000 National Biomass and Carbon Dataset (NBCD 2000) AGB estimates were used to fit a model coefficient for the MISR-derived AGB estimates for the year 2000, with AGB estimates for all subsequent years dependent on both this coefficient and MISR red band bidirectional reflectance factors (BRFs). Quality assurance (QA) files are also provided that allow users to impose criteria of varying stringency. The bidirectional reflectance distribution function (BRDF) model-fitting root mean square error (RMSE) value was used as one of the criteria to determine if the AGB estimates were reasonable. This dataset is the first example of forest AGB estimation based on a multi-angle index applied using MISR data.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json index 5242c5867c..306c3914c3 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Southern Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0_1.0.json index b655262bb4..5ac5e14db4 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Bass Strait region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0_1.0.json index 957ebdc560..63634181c5 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Baltic Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0_1.0.json index 042c2b628a..69a2e8a715 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Cape Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0_1.0.json index 9d85c0a418..09fc264670 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Gotland Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0_1.0.json index def9af7d5a..30e09805d0 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Labrador Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0_1.0.json index 01549de517..133d9f7cfd 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Marmara Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0_1.0.json index f95bb96601..273489012c 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Australian Shelf region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0_1.0.json index 959edbc1f7..17bb3d3c8a 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Pacific Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0_1.0.json index 9bb4334660..79231eae56 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the New Caledonia region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0_1.0.json index 916f922795..75858226a4 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northeast Weddell Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0_1.0.json index 2dd9e40cf2..ae451331ae 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Rockall Trough region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0_1.0.json index 6848c3df5e..7a372e6cdf 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the West Atlantic region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0_1.0.json index 9085ea71b0..be086f44e8 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the western Mediterranean Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0.json index 7546aa28ff..a8e595619c 100644 --- a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0.json +++ b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Yongala region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.", "links": [ { diff --git a/datasets/MI_Azorella_PA_201011_update_1.json b/datasets/MI_Azorella_PA_201011_update_1.json index 0547f31f64..0e93a361b0 100644 --- a/datasets/MI_Azorella_PA_201011_update_1.json +++ b/datasets/MI_Azorella_PA_201011_update_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI_Azorella_PA_201011_update_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains point location data for the presence or absence of Azorella macquariensis on Macquarie Island. The data were collected during an island wide alien plant survey during the 2010-11 season.\n\nThis dataset was updated on 2016-08-10 and a new dataset DOI created.", "links": [ { diff --git a/datasets/MI_Azorella_dieback_5x5m_1.json b/datasets/MI_Azorella_dieback_5x5m_1.json index 3692b824f9..afa8827c99 100644 --- a/datasets/MI_Azorella_dieback_5x5m_1.json +++ b/datasets/MI_Azorella_dieback_5x5m_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI_Azorella_dieback_5x5m_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises data on Azorella macquariensis dieback from four summer seasons at a range of sites across Macquarie Island: 2008-09, 2009-10, 2010-11, 2011-12. Data on the proportion of healthy and dead or dying Azorella was collected from a 5 x 5m quadrat at each site. In some years data on the health of moss in the quadrats is also provided. The file is in the form of an Excel workbook with a separate worksheet for each year.\n\nIn addition there are photographs of the sites spanning up to 4 years 2008-09 to - 2011 -12. Most photographic suites contain a North West and a South East site photographs and most are within 5- 10 m of the GPS point for the site. The site codes identify the 5 x 5m quadrats.", "links": [ { diff --git a/datasets/MI_Orchids_1976-2009_1.json b/datasets/MI_Orchids_1976-2009_1.json index aed8f4b05b..371717af2f 100644 --- a/datasets/MI_Orchids_1976-2009_1.json +++ b/datasets/MI_Orchids_1976-2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI_Orchids_1976-2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two endemic orchid species, Nematoceras dienemum and N. sulcatum, are known from sub-Antarctic Macquarie Island. Several additional orchid populations on the island are reported and cleistogamy is documented in N. dienemum for the first time. The known population sizes, habitats and locations for both orchid species are documented here, and new information on their biology and population ecology is provided.\n\nThese data are available from the biodiversity database.\n\nThere are 20 observations in the data collection.", "links": [ { diff --git a/datasets/MI_alk_clones_1.json b/datasets/MI_alk_clones_1.json index 9e5919cc78..d9a9af0307 100644 --- a/datasets/MI_alk_clones_1.json +++ b/datasets/MI_alk_clones_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI_alk_clones_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of 81 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with any word-processing or sequence analysis software.\n\nThe clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG.\n\nThe library was created from a soil sample collected at the Main Powerhouse on Macquarie Island and is Human Impacts Sample Tracking Database barcode number:52774.\n\nThese data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments.", "links": [ { diff --git a/datasets/MI_microcosm2006_microbial_data_1.json b/datasets/MI_microcosm2006_microbial_data_1.json index c398cb3adf..ab485ae393 100644 --- a/datasets/MI_microcosm2006_microbial_data_1.json +++ b/datasets/MI_microcosm2006_microbial_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MI_microcosm2006_microbial_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A microcosm experiment utilising a respirometry system and 14C-labelled hexadecane was initiated to investigate the effects of differing oxygen regimes on hydrocarbon degradation in soil from sub-Antarctic Macquarie Island. Measurements of oxygen consumed, carbon dioxide produced, total petroleum hydrocarbon degradation and nitrate and ammonium concentrations were made. The microbial community structure at the start of the experiment and after 4, 8 and 12 weeks incubation was explored using terminal restriction fragment length polymorphism and real-time PCR quantification of alkane mono-oxygenase, napthalene dioxygenase, nitrous oxide reductase and ribosomal polymerase sub-unitB. The data described here are the microbial community data only.\n\nThe download file contains an excel spreadsheet. The first sheet provides further information about the dataset.\n\nThis work was part of AAS projects 2672 and 1163.", "links": [ { diff --git a/datasets/MIvegmap_1.json b/datasets/MIvegmap_1.json index e4f40d2cea..a4fe3965a9 100644 --- a/datasets/MIvegmap_1.json +++ b/datasets/MIvegmap_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MIvegmap_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data for this map were collected as part of two ASAC projects - 488 and 956, of which Patricia Selkirk was the chief investigator. Macquarie Island (54 degrees S 159 degrees E) is a subantarctic island (c. 35km by 3 to 5km) approximately equidistant between Tasmania, New Zealand and Antarctica in the Southern Ocean. The vegetation is herbaceous, lacking shrubs and trees. Vegetation and drainage are mapped at a scale of 1:25 000 from field observations, satellite imagery and limited oblique and aerial photography. The categories adopted for mapping vegetation are based on attributes of foliage height and percentage foliage cover of the ground surface (vegetation structure), not on species distribution (floristics). The distribution of vegetation categories is strongly correlated with aspect, topography and rock type. Mires, streams and lakes form an intricate drainage pattern that is strongly influenced by the geology of this tectonically active emergent crest of the submarine Macquarie Ridge at the boundary of the Pacific and Australian plates. The drainage pattern of the whole island is represented in a map with substantially greater accuracy than in any previous map.", "links": [ { diff --git a/datasets/ML1OA_004.json b/datasets/ML1OA_004.json index 4eb40ef3b2..2bac9f76d7 100644 --- a/datasets/ML1OA_004.json +++ b/datasets/ML1OA_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1OA_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1OA_005.json b/datasets/ML1OA_005.json index be8b50cb36..c5b3acb33b 100644 --- a/datasets/ML1OA_005.json +++ b/datasets/ML1OA_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1OA_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADD_004.json b/datasets/ML1RADD_004.json index 29dfaf2d7a..a07ebb6b74 100644 --- a/datasets/ML1RADD_004.json +++ b/datasets/ML1RADD_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADD_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADD_005.json b/datasets/ML1RADD_005.json index d06735b5f3..0b595e50b8 100644 --- a/datasets/ML1RADD_005.json +++ b/datasets/ML1RADD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADG_004.json b/datasets/ML1RADG_004.json index dd16c25625..8a8c31a14d 100644 --- a/datasets/ML1RADG_004.json +++ b/datasets/ML1RADG_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADG_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADG_005.json b/datasets/ML1RADG_005.json index a01523156e..e735614a07 100644 --- a/datasets/ML1RADG_005.json +++ b/datasets/ML1RADG_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADG_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADT_004.json b/datasets/ML1RADT_004.json index 10c4d381e4..4a0c88e817 100644 --- a/datasets/ML1RADT_004.json +++ b/datasets/ML1RADT_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADT_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML1RADT_005.json b/datasets/ML1RADT_005.json index 5edd44f089..e26069a282 100644 --- a/datasets/ML1RADT_005.json +++ b/datasets/ML1RADT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML1RADT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.", "links": [ { diff --git a/datasets/ML2BRO_004.json b/datasets/ML2BRO_004.json index fca4e65009..b75a63f7ed 100644 --- a/datasets/ML2BRO_004.json +++ b/datasets/ML2BRO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2BRO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2BRO_005.json b/datasets/ML2BRO_005.json index 89f69fa372..06c433e44f 100644 --- a/datasets/ML2BRO_005.json +++ b/datasets/ML2BRO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2BRO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3CL_004.json b/datasets/ML2CH3CL_004.json index 2d7a843b3e..e0c4faa101 100644 --- a/datasets/ML2CH3CL_004.json +++ b/datasets/ML2CH3CL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3CL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3CL_005.json b/datasets/ML2CH3CL_005.json index a81605e2b9..0aac064e3d 100644 --- a/datasets/ML2CH3CL_005.json +++ b/datasets/ML2CH3CL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3CL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3CN_004.json b/datasets/ML2CH3CN_004.json index 181111d84d..b95c258de3 100644 --- a/datasets/ML2CH3CN_004.json +++ b/datasets/ML2CH3CN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3CN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3CN_005.json b/datasets/ML2CH3CN_005.json index 79e86ca109..9eb0f9e153 100644 --- a/datasets/ML2CH3CN_005.json +++ b/datasets/ML2CH3CN_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3CN_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3OH_004.json b/datasets/ML2CH3OH_004.json index 563ee0d69a..aa4946c07c 100644 --- a/datasets/ML2CH3OH_004.json +++ b/datasets/ML2CH3OH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3OH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CH3OH_005.json b/datasets/ML2CH3OH_005.json index 43eb6f0d1a..c888e36766 100644 --- a/datasets/ML2CH3OH_005.json +++ b/datasets/ML2CH3OH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CH3OH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CLO_004.json b/datasets/ML2CLO_004.json index fd7ddb2e8b..0cdaf6d4f7 100644 --- a/datasets/ML2CLO_004.json +++ b/datasets/ML2CLO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CLO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CLO_005.json b/datasets/ML2CLO_005.json index 725d64f00e..45b2fc98ff 100644 --- a/datasets/ML2CLO_005.json +++ b/datasets/ML2CLO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CLO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CO_004.json b/datasets/ML2CO_004.json index f2eda52f41..03a7619407 100644 --- a/datasets/ML2CO_004.json +++ b/datasets/ML2CO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CO_005.json b/datasets/ML2CO_005.json index 2bf85889d3..6b0b69324b 100644 --- a/datasets/ML2CO_005.json +++ b/datasets/ML2CO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00564 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2CO_NRT_005.json b/datasets/ML2CO_NRT_005.json index 4495130f9e..7ebef5b08a 100644 --- a/datasets/ML2CO_NRT_005.json +++ b/datasets/ML2CO_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2CO_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2CO_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for carbon monoxide (CO). This product contains CO profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.1 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2DGG_004.json b/datasets/ML2DGG_004.json index 3b2ed4c9e1..da84daf489 100644 --- a/datasets/ML2DGG_004.json +++ b/datasets/ML2DGG_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2DGG_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2DGG_005.json b/datasets/ML2DGG_005.json index c00b04cbfc..87dcf2e750 100644 --- a/datasets/ML2DGG_005.json +++ b/datasets/ML2DGG_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2DGG_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2DGM_004.json b/datasets/ML2DGM_004.json index 412c218f78..8575af36df 100644 --- a/datasets/ML2DGM_004.json +++ b/datasets/ML2DGM_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2DGM_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2DGM is the EOS Aura Microwave Limb Sounder (MLS) product containing the minor frame diagnostic quantities on a miscellaneous grid. These include items such as tangent pressure, chi-square describing various fits to the measured radiances, number of radiances used in various retrieval phases, etc. This product contains a second auxiliary file which includes cloud-induced radiances inferred for selected spectral channels. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGM data product should read the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF5. Each file contains sets of HDF5 dataset objects (n-dimensional arrays) for each diagnostics measurement. The dataset objects represent data and geolocation fields; included in the file are file attributes and metadata. There are two files per day (MLS-Aura_L2AUX-DGM* and MLS-Aura_L2AUX-Cloud*).", "links": [ { diff --git a/datasets/ML2DGM_005.json b/datasets/ML2DGM_005.json index c0be44bfbf..c1cecf3c78 100644 --- a/datasets/ML2DGM_005.json +++ b/datasets/ML2DGM_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2DGM_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2DGM is the EOS Aura Microwave Limb Sounder (MLS) product containing the minor frame diagnostic quantities on a miscellaneous grid. These include items such as tangent pressure, chi-square describing various fits to the measured radiances, number of radiances used in various retrieval phases, etc. This product contains a second auxiliary file which includes cloud-induced radiances inferred for selected spectral channels. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGM data product should read the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 Hierarchical Data Format, or HDF5. Each file contains sets of HDF5 dataset objects (n-dimensional arrays) for each diagnostics measurement. The dataset objects represent data and geolocation fields; included in the file are file attributes and metadata. There are two files per day (MLS-Aura_L2AUX-DGM* and MLS-Aura_L2AUX-Cloud*).", "links": [ { diff --git a/datasets/ML2GPH_004.json b/datasets/ML2GPH_004.json index 3d56d1db9d..264009d1ca 100644 --- a/datasets/ML2GPH_004.json +++ b/datasets/ML2GPH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2GPH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2GPH_005.json b/datasets/ML2GPH_005.json index 8a1cfd1d19..84809c400e 100644 --- a/datasets/ML2GPH_005.json +++ b/datasets/ML2GPH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2GPH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2H2O_004.json b/datasets/ML2H2O_004.json index 7545e9dc81..c17adc6bb5 100644 --- a/datasets/ML2H2O_004.json +++ b/datasets/ML2H2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2H2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2H2O_005.json b/datasets/ML2H2O_005.json index 8f7e4f445a..dce2343e39 100644 --- a/datasets/ML2H2O_005.json +++ b/datasets/ML2H2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2H2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2H2O_NRT_005.json b/datasets/ML2H2O_NRT_005.json index 8378adcfce..def33a3a6c 100644 --- a/datasets/ML2H2O_NRT_005.json +++ b/datasets/ML2H2O_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2H2O_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2H2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for water vapor (H2O). This product contains H2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 147 to 1 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2HCL_004.json b/datasets/ML2HCL_004.json index 59782c3d83..819a292b1c 100644 --- a/datasets/ML2HCL_004.json +++ b/datasets/ML2HCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HCL_005.json b/datasets/ML2HCL_005.json index c224a5ea2b..90d3f96af9 100644 --- a/datasets/ML2HCL_005.json +++ b/datasets/ML2HCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HCN_004.json b/datasets/ML2HCN_004.json index 1843249576..ead37987da 100644 --- a/datasets/ML2HCN_004.json +++ b/datasets/ML2HCN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HCN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HCN_005.json b/datasets/ML2HCN_005.json index 55cf89ad1a..3603a081c5 100644 --- a/datasets/ML2HCN_005.json +++ b/datasets/ML2HCN_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HCN_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HNO3_004.json b/datasets/ML2HNO3_004.json index 38a680bde7..7fd2d993c1 100644 --- a/datasets/ML2HNO3_004.json +++ b/datasets/ML2HNO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HNO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HNO3_005.json b/datasets/ML2HNO3_005.json index 2e6ba6601d..5f9b4daf60 100644 --- a/datasets/ML2HNO3_005.json +++ b/datasets/ML2HNO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HNO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HNO3_NRT_005.json b/datasets/ML2HNO3_NRT_005.json index b322f49329..84b1d24d33 100644 --- a/datasets/ML2HNO3_NRT_005.json +++ b/datasets/ML2HNO3_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HNO3_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HNO3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitric acid (HNO3). This product contains HNO3 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1.47 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2HO2_004.json b/datasets/ML2HO2_004.json index e0c42c9305..249c277ca3 100644 --- a/datasets/ML2HO2_004.json +++ b/datasets/ML2HO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HO2_005.json b/datasets/ML2HO2_005.json index 10b36f16c7..eafbd6ba1b 100644 --- a/datasets/ML2HO2_005.json +++ b/datasets/ML2HO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HOCL_004.json b/datasets/ML2HOCL_004.json index 2699ae4d8f..836811889c 100644 --- a/datasets/ML2HOCL_004.json +++ b/datasets/ML2HOCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HOCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2HOCL_005.json b/datasets/ML2HOCL_005.json index a0e4a90f47..20017ecf5c 100644 --- a/datasets/ML2HOCL_005.json +++ b/datasets/ML2HOCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2HOCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2IWC_004.json b/datasets/ML2IWC_004.json index aa2b733630..d1dbb09e85 100644 --- a/datasets/ML2IWC_004.json +++ b/datasets/ML2IWC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2IWC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2IWC_005.json b/datasets/ML2IWC_005.json index 1101dbfbd6..d5b39114f9 100644 --- a/datasets/ML2IWC_005.json +++ b/datasets/ML2IWC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2IWC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2N2O_004.json b/datasets/ML2N2O_004.json index a06eda90cb..1c850264fb 100644 --- a/datasets/ML2N2O_004.json +++ b/datasets/ML2N2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2N2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2N2O_005.json b/datasets/ML2N2O_005.json index f922618b1a..21b475cec4 100644 --- a/datasets/ML2N2O_005.json +++ b/datasets/ML2N2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2N2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2N2O_NRT_005.json b/datasets/ML2N2O_NRT_005.json index 773bc40b00..0d77ef2a0a 100644 --- a/datasets/ML2N2O_NRT_005.json +++ b/datasets/ML2N2O_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2N2O_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2N2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitrous oxide (N2O). This product contains N2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2O3_004.json b/datasets/ML2O3_004.json index 73f343613b..3db8e32464 100644 --- a/datasets/ML2O3_004.json +++ b/datasets/ML2O3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2O3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2O3_005.json b/datasets/ML2O3_005.json index b27c0bcf41..82de6dea2c 100644 --- a/datasets/ML2O3_005.json +++ b/datasets/ML2O3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2O3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2O3_NRT_005.json b/datasets/ML2O3_NRT_005.json index 12e3e08e13..c9feb825e8 100644 --- a/datasets/ML2O3_NRT_005.json +++ b/datasets/ML2O3_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2O3_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2O3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for ozone (O3). This product contains O3 profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 261 to 0.1 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2OH_004.json b/datasets/ML2OH_004.json index 6ad8248875..61dbd338db 100644 --- a/datasets/ML2OH_004.json +++ b/datasets/ML2OH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2OH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2OH_005.json b/datasets/ML2OH_005.json index 2972eec0bf..3609e2f75f 100644 --- a/datasets/ML2OH_005.json +++ b/datasets/ML2OH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2OH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 5.0. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2RHI_004.json b/datasets/ML2RHI_004.json index 887a2d553e..6b563a4945 100644 --- a/datasets/ML2RHI_004.json +++ b/datasets/ML2RHI_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2RHI_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2RHI_005.json b/datasets/ML2RHI_005.json index 32e9e1f33d..a194277b22 100644 --- a/datasets/ML2RHI_005.json +++ b/datasets/ML2RHI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2RHI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2SO2_004.json b/datasets/ML2SO2_004.json index 60fa5c1030..abba087f1a 100644 --- a/datasets/ML2SO2_004.json +++ b/datasets/ML2SO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2SO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2SO2_005.json b/datasets/ML2SO2_005.json index 8ca3016be5..735001dbfb 100644 --- a/datasets/ML2SO2_005.json +++ b/datasets/ML2SO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2SO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2SO2_NRT_005.json b/datasets/ML2SO2_NRT_005.json index 1d996b9a68..38504c9e80 100644 --- a/datasets/ML2SO2_NRT_005.json +++ b/datasets/ML2SO2_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2SO2_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2SO2_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for sulfur dioxide (SO2). This product contains SO2 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 10 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML2T_004.json b/datasets/ML2T_004.json index 4d04b4a061..a2666de7b6 100644 --- a/datasets/ML2T_004.json +++ b/datasets/ML2T_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2T_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2T_005.json b/datasets/ML2T_005.json index 33d068cd3b..aea55dff4b 100644 --- a/datasets/ML2T_005.json +++ b/datasets/ML2T_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2T_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.", "links": [ { diff --git a/datasets/ML2T_NRT_005.json b/datasets/ML2T_NRT_005.json index efafc2095e..d26bd9e958 100644 --- a/datasets/ML2T_NRT_005.json +++ b/datasets/ML2T_NRT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML2T_NRT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML2T_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for temperature. This product contains temperature profiles derived from the 118 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.001 hPa.\n\nThe MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.", "links": [ { diff --git a/datasets/ML3DBCH3CL_004.json b/datasets/ML3DBCH3CL_004.json index 78c3b57e53..9b13f9e449 100644 --- a/datasets/ML3DBCH3CL_004.json +++ b/datasets/ML3DBCH3CL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCH3CL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBCH3CL_005.json b/datasets/ML3DBCH3CL_005.json index 91274e1b90..61949f0b12 100644 --- a/datasets/ML3DBCH3CL_005.json +++ b/datasets/ML3DBCH3CL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCH3CL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBCLO_004.json b/datasets/ML3DBCLO_004.json index 7906e37401..40e1eede60 100644 --- a/datasets/ML3DBCLO_004.json +++ b/datasets/ML3DBCLO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCLO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBCLO_005.json b/datasets/ML3DBCLO_005.json index 828eaa705e..59e629ecf5 100644 --- a/datasets/ML3DBCLO_005.json +++ b/datasets/ML3DBCLO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCLO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBCO_004.json b/datasets/ML3DBCO_004.json index 5ff72b55ef..ed855c0241 100644 --- a/datasets/ML3DBCO_004.json +++ b/datasets/ML3DBCO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBCO_005.json b/datasets/ML3DBCO_005.json index f4caa215ac..3a330bea10 100644 --- a/datasets/ML3DBCO_005.json +++ b/datasets/ML3DBCO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBCO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBGPH_004.json b/datasets/ML3DBGPH_004.json index 1184c53c3f..feb0f71836 100644 --- a/datasets/ML3DBGPH_004.json +++ b/datasets/ML3DBGPH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBGPH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBGPH_005.json b/datasets/ML3DBGPH_005.json index 0b54fc4cfe..a4774b3bdb 100644 --- a/datasets/ML3DBGPH_005.json +++ b/datasets/ML3DBGPH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBGPH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBH2O_004.json b/datasets/ML3DBH2O_004.json index 23991548c8..a7a2db63d1 100644 --- a/datasets/ML3DBH2O_004.json +++ b/datasets/ML3DBH2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBH2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBH2O_005.json b/datasets/ML3DBH2O_005.json index c1703087d6..87aa97ffed 100644 --- a/datasets/ML3DBH2O_005.json +++ b/datasets/ML3DBH2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBH2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHCL_004.json b/datasets/ML3DBHCL_004.json index 3fcac4d11c..b7bac94794 100644 --- a/datasets/ML3DBHCL_004.json +++ b/datasets/ML3DBHCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHCL_005.json b/datasets/ML3DBHCL_005.json index 5e611b8c21..93a672b18a 100644 --- a/datasets/ML3DBHCL_005.json +++ b/datasets/ML3DBHCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHCN_004.json b/datasets/ML3DBHCN_004.json index 2f60b2892b..d2b910f4e3 100644 --- a/datasets/ML3DBHCN_004.json +++ b/datasets/ML3DBHCN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHCN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHCN_005.json b/datasets/ML3DBHCN_005.json index 25ff1f31d4..c451cce14b 100644 --- a/datasets/ML3DBHCN_005.json +++ b/datasets/ML3DBHCN_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHCN_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHNO3_004.json b/datasets/ML3DBHNO3_004.json index 915583507c..c3591376ce 100644 --- a/datasets/ML3DBHNO3_004.json +++ b/datasets/ML3DBHNO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHNO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHNO3_005.json b/datasets/ML3DBHNO3_005.json index 2751146bd3..dd7e728baa 100644 --- a/datasets/ML3DBHNO3_005.json +++ b/datasets/ML3DBHNO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHNO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHOCL_004.json b/datasets/ML3DBHOCL_004.json index 7db9db94b8..d4d40d4c31 100644 --- a/datasets/ML3DBHOCL_004.json +++ b/datasets/ML3DBHOCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHOCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBHOCL_005.json b/datasets/ML3DBHOCL_005.json index 3b0d29820b..d1e031af6f 100644 --- a/datasets/ML3DBHOCL_005.json +++ b/datasets/ML3DBHOCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBHOCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBIWC_004.json b/datasets/ML3DBIWC_004.json index 1f4f5de651..41ba71fd27 100644 --- a/datasets/ML3DBIWC_004.json +++ b/datasets/ML3DBIWC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBIWC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBIWC_005.json b/datasets/ML3DBIWC_005.json index b7ed36df98..0da5cfb3c6 100644 --- a/datasets/ML3DBIWC_005.json +++ b/datasets/ML3DBIWC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBIWC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBN2O_004.json b/datasets/ML3DBN2O_004.json index 308026ad3a..90b9efa34c 100644 --- a/datasets/ML3DBN2O_004.json +++ b/datasets/ML3DBN2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBN2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBN2O_005.json b/datasets/ML3DBN2O_005.json index 5cb71e8b28..bbaf601b09 100644 --- a/datasets/ML3DBN2O_005.json +++ b/datasets/ML3DBN2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBN2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBO3_004.json b/datasets/ML3DBO3_004.json index bf3c187b36..0f2147ceb5 100644 --- a/datasets/ML3DBO3_004.json +++ b/datasets/ML3DBO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBO3_005.json b/datasets/ML3DBO3_005.json index 098db7f20d..da019fadda 100644 --- a/datasets/ML3DBO3_005.json +++ b/datasets/ML3DBO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBOH_004.json b/datasets/ML3DBOH_004.json index 33be67dd43..4533a011ce 100644 --- a/datasets/ML3DBOH_004.json +++ b/datasets/ML3DBOH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBOH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBOH_005.json b/datasets/ML3DBOH_005.json index a439f99716..9807751862 100644 --- a/datasets/ML3DBOH_005.json +++ b/datasets/ML3DBOH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBOH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBRHI_004.json b/datasets/ML3DBRHI_004.json index b6129a818a..0abe06c2b4 100644 --- a/datasets/ML3DBRHI_004.json +++ b/datasets/ML3DBRHI_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBRHI_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBRHI_005.json b/datasets/ML3DBRHI_005.json index 2534b04c83..f2a9a145e0 100644 --- a/datasets/ML3DBRHI_005.json +++ b/datasets/ML3DBRHI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBRHI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBSO2_004.json b/datasets/ML3DBSO2_004.json index 1da1e17b5c..9176a32e4d 100644 --- a/datasets/ML3DBSO2_004.json +++ b/datasets/ML3DBSO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBSO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBSO2_005.json b/datasets/ML3DBSO2_005.json index 5b937a7001..ecc4a709ac 100644 --- a/datasets/ML3DBSO2_005.json +++ b/datasets/ML3DBSO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBSO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBT_004.json b/datasets/ML3DBT_004.json index 1e92b9603e..764b2b3aaf 100644 --- a/datasets/ML3DBT_004.json +++ b/datasets/ML3DBT_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBT_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DBT_005.json b/datasets/ML3DBT_005.json index 755b5246d6..aec9c65c31 100644 --- a/datasets/ML3DBT_005.json +++ b/datasets/ML3DBT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DBT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCH3CL_004.json b/datasets/ML3DZCH3CL_004.json index 7a13d1bbd9..bbbcec781d 100644 --- a/datasets/ML3DZCH3CL_004.json +++ b/datasets/ML3DZCH3CL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCH3CL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCH3CL_005.json b/datasets/ML3DZCH3CL_005.json index 3d25a0419f..4db7c883de 100644 --- a/datasets/ML3DZCH3CL_005.json +++ b/datasets/ML3DZCH3CL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCH3CL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCLO_004.json b/datasets/ML3DZCLO_004.json index 3e3c1ac63f..3e78672ed7 100644 --- a/datasets/ML3DZCLO_004.json +++ b/datasets/ML3DZCLO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCLO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCLO_005.json b/datasets/ML3DZCLO_005.json index e8f4e8c114..cf41e95faa 100644 --- a/datasets/ML3DZCLO_005.json +++ b/datasets/ML3DZCLO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCLO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCO_004.json b/datasets/ML3DZCO_004.json index 5a793cad6c..3275f868c6 100644 --- a/datasets/ML3DZCO_004.json +++ b/datasets/ML3DZCO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZCO_005.json b/datasets/ML3DZCO_005.json index 64e8634b01..05691961b1 100644 --- a/datasets/ML3DZCO_005.json +++ b/datasets/ML3DZCO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZCO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZGPH_004.json b/datasets/ML3DZGPH_004.json index b3bf85e974..5a1a6d73fd 100644 --- a/datasets/ML3DZGPH_004.json +++ b/datasets/ML3DZGPH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZGPH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZGPH_005.json b/datasets/ML3DZGPH_005.json index f1e1f5f3ae..0a065d8634 100644 --- a/datasets/ML3DZGPH_005.json +++ b/datasets/ML3DZGPH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZGPH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZH2O_004.json b/datasets/ML3DZH2O_004.json index 134ccf824c..bf4f2e509b 100644 --- a/datasets/ML3DZH2O_004.json +++ b/datasets/ML3DZH2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZH2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZH2O_005.json b/datasets/ML3DZH2O_005.json index 191bcf6a44..25ac502176 100644 --- a/datasets/ML3DZH2O_005.json +++ b/datasets/ML3DZH2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZH2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHCL_004.json b/datasets/ML3DZHCL_004.json index 8b7871bb9b..f0d5058727 100644 --- a/datasets/ML3DZHCL_004.json +++ b/datasets/ML3DZHCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHCL_005.json b/datasets/ML3DZHCL_005.json index fa4f682192..95eef5eab5 100644 --- a/datasets/ML3DZHCL_005.json +++ b/datasets/ML3DZHCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHCN_004.json b/datasets/ML3DZHCN_004.json index 5e32489dc2..eaca2435f0 100644 --- a/datasets/ML3DZHCN_004.json +++ b/datasets/ML3DZHCN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHCN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHCN_005.json b/datasets/ML3DZHCN_005.json index f51aaf152b..90eac339ee 100644 --- a/datasets/ML3DZHCN_005.json +++ b/datasets/ML3DZHCN_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHCN_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHNO3_004.json b/datasets/ML3DZHNO3_004.json index 47ff64aeb8..863ed56022 100644 --- a/datasets/ML3DZHNO3_004.json +++ b/datasets/ML3DZHNO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHNO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHNO3_005.json b/datasets/ML3DZHNO3_005.json index 21a195bf6d..d952903bc8 100644 --- a/datasets/ML3DZHNO3_005.json +++ b/datasets/ML3DZHNO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHNO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHOCL_004.json b/datasets/ML3DZHOCL_004.json index edb37dee48..2e1a7acecd 100644 --- a/datasets/ML3DZHOCL_004.json +++ b/datasets/ML3DZHOCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHOCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZHOCL_005.json b/datasets/ML3DZHOCL_005.json index 523dfdecd0..047629dd8b 100644 --- a/datasets/ML3DZHOCL_005.json +++ b/datasets/ML3DZHOCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZHOCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZIWC_004.json b/datasets/ML3DZIWC_004.json index 9144c0ed75..ddf572fd3d 100644 --- a/datasets/ML3DZIWC_004.json +++ b/datasets/ML3DZIWC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZIWC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZIWC_005.json b/datasets/ML3DZIWC_005.json index 6bc5f2f0f9..49b70a607c 100644 --- a/datasets/ML3DZIWC_005.json +++ b/datasets/ML3DZIWC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZIWC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZMBRO_004.json b/datasets/ML3DZMBRO_004.json index 776cba894f..7db42c9c9b 100644 --- a/datasets/ML3DZMBRO_004.json +++ b/datasets/ML3DZMBRO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZMBRO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.", "links": [ { diff --git a/datasets/ML3DZMBRO_005.json b/datasets/ML3DZMBRO_005.json index 682045967f..2e81c89a8c 100644 --- a/datasets/ML3DZMBRO_005.json +++ b/datasets/ML3DZMBRO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZMBRO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.", "links": [ { diff --git a/datasets/ML3DZMHO2_004.json b/datasets/ML3DZMHO2_004.json index 34b6bd9501..3f8352f9e5 100644 --- a/datasets/ML3DZMHO2_004.json +++ b/datasets/ML3DZMHO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZMHO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.", "links": [ { diff --git a/datasets/ML3DZMHO2_005.json b/datasets/ML3DZMHO2_005.json index 0f8ac8c4a9..81140dbfdf 100644 --- a/datasets/ML3DZMHO2_005.json +++ b/datasets/ML3DZMHO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZMHO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.", "links": [ { diff --git a/datasets/ML3DZN2O_004.json b/datasets/ML3DZN2O_004.json index 83b6e0c06c..e5cde8e443 100644 --- a/datasets/ML3DZN2O_004.json +++ b/datasets/ML3DZN2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZN2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZN2O_005.json b/datasets/ML3DZN2O_005.json index 5a697a4ffa..e625d6f7aa 100644 --- a/datasets/ML3DZN2O_005.json +++ b/datasets/ML3DZN2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZN2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZO3_004.json b/datasets/ML3DZO3_004.json index ade8b5b523..00a7a59200 100644 --- a/datasets/ML3DZO3_004.json +++ b/datasets/ML3DZO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZO3_005.json b/datasets/ML3DZO3_005.json index 37d513a4fc..f3c7b8734f 100644 --- a/datasets/ML3DZO3_005.json +++ b/datasets/ML3DZO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZOH_004.json b/datasets/ML3DZOH_004.json index 30902e2ad1..0bb6e76a83 100644 --- a/datasets/ML3DZOH_004.json +++ b/datasets/ML3DZOH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZOH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZOH_005.json b/datasets/ML3DZOH_005.json index ea5af66d76..376943e9ab 100644 --- a/datasets/ML3DZOH_005.json +++ b/datasets/ML3DZOH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZOH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZRHI_004.json b/datasets/ML3DZRHI_004.json index 9878692c78..2d431d8c7b 100644 --- a/datasets/ML3DZRHI_004.json +++ b/datasets/ML3DZRHI_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZRHI_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZRHI_005.json b/datasets/ML3DZRHI_005.json index 53951b8f70..07d31bbfb4 100644 --- a/datasets/ML3DZRHI_005.json +++ b/datasets/ML3DZRHI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZRHI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZSO2_004.json b/datasets/ML3DZSO2_004.json index 30cd9b2238..d9475042fb 100644 --- a/datasets/ML3DZSO2_004.json +++ b/datasets/ML3DZSO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZSO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZSO2_005.json b/datasets/ML3DZSO2_005.json index 2d41735999..6207ff496b 100644 --- a/datasets/ML3DZSO2_005.json +++ b/datasets/ML3DZSO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZSO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZT_004.json b/datasets/ML3DZT_004.json index 1d3c313bb2..c4d3b0f185 100644 --- a/datasets/ML3DZT_004.json +++ b/datasets/ML3DZT_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZT_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3DZT_005.json b/datasets/ML3DZT_005.json index 0977d81d2b..6db9651f04 100644 --- a/datasets/ML3DZT_005.json +++ b/datasets/ML3DZT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3DZT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCH3CL_004.json b/datasets/ML3MBCH3CL_004.json index 4aa70a2d65..a10872bf84 100644 --- a/datasets/ML3MBCH3CL_004.json +++ b/datasets/ML3MBCH3CL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCH3CL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCH3CL_005.json b/datasets/ML3MBCH3CL_005.json index dc8bf8754b..5d5d99ae5b 100644 --- a/datasets/ML3MBCH3CL_005.json +++ b/datasets/ML3MBCH3CL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCH3CL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCLO_004.json b/datasets/ML3MBCLO_004.json index 8f4d25a59c..7650f01e96 100644 --- a/datasets/ML3MBCLO_004.json +++ b/datasets/ML3MBCLO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCLO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCLO_005.json b/datasets/ML3MBCLO_005.json index adcdb5e467..fe7e68c48e 100644 --- a/datasets/ML3MBCLO_005.json +++ b/datasets/ML3MBCLO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCLO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCO_004.json b/datasets/ML3MBCO_004.json index b90789ccd7..1a15ea4d58 100644 --- a/datasets/ML3MBCO_004.json +++ b/datasets/ML3MBCO_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCO_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBCO_005.json b/datasets/ML3MBCO_005.json index 7b1ab3574f..9c0403dcf4 100644 --- a/datasets/ML3MBCO_005.json +++ b/datasets/ML3MBCO_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBCO_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBGPH_004.json b/datasets/ML3MBGPH_004.json index 5862b36ad6..0cc271a954 100644 --- a/datasets/ML3MBGPH_004.json +++ b/datasets/ML3MBGPH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBGPH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBGPH_005.json b/datasets/ML3MBGPH_005.json index 10ebc7ec3e..d32362c74b 100644 --- a/datasets/ML3MBGPH_005.json +++ b/datasets/ML3MBGPH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBGPH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBH2O_004.json b/datasets/ML3MBH2O_004.json index 216497a8b2..6a4b7d5b43 100644 --- a/datasets/ML3MBH2O_004.json +++ b/datasets/ML3MBH2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBH2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBH2O_005.json b/datasets/ML3MBH2O_005.json index 91829001f0..2260537a6e 100644 --- a/datasets/ML3MBH2O_005.json +++ b/datasets/ML3MBH2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBH2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHCL_004.json b/datasets/ML3MBHCL_004.json index a2542de3ac..a6ae29df3d 100644 --- a/datasets/ML3MBHCL_004.json +++ b/datasets/ML3MBHCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHCL_005.json b/datasets/ML3MBHCL_005.json index fbf72b3d0a..e5db2bcbfb 100644 --- a/datasets/ML3MBHCL_005.json +++ b/datasets/ML3MBHCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHCN_004.json b/datasets/ML3MBHCN_004.json index 1d49c736a5..13f61565c3 100644 --- a/datasets/ML3MBHCN_004.json +++ b/datasets/ML3MBHCN_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHCN_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHCN_005.json b/datasets/ML3MBHCN_005.json index 55ee6e8709..59a1b3a6df 100644 --- a/datasets/ML3MBHCN_005.json +++ b/datasets/ML3MBHCN_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHCN_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHNO3_004.json b/datasets/ML3MBHNO3_004.json index bf239bea35..0e99e48c7b 100644 --- a/datasets/ML3MBHNO3_004.json +++ b/datasets/ML3MBHNO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHNO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHNO3_005.json b/datasets/ML3MBHNO3_005.json index 9dea794189..c837759f76 100644 --- a/datasets/ML3MBHNO3_005.json +++ b/datasets/ML3MBHNO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHNO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHOCL_004.json b/datasets/ML3MBHOCL_004.json index 4cc3349283..ebcb197d3f 100644 --- a/datasets/ML3MBHOCL_004.json +++ b/datasets/ML3MBHOCL_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHOCL_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBHOCL_005.json b/datasets/ML3MBHOCL_005.json index 75b9baa7a6..990b788927 100644 --- a/datasets/ML3MBHOCL_005.json +++ b/datasets/ML3MBHOCL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBHOCL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBIWC_004.json b/datasets/ML3MBIWC_004.json index 49c59a7222..27375e1bda 100644 --- a/datasets/ML3MBIWC_004.json +++ b/datasets/ML3MBIWC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBIWC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBIWC_005.json b/datasets/ML3MBIWC_005.json index 69c1977d2a..1ce398f8be 100644 --- a/datasets/ML3MBIWC_005.json +++ b/datasets/ML3MBIWC_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBIWC_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBN2O_004.json b/datasets/ML3MBN2O_004.json index dc8f3425e0..7cb467f7f1 100644 --- a/datasets/ML3MBN2O_004.json +++ b/datasets/ML3MBN2O_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBN2O_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBN2O_005.json b/datasets/ML3MBN2O_005.json index ec26cbec02..854bd64702 100644 --- a/datasets/ML3MBN2O_005.json +++ b/datasets/ML3MBN2O_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBN2O_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBO3_004.json b/datasets/ML3MBO3_004.json index b408258848..6195bd12e4 100644 --- a/datasets/ML3MBO3_004.json +++ b/datasets/ML3MBO3_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBO3_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBO3_005.json b/datasets/ML3MBO3_005.json index 8232379456..358e95f59a 100644 --- a/datasets/ML3MBO3_005.json +++ b/datasets/ML3MBO3_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBO3_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBOH_004.json b/datasets/ML3MBOH_004.json index e9fdeb5777..8bd425d50b 100644 --- a/datasets/ML3MBOH_004.json +++ b/datasets/ML3MBOH_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBOH_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBOH_005.json b/datasets/ML3MBOH_005.json index b61cb6fd20..2771aa5e6b 100644 --- a/datasets/ML3MBOH_005.json +++ b/datasets/ML3MBOH_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBOH_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBRHI_004.json b/datasets/ML3MBRHI_004.json index 91987dd926..d8de7d1b03 100644 --- a/datasets/ML3MBRHI_004.json +++ b/datasets/ML3MBRHI_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBRHI_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBRHI_005.json b/datasets/ML3MBRHI_005.json index 39a0831fb2..11fae52b1f 100644 --- a/datasets/ML3MBRHI_005.json +++ b/datasets/ML3MBRHI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBRHI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBSO2_004.json b/datasets/ML3MBSO2_004.json index 4c347f19fc..987b49bee3 100644 --- a/datasets/ML3MBSO2_004.json +++ b/datasets/ML3MBSO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBSO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBSO2_005.json b/datasets/ML3MBSO2_005.json index 181437acd0..0afdf13f8c 100644 --- a/datasets/ML3MBSO2_005.json +++ b/datasets/ML3MBSO2_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBSO2_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBT_004.json b/datasets/ML3MBT_004.json index 9f9931f099..cb2e1ea97f 100644 --- a/datasets/ML3MBT_004.json +++ b/datasets/ML3MBT_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBT_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/ML3MBT_005.json b/datasets/ML3MBT_005.json index 37a090b052..5eedb981c5 100644 --- a/datasets/ML3MBT_005.json +++ b/datasets/ML3MBT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ML3MBT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information.\n\nThe data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.", "links": [ { diff --git a/datasets/MOCE_0.json b/datasets/MOCE_0.json index 01c2432a23..564e8d8125 100644 --- a/datasets/MOCE_0.json +++ b/datasets/MOCE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOCE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken under the Marine Optical Characterization Experiment between 1992 and 1999 off the US and Mexican Pacific coasts and central Pacific Ocean.", "links": [ { diff --git a/datasets/MOD00F_6.1NRT.json b/datasets/MOD00F_6.1NRT.json index 1d203e7648..c0168b55e2 100644 --- a/datasets/MOD00F_6.1NRT.json +++ b/datasets/MOD00F_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD00F_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Terra Near Real Time(NRT) L0 PDS Data 5-Min Swath.", "links": [ { diff --git a/datasets/MOD01_6.1.json b/datasets/MOD01_6.1.json index 38f9255b50..c9315d28bb 100644 --- a/datasets/MOD01_6.1.json +++ b/datasets/MOD01_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD01_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Raw Radiances in Counts 5-Min L1A Swath product (MOD01) containing reformatted and packaged raw instrument data. MODIS instrument data, in packetized form, is reversibly transformed to a computer data structure, along with formatted engineering and spacecraft ancillary data. The Level-1A data is separated into granules for passage to the geolocation and calibration processes. Quality indicators are added to the data to indicate missing pixels and instrument modes. This product contains MODIS digitized raw detector counts data for all 36 MODIS spectral bands, at 250 m, 500 m, or 1 km spatial resolutions including all time tags, all detector views (Earth, solar diffuser, Spectro-Radiometeric Calibration Assembly (SRCA), black body, and space view), and all engineering and ancillary data. Quality indicators are added to the data to indicate missing or bad pixels and instrument modes. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode. Visible, short-wave infrared (SWIR), and near infrared (NIR) measurements are made during daytime only, while radiances for thermal infrared (TIR) are measured during both day and night portions of the orbit.\n\nData set information:\n\nMODIS Homepage\n\nhttps://modis.gsfc.nasa.gov/data/dataprod/\n\nand\nMODIS Characterization Support Team\nhttps://mcst.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/MOD01_6.1NRT.json b/datasets/MOD01_6.1NRT.json index eb9c0e99ec..6f0f6a37ba 100644 --- a/datasets/MOD01_6.1NRT.json +++ b/datasets/MOD01_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD01_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is MODIS Level-1A Near Real Time (NRT) product containing reformatted and packaged raw instrument data. MODIS instrument data, in packetized form, is reversibly transformed to a computer data structure, along with formatted engineering and spacecraft ancillary data. The Level-1A data is separated into granules for passage to the geolocation and calibration processes. Quality indicators are added to the data to indicate missing pixels and instrument modes. This product contains MODIS digitized raw detector counts data for all 36 MODIS spectral bands, at 250 m, 500 m, or 1 km spatial resolutions including all time tags, all detector views (Earth, solar diffuser, Spectro-Radiometeric Calibration Assembly (SRCA), black body, and space view), and all engineering and ancillary data. Quality indicators are added to to the data to indicate missing or bad pixels and instrument modes. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode. Visible, SWIR, and NIR measurements are made during daytime only, while radiances for TIR are measured during both day and night portions of the orbit.", "links": [ { diff --git a/datasets/MOD021KM_6.1.json b/datasets/MOD021KM_6.1.json index 5ae1860a99..f5f1e7153f 100644 --- a/datasets/MOD021KM_6.1.json +++ b/datasets/MOD021KM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD021KM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nThe shortname for this product is MOD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size is approximately 110 MB (a day granule around 150MB and a night granule around 70 MB).\r\n\r\nIn this new version (Collection 6.1) of MOD021KM, an advanced technique is introduced to mitigate the crosstalk contamination issue among the LWIR PV bands \r\nusing data from lunar calibration events. \r\n\r\nThe electronic crosstalk contamination issue in the Terra long-wave infrared photovoltaic (LWIR PV) bands (27 -30), has existed since the beginning of the mission but become more noticeable during later half of the mission. The electronic crosstalk is where signal from each of the detectors among the LWIR PV bands can leak to the other detectors, producing a false signal contribution. This contamination has had a noticeable effect on the MODIS science products since 2010 for band 27, and since 2012 for bands 28 and 29.\r\n\r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\nor visit the Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD021KM_6.1NRT.json b/datasets/MOD021KM_6.1NRT.json index 767f7ca169..2e40ab85c4 100644 --- a/datasets/MOD021KM_6.1NRT.json +++ b/datasets/MOD021KM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD021KM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of \n\nelectromagentic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\n\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\n\nThe Shortname for this product is MOD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size would be approximately 110 MB (a day granule around 150MB and a night granule around 70 MB).\n\nIn this new version (Collection 6.1) of MOD021KM, an advanced technique is introduced to mitigate the crosstalk contamination issue among the LWIR PV bands \nusing data from lunar calibration events. \n\nThe electronic crosstalk contamination issue in the Terra long-wave infrared photovoltaic (LWIR PV) bands (27 -30), has existed since the beginning of the mission but become more noticeable during later half of the mission. The electronic crosstalk is where signal from each of the detectors among the LWIR PV bands can leak to the other detectors, producing a false signal contribution. This contamination has had a noticeable effect on the MODIS science products since 2010 for band 27, and since 2012 for bands 28 and 29.\n\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\n\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\n\nhttp://mcst.gsfc.nasa.gov/l1b/product-information\n\nor visit Science Team homepage at:\nhttp://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD02HKM_6.1.json b/datasets/MOD02HKM_6.1.json index e3616284f6..453d6201e7 100644 --- a/datasets/MOD02HKM_6.1.json +++ b/datasets/MOD02HKM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02HKM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nChannels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.\r\n \r\nSpatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. \r\n\r\nTo summarize, the MODIS L1B 500 m data product consists of:\r\n \r\n1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution\r\n \r\n2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands\r\n \r\n3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.\r\n \r\n4. Calibration data for all channels (scale and offset) \r\n \r\n5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . \r\n \r\nThe shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB.\r\n \r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD02HKM_6.1NRT.json b/datasets/MOD02HKM_6.1NRT.json index 364baada27..f163b5a477 100644 --- a/datasets/MOD02HKM_6.1NRT.json +++ b/datasets/MOD02HKM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02HKM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 500 meter MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\n\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\nChannel locations for the MODIS 500 meter data are as follows:\n \n Band Center Wavelength (um) Primary Use\n ---- ---------------------- -----------\n 1 0.620 - 0.670 Land/Cloud Boundaries\n 2 0.841 - 0.876 Land/Cloud Boundaries\n 3 0.459 - 0.479 Land/Cloud Properties\n 4 0.545 - 0.565 Land/Cloud Properties\n 5 1.230 - 1.250 Land/Cloud Properties\n 6 1.628 - 1.652 Land/Cloud Properties\n 7 2.105 - 2.155 Land/Cloud Properties\n \nChannels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.\n \nSpatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. \n\nTo summarize, the MODIS L1B 500 m data product consists of:\n \n1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution\n \n2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands\n \n3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD ttp://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf .\n \n4. Calibration data for all channels (scale and offset) \n \n5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization Users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS http://ladsweb.nascom.nasa.gov/ . \n \nThe Shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB.\n \nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\n\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\n\nhttp://mcst.gsfc.nasa.gov/l1b/product-information\n\n\nor visit Science Team homepage at:\nhttp://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD02QKM_6.1.json b/datasets/MOD02QKM_6.1.json index 82b8274eec..6cc27c31c1 100644 --- a/datasets/MOD02QKM_6.1.json +++ b/datasets/MOD02QKM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02QKM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Calibrated Radiances 5Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which have been processed to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. \r\n\r\nSeparate L1B products are available for the five 500m resolution channels (MOD02HKM) and the twenty-nine 1km resolution channels (MOD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km\r\npixel values.\r\n\r\nSpatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle.\r\n\r\nThe shortname for this product is MOD02QKM and is stored in the Earth Observing System Hierarchical\r\nData Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB.\r\n\r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\nor visit the Science Team homepage at: \r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD02QKM_6.1NRT.json b/datasets/MOD02QKM_6.1NRT.json index 6ec4045c34..e9c54def92 100644 --- a/datasets/MOD02QKM_6.1NRT.json +++ b/datasets/MOD02QKM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02QKM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 250 meter MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. \nChannel locations for the MODIS 250 meter data are as follows:\n\nBand Center Wavelength (um) Primary Use\n---- ---------------------- -----------\n1 0.620 - 0.670 Land/Cloud Boundaries\n\n2 0.841 - 0.876 Land/Cloud Boundaries\n\nSeparate L1B products are available for the five 500m resolution channels (MOD02HKM) and the twenty-nine 1km resolution channels (MOD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km\npixel values.\n\nSpatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle.\n\nThe Shortname for this product is MOD02QKM and is stored in the Earth Observing System Hierarchical\nData Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB.\n\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\n\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\n\nhttp://mcst.gsfc.nasa.gov/l1b/product-information\n\n\nor visit Science Team homepage at: \nhttp://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD02SSH_6.1.json b/datasets/MOD02SSH_6.1.json index 57b9a93fd2..6034044f6c 100644 --- a/datasets/MOD02SSH_6.1.json +++ b/datasets/MOD02SSH_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02SSH_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Level 1B Subsampled Calibrated Radiances 5km (MOD02SSH) is a subsample from the MODIS Level 1B 1-km data. Every fifth pixel is taken from the MOD021KM product and written out to MOD02SSH. The subsampling starts at the third frame, and at the third line. Here, \"frame\" and \"line\" are naming conventions for pixels along and across the scan, respectively. Since MOD02SSH is a subsampled Level 1B , many things from the Level 1B documentation apply as well. That is, the MOD02SSH data product contains calibrated and geolocated at-aperture radiances for 36 bands generated from MODIS Level 1A scans of raw radiance (MOD01). The radiance units are in W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared (SWIR), and Near Infrared (NIR) measurements are made during daytime only, while radiances for Thermal Infrared (TIR) are measured continuously. \n\nAs its parent, the MOD02SSH is in HDF-EOS format, and all metadata structures and names are preserved for better convenience. However, some relevant changes are made where appropriate (e.g., the dimension mappings are updated to reflect the new one-to-one correspondence between the data and geolocations). The latter is one of the most important differences: in the MOD02SSH, there is no offset between data and geolocation pixels. The spatial coverage is almost similar to that from MOD021KM (nominally it is 2330 by 2030 km, cross-track by along-track, respectively). The MOD02SSH is produced continuously, and thus the processing provides 2-day repeat observations of the Earth with a repeat orbit pattern every 16 days.", "links": [ { diff --git a/datasets/MOD02SSH_6.1NRT.json b/datasets/MOD02SSH_6.1NRT.json index 2a989a223c..e848d42e61 100644 --- a/datasets/MOD02SSH_6.1NRT.json +++ b/datasets/MOD02SSH_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD02SSH_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near Real Time (NRT) data type (MOD02SSH) is a subsample from the MODIS Level 1B 1-km data. Every fifth pixel is taken from the MOD021KM product and written out to MOD02SSH. The subsampling starts at the third frame, and at the third line. Here, \"frame\" and \"line\" are naming conventions for pixels along and across the scan, respectively. Since MOD02SSH is a subsampled Level 1B , many things from the Level 1B documentation apply as well. That is, the MOD02SSH data productcontains calibrated and geolocated at-aperture radiances for 36 bands generated from MODIS Level 1A scans of raw radiance (MOD 01). The radiance units are in W/(m ^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared (SWIR), and Near Infrared (NIR) measurements are made during daytime only, while radiances for Thermal Infrared (TIR) are measured continuously.As it's parent, the MOD02SSH is in HDF-EOS format, and all metadata structures and names are preserved for better convenience. However, some relevant changes are made where appropriate. E.g. the dimension mappings are updated to reflect the new one-to-one correspondance between the data and geolocations. The latter is one of the most important differences: in the MOD02SSH, there is no offset between data and geolocation pixels. The spatial coverage is almost similar to that from MOD021KM (nominally it is 2330 by 2030 km, cross-track by along-track, respectively). The MOD02SSH is produced continuously, and thus the processing provides 2-day repeat observations of the Earth with a repeat orbitpattern every 16 days.The shortname for this product is MOD02SSH", "links": [ { diff --git a/datasets/MOD03_6.1.json b/datasets/MOD03_6.1.json index 7430f772e4..d19482d323 100644 --- a/datasets/MOD03_6.1.json +++ b/datasets/MOD03_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD03_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Geolocation Fields 5Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to claculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team.\r\n\r\nThe short name for this product is MOD03. Each file is roughly 30 MB in size, and the total data volume is approximately 8 GB/day.\r\n\r\nSee the MODIS Science Team homepage for more data set\r\ninformation: https://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MOD03_6.1NRT.json b/datasets/MOD03_6.1NRT.json index 5d65f0a907..67d086d119 100644 --- a/datasets/MOD03_6.1NRT.json +++ b/datasets/MOD03_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD03_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team.The shortname for this product is MOD03.", "links": [ { diff --git a/datasets/MOD04_3K_6.1.json b/datasets/MOD04_3K_6.1.json index 19215d48b8..6de2e27427 100644 --- a/datasets/MOD04_3K_6.1.json +++ b/datasets/MOD04_3K_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD04_3K_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MODIS/Terra Aerosol 5 Min L2 Swath 3km (MOD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\r\n\r\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community.\r\n\r\nThe MOD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of \"good\" pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attemptes a \"poor quality\" retrieval while 3km algorithm does not. Everything else is the same between two products.\r\n\r\nFor more information on C6.1 changes and updates, visit the MODIS Atmosphere website at:\r\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MOD04_3K_6.1NRT.json b/datasets/MOD04_3K_6.1NRT.json index 2f96dd352a..6d17dcb3b2 100644 --- a/datasets/MOD04_3K_6.1NRT.json +++ b/datasets/MOD04_3K_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD04_3K_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MOD04_3K product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\n\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community.\n\nThe MOD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of good” pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attemptes a poor quality retrieval while 3km algorithm does not. Everything else is same in two products.\n\nFor more information on C6.1 changes and updates, visit the MODIS Atmosphere website at:\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MOD04_L2_6.1.json b/datasets/MOD04_L2_6.1.json index c7bb3a8699..de1d029a49 100644 --- a/datasets/MOD04_L2_6.1.json +++ b/datasets/MOD04_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD04_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol 5-Min L2 Swath 10km (MOD04_L2) product provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces. Both results are provided on a 10x10 pixel scale (10 km at nadir). Each MOD04_L2 product file covers a five-minute time interval. The output grid is 135 pixels in width by 203 pixels in length. Every tenth file has an output grid size of 135 by 204 pixels. MOD04_L2 product files are stored in Hierarchical Data Format (HDF-EOS).\r\n\r\nThe new Collection 6.1 (C61) MOD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\r\n\r\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5 and in earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community.\r\n\r\n\r\nFor more information visit the MODIS Atmosphere website at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/aerosol\r\n\r\nAnd, for C6.1 changes and updates, visit:\r\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MOD04_L2_6.1NRT.json b/datasets/MOD04_L2_6.1NRT.json index 95d3732531..602481540a 100644 --- a/datasets/MOD04_L2_6.1NRT.json +++ b/datasets/MOD04_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD04_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MOD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\n\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community.\n\nFor more information visit the MODIS Atmosphere website at:\nhttps://modis-atmos.gsfc.nasa.gov/products/aerosol\n\nAnd, for C6.1 changes and updates, visit:\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MOD05_L2_6.1.json b/datasets/MOD05_L2_6.1.json index 18b382cb02..a07c264065 100644 --- a/datasets/MOD05_L2_6.1.json +++ b/datasets/MOD05_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD05_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MOD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. There are two different algorithms used to derive total precipitable water vapor in this data product: a near-infrared algorithm and an infrared algorithm. The near-infrared algorithm relies on observations of reflected solar radiation in MODIS's near-infrared channels, thus, the near-infrared retrievals are only produced during the daytime over surfaces that reflect near-infrared energy. As a result, the near-infrared algorithm is only applied over clear, cloud free land areas of the globe and above clouds over both the land and ocean. Over clear ocean areas, water-vapor estimates are provided over extended sun glint areas. Data produced by the near-infrared algorithm are generated at a 1-km spatial resolution. \r\n\r\nThe other algorithm is the infrared algorithm which can be used to derive atmospheric precipitable water vapor profiles during both day and night. Data from the infrared algorithm are generated at a 5-km spatial resolution when at least nine field of views (FOVs) are cloud free. The infrared-derived precipitable water vapor is generated as a component of product MOD07, and is simply added to product MOD05 for convenience. There are two MODIS Precipitable Water Vapor products: MOD05_L2, containing data collected from the Terra platform; and MYD05_L2, containing data collected from the Aqua platform. This dataset has a short name of MOD05_L2 and provides data from the Terra platform only. \r\n\r\nThe MODIS Adaptive Processing System (MODAPS) is currently generating an improved version 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. The decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data, which have a negative impact in varying degrees in downstream products. It should be noted that the near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for Collection 6.1.\r\n\r\nFor more information, visit the MODIS Atmosphere website at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/water-vapor", "links": [ { diff --git a/datasets/MOD05_L2_6.1NRT.json b/datasets/MOD05_L2_6.1NRT.json index fd292e3299..0dc0ad4f8f 100644 --- a/datasets/MOD05_L2_6.1NRT.json +++ b/datasets/MOD05_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD05_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Adaptive Processing System (MODAPS) is currently generating an improved Collection 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. This decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data. These L1B issues had a negative impact in varying degrees in downstream products,\n\nThe MODIS level-2 atmospheric precipitable water product consists of total atmospheric column water vapor amounts (and ancillary parameters) over clear land areas of the globe, over extended clear oceanic areas with the Sun glint, and above clouds over both land and ocean. The shortname for this level-2 MODIS total precipitable water vapor product is MOD05_L2. In Collection 6, MODIS column water vapor (MOD05) datasets continue to be separately available from infrared and near-infrared methods.\n\nThe estimates based on a near-infrared algorithm uses only daytime measurements with solar zenith angle less than 72 degrees. The retrieval algorithm relies on observations of water vapor attenuation of near-infrared solar radiation reflected by surfaces and clouds. The product is produced only over areas that have reflective surfaces in the near-infrared. The near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for C6.1", "links": [ { diff --git a/datasets/MOD06_L2_6.1.json b/datasets/MOD06_L2_6.1.json index 987b9067c7..3c3b0df351 100644 --- a/datasets/MOD06_L2_6.1.json +++ b/datasets/MOD06_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD06_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Clouds 5-Min L2 Swath 1km and 5km product (MOD06_L2) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MOD06_L2. MOD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MOD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length.\r\n\r\nC6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily to ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties fill values; no updates to retrieval science are implemented.\r\n\r\n\r\nThe MODIS Cloud Product is used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution.\r\n\r\nFor more information about the MOD06_L2 product, visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud", "links": [ { diff --git a/datasets/MOD06_L2_6.1NRT.json b/datasets/MOD06_L2_6.1NRT.json index 31009162ae..6c5a2fa293 100644 --- a/datasets/MOD06_L2_6.1NRT.json +++ b/datasets/MOD06_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD06_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MOD06_L2.MOD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MOD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length.\n\nOn 18 February 2016, the Terra spacecraft, and therefore MODIS, unexpectedly entered safe hold\nmode during an inclination adjustment maneuver (IAM). After resuming operation on 24 February\n2016, several infrared (IR) channels exhibited significant degradation, namely increased electronic\ncross talk in the 8.5μm (Band 29) and immediately surrounding channels. This cross talk,\nmanifesting as a “warming” of Band 29, exacerbated a previously identified Band 29 “warming”\n(cross talk) trend that caused a spurious cloud mask trend over the tropics starting around 2010.\nFollowing extensive analysis, a cross talk correction was developed for the affected IR channels,\nand the decision was made to reprocess the entire Terra-MODIS L1B record to include this fix.\nThe Atmosphere Team subsequently decided to piggyback the Terra-MODIS L1B reprocessing\neffort to implement numerous low-impact updates to the C6 atmosphere products.\n\nC6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily\nto ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties\nfill values; no updates to retrieval science are implemented.\n\nFor more information about the MODIS Cloud product, visit the MODIS-Atmosphere site at:\n\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud\n\nFor more details regarding dataset changes read the document at https://modis-atmos.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MOD07_L2_6.1.json b/datasets/MOD07_L2_6.1.json index 29790d1f20..20f511eded 100644 --- a/datasets/MOD07_L2_6.1.json +++ b/datasets/MOD07_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD07_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MOD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free.\r\n\r\nThe MODIS total-ozone burden is an estimate of the total-column tropospheric and stratospheric ozone content. The MODIS atmospheric stability consists of three daily Level 2 atmospheric stability indices. The Total Totals (TT), the Lifted Index (LI), and the K index (K) are each computed using the infrared temperature- and moisture-profile data, also derived as part of MOD07. The MODIS temperature and moisture profiles are produced at 20 vertical levels. The MODIS atmospheric water-vapor product is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes.\r\n\r\nAdditional information is available at: \r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/atm-profile.", "links": [ { diff --git a/datasets/MOD07_L2_6.1NRT.json b/datasets/MOD07_L2_6.1NRT.json index f18a77f895..c8ba37c5e4 100644 --- a/datasets/MOD07_L2_6.1NRT.json +++ b/datasets/MOD07_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD07_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-2 MODIS Temperature and Water Vapor Profile Product MOD07_L2 consists of 30 gridded parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 FOVs are cloud free. \n\nThe atmospheric profiles are produced at 20 vertical atmospheric levels (5., 10., 20., 30., 50., 70., 100., 150., 200., 250., 300., 400., 500., 620., 700., 780., 850., 920., 950., 1000. mbar) The water vapor parameter is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes. The thermal band 9.6 micron is used for retrieving total ozone burden. \n\nThe shortname for this Level-2 MODIS atmospheric profile product is MOD07_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel ( paulm@ssec.wisc.edu).The MODIS atmospheric profile (MOD07_L2) product contains data that has a spatial resolution (pixel size) of 5 x 5 kilometers (at nadir). Each MOD07_L2 product file covers a five-minute time interval, which means the MOD07_L2 output grid is 270 5-km pixels in width and 406 5-km pixels in length for nine consecutive granules. Every tenth granule has an output grid size of 270 by 408 pixels.\n\nMOD07_L2 product files are stored in Hierarchical Data Format(HDF-EOS). Twenty eight of the 30 gridded cloud parameters(5-kilometer pixel resolution) are stored as Scientific Data Sets (SDS) within the file, the remaining two algorithmic static parameters (band number and presure level) are stored as Vdata(table arrays). Cloud Mask SDS, derived from the 1-km MOD35_L2 Cloud Mask parameter, is remapped to 5-km resolution, by using only the center 1-km pixel in the 5x5 pixel retrieval array. The remaining two (band number and static pressure levels) are stored as Vdata(table arrays) Each file is roughly 8 MB in size, and the total data volume is approximately 2 GB/day.\nMOD07_L2 Data Group and Parameters: Spatial & Temporal Resolution:\nLatitude & LongitudeScan start time\n\nSolar and Sensor Viewing Geometry:\n\nSolar zenith & Solar azimuth angleSensor zenith & Sensor azimuth angle\n\nStatic Algorithm Parameters:\nMODIS band number; Pressure levels Atmospheric & \n\nSurface Pressure:\n\nRetrieved Geopotential Height ProfileTropopause HeightSurface Elevation, Surface Pressure Atmospheric & Surface Temperature:Guess & Retrieved Temperature ProfilesBrightness Temperature and Skin Temperature\n\nAtmospheric Moisture:\n\nGuess Mixing ratio ProfileRetrieved Dew Point Temperature Profile\nAtmospheric Stability Indices:\n\nTotal Totals, Lifted Index, and K-index \n\nAtmospheric Trace Gases:\n\nTotal Ozone BurdenTotal Column Precipitable Water Vapor - IR RetrievalTotal Column Precipitable Water Vapor - Direct IR RetrievalWater Vapor(Low & High)Retrieved Water Vapour Mixing Ratio Profile Quality Assurance & Statistical Parameters:Quality Assurance Parameters Run time QA flags MODIS Cloud Mask Processing Flag These parameters are very essential in the characterization of the atmosphere, atmospheric correction of remotely sensed surface parameters, and prediction of convective clouds and thunderstorms. \n\nFor more information about the MOD07_L2 product, visit the MODIS-Atmosphere site at:\n\nhttps://modis-atmos.gsfc.nasa.gov/products/atm-profile", "links": [ { diff --git a/datasets/MOD08_D3_6.1.json b/datasets/MOD08_D3_6.1.json index 4e636e784d..eb5bd07c3b 100644 --- a/datasets/MOD08_D3_6.1.json +++ b/datasets/MOD08_D3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD08_D3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MOD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MOD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length.\r\n\r\nMOD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MOD08_D3 product, please visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/daily", "links": [ { diff --git a/datasets/MOD08_E3_6.1.json b/datasets/MOD08_E3_6.1.json index 1f0a1ebd28..86d076e17a 100644 --- a/datasets/MOD08_E3_6.1.json +++ b/datasets/MOD08_E3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD08_E3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MOD08_E3) contains 8-Day 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MOD08_E3 contains nearly 1000 statistical datasets (SDS's) that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans an 8-Day interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length.\r\n\r\nMOD08_E3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS 8-Day Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MOD08_E3 product, please visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/eight-day", "links": [ { diff --git a/datasets/MOD08_M3_6.1.json b/datasets/MOD08_M3_6.1.json index 5d7dd8a07e..9820ac4d7c 100644 --- a/datasets/MOD08_M3_6.1.json +++ b/datasets/MOD08_M3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD08_M3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MOD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MOD08_M3 contains roughly 800 statistical datasets that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are sorted into 1x1 degree cells on an equal-angle grid that spans a (calendar) monthly interval and then summarized over the globe. MOD08_M3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS monthly Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MOD08_M3 product, please visit the MODIS-Atmosphere site at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/monthly", "links": [ { diff --git a/datasets/MOD09A1G_EVI_6.json b/datasets/MOD09A1G_EVI_6.json index 0a0b36b4c1..1ee28f851c 100644 --- a/datasets/MOD09A1G_EVI_6.json +++ b/datasets/MOD09A1G_EVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1G_EVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 500m SIN Grid product, with short-name MOD09A1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 500-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.", "links": [ { diff --git a/datasets/MOD09A1G_NDVI_6.json b/datasets/MOD09A1G_NDVI_6.json index b9027557c6..ffa6586f7b 100644 --- a/datasets/MOD09A1G_NDVI_6.json +++ b/datasets/MOD09A1G_NDVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1G_NDVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 500m SIN Grid product, with short-name MOD09A1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 500-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.", "links": [ { diff --git a/datasets/MOD09A1N_6.1NRT.json b/datasets/MOD09A1N_6.1NRT.json index 1bacabdd08..a4fa91894d 100644 --- a/datasets/MOD09A1N_6.1NRT.json +++ b/datasets/MOD09A1N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Surface Reflectance Rolling-8-Day L3 Global 500m SIN Grid Near Real Time (NRT) product provides Bands 1-7 at 500-meter resolution in a daily rolling 8-day gridded level-3 product in the Sinusoidal projection. The short name of this product is MOD09A1N. Each MOD09A1N pixel contains the best possible L2G observation during an 8-day period as selected on the basis of high observation coverage, low view angle, the absence of clouds or cloud shadow, and aerosol loading. Science Data Sets (SDS) provided for this product include reflectance values for Bands 1-7, quality assessment, and the day of the year for the pixel along with solar, view, and zenith angles.", "links": [ { diff --git a/datasets/MOD09A1P_EVI_6.json b/datasets/MOD09A1P_EVI_6.json index 6ffc3f01a9..3d68e3c67c 100644 --- a/datasets/MOD09A1P_EVI_6.json +++ b/datasets/MOD09A1P_EVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1P_EVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra EVI Phenology annual L4 500m SIN Grid product, with short-name MOD09A1P_EVI is a Gap-filled Smoothed EVI created from the MOD09A1 8-day Surface Reflectance product. The spatial resolution is 500 m. MOD09A1P_NDVI is stored in Hierarchical Data Format (HDF) with sinusodial projection, same as other standard MODIS land products.", "links": [ { diff --git a/datasets/MOD09A1P_NDVI_6.json b/datasets/MOD09A1P_NDVI_6.json index ef25ce6990..bc0c52ab90 100644 --- a/datasets/MOD09A1P_NDVI_6.json +++ b/datasets/MOD09A1P_NDVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1P_NDVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra NDVI Phenology annual L4 500m SIN Grid product, with short-name MOD09A1P_NDVI is a Gap-filled Smoothed NDVI created from the MOD09A1 8-day Surface Reflectance product. The spatial resolution is 500 m. MOD09A1P_NDVI is stored in Hierarchical Data Format (HDF) with sinusodial projection, same as other standard MODIS land products.", "links": [ { diff --git a/datasets/MOD09A1_061.json b/datasets/MOD09A1_061.json index 31b71726a5..197075677f 100644 --- a/datasets/MOD09A1_061.json +++ b/datasets/MOD09A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MOD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n\nImprovements/Changes from Previous Versions \n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD09CMA_6.1NRT.json b/datasets/MOD09CMA_6.1NRT.json index a6cdf9aa3b..cbb2321139 100644 --- a/datasets/MOD09CMA_6.1NRT.json +++ b/datasets/MOD09CMA_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09CMA_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol Optical Thickness Daily L3 Global 0.05Deg CMA Neal Real Time (NRT) Product (MOD09CMA) is a daily level 3 and global product. It is in linear latitude and longitude (Plate Carre) projection with a 0.05Deg spatial resolution. This product is derived from MOD09IDN, MOD09IDT and MOD09IDS for each orbit by compositing the data on the basis of minimum band 3 (459 - 479 nm band) values (after excluding pixels flagged for clouds and high solar zenith angles).", "links": [ { diff --git a/datasets/MOD09CMG_061.json b/datasets/MOD09CMG_061.json index 84549e234a..169718c49a 100644 --- a/datasets/MOD09CMG_061.json +++ b/datasets/MOD09CMG_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09CMG_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MOD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD09CMG_6.1NRT.json b/datasets/MOD09CMG_6.1NRT.json index fc7a2122b9..00ca1ba359 100644 --- a/datasets/MOD09CMG_6.1NRT.json +++ b/datasets/MOD09CMG_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09CMG_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Surface Reflectance Daily L3 Global 0.05Deg CMG Near Real Time (NRT) product provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products. The short name for this product is MOD09CMG which provides Bands 1 through 7 in a daily level-3 product gridded on a simple 0.05 degree (5600-meter) Geographic projection. Data for each pixel is selected on the basis of low solar zenith angle, minimum Band 3 (blue) reflectance, and absence of cloud from level-3 intermediate files. Science Data Sets provided for this product include reflectance values for Bands 1???7, brightness temperatures for Bands 20, 21, 31, and 32, solar and view zenith angles, relative azimuth angle, ozone, granule time, and quality assessment.", "links": [ { diff --git a/datasets/MOD09GA_061.json b/datasets/MOD09GA_061.json index 81e061842d..82d27330bc 100644 --- a/datasets/MOD09GA_061.json +++ b/datasets/MOD09GA_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09GA_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. ", "links": [ { diff --git a/datasets/MOD09GA_6.1NRT.json b/datasets/MOD09GA_6.1NRT.json index 5290a544f7..a32b5bbf8b 100644 --- a/datasets/MOD09GA_6.1NRT.json +++ b/datasets/MOD09GA_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09GA_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid Near Real Time (NRT) product is an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products. This product, with short name MOD09GA provides Bands 1-7 in a daily gridded L2G product in the Sinusoidal projection, which includes 500-meter reflectance values and 1-kilometer observation and geolocation statistics. 500-meter Science Data Sets provided by this product include reflectance for Bands 1-7, a quality rating, observation coverage, observation number, and 250-meter scan information. 1-kilometer Science Data Sets provided include number of observations, quality state, sensor angles, solar angles, geolocation flags, and orbit pointers. ", "links": [ { diff --git a/datasets/MOD09GQ_061.json b/datasets/MOD09GQ_061.json index ee64f346cd..fafc6370cc 100644 --- a/datasets/MOD09GQ_061.json +++ b/datasets/MOD09GQ_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09GQ_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). \r\n\r\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MOD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD09GQ_6.1NRT.json b/datasets/MOD09GQ_6.1NRT.json index 2aa036365a..022ae1f785 100644 --- a/datasets/MOD09GQ_6.1NRT.json +++ b/datasets/MOD09GQ_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09GQ_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid, Near Real Time (NRT) like other Surface Reflectance products are an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products. This product, with short name MOD09GQ provides Bands 1 and 2 at a 250-meter resolution in a daily gridded L2G product in the Sinusoidal projection. Science Data Sets provided for this product include reflectance for Bands 1 and 2 a quality rating observation coverage and observation number. This product is meant to be used in conjunction with MOD09GA where important quality and viewing geometry information is stored. ", "links": [ { diff --git a/datasets/MOD09Q1G_EVI_6.json b/datasets/MOD09Q1G_EVI_6.json index 6f7ac9d601..dcbf1b29b0 100644 --- a/datasets/MOD09Q1G_EVI_6.json +++ b/datasets/MOD09Q1G_EVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1G_EVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 250m SIN Grid product, with short-name MOD09Q1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 250-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.", "links": [ { diff --git a/datasets/MOD09Q1G_NDVI_6.json b/datasets/MOD09Q1G_NDVI_6.json index 0fadb1ceeb..41fc030651 100644 --- a/datasets/MOD09Q1G_NDVI_6.json +++ b/datasets/MOD09Q1G_NDVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1G_NDVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 250m SIN Grid product, with short-name MOD09Q1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 250-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.", "links": [ { diff --git a/datasets/MOD09Q1N_6.1NRT.json b/datasets/MOD09Q1N_6.1NRT.json index 987adb4b1e..c3568d89d9 100644 --- a/datasets/MOD09Q1N_6.1NRT.json +++ b/datasets/MOD09Q1N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Surface Reflectance Rolling-8-Day L3 Global 250m SIN Grid Near Real Time (NRT) product, MOD09Q1N provides Band 1 and 2 data at 250 meter resolution in a daily rolling 8-day gridded level-3 product in the Sinusoidal projection. Each MOD09Q1N pixel contains the best possible L2G observation during an 8-day period as selected on the basis of high observation coverage low view angle the absence of clouds or cloud shadow and aerosol loading. Science Data Sets provided for this product include reflectance values for Bands 1 and 2 and a quality rating.", "links": [ { diff --git a/datasets/MOD09Q1P_EVI_6.json b/datasets/MOD09Q1P_EVI_6.json index 4d8b5aab39..ce6569fc2a 100644 --- a/datasets/MOD09Q1P_EVI_6.json +++ b/datasets/MOD09Q1P_EVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1P_EVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra EVI Phenology annual L4 250m SIN Grid product, with short-name MOD09Q1P_EVI is a Gap-filled Smoothed EVI created from the MOD09A1 8-day Surface Reflectance product. The spatial resolution is 250 m. MOD09Q1P_NDVI is stored in Hierarchical Data Format (HDF) with sinusodial projection, same as other standard MODIS land products.", "links": [ { diff --git a/datasets/MOD09Q1P_NDVI_6.json b/datasets/MOD09Q1P_NDVI_6.json index ef9ede53d3..7239995f0f 100644 --- a/datasets/MOD09Q1P_NDVI_6.json +++ b/datasets/MOD09Q1P_NDVI_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1P_NDVI_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra NDVI Phenology annual L4 250m SIN Grid product, with short-name MOD09Q1P_NDVI is a Gap-filled Smoothed NDVI created from the MOD09A1 8-day Surface Reflectance product. The spatial resolution is 250 meter. MOD09Q1P_NDVI is stored in Hierarchical Data Format (HDF) with sinusodial projection, same as other standard MODIS land products.", "links": [ { diff --git a/datasets/MOD09Q1_061.json b/datasets/MOD09Q1_061.json index 7920312183..3bb58a895c 100644 --- a/datasets/MOD09Q1_061.json +++ b/datasets/MOD09Q1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09Q1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MOD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD09_6.1.json b/datasets/MOD09_6.1.json index 7573657098..ce47efef7d 100644 --- a/datasets/MOD09_6.1.json +++ b/datasets/MOD09_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MOD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).", "links": [ { diff --git a/datasets/MOD09_6.1NRT.json b/datasets/MOD09_6.1NRT.json index 3974200bdc..2006815b89 100644 --- a/datasets/MOD09_6.1NRT.json +++ b/datasets/MOD09_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD09_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Near Real Time (NRT) L2 Surface Reflectance, 5-Min Swath 250m, 500m, and 1km (MOD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). This product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), BRDF, thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).", "links": [ { diff --git a/datasets/MOD10A1F_61.json b/datasets/MOD10A1F_61.json index f67584f096..ef1408c24f 100644 --- a/datasets/MOD10A1F_61.json +++ b/datasets/MOD10A1F_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10A1F_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells\u00a0in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided\u00a0which tracks\u00a0the number of days in each cell since the last clear-sky observation. Each data granule contains a 10\u00b0 x 10\u00b0 tile projected to the 500 m sinusoidal grid.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10A1_61.json b/datasets/MOD10A1_61.json index 3029b921a1..0b9d356175 100644 --- a/datasets/MOD10A1_61.json +++ b/datasets/MOD10A1_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10A1_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Terra Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MOD10_L2.061). Each data granule is a 10\u00b0x10\u00b0 tile projected to a 500 m sinusoidal grid.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10A2_61.json b/datasets/MOD10A2_61.json index 4f75656cf6..969458bd96 100644 --- a/datasets/MOD10A2_61.json +++ b/datasets/MOD10A2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10A2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10\u00b0 x 10\u00b0 MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061). A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10C1_61.json b/datasets/MOD10C1_61.json index bec6be435d..dc7f195a07 100644 --- a/datasets/MOD10C1_61.json +++ b/datasets/MOD10C1_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10C1_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05\u00b0 (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10C2_61.json b/datasets/MOD10C2_61.json index 3bb1e287e4..5a46e86e34 100644 --- a/datasets/MOD10C2_61.json +++ b/datasets/MOD10C2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10C2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05\u00b0 (approx. 5 km) MODIS climate modeling grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid' data set (DOI:10.5067/MODIS/MOD10A2.061).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10CM_61.json b/datasets/MOD10CM_61.json index 65640d6ca8..eb03572b5f 100644 --- a/datasets/MOD10CM_61.json +++ b/datasets/MOD10CM_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10CM_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides monthly mean snow cover extent within 0.05\u00b0 (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. This data set is derived from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG\u2019 data set (DOI:10.5067/MODIS/MOD10C1.061).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD10_L2_6.1NRT.json b/datasets/MOD10_L2_6.1NRT.json index 1d4c681467..7d0a00d0d6 100644 --- a/datasets/MOD10_L2_6.1NRT.json +++ b/datasets/MOD10_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Terra Near Real Time (NRT) Snow Cover 5-Min L2 Swath 500m (MOD10_L2) contains snow cover and quality assurance (QA) data, latitudes, and longitudes in HDF-EOS format, along with corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution, while all other fields are at 500 m resolution. MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests. ", "links": [ { diff --git a/datasets/MOD10_L2_61.json b/datasets/MOD10_L2_61.json index bd1c4e641d..3a600d5ed1 100644 --- a/datasets/MOD10_L2_61.json +++ b/datasets/MOD10_L2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD10_L2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-2 (L2) data set provides daily snow cover detected using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite: DOI:10.5067/MODIS/MOD02HKM.061 and DOI:10.5067/MODIS/MOD021KM.061. Each data granule contains 5 minutes of swath data observed at a resolution of 500 m.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD11A1_061.json b/datasets/MOD11A1_061.json index 21a5a1f129..8663775796 100644 --- a/datasets/MOD11A1_061.json +++ b/datasets/MOD11A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11A1 Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.006) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. \n\nProvided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). \n\nImprovements/Changes from Previous Versions\n\n * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. \n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ", "links": [ { diff --git a/datasets/MOD11A2_061.json b/datasets/MOD11A2_061.json index 8956b69b98..0bce096709 100644 --- a/datasets/MOD11A2_061.json +++ b/datasets/MOD11A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11A2 Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 (https://doi.org/10.5067/MODIS/MOD11A1.006) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.\r\n\r\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n", "links": [ { diff --git a/datasets/MOD11B1_061.json b/datasets/MOD11B1_061.json index e4f6b08132..52d816ffa0 100644 --- a/datasets/MOD11B1_061.json +++ b/datasets/MOD11B1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11B1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11B1 Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD11B2_061.json b/datasets/MOD11B2_061.json index a2915901b6..bc85d1f149 100644 --- a/datasets/MOD11B2_061.json +++ b/datasets/MOD11B2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11B2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11B2 Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MOD11B2 is a simple average of all the corresponding values from the LST&E values from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) product collected during that 8-day period. Each MOD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD11B3_061.json b/datasets/MOD11B3_061.json index 83e99cd677..f7a1705793 100644 --- a/datasets/MOD11B3_061.json +++ b/datasets/MOD11B3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11B3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11B3 Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MOD11B3 is a simple average of all the corresponding values from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) collected during the month period. Each MOD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n \nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD11C1_061.json b/datasets/MOD11C1_061.json index 457b5bdcd5..21deded2c4 100644 --- a/datasets/MOD11C1_061.json +++ b/datasets/MOD11C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11C1 Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD11C2_061.json b/datasets/MOD11C2_061.json index dad2fe6127..99a6f713b1 100644 --- a/datasets/MOD11C2_061.json +++ b/datasets/MOD11C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11C2 Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MOD11C2 product are derived by compositing and averaging the values from the corresponding eight MOD11C1 (https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. The MOD11C2 granule consists of 17 layers. Each MOD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD11C3_061.json b/datasets/MOD11C3_061.json index cf453cf397..f1abf1b446 100644 --- a/datasets/MOD11C3_061.json +++ b/datasets/MOD11C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of MOD11C1 (https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n\n", "links": [ { diff --git a/datasets/MOD11CM1D_005.json b/datasets/MOD11CM1D_005.json index 48cbe20194..468034a08f 100644 --- a/datasets/MOD11CM1D_005.json +++ b/datasets/MOD11CM1D_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11CM1D_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30.", "links": [ { diff --git a/datasets/MOD11CM1N_005.json b/datasets/MOD11CM1N_005.json index dcf0b9b5d7..56e7c15790 100644 --- a/datasets/MOD11CM1N_005.json +++ b/datasets/MOD11CM1N_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11CM1N_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30.", "links": [ { diff --git a/datasets/MOD11_L2_061.json b/datasets/MOD11_L2_061.json index 9d277bf6ae..d6619749d1 100644 --- a/datasets/MOD11_L2_061.json +++ b/datasets/MOD11_L2_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MOD11_L2_061", - "stac_version": "1.0.0", - "description": "The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples.\n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", + "stac_version": "1.1.0", + "description": "The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples.\r\n\r\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { "rel": "license", @@ -82,8 +82,8 @@ "license": "proprietary", "keywords": [ "EARTH SCIENCE", - "LAND SURFACE", "SURFACE THERMAL PROPERTIES", + "LAND SURFACE", "LAND SURFACE TEMPERATURE", "SURFACE RADIATIVE PROPERTIES", "EMISSIVITY" @@ -112,29 +112,21 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_Land_Surface_Temp_Day.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.04.25/BROWSE.MOD11_L2.A2001082.2350.061.2020087150434.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Terra_Land_Surface_Temp_Day.jpg", + "title": "Download BROWSE.MOD11_L2.A2001082.2350.061.2020087150434.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MOD11_L2.061/MOD11_L2.A2024197.1735.061.2024198091246/BROWSE.MOD11_L2.A2024197.1735.061.2024198091625.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.04.25/BROWSE.MOD11_L2.A2001082.2350.061.2020087150434.1.jpg", + "title": "Thumbnail", "description": "Browse Image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_Land_Surface_Temp_Day.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "gov/MOLT/MOD11_L2": { "href": "https://e4ftl01.cr.usgs.gov/MOLT/MOD11_L2.061/", "title": "Direct Download [0]", @@ -144,45 +136,25 @@ ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2343115255-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C1621100170-LPDAAC_ECS", "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MOD11_L2.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MOD11_L2_061": { - "href": "s3://lp-prod-protected/MOD11_L2.061", - "title": "lp_prod_protected_MOD11_L2_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MOD11_L2_061": { - "href": "s3://lp-prod-public/MOD11_L2.061", - "title": "lp_prod_public_MOD11_L2_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MOD11_L2.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MOD11_L2_6.1NRT.json b/datasets/MOD11_L2_6.1NRT.json index fc97e31bbf..1ee0abde55 100644 --- a/datasets/MOD11_L2_6.1NRT.json +++ b/datasets/MOD11_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD11_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra level-2 Land Surface Temperature and Emissivity (LST/E) Near Real Time (NRT) with Shortname MOD11_L2, incorporate 1 km pixels, which are produced daily at 5-minute increments using the generalized split-window algorithm. This algorithm is optimally used to separate ranges of atmospheric column water vapor and lower boundary air surface temperatures into tractable sub-ranges. The surface emissivities in bands 31 and 32 are estimated from land cover types. The data inputs include the MODIS L1B calibrated and geolocated radiances, geolocation, cloud mask, atmospheric profiles, land and snow cover. The MOD11_L2 data set comprises swath data obtained in 5-minute sensor collection periods, and includes the following Science Data Set (SDS) layers: - LST- Quality control assessment- Error estimates- Bands 31 and 32 emissivities- Zenith angle of the pixel view- Observation time- Geographic coordinates for every five scan lines and samples. Produced daily, MOD11_L2 is an unprojected level-2 product, which provides the input for the level-3 products.", "links": [ { diff --git a/datasets/MOD13A1_061.json b/datasets/MOD13A1_061.json index 437e07dfba..15437ec33e 100644 --- a/datasets/MOD13A1_061.json +++ b/datasets/MOD13A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. \n\nProvided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD13A2_061.json b/datasets/MOD13A2_061.json index fd02ff5d08..fc6b3ccc85 100644 --- a/datasets/MOD13A2_061.json +++ b/datasets/MOD13A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. \n\nProvided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD13A3_061.json b/datasets/MOD13A3_061.json index a8def14b1c..a695315417 100644 --- a/datasets/MOD13A3_061.json +++ b/datasets/MOD13A3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13A3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MOD13A2 (https://doi.org/10.5067/MODIS/MOD13A2.061) products that overlap the month and employs a weighted temporal average. \n\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.\n\nVegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion.\n\nProvided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers.\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD13A4N_6.1NRT.json b/datasets/MOD13A4N_6.1NRT.json index e87e38bb46..c502efc89e 100644 --- a/datasets/MOD13A4N_6.1NRT.json +++ b/datasets/MOD13A4N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13A4N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS level-3 Vegetation Indices Daily Rolling-8-Day Near Real Time (NRT), MOD13A4N data are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion.\r\n\r\nNote: This is a near real-time product only. Standard historical data and imagery for MOD13Q4N (250m) and MOD13A4N (500m) are not available. Users can either use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/) or access the science quality MxD09[A1/Q1] data and create the NDVI product of their own.", "links": [ { diff --git a/datasets/MOD13C1_061.json b/datasets/MOD13C1_061.json index 570774ea40..fd50beb560 100644 --- a/datasets/MOD13C1_061.json +++ b/datasets/MOD13C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD13C1 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MOD13A2 (https://doi.org/10.5067/MODIS/MOD13A2.061) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD13C2_061.json b/datasets/MOD13C2_061.json index 899208e8a7..79281cfbef 100644 --- a/datasets/MOD13C2_061.json +++ b/datasets/MOD13C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD13C2 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MOD13A2 (https://doi.org/10.5067/MODIS/MOD13A2.061) products that overlap the month and employs a weighted temporal average. Global MOD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD13Q1_061.json b/datasets/MOD13Q1_061.json index 66b968df09..545377461c 100644 --- a/datasets/MOD13Q1_061.json +++ b/datasets/MOD13Q1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13Q1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.\n\nAlong with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for all MOD13 vegetation products. Further details regarding product validation for the MOD13Q1 data product is available from the MODIS land team validation site (https://landval.gsfc.nasa.gov/ProductStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MOD13Q4N_6.1NRT.json b/datasets/MOD13Q4N_6.1NRT.json index 01b667ae22..e268a67fe1 100644 --- a/datasets/MOD13Q4N_6.1NRT.json +++ b/datasets/MOD13Q4N_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD13Q4N_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Vegetation Indices Daily Rolling-8-Day L3 Global 250m SIN Grid Near Real Time (NRT) data, MOD13Q4N, are provided everyday at 250-meter spatial resolution as a gridded leve-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion.\r\n\r\nNote: This is a near real-time product only. Standard historical data and imagery for MOD13Q4N (250m) and MOD13A4N (500m) are not available. Users can either use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/) or access the science quality MxD09[A1/Q1] data and create the NDVI product of their own.", "links": [ { diff --git a/datasets/MOD14A1_061.json b/datasets/MOD14A1_061.json index 7cb69f7afa..6257a4e129 100644 --- a/datasets/MOD14A1_061.json +++ b/datasets/MOD14A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD14A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.\n\nThe Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\n Improvements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD14A2_061.json b/datasets/MOD14A2_061.json index b3305d6e30..2e2b741e7c 100644 --- a/datasets/MOD14A2_061.json +++ b/datasets/MOD14A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD14A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MOD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition.\n\nThe Science Dataset (SDS) layers include the fire mask and pixel quality indicators.\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD14CM1_005.json b/datasets/MOD14CM1_005.json index 905fe90f63..1222731329 100644 --- a/datasets/MOD14CM1_005.json +++ b/datasets/MOD14CM1_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD14CM1_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The gridded MODIS active fire products present statistical summaries of fire pixel information (Giglio et al., 2003). The global monthly products are generated at 1x1 degree spatial resolution for time period of one calendar month. These products are derived from MODIS CMG 0.5 degree products (Giglio et al., 2006) for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on surface processes and climate modeling.", "links": [ { diff --git a/datasets/MOD14_061.json b/datasets/MOD14_061.json index 588b0e371a..2063f44aa3 100644 --- a/datasets/MOD14_061.json +++ b/datasets/MOD14_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD14_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MOD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at 1 kilometer (km) spatial resolution. The MOD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD14_6.1NRT.json b/datasets/MOD14_6.1NRT.json index 51c389f632..2418b109cc 100644 --- a/datasets/MOD14_6.1NRT.json +++ b/datasets/MOD14_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD14_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km Near Real Time (NRT), short name MOD14, product is primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (when the fire strength is sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline.MOD14 is level-2 swath data provided daily at 1-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (1:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate.", "links": [ { diff --git a/datasets/MOD15A2GFS_6.json b/datasets/MOD15A2GFS_6.json index 36b2b2aa0a..c647926166 100644 --- a/datasets/MOD15A2GFS_6.json +++ b/datasets/MOD15A2GFS_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD15A2GFS_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Gap-Filled, Smoothed Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) 8-Day L4 Global 1km SIN Grid product with short-name MOD15A2GFS, is composited every 8 days at 1-kilometer resolution on a Sinusoidal grid. The LAI variable defines the number of equivalent layers of leaves relative to a unit of ground area, whereas FPAR measures the proportion of available radiation in the photosynthetically active wavelengths that are absorbed by a canopy. Both variables are used as satellite-derived parameters for calculating surface photosynthesis, evapotranspiration, and net primary production, which in turn are used to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation.", "links": [ { diff --git a/datasets/MOD15A2H_061.json b/datasets/MOD15A2H_061.json index c5d3fc08da..ff82792974 100644 --- a/datasets/MOD15A2H_061.json +++ b/datasets/MOD15A2H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD15A2H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \u201cbest\u201d pixel available from all the acquisitions of the Terra sensor from within the 8-day period.\n\nLAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy.\n\nScience Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule.\n\nThe LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD15A2PHN_6.json b/datasets/MOD15A2PHN_6.json index edbd43f984..e1129ff0f2 100644 --- a/datasets/MOD15A2PHN_6.json +++ b/datasets/MOD15A2PHN_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD15A2PHN_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra LAI-FPAR Phenology annual L4 Global 1km SIN Grid product with short-name MOD15A2PHN, is estimated from MCD15A2 8-day products. The spatial resolution is 1-km. The MOD15PHN is stored in Hierarchical Data Format (HDF) in sinusodial projection, same as other standard MODIS land products. For the first 11 phenology parameters, only the first two seasons (marked as s1 and s2) are stored if there are more than one valid seasonal cycles detected. Valid seasonal cycles should begin within the year of interest and end before the end of the second year. There are 27 Science Data Sets (SDS) in the available phenology product.", "links": [ { diff --git a/datasets/MOD16A2GF_061.json b/datasets/MOD16A2GF_061.json index 392e4c8f19..900e858915 100644 --- a/datasets/MOD16A2GF_061.json +++ b/datasets/MOD16A2GF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD16A2GF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\n\nThe MOD16A2GF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A2GF is the improved MOD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A2GF in near-real time because it will be generated only at the end of a given year.\n\nProvided in the MOD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2GF granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", "links": [ { diff --git a/datasets/MOD16A2_061.json b/datasets/MOD16A2_061.json index ea791c3f5d..c8f8a59016 100644 --- a/datasets/MOD16A2_061.json +++ b/datasets/MOD16A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD16A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. \r\n\r\nProvided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.\r\n\r\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.\r\n\r\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", "links": [ { diff --git a/datasets/MOD16A3GF_061.json b/datasets/MOD16A3GF_061.json index d100c7bb1c..e679e7f01b 100644 --- a/datasets/MOD16A3GF_061.json +++ b/datasets/MOD16A3GF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD16A3GF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\n\nThe MOD16A3GF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A3GF is the improved MOD16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A3GF in near-real time because it will be generated only at the end of a given year.\n\nProvided in the MOD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A3GF granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. \n\n", "links": [ { diff --git a/datasets/MOD17A2HGF_061.json b/datasets/MOD17A2HGF_061.json index 5c1812c427..214b5c7769 100644 --- a/datasets/MOD17A2HGF_061.json +++ b/datasets/MOD17A2HGF_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MOD17A2HGF_061", - "stac_version": "1.0.0", - "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.\n\nThe MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.\n\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. \n\n\n", + "stac_version": "1.1.0", + "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.\r\n\r\nThe MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.\r\n\r\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. \r\n\r\n\r\n", "links": [ { "rel": "license", @@ -112,15 +112,15 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MOD17A2HGF.061/MOD17A2HGF.A2023361.h21v09.061.2024021044047/BROWSE.MOD17A2HGF.A2023361.h21v09.061.2024021044047.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2021.01.15/BROWSE.MOD17A2HGF.A2020361.h20v07.061.2021015032520.1.jpg", "type": "image/jpeg", - "title": "Download BROWSE.MOD17A2HGF.A2023361.h21v09.061.2024021044047.1.jpg", + "title": "Download BROWSE.MOD17A2HGF.A2020361.h20v07.061.2021015032520.1.jpg", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MOD17A2HGF.061/MOD17A2HGF.A2023361.h21v09.061.2024021044047/BROWSE.MOD17A2HGF.A2023361.h21v09.061.2024021044047.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2021.01.15/BROWSE.MOD17A2HGF.A2020361.h20v07.061.2021015032520.1.jpg", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ @@ -138,43 +138,23 @@ "nasa": { "href": "https://appeears.earthdatacloud.nasa.gov/", "title": "Direct Download [2]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "The Application for Extracting and Exploring Analysis Ready Samples (A\u03c1\u03c1EEARS) offers a simple and efficient way to perform data access and transformation processes.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MOD17A2HGF.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MOD17A2HGF_061": { - "href": "s3://lp-prod-protected/MOD17A2HGF.061", - "title": "lp_prod_protected_MOD17A2HGF_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MOD17A2HGF_061": { - "href": "s3://lp-prod-public/MOD17A2HGF.061", - "title": "lp_prod_public_MOD17A2HGF_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [3]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MOD17A2H.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MOD17A2H_061.json b/datasets/MOD17A2H_061.json index dbb9ef0d94..ab76045ef2 100644 --- a/datasets/MOD17A2H_061.json +++ b/datasets/MOD17A2H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD17A2H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. \r\n\r\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. \r\n", "links": [ { diff --git a/datasets/MOD17A3HGF_061.json b/datasets/MOD17A3HGF_061.json index 2b4f863082..3ffb8b6b6f 100644 --- a/datasets/MOD17A3HGF_061.json +++ b/datasets/MOD17A3HGF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD17A3HGF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products (MOD17A2H)(https://doi.org/10.5067/MODIS/MOD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).\n\nThe MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.\n\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", "links": [ { diff --git a/datasets/MOD21A1D_061.json b/datasets/MOD21A1D_061.json index 9669b48201..ef921b9fb3 100644 --- a/datasets/MOD21A1D_061.json +++ b/datasets/MOD21A1D_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21A1D_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "links": [ { diff --git a/datasets/MOD21A1N_061.json b/datasets/MOD21A1N_061.json index 2ea8c5f62a..f590b24dd0 100644 --- a/datasets/MOD21A1N_061.json +++ b/datasets/MOD21A1N_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MOD21A1N_061", - "stac_version": "1.0.0", - "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). \n", + "stac_version": "1.1.0", + "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \r\n\r\nThe MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product utilizes GEOS data replacing MERRA2. \r\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). \r\n", "links": [ { "rel": "license", @@ -82,8 +82,8 @@ "license": "proprietary", "keywords": [ "EARTH SCIENCE", - "LAND SURFACE", "SURFACE THERMAL PROPERTIES", + "LAND SURFACE", "LAND SURFACE TEMPERATURE", "SURFACE RADIATIVE PROPERTIES", "EMISSIVITY" @@ -112,29 +112,21 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_L3_Land_Surface_Temp_Daily_Night_TES.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.13/BROWSE.MOD21A1N.A2002196.h11v11.061.2020072120847.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Terra_L3_Land_Surface_Temp_Daily_Night_TES.jpg", + "title": "Download BROWSE.MOD21A1N.A2002196.h11v11.061.2020072120847.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MOD21A1N.061/MOD21A1N.A2024197.h12v12.061.2024198075157/BROWSE.MOD21A1N.A2024197.h12v12.061.2024198035157.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.13/BROWSE.MOD21A1N.A2002196.h11v11.061.2020072120847.1.jpg", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_L3_Land_Surface_Temp_Daily_Night_TES.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "gov/MOLT/MOD21A1N": { "href": "https://e4ftl01.cr.usgs.gov/MOLT/MOD21A1N.061/", "title": "Direct Download [0]", @@ -146,43 +138,23 @@ "nasa": { "href": "https://appeears.earthdatacloud.nasa.gov/", "title": "Direct Download [2]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", - "roles": [ - "data" - ] - }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MOD21A1N.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MOD21A1N_061": { - "href": "s3://lp-prod-protected/MOD21A1N.061", - "title": "lp_prod_protected_MOD21A1N_061", + "description": "The Application for Extracting and Exploring Analysis Ready Samples (A\u03c1\u03c1EEARS) offers a simple and efficient way to perform data access and transformation processes.", "roles": [ "data" ] }, - "s3_lp_prod_public_MOD21A1N_061": { - "href": "s3://lp-prod-public/MOD21A1N.061", - "title": "lp_prod_public_MOD21A1N_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov", + "title": "Direct Download [3]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MOD21A1N.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MOD21A2_061.json b/datasets/MOD21A2_061.json index aebebb6285..3b26fa0a56 100644 --- a/datasets/MOD21A2_061.json +++ b/datasets/MOD21A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "links": [ { diff --git a/datasets/MOD21C1_061.json b/datasets/MOD21C1_061.json index c17480d992..efde779931 100644 --- a/datasets/MOD21C1_061.json +++ b/datasets/MOD21C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21C1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). \n\n", "links": [ { diff --git a/datasets/MOD21C2_061.json b/datasets/MOD21C2_061.json index f0320a3a55..d125871b78 100644 --- a/datasets/MOD21C2_061.json +++ b/datasets/MOD21C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "links": [ { diff --git a/datasets/MOD21C3_061.json b/datasets/MOD21C3_061.json index 64ae5d812b..f56d1143a5 100644 --- a/datasets/MOD21C3_061.json +++ b/datasets/MOD21C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MOD21C3 dataset is a monthly composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2. \n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "links": [ { diff --git a/datasets/MOD21_061.json b/datasets/MOD21_061.json index 5fe7c118e0..2943692a6f 100644 --- a/datasets/MOD21_061.json +++ b/datasets/MOD21_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MOD21 Land Surface Temperature (LST) algorithm differs from the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) algorithm in that the MOD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). \r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product utilizes GEOS data replacing MERRA2. \r\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). \r\n\r\n\r\n\r\n\r\n", "links": [ { @@ -112,29 +112,21 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_Land_Surface_Temp_Day_TES.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.12/BROWSE.MOD21.A2002211.1820.061.2020072131212.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Terra_Land_Surface_Temp_Day_TES.jpg", + "title": "Download BROWSE.MOD21.A2002211.1820.061.2020072131212.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MOD21.061/MOD21.A2024198.0115.061.2024198132351/BROWSE.MOD21.A2024198.0115.061.2024198092357.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.12/BROWSE.MOD21.A2002211.1820.061.2020072131212.1.jpg", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Terra_Land_Surface_Temp_Day_TES.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "gov/MOLT/MOD21": { "href": "https://e4ftl01.cr.usgs.gov/MOLT/MOD21.061/", "title": "Direct Download [0]", @@ -144,45 +136,25 @@ ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2565791036-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C1621388213-LPDAAC_ECS", "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MOD21.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MOD21_061": { - "href": "s3://lp-prod-protected/MOD21.061", - "title": "lp_prod_protected_MOD21_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MOD21_061": { - "href": "s3://lp-prod-public/MOD21.061", - "title": "lp_prod_public_MOD21_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC. ", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MOD21.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MOD21_6.1NRT.json b/datasets/MOD21_6.1NRT.json index 7fa67dfb42..6d365f3ad3 100644 --- a/datasets/MOD21_6.1NRT.json +++ b/datasets/MOD21_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD21_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Land Surface Temperature/3-Band Emissivity (LST&E) 5-Min L2 1km data product, short-name MOD21 is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MOD21 Land Surface Temperature (LST) algorithm differs from the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) algorithm in that the MOD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/107/MOD21_ATBD.pdf)). \r\n\r\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and more.", "links": [ { diff --git a/datasets/MOD28C2_061.json b/datasets/MOD28C2_061.json index 711d1ded29..a3dced6f07 100644 --- a/datasets/MOD28C2_061.json +++ b/datasets/MOD28C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD28C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MOD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\n\nThe MOD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves (https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Terra satellite (MOD09Q1). \n\nThe MOD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. \n", "links": [ { diff --git a/datasets/MOD28C3_061.json b/datasets/MOD28C3_061.json index 68f2fb97da..1142b4e95d 100644 --- a/datasets/MOD28C3_061.json +++ b/datasets/MOD28C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD28C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MOD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MOD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) (https://www.sciencedirect.com/science/article/pii/S0034425720304776?via%3Dihub) via MODIS Land Surface Temperature (LST) (MOD21) and meteorological data from Global Land Data Assimilation System (GLDAS) (https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MOD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume.", "links": [ { diff --git a/datasets/MOD29E1D_61.json b/datasets/MOD29E1D_61.json index 2f9559ea70..67e0b8dbb5 100644 --- a/datasets/MOD29E1D_61.json +++ b/datasets/MOD29E1D_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD29E1D_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day\u2019 (https://doi.org/10.5067/MODIS/MOD29P1D.061) product. These data are provided daily in the EASE-Grid polar projection at a resolution of approximately 4 km.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD29P1D_61.json b/datasets/MOD29P1D_61.json index 5fca989be2..9132d3b158 100644 --- a/datasets/MOD29P1D_61.json +++ b/datasets/MOD29P1D_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD29P1D_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD29P1N_61.json b/datasets/MOD29P1N_61.json index 972dcda73a..45207ff218 100644 --- a/datasets/MOD29P1N_61.json +++ b/datasets/MOD29P1N_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD29P1N_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD29_6.1NRT.json b/datasets/MOD29_6.1NRT.json index 04867866ba..a9d5b88663 100644 --- a/datasets/MOD29_6.1NRT.json +++ b/datasets/MOD29_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD29_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km Near Real Time (NRT), short name MOD29, contains the following fields: sea ice by reflectance, sea ice by reflectance pixel quality assurance (QA), ice surface temperature (IST), IST pixel QA, sea ice by IST, combined sea ice, latitudes, and longitudes in HDF-EOS format, along with corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution, while all other fields are at 1 km resolution. The sea ice algorithm uses a Normalized Difference Snow Index (NDSI) modified for sea ice to distinguish sea ice from open ocean, based on reflective and thermal characteristics. ", "links": [ { diff --git a/datasets/MOD29_61.json b/datasets/MOD29_61.json index 837bcc1d2e..592d99f714 100644 --- a/datasets/MOD29_61.json +++ b/datasets/MOD29_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD29_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. Each data granule contains 5 minutes of swath data observed at a resolution of 1000 m.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MOD35_L2_6.1.json b/datasets/MOD35_L2_6.1.json index 8b8608eb6f..eb895fec91 100644 --- a/datasets/MOD35_L2_6.1.json +++ b/datasets/MOD35_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD35_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MOD35_L2.\r\n\r\nThe MOD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). This product consists of 9 parameters and each of these parameters are stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. \r\n\r\nFor more information about the MOD35_L2 product, visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud-mask", "links": [ { diff --git a/datasets/MOD35_L2_6.1NRT.json b/datasets/MOD35_L2_6.1NRT.json index cd5224bc4d..87b1c53d5a 100644 --- a/datasets/MOD35_L2_6.1NRT.json +++ b/datasets/MOD35_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD35_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS level-2 cloud mask product is a global product generated for both daytime and nighttime conditions at 1-km spatial resolution (at nadir) and for daytime at 250-m resolution. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. \n\n\nThe Terra MODIS Photovoltaic (PVLWIR) bands 27-30 are known to experience an electronic crosstalk contamination. The influence of the crosstalk has gradually increased over the mission lifetime, causing for example, earth surface features to become prominent in atmospheric band 27, increased detector striping, and long term drift in the radiometric bias of these bands. The drift has compromised the climate quality of C6 Terra MODIS L2 products that depend significantly on these bands, including cloud mask (MOD35), cloud fraction and cloud top properties (MOD06), and total precipitable water (MOD07). A linear crosstalk correction algorithm has been developed and tested by MCST.The electronic crosstalk correction was made to the calibration algorithm for bands 27-30 and implemented into C6.1 operational L1B processing. This implementation greatly improves the performance of the cloud mask.\n\nFor more information on C6.1 changes visit:\n\nhttps://modis-atmos.gsfc.nasa.gov/documentation/collection-61\n\nThe shortname for this Level-2 MODIS cloud mask product is MOD35_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel ( paulm@ssec.wisc.edu). MOD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). Each of the 9 gridded parameters is stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution.\n\nLink to the MODIS homepage for more data set information: \n\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud-mask", "links": [ { diff --git a/datasets/MOD44B_061.json b/datasets/MOD44B_061.json index ee50921c79..4f84fb04f7 100644 --- a/datasets/MOD44B_061.json +++ b/datasets/MOD44B_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD44B_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOD44B Version 6.1 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components. The sub-pixel mixture of ground cover estimates represents a revolutionary approach to the characterization of vegetative land cover that can be used to enhance inputs to environmental modeling and monitoring applications. \n\nThe MOD44B data product layers include percent tree cover, percent non-tree cover, percent non-vegetated, cloud cover, and quality indicators. The start date of the annual period for this product begins with day of year (DOY) 65 (March 6 except for leap year which corresponds to March 5). \n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MOD44W_061.json b/datasets/MOD44W_061.json index 6d643a6683..e8c3e3b56c 100644 --- a/datasets/MOD44W_061.json +++ b/datasets/MOD44W_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD44W_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask (MOD44W) Version 6.1 data product provides a global map of surface water at 250 meter (m) spatial resolution. The data are available annually from 2000 to present. MOD44W Version 6.1 is derived using a decision tree classifier trained with MODIS data and validated with the Version 5 MOD44W data product. A series of masks are applied to address known issues caused by terrain shadow, burn scars, cloudiness, or ice cover in oceans. Version 6.1 is the generation of a time series rather than a simple static representation of water, given that water bodies fluctuate in size and location over time due to both natural and anthropogenic causes. Provided in each MOD44W Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for land water mask and water body classification. A quality assurance (QA) layer provides users with information on the determination of water. The new seven class water classification layer provides values for shallow ocean, land, shoreline, inland water, ephemeral water, deep inland water, moderate ocean, deep ocean, and a classification deemed to fall outside of the projection.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* Additional data files will be produced annually and are scheduled for distribution in the first quarter of the calendar year. \r\n* Seven-class water body classification layer produced at 250 meter resolution is provided in tiled format.\r\n* For Collection 6.1 and beyond, Antarctica data are now being produced. \r\n", "links": [ { diff --git a/datasets/MODAODHD_6.1NRT.json b/datasets/MODAODHD_6.1NRT.json index c54831e571..409982cc1e 100644 --- a/datasets/MODAODHD_6.1NRT.json +++ b/datasets/MODAODHD_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODAODHD_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS was launched aboard the Terra satellite on December 18, 1999 (10:30 am equator crossing time) as part of NASA's Earth Observing System (EOS) mission. MODIS with its 2330 km viewing swath width provides almost daily global coverage. It acquires data in 36 high spectral resolution bands between 0.415 to 14.235 micron with spatial resolutions of 250m(2 bands), 500m(5 bands),and 1000m (29 bands). MODIS sensor counts, calibrated radiances, geolocation products and all derived geophysical atmospheric and ocean products are archived at various DAACs and has been made available to public since April 2000.\n\nThe shortname for this level-3 MODIS aerosol product is MODAODHD. The Naval Research Laboratory and the University of North Dakota developed this value-added aerosol optical depth dataset based on MODIS Level 2 aerosol products. MODAODHD is a gridded product and is specifically designed for quantitative applications including data assimilation and model validation. It is available through LANCE-MODIS. It offers several enhancements over the MODIS Level 2 data on which it is based. These enhancements include stringent filtering to reduce outliers, eliminate cloud contamination, and exclude conditions where aerosol detection is likely to be inaccurate; reduction of systematic biases over land and ocean by empirical corrections; reduction of random variation in AOD values by spatial averaging; quantitative estimation of uncertainty for each AOD data point.\n\nThe MxDAODHD granules are produced every six hours, and time-stamped 00:00, 06:00, 12:00, and 18:00 (all times UTC). Each granule includes MODIS observations from +/-3 hours from the timestamp (e.g. 12:00 product includes MODIS data from 09:00-15:00 UTC). Production is initiated as soon as the Level 2 inputs become available in the LANCE system.\n\nSee the LANCE-MODIS page for more dataset information: \n\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/modis-nrt", "links": [ { diff --git a/datasets/MODARNSS_6.1.json b/datasets/MODARNSS_6.1.json index 153a62f376..17625e05ec 100644 --- a/datasets/MODARNSS_6.1.json +++ b/datasets/MODARNSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODARNSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Atmosphere Aeronet Subsetting Product (MODARNSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over a number of Aerosol Robotic Network (AERONET) sites. These sites comprise of sites of automatic tracking Sun photometers/sky radiometers located all over the world. The process of generating cutouts involves locating and identifying a subset of sites taken from a global AERONET that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MODARNSS data set consists of subsets for around 180 AERONET sites around the globe. There is one file per site with 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc.", "links": [ { diff --git a/datasets/MODATML2_6.1.json b/datasets/MODATML2_6.1.json index 2c78f9ffbf..726450b106 100644 --- a/datasets/MODATML2_6.1.json +++ b/datasets/MODATML2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODATML2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MODATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets). The ATML2 product employs the same 5x5 pixel sampling scheme for the 1km native resolution Level 2 products as is used in the MOD08 Level 3 global aggregated product, an approach that has been shown to retain statistical integrity for multi-day aggregations. \r\n\r\nThe C6 significantly increases the number of datasets included in the ATML2 product, including the full suite of QA datasets. Since the ATML2 data granule file size is significantly smaller than the combined size of the individual L2 products, and because the 1 km pixel sampling is consistent with the L3 algorithm, the ATML2 product is a more practical means for the user community to develop research L3 algorithms for their own specific purposes.\r\n\r\nFor more information, visit the MODIS Atmosphere website at: \r\nhttps://modis-atmos.gsfc.nasa.gov/products/joint-atm", "links": [ { diff --git a/datasets/MODBMSS_6.1.json b/datasets/MODBMSS_6.1.json index 9bbe0e4749..73d6dc3975 100644 --- a/datasets/MODBMSS_6.1.json +++ b/datasets/MODBMSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODBMSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Atmosphere BELMANIP subsetting Product (MODBMSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over the Bench-mark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites. The BELMANIP sites is a network of sites, distributed globally and consist of existing networks such as Earth Observing System (EOS) Core Sites, Bigfoot, Validation of Land European Remote sensing Instruments (VALERI), a global network of micrometeorological flux measurement (FLUXNET), the aerosol robotic network (AERONET) and a set of additional sites.The process of generating cutouts for these sites involves locating and identifying a subset of sites taken from global BELMANIP sites that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MODBMSS data set consists of subsets for approximately 445 sites around the globe. There is one file per site with 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc.", "links": [ { diff --git a/datasets/MODCSR_8_6.1.json b/datasets/MODCSR_8_6.1.json index b89a30c247..7f73ecde89 100644 --- a/datasets/MODCSR_8_6.1.json +++ b/datasets/MODCSR_8_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODCSR_8_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Clear Sky Radiance 8-Day Composite Daily L3 Global 25km Equal Area (MODCSR_8) product is created from composited MODCSR_D files. Nine clear-sky radiance and reflectance statistics (bands 1-7 and 17-36, see description of the MODCSR_G product for description of statistics) are produced for day and night separately, for every calendar day from the previous eight days (eight MODCSR_D files). There must be valid clear-sky data from at least four of the eight days in order to generate a MODCSR_8 output file. The statistics include observed minus calculated data from bands 20, 22-25, and 27-36 and numbers of land vs. water observations. The data is global in extent at 25-km resolution. MODCSR_8 files are in Hierarchical Data Format (HDF).", "links": [ { diff --git a/datasets/MODCSR_B_6.1.json b/datasets/MODCSR_B_6.1.json index c39870e9dc..ea454af0a5 100644 --- a/datasets/MODCSR_B_6.1.json +++ b/datasets/MODCSR_B_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODCSR_B_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra 8-Day Clear Sky Radiance Bias Daily L3 Global 1Deg Zonal Bands (MODCSR_B) product consists of 1-degree zonal mean clear-sky biases (observed minus calculated radiance differences) and associated statistics for bands 31 and 33-36 for each calendar day from the previous eight-day period. Zonal means (5-zone moving averages) are created from the eight-day, 25-km radiance differences for daytime land, nighttime land, and ocean data separately. Day and night land data are combined south of -60 degrees latitude due to poor clear-sky sampling and the difficulty of discriminating between clear and cloudy conditions in this region. The zonal mean biases are utilized to correct clear-sky radiance calculations in the cloud top pressure (CO2 slicing) algorithm. The files are in Hierarchical Data Format (HDF).", "links": [ { diff --git a/datasets/MODFNSS_6.1.json b/datasets/MODFNSS_6.1.json index 3b187f1332..9653eeac8e 100644 --- a/datasets/MODFNSS_6.1.json +++ b/datasets/MODFNSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODFNSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Atmosphere FluxNet Subsetting Product (MODFNSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over a global network of micrometeorological flux measurement (FLUXNET) sites. The process of generating cutouts for these sites involves locating and identifying a subset of sites taken from a global FLUXNET that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MODFNSS data set consists of subsets for around 400 sites out of the total flux tower sites around the globe. There is one file per site with around 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc.", "links": [ { diff --git a/datasets/MODGRNLD_1.json b/datasets/MODGRNLD_1.json index 1dcc2e1270..4c6ab8b6de 100644 --- a/datasets/MODGRNLD_1.json +++ b/datasets/MODGRNLD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODGRNLD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This multilayer data set includes standard MODIS Collection 6.1 ice surface temperature (IST) and derived melt map, as well as MODIS Collection 6.0 albedo and water vapor for Greenland, at a spatial resolution of 0.78 km. These fields enable the relationship between IST and surface melt to be evaluated by researchers studying surface changes on the Greenland ice sheet. Water vapor is included to assist with evaluating the accuracy of the IST data and the model output. Also included is an ice mask and a basins mask for delineating drainage basins in Greenland.\n\nSurface temperature is a fundamental input for dynamical ice sheet models because it is a component of the ice sheet radiation budget and mass balance. Surface temperature also influences ice sheet processes, such as surface melt. This data set may be used as a resource for model-validation studies such as comparing MERRA-2 surface temperature with MODIS IST, and for comparing MODIS IST, albedo and water vapor with products from sensors on other satellites such as VIIRS and AIRS\n\nThe temporal coverage for this data set spans 1 March 2000 through 31 December 2019, with the exception of the IST data, which has been extended through 31 Aug 2021.", "links": [ { diff --git a/datasets/MODISA_L1_1.json b/datasets/MODISA_L1_1.json index e113db97ff..5a9dbd7f19 100644 --- a/datasets/MODISA_L1_1.json +++ b/datasets/MODISA_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L1_GEO_1.json b/datasets/MODISA_L1_GEO_1.json index 1ea7ea4293..6b5a779289 100644 --- a/datasets/MODISA_L1_GEO_1.json +++ b/datasets/MODISA_L1_GEO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L1_GEO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L2_IOP_NRT_R2022.0.json b/datasets/MODISA_L2_IOP_NRT_R2022.0.json index 7a9a29680d..cf668d6046 100644 --- a/datasets/MODISA_L2_IOP_NRT_R2022.0.json +++ b/datasets/MODISA_L2_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L2_IOP_R2022.0.json b/datasets/MODISA_L2_IOP_R2022.0.json index 3f11d86430..65a61c3194 100644 --- a/datasets/MODISA_L2_IOP_R2022.0.json +++ b/datasets/MODISA_L2_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L2_LAND_R2022.0.json b/datasets/MODISA_L2_LAND_R2022.0.json index 0257c64dae..3a6956e2a4 100644 --- a/datasets/MODISA_L2_LAND_R2022.0.json +++ b/datasets/MODISA_L2_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L2_OC_NRT_R2022.0.json b/datasets/MODISA_L2_OC_NRT_R2022.0.json index 0faac7337d..8be75fa150 100644 --- a/datasets/MODISA_L2_OC_NRT_R2022.0.json +++ b/datasets/MODISA_L2_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L2_OC_R2022.0.json b/datasets/MODISA_L2_OC_R2022.0.json index ed1dd35b87..44a5a59ec7 100644 --- a/datasets/MODISA_L2_OC_R2022.0.json +++ b/datasets/MODISA_L2_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L2_SST4_NRT_R2019.0.json b/datasets/MODISA_L2_SST4_NRT_R2019.0.json index b98c86d133..c7af4c3182 100644 --- a/datasets/MODISA_L2_SST4_NRT_R2019.0.json +++ b/datasets/MODISA_L2_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L2_SST4_R2019.0.json b/datasets/MODISA_L2_SST4_R2019.0.json index 9dc93d8461..1443516d6d 100644 --- a/datasets/MODISA_L2_SST4_R2019.0.json +++ b/datasets/MODISA_L2_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L2_SST_NRT_R2019.0.json b/datasets/MODISA_L2_SST_NRT_R2019.0.json index 4cbc516206..e3b0b66ac3 100644 --- a/datasets/MODISA_L2_SST_NRT_R2019.0.json +++ b/datasets/MODISA_L2_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L2_SST_R2019.0.json b/datasets/MODISA_L2_SST_R2019.0.json index 257721a026..2a676808e2 100644 --- a/datasets/MODISA_L2_SST_R2019.0.json +++ b/datasets/MODISA_L2_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L2_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_CHL_NRT_R2022.0.json b/datasets/MODISA_L3b_CHL_NRT_R2022.0.json index 16a58e42bc..37d2a850ca 100644 --- a/datasets/MODISA_L3b_CHL_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_CHL_R2022.0.json b/datasets/MODISA_L3b_CHL_R2022.0.json index e8a961a852..00036a9c80 100644 --- a/datasets/MODISA_L3b_CHL_R2022.0.json +++ b/datasets/MODISA_L3b_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_FLH_NRT_R2022.0.json b/datasets/MODISA_L3b_FLH_NRT_R2022.0.json index 46a218507e..d92573e7d4 100644 --- a/datasets/MODISA_L3b_FLH_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_FLH_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_FLH_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_FLH_R2022.0.json b/datasets/MODISA_L3b_FLH_R2022.0.json index dc87734889..ba9925f5f4 100644 --- a/datasets/MODISA_L3b_FLH_R2022.0.json +++ b/datasets/MODISA_L3b_FLH_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_FLH_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_IOP_NRT_R2022.0.json b/datasets/MODISA_L3b_IOP_NRT_R2022.0.json index 7d41cd2d2a..a4be84af79 100644 --- a/datasets/MODISA_L3b_IOP_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_IOP_R2022.0.json b/datasets/MODISA_L3b_IOP_R2022.0.json index f8f52d9c7b..3526577d5a 100644 --- a/datasets/MODISA_L3b_IOP_R2022.0.json +++ b/datasets/MODISA_L3b_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_KD_NRT_R2022.0.json b/datasets/MODISA_L3b_KD_NRT_R2022.0.json index 9a853f3614..2fccb9e3a7 100644 --- a/datasets/MODISA_L3b_KD_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_KD_R2022.0.json b/datasets/MODISA_L3b_KD_R2022.0.json index c28b2ab798..1dc89c3410 100644 --- a/datasets/MODISA_L3b_KD_R2022.0.json +++ b/datasets/MODISA_L3b_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_LAND_R2022.0.json b/datasets/MODISA_L3b_LAND_R2022.0.json index f241103835..39fbec2513 100644 --- a/datasets/MODISA_L3b_LAND_R2022.0.json +++ b/datasets/MODISA_L3b_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_NSST_NRT_R2019.0.json b/datasets/MODISA_L3b_NSST_NRT_R2019.0.json index b528ba11e3..9a19afb3a0 100644 --- a/datasets/MODISA_L3b_NSST_NRT_R2019.0.json +++ b/datasets/MODISA_L3b_NSST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_NSST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_NSST_R2019.0.json b/datasets/MODISA_L3b_NSST_R2019.0.json index d5657ff78c..65159a233d 100644 --- a/datasets/MODISA_L3b_NSST_R2019.0.json +++ b/datasets/MODISA_L3b_NSST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_NSST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_PAR_NRT_R2022.0.json b/datasets/MODISA_L3b_PAR_NRT_R2022.0.json index fd40bd071f..603781b816 100644 --- a/datasets/MODISA_L3b_PAR_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_PAR_R2022.0.json b/datasets/MODISA_L3b_PAR_R2022.0.json index 5539b11a17..b70082a41a 100644 --- a/datasets/MODISA_L3b_PAR_R2022.0.json +++ b/datasets/MODISA_L3b_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_PIC_NRT_R2022.0.json b/datasets/MODISA_L3b_PIC_NRT_R2022.0.json index 2fbf017cdb..2414b29234 100644 --- a/datasets/MODISA_L3b_PIC_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_PIC_R2022.0.json b/datasets/MODISA_L3b_PIC_R2022.0.json index 99fa180b82..479a055e66 100644 --- a/datasets/MODISA_L3b_PIC_R2022.0.json +++ b/datasets/MODISA_L3b_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_POC_NRT_R2022.0.json b/datasets/MODISA_L3b_POC_NRT_R2022.0.json index 8a15b0064e..cacaaff2dc 100644 --- a/datasets/MODISA_L3b_POC_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_POC_R2022.0.json b/datasets/MODISA_L3b_POC_R2022.0.json index bd3d1c282c..444af78195 100644 --- a/datasets/MODISA_L3b_POC_R2022.0.json +++ b/datasets/MODISA_L3b_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_RRS_NRT_R2022.0.json b/datasets/MODISA_L3b_RRS_NRT_R2022.0.json index 32f40f6e55..a7a4e2b480 100644 --- a/datasets/MODISA_L3b_RRS_NRT_R2022.0.json +++ b/datasets/MODISA_L3b_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_RRS_R2022.0.json b/datasets/MODISA_L3b_RRS_R2022.0.json index fd0b8c628b..9a792034eb 100644 --- a/datasets/MODISA_L3b_RRS_R2022.0.json +++ b/datasets/MODISA_L3b_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_SST4_NRT_R2019.0.json b/datasets/MODISA_L3b_SST4_NRT_R2019.0.json index f9f9de7e10..36b18dd64f 100644 --- a/datasets/MODISA_L3b_SST4_NRT_R2019.0.json +++ b/datasets/MODISA_L3b_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_SST4_R2019.0.json b/datasets/MODISA_L3b_SST4_R2019.0.json index 1ef9953f2e..2cf99c1e01 100644 --- a/datasets/MODISA_L3b_SST4_R2019.0.json +++ b/datasets/MODISA_L3b_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3b_SST_NRT_R2019.0.json b/datasets/MODISA_L3b_SST_NRT_R2019.0.json index c9daf401ec..cf8bc49cb7 100644 --- a/datasets/MODISA_L3b_SST_NRT_R2019.0.json +++ b/datasets/MODISA_L3b_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3b_SST_R2019.0.json b/datasets/MODISA_L3b_SST_R2019.0.json index feb660332f..6210f0d54a 100644 --- a/datasets/MODISA_L3b_SST_R2019.0.json +++ b/datasets/MODISA_L3b_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3b_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_CHL_NRT_R2022.0.json b/datasets/MODISA_L3m_CHL_NRT_R2022.0.json index 733c06c60e..13de347f1c 100644 --- a/datasets/MODISA_L3m_CHL_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_CHL_R2022.0.json b/datasets/MODISA_L3m_CHL_R2022.0.json index e36db5d8c6..fa1400346f 100644 --- a/datasets/MODISA_L3m_CHL_R2022.0.json +++ b/datasets/MODISA_L3m_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_FLH_NRT_R2022.0.json b/datasets/MODISA_L3m_FLH_NRT_R2022.0.json index 98b006d282..d4b86b7899 100644 --- a/datasets/MODISA_L3m_FLH_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_FLH_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_FLH_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_FLH_R2022.0.json b/datasets/MODISA_L3m_FLH_R2022.0.json index 660d54c572..f3c00069fb 100644 --- a/datasets/MODISA_L3m_FLH_R2022.0.json +++ b/datasets/MODISA_L3m_FLH_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_FLH_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_IOP_NRT_R2022.0.json b/datasets/MODISA_L3m_IOP_NRT_R2022.0.json index bb432f29c2..8d0aa57f4b 100644 --- a/datasets/MODISA_L3m_IOP_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_IOP_R2022.0.json b/datasets/MODISA_L3m_IOP_R2022.0.json index 9323551bed..a2ee7c2bd2 100644 --- a/datasets/MODISA_L3m_IOP_R2022.0.json +++ b/datasets/MODISA_L3m_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_KD_NRT_R2022.0.json b/datasets/MODISA_L3m_KD_NRT_R2022.0.json index 911c93d0eb..59f7153e3d 100644 --- a/datasets/MODISA_L3m_KD_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_KD_R2022.0.json b/datasets/MODISA_L3m_KD_R2022.0.json index 664e5b8345..6457dc3171 100644 --- a/datasets/MODISA_L3m_KD_R2022.0.json +++ b/datasets/MODISA_L3m_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_LAND_R2022.0.json b/datasets/MODISA_L3m_LAND_R2022.0.json index 2f6f1b6583..45ad8798e0 100644 --- a/datasets/MODISA_L3m_LAND_R2022.0.json +++ b/datasets/MODISA_L3m_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_NSST_NRT_R2019.0.json b/datasets/MODISA_L3m_NSST_NRT_R2019.0.json index c030f06725..15169d5e9d 100644 --- a/datasets/MODISA_L3m_NSST_NRT_R2019.0.json +++ b/datasets/MODISA_L3m_NSST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_NSST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_NSST_R2019.0.json b/datasets/MODISA_L3m_NSST_R2019.0.json index e05517f192..2c6bf5bb60 100644 --- a/datasets/MODISA_L3m_NSST_R2019.0.json +++ b/datasets/MODISA_L3m_NSST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_NSST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_PAR_NRT_R2022.0.json b/datasets/MODISA_L3m_PAR_NRT_R2022.0.json index 7049afd7c5..6a47d0f56d 100644 --- a/datasets/MODISA_L3m_PAR_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_PAR_R2022.0.json b/datasets/MODISA_L3m_PAR_R2022.0.json index 09ad7ac2d4..8b2a452d75 100644 --- a/datasets/MODISA_L3m_PAR_R2022.0.json +++ b/datasets/MODISA_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_PIC_NRT_R2022.0.json b/datasets/MODISA_L3m_PIC_NRT_R2022.0.json index bfb3576da1..ce0c93ed2f 100644 --- a/datasets/MODISA_L3m_PIC_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_PIC_R2022.0.json b/datasets/MODISA_L3m_PIC_R2022.0.json index e74f8d3a1c..118dd02e96 100644 --- a/datasets/MODISA_L3m_PIC_R2022.0.json +++ b/datasets/MODISA_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_POC_NRT_R2022.0.json b/datasets/MODISA_L3m_POC_NRT_R2022.0.json index 948fea472c..0a7077ff20 100644 --- a/datasets/MODISA_L3m_POC_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_POC_R2022.0.json b/datasets/MODISA_L3m_POC_R2022.0.json index b4e6d53b91..801bffaee9 100644 --- a/datasets/MODISA_L3m_POC_R2022.0.json +++ b/datasets/MODISA_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_RRS_NRT_R2022.0.json b/datasets/MODISA_L3m_RRS_NRT_R2022.0.json index ee5098d4d6..c2c0ed5635 100644 --- a/datasets/MODISA_L3m_RRS_NRT_R2022.0.json +++ b/datasets/MODISA_L3m_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_RRS_R2022.0.json b/datasets/MODISA_L3m_RRS_R2022.0.json index 3ec98adadb..13272a2e98 100644 --- a/datasets/MODISA_L3m_RRS_R2022.0.json +++ b/datasets/MODISA_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_SST4_NRT_R2019.0.json b/datasets/MODISA_L3m_SST4_NRT_R2019.0.json index 3c0b7a107f..a35d1e3a94 100644 --- a/datasets/MODISA_L3m_SST4_NRT_R2019.0.json +++ b/datasets/MODISA_L3m_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_SST4_R2019.0.json b/datasets/MODISA_L3m_SST4_R2019.0.json index 576500e95a..a350a12ae8 100644 --- a/datasets/MODISA_L3m_SST4_R2019.0.json +++ b/datasets/MODISA_L3m_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L3m_SST_NRT_R2019.0.json b/datasets/MODISA_L3m_SST_NRT_R2019.0.json index d979c2a929..4332192711 100644 --- a/datasets/MODISA_L3m_SST_NRT_R2019.0.json +++ b/datasets/MODISA_L3m_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODISA_L3m_SST_R2019.0.json b/datasets/MODISA_L3m_SST_R2019.0.json index 00fbf0c53b..b0f0c1ab30 100644 --- a/datasets/MODISA_L3m_SST_R2019.0.json +++ b/datasets/MODISA_L3m_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L3m_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L4b_GSM_R2022.0.json b/datasets/MODISA_L4b_GSM_R2022.0.json index 55e27ed9f2..04ee54b20e 100644 --- a/datasets/MODISA_L4b_GSM_R2022.0.json +++ b/datasets/MODISA_L4b_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L4b_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODISA_L4m_GSM_R2022.0.json b/datasets/MODISA_L4m_GSM_R2022.0.json index f81b0711e7..de17c5fb82 100644 --- a/datasets/MODISA_L4m_GSM_R2022.0.json +++ b/datasets/MODISA_L4m_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODISA_L4m_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L1_1.json b/datasets/MODIST_L1_1.json index fe6601d97e..a33eab5683 100644 --- a/datasets/MODIST_L1_1.json +++ b/datasets/MODIST_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L1_GEO_1.json b/datasets/MODIST_L1_GEO_1.json index 1271aa59e0..f565fe4e5d 100644 --- a/datasets/MODIST_L1_GEO_1.json +++ b/datasets/MODIST_L1_GEO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L1_GEO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L2_IOP_NRT_R2022.0.json b/datasets/MODIST_L2_IOP_NRT_R2022.0.json index 95fbbbf49e..a12e5951bd 100644 --- a/datasets/MODIST_L2_IOP_NRT_R2022.0.json +++ b/datasets/MODIST_L2_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L2_IOP_R2022.0.json b/datasets/MODIST_L2_IOP_R2022.0.json index 95c1078657..fa2de66b4d 100644 --- a/datasets/MODIST_L2_IOP_R2022.0.json +++ b/datasets/MODIST_L2_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L2_OC_NRT_R2022.0.json b/datasets/MODIST_L2_OC_NRT_R2022.0.json index 2eae63ec1f..bfc9e65f67 100644 --- a/datasets/MODIST_L2_OC_NRT_R2022.0.json +++ b/datasets/MODIST_L2_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L2_OC_R2022.0.json b/datasets/MODIST_L2_OC_R2022.0.json index 663cc5163c..a363fc60e0 100644 --- a/datasets/MODIST_L2_OC_R2022.0.json +++ b/datasets/MODIST_L2_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L2_SST4_NRT_R2019.0.json b/datasets/MODIST_L2_SST4_NRT_R2019.0.json index b9ce23fe73..6bd8c9db59 100644 --- a/datasets/MODIST_L2_SST4_NRT_R2019.0.json +++ b/datasets/MODIST_L2_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L2_SST4_R2019.0.json b/datasets/MODIST_L2_SST4_R2019.0.json index 95b59829b6..4a073b085f 100644 --- a/datasets/MODIST_L2_SST4_R2019.0.json +++ b/datasets/MODIST_L2_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L2_SST_NRT_R2019.0.json b/datasets/MODIST_L2_SST_NRT_R2019.0.json index 8ae5bc4d88..819c07f45f 100644 --- a/datasets/MODIST_L2_SST_NRT_R2019.0.json +++ b/datasets/MODIST_L2_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L2_SST_R2019.0.json b/datasets/MODIST_L2_SST_R2019.0.json index 81e9cb8982..526f0ca77e 100644 --- a/datasets/MODIST_L2_SST_R2019.0.json +++ b/datasets/MODIST_L2_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L2_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_CHL_NRT_R2022.0.json b/datasets/MODIST_L3b_CHL_NRT_R2022.0.json index 66d8598f2c..2232ce7113 100644 --- a/datasets/MODIST_L3b_CHL_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_CHL_R2022.0.json b/datasets/MODIST_L3b_CHL_R2022.0.json index edeb1a1809..961626fe42 100644 --- a/datasets/MODIST_L3b_CHL_R2022.0.json +++ b/datasets/MODIST_L3b_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_FLH_NRT_R2022.0.json b/datasets/MODIST_L3b_FLH_NRT_R2022.0.json index c404d6de48..274f7e1d76 100644 --- a/datasets/MODIST_L3b_FLH_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_FLH_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_FLH_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_FLH_R2022.0.json b/datasets/MODIST_L3b_FLH_R2022.0.json index f96d0396de..7af3dca92f 100644 --- a/datasets/MODIST_L3b_FLH_R2022.0.json +++ b/datasets/MODIST_L3b_FLH_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_FLH_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_IOP_NRT_R2022.0.json b/datasets/MODIST_L3b_IOP_NRT_R2022.0.json index 979c3bd887..456a148c83 100644 --- a/datasets/MODIST_L3b_IOP_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_IOP_R2022.0.json b/datasets/MODIST_L3b_IOP_R2022.0.json index aab89db78f..9b144dd107 100644 --- a/datasets/MODIST_L3b_IOP_R2022.0.json +++ b/datasets/MODIST_L3b_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_KD_NRT_R2022.0.json b/datasets/MODIST_L3b_KD_NRT_R2022.0.json index de9c53dd54..a21aef5e70 100644 --- a/datasets/MODIST_L3b_KD_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_KD_R2022.0.json b/datasets/MODIST_L3b_KD_R2022.0.json index ef6c0ecb46..b41cafd416 100644 --- a/datasets/MODIST_L3b_KD_R2022.0.json +++ b/datasets/MODIST_L3b_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_NSST_NRT_R2019.0.json b/datasets/MODIST_L3b_NSST_NRT_R2019.0.json index b1fb4c119e..4883c48567 100644 --- a/datasets/MODIST_L3b_NSST_NRT_R2019.0.json +++ b/datasets/MODIST_L3b_NSST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_NSST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_NSST_R2019.0.json b/datasets/MODIST_L3b_NSST_R2019.0.json index 0e2e2a131d..775f0545f9 100644 --- a/datasets/MODIST_L3b_NSST_R2019.0.json +++ b/datasets/MODIST_L3b_NSST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_NSST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_PAR_NRT_R2022.0.json b/datasets/MODIST_L3b_PAR_NRT_R2022.0.json index 559f7f378c..d0ed15db84 100644 --- a/datasets/MODIST_L3b_PAR_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_PAR_R2022.0.json b/datasets/MODIST_L3b_PAR_R2022.0.json index 367e2e38e5..f3b65e6d61 100644 --- a/datasets/MODIST_L3b_PAR_R2022.0.json +++ b/datasets/MODIST_L3b_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_PIC_NRT_R2022.0.json b/datasets/MODIST_L3b_PIC_NRT_R2022.0.json index ff7b305dae..20188c7ea9 100644 --- a/datasets/MODIST_L3b_PIC_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_PIC_R2022.0.json b/datasets/MODIST_L3b_PIC_R2022.0.json index 4ca1ff959c..199785eab2 100644 --- a/datasets/MODIST_L3b_PIC_R2022.0.json +++ b/datasets/MODIST_L3b_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_POC_NRT_R2022.0.json b/datasets/MODIST_L3b_POC_NRT_R2022.0.json index 0cf6ede676..c9af066b00 100644 --- a/datasets/MODIST_L3b_POC_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_POC_R2022.0.json b/datasets/MODIST_L3b_POC_R2022.0.json index cf7cd9176f..49f7166513 100644 --- a/datasets/MODIST_L3b_POC_R2022.0.json +++ b/datasets/MODIST_L3b_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_RRS_NRT_R2022.0.json b/datasets/MODIST_L3b_RRS_NRT_R2022.0.json index 194a198699..e09184b169 100644 --- a/datasets/MODIST_L3b_RRS_NRT_R2022.0.json +++ b/datasets/MODIST_L3b_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_RRS_R2022.0.json b/datasets/MODIST_L3b_RRS_R2022.0.json index 0ae2e7f618..fe70331c58 100644 --- a/datasets/MODIST_L3b_RRS_R2022.0.json +++ b/datasets/MODIST_L3b_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_SST4_NRT_R2019.0.json b/datasets/MODIST_L3b_SST4_NRT_R2019.0.json index 8ed40e79e9..3476a05eeb 100644 --- a/datasets/MODIST_L3b_SST4_NRT_R2019.0.json +++ b/datasets/MODIST_L3b_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_SST4_R2019.0.json b/datasets/MODIST_L3b_SST4_R2019.0.json index 270bee0340..433eb8788d 100644 --- a/datasets/MODIST_L3b_SST4_R2019.0.json +++ b/datasets/MODIST_L3b_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3b_SST_NRT_R2019.0.json b/datasets/MODIST_L3b_SST_NRT_R2019.0.json index a6d3511f4f..121a24fd02 100644 --- a/datasets/MODIST_L3b_SST_NRT_R2019.0.json +++ b/datasets/MODIST_L3b_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3b_SST_R2019.0.json b/datasets/MODIST_L3b_SST_R2019.0.json index 4f2db41312..06fcb4cd52 100644 --- a/datasets/MODIST_L3b_SST_R2019.0.json +++ b/datasets/MODIST_L3b_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3b_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_CHL_NRT_R2022.0.json b/datasets/MODIST_L3m_CHL_NRT_R2022.0.json index 61bf11ad2d..65ec5c9329 100644 --- a/datasets/MODIST_L3m_CHL_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_CHL_R2022.0.json b/datasets/MODIST_L3m_CHL_R2022.0.json index 948d08f8e0..8d1b58a24c 100644 --- a/datasets/MODIST_L3m_CHL_R2022.0.json +++ b/datasets/MODIST_L3m_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_FLH_NRT_R2022.0.json b/datasets/MODIST_L3m_FLH_NRT_R2022.0.json index 1c1d1682de..3e74796530 100644 --- a/datasets/MODIST_L3m_FLH_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_FLH_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_FLH_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_FLH_R2022.0.json b/datasets/MODIST_L3m_FLH_R2022.0.json index 17d2f8c834..a408e17292 100644 --- a/datasets/MODIST_L3m_FLH_R2022.0.json +++ b/datasets/MODIST_L3m_FLH_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_FLH_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_IOP_NRT_R2022.0.json b/datasets/MODIST_L3m_IOP_NRT_R2022.0.json index fe98f399a8..7283c6e011 100644 --- a/datasets/MODIST_L3m_IOP_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_IOP_R2022.0.json b/datasets/MODIST_L3m_IOP_R2022.0.json index 5c03ddf7c7..38ff24092e 100644 --- a/datasets/MODIST_L3m_IOP_R2022.0.json +++ b/datasets/MODIST_L3m_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_KD_NRT_R2022.0.json b/datasets/MODIST_L3m_KD_NRT_R2022.0.json index 64a7a6910a..cb4bb6f19c 100644 --- a/datasets/MODIST_L3m_KD_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_KD_R2022.0.json b/datasets/MODIST_L3m_KD_R2022.0.json index 0700c110f2..528223c46a 100644 --- a/datasets/MODIST_L3m_KD_R2022.0.json +++ b/datasets/MODIST_L3m_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_NSST_NRT_R2019.0.json b/datasets/MODIST_L3m_NSST_NRT_R2019.0.json index 5f1fb43b37..ea5362c4a9 100644 --- a/datasets/MODIST_L3m_NSST_NRT_R2019.0.json +++ b/datasets/MODIST_L3m_NSST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_NSST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_NSST_R2019.0.json b/datasets/MODIST_L3m_NSST_R2019.0.json index ede402c684..4f17e494e1 100644 --- a/datasets/MODIST_L3m_NSST_R2019.0.json +++ b/datasets/MODIST_L3m_NSST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_NSST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_PAR_NRT_R2022.0.json b/datasets/MODIST_L3m_PAR_NRT_R2022.0.json index 84565808fc..9420f9e081 100644 --- a/datasets/MODIST_L3m_PAR_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_PAR_R2022.0.json b/datasets/MODIST_L3m_PAR_R2022.0.json index 24dcc35e1a..8bf3ebc689 100644 --- a/datasets/MODIST_L3m_PAR_R2022.0.json +++ b/datasets/MODIST_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_PIC_NRT_R2022.0.json b/datasets/MODIST_L3m_PIC_NRT_R2022.0.json index 39fb6180d6..bc251306f8 100644 --- a/datasets/MODIST_L3m_PIC_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_PIC_R2022.0.json b/datasets/MODIST_L3m_PIC_R2022.0.json index 61e9d14a28..4ec8fc5af1 100644 --- a/datasets/MODIST_L3m_PIC_R2022.0.json +++ b/datasets/MODIST_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_POC_NRT_R2022.0.json b/datasets/MODIST_L3m_POC_NRT_R2022.0.json index a4980e0fb2..d657075bfd 100644 --- a/datasets/MODIST_L3m_POC_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_POC_R2022.0.json b/datasets/MODIST_L3m_POC_R2022.0.json index 6d44c76f2e..889a071e2a 100644 --- a/datasets/MODIST_L3m_POC_R2022.0.json +++ b/datasets/MODIST_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_RRS_NRT_R2022.0.json b/datasets/MODIST_L3m_RRS_NRT_R2022.0.json index e1a414fb6e..fa2d7d9957 100644 --- a/datasets/MODIST_L3m_RRS_NRT_R2022.0.json +++ b/datasets/MODIST_L3m_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_RRS_R2022.0.json b/datasets/MODIST_L3m_RRS_R2022.0.json index 6925b15482..aeed917805 100644 --- a/datasets/MODIST_L3m_RRS_R2022.0.json +++ b/datasets/MODIST_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_SST4_NRT_R2019.0.json b/datasets/MODIST_L3m_SST4_NRT_R2019.0.json index f5c4012236..82af8285fd 100644 --- a/datasets/MODIST_L3m_SST4_NRT_R2019.0.json +++ b/datasets/MODIST_L3m_SST4_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_SST4_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_SST4_R2019.0.json b/datasets/MODIST_L3m_SST4_R2019.0.json index 433b39f5db..f298016a93 100644 --- a/datasets/MODIST_L3m_SST4_R2019.0.json +++ b/datasets/MODIST_L3m_SST4_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_SST4_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L3m_SST_NRT_R2019.0.json b/datasets/MODIST_L3m_SST_NRT_R2019.0.json index 67b6cd9aaa..c97dd2f947 100644 --- a/datasets/MODIST_L3m_SST_NRT_R2019.0.json +++ b/datasets/MODIST_L3m_SST_NRT_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_SST_NRT_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/MODIST_L3m_SST_R2019.0.json b/datasets/MODIST_L3m_SST_R2019.0.json index 1f20c59c3a..48824deed2 100644 --- a/datasets/MODIST_L3m_SST_R2019.0.json +++ b/datasets/MODIST_L3m_SST_R2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L3m_SST_R2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L4b_GSM_R2022.0.json b/datasets/MODIST_L4b_GSM_R2022.0.json index 77c378a197..664e70548a 100644 --- a/datasets/MODIST_L4b_GSM_R2022.0.json +++ b/datasets/MODIST_L4b_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L4b_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIST_L4m_GSM_R2022.0.json b/datasets/MODIST_L4m_GSM_R2022.0.json index c0b73d2409..9222b305e0 100644 --- a/datasets/MODIST_L4m_GSM_R2022.0.json +++ b/datasets/MODIST_L4m_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIST_L4m_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.", "links": [ { diff --git a/datasets/MODIS_A-JPL-L2P-v2019.0_2019.0.json b/datasets/MODIS_A-JPL-L2P-v2019.0_2019.0.json index 3759c62676..7903237197 100644 --- a/datasets/MODIS_A-JPL-L2P-v2019.0_2019.0.json +++ b/datasets/MODIS_A-JPL-L2P-v2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_A-JPL-L2P-v2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDA-2PJ02", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json index c089a59e71..658f26e1dc 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json index faa7789c81..8cc8d6befe 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json index df90deb7ea..0ecc3b3974 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-AN4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json index 91e5c60099..b8e1fe08ce 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-AN9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json index 4f68b6375f..9382a04b9d 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-1D4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json index 009bb20e15..f4cffcc5bf 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-1D9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json index 6db384559b..b1926cf92c 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-MO4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json index 57b9c80419..04c891433c 100644 --- a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-MO9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json index e4ae96ff09..8e7a804d05 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-8D4D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json index 0af1ca2b5a..42e8c98ab5 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-8D4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json index 364e5d0506..734e79cb07 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-8D9D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json index e21db27e55..5e37503059 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-8D9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json index afc6ba12fb..2aa199a809 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-AN4D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json index 3fc45dcc20..65e6317d9f 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-AN4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json index bcf43bb820..ea77db2cbc 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-AN9D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json index 4952d17f60..22e692cee4 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-AN9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json index 3f275efc9e..a13ea8ca24 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-1D4D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json index 867ace4c9c..67421a2457 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-1D4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json index b251d77d55..8b504d51cc 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-1D9D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json index 7b91cd6898..84f2e7f53e 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-1D9N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json index 5b86274840..63a9882088 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO4D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json index 64525ecfd9..f52ca7a540 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO4N4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json index a0112e7778..13bb98f8d9 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO9D4", "links": [ { diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json index 0d8b55a355..49d62a14fb 100644 --- a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO9N4", "links": [ { diff --git a/datasets/MODIS_CCaN_NDVI_Trends_Alaska_1666_1.json b/datasets/MODIS_CCaN_NDVI_Trends_Alaska_1666_1.json index a3bc7b1479..9d5ac00667 100644 --- a/datasets/MODIS_CCaN_NDVI_Trends_Alaska_1666_1.json +++ b/datasets/MODIS_CCaN_NDVI_Trends_Alaska_1666_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_CCaN_NDVI_Trends_Alaska_1666_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.", "links": [ { diff --git a/datasets/MODIS_CR_Equal_Angle_3h_1.0.json b/datasets/MODIS_CR_Equal_Angle_3h_1.0.json index 0c34bda9cc..a7528a00c7 100644 --- a/datasets/MODIS_CR_Equal_Angle_3h_1.0.json +++ b/datasets/MODIS_CR_Equal_Angle_3h_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_CR_Equal_Angle_3h_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures.", "links": [ { diff --git a/datasets/MODIS_CR_Equal_Angle_Daily_1.0.json b/datasets/MODIS_CR_Equal_Angle_Daily_1.0.json index 01d97b7716..e877ca2e27 100644 --- a/datasets/MODIS_CR_Equal_Angle_Daily_1.0.json +++ b/datasets/MODIS_CR_Equal_Angle_Daily_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_CR_Equal_Angle_Daily_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures.", "links": [ { diff --git a/datasets/MODIS_CR_Equal_Area_3h_1.0.json b/datasets/MODIS_CR_Equal_Area_3h_1.0.json index 4b02ff5636..50c2d3e4bd 100644 --- a/datasets/MODIS_CR_Equal_Area_3h_1.0.json +++ b/datasets/MODIS_CR_Equal_Area_3h_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_CR_Equal_Area_3h_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Collection 6.1 Equal-Area Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures.", "links": [ { diff --git a/datasets/MODIS_MAIAC_Reflectance_1700_1.json b/datasets/MODIS_MAIAC_Reflectance_1700_1.json index 2be93ab4fe..e5d3eef9ab 100644 --- a/datasets/MODIS_MAIAC_Reflectance_1700_1.json +++ b/datasets/MODIS_MAIAC_Reflectance_1700_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_MAIAC_Reflectance_1700_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena.", "links": [ { diff --git a/datasets/MODIS_PAR_1140_1.json b/datasets/MODIS_PAR_1140_1.json index 88cfb2d8f5..849890ae2d 100644 --- a/datasets/MODIS_PAR_1140_1.json +++ b/datasets/MODIS_PAR_1140_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_PAR_1140_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily Moderate Resolution Imaging Spectroradiometer (MODIS) land incident photosynthetically active radiation (PAR) images over North America for the years 2003 - 2005 and was created to fill the need for daily PAR estimates. Incident PAR is the solar radiation in the range of 400 to 700 nm reaching the earth's surface and plays an important role in modeling terrestrial ecosystem productivity. The daily images were derived by integrating MODIS/Terra and MODIS/Aqua instantaneous PAR data where the instantaneous PAR data is estimated directly from Terra or Aqua MODIS 5-min L1b swath data (Liang et al., 2006 and Wang et al., 2010). The spatial distribution of this data set includes the MODIS tile subsets covering North America, Central America, portions of South America, and Greenland, available for the years 2003 - 2005. There are 45,376 *.hdf files with a spatial resolution of 4 km x 4 km in sinusoidal projection distributed by year in three compressed data files: 2003.zip, 2004.zip, and 2005.zip. Contained within each daily file are 4 separate image files: DirectPar, DiffusePAR, TotalPAR, and Observation Count. There are 46 MODIS tiles that cover the study area extent. ", "links": [ { diff --git a/datasets/MODIS_T-JPL-L2P-v2019.0_2019.0.json b/datasets/MODIS_T-JPL-L2P-v2019.0_2019.0.json index f1ff3e125e..726e7c2a18 100644 --- a/datasets/MODIS_T-JPL-L2P-v2019.0_2019.0.json +++ b/datasets/MODIS_T-JPL-L2P-v2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_T-JPL-L2P-v2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json index 2d5c2e0487..b323ffbe90 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-8D4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json index d1947bd7ef..51904b168f 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-8D9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json index 8cb3f4eb6f..8ab06465de 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-AN4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json index 4e9dae5ded..c03faff62a 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-AN9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json index eadafb81b2..1ec302e543 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-1D4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json index a909ceafa5..abdd78cf04 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-1D9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json index d04a284e13..ddecbb3276 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-MO4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json index e5d969e294..30d4da4230 100644 --- a/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-MO9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json index bd76cb9444..0ef83d3c2c 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-8D4D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json index 0e30669764..491bdf703d 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-8D4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json index 3472696c21..10d497f51e 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-8D9D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json index cd703c0a72..80bb62aa72 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_8DAY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-8D9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json index e055956bd5..e2df5e699d 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-AN4D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json index 4d872f92d5..408516bbbf 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-AN4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json index 3d780053f5..4ed45d6e05 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-AN9D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json index bb658c7de7..5b3e62d651 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-AN9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json index 7814c358d2..533d4b0031 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-1D4D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json index c8466ddfb4..124ac92d78 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_DAILY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-1D4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json index 8738c28988..20138e3f05 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-1D9D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json index cec716da9a..d8ae24aa7a 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-1D9N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json index 11910960f2..0f9b3215f2 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-MO4D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json index af14b03642..6832b2f7e8 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-MO4N4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json index 82984eb1b1..8cd938a584 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be at https://doi.org/10.5067/MODST-MO9D4", "links": [ { diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json index 0613c07a87..df62c0594a 100644 --- a/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json +++ b/datasets/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_TERRA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be at https://doi.org/10.5067/MODST-MO9N4", "links": [ { diff --git a/datasets/MODIS_emissions_758_1.json b/datasets/MODIS_emissions_758_1.json index ab17e92782..3aedb6b1a1 100644 --- a/datasets/MODIS_emissions_758_1.json +++ b/datasets/MODIS_emissions_758_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODIS_emissions_758_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The recently generated MODIS burned area product over southern Africa for the month of September 2000 was used to calculate regional biomass burning emissions from grassland and woodland fires for a number of trace gases and particulates at 1 km spatial resolution. A dynamic regional fuel load model developed for southern Africa in support of SAFARI 2000 fire emissions modeling is used to compute spatially explicit southern Africa fuel load data.", "links": [ { diff --git a/datasets/MODVI_005.json b/datasets/MODVI_005.json index 2849577df2..f51ab0020e 100644 --- a/datasets/MODVI_005.json +++ b/datasets/MODVI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MODVI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Terra Vegetation Indices Monthly product MOD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling. The vegetation indices product is generated at 1x1 degree spatial resolution starting from 2000.", "links": [ { diff --git a/datasets/MOD_L2_DC_001.json b/datasets/MOD_L2_DC_001.json index 9092f1c53f..ed09897fc3 100644 --- a/datasets/MOD_L2_DC_001.json +++ b/datasets/MOD_L2_DC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOD_L2_DC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS Terra L2 deep-convective cloud classification (DC) are part of our global MODIS Terra data from the 2017 MEaSUREs project, A Comprehensive Data Record of Marine Low-level and Deep Convective Cloud Systems Using an Object-Oriented Approach.", "links": [ { diff --git a/datasets/MOKIHANA_0.json b/datasets/MOKIHANA_0.json index 9221578ae0..2103a660ec 100644 --- a/datasets/MOKIHANA_0.json +++ b/datasets/MOKIHANA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOKIHANA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the western Pacific Ocean during September 1998.", "links": [ { diff --git a/datasets/MONTEREY_BAY_0.json b/datasets/MONTEREY_BAY_0.json index ada2d7ce62..e1003388dd 100644 --- a/datasets/MONTEREY_BAY_0.json +++ b/datasets/MONTEREY_BAY_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MONTEREY_BAY_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Monterey Bay spanning 2003 to 2006.", "links": [ { diff --git a/datasets/MOOSE_Aerodyne-Mobile-Laboratory_1.json b/datasets/MOOSE_Aerodyne-Mobile-Laboratory_1.json index af0616d7bd..fd7b7b4a2c 100644 --- a/datasets/MOOSE_Aerodyne-Mobile-Laboratory_1.json +++ b/datasets/MOOSE_Aerodyne-Mobile-Laboratory_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOOSE_Aerodyne-Mobile-Laboratory_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOOSE_Aerodyne-Mobile-Laboratory_1 is the data collected by the Aerodyne Mobile Laboratory (AML) during the Michigan-Ontario Ozone Source Experiment (MOOSE). Instruments used to collect data featured in this collection include: TDPC-GC-EI-ToFMS (Thermal Desorption Pre-Concentration - Gas Chromatograph - Electron Impact Ionization - Time of Flight Mass Spectrometer), Aerodyne Vocus PTR-ToF-MS instrument for the quantification of VOCs including BTEX, isoprene, terpenes, etc., Aerodyne TILDAS instruments for CH4, C2H6, CO, N2O, H2O, HCHO, HCOOH, NO, NO2; Aerodyne CAPS-NOx for NOx; Licor 6262 for CO2; 2B-Tech for O3; RMYoung 86000 for wind; and Hemisphere GPS Vector V103.\r\nThe Michigan-Ontario Ozone Source Experiment (MOOSE) is an international collaboration between US and Canadian agencies: the Ontario Ministry of Environment, Conservation, and Parks (MECP), the Environment and Climate Change Canada (ECCC), the US Environmental Protection Agency (EPA), and the Michigan Department of Environment, Great Lakes, and Energy (EGLE). These agencies conducted three field experiments to ensure a viable ozone attainment strategy which, due to their common goal, were given the common name MOOSE. The three field experiments that MOOSE encapsulates are: the Great Lakes Meteorology and Ozone Recirculation (GLAMOR) experiment, the Chemical Source Signatures (CHESS) experiment, and the Methane Releases from Landfills and Gas Lines (MERLIN) experiment. Field studies were conducted for MOOSE in 2021 and 2022. MOOSE consists of two phases, with the first occurring over six weeks from May to June 2021, and the second phase occurring during the summer of 2022. Both airborne and ground instruments are used in completing the campaign\u2019s main goal of aiding in the creation of an ozone attainment strategy for Southeast Michigan (SEMI). SEMI is currently designated as in-marginal nonattainment of the U.S. federal ozone standard. The campaign also has the goal of better understanding what contributes to elevated ozone levels in the Border region, the immediate area on both sides of the US-Canada border. Along with understanding the contributing factors of elevated ozone levels, the campaign aims to understand how the elevated ozone levels cause exceedances to the Canadian ambient air quality standard for ozone.\r\nIn addition to MOOSE\u2019s overarching goals, GLAMOR, CHESS, and MERLIN have their own objectives to fulfill. GLAMOR seeks to understand and simulate complex 3D flows that are associated with lake breeze circulations, the urban heat island (UHI) and its interaction with the lake breeze, and the impact of lake breezes and the UHI on ozone and ozone precursor transport. GLAMOR also aims to understand and track the influence of urban emissions and land-lake breezes on urban oxidative capacity through nitrous acid (HONO) and related reactive nitrogen species. Determining the conceptual picture (mesoscale meteorological patterns and photochemical production locations) for ozone exceedances in the Border region is what this campaign aims to achieve as well. Finally, GLAMOR aims to select representative ozone episodes for each identified mesoscale pattern, as well as conduct modeling and data analyses in support of an ozone attainment demonstration. The second sub-experiment, CHESS, has a goal to characterize the ozone precursor signatures at the key monitoring stations in the Border region where design values are highest during ozone exceedances in the typical year. CHESS will characterize emission plumes from point sources, area sources, and major industrial sectors in the Border region as well as their impacts on ozone design values on the two sides of the U.S. and Canada border. CHESS also aims to perform air quality model simulations of potential emission control strategies. The third sub-experiment, MERLIN, seeks to determine the natural gas leakage rate of pipelines or other infrastructure in SEMI. Quantifying methane, formaldehyde, and other emissions from landfills in the Border region as well as determining the contributions of large methane sources to ozone exceedances in the Border region are the two other objectives MERLIN is set to accomplish. In doing this, potential control strategies of gas emission into the atmosphere can be drafted and implemented.\r\nThe three sub-experiments are equipped with their own payloads and stations where research is conducted. GLAMOR uses ground stations and Aerodyne Networks to gather data from MECP\u2019s Windsor West air monitoring station in Ontario, EGLE\u2019s Detroit East 7 Mile PAMS Station, EGLE\u2019s Port Huron monitoring station, as well as collecting field measurements of concentration and isotopic composition of NOx, HONO, NO2, HNO3, and NO3. CHESS utilizes mobile labs, ground stations, and the NASA Gulfstream III (G-III) aircraft while working with the Aerodyne Mobile lab, University of Michigan Pollution Assessment Lab (MPAL), and MECP Mobile Lab. CHESS utilizes these tools and payloads to measure CH4, HCHO, CO2, CO, H2O, O3, SO2, and NOx. MERLIN utilizes mobile labs, drones, and ground stations to work with the University of Michigan Mobile Lab, the Colorado State University Mobile Lab, and the EPA mobile lab. Drone-mounted meteorological chemical sensors for CH4, CH2O, and O3 precursors as well as the EPA GMAP mobile platform are used to measure hydrogen sulfide, methane, benzene, toluene, ethylbenzene, m-o-p xylene, and ozone, as well as meteorological parameters.", "links": [ { diff --git a/datasets/MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json b/datasets/MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json index 64ab29f46d..6c095fcaaf 100644 --- a/datasets/MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json +++ b/datasets/MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOOSE_AircraftRemoteSensing_NASA-G3_GCAS_Data contains remotely sensed data collected by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) onboard NASA's Gulfstream-III (G-3) aircraft during the Michigan-Ontario Ozone Source Experiment (MOOSE).\r\n\r\nThe Michigan-Ontario Ozone Source Experiment (MOOSE) is an international collaboration between US and Canadian agencies: the Ontario Ministry of Environment, Conservation, and Parks (MECP), the Environment and Climate Change Canada (ECCC), the US Environmental Protection Agency (EPA), and the Michigan Department of Environment, Great Lakes, and Energy (EGLE). These agencies conducted three field experiments to ensure a viable ozone attainment strategy which, due to their common goal, were given the common name MOOSE. The three field experiments that MOOSE encapsulates are: the Great Lakes Meteorology and Ozone Recirculation (GLAMOR) experiment, the Chemical Source Signatures (CHESS) experiment, and the Methane Releases from Landfills and Gas Lines (MERLIN) experiment. Field studies were conducted for MOOSE in 2021 and 2022. MOOSE consists of two phases, with the first occurring over six weeks from May to June 2021, and the second phase occurring during the summer of 2022. Both airborne and ground instruments are used in completing the campaign\u2019s main goal of aiding in the creation of an ozone attainment strategy for Southeast Michigan (SEMI). SEMI is currently designated as in-marginal nonattainment of the U.S. federal ozone standard. The campaign also has the goal of better understanding what contributes to elevated ozone levels in the Border region, the immediate area on both sides of the US-Canada border. Along with understanding the contributing factors of elevated ozone levels, the campaign aims to understand how the elevated ozone levels cause exceedances to the Canadian ambient air quality standard for ozone.\r\n\r\nIn addition to MOOSE\u2019s overarching goals, GLAMOR, CHESS, and MERLIN have their own objectives to fulfill. GLAMOR seeks to understand and simulate complex 3D flows that are associated with lake breeze circulations, the urban heat island (UHI) and its interaction with the lake breeze, and the impact of lake breezes and the UHI on ozone and ozone precursor transport. GLAMOR also aims to understand and track the influence of urban emissions and land-lake breezes on urban oxidative capacity through nitrous acid (HONO) and related reactive nitrogen species. Determining the conceptual picture (mesoscale meteorological patterns and photochemical production locations) for ozone exceedances in the Border region is what this campaign aims to achieve as well. Finally, GLAMOR aims to select representative ozone episodes for each identified mesoscale pattern, as well as conduct modeling and data analyses in support of an ozone attainment demonstration. The second sub-experiment, CHESS, has a goal to characterize the ozone precursor signatures at the key monitoring stations in the Border region where design values are highest during ozone exceedances in the typical year. CHESS will characterize emission plumes from point sources, area sources, and major industrial sectors in the Border region as well as their impacts on ozone design values on the two sides of the U.S. and Canada border. CHESS also aims to perform air quality model simulations of potential emission control strategies. The third sub-experiment, MERLIN, seeks to determine the natural gas leakage rate of pipelines or other infrastructure in SEMI. Quantifying methane, formaldehyde, and other emissions from landfills in the Border region as well as determining the contributions of large methane sources to ozone exceedances in the Border region are the two other objectives MERLIN is set to accomplish. In doing this, potential control strategies of gas emission into the atmosphere can be drafted and implemented.\r\n\r\nThe three sub-experiments are equipped with their own payloads and stations where research is conducted. GLAMOR uses ground stations and Aerodyne Networks to gather data from MECP\u2019s Windsor West air monitoring station in Ontario, EGLE\u2019s Detroit East 7 Mile PAMS Station, EGLE\u2019s Port Huron monitoring station, as well as collecting field measurements of concentration and isotopic composition of NOx, HONO, NO2, HNO3, and NO3. CHESS utilizes mobile labs, ground stations, and the NASA Gulfstream III (G-III) aircraft while working with the Aerodyne Mobile lab, University of Michigan Pollution Assessment Lab (MPAL), and MECP Mobile Lab. CHESS utilizes these tools and payloads to measure CH4, HCHO, CO2, CO, H2O, O3, SO2, and NOx. MERLIN utilizes mobile labs, drones, and ground stations to work with the University of Michigan Mobile Lab, the Colorado State University Mobile Lab, and the EPA mobile lab. Drone-mounted meteorological chemical sensors for CH4, CH2O, and O3 precursors as well as the EPA GMAP mobile platform are used to measure hydrogen sulfide, methane, benzene, toluene, ethylbenzene, m-o-p xylene, and ozone, as well as meteorological parameters.", "links": [ { diff --git a/datasets/MOP02J_109.json b/datasets/MOP02J_109.json index 0e177164f1..0d1bd38cdb 100644 --- a/datasets/MOP02J_109.json +++ b/datasets/MOP02J_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02J_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 109 product. It consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing.\n\nMOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP02J_8.json b/datasets/MOP02J_8.json index 04fd008971..ba057844b5 100644 --- a/datasets/MOP02J_8.json +++ b/datasets/MOP02J_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02J_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02J_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 8 product. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP02J_9.json b/datasets/MOP02J_9.json index fc6c230e73..426a96c910 100644 --- a/datasets/MOP02J_9.json +++ b/datasets/MOP02J_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02J_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 9 product. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP02N_109.json b/datasets/MOP02N_109.json index 39ca51eb74..59d137dc70 100644 --- a/datasets/MOP02N_109.json +++ b/datasets/MOP02N_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02N_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02N_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Near Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration, and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. Each retrieval is accompanied by an estimated error. Version 109 products are beta versions for version 9 products, they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. \n\nMOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP02N_8.json b/datasets/MOP02N_8.json index af8d8d753a..9b9e26b7e5 100644 --- a/datasets/MOP02N_8.json +++ b/datasets/MOP02N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02N_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived carbon monoxide (CO) (Near Infrared Radiances) version 8 dataset. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product was completed in March of 2020. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP02N_9.json b/datasets/MOP02N_9.json index 70e37bb55a..6b6f3e15f9 100644 --- a/datasets/MOP02N_9.json +++ b/datasets/MOP02N_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02N_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived carbon monoxide (CO) (Near Infrared Radiances) version 9 dataset. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP02T_109.json b/datasets/MOP02T_109.json index 292295ce17..13a40a165a 100644 --- a/datasets/MOP02T_109.json +++ b/datasets/MOP02T_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02T_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. \n\nMOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft,, on December 18, 1999. The instrument was constructed by a consortium of Canadian companies and funded by the Canadian Space Agency's Space Science Division.", "links": [ { diff --git a/datasets/MOP02T_8.json b/datasets/MOP02T_8.json index 0d76dc578e..d947fbb128 100644 --- a/datasets/MOP02T_8.json +++ b/datasets/MOP02T_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02T_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02T_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Thermal Infrared Radiances) version 8 product. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP02T_9.json b/datasets/MOP02T_9.json index 5c9a52ae6b..2f9df5823a 100644 --- a/datasets/MOP02T_9.json +++ b/datasets/MOP02T_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP02T_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP02T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Thermal Infrared Radiances) version 9 product. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. Each retrieval is accompanied by an estimated error. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP03JM_109.json b/datasets/MOP03JM_109.json index cd0feab0de..42767f750b 100644 --- a/datasets/MOP03JM_109.json +++ b/datasets/MOP03JM_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03JM_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03JM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near and Thermal Infrared Radiances) version 109 product. It contains monthly mean-gridded daily L2 CO profile versions and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the L3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing.\n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03JM_8.json b/datasets/MOP03JM_8.json index d91305248e..03fc149bd2 100644 --- a/datasets/MOP03JM_8.json +++ b/datasets/MOP03JM_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03JM_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03JM_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near and Thermal Infrared Radiances) version 8 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP03JM_9.json b/datasets/MOP03JM_9.json index c4a9d9cc8b..6413f85ed7 100644 --- a/datasets/MOP03JM_9.json +++ b/datasets/MOP03JM_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03JM_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03JM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near and Thermal Infrared Radiances) version 9 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP03J_109.json b/datasets/MOP03J_109.json index a822d80a1f..106c4cf51e 100644 --- a/datasets/MOP03J_109.json +++ b/datasets/MOP03J_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03J_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Near and Thermal Infrared Radiances) version 109 product is an unvalidated beta product subject to recalibration, contains daily mean gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. \nData collection for this product is ongoing.\n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03J_8.json b/datasets/MOP03J_8.json index 0f89c1a396..9078bccaaa 100644 --- a/datasets/MOP03J_8.json +++ b/datasets/MOP03J_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03J_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03J_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near and Thermal Infrared Radiances) version 8 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. \nFor this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is completed.", "links": [ { diff --git a/datasets/MOP03J_9.json b/datasets/MOP03J_9.json index 5d2cc037b1..fbdc08cfe0 100644 --- a/datasets/MOP03J_9.json +++ b/datasets/MOP03J_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03J_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near and Thermal Infrared Radiances) version 9 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. \nFor this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product was completed in March of 2020.", "links": [ { diff --git a/datasets/MOP03NM_109.json b/datasets/MOP03NM_109.json index bf67532238..76f871cf35 100644 --- a/datasets/MOP03NM_109.json +++ b/datasets/MOP03NM_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03NM_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03NM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near Infrared Radiances) version 109 product. This product contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing.\n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03NM_8.json b/datasets/MOP03NM_8.json index a5dcb23302..90202bb2d0 100644 --- a/datasets/MOP03NM_8.json +++ b/datasets/MOP03NM_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03NM_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03NM_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near Infrared Radiances) version 8 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP03NM_9.json b/datasets/MOP03NM_9.json index bb5ebf8222..41449d2b7c 100644 --- a/datasets/MOP03NM_9.json +++ b/datasets/MOP03NM_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03NM_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03NM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near Infrared Radiances) version 9 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP03N_109.json b/datasets/MOP03N_109.json index 9571c6403c..9147e714ba 100644 --- a/datasets/MOP03N_109.json +++ b/datasets/MOP03N_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03N_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03N_109 is the Measurements of Pollution in the Troposphere (MOPITT) Beta CO gridded daily means (Near Infrared Radiances) version 109 product. It is a non-validated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing.\n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03N_8.json b/datasets/MOP03N_8.json index 052f2482a4..bc7e5b1f2e 100644 --- a/datasets/MOP03N_8.json +++ b/datasets/MOP03N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03N_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near Infrared Radiances) version 8 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP03N_9.json b/datasets/MOP03N_9.json index 713c06ead6..0dae48c547 100644 --- a/datasets/MOP03N_9.json +++ b/datasets/MOP03N_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03N_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near Infrared Radiances) version 9 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP03TM_109.json b/datasets/MOP03TM_109.json index 235e8b0d78..11a49586d8 100644 --- a/datasets/MOP03TM_109.json +++ b/datasets/MOP03TM_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03TM_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03TM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains monthly mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions for version 9 products, they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing.\n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03TM_8.json b/datasets/MOP03TM_8.json index 1a3b19de02..d1cfe6f502 100644 --- a/datasets/MOP03TM_8.json +++ b/datasets/MOP03TM_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03TM_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03TM_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Thermal Infrared Radiances) version 8 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP03TM_9.json b/datasets/MOP03TM_9.json index a0b823a662..6150b21795 100644 --- a/datasets/MOP03TM_9.json +++ b/datasets/MOP03TM_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03TM_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03TM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Thermal Infrared Radiances) version 9 data product. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. V9 is an improvement over V8 because of several scientific enhancements. These include a revision of the cloud filter to allow through a much higher number of pixels that were previously considered cloudy, a minor correction to the Forward Model to account for the long-term drift of the pressure in the gas cell, and a careful analysis of the NIR calibration process which reduces discontinuities associated with calibration events. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOP03T_109.json b/datasets/MOP03T_109.json index 57326d6e7f..412d1bc3d0 100644 --- a/datasets/MOP03T_109.json +++ b/datasets/MOP03T_109.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03T_109", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. \n\nFor a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.", "links": [ { diff --git a/datasets/MOP03T_8.json b/datasets/MOP03T_8.json index 3e9f603e99..55a55e4b75 100644 --- a/datasets/MOP03T_8.json +++ b/datasets/MOP03T_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03T_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03T_8 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Thermal Infrared Radiances) version 8 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is complete.", "links": [ { diff --git a/datasets/MOP03T_9.json b/datasets/MOP03T_9.json index 6bce48a6d8..eb763f670f 100644 --- a/datasets/MOP03T_9.json +++ b/datasets/MOP03T_9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOP03T_9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOP03T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Thermal Infrared Radiances) version 9 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.", "links": [ { diff --git a/datasets/MOPCH_007.json b/datasets/MOPCH_007.json index 7b571f3b2a..c604ffcc23 100644 --- a/datasets/MOPCH_007.json +++ b/datasets/MOPCH_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOPCH_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MOPITT Calibration History File", "links": [ { diff --git a/datasets/MOPITT_co_835_1.json b/datasets/MOPITT_co_835_1.json index 6ec75e94f0..f7e5b69cf5 100644 --- a/datasets/MOPITT_co_835_1.json +++ b/datasets/MOPITT_co_835_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MOPITT_co_835_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MOPITT (Measurements Of Pollution In The Troposphere) instrument on the NASA Terra Satellite makes measurements of infrared radiation originating from the surface of the planet and isolates the energy being radiated from carbon monoxide (CO). By using appropriate data analysis techniques, concentration profiles of CO (Level-2 (L2) data) can be obtained on a global basis at a reasonably high horizontal (~22km) and vertical resolution (~3km).The MOPITT Level-3 (L3) data products provided in this data set are a subset of the daily averages from the L2 data. This subset was produced by overlaying a global 1x1-degree grid onto the L2 data, and then clipping the data to this southern Africa subset which originates at 5 degrees longitude and -35 degrees latitude and extends to 60 degrees longitude and 35 degrees latitude. Data are reported for 2 heights, 700 and 350 hPa, from daytime swaths for the period August 1-September 30, 2000, the SAFARI 2000 Dry Season Campaign.", "links": [ { diff --git a/datasets/MQ_INVERT-AB_1.json b/datasets/MQ_INVERT-AB_1.json index 25bb5012fb..bba0bf15b0 100644 --- a/datasets/MQ_INVERT-AB_1.json +++ b/datasets/MQ_INVERT-AB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MQ_INVERT-AB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of Invertebrate abundance from soil cores on Macquarie Island.\n\nIn the summer of 1986-87, total invertebrate abundances were measured quantitatively at eight sites, representing four vegetation types: feldmark, Stilbocarpa herbfield, Pleurophyllum meadow and Poa foliosa tall tussock grassland (P. Greenslade, unpubl. data). Between 11 and 16 soil cores were sampled at each site. Each core was 5 cm wide by 5 cm deep and invertebrates were extracted using Tulgren funnels. Numbers of invertebrates from each core are expressed as animals per square metre (.m-2). The mean density for the total of 120 cores was 29702.m-2 plus or minus 3564 SE and ranged from a low site mean of 2646.m-2 plus or minus 513 SE at a feldmark site on the plateau at 250m, to high site means of 97740.m-2 plus or minus 15898 SE and 62894.m-2 plus or minus 20804 SE at two Stilbocarpa dominated, coastal eastern slopes, both at 20 m a.s.l. Poa foliosa dominated sites at 40 m and 100m a.s.l. displayed intermediate mean densities of 20599.m-2 plus or minus 4241 SE and 20567.m-2 plus or minus 2670 SE, respectively. A Pleurophyllum dominated site on the plateau at 250m a.s.l. also exhibited a low mean site density of 6,664.m-2 plus or minus 1224 m-2 SE, while one on North Head at a lower elevation of 100m a.s.l., was higher at 24107.m-2 plus or minus 4155. A higher mean density of 19417.m-2 plus or minus 3674 was also found at feldmark site on North Head at only 100 m a.s.l. These figures show that altitude appeared to have a stronger influence on invertebrate abundance than vegetation type. The total mean density is similar to those found in temperate grassland and herbfields in other parts of Australia where a mean of about 25000 invertebrates.m-2 might be expected (King and Hutchinson, 1992). Barendse and Chown (2001) found a similar mean density for feldmark of 1800.m-2 on Marion Island but rather higher mean density of 50 000.m-2 in Azorella selago cushions, a vegetation type not sampled on Macquarie Island. Collembola dominated the Macquarie Island fauna numerically, followed by Acarina. Barendse and Chown (2001) found the same groups dominated in Azorella selago cushions and bare ground on Marion. Of interest was the high density of the introduced Hypogastrura purpurescens under Stilbocarpa polaris on Macquarie Island. \n\nSee also the metadata record &Report on invertebrate field work, Macquarie Island, December 1986-January 1987& for further information.\n\nThe fields in these datasets are:\n\nEasting\nNorthing\nDescription\nSpecies\nKA/EW, Kontia andersoni and earthworms\nAV, Arthurdendyus vegrandis\nSEW, small earthworms\nDensity per square metre\nSoil Core", "links": [ { diff --git a/datasets/MRIRN2IM_001.json b/datasets/MRIRN2IM_001.json index 481a604d72..e1622778f0 100644 --- a/datasets/MRIRN2IM_001.json +++ b/datasets/MRIRN2IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MRIRN2IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MRIRN2IM is the Nimbus-2 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.4-6.9, 10-11, 14-16, 5-30, and 0.2-4.0 micrometers. There are also associated latitude grids, time, and gray scales representing different temperatures. The images are saved as JPEG 2000 digital files. About 3 weeks of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data set are contained in section 4.3.4 of the \"Nimbus II Users' Guide.\"\n\nThe MRIR experiment measured the intensity and distribution of electromagnetic radiation emitted by and reflected from the earth and its atmosphere in five selected wavelength intervals from 0.2 to 30 micrometers. Data for heat balance of the earth-atmosphere system were obtained, as well as measurements of water vapor distribution, surface or near-surface temperatures, and seasonal changes of stratospheric temperature distribution. The MRIR experiment was successful, and good data were obtained from launch on May 15 1966 until the recorder failed on July 29, 1966.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00003 (old ID 66-040A-04B).", "links": [ { diff --git a/datasets/MRIRN2L1_001.json b/datasets/MRIRN2L1_001.json index 8367ed279f..a2628cbbf9 100644 --- a/datasets/MRIRN2L1_001.json +++ b/datasets/MRIRN2L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MRIRN2L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus 2 Medium Resolution Infrared Radiometer (MRIR) was designed to measure electromagnetic radiation emitted and reflected from the earth and its atmosphere at 5 wavelengths. The five wavelengths regions are as follows:\n\n* 6.7 to 6.9 microns: This channel covers the 6.7 micron water vapor absorption band. Its purpose is to provide information on water vapor distribution in the upper troposphere and, in conjunction with the other channels to provide relative humidities at these altitudes\n\n* 10 to 11 microns: This channel measures surface or near surface temperatures over clear portions of the atmosphere. It also provides cloud cover and cloud height information (day and night).\n\n* 14 to 16 microns: This channel, centered about the strong absorption band of C02 at 15 microns, measures radiation which emanates primarily from the stratosphere.\n\n* 5 to 30 microns: This channel measures the emitted long wavelength infrared energy and, in conjunction with the reflected solar radiation channel furnishes data on the heat budget of the planet.\n\n* 0.2 to 4.0 microns: This channel covers more than 99% of the solar spectrum and yields information on the intensity of the reflected solar energy from the earth and its atmosphere.\n\nThe Nimbus 2 HRIR data are stored in a binary TAP format (proprietary Tape emulated format) .The MRIR instrument was launched on the Nimbus-2 satellite and was operational from May 15, 1966 through July 28, 1966.", "links": [ { diff --git a/datasets/MRIRN3IM_001.json b/datasets/MRIRN3IM_001.json index e21476e6ef..8aa9258fda 100644 --- a/datasets/MRIRN3IM_001.json +++ b/datasets/MRIRN3IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MRIRN3IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MRIRN3IM is the Nimbus-3 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.5-7.0, 10-11, 14.5-15.5, 5-30, and 0.2-4.0 micrometers. There are also associated latitude grids, time, and gray scales representing different temperatures. The images are saved as JPEG 2000 digital files. About 3 weeks of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data set are contained in section 4 of the \"Nimbus III Users' Guide.\"\n\nThe MRIR experiment measured the intensity and distribution of electromagnetic radiation emitted by and reflected from the earth and its atmosphere in five selected wavelength intervals from 0.2 to 30 micrometers. Data for heat balance of the earth-atmosphere system were obtained, as well as measurements of water vapor distribution, surface or near-surface temperatures, and seasonal changes of stratospheric temperature distribution. The MRIR experiment obtained data from April 15 1969 until February 4, 1970.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00184 (old ID 69-037A-05A).", "links": [ { diff --git a/datasets/MRIRN3L1_001.json b/datasets/MRIRN3L1_001.json index 8feab5de2a..c1d7ad9434 100644 --- a/datasets/MRIRN3L1_001.json +++ b/datasets/MRIRN3L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MRIRN3L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MRIRN3L1 is the Nimbus-3 Medium-Resolution Infrared Radiometer (MRIR) Level 1 Meteorological Radiance Data product and contain radiances expressed as equivalent blackbody temperature or \"brightness\" temperature, along with geolocation, time and other housekeeping information. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe MRIR instrument was designed to measure infrared electromagnetic radiation emitted and reflected from the Earth and its atmosphere at 5 wavelengths. The five wavelengths regions are as follows:\n\n(1) 6.5 to 7.0 microns - This channel covers the 6.7 micron water vapor absorption band. Its purpose is to provide information on water vapor distribution in the upper troposphere and, in conjunction with the other channels to provide data concerning relative humidities at these altitudes.\n(2) 10 to 11 microns - Operating in an atmospheric \"window,\" this channel measures surface or near-surface temperatures over clear portions of the atmosphere. It also provides cloud cover and cloud height information (day and night).\n(3) 14.5 to 15.5 microns - This channel, centered about the strong absorption band of C02 at 15 microns, measures radiation which emanates primarily from the stratosphere. The information gained here is of primary importance to in following seasonal stratospheric temperature changes.\n(4) 20 to 23 microns - This channel yields data from the spectral region containing the broad rotational absorption bands of water vapor. It will provide information similar to that of the 6.5 to 7.0 micron channel except that the flux will largely be radiated from lower in the atmosphere.\n(5) 0.2 to 4.0 microns - This channel covers more than 99% of the solar spectrum and yields information on the intensity of the reflected solar energy from the earth and its atmosphere.\n\nThese data were previously archived at NASA NSSDC as product NMRT-MRIR under the entry ID ESAD-00183 (originally 69-037A-05B).", "links": [ { diff --git a/datasets/MRLC.json b/datasets/MRLC.json index 67d9b2527d..6ad40d6f7e 100644 --- a/datasets/MRLC.json +++ b/datasets/MRLC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MRLC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Resolution Land Characteristics (MRLC) project was established to provide multi-resolution land cover data of the conterminous United States from local to regional scales. A major component of MRLC is an objective to develop a national 30-meter land cover characteristics data base using Landsat thematic mapper (TM) data. This is a cooperative effort among six programs within four U.S. Government agencies, including the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program; the U.S. Geological Survey's (USGS) National Water Quality Assessment Program; the National Biological Service's Gap Analysis Program; the USGS' Earth Resources Observation Systems (EROS) Center; the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; and the EPA's North American Landscape Characterization project.\n\nMultitemporal scenes were selected for the eastern deciduous forests, agricultural regions, and selected other regions. Multitemporal pairs were selected to be in consecutive seasons (in 1992 when possible). All scenes were previewed for image quality.\n\nThe participating agencies organized the joint purchase of a single national set of Landsat TM scenes. In addition, the cooperators developed a common definition for preprocessing the satellite data. The shared, consistently processed TM data are the foundation for the development of the national 30-meter land cover data base. The jointly acquired data are archived and distributed by EROS. A variety of products are available to MRLC participants, to their affiliated users, and to the general public.\n\nMulti-Resolution Land Characterization 2001 (MRLC 2001) At-Sensor Reflectance Dataset is a second-generation federal consortium to create an updated pool of nation-wide Landsat imagery, and derive a second-generation National Land Cover Database (NLCD 2001).\n\nThe MRLC 2001 data cover the United States, including Alaska and Hawaii. Multi-temporal scenes may also be available, depending on the location. Most of the images are of high quality, and cloud cover is generally less than ten percent. The data will also include a 30-meter Digital Elevation Model (DEM) for all scenes that do not include the Canadian or Mexican borders.", "links": [ { diff --git a/datasets/MSAQSO2L4_1.json b/datasets/MSAQSO2L4_1.json index dee19e3b46..aa86a54f5b 100644 --- a/datasets/MSAQSO2L4_1.json +++ b/datasets/MSAQSO2L4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSAQSO2L4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nThese data are superseded by newer version, DOI: 10.5067/MEASURES/SO2/DATA406 \n\n", "links": [ { diff --git a/datasets/MSAQSO2L4_2.json b/datasets/MSAQSO2L4_2.json index 3551f3c5c9..bd8836c681 100644 --- a/datasets/MSAQSO2L4_2.json +++ b/datasets/MSAQSO2L4_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSAQSO2L4_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are a part of Multi-Decadal Sulfur Dioxide (SO2) Climatology from Satellite Instruments (MEaSUREs-12-0022 project). Version 2 of the global catalogue of emissions from large SO2 point sources combines data from the Ozone Monitoring Instrument (OMI) on NASA's EOS Aura spacecraft, the Ozone Mapping and Profiler Suite (OMPS) on the NASA-NOAA Suomi National Polar-orbiting Partnership (SNPP), and the TROPOspheric Monitoring Instrument (TROPOMI) on the ESA/Copernicus Sentinel-5 Precursor (S-5P) spacecraft.\n\nThe catalogue MSAQSO2L4 file contains the site coordinates, source type, country, source name, annual SO2 emissions, annual emission uncertainties, and the number of satellite pixels in the fitting area for three satellite instruments as well as for their weighted average.\n\nThe emission estimates are based on operational version 2 OMI and OMPS Principal Component Analysis (PCA) retrieval algorithm SO2 slant column density (SCD) data (Li et al., 2020) as well as on new TROPOMI Covariance-Based Retrieval Algorithm (COBRA) SCD data (Theys et al., 2021). A single time-independent site-specific Air-Mass Factor (AMF) value for each site was calculated (McLinden et al., 2014) and applied consistently to each satellite SCD dataset to derive SO2 vertical column densities (VCDs=SCDs/AMFs). The emission estimate method is based on a fit of satellite VCDs to an empirical plume model developed to describe the SO2 spatial distribution near emission point sources. The plume model assumes that the SO2 concentrations emitted from a point source decline exponentially with distance and that they are affected by turbulent diffusion that can be described by a two-dimensional (2D) exponentially modified Gaussian function. The total SO2 mass is derived from the fit and the annual emission rate is calculated as the ratio between the total mass and the prescribed SO2 lifetime.", "links": [ { diff --git a/datasets/MSA_Ortho_1.json b/datasets/MSA_Ortho_1.json index ce70c165f6..1e32198c68 100644 --- a/datasets/MSA_Ortho_1.json +++ b/datasets/MSA_Ortho_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSA_Ortho_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthophoto is a rectified, georeferenced, corrected image of the Mawson Station Area. Original source images were collected from aerial photography.\nDistortions due to relief and camera have been removed.", "links": [ { diff --git a/datasets/MSG01-OSPO-L2P-v1.0_1.0.json b/datasets/MSG01-OSPO-L2P-v1.0_1.0.json index 99c41a4bf7..c6665c47eb 100644 --- a/datasets/MSG01-OSPO-L2P-v1.0_1.0.json +++ b/datasets/MSG01-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSG01-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. The full data records stretch from Sept. 18, 2018 to June 1, 2022. After June 1, 2022, the Meteosat-9 (MSG2) took over as the prime geostationary satellite for the Indian Ocean region (MSG02-OSPO-L2P-v1.0). Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.

\r\n\r\nThe SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.

\r\n\r\nThis L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.", "links": [ { diff --git a/datasets/MSG02-OSPO-L2P-v1.0_1.0.json b/datasets/MSG02-OSPO-L2P-v1.0_1.0.json index 272260bc77..a090f01082 100644 --- a/datasets/MSG02-OSPO-L2P-v1.0_1.0.json +++ b/datasets/MSG02-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSG02-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GHRSST L2P MSG02 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-9 (MSG2) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On June 1, 2022, the Meteosat-9 (MSG2) replaced the Meteosat-8 (MSG1) (MSG01-OSPO-L2P-v1.0) and produced the L2P SST data from June 11. 2022 to the present. This dataset will be updated every 15 minutes as a forward data stream with 3-24 hours nominal latency. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.

\r\n\r\nThe SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.

\r\n\r\nThis L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.", "links": [ { diff --git a/datasets/MSG03-OSPO-L2P-v1.0_1.0.json b/datasets/MSG03-OSPO-L2P-v1.0_1.0.json index 4f4eb18dec..a1bea1aac6 100644 --- a/datasets/MSG03-OSPO-L2P-v1.0_1.0.json +++ b/datasets/MSG03-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSG03-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteosat Second Generation (MSG-3) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 2.0.", "links": [ { diff --git a/datasets/MSG04-OSPO-L2P-v1.0_1.0.json b/datasets/MSG04-OSPO-L2P-v1.0_1.0.json index b3d51f6dbd..7a22a51a5b 100644 --- a/datasets/MSG04-OSPO-L2P-v1.0_1.0.json +++ b/datasets/MSG04-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSG04-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GHRSST L2P MSG04 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-11 (MSG4) satellite. It provides the full disk SEVIRI imagery covering the Atlantic Ocean region from its position at 0.0\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On Feb. 2, 2018 the Meteosat-11 (MSG4) took over the Meteosat-10 (MSG3) (MSG03-OSPO-L2P-v1.0) and produced the L2P SST data from Sept 10. 2018 to March 24, 2023. In March 2023, Meteosat-10 and Meteosat-11 were swapped roles and orbital positions. The MSG03 has started to produce the L2P SST data again over the Atlantic Ocean region. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.

\r\n\r\nThe SST measurements from SEVIRI are parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.

\r\n\r\nThis L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.", "links": [ { diff --git a/datasets/MSLERLSTL3d10_1.json b/datasets/MSLERLSTL3d10_1.json index b96621ff31..b47db19c13 100644 --- a/datasets/MSLERLSTL3d10_1.json +++ b/datasets/MSLERLSTL3d10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSLERLSTL3d10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Satellite Lambertian Equivalent Reflectivity (Local Satellite Time) 10-Day L3 Global 2.0x5.0deg Lat/Lon Grid data product is derived from multi-satellite Solar Backscatter UltraViolet (SBUV) observations made by the Nimbus-7 SBUV, and NOAA 9, 11, 14, 16, 17, 18 SBUV/2 instruments at 340 nm. The Local Satellite Time (LST) data are uncorrected for the drift of the local equator crossing time of the spacecraft. The table below lists the date ranges for each instrument (note A = ascending node, D = descending node):\n\nInstrument Start Date End Date \n------------------ ---------- ----------\nNimbus-7 SBUV 1978-11-01 1990-06-21\nNOAA-9 SBUV/2 (A) 1985-02-02 1991-09-03\nNOAA-9 SBUV/2 (D) 1990-04-25 1997-05-31\nNOAA-11 SBUV/2 (A) 1988-12-01 1995-03-31\nNOAA-11 SBUV/2 (D) 1997-07-15 2001-03-26\nNOAA-14 SBUV/2 (A) 1995-02-05 2002-09-11\nNOAA-14 SBUV/2 (D) 2002-04-09 2006-09-28\nNOAA-16 SBUV/2 (A) 2000-10-03 2009-09-15\nNOAA-16 SBUV/2 (D) 2008-04-28 2012-12-31\nNOAA-17 SBUV/2 2002-07-10 2012-12-31\nNOAA-18 SBUV/2 2005-06-05 2012-12-12\n\nThe scene reflectivities of the Earth at blue and ultraviolet (UV) wavelengths (320 nm to 415 nm) are low over most surfaces (except ice and snow), and are almost independent of the seasonal changes in vegetation on land and in the oceans. This makes it ideal for examining changes in radiation reflected back to space from changes in cloud and aerosol amounts, especially as affected by the start of climate change.\n\nThe MSLERLSTL3d10 data are archived in the HDF-EOS5 file format using the Grid model. The product consists of a single data file representing the entire data set containing the reflectivity data in a single time versus latitude versus longitude data field array or variable. The data array contains attributes describing the variable, and the file contains metadata stored in the HDFEOS file attribute section.", "links": [ { diff --git a/datasets/MSLERLSTL3zm_1.json b/datasets/MSLERLSTL3zm_1.json index cb6fc0d144..863a1f6f79 100644 --- a/datasets/MSLERLSTL3zm_1.json +++ b/datasets/MSLERLSTL3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSLERLSTL3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Satellite Lambertian Equivalent Reflectivity (Local Satellite Time) 1 day L3 Global 5.0deg Lat Zones data product (MSLERLSTL3zm) is derived from observations made by the Nimbus-7 SBUV, and NOAA 9, 11, 14, 16, 17, 18, 19 SBUV/2 instruments (340 nm), Nimbus-7 and Earth Probe TOMS (331 nm), OMI (340 nm) and SeaWiFS (412 nm) from 1978 to 2011. The Local Satellite Time (LST) data are uncorrected for the drift of the local equator crossing time of the spacecraft. The table below lists the date ranges for each instrument (note A = ascending node, D = descending node):\n\nInstrument Start Date End Date\n------------------ ---------- ----------\nNimbus-7 SBUV 1978-11-01 1990-06-21\nNOAA-9 SBUV/2 (A) 1985-02-02 1991-09-03\nNOAA-9 SBUV/2 (D) 1990-04-25 1997-05-31\nNOAA-11 SBUV/2 (A) 1988-12-01 1995-03-31\nNOAA-11 SBUV/2 (D) 1997-07-15 2001-03-26\nNOAA-14 SBUV/2 (A) 1995-02-05 2002-09-11\nNOAA-14 SBUV/2 (D) 2002-04-09 2006-09-28\nNOAA-16 SBUV/2 (A) 2000-10-03 2009-09-15\nNOAA-16 SBUV/2 (D) 2008-04-28 2012-12-31\nNOAA-17 SBUV/2 2002-07-10 2012-12-31\nNOAA-18 SBUV/2 2005-06-05 2012-12-12\nNimbus-7 TOMS 1978-11-01 1993-05-06\nEarthProbe TOMS 1996-07-25 2004-06-21\nAura OMI 2005-01-01 2008-12-31\nSeaStar SeaWiFS 1998-01-01 2008-12-30\n\nThe scene reflectivities of the Earth at blue and ultraviolet (UV) wavelengths (320 nm to 415 nm) are low over most surfaces (except ice and snow), and are almost independent of the seasonal changes in vegetation on land and in the oceans. This makes it ideal for examining changes in radiation reflected back to space from changes in cloud and aerosol amounts, especially as affected by the start of climate change.\n\nThe MSLERLSTL3zm data are archived in the HDF-EOS5 file format using the Zonal Average (ZA) model. The product consists of a single data file representing the entire data set containing the individual instrument data in separate time versus latitude arrays. Each data array contains attributes describing the variable, and the file contains metadata stored in the HDFEOS file attribute section", "links": [ { diff --git a/datasets/MSLERNNL3d10_1.json b/datasets/MSLERNNL3d10_1.json index c1006890a2..e11ecb2a96 100644 --- a/datasets/MSLERNNL3d10_1.json +++ b/datasets/MSLERNNL3d10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSLERNNL3d10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Satellite Lambertian Equivalent Reflectivity (Noon Normalized) 10-Day L3 Global 2.0x5.0deg Lat/Lon Grid data product is derived from multi-satellite Solar Backscatter UltraViolet (SBUV) observations made by the Nimbus-7 SBUV, and NOAA 9, 11, 14, 16, 17, 18 SBUV/2 instruments at 340 nm. The Noon Normalized (NN) data have been corrected to local noon equator crossing time. The NN correction was applied only to data between latitudes 60 degrees north and 60 degrees south due to insufficient diurnal data at higher latitudes. The table below lists the date ranges for each instrument (note A = ascending node, D = descending node):\n\nInstrument Start Date End Date\n------------------ ---------- ----------\nNimbus-7 SBUV 1978-11-01 1990-06-21\nNOAA-9 SBUV/2 (A) 1985-02-02 1991-09-03\nNOAA-9 SBUV/2 (D) 1990-04-25 1997-05-31\nNOAA-11 SBUV/2 (A) 1988-12-01 1995-03-31\nNOAA-11 SBUV/2 (D) 1997-07-15 2001-03-26\nNOAA-14 SBUV/2 (A) 1995-02-05 2002-09-11\nNOAA-14 SBUV/2 (D) 2002-04-09 2006-09-28\nNOAA-16 SBUV/2 (A) 2000-10-03 2009-09-15\nNOAA-16 SBUV/2 (D) 2008-04-28 2012-12-31\nNOAA-17 SBUV/2 2002-07-10 2012-12-31\nNOAA-18 SBUV/2 2005-06-05 2012-12-12\n\nThe scene reflectivities of the Earth at blue and ultraviolet (UV) wavelengths (320 nm to 415 nm) are low over most surfaces (except ice and snow), and are almost independent of the seasonal changes in vegetation on land and in the oceans. This makes it ideal for examining changes in radiation reflected back to space from changes in cloud and aerosol amounts, especially as affected by the start of climate change.\n\nThe MSLERNNL3d10 data are archived in the HDF-EOS5 file format using the Grid model. The product consists of a single data file representing the entire data set containing the noon-normalized reflectivites in a single latitude versus longitude versus time data field array or variable. The data array contains attributes describing the variable, and the file contains metadata stored in the HDFEOS file attribute section.", "links": [ { diff --git a/datasets/MSLERNNL3zm_1.json b/datasets/MSLERNNL3zm_1.json index 8eedba3cff..bb61f449ad 100644 --- a/datasets/MSLERNNL3zm_1.json +++ b/datasets/MSLERNNL3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSLERNNL3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Satellite Lambertian Equivalent Reflectivity (Noon Normalized) 1 day L3 Global 5.0deg Lat Zones data product (MSLERNNL3zm) is derived from observations made by the Nimbus-7 SBUV, and NOAA 9, 11, 14, 16, 17, 18, 19 SBUV/2 instruments (340 nm), Nimbus-7 and Earth Probe TOMS (331 nm), OMI (340 nm) and SeaWiFS (412 nm) from 1978 to 2011. The Noon Normalized (NN) data have been corrected to local noon equator crossing time. The NN correction was applied only to data between latitudes 60 degrees north and 60 degrees south due to insufficient diurnal data at higher latitudes. The table below lists the date ranges for each instrument (note A = ascending node, D = descending node):\n\nInstrument Start Date End Date\n------------------ ---------- ----------\nNimbus-7 SBUV 1978-11-01 1990-06-21\nNOAA-9 SBUV/2 (A) 1985-02-02 1991-09-03\nNOAA-9 SBUV/2 (D) 1990-04-25 1997-05-31\nNOAA-11 SBUV/2 (A) 1988-12-01 1995-03-31\nNOAA-11 SBUV/2 (D) 1997-07-15 2001-03-26\nNOAA-14 SBUV/2 (A) 1995-02-05 2002-09-11\nNOAA-14 SBUV/2 (D) 2002-04-09 2006-09-28\nNOAA-16 SBUV/2 (A) 2000-10-03 2009-09-15\nNOAA-16 SBUV/2 (D) 2008-04-28 2012-12-31\nNOAA-17 SBUV/2 2002-07-10 2012-12-31\nNOAA-18 SBUV/2 2005-06-05 2012-12-12\nNimbus-7 TOMS 1978-11-01 1993-05-06\nEarthProbe TOMS 1996-07-25 2004-06-21\nAura OMI 2005-01-01 2008-12-31\nSeaStar SeaWiFS 1998-01-01 2008-12-30\n\nThe scene reflectivities of the Earth at blue and ultraviolet (UV) wavelengths (320 nm to 415 nm) are low over most surfaces (except ice and snow), and are almost independent of the seasonal changes in vegetation on land and in the oceans. This makes it ideal for examining changes in radiation reflected back to space from changes in cloud and aerosol amounts, especially as affected by the start of climate change.\n\nThe MSLERNNL3zm data are archived in the HDF-EOS5 file format using the Zonal Average (ZA) model. The product consists of a single data file representing the entire data set containing the individual instrument data in separate time versus latitude arrays. Each data array contains attributes describing the variable, and the file contains metadata stored in the HDFEOS file attribute section.", "links": [ { diff --git a/datasets/MSLSP30NA_011.json b/datasets/MSLSP30NA_011.json index 93c805b9e3..1855cda1b4 100644 --- a/datasets/MSLSP30NA_011.json +++ b/datasets/MSLSP30NA_011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSLSP30NA_011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. \r\n\r\nProvided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.\r\n", "links": [ { diff --git a/datasets/MSO3L3zm5_1.json b/datasets/MSO3L3zm5_1.json index 70155c8a17..bde3370d82 100644 --- a/datasets/MSO3L3zm5_1.json +++ b/datasets/MSO3L3zm5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSO3L3zm5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The merged-satellite Solar Backscattered Ultraviolet (SBUV) Level-3 monthly zonal mean (MZM) product (MSO3L3zm5) contains 1 month zonal means for profile layer and total column ozone based on v8.6 SBUV data from the Nimbus-4 BUV, Nimbus-7 SBUV, and NOAA-9 through NOAA-18 SBUV/2 instruments. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements, and differs from the v8.0 SBUV algorithm via the use of 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n \nThe MSO3L3zm5 product is stored as a single HDF5 file, and has a size of 0.4 MB. The MZM product contains 5.0-degree-wide latitude zones with data between latitude -80.0 and 80.0 degrees. The data cover the time period from May 1970 through July 2013. Data coverage during the BUV mission from 1970 - 1977 contains many gaps after October 1973, and there are no data between November 1976 and November 1978. Continuous data coverage begins with SBUV and SBUV/2 missions starting November 1978.", "links": [ { diff --git a/datasets/MSS_1_5.json b/datasets/MSS_1_5.json index ec6c165153..8709d83d06 100644 --- a/datasets/MSS_1_5.json +++ b/datasets/MSS_1_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSS_1_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge. ", "links": [ { diff --git a/datasets/MSULST_001.json b/datasets/MSULST_001.json index 05e4bff9bc..10aa429422 100644 --- a/datasets/MSULST_001.json +++ b/datasets/MSULST_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSULST_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Sounding Unit (MSU) Lower Stratosphere Deep Layer Mean Temperature product (MSULST) provides gridded lower stratospheric temperatures for each day derived from MSU instruments on several different platforms. The temperatures are derived from MSU channel 4 using the method of Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a weighted method depending on footprint latitude. Horizontal averaging is used\nto fill some of the empty grid boxes.", "links": [ { diff --git a/datasets/MSULTT_001.json b/datasets/MSULTT_001.json index 6931a13d54..58222f057a 100644 --- a/datasets/MSULTT_001.json +++ b/datasets/MSULTT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSULTT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Sounding Unit (MSU) Lower Troposphere Deep Layer Temperature product (MSULTT) provides gridded lower tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU\nchannels 2 and 3 which has an averaging kernel that peaks near 500 hecto Pascals. The algorithm is based on Spencer and Christy (1990) with the LIMB 93 limb\ncorrection based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a\nweighted method depending on footprint latitude. Horizontal averaging is used", "links": [ { diff --git a/datasets/MSUOP_001.json b/datasets/MSUOP_001.json index 2002c0e02b..85599cd222 100644 --- a/datasets/MSUOP_001.json +++ b/datasets/MSUOP_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSUOP_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Sounding Unit (MSU) Ocean Precipitation product (MSUOP) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The precipitation estimates follow the method of Spencer (1993). Oceanic precipitation is estimated by increased warming in MSU channel 1 over a threshold. The increased warming is attributable to emission by liquid water in the lower troposphere. MSU channels 2 and 3 are used to remove warming due to air mass differences. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz.", "links": [ { diff --git a/datasets/MSUUTT_001.json b/datasets/MSUUTT_001.json index b826330c2d..a97920e892 100644 --- a/datasets/MSUUTT_001.json +++ b/datasets/MSUUTT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSUUTT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Sounding Unit (MSU) Upper Troposphere Temperature product (MSUUTT) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU channels 3 and 4 which has an averaging kernel that peaks near 250 hecto Pascals. The algorithm is based on Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a weighted method depending on footprint latitude. Horizontal averaging is used to fill some of the empty grid boxes.", "links": [ { diff --git a/datasets/MSVOLSO2L4_4.json b/datasets/MSVOLSO2L4_4.json index 813758e91a..cf81421302 100644 --- a/datasets/MSVOLSO2L4_4.json +++ b/datasets/MSVOLSO2L4_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MSVOLSO2L4_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 4 is the current version of the data set. Older versions are no longer available and have been superseded by Version 4.\n\nThese data are a part of MEaSUREs 2012 projects. The particular project, \"Multi-Decadal Sulfur Dioxide Climatology from Satellite Instruments\", is expected to produce SO2 Earth Science Data Record by means of combining measurements from backscatter Ultraviolet (BUV), thermal infrared (IR) and microwave (MLS) instruments on multiple satellites. The data represent best estimates of the volcanic and anthropogenic contribution to global atmospheric SO2 concentrations. Since SO2 is the major precursor of sulfate aerosol, which has climate and air quality impact, SO2 measurements will contribute to better understanding of the sulfate aerosol distributions and its atmospheric impact.\"\n\nThe released data file is a long-term database of volcanic SO2 emission derived from ultraviolet satellite measurements from October 31, 1978, to present.\n\nData are in a table format in simple ASCII format:\n\nColumn Descriptions:\nColumn 1 = Name of volcano.\nColumn 2 = Latitude of volcano.\nColumn 3 = Longitude of volcano.\nColumn 4 = Altitude of volcano (km).\nColumn 5 = Eruption year.\nColumn 6 = Eruption month of year.\nColumn 7 = Eruption day of month.\nColumn 8 = Eruption style: exp = explosive, eff = effusive.\nColumn 9 = Eruption volcanic explosivity index (nd = no data or undetermined).\nColumn 10 = Observed plume altitude (km) where known.\nColumn 11 = Estimated plume altitude (km) above vent: 10 km for explosive, 5 km for effusive.\nColumn 12 = Measured SO2 mass in kilotons (= 1000 metric tons).", "links": [ { diff --git a/datasets/MS_Sound_0.json b/datasets/MS_Sound_0.json index 97a298116f..5d901c019b 100644 --- a/datasets/MS_Sound_0.json +++ b/datasets/MS_Sound_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MS_Sound_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Mississippi Sound during 2005 to 2007.", "links": [ { diff --git a/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json b/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json index a59be60eda..6d30139067 100644 --- a/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json +++ b/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MTSAT2-OSPO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.", "links": [ { diff --git a/datasets/MUR-JPL-L4-GLOB-v4.1_4.1.json b/datasets/MUR-JPL-L4-GLOB-v4.1_4.1.json index c2d8029b70..c31a6c0561 100644 --- a/datasets/MUR-JPL-L4-GLOB-v4.1_4.1.json +++ b/datasets/MUR-JPL-L4-GLOB-v4.1_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MUR-JPL-L4-GLOB-v4.1_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata \"history:\" attribute to determine if a granule is near-realtime or retrospective.", "links": [ { diff --git a/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json b/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json index bbf3807363..ee8cddef92 100644 --- a/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json +++ b/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MUR25-JPL-L4-GLOB-v04.2_4.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/MURI_Camouflage_0.json b/datasets/MURI_Camouflage_0.json index 11b02b361c..6a44015f2a 100644 --- a/datasets/MURI_Camouflage_0.json +++ b/datasets/MURI_Camouflage_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MURI_Camouflage_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys.", "links": [ { diff --git a/datasets/MURI_HI_0.json b/datasets/MURI_HI_0.json index 12b7ca29aa..b7087f6367 100644 --- a/datasets/MURI_HI_0.json +++ b/datasets/MURI_HI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MURI_HI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands.", "links": [ { diff --git a/datasets/MUSE_0.json b/datasets/MUSE_0.json index cb13792296..6f399d323c 100644 --- a/datasets/MUSE_0.json +++ b/datasets/MUSE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MUSE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Monterey Bay under the MOOS Upper-water-column Science Experiment (MUSE).", "links": [ { diff --git a/datasets/MVCO_0.json b/datasets/MVCO_0.json index a70f82366f..6880e3102f 100644 --- a/datasets/MVCO_0.json +++ b/datasets/MVCO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MVCO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Martha's Vineyard Coastal Observatory (MVCO) is operated by Woods Hole Oceanographic Institution. These datasets include measurements collected from and around the Martha's Vineyard site.", "links": [ { diff --git a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json index 0308ceeff3..cacdcd4fc4 100644 --- a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json +++ b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platform and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.", "links": [ { diff --git a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1.json b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1.json index 741f3a69bb..6db59d8aee 100644 --- a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1.json +++ b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, as well as infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP and NOAA-20 satellites. These MW sensors are used through the SST production based on the sensor data availability. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Compared with the previous version 5.0 dataset, version 5.1 is processed using updated input files, VIIRS on NOAA-20 is included, the sensor-specific error statistics (SSES) for each microwave sensor are updated, and deficiencies in the OI processing have been addressed.", "links": [ { diff --git a/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json b/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json index 48f5d746ae..98fbb9f9ed 100644 --- a/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json +++ b/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MW_OI-REMSS-L4-GLOB-v5.0_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.", "links": [ { diff --git a/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json b/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json index a998068d13..83660ee56e 100644 --- a/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json +++ b/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MW_OI-REMSS-L4-GLOB-v5.1_5.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite. These MW sensors are used through the SST production based on the sensor data availability. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). This analysis does not use any in situ SST data such as drifting buoy SST. Compared with the previous version 5.0 dataset, version 5.1 is processed using updated input files, the sensor-specific error statistics (SSES) for each microwave sensor are updated, and deficiencies in the OI processing have been addressed.", "links": [ { diff --git a/datasets/MYD00F_6.1NRT.json b/datasets/MYD00F_6.1NRT.json index 54cb84e192..c18da03f80 100644 --- a/datasets/MYD00F_6.1NRT.json +++ b/datasets/MYD00F_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD00F_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Aqua Near Real Time (NRT) L0 PDS Data 5-Min Swath.", "links": [ { diff --git a/datasets/MYD01_6.1.json b/datasets/MYD01_6.1.json index c28b2e4079..8a0c63b68d 100644 --- a/datasets/MYD01_6.1.json +++ b/datasets/MYD01_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD01_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Raw Radiances in Counts 5-Min L1A Swath product (MYD01) contains reformatted and packaged raw instrument data. MODIS instrument data, in packetized form, is reversibly transformed to a computer data structure, along with formatted engineering and spacecraft ancillary data. The Level-1A data is separated into granules for passage to the geolocation and calibration processes. Quality indicators are added to the data to indicate missing pixels and instrument modes. This product contains MODIS digitized raw detector counts data for all 36 MODIS spectral bands, at 250 m, 500 m, or 1 km spatial resolutions including all time tags, all detector views (Earth, solar diffuser, Spectro-Radiometeric Calibration Assembly (SRCA), black body, and space view), and all engineering and ancillary data. Quality indicators are added to the data to indicate missing or bad pixels and instrument modes. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode. Visible, short-wave infrared (SWIR), and near infrared (NIR) measurements are made during daytime only, while radiances for thermal infrared (TIR) are measured during both day and night portions of the orbit.\n \nData set information:\n \nMODIS Homepage\n\nhttps://modis.gsfc.nasa.gov/data/dataprod/\n \nand\nMODIS Characterization Support Team\nhttps://mcst.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/MYD01_6.1NRT.json b/datasets/MYD01_6.1NRT.json index d5fe5c42f4..b028a22559 100644 --- a/datasets/MYD01_6.1NRT.json +++ b/datasets/MYD01_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD01_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is MODIS Level-1A Near Real Time (NRT) product containing reformatted and packaged raw instrument data. MODIS instrument data, in packetized form, is reversibly transformed to a computer data structure, along with formatted engineering and spacecraft ancillary data. The Level-1A data is separated into granules for passage to the geolocation and calibration processes. Quality indicators are added to the data to indicate missing pixels and instrument modes. This product contains MODIS digitized raw detector counts data for all 36 MODIS spectral bands, at 250 m, 500 m, or 1 km spatial resolutions including all time tags, all detector views (Earth, solar diffuser, Spectro-Radiometeric Calibration Assembly (SRCA), black body, and space view), and all engineering and ancillary data. Quality indicators are added to to the data to indicate missing or bad pixels and instrument modes. Only bands 20 to 36 are used to collect measurements in night mode, while all bands are used in day mode. Visible, SWIR, and NIR measurements are made during daytime only, while radiances for TIR are measured during both day and night portions of the orbit.", "links": [ { diff --git a/datasets/MYD021KM_6.1.json b/datasets/MYD021KM_6.1.json index b2c1ad05ad..0311a59044 100644 --- a/datasets/MYD021KM_6.1.json +++ b/datasets/MYD021KM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD021KM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nThe shortname for this product is MYD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size is approximately 115 MB.\r\n\r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MYD021KM_6.1NRT.json b/datasets/MYD021KM_6.1NRT.json index 1450975279..4ec30dcddb 100644 --- a/datasets/MYD021KM_6.1NRT.json +++ b/datasets/MYD021KM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD021KM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of electromagentic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for the solar reflective bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channel locations for MODIS are as follows: Band Center Wavelength (um) Primary Use---- ---------------------- -----------1 0.620 - 0.670 Land/Cloud Boundaries2 0.841 - 0.876 Land/Cloud Boundaries3 0.459 - 0.479 Land/Cloud Properties4 0.545 - 0.565 Land/Cloud Properties5 1.230 - 1.250 Land/Cloud Properties6 1.628 - 1.652 Land/Cloud Properties7 2.105 - 2.155 Land/Cloud Properties8 0.405 - 0.420 Ocean Color/Phytoplankton9 0.438 - 0.448 Ocean Color/Phytoplankton10 0.483 - 0.493 Ocean Color/Phytoplankton11 0.526 - 0.536 Ocean Color/Phytoplankton12 0.546 - 0.556 Ocean Color/Phytoplankton13 0.662 - 0.672 Ocean Color/Phytoplankton14 0.673 - 0.683 Ocean Color/Phytoplankton15 0.743 - 0.753 Ocean Color/Phytoplankton16 0.862 - 0.877 Ocean Color/Phytoplankton17 0.890 - 0.920 Atmospheric Water Vapor18 0.931 - 0.941 Atmospheric Water Vapor19 0.915 - 0.965 Atmospheric Water Vapor20 3.660 - 3.840 Surface/Cloud Temperature21 3.929 - 3.989 Surface/Cloud Temperature22 3.929 - 3.989 Surface/Cloud Temperature23 4.020 - 4.080 Surface/Cloud Temperature24 4.433 - 4.498 Atmospheric Temperature25 4.482 - 4.549 Atmospheric Temperature26 1.360 - 1.390 Cirrus Clouds27 6.535 - 6.895 Water Vapor Profile28 7.175 - 7.475 Water Vapor Profile29 8.400 - 8.700 Water Vapor Profile30 9.580 - 9.880 Ozone Overburden31 10.780 - 11.280 Surface/Cloud Temperature32 11.770 - 12.270 Surface/Cloud Temperature33 13.185 - 13.485 Cloud Top Altitude34 13.485 - 13.785 Cloud Top Altitude35 13.785 - 14.085 Cloud Top Altitude36 14.085 - 14.385 Cloud Top Altitude Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500m resolution, and the rest have 1 km resolution. However, for the MODIS L1B 1 km product, the 250 m and 500 m band radiance data and their associated uncertainties have been aggregated to 1km resolution. Thus the entire channel data set is referenced to the same spatial and geolocation scales. Separate L1B products are available for the 250 m channels (MYD02QKM) and 500 m channels (MYD02HKM) that preserve the original resolution of the data. Spatial resolution for pixels at nadir is 1 km, degrading to 4.8 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 2km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B granule will nominally contain a scene built from 203 scans (or swaths) sampled 1354 times in the cross-track direction, corresponding to approximately 5 minutes worth of data. Since an individual MODIS scan (or swath) will contain 10 along-track spatial elements, the scene will be composed of (1354 x 2030) pixels, resulting in a spatial coverage of (2330 km x 2030 km). Due to the MODIS scan geometry, there will be increasing overlap occurring beyond about 25 degrees scan angle. To summarize, the MODIS L1B 1 km data product consists of: 1. Calibrated radiances and uncertainties for (2) 250 m reflected solar bands aggregated to 1km resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands aggregated to 1 km resolution 3. Calibrated radiances and uncertainties for (13) 1 km reflected solar bands and (16) infrared emissive bands 4. Geolocation subsampled at every 5th pixel across and along track 5. Satellite and solar angles subsampled at the above frequency 6. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization. The MODIS L1B data are stored in the Earth Observing System Hierarchical Data Format (HDF-EOS) which is an extension of HDF as developed by the National Center for Supercomputer Applications (NCSA) at the University of Illinois. A typical file size will be approximately 260 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. The Shortname for this product is MYD021KM", "links": [ { diff --git a/datasets/MYD02HKM_6.1.json b/datasets/MYD02HKM_6.1.json index d65c5f80f3..dccbc62255 100644 --- a/datasets/MYD02HKM_6.1.json +++ b/datasets/MYD02HKM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02HKM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nChannels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MYD02QKM) and 1 km resolution channels (MYD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.\r\n \r\nSpatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. \r\n\r\nTo summarize, the MODIS L1B 500 m data product consists of:\r\n \r\n1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution\r\n \r\n2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands\r\n \r\n3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.\r\n \r\n4. Calibration data for all channels (scale and offset) \r\n \r\n5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MYD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . \r\n \r\nThe shortname for this product is MYD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MYD02HKM file size is approximately 135 MB.\r\n \r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MYD02HKM_6.1NRT.json b/datasets/MYD02HKM_6.1NRT.json index 71fc53d7c1..3f82416397 100644 --- a/datasets/MYD02HKM_6.1NRT.json +++ b/datasets/MYD02HKM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02HKM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 500 meter MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for these solar reflective bands through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channel locations for the MODIS 500 meter data are as follows: Band Center Wavelength (um) Primary Use ---- ---------------------- ----------- 1 0.620 - 0.670 Land/Cloud Boundaries 2 0.841 - 0.876 Land/Cloud Boundaries 3 0.459 - 0.479 Land/Cloud Properties 4 0.545 - 0.565 Land/Cloud Properties 5 1.230 - 1.250 Land/Cloud Properties 6 1.628 - 1.652 Land/Cloud Properties 7 2.105 - 2.155 Land/Cloud Properties Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500 m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MYD02QKM) and 1 km resolution channels (MYD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of: 1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands 3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD http://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf . 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization The MODIS L1B 500 m data are stored in the Earth Observing System Hierarchical Data Format (HDF-EOS) which is an extension of HDF as developed by the National Center for Supercomputer Applications (NCSA) at the University of Illinois. A typical file size will be approximately 170 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. The Shortname for this product is MYD02HKM", "links": [ { diff --git a/datasets/MYD02QKM_6.1.json b/datasets/MYD02QKM_6.1.json index d66567ef6e..9a19eae078 100644 --- a/datasets/MYD02QKM_6.1.json +++ b/datasets/MYD02QKM_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02QKM_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Calibrated Radiances 5-Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. \r\n\r\nSeparate L1B products are available for the five 500m resolution channels (MYD02HKM) and the twenty-nine 1km resolution channels (MYD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km\r\npixel values.\r\n\r\nSpatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle.\r\n\r\nThe shortname for this product is MYD02QKM and is stored in the Earth Observing System Hierarchical\r\nData Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB.\r\n\r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6.1 product information at:\r\n\r\nhttp://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit Science Team homepage at: \r\nhttp://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MYD02QKM_6.1NRT.json b/datasets/MYD02QKM_6.1NRT.json index 70c7e462b1..ed3c411101 100644 --- a/datasets/MYD02QKM_6.1NRT.json +++ b/datasets/MYD02QKM_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02QKM_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 250 meter MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W / (m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for these solar reflective bands through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. Channel locations for the MODIS 250 meter data are as follows: Band Center Wavelength (um) Primary Use ---- ---------------------- ----------- 1 0.620 - 0.670 Land/Cloud Boundaries 2 0.841 - 0.876 Land/Cloud Boundaries Separate L1B products are available for the five 500 m resolution channels (MYD02HKM) and the twenty-nine 1 km resolution channels (MYD021KM). For the 500 m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500 m resolution. Similarly, for the 1 km product, all 36 MODIS channels are available since the data from the two 250 m and five 500 m channels have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle. To summarize, the MODIS L1B 250 m data product consists of: 1. Calibrated radiances and uncertainties for (2) 250 m reflected solar bands 2. Subsampled geolocation at every 4th 250 m pixel across and along track, i.e., a geolocation point every kilometer 3. Satellite and solar angles subsampled at the above frequency 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization The MODIS L1B 250 m data are stored in the Earth Observing System Hierarchical Data Format (HDF-EOS) which is an extension of HDF as developed by the National Center for Supercomputer Applications (NCSA) at the University of Illinois. A typical file size will be approximately 170 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. The Shortname for this product is MYD02QKM", "links": [ { diff --git a/datasets/MYD02SSH_6.1.json b/datasets/MYD02SSH_6.1.json index cb2712e96a..06762cc369 100644 --- a/datasets/MYD02SSH_6.1.json +++ b/datasets/MYD02SSH_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02SSH_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Level 1B Subsampled Calibrated Radiance 5Km (MYD02SSH) product is a subsample from the MODIS Level 1B 1-km data. Every fifth pixel is taken from the MYD021KM product and written out to MYD02SSH. The subsampling starts at the third frame, and at the third line. Here, \"frame\" and \"line\" are naming conventions for pixels along and across the scan, respectively. Since MYD02SSH is a subsampled Level 1B product, many things from the Level 1B documentation apply as well. The MYD02SSH data product contains calibrated and geolocated at-aperture radiances for 36 bands generated from MODIS Level 1A scans of raw radiance (MOD 01). The radiance units are in W/(m ^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared (SWIR), and Near Infrared (NIR) measurements are made during daytime only, while radiances for Thermal Infrared (TIR) are measured continuously. \n\nAs its parent, the MYD02SSH is in HDF-EOS format, and all metadata structures and names are preserved for better convenience. However, some relevant changes are made where appropriate (e.g., the dimension mappings are updated to reflect the new one-to-one correspondence between the data and geolocations). The latter is one of the most important differences: in the MYD02SSH, there is no offset between data and geolocation pixels. The spatial coverage is almost similar to that from MYD021KM (nominally it is 2330 by 2030 km, cross-track by along-track, respectively). The MYD02SSH is produced continuously, and thus the processing provides 2-day repeat observations of the Earth with a repeat orbit pattern every 16 days.\n\nSee the MODIS Science Team homepage for more data set information: \n\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MYD02SSH_6.1NRT.json b/datasets/MYD02SSH_6.1NRT.json index 4c79894ccf..9b1d9e77f8 100644 --- a/datasets/MYD02SSH_6.1NRT.json +++ b/datasets/MYD02SSH_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD02SSH_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near Real Time (NRT) data type (MYD02SSH) is a subsample from the MODIS Level 1B 1-km data. Every fifth pixel is taken from the MYD021KM product and written out to MYD02SSH. The subsampling starts at the third frame, and at the third line. Here, \"frame\" and \"line\" are naming conventions for pixels along and across the scan, respectively. Since MYD02SSH is a subsampled Level 1B , many things from the Level 1B documentation apply as well. That is, the MYD02SSH data productcontains calibrated and geolocated at-aperture radiances for 36 bands generated from MODIS Level 1A scans of raw radiance (MOD 01). The radiance units are in W/(m ^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared (SWIR), and Near Infrared (NIR) measurements are made during daytime only, while radiances for Thermal Infrared (TIR) are measured continuously.As it's parent, the MYD02SSH is in HDF-EOS format, and all metadata structures and names are preserved for better convenience. However, some relevant changes are made where appropriate, e.g. the dimension mappings are updated to reflect the new one-to-one correspondance between the data and geolocations. The latter is one of the most important differences: in the MYD02SSH, there is no offset between data and geolocation pixels. The spatial coverage is almost similar to that from MYD021KM (nominally it is 2330 by 2030 km, cross-track by along-track, respectively). The MYD02SSH is produced continuously, and thus the processing provides 2-day repeat observations of the Earth with a repeat orbitpattern every 16 days.The shortname for this product is MYD02SSH", "links": [ { diff --git a/datasets/MYD03_6.1.json b/datasets/MYD03_6.1.json index 519ed0657b..42f317b926 100644 --- a/datasets/MYD03_6.1.json +++ b/datasets/MYD03_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD03_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Geolocation Fields 5-Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to calculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team.\r\n\r\nThe short name for this product is MYD03. Each file is roughly 30 MB in size, and the total data volume is approximately 8 GB/day.\r\n\r\nSee the MODIS Science Team homepage for more data set\r\ninformation:\r\n\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/", "links": [ { diff --git a/datasets/MYD03_6.1NRT.json b/datasets/MYD03_6.1NRT.json index 13a1b337dd..99da0eac07 100644 --- a/datasets/MYD03_6.1NRT.json +++ b/datasets/MYD03_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD03_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team.The shortname for this product is MYD03.", "links": [ { diff --git a/datasets/MYD04_3K_6.1.json b/datasets/MYD04_3K_6.1.json index 0cf4313b63..9119331c52 100644 --- a/datasets/MYD04_3K_6.1.json +++ b/datasets/MYD04_3K_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD04_3K_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MODIS/Aqua Aerosol 5 Min L2 Swath 3km (MYD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\r\n\r\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community.\r\n\r\nThe MYD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of \"good\" pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attempts a \"poor quality\" retrieval while 3km algorithm does not. Everything else is the same between two products.\r\n\r\nFor more information on C6.1 changes and updates, visit the MODIS Atmosphere website at:\r\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYD04_3K_6.1NRT.json b/datasets/MYD04_3K_6.1NRT.json index e2b91e1abd..08de660775 100644 --- a/datasets/MYD04_3K_6.1NRT.json +++ b/datasets/MYD04_3K_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD04_3K_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MYD04_3K product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\n\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community.\n\nThe MOD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box (6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of good pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attempts a poor quality retrieval while 3km algorithm does not. Everything else is same in two products.\n\nFor more information on C6.1 changes and updates, visit the MODIS Atmosphere website at:\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYD04_L2_6.1.json b/datasets/MYD04_L2_6.1.json index 55557edeb7..ac75b33c99 100644 --- a/datasets/MYD04_L2_6.1.json +++ b/datasets/MYD04_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD04_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol 5-Min L2 Swath 10km product (MYD04_L2) provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces. Both results are provided on a 10x10 pixel scale (10 km at nadir). Each MYD04_L2 product file covers a five-minute time interval. The output grid is 135 pixels in width by 203 pixels in length. Every tenth file has an output grid size of 135 by 204 pixels. MYD04_L2 product files are stored in Hierarchical Data Format (HDF-EOS).\r\n\r\nThe new Collection 6.1 (C61) MYD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\r\n\r\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5 and in earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community.\r\n\r\n\r\nFor more information visit the MODIS Atmosphere website at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/aerosol\r\n\r\nAnd, for C6.1 changes and updates, visit:\r\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYD04_L2_6.1NRT.json b/datasets/MYD04_L2_6.1NRT.json index 90831a2516..fd3d17e95d 100644 --- a/datasets/MYD04_L2_6.1NRT.json +++ b/datasets/MYD04_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD04_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new Collection 6.1 (C61) MYD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals.\n\nThe MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community.\n\nFor more information visit the MODIS Atmosphere website at:\nhttps://modis-atmos.gsfc.nasa.gov/products/aerosol\n\nAnd, for C6.1 changes and updates, visit:\nhttps://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYD05_L2_6.1.json b/datasets/MYD05_L2_6.1.json index adb3ef9b6d..48f2d35f4e 100644 --- a/datasets/MYD05_L2_6.1.json +++ b/datasets/MYD05_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD05_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MYD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. There are two different algorithms used to derive total precipitable water vapor in this data product: a near-infrared algorithm and an infrared algorithm. The near-infrared algorithm relies on observations of reflected solar radiation in MODIS's near-infrared channels, thus, the near-infrared retrievals are only produced during the daytime over surfaces that reflect near-infrared energy. As a result, the near-infrared algorithm is only applied over clear, cloud free land areas of the globe and above clouds over both the land and ocean. Over clear ocean areas, water-vapor estimates are provided over extended sun glint areas. Data produced by the near-infrared algorithm are generated at a 1-km spatial resolution. \r\n\r\nThe other algorithm is the infrared algorithm which can be used to derive atmospheric precipitable water vapor profiles during both day and night. Data from the infrared algorithm are generated at a 5-km spatial resolution when at least nine field of views (FOVs) are cloud free. The infrared-derived precipitable water vapor is generated as a component of product MYD07, and is simply added to product MYD05 for convenience. There are two MODIS Precipitable Water Vapor products: MOD05_L2, containing data collected from the Terra platform; and MYD05_L2, containing data collected from the Aqua platform. This dataset has a short name of MYD05_L2 and provides data from the Aqua platform only. \r\n\r\nThe MODIS Adaptive Processing System (MODAPS) is currently generating an improved version 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. The decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data, which have a negative impact in varying degrees in downstream products. It should be noted that the near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for Collection 6.1.\r\n\r\nFor more information, visit the MODIS Atmosphere website at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/water-vapor", "links": [ { diff --git a/datasets/MYD05_L2_6.1NRT.json b/datasets/MYD05_L2_6.1NRT.json index 89dd6c1fe5..71e3db92ce 100644 --- a/datasets/MYD05_L2_6.1NRT.json +++ b/datasets/MYD05_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD05_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Adaptive Processing System (MODAPS) is currently generating an improved Collection 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) and Level-3 (L3) Atmosphere products. This decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data. These L1B issues had a negative impact in varying degrees in downstream products.\n\nThe MODIS level-2 atmospheric precipitable water product consists of total atmospheric column water vapor amounts (and ancillary parameters) over clear land areas of the globe, over extended clear oceanic areas with the Sun glint, and above clouds over both land and ocean. The shortname for this level-2 MODIS total precipitable water vapor product is MYD05_L2. In Collection 6, MODIS column water vapor (MYD05) datasets continue to be separately available from infrared and near-infrared methods.\n\nThe estimates based on a near-infrared algorithm uses only daytime measurements with solar zenith angle less than 72 degrees. The retrieval algorithm relies on observations of water vapor attenuation of near-infrared solar radiation reflected by surfaces and clouds. The product is produced only over areas that have reflective surfaces in the near-infrared. The near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for C6.1.\nFor more information visit MODIS Atmosphere product website at:\n\nhttps://modis-atmos.gsfc.nasa.gov/products/water-vapor", "links": [ { diff --git a/datasets/MYD06_L2_6.1.json b/datasets/MYD06_L2_6.1.json index 4e0606ed19..18425275c0 100644 --- a/datasets/MYD06_L2_6.1.json +++ b/datasets/MYD06_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD06_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Clouds 5-Min L2 Swath 1km and 5km product consists of cloud optical and physical parameters. The cloud optical parameters are generated at 1km and cloud top (physical) parameters are generated at 5km resolution. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). \r\n\r\nThe shortname for this level-2 MODIS cloud product is MYD06_L2. The MYD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MYD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length.\r\n\r\nC6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily to ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties fill values; no updates to retrieval science are implemented.\r\n\r\n\r\nThe MODIS Cloud Product is used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution.\r\n\r\nFor more information about the MYD06_L2 product, visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud", "links": [ { diff --git a/datasets/MYD06_L2_6.1NRT.json b/datasets/MYD06_L2_6.1NRT.json index 594f4c5b1f..1291611cb3 100644 --- a/datasets/MYD06_L2_6.1NRT.json +++ b/datasets/MYD06_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD06_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MYD06_L2. MYD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MYD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length.\n\nC6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily\nto ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties\nfill values; no updates to retrieval science are implemented.\n\nFor more information about the MODIS Cloud product, visit the MODIS-Atmosphere site at:\n\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud\n\nFor more details regarding dataset changes read the document at https://modis-atmos.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYD07_L2_6.1.json b/datasets/MYD07_L2_6.1.json index 840aa7ce1f..8c1db239dd 100644 --- a/datasets/MYD07_L2_6.1.json +++ b/datasets/MYD07_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD07_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MYD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free.\r\n\r\nThe MODIS total-ozone burden is an estimate of the total-column tropospheric and stratospheric ozone content. The MODIS atmospheric stability consists of three daily Level 2 atmospheric stability indices. The Total Totals (TT), the Lifted Index (LI), and the K index (K) are each computed using the infrared temperature- and moisture-profile data, also derived as part of MYD07. The MODIS temperature and moisture profiles are produced at 20 vertical levels. The MODIS atmospheric water-vapor product is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes.\r\n\r\nAdditional information is available at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/atm-profile.", "links": [ { diff --git a/datasets/MYD07_L2_6.1NRT.json b/datasets/MYD07_L2_6.1NRT.json index a6f59c1c45..9f2cc920c1 100644 --- a/datasets/MYD07_L2_6.1NRT.json +++ b/datasets/MYD07_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD07_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-2 MODIS Temperature and Water Vapor Profile Product MYD07_L2 consists of 30 gridded parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 FOVs are cloud free. \n\nThe atmospheric profiles are produced at 20 vertical atmospheric levels (5., 10., 20., 30., 50., 70., 100., 150., 200., 250., 300., 400., 500., 620., 700., 780., 850., 920., 950., 1000. mbar) The water vapor parameter is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes. The thermal band 9.6 micron is used for retrieving total ozone burden. \n\nThe shortname for this Level-2 MODIS atmospheric profile product is MYD07_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel ( paulm@ssec.wisc.edu).The MYD07_L2 product contains data that has a spatial resolution (pixel size) of 5 x 5 kilometers (at nadir). Each MYD07_L2 product file covers a five-minute time interval, which means that the output grid is 270 5-km pixels in width and 406 5-km pixels in length for nine consecutive granules. Every tenth granule has an output grid size of 270 by 408 pixels.\n\nMYD07_L2 product files are stored in Hierarchical Data Format(HDF-EOS). Twenty eight of the 30 gridded cloud parameters(5-kilometer pixel resolution) are stored as Scientific Data Sets (SDS) within the file, the remaining two algorithmic static parameters (band number and presure level) are stored as Vdata(table arrays). Cloud Mask SDS, derived from the 1-km MYD35_L2 Cloud Mask parameter, is remapped to 5-km resolution, by using only the center 1-km pixel in the 5x5 pixel retrieval array. The remaining two (band number and static pressure levels) are stored as Vdata(table arrays) Each file is roughly 8 MB in size, and the total data volume is approximately 2 GB/day.\nMYD07_L2 Data Group and Parameters: \nSpatial and Temporal Resolution:\nLatitude and Longitude\nScan start time\n\nSolar and Sensor Viewing Geometry:\n\nSolar zenith and Solar azimuth angle\nSensor zenith and Sensor azimuth angle\n\nStatic Algorithm Parameters:\nMODIS band number \nPressure levels Atmospheric \n\nSurface Pressure:\n\nRetrieved Geopotential Height Profile\nTropopause Height\nSurface Elevation\nSurface Pressure \n\nAtmospheric and Surface Temperature:\nGuess and Retrieved Temperature Profiles\nBrightness Temperature and Skin Temperature\n\nAtmospheric Moisture:\n\nGuess Mixing ratio Profile\nRetrieved Dew Point Temperature Profile\n\nAtmospheric Stability Indices:\n\nTotal Totals, Lifted Index, and K-index \n\nAtmospheric Trace Gases:\n\nTotal Ozone Burden\nTotal Column Precipitable Water Vapor - IR Retrieval\nTotal Column Precipitable Water Vapor - Direct IR Retrieval\nWater Vapor(Low and High)\nRetrieved Water Vapour Mixing Ratio Profile\n\nQuality Assurance and Statistical Parameters:\n\nQuality Assurance Parameters \nRun time QA flags \nMODIS Cloud Mask Processing Flag\n\nThese parameters are very essential in the characterization of the atmosphere, atmospheric correction of remotely sensed surface parameters, and prediction of convective clouds and thunderstorms. \n\nFor more information about the MOD07_L2 product, visit the MODIS-Atmosphere site at:\n\nhttps://modis-atmos.gsfc.nasa.gov/products/atm-profile", "links": [ { diff --git a/datasets/MYD08_D3_6.1.json b/datasets/MYD08_D3_6.1.json index a361960145..edb0a220a1 100644 --- a/datasets/MYD08_D3_6.1.json +++ b/datasets/MYD08_D3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD08_D3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MYD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MYD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length.\r\n\r\nMYD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MYD08_D3 product, please visit the MODIS-Atmosphere site at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/daily", "links": [ { diff --git a/datasets/MYD08_E3_6.1.json b/datasets/MYD08_E3_6.1.json index 917f19287a..1db4db9d87 100644 --- a/datasets/MYD08_E3_6.1.json +++ b/datasets/MYD08_E3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD08_E3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MYD08_E3) contains 8-Day 1 degree x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MYD08_E3 contains nearly 1000 statistical datasets (SDS's) that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans an 8-Day interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length.\r\n\r\nMYD08_E3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS 8-Day Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MYD08_E3 product, please visit the MODIS-Atmosphere site at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/eight-day", "links": [ { diff --git a/datasets/MYD08_M3_6.1.json b/datasets/MYD08_M3_6.1.json index 8f5bfb5f15..25b5087025 100644 --- a/datasets/MYD08_M3_6.1.json +++ b/datasets/MYD08_M3_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD08_M3_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MYD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \r\n\r\nThe MYD08_M3 contains roughly 800 statistical datasets that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are sorted into 1x1 degree cells on an equal-angle grid that spans a (calendar) monthly interval and then summarized over the globe. MYD08_M3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \r\n\r\nThe MODIS monthly Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution.\r\n\r\nFor more information about the MYD08_M3 product, please visit the MODIS-Atmosphere site at:\r\nhttps://modis-atmos.gsfc.nasa.gov/products/monthly", "links": [ { diff --git a/datasets/MYD09A1_061.json b/datasets/MYD09A1_061.json index 72398c3fde..b1d86720b2 100644 --- a/datasets/MYD09A1_061.json +++ b/datasets/MYD09A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n\n Improvements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD09CMA_6.1NRT.json b/datasets/MYD09CMA_6.1NRT.json index b4f75c8b52..6f1829d10c 100644 --- a/datasets/MYD09CMA_6.1NRT.json +++ b/datasets/MYD09CMA_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09CMA_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol Optical Thickness Daily L3 Global 0.05-Deg CMA Near Real Time (NRT), short name MYD09CMA, is a daily level 3 global product. It is in linear latitude and longitude (Plate Carre) projection with a 0.05Deg spatial resolution. This product is derived from MYD09IDN, MYD09IDT and MYD09IDS for each orbit by compositing the data on the basis of minimum band 3 (459 - 479 nm band) values (after excluding pixels flagged for clouds and high solar zenith angles).", "links": [ { diff --git a/datasets/MYD09CMG_061.json b/datasets/MYD09CMG_061.json index fe22e3b7c9..c32083960e 100644 --- a/datasets/MYD09CMG_061.json +++ b/datasets/MYD09CMG_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09CMG_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n \nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD09CMG_6.1NRT.json b/datasets/MYD09CMG_6.1NRT.json index 99027e4a75..e1ed895a6a 100644 --- a/datasets/MYD09CMG_6.1NRT.json +++ b/datasets/MYD09CMG_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09CMG_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Daily L3 Global 0.05Deg CMG Near Real Time (NRT) product provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products. The short name for this product is MYD09CMG and it provides Bands 1 through 7 in a daily level-3 product gridded on a simple 0.05 degree (5600-meter) Geographic projection. Data for each pixel is selected on the basis of low solar zenith angle, minimum Band 3 (blue) reflectance, and absence of cloud from level-3 intermediate files. Science Data Sets provided for this product include reflectance values for Bands 1through 7, brightness temperatures for Bands 20, 21, 31, and 32, solar and view zenith angles, relative azimuth angle, ozone, granule time, and quality assessment.", "links": [ { diff --git a/datasets/MYD09GA_061.json b/datasets/MYD09GA_061.json index e7054bfa19..1f0414acfb 100644 --- a/datasets/MYD09GA_061.json +++ b/datasets/MYD09GA_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GA_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 km observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. \r\n\r\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD09GA_6.1NRT.json b/datasets/MYD09GA_6.1NRT.json index f41c492665..53030706a9 100644 --- a/datasets/MYD09GA_6.1NRT.json +++ b/datasets/MYD09GA_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GA_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid Near Real Time (NRT) product is an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products.MYD09GA provides Bands 1-7 in a daily gridded L2G product in the Sinusoidal projection, which includes 500-meter reflectance values and 1-kilometerobservation and geolocation statistics. 500-meter Science Data Sets provided by this product include reflectance for Bands 1-7, a quality rating, observation coverage, observation number, and 250-meter scan information.1-kilometer Science Data Sets provided include number of observations, quality state, sensor angles, solar angles, geolocation flags, and orbit pointers.", "links": [ { diff --git a/datasets/MYD09GHK_6.1NRT.json b/datasets/MYD09GHK_6.1NRT.json index 9deb31f537..bd988fd474 100644 --- a/datasets/MYD09GHK_6.1NRT.json +++ b/datasets/MYD09GHK_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GHK_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Daily L2G Global 500m SIN Grid Near Real Time (NRT) product, short name MYD09GHK, is a seven-band product computed from the MODIS Level 1B land Bands 1-7. The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The correction scheme includes corrections for the effect of atmospheric gases, aerosols, and thin cirrus clouds; it is applied to all Level 1B pixels that pass the Level 1B quality control. The correction uses Band 26 to detect cirrus clouds, the approach used in MYD04 and MYD05 for water vapor, aerosol correction, and NCEP for ozone; the best available climatology data are used if the MODIS water vapor, aerosol, or ozone products are unavailable. The Level 2G surface reflectance product is the input for the generation of several land products: 8-day Surface Reflectance, Vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).", "links": [ { diff --git a/datasets/MYD09GQK_6.1NRT.json b/datasets/MYD09GQK_6.1NRT.json index 5d64398a39..24ddcab360 100644 --- a/datasets/MYD09GQK_6.1NRT.json +++ b/datasets/MYD09GQK_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GQK_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid Near Real Time (NRT) product, short name MYD09GQK, provides MODIS bands 1 and 2, daily surface reflectance at 250 m resolution. The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The quality information for this product is provided at three different levels of detail: for individual pixels for each band and each resolution and for the whole file.", "links": [ { diff --git a/datasets/MYD09GQ_061.json b/datasets/MYD09GQ_061.json index 1e61882781..74b00c8e02 100644 --- a/datasets/MYD09GQ_061.json +++ b/datasets/MYD09GQ_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GQ_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). \r\n\r\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD09GQ_6.1NRT.json b/datasets/MYD09GQ_6.1NRT.json index 66e8807569..12f781c1b9 100644 --- a/datasets/MYD09GQ_6.1NRT.json +++ b/datasets/MYD09GQ_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GQ_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid Near Real Time (NRT) product is an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. Low-level data are corrected for atmospheric gases and aerosols, yielding a level-2 basis for several higher-order gridded level-2 (L2G) and level-3 products.MYD09GQ provides Bands 1 and 2 at a 250-meter resolution in a daily gridded L2G product in the Sinusoidal projection. Science Data Sets provided for this product include reflectance for Bands 1 and 2 a quality rating observation coverage and observation number. This product is meant to be used in conjunction with MYD09GA where important quality and viewing geometry information is stored.", "links": [ { diff --git a/datasets/MYD09GST_6.1NRT.json b/datasets/MYD09GST_6.1NRT.json index 4205f6da44..beb8fd92bb 100644 --- a/datasets/MYD09GST_6.1NRT.json +++ b/datasets/MYD09GST_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09GST_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Surface Reflectance Quality Daily L2G Global 1km SIN Grid Near Real Time (NRT) product, short name MYD09GST, is a restructured version of its primary input, the state QA data in MYD09_L2. This product summarizes the quality of the MYD09 products, specifically atmospheric and other correction states. The product contains quality assurance data pertaining to cloud and cloud shadow, land and water designations, aerosols, and the data source of corrections performed on the file. The data set also contains the number of observations for each pixel.", "links": [ { diff --git a/datasets/MYD09Q1_061.json b/datasets/MYD09Q1_061.json index c6c95c2d03..52c20efd84 100644 --- a/datasets/MYD09Q1_061.json +++ b/datasets/MYD09Q1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09Q1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD09_6.1.json b/datasets/MYD09_6.1.json index c25e8bf236..44dda8592e 100644 --- a/datasets/MYD09_6.1.json +++ b/datasets/MYD09_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MYD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).", "links": [ { diff --git a/datasets/MYD09_6.1NRT.json b/datasets/MYD09_6.1NRT.json index 2d1d2b61e7..39bdce7b07 100644 --- a/datasets/MYD09_6.1NRT.json +++ b/datasets/MYD09_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD09_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km NRT, short name MYD09, is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), BRDF, thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).", "links": [ { diff --git a/datasets/MYD10A1F_61.json b/datasets/MYD10A1F_61.json index e1e52dd2e1..19f7061b9f 100644 --- a/datasets/MYD10A1F_61.json +++ b/datasets/MYD10A1F_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10A1F_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 data set (MYD10A1F) provides daily cloud-free snow cover derived from the MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid data set (MYD10A1). Grid cells\u00a0in MYD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided\u00a0which tracks\u00a0the number of days in each cell since the last clear-sky observation. Each data granule contains a 10\u00b0 x 10\u00b0 tile projected to the 500 m sinusoidal grid.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10A1_61.json b/datasets/MYD10A1_61.json index 01f629fb02..119cc40873 100644 --- a/datasets/MYD10A1_61.json +++ b/datasets/MYD10A1_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10A1_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Aqua Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MYD10_L2.061). Each data granule is a 10\u00b0x10\u00b0 tile projected to a 500 m sinusoidal grid.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10A2_61.json b/datasets/MYD10A2_61.json index cbb1d7ce9e..132258731e 100644 --- a/datasets/MYD10A2_61.json +++ b/datasets/MYD10A2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10A2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides the maximum snow cover extent (SNE) observed over an eight-day period within 10\u00b0 x 10\u00b0 MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Aqua Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MYD10A1.061). A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10C1_61.json b/datasets/MYD10C1_61.json index a6577e6b53..8831f8fbf4 100644 --- a/datasets/MYD10C1_61.json +++ b/datasets/MYD10C1_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10C1_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05\u00b0 (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Aqua Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MYD10A1.061).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10C2_61.json b/datasets/MYD10C2_61.json index 890d723f4b..5c93ce770c 100644 --- a/datasets/MYD10C2_61.json +++ b/datasets/MYD10C2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10C2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05\u00b0 (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Aqua Snow Cover 8-Day L3 Global 500m SIN Grid' data set (DOI:10.5067/MODIS/MYD10A2.061).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10CM_61.json b/datasets/MYD10CM_61.json index bcd02bcfa5..68e08c9690 100644 --- a/datasets/MYD10CM_61.json +++ b/datasets/MYD10CM_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10CM_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides monthly average snow cover within 0.05\u00b0 (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Monthly averages are computed from daily snow cover observations in the MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG (https://doi.org/10.5067/MODIS/MYD10CM.061) data set.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD10_L2_6.1NRT.json b/datasets/MYD10_L2_6.1NRT.json index 2324e3fe62..b9ad38681a 100644 --- a/datasets/MYD10_L2_6.1NRT.json +++ b/datasets/MYD10_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Snow Cover 5-Min L2 Swath 500m Near Real Time (NRT), short name MYD10_L2, data set contains snow cover and Quality Assessment (QA) data, latitudes and longitudes in compressed Hierarchical Data Format-Earth Observing System (HDF-EOS) format, and corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution while all other fields are at 500 m resolution. There are two separate snow fields in this data set. The first field, snow cover, classifies each cloud-free land or inland water body pixel as snow-covered or snow-free, the second field, fractional snow cover, provides the percent of snow cover within each pixel for land and inland water bodies. MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests. Data are stored in HDF-EOS format.", "links": [ { diff --git a/datasets/MYD10_L2_61.json b/datasets/MYD10_L2_61.json index 585ada594b..1ce9b4c1d1 100644 --- a/datasets/MYD10_L2_61.json +++ b/datasets/MYD10_L2_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD10_L2_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-2 (L2) data set provides daily snow cover detected using Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite: DOI:10.5067/MODIS/MYD02HKM.061 and DOI:10.5067/MODIS/MYD021KM.061. Each data granule contains 5 minutes of swath data observed at a resolution of 500 m.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD11A1_061.json b/datasets/MYD11A1_061.json index 1dc96486d6..a4912a17c8 100644 --- a/datasets/MYD11A1_061.json +++ b/datasets/MYD11A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11A1 Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ", "links": [ { diff --git a/datasets/MYD11A2_061.json b/datasets/MYD11A2_061.json index e10a7431c7..7f7a0f1031 100644 --- a/datasets/MYD11A2_061.json +++ b/datasets/MYD11A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11A2 Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 (https://doi.org/10.5067/MODIS/MYD11A1.061) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.\n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: \nchanges to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD11B1_061.json b/datasets/MYD11B1_061.json index d96c039bd9..02b1b35a90 100644 --- a/datasets/MYD11B1_061.json +++ b/datasets/MYD11B1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11B1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11B1 Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD11B2_061.json b/datasets/MYD11B2_061.json index fe165441b8..6b709e63a5 100644 --- a/datasets/MYD11B2_061.json +++ b/datasets/MYD11B2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11B2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11B2 Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD11B3_061.json b/datasets/MYD11B3_061.json index b0cf5db8a3..4db89f7b8c 100644 --- a/datasets/MYD11B3_061.json +++ b/datasets/MYD11B3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11B3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11B3 Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD11C1_061.json b/datasets/MYD11C1_061.json index 976b732015..4cfca82067 100644 --- a/datasets/MYD11C1_061.json +++ b/datasets/MYD11C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11C1 Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD11C2_061.json b/datasets/MYD11C2_061.json index 5ec9959b11..d0845a431b 100644 --- a/datasets/MYD11C2_061.json +++ b/datasets/MYD11C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11C2 Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD11C3_061.json b/datasets/MYD11C3_061.json index 41f97e3b20..375b9239d8 100644 --- a/datasets/MYD11C3_061.json +++ b/datasets/MYD11C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD11CM1D_005.json b/datasets/MYD11CM1D_005.json index 0b89063cf6..3fbf4b1602 100644 --- a/datasets/MYD11CM1D_005.json +++ b/datasets/MYD11CM1D_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11CM1D_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2002-08-01 to 2015-06-30.", "links": [ { diff --git a/datasets/MYD11CM1N_005.json b/datasets/MYD11CM1N_005.json index 811856be53..9ea2605735 100644 --- a/datasets/MYD11CM1N_005.json +++ b/datasets/MYD11CM1N_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11CM1N_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2002-08-01 to 2015-06-30.", "links": [ { diff --git a/datasets/MYD11_L2_061.json b/datasets/MYD11_L2_061.json index 6b8cd66222..8f1563dc2a 100644 --- a/datasets/MYD11_L2_061.json +++ b/datasets/MYD11_L2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11_L2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples.\n\nValidation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11)\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD11_L2_6.1NRT.json b/datasets/MYD11_L2_6.1NRT.json index 72ae1271de..344136c293 100644 --- a/datasets/MYD11_L2_6.1NRT.json +++ b/datasets/MYD11_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD11_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Land Surface Temperature/Emissivity 5-Min L2 Swath 1km Near Real Time (NRT), short name MYD11_L2, incorporate 1 km pixels, which are produced daily at 5-minute increments using the generalized split-window algorithm. This algorithm is optimally used to separate ranges of atmospheric column water vapor and lower boundary air surface temperatures into tractable sub-ranges. The surface emissivities in bands 31 and 32 are estimated from land cover types. The data inputs include the MODIS L1B calibrated and geolocated radiances, geolocation, cloud mask, atmospheric profiles, land and snow cover. The MYD11_L2 data set comprises swath data obtained in 5-minute sensor collection periods, and includes the following Science Data Set (SDS) layers:- LST- Quality control assessment- Error estimates- Bands 31 and 32 emissivities- Zenith angle of the pixel view- Observation time- Geographic coordinates for every five scan lines and samples.Produced daily, MYD11_L2 is an unprojected level-2 product, which provides the input for the level-3 products.", "links": [ { diff --git a/datasets/MYD13A1_061.json b/datasets/MYD13A1_061.json index 63331b48e7..2982ec5ef9 100644 --- a/datasets/MYD13A1_061.json +++ b/datasets/MYD13A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. \n\nProvided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD13A2_061.json b/datasets/MYD13A2_061.json index e0d01ed60d..5829c8d9cc 100644 --- a/datasets/MYD13A2_061.json +++ b/datasets/MYD13A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. \n\nProvided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD13A3_061.json b/datasets/MYD13A3_061.json index 21c0c89c93..0f2a2182b2 100644 --- a/datasets/MYD13A3_061.json +++ b/datasets/MYD13A3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13A3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average. \n\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.\n\nVegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion.\n\nProvided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers.\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD13C1_061.json b/datasets/MYD13C1_061.json index a9efed7d34..9e71957657 100644 --- a/datasets/MYD13C1_061.json +++ b/datasets/MYD13C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD13C1 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD13C2_061.json b/datasets/MYD13C2_061.json index 9d8d7c985f..792b376b61 100644 --- a/datasets/MYD13C2_061.json +++ b/datasets/MYD13C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD13C2 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD13Q1_061.json b/datasets/MYD13Q1_061.json index d1ec435d1e..111255b3c2 100644 --- a/datasets/MYD13Q1_061.json +++ b/datasets/MYD13Q1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD13Q1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.\n\nAlong with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n", "links": [ { diff --git a/datasets/MYD14A1_061.json b/datasets/MYD14A1_061.json index 7c91754e5f..4139c136e0 100644 --- a/datasets/MYD14A1_061.json +++ b/datasets/MYD14A1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD14A1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.\n\nThe Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD14A2_061.json b/datasets/MYD14A2_061.json index fe34439f90..40d2731ea9 100644 --- a/datasets/MYD14A2_061.json +++ b/datasets/MYD14A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD14A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition.\n\nThe Science Dataset (SDS) layers include the fire mask and pixel quality indicators.\n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD14CM1_005.json b/datasets/MYD14CM1_005.json index c0b1d9c694..885a853a27 100644 --- a/datasets/MYD14CM1_005.json +++ b/datasets/MYD14CM1_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD14CM1_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The gridded MODIS active fire products present statistical summaries of fire pixel information (Giglio et al., 2003). The global monthly products are generated at 1x1 degree spatial resolution for time period of one calendar month. These products are derived from MODIS CMG 0.5 degree products (Giglio et al., 2006) for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on surface processes and climate modeling.", "links": [ { diff --git a/datasets/MYD14_061.json b/datasets/MYD14_061.json index adb4da131c..05c0c6e20b 100644 --- a/datasets/MYD14_061.json +++ b/datasets/MYD14_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD14_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MYD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at a 1 kilometer (km) spatial resolution. The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). \n\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD14_6.1NRT.json b/datasets/MYD14_6.1NRT.json index 19195203a8..bf148453d9 100644 --- a/datasets/MYD14_6.1NRT.json +++ b/datasets/MYD14_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD14_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km Near Real Time (NRT), short name MYD14, product is primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (when the fire strength is sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline.MYD14 is level-2 swath data provided daily at 1-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (1:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate.", "links": [ { diff --git a/datasets/MYD15A2H_061.json b/datasets/MYD15A2H_061.json index 1888886a29..0f7d0bd265 100644 --- a/datasets/MYD15A2H_061.json +++ b/datasets/MYD15A2H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD15A2H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \u201cbest\u201d pixel available from all the acquisitions of the Aqua sensor from within the 8-day period.\n\nLAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy.\n\nScience Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule.\n\nThe LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "links": [ { diff --git a/datasets/MYD16A2GF_061.json b/datasets/MYD16A2GF_061.json index ac9b814f9a..c68144af39 100644 --- a/datasets/MYD16A2GF_061.json +++ b/datasets/MYD16A2GF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD16A2GF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day \ncomposite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\n\nThe MYD16A2GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year.\n\nProvided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\n", "links": [ { diff --git a/datasets/MYD16A2_061.json b/datasets/MYD16A2_061.json index b96a78caeb..3876561999 100644 --- a/datasets/MYD16A2_061.json +++ b/datasets/MYD16A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD16A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\n\nProvided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.", "links": [ { diff --git a/datasets/MYD16A3GF_061.json b/datasets/MYD16A3GF_061.json index b82340299e..1a5ab44d8d 100644 --- a/datasets/MYD16A3GF_061.json +++ b/datasets/MYD16A3GF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD16A3GF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\n\nThe MYD16A3GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year.\n\nProvided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.\n\nValidation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.\n\nImprovements/Changes from Previous Changes\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\n", "links": [ { diff --git a/datasets/MYD17A2HGF_061.json b/datasets/MYD17A2HGF_061.json index cf76240e1a..68ea52cd7e 100644 --- a/datasets/MYD17A2HGF_061.json +++ b/datasets/MYD17A2HGF_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MYD17A2HGF_061", - "stac_version": "1.0.0", - "description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.\n\nThe MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year.\n\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\n", + "stac_version": "1.1.0", + "description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.\r\n\r\nThe MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year.\r\n\r\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\r\n", "links": [ { "rel": "license", @@ -112,15 +112,15 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MYD17A2HGF.061/MYD17A2HGF.A2023361.h10v08.061.2024022194025/BROWSE.MYD17A2HGF.A2023361.h10v08.061.2024022194026.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2021.01.15/BROWSE.MYD17A2HGF.A2020361.h19v09.061.2021015043006.1.jpg", "type": "image/jpeg", - "title": "Download BROWSE.MYD17A2HGF.A2023361.h10v08.061.2024022194026.1.jpg", + "title": "Download BROWSE.MYD17A2HGF.A2020361.h19v09.061.2021015043006.1.jpg", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MYD17A2HGF.061/MYD17A2HGF.A2023361.h10v08.061.2024022194025/BROWSE.MYD17A2HGF.A2023361.h10v08.061.2024022194026.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2021.01.15/BROWSE.MYD17A2HGF.A2020361.h19v09.061.2021015043006.1.jpg", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ @@ -138,43 +138,23 @@ "nasa": { "href": "https://appeears.earthdatacloud.nasa.gov/", "title": "Direct Download [2]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "The Application for Extracting and Exploring Analysis Ready Samples (A\u03c1\u03c1EEARS) offers a simple and efficient way to perform data access and transformation processes.", "roles": [ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MYD17A2HGF.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MYD17A2HGF_061": { - "href": "s3://lp-prod-protected/MYD17A2HGF.061", - "title": "lp_prod_protected_MYD17A2HGF_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MYD17A2HGF_061": { - "href": "s3://lp-prod-public/MYD17A2HGF.061", - "title": "lp_prod_public_MYD17A2HGF_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov/", + "title": "Direct Download [3]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MYD17A2HGF.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MYD17A2H_061.json b/datasets/MYD17A2H_061.json index b53d25928f..ae29664537 100644 --- a/datasets/MYD17A2H_061.json +++ b/datasets/MYD17A2H_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD17A2H_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. \n\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\n", "links": [ { diff --git a/datasets/MYD17A3HGF_061.json b/datasets/MYD17A3HGF_061.json index e3c0ee7402..ada11c5e6f 100644 --- a/datasets/MYD17A3HGF_061.json +++ b/datasets/MYD17A3HGF_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD17A3HGF_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H)(https://doi.org/10.5067/MODIS/MYD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).\r\n\r\nThe MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year.\r\n\r\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\r\n\r\n\r\n\r\n", "links": [ { diff --git a/datasets/MYD21A1D_061.json b/datasets/MYD21A1D_061.json index 40f9d299ee..cdb1b9443f 100644 --- a/datasets/MYD21A1D_061.json +++ b/datasets/MYD21A1D_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21A1D_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \r\n\r\nThe MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product utilizes GEOS data replacing MERRA2.\r\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).\r\n", "links": [ { diff --git a/datasets/MYD21A1N_061.json b/datasets/MYD21A1N_061.json index d4115975cb..30fb810260 100644 --- a/datasets/MYD21A1N_061.json +++ b/datasets/MYD21A1N_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21A1N_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MYD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2.\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).", "links": [ { diff --git a/datasets/MYD21A2_061.json b/datasets/MYD21A2_061.json index 2df01f61f5..def7bde355 100644 --- a/datasets/MYD21A2_061.json +++ b/datasets/MYD21A2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21A2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \r\n\r\nThe MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (httpd://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product utilizes GEOS data replacing MERRA2.\r\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).\r\n", "links": [ { diff --git a/datasets/MYD21C1_061.json b/datasets/MYD21C1_061.json index 913cf13759..0a0f5791f4 100644 --- a/datasets/MYD21C1_061.json +++ b/datasets/MYD21C1_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21C1_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).", "links": [ { diff --git a/datasets/MYD21C2_061.json b/datasets/MYD21C2_061.json index 7ac16e3be8..06ea9d7998 100644 --- a/datasets/MYD21C2_061.json +++ b/datasets/MYD21C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (https://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).\n", "links": [ { diff --git a/datasets/MYD21C3_061.json b/datasets/MYD21C3_061.json index 327bea3b89..d854bc0c32 100644 --- a/datasets/MYD21C3_061.json +++ b/datasets/MYD21C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. \n\nThe MYD21C3 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).\n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).", "links": [ { diff --git a/datasets/MYD21_061.json b/datasets/MYD21_061.json index 186114f9c5..86cd6a687b 100644 --- a/datasets/MYD21_061.json +++ b/datasets/MYD21_061.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "MYD21_061", - "stac_version": "1.0.0", - "description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). \n\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product utilizes GEOS data replacing MERRA2.\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).\n", + "stac_version": "1.1.0", + "description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). \r\n\r\nValidation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product utilizes GEOS data replacing MERRA2.\r\n* Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).\r\n", "links": [ { "rel": "license", @@ -82,8 +82,8 @@ "license": "proprietary", "keywords": [ "EARTH SCIENCE", - "LAND SURFACE", "SURFACE THERMAL PROPERTIES", + "LAND SURFACE", "LAND SURFACE TEMPERATURE", "SURFACE RADIATIVE PROPERTIES", "EMISSIVITY" @@ -112,31 +112,23 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Aqua_Land_Surface_Temp_Day_TES.jpg", + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.30/BROWSE.MYD21.A2003063.2225.061.2020087025027.1.jpg", "type": "image/jpeg", - "title": "Download MODIS_Aqua_Land_Surface_Temp_Day_TES.jpg", + "title": "Download BROWSE.MYD21.A2003063.2225.061.2020087025027.1.jpg", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/MYD21.061/MYD21.A2024198.1750.061.2024199150816/BROWSE.MYD21.A2024198.1750.061.2024199110820.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov//WORKING/BRWS/Browse.001/2020.03.30/BROWSE.MYD21.A2003063.2225.061.2020087025027.1.jpg", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/MODIS_Aqua_Land_Surface_Temp_Day_TES.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", - "roles": [ - "thumbnail" - ] - }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2565805776-LPCLOUD", + "href": "https://search.earthdata.nasa.gov/search?q=C1621444178-LPDAAC_ECS", "title": "Direct Download [0]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ @@ -151,38 +143,18 @@ "data" ] }, - "provider_metadata": { - "href": "https://doi.org/10.5067/MODIS/MYD21.061", - "title": "Provider Metadata", - "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", - "roles": [ - "metadata" - ] - }, - "s3_lp_prod_protected_MYD21_061": { - "href": "s3://lp-prod-protected/MYD21.061", - "title": "lp_prod_protected_MYD21_061", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_MYD21_061": { - "href": "s3://lp-prod-public/MYD21.061", - "title": "lp_prod_public_MYD21_061", + "usgs": { + "href": "https://earthexplorer.usgs.gov", + "title": "Direct Download [2]", + "description": "USGS EarthExplorer provides users the ability to query, search, and order products available from the LP DAAC.", "roles": [ "data" ] }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", + "provider_metadata": { + "href": "https://doi.org/10.5067/MODIS/MYD21.061", + "title": "Provider Metadata", + "description": "The LP DAAC product page provides information on Science Data Set layers and links for user guides, ATBDs, data access, tools, customer support, etc.", "roles": [ "metadata" ] diff --git a/datasets/MYD21_6.1NRT.json b/datasets/MYD21_6.1NRT.json index a5ba1051cd..30533cc812 100644 --- a/datasets/MYD21_6.1NRT.json +++ b/datasets/MYD21_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD21_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Land Surface Temperature/3-Band Emissivity (LST&E) 5-Min L2 1km data product, short-name MYD21 is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/107/MOD21_ATBD.pdf)). \r\n\r\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and more.", "links": [ { diff --git a/datasets/MYD28C2_061.json b/datasets/MYD28C2_061.json index 57c0e3611c..e8430687e3 100644 --- a/datasets/MYD28C2_061.json +++ b/datasets/MYD28C2_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD28C2_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\r\n\r\nThe MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves (https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Aqua satellite (MYD09Q1). \r\n\r\nThe MYD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. \r\n", "links": [ { diff --git a/datasets/MYD28C3_061.json b/datasets/MYD28C3_061.json index 321a607bbb..80e0ffd704 100644 --- a/datasets/MYD28C3_061.json +++ b/datasets/MYD28C3_061.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD28C3_061", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MYD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) (https://www.sciencedirect.com/science/article/pii/S0034425720304776?via%3Dihub) via MODIS Land Surface Temperature (LST) (MYD21) and meteorological data from Global Land Data Assimilation System (GLDAS) (https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MYD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume.", "links": [ { diff --git a/datasets/MYD29E1D_61.json b/datasets/MYD29E1D_61.json index b1812ff9f5..867f9aaabc 100644 --- a/datasets/MYD29E1D_61.json +++ b/datasets/MYD29E1D_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD29E1D_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day\u2019 (https://doi.org/10.5067/MODIS/MYD29P1D.061) product. These data are provided daily in the EASE-Grid polar projection at a resolution of approximately 4 km.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD29P1D_61.json b/datasets/MYD29P1D_61.json index 58af368eda..660d9177ad 100644 --- a/datasets/MYD29P1D_61.json +++ b/datasets/MYD29P1D_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD29P1D_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10\u00b0 x 10\u00b0 of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD29P1N_61.json b/datasets/MYD29P1N_61.json index ef839e2a71..d538de4201 100644 --- a/datasets/MYD29P1N_61.json +++ b/datasets/MYD29P1N_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD29P1N_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD29_6.1NRT.json b/datasets/MYD29_6.1NRT.json index 1ad93f13d2..91e09d9442 100644 --- a/datasets/MYD29_6.1NRT.json +++ b/datasets/MYD29_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD29_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km Near Real Time (NRT), short name MYD29, contains the following fields: sea ice by reflectance, sea ice by reflectance pixel quality assurance (QA), ice surface temperature (IST), IST pixel QA, sea ice by IST, combined sea ice, latitudes, and longitudes in HDF-EOS format, along with corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution, while all other fields are at 1 km resolution. The sea ice algorithm uses a Normalized Difference Snow Index (NDSI) modified for sea ice to distinguish sea ice from open ocean, based on reflective and thermal characteristics. ", "links": [ { diff --git a/datasets/MYD29_61.json b/datasets/MYD29_61.json index 8a8c560ab4..9dc87b4e3d 100644 --- a/datasets/MYD29_61.json +++ b/datasets/MYD29_61.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD29_61", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. Each data granule contains 5 minutes of swath data observed at a resolution of 1000 m.\n\nThe terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.", "links": [ { diff --git a/datasets/MYD35_L2_6.1.json b/datasets/MYD35_L2_6.1.json index 4df818b8b7..38e9a5e648 100644 --- a/datasets/MYD35_L2_6.1.json +++ b/datasets/MYD35_L2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD35_L2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2.\r\n\r\nThe MYD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). This product consists of 9 parameters and each of these parameters are stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. \r\n\r\nFor more information about the MYD35_L2 product, visit the MODIS-Atmosphere site at:\r\n\r\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud-mask", "links": [ { diff --git a/datasets/MYD35_L2_6.1NRT.json b/datasets/MYD35_L2_6.1NRT.json index a42deb2d9b..01f02ec76a 100644 --- a/datasets/MYD35_L2_6.1NRT.json +++ b/datasets/MYD35_L2_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD35_L2_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS level-2 cloud mask product is a global product generated for both daytime and nighttime conditions at 1-km spatial resolution (at nadir) and for daytime at 250-m resolution. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. \n\nThe Terra MODIS Photovoltaic (PVLWIR) bands 27-30 are known to experience an electronic crosstalk contamination. The influence of the crosstalk has gradually increased over the mission lifetime, causing for example, earth surface features to become prominent in atmospheric band 27, increased detector striping, and long term drift in the radiometric bias of these bands. The drift has compromised the climate quality of C6 Terra MODIS L2 products that depend significantly on these bands, including cloud mask (MOD35), cloud fraction and cloud top properties (MOD06), and total precipitable water (MOD07). A linear crosstalk correction algorithm has been developed and tested by MCST.The electronic crosstalk correction was made to the calibration algorithm for bands 27-30 and implemented into C6.1 operational L1B processing. This implementation greatly improves the performance of the cloud mask.\n\nThe shortname for this Level-2 MODIS cloud mask product is MYD35_L2 and the principal investigator for this product is MODIS scientist Dr. Paul Menzel ( paulm@ssec.wisc.edu). MYD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). Each of the 9 gridded parameters is stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. \n\nMYD35_L2 Data Group and Parameters:\n\nSpatial and Temporal Resolution:\n\nLatitude and Longitude\nScan start time\n\nSolar and Sensor Viewing Geometry:\n\nSolar zenith and Solar azimuth angle\nSensor zenith and Sensor azimuth angle\n\nScience Parameters:\nCloud Mask (1km) \nCloud Mask (250 m) \n\nQuality Assurance Parameters: \nQuality Assurance Flags (1km)\n\n\nLink to the MODIS homepage for more data set information: \n\nhttps://modis-atmos.gsfc.nasa.gov/products/cloud-mask\n\nLink to the MODIS homepage for C6.1 changes:\n\n\nhttps://modis-atmos.gsfc.nasa.gov/documentation/collection-61", "links": [ { diff --git a/datasets/MYDAODHD_6.1NRT.json b/datasets/MYDAODHD_6.1NRT.json index bab59ece05..f934fc47dd 100644 --- a/datasets/MYDAODHD_6.1NRT.json +++ b/datasets/MYDAODHD_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDAODHD_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS was launched aboard the Terra satellite on December 18, 1999 (10:30 am equator crossing time) as part of NASA's Earth Observing System (EOS) mission. MODIS with its 2330 km viewing swath width provides almost daily global coverage. It acquires data in 36 high spectral resolution bands between 0.415 to 14.235 micron with spatial resolutions of 250m(2 bands), 500m(5 bands),and 1000m (29 bands). MODIS sensor counts, calibrated radiances, geolocation products and all derived geophysical atmospheric and ocean products are archived at various DAACs and has been made available to public since April 2000.\n\nThe shortname for this level-3 MODIS aerosol product is MYDAODHD. The Naval Research Laboratory and the University of North Dakota developed this value-added aerosol optical depth dataset based on MODIS Level 2 aerosol products. The MYDAODHD is a gridded product and is specifically designed for quantitative applications including data assimilation and model validation. It is available through LANCE-MODIS. It offers several enhancements over the MODIS Level 2 data on which it is based. These enhancements include stringent filtering to reduce outliers, eliminate cloud contamination, and exclude conditions where aerosol detection is likely to be inaccurate; reduction of systematic biases over land and ocean by empirical corrections; reduction of random variation in AOD values by spatial averaging; quantitative estimation of uncertainty for each AOD data point.\n\nThe MxDAODHD granules are produced every six hours, and time-stamped 00:00, 06:00, 12:00, and 18:00 (all times UTC). Each granule includes MODIS observations from +/-3 hours from the timestamp (e.g. 12:00 product includes MODIS data from 09:00-15:00 UTC). Production is initiated as soon as the Level 2 inputs become available in the LANCE system.\n\nSee the LANCE-MODIS page for more dataset information: \n\nhttps://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/modis-nrt", "links": [ { diff --git a/datasets/MYDARNSS_6.1.json b/datasets/MYDARNSS_6.1.json index cb5adcfd5f..383604bdda 100644 --- a/datasets/MYDARNSS_6.1.json +++ b/datasets/MYDARNSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDARNSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Atmosphere Aeronet Subsetting Product (MYDARNSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over a number of Aerosol Robotic Network (AERONET) sites. These sites comprise of sites of automatic tracking Sun photometers/sky radiometers located all over the world. The process of generating cutouts involves locating and identifying a subset of sites taken from a global AERONET that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MYDARNSS data set consists of subsets for around 180 AERONET sites around the globe. There is one file per site with 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc.", "links": [ { diff --git a/datasets/MYDATML2_6.1.json b/datasets/MYDATML2_6.1.json index 08af1b15e0..ff321c2216 100644 --- a/datasets/MYDATML2_6.1.json +++ b/datasets/MYDATML2_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDATML2_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MYDATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets). The ATML2 product employs the same 5x5 pixel sampling scheme for the 1km native resolution Level 2 products as is used in the MOD08 Level 3 global aggregated product, an approach that has been shown to retain statistical integrity for multi-day aggregations. \r\n\r\nThe C6 significantly increases the number of datasets included in the ATML2 product, including the full suite of QA datasets. Since the ATML2 data granule file size is significantly smaller than the combined size of the individual L2 products, and because the 1 km pixel sampling is consistent with the L3 algorithm, the ATML2 product is a more practical means for the user community to develop research L3 algorithms for their own specific purposes.\r\n\r\nFor more information, visit the MODIS Atmosphere website at: \r\nhttps://modis-atmos.gsfc.nasa.gov/products/joint-atm", "links": [ { diff --git a/datasets/MYDBMSS_6.1.json b/datasets/MYDBMSS_6.1.json index f3d14915b8..a8e28d28ba 100644 --- a/datasets/MYDBMSS_6.1.json +++ b/datasets/MYDBMSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDBMSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Atmosphere BELMANIP subsetting Product (MYDBMSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over the Bench-mark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites. The BELMANIP sites is a network of sites, distributed globally and consist of existing networks such as Earth Observing System (EOS) Core Sites, Bigfoot, Validation of Land European Remote sensing Instruments (VALERI), a global network of micrometeorological flux measurement (FLUXNET), the aerosol robotic network (AERONET) and a set of additional sites.The process of generating cutouts for these sites involves locating and identifying a subset of sites taken from global BELMANIP sites that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MODBMSS data set consists of subsets for approximately 445 sites around the globe. There is one file per site with 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc.", "links": [ { diff --git a/datasets/MYDCSR_8_6.1.json b/datasets/MYDCSR_8_6.1.json index 4ec5e628fc..cb264d7874 100644 --- a/datasets/MYDCSR_8_6.1.json +++ b/datasets/MYDCSR_8_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDCSR_8_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Clear Sky Radiance 8-Day Composite Daily L3 Global 25km Equal Area (MYDCSR_8) product is created from composited MYDCSR_D files. Nine clear-sky radiance and reflectance statistics (bands 1-7 and 17-36, see description of the MYDCSR_G product for description of statistics) are produced for day and night separately, for every calendar day from the previous eight days (eight MYDCSR_D files). There must be valid clear-sky data from at least four of the eight days in order to generate a MYDCSR_8 output file. The statistics include observed minus calculated data from bands 20, 22-25, and 27-36 and numbers of land vs. water observations. The data is global in extent at 25-km resolution. MYDCSR_8 files are in Hierarchical Data Format (HDF).", "links": [ { diff --git a/datasets/MYDCSR_B_6.1.json b/datasets/MYDCSR_B_6.1.json index 910d40cfa2..6792d00b8c 100644 --- a/datasets/MYDCSR_B_6.1.json +++ b/datasets/MYDCSR_B_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDCSR_B_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua 8-Day Clear Sky Radiance Bias Daily L3 Global 1Deg Zonal Bands (MYDCSR_B) product consists of 1-degree zonal mean clear-sky biases (observed minus calculated radiance differences) and associated statistics for bands 31 and 33-36 for each day from the previous eight-day period. Zonal means (5-zone moving averages) are created from the eight-day, 25-km radiance differences for daytime land, nighttime land, and ocean data separately. Day and night land data are combined south of -60 degrees latitude due to poor clear-sky sampling and the difficulty of discriminating between clear and cloudy conditions in this region. The zonal mean biases are utilized to correct clear-sky radiance calculations in the cloud top pressure (CO2 slicing) algorithm. The files are in Hierarchical Data Format (HDF).", "links": [ { diff --git a/datasets/MYDFNSS_6.1.json b/datasets/MYDFNSS_6.1.json index aa1dcc27bb..1beb343220 100644 --- a/datasets/MYDFNSS_6.1.json +++ b/datasets/MYDFNSS_6.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDFNSS_6.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Atmosphere FluxNet Subsetting Product (MODFNSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over a global network of micrometeorological flux measurement (FLUXNET) sites. The process of generating cutouts for these sites involves locating and identifying a subset of sites taken from a global FLUXNET that are within the spatial coverage of a 5 minute Level 2 MODIS granule and extracting 0.5 x 0.5 degree cutouts. The MODFNSS data set consists of subsets for around 400 sites out of the total flux tower sites around the globe. There is one file per site with around 55 Science Data Sets (SDS) such as at-aperture radiances for 36 discrete MODIS bands, Cloud Mask, and Water Vapor, etc", "links": [ { diff --git a/datasets/MYDGB0_6.1NRT.json b/datasets/MYDGB0_6.1NRT.json index 10ed2a8f67..77294f616a 100644 --- a/datasets/MYDGB0_6.1NRT.json +++ b/datasets/MYDGB0_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDGB0_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS/Aqua Near Real Time (NRT) 5-minute GBAD data in L0 format.", "links": [ { diff --git a/datasets/MYDVI_005.json b/datasets/MYDVI_005.json index f77276a3b2..2bef021aa7 100644 --- a/datasets/MYDVI_005.json +++ b/datasets/MYDVI_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYDVI_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Aqua Vegetation Indices Monthly product MYD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling. The vegetation indices product is generated at 1x1 degree spatial resolution starting from July 2002.", "links": [ { diff --git a/datasets/MYD_L2_CB_001.json b/datasets/MYD_L2_CB_001.json index 71534bfc97..1b18cdd370 100644 --- a/datasets/MYD_L2_CB_001.json +++ b/datasets/MYD_L2_CB_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD_L2_CB_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is composed of a beta version for a product from the MODerate resolution Imaging Spectrometer (MODIS) on board the Aqua satellite.\n\nMODIS Aqua L2 chopped blocks (CB) are part of our global MODIS Aqua data from the 2017 MEaSUREs project, A Comprehensive Data Record of Marine Low-level and Deep Convective Cloud Systems Using an Object-Oriented Approach.\n\nThese data are individual MODIS Aqua granules chopped into small blocks in shape (np_x, np_y), where np_x = 128 pixels and np_y = 128 pixels. This file provides the geolocations of all the chopped blocks in the MODIS Aqua granule data. Only daytime granule data are included and blocks with sensor zenith angle > 45 degrees are excluded. The geolocations for each block consist of five latitude and longitude pairs for its four corners and center with a low cloud flag that indicates whether the chopped block is or is not low cloud dominated.\n\nThe DOIs of the related datasets of this project are:\nMYD_L2_MPLCT_001 DOI: 10.5067/8TDZURGRLN9I\nMYD_L3_OFLCT_001 DOI: 10.5067/3FAIC739DQRH", "links": [ { diff --git a/datasets/MYD_L2_DC_001.json b/datasets/MYD_L2_DC_001.json index 2ca64bd7ad..84c2be1dc5 100644 --- a/datasets/MYD_L2_DC_001.json +++ b/datasets/MYD_L2_DC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD_L2_DC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MODIS Aqua L2 deep-convective cloud classification (DC) are part of our global MODIS Aqua data from the 2017 MEaSUREs project, A Comprehensive Data Record of Marine Low-level and Deep Convective Cloud Systems Using an Object-Oriented Approach.", "links": [ { diff --git a/datasets/MYD_L2_MPLCT_001.json b/datasets/MYD_L2_MPLCT_001.json index 3d1dd69c8d..88d18cf30f 100644 --- a/datasets/MYD_L2_MPLCT_001.json +++ b/datasets/MYD_L2_MPLCT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD_L2_MPLCT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is composed of a beta version for a product from the MODerate resolution Imaging Spectrometer (MODIS) on board the Aqua satellite.\n\nThis dataset contains model predicted low cloud morphology type classifications (MPLCT) of each of the chopped blocks as part of our global MODIS Aqua data from the 2017 MEaSUREs project, A Comprehensive Data Record of Marine Low-level and Deep Convective Cloud Systems Using an Object-Oriented Approach.\n\nThese data are the model predictions of cloud types for low-cloud-dominated blocks over the oceans for individual MODIS Aqua granule data, chopped into small blocks in shape (np_x, np_y), where np_x = 128 pixels and np_y = 128 pixels. These low-cloud-dominated blocks are defined by the conditions: the ratio of high-cloud fraction and low-cloud fraction is smaller than 0.2, with high-cloud fraction < 0.3 and low-cloud fraction > 0.05. Only daytime granule data are included and blocks with sensor zenith angle > 45 and blocks over land are excluded.\n\nThe variables include:\nblock_low: the name of the low-cloud-dominated block, based on which the location of the chopped block in the granule data can be found.\npred_cat: the predicted cloud type of each block.\npred_prob: the prediction probability of cloud type\nlcf: the low-cloud fraction of the low-cloud-dominated block.\nsensor_zenith: the sensor zenith angle at the center of the low-cloud-dominated block\nFive latitude and longitude points for the four corners and center of the chopped blocks\n\nThe DOIs of the related datasets in this project are:\nMYD_L2_CB_001 DOI: 10.5067/DFDGJR6707D8\nMYD_L3_OFLCT_001 DOI: 10.5067/3FAIC739DQRH\n", "links": [ { diff --git a/datasets/MYD_L3_OFLCT_001.json b/datasets/MYD_L3_OFLCT_001.json index 3a828778b6..12cf2db04b 100644 --- a/datasets/MYD_L3_OFLCT_001.json +++ b/datasets/MYD_L3_OFLCT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MYD_L3_OFLCT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is composed of a beta version for a product from the MODerate resolution Imaging Spectrometer (MODIS) on board the Aqua satellite.\n\nMODIS Aqua L3 occurrence frequency of low cloud types (OFLCT) is part of our global MODIS Aqua data from the 2017 MEaSUREs project, A Comprehensive Data Record of Marine Low-level and Deep Convective Cloud Systems Using an Object-Oriented Approach.\n\nThis file provides the aggregated occurrence frequency of low-cloud types for the year 2007 at monthly and annual intervals. The latitude and longitude variables from MYD_L2_CB; and latitude, longitude and pred_cat from MYD_L2_MPLCT were used to calculate the occurrence frequency of six low-cloud types in 2.0\u00b0 x 2.0\u00b0 grids for 60\u00b0S-60\u00b0N and 180\u00b0W-180\u00b0E. The six low cloud types are Closed-cellular MCC, Clustered Cumulus, Disorganized MCC, Open-cellular MCC, Solid Stratus, and Suppressed Cumulus.\n\nThe DOIs of the related datasets in this project are:\nMYD_L2_CB_001 DOI: 10.5067/DFDGJR6707D8\nMYD_L2_MPLCT_001 DOI: 10.5067/8TDZURGRLN9I\n", "links": [ { diff --git a/datasets/Macca_DSAM_1.json b/datasets/Macca_DSAM_1.json index 73dcc06376..ff3b492cea 100644 --- a/datasets/Macca_DSAM_1.json +++ b/datasets/Macca_DSAM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macca_DSAM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.\n\nThe dataset conforms to the SCAR Feature Catalogue which includes data quality information.\n\nThe data is included in the data available for download from a Related URL below.\nThe data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 25.\nEach feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.\n\nChanges have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added.\nAs a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).", "links": [ { diff --git a/datasets/Macca_Penguins_1911-1980_1.json b/datasets/Macca_Penguins_1911-1980_1.json index 88853d104e..cdf6b145ba 100644 --- a/datasets/Macca_Penguins_1911-1980_1.json +++ b/datasets/Macca_Penguins_1911-1980_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macca_Penguins_1911-1980_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains information on the distribution of Penguins and their breeding colonies in the Australian Antarctic sector, as of 1983. It forms Australia's contribution to the International Survey of Antarctic Seabirds (ISAS). The results are listed in the documentation. These include counts of chicks, adults and nests, as well as colony distribution maps. The survey includes Emperor Penguins, Adelie Penguins, King Penguins, Gentoo Penguins, Macaroni Penguins, Rockhopper Penguins, Chinstrap Penguins and Royal Penguins.\n\nOriginal data were taken from ANARE Research Notes 9.\n\nOnly data from the Australian Antarctic Territory is described in this metadata record.\n\nImages of rough maps detailing the locations of each of the colonies are available for download from the url given below. Observation and count data have been incorporated into the Australian Antarctic Data Centre's Biodiversity Database. \n\nThe data are presented in the format of Croxall and Kirkwood (1979) as recommended by the Report of the Subcommittee on Bird Biology held in Pretoria. In the tables all counts are estimates of the number of breeding pairs except where otherwise indicated. The numerical estimates and counts are of three kinds, indicated by the coded N, C or A:\n\nNESTS (N = count of NESTS or breeding/incubating pairs)\nThe most accurate count of breeding pairs is that derived from a count of nests. This is usually carried out during incubation, but may also be made while chicks are still in the nest, before creches are formed. Such counts are only underestimates of breeding pairs by the number of breeding failures sustained between egg laying and the date of the count.\n\nCHICKS (C = count of CHICKS)\nLate in the breeding season the only counts possible are those of chicks. In general most pygosceild penguins raise one chick per pair per season, so a count of chicks gives a reasonable approximation of the original number of breeding pairs. However, season to season variation in breeding success can often be considerable. For example Yeates (1968) reports breeding success in Adelie Penguins at Cape Royds of twenty-six per cent, forty-seven per cent and sixty-eight per cent ever three seasons. Also, Macaroni Penguins only raise approximately 0.5 chicks per pair per season, so that chick counts of this species may be a considerable underestimate of the true breeding population.\n\nADULTS (A = count of ADULTS)\nMany colony counts and estimates were expressed as total number of birds or adults. These figures are difficult to interpret as they depend on the time during the breeding season at which they were made. For some days prior to and until laying is finished, both birds of a pair will be present at the nest site while during incubation it is more likely that only one bird will be present. A further problem with counts of 'birds' is that they may include individuals who are not breeding and this gives an overestimate of the true breeding population. The counts of 'birds' or 'adults' which appear unqualified in log books have been divided by two to give an estimate of the number of breeding pairs. It must be stressed therefore that these counts are the least accurate.\n\nThe degree of accuracy of these counts is inevitably highly variable and it is often difficult to ascertain on what basis a figure was arrived at. For the present survey counts have been allocated to one of five degrees of accuracy.\n\n1. Pairs/nests essentially individually counted. The count is probably accurate to better than + 5 per cent.\n\n2. Numbers of pairs in a known area counted individually and knowing the total area of the colony, the overall total calculated. This technique is useful for very large colonies.\n\n3. Accurate estimates; + 10-15 per cent accuracy.\n\n4. Rough estimate; accurate to 25-50 per cent.\n\n5. Guesstimate; to nearest order of magnitude.\n\nMany references are in the form ANARE (Johnstone) or simply ANARE. These refer to unpublished reports extracted from ANARE station biology logs. Those in the form Budd (1961) refer to published records and are listed in the references at the end of this publication.\n\nThe locations of some colonies are indicated on maps. Place names that (as of 1983) have not yet been approved are shown in the tables and on the maps in parentheses, for example: (ROCKERY ISLAND).", "links": [ { diff --git a/datasets/Macca_bathy_500k_1.json b/datasets/Macca_bathy_500k_1.json index 201628fa42..635eb78632 100644 --- a/datasets/Macca_bathy_500k_1.json +++ b/datasets/Macca_bathy_500k_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macca_bathy_500k_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A database containing sounding data around Macquarie Island.\nTrack line data for each data source is included.", "links": [ { diff --git a/datasets/Macca_vegsur_gis_1.json b/datasets/Macca_vegsur_gis_1.json index ef02bfebb6..99e721cbed 100644 --- a/datasets/Macca_vegsur_gis_1.json +++ b/datasets/Macca_vegsur_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macca_vegsur_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes Simone Ingham's survey of vegetation sampling sites on Macquarie Island in the summer of 2001/2002.\nThe Biolab base station needs to be accurately surveyed from the AUSLIG (now Geoscience Australia) GPS base station (AUS211) and the GPS data re-processed using new coordinates. The re-processing is required to establish the relative and absolute values of Simone's survey. Checks will be need to be made in the field with GPS dual frequency receivers on permanent markers surveyed by Simone. This will serve as a check on the survey.\nThis report has been compiled from Simone's report and email's from Paul Standen / Ultimate Positioning. Paul's report is not definitive regarding the relative and absolute accuracies of Simone's survey.\n\nA pdf copy of the report is available for download from the URL given below.\n\nCopies of the GPS data, and an excel spreadsheet summarising the locations of the various monitored sites are available for download at the URL given below.\n\nThis work was completed as part of ASAC project 1015 (ASAC_1015).\n\nThe fields in the excel spreadsheet are:\n\nSite name\nDescription\nLocation\nLatitude\nLongitude\nAltitude\nComments\nMeasured\nOutlines mapped", "links": [ { diff --git a/datasets/Macquarie_Island_Notes_1948_1949_1.json b/datasets/Macquarie_Island_Notes_1948_1949_1.json index 8feb9fe9e1..457788124a 100644 --- a/datasets/Macquarie_Island_Notes_1948_1949_1.json +++ b/datasets/Macquarie_Island_Notes_1948_1949_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Island_Notes_1948_1949_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains 22 pages of scanned typewritten text, and 1 page of a sketched map. The document describes the establishment of Macquarie Island station in 1948, but the first ANARE (Australian National Antarctic Research Expeditions) party. It was written by Peter Wylie King on October 20, 1951.\n\nThe document details activities, living conditions, and other facets of life on Macquarie Island, including the construction of the early station, details about the sheep, goats and dog brought to the island, the rabbits already present on the island, exploration of the island, and the death of engineer Charlie Scoble.", "links": [ { diff --git a/datasets/Macquarie_Island_Vegetation_photo_interpretation_1.json b/datasets/Macquarie_Island_Vegetation_photo_interpretation_1.json index 7cd9821ec6..b49ebdf6bb 100644 --- a/datasets/Macquarie_Island_Vegetation_photo_interpretation_1.json +++ b/datasets/Macquarie_Island_Vegetation_photo_interpretation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Island_Vegetation_photo_interpretation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a spreadsheet containing plant species coverage for the canopies of vegetation plots on Macquarie Island in the summers of 2008/09 and 2009/10. It was collected as part of AAS 3095, for Phillippa Bricher's PhD thesis.\n \n350 sites were chosen using a proportional stratified random sampling protocol. We stratified Macquarie Island into seven relatively homogeneous landform classes using an unsupervised fuzzy c-means classification based on variables derived from a 5 m digital elevation model (DEM) and a 2.4 m resolution orthorectified multispectral QuickBird satellite image, captured 15 March 2005. The digital elevation model is described by the metadata record 'Macquarie Island AIRSAR DEM (Digital Elevation Model)' with Entry ID: macca_dem_gis. \nThe satellite image was provided by Dr Arko Lucieer of the University of Tasmania.\n\nElevation, slope, wetness index, solar radiation and surface curvature were calculated from the DEM, and a Normalised Difference Vegetation Index was calculated from the satellite image. The proportion of sites in each land form class was determined on the basis of three criteria: area; standard deviation of the NDVI (as a proxy for chlorophyll levels); and a subjective assessment of the likelihood of significant vegetation change. Of the 350 random sites, 288 were visited over the two seasons. The majority of the non-visited sites were inaccessible because they were either on steep slopes or close to breeding seabirds. A few sites were not visited due to time constraints.\n\nThe dataset also includes data from 54 sites that had been previously sampled as part of ongoing vegetation studies under AAS 3095 or AAS 1015 and 8 sites purposely selected to capture rare plant communities. 72 sites were visited in both seasons to monitor inter-annual change. The table below shows the number of sites in each category visited in the two field seasons.\n\n Total Random AAS 3095/ AAS 1015 Purpose\n2008/09 215 159 52 4\n2009/10 207 175 26 6\nBoth years 350 288 54 8\n\nAt each site, a 10 x 10 m plot was laid out and a vertical photograph taken of each corner from a height of 2.7 m. Each photograph covers an area approximately 2.9 x 4.3 m. For each site, the photographs cover a total of 49.9 m2, or half the plot.\nA point intercept method was used to estimate percentage cover for each cover class using Coral Point Count (CPCe) software. 100 random points were laid over each photograph, and the cover class under each point was manually identified. Cover classes for the photographs are shown in the data dictionary (file_name.csv). These classes include most vascular plant species (two taxa were identified only to genus-level); and higher-order classes for bryophytes, fungi, algae, lichens, and bare ground. Percentage cover was calculated for all cover classes for each site, and is presented in this dataset.\n\nA data dictionary (available for download with the dataset) describes the fields in the main spreadsheet.\n\nThis dataset was collected as part of AAS projects 3095 and 3130. More specifically it relates to:\n\n3095 - Objective 1\nQuantify change in terrestrial ecosystems at a range of spatial and temporal scales on Heard and McDonald Islands and Macquarie Island. \n\n3130 - Objective 1 and 5\nCollate and collect spatial data in order to establish a baseline map of, and detect changes in vegetation communities on the Windmill Islands and Macquarie Island.\nCombine detailed plot-scale data and field photographs with terrain information and high-resolution satellite imagery to identify and map changes in both plant communities and plant stress more efficiently.", "links": [ { diff --git a/datasets/Macquarie_Island_quickbird_digitising_1.json b/datasets/Macquarie_Island_quickbird_digitising_1.json index 3a50f142f0..ae8b3d0b10 100644 --- a/datasets/Macquarie_Island_quickbird_digitising_1.json +++ b/datasets/Macquarie_Island_quickbird_digitising_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Island_quickbird_digitising_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The topographical features were digitised from the Macquarie Island Quickbird orthorectifed satellite imagery (5 March 2005) with metadata record:\nEntry ID: macquarie_quickbird_5March2005_mapping\n\nThe features were digitised at a scale of 1:2500 between April and August 2012.\nAll lakes that could be identified were digitised but many creeks, ridges and parts of the escarpment remain to be mapped.\nIt was difficult to determine where creeks became dry gullies and dry gullies became creeks so identification is not absolute.\nWaterbodies (lakes and lagoons) between the west coast and the base of the escarpment have been attributed as lakes but some or all may only be lagoons or wide shallow creeks - from the imagery it is difficult to determine the type of waterbody they are.\nOnly the major escarpment and ridges have been digitised.", "links": [ { diff --git a/datasets/Macquarie_Quickbird_15Mar2005_1.json b/datasets/Macquarie_Quickbird_15Mar2005_1.json index 500c638dfa..119ea8c134 100644 --- a/datasets/Macquarie_Quickbird_15Mar2005_1.json +++ b/datasets/Macquarie_Quickbird_15Mar2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Quickbird_15Mar2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quickbird image of Macquarie Island acquired on 15 March 2005. The image has been orthorectified, i.e. all geometric distortions caused by the earth curvature and relief displacement have been corrected. \nTwenty six ground control points spread out over the image were identified, based on survey control marks and corresponding pixels in the Panchromatic band of the Quickbird image. This was done in collaboration with Henk Brolsma, Australian Antarctic Data Centre.\n\nThe image was transformed based on the Quickbird RPC coefficients (sensor platform parameters) and the ground control points. \n\nThe 5m AIRSAR DEM acquired by NASA (metadata record - Macquarie Island AIRSAR DEM (Digital Elevation Model / Entry ID: macca_dem_gis ) was used to correct the image for topographic relief distortions. The DEM was resampled with the bilinear resampling algorithm; the image was resampled with the nearest neighbour algorithm to retain the original pixel values. \n\nThe horizontal accuracy is estimated to be within 5m or 2 pixels, but in places this may be greater. \nCoordinate system: WGS84 datum UTM zone 57S projection.\n\nThe orthorectification was done by Dr Arko Lucieer, Centre for Spatial Information Science (CenSIS), School of Geography and Environmental Studies, University of Tasmania using ENVI 4.3 and the new image was exported to a GeoTIFF and a JPEG2000 (lossless compression) image.\n\nQuickbird imagery consists of four multi-spectral bands: Blue, Green, Red, Near-Infrared at 2.4m resolution (pixel size). \n\nQuickbird also acquires a panchromatic image (grey scale) at 0.6m resolution.\n\nTwo colour composites have been included:\n1. Visible bands (RGB = band 3, 2, 1) corresponding to the way the human visual system sees colours and very similar to an aerial photograph.\n2. False colour composite (RGB = band 4, 3, 2). This colour composite includes the Near-Infrared band (band 4) to highlight vegetation in red. Cholorphyll in vegetation causes a high reflectance of NIR wavelength \nenergy which shows up as bright red in the image. Vegetation that has been grazed or dies back looses its red colour.\n\nTwo image formats have been included:\n1. GeoTIFF (.tif and .tfw). This image is uncompressed and can be opened in ArcView 3.x or ArcMap without loading an extension.\n2. JPEG2000. This image format is highly compressed without loosing image quality. This image is exactly the same as the GeoTIFF but has a smaller file size. \nJPEG2000 can be opened in ArcView with the ECW extension from ERMapper.\n\nIn the file names:\nms=multispectral (2.4m)\n\nps=pansharpened (0.6m)\n\norc=orthorectified\n\nvis=visible colour composite (RGB, 8-bit: 0-255)\n\nfc=false colour composite (NIR,R,G 8-bit: 0-255)\n\nAll images without fc or vis in the filename have four bands and have a 16-bit data type (0 - 65535). Quickbird collects image data in 11-bit (0-2048), so in order to display these images the image values need to be stretched. \n\nFor GIS use Arko recommends using the fc and vis files, because they have been stretched already. Keep in mind that ArcGIS has to build image pyramids and image statistics before display.\n\nIn 2012 lakes, creeks, escarpments and ridges were digitised from the orthorectifed image. For details about the digitising and access to the resulting dataset, refer to the metadata record 'Macquarie Island - Digitising topographic features', Entry ID: Macquarie_Island_quickbird_digitising.", "links": [ { diff --git a/datasets/Macquarie_Quickbird_2Nov2010_1.json b/datasets/Macquarie_Quickbird_2Nov2010_1.json index 558dbe0064..c2a78888d7 100644 --- a/datasets/Macquarie_Quickbird_2Nov2010_1.json +++ b/datasets/Macquarie_Quickbird_2Nov2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Quickbird_2Nov2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Quickbird (2 Nov 2010) images purchased by the AAD in May 2011 were orthorectified to correct for geometric distortions caused by relief displacement. Additionally, for change detection analysis the Quickbird images were co-registered to the Quickbird (15 Mar2005) imagery of Macquarie Island (satellite imagery catalogue 457).", "links": [ { diff --git a/datasets/Macquarie_Royals_1962-1968_1.json b/datasets/Macquarie_Royals_1962-1968_1.json index 365bcf3b95..33374a95bf 100644 --- a/datasets/Macquarie_Royals_1962-1968_1.json +++ b/datasets/Macquarie_Royals_1962-1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Royals_1962-1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scans from one or more field books from observations made at Macquarie Island between 1962 and 1968. The observations were of Royal Penguins, and also of Skua predation and band resights.\n\nThe following names have been mentioned in the scans:\n\nSusan Ingham\nJohn Warham\nJohn Ling\nDavid Nicolls\nI.T. Simpson\nDuncan Mackenzie\nPeter Shaughnessy\nD. Edwards\nR.Carrick\nMerilees\nKerry\nPeter Ormay\nSchmidt\nMajor\nS. Harris", "links": [ { diff --git a/datasets/Macquarie_Tide_Gauges_2.json b/datasets/Macquarie_Tide_Gauges_2.json index 9960e2c233..c07cee55dd 100644 --- a/datasets/Macquarie_Tide_Gauges_2.json +++ b/datasets/Macquarie_Tide_Gauges_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Macquarie_Tide_Gauges_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Over time there have been a number of tide gauges deployed at Macquarie Island Station. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1993 to 2007. More recent data are described elsewhere.\n\nMacquarie Island used Aquatrak and Druck tide gauges during this period.\n\nDocumentation from the older metadata record:\nDocumentation dated 2001-06-12\nThe Macquarie Island Tide Gauge System\n\nThe Macquarie Island Tide Gauge was first commissioned in November 1993. Since then every year attempts have been made to improve the performance of the system.\nThe next improvement involves the installation of radio modems to effect a network link to the tide gauge dataloggers. Other improvements planned are include using the wave guide temperatures to correct the water heights for variations in the velocity of sound in air due to temperature gradients in the waveguide. \nThe system consists of two separate sensors contained in separate housings on a rock shelf on the northern side of Garden Cove. One of the sensors is an Aquatrack acoustic type and the other is a Druck pressure transducer. Both housings contain a Platypus Engineering data logger and a battery. The housings consist each of an Admiralty Bronze ring bolted down to a concrete plinth and a glass fibre reinforced cover held down by a single central bolt and nut. \nPrimary power for both installations comes from a solar panel array mounted on the northern side of the rock ridge behind the rock shelf. The solar panels are attached to an aluminium frame which is bolted to a galvanized steel frame cemented into holes in the rock face. The bolts are made of nylon with nylon washers so that the aluminium frame is not in contact with the galvanized frame.\nMounted below the panels is a sealed plastic box with a hinged door. A multicore data cable runs from this box to the tide gauge housings. This cable is run inside a length of plastic conduit along with the power cable. The conduit is concealed in the vegetation and at the lower level is cemented into slots cut into the rock\nThe batteries in the housing are kept charged by the solar panels but are isolated via power diodes, one in each housing. Either or both of the housing batteries or only the solar panel battery may be removed without interruption to data logging. The voltage of either housing battery may be found by interrogation of the appropriate data logger.\n\nTide Gauge Bore Holes.\n\nBoth gauges obtain access to the ocean via an inclined hole about 12 metres long inclined at approximately 34 and 39 degrees to the horizontal. Both holes are lined with a plastic pipe which is normally not removable. In the Aquatrack sensor hole a 50mm ABS pressure pipe runs down inside the liner and is fitted with a brass strainer and orifice at the lower end. This strainer protrudes into the ocean somewhat clear of the sea floor (see figure). Inside the 50mm pipe runs a 15mm diameter plastic pipe. The bottom end of this is fitted with a 600mm length of red brass tubing and stops about 100mm from the orifice at the bottom of the pipe. The 15mm pipe is held central in the 50mm pipe by three armed spiders placed about every metre down the pipe. The top end of both pipes is secured by a flange with two O rings and stainless steel screws. On top of the 15mm pipe is mounted the Aquatrack acoustic sensor the 15mm pipe acting as a waveguide for sound pulses from the sensor (see figure ). The Aquatrack sensor measures the distance of the water surface from a reference point on the sensor. About one metre down the wave guide is a small hole. This has two functions. One is to act as vent to allow water to rise and fall in the wave guide and the other is to provide an acoustic reflection at a known distance down the wave guide. This allows compensation for velocity of sound changes due to temperature changes.\nThe Aquatrak wave guide has a series of thermistors placed along its length. The bottom one is always submerged and is used to measure the seawater temperature..The top one is placed just below the Sensor and the others evenly spaced along the length of the waveguide. The temperature readings from these can be used to compensate for the change in the velocity of sound due to density changes. This feature has not yet been used.\nThe Druck Sensor has a single thermistor placed beside it which measures seawater temperature.\n\nSystem Components. \n\nThe Aquatrak Installation houses four main components.\n1. The Aquatrack Sensor and Waveguide Assembly. The sensor itself is in a waterproof plastic tube with a cable with a waterproof connector which plugs into the Bartek controller.\n2. The Bartek Controller, housed in a waterproof diecast box with waterproof connectors. This lies in the centre of the installation housing.\n3. The Platypus Engineering Datalogger \n4. The Battery, a 15 Ah, 12 volt sealed gel cell lead acid battery. It is charged from the solar a diode. The battery lies in the main housing opposite the Datalogger .\n\nThe Druck Installation houses four main components\n1. The Druck Pressure Sensor, fitted to the end of a 13 metre cable, submerged in seawater about 10 metres down the borehole.\nThe cable has five conductors and an air vent enclosed within it.\n2. The Pressure Sensor Amplifier housed in a waterproof diecast box. This box has a vent leading to a vented bottle filled with silica gel to keep the transducer air vent dry.\n3. A Datalogger As above.\n4. A battery as above \n\nThe Solar Panel Installation has three main parts.\n1. Three Photo Voltaic Solar Panels, two 60 Watt and one 30 Watt. These are mounted on an aluminium frame attached to a hotdip galvanised steel frame with insulating bolts. \n2. A sealed plastic box mounted below the panels containing a12V 24 Ah Battery and a regulator and the radio modem equipment. (The modems are not currently fitted.)\n3. Antennae and cables protected with flexible conduit.\nData Retrieval\n\nData have been retrieved at approximately 30 day intervals from the Garden Cove gauges by using a portable computer to download the data loggers. The connector for this is in the enclosure by the solar panels allowing the loggers to be accessed during bad weather.\n\nDocumentation dated 2008-10-17\n1. In April 2007, the dataloggers and radio modems at Macquarie Island Tide Gauge site were replaced with Campbell Scientific CR1000 dataloggers.\n2. This change enabled data to be streamed from the pressure sensor datalogger every 30 seconds.\n3. There has been no change to scaling of records from the Aquatrak sensor as generation of ranges is done by the Aquatrak controller, the datalogger only saving and transmitting the records.\n Records from the pressure sensor however are now not converted to heights but saved and streamed as raw A/D conversion values.\n It is intended that appropriate scales and offsets for this sensor be derived after a Floating GPS Buoy exercise.\n4. Data is streamed from the pressure sensor logger as this is the only sensor that can be supply 30 seconds average values.\n This logger also streams 3 minute average values.\n5. The aquatrak sensor logger streams 3 minute average value ranges.\n6. Data is streamed in NVP (name/Value Pair) format as defined by BoM.\n7. Embedded in the streams are battery voltage and aquatrak waveguide temperature values.", "links": [ { diff --git a/datasets/MagMix_0.json b/datasets/MagMix_0.json index 89e5a39dd1..e28ce79219 100644 --- a/datasets/MagMix_0.json +++ b/datasets/MagMix_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MagMix_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estuarine and coastal systems play important roles in society, serving as port facilities, productive fisheries and rookeries, and scenic recreational areas. However, these same values to society mean that these areas can be significantly affected by human activities. Inputs of nutrients, organic matter, and trace metals are among these impacts. The MagMix project seeks to understand the transport and cycling of nutrients and trace elements and relate that to biogeochemical and optical properties in river-dominated coastal systems. The area of study is the outflow region of the Mississippi and Atchafalaya rivers in the northern Gulf of Mexico. The Mississippi River carries high concentrations of plant nutrients derived from fertilizer use on farms in the heartland of the US. These excess nutrients stimulate plant growth in the surface waters of the Louisiana Shelf. These plants, in turn, sink to the bottom waters of the shelf where they serve as food for respiring organisms. The input of this excess food then stimulates an excess of respiration thereby depleting the shelf bottom waters of oxygen during the summer. These oxygen-depleted (or hypoxic) waters then become a dead zone avoided by animals. The overall goal of this research project is to better understand the mixing processes and their relationship to optical and biogeochemical properties as the waters of the Mississippi River and the Atchafalaya River enter the Gulf of Mexico.", "links": [ { diff --git a/datasets/Main_Melt_Onset_Dates_1841_1.1.json b/datasets/Main_Melt_Onset_Dates_1841_1.1.json index 83d6696304..0e92ca2b34 100644 --- a/datasets/Main_Melt_Onset_Dates_1841_1.1.json +++ b/datasets/Main_Melt_Onset_Dates_1841_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Main_Melt_Onset_Dates_1841_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.", "links": [ { diff --git a/datasets/MaineInvasives.json b/datasets/MaineInvasives.json index 2140f21b80..fabddcd150 100644 --- a/datasets/MaineInvasives.json +++ b/datasets/MaineInvasives.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MaineInvasives", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities.", "links": [ { diff --git a/datasets/Maps_AGB_North_Slope_AK_1565_1.json b/datasets/Maps_AGB_North_Slope_AK_1565_1.json index 5c63357117..470cde1b16 100644 --- a/datasets/Maps_AGB_North_Slope_AK_1565_1.json +++ b/datasets/Maps_AGB_North_Slope_AK_1565_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Maps_AGB_North_Slope_AK_1565_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.", "links": [ { diff --git a/datasets/Marine Debris Archive (MARIDA)_1.json b/datasets/Marine Debris Archive (MARIDA)_1.json index f69b12ae5b..cba90407b3 100644 --- a/datasets/Marine Debris Archive (MARIDA)_1.json +++ b/datasets/Marine Debris Archive (MARIDA)_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marine Debris Archive (MARIDA)_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nMarine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features (clear & turbid water, waves, etc.) and floating materials (Sargassum macroalgae, ships, natural organic material, etc) that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task.\n", "links": [ { diff --git a/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json b/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json index 731b94c7a5..e5cdd13de6 100644 --- a/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json +++ b/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marine Debris Dataset for Object Detection in Planetscope Imagery_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\nFloating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris on satellite imagery and pave the way to a global monitoring system for controlling and preventing the accumulation of marine debris in oceans.\nThis dataset consists of images of marine debris which are 256 by 256 pixels in size and labels which are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery which has a spatial resolution of approximately 3 meters. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items. Several studies were used for data collection and validation. While a small percentage of the dataset represents the coastlines of Ghana and Greece, most of the observations surround the Bay Islands in Honduras. The marine debris detection models created and the relevant code for using this dataset can be found [here](https://github.com/NASA-IMPACT/marine_debris_ML).\n", "links": [ { diff --git a/datasets/Marine_Debris_Bibliography_1.json b/datasets/Marine_Debris_Bibliography_1.json index 330c45094e..62b76e7ed0 100644 --- a/datasets/Marine_Debris_Bibliography_1.json +++ b/datasets/Marine_Debris_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marine_Debris_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine Debris Bibliography compiled by Frederique Olivier contains 210 records.\n\nThe fields in this dataset are:\nBibliography index\nSubset\nDate of Publication\nAuthor/s \nTitle\nSource\nArea\nKeywords \nAbstract", "links": [ { diff --git a/datasets/Marine_Plastics_Heard_Macquarie_1.json b/datasets/Marine_Plastics_Heard_Macquarie_1.json index 988cc331a6..9e79ffaa16 100644 --- a/datasets/Marine_Plastics_Heard_Macquarie_1.json +++ b/datasets/Marine_Plastics_Heard_Macquarie_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marine_Plastics_Heard_Macquarie_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project monitored plastics at the four-bays area on Heard Island and at Sandell Bay on Macquarie Island. It characterised plastics by infra-red spectroscopy both from the beach collection and small pieces from fur-seal stomachs and cormorant boluses. The aim was to assess human impact on the ocean by measuring plastic abundance and type.", "links": [ { diff --git a/datasets/Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1.json b/datasets/Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1.json index 20401a2eba..b8e1a2893f 100644 --- a/datasets/Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1.json +++ b/datasets/Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples were collected during the SAZ-Sense cruise (January - February 2007) in the Southern Ocean south of Tasmania, Australia on board RV Aurora Australis. Twenty four stations were sampled in an area between 43 oS to 54 oS and 140 oE to 155 oE. At 3 of the stations designated Process Stations 1, 2 and 3 repeated sampling was completed over a number of days to examine temporal variation. Process Stations 1 to 3 were located in the SAZ to the southwest of Tasmania, the PFZ and in the productive SAZ region southeast of Tasmania respectively, the latter being potentially representative of the future SAZ. Abundances of algae, bacteria, viruses and heterotrophic nanoflagellates were measured using flow cytometry and viral production was determined by an incubation based method. A dilution method was also used to determine grazing and viral lysis of the algae.", "links": [ { diff --git a/datasets/Marlon_Lewis_92_0.json b/datasets/Marlon_Lewis_92_0.json index 84a96ff692..4ed05f2938 100644 --- a/datasets/Marlon_Lewis_92_0.json +++ b/datasets/Marlon_Lewis_92_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marlon_Lewis_92_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from 3 drifting buoys deployed in fall, 1992. Two of the buoys were air launched near 140W, -999 degrees in the Pacific Ocean, and one was deployed in Monterey Bay attached to a fixed mooring. The fixed mooring was recovered and subjected to post-calibration.", "links": [ { diff --git a/datasets/Marn10k_1.json b/datasets/Marn10k_1.json index e4d508bfd5..8ee7620c04 100644 --- a/datasets/Marn10k_1.json +++ b/datasets/Marn10k_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Marn10k_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset details features of Marine Plain in the Vestfold Hills, Antarctica. The dataset includes coastline, 5 metre interval contours and lake shores. \nThese data were captured from aerial photography and are the basis of the Marine Plain Orthophoto Map published for the Australian Antarctic Division in 1993. This map is available from a URL provided in this metadata record.", "links": [ { diff --git a/datasets/Maryland_Temperature_Humidity_1319_1.json b/datasets/Maryland_Temperature_Humidity_1319_1.json index 06743c7647..b63b4a1a0c 100644 --- a/datasets/Maryland_Temperature_Humidity_1319_1.json +++ b/datasets/Maryland_Temperature_Humidity_1319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Maryland_Temperature_Humidity_1319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting.", "links": [ { diff --git a/datasets/MassBay_LongTerm.json b/datasets/MassBay_LongTerm.json index b8f102fbb0..99f8e22be6 100644 --- a/datasets/MassBay_LongTerm.json +++ b/datasets/MassBay_LongTerm.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassBay_LongTerm", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42\u00b0 22.6' N., 70\u00b0 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42\u00b0 9.8' N., 70\u00b0 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQHMOSAICSCDS_POLY.json b/datasets/MassGIS_GISDATA.COQHMOSAICSCDS_POLY.json index 0370f2d23c..c887efd6b7 100644 --- a/datasets/MassGIS_GISDATA.COQHMOSAICSCDS_POLY.json +++ b/datasets/MassGIS_GISDATA.COQHMOSAICSCDS_POLY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQHMOSAICSCDS_POLY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CD-ROM index scheme for the 2001 color ortho image MrSID mosaics.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm.json b/datasets/MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm.json index f678acf863..b043bb8282 100644 --- a/datasets/MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm.json +++ b/datasets/MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DVD index scheme for the 2001 color ortho image MrSID mosaics.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQHMOSAICS_POLY.json b/datasets/MassGIS_GISDATA.COQHMOSAICS_POLY.json index 4bf18408c0..4f2303607a 100644 --- a/datasets/MassGIS_GISDATA.COQHMOSAICS_POLY.json +++ b/datasets/MassGIS_GISDATA.COQHMOSAICS_POLY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQHMOSAICS_POLY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQMOSAICS2005_POLY.json b/datasets/MassGIS_GISDATA.COQMOSAICS2005_POLY.json index 4b3883f396..059863c828 100644 --- a/datasets/MassGIS_GISDATA.COQMOSAICS2005_POLY.json +++ b/datasets/MassGIS_GISDATA.COQMOSAICS2005_POLY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQMOSAICS2005_POLY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Index scheme for the 2005 color ortho image MrSID mosaics.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQMOSAICSCDS2005_POLY..json b/datasets/MassGIS_GISDATA.COQMOSAICSCDS2005_POLY..json index e01a418947..b86a8dada7 100644 --- a/datasets/MassGIS_GISDATA.COQMOSAICSCDS2005_POLY..json +++ b/datasets/MassGIS_GISDATA.COQMOSAICSCDS2005_POLY..json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQMOSAICSCDS2005_POLY.", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CD-ROM index scheme for the 2005 color ortho image MrSID mosaics.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY.json b/datasets/MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY.json index c0b45decc2..6f18318bd7 100644 --- a/datasets/MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY.json +++ b/datasets/MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DVD index scheme for the 2005 color ortho image MrSID mosaics.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.IMG_BWORTHOS.json b/datasets/MassGIS_GISDATA.IMG_BWORTHOS.json index 3b2f255411..a3328b1318 100644 --- a/datasets/MassGIS_GISDATA.IMG_BWORTHOS.json +++ b/datasets/MassGIS_GISDATA.IMG_BWORTHOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.IMG_BWORTHOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These medium resolution images provide a high-quality \"basemap\" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.IMG_COQ2001.json b/datasets/MassGIS_GISDATA.IMG_COQ2001.json index bae87085b1..0bb9c703c6 100644 --- a/datasets/MassGIS_GISDATA.IMG_COQ2001.json +++ b/datasets/MassGIS_GISDATA.IMG_COQ2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.IMG_COQ2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These medium resolution true color images are considered the new \"basemap\" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.IMG_COQ2005.json b/datasets/MassGIS_GISDATA.IMG_COQ2005.json index 6b0795e1ed..6c8a37a47b 100644 --- a/datasets/MassGIS_GISDATA.IMG_COQ2005.json +++ b/datasets/MassGIS_GISDATA.IMG_COQ2005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.IMG_COQ2005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These medium resolution true color images are considered the new \"basemap\" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health.", "links": [ { diff --git a/datasets/MassGIS_GISDATA.VCPEATLAND_POLY.json b/datasets/MassGIS_GISDATA.VCPEATLAND_POLY.json index 0c9ce23bd9..65807e6e2b 100644 --- a/datasets/MassGIS_GISDATA.VCPEATLAND_POLY.json +++ b/datasets/MassGIS_GISDATA.VCPEATLAND_POLY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MassGIS_GISDATA.VCPEATLAND_POLY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acidic Peatland Community Systems include evergreen forest and shrub bogs, Atlantic White Cedar (AWC) swamps and bogs, and shrub and graminoid fens. This data was created by starting with the DEP Wetlands, creating a new set of just the bog, coniferous and mixed forested wetland types, and then adding, deleting and changing polygon shapes and labels based on aerial photo interpretation of the 1999/2000 photos and field information. In some areas where this wetland layer did not exist, the wetlands were interpreted and digitized from the aerial photos. The Acidic Peatland datalayer is named VCPEATLAND_POLY in ArcSDE. This layer is part of the MassGIS Priority Natural Vegetation Communities dataset, which depicts the distribution of the eight natural community systems identified by the Massachusetts Natural Heritage and Endangered Species Program (NHESP) as most critical to the conservation of the Commonwealth\u201a\u00c4\u00f4s biological diversity (Barbour et al., 1998).", "links": [ { diff --git a/datasets/MatthewsVegetation_419_1.json b/datasets/MatthewsVegetation_419_1.json index f43c32f988..c99284b38a 100644 --- a/datasets/MatthewsVegetation_419_1.json +++ b/datasets/MatthewsVegetation_419_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MatthewsVegetation_419_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global digital data base of vegetation was compiled at 1 degree latitude by 1 degree longitude resolution, drawing on approximately 100 published sources. Vegetation data from varied sources were consistently recorded using the hierarchical UNESCO classification system. The raw data base distinguishes about 180 vegetation types that have been collapsed to 32.", "links": [ { diff --git a/datasets/Mawson_Escarpment_Geo_1.json b/datasets/Mawson_Escarpment_Geo_1.json index efabed2951..390c37325b 100644 --- a/datasets/Mawson_Escarpment_Geo_1.json +++ b/datasets/Mawson_Escarpment_Geo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mawson_Escarpment_Geo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are several ArcInfo coverages described by this metadata record - FRAME, GEOL, MAPGRID, SITES, STRLINE and STRUC (in that order). Each coverage is described below. The data is also provided as shapefiles and ArcInfo interchange files. \nThe data was used for the Mawson Escarpment Geology map published in 1998. This map is available from a URL provided in this metadata record. \n\nFRAME:\n\nThe coverage FRAME contains (arcs) and (polygon, label) and forms the limits of the data sets or map coverage of the MAWSON ESCARPMENT area of the AUSTRALIAN ANTARCTIC TERRITORY.\n\nThe purpose or intentions for this dataset is to form a cookie cutter for future data which may be aquired and require clipping to the map/data area.\n\nGEOL:\n\nThe coverage GEOL is historical geological data covering the MAWSON ESCARPMENT area.\n\nThe data were captured in ARC/INFO format and combined with geological outcrops that were accurately digitised over a March 1989 Landsat Thematic Mapper image at a scale of 1:100000.\nIt is not recomended that this data be used beyond this scale.\n\nThe coverage contains Arcs (lines) and polygons (polygon labels). These object are attributed as fully as possible in their .aat file for arcs and .pat for polygon labels and conform with the Geoscience Australia Geoscience Data Dictionary Version 98.04\n\nThe purpose or intentions for the dataset is that it become part of a greater geological database of the Australian Antarctic Territory.\n\n(1998-04-10 - 1998-06-30)\n\nMAPGRID:\n\nMAPGRID is a graticule that was generated as a 5 minute by 5 minute grid mainly to allow for good location/registration of source materials for digitising and adding some locational anno.mapgrat\n\nThis covers other function was to be used for a proof plot.\n\n(1998-04-22 - 1998-06-30)\n\nSITES:\n\nThe purpose or intentions for this dataset is to provide the approximate location of this historic data on sample sites in the MAWSON ESCARPMENT region of the AUSTRALIAN ANTARCTIC TERRITORY, for future expansion or more accurate positioning when improved records of location are found.\n\n(1998-05-11 - 1998-06-30)\n\nSTRLINE:\n\nThis Structural lines for geology coverage is named (STRLINE).\n\nThe purpose or intentions for the dataset is to have the linear structural features in their own coverage containing only structure which does not form polygon boundaries.\n\n(1998-05-28 - 1998-06-30)\n\nSTRUC:\n\nThis coverage called STRUC for structural measurements is a point coverage. It can be described as Mesoscopic structures at a site or outcrop.\n\nThe purpose or intentions for the dataset is to provide all the known structural point data information in the one coverage.\n\n(1998-05-28 - 1998-06-30)", "links": [ { diff --git a/datasets/Mawson_SAM_1.json b/datasets/Mawson_SAM_1.json index d1ee5f1bde..a700ab9a2a 100644 --- a/datasets/Mawson_SAM_1.json +++ b/datasets/Mawson_SAM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mawson_SAM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents topographic features and facilities at Mawson and its immediate environs. Feature types include buildings, masts, tanks, roads, coastline and contours (1 metre interval).\nThe data are included in the data available for download from a Related URL below.\nThe data conform to the SCAR Feature Catalogue which include data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 111.\nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.\n\nChanges have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added.\nAs a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).", "links": [ { diff --git a/datasets/Mawson_Tide_Gauges_2.json b/datasets/Mawson_Tide_Gauges_2.json index f0cdcaad3c..df7c7eb1a0 100644 --- a/datasets/Mawson_Tide_Gauges_2.json +++ b/datasets/Mawson_Tide_Gauges_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mawson_Tide_Gauges_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Over time there have been a number of tide gauges deployed at Mawson Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1992 to 2016. More recent data are described elsewhere.\n\nTide Gauge 1 (TG001)\n1992-03-05 - 1992-05-13\nThis folder contains monthly download files from the first deployment of a submerged tide gauge at Mawson in March 1992.\nThese files are ASCII hexadecimal files. They need to be converted to decimal.\nThe resultant values are absolute seawater pressures in mbar.\n\nTide Gauge 4 (TG004)\n1993-03-22 - 1999-12-29\nThis folder contains the following folders:-\nold_tidedata \n\tmonthly download files from the second deployment of a submerged tide gauge at Mawson in March 1993.\n\tThese files are ASCII hexadecimal files. They need to be converted to decimal.\n\tThe resultant values are absolute seawater pressures in mbar.\n\t\nraw\n\tmemory images from submerged tide gauge. file extension is memory bank number. \n\tThese files are processed by a utility called tgxtract.exe which creates files in same format as those in old_tidedata folder.\n\tThese file have extension .srt. They are then converted to decimal pressure values.\n\t\ninterim\n\tfiles produced during processing of .raw files.\noutput\n\toutput file (.srt) which have been sent to BoM.\n\nTide Gauge 13 (TG013)\n2014-06-04 - 2016-11-04\n\nTide Gauge 20 (TG020)\n1999-11-05 - 2009-12-21\nThis folder contains the following folders:-\n\nraw\n\tmemory images from submerged tide gauge. file extension is memory bank number. \n\tThese files are processed by a utility called tgxtract.exe which creates files in same format as original download format.\n\tThese file have extension .srt. \n\tThese files are ASCII hexadecimal files. They need to be converted to decimal.\n\tThe resultant values are absolute seawater pressures in mbar.\n\ninterim\n\tfiles produced during processing of .raw files.\noutput\n\toutput file (.srt) which have been sent to BoM.\n\nTide Gauge 41 (TG041)\n2008-03-02 - 2010-11-16\nThis folder contains the following folders:-\n\nraw\n\tmemory images from submerged tide gauge. file extension is memory bank number. \n\tThese files are processed by a utility called tgxtract.exe which creates files in same format as original download format.\n\tThese file have extension .srt. \n\tThese files are ASCII hexadecimal files. They need to be converted to decimal.\n\tThe resultant values are absolute seawater pressures in mbar.\n\ninterim\n\tfiles produced during processing of .raw files.\noutput\n\toutput file (.srt) which have been sent to BoM.\n\t\nDocumentation from older metadata record:\nDocumentation dated 2001-03-26\n\nMawson Submerged Tide Gauge\n\nThe gauge used at Mawson was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania.\nIt was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid.\nThe gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature range.\nThe overall accuracy of the system is better than +/- 3 mm for a known water density.\nData is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit ,a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved.\nThe first of these gauges were first deployed Mawson in early 1992 in a a mooring in Horseshoe Harbour. The gauge was found to have some communications problems and was removed in May 1992. Tidal records from 6/3/92 to present have been retrieved from it.\nA new gauge was deployed at Mawson in March 1993.\nData has been retrieved from these gauges irregularly since then. The records are complete since deployment except for a few days in late 1995. The loss was caused by a fault in the software which allows directory entries to overwrites data when the directory memory has been filled.\nThe first gauge used at Mawson in 1992 was refitted with a higher pressure transducer and was later deployed at Heard Island in Atlas Cove.\nConversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC .\nAs the current gauge is expected to require a new battery soon, a new mooring has been placed close to the original and a new gauge has been deployed. \nLevelling\nSeveral attempts have been made at precise levelling of the gauge.\nThe first was in the Summer of 1995/6. Roger Handsworth, Tom Gordon and Natasha Adams physically measured the level of the top of the gauge in its mooring and derived a reading when a known column of water was over the gauge. \nThe next attempt was in the Summer of 1996/7 when Roger Handsworth and Paul Delaney made timed water level measurements close to the gauge and the tide gauge benchmark. From this work, and from tidal records, a value for MSL for Mawson was derived.\n\nPermanent Gauge\n\nIn the summer of 1995/6 two possible sites for a permanent Aquatrak type tide gauge were identified.\nAs neither of these sites were approved, a survey in the Summer of 1996/7 identified two more suitable sites. One of these, the site at the base of East arm, near the Variometer Building, was approved and a bore hole was drilled to exit about 6 metres below MSL. A power cable was run from the variometer building to provide two phase 240V power to the site.\nA heated borehole liner containing an Aquatrak wave guide and a Druck pressure transducer was inserted into the bore hole. Two datalogger will be added to the installation in 2001 to complete the installation.\nA radio modem will be used to link the dataloggers to the AAD network.\n\nDocumentation dated 2008-10-17\nMawson\nA new submerged gauge ,TG41, was deployed at Mawson on 2008-03-03.\nSubmerged Tide gauge TG20 was removed on 2008-08-26.\nThere is a useful overlap of data between the gauges of about 104 days.\n\nThe dataloggers used in the shored based tide gauge installation have been replaced with Campbell Scientific CR1000 dataloggers.\n\nThe aquatrak shore based gauge at Mawson has not been operating since march 2008.\nThe shore base pressure gauge is still operating.", "links": [ { diff --git a/datasets/MawsonsHuts2008_2009_1.json b/datasets/MawsonsHuts2008_2009_1.json index e76d087486..2d0962392a 100644 --- a/datasets/MawsonsHuts2008_2009_1.json +++ b/datasets/MawsonsHuts2008_2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MawsonsHuts2008_2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "723 images where loaded into the AAD image library, \"Image Antarctica\" and attached to records in the Antarctic Heritage Register database. The images documented the condition of the interior and exterior of Mawsons Huts located at Cape Denison including the main hut, the absolute hut, the magnetograph hut and the transit hut during the 2007/2008 season and the 2008/2009 season. The images were taken in both high resolution jpgs as well as raw files. The camera used was a Nikon D80. Also included were images of conserved artefacts as well as details of the conservation treatments uploaded to the Antarctic Heritage Register Database and linked to specific catalogue records.\n\n2011-04-21 - the record was updated to include a file of data from the 2009/2010 season. Raw data from 2008/2009 and 2009/2010 have also been archived in the AADC servers, and are available to AAD personnel upon request.", "links": [ { diff --git a/datasets/Mawsons_Huts_Dataloggers_2.json b/datasets/Mawsons_Huts_Dataloggers_2.json index c3e6c04e6c..3335d6d2b3 100644 --- a/datasets/Mawsons_Huts_Dataloggers_2.json +++ b/datasets/Mawsons_Huts_Dataloggers_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mawsons_Huts_Dataloggers_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataloggers were installed in a number of locations inside and outside Mawson's Huts at Cape Denison. The dataloggers measure temperature and relative humidity for the purpose of helping gauge corrosivity in the huts. The data are used to assess whether the removal of ice and snow from inside the Hut is affecting the internal microclimate and, therefore, the condition of the building fabric and other artefacts. Currently the data are downloaded by the Research Centre for Materials Conservation and the Built Environment at the Australian Museum, Sydney. Copies of the data are stored in the Australian Antarctic Data Centre.\n\nThe fields in this dataset are:\n\nDate\nTime\nTemperature\nRelative Humidity\nThermocouple\nSite", "links": [ { diff --git a/datasets/Maxwell_Bay_Beaches_data.json b/datasets/Maxwell_Bay_Beaches_data.json index 8f8430c462..e154a904da 100644 --- a/datasets/Maxwell_Bay_Beaches_data.json +++ b/datasets/Maxwell_Bay_Beaches_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Maxwell_Bay_Beaches_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight.", "links": [ { diff --git a/datasets/McMurdo_Predator_Prey_Acoustics.json b/datasets/McMurdo_Predator_Prey_Acoustics.json index 6bdc0428af..613ee93ef9 100644 --- a/datasets/McMurdo_Predator_Prey_Acoustics.json +++ b/datasets/McMurdo_Predator_Prey_Acoustics.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "McMurdo_Predator_Prey_Acoustics", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica", "links": [ { diff --git a/datasets/McMurdo_Predator_Prey_Adelie_Penguins.json b/datasets/McMurdo_Predator_Prey_Adelie_Penguins.json index b42763858f..9f767d89fb 100644 --- a/datasets/McMurdo_Predator_Prey_Adelie_Penguins.json +++ b/datasets/McMurdo_Predator_Prey_Adelie_Penguins.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "McMurdo_Predator_Prey_Adelie_Penguins", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/).", "links": [ { diff --git a/datasets/Mean_Seasonal_LAI_1653_1.json b/datasets/Mean_Seasonal_LAI_1653_1.json index d57ff57fbe..9770e840e6 100644 --- a/datasets/Mean_Seasonal_LAI_1653_1.json +++ b/datasets/Mean_Seasonal_LAI_1653_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mean_Seasonal_LAI_1653_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2). The LAI3g version 2 (raw) data were first regridded from 1/12 x 1/12 degree to 0.25 x 0.25 degree resolution, then processed to remove missing and unreasonable values, scaled to obtain LAI values, and the bi-weekly LAI values were averaged for every month. Finally, the monthly long-term mean LAI (1981-2015) was calculated.", "links": [ { diff --git a/datasets/Medit_Ligurian_0.json b/datasets/Medit_Ligurian_0.json index 5a9c3ab8a7..b08cc2d9b9 100644 --- a/datasets/Medit_Ligurian_0.json +++ b/datasets/Medit_Ligurian_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Medit_Ligurian_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Mediterranean Sea, the Ligurian Sea near Northern Italy and Southern France, and off the western coast of South Africa.", "links": [ { diff --git a/datasets/Menz50k_1.json b/datasets/Menz50k_1.json index 56979a79a9..6c3d3263b5 100644 --- a/datasets/Menz50k_1.json +++ b/datasets/Menz50k_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Menz50k_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mount Menzies dataset is a topographic database.\nMount Menzies is situated within the Southern Prince Charles Mountains, surrounded by the Fisher Glacier.\nThe database contains natural features captured at a density appropriate to 1:50,000 scale. Features are represented as lines, points and polygons. The dataset includes a 20 metre interval contour coverage.\nThe data is available for download as shapefiles from a Related URL below.\n\nThe data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nEach feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/MetOpA_GOME2_SIF_V2_2292_2.json b/datasets/MetOpA_GOME2_SIF_V2_2292_2.json index 5690471111..47d029c52d 100644 --- a/datasets/MetOpA_GOME2_SIF_V2_2292_2.json +++ b/datasets/MetOpA_GOME2_SIF_V2_2292_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MetOpA_GOME2_SIF_V2_2292_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-A with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land on an orbital basis at a resolution of approximately 40 km x 80 km (before 15 July 2013) or 40 km x 40 km (since 15 July 2013). Data are provided for the period from 2007-02-01 to 2018-02-01. Each file contains daily raw and bias-adjusted solar-induced fluorescence, quality control information, and ancillary data. SIF measurements can provide information on vegetation's functional status, including light-use efficiency and global primary productivity, which can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/MetOpB_GOME2_SIF_2182_1.json b/datasets/MetOpB_GOME2_SIF_2182_1.json index cd637d6935..5452f329c3 100644 --- a/datasets/MetOpB_GOME2_SIF_2182_1.json +++ b/datasets/MetOpB_GOME2_SIF_2182_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MetOpB_GOME2_SIF_2182_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-B with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land (observations up to 75-degree solar zenith angle) at a resolution of approximately 40 km x 80. Data are provided for the period from 2013-04-01 to 2021-06-07. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data. SIF measurements can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy owing to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF (*.nc) format.", "links": [ { diff --git a/datasets/Meteorological_1065_1.json b/datasets/Meteorological_1065_1.json index de06928af7..b586eb93f8 100644 --- a/datasets/Meteorological_1065_1.json +++ b/datasets/Meteorological_1065_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Meteorological_1065_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model. ", "links": [ { diff --git a/datasets/Meteorology_Log_Commonwealth_Bay_1977_1978_1.json b/datasets/Meteorology_Log_Commonwealth_Bay_1977_1978_1.json index 4c57dd257d..c5ade8999b 100644 --- a/datasets/Meteorology_Log_Commonwealth_Bay_1977_1978_1.json +++ b/datasets/Meteorology_Log_Commonwealth_Bay_1977_1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Meteorology_Log_Commonwealth_Bay_1977_1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914.\n\nThe hard copy of the log has been archived by the Australian Antarctic Division library.", "links": [ { diff --git a/datasets/Methane_Ebullition_Lakes_AK_1861_1.json b/datasets/Methane_Ebullition_Lakes_AK_1861_1.json index 34a30c721a..b9d0fc2b50 100644 --- a/datasets/Methane_Ebullition_Lakes_AK_1861_1.json +++ b/datasets/Methane_Ebullition_Lakes_AK_1861_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Methane_Ebullition_Lakes_AK_1861_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils.", "links": [ { diff --git a/datasets/Methane_Ethane_MA_NH_1982_1.json b/datasets/Methane_Ethane_MA_NH_1982_1.json index 12b8978d50..a537cde57f 100644 --- a/datasets/Methane_Ethane_MA_NH_1982_1.json +++ b/datasets/Methane_Ethane_MA_NH_1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Methane_Ethane_MA_NH_1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS. Background CH4 concentrations for the urban sites were determined using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model trajectories at the boundary of the study region based on measurements at three boundary sites and wind direction from the North American Mesoscale Forecast System (NAM) 12-kilometer meteorology.", "links": [ { diff --git a/datasets/Methane_Flaring_Sites_VIIRS_1874_1.json b/datasets/Methane_Flaring_Sites_VIIRS_1874_1.json index cee205c4e5..d5dfe640fc 100644 --- a/datasets/Methane_Flaring_Sites_VIIRS_1874_1.json +++ b/datasets/Methane_Flaring_Sites_VIIRS_1874_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Methane_Flaring_Sites_VIIRS_1874_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains annual global flare site surveys from 2012-2019 derived from Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (SNPP) satellite. Gas flaring sites were identified from heat anomalies first estimated by the VIIRS Nightfire (VNF) algorithm from which high-temperature biomass burning and low-temperature gas flaring were separated based on temperature and persistence. Nightly observations for each flare site were drawn to determine their activity in the given calendar year. Data include flare location, temperature, and estimated flared gas volume; flaring data summarized by country; and KMZ files for viewing flaring locations in Google Earth. This dataset is valuable for measuring the current status of global gas flaring, which can have significant environmental impacts.", "links": [ { diff --git a/datasets/Microbiome_0.json b/datasets/Microbiome_0.json index 0edacee0a1..1f15cc5dd2 100644 --- a/datasets/Microbiome_0.json +++ b/datasets/Microbiome_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Microbiome_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tara microbiome is the latest Tara expedition focused on plankton. The Microbiome Mission will help us understand the services provided by this essential ecosystem of the Ocean, its microbiome, an increasingly crucial challenge for scientific research and is done in conjunction with the AtlantECO program where additional ships will collect similar variables.", "links": [ { diff --git a/datasets/Mid-latitude_soils_705_2.json b/datasets/Mid-latitude_soils_705_2.json index 045cc33842..cd8f23d0b3 100644 --- a/datasets/Mid-latitude_soils_705_2.json +++ b/datasets/Mid-latitude_soils_705_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Mid-latitude_soils_705_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid-Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values.", "links": [ { diff --git a/datasets/Missouri_Reservoirs_RSWQ_0.json b/datasets/Missouri_Reservoirs_RSWQ_0.json index 20f08025f5..7eb82ee6f1 100644 --- a/datasets/Missouri_Reservoirs_RSWQ_0.json +++ b/datasets/Missouri_Reservoirs_RSWQ_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Missouri_Reservoirs_RSWQ_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset comprises in-situ hyperspectral data acquired using the on-water approach (aka skylight-blocked approach), using a combination of a downwelling irradiance sensor and an upwelling radiance sensor. These sensors are specifically TriOS RAMSES hyperspectral radiometers, each associated with two calibration files. The data collection was conducted across different reservoirs in the state of Missouri USA. This NASA-funded project directly addresses how Earth-observing satellite data can better inform critical links between the biogeochemical and optical properties of inland waters. It achieves this by using satellite imagery and in-situ measurements from two long-running water quality monitoring programs in the state of Missouri that annually record more than one thousand measurements of nitrogen, phosphorus, chlorophyll-a, Secchi depth, particulate organic and inorganic matter, and cyanotoxins across 100 reservoirs.", "links": [ { diff --git a/datasets/MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3.json b/datasets/MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3.json index 97ce70f649..d0176706d1 100644 --- a/datasets/MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3.json +++ b/datasets/MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.3 is an updated product of WetCHARTs v1.3.1 dataset. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/Monthly_Hydrological_Fluxes_1647_1.json b/datasets/Monthly_Hydrological_Fluxes_1647_1.json index 6e95fc5b64..83a1b9aa66 100644 --- a/datasets/Monthly_Hydrological_Fluxes_1647_1.json +++ b/datasets/Monthly_Hydrological_Fluxes_1647_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Monthly_Hydrological_Fluxes_1647_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average.", "links": [ { diff --git a/datasets/MultiInstrumentFusedXCO2_3.json b/datasets/MultiInstrumentFusedXCO2_3.json index 3b7cd32440..b55a4f78ed 100644 --- a/datasets/MultiInstrumentFusedXCO2_3.json +++ b/datasets/MultiInstrumentFusedXCO2_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MultiInstrumentFusedXCO2_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.\n", "links": [ { diff --git a/datasets/MultiInstrumentFusedXCO2_4.json b/datasets/MultiInstrumentFusedXCO2_4.json index ca93dbbda3..d9a0b90c49 100644 --- a/datasets/MultiInstrumentFusedXCO2_4.json +++ b/datasets/MultiInstrumentFusedXCO2_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MultiInstrumentFusedXCO2_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.\n", "links": [ { diff --git a/datasets/MumfordCove_0.json b/datasets/MumfordCove_0.json index 28c4e30892..e573d1acd9 100644 --- a/datasets/MumfordCove_0.json +++ b/datasets/MumfordCove_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "MumfordCove_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in and around Mumford Cove, Connecticut since 2015.", "links": [ { diff --git a/datasets/N01_0.json b/datasets/N01_0.json index 27ebd84189..7ab4da28c2 100644 --- a/datasets/N01_0.json +++ b/datasets/N01_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N01_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken along a zonal transect from Hawaii across the western Pacific Ocean in 2011.", "links": [ { diff --git a/datasets/N07_AVH02C1_6.json b/datasets/N07_AVH02C1_6.json index dbf9b9d801..179c20fffb 100644 --- a/datasets/N07_AVH02C1_6.json +++ b/datasets/N07_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N07_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-07 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05 Deg. CMG, short-name N07_AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N07_AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N07_AVH09C1_6.json b/datasets/N07_AVH09C1_6.json index 3362b50936..9746352c40 100644 --- a/datasets/N07_AVH09C1_6.json +++ b/datasets/N07_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N07_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-07 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N07_AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N07_AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N07_AVH13C1_6.json b/datasets/N07_AVH13C1_6.json index 96ed34a66f..bedb0d4e9a 100644 --- a/datasets/N07_AVH13C1_6.json +++ b/datasets/N07_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N07_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-07 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N07_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N07_AVH01C1). The N07_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N09_AVH02C1_6.json b/datasets/N09_AVH02C1_6.json index 9f477c6473..5b5f59f5d5 100644 --- a/datasets/N09_AVH02C1_6.json +++ b/datasets/N09_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N09_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-09 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05 Deg. CMG, short-name N09_AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N09_AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N09_AVH09C1_6.json b/datasets/N09_AVH09C1_6.json index a171aa1a74..1a06e08041 100644 --- a/datasets/N09_AVH09C1_6.json +++ b/datasets/N09_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N09_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-09 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N09_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N09_AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N09_AVH13C1_6.json b/datasets/N09_AVH13C1_6.json index c31b84a692..99b301311e 100644 --- a/datasets/N09_AVH13C1_6.json +++ b/datasets/N09_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N09_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-09 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N09_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N09_AVH01C1). The N09_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N11_AVH02C1_6.json b/datasets/N11_AVH02C1_6.json index 83fbd82bce..d84d2810fa 100644 --- a/datasets/N11_AVH02C1_6.json +++ b/datasets/N11_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N11_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-11 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05 Deg. CMG, short-name N11_AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N11_AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N11_AVH09C1_6.json b/datasets/N11_AVH09C1_6.json index 97e593356c..63934ed2d0 100644 --- a/datasets/N11_AVH09C1_6.json +++ b/datasets/N11_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N11_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-11 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg. CMG, short-name N11_AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The M1_AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N11_AVH13C1_6.json b/datasets/N11_AVH13C1_6.json index 0aa58716c2..1553877702 100644 --- a/datasets/N11_AVH13C1_6.json +++ b/datasets/N11_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N11_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-11 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N11_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N11_AVH01C1). The N11_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N14_AVH02C1_6.json b/datasets/N14_AVH02C1_6.json index 5dee9f0dd4..7e3816219c 100644 --- a/datasets/N14_AVH02C1_6.json +++ b/datasets/N14_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N14_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-14 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05Deg CMG, short-name N14_AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N14_AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N14_AVH09C1_6.json b/datasets/N14_AVH09C1_6.json index b379461db3..19f764cb5a 100644 --- a/datasets/N14_AVH09C1_6.json +++ b/datasets/N14_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N14_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-14 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N14_AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N14_AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N14_AVH13C1_6.json b/datasets/N14_AVH13C1_6.json index 9b683f0073..e0bdea5eef 100644 --- a/datasets/N14_AVH13C1_6.json +++ b/datasets/N14_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N14_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-14 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N14_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N14_AVH01C1). The N14_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N16_AVH02C1_6.json b/datasets/N16_AVH02C1_6.json index 0252494746..37349b89e2 100644 --- a/datasets/N16_AVH02C1_6.json +++ b/datasets/N16_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N16_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-16 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05Deg CMG, short-name N16_ AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N16_ AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N16_AVH09C1_6.json b/datasets/N16_AVH09C1_6.json index 9b17eea0e9..bad8d3b028 100644 --- a/datasets/N16_AVH09C1_6.json +++ b/datasets/N16_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N16_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-16 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N16_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N16_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N16_AVH13C1_6.json b/datasets/N16_AVH13C1_6.json index 7c1ac408e5..667f8b002a 100644 --- a/datasets/N16_AVH13C1_6.json +++ b/datasets/N16_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N16_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-16 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N16_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N16_AVH01C1). The N16_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N18_AVH02C1_6.json b/datasets/N18_AVH02C1_6.json index ea3af6afc8..8ec064db20 100644 --- a/datasets/N18_AVH02C1_6.json +++ b/datasets/N18_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N18_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-18 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05Deg CMG, short-name N18_ AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N18_ AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N18_AVH09C1_6.json b/datasets/N18_AVH09C1_6.json index 6037d48300..eb33ab5a78 100644 --- a/datasets/N18_AVH09C1_6.json +++ b/datasets/N18_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N18_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-18 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N18_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N18_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N18_AVH13C1_6.json b/datasets/N18_AVH13C1_6.json index ba09e2a71b..4a26583bf9 100644 --- a/datasets/N18_AVH13C1_6.json +++ b/datasets/N18_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N18_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-18 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05Deg CMG, short-name N18_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N18_AVH01C1). The N18_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N19_AVH02C1_6.json b/datasets/N19_AVH02C1_6.json index 3a6cb8d84d..85ad4e5e8c 100644 --- a/datasets/N19_AVH02C1_6.json +++ b/datasets/N19_AVH02C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N19_AVH02C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-19 AVHRR Top-of-Atmosphere Reflectance Daily L3 Global 0.05Deg CMG, short-name N19_ AVH02C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N19_ AVH02C1 consist of Top-of-atmosphere reflectance for bands 1 and 2, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), thermal data (thermal bands 3, 4 and 5), and additional data (scan time).\r\n", "links": [ { diff --git a/datasets/N19_AVH09C1_6.json b/datasets/N19_AVH09C1_6.json index d05c3e4b72..02f537bdb5 100644 --- a/datasets/N19_AVH09C1_6.json +++ b/datasets/N19_AVH09C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N19_AVH09C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\n\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-19 AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05Deg CMG, short-name N19_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The N19_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N19_AVH13C1_6.json b/datasets/N19_AVH13C1_6.json index 6af295ba1a..56e3714109 100644 --- a/datasets/N19_AVH13C1_6.json +++ b/datasets/N19_AVH13C1_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N19_AVH13C1_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: \r\nNOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.\r\nCurrently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.\r\n\r\nThe NOAA-19 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N19_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N19_AVH01C1). The N19_AVH13C1 product is available in HDF4 file format.\r\n", "links": [ { diff --git a/datasets/N21-VIIRS-L2P-ACSPO-v2.80_2.80.json b/datasets/N21-VIIRS-L2P-ACSPO-v2.80_2.80.json index 2465a6473b..0dcb2ed8fc 100644 --- a/datasets/N21-VIIRS-L2P-ACSPO-v2.80_2.80.json +++ b/datasets/N21-VIIRS-L2P-ACSPO-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N21-VIIRS-L2P-ACSPO-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.

\r\n\r\nVIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system (Jonasson et al. 2022). Data are reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. In ACSPO products, SSTs are derived using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels with quality level QL=5 are recommended. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5.

\r\n\r\nThe ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO N21 VIIRS L3U product is also available (10.5067/GHV21-3U280) (Ignatov et al., 2017).", "links": [ { diff --git a/datasets/N21-VIIRS-L3U-ACSPO-v2.80_2.80.json b/datasets/N21-VIIRS-L3U-ACSPO-v2.80_2.80.json index ca686ef7ee..091f539e9c 100644 --- a/datasets/N21-VIIRS-L3U-ACSPO-v2.80_2.80.json +++ b/datasets/N21-VIIRS-L3U-ACSPO-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "N21-VIIRS-L3U-ACSPO-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.

\r\n\r\nThe ACSPO N21 VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N21 VIIRS L2P product, also available at PO.DAAC (10.5067/GHV21-2P280). The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. There are 144 granules per 24 hour interval, with a total data volume of 0.6GB/day. Fill values are reported at all invalid pixels, including pixels >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SST, a subset of variable l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and the SST minus reference CMC SST (Canadian Met Centre 0.1deg L4 SST, 10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST.

\r\n\r\nThe ACSPO VIIRS L3U product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM).", "links": [ { diff --git a/datasets/NAAMES_0.json b/datasets/NAAMES_0.json index beb8a3b306..1838454921 100644 --- a/datasets/NAAMES_0.json +++ b/datasets/NAAMES_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the NAAMES (North Atlantic Aerosols and Marine Ecosystems Study) program.INTERNAL LINKS (Special datasets*)*Special datasets are not in SeaBASS format, and are thus only accesible via these links (they do not appear in the file search or lists of metadata)Altimetry re-analyses (maps of diagnostics e.g., integration of altimetry & maps of masks of water origin)Ship Underway Data (systems include: IMU, SAMOS, and SSW)EXTERNAL LINKSAircraft dataSatellite dataSeaBASS NAAMES pageSee URL below for the primary NAAMES website", "links": [ { diff --git a/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json b/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json index ce1fcbc059..37aefc7ceb 100644 --- a/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json +++ b/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_AerosolCloud_AircraftRemoteSensing_Data are remotely sensed cloud, aerosol and ocean optical measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json b/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json index 69edf0988b..eeb30cf280 100644 --- a/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json +++ b/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Aerosol_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Aerosol_AircraftInSitu_Data are in situ aerosol measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Aerosol_ShipInSitu_Data_1.json b/datasets/NAAMES_Aerosol_ShipInSitu_Data_1.json index acd4fced65..6ff24b1ccf 100644 --- a/datasets/NAAMES_Aerosol_ShipInSitu_Data_1.json +++ b/datasets/NAAMES_Aerosol_ShipInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Aerosol_ShipInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Aerosol_ShipInSitu_Data are in situ aerosol measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Cloud_AircraftInSitu_Data_1.json b/datasets/NAAMES_Cloud_AircraftInSitu_Data_1.json index 04e63cc286..271fadb209 100644 --- a/datasets/NAAMES_Cloud_AircraftInSitu_Data_1.json +++ b/datasets/NAAMES_Cloud_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Cloud_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Cloud_AircraftInSitu_Data are in situ cloud measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete. \r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Merge_Data_1.json b/datasets/NAAMES_Merge_Data_1.json index 19c3b90aba..8a2c4169d9 100644 --- a/datasets/NAAMES_Merge_Data_1.json +++ b/datasets/NAAMES_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Merge_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) pre-generated aircraft merge data files created using data collected during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_MetNav_AircraftInSitu_Data_1.json b/datasets/NAAMES_MetNav_AircraftInSitu_Data_1.json index c849b8188a..5c908e6006 100644 --- a/datasets/NAAMES_MetNav_AircraftInSitu_Data_1.json +++ b/datasets/NAAMES_MetNav_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_MetNav_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_MetNav_ShipInSitu_Data_1.json b/datasets/NAAMES_MetNav_ShipInSitu_Data_1.json index f983e1ef75..2f9e1f52df 100644 --- a/datasets/NAAMES_MetNav_ShipInSitu_Data_1.json +++ b/datasets/NAAMES_MetNav_ShipInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_MetNav_ShipInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_MetNav_ShipInSitu_Data are in situ meteorological and navigational measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Met_SondeInSitu_Data_1.json b/datasets/NAAMES_Met_SondeInSitu_Data_1.json index 72a8caa0af..85f5333b90 100644 --- a/datasets/NAAMES_Met_SondeInSitu_Data_1.json +++ b/datasets/NAAMES_Met_SondeInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Met_SondeInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Met_SondeInSitu_Data are meteorological radiosonde measurements collected via radiosonde launches during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015 and May 11, 2016 \u2013 June 5 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Misc_Ship_Data_1.json b/datasets/NAAMES_Misc_Ship_Data_1.json index ac25863df5..adbfa452e2 100644 --- a/datasets/NAAMES_Misc_Ship_Data_1.json +++ b/datasets/NAAMES_Misc_Ship_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Misc_Ship_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Misc_Ship_Data are miscellaneous ship measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Ocean_AircraftRemoteSensing_Data_1.json b/datasets/NAAMES_Ocean_AircraftRemoteSensing_Data_1.json index aa8cfbee38..96589aded9 100644 --- a/datasets/NAAMES_Ocean_AircraftRemoteSensing_Data_1.json +++ b/datasets/NAAMES_Ocean_AircraftRemoteSensing_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Ocean_AircraftRemoteSensing_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Ocean_AircraftRemoteSensing_Data are remotely sensed ocean measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Related ocean property measurements are available in the NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_Radiation_AircraftInSitu_Data_1.json b/datasets/NAAMES_Radiation_AircraftInSitu_Data_1.json index 7e61511234..92e2d9c57c 100644 --- a/datasets/NAAMES_Radiation_AircraftInSitu_Data_1.json +++ b/datasets/NAAMES_Radiation_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_Radiation_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_Radiation_AircraftInSitu_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in-situ radiation data collected onboard the C-130 aircraft during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_TraceGas_AircraftInSitu_Data_1.json b/datasets/NAAMES_TraceGas_AircraftInSitu_Data_1.json index 301ebb58d1..c2614e9a3a 100644 --- a/datasets/NAAMES_TraceGas_AircraftInSitu_Data_1.json +++ b/datasets/NAAMES_TraceGas_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_TraceGas_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_TraceGas_AircraftInSitu_Data are in situ trace gas measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete. \r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAAMES_TraceGas_ShipInSitu_Data_1.json b/datasets/NAAMES_TraceGas_ShipInSitu_Data_1.json index 4ba0ea7e01..fc88b505b1 100644 --- a/datasets/NAAMES_TraceGas_ShipInSitu_Data_1.json +++ b/datasets/NAAMES_TraceGas_ShipInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAAMES_TraceGas_ShipInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NAAMES_TraceGas_ShipInSitu_Data are in situ trace gas measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.\r\n\r\nThe NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.", "links": [ { diff --git a/datasets/NAB08_0.json b/datasets/NAB08_0.json index c019132d6b..b2b4b2f716 100644 --- a/datasets/NAB08_0.json +++ b/datasets/NAB08_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAB08_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the North Atlantic Bight in 2008.", "links": [ { diff --git a/datasets/NABE_0.json b/datasets/NABE_0.json index ed6a626e9b..e2c2bde3a1 100644 --- a/datasets/NABE_0.json +++ b/datasets/NABE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NABE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the Atlantic Coast of Europe as part of the North Atlantic Bloom Experiment (NABE) in 1989.", "links": [ { diff --git a/datasets/NACP_ACES_V2_1943_2.json b/datasets/NACP_ACES_V2_1943_2.json index 7447ce6a87..60dce599f4 100644 --- a/datasets/NACP_ACES_V2_1943_2.json +++ b/datasets/NACP_ACES_V2_1943_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_ACES_V2_1943_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels at 1-km resolution for the coterminous United States (CONUS) covering the years 2012 through 2017. Emissions from the ACES model are reported for ten distinct emissions source sectors: Airports and Aircraft, Commercial Buildings, Electric Power Generation facilities, Industrial point and non-point sources, Commercial Marine Vessels, Nonroad vehicles and equipment, Oil and Gas wells and facilities, Onroad vehicles, Railway engines and yards, and Residential buildings. All emissions are reported hourly on a 1-km x 1-km spatial grid. The data are provided in NetCDF version 4 format.", "links": [ { diff --git a/datasets/NACP_BlackSpruce_Burn-Severity_1331_1.json b/datasets/NACP_BlackSpruce_Burn-Severity_1331_1.json index 7c7309a302..aec2ff1502 100644 --- a/datasets/NACP_BlackSpruce_Burn-Severity_1331_1.json +++ b/datasets/NACP_BlackSpruce_Burn-Severity_1331_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_BlackSpruce_Burn-Severity_1331_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides organic soil layer characteristics, estimated carbon content, and soil depth measurements made at four black spruce stands in interior Alaska that had burned twice in the last 37-52 years (intermediate-interval fire events). The most recent fires occurred in 2004, 2005, and 2010. Measurements of soil depth and distance from the adventitious roots to the soil, and total organic matter are also included for unburned black spruce sites adjacent to the burned sites dominated by live, intermediate-aged (~37-52 years) black spruce trees.", "links": [ { diff --git a/datasets/NACP_Boreal_Biome_Biomass_1273_1.json b/datasets/NACP_Boreal_Biome_Biomass_1273_1.json index daf7b97412..76bc5adc26 100644 --- a/datasets/NACP_Boreal_Biome_Biomass_1273_1.json +++ b/datasets/NACP_Boreal_Biome_Biomass_1273_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Boreal_Biome_Biomass_1273_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of Alaska and western and eastern Canada, roughly between 45 and 70 degrees N. The study focused on within-growing-season data, i.e. leaf-on conditions.The AGB estimates were derived from a series of models that first related ground-based measured biomass to Portable Airborne Laser System (PALS) LiDAR measurements, and a second set of models that related the airborne estimates of biomass to Geoscience Laser Altimeter System (GLAS) LiDAR canopy structure measurements. The GLAS LiDAR biomass estimates were extrapolated by land cover types and ecoregions across the entire biome area.The study compiled remotely sensed forest structure data collected in June of 2005 and 2006 from the GLAS LiDAR instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite and from the PALS airborne instrument flown at various times from 2005-2009 over both the ground plots and the ICESat GLAS flight path. For a consistent biome-level analysis, ecoregions contained within the boreal forest biome were identified by the World Wildlife Fund's (WWF) ecoregion map of the world (Olson et al., 2001). Land cover maps were used to identify land cover types for stratification purposes within eco-regions. Land cover data for Canada were provided by the Earth Observations for Sustainable Development (EOSD) project centered on year 2000, with images from 1999 to 2002. The National Land Cover Data (NLCD) 2001 classification was used for Alaska based on data collected between 1999 and 2004. The ground-based measurements are not provided with this data set.", "links": [ { diff --git a/datasets/NACP_Forest_Biophysical_1046_1.json b/datasets/NACP_Forest_Biophysical_1046_1.json index 4533c412a7..112bc0e6e6 100644 --- a/datasets/NACP_Forest_Biophysical_1046_1.json +++ b/datasets/NACP_Forest_Biophysical_1046_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Forest_Biophysical_1046_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes biophysical measurements collected in 2009 from five New England experimental forest stations: Bartlett Experimental Forest, Harvard Forest, Howland Research Forest, Hubbard Brook Experimental Forest, and the Penobscot Experimental Forest. Howland measurements were repeated in 2010 and one site in the Sierra National Forest, California, was surveyed in 2008. Biomass in respective measurement plots was calculated with allometric equations using measured diameter at breast height (DBH) for trees greater than 10 cm and species identification. Within selected subplots, the number of stems with diameters less than 10 cm were counted and classified to allow for an estimate of biomass for these stems. There are 16 data files provided that present the biophysical measurement results and the biomass estimates in ASCII comma-separated format. For a subset of sites and plots (Bartlett Experimental Forest, Harvard Forest and Howland Research Forest), more intensive inventories were done in coordination with Echidna lidar imaging (Strahler et al., 2008). In these intensive collections, the stem location, species, DBH and live/dead status were recorded for all stems with total stem height and canopy dimensions recorded for every tenth stem. In addition, for stems below 10 cm DBH, species and count were recorded in a subplot of each intensive inventory plot. See the related data set Strahler et al., 2011.Investigators from Federal and university laboratories conducted these field campaign to make estimates of forest biophysical attributes that will prove useful in comparisons with airborne lidar (LVIS) and UAVSAR remote sensing acquisitions. The North American Carbon Program (NACP) is a multi-disciplinary research program designed to obtain scientific understanding of North America's carbon sources and sinks and of the changes in carbon stocks needed to meet societal concerns, and to provide tools for decision makers. NACP began in 2002 and continues to date. The NACP data collection contains continental carbon budgets, dynamics, processes, and management of the sources and sinks of carbon dioxide, methane, and carbon monoxide in North America and in adjacent ocean regions. ", "links": [ { diff --git a/datasets/NACP_Forest_Conservation_1662_1.json b/datasets/NACP_Forest_Conservation_1662_1.json index 6de713bf35..2828c8641e 100644 --- a/datasets/NACP_Forest_Conservation_1662_1.json +++ b/datasets/NACP_Forest_Conservation_1662_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Forest_Conservation_1662_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains annual estimates of carbon stocks, fluxes, and productivity over forested land in 11 states of the western USA (Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). The estimates were produced from multiple simulations using the Community Land Model (v4.5) with different climate forcing data and prescribed harvest rates. Business as usual (BAU) scenarios were run to ensure that the simulations represented present-day stand ages. The estimates span two modeled time periods, 1979-2014 and 2015-2099, at 1/24-degree (4 km x 4 km) resolution. Variables included are gross primary production (GPP), net ecosystem exchange (NEE), net ecosystem productivity (NEP), net primary production (NPP), autotrophic respiration (RA), heterotrophic respiration (RH), transpiration factor, aboveground live tree carbon, carbon loss from fire, allocation to stem carbon, and burned area fraction over forested areas of the western USA.", "links": [ { diff --git a/datasets/NACP_GHG_Data_Compilation_1206_1.json b/datasets/NACP_GHG_Data_Compilation_1206_1.json index fdadabf1ea..cf429c99b1 100644 --- a/datasets/NACP_GHG_Data_Compilation_1206_1.json +++ b/datasets/NACP_GHG_Data_Compilation_1206_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_GHG_Data_Compilation_1206_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a collection of measurements of carbon dioxide (CO2) and non-CO2 greenhouse gases made across North America by nine independent atmospheric monitoring networks from 2000 - 2009. During this North American Carbon Program (NACP) sponsored activity, data were compiled from the following networks: AGAGE, COBRA, CSIRO, INTEX-A, INTEX B, Irvine Latitude Network, NOAA CMDL, SCRIPPS, and Stanley Tyler-UC Irvine. The files presented here are the products of merging multiple original measurement results files for selected sites across North America from each monitoring network. The primary focus of this effort was the compilation of non-CO2 greenhouse gases over North America, but numerous CO2 observations are also included. The data files for each network are accompanied by detailed readme documentation files prepared by the respective network investigators. Project descriptions, objectives, references, sampling and analysis methods, and data file descriptions are included in these READMEs. Table 1 in the documentation displays the monitoring network sites, sample types, analytes, and links to the detailed network README files. Network- and laboratory-specific data citations are included in the README documentation and should be used to acknowledge the use of these data as appropriate. The data files for each monitoring network and each sampling type (continuous or flasks) have been combined into one compressed (*.zip) file along with the detailed README document. There are 17 compressed files that when expanded contain data files which represent one years data for that specific campaign and sampling method. The number of annual files that were compiled from a network into this collection varies.", "links": [ { diff --git a/datasets/NACP_MCI_CO2_Inventory_1205_1.json b/datasets/NACP_MCI_CO2_Inventory_1205_1.json index d94dc0b3a9..9c5a784b1f 100644 --- a/datasets/NACP_MCI_CO2_Inventory_1205_1.json +++ b/datasets/NACP_MCI_CO2_Inventory_1205_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MCI_CO2_Inventory_1205_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign. Emissions for the MCI region were compiled from these resources into nine inventory sources (Table 1):(1) forest biomass and soil carbon, harvested woody products carbon, and agricultural soil carbon from the U.S. Greenhouse Gas (GHG) Inventory (EPA, 2010; Heath et al., 2011);(2) high resolution data on fossil and biofuel CO2 emissions from Vulcan (Gurney et al,. 2009); (3) CO2 uptake by agricultural crops, lateral transport in crop biomass harvest, and livestock CO2 emissions using USDA statistics (West et al., 2011); (4) agricultural residue burning (McCarty et al., 2011);(5) CO2 emissions from landfills (EPA, 2012);(6) and CO2 losses from human respiration using U.S. Census data (West et al., 2009). The CO2 inventory in the MCI region was dominated by fossil fuel combustion, carbon uptake during crop production, carbon export in biomass (commodities) from the region, and to a lesser extent, carbon sinks in forest growth and incorporation of carbon into timber products. ", "links": [ { diff --git a/datasets/NACP_MCI_CO2_Inversions_1204_1.json b/datasets/NACP_MCI_CO2_Inversions_1204_1.json index ca023ab163..eb76677000 100644 --- a/datasets/NACP_MCI_CO2_Inversions_1204_1.json +++ b/datasets/NACP_MCI_CO2_Inversions_1204_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MCI_CO2_Inversions_1204_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of Net Ecosystem Exchange (NEE) flux for the U.S. Upper Midwest at 0.5-degree resolution for the year 2007. Estimates were produced by two atmospheric CO2 inversion systems (top-down), referenced as the continental Colorado State University (CSU) inversion and the mesoscale Pennsylvania State University (PSU) inversion. This modeling work was performed in support of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) experimental campaign in the U.S. Upper Midwest designed to evaluate innovative methods for CO2 flux inversion and data assimilation. The experiment was performed over a relatively flat, heavily managed agricultural landscape which features a high density of atmospheric CO2 observation measurements. Among the CO2 observations used by the inversion systems were results from a network of instrumented tall towers in the region. The NEE estimates were produced for comparison with CO2 fluxes derived from bottom-up inventory estimates.There are five data files with this data set. The NEE estimates are provided in two NetCDF files, one for each inversion system. Boundary CO2 inflow data used by each inversion system are provided in three comma-separated-format files (.csv).", "links": [ { diff --git a/datasets/NACP_MCI_CO2_Measurements_1202_1.json b/datasets/NACP_MCI_CO2_Measurements_1202_1.json index 994d6d1283..550e874a85 100644 --- a/datasets/NACP_MCI_CO2_Measurements_1202_1.json +++ b/datasets/NACP_MCI_CO2_Measurements_1202_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MCI_CO2_Measurements_1202_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides high precision and high accuracy atmospheric CO2 data from seven instrumented communication towers located in the U.S. Upper Midwest. The overall sampling period was from January 2007 through December 2009 although actual sampling dates vary within this time period for individual towers and sampling heights above ground level. The measurements were obtained in support of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign.The sampling network included: the five Ring 2 towers (Centerville (Iowa), Galesville (Wisconsin), Kewanee (Illinois), Mead (Nebraska), and Round Lake (Minnesota)) deployed and operated by PSU; the Missouri Ozarks (Missouri) co-located AmeriFlux site (PSU/Oak Ridge National Laboratory (ORNL)); and the Rosemount (Minnesota) tall tower trace gas observatory (University of Minnesota, Rosemount Research and Outreach Center (RROC)). Hourly CO2 dry mole fractions (in ppm) were averaged from measurements made at different above-ground levels on the towers and are reported in Coordinated Universal Time (UTC). For the five Ring 2 sites, daily daytime average CO2 dry mole fractions were also calculated, from hourly values between 12:00-17:00 local standard time and reported in UTC. There are seven compressed (.zip) data files and one comma-separated (.csv) file with this data set. Data quality flags are provided in each file. ", "links": [ { diff --git a/datasets/NACP_MCI_Crop_GPP_1217_1.json b/datasets/NACP_MCI_Crop_GPP_1217_1.json index 52ddf6038f..b2d5c39718 100644 --- a/datasets/NACP_MCI_Crop_GPP_1217_1.json +++ b/datasets/NACP_MCI_Crop_GPP_1217_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MCI_Crop_GPP_1217_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an integrated collection of (1) ground-based meteorological, radiometric, and vegetation measurements, (2) flux-based estimates of gross primary production (GPP), and (3) numerous vegetation indices derived from satellite imagery for three eddy covariance flux tower locations near Lincoln, Nebraska, USA. Land use surrounding the towers is cropland with corn and soybeans. Data are reported for selected days during the growing seasons of 2001 through 2008 only when ground-based crop canopy reflectance was measured. Algorithms developed to relate ground-based and satellite spectral information to GPP of the cropland adjacent to the towers are provided. AmeriFlux tower-based Level 2 measurements included photosynthetically active radiation (PAR), heat flux, and GPP estimates; see Section 2 for specific towers.Ground-based measurements on the corn and soybean vegetation surrounding the towers included total chlorophyll content (Chl) and leaf area index (LAI). Ground-based crop canopy reflectance was measured at 5.4 m above the corn and soybean canopy using hyperspectral radiometers (range 400 to 1100 nm) during the growing season from May to October in eight different years (2001-2008). This resulted in 173 measurement campaigns (18 in 2001, 31 in 2002, 34 in 2003, 31 in 2004, 21 in 2005, 15 in 2006, 14 in 2007, and 9 in 2008). Spectral bands from Landsat TM and ETM+, MERIS , and MODIS instruments were used to calculate vegetation indices. Vegetation indices related to chlorophyll can be used as a proxy for GPP because of the observed close relationship between GPP and Chl content in crops. Algorithms developed to relate spectral information to the GPP of the cropland adjacent to the towers are provided as companion files.", "links": [ { diff --git a/datasets/NACP_Modeled_NEE_NEP_Fluxes_1203_1.json b/datasets/NACP_Modeled_NEE_NEP_Fluxes_1203_1.json index 294058d0e3..a9287411c2 100644 --- a/datasets/NACP_Modeled_NEE_NEP_Fluxes_1203_1.json +++ b/datasets/NACP_Modeled_NEE_NEP_Fluxes_1203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Modeled_NEE_NEP_Fluxes_1203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides modeled carbon flux estimates at 8-km spatial resolution over North America for the year 2004 of (1) net ecosystem exchange (NEE) of carbon dioxide (CO2), (2) net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration), (3) stream evasion (CO2 emitted from streams and rivers), (4) emissions from harvested forest and agricultural products, and (5) emissions from biomass burning.Annual estimates, in g C/m2/year, are provided for all five fluxes. Daily estimates, in g C/m2/day, are provided for NEP and stream evasion fluxes. Fluxes for fire emissions, harvest decomposition/respiration, stream evasion, and NEP were derived as described in Section 5.NEE fluxes were estimated using a full bottom-up accounting of NEE produced by integrating emissions from harvested forest and agricultural products, CO2 emitted from streams and rivers, and biomass burning in the CarbonTracker (version 2011_oi) modeling system. NEE estimates were run in the forward mode through the CarbonTracker inversion setup that calculates CO2 uptake and release at the Earth's surface over time. Refer to Turner et al.(2013) for details.There are seven data files in NetCDF (.nc) format with this data set, including: five annual files for fire emissions, harvest decomposition/respiration, stream evasion, NEP, and NEE fluxes; and two daily files for NEP and stream evasion fluxes.", "links": [ { diff --git a/datasets/NACP_MsTMIP_Model_Driver_1220_1.json b/datasets/NACP_MsTMIP_Model_Driver_1220_1.json index b2ceeb46ba..de885a2018 100644 --- a/datasets/NACP_MsTMIP_Model_Driver_1220_1.json +++ b/datasets/NACP_MsTMIP_Model_Driver_1220_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MsTMIP_Model_Driver_1220_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental data that have been standardized and aggregated for use as input to carbon cycle models at global (0.5-degree resolution) and regional (North America at 0.25-degree resolution) scales. The data were compiled from selected sources (Table 2) and integrated into gridded global and regional collections of climatology variables (precipitation, air temperature, air specific humidity, air relative humidity (NA only), pressure, downward longwave radiation, downward shortwave radiation, and wind speed), time-varying atmospheric CO2 concentrations, time-varying nitrogen deposition, biome fraction and type, land-use and land-cover change, C3/C4 grasses fractions, major crop distribution, phenology, multiple soil characteristics, and a land-water mask. The temporal ranges of the data are sufficient for carbon cycle model simulations from 1801 to 2010. These data were compiled specifically for the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as the prescribed model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The standardized data provided consistent model inputs to minimize the inter-model variability caused by differences in environmental drivers and initial conditions. Together with the sensitivity simulations, the standardized input data enable better interpretation and quantification of structural and parameter uncertainties of model estimates. Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF-4 file formats. There are 3,152 *.nc4 data files with this data set. ", "links": [ { diff --git a/datasets/NACP_MsTMIP_Model_Structure_1228_1.json b/datasets/NACP_MsTMIP_Model_Structure_1228_1.json index 841c7dd0fc..ebffc5912d 100644 --- a/datasets/NACP_MsTMIP_Model_Structure_1228_1.json +++ b/datasets/NACP_MsTMIP_Model_Structure_1228_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MsTMIP_Model_Structure_1228_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a summary of the model structure and characteristics of participating models in the North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP), a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and the specific ways these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. These data are the result of a comprehensive survey of investigators responsible for each MsTMIP participating model. For a given characteristic (i.e., process/attribute), a model was assigned a binary value (0 or 1) indicating whether it included a particular characteristic; a value of one (1) was given if it considered or included that process, or a zero (0) if it did not. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. There is one data file with this data set in .csv format.", "links": [ { diff --git a/datasets/NACP_MsTMIP_TBMO_1225_1.json b/datasets/NACP_MsTMIP_TBMO_1225_1.json index 595b16687e..1f2258bf51 100644 --- a/datasets/NACP_MsTMIP_TBMO_1225_1.json +++ b/datasets/NACP_MsTMIP_TBMO_1225_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MsTMIP_TBMO_1225_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global gridded estimates of carbon, energy, and hydrologic fluxes between the land and atmosphere from 15 Terrestrial Biosphere Models (TBMs) in a standard format. Model estimates are at monthly and yearly time steps for the period 1900 to 2010, with a spatial resolution of 0.5 degree x 0.5 degree globally, excluding Antarctica.", "links": [ { diff --git a/datasets/NACP_MsTMIP_Unified_NA_SoilMap_1242_1.json b/datasets/NACP_MsTMIP_Unified_NA_SoilMap_1242_1.json index 2048313b8a..35caa9ff73 100644 --- a/datasets/NACP_MsTMIP_Unified_NA_SoilMap_1242_1.json +++ b/datasets/NACP_MsTMIP_Unified_NA_SoilMap_1242_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_MsTMIP_Unified_NA_SoilMap_1242_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.", "links": [ { diff --git a/datasets/NACP_NAM_HYSPLIT_Footprints_1586_1.json b/datasets/NACP_NAM_HYSPLIT_Footprints_1586_1.json index e221d544bd..5bd26369e9 100644 --- a/datasets/NACP_NAM_HYSPLIT_Footprints_1586_1.json +++ b/datasets/NACP_NAM_HYSPLIT_Footprints_1586_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_NAM_HYSPLIT_Footprints_1586_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset reports continuous atmospheric measurements of CO2 from two receptor sites and three boundary sites in and around Boston, Massachusetts, USA, that were combined with high-resolution CO2 emissions estimates and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to estimate regional CO2 emissions from September 2013 to December 2014. The HYSPLIT model followed an ensemble of 1,000 particles released at the urban CO2 measurement sites backward in time based on wind fields and turbulence from the North American Mesoscale Forecast System (NAM) at 12-km resolution to the boundary CO2 measurement sites to derive footprint values and CO2 enhancements expected from the prior emissions based on the Anthropogenic Carbon Emissions System (ACES) inventory and the urban-Vegetation Photosynthesis Respiration Model (urbanVPRM). This dataset contains three sets of data products: (1) observed hourly mean CO2 observations for two urban receptor sites in Boston, MA (Boston University (BU) and Copley Square (COP)), (2) observed hourly mean CO2 and calculated vertical profiles (50 - 5000 m) for three boundary sites around Boston including Harvard Forest at Petersham, MA (HF), Canaan, NH (CA), and Martha's Vineyard, MA (MVY), and modeled mean boundary CO2 concentrations for particles released from BU and COP, and (3) particle trajectory files including footprint values and CO2 enhancements above boundary CO2 concentrations from the HYSPLIT model.", "links": [ { diff --git a/datasets/NACP_PNW_Carbon_Balance_1317_1.json b/datasets/NACP_PNW_Carbon_Balance_1317_1.json index d87e81f359..2f4949c1c0 100644 --- a/datasets/NACP_PNW_Carbon_Balance_1317_1.json +++ b/datasets/NACP_PNW_Carbon_Balance_1317_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_PNW_Carbon_Balance_1317_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Biome-BGC modeled estimates of carbon stocks and fluxes in the U.S. Pacific Northwest for the years 1986-2010. Fluxes include net ecosystem production (NEP), and net aboveground wood growth. Stocks include aboveground wood mass. Also present are mapped distributions of associated forest disturbances, distinguished by disturbance type (harvest, fire, pest/pathogen). The data are presented in a mapped form as well as in tabular summaries broken out by ownership and ecoregion. Maps of annual precipitation and temperature data are included for the years 1980-2010.", "links": [ { diff --git a/datasets/NACP_PalEON_MIP_1779_1.json b/datasets/NACP_PalEON_MIP_1779_1.json index 3c9aa2739c..1a668ac7c3 100644 --- a/datasets/NACP_PalEON_MIP_1779_1.json +++ b/datasets/NACP_PalEON_MIP_1779_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_PalEON_MIP_1779_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset from the PalEON Ecosystem Model Intercomparison Project (PEMIP) provides harmonized regional environmental and meteorological drivers at a resolution of 0.5 degrees for the North-central and Northeastern U.S. over the time period 0850-01-01 to 2010-12-31. This dataset consists of the regional environmental and meteorological drivers. The environmental drivers include (1) dominant biome type, (2) plant functional type, (3) annual carbon dioxide concentration, (4) monthly carbon dioxide concentration, (5) land use-land cover change, (6) nitrogen concentrations, and (7) soil measurements. The meteorological drivers include (1) incident longwave radiation, (2) incident shortwave radiation, (3) precipitation, (4) surface pressure, (5) specific humidity, (6) air temperature, and (7) wind speed. The PEMIP is a coordinated effort to develop a set of terrestrial ecosystem model simulations with the ability to evaluate high-resolution ecophysiological causes and consequences of forest responses to climatic variability and change over the past millennium.", "links": [ { diff --git a/datasets/NACP_Peatland_Burn-Severity_1283_1.json b/datasets/NACP_Peatland_Burn-Severity_1283_1.json index 9a997452ac..342df6f667 100644 --- a/datasets/NACP_Peatland_Burn-Severity_1283_1.json +++ b/datasets/NACP_Peatland_Burn-Severity_1283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Peatland_Burn-Severity_1283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides landcover maps of (1) peatland type (bog, fen, marsh, swamp) with levels of biomass (open, forested) and (2) Burn Severity Index (BSI) (Dyrness and Norum, 1983) for four wildfire areas in northern Alberta, Canada. The four wildfire sites include the Utikuma fire site of 2011, Kidney Lake fire site of 2011, Fort McMurray west fire site of 2009, and Fort McMurray east fire site of 2009. The peatland classification at 12.5-m resolution (fen vs. bog including treed vs. open vs. shrubby) at each wildfire site was based on a pre-burn 2007 multi-date, multi-sensor fusion (Optical-IR, C-band and L-band SAR) approach. Over 350 field locations were sampled in central Alberta to train and validate the peatland type maps. The additional site, Wabasca, was an unburned site. Burn severity was measured in the field using the Burn Severity Index (BSI) (Dyrness and Norum 1987), a qualitative assessment of burnt moss that uses a 1-5 scale, with 1 being unburnt and 5 being severely burnt. The field data of ground consumption were correlated with Landsat pre- and post-burn imagery, specific to peatlands, to develop multivariate models for calculating burn severity and %-not-sphagnum-moss. These models were used to generate the Burn Severity Maps at 30-m resolution (percent unburned moss, and the burn severity index (BSI)). All sites were visited in 2013 for field measurements and the Utikuma site was also visited in 2012 for field measurements. Additional biophysical data for the various peatlands (aboveground biomass \u0096 tree and shrub, plant heights, density, etc. were collected and will be provided in another data set. ", "links": [ { diff --git a/datasets/NACP_Peatland_Land_Cover_MI_1513_1.json b/datasets/NACP_Peatland_Land_Cover_MI_1513_1.json index 52837478d7..9dbbc11933 100644 --- a/datasets/NACP_Peatland_Land_Cover_MI_1513_1.json +++ b/datasets/NACP_Peatland_Land_Cover_MI_1513_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Peatland_Land_Cover_MI_1513_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).", "links": [ { diff --git a/datasets/NACP_Regional_Model_GHG_Aggr_1179_1.json b/datasets/NACP_Regional_Model_GHG_Aggr_1179_1.json index 0960cc9055..fb91660988 100644 --- a/datasets/NACP_Regional_Model_GHG_Aggr_1179_1.json +++ b/datasets/NACP_Regional_Model_GHG_Aggr_1179_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Regional_Model_GHG_Aggr_1179_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided. The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files. Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models. ", "links": [ { diff --git a/datasets/NACP_Regional_Obs_Model_Grid_1157_1.json b/datasets/NACP_Regional_Obs_Model_Grid_1157_1.json index f96f6f467f..7bfc830cd8 100644 --- a/datasets/NACP_Regional_Obs_Model_Grid_1157_1.json +++ b/datasets/NACP_Regional_Obs_Model_Grid_1157_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Regional_Obs_Model_Grid_1157_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that were used in the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides five observation data files (MODIS GPP, MODIS NPP, FIA forest biomass/forest area, NASS crop NPP, and NASS agricultural land fraction) and simulation results from 18 terrestrial biosphere models (TBM) (28 variables; 114 files) and seven inverse models (IM) (two variables; 8 files). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) resampled original model simulation results and observation measurement data to 1-degree spatial resolution for North American region, interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format. ", "links": [ { diff --git a/datasets/NACP_Regional_Obs_Model_Orig_1193_1.json b/datasets/NACP_Regional_Obs_Model_Orig_1193_1.json index d0a375c35c..81e74a525d 100644 --- a/datasets/NACP_Regional_Obs_Model_Orig_1193_1.json +++ b/datasets/NACP_Regional_Obs_Model_Orig_1193_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Regional_Obs_Model_Orig_1193_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various investigator teams contributed to the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides nine (9) data packages of remote sensing and ground observation measurements (OM) (MODIS gross primary productivity (GPP), MODIS net primary production (NPP), MODIS fraction of photosynthetically active radiation (fPar), MODIS leaf area index (LAI), MODIS enhanced vegetation index (EVI), MODIS normalize difference vegetation index (NDVI), Forest Inventory and Analysis (FIA) forest biomass, National Agricultural Statistics Service (NASS) crop NPP, and Flux Anomaly). The data set also provides data packages of simulation results from 19 terrestrial biosphere models (TBM) and eight (8) inverse models (IM). The data packages are respectively OM, TBM, and IM data files listed in Tables 4-6. Each OM, TBM, and IM data package contains all of the original data (and documentation, if any) that the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) acquired or received. These originally-submitted data were processed by the MAST-DC to produce the three standardized gridded data sets of carbon flux for inter-comparison purposes (see Related Data Products below). These original data and documentation are provided to allow users of the standardized gridded data products to be able to trace back to the data origins when needed. The Data Center (ORNL DAAC) transformed some of the originally-submitted data files to file formats that are more suitable for long-term archiving. For example, *.xlsx files were saved as *.csv, ERDAS Imagine files were converted to GeoTIFFs, and MATLAB files were converted to GeoTIFF and NetCDF formats as appropriate. Files received in NetCDF, GeoTIFF, and HDF formats were not transformed. ", "links": [ { diff --git a/datasets/NACP_Regional_Obs_Model_Suppl_1158_1.json b/datasets/NACP_Regional_Obs_Model_Suppl_1158_1.json index ddd516ca6a..62c9a89c51 100644 --- a/datasets/NACP_Regional_Obs_Model_Suppl_1158_1.json +++ b/datasets/NACP_Regional_Obs_Model_Suppl_1158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Regional_Obs_Model_Suppl_1158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains standardized gridded observation data, terrestrial biospheric model output, and inverse model simulations that were compiled but not used in the North American Carbon Program (NACP) Regional Synthesis activities, thus the supplemental designation. The data set provides six (6) observation data packages (9 variables - MODIS LAI, MODIS FPAR, MODIS NDVI, MODIS EVI, FIA forest biomass, forest area, GPP Anomaly, NEE Anomaly, Reco Anomaly; 8 data files), output results from three terrestrial biosphere models (TBM) (14 variables; 214 files), and simulations from one inverse model (IM) (one variable; 1 file). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) original data files were resampled to 1-degree spatial resolution for North American region (except for FIA Forest Biomass which was resampled to 0.5-degree resolution), interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format.", "links": [ { diff --git a/datasets/NACP_Site_Model_Data_Orig_Fmt_1192_1.json b/datasets/NACP_Site_Model_Data_Orig_Fmt_1192_1.json index 553220c325..5e3e08c0e4 100644 --- a/datasets/NACP_Site_Model_Data_Orig_Fmt_1192_1.json +++ b/datasets/NACP_Site_Model_Data_Orig_Fmt_1192_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Site_Model_Data_Orig_Fmt_1192_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon Program (NACP) Site-Level Synthesis. The model teams generated estimates for, but not limited to, a minimum of six variables, including gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) for each of 47 selected eddy covariance flux tower sites across North America. Participating modeling teams followed the NACP Site Synthesis Protocol (site_synthesis_protocol_v7.pdf), which covers procedures, plans, and infrastructure for the site-level analyses. File format and units conversions of several data submissions were made by the MAST-DC to produce NetCDF files of consistent content and structure for all 24 TBM outputs. The model outputs are structured as described in Appendix A: Model Output Variables, of the Site Synthesis Protocol. In addition, MAST-DC processed these original model submissions to derive uniquely processed and formatted data files for model inter-comparison and evaluation (NACP Site: Terrestrial Biosphere Model and Aggregated Flux Data in Standard Format). This related data set provides GPP, NEE, LAI, Re, LE, and sensible heat (H) model output variables at the native half-hourly time step, and in daily, monthly, and annual aggregations. The related data set also contains gap-filled observations and total uncertainty estimates at the same time steps.There are 24 compressed (*.zip) files with this data set -- one file for each model. When expanded, the .zip files contain model output data files for flux tower sites in NetCDF and some in text formats.", "links": [ { diff --git a/datasets/NACP_Site_Model_Flux_Std_Fmt_1183_1.json b/datasets/NACP_Site_Model_Flux_Std_Fmt_1183_1.json index 4a1833daf3..de88cb4d7a 100644 --- a/datasets/NACP_Site_Model_Flux_Std_Fmt_1183_1.json +++ b/datasets/NACP_Site_Model_Flux_Std_Fmt_1183_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Site_Model_Flux_Std_Fmt_1183_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides standardized output variables for gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) from 24 terrestrial biosphere models for 47 eddy covariance flux tower sites in North America. Each model used standardized input data for each flux tower site (i.e., gap-filled, locally observed weather; land use history; and other site specific data) and followed standard model setup and spinup procedures. The files also contain gap-filled observations and total uncertainty estimates. The data set was compiled for the North American Carbon Program (NACP) Site-Level Synthesis for use in model inter-comparison and assessment of how well the models simulate carbon processes across vegetation types and environmental conditions in North America. There is one compressed (.zip) file with this data set. When expanded, the .zip file contains model output data for one variable at one site. The model output and observations are available at the native half-hourly time step, or in daily, monthly, and annual aggregations, in comma-separated text (.csv) format. ", "links": [ { diff --git a/datasets/NACP_Site_Tower_Met_and_Flux_1178_1.json b/datasets/NACP_Site_Tower_Met_and_Flux_1178_1.json index 705c6b687b..2f08fe5bc2 100644 --- a/datasets/NACP_Site_Tower_Met_and_Flux_1178_1.json +++ b/datasets/NACP_Site_Tower_Met_and_Flux_1178_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Site_Tower_Met_and_Flux_1178_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological, carbon cycle flux, phenology, and ancillary data measured at 47 eddy covariance flux tower sites across North America. The data were used by North American Carbon Program (NACP) Site-Level Synthesis as model driver data and for assessing how well 24 Terrestrial Biosphere Models simulated carbon processes across vegetation types and environmental conditions.*The meteorology data include eight variables: air temperature (K), specific humidity (kg/kg), wind speed (m/s), precipitation (kg/m2/s), surface pressure (Pa), surface incident shortwave radiation (W/m2), surface incident longwave radiation (W/m2), and CO2 concentration (ppm). Gap-filled data were used by modeling teams as input model driver data.*Measured fluxes of net ecosystem exchange (NEE) and derived gross primary productivity (GPP) and respiration (R) and respective calculated uncertainty estimates are provided for each tower site at the native time resolution of the observations (30 or 60-minute) as well as the diurnal, seasonal, and annual time scales. The data were gap-filled following a standard protocol. Components of uncertainty include uncertainties resulting from turbulence, gap-filling, flux partitioning, and u* threshold determination. Flux observations and uncertainty data were used to assess how well models simulated carbon processes.*Remotely sensed NDVI, LAI, and fPAR phenology data were derived from the GIMMS version g NDVI data set for each flux tower site. Phenology data were used by some modeling teams as input model driver data.*Ancillary data and information describe tower location and physical characteristics, disturbance history, and biological and ecological attributes of the vegetation, litter, and soil. These ancillary data were used by modeling teams as input model driver data.The data files are in both ASCII text and NetCDF formats (ALMA standard). The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). ", "links": [ { diff --git a/datasets/NACP_TERRA-PNW_1292_1.json b/datasets/NACP_TERRA-PNW_1292_1.json index f12c731606..4a80be66b9 100644 --- a/datasets/NACP_TERRA-PNW_1292_1.json +++ b/datasets/NACP_TERRA-PNW_1292_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_TERRA-PNW_1292_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements and estimates of leaf, tree, and soil data from six projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across forests in Oregon and Northern California. Included are standardized, integrated measurements and estimates of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. Plot-level measurements of forest composition, structure (e.g. tree biomass), and productivity estimates, as well as measurements of soil structure (e.g. bulk density) and chemistry (e.g. carbon) are also included.", "links": [ { diff --git a/datasets/NACP_Vista_CA_CH4_Inventory_1726_1.json b/datasets/NACP_Vista_CA_CH4_Inventory_1726_1.json index b9dcdc2fa6..9b5aac661d 100644 --- a/datasets/NACP_Vista_CA_CH4_Inventory_1726_1.json +++ b/datasets/NACP_Vista_CA_CH4_Inventory_1726_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Vista_CA_CH4_Inventory_1726_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides spatial data products with identified and organized locations of potential methane (CH4) emitting facilities and infrastructure spanning the State of California. These data products form a GIS-based mapping database designed to address shortcomings in current CH4 source inventories and is known as Vista California (Vista-CA). Vista-CA consists of detailed spatial maps for facilities and infrastructure in California that are known or expected sources of CH4 emissions and illustrates the spatial distribution of potential CH4 sources. Vista-CA spatial data sets were created utilizing an assortment of publicly available data sources from local, state, and federal agencies for the years 2005 to 2019. The final Vista-CA database contains over 230,000 entries, which are presented as fifteen CH4 emitting infrastructure maps. The database was used to support flight planning and source attribution for the California Methane Survey project.", "links": [ { diff --git a/datasets/NACP_Vista_LA_CH4_Inventory_1525_1.json b/datasets/NACP_Vista_LA_CH4_Inventory_1525_1.json index eee6959a6e..ef2caabf04 100644 --- a/datasets/NACP_Vista_LA_CH4_Inventory_1525_1.json +++ b/datasets/NACP_Vista_LA_CH4_Inventory_1525_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Vista_LA_CH4_Inventory_1525_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides spatial data products with identified and classified locations of potential methane (CH4) emitting facilities and infrastructure in the South Coast Air Basin (SoCAB). These data products form a GIS-based mapping database designed to address shortcomings in current urban CH4 source inventories and is known as Vista Los Angeles (Vista-LA). SoCAB is the air shed for the greater Los Angeles urban area, which includes urbanized portions of the Los Angeles, Orange, Riverside, and San Bernardino Counties, California, USA. Vista-LA consists of detailed spatial maps for facilities and infrastructure in the SoCAB that are known or expected sources of CH4 emissions and illustrates the spatial distribution of potential CH4 sources, representing a first step towards developing an urban-scale CH4 emissions gridded inventory for the SoCAB. Vista-LA spatial data sets were created utilizing an assortment of publicly available data sources from local, state, and federal agencies for the years 2012 to 2017. The final Vista-LA database contains over 33,000 entries, which are presented as thirteen CH4 emitting infrastructure maps.", "links": [ { diff --git a/datasets/NACP_Wild_Cropland_Fuel_Map_1163_1.json b/datasets/NACP_Wild_Cropland_Fuel_Map_1163_1.json index f131e3949a..1c68d67e7c 100644 --- a/datasets/NACP_Wild_Cropland_Fuel_Map_1163_1.json +++ b/datasets/NACP_Wild_Cropland_Fuel_Map_1163_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Wild_Cropland_Fuel_Map_1163_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set provides a 30-m comprehensive fuelbed characteristics map for both the wildland and cropland areas of the conterminous United States (CONUS) for 2010. This integrated product is the result of combining the spatially discrete Fuel Characteristic Classification System (FCCS) data of the US Forest Service (USFS) with the crop-and grassland-specific information of the US Department of Agricultures (USDA's) Cropland Data Layer (CDL). By combining the spatially discrete details of the FCCS data set with the crop-and grassland-specific information of the CDL, a more robust map of fuelbed characteristics is available. The merged product has an advantage over the original FCCS map for estimating emissions from burned areas due to the integration of the fuelbed characteristics for agricultural areas from the CDL.There are three GeoTIFF format files and three comma-separated companion files distributed with this data set. The three tif files provided are very large and exceed the size limits of a standard GeoTIFF file format (4 GB). File sizes range from 20 to 30 GB. Compressed file sizes range from 2 to 3 GB. They are in a format that is called a BigTIFF file. ArcGIS 10.0 and ERDAS Imagine are able to read these files.", "links": [ { diff --git a/datasets/NACP_Woody_Veg_N_Slope_AK_V2_1365_2.json b/datasets/NACP_Woody_Veg_N_Slope_AK_V2_1365_2.json index 1766987df9..fbf8c354ca 100644 --- a/datasets/NACP_Woody_Veg_N_Slope_AK_V2_1365_2.json +++ b/datasets/NACP_Woody_Veg_N_Slope_AK_V2_1365_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NACP_Woody_Veg_N_Slope_AK_V2_1365_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of (1) field measurements of woody vegetation (shrubs) at 26 diverse sites across the North Slope of Alaska during 2010 and 2011, (2) field-based statistical estimates of site shrub structural characteristics, (3) high-resolution panchromatic satellite imagery-based estimates of field site shrub characteristics using the Canopy Analysis with Panchromatic Imagery (CANAPI) model, and (4) adjusted CANAPI estimates of shrub characteristics at 1,013 selected sites widely distributed across the North Slope.", "links": [ { diff --git a/datasets/NAFD-NEX_Attribution_1799_1.json b/datasets/NAFD-NEX_Attribution_1799_1.json index 7c09dfb388..f384729b1f 100644 --- a/datasets/NAFD-NEX_Attribution_1799_1.json +++ b/datasets/NAFD-NEX_Attribution_1799_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAFD-NEX_Attribution_1799_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Characterizing the cause of forest canopy changes through time is fundamental to understanding current and future forest functions. A better understanding of forest dynamics can help build linkages between patterns and processes. The North American Forest Dynamics (NAFD) products provided in this dataset predict characteristics related to the cause of forest canopy cover losses for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. The characteristics are summarized in four separate data layers. The first layer labels the type of change event (stable-no change, removals, fire, stress, wind, conversion, other), the second labels the year of the event, the third and fourth layers measure dominance and diversity, measures of qualitative confidence metrics derived from the model predictions. For each pixel the maps depict the greatest magnitude event occurring between 1986-2010.", "links": [ { diff --git a/datasets/NAFD-NEX_Forest_Disturbance_1290_1.json b/datasets/NAFD-NEX_Forest_Disturbance_1290_1.json index ef541c6036..b0238c39f0 100644 --- a/datasets/NAFD-NEX_Forest_Disturbance_1290_1.json +++ b/datasets/NAFD-NEX_Forest_Disturbance_1290_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAFD-NEX_Forest_Disturbance_1290_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.", "links": [ { diff --git a/datasets/NAFD_Disturbance_1077_1.json b/datasets/NAFD_Disturbance_1077_1.json index db4970a745..8fecffa15f 100644 --- a/datasets/NAFD_Disturbance_1077_1.json +++ b/datasets/NAFD_Disturbance_1077_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAFD_Disturbance_1077_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of time-series analyses of Landsat imagery for 55 selected forested sites across the conterminous U.S.A. The output is a pair of disturbance data products for each site, one showing the first year of disturbance in the time series, the other showing the last year of disturbance. Each data pixel is labeled as either a static land class (persistent non-forest, persistent forest, or persistent water) or with the year of change for disturbed forest pixels. The time period analyzed is approximately 1984-2009.These forest disturbance data are distributed as a single band GeoTiff, with appropriate projection information defined within the file. The analyses were performed in three phases: 5 sites during the Prototype/Focal phase; 23 sites in Phase I; and 27 sites in Phase II. The spatial resolution of the Prototype/Focal and Phase I data is 28.5 meters. The spatial resolution of the Phase II data is 30 meters. The temporal resolution is nominally biennial. The mapped area for each forested site is approximately 185 km x 185 km. There are a total of 110 GeoTiff files - a first year and a last year disturbance file for each of the 55 sites.", "links": [ { diff --git a/datasets/NAIP.json b/datasets/NAIP.json index dd013d265c..7d2b1905dd 100644 --- a/datasets/NAIP.json +++ b/datasets/NAIP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAIP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.\n \nNAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This \"leaf-on\" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.", "links": [ { diff --git a/datasets/NALC.json b/datasets/NALC.json index 3e1fa71bb5..3086102dce 100644 --- a/datasets/NALC.json +++ b/datasets/NALC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NALC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The North American Landscape Characterization (NALC) project is a component of the Landsat Pathfinder Program, which is part of a larger Pathfinder Program initiated by the National Aeronautics and Space Administration (NASA) in 1989. The NALC project is a cooperative effort between NASA, the U.S. Environmental Protection Agency, and the U.S. Geological Survey to make Landsat data available to the widest possible user community for scientific research and for the general public interest. The objectives of the NALC project are to develop standardized remotely sensed data sets and analysis methods in support of investigations of changes in land cover, to develop inventories of terrestrial carbon stocks, to assess carbon cycling dynamics, and to map terrestrial sources of greenhouse gas (CO, CO2, CH4, and N2) emissions. The NALC data set is comprised of hundreds of triplicates (i.e., multispectral scanner (MSS) data acquired in the years 1973, 1986, and 1991 plus or minus 1 year, thus, the name triplicate). The NALC triplicates also include digital elevation model data. The specific temporal windows vary for geographical regions based on the seasonal characteristics of the vegetation cover. In accordance with the Landsat Pathfinder Program concept, the Pathfinder basic data sets are to be comprised of data which have had systematic radiometric and systematic geometric corrections applied to them. The NALC triplicates, however, are precision corrected for geocoding purposes.\n", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA_1.json index 8cb8018edb..76d56d1b0b 100644 --- a/datasets/NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 Air Chemistry, Particulate Matter (PM), and Meteorological Data product. This data product was obtained from July to September 1999 during the Atlanta Experiment of the U.S. EPA Particulate Matter Supersites Program. \r\n\r\nThe EPS selected Atlanta as one of the first Supersites Programs dedicated to the study of fine particles (or Particulate Matter (PM) 2.5). The Southern Oxidants Study (SOS) in conjunction with the Georgia Institute of Technology, Earth and Atmospheric Sciences Department developed and implemented the scientific research plan for this initial Supersites Program effort. The Atlanta field experiment was a 4-week long campaign aimed at comprehensively addressing issues related to the measurement and characterization of fine particles in the polluted or urban atmosphere. The experiment took place during the August 1999 and deployed a wide array of instrumentation at a measurement site located on Jefferson Street in Midtown Atlanta.\r\n\r\nGoals of the Atlanta Supersite Program were twofold: first, to provide a platform for testing and contrasting some of the newer particle measurement techniques; and second, to provide data to advance our scientific understanding of atmospheric processes regarding atmospheric particles. Specific objectives were: (1) to characterize the performance of emerging and/or state-of-the-science PM Measurements; (2) to compare and contrast similar and dissimilar PM Measurements; (3) to evaluate the precision, accuracy, and completeness of information that can be gained from the planned EPA PM mass and chemical composition networks; (4) to evaluate the scientific information gained by combining various independent and complementary PM Measurements; and (5) to address various scientific issues and their ozone- and PM-related policy implications with this data base.\r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA_1.json b/datasets/NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA_1.json index 4d30e95ae9..5f65866453 100644 --- a/datasets/NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 University of Alabama-Huntsville (UAH) Mobile Integrated Profiling System (MIPS) Wind Data product. Files for this data product were obtained from July to September 1999 during the Atlanta Experiment of the EPA Particulate Matter Supersites Program. The UAH MIPS Doppler profiler (915 MHz radar) was used to estimate the vertical distribution of horizontal wind speed and wind direction. Radial velocity along six beams was used to obtain the horizontal wind speed and wind direction. The consensus averaging time was 55 minutes, the number of beams is 6, and the number of range gates was 41. For beam 1, the number of records required to make consensus was 16, the total number of records was 26, and the consensus window size was 4 m/s. For beams 2 and 3, the number of records required to make consensus was 13, the total number of records was 26, and the consensus window size was 3 m/s. There was no data for beams 4 and 5. For beam 6, the number of records required to make consensus was 16, the total number of records was 26 and the consensus window size was 3 m/s. The azimuth and elevation for beams 1 to 6 were: 358 and 90; 88 and 66.4; 178 and 66.4; none; none; 88 and 90.\r\n\r\nThe EPA selected Atlanta as one of the first Supersites Programs dedicated to the study of fine particles (or Particulate Matter (PM) 2.5). The Southern Oxidants Study (SOS) in conjunction with the Georgia Institute of Technology, Earth and Atmospheric Sciences Department developed and implemented the scientific research plan for this initial Supersites Program effort. The Atlanta field experiment was a 4-week long campaign aimed at comprehensively addressing issues related to the measurement and characterization of fine particles in the polluted or urban atmosphere. The experiment took place during the August 1999 and deployed a wide array of instrumentation at a measurement site located on Jefferson Street in Midtown Atlanta. \r\n\r\nGoals of the Atlanta Supersite Program were twofold: first, to provide a platform for testing and contrasting some of the newer particle measurement techniques; and second, to provide data to advance our scientific understanding of atmospheric processes regarding atmospheric particles. Specific objectives were: (1) to characterize the performance of emerging and/or state-of-the-science PM Measurements; (2) to compare and contrast similar and dissimilar PM Measurements; (3) to evaluate the precision, accuracy, and completeness of information that can be gained from the planned EPA PM mass and chemical composition networks; (4) to evaluate the scientific information gained by combining various independent and complementary PM Measurements; and (5) to address various scientific issues and their ozone- and PM-related policy implications with this data base.\r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA_1.json b/datasets/NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA_1.json index 6e34817ab4..7b749be4af 100644 --- a/datasets/NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 Rapid Single-Particle Mass Spectrometer (SPMS) Data product. Data for this product was obtained in August 1999 during the Atlanta Experiment of the EPA Particulate Matter (PM) Supersites Program. \r\n\r\nDuring a month in the summer of 1999, individual aerosol particles were sized and analyzed using a Rapid Single-particle Mass Spectrometer (RSMS) in Atlanta. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a single time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were initiated periodically, and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range. \r\n\r\nSingle particle data are valuable for the following reasons:\r\n-\tthey are collected and analyzed real time so have excellent temporal resolution, \r\n-\tthe particle-to-particle composition variations (external mixing properties) can be assessed, and \r\n-\tkey particle sources are easily identified since the particles retain source characteristics. \r\n\r\nThe data resulting from these measurements consist of an aerodynamic particle size and a positive mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy. Support for RSMS measurements was provided by the EPA Supersite program and additional funding from the EPA and National Science Foundation (NSF).\r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA_1.json b/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA_1.json index 983d12b9ac..00f2cf18ad 100644 --- a/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_BALTIMORE_JHU_LIDAR_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Johns Hopkins University (JHU) LIDAR Backscatter and Mixing Height Data product. This product contains measurements that were taken from May 2001 to September 2002 during the Baltimore Experiment of the EPA Particulate Matter (PM) Supersites Program by the JHU, Department of Geography and Environmental Engineering. A miniature elastic backscatter LIDAR was operated at several Baltimore locations in the vertical mode with typical resolution range of 3 m, typical time steps of 5 seconds, and ranges of 4.5-8 km. All vertical profile measurements of aerosol backscatter were taken during daytime with continuous sampling and can be used to describe composition, dynamics, and extent of the mixing layer and the air aloft. Mixing heights were determined from profile data under cloud-free conditions. Included in this data set are the large ASCII files of the aerosol backscatter data, the calculated mixing height data, and a companion HTML application with color images of the LIDAR profiles of the backscatter signals from aerosols. The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA_1.json index b50b426217..2edf30bf8c 100644 --- a/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Johns Hopkins University Meteorolgical Data product. This product contains\r\nmeteorological and turbulence measurements that were recorded using a diverse array of instruments by the Parlange Environmental Fluid Mechanics Group, Department of Geography and Environmental Engineering, JHU at the EPA Baltimore Supersite. Measurements were made at three Baltimore locations over the indicated time intervals: FMC Corporation (May 26 - June 15, 2001), Clifton Park (July 1 - September 14, 2001), and Ponca Street (February 13, 2002 - March 15, 2003).\r\n\r\nThe instruments were mounted on an 11m tall meteorological tower on the site. The instrumentation consisted of a 3d sonic anemometer-thermometer, pyranometer, wind vane, tipping bucket rain collector, 2 cup anemometers, temperature and relative humidity probe and pressure sensor. The data were collected on a continuous basis and were subsequently subjected to multiple cycles of data validation to ensure correctness and accuracy. The validated data was then averaged over a 5 minute interval to create the final data set. The data set is organized to provide a unique data file for any given day within the operating time duration. Each file contains the variables temperature, relative humidity, mean horizontal wind speed (at 10.39m), horizontal resultant vector mean wind speed, mean horizontal wind speed (at 5.87m), mean horizontal wind angle, std deviation of the wind angle, precipitation, friction velocity, Obukhov length, sensible vertical heat flux, solar radiation, atmospheric pressure, virtual potential temperature, specific humidity and wind angle from sonic anemometer. In addition to usual meteorological variables, this data set also provides information on turbulent mixing (parameterized by the friction velocity) and atmospheric stability (parameterized by the Obukhov length). \r\n\r\nThe Baltimore Supersite collected high-quality ambient air quality measurements with unprecedented temporal resolution at an industrially influenced urban site and two intensive measurement campaigns. A data set of project results was constructed to take advantage of advanced multivariate statistical techniques. Data were collected on the sources and nature of organic aerosol for the region, and large quantities of urban particulate matter (PM) were collected for retrospective chemical, physical, and biological analyses and for toxicological testing. These data provided important information on the potential health effects of particles to support exposure and epidemiological studies for enhanced evaluation of health outcome, pollutant, and source relationships. \r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA_1.json b/datasets/NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA_1.json index 4f6f042cd7..5ab77ce706 100644 --- a/datasets/NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Rapid Single-Particle Mass Spectrometer (RSMS) Data product. This data product was\r\nobtained in 2002 at the Baltimore Supersite. For 7 months, starting in May 2002, individual aerosol particles were sized and analyzed using a RSMS in Baltimore. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a dual time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were periodically initiated and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range. \r\n\r\nSingle particle data are valuable because for the following reasons: \r\n-\tthey are collected and analyzed real time so have excellent temporal resolution, \r\n-\tthe particle-to-particle composition variations (external mixing properties) can be assessed, and \r\n-\tkey particle sources are easily identified since the particles retain source characteristics.\r\n\r\nThe data resulting from these measurements consist of an aerodynamic particle size and a positive and negative mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy. Support for RSMS measurements were provided by the EPA Supersites program and additional funding from the EPA.\r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES_1.json b/datasets/NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES_1.json index 26f7973d8d..c12bbc20e0 100644 --- a/datasets/NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES_1.json +++ b/datasets/NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_BALTIMORE_SEAS_PM25_METAL_CYTOKINES is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Semicontinuous Elements in Aerosol Sampler (SEAS) Particulate Matter (PM) 25 Metal Cytokines Data product. This data product was obtained between August 27 to September 10, 2001 and July 6 to November 27, 2002 during the University of Maryland SEAS II employed at the Baltimore Supersite. Thirty minute samples were collected at the 3 Baltimore monitoring locations for elemental analyses and samples were co-collected for cytokine assays. Simultaneous multi-element graphite furnace atomic absorption spectrometry was used to determine Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Sb, Se, and Zn in ambient air sampled at 90 L/min for 30 min and collected as a slurry after dynamic preconcentration. A bioassay for testing highly time resolved PM2.5 samples for their ability to stimulate the release of immune mediators of the inflammation was successfully developed through this project. The release of cytokines and chemokines by cultured alveolar epithelial cells and monocytes stimulated by PM2.5 samples collected over time periods as short as 30 minutes was detectable and responsive to PM2.5 samples of different chemical compositions. Results obtained from the bioassay system in both cell types were reproducible and of sufficient precision to allow detection of differences between PM2.5 samples collected over short time intervals. \r\n\r\nThe Baltimore Supersite collected high-quality ambient air quality measurements with unprecedented temporal resolution at industrially influenced urban sites from August of 2001 to November of 2002 with two intensive measurement campaigns. A data set of project results was constructed to take advantage of advanced multivariate statistical techniques. Data were collected on the sources and nature of organic aerosol for the region, and large quantities of urban PM were collected for retrospective chemical, physical, and biological analyses and for toxicological testing. These data provided important information on the potential health effects of particles to support exposure and epidemiologic studies for enhanced evaluation of health outcome, pollutant, and source relationships.\r\n\r\nThe EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON_1.json b/datasets/NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON_1.json index 7572d011ba..f10efb43c1 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_AETHALOM_MULTI_WL_CARBON is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Aethalometer Multi-Wavelength Carbon Data product. This data was obtained between May 1999 and December 2006 at the Fresno supersite. A multiwavelength aethalometer (Model AE30S) operated at the Fresno supersite from May 12, 1999 to December 31, 2006. The collected aerosol sample was illuminated with light from seven light emitting diodes at wavelengths of 370, 470, 520, 590, 660, 880, and 950 nm. Aerosol samples were collected for five minute periods. The air sample was collected through a sharp cut size-selective cyclone to limit the size of particles to aerodynamic diameters of 2.5 um and less. The concentration of black carbon corresponded to the 880 nm measurement. The black carbon equivalents at the other six wavelengths were also determined.\r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_BAM_PM_MASS_1.json b/datasets/NARSTO_EPA_SS_FRESNO_BAM_PM_MASS_1.json index 276a103ec5..7ea62b4ad1 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_BAM_PM_MASS_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_BAM_PM_MASS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_BAM_PM_MASS FRACTION is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Beta Attenuation Monitors (BAM), Particulate Mass Concentration Data product. This data set contains measurements taken from two BAMs, PM10, and PM2.5, operated at the Fresno Supersite. The MetOne BAM Monitor measured the attenuation of a beam of beta particles (electrons) generated by a 14\u00baC source transmitted through an aerosol sample collected on a glass fiber filter tape. Before sample collection, the beta attenuation was measured through a clean part of the tape to obtain a baseline. A sample was collected on the same location on the tape. After sample collection, the beta attenuation was measured through the exposed part of the tape. The net attenuation is proportional to the amount of mass collected on the filter. A mass flow controller controls the flow rate during sample collection at a flow rate of approximately 16.7 l/min. The mass concentration of the collected aerosol was determined from the net attenuation, the sample air flow, the sample time, and the attenuation coefficient for the instrument. \r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION_1.json b/datasets/NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION_1.json index 27dfa0cbd2..6ccb255889 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_EC_PM25_FRACTION is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Elemental Carbon in 2.5 um Aerosol Fraction Data product. This data set contains the measurements taken with a single and dual wavelength aethalometer. The single wavelength aethalometer (model AE14) was operated at the Fresno supersite from December 17, 1999 to September 27, 2002. This instrument used a broad spectrum incandescent lamp to illuminate the collected aerosol. Aerosol samples were collected for five minute periods. The air sample was collected through a sharp cut size-selective cyclone to limit the size of particles to aerodynamic diameters of 2.5 m or less. A single concentration of black carbon was determined for each five minute period. A dual-wavelength aethalometer (model AE21) operated at the Fresno supersite from February 25, 2003 to December 31, 2006. The collected aerosol sample is illuminated with light from two light emitting diodes at wavelengths of 370 and 880 nm. Aerosol samples are collected for five minute periods. The air sample is collected through a sharp cut size-selective cyclone to limit the size of particles to aerodynamic diameters of 2.5 m or less. The concentration of black carbon corresponds to the 880 nm measurement. The black carbon equivalent at the ultraviolet wavelength was also determined.\r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_FRESNO_MET_DATA_1.json index a099b97232..2c09b6e4cb 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_MET_DATA is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Beta Attenuation Monitors (BAM) Meteorological Data. This data set contains measurements taken from six meteorological instruments operated at the Fresno supersite from May 24, 2000 to December 31, 2006. The ambient temperature was measured by a Met One Instruments aspirated thermistor, Model 060A-2. The barometric pressure was measured by a Met One pressure transducer, Model 090D. The relative humidity was measured by a Met One aspirated thin film capacitor, Model 083V. The solar radiation was measured by a LI-COR Inc. pyranometer, Model LI-200SA. The wind speed was measured by a Met One Instruments 3-cup anemometer, Model 010-SC. The wind direction was measured by a Met One Instruments High-Sensitivity wind vane, Model 025-5C. All six instruments reported 5 minute samples.\r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA_1.json b/datasets/NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA_1.json index ec79d58614..15ec6580f0 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_PARTICLE_PAC_DATA is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Particle-bound Polycyclic Aromatic Compound Data. This data set contains measurements of particle-bound polycyclic aromatic compounds (PAC) from a Photoelectric Aerosol Sensor (PAS) monitor operated at the Fresno supersite from September 30, 1999 to December 31, 2006. The ambient sample was measured continuously and averaged for five minute periods. The sample inlet was a tube with an inverted funnel to protect the inlet from rain but has no specified particle size separation.\r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4_1.json b/datasets/NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4_1.json index 3ce413fc4d..69fca82d0f 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_FRESNO_PM25_NO3_SO4 is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Particulate Matter (PM) 2.5 Particulate Nitrate and Sulfate Data. This data set contains measurements taken from two nitrate monitors and one sulfate monitor operated at the Fresno Supersite. The sample collection time for all instruments was 8 minutes. The sample analysis time was 2 minutes. Data were output once every 10 minutes. The Rupprecht and Patashnick (R&P) Ambient Particulate Nitrate Monitor measured the amount of particulate nitrate in an air sample at a nearly continuous rate. The Rupprecht and Patashnick (R&P) Ambient Particulate Sulfate Monitor measured the amount of particulate sulfate in an air sample at a nearly continuous rate. The ambient aerosol was collected by impaction on a small metallic strip. At the end of collection, the strip was heated to vaporize and decompose the particulate matter into oxides which were then measured. \r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_PM25_OC_EC_1.json b/datasets/NARSTO_EPA_SS_FRESNO_PM25_OC_EC_1.json index 89528d0fd4..3bb96b0e5b 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_PM25_OC_EC_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_PM25_OC_EC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_PM25_OC_EC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_PM25_OC_EC is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Particulate Matter (PM) 2.5 Organic and Elemental Carbon Data. This data set contains measurements taken from a continuous carbon monitor, Model Rupprecht and Patashnick (R&P) 5400C operated from January 13, 2000 to March 31, 2005, and a Sunset Carbon Analyzer at the Fresno supersite. The sample collection time was 1 hour; the sample analysis time was one hour. Data were output once an hour, two hours after the start of sample collection. \r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS_1.json b/datasets/NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS_1.json index 6545cbcc91..5265b0df6d 100644 --- a/datasets/NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS_1.json +++ b/datasets/NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_FRESNO_TEOM_PM_MASS is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, tapered element oscillating microbalance (TEOM) Particulate Mass Concentration Data product. This data set contains measurements taken from two TEOM operated at the Fresno supersite from July 10, 1999. One TEOM samples through an impactor size-selective inlet to collect particles with aerodynamic diameters less than 10 m at a flow rate of 16.7 liters/min. The other TEOM samples through a cyclone size-selective inlet to collect particles with aerodynamic diameters less than 2.5 m at a flow rate of 16.7 liters/ min. Both TEOMs operate with inlets heated to 50\u00ba C to remove water vapor and other volatile species so that the measured concentrations are for the dry ambient aerosol. Both TEOMs report 5 minute samples.\r\n\r\nThe Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_DMA_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_DMA_DATA_1.json index 105722f687..fb0cfa2b67 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_DMA_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_DMA_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_DMA_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_DMA_DATA measurements consist of aerosol size distributions and number concentrations collected in Houston, Texas, beginning in August 2000 and ending in November 2001. Data were collected at two sites throughout this period (Aldine and HRM3), and a third instrument sampled at two sites (La Porte: Aug-Sep 2000; Deer Park: Sep 2000-Oct 2001). These measurements were collected as part of the EPA Supersite program. High-flow differential mobility analyzers (DMAs) were used to collect these data.The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA_1.json index 78e3b43e3c..1021bf1b73 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_NH3_HNO3_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_NH3_HNO3_is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Ammonia and Nitric Acid Data product. It contains 24-hour integrated measurements of ammonia and nitric acid collected during September 30, 2000 through May 22, 2001 at the Houston Regional Monitoring (HRM) Site 3, Aldine, and Deer Park Houston Supersite monitoring locations. Samples were collected using a coated annular diffusion denuder downstream of a Teflon filter and analyzed by ion chromatography. Data set change history: Measurements of ammonia and nitric acid were quantified as ammonium and nitrate using ion chromatography. \r\n\r\nEffective August 27, 2004, the TABLE COLUMN NAME and TABLE COLUMN CAS IDENTIFIER values in the main data table were changed from ammonium and nitrate to ammonia and nitric acid to clarify that these are gas-phase measurements. The data set name was changed from NARSTO EPA_SS_HOUSTON Ammonium and Nitrate Data to NARSTO EPA_SS_HOUSTON Ammonia and Nitric Acid Data. No data values were changed.\r\n\r\nThe Houston Supersite is one of several Supersites that was established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of PM in Southeastern Texas, to develop and test new methods for characterizing fine PM, and to collect data on the physical and chemical characterization of fine PM that can be used to support exposure and health effects studies.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA_1.json index b8c8ad5e63..395b066919 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_NO3_SO4_C_DATA files contain continuous measurements of PM2.5 nitrate, PM2.5 sulfate, and PM2.5 carbon collected during August 12, 2000 through November 5, 2001 at the Aldine, Deer Park, and LaPorte Houston Supersite monitoring locations. Nitrate measurements were collected using the R&P 8400N Method. Sulfate and carbon measurements were collected using the Prototype ADI Particulate Sulfate and Carbon Monitor Method.The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA_1.json index 3d4b016628..f4c3ee33b2 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_HOUSTON_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Rapid Single-Particle Mass Spectrometer Data. This product contains individual aerosol particles which were sized and analyzed using a Rapid Single-particle Mass Spectrometer (RSMS) in Houston during the summer of 2000. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a single time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were initiated periodically, and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range. \r\n\r\nSingle particle data are valuable because they:\r\n- are collected and analyzed real time so have excellent temporal resolution,\r\n- enable assessment of particle-to-particle composition variations (external mixing properties), \r\n- allow for easy identification of key particle sources since the particles retain source characteristics. \r\n\r\nThe data resulting from these measurements consisted of an aerodynamic particle size and a positive mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy. Support for RSMS measurements was provided by the EPA Supersite program and additional funding from the EPA. The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of PM in Southeastern Texas, to develop and test new methods for characterizing fine PM, and to collect data on the physical and chemical characterization of fine PM that can be used to support exposure and health effects studies.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA_1.json index e9971ca11a..2987e6e0ac 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_CAMS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_HOUSTON_TEXAQS2000_CAMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Texas Natural Resource Conservation Commission (TNRCC) continuous ambient monitoring stations (CAMS) Air Quality Data. This data set contains 5-minute air quality measurements collected in Texas during August and September 2000 at 85 CAMS during TEXAQS2000. Measurements include carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), total reactive nitrogen species (NOy), ozone, particulate matter (PM) 2.5 mass, hydrogen sulfide (H2S), wind speed, wind direction, maximum wind gust, air temperature, dewpoint temperature, humidity, precipitation, surface pressure, radiation, and visibility. CAMS are operated by the Texas Commission on Environmental Quality (TCEQ), local city or county governments, or private monitoring networks. Important monitoring site information: The site information data table in each of the 85 data files may not contain the latest TCEQ site information. A companion file site information spreadsheet (.csv) that lists data for all 85 sites is the latest TCEQ site information. The site information data tables in the 85 data files will not be updated. The 85 site spreadsheet companion document is the official source of site data, and this data is listed in the TEXAQS2000 CAMS guide document.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA_1.json index 7068f020c7..b3372b7b0e 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_DOE_G-1_DATA is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Department of Energy (DOE) G-1 Air Chemistry, Aerosol, and Met Data. \r\n\r\nTwenty research flights were made from August 18 to September 12, 2000.The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA_1.json index 9fd1a86fbc..73adb3db8e 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_HCHO_H2O2_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Formaldehyde and Hydrogen Peroxide Data. It contains continuous formaldehyde (HCHO) and hydrogen peroxide (H2O2) measurements collected in August - September 2000 during TEXAQS2000 at the Houston Regional Monitoring (HRM) Site 3 monitoring station. Integrated single point measurements of 3-minute samples were collected every 10 minutes. \r\n\r\nThe Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of PM in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine PM that can be used to support exposure and health effects studies. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA_1.json index f39e26712b..09ccfc29ea 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM25_ORG_DATA is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Particulate Matter (PM) 2.5 Organic Speciation Data. This file contains 24-hour integrated organic speciation of fine particulate matter (PM2.5) collected August 15, 2000 through September 30, 2000 at the HRM Site 3, Aldine, and La Porte Houston Supersite monitoring locations during TexAQS2000. The filters were extracted with hexane and benzene: isopropanol. Polar compounds were analyzed after derivatization with either diazomethane or bis-trimethylsilyl-trifluoroacetamide. All compounds were quantified by gas chromatography-mass spectrometry. The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR_1.json index 463db4ff48..7231ec1653 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_FTIR measurement data consist of absolute absorbance areas for organonitrates, sulfate, aliphatic carbon and carbonyl compounds for size segregated particulate matter collected using a Herring Low Pressure Impactor (LPI). These data were collected during August and September 2000 at the Houston PM Supersite locations (LaPorte, HRM3, and Aldine) during the Texas Air Quality Study 2000 (TexAQS).The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS_1.json index ecb1968d13..a378504aa5 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Size-specific Particulate Matter (PM) Mass Concentration Data. This file reports size segregated mass particulate data collected with a micro-orifice uniform deposit impactors (MOUDI) sampler during the TexAQS2000 at the Houston Regional Monitoring (HRM) Site 3 and LaPorte Houston Supersite monitoring locations. Daily MOUDI sampling began on August 17, 2000 and ended on September 13, 2000. The MOUDI is a model 100 rotating micro-orifice uniform deposit impactor from MSP Corporation. The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended PM. The overall goals were to characterize the composition and identify the sources of PM in Southeastern Texas, to develop and test new methods for characterizing fine PM, and to collect data on the physical and chemical characterization of fine PM that can be used to support exposure and health effects studies. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL_1.json b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL_1.json index c455342070..c170bf4767 100644 --- a/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL_1.json +++ b/datasets/NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_HOUSTON_TEXAQS2000_WB_TUNNEL data contain gas and particle phase measurements collected in a tunnel in the Houston area during the summer of 2000. The primary objective of this study was to provide data for estimating vehicular emission factors and composition profiles as part of the TexAQS2000 program. Measurements were collected on each day from August 29, 2000 (Tuesday) through September 1, 2000 (Friday). Sampling was conducted during the 1200 - 1400 CDT and 1600 - 1800 CDT time periods each day. Measurements collected during the study included nitrogen oxides, carbon dioxide, carbon monoxide, ammonia, fine particulate matter (PM2.5), and individual hydrocarbon species.The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of particulate matter in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine particulate matter that can be used to support exposure and health effects studies.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA_1.json index 171bf78069..99af69de4a 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_LOS_ANGELES_AETHALOMETER_EC_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Los Angeles Aethalometer Elemental Carbon Data. Data was collected between September 2000 to October 2003 at Claremont, Downey, Riverside, Rubidoux, and the University of Southern California (USC) in Los Angeles County, California. The Magee Scientific AE-2 series dual beam aethalometer was used in a mobile trailer to collect mass concentrations of optically absorbing black carbon particles in the submicron size range during September 15, 2000 to October 16, 2003. The Aethalometer collected aerosol continuously on quartz fiber paper and determined the increment of optically absorbing black carbon per unit volume of sampled air every 5 minutes. The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) was to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin (LAB ). The EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_APS_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_APS_DATA_1.json index b98f740790..a039f368f2 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_APS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_APS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_APS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_LOS_ANGELES_APS_DATA were collected between December 2000 and September 2001. At several locations in Los Angeles County, California, a TSI Aerodynamic Particle Sizer (APS) was used in a mobile trailer to collect size characteristics of particles ranging from about 0.5 to 20 mm. Based on the time-of-flight principle, the APS measured particle count concentrations for 52 channels that cover sizes from 0.5 to 20 mm in every 15 minutes. Note that the first channel reports particle count concentrations for sizes < 0.523 mm.The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) is to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS_1.json index b5d363dc1f..78baddc2db 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_LOS_ANGELES_HEADS_PART_IONS_MASS is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Los Angeles Harvard/EPA Annular Denuder System (HEADS) Data product. This product was collected between December 2001 and June 2003. The HEADS model URG-2000-30DI was used to collect the Particulate Matter (PM) 2.5 mass concentration data episodically from December 6, 2001 - August 21, 2002. It was also used to collect sulfate and nitrate ions at Claremont from September 28, 2001 - August 6, 2002, at Riverside from March 14 - June 6 2001, and the University of Southern California from October 8, 2002 - June 11, 2003. HEADS uses chemically coated annular denuder tubes to selectively remove gaseous pollutants before PM. The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) was to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin (LAB). The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA_1.json index b7d9549b6a..37756a6c8d 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_LOS_ANGELES_MOUDI_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Los Angeles Size-Fractionated Particulate Matter (PM) Composition - micro-orifice uniform deposit impactors (MOUDI) Data product. Data for this collection was collected between late 2000 and late 2003 from sites at Downey, Claremont, Riverside, Rubidoux, and the University of Southern California (USC). Samples were typically collected for a one-day period, but in some cases, duration was less than or more than one day. Element/metals, carbon, nitrate/sulfate ion, and mass concentration data were obtained. The MOUDI is a multiple stage inertial cascade impactor. At each stage, particles larger than the cut point of the stage are collected on the impaction plate while smaller particles pass through to the next stage. This continues through the cascade impactor until the smallest particles are collected on the after filter. At Downey, a size range of 10um to 0um was collected (10.0-2.5um, 2.5-1.0um, 1.0-0.32um, 0.32-0um). Most of the 10.0-2.5um size range samples were eliminated at Claremont, Riverside, Rubidoux, and USC because this size range was collected using the Partisol sampler. All samples were analyzed using X-ray florescence and mass concentration analysis at an independent laboratory. \r\n\r\nThe overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) was to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin. The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. \r\n\r\nEight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA_1.json index 7da240afe9..3fcc382ed3 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_LOS_ANGELES_PARTISOL_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Los Angeles Particulate Matter (PM) 2.5-10 Composition and Mass Data product. Data was collected using Partisol Model 2025-D samplers between late 2000 and late 2003 from sites at Downey, Claremont, Riverside, Rubidoux, and the University of Southern California (USC). Samples were collected episodically, frequently for a 24-hour per period, but in some cases multiple samples were collected over the course of a day. Element/metals, nitrate/sulfate ion, and mass concentration data were obtained. The Partisol is a dichotomous sequential multi-filter air sampler. It uses a virtual impactor to divide the air stream to facilitate the collection of fine (0.0-2.5um) and coarse (2.5-10.0um) particles onto a filter media over a pre-programmed collection period. The coarse fraction was analyzed using X-ray fluorescence and mass concentration analysis. Ion chromatography and mass concentration analyses were performed on the fine fraction. The overall objective of the Southern California Supersite (SCS) was to conduct research and monitoring that contributed to a better understanding of the measurement, sources, size distribution, chemical composition, physical state, spatial and temporal variability, and health effects of suspended PM in the Los Angeles Basin (LAB). Intensive aerosol measurements, well beyond the traditional PM2.5 mass, sulfate and nitrate concentrations, were conducted in several areas of the LAB. These included particle number concentrations, size distributions, and detailed PM chemical composition as a function of particle size. Sampling locations were chosen to provide wide geographical and seasonal coverage, including urban source sites and downwind receptor sites. \r\n\r\nThe primary sampling facility, a mobile Particle Instrumentation Unit (PIU), was deployed to several locations to conduct a wide range of PM measurements. Sampling in each site lasted for 6-12 months. Intensive PM measurements were also conducted up and downwind of several freeways of the LAB, to characterize near-roadway exposure environments and to support several in vivo and in vitro health studies. The monitoring activities of the SCS were linked with toxicology studies in the LAB using a mobile PM Concentrator facility to investigate health effects associated with exposures to ultrafine, fine and coarse particles. Finally, the PIU facility was successfully used as a platform to develop, test, and evaluate numerous PM measurement instruments and sampling technologies, including several monitors for semi-continuous size fractionated mass and chemistry, personal PM exposure monitors, particle concentration technologies, and particle counting devices.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON_1.json index dc3aa99283..e0320c96de 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_LOS_ANGELES_PM25_CARBON data were collected between January and May 2002. At Claremont (Los Angeles County, California), Cascaded Integrated Collection and Vaporization System for Particulate Carbon (ICVS for Carbon) was used in a mobile trailer to collect PM2.5 particulate carbon data during January 14, 2002 to May 24, 2002. The ICVS for Carbon measured PM2.5 particulate carbon data that cover sizes from 0.1-2.5um in every 10 minutes.The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) is to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE_1.json index f7dca05814..7767f95cac 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_LOS_ANGELES_PM25_NITRATE data were collected between July 2001 and January 2002. At Claremont and Rubidoux (Los Angeles County, California), Cascaded Integrated Collection and Vaporization System for Particulate Nitrate (ICVS for Nitrate) was used in a mobile trailer to collect PM2.5 particulate nitrate data during July 11, 2001 to January 11, 2002. The ICVS for Nitrate measured PM2.5 particulate nitrate data that cover sizes from 0.1-2.5um in every 10 minutes.The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) is to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA_1.json index 99f835a747..4c6db0b1b6 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_LOS_ANGELES_SMPS_DATA were collected between December 2000 and February 2002. At Claremont, Downey, Riverside, Rubidoux (Los Angeles County, California), TSI Scanning mobility particle sizer (SMPS) was used in a mobile trailer to collect size characteristics of particles ranging from about 0.014 to 0.673 mm during December 8, 2000 to February 22, 2002. The SMPS measured particle count concentrations for 54 to 108 channels that cover sizes from 0.014 to 0.673 mm in every 15 minutes.The overall objective of the Los Angeles Supersite in Southern California Particle Center and Supersite (SCPCS) is to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA_1.json b/datasets/NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA_1.json index 893a4f2bdd..9aae9e402f 100644 --- a/datasets/NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_LOS_ANGELES_TEOM_PM25_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Los Angeles Tapered-Element Oscillating Microbalance (TEOM) Particulate Matter (PM) 2.5 Mass Concentration Data. It was collected between December 2000 and September 2002 using a Tapered-Element Oscillating Microbalance (TEOM). At Downey and Riverside (Los Angeles County, California), the standard TEOM Model 1400a was used in a mobile trailer to collect PM2.5 mass concentration data every 30 minutes during December 19, 2000 to May 22, 2001. At Claremont and Rubidoux (Los Angeles County, California), Differential TEOM (proto-type) was used in a mobile trailer to collect hourly PM2.5 mass concentration data during August 17, 2001 to September 3, 2002. The overall objective of the Los Angeles Super Site in Southern California Particle Center and Supersite (SCPCS) is to conduct monitoring and research that contributes to a better understanding of the measurement, sources, size distribution, chemical composition and physical state, spatial and temporal variability, and linkages to health effects of airborne particulate matter in the Los Angeles Basin.\r\n\r\nThe U.S. EPA Particulate Matter (PM) Super Sites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA_1.json index 156cd78300..f1b3d9c6e9 100644 --- a/datasets/NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_NEW_YORK_AIR_CHEM_PM_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) New York Air Chemistry, Particulate Matter, and Meteorological Data. It was collected between 2001 and 2006 during the PM2.5 Technology Assessment and Characterization Study in New York State (PMTACS-NY). Data files from all components of the PMTACS-NY Supersite program are archived in this single data set. The PMTACS-NY Supersite program provided a unique and unparalleled opportunity to enhance our understanding of ozone/PM2.5-precursor relationships and track progress in current precursor emission control programs and assess their effectiveness in achieving expected air quality responses. The impact of this research is highly significant, providing a sound scientific basis for informed effective decisions in the management of air quality in New York and significant benefit to its citizens - both environmentally and economically. The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA_1.json b/datasets/NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA_1.json index e7d0810805..2d213f4f25 100644 --- a/datasets/NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_PITTSBURGH_GAS_PM_PROPERTY_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite Pittsburgh Gas Concentration and Particulate matter (PM) Physical Properties Data product. Data was obtained between May 23, 2001 and September 1, 2002 during the Pittsburgh Air Quality Study (PAQS). The data set provides Particulate Matter Composition Data of the following types: \r\n1) Total, Organic, and Hydrogen Peroxide data \r\n2) Filter based measurement of PM10 and PM2.5 Mass concentration using a Dichotomous sampler \r\n3) Epiphaniometer total particle active surface area\r\n4) Filter based measurement of PM2.5 Mass using the Federal Reference Method\r\n5) Integrating nephelometer based measurement of PM2.5 light scattering\r\n6) TSI Scanning Mobility Particle Sizer (Long-column/model 3936L10)\r\n7) Measurements of PM mass size distribution using a MOUDI cascade impactor\r\n8) In-situ VOC measurements by pre-concentration and gc/msd/fid9) Surface air concentrations of O3, NO, NOx, SO2, CO, and PM2.5 mass. \r\n\r\nPittsburgh Air Quality Study (PAQS), along with the Pittsburgh Supersite Program, was a comprehensive, multi-disciplinary investigation to characterize the ambient PM in the Pittsburgh region, to improve understanding the links between ambient PM and public health, and to develop new instrumentation for PM measurements. The Pittsburgh supersite was designed to achieve several objectives: to determine the physical and chemical characteristics of PM in the Pittsburgh region; to develop and evaluate the next generation of atmospheric aerosol monitoring techniques; to update emission profiles for important regional sources; to quantify the impact of the various sources on the local PM concentrations; and to predict changes in the PM characteristics due to proposed changes in emissions. The last objective was based on concurrent modeling studies and was designed to support the development of regulations. These objectives were addressed through four components of the research: (1) ambient monitoring at a central site and a set of satellite sites in the region; (2) an instrument development and evaluation study; (3) a data analysis and synthesis component; and (4) a comprehensive modeling component. \r\n\r\nThe central supersite was located on a grassy hill in a large urban park adjacent to the Carnegie Mellon University campus, approximately 6km east of downtown Pittsburgh. It was separated from the city in the predominant upwind direction (south and west) by roughly 1km of parkland. It was at least several hundred meters from any other major source of air pollution: the site was positioned approximately 50m past the end of a dead end street, and several hundred meters from the nearest heavily traveled street. Five additional sites were operated as Satellite sites to character the spatial variation of the PM. The measurement campaign lasted for 14 months (July 2001-September 2002). Intensive monitoring was performed during two periods, from 1 July to 3 August, 2001 (ESP01) and 1 January to 15 January, 2002 (ESP02). Baseline monitoring was conducted for the rest of the study. Baseline measurements included daily filter samples for fine particle mass and composition (OC/EC, major ions, elemental composition). The U.S. EPA Particulate Matter (PM) super sites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_PITTSBURGH_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_PITTSBURGH_MET_DATA_1.json index 2935d555da..b3bb1763a4 100644 --- a/datasets/NARSTO_EPA_SS_PITTSBURGH_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_PITTSBURGH_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_PITTSBURGH_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_PITTSBURGH_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Pittsburgh Meteorological Data product. It was obtained between July 1, 2001 and November 1, 2002 during the Pittsburgh Supersite Program. Ambient monitoring at the central super site and a set of satellite sites in the Pittsburgh region included numerous meteorological measurements. Meteorological parameters measured during the sampling period included temperature, relative humidity, precipitation, wind speed and direction, UV intensity, and solar intensity. The Pittsburgh Super Site Program was a comprehensive, multi-disciplinary investigation to characterize the ambient Particulate Matter (PM) in the Pittsburgh region, to improve understanding the links between ambient PM and public health, and to develop new instrumentation for PM measurements. The central super site was located next to the Carnegie Mellon University campus near downtown Pittsburgh. Five additional sites served as Satellite sites. The measurement campaign lasted for 18 months (May 2001-October 2002). \r\n\r\nThe specific objectives were to: Characterize the PM with regard to size, surface, and volume distribution; chemical composition as a function of size and on a single particle basis; temporal and spatial variability. Develop and evaluate the current and next generation atmospheric aerosol monitoring techniques including single particle measurements, continuous measurements, ultra-fine aerosol measurements, improved organic component characterization, and others. Quantify the impact of the various sources of PM concentrations in the area including transportation, power plants, natural, etc. Combine the ambient monitoring study with the proposed indoor, health, and modeling studies to elucidate of the links between PM characteristics and their health impacts in this area. \r\n\r\nThe EPA PM Super sites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA_1.json b/datasets/NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA_1.json index f66736dfb1..a54c877768 100644 --- a/datasets/NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_PITTSBURGH_PM_COMPOSITION_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Pittsburgh Particulate Matter (PM) Composition Data. It was obtained between June 30 and September 1, 2001 during the Pittsburgh Air Quality Study (PAQS). The data set provides PM Composition Data of the following types:1) PM2.5 nitrate and PM2.5 sulfate.2) Semi-Continuous Organic and Elemental Carbon Measurements.3) Air concentrations of water soluble PM2.5 aerosol species and water soluble gases, as measured with the CMU steam sampler - IC combination.4) Manual filter-based PM2.5 element measurements from microwave decomposition of filters followed by Inductively Coupled Plasma Mass Spectrometer analysis.5) Manual filter-based PM10 element measurements from microwave decomposition of filters and Inductively Coupled Plasma Mass Spectrometer analysis.6) Manual filter-based PM2.5 inorganic composition with analysis performed using ion chromatography.7) Manual filter-based PM2.5 organic and elemental carbon measurements with analysis performed using a Thermal Optical Transmission carbon analyzer.8) Measurements of PM composition size distributions using a MOUDI cascade impactor.9) PM2.5 organic and elemental carbon concentrations from an activated carbon denuder/quartz filter/charcoal impregnated fiber filter backup combination. Quartz filters analyzed using a Thermal/Optical transmittance carbon analyzer.10) Fog chemistry dataPAQS, along with the Pittsburgh Supersite Program, was a comprehensive, multi-disciplinary investigation to characterize the ambient PM in the Pittsburgh region, to improve understanding the links between ambient PM and public health, and to develop new instrumentation for PM measurements. The Pittsburgh Supersite was designed to achieve several objectives: to determine the physical and chemical characteristics of PM in the Pittsburgh region; to develop and evaluate the next generation of atmospheric aerosol monitoring techniques; to update emission profiles for important regional sources; to quantify the impact of the various sources on the local PM concentrations; and to predict changes in the PM characteristics due to proposed changes in emissions. The last objective was based on concurrent modeling studies and was designed to support the development of regulations. These objectives were addressed through four components of the research: (1) ambient monitoring at a central site and a set of satellite sites in the region; (2) an instrument development and evaluation study; (3) a data analysis and synthesis component; and (4) a comprehensive modeling component.\r\n\r\nThe central supersite was located on a grassy hill in a large urban park adjacent to the Carnegie Mellon University campus, approximately 6km east of downtown Pittsburgh. It was separated from the city in the predominant upwind direction (south and west) by roughly 1km of parkland. It was at least several hundred meters from any other major source of air pollution: the site was positioned approximately 50m past the end of a dead end street, and several hundred meters from the nearest heavily traveled street. Five additional sites were operated as Satellite sites to character the spatial variation of the PM. The measurement campaign lasted for 14 months (July 2001-September 2002). Intensive monitoring was performed during two periods, from 1 July to 3 August 2001 (ESP01) and 1 January to 15 January, 2002 (ESP02). Baseline monitoring was conducted for the rest of the study. Baseline measurements included daily filter samples for fine particle mass and composition (OC/EC, major ions, elemental composition). The U.S. EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA_1.json b/datasets/NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA_1.json index c16b559b30..6e575073da 100644 --- a/datasets/NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_EPA_SS_PITTSBURGH_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Pittsburgh Rapid Single-Particle Mass Spectrometer Data product. It was obtained between September 20 and December 27, 2001 during the Pittsburgh Air Quality Study (PAQS). During 12 months, starting September 2001, individual aerosol particles were sized and analyzed using a Rapid Single-particle Mass Spectrometer (RSMS) in Pittsburgh. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a dual time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were initiated periodically, and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range. Single particle data were valuable because a) they were collected and analyzed real time so have excellent temporal resolution, b) the particle-to-particle composition variations (external mixing properties) could be assessed, and c) key particle sources were easily identified since the particles retain source characteristics. The data resulting from these measurements consist of an aerodynamic particle size and a positive and negative mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy.\r\n\r\nThe U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA_1.json b/datasets/NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA_1.json index 588e0c56ca..6a266dbc71 100644 --- a/datasets/NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA_1.json +++ b/datasets/NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA were obtained between April 11, 2001 and July 21, 2003 during the St. Louis - Midwest Supersite program.The overall goal of the St. Louis - Midwest Supersite was to conduct aerosol physical and chemical measurements needed by the health effects community, the atmospheric science community and the regulatory community to properly assess the impact of particulate matter exposure on human health and to develop control strategies to mitigate these effects. Metropolitan St. Louis is a major population center well isolated from other urban centers of even moderate size, and is impacted by both distant and local sources. Local industry includes manufacturing,refining, and chemical plants. St. Louis is climatologically representative of the country's eastern interior, affected by a wide range of synoptic weather patterns and free of localized influences from the Great Lakes, Ocean, Gulf, and mountains. It accordingly provides an ideal environment for studying the sources, transport, and properties of ambient particles.The initial data types included:1) 5-minute PM 2.5 black carbon (880 nm) and uv-absorbing carbon (370 nm) measured by a Magee Scientific Aethalometer (Model AE-21).2) 1-hour PM 2.5 elemental carbon and blank-corrected organic carbon from semicontinuous thermo-optical analysis by the ACE-ASIA method.3) 24-hour PM 2.5 elemental carbon and organic carbon (both blank-corrected) from integrated filter with offline thermo-optical analysis by the ACE-ASIA method.4) 30-minute PM 2.5 metal composition from samples collected with a Semicontinuous Elements in Aerosol Sampler (SEAS) II.5) 5-minute meteorological data (wind, temperature, RH, solar radiation, atmospheric pressure, and precipitation) measured with a Climatronics anemometer, wind vane, thermocouple, lithium chloride sensor, pyranometer, barometer, and tipping bucket.6) 24-hour PM 1.0 filter mass concentration measured by sharp cut cyclone and gravimetric analysis.7) 1-hour PM 2.5 mass measured by an Andersen Continuous Ambient Mass Monitoring System (CAMMS).8) 24-hour PM 2.5 and PM 10 filter mass by Harvard Impactors and laboratory gravimetric analysis.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods. NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA_1.json b/datasets/NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA_1.json index b60dc7074e..0de3191803 100644 --- a/datasets/NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA_1.json +++ b/datasets/NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_ICARTT_NEAX_2004_DOE_G-1_DATA is the NARSTO_NE_MODEL is the North American Research Strategy for Tropospheric Ozone (NARSTO) ICARTT NEAX 2004 DOE G-1 Air Chemistry, Aerosol, and Met Data collected in July and August, 2004 during the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT), NorthEast Aerosol eXperiment (NEAX). The DOE Gulfstream G-1 aircraft operated within about 300 nautical miles from Latrobe, PA from about July 19 - August 15, 2004. There were 13 total flights on twelve different days. Data were reported for both 1 second and averaged 10 second sampling intervals.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_NE_MODEL_1.json b/datasets/NARSTO_NE_MODEL_1.json index 0a85dd247a..d0ad796610 100644 --- a/datasets/NARSTO_NE_MODEL_1.json +++ b/datasets/NARSTO_NE_MODEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_NE_MODEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_NE_MODEL is the North American Research Strategy for Tropospheric Ozone (NARSTO) 1998 Model-Intercomparison Study Verification Data: NARSTO-Northeast 1995 Surface Ozone, NO, and NOx Langley Data Center Data Set. It supports the NARSTO Model Intercomparison activity described in the report of a workshop that was held in Washington, DC on May 27-28, 1998. The intercomparison activity will compare meteorological, emissions, and air quality models that are used to estimate ozone concentrations in the northeastern United States. The air quality models are used to estimate how ambient ozone concentrations will change in response to changes in VOC and NOx emissions. These data are a subset of the measurements made during the NARSTO-Northeast 1995 intensive field campaign and will be used to verify model predictions. Included are surface one-hour average O3, NO, and NOx measurement results from all reporting sources for 1995. ASCII data files are available for specific time intervals and the full monitoring period. A measurement station description file is included.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_NE_NEXRAD_IMAGES_1.json b/datasets/NARSTO_NE_NEXRAD_IMAGES_1.json index d998ddff4e..57e73b282e 100644 --- a/datasets/NARSTO_NE_NEXRAD_IMAGES_1.json +++ b/datasets/NARSTO_NE_NEXRAD_IMAGES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_NE_NEXRAD_IMAGES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_NE_NEXRAD_IMAGES data set contains selected NEXt Generation RADar (NEXRAD) weather images from the northeastern United States for the summer of 1995. The NEXRAD weather radar images were used to supplement the air quality monitoring, meteorological, and emissions data collected across the northeastern United States.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA_1.json b/datasets/NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA_1.json index 9961908d59..4b8012298a 100644 --- a/datasets/NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA_1.json +++ b/datasets/NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_CASSIAR_TUNNEL_GAS_PM_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Cassiar Tunnel Gaseous and Particle Mass and Composition Data product. This data product was obtained from August 8-15, 2001 during the PAC2001.The Cassiar Tunnel site is located at 49 17' 01.9 N and 123 01' 54.2 W, at 40 m above sea level (a.s.l.). The tunnel is used mostly by light duty traffic with peak traffic volumes at rush hours. \r\n\r\nThe goal of measurements at this site was to reduce the uncertainty in mobile source inventory for gas and particle emissions from light duty traffic sources, emphasizing the emissions of the precursors to Particulate Matter (PM) formation and primary PM emissions for comparison with tailpipe emissions data from traditional testing conducted in laboratory on mobile source emissions, particularly for mass emission rates and chemical profiles. For several measurements, different sampling and analytical techniques were used as a check on the accuracy of the measurements. For most gas measurements, two sets of instruments were deployed, one at each end of the tunnel, whereas most of the PM measurements were conducted at the exit end of the tunnel. Gas measurements included the typical pollution gases (SF6 as the tracer, NOx, N2O, CO, CO2, methane, SO2, VOCs, carbonyls, organic acids, NH3, Graham and Gray, 2002). \r\n\r\nThe PM chemical and physical properties were measured in great details. For physical properties, particle number size distributions from 10 nm to 3 um were measured, and hygroscopic properties were measured at two sizes (Prenni et al., 2002). Chemical measurements characterized the mass, inorganic and carbonaceous compositions of the primary particles. The PAC2001 was conducted from August 1 until September 31, 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were (1) Cassiar Tunnel, (2) Slocan Park, (3) Langley Ecole Lochiel, (4) Sumas Eagle Ridge, and (5) Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA_1.json b/datasets/NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA_1.json index b32b3e0276..7458106fc0 100644 --- a/datasets/NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA_1.json +++ b/datasets/NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_PAC2001_CESSNA_VOC_PM_OZONE_MET_DATA were obtained between August 14 and August 31, 2001 during the Pacific 2001 Air Quality Study (PAC2001).The missions of the Canadian Forest Service (CFS) Cessna 188 were to support the ground-based measurements at the Slocan Park (SL) site, the Langley Ecole Lochiel (LEL) site, and the Eagle Ridge site on Sumas Mountain (SER). Integration of the measurements on the Cessna with ground measurements was envisioned to provide the vertical chemical and thermal structure of the lowest part of the boundary layer at the sites, and how particle characteristics changes with altitude within the boundary layer. The Cessna flights included profiling and specialized flight patterns. The profiling was made over the sites and at the model boundaries. The profiling provided vertical profiles of O3, particle number size distribution from 0.12 to and total particle counts, VOCs, and meteorological parameters at these locations. During race-track flight patterns, filters were collected at 50, 100, and 300 m altitudes, for inorganic and OC/EC components. On August 20, based on forecast forward trajectories, the Cessna flew along the trajectories starting from the LEL site at the 500 m altitude in an attempt to understanding the time evolution of particles.The Pacific 2001 Air Quality Study (PAC2001) was conducted from 1 August to 31 September, 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were (1) Cassiar Tunnel, (2) Slocan Park, (3) Langley Ecole Lochiel, (4) Sumas Eagle Ridge, and (5) Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA_1.json b/datasets/NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA_1.json index d647d3980c..4b896e6fe4 100644 --- a/datasets/NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA_1.json +++ b/datasets/NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_CONVAIR_PM_OZONE_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Convair Particulate Matter (PM) Ozone (O3) Meteorological Data product. Data was obtained between August 14 and August 30, 2001 during PAC2001. The main mission for the National Research Council (NRC) - Institute for Aerospace Research (IAR) Convair 580 was to map the particle spatial distribution in the valley through remote sensing as well as provide critical meteorological data, particle number size distribution, and O3 profiles. \r\n\r\nThe flights followed mostly meridional and two approximately east-west tracks at 4800 m over the valley for remote sensing using two LIDARs (Strapp and Chevrier, 2001). Spirals from 150-6000 m, for vertical profiles of O3, particle number size distribution, and meteorological parameters, were conducted at the model western boundary at 49.20'N and 123.45' W, at the model southern boundary of 48.25' N and 123.W, as well as during takeoff and landing. A typical flight covered the valley in eight meridional legs, approximately equally spaced, with three of them directly over the ground sites Slocan Park (SP), Langley Ecole Lochiel (LEL), and Sumas Eagle Ridge (SER). East-west flight tracks were flown north and south of the Fraser River, covering most of the urban centers of the valley to probe the urban-suburban-rural gradient, with additional East-West tracks over the North shore lakes to help understand the valley flow situation. The remote sensing was based on aerosol backscattering using upward- and downward-looking LIDARs at the 1064 nm wavelength with a depolarization channel (Strawbridge and Snyder, 2004a). \r\n\r\nThe profiles were obtained during aircraft spirals, specifically located at the western and southern boundaries of the domains of air quality models Urban Airshed Model Variable Grid (UAM-V ) and Model 3/Community Multiscale Air Quality (CMAQ) and were intended as the input as boundary conditions for further modeling. The Convair mission flights covered an area with boundaries roughly corresponding to the model domain of Model 3/CMAQ application to the region, with the eastern boundary at 121.52' 30 W and the western boundary at 123.50' 13 W and extended from 48.30' N to 49.30' N over the mountain tops. On August 26 and August 29, night missions were flown from approximately 9 p.m. to 2 a.m. the next morning, primarily to map the nighttime movement of the urban plume in the main and secondary valleys (Strawbridge and Snyder, 2004b). The ground site overflights provided an assessment of the vertical thermal structure and the extent of particle spatial distribution over the sites. The Pacific 2001 Air Quality Study (PAC2001) was conducted from 1 August to 31 September 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. \r\n\r\nThe study consisted of individual research projects organized to address several issues on ambient PM and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. Thre were five ground sampling sites during the study, which included: Cassiar Tunnel, Slocan Park, Langley Ecole Lochiel, Sumas Eagle Ridge, and Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA_1.json b/datasets/NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA_1.json index 89fa587d98..248139d0cb 100644 --- a/datasets/NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA_1.json +++ b/datasets/NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_GOLDEN_EARS_GAS_PM_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Golden Ears Gaseous, Particulate Matter (PM), and Meteorological Data product. Data was obtained from August 3-11, 2001 during PAC2001. The Golden Ears Provincial Park (GEP) site was situated at 49.27783 N, 120.51544 W, and 220 m above sea level (a.s.l.), about 45 km east of Vancouver in the Coastal Mountains. The sampling site, located at the park ranger headquarters compound, was in a small forest clearing of about 65 x 130 m and was surrounded by tall coniferous trees (dominated by Western Hemlock, Western Red Cedar, and Douglas Fir, typically seen in the Coastal Mountains) with canopy heights about 10-15 m near the site but rising to over 30 m in the park in general. The temporary labs were about 10 m away from the closest trees. The closest urban area, Maple Ridge, is about 8 km to the south. \r\n\r\nDifferent from the other sites, the Golden Ears Provincial Park site was dedicated to the question of secondary biogenic particles production from forestry precursors. The 1995 emission inventories for the LFV indicate strong monoterpene emissions from forests in the Coastal Mountains and the Cascade Ranges. A previous study here showed significant ambient concentrations of terpenes. Monoterpenes are converted into particles in the gas phase with high yields. While the forests were known to release monoterpenes, the magnitude of the contribution to fine particles in the LFV was not clear. Measurements at the GEP site were designed to provide information on secondary biogenic particle production from forestry precursors, such as monoterpenes. \r\n\r\nPAC2001 was conducted from August 1 to September 31, 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient PM and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. There were 5 ground sampling sites during the study, which included: Cassiar Tunnel, Slocan Park, Langley Ecole Lochiel, Sumas Eagle Ridge, and Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA_1.json b/datasets/NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA_1.json index 99f5bd78c3..57404cc5d9 100644 --- a/datasets/NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA_1.json +++ b/datasets/NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Greater Vancouver Regional District (GVRD) and and Canadian Air and Precipitation Monitoring Network (CAPMoN) Supplemental Air Quality Data product. Data was obtained from January 1, 2001 to January 1, 2002. Air quality monitoring data routinely collect by the GVRD CAPMoN during the sampling period of PAC2001, are included as supplemental data for PAC2001.\r\n\r\nThe GVRD monitoring network of 20 sites continued operation during the PAC2001 field study period, with enhanced quality assurance (QA) and quality control (QC) activities. At all sites, meteorological measurements were carried out at a 5-min time resolution. At a few specially equipped sites, particle mass PM10 were measured using tapered element oscillating microbalances (TEOMs). The network data complements the special study sites and form a spatial distribution of the pollutants. CAPMoN is a non-urban air quality monitoring network with siting criteria designed to ensure that the measurement locations are regionally representative (not affected by local sources of air pollution).\r\n\r\nThe objectives were to determine the spatial patterns and establish the temporal trends of pollutants related to acid rain; provide for long-range transport model evaluations and effects research (aquatic, terrestrial, building materials and health); ensure the compatibility of federal, provincial and U.S. measurements; and study atmospheric processes. Scientists involved with the measurement of atmospheric pollution in urban centers would consider most CAPMoN sites to be remote and pristine. There are currently 19 measurement sites in Canada and 1 in the U.S. The Saturna Island site is located in the PAC2001 area of interest. \r\n\r\nPAC2001 was conducted from 1 August to 31 September 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were (1) Cassiar Tunnel, (2) Slocan Park, (3) Langley Ecole Lochiel, (4) Sumas Eagle Ridge, and (5) Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA_1.json b/datasets/NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA_1.json index 225e30dd7e..97d32190e2 100644 --- a/datasets/NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA_1.json +++ b/datasets/NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_LANGLEY_GAS_PM_MET_DATA was obtained between August 8 and September 2, 2001 during the Pacific 2001 Air Quality Study (PAC2001).The Langley Ecole Lochiel (LEL) site was at 49.0289 N and -122.6025 W and at 90m above sea level (a.s.l). The site was surrounded by hobby farms and by relatively few country roads that are lined with both coniferous and deciduous trees, with little change in terrain heights within a radius of 15 km. Nontraditional agricultural practices, such as mushroom and chicken farming and small orchards, are common within this radius of the site. The nearest small urban center, Langley, is about 6 km north of the site. The site was approximately 10 km to the major expressways of Highway 1 in Canada and I-5 in the US and was approximately 6km to Highway 1A in Canada. Particle sampling was done in the center of an unobstructed field of approximately 30-50m2 about 2.5m from ground. On-site measurements were conducted from five temporary labs with inlets about 5m above ground. \r\n\r\nMeasurements at this site, from August 13th to 31st, were intended to address the unknowns related to particles and ozone, with an emphasis on the transition from the urban mix to a suburban/rural setting, particularly the impact of agricultural sources on the particulate matter formation and evolution. Similar to the instrumentation package at Slocan Park site, the instrumentation package includes measurements in five categories.1) Measurements related to the precursors of fine PM and the oxidation environment in which the fine PM is formed. 2) Measurements related to the characterization of fine PM and the evolution process of PM.3) Measurements related to the emission of fine PM and its precursors in the valley.4) Measurements related to the mapping of fine PM horizontal and vertical distribution in the valley.5) Measurements of meteorological parameters in the valley. Measurements included detailed gas phase measurements of NOx=NOy (total and speciated), CO, O3, SO2, VOCs, OVOCs, carbonyls, NH3, HOx, and NH3 intended for a detailed understanding of the oxidation environment and chemical processes in which both O3 and secondary particulate matter are formed. Detailed measurements were made on size distributed inorganic ionic components, organic carbon, elemental carbon, and mass from 0.05 to 18 mm AD twice a day. High-time resolution measurements using a second AMS were made, measuring the size distribution of inorganic species and homologues of organic species from 0.06 to 0.7 mm. Detailed organic carbon speciation measurements, carbon isotope characterization, sulfur isotope characterization, and amorphous carbon were made for particles 2.5 mm on 10-h day samples collected twice daily. The gas-particle partitioning of semi-volatile organic compounds was studied using a Hi-cap denuder sampling system and detailed lab organic analyses. Continuous mass measurements for particles 2.5 mm were made using a tapered element oscillating microbalance (TEOM)\r\nwith a diffusion dryer on the inlet. Particle number size distributions were measured from 0.01 to 3 mm using a DMA and an optical probe. Hygroscopic properties of particles were measured at two particle sizes using two DMAs in tandem. For NH3, HNO2, HNO3, HCHO, and PM 2.5 mm mass measurements and the particle chemical size distributions, more than one technique were deployed at this site. The multiple measurements of these species provided a test of the performance and validation of the different techniques and ensure that instrument biases were corrected. They also provide complementing data of different characteristics, such as better sensitivities versus time resolution. The diurnal evolution of the boundary layer height was studied using a scanning LIDAR that scanned the north, east and west quadrants. Radiation measurements, both UV and visible, were done using an Eppley and a CIMEL sun photometer. Vertical distribution of certain parameters, such as O3 and meteorological parameters, in the lower part of the atmosphere were also assessed from tethered balloons at Langley Poppy High School, 7.9km northeast of the Langley Ecole Lochiel site. Number size distribution between 0.25 and 10 mm were done from ground the Langley Ecole Lochiel site. This was further aided by a scanning lidar that based at the Langley Ecole Lochiel site. The Pacific 2001 Air Quality Study (PAC2001) was conducted from 1 August to 31 September 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were (1) Cassiar Tunnel, (2) Slocan Park, (3) Langley Ecole Lochiel, (4) Sumas Eagle Ridge, and (5) Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA_1.json b/datasets/NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA_1.json index 6e8b26ea93..ab266ba6fe 100644 --- a/datasets/NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA_1.json +++ b/datasets/NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_SLOCAN_PARK_GAS_PM_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Slocan Park (SLPK) Slocan Park Site Gaseous, Partculate Matter (PM), and Meteorological Data product. Data was collected between August 11 and September 01, 2001 during PAC2001.\r\n\r\nThe SLPK site, at 49.23417 N and -123.0475 W and at 85 m above sea level (a.s.l.), was in a typical urban park in a residential neighborhood in Vancouver with an open field of approximately 150 x 300 m2. Residences of one to two stories surround the park. The site had good fetch in all directions with no major point sources within a radius of 3 km. Like, in much of Vancouver, both deciduous and coniferous trees lined the streets around the site. Traffic in the nearby streets was typical of light volume and light duty transportation. The closest street (29th Avenue), approximately 50 m away, was a secondary traffic route with light volume rush hour traffic. The closest major highway, Highway 1A, was about 600 m away where congested rush hour traffic is typical. Measurements at this site were designed to study the urban mixture of primary particles and secondary particles that are expected from conversion of precursors, such as anthropogenic hydrocarbons. Emphasis was also placed on chemical characterization of PM with an eventual goal of receptor modeling, particularly for organic carbon components. Measurements made at this site included those for gases, such as O3, NOx, total and speciated NOy, SO2, CO, NH3, NMHCs (including mono-terpenes), HCHO and CH3CHO. Particle chemical measurements included size distributed inorganic composition, organic and elemental carbon, and mass from <0.05 to 18 um aerodynamic diameter (AD) using impactors that were sampled twice daily, and size distributed chemical composition from 0.06 to 0.7 um AD at high time resolution using an Aerodyne Aerosol Mass Spectrometer. \r\n\r\nDetailed organic carbon speciation for many solvent-extractable polar and non-polar homologues of organic compounds were conducted with twice daily high-volume sampling and detailed lab analyses. Black carbon was determined using filter-based optical absorption methods. Sulfur isotope was characterized in PM<2.5 um, twice daily on high volume filter samples. Detailed mass measurements were made using several techniques, primarily to assess the performance of the techniques. Particle number size distributions were measured from 0.12 to 0.3 um using an optical probe. Tethered balloon measurements were made at this site. Vertical profiles, from ground level to 300 m for O3, wind direction and speed, T, P, and RH, were measured four times daily. \r\n\r\nPAC2001 was conducted from August 1 to September 31, 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. There were 5 ground sampling sites during the study, which included: Cassiar Tunnel, Slocan Park, Langley Ecole Lochiel, Sumas Eagle Ridge, and Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.\r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA_1.json b/datasets/NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA_1.json index 037086f3b7..9ee8b40131 100644 --- a/datasets/NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA_1.json +++ b/datasets/NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_PAC2001_SUMAS_MTN_GAS_PM_MET_DATA was obtained between August 13 and September 5, 2001 during the Pacific 2001 Air Quality Study (PAC2001). Measurements were collected at the Sumas Eagle Ridge (SER) site. The SER site was located at 49.05166 N and -122.24666 W, at 300 m above sea level (a.s.l.) and approximately 250 m above the surrounding valley floor. The site was in a forest clearing of about 85 - 95m2 on top of a concrete-covered reservoir and surrounded by a mixture of coniferous and deciduous trees. The shortest distance from the site to residential area was about 1 km and was about 3 km to the edge of city of Abbotsford and the nearby major traffic route of Highway 1 in the valley floor. About 3 km to the south of the site, where the elevation drops to about 50 m a.s.l. in the valley floor, NH3 emissions are strong from agricultural sources, and their impact of particle formation and hence the visibility reduction is expected to be significant. Because the site was elevated, the boundary layer did not reach the site each day until midmorning, as indicated by NO and CO. Hence, it was a unique site to study changes in gas and particle chemistry from light to dark hours, the nighttime chemistry and the interaction between biogenic emissions and urban pollution. The site was chosen also to characterize particles for optical, chemical and physical properties since PM in this area of the valley appears to be optically different from those typically observed over the urban areas in Vancouver. \r\n\r\nThe main objectives were to: obtain mass and optical closure in order to better attribute aerosol types and sources to the issues of PM and visibility, and to determine the contribution of non-volatile organic compounds (VOCs), biogenic VOCs, and NH3 to particle mass. Gas phase measurements included oxidant related species: O3, NOx , total and speciated NOy, H2O2, CO, SO2, VOCs, including terpenes and some of their oxidation products, carbonyls, and NH3. Nighttime NO3 was measured at a site near this main site by differential optical absorption spectroscopy. Particle chemical characterization measurements included size-distributed mass, inorganic composition, and organic carbon and elemental carbon (using quartz filters and thermal optical transmittance measurements from 0:05 to 18 mm AD. High-time resolution measurements using an AMS were carried out for the last 5 days during this period, covering the size distribution of inorganic and organic species from 0.06 to 0:7 mm AD. Carbon isotope and detailed speciation of organic carbon in particles 2:5 mm were done on high volume samples on quartz filters that were collected twice daily. Continuous mass measurements for particles 10 mm were made using a tapered element oscillating microbalance (TEOM) that operated at 50C. Particle physical measurements were made to characterize the particle evolution at this site. This included concentration of particles 40:015 mm, number size distribution measurements from 0.003 to 0:20 mm using ultrafine Dynamic mechanical analysis (DMAs). Standard meteorological measurements were carried out at this site during the measurement period. The Pacific 2001 Air Quality Study (PAC2001) was conducted from 1 August to 31 September 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were Cassiar Tunnel, Slocan Park, Langley Ecole Lochiel, Sumas Eagle Ridge, and Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets. \r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA_1.json b/datasets/NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA_1.json index 7ba09b6783..847b4244ac 100644 --- a/datasets/NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA_1.json +++ b/datasets/NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SHEMP_CANADA_PM_COMPOSITION_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_SHEMP_CANADA_PM_COMPOSTION_DATA was obtained between February 14, 2000 and August 1, 2002 during the Study of the Health Effects of the Mix of Urban Air Pollutants (SHEMP). SHEMP was a three-year Toxic Substances Research Initiative study undertaken to advance Canadian knowledge on the possible relationship between PM 2.5 composition and co-pollutants (e.g., NO2, O3) and health effects and on the behavior of PM 2.5 in Canadian cities. The main objectives of this study were to obtain new information on the sources, formation and chemical make-up of PM 2.5 and the critical components of the air pollution mix responsible for health effects. \r\n\r\nField measurement studies in Toronto and Vancouver were designed to meet these objectives and to provide the data to test the hypothesis that the organic fraction of PM 2.5 was a critical component with respect to cardio-respiratory disease. In this study, the sources, formation and chemical content of breathable particles in air and the co-occurrence of other air pollutants were investigated. Air samples were collected daily from sites in Toronto and Vancouver over 2-3 years. Chemical content and particle size were determined. Data were also collected on the presence of other air pollutants present in the form of gases (co-pollutants). A small number of samples were analyzed to determine whether the content of specific indicator chemicals in air particles could help find their pollution source (vehicle exhaust, cooking, wood burning etc.). \r\n\r\nSpecific chemicals of breathable particles produced by different types of sources were identified. Similarly, a small number of samples were collected to assess if semi-volatile organic compounds (i.e., chemicals that may evaporate from the particle and are thus often not measured properly) were an important contributor to the mass of breathable particles. The SHEMP study was led by the Air Quality Research Branch of the Meteorological Service of Canada (MSC). The main collaborators were the Environmental Technology Centre of the Environmental Protection Service and the Chemistry Department of the University of Toronto. The breadth of the study also necessitated that many other organizations provide support, such as the Greater Vancouver Regional District, the Pacific Environmental Science Centre and the Ontario Ministry of the Environment. North American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA_1.json b/datasets/NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA_1.json index 6cf96379ca..986f4260a6 100644 --- a/datasets/NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA_1.json +++ b/datasets/NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_SOS99NASH_G-1_AIR_CHEMISTRY_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) SOS99 Nashville Department of Energy (DOE) G-1 Air Chemistry Data product. Data was collected via the G-1 aircraft deployed during the 1999 campaign to make measurements within the Nashville urban plume. These in situ, semi-Lagrangian measurements, in conjunction with surface-based observations independently made at the Polk Building and at the Cornelia Fort site, allowed quantification of the following:\r\na) ozone production/loss rates, \r\nb) ozone production efficiency and \r\nc) NOx loss rates within this plume. \r\nMechanical problems with the G-1 aircraft precluded making additional measurements. \r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA_1.json b/datasets/NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA_1.json index a08de4183a..c00fcc6cf5 100644 --- a/datasets/NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA_1.json +++ b/datasets/NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_SOS99NASH_SURFACE_MET_CHEM_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) SOS99 Nashville Tennessee Valley Authority (TVA) Surface Meteorology and Chemistry Data product. Data was collected between June 15 and July 16, 1999 during the 1999 Nashville/Middle Tennessee Field Study. TVA operated two enhanced (Level 2) surface-level monitoring stations during the study. One of the level 2 stations was located on the top of the James K. Polk Building in downtown Nashville. The other level 2 station was located about 40 miles west of Nashville near Cumberland Furnace in Dickson County. Gas measurements (5-minute averaged concentrations) included sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NO), nitrogen dioxide (NO2), and total oxides of nitrogen (NOY). The meteorological measurement package included wind speed (WS), wind direction (WD), temperature (T), relative humidity (RH), and solar radiation (RAD). \r\n\r\nNorth American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_SOS99NASH_WIND_PROFILER_DATA_1.json b/datasets/NARSTO_SOS99NASH_WIND_PROFILER_DATA_1.json index eea4f76d42..3abb022422 100644 --- a/datasets/NARSTO_SOS99NASH_WIND_PROFILER_DATA_1.json +++ b/datasets/NARSTO_SOS99NASH_WIND_PROFILER_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SOS99NASH_WIND_PROFILER_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_SOS99NASH_WIND_PROFILER_DATA were obtained between May 19 and August 4, 1999. Wind components (u and v) were collected from five 915-MHz radar wind profilers. Availability of data for each day varies among the profilers, especially at the beginning and end of the project.The profilers and their locations were:Cornelia Fort Airpark (CFA) 36.19N, 86.70 W, 126 m MSLDickson (DIK) 36.25N, 87.37W, 225 m MSLEagleville (EGV) 35.73N, 86.60W, 228 m MSLGallatin (GAL) 36.33N, 86.40W, 171 m MSLCumberland (CMB) 36.38N, 87.65W, 136 m MSLThe number and location of range gates (vertical location of the wind measurements) was:CFA: 1st gate 146 m AGL, 64 gatesDIK, EGV, GAL: 1st gate 96 m AGL, 50 gatesCMB: 1st gate 165 m AGL, 64 gatesAll sites use 58 m range gates.Mixing depth (convective boundary layer height or zi) is given for daytime hours at each site as derived from a manual inspection of profiler reflectivity patterns. Data may be unavailable for a variety of reasons including rain, poorly defined boundary layer, or instrument outage. Data in late afternoon should be used with care even when available, since the afternoon transition is poorly understood.NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA_1.json b/datasets/NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA_1.json index bdc1b925f9..c731a225af 100644 --- a/datasets/NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA_1.json +++ b/datasets/NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_SOS99NASH_WP3D_CHEMISTRY_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) SOS99 Nashville WP-3D Orion Air Chemistry Data product. It was obtained between June 26 and July 19, 1999 during the WP3-D aircraft component of the Nashville 1999 study sponsored in part by the Southern Oxidant Study. The organizations participating in the study included Aircraft Operations Center, National Oceanic and Atmospheric Administration (NOAA), U.S. Dept of Commerce; Aeronomy Laboratory, U.S. Dept of Commerce; Cooperative Institute for Research in Environmental Sciences, University of Colorado; National Center for Atmospheric Research (NCAR); Brookhaven National Laboratory; and University of Denver. \r\n\r\nThere were 12 flights in the mission. Measurements focused on obtaining an improved understanding of the processes that control the formation and distribution of fine particles and ozone. Measurements included in the data files are: aircraft location data, aerosol particle characteristics; upper air meteorology; CO, ozone, NO, NO2, NOy, HNO3, SO2, CO2; NMHCs; photolysis rate coefficients from Actinic flux measurements; PAN, PPN, and MPAN; and formaldehyde. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION_1.json b/datasets/NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION_1.json index 9f2ed8ced0..65118a2040 100644 --- a/datasets/NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION_1.json +++ b/datasets/NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NARSTO_SOS_SC_UPSTATE_PM25_COMPOSITION data were collected during July 2001 and January of 2002 to elucidate the seasonal variability of the aerosols. Samples were collected at a rural location in South Carolina, beginning and ending at midnight in order to associate each sampling event with a calendar day. In all, 40 samples per month were collected (including blanks).The purpose of the study was to determine experimentally the concentration and chemical composition of fine particulate matter (PM2.5, particles with a diameter less than 2.5 um) in South Carolina. The collection of PM2.5 samples on Teflon filters was carried out using a cyclone-based system. Ion chromatography analysis for anions and cations was performed, as well as x-ray fluorescence (XRF) analysis for crustal metals. PM2.5 samples on quartz filters were also collected in order to determine the organic and elemental carbon (EC/OC) particle concentration.The average concentration for PM2.5 during July of 2001 was 20.85 mg/m3. The major components of the aerosol were organic compounds (38.5%) and sulfates (34.7%). During January of 2002, the average concentration for PM2.5 was 9.4 mg/m3. Again, the major components of the aerosol were organic compounds (64.1%) and sulfates (21.9%).NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.", "links": [ { diff --git a/datasets/NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA_1.json b/datasets/NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA_1.json index 8e5d8a91f0..49de3d97d5 100644 --- a/datasets/NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA_1.json +++ b/datasets/NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_STIFS_CANADA_PM_INORG_VOC_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Supersite Transboundary Intensive Field Study (STIFS) Particulate Matter (PM) Inorganic Volatile Organic Compounds Data\r\n\r\nData was collected in the Summer-Winter-Summer of 2001-2002 during the The goal of STIFS was the measurement of PM2.5 composition and related pollutants to improve estimates of the local vs long-range transport contribution to particles. The regions of interest were from SW Ontario to SW Quebec, and the Saturna/Vancouver area. In addition to the long-range transport emphasis, the improved time resolution in the data sets provide more detail for a variety of purposes, especially process studies and model development. STIFS particle-related measurements included: daily ambient meteorological measurements; real-time single particle size and chemistry; high time resolution OC, BC and particle nitrate; high time resolution particle sulfate; size-distribution of organic and element carbon; organic speciation; mass and water soluble organics and inorganics; particle mass and trace metals; and particle mass and inorganic ions. A companion document describing the project in greater detail and showing monitoring locations is available. Data archived at this time are the mass and water soluble organics and inorganics, speciated volatile organic carbon gas phase measurements, PM2.5 and PM2.5-10 mass concentration, and elemental and organic carbon mass concentrations. It is known that U.S. sources contribute significantly to the regional particle levels during certain time periods and likely have a significant impact on the annual average. However, sources within Ontario, Quebec and B.C. also play a role and better information on the relative importance of these sources vs U.S. sources is critical to policy development. Improved information on this issue can be obtained through more detailed ambient measurements in urban and rural areas and through the use of models. The study provided the measurements needed to infer more about the sources of particles in areas impacted by regional transport and to improve regional models (e.g., AURAMS and Models-3/CMAQ) for future application. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_1.json b/datasets/NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_1.json index 87124f78cc..7f7729a5a3 100644 --- a/datasets/NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_1.json +++ b/datasets/NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NARSTO_Texas_PM2.5_Sampling_and_Analysis_Study_1997-1998_ is the North American Research Strategy for Tropospheric Ozone (NARSTO) Texas Particulate Matter (PM) 2.5 Sampling and Analysis Study: 1997-1998 Data. The data for this product was collected from March 11, 1997 to March 12, 1998. The City of Houston, the Texas Natural Resource Conservation Commission (TNRCC), and the Houston Regional Monitoring Network sponsored sampling and analysis of PM2.5 samples taken over the course of one year, from March 11, 1997 to March 12, 1998. Objectives of the study were to determine the levels and chemical composition of PM2.5 in Houston and other cities in Texas and to determine the background levels and chemical composition of PM2.5 transported into Houston. During the sampling effort, 24-hour PM2.5 mass measurements were acquired from 15 sites throughout the state of Texas, using DRI's MEDVOL particle samples. All of the Teflon filters were analyzed for mass by gravimetry and a selected subset of the Teflon and quartz fiber filters were subjected to full chemical analysis. These measurements were taken in anticipation of the U.S. EPA revising PM2.5 and PM10 NAAQS. These results could be used to establish background PM conditions and determine compliance with new PM standards. Various sampler configurations allow evaluation of data precision, accuracy, and validity. \r\n\r\nNARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.", "links": [ { diff --git a/datasets/NASA Flood Extent Detection_1.json b/datasets/NASA Flood Extent Detection_1.json index 092f741f70..d7c2b3a33d 100644 --- a/datasets/NASA Flood Extent Detection_1.json +++ b/datasets/NASA Flood Extent Detection_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASA Flood Extent Detection_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains synthetic aperture radar (SAR) raster imagery for various flood events acquired from the European Space Agencys Sentinel-1A and Sentinel-1B missions, providing C-Band dual-polarized imagery that spans geographical areas of interest in the United States and Bangladesh. The main emphasis was on the labeling of open water areas where specular reflection of the radar signal off of the relatively still, flat open water surface results in reduced backscatter, low amplitude, and an overall darkened appearance within the image. The labels for the water surface reflectance are also provided in GeoTiff rasterized file format in scenes aligned with the SAR source raster imagery.", "links": [ { diff --git a/datasets/NASADEM_HGT_001.json b/datasets/NASADEM_HGT_001.json index 55dcad53dd..20eaec228a 100644 --- a/datasets/NASADEM_HGT_001.json +++ b/datasets/NASADEM_HGT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_HGT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_HGT) dataset, which provides global elevation data at 1 arc second spacing.\r\n\r\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \r\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\r\n\r\nNASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \r\n\r\nNASADEM_HGT data product layers include DEM, number of scenes (NUM), and an updated SRTM water body dataset (water mask). The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data. A low-resolution browse image showing elevation is also available for each NASADEM_HGT granule.\r\n", "links": [ { diff --git a/datasets/NASADEM_NC_001.json b/datasets/NASADEM_NC_001.json index 023e432a48..07b371be25 100644 --- a/datasets/NASADEM_NC_001.json +++ b/datasets/NASADEM_NC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_NC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_NC) dataset, which provides global elevation data at 1 arc second spacing.\r\n\r\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \r\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\r\n\r\nNASADEM are distributed in 1\u00b0 by 1\u00b0 tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \r\n\r\nNASADEM_NC data product layer includes DEM. The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data. The source of each elevation pixel in the corresponding NASADEM_NUMNC product.\r\n", "links": [ { diff --git a/datasets/NASADEM_NUMNC_001.json b/datasets/NASADEM_NUMNC_001.json index 005f36642b..f4eafccb04 100644 --- a/datasets/NASADEM_NUMNC_001.json +++ b/datasets/NASADEM_NUMNC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_NUMNC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_NC) dataset, which provides global elevation data at 1 arc second spacing.\r\n\r\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \r\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\r\n\r\nNASADEM are distributed in 1\u00b0 by 1\u00b0 tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \r\n\r\nNASADEM_NC data product layer includes DEM. The NASADEM_NCNUM layer indicates the number of scenes that were processed for each pixel and the source of the data.\r\n\r\nThe global 1 arc second NASADEM product is also available in NetCDF4 format as the NASADEM_NC dataset with the source of each elevation pixel in the corresponding NASADEM_NUMNC product.\r\n", "links": [ { diff --git a/datasets/NASADEM_SC_001.json b/datasets/NASADEM_SC_001.json index a2a4d228dc..d75c050004 100644 --- a/datasets/NASADEM_SC_001.json +++ b/datasets/NASADEM_SC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_SC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SC) dataset, which provides global slope and curvature elevation data at 1 arc second spacing.\r\n\r\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \r\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\r\n\r\nNASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \r\n\r\nNASADEM_SC data product layers include slope, aspect angle, profile curvature, plan curvature, and an updated SRTM water body dataset (water mask). A low-resolution browse image showing slope is also available for each NASADEM_SC granule.\r\n", "links": [ { diff --git a/datasets/NASADEM_SHHP_001.json b/datasets/NASADEM_SHHP_001.json index 9cffbf2e0f..f37a3cdc2d 100644 --- a/datasets/NASADEM_SHHP_001.json +++ b/datasets/NASADEM_SHHP_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_SHHP_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SHHP) dataset, which provides Shuttle Radar Topography Mission (SRTM) global elevation height data at 1 arc second spacing.\n\nNASADEM data products were derived from original telemetry data from SRTM, a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \n\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\n\nNASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \n\nNASADEM_SHHP data product layers include SRTM-only floating-point DEM and height error. A low-resolution browse image showing the SRTM-only elevation is also available for each NASADEM_SHHP granule.\n", "links": [ { diff --git a/datasets/NASADEM_SIM_001.json b/datasets/NASADEM_SIM_001.json index d6b11be764..eba3aa0fd1 100644 --- a/datasets/NASADEM_SIM_001.json +++ b/datasets/NASADEM_SIM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_SIM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SIM) dataset, which provides global Shuttle Radar Topography Mission (SRTM) image mosaic data at 1 arc second spacing.\r\n\r\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \r\nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\r\n\r\nNASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \r\n\r\nNASADEM_SIM data product layers include radar combined images and a NUM file associated with combined images. A low-resolution browse image showing the SRTM image mosaic elevation is also available for each NASADEM_SIM granule.\r\n", "links": [ { diff --git a/datasets/NASADEM_SSP_001.json b/datasets/NASADEM_SSP_001.json index f4ad84986a..ab8efdea85 100644 --- a/datasets/NASADEM_SSP_001.json +++ b/datasets/NASADEM_SSP_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASADEM_SSP_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SSP) dataset, which provides global Shuttle Radar Topography Mission (SRTM) sub-swath elevation data at 1 arc second spacing.\n\nNASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. \nIn addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.\n\nNASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \n\nNASADEM_SSP data product layers include radar total correlation, radar volumetric correlation, radar individual images, radar incidence angle (relative to ellipsoid), and radar incidence angle (local). A low-resolution browse image showing sub-swath elevation is also available for each NASADEM_SSP granule.\n", "links": [ { diff --git a/datasets/NASAPHOTOS.json b/datasets/NASAPHOTOS.json index a2e625a72b..880c13ddda 100644 --- a/datasets/NASAPHOTOS.json +++ b/datasets/NASAPHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASAPHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Aeronautics and Space Administration (NASA) Aerial Photography\n data set is a film archive of photographs from the Lyndon B. Johnson Space\n Center (JSC) in Houston, Texas, and the NASA Ames Research Center in Moffett\n Field, California. In 1965, the JSC initiated the Earth Resources Aircraft\n Program and began flying photographic missions for Federal Government agencies\n and other entities involved in remote sensing experiments. Beginning in 1966,\n NASA conducted an Earth Observations Program, including Earth surveys using\n aircraft platforms.\n \n Photographs from a variety of NASA programs provide project-specific coverage\n over the United States, Grand Bahama, Jamaica, and Central America at base\n scales ranging from 1:16,000 scale to 1:450,000 scale. Film types, scales,\n acquisition schedules, flight altitudes, and end products differ, according to\n project requirements.", "links": [ { diff --git a/datasets/NASASatellite_Dev_Applications_2293_1.json b/datasets/NASASatellite_Dev_Applications_2293_1.json index c543b61846..d45618a271 100644 --- a/datasets/NASASatellite_Dev_Applications_2293_1.json +++ b/datasets/NASASatellite_Dev_Applications_2293_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASASatellite_Dev_Applications_2293_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.", "links": [ { diff --git a/datasets/NASA_Airborne_Lidar_Flights_1.json b/datasets/NASA_Airborne_Lidar_Flights_1.json index 5da972611f..93c7b316ea 100644 --- a/datasets/NASA_Airborne_Lidar_Flights_1.json +++ b/datasets/NASA_Airborne_Lidar_Flights_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASA_Airborne_Lidar_Flights_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from the 1982 NASA Langley Airborne Lidar flights following the eruption of El Chichon beginning in July 1982 and continuing to January 1984. Data in ASCII format.", "links": [ { diff --git a/datasets/NASA_OMI_3.0.json b/datasets/NASA_OMI_3.0.json index cfefafa04c..7f07e4bd88 100644 --- a/datasets/NASA_OMI_3.0.json +++ b/datasets/NASA_OMI_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASA_OMI_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI observations provide the following capabilities and features: \u2022 A mapping of ozone columns at 13 km x 24 km and profiles at 13 km x 48 km \u2022 A measurement of key air quality components: NO2, SO2, BrO, HCHO, and aerosol \u2022 The ability to distinguish between aerosol types, such as smoke, dust and sulfates \u2022 The ability to measure aerosol absorption capacity in terms of aerosol absorption optical depth or single scattering albedo \u2022 A measurement of cloud pressure and coverage \u2022 A mapping of the global distribution and trends in UV-B radiation The OMI data are available in the following four levels: Level 0, Level 1B, Level 2, and Level 3. \u2022 Level 0 products are raw sensor counts. Level 0 data are packaged into two-hour "chunks" of observations in the life of the spacecraft (and the OMI aboard it) irrespective of orbital boundaries. They contain orbital swath data. \u2022 Level 1B processing takes Level 0 data and calibrates, geo-locates and packages the data into orbits. They contain orbital swath data. \u2022 Level 2 products contain orbital swath data. \u2022 Level 3 products contain global data that are composited over time (daily or monthly) or over space for small equal angle (latitude longitude) grids covering the whole globe.", "links": [ { diff --git a/datasets/NASMo_TiAM_250m_2326_1.json b/datasets/NASMo_TiAM_250m_2326_1.json index 8d5ec7079d..f8611260d7 100644 --- a/datasets/NASMo_TiAM_250m_2326_1.json +++ b/datasets/NASMo_TiAM_250m_2326_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NASMo_TiAM_250m_2326_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.", "links": [ { diff --git a/datasets/NAWQA.json b/datasets/NAWQA.json index 486303b7ce..d2c2e6addd 100644 --- a/datasets/NAWQA.json +++ b/datasets/NAWQA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAWQA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a coverage of the boundaries and codes used for the U.S. Geological\nSurvey National Water-Quality Assessment (NAWQA) Program Study-Unit\ninvestigations for the conterminous United States, excluding the High Plains\nRegional Ground-Water Study.\n\nThe National Water-Quality Assessment Program is designed to describe the\nstatus and trends in the quality of the Nation's ground- and surface-water\nresources and to provide a sound understanding of the natural and human factors\nthat affect the quality of these resources (Leahy and others, 1990). A \"Study\nUnit\" is a major hydrologic system in which NAWQA studies are focused. Study\nUnits are geographically defined by a combination of ground- and surface-water\nfeatures (Gilliom and others, 1995).\n\nAs part of the NAWQA program, Study-Unit investigations were planned for 60\nareas throughout the Nation to provide a framework for national and regional\nwater-quality assessments (Leahy and others, 1990). The 60 planned Study-Units\nwere divided into three groups of 20. Each group would be intensively studied\non a rotational basis with 20 studies beginning in fiscal year 1991 (FY 1991\nruns from October 1990-September 1991), 20 more studies beginning in fiscal\nyear 1994 (October 1993-September 1994), and the final 20 studies beginning in\nfiscal year 1997 (October 1996-September 1997). Each study cycle would span 10\nyears. In 1996, the number of Study-Units was scaled back to 59 when two of the\noriginal 60 Study Units combined. Also, because of budgetary restraints, some\nof the original planned Study Units have been scheduled to start later than\noriginally planned and others have not even been scheduled to start yet.\n\nThis coverage contains the boundaries for the 57 Study Units within the\nconterminous United States, excluding the High Plains Regional Ground\nWater-Study, which was conceived in late 1997. The coverage also includes the\nname, starting date, and NAWQA standard abbreviation of each Study Unit plus\nvarious codes to help display the data. This data set is used primarily to\ndisplay the location of NAWQA Study Units and for analysis of data at the\nnational scale. It is not recommended for either local or regional analysis due\nto the small scale of most of the features.\n\nThis coverage can be used in conjunction with other NAWQA datasets including\nthe point coverage of NAWQA Trace Element Sampling Sites (NAWQA_TE) and the\npoint coverage of NAWQA Nutrients Sampling Sites (NAWQA_NU). Detailed\ninformation on these two coverages can be found in their respective metadata.\n\nOriginally, Study-Unit boundaries in this coverage were composed of 1:\n2,000,000-scale hydrologic unit boundaries (Allord, 1992) and state boundaries\n(Negri, 1994). As the NAWQA project has progressed and Study-Unit\nInvestigations have gotten underway, many Study-Unit boundaries have been\nmodified. In addition, Study Units have enhanced their boundary coverages with\nfeatures at higher resolutions. As these modifications are made, Study Units\nsubmit their new boundary coverages to National Synthesis teams, who are\nresponsible for summarizing the results from all of the Study Units, and the\nchanges are incorporated into this coverage. As a result, this coverage is\ncomposed of linear features at various scales (for example, 1: 100,000, 1:\n250,000), but the majority remain at the 1: 2,000,000 scale.\n\nThe original version of this coverage was generated by the the USGS\nCartographic and Publishing Program (CAPP) in Madison, Wisconsin, in the fall\nof 1991. The procedures used to create this coverage are described below. Each\nNAWQA Study Unit was asked for a description of their boundary definition. Once\nthis information was gathered, CAPP created the coverage by extracting digital\nfeatures from the 1: 2,000,000 Hydrologic Unit boundaries coverage and the 1:\n2,000,000 state boundaries coverage. Since the majority of Study-Unit\nboundaries are defined from hydrologic unit boundaries, most of the features\nwere directly copied from the Hydrologic Units coverage. An exception to this\nwas the boundary defining the Georgia-Florida Coastal Plain Study Unit where\nthe northern boundary was defined by the northern edge of the Florida Aquifer.\nTo incorporate this boundary into the coverage, the aquifer boundary was\ndigitized from the U.S. Geological Survey's \"Ground-Water Atlas of the United\nStates\", HA-730 (G) (Miller, 1990). In November 1991, responsibility for\nmaintaining the coverage was transferred to NAWQA's National Synthesis staff.\nMajor milestones in the development of the coverage and various revisions to\nthe coverage are listed under the Lineage section.\n\nThe NAWQA Program has used the coverage for various analyses and displays and\nfor various published reports, for example, Leahy and Thompson (1994) and\nGilliom and others (1995).\n\nThe coverage is reviewed by one of the NAWQA National Synthesis GIS staff\nmembers prior to release. Related_Spatial_and_Tabular_Data_Sets:\n\nAlaska (Cook Inlet) and Hawaii (Oahu) NAWQA Study-Unit boundaries are\nmaintained in separate data sets.\n\nThe High Plains Regional Ground-Water Study boundary is in a separate data set.\n\nCook, Oahu, and High Plains study boundaries should be used with this data set\nto give the full picture of NAWQA Study Units nationwide.\n\n[Summary provided by EPA]", "links": [ { diff --git a/datasets/NAWQAHIS.json b/datasets/NAWQAHIS.json index 47bc888f8c..ac49c6b308 100644 --- a/datasets/NAWQAHIS.json +++ b/datasets/NAWQAHIS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NAWQAHIS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The retrospective database is a compilation of historical water-quality and\nancillary data collected before NAWQA Study Units initiated sampling in 1993.\nThis coverage contains the point locations of monitoring locations where\nhistorical water-quality data was collected. Water-quality data were obtained\nby study-unit personnel from the U.S. Geological Survey (USGS) National Water\nInformation System (NWIS), from records of State water-resource agencies, and\nfrom STORET, the U.S. Environmental Protection Agency national database.\nAncillary data describing characteristics of sampled sites were compiled by\nNAWQA Study Units or obtained from national-scale digital maps.\n\nMueller and others (1995) used this data to determine preexisting water-quality\nconditions in the first 20 NAWQA Study Units that began in 1991. Also, Nolan\nand Ruddy (1996) used the data to describe areas of the United States at risk\nof nitrate contamination of ground water.\n\nSupplemental_Information:\n\nThe retrospective database includes over 22,000 surface-water samples. The\nsurface-water data are for samples collected during 1980-90 at sites that had a\nminimum of 25 monthly samples. Year of sampling is included in the\nretrospective database because it was reported most often by the various Study\nUnits. Year of sampling also is convenient because some Study Units reported\nmedian constituent concentrations. If sampling date ranges for median values\nfell within a single year, then year of sampling was retained in the national\ndata set for that sample.\n\nBecause sampling, preservation, and analytical techniques associated with these\nhistorical data changed during the period of record and are different for\ndifferent agencies, reported nutrient concentrations were aggregated into the\nfollowing groups: (1) ammonia as N, (2) nitrate as N, (3) total nitrogen, (4)\northophosphate as P, and (5) total phosphorus. For example, ammonia includes\nboth ammonium ions and un-ionized ammonia. More information on methods used to\naggregate constituent data is available in the report by Mueller and others\n(1995).\n\nMuch of the ancillary data, such as well and aquifer descriptions and land-use\nclassification for surface-water drainage basins, were provided by NAWQA Study\nUnits. Data evaluated at the national scale include land use, soil hydrologic\ngroup, nitrogen input to the land surface, and the ratios of pasture or\nwoodland to cropland.\n\nLand-use classification of surface-water sites is based on Anderson Level I\ncategories (Anderson and others, 1976). Land use at surface-water sites was\nclassified by NAWQA Study Unit personnel based on the Anderson Level I\ncategories. Many surface-water sites were affected by mixed land uses, such as\nForest and Agricultural, or Agricultural and Urban. Surface-water sites with\nvery large drainage areas (greater than 10,000 square miles) were considered to\nbe affected by multiple land uses, and were designated as Integrated land use.\nMore detailed descriptions of the land-use categories in the retrospective\ndatabase are given by Mueller and others (1995).\n\nSoil hydrologic group was determined from digital maps compiled by the U.S.\nSoil Conservation Service (1993). The categorical values (A, B, C, and D) from\nthe digital maps were converted to numbers to permit aggregation (Mueller and\nothers, 1995). Surface-water sites were assigned the area-weighted mean for\nsoil mapping units in the upstream drainage basin. Many surface-water sites did\nnot have digitized basin boundaries available, so hydrologic group could not be\nevaluated.\n\nFertilizer and manure applications were estimated from national databases of\nfertilizer sales (U.S. Environmental Protection Agency, 1990) and animal\npopulations (U.S. Bureau of the Census, 1989). Nitrogen input by atmospheric\ndeposition was derived from data provided by the National Atmospheric\nDeposition Program/National Trends Network (1992).\n\nPopulation data were obtained from the U.S. Bureau of the Census (1991). Total\npopulation in the upstream drainage was compiled for the surface-water data\nset.\n\nWithin the database, concentrations less than detection are reported as\nnegative values of the detection limit. Missing values are indicated by a\ndecimal point. (During processing of the tabular data, these decimal points\nwere replaced will NULL values; See Data_Quality_Information section.\n\nHistorical data can be of limited use in national assessments because of\ninconsistencies between and within agencies in database structure and format\nand in sample collection, preservation, and analytical procedures. For example,\nchanges in sample collection and analytical procedures can cause shifts in\nconstituent concentrations that are unrelated to possible changes in\nenvironmental factors. See Mueller and others (1995) for assumptions and\nlimitations associated with the retrospective database. \n\n[Summary provided by the EPA.]", "links": [ { diff --git a/datasets/NA_MODIS_Surface_Biophysics_1210_1.json b/datasets/NA_MODIS_Surface_Biophysics_1210_1.json index 8fdf61253c..2df6b36aea 100644 --- a/datasets/NA_MODIS_Surface_Biophysics_1210_1.json +++ b/datasets/NA_MODIS_Surface_Biophysics_1210_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NA_MODIS_Surface_Biophysics_1210_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides MODIS-derived surface biophysical climatologies of bidirectional distribution function (BRDF), BDRF/albedo, land surface temperature (LST), leaf area index (LAI), and evapotranspiration (ET) as separate files for each of the MODIS land cover types, and four radiative forcing data files for four scenarios of potential vegetation shifts in North America. Each biophysical variable has temporal periods that represent the average of all 8-day periods from the years 2000-2012. The data have a spatial resolution of 0.05 degree (~5 km) and a temporal resolution of eight days. Additionally, a file containing diffuse fraction of surface downward solar radiation (DiffuseFraction) at a monthly scale, and a file containing snow water equivalent (SWE) are provided. The extent of the data covers the land area of North America, from 20 to 60 degrees N. The land-cover map used was synthesized from nine yearly 500-m MODIS land-cover layers (MCD12 Q1 Collection 5) for 2001-2008. These high-resolution land data were originally developed for quantifying biophysical forcing from land-use changes associated with forestry activities, such as radiative forcing from altered surface albedo. ", "links": [ { diff --git a/datasets/NA_TreeAge_1096_1.json b/datasets/NA_TreeAge_1096_1.json index 287048bc71..75484735bc 100644 --- a/datasets/NA_TreeAge_1096_1.json +++ b/datasets/NA_TreeAge_1096_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NA_TreeAge_1096_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from the NASA Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. These input data products have various sources and creation dates as described in the source paper by Pan et al. (2011). Canadian maps were produced with data available through 2004 and U.S.A. maps with data available through 2006. A supplementary map of the standard deviations for age estimates was developed for quantifying uncertainty.Note that the Pan et al. (2011) paper is included as a companion file with this data set and was the source of descriptions in the guide.Forest age, implicitly reflecting the past disturbance legacy, is a simple and direct surrogate for the time since disturbance and may be used in various forest carbon analyses that concern the impact of disturbances. By combining geographic information about forest age with estimated carbon dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status.", "links": [ { diff --git a/datasets/NBCD2000_V2_1161_2.json b/datasets/NBCD2000_V2_1161_2.json index b29792200b..4a3ec28ea1 100644 --- a/datasets/NBCD2000_V2_1161_2.json +++ b/datasets/NBCD2000_V2_1161_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBCD2000_V2_1161_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project. ", "links": [ { diff --git a/datasets/NBId0001_101.json b/datasets/NBId0001_101.json index ffb26eee14..1c5368a71d 100644 --- a/datasets/NBId0001_101.json +++ b/datasets/NBId0001_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0001_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries.", "links": [ { diff --git a/datasets/NBId0006_101.json b/datasets/NBId0006_101.json index 69cf1ac3ad..643208cbb9 100644 --- a/datasets/NBId0006_101.json +++ b/datasets/NBId0006_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0006_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI06\n\nDataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation\n\nThe Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by\nFood and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used\nwere hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution\nof 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules.", "links": [ { diff --git a/datasets/NBId0007_101.json b/datasets/NBId0007_101.json index 3f5e33259b..991f74df9a 100644 --- a/datasets/NBId0007_101.json +++ b/datasets/NBId0007_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0007_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI07\n\nThis dataset shows adminstrative boundries of Africa at continental,\nnational, second and third levels in lat/long.\n\n\nThe Administrative units Dataset documentation\n\nFiles: ADMINLL.E00 Code: 100012-002\n\nVector Member\n\nThe files are in Arc/Info Export format and should be imported\nwith the Arc/Info command Import cover In-Filename Out-Filename.\n\nThe administrative units dataset is part of the UNEP/FAO/ESRI\nDatabase project that covers the entire world but focuses here on\nAfrica. The maps were prepared by Environmental Systems Research\nInstitute (ESRI), USA. Most data for the database were provided by the\nSoil Resources, Management and Conservation Service Land and Water\nDevelopment Division of the Food and Agriculture Organization (FAO),\nItaly. The database was developed by the United Nations Environment\nProgram (UNEP as part of a project initiated by UNEP.\n\nThe base maps used were the FAO/UNESCO Soil Map of the World\n(1977) in Miller Oblated Stereographic projection, FAO Maps and\nStatistical Data by Administrative Unit (1983), and the Rand-McNally\nNew International Atlas (1982). All sources were re-registered to the\nbasemap by comparing known features on the basemap and the source\nmaps. The digitizing was done with a spatial resolution of 0.002\ninches. The maps were then transformed from inch coordinates to\nlatitude/longitude degrees. The transformation was done by an\nunpublished algorithm (by US Geological Survey and ESRI) to create\ncoverage\"'\"s for one-degree graticules.\n\nContact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil\nResources, Management and Conservation Service 00100, Rome, Italy.\nESRI, 380 New York Street, Redlands, CA 92373, USA\n\n\nThe ADMINLL file shows adminstrative boundries at continental, national,\nsecond and third levels in lat/long\n\nReferences:\n\nESRI. Final Report UNEP/FAO world and Africa GIS data base (1984).\nInternal Publication ESRI, FAO and UNEP\n\nFAO, UNESCO. Soil Map of the World (1977).\nScale 1:5000000. UNESCO, Paris\n\nDefence Mapping Agency. Global Navigation and Planning Charts for Africa\n(various dates: 1976-1982). Scale 1:5000000. Washington DC.\n\nG.M.Grosvenor. National Geographic Atlas of the World (1975).\nScale 1:8500000. National Geographic Society Washington DC.\n\n\nSource : FAO Soil Map of the World, scale 1:5000000\nPublication Date : Dec 1984\nProjection : Geographic Lat/Long\nType : Polygon\nFormat : Arc/Info Export non-compressed\nRelated Datasets : All UNEP/FAO/ESRI Datasets\nTOWNS2 100022-002, Human settlements and airports\nROADS2 100021-001, major roads", "links": [ { diff --git a/datasets/NBId0012_101.json b/datasets/NBId0012_101.json index 1595d3768f..f1adbbeedb 100644 --- a/datasets/NBId0012_101.json +++ b/datasets/NBId0012_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0012_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cattle and Buffalo distribution dataset shows cattle and buffalo distribution for sub-Saharan, East and Central Africa. It is part of the East Coast Fever (ECF) dataset. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic\ncattle by Buffalo. Buffalo is the main wildlife host of the ECF. The study was carried out in Nairobi in collaboration with United Nations Environment Program, Global Resource Information Database (UNEP/GRID)\nand the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI).", "links": [ { diff --git a/datasets/NBId0016_101.json b/datasets/NBId0016_101.json index d0a065a1e7..2722c99ef5 100644 --- a/datasets/NBId0016_101.json +++ b/datasets/NBId0016_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0016_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI16\n\nAgro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.\n\nThe Africa Agro-ecological Zones Dataset documentation\n\nFiles: AEZBLL08.E00\nCode: 100025-011\nAEZBLL09.E00\n100025-012\nAEZBLL10.E00\n100025-013\n\nVector Members\nThe E00 files are in Arc/Info Export format and should be imported\nwith the Arc/Info command Import cover In-Filename Out-Filename.\n\nThe Africa agro-ecological zones dataset is part of the\nUNEP/FAO/ESRI Database project that covers the entire world but\nfocuses on Africa. The maps were prepared by Environmental Systems\nResearch Institute (ESRI), USA. Most data for the database were\nprovided by Food and Agriculture Organization (FAO), the Soil\nResources, Management and Conservation Service Land and Water\nDevelopment Division, Italy. The daset was developed by United Nations\nEnvironment Program (UNEP), Kenya. The base maps that were used were\nthe UNESCO/FAO Soil Map of the world (1977) in Miller Oblated\nStereographic projection, the Global Navigation and Planning Charts\n(various 1976-1982) and the National Geographic Atlas of the World\n(1975). basemap and the source maps. The digitizing was done with a\nspatial resolution of 0.002 inches. The maps were then transformed\nfrom inch coordinates to latitude/longitude degrees. The\ntransformation was done by an unpublished algorithm (by US Geological\nSurvey and ESRI) to create coverages for one-degree graticules. This\nedit step required appending the country boundaries from\nAdministrative Unit map and then producing the computer plot.\n\n\nContact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil\nResources, Management and Conservation Service, 00100, Rome, Italy\nESRI, 380 New York Street, Redlands, CA 92373, USA\n\nThe AEZBLL08 data covers North-West of African continent\nThe AEZBLL09 data covers North-East of African continent\nThe AEZBLL10 data covers South of African continent\n\nReferences:\n\nESRI. Final Report UNEP/FAO world and Africa GIS data base (1984).\nInternal Publication by ESRI, FAO and UNEP\n\nFAO/UNESCO. Soil Map of the World (1977).\nScale 1:5000000. UNESCO, Paris\n\nDefence Mapping Agency. Global Navigation and Planning Charts for Africa\n(various dates:1976-1982). Scale 1:5000000. Washington DC.\n\nG.M. Grosvenor. National Geographic Atlas of the World (1975).\nScale 1:8500000. National Geographic Society, Washington DC.\n\nFAO. Statistical Data on Existing Animal Units by Agro-ecological\nZones for Africa (1983). Prepared by Todor Boyadgiev of the Soil\nResources, Management and Conservation Services Division.\n\n\nFAO. Statistical Data on Existing and Potential Populations by\nAgro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of\nthe Soil Resources, Management and Conservation Services Division.\nFAO. Report on the Agro-ecological Zones Project. Vol.I (1978),\nMethodology & Result for Africa. World Soil Resources No.48.\n\n\nSource : UNESCO/FAO Soil Map of the World, scale 1:5000000\nPublication Date : Dec 1984\nProjection : Miller\nType : Polygon\nFormat : Arc/Info Export non-compressed\nRelated Datasets : All UNEP/FAO/ESRI Datasets,\nLanduse (100013/05, New-ID: 05\nFAO Irrigable Soils Datasets and Water balance (100050/53)", "links": [ { diff --git a/datasets/NBId0018_101.json b/datasets/NBId0018_101.json index 589313343e..9f46bbf9bf 100644 --- a/datasets/NBId0018_101.json +++ b/datasets/NBId0018_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0018_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI18\n\nThe Africa Major Infrastructure and Human Settlements Dataset\n\nFiles: TOWNS2.E00\nCode: 100022-002\nROADS2.E00\n100021-002\n\nVector Members:\nThe E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename\n\nThe Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for\nthe database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed\nin collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global\nNavigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree\ngraticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box\n30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent\n\nReferences:\n\nESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP\n\nFAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris\n\nDefence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC.\n\nGrosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC.\n\nDMA. Topographic Maps of Africa (various dates).\nScale 1:2000000 Washington DC.\n\nRand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago\n\nSource: FAO Soil Map of the World. Scale 1:5000000\nPublication Date: Dec 1984\nProjection: Miller\nType: Points\nFormat: Arc/Info export non-compressed\nRelated Datasets: All UNEP/FAO/ESRI Datasets\nADMINLL (100012-002) administrative boundries\nAFURBAN (100082) urban percentage coverage\nComments: There is no outline of Africa", "links": [ { diff --git a/datasets/NBId0019_101.json b/datasets/NBId0019_101.json index 5b6424ac68..b8accc6015 100644 --- a/datasets/NBId0019_101.json +++ b/datasets/NBId0019_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0019_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI19\n\nThe Africa Major Elevation Zones Dataset documentation\n\nFile: ELEVLL\nCode: 100070-003\n\nVector Member\nThe above file is in Arc/Info Export format and should be imported\nusing the Arc/Info command Import cover In-Filename Out-Filename The\nAfrica elevation major zones dataset is part of the UNEP/FAO/ESRI\nDatabase project that covers the entire world but focuses here on\nAfrica. The maps were prepared by Environmental Systems Research\nInstitute (ESRI), USA. Most data for the database were provided by\nthe Soil Resources, Management and Conservation Service Land and Water\nDevelopment Division of the Food and Agriculture Organization (FAO),\nItaly. This dataset was developed in collaboration with the United\nNations Environment Program (UNEP), Kenya. The manuscript derived\nfrom the topographic film separates of the UNESCO/FAO Soil Map of the\nWorld (1977) in Miller Oblated Stereographic projection was used to\nprovide a generalized coverage of elevation values providing\ninformation as both line-related and polygonal form. The map was\nprepared by overlaying the topography film separate with a matte\ndrafting film and then delineating the selected elevation contours.\nSome of the line crenulation was removed during the delineation\nprocess, because this map was designed to define general elevation\nzones rather than constitute a true topographic base. Code values\nwere recorded directly on the map and were key-entered during the\ndigitizing process with a spatial resolution of 0.002 inches, as part\nof the polygon or line sequence indentification number. The map was\nthen transformed from inch coordinates to latitude/longitude\ndegrees. The transformation was done using an unpublished algorithm of\nthe US Geological Survey and ESRI to create coverages for one-degree\ngraticules. Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya\nFAO, Soil Resources, Management and Conservation Service, 00100, Rome,\nItaly.\nESRI, 380 New York Street, Redlands, CA 92373, USA\n\n\nThe ELEVLL2 data shows Major Elevation zones of Africa, in lat/lon\n\nReferences:\n\nESRI. Final Report UNEP/FAO World and Africa GIS data base (1984).\nInternal Publication by ESRI, FAO and UNEP FAO. UNESCO/FAO Soil Map\nof the World(1977). Scale 1:5000000. UNESCO, Paris DMA. Topographic\nMaps of Africa (various dates). Scale 1:2000000 Washington DC.\n\nG.M. Grosvenor. National Geographic Atlas of the World (1975).\nScale 1:8500000. National Geographic Society Washington DC.\n\n\nSource: FAO Soil Map of the World, scale 1:5000000\nPublication Date: Dec 1984\nProjection: Miller\nType: Polygon and line\nFormat: Arc/Info export non compressed\nRelated Datasets: All UNEP/FAO/ESRI Datasets\nAFELBA elevation and Bathymetry (100048)", "links": [ { diff --git a/datasets/NBId0020_101.json b/datasets/NBId0020_101.json index 49343adf28..93cf22bcf0 100644 --- a/datasets/NBId0020_101.json +++ b/datasets/NBId0020_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0020_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI20\n\nCountries, Coasts and Islands Dataset documentation\n(Micro World Data Bank II)\n\nFiles: COASTS.E00\nCode: 100051-001\nCOUNTRY.E00\n100052-001\nISLANDS.E00\n100054-001\n\nVector Members\nOriginal files were in IDRISI VEC format coverted to Arc/Info.\nThe E00 files are in Arc/Info Export format and should be imported\nwith the Arc/Info command Import cover In-Filename Out-Filename.\nMicro World Data Bank II (MWDB-II) comprising Coastlines, Country\nboundries and Islands data sets is part of NOAA project that was\ndeveloped by the World Data Center-A (WDC-A) for Solid Earth\nGeophysics, operated by the U.S. National Geophysical Data Center\n(NGDC). The dataset is part of the World Data Bank II and is provided\non a diskette called The Global Change Data Base. The Data Bank II is\npart of larger project called Global Ecosystems Database Project. This\nis a cooperation between the National Oceanic and Atmospheric\nAdministration (NOAA), NGDC and the U.S. Environmental Protection\nAgency (EPA). The National Center for Geographic Information and\nAnalyses (NCGIA) in Santa Barbara, California joined the project to\nassist with training and evaluation. A scale was chosen that\ncorresponds closely with the resolution of global AVHRR coverage to\nprovide compatibility with other scales. All data are provided in\ngeographic (longitude/latitude) projection. The dataset is accompanied\nby an ASCII documentation file which contains information necessary\nfor use of the dataset in a GIS or other software.\n\nContact: NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA\n\nThe COASTS file shows African Coastlines The COUNTRY file shows\nAfrican Country Boundaries without coast, no names - only lines The\nISLANDS file shows African Islands\n\nReferences:\n\nNOAA. Global Change Data Base, Digital Data with Documentation (1992).\nNational Oceanic and Atmospheric Administration, National Geophysical Data\nCenter, Boulder, Colorado.\n\nHastings, David A., and Liping Di. Modeling of global change\nphenomena with GIS using the Global Change Data Base (1992). Remote\nsensing of environment, in review. Clark, David M., Hastings, David\nA. and Kineman, John J. Global databases and their implications for\nGIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind,\nDavid W., eds. Geographical Information Systems: Overview, Principles\nand Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2,\npp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data\nintegration and modelling for global change: An international\nexperiment (1990). Proceeding of the International Conference and\nworkshop on Global Natural Resource Monitoring and\nAssessments. Preparing for the 21st Century (Venice, Italy, 24-30\nSeptember 1989). Bethesda, Maryland, American Society of\nPhotogrammetry and Remote Sensing, Vol. 2, pp. 660-669. CERL. The\nGeographic Resources Analysis Support System (GRASS-GIS) version 4.0\n(1991). U.S. Army Corps of Engineers, Construction Engineering\nResearch Laboratory, Champaign, Illinois.\n\nSource map: digitized from available sources\nPublication Date: Jun 1992\nProjection: Lat/Lon\nType: Polygon and line\nFormat: Arc/Info Export non-compressed", "links": [ { diff --git a/datasets/NBId0022_101.json b/datasets/NBId0022_101.json index 9613e49fa8..8bc6e3614f 100644 --- a/datasets/NBId0022_101.json +++ b/datasets/NBId0022_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0022_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI22\n\nOLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION\n\nFile: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid\nEarth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric\nAdministration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic\nInformation and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other\nscales and because this corresponds closely with the resolution of global AVHRR coverage.\n\nAll data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software.\n\nContact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4\n\nReferences:\n\nOlson, J.S. Earth\"'\"s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge,\nTennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map).\n\nSource map : from available maps and observations.\nPublication Date : 1989\nProjection : lat/lon.\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0023_101.json b/datasets/NBId0023_101.json index c89f488258..88aa5f9e94 100644 --- a/datasets/NBId0023_101.json +++ b/datasets/NBId0023_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0023_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI23\n\nHoldridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa.", "links": [ { diff --git a/datasets/NBId0024_101.json b/datasets/NBId0024_101.json index 366a9103c3..7c4936f7f8 100644 --- a/datasets/NBId0024_101.json +++ b/datasets/NBId0024_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0024_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI24\n\nWilson and Henderson-Sellers soil classes and soil class\nreliability. The Wilson and Henderson-Sellers Soil Classes Dataset\nFiles: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster\nMembers The IMG files are in IDRISI format. The Wilson and\nHenderson-Sellers soils data set is part of Wilson Henderson-Sellers\nland cover and soils for global circulation modeling project was\ndeveloped by the World Data Center-A (WDC-A) for Solid Earth\nGeophysics, operated by the U.S. National Geophysical Data Center\n(NGDC). The dataset is part of the World Data Bank II. This data Bank\nis provided on a Database on diskette called The Global Change Data\nBase. The Data Bank II is part of larger project called Global\nEcosystems Database Project. This is a cooperation between the\nNational Oceanic and Atmospheric Administration (NOAA), NGDC and the\nU.S. Environmental Protection Agency (EPA). The National Center for\nGeographic Information and Analyses (NCGIA) in Santa Barbara,\nCalifornia joined the project to assist with training and evaluation.\nA nominal 10 arc-minute scale was chosen to provide compatibility with\nother scales and because this corresponds closely with the resolution\nof global AVHRR coverage. All data are provided in geographic\n(longitude/latitude) projection. The dataset is accompanied by an\nASCII documentation file which contains information necessary for use\nof the dataset in a GIS or other software.\n\nContact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000\n\nThe AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes\nThe ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability\n\nReferences:\nWilson, M.F/ and A. Henderson-Sellers. A global archive of land\ncover and soils data for use in general ciruclation climate\nmodels. Journal of Climatology, vol.5, pp.119-143.\n\nSource : Digitized from available sources: FAO/UNESCO Soil Map of the\nWorld. Oxford Regional Economic Atlas of USSR and Eastern Europe\nPublication Date : 1985\nProjection : Lat/Lon\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0025_101.json b/datasets/NBId0025_101.json index ac99be4915..a2b4f1cd98 100644 --- a/datasets/NBId0025_101.json +++ b/datasets/NBId0025_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0025_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI25\n\nAfrica ZOBLER Soil Type, Soil Texture, Surface Slope Classes\nDataset Documentation\n\nFiles: AFZSOILS.IMG\nCode: 100090-001\nAFZTEX.IMG\n100090-002\nAFZSUBSD.IMG\n100090-003\nAFZSP3.IMG\n100090-004\nAFZPHS.IMG\n100090-005\nAFZSLOPE.IMG\n100092-001\n\nRaster Members\nThe IMG files are in IDRISI format The Zobler soil type, soil\ntexture and surface slope dataset was developed by the World Data\nCenter-A (WDC-A) for Solid Earth Geophysics, operated by the U.S.\nNational Geophysical Data Center (NGDC). The dataset is part of the\nWorld Data Bank II provided on a diskette called The Global Change\nData Base. The Data Bank II is part of a larger project called Global\nEcosystems Database Project. This is a cooperation between the\nNational Oceanic and Atmospheric Administration (NOAA), NGDC and the\nU.S. Environmental Protection Agency (EPA). The National Center for\nGeographic Information and Analyses (NCGIA) in Santa Barbara,\nCalifornia joined the project to assist with training and evaluation.\nA nominal 10 arc-minute scale was chosen to provide compatibility with\nother scales and because this corresponds closely with the resolution\nof global AVHRR coverage. All data are provided in geographic\n(longitude/latitude) projection.\nThe dataset is accompanied by an ASCII documentation file which contains\ninformation necessary for use of the dataset in a GIS or other software.\n\nContact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA\n\nThe AFZSOILS file shows Zobler soil types\nThe AFZTEX file shows Zobler soil texture\nThe AFZSUBSD file shows subsidiary soil units\nThe AFZSP3 file shows Zobler special codes\nThe AFZPHS file shows Zobler phase codes\nThe AFZSLOPE file shows Zobler surface slope\n\nReferences:\n\nFAO. FAO-UNESCO Soil Map of the World (1974). Scale\n1:5000000. UNESCO, Paris.\nStaub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of\nSoil Type, Soil Texture, Surface Slope, and other properties:\nDocumentation of Archived Tape Data. NASA Technical Memorandum\nNo.100685.\n\nHenderson-Sellers, A., M.F. Wilson, G. Thomas,\nR.E. Dickinson. Current Global Land Surface Data Sets for Use in\nClimate-Related Studies. (1986). Matthews, E. Global vegetation and\nland use: New high resolution data bases for climate studies (1983).\nJ. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation,\nLand-use and Seasonal Albedo Data Sets: Documentation of Archived Data\nTape (1984). NASA Technical Memorandum. No.86107.\n\nWilson. M.F. and A. Henderson-Sellers. A global archive of land\ncover and soils data for use in general circulation climate models\n(1985). Journal of Climatology, vol.5, pp.119-143.\n\nSource map : various\nPublication Date : 1987\nProjection : Lat/lon\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0036_101.json b/datasets/NBId0036_101.json index dd0d43fc02..59131bef05 100644 --- a/datasets/NBId0036_101.json +++ b/datasets/NBId0036_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0036_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI36\n\nAfrica Lakes and Rivers.\n\nLakes and Rivers Dataset documentation\n(Micro World Data Bank II)\n\nFiles: LAKES.VEC Code:\n100055-001\nRIVERS.VEC\n100061-001\nAFRIVER.IMG\n100002-001\n\nRaster Members\nThe VEC and IMG files are in IDRISI format Africa lakes and rivers\ndatasets are part of the NOAA project that was developed by the World\nData Center-A (WDC-A) for Solid Earth Geophysics, operated by the\nU.S. National Geophysical Data Center (NGDC). The dataset is part of\nthe World Data Bank II provided on a diskette called The Global Change\nData Base. The Data Bank II is part of larger project called Global\nEcosystems Database Project. This is a cooperation between the\nNational Oceanic and Atmospheric Administration (NOAA), NGDC and the\nU.S. Environmental Protection Agency (EPA). The National Center for\nGeographic Information and Analyses (NCGIA) in Santa Barbara,\nCalifornia joined the project to assist with training and evaluation.\nA scale was chosen that corresponds closely with the resolution of\nglobal AVHRR coverage was chosen to provide compatibility with other\nscales. All data are provided in geographic (longitude/latitude)\nprojection. The dataset is accompanied by an ASCII documentation file\nwhich contains information necessary for use of the dataset in a GIS\nor other software.\n\nContact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA\n\nThe LAKES file shows African lakes\nThe RIVERS file shows African rivers\nThe AFRIVER file shows African rivers\n\nReferences:\n\nNOAA. Global Change Data Base, Digital Data with Documentation\n(1992). National Oceanic and Atmospheric Administration, National\nGeophysical Data Center, Boulder, Colorado.\n\nHastings, David A., and Liping Di. Modeling of global change\nphenomena with GIS using the Global Change Data Base (1992). Remote\nsensing of environment, in review.\n\nClark, David M., Hastings, David A. and Kineman, John J. Global\ndatabases and their implications for GIS (1991). IN Maguire, David J.,\nGoodchild, Michael F., and Rhind, David W., eds. Geographical\nInformation Systems: Overview, Principles and Applications. Burnt\nMill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231.\n\n\nKineman, J.J., Clark, D.M., and Croze, H. Data integration and\nmodelling for global change: An international experiment\n(1990). Proceeding of the International Conference and workshop on\nGlobal Natural Resource Monitoring and Assessments. Preparing for the\n21st Century (Venice, Italy, 24-30 September 1989). Bethesda,\nMaryland, American Society of Photogrammetry and Remote Sensing,\nvol. 2, pp. 660-669.\n\nCERL. The Geographic Resources Analysis Support System (GRASS-GIS)\nversion 4.0 (1991). U.S. Army Corps of Engineers, Construction\nEngineering Research Laboratory, Champaign, Illinois.\n\nSource map : digitized from available sources\nPublication Date : 1988\nProjection : Lat/lon\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0041_101.json b/datasets/NBId0041_101.json index ad9f65f490..cab983416d 100644 --- a/datasets/NBId0041_101.json +++ b/datasets/NBId0041_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0041_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI41\n\nAfrica FNOC Elevation (meters), Terrain and Surface characteristics.\n\nAfrica Elevation (meters), Terrain, and Surface Characteristics Dataset\nDocumentation\n\nFiles: AFMAX.IMG Code: 100082-001\nAFMIN.IMG 100082-002\nAFMOD.IMG 100082-003\n\nRaster Members\nThe IMG files are in IDRISI format\n\nAfrica elevation dataset is part of the revised FNOC elevation,\nterrain and surface characteritics. It formed part of the NOAA\nproject that was developed by the World Data Center-A (WDC-A) for\nSolid Earth Geophysics, operated by the U.S. National Geophysical Data\nCenter (NGDC). The dataset is part of the World Data Bank II provided\non a diskette called The Global Change Data Base. The Data Bank II is\npart of larger project called Global Ecosystems Database Project. This\nis a cooperation between the National Oceanic and Atmospheric\nAdministration (NOAA), NGDC and the U.S. Environmental Protection\nAgency (EPA). The National Center for Geographic Information and\nAnalyses (NCGIA) in Santa Barbara, California joined the project to\nassist with training and evaluation. A scale was chosen that\ncorresponds closely with the resolution of global AVHRR coverage was\nchosen to provide compatibility with other scales. All data are\nprovided in geographic (longitude/latitude) projection. The dataset is\naccompanied by an ASCII documentation file which contains information\nnecessary for use of the dataset in a GIS or other software.\n\n\nContact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA\n\nThe AFMAX file shows maximum elevation (meters)\nThe AFMIN file shows minimum elevation (meters)\nThe AFMOD shows modal elevation (meters)\n\nReference:\n\nCuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC\nSystem for Terrain Data Extraction and Processing\n(1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet\nNumerical Oceanography Center (Monterey, CA). Published by\nMeteorology International Incorporated. Source map : Digitized from\navailable maps and reprocessed: US Defense Operational Navigation\nCharts (ONC), scale 1:1000000; some World Aeronautical Charts and\ncharts from Jet Navigation.\nPublication Date : 1985 Projection :\nLat/Lon Type : Raster\n\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0042_101.json b/datasets/NBId0042_101.json index bfb2bb36db..e3f7d504ef 100644 --- a/datasets/NBId0042_101.json +++ b/datasets/NBId0042_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0042_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI42\n\nNOAA monthly Normalized Vegetation Index (NDVI) for Africa.\n\nNOAA Monthly 10-Min Normalized Vegetation Index Dataset\n(APRIL 1985 - DECEMBER 1988)\n\nFiles: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001\nAFJAN86.IMG-AFDEC86.IMG 100041-001\nAFJAN87.IMG-AFDEC87.IMG 100041-001\nAFJAN88.IMG-AFDEC88.IMG 100041-001\n\nRaster Members\nThe IMG files are in IDRISI format\n\nAfrica monthly 10-min normalized difference vegetation index\ndataset is part of the NOAA project that was developed by the World\nData Center-A (WDC-A) for Solid Earth Geophysics, operated by the\nU.S. National Geophysycal Data Center (NGDC). The dataset is part of\nthe World Data Bank II provided on a diskette called The Global Change\nData Base. The Data Bank II is part of larger project called Global\nEcosystems Database Project. This is a cooperation between the\nNational Oceanic and Atmospheric Administration (NOAA), NGDC and the\nU.S. Environmental Protection Agency (EPA). The National Center for\nGeographic Information and Analyses (NCGIA) in Santa Barbara,\nCalifornia joined the project to assist with training and evaluation.\nA scale was chosen to provide compatibility with other scales and\nbecause this corresponds closely with the resolution of global AVHRR\ncoverage. All data are provided in geographic (longitude/latitude)\nprojection. The dataset is accompanied by an ASCII documentation file\nwhich contains information necessary for use of the dataset in a GIS\nor other software.\n\nContact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA\n\nAFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI)\n\nReferences:\n\nKidwell, Katherin B. (ed.). Global Vegetion Index User\"'\"s Guide (1990).\nNOAA/NHESDIS/SDSD.\n\nfor additional references see Appendix A-26-A32 of the Global\nChange Data Base documentation\n\n\nSource map : digitized from available maps and reprocessed\nPublication Date : Jun 1992\nProjection : Lat/lon\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0043_101.json b/datasets/NBId0043_101.json index 6baafc4de1..4e6094a1ce 100644 --- a/datasets/NBId0043_101.json +++ b/datasets/NBId0043_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0043_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI43\n\nAfrica Integrated Elevation and Bathymetry (feet).\n\nIntegrated Elevation and Bathymetry Dataset Documentation\n\nFile: AFELBA.IMG Code: 100048-001\n\nRaster Member\nThis IMG file is in IDRISI format\n\nIntegrated elevation and bathymetry data set is part of\nUNEP-GRID/FAO Africa data base incorporated into World Data Bank II by\nthe World Data Center-A (WDC-A) for Solid Earth Geophysics, operated\nby the U.S. National Geophysical Data Center (NGDC). The dataset is\nprovided on a diskette called The Global Change Data Base. The Data\nBank II is part of larger project called Global Ecosystems Database\nProject. This is a cooperation between the National Oceanic and\nAtmospheric Administration (NOAA), NGDC and the U.S. Environmental\nProtection Agency (EPA). The National Center for Geographic\nInformation and Analyses (NCGIA) in Santa Barbara, California joined\nthe project to assist with training and evaluation. Sources used were\nthe USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA\nTopographic Maps of Africa, Raize Landform Map of North Africa, and\nLandsat mosaics. A scale was chosen that corresponds closely with the\nresolution of global AVHRR coverage was chosen to provide\ncompatibility with other scales. All data are provided in geographic\n(longitude/latitude) projection. The dataset is accompanied by an\nASCII documentation file which contains information necessary for use\nof the dataset in a GIS or other software. Contact : NGDC, 325\nBroadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows\nintegrated elevation and bathymetry (feet)\n\nReferences:\n\nEdwards, Margaret Helen. Digital Image Processing of Local and\nGlobal Bathymetric Data (1986). Master\"'\"s Thesis. Washington\nUniversity, Dept. of Earch and Planetary Sciences, St. Louis,\nMissouri, p.106.\n\nHaxby, W.F., et al. Digital Images of Combined Oceanic and\nContinental Data Sets and Their Use in Tectonic Studies (1983). EOS\nTransaction of the American Geophysical Union, vol.64, no.52,\npp.995-1004.\n\n\nNOAA. Global Change Data Base, Digital Data with Documentation\n(1992). National Oceanic and Atmospheric Administration, National\nGeophysical Data Center, Boulder, Colorado.\n\nHastings, David A., and Liping Di. Modeling of global change\nphenomena with GIS using the Global Change Data Base (1992). Remote\nsensing of environment, in review.\n\nClark, David M., Hastings, David A. and Kineman, John J. Global\ndatabases and their implications for GIS (1991). IN Maguire, David J.,\nGoodchild, Michael F., and Rhind, David W., eds., Geographical\nInformation Systems: Overview, Principles and Applications. Burnt\nMill, Essex, United Kingdom, Longman. V.2, pp. 217-231.\n\n\nKineman, J.J., Clark, D.M., and Croze, H. Data integration and\nmodelling for global change: An international experiment\n(1990). Proceeding of the International Conference and workshop on\nGlobal Natural Resource Monitoring and Assessments. Preparing for the\n21st Century (Venice, Italy, 24-30 September 1989). Bethesda,\nMaryland, American Society of Photogrammetry and Remote Sensing,\nvol. 2, pp. 660-669.\n\nCERL. The Geographic Resources Analysis Support System (GRASS-GIS)\nversion 4.0 (1991). U.S. Army Corps of Engineers, Construction\nEngineering Research Laboratory, Champaign, Illinois.\n\n\nSource map : various sources\nPublication Date : Jun1992\nProjection : Miller Oblated Stereographic resampled to lat/lon.\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0044_101.json b/datasets/NBId0044_101.json index 0a6e7f8fc7..23312ce0ea 100644 --- a/datasets/NBId0044_101.json +++ b/datasets/NBId0044_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0044_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI44\n\nOcean mask for Africa.\n\nIntegrated Elevation and Bathymetry Dataset Documentation\n\nFile: AFELBA.IMG Code: 100048-001\n\nRaster Member\nThis IMG file is in IDRISI format\n\nIntegrated elevation and bathymetry data set is part of\nUNEP-GRID/FAO Africa data base incorporated into World Data Bank II by\nthe World Data Center-A (WDC-A) for Solid Earth Geophysics, operated\nby the U.S. National Geophysical Data Center (NGDC). The dataset is\nprovided on a diskette called The Global Change Data Base. The Data\nBank II is part of larger project called Global Ecosystems Database\nProject. This is a cooperation between the National Oceanic and\nAtmospheric Administration (NOAA), NGDC and the U.S. Environmental\nProtection Agency (EPA). The National Center for Geographic\nInformation and Analyses (NCGIA) in Santa Barbara, California joined\nthe project to assist with training and evaluation. Sources used were\nthe USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA\nTopographic Maps of Africa, Raize Landform Map of North Africa, and\nLandsat mosaics. A scale was chosen that corresponds closely with the\nresolution of global AVHRR coverage was chosen to provide\ncompatibility with other scales. All data are provided in geographic\n(longitude/latitude) projection. The dataset is accompanied by an\nASCII documentation file which contains information necessary for use\nof the dataset in a GIS or other software. Contact : NGDC, 325\nBroadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows\nintegrated elevation and bathymetry (feet)\n\nReferences:\n\nEdwards, Margaret Helen. Digital Image Processing of Local and\nGlobal Bathymetric Data (1986). Master\"'\"s Thesis. Washington\nUniversity, Dept. of Earch and Planetary Sciences, St. Louis,\nMissouri, p.106.\n\n\nHaxby, W.F., et al. Digital Images of Combined Oceanic and\nContinental Data Sets and Their Use in Tectonic Studies (1983). EOS\nTransaction of the American Geophysical Union, vol.64, no.52,\npp.995-1004.\n\nNOAA. Global Change Data Base, Digital Data with Documentation\n(1992).\nNational Oceanic and Atmospheric Administration, National Geophysical Data\nCenter, Boulder, Colorado.\n\nHastings, David A., and Liping Di. Modeling of global change\nphenomena with GIS using the Global Change Data Base (1992). Remote\nsensing of environment, in review.\n\nClark, David M., Hastings, David A. and Kineman, John J. Global\ndatabases and their implications for GIS (1991). IN Maguire, David J.,\nGoodchild, Michael F., and Rhind, David W., eds., Geographical\nInformation Systems: Overview, Principles and Applications. Burnt\nMill, Essex, United Kingdom, Longman. V.2, pp. 217-231.\n\n\nKineman, J.J., Clark, D.M., and Croze, H. Data integration and\nmodelling for global change: An international experiment\n(1990). Proceeding of the International Conference and workshop on\nGlobal Natural Resource Monitoring and Assessments. Preparing for the\n21st Century (Venice, Italy, 24-30 September 1989). Bethesda,\nMaryland, American Society of Photogrammetry and Remote Sensing,\nvol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support\nSystem (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers,\nConstruction Engineering Research Laboratory, Champaign, Illinois.\n\n\nSource map : various sources\nPublication Date : Jun1985\nProjection : Miller Oblated Stereographic resampled to lat/lon.\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0053_101.json b/datasets/NBId0053_101.json index d90a27ab8e..e684ac007e 100644 --- a/datasets/NBId0053_101.json +++ b/datasets/NBId0053_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0053_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI53\n\nAfrica Revised FNOC Percent Water Cover Dataset Documentation\n\nFile: AFWATER.IMG Code: 100082-005\n\nRaster Member\nThe IMG file is in IDRISI format\n\nThe percent water cover dataset is part of the revised FNOC\nelevation, terrain and surface characteritics. It formed part of the\nNOAA project that was developed by the World Data Center-A (WDC-A) for\nSolid Earth Geophysics, operated by the U.S. National Geophysical Data\nCenter (NGDC). The dataset is part of the World Data Bank II provided\non a diskette called The Global Change Data Base. The Data Bank II is\npart of larger project called Global Ecosystems Database Project. This\nis a cooperation between the National Oceanic and Atmospheric\nAdministration (NOAA), NGDC and the U.S. Environmental Protection\nAgency (EPA). The National Center for Geographic Information and\nAnalyses (NCGIA) in Santa Barbara, California joined the project to\nassist with training and evaluation. A scale was chosen that\ncorresponds closely with the resolution of global AVHRR coverage was\nchosen to provide compatibility with other scales. All data are\nprovided in geographic (longitude/latitude) projection. The dataset is\naccompanied by an ASCII documentation file which contains information\nnecessary for use of the dataset in a GIS or other software. Contact :\nNGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file\nshows the revised FNOC percent water cover for Africa.\n\nReference:\n\nCuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC\nSystem for Terrain Data Extraction and Processing\n(1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet\nNumerical Oceanography Center (Monterey, CA). Published by\nMeteorology International Incorporated. Source map : Digitized from\navailable maps and reprocessed: US Defense\nOperational Navigation Charts (ONC), scale 1:1000000; some World\nAeronautical Charts and charts from Jet Navigation.\nPublication Date : 1985\nProjection : Lon/lat\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0079_101.json b/datasets/NBId0079_101.json index e5d09b234c..7c81765665 100644 --- a/datasets/NBId0079_101.json +++ b/datasets/NBId0079_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0079_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lake Chad Dataset which is a detailed case study of the\nUNEP/FAO/ESRI Family was developed by UNEP/GRID, on behalf of the\nUNEP/Fresh Water Unit for the Lake Chad Commission on Sustainable\nDevelopment. Lake Chad Dataset covers parts of 7 countries: Cameroon,\nChad, Nigeria and Niger, Sudan, Central African Republic and Libya and\nis a clip (regional version) of Africa Outline Dataset (NBI01).\n\nThe base maps used for the continental version were the FAO/UNESCO\nSoil Map of the World (1977) in Miller Oblated Stereographic\nprojection, FAO Maps and Statistical Data by Administrative Unit and\nthe Rand-McNally New International Atlas (1982) to clarify unit\nboundaries.\n\nFiles: ADMIN.E00 Code: 115001-001\nBASE.E00 115002-001\nCOUNTRIES.E00 115003-001\n\nVector Members\nThe E00 files are in Arc/Info Export format and should be imported\nwith the Arc/Info command Import cover In-Filename Out-Filename.\nThe ADMIN polygon dataset showing administrative areas for 7 countries\naround Lake Chad.\nThe BASE is a polygon Dataset showing the countries with inland water\nbodies. The COUNTRIES is a polygon Dataset showing only the country\nboundaries.\n\nReferences:\n\nESRI. Final Report UNEP/FAO world and Africa GIS data base\n(1984). Internal Publication by ESRI, FAO and UNEP\n\nFAO. FAO/UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO,\nParis\n\nFAO. Maps and Statistical Data by Administrative Unit (unpublished)\n\nRand-McNally. New International Atlas (1982). Rand-McNally &\nCompany. Chicago\n\nSource: FAO/UNESCO Soil Map of the World. Scale 1:5000000\nPublication Date: Dec 1988\nProjection: Miller\nType: Polygon and line\nFormat: Arc/Info Export, non-compressed\nRelated Datasets: All the Lake Chad Datasets of the UNEP/FAO/ESRI\nfamily.", "links": [ { diff --git a/datasets/NBId0083_101.json b/datasets/NBId0083_101.json index 55d5adb90d..7aff78f3d3 100644 --- a/datasets/NBId0083_101.json +++ b/datasets/NBId0083_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0083_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Description:\nThese datasets (Administrative Units, Drainage, Agro-Climatic Zones,\nRainfall, Infrastructure) were scanned by the Canadian Land data\nSystems Division, Land Directorate, Dept of Environment, Ottawa,\nCanada. This was in response to the request to GRID by the Kenya\nMinistry of Agriculture to assist in creating the datasets.\n\nThe source information and scales are varied; Rivers, Agroecological\nZones, Soils, Boundaries, Towns, Lakes, Transport, and the Districts,\nProvinces (administrative boundary), Elevation were based on the scale\nof 1: 1 000 000 and of which the source information was derived from\nMinistry of Agriculture and Survey of Kenya maps. The Landuse dataset\nwas based on the Kenya Rangeland Ecological Monitoring Unit (KREMU now\nDRSRS) map at the scale of 1: 1 000 000.The Mean Annual Rainfall\ndataset was based on an East Africa map(1966) at the scale of 1: 2 000\n000\n\nRainfall data was originally provided by Kenya Meteorological\nDepartment. These were collected from a total of 79 Stations for the\nperiod between 1982-1988. More records were added by GRID which\nextended the period to 1991 The data consists of the\nrainfall,Potential Evapotranspiration (PET) and Temperature\ninformation.\n\nSample\nFiles:\nRAINFALL.E00\nFILL8291.PLU, PETALL.DBF/.NDX, ADD82,83,84,85,86.DAT\n(Others available on request)\n\nVector Members:\n- Files are in an ArcInfo Export format", "links": [ { diff --git a/datasets/NBId0089_101.json b/datasets/NBId0089_101.json index 2412018c02..68141da8bc 100644 --- a/datasets/NBId0089_101.json +++ b/datasets/NBId0089_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0089_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI89\n\nSOIL MAP OF KENYA.\n\nProduced by the Republic of Kenya, Kenya Soil Survey in the Ministry\nof Agriculture Nairobi. Agro-climatic classification and map\npreparation was done by H. M. H. Braun and other staff of the Kenya\nsoil survey. Cartography and lithography was done by the Soil Survey\nInsitute Wageningen, The Netherlands.\n\nThere are three items in the info table which are of importance namely\nTYPE1, TYPE2 and SOIL. TYPE1 and TYPE2 are an alpha-numeric code\nwhich represent the soil type in the item SOIL. This code was given\nin order to facilitate manipulation and calculations of the info\ntables, which is more easily done using integers rather than using\ncharacter strings. TYPE1 is the first part of the character string in\nthe item SOIL and TYPE2 is the second part of the character string in\nthe item SOIL, as seen in the info table below in SOIL# 19.\nFor details on the actual soil types and associated information see the\ndocumentation \"Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980.\n\nMAP TITLE\n\nExploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980.\n\n\nArc/info table\n\nAREA PERIMETER SOIL# SOIL-ID TYPE1 TYPE2 SOIL\n-47.552 39.567 1 0 0 0 ''\n0.068 3.258 2 9009 479 0 ' H9'\n0.013 0.634 3 9010 645 0 ' Y5'\n0.000 0.053 4 9011 60937 0 ' Ux7'\n0.001 0.132 5 9012 403 0 ' A3'\n0.002 0.284 6 9013 645 0 ' Y5'\n0.009 0.524 7 9014 60937 0 ' Ux7'\n0.001 0.150 8 9015 479 0 ' H9'\n0.009 0.602 9 9016 516 0 ' L6'\n0.052 1.562 10 9017 645 0 ' Y5'\n0.022 0.975 11 9018 558821 0 ' Ps21'\n0.127 2.573 12 9019 558821 0 ' Ps21'\n0.000 0.085 13 9020 479 0 ' H9'\n0.073 4.595 14 9021 403 0 ' A3'\n0.238 5.943 15 9022 60937 0 ' Ux7'\n0.002 0.231 16 9023 458 0 ' F8'\n0.142 3.913 17 9024 408 0 ' A8'\n0.004 0.263 18 9025 479 0 ' H9'\n0.004 0.249 19 9026 431 55813 ' D1 + Pl3'\n0.018 0.855 20 9027 408 0 ' A8'\n0.044 1.360 21 9028 479 0 ' H9'", "links": [ { diff --git a/datasets/NBId0093_101.json b/datasets/NBId0093_101.json index b83f117821..a024465f59 100644 --- a/datasets/NBId0093_101.json +++ b/datasets/NBId0093_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0093_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into Line and Point attribute codes were assigned\ninteractively at the time of initial data capture. Polygon attributes\nwere assigned after topology had been reached. Editing was carried out\nto eliminate obvious errors, after which the data was plotted at scale\nand then compared to the source correctness. All of the data layers\nwere checked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0098_101.json b/datasets/NBId0098_101.json index 4d0ab3c501..a48ea9cca0 100644 --- a/datasets/NBId0098_101.json +++ b/datasets/NBId0098_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0098_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\n\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0106_101.json b/datasets/NBId0106_101.json index cabb4d0837..77858fde7b 100644 --- a/datasets/NBId0106_101.json +++ b/datasets/NBId0106_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0106_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\n\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\n\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0110_101.json b/datasets/NBId0110_101.json index baeb8bc11c..71e3554e4c 100644 --- a/datasets/NBId0110_101.json +++ b/datasets/NBId0110_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0110_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0115_101.json b/datasets/NBId0115_101.json index 7b649d2ea3..3f5d6492e7 100644 --- a/datasets/NBId0115_101.json +++ b/datasets/NBId0115_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0115_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0127_101.json b/datasets/NBId0127_101.json index 6e70a116f5..eb953f1ba7 100644 --- a/datasets/NBId0127_101.json +++ b/datasets/NBId0127_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0127_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\n\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0128_101.json b/datasets/NBId0128_101.json index 96cdc08011..81e5a7d822 100644 --- a/datasets/NBId0128_101.json +++ b/datasets/NBId0128_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0128_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0129_101.json b/datasets/NBId0129_101.json index bc4ded7b4a..6bd03f002f 100644 --- a/datasets/NBId0129_101.json +++ b/datasets/NBId0129_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0129_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0131_101.json b/datasets/NBId0131_101.json index 0763f36d21..9a3fcfd4b2 100644 --- a/datasets/NBId0131_101.json +++ b/datasets/NBId0131_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0131_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0136_101.json b/datasets/NBId0136_101.json index 578ee5c512..5218433806 100644 --- a/datasets/NBId0136_101.json +++ b/datasets/NBId0136_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0136_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Eastern African Coastal and Marine Environment Resource Database\nis a comprehensive 1:250,000-scale vector database of the Kenya\nCoastal Zone. It consists of geographic, attribute, and textual data\nwhich can be accessed, queried, displayed, and modified. The database\nwas developed under the Eastern African Action Plan, with the\ncollaboration of UNEP/GRID-PAC, UNEP/OCA-PAC, and KEMFRI. The primary\nsources of data are the Survey of Kenya 1:250,000 series, National\nMuseums of Kenya, Kenya Ports Authority, Coastal Development\nAuthority, Kenya Wildlife Service, and Kenya Government Departments:\nFisheries, Agriculture, Meteorology, Mines and Geology.\n\nData Sources\nThe primary sources of data are the Survey of Kenya 1:250,000 series,\nLandsat Thematic Mapper images, and socio-economic data from various\nGovernment ministries, departments, and institutes.\n\nNaming Conventions\nA naming convention was established to allow users to identify the\ncontents of layers based on their name. Coverage names begin with a\ntwo letter abbreviation representing the country code (for example,\nKE:-Kenya) followed by a descriptive term representing the theme. The\ncountry code being two characters in length plus the descriptive term,\nwhich is usually six characters long, conforms to the Direct Operating\nSystem (DOS) naming convention. An example of this would be as\nfollows:- the elevation coverage would be called KEELEVAT, the KE\nrepresenting the abbreviation for Kenya and the ELEVAT representing\nelevation. A full list of country codes for the East African region\nis as follows:-\n\nList by country name\nCountry Name Data Code\nComoros CN\nKenya KE\nMadagascar MA\nMauritius MP\nMozambique MZ\nReunion RE\nSeychelles SE\nSomalia SO\nTanzania TZ\n\nEdgematching\nEdgematching was done manually working from the top most map sheet\n(Kolbio: SA-37-8) to the bottom most (Mombasa: SB-37-3). In this way\nany errors would be distributed in a systematic way. The greatest\nerrors are in the order of 750 meters on the ground which is 3\nmillimeters on the map. These errors occurred between the following\nmap sheets:- Voi (SA-37-14) / Kilifi (SA-37-15)and Voi (SA-37-14) /\nLushoto (SB-37-2).\n\nAnnotation\nAll feature names which include points, lines or polygons have an\nentry in the attribute table describing the feature. Additional\nattributes may also exist for the particular feature, however this\nvaries from feature to feature.\n\nGeneral production process\nCoverages developed for the database were derived from three sources:\n1:250,000 scale paper maps prepared by the Survey of Kenya, Landsat\nThematic Mapper Data, and socio-economic data from the Kenya Marine\nFisheries Institute (KMFI), the Kenya Wildlife Service (KWS) and other\nGovernment Ministries and NGO's.\n\n1:250,000 paper maps\nTwelve TIC's (control points) were selected from the map sheet, based\non the latitude/longitude grid on the sheet. The reason for this is\nthat one of the other sheets only has a latitude/longitude grid where\nas the others have both a latitude/longitude grid as well as a UTM\ngrid. In this way, consistency is being maintained between all the\nmap sheets covering the Kenya coastal zone.\n\nArc/info conversion MACRO PROGRAMME was used to convert a\nstandard ASCII text file of Latitude/Longitude coordinates into\nUniversal Transverse Mercator coordinates.\n\nSML TO PROJECT DECIMAL DEGREES FILE INTO UTM ZONE 37\nINPUT\nPROJECTION GEOGRAPHIC\nSET INPUT UNITS AS DECIMAL DEGREES\nUNITS DD\nSPHEROID CLARKE1880\nPARAMETERS\nOUTPUT\nPROJECTION UTM\nUNITS METERS\nYSHIFT 10000000\nPARAMETERS\n39 00 00\n00 00 00\nEND\n[ARC] Createcov ***tic\n{ARC} Tables\n> Select ***tic.tic\n> Add\n> TICID = ?\n> XTIC = ?\n> YTIC = ?\nThis is done for all 12 UTM Tic Ids and coordinates.\n>List\nThis is done to check that the Tic Ids and coordinates are correct.\n> Q stop\nA tile boundary was then added to the coverage to help in the\ndigitization of the map sheet. This coverage <***tic> was then used\nin the creation of all the other coverage layers that were digitized\nfrom the map sheet.\n\n1:250,000 paper maps were used due to lack of stable mylar or acetate\ncopies. All features:- points, lines, and polygons were digitized\nusing PC Arc/Info 3.4D Plus software running on a Gateway 2000 P5-90\nPC and a CalComp 9100 digitizing board. Editing was carried out to\neliminate obvious errors, after which the data was plotted at scale\nusing a Hewlett Packard Design Jet 650 C inkjet plotter and then\ncompared to the source for positional accuracy, completeness, and\ntopological correctness. All of the data layers were checked using\nthis method and all edits were verified.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assign after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nLandsat Thematic Mapper Data\nTwo full scenes and one quad were used in the land cover\nclassification, more details concerning the methodology used in the\nclassification can be found in Annex 3.\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data cature. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.\n\nSocio-economic Data\nThese data came in various forms (from digital data to reports), which\nwere then converted into\n\nLine and Point attribute codes were assigned interactively at the time\nof initial data capture. Polygon attributes were assigned after\ntopology had been reached. Editing was carried out to eliminate\nobvious errors, after which the data was plotted at scale and then\ncompared to the source correctness. All of the data layers were\nchecked using this method and all edits were verified.\n\nAll the data was finally plotted at scale in a single composite and\nattribute code value validity, attribute code consistency, topology\nerrors, attribute field definition correctness, and internal data\nstructure correctness were checked. In addition Arc/View 2.1 was used\nto carry out a quick visual check on the data.", "links": [ { diff --git a/datasets/NBId0153_101.json b/datasets/NBId0153_101.json index 5c60bdcdd6..7e195e2550 100644 --- a/datasets/NBId0153_101.json +++ b/datasets/NBId0153_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0153_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI153\n\nThe Equatorial Guinea Benito River dataset documentation\n\nFile: EGRIVER.E00\nCode:\n121001-001\n\nVector Member\nThe E00 file is in Arc/info format and should be imported with the\nArc/Info command Import cover In-Filename Out-Filename.\n\nThe Equatorial Guinea Benito River dataset was generated for the\nElephant Database that was completed in 1991, as a result of\ncooperation between European Communities (EEC), Elsa Wild Animal\nAppeal (EWAA) and UNEP/GRID.\n\nSource map used was from the Institute Geographic National, Paris of\nunknown scale. The dataset was then projected into Miller Oblated\nStereographic at 1:1000000.\n\nContact : UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya\nFAO, Soil Resources, Management and Conservation Service 00100,\nRome, Italy.\nESRI, 380 New York Street, Redlands, CA 92373, USA\n\nFile EGRIVER represents Benito River of Equatorial Guinea REMARK: GET\nROAD-CODE = 5 from EGRDS.E00 for complete set of rivers of Equatorial\nGuinea.\n\nReference:\nUNEP/EEC/EWAA Technical Report on the African Elephant Database (1991)\n\nSource map : from Institut Geographic National, scale 1:1000000\nPublication Date : September 1991\nProjection : Miller\nType : Line\nFormat : Arc/Info Export non-compressed\nRelated dataset : EGRDS.E00\nVector:\nThe EGRIVER data per area is: Benito River\n\nDescription of SINGLE precision coverage egriver\n\nARCS POLYGONS\nArcs = 12 Polygons = 1\nSegments = 118 Polygon Topology is present.\n28 bytes of Arc Attribute Data 16 bytes of Polygon Attribute\nData\n\nNODES POINTS\nNodes = 13 Label Points = 0\n0 bytes of Node Attribute Data\n\nTOLERANCES SECONDARY FEATURES\nFuzzy = 0.002 N Tics = 4\nDangle = 0.000 N Links = 0\n\nCOVERAGE BOUNDARY\nXmin = 10.558 Ymin =\n17.816\nXmax = 14.347 Ymax =\n18.817\n\nSTATUS\nThe coverage has not been Edited since the last BUILD or CLEAN.\nNO COORDINATE SYSTEM DEFINED\n\nLines\nCOLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC ALTERNATE NAME\nINDEXED?\n1 FNODE# 4 5 B -\n-\n5 TNODE# 4 5 B -\n-\n9 LPOLY# 4 5 B - -\n13 RPOLY# 4 5 B -\n-\n17 LENGTH 4 12 F 3\n-\n21 EGRIVER# 4 5 B -\n-\n25 ROAD-CODE 4 5 B -\n-\n\ncolunm 25 ROAD-CODE = 5 = BENITO River", "links": [ { diff --git a/datasets/NBId0161_101.json b/datasets/NBId0161_101.json index 3fe3a8df1c..fb1a40ecf5 100644 --- a/datasets/NBId0161_101.json +++ b/datasets/NBId0161_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0161_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New-ID: NBI161\n\nThe Climate Dataset of Senegal documentation\n\nFiles: SENEGAL4.IMG\nCode: 146005-001\nSENEGAL5.IMG\n146006-001\nSENEGAL6.IMG\n146007-001\n\nRaster Members\nIMG files are in IDRISI format\n\nThe Senegal Climate Dataset was originally digitized for the\nUNEP/FAO/ESRI Database for Africa from hand-drawn maps provided by FAO\nfor the Desertification Hazard Mapping project. GRID-Geneva rasterized\nthe coverages for UNEP/GRID/WHO/CISFAM Senegal Database with a cell\nsize of 30 seconds and two minutes lat/lon (approximately one- and\nfour kilometeter-square pixels, respectively).\n\nContact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya\nFAO, Soil Resources, Management and Conservation Service, 00100,\nRome, Italy\n\nThe SENEGAL4 file shows mean annual wind velocity meters per second (8\nclasses).\nThe SENEGAL5 file shows number of wet days per year (6 classes).\nThe SENEGAL6 file shows mean annual rainfall in millimeters (10\nclasses).\nREMARK: file may have limited applicability at national scale as was\nextracted from continental.\n\nReferences:\nESRI. Final Report UNEP/FAO World and Africa GIS data base\n(1984). Internal Publication by ESRI, FAO and UNEP.\n\nCISFAM. Consolidated Information System for Famine Management in\nAfrica, Phase I Report (Apr. 1987), Univ. of Louvain, Brussels,\nBelgium.\n\nSource and scale : unknown\nReport Publication Date : Dec 1988\nProjection : lat/lon\nType : Raster\nFormat : IDRISI\nRelated Datasets : All UNEP/FAO/ESRI climate Datasets", "links": [ { diff --git a/datasets/NBId0169_101.json b/datasets/NBId0169_101.json index 8da5c887d2..dee03c3dab 100644 --- a/datasets/NBId0169_101.json +++ b/datasets/NBId0169_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0169_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP\nProvisional Methodology for Assessment and Mapping of Desertification,\nand to recommend an effective, simple methodology for desertification\nassessment within Kenya.\n\nThe FAO/UNEP Provisional Methodology (1984) proposes seven processes\nfor consideration in desertification assessment: degradation of\nvegetation, water erosion, wind erosion, salinization, reduction of\norganic content, soil crusting and compaction.\n\nIn late 1985, a pilot project for the assessment of the FAO/UNEP\nMethodology within Kenya was proposed, and in 1987 a memorandum of\nunderstanding between the Government of Kenya and UNEP for the\nimplementation of that study was signed.\n\nThe study areas were:\n1) Models can be useful to assist in desertification assessment.\nModels can be developed from FAO/UNEP Methodology.\n\n2) Any modeling output requires verification.\n\n3) Ground survey and remote sensing can be important sources of data.\n\n4) An evaluation of data and methodologies necessary to allow\nverification of desertification assessment modeling is required.\n\n5) A human use component should be incorporated into desertification\nassessment that considers management implications and social, as well\nas, economic context.\n\n6) Computer implementation of desertificaiton assessment can be\neffective, however, procedures should be well defined.\n\nThis study within the Baringo Study Area was designed to address these\nconcerns.\n\nThe Baringo Study Area identified in this study would be typical of\nsuch a training area. The models developed during this study could be\napplied to the general region.\n\nThe study area lies between 0 15'-1 N and 35 30' -36 30' E.\n\nIt is located between the Laikipia escarpment to the East and the\nTugen Hills to the West. Topographic elevations vary from 900m on the\nNjemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The\nsize of the study area is approximately 15ookm2.\n\n\n4.0 DATA COLLECTION\n\nA wide variety of data was collected. Detailed data was required to\nprovide a basis for evaluating more general cost effective data\ngathering techniques and to provide a basis for model verification,\nparticularly the socio/economic data.\n\nPhysical Environment\nTopographic Data\nTopographic contours were digitized directly from 1:250,000 Survey of\nKenya topographic maps. The contour interval was 200 feet. A digital\nelevation model was constructed using triangular irregular networks\n(TIN).\n\nSoil Data\nSoil types were mapped at 1:100,000 scale using existing soil maps,\nmanual interpretation of SPOT imagery, and field investigations\n(Figure 3). During field trips, soil samples were taken from each\nsoil unit and analyzed by the Kenya National Agricultural Center.\n\n4.2 Climate Data\n4.2.1 Rainfall Data\nRainfall data from the Kenya Meteorological Department was analyzed\nfor 33 stations within and surrounding the study area. A rainfall\nerosivity index was calculated based on the Fourier Index (R).\n12\nRE (p /P)\n12\nwhere P = annual rainfall\np = monthly rainfall\n\nA relationship between this erosivity index and the annual rainfall\nfor each station was calculated using linear regression (Bake, 1988).\nA map of rainfall erosivity was generated for the study area by\nrelating annual rainfall isoheyts to the following:\n\ny = 0.108x - 0.68\n\nThis data was coded and digitized.\n\nWind Erosion Potential\nThe following required conditions were determined to create high wind\nerosion potential (Kinuthia, 1989):\n\n1) Annual rainfall less than 300mm.\n2) P/E greater than zero and less than 1, where:\nP=mean monthly rainfall (cm).\nE=mean monthly PET (cm).\n3) Wind velocity greater than 4 m/s at 10m height.\n\nVegetation Data\nA vegetation map for the study area was produced at a scale of\n1:100,000 through manual interpretation of a SPOT image and field\ninvestigations (Figure 6). A structural classification system as\nadopted by DRSRS was used for naming vegetation types (Grunb).\n\nSystematic Reconnaissance Flight Data Since 1977, DRSRS has been\nconducting aerial surveys of Kenyan rangelands. In addition to data\non the number of wildlife and livestock, observations of land use and\nenvironmental condition are also made.\n\nSocio/economic Data\nSocial Factors\n\nA wide variety of data was collected through literature review and a\nfield administered questionnaire. Nutritional status was estimated by\nmeasurement of childrens' mid upper arm. Such data is useful for a\nLevel 1 type assessment.\n\nPermanent Structures Data\nFor the Level 2 assessment, data on permanent structures was extracted\nfrom DRSRS SRF data. This data was used to indicate presence and\nconcentration of sedentary populations.\n\nExample\nFiles: VDS.E00 (Vegetation degradation)\nDES.E00 (Plant Species)\nOthers available on request.", "links": [ { diff --git a/datasets/NBId0177_101.json b/datasets/NBId0177_101.json index c1c6f068e2..6acb82b376 100644 --- a/datasets/NBId0177_101.json +++ b/datasets/NBId0177_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0177_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laikipia Research Programme GIS Datasets are divided into two main\ndifferent study area scales: the Regional level [Laikipia district,\nthe Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s),\ncatchments of a few kilometer square].\n\nCoordinate Reference System\n\nCoverage data is organized thematically as a series of layers. The\ncoordinate reference systems used in LRP dataset are:- (a) global\ncoordinate system - Universal Transverse Mercator (UTM), (b) Local\ncoordinate system.\n\nDigitizing Scale and Fuzzy Tolerance\n\nThe initial digitizing scale for the LRP GIS Dataset is dependent on\nthe scale of the study areas. There are two major research levels\ncarried by LRP namely Regional and Local. The scales used for regional\nlevel are 1:250,000 and 1:50,000.\n\nFUZZY TOLERANCE is the minimum distance between coordinates in a\ncoverage. The resolution of a coverage is defined by the minimum\ndistance separating the coordinates used to store coverage\nfeatures. Resolution is limited by the map scale in initial\ndigitizing. The fuzzy tolerance can be calculated as follows for\ndigitizing table:\n\nInitial Scale for Coverage of Fuzzy Tolerance\nDigitizing Units Value\n1;250,000 Meters 6.35\n1:50,000 Meters 1.25\n1:10,000 Meters 0.25\n1:5,000 Meters 0.125\n1:2,500 Meters 0.0625\n\nFiles: Roads.E00 (Roads)\nSettle.E00 (Settlement Pattern)\nCentres.E00 (Urban Centres)\n(other files exist also)", "links": [ { diff --git a/datasets/NBId0203_101.json b/datasets/NBId0203_101.json index 4377417bc0..fa3fbdf391 100644 --- a/datasets/NBId0203_101.json +++ b/datasets/NBId0203_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0203_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Water Balance data set which is prepared by watershed and\n by country, belongs to the group of \"Irrigation and Water Resources\n Potential\" study. It covers 55 countries and 25 major basins which\n contain 335 watersheds. The digitized data base for Africa and the\n World was originally prepared for an FAO/UNEP project on\n Desertification in 1982-1984. UNEP financed preparation and analysis\n of the digitized map data and FAO prepared the data and methodology.\n The main input maps (all in Miller Oblated Stereographic projection)\n are the 1975 UNESCO Geological Map of Africa (originally at a scale of\n 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall\n Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and\n Administrative Units map - all at a scale 1:5 m.\n \n The methodology was based on water balance approach. This determines\n the suitability of the soil for irrigation and estimates the amount of\n water the soil requires. Estimates of the surface and groundwater are\n then compared to the potential irrigation use. If use exceeds\n available water resources, the irrigable area is correspondingly\n reduced; in the event of water surplus, some of the water is routed to\n the downstream basin.\n \n The classification was done for two major crop types: lowland crops\n (flooded rice), and upland crops (for all other irrigated crops except\n lowland crops).\n \n For further details refer to FAO contact for the 1987 FAO Irrigation\n and Water Resources Potential for Africa AGL/MISC/11/87.\n \n FAO, Land and Water Development Division via Delle Terme di Caracalla,\n 00100, Rome, Italy\n \n Vector Member\n The file is in Arc/Info Export format.\n \n Reference:\n FAO. Irrigation and Water Resources Potential for Africa. (1987)\n FAO. Final Report UNEP/FAO world and Africa GIS data base (1984),\n unpublished publication of ESRI, FAO and UNEP.\n UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000.", "links": [ { diff --git a/datasets/NBId0207_101.json b/datasets/NBId0207_101.json index 2060d49d70..ea799a237a 100644 --- a/datasets/NBId0207_101.json +++ b/datasets/NBId0207_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0207_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The IGADD (Inter-Governmental Authority on Drought and Development)\ncrop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The\nmaps were prepared by Environmental Systems Research Institute (ESRI),\nUSA. The data was provided by Food and Agriculture Organization\n(FAO), the Soil Resources, Management and Conservation Service, Land\nand Water Development Division, Italy. The datasets were then\ndeveloped in collaboration with the United Nations Environment Program\n(UNEP), Kenya.\n\nThe base maps used were the UNESCO/FAO Soil Map of the world (1977) in\nMiller Oblated Stereographic projection, the Administrative Units map\nand the World Atlas of Agriculture (1969). All sources were\nre-registered to the base map by comparing known features on the base\nmap and the source maps. In the original Database (Africa), a\nconsiderable study was made of crop water requirements for a range of\ncrops in the various African climates during the time of the year when\nirrigation would be required. It was found that a relatively simple\nrelationship exists between annual rainfall and the crop irrigation\nwater requirements for the African food grain crops. It was also\nobserved that water requirements for food grains vary between fruit\nand vegetable crops on the one side and fiber crops and fodder on the\nother. No attempt was made to produce complex crop patterns. There\nis a maximum of 13 crop types in one country.\n\nReferences:\nESRI. Final Report UNEP/FAO world and Africa GIS data base (1984).\nInternal Publication by ESRI, FAO and UNEP\nFAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO,\nParis.\nFAO. Administration units map. Scale 1:5 000 000. Rome.\nFAO. Irrigation and Water Resources Potential for Africa. (1987)\n\nSource :UNESCO/FAO Soil Map of the World. Scale 1:5000000\nPublication Date :Nov 1987\nProjection :Miller\nType :Polygon\nFormat :Arc/Info Export non-compressed\nRelated Data sets :All UNEP/FAO/ESRI Data sets\nFAO Irrigable Data sets 100050:\n\" IRRIGLB lowland crops, best soils\n\" IRRIGLT lowland crops, best plus suitable soils\n\" IRRIGUB upland crops, best soils\n\" IRRIGUT upland crops, best plus suitable soils\nFAO Soil water balance 100053:\n\" WATBALLB lowland crops, best soils\n\" WATBALLT lowland crops, best plus suitable soils\n\" WATBALUB upland crops, best soils\n\" WATBALUT upland crops, best plus suitable soils\nFAO Agro-ecological zones\nAEZBLL08 North-west of continent\nAEZBLL09 North-east of continent\nAEZBLL10 South of continent", "links": [ { diff --git a/datasets/NBId0208_101.json b/datasets/NBId0208_101.json index e102d658e4..7ab34b89dd 100644 --- a/datasets/NBId0208_101.json +++ b/datasets/NBId0208_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0208_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Human Settlements and Landuse data sets form part of the\nUNEP/FAO/ESRI Database project that covers the entire world but\nfocuses here on Africa. The maps were prepared by Environmental\nSystems Research Institute (ESRI), USA. Most data for the database\nwere provided by the Soil Resources, Management and Conservation\nService, Land and Water Development Division of the Food and\nAgriculture Organization (FAO), Italy. This data set was developed in\ncollaboration with the United Nations Environment Program (UNEP),\nKenya.\n\nThe base maps used were the UNESCO/FAO Soil Map of the world (1977) in\nMiller Oblated Stereographic projection, the DMA Global Navigation and\nPlanning charts for Africa (various dates: 1976-1982) and the\nRand-McNally, New International Atlas (1982). All sources were\nre-registered to the basemap by comparing known features on the base\nmap those of the source maps. The digitizing was done with a spatial\nresolution of 0.002 inches. The maps were then transformed from inch\ncoordinates to latitude/longitude degrees. The transformation was done\nusing an unpublished algorithm of the US Geological Survey and ESRI to\ncreate coverages for one-degree graticules. The Population Centers\nwere selected based upon their inclusion in the list of major cities\nand populated areas in the Rand McNally New International Atlas.\n\nReferences:\nESRI. Final Report UNEP/FAO World and Africa GIS data base (1984).\nInternal Publication by ESRI, FAO and UNEP\nFAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO,\nParis\nDefence Mapping Agency. Global Navigation and Planning charts for\nAfrica (various dates: 1976-1982). Scale 1:5000000. Washington DC.\nGrosvenor. National Geographic Atlas of the World (1975). Scale\n1:850000. National Geographic Society Washington DC.\nDMA. Topographic Maps of Africa (various dates). Scale 1:2000000\nWashington DC.\nRand-McNally. The new International Atlas (1982). Scale\n1:6,000,000. Rand McNally & Co.Chicago\n\nSource :FAO Soil Map of the World. Scale 1:5000000\nPublication Date :Dec 1984\nProjection :Miller\nType :Points\nFormat :Arc/Info export non-compressed\nRelated Data sets :All UNEP/FAO/ESRI Data sets\nADMINLL (100012-002) administrative boundries\nAFURBAN (100082) urban percentage coverage\n\nComments : no outline of Africa", "links": [ { diff --git a/datasets/NBId0211_101.json b/datasets/NBId0211_101.json index e6027f22c1..9239abbee6 100644 --- a/datasets/NBId0211_101.json +++ b/datasets/NBId0211_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0211_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Irrigation Potential data set, which represents the best\n soils suitable for upland, is part of the FAO Irrigation and Water\n Resources Potential Database. The main input maps were the 1977\n FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas\n (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS\n climate maps, Template with water related features, Administrative\n Units map, and Watersheds map. All maps, apart from where specified\n were at a scale of 1:5 million, and all in Miller Oblated\n Stereographic projection.\n \nThe soil suitability for irrigation was determined by evaluating the\n properties of all soil components: dominant soil, associations and\n inclusions, phases, slope, drainage, and texture. The classification\n was done for two major crop types: lowland crops (flooded rice), and\n upland crops (for all other irrigated crops except the lowland crops).\n \n The soils source includes a list of attributes for each soil unit\n including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU\n 100004). Then for both cases (lowland crops (flooded rice), and upland\n crops (for all other irrigated crops except the lowland crops)), two\n maps were generated. One with all soils which are suitable, and one\n where slope, texture, drainage and phase were considered.\n \n Each different soil type is classed according to suitability, S1\n irrigation with no constraints, S2 irrigation with some constraints,\n N1 not suitable without major improvements, N2 permanently not\n suitable. Because one soil unit can consist of more soil components\n (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed\n in percentage of the unit that is suitable (1 >50% suitable, 2 =\n 25-50% etc.). Then the soil characteristics are used to refine the\n ranking. This refining is done were the original soil rank is\n increased decreased or changed from their original suitability to a\n new suitability (so or soil gets new class S1, N1 etc. or ranking\n changes like, -1 lower soil rank by one, +1 raise soil rank with one).\n \n The Ranking of Soils is as follows\n The soils considered not suitable are:\n \n Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic\n Fluvisols,\n \n Miscellaneous land units such as rock debris, desert debris,\n \n Gypsum units,\n \n Soils with stonic, lythic or petrogypsic phase.", "links": [ { diff --git a/datasets/NBId0216_101.json b/datasets/NBId0216_101.json index 1e1363aa73..d5e799bda4 100644 --- a/datasets/NBId0216_101.json +++ b/datasets/NBId0216_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0216_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules.\n\nReferences:\nESRI. Final Report UNEP/FAO world and Africa GIS data base (1984).\n\"Internal Publication from ESRI, FAO and UNEP\n\"FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO,\nParis\n\"FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet\nfor Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources,\nManagement and Conservation Service. FAO, Rome\n\"FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale\n1:5000000. Todor Boyadgiev, Soil Resourcs, Management and\nConservation Service. FAO, Rome\"\n\"FAO. Maps of Number of Wet Days per Year (1983). Scale\n1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation\nService. FAO, Rome\n\nSource :FAO Soil Map of the World. Scale 1:5000000\nPublication Date :Dec 1984\nProjection :Geographic (lat/lon)\nType :Polygon and line\nFormat :Arc/Info Export non-compressed\nRelated Datasets :All UNEP/FAO/ESRI Data sets", "links": [ { diff --git a/datasets/NBId0218_101.json b/datasets/NBId0218_101.json index a6e445c534..7b1c62a064 100644 --- a/datasets/NBId0218_101.json +++ b/datasets/NBId0218_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0218_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The First-Third Order Stream Network member of the African Surface\nHydrography data set is part of the UNEP/FAO/ESRI Database project\nthat covers the entire world but focuses here on Africa. The maps were\nprepared by Environmental Systems Research Institute (ESRI), USA. Most\ndata for the database were provided by the Land and Water Development\nDivision of the Food and Agriculture Organization (FAO), Italy. The\ndatabase was developed by the United Nations Environment Program\n(UNEP), as part of a project initiated by the same. The base map used\nwas the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in\nMiller Oblated Stereographic projection. All sources were\nre-registered to the base map by comparing known features on the base\nmap and the source maps. The digitizing was done with a spatial\nresolution of 0.002 inches. The maps were then transformed from inch\ncoordinates to latitude/longitude degrees. The transformation was done\nusing an unpublished algorithm by US Geological Survey and ESRI) to\ncreate coverage for one-degree graticules.\n\n\nReferences:\nESRI. Final Report UNEP/FAO World and Africa GIS data base (1977).\nInternal Publication by ESRI, FAO and UNEP\nFAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO,\nParis\n\nSource :FAO/UNESCO Soil Map of the World. Scale 1:5000000\nPublication Date :Dec 1984\nProjection :Geographic (lat/lon)\nFeature type :line\nRelated Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa\nOUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins\n\nComment : No boundary (outline) for Africa.", "links": [ { diff --git a/datasets/NBId0220_101.json b/datasets/NBId0220_101.json index c7f25e1c41..cb3bef1f86 100644 --- a/datasets/NBId0220_101.json +++ b/datasets/NBId0220_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0220_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Rain Measuring Stations data set, for monthly rainfall is\npart of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database\nproject. The point data was reformatted (Miller, scale 1:5 000 000)\nfrom CIAT tabular data based on 12 average monthly rainfall,\nevaporation, and minimum/maximum temperature. The data was used in\nthe calculation of interpolated surfaces for rainfall and temperature\ndistribution as the basis for modeling of climatic stress factors that\nconstrain the distribution of ticks that transfer ECF.\n\nVector Member\nThe file is in Arc/Info Export format.\nThe RAINSTNS point data represents rainfall measuring stations (12\naverage monthly) should go with file DATREAD.ME\n\nReferences:\nP. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan,\nK. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information\nSystem for studying the Epidemiology of East Coast Fever (Theileria\nparva) (1989).\nK. Kundert. Isolating East Coast Fever High risk Areas (1989).\nArc/Info European User Conference, Rome, October 1989.\nCSIRO. Users guide to CLIMEX, A computer program for comparing\nclimates in ecology. CSIRO Aust. Div Rep No.35, pp.-29\n\nSource : CIAT tabular data\nPublication Date :Jan 1989\nProjection :Miller\nType :Point\nFormat :Arc/Info Export non-compressed\n\"Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP,\nTICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS,\nRNPNTS and RZPNTS.\n\nComment : No boundary (outline) for Africa", "links": [ { diff --git a/datasets/NBId0223_101.json b/datasets/NBId0223_101.json index ea4db4a9dd..415ca124bd 100644 --- a/datasets/NBId0223_101.json +++ b/datasets/NBId0223_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0223_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Zobler soil datasets were developed by the World Data Center-A\n(WDC-A) for Solid Earth Geophysics, operated by the U.S. National\nGeophysical Data Center (NGDC). The data set is part of the World Data\nBank II and is part of \"The Global Change Data Base\". The World Data\nBank II is part of a larger project called \"Global Ecosystems Database\nProject\". The project was a joint effort between the National Oceanic\nand Atmospheric Administration (NOAA), NGDC and the U.S. Environmental\nProtection Agency (EPA). The National Center for Geographic\nInformation and Analyses (NCGIA) in Santa Barbara, California joined\nthe project to assist with training and evaluation. A nominal 10\narc-minute scale was chosen to provide compatibility with other scales\nand because this corresponds closely with the resolution of global\nAVHRR coverage. All data are provided in geographic\n(longitude/latitude) projection. The dataset is accompanied by an\nASCII documentation file which contains information necessary for use\nof the dataset in a GIS or other software.\n\nThe texture data is based on the FAO Soil Map of the World, and\ncompiled into digital form by Zobler. Each matrix element represents\nthe near-surface texture (upper 30 cm) of the dominant soil unit in a\none-degree square cell of the earth's surface. The data conforms in\nlocation, and nominal classification (land, land-ice, water) to\nMatthew's vegetation data set.\n\nReferences:\nFAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO,\nParis.\nStaub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil\nType, Soil Texture, Surface Slope, and other properties: Documentation\nof Archived Tape Data. NASA Technical Memorandum No.100685.\nHenderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current\nGlobal Land Surface Data Sets for Use in Climate-Related\nStudies. (1986).\nMatthews, E. Global vegetation and land use: New high resolution data\nbases for climate studies (1983). J. Clim. Appl. Meteor., vol.22,\npp.474-487.\nVegetation, Land-use and Seasonal Albedo Data Sets: Documentation of\nArchived Data Tape (1984). NASA Technical Memorandum. No.86107.\nWilson. M.F. and A. Henderson-Sellers. A global archive of land cover\nand soils data for use in general circulation climate models\n(1985). Journal of Climatology, vol.5, pp.119-143.\n\nSource map :FAO/UNESCO Soil Map of the World\nPublication Date :1987\nProjection :lat/lon\nType :Raster\nFormat :IDRISI", "links": [ { diff --git a/datasets/NBId0233_101.json b/datasets/NBId0233_101.json index 933af6d490..8bc9bc03f0 100644 --- a/datasets/NBId0233_101.json +++ b/datasets/NBId0233_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0233_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Africa Population density model represents ranges of population\ndensity of inhabitants per square kilometer. The estimated population\ndensities are expressed on a regularly spaced latitude/longitude\nraster grid covering Africa with an approximate resolution of 10 km x\n10 km at the Equator. The data set which is an assessment of one of\nthe factors causing soil degradation, namely the spatial distribution\nand density of population. It was developed for the GEMS/UNITAR Africa\nDatabase and later used for GLASOD.\n\nThe data sources include: 600 African towns and cities with figures\nstandardized to 1988 values ( a combination of 479 cities from\nBirkbeck College and 363 cities in 51 African countries from PC Globe\n3.0); UNEP/FAO population data from the 1984 Africa database; the\nSierra Club Wilderness Area IUCN Protected Areas, used to delimit\nareas with extremely sparse populations and treated as having a\ndensity of less than one person per square kilometer.\n\nFor methodology and further detail refer to references listed: UN\nInstitute for Training & Research (UNITAR). GEMS/UNITAR Africa\nDatabase. Deichmann, U. and Lars Eklundh. Global Digital Datasets for\nLand Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID,\nNairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold:\nA division of Hodder and Stoughton, London.\n\nProjection :Geographic\nType :Raster\nFormat :IDRISI\nRelated files :POPDENSL.E00, POPDENGR.E00\nAssociated files :POPDENS.DOC and POPDENS.PAL", "links": [ { diff --git a/datasets/NBId0236_101.json b/datasets/NBId0236_101.json index 9554a588c6..0a5509809c 100644 --- a/datasets/NBId0236_101.json +++ b/datasets/NBId0236_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0236_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the\ndisease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on\nAnimal Diseases (ILRAD), now called International Livestock Research Institute (ILRI).", "links": [ { diff --git a/datasets/NBId0248_101.json b/datasets/NBId0248_101.json index f8a55259b4..5a2d7a0d2c 100644 --- a/datasets/NBId0248_101.json +++ b/datasets/NBId0248_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0248_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the \"Wilson Henderson-Sellers land cover and soils for global circulation modeling project \" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called \"\"The Global Change Data Base\"\". The Data Bank II is part of larger project called \"Global Ecosystems Database Project\". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software.\n\nReferences:\nWilson, M.F./ and A. Henderson-Sellers. A global archive of land cover\nand soils data for use in general circulation climate models. Journal\nof Climatology, vol.5, pp.119-143.\n\nSource : Digitized from available sources:\nFAO/UNESCO Soil Map of the World\nPublication Date : 1985\nProjection : lat/lon\nType : Raster\nFormat : IDRISI", "links": [ { diff --git a/datasets/NBId0270_101.json b/datasets/NBId0270_101.json index 3fe163478a..ee0591c7e5 100644 --- a/datasets/NBId0270_101.json +++ b/datasets/NBId0270_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0270_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTRODUCTION\n Desertification/Land Degradation - The Background\n \n More than 6.1 billion hectares, over one third of the Earth's land\n area, is dryland. Nearly one billion hectares of this area are\n naturally hvperarid deserts, with very low biological\n productivity. The remaining 5.1 billion hectares are made up of arid,\n semiarid and dry subhumid areas, part of which have become desert\n since the dawn of civilization while other parts of these areas are\n still being degraded by human action today. These lands are the\n habitat and the source of livelihood for one quarter of the world's\n population. They are areas characterized by the persistent natural\n menace of recurrent drought, a natural hazard accentuated by\n imbalanced management of natural resources. Particularly acute\n drought years in the Sahelian region of Africa from 1968 to 1973, and\n their tragic effects on the peoples of the region, drew worldwide\n attention to the problems of human survival and development in\n drylands, particularly on desert margins. These problems have been\n addressed by the United Nations (UN) General Assembly, in conformity\n with the Charter of the United Nations. The UN General Assembly's\n Resolution 3202 (vi) of 1 May 1974 recommended that the international\n community undertake concrete and speedy measures to arrest\n desertification and assist the economic development of affected\n areas. The Economic and Social Council's Resolution 1878 (LVII) of 16\n July 1974 requested all the concerned organizations of the UN system\n to pursue a broad attack on the drought problem. Decisions of the\n Governing Councils of the UN Development Programme (UNDP) and the UN\n Environment Programme (UNEP) emphasized the need for undertaking\n measures to check the spread of desert conditions. The General\n Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974,\n to initiate concerted international action to combat desertification\n and, in order to provide an impetus to this action, to convene a UN\n Conference on Desertification (UNCOD), between 29 August and 9\n September 1977 in Nairobi, Kenya, which would produce an effective,\n comprehensive and coordinated programme for solving the problem. For\n the purposes of this atlas, desertification/land degradation is\n defined as: Land degradation in arid, semiarid and dry subhumid areas\n resulting mainly from adverse human impact.", "links": [ { diff --git a/datasets/NBId0288_101.json b/datasets/NBId0288_101.json index a35e4cfb51..1dc661ddaf 100644 --- a/datasets/NBId0288_101.json +++ b/datasets/NBId0288_101.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBId0288_101", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INTRODUCTION\n Desertification/Land Degradation - The Background\n \n More than 6.1 billion hectares, over one third of the Earth's land\n area, is dryland. Nearly one billion hectares of this area are\n naturally hvperarid deserts, with very low biological\n productivity. The remaining 5.1 billion hectares are made up of arid,\n semiarid and dry subhumid areas, part of which have become desert\n since the dawn of civilization while other parts of these areas are\n still being degraded by human action today. These lands are the\n habitat and the source of livelihood for one quarter of the world's\n population. They are areas characterized by the persistent natural\n menace of recurrent drought, a natural hazard accentuated by\n imbalanced management of natural resources. Particularly acute\n drought years in the Sahelian region of Africa from 1968 to 1973, and\n their tragic effects on the peoples of the region, drew worldwide\n attention to the problems of human survival and development in\n drylands, particularly on desert margins. These problems have been\n addressed by the United Nations (UN) General Assembly, in conformity\n with the Charter of the United Nations. The UN General Assembly's\n Resolution 3202 (vi) of 1 May 1974 recommended that the international\n community undertake concrete and speedy measures to arrest\n desertification and assist the economic development of affected\n areas. The Economic and Social Council's Resolution 1878 (LVII) of 16\n July 1974 requested all the concerned organizations of the UN system\n to pursue a broad attack on the drought problem. Decisions of the\n Governing Councils of the UN Development Programme (UNDP) and the UN\n Environment Programme (UNEP) emphasized the need for undertaking\n measures to check the spread of desert conditions. The General\n Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974,\n to initiate concerted international action to combat desertification\n and, in order to provide an impetus to this action, to convene a UN\n Conference on Desertification (UNCOD), between 29 August and 9\n September 1977 in Nairobi, Kenya, which would produce an effective,\n comprehensive and coordinated programme for solving the problem. For\n the purposes of this atlas, desertification/land degradation is\n defined as: Land degradation in arid, semiarid and dry subhumid areas\n resulting mainly from adverse human impact.", "links": [ { diff --git a/datasets/NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1.json b/datasets/NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1.json index 998cfd87f7..0c106acdb1 100644 --- a/datasets/NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1.json +++ b/datasets/NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). \n \n A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08).", "links": [ { diff --git a/datasets/NCALDAS_NOAH0125_D_2.0.json b/datasets/NCALDAS_NOAH0125_D_2.0.json index 43694efccd..5f35cb09d4 100644 --- a/datasets/NCALDAS_NOAH0125_D_2.0.json +++ b/datasets/NCALDAS_NOAH0125_D_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCALDAS_NOAH0125_D_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate.\n\nAn overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document.\n\nThis NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0.\n\nNCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product.", "links": [ { diff --git a/datasets/NCALDAS_NOAH0125_Trends_2.0.json b/datasets/NCALDAS_NOAH0125_Trends_2.0.json index 9ea33a294c..fc77e5be00 100644 --- a/datasets/NCALDAS_NOAH0125_Trends_2.0.json +++ b/datasets/NCALDAS_NOAH0125_Trends_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCALDAS_NOAH0125_Trends_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate.\n\nThis dataset consists of a suite of historical trends in terrestrial hydrology over the conterminous United States estimated for the water years of 1980-2015 using the NCA-LDAS daily reanalysis. NCA-LDAS provides gridded daily outputs from the uncoupled Noah version 3.3 land surface model (LSM) at 1/8th degree resolution forced with NLDAS-2 meteorology (Xia et al., 2012), rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products (Jasinski et al., 2019; Kumar et al., 2019).\n\nTrends in annual hydrologic indicators are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. An additional precipitation trend field (annual total), with no significance test applied, is included for comparison purposes. Collectively, these fields represent the bulk of the results presented in Jasinski et al. (2019).", "links": [ { diff --git a/datasets/NCAR_DS474.0.json b/datasets/NCAR_DS474.0.json index cc263fd2cb..3e4b7f98d8 100644 --- a/datasets/NCAR_DS474.0.json +++ b/datasets/NCAR_DS474.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCAR_DS474.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC).", "links": [ { diff --git a/datasets/NCAR_DS510.5.json b/datasets/NCAR_DS510.5.json index 7683d801b1..22757b59e6 100644 --- a/datasets/NCAR_DS510.5.json +++ b/datasets/NCAR_DS510.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCAR_DS510.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s.", "links": [ { diff --git a/datasets/NCAR_DS744.7.json b/datasets/NCAR_DS744.7.json index ff7995b30b..8144b3e4bf 100644 --- a/datasets/NCAR_DS744.7.json +++ b/datasets/NCAR_DS744.7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCAR_DS744.7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset.", "links": [ { diff --git a/datasets/NCAR_DS871.0.json b/datasets/NCAR_DS871.0.json index 80735243ea..a95b5891d3 100644 --- a/datasets/NCAR_DS871.0.json +++ b/datasets/NCAR_DS871.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCAR_DS871.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments.", "links": [ { diff --git a/datasets/NCEI DSI 1167_01_Not Applicable.json b/datasets/NCEI DSI 1167_01_Not Applicable.json index 1227abf3cf..da83d82244 100644 --- a/datasets/NCEI DSI 1167_01_Not Applicable.json +++ b/datasets/NCEI DSI 1167_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 1167_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file.", "links": [ { diff --git a/datasets/NCEI DSI 2001_01_Not Applicable.json b/datasets/NCEI DSI 2001_01_Not Applicable.json index 08494f20a4..034157bfde 100644 --- a/datasets/NCEI DSI 2001_01_Not Applicable.json +++ b/datasets/NCEI DSI 2001_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 2001_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format.", "links": [ { diff --git a/datasets/NCEI DSI 2002_01_Not Applicable.json b/datasets/NCEI DSI 2002_01_Not Applicable.json index 2540bb4f60..dc7ba89a17 100644 --- a/datasets/NCEI DSI 2002_01_Not Applicable.json +++ b/datasets/NCEI DSI 2002_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 2002_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The CFSv2 Operational Analysis or Climate Data Assimilation System (CDAS), consist of all 6-Hourly CDAS, and the monthly CDAS monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format.", "links": [ { diff --git a/datasets/NCEI DSI 3298_01 (original)_Not Applicable.json b/datasets/NCEI DSI 3298_01 (original)_Not Applicable.json index fe67137577..2e3058c6a8 100644 --- a/datasets/NCEI DSI 3298_01 (original)_Not Applicable.json +++ b/datasets/NCEI DSI 3298_01 (original)_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 3298_01 (original)_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Climate Record Books (CRB) Data were keyed as part of the Climate Database Modernization Program (CDMP). These original keyed files as well as documentation relating to the format and keying process is available within the 3298_01 archive. The Northeast Regional Climate Center (NRCC) reformatted and performed quality control checks on the data, ensuring that the data could be used in high quality datasets and applications. Data and documentation for this data is available within the 3298_02 archive. The dataset consists of 171 stations that are located throughout the US. Variables include: maximum temperature, minimum temperature, average temperature, precipitation, and snowfall. Temporal resolution is daily, but observation times are not available for this dataset. However, data coverage varies by station. The records for individual stations range in length from 9 months to 121 years. Parts of the records may be duplicated in other, higher-priority ACIS data sources.", "links": [ { diff --git a/datasets/NCEI DSI 3341_Not Applicable.json b/datasets/NCEI DSI 3341_Not Applicable.json index 008d982c87..8ce6bc3060 100644 --- a/datasets/NCEI DSI 3341_Not Applicable.json +++ b/datasets/NCEI DSI 3341_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 3341_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASOS Special Inventory Hourly Precipitation Data is historical digital data set DSI-3341, archived at the National Climatic Data Center (NCDC). DSI-3341 is the daily inventory for the subset of stations in data set DSI-3240, Hourly Precipitation Data, that are Automated Surface Observing System (ASOS) stations. Areal coverage is the United States excluding Hawaii. Years covered are 1995-8.", "links": [ { diff --git a/datasets/NCEI DSI 3610_01_Version 1.json b/datasets/NCEI DSI 3610_01_Version 1.json index 50fca67360..9dec68a12b 100644 --- a/datasets/NCEI DSI 3610_01_Version 1.json +++ b/datasets/NCEI DSI 3610_01_Version 1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 3610_01_Version 1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BASE Temperature Data Record (TDR) dataset from Colorado State University (CSU) is a collection of the raw unprocessed antenna temperature data that has been written into single orbit granules and reformatted into netCDF-4. The temperature data are from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) series of passive microwave radiometers carried onboard the Defense Meteorological Satellite Program (DMSP) satellites. This dataset encompasses data from a total of nine satellites including the SSM/I sensors on board DMSP satellites F08, F10, F11, F13, F14, and F15 as well as the SSMIS sensors on board DMSP satellites F16, F17, and F18. The data record covers the time period from July 1987 through the present with a 7 to 10 day latency. The spatial and temporal resolutions of the BASE files correspond to the original resolution of the raw source TDR observations. There are roughly 15 orbits per day with a swath width of approximately 1400 km resulting in nearly global daily coverage. The spatial resolution of the data is a function of the sensor/channel and varies from approximately ~50 km for the lowest frequency channels to ~15km for the high-frequency channels. These files contain all of the information from the original source TDR files with the following changes/additions. The BASE files have been reorganized into single orbit granules with duplicate scans removed, and spacecraft position and velocity based on the TLE (two line element) data have been added for calculating geolocation. With the exception of duplicate scans, none of data from the original TDR files was changed or removed. This BASE TDR dataset is used by CSU as input for the subsequent processing of the final intercalibrated Fundamental Climate Data Record (FCDR). The file format is netCDF-4 with added metadata that follow the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD).", "links": [ { diff --git a/datasets/NCEI DSI 6190_01_Not Applicable.json b/datasets/NCEI DSI 6190_01_Not Applicable.json index c12edd5898..c0c02be748 100644 --- a/datasets/NCEI DSI 6190_01_Not Applicable.json +++ b/datasets/NCEI DSI 6190_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6190_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NCEP Climate Forecast System Reanalysis (CFSR) was initially completed for the 31-year period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system to provide the best estimate of the state of these coupled domains over this 31-year period. The CFSR has also been extended as an operational, real time product into the future. New features of the CFSR include: (1) coupling of atmosphere and ocean during the generation of the 6 hour guess field; (2) an interactive sea-ice model; and (3) assimilation of satellite radiances by the Grid-point Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is approximately 38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25 deg at the equator, extending to a global 0.5 deg beyond the tropics, with 40 levels to a depth of 4737m. The global land surface model has four4 soil levels and the global sea ice model has 3 layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979-2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in-situ and satellite observation data were included in the CFSR. Satellite-based radiance observations were bias corrected with spin-up runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled smooth transitions of the observation record due to evolutionary changes in satellite observing systems. The CFSR atmospheric, oceanic and land surface output products are available at an hourly time resolution and at a 0.5 deg x 0.5 deg latitude and longitude resolution. In total, there are 10 data products available from the National Climatic Data Center that make up the CFS Reanalysis collection: MON - Monthly Means; TIME - Parameter Timeseries; PGB - 3-D Pressure Level Data; FLX - Surface and Radiative Fluxes; OCN - 3-D Ocean Data; IPV - 3-D Isentropic Level Data; DIAB - 3-D Diabatic Heating Data; GRBLOW - Low-Resolution Data; HIC - High-Res Initial Conditions; LIC - Low-Res Initial Conditions. All data are in GRIB-2 format, except for the initial condition data which are in native binary formats. Total CFSR data volume is approximately 200 TB.", "links": [ { diff --git a/datasets/NCEI DSI 6192_02_Not Applicable.json b/datasets/NCEI DSI 6192_02_Not Applicable.json index bd22b3cf66..3fcbaefc8a 100644 --- a/datasets/NCEI DSI 6192_02_Not Applicable.json +++ b/datasets/NCEI DSI 6192_02_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6192_02_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NCEP Climate Forecast System Reanalysis (CFSR) was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system to provide the best estimate of the state of these coupled domains over the 31-year period of 1979 to 2009. A complete Reforecast of CFS version 2, over the 30-year period (1981-2011) has been created in order to provide stable calibration and skill estimates of the new system, for operational seasonal and sub seasonal prediction at NCEP. Coupled full 9-month forecasts from initial conditions every 5 days apart (for all 4 cycles on that day) have been made for each calendar year with the T126L64 GFS with half-hourly coupling to the ocean (MOM4 at 0.25 degree equatorial, 0.5 degree global). Total number of 9-month forecasts is 73x4 for each year, amounting to 8468 forecast runs for the full period. In addition to the 9-month runs, there is a full season run from every 0Z cycle over a 12-year period (1999-2010) for a total of 4380 runs. There is also a short 45-day forecast from every 6Z, 12Z and 18Z cycle over the same 12-year period (1999-2010) for a total of 13140 runs.", "links": [ { diff --git a/datasets/NCEI DSI 6307_Not Applicable.json b/datasets/NCEI DSI 6307_Not Applicable.json index 5873282b9a..0c634ca58a 100644 --- a/datasets/NCEI DSI 6307_Not Applicable.json +++ b/datasets/NCEI DSI 6307_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6307_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CARDS Monthly Statistics is digital data set DSI-6307, archived at the National Climatic Data Center (NCDC). This data set uses data from Comprehensive Aerological Data Set (CARDS) (DSI-6305), also archived at NCDC. DSI-6307 is similar in concept and format to Monthly Aerological Data Set (MONADS) (DSI-6220), another digital data set archived at NCDC. DSI-6305 and DSI-6220 are monthly upper air statistics. DSI-6307 data are for surface, tropopause, and mandatory pressure levels. At each level, monthly statistical parameters are provided for geopotential height or pressure, temperature, relative humidity, specific humidity, dew point temperature, wind speed, zonal wind speed, and meridional wind speed. Those statistical parameters are: mean value; standard deviation; minimum value; maximum value; first, second, and third quartile values, and number of non-missing observations used in the calculations. Data are global, from 1948 through 2001. NCDC maintains this data set in archive but no longer updates nor actively distributes it. It has been superseded by the Integrated Global Radiosonde Archive (IGRA) (C00616).", "links": [ { diff --git a/datasets/NCEI DSI 6316_01_Not Applicable.json b/datasets/NCEI DSI 6316_01_Not Applicable.json index 5279a28e8f..1658c2fb8b 100644 --- a/datasets/NCEI DSI 6316_01_Not Applicable.json +++ b/datasets/NCEI DSI 6316_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6316_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Argentina Upper Air is historical digital data set DSI-6316, archived at the National Climatic Data Center (NCDC). This is meteorological upper air data. This is a historical data set of upper air data for Argentina that was assembled by that nation. There were 21 reporting stations. Data is for the period 1958-91, although the period of record varies by station. DSI-6316 was included in the larger Comprehensive Aerological Data Set (CARDS) Upper Air data set, DSI-6305, which was quality controlled as it was assembled from many smaller data sets. DSI-6316 itself was not quality controlled at the NCDC. Most users should not request DSI-6316, but should instead opt for DSI-6305. Major parameters in upper air data sets are: pressure, temperature, relative humidity, and wind speed and direction.", "links": [ { diff --git a/datasets/NCEI DSI 6322_01_Not Applicable.json b/datasets/NCEI DSI 6322_01_Not Applicable.json index cdd3448619..fe948943ce 100644 --- a/datasets/NCEI DSI 6322_01_Not Applicable.json +++ b/datasets/NCEI DSI 6322_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6322_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Australia GTS Upper Air is historical digital data set DSI-6322, archived at the National Climatic Data Center (NCDC). This is meteorological upper air data. This is a small historical data set of upper air data for scattered stations around the world for 1990-3. The data was orginally received in Australia from the Global Telecommunications System (GTS). DSI-6322 was included in the larger Comprehensive Aerological Data Set (CARDS) Upper Air data set, DSI-6305, which was quality controlled as it was assembled from many smaller data sets. DSI-6322 itself was not quality controlled at the NCDC. Most users should not request DSI-6322, but should instead opt for DSI-6305. Major parameters in upper air data sets are: pressure, temperature, relative humidity, and wind speed and direction.", "links": [ { diff --git a/datasets/NCEI DSI 6323_01_Not Applicable.json b/datasets/NCEI DSI 6323_01_Not Applicable.json index 12f1d1bdf6..1a00ff59e8 100644 --- a/datasets/NCEI DSI 6323_01_Not Applicable.json +++ b/datasets/NCEI DSI 6323_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6323_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Australia Upper Air Thermo/Winds Merged is historical digital data set DSI-6323, archived at the National Climatic Data Center (NCDC). This is meteorological upper air data. This is a historical data set of upper air data for 1950-93 that was received from Australia. Data is mostly from Australia and New Guinea, but includes a few other stations scattered around the world. DSI-6323 was included in the larger Comprehensive Aerological Data Set (CARDS) Upper Air data set, DSI-6305, which was quality controlled as it was assembled from many smaller data sets. DSI-6323 itself was not quality controlled at the NCDC. Most users should not request DSI-6323, but should instead opt for DSI-6305. Major parameters in upper air data sets are: pressure, temperature, relative humidity, and wind speed and direction.", "links": [ { diff --git a/datasets/NCEI DSI 6324_01_Not Applicable.json b/datasets/NCEI DSI 6324_01_Not Applicable.json index b1e77fdba7..815f4d93af 100644 --- a/datasets/NCEI DSI 6324_01_Not Applicable.json +++ b/datasets/NCEI DSI 6324_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6324_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Brazil Upper Air is historical digital data set DSI-6324, archived at the National Climatic Data Center (NCDC). This is meteorological upper air data. This is a historical data set of upper air data for 1951-81 from Brazil. DSI-6324 was included in the larger Comprehensive Aerological Data Set (CARDS) Upper Air data set, DSI-6305, which was quality controlled as it was assembled from many smaller data sets. DSI-6324 itself was not quality controlled at the NCDC. Most users should not request DSI-6324, but should instead opt for DSI-6305. Major parameters in upper air data sets are: pressure, temperature, relative humidity, and wind speed and direction.", "links": [ { diff --git a/datasets/NCEI DSI 6402_Not Applicable.json b/datasets/NCEI DSI 6402_Not Applicable.json index a6bd3d7f5e..963d7694e2 100644 --- a/datasets/NCEI DSI 6402_Not Applicable.json +++ b/datasets/NCEI DSI 6402_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6402_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASOS Surface System Log (SYSLOG) is digital data set DSI-6402, archived at the National Climatic Data Center (NCDC). SYSLOG is an electronic systems messages logbook from the Automated Surface Observing System (ASOS). System Log Error Messages are generated by the ASOS when an error is detected by the continuous system self-test. When a faulty Field Replaceable Unit (FRU) is identified, the corrective action taken is to replace it. Messages not associated with the FRUs are for general information use only and require no corrective action. Error codes are assigned sequentially, unless otherwise specified. Major parameters in SYSLOG include ASOS station identification and time information, the message code, and a remarks field.", "links": [ { diff --git a/datasets/NCEI DSI 6403_Not Applicable.json b/datasets/NCEI DSI 6403_Not Applicable.json index 45e8e26c86..c7395e6318 100644 --- a/datasets/NCEI DSI 6403_Not Applicable.json +++ b/datasets/NCEI DSI 6403_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6403_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASOS 5-Minute Weather Duration (DSI-6403) is digital data set DSI-6403, archived at the National Climatic Data Center (NCDC). ASOS is Automated Surface Observing System, which is used at several hundred National Weather Service (NWS), Federal Aviation Administration (FAA), and US military stations in the United States of America including Alaska and Hawaii, and Puerto Rico. The earliest data is from June, 1998. DSI-6403 contains a record of all weather events for the reporting site. Reporting sites include all ASOS stations across the US. The beginning and ending time of the weather event is also reported. Weather duration data are derived from DSI-6401, ASOS 5-Minute Data. Weather duration data are used as input to create a climatological product known as the Monthly Airways Extract (MAE), DSI-6407, for those sites. Major parameters include the type of weather event and its begin and end times.", "links": [ { diff --git a/datasets/NCEI DSI 6404_Not Applicable.json b/datasets/NCEI DSI 6404_Not Applicable.json index c355000957..b6ed3080e8 100644 --- a/datasets/NCEI DSI 6404_Not Applicable.json +++ b/datasets/NCEI DSI 6404_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 6404_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASOS 30-Second Ceilometer Data is digital data set DSI-6404, archived at the National Climatic Data Center (NCDC). A major part of the National Weather Service (NWS) modernization effort in the 1990s is the implementation of the Automated Surface Observing System (ASOS). The ASOS Cloud Height Indicator (CHI) is a laser ceilometer that features a rapid pulse and sampling rate. The pulse rate varies from 620 Hz to 1,120 Hz according to ambient air temperature. At a nominal pulse rate of 770 Hz, the ceilometer outputs 9,240 pulses during a 12-second sampling period. The vertical resolution is 50 feet up to 12,600 feet above ground level (AGL). The maximum reporting height is 12,000 ft. The ceilometer data are sampled by the ASOS software once every 30 seconds. The accumulated 30-second data are arranged by height and averaged over a time-weighted 30-minute period to determine up to three cloud layers for each observation. Because ceilometer data is high-resolution and high-volume, at present only 25 ASOS sites contribute data, out of hundreds that currently exist. All are in the 48 contiguous United States of America. The earliest data is from June, 1998. Meteorological parameters include how many cloud layer bases, if any, were detected, and the height and thickness of each cloud layer. Other parameters include ceilometer status information and quality control parameters. This 30-second Ceilometer data set contains the 30-second samples and sensor status information for twenty-five ASOS reference sites below: Station and Call Sign Astoria, OR AST Atlanta, GA ATL Atlantic City, NJ ACY Brownsville, TX BRO Bismarck, ND BIS Charleston, SC CHS Dallas-Ft. Worth, TX DFW Denver, CO DEN Grand Rapids, MI GRR Great Falls, MT GTF Lincoln, NE LNK Los Angeles, CA LAX Minneapolis-St. Paul, MN MSP Mobile, AL MOB Paducah, KY PAH Pittsburgh, PA PIT Portland, ME PWM Raleigh-Durham, NC RDU Salt Lake City, UT SLC San Francisco, CA SFO Sault Ste. Marie, MI CUI Syracuse, NY SYR Tucson, AZ TUS Tulsa, OK TUL West Palm Beach, FL PBI", "links": [ { diff --git a/datasets/NCEI DSI 9670_Not Applicable.json b/datasets/NCEI DSI 9670_Not Applicable.json index beb0cef871..862acd39e3 100644 --- a/datasets/NCEI DSI 9670_Not Applicable.json +++ b/datasets/NCEI DSI 9670_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9670_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BOMEX - Miscellaneous Data is a historical digital data set archived at the National Climatic Data Center (NCDC). BOMEX Archive includes, data collected during the Barbados Oceanographic and Meteorological Experiment (BOMEX) in 1969. Parameters included in this dataset are: boundary layer and surface air temperature, wet bulb temperature, humidity, and winds; clouds; visibility; precipitation; sea surface temperature; and waves. With the cooperation of the Government of Barbados and with the National Oceanic and Atmospheric Administration as lead agency, the Barbados Oceanographic and Meteorological Experiment (BOMEX) was conducted over the tropical Atlantic East of Barbados in the summer of 1969. The field operations for this multiagency national study of the ocean-atmosphere system were divided into four observation periods: May 3 to 15, May 24 to June 10, June 19 to July 2, and July 11 to July 28. The first three were devoted to the Sea Air Interaction Program--the BOMEX 'Core Experiment'--within a 500-km by 500-km square ship array. During the fourth period, the array was extended southward to incorporate the Intertropical Convergence Zone. The following is a list of the 8 different records and their respected data sets for this project. Miscellaneous Data (DSI-9670) - C00598 Rawinsonde and Radiometersonde Data (DSI-9671) - C00302 Boom Surface Meteorological Data (DSI-9672) - C00303 Salinity-Temperature-Depth (STD) Data (DSI-9673) - C00599 Aircraft Data (DSI-9674) - C00600 Boundary Layer Instrument Package (BLIP) Data (DSI-9675) - C00304 Surface Radar Data (DSI-9676) - C00601 Dropsonde Data (DSI-9677) - C00602", "links": [ { diff --git a/datasets/NCEI DSI 9673_Not Applicable.json b/datasets/NCEI DSI 9673_Not Applicable.json index eb0dea4576..5392b8b302 100644 --- a/datasets/NCEI DSI 9673_Not Applicable.json +++ b/datasets/NCEI DSI 9673_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9673_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BOMEX - Salinity-Temperature-Depth (STD) Data is a historical digital data set archived at the National Climatic Data Center (NCDC). BOMEX Archive includes, data collected during the Barbados Oceanographic and Meteorological Experiment (BOMEX) in 1969. Parameters included in this dataset are: sea temperature, salinity, and depth. With the cooperation of the Government of Barbados and with the National Oceanic and Atmospheric Administration as lead agency, the Barbados Oceanographic and Meteorological Experiment (BOMEX) was conducted over the tropical Atlantic East of Barbados in the summer of 1969. The field operations for this multiagency national study of the ocean-atmosphere system were divided into four observation periods: May 3 to 15, May 24 to June 10, June 19 to July 2, and July 11 to July 28. The first three were devoted to the Sea Air Interaction Program--the BOMEX 'Core Experiment'--within a 500-km by 500-km square ship array. During the fourth period, the array was extended southward to incorporate the Intertropical Convergence Zone. The following is a list of the 8 different records and their respected data sets for this project. Miscellaneous Data (DSI-9670) - C00598 Rawinsonde and Radiometersonde Data (DSI-9671) - C00302 Boom Surface Meteorological Data (DSI-9672) - C00303 Salinity-Temperature-Depth (STD) Data (DSI-9673) - C00599 Aircraft Data (DSI-9674) - C00600 Boundary Layer Instrument Package (BLIP) Data (DSI-9675) - C00304 Surface Radar Data (DSI-9676) - C00601 Dropsonde Data (DSI-9677) - C00602 STD DATA Salinity-Temperature-Depth Data Set (STD) instruments were casts from the surface to 1,000m. The instrument's underwater signals were frequency modulated and multiplexed so that salinity, temperature, and depth measurements were transmitted through the lowering cable as a single composite wave form. These were scheduled for the Discoverer, Oceanographer, and Rockaway at 0100, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 GMT; during Period IV, however, the first sounding from the Discoverer was made at 0000 rather than 0100. Soundings from the Mt. Mitchell and Rainier were scheduled at 0100, 0600, 1200, and 1800 GMT. All schedules were adhered to within +/-30 min. The sensor package was soaked at the surface for 5 min., lowered at a rate of approximately 20m/min to 100m, and then allowed to descend at 40 to 50m/min. The depths were determined from the STD strip-chart recorder on deck. Data were recorded during each descent only.", "links": [ { diff --git a/datasets/NCEI DSI 9691_01_Not Applicable.json b/datasets/NCEI DSI 9691_01_Not Applicable.json index 914e9b8845..08f01d422d 100644 --- a/datasets/NCEI DSI 9691_01_Not Applicable.json +++ b/datasets/NCEI DSI 9691_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9691_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digitized data taken from original weather observations taken at Cape Kennedy Air Force Station, Florida. Elements recorded are wind speed and direction, wind speed minimum, mean, and maximum, temperature, delta temperature between levels, pressure, radiation, dew point, precipitation, stability index and precipitable water, and vertical winds. Observations were taken simultaneously at nine levels, using instrumentation mounted on two towers: Tower 1 (18m tall): 3m, 10m, 18m Tower 2 (150m tall): 18m, 30m, 60m, 90m, 120m, 150m", "links": [ { diff --git a/datasets/NCEI DSI 9692_01_Not Applicable.json b/datasets/NCEI DSI 9692_01_Not Applicable.json index 27afccea12..075e122c66 100644 --- a/datasets/NCEI DSI 9692_01_Not Applicable.json +++ b/datasets/NCEI DSI 9692_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9692_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digitized data taken from original weather observations taken at Cape Kennedy Air Force Station, Florida. Elements recorded are wind speed and direction, temperature, dew point, pressure, and possibly more. Observations were taken every three hours. Records are contained in standard 80 character record lengths. The reference manual to indicate the individual fields and their lengths has not been found. It is possible to identify most of the fields, but some, particularly later in the record, have not been identified.", "links": [ { diff --git a/datasets/NCEI DSI 9693_01_Not Applicable.json b/datasets/NCEI DSI 9693_01_Not Applicable.json index a4d5420e39..a6d5fa6517 100644 --- a/datasets/NCEI DSI 9693_01_Not Applicable.json +++ b/datasets/NCEI DSI 9693_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9693_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cape Kennedy Thunderstorms Data contains an account of all thunderstorms reported in weather observations taken at Cape Kennedy Air Force Station, Florida between 1957 and 1972. Elements recorded include date and time thunderstorm began and ended, quadrant(s) in which the storm was first and last observed, direction of movement, intensity, frequency of thunder, presence of more than one storm, lightning characteristics and intensity. Wind speed (sustained and gusts), wind shift, pressure tendency, and minimum ceiling hight and visibility are also included.", "links": [ { diff --git a/datasets/NCEI DSI 9694_01_Not Applicable.json b/datasets/NCEI DSI 9694_01_Not Applicable.json index 9f59967835..5c12ff17b2 100644 --- a/datasets/NCEI DSI 9694_01_Not Applicable.json +++ b/datasets/NCEI DSI 9694_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9694_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A meteorological data system was designed, assembled, and installed to obtain, on a continuous basis, wind and temperature information at 12 levels on a television transmitting tower 1434 ft in height. Measurement and recording of atmospheric variables was accomplished entirely automatically, the output being in the form of punched paper tape and a record prepared by an electric typewriter. The tower used as an instrument support was triangular in cross section, measuring 12 ft on a side with no taper, was extensively guyed, contained a 2000-lb capacity elevator, and was capped by a triangular superstructure 75 ft on a side and about 14 ft high.", "links": [ { diff --git a/datasets/NCEI DSI 9715_01_Not Applicable.json b/datasets/NCEI DSI 9715_01_Not Applicable.json index 36943ab059..05a11a3dc9 100644 --- a/datasets/NCEI DSI 9715_01_Not Applicable.json +++ b/datasets/NCEI DSI 9715_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9715_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are keyed (digitized) data from the images of the Climatological Data National Summary containing monthly summaries for cities in the United States (and territories). Variables include temperature, precipitation, station and sea level pressure, average dew point, average relative humidity, weather occurrence, wind, cloudiness/sunshine and degree days. Period of record is 1961-1964.", "links": [ { diff --git a/datasets/NCEI DSI 9795_01_Not Applicable.json b/datasets/NCEI DSI 9795_01_Not Applicable.json index 5ffa3bdb4a..d6f8776ce9 100644 --- a/datasets/NCEI DSI 9795_01_Not Applicable.json +++ b/datasets/NCEI DSI 9795_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9795_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climatic Diagnostics Database, DSI-9795, is a historical data set created by the Climate Analysis Center using global climatic data from the period October 1, 1978 through September 30, 1983. The Climate Diagnostics Database contains monthly averages of selected fields from the National Meteorological Center's (NMC; now National Centers for Environmental Prediction, NCEP) Global Data Assimilation System (GDAS). The major parameters are monthly averages of the following elements for constant pressure levels of 1000-, 850-, 700-, 500-, 300-, 250-, 200-, 100-, and 50-millibars: 1. U (West/East) component of wind (meters/second), 2. V (South/North) component of wind (meters/second), 3. Temperature (Deg. K), 4. Geopotential height (geopotential meters), 5. Vertical velocity (millibars/second), 6. Specific humidity (grams/kilogram) 7. Vorticity (seconds-1), 8. Pressure (millibars), 9. Sums squared of U (West/East) component of wind (meters/second), 10. Sums squared of V (South/North) component of wind (meters/second), 11. Sums squared of temperature (K), 12. Sums squared of geopotential height (geopotential meters). 13. Sums squared of vertical velocity (millibars/second), 14. Sums squared of specific humidity (grams/kilogram), 15. Sums squared of vertical velocity (seconds-1), 16. Sum of cross product UV wind components (m2s-2), East-West transport of poleward momentum, 17. Sum of cross product U and temperature (ms-1K), East-West transport of heat, 18. Sum of cross product U and geopotential height (ms-1gpm), East-West transport of mass, 19. Sum of cross product U and vertical velocity (mmbs-2), East-West transport of vertical momentum, 20. Sum of cross product U and specific humidity (mgs-1Kg-1), East-West transport of moisture, 21. Sum of cross product U and vorticity (ms-2), East-West transport of relative vorticity, 22. Sum of cross product V and temperature, North-South transport of heat, 23. Sum of cross product V and geopotential height (ms-1gpm), North-South transport of mass, 24. Sum of cross product V and vertical velocity (mmbs-2), North-South transport of vertical momentum, 25. Sum of cross products V and specific humidity (mgs-1Kg-1), North-South transport of moisture, 26. Sum of cross products V and vorticity (ms-2), North-South transport of relative vorticity, 27. Stretching of vortex tubes (s-2).", "links": [ { diff --git a/datasets/NCEI DSI 9796_01_Not Applicable.json b/datasets/NCEI DSI 9796_01_Not Applicable.json index 1ff4e5d76f..e5f053db62 100644 --- a/datasets/NCEI DSI 9796_01_Not Applicable.json +++ b/datasets/NCEI DSI 9796_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9796_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric Handbook Data Tables consists of one combined file containing 226 data files. The files contains information, programs, and data largely taken from results published in scientific journals. In general, sections of files are grouped according to the atmospheric area. Atmospheric data tables in this data set are described in World Data Center A for Meteorology and World Data Center A for Solar Terrestrial Physics Report UAG-89. Data areas cover attenuation coefficients for the atmosphere and H2O; 1962 standard atmospheres; cloud drop size distributions for water and ice spheres; solar spectral irradiance (NIMBUS and SMM satellite solar irradiance data); sky spectral radiance; Rayleigh coefficients for air; refractive indices for air, ice, liquid H2O, and various atmospheric aerosols; and relative reflectance for ice and H2O.", "links": [ { diff --git a/datasets/NCEI DSI 9799_Not Applicable.json b/datasets/NCEI DSI 9799_Not Applicable.json index f4894c08e0..ad7a6bb007 100644 --- a/datasets/NCEI DSI 9799_Not Applicable.json +++ b/datasets/NCEI DSI 9799_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9799_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm).", "links": [ { diff --git a/datasets/NCEI DSI 9873_01_Not Applicable.json b/datasets/NCEI DSI 9873_01_Not Applicable.json index 0933885129..37d427d7cd 100644 --- a/datasets/NCEI DSI 9873_01_Not Applicable.json +++ b/datasets/NCEI DSI 9873_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9873_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. The \"STAR\" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as \"Baseline sites\". As the NCEI archive only contains a subset (The \"STAR\" stations continue to operate, so their data set does extend beyond 2008), users are encouraged to contact the ESRL Global Monitoring Division for the most up-to-date information. \n \n\nPer MACI team: \nThe dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. Dave Longenecker is the data manager in Boulder and he provides the data to the global network (see online resource URL). In a phone conversation with Mara Sprain, 22 Aug 2016, Dave related that he didn't know we had this small subset. He had no direction to provide us with additional data. \n\nThis dataset needs a submission agreement (if it's to be maintained) or it should be a candidate for removal. It's duplicated both in Boulder (FTP) and Germany (FTP and PANGAEA). \n\nFrom John Augustine email, 19 Aug 2016: The \"STAR\" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as \"Baseline sites\". When NCDC found out about these measurements (circa 2008), they requested that their data be submitted there. I wrote a program for Ells to do that and several years of data were submitted. I am not sure how up-to-date those submissions are because I don't do them. If you want metadata on the Baseline sites, you will have to contact Dave Longenecker (david.u.longenecker@noaa.gov). He has been the data manager for them for many years. Bermuda and Kwajalein have been supported by NASA, but they cut those funds this year. I am not sure whether they will continue. Bermuda has not operated for about three years because of communication problems and other issues. It will be brought back up soon. The \"STAR\" stations continue to operate, so their data set does extend beyond 2008. \n\nData are also (?) held in Colorado archive.", "links": [ { diff --git a/datasets/NCEI DSI 9926_01_Not Applicable.json b/datasets/NCEI DSI 9926_01_Not Applicable.json index cc224d9d61..f10b26cc82 100644 --- a/datasets/NCEI DSI 9926_01_Not Applicable.json +++ b/datasets/NCEI DSI 9926_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9926_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly station summaries of precipitation (including snowfall), maximum temperature and minimum temperature are provided. Also included are number of days with temperature and precipitation meeting defined threshold values. Also included are extreme highest and lowest temperature, and years of record. Period of record is generally 1891-1960, with coverage in the United States, Puerto Rico, the U.S. Virgin Islands and the Pacific islands.", "links": [ { diff --git a/datasets/NCEI DSI 9949_01_Not Applicable.json b/datasets/NCEI DSI 9949_01_Not Applicable.json index e0902dadf2..20be5914f4 100644 --- a/datasets/NCEI DSI 9949_01_Not Applicable.json +++ b/datasets/NCEI DSI 9949_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI 9949_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Service Records and Retention System (SRRS) is historical digital data set DSI-9949, a collection of products created by the U.S. National Weather Service (NWS) and archived at the National Centers for Environmental Information (NCEI) [formerly National Climatic Data Center (NCDC)]. SRRS was a network of computers and associated hardware whose purpose was to transmit and store a large number of NWS products and make them available as needed. Basic meteorological and hydrological data, analyses, forecasts, and warnings are distributed among NWS offices over the AFOS (Automation of Field Operations and Services) communications system since 1978. These include PIREP (aircraft reports from pilots), AIRMET (aeronautical meteorological bulletins), SIGMET (significant meteorological information), surface and upper air plotted unanalyzed maps, air stagnation, precipitable water, Forecasts such as wind and temperature aloft, thickness and analysis, fire weather, area, local, zone, state, agricultural advisory, and terminal; and Warnings such as marine, severe weather, hurricane and tornado. The AFOS system was developed to increase the productivity and effectiveness of NWS personnel and to increase the timeliness and quality of their warning and forecasting services. \n\nThis format version of the SRRS data was archived at NCEI from 1983 to 2001 (when a new format was created). The NCEI can service requests for products from the SRRS; two types of products are available to the user: 1) graphic displays of meteorological analyses and forecast charts (limited), and 2) alphanumeric displays of narrative summaries and meteorological/hydrological data. The following is a partial list of historical SRRS products available through the NCDC: rawinsonde data above 100 MB; AIREPS buoy reports; coastal flood warning; Coast Guard surface report; climatological report (daily and misc, incl monthly reports); weather advisory Coastal Waters Forecast Center (CWSU); weather statement; 3- to 5-day extended forecast; average 6- to 10-day weather outlook (local and national); aviation area forecast winds aloft forecast; flash flood statements, watches and warnings; flood statement; flood warning forecast; medium range guidance; FOUS relative humidity/temperature guidance; FOUS prog max/min temp/POP guidance; FOUS wind/cloud guidance; Great Lakes forecast; hurricane local statement; high seas forecast; international aviation observations; local forecast; local storm report; rawinsonde observation - mandatory levels;, METAR formatted surface weather observation; marine weather statement; short term rorecast; non-precipitation warnings/watches/advisories; nearshore marine forecast (Great Lakes only), offshore aviation area forecast; offshore forecast; other marine products, other surface weather observations, pilot report plain language, ship report, state pilot report, collective recreational report; narrative radar summary radar observation; hydrology-meteorology data report; river summary; river forecast; miscellaneous river product; river recreation statement; ; regional weather summary; surface aviation observation; preliminary notice of watch and canc msg SVR; local storm watch and warning; cancelation msg SELS watch; point information message; state forecast discussion ; state forecast rawinsonde observation - significant levels; surface ship report at intermediate synoptic time; surface ship report at non-synoptic time; surface ship report at synoptic time; special weather statement international; SIGMET severe local storm watch and area outline; special marine warning; intermediate surface synoptic observation; main surface synoptic observation; severe thunderstorm warning; severe weather statement; severe storm outlook; narrative state weather summary; terminal forecast; tropical cyclone discussion; marine/aviation tropical cyclone advisory; public tropical cyclone advisory; tornado warning; transcribed weather broadcast; tropical weather discussion; tropical weather outlook and summary; AIRMET SIGMET zone forecast; terminal forecast (prior to 7/1/96); winter weather warnings, watches, advisories; marine advisory/warning; special marine warning; miscellaneous product convective SIGMET ; local ice forecast; area forecast discussion; public information statement. SRRS (DSI-9949) by the Gateway SRRS (DSI-9957; C00583). NWS products after 2001 can be obtained from those systems, from NCEI.", "links": [ { diff --git a/datasets/NCEI DSI: 2017_01_Not Applicable.json b/datasets/NCEI DSI: 2017_01_Not Applicable.json index 17dcf00cc5..561d858ad9 100644 --- a/datasets/NCEI DSI: 2017_01_Not Applicable.json +++ b/datasets/NCEI DSI: 2017_01_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI DSI: 2017_01_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These BP Public Release data were gathered and utilized during the Response and Assessment phases of the Deepwater Horizon oil spill in the Gulf of Mexico. These data include datasets made public by BP that were standardize and integrated into NOAA's DIVER database. It includes discrete samples. The data were compiled by the NOAA Office of Response and Restoration (OR&R) and Trustees in the Data Integration, Visualization, Exploration, and Reporting (DIVER) data warehouse prior to being archived by the NOAA National Centers for Environmental Information (NCEI). The collection of files include environmental data used to determine the extent and magnitude of injury to the Gulf of Mexico ecosystem from the Deepwater Horizon oil spill. These data were used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP) developed through the Natural Resource Damage Assessment (NRDA) conducted as a result of the April 20, 2010 explosion and subsequent sinking of the Deepwater Horizon offshore drilling rig in the Gulf of Mexico, about 40 miles (60 km) southeast off the Louisiana coast, that led to a major oil spill in the region.", "links": [ { diff --git a/datasets/NCEI WebARTIS: CARN_Not Applicable.json b/datasets/NCEI WebARTIS: CARN_Not Applicable.json index 3213726263..09b2a36d38 100644 --- a/datasets/NCEI WebARTIS: CARN_Not Applicable.json +++ b/datasets/NCEI WebARTIS: CARN_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI WebARTIS: CARN_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Department of Terrestrial Magnetism at the Carnegie Institute of Science conducted observations of atmospheric electricity and magnetic storms. In addition to observatories in Washington DC and Tucson AZ, the Department operated observatories in Watheroo, Australia, Huancayo, Peru, and Apia, Samoa. Included are climatological records as well as potential gradient and conductivity data. Observations were conducted between 1916-1956, contained in 92 boxes. In addition to monitoring magnetic events, the observatories initially studied the variation of the electric potential and conductivity of the air, earth currents, cosmic rays, and disturbances in the Sun's chromosphere. They also provided meteorological information for the benefit of the local regions. DTM developed and supplied equipment for Huancayo and Watheroo for magnetic, electrical, cosmic ray, and seismic investigations.", "links": [ { diff --git a/datasets/NCEI WebARTIS: CCSP_Not Applicable.json b/datasets/NCEI WebARTIS: CCSP_Not Applicable.json index 3e516a0870..fa23f03b16 100644 --- a/datasets/NCEI WebARTIS: CCSP_Not Applicable.json +++ b/datasets/NCEI WebARTIS: CCSP_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI WebARTIS: CCSP_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Change Science Program (CCSP) Collection consists of publications and other resources produced between 2007 and 2009 by the CCSP with the intention of providing sound climate science for national and international consideration to mitigate potential global change risks. The CCSP worked with a number of United States Agencies to collect climate data and research, culminating in 21 separate assessments, discussing the current state of the climate as well as expected changes and impacts. The archive only maintains a subset of these assessments. In 2009, the Program name changed to the US Global Change Research Program (USGCRP). Since 2009, USGCRP has released updated assessments to address climate change and impacts the global ecosystem.", "links": [ { diff --git a/datasets/NCEI WebARTIS: WBAN31_Not Applicable.json b/datasets/NCEI WebARTIS: WBAN31_Not Applicable.json index fa10aca6ca..453fa2372a 100644 --- a/datasets/NCEI WebARTIS: WBAN31_Not Applicable.json +++ b/datasets/NCEI WebARTIS: WBAN31_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NCEI WebARTIS: WBAN31_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WBAN-31 is a form on which the Weather Bureau, Army and Navy recorded weather observations in the upper air as observed by rawinsonde and radiosonde. The collection includes thousands of these Adiabatic Charts, with the physical archive collection beginning primarily in the 1930s and ending in the mid 1990s and represents stations located throughout the world. The major parameters presented are pressure (Mb), height of pressure level, temperature (degrees C), dew point depression (degrees C), wind direction, and wind speed (knots). In the mid-1970s, the plotting of adiabatic charts was transitioned from paper forms to digital records. Many of the records in the latter part of the collection are computer printouts rather than the historical analog forms of the early 20th century. The bulk of this collection is available only on microfilm.", "links": [ { diff --git a/datasets/ND01_Age_Maps_1184_1.json b/datasets/ND01_Age_Maps_1184_1.json index 7ab2a83e2c..5298630820 100644 --- a/datasets/ND01_Age_Maps_1184_1.json +++ b/datasets/ND01_Age_Maps_1184_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Age_Maps_1184_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Paranao areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000). Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978. Data are provided as three GeoTiff (*.tif) images, one for each of the three areas. ", "links": [ { diff --git a/datasets/ND01_Land_Cover_Maps_1259_1.json b/datasets/ND01_Land_Cover_Maps_1259_1.json index 21008d3592..c8668d5296 100644 --- a/datasets/ND01_Land_Cover_Maps_1259_1.json +++ b/datasets/ND01_Land_Cover_Maps_1259_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Land_Cover_Maps_1259_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a time series of land cover classifications for Ariquemes, Ji-Parana, and Luiza, research sites in Rondonia, Brazil. The land cover classifications are derived from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) sensors. The time period ranges from June 1975 through June 2000, but all areas do not have images for all the years. The images were classified into the following categories: 1. Primary upland forest, representing the dominant natural vegetation in the area; 2. Pasture and green pasture; 3. Second growth, dominated by small trees and shrubs with low species diversity and biomass relative to primary forest; 4. Soil/urban; 5. Rock/savanna; 6. Water; and 7. Cloud and smoke obscured. In addition, areas covered by rock and savanna were mapped and all areas outside of the overlap zone between all dates within a scene, and scene edges, were masked.There are 75 GeoTIFF files (.tif) with this data set which includes: classified images (*ful.tif) and a corresponding image mask (*ful_mask .tif) for each date (with the exception of 1978 and 1996 images for Ji-Parana, for which there are only ful_mask.tif files), and three mask files for rock, savannah, and scene edges, for each area. By area, there are 31 images for Ariquemes, 23 images for Ji-Parana, and 21 images for Luiza. ", "links": [ { diff --git a/datasets/ND01_Pasture_Nutrients_1135_1.json b/datasets/ND01_Pasture_Nutrients_1135_1.json index 84fd5c4620..c8bc554001 100644 --- a/datasets/ND01_Pasture_Nutrients_1135_1.json +++ b/datasets/ND01_Pasture_Nutrients_1135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Pasture_Nutrients_1135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides soil physical and chemical properties, and grass nutrient measurements of samples collected from 17 pasture sites located within the state of Rondonia in the southwestern Brazilian Amazon. Soil data includes bulk density, class, texture, and measurements of carbon (C), phosphorus (P), calcium (Ca), magnesium (mg), and potassium (K) concentrations. Foliar data includes nitrogen (N), P, Ca, Mg, and K concentrations.The 17 pasture sites were cattle ranches selected within the region between Porto Velho and Presidente Medici of Rondonia. Four of the ranches with Ultisols support dairy cattle, and the rest have beef cattle pastures dispersed across three soil orders: Oxisols, Ultisols, and Alfisols. Nearby primary forest sites were also sampled to provide data on the original soil properties for each soil order. Soil samples were collected in May 2003, July through August 2003, and May 2004, which covered the late rainy season (May) and the dry season (July through August). Grass species sampled included Brachiaria brizantha, Brachiaria decumbens, B. brizantha, and Pennestum clandestinum, and represented three phenologically distinct grass materials: wet-season live grass, collected in May 2004, dry-season live grass and dry-season senesced grass, both collected between the end of July and the beginning of August 2003. There are 4 comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/ND01_Pasture_Spectra_1154_1.json b/datasets/ND01_Pasture_Spectra_1154_1.json index 81438fc66c..d2a1567407 100644 --- a/datasets/ND01_Pasture_Spectra_1154_1.json +++ b/datasets/ND01_Pasture_Spectra_1154_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Pasture_Spectra_1154_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of spectral reflectance (350 to 2,500 nm at 1-nm increments) and biophysical measurements on grass pastures in eight cattle ranches in the state of Rondonia, located in the southwestern Brazilian Amazon. The ranches are located near the cities of Porto Velho, Ariquemes, Ouro Preto, Ji-Parana, and Presidente Medici. Field measurements were collected in July and August 2003. The primary grass species sampled were Brachiaria brizantha and Brachiaria decumbens. Spectrometer measurements were taken at 5-m intervals along 100 m transects on the pastures - fourteen total transects. Vegetation was sampled at 20-m intervals along the transects. All standing biomass and litter on the soil surface were collected and separated into live and senesced biomass and then dried to calculate water content. Sixty-eight reflectance spectra coincided with grass biophysical samples.Note that the research was done on private lands in Rondonia, and to protect the privacy of those land owners no geographic information is associated with the reported measurements. Three data files are included: an ENVI spectral library file with reflectance data for 484 pasture sampling points, an ASCII comma-separated file with reflectance data for the 484 pasture sampling points, and an ASCII comma-separated file with the biophysical measurements.", "links": [ { diff --git a/datasets/ND01_Registered_TM_MSS_1197_1.json b/datasets/ND01_Registered_TM_MSS_1197_1.json index b07454b39b..bfb15feb2d 100644 --- a/datasets/ND01_Registered_TM_MSS_1197_1.json +++ b/datasets/ND01_Registered_TM_MSS_1197_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Registered_TM_MSS_1197_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a time series of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) scenes for five (Path/Row) areas in Rondonia, Brazil. The scenes are from the period June 1975 through June 2000, but all areas do not have scenes for all the years. The areas and Landsat Path/Rows included are as follows: Ariquemes (P232,R67), Ji-Parana (P231, R67), Luiza (P231, R68), Cacoal (P230, R68), and Porto Velho (P232, R66). TM images are available for all five areas. Because of a paucity of digital Landsat MSS imagery from the 1970s, only two scenes could be included, a 1975 scene from Ariquemes and a 1978 scene from Ji-Parana. Each of the Landsat scenes has been coregistered to a Path/Row-specific georectified PRODES Landsat file obtained from the Brazilian Government's National Institute for Space Research (INPE) program. For each scene, the coregistration is accurate to within (plus or minus)1 pixel (30-m Landsat resolution) in most places. The five INPE PRODES Landsat scenes used in the georectification process are included with this data set. There are five compressed files (tar.gz format) with this data set. When expanded, each compressed file (which corresponds to one of the five areas) contains a directory for each scene with GeoTIFF files for individual Landsat bands, a text file of tie points, and another text file of slope and intercept values for converting radiance to reflectance. There are two dates for Landsat MSS scenes, 45 dates for TM scenes, and six dates for ETM+ scenes.", "links": [ { diff --git a/datasets/ND01_Spectral_Mixture_Models_1188_1.json b/datasets/ND01_Spectral_Mixture_Models_1188_1.json index 8f20adad29..0a7c4c58df 100644 --- a/datasets/ND01_Spectral_Mixture_Models_1188_1.json +++ b/datasets/ND01_Spectral_Mixture_Models_1188_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Spectral_Mixture_Models_1188_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides fractional land cover type images for shade, green vegetation (GV), non-photosynthetic vegetation (NPV), and soil for the regions of JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal in the state of Rondonia, Brazil, for the period 1984 to 2000. The images were derived with a spectral mixture analysis (SMA) of Landsat Thematic Mapper (TM) time series scenes for each of these areas. There were 249 TM scenes and one Landsat Multispectral Scanner (MSS) scene acquired for these analyses. The images are 30-m Landsat resolution and were georectified to the Brazilian space agency 1998 and 1999 PRODES imagery. There are 250 GeoTIIF image files (*.tif) in this data set. Files are grouped by region and year/month/day scene was taken. ", "links": [ { diff --git a/datasets/ND01_Stream_Chemistry_1119_1.json b/datasets/ND01_Stream_Chemistry_1119_1.json index ddbe4b38d3..ef5b10de3a 100644 --- a/datasets/ND01_Stream_Chemistry_1119_1.json +++ b/datasets/ND01_Stream_Chemistry_1119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND01_Stream_Chemistry_1119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of (1) synoptic streamwater sampling and analyses from numerous sites across Rondonia and (2) corresponding watershed characteristics derived from remote sensing and historical/available data sources. Sixty streams, in both forested and non-forested sites, were sampled once during the dry season in August of 1998 and 49 of the same streams were sampled again during the wet season in January-February of 1999. Analyses included sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), silica (Si), chloride (Cl), sulfate, pH, and acid neutralizing capacity. Watershed characteristics, including soil cation content, pH, watershed lithology, area, percent deforested, and urban watershed population density, were derived and calculated from digitized soil maps and available soil profile analyses, digitized topographic maps, land use mosaics from Landsat Thematic Mapper (TM) images, and Brazilian census data. The objective of the study was to determine the relative influence of watershed soil exchangeable cation content, rock type, deforestation, and urban population density on stream concentrations of base cations, dissolved silicon, chloride and sulfate in both the dry and wet seasons in a humid tropical region undergoing regional land use transformation. There are three comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/ND02_Fertilization_Experiment_954_1.json b/datasets/ND02_Fertilization_Experiment_954_1.json index 26938cca28..062f4bc6b8 100644 --- a/datasets/ND02_Fertilization_Experiment_954_1.json +++ b/datasets/ND02_Fertilization_Experiment_954_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Fertilization_Experiment_954_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil emissions of nitric oxide (NO), carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) measured in plots of a secondary-growth forest fertilization experiment located 6.5-km northwest of the town of Paragominas, Para, Brazil during 1999-2001. Control, pre-, and post-treatment plot trace gas emission results are reported in a single comma-separated file.A highly degraded former pasture with secondary-growth forest (capoeira -- fallow vegetation) at Fazenda Vitoria, 6.5-km northwest of the town of Paragominas, Para, was chosen for this fertilization experiment. Twelve 20m x 20m plots were established: three were fertilized with nitrogen (only), three were fertilized with phosphorus (only), and three were fertilized with both nitrogen and phosphorus. The remaining three plots served as the control for these treatments. Application of the fertilizers occurred Jan 19, 2000 and February 2001, at the beginning of the rainy season. ", "links": [ { diff --git a/datasets/ND02_Landsat_TM_MSS_Para_1156_1.json b/datasets/ND02_Landsat_TM_MSS_Para_1156_1.json index f44f3c8ca3..acc6e80798 100644 --- a/datasets/ND02_Landsat_TM_MSS_Para_1156_1.json +++ b/datasets/ND02_Landsat_TM_MSS_Para_1156_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Landsat_TM_MSS_Para_1156_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Landsat images of the county of Sao Francisco do Para located in the Bragantina region of Para, Brazil, the oldest agriculture frontier in Amazonia. These images are subsets for the municipio (county) and immediate region. There are seven GeoTIFF files (.tif) with this data set which includes two for July 24, 1984 multispectral scanner (MSS), one for June 21, 1994 thematic mapper Landsat 5 (TM5), three for July 13, 1999 thematic mapper Landsat 7 (TM7), and one TM for June 21, 1994.", "links": [ { diff --git a/datasets/ND02_Mulching_Experiment_950_1.json b/datasets/ND02_Mulching_Experiment_950_1.json index 032a2e40c7..042fc3cee7 100644 --- a/datasets/ND02_Mulching_Experiment_950_1.json +++ b/datasets/ND02_Mulching_Experiment_950_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Mulching_Experiment_950_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of a study to measure soil emissions of the carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and nitric oxide (NO) throughout an entire cropping cycle in (1) slash-and-burn and (2) chop-and-mulch prepared agricultural fields from 2001-2004. An adjacent 15-year-old fallow field with secondary forest vegetation served as the control. The study site is within the municipality of Igarape Acu, Para, Brazil, at the Experimental Farm of the Federal Rural University of Amazonia. Flux data are reported in one comma-separated file.", "links": [ { diff --git a/datasets/ND02_Non_Woody_Biomass_1115_1.json b/datasets/ND02_Non_Woody_Biomass_1115_1.json index 0d21ea60fa..d061dcdbc0 100644 --- a/datasets/ND02_Non_Woody_Biomass_1115_1.json +++ b/datasets/ND02_Non_Woody_Biomass_1115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Non_Woody_Biomass_1115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports biomass from small stems and non-woody vegetation measured from 1999 to 2005 in plots of a secondary-growth forest fertilization experiment. The study location was Fazenda Vitoria, 6.5-km northwest of the town of Paragominas, Para, Brazil, in a 6-year old secondary-growth forest. Vegetation life-forms with diameters less than or equal to 2 cm (grasses, herbs, vines and dead material) were destructively sampled in November 1999, June 2000, June 2001, July 2003, July 2004, and July 2005. All data are provided in a single comma-separated file. The site was divided into three blocks with four treatment plots (each 20m x 20m) located in each block (3 reps x 4 treatments = 12 plots). Three of the twelve plots were fertilized with nitrogen (100 kg N/ha as urea), three were fertilized with phosphorus (50 kg P/ha as superphosphate), three were fertilized with both nitrogen and phosphorus. The remaining three plots were not fertilized and served as the experimental control.", "links": [ { diff --git a/datasets/ND02_REE_Soil_VWC_1061_1.json b/datasets/ND02_REE_Soil_VWC_1061_1.json index 9fb807ee42..a699a4c933 100644 --- a/datasets/ND02_REE_Soil_VWC_1061_1.json +++ b/datasets/ND02_REE_Soil_VWC_1061_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_REE_Soil_VWC_1061_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports monthly measured soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002). There are two ASCII comma delimited files with measured VWC, one for the control plot and one for the rainfall exclusion plot.These measured values were used by the authors to develop a model of daily changes in the distribution of water through the soil layers. The simulated daily VWC values are also provided in the file with the measured VWC. For comparison, results of VWC simulation for the control and treatment plots using a STELLA model which incorporates rainfall and plant water uptake are provided. There are two ASCII comma delimited files of simulated results. See Belk et. al., 2007 for details.", "links": [ { diff --git a/datasets/ND02_REE_Trace_Gas_Tapajos_955_1.json b/datasets/ND02_REE_Trace_Gas_Tapajos_955_1.json index bb660394be..1972e61b8b 100644 --- a/datasets/ND02_REE_Trace_Gas_Tapajos_955_1.json +++ b/datasets/ND02_REE_Trace_Gas_Tapajos_955_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_REE_Trace_Gas_Tapajos_955_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of a rainfall exclusion experiment in the Tapajos National Forest (Flona-Tapajos) at km 67 along the Santarem-Cuiaba BR-163 highway. From December 1999 through April 2005, following a one-year pre-treatment phase, rainfall was excluded from one of two 1-hectare plots of seasonally dry humid tropical forest. Soil emissions of carbon dioxide (CO2), nitric oxide (NO), nitrous oxide (N2O), and methane (CH4) were monitored in order to determine the likely effect of increasingly frequent El Nino drought episodes in the Amazon basin. Soil trace gas flux data are provided in one comma-separated data file.", "links": [ { diff --git a/datasets/ND02_Soil_CO2_Extracts_1074_1.json b/datasets/ND02_Soil_CO2_Extracts_1074_1.json index 4daf55e108..1321962199 100644 --- a/datasets/ND02_Soil_CO2_Extracts_1074_1.json +++ b/datasets/ND02_Soil_CO2_Extracts_1074_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Soil_CO2_Extracts_1074_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a time series of calcium, magnesium, and potassium extracted from soil samples from a laboratory column extraction study conducted in 2002. Soils used in the columns were originally collected in 1998 in Fazenda Vitoria, a cattle ranch 6 km north of the town of Paragominas, Para, Brazil. The soils were from contrasting land uses of primary forest (mata), secondary forest (capoeira), or pasture (pasto). Water equilibrated with increasing concentrations of CO2 was used to extract cations from the soil columns. Data represent the time series of cation concentrations in the extract solutions as well as the total content of cations removed from the soils. There is one comma-delimited ASCII file with this data set.", "links": [ { diff --git a/datasets/ND02_Soil_CO2_Flux_1066_1.json b/datasets/ND02_Soil_CO2_Flux_1066_1.json index a897085e9b..f1a9525d7e 100644 --- a/datasets/ND02_Soil_CO2_Flux_1066_1.json +++ b/datasets/ND02_Soil_CO2_Flux_1066_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Soil_CO2_Flux_1066_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports soil CO2 flux and results of physical and chemical characterization of soils from pastures, secondary forests, and mature forests near Rio Branco, Acre, Brazil. CO2 flux measurements were made in the field on a monthly basis at 16 sites from June of 1999 to January 2001. In addition, litter was collected monthly from 2001-2002 at each of the mature forest sites and at 4 of the secondary forest sites, and mean litter mass is reported. Soil samples were collected and analyzed from several land cover types at two sites during this same time period. There are four comma-delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/ND02_Soil_Gas_Flux_Apeu_953_1.json b/datasets/ND02_Soil_Gas_Flux_Apeu_953_1.json index b689a9db43..405ceba6ea 100644 --- a/datasets/ND02_Soil_Gas_Flux_Apeu_953_1.json +++ b/datasets/ND02_Soil_Gas_Flux_Apeu_953_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Soil_Gas_Flux_Apeu_953_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of a study to quantify the effects of moisture and substrate availability on soil trace gas fluxes in a regrowth forest in Eastern Amazonia, Apeu, Para, Brazil, from 1999-2003. The efflux of carbon dioxide (CO2), nitric oxide (NO), nitrous oxide (N2O), and methane (CH4) from soil was measured as a response to (1) increased soil moisture availability during the dry season by irrigation and (2) decreased substrate availability by continuous removal of aboveground litter and compared to (3) untreated control plots. Soil gas fluxes are reported in one comma-separated data file.", "links": [ { diff --git a/datasets/ND02_Soil_Gases_REE_1117_1.json b/datasets/ND02_Soil_Gases_REE_1117_1.json index 565ec19848..806be2fe7f 100644 --- a/datasets/ND02_Soil_Gases_REE_1117_1.json +++ b/datasets/ND02_Soil_Gases_REE_1117_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Soil_Gases_REE_1117_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports soil carbon dioxide (CO2) and nitrous oxide (N2O) concentrations and soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two to three months. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002).Data provided are from December 9, 1999, and April 2, 2000-June 14, 2002. There is one comma-delimited data file with this data set. ", "links": [ { diff --git a/datasets/ND02_Soil_Hydraulic_Conductivity_1075_1.json b/datasets/ND02_Soil_Hydraulic_Conductivity_1075_1.json index 0b9b0340b5..125e79ba4d 100644 --- a/datasets/ND02_Soil_Hydraulic_Conductivity_1075_1.json +++ b/datasets/ND02_Soil_Hydraulic_Conductivity_1075_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Soil_Hydraulic_Conductivity_1075_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports field estimated saturated hydraulic conductivity measurements from June 12 through June 20, 2001. This study was part of a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil. The objective of this component of the study was to develop an understanding of the physical processes driving the observed soil water dynamics at the site. There is one comma-delimited ASCII data file with this data set.", "links": [ { diff --git a/datasets/ND02_Tree_Heights_DBH_951_1.json b/datasets/ND02_Tree_Heights_DBH_951_1.json index 6fd031a91a..479b47968f 100644 --- a/datasets/ND02_Tree_Heights_DBH_951_1.json +++ b/datasets/ND02_Tree_Heights_DBH_951_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Tree_Heights_DBH_951_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides tree diameters and heights measured from 1999 to 2005 in plots of a secondary-growth forest fertilization experiment located 6.5-km northwest of the town of Paragominas, Para, Brazil. In the 6-year old secondary-growth forest, all trees greater than 2 cm diameter at breast height (DBH) were tagged, identified, and measured for diameter and height in November 1999. Fertilizer was applied to selected plots in January 2000 and February 2001. Tree heights and diameters were remeasured in May 2000, June 2001, July 2002, July 2004, and July 2005. All data are provided in a single comma-separated file.The site was divided into three blocks, with four treatment plots (each 20m x 20m) located in each block (3 reps x 4 treatments = 12 plots). Three of the twelve plots were fertilized with nitrogen (100 kg N/ha as urea); three were fertilized with phosphorus (50 kg P/ha as superphosphate); three were fertilized with both nitrogen and phosphorus. The remaining three plots were not fertilized and served as the experimental control..", "links": [ { diff --git a/datasets/ND02_Water_Chemistry_Paragominas_1067_1.json b/datasets/ND02_Water_Chemistry_Paragominas_1067_1.json index 4e384493ab..4acd04f3bd 100644 --- a/datasets/ND02_Water_Chemistry_Paragominas_1067_1.json +++ b/datasets/ND02_Water_Chemistry_Paragominas_1067_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND02_Water_Chemistry_Paragominas_1067_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes measurements of dissolved nutrient and organic carbon concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, pH, and discharge from three streams located in mixed land use (crop fields, pastures, secondary vegetation, and forest) and two streams in entirely forested landscapes near Paragominas in the state of Para, Brazil. Stream water samples were collected during two different periods: 1) weekly from August 1999 to July 2001 at location Igarape 54, Station 5 and 2) monthly from April 2003 through October 2005 at all of the stations. The exact start date and suite of measurements vary by location. In addition, samples from precipitation collectors at the Paragominas Meteorological Station were measured for nutrient concentrations every two weeks from 1999 to 2001. There are two comma delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/ND03_Flowpath_Chemistry_1076_1.json b/datasets/ND03_Flowpath_Chemistry_1076_1.json index 00641be85d..cc7138a4f3 100644 --- a/datasets/ND03_Flowpath_Chemistry_1076_1.json +++ b/datasets/ND03_Flowpath_Chemistry_1076_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND03_Flowpath_Chemistry_1076_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of water chemistry data from streams, wells, rainwater, and canopy throughfall samples. The field measurements were carried out at Rancho Grande in the Brazilian state of Rondonia, in the southwestern Brazilian Amazon basin, at two adjacent watersheds, a forest (1.37 ha), and pasture (0.73 ha). Samples were collected during one entire rainy season starting in August 2004 and ending in April 2005. There is one comma-delimited data file with this data set.Ridge, Tennessee, U.S.A.", "links": [ { diff --git a/datasets/ND03_Streams_Soilwater_1113_1.json b/datasets/ND03_Streams_Soilwater_1113_1.json index d659a4afbc..4c3255e6a0 100644 --- a/datasets/ND03_Streams_Soilwater_1113_1.json +++ b/datasets/ND03_Streams_Soilwater_1113_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND03_Streams_Soilwater_1113_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of (1) the physical and chemical characterization of streams and (2) comparable chemical analyses of extracted soil water in the Aldeia River basin at Fazenda Nova Vida, a large cattle ranch 50 km from the city of Ariquemes, in central Rondonia, Brazil, from 1994-2001. Data are provided on the stream beds including cross-sectional depth and stream bed surface type. Stream discharge is reported. Streamwater was sampled and analyzed periodically over the eight year duration of the study at numerous steam locations. Soil solution samples were collected at the same frequency with lysimeters placed at 30 cm and 100 cm depths on the floodplain and at upland forest and pasture sites in the Aldeia River watershed. There are five comma-delimited data files in this data set. ", "links": [ { diff --git a/datasets/ND04_C_Nutrient_Stocks_1069_1.json b/datasets/ND04_C_Nutrient_Stocks_1069_1.json index 1955dc7b1b..b6c43d05e4 100644 --- a/datasets/ND04_C_Nutrient_Stocks_1069_1.json +++ b/datasets/ND04_C_Nutrient_Stocks_1069_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND04_C_Nutrient_Stocks_1069_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the carbon and nutrient stocks of above-ground vegetation and soil pools at three locations where post-pasture secondary forest recovery ranged from 0 to 14 years since abandonment. These sites are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53) and Fazenda Dimona (km 72). From September 2000 to July 2001, measurements were obtained for aboveground biomass (cite ND-04 Sec For Recovery), foliage and wood samples were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations, and soil samples from 0 to 45 cm depth were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations. Total carbon (C) and nutrient stocks were calculated for various vegetation and soil pools to gain an understanding of the dynamics of nutrient and C buildup in regenerating secondary forests in central Amazonia (Feldpausch et al., 2004). There are 2 comma-delimited ASCII data files with this data set.", "links": [ { diff --git a/datasets/ND04_Secondary_Forest_Recovery_1068_1.json b/datasets/ND04_Secondary_Forest_Recovery_1068_1.json index 003bd639d8..457dce6eeb 100644 --- a/datasets/ND04_Secondary_Forest_Recovery_1068_1.json +++ b/datasets/ND04_Secondary_Forest_Recovery_1068_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND04_Secondary_Forest_Recovery_1068_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports measurements of the canopy and structure of secondary forests regenerating from abandoned pastures. These secondary forests are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53), and Fazenda Dimona (km 72). Ten secondary forest study sites were selected within the three fazendas where post-pasture forest recovery ranged from 0 to 14 years since abandonment.From 2000-2001 estimates of leaf area index (LAI) and canopy cover were derived from hemispherical canopy digital photographs, and estimates of aboveground biomass and basal area were derived utilizing allometric equations from diameter at breast height (DBH) measurements. Estimates were classified by growth-form and diameter class. See Feldpausch et al. (2005) for more information. There are four comma-delimited data files with this data set and one companion file with information regarding the allometric equations relating diameter at breast height (for dbh > 5 cm) to dry weight for biomass calculations. ", "links": [ { diff --git a/datasets/ND04_Soil_H2O_Manaus_1246_1.json b/datasets/ND04_Soil_H2O_Manaus_1246_1.json index fd53ff6a04..ec8f9438b1 100644 --- a/datasets/ND04_Soil_H2O_Manaus_1246_1.json +++ b/datasets/ND04_Soil_H2O_Manaus_1246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND04_Soil_H2O_Manaus_1246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil water measurements to a depth of 3 meters for the years 1999, 2000, and 2001, and total monthly precipitation data for 1999-2000. The data were collected from a pasture site located at the Embrapa Pasture Research Site, a former cattle research station 54 km north of Manaus on the highway BR 174 Manaus-Boa Vista, Brazil. There are three comma-separated data files (.csv) with this data set.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: There is no associated research documentation and the units were not provided with the data. ", "links": [ { diff --git a/datasets/ND04_Termite_Mounds_1072_1.json b/datasets/ND04_Termite_Mounds_1072_1.json index e32ab2112c..f67a5d99ca 100644 --- a/datasets/ND04_Termite_Mounds_1072_1.json +++ b/datasets/ND04_Termite_Mounds_1072_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND04_Termite_Mounds_1072_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of a comprehensive study of mound building termites at the Embrapa research station in the Distrito Agropecuario da SUFRAMA, located at km 53 of the federal highway BR 174 outside Manaus, Amazonas, Brazil. Study areas included a primary forest site, an adjacent 7-8 year old secondary forest site, and two abandoned pasture sites which were being used for agroforest purposes.Reported are (1) the termite species occurrence and areal abundance of mounds, (2) characterization of the mound soil microbiological community, root biomass, seedling emergence success, soil respiration, nitrogen mineralization, and (3) the characterization of the termite mound soil physical, chemical, and hydraulic properties. Analyses were also performed on samples from adjacent control soils for comparison. This data set contains 15 comma-delimited data files.", "links": [ { diff --git a/datasets/ND06_LandUse_Studies_1130_1.json b/datasets/ND06_LandUse_Studies_1130_1.json index 197c5de684..e3616b3073 100644 --- a/datasets/ND06_LandUse_Studies_1130_1.json +++ b/datasets/ND06_LandUse_Studies_1130_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND06_LandUse_Studies_1130_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of soil properties compiled from 39 studies on nutrient dynamics in natural forests and forest-derived land uses (pasture, shifting cultivation and tree plantations) conducted in Amazonia over the period of 1950-2001. The initial literature survey for the data consisted of more than 100 studies conducted during this period.The objectives of this project were to compare soil data from major land uses across Amazonia and identify gaps in present knowledge that offer direction for future research. Five widely cited hypotheses were tested concerning the effects of land-use change on soil properties by analyzing data compiled from 39 studies in multi-factorial ANOVA models:-- effective cation exchange capacity (ECEC), and exchangeable calcium (Ca) concentrations rise and remain elevated following the slash-and-burn conversion of forest to pasture or crop fields soil contents of total carbon (C), nitrogen (N), and inorganic readily (i.e., Bray, Mehlich I or resin) extractable phosphorus (Pi) decline following forest-to-pasture conversion-- soil concentrations of total C, N, and Pi increase in secondary forests with time since abandonment from agricultural activities-- soil nutrient conditions under all tree-dominated land-use systems (natural or not) remain the same-- higher efficiencies of nutrient utilization occur where soil nutrient pools are lower There is one comma-delimited ASCII file (.csv) with this data set and a list of the 39 studies used in this data set provided as a companion file in text format.", "links": [ { diff --git a/datasets/ND07_15N_Leaves_Soils_1121_1.json b/datasets/ND07_15N_Leaves_Soils_1121_1.json index caa1647813..7bba5c8440 100644 --- a/datasets/ND07_15N_Leaves_Soils_1121_1.json +++ b/datasets/ND07_15N_Leaves_Soils_1121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND07_15N_Leaves_Soils_1121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides (1) delta 15N ratios and nitrogen concentrations for foliar samples and (2) delta 13C and delta 15N ratios as well as carbon and nitrogen concentrations for soil samples collected from cerrado sites within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil. Foliar samples, collected from 320 individuals representing 45 woody tree and shrub species, and soil samples were collected from 5 cerrado locations (2 in campo sujo, 2 in cerrado denso and 1 in cerrado). Soil samples were collected to 450 cm depth in the campo sujo and 800 cm depth elsewhere. Samples were collected during the period December 1999 to September 2000. Eiten (1972) described campo sujo as an open savanna with scattered trees and shrubs, cerrado sensu stricto as a savanna woodland with abundant evergreen and deciduous trees and shrubs and an herbaceous understory, and cerrado denso as medium to tall woodlands with closed or semiclosed canopies (Bustamante et al., 2004).There are two comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/ND07_NO_Flux_Cerrado_1124_1.json b/datasets/ND07_NO_Flux_Cerrado_1124_1.json index cfa44fa5ea..feda84747a 100644 --- a/datasets/ND07_NO_Flux_Cerrado_1124_1.json +++ b/datasets/ND07_NO_Flux_Cerrado_1124_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND07_NO_Flux_Cerrado_1124_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of soil nitric oxide (NO) flux, soil moisture, and soil nitrate (NO3) and ammonium (NH4) concentration measurements on Cerrado soils receiving nitrogen fertilization. Measurements and samples were collected from control and fertillized experimental plots on Cerrado soils within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil. Sampling dates were from March 26, 2004 to November 25, 2004. The soils had received nitrogen and phosphorus fertilization treatments which began in 1998. The objective of this project was to determine the long-term effects of nutrient addition (N and N+P) in native Cerrado area on N oxide fluxes from soil to the atmosphere. There is one comma delimited (.csv) ASCII file with this data set.", "links": [ { diff --git a/datasets/ND07_PLFA_Soils_Microbial_Biomass_1017_1.json b/datasets/ND07_PLFA_Soils_Microbial_Biomass_1017_1.json index 65cd717ccc..22732a5d5d 100644 --- a/datasets/ND07_PLFA_Soils_Microbial_Biomass_1017_1.json +++ b/datasets/ND07_PLFA_Soils_Microbial_Biomass_1017_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND07_PLFA_Soils_Microbial_Biomass_1017_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the microbial biomass in soil samples collected from the Cerrado, a woodlands-savannah area, in Brasilia, Brazil. Microbial biomass was determined as the total concentration of phospholipid fatty acids (PLFAs). Soil samples (0-5 cm) were collected from June, 2000 to June, 2001 in two native areas of Cerrado that were subjected to a range of fire regimes. Two plots were protected from fire since 1973, another two plots were subjected to prescribed fires every two years since 1992, and a fifth plot was in a 20 year-old active pasture (Brachiaria brizantha). The analyses were conducted to determine the effects of fire regimes and changes in vegetation cover on the microbial communities of Cerrada soils. There is one comma-separated ASCII data file with this data set. ", "links": [ { diff --git a/datasets/ND07_Stream_Chemistry_Brasilia_1018_1.json b/datasets/ND07_Stream_Chemistry_Brasilia_1018_1.json index e7f9ecdd0b..12417979ff 100644 --- a/datasets/ND07_Stream_Chemistry_Brasilia_1018_1.json +++ b/datasets/ND07_Stream_Chemistry_Brasilia_1018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND07_Stream_Chemistry_Brasilia_1018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports on dissolved nutrient concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, and pH measured in water samples collected from nine streams located in the state of Brasilia, Brazil, between September, 2004 and December, 2006. Streams were located in different land cover types including natural (forest), rural (agricultural), and developed landscapes. In addition, water samples from wells, lysimeters, surface runoff, and precipitation were collected from four sites, 2 natural and 2 rural, and analyzed for nutrient concentrations. Streams were sampled every 2-4 weeks; rain water was collected approximately monthly during the wet season and once during a dry season; wells and lysimeters were sampled monthly; and surface runoff collections were event based. There are three comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/ND07_Trace_Gas_Land_Use_1016_1.json b/datasets/ND07_Trace_Gas_Land_Use_1016_1.json index 163eb92918..141ed3e42a 100644 --- a/datasets/ND07_Trace_Gas_Land_Use_1016_1.json +++ b/datasets/ND07_Trace_Gas_Land_Use_1016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND07_Trace_Gas_Land_Use_1016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports on soil-atmosphere fluxes of trace carbon dioxide, carbon monoxide, nitrous oxide, and nitric oxide (CO2, CO, N2O, NO) under various natural and manipulated land use conditions. The studies were conducted near Brasilia, Brazil in pastures and agricultural areas under a variety of management regimes and in more natural areas of cerrado (20-50% canopy cover) and campo sujo (open, grass-dominated), which were either burned every 2 years or protected from fire. Results provide data and relationships needed for regional trace gas models. There are nine comma-separated ASCII data files with this data set.", "links": [ { diff --git a/datasets/ND08_Biomass_Jari_1148_1.json b/datasets/ND08_Biomass_Jari_1148_1.json index bba67671fc..854ab21a2a 100644 --- a/datasets/ND08_Biomass_Jari_1148_1.json +++ b/datasets/ND08_Biomass_Jari_1148_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND08_Biomass_Jari_1148_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the concentrations of the nutrients nitrogen (N), phosphorus (P), magnesium (Mg), calcium (Ca), and potassium (K) in roots, litterfall, leaves, and twigs, biomass of fine roots and litterfall, and the decomposition of leaves and twigs in samples that were collected on the property of Jari Celulose, Monte Dourado, Para, Brazil, from 1999-2001.Samples were collected from two study sites, a eucalyptus plantation and an adjacent primary forest, during both rainy and dry seasons. Roots were sampled from three depths (0-15 cm, 35-50 cm, and 85-100 cm).There are five comma-delimited data files with this data set.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.KNOWN PROBLEMS: The data files do not identify the year in which samples were collected. The methods for nutrient, decomposition, and biomass sampling and analyses were not provided. The data file descriptions indicate that samples were collected from two soil types (sandy and clay) but there is no documentation of which data field provides that information. Also, there is no documentation for the Location or Block fields in the data files.", "links": [ { diff --git a/datasets/ND08_Soil_Respiration_1250_1.json b/datasets/ND08_Soil_Respiration_1250_1.json index 93f5d4890f..ff1f25915a 100644 --- a/datasets/ND08_Soil_Respiration_1250_1.json +++ b/datasets/ND08_Soil_Respiration_1250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND08_Soil_Respiration_1250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides (1) carbon (C) and nitrogen (N) concentration measurements of two soil aggregate fractions (250-2000 micon, small macro-aggregates (SMAG)), and (53-250 micron (micro-aggregates (mico)) and (2) in situ soil respiration measurements (January-March 2003) on sand and clay soils from a Eucalyptus plantation and an adjacent primary forest. The soils for fractionation were sampled in July 2001 from 0-20 cm and 30-50 cm depths. The research site was on the property of Jari Celulose, Monte Dourado, Para, Brazil. There are two files with this data set in comma-delimited (.csv) format.", "links": [ { diff --git a/datasets/ND10_Soil_Chemistry_1171_1.json b/datasets/ND10_Soil_Chemistry_1171_1.json index 9deb22534e..5e6aaaabb5 100644 --- a/datasets/ND10_Soil_Chemistry_1171_1.json +++ b/datasets/ND10_Soil_Chemistry_1171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND10_Soil_Chemistry_1171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of soil physical property and chemical measurements of samples collected from two pasture chronosequences (years since conversion from primary forest) located on two ranches south of Santarem, Para, Brazil, and east of the Tapajos River. Soil data includes soil classification, bulk density, texture, and mean concentrations of total nitrogen (N), carbon (C), phosphorus (P), and P fractions. The soils were high clay oxisols and highly sandy entisols.One chronosequence of sites was established on oxisol soils dating 2, 7, and 15 years since conversion from primary forest. A second set of sites, 1, 7, and 15 years old was established on the sandy entisols. Five of the six pasture sites were on a single ranch; the 2-year-old oxisol pasture was the exception. Ten soil samples per site were collected from 0-10 cm depth along random intervals within 100-m transects in August 1997.There are two comma-delimited (.csv) data files with this data set.", "links": [ { diff --git a/datasets/ND11_Carbon_Export_CPOM_913_1.json b/datasets/ND11_Carbon_Export_CPOM_913_1.json index 81b8569486..c830751115 100644 --- a/datasets/ND11_Carbon_Export_CPOM_913_1.json +++ b/datasets/ND11_Carbon_Export_CPOM_913_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Carbon_Export_CPOM_913_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains stream water exports of coarse particulate organic matter (CPOM) and coarse particulate organic carbon (CPOC) during 2003-2004 from four forested headwater streams near Juruena, Mato Grosso, Brazil (Selva et al. (2007) and Johnson et al. (2006) . Data are reported in a single comma-separated ASCII file as watershed exports in mass units, carbon content, and watershed exports per watershed area over the reported sampling intervals.Resolving the carbon balance in the Amazonian forest depends on an improved quantification of production and losses of particulate C from forested landscapes via stream export. The export of coarse organic particulate matter (>2 mm) was quantified for one year in four small watersheds (1-2 ha) under native forest in southern Amazonia near Juruena, Mato Grosso, Brazil. Stream-water exports of particulate C were positively correlated with stream flow, increasing in the rainiest months. The export of particulate C in stream flow was found to be a small (less than 1%) percentage of the amount of litterfall produced. ", "links": [ { diff --git a/datasets/ND11_Logging_Damage_MT_977_1.json b/datasets/ND11_Logging_Damage_MT_977_1.json index be5cc0b56b..269eacb8d1 100644 --- a/datasets/ND11_Logging_Damage_MT_977_1.json +++ b/datasets/ND11_Logging_Damage_MT_977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Logging_Damage_MT_977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data were collected in the logging concession at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Estimates of damage associated with logging operations were made after logging operations were complete in 2003 and 2004. Damage associated with gaps created by felling single trees was estimated in 54 individual gaps. Characteristics of the single harvested tree were recorded and included species, DBH, commercial height, total height, and canopy proportions. Damage to all surrounding trees was recorded. Stratified transects in two logging blocks were used to estimate damage associated with road building and skid trails. Twenty-six transects were established in Block 5 and 21 transects in Block 18 to assess the frequency of damage by log skidders and tree felling. The boundaries between different types of damage were noted along the transect and the length in meters of that damage type along the transect was recorded. From this information, the area of the logging block affected by road building and skid trails was determined.The Gap Survey and the Logging Damage Transects Survey data are provided in comma-separated ASCII files. A third file provides the coordinates of the starting points for the Survey Transects. ", "links": [ { diff --git a/datasets/ND11_Nitrogen_Transfer_Leaf_Litter_915_1.json b/datasets/ND11_Nitrogen_Transfer_Leaf_Litter_915_1.json index 68126e497a..1b8428230d 100644 --- a/datasets/ND11_Nitrogen_Transfer_Leaf_Litter_915_1.json +++ b/datasets/ND11_Nitrogen_Transfer_Leaf_Litter_915_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Nitrogen_Transfer_Leaf_Litter_915_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of an experiment to determine litter decomposition and dynamics of carbon and nitrogen release from plant litter of differing qualities which occur in combination in agroforestry systems. Reported in five ASCII files are (1) the initial values of soil and plant litter macronutrients, nitrogen; and carbon contents; (2) descriptions of the various experimental treatments; and (3) the final nitrogen and carbon composition of the plant litter and soil and calculated releases from the litter.The study was conducted at the Empresa Brasileira de Pesquisa Agropecuaria-Centro da Pesquisa Agroflorestal (EMBRAPA-CPAA) experimental station located north of Manaus on the BR 174 highway in the central Amazon Basin. The experimental plot, located in an open grassy field, was selected based on low, homogeneous soil C and nutrient contents, minimal prior disturbance, and no previous fertilizer application. The soil was a degraded typic Hapludox with the following soil properties for the upper 0-3 cm: pH in water of 5.05, 1.3 mg N/g, 18.0 mg C/g (automatic CN Analyzer, Elementar), 2.05 mg P/kg, 55.0 mg K/kg (Mehlich-1 extractable), 11.7 mg Ca/kg, and 4.6 mg Mg/kg (KCl extractable). The experiment was conducted in the field in small plots with treatments that vary in the ratio of plant litter of two plants: gliricidia (Gliricidia sepium (Jacq.) Kunth. ex Walp.) and cupuacu (Theobroma grandiflorum). Soil and litter samples were collected prior to and during the experiment. Resin bags were used to retain mineral nitrogen released from the plant litter.", "links": [ { diff --git a/datasets/ND11_Regeneration_Succession_965_1.json b/datasets/ND11_Regeneration_Succession_965_1.json index ecef8ef9c1..5b0d42723a 100644 --- a/datasets/ND11_Regeneration_Succession_965_1.json +++ b/datasets/ND11_Regeneration_Succession_965_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Regeneration_Succession_965_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of field surveys to determine: regeneration diversity and size distribution of plants in primary undisturbed forest; and regeneration diversity and size distribution of trees in a one hundred hectare block, six years after reduced impact logging treatment (vine removal) was applied in 1998. In addition, wood density and carbon concentrations in commercially harvested species are reported. All surveys were performed in 2003 and 2004 within block 5 of the logging concession at the Fazenda Rohsamar in the municipality of Jurena in northwestern Mato Grosso, Brazil. The data are reported in three comma separated files.", "links": [ { diff --git a/datasets/ND11_Soil_Nitrate_Moisture_MT_976_1.json b/datasets/ND11_Soil_Nitrate_Moisture_MT_976_1.json index 9cdb4d42f0..4ae77fe9c2 100644 --- a/datasets/ND11_Soil_Nitrate_Moisture_MT_976_1.json +++ b/datasets/ND11_Soil_Nitrate_Moisture_MT_976_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Soil_Nitrate_Moisture_MT_976_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of the analysis of soil samples for Nitrate (NO3) and physical properties that were collected for one year following reduced impact logging in logging concessions at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Sample locations were randomly selected from stratified regions of the 1,400 ha Block 5 to account for local scale soil variability. Soil samples were collected to 8-m depth in (1) nine gaps formed by single tree removal and (2) nine areas of undisturbed primary forest. Areas of undisturbed forest were confined to patches of forest within Block 5 that were protected from logging. An additional 3 forested areas were sampled to 3-m depth that contained high sand content. These results quantified the effects of reduced impact logging, to test whether nitrogen (N) loss from leaves and coarse woody debris under reduced impact logging results in a significant accumulation of subsoil nitrate (Feldpausch et al., 2009). One comma separated data file contains the soil moisture results and a second file the soil NO3 content and soil physical properties.", "links": [ { diff --git a/datasets/ND11_Soil_Spatial_Variability_914_1.json b/datasets/ND11_Soil_Spatial_Variability_914_1.json index 7c33b0c68a..105f076d80 100644 --- a/datasets/ND11_Soil_Spatial_Variability_914_1.json +++ b/datasets/ND11_Soil_Spatial_Variability_914_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Soil_Spatial_Variability_914_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The results of the analysis of soil chemical parameters, texture, and color are reported for 185 georeferenced soil profile sample points over four forested headwater catchments near Juruena, Mato Grosso, Brazil (Novaes Filho, et al., 2007a and Novaes Filho, et al., 2007b). Samples were collected from an approximately 20 x 20 m grid over each watershed from 2004/05/01 to 2004/08/18. By sampling each location at depths of 0-20 and 40-60 cm it was possible to distinguish and map the principle soil classes found in the study area to the 2nd category level of the Brazilian System of Soil Classification (Cooper et al., 2005) associated with the topographic relief. The data set contains one comma separated ASCII data file with spatially referenced soil nutrient and organic carbon data from 0-20 cm (A layer, topsoil) and 40-60 cm (B layer, subsoil)depths for the Juruena watersheds study area.A satisfactory relationship between the redness index of the diagnostic horizons and the soil class colors was also found. In spite of the apparent homogeneity of the visible landscape characteristics such as slope, soil color, and vegetation, the carbon and soil clay attributes were found to vary greatly. This variability over small distances demonstrates that extrapolation of soil characteristics and soil carbon stocks to larger areas could produce erroneous results if the spatial variability of the soil attributes is not taken into consideration.", "links": [ { diff --git a/datasets/ND11_Soil_Water_Pressure_851_1.json b/datasets/ND11_Soil_Water_Pressure_851_1.json index 84b97d239c..cb0fd31951 100644 --- a/datasets/ND11_Soil_Water_Pressure_851_1.json +++ b/datasets/ND11_Soil_Water_Pressure_851_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Soil_Water_Pressure_851_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains information that can be used to examine water fluxes in soils beneath tree crops in an Amazonian agroforest. The data consists of repeated measurements of soil matrix pressure and soil moisture content at several depths. The study was carried out at the Empresa Brasileira de Pesquisa Agropecuaria (Embrapa)-Amazonia Ocidental, 29 km North of Manaus, Brazil (3d 8m S, 59d 52m W, 40 - 50 m above sea level), in 1998 and 1999.Microaggregated tropical soils have shown high water conductivity even under unsaturated conditions in laboratory experiments. It is not clear, however, what depth the infiltrating soil water reaches during storm events under humid tropical conditions. Dynamics and fluxes of water were determined with high temporal resolution to a depth of 5 m in a Xanthic Hapludox of central Amazonia, Brazil. The soil water percolated to a depth of 0.9 m within 2 h of a rainfall event of 48 mm. Water fluxes were significantly slower below 0.9 m (17% of infiltration at 0 - 0.9 m) due to higher bulk densities. Percolation not only started rapidly after a rainfall event when soil water suction reached a certain threshold (ca. 20 - 30 hPa) but was also reduced to background levels less than 1 h after the rain had ended. The demonstrated extremely short-term dynamics of water fluxes have implications for measurement design of water availability and solute leaching in microaggregated tropical soil that require correct time integrals of solution concentrations and soil water dynamics. Measurement intervals of 30 min or less were necessary in our study. Rapid water flows may explain the observed high nutrient losses from the topsoil of microaggregated tropical soil and the large accumulation of nutrients in the deep soil (> 5 m).", "links": [ { diff --git a/datasets/ND11_Stream_Nutrients_921_1.json b/datasets/ND11_Stream_Nutrients_921_1.json index 58f20d29a0..b8746b0a6d 100644 --- a/datasets/ND11_Stream_Nutrients_921_1.json +++ b/datasets/ND11_Stream_Nutrients_921_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Stream_Nutrients_921_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains baseflow streamwater concentrations of pH, specific conductivity, base cations, carbon (dissolved organic carbon (DOC), particulate organic carbon (POC) and bicarbonate alkalinity) and silica for four headwater streams in the seasonally dry Amazon (Johnson et al. (2006a) and Johnson et al. (2006b). Data are provided in one comma-separated ASCII file.This hydrologic study of four headwater watersheds was conducted in an undisturbed forest near Juruena, Mato Grosso in the seasonally dry, southern Amazon. The small catchments range in size from 0.85 to 1.9 ha. Stream water samples were collected weekly during rainy seasons and biweekly during the dry seasons. Baseflow stream water concentrations of base cations, silica, electrical conductivity, DOC, and alkalinity varied inversely with discharge. While there was variation among the watersheds, the concentration-discharge patterns were consistent for each of the four watersheds. Baseflow discharge data are not included in this data set and will be archived separately.", "links": [ { diff --git a/datasets/ND11_Tree_Vine_Biomass_MT_922_1.json b/datasets/ND11_Tree_Vine_Biomass_MT_922_1.json index c75544f298..b5be055cab 100644 --- a/datasets/ND11_Tree_Vine_Biomass_MT_922_1.json +++ b/datasets/ND11_Tree_Vine_Biomass_MT_922_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Tree_Vine_Biomass_MT_922_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree and liana (vine) measurements were collected in a logging concession at the Fazenda Rohsamar in the municipality of Jurena in northwestern Mato Grosso. Tree identification and diameter measurements were collected between July 31, 2003 and October 14, 2003 on 10-m x 1000-m transects and the liana measurements were collected between August 5, 2003 and October 14, 2003 on 10 2-m x 1000-m transects within a 1400 ha logging block ( Feldpauschh et al. 2006). Liana transects were nested within tree census transects to relate total species data to the tree inventory. The biomass of lianas was calculated using two different allometric equations derived for lianas in Amazonian forests ( Gerwing and Farias, 2000; Gerwing et al. 2004). Comma-separated data files of measurements of (1) tree species (diameter >10 cm), and forest characteristics, (2) measurements of liana diameter, forest characteristics, and calculated biomass, and (3) georeference points for the liana sampling transects are provided.Selective logging has become a dominant land-use in Brazilian Amazonia. Published data on forest biomass in southern Amazonia is sparse. As part of a larger study to evaluate the effect of reduced impact logging on carbon dynamics and nutrient stocks, forest structure, and forest regeneration potential, we conducted a pre-harvest campaign to estimate tree and liana biomass in a parcel of managed forest in northwestern Mato Grosso. Prior to logging in 2003, a scientific inventory was conducted in Block 5 of the logging concession(Figure 1). Tree characteristics for all trees and palms > 10 cm DBH was measured by stratified sampling across the block to account for differences in tree densities (trees/ha). Transects were located using a commercial timber inventory to identify tree trunks approximately 10 cm DBH, lower and upper canopy height, species, and location of all individuals to the nearest 10 cm on an x-y grid. Diameter of all liana stems were included if their ultimate rooting point before ascending into the canopy fell within the transect. Lianas that had been cut due to reduced impact logging practices were also measured. Distance along the transect was recorded for each stem.", "links": [ { diff --git a/datasets/ND11_Veg_Biomass_MT_964_1.json b/datasets/ND11_Veg_Biomass_MT_964_1.json index 9d74491fc4..048a50d82a 100644 --- a/datasets/ND11_Veg_Biomass_MT_964_1.json +++ b/datasets/ND11_Veg_Biomass_MT_964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND11_Veg_Biomass_MT_964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of a vegetation survey along transects across the transition zones between the three major forest types in the seasonally dry forest of the southwestern Amazon Basin in the municipality of Juruena, Mato Grosso in 2004. The major forest types are differentiated by tree species composition, biomass, soil type, and landscape position: (1) campinarana; high stem density and low biomass on sandstone outcrops, (2) palm forest; low-lying seasonally inundated areas dominated by palms and, (3) terra firme; low stem density and high biomass. Along these 10 x 100 m transects, all trees, palms, and lianas >= 10 cm DBH in 10 x 10 m plots and, in nested 2 x 10 m subplots, all trees, palms and lianas >=1 cm DBH were measured, identified and georeferenced. Plot locations and survey data are reported in two comma separated files.", "links": [ { diff --git a/datasets/ND30_Litter_Para_1129_1.json b/datasets/ND30_Litter_Para_1129_1.json index b3247234a6..24892dcfad 100644 --- a/datasets/ND30_Litter_Para_1129_1.json +++ b/datasets/ND30_Litter_Para_1129_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND30_Litter_Para_1129_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides fine litterfall mass and nutrient concentrations from samples collected at chronosequences established at Sao Francisco do Para and Capitao Poco, Para, Brazil. Nitrogen (N) and phosphorus (P) concentrations were determined for litterfall samples from the Sao Francisco do Para, and N, P, potassium (K), calcium (Ca), and magnesium (Mg) concentrations are reported for samples from the Capitao Poco. In addition, carbon (C), N, delta C13, and delta N15 values were determined for leaves from the dominant species of the forests at Sao Francisco do Para; soil physical and chemical characteristics were determined for a subset of the chronosequence plots at the two study sites; and soil trace gas fluxes were determined from the Sao Francisco do Para site. All samples were collected between March 2001-February 2005. Trace gas fluxes were measured 10 times between October 2000 and June 2002 with 5 sample periods in dry season and 5 in wet season months. There are five comma-delimited data files with this data set.", "links": [ { diff --git a/datasets/ND30_Pasture_Degradation_1164_1.json b/datasets/ND30_Pasture_Degradation_1164_1.json index 3975aac808..f6b4433a24 100644 --- a/datasets/ND30_Pasture_Degradation_1164_1.json +++ b/datasets/ND30_Pasture_Degradation_1164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND30_Pasture_Degradation_1164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains images of fractional cover estimates of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and exposed soils (S) derived from Landsat images (30-m resolution) obtained for two ranches in the Brazilian Amazon from 1996 to 2002. The Fazenda Vitoria ranch is located in eastern Para near the city of Paragominas and is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography. The Fazenda Nova Vida ranch is located in the state of Rondonia in western Amazonia and is a mosaic of primary forest, logged forest, and pastures. For Fazenda Vitoria, two dry-season Landsat images were obtained, subset, and analyzed. For Nova Vida three dry-season images and one end-of-wet-season image were obtained, subset, and analyzed. Spectral mixture analysis, which decomposes individual satellite pixels into constituent cover fractions of surface materials, was used with a general probabilistic modeling approach to derive subpixel cover fractions of PV, NPV, and S. There are six GeoTIFF (.tif) files with this data set. ", "links": [ { diff --git a/datasets/ND30_REE_Water_Chemistry_1131_1.json b/datasets/ND30_REE_Water_Chemistry_1131_1.json index 8611936fb8..6a11eac7aa 100644 --- a/datasets/ND30_REE_Water_Chemistry_1131_1.json +++ b/datasets/ND30_REE_Water_Chemistry_1131_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ND30_REE_Water_Chemistry_1131_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of chemical analyses of rainfall, throughfall, litter leachate, and soil water samples collected before, during, and after a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two weeks from May 17, 1999 through May 10, 2006. Measurements included alkalinity, conductivity, pH, and selected anions and cations analyzed by ion chromatography.The exclusion treatment, began in late January 2000 and continued through December 2004, involved diverting about 60% of throughfall (equivalent to approximately half the rainfall) from a 1-hectare plot using plastic panels installed in the understory. The comparable 1-hectare control plot was unaltered. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad et al., 2002 and Nepstad et al., 2007). There are five comma-delimited data files with this data set. ", "links": [ { diff --git a/datasets/NDVI_Forest_Structure_1797_1.json b/datasets/NDVI_Forest_Structure_1797_1.json index f8dbe007b2..27e806861d 100644 --- a/datasets/NDVI_Forest_Structure_1797_1.json +++ b/datasets/NDVI_Forest_Structure_1797_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NDVI_Forest_Structure_1797_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession. The data are from 102 sites surveyed in 2017 and 2018 and include locations with and without a fire since 1940. A time series of NDVI was developed from Landsat (1999-2018) to measure NDVI trends. The field data cover the period 2017-08-29 to 2018-08-20. The surveyed forest stands spanned a distance of over 425 km across interior Alaska. The sites were selected before visiting the field to include locations with and without a fire since 1940. Recently burned sites were selected to span a range of years since fire, while sites without a recent fire were selected to include a range of Landsat NDVI trends. For each year, the median NDVI during the growing season was calculated. Then, a simple linear regression trend was calculated for years 1999-2018.", "links": [ { diff --git a/datasets/NEMSN5L2_001.json b/datasets/NEMSN5L2_001.json index 55d3fd3fab..f070171901 100644 --- a/datasets/NEMSN5L2_001.json +++ b/datasets/NEMSN5L2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEMSN5L2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NEMSN5L2 is the Nimbus-5 or Nimbus-E Microwave Spectrometer (NEMS) Level-2 Output Data product and contains surface reflectivity, water vapor, liquid water, layer thickness, temperature at standard pressure levels, surface brightness temperature, and surface type information, as well as the input antenna and brightness temperatures at 5 microwave channels (H2O channels 22.235 and 31.4 GHz, and O2 channels 53.65, 54.9 and 58.8 GHz). The NEMS instrument views the nadir with a footprint is a 180-km diameter circle on the earth's surface. Data are available for the time period from 1972-12-17 to 1973-10-31 with data for about five days stored in a single binary data file.\n\nThe principal investigator for the NEMS experiment was David H. Staelin from MIT. An advanced version of this instrument, the Scanning Microwave Spectrometer (SCAMS) was flown on the subsequent Nimbus-6 satellite.", "links": [ { diff --git a/datasets/NES-LTER_0.json b/datasets/NES-LTER_0.json index eeff8739f5..9c7e9b3ddd 100644 --- a/datasets/NES-LTER_0.json +++ b/datasets/NES-LTER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NES-LTER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing, and how those changes impact the productivity of higher trophic levels. The NES-LTER is co-located with the Northeast U.S. Continental Shelf Large Marine Ecosystem, spanning the Middle Atlantic Bight and Gulf of Maine. Our focal cross-shelf transect extends about 150 km southward from Martha's Vineyard, MA, to just beyond the shelf break.", "links": [ { diff --git a/datasets/NESP_2015_SRW.json b/datasets/NESP_2015_SRW.json index 7ee5071e73..b5b940d0d0 100644 --- a/datasets/NESP_2015_SRW.json +++ b/datasets/NESP_2015_SRW.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2015_SRW", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.", "links": [ { diff --git a/datasets/NESP_2015_SRW_3.json b/datasets/NESP_2015_SRW_3.json index 83e83ef37f..dca2c5ce79 100644 --- a/datasets/NESP_2015_SRW_3.json +++ b/datasets/NESP_2015_SRW_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2015_SRW_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.\n\nA data update was provided in August, 2020 to correct some incorrectly given longitude values.", "links": [ { diff --git a/datasets/NESP_2016_SRW_3.json b/datasets/NESP_2016_SRW_3.json index a2df4251ad..6d1c744bf6 100644 --- a/datasets/NESP_2016_SRW_3.json +++ b/datasets/NESP_2016_SRW_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2016_SRW_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.\n\nA data update was provided in August, 2020 to correct some incorrectly given longitude values.", "links": [ { diff --git a/datasets/NESP_2017_SRW_1.json b/datasets/NESP_2017_SRW_1.json index a2cc4559da..8cea63b9fd 100644 --- a/datasets/NESP_2017_SRW_1.json +++ b/datasets/NESP_2017_SRW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2017_SRW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future", "links": [ { diff --git a/datasets/NESP_2018_SRW_1.json b/datasets/NESP_2018_SRW_1.json index 5f6f8fb38a..594565d069 100644 --- a/datasets/NESP_2018_SRW_1.json +++ b/datasets/NESP_2018_SRW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2018_SRW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.", "links": [ { diff --git a/datasets/NESP_2019_SRW_1.json b/datasets/NESP_2019_SRW_1.json index e55fb7a69a..641736220e 100644 --- a/datasets/NESP_2019_SRW_1.json +++ b/datasets/NESP_2019_SRW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NESP_2019_SRW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.", "links": [ { diff --git a/datasets/NEUROST_SSH-SST_L4_V2024.0_2024.0.json b/datasets/NEUROST_SSH-SST_L4_V2024.0_2024.0.json index 463bcb7ec3..5ab31cf21f 100644 --- a/datasets/NEUROST_SSH-SST_L4_V2024.0_2024.0.json +++ b/datasets/NEUROST_SSH-SST_L4_V2024.0_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEUROST_SSH-SST_L4_V2024.0_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Daily NeurOST Level 4 Sea Surface Height and Surface Geostrophic Currents analysis product from the University of Washington and JPL was mapped by a neural network trained with sparse Level 3 nadir altimetry observations (CMEMS, E.U. Copernicus Marine Service Information) and the MUR Level 4 gridded sea surface temperature product (PO.DAAC).", "links": [ { diff --git a/datasets/NEWS_WEB_ACLIM_1.0.json b/datasets/NEWS_WEB_ACLIM_1.0.json index d5fae33b1b..edbe9b93ba 100644 --- a/datasets/NEWS_WEB_ACLIM_1.0.json +++ b/datasets/NEWS_WEB_ACLIM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEWS_WEB_ACLIM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. \n\nThe NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the annual climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010.\n\nThe continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies.\n\nThe data are distributed with four different units (1000 km^3/year, W/m^2, cm/year, and mm/day), in three formats (NetCDF, xlsx, and csv).", "links": [ { diff --git a/datasets/NEWS_WEB_MCLIM_1.0.json b/datasets/NEWS_WEB_MCLIM_1.0.json index de58156adf..275b86802b 100644 --- a/datasets/NEWS_WEB_MCLIM_1.0.json +++ b/datasets/NEWS_WEB_MCLIM_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEWS_WEB_MCLIM_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. \n\nThe NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the monthly climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010.\n\nThe continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies.\n\nThe data are distributed with four different units (1000 km^3/month, W/m^2, cm/month, and mm/day), in three formats (NetCDF, xlsx, and csv).", "links": [ { diff --git a/datasets/NEX-DCP30_1.json b/datasets/NEX-DCP30_1.json index 1c8a03d4be..3c2a360bfd 100644 --- a/datasets/NEX-DCP30_1.json +++ b/datasets/NEX-DCP30_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEX-DCP30_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NASA dataset is provided to assist the science community in conducting studies of climate\r\nchange impacts at local to regional scales, and to enhance public understanding of possible future\r\nclimate patterns and climate impacts at the scale of individual neighborhoods and communities.\r\nThis dataset is intended for use in scientific research only, and use of this dataset for other\r\npurposes, such as commercial applications, and engineering or design studies is not\r\nrecommended without consultation with a qualified expert. Community feedback to improve and\r\nvalidate the dataset for modeling usage is appreciated. Email comments to\r\nbridget@climateanalyticsgroup.org.\r\nDataset File Name: NASA Earth Exchange (NEX) Downscaled Climate Projections (NEXDCP30),\r\nhttps://portal.nccs.nasa.gov/portal_home/published/NEX.html", "links": [ { diff --git a/datasets/NEX-GDDP_1.json b/datasets/NEX-GDDP_1.json index 88137aca02..175fbe23cb 100644 --- a/datasets/NEX-GDDP_1.json +++ b/datasets/NEX-GDDP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NEX-GDDP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds.\r\n\r\nEach of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). ", "links": [ { diff --git a/datasets/NFRDI_0.json b/datasets/NFRDI_0.json index 7d794b4754..d3d5d849b5 100644 --- a/datasets/NFRDI_0.json +++ b/datasets/NFRDI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NFRDI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the National Fisheries Research and Development Institute (NFRDI), Ministry of Oceans and Fisheries for Korea, in the East China Sea in 2000.", "links": [ { diff --git a/datasets/NGLI_Lake_Bourne_0.json b/datasets/NGLI_Lake_Bourne_0.json index d8c7044f33..716a65d78c 100644 --- a/datasets/NGLI_Lake_Bourne_0.json +++ b/datasets/NGLI_Lake_Bourne_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NGLI_Lake_Bourne_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Northern Gulf Littoral Initiative (NGLI) in the Gulf of Mexico near the Mississippi River outflow region in 2001.", "links": [ { diff --git a/datasets/NHAP.json b/datasets/NHAP.json index d831437159..7dab6a74d6 100644 --- a/datasets/NHAP.json +++ b/datasets/NHAP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NHAP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National High Altitude Photography (NHAP) program, which was operated from 1980 - 1989, was coordinated by the U.S. Geological Survey as an interagency project to eliminate duplicate photography in various Government programs. The aim of the program was to cover the 48 conterminous states of the USA over a 5-year span. In the NHAP program, black-and-white and color-infrared aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. The color-infrared photographs are at a scale of 1:58,000 (1 inch equals about .9 miles) and the black-and-white photographs are at a scale of 1:80,000 (1 inch equals about 1.26 miles).", "links": [ { diff --git a/datasets/NHICEM_001.json b/datasets/NHICEM_001.json index 5b1a2f6126..7cbb3b8e06 100644 --- a/datasets/NHICEM_001.json +++ b/datasets/NHICEM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NHICEM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. \n \n The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014.\n \n The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).", "links": [ { diff --git a/datasets/NHS.json b/datasets/NHS.json index 035d396f69..6d4f82c87d 100644 --- a/datasets/NHS.json +++ b/datasets/NHS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NHS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Hydrological and Hydrometeorological Services of the\n World Meteorological Organization (WMO) provides data on hydrology and\n water resources assessment activities.\n \n The data are available regionally:\n \n Region I - Africa\n \n Benin - Service de l'Hydrologie\n Botswana - Department of Water Affairs\n Burkina Faso - Direction G?n?rale de l'Hydraulique\n Cameroun - Centre de Recherches Hydrologiques\n Congo - Direction G?n?rale de la Recherche Scientifique et Technique\n Egypt - Ministry of Public Work and Water Resources\n Guinee - Direction nationale de la gestion des ressources en eau\n Mali - Direction Nationale de l'Hydraulique et de l'Energie\n Morocco - Direction G?n?rale de l'Hydraulique\n Mozambique - Direc??o nacional de ?guas\n Niger - Direction des ressources en eau\n Republique Centrafricaine - Direction de la M?t?orologie Nationale,\n Service de l'Hydrologie\n South Africa - Department of Water Affairs and Forestry\n Tanzania - Ministry of Water\n Tchad - Direction des Ressources en Eau et de la M?t?orologie\n Uganda - Directorate of Water Development\n \n Region II - Asia\n \n Bangladesh Water Development Board - Flood Forecasting and Warning\n Centre\n China - Ministry of Water Resources\n India - Central Water Commission\n Islamic Republic of Iran - Water Resources Management Organization\n Japan River Bureau\n Mongolia Institute of Meteorology and Hydrology\n Nepal Department of Hydrology and Meteorology\n Pakistan Flood Forecasting Bureau\n Republic of Korea Water Resources Bureau\n Saudi Arabia Ministry of Agriculture and Water\n \n Region III - South America\n \n Argentina - Instituto nacional del agua\n Bolivia Servicio nacional de meteorolog?a e hidrolog?a\n Brazil ANA - National Water Agency (in Portuguese)\n Chile - Direcci?n General de Aguas\n Colombia IDEAM - Institute of Hydrology, Meteorology and Environment\n Studies\n Ecuador INAMHI - National Institute of Meteorology and Hydrology\n Guyana Hydrometeorological Service\n Peru SENAMHI - National Meteorological and Hydrological Service\n Venezuela Ministry of Environment and Natural Resources\n \n Region IV - North and Central America\n \n Bahamas - Water and Sewerage Corporation\n British Caribbean Territories - Caribbean Institute for Meteorology\n and Hydrology\n Canada Environment Canada\n Dominica - Caribbean Institute for Meteorology and Hydrology\n El Salvador Servicio Nacional De Estudios Territoriales\n Jamaica Water Resources Authority\n Mexico Comisi?n nacional del agua\n Panama Departamento de Hidrometeorolog?a\n Republica Dominicana INDRHI - Instituto Nacional de Recursos\n Hidraulicos\n USA United States Geological Survey\n \n Region V - South-West Pacific\n Australia Hydrometeorological Advisory Service (HAS) - Bureau of\n Meteorology\n \n Malaysia Department of Irrigation and Drainage\n New Zealand National Institute of Water and Atmospheric Research\n Philippines National Water Resources Board\n \n Region VI - Europe (including Middle East)\n \n Armenia Department of Hydrometeorology ARMHYDROMET\n Austria BMLF Hydrological Service\n Azerbaijan State Hydrometeorological Committee of the Azerbaijan Republic\n Bosnia and Herzegovina - Federal Meteorological Institute\n Bulgaria National Institute of Meteorology and Hydrology\n Croatia Meteorological and Hydrological Service\n Cyprus Water Development Sector\n Czech Republic Czech Hydrometeorological Institute\n Denmark Geological Survey of Denmark and Greenland\n Estonia Estonian Meteorological and Hydrological Institute\n Finland Finnish Environment Institute - Hydrology and Water Management\n Division\n France R?seau National des Donn?es sur l'Eau\n Germany BfG - Federal Institute for Hydrology\n Hungary VITUKI RT (mainly in Hungarian)\n Iceland Hydrological Service\n Ireland Office of Public Works\n Italy National Hydrographic and Oceanographic Service (in Italian)\n Latvia Latvian Hydrometeorological Agency\n Lithuania Lithuanian Hydrometeorological Service\n The former Yugoslav Republic of Macedonia Hydrometeorological Institute\n Malta Water Services Corporation\n Netherlands Institute for Inland Watermanagement and Wastewater\n Treatment (RIZA)\n \n Norway NVE - Norwegian Water Resources and Energy Administration\n Poland IMGW - Institute of Meteorology and Water Management\n Portugal Instituto da ?gua - Water Institute\n Romania National Institute of Meteorology and Hydrology\n Russian Federation State Hydrological Institute\n Slovakia Slovak Hydrometeorological Institute\n Slovenia Hydrometeorological Service\n Spain Ministerio de Medio Ambiente\n Sweden Swedish Meteorological and Hydrological Institute\n Switzerland Swiss Federal Office for Water and Geology\n Turkey DSI General Directorate of State Hydraulic Works\n United Kingdom Centre for Ecology and Hydrology\n Yugoslavia Federal Hydrometeorological Institute\n \n Information taken from\n \"http://www.wmo.ch/web/homs/links/linksnhs.html\"\n \n Data link: \"http://www.wmo.ch/web/homs/links/linksnhs.html\"", "links": [ { diff --git a/datasets/NHSNOWM_001.json b/datasets/NHSNOWM_001.json index 0724b470a5..f8f339e7a7 100644 --- a/datasets/NHSNOWM_001.json +++ b/datasets/NHSNOWM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NHSNOWM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product is Snow Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. \n \n The product includes the monthly snow statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period from January 2000 to November 2014.\n \n Monthly data were derived from daily snow cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).", "links": [ { diff --git a/datasets/NIH-NSF_Lake_Erie_0.json b/datasets/NIH-NSF_Lake_Erie_0.json index 6bc9b38850..db339e33e6 100644 --- a/datasets/NIH-NSF_Lake_Erie_0.json +++ b/datasets/NIH-NSF_Lake_Erie_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIH-NSF_Lake_Erie_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Lake Erie funded by National Institutes of Health and the National Science Foundation.", "links": [ { diff --git a/datasets/NIMBUS7_ERB_Ch10C_TSI_NAT_1.json b/datasets/NIMBUS7_ERB_Ch10C_TSI_NAT_1.json index 95f6c71fb1..d3f85fdbbe 100644 --- a/datasets/NIMBUS7_ERB_Ch10C_TSI_NAT_1.json +++ b/datasets/NIMBUS7_ERB_Ch10C_TSI_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIMBUS7_ERB_Ch10C_TSI_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NIMBUS7_ERB_Ch10C_TSI_NAT data set is the Nimbus-7 Channel 10C (Ch10C) Total Solar Irradiance (TSI) aboard the Earth Radiation Budget (ERB) satellite Data in Native (NAT) format.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.", "links": [ { diff --git a/datasets/NIMBUS7_ERB_SEFDT_1.json b/datasets/NIMBUS7_ERB_SEFDT_1.json index 700fced821..a013cd25c4 100644 --- a/datasets/NIMBUS7_ERB_SEFDT_1.json +++ b/datasets/NIMBUS7_ERB_SEFDT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIMBUS7_ERB_SEFDT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NIMBUS7_ERB_SEFDT data set is the Solar and Earth Flux Data Tape (SEFDT) generated from Nimbus-7 Earth Radiation Budget (ERB) instrument data. The main purpose of the SEFDT program was to produce a tape containing the solar data and the wide angle terrestrial flux data only. On Nimbus-7, the ERB had two total irradiance channels, Channel 3 and Channel 10C.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.", "links": [ { diff --git a/datasets/NIMBUS7_NFOV_MLCE_1.json b/datasets/NIMBUS7_NFOV_MLCE_1.json index 7663aa5c73..97827e2e7f 100644 --- a/datasets/NIMBUS7_NFOV_MLCE_1.json +++ b/datasets/NIMBUS7_NFOV_MLCE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIMBUS7_NFOV_MLCE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NIMBUS7_NFOV_MLCE data are Nimbus 7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format.The NIMBUS7_NFOV_MLCE data set uses the Nimbus-7 measurements and the MLCE algorithm for better regional and temporal resolution. The Earth Radiation Budget (ERB) parameters, derived from the Nimbus-7 scanner measurements, were rederived in 1990 using a Maximum Likelihood Cloud Estimation (MLCE) algorithm similar, but not identical, to the Earth Radiation Budget Experiment (ERBE) algorithm. Daily and monthly means are presented on two commensurate equal area world grids: (167 km by 167 km) and (500 km by 500 km). The MLCE procedure also yielded a rough estimate of the regional cloud cover.The scanner took measurements from November 16, 1978 through June 20, 1980; however, only 13 months (May 1979 through May 1980) of data sampling were reprocessed using the Sorting into Angular Bins and MLCE algorithms. There was poorer temporal sampling during the first five months of the experiment.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.", "links": [ { diff --git a/datasets/NIPR-GEO-1.json b/datasets/NIPR-GEO-1.json index 213ec1e991..f8d5798e48 100644 --- a/datasets/NIPR-GEO-1.json +++ b/datasets/NIPR-GEO-1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIPR-GEO-1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital data which can be supplied are total intensity raw data,\n and not reduced to magnetic anomaly data. However, the user can\n analyze the data by him/herself with the Data Reports. The data\n processing is still being made at NIPR.", "links": [ { diff --git a/datasets/NIPR_GEO_SEIS_SEAL_MIZUHO.json b/datasets/NIPR_GEO_SEIS_SEAL_MIZUHO.json index a47327e237..6239b62516 100644 --- a/datasets/NIPR_GEO_SEIS_SEAL_MIZUHO.json +++ b/datasets/NIPR_GEO_SEIS_SEAL_MIZUHO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIPR_GEO_SEIS_SEAL_MIZUHO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Deep Seismic Surveys (DSS) were carried out in 2000 and 2002 austral summers on the continental ice-sheet of the Lutzow-Holm Complex (LHC), Eastern Dronning Maud Land, East Antarctica . The surveys were carried out as a program of the \"Structure and Evolution of the East Antarctic Lithosphere (SEAL)\" by JARE. Detailed crustal velocity models and reflection sections were obtained in the LHC. In both surveys, more than 170 plant-type 2 Hz geophones were installed on the continental ice-sheet totally 190 km in length. A total of 8,300kg dynamite charge at the fourteen sites on the Mizuho Plateau gave information concerning the deep structure of a continental margin of the LHC. Archived digital waveforms are available from Library Server of Polar Data Center of NIPR.", "links": [ { diff --git a/datasets/NIPR_PMG_AIR_ARCHIVE_ANT.json b/datasets/NIPR_PMG_AIR_ARCHIVE_ANT.json index f46807dd5c..96f4d20629 100644 --- a/datasets/NIPR_PMG_AIR_ARCHIVE_ANT.json +++ b/datasets/NIPR_PMG_AIR_ARCHIVE_ANT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIPR_PMG_AIR_ARCHIVE_ANT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air samples for archive", "links": [ { diff --git a/datasets/NIPR_UAP_ASI_SOUTHPOLE.json b/datasets/NIPR_UAP_ASI_SOUTHPOLE.json index 92ed193717..c3d6879fad 100644 --- a/datasets/NIPR_UAP_ASI_SOUTHPOLE.json +++ b/datasets/NIPR_UAP_ASI_SOUTHPOLE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NIPR_UAP_ASI_SOUTHPOLE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All-sky Imager Observation at South Pole Station", "links": [ { diff --git a/datasets/NISE_2.json b/datasets/NISE_2.json index b40fb16d57..505fb8edbd 100644 --- a/datasets/NISE_2.json +++ b/datasets/NISE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NISE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.\n\nThis NISE Version 2 product contains SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F13 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For DMSP-F18, SSMIS-derived data, see NISE Version 5.", "links": [ { diff --git a/datasets/NISE_3.json b/datasets/NISE_3.json index fc1757c3c5..0ad76ca0cb 100644 --- a/datasets/NISE_3.json +++ b/datasets/NISE_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NISE_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.\n\nThis NISE Version 3 product contains DMSP-F16, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F16 satellite. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2.", "links": [ { diff --git a/datasets/NISE_4.json b/datasets/NISE_4.json index 9647c20e88..451963a415 100644 --- a/datasets/NISE_4.json +++ b/datasets/NISE_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NISE_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. \n\nThis NISE Version 4 product contains DMSP-F17, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F17 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2.", "links": [ { diff --git a/datasets/NISE_5.json b/datasets/NISE_5.json index 4d37937639..d9dbc628c3 100644 --- a/datasets/NISE_5.json +++ b/datasets/NISE_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NISE_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.\n\nThis NISE Version 5 product contains DMSP-F18, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F18 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2.", "links": [ { diff --git a/datasets/NLDAS_FORA0125_H_002.json b/datasets/NLDAS_FORA0125_H_002.json index 62fef5bc8a..499c912dd5 100644 --- a/datasets/NLDAS_FORA0125_H_002.json +++ b/datasets/NLDAS_FORA0125_H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the primary forcing data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1. \n\nDetails about the generation of the NLDAS-2 forcing data sets can be found in Xia et al. (2012). \n\nThe non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature, and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and\nNLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are those employed in NLDAS-1, as presented by Cosgrove et al. (2003).\n\nThe surface downward shortwave radiation field in \"File A\" is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003). The potential evaporation field in \"File A\" is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984).\n\nThe precipitation field in \"File A\" is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology. The precipitation is temporally disaggregated into hourly fields by deriving hourly disaggregation weights from either WSR-88D Doppler radar-based precipitation estimates, 8-km CMORPH hourly precipitation analyses, or NARR-simulated precipitation (based on availability, in order). The latter fields from radar, CMORPH, and NARR are used only to derive disaggregation weights and do not change the daily total precipitation. The field in \"File A\" that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in \"File B\"): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from NARR.\n\nThe hourly land surface forcing fields for NLDAS-2 are grouped into two GRIB files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORA0125_H_2.0.json b/datasets/NLDAS_FORA0125_H_2.0.json index 33587c8354..0950a1cdc8 100644 --- a/datasets/NLDAS_FORA0125_H_2.0.json +++ b/datasets/NLDAS_FORA0125_H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the primary forcing hourly data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB data files).\n\nThe non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature, and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and NLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are presented by Cosgrove et al. (2003).\n\nThe surface downward shortwave radiation field in \"File A\" is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003). The potential evaporation field in \"File A\" is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984).\n\nThe precipitation field in \"File A\" is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology. The precipitation is temporally disaggregated into hourly fields by deriving hourly disaggregation weights from either WSR-88D Doppler radar-based precipitation estimates, 8-km CMORPH hourly precipitation analyses, or NARR-simulated precipitation (based on availability, in order). The latter fields from radar, CMORPH, and NARR are used only to derive disaggregation weights and do not change the daily total precipitation. The field in \"File A\" that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in \"File B\"): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from NARR.\n\nThe hourly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012). \n", "links": [ { diff --git a/datasets/NLDAS_FORA0125_MC_002.json b/datasets/NLDAS_FORA0125_MC_002.json index 847375b482..d3b188b3f5 100644 --- a/datasets/NLDAS_FORA0125_MC_002.json +++ b/datasets/NLDAS_FORA0125_MC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_MC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the monthly climatology data of the primary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. \n\nA brief description about the NLDAS-2 hourly and monthly primary forcing data can be found from the GCMD DIFs for GES_DISC_NLDAS_FORA0125_H_V002 and GES_DISC_NLDAS_FORA0125_M_V002. \n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). \n\nThe monthly climatology land surface forcing fields for NLDAS-2 are grouped into two GRIB files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven fields. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORA0125_MC_2.0.json b/datasets/NLDAS_FORA0125_MC_2.0.json index 877753c86c..99110b4e6f 100644 --- a/datasets/NLDAS_FORA0125_MC_2.0.json +++ b/datasets/NLDAS_FORA0125_MC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_MC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the monthly climatology data of the primary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.\n\nA brief description about the NLDAS-2 hourly and monthly primary forcing data can be found from the NLDAS_FORA0125_H_2.0 and NLDAS_FORA0125_M_2.0 landing pages.\n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORA0125_M_002.json b/datasets/NLDAS_FORA0125_M_002.json index 15a26da267..b96dfe6a29 100644 --- a/datasets/NLDAS_FORA0125_M_002.json +++ b/datasets/NLDAS_FORA0125_M_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_M_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the monthly primary forcing data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. \n\nThe NLDAS-2 monthly primary forcing data were generated from the NLDAS-2 hourly primary forcing data, as monthly accumulation for total precipitation, convective precipitation, and potential evaporation, and monthly average for other variables. The convective precipitation monthly total is the hourly convective fraction multiplied by the hourly precipitation (both from the NLDAS-2 \"File A\" files), and then summed over all hours of the month. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. \n\nA brief description about the NLDAS-2 hourly primary forcing data can be found from the GCMD DIF for GES_DISC_NLDAS_FORA0125_H_V002. \n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n\nThe monthly land surface forcing fields for NLDAS-2 are grouped into two GRIB files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven fields. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORA0125_M_2.0.json b/datasets/NLDAS_FORA0125_M_2.0.json index cda5029f47..69c0eed08f 100644 --- a/datasets/NLDAS_FORA0125_M_2.0.json +++ b/datasets/NLDAS_FORA0125_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORA0125_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains the monthly primary forcing data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB data files). \n\nThe NLDAS-2 monthly primary forcing data were generated from the NLDAS-2 hourly primary forcing data, as monthly accumulation for total precipitation, convective precipitation, and potential evaporation, and monthly average for other variables. The convective precipitation monthly total is the hourly convective fraction multiplied by the hourly precipitation (both from the NLDAS-2 \"File A\" files), and then summed over all hours of the month. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. \n\nThe monthly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).", "links": [ { diff --git a/datasets/NLDAS_FORB0125_H_002.json b/datasets/NLDAS_FORB0125_H_002.json index 7feb3e863c..041ccd8a4c 100644 --- a/datasets/NLDAS_FORB0125_H_002.json +++ b/datasets/NLDAS_FORB0125_H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the secondary forcing data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1. \n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). \n\nThe non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. NLDAS-2 is providing a second forcing file, \"File B\", in which the surface temperature, humidity, and wind fields are represented not at 2-meters and 10-meters above the height of the NLDAS terrain, but rather at the same height above the NLDAS terrain as the height above the NARR terrain of the lowest prognostic level of the NARR assimilation system (namely, the same height above the model terrain as the lowest prognostic level of the mesoscale Eta model, which is the assimilating model in NARR). \n\nThe surface downward surface radiation field in \"File B\" is taken directly from NARR, without any bias correction. The precipitation and convective precipitation fields in \"File B\" are also taken directly from NARR, and are used to calculate the convective fraction provided in \"File A\". The aerodynamic conductance is \"File B\" is also taken from NARR.\n\nThe hourly land surface forcing fields for NLDAS-2 are grouped into two GRIB files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORB0125_H_2.0.json b/datasets/NLDAS_FORB0125_H_2.0.json index 7b27df9852..1c3ac2b1c0 100644 --- a/datasets/NLDAS_FORB0125_H_2.0.json +++ b/datasets/NLDAS_FORB0125_H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the secondary forcing hourly data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format isnetCDF (converted from the GRIB data files).\n\nThe non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. NLDAS-2 is providing a second forcing file, \"File B\", in which the surface temperature, humidity, and wind fields are represented not at 2-meters and 10-meters above the height of the NLDAS terrain, but rather at the same height above the NLDAS terrain as the height above the NARR terrain of the lowest prognostic level of the NARR assimilation system (namely, the same height above the model terrain as the lowest prognostic level of the mesoscale Eta model, which is the assimilating model in NARR). \n\nThe surface downward surface radiation field in \"File B\" is taken directly from NARR, without any bias correction. The precipitation and convective precipitation fields in \"File B\" are also taken directly from NARR, and are used to calculate the convective fraction provided in \"File A\". The aerodynamic conductance is \"File B\" is also taken from NARR.\n\nThe hourly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten meteorological forcing fields. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). ", "links": [ { diff --git a/datasets/NLDAS_FORB0125_MC_002.json b/datasets/NLDAS_FORB0125_MC_002.json index 9b5131c0c7..8ac68a7217 100644 --- a/datasets/NLDAS_FORB0125_MC_002.json +++ b/datasets/NLDAS_FORB0125_MC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_MC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the monthly climatology data of the secondary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. \n\nA brief description about the NLDAS-2 hourly and monthly primary forcing data can be found from the GCMD DIFs for GES_DISC_NLDAS_FORB0125_H_V002 and GES_DISC_NLDAS_FORB0125_M_V002. \n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n\nThe NLDAS-2 monthly climatology land surface forcing fields are grouped into two GRIB files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORB0125_MC_2.0.json b/datasets/NLDAS_FORB0125_MC_2.0.json index 91a4f73296..a630fc1d40 100644 --- a/datasets/NLDAS_FORB0125_MC_2.0.json +++ b/datasets/NLDAS_FORB0125_MC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_MC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the monthly climatology (MC) data of the secondary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.\n\nA brief description about the NLDAS-2 hourly and monthly secondary forcing data can be found from the NLDAS_FORB0125_H_2.0 and NLDAS_FORB0125_M_2.0 landing pages.\n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORB0125_M_002.json b/datasets/NLDAS_FORB0125_M_002.json index 69ac10f109..5e3f0593c4 100644 --- a/datasets/NLDAS_FORB0125_M_002.json +++ b/datasets/NLDAS_FORB0125_M_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_M_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains the monthly secondary forcing data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly secondary forcing data were generated from the NLDAS-2 hourly secondary forcing data, as monthly accumulation for precipitation and convective precipitation and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. \n\nA brief description about the NLDAS-2 hourly secondary forcing data can be found from the GCMD DIF for GES_DISC_NLDAS_FORB0125_H_V002. \n\nDetails about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n\nThe NLDAS-2 monthly land surface forcing fields are grouped into two GRIB files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_FORB0125_M_2.0.json b/datasets/NLDAS_FORB0125_M_2.0.json index 8320628e24..0d1cb2c8a5 100644 --- a/datasets/NLDAS_FORB0125_M_2.0.json +++ b/datasets/NLDAS_FORB0125_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_FORB0125_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the monthly secondary forcing data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). \n\nThe NLDAS-2 monthly secondary forcing data were generated from the NLDAS-2 hourly secondary forcing data, as monthly accumulation for precipitation and convective precipitation and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. \n\nThe monthly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).", "links": [ { diff --git a/datasets/NLDAS_MOS0125_H_002.json b/datasets/NLDAS_MOS0125_H_002.json index 244d7f2a17..2140e04564 100644 --- a/datasets/NLDAS_MOS0125_H_002.json +++ b/datasets/NLDAS_MOS0125_H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1. \n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). \n\nMosaic was developed by Koster and Suarez (1994, 1996) to account for subgrid vegetation variability with a tile approach. Each vegetation tile carries its own energy and water balance and soil moisture and temperature. Each tile has three soil layers, with the first two in the root zone. In NLDAS, Mosaic is configured to support a maximum of 10 tiles per grid cell with a 5% cutoff that ignores vegetation classes covering less than 5% of the cell. Additionally in NLDAS, all tiles of Mosaic in a grid cell have a predominant soil type and three soil layers with fixed thickness values of 10, 30, and 160 cm (hence constant rooting depth of 40 cm and constant total column depth of 200 cm). \n\nThe Mosaic LSM was forced by the hourly NLDAS-2 forcing \"File A\" files, and contains thirty-seven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086) and Soil Temperature (PDS 085), please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_MOS0125_H_2.0.json b/datasets/NLDAS_MOS0125_H_2.0.json index 5cea88b8f8..8e855a3bd6 100644 --- a/datasets/NLDAS_MOS0125_H_2.0.json +++ b/datasets/NLDAS_MOS0125_H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains thirty-eight fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format).\n\nMosaic was developed by Koster and Suarez (1994, 1996) to account for subgrid vegetation variability with a tile approach. Each vegetation tile carries its own energy and water balance and soil moisture and temperature. Each tile has three soil layers, with the first two in the root zone. In NLDAS, Mosaic is configured to support a maximum of 10 tiles per grid cell with a 5% cutoff that ignores vegetation classes covering less than 5% of the cell. Additionally in NLDAS, all tiles of Mosaic in a grid cell have a predominant soil type and three soil layers with fixed thickness values of 10, 30, and 160 cm (hence constant rooting depth of 40 cm and constant total column depth of 200 cm). \n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). ", "links": [ { diff --git a/datasets/NLDAS_MOS0125_MC_002.json b/datasets/NLDAS_MOS0125_MC_002.json index 1c5e31b175..eed05391d7 100644 --- a/datasets/NLDAS_MOS0125_MC_002.json +++ b/datasets/NLDAS_MOS0125_MC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_MC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. \n\nA brief description about the NLDAS-2 hourly and monthly Mosaic LSM data can be found from the GCMD DIFs for GES_DISC_NLDAS_MOS0125_H_V002 and GES_DISC_NLDAS_MOS0125_M_V002. \n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). \n\nThe NLDAS-2 Mosaic model monthly climatology data set contains thirty-seven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nThere are six vertical levels for the Soil Moisture (PDS 086) in the Mosaic GRIB files. For more information, please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_MOS0125_MC_2.0.json b/datasets/NLDAS_MOS0125_MC_2.0.json index 70aff5cab0..9e6196f654 100644 --- a/datasets/NLDAS_MOS0125_MC_2.0.json +++ b/datasets/NLDAS_MOS0125_MC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_MC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.\n\nA brief description about the NLDAS-2 hourly and monthly Mosaic LSM data can be found from the NLDAS_MOS0125_H_2.0 and NLDAS_MOS0125_M_2.0 landing pages.\n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_MOS0125_M_002.json b/datasets/NLDAS_MOS0125_M_002.json index a70b5380b2..b7ea409d42 100644 --- a/datasets/NLDAS_MOS0125_M_002.json +++ b/datasets/NLDAS_MOS0125_M_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_M_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly Mosaic model data were generated from the NLDAS-2 hourly Mosaic model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, and snow melt, and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, except the first month (Jan. 1979) that starts from 00Z 02 Jan 1979. \n\nA brief description about the NLDAS-2 monthly Mosaic model can be found from the GCMD DIF for NLDAS-2 hourly Mosaic data GES_DISC_NLDAS_MOS0125_H_V002. \n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).\n\nThe NLDAS-2 monthly Mosaic model data contain thirty-seven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086) and Soil Temperature (PDS 085), please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_MOS0125_M_2.0.json b/datasets/NLDAS_MOS0125_M_2.0.json index b9073637f7..4db62d0f50 100644 --- a/datasets/NLDAS_MOS0125_M_2.0.json +++ b/datasets/NLDAS_MOS0125_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_MOS0125_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains thirty-seven fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). \n\nThe NLDAS-2 monthly Mosaic model data, containing thirty-seven fields, were generated from the NLDAS-2 hourly Mosaic model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, and snow melt, and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, except the first month (Jan. 1979) that starts from 00Z 02 Jan 1979. \n\nDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_H_002.json b/datasets/NLDAS_NOAH0125_H_002.json index d4447c4119..3772551bdb 100644 --- a/datasets/NLDAS_NOAH0125_H_002.json +++ b/datasets/NLDAS_NOAH0125_H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1. \n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012). \n\nThe Noah model was developed as the land component of the NOAA NCEP mesoscale Eta model [Betts et al. (1997); Chen et al. (1997); Ek et al. (2003)]. As used in NLDAS-2, recent modifications were made to Noah's cold-season [Livneh et al. (2010)] and warm-season [Wei et al. (2012)] parameterizations. Noah serves as the land component in the evolving Weather Research and Forecasting (WRF) regional atmospheric model, the NOAA NCEP coupled Climate Forecast System (CFS), and the Global Forecast System (GFS). The model simulates the soil freeze-thaw process and its impact on soil heating/cooling and transpiration, following Koren et al. (1999). The model has four soil layers with spatially invariant thicknesses of 10, 30, 60, and 100 cm. The first three layers form the root zone in non-forested regions, with the fourth layer included in forested regions. \n\nThe Noah LSM was forced by the hourly NLDAS-2 forcing \"File A\" files, and contains fifty-two fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151), please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_H_2.0.json b/datasets/NLDAS_NOAH0125_H_2.0.json index 97c694c0fc..24f2253fca 100644 --- a/datasets/NLDAS_NOAH0125_H_2.0.json +++ b/datasets/NLDAS_NOAH0125_H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains fifty-three fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). \n\nThe Noah model was developed as the land component of the NOAA NCEP mesoscale Eta model [Betts et al. (1997); Chen et al. (1997); Ek et al. (2003)]. As used in NLDAS-2, recent modifications were made to Noah's cold-season [Livneh et al. (2010)] and warm-season [Wei et al. (2012)] parameterizations. Noah serves as the land component in the evolving Weather Research and Forecasting (WRF) regional atmospheric model, the NOAA NCEP coupled Climate Forecast System (CFS), and the Global Forecast System (GFS). The model simulates the soil freeze-thaw process and its impact on soil heating/cooling and transpiration, following Koren et al. (1999). The model has four soil layers with spatially invariant thicknesses of 10, 30, 60, and 100 cm. The first three layers form the root zone in non-forested regions, with the fourth layer included in forested regions. \n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012). ", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_MC_002.json b/datasets/NLDAS_NOAH0125_MC_002.json index 6ab9b56140..50a111e871 100644 --- a/datasets/NLDAS_NOAH0125_MC_002.json +++ b/datasets/NLDAS_NOAH0125_MC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_MC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. \n\nA brief description about the NLDAS-2 hourly and monthly Noah LSM data can be found from the dataset landing pages for NLDAS_NOAH0125_H_002 and NLDAS_NOAH0125_M_002, and the NLDAS-2 README document.\n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).\n\nThe NLDAS-2 Noah monthly climatology data contain fifty-two fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.\n\nThere are four vertical levels for the Soil Moisture Content (PDS 086) and Soil Temperature (PDS 085) in the Noah GRIB files. For more information, please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_MC_2.0.json b/datasets/NLDAS_NOAH0125_MC_2.0.json index 073560fa93..206eb659e4 100644 --- a/datasets/NLDAS_NOAH0125_MC_2.0.json +++ b/datasets/NLDAS_NOAH0125_MC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_MC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.\n\nA brief description about the NLDAS-2 hourly and monthly Noah LSM data can be found from the dataset landing pages for NLDAS_NOAH0125_H_2.0 and NLDAS_NOAH0125_M_2.0.\n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_M_002.json b/datasets/NLDAS_NOAH0125_M_002.json index f9a337138c..4b67ed95a8 100644 --- a/datasets/NLDAS_NOAH0125_M_002.json +++ b/datasets/NLDAS_NOAH0125_M_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_M_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly Noah model data were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. The monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (Jan 1979) which starts at 00Z 02 Jan 1979. Also for the first month (Jan 1979), because the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 Jan 1979, and this one hour is not included in the average for this month only. \n\nA brief description about the NLDAS-2 monthly Noah model can be found from the dataset landing page for NLDAS_NOAH0125_H_002 and the NLDAS-2 README document. \n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012). \n\nThe NLDAS-2 Noah monthly data contain fifty-two fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151) please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_NOAH0125_M_2.0.json b/datasets/NLDAS_NOAH0125_M_2.0.json index 303cc1afde..18632ba6bd 100644 --- a/datasets/NLDAS_NOAH0125_M_2.0.json +++ b/datasets/NLDAS_NOAH0125_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_NOAH0125_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains fifty-two fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). \n\nThe NLDAS-2 monthly Noah model data, containing fifty-two fields, were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. The monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (Jan 1979) which starts at 00Z 02 Jan 1979. Also for the first month (Jan 1979), because the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 Jan 1979, and this one hour is not included in the average for this month only. \n\nDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012). ", "links": [ { diff --git a/datasets/NLDAS_VIC0125_H_002.json b/datasets/NLDAS_VIC0125_H_002.json index 849c350498..6837a2717d 100644 --- a/datasets/NLDAS_VIC0125_H_002.json +++ b/datasets/NLDAS_VIC0125_H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1. \n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). \n\nThe VIC model was developed at the University of Washington and Princeton University as a macroscale, semi-distributed, grid-based, hydrologic model [Liang et al., 1994; Wood et al., 1997]. The full water and energy balance modes of VIC were used for NLDAS-2. VIC uses three soil layers, with thicknesses that vary spatially. The root zone depends on the vegetation type and its root distribution, and can span all three soil layers. The VIC model includes a two-layer energy balance snow model [Cherkauer et al., 2003]. \n\nThe VIC LSM was forced by the hourly NLDAS-2 forcing \"File A\" files, and contains forty-three fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151), please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_VIC0125_H_2.0.json b/datasets/NLDAS_VIC0125_H_2.0.json index e309c16e4a..0021ddc1a1 100644 --- a/datasets/NLDAS_VIC0125_H_2.0.json +++ b/datasets/NLDAS_VIC0125_H_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_H_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains forty-four fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). \n\nThe VIC model was developed at the University of Washington and Princeton University as a macroscale, semi-distributed, grid-based, hydrologic model [Liang et al., 1994; Wood et al., 1997]. The full water and energy balance modes of VIC were used for NLDAS-2. VIC uses three soil layers, with thicknesses that vary spatially. The root zone depends on the vegetation type and its root distribution, and can span all three soil layers. The VIC model includes a two-layer energy balance snow model [Cherkauer et al., 2003]. \n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). ", "links": [ { diff --git a/datasets/NLDAS_VIC0125_MC_002.json b/datasets/NLDAS_VIC0125_MC_002.json index 5d67b82d63..12d726d0bb 100644 --- a/datasets/NLDAS_VIC0125_MC_002.json +++ b/datasets/NLDAS_VIC0125_MC_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_MC_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nAbstract: This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The file format is WMO GRIB-1. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980-2009) of the NLDAS-2 monthly data. \n\nA brief description about the NLDAS-2 hourly and monthly VIC LSM data can be found from the GCMD DIFs for GES_DISC_NLDAS_VIC0125_H_V002 and GES_DISC_NLDAS_VIC0125_M_V002. \n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). \n\nThe NLDAS-2 VIC monthly climatology data contain forty-three fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151), please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_VIC0125_MC_2.0.json b/datasets/NLDAS_VIC0125_MC_2.0.json index 2dc36e906a..eb3907306e 100644 --- a/datasets/NLDAS_VIC0125_MC_2.0.json +++ b/datasets/NLDAS_VIC0125_MC_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_MC_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.\n\nA brief description about the NLDAS-2 hourly and monthly VIC LSM data can be found from the NLDAS_VIC0125_H_2.0 and NLDAS_VIC0125_M_2.0 landing pages.\n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003).\n\nFor more information, please see the README Document.", "links": [ { diff --git a/datasets/NLDAS_VIC0125_M_002.json b/datasets/NLDAS_VIC0125_M_002.json index 6aa750c2e4..3a2590852b 100644 --- a/datasets/NLDAS_VIC0125_M_002.json +++ b/datasets/NLDAS_VIC0125_M_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_M_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NLDAS-2.0 netCDF data serve as a replacement for all NLDAS-002 GRIB-1 products. The NLDAS-2.0 hourly products are produced in the forward stream (updated daily) with ~4 day latency, while the latest NLDAS-2.0 monthly products will be released around the 6th of the following month. On August 1, 2024, the NLDAS-002 GRIB-1 forward stream will end; all NLDAS-002 GRIB-1 products will be decommissioned and removed from public access on November 1, 2024. NLDAS-2 users are encouraged to use the updated NLDAS version 2.0 netCDF data, which are currently publicly accessible and will continue to be updated.\n\nThis data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from January 1979 to present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly Noah model data were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. Each monthly period is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (January 1979), which starts at 00Z 02 January 1979. Also, for the first month (January 1979), since the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 January 1979, this one hour is not included in the average for this one month. \n\nA brief description about the NLDAS-2 monthly VIC model data can be found from the dataset landing page for NLDAS_VIC0125_H_002 and the NLDAS-2 README document. \n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). \n\nThe NLDAS-2 VIC monthly data contain forty-three fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. \n\nFor information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content, please see the README Document or the GrADS ctl file.", "links": [ { diff --git a/datasets/NLDAS_VIC0125_M_2.0.json b/datasets/NLDAS_VIC0125_M_2.0.json index 790b2bd0df..235454f874 100644 --- a/datasets/NLDAS_VIC0125_M_2.0.json +++ b/datasets/NLDAS_VIC0125_M_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDAS_VIC0125_M_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains forty-three fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from January 1979 to present. The temporal resolution is monthly. The file format is netCDF (converted from GRIB format). \n\nThe NLDAS-2 monthly Noah model data were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. Each monthly period is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (January 1979), which starts at 00Z 02 January 1979. Also, for the first month (January 1979), since the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 January 1979, this one hour is not included in the average for this one month. \n\nDetails about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012).", "links": [ { diff --git a/datasets/NLDC.json b/datasets/NLDC.json index 00ad23a2f2..cf003fd312 100644 --- a/datasets/NLDC.json +++ b/datasets/NLDC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NLDC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Landsat Data Collection (NLDC) is a compilation of Landsat multispectral scanner (MSS) scenes and Landsat thematic mapper (TM) scenes. This compilation of scenes represents data collections from four distinct projects including: (1) the Global Change Landsat Data Collection (GCLDC);(2) the Humid Tropical Forest Project (HTFP) collection of source scenes and products; (3) a collection of data from the Committee on Environment and Natural Resources Research [formerly the Committee on Earth and Environmental Sciences (CEES)] that is historically referred to as the CEES collection; and (4) ongoing Landsat data purchases by NASA-funded investigators, starting with the 1996 fiscal year. The NLDC scenes have been screened for cloud cover and band quality resulting in a high grade,high quality data compilation. The GCLDC collection contains Landsat TM scenes that were purchased by NASA from Space Imaging, formerly the Earth Observation Satellite Company,under a special agreement to promote the use of shared data in global change research. The HTFP, the largest component of NASA's Landsat Pathfinder Program, contains Landsat MSS and TM scenes collected over the past 20 years. The goal of the HTFP is to globally map deforestation in the humid tropical forests. The CEES collection is the result of an effort to coordinate data needs among several Federal agencies (e.g.,Environmental Protection Agency, Department of the Interior agencies, National Oceanic and Atmospheric Administration, Department of Defense). These Landsat TM scenes were collected for a variety of research projects. Ongoing NASA purchases of Landsat TM data support NASA scientists and their affiliated researchers in programs and projects including the NASA Research and Analysis Program; the Global Land Cover Test Sites Project; the HTFP, the International Biosphere-Geosphere Programme, the NASA Applications Program; and the Landsat-7 Science Team.\n", "links": [ { diff --git a/datasets/NMMIEAI-L2-NRT_2.json b/datasets/NMMIEAI-L2-NRT_2.json index 093ec0a20d..9657dba1be 100644 --- a/datasets/NMMIEAI-L2-NRT_2.json +++ b/datasets/NMMIEAI-L2-NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NMMIEAI-L2-NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Aerosol Index swath orbital V2 for Near Real Time. For the standard product see the OMPS_NPP_NMMIEAI_L2 product in CMR .The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. ", "links": [ { diff --git a/datasets/NMSO2-PCA-L2-NRT_2.json b/datasets/NMSO2-PCA-L2-NRT_2.json index 58d17b6898..8535caa915 100644 --- a/datasets/NMSO2-PCA-L2-NRT_2.json +++ b/datasets/NMSO2-PCA-L2-NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NMSO2-PCA-L2-NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A Principle Component Analysis (PCA) algorithm is used to retrieve the SO2 total column amount and column amounts in the lower (centered at 2.5 km), middle (centered at 7.5 km) and upper (centered at 11 km) troposphere, as well as the lower stratosphere (centered at 16 km). Each granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 35 pixels in the cross-track direction, with a pixel resolution of about 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/NMTO3NRT_2.json b/datasets/NMTO3NRT_2.json index ca575f5907..fdced31d8a 100644 --- a/datasets/NMTO3NRT_2.json +++ b/datasets/NMTO3NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NMTO3NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite.The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The L2 NM Ozone data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/NOAA_0.json b/datasets/NOAA_0.json index fc4388964e..a1ae82bb06 100644 --- a/datasets/NOAA_0.json +++ b/datasets/NOAA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOAA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA measurements from 1996 to 1999 along the Eastern US coastal region.", "links": [ { diff --git a/datasets/NOAA_CDR_NDVI.json b/datasets/NOAA_CDR_NDVI.json index 4caa657bba..e17ce725d0 100644 --- a/datasets/NOAA_CDR_NDVI.json +++ b/datasets/NOAA_CDR_NDVI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOAA_CDR_NDVI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Oceanic and Atmospheric Administration (NOAA) Climate Data Records (CDR) provide historical climate information using data from weather satellites. This dataset contains daily Normalized Difference Vegetation Index (NDVI) derived from surface reflectance data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor. This long-term record spans from 1981 to 2013 and utilizes AVHRR data from seven NOAA polar orbiting satellites: NOAA 7, 9, 11, 14, 16, and 18. This NDVI collection provides the global change and resource management communities with vegetation data for historical trend analysis and vegetation monitoring studies for land surfaces around the globe.", "links": [ { diff --git a/datasets/NOAA_ToF_CIMS_Instrument_Data_1921_2.json b/datasets/NOAA_ToF_CIMS_Instrument_Data_1921_2.json index c3021df645..d777a456ca 100644 --- a/datasets/NOAA_ToF_CIMS_Instrument_Data_1921_2.json +++ b/datasets/NOAA_ToF_CIMS_Instrument_Data_1921_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOAA_ToF_CIMS_Instrument_Data_1921_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the mixing ratios of reactive nitrogen and halogen species measured by the NOAA Iodide Ion Time-of-Flight Chemical Ionization Mass Spectrometer (NOAA CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission for ATom-3 and ATom-4 campaigns. The NOAA CIMS uses chemical ionization mass spectrometric detection of gas phase organic and inorganic analytes via I- adduct formation. Measurements for ATom include N2O5 (dinitrogen pentoxide), ClNO2 (chloro nitrite), Cl2 (Chlorine), HCOOH (formic acid), C2H4O3S (hydroperoxymethyl thioformate), BrCl (bromine monochloride), BrCN (cyanogen bromide), and BrO (bromine monoxide). ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2-13 km altitude. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.", "links": [ { diff --git a/datasets/NOBM_DAY_R2017.json b/datasets/NOBM_DAY_R2017.json index a77911fc59..a641b8cc6f 100644 --- a/datasets/NOBM_DAY_R2017.json +++ b/datasets/NOBM_DAY_R2017.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOBM_DAY_R2017", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).", "links": [ { diff --git a/datasets/NOBM_MON_R2017.json b/datasets/NOBM_MON_R2017.json index d55f1d9928..c362412d42 100644 --- a/datasets/NOBM_MON_R2017.json +++ b/datasets/NOBM_MON_R2017.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOBM_MON_R2017", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).", "links": [ { diff --git a/datasets/NOPP_float_0.json b/datasets/NOPP_float_0.json index f07d85b4d2..7fb19f2738 100644 --- a/datasets/NOPP_float_0.json +++ b/datasets/NOPP_float_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NOPP_float_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the NOPP (National Oceanographic Partnership Program) as collected by ARGO profiling floats.", "links": [ { diff --git a/datasets/NORBAL_0.json b/datasets/NORBAL_0.json index 55aa2e10e2..dd112dd9a4 100644 --- a/datasets/NORBAL_0.json +++ b/datasets/NORBAL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NORBAL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Northern Balearic Sea in 2002 and 2003.", "links": [ { diff --git a/datasets/NPBUVO3-L2-NRT_2.json b/datasets/NPBUVO3-L2-NRT_2.json index 283aed1e2f..b189b18cb9 100644 --- a/datasets/NPBUVO3-L2-NRT_2.json +++ b/datasets/NPBUVO3-L2-NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPBUVO3-L2-NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite in Near Real Time. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The L2 NP Ozone data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/NPP_ATH_577_2.json b/datasets/NPP_ATH_577_2.json index 9cb1782364..342e4ba966 100644 --- a/datasets/NPP_ATH_577_2.json +++ b/datasets/NPP_ATH_577_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_ATH_577_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains eight data files (.txt format): three net primary productivity (NPP) data files and five climate data files. The NPP estimates are based on field measurements of litterfall accumulation at three tropical rainforest sub-sites near Atherton, Queensland, in northeast Australia. Additional NPP component data include standing litter biomass, leaf decomposition rates, and nutrient concentrations in litter, where available. Precipitation and temperature data are provided from measurements at two of the study sites, nearby weather stations, and from published literature sources. Annual litterfall estimates are based on weekly or bi-weekly measurements for 3-4 years (1974/5-1978) at the Wongabel (17.32 S 145.50 E) and the Gadgarra (17.30 S 145.72 E) State Forest Reserves, and weekly measurements for 5 years (1980-1985) at Tableland (17.28 S 145.57 E) near Atherton. Additional measurements were made at Wongabel and Gadgarra including branchfall (monthly); litter on the forest floor (quarterly); rainfall and throughfall (weekly); and trace element concentrations in litterfall (weekly or bi-weekly). In a related study at Gadgarra, litterfall and standing litter crop were sampled at approximately 19-day intervals from 1974 to 1976 and at approximately 3-month intervals 1976 to 1978. Total litterfall averaged 905 g/m2/year at Wongabel, 987 g/m2/year at Gadgarra, and 1,103 g/m2/year at Tableland, giving minimum estimates of above-ground NPP for this region of Australia and an indication of tropical forest productivity south of the Equator. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/NPP_BCN_204_2.json b/datasets/NPP_BCN_204_2.json index 02c8b64ab7..2c686eb566 100644 --- a/datasets/NPP_BCN_204_2.json +++ b/datasets/NPP_BCN_204_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BCN_204_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII text files, one providing productivity measurements for a chalk grassland on Beacon Hill, West Sussex, U.K. (50.92 N, -0.85 W) and the other containing climate data from a weather station at the former King's College London, Rogate Field Centre, 6 km distant (51.01 N, -0.85 W). Measurements of above-ground live biomass and total dead matter were made by harvesting 0.25 m2 quadrats in the 20 x 20-m study area at eight to ten week intervals from March 1972 to April 1973. Precipitation amount and minimum/maximum temperature were recorded from 1969 through 1993.Above-ground net primary production (ANPP) was estimated by several methods: 332 g/m2/year (annual increase in living biomass, sum of species); 355 g/m2/year (peak or maximum live biomass, plant dry matter weight); 773 g/m2/year (maximum live + dead biomass); 310 g/m2/year (annual increase in living biomass carbon by summing positive increments in biomass); and 691 g/m2/year (annual net production accounting for leaf turnover). The carbon content of ANPP (accounting for leaf turnover) was estimated to be 310 gC/m2/year using a conversion factor of 0.45. Below-ground production was not measured.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_BDK_203_2.json b/datasets/NPP_BDK_203_2.json index 5a923c208d..1916c24160 100644 --- a/datasets/NPP_BDK_203_2.json +++ b/datasets/NPP_BDK_203_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BDK_203_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains estimates of above-ground live phytomass for an ephemeral desert steppe located in the Badkhyz Nature Reserve in southern Turkmenistan. Monthly measurements of plant biomass were made by harvest during the growing season (January-May) from 1948 through 1963. Afterwards, an annual measurement of peak live biomass was made in May from 1964 through 1982, with gaps for years 1973-1976. The second file contains monthly and annual climate data recorded at the study site from 1941 through 1982. Above-ground net production (ANPP) for the Badkhyz site is the lowest among the eight Eurasian grassland sites, at 100 g/m2/yr. Below-ground NPP for Badkhyz (1,745 g/m2/yr) was conservatively estimated from a similar dry, semiarid continental steppe in Northern Kazakhstan (i.e., Shortandy). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_BOREAL_611_2.json b/datasets/NPP_BOREAL_611_2.json index 3a23a6bad8..2c757c87b5 100644 --- a/datasets/NPP_BOREAL_611_2.json +++ b/datasets/NPP_BOREAL_611_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BOREAL_611_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of above- and below-ground biomass, above- and below-ground NPP (ANPP and BNPP), and total NPP(TNPP) for selected North American and Eurasian boreal forests located between 66.37 degrees N and 47.5 degrees N. Each stand was selected through a review of published literature and classified into one of three classes, depending upon completeness of NPP budget, ancillary site data, and stand information. Within the overall 1965-1995 temporal range, data available for individual sites varies widely.There are two ASCII files (comma-separated-value format) of NPP data.\tThe first file provides carbon distribution in above- and below-ground vegetation biomass, above- and below-ground net primary production, and mean annual biomass increment for twenty-four (24) Class I sites which have complete NPP budgets (ANPP + BNPP). Information about site characteristics and NPP measurement approaches are also provided.\tThe second file provides stand information, carbon distribution in above-ground vegetation biomass, and ANPP data for forty-five (45) Class II boreal forest stands that have incomplete NPP budgets. Revision Notes:Above- and below-ground biomass, ANPP, and TNPP values for several sites have been corrected to agree with primary published sources and related data sets. The temporal coverage for both has been corrected to agree with primary published sources. Please see the Data Set Revision section of this document for detailed information.", "links": [ { diff --git a/datasets/NPP_BRD_205_2.json b/datasets/NPP_BRD_205_2.json index 21d044521c..f09df9dd84 100644 --- a/datasets/NPP_BRD_205_2.json +++ b/datasets/NPP_BRD_205_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BRD_205_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII text files for the Bridger grassland study site in the Rocky Mountains (45.78 N, -110.78 W, Elevation 2,340 m). Two files contain above- and below-ground biomass data, one for each treatment (ungrazed and moderately grazed). The third file contains climate data from the Big Timber weather station (45.80 N, -110.00 W, Elevation 1,249 m) near Bridger. Dynamics of above- and below-ground plant biomass were monitored by harvest technique at roughly 2-week intervals during the growing season for the years 1970, 1972, and 1973. Data on above-ground live biomass, standing dead matter, and litter are provided for each sampling date. Below-ground biomass (roots and crowns) are provided for 0-30 cm and 0-50 cm depths. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States under the U.S. Grassland Biome Project of the International Biological Program (IBP). Above-ground net primary production (ANPP) was estimated, conservatively, by summing peak biomass of individual species. Below-ground net primary production (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns). Values varied according to treatment.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_BRR_157_2.json b/datasets/NPP_BRR_157_2.json index de3a89549e..d768081c25 100644 --- a/datasets/NPP_BRR_157_2.json +++ b/datasets/NPP_BRR_157_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BRR_157_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). One file provides net primary productivity (NPP) data for the moist lowland tropical forest on Barro Colorado Island, Panama. NPP estimates are based on field measurements of litterfall accumulation, tree growth and mortality, and herbivory. Above-ground biomass and LAI are also reported. The other two files provide climate data recorded onsite.Annual litterfall accumulation (leaf + twig + other litterfall) averaged 1,064 g/m2/year, excluding losses to herbivory, on the central plateau of the island and in the Lutz catchment (1969-1979) and 1,246 g/m2/year at Poacher's Peninsula (1986-1990). Herbivory due to insects (about 50 g/m2/year) was estimated from leaf litterfall (1974-1977) by measuring holes and gaps in fallen leaves. An additional 30 g/m2/year may be lost to vertebrate herbivores which leave no identifiable traces in litter traps. Coarse wood litterfall due to tree damage may represent an additional 46 g/m2/year. Above-ground biomass averaged 27,425 g/m2 based on inventory data collected every 5 years from 1985 to 2000 and allometric regression equations. Tree growth of 554 g/m2/year was based on above-ground biomass changes during the three census intervals. Tree mortality of 2-3% was estimated by recording dead or missing trees (1982-1990). LAI of 7.3 was based on the average area of leaves that fell per area of ground per year. Overall, above-ground NPP for Barro Colorado Island was estimated at 1,800 g/m2/year. ", "links": [ { diff --git a/datasets/NPP_BRW_580_2.json b/datasets/NPP_BRW_580_2.json index dcea90df14..64b5a5a474 100644 --- a/datasets/NPP_BRW_580_2.json +++ b/datasets/NPP_BRW_580_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_BRW_580_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three data files. One file (.csv format) provides above- and below-ground biomass and leaf area index (LAI) data for a wet arctic tundra meadow (Biome research site 2, Dupontia meadow, vegetation type V) studied from 1970 to 1971 at Point Barrow, Alaska, USA, (71.30 N -156.67 W Elevation 5 m). The second file, also in .csv format, provides net primary productivity (NPP) estimates for different plant growth forms for eight vegetation types recognized in the coastal tundra at Barrow. The third file (.txt format) provides climate data from the weather station at Barrow, Alaska (71.30 N -156.78 W Elevation 31 m). Measurements of above- and below-ground living and dead biomass were made at 10-day intervals during the growing season (mid June to end of August) by harvest methods in 6 x 6 m study plots of undisturbed vegetation. LAI was estimated at 10-day intervals with inclined point quadrats and other methods. NPP estimates are based on harvest at the period of peak above-ground vascular biomass and seasonal CO2 gas exchange estimates in 1972. The studies were conducted as part of the International Biological Program (IBP) U.S. Tundra Biome program. Average total NPP for the eight vegetation communities recognized for the coastal tundra at Barrow was 230 g/m2/year (110 g/m2/year ANPP plus 120 g/m2/year BNPP). Values varied by vegetation community type. Revision Notes: This data set has been revised to correct several values of average below-ground plant standing crop. A second NPP data file has been added to provide NPP estimates for the different vegetation types at the coastal tundra study site from measurements made in 1972. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_CBAY_473_2.json b/datasets/NPP_CBAY_473_2.json index eac439d929..d0b523964f 100644 --- a/datasets/NPP_CBAY_473_2.json +++ b/datasets/NPP_CBAY_473_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CBAY_473_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). One data file contains above-ground biomass, litter, litterfall, herbivory, biomass change, and above-ground net primary productivity (ANPP) estimates for a late secondary moist subtropical forest based on measurements from 16 permanent study plots located along an elevational (60-290 m) and topological gradient within the 132-ha Cinnamon Bay watershed on St. John, U.S. Virgin Islands. The purpose of the study was to provide information on forest structure, species composition, and forest productivity along environmental gradients, including the effects of hurricanes and drought. The other two files provide climate records from nearby weather stations (1917-1981).Above-ground biomass was measured every 5 years (1983-2003). Litterfall accumulation was determined in 1992-1993. In 1983, total above-ground biomass on all plots combined averaged 13,870 g/m2; by 2003 during a post-hurricane recovery period, it had declined by nearly 7 percent. In 1983, biomass was greatest on the summit, intermediate on slopes and valleys, and least on ridges; by 2003, the quantities for all sites had converged except on the summit plot.In 1992, ANPP was estimated based on annual litterfall accumulation (897 g/m2/year) plus biomass change due to delayed mortality (142 g/m2/year) plus estimated herbivory (25 g/m2/year), giving a total ANPP of 1,064 g/m2/year. Periodic storms and drought appear to maintain the forest in a disturbed state.", "links": [ { diff --git a/datasets/NPP_CHM_578_2.json b/datasets/NPP_CHM_578_2.json index 4ba97a24df..4fff0684b1 100644 --- a/datasets/NPP_CHM_578_2.json +++ b/datasets/NPP_CHM_578_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CHM_578_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains five data files (.txt format). Three data files provide net primary productivity (NPP) estimates for a tropical dry deciduous forest within the 3,300-ha Chamela Biological Station, Mexico. There is one file for each of the three permanent watershed plots located along an elevational gradient from 60 to 160-m above sea level. NPP was estimated from field measurements obtained during wet and dry seasons between 1982 and 1995. A fourth NPP data file provides average nutrient fluxes into and out of five watersheds. The fifth file provides precipitation and minimum/maximum temperature data from measurements obtained onsite. Detailed data are available for above-ground NPP (ANPP) (fine litterfall, wood increment, and leaf herbivory plus an estimation of understory production), and below-ground NPP (BNPP) (fine root production and root increment). Biomass data and nutrient inputs/outputs (P, K, Ca, Mg) averaged from five watersheds are also included in the data set. Estimated ANPP ranged from 611 to 808 g/m2/year between the three sub-sites (average 682 g/m2/year), and total NPP ranged from 1,119 to 1,353 g/m2/year (average 1,206 g/m2/year). These estimates are thought to represent the lower bounds of NPP because root and stem herbivory have not been taken into account, although leaf herbivory is included. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/NPP_CHR_468_2.json b/datasets/NPP_CHR_468_2.json index 0b274b58bf..841a36d532 100644 --- a/datasets/NPP_CHR_468_2.json +++ b/datasets/NPP_CHR_468_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CHR_468_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). One file provides above- and below-ground biomass, productivity, litterfall, and bioelement data for a native C3 grassland near Charleville (-26.40 S, 146.27 E, Elevation 304 m) in southern Queensland, northeast Australia. The second file provides above- and below-ground biomass and productivity estimates for an introduced C4 grassland near Charleville. The third file contains climate data (precipitation and maximum/minimum temperature) recorded a weather station located at the Charleville Airport for the period 1942-1994. The NPP studies were carried out over a 12-month period from 1973 to 1974 using harvest techniques with a view to parameterizing a simulation model of primary production and livestock carrying capacity. Peak above-ground standing crop at the end of the summer season was 122 g/m2 and 154 g/m2 for the native and introduced grasslands, respectively. Maximum below-ground standing crop was markedly different, at 110 g/m2 and 400 g/m2, respectively, suggesting a significant difference in shoot/root allocation. Annual net primary production was estimated as the sum of above-ground peak standing crop (live + dead) and root increment. These values were 182 and 319 g/m2/yr for the native and introduced grasslands, respectively. Additional data on litter production and nutrient dynamics are available for the native grassland site. Data on soil moisture, determined gravimetrically with each biomass harvest, are available in the literature. ", "links": [ { diff --git a/datasets/NPP_CLB_206_2.json b/datasets/NPP_CLB_206_2.json index c771a25f09..8197878879 100644 --- a/datasets/NPP_CLB_206_2.json +++ b/datasets/NPP_CLB_206_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CLB_206_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains four ASCII text files for a 260-hectare humid Trachypogon savanna at the Estacion Biologica de Los Llanos, Calabozo, Venezuela (8.93 N, -67.42 W, Elevation 98 m). Two data files contain monthly estimates of above-ground biomass for the period January 1969 to October 1969, one file for each treatment (burned and unburned plots). These files also provide one estimate of below-ground biomass. Another NPP data file contains monthly estimates of above- and below-ground biomass, LAI, and nitrogen content of living and dead leaves and stems and below-ground biomass for an unburned area of the Calabozo savanna for March 1986 to April 1987. The fourth data file contains precipitation and maximum/minimum temperature data from a nearby weather station at Estacion Biologica de Los Llanos (8.88 N, -67.32 W, Elevation 86 m) for the period 1968-1986. Harvest methods were used to estimate above- and below-ground biomass. Total NPP (above- plus below-ground productivity) was estimated at 682 g/m2/yr for unburned and 755 g/m2/yr for burned grassland plots in 1969. Later TNPP estimates (1986/87) for the unburned grassland ranged from 823 to 1,310 g/m2/yr.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_CNL_465_2.json b/datasets/NPP_CNL_465_2.json index f69f87366c..fb972fb3ad 100644 --- a/datasets/NPP_CNL_465_2.json +++ b/datasets/NPP_CNL_465_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CNL_465_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files (.txt). One file contains stand characteristics, soil characteristics, biomass distribution, and production allocation data measured during the 1984 growing season in four lodgepole pine stands (Pinus contorta var. latifolia) located near Canal Flats, British Columbia, Canada (50.2 N -115.5 W Elevation 1,300-1,380 m). The second file contains climate data from a nearby weather station at Kananaskis Boundary, Alberta (50.98 N -115.12 W Elevation 1,463 m). Two lodgepole pine stands were growing on xeric sites and two stands were growing on mesic sites. The stands were 70-78 years old, were unmanaged, and had regenerated naturally following wildfire. They were studied to determine the influence of soil water content on resource allocation to above-ground versus below-ground plant components. Above-ground NPP of the two xeric stands was 350 and 330 g/m2/yr, and below-ground NPP was 430 and 630 g/m2/yr, respectively, giving a range of total NPP from 780 to 960 g/m2/yr. ANPP of the two mesic stands was 640 and 740 g/m2/yr, and BNPP was 550 and 450 g/m2/yr, respectively, giving total NPP of 1,190 g/m2/yr in each case. Although the ANPP of the mesic stands was approximately double that of the two xeric stands, total NPP was only 36% greater for the mesic stands than for the xeric stands. Production allocation was in the following order: fine and small roots > stems > foliage > coarse roots > branches, for all but the wettest site, where stem production exceeded fine and small root production. Revision Notes: This data set has been revised to correct the sampling date (month) when above-ground biomass samples were collected. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_CNS_191_2.json b/datasets/NPP_CNS_191_2.json index ba4f0100f4..efae54c054 100644 --- a/datasets/NPP_CNS_191_2.json +++ b/datasets/NPP_CNS_191_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CNS_191_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII text files; one providing above-ground biomass, productivity, and bioelement concentration data for a derived savanna at Canas (10.4 N 85.1 W Elevation 45 m) in northwestern Costa Rica, and the other providing climate data from the La Pacifica weather station near Canas and rom other sources. Monthly dynamics of above-ground plant matter were monitored from July 1969 to June 1970 using harvest procedures within an exclosure to restrict grazing. The climate data are available from three time periods: 1951-1960, 1963, and 1969-1970. The climate is characterized by a dry season from late November to April, with little seasonal differences in temperature. The Canas study site is dominated almost exclusively by Hyparrhenia rufa, a perennial grass of African origin introduced extensively throughout the tropics. The original vegetation cover was closed semideciduous forest. The study area was deforested and converted to a grass savanna around January 1947, 22.5 years before the start of the present study. Light grazing by cattle and horses is accompanied by burning of the Canas savanna annually between December and April. Above-ground net primary productivity (ANPP) was estimated by two methods: maximum standing crop of herbaceous shoot tissue (peak live + dead matter) (968 g/m2/year); and the sum of monthly estimates of shoot production (including estimated mortality) (1,387 g/m2/year). End-of-season (November, 1969) live root biomass estimates of 1,220 g/m2 at 0-20 cm depth and 2,254 g/m2 at 0-100 cm depth are available in the literature. ", "links": [ { diff --git a/datasets/NPP_CPR_145_2.json b/datasets/NPP_CPR_145_2.json index 05a18ad2d8..444c5f7495 100644 --- a/datasets/NPP_CPR_145_2.json +++ b/datasets/NPP_CPR_145_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_CPR_145_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set records the productivity of a semiarid shortgrass prairie steppe located in the Central Plains Experimental Reserve (CPER)/Pawnee National Grassland in north-central Colorado. There are nine data files (.txt). Four files contain measurements of monthly dynamics of harvested above-ground plant biomass, one file each for untreated, irrigated, fertilized, and irrigated + fertilized plots for the period 1970 to 1975. The fifth file contains annual above-ground NPP estimates for the untreated plot for the period 1970-1974. The sixth file contains long-term ANPP estimated from field harvest measurements made between 1970 and 1990 and by correlation with forage production measurements made between 1939 and 1990. Two additional files provide estimates of above- and below-ground NPP based on peak growing season harvests; one record covers 1970-1972 from the Pawnee site and the other covers 1985-1988 from CPER. The ninth file contains climate data for 1912-1990 from a weather station located at CPER.Revision Notes: This data set has been revised to correct the study site elevation, extend the temporal coverage, and add four data files containing estimates of NPP. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_DCK_207_2.json b/datasets/NPP_DCK_207_2.json index 3de4c40808..a60598c2ed 100644 --- a/datasets/NPP_DCK_207_2.json +++ b/datasets/NPP_DCK_207_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_DCK_207_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a northern mixed prairie grassland, one file for an ungrazed treatment and the other for a heavily grazed treatment. The study site (46.90 N, -102.82 W, Elevation 784 m) is located in the northern Great Plains, near the city of Dickinson, about 160-km west of Bismarck, North Dakota. The third file contains climate data for the period 1891-1994 obtained from a weather station near Dickinson (46.88 N, -102.80 W, Elevation 750 m). Dynamics of above-ground living and dead plant biomass were monitored by the harvest technique at roughly 2-week intervals during the growing season of 1970. Total below-ground biomass was sampled at the same intervals by manual coring within the harvested plots to a depth sufficient to include at least 90% of the root mass. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP).Above-ground net primary productivity (ANPP) was estimated conservatively by summing peak biomass of individual species. These values were 351 g/m2/year for ungrazed and 302 g/m2/year for grazed grassland plots. Below-ground net primary productivity (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns); 932 g/m2/year for ungrazed and 958 g/m2/year for grazed grassland plots. Revision Notes: Only the documentation for this data set has been modified. The files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_DRN_215_2.json b/datasets/NPP_DRN_215_2.json index 649375d5bb..69b8a10e01 100644 --- a/datasets/NPP_DRN_215_2.json +++ b/datasets/NPP_DRN_215_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_DRN_215_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NPP data set contains one ASCII file (.txt format). The data file contains above- and below-ground biomass, litterfall, LAI, vegetation/soil micro-nutrient content (P, K, Ca, Mg, etc.), and above-ground net primary productivity (ANPP) estimates for transitional moist/dry tropical forests at Rio Lara (wet season site) and Rio Sabana (dry season site) in Darien Province, Panama. Field measurements were made in 1967 and 1968. No climate data are provided.Apart from litter quantity, most of the differences between these data reflect variations between the two plots sampled, rather than seasonal changes. Both plots were considered representative of the surrounding forest. The area was thought to have been forested for the previous 400 years, following abandonment of open savanna lands maintained by the Precolumbian Indians.Total annual leaf and branch fall averaged for the two sites was 1,137 g/m2/yr, representing a minimum ANPP estimate. Litter decomposition over the 9-month wet season was around 90%. LAI at both sites was high; however, the index for the dry site (10.6 m2/m2) was only half that of the wet season site (22.4 m2/m2). ", "links": [ { diff --git a/datasets/NPP_DZH_192_2.json b/datasets/NPP_DZH_192_2.json index dfb9b07320..9313f05676 100644 --- a/datasets/NPP_DZH_192_2.json +++ b/datasets/NPP_DZH_192_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_DZH_192_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1955 and 1989 on a semi-desert steppe at the Dzhanybek Research Station in Kazakhstan (49.33 N 46.78 E Elevation 20 m). The second file contains monthly and annual climate data for the study site for the period 1953-1989.Peak live biomass measurements were made from 1955 to 1989 (excluding 1976) and additional measurements of above-ground live biomass and dead matter were made seasonally from 1985 to 1989. Averaged over the time series, above-ground live phytomass and standing dead were estimated to be 137 g/m2 and 32 g/m2 (dry matter weight), respectively, while below-ground phytomass dry weight was 1,750 g/m2. ANPP was estimated to be 201 g/m2/yr. BNPP and TNPP were not estimated.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1997.", "links": [ { diff --git a/datasets/NPP_EMDI_615_3.json b/datasets/NPP_EMDI_615_3.json index 71790d2042..81ccf7049e 100644 --- a/datasets/NPP_EMDI_615_3.json +++ b/datasets/NPP_EMDI_615_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_EMDI_615_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set represents a refined set of global net primary productivity (NPP) estimates and model driver data that are the results of the Ecosystem Model-Data Intercomparison (EMDI) workshop review and outlier analyses undertaken to assess the accuracy of global model forecasts of terrestrial carbon cycling. EMDI builds upon the accomplishments of the original worldwide synthesis of NPP measurements and associated model driver data prepared by the Global Primary Production Data Initiative (GPPDI) (Olson et al., 2001; 2013). The EMDI review and analyses produced NPP, climate, NDVI, land cover, vegetation, and soil data for a sub-set of GPPDI data: 81 Class A sites, 933 Class B sites, and 3,855 Class C 0.5-degree cell grids. Class A sites represent well-documented study sites that have complete above- and below-ground NPP measurements. Class B sites represent more numerous extensive sites with less documentation and site-specific information available. Class C cells represent estimates of NPP for 0.5-degree grid cells for which inventory, modeling, or remote-sensing tools were used to scale up the point measurements. The data files are in comma-separated-value (.csv) format: 18 data files for Class A sites which includes 12 comma-separated files (*.csv) and six compressed files (*.zip) 11 data files for Class B sites in comma-separated format (*.csv).9 data files for Class C grid cells in comma-separated format (*.csv).This document and a companion file (Olson et al., 2001) describe the compilation of NPP estimates under the GPPDI and the EMDI review and outlier analyses that produced this refined set of NPP estimates and model driver data. Revision Notes: This data set has been revised to correct previously reported NPP estimates for three OTTER Transect sites, USA, in the Class A NPP data file. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_FLK_201_2.json b/datasets/NPP_FLK_201_2.json index b72dc43cc5..cad594d00d 100644 --- a/datasets/NPP_FLK_201_2.json +++ b/datasets/NPP_FLK_201_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_FLK_201_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three files (.txt format) for an established 8.25 ha boreal forest dominated by Norway spruce, Picea abies, at Flakaliden (64.12 N 19.45 E) in northern Sweden. Two data files contain stand characteristics, above- and below-ground biomass, and Net Primary Productivity (NPP) allocation data (one file for plots fertilized and irrigated during the growing season and one file for control plots). The third file provides climate data for the period 1991-1995 from a weather station established at the study site. The experimental forest was established in 1963 by planting 4-year-old P. abies seedlings after clear-felling, burning, and soil scarification. A yield optimization study was started in 1986 to compare the productivity of the boreal forest under four types of treatment (only the results of fertilization/irrigation and no treatment are presented herein). Treatments began in 1987 and continued through the 1996 growing season. Field measurements were made by inventory and harvest methods. After three years of treatment, height and diameter growth in the fertilized/irrigated stands were double that of the control stand. After 10 years, volume growth of fertilized/irrigated stands were almost four times that of the control. Total net primary production (TNPP) of the 36-year-old untreated stand in 1995 was 291 g/m2/year. TNPP in the fertilized/irrigated stand (902 g/m2/year) was more than three times that of the control, confirming earlier findings that nutrient availability is a major constraint on forest production in Sweden.", "links": [ { diff --git a/datasets/NPP_GNN_474_2.json b/datasets/NPP_GNN_474_2.json index 1f119c3b64..254d94e42f 100644 --- a/datasets/NPP_GNN_474_2.json +++ b/datasets/NPP_GNN_474_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_GNN_474_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains seven ASCII data files (.txt format). Four files provide NPP data for contrasting lowland rainforests within Gunung Mulu National Park on the island of Borneo, Malaysia. Three files provide climate data from weather stations near Gunung Mulu.The study areas are located along an environmental gradient of varying soil types at elevations ranging from 50 to 300 m within the 544 km2 Park. The study sites are primary lowland evergreen rainforests, each about 1.0 hectare in size. They include: an alluvial forest (at 50 m elevation and often inundated during the wet season); a heath forest (on a sandy terrace at about 170 m elevation); a mixed dipterocarp forest (on ridge slopes at 200-250 m elevation); and a limestone forest (on shallow soils over limestone at the base of cliffs and ravines at 300 m elevation).The NPP files contain estimates of above-ground biomass, annual litterfall accumulation, standing litter crop, and nutrient content of different vegetation components and soils. The scientific expedition was carried out between June 1977 and September 1978. Estimates of litterfall, ranging from 886 g/m2/year to 1,203 g/m2/year, give a minimum estimate of above-ground production.The climate record for the study sites extends from 1915 through 1990. The area's climate is controlled largely by the Indo-Australian monsoon system with wet northeast monsoon from December to March and slightly drier southwest monsoon from May to October. Mean annual precipitation is around 3,000 mm and mean average temperature is about 27 C.", "links": [ { diff --git a/datasets/NPP_GPPDI_617_3.json b/datasets/NPP_GPPDI_617_3.json index 5e6fee4476..5b333026cc 100644 --- a/datasets/NPP_GPPDI_617_3.json +++ b/datasets/NPP_GPPDI_617_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_GPPDI_617_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Net primary productivity (NPP) estimates were compiled by the Global Primary Production Data Initiative (GPPDI). The database covers 2,523 individual sites and 5,164 half-degree grid cells and underwent extensive review under the Ecosystem Model-Data Intercomparison (EMDI) process. The GPPDI database includes NPP measurements that were collected over a long time period by many investigators using a variety of methods. The measurements are categorized as either Class A, from intensively studied sites; Class B, from extensive sites; or reported as Class C, 0.5 latitude-longitude grid cells. The data set contains six comma-separated files (.csv format). There are two files for each class. One file for each class contains site locations, elevation, NPP estimates, climate data, biome and dominant species information, and references. The other file for each class contains model validation outlier flags derived from site-specific reviews. This document and a companion file (Olson et al., 2001) describe the compilation of NPP estimates under the GPPDI. The results of the EMDI review and outlier analysis produced a refined set of NPP estimates and model driver data (the EMDI database; Olson et al., 2001; 2013). Another ORNL DAAC data set (Zheng et al., 2013) contributed to the compilation of GPPDI. Revision Notes: This data set has been revised to correct previously reported ANPP, BNPP, and TNPP estimates for three OTTER Transect sites, USA, in the Class A NPP data file and BNPP, and TNPP estimates for Vindhyan, India, in the Class B NPP data file. Please see the Data Set Revisions section of this document for detailed information.", "links": [ { diff --git a/datasets/NPP_GRIDDED_GPPDI_614_4.json b/datasets/NPP_GRIDDED_GPPDI_614_4.json index 7b69cc9e08..95a03e9969 100644 --- a/datasets/NPP_GRIDDED_GPPDI_614_4.json +++ b/datasets/NPP_GRIDDED_GPPDI_614_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_GRIDDED_GPPDI_614_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files (.csv format) containing gridded (0.5-degree) estimates of net primary productivity (NPP), elevation, temperature, precipitation, NDVI, and biome type for selected terrestrial regions of the world. The field data used to develop NPP estimates came from 15 worldwide data sources in several different biomes covering the period 1954-1998. NPP values were developed from natural resource field inventories (e.g., forest, rangeland, crop) at different scales, from plot to county; from data compiled from published literature and high resolution maps; from simulation models using key independent variables; from regression analyses with environmental variables; and by using relationships between remotely-sensed spectral vegetation indices and field observations. The first file, NPP_Gridded_3654_cells_R3.csv with 3,654 0.5-degree grid cells, is suitable for biome level and overall analyses because of a larger sample size. In this file, 36 cells have above-ground net primary production (ANPP) only, 320 cells have total net promary production (TNPP) only, and 3,298 cells have both TNPP and ANPP. The second file, NPP_Gridded_2335_unique_cell_R3s.csv, was derived from the larger file and contains 2,335 0.5-degree grid cells after outliers were excluded, replicate measurements were averaged out for each unique geographic location, and cells classified as water, bare ground, and urban were excluded. This smaller data file is more appropriate for model/data inter-comparisons. Overall, gridded ANPP values ranged from 3 to 890 gC/m2/yr, and gridded TNPP values ranged from 3 to 1,235 gC/m2/yr. The lowest values are for sparsely vegetated ground (e.g., open shrublands) and the highest values are for forests. Revision Notes: This data set has been revised to correct the previously reported temporal coverage of field data measurements that were used to develop NPP estimates and the data set title was also corrected to reflect the date change. Missing value codes were added. Please see the Data Set Revisions section of this document for detailed information.", "links": [ { diff --git a/datasets/NPP_GSM_804_2.json b/datasets/NPP_GSM_804_2.json index 1c2a0789e4..563c4d5ca3 100644 --- a/datasets/NPP_GSM_804_2.json +++ b/datasets/NPP_GSM_804_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_GSM_804_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two data files (.csv format). One file contains site characteristics, stand descriptors, and above-ground biomass and ANPP data for seven old-growth temperate forest stands and one young cove forest stand in the Great Smoky Mountains of Tennessee. The old-growth stands (> 200 years old) span several watersheds on the north slope of the mountains at elevations ranging from 720 to 1,140 m. The younger stand (48-63 years old, elevation 910 m) developed after agricultural abandonment. The second file contains monthly mean climate data averaged over four years (1947-1950) from four climate stations located along an elevational gradient (445-1,920 m) in Great Smoky Mountains National Park. DBH measurements were made at the beginning of the study and biomass increment was measured from a subset of trees. ANPP was estimated using regional species-specific allometric relationships for tree mass. Biomass, volume, and annual input of coarse woody detritus are also reported. Live biomass in the old-growth stands (32,600-47,100 g/m2) is among the highest reported for temperate forests of eastern North America while ANPP is moderate (630-1,010 g/m2/yr). ANPP in the younger stand was higher (1,180-1,310 g/m2/yr). In comparison with forests worldwide, inputs of coarse woody debris is moderate. Revision Notes: Previously reported field collection dates have been corrected in the NPP file. Biomass and ANPP values were converted from Mg/ha and Mg/ha/yr to g/m2 and g/m2/yr, respectively, consistent with units in other files in the NPP collection. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_Grassland_31_654_2.json b/datasets/NPP_Grassland_31_654_2.json index 31fe8cf994..cb5a1461db 100644 --- a/datasets/NPP_Grassland_31_654_2.json +++ b/datasets/NPP_Grassland_31_654_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_Grassland_31_654_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes monthly grassland biomass data, net primary productivity (NPP) estimates, and climate (rainfall amounts and temperature) data for multiple study sites in major grassland types worldwide. Field measurements of biomass and associated environmental data were compiled for the multiple grassland study sites. When sufficient biomass data were available, NPP was estimated by six different algorithms for 31 grassland sites to examine potential bias associated with the algorithms (Scurlock et al. 2002).The data consisted of monthly measurements of biomass components including aboveground live material, standing dead, litter, belowground biomass, and belowground dead material. However, many of the sites did not collect all of the components. There are 1,477 field measurements of some component of NPP, all sites having at least aboveground biomass measurements. Of the 31 sites, 20 also measured standing dead and litter or total live plus dead material. In addition, 17 sites measured total belowground biomass, and six of these sites provided separate measurements of live and dead root components. The study sites had from 1 to 29 years of biomass data with an average of three years per site. Five ecoregions were represented, including cold desert steppe, temperate dry steppe, humid savanna, humid temperate, and savanna. The selection of study sites was based on the availability of complete and consistent information on NPP or at least partial NPP, together with the dynamics of live biomass and dead matter for at least the growing season (Scurlock et al. 2002). Site-description metadata, such as latitude, longitude, elevation, and information on vegetation type (biome), soil type, and land-use history were also desirable for inclusion for study sites in the compilation. Study sites were included that had at least one reference from the peer-reviewed literature.There are two data files in comma-separated (.csv) format with this data set.Revision Notes: Only the documentation for this data set has been modified from the original data set publication. ", "links": [ { diff --git a/datasets/NPP_Grassland_613_2.json b/datasets/NPP_Grassland_613_2.json index 94a2966a41..45a796c622 100644 --- a/datasets/NPP_Grassland_613_2.json +++ b/datasets/NPP_Grassland_613_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_Grassland_613_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In many grasslands, aboveground net primary productivity (ANPP) is commonly estimated by measuring peak aboveground biomass. Estimates of belowground net primary productivity (BNPP), and consequently, total net primary productivity (NPP), are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems - to develop simple models or algorithms to estimate missing components of total system NPP.", "links": [ { diff --git a/datasets/NPP_HYS_208_2.json b/datasets/NPP_HYS_208_2.json index 32a52cd5a2..3b8371552a 100644 --- a/datasets/NPP_HYS_208_2.json +++ b/datasets/NPP_HYS_208_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_HYS_208_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a mixed prairie grassland, one file for an ungrazed treatment and the other for a moderately grazed treatment. The study site (38.87 N, - 99.38 W, Elevation 714 m) is located in the central Great Plains near the city of Hays, Kansas, about 400-km west of Kansas City. The third file contains monthly and annual climate data for the period 1891-1994 obtained from a weather station (38.87 N, -99.38 W, Elevation 613 m) located at the Hays grassland study site. Dynamics of above-ground living and dead plant biomass were monitored by the harvest technique at roughly 2-week intervals during the growing season of 1970. Total below-ground biomass was sampled at the same intervals by manual coring within the harvested plots to a depth sufficient to include at least 90% of the root mass. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP).Annual above-ground net primary production (ANPP) was estimated conservatively by summing peak biomass of individual species. These values were 363 g/m2/year for ungrazed and 372 g/m2/year for grazed grassland plots. Annual below-ground net primary production (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns); 1,062 g/m2/year for ungrazed and 855 g/m2/year for grazed grassland plots.", "links": [ { diff --git a/datasets/NPP_IBP_198_2.json b/datasets/NPP_IBP_198_2.json index 42c078b9fa..bb0edfd328 100644 --- a/datasets/NPP_IBP_198_2.json +++ b/datasets/NPP_IBP_198_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_IBP_198_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides four data files containing net primary productivity (NPP) data, edaphic characteristics, average climatic conditions, and basic descriptive and quantitative information on vegetation for 117 globally-distributed terrestrial forest sites. The data set was derived from the IBP (International Biological Programme) Woodlands Data Set of DeAngelis et al. (1981). The data were collected from the mid 1950s to the early 1970s and were compiled into an electronic data set at the Oak Ridge National Laboratory to facilitate comparisons involving the diverse woodland ecosystems. One data file provides a complete synthesis of NPP, vegetation, edaphic, and climate data and data-source references for each of the 117 sites as published in DeAngelis et al. (1981) for a total of 5,887 records. The second file provides site location, biome, and selected forest productivity and soils data for the 117 sites. The third file provides summary climate data (temperature, precipitation, radiation, growing season length) for each site, and the fourth file provides forest type, investigator(s), and years of the study for each site. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally archived in 1997 (DeAngelis, et al, 1997.) ", "links": [ { diff --git a/datasets/NPP_JDR_202_2.json b/datasets/NPP_JDR_202_2.json index eb241e95ad..a967885228 100644 --- a/datasets/NPP_JDR_202_2.json +++ b/datasets/NPP_JDR_202_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_JDR_202_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three files (.txt format). Two of the files contain stand characteristics, above- and below-ground biomass, and above- and below-ground production allocation data for Scots pine (Pinus sylvestris) forests near Jadraas, Sweden. One file is for a young regenerating forest (14-20 years old) and the other is for an old-growth forest (120-125 years old). The field measurements were made by destructive and non-destructive methods between 1973 and 1983. The third file contains climate data recorded at the Jadraas site from 1974 through 1990.The research was conducted under the auspices of SWECON (Swedish Coniferous Forest Biome) Project to enhance understanding of plant biomass dynamics and factors regulating plant growth. Most of the research concerned plant and vegetation processes, but particular interest was also given to soil processes, consumption processes, and energy and water exchange in the canopy and the soil.Total net primary production (NPP) in the young forest was estimated at 860 g/m2/year (above-ground = 372 g/m2/year; below-ground = 488 g/m2/year). NPP for the old-growth forest was not calculated; however, the data set contains estimates of branch and trunk growth (43 g/m2/year and 106 g/m2/year, respectively), annual litterfall (135-162 g/m2/year), needle grazing loss (1.55 g/m2/year), fine tree root production (188 g/m2/year), and understory root production (30 g/m2/year).Revision Notes: The NPP data file has been split into two files, one for the young regenerating forest and one for old-growth forest. The data files have been revised to correct previously reported data and information and to add new data from published sources. Please see the ORNL DAAC Data Set Change Information file for more information. ", "links": [ { diff --git a/datasets/NPP_JHN_475_2.json b/datasets/NPP_JHN_475_2.json index 88e717e98c..0c06ef7fe0 100644 --- a/datasets/NPP_JHN_475_2.json +++ b/datasets/NPP_JHN_475_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_JHN_475_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains five NPP data files and three climate data files (ASCII .txt format). There is one NPP file for each of the five sub-types of upper montane tropical forest located along John Crow Ridge in the Blue Mountains of Jamaica. Biomass, productivity, tree mortality, standing crop of litter, LAI, and nutrient content of leaf fall were measured from 1974 to 1978. Long-term climate data are available from Cinchona Botanic Gardens, approximately 3 km south of the John Crow Ridge study area and at similar elevation.The John Crow Ridge study areas (18.08 N 76.65 W) consist of 8 to 10 contiguous 10 m x 10 m permanent plots. The forests are of low stature and appear to be completely undisturbed. The forest floor at one sub-site (Mor Ridge) was overlain by a 30-50 cm layer of mor humus with a high C/N ratio.ANPP was estimated to be between 654-997 g/m2/year (sum of litterfall and trunk/branch increment), or 854-1,057 g/m2/year (including tree mortality). These figures are lower than for lowland tropical forests with greater leaf turnover and reflect the relatively low stature of the upper montane forest plots. Above-ground biomass was estimated by both destructive methods and nondestructive regression analysis. Values ranged from 23,000 to 34,000 g/m2. Below-ground biomass (5,370 g/m2) was determined for one site by harvesting roots to a depth of 50 cm. LAI for trees plus understory, where measured, was 5.5-5.7 m2/m2.", "links": [ { diff --git a/datasets/NPP_JRN_209_2.json b/datasets/NPP_JRN_209_2.json index 65c107edb4..8a7f175426 100644 --- a/datasets/NPP_JRN_209_2.json +++ b/datasets/NPP_JRN_209_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_JRN_209_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a desert grassland in the Jornada Experimental Range, New Mexico, one file for an ungrazed treatment and the other for a light to moderately grazed treatment. The study site (32.60 N, -106.85 W, Elevation 1,350 m) is located in the Basin and Range geomorphic province at the northernmost extent of the Chihuahuan Desert, near the city of Las Cruces, New Mexico, about 60-km northwest of El Paso, Texas. The third file contains climate data for the period 1954-1992 obtained from a weather station located near the study site (32.62 N, -106.73 W, Elevation 1,300 m).Dynamics of above-and below-ground plant biomass were monitored at roughly 2-week intervals during the growing season from 1970 to 1972. Data on above-ground live biomass, recent and old dead matter, and root-crown biomass are available for one to two replications of grazed and \"ungrazed\" (relatively undisturbed) treatments. Total below-ground biomass was also sampled. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US GrasslandBiome Project of the International Biological Program (IBP).Annual above-ground net primary production (ANPP) was estimated, conservatively, by summing peak biomass of individual species, and annual below-ground net primary production (BNPP) estimated as the sum of positive increments in total root biomass. ", "links": [ { diff --git a/datasets/NPP_KDE_216_2.json b/datasets/NPP_KDE_216_2.json index 4942934def..9a04b58248 100644 --- a/datasets/NPP_KDE_216_2.json +++ b/datasets/NPP_KDE_216_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KDE_216_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one NPP data file and two climate data files (ASCII .txt format). The NPP file contains above- and below- ground biomass, litterfall, standing litter crop, and nutrient content data for a moist semi-deciduous secondary tropical forest at the Kade Agricultural Research Station (6.15 N 0.92 W), Ghana, spanning several collections periods between 1957 and 1972. Climate data come from weather stations at Kade near the study site (1958-1997) and at Kumasi near Kade (1945-1990).The Kade study site is typical of an old secondary forest which has probably not been cultivated or harvested since around 1915-1925. Tree basal area measured in 1957 was quite high at 33.7 m2/ha; measurements in 1968 of a plot a few hundred meters away gave 30.6 m2/ha.Detailed above- and below-ground biomass data are provided from a single clear-felling made in 1957. Nutrient content for lianas, leaves and twigs, branches, large wood, standing dead wood, stumps, litter, and roots is also provided. Total live + dead biomass was 36,102 g/m2, of which 5,414 g/m2 (15%) was below-ground live biomass and 23,568 g/m2 was above-ground live biomass. Monthly litterfall is available for 26 months (1970-72).Total annual NPP was estimated in the late 1950s at about 2,400 g/m2/year based on litterfall of 1,054 g/m2/year plus rough estimates of timber fall (1,070-1,121 g/m2/year) and root production (258 g/m2/year). In the 1970s, NPP was recalculated at 2,200-2,500 g/m2/year based on additional measurements of litter, wood fall, and decomposition. ", "links": [ { diff --git a/datasets/NPP_KHC_217_2.json b/datasets/NPP_KHC_217_2.json index 1c995f4c53..0ce9d052c3 100644 --- a/datasets/NPP_KHC_217_2.json +++ b/datasets/NPP_KHC_217_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KHC_217_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one net primary productivity (NPP) data file and three climate data files (.txt format) for a fully closed tropical rainforest in the Khao Chong Reserve (7.58 N 99.8 E) in southern Thailand. The Reserve comprises 500 ha of well-preserved rainforest considered typical of the region, although maximum tree height (36 m) and biodiversity were less than in Malaysian forests. Net primary productivity (NPP) was estimated as the sum of annual net above-and below-ground biomass increase plus extrapolated annual litterfall and tree mortality. Biomass increment for trees > 4.5 cm diameter at breast height (DBH) was monitored between 1962 to 1965, and daily litterfall was measured for one month in 1962. Total NPP was estimated at 2,860 g/m2/year. This value includes a possible over-estimate of litterfall (2,330 g/m2/year) plus above-ground woody biomass increment and turnover as mortality combined (489 g/m2/year) plus below-ground woody biomass increment (41 g/m2/year). Fine root turnover and herbivory were not included in these estimates. Allometric relationships for estimating above-ground biomass were checked by destructive harvest. Leaf area index was relatively high at 11.4-12.3 m2/m2. Long-term climate data for Khao Chong are available from weather stations at Songkhla, Thailand (7.2 N 100.6 E) and Trang, Thailand (7.52 N 99.62 E). Depending on station location and temporal coverage, mean annual temperature is 27.2-27.4 C and mean annual precipitation is between 1,928 and 2,696 mm. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.", "links": [ { diff --git a/datasets/NPP_KHM_146_2.json b/datasets/NPP_KHM_146_2.json index dfec75c63e..08998a0175 100644 --- a/datasets/NPP_KHM_146_2.json +++ b/datasets/NPP_KHM_146_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KHM_146_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains biomass measurements made in 1948 and between 1967 and 1970 for a humid temperate steppe in the Khomutovskaya Steppe Nature Reserve in the Donezk Region of Ukraine. The second file contains monthly and annual climate data for the study site for the period 1955-1972.Biomass measurements were made once in July of 1948 and biweekly to monthly during the growing season (April-August/September) from 1967 to 1970 at the permanent Khomutovskaya research station. ANPP was calculated for each sampling date and cumulatively over the four years, 1967-1970. Averaged over the time series, above-ground live phytomass, standing dead, and litter biomass were estimated to be 340, 90, and 240 g/m2 (dry matter weight), respectively, while below-ground phytomass and mortmass were estimated to be 1,675 and 792 g/m2 (dry matter weight), respectively. ANPP was estimated to be 460 g/m2/yr. BNPP was estimated to be 1,340 g/m2/yr.", "links": [ { diff --git a/datasets/NPP_KLN_147_2.json b/datasets/NPP_KLN_147_2.json index b9843b4562..ff8647ac97 100644 --- a/datasets/NPP_KLN_147_2.json +++ b/datasets/NPP_KLN_147_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KLN_147_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three data files in text format (.txt). One file contains monthly biomass measurements and Net Primary Productivity (NPP) estimates made between April 1984 and July 1990 on a semi-natural tropical monsoon grassland in southern Thailand. The second file contains a year-long record of monthly biomass measurements and NPP estimates made on a portion of the same grassland that was accidentally burned in January 1985. The third file contains monthly and annual climate data for the study site for the period 1973-1989.Two accidental fires occurred during the course of the study; the one in January 1985 burnt 40% of the study area and the other in February 1986 burnt most of the site. During 1985, sampling occurred simultaneously on unburned primary and burned secondary portions of the grassland; afterwards sampling continued on the primary site. Measurements of above- and below-ground live and dead biomass were made on the 15th day of each month. NPP estimates were calculated from changes in biomass and decomposition rates in unburned areas from 1984 through 1986. From April 1984 to April 1985, NPP was 2,036 g/m2/yr, with below-ground organs contributing 23% of this total. Production in the unburned area in 1985 was substantially lower (1,677 g/m2/yr) which coincided with a 35% decrease in mean leaf area index. NPP in the 1985 burned area was only slightly lower (1,524 g/m2/yr); however, NPP was substantially lower after the second fire in 1986 (134 g/m2/yr).Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.", "links": [ { diff --git a/datasets/NPP_KNZ_148_2.json b/datasets/NPP_KNZ_148_2.json index c51c585fe8..af90bd981c 100644 --- a/datasets/NPP_KNZ_148_2.json +++ b/datasets/NPP_KNZ_148_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KNZ_148_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). Two files contain above-ground biomass and productivity data for a humid temperate tall-grass prairie grassland located in the Konza Prairie Natural Research Area, Kansas. One file provides data for an unburned treatment and the other for a burned treatment for 1975 to 1990. The third file contains climate data for the period 1891-1988 obtained from a weather station at Konza. The above-ground net primary productivity measurement presented here (394 g/m2/year) is a 10-year average (1975-1984) based on peak seasonal live biomass values averaged for burned and unburned lowland and upland grasslands. The Konza study site (39.10 N, - 96.61 W, Elevation 400 m) is situated near the town of Manhattan in north-eastern Kansas, about 170-km west of Kansas City. The Konza research program is built upon a long-term database on ecological pattern and process data derived from a fully replicated watershed-level experimental design, in place at the Konza Prairie Biological Station since 1977. This design includes replicate watersheds subject to different fire and grazing treatments. Within the watersheds, permanent sampling transects are replicated at various topographic positions, where plant species composition, plant and consumer populations, above-ground net primary production (ANPP), soil properties, and other key above- and below-ground processes are measured. In addition to these watershed-level studies, the Konza Long Term Ecological Research (LTER) program includes a number of long-term plot-level experiments. ", "links": [ { diff --git a/datasets/NPP_KRK_193_2.json b/datasets/NPP_KRK_193_2.json index 721a62f196..d4c572f12a 100644 --- a/datasets/NPP_KRK_193_2.json +++ b/datasets/NPP_KRK_193_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KRK_193_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII files (.txt format). One file contains above- and below-ground biomass (including standing dead material and litter) and productivity data for a tropical grassland at Kurukshetra University (29.97 N, 76.85 E, Elevation 247 m) in northern India, about 150-km north-northwest of Delhi. The second file contains climate data from a weather station located at the study site. Biomass measurements were made monthly by harvest methods from mid-May 1970 to mid-May 1971. Annual net primary productivity (NPP) was calculated for the grassland according to several methods, with preference shown for the estimate given by summing positive increases in biomass and accounting for mortality. Total NPP was estimated at 3,538 g/m2/yr, with above-ground net primary productivity (ANPP) of 2,407 g/m2/yr and below-ground net primary productivity (BNPP) of 1,131 g/m2/yr. Seasonal changes in the vegetation were studied through tiller analysis. Examination of vertical distribution of above-ground biomass showed that different layers of vegetation were dominated by different species in different months. ANPP was maximum during the rainy season (1,706 g/m2) and BNPP was maximum during the dry winter season (785 g/m2). Production was more directed above ground during the rainy season and below ground during the dry season. Apparent efficiency of energy conversion was calculated at 1.66% on the basis of 50% total solar radiation. ", "links": [ { diff --git a/datasets/NPP_KRS_149_2.json b/datasets/NPP_KRS_149_2.json index ff5bfe0ff0..4bad8cb29c 100644 --- a/datasets/NPP_KRS_149_2.json +++ b/datasets/NPP_KRS_149_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KRS_149_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1954 and 1983 on a virgin meadow steppe in the Central-Chernozem V.V. Alyekhin Natural Reserve, Kursk Region, Russia. The second file contains monthly and annual climate data for the study site for the period 1947-1983.Above-ground live biomass measurements were made at biweekly to monthly intervals over the entire 30-year time series. Discontinuous measurements of above-ground standing dead matter and litter biomass (1956-1983) and below-ground live and dead biomass (1972-1973 and 1981-1983) were also made. Cumulative ANPP was estimated at the end of the growing season (1956-1963 and 1972-1973) and monthly (1982-1983). Averaged over the time series, above-ground live phytomass, standing dead, and litter biomass were estimated to be 362, 344, and 424 g/m2 (dry matter weight), respectively, while below-ground phytomass and mortmass were 910 and 1,370 g/m2 (dry matter weight), respectively. ANPP was estimated to be 774 g/m2/yr and BNPP was estimated to be 1,700 g/m2/yr for a TNPP estimate of 2,474 g/m2/yr. The study site is one of eight major grassland types of Eurasia which encompass an extremely wide climatic gradient in the direction of increasing maximum summer temperatures and continentality and decreasing precipitation in a north-west to the south-east band of steppes in the European and Asian parts of the former USSR (Commonwealth of Independent States). Kurst, on rich loamy chernozem soil, is one of the most productive upland grassland ecosystems of Russia with annual mean maximum/minimum temperatures of 24.8/-14.4 C and annual mean precipitation of 582.7 mm for the period 1947-1983. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_KSM_466_2.json b/datasets/NPP_KSM_466_2.json index 762ddce960..53af563314 100644 --- a/datasets/NPP_KSM_466_2.json +++ b/datasets/NPP_KSM_466_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_KSM_466_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three files (.txt format). One file provides stand characteristics, biomass, and production allocation data for an old-growth boreal forest near Kuusamo, Finland. The research was conducted during the 1967-1972 growing seasons. The other two files provide climate data from a weather station about 60 km south of the forest. One record contains precipitation and mean average temperature data for the 1961-1994 period (excluding 1971-1980) and the other contains precipitation data for 1908-1994. The Kuusamo research site is located just south of the Arctic Circle (66.37 N 29.32 E) and belongs to the northern boreal zone of taiga forests. The forest is an old Hylocomium-Myrtillus type spruce forest which has remained in a natural state and reached climatic climax long ago. The average age of the dominant spruces (Picea abies) is about 260 years. There is a well-developed ground layer of vegetation, chiefly dwarf scrub and mosses (dominant species: Vaccinium myrtillus, V. vitis-idaea, Hylocomium splendens, and Pleurozium schreberi). The northerly location of the forest and the age of its trees are the main factors responsible for low biomass and net production figures in comparison with spruce forests further south. Total above-ground biomass (including tree, understory, and moss layers) was determined by harvest methods and estimated to be 10,194 g/m2. Below-ground tree biomass estimates, also determined by harvest methods, are less reliable, at 3,753 g/m2. Total net primary productivity (NPP) for this site was estimated to be 441 g/m2/yr (421 g/m2/yr above-ground, 20 g/m2/yr below-ground).", "links": [ { diff --git a/datasets/NPP_LMT_150_2.json b/datasets/NPP_LMT_150_2.json index 3d8eeda432..fdc86a8634 100644 --- a/datasets/NPP_LMT_150_2.json +++ b/datasets/NPP_LMT_150_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_LMT_150_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three data files in text format (.txt). One file contains monthly above-ground biomass measurements made in 1965 in a humid grass savanna at the Lamto Research Station, Cote Ivoire, Africa. The second file contains monthly above- and below-ground biomass measurements and calculations of carbon/nitrogen ratio of above-ground live and dead biomass and below-ground biomass from the same site for 1969-1987. The third file contains monthly and annual climate data for the study site for the period 1969-1990.Total net primary production (NPP) of the Loudetia simplex grass savanna was estimated at 2,150 g/m2/yr, of which 1,320 g/m2/yr (61%) was below-ground production. Normally 50-90% of above-ground grass biomass is burned annually,Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_LQL_476_2.json b/datasets/NPP_LQL_476_2.json index c855fdeb60..f6479f2486 100644 --- a/datasets/NPP_LQL_476_2.json +++ b/datasets/NPP_LQL_476_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_LQL_476_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ten ASCII files (.txt format), one NPP file for each of the nine different montane tropical rainforest sites within the Luquillo Experimental Forest (LEF) of Puerto Rico and one file containing climate data. The NPP study sites are located along an environmental gradient of different soils, elevation (100-1,000 m), develop stage, and mean annual rainfall. Field measurements were carried out from 1946 through 1994.", "links": [ { diff --git a/datasets/NPP_MDL_210_2.json b/datasets/NPP_MDL_210_2.json index d012d81fdb..1aa94d5127 100644 --- a/datasets/NPP_MDL_210_2.json +++ b/datasets/NPP_MDL_210_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MDL_210_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII files (.txt format). One file provides monthly above-ground live biomass, dead matter, and litter data and daily above-ground net primary productivity (ANPP) data for a temperate grassland steppe at the Media Luna Ranch in Patagonia, Argentina (45.60 S, 71.42 W, Elevation 630 m) for the period May 1981 to March 1983. The second file contains climate data recorded at Media Luna Ranch from 1981 through 1985. Productivity of the steppe was monitored at monthly or bi-monthly intervals during two growing seasons. The ANPP measurement presented here is the sum of the increase in above-ground live biomass, dead matter, and litter. ANPP was estimated to be 0.00-0.15 g/m2/day in winter months (May-September) and 0.22-0.94 g/m2/day in the warmer months. Annual ANNP was estimated to be 35 g/m2/year. The study site is a 2.5-hectare exclosure situated on the Rio Mayo terraces. The steepe represents one of the most important grassland areas of Patagonia which occupy a narrow belt in the foothills of the Andes. The region has been over-grazed by introduced livestock since the early 1900s, and is currently used for sheep production. ", "links": [ { diff --git a/datasets/NPP_MGD_477_2.json b/datasets/NPP_MGD_477_2.json index 28a57ea841..b8394782d5 100644 --- a/datasets/NPP_MGD_477_2.json +++ b/datasets/NPP_MGD_477_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MGD_477_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two NPP data files and one climate data file (ASCI .txt format). The NPP files contain data for above-ground biomass, litterfall, and nutrient content of above-ground vegetation, organic surface layer, and soils measured during an 18-month period in 1970 and 1971 at two contrasting tropical seasonal evergreen forests in Magdalena Valley, Colombia. The climate record provides mean monthly and annual precipitation (1951-1992) and mean monthly and annual average temperature (1970-1997) from Barranca Bermeja (7.00 N 73.80 W) near the Magdalena Valley sites. ", "links": [ { diff --git a/datasets/NPP_MMG_802_2.json b/datasets/NPP_MMG_802_2.json index 7c472e0fe9..030974cd19 100644 --- a/datasets/NPP_MMG_802_2.json +++ b/datasets/NPP_MMG_802_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MMG_802_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one data file (.csv format) that quantifies net primary productivity (NPP) as a function of rainfall in mesic to wet montane rainforests on the island of Maui, Hawaii, U.S.A. The NPP data were collected at six mature forests stands that comprise the Maui Moisture Gradient, a sequence of sites located on Maui where mean annual precipitation ranges from 2,200 mm to 5,050 mm while temperature and all other state factors (parent material, substrate age, organisms, and topography) that control NPP remain relatively constant. ", "links": [ { diff --git a/datasets/NPP_MNS_579_2.json b/datasets/NPP_MNS_579_2.json index 6ea9226987..898127d65c 100644 --- a/datasets/NPP_MNS_579_2.json +++ b/datasets/NPP_MNS_579_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MNS_579_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes six ASCII files (.txt format). Five files contain productivity values for several types of tropical Amazon rainforest near Manaus, Brazil studied between 1963 and 1990, and one file contains monthly and annual climate data for the period 1910-1993.", "links": [ { diff --git a/datasets/NPP_MNT_413_2.json b/datasets/NPP_MNT_413_2.json index c0cb7cb1bb..3520cc6f00 100644 --- a/datasets/NPP_MNT_413_2.json +++ b/datasets/NPP_MNT_413_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MNT_413_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three data files in text format (.txt). One file contains monthly biomass measurements and net primary productivity (NPP) estimates made between June 1984 and December 1994 on an ungrazed saline grassland (MNT1) that was accidently burned in February 1986 at the Colegio de Postgraduados field station site, Montecillo, Mexico. The second file contains a shorter time series of monthly biomass measurements and NPP estimates on an adjacent saline grassland (MNT2) that was burned in May 1989, probably by local farmers following normal burning practices. Both files also contain above-and below-ground dead matter decomposition rates. There are data gaps in both files. The third file contains monthly and annual climate data for the period 1963-1989 from the Chapingo meteorological station located 5 km northeast for the study area.Annual NPP at MNT1 was calculated for 1985-1987 to determine impacts of the unexpected fire. Above-ground NPP (NPP) averaged 669.2 g/m2/yr despite post-fire decline and recovery and variable rainfall over the period. Below-ground NPP (BNPP) was higher, averaging 1,007 g/m2/yr, but with a similar post-fire decline and recovery pattern. Total NPP reflected the yearly variation in ANPP and BNPP, averaging 1,676 g/m2/yr over the 3 year period. Monthly ANPP and BNPP values for MNT2 were lower overall but were not analyzed to determine annual trends.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_MRF_478_2.json b/datasets/NPP_MRF_478_2.json index 751d59728b..2fc218f20b 100644 --- a/datasets/NPP_MRF_478_2.json +++ b/datasets/NPP_MRF_478_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MRF_478_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII files (.txt format), one providing net primary production (NPP) component data for a lower montane rainforest and the other providing climate data. The NPP studies were conducted at Marafunga (6.00 S 145.18 E) in the highlands of Papua New Guinea to the east of Mount Kerigomna, about 25 km west of the town of Goroka. LAI, litterfall, litter standing crop and decomposition, and nutrient content of different vegetation components were measured from November 1970 through December 1971 at four representative forest stands: Ridge Top; Ridge Gap; Valley; and Slope. Forest inventories and field measurements of above- and below-ground biomass were made by destructive harvest at a fifth stand (Ridge Top) during October-December 1970 and April-August 1971. The results of these studies are given for the forest at large. The only component of NPP determined at Marafunga was litterfall (755 g/m2/year). ", "links": [ { diff --git a/datasets/NPP_MSS_572_2.json b/datasets/NPP_MSS_572_2.json index 0ca687711b..3413c1919c 100644 --- a/datasets/NPP_MSS_572_2.json +++ b/datasets/NPP_MSS_572_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MSS_572_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files (.txt format) for study sites in the Mississagi River area of Ontario, Canada (46.35 N -83.38 W elevation 860 m). One file summarizes the results of a series of investigations on the nutrition of jack pine ecosystems, including stand characteristics, above-ground biomass, and where available, litterfall amount and nutrient content of vegetation, litterfall, and soil horizons. Field data were collected in three jack pine (Pinus banksiana) study plots between 1969 and 1973. The stands were 20, 30, and 65 years of age. The second file contains climate data recorded at two weather stations near the Mississagi sites. Precipitation data were obtained from Saulte Sainte Marie, Michigan, USA (46.5 N -84.4 W elevation 218 m) and temperature data were obtained from Saulte Sainte Marie, Ontario, Canada (46.5 N -84.5 W elevation 188 m). Net primary productivity (NPP) was not directly measured, but is estimated based on above-ground tree growth and litterfall. For the different aged stands, above-ground tree growth was estimated at 262 g/m2/yr (0-20 years), 289 g/m2/yr (20-30 years), and 93 g/m2/yr (30-65 years). Annual tree litter production for the 30-year-old stand averaged 372.9 g/m2/yr over the course of 3 years. Understory litterfall production for the 30-year-old stand in one year was 33.1 g/m2/yr. Revision Notes: The NPP and climate data files for Mississagi have been revised to correct previously reported temporal coverage and litterfall data. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_MTD_469_2.json b/datasets/NPP_MTD_469_2.json index 325fb7e777..66e92e876f 100644 --- a/datasets/NPP_MTD_469_2.json +++ b/datasets/NPP_MTD_469_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MTD_469_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains five ASCII files (.txt format). Three files contain productivity data for a mixed prairie at the Matador Field Station, Canada, and two files contain climate data. The 8-km2 Matador Field Station (50.70 N, -107.72 W, Elevation 676 m) is located approximately 47-km north of the city of Swift Current in southern Saskatchewan Province at the northern limit of the \"mixed prairie\" portion of the North American Great Plains. The study area is located on the bed of a former glacial lake. One NPP file contains monthly measurements of above-ground standing live and dead biomass and litter made in \"Section 16\" by harvest methods during the growing season from March or April 1968 to October or November 1972. The second NPP file contains monthly measurements of root biomass made at different depths in \"Section 16\" during the growing season from April 1968 to July 1971. The third NPP file contains monthly above-ground biomass estimates for a secondary study area during the growing season from May 1970 to August 1972. The climate data are reported from two locations, one file from a weather station at Swift Current (1938-1990) and the other from a weather station at the Field Station during the study period (1968-1972). The above- and below-ground net primary productivity (ANPP and BNPP, respectively) were calculated for \"Section 16\" only. ANPP (363 g/m2/yr) was sum of the increase in green biomass over the growing season, plus increase in dead biomass and litter. BNPP (600 g/m2/yr) was the increase in root biomass over the growing season.", "links": [ { diff --git a/datasets/NPP_MULTIBIOME_653_2.json b/datasets/NPP_MULTIBIOME_653_2.json index 8f29803040..35f3314182 100644 --- a/datasets/NPP_MULTIBIOME_653_2.json +++ b/datasets/NPP_MULTIBIOME_653_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_MULTIBIOME_653_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one data file (.csv format) that provides net primary productivity (NPP) estimates for 34 grasslands, 14 tropical forests, and 5 boreal forest sites distributed worldwide. The NPP data were compiled from published literature. In addition to above- and below-ground NPP, and total NPP estimates, the file includes site name and location, biome type, mean annual precipitation, and mean annual temperature, where available. Aboveground net primary production (ANPP), ranged from 35 to 2,320 g/m2/year, belowground net primary production (BNPP) ranged from 20 to 1,832 g/m2/year, and total net primary production (TNPP) ranged from 182 to 3,538 g/m2/year. Revision Notes: This data file has been revised to add a negative sign to south latitude and west longitude decimal degree coordinates, and the compass direction (N, S, E, W) for coordinates has been removed. NPP data for Vindhyan, India; Atherton, Australia; John Crow Ridge, Jamaica; and La Selva, Costa Rica, have been revised to correct previously reported values. Additional data references for Kuusamo, Finland, and La Selva, Costa Rica, have been added. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_Multi-Biome_125_Sites_1352_1.json b/datasets/NPP_Multi-Biome_125_Sites_1352_1.json index 6d7b6a9831..e4dcfaf1cb 100644 --- a/datasets/NPP_Multi-Biome_125_Sites_1352_1.json +++ b/datasets/NPP_Multi-Biome_125_Sites_1352_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_Multi-Biome_125_Sites_1352_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936-2006, contains a single shapefile that provides site-level summary statistics from 125 sites in five biomes: boreal forest, grassland, temperate forest, tropical forest, and tundra. The spatial coverage is global and spans the time period from 1936 through 2006. Study periods, and both spatial and temporal resolution vary by site. Data include georeferenced location, elevation, mean annual precipitation, mean annual minimum and maximum air temperature, dominant soil type, ecoregion type, dominant plant species, general vegetation types, annual mean or peak living above- and below-ground biomass, average annual above- and below-ground Net Primary Productivity (NPP), and reference information. Additionally study sampling period and intervals, plot management, and long-term site management history are also provided.", "links": [ { diff --git a/datasets/NPP_NLS_194_2.json b/datasets/NPP_NLS_194_2.json index 6d94efce9b..1ac7ab046a 100644 --- a/datasets/NPP_NLS_194_2.json +++ b/datasets/NPP_NLS_194_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_NLS_194_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains five data files in text format (.txt). Three files contain biomass dynamics data for a broad-leaved savanna located in the 800-hectare Nylsvley study site 200 km north of Johannesburg, South Africa. One net primary productivity (NPP) file contains monthly above-ground biomass data from harvests made between mid-October 1974 and mid-September 1977. A second NPP file contains three-year mean monthly values for above-ground, standing dead, and litter biomass. The third NPP file contains monthly below-ground living and dead biomass data from excavations made from August 1988 to November 1989. Climate data are provided in the other two files. One file contains air temperature data measured at the study site (1975-1983). The other file contains rainfall data measured at a nearby farmhouse (1917-1995).Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.", "links": [ { diff --git a/datasets/NPP_NRB_151_2.json b/datasets/NPP_NRB_151_2.json index 5ce7db9aac..1c771810b1 100644 --- a/datasets/NPP_NRB_151_2.json +++ b/datasets/NPP_NRB_151_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_NRB_151_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three data files in text format (.txt). One file contains a long time series of monthly biomass measurements and net primary productivity (NPP) estimates made between July 1984 and November 1994 on an ungrazed tropical dry savanna grassland in the Nairobi National Park, Kenya. The second file contains a shorter time series (October 1989-June 1991) of monthly biomass measurements made on an adjacent savanna that had been clipped in September 1989. The third file contains monthly and annual climate data for the study site for the period 1969-1989.Measurements of above-ground live phytomass, standing dead, and litter biomass were made on the 15th day of each month on the ungrazed grassland over the time series (except during 1987 and at a few other times). The time series for below-ground biomass (BNPP) measurements (live and dead) is more discontinuous with large gaps in 1987-1989. NPP estimates are only available for part of 1984 through 1986 on the ungrazed savanna. The above-ground NPP (ANPP) values averaged 1,004 g/m2/yr and BNPP averaged 875 g/m2/yr. The record of above- and below-ground biomass measurements for the clipped savanna is complete except for a few months in 1990. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.", "links": [ { diff --git a/datasets/NPP_ODS_214_2.json b/datasets/NPP_ODS_214_2.json index b30d1e5dce..afa8cbbca8 100644 --- a/datasets/NPP_ODS_214_2.json +++ b/datasets/NPP_ODS_214_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_ODS_214_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three files. The first file provides net primary productivity (NPP) estimates, vegetation characteristics, and summary climate data for 720 globally-distributed terrestrial sites. Each site is geographically referenced (latitude/longitude) and classified according to biome (i.e., cropland, desert, forest, grassland, Mediterranean, pasture, plantation, savanna, tundra, and wetlands), where known. The data were extracted and synthesized from scientific literature dating from 1869 to 1982. The majority of references were published in the 1960s and 1970s. The second file provides a summary of climate, vegetation type, species, and type for the 720 records. The third file provides the bibliography of 858 original-source references of data on NPP from Esser et al. (1997). Literature that is not directly cross-referenced to the NPP data set records is marked with an asterisk (*). Of the 720 unique NPP records, about two-thirds have above-ground net primary production (ANPP) estimates that range between 1 and 8,530 g/m2/year dry matter, one-fourth of the sites have estimates for below-ground NPP that range between 0 and 5,828 g/m2/year, and more than half of the sites have total NPP estimates that range from 3 to 9,320 g/m2/year dry matter. The high-range estimates are lower when doubtful values, wetlands estimates, and estimates for crops/pastures and other managed systems are excluded from calculations. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_OLK_195_2.json b/datasets/NPP_OLK_195_2.json index fffa28f837..47af330cae 100644 --- a/datasets/NPP_OLK_195_2.json +++ b/datasets/NPP_OLK_195_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_OLK_195_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII files (.txt format). One file contains monthly above-ground biomass data (total live biomass plus dead matter) for May 1956 to February 1958 for an annually burned, humid derived savanna in the Olokemeji Forest Reserve, Nigeria (7.42 N, 3.55 E) . This file also contains single measurements of above-ground biomass for years 1963 and 1964, single measurements of above-ground biomass at a nearby area for years 1960 and 1964, a single measurement of peak herbaceous leaf area index (LAI) for 1963, and a single measurement of peak tree/shrub LAI for 1964. Harvest procedures were used to measure biomass. LAI was determined by direct measurements. The second file contains climate data (precipitation amount and maximum/minimum temperature) from a weather station at the study site for the period 1956/01/01 through 1964/12/31.Annual above-ground net primary production (ANPP) estimates presented here are the sum of the increase in above-ground plant matter accumulation (total live biomass plus dead matter). ANPP of the herbaceous layer was estimated in 1957 to be around 680 g/m2/yr based on peak total clipped matter.", "links": [ { diff --git a/datasets/NPP_OSG_211_2.json b/datasets/NPP_OSG_211_2.json index a26d6f59c9..79bf82dec0 100644 --- a/datasets/NPP_OSG_211_2.json +++ b/datasets/NPP_OSG_211_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_OSG_211_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for the Osage tallgrass prairie study site (36.95 N, -96.55 W, Elevation 392 m) in the U.S. Central Lowlands. There is one file for each treatment area (ungrazed and lightly grazed). The third file contains climate data from weather station at Pawhuska, Oklahoma (36.67 N, -96.35 W, Elevation of 255 m) near Osage.Dynamics of above- and below-ground plant biomass were monitored by harvest technique at roughly 2-week intervals during the growing season for the years 1970-1972. Data on above-ground live biomass, standing dead matter, and litter are available for two replications each at recently grazed and an \"ungrazed\" (relatively undisturbed) grassland sites at Osage. Below-ground biomass was sampled at 0-30 cm and 0-90 cm depths. The data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP). Annual above-ground net primary production (ANPP) was estimated conservatively by summing peak biomass of individual species (346 g/m2/yr), and annual below-ground net primary production (BNPP) was estimated as the sum of positive increments in root biomass (including crown biomass) (542 g/m2/yr). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998. ", "links": [ { diff --git a/datasets/NPP_OTR_152_2.json b/datasets/NPP_OTR_152_2.json index ba06e5e4df..2811aa825c 100644 --- a/datasets/NPP_OTR_152_2.json +++ b/datasets/NPP_OTR_152_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_OTR_152_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides three data files in text format (.txt). Two files contain biomass and above-ground net primary production (ANPP) estimates for two upland meadows with contrasting soil types at the Otradnoe research station of the V.L. Komarov Botanical Institute of the Russian Academy of Sciences located on the Karelian peninsula 100-km to the north of St. Petersburg, Russia. The third file contains monthly and annual climate data recorded at the study site for the period 1968-1973. Measurements of above- and below-ground live and dead biomass were made at a sandy meadow (OTRS) from 1969 to 1972 and at a loamy meadow (OTRL) from 1969 to 1973. Additional biomass measurements were made at OTRS in June 1972 and at OTRL in May 1973. Monthly N, P, and S content of above-ground live biomass were measured 1969-1971 at OTRS.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_OTTER_472_2.json b/datasets/NPP_OTTER_472_2.json index 185e7538c8..a7c20fc3d8 100644 --- a/datasets/NPP_OTTER_472_2.json +++ b/datasets/NPP_OTTER_472_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_OTTER_472_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides net primary productivity (NPP) estimates and associated field measurements for six sites located along the 250-km, west-east transect of the Oregon Transect Ecosystem Research Project (OTTER) in the Pacific Northwest. Leaf area indices, biomass, and NPP vary about 10-fold across the OTTER transect. Leaf area index (LAI) ranges from 0.4 m2/m2 at the Juniper/Sisters site to 8.6 m2/m2 at the Scio western Cascade site. Total NPP follows a similar trend with the Juniper/Sisters site having the lowest NPP value (300 g/m2/yr) and the Scio site having the highest (2,250-2,570 g/m2/yr). Total tree biomass across the transect ranges from to 1,080 g/m2 at Juniper/Sisters to 71,080 g/m2 at Cascade Head. Vegetation intercepts 22% to 99.5% of incident photosynthetically active radiation along the transect.There is one data file (.csv format) with this data set. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1999. ", "links": [ { diff --git a/datasets/NPP_PIK_575_2.json b/datasets/NPP_PIK_575_2.json index 45a8a86497..3d0f0ecdd4 100644 --- a/datasets/NPP_PIK_575_2.json +++ b/datasets/NPP_PIK_575_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_PIK_575_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There is one comma-separated (.csv) data file and one text (.txt) file (bibliographic information) with this data set. This data set provides above-ground net primary production (ANPP) and total net primary producitivity (NPP) [expressed in grams of carbon per square meter per year (gC/m2/year)], and the C fraction used to convert dry biomass weight to carbon content, for 127 unique study sites in northern Eurasia. The sites are classified by ecozone (i.e., tundra, forest-tundra, taiga, mixed forest, broadleaf forest, small-leaved secondary forests, forest bogs, meadows, steppe, semi-desert, and polar desert) and plant community (phytocoenosis). Each study location is georeferenced (latitude/longitude) with elevation and zonal/interzonal information. References to original author, year of publication, and table/record in Bazilevich (1993) are also included. The data set also provides a bibliography of 274 original-source references (in Russian) to accompany the 127 data records on NPP from Bazilevich (1993). The data are a subset of data adapted from Bazilevich, N.I. 1993. Biological Productivity of Ecosystems of Northern Eurasia. Nauka Publishers, Moscow. 293 pp. (in Russian). The data set originated from field measurements of primary productivity collected between 1940 and 1988 for most of the terrestrial vegetation types in northern Eurasia. The NPP data collection contains field measurements of biomass, estimated NPP, and climate data for terrestrial grassland, tropical forest, temperate forest, boreal forest, and tundra sites worldwide. Data were compiled from the published literature for intensively studied and well-documented individual field sites and from a number of previously compiled multi-site, multi-biome data sets of georeferenced NPP estimates. The principal compilation effort (Olson et al., 2001) was sponsored by the NASA Terrestrial Ecology Program. For more information, please visit the NPP web site at http://daac.ornl.gov/NPP/npp_home.html.", "links": [ { diff --git a/datasets/NPP_PMP_212_2.json b/datasets/NPP_PMP_212_2.json index 834beb7d34..09662c5e9a 100644 --- a/datasets/NPP_PMP_212_2.json +++ b/datasets/NPP_PMP_212_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_PMP_212_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two ASCII files (.txt format). One file contains monthly productivity data measured on an arid dwarf-shrub steppe in northern Patagonia, Argentina from August 1980 to March 1982. The second file contains climate data recorded at a weather station set up onsite for the duration of the NPP study.Dynamics of above-ground biomass, dead matter, and litter were monitored at Pampa de Leman (-45.43 S, -69.83 W, Elevation 400 m) at monthly or bi-monthly intervals in a 1.5-hectare exclosure area protected from sheep grazing. The vegetative community is dominated by the dwarf shrub Nassauvia glomerulosa, with two grasses (Poa dusenii and Hordeum comosum).Annual above-ground net primary productivity (ANPP) of 78 g/m2/yr was estimated from the sum of the increase in above-ground biomass, dead matter, and litter.", "links": [ { diff --git a/datasets/NPP_PSH_219_2.json b/datasets/NPP_PSH_219_2.json index c1fb981cb9..8d0183b58b 100644 --- a/datasets/NPP_PSH_219_2.json +++ b/datasets/NPP_PSH_219_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_PSH_219_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains four ASCII data files (.txt format), one providing net primary production (NPP) component data and three providing climate data. The NPP studies were conducted in a lowland tropical rainforest in the Pasoh Forest Reserve, Malaysia (2.98 N 102.31 E) from 1971 through 1973. Precipitation and temperature data are available from weather stations located about 25 km from the study sites.", "links": [ { diff --git a/datasets/NPP_REDWOOD_803_2.json b/datasets/NPP_REDWOOD_803_2.json index 46c71951ca..4b6f865f13 100644 --- a/datasets/NPP_REDWOOD_803_2.json +++ b/datasets/NPP_REDWOOD_803_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_REDWOOD_803_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains site characteristics, stand descriptors, and measured and calculated above-ground biomass, above-ground net primary production (ANPP), and woody detritus input data for an old Sequoia sempervirens stand at Bull Creek in Humboldt Redwoods State Park, California. There is one data file (.csv format) with this data set. Productivity of the Sequoia stand was studied via tree re-measurement (1972 and 2001) and allometric relationships. Measurements of tree circumference at 1.7 m above ground were made at the beginning and the end of the study. A 1972 stem map of the stand allowed the investigators to identify and re-measure trees >10 cm in diameter. ANPP was estimated using a range of specific gravities and several allometric relationships for tree volume. Estimation procedures were outlined by Busing and Fujimori (2005). Tree loss to mortality over the study interval was included in the analysis. Estimates of total tree ANPP ranged from 600 to 1,400 g/m2/yr. However, ANPP values in the range of 700-1,000 g/m2/yr were considered to be the most reasonable estimate because of the accuracy of the particular equations, specific gravities, and assumptions used to obtain them (Busing and Fujimori, 2005). Above-ground total tree biomass was extremely high (> 300,000 g/m2). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2005. ", "links": [ { diff --git a/datasets/NPP_RMY_574_2.json b/datasets/NPP_RMY_574_2.json index 928f1c3dd9..06900a8920 100644 --- a/datasets/NPP_RMY_574_2.json +++ b/datasets/NPP_RMY_574_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_RMY_574_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three data files in text format (.txt) for a temperate dry steppe at Rio Mayo, Argentia. One file contains quarterly above-ground biomass data for grasses on the steepe (May 1984-May 1985). The second file contains average annual above-ground primary production (ANPP) data for grasses and shrubs for years 1972-1997 based upon peak above-ground biomass estimates. The third file contains precipitation and maximum/minimum temperature data for the Rio Mayo site for the period 1968 through 1990.Rio Mayo is located in the Patagonia region of Argentina. The vegetation is chiefly composed of grasses and shrubs. Harvest methods were used to estimate grass and shrub production. Between 1972 and 1997, peak annual ANPP of grasses plus shrubs ranged from 21 to 75 g/m2/yr, with an average of about 60 g/m2/yr. Grasses accounted for about two-thirds of the productivity. ANPP was reduced in a drought year, but did not increase in relatively wet years, suggesting that it may not be linearly related to precipitation. ANPP for 1984-1985 was slightly higher (79 g/m2/yr) when a different algorithm was used for estimation. Revision Notes: The original npp data file (rmy_npp.txt) has been split into two files, one file containing seasonal biomass and the other containing annual ANPP estimates. The data file containing annual ANPP estimates has been revised to extend temporage coverage and add additional annual ANPP data. See the Revisions section in this document for details.", "links": [ { diff --git a/datasets/NPP_SCH_573_2.json b/datasets/NPP_SCH_573_2.json index 8e41ba2409..b444208066 100644 --- a/datasets/NPP_SCH_573_2.json +++ b/datasets/NPP_SCH_573_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SCH_573_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files (.txt format). One file provides above- and below-ground biomass, soil, and nutrient data for a mature boreal ecosystem (subarctic Picea mariana/lichen woodland) near Schefferville, Canada (54.72 N, -67.70) for the 1974 growing season. The second data file contains climate data (precipitation amount and maximum/minimum temperature) from a weather station located 22 km northeast of the study site for the 1948-1990 period. The black spruce/lichen woodland is a vegetation type found in the transitional zone between boreal forest and tundra on well-drained, nutrient-poor podzolic soils. These spruce/lichen woodlands are generally not subject to attack by herbivory, but natural fires are common. The study forest was estimated to be 110 years old, based on annual tree ring data which showed the number of years since it was last burned. Biomass estimates were determined by harvesting trees, shrubs, and ground cover in the 0.2 ha study plot. To confirm the \"typical\" nature of the site, species composition and density were evaluated for the principal plot and compared to that of fifteen other plots. Organic and mineral soils were also extracted. The plant and soil samples were evaluated for nutrient and mineral content. Living tree, shrub, and lichen components contributed a total biomass of 2,636, 833, and 939 g/m2, respectively. NPP was estimated by the Terrestrial Ecosystem Model (TEM) to be about 340 g/m2/yr. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/NPP_SCR_479_2.json b/datasets/NPP_SCR_479_2.json index a4c4ef0af5..c779b69c6f 100644 --- a/datasets/NPP_SCR_479_2.json +++ b/datasets/NPP_SCR_479_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SCR_479_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes five ASCII files (.txt format). Three files contain above- and below-ground biomass and net primary productivity (NPP) data, one file for each tropical forest study site near San Carlos de Rio Negro, Venezuela. The study sites are located along an ecosystem gradient from riverine to lateritic hill: Tall Amazon Caatinga forest on coarse sandy spodosols close to river level; Bana vegetation on sandy soils less prone to flooding; and Tierra Firme mixed forest on clay oxisols of higher ground. Bioelement concentrations are also provided. The other two files contain climate data from a weather station in San Carlos village.", "links": [ { diff --git a/datasets/NPP_SES_480_2.json b/datasets/NPP_SES_480_2.json index 8e0f1ac40c..2b140ed7fa 100644 --- a/datasets/NPP_SES_480_2.json +++ b/datasets/NPP_SES_480_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SES_480_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ASCII data files (.txt format), one for net primary production (NPP) component data and two for climate data. The NPP studies were conducted in a tropical montane forest in the Sierra de Merida at San Eusebio (8.62 N 71.35 W) in northwestern Venezuela. The forest is mostly primary in character, with some selective logging having taken place in the past. Biomass, litterfall, and nutrient content of above- and below-ground vegetation and soil were determined in 1973-1974.", "links": [ { diff --git a/datasets/NPP_SHR_153_2.json b/datasets/NPP_SHR_153_2.json index 0c0b7b89b6..4805e99ec6 100644 --- a/datasets/NPP_SHR_153_2.json +++ b/datasets/NPP_SHR_153_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SHR_153_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP calculations made between 1977 and 1980 on a dry continental steppe at Shortandy Biological Station in Kazakhstan. The second file contains monthly and annual climate data for the study site for the period 1976-1980.Measurements of above- and below-ground live and dead matter were made at biweekly to monthly intervals during the growing season at Shortandy from 1977 to 1980. Cumulative ANPP estimates are calculated from these measurements. The study site is one of eight major grassland types of Eurasia which encompass an extremely wide climatic gradient in the direction of increasing maximum summer temperatures and continentality and decreasing precipitation in a north-west to the south-east band of steppes in the European and Asian parts of the former USSR (Commonwealth of Independent States). Shortandy represents the semiarid continental grass-forb steppe found on the southern chernozem soils of northern Kazakhstan. The site had annual mean maximum/minimum temperatures of 27.7/-24.6 degrees C and annual mean precipitation of 349.8 mm for the period 1976-1980. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_SLV_218_2.json b/datasets/NPP_SLV_218_2.json index 9182f07c10..24f84f044e 100644 --- a/datasets/NPP_SLV_218_2.json +++ b/datasets/NPP_SLV_218_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SLV_218_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are two data files with this data set in (.txt) format. The files contain net primary productivity (NPP) data and climate data for a mature tropical lowland rainforest at the La Selva Biological Station, Costa Rica. The La Selva forest reserve (10.43 N, 83.98 W) covers over 1,500 ha, of which 53% is primary forest and the rest is in various types of secondary forest and abandoned land.Above- and below-ground biomass, litterfall, root production, and nutrient content of different vegetation components and soils were determined in different areas of the biological station on different occasions between 1975 and 1994. Work has continued to the present day. Precipitation (1984-1997) and maximum/minimum temperature (1992-1997) were measured at the forest study site.NPP has not been completely estimated although detailed data on forest dynamics are available from a variety of published and unpublished sources. A crude minimum estimate of NPP for La Selva was obtained by summing the estimates of litterfall [850 g/m2/year predicted for average annual temperature and precipitation conditions at La Selva by the equation of Brown and Lugo (1982)] and unpublished data on root production (550-1,250 g/m2/year), giving a NPP range of about 1,400-2,100 g/m2/year.Revision Notes: The NPP data file has been revised to add additional root biomass estimates, correct temporal coverage of data series, correct parameter label for potassium concentration in leaf litterfall, and add additional References/Comments. Please see the Data Set Revisions section of this document for detailed information.", "links": [ { diff --git a/datasets/NPP_SNF_190_2.json b/datasets/NPP_SNF_190_2.json index e48ff7c23a..124da26e03 100644 --- a/datasets/NPP_SNF_190_2.json +++ b/datasets/NPP_SNF_190_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SNF_190_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files (.txt format). One file provides ground-based biophysical measurements and above-ground net primary productivity (ANPP) estimates for 31 black spruce (Picea mariana) and 30 quaking aspen (Populus tremuloides) stands in Superior National Forest (SNF) in northeastern Minnesota, U.S.A. (-92 W 48 N). The measurements were obtained during a 1983-1984 intensive field campaign. Non-destructive measurements were made in over 100 forest plots covering a 50 x 50 km area. Trees sacrificed for biomass and annual increment measurements were taken outside the plots. The second file provides climate data from nearby weather stations for the period 1976-1986. The data set provides stand structural measurements (diameter at breast height, tree height, crown depth, and stem density), above-ground biomass, leaf area index, bark area index, and ANPP estimates. ANPP data are based on a combination of allometric relationships and annual tree-ring (radial) increments for the 5-year period 1979-1983. In the spruce stands, above-ground biomass ranged from 700-15,100 g/m2, LAI varied between 0.5-4.3, and ANPP ranged from 39-572 g/m2/yr. In comparison, above-ground biomass among aspen stands ranged from 600-22,000 g/m2, LAI varied between 1.3-4.0, and ANPP ranged from 213-1,199 g/m2/yr. The purpose of the SNF campaign was to investigate the ability of remote sensing to provide estimates of ecosystem biophysical properties. In addition to the results presented herein, satellite, aircraft, and helicopter observations and other ground measurements for the study sites are available from the ORNL DAAC Superior National Forest (SNF) Project web site [http://daac.ornl.gov/SNF/snf.shtml]. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1997. ", "links": [ { diff --git a/datasets/NPP_SSP_467_2.json b/datasets/NPP_SSP_467_2.json index 615ca12b43..f1f92cc575 100644 --- a/datasets/NPP_SSP_467_2.json +++ b/datasets/NPP_SSP_467_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_SSP_467_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two files (comma-separated-value format). One file provides components of net primary productivity, standing biomass, age and stand structure, and litterfall data for 11 stands of Scots pine (Pinus sylvestris) in the Tomsk Region of Russia (approx. 58 N 83 E). The second file contains data for the same types of variables for three stands of Scots pine in the Irkutsk Region of Siberia (approx. 53 N 103 E). Field measurements were made in 0.3-0.4 ha forest plots between 1968 and 1974. The forest plots range in age from 25 to 122 years old. Tree biomass was determined from volume and density measurements and selective harvest. Understory and ground cover was harvested in 0.25m2 plots. Root mass has determined from harvested trees and soil monoliths. Wood increment was measured from annual rings. Root production was based on species-specific turnover rates. Leaf litterfall was measured in 0.5-1.0 m2 traps, and branch litterfall was estimated from 4.0 m2 plots. Revision Notes: The NPP data file has been split into two files, one for the Tomask forests and one for the Irkutsk forests. The data files have been revised to rearrange columns, add a total ANPP column, and correct previously reported data, where needed. Please see the Data Set Revisions section of this document for detailed information. The Net Primary Productivity (NPP) data collection contains field measurements of biomass, estimated NPP, and climate data for terrestrial grassland, tropical forest, temperate forest, boreal forest, and tundra sites worldwide. Data were compiled from the published literature for intensively studied and well-documented individual field sites and from a number of previously compiled multi-site, multi-biome data sets of georeferenced NPP estimates. The principal compilation effort (Olson et al., 2001) was sponsored by the NASA Terrestrial Ecology Program. For more information, please visit the NPP web site at http://daac.ornl.gov/NPP/npp_home.html.", "links": [ { diff --git a/datasets/NPP_TEM_471_2.json b/datasets/NPP_TEM_471_2.json index ca520fdb9c..772c19b5e3 100644 --- a/datasets/NPP_TEM_471_2.json +++ b/datasets/NPP_TEM_471_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TEM_471_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one data file (.csv format) that is known as the Terrestrial Ecosystem Model (TEM) data set. The data provide pool sizes and fluxes of carbon (C) and nitrogen (N) for 16 globally distributed field sites that represent a wide range of terrestrial biomes, tundra to tropical forest, but exclude wetlands. The net primary productivity (NPP) data were extracted from the literature. They were not previously widely available to the ecosystem modeling community in electronic form until this data set and additional NPP data sets were published by the ORNL DAAC. The data were used to calibrate the ecosystem process-based TEM. The TEM was developed by staff at the Ecosystem Center, Marine Biological Laboratory, Woods Hole, Massachusetts, USA, to estimate the spatial and temporal distribution of major carbon (C) and nitrogen (N) fluxes and pool sizes at continental to global scales (resolution: 0.5 degrees latitude x 0.5 degrees longitude). Eight of the TEM calibration sites are also included in the ORNL DAAC NPP data collection as individual site NPP data sets. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1999. ", "links": [ { diff --git a/datasets/NPP_TLK_581_2.json b/datasets/NPP_TLK_581_2.json index 6da6c9058f..3119116b6b 100644 --- a/datasets/NPP_TLK_581_2.json +++ b/datasets/NPP_TLK_581_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TLK_581_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one text file (.csv format) that provides productivity data for four contrasting tundra vegetation types studied during 1982 near Toolik Lake in the northern foothills of the Brooks Range on the North Slope of Alaska (68.63 N 149.72 W). The vegetation types include a \"tussock\" tundra containing graminoids, deciduous shrubs, and evergreen shrubs; a \"shrub\" tundra dominated by deciduous willow shrubs; a \"heath\" tundra of evergreen shrubs; and a \"wet\" tundra site containing rhizomatous graminoids. The study sites were selected to represent extreme examples of the wide range of local plant growth forms in the region.Living above- and below-ground biomass were sampled on three occasions during the growing season using randomly located quadrats ranging in size from 10 cm x 20 cm to 50 cm x 50 cm. Production and biomass of most tissues were determined by harvest methods, with additional separate determinations of stem secondary growth and below-ground rhizome growth as components of net primary production (NPP). Elemental content of above-ground samples was analyzed. Production, biomass, and elemental content of roots were not determined. Leaf area index (LAI) was measured using a LI-COR leaf area meter.NPP, excluding fine roots, was estimated at 37, 99, 278, and 475 g/m2/year, at the heath, wet, tussock, and shrub sites, respectively. Inclusion of estimated production by roots increased these NPP figures to 140, 200, 430, and 780 g/m2/year, respectively. Leaf area index was similarly ranked.", "links": [ { diff --git a/datasets/NPP_TLL_196_2.json b/datasets/NPP_TLL_196_2.json index 1509b48816..4c479689d1 100644 --- a/datasets/NPP_TLL_196_2.json +++ b/datasets/NPP_TLL_196_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TLL_196_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains three ACSII files (.txt format). Two files contain above-ground biomass data for two ungrazed seashore meadow plots dominated by the saltmeadow rush Juncus gerardii at Tullgarnsnaset, near Stockholm, Sweden (approximately 59.20 N, 17.50 E). There is one file for each plot. The third data file contains monthly and annual climate data from weather station near Stockholm (59.4 N, 18.0 E) for the period 1951-1990. Measurements of above-ground live biomass and total dead matter were made approximately monthly from April 1968 to April 1969. Below-ground biomass was also measured, but the data are not reported in this data set. Annual above-ground net primary production (ANPP) was estimated by several calculation methods, including peak total live plus current dead matter; sum of species maxima (biomass + dead material); single square clippings; and variations of these equations. The rate of disappearance of dead material and mortality were also determined. Mean ANPP estimates ranged from 324 g/m2/yr (max live + dead) to 430 g/m2/yr (taken as the mean of the two sites accounting for disappearance of dead matter). ", "links": [ { diff --git a/datasets/NPP_TMG_470_2.json b/datasets/NPP_TMG_470_2.json index e3c4dc7c75..40cc7a43ed 100644 --- a/datasets/NPP_TMG_470_2.json +++ b/datasets/NPP_TMG_470_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TMG_470_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains four ASCII files (.txt format). Three files contain monthly above- and below-ground biomass data, one data file for each cold meadow steppe studied from 1981 to 1990 at Tumugi, Xingan League, in eastern Inner Mongolia, China (approximately 46.10 N 123.00 E Elevation 191 m). The fourth file contains climate data recorded at a weather station located in the study area for the length of the study.The Tumugi study sites consist of three different natural steppes dominated by Filifolium sibiricum, Stipa baicalensis, and Leymus chinense, respectively. Measurements of above- and below-ground live biomass were made monthly throughout the growing season (March to November) by clipping 1.0 m2 quadrats and sampling 1.0 m2 soil pits to a depth of 1.0 m, respectively. The study areas had been protected from grazing since 1976.Above-ground net primary productivity (ANPP) was estimated at 155 g/m2/year (average for the three steppes, based on peak above-ground living biomass). Peak live below-ground biomass was used to estimate below-ground net primary productivity (BNPP): 968 g/m2/year for the F. sibiricum steppe; 983 g/m2/year the Stipa baicalensis steppe; and 1,022 g/m2/year for the L. chinense steppe. Above- and below-ground biomass data were compared with simulation results from the CENTURY model. Simulated data agreed reasonably well with the observed data (within +25%). ", "links": [ { diff --git a/datasets/NPP_TMN_154_2.json b/datasets/NPP_TMN_154_2.json index 7df96c5ab7..288d03b308 100644 --- a/datasets/NPP_TMN_154_2.json +++ b/datasets/NPP_TMN_154_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TMN_154_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains tri-monthly measurements of above-ground biomass made during the growing season between July 1982 and August 1990 on a dry, cold Eurasian steppe dominated by Stipa grandis at the Tumentsogt Research Station in Mongolia. The second file contains monthly and annual climate data recorded at the study site from 1963 through 1983. Mongolian steppes occupy a major part of eastern Mongolia and northern China and are characterized climatically by low mean annual rainfall and temperature, with a highly seasonal pattern in both. The beginning of spring rainfall and warming are strongly correlated, and the onset of the growing season rainfall triggers the green-up in the region. Land use is dominated by grazing, historically by nomadic pastoralists and more recently for cooperative livestock production. Privatization of grazing land and cropland conversions have been increasing since 1990. Ecosystem degradation such as deterioration of vegetation (e.g., vegetation removal and replacement) and soil (e.g., erosion) is becoming widespread. Peak above-ground biomass at Tumentsogt occurs during a short rainy season (June-August). The amount of biomass fluctuates from year-to-year coherently with rainfall variation. Above-ground net primaryp roductivity (ANPP) estimates are relatively low in comparison to other temperate grasslands, ranging from 72 to 160 g/m2/yr. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.", "links": [ { diff --git a/datasets/NPP_TROPICAL_616_2.json b/datasets/NPP_TROPICAL_616_2.json index d731eac398..6b139475f4 100644 --- a/datasets/NPP_TROPICAL_616_2.json +++ b/datasets/NPP_TROPICAL_616_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TROPICAL_616_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains documented field measurements of NPP components for 39 old-growth tropical forests distributed worldwide between latitudes 23.58 N and 23.58 S. The data were compiled from published literature and other extant sources. The data are georeferenced to each intensive study site and include above- and below-ground biomass, fine root biomass, litterfall, branchfall, above-ground biomass increment, and herbivory estimates, where available. Other site characteristics are included, such as elevation, forest type and age, soil type, and climate summaries. Key references are provided. Estimates of above-ground net primary productivity (ANPP) for the 39 sites were made based on the sum of (1) measured or estimated above-ground biomass increment, (2) measured or estimated fine litterfall accumulation, (3) estimated losses to consumers, and (4) estimated biogenic volatile organic compound emissions. Estimates of below-ground NPP were made based on professional judgment that below-ground production is 0.2 x ANPP (lower bounds) or 1.2 x ANPP (upper bounds). TNPP was calculated as the range between the low and high values of ANPP + BNPP. Average BNPP and TNPP estimates were also calculated. Across the broad spectrum of the tropical forests studied (dry to wet, lowland to montane, nutrient-rich to nutrient-poor soils), the estimates of total NPP range from 3.4 to 34.4 Mg/ha/yr (lower bounds) and from 6.2 to 63.0 Mg/ha/yr (upper bounds). There is one comma-separated data file (.csv) with this data set. The ORNL DAAC [http://daac.ornl.gov] NPP Collection for tropical forests contains additional biomass and NPP component estimates and climate data for 28 of the intensive study sites in this data set. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/NPP_TVA_155_2.json b/datasets/NPP_TVA_155_2.json index d645e8879e..04f821e346 100644 --- a/datasets/NPP_TVA_155_2.json +++ b/datasets/NPP_TVA_155_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TVA_155_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP estimates made between 1978 and 1985 at an ultracontinental steppe at the Tuva Research Station in Russia. The second file contains monthly and annual climate data for the study site for 1976-1985.Monthly measurements of above-ground live phytomass, standing dead, and litter biomass were made during each growing season (May-August) of the eight-year study period. Harvests of below-ground biomass were made at the end of the growing season in some years. A year-end measurement of above-ground biomass (particularly standing dead and litter) was also made in 1980. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_TWM_213_2.json b/datasets/NPP_TWM_213_2.json index 490881578e..26c6523fd1 100644 --- a/datasets/NPP_TWM_213_2.json +++ b/datasets/NPP_TWM_213_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_TWM_213_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides seven data files in text format (.txt). The files provide biomass estimates, soil carbon (C), nitrogen (N), and phosphorus (P) measurements made at an artificially-established grassland savanna study site in Towoomba, South Africa. The study site was part of a long-term experiment to test the effect of fertilizer application. Biomass data are available for the years 1950-1981 (data are not available for 1976 or 1977); soil C, N, and P data are available for the years 1949, 1962, 1980, and 1990. The 1949 estimates were inferred in 1990 from undisturbed savanna adjacent to the experiment. The savanna-fertilizer experiment was several hectares in extent, with five levels of nitrogen and three levels of phosphorus laid out in a randomized block design on an area from which all trees were removed. The ammonium sulphate and superphosphate fertilizers were added during November (50% of total), January (25%) and February (25%). The response to N fertilizer saturated at higher levels, so data from only six (3 x 2) of the 15 possible treatment combinations are provided.Above-ground biomass was sampled by mowing to 5-cm height. It was assumed that 50 g/m2 was left in the field, plus an additional 5% of the mowed dry weight. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published. ", "links": [ { diff --git a/datasets/NPP_VAST_576_2.json b/datasets/NPP_VAST_576_2.json index 742b167325..0521a40934 100644 --- a/datasets/NPP_VAST_576_2.json +++ b/datasets/NPP_VAST_576_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_VAST_576_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains one data file in comma-delimited (.csv format) that provides observations from Australia for use in parameterizing the Vegetation and Soil-carbon Transfer (VAST) Model (version 1.1). The observations include net primary productivity (NPP), biomass, litter mass, surface horizon soil carbon concentration (i.e., mass fraction) and bulk density, and soil carbon and bulk density measurements at various depths. The data consist of 33 estimates of above-ground NPP based on cut grass swards and visual assessment of growth, 150 measurements of litterfall (leaf and fine twig), 76 measurements of above-ground biomass (phytomass), 91 determinations of fine litter mass, 341 measurements of soil carbon concentration in surface layers (to 15 cm depth), and 50 determinations of soil bulk density (to 15 cm depth). All these data were derived from 174 original literature references describing study sites throughout Australia. VAST is a conceptual carbon (C) cycle model that depicts large scale dynamics of terrestrial C pools and the net exchange of C between the land surface and the atmosphere at a resolution of 0.05 degrees. The model consists of 10 C pools comprising two above-ground biomass pools, two litter pools, and three pools each of below-ground biomass and soil C. Below-ground pools are distributed among three soil layers (0-20, 20-50, and 50-100 cm). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/NPP_VND_197_2.json b/datasets/NPP_VND_197_2.json index 3c7966fa2f..28549b8fa9 100644 --- a/datasets/NPP_VND_197_2.json +++ b/datasets/NPP_VND_197_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_VND_197_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains four data files in text format (.txt). Three files provide above- and below-ground productivity data for three derived savannas on the Vindhyan plateau in northern India from 1986 to 1989, one file for each of three treatments. Each study site (Ranitali, Hathinala, Telburva) contains three treatment areas: ungrazed; grazed annually for 30-40 years; and grazed but temporarily fenced for 2-6 years prior to the study. The fourth file provides climate data from a weather station at Daltonganj, India, for the period 1893-1990. Monthly dynamics of above- and below-ground biomass were measured by harvest methods in each treatment area at each site for two annual cycles (1986/1987 and 1987/1988). Additional above-ground peak biomass data (live shoot + dead shoot) for October 1988 from ungrazed and grazed plots represent the 1988/1989 annual cycle.Annual above-ground net primary production (ANPP) was estimated using trough-peak analysis of increments in live biomass, standing dead matter, and litter. Annual below-ground production (BNPP) was estimated from biomass increments combined with root in-growth studies. Mean ANPP for the ungrazed treatment areas on the three sites ranged from 377 to 664 g/m2/yr over the 1986 to 1989 period. Mean BNPP in the same areas was estimated at 510 and 727 g/m2/yr for 1986/1987 and 1987/1988, respectively. Mean total ungrazed NPP (ANPP + BNPP) was estimated at 1,082 and 1,391 g/m2/yr for 1986/1987 and 1987/1988, respectively.Revision Notes: The NPP data for the temporarily fenced grassland sites for the 1987/1988 annual cycle have been revised to correct previously reported BNPP estimates. Please see the Data Set Revisions section of this document for detailed information. ", "links": [ { diff --git a/datasets/NPP_WBW_819_2.json b/datasets/NPP_WBW_819_2.json index bb2eda8fe5..6e44c0c9cb 100644 --- a/datasets/NPP_WBW_819_2.json +++ b/datasets/NPP_WBW_819_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_WBW_819_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains five data files, in comma-separated format (.csv), derived from the Walker Branch Watershed (WBW) vegetation inventory in eastern Tennessee. Field studies of permanent vegetation plots were conducted using one sampling design over a 40-year period (1967 to 2006). The data set contains long-term measurements of diameter at breast height (DBH) determined on stratified randomly-located inventory plots within the 4 different vegetation types (oak-hickory, pine-oak-hickory, pine, and mesophytic hardwoods) located in the WBW in 1967. The WBW plot-level vegetation DBH data are provided in four files. One file contains the complete set of inventory records (139,806 observations). To accommodate spreadsheet use, the complete inventory is split into three files, one containing 52,110 observations and the other two containing 48,231 and 39,465 observations, respectively. The fifth file contains the WBW vegetation species inventory with species names, the numeric species code for each species, a species group designation, the scientific name for each species, and the literature-derived ratio of g lignin/g N for leaves of each species. NPP values have been reported for various forest stands at different locations within the WBW by Olson et al. (2012a, b; DeAngelis et al. (1997); and Esser (1998). Total NPP values range from 380 gC/m2/yr for forest stands dominated by yellow poplar to 790 gC/m2/yr for forest stands dominated by oaks. Revision Notes: This updated vegetation inventory data set includes results of the 2006 survey and updates to previous results based on the latest survey. The 1967-2006 data set completely supersedes the 1967-1997 data set. If you downloaded the 1967-1997 data set before September 3, 2013, you should download the 1967-2006 version at your earliest convenience.", "links": [ { diff --git a/datasets/NPP_WOODY_655_2.json b/datasets/NPP_WOODY_655_2.json index 288bbf2355..3690ae95e4 100644 --- a/datasets/NPP_WOODY_655_2.json +++ b/datasets/NPP_WOODY_655_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_WOODY_655_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are two data files (tab-delimited .txt format) with this data set that provide estimates of above-ground biomass per county; county-level annual above-ground biomass growth, removals (harvest), and mortality of woody biomass per hectare; county-level total annual above-ground woody biomass production per hectare; forest area per county; mortality (%) in forests within each county; and total annual production and mortality per county. The data provide annual mean above-ground wood increments for temperate forests in 1,956 counties of the 28 eastern US states. The data are derived from forest inventory data from 1960s to 1990s that were collected from an extensive network of permanent inventory plots as part of the US Department of Agriculture Forest Service Forest Inventory and Analysis (FIA). Based on the analysis of the above-ground production data (Brown and Schroeder, 1999), above-ground production of woody biomass (APWB) for hardwood forests ranged from 0.6 to 28 Mg/ha/yr and averaged 5.2 Mg/ha/yr. For softwood forests, APWB ranged from 0.2 to 31 Mg/ha/yr and averaged 4.9 Mg/ha/yr. APWB was generally highest in southeastern and southern counties, mostly along an arc from southern Virginia to Louisiana and eastern Texas. No clear spatial pattern of mortality of woody biomass (MWB) existed, except for a distinct area of high mortality in South Carolina as a result of Hurricane Hugo in 1989. For hardwood forests, MWB ranged from 0 to 15 Mg/ha/yr and averaged 1.1 Mg/ha/yr. The average MWB for softwood forests was 0.6 Mg/ha/yr with a range of 0 to 10 Mg/ha/yr. The rate of above-ground MWB averaged <1%/yr for both hardwood and softwood forests. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2003. ", "links": [ { diff --git a/datasets/NPP_XLN_156_2.json b/datasets/NPP_XLN_156_2.json index 47f80fd61d..c93a0bb8d4 100644 --- a/datasets/NPP_XLN_156_2.json +++ b/datasets/NPP_XLN_156_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_XLN_156_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two data files in text format (.txt). One file contains bi-weekly measurements of above-ground live biomass recorded during the growing season (early May to early October) from 1980 through 1989 on a cold desert steppe at the Inner Mongolia Grassland Research Station of the Chinese Academy of Sciences within the Xilingol Biosphere Reserve. The second file contains monthly and annual climate data recorded at the study site from 1978 through 1989. The study site contains grassland steppes of Leymus chinense and Stipa grandis which are the dominant vegetation types, respectively, in the Eastern Eurasian steppe zone (semi-arid and sub-humid) and the middle Eurasian steppe zone (semi-arid). Both steppes provide good livestock forage and are used mainly as natural grazing lands. Above-ground net primary production (ANPP) was estimated by summing peak live biomass of each of 5 species categories. Peak live biomass of L. chinense steppe occurred between late July and late August and averaged 182.68 g/m2 between 1980 and 1988 while that of S. grandis steppe occurred in mid August to early September and averaged 144.43 g/m2 over the same time period. Mean ANPP for L. chinense steppe during 1980-1989 was 248.63 g/m2/yr. ANPP for S. grandis steppe was not calculated. Data are only provided for the Leymus chinense steppe in this data set.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. ", "links": [ { diff --git a/datasets/NPP_surfaces_750_1.json b/datasets/NPP_surfaces_750_1.json index 40c8ea9887..52a4b48515 100644 --- a/datasets/NPP_surfaces_750_1.json +++ b/datasets/NPP_surfaces_750_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPP_surfaces_750_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.", "links": [ { diff --git a/datasets/NPS_YNP_30M_DEM.json b/datasets/NPS_YNP_30M_DEM.json index 8ca2968b6b..6e3e91ee32 100644 --- a/datasets/NPS_YNP_30M_DEM.json +++ b/datasets/NPS_YNP_30M_DEM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPS_YNP_30M_DEM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Elevation Models are useful for deriving elevations;\nmodeling 3D surfaces; creating derived products such as\nslope, aspect, and relief layers; creating watersheds and\nconducting watershed analyses; and conducting other types of\nterrain analyses.\n\nDigital Elevation Model (DEM) is the terminology adopted by\nthe USGS to describe terrain elevation data sets in a digital\nraster form. The 7.5-minute DEM (30- by 30-m cell size, in a\nUniversal Transverse Mercator (UTM) projection) provides\ncoverage in 7.5- by 7.5-minute blocks. Each product provides\nthe same coverage as a standard USGS 7.5-minute quadrangle\nwithout over edge. The DEM data are stored as a series of\nprofiles in which the spacing of the elevations along and\nbetween each profile is in regular whole number intervals.The\nYellowstone National Park 30 m DEM was compiled from a\ncombination of of Level I and II USGS 30 m DEMs, elevation\nvalues range from 1528 to 4186, describing 1528 - 4186\nmeters.\n\nThe parkwide DEM was compiled by Lisa Landenburger,\nGeographic Information and Analysis Center (GIAC), Montana\nState University, Bozeman, MT. This data set is unpublished.\nmaterial.\n\nThis summary was abstracted from the FGDC metadata file.", "links": [ { diff --git a/datasets/NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002.json b/datasets/NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002.json index 676b906d0a..b9a37aaafc 100644 --- a/datasets/NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002.json +++ b/datasets/NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alien Plants Ranking System (APRS) is a computer-implemented system to help\nland managers make difficult decisions concerning invasive nonnative plants.\nThe management of invasive plants is difficult, expensive, and requires a\nlong-term commitment. Therefore, land managers must focus their limited\nresources, targeting the species that cause major impacts or threats to\nresources within their management, or the species that impede attainment of\nmanagement goals. APRS provides an analytical tool to separate the innocuous\nspecies from the invasive ones (typically around 10% of the nonnative species).\nAPRS not only helps identify those species that currently impact a site, but\nalso those that have a high potential do so in the future. Finally, the system\naddresses the feasibility of control of each species, enabling the manager to\nweigh the costs of control against the level of impact. This system has been\ndeveloped and tested primarily in grassland and prairie parks in the central\nUnited States.", "links": [ { diff --git a/datasets/NPWRC_effectsoffireonbirdpops.json b/datasets/NPWRC_effectsoffireonbirdpops.json index d153eb3c2d..90fa551d7e 100644 --- a/datasets/NPWRC_effectsoffireonbirdpops.json +++ b/datasets/NPWRC_effectsoffireonbirdpops.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NPWRC_effectsoffireonbirdpops", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The mixed-grass prairie is one of the largest ecosystems in North America,\noriginally covering about 69 million hectares (Bragg and Steuter 1995).\nAlthough much of the natural vegetation has been replaced by cropland and other\nuses (Samson and Knopf 1994, Bragg and Steuter 1995), significant areas have\nbeen preserved in national wildlife refuges, waterfowl production areas, state\ngame management areas, and nature preserves. Mixed-grass prairie evolved with\nfire (Bragg 1995), and fire is frequently used as a management tool for prairie\n(Berkey et al. 1993). \n\nMuch of the mixed-grass prairie that has been protected is managed to enhance\nthe reproductive success of waterfowl and other gamebirds, but nongame birds\nnow are receiving increasing emphasis. Despite the importance of the area to\nnumerous species of birds and the aggressive management applied to many sites,\nrelatively little is known about the effects of fire on the suitability of\nmixed-grass prairie for breeding birds. Several studies have examined effects\nof fire on breeding birds in the tallgrass prairie (e.g., Tester and Marshall\n1961, Eddleman 1974, Halvorsen and Anderson 1983, Westenmeier and Buhnerkempe\n1983, Zimmerman 1992, Herkert 1994), in western sagebrush grasslands (Peterson\nand Best 1987), and in shrubsteppe (Bock and Bock 1987). \n\nStudies of fire effects in the mixed-grass prairie are limited. Huber and\nSteuter (1984) examined the effects on birds during the breeding season\nfollowing an early-May prescribed burn on a 122-ha site in South Dakota. They\ncontrasted the bird populations on that site to those on a nearby 462-ha\nunburned site that had been lightly grazed by bison (Bison bison). Pylypec\n(1991) monitored breeding bird populations occurring in fescue prairies of\nCanada on a single 12.9-ha burned area and on an adjacent 5.6-ha unburned\nfescue prairie for three years after a prescribed burn.", "links": [ { diff --git a/datasets/NRMSC_carnivorerecolonisation.json b/datasets/NRMSC_carnivorerecolonisation.json index 66576a3676..d5db074119 100644 --- a/datasets/NRMSC_carnivorerecolonisation.json +++ b/datasets/NRMSC_carnivorerecolonisation.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRMSC_carnivorerecolonisation", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Most large native carnivores have experienced range contractions due to\nconflicts with humans, although neither rates of spatial collapse nor expansion\nhave been well characterized. In North America, the grizzly bear (Ursus\narctos) once ranged from Mexico northward to Alaska, however its range in the\ncontinental United States has been reduced by 95-98%. Under the U.S.\nEndangered Species Act, the Yellowstone grizzly bear population has\nre-colonized habitats outside Yellowstone National Park. We analyzed\nhistorical and current records, including data on radio-collared bears, (i) to\nevaluate changes in grizzly bear distribution in the southern Greater\nYellowstone Ecosystem over a 100-year period, (ii) to utilise historical rates\nof recolonization to project future expansion trends and (iii) to evaluate the\nreality of future expansion based on human limitations and land use. Analysis\nof distribution in 20-year increments reflects range reduction from south to\nnorth (1900-1940) and expansion to the south (1940-2000). Expansion was\nexponential and the area occupied by grizzly bears doubled approximately every\n20 years. A complementary analysis of bear occurrence in Grand Teton National\nPark also suggests an unprecedented period of rapid expansion during the last\n20-30 years. The grizzly bear population currently has re-occupied about 50%\nof the southern GYE. Based on assumptions of continued protection and\necological stasis, our model suggests total occupancy in 25 years. \nAlternatively, extrapolation of linear expansion rates from the period prior to\nprotection suggests total occupancy could take > 100 years. Analyses of\nhistorical trends can be useful as a restoration tool because they enable a\nframework and timeline to be constructed to pre-emptively address the social\nchallenges affecting future carnivore recovery.\n\nOne of the purposes of the dataset is to predict when grizzly bear occupation\nof Southern Yellowstone Ecosystem will be total.\n\nWe focused on a 24,000 square kilometer mosaic of mostly public land that is\nmanaged by various federal and state agencies. Our analysis of changes in\ngrizzly bear distribution during 1900-2000 was divided into 20-year periods. \nFor each, we used various data sources for grizzly bear occurrence to create\ndigital maps of bear distribution using ArcView GIS 32. (ESRI, Redlands, CA) \nWe digitized reports, interviews, conflicts, mortalities and observations as\npoints. We created a polygon for the 1920 source data, a hand-drawn\ndistribution map by Merriam (1922). More methodology given in Pyare, 2004\npaper.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ FAPAR_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_ FAPAR_MODIS_0.05D_11.json index 67eb405640..ab60cf47b0 100644 --- a/datasets/NRSCC_GLASS_ FAPAR_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_ FAPAR_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ FAPAR_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was generated using MODIS products.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ FAPAR_MODIS_1KM_11.json b/datasets/NRSCC_GLASS_ FAPAR_MODIS_1KM_11.json index 8418e75f36..5984d64595 100644 --- a/datasets/NRSCC_GLASS_ FAPAR_MODIS_1KM_11.json +++ b/datasets/NRSCC_GLASS_ FAPAR_MODIS_1KM_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ FAPAR_MODIS_1KM_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ LAI_AVHRR_0.05D_11.json b/datasets/NRSCC_GLASS_ LAI_AVHRR_0.05D_11.json index dcc938539c..77fa300c40 100644 --- a/datasets/NRSCC_GLASS_ LAI_AVHRR_0.05D_11.json +++ b/datasets/NRSCC_GLASS_ LAI_AVHRR_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ LAI_AVHRR_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using AVHRR datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ LAI_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_ LAI_MODIS_0.05D_11.json index 362c09e134..3583796c8f 100644 --- a/datasets/NRSCC_GLASS_ LAI_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_ LAI_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ LAI_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_Albedo_AVHRR_11.json b/datasets/NRSCC_GLASS_Albedo_AVHRR_11.json index e9cd46e6c3..66bc4463b8 100644 --- a/datasets/NRSCC_GLASS_Albedo_AVHRR_11.json +++ b/datasets/NRSCC_GLASS_Albedo_AVHRR_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_Albedo_AVHRR_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global high-resolution land surface albedo data from NOAA/AVHRR, generated by Global LAnd Surface Satellite (GLASS) Dataset production team.", "links": [ { diff --git a/datasets/NRSCC_GLASS_Albedo_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_Albedo_MODIS_0.05D_11.json index 7149dd0147..1e5f35c741 100644 --- a/datasets/NRSCC_GLASS_Albedo_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_Albedo_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_Albedo_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 0.05 Degree.", "links": [ { diff --git a/datasets/NRSCC_GLASS_Albedo_MODIS_1KM_11.json b/datasets/NRSCC_GLASS_Albedo_MODIS_1KM_11.json index d8d68d44f5..0319b403ff 100644 --- a/datasets/NRSCC_GLASS_Albedo_MODIS_1KM_11.json +++ b/datasets/NRSCC_GLASS_Albedo_MODIS_1KM_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_Albedo_MODIS_1KM_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 1KM.", "links": [ { diff --git a/datasets/NRSCC_GLASS_BBE_AVHRR_11.json b/datasets/NRSCC_GLASS_BBE_AVHRR_11.json index fac2c594e2..d9b5c0e096 100644 --- a/datasets/NRSCC_GLASS_BBE_AVHRR_11.json +++ b/datasets/NRSCC_GLASS_BBE_AVHRR_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_BBE_AVHRR_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from AVHRR.", "links": [ { diff --git a/datasets/NRSCC_GLASS_BBE_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_BBE_MODIS_0.05D_11.json index 297b92799e..5915c7b32e 100644 --- a/datasets/NRSCC_GLASS_BBE_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_BBE_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_BBE_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from MODIS. The horizontal resolution is 0.05 Degree.", "links": [ { diff --git a/datasets/NRSCC_GLASS_BBE_MODIS_1KM_11.json b/datasets/NRSCC_GLASS_BBE_MODIS_1KM_11.json index 68ffa750da..0b96e5a9ce 100644 --- a/datasets/NRSCC_GLASS_BBE_MODIS_1KM_11.json +++ b/datasets/NRSCC_GLASS_BBE_MODIS_1KM_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_BBE_MODIS_1KM_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NRSCC_GLASS_BBE_MODIS_1KM", "links": [ { diff --git a/datasets/NRSCC_GLASS_DSR_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_DSR_MODIS_0.05D_11.json index 42a525d654..fbbae5be69 100644 --- a/datasets/NRSCC_GLASS_DSR_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_DSR_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_DSR_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Downward Shortwave Radiation (DSR) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ET_AVHRR_0.05D_11.json b/datasets/NRSCC_GLASS_ET_AVHRR_0.05D_11.json index 9d7ae62c21..70f29013ae 100644 --- a/datasets/NRSCC_GLASS_ET_AVHRR_0.05D_11.json +++ b/datasets/NRSCC_GLASS_ET_AVHRR_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ET_AVHRR_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Latent Heat (ET) product derived from AVHRR. The horizontal resolution is 0.05 Decimal Degrees.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ET_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_ET_MODIS_0.05D_11.json index 0e06275727..1720266e99 100644 --- a/datasets/NRSCC_GLASS_ET_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_ET_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ET_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Latent heat (ET) product derived from MODIS. The horizontal resolution is 0.05 Decimal Degrees.", "links": [ { diff --git a/datasets/NRSCC_GLASS_ET_MODIS_1KM_11.json b/datasets/NRSCC_GLASS_ET_MODIS_1KM_11.json index 95011b5861..d80952f3e0 100644 --- a/datasets/NRSCC_GLASS_ET_MODIS_1KM_11.json +++ b/datasets/NRSCC_GLASS_ET_MODIS_1KM_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_ET_MODIS_1KM_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Latent heat (ET) product derived from MODIS. The horizontal resolution is 1 KM.", "links": [ { diff --git a/datasets/NRSCC_GLASS_FAPAR_AVHRR_0.05D_11.json b/datasets/NRSCC_GLASS_FAPAR_AVHRR_0.05D_11.json index b5756e6fdb..a2fa492287 100644 --- a/datasets/NRSCC_GLASS_FAPAR_AVHRR_0.05D_11.json +++ b/datasets/NRSCC_GLASS_FAPAR_AVHRR_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_FAPAR_AVHRR_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product derived from AVHRR. The horizontal resolution is 0.05 Decimal Degrees.", "links": [ { diff --git a/datasets/NRSCC_GLASS_FVC_AVHRR_0.05D_11.json b/datasets/NRSCC_GLASS_FVC_AVHRR_0.05D_11.json index c994a1f889..dc618ff343 100644 --- a/datasets/NRSCC_GLASS_FVC_AVHRR_0.05D_11.json +++ b/datasets/NRSCC_GLASS_FVC_AVHRR_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_FVC_AVHRR_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Fractional vegetation cover (FVC) product was developed using AVHRR datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_FVC_MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_FVC_MODIS_0.05D_11.json index 4baf7ecd50..6e80fff650 100644 --- a/datasets/NRSCC_GLASS_FVC_MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_FVC_MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_FVC_MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Fractional vegetation cover (FVC) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_FVC_MODIS_500M_11.json b/datasets/NRSCC_GLASS_FVC_MODIS_500M_11.json index 80836986e1..8127b47e6c 100644 --- a/datasets/NRSCC_GLASS_FVC_MODIS_500M_11.json +++ b/datasets/NRSCC_GLASS_FVC_MODIS_500M_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_FVC_MODIS_500M_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Fractional vegetation cover (FVC) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_11.json b/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_11.json index 9603955ab5..f7045bb9dd 100644 --- a/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_11.json +++ b/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_GPP_AVHRR_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Gross Primary Production (GPP) product was developed using AVHRR datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_YEARLY_11.json b/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_YEARLY_11.json index 3242888a09..e94fa54350 100644 --- a/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_YEARLY_11.json +++ b/datasets/NRSCC_GLASS_GPP_AVHRR_0.05D_YEARLY_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_GPP_AVHRR_0.05D_YEARLY_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Gross Primary Productivity (GPP) yearly product was developed using AVHRR datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_GPP_MODIS_500M_11.json b/datasets/NRSCC_GLASS_GPP_MODIS_500M_11.json index 817b11330b..60f693f336 100644 --- a/datasets/NRSCC_GLASS_GPP_MODIS_500M_11.json +++ b/datasets/NRSCC_GLASS_GPP_MODIS_500M_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_GPP_MODIS_500M_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Gross Primary Production (GPP) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_GPP_MODIS_500M_YEARLY_11.json b/datasets/NRSCC_GLASS_GPP_MODIS_500M_YEARLY_11.json index b3c9d1ee2a..c5a0b42b25 100644 --- a/datasets/NRSCC_GLASS_GPP_MODIS_500M_YEARLY_11.json +++ b/datasets/NRSCC_GLASS_GPP_MODIS_500M_YEARLY_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_GPP_MODIS_500M_YEARLY_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Gross Primary Productivity (GPP) yearly product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_GLASS_LAI_MODIS_1KM_4.json b/datasets/NRSCC_GLASS_LAI_MODIS_1KM_4.json index 0ba8109876..52263e86f0 100644 --- a/datasets/NRSCC_GLASS_LAI_MODIS_1KM_4.json +++ b/datasets/NRSCC_GLASS_LAI_MODIS_1KM_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_LAI_MODIS_1KM_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global LAnd Surface Satellite (GLASS) Dataset was produced by NRSCC, Beijing Normal University and Wuhan University. This long-term LAI product was derived from NASA MODIS. The resolution is 1KM.", "links": [ { diff --git a/datasets/NRSCC_GLASS_PAR_ MODIS_0.05D_11.json b/datasets/NRSCC_GLASS_PAR_ MODIS_0.05D_11.json index 7e7c7d85d4..cef8ecfbdb 100644 --- a/datasets/NRSCC_GLASS_PAR_ MODIS_0.05D_11.json +++ b/datasets/NRSCC_GLASS_PAR_ MODIS_0.05D_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_GLASS_PAR_ MODIS_0.05D_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Global LAnd Surface Satellite (GLASS) Photosynthetically Active Radiation (PAR) product was developed using MODIS datasets.", "links": [ { diff --git a/datasets/NRSCC_NODA_ZhangHeng_EFD_1.json b/datasets/NRSCC_NODA_ZhangHeng_EFD_1.json index 82c39865af..532d6346e3 100644 --- a/datasets/NRSCC_NODA_ZhangHeng_EFD_1.json +++ b/datasets/NRSCC_NODA_ZhangHeng_EFD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_NODA_ZhangHeng_EFD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is for the observation of time-varying magnetic fields retrieved by the Electric Field Detector (EFD) instrument onboard China Seismo-Electromagnetic Satellite Mission (CSES, or Zhangheng-1). It is archived in the ChinaGEOSS site.", "links": [ { diff --git a/datasets/NRSCC_NODA_ZhangHeng_HEP_1.json b/datasets/NRSCC_NODA_ZhangHeng_HEP_1.json index 40cc0dee16..77783c1c2a 100644 --- a/datasets/NRSCC_NODA_ZhangHeng_HEP_1.json +++ b/datasets/NRSCC_NODA_ZhangHeng_HEP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_NODA_ZhangHeng_HEP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is for the observation of time-varying magnetic fields retrieved by the High-Energe Particle Package (HEP) instrument onboard China Seismo-Electromagnetic Satellite Mission (CSES, or Zhangheng-1). It is archived in the ChinaGEOSS site.", "links": [ { diff --git a/datasets/NRSCC_NODA_ZhangHeng_HPM_1.json b/datasets/NRSCC_NODA_ZhangHeng_HPM_1.json index 42c3a626ad..586a941ecd 100644 --- a/datasets/NRSCC_NODA_ZhangHeng_HPM_1.json +++ b/datasets/NRSCC_NODA_ZhangHeng_HPM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_NODA_ZhangHeng_HPM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is for the observation of time-varying magnetic fields retrieved by the High Precision Magnetometer (HPM) instrument onboard China Seismo-Electromagnetic Satellite Mission (CSES, or Zhangheng-1). It is archived in the ChinaGEOSS site.", "links": [ { diff --git a/datasets/NRSCC_NODA_ZhangHeng_LAP_1.json b/datasets/NRSCC_NODA_ZhangHeng_LAP_1.json index 70532a1f37..e0fa2295de 100644 --- a/datasets/NRSCC_NODA_ZhangHeng_LAP_1.json +++ b/datasets/NRSCC_NODA_ZhangHeng_LAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_NODA_ZhangHeng_LAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is for the observation of time-varying magnetic fields retrieved by the Langmuir Probe (LAP) instrument onboard China Seismo-Electromagnetic Satellite Mission (CSES, or Zhangheng-1). It is archived in the ChinaGEOSS site.", "links": [ { diff --git a/datasets/NRSCC_NODA_ZhangHeng_SCM_1.json b/datasets/NRSCC_NODA_ZhangHeng_SCM_1.json index 7ac4286286..fde071a876 100644 --- a/datasets/NRSCC_NODA_ZhangHeng_SCM_1.json +++ b/datasets/NRSCC_NODA_ZhangHeng_SCM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRSCC_NODA_ZhangHeng_SCM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is for the observation of time-varying magnetic fields in the Ultra-Low Frequency (ULF), Extremely Low frequency (ELF), and Very Low Frequency\uff08VLF\uff09ranges that are retrieved by Search-Coil Magnetometer (SCM) instrument onboard China Seismo-Electromagnetic Satellite Mission (CSES, or Zhangheng-1). It is archived in the ChinaGEOSS site.", "links": [ { diff --git a/datasets/NRT_Open_4.0.json b/datasets/NRT_Open_4.0.json index 338dfc3108..22465f7335 100644 --- a/datasets/NRT_Open_4.0.json +++ b/datasets/NRT_Open_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NRT_Open_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMOS Near Real Time products include Level 1 geo-located brightness temperature and Level 2 geo-located soil moisture estimation. The SMOS NRT L1 Light BUFR product contains brightness temperature geo-located on a reduced Gaussian grid (T511/N256), only for "land" pixels but keeping the full angular resolution. The pixels are consolidated in a full orbit dump segment (i.e. around 100 minutes of sensing time) with a maximum size of about 30MB per orbit. Spatial resolution is in the range of 30-50 km. This product is distributed in BUFR format. The SMOS NRT L2 Soil Moisture Neural Network (NN) product provides NRT soil moisture data based on the statistical coefficients estimated by a neural network. It is provided in the SMOS DGG grid and only at the satellite track. It also provides an estimation of the uncertainty of the estimated soil moisture product, and the probability that a soil moisture value is contaminated by Radio Frequency Interference (RFI). This product is distributed in NetCDF format. The L2 data product is also distributed via the EUMETCast Europe Service (DVB), upon registration on the EUMETSAT Earth Observation Portal (https://eoportal.eumetsat.int/userMgmt/gateway.faces). The Ku-band DVB reception station must be situated within the service coverage in Europe. SMOS NRT data is also regularly delivered to the UK Met-Office, then made available to operational agencies and research and development institutes via the WMO GTS Network. For an optimal exploitation of the SMOS NRT products please consult the read-me-first notes available in the Resources section below.", "links": [ { diff --git a/datasets/NSCAT_25KM_MGDR_V2_2.json b/datasets/NSCAT_25KM_MGDR_V2_2.json index 5afb1b2240..06e190f760 100644 --- a/datasets/NSCAT_25KM_MGDR_V2_2.json +++ b/datasets/NSCAT_25KM_MGDR_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_25KM_MGDR_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Scatterometer (NSCAT) Level 2.5 high-resolution merged ocean wind vectors and sigma-0 in 25 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0, and sigma-0 values are preserved where measurements are available. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.", "links": [ { diff --git a/datasets/NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL_2.json b/datasets/NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL_2.json index b416925284..b62833cd1f 100644 --- a/datasets/NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL_2.json +++ b/datasets/NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the NASA Scatterometer (NSCAT) Level 2 ocean wind vector ambiguity overlay files for the NSCAT MGDR version 2 dataset, referenced for 25 km wind vector cells (WVC). The dataset is derived from the results of a study which used a 2-D variational analysis method (VAM) to select a wind solution from the NSCAT ambiguous winds (Hoffman et al. 2003). Hoffman et al. chose the ambiguity closest in direction to the VAM surface wind analysis. No ambiguity was chosen for poor quality wind vector cells (WVCs). ECMWF analyses were used as the background field for the VAM. Their choice of ambiguity selection is compared with that of JPL, which used a median filter initialized with NCEP analysis fields. Ambiguity selection is changed in ~5% of the dataset, often improving the depiction of meteorological features where the surface wind is strongly curved or sheared. See Hoffman et al. (2003) for more on the method and results. Additional work by Henderson et al. (2003) compares the results of median filtering (JPL) vs. the 2d-VAR method (Hoffman et al., 2003) using 51 days of NSCAT data, supplemented by the NCEP 1000 hPa wind analyses as background fields.", "links": [ { diff --git a/datasets/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1.json b/datasets/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1.json index a139e9c18b..2f464f066e 100644 --- a/datasets/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1.json +++ b/datasets/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 4.45 km pixel resolution stored in SIR files; polar regions are gridded using a polar-stereographic technique. A non-enhanced version is provided at 22.25 km pixel resolution in a format known as GRD files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, polarization, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In additional to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. For more information, please visti: http://www.scp.byu.edu/docs/NSCAT_user_notes.html", "links": [ { diff --git a/datasets/NSCAT_LEVEL_1.7_V2_2.json b/datasets/NSCAT_LEVEL_1.7_V2_2.json index e2d877657a..10776201d0 100644 --- a/datasets/NSCAT_LEVEL_1.7_V2_2.json +++ b/datasets/NSCAT_LEVEL_1.7_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_LEVEL_1.7_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Scatterometer (NSCAT) Level 1.7 ocean sigma-0 referenced to 50 km wind vector cells (WVC) contains daily backscatter (sigma-0) data from ascending and descending passes. Rain flagging information is not included. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; re-processing had only minor impacts on the Level 1.7 data.", "links": [ { diff --git a/datasets/NSCAT_LEVEL_2_V2_2.json b/datasets/NSCAT_LEVEL_2_V2_2.json index c2b6bdbad2..b813009094 100644 --- a/datasets/NSCAT_LEVEL_2_V2_2.json +++ b/datasets/NSCAT_LEVEL_2_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_LEVEL_2_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Scatterometer (NSCAT) Level 2 ocean wind vectors in 50 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. Winds are calculated using the NSCAT-2 model function. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.", "links": [ { diff --git a/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json b/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json index f877d74eca..6ccf002297 100644 --- a/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json +++ b/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_LEVEL_3_BROWSE_IMAGES_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203.", "links": [ { diff --git a/datasets/NSCAT_LEVEL_3_V2_2.json b/datasets/NSCAT_LEVEL_3_V2_2.json index 63c89cd431..73560da1d5 100644 --- a/datasets/NSCAT_LEVEL_3_V2_2.json +++ b/datasets/NSCAT_LEVEL_3_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_LEVEL_3_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.", "links": [ { diff --git a/datasets/NSCAT_W25_RMGDR_V2_2.json b/datasets/NSCAT_W25_RMGDR_V2_2.json index 2d9715958c..da2729e8ee 100644 --- a/datasets/NSCAT_W25_RMGDR_V2_2.json +++ b/datasets/NSCAT_W25_RMGDR_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSCAT_W25_RMGDR_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function.", "links": [ { diff --git a/datasets/NSF-ANT-1142074-penguins_1.0.json b/datasets/NSF-ANT-1142074-penguins_1.0.json index 6b40b3088e..d3c3c88917 100644 --- a/datasets/NSF-ANT-1142074-penguins_1.0.json +++ b/datasets/NSF-ANT-1142074-penguins_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT-1142074-penguins_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics..", "links": [ { diff --git a/datasets/NSF-ANT02-28842.json b/datasets/NSF-ANT02-28842.json index 361c7c7a77..78b07fc68f 100644 --- a/datasets/NSF-ANT02-28842.json +++ b/datasets/NSF-ANT02-28842.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT02-28842", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award, provided by the Antarctic Geology and Geophysics Program of the Office of Polar Programs, supports a project to investigate the role and fate of Boron in high-grade metamorphic rocks of the Larsemann Hills region of Antarctica. Trace elements provide valuable information on the changes sedimentary rocks undergo as temperature and pressure increase during burial. One such element, boron, is particularly sensitive to increasing temperature because of its affinity for aqueous fluids, which are lost as rocks are buried. Boron contents of unmetamorphosed pelitic sediments range from 20 to over 200 parts per million, but rarely exceed 5 parts per million in rocks subjected to conditions of the middle and lower crust, that is, temperatures of 700 degrees C or more in the granulite-facies, which is characterized by very low water activities at pressures of 5 to 10 kbar (18-35 km burial). Devolatization reactions with loss of aqueous fluid and partial melting with removal of melt have been cited as primary causes for boron depletion under granulite-facies conditions. Despite the pervasiveness of both these processes, rocks rich in boron are locally found in the granulite-facies, that is, there are mechanisms for retaining boron during the metamorphic process. The Larsemann Hills, Prydz Bay, Antarctica, are a prime example. More than 20 lenses and layered bodies containing four borosilicate mineral species crop out over a 50 square kilometer area, which thus would be well suited for research on boron-rich granulite-facies metamorphic rocks. While most investigators have focused on the causes for loss of boron, this work will investigate how boron is retained during high-grade metamorphism. Field observations and mapping in the Larsemann Hills, chemical analyses of minerals and their host rocks, and microprobe age dating will be used to identify possible precursors and deduce how the precursor materials recrystallized into borosilicate rocks under granulite-facies conditions. \n\nThe working hypothesis is that high initial boron content facilitates retention of boron during metamorphism because above a certain threshold boron content, a mechanism 'kicks in' that facilitates retention of boron in metamorphosed rocks. For example, in a rock with large amounts of the borosilicate tourmaline, such as stratabound tourmalinite, the breakdown of tourmaline to melt could result in the formation of prismatine and grandidierite, two borosilicates found in the Larsemann Hills. This situation is rarely observed in rocks with modest boron content, in which breakdown of tourmaline releases boron into partial melts, which in turn remove boron when they leave the system. Stratabound tourmalinite is associated with manganese-rich quartzite, phosphorus-rich rocks and sulfide concentrations that could be diagnostic for recognizing a tourmalinite protolith in a highly metamorphosed complex where sedimentary features have been destroyed by deformation. Because partial melting plays an important role in the fate of boron during metamorphism, our field and laboratory research will focus on the relationship between the borosilicate units, granite pegmatites and other granitic intrusives. The results of our study will provide information on cycling of boron at deeper levels in the Earth's crust and on possible sources of boron for granites originating from deep-seated rocks. An undergraduate student will participate in the electron microprobe age-dating of monazite and xenotime as part of a senior project, thereby integrating the proposed research into the educational mission of the University of Maine. In response to a proposal for fieldwork, the Australian Antarctic Division, which maintains Davis station near the Larsemann Hills, has indicated that they will support the Antarctic fieldwork.", "links": [ { diff --git a/datasets/NSF-ANT04-36190_1.json b/datasets/NSF-ANT04-36190_1.json index d4985deabc..87d82557cd 100644 --- a/datasets/NSF-ANT04-36190_1.json +++ b/datasets/NSF-ANT04-36190_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT04-36190_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Patterns of biodiversity, as revealed by basic research in organismal biology, may be derived from ecological and evolutionary processes expressed in unique settings, such as Antarctica. The polar regions and their faunas are commanding increased attention as declining species diversity, environmental change, commercial fisheries, and resource management are now being viewed in a global context. Commercial fishing is known to have a direct and pervasive effect on marine biodiversity, and occurs in the Southern Ocean as far south as the Ross Sea. The nature of fish biodiversity in the Antarctic is different than in all other ocean shelf areas. Waters of the Antarctic continental shelf are ice covered for most of the year and water temperatures are nearly constant at -1.5 C. In these waters components of the phyletically derived Antarctic clade of Notothenioids dominate fish diversity. In some regions, including the southwestern Ross Sea, Notothenioids are overwhelmingly dominant in terms of number of species, abundance, and biomass. Such dominance by a single taxonomic group is unique among shelf faunas of the world. In the absence of competition from a taxonomically diverse fauna, Notothenioids underwent a habitat or depth related diversification keyed to the utilization of unfilled niches in the water column, especially pelagic or partially pelagic zooplanktivory and piscivory. This has been accomplished in the absence of a swim bladder for buoyancy control. They also may form a special type of adaptive radiation known as a species flock, which is an assemblage of a disproportionately high number of related species that have evolved rapidly within a defined area where most species are endemic. Diversification in buoyancy is the hallmark of the notothenioid radiation. Buoyancy is the feature of notothenioid biology that determines whether a species lives on the substrate, in the water column or both. Buoyancy also influences other key aspects of life history including swimming, feeding and reproduction and thus has implications for the role of the species in the ecosystem. With similarities to classic evolutionary hot spots, the Antarctic shelf and its Notothenioid radiation merit further exploration. The 2004 'International Collaborative Expedition to collect and study Fish Indigenous to Sub-Antarctic Habitats,' or, 'ICEFISH,' provided a platform for collection of notothenioid fishes from sub-Antarctic waters between South America and Africa, which will be examined in this project. This study will determine buoyancy for samples of all notothenioid species captured during the ICEFISH cruise. This essential aspect of the biology is known for only 19% of the notothenioid fauna. Also, the gross and microscopic anatomy of brains and sense organs of the phyletically basal families Bovichtidae, Eleginopidae, and of the non-Antarctic species of the primarily Antarctic family Nototheniidae will be examined. The fish biodiversity and endemicity in poorly known localities along the ICEFISH cruise track, seamounts and deep trenches will be quantified. Broader impacts include improved information for comprehending and conserving biodiversity, a scientific and societal priority.", "links": [ { diff --git a/datasets/NSF-ANT04-39906_1.json b/datasets/NSF-ANT04-39906_1.json index 560bb45ae5..d6d65a5275 100644 --- a/datasets/NSF-ANT04-39906_1.json +++ b/datasets/NSF-ANT04-39906_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT04-39906_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses.\n\nBecause of extreme isolation of the Antarctic continent since the \nEarly Oligocene, one expects a unique invertebrate benthic fauna with \na high degree of endemism. Yet some invertebrate taxa that constitute \nimportant ecological components of sedimentary benthic communities \ninclude more than 40 percent non-endemic species (e.g., benthic \npolychaetes). To account for non-endemic species, intermittent genetic \nexchange must occur between Antarctic and other (e.g. South American) \npopulations. The most likely mechanism for such gene flow, at least \nfor in-faunal and mobile macrobenthos, is dispersal of planktonic \nlarvae across the sub- Antarctic and Antarctic polar fronts. To test \nfor larval dispersal as a mechanism of maintaining genetic continuity \nacross polar fronts, the scientists propose to (1) take plankton \nsamples along transects across Drake passage during both the austral \nsummer and winter seasons while concurrently collecting the \nappropriate hydrographic data. Such data will help elucidate the \nhydrographic mechanisms that allow dispersal across Drake Passage. \nUsing a molecular phylogenetic approach, they will (2) compare \nseemingly identical adult forms from Antarctic and South America \ncontinents to identify genetic breaks, historical gene flow, and \ncontrol for the presence of cryptic species. (3) Similar molecular \ntools will be used to relate planktonic larvae to their adult forms. \nThrough this procedure, they propose to link the larval forms \nrespectively to their Antarctic or South America origins. The proposed \nwork builds on previous research that provides the basis for this \neffort to develop a synthetic understanding of historical gene flow \nand present day dispersal mechanism in South American/Drake Passage/ \nAntarctic Peninsular region. Furthermore, this work represents one of \nthe first attempts to examine recent gene flow in Antarctic benthic \ninvertebrates. Graduate students and a postdoctoral fellow will be \ntrained during this research\n", "links": [ { diff --git a/datasets/NSF-ANT04-53680.json b/datasets/NSF-ANT04-53680.json index db16ca1aac..121aac9c29 100644 --- a/datasets/NSF-ANT04-53680.json +++ b/datasets/NSF-ANT04-53680.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT04-53680", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Southern Ocean may play a central role in causing ice ages and general global climate change. This work will reveal key characteristics of the glacial ocean, and may explain the cause of glacial/interglacial cycles by measuring the abundances of certain isotopes of nitrogen found in fossil diatoms from Antarctic marine sediments. Diatom-bound N is a potentially important recorder of nutrient utilization. The Southern Ocean's nutrient status, productivity and circulation may be central to setting global atmospheric CO2 contents and other aspects of climate. Previous attempts to make these measurements have yielded ambiguous results. This project includes both technique development and analyses, including measurements on diatoms from both sediment traps and culture experiments. With regard to broader impacts, this grant is focused around the education and academic development of a graduate student, by coupling their research with mentorship of an undergraduate researcher.", "links": [ { diff --git a/datasets/NSF-ANT05-37371.json b/datasets/NSF-ANT05-37371.json index e465053963..7a5aa6d0d4 100644 --- a/datasets/NSF-ANT05-37371.json +++ b/datasets/NSF-ANT05-37371.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT05-37371", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach.", "links": [ { diff --git a/datasets/NSF-ANT05-37609_1.json b/datasets/NSF-ANT05-37609_1.json index cecbe685b5..1ed5a8388a 100644 --- a/datasets/NSF-ANT05-37609_1.json +++ b/datasets/NSF-ANT05-37609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT05-37609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project studies remnant magnetization in igneous rocks from the Dufek igneous complex, Antarctica. Its primary goal is to understand variations in the Earth's magnetic field during the Mesozoic Dipole Low (MDL), a period when the Earth's magnetic field underwent dramatic weakening and rapid reversals. This work will resolve the MDL's timing and nature, and assess connections between reversal rate, geomagnetic intensity and directional variability, and large-scale geodynamic processes. The project also includes petrologic studies to determine cooling rate effects on magnetic signatures, and understand assembly of the Dufek as an igneous body. Poorly studied, the Dufek is amongst the world's largest intrusions and its formation is connected to the break-up of Gondwana. The broader impacts of this project include graduate and undergraduate education and international collaboration with a German and Chilean IPY project.", "links": [ { diff --git a/datasets/NSF-ANT05-38580.json b/datasets/NSF-ANT05-38580.json index 42a7fd7c5b..542aeb9c77 100644 --- a/datasets/NSF-ANT05-38580.json +++ b/datasets/NSF-ANT05-38580.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT05-38580", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project studies sediment from the ocean floor to understand Antarctica's geologic history. Glacially eroded from the Antarctic continent, these sediments may offer insight into the 99% Antarctica covered by ice. The work's central focus is determining crust formation ages and thermal histories for three key areas of East Antarctica--Prydz Bay, eastern Weddell Sea, and Wilkes Land--through a combination of petrography, bulk sediment geochemistry and radiogenic isotopes, as well as isotope chronology of individual mineral grains. One specific objective is characterizing the composition of the Gamburtsev Mountains through studies of Eocene fluvial sediments from Prydz Bay. In addition to furthering our understanding of the hidden terrains of Antarctica, these terrigenous sediments will also serve as a natural laboratory to evaluate the effects of continental weathering on the Hf/Nd isotope systematics of seawater. An important broader impact of the project is providing exciting research projects for graduate and postdoctoral students using state of the art techniques in geochemistry.", "links": [ { diff --git a/datasets/NSF-ANT06-36850.json b/datasets/NSF-ANT06-36850.json index b8bc57abb1..b968767ae2 100644 --- a/datasets/NSF-ANT06-36850.json +++ b/datasets/NSF-ANT06-36850.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT06-36850", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project studies the opening of the Drake Passage between South America and Antarctica through a combined marine geophysical survey and geochemical study of dredged ocean floor basalts. Dating the passage's opening is key to understanding the formation of the circum-Antarctic current, which plays a major role in worldwide ocean circulation, and whose formation is connected with growth of the Antarctic ice sheet. Dredge samples will undergo various geochemical studies to determine their age and constrain mantle flow beneath the region. Broader impacts include support for graduate education, as well as undergraduate and K12 teacher involvement in a research cruise. The project also involves international collaboration with the UK and is part of IPY Project #77: Plates&Gates, which aims to reconstruct the geologic history of polar ocean basins and gateways for computer simulations of climate change. See http://www.ipy.org/index.php?/ipy/detail/plates_gates/ for more information.", "links": [ { diff --git a/datasets/NSF-ANT06-36899_1.json b/datasets/NSF-ANT06-36899_1.json index d902720dd2..603e1af149 100644 --- a/datasets/NSF-ANT06-36899_1.json +++ b/datasets/NSF-ANT06-36899_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT06-36899_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Auroral protons are not energized by electric fields directly above the auroral atmosphere and therefore they are a much better diagnostic of processes deep in the magnetosphere. It has been shown from measurements from space by the IMAGE spacecraft that the dayside hydrogen emission is directly related to dayside reconnection processes. A four channel all-sky images had been operating at South Pole during 2004-2007 to observe auroral features in specific wavelengths channels that allowed a quantitative investigation of proton aurora. This was accomplished by measuring the Hydrogen Balmer beta line at 486.1 nm and by monitoring another wavelength band for subtracting non proton produced background emissions. South Pole allows these measurements because of the 24 hour darkness and favorable conditions even on the dayside. To increase the scientific return it was also attempted to measure the Doppler shift of the hydrogen emissions because that provides diagnostics regarding the energy of the protons. Thus the proton camera measured 3 wavelength bands simultaneously in the vicinity of the Balmer beta line to provide the line intensity near zero Doppler shift, at a substantial Doppler shift and a third channel for background. \n\nThe 4-channel all-sky camera at South Pole was modified in 2008 in order to observe several types of auroras, and to distinguish the cusp reconnection aurora from the normal plasma sheet precipitation. The camera simultaneously operates in four wavelength regions that allow a distinction between auroras that are created by higher energy electrons (greater than 1 keV) and those created by low energy (less than 500 eV) precipitation. The cusp is the location where plasma enters the magnetosphere through the process of magnetic reconnection. This reconnection occurs where the Interplanetary Magnetic Field (IMF) and the terrestrial magnetic field are oriented in opposite directions. \n\nThe data are represented as keograms (geomagnetic north-south slices through the time series of images) for the four different wavelengths. The top of the keogram points to the magnetic south pole. The time series allows a very quick assessment about the presence of aurora, motion, intensity, and brightness differences in the four simultaneously registered channels.", "links": [ { diff --git a/datasets/NSF-ANT06-36928.json b/datasets/NSF-ANT06-36928.json index 5c6fc96f10..fa1a642ccf 100644 --- a/datasets/NSF-ANT06-36928.json +++ b/datasets/NSF-ANT06-36928.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT06-36928", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This proposal seeks funding to resume operation of the VLF Beacon Transmitter at the South Pole Station used to quantify temporal and spatial variations in the state of the lower ionosphere between the polar cap and subauroral zone, to determine the ionosphere's response to precipitation of highly energetic radiation belt electrons and solar protons, and to monitor the loss of these particles into the atmosphere. Although fluctuations in the relativistic particle population are extensively observed on satellites, little is known about the extent of associated precipitation into the ionosphere. Upon precipitation, these highly energetic particles penetrate to altitudes as low as 30-40 km, producing ionization, X-rays, and possibly affecting chemical reactions involving ozone production. It is proposed to continue recording the VLF beacon's signal at various Antarctic coastal stations (Palmer, Halley, etc). The broader impact of the proposed program includes the synergistic use of the South Pole VLF beacon with ongoing satellite-based measurements of trapped and precipitating high-energy electrons both at low and high altitudes and with other Antarctic Upper Atmospheric research efforts, such as the Automatic Geophysical Observatory programs and routine upper atmospheric observations at manned bases. The proposed project also promotes international collaboration via multi-points recording of the South Pole VLF beacon signal while providing the basis of a graduate or doctoral student thesis.", "links": [ { diff --git a/datasets/NSF-ANT06-49609_1.json b/datasets/NSF-ANT06-49609_1.json index d37abbf096..348a1b7dca 100644 --- a/datasets/NSF-ANT06-49609_1.json +++ b/datasets/NSF-ANT06-49609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT06-49609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objectives of this research are to investigate the proximate effects of aging on diving capability in the Weddell Seal and to describe mechanisms by which aging may influence foraging ecology, through physiology and behavior. This model pinniped species has been the focus of three decades of research in McMurdo Sound, Antarctica. Compared to the knowledge of pinniped diving physiology and ecology during early development and young adulthood, little is known about individuals nearing the upper limit of their normal reproductive age range. Evolutionary aging theories predict that elderly diving seals should exhibit senescence. This should be exacerbated by surges in the generation of oxygen free radicals via hypoxia-reoxygenation during breath-hold diving and hunting, which are implicated in age-related damage to cellular mitochondria. Surprisingly, limited observations of non-threatened pinniped populations indicate that senescence does not occur to a level where reproductive output is affected. The ability of pinnipeds to avoid apparent senescence raises two major questions: what specific physiological and morphological changes occur with advancing age in pinnipeds; and what subtle adjustments are made by these animals to cope with such changes? This investigation will focus on specific, functional physiological and behavioral changes relating to dive capability with advancing age. Data will be compared between Weddell seals in the peak, and near the end, of their reproductive age range. The investigators will quantify age-related changes in general health and body condition, combined with fine scale assessments of external and internal ability to do work in the form of diving. Specifically, patterns of muscle morphology, oxidant status and oxygen storage with age will be examined. The effects of age on skeletal muscular function and exercise performance will also be examined. The investigators hypothesize that senescence does occur in Weddell seals at the level of small-scale, proximate physiological effects and performance, but that behavioral plasticity allows for a given degree of compensation. Broader impacts include the training of students and outreach activities including interviews and articles written for the popular media. This study should also establish diving seals as a novel model for the study of cardiovascular and muscular physiology of aging and develop a foundation for similar research on other species. Advancement of the understanding of aging by medical science has been impressive in recent years but basic mammalian aging is an area of study the still requires considerable effort. The development of new models for the study of aging has tremendous potential benefits to society at large.", "links": [ { diff --git a/datasets/NSF-ANT07-32625_1.json b/datasets/NSF-ANT07-32625_1.json index 3f3f65dd97..7dcdc71e01 100644 --- a/datasets/NSF-ANT07-32625_1.json +++ b/datasets/NSF-ANT07-32625_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT07-32625_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a research cruise to perform geologic studies in the area under and surrounding the former Larsen B ice shelf, on the Antarctic Peninsula. The ice shelf's disintegration in 2002 coupled with the unique marine geology of the area make it possible to understand the conditions leading to ice shelf collapse. Bellwethers of climate change that reflect both oceanographic and atmospheric conditions, ice shelves also hold back glacial flow in key areas of the polar regions. Their collapse results in glacial surging and could cause rapid rise in global sea levels. This project characterizes the Larsen ice shelf's history and conditions leading to its collapse by determining: 1) the size of the Larsen B during warmer climates and higher sea levels back to the Eemian interglacial, 125,000 years ago; 2) the configuration of the Antarctic Peninsula ice sheet during the LGM and its subsequent retreat; 3) the causes of the Larsen B's stability through the Holocene, during which other shelves have come and gone; 4) the controls on the dynamics of ice shelf margins, especially the roles of surface melting and oceanic processes, and 5) the changes in sediment flux, both biogenic and lithogenic, after large ice shelf breakup.\n\nThe broader impacts include graduate and undergraduate education through research projects and workshops; outreach to the general public through a television documentary and websites, and international collaboration with scientists from Belgium, Spain, Argentina, Canada, Germany and the UK. The work also has important societal relevance. Improving our understanding of how ice shelves behave in a warming world will improve models of sea level rise.\n\nThe project is supported under NSF's International Polar Year (IPY) research emphasis area on 'Understanding Environmental Change in Polar Regions'.", "links": [ { diff --git a/datasets/NSF-ANT07-32651.json b/datasets/NSF-ANT07-32651.json index ebafe79a88..b07b679bdd 100644 --- a/datasets/NSF-ANT07-32651.json +++ b/datasets/NSF-ANT07-32651.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT07-32651", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scambos/0732921,Pettit/0732738,Gordon/0732651,Truffer/0732602,Mosley-Thompson/0732655. Like no other region on Earth, the northern Antarctic Peninsula represents a spectacular natural laboratory of climate change and provides the opportunity to study the record of past climate and ecological shifts alongside the present-day changes in one of the most rapidly warming regions on Earth. This award supports the cryospheric and oceano-graphic components of an integrated multi-disciplinary program to address these rapid and fundamental changes now taking place in Antarctic Peninsula (AP). By making use of a marine research platform (the RV NB Palmer and on-board helicopters) and additional logistical support from the Argentine Antarctic program, the project will bring glaciologists, oceanographers, marine geologists and biologists together, working collaboratively to address fundamentally interdisciplinary questions regarding climate change. The project will include gathering a new, high-resolution paleoclimate record from the Bruce Plateau of Graham Land, and using it to compare Holocene- and possibly glacial-epoch climate to the modern period; investigating the stability of the remaining Larsen Ice Shelf and rapid post-breakup glacier response ? in particular, the roles of surface melt and ice-ocean interactions in the speed-up and retreat; observing the contribution of, and response of, oceanographic systems to ice shelf disintegration and ice-glacier interactions. Helicopter support on board will allow access to a wide range of glacial and geological areas of interest adjacent to the Larsen embayment. At these locations, long-term in situ glacial monitoring, isostatic uplift, and ice flow GPS sites will be established, and high-resolution ice core records will be obtained using previously tested lightweight drilling equipment. Long-term monitoring of deep water outflow will, for the first time, be integrated into changes in ice shelf extent and thickness, bottom water formation, and multi-level circulation by linking near-source observations to distal sites of concentrated outflow. The broader impacts of this international, multidisciplinary effort are that it will significantly advance our understanding of linkages amongst the earth's systems in the Polar Regions, and are proposed with international participation (UK, Spain, Belgium, Germany and Argentina) and interdisciplinary engagement in the true spirit of the International Polar Year (IPY). It will also provide a means of engaging and educating the public in virtually all aspects of polar science and the effects of ongoing climate change. The research team has a long record of involving undergraduates in research, educating high-performing graduate students, and providing innovative and engaging outreach products to the K-12 education and public media forums. Moreover, forging the new links both in science and international Antarctic programs will provide a continuing legacy, beyond IPY, of improved understanding and cooperation in Antarctica.\n", "links": [ { diff --git a/datasets/NSF-ANT07-32983_1.json b/datasets/NSF-ANT07-32983_1.json index d57bd81576..af2223ba0b 100644 --- a/datasets/NSF-ANT07-32983_1.json +++ b/datasets/NSF-ANT07-32983_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT07-32983_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborative Research in IPY: Abrupt Environmental Change in the Larsen Ice Shelf System, a Multidisciplinary Approach - Marine Ecosystems. A profound transformation in ecosystem structure and function is occurring in coastal waters of the western Weddell Sea, with the collapse of the Larsen B ice shelf. This transformation appears to be yielding a redistribution of energy flow between chemoautotrophic and photosynthetic production, and to be causing the rapid demise of the extraordinary seep ecosystem discovered beneath the ice shelf. This event provides an ideal opportunity to examine fundamental aspects of ecosystem transition associated with climate change. We propose to test the following hypotheses to elucidate the transformations occurring in marine ecosystems as a consequence of the Larsen B collapse: (1) The biogeographic isolation and sub-ice shelf setting of the Larsen B seep has led to novel habitat characteristics, chemoautotrophically dependent taxa and functional adaptations. (2) Benthic communities beneath the former Larsen B ice shelf are fundamentally different from assemblages at similar depths in the Weddell sea-ice zone, and resemble oligotrophic deep-sea communities. Larsen B assemblages are undergoing rapid change. (3) The previously dark, oligotrophic waters of the Larsen B embayment now support a thriving phototrophic community, with production rates and phytoplankton composition similar to other productive areas of the Weddell Sea. To document rapid changes occurring in the Larsen B ecosystem, we will use a remotely operated vehicle, shipboard samplers, and moored sediment traps. We will characterize microbial, macrofaunal and megafaunal components of the seep community; evaluate patterns of surface productivity, export flux, and benthic faunal composition in areas previously covered by the ice shelf, and compare these areas to the open sea-ice zone. These changes will be placed within the geological, glaciological and climatological context that led to ice-shelf retreat, through companion research projects funded in concert with this effort. Together these projects will help predict the likely consequences of ice-shelf collapse to marine ecosystems in other regions of Antarctica vulnerable to climate change. The research features international collaborators from Argentina, Belgium, Canada, Germany, Spain and the United Kingdom. The broader impacts include participation of a science writer; broadcast of science segments by members of the Jim Lehrer News Hour (Public Broadcasting System); material for summer courses in environmental change; mentoring of graduate students and postdoctoral fellows; and showcasing scientific activities and findings to students and public through podcasts.\n", "links": [ { diff --git a/datasets/NSF-ANT07-39464_1.json b/datasets/NSF-ANT07-39464_1.json index 69f2d9c3a0..e8bc83dddd 100644 --- a/datasets/NSF-ANT07-39464_1.json +++ b/datasets/NSF-ANT07-39464_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT07-39464_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic polynyas are the ice free zones often persisting in continental sea ice. Characterization of the lower atmosphere properties, air-sea surface heat fluxes and corresponding ocean depth profiles of Antarctic polynyas, especially during strong wind events, is needed for a more detailed understanding of the role of polynya in the production of latent-heat type sea ice and the formation, through brine rejection, of dense ocean bottom waters. Broader impacts: A key technological innovation, the use of instrumented uninhabited aircraft systems (UAS), will be employed to enable the persistent and safe observation of the interaction of light and strong katabatic wind fields with the Terra Nova Bay (Victoria Land, Antarctica) polynya waters during late winter and early summer time frames. The use of UAS observational platforms on the continent to date has to date been modest, but demonstration of their versatility and effectiveness in surveying and observing mode is a welcome development. The projects use of UAS platforms by University of Colorado and LDEO (Columbia) researchers is both high risk, and potentially transformative for the systematic data measurement tasks that many Antarctic science applications increasingly require.", "links": [ { diff --git a/datasets/NSF-ANT08-37988.json b/datasets/NSF-ANT08-37988.json index ebcb7fc8ed..761315a9f0 100644 --- a/datasets/NSF-ANT08-37988.json +++ b/datasets/NSF-ANT08-37988.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT08-37988", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This award supports a project to reconstruct the past physical and chemical climate of Antarctica, with an emphasis on the region surrounding the Ross Sea Embayment, using >60 ice cores collected in this region by US ITASE and by Australian, Brazilian, Chilean, and New Zealand ITASE teams. The ice core records are annually resolved and exceptionally well dated, and will provide, through the analyses of stable isotopes, major soluble ions and for some trace elements, instrumentally calibrated proxies for past temperature, precipitation, atmospheric circulation, chemistry of the atmosphere, sea ice extent, and volcanic activity. These records will be used to understand the role of solar, volcanic, and human forcing on Antarctic climate and to investigate the character of recent abrupt climate change over Antarctica in the context of broader Southern Hemisphere and global climate variability. The intellectual merit of the project is that ITASE has resulted in an array of ice core records, increasing the spatial resolution of observations of recent Antarctic climate variability by more than an order of magnitude and provides the basis for assessment of past and current change and establishes a framework for monitoring of future climate change in the Southern Hemisphere. This comes at a critical time as global record warming and other impacts are noted in the Southern Ocean, the Antarctic Peninsula, and on the Antarctic ice sheet. The broader impacts of the project are that Post-doctoral and graduate students involved in the project will benefit from exposure to observational and modeling approaches to climate change research and working meetings to be held at the two collaborating institutions plus other prominent climate change institutions. The results are of prime interest to the public and the media Websites hosted by the two collaborating institutions contain climate change position papers, scientific exchanges concerning current climate change issues, and scientific contribution series.", "links": [ { diff --git a/datasets/NSF-ANT08-38955_1.json b/datasets/NSF-ANT08-38955_1.json index eaefcd4489..c22b6a0296 100644 --- a/datasets/NSF-ANT08-38955_1.json +++ b/datasets/NSF-ANT08-38955_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT08-38955_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).\n\nMost organisms meet their carbon and energy needs using photosynthesis (phototrophy) or ingestion/assimilation of organic substances (heterotrophy). However, a nutritional strategy that combines phototrophy and heterotrophy - mixotrophy - is geographically and taxonomically widespread in aquatic systems. While the presence of mixotrophs in the Southern Ocean is known only recently, preliminary evidence indicates a significant role in Southern Ocean food webs. Recent work on Southern Ocean dinoflagellate, Kleptodinium, suggests that it sequesters functional chloroplasts of the bloom-forming haptophyte, Phaeocystis antarctica. This dinoflagellate is abundant in the Ross Sea, has been reported elsewhere in the Southern Ocean, and may have a circumpolar distribution. By combining nutritional modes. mixotrophy may offer competitive advantages over pure autotrophs and heterotrophs.\n\nThe goals of this project are to understand the importance of alternative nutritional strategies for Antarctic species that combine phototrophic and phagotrophic processes in the same organism. The research will combine field investigations of plankton and ice communities in the Southern Ocean with laboratory experiments on Kleptodinium and recently identified mixotrophs from our Antarctic culture collections. The research will address: 1) the relative contributions of phototrophy and phagotrophy in Antarctic mixotrophs; 2) the nature of the relationship between Kleptodinium and its kleptoplastids; 3) the distributions and abundances of mixotrophs and Kleptodinium in the Southern Ocean during austral spring/summer; and 4) the impacts of mixotrophs and Kleptodinium on prey populations, the factors influencing these behaviors and the physiological conditions of these groups in their natural environment. \n\nThe project will contribute to the maintenance of a culture collection of heterotrophic, phototrophic and mixotrophic Antarctic protists that are available to the scientific community, and it will train graduate and undergraduate students at Temple University. Research findings and activities will be summarized for non-scientific audiences through the PIs' websites and through other public forums, and will involve middle school teachers via collaboration with COSEE-New England.\n", "links": [ { diff --git a/datasets/NSF-ANT08-38996_1.json b/datasets/NSF-ANT08-38996_1.json index b07f062ca8..fa971f91e6 100644 --- a/datasets/NSF-ANT08-38996_1.json +++ b/datasets/NSF-ANT08-38996_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT08-38996_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ammonia oxidation is the first step in the conversion of regenerated nitrogen to dinitrogen gas, a 3-step pathway mediated by 3 distinct guilds of bacteria and archaea. Ammonia oxidation and the overall process of nitrification-denitrification have received relatively little attention in polar oceans where the effects of climate change on biogeochemical rates are likely to be pronounced. Previous work on Ammonia Oxidizing Archaea (AOA) in the Palmer LTER study area West of the Antarctic Peninsula (WAP), has suggested strong vertical segregation of crenarchaeote metabolism, with the 'winter water' (WW, ~50-100 m depth range) dominated by non-AOA crenarchaeotes, while Crenarchaeota populations in the 'circumpolar deep water' (CDW), which lies immediately below the winter water (150-3500 m), are dominated by AOA. Analysis of a limited number of samples from the Arctic Ocean did not reveal a comparable vertical segregation of AOA, and suggested that AOA and Crenarchaeota abundance is much lower there than in the Antarctic. These findings led to 3 hypotheses that will be tested in this project: 1) the apparent low abundance of Crenarchaeota and AOA in Arctic Ocean samples may be due to spatial or temporal variability in populations; 2) the WW population of Crenarchaeota in the WAP is dominated by a heterotroph; 3) the WW population of Crenarchaeota in the WAP 'grows in' during spring and summer after this water mass forms.\n\nThe study will contribute substantially to understanding an important aspect of the nitrogen cycle in the Palmer LTER (Long Term Ecological Research) study area by providing insights into the ecology and physiology of AOA. The natural segregation of crenarchaeote phenotypes in waters of the WAP, coupled with metagenomic studies in progress in the same area by others (A. Murray, H. Ducklow), offers the possibility of major breakthroughs in understanding of the metabolic capabilities of these organisms. This knowledge is needed to model how water column nitrification will respond to changes in polar ecosystems accompanying global climate change. The Principal Investigator will participate fully in the education and outreach efforts of the Palmer LTER, including making highlights of our findings available for posting to their project web site and participating in outreach (for example, Schoolyard LTER). The research also will involve undergraduates (including the field work if possible) and will support high school interns in the P.I.'s laboratory over the summer.\n", "links": [ { diff --git a/datasets/NSF-ANT09-44042.json b/datasets/NSF-ANT09-44042.json index f17ce75d04..ffd88d9c5e 100644 --- a/datasets/NSF-ANT09-44042.json +++ b/datasets/NSF-ANT09-44042.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44042", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component.", "links": [ { diff --git a/datasets/NSF-ANT09-44358.json b/datasets/NSF-ANT09-44358.json index a2d0244076..510f685220 100644 --- a/datasets/NSF-ANT09-44358.json +++ b/datasets/NSF-ANT09-44358.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44358", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources.", "links": [ { diff --git a/datasets/NSF-ANT09-44411.json b/datasets/NSF-ANT09-44411.json index c7205cef69..595469aa0c 100644 --- a/datasets/NSF-ANT09-44411.json +++ b/datasets/NSF-ANT09-44411.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44411", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad\u00e9lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources.\n", "links": [ { diff --git a/datasets/NSF-ANT09-44532.json b/datasets/NSF-ANT09-44532.json index 384468f6b7..2c50526328 100644 --- a/datasets/NSF-ANT09-44532.json +++ b/datasets/NSF-ANT09-44532.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44532", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intellectual Merit: The goal of this project is to address relationships between foreland basins and their tectonic settings by combining detrital zircon isotope characteristics and sedimentological data. To accomplish this goal the PIs will develop a detailed geochronology and analyze Hf- and O-isotopes of detrital zircons in sandstones of the Devonian Taylor Group and the Permian-Triassic Victoria Group. These data will allow them to better determine provenance and basin fill, and to understand the nature of the now ice covered source regions in East and West Antarctica. The PIs will document possible unexposed/unknown crustal terrains in West Antarctica, investigate sub-glacial terrains of East Antarctica that were exposed to erosion during Devonian to Triassic time, and determine the evolving provenance and tectonic history of the Devonian to Triassic Gondwana basins in the central Transantarctic Mountains. Detrital zircon data will be interpreted in the context of fluvial dispersal/drainage patterns, sandstone petrology, and sequence stratigraphy. This interpretation will identify source terrains and evolving sediment provenances. Paleocurrent analysis and sequence stratigraphy will determine the timing and nature of changing tectonic conditions associated with development of the depositional basins and document the tectonic history of the Antarctic sector of Gondwana. Results from this study will answer questions about the Panthalassan margin of Gondwana, the Antarctic craton, and the Beacon depositional basin and their respective roles in global tectonics and the geologic and biotic history of Antarctica. The Beacon basin and adjacent uplands played an important role in the development and demise of Gondwanan glaciation through modification of polar climates, development of peat-forming mires, colonization of the landscape by plants, and were a migration route for Mesozoic vertebrates into Antarctica. \n\nBroader impacts: This proposal includes support for two graduate students who will participate in the fieldwork, and also support for other students to participate in laboratory studies. Results of the research will be incorporated in classroom teaching at the undergraduate and graduate levels and will help train the next generation of field geologists. Interactions with K-12 science classes will be achieved by video/computer conferencing and satellite phone connections from Antarctica. Another outreach effort is the developing cooperation between the Byrd Polar Research Center and the Center of Science and Industry in Columbus.\n", "links": [ { diff --git a/datasets/NSF-ANT09-44653_1.json b/datasets/NSF-ANT09-44653_1.json index 9625f9510b..1b4d78bd44 100644 --- a/datasets/NSF-ANT09-44653_1.json +++ b/datasets/NSF-ANT09-44653_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44653_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a project to broaden the knowledge of annual accumulation patterns over the West Antarctic Ice Sheet by processing existing near-surface radar data taken on the US ITASE traverse in 2000 and by gathering and validating new ultra/super-high-frequency (UHF) radar images of near surface layers (to depths of ~15 m), expanding abilities to monitor recent annual accumulation patterns from point source ice cores to radar lines. Shallow (15 m) ice cores will be collected in conjunction with UHF radar images to confirm that radar echoed returns correspond with annual layers, and/or sub-annual density changes in the near-surface snow, as determined from ice core stable isotopes. This project will additionally improve accumulation monitoring from space-borne instruments by comparing the spatial-radar-derived-annual accumulation time series to the passive microwave time series dating back over 3 decades and covering most of Antarctica. The intellectual merit of this project is that mapping the spatial and temporal variations in accumulation rates over the Antarctic ice sheet is essential for understanding ice sheet responses to climate forcing. Antarctic precipitation rate is projected to increase up to 20% in the coming century from the predicted warming. Accumulation is a key component for determining ice sheet mass balance and, hence, sea level rise, yet our ability to measure annual accumulation variability over the past 5 decades (satellite era) is mostly limited to point-source ice cores. Developing a radar and ice core derived annual accumulation dataset will provide validation data for space-born remote sensing algorithms, climate models and, additionally, establish accumulation trends. The broader impacts of the project are that it will advance discovery and understanding within the climatology, glaciology and remote sensing communities by verifying the use of UHF radars to monitor annual layers as determined by visual, chemical and isotopic analysis from corresponding shallow ice cores and will provide a dataset of annual to near-annual accumulation measurements over the past ~5 decades across WAIS divide from existing radar data and proposed radar data. By determining if temporal changes in the passive microwave signal are correlated with temporal changes in accumulation will help assess the utility of passive microwave remote sensing to monitor accumulation rates over ice sheets for future decades. The project will promote teaching, training and learning, and increase representation of underrepresented groups by becoming involved in the NASA History of Winter project and Thermochron Mission and by providing K-12 teachers with training to monitor snow accumulation and temperature here in the US, linking polar research to the student's backyard. The project will train both undergraduate and graduate students in polar research and will encouraging young investigators to become involved in careers in science. In particular, two REU students will participate in original research projects as part of this larger project, from development of a hypothesis to presentation and publication of the results. The support of a new, young woman scientist will help to increase gender diversity in polar research.\n", "links": [ { diff --git a/datasets/NSF-ANT09-44727.json b/datasets/NSF-ANT09-44727.json index 1c85bb6068..d12eea210e 100644 --- a/datasets/NSF-ANT09-44727.json +++ b/datasets/NSF-ANT09-44727.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT09-44727", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASPIRE is an NSF-funded project that will examine the ecology of the Amundsen Sea during the Austral summer of 2010. ASPIRE includes an international team of trace metal and carbon chemists, phytoplankton physiologists, microbial and zooplankton ecologists, and physical oceanographers, that will investigate why and how the Amundsen Sea Polynya is so much more productive than other polynyas and whether interannual variability can provide insight to climate-sensitive mechanisms driving carbon fluxes. This project will compliment the existing ASPIRE effort by using 1) experimental manipulations to understand photoacclimation of the dominant phytoplankton taxa under conditions of varying light and trace metal abundance, 2) nutrient addition bioassays to determine the importance of trace metal versus nitrogen limitation of phytoplankton growth, and 3) a numerical ecosystem model to understand the importance of differences in mixing regime, flow field, and Fe sources in controlling phytoplankton bloom dynamics and community composition in this unusually productive polynya system. The research strategy will integrate satellite remote sensing, field-based experimental manipulations, and numerical modeling. Outreach and education include participation in Stanford's Summer Program for Professional Development for Science Teachers, Stanford's School of Earth Sciences high school internship program, and development of curriculum for local science training centers, including the Chabot Space and Science Center. Undergraduate participation and training will include support for both graduate students and undergraduate assistants.", "links": [ { diff --git a/datasets/NSF-ANT10-43145_1.json b/datasets/NSF-ANT10-43145_1.json index a54f5eb549..a23ba8c3bd 100644 --- a/datasets/NSF-ANT10-43145_1.json +++ b/datasets/NSF-ANT10-43145_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-43145_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A range of chemical and microphysical pathways in polar latitudes, including spring time (tropospheric) ozone depletion, oxidative pathways for mercury, and cloud condensation nuclei (CCN) production leading to changes in the cloud cover and attendant surface energy budgets, have been invoked as being dependent upon the emission of halogen gases formed in sea-ice.\nThe prospects for climate warming induced reductions in sea ice extent causing alteration of these incompletely known surface-atmospheric feedbacks and interactions requires confirmation of mechanistic details in both laboratory studies and field campaigns. One such mechanistic question is how bromine (BrO and Br) enriched snow migrates or is formed through processes in sea-ice, prior to its subsequent mobilization as an aerosol fraction into the atmosphere by strong winds. Once aloft, it may react with ozone and other atmospheric species. Dartmouth researchers will collect snow from the surface of sea ice, from freely blowing snow and in sea-ice cores from Cape Byrd, Ross Sea. A range of spectroscopic, microanalytic and and microstructural approaches will be subsequently used to determine the Br distribution gradients through sea-ice, in order to shed light on how sea-ice first forms and then releases bromine species into the polar atmospheric boundary layer.", "links": [ { diff --git a/datasets/NSF-ANT10-43485_1.json b/datasets/NSF-ANT10-43485_1.json index 6f99ce9e39..c0582deb57 100644 --- a/datasets/NSF-ANT10-43485_1.json +++ b/datasets/NSF-ANT10-43485_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-43485_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation.\n", "links": [ { diff --git a/datasets/NSF-ANT10-43517.json b/datasets/NSF-ANT10-43517.json index 99f131ab6f..fb17c0bd15 100644 --- a/datasets/NSF-ANT10-43517.json +++ b/datasets/NSF-ANT10-43517.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-43517", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation.", "links": [ { diff --git a/datasets/NSF-ANT10-43554_1.json b/datasets/NSF-ANT10-43554_1.json index 1dfcd6a1b8..23f4b6757e 100644 --- a/datasets/NSF-ANT10-43554_1.json +++ b/datasets/NSF-ANT10-43554_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-43554_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events.\nThis study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete.", "links": [ { diff --git a/datasets/NSF-ANT10-43621.json b/datasets/NSF-ANT10-43621.json index e41431fc64..f35bd024ae 100644 --- a/datasets/NSF-ANT10-43621.json +++ b/datasets/NSF-ANT10-43621.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-43621", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input.\n", "links": [ { diff --git a/datasets/NSF-ANT10-44978.json b/datasets/NSF-ANT10-44978.json index d0d0a9558e..be48325644 100644 --- a/datasets/NSF-ANT10-44978.json +++ b/datasets/NSF-ANT10-44978.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-44978", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole. The proposed work is a four-year program of research activities directed toward upgrading the BICEP (Background Imaging of Cosmic Extragalactic Polarization) telescope operating at South Pole since early 2006 to reach far =stretching goals of detection of the Cosmic Gravitational-wave Background (CGB). This telescope is a first Cosmic Microwave Background (CMB) B-mode polarimeter, specifically designed to search for CGB signatures while mapping ~2% of the southern sky that is free of the Milky Way foreground galactic radiation at 100 GH and 150 GHz. The BICEP1 telescope will reach its designed sensitivity by the end of 2008. A coordinated series of upgrades to BICEP1 will provide the increased sensitivity and more exacting control of instrumental effects and potential confusion from galactic foregrounds necessary to search for the B-mode signal more deeply through space. A powerful new 150 GHz receiver, BICEP2, will replace the current detector at the beginning of 2009, increasing the mapping speed almost ten-fold. In 2010, the first of a series of compact, mechanically-cooled receivers (called SPUD - Small Polarimeter Upgrade for DASI) will be deployed on the existing DASI mount and tower, providing similar mapping speed at 100 GHz in parallel with BICEP2. The latter instrument will reach (and exceed with the addition of a SPUD polarimeter) the target sensitivity r = 0.15 set forth by the Interagency (NSF/NASA/DoE) Task Force on CMB Research for a future space mission dedicated to the detection and characterization of primordial gravitational waves. This Task Force has identified detection of the Inflation's gravitational waves as the number one priority for the modern cosmology. More broadly, as the cosmology captures a lot of the public imagination, it is a remarkably effective vehicle for stimulating interest in basic science. The CGB detection would be to Inflation what the discovery of the CMB radiation was to the Big Bang. The project will contribute to the training of the next generation of cosmologists by integrating graduate and undergraduate education with the technology and instrumentation development, astronomical observations and scientific analysis. Sharing of the forefront research results with public extends the new knowledge beyond the universities. This project will be undertaken in collaboration between the California Institute of Technology and the University of Chicago.", "links": [ { diff --git a/datasets/NSF-ANT10-48343_1.json b/datasets/NSF-ANT10-48343_1.json index eae7bf811d..6ee2c6f20f 100644 --- a/datasets/NSF-ANT10-48343_1.json +++ b/datasets/NSF-ANT10-48343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-48343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intellectual Merit: The PI proposes a high-resolution paleoenvironmental study of pollen, spore, fresh-water algae, and dinoflagellate cyst assemblages to investigate the palynological record of sudden warming events in the Antarctic as recorded by the ANDRILL SMS drill core and terrestrial sections. These data will be used to derive causal mechanisms for these rapid climate events. Terrestrial samples will be obtained at various altitudes in the Dry Valleys region. The pollen and spores will provide data on atmospheric conditions, while the algae will provide data on sea-surface conditions. These data will help identify the triggers for sudden climatic shifts. If they are caused by changes in oceanic currents, a signal will be visible in the dinocyst assemblages first as currents influence their distribution. Conversely, if these shifts are triggered by atmospheric factors, then the shifts will first affect plants and be visible in the pollen record. Broader impacts: The PI proposes a suite of activities to bring field-based climate change research to a broader audience. The PI will advise a diverse group of students and educators. The palynological data collected as part of this research will be utilized, in part, to develop new lectures on Antarctic palynology and these new lectures will be made available via a collaboration with the LSU HHMI program. In addition, the PI will direct three Louisiana middle-school teachers as they pursue a Masters of Natural Science for science educators. These teachers will help the PI develop a professional development program for science teachers. Community-based activities will be organized to raise science awareness and alert students and the public of opportunities in science.", "links": [ { diff --git a/datasets/NSF-ANT10-63592_1.json b/datasets/NSF-ANT10-63592_1.json index d4b31e05c7..234e3486df 100644 --- a/datasets/NSF-ANT10-63592_1.json +++ b/datasets/NSF-ANT10-63592_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT10-63592_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phaeocystis antarctica is capable of forming blooms that are denser and more extensive than any other member of the Southern Ocean phytoplankton community. The factors that enable P Antarctica to dominate its competitors are not clear but are likely related to its colonial lifestyle. The goal of the project is to map all the reactions in metabolic pathways that are key to defining the ecological niche of Phaeocystis antarctica by developing a Pathway/Genome Database (PGDB) using Pathway Tools software. The investigators will assign proteins and enzymes to key pathways in P. Antarctica, continually improve and edit the database as the full Phaeocystis genome comes online, and host the database on the BioCyc webpage. The end product will be the first database for a eukaryotic phytoplankton genome where researchers can query extant metabolic pathways and place new proteins and enzymes of interest within metabolic networks. The risk is that a substantial percentage of catalytic enzymes may belong to pathways that are poorly characterized. The science impact is to link genomes to metabolic potential in the context of Phaeocystis life history but also in comparison to other organisms across the tree of life. The education and outreach includes work with a high school teacher and intern and curriculum development.", "links": [ { diff --git a/datasets/NSF-ANT11-42018_1.json b/datasets/NSF-ANT11-42018_1.json index e7742a7481..8000449918 100644 --- a/datasets/NSF-ANT11-42018_1.json +++ b/datasets/NSF-ANT11-42018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT11-42018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students.", "links": [ { diff --git a/datasets/NSF-ANT11-42102.json b/datasets/NSF-ANT11-42102.json index cc25bb33b5..3b04822918 100644 --- a/datasets/NSF-ANT11-42102.json +++ b/datasets/NSF-ANT11-42102.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT11-42102", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The McMurdo Dry Valleys in Antarctica are among the coldest, driest habitats on the planet. Previous research has documented the presence of surprisingly diverse microbial communities in the soils of the Dry Valleys despite these extreme conditions. However, the degree to which these organisms are active is unknown; it is possible that much of this diversity reflects microbes that have blown into this environment that are subsequently preserved in these cold, dry conditions. This research will use modern molecular techniques to answer a fundamental question regarding these communities: which organisms are active and how do they live in such extreme conditions? The research will include manipulations to explore how changes in water, salt and carbon affect the microbial community, to address the role that these organisms play in nutrient cycling in this environment. The results of this work will provide a broader understanding of how life adapts to such extreme conditions as well as the role of dormancy in the life history of microorganisms. Results will be widely disseminated through publications as well as through presentations at national and international meetings; raw data will be made available through a high-profile web-based portal. The research will support two graduate students, two undergraduate research assistants and a postdoctoral fellow. The results will be incorporated into a webinar targeted to secondary and post-secondary educators and a complimentary hands-on class activity kit will be developed and made available to various teacher and outreach organizations.", "links": [ { diff --git a/datasets/NSF-ANT12-41487.json b/datasets/NSF-ANT12-41487.json index 65fd15018d..4b93a104bc 100644 --- a/datasets/NSF-ANT12-41487.json +++ b/datasets/NSF-ANT12-41487.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT12-41487", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines.", "links": [ { diff --git a/datasets/NSF-ANT13-55533_1.json b/datasets/NSF-ANT13-55533_1.json index 754e789e71..6a2af10115 100644 --- a/datasets/NSF-ANT13-55533_1.json +++ b/datasets/NSF-ANT13-55533_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT13-55533_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences.\nThis work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis.", "links": [ { diff --git a/datasets/NSF-ANT90-24544.json b/datasets/NSF-ANT90-24544.json index 6df39c2a2d..53863f56d3 100644 --- a/datasets/NSF-ANT90-24544.json +++ b/datasets/NSF-ANT90-24544.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-ANT90-24544", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Location: Ice camp on perennial sea ice in the southwestern corner of the Weddell Sea, Antarctic\n\nThe first direct radiative and turbulent surface flux measurements ever made over floating Antarctic sea ice. The data are from Ice Station Weddell as it drifted in the western Weddell Sea from February to late May 1992.\n\nData Types:\n\nHourly measurements of the turbulent surface fluxes of momentum and sensible and latent heat by eddy covariance at a height of 4.65 m above snow-covered sea ice. Instruments were a 3-axis sonic anemometer/thermometer and a Lyman-alpha hygrometer.\n\nHourly, surface-level measurements of the four radiation components: in-coming and out-going longwave and shortwave radiation. Instruments were hemispherical pyranometers and pyrgeometers.\n\nHourly mean values of standard meteorological variables: air temperature, dew point temperature, wind speed and direction, barometric pressure, surface temperature. Instruments were a propeller-vane for wind speed and direction and cooled-mirror dew-point hygrometers and platinum resistance thermometers for dew-points and temperatures. Surface temperature came from a Barnes PRT-5 infrared thermometer.\n\nFlux Data\nThe entire data kit is bundled as a zip file named ISW_Flux_Data.zip\nThe main data file is comma delimited.\nThe README file is ASCII.\nThe associated reprints of publications are in pdf.\n\nRadiosounding data: On Ice Station Weddell, typically twice a day from 21 February through 4 June 1992 made with both tethered (i.e., only boundary-layer profiles) and (more rarely) free-flying sondes that did not measure wind speed. (168 soundings).\n\nISW Radiosoundings\nThe entire data kit is bundled as a zip file named ISW_Radiosounding.zip.\nThe README file is in ASCII.\nTwo summary files that include the list of sounding and the declinations are in ASCII.\nThe 168 individual sounding files are in ASCII.\nTwo supporting publications that describe the data and some analyses are in pdf.\n\nRadiosounding data collected from the Russian ship Akademic Fedorov from 26 May through 5 June 1992 at 6-hourly intervals as it approached Ice Station Weddell from the north. These soundings include wind vector, temperature, humidity, and pressure. (40 soundings)\n\nAkademic Federov Radiosoundings\nThe entire data kit is bundled as a zip file named Akad_Federov_Radiosounding.zip.\nThe README file is in ASCII.\nA summary file that lists the soundings is in ASCII.\nThe 40 individual sounding files are in ASCII.\nTwo supporting publications that describe the data and some analyses are in pdf.\n\n\nDocumentation:\n\nAndreas, E. L, and K. J. Claffey, 1995: Air-ice drag coefficients in the western Weddell Sea: 1. Values deduced from profile measurements. Journal of Geophysical Research, 100, 4821\u20134831.\n\nAndreas, E. L, K. J. Claffey, and A. P. Makshtas, 2000: Low-level atmospheric jets and inversions over the western Weddell Sea. Boundary-Layer Meteorology, 97, 459\u2013486.\n\nAndreas, E. L, R. E. Jordan, and A. P. Makshtas, 2004: Simulations of snow, ice, and near-surface atmospheric processes on Ice Station Weddell. Journal of Hydrometeorology, 5, 611\u2013624.\n\nAndreas, E. L, R. E. Jordan, and A. P. Makshtas, 2005: Parameterizing turbulent exchange over sea ice: The Ice Station Weddell results. Boundary-Layer Meteorology, 114, 439\u2013460.\n\nAndreas, E. L, P. O. G. Persson, R. E. Jordan, T. W. Horst, P. S. Guest, A. A. Grachev, and C. W. Fairall, 2010: Parameterizing turbulent exchange over sea ice in winter. Journal of Hydrometeorology, 11, 87\u2013104.\n\nClaffey, K. J., E. L Andreas, and A. P. Makshtas, 1994: Upper-air data collected on Ice Station Weddell. Special Report 94-25, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, 62 pp.\n\nISW Group, 1993: Weddell Sea exploration from ice station. Eos, Transactions, American Geophysical Union, 74, 121\u2013126.\n\nMakshtas, A. P., E. L Andreas, P. N. Svyaschennikov, and V. F. Timachev, 1999: Accounting for clouds in sea ice models. Atmospheric Research, 52, 77\u2013113.", "links": [ { diff --git a/datasets/NSF-BWZ_0.json b/datasets/NSF-BWZ_0.json index 527a5e842b..1164f5f548 100644 --- a/datasets/NSF-BWZ_0.json +++ b/datasets/NSF-BWZ_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF-BWZ_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Blue Water Zone (BWZ) under NSF funding near Antarctica and Drakes Passage in 2004 to 2006.", "links": [ { diff --git a/datasets/NSF_Gulf_Rapid_0.json b/datasets/NSF_Gulf_Rapid_0.json index 85bc39f04b..784ac3d686 100644 --- a/datasets/NSF_Gulf_Rapid_0.json +++ b/datasets/NSF_Gulf_Rapid_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSF_Gulf_Rapid_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collaborative Research: A RAPID response to Hurricane Harvey's impacts on coastal carbon cycle, metabolic balance and ocean acidification.", "links": [ { diff --git a/datasets/NSIDC-0001_6.json b/datasets/NSIDC-0001_6.json index 8524115368..5db0fb9a69 100644 --- a/datasets/NSIDC-0001_6.json +++ b/datasets/NSIDC-0001_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0001_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides daily gridded brightness temperatures derived from passive microwave sensors and distributed in a polar stereographic projection. NSIDC produces daily gridded brightness temperatures from orbital swath data generated by the Special Sensor Microwave/Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F8, F11, and F13 platforms and the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP F17 and F18. The SSM/I and SSMIS channels used to calculate brightness temperatures include 19.3 GHz vertical and horizontal, 22.2 GHz vertical, 37.0 GHz vertical and horizontal, 85.5 GHz vertical and horizontal (on SSM/I), and 91.7 GHz vertical and horizontal (on SSMIS). Data at 85.5 GHz and 91.7 GHz are gridded at a resolution of 12.5 km, with all other frequencies at a resolution of 25 km. Orbital data for each 24-hour period are mapped to respective grid cells using a simple sum-and-average method, also known as the drop-in-the-bucket method. Data coverage began on 09 July 1987 and is ongoing through the most current processing, with updated data processed several times annually.", "links": [ { diff --git a/datasets/NSIDC-0020_1.json b/datasets/NSIDC-0020_1.json index e3fecd156a..4ddd04452f 100644 --- a/datasets/NSIDC-0020_1.json +++ b/datasets/NSIDC-0020_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0020_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CEAREX was a multi-platform field program conducted in the Norwegian Seas and Greenland north to Svalbard from September 1988 through May 1989. Canada, Denmark, France, Norway and the United States participated in the experiment.", "links": [ { diff --git a/datasets/NSIDC-0026_1.json b/datasets/NSIDC-0026_1.json index e6f15e3e6b..246c896177 100644 --- a/datasets/NSIDC-0026_1.json +++ b/datasets/NSIDC-0026_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0026_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of AVHRR imagery selected from hard copy 'quick look' images to provide the best coverage possible over the Arctic approximately every three days for a three-year period. Level-1B data from NOAA/SDSD have been calibrated and mapped to earth locations, then gridded to 1 km pixels on a basin scale to the polar stereographic projection. The projection is similar to that used by NSIDC to produce DMSP SSM/I polar brightness temperature and sea ice products. Each image was ranked for areal coverage of particular seas and for degree of cloud coverage. Passes covering a large area are generally favored over shorter passes with less cloud cover. The data set was developed in support of the Office Of Naval Research Arctic Leads Accelerated Research Initiative (Arctic Leads ARI). The aim of the Initiative was to develop a more thorough understanding of the oceanography, meteorology, and ice dynamics surrounding formation and evolution of leads in sea ice. The leads ARI field experiment took place from March to April 1992.\n\nA spreadsheet containing the image rankings is available in hard copy (NOARL Tech. Note 118, April 1991); paper copies of the spreadsheet are available on request. Data set information is available on-line. Data are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0033_1.json b/datasets/NSIDC-0033_1.json index 9e73b22cd0..e5d0884fe8 100644 --- a/datasets/NSIDC-0033_1.json +++ b/datasets/NSIDC-0033_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0033_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A gridded climatological monthly-mean data base of Arctic water vapor characteristics has been assembled by combining fixed station data with data from soundings taken over the Arctic Ocean from ships and Russian drifting stations.\n\nVariables provided include temperature, specific humidity, zonal vapor flux, meridional vapor flux, zonal wind speed, and meridional wind speed, available for 15 pressure levels extending from the surface (1,000 mb) to 300 mb. Sea level pressure and geopotential height are provided for the 850 mb, 700 mb, 500 mb and 300 mb levels. Precipitable water, vertically integrated zonal vapor flux and vertically integrated meridional vapor flux are available for five layers: surface to 850 mb, 850 to 700 mb, 700 to 500 mb, 500 to 400 mb and 400 to 300 mb.\n\nCoverage of the rawinsonde archives extends from 1954 through 1990 for data from the Russian North Pole series of drifting ice stations over the Arctic Ocean; from 1976 through 1991 for fixed-station data obtained from the National Center for Atmospheric Research; and from about 1958 through 1991 for fixed-station data obtained from the Historical Arctic Rawinsonde Archive.\n\nAll variables were obtained through interpolation of the raw sounding data, with the exception of sea level pressure and geopotential height. The files are structured in monthly data arrays over a subsection of the National Meteorological Center grid (octagonal grid format) centered over the pole, extending to approximately 65 degrees North on each side and about 55 degrees North at the corners.", "links": [ { diff --git a/datasets/NSIDC-0051_2.json b/datasets/NSIDC-0051_2.json index a813bdd5a0..b94b8db153 100644 --- a/datasets/NSIDC-0051_2.json +++ b/datasets/NSIDC-0051_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0051_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km. Data coverage began on 26 October 1978 and is ongoing through the most current processing, with updated data processed several times annually.", "links": [ { diff --git a/datasets/NSIDC-0052_1.json b/datasets/NSIDC-0052_1.json index c6b1f952e2..c368788109 100644 --- a/datasets/NSIDC-0052_1.json +++ b/datasets/NSIDC-0052_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0052_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Digital SAR Mosaic and Elevation Map of the Greenland Ice Sheet combines the most detailed synthetic aperture radar (SAR) image mosaic available with the best current digital elevation model. The mosaic image shows both the location of the ice edge and the distribution of melt-related 'scatterers' on the surface. These scatterers include ice lenses and complex layered structure in the percolation zone and bare ice of the ablation zone. Other melt-related features that can be seen include lake and surface meltwater stream channels at lower elevations, as well as ice-marginal lakes.\n\nThis characterization of the ice sheet provides a reference against which future change can be measured. Changing conditions resulting from climatic variation should show up as changes in the ice margin and shifts in the hydrologic zones. It is hoped that the standard reference provided by this data set can facilitate activities aimed at change detection and promote other work aimed at understanding the processes operating on the ice sheet.\n\nThe image data are derived from SAR image swaths acquired by the ERS-1 satellite during August of 1992. The mosaic was assembled at the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). Its component images are a copyrighted product of the European Space Agency. The mosaic, a value-added derived product, is available to individuals and non-profit organizations for research oriented purposes only. The Danish geodetic and cadastral agency Kort-og Matrikelstyrelsen (KMS) compiled the elevation data provided with the product from a number of sources, including field surveys, aerial photographs, and the ERS-1 radar altimeter.", "links": [ { diff --git a/datasets/NSIDC-0057_1.json b/datasets/NSIDC-0057_1.json index bc0c26d535..b79ee3cd39 100644 --- a/datasets/NSIDC-0057_1.json +++ b/datasets/NSIDC-0057_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0057_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Comprehensive Ocean - Atmosphere Data Set (COADS) Long Marine Reports Fixed-Length (LMRF) Arctic subset contains marine surface weather reports for regions north of 65 degrees N from ships, drifting ice stations, and buoys. The COADS LMRF Arctic subset contains data collected over the years 1950 to 1995 and includes the following parameters: air and sea temperature, cloudiness, humidity, and winds. The data are in the form of individual marine reports with a given latitude and longitude.", "links": [ { diff --git a/datasets/NSIDC-0060_1.json b/datasets/NSIDC-0060_1.json index a14b01c2b5..85dc084ec3 100644 --- a/datasets/NSIDC-0060_1.json +++ b/datasets/NSIDC-0060_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0060_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archive of daily rawinsonde measurements of wind direction and speed, atmospheric pressure, humidity, air temperature, and geopotential height as well as surface-based observation of cloud cover (amount, type and height) from Soviet North Pole drifting stations was assembled under the direction of Dr. J. Kahl, with funding from the National Oceanic and Atmospheric Administration, the National Science Foundation, and the Electric Power Research Institute. Soundings were recorded from April 19, 1954 to July 31, 1990 at drifting stations located in the Arctic Ocean, north of approximately 70 degrees North. Data were obtained from several different sources. All of these data are ultimately derived from the set of bound volumes of handwritten tables kept at the Arctic and Antarctic Research Institute (AARI) in St. Petersburg, Russia. Data are in 21 ASCII text format files with an average size of under 10 MB.", "links": [ { diff --git a/datasets/NSIDC-0063_1.json b/datasets/NSIDC-0063_1.json index 54f2615e80..e2b74aff93 100644 --- a/datasets/NSIDC-0063_1.json +++ b/datasets/NSIDC-0063_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0063_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The total annual freezing and thawing indices are defined as the\ncumulative number of degree-days when air temperatures are below and\nabove zero degrees Celsius. The total annual freezing index has been\nwidely used to predict permafrost distribution; estimate the\nmaximum thickness of sea, lake, and river ice, and the maximum depth of\nground-frost penetration; and classify snow types. The annual total\nthawing index has been used to predict permafrost distribution and to\nestimate the maximum depth of thaw in frozen ground. Both total\nfreezing and thawing indices are important parameters for engineering\ndesign in cold regions.\n\nData coverage is global. The data set contains the total annual freezing and thawing indices with a spatial resolution of 0.5 degrees\nlatitude by 0.5 degrees longitude. Two data files are available, for\nthe freeze and thaw indices respectively, in flat binary format. Each\nfile is approximately 1 MB in size. The total annual freezing and\nthawing indexes were calculated based upon the monthly mean air\ntemperature by Legates and Willmott (1990).", "links": [ { diff --git a/datasets/NSIDC-0070_1.json b/datasets/NSIDC-0070_1.json index 27861bc3aa..2046a879a0 100644 --- a/datasets/NSIDC-0070_1.json +++ b/datasets/NSIDC-0070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This compilation of recent ice velocity data of the Antarctic ice sheet is intended for use by the polar scientific community. The data are presented in tabular form (ASCII), containing latitude, longitude, speed, bearing, and error ranges. A metadata header describes the source of the data, the time of measurement, and gives details on measurement accuracy and precision. The tables are available for ftp transfer. Web pages developed specifically for this data set provide detailed information for viewing and selecting the velocity data. These pages contain large satellite image maps (available as jpeg files). The data sets used to create these images were contributed by several investigators, generally from already published work. Both in situ and image-based methods are used. References for the data sets are included with the data tables. If you have well-characterized Antarctic ice velocity data you would like to contribute to this site, please contact teds@icehouse.colorado.edu. If you have any questions concerning the relevance of these data to your work please contact NSIDC User Services.", "links": [ { diff --git a/datasets/NSIDC-0071_1.json b/datasets/NSIDC-0071_1.json index 7961081fc2..1345c9e1bb 100644 --- a/datasets/NSIDC-0071_1.json +++ b/datasets/NSIDC-0071_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0071_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of brightness temperatures acquired from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 Pathfinder satellite. The brightness temperatures are gridded onto the Equal-Area Scalable Earth Grid (EASE-Grid) and are presented in three different projections: Northern Hemisphere, Southern Hemisphere, and global.", "links": [ { diff --git a/datasets/NSIDC-0075_1.json b/datasets/NSIDC-0075_1.json index 8f114ba3cf..75f365dc2c 100644 --- a/datasets/NSIDC-0075_1.json +++ b/datasets/NSIDC-0075_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0075_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic atlas consists of 28 digital elevation maps which cover all of Antarctica north of 72.1 degrees south at a resolution of three kilometers. Each map contains surface elevations and coordinates for one atlas page covering 16 degrees of longitude. Data were acquired by the Geodetic Satellite (GEOSAT) Geodetic Mission (GM) from March 1985 through September 1986 and are available in both Universal Transverse Mercator (UTM) coordinates, and in latitude and longitude coordinates.\n\nData were mapped using the UTM projection in atlas form to decrease the distortion that usually occurs at the poles. Many features of the Antarctic Ice Sheet are shown in more detail than in previous digital elevation models, especially along the margin of the East Antarctic Ice Sheet. A geostatistical mapping technique (Herzfeld et al. 1993) improved the accuracy of surface elevations compared to previous GEOSAT elevation data sets. This atlas will facilitate the monitoring of changes in surface elevation that could indicate mass changes in the Antarctic Ice Sheet.", "links": [ { diff --git a/datasets/NSIDC-0076_1.json b/datasets/NSIDC-0076_1.json index a51ac222a7..953c5adc9b 100644 --- a/datasets/NSIDC-0076_1.json +++ b/datasets/NSIDC-0076_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0076_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a Digital Elevation Model (DEM) for Antarctica to 81.5 degrees south latitude, at a resolution of 5 km. Approximately twenty million data points were used to generate this data set. Data points were derived from ERS-1 radar altimetry during the geodetic phase from March 1994 to May 1995.", "links": [ { diff --git a/datasets/NSIDC-0077_2.json b/datasets/NSIDC-0077_2.json index f35963cb1c..862dcde2aa 100644 --- a/datasets/NSIDC-0077_2.json +++ b/datasets/NSIDC-0077_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0077_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) data set consists of gridded brightness temperature arrays for the Arctic and Antarctic, spanning 11 December 1972 through 16 May 1977. The ESMR instrument senses horizontally polarized radiation at a frequency of 19 GHz. The data are gridded to a polar stereographic projection at 25 km resolution and adjusted to partially remove instrument drift and sensitivity shifts. Daily data that could not be adjusted are missing from this data set. Data are in 2-byte integer flat-binary format.", "links": [ { diff --git a/datasets/NSIDC-0079_4.json b/datasets/NSIDC-0079_4.json index 16ce7683a8..eb66d209d3 100644 --- a/datasets/NSIDC-0079_4.json +++ b/datasets/NSIDC-0079_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0079_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This sea ice concentration data set was derived using measurements from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite and from the Special Sensor Microwave/Imager (SSM/I) sensors on the Defense Meteorological Satellite Program's (DMSP) -F8, -F11, and -F13 satellites. Measurements from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP-F17 are also included. The data set has been generated using the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) Bootstrap Algorithm with daily varying tie-points. Daily (every other day prior to July 1987) and monthly data are available for both the north and south polar regions. Data are gridded on the SSM/I polar stereographic grid (25 x 25 km) and provided in two-byte integer format. Data coverage began on 01 November 1978 and is ongoing through the most current processing, with updated data processed several times annually.", "links": [ { diff --git a/datasets/NSIDC-0080_2.json b/datasets/NSIDC-0080_2.json index c2ad7fe54d..df23d1f4a8 100644 --- a/datasets/NSIDC-0080_2.json +++ b/datasets/NSIDC-0080_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0080_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperature product provides near-real-time polar stereographic gridded daily brightness temperatures for both the Northern and Southern Hemispheres.", "links": [ { diff --git a/datasets/NSIDC-0081_2.json b/datasets/NSIDC-0081_2.json index f8e324339b..ebfe19b8eb 100644 --- a/datasets/NSIDC-0081_2.json +++ b/datasets/NSIDC-0081_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0081_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a Near-Real-Time (NRT) map of sea ice concentrations for both the Northern and Southern Hemispheres.", "links": [ { diff --git a/datasets/NSIDC-0092_1.json b/datasets/NSIDC-0092_1.json index 29f5a56c4f..50ddcc4f25 100644 --- a/datasets/NSIDC-0092_1.json +++ b/datasets/NSIDC-0092_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0092_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM), ice thickness grid, and bedrock elevation grid of Greenland, acquired as part of the PARCA program. DEM data are a combination of ERS-1 and Geosat satellite radar altimetry data, Airborne Topographic Mapper (ATM) data, and photogrammetric digital height data. Ice thickness data are based on approximately 700,000 data points collected in the 1990s from a University of Kansas airborne Ice Penetrating Radar (IPR). Nearly 30,000 data points were collected in the 1970s from a Technical University of Denmark (TUD) airborne echo sounder.The ice thickness grid was subtracted from the DEM to produce a grid of bedrock elevation values. Data set applications include studies of gravitational driving stress and ice volume (mass balance) of the Greenland Ice Sheet.", "links": [ { diff --git a/datasets/NSIDC-0093_1.json b/datasets/NSIDC-0093_1.json index f2e19d5c40..fe229aa898 100644 --- a/datasets/NSIDC-0093_1.json +++ b/datasets/NSIDC-0093_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0093_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glaciochemical and accumulation rate records developed from four ice cores in central West Antarctica are used to reconstruct former atmospheric circulation patterns in this region for the last 40 years with extended records (150-250 years) at two sites. The sites lie on a 200 km traverse from 82 degrees 22 minutes south, 119 degrees 17 minutes west to 81 degrees 22 minutes south, 107 degrees 17 minutes west, gaining elevation from 950 to 1930 m. The glaciochemical records represent the major ionic species present in Antarctic snow: sodium, potassium, magnesium, calcium, chloride, nitrate, and sulfate.", "links": [ { diff --git a/datasets/NSIDC-0108_1.json b/datasets/NSIDC-0108_1.json index a07470adc5..eb7f28a21b 100644 --- a/datasets/NSIDC-0108_1.json +++ b/datasets/NSIDC-0108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data describe the concentration and carbon-isotopic composition (d13CO2) of atmospheric CO2 from air trapped in ice between 27,000 and 1,300 years before present from Taylor Dome, Antarctica. Data are used to investigate the causes of the CO2 concentration increase that occurred during the transition between the last glacial maximum (LGM) and the Holocene. Data are in tab-delimited ASCII and Excel formats, and are available via ftp.", "links": [ { diff --git a/datasets/NSIDC-0114_1.json b/datasets/NSIDC-0114_1.json index 9dbf737e25..c32411eef9 100644 --- a/datasets/NSIDC-0114_1.json +++ b/datasets/NSIDC-0114_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0114_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the result of a study of volcanic ash and rock fragment (tephra) layers in exposed blue ice areas on Brimstone Peak (75.888S 158.55E) in East Antarctica. Tephra samples were collected between 15 November 1996 and 15 January 1997. The Antarctic ice sheets preserve a record of the volcanic ash layers and chemical aerosol signatures of local and distant volcanic eruptions. Correlation of individual tephra layers, or sets of layers, in blue ice areas will allow a better understanding of the geometry of ice flow in these areas. Tephra layers in deep ice cores can also provide unique time-stratigraphic markers in cores that are difficult to date. Data include the following information for each sample site: a general description, electron microprobe analysis, GPS location, neutron activation analysis, and a visual description of the petrography.Data are provided as Excel 97 data files, JPG map files, and GIF-formatted BSE images. Data are available via ftp.", "links": [ { diff --git a/datasets/NSIDC-0115_1.json b/datasets/NSIDC-0115_1.json index 8abdfe2683..13b9d8e1f8 100644 --- a/datasets/NSIDC-0115_1.json +++ b/datasets/NSIDC-0115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the result of a study of volcanic ash and rock fragment (tephra) layers in exposed blue ice areas on Mt. DeWitt, Antarctica (77.12 deg S, 159.51 deg E). Tephra samples were collected between 15 November 1996 and 15 January 1997. Data include the following information for each sample site: a general description, electron microprobe analysis, GPS location, neutron activation analysis, and a visual description of the petrography. Data are provided as an Excel 97 data file, (this file is also divided into various text files) and TIF images. Data are available via ftp. Antarctic ice sheets preserve a record of the volcanic ash layers and chemical aerosol signatures of local and distant volcanic eruptions. Correlation of individual tephra layers, or sets of layers, in blue ice areas will allow a better understanding of the geometry of ice flow in these areas. Tephra layers in deep ice cores can also provide unique time-stratigraphic markers in cores that are difficult to date.", "links": [ { diff --git a/datasets/NSIDC-0118_1.json b/datasets/NSIDC-0118_1.json index 49383281ae..a6055146ec 100644 --- a/datasets/NSIDC-0118_1.json +++ b/datasets/NSIDC-0118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Declassified Intelligence Satellite Photographs (DISP) Yearly Satellite Photographic Mosaics of Greenland are composites of black-and-white photographs of Greenland taken from American satellites in 1962 and 1963. The mosaics provide details of ice sheet morphology, glaciers, rock outcrops, the coastline, and other features. The image mosaics are useful for comparing the extent and internal configuration of the Greenland ice sheet with current satellite data. The data set consists of one tagged image file (.TIF) for each year.\n\nThe files are large-- the 1962 mosaic image dimensions are 17,092 by 28,484 pixels and the file size is 464.3 MB. The 1963 image dimensions are 17,792 by 27,805 pixels and the file size is 471.8 MB.", "links": [ { diff --git a/datasets/NSIDC-0144_1.json b/datasets/NSIDC-0144_1.json index b715dec8b4..3caff444bc 100644 --- a/datasets/NSIDC-0144_1.json +++ b/datasets/NSIDC-0144_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0144_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes Defense Meteorological Satellite Program (DMSP) F-13 passive microwave brightness temperatures gridded to the geographic (lat/long) and UTM grids of the Cold Land Processes Field Experiment (CLPX) Large Regional Study Area (LRSA).", "links": [ { diff --git a/datasets/NSIDC-0145_1.json b/datasets/NSIDC-0145_1.json index 754c040c2f..7a25ac5ab6 100644 --- a/datasets/NSIDC-0145_1.json +++ b/datasets/NSIDC-0145_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0145_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes Aqua Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) passive microwave brightness temperatures gridded to the geographic (lat/long) and UTM grids of the Large Regional Study Area (LRSA) of the NASA Cold Land Processes Field Experiement (CLPX).", "links": [ { diff --git a/datasets/NSIDC-0148_1.json b/datasets/NSIDC-0148_1.json index 2af3ac06aa..fffb3d4256 100644 --- a/datasets/NSIDC-0148_1.json +++ b/datasets/NSIDC-0148_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0148_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of apparent surface reflectance, subpixel snow-covered area, and grain size collected from the Hyperion hyperspectral imager. The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines).", "links": [ { diff --git a/datasets/NSIDC-0149_1.json b/datasets/NSIDC-0149_1.json index b6e424c311..756cd4d848 100644 --- a/datasets/NSIDC-0149_1.json +++ b/datasets/NSIDC-0149_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0149_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of Landsat thematic mapper imagery collected over the Cold Land Processes Field Experiment (CLPX) Large Regional Study Area (LRSA), located between 38.5-42 N and 104-108.5 W. Data consist of Level 1G imagery products (radiance) that have been radiometrically and geometrically corrected.", "links": [ { diff --git a/datasets/NSIDC-0150_1.json b/datasets/NSIDC-0150_1.json index 41fc049767..1acaf7f2d8 100644 --- a/datasets/NSIDC-0150_1.json +++ b/datasets/NSIDC-0150_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0150_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes Level-1B2 georectified terrain, Level-2 land surface, and an ancillary geographic product for the Large Regional Study Area (LRSA) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado and southern Wyoming.", "links": [ { diff --git a/datasets/NSIDC-0151_1.json b/datasets/NSIDC-0151_1.json index a0eccf4862..e828e769f4 100644 --- a/datasets/NSIDC-0151_1.json +++ b/datasets/NSIDC-0151_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0151_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) data as part of the Cold Land Processes Field Experiment (CLPX). Parameters include radiances, surface reflectance, snow cover, land surface temperature/emissivity, and vegetation indices.", "links": [ { diff --git a/datasets/NSIDC-0152_1.json b/datasets/NSIDC-0152_1.json index 6552f14f89..ce2172599b 100644 --- a/datasets/NSIDC-0152_1.json +++ b/datasets/NSIDC-0152_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0152_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes AVHRR/HRPT (Advanced Very High Resolution Radiometer/High Resolution Picture Transmission) brightness temperatures and reflectances over the NASA Cold Land Processes Field Experiment (CLPX) Large Regional Study Area (LRSA).", "links": [ { diff --git a/datasets/NSIDC-0153_1.json b/datasets/NSIDC-0153_1.json index 2be11ae33a..868fa08e47 100644 --- a/datasets/NSIDC-0153_1.json +++ b/datasets/NSIDC-0153_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0153_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne Synthetic Aperture Radar (AIRSAR) is a side-looking imaging radar that is able to collect data irrespective of daylight or cloud cover. The AIRSAR instrument operated in two modes over each Cold Land Processes Field Experiment (CLPX) Meso-cell Study Area (MSA). In the first mode (POLSAR), polarimetric radar data were collected at P-, L-, and C-bands. In the second mode (TOPSAR), cross-track interferometry data were collected at C- and L-bands.", "links": [ { diff --git a/datasets/NSIDC-0154_1.json b/datasets/NSIDC-0154_1.json index 9a074be548..a9b89f6af4 100644 --- a/datasets/NSIDC-0154_1.json +++ b/datasets/NSIDC-0154_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0154_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of apparent surface reflectance, subpixel snow-covered area and grain size inferred from data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).", "links": [ { diff --git a/datasets/NSIDC-0155_1.json b/datasets/NSIDC-0155_1.json index 0cecf8a8a9..396bf222ba 100644 --- a/datasets/NSIDC-0155_1.json +++ b/datasets/NSIDC-0155_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0155_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides multiband polarimetric brightness temperature images over three 25 x 25 km mesoscale study areas (MSAs) in Northern Colorado.", "links": [ { diff --git a/datasets/NSIDC-0157_1.json b/datasets/NSIDC-0157_1.json index 561b98b895..e46d53baa5 100644 --- a/datasets/NSIDC-0157_1.json +++ b/datasets/NSIDC-0157_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0157_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of color infrared orthophotography (TerrainVision\u00ae - High resolution Topographic Mapping & Aerial Photography, with 6-inch pixel resolution), lidar elevation returns (raw/combined, filtered to bare ground/snow, and filtered to top of vegetation), elevation contours (0.5 meter) and snow depth contours (0.1 meter).", "links": [ { diff --git a/datasets/NSIDC-0158_1.json b/datasets/NSIDC-0158_1.json index d64d1e6a31..fab10b96fb 100644 --- a/datasets/NSIDC-0158_1.json +++ b/datasets/NSIDC-0158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne gamma surveys were conducted over each of the three Cold Land Processes Field Experiment (CLPX) Meso-cell Study Areas (MSAs) in northern Colorado, USA, during September 2001 and 2002, and during the three Intensive Observation Periods (IOPs) in February 2002 (IOP1), February 2003 (IOP3) and March 2003 (IOP4). Data collected in September 2001 and 2002 provided background gamma radiation measurements necessary to calculate measurements of snow water equivalent (SWE) and soil moisture during subsequent winters.", "links": [ { diff --git a/datasets/NSIDC-0159_1.json b/datasets/NSIDC-0159_1.json index 61428fc6cc..8e128b612b 100644 --- a/datasets/NSIDC-0159_1.json +++ b/datasets/NSIDC-0159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Ku-band polarimetric scatterometer (POLSCAT) data collected as part of the Cold Land Processes Field Experiment (CLPX) to enable the development of a retrieval algorithm for snow water equivalent (SWE).", "links": [ { diff --git a/datasets/NSIDC-0161_1.json b/datasets/NSIDC-0161_1.json index 0a81157831..744a031600 100644 --- a/datasets/NSIDC-0161_1.json +++ b/datasets/NSIDC-0161_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0161_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents more than 400 sub-canopy digital thermograms collected at the Fraser Experimental Forest (Fraser, Colorado, USA) using an Infrared Solutions IR SnapShot\u2122 thermoelectric thermal infrared (IR) imaging radiometer that was cold-weather modified.", "links": [ { diff --git a/datasets/NSIDC-0164_1.json b/datasets/NSIDC-0164_1.json index d73bdbc683..bec0b30ab2 100644 --- a/datasets/NSIDC-0164_1.json +++ b/datasets/NSIDC-0164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is comprised of FMCW radar measurements, which are sensitive to electromagnetic discontinuities in the snowpack made during the 2002 and 2003 Cold Land Processes Field Experiment (CLPX).", "links": [ { diff --git a/datasets/NSIDC-0165_1.json b/datasets/NSIDC-0165_1.json index 8238671d85..db275b5e0e 100644 --- a/datasets/NSIDC-0165_1.json +++ b/datasets/NSIDC-0165_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0165_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperature observations of the snow cover at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in Fraser, Colorado, USA.", "links": [ { diff --git a/datasets/NSIDC-0166_1.json b/datasets/NSIDC-0166_1.json index c80aaeab2c..504af7c118 100644 --- a/datasets/NSIDC-0166_1.json +++ b/datasets/NSIDC-0166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes ground-based radar observations carried out at the Fraser Experimental Forest Headquarters, Colorado, USA.", "links": [ { diff --git a/datasets/NSIDC-0167_1.json b/datasets/NSIDC-0167_1.json index 1f23703f9f..09520bf856 100644 --- a/datasets/NSIDC-0167_1.json +++ b/datasets/NSIDC-0167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains microwave radiometry data collected at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in Colorado, USA, during IOP4 (March-April 2003).", "links": [ { diff --git a/datasets/NSIDC-0168_1.json b/datasets/NSIDC-0168_1.json index 93b2e5e455..199f4045b8 100644 --- a/datasets/NSIDC-0168_1.json +++ b/datasets/NSIDC-0168_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0168_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes two sets of soil temperature profiles, two sets of soil moisture profiles, two sets of soil heat flux profiles (in dense pine and open pine areas), and one set of a snow temperature profile, air temperature, and relative humidity measurements. Measurements were made at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado.", "links": [ { diff --git a/datasets/NSIDC-0169_1.json b/datasets/NSIDC-0169_1.json index cfdd9d7a75..dda842dd17 100644 --- a/datasets/NSIDC-0169_1.json +++ b/datasets/NSIDC-0169_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0169_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents snow depth, snow water equivalence (SWE), snow wetness data, and snow pit data from two pine sites and a small clearing at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado.", "links": [ { diff --git a/datasets/NSIDC-0170_1.json b/datasets/NSIDC-0170_1.json index a04776c9a8..16e683560c 100644 --- a/datasets/NSIDC-0170_1.json +++ b/datasets/NSIDC-0170_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0170_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of solar and longwave radiation data from beneath two pine canopies (one uniform, one discontinuous) at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado.", "links": [ { diff --git a/datasets/NSIDC-0172_1.json b/datasets/NSIDC-0172_1.json index 4c3cacddb5..43c2850c5e 100644 --- a/datasets/NSIDC-0172_1.json +++ b/datasets/NSIDC-0172_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0172_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological observations at ten sites throughout the Small Regional Study Area (SRSA) of the Cold Land Processes Field Experiment (CLPX) in Fraser, Colorado, USA.", "links": [ { diff --git a/datasets/NSIDC-0173_1.json b/datasets/NSIDC-0173_1.json index 8fcaa468de..587934e511 100644 --- a/datasets/NSIDC-0173_1.json +++ b/datasets/NSIDC-0173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological observations at 36 sites throughout the Small Regional Study Area (SRSA) of the NASA Cold Land Processes Field Experiment (CLPX) in Colorado, USA.", "links": [ { diff --git a/datasets/NSIDC-0175_2.json b/datasets/NSIDC-0175_2.json index 34647091b9..97d9a6c24e 100644 --- a/datasets/NSIDC-0175_2.json +++ b/datasets/NSIDC-0175_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0175_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of snow depth data from nine study areas, within three larger-scale areas in northern Colorado (Fraser, North Park, and Rabbit Ears Meso-cell Study Areas (MSAs)). The study areas range from low-relief (flat topography) unforested areas with shallow snow covers, to high-relief (complex topography) densely forested areas with deep snow covers.", "links": [ { diff --git a/datasets/NSIDC-0176_2.json b/datasets/NSIDC-0176_2.json index aef9ee96bb..6d51f06b6a 100644 --- a/datasets/NSIDC-0176_2.json +++ b/datasets/NSIDC-0176_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0176_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of snow pit data from nine study areas, within three larger-scale areas in northern Colorado (Fraser, North Park, and Rabbit Ears Meso-cell Study Areas (MSAs)). The study areas range from low-relief (flat topography) unforested areas with shallow snow covers, to high-relief (complex topography) densely forested areas with deep snow covers.", "links": [ { diff --git a/datasets/NSIDC-0178_1.json b/datasets/NSIDC-0178_1.json index 648d727de9..f0374e524b 100644 --- a/datasets/NSIDC-0178_1.json +++ b/datasets/NSIDC-0178_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0178_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of in-situ point measurements of soil moisture within three 25-km by 25-km Meso-cell Study Areas (MSAs) in northern Colorado (Fraser, North Park , and Rabbit Ears).", "links": [ { diff --git a/datasets/NSIDC-0179_1.json b/datasets/NSIDC-0179_1.json index f02c4e7814..125d6eb567 100644 --- a/datasets/NSIDC-0179_1.json +++ b/datasets/NSIDC-0179_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0179_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Local Analysis and Prediction System (LAPS), run by the NOAA's Forecast Systems Laboratory (FSL), combines numerous observed meteorological data sets into a collection of atmospheric analyses.", "links": [ { diff --git a/datasets/NSIDC-0180_1.json b/datasets/NSIDC-0180_1.json index 1b0698ed63..d29779229a 100644 --- a/datasets/NSIDC-0180_1.json +++ b/datasets/NSIDC-0180_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0180_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Rapid Update Cycle analysis/model system at a 20-km horizontal resolution (RUC20) provides short-range numerical weather guidance for general forecasting, as well as for the special short-term needs of aviation and severe-weather forecasting. This data set consists of 60 meteorological and soil parameters at 50 computational levels.", "links": [ { diff --git a/datasets/NSIDC-0181_1.json b/datasets/NSIDC-0181_1.json index 526389f28d..d14032c781 100644 --- a/datasets/NSIDC-0181_1.json +++ b/datasets/NSIDC-0181_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0181_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LDAS data set contains 43 model and observation-based fields produced by the LDAS uncoupled modeling system at the NASA Goddard Space Flight Center using the Mosaic Land Surface Model (LSM).", "links": [ { diff --git a/datasets/NSIDC-0194_1.json b/datasets/NSIDC-0194_1.json index 8eb0a586fd..996cf6e44c 100644 --- a/datasets/NSIDC-0194_1.json +++ b/datasets/NSIDC-0194_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0194_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Wakasa Bay Field Campaign was conducted to validate rainfall algorithms developed for the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E).", "links": [ { diff --git a/datasets/NSIDC-0195_1.json b/datasets/NSIDC-0195_1.json index f48bb41f11..616bd369d8 100644 --- a/datasets/NSIDC-0195_1.json +++ b/datasets/NSIDC-0195_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0195_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Airborne Second Generation Precipitation Radar (APR-2) collected data in the Wakasa Bay AMSR-E validation campaign over the sea of Japan on board a NASA P-3 aircraft. Data were collected on all P-3 flights that encountered precipitation.", "links": [ { diff --git a/datasets/NSIDC-0201_1.json b/datasets/NSIDC-0201_1.json index a9331e3f32..644978f699 100644 --- a/datasets/NSIDC-0201_1.json +++ b/datasets/NSIDC-0201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a continuous, high-resolution record of biogenic sulfur (methanesulfonate, known as MSA and CH3SO3-) in the 1000 m deep Siple Dome A (SDMA) core, covering 100,000 to 20 years BP. The analysis was done on between August 2002 and November 2003 at the University of California, Irvine. Investigators used a mass spectrometer to measure methanesulfonate. Measurements are given as MSA concentration at various depths. Estimated age of the ice at each depth is also given. This project was a part of the West Antarctic Ice Sheet Cores (WAISCORES) project for deep ice coring in West Antarctica. WAISCORES is supported by the Office of Polar Programs, National Science Foundation (NSF).", "links": [ { diff --git a/datasets/NSIDC-0202_1.json b/datasets/NSIDC-0202_1.json index 732cfc51d4..20312f47d1 100644 --- a/datasets/NSIDC-0202_1.json +++ b/datasets/NSIDC-0202_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0202_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are CO2 concentrations of the air occluded in Siple Dome ice core, Antarctica. The study was conducted between January 2001 and March 2003 on a deep ice core from Siple Dome Core A, located at 81.66 S, 148.82 W.", "links": [ { diff --git a/datasets/NSIDC-0209_1.json b/datasets/NSIDC-0209_1.json index f7d5160bb5..9d2a7fba9b 100644 --- a/datasets/NSIDC-0209_1.json +++ b/datasets/NSIDC-0209_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0209_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes non-Doppler polar volume reflectivity data from the Baltic Sea Experiment (BALTEX). Data were collected on Sweden's Gotland Island, using an Ericsson radar mounted at 56 m above sea level.", "links": [ { diff --git a/datasets/NSIDC-0210_1.json b/datasets/NSIDC-0210_1.json index 0d624b8fb4..b3cfd4be09 100644 --- a/datasets/NSIDC-0210_1.json +++ b/datasets/NSIDC-0210_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0210_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes rainfall data from 25 sites in Iowa, centered on the Iowa City Municipal Airport.", "links": [ { diff --git a/datasets/NSIDC-0211_1.json b/datasets/NSIDC-0211_1.json index d925969061..1988384689 100644 --- a/datasets/NSIDC-0211_1.json +++ b/datasets/NSIDC-0211_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0211_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Rapid Update Cycle, version 2 at 40km (RUC-2, known to the Cold Land Processes community as RUC40) model is a Mesoscale Analysis and Prediction System (MAPS) data set that uses the Model Output Reduced Data Set (MORDS) version. This data set has been subsetted for use in the Cold Land Processes Field Experiment (CLPX).", "links": [ { diff --git a/datasets/NSIDC-0212_1.json b/datasets/NSIDC-0212_1.json index 95fa00ec94..cf13326dc2 100644 --- a/datasets/NSIDC-0212_1.json +++ b/datasets/NSIDC-0212_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0212_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands.", "links": [ { diff --git a/datasets/NSIDC-0218_1.json b/datasets/NSIDC-0218_1.json index 079f681705..04f52b636e 100644 --- a/datasets/NSIDC-0218_1.json +++ b/datasets/NSIDC-0218_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0218_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Greenland ice sheet melt extent data, acquired as part of the NASA Program for Arctic Regional Climate Assessment (PARCA), is a daily (or every other day, prior to August 1987) estimate of the spatial extent of wet snow on the Greenland ice sheet since 1979. It is derived from passive microwave satellite brightness temperature characteristics using the Cross-Polarized Gradient Ratio (XPGR) of Abdalati and Steffen (1997). It is physically based on the changes in microwave emission characteristics observable in data from the Scanning Multi-channel Microwave Radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) instruments when surface snow melts. It is not a direct measure of the snow wetness but rather is a binary indicator of the state of melt of each SMMR and SSM/I pixel on the ice sheet for each day of observation. It is, however, a useful proxy for the amount of melt that occurs on the Greenland ice sheet. The data are provided in a variety of formats including raw data in ASCII format, gridded daily data in binary format, and annual and complete time series climatologies in gridded binary and GeoTIFF format. All data are in a 60 x 109 pixel subset of the standard Northern Hemisphere polar stereographic grid with a 25 km resolution and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0223_1.json b/datasets/NSIDC-0223_1.json index 91ce53e0c7..4e84164757 100644 --- a/datasets/NSIDC-0223_1.json +++ b/datasets/NSIDC-0223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells.", "links": [ { diff --git a/datasets/NSIDC-0240_1.json b/datasets/NSIDC-0240_1.json index f6b8072fb2..d89d1b126a 100644 --- a/datasets/NSIDC-0240_1.json +++ b/datasets/NSIDC-0240_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0240_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data that the Support Office for Aerogeophysical Research (SOAR) provides include various aerogeophysical measurements taken in the West Antarctic Ice Shelf (WAIS) from 1994 to 2001. \r\n\r\nThe instruments used in experiments include ice-penetrating radar, laser altimetry and magnetics, and an integrated aerogeophysical platform that includes airborne gravity with carrier-phase GPS to support kinematic differential positioning.\r\n\r\nSOAR is a part of the University of Texas Institute for Geophysics (UTIG) and provides several types of data associated with various campaigns over the years. This material is based on work supported by the National Science Foundation under Grants: OPP-9120464, 9319369, 9319379, and 9911617.", "links": [ { diff --git a/datasets/NSIDC-0247_1.json b/datasets/NSIDC-0247_1.json index bb0db47c55..b4d93a947c 100644 --- a/datasets/NSIDC-0247_1.json +++ b/datasets/NSIDC-0247_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0247_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of microparticle and chemistry data from Byrd Ice Core, the first ice core to reach bedrock in Antarctica. The core was drilled with a cable-suspended electromechanical rotary drill at Byrd Station, Antarctica. The vertical thickness of the ice was 2164 meters and more than 99 percent of the core was recovered. Cores were sought for investigations of the physical properties of the ice sheet, the nature of the ice-rock contact, and the composition of the underlying bedrock.", "links": [ { diff --git a/datasets/NSIDC-0253_1.json b/datasets/NSIDC-0253_1.json index b50d5ee938..1f2e585803 100644 --- a/datasets/NSIDC-0253_1.json +++ b/datasets/NSIDC-0253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set compares global atmospheric concentration of methane from ice cores taken on the ice sheets of Antarctica and Greenland. The data come from multiple ice cores on each continent, including Greenland Ice Core Project (GRIP) and Greenland Ice Sheet Project (GISP) ice cores and the Byrd and Vostok cores from Antarctica.", "links": [ { diff --git a/datasets/NSIDC-0271_1.json b/datasets/NSIDC-0271_1.json index 9a15376059..671e2c683d 100644 --- a/datasets/NSIDC-0271_1.json +++ b/datasets/NSIDC-0271_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0271_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises global, monthly satellite-derived Snow Water Equivalent (SWE) climatologies from November 1978 through May 2007, with periodic updates released as resources permit. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). Global snow water equivalent is derived from Scanning Multichannel Microwave Radiometer (SMMR) and selected Special Sensor Microwave/Imagers (SSM/I). Northern Hemisphere data are enhanced with snow cover frequencies derived from the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data (these data were not produced for the Southern Hemisphere). The data are binary data files and PNG images, and are available via HTTPS.", "links": [ { diff --git a/datasets/NSIDC-0272_1.json b/datasets/NSIDC-0272_1.json index 6d78766666..baa54adc70 100644 --- a/datasets/NSIDC-0272_1.json +++ b/datasets/NSIDC-0272_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0272_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Land Ice Measurements from Space (GLIMS) is an international initiative with the goal of repeatedly surveying the world's estimated 200,000 glaciers. GLIMS uses data collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard the Terra satellite and the LANDSAT series of satellites, along with historical observations. \n\nThe GLIMS initiative has created a unique glacier inventory, storing information about the extent and rates of change of all the world's mountain glaciers and ice caps. The GLIMS Glacier Database was built up from data contributions from many glaciological institutions, which are managed by Regional Coordinators, who coordinate the production of glacier mapping results for their particular region. The GLIMS Glacier Database provides students, educators, scientists, and the public with reliable glacier data from these analyses. New glacier data are continually being added to the database.\n\nThe GLIMS Glacier Viewer was developed to provide the public with easy access to the GLIMS Glacier Database. This Web application allows users to view and query several thematic layers, including glacier outlines, Regional Coordinator institution locations, the World Glacier Inventory, and more. GLIMS data can be downloaded into a number of GIS-compatible formats, including ESRI Shapefiles, MapInfo tables, Geographic Mark-up Language (GML), and Keyhole Mark-up Language (KML) suitable for viewing in Google Earth.", "links": [ { diff --git a/datasets/NSIDC-0279_1.json b/datasets/NSIDC-0279_1.json index d19804b4e4..b71df23acd 100644 --- a/datasets/NSIDC-0279_1.json +++ b/datasets/NSIDC-0279_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0279_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the WAISCORES (West Antarctic Ice Sheet cores) project, research funded by the National Science Foundation (NSF) and designed to improve understanding of how the West Antarctic ice sheet influences climate and sea level change. WAISCORES investigators acquired and analyzed ice cores from the Siple Dome, in the Siple Coast region, West Antarctica. These data provide researchers with a record of natural climatic variability and anthropogenic influence on biogeochemical cycles. Because ice cores contain an archive of preindustrial air, a baseline can be established, and the extent of human impact on the climate can be ascertained. \n\nThis data set includes mixing ratios of carbonyl sulfide (COS), methyl chloride (CH3Cl), and methyl bromide (CH3Br). Data samples were retrieved from the Siple C ice core, which was drilled at 81.65\ufffd S, 148.81\ufffd W in December 1995. The core site sits 620 m above sea level near the edge of the Ross Ice Shelf where there is a mean annual temperature of -25.4 \ufffdC.\n\nData are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0281_1.json b/datasets/NSIDC-0281_1.json index fd590dccb3..a3bc347ab8 100644 --- a/datasets/NSIDC-0281_1.json +++ b/datasets/NSIDC-0281_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0281_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow pit measurements of oxygen isotopes, 17O and 18O, in nitrate and ion concentrations, and surface measurements of oxygen isotopes in nitrate and in nitrate aerosols from the Amundsen-Scott South Pole Station, Antarctica. The 6-meter snow pit provides investigators with a 25-year record of nitrate isotope variations and ion concentrations for a period spanning from 1979 to 2004. Monthly surface snow and weekly aerosol collections yield a year-long record of nitrate isotopic composition starting 01 December 2003 and ending 31 December 2004.\n\nLittle is known about the past denitrification of the stratosphere in high latitude regions. Such knowledge is important to understanding the chemical state of the ancient atmospheres and evaluating the present climate models. With this research, investigators aim to understand the denitrification of the Antarctic stratosphere and quantify the sources of nitrate aerosols over time.\n\nData are in Microsoft Excel format and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0283_1.json b/datasets/NSIDC-0283_1.json index 552e71352f..209a84c710 100644 --- a/datasets/NSIDC-0283_1.json +++ b/datasets/NSIDC-0283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic megadune research was conducted during two field seasons, one in November 2002 and the other during the period of December 2003 through January 2004. The megadune field site is located on the East Antarctic Plateau, southeast of Vostok station. The objectives of this multi-facetted research are 1) to determine the physical characteristics of the firn across the dunes including typical climate indicators such as stable isotopes and major chemical species and 2) to install instruments to measure the time variation of near-surface wind and temperature with depth, to test and refine hypotheses for megadune formation. It is important to improve our current understanding of the megadunes because of their extreme nature, their broad extent, and their potential impact on the climate record. Megadunes are a manifestation of an extreme terrestrial climate and may provide insight on the past terrestrial climate or on processes active on other planets.\n\nSnow megadunes are undulating variations in accumulation and surface texture with wavelengths of 2 to 5 km and amplitudes up to 5 meters. The features cover 500,000 km2 of the East Antarctic plateau, occurring in areas of moderate regional slope and low accumulation on the flanks of the ice sheet between 2500 and 3800 meters elevation. Landsat images and aerial photography indicate the dunes consist of alternating surfaces of glaze and rough sastrugi, with gradational boundaries. This pattern is oriented perpendicular to the mean wind direction, as modeled in katabatic wind studies. Glazed surfaces cover the leeward faces and troughs; rough sastrugi cover the windward faces and crests. The megadune pattern is crossed by smooth to eroded wind-parallel longitudinal dunes. Wind-eroded longitudinal dunes form spectacular 1-meter-high sastrugi in nearby areas.\n\nThis data set contains automated weather station (AWS) data from two sites. The Mac site was oriented on the rough sastrugi-covered windward face and the Zoe site was on the glazed leeward face. The AWSs collected data throughout the year from 16 January 2004 to 17 November 2004. Investigators received data from the two field sites via the ARGOS Satellite System (http://www.argosinc.com/). Data are provided in space-delimited ASCII text format and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0291_1.json b/datasets/NSIDC-0291_1.json index 4797aa0ef0..015d4a659c 100644 --- a/datasets/NSIDC-0291_1.json +++ b/datasets/NSIDC-0291_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0291_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of three-dimensional meteorological analyses for the entire cold season 2002-2003 for the three CLPX Meteorological Study Areas (MSAs) in northern Colorado (North Park, Fraser and Rabbit Ears) using high-resolution (500 m horizontal grid spacing).", "links": [ { diff --git a/datasets/NSIDC-0301_1.json b/datasets/NSIDC-0301_1.json index 5dc905965f..5780898a32 100644 --- a/datasets/NSIDC-0301_1.json +++ b/datasets/NSIDC-0301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of the Earth. NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (AE_L2A) input source data are used.\n\nThese data are provided in three EASE-Grid projections (north and south Lambert azimuthal and global cylindrical) at 25 km resolution, and in one global cylindrical, equidistant latitude-longitude projection at 0.25 degree (quarter-degree) resolution.", "links": [ { diff --git a/datasets/NSIDC-0302_1.json b/datasets/NSIDC-0302_1.json index 2e36ab8df0..ba5aa05311 100644 --- a/datasets/NSIDC-0302_1.json +++ b/datasets/NSIDC-0302_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0302_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of the Earth. NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an inverse-distance squared method. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (AE_L2A) input source data are used to create the gridded brightness temperature data.", "links": [ { diff --git a/datasets/NSIDC-0304_1.json b/datasets/NSIDC-0304_1.json index 30c03e91ca..473aceb452 100644 --- a/datasets/NSIDC-0304_1.json +++ b/datasets/NSIDC-0304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Antarctica is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.\n\nThis DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 500 m grid spacing. The grid covers all of Antarctica north of 86\u00b0 S. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.\n\nThe data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0305_1.json b/datasets/NSIDC-0305_1.json index b0162458f4..7a2617c9cf 100644 --- a/datasets/NSIDC-0305_1.json +++ b/datasets/NSIDC-0305_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0305_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and land Elevation Satellite (ICESat) provides global measurements of elevation, and repeats measurements along nearly-identical tracks; its primary mission is to measure changes in ice volume (mass balance) over time. This digital elevation model (DEM) of Greenland is derived from GLAS/ICESat laser altimetry profile data and provides new surface elevation grids of the ice sheets and coastal areas, with greater latitudinal extent and fewer slope-related effects than radar altimetry.\n\nThis DEM is generated from the first seven operational periods (from February 2003 through June 2005) of the GLAS instrument. It is provided on polar stereographic grids at 1 km grid spacing. The grid covers all of Greenland south of 83\u00b0 N. Elevations are reported as centimeters above the datums, relative to both the WGS 84 ellipsoid and the EGM96 geoid, in two separate elevation data files. A data quality map of the interpolation distance is distributed in addition to the elevation data. ENVI header files are also provided.\n\nThe data are in 4-byte (long) signed integer binary files (big endian byte order) and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0310_1.json b/datasets/NSIDC-0310_1.json index b950a45390..b3bd3d8212 100644 --- a/datasets/NSIDC-0310_1.json +++ b/datasets/NSIDC-0310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes records of the delta carbon-13 (δ13C) of methane (CH4) in firn air from the South Pole and trapped in bubbles in a short ice core from Siple Dome, Antarctica. Using two firn air samples, one from January 1995 and the other from January 2001, investigators reconstructed records of the isotopic composition of paleoatmospheric methane covering the last 2 centuries, from 1820 to 2001. \n\nData are in Microsoft Excel and Microsoft Word formats and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0312_1.json b/datasets/NSIDC-0312_1.json index e20bd912b7..37f12b60b0 100644 --- a/datasets/NSIDC-0312_1.json +++ b/datasets/NSIDC-0312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes a nested model, that starts at low resolution for the whole Antarctic Ice Sheet, and then embeds higher resolution data at limited domains. There are at least three levels of nesting: whole, regional, and specific ice streams. Investigators focused on the Thwaites Glacier and the Pine Island Glacier. The model was produced using data from (Holt et al. 2006) and (Vaughan et al. 2006). Data are in Network Common Data Form (NetCDF) format and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0313_1.json b/datasets/NSIDC-0313_1.json index a9043b825d..e17610b2f7 100644 --- a/datasets/NSIDC-0313_1.json +++ b/datasets/NSIDC-0313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is an analysis of methyl chloride (CH3Cl) and methyl bromide (CH3Br) in Antarctic ice core samples. Investigators reported mixing ratios of methyl chloride gas extracted from samples taken from the South Pole Remote Earth Science and Seismological Observatory (SPRESSO) core, drilled as part of the International Trans Antarctic Science Expedition (ITASE). This data covers an age range of 2159 - 140 years before present (Y.B.P.) where the year 2000 was used as present. Investigators analyzed trace gases in ice core samples from Siple Dome, West Antarctica (dry-drilled C core and deep, fluid-drilled A core) and from South Pole, Antarctica (300 m dry drilled SPRESSO core). Data are available in Microsoft Excel format and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0314_1.json b/datasets/NSIDC-0314_1.json index bea443adb0..95cebd852a 100644 --- a/datasets/NSIDC-0314_1.json +++ b/datasets/NSIDC-0314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reconstructions of ancient atmospheric CO2 variations help us better understand how the global carbon cycle and climate are linked. This data set compares CO2 variations on millennial time scales between 20,000 and 90,000 years with an Antarctic temperature proxy and records of abrupt climate change in the Northern hemisphere.", "links": [ { diff --git a/datasets/NSIDC-0315_1.json b/datasets/NSIDC-0315_1.json index 0e8546d1e4..04deee6618 100644 --- a/datasets/NSIDC-0315_1.json +++ b/datasets/NSIDC-0315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Using new and existing ice core CO2 data from 65 - 30 ka BP a new chronology for Taylor Dome ice core CO2 is established and synchronized with Greenland ice core records to study how high latitude climate change and the carbon cycle were linked during the last glacial period. The new data and chronology should provide a better target for models attempting to explain CO2 variability and abrupt climate change.", "links": [ { diff --git a/datasets/NSIDC-0318_1.json b/datasets/NSIDC-0318_1.json index 2289c18bc7..bf25aa9980 100644 --- a/datasets/NSIDC-0318_1.json +++ b/datasets/NSIDC-0318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations.\n\nThe 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low.\n\nData are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP.", "links": [ { diff --git a/datasets/NSIDC-0321_1.json b/datasets/NSIDC-0321_1.json index d06741a1f7..f551b8d6e1 100644 --- a/datasets/NSIDC-0321_1.json +++ b/datasets/NSIDC-0321_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0321_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises global, 8-day Snow-Covered Area (SCA) and Snow Water Equivalent (SWE) data from 2000 through 2008. Global SWE data are derived from the Special Sensor Microwave Imager (SSM/I) and are enhanced with MODIS/Terra Snow Cover 8-Day Level 3 Global 0.05 degree Climate Modeling Grid (CMG) data. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). These data are suitable for continental-scale time-series studies of snow cover and snow water equivalent.", "links": [ { diff --git a/datasets/NSIDC-0326_1.json b/datasets/NSIDC-0326_1.json index c6e9ee002b..6902683668 100644 --- a/datasets/NSIDC-0326_1.json +++ b/datasets/NSIDC-0326_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0326_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. \n\nData are available via FTP in space-delimited ASCII format.", "links": [ { diff --git a/datasets/NSIDC-0334_1.json b/datasets/NSIDC-0334_1.json index 72a59c5ee9..d0806c4ba3 100644 --- a/datasets/NSIDC-0334_1.json +++ b/datasets/NSIDC-0334_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0334_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of \ufffd20 cm; a primary subset has an error of \ufffd8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0336_1.json b/datasets/NSIDC-0336_1.json index c0e8beb388..eb45edfb84 100644 --- a/datasets/NSIDC-0336_1.json +++ b/datasets/NSIDC-0336_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0336_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is an Antarctic radar-based subglacial lake classification collection, which focuses on the radar reflection properties of each given lake.\n\nThe Subglacial lakes are separated into four categories specified by radar reflection properties. Additional information includes: latitude, longitude, length (in kilometers), hydro-potential (in meters), bed elevation (in meters above WGS84), and ice thickness (in meters).\n\nSource data used to compile this data set were collected between 1998 and 2001. Data are available via FTP as a Microsoft Excel Spreadsheet (XLS), and Tagged Image File Format (TIF).", "links": [ { diff --git a/datasets/NSIDC-0393_1.json b/datasets/NSIDC-0393_1.json index df30e7faef..54e139f45c 100644 --- a/datasets/NSIDC-0393_1.json +++ b/datasets/NSIDC-0393_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0393_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of sea ice freeboard and sea ice thickness for the Arctic region. The data were derived from measurements made by from the Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) instrument, the Special Sensor Microwave/Imager (SSM/I), and climatologies of snow and drift of ice.", "links": [ { diff --git a/datasets/NSIDC-0394_1.json b/datasets/NSIDC-0394_1.json index 7f59142e24..e08660de8d 100644 --- a/datasets/NSIDC-0394_1.json +++ b/datasets/NSIDC-0394_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0394_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains atmospheric mixing ratios of hydrogen peroxide and methylhydroperoxide at 21 sites on the West Antarctic Ice Sheet (WAIS) were obtained from 2000 to 2003 during the US International Trans-Antarctic Scientific Expedition (US ITASE) deployments. Sample location from the WAIS region (76-90\ufffdS / 84-124\ufffdW) were approximately 100-300 km apart and correspond to US ITASE ice core sites. At each site, ambient air from 1 m above the snow surface was sampled between two to five days. Atmospheric hydroperoxides (ROOH) were continuously scrubbed from the sample air with a glass coil scrubber and subsequently quantified using a fluorescence detection method.\n\nData are available via FTP as ASCII text files (.txt).", "links": [ { diff --git a/datasets/NSIDC-0414_1.json b/datasets/NSIDC-0414_1.json index 30d14c48e9..03ef6387c6 100644 --- a/datasets/NSIDC-0414_1.json +++ b/datasets/NSIDC-0414_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0414_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Center for Remote Sensing of Ice Sheets (CReSIS) data from the 2002 and 2006 Flight missions.\n\nBasic Processing has been done to obtain the radar echograms and derived ice thickness data. Algorithms are being developed to reduce the multiples to obtain accurate measurement of ice sheet thickness over some areas. Data are available in Matlab (.mat) and Adobe .pdf formats.", "links": [ { diff --git a/datasets/NSIDC-0422_1.json b/datasets/NSIDC-0422_1.json index 3f974d29f7..7e05ec9b94 100644 --- a/datasets/NSIDC-0422_1.json +++ b/datasets/NSIDC-0422_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0422_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0424_1.json b/datasets/NSIDC-0424_1.json index 6a3ed10c32..7ee09522e3 100644 --- a/datasets/NSIDC-0424_1.json +++ b/datasets/NSIDC-0424_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0424_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains snow depth measurements collected over sea ice in the Barrow, Alaska area and at the Navy Ice Camp in the main pack ice of the Arctic Ocean as part of the joint in situ and aircraft AMSRIce03 campaign.", "links": [ { diff --git a/datasets/NSIDC-0426_1.json b/datasets/NSIDC-0426_1.json index a272490803..a9e803eba3 100644 --- a/datasets/NSIDC-0426_1.json +++ b/datasets/NSIDC-0426_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0426_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains ice thickness measurements collected over sea ice in the Barrow, Alaska area and at the Navy Ice Camp in the main pack ice of the Arctic Ocean as part of the joint in situ and aircraft AMSRIce03 campaign conducted in March 2003.", "links": [ { diff --git a/datasets/NSIDC-0427_1.json b/datasets/NSIDC-0427_1.json index 0062894af8..b26d3e6593 100644 --- a/datasets/NSIDC-0427_1.json +++ b/datasets/NSIDC-0427_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0427_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains surface roughness measurements collected over sea ice in the Barrow, Alaska USA area as part the joint in situ and aircraft AMSRIce03 campaign.", "links": [ { diff --git a/datasets/NSIDC-0428_1.json b/datasets/NSIDC-0428_1.json index c04c482ace..8c60a3d1cc 100644 --- a/datasets/NSIDC-0428_1.json +++ b/datasets/NSIDC-0428_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0428_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains snow ice temperature measurements collected over sea ice in the Barrow, Alaska USA area as part of the joint in situ and aircraft AMSRIce03 campaign.", "links": [ { diff --git a/datasets/NSIDC-0429_1.json b/datasets/NSIDC-0429_1.json index 94636c3e6f..a1193ad7b2 100644 --- a/datasets/NSIDC-0429_1.json +++ b/datasets/NSIDC-0429_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0429_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains snow pit data collected over sea ice in the Barrow, Alaska, USA area and nearby at the Navy Ice Camp in the main pack ice of the Arctic Ocean.", "links": [ { diff --git a/datasets/NSIDC-0430_1.json b/datasets/NSIDC-0430_1.json index d517de9333..e148883a61 100644 --- a/datasets/NSIDC-0430_1.json +++ b/datasets/NSIDC-0430_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0430_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains photographic mosaics of sea ice in the Barrow, Alaska USA area as part of the joint in situ and aircraft AMSRIce03.", "links": [ { diff --git a/datasets/NSIDC-0431_1.json b/datasets/NSIDC-0431_1.json index 8f2fa4283c..ce2bb94331 100644 --- a/datasets/NSIDC-0431_1.json +++ b/datasets/NSIDC-0431_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0431_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Landsat-7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery of the Bering Sea and Chukchi Sea areas to complement the joint in situ and aircraft Advanced Microwave Scanning Radiometer Sea Ice Product Validation (AMSRIce03) campaign.", "links": [ { diff --git a/datasets/NSIDC-0432_1.json b/datasets/NSIDC-0432_1.json index d098c7d721..2a0ef0ed4d 100644 --- a/datasets/NSIDC-0432_1.json +++ b/datasets/NSIDC-0432_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0432_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Moderate Resolution Imaging Spectroradiometer (MODIS) imagery of the Alaska, USA area, complementing the joint in situ and aircraft Advanced Microwave Scanning Radiometer Sea Ice Product Validation (AMSRIce03) campaign.", "links": [ { diff --git a/datasets/NSIDC-0437_1.json b/datasets/NSIDC-0437_1.json index a9bb129122..5c371b8409 100644 --- a/datasets/NSIDC-0437_1.json +++ b/datasets/NSIDC-0437_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0437_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides incoming shortwave radiation measurements from fourteen stations of the Greenland Climate Network (GC-Net) distributed over the Greenland Ice Sheet. The original data were obtained from the GC-Net and subsequently quality controlled. The data span from 01 January 1995 through 09 May 2008. The data set is approximately 15 MB comprised of fourteen Network Common Data Form (netCDF) files. The data are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0447_1.json b/datasets/NSIDC-0447_1.json index b78644e317..c297f72b59 100644 --- a/datasets/NSIDC-0447_1.json +++ b/datasets/NSIDC-0447_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0447_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a Northern Hemisphere subset of the Canadian Meteorological Centre (CMC) operational global daily snow depth analysis. Data include daily analyzed snow depths, as well as monthly means and climatologies of snow depth and estimated snow water equivalent (SWE).", "links": [ { diff --git a/datasets/NSIDC-0450_1.json b/datasets/NSIDC-0450_1.json index 01e45c8d7b..879d9d2115 100644 --- a/datasets/NSIDC-0450_1.json +++ b/datasets/NSIDC-0450_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0450_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users:\nThe documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains the following spatially and temporally co-registered data: Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) brightness temperatures for all channels; Quick Scatterometer (QuikSCAT) backscattering coefficients; and World Meteorological Organization (WMO) ground observations acquired from more than two thousand stations. There is a large, increasing interest in the potential arising from the combination of active and passive microwave data for the extraction of geophysical parameters from spaceborne platforms. Often, one of the major obstacles is the generation of spatially and temporally co-registered data sets for testing hypotheses, validating models, and developing retrieval approaches. The temporal coverage of this data set spans from 01 January 2002 through 19 March 2009 with AMSR-E data included for the 19 June 2002 through 19 March 2009 time period. The volume of the data set is approximately two gigabytes. Data are provided in tab-delimited ASCII text files and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0451_3.json b/datasets/NSIDC-0451_3.json index 603b415959..a228c44ff5 100644 --- a/datasets/NSIDC-0451_3.json +++ b/datasets/NSIDC-0451_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0451_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains satellite-retrieved geophysical parameter files generated from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the National Aeronautics and Space Administration (NASA) Aqua satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on the JAXA GCOM-W1 satellite. The geophysical parameters include daily air surface temperature, fractional open water cover estimates, vegetation optical depth, surface volumetric soil moisture, and atmosphere total column precipitable water vapor. The global retrievals were derived over land for non-precipitating, non-snow, and non-ice covered conditions.", "links": [ { diff --git a/datasets/NSIDC-0459_1.json b/datasets/NSIDC-0459_1.json index 62380e6bd2..3df81c01e1 100644 --- a/datasets/NSIDC-0459_1.json +++ b/datasets/NSIDC-0459_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0459_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains photographs of sea ice in the Chukchi and Beaufort Seas of the Arctic Ocean, and of snow cover off the northern coast of Alaska, USA. Photographs were taken from a P3 aircraft using two Kodak digital DC4800 cameras.", "links": [ { diff --git a/datasets/NSIDC-0461_1.json b/datasets/NSIDC-0461_1.json index 9f8e8d5ffb..653ba6edf4 100644 --- a/datasets/NSIDC-0461_1.json +++ b/datasets/NSIDC-0461_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0461_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.\n\nThis data set contains Lidar measurements of sea ice in the Chukchi and Beaufort Seas of the Arctic Ocean, and of snow cover off the northern coast of Alaska, USA. The Lidar data were obtained by the Airborne Topographic Mapper (ATM) instrument mounted on a P3 aircraft.", "links": [ { diff --git a/datasets/NSIDC-0466_1.json b/datasets/NSIDC-0466_1.json index ed7535e9fa..4c7e59058a 100644 --- a/datasets/NSIDC-0466_1.json +++ b/datasets/NSIDC-0466_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0466_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes a variety of station data from two Antarctic icebergs. In 2006, researchers installed specialized weather stations called Automated Meteorological Ice Geophysical Observing Stations (AMIGOS) on two icebergs, A22A and UK211 (nicknamed Amigosberg), near Marambio Station in Antarctica.The AMIGOS stations were outfitted with Global Positioning System (GPS) sensors, cameras, and an electronic thermometer. They collected data from their installation in March 2006 until the icebergs crumbled into the ocean, in 2006 (Amigosberg) and 2007 (A22A). Available data include GPS, temperature and ablation measurements, and photographs of the station base and of flag lines extending out to the edges of the icebergs. Snow pit data from iceberg A22A is also included.\n\nThis data set was collected as part of a National Science Foundation Office of Polar Programs Special Grant for Exploratory Research, to explore the possibility of using drfting icebergs to investigate ice shelf evolution caused by climate change. The expedition, nicknamed IceTrek, was conducted jointly with Argentine scientists. The data are available via FTP in ASCII text (.txt) and Joint Photographic Experts Group (.jpg) formats.", "links": [ { diff --git a/datasets/NSIDC-0468_1.json b/datasets/NSIDC-0468_1.json index 1f13730db9..d2db4eb024 100644 --- a/datasets/NSIDC-0468_1.json +++ b/datasets/NSIDC-0468_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0468_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of scripts and code designed for modeling the properties of boreholes in polar ice sheets, under a range of variations in the borehole geometry, firn layering, and camera pointing and position. The data set contains two folders. One includes two perl scripts and a piece of C code, along with directions for setting up and running a Monte Carlo model of photons traveling to and from a borehole in the firn. The second includes scripts for generating ray-tracing input files to be used with the POV-Ray package (a standard, free raytracing package) to generate simulated borehole video frames based on the results of the Monte Carlo model. The project was conducted between February 2005 and April 2010.\n\nThe codes to run the models are available via FTP, in Perlscript (.pl) and C code.", "links": [ { diff --git a/datasets/NSIDC-0477_5.json b/datasets/NSIDC-0477_5.json index 83ef6eafca..5e57bfd0be 100644 --- a/datasets/NSIDC-0477_5.json +++ b/datasets/NSIDC-0477_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0477_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a global record of the daily freeze/thaw status of the landscape. The record is derived from radiometric brightness temperatures acquired between 1979 and 2021 by four satellite-based, passive microwave sensors: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager/Sounder (SSMIS), and the Advanced Microwave Scanning Radiometer 2 (AMSR2).", "links": [ { diff --git a/datasets/NSIDC-0478_2.json b/datasets/NSIDC-0478_2.json index 05b747df63..3f5c4f2045 100644 --- a/datasets/NSIDC-0478_2.json +++ b/datasets/NSIDC-0478_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0478_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains seasonal (winter) ice sheet-wide velocity maps for Greenland. The maps are derived from Interferometric Synthetic Aperture Radar (InSAR) data obtained by the Canadian Space Agency's (CSA) RADARSAT-1, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observation Satellite (ALOS), and the German Aerospace Center's (DLR) TerraSAR-X/TanDEM-X (TSX/TDX) satellites, as well as from the European Space Agency's (ESA) C-band Synthetic Aperture Radar data from Copernicus Sentinel-1A and -1B.\n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0481_4.json b/datasets/NSIDC-0481_4.json index 6085fd53f7..4976a7ec76 100644 --- a/datasets/NSIDC-0481_4.json +++ b/datasets/NSIDC-0481_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0481_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data.\n\nSee Greenland Ice Mapping Project (GrIMP) for more related data.", "links": [ { diff --git a/datasets/NSIDC-0484_2.json b/datasets/NSIDC-0484_2.json index ccb39c69cf..925edf375f 100644 --- a/datasets/NSIDC-0484_2.json +++ b/datasets/NSIDC-0484_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0484_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 were used as needed to maximize coverage.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0498_2.json b/datasets/NSIDC-0498_2.json index 1de6442ea1..31569d4eea 100644 --- a/datasets/NSIDC-0498_2.json +++ b/datasets/NSIDC-0498_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0498_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 22 years of comprehensive high-resolution mapping of grounding lines in Antarctica from 1992 to 2014. The data were derived using differential satellite synthetic aperture radar interferometry (DInSAR) measurements from the following platforms: Earth Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2), RADARSAT-1, RADARSAT-2, the Advanced Land Observing System Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), Cosmo Skymed, and Copernicus Sentinel-1.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0504_1.json b/datasets/NSIDC-0504_1.json index 3c2bbde6a5..d56165dc1f 100644 --- a/datasets/NSIDC-0504_1.json +++ b/datasets/NSIDC-0504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases.\n\nData are available via FTP in Microsoft Excel (.xls) format.", "links": [ { diff --git a/datasets/NSIDC-0515_1.json b/datasets/NSIDC-0515_1.json index 031be366ee..8cb30e0bd0 100644 --- a/datasets/NSIDC-0515_1.json +++ b/datasets/NSIDC-0515_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0515_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Researchers gathered data on annual snow layers at Siple Dome, Antarctica, using borehole optical stratigraphy. This data set contains annual layer depths and firn optical brightness. The brightness log is a record of reflectivity of the firn, and peaks in brightness are interpreted to be fine-grained high-density winter snow, as part of the wind slab depth-hoar couplet. \n\nData are available via FTP in ASCII text (.txt) format", "links": [ { diff --git a/datasets/NSIDC-0516_1.json b/datasets/NSIDC-0516_1.json index d7a3e72949..5528acc654 100644 --- a/datasets/NSIDC-0516_1.json +++ b/datasets/NSIDC-0516_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0516_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a 100 meter resolution surface topography Digital Elevation Model (DEM) of the Antarctic Peninsula. The DEM is based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) data.", "links": [ { diff --git a/datasets/NSIDC-0517_1.json b/datasets/NSIDC-0517_1.json index 8bf7c57ef3..6fc799da62 100644 --- a/datasets/NSIDC-0517_1.json +++ b/datasets/NSIDC-0517_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0517_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. \n\nData are available via FTP in space-delimited ASCII format.", "links": [ { diff --git a/datasets/NSIDC-0522_1.json b/datasets/NSIDC-0522_1.json index 32718a184b..caca3f1fd9 100644 --- a/datasets/NSIDC-0522_1.json +++ b/datasets/NSIDC-0522_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0522_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a coastline history of the eastern Amundsen Sea Embayment and terminus histories of its outlet glaciers derived from those coastlines. These outlet glaciers include Smith, Haynes, Thwaites, and Pine Island Glaciers. The coastlines were derived from detailed tracing of Landsat imagery between late 1972 and late 2011 (at a scale of 1:50,000). The data set also uses some additional data from other sources. The terminus histories are calculated as the intersections between these coastlines and 1996 flowlines.\n\nData are available via FTP in ESRI shapefile and comma separated value (.csv) formats.", "links": [ { diff --git a/datasets/NSIDC-0525_1.json b/datasets/NSIDC-0525_1.json index 71f8e90d4a..16814f28f5 100644 --- a/datasets/NSIDC-0525_1.json +++ b/datasets/NSIDC-0525_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0525_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two high-resolution digital mosaics of ice motion in Central Antarctica. The mosaics were assembled from satellite interferometric synthetic-aperture radar (InSAR) data acquired by RADARSAT-1 in 1997 and by RADARSAT-2 in 2009.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0530_1.json b/datasets/NSIDC-0530_1.json index b0b7531b80..1f2ecc409d 100644 --- a/datasets/NSIDC-0530_1.json +++ b/datasets/NSIDC-0530_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0530_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively. The fourth variable merges the three source products into a single representation of snow cover.", "links": [ { diff --git a/datasets/NSIDC-0531_1.json b/datasets/NSIDC-0531_1.json index 059c6b1fdc..04bbcb77d8 100644 --- a/datasets/NSIDC-0531_1.json +++ b/datasets/NSIDC-0531_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0531_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users weekly 100 km Northern Hemisphere snow cover extent represented by three different variables. Two of the variables are derived from individual source products: the NOAA/NCDC Northern Hemisphere Snow Cover Extent Climate Data Record and Defense Meteorological Satellite Program (DMSP) passive microwave brightness temperatures, respectively. The third variable merges the other two into a single representation of snow cover.", "links": [ { diff --git a/datasets/NSIDC-0532_1.json b/datasets/NSIDC-0532_1.json index ea9390728c..bf7979bde6 100644 --- a/datasets/NSIDC-0532_1.json +++ b/datasets/NSIDC-0532_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0532_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset. Data are gridded in the 25 km Equal-Area Scalable Earth Grid (EASE-Grid) 2.0 and provided as netCDF files.", "links": [ { diff --git a/datasets/NSIDC-0533_1.json b/datasets/NSIDC-0533_1.json index 7ab4b64cc2..ff14531a09 100644 --- a/datasets/NSIDC-0533_1.json +++ b/datasets/NSIDC-0533_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0533_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired during a 34 year span by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).", "links": [ { diff --git a/datasets/NSIDC-0534_1.json b/datasets/NSIDC-0534_1.json index c0ceeb657a..e152c02a5e 100644 --- a/datasets/NSIDC-0534_1.json +++ b/datasets/NSIDC-0534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources.", "links": [ { diff --git a/datasets/NSIDC-0535_1.json b/datasets/NSIDC-0535_1.json index 486816eb7a..cb0525cde2 100644 --- a/datasets/NSIDC-0535_1.json +++ b/datasets/NSIDC-0535_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0535_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources.", "links": [ { diff --git a/datasets/NSIDC-0538_1.json b/datasets/NSIDC-0538_1.json index 4321981177..47e4e5e686 100644 --- a/datasets/NSIDC-0538_1.json +++ b/datasets/NSIDC-0538_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0538_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes bubble number-density measured at depths from 120 meters to 560 meters at 20-meter intervals in both horizontal and vertical samples. The data set also includes modeled temperature reconstructions based on the model developed by Spencer and others (2006).", "links": [ { diff --git a/datasets/NSIDC-0539_1.json b/datasets/NSIDC-0539_1.json index 88fac18bca..b3766a5054 100644 --- a/datasets/NSIDC-0539_1.json +++ b/datasets/NSIDC-0539_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0539_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes.", "links": [ { diff --git a/datasets/NSIDC-0541_1.json b/datasets/NSIDC-0541_1.json index dd5a4bf6ef..26e9c97017 100644 --- a/datasets/NSIDC-0541_1.json +++ b/datasets/NSIDC-0541_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0541_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes stable water isotope values at 10 m resolution along an approximately 5 km transect through the main icefield of the Allan Hills Blue Ice Area, and at 15 cm within a 225 m core drilled at the midpoint of the transect.", "links": [ { diff --git a/datasets/NSIDC-0543_1.json b/datasets/NSIDC-0543_1.json index 812d32da96..bf7f239865 100644 --- a/datasets/NSIDC-0543_1.json +++ b/datasets/NSIDC-0543_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0543_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). The data set complements existing land emissivity products from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU) by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Land surface emissivity estimates for this data set were collected at the following vertically and horizontally polarized (V-pol and H-pol) frequencies: 6.9, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, were obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties were obtained from TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. The data set is in monthly format that is extracted from instantaneous emissivity estimates. Data are stored in HDF4 files and are available via FTP.", "links": [ { diff --git a/datasets/NSIDC-0545_1.json b/datasets/NSIDC-0545_1.json index 65fbc765e3..0d45a10a1b 100644 --- a/datasets/NSIDC-0545_1.json +++ b/datasets/NSIDC-0545_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0545_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides high-resolution, digital mosaics of ice motion in the Amundsen Sea Embayment (ASE) and West Antarctica, including the Pine Island, Thwaites, Haynes, Pope, Smith, and Kohler glaciers. The mosaics were assembled from interferometric synthetic-aperture radar (InSAR) data acquired in 1996, 2000, 2002, and 2006-2012 by various satellites.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0547_2.json b/datasets/NSIDC-0547_2.json index 71fc7c6844..c1e606340a 100644 --- a/datasets/NSIDC-0547_2.json +++ b/datasets/NSIDC-0547_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0547_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two digital Greenland image maps each for the 2005, 2010, and 2015 measurement periods: the MOG Surface Morphology Image Map and the MOG Grain Size Image Map. The image maps are constructed from MODIS imagery acquired during 2005, 2010, and 2015 and provide nearly cloud-free views of all land areas and islands larger than a few hundred meters, including the ice caps on Baffin Island, Devon Island, Axel Heiberg Island, and Ellesmere Island.", "links": [ { diff --git a/datasets/NSIDC-0599_1.json b/datasets/NSIDC-0599_1.json index 6efd9c07ba..d68f35e08f 100644 --- a/datasets/NSIDC-0599_1.json +++ b/datasets/NSIDC-0599_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0599_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set includes carbonyl sulfide (COS) measurements made on air extracted from 53 samples from the Taylor Dome M3C1 ice core. COS was measured in air from the Taylor Dome ice core to reconstruct an atmospheric record for the Holocene (11-0 kyr B.P.) and part of the last glacial period (50-30 kyr B.P.).", "links": [ { diff --git a/datasets/NSIDC-0603_1.json b/datasets/NSIDC-0603_1.json index dd030c201e..06aa4e5ca8 100644 --- a/datasets/NSIDC-0603_1.json +++ b/datasets/NSIDC-0603_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0603_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes ~50 m averaged annual layer thicknesses down to 3403 m depth at the West Antarctic Ice Sheet (WAIS) Divide ice core as observed visually using diffuse transmitted light opposite a planed surface in a light-shielded booth in the core-processing line at the National Ice Core Lab in Denver, CO.", "links": [ { diff --git a/datasets/NSIDC-0605_1.json b/datasets/NSIDC-0605_1.json index 3dcbbfaead..01236c9533 100644 --- a/datasets/NSIDC-0605_1.json +++ b/datasets/NSIDC-0605_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0605_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains c-axis fabric measurements and grain area from the physical properties samples taken from the main West Antarctic Ice Sheet (WAIS) Divide ice core, WDC06A , Antarctica.", "links": [ { diff --git a/datasets/NSIDC-0607_1.json b/datasets/NSIDC-0607_1.json index 7c05770424..f3fa41951d 100644 --- a/datasets/NSIDC-0607_1.json +++ b/datasets/NSIDC-0607_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0607_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Land-Ocean-Coastline-Ice (LOCI) files provide land classification masks derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The masks are available in various EASE-Grid azimuthal and global projections, at 12.5 km and 25 km spatial resolutions. The masks are in flat binary, 1 byte files stored by row. Quick-look browse images of the masks are also available in PNG (.png) format.", "links": [ { diff --git a/datasets/NSIDC-0608_1.json b/datasets/NSIDC-0608_1.json index 50d88bc73e..bc7005375e 100644 --- a/datasets/NSIDC-0608_1.json +++ b/datasets/NSIDC-0608_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0608_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are available in various EASE-Grid azimuthal and global projections, in 12.5 km and 25 km spatial resolutions. The data are in flat binary, 1 byte files that are stored by row.", "links": [ { diff --git a/datasets/NSIDC-0609_1.json b/datasets/NSIDC-0609_1.json index 66d3062f1b..99b92bc6d9 100644 --- a/datasets/NSIDC-0609_1.json +++ b/datasets/NSIDC-0609_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0609_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These Land-Ocean-Coastline-Ice (LOCI) files provide land classification masks derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The masks are available in various EASE-Grid 2.0 azimuthal and global projections, at various spatial resolutions ranging from 3 km to 100 km. The masks are in flat binary, 1 byte files stored by row. Quick-look browse images of the masks are also available in PNG (.png) format.", "links": [ { diff --git a/datasets/NSIDC-0610_1.json b/datasets/NSIDC-0610_1.json index 6d9d81ec7f..8232019ba7 100644 --- a/datasets/NSIDC-0610_1.json +++ b/datasets/NSIDC-0610_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0610_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are available in various EASE-Grid 2.0 azimuthal and global projections, in multiple spatial resolutions ranging from 3 km to 100 km. The data are in flat binary, 1 byte files that are stored by row.", "links": [ { diff --git a/datasets/NSIDC-0611_4.json b/datasets/NSIDC-0611_4.json index b98bda1f2d..125bd40834 100644 --- a/datasets/NSIDC-0611_4.json +++ b/datasets/NSIDC-0611_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0611_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides weekly estimates of sea ice age for the Arctic Ocean derived from remotely sensed sea ice motion and sea ice extent. For more recent data, see the Quicklook Arctic Weekly EASE-Grid Sea Ice Age data product (https://nsidc.org/data/nsidc-0749).", "links": [ { diff --git a/datasets/NSIDC-0627_1.json b/datasets/NSIDC-0627_1.json index c885fbc827..91c9af67c7 100644 --- a/datasets/NSIDC-0627_1.json +++ b/datasets/NSIDC-0627_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0627_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a time series of borehole temperatures at different depths from three thermistor strings deployed in three boreholes drilled through the Pine Island Glacier ice shelf, Antarctica.", "links": [ { diff --git a/datasets/NSIDC-0630_1.json b/datasets/NSIDC-0630_1.json index 8e9fa46486..895675b841 100644 --- a/datasets/NSIDC-0630_1.json +++ b/datasets/NSIDC-0630_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0630_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, is an improved, enhanced-resolution, gridded passive microwave Earth System Data Record (ESDR) for monitoring cryospheric and hydrologic time series from SMMR, SSM/I-SSMIS, and AMSR-E. It is derived from the most mature and available Level-2 satellite passive microwave records from 1978 through the present.", "links": [ { diff --git a/datasets/NSIDC-0630_2.json b/datasets/NSIDC-0630_2.json index 2a0a1c4c77..857808e5c3 100644 --- a/datasets/NSIDC-0630_2.json +++ b/datasets/NSIDC-0630_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0630_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery. Inputs include brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2).", "links": [ { diff --git a/datasets/NSIDC-0634_1.json b/datasets/NSIDC-0634_1.json index ee0f596ed4..7b0575a22a 100644 --- a/datasets/NSIDC-0634_1.json +++ b/datasets/NSIDC-0634_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0634_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images.", "links": [ { diff --git a/datasets/NSIDC-0637_1.json b/datasets/NSIDC-0637_1.json index 299740149e..1fdf7cc3c3 100644 --- a/datasets/NSIDC-0637_1.json +++ b/datasets/NSIDC-0637_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0637_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes borehole temperature measurements performed in January 2008 and January 2009 at the West Antarctic Ice sheet divide from the 300 m hole WDC05A.", "links": [ { diff --git a/datasets/NSIDC-0642_2.json b/datasets/NSIDC-0642_2.json index 5589c0f21b..54dccc35ea 100644 --- a/datasets/NSIDC-0642_2.json +++ b/datasets/NSIDC-0642_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0642_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8. Although temporal coverage varies by glacier, data are available for the winter seasons 1972\u20131973 through 2020\u20132021. Data are provided as shapefiles.\n\nSee Greenland Ice Mapping Project (GrIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0644_1.json b/datasets/NSIDC-0644_1.json index 668c5235af..0644213b33 100644 --- a/datasets/NSIDC-0644_1.json +++ b/datasets/NSIDC-0644_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0644_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports mean annual snow accumulation rates in meters water equivalent (m\u00b7w.e.) from 1959 to 2004 along a 250 km segment of the Exp\u00e9ditions Glaciologiques Internationales au Groenland (EGIG) line. Accumulation rates are derived from Airborne SAR/Interferometric Radar Altimeter System (ASIRAS) data and high resolution neutron-probe (NP) density profiles.", "links": [ { diff --git a/datasets/NSIDC-0645_1.json b/datasets/NSIDC-0645_1.json index c1790b81a9..6ab7698d2e 100644 --- a/datasets/NSIDC-0645_1.json +++ b/datasets/NSIDC-0645_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0645_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet. It was constructed by combining ASTER and SPOT 5 DEMs over the ice sheet periphery and margin with AVHRR photoclinometry for the interior and far north, and calibrating the data to approximate mean ICESat/GLAS elevations from 2003 to 2009. \n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0646_3.json b/datasets/NSIDC-0646_3.json index e2758bf4c5..da6ed75830 100644 --- a/datasets/NSIDC-0646_3.json +++ b/datasets/NSIDC-0646_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0646_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of mean monthly velocity maps for selected glacier outlet areas. The maps are generated by tracking visible features between optical image pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM), the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8 Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).\n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0666_1.json b/datasets/NSIDC-0666_1.json index 701029ebe8..698ba6ad52 100644 --- a/datasets/NSIDC-0666_1.json +++ b/datasets/NSIDC-0666_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0666_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains data obtained by the Passive Active L- and S-band (PALS) microwave aircraft instrument that are matched up with a variety of soil moisture campaign data. The data were collected as part of four different campaigns: Southern Great Plains 1999 (SGP99), Cloud and Land Surface Interaction Campaign 2007 (CLASIC07), Soil Moisture Experiment 2002 (SMEX02), and the SMAP Validation Experiment 2008 (SMAPVEX08).", "links": [ { diff --git a/datasets/NSIDC-0668_2.json b/datasets/NSIDC-0668_2.json index f236b9353b..431f61c1ef 100644 --- a/datasets/NSIDC-0668_2.json +++ b/datasets/NSIDC-0668_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0668_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a daily gridded terrestrial snow water equivalent (SWE) dataset based on five component SWE products:\n", "links": [ { diff --git a/datasets/NSIDC-0670_1.json b/datasets/NSIDC-0670_1.json index 475367ddfb..fd310bc07e 100644 --- a/datasets/NSIDC-0670_1.json +++ b/datasets/NSIDC-0670_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0670_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a multi-year ice-sheet-wide velocity mosaic for Greenland, derived from Interferometric Synthetic Aperture Radar (InSAR), Synthetic Aperture Radar (SAR), and Landsat 8 optical imagery data.\n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0690_1.json b/datasets/NSIDC-0690_1.json index 499305d4b8..77d599f0d0 100644 --- a/datasets/NSIDC-0690_1.json +++ b/datasets/NSIDC-0690_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0690_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fields of long-term mean spring ice thickness derived from ERS-1 (1993-2001) and CryoSat (2011 to 2013) radar altimeters, ICESat laser altimeter (2004 to 2009), Operation IceBridge airborne altimeter and snow radar (2009 to 2014), and submarine upward looking sonar (1986-) are provided on 100 km EASE grids. All satellite-derived ice thickness fields were regridded as needed from their original gridded format to 100-km EASE grids using a drop-in-the-bucket averaging. IceBridge and submarine thickness estimates within 70 km of a 100 km EASE grid box center were averaged to give a grid cell mean thickness.", "links": [ { diff --git a/datasets/NSIDC-0707_1.json b/datasets/NSIDC-0707_1.json index ba720a578c..5ac7d57645 100644 --- a/datasets/NSIDC-0707_1.json +++ b/datasets/NSIDC-0707_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0707_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains sea ice thickness and other derived geophysical parameters measured over the Arctic sea ice cover. The data are primarily based on ESA's CryoSat-2 Level-1B SAR/SarIn Baseline B data products for March 2014 and March 2015. Ancillary data include snow depth, snow density, sea ice concentration, sea ice freeboard, and sea ice roughness. This quick look product is experimental and designed to aid in seasonal sea ice forecasting and other time-sensitive projects. The data were collected as part of Operation IceBridge funded campaigns.\n\nNote: This data set is the Quick Look counterpart to the CryoSat-2 Level-4 Sea Ice Elevation, Freeboard, and Thickness data set.", "links": [ { diff --git a/datasets/NSIDC-0709_2.json b/datasets/NSIDC-0709_2.json index 740d54964e..fea9dcd651 100644 --- a/datasets/NSIDC-0709_2.json +++ b/datasets/NSIDC-0709_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0709_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides maps of Antarctic ice shelves, Antarctic basins, and the Antarctic coastline. The maps are assembled from 2008-2009 ice-front data from the Japan Aerospace Exploration Agency's (JAXA) ALOS PALSAR and European Space Agency's ENVISAT ASAR data, acquired during International Polar Years 2007-2009 (IPY); the InSAR-based grounding line data (MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry), augmented with other grounding line sources; the Antarctic ice velocity map (MEaSUREs InSAR-Based Antarctica Ice Velocity Map); and the Bedmap-2 DEM.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0710_1.json b/datasets/NSIDC-0710_1.json index 351ec8a4c0..d3dc18301f 100644 --- a/datasets/NSIDC-0710_1.json +++ b/datasets/NSIDC-0710_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0710_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a compilation of ice velocity mappings generated from pairs of Landsat 8 panchromatic images acquired from May 2013 to present covering all terrestrial permanent ice within the latitude range 82\u00b0S to 82\u00b0N that is larger than 5 km2 in area. The data are updated monthly with new images acquired by Landsat 8 that are then paired with older images acquired within 400 days of the new acquisition for the Antarctic ice sheet, 112 days for the Greenland ice sheet, and 96 days for all other glacierized areas. The data are generated by an image correlation algorithm that produces grids of ice displacement referenced to in-image rock outcrops, slow moving ice, or if lacking that, using the satellite's geo-positioning (accurate to +/- 5 m). Velocity vector grids are generated at a sample spacing of 300 m from small sub-images that are either 300 m or 600 m on a side, depending on the region. For example, ice sheet areas are mapped with 600 m x 600 m sub-images, and mountain glaciers are mapped with 300 m x 300 m sub-images. Accuracy of the velocity data varies depending on the time separation between the images, ranging between ~1 m/d per day to 0.02 m/d per day.", "links": [ { diff --git a/datasets/NSIDC-0712_1.json b/datasets/NSIDC-0712_1.json index 13e2fbb353..47330db7f6 100644 --- a/datasets/NSIDC-0712_1.json +++ b/datasets/NSIDC-0712_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0712_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP radiometer and radar soil moisture data products are matched with in situ-based soil moisture estimates from core validation sites to produce this data set. These data provide performance assessments of various SMAP soil moisture products.", "links": [ { diff --git a/datasets/NSIDC-0713_1.json b/datasets/NSIDC-0713_1.json index 77c7eb9606..6564028514 100644 --- a/datasets/NSIDC-0713_1.json +++ b/datasets/NSIDC-0713_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0713_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete 15 m resolution image mosaic of the Greenland ice sheet, derived from USGS Landsat 7 ETM+ imagery and Canadian Space Agency's (CSA) RADARSAT-1 imagery from the years 1999 to 2002. Additional bands (some at 30 m resolution) are provided for each tile in the mosaic and are useful for understanding surface properties, such as snow grain size, bedrock outcrops, mapping layering in the snow, and blue ice or lake-filled regions, during the spring and summer months. The panchromatic (band 8) mosaic provides the highest-resolution view of the ice sheet surface at 15 m, resolving topographic features, large crevasses, and other geophysical structures.\n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0714_1.json b/datasets/NSIDC-0714_1.json index 9645b0c80d..cb4d54bc35 100644 --- a/datasets/NSIDC-0714_1.json +++ b/datasets/NSIDC-0714_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0714_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete land ice and ocean classification mask for the Greenland ice sheet, that was mapped using a combination of USGS Landsat 7 ETM+ panchromatic band imagery, and the Canadian Space Agency's (CSA) RADARSAT-1 Synthetic Aperture Radar (SAR) amplitude images. \n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0715_2.json b/datasets/NSIDC-0715_2.json index 19f6224ef5..e9bf093f87 100644 --- a/datasets/NSIDC-0715_2.json +++ b/datasets/NSIDC-0715_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0715_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet, derived from sub-meter resolution, panchromatic stereoscopic imagery collected by the GeoEye-1, WorldView-1, -2, and -3 satellites operated by Maxar Technologies.\n\nThe DEM was created from in-track image pairs (i.e., both images collected minutes apart along the same orbital pass) and cross-track images (i.e., from different orbits) within the in-track imaging geometry and maximum time separation criteria. The DEM is registered to ATLAS/ICESat-2 L3A Land Ice Height, Version 5 (ATL06, V5) data collected in the summers of 2019 and 2020.\n\nSee Greenland Ice Mapping Project (GrIMP) for related data", "links": [ { diff --git a/datasets/NSIDC-0719_1.json b/datasets/NSIDC-0719_1.json index bcc292ebd5..52c2909fd4 100644 --- a/datasets/NSIDC-0719_1.json +++ b/datasets/NSIDC-0719_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0719_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides daily 4 km snow water equivalent (SWE) and snow depth over the conterminous United States. It was developed at the University of Arizona (UA) under the support of the NASA MAP and SMAP Programs. The data were created by assimilating in-situ snow measurements from the National Resources Conservation Service's SNOTEL network and the National Weather Service's COOP network with modeled, gridded temperature and precipitation data from PRISM.", "links": [ { diff --git a/datasets/NSIDC-0720_1.json b/datasets/NSIDC-0720_1.json index 764809c9b6..64d4781828 100644 --- a/datasets/NSIDC-0720_1.json +++ b/datasets/NSIDC-0720_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0720_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides annual maps of Antarctic ice velocity. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM \u2013X (TDX), and the U.S. Geological Survey's (USGS) Landsat-8 optical imagery..\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0722_1.json b/datasets/NSIDC-0722_1.json index fb07e559a5..701a2c9939 100644 --- a/datasets/NSIDC-0722_1.json +++ b/datasets/NSIDC-0722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily snow depths and snow-water equivalents (SWEs) estimated from GPS signal-to-noise ratios (SNRs).\u00a0Snow depth is determined by\u00a0calculating the relative change of the effective multipath reflector height (i.e. the snow surface) with respect to the snow free surface. SWEs are determined using GPS snow depths and, when available, density observations from nearby SNOpack TELemetry (SNOTEL) stations. When a nearby SNOTEL station\u00a0is not available, density is estimated using GPS snow depth and\u00a0climate classes, which account for\u00a0variables such as location and time of the year.", "links": [ { diff --git a/datasets/NSIDC-0723_4.json b/datasets/NSIDC-0723_4.json index b5411497d4..cd6443cbaa 100644 --- a/datasets/NSIDC-0723_4.json +++ b/datasets/NSIDC-0723_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0723_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 6-day and 12-day 50 m resolution image mosaics of the Greenland coastline and ice sheet periphery. The mosaics are derived from C-band Synthetic Aperture Radar (C-SAR) acquired by the Copernicus Sentinel-1A and -1B satellites.\n\nSee Greenland Ice Mapping Project (GrIMP) for related data sets.", "links": [ { diff --git a/datasets/NSIDC-0724_1.json b/datasets/NSIDC-0724_1.json index aeeee65ec8..6dec5c8fe2 100644 --- a/datasets/NSIDC-0724_1.json +++ b/datasets/NSIDC-0724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of monthly image mosaics of the Greenland coastline and ice sheet periphery constructed from composited MODIS imagery.\n\nSee Greenland Ice Mapping Project (GIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0725_5.json b/datasets/NSIDC-0725_5.json index edfd9379c9..85b21f46a5 100644 --- a/datasets/NSIDC-0725_5.json +++ b/datasets/NSIDC-0725_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0725_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains annual ice velocity mosaics for the Greenland Ice Sheet. Velocities are derived from synthetic aperture radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9. See Greenland Ice Mapping Project (GrIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0726_1.json b/datasets/NSIDC-0726_1.json index 35575963aa..86b01171e5 100644 --- a/datasets/NSIDC-0726_1.json +++ b/datasets/NSIDC-0726_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0726_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Daily Lake Ice Phenology Time Series Derived from AMSR-E and AMSR2 provides 5 km ice phenology retrievals describing daily lake ice conditions (ice-on/ice-off) over the Northern Hemisphere. This satellite-based data set allows for rapid assessment and regional monitoring of seasonal ice coverage over large lakes with resulting accuracy suitable for global change studies. Data are provided in the 5 km Northern Hemisphere Equal-Area Scalable Earth Grid 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/NSIDC-0727_5.json b/datasets/NSIDC-0727_5.json index a3f673eac9..28676d6f54 100644 --- a/datasets/NSIDC-0727_5.json +++ b/datasets/NSIDC-0727_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0727_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains quarterly (three-month interval) ice velocity mosaics for the Greenland Ice Sheet. This data set is derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.\n\nSee Greenland Ice sheet Mapping Project (GrIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0731_5.json b/datasets/NSIDC-0731_5.json index efe28dbf7d..980c006b72 100644 --- a/datasets/NSIDC-0731_5.json +++ b/datasets/NSIDC-0731_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0731_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly ice velocity mosaics for the Greenland Ice Sheet. The data are derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.\n\nSee Greenland Ice sheet Mapping Project (GrIMP) for related data.", "links": [ { diff --git a/datasets/NSIDC-0738_2.json b/datasets/NSIDC-0738_2.json index 8e9eb23d35..e166bb3ae4 100644 --- a/datasets/NSIDC-0738_2.json +++ b/datasets/NSIDC-0738_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0738_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains twice-daily, enhanced-resolution brightness temperature data derived from the SMAP radiometer. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and on the 3 km, 3.125 km, 9 km, 25 km, and 36 km resolution grids. This data set applies the same SIR technique used to derive brightness temperatures from the SMMR, AMSR-E, and SSM/I-SSMIS sensors and is a companion product for the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR data set (DOI: 10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001).", "links": [ { diff --git a/datasets/NSIDC-0747_1.json b/datasets/NSIDC-0747_1.json index be29c6e98a..8f79cc4ba6 100644 --- a/datasets/NSIDC-0747_1.json +++ b/datasets/NSIDC-0747_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0747_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product contains melt-season indicators that can be used to delineate various stages in the summer melt and freeze-up period of sea ice. The data were primarily derived using Sea Ice Concentration (SIC) observations from the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration and brightness temperature observations from the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures; both input data sets are archived at NSIDC. The main parameters for this data set include the dates of melt onset, early melt onset, and continuous melt onset; dates of early and continuous freeze onset; day of opening (last day SIC is above 80%); day of retreat (last day SIC drops below 15%); day of advance (first day SIC increases above 15%); day of closing (first day SIC increases above 80%); total outer ice-free period; total inner ice-free period; seasonal loss-of-ice period; seasonal gain-of-ice period; and the seasonal ice zone.\n\nThese data are available for 1979 through 2017. They are gridded on the NSIDC northern hemisphere polar stereographic grid at 25 km.", "links": [ { diff --git a/datasets/NSIDC-0754_1.json b/datasets/NSIDC-0754_1.json index cb527e7274..a06ad850a9 100644 --- a/datasets/NSIDC-0754_1.json +++ b/datasets/NSIDC-0754_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0754_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, combines interferometric phases from multiple satellite interferometric synthetic-aperture radar systems to derive the first comprehensive phase-based map of Antarctic ice velocity. The precision in ice speed and flow direction over 80% of Antarctica is better than prior mappings based on feature and speckle tracking by a factor of 10. Phase-derived velocity mostly covers the years between 2007 and 2018, while tracking-derived velocity (for regions along the coasts) is mostly found in the years from 2013 to 2017. Additional data acquired between 1996 and 2018 were used as needed to maximize coverage.\n\nSee Antarctic Ice Sheet Velocity and Mapping Data for related data.", "links": [ { diff --git a/datasets/NSIDC-0756_3.json b/datasets/NSIDC-0756_3.json index 100607a4f9..b6d4490c2f 100644 --- a/datasets/NSIDC-0756_3.json +++ b/datasets/NSIDC-0756_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0756_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography/bathymetry map of Antarctica based on mass conservation, streamline diffusion, and other methods. The data set also includes ice thickness, surface elevation, an ice/ocean/land mask, ice thickness estimation errors, and a map showing where each method was utilized.", "links": [ { diff --git a/datasets/NSIDC-0761_1.json b/datasets/NSIDC-0761_1.json index e1dcac5599..e1f899b918 100644 --- a/datasets/NSIDC-0761_1.json +++ b/datasets/NSIDC-0761_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0761_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of three as-complete-as-possible mosaic maps of velocities on the Antarctic ice sheet for the time periods 1995\u20132001, 2007\u20132009, and 2014\u20132017. The maps are posted at 450 m in the WGS 84/Antarctic Polar Stereographic projection.\n \nIn addition to ice velocity, the data set provides maps of velocity error and standard deviation; counts of velocity estimates used per pixel; date ranges; and masks that delineate the ice fronts and grounding lines for the each period.", "links": [ { diff --git a/datasets/NSIDC-0764_1.json b/datasets/NSIDC-0764_1.json index 2c1dfd953d..dfdbee25b0 100644 --- a/datasets/NSIDC-0764_1.json +++ b/datasets/NSIDC-0764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains shapefiles of Greenland\u2019s glacial termini and basins for the years 1972 to 2019. These vector data were created from Landsat 1-8 satellite imagery using the Calving Front Machine (CALFIN) an automated processing workflow utilizing neural networks for extracting calving fronts from satellite images of marine-terminating glaciers.", "links": [ { diff --git a/datasets/NSIDC-0766_1.json b/datasets/NSIDC-0766_1.json index 5b776ab983..fbf9bd3021 100644 --- a/datasets/NSIDC-0766_1.json +++ b/datasets/NSIDC-0766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery derived from images acquired between 2015\u20132021 by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.", "links": [ { diff --git a/datasets/NSIDC-0766_2.json b/datasets/NSIDC-0766_2.json index 5a481c5efc..ebac1363b0 100644 --- a/datasets/NSIDC-0766_2.json +++ b/datasets/NSIDC-0766_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0766_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery. Velocities are derived from images acquired by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.\n\nSee Greenland Ice Mapping Project (GrIMP) for related data sets.", "links": [ { diff --git a/datasets/NSIDC-0768_1.json b/datasets/NSIDC-0768_1.json index 304f52038e..9517e60dfe 100644 --- a/datasets/NSIDC-0768_1.json +++ b/datasets/NSIDC-0768_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0768_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of global, seasonal snow classifications\u2014e.g., tundra, boreal forest, maritime, ephemeral, prairie, montane forest, and ice\u2014determined from air temperature, precipitation, and wind speed climatologies. Data are available on global, North America, and Eurasia latitude-longitude grids at four resolutions: 10 arcsec (~300 m), 30 arcsec (~1 km), 2.5 arcmin (~5 km), and 0.5\u00b0 (~50 km).", "links": [ { diff --git a/datasets/NSIDC-0772_1.json b/datasets/NSIDC-0772_1.json index 7ca39ae7ab..dc46773986 100644 --- a/datasets/NSIDC-0772_1.json +++ b/datasets/NSIDC-0772_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0772_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "EASE-Grids 2.0 provide arrays of the latitude and longitude at the center of each grid cell. New products use the EASE-Grid 2.0, but many existing data sets still use the original EASE-Grids.", "links": [ { diff --git a/datasets/NSIDC-0774_1.json b/datasets/NSIDC-0774_1.json index d206f9d01c..1f3093aaeb 100644 --- a/datasets/NSIDC-0774_1.json +++ b/datasets/NSIDC-0774_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0774_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains twice-daily synthetic aperture radar (SAR) and enhanced-resolution scatterometer radar backscatter derived from SMAP radar data. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and as either 25 km or 3.125 km resolution grids. This new product uses the drop-in-the bucket gridding (GRD) algorithm and Scatterometer Image Reconstruction (SIR) algorithm to process the individual swath-based data from the input data sets into twice-daily images, producing SAR images (high-resolution), slice and footprint images (both lower-resolution), respectively. Note, that as a companion product, the radiometer form of the SIR algorithm (rSIR) was used to derive brightness temperatures from SMAP radiometer data for the SMAP Radiometer Twice-Daily rSIR-Enhanced EASE-Grid 2.0 Brightness Temperatures, Version 2, data set (NSIDC-0738).", "links": [ { diff --git a/datasets/NSIDC-0775_1.json b/datasets/NSIDC-0775_1.json index 086875b35c..3c2fea303b 100644 --- a/datasets/NSIDC-0775_1.json +++ b/datasets/NSIDC-0775_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0775_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of ice velocities at 240 m resolution, generated from Landsat 4, 5, 7, and 8 optical image pairs. Velocities were derived using the autonomous Repeat Image Feature Tracking algorithm (autoRIFT) processing chain. Data are available for all land ice areas larger than 5 square km, spanning the period from 1985 to 2018 (subject to image availability and quality). Data scarcity and/or low radiometric quality are significant limiting factors for many regions during the earlier years of the data record. Annual, global coverage is nearly complete after the 2013 launch of Landsat 8.", "links": [ { diff --git a/datasets/NSIDC-0776_1.json b/datasets/NSIDC-0776_1.json index 1d4346ad0a..8c8358a5a5 100644 --- a/datasets/NSIDC-0776_1.json +++ b/datasets/NSIDC-0776_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0776_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set from the Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of regionally compiled, mean annual surface velocities for major glacier-covered regions, derived from Landsat 4, 5, 7, and 8 imagery. Data are available from 1985 to 2018. Data availability is significantly limited prior to 2013 due to image scarcity and low image quality; however annual coverage is nearly complete for the years following the Landsat 8 launch in 2013.", "links": [ { diff --git a/datasets/NSIDC-0777_1.json b/datasets/NSIDC-0777_1.json index 81bd7d659d..72c67e3833 100644 --- a/datasets/NSIDC-0777_1.json +++ b/datasets/NSIDC-0777_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0777_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of surface velocity estimates for selected Greenland Ice Sheet outlet glaciers. Velocity fields were generated by tracking visible features in optical images acquired by the U.S. Geological Survey (USGS) Landsat 8 Operational Land Imager (OLI) and the European Space Agency (ESA) Copernicus Sentinel-2A and Sentinel-2B satellites.", "links": [ { diff --git a/datasets/NSIDC-0778_1.json b/datasets/NSIDC-0778_1.json index 6f33bb3bf7..89871096d3 100644 --- a/datasets/NSIDC-0778_1.json +++ b/datasets/NSIDC-0778_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0778_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a comprehensive map for the Antarctic Ice Sheet of the short-term zone of migration of the grounding line (i.e., the transition boundary between grounded ice and ice floating in the ocean waters) over a given period due to changes in oceanic tide. This short-term variation in the grounding line is referred to in this data set as the \u201cgrounding zone.\u201d The grounding zone is presented as polylines in an ESRI shapefile indicating the upstream and downstream bound of the variation in the grounding line for a given year. The data is based on an automatic delineation of thousands of grounding lines using Sentinel-1 A/B interferometric synthetic aperture radar (InSAR) data with a machine learning algorithm and supplemented by grounding lines from COSMO SkyMed InSAR data.", "links": [ { diff --git a/datasets/NSIDC-0779_1.json b/datasets/NSIDC-0779_1.json index 44cb749f43..06ad45dc07 100644 --- a/datasets/NSIDC-0779_1.json +++ b/datasets/NSIDC-0779_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0779_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture. The data set is validated by in situ soil moisture measurements from dense soil moisture networks representing different global land cover types.", "links": [ { diff --git a/datasets/NSIDC-0780_1.json b/datasets/NSIDC-0780_1.json index e6c2314cf1..6123c7bb5a 100644 --- a/datasets/NSIDC-0780_1.json +++ b/datasets/NSIDC-0780_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0780_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product is a set of polar ocean region masks. The masks have been developed primarily to support sea ice products, but they are potentially useful for other applications.", "links": [ { diff --git a/datasets/NSIDC-0781_1.json b/datasets/NSIDC-0781_1.json index 3711bd7680..e46fa8642f 100644 --- a/datasets/NSIDC-0781_1.json +++ b/datasets/NSIDC-0781_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0781_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of sub-seasonal, digitized (polyline) ice front positions for 219 outlet glaciers in Greenland. For 199 glaciers, ice front positions are digitized at a monthly resolution. For 20 glaciers in northwestern Greenland, ice front positions are digitized at a 6-12 day resolution, depending on the availability of satellite imagery. Ice front positions are derived from Sentinel-1A and Sentinel-1B synthetic aperture radar (SAR) mosaics.", "links": [ { diff --git a/datasets/NSIDC-0782_1.json b/datasets/NSIDC-0782_1.json index c489c585b1..830a1219ce 100644 --- a/datasets/NSIDC-0782_1.json +++ b/datasets/NSIDC-0782_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0782_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product contains monthly ice sheet elevation change data for Antarctica derived from five radar altimetry missions (Geosat, ERS-1 and -2, Envisat and CryoSat-2) and two laser altimetry missions (ICESat and ICESat-2). Each time step and grid node includes relative error estimates and a quality flag that can be used to filter the data in space and time. The product is also provided with an estimate of static topography in the form of a digital elevation model (DEM), which was used to estimate monthly ice sheet elevation change. With a temporal coverage of 17 April 1985 to 16 December 2020, this product can be used to determine changes in ice sheet mass balance over time.", "links": [ { diff --git a/datasets/NSIDC-0783_1.json b/datasets/NSIDC-0783_1.json index ca000e8d79..ecc2619a9a 100644 --- a/datasets/NSIDC-0783_1.json +++ b/datasets/NSIDC-0783_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0783_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Extreme Ice Survey (EIS) Glacier Image Archive, 2007-2022 is a collection of images capturing changes in arctic and alpine landscapes. Camera sites were established and maintained at scientifically significant cryospheric locations around the world. The 1.5 million images in this collection provide crucial data on the speed and extent of glacial retreat.", "links": [ { diff --git a/datasets/NSIDC-0786_1.json b/datasets/NSIDC-0786_1.json index 802d33944f..a251fd412c 100644 --- a/datasets/NSIDC-0786_1.json +++ b/datasets/NSIDC-0786_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0786_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides twice-daily enhanced-resolution radar backscatter images from 14 GHz NASA Scatterometer (NSCAT) observations by applying the Scatterometer Image Reconstruction with Filtering (SIRF) algorithm. The algorithm incorporates a median filter and a simplified spatial response function. Multiple passes of the spacecraft are combined to produce a higher spatial resolution and fill in coverage gaps between the individual measurement footprints, which are not contiguous and have six-sided shapes. Overlapping imaging periods start each day and extend through 4- and 8-day periods. Data are gridded to Northern Hemisphere, Southern Hemisphere, and Temperate EASE-Grid 2.0 projections at 3.125 km and 25 km resolutions.", "links": [ { diff --git a/datasets/NSIDC-0787_1.json b/datasets/NSIDC-0787_1.json index 40945271b1..390d6a1240 100644 --- a/datasets/NSIDC-0787_1.json +++ b/datasets/NSIDC-0787_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0787_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains twice-daily radar backscatter data collected at 14.6 GHz for horizontal-horizontal and vertical-vertical receive radar channels from the SeaSat-A satellite scatterometer (SASS) sensor. Data are available on the Northern Hemisphere, Southern Hemisphere, and Temperate EASE-Grid 2.0 projections as either 25 km or 3.125 km resolution grids. The data set uses the drop-in-the bucket gridding (GRD) and Scatterometer Image Reconstruction (SIR) algorithms to process the individual swath-based data into twice-daily morning and evening images. The GRD algorithm produces SAR images (high-resolution) on a 25 km grid and the SIR algorithm produces slice and footprint images (both lower-resolution) on a 3.125 km grid, provided at 8-,16-, 32-day imaging intervals. The data coverage is global from 7 July 1978 through 10 Oct 1978.", "links": [ { diff --git a/datasets/NSIDC-0788_1.json b/datasets/NSIDC-0788_1.json index ecfb90d13d..f85a0d038a 100644 --- a/datasets/NSIDC-0788_1.json +++ b/datasets/NSIDC-0788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the Greenland Ice Sheet Mapping Project (GrIMP) ice masks.", "links": [ { diff --git a/datasets/NSIDC-0791_1.json b/datasets/NSIDC-0791_1.json index 9067444afe..a654cec7f8 100644 --- a/datasets/NSIDC-0791_1.json +++ b/datasets/NSIDC-0791_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0791_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents new global snow cover classification regimes derived from the MODIS Terra cloud gap-filled NDSI data (MOD10A1F), elevation, and temperature climatology inputs. The six data granules are available as NetCDF (.nc) files, with each containing a unique snow cover classification spanning 2001 to 2023. The six classifications included in this data set are: (1) snow class climatology (SSC), (2) core snow season length (CSS), (3) snow cover duration (SCD), (4) full snow season length (FSS), (5) snow persistence (SP), and (6) snow season persistence (SSP).", "links": [ { diff --git a/datasets/NSIDC-0792_1.json b/datasets/NSIDC-0792_1.json index 119c0224ec..221b5112c5 100644 --- a/datasets/NSIDC-0792_1.json +++ b/datasets/NSIDC-0792_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0792_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution.\n\nThe data were generated from four European Space Agency (ESA) satellite radar altimetry missions\u2014ERS-1, ERS-2, Envisat, and CryoSat-2\u2014using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).", "links": [ { diff --git a/datasets/NSIDC-0793_1.json b/datasets/NSIDC-0793_1.json index 72dd1c00b2..0c50dca78a 100644 --- a/datasets/NSIDC-0793_1.json +++ b/datasets/NSIDC-0793_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0793_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly, 120 m resolution ice masks for the Greenland Ice Sheet from 1972 to 2022. The presence of ice was determined from 237,556 manually and AI-derived terminus positions acquired by satellite optical and radar observations. Months with no observations have been gap-filled using past and future observations of terminus positions and advance rates constrained by the average flow speed of the glacier.\n\nAnimations are also available for 206 catchments that show how the ice front positions have changed over the course of the time series and can be used as a quality control check.", "links": [ { diff --git a/datasets/NSIDC-0794_1.json b/datasets/NSIDC-0794_1.json index 3e7141f188..b11c5bd34a 100644 --- a/datasets/NSIDC-0794_1.json +++ b/datasets/NSIDC-0794_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0794_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided.", "links": [ { diff --git a/datasets/NSIDC-0796_1.json b/datasets/NSIDC-0796_1.json index c143efcae1..d308411a9c 100644 --- a/datasets/NSIDC-0796_1.json +++ b/datasets/NSIDC-0796_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0796_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images.\n\nFjord data were sourced from USGS EarthExplorer, Copernicus Open Access Hub, and the NSIDC. Landsat-8 and MODIS imagery for ice identification were collected from NASA Worldview and USGS EarthExplorer. Fjord, fast ice, and glacial ice boundaries were manually delineated using ArcGIS. Glacial ice was further categorized as dense glacial melange (Type 3), substantial glacial ice with large icebergs (Type 2), low-density glacial ice with large icebergs (Type 1), consistent small ice surface without large icebergs (Type 0), or glacier surface (Type 99).", "links": [ { diff --git a/datasets/NSIDC-0797_1.json b/datasets/NSIDC-0797_1.json index a0702839f6..c6722548c2 100644 --- a/datasets/NSIDC-0797_1.json +++ b/datasets/NSIDC-0797_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NSIDC-0797_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the SMAP baseline active-passive TB algorithm. The SMAP Single Channel Algorithm \u2013 Vertical polarization (SCA-V) is then used to derive soil moisture from SMAP/CYGNSS Tb data. The main parameter of this data set is surface soil moisture presented on the Global EASE-Grid 2.0 projection, with each data point representing the top 5 cm of the soil column. For SMAP-derived data, see SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5; for CYGNSS-derived data, see CYGNSS Level 1, Version 2.1.", "links": [ { diff --git a/datasets/NURE_SEDIMENT_CHEM.json b/datasets/NURE_SEDIMENT_CHEM.json index 707bbf85b4..3e654664ae 100644 --- a/datasets/NURE_SEDIMENT_CHEM.json +++ b/datasets/NURE_SEDIMENT_CHEM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NURE_SEDIMENT_CHEM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From NURE Sediment Chemistry FAQ:\n\nThese maps are derived from a subset of the National Uranium Resource\nEvaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR)\ndata. Approximately 260,000 samples were analyzed in the continental U.S. and\nconsisted of solid samples, including stream, lake, pond, spring, and playa\nsediments, and soils. Data for eleven elements: Na, Ti, Fe, Cu, Zn, As, Ce, Hf,\nPb, Th, and U were analyzed and included on the National Geochemical Atlas CD\nand the digital release NURE Sediment Chemistry. These publications are intended to allow\nthe rapid visualization of the geochemical landscape of the conterminous U.S.\nusing NURE HSSR data.\n\nThe raw data used in the production of these publications are available on the\nfollowing CD-ROM: Hoffman, J.D., Kim P. Buttleman, Russell A. Ambroziak, and\nChristine A. Cook, 1996, National Uranium Resource Evaluation (NURE)\nHydrogeochemical and Stream Sediment Reconnaissance (HSSR) data. available\n", "links": [ { diff --git a/datasets/NVAP_CLIMATE_Layered-Precipitable-Water_1.json b/datasets/NVAP_CLIMATE_Layered-Precipitable-Water_1.json index 02f5386d24..6fecea0d36 100644 --- a/datasets/NVAP_CLIMATE_Layered-Precipitable-Water_1.json +++ b/datasets/NVAP_CLIMATE_Layered-Precipitable-Water_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NVAP_CLIMATE_Layered-Precipitable-Water_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NVAP_CLIMATE_Layered-Precipitable-Water data set is designed to provide the most stable water vapor data set over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.", "links": [ { diff --git a/datasets/NVAP_CLIMATE_Total-Precipitable-Water_1.json b/datasets/NVAP_CLIMATE_Total-Precipitable-Water_1.json index dcb3cbee08..6db2ab8414 100644 --- a/datasets/NVAP_CLIMATE_Total-Precipitable-Water_1.json +++ b/datasets/NVAP_CLIMATE_Total-Precipitable-Water_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NVAP_CLIMATE_Total-Precipitable-Water_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NVAP_CLIMATE_Total-Precipitable-Water data set is designed to provide the most stable water vapor dataset over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.", "links": [ { diff --git a/datasets/NVAP_OCEAN_Total-Precipitable-Water_1.json b/datasets/NVAP_OCEAN_Total-Precipitable-Water_1.json index 15f11c68e9..207519bc35 100644 --- a/datasets/NVAP_OCEAN_Total-Precipitable-Water_1.json +++ b/datasets/NVAP_OCEAN_Total-Precipitable-Water_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NVAP_OCEAN_Total-Precipitable-Water_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NVAP_OCEAN_Total-Precipitable-Water data set includes only data from the Special Sensor Microwave/Imager (SSM/I) and intends to mirror other available SSM/I-only water vapor data sets. The data set is used for studies of climate change, interannual variability, and independent comparison to other ocean-only data sets. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.", "links": [ { diff --git a/datasets/NVAP_WEATHER_Layered-Precipitable-Water_1.json b/datasets/NVAP_WEATHER_Layered-Precipitable-Water_1.json index 5628880c8a..733d51dfd5 100644 --- a/datasets/NVAP_WEATHER_Layered-Precipitable-Water_1.json +++ b/datasets/NVAP_WEATHER_Layered-Precipitable-Water_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NVAP_WEATHER_Layered-Precipitable-Water_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NVAP_WEATHER_Layered-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Land GPS sites were added beginning in 1997. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.", "links": [ { diff --git a/datasets/NVAP_WEATHER_Total-Precipitable-Water_1.json b/datasets/NVAP_WEATHER_Total-Precipitable-Water_1.json index 7411e36337..4350a6ce4f 100644 --- a/datasets/NVAP_WEATHER_Total-Precipitable-Water_1.json +++ b/datasets/NVAP_WEATHER_Total-Precipitable-Water_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NVAP_WEATHER_Total-Precipitable-Water_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NVAP_WEATHER_Total-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.", "links": [ { diff --git a/datasets/NWS0007.json b/datasets/NWS0007.json index fc9124e8ba..88d527a878 100644 --- a/datasets/NWS0007.json +++ b/datasets/NWS0007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NWS0007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data sets are based on an area-by-area study of the Pacific Basin to\ndocument historical tsunamis and quantify historical coastal damage both near\nthe source and at far-field locations. An operational modification of the\nImamura-Iida Scale is used for this purpose.", "links": [ { diff --git a/datasets/NWT_Burn_Severity_Maps_1694_1.json b/datasets/NWT_Burn_Severity_Maps_1694_1.json index e093257afe..2dbcc61519 100644 --- a/datasets/NWT_Burn_Severity_Maps_1694_1.json +++ b/datasets/NWT_Burn_Severity_Maps_1694_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NWT_Burn_Severity_Maps_1694_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.", "links": [ { diff --git a/datasets/NW_microcosm_results_1.json b/datasets/NW_microcosm_results_1.json index 69002b5ebf..cf6d55d025 100644 --- a/datasets/NW_microcosm_results_1.json +++ b/datasets/NW_microcosm_results_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NW_microcosm_results_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geochemical, microbial and 14C data on remediation of petroleum hydrocarbons in Antarctica.\nThis record is part of ASAC project 1163 (ASAC_1163).\nMicrocosm study using Old Casey petroleum hydrocarbon contaminated sediment investgating the effect of water, nutrients and freze/thaw cycles on biodegradation. Temperature range -4 to 28 degrees. Microcosms with three different levels of nutrients and three different levels of water were investigated. The experiment was run over 95 days.\nDegradation was traced by radiometric methods and total aliphatic hydrocarbons were measured by gas chromatography.\nRadiometric data in file radiometric_01.xls, Gas Chromatography data in file gc_01.xls.\n\nThis work was completed as part of ASAC project 1163 (ASAC_1163).\n\nThe radiometric spreadsheet is divided up as follows:\nCODES is a summary of what went into each microcosm.\nCALCULATIONS is how much nutrients, water, radioactivity was added to the sediment.\nSUMMARY is what went into each microcosm flask.\nCT1, CT2 etc is the raw data, what was measured and calculations of radioactivity and recovery of isotope.\n\nNote that the Evaporation flasks (i.e., E10a) the number refers to the temperature that the flasks were incubated at, 'a' and 'b' refer to duplicates.\n\nAVERAGE is the average recoveries and first order rates of the triplicate microcosm for each treatment. GRAPHS is the graphs.\n\nThe fields in this dataset are:\nDays\nHours\nInitial flask weight\nNaOH removed\nNaOH added\nWeight of NaOH (g)\nCount (dpm)\nDiscarded dpm's\nVolume NaOH (ml)\ndpm in trap\nAbsolute dpm's\n%dpm recovered\nmillimole octadecane mineralised", "links": [ { diff --git a/datasets/NatalMuseum.json b/datasets/NatalMuseum.json index fbb610956a..fa546a8f46 100644 --- a/datasets/NatalMuseum.json +++ b/datasets/NatalMuseum.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NatalMuseum", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Natal Museum's Department of Mollusca had its origins in the shell\n collection and library of Henry Burnup, a dedicated amateur who was honorary\n curator of molluscs until his death in 1928. Subsequently, the collection has\n been expanded many times over through field work, donation, exchange and\n purchase. Its historical value was greatly increased by absorption of important\n shell collections housed the Transvaal Museum (1978) and Albany Museum (1980),\n as well as the Rodney Wood collection from the Seychelles received from the\n Mutare Museum in Zimbabwe and the Kurt Grosch collection, built up over 25\n years of residence in northern Mozambique. The mollusc collection now ranks\n among the 15 largest in the world and is certainly the largest both in Africa\n and on the Indian Ocean rim. It currently contains 7233 Bivalvia records, and\n 20112 Gastropoda records (total 27345 records of 282 families). The collection\n will be updated in the near future.", "links": [ { diff --git a/datasets/Nested_DGGE_1.json b/datasets/Nested_DGGE_1.json index 3604cc46d5..b9cbbd6346 100644 --- a/datasets/Nested_DGGE_1.json +++ b/datasets/Nested_DGGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Nested_DGGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment samples which were originally collected as part of ASAC 868 (ASAC_868) are now being investigated using molecular microbial techniques as part of ASAC 1228 (ASAC_1228). Samples were collected in a nested survey design in two hydrocarbon impacted areas and two unimpacted areas. Denaturing gradient gel electrophoresis (DGGE) of a region of the 16S RNA gene was used to investigate the microbial community structure. Banding patterns obtained from the DGGE were transformed into a presence / absence matrix and analysed with a multivariate statistical approach.\n\nThe download file contains an excel spreadsheet, a csv version of the data, plus a readme file.", "links": [ { diff --git a/datasets/NetCDF.GOMOS_UFP_6.0.json b/datasets/NetCDF.GOMOS_UFP_6.0.json index 356d2f611c..a7caeb40cd 100644 --- a/datasets/NetCDF.GOMOS_UFP_6.0.json +++ b/datasets/NetCDF.GOMOS_UFP_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NetCDF.GOMOS_UFP_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product describes atmospheric constituents profiles: In particular the vertical and line density profiles of ozone, NO2, NO3, O2, H2O, air, aerosols, temperature, turbulence. Coverage is as follows: Elevation range: +62 deg to +68 deg Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The GOMOS data are now also available as user friendly products in the NetCDF4-format. These files are occultation based (dark and bright) and include all GOMOS Level 2 constituent profiles and HRTP profiles with all the essential parameters. For further information, please see the news published on 1 March 2017 and 1 August 2017.", "links": [ { diff --git a/datasets/NetCDF.GOMOS_UFP_Gridded_6.0.json b/datasets/NetCDF.GOMOS_UFP_Gridded_6.0.json index 602779c5f5..d6e9417f5f 100644 --- a/datasets/NetCDF.GOMOS_UFP_Gridded_6.0.json +++ b/datasets/NetCDF.GOMOS_UFP_Gridded_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NetCDF.GOMOS_UFP_Gridded_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product describes atmospheric constituents profiles: In particular the vertical and line density profiles of ozone, NO2, NO3, O2, H2O, air, aerosols, temperature, turbulence. Coverage is as follows: Elevation range: +62 deg to +68 deg Azimuth range: +90 deg to +190 deg (with respect to the flight direction) The GOMOS data are now also available as user friendly products in the NetCDF4-format. These files are Level 2 constituent profiles and are altitude gridded. These Level 2 files include quality flags and are based and collected on a yearly basis.", "links": [ { diff --git a/datasets/Neutron-Monitor-era-annual-10Be_1.json b/datasets/Neutron-Monitor-era-annual-10Be_1.json index 5f8a065be5..e520248267 100644 --- a/datasets/Neutron-Monitor-era-annual-10Be_1.json +++ b/datasets/Neutron-Monitor-era-annual-10Be_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Neutron-Monitor-era-annual-10Be_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annually-resolved 10Be concentrations, stable water isotope ratios and accumualtion rate data from the DSS site on Law Dome, East Antarctica (spanning 1936-2009) and the Das2 site, south-east Greenland (1936-2002). A composite record constructed from these records and previously published records from NGRIP, Renland and Dye 3 (Greenland) and Dronning Maud Land (Antarctica) is also provided. \n\nLaw Dome Summit South (DSS), Antarctica 66 degrees 46 degrees S 112 degrees 48 degrees E, 1370 m asl\nDas2 Greenland, 67 degrees 32'N, 36 degrees 04'W, 2936 m asl", "links": [ { diff --git a/datasets/New_England_CH4_1311_1.json b/datasets/New_England_CH4_1311_1.json index b7a8490fa6..ae3f6d3f7c 100644 --- a/datasets/New_England_CH4_1311_1.json +++ b/datasets/New_England_CH4_1311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "New_England_CH4_1311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains an inventory of natural and anthropogenic methane emissions for all counties in the six New England states of Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine. The inventory represents a snapshot in time (circa 1990-1994) and provides emission estimates for multiple sources including wetlands, landfills, ruminant animals, residential wood combustion, fossil fuel combustion and use, animal manure, wastewater treatment, and natural gas transmission pipelines. Also included is the uptake or sink of methane in relatively well-drained upland soils.", "links": [ { diff --git a/datasets/New_Hampshire_Landcover_1305_1.json b/datasets/New_Hampshire_Landcover_1305_1.json index d4225bd19d..6ab7923dc7 100644 --- a/datasets/New_Hampshire_Landcover_1305_1.json +++ b/datasets/New_Hampshire_Landcover_1305_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "New_Hampshire_Landcover_1305_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The New Hampshire Geographically Referenced Analysis and Information Transfer System (GRANIT) land cover data set provides a land cover and land use product at 30-m resolution with 23 individual classes across the state. The classification is based largely on the analysis of 12 Landsat Thematic Mapper (TM and ETM+) images. Over 1,400 new classification training site data points were collected to supplement 1,200 archived sites from previous projects. The classification represents a snapshot in time from 1996 to 2001. This time range spans the dates of the most recent acquisitions of a TM scene for each region of the state and the dates of the most recent field data collection.", "links": [ { diff --git a/datasets/Niwot_Ridge_CNPAM_Fluorescence_1722_1.json b/datasets/Niwot_Ridge_CNPAM_Fluorescence_1722_1.json index 2efc552eb6..01ab7f87d8 100644 --- a/datasets/Niwot_Ridge_CNPAM_Fluorescence_1722_1.json +++ b/datasets/Niwot_Ridge_CNPAM_Fluorescence_1722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Niwot_Ridge_CNPAM_Fluorescence_1722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides chlorophyll fluorescence measurements made on pine and spruce needle tissues at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Two types of measurements were made using pulse-amplitude-modulation (PAM) fluorometry: the photosystem II (PSII) operating efficiency in the light (Fq'/Fm' at variable light levels), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) on dark-acclimated tissues. Chlorophyll fluorescence measurements were made to determine seasonality of photosynthetic performance at the needle level.", "links": [ { diff --git a/datasets/Niwot_Ridge_Pigment_1723_1.json b/datasets/Niwot_Ridge_Pigment_1723_1.json index 072134c429..df273b0837 100644 --- a/datasets/Niwot_Ridge_Pigment_1723_1.json +++ b/datasets/Niwot_Ridge_Pigment_1723_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Niwot_Ridge_Pigment_1723_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides concentrations of pigments in pine and spruce needle tissues collected at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Pigments measured included Chlorophyll A and B, Violaxanthin, Antheraxanthin, Zeaxanthin, Neoxanthin, Lutein, and beta-Carotene. Measurements were made on sun foliage from two canopy-access towers near the main flux tower, and in the laboratory on branches collected from those towers, every 4-8 weeks over the annual cycle. Due to canopy structure, a limited number of trees were accessible from the towers, preventing extensive replication. Pigments were extracted in acetone and analyzed by HPLC. The measurements were made to evaluate seasonal changes associated with the down-regulation of photosynthesis.", "links": [ { diff --git a/datasets/NmAVCS1H_1.json b/datasets/NmAVCS1H_1.json index fbc1481299..8b9e965757 100644 --- a/datasets/NmAVCS1H_1.json +++ b/datasets/NmAVCS1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmAVCS1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus Advanced Vidicon Camera System Visible Imagery L1, HDF5 (NmAVCS1H) data set consists of black-and-white images captured by the Advanced Vidicon Camera Systems onboard the Nimbus 1 (1964) and Nimbus 2 (1966) satellites. Data are provided as HDF5-formatted files. Browse images are also available.", "links": [ { diff --git a/datasets/NmAVCS3G_1.json b/datasets/NmAVCS3G_1.json index 4adb80922d..db74257158 100644 --- a/datasets/NmAVCS3G_1.json +++ b/datasets/NmAVCS3G_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmAVCS3G_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmAVCS3G) consists of daily image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery for the region between 60 N and 60 S. Data are provided as GeoTIFFs. For the HDF5 formatted version of these data, see Nimbus Advanced Vidicon Camera System Remapped Visible Imagery Daily L3, HDF5.", "links": [ { diff --git a/datasets/NmAVCS3H_1.json b/datasets/NmAVCS3H_1.json index 5076333171..785e406ac9 100644 --- a/datasets/NmAVCS3H_1.json +++ b/datasets/NmAVCS3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmAVCS3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmAVCS3H) consists of daily, global image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 5 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 10 km equidistant grid for the region between 60 N and 60 S.", "links": [ { diff --git a/datasets/NmHRIR1H_1.json b/datasets/NmHRIR1H_1.json index 11bd5eac05..75f087e0ba 100644 --- a/datasets/NmHRIR1H_1.json +++ b/datasets/NmHRIR1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmHRIR1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus High Resolution Infrared Radiometer Digital Swath Data L1, HDF data set (NmHRIR1H) consists of High Resolution Infrared Radiometer (HRIR) brightness temperatures obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. A correction has been applied to minimize seemingly random alignment errors that caused clouds edges and land features to appear jagged in the original 1960s data.", "links": [ { diff --git a/datasets/NmHRIR1T_1.json b/datasets/NmHRIR1T_1.json index 421a2a0a7b..f776744f5f 100644 --- a/datasets/NmHRIR1T_1.json +++ b/datasets/NmHRIR1T_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmHRIR1T_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 3.4 to 4.2 \u00b5m window. These data were detected by the High Resolution Infrared Radiometer (HRIR) on board the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969-1970. The Nimbus HRIR sensor was used to map the earth's nighttime cloud cover and to measure cloud top temperatures or surface temperatures. Note: This data set is not georeferenced and contains some gaps in temporal coverage because of missing data.", "links": [ { diff --git a/datasets/NmHRIR3G_1.json b/datasets/NmHRIR3G_1.json index 53b54f928d..b1ff0c89a8 100644 --- a/datasets/NmHRIR3G_1.json +++ b/datasets/NmHRIR3G_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmHRIR3G_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969. Data are available as GeoTIFFs and browse images. For the HDF5 formatted version of these data, see the Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, HDF5 data set.", "links": [ { diff --git a/datasets/NmHRIR3H_1.json b/datasets/NmHRIR3H_1.json index d88ac123d0..de672092af 100644 --- a/datasets/NmHRIR3H_1.json +++ b/datasets/NmHRIR3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmHRIR3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmHRIR3H) consists of daily, global composites of High Resolution Infrared Radiometer (HRIR) data obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 10 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 20 km equidistant grid for the region between 60 N and 60 S. Browse images are also available.", "links": [ { diff --git a/datasets/NmIDCS1H_1.json b/datasets/NmIDCS1H_1.json index 665e5d74a7..d0fe9ee960 100644 --- a/datasets/NmIDCS1H_1.json +++ b/datasets/NmIDCS1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmIDCS1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus Image Dissector Camera System Visible Imagery L1, HDF5 (NmIDCS1H) data set consists of black-and-white images captured by the Image Dissector Camera Systems (IDCSs) onboard the Nimbus 3 and Nimbus 4 satellites. Data are provided as HDF5-formatted files. Browse images are also available.", "links": [ { diff --git a/datasets/NmIDCS3G_1.json b/datasets/NmIDCS3G_1.json index aea4bc74e9..7b42887a19 100644 --- a/datasets/NmIDCS3G_1.json +++ b/datasets/NmIDCS3G_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmIDCS3G_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmIDCS3G) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery for the region between 60 N and 60 S. Images were acquired between 23 April, 1969 - 04 January, 1971. Data are available as GeoTIFFs and browse images. For HDF5 formatted version of these data, see Nimbus Image Dissector Camera System Remapped Visible Imagery Daily L3, HDF5.", "links": [ { diff --git a/datasets/NmIDCS3H_1.json b/datasets/NmIDCS3H_1.json index 0a1f5f9ce5..fe186c9642 100644 --- a/datasets/NmIDCS3H_1.json +++ b/datasets/NmIDCS3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmIDCS3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmIDCS3H) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery captured from 23 April, 1969 - 04 April, 1971.", "links": [ { diff --git a/datasets/NmIcEdg2_1.json b/datasets/NmIcEdg2_1.json index 317461a3d3..6f3663a7bb 100644 --- a/datasets/NmIcEdg2_1.json +++ b/datasets/NmIcEdg2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmIcEdg2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmIcEdg2) estimates the location of the North and South Pole sea ice edges at various times during the mid to late 1960s, based on recovered Nimbus 1 (1964), Nimbus 2 (1966), and Nimbus 3 (1969) visible imagery.", "links": [ { diff --git a/datasets/NmTHIR115-1H_1.json b/datasets/NmTHIR115-1H_1.json index 3caa215d66..6db41bdd77 100644 --- a/datasets/NmTHIR115-1H_1.json +++ b/datasets/NmTHIR115-1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR115-1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR115-1H) consists of daily, global radiative temperatures measured in the 11.5 \u00b5m window (10.5 \u00b5m - 12.5 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures.", "links": [ { diff --git a/datasets/NmTHIR115-1T_1.json b/datasets/NmTHIR115-1T_1.json index f4a1c84436..0a920d4584 100644 --- a/datasets/NmTHIR115-1T_1.json +++ b/datasets/NmTHIR115-1T_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR115-1T_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 11.5 \u00b5m window (10.5 \u00b5m - 12.5 \u00b5m). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975 and 1975. The Nimbus satellites used the THIR 11.5 \u00b5m window to measure cloud top or surface temperatures. Note: This data set is not georeferenced and contains some gaps in temporal coverage because of missing data.", "links": [ { diff --git a/datasets/NmTHIR115-3G_1.json b/datasets/NmTHIR115-3G_1.json index b24d03d38c..a546b7406c 100644 --- a/datasets/NmTHIR115-3G_1.json +++ b/datasets/NmTHIR115-3G_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR115-3G_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR115-3G) consists of daily, global composites of radiative temperatures obtained in the 11.5 \u00b5m window (10.5 \u00b5m - 12.5 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file only from the Nimbus Temperature-Humidity Infrared Radiometer 11.5 \u00b5m Remapped Digital Data Daily L3, HDF5 (NmTHIR115-3H) data set.", "links": [ { diff --git a/datasets/NmTHIR115-3H_1.json b/datasets/NmTHIR115-3H_1.json index 6b54b9005c..c7120cb7cb 100644 --- a/datasets/NmTHIR115-3H_1.json +++ b/datasets/NmTHIR115-3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR115-3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR115-3H) consists of daily, global composites of radiative temperatures obtained in the 11.5 \u00b5m window (10.5 \u00b5m - 12.5 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR swath data and show cloud top or surface temperatures.", "links": [ { diff --git a/datasets/NmTHIR67-1H_1.json b/datasets/NmTHIR67-1H_1.json index ab47e02543..33afa83316 100644 --- a/datasets/NmTHIR67-1H_1.json +++ b/datasets/NmTHIR67-1H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR67-1H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR67-1H) consists of daily, global radiative temperatures measured in the 6.7 \u00b5m window (6.5 \u00b5m - 7.0 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The THIR 6.7 \u00b5m window was used to map the water vapor distribution in the upper troposphere and stratosphere.", "links": [ { diff --git a/datasets/NmTHIR67-1T_1.json b/datasets/NmTHIR67-1T_1.json index 606d1449db..0e1bd353aa 100644 --- a/datasets/NmTHIR67-1T_1.json +++ b/datasets/NmTHIR67-1T_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR67-1T_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 6.7 \u00b5m window (6.5 \u00b5m - 7.0 \u00b5m). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975, and 1975. The Nimbus satellites used the THIR 6.7 \u00b5m window to map the water vapor distribution in the upper troposphere and stratosphere. Note: This data set is not georeferenced and there are some gaps in temporal coverage because of missing data.", "links": [ { diff --git a/datasets/NmTHIR67-3G_1.json b/datasets/NmTHIR67-3G_1.json index 78da7fbee7..38c944fb65 100644 --- a/datasets/NmTHIR67-3G_1.json +++ b/datasets/NmTHIR67-3G_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR67-3G_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR67-3G) consists of daily, global composites of radiative temperatures obtained in the 6.7 \u00b5m water vapor window (6.5 \u00b5m - 7.0 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The THIR 6.7 \u00b5m window was used to map the water vapor distribution in the upper troposphere and stratosphere. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file only from the Nimbus Temperature-Humidity Infrared Radiometer 6.7 \u00b5m Water Vapor Remapped Digital Data Daily, HDF5 (NmTHIR67-3H) data set.", "links": [ { diff --git a/datasets/NmTHIR67-3H_1.json b/datasets/NmTHIR67-3H_1.json index 818ea352d3..2b8729df7f 100644 --- a/datasets/NmTHIR67-3H_1.json +++ b/datasets/NmTHIR67-3H_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIR67-3H_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set (NmTHIR67-3H) consists of daily, global composites of radiative temperatures measured in the 6.7 \u00b5m water vapor window (6.5 \u00b5m - 7.0 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) onboard the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR swath data and show water vapor distribution in the upper troposphere and stratosphere.", "links": [ { diff --git a/datasets/NmTHIRmtg-1T_1.json b/datasets/NmTHIRmtg-1T_1.json index a53e9e5bdd..7f5a486c0c 100644 --- a/datasets/NmTHIRmtg-1T_1.json +++ b/datasets/NmTHIRmtg-1T_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NmTHIRmtg-1T_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of daily, global grayscale TIFF images measured in the 6.7 \u00b5m window (6.5 \u00b5m - 7.0 \u00b5m) and the 11.5 \u00b5m window (10.5 \u00b5m - 12.5 \u00b5m) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 7 satellite. Each data granule is a daytime or nighttime global composite of all the swaths in a day. Note: This data set is not georeferenced and there are some gaps in the temporal coverage because of missing data.", "links": [ { diff --git a/datasets/Nome_Veg_Plots_1372_1.json b/datasets/Nome_Veg_Plots_1372_1.json index c33fb4cd52..b5dac49d2b 100644 --- a/datasets/Nome_Veg_Plots_1372_1.json +++ b/datasets/Nome_Veg_Plots_1372_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Nome_Veg_Plots_1372_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska.", "links": [ { diff --git a/datasets/Non-Forest_Trees_Sahara_Sahel_1832_1.json b/datasets/Non-Forest_Trees_Sahara_Sahel_1832_1.json index a297686bd7..b05906f412 100644 --- a/datasets/Non-Forest_Trees_Sahara_Sahel_1832_1.json +++ b/datasets/Non-Forest_Trees_Sahara_Sahel_1832_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Non-Forest_Trees_Sahara_Sahel_1832_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time.", "links": [ { diff --git a/datasets/Nongrowing_Season_CO2_Flux_1692_1.json b/datasets/Nongrowing_Season_CO2_Flux_1692_1.json index a9e46c54c1..20552a0387 100644 --- a/datasets/Nongrowing_Season_CO2_Flux_1692_1.json +++ b/datasets/Nongrowing_Season_CO2_Flux_1692_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Nongrowing_Season_CO2_Flux_1692_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources.", "links": [ { diff --git a/datasets/NorthSlope_NEE_TVPRM_1920_1.json b/datasets/NorthSlope_NEE_TVPRM_1920_1.json index e2e17c12d8..3239f33bcf 100644 --- a/datasets/NorthSlope_NEE_TVPRM_1920_1.json +++ b/datasets/NorthSlope_NEE_TVPRM_1920_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "NorthSlope_NEE_TVPRM_1920_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC).", "links": [ { diff --git a/datasets/North_Carolina_Coast_0.json b/datasets/North_Carolina_Coast_0.json index 5effe8bd77..f4317bf840 100644 --- a/datasets/North_Carolina_Coast_0.json +++ b/datasets/North_Carolina_Coast_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "North_Carolina_Coast_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the North Carolina coast.", "links": [ { diff --git a/datasets/North_Carolina_Sabrina_0.json b/datasets/North_Carolina_Sabrina_0.json index 242a4ab488..504ee047b0 100644 --- a/datasets/North_Carolina_Sabrina_0.json +++ b/datasets/North_Carolina_Sabrina_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "North_Carolina_Sabrina_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken by the research vessel Sabrina in the Outer Banks and coastal regions of North Carolina in 2002 and 2003.", "links": [ { diff --git a/datasets/North_Sea_0.json b/datasets/North_Sea_0.json index 35f1104e0e..8e5fc26c29 100644 --- a/datasets/North_Sea_0.json +++ b/datasets/North_Sea_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "North_Sea_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the North Sea in 1994.", "links": [ { diff --git a/datasets/North_Slope_Transect_Veg_Maps_1386_1.json b/datasets/North_Slope_Transect_Veg_Maps_1386_1.json index 7d7960584e..af05210170 100644 --- a/datasets/North_Slope_Transect_Veg_Maps_1386_1.json +++ b/datasets/North_Slope_Transect_Veg_Maps_1386_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "North_Slope_Transect_Veg_Maps_1386_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.", "links": [ { diff --git a/datasets/North_Slope_Veg_Plots_1536_1.json b/datasets/North_Slope_Veg_Plots_1536_1.json index ade3923e95..38f27eb0ea 100644 --- a/datasets/North_Slope_Veg_Plots_1536_1.json +++ b/datasets/North_Slope_Veg_Plots_1536_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "North_Slope_Veg_Plots_1536_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation cover and environmental plot and soil data collected at flux tower sites of the North Slope Arctic System Science/Land-Atmosphere-Ice Interactions (ARCSS/LAII) project in August of 1995 and 1996. The 19 ARCSS/LAII flux tower sites are located along a North-South transect from near Prudhoe Bay to the foothills of the Brooks Range on the North Slope of Alaska. At 17 of the flux tower sites, one or more vegetation plots (29 total) were established and measurements including (1) plant species cover for the major vegetation types using the Braun-Blanquet approach, (2) plot environmental data, and (3) soil profile descriptions were recorded. In addition, at all 19 sites, plant growth form composition and cover were surveyed using a point sampling technique along multiple transects within selected plots.", "links": [ { diff --git a/datasets/Northern_Alaska_Veg_Maps_1359_1.json b/datasets/Northern_Alaska_Veg_Maps_1359_1.json index 90182f838b..86e4ba8cb0 100644 --- a/datasets/Northern_Alaska_Veg_Maps_1359_1.json +++ b/datasets/Northern_Alaska_Veg_Maps_1359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Northern_Alaska_Veg_Maps_1359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides four land cover and ecosystem classification maps for northern Alaska. The maps were produced for several projects and from different data sources including Landsat imagery and existing maps and models, and cover a range of ecosystem and vegetation classes. The data used to derive the maps covered the period 1976-08-04 to 2014-09-01.", "links": [ { diff --git a/datasets/O2_OCM_STUC00GHD_1.0.json b/datasets/O2_OCM_STUC00GHD_1.0.json index d2cafdc8aa..5ffda13507 100644 --- a/datasets/O2_OCM_STUC00GHD_1.0.json +++ b/datasets/O2_OCM_STUC00GHD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "O2_OCM_STUC00GHD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Local Area Coverage (LAC) Ocean Color Monitor Radiance products with 360 m x 360 m resolution.", "links": [ { diff --git a/datasets/OAHU_0.json b/datasets/OAHU_0.json index 81db702eda..b717a6562a 100644 --- a/datasets/OAHU_0.json +++ b/datasets/OAHU_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OAHU_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the west-central Pacific near Wake Island in 2007.", "links": [ { diff --git a/datasets/OASIS_Moorings_0.json b/datasets/OASIS_Moorings_0.json index 9eb03590ea..eb1195df9a 100644 --- a/datasets/OASIS_Moorings_0.json +++ b/datasets/OASIS_Moorings_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OASIS_Moorings_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the OASIS moorings at the mouth of the Monterey Bay in 1995.", "links": [ { diff --git a/datasets/OCEANSAT-2_0.json b/datasets/OCEANSAT-2_0.json index 4dbeec8b62..ecf3d0ffaa 100644 --- a/datasets/OCEANSAT-2_0.json +++ b/datasets/OCEANSAT-2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCEANSAT-2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the monsoonal Arabian Sea in 2009 to validate the Indian Satellite, OceanSat-2.", "links": [ { diff --git a/datasets/OCEAN_LIDAR_0.json b/datasets/OCEAN_LIDAR_0.json index 0591f9979a..94af47de9d 100644 --- a/datasets/OCEAN_LIDAR_0.json +++ b/datasets/OCEAN_LIDAR_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCEAN_LIDAR_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Tropical Western Pacific Ocean between 1994 and 2001.", "links": [ { diff --git a/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json b/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json index 8bf279c19c..e29e3317db 100644 --- a/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json +++ b/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.1Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.", "links": [ { diff --git a/datasets/OCFLEXPART_1.json b/datasets/OCFLEXPART_1.json index 4d1b42edb9..a1f5ef9a29 100644 --- a/datasets/OCFLEXPART_1.json +++ b/datasets/OCFLEXPART_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCFLEXPART_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a global simulation of organic carbon (OC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available. \n", "links": [ { diff --git a/datasets/OCO2GriddedXCO2_3.json b/datasets/OCO2GriddedXCO2_3.json index 38c828148d..431fdc7bc3 100644 --- a/datasets/OCO2GriddedXCO2_3.json +++ b/datasets/OCO2GriddedXCO2_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2GriddedXCO2_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.", "links": [ { diff --git a/datasets/OCO2GriddedXCO2_4.json b/datasets/OCO2GriddedXCO2_4.json index 3d1f19c28b..1fe114b070 100644 --- a/datasets/OCO2GriddedXCO2_4.json +++ b/datasets/OCO2GriddedXCO2_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2GriddedXCO2_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.", "links": [ { diff --git a/datasets/OCO2GriddedXCO2_SIF_4.json b/datasets/OCO2GriddedXCO2_SIF_4.json index ad45837588..9a5fd3eb36 100644 --- a/datasets/OCO2GriddedXCO2_SIF_4.json +++ b/datasets/OCO2GriddedXCO2_SIF_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2GriddedXCO2_SIF_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.", "links": [ { diff --git a/datasets/OCO2_Att_10.json b/datasets/OCO2_Att_10.json index a15a6bea81..cb58ad527d 100644 --- a/datasets/OCO2_Att_10.json +++ b/datasets/OCO2_Att_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Att_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Att_10r.json b/datasets/OCO2_Att_10r.json index 48b9afcc1d..4d4002c556 100644 --- a/datasets/OCO2_Att_10r.json +++ b/datasets/OCO2_Att_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Att_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nVersion 8r is the current version of the data set. Version 7r has been superseded by Version 8r.\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_Att_11.2.json b/datasets/OCO2_Att_11.2.json index 2e09ca6fd9..3e5d8f6651 100644 --- a/datasets/OCO2_Att_11.2.json +++ b/datasets/OCO2_Att_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Att_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Att_11.json b/datasets/OCO2_Att_11.json index b5dc80b72d..33146967c5 100644 --- a/datasets/OCO2_Att_11.json +++ b/datasets/OCO2_Att_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Att_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Att_11r.json b/datasets/OCO2_Att_11r.json index af9ea83dce..12bef86ee6 100644 --- a/datasets/OCO2_Att_11r.json +++ b/datasets/OCO2_Att_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Att_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_Eph_10.json b/datasets/OCO2_Eph_10.json index 7c416394be..63663d3c24 100644 --- a/datasets/OCO2_Eph_10.json +++ b/datasets/OCO2_Eph_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Eph_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Eph_10r.json b/datasets/OCO2_Eph_10r.json index bf37713bca..89c3aa8397 100644 --- a/datasets/OCO2_Eph_10r.json +++ b/datasets/OCO2_Eph_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Eph_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nVersion 8r is the current version of the data set. Version 7r has been superseded by Version 8r.\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_Eph_11.2.json b/datasets/OCO2_Eph_11.2.json index 2d440c30f5..1a27749e7c 100644 --- a/datasets/OCO2_Eph_11.2.json +++ b/datasets/OCO2_Eph_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Eph_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Eph_11.json b/datasets/OCO2_Eph_11.json index e36384c3d8..4267f9a667 100644 --- a/datasets/OCO2_Eph_11.json +++ b/datasets/OCO2_Eph_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Eph_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.", "links": [ { diff --git a/datasets/OCO2_Eph_11r.json b/datasets/OCO2_Eph_11r.json index aedc0c9374..6a18f105e7 100644 --- a/datasets/OCO2_Eph_11r.json +++ b/datasets/OCO2_Eph_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_Eph_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_GEOS_L3CO2_DAY_10r.json b/datasets/OCO2_GEOS_L3CO2_DAY_10r.json index c97745f14e..ec7bdeea50 100644 --- a/datasets/OCO2_GEOS_L3CO2_DAY_10r.json +++ b/datasets/OCO2_GEOS_L3CO2_DAY_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_GEOS_L3CO2_DAY_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " This is the Gridded Daily OCO-2 Carbon Dioxide assimilated dataset. \n\n The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2\u2019s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth\u2019s carbon cycle and atmospheric transport. \n\n OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA\u2019s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem. ", "links": [ { diff --git a/datasets/OCO2_GEOS_L3CO2_MONTH_10r.json b/datasets/OCO2_GEOS_L3CO2_MONTH_10r.json index 7a0d3a1619..4a0841ba06 100644 --- a/datasets/OCO2_GEOS_L3CO2_MONTH_10r.json +++ b/datasets/OCO2_GEOS_L3CO2_MONTH_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_GEOS_L3CO2_MONTH_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Gridded Monthly OCO-2 Carbon Dioxide assimilated dataset. \n\n The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2\u2019s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth\u2019s carbon cycle and atmospheric transport. \n\n OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA\u2019s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.", "links": [ { diff --git a/datasets/OCO2_L1B_Calibration_10r.json b/datasets/OCO2_L1B_Calibration_10r.json index 6080de0a7e..410f8f4332 100644 --- a/datasets/OCO2_L1B_Calibration_10r.json +++ b/datasets/OCO2_L1B_Calibration_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Calibration_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nVersion 8r is the current version of the data set. Version 7r has been superseded by Version 8r.\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1B_Calibration_11.2.json b/datasets/OCO2_L1B_Calibration_11.2.json index 826beb82de..d1dc45ee6f 100644 --- a/datasets/OCO2_L1B_Calibration_11.2.json +++ b/datasets/OCO2_L1B_Calibration_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Calibration_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.", "links": [ { diff --git a/datasets/OCO2_L1B_Calibration_11.2r.json b/datasets/OCO2_L1B_Calibration_11.2r.json index e993de49f8..b237475469 100644 --- a/datasets/OCO2_L1B_Calibration_11.2r.json +++ b/datasets/OCO2_L1B_Calibration_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Calibration_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.", "links": [ { diff --git a/datasets/OCO2_L1B_Calibration_11r.json b/datasets/OCO2_L1B_Calibration_11r.json index f7e9d62b44..8422464df4 100644 --- a/datasets/OCO2_L1B_Calibration_11r.json +++ b/datasets/OCO2_L1B_Calibration_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Calibration_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1B_Science_10r.json b/datasets/OCO2_L1B_Science_10r.json index 9f317698c4..2c0f8e1578 100644 --- a/datasets/OCO2_L1B_Science_10r.json +++ b/datasets/OCO2_L1B_Science_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Science_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the output from the Level 1B process. It converts the raw instrument data numbers into calibrated radiances. This conversion is based upon files of instrument characteristics and algorithm parameters that may vary over time. In addition to calibrated radiances, the Level 1B output products have geolocation information recorded for each measurement for use in higher-level processes.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1B_Science_11.2.json b/datasets/OCO2_L1B_Science_11.2.json index 513c3cf195..6dc2a447ae 100644 --- a/datasets/OCO2_L1B_Science_11.2.json +++ b/datasets/OCO2_L1B_Science_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Science_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the output from the Level 1B process. It converts the raw instrument data numbers into calibrated radiances. This conversion is based upon files of instrument characteristics and algorithm parameters that may vary over time. In addition to calibrated radiances, the Level 1B output products have geolocation information recorded for each measurement for use in higher-level processes.", "links": [ { diff --git a/datasets/OCO2_L1B_Science_11.2r.json b/datasets/OCO2_L1B_Science_11.2r.json index ad057bce20..72f69a7293 100644 --- a/datasets/OCO2_L1B_Science_11.2r.json +++ b/datasets/OCO2_L1B_Science_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Science_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the output from the Level 1B process. It converts the raw instrument data numbers into calibrated radiances. This conversion is based upon files of instrument characteristics and algorithm parameters that may vary over time. In addition to calibrated radiances, the Level 1B output products have geolocation information recorded for each measurement for use in higher-level processes.", "links": [ { diff --git a/datasets/OCO2_L1B_Science_11r.json b/datasets/OCO2_L1B_Science_11r.json index d140471b75..b06cfd4f0b 100644 --- a/datasets/OCO2_L1B_Science_11r.json +++ b/datasets/OCO2_L1B_Science_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1B_Science_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the output from the Level 1B process. It converts the raw instrument data numbers into calibrated radiances. This conversion is based upon files of instrument characteristics and algorithm parameters that may vary over time. In addition to calibrated radiances, the Level 1B output products have geolocation information recorded for each measurement for use in higher-level processes.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Pixel_10.json b/datasets/OCO2_L1aIn_Pixel_10.json index 510bffd265..37c0a6e700 100644 --- a/datasets/OCO2_L1aIn_Pixel_10.json +++ b/datasets/OCO2_L1aIn_Pixel_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Pixel_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Pixel_10r.json b/datasets/OCO2_L1aIn_Pixel_10r.json index 90e136f03d..57bc71bb9e 100644 --- a/datasets/OCO2_L1aIn_Pixel_10r.json +++ b/datasets/OCO2_L1aIn_Pixel_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Pixel_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nVersion 8r is the current version of the data set. Version 7r has been superseded by Version 8r.\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Pixel_11.2.json b/datasets/OCO2_L1aIn_Pixel_11.2.json index 063198690b..eee198d216 100644 --- a/datasets/OCO2_L1aIn_Pixel_11.2.json +++ b/datasets/OCO2_L1aIn_Pixel_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Pixel_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Pixel_11.json b/datasets/OCO2_L1aIn_Pixel_11.json index 2c15e69524..4f4d003903 100644 --- a/datasets/OCO2_L1aIn_Pixel_11.json +++ b/datasets/OCO2_L1aIn_Pixel_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Pixel_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Pixel_11r.json b/datasets/OCO2_L1aIn_Pixel_11r.json index f808e27056..ea23bd0eaa 100644 --- a/datasets/OCO2_L1aIn_Pixel_11r.json +++ b/datasets/OCO2_L1aIn_Pixel_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Pixel_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Sample_10.json b/datasets/OCO2_L1aIn_Sample_10.json index 1272448489..d2cbc1c79f 100644 --- a/datasets/OCO2_L1aIn_Sample_10.json +++ b/datasets/OCO2_L1aIn_Sample_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Sample_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Sample_10r.json b/datasets/OCO2_L1aIn_Sample_10r.json index 4015071434..15c8a364a6 100644 --- a/datasets/OCO2_L1aIn_Sample_10r.json +++ b/datasets/OCO2_L1aIn_Sample_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Sample_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nVersion 8r is the current version of the data set. Version 7r has been superseded by Version 8r.\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Sample_11.2.json b/datasets/OCO2_L1aIn_Sample_11.2.json index 7eec32689a..fe6036b1a5 100644 --- a/datasets/OCO2_L1aIn_Sample_11.2.json +++ b/datasets/OCO2_L1aIn_Sample_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Sample_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Sample_11.json b/datasets/OCO2_L1aIn_Sample_11.json index 190bdcce2a..b06eda18e7 100644 --- a/datasets/OCO2_L1aIn_Sample_11.json +++ b/datasets/OCO2_L1aIn_Sample_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Sample_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.", "links": [ { diff --git a/datasets/OCO2_L1aIn_Sample_11r.json b/datasets/OCO2_L1aIn_Sample_11r.json index b62ff3fc8e..45e961c2ee 100644 --- a/datasets/OCO2_L1aIn_Sample_11r.json +++ b/datasets/OCO2_L1aIn_Sample_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L1aIn_Sample_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_ABand_10r.json b/datasets/OCO2_L2_ABand_10r.json index 3ebe1dc32b..0339315267 100644 --- a/datasets/OCO2_L2_ABand_10r.json +++ b/datasets/OCO2_L2_ABand_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_ABand_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_ABand_11.2.json b/datasets/OCO2_L2_ABand_11.2.json index c7f36ec57c..2059d99a23 100644 --- a/datasets/OCO2_L2_ABand_11.2.json +++ b/datasets/OCO2_L2_ABand_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_ABand_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_ABand_11.2r.json b/datasets/OCO2_L2_ABand_11.2r.json index f2e2fe447c..e91af5ac52 100644 --- a/datasets/OCO2_L2_ABand_11.2r.json +++ b/datasets/OCO2_L2_ABand_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_ABand_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_ABand_11r.json b/datasets/OCO2_L2_ABand_11r.json index 1a4ea0b977..8121d05ff0 100644 --- a/datasets/OCO2_L2_ABand_11r.json +++ b/datasets/OCO2_L2_ABand_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_ABand_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_CO2Prior_10r.json b/datasets/OCO2_L2_CO2Prior_10r.json index 5380c9e8b5..eb38434c87 100644 --- a/datasets/OCO2_L2_CO2Prior_10r.json +++ b/datasets/OCO2_L2_CO2Prior_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_CO2Prior_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_CO2Prior_11.2.json b/datasets/OCO2_L2_CO2Prior_11.2.json index 069389c6d9..698d57bf6e 100644 --- a/datasets/OCO2_L2_CO2Prior_11.2.json +++ b/datasets/OCO2_L2_CO2Prior_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_CO2Prior_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_CO2Prior_11.2r.json b/datasets/OCO2_L2_CO2Prior_11.2r.json index 9df3ccc945..2c205c254c 100644 --- a/datasets/OCO2_L2_CO2Prior_11.2r.json +++ b/datasets/OCO2_L2_CO2Prior_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_CO2Prior_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_CO2Prior_11r.json b/datasets/OCO2_L2_CO2Prior_11r.json index 8219f9305a..97f8ce2b82 100644 --- a/datasets/OCO2_L2_CO2Prior_11r.json +++ b/datasets/OCO2_L2_CO2Prior_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_CO2Prior_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\n", "links": [ { diff --git a/datasets/OCO2_L2_Diagnostic_10r.json b/datasets/OCO2_L2_Diagnostic_10r.json index 2bb7e27d4c..fdc373ab5d 100644 --- a/datasets/OCO2_L2_Diagnostic_10r.json +++ b/datasets/OCO2_L2_Diagnostic_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Diagnostic_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_Diagnostic_11.2.json b/datasets/OCO2_L2_Diagnostic_11.2.json index 9aace5d126..3de671afa6 100644 --- a/datasets/OCO2_L2_Diagnostic_11.2.json +++ b/datasets/OCO2_L2_Diagnostic_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Diagnostic_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.", "links": [ { diff --git a/datasets/OCO2_L2_Diagnostic_11.2r.json b/datasets/OCO2_L2_Diagnostic_11.2r.json index 298d573aee..7b712f2734 100644 --- a/datasets/OCO2_L2_Diagnostic_11.2r.json +++ b/datasets/OCO2_L2_Diagnostic_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Diagnostic_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.", "links": [ { diff --git a/datasets/OCO2_L2_Diagnostic_11r.json b/datasets/OCO2_L2_Diagnostic_11r.json index 1a83ab91ea..74c5f14c60 100644 --- a/datasets/OCO2_L2_Diagnostic_11r.json +++ b/datasets/OCO2_L2_Diagnostic_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Diagnostic_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_IMAPDOAS_10r.json b/datasets/OCO2_L2_IMAPDOAS_10r.json index ba4b35dc6c..df80452c88 100644 --- a/datasets/OCO2_L2_IMAPDOAS_10r.json +++ b/datasets/OCO2_L2_IMAPDOAS_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_IMAPDOAS_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_IMAPDOAS_11.2.json b/datasets/OCO2_L2_IMAPDOAS_11.2.json index 28b0b87c32..978e7a725f 100644 --- a/datasets/OCO2_L2_IMAPDOAS_11.2.json +++ b/datasets/OCO2_L2_IMAPDOAS_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_IMAPDOAS_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.", "links": [ { diff --git a/datasets/OCO2_L2_IMAPDOAS_11.2r.json b/datasets/OCO2_L2_IMAPDOAS_11.2r.json index f21e054d5e..219b8f5bc0 100644 --- a/datasets/OCO2_L2_IMAPDOAS_11.2r.json +++ b/datasets/OCO2_L2_IMAPDOAS_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_IMAPDOAS_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.", "links": [ { diff --git a/datasets/OCO2_L2_IMAPDOAS_11r.json b/datasets/OCO2_L2_IMAPDOAS_11r.json index 5a91cf4be8..faa96a33aa 100644 --- a/datasets/OCO2_L2_IMAPDOAS_11r.json +++ b/datasets/OCO2_L2_IMAPDOAS_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_IMAPDOAS_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_Lite_FP_10r.json b/datasets/OCO2_L2_Lite_FP_10r.json index 66978e8b8f..49abc86235 100644 --- a/datasets/OCO2_L2_Lite_FP_10r.json +++ b/datasets/OCO2_L2_Lite_FP_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_FP_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.", "links": [ { diff --git a/datasets/OCO2_L2_Lite_FP_11.1r.json b/datasets/OCO2_L2_Lite_FP_11.1r.json index f101a84c80..4146d8c896 100644 --- a/datasets/OCO2_L2_Lite_FP_11.1r.json +++ b/datasets/OCO2_L2_Lite_FP_11.1r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_FP_11.1r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.", "links": [ { diff --git a/datasets/OCO2_L2_Lite_FP_11.2r.json b/datasets/OCO2_L2_Lite_FP_11.2r.json index 1097c54bcb..1018314ab4 100644 --- a/datasets/OCO2_L2_Lite_FP_11.2r.json +++ b/datasets/OCO2_L2_Lite_FP_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_FP_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.", "links": [ { diff --git a/datasets/OCO2_L2_Lite_SIF_10r.json b/datasets/OCO2_L2_Lite_SIF_10r.json index 959ab04d7a..69acdece89 100644 --- a/datasets/OCO2_L2_Lite_SIF_10r.json +++ b/datasets/OCO2_L2_Lite_SIF_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_SIF_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. \n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\n This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline. \n", "links": [ { diff --git a/datasets/OCO2_L2_Lite_SIF_11.2r.json b/datasets/OCO2_L2_Lite_SIF_11.2r.json index 0c110e4e18..101b10357e 100644 --- a/datasets/OCO2_L2_Lite_SIF_11.2r.json +++ b/datasets/OCO2_L2_Lite_SIF_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_SIF_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. \n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\nThis collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline. \n", "links": [ { diff --git a/datasets/OCO2_L2_Lite_SIF_11r.json b/datasets/OCO2_L2_Lite_SIF_11r.json index bbf1ac15e0..515c5ac372 100644 --- a/datasets/OCO2_L2_Lite_SIF_11r.json +++ b/datasets/OCO2_L2_Lite_SIF_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Lite_SIF_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\n The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. \n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\nThis collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline. \n", "links": [ { diff --git a/datasets/OCO2_L2_Met_10r.json b/datasets/OCO2_L2_Met_10r.json index 0f505f62c7..e8e177519e 100644 --- a/datasets/OCO2_L2_Met_10r.json +++ b/datasets/OCO2_L2_Met_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Met_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\nThis collection encompass meteorological parameters interpolated from global assimilation model for each sounding.\n\n", "links": [ { diff --git a/datasets/OCO2_L2_Met_11.2.json b/datasets/OCO2_L2_Met_11.2.json index 81133a0c9a..b10884d77f 100644 --- a/datasets/OCO2_L2_Met_11.2.json +++ b/datasets/OCO2_L2_Met_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Met_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\nThis collection encompass meteorological parameters interpolated from global assimilation model for each sounding.\n\n", "links": [ { diff --git a/datasets/OCO2_L2_Met_11.2r.json b/datasets/OCO2_L2_Met_11.2r.json index a359cde107..5d764e4cf5 100644 --- a/datasets/OCO2_L2_Met_11.2r.json +++ b/datasets/OCO2_L2_Met_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Met_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\nThis collection encompass meteorological parameters interpolated from global assimilation model for each sounding.\n\n", "links": [ { diff --git a/datasets/OCO2_L2_Met_11r.json b/datasets/OCO2_L2_Met_11r.json index cac6a18e20..659e947efa 100644 --- a/datasets/OCO2_L2_Met_11r.json +++ b/datasets/OCO2_L2_Met_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Met_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\nThis collection encompass meteorological parameters interpolated from global assimilation model for each sounding.\n\n", "links": [ { diff --git a/datasets/OCO2_L2_Met_9r.json b/datasets/OCO2_L2_Met_9r.json index 2e92edf3ae..d3ebe26be3 100644 --- a/datasets/OCO2_L2_Met_9r.json +++ b/datasets/OCO2_L2_Met_9r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Met_9r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 9r is the current version of the data set. Previous versions have been superseded by Version 9r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. \n\nThis collection encompass meteorological parameters interpolated from global assimilation model for each sounding.\n\n", "links": [ { diff --git a/datasets/OCO2_L2_Standard_10r.json b/datasets/OCO2_L2_Standard_10r.json index b3f7f43f34..de85044eb7 100644 --- a/datasets/OCO2_L2_Standard_10r.json +++ b/datasets/OCO2_L2_Standard_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Standard_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nIn early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO2_L2_Standard_11.2.json b/datasets/OCO2_L2_Standard_11.2.json index bad45f30b4..69a5b30ebc 100644 --- a/datasets/OCO2_L2_Standard_11.2.json +++ b/datasets/OCO2_L2_Standard_11.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Standard_11.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).", "links": [ { diff --git a/datasets/OCO2_L2_Standard_11.2r.json b/datasets/OCO2_L2_Standard_11.2r.json index 194e723316..619ff51680 100644 --- a/datasets/OCO2_L2_Standard_11.2r.json +++ b/datasets/OCO2_L2_Standard_11.2r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Standard_11.2r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).", "links": [ { diff --git a/datasets/OCO2_L2_Standard_11r.json b/datasets/OCO2_L2_Standard_11r.json index 0abb893209..55186eceb9 100644 --- a/datasets/OCO2_L2_Standard_11r.json +++ b/datasets/OCO2_L2_Standard_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO2_L2_Standard_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO3_L1B_Calibration_10.json b/datasets/OCO3_L1B_Calibration_10.json index f2fd14d223..1f61d41b62 100644 --- a/datasets/OCO3_L1B_Calibration_10.json +++ b/datasets/OCO3_L1B_Calibration_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Calibration_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. \n\nThe OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.\n\nThis L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO3_L1B_Science (L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the direction of the boresight vector.\n\n\n", "links": [ { diff --git a/datasets/OCO3_L1B_Calibration_10r.json b/datasets/OCO3_L1B_Calibration_10r.json index e397f39d34..18b9263fe2 100644 --- a/datasets/OCO3_L1B_Calibration_10r.json +++ b/datasets/OCO3_L1B_Calibration_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Calibration_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. \n\nThe OCO-3 incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.\n\nThis L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO3_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n", "links": [ { diff --git a/datasets/OCO3_L1B_Calibration_11.json b/datasets/OCO3_L1B_Calibration_11.json index 82c1e22114..d6007d96fd 100644 --- a/datasets/OCO3_L1B_Calibration_11.json +++ b/datasets/OCO3_L1B_Calibration_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Calibration_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. \n\nThe OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.\n\nThis L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO3_L1B_Science (L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the direction of the boresight vector.\n\n\n", "links": [ { diff --git a/datasets/OCO3_L1B_Calibration_11r.json b/datasets/OCO3_L1B_Calibration_11r.json index 801dbca892..1ee63c9231 100644 --- a/datasets/OCO3_L1B_Calibration_11r.json +++ b/datasets/OCO3_L1B_Calibration_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Calibration_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. \n\nThe OCO-3 incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.\n\nThis L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO3_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n\nThis is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n", "links": [ { diff --git a/datasets/OCO3_L1B_Science_10.json b/datasets/OCO3_L1B_Science_10.json index f90eda0cca..1685ec158e 100644 --- a/datasets/OCO3_L1B_Science_10.json +++ b/datasets/OCO3_L1B_Science_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Science_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L1B_Science_10r.json b/datasets/OCO3_L1B_Science_10r.json index e9fd1d3321..317f0c281d 100644 --- a/datasets/OCO3_L1B_Science_10r.json +++ b/datasets/OCO3_L1B_Science_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Science_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L1B_Science_11.json b/datasets/OCO3_L1B_Science_11.json index cfac47d29d..dbdcabb4a2 100644 --- a/datasets/OCO3_L1B_Science_11.json +++ b/datasets/OCO3_L1B_Science_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Science_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L1B_Science_11r.json b/datasets/OCO3_L1B_Science_11r.json index 3205c0611b..f6584f0b81 100644 --- a/datasets/OCO3_L1B_Science_11r.json +++ b/datasets/OCO3_L1B_Science_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1B_Science_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L1aAE_11.json b/datasets/OCO3_L1aAE_11.json index f28947f2c3..2abf26d161 100644 --- a/datasets/OCO3_L1aAE_11.json +++ b/datasets/OCO3_L1aAE_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aAE_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO3_L1aAE_11r.json b/datasets/OCO3_L1aAE_11r.json index af0ceeaa71..f9dc5562de 100644 --- a/datasets/OCO3_L1aAE_11r.json +++ b/datasets/OCO3_L1aAE_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aAE_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO3_L1aIn_Pixel_11.json b/datasets/OCO3_L1aIn_Pixel_11.json index f32266dc9a..9b1b2d1744 100644 --- a/datasets/OCO3_L1aIn_Pixel_11.json +++ b/datasets/OCO3_L1aIn_Pixel_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aIn_Pixel_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.", "links": [ { diff --git a/datasets/OCO3_L1aIn_Pixel_11r.json b/datasets/OCO3_L1aIn_Pixel_11r.json index bf38bd3948..4c7eee1883 100644 --- a/datasets/OCO3_L1aIn_Pixel_11r.json +++ b/datasets/OCO3_L1aIn_Pixel_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aIn_Pixel_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO3_L1aIn_Sample_11.json b/datasets/OCO3_L1aIn_Sample_11.json index 90fb87ef81..8f720974a3 100644 --- a/datasets/OCO3_L1aIn_Sample_11.json +++ b/datasets/OCO3_L1aIn_Sample_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aIn_Sample_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.", "links": [ { diff --git a/datasets/OCO3_L1aIn_Sample_11r.json b/datasets/OCO3_L1aIn_Sample_11r.json index c6eeeba258..03797c5d21 100644 --- a/datasets/OCO3_L1aIn_Sample_11r.json +++ b/datasets/OCO3_L1aIn_Sample_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L1aIn_Sample_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.", "links": [ { diff --git a/datasets/OCO3_L2_ABand_10.json b/datasets/OCO3_L2_ABand_10.json index 5e061a6970..1967ae11af 100644 --- a/datasets/OCO3_L2_ABand_10.json +++ b/datasets/OCO3_L2_ABand_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_ABand_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_ABand_10r.json b/datasets/OCO3_L2_ABand_10r.json index 0fdcf095fb..c1fa028f00 100644 --- a/datasets/OCO3_L2_ABand_10r.json +++ b/datasets/OCO3_L2_ABand_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_ABand_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_ABand_11.json b/datasets/OCO3_L2_ABand_11.json index 4e69d7da91..d5ba2126a9 100644 --- a/datasets/OCO3_L2_ABand_11.json +++ b/datasets/OCO3_L2_ABand_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_ABand_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_ABand_11r.json b/datasets/OCO3_L2_ABand_11r.json index 96f6986b6e..2b2004fd5c 100644 --- a/datasets/OCO3_L2_ABand_11r.json +++ b/datasets/OCO3_L2_ABand_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_ABand_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_CO2Prior_10.json b/datasets/OCO3_L2_CO2Prior_10.json index 98af9d6666..03a71b9530 100644 --- a/datasets/OCO3_L2_CO2Prior_10.json +++ b/datasets/OCO3_L2_CO2Prior_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_CO2Prior_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_CO2Prior_10r.json b/datasets/OCO3_L2_CO2Prior_10r.json index f9898f10ce..d8b3d9d575 100644 --- a/datasets/OCO3_L2_CO2Prior_10r.json +++ b/datasets/OCO3_L2_CO2Prior_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_CO2Prior_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_CO2Prior_11.json b/datasets/OCO3_L2_CO2Prior_11.json index a926959541..197fd178cb 100644 --- a/datasets/OCO3_L2_CO2Prior_11.json +++ b/datasets/OCO3_L2_CO2Prior_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_CO2Prior_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_CO2Prior_11r.json b/datasets/OCO3_L2_CO2Prior_11r.json index 673194ffdf..d14133d655 100644 --- a/datasets/OCO3_L2_CO2Prior_11r.json +++ b/datasets/OCO3_L2_CO2Prior_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_CO2Prior_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Diagnostic_10.json b/datasets/OCO3_L2_Diagnostic_10.json index fc52426759..6410f1bf9e 100644 --- a/datasets/OCO3_L2_Diagnostic_10.json +++ b/datasets/OCO3_L2_Diagnostic_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Diagnostic_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Diagnostic_10r.json b/datasets/OCO3_L2_Diagnostic_10r.json index df46f6eb5e..9665a1599e 100644 --- a/datasets/OCO3_L2_Diagnostic_10r.json +++ b/datasets/OCO3_L2_Diagnostic_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Diagnostic_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Diagnostic_11.json b/datasets/OCO3_L2_Diagnostic_11.json index 3699d3370c..1a8421d22a 100644 --- a/datasets/OCO3_L2_Diagnostic_11.json +++ b/datasets/OCO3_L2_Diagnostic_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Diagnostic_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Diagnostic_11r.json b/datasets/OCO3_L2_Diagnostic_11r.json index 98877b8d87..eb5ebaf937 100644 --- a/datasets/OCO3_L2_Diagnostic_11r.json +++ b/datasets/OCO3_L2_Diagnostic_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Diagnostic_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_IMAPDOAS_10.json b/datasets/OCO3_L2_IMAPDOAS_10.json index ec4f249811..7346f2db56 100644 --- a/datasets/OCO3_L2_IMAPDOAS_10.json +++ b/datasets/OCO3_L2_IMAPDOAS_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_IMAPDOAS_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_IMAPDOAS_10r.json b/datasets/OCO3_L2_IMAPDOAS_10r.json index 557e885fe0..d4aa2280a1 100644 --- a/datasets/OCO3_L2_IMAPDOAS_10r.json +++ b/datasets/OCO3_L2_IMAPDOAS_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_IMAPDOAS_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_IMAPDOAS_11.json b/datasets/OCO3_L2_IMAPDOAS_11.json index 0928d8670e..ea23b2ac85 100644 --- a/datasets/OCO3_L2_IMAPDOAS_11.json +++ b/datasets/OCO3_L2_IMAPDOAS_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_IMAPDOAS_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_IMAPDOAS_11r.json b/datasets/OCO3_L2_IMAPDOAS_11r.json index 9fc64b73ed..4337c6049b 100644 --- a/datasets/OCO3_L2_IMAPDOAS_11r.json +++ b/datasets/OCO3_L2_IMAPDOAS_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_IMAPDOAS_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Lite_FP_10.4r.json b/datasets/OCO3_L2_Lite_FP_10.4r.json index ca0f16e3da..e440fd96a2 100644 --- a/datasets/OCO3_L2_Lite_FP_10.4r.json +++ b/datasets/OCO3_L2_Lite_FP_10.4r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Lite_FP_10.4r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10.4r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.4r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Lite_FP_11r.json b/datasets/OCO3_L2_Lite_FP_11r.json index e945bc193a..4ddce089c7 100644 --- a/datasets/OCO3_L2_Lite_FP_11r.json +++ b/datasets/OCO3_L2_Lite_FP_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Lite_FP_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Lite_SIF_10r.json b/datasets/OCO3_L2_Lite_SIF_10r.json index 420dea341f..37e80818a4 100644 --- a/datasets/OCO3_L2_Lite_SIF_10r.json +++ b/datasets/OCO3_L2_Lite_SIF_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Lite_SIF_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Lite_SIF_11r.json b/datasets/OCO3_L2_Lite_SIF_11r.json index 387c5d95c4..0f912f19b8 100644 --- a/datasets/OCO3_L2_Lite_SIF_11r.json +++ b/datasets/OCO3_L2_Lite_SIF_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Lite_SIF_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Met_10.json b/datasets/OCO3_L2_Met_10.json index f9ed7300b2..8e26eb4b67 100644 --- a/datasets/OCO3_L2_Met_10.json +++ b/datasets/OCO3_L2_Met_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Met_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Met_10r.json b/datasets/OCO3_L2_Met_10r.json index 5bedd77d58..ff641013d8 100644 --- a/datasets/OCO3_L2_Met_10r.json +++ b/datasets/OCO3_L2_Met_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Met_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Met_11.json b/datasets/OCO3_L2_Met_11.json index cb48408149..0a1b446c57 100644 --- a/datasets/OCO3_L2_Met_11.json +++ b/datasets/OCO3_L2_Met_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Met_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Met_11r.json b/datasets/OCO3_L2_Met_11r.json index 80f4398991..6429c00054 100644 --- a/datasets/OCO3_L2_Met_11r.json +++ b/datasets/OCO3_L2_Met_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Met_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Standard_10.json b/datasets/OCO3_L2_Standard_10.json index a60d220d03..9170b43758 100644 --- a/datasets/OCO3_L2_Standard_10.json +++ b/datasets/OCO3_L2_Standard_10.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Standard_10", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Standard_10r.json b/datasets/OCO3_L2_Standard_10r.json index 2b5801eb59..a6d60a8ad3 100644 --- a/datasets/OCO3_L2_Standard_10r.json +++ b/datasets/OCO3_L2_Standard_10r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Standard_10r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Standard_11.json b/datasets/OCO3_L2_Standard_11.json index ded7123e17..876dda3703 100644 --- a/datasets/OCO3_L2_Standard_11.json +++ b/datasets/OCO3_L2_Standard_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Standard_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCO3_L2_Standard_11r.json b/datasets/OCO3_L2_Standard_11r.json index f77a253637..9658430011 100644 --- a/datasets/OCO3_L2_Standard_11r.json +++ b/datasets/OCO3_L2_Standard_11r.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCO3_L2_Standard_11r", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.\n\nThe Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.\n\nThe Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.\n\nOCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. \n\nOxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.\n\nThe OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 \u00b5m. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. \n\n", "links": [ { diff --git a/datasets/OCTS_L1_1.json b/datasets/OCTS_L1_1.json index 6d907689ea..1ca27bb9b0 100644 --- a/datasets/OCTS_L1_1.json +++ b/datasets/OCTS_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L1_2.json b/datasets/OCTS_L1_2.json index ebf9053d1c..516e719207 100644 --- a/datasets/OCTS_L1_2.json +++ b/datasets/OCTS_L1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L2_IOP_2014.json b/datasets/OCTS_L2_IOP_2014.json index df99d58893..02f162436b 100644 --- a/datasets/OCTS_L2_IOP_2014.json +++ b/datasets/OCTS_L2_IOP_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L2_IOP_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L2_IOP_2022.0.json b/datasets/OCTS_L2_IOP_2022.0.json index 34c7d8033d..679c27715b 100644 --- a/datasets/OCTS_L2_IOP_2022.0.json +++ b/datasets/OCTS_L2_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L2_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L2_OC_2014.json b/datasets/OCTS_L2_OC_2014.json index 30b5b474a2..00f37af235 100644 --- a/datasets/OCTS_L2_OC_2014.json +++ b/datasets/OCTS_L2_OC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L2_OC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L2_OC_2022.0.json b/datasets/OCTS_L2_OC_2022.0.json index bd2bcd04c8..a38a213ba6 100644 --- a/datasets/OCTS_L2_OC_2022.0.json +++ b/datasets/OCTS_L2_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L2_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_CHL_2014.json b/datasets/OCTS_L3b_CHL_2014.json index abf57fe98f..4f6dd1f7fc 100644 --- a/datasets/OCTS_L3b_CHL_2014.json +++ b/datasets/OCTS_L3b_CHL_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_CHL_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_CHL_2022.0.json b/datasets/OCTS_L3b_CHL_2022.0.json index cc6e2d5d17..8efc451fb4 100644 --- a/datasets/OCTS_L3b_CHL_2022.0.json +++ b/datasets/OCTS_L3b_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_IOP_2014.json b/datasets/OCTS_L3b_IOP_2014.json index c407658195..c45c12a2da 100644 --- a/datasets/OCTS_L3b_IOP_2014.json +++ b/datasets/OCTS_L3b_IOP_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_IOP_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_IOP_2022.0.json b/datasets/OCTS_L3b_IOP_2022.0.json index 9e138d0824..5c21a0225a 100644 --- a/datasets/OCTS_L3b_IOP_2022.0.json +++ b/datasets/OCTS_L3b_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_KD_2014.json b/datasets/OCTS_L3b_KD_2014.json index e5419607be..71cf8525b4 100644 --- a/datasets/OCTS_L3b_KD_2014.json +++ b/datasets/OCTS_L3b_KD_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_KD_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_KD_2022.0.json b/datasets/OCTS_L3b_KD_2022.0.json index e20d70882a..b886c43d12 100644 --- a/datasets/OCTS_L3b_KD_2022.0.json +++ b/datasets/OCTS_L3b_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_PAR_2014.json b/datasets/OCTS_L3b_PAR_2014.json index 3c9ba98547..c864f6ded9 100644 --- a/datasets/OCTS_L3b_PAR_2014.json +++ b/datasets/OCTS_L3b_PAR_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_PAR_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_PAR_2022.0.json b/datasets/OCTS_L3b_PAR_2022.0.json index 6f95fe1bbe..efb6a05d02 100644 --- a/datasets/OCTS_L3b_PAR_2022.0.json +++ b/datasets/OCTS_L3b_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_PIC_2014.json b/datasets/OCTS_L3b_PIC_2014.json index c99dfba2ee..0e063177d5 100644 --- a/datasets/OCTS_L3b_PIC_2014.json +++ b/datasets/OCTS_L3b_PIC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_PIC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_PIC_2022.0.json b/datasets/OCTS_L3b_PIC_2022.0.json index 5b4d96187f..fb072f4485 100644 --- a/datasets/OCTS_L3b_PIC_2022.0.json +++ b/datasets/OCTS_L3b_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_POC_2014.json b/datasets/OCTS_L3b_POC_2014.json index 911e7b99a0..6902417fcb 100644 --- a/datasets/OCTS_L3b_POC_2014.json +++ b/datasets/OCTS_L3b_POC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_POC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_POC_2022.0.json b/datasets/OCTS_L3b_POC_2022.0.json index 455c6132d3..02ca0d0e44 100644 --- a/datasets/OCTS_L3b_POC_2022.0.json +++ b/datasets/OCTS_L3b_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_RRS_2014.json b/datasets/OCTS_L3b_RRS_2014.json index 37f62a141e..790678849d 100644 --- a/datasets/OCTS_L3b_RRS_2014.json +++ b/datasets/OCTS_L3b_RRS_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_RRS_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3b_RRS_2022.0.json b/datasets/OCTS_L3b_RRS_2022.0.json index 81f07730df..6ab8823c3a 100644 --- a/datasets/OCTS_L3b_RRS_2022.0.json +++ b/datasets/OCTS_L3b_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3b_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_CHL_2014.json b/datasets/OCTS_L3m_CHL_2014.json index 989a9bab5f..c7e90a864f 100644 --- a/datasets/OCTS_L3m_CHL_2014.json +++ b/datasets/OCTS_L3m_CHL_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_CHL_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_CHL_2022.0.json b/datasets/OCTS_L3m_CHL_2022.0.json index 80d23b8b30..9e6404e695 100644 --- a/datasets/OCTS_L3m_CHL_2022.0.json +++ b/datasets/OCTS_L3m_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_IOP_2014.json b/datasets/OCTS_L3m_IOP_2014.json index 3f728ca68c..6fa1c56bca 100644 --- a/datasets/OCTS_L3m_IOP_2014.json +++ b/datasets/OCTS_L3m_IOP_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_IOP_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_IOP_2022.0.json b/datasets/OCTS_L3m_IOP_2022.0.json index 7c50285062..49f5996e78 100644 --- a/datasets/OCTS_L3m_IOP_2022.0.json +++ b/datasets/OCTS_L3m_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_KD_2014.json b/datasets/OCTS_L3m_KD_2014.json index 4dc4c14b78..5e36cc6543 100644 --- a/datasets/OCTS_L3m_KD_2014.json +++ b/datasets/OCTS_L3m_KD_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_KD_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_KD_2022.0.json b/datasets/OCTS_L3m_KD_2022.0.json index dd3022ecc4..6405c5d52c 100644 --- a/datasets/OCTS_L3m_KD_2022.0.json +++ b/datasets/OCTS_L3m_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_PAR_2014.json b/datasets/OCTS_L3m_PAR_2014.json index 065dbfd423..0b4208c923 100644 --- a/datasets/OCTS_L3m_PAR_2014.json +++ b/datasets/OCTS_L3m_PAR_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_PAR_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_PAR_2022.0.json b/datasets/OCTS_L3m_PAR_2022.0.json index 74dd90915b..11f61fa54d 100644 --- a/datasets/OCTS_L3m_PAR_2022.0.json +++ b/datasets/OCTS_L3m_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_PIC_2014.json b/datasets/OCTS_L3m_PIC_2014.json index c4c0d6c379..f279638013 100644 --- a/datasets/OCTS_L3m_PIC_2014.json +++ b/datasets/OCTS_L3m_PIC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_PIC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_PIC_2022.0.json b/datasets/OCTS_L3m_PIC_2022.0.json index 172d913cf9..bd603233a5 100644 --- a/datasets/OCTS_L3m_PIC_2022.0.json +++ b/datasets/OCTS_L3m_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_POC_2014.json b/datasets/OCTS_L3m_POC_2014.json index 1398ee151b..bc3df3f6e1 100644 --- a/datasets/OCTS_L3m_POC_2014.json +++ b/datasets/OCTS_L3m_POC_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_POC_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_POC_2022.0.json b/datasets/OCTS_L3m_POC_2022.0.json index 31cb4078ea..e24ada9d19 100644 --- a/datasets/OCTS_L3m_POC_2022.0.json +++ b/datasets/OCTS_L3m_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_RRS_2014.json b/datasets/OCTS_L3m_RRS_2014.json index 8f9e2d285a..c21cde089d 100644 --- a/datasets/OCTS_L3m_RRS_2014.json +++ b/datasets/OCTS_L3m_RRS_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_RRS_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency)\nlaunched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun\nsynchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the\ninstruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature\nScanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near\ninfrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are\nthe only bands calibrated and processed by the OBPG) OCTS has a swath width of\napproximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated\nat three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/OCTS_L3m_RRS_2022.0.json b/datasets/OCTS_L3m_RRS_2022.0.json index 7b856ff3c4..285013a043 100644 --- a/datasets/OCTS_L3m_RRS_2022.0.json +++ b/datasets/OCTS_L3m_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OCTS_L3m_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS.", "links": [ { diff --git a/datasets/ODIN.SMR_5.0.json b/datasets/ODIN.SMR_5.0.json index 7b6c166ed2..5971e595ad 100644 --- a/datasets/ODIN.SMR_5.0.json +++ b/datasets/ODIN.SMR_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ODIN.SMR_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The latest Odin Sub-Millimetre Radiometer (SMR) datasets have been generated by Chalmers University of Technology and Molflow within the Odin-SMR Recalibration and Harmonisation project (http://odin.rss.chalmers.se/), funded by the European Space Agency (ESA) to create a fully consistent and homogeneous dataset from the 20 years of satellite operations. The Odin satellite was launched in February 2001 as a joint undertaking between Sweden, Canada, France and Finland, and is part of the ESA Third Party Missions (TPM) programme since 2007. The complete Odin-SMR data archive was reprocessed applying a revised calibration scheme and upgraded algorithms. The Level 1b dataset is entirely reconsolidated, while Level 2 products are regenerated for the main mesospheric and stratospheric frequency modes (i.e., FM 01, 02, 08, 13, 14, 19, 21, 22, 24). The resulting dataset represents the first full-mission reprocessing campaign of the mission, which is still in operation.", "links": [ { diff --git a/datasets/ODU_CBM_0.json b/datasets/ODU_CBM_0.json index 700daa47fd..9c9237b80c 100644 --- a/datasets/ODU_CBM_0.json +++ b/datasets/ODU_CBM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ODU_CBM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made of the Chesapeake Bay Mouth (CBM) by Old Dominion University (ODU) between 2004 and 2006.", "links": [ { diff --git a/datasets/OFR_94-212.json b/datasets/OFR_94-212.json index b9bddf5e03..9ca9f72c4d 100644 --- a/datasets/OFR_94-212.json +++ b/datasets/OFR_94-212.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OFR_94-212", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for\nsulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A\ncorrelation spectrometer, or COSPEC, was used to measure sulfur dioxide in\nMount St. Helens' plume. The upward-looking COSPEC was mounted in a \nfixed-wing aircraft and flown below and at right angles to the plume.\nTypically, three to six traverses were made underneath the plume to determine\nthe SO2 burden (concentration x pathlength) within a cross-section of the\nplume. Knowing the burden along with the plume width and plume velocity\n(assumed to be the same as ambient wind speed), we could then calculate the\nemission rate of SO2. The use of correlation spectroscopy for determining the\nsulfur dioxide output of volcanoes is well established and the technique has\nbeen discussed in detail by a number of investigators (Malinconico, 1979;\nCasadevall and others, 1981; Stoiber and others, 1983).\n\n Carbon dioxide in the Mount St. Helens plume was measured by an infrared\nspectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube\nwas attached to the fuselage of a twin-engine aircraft to deliver outside air\nto the gas cell of the spectrometer. The aircraft was then flown at several\ndifferent elevations through the plume at right angles to plume trajectory to\ndefine plume area and carbon dioxide concentration in a vertical cross-section\nof the plume. These two parameters along with the density of CO2 for the\naltitude of the plume and the plume velocity (assumed as above to be equal to\nambient wind speed) were then used to calculate the CO2 emission rate (Harris\nand others, 1981).", "links": [ { diff --git a/datasets/OFR_95-55.json b/datasets/OFR_95-55.json index 7e60f7d8c7..481186a0af 100644 --- a/datasets/OFR_95-55.json +++ b/datasets/OFR_95-55.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OFR_95-55", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report contains all of the available daily sulfur dioxide and carbon\ndioxide emission rates from Cook Inlet volcanoes as determined by the U.S. \nGeological Survey (USGS) from March 1990 through July 1994. Airborne sulfur\ndioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and\nAugustine) began in 1986 when several measurements were carried out at\nAugustine volcano during the eruption of 1986. Systematic monitoring for\nsulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and\ncontinues to the present. Intermittent measurements at Augustine and Iliamna\nvolcanoes began in 1990 and continues to the present. Intermittent\nmeasurements began at Spurr volcano in 1991, and were continued at more regular\nintervals from June, 1992 through the 1992 eruption at the Crater Peak vent to\nthe present.", "links": [ { diff --git a/datasets/OFR_95-78_1.json b/datasets/OFR_95-78_1.json index 3c1a9b168f..c1a65969f8 100644 --- a/datasets/OFR_95-78_1.json +++ b/datasets/OFR_95-78_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OFR_95-78_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological data files pertaining to the Gold Spring\nGeomet research site. Documentation files and data-accessing display software\nare also included. The meteorological data are wind speed, peak gust, wind\ndirection, precipitation, air temperature, soil temperature, barometric\npressure, and humidity. Data from the monitoring station are voluminous; 14\nobservations from each station are made as often as ten times per hour,\ntotaling more than a million observations per station per year.", "links": [ { diff --git a/datasets/OISSS_L4_multimission_7day_v1_1.0.json b/datasets/OISSS_L4_multimission_7day_v1_1.0.json index a5ac58bba4..8ccd658232 100644 --- a/datasets/OISSS_L4_multimission_7day_v1_1.0.json +++ b/datasets/OISSS_L4_multimission_7day_v1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISSS_L4_multimission_7day_v1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.", "links": [ { diff --git a/datasets/OISSS_L4_multimission_7day_v2_2.0.json b/datasets/OISSS_L4_multimission_7day_v2_2.0.json index ed60e76d5c..0438077003 100644 --- a/datasets/OISSS_L4_multimission_7day_v2_2.0.json +++ b/datasets/OISSS_L4_multimission_7day_v2_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISSS_L4_multimission_7day_v2_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.", "links": [ { diff --git a/datasets/OISSS_L4_multimission_monthly_v1_1.0.json b/datasets/OISSS_L4_multimission_monthly_v1_1.0.json index d0043b5cb4..c97a3e27c8 100644 --- a/datasets/OISSS_L4_multimission_monthly_v1_1.0.json +++ b/datasets/OISSS_L4_multimission_monthly_v1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISSS_L4_multimission_monthly_v1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide and Addendum I to the product Technical Notes.", "links": [ { diff --git a/datasets/OISSS_L4_multimission_monthly_v2_2.0.json b/datasets/OISSS_L4_multimission_monthly_v2_2.0.json index 0d86791358..75f42bc699 100644 --- a/datasets/OISSS_L4_multimission_monthly_v2_2.0.json +++ b/datasets/OISSS_L4_multimission_monthly_v2_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISSS_L4_multimission_monthly_v2_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.", "links": [ { diff --git a/datasets/OISST_HR_NRT-GOS-L4-BLK-v2.0_2.0.json b/datasets/OISST_HR_NRT-GOS-L4-BLK-v2.0_2.0.json index e57529da80..79f8194dff 100644 --- a/datasets/OISST_HR_NRT-GOS-L4-BLK-v2.0_2.0.json +++ b/datasets/OISST_HR_NRT-GOS-L4-BLK-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISST_HR_NRT-GOS-L4-BLK-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.", "links": [ { diff --git a/datasets/OISST_HR_NRT-GOS-L4-MED-v2.0_2.0.json b/datasets/OISST_HR_NRT-GOS-L4-MED-v2.0_2.0.json index ef97e12ed3..5962a4ec67 100644 --- a/datasets/OISST_HR_NRT-GOS-L4-MED-v2.0_2.0.json +++ b/datasets/OISST_HR_NRT-GOS-L4-MED-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISST_HR_NRT-GOS-L4-MED-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625deg. x 0.0625deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.", "links": [ { diff --git a/datasets/OISST_UHR_NRT-GOS-L4-BLK-v2.0_2.0.json b/datasets/OISST_UHR_NRT-GOS-L4-BLK-v2.0_2.0.json index c159309b0d..ba999c6553 100644 --- a/datasets/OISST_UHR_NRT-GOS-L4-BLK-v2.0_2.0.json +++ b/datasets/OISST_UHR_NRT-GOS-L4-BLK-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISST_UHR_NRT-GOS-L4-BLK-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.", "links": [ { diff --git a/datasets/OISST_UHR_NRT-GOS-L4-MED-v2.0_2.0.json b/datasets/OISST_UHR_NRT-GOS-L4-MED-v2.0_2.0.json index d54bfc99ec..be8864a7a0 100644 --- a/datasets/OISST_UHR_NRT-GOS-L4-MED-v2.0_2.0.json +++ b/datasets/OISST_UHR_NRT-GOS-L4-MED-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OISST_UHR_NRT-GOS-L4-MED-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg. horizontal resolution over the Mediterranean \r\nSea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.", "links": [ { diff --git a/datasets/OLCIS3A_L1_EFR_1.json b/datasets/OLCIS3A_L1_EFR_1.json index 53fb2c98b3..e24593d0a3 100644 --- a/datasets/OLCIS3A_L1_EFR_1.json +++ b/datasets/OLCIS3A_L1_EFR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L1_EFR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2° and a 0.6° overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.", "links": [ { diff --git a/datasets/OLCIS3A_L1_ERR_1.json b/datasets/OLCIS3A_L1_ERR_1.json index a8f1f67215..8836192b06 100644 --- a/datasets/OLCIS3A_L1_ERR_1.json +++ b/datasets/OLCIS3A_L1_ERR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L1_ERR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2° and a 0.6° overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.", "links": [ { diff --git a/datasets/OLCIS3A_L2_EFR_IOP_NRT_R2022.0.json b/datasets/OLCIS3A_L2_EFR_IOP_NRT_R2022.0.json index 5b79179201..69ab93ac4f 100644 --- a/datasets/OLCIS3A_L2_EFR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L2_EFR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_EFR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L2_EFR_IOP_R2022.0.json b/datasets/OLCIS3A_L2_EFR_IOP_R2022.0.json index 4b9c895170..c520f3989b 100644 --- a/datasets/OLCIS3A_L2_EFR_IOP_R2022.0.json +++ b/datasets/OLCIS3A_L2_EFR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_EFR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L2_EFR_OC_NRT_R2022.0.json b/datasets/OLCIS3A_L2_EFR_OC_NRT_R2022.0.json index 953892da90..2b55910ba2 100644 --- a/datasets/OLCIS3A_L2_EFR_OC_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L2_EFR_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_EFR_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L2_EFR_OC_R2022.0.json b/datasets/OLCIS3A_L2_EFR_OC_R2022.0.json index 8c8b22f0be..6f3f136d29 100644 --- a/datasets/OLCIS3A_L2_EFR_OC_R2022.0.json +++ b/datasets/OLCIS3A_L2_EFR_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_EFR_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L2_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3A_L2_ERR_IOP_NRT_R2022.0.json index 726a56765b..1c3b9e537b 100644 --- a/datasets/OLCIS3A_L2_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L2_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L2_ERR_IOP_R2022.0.json b/datasets/OLCIS3A_L2_ERR_IOP_R2022.0.json index b6ab51fa7e..1ad4754c16 100644 --- a/datasets/OLCIS3A_L2_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3A_L2_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L2_ERR_OC_NRT_R2022.0.json b/datasets/OLCIS3A_L2_ERR_OC_NRT_R2022.0.json index 5257ba8fc2..bbe45c8f33 100644 --- a/datasets/OLCIS3A_L2_ERR_OC_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L2_ERR_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_ERR_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L2_ERR_OC_R2022.0.json b/datasets/OLCIS3A_L2_ERR_OC_R2022.0.json index a2595bc403..89c3bbecaf 100644 --- a/datasets/OLCIS3A_L2_ERR_OC_R2022.0.json +++ b/datasets/OLCIS3A_L2_ERR_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_ERR_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L2_ILW_4.json b/datasets/OLCIS3A_L2_ILW_4.json index dc3a5998bf..fae227953e 100644 --- a/datasets/OLCIS3A_L2_ILW_4.json +++ b/datasets/OLCIS3A_L2_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L2_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_CYANTC_5.0.json b/datasets/OLCIS3A_L3b_CYANTC_5.0.json index 70db6db8a4..9bf6ab91a2 100644 --- a/datasets/OLCIS3A_L3b_CYANTC_5.0.json +++ b/datasets/OLCIS3A_L3b_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_CYANTC_NRT_5.0.json b/datasets/OLCIS3A_L3b_CYANTC_NRT_5.0.json index 30317bfa46..c813545ee4 100644 --- a/datasets/OLCIS3A_L3b_CYANTC_NRT_5.0.json +++ b/datasets/OLCIS3A_L3b_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_CYAN_5.0.json b/datasets/OLCIS3A_L3b_CYAN_5.0.json index 35273d8fed..fd62ab01d7 100644 --- a/datasets/OLCIS3A_L3b_CYAN_5.0.json +++ b/datasets/OLCIS3A_L3b_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_CYAN_NRT_5.0.json b/datasets/OLCIS3A_L3b_CYAN_NRT_5.0.json index 89c47ad19b..d4a9fd5146 100644 --- a/datasets/OLCIS3A_L3b_CYAN_NRT_5.0.json +++ b/datasets/OLCIS3A_L3b_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_CHL_NRT_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_CHL_NRT_R2022.0.json index a88d88b578..0cba4e937a 100644 --- a/datasets/OLCIS3A_L3b_ERR_CHL_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_CHL_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_CHL_R2022.0.json index af16817d31..bcf978482e 100644 --- a/datasets/OLCIS3A_L3b_ERR_CHL_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_IOP_NRT_R2022.0.json index ef63835111..6a7a8b7c0b 100644 --- a/datasets/OLCIS3A_L3b_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_IOP_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_IOP_R2022.0.json index 7be0202ee8..adb64ae5f6 100644 --- a/datasets/OLCIS3A_L3b_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_KD_NRT_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_KD_NRT_R2022.0.json index f2fa7843a9..b69bb3636a 100644 --- a/datasets/OLCIS3A_L3b_ERR_KD_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_KD_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_KD_R2022.0.json index 363488977c..654ea15d3e 100644 --- a/datasets/OLCIS3A_L3b_ERR_KD_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_RRS_NRT_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_RRS_NRT_R2022.0.json index fb11a0ebdb..1d332467ad 100644 --- a/datasets/OLCIS3A_L3b_ERR_RRS_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ERR_RRS_R2022.0.json b/datasets/OLCIS3A_L3b_ERR_RRS_R2022.0.json index 6d059cc78c..a33079fcc8 100644 --- a/datasets/OLCIS3A_L3b_ERR_RRS_R2022.0.json +++ b/datasets/OLCIS3A_L3b_ERR_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ERR_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3b_ILW_4.json b/datasets/OLCIS3A_L3b_ILW_4.json index 1989c2a426..477e514e4f 100644 --- a/datasets/OLCIS3A_L3b_ILW_4.json +++ b/datasets/OLCIS3A_L3b_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3b_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_CYANTC_5.0.json b/datasets/OLCIS3A_L3m_CYANTC_5.0.json index bf46411247..ef2063f15e 100644 --- a/datasets/OLCIS3A_L3m_CYANTC_5.0.json +++ b/datasets/OLCIS3A_L3m_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_CYANTC_NRT_5.0.json b/datasets/OLCIS3A_L3m_CYANTC_NRT_5.0.json index 8ca2d209cf..ddc33004b1 100644 --- a/datasets/OLCIS3A_L3m_CYANTC_NRT_5.0.json +++ b/datasets/OLCIS3A_L3m_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_CYAN_5.0.json b/datasets/OLCIS3A_L3m_CYAN_5.0.json index ae399bdf50..bb316faac9 100644 --- a/datasets/OLCIS3A_L3m_CYAN_5.0.json +++ b/datasets/OLCIS3A_L3m_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_CYAN_NRT_5.0.json b/datasets/OLCIS3A_L3m_CYAN_NRT_5.0.json index 27ffb03047..64a8199fa5 100644 --- a/datasets/OLCIS3A_L3m_CYAN_NRT_5.0.json +++ b/datasets/OLCIS3A_L3m_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_CHL_NRT_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_CHL_NRT_R2022.0.json index 9ee5a02870..066af458f5 100644 --- a/datasets/OLCIS3A_L3m_ERR_CHL_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_CHL_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_CHL_R2022.0.json index fee5c7cc52..ed7ce76e58 100644 --- a/datasets/OLCIS3A_L3m_ERR_CHL_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_IOP_NRT_R2022.0.json index 76e9f013e6..b8477685fb 100644 --- a/datasets/OLCIS3A_L3m_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_IOP_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_IOP_R2022.0.json index 3257b702f9..c07d09205c 100644 --- a/datasets/OLCIS3A_L3m_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_KD_NRT_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_KD_NRT_R2022.0.json index 12211b8ce9..6f28a75e3e 100644 --- a/datasets/OLCIS3A_L3m_ERR_KD_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_KD_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_KD_R2022.0.json index 5352001869..23fda3b222 100644 --- a/datasets/OLCIS3A_L3m_ERR_KD_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_RRS_NRT_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_RRS_NRT_R2022.0.json index 82701d3089..bd703c1102 100644 --- a/datasets/OLCIS3A_L3m_ERR_RRS_NRT_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ERR_RRS_R2022.0.json b/datasets/OLCIS3A_L3m_ERR_RRS_R2022.0.json index cd590879cc..4ed22a3c4b 100644 --- a/datasets/OLCIS3A_L3m_ERR_RRS_R2022.0.json +++ b/datasets/OLCIS3A_L3m_ERR_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ERR_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3A_L3m_ILW_4.json b/datasets/OLCIS3A_L3m_ILW_4.json index aa5d31ebe7..64aaebc54f 100644 --- a/datasets/OLCIS3A_L3m_ILW_4.json +++ b/datasets/OLCIS3A_L3m_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3A_L3m_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLCIS3B_L1_EFR_1.json b/datasets/OLCIS3B_L1_EFR_1.json index c4f60c2a9d..b0ef83551c 100644 --- a/datasets/OLCIS3B_L1_EFR_1.json +++ b/datasets/OLCIS3B_L1_EFR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L1_EFR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.", "links": [ { diff --git a/datasets/OLCIS3B_L1_ERR_1.json b/datasets/OLCIS3B_L1_ERR_1.json index 2fe4dfd7ca..86d4c3f332 100644 --- a/datasets/OLCIS3B_L1_ERR_1.json +++ b/datasets/OLCIS3B_L1_ERR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L1_ERR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2° and a 0.6° overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.", "links": [ { diff --git a/datasets/OLCIS3B_L2_EFR_IOP_NRT_R2022.0.json b/datasets/OLCIS3B_L2_EFR_IOP_NRT_R2022.0.json index 6115189e18..d9c38f700f 100644 --- a/datasets/OLCIS3B_L2_EFR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L2_EFR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_EFR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L2_EFR_IOP_R2022.0.json b/datasets/OLCIS3B_L2_EFR_IOP_R2022.0.json index 7d3fe1b994..a6a1f7c634 100644 --- a/datasets/OLCIS3B_L2_EFR_IOP_R2022.0.json +++ b/datasets/OLCIS3B_L2_EFR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_EFR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L2_EFR_OC_NRT_R2022.0.json b/datasets/OLCIS3B_L2_EFR_OC_NRT_R2022.0.json index b1a9a8af75..611c41d740 100644 --- a/datasets/OLCIS3B_L2_EFR_OC_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L2_EFR_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_EFR_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L2_EFR_OC_R2022.0.json b/datasets/OLCIS3B_L2_EFR_OC_R2022.0.json index 0de592f7d0..5a874ef4ff 100644 --- a/datasets/OLCIS3B_L2_EFR_OC_R2022.0.json +++ b/datasets/OLCIS3B_L2_EFR_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_EFR_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L2_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3B_L2_ERR_IOP_NRT_R2022.0.json index 3702a841d0..8ea0563cef 100644 --- a/datasets/OLCIS3B_L2_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L2_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L2_ERR_IOP_R2022.0.json b/datasets/OLCIS3B_L2_ERR_IOP_R2022.0.json index 27dc2edfbb..fc337e0ce4 100644 --- a/datasets/OLCIS3B_L2_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3B_L2_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L2_ERR_OC_NRT_R2022.0.json b/datasets/OLCIS3B_L2_ERR_OC_NRT_R2022.0.json index 96e91797b3..6b5bc611f7 100644 --- a/datasets/OLCIS3B_L2_ERR_OC_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L2_ERR_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_ERR_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L2_ERR_OC_R2022.0.json b/datasets/OLCIS3B_L2_ERR_OC_R2022.0.json index 1a9be8c8e5..7d1b8d0243 100644 --- a/datasets/OLCIS3B_L2_ERR_OC_R2022.0.json +++ b/datasets/OLCIS3B_L2_ERR_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_ERR_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L2_ILW_4.json b/datasets/OLCIS3B_L2_ILW_4.json index f5336300af..c830552a15 100644 --- a/datasets/OLCIS3B_L2_ILW_4.json +++ b/datasets/OLCIS3B_L2_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L2_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_CYANTC_5.0.json b/datasets/OLCIS3B_L3b_CYANTC_5.0.json index 1fdeea02dd..927cdbf618 100644 --- a/datasets/OLCIS3B_L3b_CYANTC_5.0.json +++ b/datasets/OLCIS3B_L3b_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_CYANTC_NRT_5.0.json b/datasets/OLCIS3B_L3b_CYANTC_NRT_5.0.json index 0bc2b1831a..cb97c06520 100644 --- a/datasets/OLCIS3B_L3b_CYANTC_NRT_5.0.json +++ b/datasets/OLCIS3B_L3b_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_CYAN_5.0.json b/datasets/OLCIS3B_L3b_CYAN_5.0.json index efba825c62..280fecd11c 100644 --- a/datasets/OLCIS3B_L3b_CYAN_5.0.json +++ b/datasets/OLCIS3B_L3b_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_CYAN_NRT_5.0.json b/datasets/OLCIS3B_L3b_CYAN_NRT_5.0.json index d62cc3c67b..09b74d20f2 100644 --- a/datasets/OLCIS3B_L3b_CYAN_NRT_5.0.json +++ b/datasets/OLCIS3B_L3b_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_CHL_NRT_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_CHL_NRT_R2022.0.json index 51e27e3d2f..609b89b105 100644 --- a/datasets/OLCIS3B_L3b_ERR_CHL_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_CHL_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_CHL_R2022.0.json index 127ee0f779..4a9cb18326 100644 --- a/datasets/OLCIS3B_L3b_ERR_CHL_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_IOP_NRT_R2022.0.json index 43ac6dd578..b9b043585e 100644 --- a/datasets/OLCIS3B_L3b_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_IOP_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_IOP_R2022.0.json index 087c5a8cc5..72d97d8673 100644 --- a/datasets/OLCIS3B_L3b_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_KD_NRT_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_KD_NRT_R2022.0.json index dba69dc4e7..9f2bd7bf69 100644 --- a/datasets/OLCIS3B_L3b_ERR_KD_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_KD_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_KD_R2022.0.json index 63f4293f03..b7b3666009 100644 --- a/datasets/OLCIS3B_L3b_ERR_KD_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_RRS_NRT_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_RRS_NRT_R2022.0.json index 96a00e4ef8..492974efca 100644 --- a/datasets/OLCIS3B_L3b_ERR_RRS_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ERR_RRS_R2022.0.json b/datasets/OLCIS3B_L3b_ERR_RRS_R2022.0.json index 0b2ca1e2ea..f911372edc 100644 --- a/datasets/OLCIS3B_L3b_ERR_RRS_R2022.0.json +++ b/datasets/OLCIS3B_L3b_ERR_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ERR_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3b_ILW_4.json b/datasets/OLCIS3B_L3b_ILW_4.json index 3509539541..95ec80f37a 100644 --- a/datasets/OLCIS3B_L3b_ILW_4.json +++ b/datasets/OLCIS3B_L3b_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3b_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_CYANTC_5.0.json b/datasets/OLCIS3B_L3m_CYANTC_5.0.json index 394323a3ca..6853844f1a 100644 --- a/datasets/OLCIS3B_L3m_CYANTC_5.0.json +++ b/datasets/OLCIS3B_L3m_CYANTC_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_CYANTC_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_CYANTC_NRT_5.0.json b/datasets/OLCIS3B_L3m_CYANTC_NRT_5.0.json index 82959b0a44..c5c22a3c89 100644 --- a/datasets/OLCIS3B_L3m_CYANTC_NRT_5.0.json +++ b/datasets/OLCIS3B_L3m_CYANTC_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_CYANTC_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_CYAN_5.0.json b/datasets/OLCIS3B_L3m_CYAN_5.0.json index 2cbddc0db0..d200240c99 100644 --- a/datasets/OLCIS3B_L3m_CYAN_5.0.json +++ b/datasets/OLCIS3B_L3m_CYAN_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_CYAN_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_CYAN_NRT_5.0.json b/datasets/OLCIS3B_L3m_CYAN_NRT_5.0.json index bc3ba101f7..837c910bde 100644 --- a/datasets/OLCIS3B_L3m_CYAN_NRT_5.0.json +++ b/datasets/OLCIS3B_L3m_CYAN_NRT_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_CYAN_NRT_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of\nancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than\noptimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_CHL_NRT_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_CHL_NRT_R2022.0.json index f0a0b7500d..a029ac8a2b 100644 --- a/datasets/OLCIS3B_L3m_ERR_CHL_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_CHL_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_CHL_R2022.0.json index 28e58bf990..63fc85a091 100644 --- a/datasets/OLCIS3B_L3m_ERR_CHL_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_IOP_NRT_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_IOP_NRT_R2022.0.json index 0e3ed452bb..e2dc8c1bd0 100644 --- a/datasets/OLCIS3B_L3m_ERR_IOP_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_IOP_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_IOP_R2022.0.json index 3b3b878bbc..c492ff2ab7 100644 --- a/datasets/OLCIS3B_L3m_ERR_IOP_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_KD_NRT_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_KD_NRT_R2022.0.json index 5d732ac4a1..b4409d6b07 100644 --- a/datasets/OLCIS3B_L3m_ERR_KD_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_KD_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_KD_R2022.0.json index 59a7ef46b5..2be3d6fe2a 100644 --- a/datasets/OLCIS3B_L3m_ERR_KD_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_RRS_NRT_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_RRS_NRT_R2022.0.json index f47ff1a15f..817aa43bf1 100644 --- a/datasets/OLCIS3B_L3m_ERR_RRS_NRT_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ERR_RRS_R2022.0.json b/datasets/OLCIS3B_L3m_ERR_RRS_R2022.0.json index debf2e0b84..cf749a63bd 100644 --- a/datasets/OLCIS3B_L3m_ERR_RRS_R2022.0.json +++ b/datasets/OLCIS3B_L3m_ERR_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ERR_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2\u00b0 and a 0.6\u00b0 overlap with its neighbors. The whole field of view is shifted across track by 12.6\u00b0 away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, refered to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km. ", "links": [ { diff --git a/datasets/OLCIS3B_L3m_ILW_4.json b/datasets/OLCIS3B_L3m_ILW_4.json index 3452733766..83c28faf3a 100644 --- a/datasets/OLCIS3B_L3m_ILW_4.json +++ b/datasets/OLCIS3B_L3m_ILW_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLCIS3B_L3m_ILW_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.", "links": [ { diff --git a/datasets/OLIPAC_0.json b/datasets/OLIPAC_0.json index 809c0c375c..d714f5d037 100644 --- a/datasets/OLIPAC_0.json +++ b/datasets/OLIPAC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLIPAC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Southern Pacific Ocean by the French research vessel, the Latalante, in 1994", "links": [ { diff --git a/datasets/OLVIS1A_1.json b/datasets/OLVIS1A_1.json index 6b99f63b7d..9d97946611 100644 --- a/datasets/OLVIS1A_1.json +++ b/datasets/OLVIS1A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OLVIS1A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geotagged images captured by NASA Digital Mapping Cameras, which were mounted alongside the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.", "links": [ { diff --git a/datasets/OMAEROG_003.json b/datasets/OMAEROG_003.json index a46f8b57f1..4a29c4f542 100644 --- a/datasets/OMAEROG_003.json +++ b/datasets/OMAEROG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAEROG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMAEROG is based on the pixel level OMI Level-2 Aerosol product OMAERO, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAEROG is a special Level-2 gridded product where pixel level products are binned into 0.25 x 0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMAEROG data product contains almost all parameters that are in OMAERO. For example, in addition to the extinction optical depth and single scattering albedo, it also contains aerosol indices, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags.\n\nThe OMAEROG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROG data product is about 78 Mbytes.", "links": [ { diff --git a/datasets/OMAEROZ_003.json b/datasets/OMAEROZ_003.json index 14ef121cee..0c5e97ae46 100644 --- a/datasets/OMAEROZ_003.json +++ b/datasets/OMAEROZ_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAEROZ_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reprocessed OMI/Aura Level-2 Zoomed Aerosol data product OMAEROZ at 13x12 km resolution have been made available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access in March 2012. There are two Level-2 Aura OMI aerosol products OMAERO and OMAERUV. The OMAERUV product uses the near-UV algorithm. The OMAERO (13x24 km resolution) and OMAEROZ (13x12 km resolution) is based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The multi-wavelength retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAEROZ products contain Aerosol Optical Depths, Single Scattering Albedo, Aerosol Type, Aerosol Layer Height, and other intermediate and ancillary parameters and geolocation information.\n\nThe OMAEROZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMAEROZ data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMAEROZ data is about 11 Mbytes.\n\nA Readme document containing brief algorithm description and known data quality related issues and file specifications are provided by the OMAERO Algorithm lead.", "links": [ { diff --git a/datasets/OMAERO_003.json b/datasets/OMAERO_003.json index e0bdb67ce8..894fd5828e 100644 --- a/datasets/OMAERO_003.json +++ b/datasets/OMAERO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003. The data was re-processed in late 2011 using an improved algorithm (processing version 1.2.3.1). After some quick validation the reprocessed data was released to the public in March 2012. The shortname for this Level-2 Aerosol Product is OMAERO_V003. There are two Level-2 Aura OMI aerosol products OMAERUV and OMAERO. The OMAERUV product uses the near-UV algorithm. The OMAERO product is based on the multi-wavelength algorithm and that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAERO retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAERO product contains Aerosol Optical Depths, Single Scattering Albedo, and other ancillary and geolocation information.\n\nThe OMAERO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERO data product is about 6 Mbytes.", "links": [ { diff --git a/datasets/OMAEROe_003.json b/datasets/OMAEROe_003.json index 933aac9d89..81de3f737e 100644 --- a/datasets/OMAEROe_003.json +++ b/datasets/OMAEROe_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAEROe_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle).\n\nThe OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe)", "links": [ { diff --git a/datasets/OMAERUVG_003.json b/datasets/OMAERUVG_003.json index 77ff16e5f5..f167ae5bb4 100644 --- a/datasets/OMAERUVG_003.json +++ b/datasets/OMAERUVG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERUVG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes.", "links": [ { diff --git a/datasets/OMAERUV_003.json b/datasets/OMAERUV_003.json index 5c19b34d25..9a25dea530 100644 --- a/datasets/OMAERUV_003.json +++ b/datasets/OMAERUV_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERUV_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km).\n\nThe OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes.", "links": [ { diff --git a/datasets/OMAERUV_004.json b/datasets/OMAERUV_004.json index 3532ce3125..3b03515191 100644 --- a/datasets/OMAERUV_004.json +++ b/datasets/OMAERUV_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERUV_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Optical Depth, Aerosol Single Scattering Albedo, Absorption Optical Depth, UV Aerosol Index, and Aerosol Optical Depth over clouds at three wavelengths (354, 388, and 500 nm), and other ancillary and geolocation parameters, in the OMI field of view (13x24 km).\n\nThe OMAERUV files are stored in the version 4.0 Network Common Data Form (NetCDF). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 17 Mbytes.", "links": [ { diff --git a/datasets/OMAERUV_CPR_003.json b/datasets/OMAERUV_CPR_003.json index 4fea800715..cd7285f50f 100644 --- a/datasets/OMAERUV_CPR_003.json +++ b/datasets/OMAERUV_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERUV_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n \n(The shortname for this CloudSat-collocated OMI Level 2 near-UV aerosol subset is OMAERUV_CPR_003)", "links": [ { diff --git a/datasets/OMAERUVd_003.json b/datasets/OMAERUVd_003.json index 48a3d79846..f0f9260a5c 100644 --- a/datasets/OMAERUVd_003.json +++ b/datasets/OMAERUVd_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMAERUVd_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm). \n\nThe OMAERUVd files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAERUVd data product is about 0.2 Mbytes.", "links": [ { diff --git a/datasets/OMBRO_003.json b/datasets/OMBRO_003.json index c81dda4c3d..1ce80374bb 100644 --- a/datasets/OMBRO_003.json +++ b/datasets/OMBRO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMBRO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) collection-3 Bromine Monoxide Product OMBRO from the Aura-OMI, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column BrO product is OMBRO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMBRO product contains total vertical column BrO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information.\n\nThe OMBRO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMBRO data product is about 5 Mbytes.", "links": [ { diff --git a/datasets/OMCLDO2G_003.json b/datasets/OMCLDO2G_003.json index d3dea18cb9..21a3b62aef 100644 --- a/datasets/OMCLDO2G_003.json +++ b/datasets/OMCLDO2G_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDO2G_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMCLDO2G is based on the pixel level OMI Level-2 CLDO2 product OMCLDO2. This level-2G global cloud product (OMCLDO2G) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2 and Ozone, Ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The short name for this Level-2 OMI cloud product is OMCLDO2G and the lead scientist for this product and for OMCLDO2 (the data source of OMCLDO2G) is KNMI scientist Dr. Pepijn Veefkind. OMCLDO2G data product is a special Level-2 Global Gridded Product where pixel level data (OMCLDO2) are binned into 0.25x0.25 degree global grids. It contains the OMCLDO2 data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid are saved 'Without Averaging'. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMCLDO2G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily data file contains data from the day lit portion of the orbits (~14 orbits) and is roughly 85 MB in size.", "links": [ { diff --git a/datasets/OMCLDO2Z_003.json b/datasets/OMCLDO2Z_003.json index 34d4559b34..734999a758 100644 --- a/datasets/OMCLDO2Z_003.json +++ b/datasets/OMCLDO2Z_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDO2Z_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 zoomed cloud data product OMCLDO2Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method and O2-O2 absorption method using the DOAS technique. This level-2 zoomed cloud product at the pixel resolution (13x12 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains effective cloud pressure, effective cloud fraction, slant column O2-O2; uncertainties in derived, parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Zoomed cloud product is OMCLDO2Z. The lead scientist for this product is Dr. Pepijn Veefkind.\n\nThe OMCLDO2Z files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 20 MB in size. OMCLDO2Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one zoomed cloud product per month.", "links": [ { diff --git a/datasets/OMCLDO2_003.json b/datasets/OMCLDO2_003.json index 3c58cf00b1..185a64f966 100644 --- a/datasets/OMCLDO2_003.json +++ b/datasets/OMCLDO2_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDO2_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind.\n\nThe OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day.", "links": [ { diff --git a/datasets/OMCLDO2_CPR_003.json b/datasets/OMCLDO2_CPR_003.json index 92837fef7b..27fcd00c5c 100644 --- a/datasets/OMCLDO2_CPR_003.json +++ b/datasets/OMCLDO2_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDO2_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n \n(The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR)", "links": [ { diff --git a/datasets/OMCLDRRG_003.json b/datasets/OMCLDRRG_003.json index 781462e2f3..9cc0246aa2 100644 --- a/datasets/OMCLDRRG_003.json +++ b/datasets/OMCLDRRG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDRRG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.", "links": [ { diff --git a/datasets/OMCLDRR_003.json b/datasets/OMCLDRR_003.json index b209234c22..6341a5a2eb 100644 --- a/datasets/OMCLDRR_003.json +++ b/datasets/OMCLDRR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDRR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.\n\nThe OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.", "links": [ { diff --git a/datasets/OMCLDRR_004.json b/datasets/OMCLDRR_004.json index 853c5e7932..848cae7874 100644 --- a/datasets/OMCLDRR_004.json +++ b/datasets/OMCLDRR_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDRR_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.\n\nThe OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.", "links": [ { diff --git a/datasets/OMCLDRR_CPR_003.json b/datasets/OMCLDRR_CPR_003.json index 3e15acfad9..f254ef7ba2 100644 --- a/datasets/OMCLDRR_CPR_003.json +++ b/datasets/OMCLDRR_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMCLDRR_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n\n(The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR)", "links": [ { diff --git a/datasets/OMDOAO3G_003.json b/datasets/OMDOAO3G_003.json index 0d874ea774..f9b87c6fa1 100644 --- a/datasets/OMDOAO3G_003.json +++ b/datasets/OMDOAO3G_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMDOAO3G_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI.\n\nThe OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size.", "links": [ { diff --git a/datasets/OMDOAO3Z_003.json b/datasets/OMDOAO3Z_003.json index 3e7f618375..212b859e61 100644 --- a/datasets/OMDOAO3Z_003.json +++ b/datasets/OMDOAO3Z_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMDOAO3Z_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 Zoomed Ozone data product OMDOAO3Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two sets of total column ozone products OMTO3 and OMDOAO3 which are based on two different algorithms. OMTO3 product is based on TOMS like ozone retrieval algorithm whereas OMDOAO3 total column ozone product is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. The DOAS retrieval algorithm is developed by the KNMI OMI Scientist, Dr Pepijn Veefkind. Based on spatial resolutions, there are two DOAS algorithm based ozone products, OMDOAO3 (at 13x24 km resolution) and OMDOAO3Z (13x12 km resolution). In addition to the total ozone column values these DOAS based ozone products also contain some auxiliary derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI Zoomed Ozone product is OMDOAO3Z.\n\nThe OMDOAO3Z files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMDOAO3Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMDOAO3Z data is approximately 30 MB.", "links": [ { diff --git a/datasets/OMDOAO3_003.json b/datasets/OMDOAO3_003.json index 36db482f5e..230ac5e773 100644 --- a/datasets/OMDOAO3_003.json +++ b/datasets/OMDOAO3_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMDOAO3_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The second release of Aura Ozone Monitoring Instrument (OMI) Version 003 OMI/Aura Level-2 Total Column Ozone Data Product OMDOAO3 is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The data were processed in late 2011 using Algorithm or PGE version 1.2.3 and released in March 2012. OMI provides two total column ozone products based on two different algorithms. This level-2 global total column ozone product at the pixel resolution (13x24 km at nadir), is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI UV radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains auxiliary , derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI total column ozone product is OMDOAO3. The lead scientist for this product is Dr. J. Pepijn Veefkind.\n\nThe OMDOAO3 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes) and is approximately 11 MB in size. There are approximately 14 orbits per day.", "links": [ { diff --git a/datasets/OMDOAO3e_003.json b/datasets/OMDOAO3e_003.json index 3bb4ab68e5..3cf80f98ea 100644 --- a/datasets/OMDOAO3e_003.json +++ b/datasets/OMDOAO3e_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMDOAO3e_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 Aura/OMI Global OMDOAO3e Data Products (0.25deg Lat/Lon grids). This Level-3 global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column (best quality data, satisfying the shortest path length) and its precision this product also contains some ancillary parameters such as cloud fraction, cloud height, etc. The short name for this Level-3 OMI ozone product is OMDOAO3e and the lead Algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3e) is Dr. Pepijn Veefkind from KNMI.\n\nThe OMDOAO3e product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 8 MB in size.", "links": [ { diff --git a/datasets/OMEXII_0.json b/datasets/OMEXII_0.json index cbb4a9e1b8..1f253a2d32 100644 --- a/datasets/OMEXII_0.json +++ b/datasets/OMEXII_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMEXII_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the OMEX-II project were made along the Northwest European continental shelf between 1997 and 1999.", "links": [ { diff --git a/datasets/OMGLER_003.json b/datasets/OMGLER_003.json index 6c4ef91b05..e1bc997cc5 100644 --- a/datasets/OMGLER_003.json +++ b/datasets/OMGLER_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMGLER_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI/Aura Global Geometry-Dependent Surface LER 1-Orbit L2 Swath 13x24km product, or OMGLER, provides GLER, and the computed top-of-atmosphere (TOA) radiance from which GLER is derived, for the OMI field of view. The OMGLER data also contain a number of ancillary/input parameters for each OMI pixel used to compute TOA radiance. The primary intended use of the product is to provide surface reflectance information for OMI cloud, aerosol and trace gas algorithms. GLER is designed to easily replace commonly used LER climatologies within existing OMI algorithms. The product lead is Joanna J. Joiner (OMI US science team leader). The algorithm developer is Wenhan Qin.\n\nThe OMGLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains GLER data for the daylit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. Files are roughly 9 MB in size.", "links": [ { diff --git a/datasets/OMG_L1B_AIRGRAV_1.json b/datasets/OMG_L1B_AIRGRAV_1.json index 4351e9d22c..29b1cb2a13 100644 --- a/datasets/OMG_L1B_AIRGRAV_1.json +++ b/datasets/OMG_L1B_AIRGRAV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L1B_AIRGRAV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides level 1B gravity data products from airborne gravity surveys for the NASA Oceans Melting Greenland (OMG) mission.", "links": [ { diff --git a/datasets/OMG_L1_FLOAT_ALAMO_1.json b/datasets/OMG_L1_FLOAT_ALAMO_1.json index f3cbf4658e..17a297dc4c 100644 --- a/datasets/OMG_L1_FLOAT_ALAMO_1.json +++ b/datasets/OMG_L1_FLOAT_ALAMO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L1_FLOAT_ALAMO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling Alamo floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly. Conductivity, Temperature and Depth sensors (CTDs) allow them to collect vertical profiles of temperature and salinity. This provided measurements of the ocean's physical characteristics around Greenland. The floats wer deployed as part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.", "links": [ { diff --git a/datasets/OMG_L1_FLOAT_APEX_1.json b/datasets/OMG_L1_FLOAT_APEX_1.json index d38b364d90..eb20376fec 100644 --- a/datasets/OMG_L1_FLOAT_APEX_1.json +++ b/datasets/OMG_L1_FLOAT_APEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L1_FLOAT_APEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling APEX floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly. Conductivity, Temperature and Depth sensors (CTDs) allow them to collect vertical profiles of temperature and salinity. This provided measurements of the ocean's physical characteristics around Greenland. The floats wer deployed as part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.", "links": [ { diff --git a/datasets/OMG_L2_AXBT_1.json b/datasets/OMG_L2_AXBT_1.json index 79771f745e..2ea6f317d7 100644 --- a/datasets/OMG_L2_AXBT_1.json +++ b/datasets/OMG_L2_AXBT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L2_AXBT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2 in situ temperature profile measurements from the Airborne eXpendable BathyThermograph (AXBT) probes. It provides science quality temperature measurements as a function of depth in the water column. The AXBTs were jettisoned from a plane to collect temperature readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXBTs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature as it falls. An equation is used to determine the depth of the measurements as the probe falls. The AXBTs are part of the Oceans Melting Greenland (OMG) mission. In the fall of 2020 and 2021, the AXBT probes were used to supplement the ocean-per-year survey of ocean properties on the continental shelf surrounding Greenland. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.", "links": [ { diff --git a/datasets/OMG_L2_AXCTD_1.json b/datasets/OMG_L2_AXCTD_1.json index 4bc6708e7a..c1000f0f32 100644 --- a/datasets/OMG_L2_AXCTD_1.json +++ b/datasets/OMG_L2_AXCTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L2_AXCTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in situ profile measurements from Airborne eXpendable Conductivity Temperature Depth (AXCTD) probes. It provides salinity, density, temperature and sound velocity as a function of depth in the water column. The AXCTDs were jettisoned from a plane to collect temperature and salinity readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXCTDs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature and conductivity as it falls. An equation is used to determine the depth of the measurements as the probe falls, and another equation is used to convert temperature, depth and conductivity into salinity. These probes provided measurements of the ocean's physical characteristics around Greenland, where a ship would have had difficulties maneuvering. The AXCTDs are part of the Oceans Melting Greenland (OMG) mission. The AXCTDs were deployed in the fall from 2016 through 2021, covering the entire continental shelf surrounding Greenland as part of a once-per-year survey. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.", "links": [ { diff --git a/datasets/OMG_L2_Bathy_MBES_Gridded_1.json b/datasets/OMG_L2_Bathy_MBES_Gridded_1.json index 5cbb111154..d99b52b1e5 100644 --- a/datasets/OMG_L2_Bathy_MBES_Gridded_1.json +++ b/datasets/OMG_L2_Bathy_MBES_Gridded_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L2_Bathy_MBES_Gridded_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains level 2 in situ depth measurements from Multibeam Echo Sounder System (MBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The MBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions. Bathymetry was also measured with the Singlebeam Echo Sounder System (SBES) in areas where the MBES could not go, but has less spatial coverage.", "links": [ { diff --git a/datasets/OMG_L2_Bathy_SBES_Gridded_1.json b/datasets/OMG_L2_Bathy_SBES_Gridded_1.json index a55cc9a57d..5ac148a869 100644 --- a/datasets/OMG_L2_Bathy_SBES_Gridded_1.json +++ b/datasets/OMG_L2_Bathy_SBES_Gridded_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L2_Bathy_SBES_Gridded_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in situ depth measurements from Singlebeam Echo Sounder System (SBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The SBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions.", "links": [ { diff --git a/datasets/OMG_L2_CTD_1.json b/datasets/OMG_L2_CTD_1.json index c0624c50b0..3cd81787db 100644 --- a/datasets/OMG_L2_CTD_1.json +++ b/datasets/OMG_L2_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L2_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in situ measurements from Conductivity Temperature Depth (CTD) casts and tows. It provides salinity, density, temperature and sound velocity of the water column. The CTDs were deployed from a ship either as single profile casts or towed yo-yo behind the ship to measure the physical properties of the water. This provided measurements of the ocean's physical characteristics around Greenland. The CTDs are part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.", "links": [ { diff --git a/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json b/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json index 588992758c..cd74a36192 100644 --- a/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json +++ b/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_L3_ICE_ELEV_GLISTINA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs", "links": [ { diff --git a/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json b/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json index cc6ceaa77b..02decd6f7e 100644 --- a/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json +++ b/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This OMG Narwhals L3 dataset contains daily-averaged temperature and salinity measurements from CTD and temperature loggers from the same mooring. \r\n

\r\nNASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Between 2018 and 2020, five bottom-mounted moorings with marine mammal acoustic recorders and oceanographic instruments were deployed year-round near three glacier fronts: Sverdrup Glacier, Kong Oscar Glacier, and Rink Glacier.", "links": [ { diff --git a/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json b/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json index 89f9f034fc..fdf3dd73f3 100644 --- a/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json +++ b/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This OMG Narwhals dataset contains measurements from the ship based full water column CTD profiles that were obtained during summer mooring deployment/recovery cruises. \r\n

\r\nNASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Seafloor-mounted ocean moorings with marine mammal acoustic recorders and oceanographic instruments were deployed near three glacier fronts with known narwhal presence in Melville Bay, northwest Greenland.", "links": [ { diff --git a/datasets/OMHCHOG_003.json b/datasets/OMHCHOG_003.json index 17ffb1cb70..9f2e78c91e 100644 --- a/datasets/OMHCHOG_003.json +++ b/datasets/OMHCHOG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMHCHOG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMHCHOG is based on the pixel level OMI Level-2 HCHO product OMHCHO. OMHCHOG data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree grids. It contains data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging (third dimension provides indexing for the data points in each small grid). Scientists can apply a data filtering scheme of their choice and create Level-3 global gridded products. The OMHCHOG data product contains almost all parameters (e.g. total vertical column HCHO, standard errors, quality flags, geolocation and ancillary information) that are contained in the OMHCHO product.\n\nThe OMHCHOG data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portions of 14 to 15 orbits that cover the globe in a day. The average file size for the OMGCHOG data product is about 55 Mbytes.", "links": [ { diff --git a/datasets/OMHCHO_003.json b/datasets/OMHCHO_003.json index 208c5b0a85..a9f95ae92b 100644 --- a/datasets/OMHCHO_003.json +++ b/datasets/OMHCHO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMHCHO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Version-3 Formaldehyde Product OMHCHO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column Formaldehyde product is OMHCHO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMHCHO product contains total vertical column HCHO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information.\n\nThe OMHCHO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMHCHO data product is about 5 MB.", "links": [ { diff --git a/datasets/OMHCHOd_003.json b/datasets/OMHCHOd_003.json index 6a33564c38..d0be4d7600 100644 --- a/datasets/OMHCHOd_003.json +++ b/datasets/OMHCHOd_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMHCHOd_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted Mean Global 0.1deg Lat/Lon Grid (OMHCHOd). The formaldehyde values in each file are the average for 0.1 x 0.1 degree grid cell of cloud-screened total HCHO columns for a single day.\n\nOther variables included in the files are the weight of each grid cell, the standard error of column averages, mean albedo, mean cloud fraction, mean cloud pressure, and surface height. The weight information is useful for combining data from several files and reducing the noise of the retrievals by co-adding in the temporal or spatial dimensions.\n\nThe OMHCHOd files are in the netCDF4 format which is compatible with most HDF5 readers and tools. Each file contains daily data from approximately 15 orbits. The maximum file size for the OMHCHOd data product is about 80 Mbytes.", "links": [ { diff --git a/datasets/OMIAuraAER_1.json b/datasets/OMIAuraAER_1.json index e5cb6db3b1..3a36906bfc 100644 --- a/datasets/OMIAuraAER_1.json +++ b/datasets/OMIAuraAER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMIAuraAER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 32-year record of satellite near-UV observations by three sensors. \n\nThe OMI/Aura Near UV (version 1) Aerosol Index, Optical Depth and Single Scattering Albedo data product consists of aerosol absorption optical depth, aerosol optical depth, aerosol single scattering albedo, cloud fraction, cloud optical depth, refractive index, radiance, reflectivity and residue at approximately 13x24km. This product also contains ancillary data for ocean corrected surface albedo and terrain pressure.\n\nThese Level-2 data are stored in the Hierarchical Data Format 5 (HDF5) and are available from the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).", "links": [ { diff --git a/datasets/OMI_MINDS_NO2G_1.1.json b/datasets/OMI_MINDS_NO2G_1.1.json index caa1dbe560..4935cf232a 100644 --- a/datasets/OMI_MINDS_NO2G_1.1.json +++ b/datasets/OMI_MINDS_NO2G_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMI_MINDS_NO2G_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2011), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.", "links": [ { diff --git a/datasets/OMI_MINDS_NO2_1.1.json b/datasets/OMI_MINDS_NO2_1.1.json index 58541d579a..38dfb3cd2a 100644 --- a/datasets/OMI_MINDS_NO2_1.1.json +++ b/datasets/OMI_MINDS_NO2_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMI_MINDS_NO2_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2011), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.", "links": [ { diff --git a/datasets/OMI_MINDS_NO2d_1.1.json b/datasets/OMI_MINDS_NO2d_1.1.json index 5f7a8203b6..6bc3a24a46 100644 --- a/datasets/OMI_MINDS_NO2d_1.1.json +++ b/datasets/OMI_MINDS_NO2d_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMI_MINDS_NO2d_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2011), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.", "links": [ { diff --git a/datasets/OML1BIRR_003.json b/datasets/OML1BIRR_003.json index 27435339ee..b8f327f9f0 100644 --- a/datasets/OML1BIRR_003.json +++ b/datasets/OML1BIRR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BIRR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI Level 1B solar irradiance product is the radiometrically calibrated and geolocated measurements of the UV and Visible channels of the spectral solar irradiance. It is the averaged measurements of the solar irradiances over a single solar observation in the wavelength ranges of UV1 (264-311 nm, 159 channels), UV2 (307-383 nm, 557 channels) and VIS (349-504 nm, 751 channels). The data contain solar measurement products for both the global and the spatial zoom-in mode. This product only contains measurements obtained with the quartz volume diffuser and provides average of the individual measurements made along track to average out the solar elevation dependent bidirectional reflectance distribution function (BRDF) features of the diffuser. The shortname for this OMI Level-1B Product is OML1BIRR. The lead algorithm scientists for this product is Dr. Marcel Dobber from the Roayl Netherlands Meteorological Institude (KNMI).\n\nOMI calibrated and geolocated radiances for the UV and Visible channels, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products are archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). OML1BIRR files are stored in the HDF4 based EOS Hierarchical Data Format. The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.", "links": [ { diff --git a/datasets/OML1BIRR_004.json b/datasets/OML1BIRR_004.json index 1a6157e4fe..b04a2910e7 100644 --- a/datasets/OML1BIRR_004.json +++ b/datasets/OML1BIRR_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BIRR_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) UV Averaged Solar Irradiances product (shortname OML1BIRR) contains the averaged radiometrically calibrated irradiance measurements from the UV and VIS detectors. The OMI UV Band 1 (264-311 nm) has 159 wavelength bins, the UV Band 2 (307-383 nm) has 559 wavelength bins, and the VIS Band 3 (349-504 nm) has 751 wavelength bins. The data files are written in netCDF version 4 format and are usually made once per day when the Sun is within the solar port field-of-view just before the spacecraft moves into the night shadow at the north end of an orbit. The lead algorithm scientist for this product is Quintus Kleipool from the Royal Netherlands Meteorological Institude (KNMI).", "links": [ { diff --git a/datasets/OML1BRUG_003.json b/datasets/OML1BRUG_003.json index 6f62ce7628..06a184526d 100644 --- a/datasets/OML1BRUG_003.json +++ b/datasets/OML1BRUG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRUG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Global-Mode (OML1BRUG) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath for each of the 557 channels of UV2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of UV1 (264-311 nm). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 180 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRUG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI).\n\nThe OML1BRUG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.", "links": [ { diff --git a/datasets/OML1BRUG_004.json b/datasets/OML1BRUG_004.json index 9368f2e9a9..8425affe81 100644 --- a/datasets/OML1BRUG_004.json +++ b/datasets/OML1BRUG_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRUG_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Global-mode (shortname OML1BRUG) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRUG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).", "links": [ { diff --git a/datasets/OML1BRUZ_003.json b/datasets/OML1BRUZ_003.json index d20fbc8173..690a68859d 100644 --- a/datasets/OML1BRUZ_003.json +++ b/datasets/OML1BRUZ_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRUZ_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Zoom-in-Mode (OML1BRUZ) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 215 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. The shortname for this OMI Level-1B Product is OML1BRUZ. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI).\n\nThe OML1BRUZ files are stored in HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.", "links": [ { diff --git a/datasets/OML1BRUZ_004.json b/datasets/OML1BRUZ_004.json index 903d72c8cd..5107eebb97 100644 --- a/datasets/OML1BRUZ_004.json +++ b/datasets/OML1BRUZ_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRUZ_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Zoom-mode (shortname OML1BRUZ) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUZ zoom-in mode product is only available about once a month. The data in the OML1BRUZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).", "links": [ { diff --git a/datasets/OML1BRVG_003.json b/datasets/OML1BRVG_003.json index a14a7c44e3..dbb139f166 100644 --- a/datasets/OML1BRVG_003.json +++ b/datasets/OML1BRVG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRVG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Global-Mode (OML1BRVG) Version-3 product contains geo-located Earth view spectral radiances from the VIS detector in the wavelength range of 349 to 504 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (13 km x 48 km at nadir). Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 200 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRVG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI).\n\nThe OML1BRVG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16-bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.", "links": [ { diff --git a/datasets/OML1BRVG_004.json b/datasets/OML1BRVG_004.json index 3a9086037f..d0ef5f4725 100644 --- a/datasets/OML1BRVG_004.json +++ b/datasets/OML1BRVG_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRVG_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine VIS Radiance, Global-mode (shortname OML1BRVG) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRVG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).", "links": [ { diff --git a/datasets/OML1BRVZ_003.json b/datasets/OML1BRVZ_003.json index c7fc115ef6..3d69456e4e 100644 --- a/datasets/OML1BRVZ_003.json +++ b/datasets/OML1BRVZ_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRVZ_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Zoom-in-Mode (OML1BRVZ) Version-3 product contains geo-located Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 190 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains VIS measurements that are rebinned from the spatial zoom-in measurements. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI).\n\nThe OML1BRVZ files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.", "links": [ { diff --git a/datasets/OML1BRVZ_004.json b/datasets/OML1BRVZ_004.json index d9305c04f1..ced7adc0b0 100644 --- a/datasets/OML1BRVZ_004.json +++ b/datasets/OML1BRVZ_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OML1BRVZ_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Zoom-in Earthshine UV Radiance, Zoom-mode (shortname OML1BRVZ) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the zoom-in measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVZ zoom-in mode product is only available about once a month. The data in the OML1BRVZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).", "links": [ { diff --git a/datasets/OMLER_003.json b/datasets/OMLER_003.json index 8fd6a2cc4a..c1bfc7ad2c 100644 --- a/datasets/OMLER_003.json +++ b/datasets/OMLER_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMLER_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI Earth Surface Reflectance Climatology product, OMLER (Global 0.5 degrees Lat/Lon grid) which is based on Version 003 Level-1B top of atmosphere upwelling radiance and incoming irradiance. OMI calibrated and geolocated radiances from 159 channels in UV1(264-311 nm), 557 channels in UV2 (307-383 nm) and 751 channels in VIS (349-504) spectral regions, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products (Level-2 and 3) are archived at the NASA Goddard DAAC.\n\nOMLER spectral surface reflectance product contains monthly and yearly climatology of the Earth's surface Lambert Equivalent Reflectance (LER) for 23 wavelengths in the spectral range 309 to 500 nm, at a spatial resolution of 0.5 by 0.5 degrees. This LER is defined as the required reflectance of an isotropic surface needed to match the observed top of the atmosphere (TOA) reflectance in a pure Rayleigh scattering atmosphere under cloud free conditions and no aerosols. The climatology is based on statistical analysis of the three years of OMI version 03 radiance data (Oct 2004-Oct 2007). This product also provides minimum spectral surface reflectivity observed during the three year period.\n\nThe OMLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). It is roughly 300 MB in size.", "links": [ { diff --git a/datasets/OMMYDAGEO_003.json b/datasets/OMMYDAGEO_003.json index 405861f169..d39845d25e 100644 --- a/datasets/OMMYDAGEO_003.json +++ b/datasets/OMMYDAGEO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMMYDAGEO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI/Aura and MODIS/Aqua Aerosol Geo-colocation Product 1-Orbit L2 Swath 13x24 km (OMMYDAGEO) is a Level-2 orbital data product that links the MODIS/Aqua aerosol geo-coordinates at 3 and 10 km with the OMI indices along the OMI orbital track. This product allows users to match up MODIS granules with the OMI orbit for analysis and validation. It co-locates MODIS and OMI cloud and radiance information onto the OMI pixel.\n\nThe OMMYDAGEO data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data for 5 minutes, corresponding to the MODIS granule from the daylit side of the orbit. There are on the order of about 140 swath files per day. The file size for the OMMYDAGEO data product is about 12 Megabytes.", "links": [ { diff --git a/datasets/OMMYDCLD_003.json b/datasets/OMMYDCLD_003.json index 70f275d66e..3cb4cb5082 100644 --- a/datasets/OMMYDCLD_003.json +++ b/datasets/OMMYDCLD_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMMYDCLD_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km (OMMYDCLD) is a Level-2 orbital product that combines cloud parameters retrieved by the Ozone Mapping Instrument (OMI) on the Aura satellite with collocated statistical information for cloud parameters retrieved by the Moderate Resolution Imaging Spectrometer (MODIS) on the Aqua spacecraft. This product is designed to take advantage of the synergy between OMI and MODIS, which both fly on satellites in the NASA A-Train constellation of Earth-observing satellites that follow similar orbital tracks and collect near-simultaneous observations. This product can be used for cloud-clearing, detection of multi-layered clouds, and other applications that may exploit these multi-spectral measurements.\n\nThe algorithm for the OMMYDCLD product co-locates daytime cloud parameters from MODIS onto the OMI visible (VIS) pixel for a given OMI orbit and generates statistical information from the collocated MODIS pixels. For each OMI granule, the orbit start and end times are used to select the corresponding 5-minute MODIS granules for processing. A contiguous list of MODIS granules spanning the full duration of the OMI orbit are selected based on the relative time lag between Aqua and Aura. The algorithm lead for this product is NASA OMI scientist Dr. Joanna Joiner.\n\nThe OMMYDCLD data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data from the day lit portion of an orbit (about 53 minutes). There are approximately 14 orbits per day. The file size for the OMMYDCLD data product is about 8 Mbytes.", "links": [ { diff --git a/datasets/OMNO2G_003.json b/datasets/OMNO2G_003.json index 06684dc654..1941f1f5ad 100644 --- a/datasets/OMNO2G_003.json +++ b/datasets/OMNO2G_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2G_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMNO2G is based on the pixel level OMI Level-2 NO2 product OMNO2. OMNO2G data product is a special Level-2 Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a rid box are saved Without Averaging. Nitrogen dioxide is an important chemical species in both the stratosphere, where it plays a key role in ozone chemistry, and in the troposphere, where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2G data product contains almost all parameters that are contained in OMNO2 product.\n\nThe OMNO2G data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2G data product is about 115 Mbytes.", "links": [ { diff --git a/datasets/OMNO2_003.json b/datasets/OMNO2_003.json index e501a2c4ed..5414a1c706 100644 --- a/datasets/OMNO2_003.json +++ b/datasets/OMNO2_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI \ufb01eld of view speci\ufb01c geometry dependent surface Lambertian Equivalent Re\ufb02ectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (e\ufb00ective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain re\ufb02ectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d.\n\nThe OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.", "links": [ { diff --git a/datasets/OMNO2_004.json b/datasets/OMNO2_004.json index 264fd6b661..e0f583c4ce 100644 --- a/datasets/OMNO2_004.json +++ b/datasets/OMNO2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Collection 4, Version 5 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major updates include: (1) use of a new daily and OMI \ufb01eld of view speci\ufb01c geometry dependent surface Lambertian Equivalent Re\ufb02ectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (e\ufb00ective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain re\ufb02ectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d.\n\nThe OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.", "links": [ { diff --git a/datasets/OMNO2_CPR_003.json b/datasets/OMNO2_CPR_003.json index 8cb911ec55..35d6da9a27 100644 --- a/datasets/OMNO2_CPR_003.json +++ b/datasets/OMNO2_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a CloudSat-collocated subset of the original product OMNO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n\n(The shortname for this CloudSat-collocated OMI Level 2 NO2 subset is OMNO2_CPR_V003)", "links": [ { diff --git a/datasets/OMNO2d_003.json b/datasets/OMNO2d_003.json index db1da05383..16eee2f266 100644 --- a/datasets/OMNO2d_003.json +++ b/datasets/OMNO2d_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2d_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent.\n\nNitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality.\n\nThe OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes.", "links": [ { diff --git a/datasets/OMNO2d_004.json b/datasets/OMNO2d_004.json index 6d6a0e2058..d346dc11d3 100644 --- a/datasets/OMNO2d_004.json +++ b/datasets/OMNO2d_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMNO2d_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent.\n\nNitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality.\n\nThe OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes.", "links": [ { diff --git a/datasets/OMO3PR_003.json b/datasets/OMO3PR_003.json index d25dfdf6cf..b1d99b8a2e 100644 --- a/datasets/OMO3PR_003.json +++ b/datasets/OMO3PR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMO3PR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument Level-2 Ozone Profile data product OMO3PR (Version 003) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI Level-2 ozone profile product, OMO3PR at the pixel resolution 13x 48 km (at nadir), is based on the optimal estimation algorithm (Rodgers, 2000) with climatological ozone profiles as a-priori information. The OMO3PR retrieval algorithm uses spectral radiance values from the UV1 channel (270 nm to 308.5 nm) and from the first part of the UV2 channel (311.5 nm to 33 0 nm). OMO3PR product provides ozone values (in Dobson unit) for 18 atmospheric layers. It also provides a-priori ozone profile values, error covariance matrix, averaging kernel and some ancillary information such as time, latitude, longitude, solar zenith and viewing zenith angles and quality flags. The short name for this Level-2 OMI ozone profile product is OMO3PR. The lead scientist for this product is Dr. Johan de Haan.\n\nThe OMO3PR product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes). There are approximately 14 orbits per day thus the total data volume is approximately 150 GB/day.", "links": [ { diff --git a/datasets/OMOCLO_003.json b/datasets/OMOCLO_003.json index 1b5d04b445..84dae65c58 100644 --- a/datasets/OMOCLO_003.json +++ b/datasets/OMOCLO_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMOCLO_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) collection-3 Chlorine Dioxide Product OMOCLO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI total column OClO product is OMOCLO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMOCLO product contains slant column OClO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information.\n\nThe OMOCLO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMOCLO data product is about 20 MB.", "links": [ { diff --git a/datasets/OMPIXCORZ_003.json b/datasets/OMPIXCORZ_003.json index 12070594fa..03442a0079 100644 --- a/datasets/OMPIXCORZ_003.json +++ b/datasets/OMPIXCORZ_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPIXCORZ_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product in zoom-in mode, OMPIXCORZ, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCORZ. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA.\n\nThe OMPIXCORZ product contains ground locations of the OMI pixel corners in the zoom-in scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few.\n\nThe OMPIXCORZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits approximately one day per month. The average file size for the OMPIXCORZ data product is about 8 Mbytes.", "links": [ { diff --git a/datasets/OMPIXCOR_003.json b/datasets/OMPIXCOR_003.json index b2a12918e7..20aedb7166 100644 --- a/datasets/OMPIXCOR_003.json +++ b/datasets/OMPIXCOR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPIXCOR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product, OMPIXCOR, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCOR. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA.\n\nThe OMPIXCOR product contains ground locations of the OMI pixel corners in the global scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few.\n\nThe OMPIXCOR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits per day. The average file size for the OMPIXCOR data product is about 5 Mbytes.", "links": [ { diff --git a/datasets/OMPS_N20_NMHCHO_L2_1.json b/datasets/OMPS_N20_NMHCHO_L2_1.json index d0b78a4775..30f1ed3bba 100644 --- a/datasets/OMPS_N20_NMHCHO_L2_1.json +++ b/datasets/OMPS_N20_NMHCHO_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N20_NMHCHO_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N20 L2 NM Formaldehyde (HCHO) Total Column swath orbital product provides formaldehyde measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the NOAA-20 (JPSS-1) satellite. The total column HCHO amount is derived from radiances at wavelengths between 328.5 and 356.5 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day. Each has typically 1201 swaths. The swath width of the NM is about 2800 km. Prior to orbit 6419 on 13 February 2019, each swath has 104 scenes, or pixels, with a footprint size of 17 km x 17 km at nadir. Orbits 6419 and later have 140 scenes across the swath, with a footprint size of 12 km x 17 km. The files are written in the new netCDF version 4 format.", "links": [ { diff --git a/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_1.json b/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_1.json index 9f98bb434b..7179003b05 100644 --- a/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_1.json +++ b/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N20_NMSO2_PCA_L2_Step1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N20 NM PCA SO2 Step1 Total Column 1-Orbit L2 Swath 17x13km collection 1 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the NOAA-20 (JPSS-1) satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017) used to retrieve the SO2 total column amounts assuming different SO2 plume heights, including the boundary layer (lowest 1 km of the atmosphere), the lower (centered at 3 km), middle (centered at 8 km) and upper (centered at 13 km) troposphere, as well as the lower stratosphere (centered at 18 km).\n\nEach granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 104 pixels in the cross-track direction before February 13, 2019 with a pixel resolution of about 17 km x 17 km at nadir. Since then, the pixel resolution has been enhanced to 17 km x 13 km at nadir, with 140 pixels in the cross-track direction. The files are written using netCDF version 4.", "links": [ { diff --git a/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_NRT_1.json b/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_NRT_1.json index 6d293c6979..2f8780e41b 100644 --- a/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_NRT_1.json +++ b/datasets/OMPS_N20_NMSO2_PCA_L2_Step1_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N20_NMSO2_PCA_L2_Step1_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N20 NM PCA SO2 Step1 Total Column 1-Orbit L2 Swath 17x13km collection 1 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the NOAA-20 (JPSS-1) satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017) used to retrieve the SO2 total column amounts assuming different SO2 plume heights, including the boundary layer (lowest 1 km of the atmosphere), the lower (centered at 3 km), middle (centered at 8 km) and upper (centered at 13 km) troposphere, as well as the lower stratosphere (centered at 18 km). Each granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 104 pixels in the cross-track direction before February 13, 2019 with a pixel resolution of about 17 km x 17 km at nadir. Since then, the pixel resolution has been enhanced to 17 km x 13 km at nadir, with 140 pixels in the cross-track direction. The files are written using netCDF version 4. ", "links": [ { diff --git a/datasets/OMPS_N20_NMUVAI_L2_NRT_2.json b/datasets/OMPS_N20_NMUVAI_L2_NRT_2.json index e23d9bfc89..faa64bee2d 100644 --- a/datasets/OMPS_N20_NMUVAI_L2_NRT_2.json +++ b/datasets/OMPS_N20_NMUVAI_L2_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N20_NMUVAI_L2_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N20 L2 NM Aerosol Index swath orbital V2 for Near Real Time. For the standard product see the OMPS_N20_NMUVAI_L2 product in CMR .The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. ", "links": [ { diff --git a/datasets/OMPS_N21_LP_L1G_EV_1.0.json b/datasets/OMPS_N21_LP_L1G_EV_1.0.json index a596c71ee9..5618b1de1c 100644 --- a/datasets/OMPS_N21_LP_L1G_EV_1.0.json +++ b/datasets/OMPS_N21_LP_L1G_EV_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_LP_L1G_EV_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N21 L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA 21 (JPSS-2) satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1-2 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json b/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json index e55daa7e2c..89d5a5152a 100644 --- a/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json +++ b/datasets/OMPS_N21_LP_L2_AER_DAILY_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_LP_L2_AER_DAILY_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N21 L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_N21_LP_L2_O3_DAILY_1.0.json b/datasets/OMPS_N21_LP_L2_O3_DAILY_1.0.json index 25738107af..d47c296eb6 100644 --- a/datasets/OMPS_N21_LP_L2_O3_DAILY_1.0.json +++ b/datasets/OMPS_N21_LP_L2_O3_DAILY_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_LP_L2_O3_DAILY_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N21 L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). \n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_N21_LP_L2_O3_NRT_1.json b/datasets/OMPS_N21_LP_L2_O3_NRT_1.json index a6141d15da..87608ff330 100644 --- a/datasets/OMPS_N21_LP_L2_O3_NRT_1.json +++ b/datasets/OMPS_N21_LP_L2_O3_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_LP_L2_O3_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2.6 is the current version of this data product, and supersedes all previous versions.The OMPS-N21 L2 LP Ozone (O3) Vertical Profile swath daily Center slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-N21 satellite in Near Real Time (NRT). The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). See the README for full description of the product and updated retrieval algorithm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.The data are written using the Hierarchical Data Format Version 5 or HDF5. ", "links": [ { diff --git a/datasets/OMPS_N21_LP_NRT_AER_1.json b/datasets/OMPS_N21_LP_NRT_AER_1.json index fb55fc3a94..1dae919b78 100644 --- a/datasets/OMPS_N21_LP_NRT_AER_1.json +++ b/datasets/OMPS_N21_LP_NRT_AER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_LP_NRT_AER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N21 Level 2 LP Aerosol Extinction Vertical Profile swath level 2 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-N21 satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product. Each granule contains data from the daylight portion of each orbit. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The files are written using the Hierarchical Data Format Version 5 or HDF5. The /ProfileFields/RetrievedExtCoeff dataset is produced via a neural network trained on LP-L2-AER data for NRT processing. ", "links": [ { diff --git a/datasets/OMPS_N21_NMUVAI_L2_NRT_2.json b/datasets/OMPS_N21_NMUVAI_L2_NRT_2.json index a62bc96294..84f2a3da15 100644 --- a/datasets/OMPS_N21_NMUVAI_L2_NRT_2.json +++ b/datasets/OMPS_N21_NMUVAI_L2_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_N21_NMUVAI_L2_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-N21 L2 NM Aerosol Index swath orbital V2 for Near Real Time. For the standard product see the OMPS_N21_NMUVAI_L2 product in CMR .The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. ", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L1G_EV_2.6.json b/datasets/OMPS_NPP_LP_L1G_EV_2.6.json index d4b987cbdd..76b310fcf6 100644 --- a/datasets/OMPS_NPP_LP_L1G_EV_2.6.json +++ b/datasets/OMPS_NPP_LP_L1G_EV_2.6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L1G_EV_2.6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1-2 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L1G_EV_2.json b/datasets/OMPS_NPP_LP_L1G_EV_2.json index 46ff2e96cd..92831336de 100644 --- a/datasets/OMPS_NPP_LP_L1G_EV_2.json +++ b/datasets/OMPS_NPP_LP_L1G_EV_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L1G_EV_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1-2 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L2_AER_DAILY_2.json b/datasets/OMPS_NPP_LP_L2_AER_DAILY_2.json index 9a70de5c19..d5c07c644b 100644 --- a/datasets/OMPS_NPP_LP_L2_AER_DAILY_2.json +++ b/datasets/OMPS_NPP_LP_L2_AER_DAILY_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L2_AER_DAILY_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.6.json b/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.6.json index 60b6aeabbb..451b9523bf 100644 --- a/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.6.json +++ b/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L2_O3_DAILY_2.6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2.6 is the current version of this data product, and supersedes all previous versions.\n\nThe OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily Center slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). See the README for full description of the product and updated retrieval algorithm. \n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.json b/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.json index 0e3313c15b..85cd77a7de 100644 --- a/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.json +++ b/datasets/OMPS_NPP_LP_L2_O3_DAILY_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L2_O3_DAILY_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). \n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L2_O3_NRT_1.json b/datasets/OMPS_NPP_LP_L2_O3_NRT_1.json index 0cb0bb33e2..f8dcd4c5c0 100644 --- a/datasets/OMPS_NPP_LP_L2_O3_NRT_1.json +++ b/datasets/OMPS_NPP_LP_L2_O3_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L2_O3_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2.6 is the current version of this data product, and supersedes all previous versions.The OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily Center slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite in Near Real Time (NRT). The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). See the README for full description of the product and updated retrieval algorithm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.The data are written using the Hierarchical Data Format Version 5 or HDF5. ", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L2_Temp_DAILY_1.0.json b/datasets/OMPS_NPP_LP_L2_Temp_DAILY_1.0.json index 174cbeddcc..31091b4e13 100644 --- a/datasets/OMPS_NPP_LP_L2_Temp_DAILY_1.0.json +++ b/datasets/OMPS_NPP_LP_L2_Temp_DAILY_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L2_Temp_DAILY_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP LP L2 Temperature Vertical Profile swath daily 3-slit collection contains the temperature profile measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The product measures the temperature in the stratosphere and lower mesosphere.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_L3_AER_MONTHLY_1.json b/datasets/OMPS_NPP_LP_L3_AER_MONTHLY_1.json index 342c61a63d..fa560fc985 100644 --- a/datasets/OMPS_NPP_LP_L3_AER_MONTHLY_1.json +++ b/datasets/OMPS_NPP_LP_L3_AER_MONTHLY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_L3_AER_MONTHLY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L3 LP Aerosol Extinction Vertical Profile 5 x 15 deg lat-lon grid multi-wavelength monthly (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone.\n\nEach granule contains data from the daylight portion of each orbit averaged over an entire month in 5 x 15 deg lat-lon grids. Spatial coverage is global (-90 to 90 degrees latitude). The profiles are gridded from the ground up to about 41 km with a vertical resolution of the 1.0 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_LP_NRT_AER_1.json b/datasets/OMPS_NPP_LP_NRT_AER_1.json index 5a931543e1..2de3061e8a 100644 --- a/datasets/OMPS_NPP_LP_NRT_AER_1.json +++ b/datasets/OMPS_NPP_LP_NRT_AER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_LP_NRT_AER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 LP Aerosol Extinction Vertical Profile swath l2 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The files are written using the Hierarchical Data Format Version 5 or HDF5. The /ProfileFields/RetrievedExtCoeff dataset is produced via a neural network trained on LP-L2-AER data for NRT processing. ", "links": [ { diff --git a/datasets/OMPS_NPP_NMCLDRR_L2_2.json b/datasets/OMPS_NPP_NMCLDRR_L2_2.json index 830080e6a5..28bce94b66 100644 --- a/datasets/OMPS_NPP_NMCLDRR_L2_2.json +++ b/datasets/OMPS_NPP_NMCLDRR_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMCLDRR_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Cloud Pressure and Fraction swath orbital product provides effective cloud fraction and effective cloud pressure retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The cloud pressure algorithm retrieves effective cloud pressus, a.k.a optical centroid pressure (OCP) and effective cloud fraction (ECF) using a concept of Mixed Lambert Equivalent Reflectivity (MLER) at 354.1 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km.\n\nThe files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NMEV_L1B_2.json b/datasets/OMPS_NPP_NMEV_L1B_2.json index 71b00dc4d3..2ff1826da2 100644 --- a/datasets/OMPS_NPP_NMEV_L1B_2.json +++ b/datasets/OMPS_NPP_NMEV_L1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMEV_L1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L1B NM Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Mapper sensor on the Suomi-NPP satellite.\n\nEach granule contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each with a swath width of 2600 km.", "links": [ { diff --git a/datasets/OMPS_NPP_NMHCHO_L2_1.json b/datasets/OMPS_NPP_NMHCHO_L2_1.json index b64c03fc61..03ab7310bb 100644 --- a/datasets/OMPS_NPP_NMHCHO_L2_1.json +++ b/datasets/OMPS_NPP_NMHCHO_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMHCHO_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Formaldehyde (HCHO) Total Column swath orbital product provides formaldehyde measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The total column HCHO amount is derived from radiances at wavelengths between 328.5 and 356.5 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day. Each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The files are written in the new netCDF version 4 format.", "links": [ { diff --git a/datasets/OMPS_NPP_NMMIEAI_L2_2.json b/datasets/OMPS_NPP_NMMIEAI_L2_2.json index 79a15cf447..a6ecdfa327 100644 --- a/datasets/OMPS_NPP_NMMIEAI_L2_2.json +++ b/datasets/OMPS_NPP_NMMIEAI_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMMIEAI_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product. The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir.\n\nThe data are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NMNO2_L2_2.json b/datasets/OMPS_NPP_NMNO2_L2_2.json index 4d033d69f4..16727a5363 100644 --- a/datasets/OMPS_NPP_NMNO2_L2_2.json +++ b/datasets/OMPS_NPP_NMNO2_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMNO2_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Nitrogen Dioxide (NO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved nitrogen dioxide (NO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the NO2 total column amount and a new spatial technique is applied to separate the stratospheric and tropospheric amounts.\n\nEach granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 35 pixels in the cross-track direction, with a pixel resolution of about 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NMSO2_L2_2.json b/datasets/OMPS_NPP_NMSO2_L2_2.json index 27aafb0e4a..1fb126d3e9 100644 --- a/datasets/OMPS_NPP_NMSO2_L2_2.json +++ b/datasets/OMPS_NPP_NMSO2_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMSO2_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the SO2 total column amount and column amounts in the lower (centered at 2.5 km), middle (centered at 7.5 km) and upper (centered at 11 km) troposphere, as well as the lower stratosphere (centered at 16 km).\n\nEach granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 35 pixels in the cross-track direction, with a pixel resolution of about 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NMSO2_PCA_L2_2.json b/datasets/OMPS_NPP_NMSO2_PCA_L2_2.json index 5fa4d1df57..f9cc5381a2 100644 --- a/datasets/OMPS_NPP_NMSO2_PCA_L2_2.json +++ b/datasets/OMPS_NPP_NMSO2_PCA_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMSO2_PCA_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS_NPP_NMSO2_PCA_L2 product is part of the MEaSUREs (Making Earth Science Data Records for Use in Research Environments) suite of products.\n\nIt is retrieved from the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) spectrometer and provides contiguous daily global monitoring of anthropogenic and volcanic sulfur dioxide (SO2), an important pollutant and aerosol precursor that affects both air quality and the climate. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017), and continues (Zhang et al., 2017) NASA's Earth Observing System (EOS) standard Aura/Ozone Monitoring Instrument SO2 product (OMSO2). \n\nThe latest OMPS_NPP_NMSO2_PCA_L2 V2 product uses new Jacobian lookup tables and more realistic model based a priori profiles in anthropogenic SO2 retrievals. This helps to more accurately account for the pixel-to-pixel variation in SO2 sensitivity due to different factors such as the vertical distribution of SO2, solar and viewing angles, surface reflectivity, and cloudiness. As compared with the previous OMPS_NPP_NMSO2_PCA_L2 V1.2 product that assumes the same SO2 sensitivity for all OMPS pixels, the new V2 anthropogenic SO2 retrievals have reduced retrieval biases especially over background regions (see Figure 1 for an example). The same updated PCA SO2 retrieval algorithm (Li et al., 2020) is also used to produce the recently released OMSO2 V2 product (doi:10.5067/Aura/OMI/DATA2022). The new OMPS_NPP_NMSO2_PCA_L2 V2 product thus offers enhanced consistency between the NASA EOS standard (OMI) and continuity (OMPS) SO2 data records\n\nSulfur Dioxide (SO2) is a short-lived gas primarily produced by volcanoes, power plants, refineries, metal smelting and burning of fossil fuels. Where SO2 remains near the Earth's surface, it is toxic, causes acid rain, and degrades air quality. Where SO2 is lofted into the free troposphere, it forms aerosols that can alter cloud reflectivity and precipitation. In the stratosphere, volcanic SO2 forms sulfate aerosols that can result in climate change.", "links": [ { diff --git a/datasets/OMPS_NPP_NMSO2_PCA_L3_DAILY_1.json b/datasets/OMPS_NPP_NMSO2_PCA_L3_DAILY_1.json index 065900e82b..5d562182a6 100644 --- a/datasets/OMPS_NPP_NMSO2_PCA_L3_DAILY_1.json +++ b/datasets/OMPS_NPP_NMSO2_PCA_L3_DAILY_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMSO2_PCA_L3_DAILY_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L3 NM PCA Sulfur Dioxide (SO2) Total Column Daily Best Pixel Global Gridded 0.25 x 0.25 degree product contains sulfur dioxide amounts gridded at 0.25 x 0.25 degree resolution using the 'best' pixel method. Data are measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017).\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and the spatial resolution is 0.25 x 0.25 degrees.\n\nThe files are written using netCDF version 4.", "links": [ { diff --git a/datasets/OMPS_NPP_NMTO3_L2_2.json b/datasets/OMPS_NPP_NMTO3_L2_2.json index eed32d44c4..0c7172f0ae 100644 --- a/datasets/OMPS_NPP_NMTO3_L2_2.json +++ b/datasets/OMPS_NPP_NMTO3_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMTO3_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NMTO3_L3_DAILY_2.json b/datasets/OMPS_NPP_NMTO3_L3_DAILY_2.json index f52b5c375f..7a108137d5 100644 --- a/datasets/OMPS_NPP_NMTO3_L3_DAILY_2.json +++ b/datasets/OMPS_NPP_NMTO3_L3_DAILY_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NMTO3_L3_DAILY_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L3 NM Ozone (O3) Total Column 1.0 deg grid daily product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite.\nThe level-3 gridding algorithm is used to combine the orbital OMPS cross track measurements into a daily map product with a fixed global grid. Grid cells are computed as weighted averages of a given parameter derived for the field-of-views that overlay the given cell. The current version of this product includes UV aerosol index and reflectivity at 331 nm retrievals as well. \n\nEach granule contains data for a full day. Spatial coverage is global (-90 to 90 degrees latitude), with a resolution of 1.0 degree in longitude and 1.0 degree in latitude, and array size of 360 by 180. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NPBUVO3_L2_2.9.json b/datasets/OMPS_NPP_NPBUVO3_L2_2.9.json index caa902fc32..29abf1bbf3 100644 --- a/datasets/OMPS_NPP_NPBUVO3_L2_2.9.json +++ b/datasets/OMPS_NPP_NPBUVO3_L2_2.9.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NPBUVO3_L2_2.9", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NPBUVO3_L2_2.json b/datasets/OMPS_NPP_NPBUVO3_L2_2.json index c944471809..fb9f789f7c 100644 --- a/datasets/OMPS_NPP_NPBUVO3_L2_2.json +++ b/datasets/OMPS_NPP_NPBUVO3_L2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NPBUVO3_L2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm.\n\nEach granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.", "links": [ { diff --git a/datasets/OMPS_NPP_NPEV_L1B_2.json b/datasets/OMPS_NPP_NPEV_L1B_2.json index 11259218a1..84d1b6782a 100644 --- a/datasets/OMPS_NPP_NPEV_L1B_2.json +++ b/datasets/OMPS_NPP_NPEV_L1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMPS_NPP_NPEV_L1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite.\n\nEach granule typically contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is nearly global (-82 to 82 degrees latitude), and there are about 14.5 orbits per day each with a single nadir measurement along the satellite track.", "links": [ { diff --git a/datasets/OMSO2G_003.json b/datasets/OMSO2G_003.json index cad9e49bd1..c068f47f81 100644 --- a/datasets/OMSO2G_003.json +++ b/datasets/OMSO2G_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMSO2G_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags.\n\nThe OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.", "links": [ { diff --git a/datasets/OMSO2_003.json b/datasets/OMSO2_003.json index d80802fc1e..592cff5072 100644 --- a/datasets/OMSO2_003.json +++ b/datasets/OMSO2_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMSO2_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li.\n\nThe OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.", "links": [ { diff --git a/datasets/OMSO2_CPR_003.json b/datasets/OMSO2_CPR_003.json index 2a5703c481..acd80ab2e3 100644 --- a/datasets/OMSO2_CPR_003.json +++ b/datasets/OMSO2_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMSO2_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n\n(The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003)\n \nThis document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2.\n\nThe product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the \"zoom mode\" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: \n\n1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km.\n2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km.\n3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, \n4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. \n\nThe accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). \n\nOMSO2 files are stored in EOS Hierarchical Data Format\n(HDF-EOS5). Each file contains data from the day lit portion of an\norbit (53 minutes). There are approximately 14 orbits per day. The\nmaximum file size for the OMSO2 data product is about 9 Mbytes.", "links": [ { diff --git a/datasets/OMSO2e_003.json b/datasets/OMSO2e_003.json index ad2854d626..913956e99c 100644 --- a/datasets/OMSO2e_003.json +++ b/datasets/OMSO2e_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMSO2e_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the \"best pixel\", selected from all \"good\" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid.\n\nThe OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model.", "links": [ { diff --git a/datasets/OMTO3G_003.json b/datasets/OMTO3G_003.json index bc50977cef..ed388f031a 100644 --- a/datasets/OMTO3G_003.json +++ b/datasets/OMTO3G_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMTO3G_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags.\n\nThe OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.", "links": [ { diff --git a/datasets/OMTO3_003.json b/datasets/OMTO3_003.json index b5924dafb7..b229cb5fd4 100644 --- a/datasets/OMTO3_003.json +++ b/datasets/OMTO3_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMTO3_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia.\n\nThe OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.", "links": [ { diff --git a/datasets/OMTO3_CPR_003.json b/datasets/OMTO3_CPR_003.json index 508ebdcbf9..c2cbc34850 100644 --- a/datasets/OMTO3_CPR_003.json +++ b/datasets/OMTO3_CPR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMTO3_CPR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.\n \n(The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003)", "links": [ { diff --git a/datasets/OMTO3d_003.json b/datasets/OMTO3d_003.json index bdc10f158b..aaae59ee2a 100644 --- a/datasets/OMTO3d_003.json +++ b/datasets/OMTO3d_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMTO3d_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids.\n\nThe OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.", "links": [ { diff --git a/datasets/OMTO3e_003.json b/datasets/OMTO3e_003.json index 0cc6fcc672..7778de55e9 100644 --- a/datasets/OMTO3e_003.json +++ b/datasets/OMTO3e_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMTO3e_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles.\n\nThe OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.", "links": [ { diff --git a/datasets/OMUANC_004.json b/datasets/OMUANC_004.json index 1f80ff3fe8..e91e9ebc19 100644 --- a/datasets/OMUANC_004.json +++ b/datasets/OMUANC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUANC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMUFPITMET_003.json b/datasets/OMUFPITMET_003.json index 0dfc290c23..e349c2d740 100644 --- a/datasets/OMUFPITMET_003.json +++ b/datasets/OMUFPITMET_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUFPITMET_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. \n\nThe fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere. \n\nThe OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMUFPMET_004.json b/datasets/OMUFPMET_004.json index f9e327f656..e025a380f2 100644 --- a/datasets/OMUFPMET_004.json +++ b/datasets/OMUFPMET_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUFPMET_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMUFPSLV_004.json b/datasets/OMUFPSLV_004.json index 176c172d9f..3e0a120326 100644 --- a/datasets/OMUFPSLV_004.json +++ b/datasets/OMUFPSLV_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUFPSLV_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMUFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMUVBG_003.json b/datasets/OMUVBG_003.json index 9b2cdc6cea..34d638b2aa 100644 --- a/datasets/OMUVBG_003.json +++ b/datasets/OMUVBG_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUVBG_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Level-2G daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBG). The OMUVBG is a special Level-2 Global Gridded type data Product (referred as Level 2G or L2G) where Level-2 or swath pixel data are binned (but not averaged)into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All ancillary parameters such as latitude, longitude, time, solar and viewing angles are also saved for each pixel. First two dimensions of each parameter correspond to spatial (Lat/Lon based) Grid ID and third dimension identifies the pixel or observed scene (referred as 'candidates' ID). Scientists can apply a data filtering scheme of their choice, average good quality pixels data in each grid and create their Level-3 products.\n\nThe OMUVBG files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBG data product is about 128 MBytes.", "links": [ { diff --git a/datasets/OMUVB_003.json b/datasets/OMUVB_003.json index 2115f9b988..3ca12f55d7 100644 --- a/datasets/OMUVB_003.json +++ b/datasets/OMUVB_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUVB_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aura Ozone Monitoring Instrument (OMI) Version 003 Surface UV Irradiance Product (OMUVB) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI Surface UVB product is OMUVB. The algorithm scientists for this product are: Dr. Jari Hovila, Dr. Antii Arola and Dr. Johanna Taminnen. The OMUVB product contains erythemally weighted daily dose and dose rate, and spectral irradiances at 305, 310, 324, and 380 nm. It also contains quality flags, cloud optical depth, Lambertian Equivalent Reflectivity, Total Column Ozone amount, and other ancillary information.\n\nThe OMUVB files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMUVB data product is about 10 Mbytes.", "links": [ { diff --git a/datasets/OMUVBd_003.json b/datasets/OMUVBd_003.json index 0d2259103e..706357b8ee 100644 --- a/datasets/OMUVBd_003.json +++ b/datasets/OMUVBd_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMUVBd_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is Level-3 daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBd). The OMUVBd product contains global erythemally weighted daily dose and erythemal dose rate at local solar noon at 1.0x1.0 deg grids.\n\nThe OMUVBd files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBd data product is about 5 MBytes.", "links": [ { diff --git a/datasets/OMVANC_004.json b/datasets/OMVANC_004.json index 0208ee0853..212aba7d33 100644 --- a/datasets/OMVANC_004.json +++ b/datasets/OMVANC_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMVANC_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMVANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMVFPITMET_003.json b/datasets/OMVFPITMET_003.json index 0910af9710..7c08a4d40c 100644 --- a/datasets/OMVFPITMET_003.json +++ b/datasets/OMVFPITMET_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMVFPITMET_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km (OMVFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI VIS swath. \n\nThe fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI UV-2 swath, OMUFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI VIS spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMVFPITMET by combining layers above the troposphere. \n\nThe OMVFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMVFPMET_004.json b/datasets/OMVFPMET_004.json index 5664306b08..8baca3a965 100644 --- a/datasets/OMVFPMET_004.json +++ b/datasets/OMVFPMET_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMVFPMET_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMVFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/OMVFPSLV_004.json b/datasets/OMVFPSLV_004.json index 7180942736..6d1c9c47cf 100644 --- a/datasets/OMVFPSLV_004.json +++ b/datasets/OMVFPSLV_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OMVFPSLV_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.\n\nThe fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.\n\nThe OMVFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.", "links": [ { diff --git a/datasets/ONDEQUE_0.json b/datasets/ONDEQUE_0.json index ef464a7228..25ec229577 100644 --- a/datasets/ONDEQUE_0.json +++ b/datasets/ONDEQUE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ONDEQUE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken along the mid-Atlantic coast and near Bermuda as part of the ONDEQUE program in 2007 and 2008.", "links": [ { diff --git a/datasets/ONICE_Chlorophyll_1.json b/datasets/ONICE_Chlorophyll_1.json index ebee031504..bdbdbedc14 100644 --- a/datasets/ONICE_Chlorophyll_1.json +++ b/datasets/ONICE_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ONICE_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 2, 1997-1998 - the ONICE cruise. Samples were collected from September-November of 1997. \n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/ONR-MAB_0.json b/datasets/ONR-MAB_0.json index 4b76a4f5b5..734916c204 100644 --- a/datasets/ONR-MAB_0.json +++ b/datasets/ONR-MAB_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ONR-MAB_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mid-Atlantic Bight measurements taken between 1996 and 1999.", "links": [ { diff --git a/datasets/ONR_PenobscotRiverSystem_0.json b/datasets/ONR_PenobscotRiverSystem_0.json index b04d0ba005..562c219cf6 100644 --- a/datasets/ONR_PenobscotRiverSystem_0.json +++ b/datasets/ONR_PenobscotRiverSystem_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ONR_PenobscotRiverSystem_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Penobscot River System in Maine taken between 2006 and 2009.", "links": [ { diff --git a/datasets/OPERA_L2_CSLC-S1-STATIC_V1_1.json b/datasets/OPERA_L2_CSLC-S1-STATIC_V1_1.json index 00fb4b2acc..23d583ed6d 100644 --- a/datasets/OPERA_L2_CSLC-S1-STATIC_V1_1.json +++ b/datasets/OPERA_L2_CSLC-S1-STATIC_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L2_CSLC-S1-STATIC_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission\u2019s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static layers generation algorithm. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset.", "links": [ { diff --git a/datasets/OPERA_L2_CSLC-S1_V1_1.json b/datasets/OPERA_L2_CSLC-S1_V1_1.json index 931b5e3a0c..b96f28fc5b 100644 --- a/datasets/OPERA_L2_CSLC-S1_V1_1.json +++ b/datasets/OPERA_L2_CSLC-S1_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L2_CSLC-S1_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform\u2019s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data, provided by the European Space Agency. The CSLC images are precisely aligned or \u201ccoregistered\u201d to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively. Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. Due to the S1 mission\u2019s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).", "links": [ { diff --git a/datasets/OPERA_L2_RTC-S1-STATIC_V1_1.json b/datasets/OPERA_L2_RTC-S1-STATIC_V1_1.json index 45e0d2b105..513c750d70 100644 --- a/datasets/OPERA_L2_RTC-S1-STATIC_V1_1.json +++ b/datasets/OPERA_L2_RTC-S1-STATIC_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L2_RTC-S1-STATIC_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission\u2019s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID. The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica). The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset.", "links": [ { diff --git a/datasets/OPERA_L2_RTC-S1_V1_1.json b/datasets/OPERA_L2_RTC-S1_V1_1.json index a7acadb282..70d4565f9c 100644 --- a/datasets/OPERA_L2_RTC-S1_V1_1.json +++ b/datasets/OPERA_L2_RTC-S1_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L2_RTC-S1_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data, provided by the European Space Agency, with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product. The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, \u02630, obtained from the original radar brightness beta-naught, \u03b20, through radiometric terrain correction. The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. Due to the S1 mission\u2019s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.", "links": [ { diff --git a/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json b/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json index 9641ba1152..2ba40ad864 100644 --- a/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json +++ b/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L3_DIST-ALERT-HLS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A and Sentinel-2B Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.\r\nThe OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document for a more detailed description of the individual layers provided in the DIST-ALERT product.\r\n", "links": [ { diff --git a/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json b/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json index 176b711d55..9ae0c271bc 100644 --- a/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json +++ b/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L3_DIST-ANN-HLS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).\r\nThe OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product. \r\n", "links": [ { diff --git a/datasets/OPERA_L3_DSWX-HLS_V1_1.0.json b/datasets/OPERA_L3_DSWX-HLS_V1_1.0.json index 67c371253d..a1c8aa0b44 100644 --- a/datasets/OPERA_L3_DSWX-HLS_V1_1.0.json +++ b/datasets/OPERA_L3_DSWX-HLS_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L3_DSWX-HLS_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km \u00d7 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.\n

\nThe digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 \u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM.\n

\nThe OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).\n

\nTo access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov ", "links": [ { diff --git a/datasets/OPERA_L3_DSWX-S1_V1_1.0.json b/datasets/OPERA_L3_DSWX-S1_V1_1.0.json index ee42044e3b..616a763533 100644 --- a/datasets/OPERA_L3_DSWX-S1_V1_1.0.json +++ b/datasets/OPERA_L3_DSWX-S1_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OPERA_L3_DSWX-S1_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinal-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024. Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.\n

\nThe OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).\n

\nTo access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov ", "links": [ { diff --git a/datasets/ORACLES_AerosolCloud_AircraftRemoteSensing_Data_1.json b/datasets/ORACLES_AerosolCloud_AircraftRemoteSensing_Data_1.json index 1651abd981..b021a69041 100644 --- a/datasets/ORACLES_AerosolCloud_AircraftRemoteSensing_Data_1.json +++ b/datasets/ORACLES_AerosolCloud_AircraftRemoteSensing_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_AerosolCloud_AircraftRemoteSensing_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_AerosolCloud_AircraftRemoteSensing_Data are remotely sensed aerosol and cloud measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_Aerosol_AircraftInSitu_Data_1.json b/datasets/ORACLES_Aerosol_AircraftInSitu_Data_1.json index 7a992ff02d..9204ce789c 100644 --- a/datasets/ORACLES_Aerosol_AircraftInSitu_Data_1.json +++ b/datasets/ORACLES_Aerosol_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_Aerosol_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_Aerosol_AircraftInSitu_Data are in situ aerosol measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_Cloud_AircraftInSitu_Data_1.json b/datasets/ORACLES_Cloud_AircraftInSitu_Data_1.json index 4d206d6974..3533a356d7 100644 --- a/datasets/ORACLES_Cloud_AircraftInSitu_Data_1.json +++ b/datasets/ORACLES_Cloud_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_Cloud_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_Cloud_AircraftInSitu_Data are in situ cloud measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_Merge_Data_1.json b/datasets/ORACLES_Merge_Data_1.json index 2efc8d6263..c48409e588 100644 --- a/datasets/ORACLES_Merge_Data_1.json +++ b/datasets/ORACLES_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_Merge_Data are pre-generated aircraft merge data files created utilizing data collected during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_MetNav_AircraftInSitu_Data_1.json b/datasets/ORACLES_MetNav_AircraftInSitu_Data_1.json index 0aeb9d3d9a..64975b8bec 100644 --- a/datasets/ORACLES_MetNav_AircraftInSitu_Data_1.json +++ b/datasets/ORACLES_MetNav_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_MetNav_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_Model_Data_1.json b/datasets/ORACLES_Model_Data_1.json index 5b2abd091a..b7d80a3406 100644 --- a/datasets/ORACLES_Model_Data_1.json +++ b/datasets/ORACLES_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_Model_Data consists of model-derived estimates for aerosol time since emissions (days), calculated with the Weather Research and Aerosol Aware Microphysics (WRF-AAM) Model collected for the ObseRvations of Aerosols above CLouds and their intEractions sub-orbital (ORACLES) campaign. Data collection is complete, and data is available for all three deployments (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018).\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES uses two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_Radiation_AircraftInSitu_Data_1.json b/datasets/ORACLES_Radiation_AircraftInSitu_Data_1.json index 24f809eae1..d60c11878a 100644 --- a/datasets/ORACLES_Radiation_AircraftInSitu_Data_1.json +++ b/datasets/ORACLES_Radiation_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_Radiation_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_Radiation_AircraftInSitu_Data are in situ radiation measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORACLES_TraceGas_AircraftInSitu_Data_1.json b/datasets/ORACLES_TraceGas_AircraftInSitu_Data_1.json index 0dc287b7fe..e4a27f400f 100644 --- a/datasets/ORACLES_TraceGas_AircraftInSitu_Data_1.json +++ b/datasets/ORACLES_TraceGas_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORACLES_TraceGas_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ORACLES_TraceGas_AircraftInSitu_Data are in situ trace gas measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 \u2013 October 27, 2016, August 1, 2017 \u2013 September 4, 2017 and September 21, 2018 \u2013 October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.\r\n\r\nSouthern Africa produces almost one-third of the Earth\u2019s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 \u2013 October 27, 2016; August 1, 2017 \u2013 September 4, 2017; September 21, 2018 \u2013 October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.", "links": [ { diff --git a/datasets/ORINOCO_0.json b/datasets/ORINOCO_0.json index 30017b1da6..c506e7b8aa 100644 --- a/datasets/ORINOCO_0.json +++ b/datasets/ORINOCO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ORINOCO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near the Orinoco River outflow region from 1998 to 2000.", "links": [ { diff --git a/datasets/OS2_GAC_RAD_1.0.json b/datasets/OS2_GAC_RAD_1.0.json index 7e1ae6724f..b7c685a708 100644 --- a/datasets/OS2_GAC_RAD_1.0.json +++ b/datasets/OS2_GAC_RAD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OS2_GAC_RAD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Area Coverage (GAC) Ocean Color Monitor Radiance products with 1 km x 1 km resolution.", "links": [ { diff --git a/datasets/OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2_2.json b/datasets/OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2_2.json index 48d98471c7..0fa879c6d3 100644 --- a/datasets/OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2_2.json +++ b/datasets/OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of the version 2 Level 2B science-quality ocean surface wind vector retrievals from the Oceansat-2 scatterometer (OSCAT), which was designed and launched by the Indian Space Research Organization (ISRO) 23 September 2009. This Level 2B dataset is produced by the Jet Propulsion Laboratory (JPL) QuikSCAT Project in cooperation with ISRO. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. This resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. This newest version contains an improved geophysical model function (GMF), known as QSCAT12, consistent with the Remote Sensing Systems Ku2011 GMF, and an improved rain detection and flagging algorithm; these algorithms are consistent with the latest reprocessed version 3 QuikSCAT L2B dataset. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. As a Ku-band dual pencil-beam rotating scatterometer, OSCAT design specs bear a strong resemblence of the Ku-band SeaWinds scatterometers on QuikSCAT and Midori-II (ADEOS-II). The primary difference between OSCAT and SeaWinds is the ~4 degree increase in the OSCAT incidence angle, which acts as an offset to the relatively lower altitude of Oceansat-2 to help provide a nearly identical swath width to SeaWinds. In the early phase of cal/val, the JPL QuikSCAT Project identified several problems, most of which have been corrected in this latest L2B version. This dataset release is expected to have an accuracy similar to that of the version 3 L2B QuikSCAT product, with minor caveats, all of which are described by Jaruwatanadilok et al. (2014) and summarized in the User Guide document which is made available in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/docs/ . Read software is made available in MATLAB, Python, R, and IDL and is accessible in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/sw/ .", "links": [ { diff --git a/datasets/OS2_SCAT_L2B_1.json b/datasets/OS2_SCAT_L2B_1.json index 2c54ccd465..49693cb679 100644 --- a/datasets/OS2_SCAT_L2B_1.json +++ b/datasets/OS2_SCAT_L2B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OS2_SCAT_L2B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scatterometer provides wind vector data products for weather forecasting, cyclone detection and tracking services to the users", "links": [ { diff --git a/datasets/OS2_SCAT_L3SH_1.json b/datasets/OS2_SCAT_L3SH_1.json index cea37e9999..446f970695 100644 --- a/datasets/OS2_SCAT_L3SH_1.json +++ b/datasets/OS2_SCAT_L3SH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OS2_SCAT_L3SH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All sigma-0 measurements (forward and aft looking) for Veritcal polarization and falling within a grid cell are averaged. A separate product is generated for Veritcal polarization.", "links": [ { diff --git a/datasets/OS2_SCAT_L3SV_1.json b/datasets/OS2_SCAT_L3SV_1.json index 463c4ec4d6..5b87933af0 100644 --- a/datasets/OS2_SCAT_L3SV_1.json +++ b/datasets/OS2_SCAT_L3SV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OS2_SCAT_L3SV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All sigma-0 measurements (forward and aft looking) for Horizantal polarization and falling within a grid cell are averaged. A separate product is generated for Horizontal polarization.", "links": [ { diff --git a/datasets/OSCAR_L4_OC_FINAL_V2.0_2.0.json b/datasets/OSCAR_L4_OC_FINAL_V2.0_2.0.json index cee1c26f5b..9ae3587307 100644 --- a/datasets/OSCAR_L4_OC_FINAL_V2.0_2.0.json +++ b/datasets/OSCAR_L4_OC_FINAL_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OSCAR_L4_OC_FINAL_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day. OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/. More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.", "links": [ { diff --git a/datasets/OSCAR_L4_OC_INTERIM_V2.0_2.0.json b/datasets/OSCAR_L4_OC_INTERIM_V2.0_2.0.json index 4adc5eff15..16b17ff3cc 100644 --- a/datasets/OSCAR_L4_OC_INTERIM_V2.0_2.0.json +++ b/datasets/OSCAR_L4_OC_INTERIM_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OSCAR_L4_OC_INTERIM_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day. OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/. More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.", "links": [ { diff --git a/datasets/OSCAR_L4_OC_NRT_V2.0_2.0.json b/datasets/OSCAR_L4_OC_NRT_V2.0_2.0.json index 14a09d6514..c06fd0878f 100644 --- a/datasets/OSCAR_L4_OC_NRT_V2.0_2.0.json +++ b/datasets/OSCAR_L4_OC_NRT_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OSCAR_L4_OC_NRT_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day. OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/. More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.", "links": [ { diff --git a/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json b/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json index 42e9de7aaf..b4ed543009 100644 --- a/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json +++ b/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Operational Sea Surface Temperature and Sea Ice Analysis Reprocessed (OSTIA-REP) is a GHRSST reprocessed Level-4 sea surface temperature and ice-concentration analysis produced by the UK Met Office (UKMO) using optimal interpolation (OI) on a global 0.05 degree grid. It is a sister product of the Near Real Time version (OSTIA-NRT), but incorporates satellite data from over 25 different SST sensors as well as in situ data from drifting and moored buoys. The OSTIA-REP is produced on a biannual frequency when more satellite and climatology observations are available from existing geostationary IR, and polar orbiting IR and MW satellites in addition to the data used in OSTIA-NRT.

\r\nWhile OSTIA-NRT is produced to mainly serve as a lower boundary condition in Numerical Weather Prediction (NWP) models, this OSTIA-REP aims to provide a more accurate and consistent record of SST measurements over time, which is crucial for detecting long-term climate trends and variability. Both versions follow GHRSST Data Processing Specification (GDS) version 2 format guidelines.

\r\nData to June 2022 are also distributed through the E.U. Copernicus Marine Service Information (https://marine.copernicus.eu/, DOI: https://doi.org/10.48670/moi-00168 with the following license). Please refer to the user guide for more information. ", "links": [ { diff --git a/datasets/OSTIA-UKMO-L4-GLOB-v2.0_2.0.json b/datasets/OSTIA-UKMO-L4-GLOB-v2.0_2.0.json index 04075e213b..dc486588fc 100644 --- a/datasets/OSTIA-UKMO-L4-GLOB-v2.0_2.0.json +++ b/datasets/OSTIA-UKMO-L4-GLOB-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OSTIA-UKMO-L4-GLOB-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.05x0.05 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from over 10 unique sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferometer (IASI), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications and is updated daily with 24-hours nominal latency in a Near Real Time (NRT) mode. UKMO also produces the higher quality reprocessed OSTIA L4 SST using more sensors and data with a biannual latency (https://podaac.jpl.nasa.gov/dataset/OSTIA-UKMO-L4-GLOB-REP-v2.0).", "links": [ { diff --git a/datasets/OTZ_WHOI_0.json b/datasets/OTZ_WHOI_0.json index 092a7521f2..9e6e55d33a 100644 --- a/datasets/OTZ_WHOI_0.json +++ b/datasets/OTZ_WHOI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OTZ_WHOI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Twilight Zone (OTZ) program at the Woods Hole Oceanographic Institution is a comprehensive exploration of the twilight zone focused on this important ecosystem and its role in the C cycle and climate, laying the groundwork for long-term sustainability. The project comprises: scientific discovery, technological innovation, and public engagement. The OTZ program partnered with NASA EXPORTS during field sampling in the North Atlantic in the Spring of 2021.", "links": [ { diff --git a/datasets/OWLETS1_Model_Data_1.json b/datasets/OWLETS1_Model_Data_1.json index 002e7926a9..313c454c16 100644 --- a/datasets/OWLETS1_Model_Data_1.json +++ b/datasets/OWLETS1_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS1_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS1_Model_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) analysis model data utilized during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS1_Pandora_Data_1.json b/datasets/OWLETS1_Pandora_Data_1.json index fa35b2f958..e6432c7ddb 100644 --- a/datasets/OWLETS1_Pandora_Data_1.json +++ b/datasets/OWLETS1_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS1_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS1_Pandora_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone and nitrogen dioxide data collected by the NASA GSFC Pandora Spectrometer Project located at NASA Langley Research Center, the Chesapeake Bay Bridge Tunnel, SERC Research Vessel, Virginia Commonwealth University (VCU) and Wallops Flight Facility during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS1_SherpaAircraft_Data_1.json b/datasets/OWLETS1_SherpaAircraft_Data_1.json index 87b9476af9..499d830f14 100644 --- a/datasets/OWLETS1_SherpaAircraft_Data_1.json +++ b/datasets/OWLETS1_SherpaAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS1_SherpaAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS1_SherpaAircraft_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) data collected on board the Sherpa Aircraft. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data include trace gas measurements, greenhouse gases, and aircraft navigational and housekeeping data collected via remote sensing and in-situ instrumentation. This collection features data from the GeoTASO instrument, a pre-cursor to the TEMPO satellite. Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS1_Sondes_Data_1.json b/datasets/OWLETS1_Sondes_Data_1.json index 5dc6a50ea8..e29056266a 100644 --- a/datasets/OWLETS1_Sondes_Data_1.json +++ b/datasets/OWLETS1_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS1_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS1_Sondes_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone data collected via synchronous ozonesonde launches at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS1_SurfaceLidar_Data_1.json b/datasets/OWLETS1_SurfaceLidar_Data_1.json index ab0a8f8bf7..5c77f15ace 100644 --- a/datasets/OWLETS1_SurfaceLidar_Data_1.json +++ b/datasets/OWLETS1_SurfaceLidar_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS1_SurfaceLidar_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS1_SurfaceLidar_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) lidar data collected at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_Aerosol_Data_1.json b/datasets/OWLETS2_Aerosol_Data_1.json index 9ae84a5946..76447ee2e1 100644 --- a/datasets/OWLETS2_Aerosol_Data_1.json +++ b/datasets/OWLETS2_Aerosol_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_Aerosol_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_Aerosol_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) aerosol data collected at various ground sites during the OWLETS-2 field campaign via ceilometers and PILS-IC. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_Pandora_Data_1.json b/datasets/OWLETS2_Pandora_Data_1.json index 8d008f4411..b58035098f 100644 --- a/datasets/OWLETS2_Pandora_Data_1.json +++ b/datasets/OWLETS2_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_Pandora_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) ozone and nitrogen dioxide data collected by the NASA GSFC Pandora Spectrometer Project located at various ground sites and onboard the SERC research vessel during the OWLETS-2 field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_Ship_Data_1.json b/datasets/OWLETS2_Ship_Data_1.json index 675305ea0c..c62f7545a2 100644 --- a/datasets/OWLETS2_Ship_Data_1.json +++ b/datasets/OWLETS2_Ship_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_Ship_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_Ship_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) data collected onboard the Smithsonian Environmental Research Center (SERC) Vessel. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data includes ozone and nitrogen dioxide measurements, meteorological parameters, and ship navigational data collected via in-situ instrumentation. OWLETS and OWLETS-2 were supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_Sondes_Data_1.json b/datasets/OWLETS2_Sondes_Data_1.json index 0eaa048ea8..3b69486cdf 100644 --- a/datasets/OWLETS2_Sondes_Data_1.json +++ b/datasets/OWLETS2_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_Sondes_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone data collected via synchronous ozonesonde launches at the Howard University Beltsville (HUBV) and University of Maryland Baltimore County over-land sites and Hart Miller Island over-water site during the OWLETS-2 field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_SurfaceLidar_Data_1.json b/datasets/OWLETS2_SurfaceLidar_Data_1.json index fd7514e6cf..1a3ae4d20f 100644 --- a/datasets/OWLETS2_SurfaceLidar_Data_1.json +++ b/datasets/OWLETS2_SurfaceLidar_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_SurfaceLidar_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_SurfaceLidar_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) NASA GSFC TROPOZ, NASA LMOL, and wind lidar data collected at Hart Miller Island site and UMBC site during the OWLETS-2 field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_Surface_Data_1.json b/datasets/OWLETS2_Surface_Data_1.json index 08ab96c02d..e97f11c27e 100644 --- a/datasets/OWLETS2_Surface_Data_1.json +++ b/datasets/OWLETS2_Surface_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_Surface_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_Surface_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) data collected via in-situ and remote sensing instrumentation at various ground/surface sites during the OWLETS-2 field campaign. OWLETS and OWLETS-2 were supported by the NASA Science Innovation Fund (SIF). Data includes ozone measurements. Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OWLETS2_UMDAircraft_Data_1.json b/datasets/OWLETS2_UMDAircraft_Data_1.json index f33ff5e8a7..d1c0a1ae43 100644 --- a/datasets/OWLETS2_UMDAircraft_Data_1.json +++ b/datasets/OWLETS2_UMDAircraft_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OWLETS2_UMDAircraft_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OWLETS2_UMDAircraft_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) data collected onboard the University of Maryland Cessna Aircraft. Data include trace gas measurements, greenhouse gases, aerosols, and aircraft navigational and housekeeping data collected via remote sensing and in-situ instrumentation. This collection features data from the GeoTASO instrument, a pre-cursor to the TEMPO satellite. OWLETS and OWLETS-2 were supported by the NASA Science Innovation Fund (SIF). Data collection is complete.\r\n\r\nCoastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA\u2019s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 \u2013 August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 \u2013 July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.", "links": [ { diff --git a/datasets/OceanSat-2.NRT.data_9.0.json b/datasets/OceanSat-2.NRT.data_9.0.json index 2a128a241b..98d12fbee2 100644 --- a/datasets/OceanSat-2.NRT.data_9.0.json +++ b/datasets/OceanSat-2.NRT.data_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "OceanSat-2.NRT.data_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESA, in collaboration with GAF AG, acquired and processed every day OceanSat-2 passes over Neutrelitz reception station from January 2016 to November 2021. All passes were systematically processed to levels 1B, 2B and 2C, and available to users in NRT (< 3 hours). Products are available in: \u2022 Level 1B: Geophysical Data containing Radiance Data for all 8 Bands of OCM-2 \u2022 Level 2B: Geophysical Data L2B for given Geo physical parameter. Geo physical parameters: Chlorophyll, Aerosol Depth, Different Attenuation, Total Suspended Sediments \u2022 Level 2C: Georeferenced Radiance Data for given geo physical parameter. Geo physical parameters: Chlorophyll, Aerosol Depth, Different Attenuation, Total Suspended Sediments", "links": [ { diff --git a/datasets/Ocean_Color_Cal_Val_0.json b/datasets/Ocean_Color_Cal_Val_0.json index 69c00d505b..d8564ccea6 100644 --- a/datasets/Ocean_Color_Cal_Val_0.json +++ b/datasets/Ocean_Color_Cal_Val_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ocean_Color_Cal_Val_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the New Jersey and New York coasts between 2005 and 2009.", "links": [ { diff --git a/datasets/Oceania_0.json b/datasets/Oceania_0.json index 379716dbe7..01d39f79fb 100644 --- a/datasets/Oceania_0.json +++ b/datasets/Oceania_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Oceania_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken during 1998 to 2000 in the Norwegian Sea.", "links": [ { diff --git a/datasets/Odin.OSIRIS_4.0.json b/datasets/Odin.OSIRIS_4.0.json index 7433bf82c7..5dbe19d689 100644 --- a/datasets/Odin.OSIRIS_4.0.json +++ b/datasets/Odin.OSIRIS_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Odin.OSIRIS_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Odin OSIRIS (Optical Spectrograph and Infra-Red Imaging System) data provides vertical profiles measures of spectrally dispersed, limb scattered sunlight from the upper troposphere into the lower mesosphere. The data products are regularly processed and provide Ozone density vertical profiles (both Level 2 and Level 3), vertical profiles of stratospheric Aerosol (both Level 2 and Level 3), slant column densities of nitrogen dioxide NO2 profiles (Level 2), stratospheric BrO profiles (Level 2)", "links": [ { diff --git a/datasets/Odyssee_Saint_Laurent_0.json b/datasets/Odyssee_Saint_Laurent_0.json index a7f9a6e0e3..486694772a 100644 --- a/datasets/Odyssee_Saint_Laurent_0.json +++ b/datasets/Odyssee_Saint_Laurent_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Odyssee_Saint_Laurent_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From Réseau Québec maritime (RQM)'s Odyssée Saint-Laurent research program, this mission used the icebreaker C.C.G.S. Amundsen to gain knowledge on the St. Lawrence system (eastern Canada) in winter 2019.", "links": [ { diff --git a/datasets/Okeechobee_0.json b/datasets/Okeechobee_0.json index 4e237bc714..0f564e178e 100644 --- a/datasets/Okeechobee_0.json +++ b/datasets/Okeechobee_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Okeechobee_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optical measurements made in Lake Okeechobee, Florida, between 1997 and 1999", "links": [ { diff --git a/datasets/Open Cities AI Challenge Dataset_1.json b/datasets/Open Cities AI Challenge Dataset_1.json index c31de11b31..2eac01f98a 100644 --- a/datasets/Open Cities AI Challenge Dataset_1.json +++ b/datasets/Open Cities AI Challenge Dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Open Cities AI Challenge Dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed as part of a challenge to segment building footprints from aerial imagery. The goal of the challenge was to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities [Read more about the [challenge](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)]. The data consists of drone imagery from 10 different cities and regions across Africa", "links": [ { diff --git a/datasets/Opt_Sed_Trap_Cal_0.json b/datasets/Opt_Sed_Trap_Cal_0.json index 2dd791cdf5..1c46c8d88c 100644 --- a/datasets/Opt_Sed_Trap_Cal_0.json +++ b/datasets/Opt_Sed_Trap_Cal_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Opt_Sed_Trap_Cal_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Opt_Sed_Trap_Cal (Optical Sediment Trap Calibration) project off the New England coast.", "links": [ { diff --git a/datasets/Optical_Layers_0.json b/datasets/Optical_Layers_0.json index bf265fcb48..2b32b8c620 100644 --- a/datasets/Optical_Layers_0.json +++ b/datasets/Optical_Layers_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Optical_Layers_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the coast of Louisiana in the Gulf of Mexico by the Naval Research Lab.", "links": [ { diff --git a/datasets/Optique_St_Laurent_0.json b/datasets/Optique_St_Laurent_0.json index 2ad4cdc5ef..5382c70065 100644 --- a/datasets/Optique_St_Laurent_0.json +++ b/datasets/Optique_St_Laurent_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Optique_St_Laurent_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The St. Lawrence ecosystem is a complex environment influenced by a variety of physical forces (runoff, winds, tides, bathymetry) that sustains a diverse food web going from phytoplankton to whales. Chlorophyll concentration is thus an important variable to measure at the scale of the ecosystem. Because of its large size, remote sensing is the only available tool to measure chlorophyll distribution in the St. Lawrence using ocean color imagery. To fully utilize this type of data, it is however important to have a sound knowledge of the bio-optical properties of the different water masses in the system. A St. Lawrence SeaWiFS program was thus built to gather this knowledge beginning in 1997.", "links": [ { diff --git a/datasets/Oumalik_Veg_plots_1506_1.json b/datasets/Oumalik_Veg_plots_1506_1.json index 2c096d87f0..84c5203c1d 100644 --- a/datasets/Oumalik_Veg_plots_1506_1.json +++ b/datasets/Oumalik_Veg_plots_1506_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Oumalik_Veg_plots_1506_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected between 1983 and 1985 from 87 study plots near an abandoned test oil well in Oumalik, Alaska. Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources. The vegetation sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions.", "links": [ { diff --git a/datasets/P380108_0.json b/datasets/P380108_0.json index 382ee457c5..61cd4909db 100644 --- a/datasets/P380108_0.json +++ b/datasets/P380108_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "P380108_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the north Pacific Ocean from Hawaii toward the Gulf of Alaska in 2001.", "links": [ { diff --git a/datasets/P6_AWIF_STUC00GTD_1.0.json b/datasets/P6_AWIF_STUC00GTD_1.0.json index 0b24fc716c..bb953492d9 100644 --- a/datasets/P6_AWIF_STUC00GTD_1.0.json +++ b/datasets/P6_AWIF_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "P6_AWIF_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coarse resolution multi-spectral sensor, AWIFS operates in four spectral bands - B2, B3, B4, B5 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 56m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/P6_LIS3_STUC00GTD_1.0.json b/datasets/P6_LIS3_STUC00GTD_1.0.json index 3ddbc62197..f1c3224d56 100644 --- a/datasets/P6_LIS3_STUC00GTD_1.0.json +++ b/datasets/P6_LIS3_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "P6_LIS3_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/PACE-PAX_0.json b/datasets/PACE-PAX_0.json index f983d73cb3..db0a248dbc 100644 --- a/datasets/PACE-PAX_0.json +++ b/datasets/PACE-PAX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE-PAX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PACE-PAX SeaBASS DOI description: The Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) was a field campaign to support validation of the PACE mission through a combination of multiple platforms including aircraft (ER-2, CIRPAS Twin Otter), ships (R/V Shearwater, R/V Blissfully), autonomous ocean- and land-based instruments, and collaboration with the PACE Validation Science Teams (e.g.PVST-SBCR and PVST-CALCOFI) and PACE Vicarious Calibration systems (HyperNAV). The PACE-PAX mission took place during the month of September 2024 in Southern and Central California and nearby coastal regions. All PACE-PAX data are to be archived in 3 main data repositories: (1) NASA AIR-LARC for all aircraft data (until final data submission in March 2025 then migrated to the Langley Airborne DAAC), (2) AERONET-MAN for Microtops data, and (3) SeaBASS for all ocean optical, biogeochemical and phytoplankton data. The SeaBASS PACE-PAX DOI is split into 4 main cruises: PACE-PAX_Shearwater (cruise_id=RFDDMM-RS), PACE-PAX_Blissfully (cruise_id=RFDDMM-RB), PACE-PAX_SBCR (cruise_id=RFMMDD-SB), PACE-PAX_CALCOFI (cruise_id=RFMMDD-CL) where DDMM, represent the day and month collection date.", "links": [ { diff --git a/datasets/PACE_ABSclosure_0.json b/datasets/PACE_ABSclosure_0.json index 1fa9597d8d..a20bdef70a 100644 --- a/datasets/PACE_ABSclosure_0.json +++ b/datasets/PACE_ABSclosure_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_ABSclosure_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the PACE Absorbance Closure project off the coast of Florida.", "links": [ { diff --git a/datasets/PACE_EPH_DEF_1.json b/datasets/PACE_EPH_DEF_1.json index 3c9aa86e08..d653493d9d 100644 --- a/datasets/PACE_EPH_DEF_1.json +++ b/datasets/PACE_EPH_DEF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_EPH_DEF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. ", "links": [ { diff --git a/datasets/PACE_HARP2_L0_D1_1.json b/datasets/PACE_HARP2_L0_D1_1.json index cfa73f4e28..6ad56f0e9d 100644 --- a/datasets/PACE_HARP2_L0_D1_1.json +++ b/datasets/PACE_HARP2_L0_D1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L0_D1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HARP2_L0_D2_1.json b/datasets/PACE_HARP2_L0_D2_1.json index ac6fbc500a..07ee3cc5f2 100644 --- a/datasets/PACE_HARP2_L0_D2_1.json +++ b/datasets/PACE_HARP2_L0_D2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L0_D2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HARP2_L0_D3_1.json b/datasets/PACE_HARP2_L0_D3_1.json index 8540b930fd..f1fe464e34 100644 --- a/datasets/PACE_HARP2_L0_D3_1.json +++ b/datasets/PACE_HARP2_L0_D3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L0_D3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HARP2_L1A_SCI_2.json b/datasets/PACE_HARP2_L1A_SCI_2.json index 4048b54d2d..558a377e3b 100644 --- a/datasets/PACE_HARP2_L1A_SCI_2.json +++ b/datasets/PACE_HARP2_L1A_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L1A_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HARP2_L1B_SCI_2.json b/datasets/PACE_HARP2_L1B_SCI_2.json index 8fdc1f6251..a5522cd107 100644 --- a/datasets/PACE_HARP2_L1B_SCI_2.json +++ b/datasets/PACE_HARP2_L1B_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L1B_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HARP2_L1C_SCI_2.json b/datasets/PACE_HARP2_L1C_SCI_2.json index 1ac0a4ecbd..1ae4fda1e8 100644 --- a/datasets/PACE_HARP2_L1C_SCI_2.json +++ b/datasets/PACE_HARP2_L1C_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HARP2_L1C_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.", "links": [ { diff --git a/datasets/PACE_HKT_1.json b/datasets/PACE_HKT_1.json index f02a1baba0..67cd90f9f5 100644 --- a/datasets/PACE_HKT_1.json +++ b/datasets/PACE_HKT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HKT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. ", "links": [ { diff --git a/datasets/PACE_HSK_1.json b/datasets/PACE_HSK_1.json index 8845a0bfad..b889564136 100644 --- a/datasets/PACE_HSK_1.json +++ b/datasets/PACE_HSK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_HSK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. ", "links": [ { diff --git a/datasets/PACE_OCI_L0_DIAG_1.json b/datasets/PACE_OCI_L0_DIAG_1.json index 66087560db..280d0f8ee0 100644 --- a/datasets/PACE_OCI_L0_DIAG_1.json +++ b/datasets/PACE_OCI_L0_DIAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L0_DIAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "links": [ { diff --git a/datasets/PACE_OCI_L0_SCI_1.json b/datasets/PACE_OCI_L0_SCI_1.json index e6bde0fa1a..f87acbcbaf 100644 --- a/datasets/PACE_OCI_L0_SCI_1.json +++ b/datasets/PACE_OCI_L0_SCI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L0_SCI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "links": [ { diff --git a/datasets/PACE_OCI_L1A_SCI_2.json b/datasets/PACE_OCI_L1A_SCI_2.json index f32e135d4a..d45fb9a983 100644 --- a/datasets/PACE_OCI_L1A_SCI_2.json +++ b/datasets/PACE_OCI_L1A_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L1A_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "links": [ { diff --git a/datasets/PACE_OCI_L1B_SCI_2.json b/datasets/PACE_OCI_L1B_SCI_2.json index e01fe21bdb..9ea1b9c203 100644 --- a/datasets/PACE_OCI_L1B_SCI_2.json +++ b/datasets/PACE_OCI_L1B_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L1B_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "links": [ { diff --git a/datasets/PACE_OCI_L1C_SCI_2.json b/datasets/PACE_OCI_L1C_SCI_2.json index 2ac6fbda80..5fb52a5ea0 100644 --- a/datasets/PACE_OCI_L1C_SCI_2.json +++ b/datasets/PACE_OCI_L1C_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L1C_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "links": [ { diff --git a/datasets/PACE_OCI_L2_AOP_NRT_2.0.json b/datasets/PACE_OCI_L2_AOP_NRT_2.0.json index 2dc21ea6aa..47a423812a 100644 --- a/datasets/PACE_OCI_L2_AOP_NRT_2.0.json +++ b/datasets/PACE_OCI_L2_AOP_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L2_AOP_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L2_BGC_NRT_2.0.json b/datasets/PACE_OCI_L2_BGC_NRT_2.0.json index c41be2ff2f..40f9007254 100644 --- a/datasets/PACE_OCI_L2_BGC_NRT_2.0.json +++ b/datasets/PACE_OCI_L2_BGC_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L2_BGC_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L2_IOP_NRT_2.0.json b/datasets/PACE_OCI_L2_IOP_NRT_2.0.json index e1994d250d..57beccf4fa 100644 --- a/datasets/PACE_OCI_L2_IOP_NRT_2.0.json +++ b/datasets/PACE_OCI_L2_IOP_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L2_IOP_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L2_PAR_NRT_2.0.json b/datasets/PACE_OCI_L2_PAR_NRT_2.0.json index 63979ef9ce..bcae554717 100644 --- a/datasets/PACE_OCI_L2_PAR_NRT_2.0.json +++ b/datasets/PACE_OCI_L2_PAR_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L2_PAR_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L2_SFREFL_NRT_2.0.json b/datasets/PACE_OCI_L2_SFREFL_NRT_2.0.json index 5e6b5ba734..e3ff3f9223 100644 --- a/datasets/PACE_OCI_L2_SFREFL_NRT_2.0.json +++ b/datasets/PACE_OCI_L2_SFREFL_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L2_SFREFL_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_CHL_NRT_2.0.json b/datasets/PACE_OCI_L3B_CHL_NRT_2.0.json index d97f10625e..8ed6bbed21 100644 --- a/datasets/PACE_OCI_L3B_CHL_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_CHL_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_CHL_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_IOP_NRT_2.0.json b/datasets/PACE_OCI_L3B_IOP_NRT_2.0.json index 79d5482344..e9fbd4d9b9 100644 --- a/datasets/PACE_OCI_L3B_IOP_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_IOP_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_IOP_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_KD_NRT_2.0.json b/datasets/PACE_OCI_L3B_KD_NRT_2.0.json index 384635e262..4526397dcd 100644 --- a/datasets/PACE_OCI_L3B_KD_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_KD_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_KD_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_PAR_NRT_2.0.json b/datasets/PACE_OCI_L3B_PAR_NRT_2.0.json index 2df1b5db3e..2870c11878 100644 --- a/datasets/PACE_OCI_L3B_PAR_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_PAR_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_PAR_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_POC_NRT_2.0.json b/datasets/PACE_OCI_L3B_POC_NRT_2.0.json index df07b77e1f..a1e19a0628 100644 --- a/datasets/PACE_OCI_L3B_POC_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_POC_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_POC_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_RRS_NRT_2.0.json b/datasets/PACE_OCI_L3B_RRS_NRT_2.0.json index d6be3c1f0e..c086a25432 100644 --- a/datasets/PACE_OCI_L3B_RRS_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_RRS_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_RRS_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3B_SFREFL_NRT_2.0.json b/datasets/PACE_OCI_L3B_SFREFL_NRT_2.0.json index 34807118e3..87845f9c48 100644 --- a/datasets/PACE_OCI_L3B_SFREFL_NRT_2.0.json +++ b/datasets/PACE_OCI_L3B_SFREFL_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3B_SFREFL_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_AVW_NRT_2.0.json b/datasets/PACE_OCI_L3M_AVW_NRT_2.0.json index 01318ad0e4..5e538f43ec 100644 --- a/datasets/PACE_OCI_L3M_AVW_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_AVW_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_AVW_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_CHL_NRT_2.0.json b/datasets/PACE_OCI_L3M_CHL_NRT_2.0.json index 64d1573aa4..ed8e429cc8 100644 --- a/datasets/PACE_OCI_L3M_CHL_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_CHL_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_CHL_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_IOP_NRT_2.0.json b/datasets/PACE_OCI_L3M_IOP_NRT_2.0.json index fe72f571c2..8fd32601f5 100644 --- a/datasets/PACE_OCI_L3M_IOP_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_IOP_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_IOP_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_KD_NRT_2.0.json b/datasets/PACE_OCI_L3M_KD_NRT_2.0.json index 4052c9f104..5169a73d67 100644 --- a/datasets/PACE_OCI_L3M_KD_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_KD_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_KD_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_PAR_NRT_2.0.json b/datasets/PACE_OCI_L3M_PAR_NRT_2.0.json index 2dda222455..616815e3a3 100644 --- a/datasets/PACE_OCI_L3M_PAR_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_PAR_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_PAR_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_POC_NRT_2.0.json b/datasets/PACE_OCI_L3M_POC_NRT_2.0.json index 46ccf58715..dced817d7b 100644 --- a/datasets/PACE_OCI_L3M_POC_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_POC_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_POC_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_RRS_NRT_2.0.json b/datasets/PACE_OCI_L3M_RRS_NRT_2.0.json index 342ba90f57..bec0ce5091 100644 --- a/datasets/PACE_OCI_L3M_RRS_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_RRS_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_RRS_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_OCI_L3M_SFREFL_NRT_2.0.json b/datasets/PACE_OCI_L3M_SFREFL_NRT_2.0.json index 81878d86a9..0c15c21e58 100644 --- a/datasets/PACE_OCI_L3M_SFREFL_NRT_2.0.json +++ b/datasets/PACE_OCI_L3M_SFREFL_NRT_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_OCI_L3M_SFREFL_NRT_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L0_1.json b/datasets/PACE_SPEXONE_L0_1.json index 3667546a40..682c6fad1c 100644 --- a/datasets/PACE_SPEXONE_L0_1.json +++ b/datasets/PACE_SPEXONE_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L1A_SCI_3.json b/datasets/PACE_SPEXONE_L1A_SCI_3.json index a4f3a7e952..d09adca057 100644 --- a/datasets/PACE_SPEXONE_L1A_SCI_3.json +++ b/datasets/PACE_SPEXONE_L1A_SCI_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L1A_SCI_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L1B_SCI_2.json b/datasets/PACE_SPEXONE_L1B_SCI_2.json index 74714c76a3..bb6f9bd836 100644 --- a/datasets/PACE_SPEXONE_L1B_SCI_2.json +++ b/datasets/PACE_SPEXONE_L1B_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L1B_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L1B_SCI_3.json b/datasets/PACE_SPEXONE_L1B_SCI_3.json index 38e4157dd2..126d9647c1 100644 --- a/datasets/PACE_SPEXONE_L1B_SCI_3.json +++ b/datasets/PACE_SPEXONE_L1B_SCI_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L1B_SCI_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L1C_SCI_2.json b/datasets/PACE_SPEXONE_L1C_SCI_2.json index e8f2342eb9..9a96cd4a08 100644 --- a/datasets/PACE_SPEXONE_L1C_SCI_2.json +++ b/datasets/PACE_SPEXONE_L1C_SCI_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L1C_SCI_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PACE_SPEXONE_L1C_SCI_3.json b/datasets/PACE_SPEXONE_L1C_SCI_3.json index eaae2b7a86..1406e62821 100644 --- a/datasets/PACE_SPEXONE_L1C_SCI_3.json +++ b/datasets/PACE_SPEXONE_L1C_SCI_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PACE_SPEXONE_L1C_SCI_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.", "links": [ { diff --git a/datasets/PAC_DFO_PAR_BUOY_0.json b/datasets/PAC_DFO_PAR_BUOY_0.json index d6b255d4c3..4c849f4d48 100644 --- a/datasets/PAC_DFO_PAR_BUOY_0.json +++ b/datasets/PAC_DFO_PAR_BUOY_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAC_DFO_PAR_BUOY_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optical sensors were added to some of the 17 meteorological ODAS (Ocean Data Acquisition System) buoys which provide weather and ocean data along and off the west coast of Canada for the Environment (EC) and the Fisheries and Ocean departments (DFO) of the Canadian federal government. This dataset includes Photosynthetically Active Radiation (PAR) data from two locations: Halibut Bank (buoy code 46146) and Saanich Inlet near the Institute of Ocean Sciences (buoy code 46134). For additional information please refer to Gower et al., 1999 (DOI:10.1109/OCEANS.1999.800169)", "links": [ { diff --git a/datasets/PAD_2011_1133_2.json b/datasets/PAD_2011_1133_2.json index e5c00c7c9b..6c1f02b079 100644 --- a/datasets/PAD_2011_1133_2.json +++ b/datasets/PAD_2011_1133_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAD_2011_1133_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Peace-Athabasca Delta (PAD) is a hydrologically complex and ecologically diverse freshwater delta formed by the confluence of the Peace, Athabasca, and Birch Rivers near the western end of Lake Athabasca, Alberta, Canada. This data set includes 3 comma-delimited ASCII files: one containing water quality data and site characteristics from June and July 2010, a second containing water quality data and site characteristics for June and July 2011, and a third containing spectral reflectance of the water surface for 2011. The 2010 data file has measurements from 62 unique sites, the majority of which were revisited in 2011. Both of the 2011 data files have measurements from 99 unique sites visited 1-4 times. ", "links": [ { diff --git a/datasets/PAD_935_1.json b/datasets/PAD_935_1.json index 31031dc50a..696e042565 100644 --- a/datasets/PAD_935_1.json +++ b/datasets/PAD_935_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAD_935_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Peace-Athabasca Delta (PAD) is a large boreal wetland located in northeastern Alberta, Canada at the confluence of the Peace and Athabasca Rivers with Lake Athabasca (Figures 1 and 2). A Ramsar Convention wetland and UNESCO World Heritage Site, it is among the world's most ecologically significant wetlands. This data set contains four comma-delimited ASCII files, two of which contain water surface elevation site and measurement information and two contain water quality and ancillary parameter location and measurement data for 120 sites within the PAD.Data archived include water surface elevation and water quality parameters measured at points throughout the Delta during summers 2006 and 2007. These data sets were originally collected to improve understanding of hydrologic recharge processes in low-relief environments and to provide ground-based measurements to validate satellite observations of inundation and sediment transport. All work was supported by the NASA Terrestrial Hydrology Program under grant NNG06GE05G to the Department of Geography, University of California-Los Angeles, Los Angeles, California. ", "links": [ { diff --git a/datasets/PAGESAntTemp2013_1.json b/datasets/PAGESAntTemp2013_1.json index 9b081a941c..b81fc327f6 100644 --- a/datasets/PAGESAntTemp2013_1.json +++ b/datasets/PAGESAntTemp2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAGESAntTemp2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of a larger reconstruction of global temperatures over the last 2000 years, work was done to bring together all the Antarctic temperature datasets into one combined dataset.\n\nTaken from the PAGES website:\nAntarctica and the Southern Ocean play a key role in the global climate system (e.g. Mayewski et al., 2009; Convey et al., 2009). The processes that occur at these high southern latitudes play a pivotal role in global atmospheric and oceanic circulation, oceanic uptake of heat and carbon, and planetary energy balance, through the ice-albedo feedback.\n\nThe ability to detect and attribute climate change in the Antarctic and Southern Ocean is dependent upon climate observations; however, this region is the most observation-sparse and record-length-limited part of the globe. There are few systematic observations extending back before the mid-20th century and good coverage is only available since the satellite era (i.e. the last 3-4 decades).\n\nIn this context, key questions of the PAGES 2k Network underscore an acute need for good high resolution palaeoclimate data extending out to 2000 years before the present, but also with good coverage through the instrumental period so as to permit proxy calibration. Obtaining well-resolved ice cores over large parts of Antarctica is a challenge, but one that is becoming more tractable with the use of new technology. Antarctica2k seeks to integrate such records with other available proxies in order to address the goals of the 2k Network.", "links": [ { diff --git a/datasets/PAL-LTER_0.json b/datasets/PAL-LTER_0.json index b99b7addfd..349b5be83b 100644 --- a/datasets/PAL-LTER_0.json +++ b/datasets/PAL-LTER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAL-LTER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Long Term Ecological Research Network (LTER) Palmer Station Antarctica (PAL) program.", "links": [ { diff --git a/datasets/PARASOLRB_CPR_001.json b/datasets/PARASOLRB_CPR_001.json index 854447c0d4..194c5e68a3 100644 --- a/datasets/PARASOLRB_CPR_001.json +++ b/datasets/PARASOLRB_CPR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PARASOLRB_CPR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage.\n \n All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well.", "links": [ { diff --git a/datasets/PASSCAL_ABBA.json b/datasets/PASSCAL_ABBA.json index 5a83e4451d..a95e8de3ea 100644 --- a/datasets/PASSCAL_ABBA.json +++ b/datasets/PASSCAL_ABBA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PASSCAL_ABBA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Objective: Determination of anistropy and depth/characteristics of\ndiscontinuties in the mantle and the Moho beneath the Adirondacks.\n\nPreliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a\ndelay time of about 1 s. Discontinuity studies are still in progress.", "links": [ { diff --git a/datasets/PASSCAL_ALAR.json b/datasets/PASSCAL_ALAR.json index 3b83459951..ba7f255120 100644 --- a/datasets/PASSCAL_ALAR.json +++ b/datasets/PASSCAL_ALAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PASSCAL_ALAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "27 instruments were deployed at 18 different locations in the Aleutian\nIslands to record the airguns from the Ewing as it shot offshore.\n\nThe full data report is available in PDF at the following URL:\n\"http://www.iris.edu/data/reports/1996/96-016.pdf\"", "links": [ { diff --git a/datasets/PASSCAL_KRAFLA.json b/datasets/PASSCAL_KRAFLA.json index adf959eb2d..aad7589a5f 100644 --- a/datasets/PASSCAL_KRAFLA.json +++ b/datasets/PASSCAL_KRAFLA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PASSCAL_KRAFLA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Thirty-eight instruments were used to shoot two perpendicular\nrefraction profiles across the Krafla central volcano. The North/South\nprofile is 20 km long while the East/West profile is 55 km\nlong. Average station spacing was 500 m in the caldera and 1-4 km\nelswhere. A total of three shots were used in the NS profile and 6\nshots were used in the EW profile.", "links": [ { diff --git a/datasets/PASSCAL_WABASH.json b/datasets/PASSCAL_WABASH.json index 6e574bb716..52f31b3ab2 100644 --- a/datasets/PASSCAL_WABASH.json +++ b/datasets/PASSCAL_WABASH.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PASSCAL_WABASH", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recent paleoseismic evidence had shown there were 5-8 magnitude\ngreater than 6 earthquakes in this region in the past 20,000\nyears. The study area has always been at the fringe of previously\noperated seismic networks. A focused, short-term deployment was\ndesigned to lower the detection threshold to determine seismicity\nrates for the region for comparison with estimates derived from\npaleoseismicity. The researchers hoped to relate observed seismicity to faults\nmapped in the subsurface through new seismic reflection data made\navailable to the Illinois Basin Consortium.", "links": [ { diff --git a/datasets/PATEX_0.json b/datasets/PATEX_0.json index 0dc6e5e18f..76e2bf8e7d 100644 --- a/datasets/PATEX_0.json +++ b/datasets/PATEX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PATEX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PATagonia EXperiment (PATEX) Project is a Brazilian research project, which has the overall objective of characterizing the environmental constraints, phytoplankton assemblages, primary production rates, bio-optical characteristics, and air-sea CO2 fluxes waters along the Argentinean shelf-break during austral spring and summer. A set of seven PATEX cruises were conducted from 2004 to 2009. Garcia et al., 2011 (doi:10.1029/2010JC006595)", "links": [ { diff --git a/datasets/PAZ.ESA.archive_16.0.json b/datasets/PAZ.ESA.archive_16.0.json index 01e95685e0..e4be550ad2 100644 --- a/datasets/PAZ.ESA.archive_16.0.json +++ b/datasets/PAZ.ESA.archive_16.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAZ.ESA.archive_16.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The PAZ ESA archive collection consists of PAZ Level 1 data previously requested by ESA supported projects over their areas of interest around the world and, as a consequence, the products are scattered and dispersed worldwide and in different time windows. The dataset regularly grows as ESA collects new products over the years. Available modes are: \u2022\tStripMap mode (SM): SSD less than 3m for a scene 30km x 50km in single polarization or 15km x 50km in dual polarisation \u2022\tScanSAR mode (SC): the scene is 100 x 150 km2, SSD less than 18m in signle pol only \u2022\tWide ScanSAR mode (WS): single polarisation only, with SS less than 40m and scene size of 270 x 200 km2 \u2022\tSpotlight modes (SL): SSD less than 2m for a scene 10km x 10km, both single and dual polarization are available \u2022\tHigh Resolution Spotlight mode (HS): in both single and dual polarisation, the scene is 10x5 km2, SSD less than 1m \u2022\tStaring Spotlight mode (ST): SSD is 25cm, the scene size is 4 x 4 km2, in single polarisation only. The available geometric projections are: \u2022\tSingle Look Slant Range Complex (SSC): single look product, no geocoding, no radiometric artifact included, the pixel spacing is equidistant in azimuth and in ground range \u2022\tMulti Look Ground Range Detected (MGD): detected multi look product, simple polynomial slant-to-ground projection is performed in range, no image rotation to a map coordinate system is performed \u2022\tGeocoded Ellipsoid Corrected (GEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid with no terrain corrections \u2022\tEnhanced Ellipsoid Corrected (EEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid, the image distortions caused by varying terrain height are corrected using a DEM The following table summarises the offered product types EO-SIP product type\tOperation Mode\tGeometric Projection PSP_SM_SSC\tStripmap (SM)\tSingle Look Slant Range Complex (SSC) PSP_SM_MGD\tStripmap (SM)\tMulti Look Ground Range Detected (MGD) PSP_SM_GEC\tStripmap (SM)\tGeocoded Ellipsoid Corrected (GEC) PSP_SM_EEC\tStripmap (SM)\tEnhanced Ellipsoid Corrected (EEC) PSP_SC_MGD\tScanSAR (SC)\tSingle Look Slant Range Complex (SSC) PSP_SC_GEC\tScanSAR (SC)\tMulti Look Ground Range Detected (MGD) PSP_SC_EEC\tScanSAR (SC)\tGeocoded Ellipsoid Corrected (GEC) PSP_SC_SSC\tScanSAR (SC)\tEnhanced Ellipsoid Corrected (EEC) PSP_SL_SSC\tSpotlight (SL)\tSingle Look Slant Range Complex (SSC) PSP_SL_MGD\tSpotlight (SL)\tMulti Look Ground Range Detected (MGD) PSP_SL_GEC\tSpotlight (SL)\tGeocoded Ellipsoid Corrected (GEC) PSP_SL_EEC\tSpotlight (SL)\tEnhanced Ellipsoid Corrected (EEC) PSP_HS_SSC\tHigh Resolution Spotlight (HS)\tSingle Look Slant Range Complex (SSC) PSP_HS_MGD\tHigh Resolution Spotlight (HS)\tMulti Look Ground Range Detected (MGD) PSP_HS_GEC\tHigh Resolution Spotlight (HS)\tGeocoded Ellipsoid Corrected (GEC) PSP_HS_EEC\tHigh Resolution Spotlight (HS)\tEnhanced Ellipsoid Corrected (EEC) PSP_ST_SSC\tStaring Spotlight (ST)\tSingle Look Slant Range Complex (SSC) PSP_ST_MGD\tStaring Spotlight (ST)\tMulti Look Ground Range Detected (MGD) PSP_ST_GEC\tStaring Spotlight (ST)\tGeocoded Ellipsoid Corrected (GEC) PSP_ST_EEC\tStaring Spotlight (ST)\tEnhanced Ellipsoid Corrected (EEC) PSP_WS_SSC\tWide ScanSAR (WS)\tSingle Look Slant Range Complex (SSC) PSP_WS_MGD\tWide ScanSAR (WS)\tMulti Look Ground Range Detected (MGD) PSP_WS_GEC\tWide ScanSAR (WS)\tGeocoded Ellipsoid Corrected (GEC) PSP_WS_EEC\tWide ScanSAR (WS)\tEnhanced Ellipsoid Corrected (EEC)", "links": [ { diff --git a/datasets/PAZ.Full.Archive.and.New.Tasking_7.0.json b/datasets/PAZ.Full.Archive.and.New.Tasking_7.0.json index 67d6df98e4..50e40e87f2 100644 --- a/datasets/PAZ.Full.Archive.and.New.Tasking_7.0.json +++ b/datasets/PAZ.Full.Archive.and.New.Tasking_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PAZ.Full.Archive.and.New.Tasking_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PAZ Image Products can be acquired in 8 image modes with flexible resolutions (from 1 m to 40 m) and scene sizes. Thanks to different polarimetric combinations and processing levels the delivered imagery can be tailored specifically to meet the requirements of the application. Available modes are: \u2022 StripMap mode (SM) in single and dual polarisation: The ground swath is illuminated with a continuous train of pulses while the antenna beam is pointed to a fixed angle, both in elevation and in azimuth. \u2022 ScanSAR mode (SC) in single polarisation: the swath width is increased respecting to the StripMap mode, it is composed of four different sub-swaths, which are obtained by antenna steering in elevation direction. \u2022 Wide ScanSAR mode (WS), in single polarisation: the usage of six sub-swaths allows to obtain a higher swath coverage product. \u2022 Spotlight modes: in single and dual polarisation: Spotlight modes take advantage of the beam steering capability in the azimuth plane to illuminate for a longer time the area of interest: a sensible improvement of the azimuth resolution is achieved at the expense of a shorter scene size. Spotlight mode (SL) is designed to maximise the azimuth scene extension at the expense of the spatial resolution, and High Resolution Spotlight mode (HS) is designed to maximize the spatial resolutions at the expense of the scene extension. \u2022 Staring Spotlight mode (ST), in single polarisation: The virtual rotation point coincides with the center of the beam: the image length in the flight direction is constrained by the projection on- ground of the azimuth beamwidth and it leads to a target azimuth illumination time increment and to achieve the best azimuth resolution. There are two main classes of products: \u2022 Spatially Enhanced products (SE): designed with the target of maximize the spatial resolution in pixels with squared size, so the larger resolution value of azimuth or ground range determines the square pixel size, and the smaller resolution value is adjusted to this size and the corresponding reduction of the bandwidth is used for speckle reduction. \u2022 Radiometrically Enhanced products (RE): designed with the target of maximize the radiometry, so the range and azimuth resolutions are intentionally decreased to significantly reduce speckle by averaging several looks. The following geometric projections are offered: \u2022 Single Look Slant Range Complex (SSC): single look product of the focused radar signal: the pixels are spaced equidistant in azimuth and in slant range. No geocoding is available, no radiometric artifacts included. Product delivered in the DLR-defined binary COSAR format. The SSC product is intended for applications that require the full bandwidth and phase information, e.g. for SAR interferometry and polarimetry. \u2022 Multi Look Ground Range Detected (MGD): detected multi look product in GeoTiff format with reduced speckle and approximately square resolution cells on ground. The image coordinates are oriented along flight direction and along ground range; the pixel spacing is equidistant in azimuth and in ground range. A simple polynomial slant to ground projection is performed in range using a WGS84 ellipsoid and an average, constant terrain height parameter. No image rotation to a map coordinate system is performed and interpolation artifacts are thus avoided. \u2022 Geocoded Ellipsoid Corrected (GEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid assuming one average terrain height. No terrain correction performed. UTM is the standard projection, for polar regions UPS is applied. \u2022 Enhanced Ellipsoid Corrected (EEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid. The image distortions caused by varying terrain height are corrected using an external DEM; therefore the pixel localization in these products is highly accurate. UTM is the standard projection, for polar regions UPS is applied. StripMap Single Mode ID: SM-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 30 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2.99 - 3.52 at (45\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 6.53 - 7.65 at (45\u00b0 - 20\u00b0) - SSC[Slant range] 1.1 (150 MHz bandwidth) 1.7 (100 MHz bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.05 - MGD, GEC, EEC (RE) 6.53 - 7.60 at (45\u00b0 - 20\u00b0) - SSC 3.01 StripMap Dual Mode ID: SM-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 15 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 6 - MGD, GEC, EEC (RE)[Ground range] 7.51 - 10.43 at (45\u00b0 - 20\u00b0) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 6.11 - MGD, GEC, EEC (RE) 7.52 - 10.4 at (45\u00b0 - 20\u00b0) - SSC ScanSAR Mode ID: SC Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 100 x 150 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 16.79 - 18.19 at (45\u00b0 - 20\u00b0) - SSC[Slant range] 1.17 - 3.4 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 17.66 - 18.18 at (45\u00b0 - 20\u00b0) - SSC 18.5 Wide ScanSAR Mode ID: WS Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [273-196] x 208 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 35 - SSC[Slant range] 1.75 - 3.18 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 39 - SSC 38.27 Spotlight Single Mode ID: SL-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1.55 - 3.43 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 3.51 - 5.43 at (55\u00b0 - 20\u00b0) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1.56 - 2.9 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE) 3.51 - 5.4 at (55\u00b0 - 20\u00b0) - SSC 1.46 Spotlight Dual Mode ID: SL-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 3.09 - 3.5 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 4.98 - 7.63 at (55\u00b0 - 20\u00b0) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.53 - MGD, GEC, EEC (RE) 4.99 - 7.64 at (55\u00b0 - 20\u00b0) - SSC 3.1 HR Spotlight Single Mode ID: HS-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10-6 x 5 (depending on incident angle) Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1 - 1.76 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 2.83 - 3.11 at (55\u00b0 - 20\u00b0) - SSC[Slant range] 0.6 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1 - 1.49 at (55 \u00b0- 20\u00b0) - MGD, GEC, EEC (RE) 2.83 - 3.13 at (55\u00b0 - 20\u00b0) - SSC 1.05 HR Spotlight Dual Mode ID: HS-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 5 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2 - 3.5 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 4 - 6.2 at (55\u00b0 - 20\u00b0) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 2.38 - 2.93 at (55\u00b0 - 20\u00b0) - MGD, GEC, EEC (RE) 4 - 6.25 at (55\u00b0 - 20\u00b0) - SSC 2.16 Staring Spotlight Mode ID: ST Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [9-4.6] x [2.7-3.6] Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 0.96 - 1.78 at (45\u00b0- 20\u00b0) - MGD, GEC, EEC (RE)[Ground range] 0.97 - 1.78 at (45\u00b0-20\u00b0) - SSC[Slant range] 0.59 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 0.38 - 0.7 at (45\u00b0-20\u00b0) - MGD, GEC, EEC (RE) 0.97 - 1.42 at (45\u00b0-20\u00b0) - SSC 0.22 All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. For archive data, the user is invited to search PAZ products by using the USP (User Service Provider) web portal (http://www.geos.hisdesat.es/) (self registration required) in order to verify the availability over the Area of Interest in the Time of Interest.", "links": [ { diff --git a/datasets/PC06_ECMWF_LBA_1141_1.json b/datasets/PC06_ECMWF_LBA_1141_1.json index 47f131b6cf..c8d1006ba3 100644 --- a/datasets/PC06_ECMWF_LBA_1141_1.json +++ b/datasets/PC06_ECMWF_LBA_1141_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PC06_ECMWF_LBA_1141_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model.The model outputs were 12- to 36-hour short-range forecasts, run at a triangular truncation of T319 and a vertical resolution of 60 levels, from each daily 1200 (UTC) analysis. The version of the forecast model used to prepare this data product was the operational ECMWF model in fall 2000, which included the tiled land-surface scheme (TESSEL) (Van den Hurk et al., 2000) and recent revisions to the convection, radiation, and cloud schemes described by Gregory et al., (2000). The ECMWF model was run for two Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) campaigns conducted in Rondonia, Brazil, during January and February of 1999: the Wet Season Atmospheric Mesoscale Campaign (WETAMC) and the Tropical Rainfall Measuring Mission (TRMM). See Silva Dias et al.,(2002) for additional information regarding the WETAMAC and TRMM campaigns. There are two comma-delimited data files with this data set: the ECMWF model output data and a file containing the mean hourly precipitation observations used to check the model output for biases.", "links": [ { diff --git a/datasets/PCD_INPE_web.json b/datasets/PCD_INPE_web.json index 1bb25bf834..98e03f6c3c 100644 --- a/datasets/PCD_INPE_web.json +++ b/datasets/PCD_INPE_web.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PCD_INPE_web", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Web access to data of a network of Meteorological Automatic Stations covering\nthe Brazilian area", "links": [ { diff --git a/datasets/PEACETIME_0.json b/datasets/PEACETIME_0.json index 826c737e28..abfdec4c64 100644 --- a/datasets/PEACETIME_0.json +++ b/datasets/PEACETIME_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PEACETIME_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the PEACETIME (ProcEss studies at the Air-sEa Interface after dust deposition in the MEditerranean sea) project in the Mediterranean Sea to characterize biogeochemical processes in the atmosphere, at the air-sea boundary layer, and in the water.", "links": [ { diff --git a/datasets/PERU_0.json b/datasets/PERU_0.json index c9658c4dab..2539cd2b48 100644 --- a/datasets/PERU_0.json +++ b/datasets/PERU_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PERU_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the northern coast of Peru in 2003.", "links": [ { diff --git a/datasets/PET_PU_3H025_001.json b/datasets/PET_PU_3H025_001.json index fff50f7f73..650c569c2a 100644 --- a/datasets/PET_PU_3H025_001.json +++ b/datasets/PET_PU_3H025_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PET_PU_3H025_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Princeton University MEaSUREs Potential Evapotranspiration (PET) dataset provides a set of estimates of PET based on near surface meteorology and surface radiation data derived from a combination of reanalysis, satellite and gridded gauge data. The rationale of the project is to reduce the error from the input meteorological forcing and provide a variety of widely-used PET methods for different research and application purposes.\n\nPET is estimated using three methods: Penman open-water method (Penman), Priestley-Taylor method (PT), Reference crop evapotranspiration using the UN Food and Agricultural Organization approach (FAO). The Penman equation assumes PET occurs from an open water surface and calculates PET based on observations of surface net radiation, near-surface air temperature, wind speed, and specific humidity (Shuttleworth, 1993). The PT equation calculates PET based on surface net radiation and near-surface air temperature and does not account for the aerodynamic component (Priestley and Taylor, 1972). The FAO equation is a specific application of the Penman-Monteith equation for crop and short-grass reference surfaces and is based on surface net radiation, near-surface air temperature, wind speed, and specific humidity (Allen, 1998). \n\nThis first version of dataset is estimated at a 3 hourly temporal resolution and 0.25x0.25 degrees spatial resolution globally, spanning the 23-year period 1984-2006. Datasets are stored as a 3-dimensional array with dimension 720 x 1440 x 8 for each day, in NetCDF-4 format.", "links": [ { diff --git a/datasets/PET_PU_3H025_002.json b/datasets/PET_PU_3H025_002.json index d3ecd3d7b9..822ba866ab 100644 --- a/datasets/PET_PU_3H025_002.json +++ b/datasets/PET_PU_3H025_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PET_PU_3H025_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is version 2 of Princeton University MEaSUREs Potential Evapotranspiration (PET) dataset, which provides a set of estimates of PET based on near surface meteorology and surface radiation data derived from a combination of reanalysis, satellite and gridded gauge data. The rationale of the project is to reduce the error from the input meteorological forcing and provide a variety of widely-used PET methods for different research and application purposes.\n\nPET is estimated using three methods: Penman open-water method (Penman), Priestley-Taylor method (PT), Reference crop evapotranspiration using the UN Food and Agricultural Organization approach (FAO). The Penman equation assumes PET occurs from an open water surface and calculates PET based on observations of surface net radiation, near-surface air temperature, wind speed, and specific humidity (Shuttleworth, 1993). The PT equation calculates PET based on surface net radiation and near-surface air temperature and does not account for the aerodynamic component (Priestley and Taylor, 1972). The FAO equation is a specific application of the Penman-Monteith equation for crop and short-grass reference surfaces and is based on surface net radiation, near-surface air temperature, wind speed, and specific humidity (Allen, 1998). As a follow-on to PET_PU_3H025 Version 1, PET_PU_3H025 Version 2 seeks to continue to reduce the error from the input meteorological forcing and provide a variety of widely-used PET methods for different research and application purposes. The modifications include the addition of the FAO tall reference crop equation, an extension of the radiation data and temporal coverage, the usage of the GLASS satellite albedo to estimate upward short-wave radiation and the estimation of ground heat flux based on diurnal phase shift of net radiation.\n\nThis second version of the dataset is estimated at a 3 hourly temporal resolution and 0.25x0.25 degrees spatial resolution globally, spanning the 33-year period 1984-2016. Datasets are stored as a 3-dimensional array with dimension 720 x 1440 x 8 for each day, in NetCDF-4 format.\n\nThe DOI for version 1 of the data is: 10.5067/GPZDZYELYG1A.", "links": [ { diff --git a/datasets/PICOLO_0.json b/datasets/PICOLO_0.json index 732da49657..e9fe54b80c 100644 --- a/datasets/PICOLO_0.json +++ b/datasets/PICOLO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PICOLO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the PICOLO experiment in the Central Eastern Atlantic Ocean off the African Coast from 1997.", "links": [ { diff --git a/datasets/PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019_1.json b/datasets/PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019_1.json index 7028ebc7ce..eb58a1bbc4 100644 --- a/datasets/PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019_1.json +++ b/datasets/PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018-2019 autonomous platform ocean data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using Sounding Oceanographic Lagrangian Observer II (SOLO-II) instruments. Data collection is complete.\r\n\r\nThe PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises, deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. \r\n\r\nFor characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). \r\n\r\nThere are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 (this doi)\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001\r\n\r\nThe CAMP2Ex 2019 data DOI is:\r\nhttps://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001\r\nThe CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives.", "links": [ { diff --git a/datasets/PISTON-ONR-NOAA_Islands_2018-2019_1.json b/datasets/PISTON-ONR-NOAA_Islands_2018-2019_1.json index f694133360..3b66541931 100644 --- a/datasets/PISTON-ONR-NOAA_Islands_2018-2019_1.json +++ b/datasets/PISTON-ONR-NOAA_Islands_2018-2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PISTON-ONR-NOAA_Islands_2018-2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PISTON-ONR-NOAA_Islands_2018-2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018-2019 island rawinsonde data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data collection is complete.\r\n\r\nThe PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises, deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. \r\n\r\nFor characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). \r\n\r\nThere are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001 (this doi)\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001\r\n\r\nThe CAMP2Ex 2019 data DOI is:\r\nhttps://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001\r\nThe CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives.", "links": [ { diff --git a/datasets/PISTON-ONR-NOAA_RVMirai_2018_1.json b/datasets/PISTON-ONR-NOAA_RVMirai_2018_1.json index 9390d5355e..6f50c6e093 100644 --- a/datasets/PISTON-ONR-NOAA_RVMirai_2018_1.json +++ b/datasets/PISTON-ONR-NOAA_RVMirai_2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PISTON-ONR-NOAA_RVMirai_2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PISTON-ONR-NOAA_RVMirai_2018 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018 Research Vessel (RV) Mirai data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using multiple instruments on the RV Thompson platform including C-band radar and rawinsondes. Data collection is complete.\r\n\r\nThe PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises, deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai (this doi). The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. \r\n\r\nFor characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). \r\n\r\nThere are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001 (this doi)\r\n\r\nThe CAMP2Ex 2019 data DOI is:\r\nhttps://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001\r\nThe CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives.", "links": [ { diff --git a/datasets/PISTON-ONR-NOAA_RVSallyRide_2019_1.json b/datasets/PISTON-ONR-NOAA_RVSallyRide_2019_1.json index 957598f28c..b9e839596d 100644 --- a/datasets/PISTON-ONR-NOAA_RVSallyRide_2019_1.json +++ b/datasets/PISTON-ONR-NOAA_RVSallyRide_2019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PISTON-ONR-NOAA_RVSallyRide_2019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PISTON-ONR-NOAA_RVSallyRide_2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2019 Research Vessel (RV) Sally Ride data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using multiple instruments on the RV platform including: Conductivity, Temperature, Depth (CTD), Acoustic Doppler Current Profiler (ADCP), SEA-going POLarimetric Doppler Radar (SEA-POL), Chameleon Microstructure Profiler(CHAM), and SurfOtter Platform (SO). Data collection is complete.\r\n\r\nThe PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises (this doi), deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019 (this doi). The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. \r\n\r\nFor characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). \r\n\r\nThere are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001 (this doi)\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001\r\n\r\n\r\nThe CAMP2Ex 2019 data DOI is:\r\nhttps://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001\r\nThe CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives. \r\n", "links": [ { diff --git a/datasets/PISTON-ONR-NOAA_RVThompson_2018_1.json b/datasets/PISTON-ONR-NOAA_RVThompson_2018_1.json index 9ec7416e0d..af1a1a48db 100644 --- a/datasets/PISTON-ONR-NOAA_RVThompson_2018_1.json +++ b/datasets/PISTON-ONR-NOAA_RVThompson_2018_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PISTON-ONR-NOAA_RVThompson_2018_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PISTON-ONR-NOAA_RVThompson_2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018 Research Vessel (RV) Thompson data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using multiple instruments on the RV Thompson platform.\r\n\r\nThe PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises (this doi), deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 (this doi) and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. \r\n\r\nFor characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). \r\n\r\nThere are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 (this doi)\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 \r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001\r\nhttps://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001\r\n\r\n\r\nThe CAMP2Ex 2019 data DOI is:\r\nhttps://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001\r\nThe CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives. ", "links": [ { diff --git a/datasets/PM1ATTNR_NRT_6.1NRT.json b/datasets/PM1ATTNR_NRT_6.1NRT.json index b43c9ca07c..740653b18e 100644 --- a/datasets/PM1ATTNR_NRT_6.1NRT.json +++ b/datasets/PM1ATTNR_NRT_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PM1ATTNR_NRT_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PM1ATTNR is the Aqua Near Real Time (NRT) daily spacecraft attitude data file in native format. This is MODIS Ancillary Data. The data collection consists of PM1 Platform Attitude Data that has been preprocessed by ECS to an internal standard supported by the ECS SDP Toolkit. This data is typically used in determining the geolocation of earth remote sensing observations.The file name format is the following:PM1ATTNR_NRT.Ayyyyddd.hhmm.vvv where from left to right: PM1 = PM1 (Aqua); ATT = spacecraft attitude; N = Native format; R = Refined; A = Acquisition; yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID.", "links": [ { diff --git a/datasets/PM1EPHND_NRT_6.1NRT.json b/datasets/PM1EPHND_NRT_6.1NRT.json index 67c47d5e82..b65689aabd 100644 --- a/datasets/PM1EPHND_NRT_6.1NRT.json +++ b/datasets/PM1EPHND_NRT_6.1NRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PM1EPHND_NRT_6.1NRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PM1EPHND is the Aqua Near Real Time (NRT) daily spacecraft definitive ephemeris data file in native format. This is MODIS Ancillary Data. The data collection consists of PM1 Platform Attitude Data that has been preprocessed by ECS to an internal standard supported by the ECS SDP Toolkit. This data is typically used in determining the geolocation of earth remote sensing observations.The file name format is the following:\n\nPM1EPHND_NRT.Ayyyyddd.hhmm.vvv \n\nwhere from left to right:\nPM1 = PM1 (Aqua);\nEPH = Spacecraft Ephemeris; \nN = Native format; \nD = Definitive; \nA = Acquisition; \nyyyy = data year, \nddd = Julian data day, \nhh = data hour, \nmm = data minute; \nvvv = Version ID.", "links": [ { diff --git a/datasets/PMNS_0.json b/datasets/PMNS_0.json index 3876204748..3f60335a94 100644 --- a/datasets/PMNS_0.json +++ b/datasets/PMNS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PMNS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made during 1993 and 1994 in the North Sea as part of Phytoplankton Monitoring in the North Sea (PMNS).", "links": [ { diff --git a/datasets/PMRN6L1RAD_CDROM_001.json b/datasets/PMRN6L1RAD_CDROM_001.json index 24a99ccf63..4b6660dc15 100644 --- a/datasets/PMRN6L1RAD_CDROM_001.json +++ b/datasets/PMRN6L1RAD_CDROM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PMRN6L1RAD_CDROM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PMRN6L1RAD_CDROM is the gridded Nimbus-6 Pressure Modulated Radiometer (PMR) Level 1 Radiance Data Product. The radiances are measured at CO2 lines in the 15 micron band. The purpose of the PMR experiment is to measure the temperature of the upper stratosphere and mesosphere from 40 to 90 km with a vertical resolution of about 10 km, and 500 km horizontal resolution. This product contains radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as copies of the original tapes. The data for this product are available from 16 June 1975 to 24 June 1978. The principal investigator for the PMR experiment was Dr. John T. Houghton from Oxford University.\n\nThis product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on two CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the gridded radiance data and a few original tape data files, the second CD-ROM contains the remaining compressed copies of the original data tapes. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.", "links": [ { diff --git a/datasets/PM_Bibliography_1.json b/datasets/PM_Bibliography_1.json index 8a1bfa7071..1a31d485e9 100644 --- a/datasets/PM_Bibliography_1.json +++ b/datasets/PM_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PM_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of papers on microrganisms from polar areas. Publication dates of papers in the collection range from 1847 to 2002. The bibliography was compiled by Dr David Wynn Williams of the British Antarctic Survey (BAS). Dr Williams is now deceased.", "links": [ { diff --git a/datasets/POAM2_VER6_1.json b/datasets/POAM2_VER6_1.json index 7094101188..c075daf2bb 100644 --- a/datasets/POAM2_VER6_1.json +++ b/datasets/POAM2_VER6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POAM2_VER6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POAM2_VER6 data are Polar Ozone and Aerosol Measurement II Version 6.0 data. The Polar Ozone and Aerosol Measurement (POAM) II instrument measures the vertical distribution of atmospheric ozone, nitrogen dioxide, and aerosol extinction. The instrument was developed by the Naval Research Laboratory (NRL).POAM II measures solar extinction in nine narrow band channels, covering the spectral range from approximately 350 to 1060 nm. Solar extinction by the atmosphere is measured using the solar occultation technique; the sun is observed through the Earth's atmosphere as it rises and sets as viewed from the satellite.POAM II was launched aboard the French SPOT-3 satellite on 26 September, 1993 into a Sun synchronous polar orbit. As seen from the satellite, the Sun rises in the north polar region and sets in the south polar region 14.2 times per day. Sunrise measurements are made in a latitude band from 55-71 degrees north while sunsets occur between 63-88 degrees south. The POAM II mission was interrupted by the failure of the SPOT-3 satellite in November of 1996.Each data granule contains one month of data for a particular hemisphere, taken at approximately 101 minute intervals. The latitudinal extent of data for the northern hemisphere is 54.68 to 71.01 and -62.55 to -88.11 for the southern hemisphere. The longitude ranges from 0 to 360. Several images in 'gif' format are also available. The images represent daily average ozone (altitude .vs. time) for the northern and southern hemispheres, daily average aerosol extinction for the southern hemisphere and latitudinal limits of data samples are also provided. The data consists of profiles of ozone and nitrogen dioxide (NO2) concentration and aerosol extinction at 1.06um.", "links": [ { diff --git a/datasets/POAM3_1.json b/datasets/POAM3_1.json index 30f98de1d2..401b5c4f66 100644 --- a/datasets/POAM3_1.json +++ b/datasets/POAM3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POAM3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POAM3 data are Polar Ozone and Aerosol Measurement III Version 3.0. The Polar Ozone and Aerosol Measurement (POAM) III instrument measures the vertical distribution of atmospheric ozone, water vapor, nitrogen dioxide, and aerosol extinction. The instrument was developed by the Naval Research Laboratory (NRL). POAM III was launched aboard the French SPOT-4 satellite in March 1998 into a Sun synchronous polar orbit.The POAM III instrument was developed by the Naval Research Laboratory (NRL) to measure the vertical distribution of atmospheric ozone, water vapor, nitrogen dioxide, aerosol extinction, and temperature. POAM III measures solar extinction in nine narrow band channels, covering the spectral range from 354 to 1018 nm. Solar extinction by the atmosphere is measured using the solar occultation technique; the sun is observed through the Earth's atmosphere as it rises and sets, as viewed from the satellite.POAM III was launched aboard the French SPOT-4 satellite in March 1998 into a Sun synchronous polar orbit. As seen from the satellite, the Sun rises in the north polar region and sets in the south polar region 14.2 times per day. Sunrise measurements are made in a latitude band from 55-71 degrees north while sunsets occur between 63-88 degrees south.Each data granule contains one month of data for a particular hemisphere, taken at approximately 101 minute intervals. The latitudinal extent of data for the northern hemisphere is 54.68 to 71.01 and -62.55 to -88.11 for the southern hemisphere. The longitude ranges from 0 to 360. The data consists of profiles of ozone, nitrogen dioxide (NO2), and water vapor concentration, and aerosol extinction at 442 nm and 1018 nm.", "links": [ { diff --git a/datasets/POLARIS_Aerosol_AircraftInSitu_ER2_Data_1.json b/datasets/POLARIS_Aerosol_AircraftInSitu_ER2_Data_1.json index a7fc8c1da1..3157cd6585 100644 --- a/datasets/POLARIS_Aerosol_AircraftInSitu_ER2_Data_1.json +++ b/datasets/POLARIS_Aerosol_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Aerosol_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_Aerosol_AircraftInSitu_ER2_Data is the in-situ trace aerosol data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Multiple-Angle Aerosol Spectrometer (MASP), Wing Tip Air Particulate Sampler (APS), Condensation Nuclei Counter (CNC), and the Focused Cavity Aerosol Spectrometer (FCAS) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_Analysis_ER2_Data_1.json b/datasets/POLARIS_Analysis_ER2_Data_1.json index 282c8d1d64..ba9e4aa551 100644 --- a/datasets/POLARIS_Analysis_ER2_Data_1.json +++ b/datasets/POLARIS_Analysis_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Analysis_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_Analysis_ER2_Data is the modeled trajectories and meteorological data along the flight path for the ER-2 aircraft collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_Ground_Data_1.json b/datasets/POLARIS_Ground_Data_1.json index dafdad4359..532324d0c6 100644 --- a/datasets/POLARIS_Ground_Data_1.json +++ b/datasets/POLARIS_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_Ground_Data is the ground site data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Composition and Photo-Dissociative Flux Measurement (CPFM) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_MetNav_AircraftInSitu_ER2_Data_1.json b/datasets/POLARIS_MetNav_AircraftInSitu_ER2_Data_1.json index d2dd7af5f6..13a7be285e 100644 --- a/datasets/POLARIS_MetNav_AircraftInSitu_ER2_Data_1.json +++ b/datasets/POLARIS_MetNav_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_MetNav_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_MetNav_AircraftInSitu_ER2_Data is the in-situ meteorological and navigational data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Meteorological Measurement System (MMS), ER-2 Nav Recorder (NavRec), Microwave Temperature Profiler (MTP), JPL Laser Hygrometer (JLH), and the Composition and Photo-Dissociative Flux Measurement (CPFM) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_Model_Data_1.json b/datasets/POLARIS_Model_Data_1.json index b218afac46..a10673b675 100644 --- a/datasets/POLARIS_Model_Data_1.json +++ b/datasets/POLARIS_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_Model_Data is the model data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_Satellite_Data_1.json b/datasets/POLARIS_Satellite_Data_1.json index fc7c1b5de9..c9ac3b30f4 100644 --- a/datasets/POLARIS_Satellite_Data_1.json +++ b/datasets/POLARIS_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_Satellite_Data is the supplementary satellite data for the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite 9 (GOES-9), and the Geostationary Meteorological Satellite 5 (GMS-5) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_Sondes_Data_1.json b/datasets/POLARIS_Sondes_Data_1.json index 0028dd76ad..c032c8f7ea 100644 --- a/datasets/POLARIS_Sondes_Data_1.json +++ b/datasets/POLARIS_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_MetNav_AircraftInSitu_ER2_Data is the balloonsonde and ozonesonde data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_TraceGas_AircraftInSitu_ER2_Data_1.json b/datasets/POLARIS_TraceGas_AircraftInSitu_ER2_Data_1.json index 77c5ee72c5..ba972b0c3c 100644 --- a/datasets/POLARIS_TraceGas_AircraftInSitu_ER2_Data_1.json +++ b/datasets/POLARIS_TraceGas_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_TraceGas_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_TraceGas_AircraftInSitu_ER2_Data is the in-situ trace gas data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the High-Sensitivity Fast-Response CO2 Analyzer (Harvard CO2), Advanced Whole Air Sampler (AWAS), Airborne Chromatograph for Atmospheric Trace Species (ACATS), NOAA NOy instrument, Harvard Hydroxyl Experiment (HOx), Airborne Tunable Laser Absorption Spectrometer (ATLAS), Chlorine Nitrate Instrument (ClONO2), NOAA O3 Classic instrument, Submillimeter Limb Sounder (SLS), and the Aircraft Laser Infrared Absorption Spectrometer (ALIAS) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLARIS_jValue_AircraftInSitu_ER2_Data_1.json b/datasets/POLARIS_jValue_AircraftInSitu_ER2_Data_1.json index 55844f81fd..9091c7aba3 100644 --- a/datasets/POLARIS_jValue_AircraftInSitu_ER2_Data_1.json +++ b/datasets/POLARIS_jValue_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLARIS_jValue_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "POLARIS_jValue_AircraftInSitu_ER2_Data is the photolysis frequencies (j-values) collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the the Composition and Photo-Dissociative Flux Measurement (CPFM) is featured in this collection. Data collection for this product is complete.\r\n\r\nThe POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.", "links": [ { diff --git a/datasets/POLYNYA_ship_1.json b/datasets/POLYNYA_ship_1.json index 0b614d94ce..b2f70f9f11 100644 --- a/datasets/POLYNYA_ship_1.json +++ b/datasets/POLYNYA_ship_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POLYNYA_ship_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the vicinity of the Mertz Polynya, encompassing 2 consecutive seasonal cycles from 1998 to 2000. In the southern winter of 1999, a total of 92 CTD/LADCP vertical profile stations were taken, most to within 20 m of the bottom, with 3 laps completed around the boundary of a box adjacent to the Mertz Glacier. Over 700 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, oxygen 18, dimethyl sulphide, and biological parameters, using a 12 bottle rosette sampler mounted on a 24 bottle frame. Additional CTD vertical profiles were taken in April 1998, July 1998 and February 2000. Near surface current data were collected on all cruises using ship mounted ADCP. Two mooring arrays comprising thermosalinographs, current meters and upward looking sonars were deployed in the region of the Polynya. The first array of 7 moorings was deployed in April 1998. The second array of 4 moorings was deployed in the winter of 1999. All 11 Polynya moorings were recovered in February 2000. A summary of all data and data quality is presented in the data report.\n\nThis work was completed as part of ASAC projects 2223 and 189.", "links": [ { diff --git a/datasets/POMME_0.json b/datasets/POMME_0.json index 07247e250c..125dba571e 100644 --- a/datasets/POMME_0.json +++ b/datasets/POMME_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POMME_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made during the Programme Ocean Multidisciplinaire Meso-Echelle (POMME) or Multidisciplinary middle-level ocean program in 2001.", "links": [ { diff --git a/datasets/POSTER-03CYCLONE_Not Applicable.json b/datasets/POSTER-03CYCLONE_Not Applicable.json index 310cdac97c..cc85ee360f 100644 --- a/datasets/POSTER-03CYCLONE_Not Applicable.json +++ b/datasets/POSTER-03CYCLONE_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POSTER-03CYCLONE_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36\"x 27\".", "links": [ { diff --git a/datasets/POSTER-2004 Hurricanes_Not Applicable.json b/datasets/POSTER-2004 Hurricanes_Not Applicable.json index a099fde82f..cd8e938dae 100644 --- a/datasets/POSTER-2004 Hurricanes_Not Applicable.json +++ b/datasets/POSTER-2004 Hurricanes_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POSTER-2004 Hurricanes_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34\"x27\".", "links": [ { diff --git a/datasets/POSTER-2005 Atl Hurricanes_Not Applicable.json b/datasets/POSTER-2005 Atl Hurricanes_Not Applicable.json index 16d792cb27..f9f5ed3648 100644 --- a/datasets/POSTER-2005 Atl Hurricanes_Not Applicable.json +++ b/datasets/POSTER-2005 Atl Hurricanes_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POSTER-2005 Atl Hurricanes_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35\"x30\".", "links": [ { diff --git a/datasets/POSTER-2005 Sig Hurricanes_Not Applicable.json b/datasets/POSTER-2005 Sig Hurricanes_Not Applicable.json index 6d797e23f3..8f17dec862 100644 --- a/datasets/POSTER-2005 Sig Hurricanes_Not Applicable.json +++ b/datasets/POSTER-2005 Sig Hurricanes_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "POSTER-2005 Sig Hurricanes_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36\"x32\".", "links": [ { diff --git a/datasets/PRECIP_AMSR2_GCOMW1_1.json b/datasets/PRECIP_AMSR2_GCOMW1_1.json index 94376b1d29..8feb5a551c 100644 --- a/datasets/PRECIP_AMSR2_GCOMW1_1.json +++ b/datasets/PRECIP_AMSR2_GCOMW1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_AMSR2_GCOMW1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) flown on the Global Climate Observing Mission-Water 1 (GCOM-W1). Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2012 to 2020 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_AMSRE_AQUA_1.json b/datasets/PRECIP_AMSRE_AQUA_1.json index 85ed3a307a..8211368b80 100644 --- a/datasets/PRECIP_AMSRE_AQUA_1.json +++ b/datasets/PRECIP_AMSRE_AQUA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_AMSRE_AQUA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-E (AMSR-E) flown on the AQUA satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2002 to 2011 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_GMI_GPM_1.json b/datasets/PRECIP_GMI_GPM_1.json index 82909a8a26..55c68320bb 100644 --- a/datasets/PRECIP_GMI_GPM_1.json +++ b/datasets/PRECIP_GMI_GPM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_GMI_GPM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) flown on the GPM satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2014 to 2020 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SMMR_NIMBUS7_1.json b/datasets/PRECIP_SMMR_NIMBUS7_1.json index 1f811e6c84..b46820d484 100644 --- a/datasets/PRECIP_SMMR_NIMBUS7_1.json +++ b/datasets/PRECIP_SMMR_NIMBUS7_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SMMR_NIMBUS7_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Scanning Multichannel Microwave Radiometer (SMMR) flown on the Nimbus-7 satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1979 to 1987 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMIS_F16_1.json b/datasets/PRECIP_SSMIS_F16_1.json index 98177efed9..784763a631 100644 --- a/datasets/PRECIP_SSMIS_F16_1.json +++ b/datasets/PRECIP_SSMIS_F16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMIS_F16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F16 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2005 to 2020 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMIS_F17_1.json b/datasets/PRECIP_SSMIS_F17_1.json index d7c111184b..f7d958ecb5 100644 --- a/datasets/PRECIP_SSMIS_F17_1.json +++ b/datasets/PRECIP_SSMIS_F17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMIS_F17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F17 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2008 to 2020 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMIS_F18_1.json b/datasets/PRECIP_SSMIS_F18_1.json index a0ba86222e..133ca86b95 100644 --- a/datasets/PRECIP_SSMIS_F18_1.json +++ b/datasets/PRECIP_SSMIS_F18_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMIS_F18_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F18 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2010 to 2020 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMIS_F19_1.json b/datasets/PRECIP_SSMIS_F19_1.json index 0043198e96..a95c0e77eb 100644 --- a/datasets/PRECIP_SSMIS_F19_1.json +++ b/datasets/PRECIP_SSMIS_F19_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMIS_F19_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F19 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2014 to 2016 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F08_1.json b/datasets/PRECIP_SSMI_F08_1.json index fffb14ee2e..ddcdb73c58 100644 --- a/datasets/PRECIP_SSMI_F08_1.json +++ b/datasets/PRECIP_SSMI_F08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F08 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1987 to 1991 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F10_1.json b/datasets/PRECIP_SSMI_F10_1.json index 432e2ce9fd..9248497af9 100644 --- a/datasets/PRECIP_SSMI_F10_1.json +++ b/datasets/PRECIP_SSMI_F10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F10 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1990 to 1997 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F11_1.json b/datasets/PRECIP_SSMI_F11_1.json index 5b2570ef31..e8a939fba2 100644 --- a/datasets/PRECIP_SSMI_F11_1.json +++ b/datasets/PRECIP_SSMI_F11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F11 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1991 to 2000 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F13_1.json b/datasets/PRECIP_SSMI_F13_1.json index 28132434cf..7574217df5 100644 --- a/datasets/PRECIP_SSMI_F13_1.json +++ b/datasets/PRECIP_SSMI_F13_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F13_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F13 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1995 to 2009 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F14_1.json b/datasets/PRECIP_SSMI_F14_1.json index 2d492178d3..b2b7dc386d 100644 --- a/datasets/PRECIP_SSMI_F14_1.json +++ b/datasets/PRECIP_SSMI_F14_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F14_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F14 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1997 to 2008 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_SSMI_F15_1.json b/datasets/PRECIP_SSMI_F15_1.json index 6339f32424..d4c57d1ea3 100644 --- a/datasets/PRECIP_SSMI_F15_1.json +++ b/datasets/PRECIP_SSMI_F15_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_SSMI_F15_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave/Imager (SSM/I) flown on the US Defense Meteorological Satellite Program (DMSP) F15 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2000 to 2006 with one file per orbit.", "links": [ { diff --git a/datasets/PRECIP_TMI_TRMM_1.json b/datasets/PRECIP_TMI_TRMM_1.json index 250a42589e..876c16a2b2 100644 --- a/datasets/PRECIP_TMI_TRMM_1.json +++ b/datasets/PRECIP_TMI_TRMM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRECIP_TMI_TRMM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) flown on the TRMM satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 1997 to 2015 with one file per orbit.", "links": [ { diff --git a/datasets/PREFIRE_SAT1_0-BUS-TLM_R01.json b/datasets/PREFIRE_SAT1_0-BUS-TLM_R01.json index 6ca5a3ae2f..7030df230f 100644 --- a/datasets/PREFIRE_SAT1_0-BUS-TLM_R01.json +++ b/datasets/PREFIRE_SAT1_0-BUS-TLM_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT1_0-BUS-TLM_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Telemetry (PREFIRE_SAT1_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\n\nThis collection contains the time, beta angle, orbit position and velocity, and quaternion of PREFIRE Satellite 1 (PREFIRE-SAT1). Combined with a Digital Elevation Map, these telemeters are used to geolocate PREFIRE data on the Earth\u2019s surface.\n\nData retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data is retrieved at a frequency of 1Hz and is available in CSV format.\n\nPositioning and pointing information for the sister satellite, PREFIRE-SAT2, can be found in the PREFIRE_SAT2_0-BUS-TLM collection.", "links": [ { diff --git a/datasets/PREFIRE_SAT1_0-PAYLOAD-TLM_R01.json b/datasets/PREFIRE_SAT1_0-PAYLOAD-TLM_R01.json index d49a7869b7..c80135fdd1 100644 --- a/datasets/PREFIRE_SAT1_0-PAYLOAD-TLM_R01.json +++ b/datasets/PREFIRE_SAT1_0-PAYLOAD-TLM_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT1_0-PAYLOAD-TLM_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Curated Payload (PREFIRE_SAT1_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\r\n\r\nThis collection contains the raw, curated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 1 (PREFIRE-SAT1). Data retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format.\r\n\r\nThe PREFIRE_SAT2_0-PAYLOAD-TLM collection contains raw, curated digital number counts for the sister instrument aboard PREFIRE-SAT2.\r\n", "links": [ { diff --git a/datasets/PREFIRE_SAT1_0-PAYLOAD_R01.json b/datasets/PREFIRE_SAT1_0-PAYLOAD_R01.json index efcf71ed26..54435fe636 100644 --- a/datasets/PREFIRE_SAT1_0-PAYLOAD_R01.json +++ b/datasets/PREFIRE_SAT1_0-PAYLOAD_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT1_0-PAYLOAD_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Payload (PREFIRE_SAT1_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\r\n\r\nThis collection contains the raw and uncurated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 1 (PREFIRE-SAT1). Data retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format.\r\n\r\nRaw, uncurated digital number counts for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_0-PAYLOAD collection.", "links": [ { diff --git a/datasets/PREFIRE_SAT2_0-BUS-TLM_R01.json b/datasets/PREFIRE_SAT2_0-BUS-TLM_R01.json index 85c391cba8..7f96dfc002 100644 --- a/datasets/PREFIRE_SAT2_0-BUS-TLM_R01.json +++ b/datasets/PREFIRE_SAT2_0-BUS-TLM_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT2_0-BUS-TLM_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Telemetry (PREFIRE_SAT2_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\r\n\r\nThis collection contains the time, beta angle, orbit position and velocity, and the quaternion of PREFIRE Satellite 2 (PREFIRE-SAT2). Combined with a Digital Elevation Map, these telemeters geolocate PREFIRE data on the Earth\u2019s surface. \r\n\r\nData retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data is retrieved at a frequency of 1Hz and is available in CSV format.\r\n\r\nPositioning and pointing information for the sister satellite, PREFIRE-SAT1, can be found in the PREFIRE_SAT1_0-BUS-TLM collection.\r\n\r\n", "links": [ { diff --git a/datasets/PREFIRE_SAT2_0-PAYLOAD-TLM_R01.json b/datasets/PREFIRE_SAT2_0-PAYLOAD-TLM_R01.json index ad8f5eaf08..a4178e19ef 100644 --- a/datasets/PREFIRE_SAT2_0-PAYLOAD-TLM_R01.json +++ b/datasets/PREFIRE_SAT2_0-PAYLOAD-TLM_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT2_0-PAYLOAD-TLM_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Curated Radiance (PREFIRE_SAT2_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\r\n\r\nThis collection contains the raw, curated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 2 (PREFIRE-SAT2). Data retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format.\r\n\r\nRaw, curated digital number counts for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_0-PAYLOAD-TLM collection.\r\n", "links": [ { diff --git a/datasets/PREFIRE_SAT2_0-PAYLOAD_R01.json b/datasets/PREFIRE_SAT2_0-PAYLOAD_R01.json index 17436ed3a8..462d196a2e 100644 --- a/datasets/PREFIRE_SAT2_0-PAYLOAD_R01.json +++ b/datasets/PREFIRE_SAT2_0-PAYLOAD_R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREFIRE_SAT2_0-PAYLOAD_R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Payload (PREFIRE_SAT2_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 \u00b5m. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.\r\n\r\nThis collection contains the raw and uncurated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 2 (PREFIRE-SAT2). Data retrieval started in May TBD, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format.\r\n\r\nRaw, uncurated digital number counts for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_0-PAYLOAD collection.", "links": [ { diff --git a/datasets/PREPP_0.json b/datasets/PREPP_0.json index c18529bef1..965c74df54 100644 --- a/datasets/PREPP_0.json +++ b/datasets/PREPP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PREPP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Pearl River Estuary Pollution Project (PREPP) near Hong Kong in 2001.", "links": [ { diff --git a/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json b/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json index b72cc675e8..670f5ecdc6 100644 --- a/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json +++ b/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS-1, ERS-2, TOPEX/Poseidon OSTM/Jason-2 and Envisat that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.", "links": [ { diff --git a/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json b/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json index fb1b8ec934..e623f6b32d 100644 --- a/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json +++ b/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global River Radar Altimeter Time Series (GRRATS) are simulated river gauge data that are derived from ERS-1, ERS-2, TOPEX/Poseidon OSTM/Jason-2, Jason-3 and Envisat altimetric measurements. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.If you are looking for version 1 it can be found at https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V1 however this version should be used with caution. Version 2, this page, is the most recent version with the most accurate algorithms used for producing river heights.", "links": [ { diff --git a/datasets/PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2.json b/datasets/PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2.json index 3387b43c36..6834a54e03 100644 --- a/datasets/PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2.json +++ b/datasets/PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Lake/Reservoir Surface Inland Water Height Time Series is derived from the G-REALM10 lake level product https://ipad.fas.usda.gov/cropexplorer/global_reservoir/ The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. Water level variation is also a key component required for the determination of surface water storages and fluxes. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.", "links": [ { diff --git a/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json b/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json index fe6e4cf365..8133c0e3d6 100644 --- a/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json +++ b/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Lake/Reservoir Surface Inland Water Extent Mask Time Series are derived from the MODIS instruments. The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs throughout the globe, with a base temporal resolution of 8 days and a spatial resolution of 500 meters. With the exception of periods of low-quality input data, these time series will extend across the lifespan of the MODIS multispectral reflectance products, from roughly 2000 to present. These time series will allow us to satisfy the project goal to produce ESDRs of suitable quality to support long-term trend analysis and global water dynamics models for the longest length possible (in most cases, about 20 years, the length of the altimetry record) of key measures of surface water storages and fluxes. This product should be accessible and of direct use to both water managers and the scientific community worldwide, and will allow for improved assessment and modeling of human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.", "links": [ { diff --git a/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json b/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json index 76dc0d132c..ab9cc71219 100644 --- a/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json +++ b/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products. The purpose of this dataset is to provide surface water storage estimates for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.", "links": [ { diff --git a/datasets/PRIM_SMAP_L2_V1_1.0.json b/datasets/PRIM_SMAP_L2_V1_1.0.json index f30d18731f..4d01030b42 100644 --- a/datasets/PRIM_SMAP_L2_V1_1.0.json +++ b/datasets/PRIM_SMAP_L2_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRIM_SMAP_L2_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the PI-produced SMAP sea water salinity, level 2 v1.0 orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Parameterized Rain Impact Model (PRIM) developed at the University of Central Florida (UCF) Central Florida Remote Sensing Lab (CFRSL), Orlando, FL; University of Washington (UW) Applied Physics Lab (APL), Seattle, WA.

\r\nThe PRIM product range extended from March 31, 2015 to September 30, 2021. It includes data for a range of parameters: derived SMAP sea water salinity at surface, 1m depth and 5m depth, and probability of salinity stratification (PSS), rainfall rate and wind speed data. Each data file covers one 98-minute orbit (15 files per day), and corresponds to a JPL SMAP Level 2B CAP Sea Surface Salinity V5.0 file which corresponds to a single orbit on a given day.

\r\nThe SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.", "links": [ { diff --git a/datasets/PRISM_CORAL_L1_1.0.json b/datasets/PRISM_CORAL_L1_1.0.json index e08db3992a..d2a7fcd4ef 100644 --- a/datasets/PRISM_CORAL_L1_1.0.json +++ b/datasets/PRISM_CORAL_L1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRISM_CORAL_L1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flight line reflectance measurements from the Portable Remote Imaging Spectrometer (PRISM) instrument aboard the Tempus Applied Solutions Gulfstream-IV (G-IV) aircraft, taken as part of the NASA COral Reef Airborne Laboratory (CORAL) Earth Venture Suborbital-2 (EVS-2) mission designed to provide an extensive, uniform picture of coral reef composition. The CORAL mission surveyed parts of the reefs surrounding the Mariana Islands, Palau, portions of the Great Barrier Reef, the main Hawaiian Islands, and the Florida Keys.", "links": [ { diff --git a/datasets/PRISM_CORAL_L2_1.0.json b/datasets/PRISM_CORAL_L2_1.0.json index cf27e87622..6b25185e4c 100644 --- a/datasets/PRISM_CORAL_L2_1.0.json +++ b/datasets/PRISM_CORAL_L2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRISM_CORAL_L2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flight line benthic cover measurements from the Portable Remote Imaging Spectrometer (PRISM) instrument aboard the Tempus Applied Solutions Gulfstream-IV (G-IV) aircraft, taken as part of the NASA COral Reef Airborne Laboratory (CORAL) Earth Venture Suborbital-2 (EVS-2) mission designed to provide an extensive, uniform picture of coral reef composition. The CORAL mission surveyed parts of the reefs surrounding the Mariana Islands, Palau, portions of the Great Barrier Reef, the main Hawaiian Islands, and the Florida Keys.", "links": [ { diff --git a/datasets/PRISM_Elkhorn_Slough_0.json b/datasets/PRISM_Elkhorn_Slough_0.json index 349dc10372..75e9bd7604 100644 --- a/datasets/PRISM_Elkhorn_Slough_0.json +++ b/datasets/PRISM_Elkhorn_Slough_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRISM_Elkhorn_Slough_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The estuarine waters of Elkhorn Slough terminating in Monterey Bay, California present an excellent study site for testing the limits of hyperspectral imaging spectroscopy in a region with turbid sediment-laden waters and diverse coastal habitats including eelgrass and salt marsh. In July 2012, we undertook a field validation in this region of the Portable Remote Imaging SpectroMeter (PRISM), a new imaging sensor package optimized for coastal ocean processes. PRISM provides spatial resolutions up to 30 cm and spectral resolutions of 3 nm. In-situ sampling was conducted concurrent to the PRISM flights to measure inherent optical properties of the water column and sample selected benthic and coastal habitat spectral targets.", "links": [ { diff --git a/datasets/PROBA.CHRIS.1A_7.0.json b/datasets/PROBA.CHRIS.1A_7.0.json index de8304d36f..be2f413af7 100644 --- a/datasets/PROBA.CHRIS.1A_7.0.json +++ b/datasets/PROBA.CHRIS.1A_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PROBA.CHRIS.1A_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CHRIS acquires a set of up to five images of each target during each acquisition sequence, these images are acquired when Proba-1 is pointing at distinct angles with respect to the target. CHRIS Level 1A products (supplied in HDF data files, version 4.1r3) include five formal CHRIS imaging modes, classified as modes 1 to 5: \u2022 MODE 1: Full swath width, 62 spectral bands, 773nm / 1036nm, nadir ground sampling distance 34m @ 556km \u2022 MODE 2 WATER BANDS: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 3 LAND CHANNELS: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 4 CHLOROPHYL BAND SET: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 5 LAND CHANNELS: Half swath width, 37 spectral bands, nadir ground sampling distance 17m @ 556km All Proba-1 passes are systematically acquired according to the current acquisition plan, CHRIS data are processed every day to Level 1A and made available to ESA users. Observation over a new specific area can be performed by submitting the request to add a new site to the acquisition plan. Valuable indication whether the acquisition was successfully, cloudy, failed or programmed is reported in the _$$Proba-CHRIS Actual Acquisitions$$ http://www.rsacl.co.uk/chris/excel/active/", "links": [ { diff --git a/datasets/PROBA.HRC.1A_6.0.json b/datasets/PROBA.HRC.1A_6.0.json index 457e77bd64..b016d1bddd 100644 --- a/datasets/PROBA.HRC.1A_6.0.json +++ b/datasets/PROBA.HRC.1A_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PROBA.HRC.1A_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HRC Level 1A product is an image images with a pixel resolution of 8m. The data are grey scale images, an image contains 1026 x 1026 pixels and covers an area of 25 km2. HRC data is supplied in BMP format. All Proba-1 passes are systematically acquired according to the current acquisition plan, HRC data are processed every day to Level 1A and made available to ESA users.", "links": [ { diff --git a/datasets/PRONEX_0.json b/datasets/PRONEX_0.json index 02b6b3e77c..f63367ef8c 100644 --- a/datasets/PRONEX_0.json +++ b/datasets/PRONEX_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PRONEX_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the coast of southern Brazil from 2005 to 2007 under PRONEX.", "links": [ { diff --git a/datasets/PSD1-Nottingham_1.json b/datasets/PSD1-Nottingham_1.json index 85266bcac4..18f06cbc84 100644 --- a/datasets/PSD1-Nottingham_1.json +++ b/datasets/PSD1-Nottingham_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PSD1-Nottingham_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctica is a desert continent dominated by micro-organisms. The seals and penguins, which are conspicuous around its margins, depend upon the sea for their food resources and are effectively part of the marine food chain. Life depends upon the availability of free water. In Antarctica water is usually locked up in ice, only in summer is there free water in the terrestrial environment. Not only is water limited, but low temperatures and low levels of nutrients severely limit the scope for growth among the micro-organisms that have managed to colonise the continent. Propagules are brought to the continent in a number of ways. Some arrive in the air masses that flow around the Earth. Once deposited some simply cannot survive the extreme conditions, while others may become established. Other species of micro-organism may be introduced by Man around the scientific stations on the continent. During the ice-ages which have occurred repeatedly through geological history, micro-organisms may have survived in refugia offered by nunataks or in the ice, and have recolonised more widely following ice retreat.\n\nThis project will concentrate on one group of micro-organisms, the fungi. We will use special air samplers to determine which species are brought to the continent in the winds and we will compare the propagules from these samplers with the species living in the 'soil' and samples grown up from ice samples, where the resting spores can remain dormant of hundreds of years. We will analyse the communities of fungi that are found in the proximity of scientific stations and compare them with 'natural' communities in Antarctica, to determine what impact Man has had on introducing fungal species. The data generated will provide us with an insight into the colonisation of Antarctica by fungi. As global warming continues, species hitherto unable to establish may be able to do so. It is important to have a baseline on what is currently living on the continent, so that we can monitor the establishment of new species in the future.\n\nThalli of the lichens Buellia frigida and Xanthoria elegans were collected from five different locations each 5-15 km apart in the Vestfold Hills, Princess Elizabeth Land, eastern Antarctica. A further collection was made from Mawson Station, Mac Robertson Land, eastern Antarctica 660 km away. DNA was extracted from whole thalli and the ribosomal ITS region amplified by PCR using fungal specific primers. Resulting products were sequenced to gain an indication of whether or not variation was present within populations of lichen-forming fungi from continental Antarctica, and therefore of the availability of genetic resources to react to pressures such as climate change. Three genotypes of B. frigida and two of X. elegans were detected in the Vestfold Hill collections. However, these differed by only one nucleotide position suggesting the presence of relatively little genetic variation if the ITS region is indicative of the overall genome. B. frigida collected from Mawson Station had an identical ITS region sequence to the most common Vestfold Hills genotype, indicating that this species may have a low level of genetic variation across much of eastern Antarctica. In contrast, X. elegans collected from Mawson showed considerable genetic variation from the Vestfolds thalli, differing at 14.2 % of nucleotide positions and had an identical ITS region sequence to an isolate from maritime Antarctica 4960 km away. Samples from the Vestfold Hills formed a distinct cluster in a phylogenetic analysis of ITS sequences from a worldwide collection of X. elegans isolates.\n\nThe collection sites used in this study were:\nLichen Valley, Vestfold Hills\nStalker Hill, Vestfold Hills\nEllis Rapids, Vestfold Hills\nTrajer Ridge, Vestfold Hills\nBoulder Hill, Vestfold Hills\nMawson Station.\n\nThe DNA sequences arising from the lichens can be accessed from Genbanks Entrez Nucleotide Sequence Search, the accession numbers are:\nAF276066-AF276070\nAF281306-AF281307\nAF278753-AF278757\n\nThis work was carried out as part of ASAC project 1201 (ASAC_1201).", "links": [ { diff --git a/datasets/PSScene3Band_1.json b/datasets/PSScene3Band_1.json index 5dacd379d9..2246c50a28 100644 --- a/datasets/PSScene3Band_1.json +++ b/datasets/PSScene3Band_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PSScene3Band_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Planet Scope 3 band collection contains satellite imagery obtained from Planet Labs, Inc by the Commercial SmallSat Data Acquisition (CSDA) Program. This satellite imagery is in the visible waveband range with data in the red, green, and blue wavelengths. These data are collected by Planets Dove, Super Dove, and Blue Super Dove instruments collected from across the global land surface from June 2014 to present. Data have a spatial resolution of 3.7 meters at nadir and provided in GeoTIFF format. Data access are restricted to US Government funded investigators approved by the CSDA Program.", "links": [ { diff --git a/datasets/PTLO_0.json b/datasets/PTLO_0.json index d5a06f8a12..edaffbc00b 100644 --- a/datasets/PTLO_0.json +++ b/datasets/PTLO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PTLO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from Monterey Bay from 2001 to 2003.", "links": [ { diff --git a/datasets/PVST_HOTPACE_0.json b/datasets/PVST_HOTPACE_0.json index 65f5c25c86..612cb22cac 100644 --- a/datasets/PVST_HOTPACE_0.json +++ b/datasets/PVST_HOTPACE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_HOTPACE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In this project, we leverage the near-monthly, 5-day long expeditions to oligotrophic waters approximately 100 km from Oahu, Hawaii, conducted by the Hawaii Ocean Time-series (HOT) program. We will participate in ~30 HOT cruises across all seasons over a 3-year period, amounting to ~ 150 days of high-quality collection of a suite of essential parameters for PACE Mission validation.", "links": [ { diff --git a/datasets/PVST_NORTHERN_INDIAN_OCEAN_0.json b/datasets/PVST_NORTHERN_INDIAN_OCEAN_0.json index 6a4bbff0e6..9118990da4 100644 --- a/datasets/PVST_NORTHERN_INDIAN_OCEAN_0.json +++ b/datasets/PVST_NORTHERN_INDIAN_OCEAN_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_NORTHERN_INDIAN_OCEAN_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will collect high-quality, bio-optical, and biogeochemical data for validation of advanced satellite products from PACE OCI for the Arabian Sea, a highly under-sampled region of the worlds oceans, now experiencing dramatic ecosystem changes from human activities and climate-change. Over the past two decades, the base of the food chain of this monsoonal-driven ecosystem has transitioned from diatoms to one dominated by the mixotrophic dinoflagellate, Noctiluca scintillans (Noctiluca) that forms intense and widespread blooms visible from space. Capturing such phytoplankton transitions has been the pursuit of ocean color missions for more than three decades, and with its hyperspectral capabilities, NASAs PACE mission can now provide unprecedented insight into the response of phytoplankton communities to global pressures. Despite the dramatic rates at which the Arabian Sea has been changing, it remains among the most optically under-sampled of global water bodies. As part of this effort, we will leverage our long-standing ties with colleagues in India to collect high quality, high resolution (sub-pixel scale), continuous, underway and discrete bio-optical measurements to validate standard and advanced ocean products from PACE, essential to advance our understanding of vulnerable marine ecosystems and their response to anthropogenic change. As part of this activity, we plan to participate in one pre-monsoon cruise (2025) led by Space Applications Centre, ISRO, India, and two post-bloom ONR led cruises in April-May of 2024 and in April-May 2025. The pre-monsoon cruises are being undertaken as part of an Indo-US study focused on establishing triggers of the southwest monsoon rainfall season over the Indian sub-continent. Some of the data shared under this DOI is part of the Arabian Sea Marine environment through Science and Advanced Training (EKAMSAT) collaborative effort between the Ministry of Earth Sciences, Govt. of India and the Office of Naval Research. EKAMSAT commenced with a pilot study in June 2023. The pilot data is being archived under the SeaBASS experiment EKAMSAT_Pilot_ASTRAL (DOI: 10.5067/SeaBASS/EKAMSAT_Pilot_ASTRAL/DATA001) and can downloaded here: https://seabass.gsfc.nasa.gov/experiment/EKAMSAT_Pilot_ASTRAL.", "links": [ { diff --git a/datasets/PVST_POL_0.json b/datasets/PVST_POL_0.json index b1c84c28e3..ca8199f6a3 100644 --- a/datasets/PVST_POL_0.json +++ b/datasets/PVST_POL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_POL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Naval Research Laboratory (NRL) and the City College of New York (CCNY) operate a comprehensive suite of instrumentation to measure and characterize the uncertainty of polarized radiation, as well\u00c2\u00a0as particle size distributions (PSDs), particle shape characterization and imaging, advanced chlorophyll fluorescence analysis, and seawater inherent optical properties (IOPs) and constituents. Measured attenuation (c) and absorption (a) coefficients of water and the degree of linear polarization (DoLP) above the surface derived from measurements of polarized radiances at Hyper-Angular Research Polarimeter 2 (HARP2) wavelengths will be used for the validation of a newly developed neural network algorithm for the retrieval of the c/a ratio from combined Ocean Color Instrument (OCI) and HARP-2 measurements (Agagliate et al., Frontiers in Remote Sensing, 2023).", "links": [ { diff --git a/datasets/PVST_PRINGLS_0.json b/datasets/PVST_PRINGLS_0.json index e7abafcd0a..b8156f6133 100644 --- a/datasets/PVST_PRINGLS_0.json +++ b/datasets/PVST_PRINGLS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_PRINGLS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Laurentian Great Lakes provide extensive optical and trophic variability across diverse ecosystems, from environments challenged by current and legacy nutrient pollution and continuing water quality impairments, e.g., harmful algal blooms (HABs) and hypoxia, to relatively pristine aquatic systems with emerging water quality concerns such as Lake Superior. This project will conduct high density sampling of the Great Lakes in space and time to provide a diversity of reflectance spectra broadly representative of the fundamental inherent optical properties (IOPs) and biogeochemical conditions observed across inland and coastal systems globally. Station locations are responsive to in situ conditions and potential for PACE validation, but are generally located in Lake Eries western and central basins, southwestern Lake Michigan and Green Bay, and the central basin and the western arm of Lake Superior. Sampling uses coastal class vessels that enable rapid transit between stations and range in size from 25 to 70 ft. Data collected includes above water radiometry (SVC HR-512i), discrete spectral absorption (ap, aph, aNAP, aCDOM), hyperspectral backscattering (Sequoia hyper-bb), physical and optical biogeochemical variability (YSI EXO2 water quality sonde), and discrete biogeochemical parameters (HPLC pigments, DOC, POC, and SPM). Data will be collected in every month of the year over the course of a three-year period, ensuring seasonal matchups for PACE OCI science data products in the winter and shoulder seasons that remain chronically under observed.", "links": [ { diff --git a/datasets/PVST_SBCR_0.json b/datasets/PVST_SBCR_0.json index 0dfce98d90..b34eaa696b 100644 --- a/datasets/PVST_SBCR_0.json +++ b/datasets/PVST_SBCR_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_SBCR_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rapid response field campaigns (small boats, small crew) to measure radiometry in the Santa Barbara Channel when sea and sky conditions are optimal for matchups. Process and deliver in situ high quality radiometric data for the validation of the reflectance data product of the PACE OCI instrument.", "links": [ { diff --git a/datasets/PVST_SMARTS_0.json b/datasets/PVST_SMARTS_0.json index 67c95f18ba..baad71f054 100644 --- a/datasets/PVST_SMARTS_0.json +++ b/datasets/PVST_SMARTS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_SMARTS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multiple units of in-house built SMART-s (Spectral Measurements for Atmospheric Radiative Transfer-spectrometer, 330870 nm at ~0.8 nm resolution), as a part of NASA/Pandora network with extended spectral range, will be deployed to support PACE validation over oceanic waters (Eureka Oil Platform, CA; ~8 miles off the coast of Long Beach, CA) and seasonal transported Asian dust, southeastern biomass-burning smoke, and locally generated industrial air pollutants such as trace gases, precursors, and aerosols (Taiwan) sites. Specifically, we propose to accomplish the following two tasks:1. To evaluate OCI's atmospheric-corrected water-leaving radiance/reflectance: Since the scans of SMART-s are very flexible and programmable, we will initially adopt the AERONET-OC operational criteria (e.g., IOCCG, 2019; Zibordi et al., 2021) for data continuity and consistency checks; then, after accumulating enough lessons learned from SeaPRISM, the advantage of SMART-s spectrometry will help improving the spatial-spectral-temporal sampling efficiency and effectiveness for PACE/OCI intercomparison (validation) and application. These water-leaving radiance/reflectance will be integrated with OCI's spectral response functions to meet their spectral range (i.e., 17 bands in 350710 nm at 15 nm bandwidth; 665/678 nm at 10 nm bandwidth) and uncertainty requirements.2. To validate PACEs aerosol and cloud products: We will utilize well-calibrated SMART-s' direct-Sun and sky measurements with SMART-s published methods (Jeong et al., 2018, 2020, and 2022) to retrieve columnar properties of aerosols (e.g., spectral AOD, single-scattering albedo, and ngstrm exponent, fine-mode fraction of complex index of refraction) and abundance of trace gases (O3, NO2, H2Ovapor). By leveraging the assets of the upcoming 7-SEAS (Seven SouthEast Asian Studies, 20242026, Taiwan in collaborating NASA AERONET/ MPLNET) international field campaigns, SMART-s measurements can be maximized for improving scientific understanding and validating PACE/OCI products.", "links": [ { diff --git a/datasets/PVST_VDIUP_0.json b/datasets/PVST_VDIUP_0.json index 63783e47c1..936acaaf13 100644 --- a/datasets/PVST_VDIUP_0.json +++ b/datasets/PVST_VDIUP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PVST_VDIUP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project contributes to the validation of global surface radiation products and diffuse attenuation coefficients (Kd) generated by the PACE mission, essential for quantifying net primary production. The radiation products include instantaneous, daily mean, planar, and scalar fluxes products, in particular daily mean photosynthetically available radiation (PAR). In-situ observations are gathered through a network of automatic stations measuring hyperspectral downward planar irradiance (Ed(0+)) at selected AERONET-OC sites, and BGC-Argo profilers equipped with hyperspectral Ed sensors. BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System https://doi.org/10.17882/42182. Link to BGC-Argo GDAC for raw float data: https://data-argo.ifremer.fr/aux/coriolis/.", "links": [ { diff --git a/datasets/PanamaCity_0.json b/datasets/PanamaCity_0.json index 3009dd687e..601d0674e6 100644 --- a/datasets/PanamaCity_0.json +++ b/datasets/PanamaCity_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PanamaCity_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Gulf of Mexico near Panama City, Florida in 1993.", "links": [ { diff --git a/datasets/Panhandle_OWQ_0.json b/datasets/Panhandle_OWQ_0.json index bde5d24a83..a41f4258b3 100644 --- a/datasets/Panhandle_OWQ_0.json +++ b/datasets/Panhandle_OWQ_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Panhandle_OWQ_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Florida Panhandle estuaries in partnership with USF and FWC-FWRI.", "links": [ { diff --git a/datasets/Passive_Microwave_Snowoff_Data_1711_1.1.json b/datasets/Passive_Microwave_Snowoff_Data_1711_1.1.json index f662660c42..f35852a61b 100644 --- a/datasets/Passive_Microwave_Snowoff_Data_1711_1.1.json +++ b/datasets/Passive_Microwave_Snowoff_Data_1711_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Passive_Microwave_Snowoff_Data_1711_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.", "links": [ { diff --git a/datasets/Patagonian_Coastal_0.json b/datasets/Patagonian_Coastal_0.json index f80f38ae66..37aee8f907 100644 --- a/datasets/Patagonian_Coastal_0.json +++ b/datasets/Patagonian_Coastal_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Patagonian_Coastal_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the South Atlantic Ocean in 2008 and 2009 off the Argentinian coast near Drakes Passage.", "links": [ { diff --git a/datasets/Peatland_carbon_balance_1382_1.json b/datasets/Peatland_carbon_balance_1382_1.json index d096ec94fb..1e3e1ea137 100644 --- a/datasets/Peatland_carbon_balance_1382_1.json +++ b/datasets/Peatland_carbon_balance_1382_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Peatland_carbon_balance_1382_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes.", "links": [ { diff --git a/datasets/Pelican_PCO2_0.json b/datasets/Pelican_PCO2_0.json index b291804e38..c7d8373d2e 100644 --- a/datasets/Pelican_PCO2_0.json +++ b/datasets/Pelican_PCO2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pelican_PCO2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Pelican research vessel made off the southern coast of Louisiana in the Gulf of Mexico from 2006.", "links": [ { diff --git a/datasets/PenBaySurvey_0.json b/datasets/PenBaySurvey_0.json index d94b0401fc..59ff72e2aa 100644 --- a/datasets/PenBaySurvey_0.json +++ b/datasets/PenBaySurvey_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PenBaySurvey_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Penobscot Bay between 2007 and 2008.", "links": [ { diff --git a/datasets/PermafrostThaw_CarbonEmissions_1872_1.json b/datasets/PermafrostThaw_CarbonEmissions_1872_1.json index 1aa839cde2..07bc8c8e54 100644 --- a/datasets/PermafrostThaw_CarbonEmissions_1872_1.json +++ b/datasets/PermafrostThaw_CarbonEmissions_1872_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PermafrostThaw_CarbonEmissions_1872_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions.", "links": [ { diff --git a/datasets/Permafrost_ActiveLayer_NSlope_1759_1.json b/datasets/Permafrost_ActiveLayer_NSlope_1759_1.json index e4754f4cc7..f8a922d74e 100644 --- a/datasets/Permafrost_ActiveLayer_NSlope_1759_1.json +++ b/datasets/Permafrost_ActiveLayer_NSlope_1759_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Permafrost_ActiveLayer_NSlope_1759_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.", "links": [ { diff --git a/datasets/Permafrost_Thaw_Depth_YK_1598_1.json b/datasets/Permafrost_Thaw_Depth_YK_1598_1.json index 652fc56ac3..4f3a6eda79 100644 --- a/datasets/Permafrost_Thaw_Depth_YK_1598_1.json +++ b/datasets/Permafrost_Thaw_Depth_YK_1598_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Permafrost_Thaw_Depth_YK_1598_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy.", "links": [ { diff --git a/datasets/PhenoCam_V2_1674_2.json b/datasets/PhenoCam_V2_1674_2.json index 35b437c84d..979c84ba71 100644 --- a/datasets/PhenoCam_V2_1674_2.json +++ b/datasets/PhenoCam_V2_1674_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PhenoCam_V2_1674_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals.", "links": [ { diff --git a/datasets/Phenocam_Images_V2_1689_2.json b/datasets/Phenocam_Images_V2_1689_2.json index 3e6eb77fc3..4f9f6cf031 100644 --- a/datasets/Phenocam_Images_V2_1689_2.json +++ b/datasets/Phenocam_Images_V2_1689_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Phenocam_Images_V2_1689_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0. ", "links": [ { diff --git a/datasets/Phenology_AmeriFlux_Neon_Sites_2033_1.json b/datasets/Phenology_AmeriFlux_Neon_Sites_2033_1.json index 06dd026dac..d1cde9082e 100644 --- a/datasets/Phenology_AmeriFlux_Neon_Sites_2033_1.json +++ b/datasets/Phenology_AmeriFlux_Neon_Sites_2033_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Phenology_AmeriFlux_Neon_Sites_2033_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats.", "links": [ { diff --git a/datasets/Phenology_Deciduous_Forest_1570_1.json b/datasets/Phenology_Deciduous_Forest_1570_1.json index 213783faf8..b65d890a70 100644 --- a/datasets/Phenology_Deciduous_Forest_1570_1.json +++ b/datasets/Phenology_Deciduous_Forest_1570_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Phenology_Deciduous_Forest_1570_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Landsat phenology algorithm (LPA) derived start and end of growing seasons (SOS and EOS) at 500-m resolution for deciduous and mixed forest areas of 75 selected Landsat sidelap regions across the Eastern United States and Canada. The data are a 30-year time series (1984-2013) of derived spring and autumn phenology for forested areas of the Eastern Temperate Forest, Northern Forest, and Taiga ecoregions.", "links": [ { diff --git a/datasets/Photos_ThermokarstLakes_AK_1845_1.json b/datasets/Photos_ThermokarstLakes_AK_1845_1.json index c0ad62b979..ce2571f28a 100644 --- a/datasets/Photos_ThermokarstLakes_AK_1845_1.json +++ b/datasets/Photos_ThermokarstLakes_AK_1845_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Photos_ThermokarstLakes_AK_1845_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions.", "links": [ { diff --git a/datasets/Pingo_Veg_Plots_1507_1.json b/datasets/Pingo_Veg_Plots_1507_1.json index a160f0bd90..036dbcde53 100644 --- a/datasets/Pingo_Veg_Plots_1507_1.json +++ b/datasets/Pingo_Veg_Plots_1507_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pingo_Veg_Plots_1507_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation species and vegetation plot data collected between 1983 and 1985 from 293 study plots on 41 pingos on the North Slope of Alaska. The pingos were located within the Arctic Coastal Plain in the Kuparuk, Prudhoe Bay, Kadleroshilik, and Toolik River areas. Specific attributes include dominant vegetation species, cover, soil pH, moisture, and physical characteristics of the plots.", "links": [ { diff --git a/datasets/PlanetScope.Full.Archive_7.0.json b/datasets/PlanetScope.Full.Archive_7.0.json index 28777f1c4d..ce033bf131 100644 --- a/datasets/PlanetScope.Full.Archive_7.0.json +++ b/datasets/PlanetScope.Full.Archive_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PlanetScope.Full.Archive_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer.\r\rThe Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user.\rThis kind of product is designed for users with advanced image processing and geometric correction capabilities.\r\rBasic Scene Product Components and Format\rProduct Components\t\rImage File (GeoTIFF format)\rMetadata File (XML format)\rRational Polynomial Coefficients (XML format)\rThumbnail File (GeoTIFF format)\rUnusable Data Mask UDM File (GeoTIFF format)\rUsable Data Mask UDM2 File (GeoTIFF format)\rBands\t4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared)\rGround Sampling Distance\tApproximate, satellite altitude dependent\rDove-C: 3.0 m-4.1 m\rDove-R: 3.0 m-4.1 m\rSuperDove: 3.7 m-4.2 m\rAccuracy\t<10 m RMSE\r \r\rThe Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres.\rOrtho Scene Product Components and Format\rProduct Components\t\rImage File (GeoTIFF format)\rMetadata File (XML format)\rThumbnail File (GeoTIFF format)\rUnusable Data Mask UDM File (GeoTIFF format)\rUsable Data Mask UDM2 File (GeoTIFF format)\rBands\t3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared)\rGround Sampling Distance\tApproximate, satellite altitude dependent\rDove-C: 3.0 m-4.1 m\rDove-R: 3.0 m-4.1 m\rSuperDove: 3.7 m-4.2 m\rProjection\tUTM WGS84\rAccuracy\t<10 m RMSE\r \r\rPlanetScope Ortho Scene product is available in the following: \r\rPlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite.\rPlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications.\rPlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/PlanetScopeESAarchive_8.0.json b/datasets/PlanetScopeESAarchive_8.0.json index 9934e5c8f8..7e1d564f52 100644 --- a/datasets/PlanetScopeESAarchive_8.0.json +++ b/datasets/PlanetScopeESAarchive_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PlanetScopeESAarchive_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The PlanetScope ESA archive collection consists of PlanetScope products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products.\r\rThree product lines for PlanetScope imagery are offered, for all of them the Ground Sampling Distance at nadir is 3.7 m (at reference altitude 475 km).\r\rEO-SIP Product Type\tProduct description\tProcessing Level\rPSC_DEF_S3\t3 bands \u2013 Analytic and Visual - Basic and Ortho Scene\tlevel 1B and 3B\rPSC_DEF_S4\t4 bands \u2013 Analytic and Visual - Basic and Ortho Scene\tlevel 1B and 3B\rPSC_DEF_OT\t3 bands, 4 bands and 5 bands \u2013 Analytic and Visual - Ortho Tile\tlevel 3A\r \r\rThe Basic Scene product is a single-frame scaled Top of Atmosphere Radiance (at sensor) and sensor-corrected product. The product is not orthorectified or corrected for terrain distortions, radiometric and sensor corrections are applied to the data.\rThe Ortho Scenes product is a single-frame scaled Top of Atmosphere Radiance (at sensor) or Surface Reflectance image product. The product is radiometrically, sensor and geometrically corrected and is projected to a cartographic map (UTM/WGS84).\rThe Ortho Tiles are multiple orthorectified scenes in a single strip that have been merged and then divided according to a defined grid. Radiometric and sensor corrections are applied, the imagery is orthorectified and projected to a UTM projection.\rSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/PlanetScope available on the Third Party Missions Dissemination Service.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/PlantVillage Crop Type Kenya_1.json b/datasets/PlantVillage Crop Type Kenya_1.json index 2e5d48396d..200a8d036a 100644 --- a/datasets/PlantVillage Crop Type Kenya_1.json +++ b/datasets/PlantVillage Crop Type Kenya_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PlantVillage Crop Type Kenya_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains field boundaries and crop type information for fields in Kenya. PlantVillage app is used to collect multiple points around each field and collectors have access to basemap imagery in the app during data collection. They use the basemap as a guide in collecting and verifying the points.\n

\nPost ground data collection, Radiant Earth Foundation conducted a quality control of the polygons using Sentinel-2 imagery of the growing season as well as Google basemap imagery. Two actions were taken on the data 1)several polygons that had overlapping areas with different crop labels were removed, 2) invalid polygons where multiple points were collected in corners of the field (within a distance of less than 0.5m) and the overall shape was not convex, were corrected. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item). ", "links": [ { diff --git a/datasets/Pleiades.ESA.archive_9.0.json b/datasets/Pleiades.ESA.archive_9.0.json index b3278a3751..29c5d07682 100644 --- a/datasets/Pleiades.ESA.archive_9.0.json +++ b/datasets/Pleiades.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pleiades.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Pl\u00e9iades ESA archive is a dataset of Pl\u00e9iades-1A and 1B products that ESA collected over the years. The dataset regularly grows as ESA collects new Pl\u00e9iades products.\r\rPl\u00e9iades Primary and Ortho products can be available in the following modes:\r\r \u2022 Panchromatic image at 0.5 m resolution\r \u2022 Pansharpened colour image at 0.5 m resolution\r \u2022 Multispectral image in 4 spectral bands at 2 m resolution\r \u2022 Bundle (0.5 m panchromatic image + 2 m multispectral image)\r\rSpatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service. \r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Pleiades.HiRI.archive.and.new_9.0.json b/datasets/Pleiades.HiRI.archive.and.new_9.0.json index fed6fff0f3..27409e0287 100644 --- a/datasets/Pleiades.HiRI.archive.and.new_9.0.json +++ b/datasets/Pleiades.HiRI.archive.and.new_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pleiades.HiRI.archive.and.new_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Pl\u00e9iades twins (1A and 1B) deliver very high-resolution optical data (up to 0.5 m resolution Panchromatic and Colour and 2 m Multispectral) and offer a daily revisit capability to any point on the globe. The swath width is approximately 20 km (with a nadir footprint).\rThe ortho-products are automatically generated by the Pl\u00e9iades ground segment, based on SRTM or Reference3D database. The projection available for Pl\u00e9iades ortho-products is UTM, datum WGS84.\rBands combinations::\r\u2022\tPanchromatic: black&white image at 50cm resolution\r\u2022\tPansharpened: 3-bands or 4 bands colour image at 50cm resolution\r\u2022\tMultispectral: 4 bands image at 2m resolution \r\u2022\tBundle: 0.5 m panchromatic image + 2 m multispectral image, co-registered\rProcessing levels:\r\u2022\tPrimary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion.\r\u2022\tOrtho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects. Available in MONO acquisition mode only.\rAcquisition modes:\r\u2022\tMono\r\u2022\tStereo\r\u2022\tTristero\r\rTo complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download.\rThe Living Library consist of\r\u2022\tless-than-18-months-old imagery\r\u2022\ta curation of SPOT images with no cloud cover and less than 30\u00b0 incidence angle\r\u2022\tPl\u00e9iades images acquired worldwide with maximum 15% cloud cover and 30\u00b0 Incidence Angle\r\u2022\tPl\u00e9iades Neo premium imagery selection with 2% cloud cover and 30\u00b0 incidence angle\rThese are the available subscription packages (to be consumed withing one year from the activation) \rOneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming\rOneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming\rOneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming\rAll details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPOT-Pleiades-data-terms-of-applicability.pdf available in the Resources section.\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Pleiades.Neo.full.archive.and.tasking_9.0.json b/datasets/Pleiades.Neo.full.archive.and.tasking_9.0.json index 5ea42ba117..a0ef617875 100644 --- a/datasets/Pleiades.Neo.full.archive.and.tasking_9.0.json +++ b/datasets/Pleiades.Neo.full.archive.and.tasking_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pleiades.Neo.full.archive.and.tasking_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very High Resolution optical Pl\u00e9iades Neo data at 30 cm PAN resolution (1.2 m 6-bands Multispectral) are available as part of the Airbus provision with twice daily revisit capability over the entire globe. The swath width is 14 km (footprint at nadir).\rBand combinations:\r\u2022\tPanchromatic one band Black & White image at 0.3 m resolution\r\u2022\tPansharpened colour image at 0.3 m resolution: Natural colour (3 bands RGB), false colour (3 bands NIRRG), 4 bands (RGB+NIR), 6 bands\r\u2022\tMultispectral colour image in 4 bands (RGB+NIR) or 6 bands (also Deep blue and Red Edge) at 1.2 m resolution\r\u2022\tBundle 0.3 m panchromatic image and 1.2 m multispectral image (4 or 6 bands) simultaneously acquired\rGeometric processing levels:\r\u2022\tPrimary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion.\r\u2022\tOrtho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects.\r Acquisition modes:\r\u2022\tMono\r\u2022\tStereo\r\u2022\tTristereo\r\u2022\tHD15: 15cm resolution for Panchromatic, 60cm resolution for Multispectral: Mono image resampled by using machine learning model which increase sharpness and fineness of details without introducing any fake data.\r\rTo complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download.\rThe Living Library consist of:\r\u2022\tless-than-18-months-old Pansharpened and Bundle imagery\r\u2022\ta curation of SPOT images with no cloud cover and less than 30\u00b0 incidence angle\r\u2022\tPl\u00e9iades images acquired worldwide with maximum 15% cloud cover and 30\u00b0 Incidence Angle\r\u2022\tPl\u00e9iades Neo premium imagery selection with 2% cloud cover and 30\u00b0 incidence angle\rThese are the available subscription packages (to be consumed withing one year from the activation) \rOneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming\rOneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming\rOneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming\r\rAll details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPOT-Pleiades-data-terms-of-applicability.pdf available in the Resources section.\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Plot_Data_Noatak_Seward_AK_1919_1.json b/datasets/Plot_Data_Noatak_Seward_AK_1919_1.json index 837461db74..22171c4498 100644 --- a/datasets/Plot_Data_Noatak_Seward_AK_1919_1.json +++ b/datasets/Plot_Data_Noatak_Seward_AK_1919_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Plot_Data_Noatak_Seward_AK_1919_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included.", "links": [ { diff --git a/datasets/Plumes_and_Blooms_0.json b/datasets/Plumes_and_Blooms_0.json index 7e148d5280..f70a747ec6 100644 --- a/datasets/Plumes_and_Blooms_0.json +++ b/datasets/Plumes_and_Blooms_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Plumes_and_Blooms_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Plumes and Blooms program is a joint collaboration among UCSB faculty, student and staff researchers at the Institute of Computational Earth System Science (ICESS), NOAA researchers at the Coastal Services Center (Charleston, SC) and the NOAA sanctuary managers of the Channel Islands National Marine Sanctuary (CINMS). Since August, 1996, monthly research cruises have been conducted to collect measurements. These measurements include temperature and salinity, ocean color spectra, and water column profiles of red light transmission and chlorophyll fluorescence (indexes of suspended particulate load and phytoplankton abundance). The transect observations begin at the shelf waters north of Santa Rosa island and end at an area off Goleta Point. These repeat observations are combined with satellite imagery to build a time-series of the changing ocean color conditions in the Santa Barbara Channel.", "links": [ { diff --git a/datasets/PolInSAR_Canopy_Height_1589_1.json b/datasets/PolInSAR_Canopy_Height_1589_1.json index d5e3121654..ab1c4d6777 100644 --- a/datasets/PolInSAR_Canopy_Height_1589_1.json +++ b/datasets/PolInSAR_Canopy_Height_1589_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PolInSAR_Canopy_Height_1589_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency.", "links": [ { diff --git a/datasets/Polar-VPRM_Alaskan-NEE_1314_1.json b/datasets/Polar-VPRM_Alaskan-NEE_1314_1.json index f22c7dce7f..b3dfd732b6 100644 --- a/datasets/Polar-VPRM_Alaskan-NEE_1314_1.json +++ b/datasets/Polar-VPRM_Alaskan-NEE_1314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Polar-VPRM_Alaskan-NEE_1314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution. The PolarVPRM produces high-frequency estimates of GEE of CO2 for North American biomes from remotely-sensed data sets. For Alaska, the model used meteorological inputs from the North American regional re-analysis (NARR) and inputs of fractional snow cover and land surface water index (LSWI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Land surface greenness was factored into the model from three sources: 1) Enhanced Vegetation Index (EVI) from MODIS; 2) Solar Induced Florescence (SIF) from the Orbiting Carbon Observatory 2 (OCO-2); and 3) SIF from the Global Ozone Monitoring Experiment 2 (GOME-2). Three independent estimates of GEE are included in the data set, one for each source of greenness observations.", "links": [ { diff --git a/datasets/PolarWindsII_DAWN_DC8_1.json b/datasets/PolarWindsII_DAWN_DC8_1.json index 8ba22044ea..08494cc384 100644 --- a/datasets/PolarWindsII_DAWN_DC8_1.json +++ b/datasets/PolarWindsII_DAWN_DC8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PolarWindsII_DAWN_DC8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PolarWindsII_DAWN_DC8_1 is the Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 data product. Data collection for this product is complete. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap.\r\n\r\nPolar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds.\r\n\r\nThe focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA C-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements.", "links": [ { diff --git a/datasets/PolarWindsI_DAWN_KingAirUC-12B_1.json b/datasets/PolarWindsI_DAWN_KingAirUC-12B_1.json index 01b3f4c004..0f95612812 100644 --- a/datasets/PolarWindsI_DAWN_KingAirUC-12B_1.json +++ b/datasets/PolarWindsI_DAWN_KingAirUC-12B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PolarWindsI_DAWN_KingAirUC-12B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PolarWindsI_DAWN_KingAirUC-12B is the Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B data product. Data for this was collected using the DAWN instrument flown on the NASA Langley Beechcraft UC-12B Huron aircraft. Data collection for this product is complete. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds.\r\n\r\nThe focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA UC-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements.\r\n\r\nBeginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap.", "links": [ { diff --git a/datasets/Polarimetric_CT_1601_1.json b/datasets/Polarimetric_CT_1601_1.json index 5e8072b935..65243650b7 100644 --- a/datasets/Polarimetric_CT_1601_1.json +++ b/datasets/Polarimetric_CT_1601_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Polarimetric_CT_1601_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign.", "links": [ { diff --git a/datasets/Polarimetric_height_profile_1577_1.json b/datasets/Polarimetric_height_profile_1577_1.json index 3155f11ca9..b650f94579 100644 --- a/datasets/Polarimetric_height_profile_1577_1.json +++ b/datasets/Polarimetric_height_profile_1577_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Polarimetric_height_profile_1577_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI).", "links": [ { diff --git a/datasets/Poplar_Veg_Plots_1376_1.json b/datasets/Poplar_Veg_Plots_1376_1.json index 8850c75781..b5a2bc5261 100644 --- a/datasets/Poplar_Veg_Plots_1376_1.json +++ b/datasets/Poplar_Veg_Plots_1376_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Poplar_Veg_Plots_1376_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale. Plot data includes moisture, topographic position, slope, aspect, shape, and soil data.", "links": [ { diff --git a/datasets/PostFire_Tree_Regeneration_1955_1.1.json b/datasets/PostFire_Tree_Regeneration_1955_1.1.json index 890d0b2933..eee2f159a1 100644 --- a/datasets/PostFire_Tree_Regeneration_1955_1.1.json +++ b/datasets/PostFire_Tree_Regeneration_1955_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PostFire_Tree_Regeneration_1955_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Post_Fire_C_Emissions_1787_1.json b/datasets/Post_Fire_C_Emissions_1787_1.json index 3be259c8f5..6a4db16e67 100644 --- a/datasets/Post_Fire_C_Emissions_1787_1.json +++ b/datasets/Post_Fire_C_Emissions_1787_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Post_Fire_C_Emissions_1787_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis.", "links": [ { diff --git a/datasets/Post_Fire_SOC_NWT_2235_1.json b/datasets/Post_Fire_SOC_NWT_2235_1.json index 4571d7b670..4d7fcd65dc 100644 --- a/datasets/Post_Fire_SOC_NWT_2235_1.json +++ b/datasets/Post_Fire_SOC_NWT_2235_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Post_Fire_SOC_NWT_2235_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/PreABoVE_AirMOSS_L1_Alaska_1678_1.json b/datasets/PreABoVE_AirMOSS_L1_Alaska_1678_1.json index d6b88a8574..54c0afe7ef 100644 --- a/datasets/PreABoVE_AirMOSS_L1_Alaska_1678_1.json +++ b/datasets/PreABoVE_AirMOSS_L1_Alaska_1678_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreABoVE_AirMOSS_L1_Alaska_1678_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015. The acquired L1 P-band radar backscatter data will be used to derive estimates of soil water content and permafrost state at the study sites.", "links": [ { diff --git a/datasets/PreDeltaX_ADCP_Measurements_1806_1.json b/datasets/PreDeltaX_ADCP_Measurements_1806_1.json index 81f4431c46..04589c1f09 100644 --- a/datasets/PreDeltaX_ADCP_Measurements_1806_1.json +++ b/datasets/PreDeltaX_ADCP_Measurements_1806_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_ADCP_Measurements_1806_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides river discharge measurements collected at selected locations across the Atchafalaya River Basin, within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign on October 15 to 20, 2016. Seventy-five channel surveys were conducted with a SonTek RiverSurveyor M9 acoustic doppler current profiler (ADCP) on selected wide channels (~100 m) and a few selected (~10 m) narrow channels. ADCP data provide near-instantaneous estimates of river discharge across the sampled channels. Sites coincided with AirSWOT swaths in the Atchafalaya River Basin and water level measurement locations. This in situ dataset was used to calibrate and validate Delta-X hydrodynamic models.", "links": [ { diff --git a/datasets/PreDeltaX_ASO_LiDAR_WaterLevel_1820_1.json b/datasets/PreDeltaX_ASO_LiDAR_WaterLevel_1820_1.json index dbd77c722f..36e1889b1a 100644 --- a/datasets/PreDeltaX_ASO_LiDAR_WaterLevel_1820_1.json +++ b/datasets/PreDeltaX_ASO_LiDAR_WaterLevel_1820_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_ASO_LiDAR_WaterLevel_1820_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains lidar-derived water surface elevation profiles for river channels between Wax Lake, in the Atchafalaya River Basin of the Mississippi River Delta, and the Gulf of Mexico. The provided elevation profiles (i.e., water levels) were estimated using remotely sensed lidar data in combination with in situ field measurements of water levels for elevation calibration and to quantify uncertainty in estimates. The lidar data were collected during the Fall 2016 Pre-Delta-X Campaign using an Airborne Snow Observatory (ASO) lidar instrument. The results are time-specific water levels measured as elevation in meters with respect to the North American Vertical Datum 1988 (NAVD 88) geoid (or orthometric height) and the World Geodetic System 1984 (WGS 84) ellipsoidal surface (or ellipsoidal/GPS height) along the water channels in this drainage system.", "links": [ { diff --git a/datasets/PreDeltaX_Insitu_Reflectance_1804_1.json b/datasets/PreDeltaX_Insitu_Reflectance_1804_1.json index a70f8b02a9..f9b7349ffd 100644 --- a/datasets/PreDeltaX_Insitu_Reflectance_1804_1.json +++ b/datasets/PreDeltaX_Insitu_Reflectance_1804_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_Insitu_Reflectance_1804_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of in situ remote-sensing reflectance (Rrs; per steradian) of surface water across Atchafalaya Basin, southern coastal Louisiana, USA within Mississippi River Delta (MRD) floodplain. The in situ spectral reflectance measurements were made during the Pre-Delta-X campaign in Fall 2016 (October 14 to 2). Hand-held spectrometer measurements were collected from a boat at 35 locations selected to represent a range of suspended sediment concentrations and properties from a variety of hydrodynamic and physical settings typically encountered across the Atchafalaya basin. These 35 spectral reflectance measurements were collected at 24 unique sites that coincide with measurements of total suspended solids (TSS). The data serves two main purposes, to ground-truth the remote-sensing reflectance derived from NASA's Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) instrument, and to calibrate and validate algorithms for the retrieval of TSS from AVIRIS-NG.", "links": [ { diff --git a/datasets/PreDeltaX_L2_AVIRIS_SR_1826_1.json b/datasets/PreDeltaX_L2_AVIRIS_SR_1826_1.json index 8867cdebe5..b1731c4ff2 100644 --- a/datasets/PreDeltaX_L2_AVIRIS_SR_1826_1.json +++ b/datasets/PreDeltaX_L2_AVIRIS_SR_1826_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_L2_AVIRIS_SR_1826_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 2 (L2) dataset provides surface spectral reflectance data acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA, in 2015-2016. The data include georectified images with Lambertian-equivalent surface reflectance and composite mosaics with adjustments for Bidirectional Reflectance Distribution Function (BRDF) effects. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG), as part of the Pre-Delta-X campaign.", "links": [ { diff --git a/datasets/PreDeltaX_L2_AirSWOT_1818_1.json b/datasets/PreDeltaX_L2_AirSWOT_1818_1.json index 31354ad59a..feff1b8c0f 100644 --- a/datasets/PreDeltaX_L2_AirSWOT_1818_1.json +++ b/datasets/PreDeltaX_L2_AirSWOT_1818_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_L2_AirSWOT_1818_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides water surface elevations over the Wax Lake Delta in the Atchafalaya Basin in coastal Louisiana, USA, in May 2015. These Level 2 (L2) data were collected by AirSWOT, an airborne instrument employing near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data at 3.6 m resolution. Along with elevation estimates, the dataset includes measures of estimation errors, sensitivity, incidence angle, backscatter, and interferometric correlation. For this application, in situ water level data were added into the AirSWOT phase calibration procedure. These L2 data consist of a set of rasters in Universal Transverse Mercator (UTM) map coordinates for each of the 39 AirSWOT flight lines. These elevation data were later used for calculating elevation and slopes along the main channels in this wetland system, as well as tying observations to ocean tidal conditions.", "links": [ { diff --git a/datasets/PreDeltaX_L3_AVIRIS_Biomass_1821_1.json b/datasets/PreDeltaX_L3_AVIRIS_Biomass_1821_1.json index 864bef106c..db8fbd9e80 100644 --- a/datasets/PreDeltaX_L3_AVIRIS_Biomass_1821_1.json +++ b/datasets/PreDeltaX_L3_AVIRIS_Biomass_1821_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_L3_AVIRIS_Biomass_1821_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model. The AVIRIS-NG surface reflectance data were also combined with L-band Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) HV backscatter and scattering component values from coincident vegetation sample sites to develop and test AGB models for emergent herbaceous and forested wetland vegetation. This study used the integrated airborne data from AVIRIS-NG and UAVSAR to assess the instruments' unique capabilities in combination for estimating AGB in coastal deltaic wetlands. The 5.4 m resolution vegetation classification map for the WLD study area was then used to apply the best models to estimate AGB across the WLD.", "links": [ { diff --git a/datasets/PreDeltaX_L3_AVIRIS_Sediment_1822_1.json b/datasets/PreDeltaX_L3_AVIRIS_Sediment_1822_1.json index 23cd5953a1..50c5f486fc 100644 --- a/datasets/PreDeltaX_L3_AVIRIS_Sediment_1822_1.json +++ b/datasets/PreDeltaX_L3_AVIRIS_Sediment_1822_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_L3_AVIRIS_Sediment_1822_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes total suspended solids (TSS) at the water surface across the Atchafalaya and Terrebonne Basins in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. AVIRIS-NG, the Next Generation Airborne Visible to Infrared Imaging Spectrometer, acquired data over the study area in 2015 and 2016. The remote imageries were combined with coincident field measurements to develop and validate spatially explicit estimates at 3.7-5.4 m resolution of the concentration (mg/L) of TSS.", "links": [ { diff --git a/datasets/PreDeltaX_L3_AirSWOT_WL_1819_1.json b/datasets/PreDeltaX_L3_AirSWOT_WL_1819_1.json index 4d95371031..fc67d569ab 100644 --- a/datasets/PreDeltaX_L3_AirSWOT_WL_1819_1.json +++ b/datasets/PreDeltaX_L3_AirSWOT_WL_1819_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_L3_AirSWOT_WL_1819_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains water level profiles generated from the AirSWOT data collected in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Part of the Pre-Delta-X Campaign, AirSWOT used near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and uncertainty in May 2015. This Level 3 (L3) AirSWOT dataset is in the form of numerous profiles of water level along the Wax Lake Outlet.", "links": [ { diff --git a/datasets/PreDeltaX_Sonar_Bathymetry_1807_1.json b/datasets/PreDeltaX_Sonar_Bathymetry_1807_1.json index c30fbe269f..e7c27b7a99 100644 --- a/datasets/PreDeltaX_Sonar_Bathymetry_1807_1.json +++ b/datasets/PreDeltaX_Sonar_Bathymetry_1807_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_Sonar_Bathymetry_1807_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides water depths and water surface elevations collected during bathymetric surveys of the main channel of the Wax Lake Delta within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X Campaign in Fall 2016. The in situ continuous (1 Hz) surveys of channel bathymetry were conducted using a SonarMite Hydrolite Single Beam Echo Sounder mounted on the side of the research boat. The sounder was located directly beneath the Septentrio global navigation satellite system (GNSS) antenna, about 30 cm below the water surface. The sounder depth observations were integrated with the GNSS location and elevation data into one data file per day for October 16-20. These bathymetry measurements were used to generate a merged digital elevation model (DEM) through interpolation with ancillary DEMs to expand the existing wetland DEM to include channels. The merged DEM product is distributed in GeoTIFF format.", "links": [ { diff --git a/datasets/PreDeltaX_TSS_Concentration_1802_1.json b/datasets/PreDeltaX_TSS_Concentration_1802_1.json index 25ec351a98..c8b85d2628 100644 --- a/datasets/PreDeltaX_TSS_Concentration_1802_1.json +++ b/datasets/PreDeltaX_TSS_Concentration_1802_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_TSS_Concentration_1802_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the total suspended solids (TSS) concentration of in situ water samples collected at selected sites across the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2016. The sampling sites spanned large and small channels at locations chosen to cover a representative range of suspended sediment concentrations from a variety of hydrodynamic and physical settings typically encountered across the basins. Water samples were collected by bottle just beneath the surface and stored on ice until filtering. The TSS concentration was calculated as the difference of the filter weight (before and after filtration) divided by the volume of sample filtered. Both TSS and in situ spectral reflectance measurements were collected at some sampling sites. Pre-Delta-X sampling focused on surface waters, where the TSS data are used as inputs for hydrodynamic models for sediment transport and to calibrate the model to convert AVIRIS-NG spectral reflectance measurements into TSS.", "links": [ { diff --git a/datasets/PreDeltaX_UAVSAR_Channel_Maps_1954_1.json b/datasets/PreDeltaX_UAVSAR_Channel_Maps_1954_1.json index 7afa2da7bd..c96db36931 100644 --- a/datasets/PreDeltaX_UAVSAR_Channel_Maps_1954_1.json +++ b/datasets/PreDeltaX_UAVSAR_Channel_Maps_1954_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_UAVSAR_Channel_Maps_1954_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides spatial data on water channels in the estuary of the Atchafalaya Basin of the Mississippi River Delta of coastal Louisiana. These Level-3 (L3) channel maps were developed from interferograms derived from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data collected on 2016-10-16 and 2016-10-17 during low and high tides. The channel maps define open water paths in hydrodynamic models and are used to evaluate model performance.", "links": [ { diff --git a/datasets/PreDeltaX_UAVSAR_SLC_1816_1.json b/datasets/PreDeltaX_UAVSAR_SLC_1816_1.json index 3abc5fd4f3..c6b7042821 100644 --- a/datasets/PreDeltaX_UAVSAR_SLC_1816_1.json +++ b/datasets/PreDeltaX_UAVSAR_SLC_1816_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_UAVSAR_SLC_1816_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 1 (L1) dataset includes single look complex (SLC) stack products and co-registered interferograms in the HH (horizontal transmit and horizontal receive) polarization for the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. The data were collected in October 2016 by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III aircraft as part of the Pre-Delta-X campaign. A single study region, flight line \"gulfco_12011\", was sampled six times at approximately 30-minute intervals to monitor changing water levels. The SLC stack product is a standard UAVSAR product delivered by the UAVSAR processing team. The L1 interferograms were generated from the SLC stacks.", "links": [ { diff --git a/datasets/PreDeltaX_UAVSAR_WaterLevel_1823_1.json b/datasets/PreDeltaX_UAVSAR_WaterLevel_1823_1.json index 345937f121..a7f8ae696d 100644 --- a/datasets/PreDeltaX_UAVSAR_WaterLevel_1823_1.json +++ b/datasets/PreDeltaX_UAVSAR_WaterLevel_1823_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_UAVSAR_WaterLevel_1823_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains five maps of cumulative changes in water levels at 30-minute intervals over a 150-minute period on 2016-10-16 in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Water surface elevations were measured on six flights at 30-minute intervals, with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft. The five georeferenced maps at 6 m resolution show the cumulative change of water levels (cm) every 30 minutes relative to the first sampling flight. These Level 3 maps were generated using the InSAR time series Small Baseline Subsets (SBAS) algorithm implemented in the Generic InSAR Analysis Toolbox (GIAnT) toolbox and served to evaluate and compare hydrodynamic models.", "links": [ { diff --git a/datasets/PreDeltaX_Vegetation_Structure_1805_1.json b/datasets/PreDeltaX_Vegetation_Structure_1805_1.json index edf4d895c1..4d22f281ef 100644 --- a/datasets/PreDeltaX_Vegetation_Structure_1805_1.json +++ b/datasets/PreDeltaX_Vegetation_Structure_1805_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_Vegetation_Structure_1805_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation species, height, stem density and diameter, and species aboveground biomass (AGB) measurements collected at herbaceous and forested wetland sites across the Atchafalaya and Terrebonne basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2015. Vegetation height and density and diameter data are only provided for forested Atchafalaya sites during the spring collections. At the nine herbaceous wetland sites, a transect was established perpendicular to the wetland edge with replicate sample plots (0.25 m2, 5 m apart) located at 50, 100, and 150 m from the wetland edge to capture the range of vegetation structure, zonation, and composition. AGB was harvested inside the duplicate plots at each sampling location. At the six forested wetland sites, duplicate circular plots (10 m radius, 50 m apart) were established inside the forest approximately 30 m from the wetland edge. All trees with a diameter at breast height (DBH at 1.3 m) > 2.5 cm were measured within each plot and identified to species. The height of trees was measured with a laser range finder. AGB was estimated using species-specific allometric equations. Measurements were used to generate marsh and forested wetland coverage and biomass in response to seasonality within both basins. The data will be used to calibrate remote sensing data (e.g., UAVSAR, AVIRIS-NG) and hydrodynamics and sediment transport models.", "links": [ { diff --git a/datasets/PreDeltaX_Water_Level_Data_1801_1.json b/datasets/PreDeltaX_Water_Level_Data_1801_1.json index 1b992146ed..e6dfbae772 100644 --- a/datasets/PreDeltaX_Water_Level_Data_1801_1.json +++ b/datasets/PreDeltaX_Water_Level_Data_1801_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "PreDeltaX_Water_Level_Data_1801_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016. Relative water level measurements were recorded every five minutes during a one-week period using in situ pressure transducers (Solinst) to measure water surface elevation change with millimeter accuracy. The Solinst system combines a total pressure transducer (TPT) and a temperature detector. Once underwater, the TPT measures the sum of the atmosphere and water pressure above the sensor. Atmospheric pressure fluctuations must be accounted for to obtain the height of the water column above the TPT. An absolute elevation correction was applied to the water level data using an iterative approach with the USGS Calumet Station water level height and Airborne Snow Observatory (ASO) lidar water level profiles. These Pre-Delta-X water level measurements served to calibrate and validate the campaign's remote sensing observations and hydrodynamic models.", "links": [ { diff --git a/datasets/Pre_LBA_ABRACOS_899_1.1.json b/datasets/Pre_LBA_ABRACOS_899_1.1.json index cab8385668..80165ea54d 100644 --- a/datasets/Pre_LBA_ABRACOS_899_1.1.json +++ b/datasets/Pre_LBA_ABRACOS_899_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Pre_LBA_ABRACOS_899_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture. The objectives of the ABRACOS were to monitor Amazonian climate and improve the understanding of the consequences of deforestation and to provide data for the calibration and validation of GCMs and GCM sub-models of Amazonian forest and post-deforestation pasture (Shuttleworth et al, 1991). Three areas were instrumented, each with different soils, dry season intensities and deforestation densities (Gash et al, 1996). In each area, an automatic weather station and soil moisture measurement equipment were installed: in a primary forest site and in nearby cattle pasture, for monitoring climate and soil status throughout the year. Additional intensive periods of study (or Missions), of varying duration, were operated at these sites: for calibration purposes, to understand the physical processes relevant to each site, and for detailed comparisons between sites. These data were collected under the ABRACOS project and made available by the UK Institute of Hydrology and the Instituto Nacional de Pesquisas Espaciais (Brazil). ABRACOS is a collaboration between the Agencia Brasileira de Cooperacao and the UK Overseas Development Administration. The processed, quality controlled and integrated data in the documented Pre-LBA data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.", "links": [ { diff --git a/datasets/Proantar_0.json b/datasets/Proantar_0.json index 9a79f79235..de4153ccb2 100644 --- a/datasets/Proantar_0.json +++ b/datasets/Proantar_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Proantar_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off James Ross Island near Antarctica in 2005.", "links": [ { diff --git a/datasets/Profile_based_PBL_heights_1706_1.1.json b/datasets/Profile_based_PBL_heights_1706_1.1.json index c87091ccb1..a0108ef942 100644 --- a/datasets/Profile_based_PBL_heights_1706_1.1.json +++ b/datasets/Profile_based_PBL_heights_1706_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Profile_based_PBL_heights_1706_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions.", "links": [ { diff --git a/datasets/Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1.json b/datasets/Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1.json index b8ca32c921..169e6052e9 100644 --- a/datasets/Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1.json +++ b/datasets/Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance.", "links": [ { diff --git a/datasets/Prudhoe_Bay_Veg_Maps_1387_1.json b/datasets/Prudhoe_Bay_Veg_Maps_1387_1.json index 491fe046e0..644b6efed2 100644 --- a/datasets/Prudhoe_Bay_Veg_Maps_1387_1.json +++ b/datasets/Prudhoe_Bay_Veg_Maps_1387_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Prudhoe_Bay_Veg_Maps_1387_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development.", "links": [ { diff --git a/datasets/Prudhoe_Bay_Veg_Plots_1360_1.json b/datasets/Prudhoe_Bay_Veg_Plots_1360_1.json index 6d1efc2bb4..5d02cce7bc 100644 --- a/datasets/Prudhoe_Bay_Veg_Plots_1360_1.json +++ b/datasets/Prudhoe_Bay_Veg_Plots_1360_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Prudhoe_Bay_Veg_Plots_1360_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska.", "links": [ { diff --git a/datasets/Prudhoe_Freshets_0.json b/datasets/Prudhoe_Freshets_0.json index 386172356f..06d2ec36b3 100644 --- a/datasets/Prudhoe_Freshets_0.json +++ b/datasets/Prudhoe_Freshets_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Prudhoe_Freshets_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A three-year observational study into the influence of spring freshets entering Stefansson Sound (coastal Alaskan lagoon system) using a combination of autonomous and direct sampling approaches applying hydrographic, optical, acoustic, and biogeochemical measurements.", "links": [ { diff --git a/datasets/Prydz_Bay_Foraminiferida_1.json b/datasets/Prydz_Bay_Foraminiferida_1.json index 38df2fc1a9..6183f9b17f 100644 --- a/datasets/Prydz_Bay_Foraminiferida_1.json +++ b/datasets/Prydz_Bay_Foraminiferida_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Prydz_Bay_Foraminiferida_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are linked to what appears to be an unfinished report/paper by Pat Quilty. An extract of the unfinished report is available below, and the full document is included in the data download.\n\nThese data are also linked to a collection in the biodiversity database, and are also related to another record (both listed at the provided URLs).\n\nForaminiferids are recorded from samples collected on Mac. Robertson Shelf and Prydz Bay, East Antarctica in 1982, 1995 and 1997. Most are identifiable from previous literature but a new enrolled biserial agglutinated genus is noted but not defined. Distribution is related to oceanographic factors.\n\nThe Mac. Robertson Shelf-Prydz Bay region off the East Antarctic coast is that segment of the southern Indian Ocean between latitudes 66 degrees and almost 70 degrees S, and longitudes 60 degrees and 80 degrees E. It includes Mac. Robertson Shelf, the continental shelf, bounded seaward by the 500 m isobath, and Prydz Bay, the deepest re-entrant into the east Antarctic shield and the outlet for the Lambert Glacier at its southern end. The Lambert Glacier is the world\u2019s largest glacier and drains some 1 000 000 km2 of East Antarctica. The marine region studied here covers some 140 000 km2.\n\nSeveral research cruises to the region have collected sediment samples that yielded modern and recycled foraminiferid faunas. The modern component of the faunas has not been recorded in detail previously. \n\nThis paper records the details of the taxonomy and distribution of species collected during marine geology/geophysics cruises that provided the foraminiferids discussed in Quilty (1985, 2001), O\u2019Brien (1992), O\u2019Brien et al. (1993, 1995) and Harris et al. (1997). The geophysical results and interpretations of the 1982 voyage of MV Nella Dan are described by Stagg (1985) and this provides also the general setting and nomenclature of Prydz Bay. Two cruises (1995 and 1997) of RSV Aurora Australis collected samples and these provided the basis for Quilty\u2019s records of foraminiferids and other components on a sample-by-sample basis in O\u2019Brien et al. (1995) from 51 samples, and from a further 27 samples reported in Harris et al. (1997). The 1995 cruise also yielded the recycled foraminifera recorded by Quilty (2001) and the Mesozoic material documented by Truswell et al. (1999). Neither of these cruise records provided details of the faunas to the level covered here. Further studies for the region are given in the results of ODP Legs 119 and 188.\n\nThe impetus for conducting this review comes from two sources. Firstly, few foraminiferids have been documented from this region, and even fewer have been figured. Secondly, 2007-2008 was designated the [fourth] International Polar Year (IPY) and one of the major programs is the Census of Antarctic Marine Life (CAML), a component of the global Census of Marine Life (CML). This paper is a contribution to that project. Included in the review are faunas from the modern environment and some which may be \u2018Late Cenozoic\u2019 in which the faunas are of the same species as the modern and in which data from the modern can be, and have been, used to infer past environments (Fillon 1974, Kellogg et al. 1979, Ward and Webb 1986).\n\t\t\nThe aims of this paper are: \n- to document the species of foraminifera recovered from geology/geophysics cruises to the Mac. Robertson Shelf and Prydz Bay region, offshore East Antarctica (Fig. 1);\n- to make the nomenclature of species recorded consistent with latest taxonomic practice; \n- to characterise the faunas by diversity and dominance factors; and\n- to discuss the controls on the distribution of faunas recorded.", "links": [ { diff --git a/datasets/QB02_MSI_L1B_1.json b/datasets/QB02_MSI_L1B_1.json index f7cfd8e58e..e60bfb9024 100644 --- a/datasets/QB02_MSI_L1B_1.json +++ b/datasets/QB02_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QB02_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The QuickBird Level 1B Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe QuickBird-2 satellite using the Ball High Resolution Camera 60 across the global land surface from October 2001 to January 2015. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The spatial resolution is 2.16m at nadir and the temporal resolution is 2.5 to 5.6 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/QB02_Pan_L1B_1.json b/datasets/QB02_Pan_L1B_1.json index 364d472923..6c1bbd191b 100644 --- a/datasets/QB02_Pan_L1B_1.json +++ b/datasets/QB02_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QB02_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The QuickBird Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe QuickBird-2 satellite using the Ball High Resolution Camera 60 across the global land surface from October 2001 to January 2015. This data product includes panchromatic imagery with a spatial resolution of 0.55m at nadir and a temporal resolution of 2.5 to 5.6 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0.json b/datasets/QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0.json index b55f137100..b27b9fb7d1 100644 --- a/datasets/QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0.json +++ b/datasets/QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the first provisional release of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the QuikSCAT Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These ancillary files include: i) ocean surface wind fields from ERA-5 short-term forecasts (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobeCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. The primary purpose of this release is for provisional evaluation to be provided by the NASA International Ocean Vector Winds Science Team (IOVWST). As such, this release is not intended for science-quality research, and is subject to future revision based on feedback provided by the IOVWST. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 QuikSCAT sampled locations at 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.", "links": [ { diff --git a/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json b/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json index 826cb05ae5..3b649ac100 100644 --- a/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json +++ b/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function. The averaging of the L1B input data combined with the wind vector processing results are both contained in this product referred to hereafter as Level 1C (L1C). The fixed and repointed beam processing is relative to either the one corresponding to the vertically polarized \"outer\" beam or the other corresponding to the horizontally polarized \"inner\" beam. The Sigma-0 values from the fixed operating beam for each frame are averaged to a single value representing approximately 50 samples. The data points are land flagged, collocated with ECMWF surface winds, and have climatological nadir attenuations provided for the location and time of the data (not applied to the sigma0). The following enhancements have been applied in the Version 2 re-processing: 1) the GMF has been updated (QNS2016a) to make use of ECMWF nowcast 1x1 degree resolution wind direction information for the entire historical data record; 2) the new QNS2016a GMF leverages a calibration adjustment from Remote Sensing Systems (RSS) resulting in a consistently lower Normalized Radar Cross Section (NRCS or Sigma-0) measurements that establishes a Sigma-0 bias of -0.25 dB (-5.9% linear scale) compared to the L1C Version 1 data; 3) the new QNS2016a GMF also applies an azimuthal modulation that is decreased by several tenths of a dB (for Sigma-0) in variation with wind speed; this results in a more consistent wind speed retrieval comparison between \"non-spinning\" and \"spinning\" modes of the QuikSCAT instrument; 4) spacecraft attitude was re-estimated using slice data over multiple orbits as a replacement for lost echo-tracking capability during the \"non-spinning\" mode of the instrument; this new attitude estimation follows an unpublished manual technique that leverages the echo power of individual slice observations; since only a small subset of slice observations are analyzed, rapid variations in attitude are not captured; 5) continues data production beyond October 2016 through the end of mission on 30 August 2018. Retrieved wind directions are only slightly different from ECMWF values and should not be considered an independent measurement of wind direction. Retrieved wind speeds do not depend significantly on ECMWF speeds as evidenced by the fact that they agree closely with WindSAT polarimetric radiometer speeds whenever WindSAT and ECMWF disagree. The Sigma0 values have also been corrected for scan loss (due to the fact that the antenna does not scan) and for X-factor changes due to repointing.", "links": [ { diff --git a/datasets/QSCAT_LEVEL_1B_V2_2.json b/datasets/QSCAT_LEVEL_1B_V2_2.json index e703acf9d9..479a388fb3 100644 --- a/datasets/QSCAT_LEVEL_1B_V2_2.json +++ b/datasets/QSCAT_LEVEL_1B_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_LEVEL_1B_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. This dataset represents the second reprocessed version of the Level 1B release. Special note: QuikSCAT went into a \"non-spinning\" mode on 22 November 2009. The final rev number in the nominal Operational \"spinning\" mode is 54296; the \"non-spinning\" mode of the instrument continued predominantly until the end of the time series. There were some brief periods of \"spinning\" in between, which include the following days and rev numbers (identified in parenthesis): 1) 29 January 2013 to 5 February 2013 (7909-71011), 2) 14 March 2013 (71536-71549), 3) 18 March 2013 to 21 March 2013 (71590-71634), and 4) 28 March 2013 to 31 March 2013 (71735-71769). Data during the \"non-spinning\" mode is not consistently calibrated with data from the \"spinning\" mode. Furthermore, incidence angles change periodically during the \"non-spinning\" mode. It is therefore advised that only \"expert\" users attempt using the data during the \"non-spinning\" mode. For standard L1B data users who wish to access consistently calibrated L1B data during the \"non-spinning\" mode, please consider using the L1B Averaged Sigma-0 dataset as alternative, which may be accessed by contacting podaac@podaac.jpl.nasa.gov", "links": [ { diff --git a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json index a43dd26d32..56e59d19fa 100644 --- a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json +++ b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_LEVEL_2B_OWV_COMP_12_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2014): PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/quikscat/L2B12/docs/fore_et_al_ieee_2014.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/quikscat/open/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf.", "links": [ { diff --git a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1_4.1.json b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1_4.1.json index 60a549d486..d73cfc12a1 100644 --- a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1_4.1.json +++ b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the latest reprocessed version 4.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is an official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). Version 4.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 4.0 processing. This new version has a number of key improvements and changes over the previous version 4.0, including: 1) winds are now retrieved to within 5-km and 10-km of the coast within oceans/seas and lakes respectively; 2) coastal winds are now flagged as poor coastal quality and likely corrupted in orbits with estimated spacecraft pitch error greater than 0.04 degrees, which affects 150 orbits of data where coastal winds are severely contaminated by land due to poor attitude knowledge (note: attitude error tracking can identify pitch error but not yaw error, so when estimated pitch error is far from zero, it implies yaw error is large and uncorrected); 3) coastal winds are flagged based upon the long term mean wind speed and standard deviation of wind speed for each place on the ground; 4) four quantities, means and standard deviations computed with and without the land contamination correction algorithm applied (note: higher mean and smaller standard deviation are evidence of land contamination), are used to estimate the expected wind speed bias with respect to neighboring wind vector cells over open water; 5) wind vector cells with estimated speed bias greater than 0.4 m/s are flagged as poor coastal quality and likely corrupted; 6) winds within 5-km of the coast of an ocean/sea and 10-km of the coast of a lake are flagged as poor coastal quality and likely corrupted; the larger distance threshold for lakes is due to higher variability in lake water levels.", "links": [ { diff --git a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1_3.1.json b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1_3.1.json index 529c434190..a8e4b487df 100644 --- a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1_3.1.json +++ b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1_3.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1_3.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the latest reprocessed version 3.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 3.0 processing. Version 3.1 improves upon the previous Version 3.0 processing by incorporating enhanced coastal processing using a Land Contamination Ratio (LCR) method with a fixed threshold. The 12.5 km binning resolution combined with the LCR processing enables this dataset to provide wind vector retrievals with approximately half the coastal gap as compared to the Version 3.0 12.5 km L2B dataset. The geophysical model function used to produce the wind vector cell retrievals remains unchanged between Version 3.0 and 3.1. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). More details to the processing changes and improvements are to be published in the near future, but for now can be referenced by the following presentation: https://mdc.coaps.fsu.edu/scatterometry/meeting/docs/2016/Thu_AM/coastal-poster.pdf .", "links": [ { diff --git a/datasets/QUICKBIRD_CAPSIZE_COMPTON_GMS_1.json b/datasets/QUICKBIRD_CAPSIZE_COMPTON_GMS_1.json index 5288043227..497ba06677 100644 --- a/datasets/QUICKBIRD_CAPSIZE_COMPTON_GMS_1.json +++ b/datasets/QUICKBIRD_CAPSIZE_COMPTON_GMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QUICKBIRD_CAPSIZE_COMPTON_GMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multispectral Quickbird Image of eastern Heard Island. This image was derived from Quickbird satellite imagery captured on 17 January 2003.\n\nMore information about the images used, the processing, and the feature mapping are documented in an image report available for download at the url given below.", "links": [ { diff --git a/datasets/QUICKBIRD_HEARD_EAST_FEATURES_1.json b/datasets/QUICKBIRD_HEARD_EAST_FEATURES_1.json index b3f7abf7b1..2c26be1140 100644 --- a/datasets/QUICKBIRD_HEARD_EAST_FEATURES_1.json +++ b/datasets/QUICKBIRD_HEARD_EAST_FEATURES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QUICKBIRD_HEARD_EAST_FEATURES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Features mapped from two pan sharpened multi spectral satellite images of eastern Heard Island, from Shag Island to Compton Lagoon to Capsize Beach. The images were captured from Quickbird on 17 January 2003. The features include coastline, glaciers, lagoons, moraines, snow, vegetation, lakes and watercourses.\n\nMore information about the images used, the processing, and the feature mapping are documented in an image report available for download at the url given below.", "links": [ { diff --git a/datasets/QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0_1.0.json b/datasets/QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0_1.0.json index 70c92f53d5..29d2e99db2 100644 --- a/datasets/QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0_1.0.json +++ b/datasets/QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QUIKSCAT_ESDR_L2_WIND_STRESS_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the first provisional release of the MEaSUREs-funded Earth Science Data Record (ESDR) of inter-calibrated ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from QuikSCAT scatterometer observations. The primary purpose of this release is for provisional evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). As such, this release is not intended for science-quality research, and is subject to future revision based on feedback provided by the IOVWST. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.", "links": [ { diff --git a/datasets/QuickBird-2.ESA.archive_6.0.json b/datasets/QuickBird-2.ESA.archive_6.0.json index b9644a9809..57279c1a93 100644 --- a/datasets/QuickBird-2.ESA.archive_6.0.json +++ b/datasets/QuickBird-2.ESA.archive_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QuickBird-2.ESA.archive_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The QuickBird-2 archive collection consists of QuickBird-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Panchromatic (up to 61 cm resolution) and 4-Bands (up to nominal value of 2.44m resolution, reduced to 1.63m when at the end of the mission the orbit altitude was lowered to 300km) products are available; the 4-Bands includes various options such as Multispectral (separate channel for BLUE, GREEN, RED, NIR1), Pan-sharpened (BLUE, GREEN, RED, NIR1), Bundle (separate bands for PAN, BLUE, GREEN, RED, NIR1), Natural Color (pan-sharpened BLUE, GREEN, RED), Colored Infrared (pan-sharpened GREEN, RED, NIR1), Natural Colour / Coloured Infrared (3-Band pan-sharpened) The processing levels are: \u2022 STANDARD (2A): normalized for topographic relief \u2022 VIEW READY STANDARD (OR2A): ready for orthorectification \u2022 VIEW READY STEREO: collected in-track for stereo viewing and manipulation \u2022 MAP-READY (ORTHO) 1:12.000 Orthorectified: additional processing unnecessary \u2022 MAP-READY (ORTHO) 1:15.000 Orthorectified: additional processing unnecessary", "links": [ { diff --git a/datasets/QuickBird.full.archive_5.0.json b/datasets/QuickBird.full.archive_5.0.json index 87ec143344..bd370c7e70 100644 --- a/datasets/QuickBird.full.archive_5.0.json +++ b/datasets/QuickBird.full.archive_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "QuickBird.full.archive_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "QuickBird high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rIn particular, QuickBird offers archive panchromatic products up to 0.60 m GSD resolution and 4-Bands Multispectral products up to 2.4 m GSD resolution.\r\rBand Combination\tData Processing Level\tResolution\rPanchromatic and 4-bands\tStandard(2A)/View Ready Standard (OR2A)\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap-Ready (Ortho) 1:12,000 Orthorectified\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\r \r\r4-Bands being an option from:\r\r4-Band Multispectral (BLUE, GREEN, RED, NIR1)\r4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1)\r4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1)\r3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED)\r3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1)\rNatural Colour / Coloured Infrared (3-Band pan-sharpened)\rNative 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique intelligently increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details.", "links": [ { diff --git a/datasets/R2A_LIS3_STUC00GTD_1.0.json b/datasets/R2A_LIS3_STUC00GTD_1.0.json index e9b40402ec..2ba2eb3048 100644 --- a/datasets/R2A_LIS3_STUC00GTD_1.0.json +++ b/datasets/R2A_LIS3_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "R2A_LIS3_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/R2A_LIS4_FMX_STUC00GTD_1.0.json b/datasets/R2A_LIS4_FMX_STUC00GTD_1.0.json index 1f2fa1f358..06d6117f5b 100644 --- a/datasets/R2A_LIS4_FMX_STUC00GTD_1.0.json +++ b/datasets/R2A_LIS4_FMX_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "R2A_LIS4_FMX_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coarse resolution multi-spectral sensor, LIS4 FMX operates in four spectral bands - B2, B3, B4, B5 in visible near infrared (VNIR) and B5 in Short Wave Infrared (SWIR) providing data with 5.8m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/RADAMBrasil_941_1.json b/datasets/RADAMBrasil_941_1.json index 02571b5484..6dd36e29a3 100644 --- a/datasets/RADAMBrasil_941_1.json +++ b/datasets/RADAMBrasil_941_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RADAMBrasil_941_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RADAMBRASIL project extensively mapped the Amazon soils using a combination of soil pit information, aerial photography, and geologic maps. During the project, 1,153 soil pits, distributed basin-wide, were described and sampled by horizon and analyzed for texture and chemical composition.This data set, which consists of one file in ASCII comma separated format, contains soil profile descriptions for locations throughout Brazilian Amazonia. These data are based on RADAMBRASIL surveys from the Soil Profiles of Amazonia (Source: IPAM, Brazil/WHRC, USA). See the companion file Soil Profiles of Amazonia.pdf", "links": [ { diff --git a/datasets/RADARSAT.SAR.F_6.0.json b/datasets/RADARSAT.SAR.F_6.0.json index bec5ee0ec1..f8688331f1 100644 --- a/datasets/RADARSAT.SAR.F_6.0.json +++ b/datasets/RADARSAT.SAR.F_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RADARSAT.SAR.F_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": ""RADARSAT-1&2 full archive and new tasking products are available in several different beam modes. RADARSAT-1 PRODUCTS The Standard beam mode operates with any one of seven beam positions, referred to as S1 to S7. The nominal incidence angle range covered by the full set of Standard beams is from 20 degrees (at the inner edge of S1) to 49 degrees (at the outer edge of S7). Each individual beam covers a minimum ground swath of 100 km within the total 500 km accessibility swath of the full set of Standard beams. The nominal spatial resolution in the range direction is 26 m for S1 at near range to 20 m for S7 at far range. The nominal azimuth resolution is the same, 27 m, for all beam positions. The Wide beam modes are similar to the Standard beams except that the swath width achieved by this beam is 150 km rather than 100 km. As a result, only three Wide beams, W1, W2 and W3 are necessary to provide coverage of almost all of the 500 km swath range. They provide comparable resolution to the standard beam mode, though the increased ground swath coverage is obtained at the expense of a slight reduction in overall image quality. In the Fine beam mode the nominal azimuth resolution is 8.4 m, with range resolution 9.1 m to 7.8 m from F1 to F5. Since the radar operates with a higher sampling rate in this mode than in any of the other beam mode, the ground swath coverage has to be reduced to approximately 50 km in order to keep the downlink signal within its allocated bandwidth. Originally, five Fine beam positions, F1 to F5, were available to cover the far range of the swath with an incidence angle range from 37 to 47 degrees. By modifying timing parameters, 10 new positions have been added with offset ground coverage. Each original Fine beam position can either be shifted closer to or further away from Nadir. In Extended High beam mode six positions, EH1 to EH6, are available for collection of data in the 49 to 60 degree incidence angle range. Since this beam mode operates outside the optimum scan angle range of the SAR antenna, some minor degradation of image quality can be expected when compared with the Standard beam mode. Swath widths are restricted to a nominal 80 km for the inner three positions, and 70 km for the outer three positions. In Extended Low beam mode one position, EL1, is provided for imaging in the incidence angle range 10 to 23 degrees with nominal ground swath coverage of 170 km. As with the Extended High beam mode, some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum elevation angle range. In ScanSAR mode, combinations of two, three or four single beams are used during data collection. Each beam is selected sequentially so that data is collected from a wider swath than possible with a single beam. The beam switching rates are chosen to ensure at least one "look" at the Earth's surface for each beam within the along track illumination time or dwell time of the antenna beam. In practice, the radar beam switching is adjusted to provide two looks per beam. The beam multiplexing inherent in ScanSAR operation reduces the effective sampling rate within each of the component beams; hence the increased swath coverage is obtained at the expense of spatial resolution. The ScanSAR Narrow mode combines two beams (incidence angle range of 20 to 39 degrees) or three beams (incidence angle from 31 to 46 degrees) and provides coverage of a nominal 300 km ground swath, with spatial resolution of 50 m. The ScanSAR Wide mode combines four beams, provides coverage of either 500 km (with incidence angle range of 20 to 49 degrees) or 450 km (incidence angle range from 20 to 46 degrees) nominal ground swaths depending on the beam combination. Beam Mode| Product| Ground coverage (km2)| Nominal resolution (m)| Polarisation| ScanSAR wide| SCW, SCF, SCS| 500 x 500| 100| Single and dual| ScanSAR narrow| SCN, SCF, SCS| 300 x 300| 60| Single and dual| Wide| SGF, SGX, SLC, SSG, SPG| 150 x 150| 24| Single and dual| Standard| SGF, SGX, SLC, SSG, SPG| 100 x 100| 24| Single| Extended low| SGF, SGX, SLC, SSG, SPG| 170 x 170| 24| Single| Extended high| SGF, SGX, SLC, SSG, SPG| 75 x 75| 24| Single| Fine| SGF, SGX, SLC, SSG, SPG| 50 x 50| 8| Single| RADARSAT-2 PRODUCTS The Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8. The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. The Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. The Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. In the Standard Quad Polarization Beam Mode, the radar transmits pulses alternately in horizontal (H) and vertical (V) polarisations, and receives the return signals from each pulse in both H and V polarisations separately but simultaneously. This beam mode therefore enables full polarimetric (HH+VV+HV+VH) image products to be generated. The Standard Quad Polarization Beam Mode operates with the same pulse bandwidths as the Standard Beam Mode. Products with swath widths of approximately 25 km can be obtained covering any area within the region from an incidence angle of 18 degrees to at least 49 degrees. The Wide Standard Quad Polarization Beam Mode operates the same way as the Standard Quad Polarization Beam Mode but with higher data acquisition rates, and offers wider swaths of approximately 50 km at equivalent spatial resolution. 21 beams are available covering any area from 18 degrees to 42 degrees, ensuring overlaps of about 50% between adjacent swaths. The Fine Quad Polarization Beam Mode provides full polarimetric imaging with the same spatial resolution as the Fine Resolution Beam Mode. Fine Quad Polarization Beam Mode products with swath widths of approximately 25 km can be obtained covering any area within the region from an incidence angle of 18 degrees to at least 49 degrees. The Wide Fine Quad Polarization Beam Mode operates the same way as the Fine Quad Polarization Beam Mode but with higher data acquisition rates, and offers a wider swath of approximately 50 km at equivalent spatial resolution. 21 beams are available covering any area from 18 degrees to 42 degrees, ensuring overlaps of about 50% between adjacent swaths. The Multi-Look Fine Resolution Beam Mode covers the same swaths as the Fine Resolution Beam Mode. Products with multiple looks in range and azimuth are generated at approximately the same spatial resolution as Fine Resolution Beam mode products, but with multiple looks and therefore improved radiometric resolution. Single look products are generated at finer spatial resolutions than Fine Resolution Beam Mode products. In order to obtain the multiple looks without a reduction in swath width, this beam mode operates with higher data acquisition rates and noise levels than Fine Resolution Beam Mode. As with the Fine Resolution Beam Mode, nine physical beams are available to cover the incidence angle range from 30 to 50 degrees, and additional near and/or far range swath positioning choices are available to provide additional overlap. The Wide Multi-Look Fine Resolution Beam Mode offers a wider coverage alternative to the regular Multi-Look Fine Beam Mode, while preserving the same spatial and radiometric resolution, but at the expense of higher data compression ratios (which leads to higher signal-dependent noise levels). The nominal swath width is 90 km compared to 50 km for the Multi-Look Fine Beam Mode. The nine physical beams are the same as in the Multi-Look Fine Beam Mode, covering incidence angles from approximately 30 to 50 degrees, but the additional near and far range swath positioning choices available in the Multi-Look Fine Beam Mode are not needed because the beam centered swaths are wide enough to overlap by more than 50%. The Ultra-Fine Resolution Beam Mode is intended for applications which require very high spatial resolution. The set of Ultra-Fine Resolution Beams cover any area within the incidence angle range from 20 to 50 degrees (soon to be extended to 54 degrees). Each beam within the set images a swath width of at least 20 km. The Wide Ultra-Fine Resolution Beam Mode provides the same spatial resolution as the Ultra-Fine mode as well as wider coverage, but at the expense of higher data compression ratios (which leads to higher signal-dependent noise levels). The set of Wide Ultra-Fine Resolution Beams cover any area within the incidence angle range from 30 to 50 degrees. Each beam within the set images a swath width of approximately 50 km. The Wide Fine Resolution Beam Mode is intended for applications which require both a finer spatial resolution and a wide swath. Products from this beam mode have a nominal ground swath equivalent to the ones offered by the Wide Swath Beam Mode (170 km, 150 km and 120 km) and a spatial resolution equivalent to the ones offered by the Fine Resolution Beam Mode, at the expense of somewhat higher noise levels. Three Wide Fine Resolution beam positions, F0W1 to F0W3 are available to cover the incidence angle range from 20 to 45 degrees. The Extra-Fine Resolution Beam Mode nominally provides similar swath width and incidence angle coverage as the Wide Fine Beam Mode, at even finer resolutions, but with higher data compression ratios and noise levels. The four Extra-Fine beams provide coverage of swaths of approximately 160 km, 124 km, 120 km and 108 km in width respectively, and collectively span a total incidence angle range from 22 to 49 degrees. This beam mode also offers additional optional processing parameter selections that allow for reduced-bandwidth single-look products, 4-look, and 28-look products. In Spotlight Beam Mode, the beam is steered electronically in order to dwell on the area of interest over longer aperture times, which allows products to be processed to finer azimuth resolution than in other modes. Unlike in other modes, Spotlight images are of fixed size in the along track direction. The set of Spotlight beams cover any area within the incidence angle range from 20 to 50 degrees (soon to be extended to 54 degrees). Each beam within the set images a swath width of at least 18 km. Beam Mode| Product| Nominal Pixel Spacing [Range x Azimuth](metres)| Nominal Resolution (metres)| Resolution [Range x Azimuth](metres)| Nominal Scene Size [Range x Azimuth](kilometres)| Range of Angle of Incidence [Range](degrees)| Number of Looks [Range x Azimuth]| Polarisations Options| Spotlight| SLC |1.3 x 0.4| <1| 1.6 x 0.8| 18 x 8| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Spotlight| SGX |1 or 0.8 x 1/3| <1|4.6 - 2.0 x 0.8|18 x 8| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Spotlight| SGF |0.5 x 0.5| <1|4.6 - 2.0 x 0.8|18 x 8| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Spotlight| SSG, SPG|0.5 x 0.5| <1|4.6 - 2.0 x 0.8|18 x 8| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Ultra-fine| SLC| 1.3 x 2.1| 3| 1.6 x 2.8| 20 x 20| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Ultra-fine| SGX| 1 x 1 or 0.8 x 0.8| 3| 3.3 \u2013 2.1 x 2.8| 20 x 20| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Ultra-fine| SGF| 1.56 x 1.56| 3| 3.3 \u2013 2.1 x 2.8| 20 x 20| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Ultra-fine| SSG, SPG| 1.56 x 1.56| 3| 3.3 \u2013 2.1 x 2.8| 20 x 20| 20 to 54| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Wide Ultra-fine| SLC| 1.3 x 2.1| 3| 3.1 x 4.6| 50 x 50 29 to 50 1 x 1 Single Co or Cross (HH or VV or HV or VH)| Wide Ultra-fine| SGX| 1 x 1| 3| 3.3 - 2.1 x 2.8| 50 x 50 29 to 50 1 x 1 Single Co or Cross (HH or VV or HV or VH)| Wide Ultra-fine| SGF| 1.56 x 1.56| 3| 3.3 - 2.1 x 2.8| 50 x 50 29 to 50 1 x 1 Single Co or Cross (HH or VV or HV or VH)| Wide Ultra-fine| SSG, SPG| 1.56 x 1.56| 3| 3.3 - 2.1 x 2.8| 50 x 50 29 to 50 1 x 1 Single Co or Cross (HH or VV or HV or VH)| Multi-look fine| SLC| 2.7 x 2.9| 8| 3.1 x 4.6| 50 x 50| 30 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Multi-look fine| SGX| 3.13 x 3.13| 8| 10.4 - 6.8 x 7.6| 50 x 50| 30 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Multi-look fine| SGF| 6.25 x 6.25| 8| 10.4 - 6.8 x 7.6| 50 x 50| 30 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Multi-look fine| SSG, SPG| 6.25 x 6.25| 8| 10.4 - 6.8 x 7.6| 50 x 50| 30 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Wide Multi-look fine| SLC| 2.7 x 2.9| 8| 3.1 x 4.6| 90 x 50| 29 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Wide Multi-look fine| SGX| 3.13 x 3.13| 8| 10.8 - 6.8 x 7.6| 90 x 50| 29 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Wide Multi-look fine| SGF| 6.25 x 6.25| 8| 10.8 - 6.8 x 7.6| 90 x 50| 29 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Wide Multi-look fine| SSG, SPG| 6.25 x 6.25| 8| 10.8 - 6.8 x 7.6| 90 x 50| 29 to 50| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SLC| (Full resolution)| 2.7 x 2.9| 5| 3.1 x 4.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SLC| (fine resolution)| 4.3 x 5.8| 5| 5.2 x 7.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SLC| (standard resolution)| 7.1 x 5.8| 5| 8.9 x 7.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SLC| (wide resolution)| 10.6 x 5.8| 5| 13.3 x 7.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGX| (1 look)| 2.0 x 2.0| 5| 8.4 \u2013 4.1 x 4.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGX| (4 looks)| 3.13 x 3.13| 5| 14 \u2013 6.9 x 7.6| 125 x 125| 22 to 49| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGX| (28 looks)| 5.0 x 5.0| 5| 24 - 12 x 23.5| 125 x 125| 22 to 49| 4 x 7| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGF| (1 look)| 3.13 x 3.13| 5| 8.4 - 4.1 x 4.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGF| (4 looks)| 6.25 x 6.25| 5| 14 - 6.9 x 7.6| 125 x 125| 22 to 49| 2 x 2| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SGF| (28 looks)| 8.0 x 8.0| 5| 24 - 12 x 23.5| 125 x 125| 22 to 49| 4 x 7| Single Co or Cross (HH or VV or HV or VH)| Extra-fine| SSG, SPG| 3.13 x 3.13| 5| 8.4 - 4.1 x 4.6| 125 x 125| 22 to 49| 1 x 1| Single Co or Cross (HH or VV or HV or VH)| Fine| SLC| 4.7 x 5.1| 8| 5.2 x 7.7| 50 x 50| 30 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Fine| SGX| 3.13 x 3.13| 8| 10.4 \u2013 6.8 x 7.7| 50 x 50| 30 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Fine| SGF| 6.25 x 6.25| 8| 10.4 \u2013 6.8 x 7.7| 50 x 50| 30 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Fine| SSG, SPG| 6.25 x 6.25| 8| 10.4 \u2013 6.8 x 7.7| 50 x 50| 30 to 50| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide Fine| SLC| 4.7 x 5.1| 8| 5.2 x 7.7| 150 x 150| 20 to 45| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide Fine| SGX| 3.13 x 3.13| 8| 14.9 - 7.3 x 7.7| 150 x 150| 20 to 45| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide Fine| SGF| 6.25 x 6.25| 8| 14.9 - 7.3 x 7.7| 150 x 150| 20 to 45| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide Fine| SSG, SPG| 6.25 x 6.25| 8| 14.9 - 7.3 x 7.7| 150 x 150| 20 to 45| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Standard| SLC| 8.0 or 11.8 x 5.1| 25| 9.0 or 13.5 x 7.7| 100 x 100| 20 - 52| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Standard| SGX| 8 x 8| 25| 26.8 - 17.3 x 24.7| 100 x 100| 20 - 52| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Standard| SGF| 12.5 x 12.5| 25| 26.8 - 17.3 x 24.7| 100 x 100| 20 - 52| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Standard| SSG, SPG| 12.5 x 12.5| 25| 26.8 - 17.3 x 24.7| 100 x 100| 20 - 52| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide| SLC| 11.8 x 5.1| 30| 13.5 x 7.7| 150 x 150| 20 - 45| 1 x 1| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide| SGX| 10 x 10| 30| 40.0 - 19.2 x 24.7| 150 x 150| 20 - 45| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide| SGF| 12.5 x 12.5| 30| 40.0 - 19.2 x 24.7| 150 x 150| 20 - 45| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Wide| SSG, SPG| 12.5 x 12.5| 30| 40.0 - 19.2 x 24.7| 150 x 150| 20 - 45| 1 x 4| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| Extended High| SLC| 11.8 x 5.1| 25| 13.5 x 7.7| 75 x 75| 49 - 60| 1 x 1| Single (HH only)| Extended High| SGX| 8 x 8| 25| 18.2 - 15.9 x 24.7| 75 x 75| 49 - 60| 1 x 4| Single (HH only)| Extended High| SGF| 12.5 x 12.5| 25| 18.2 - 15.9 x 24.7| 75 x 75| 49 - 60| 1 x 4| Single (HH only)| Extended High| SSG, SPG| 12.5 x 12.5| 25| 18.2 - 15.9 x 24.7| 75 x 75| 49 - 60| 1 x 4| Single (HH only)| Extended Low| SLC| 8.0 x 5.1| 25| 9.0 x 7.7| 170 x 170| 10 - 23| 1 x 1| Single (HH only)| Extended Low| SGX| 10 x 10| 25| 52.7 \u2013 23.3 x 24.7| 170 x 170| 10 - 23| 1 x 4| Single (HH only)| Extended Low| SGF| 12.5 x 12.5| 25| 52.7 \u2013 23.3 x 24.7| 170 x 170| 10 - 23| 1 x 4| Single (HH only)| Extended Low| SSG, SPG| 12.5 x 12.5| 25| 52.7 \u2013 23.3 x 24.7| 170 x 170| 10 - 23| 1 x 4| Single (HH only)| Fine Quad-Pol| SLC| 4.7 x 5.1| 8| 5.2 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Fine Quad-Pol| SGX| 3.13 x 3.13| 8| 16.5 \u2013 6.8 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Fine Quad-Pol| SSG, SPG| 3.13 x 3.13| 8| 16.5 \u2013 6.8 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Wide Fine Quad-Pol| SLC| 4.7 x 5.1| 8| 5.2 x 7.6| 50 x 25| 18 - 42| 1 x 1 Quad (HH+VV+HV+VH)| Wide Fine Quad-Pol| SGX| 3.13 x 3.13| 8| 17.3\u20137.8 x 7.6| 50 x 25| 18 - 42| 1 x 1 Quad (HH+VV+HV+VH)| Wide Fine Quad-Pol| SSG, SPG| 3.13 x 3.13| 8| 17.3\u20137.8 x 7.6| 50 x 25| 18 - 42| 1 x 1 Quad (HH+VV+HV+VH)| Standard Quad-Pol| SLC| 8 or 11.8 x 5.1| 25| 9.0 or 13.5 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Standard Quad-Pol| SGX| 8 x 3.13| 25| 28.6 \u2013 17.7 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Standard Quad-Pol| SSG, SPG| 8 x 3.13| 25| 28.6 \u2013 17.7 x 7.6| 25 x 25| 18 - 49| 1 x 1| Quad (HH+VV+HV+VH)| Wide Standard Quad-Pol| SLC| 8 or 11.8 x 5.1| 25| 9.0 or 13.5 x 7.6| 50 x 25| 18 - 42| 1 x 1| Quad (HH+VV+HV+VH)| Wide Standard Quad-Pol| SGX| 8 x 3.13| 25| 30.0 \u201316.7 x 7.6| 50 x 25| 18 - 42| 1 x 1| Quad (HH+VV+HV+VH)| Wide Standard Quad-Pol| SSG, SPG| 8 x 3.13| 25| 30.0 \u201316.7 x 7.6| 50 x 25| 18 - 42| 1 x 1| Quad (HH+VV+HV+VH)| ScanSAR Narrow| SCN, SCF, SCS| 25 x 25| 50| 81\u201338 x 40-70| 300 x 300| 20 to 46| 2 x 2| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| ScanSAR Wide| SCW, SCF, SCS| 50 x 50| 100| 163-73 x 78-106| 500 x 500| 20 to 49| 4 x 2| Single Co or Cross (HH or VV or HV or VH) or Dual (HH+HV or VV+VH)| These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track The products are available as part of the MDA provision from RADARSAT missions with worldwide coverage: the EODMS catalogue (https://www.eodms-sgdot.nrcan-rncan.gc.ca/index_en.jsp) can be accessed (registration required only for ordering) to discover and check the data readiness. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.", "links": [ { diff --git a/datasets/RAIN_ARKIN_1.json b/datasets/RAIN_ARKIN_1.json index 92747bfd44..4a6b590761 100644 --- a/datasets/RAIN_ARKIN_1.json +++ b/datasets/RAIN_ARKIN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RAIN_ARKIN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are transitioned to a state of permanent preservation. They are available upon request.\nMore advanced datasets have been developed since. \nOne recommended replacement is the GPCP (doi: 10.5067/DBVUO4KQHXTK) product developed under the MEaSUREs project. \n\nThe Arkin and Janowiak GPI (GOES Precipitation Index) was the infrared-based monthly rainfall estimate produced by the early GPCP (Global Precipitation Climatology Project) algorithms. The infrared observations from geostationary satellites (GOES, GMS, Meteosat) are used to produce these monthly mean rainfall totals on a 2.5 deg by 2.5 deg grid from 40 N to 40 S for the period Jan 1986 to Dec 1995.", "links": [ { diff --git a/datasets/RAIN_CHANG_2.3.json b/datasets/RAIN_CHANG_2.3.json index 4689f4c7d4..16f1202348 100644 --- a/datasets/RAIN_CHANG_2.3.json +++ b/datasets/RAIN_CHANG_2.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RAIN_CHANG_2.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are transitioned to a state of permanent preservation. They are available upon request.\nMore advanced datasets have been developed since. \nOne recommended replacement is the GPCP (doi: 10.5067/DBVUO4KQHXTK) product developed under the MEaSUREs project.\n\nFuthermore, the NASA Precipitation Measurement Missions Project released newly processed SSM/I datasets as output from the GPROF (Goddard Profiling Algorithm).\n(doi: 10.5067/GPM/SSMI/F11/GPROFCLIM/2A/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-MONTH/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-DAY/05)\n\n\nThe \"RAIN_CHANG\" SSM/I Derived Oceanic Monthly Rainfall Indices data set was an early Global Precipitation Climatology Project (GPCP) product. Monthly rainfall indices over the oceans were derived from Special Sensor Microwave Imager (SSM/I) data from the Defense Meteorological Satellite Program (DMSP) satellites F8 and F11, on channels 19 and 22 V. The data set covered the period from July 1987 to December 1995.\n \n The monthly rainfall indices are on a 5 degree by 5 degree grid extending from 50 N to 50 S. The Wilheit, Chang and Chiu (1991) method used to derive the indices gives valid values only over ocean areas. Land pixels (including island pixels) and erroneous pixels return a -10 flag. The data are stored on a 72 x 20 grid. Grid point (1,1) contains the index for 45-50 N, 0-5 E, grid point (2,1) contains the index for 45-50 N, 5-10 E, ... and grid point (72,20) contains the index for 45-50 S, 175-180 W.\n \n In the data set, each month starts with an ASCII header to identify the year and month. The data is in 10F8.1 format. Each value is the average of AM and PM estimates and corrected for beam filling error. The equation used is: (AM PM)/2.0 * 1.8. Land pixels are set to -10.0. Also there are 33 pixels blocked out due to island contamination (-10.0). If the rain retrieval did not converge, a -10.0 is assigned to the pixel.\n \n The objective of this data set was to provide a long term monthly rainfall data set to be used in EOS global change and GEWEX related research.\n\n", "links": [ { diff --git a/datasets/RAIN_JAEGER_1.json b/datasets/RAIN_JAEGER_1.json index dd15d33255..7a30ab8a64 100644 --- a/datasets/RAIN_JAEGER_1.json +++ b/datasets/RAIN_JAEGER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RAIN_JAEGER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Jaeger Surface Rain Gauge Observations data set consists of gridded mean monthly global precipitation values for 1931 to 1960 over the continents and 1955 to 1965 over the oceans. \n\nIn order to calculate monthly, seasonal, and annual variations of precipitation over the whole globe, both hemispheres, and various meridional zones, a gridding technique was used on data spanning 1931 to\n1960 over the continents, and 1955 to 1965 over the oceans. For the continental regions, the grid point values were obtained as eye estimates from isopleth maps prepared from up-to-date climatic atlases containing annual and monthly rainfall values, supplemented by other data sets.\nAlthough it was initially intended to use data for the standard period 1931-1960, this did not prove possible for all regions.\n\nMoller's (1951) method for estimating rainfall frequencies was adopted to provide ocean precipitation data. Monthly percentage frequencies were extracted from the mapped isolines of the US Marine Climatic Atlas (US Naval Weather Service 1955-1965) and interpolated to the grid points. After re-expressing the monthly frequencies as annual percentages, the values were scaled to rainfall depth units using Geiger's (1965) precipitation map to yield monthly precipitation means.", "links": [ { diff --git a/datasets/RAIN_LEGATES_1.json b/datasets/RAIN_LEGATES_1.json index 3ee81f426a..08488eb489 100644 --- a/datasets/RAIN_LEGATES_1.json +++ b/datasets/RAIN_LEGATES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RAIN_LEGATES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Legates Surface and Shipboard Rain Gauge Observations data set consists of a global climatology of monthly mean precipitation values. A global climatology of mean monthly precipitation was developed using traditional land-based gauge measurements as well as extrapolations of oceanic precipitation from coastal and island observations. Data were obtained from a variety of source archives. These data were screened for coding errors, merged, and redundant stations were removed. The resulting data base contains 24,635 independent terrestrial station records and 2223 oceanic gridpoint estimates.\n \n Precipitation gauge catches, however, are known to underestimate actual precipitation. Errors in the gauge catch result from wind-field deformation above the orifice of the gauge, wetting losses, and evaporation from the gauge and amount globally to nearly 8, 2, and 1 percent of the catch, respectively. A procedure was developed to estimate these errors and was used to obtain better estimates of global precipitation. Spatial variations in gauge type, air temperature, wind speed, and natural vegetation have been interpolated to the nodes of a 0.5 degrees of latitude by 0.5 degrees of longitude lattice using a spherically-based interpolation algorithm.\n \n The data set is used to validate general circulation model simulations of the present-day precipitation climate, for ground-based comparison with satellite-derived precipitation estimates, and as a basis for global water balance studies.", "links": [ { diff --git a/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json b/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json index f0a1ed1af9..3bb729f440 100644 --- a/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json +++ b/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RAMSSA_09km-ABOM-L4-AUS-v01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.", "links": [ { diff --git a/datasets/RBLE_917_1.json b/datasets/RBLE_917_1.json index cf9ec0afd4..086d42c7f6 100644 --- a/datasets/RBLE_917_1.json +++ b/datasets/RBLE_917_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RBLE_917_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The atmospheric boundary layer (ABL) is the layer of air closest to the ground which is directly influenced on a daily basis by the heating and cooling of the earth's surface. The exact depth of the ABL varies according synoptic weather conditions and the time of day. During the daytime it is usually between 1 and 3 km; during the night it is much shallower. The ABL is important because it links the fluxes of heat and water vapor observed at the surface to the general circulation of the atmosphere. To model climate correctly, it is necessary for the ABL to be well understood and represented in the model. Because the air in the ABL is turbulent, small scale variations (about 1 km or less) in evaporation and heat flux at the surface are smoothed, with the temperature, humidity and depth of the ABL being uniform over the entire area. Larger scale variations (on the scale of 10 km or more) may lead to differences in ABL properties between the different surface types. Such differences may cause local atmospheric circulations to develop which may be important for the local climate of an area. During ABRACOS, three ABL measurement campaigns were carried out. These campaigns were called the Rondonia Boundary Layer Experiment (RBLE) 1, 2 and 3 and were held at Ji-Parana where the scale of the forested and deforested areas is large enough for each surface type to develop its own ABL. Refer to the related data set, Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data, for additional information.The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. The campaigns were held during the dry season when the difference in evaporation between the two surfaces types, forest and pasture, is at its greatest. Measurements were made with both free-flying radiosondes which measure temperature, humidity, and wind up to about 12 km and with a tethered balloon which makes more detailed measurements in the lowest 1 km of the atmosphere. Measurements were made at both the forest and clearing sites. Profiles of potential temperature measured during RBLE2 show that the daytime ABL was deeper over the clearing than the forest. The data have been used to test several models of ABL development. It appears that the ABL over pastures or over clearings grows more rapidly than predicted by the models, possibly because of the increased turbulence generated by the strips of forest typical of this area. The data have also been used to initialize one-dimensional climate models used in experiments to investigate the sensitivity of climate to land surface parameters, and to initialize a mesoscale model which can predict local effects on climate caused by the pattern of deforestation in this area.", "links": [ { diff --git a/datasets/RDBTS4_2.json b/datasets/RDBTS4_2.json index c32f330a28..1c7da6292f 100644 --- a/datasets/RDBTS4_2.json +++ b/datasets/RDBTS4_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDBTS4_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Likely Basal Thermal State of the Greenland Ice Sheet (GrIS) product contains key data sets that show how the likely basal thermal state was inferred from existing airborne and satellite data sets and recent methods, and provides a synthesis mask of the likely basal thermal state over the Greenland Ice Sheet.", "links": [ { diff --git a/datasets/RDEFT4_1.json b/datasets/RDEFT4_1.json index 42155f0a38..380675680b 100644 --- a/datasets/RDEFT4_1.json +++ b/datasets/RDEFT4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDEFT4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains estimates of Arctic sea ice thickness and concentration, ice freeboard and surface roughness, as well as snow density and depth, derived from the ESA CryoSat-2 Synthetic Aperture Interferometric Radar Altimeter (SIRAL). The data are provided daily on a 25 km grid as 30-day averages for the months between September and May.", "links": [ { diff --git a/datasets/RDGBV4_1.json b/datasets/RDGBV4_1.json index a28a451129..d0b91b76d7 100644 --- a/datasets/RDGBV4_1.json +++ b/datasets/RDGBV4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDGBV4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains calculated balance velocity of the Greenland Ice Sheet during the last three quarters of the Holocene epoch (9ka).", "links": [ { diff --git a/datasets/RDK1_GTNL1_0.1.json b/datasets/RDK1_GTNL1_0.1.json index 4e6e4959c0..9daaa57c0c 100644 --- a/datasets/RDK1_GTNL1_0.1.json +++ b/datasets/RDK1_GTNL1_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDK1_GTNL1_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoton-L1 multispectral images from Resurs-DK\n\nMultispectral highly detailed-resolution optoelectronic sensor from \"Resurs-DK\" satellite that has circular sun synchronous orbit. Archival satellite images are presented by panchromatic data (580-800 nm) with 2,8 m spatial resolution and multispectral data (green - 500-600 nm, red - 600-700 nm, near infrared - 700-800 nm) with 3-5 m spatial resolution. Swath with of the system is 8-16 km. Data can be used for solving disparate issues in agriculture, ecology, cartography, construction, forestry, natural resources inventory and emergency situations monitoring.", "links": [ { diff --git a/datasets/RDSISC4_1.json b/datasets/RDSISC4_1.json index 4b880db095..03314bb395 100644 --- a/datasets/RDSISC4_1.json +++ b/datasets/RDSISC4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDSISC4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. The images are provided as TIFF files (.tif). Additional metadata are provided as CSV text files (.csv), which are available as a single zip file named RDSISC4_metadata.zip. An orthorectified version of this data set is available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images.", "links": [ { diff --git a/datasets/RDSISCO4_1.json b/datasets/RDSISCO4_1.json index 282fbc931f..c274e3d262 100644 --- a/datasets/RDSISCO4_1.json +++ b/datasets/RDSISCO4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDSISCO4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM collection. The standard (non-orthorectified) images are available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images.", "links": [ { diff --git a/datasets/RDWES1B_1.json b/datasets/RDWES1B_1.json index 799366463e..37d354fe5c 100644 --- a/datasets/RDWES1B_1.json +++ b/datasets/RDWES1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RDWES1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface elevations from retracked CryoSat-2 waveforms, as well as model fitting parameters used to retrack the waveform. The primary data set used in the production of these data come from the ESA CryoSat-2 satellite.", "links": [ { diff --git a/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json b/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json index aecfb6b130..42d0e71124 100644 --- a/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json +++ b/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RECON_SEA_LEVEL_OST_L4_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges. The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993. Tide gauges have measured sea level over the last 200 years, with some records extending back to 1807, but they only provide regional coverage, not global. Combining satellite altimetry with tide gauges, using a technique known as sea level reconstruction, results in a dataset with the record length of the tide gauges and the near-global coverage of satellite altimetry. Cyclostationary empirical orthogonal functions (CSEOFs), derived from satellite altimetry, are combined with historical sea level measurements from tide gauges to create the Reconstructed Sea Level dataset spanning from 1950 through 2009. Combining the altimetric and tide gauge records alleviates the difficulties caused by the short record length and poor spatial distribution of the satellite altimetry and tide gauges, respectively. Previous sea level reconstructions have utilized empirical orthogonal functions (EOFs) as basis functions, but by using CSEOFs and by addressing other aspects of the reconstruction procedure, an alternative sea level reconstruction can be computed. The resulting reconstructed sea level dataset has weekly temporal resolution and half-degree spatial resolution. For specific information on the algorithm and how the CSEOFs are applied to the tide gauge data please see Hamlington et al. 2011.", "links": [ { diff --git a/datasets/RED_TIDE_0.json b/datasets/RED_TIDE_0.json index ceac059f6c..38ff7f35ac 100644 --- a/datasets/RED_TIDE_0.json +++ b/datasets/RED_TIDE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RED_TIDE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Central Florida Gulf Coast in 1998 and 2005.", "links": [ { diff --git a/datasets/REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0_1.0.json b/datasets/REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0_1.0.json index 4289dbb3f3..58ad28d875 100644 --- a/datasets/REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0_1.0.json +++ b/datasets/REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis by the Oceanographic Modeling and Observation Network (REMO) at Applied Meteorology Laboratory/Federal University of Rio de Janeiro (LMA/UFRJ) using the Barnes sub optimal interpolation (OI) technique on a regional 0.05 degree grid. REMO uses Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) satellites series (NOAA 15, NOAA 16, NOAA 17, NOAA 18 and NOAA 19) and Microwave Imager (TMI) data from Tropical Rainfall Measuring Mission (TRMM) which is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) to generate 0.05 degree daily cloud free blended (infrared and microwave) SST products (approximately 5.5 km). The data lies between latitudes 45 S and 15 N and longitudes 70 W and 15 W region and are fully validated by in situ measurements from eleven buoys of Prediction and Research Moored Array in the Tropical Atlantic (PIRATA).AVHRR is a scanning radiometer capable of detecting energy from land, ocean and atmosphere. It operates with six spectral bands arranged in the regions of visible and infrared region. TRMM was launched in December, 1997, having an orbital inclination of 53 degree and altitude 350 km, an equatorial orbit that ranges from 40 N to 40 S and a spatial resolution of 0.25 degree (∼27.75 km). Although infrared AVHRR SST data have high spatial resolution, they are contaminated by cloud cover and aerosols, while lower resolution microvwave TMI data are barely influenced by these.", "links": [ { diff --git a/datasets/RESOLUTE_0.json b/datasets/RESOLUTE_0.json index f82975790f..fbca2adcc9 100644 --- a/datasets/RESOLUTE_0.json +++ b/datasets/RESOLUTE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RESOLUTE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken off Cornwallis Island off Resolute in the mid to late 90s.", "links": [ { diff --git a/datasets/REYNOLDS_NCDC_L4_MONTHLY_V5_5.json b/datasets/REYNOLDS_NCDC_L4_MONTHLY_V5_5.json index 57717c044e..44fbdc623f 100644 --- a/datasets/REYNOLDS_NCDC_L4_MONTHLY_V5_5.json +++ b/datasets/REYNOLDS_NCDC_L4_MONTHLY_V5_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "REYNOLDS_NCDC_L4_MONTHLY_V5_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 5 of this dataset implements release 3.0 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) system data. As for the prior ERSST version, v5 implements Empirical Orthogonal Teleconnection analysis (EOT) but with an improved tuning method for sparsely sampled regions and periods. ERSST anomalies are computed with respect to a 1971-2000 monthly climatology. The version 5 has been improved from previous version 4. Major improvements in v5 include: 1) Inclusion and use of new sources and new versions of input datasets, such as data from Argo floats (new source), ICOADS R3.0 (from R2.5), HadISST2 (from HadISST1) sea ice concentration, and 2) Improved methodologies, such as inclusion of additional statistical modes, less spatial-temporal smoothing, better quality control method, and bias correction with baseline to modern buoy observations. The new version improves the spatial structures and magnitudes of El Nino and La Nina events. The ERSST v5 in netCDF format contains extended reconstructed sea surface temperature, SST anomaly, and associated estimated SST error standard deviation fields, in compliance with CF1.6 standard metadata.", "links": [ { diff --git a/datasets/RGGRV1B_1.json b/datasets/RGGRV1B_1.json index a0a8b27f76..90a1f78bf9 100644 --- a/datasets/RGGRV1B_1.json +++ b/datasets/RGGRV1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RGGRV1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Greenland and Antarctica gravity measurements taken from the Sander Geophysics AIRGrav airborne gravity system.", "links": [ { diff --git a/datasets/RICMIAAE_002.json b/datasets/RICMIAAE_002.json index 03234321f7..7a200e029d 100644 --- a/datasets/RICMIAAE_002.json +++ b/datasets/RICMIAAE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMIAAE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 Aerosol Product.It contains Aerosol optical depth and particle type, with associated atmospheric data over the RICO region.", "links": [ { diff --git a/datasets/RICMIB2E_002.json b/datasets/RICMIB2E_002.json index 5826195fb6..b05098dc8a 100644 --- a/datasets/RICMIB2E_002.json +++ b/datasets/RICMIB2E_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMIB2E_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the ellipsoid projected TOA Radiance over the RICO region,resampled to WGS84 ellipsoid corrected", "links": [ { diff --git a/datasets/RICMIRCM_004.json b/datasets/RICMIRCM_004.json index 2469f45059..9046936a1a 100644 --- a/datasets/RICMIRCM_004.json +++ b/datasets/RICMIRCM_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMIRCM_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the Radiometric camera-by-camera Cloud Mask dataset over the RICO region. It is used to determine whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over ocean. This version of the ESDT is used by MISR PGE 13.", "links": [ { diff --git a/datasets/RICMITAL_002.json b/datasets/RICMITAL_002.json index e6a80ea014..d0f8ae06df 100644 --- a/datasets/RICMITAL_002.json +++ b/datasets/RICMITAL_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMITAL_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 TOA/Cloud Albedo Product subset for the RICO region. It contains local,restrictive, and expansive albedo, with associated data.", "links": [ { diff --git a/datasets/RICMITCL_003.json b/datasets/RICMITCL_003.json index 14c98b3e36..7349321a75 100644 --- a/datasets/RICMITCL_003.json +++ b/datasets/RICMITCL_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMITCL_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 TOA/Cloud Classifiers Product subset for the RICO region. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.", "links": [ { diff --git a/datasets/RICMITST_002.json b/datasets/RICMITST_002.json index c589f6a9a6..eec03f9b31 100644 --- a/datasets/RICMITST_002.json +++ b/datasets/RICMITST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RICMITST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 TOA/Cloud Stereo Product subset for the RICO region. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.", "links": [ { diff --git a/datasets/RIO-SFE_0.json b/datasets/RIO-SFE_0.json index 991f56be77..39c445a228 100644 --- a/datasets/RIO-SFE_0.json +++ b/datasets/RIO-SFE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RIO-SFE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Remote and In Situ Observations - San Francisco Bay and Delta Ecosystem (RIO-SFE)", "links": [ { diff --git a/datasets/ROAVERRS_0.json b/datasets/ROAVERRS_0.json index 4960d55010..cf8fa04ce9 100644 --- a/datasets/ROAVERRS_0.json +++ b/datasets/ROAVERRS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ROAVERRS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken off the Antarctic coast in the Ross Sea between 1996 and 1998 under the Research on Ocean-Atmosphere Variability and Ecosystem Response in the Ross Sea (ROAVERRS).", "links": [ { diff --git a/datasets/RONGOWAI_L1_SDR_V1.0_1.0.json b/datasets/RONGOWAI_L1_SDR_V1.0_1.0.json index 303246c023..a079866001 100644 --- a/datasets/RONGOWAI_L1_SDR_V1.0_1.0.json +++ b/datasets/RONGOWAI_L1_SDR_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RONGOWAI_L1_SDR_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.

\r\n\r\nThis Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.\r\n\r\nEach netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.", "links": [ { diff --git a/datasets/RON_BROWN_0.json b/datasets/RON_BROWN_0.json index 3947b75a28..c4e567b791 100644 --- a/datasets/RON_BROWN_0.json +++ b/datasets/RON_BROWN_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RON_BROWN_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by the NOAA research vessel, the Ron H. Brown between 2000 and 2002.", "links": [ { diff --git a/datasets/RP1_GSA_0.1.json b/datasets/RP1_GSA_0.1.json index c27d0882ed..5a1ad1d45b 100644 --- a/datasets/RP1_GSA_0.1.json +++ b/datasets/RP1_GSA_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RP1_GSA_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hyperspectral imaging from Resurs-P N1\n\nHyperspectral sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor surveys earth surface in the 96 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. The data can be used for solving wide variety of problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. Collected data allows identifying vegetation composition, pollution films composition, mineral composition of soils, subsoils, rocks and other parameters of natural and anthropogenic objects.", "links": [ { diff --git a/datasets/RP1_GTNL1_0.1.json b/datasets/RP1_GTNL1_0.1.json index 533398ff5e..59187c3bc4 100644 --- a/datasets/RP1_GTNL1_0.1.json +++ b/datasets/RP1_GTNL1_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RP1_GTNL1_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoton-L1 multispectral images from Resurs-P N1\n\nMultispectral high-resolution optoelectronic sensor from ?Resurs-P N1? satellite that has circular sun synchronous orbit. The sensor can survey earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory and emergency situation monitoring.", "links": [ { diff --git a/datasets/RP2_GSA_0.1.json b/datasets/RP2_GSA_0.1.json index 6d7512bb17..7fded69ab0 100644 --- a/datasets/RP2_GSA_0.1.json +++ b/datasets/RP2_GSA_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RP2_GSA_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hyperspectral imaging from Resurs-P N2\n\nHyperspectral sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in the 96-255 narrow spectral bands from 400 to 1100 nm with 30 m spatial resolution and 3 days revisit frequency. Swath with of the sensor is 25 km. Received data can be used to address different problems in agriculture, ecology, cartography, construction, forestry, natural resources inventory, emergency situations monitoring. This data allows identification of vegetation composition, pollution, mineral composition of soils, subsoils, rock. Many other parameters of natural and anthropogenic objects can also be determined.", "links": [ { diff --git a/datasets/RP2_GTNL1_0.1.json b/datasets/RP2_GTNL1_0.1.json index 09806dcb08..0bda118d41 100644 --- a/datasets/RP2_GTNL1_0.1.json +++ b/datasets/RP2_GTNL1_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RP2_GTNL1_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoton-L1 multispectral images from Resurs-P N2\n\nMultispectral high resolution optoelectronic sensor from ?Resurs-P N2? satellite that has circular sun synchronous orbit. The sensor allows surveying earth surface in 7 spectral bands (450-520 nm, 520-600 nm, 610-680 nm, 670-700 nm, 700-730nm, 720-800 nm, 800-900 nm) with 3 m spatial resolution and panchromatic band (580-800 nm) with 1 m spatial resolution. Swath with of the sensor is 38 km. The revisit frequency is less than 3 days. Data from the sensor can be used for infrastructure monitoring, cadastral and topographical surveying, engineering surveying, natural resources inventory, emergency situation monitoring.", "links": [ { diff --git a/datasets/RRRAG4_1.json b/datasets/RRRAG4_1.json index 02baeb6027..4aebe3fdcd 100644 --- a/datasets/RRRAG4_1.json +++ b/datasets/RRRAG4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RRRAG4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the traced deep radiostratigraphy of the Greenland Ice Sheet from airborne deep ice-penetrating radar data collected by The University of Kansas Improved Coherent Radar Depth Sounder (ICORDS), Advanced Coherent Radar Depth Sounder (ACORDS), Multi-Channel Radar Depth Sounder (MCRDS), and Multichannel Coherent Radar Depth Sounder (MCoRDS) instruments between 1993 and 2013. This is an IceBridge-related data set.", "links": [ { diff --git a/datasets/RS2_AWIF_STUC00GTD_1.0.json b/datasets/RS2_AWIF_STUC00GTD_1.0.json index 1c40a54b08..2aeb39bd02 100644 --- a/datasets/RS2_AWIF_STUC00GTD_1.0.json +++ b/datasets/RS2_AWIF_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RS2_AWIF_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coarse resolution multi-spectral sensor, AWIFS operates in four spectral bands - B2, B3, B4, B5 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 56m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/RS2_LIS3_STUC00GTD_1.0.json b/datasets/RS2_LIS3_STUC00GTD_1.0.json index a6333e5e31..47f2315ca7 100644 --- a/datasets/RS2_LIS3_STUC00GTD_1.0.json +++ b/datasets/RS2_LIS3_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RS2_LIS3_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared \r\n(SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/RS2_LIS4_FMX_STUC00GTD_1.0.json b/datasets/RS2_LIS4_FMX_STUC00GTD_1.0.json index e815756205..bdb8631523 100644 --- a/datasets/RS2_LIS4_FMX_STUC00GTD_1.0.json +++ b/datasets/RS2_LIS4_FMX_STUC00GTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RS2_LIS4_FMX_STUC00GTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coarse resolution multi-spectral sensor, LIS4 FMX operates in four spectral bands - B2, B3, B4, B5 in visible near infrared (VNIR) and B5 in Short Wave Infrared (SWIR) providing data with 5.8m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit products.", "links": [ { diff --git a/datasets/RSAT-1_L0_1.json b/datasets/RSAT-1_L0_1.json index b5d561a3e9..71927fb23b 100644 --- a/datasets/RSAT-1_L0_1.json +++ b/datasets/RSAT-1_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSAT-1_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "RADARSAT-1 Level 0", "links": [ { diff --git a/datasets/RSAT-1_L1_1.json b/datasets/RSAT-1_L1_1.json index 50e2886f91..3f1cbd34f9 100644 --- a/datasets/RSAT-1_L1_1.json +++ b/datasets/RSAT-1_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSAT-1_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "RADARSAT-1 Level 1 Amplitude Images", "links": [ { diff --git a/datasets/RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1_1.0.json b/datasets/RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1_1.0.json index d6762099f4..9266571ee9 100644 --- a/datasets/RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1_1.0.json +++ b/datasets/RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the multi-sourced microwave radiometer wind speed, rain and cloud liquid water data collocated to RapidScat Level 2B wind vector cell (WVC) locations. The corresponding NASA mission is officially referred to as ISS-RapidScat. This dataset is produced by Remote Sensing Systems (RSS) with direct funding from the JPL RapidScat project. All of the collocated radiometer data is produced by RSS. The co-located radiometer sources include: 1) DMSP SSM/I (F15) and SSMIS (F16/F17), 2) Coriolis WindSat, 3) GCOM-W1 AMSR2 and 4) GPM Core GMI; more details on these radiometer sources and sensors can be extracted by scrolling down to the \"Platform/Sensor\" section below this description. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-4 file format that follows the netCDF \"classic\" model and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.", "links": [ { diff --git a/datasets/RSCAT_L1B_V2.0_2.0.json b/datasets/RSCAT_L1B_V2.0_2.0.json index 3bf13b9752..c1d470233d 100644 --- a/datasets/RSCAT_L1B_V2.0_2.0.json +++ b/datasets/RSCAT_L1B_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_L1B_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the ISS-RapidScat Version 2.0 Level 1B geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. Version 2.0 represents a complete historical re-processing of the L1B data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). The Version 2.0 is also the dataset used to derive the Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. This dataset is intended for expert use only. If you must use RapidScat Sigma-0 data but you are unsure about how to use the L1B data record, please consider using either of the following L2A datasets: 1) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_25KM_V2.0 or 2) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_12KM_V2.0. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the ISS Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. ", "links": [ { diff --git a/datasets/RSCAT_L2A_12KM_V2.0_2.0.json b/datasets/RSCAT_L2A_12KM_V2.0_2.0.json index 3c783aab5b..9268ca59b2 100644 --- a/datasets/RSCAT_L2A_12KM_V2.0_2.0.json +++ b/datasets/RSCAT_L2A_12KM_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_L2A_12KM_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 2.0 ISS-RapidScat on Level 2A 12.5 km science data record, which provides surface-flagged sigma-0 in 12.5 km Wind Vector Cells processed using the pulse \"slice\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the RapidScat instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.", "links": [ { diff --git a/datasets/RSCAT_L2A_25KM_V2.0_2.0.json b/datasets/RSCAT_L2A_25KM_V2.0_2.0.json index 10bc35bace..4cc6036bfd 100644 --- a/datasets/RSCAT_L2A_25KM_V2.0_2.0.json +++ b/datasets/RSCAT_L2A_25KM_V2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_L2A_25KM_V2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the Version 2.0 ISS-RapidScat Level 2A 25km science data record, which provides surface-flagged sigma-0 in 25km Wind Vector Cells processed using the pulse \"egg\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the SeaWinds instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.", "links": [ { diff --git a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json index 1c6c5e7bc0..031dd5b4f8 100644 --- a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json +++ b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via Direct Download and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This climate quality data set differs from the nominal \"slice\" L2B dataset as follows: 1) it uses full antenna footprint measurements (~20-km) without subdividing by range (~7-km) and 2) the absolute calibration has been modified for the two different low signal-to-noise ratio (SNR) mode data sets: LowSNR1 14 August 2015 to 18 September 2015; LowSNR2 6 October 2015 to 7 February 2016. The above enhancements allow this dataset to provide consistent calibration across all SNR states. Low SNR periods and other key quality control (QC) issues are tracked and kept up-to-date in PO.DAAC Drive at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/rapidscat/open/L1B/docs/revtime.csv. If you have any questions, please visit our user forums: https://podaac.jpl.nasa.gov/forum/.", "links": [ { diff --git a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json index fdecf4f034..303d6556d0 100644 --- a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json +++ b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the RapidScat Level 2B 12.5km Version 2.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. The new version has two important improvements over the previous version 1.0. First, an SST-dependent GMF developed by Lucrezia Ricciardulli of Remote Sensing Systems is used in wind retrieval in order to fix persistent speed biases in Ku-band data over cold ocean. Second, flagging is simplified and extra flags are provided. All the previously existing flags are still there and still reflect the same meaning and purpose. A new single bit wind_retrieval_likely_corrupted_flag specifies the approximately 3% of the data which is known to have suboptimal performance due to rain, ice, or a few other rare anomalous cases. Another bit wind_retrieval_possibly_corrupted_flag specifies the approximately 15% of the data near rain, near ice, or near the coast, that is thought to be high quality but may not match up well with numerical wind models due to either remaining rain/ice/land contamination or variability in the winds near ice, rain, and coasts that are not reflected in the NWPs. In addition to these two new bits, copious quality information is provided in the data to allow users to tailor flags to meet their own needs. There is also an added a global attribute called rev_status that specifies whether the RapidScat Instrument was in the original (highest data quality) high SNR mode, or one of the four low SNR time periods, the latter of which indicates the accuracy of winds below 5 m/s is degraded. This attribute also serves to identify MARGINAL orbits in which there are large gaps in the data record due to suboptimal spacecraft attitude. Other than gaps in the data, the accuracy of the winds in the MARGINAL orbits are similar to other orbits. This dataset is provided in netCDF-4 format and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.", "links": [ { diff --git a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1.json b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1.json index 3d41e4a112..0766bbd4ec 100644 --- a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1.json +++ b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.1 dataset differs from the previous Version 1 dataset as follows: 1) A new neural network approach for high wind speeds provided rain corrections for the \"retrieve_wind_speed\" variable for wind speeds in excess of 15 m/s. 2) The data variables containing the number of measurements of each type for each wind vector cell have been corrected; these variables include \"number_in_aft\", \"number_in_fore\", \"number_out_aft\", and \"number_out_fore\". 3) The \"wind_obj\" data variable has been corrected to include the proper data for the conditional probability for the objective DIRTH function values. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions, please contact podaac@podaac.jpl.nasa.gov", "links": [ { diff --git a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2.json b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2.json index bd2d93661b..2b794a0eb3 100644 --- a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2.json +++ b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.", "links": [ { diff --git a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3.json b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3.json index f315da5393..79fa03f0cc 100644 --- a/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3.json +++ b/datasets/RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.", "links": [ { diff --git a/datasets/RSES_PCM_1.json b/datasets/RSES_PCM_1.json index 0d6d543395..78cd4e44a2 100644 --- a/datasets/RSES_PCM_1.json +++ b/datasets/RSES_PCM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSES_PCM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of cosmogenic exposure ages for samples collected by Research School of Earth Sciences in the Prince Charles Mountains and vicinity.\n\nThus far work has been carried out in the 2001/2002, 2002/2003, 2003/2004 and 2004/2005 field seasons.\n\nCurrently, the only data publicly available is an excel spreadsheet detailing sampling locations.\n\nThe objectives of this project were:\n\nTo develop a comprehensive understanding of the Lambert Glacier of East Antarctica, from the time of the last maximum glaciation to the present, through an integrated and interdisciplinary study combining new field evidence - ice retreat history from cosmogenic exposure dating, geodetic measurements of crustal rebound, satellite measurements of present ice heights and changes therein - with other geological and glaciological data and numerical geophysical modelling advances. The project contributes to the quantitative characterisation of the complex interactions between ice-sheets, oceans and solid earth within the climate system. Outcomes have implications for geophysics, glaciology, geomorphology, climate, and past and future sea-level change.\n\nThis work was completed as part of ASAC projects 2502 and 2516 (ASAC_2502 and ASAC_2516).\n\nThe fields in this dataset are:\n\nSample\nDate\nCollector\nType\nLithology\nLocation\nElevation\nLatitude\nLongitude", "links": [ { diff --git a/datasets/RSFDCE_KLIM4.json b/datasets/RSFDCE_KLIM4.json index 0867c45db5..e3877ed54b 100644 --- a/datasets/RSFDCE_KLIM4.json +++ b/datasets/RSFDCE_KLIM4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSFDCE_KLIM4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrometeorological data on the conditions of the environment are held\n by the Russian State Fund of data. This dataset was created by West\n Sybiria Computer Centre in 1977 and containes data from 1078 stations\n of the USSR. Data is currently stored on magnetic tape (800 bit/inch).", "links": [ { diff --git a/datasets/RSFDCE_KLIM5.json b/datasets/RSFDCE_KLIM5.json index a9b9818d38..2668dc60e3 100644 --- a/datasets/RSFDCE_KLIM5.json +++ b/datasets/RSFDCE_KLIM5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSFDCE_KLIM5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrometeorological data on the conditions of the environment are held\n by the Russian State Fund of data. This dataset was created by West\n Subiria Computer Centre in 1977 and containes data from 1078 stations\n of the USSR. Data is currently stored on magnetic tape (800 bit/inch).", "links": [ { diff --git a/datasets/RSS18_AVIRIS_L1B_449_1.json b/datasets/RSS18_AVIRIS_L1B_449_1.json index 6296e5a159..bf118d6aa9 100644 --- a/datasets/RSS18_AVIRIS_L1B_449_1.json +++ b/datasets/RSS18_AVIRIS_L1B_449_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSS18_AVIRIS_L1B_449_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada. BOREAS focused on improving the understanding of exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. NASA's AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. For these data, AVIRIS-Classic was deployed on NASA's ER-2 high altitude aircraft. These spectra are acquired as images with 20-meter spatial resolution, 11 km swath width, and flight lines up to 800 km in length. The measurements are spectrally, radiometrically, and geometrically calibrated. There are seven flight lines subdivided into 66 scenes. The dataset includes the radiance imagery cube for each scene along with calibration and navigation information. The radiance data are in instrument coordinates, georeferenced by center of each scan line, and provided in a binary file. Metadata are included in a mixture of binary and text file formats.", "links": [ { diff --git a/datasets/RSS_WindSat_L1C_TB_V08.0_8.0.json b/datasets/RSS_WindSat_L1C_TB_V08.0_8.0.json index c1b263e71d..4a17a3678f 100644 --- a/datasets/RSS_WindSat_L1C_TB_V08.0_8.0.json +++ b/datasets/RSS_WindSat_L1C_TB_V08.0_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RSS_WindSat_L1C_TB_V08.0_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run.", "links": [ { diff --git a/datasets/Radarsat-2_8.0.json b/datasets/Radarsat-2_8.0.json index 72b12461e7..e1dc27ce7a 100644 --- a/datasets/Radarsat-2_8.0.json +++ b/datasets/Radarsat-2_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Radarsat-2_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. BEAM MODE: Standard PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 or 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 9.0 or 13.5 x 7.7 (SLC), 26.8 - 17.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 100 x 100 Range of Angle of Incidence (deg): 20 - 52 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. BEAM MODE: Wide PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 10 x 10 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 40.0 - 19.2 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 150 x 150 Range of Angle of Incidence (deg): 20 - 45 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: Fine PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 4.7 x 5.1 (SLC), 3.13 x 3.13 (SGX), 6.25 x 6.25 (SSG, SPG) Resolution - Range x Azimuth (m): 5.2 x 7.7 (SLC), 10.4 - 6.8 x 7.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 50 x 50 Range of Angle of Incidence (deg): 30 - 50 No. of Looks - Range x Azimuth: 1 x 1 (SLC,SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. BEAM MODE: Extended Low PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 x 5.1 (SLC), 10.0 x 10.0 (SGX), 12.5 x 12.5 (SSG, SPG) Nominal Resolution - Range x Azimuth (m): 9.0 x 7.7 (SLC), 52.7 - 23.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 170 x 170 Range of Angle of Incidence (deg): 10 - 23 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. BEAM MODE: Extended High PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 18.2 - 15.9 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 75 x 75 Range of Angle of Incidence (deg): 49 - 60 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: ScanSAR Narrow PRODUCT: SCN, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 25 x 25 Nominal Resolution - Range x Azimuth (m):81-38 x 40-70 Nominal Scene Size - Range x Azimuth (km): 300 x 300 Range of Angle of Incidence (deg): 20 - 46 No. of Looks - Range x Azimuth: 2 x 2 Polarisations - Options: \u2022 Single Co or Cross: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. Polarisation can be single and dual. BEAM MODE: ScanSAR Wide PRODUCT: SCW, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 50 x 50 Resolution - Range x Azimuth (m): 163.0 - 73 x 78-106 Nominal Scene Size - Range x Azimuth (km): 500 x 500 Range of Angle of Incidence (deg): 20 - 49 No. of Looks - Range x Azimuth: 4 x 2 Polarisations - Options: \u2022 Single Co or Cross: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track", "links": [ { diff --git a/datasets/Radial_Growth_PRI_1781_1.json b/datasets/Radial_Growth_PRI_1781_1.json index e625da5614..042b0d94d4 100644 --- a/datasets/Radial_Growth_PRI_1781_1.json +++ b/datasets/Radial_Growth_PRI_1781_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Radial_Growth_PRI_1781_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics.", "links": [ { diff --git a/datasets/Rain-on-Snow_Data_1611_1.json b/datasets/Rain-on-Snow_Data_1611_1.json index c5c900bd87..ecd0d2c2a8 100644 --- a/datasets/Rain-on-Snow_Data_1611_1.json +++ b/datasets/Rain-on-Snow_Data_1611_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Rain-on-Snow_Data_1611_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz.", "links": [ { diff --git a/datasets/RapidEye.ESA.archive_7.0.json b/datasets/RapidEye.ESA.archive_7.0.json index 0816953351..e127de2398 100644 --- a/datasets/RapidEye.ESA.archive_7.0.json +++ b/datasets/RapidEye.ESA.archive_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RapidEye.ESA.archive_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products.", "links": [ { diff --git a/datasets/RapidEye.Full.archive_6.0.json b/datasets/RapidEye.Full.archive_6.0.json index b9243479ed..dd04cc2f86 100644 --- a/datasets/RapidEye.Full.archive_6.0.json +++ b/datasets/RapidEye.Full.archive_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RapidEye.Full.archive_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RapidEye Level 3A Ortho Tile, both Visual (in natural colour) and Analytic (multispectral), full archive and new tasking products are available as part of Planet imagery offer. The RapidEye Ortho Tile product (L3A) is radiometric, sensor and geometrically corrected (by using DEMs with a post spacing of between 30 and 90 meters) and aligned to a cartographic map projection. Ground Control Points (GCPs) are used in the creation of every image and the accuracy of the product will vary from region to region based on available GCPs. Product Components and Format: \u2022 Image File \u2013 GeoTIFF file that contains image data and geolocation information \u2022 Metadata File \u2013 XML format metadata file \u2022 Unusable Data Mask (UDM) file \u2013 GeoTIFF format Bands: 3-band natural color (blue, green, red) or 5-band multispectral image (blue, green, red, red edge, near-infrared) Ground Sampling Distance (nadir): 6.5 m at nadir (average at reference altitude 475 km) Projection: UTM WGS84 Accuracy: depends on the quality of the reference data used (GCPs and DEMs) The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations.RapidEye collection has worldwide coverage: the Planet Explorer Catalogue (https://www.planet.com/explorer/) can be accessed (Planet registration requested) to discover and check the data readiness. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf).", "links": [ { diff --git a/datasets/RapidEye.South.America_6.0.json b/datasets/RapidEye.South.America_6.0.json index 2f0c208eb8..a3ab3d8626 100644 --- a/datasets/RapidEye.South.America_6.0.json +++ b/datasets/RapidEye.South.America_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RapidEye.South.America_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESA, in collaboration with BlackBridge, has collected this RapidEye dataset of level 3A tiles covering more than 6 million km2 of South American countries: Paraguay, Ecuador, Chile, Bolivia, Peru, Uruguay and Argentina. The area is fully covered with low cloud coverage", "links": [ { diff --git a/datasets/RapidEye.time.series.for.Sentinel-2_6.0.json b/datasets/RapidEye.time.series.for.Sentinel-2_6.0.json index 4a9114cde0..96d5eecef6 100644 --- a/datasets/RapidEye.time.series.for.Sentinel-2_6.0.json +++ b/datasets/RapidEye.time.series.for.Sentinel-2_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RapidEye.time.series.for.Sentinel-2_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The European Space Agency, in collaboration with BlackBridge collected 2 time series datasets with a 5 day revisit at high resolution: \u2022 February to June 2013 over 14 selected sites around the world \u2022 April to September 2015 over 10 selected sites around the world The RapidEye Earth Imaging System provides data at 5 m spatial resolution (multispectral L3A orthorectified). The products are radiometrically and sensor corrected similar to the 1B Basic product, but have geometric corrections applied to the data during orthorectification using DEMs and GCPs. The product accuracy depends on the quality of the ground control and DEMs used. The imagery is delivered in GeoTIFF format with a pixel spacing of 5 metres. The dataset is composed of data over: \u2022 14 selected sites in 2013: Argentina, Belgium, Chesapeake Bay, China, Congo, Egypt, Ethiopia, Gabon, Jordan, Korea, Morocco, Paraguay, South Africa and Ukraine. \u2022 10 selected sites in 2015: Limburgerhof, Railroad Valley, Libya4, Algeria4, Figueres, Libya1, Mauritania1, Barrax, Esrin, Uyuni Salt Lake.", "links": [ { diff --git a/datasets/Rauer_Group_Geomorphic_Map_1.json b/datasets/Rauer_Group_Geomorphic_Map_1.json index 507a43f6a3..39e1d2ff5e 100644 --- a/datasets/Rauer_Group_Geomorphic_Map_1.json +++ b/datasets/Rauer_Group_Geomorphic_Map_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Rauer_Group_Geomorphic_Map_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GIS data was produced following a three week expedition to the region onboard the Alfred Wegener Institute research vessel Polarstern (cruise ANT-XXIII/9) in 2007, and subsequent analysis of aerial and field photography on return to Australia. The results are discussed in the expedition report and the following manuscripts. Cosmogenic exposure samples that will further constrain the age of ice retreat in the region are expected to be finalised during 2012. The data are intended to accompany existing polygons that detail the lakes, bedrock and penguin breeding sites (i.e. areas of biogenic sediment) from the area that are already present in the AAD digital database.\n\nSee the word document in the download file for further information.", "links": [ { diff --git a/datasets/RdlP_PT_0.json b/datasets/RdlP_PT_0.json index 4a0a50d24b..e8bb1bd94e 100644 --- a/datasets/RdlP_PT_0.json +++ b/datasets/RdlP_PT_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RdlP_PT_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements in the north coast of the Rio de la Plata estuary,\u00a0near Punta del Tigre, San Jose", "links": [ { diff --git a/datasets/ReSALT_ALT_GPR_1265_1.json b/datasets/ReSALT_ALT_GPR_1265_1.json index d56aea7121..aaf6e83af4 100644 --- a/datasets/ReSALT_ALT_GPR_1265_1.json +++ b/datasets/ReSALT_ALT_GPR_1265_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ReSALT_ALT_GPR_1265_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes estimates of permafrost Active Layer Thickness (ALT; cm), and calculated uncertainties, derived using a ground-penetrating radar (GPR) system in the field in August 2014 near Toolik Lake and Happy Valley on the North Slope of Alaska. GPR measurements were taken along 10 transects of varying length (approx. 1 to 7 km). Traditional ALT estimates from mechanical probing every 100 to 500 m along each transect are also included. These data are suitable for future studies of how ALT varies over relatively large geological features, such as hills and valleys, wetland areas, and drained lake basins.", "links": [ { diff --git a/datasets/ReSALT_InSAR_Barrow_1266_1.json b/datasets/ReSALT_InSAR_Barrow_1266_1.json index 57d1656ee0..59f3ffa60a 100644 --- a/datasets/ReSALT_InSAR_Barrow_1266_1.json +++ b/datasets/ReSALT_InSAR_Barrow_1266_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ReSALT_InSAR_Barrow_1266_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface. This data set includes the mean Remotely Sensed Active Layer Thickness (ReSALT) over years 2006 to 2011 for the region near Barrow, Alaska. The data were produced by an Interferometric Synthetic Aperture Radar (InSAR) technique that measures seasonal surface subsidence and infers ALT. ReSALT estimates were validated by comparison with ground-based ALT obtained using probing and Ground Penetrating Radar at multiple sites. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain.These data provide gridded (30-m) estimates of active layer thickness (cm; ALT) and seasonal subsidence (cm), as well as calculated uncertainty in each of these parameters. This data set was developed in support of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign.The data are presented in one netCDF (*.nc) file. ", "links": [ { diff --git a/datasets/Reflectance_Spectra_Alaska_1685_1.json b/datasets/Reflectance_Spectra_Alaska_1685_1.json index 47058dc1d8..9a3afbfaa0 100644 --- a/datasets/Reflectance_Spectra_Alaska_1685_1.json +++ b/datasets/Reflectance_Spectra_Alaska_1685_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Reflectance_Spectra_Alaska_1685_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset reports full-spectrum (350-2500 nm) reflectance measurements of diverse plant communities at the plot-level and individual plant species at the leaf-level, at multiple sites across northern Alaska during the 2017 and 2018 summer field seasons. Plot-level reflectance data (1 m2) include an assemblage of vascular and non-vascular species comprising tundra plant communities, while leaf-level scans are specific to one particular tundra species. Reflectance measurements were collected using a HR-1024i spectrometer and data were calibrated using a Spectralon white reference panel during sampling to correct for changing light conditions. Sampling methods and data and metadata structure follow that of the Ecological Spectral Information System (EcoSIS) Spectral Library.", "links": [ { diff --git a/datasets/RemSensPOC_0.json b/datasets/RemSensPOC_0.json index 10f929b9b1..04e3ec7a73 100644 --- a/datasets/RemSensPOC_0.json +++ b/datasets/RemSensPOC_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RemSensPOC_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken for the purpose of validating remote-sensing-derived particulate organic carbon.", "links": [ { diff --git a/datasets/ResourceSat-1-IRS-P6.archive_6.0.json b/datasets/ResourceSat-1-IRS-P6.archive_6.0.json index b1611bb3e5..e8698bf0db 100644 --- a/datasets/ResourceSat-1-IRS-P6.archive_6.0.json +++ b/datasets/ResourceSat-1-IRS-P6.archive_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ResourceSat-1-IRS-P6.archive_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ResourceSat-1 (also known as IRS-P6) archive products are available as below. \u2022 LISS-IV MN: Mono-Chromatic, Resolution 5 m, Coverage 70 km x 70 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2010, Global Archive 2003 - 2013 \u2022 LISS-III: Multi-spectral, Resolution 20 m, Coverage 140 km x 140 km, Radiometrically and Ortho (DN) corrected (ortho delivered without Band 5), Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 \u2022 AWiFS: Multi-spectral, Resolution 60 m, Coverage 370 km x 370 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 Note: \u2022 LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used. \u2022 For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. \u2018ResourceSat-1 archive\u2019 collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf).", "links": [ { diff --git a/datasets/ResourceSat-2.archive.and.tasking_6.0.json b/datasets/ResourceSat-2.archive.and.tasking_6.0.json index c5c3ecad4a..d17d2b45aa 100644 --- a/datasets/ResourceSat-2.archive.and.tasking_6.0.json +++ b/datasets/ResourceSat-2.archive.and.tasking_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ResourceSat-2.archive.and.tasking_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ResourceSat-2 (also known as IRS-R2) archive and tasking products are available as below: Sensor: LISS-IV Type: Mono-Chromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected and Ortho corrected (DN) Neustralitz archive: 2014 Global archive: 2011 Sensor: LISS-III Type: Multi-spectral Resolution (m): 20 Coverage (km x km): 140 x 140 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Sensor: AWiFS Type: Multi-spectral Resolution (m): 60 Coverage (km x km): 370 x 370 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Note: \u2022 LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used.For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. \u2018ResourceSat-2 archive and tasking\u2019 collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf).", "links": [ { diff --git a/datasets/Respiration_622_1.json b/datasets/Respiration_622_1.json index 0ef8480424..41a3f941e8 100644 --- a/datasets/Respiration_622_1.json +++ b/datasets/Respiration_622_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Respiration_622_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates.", "links": [ { diff --git a/datasets/RiSCC_Outcomes_Bibliography_1.json b/datasets/RiSCC_Outcomes_Bibliography_1.json index d3b463f05c..b4b525349d 100644 --- a/datasets/RiSCC_Outcomes_Bibliography_1.json +++ b/datasets/RiSCC_Outcomes_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RiSCC_Outcomes_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references.", "links": [ { diff --git a/datasets/RiSCC_Research_Support_Bibliography_1.json b/datasets/RiSCC_Research_Support_Bibliography_1.json index 9ec5193e61..56ade43e48 100644 --- a/datasets/RiSCC_Research_Support_Bibliography_1.json +++ b/datasets/RiSCC_Research_Support_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RiSCC_Research_Support_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references.", "links": [ { diff --git a/datasets/River_Ice_Breakup_Freezeup_1697_1.json b/datasets/River_Ice_Breakup_Freezeup_1697_1.json index 4e93a8b311..52a6eca564 100644 --- a/datasets/River_Ice_Breakup_Freezeup_1697_1.json +++ b/datasets/River_Ice_Breakup_Freezeup_1697_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "River_Ice_Breakup_Freezeup_1697_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue.", "links": [ { diff --git a/datasets/RoyalPenguin1955-1969_1.json b/datasets/RoyalPenguin1955-1969_1.json index 9b788b7fb7..48f23a607c 100644 --- a/datasets/RoyalPenguin1955-1969_1.json +++ b/datasets/RoyalPenguin1955-1969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "RoyalPenguin1955-1969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are contained in a number of log books in hand written form (now scanned onto CD ROM. They were gathered according to a protocol updated annually by the Principal Investigator, DR Robert Carrick (now deceased). Details are contained in the paper Carrick R (1972) Population ecology of the Australian black-backed magpie, royal penguin, and silver gull. in: Population ecology of migratory birds - A symposium. US Dept of the Interior, Fish and wildlife service. Wildlife Research Report 2. pp 41-99. The only other information on the Royal penguin population to come from these investigations is the PhD Thesis of G.T. Smith, Studies on the behaviour and reproduction of the Royal penguin Eudyptes chrysolophus schlegeli. Australian National University April 1970.\n\nThe log books contain a vast array of observations on the Royal penguin. Major observations/studies include banding of chicks and adults, breeding chronology, egg laying, breeding success, arrival weights, movements within and between colonies.\n\nThe protocols for the collection of the data are missing although some instructions and notes are included in the volumes.\n \nSome data have also been entered into an excel spreadsheet.", "links": [ { diff --git a/datasets/Ruker_rymill_sat_1.json b/datasets/Ruker_rymill_sat_1.json index aa33f686f4..72a7d1eb55 100644 --- a/datasets/Ruker_rymill_sat_1.json +++ b/datasets/Ruker_rymill_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Ruker_rymill_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two satellite images maps of Mt Ruker and Mt Rymill in the Australian Antarctic Territory were produced by the Australian Antarctic Division in 1998. Both maps are at a scale of 1:100 000 using Landsat TM imagery.\n\nData source: \nMount Ruker - Landsat TM imagery, scenes 128/112, acquired 29 November 1989. \nMount Rymill - Landsat TM imagery, scenes 128/111 and 128/112, acquired 18 March 1989 and 29 November 1989 respectively.\n\nNomenclature: Names have been approved by the Antarctic Names Committee of Australia. Please see the URL link for details on the images and processes used to produce these maps.", "links": [ { diff --git a/datasets/Russian_Forest_Disturbance_1294_1.json b/datasets/Russian_Forest_Disturbance_1294_1.json index 4ea87f8f70..e78c0b955f 100644 --- a/datasets/Russian_Forest_Disturbance_1294_1.json +++ b/datasets/Russian_Forest_Disturbance_1294_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Russian_Forest_Disturbance_1294_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period. Forest pixels were classified by year of latest disturbance or as undisturbed.", "links": [ { diff --git a/datasets/Rwanda Field Boundary Competition Dataset_1.json b/datasets/Rwanda Field Boundary Competition Dataset_1.json index 3951bc5a96..957987cea9 100644 --- a/datasets/Rwanda Field Boundary Competition Dataset_1.json +++ b/datasets/Rwanda Field Boundary Competition Dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Rwanda Field Boundary Competition Dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains field boundaries for smallholder farms in eastern Rwanda. The Nasa Harvest program funded a team of annotators from TaQadam to label Planet imagery for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet's NICFI program. Planet's basemap imagery is provided for six months (March, April, August, October, November and December). The paired dataset is provided in 256x256 chips for a total of 70 tiles covering 1532 individual fields.

Input imagery consists of a time series of planet Basemaps from the NICFI program (monthly composite) data.

Imagery Copyright 2021 Planet Labs Inc. All use subject to the Participant License Agreement.", "links": [ { diff --git a/datasets/S2-16D-2_NA.json b/datasets/S2-16D-2_NA.json index 4fbda77538..cf9cfae433 100644 --- a/datasets/S2-16D-2_NA.json +++ b/datasets/S2-16D-2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S2-16D-2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth Observation Data Cube generated from Copernicus Sentinel-2/MSI Level-2A product over Brazil. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid Version 2 (BDC_SM V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.", "links": [ { diff --git a/datasets/S2K_EACM_Subset_623_1.json b/datasets/S2K_EACM_Subset_623_1.json index 1684dd5b67..004748fcde 100644 --- a/datasets/S2K_EACM_Subset_623_1.json +++ b/datasets/S2K_EACM_Subset_623_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S2K_EACM_Subset_623_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5-degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.", "links": [ { diff --git a/datasets/S2_L1C_BUNDLE-1_NA.json b/datasets/S2_L1C_BUNDLE-1_NA.json index bec8ab1d03..b58fba0267 100644 --- a/datasets/S2_L1C_BUNDLE-1_NA.json +++ b/datasets/S2_L1C_BUNDLE-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S2_L1C_BUNDLE-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copernicus Sentinel-2/MSI Level-1C product over Brazil. Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance images.", "links": [ { diff --git a/datasets/S2_L2A-1_NA.json b/datasets/S2_L2A-1_NA.json index c7de3cf578..5f983eaf04 100644 --- a/datasets/S2_L2A-1_NA.json +++ b/datasets/S2_L2A-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S2_L2A-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copernicus Sentinel-2/MSI Level-2A product over Brazil. Level-2A product provides orthorectified surface reflectance images (Bottom-Of-Atmosphere - BOA). This dataset is provided as Cloud Optimized GeoTIFF (COG).", "links": [ { diff --git a/datasets/S2_L2A_BUNDLE-1_NA.json b/datasets/S2_L2A_BUNDLE-1_NA.json index 3dbb83dc31..d4a1cb0df5 100644 --- a/datasets/S2_L2A_BUNDLE-1_NA.json +++ b/datasets/S2_L2A_BUNDLE-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S2_L2A_BUNDLE-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copernicus Sentinel-2/MSI Level-2A product over Brazil. Level-2A product provides orthorectified surface reflectance images (Bottom-Of-Atmosphere - BOA).", "links": [ { diff --git a/datasets/S3A_OL_1_EFR_1.json b/datasets/S3A_OL_1_EFR_1.json index 4545cd1c5b..2d6b349f48 100644 --- a/datasets/S3A_OL_1_EFR_1.json +++ b/datasets/S3A_OL_1_EFR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3A_OL_1_EFR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance product, S3A_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. \r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3A_OL_1_EFR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.\r\n\r\n\r\nThe OL_1_EFR product package is described below:\r\n\r\nElement name \t Description\r\nManifest.safe \t SENTINEL-SAFE product manifest\r\nOa##_radiance.nc \tRadiance for OLCI acquisition bands 01 to 21\r\nRemoved_pixels.nc \tRemoved pixels information needed for Level-1C generation\r\nTime_coordinates.nc \tTime stamp annotations\r\nGeo_coordinates.nc \tHigh resolution georeferencing data\r\nQuality_flags.nc \tClassification and quality flags\r\nTie_geo_coordinates.nc \tLow resolution georeferencing data\r\nTie_geometries.nc \tSun and view angles\r\nTie_meteo.nc \t ECMWF meteorology data\r\nInstrument_data.nc \tInstrument data\r\n\r\nnote: Oa## represents all the OLCI channels (Oa1 to Oa21).\r\n\r\n\r\nFor more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci.", "links": [ { diff --git a/datasets/S3A_OL_1_ERR_1.json b/datasets/S3A_OL_1_ERR_1.json index ab863ebcb9..a4e84216d3 100644 --- a/datasets/S3A_OL_1_ERR_1.json +++ b/datasets/S3A_OL_1_ERR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3A_OL_1_ERR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI/Sentinel-3A L1 Reduced Resolution Top of Atmosphere Reflectance, S3A_OL_1_ERR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. \r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3A_OL_1_ERR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.\r\n\r\n\r\nThe OL_1_EFR product package is described below:\r\n\r\nElement name \t Description\r\nManifest.safe \t SENTINEL-SAFE product manifest\r\nOa##_radiance.nc \tRadiance for OLCI acquisition bands 01 to 21\r\nTime_coordinates.nc \tTime stamp annotations\r\nGeo_coordinates.nc \tHigh resolution georeferencing data\r\nQuality_flags.nc \tClassification and quality flags\r\nTie_geo_coordinates.nc \tLow resolution georeferencing data\r\nTie_geometries.nc \tSun and view angles\r\nTie_meteo.nc \t ECMWF meteorology data\r\nInstrument_data.nc \tInstrument data\r\n\r\nnote: Oa## represents all the OLCI channels (Oa1 to Oa21).\r\n\r\n\r\nFor more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci", "links": [ { diff --git a/datasets/S3A_SL_1_RBT_1.json b/datasets/S3A_SL_1_RBT_1.json index 90df4c6f23..c249730c99 100644 --- a/datasets/S3A_SL_1_RBT_1.json +++ b/datasets/S3A_SL_1_RBT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3A_SL_1_RBT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SLSTR/Sentinel-3A L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3A_SL_1_RBT, is generated from the data aquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments. The SLSTR instrument design incorporates the basic functionality of AATSR in addition to new, more advanced features including a wider swath, new channels (including two channels dedicated to fire detection), and higher resolution in some channels. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications.\r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3A_SL_1_RBT is a Level 1B product which consist of full resolution, geolocated, co-located nadir and along track view, Top of Atmosphere (TOA) brightness temperatures (in the case of thermal IR channels) or radiances (in the case of visible, NIR and SWIR channels) from all SLSTR channels. It also contains quality flags, pixel classification information and meteorological annotations. Based on components activated by configuration which are not part of the operational production baseline, the S3A_SL_1_RBT may contain 77 or 111 files. Out of the these files, 22 or 34 files contain the actual measurements, where the other 54 or 76 files contain the annotations data.\r\n\r\nFor more information about the product, read the SENTINEL-3 SLSTR User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr", "links": [ { diff --git a/datasets/S3A_SY_2_SYN_1.json b/datasets/S3A_SY_2_SYN_1.json index 44816a3a29..8b30246790 100644 --- a/datasets/S3A_SY_2_SYN_1.json +++ b/datasets/S3A_SY_2_SYN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3A_SY_2_SYN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI+SLSTR/Sentinel-3A L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications.\r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3A_SY_2_SYN is a Level 2 product which consist of surface reflectances for all SYN channels and aerosol parameters over Land. There are 29 Measurement Data Files and 9 Annotation Data Files included in this product. All measurement datasets are provided on the OLCI image grid, similar to the one included in the OLCI L1b product. \r\n\r\nFor more information about the product, read the SENTINEL-3 Synergy User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-synergy", "links": [ { diff --git a/datasets/S3B_OL_1_EFR_1.json b/datasets/S3B_OL_1_EFR_1.json index 8d98531af0..6a3ead65ef 100644 --- a/datasets/S3B_OL_1_EFR_1.json +++ b/datasets/S3B_OL_1_EFR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3B_OL_1_EFR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI/Sentinel-3B L1 Full Resolution Top of Atmosphere Reflectance product, S3B_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. \r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3B_OL_1_EFR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) for Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information pertaining to illumination and observation geometry, environmental data (meteorological data), and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.\r\n\r\nThe OL_1_EFR product package is described below:\r\n\r\nElement name \t Description\r\nxfdumanifest.xml \tSENTINEL-SAFE product manifest\r\nOa##_radiance.nc \tRadiance for OLCI acquisition bands 01 to 21\r\nremoved_pixels.nc \tRemoved pixels information needed for Level-1C generation\r\ntime_coordinates.nc \tTime stamp annotations\r\ngeo_coordinates.nc \tHigh resolution georeferencing data\r\nqualityFlags.nc \tClassification and quality flags\r\ntie_geo_coordinates.nc \tLow resolution georeferencing data\r\ntie_geometries.nc \tSun and view angles\r\ntie_meteo.nc \t ECMWF meteorology data\r\ninstrument_data.nc \tInstrument data\r\n\r\nnote: Oa## represents all the OLCI channels (Oa1 to Oa21).\r\n\r\nFor more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci.", "links": [ { diff --git a/datasets/S3B_OL_1_ERR_1.json b/datasets/S3B_OL_1_ERR_1.json index 2037916157..f8ea1a9a84 100644 --- a/datasets/S3B_OL_1_ERR_1.json +++ b/datasets/S3B_OL_1_ERR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3B_OL_1_ERR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI/Sentinel-3B L1 Reduced Resolution Top of Atmosphere Reflectance product, S3B_OL_1_ERR is generated from the data acquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. \r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3B_OL_1_ERR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.\r\n\r\nThe OL_1_EFR product package is described below:\r\n\r\nElement name Description\r\nxfdumanifest.xml SENTINEL-SAFE product manifest\r\nOa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21\r\nTime_coordinates.nc Time stamp annotations\r\nGeo_coordinates.nc High resolution georeferencing data\r\nQuality_flags.nc Classification and quality flags\r\nTie_geo_coordinates.nc Low resolution georeferencing data\r\nTie_geometries.nc Sun and view angles\r\nTie_meteo.nc ECMWF meteorology data\r\nInstrument_data.nc Instrument data\r\n\r\nnote: Oa## represents all the OLCI channels (Oa1 to Oa21).\r\n\r\n\r\nFor more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci", "links": [ { diff --git a/datasets/S3B_SL_1_RBT_1.json b/datasets/S3B_SL_1_RBT_1.json index 5097e67127..8413b20bd2 100644 --- a/datasets/S3B_SL_1_RBT_1.json +++ b/datasets/S3B_SL_1_RBT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3B_SL_1_RBT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SLSTR/Sentinel-3B L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3B_SL_1_RBT, is generated from the data acquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments. The SLSTR instrument design incorporates the basic functionality of AATSR in addition to new, more advanced features including a wider swath, new channels (including two channels dedicated to fire detection), and higher resolution in some channels. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications.\r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3B_SL_1_RBT is a Level 1B product which consist of full resolution, geolocated, co-located nadir and along track view, Top of Atmosphere (TOA) brightness temperatures (in the case of thermal IR channels) or radiances (in the case of visible, NIR and SWIR channels) from all SLSTR channels. It also contains quality flags, pixel classification information and meteorological annotations. Based on components activated by configuration which are not part of the operational production baseline, the S3B_SL_1_RBT may contain 77 or 111 files. Out of the these files, 22 or 34 files contain the actual measurements, where the other 54 or 76 files contain the annotations data.\r\n\r\nFor more information about the product, read the SENTINEL-3 SLSTR User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr", "links": [ { diff --git a/datasets/S3B_SY_2_SYN_1.json b/datasets/S3B_SY_2_SYN_1.json index 365e413d4e..8522a4e703 100644 --- a/datasets/S3B_SY_2_SYN_1.json +++ b/datasets/S3B_SY_2_SYN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S3B_SY_2_SYN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLCI+SLSTR/Sentinel-3B L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications.\r\n\r\nFor more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. \r\n\r\nThe S3B_SY_2_SYN is a Level 2 product which consist of surface reflectances for all SYN channels and aerosol parameters over Land. There are 29 Measurement Data Files and 9 Annotation Data Files included in this product. All measurement datasets are provided on the OLCI image grid, similar to the one included in the OLCI L1b product. \r\n\r\nFor more information about the product, read the SENTINEL-3 Synergy User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-synergy", "links": [ { diff --git a/datasets/S4_211a_1.json b/datasets/S4_211a_1.json index 2ee28df2c2..37fcc5f07b 100644 --- a/datasets/S4_211a_1.json +++ b/datasets/S4_211a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S4_211a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current meter S4_211a is one of four current meters deployed off the coast of Casey Station, Australian Antarctic Territory. S4_211a was located in Shannon Bay at 66 degrees 16.727 minutes South, 110 degrees 31.434 minutes West. Further deployment details can be found in the 'Mooring Details' section of the data, as well as a 'Location Map'. The data includes: current speed components, current speed and current direction, a progressive vector diagram of displacement, and water temperature. The data were recorded by the Australian Antarctic Division, and processed by Oceanographic Field Services Pty Ltd. Data was recorded between 3:30am 18 November 1997 (GMT) and 7:30am 29 December 1998 (GMT).\n\nThe fields in this dataset include:\n\nDate\nTime\nSpeed (centimetres per second)\nDirection (degrees)\nTemperature (degrees)", "links": [ { diff --git a/datasets/S4_211b_1.json b/datasets/S4_211b_1.json index 64562fc458..79e334abab 100644 --- a/datasets/S4_211b_1.json +++ b/datasets/S4_211b_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S4_211b_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current meter S4_211b is one of four current meters deployed off the coast of Casey Station, Australian Antarctic Territory. S4_211a was located in Shannon Bay at 66 degrees 16.727 minutes South, 110 degrees 31.434 minutes West. Further deployment details can be found in the 'Mooring Details' section of the data, as well as a 'Location Map'. The data includes: current speed components, current speed and current direction, a progressive vector diagram of displacement, and water temperature. The data were recorded by the Australian Antarctic Division, and processed by Oceanographic Field Services Pty Ltd. Data was recorded between 3:30am 18 November 1997 (GMT) and 7:30am 29 December 1998 (GMT).\n\nThe fields in this dataset include:\n\nDate\nTime\nSpeed (centimetres per second)\nDirection (degrees)\nTemperature (degrees)", "links": [ { diff --git a/datasets/S4_212a_1.json b/datasets/S4_212a_1.json index e3be999266..33df65daf1 100644 --- a/datasets/S4_212a_1.json +++ b/datasets/S4_212a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S4_212a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current meter S4_212a is one of four current meters deployed off the coast of Casey Station, Australian Antarctic Territory. S4_211a was located in Shannon Bay at 66 degrees 16.727 minutes South, 110 degrees 31.434 minutes West. Further deployment details can be found in the 'Mooring Details' section of the data, as well as a 'Location Map'. The data includes: current speed components, current speed and current direction, a progressive vector diagram of displacement, and water temperature. The data were recorded by the Australian Antarctic Division, and processed by Oceanographic Field Services Pty Ltd. Data was recorded between 3:30am 18 November 1997 (GMT) and 7:30am 29 December 1998 (GMT).\n\nThe fields in this dataset include:\n\nDate\nTime\nSpeed (centimetres per second)\nDirection (degrees)\nTemperature (degrees)", "links": [ { diff --git a/datasets/S4_212b_1.json b/datasets/S4_212b_1.json index fcbdb1cb05..dff9282bf9 100644 --- a/datasets/S4_212b_1.json +++ b/datasets/S4_212b_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S4_212b_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current meter S4_212b is one of four current meters deployed off the coast of Casey Station, Australian Antarctic Territory. S4_211a was located in Shannon Bay at 66 degrees 16.727 minutes South, 110 degrees 31.434 minutes West. Further deployment details can be found in the 'Mooring Details' section of the data, as well as a 'Location Map'. The data includes: current speed components, current speed and current direction, a progressive vector diagram of displacement, and water temperature. The data were recorded by the Australian Antarctic Division, and processed by Oceanographic Field Services Pty Ltd. Data was recorded between 3:30am 18 November 1997 (GMT) and 7:30am 29 December 1998 (GMT).\n\nThe fields in this dataset include:\n\nDate\nTime\nSpeed (centimetres per second)\nDirection (degrees)\nTemperature (degrees)", "links": [ { diff --git a/datasets/S5P_L1B_IR_SIR_1.json b/datasets/S5P_L1B_IR_SIR_1.json index 55047510f2..958dc772b0 100644 --- a/datasets/S5P_L1B_IR_SIR_1.json +++ b/datasets/S5P_L1B_IR_SIR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_IR_SIR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_IR_SIR_2.json b/datasets/S5P_L1B_IR_SIR_2.json index 9e62a42499..44927ca48e 100644 --- a/datasets/S5P_L1B_IR_SIR_2.json +++ b/datasets/S5P_L1B_IR_SIR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_IR_SIR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_IR_UVN_1.json b/datasets/S5P_L1B_IR_UVN_1.json index 02d6a93923..a05a19966a 100644 --- a/datasets/S5P_L1B_IR_UVN_1.json +++ b/datasets/S5P_L1B_IR_UVN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_IR_UVN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_IR_UVN_2.json b/datasets/S5P_L1B_IR_UVN_2.json index 1da75c599a..b8d6121360 100644 --- a/datasets/S5P_L1B_IR_UVN_2.json +++ b/datasets/S5P_L1B_IR_UVN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_IR_UVN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD1_1.json b/datasets/S5P_L1B_RA_BD1_1.json index a34e2f4a15..8137de7883 100644 --- a/datasets/S5P_L1B_RA_BD1_1.json +++ b/datasets/S5P_L1B_RA_BD1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD1_HiR_1.json b/datasets/S5P_L1B_RA_BD1_HiR_1.json index 314612386d..5084999aca 100644 --- a/datasets/S5P_L1B_RA_BD1_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD1_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD1_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD1_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD1_HiR_2.json b/datasets/S5P_L1B_RA_BD1_HiR_2.json index 3cc4fb6cbb..7bc81a85fc 100644 --- a/datasets/S5P_L1B_RA_BD1_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD1_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD1_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD1_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD2_1.json b/datasets/S5P_L1B_RA_BD2_1.json index 6be64ca587..99d5156296 100644 --- a/datasets/S5P_L1B_RA_BD2_1.json +++ b/datasets/S5P_L1B_RA_BD2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD2_HiR_1.json b/datasets/S5P_L1B_RA_BD2_HiR_1.json index ebc79e6daf..b4f9d89e3f 100644 --- a/datasets/S5P_L1B_RA_BD2_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD2_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD2_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD2_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD2_HiR_2.json b/datasets/S5P_L1B_RA_BD2_HiR_2.json index 9739af3cf1..91c0c9d6ab 100644 --- a/datasets/S5P_L1B_RA_BD2_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD2_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD2_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD2_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD3_1.json b/datasets/S5P_L1B_RA_BD3_1.json index 065577e1f8..ed5df8ac0f 100644 --- a/datasets/S5P_L1B_RA_BD3_1.json +++ b/datasets/S5P_L1B_RA_BD3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD3 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD3_HiR_1.json b/datasets/S5P_L1B_RA_BD3_HiR_1.json index 49fc3e997e..c17bb909e7 100644 --- a/datasets/S5P_L1B_RA_BD3_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD3_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD3_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD3_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD3_HiR_2.json b/datasets/S5P_L1B_RA_BD3_HiR_2.json index c45c9a287a..f2734acf8b 100644 --- a/datasets/S5P_L1B_RA_BD3_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD3_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD3_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD3_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD4_1.json b/datasets/S5P_L1B_RA_BD4_1.json index 5af5125f2f..a36740e8f9 100644 --- a/datasets/S5P_L1B_RA_BD4_1.json +++ b/datasets/S5P_L1B_RA_BD4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD4 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD4_HiR_1.json b/datasets/S5P_L1B_RA_BD4_HiR_1.json index 8cfa220603..9e2d02f4ff 100644 --- a/datasets/S5P_L1B_RA_BD4_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD4_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD4_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD4_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD4_HiR_2.json b/datasets/S5P_L1B_RA_BD4_HiR_2.json index 61f2e7278c..f786c59fac 100644 --- a/datasets/S5P_L1B_RA_BD4_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD4_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD4_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD4_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD5_1.json b/datasets/S5P_L1B_RA_BD5_1.json index 50951e6c0d..c56e8744ed 100644 --- a/datasets/S5P_L1B_RA_BD5_1.json +++ b/datasets/S5P_L1B_RA_BD5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD5 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD5_HiR_1.json b/datasets/S5P_L1B_RA_BD5_HiR_1.json index 39b9353473..6765c837ab 100644 --- a/datasets/S5P_L1B_RA_BD5_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD5_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD5_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD5_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD5_HiR_2.json b/datasets/S5P_L1B_RA_BD5_HiR_2.json index 7ee81e2ce5..b1f48f2b16 100644 --- a/datasets/S5P_L1B_RA_BD5_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD5_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD5_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD5_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD6_1.json b/datasets/S5P_L1B_RA_BD6_1.json index 1bfe2901ba..9f6f89c0aa 100644 --- a/datasets/S5P_L1B_RA_BD6_1.json +++ b/datasets/S5P_L1B_RA_BD6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD6 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD6_HiR_1.json b/datasets/S5P_L1B_RA_BD6_HiR_1.json index 14763785f6..2bafe42459 100644 --- a/datasets/S5P_L1B_RA_BD6_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD6_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD6_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD6_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD6_HiR_2.json b/datasets/S5P_L1B_RA_BD6_HiR_2.json index 0a3f0fed7b..5dcff530a4 100644 --- a/datasets/S5P_L1B_RA_BD6_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD6_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD6_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD6_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD7_1.json b/datasets/S5P_L1B_RA_BD7_1.json index 0a791a2105..c0199e27e1 100644 --- a/datasets/S5P_L1B_RA_BD7_1.json +++ b/datasets/S5P_L1B_RA_BD7_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD7_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD7 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD7_HiR_1.json b/datasets/S5P_L1B_RA_BD7_HiR_1.json index 1e02b890eb..40d713975f 100644 --- a/datasets/S5P_L1B_RA_BD7_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD7_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD7_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD7_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD7_HiR_2.json b/datasets/S5P_L1B_RA_BD7_HiR_2.json index d16eb5da5c..4d48582270 100644 --- a/datasets/S5P_L1B_RA_BD7_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD7_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD7_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD7_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD8_1.json b/datasets/S5P_L1B_RA_BD8_1.json index 022ffe79d7..c8aa7933f5 100644 --- a/datasets/S5P_L1B_RA_BD8_1.json +++ b/datasets/S5P_L1B_RA_BD8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L1B_RA_BD8 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD8_HiR_1.json b/datasets/S5P_L1B_RA_BD8_HiR_1.json index c5a64b70b8..2f54b214df 100644 --- a/datasets/S5P_L1B_RA_BD8_HiR_1.json +++ b/datasets/S5P_L1B_RA_BD8_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD8_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD8_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L1B_RA_BD8_HiR_2.json b/datasets/S5P_L1B_RA_BD8_HiR_2.json index aa8977c8f2..038c8b4672 100644 --- a/datasets/S5P_L1B_RA_BD8_HiR_2.json +++ b/datasets/S5P_L1B_RA_BD8_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L1B_RA_BD8_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L1B_RA_BD8_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).\nTROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.", "links": [ { diff --git a/datasets/S5P_L2__AER_AI_1.json b/datasets/S5P_L2__AER_AI_1.json index 7e3ae215f9..81bacd025e 100644 --- a/datasets/S5P_L2__AER_AI_1.json +++ b/datasets/S5P_L2__AER_AI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_AI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__AER_AI data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060.", "links": [ { diff --git a/datasets/S5P_L2__AER_AI_HiR_1.json b/datasets/S5P_L2__AER_AI_HiR_1.json index d53189bbf0..b6c1eb0a90 100644 --- a/datasets/S5P_L2__AER_AI_HiR_1.json +++ b/datasets/S5P_L2__AER_AI_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_AI_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__AER_AI_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060.", "links": [ { diff --git a/datasets/S5P_L2__AER_AI_HiR_2.json b/datasets/S5P_L2__AER_AI_HiR_2.json index 389fcfbbf6..ce31727346 100644 --- a/datasets/S5P_L2__AER_AI_HiR_2.json +++ b/datasets/S5P_L2__AER_AI_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_AI_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__AER_AI_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060.", "links": [ { diff --git a/datasets/S5P_L2__AER_LH_1.json b/datasets/S5P_L2__AER_LH_1.json index 84b6a3e30c..cc8403da78 100644 --- a/datasets/S5P_L2__AER_LH_1.json +++ b/datasets/S5P_L2__AER_LH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_LH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__AER_LH data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P TROPOMI aerosol layer height algorithm applies a forward model, which includes DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval, a Layer-Based Orders of Scattering algorithm) to calculate monochromatic reflectance, and also employs a neural network scheme for speedy processor performance. Data retrieval uses the Optimal Estimation Method (OEM) for spectral fitting with various aerosol layer pressures and aerosol optical thicknesses in the Oxygen-A band (758 -770nm). The aerosol baseline model assumes a single uniformly distributed aerosol layer with a fixed pressure thickness and a constant aerosol volume extinction coefficient and single scattering albedo. The aerosol parameterization is particularly suitable for elevated non-scattering aerosols such as volcanic ash, desert dust and biomass burning plume. The product main outputs include the mid-pressure and mid-altitude of aerosol layers, aerosol optical thickness at 760nm, error estimates, and other relevant diagnostics.", "links": [ { diff --git a/datasets/S5P_L2__AER_LH_HiR_1.json b/datasets/S5P_L2__AER_LH_HiR_1.json index bae129ee9b..334d8c50c1 100644 --- a/datasets/S5P_L2__AER_LH_HiR_1.json +++ b/datasets/S5P_L2__AER_LH_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_LH_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__AER_LH_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P TROPOMI aerosol layer height algorithm applies a forward model, which includes DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval, a Layer-Based Orders of Scattering algorithm) to calculate monochromatic reflectance, and also employs a neural network scheme for speedy processor performance. Data retrieval uses the Optimal Estimation Method (OEM) for spectral fitting with various aerosol layer pressures and aerosol optical thicknesses in the Oxygen-A band (758 -770nm). The aerosol baseline model assumes a single uniformly distributed aerosol layer with a fixed pressure thickness and a constant aerosol volume extinction coefficient and single scattering albedo. The aerosol parameterization is particularly suitable for elevated non-scattering aerosols such as volcanic ash, desert dust and biomass burning plume. The product main outputs include the mid-pressure and mid-altitude of aerosol layers, aerosol optical thickness at 760nm, error estimates, and other relevant diagnostics.\n\nStarting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the aerosol layer height product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM).", "links": [ { diff --git a/datasets/S5P_L2__AER_LH_HiR_2.json b/datasets/S5P_L2__AER_LH_HiR_2.json index 67e5c6c260..43d2bea194 100644 --- a/datasets/S5P_L2__AER_LH_HiR_2.json +++ b/datasets/S5P_L2__AER_LH_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__AER_LH_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__AER_LH_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P TROPOMI aerosol layer height algorithm applies a forward model, which includes DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval, a Layer-Based Orders of Scattering algorithm) to calculate monochromatic reflectance, and also employs a neural network scheme for speedy processor performance. Data retrieval uses the Optimal Estimation Method (OEM) for spectral fitting with various aerosol layer pressures and aerosol optical thicknesses in the Oxygen-A band (758 -770nm). The aerosol baseline model assumes a single uniformly distributed aerosol layer with a fixed pressure thickness and a constant aerosol volume extinction coefficient and single scattering albedo. The aerosol parameterization is particularly suitable for elevated non-scattering aerosols such as volcanic ash, desert dust and biomass burning plume. The product main outputs include the mid-pressure and mid-altitude of aerosol layers, aerosol optical thickness at 760nm, error estimates, and other relevant diagnostics.\n\nStarting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the aerosol layer height product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM).", "links": [ { diff --git a/datasets/S5P_L2__CH4____1.json b/datasets/S5P_L2__CH4____1.json index 678b6f7ee8..c4f50b7837 100644 --- a/datasets/S5P_L2__CH4____1.json +++ b/datasets/S5P_L2__CH4____1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CH4____1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__CH4___ data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI methane product is a physics based method that uses the Oxygen-A band (760 nm) and absorption bands in shortwave infrared spectrum. In Sentinel-5P/TROPOMI methane algorithm, the atmosphere forward model simulates the absorbing gas (oxygen, methane, water vapor, and carbon monoxide) optical properties, as well as aerosol optical properties with size distribution, refractive index, and number concentration. The inversion is performed based on the forward calculation, the measurement, and the prior information. Cloud filtering is critical in methane retrieval, S5P methane algorithm applies re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask data. Additional cloud filters based on S5P/TROPOMI measurements and the FRESCO apparent surface pressure will be applied when VIIRS cloud data are not available. Other current data filters in the retrieval algorithm include land-only pixels (excluding mountainous areas), spectrum intensity, solar zenith angle and instrument zenith angle. \n\nThe S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. \n\nThe data are stored in an enhanced netCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__CH4____HiR_1.json b/datasets/S5P_L2__CH4____HiR_1.json index ddec41cb48..29e1b80dc3 100644 --- a/datasets/S5P_L2__CH4____HiR_1.json +++ b/datasets/S5P_L2__CH4____HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CH4____HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__CH4____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI methane product is a physics based method that uses the Oxygen-A band (760 nm) and absorption bands in shortwave infrared spectrum. In Sentinel-5P/TROPOMI methane algorithm, the atmosphere forward model simulates the absorbing gas (oxygen, methane, water vapor, and carbon monoxide) optical properties, as well as aerosol optical properties with size distribution, refractive index, and number concentration. The inversion is performed based on the forward calculation, the measurement, and the prior information. Cloud filtering is critical in methane retrieval, S5P methane algorithm applies re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask data. Additional cloud filters based on S5P/TROPOMI measurements and the FRESCO apparent surface pressure will be applied when VIIRS cloud data are not available. Other current data filters in the retrieval algorithm include land-only pixels (excluding mountainous areas), spectrum intensity, solar zenith angle and instrument zenith angle. \n\nThe S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. \n\nStarting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the methane product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM).\n\nThe data are stored in an enhanced netCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__CH4____HiR_2.json b/datasets/S5P_L2__CH4____HiR_2.json index 6c776d8346..f831e71053 100644 --- a/datasets/S5P_L2__CH4____HiR_2.json +++ b/datasets/S5P_L2__CH4____HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CH4____HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__CH4____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI methane product is a physics based method that uses the Oxygen-A band (760 nm) and absorption bands in shortwave infrared spectrum. In Sentinel-5P/TROPOMI methane algorithm, the atmosphere forward model simulates the absorbing gas (oxygen, methane, water vapor, and carbon monoxide) optical properties, as well as aerosol optical properties with size distribution, refractive index, and number concentration. The inversion is performed based on the forward calculation, the measurement, and the prior information. Cloud filtering is critical in methane retrieval, S5P methane algorithm applies re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask data. Additional cloud filters based on S5P/TROPOMI measurements and the FRESCO apparent surface pressure will be applied when VIIRS cloud data are not available. Other current data filters in the retrieval algorithm include land-only pixels (excluding mountainous areas), spectrum intensity, solar zenith angle and instrument zenith angle. \n\nThe S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. \n\nStarting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the methane product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM).\n\nThe data are stored in an enhanced netCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__CLOUD__1.json b/datasets/S5P_L2__CLOUD__1.json index 0a7cd3fad6..2e8b0730c4 100644 --- a/datasets/S5P_L2__CLOUD__1.json +++ b/datasets/S5P_L2__CLOUD__1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CLOUD__1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__CLOUD__1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__CLOUD__HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__CLOUD__HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Cloud products are retrieved by two algorithms. The S5P_CLOUD_OCRA is based on the Optical Cloud Recognition Algorithm which generates the fractional cloud cover. The S5P_CLOUD_ROCINN (the Retrieval Of Cloud Information through Neural Networks algorithm) is built on measurements in and around the O2 A-band, which produces the cloud optical thickness and the cloud top height.", "links": [ { diff --git a/datasets/S5P_L2__CLOUD__HiR_1.json b/datasets/S5P_L2__CLOUD__HiR_1.json index f6e2c8e514..6f6b504bff 100644 --- a/datasets/S5P_L2__CLOUD__HiR_1.json +++ b/datasets/S5P_L2__CLOUD__HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CLOUD__HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__CLOUD__1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__CLOUD__HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__CLOUD__HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Cloud products are retrieved by two algorithms. The S5P_CLOUD_OCRA is based on the Optical Cloud Recognition Algorithm which generates the fractional cloud cover. The S5P_CLOUD_ROCINN (the Retrieval Of Cloud Information through Neural Networks algorithm) is built on measurements in and around the O2 A-band, which produces the cloud optical thickness and the cloud top height.", "links": [ { diff --git a/datasets/S5P_L2__CLOUD__HiR_2.json b/datasets/S5P_L2__CLOUD__HiR_2.json index e19a8c91b7..fe9ab67b21 100644 --- a/datasets/S5P_L2__CLOUD__HiR_2.json +++ b/datasets/S5P_L2__CLOUD__HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CLOUD__HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__CLOUD__1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__CLOUD__HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__CLOUD__HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Cloud products are retrieved by two algorithms. The S5P_CLOUD_OCRA is based on the Optical Cloud Recognition Algorithm which generates the fractional cloud cover. The S5P_CLOUD_ROCINN (the Retrieval Of Cloud Information through Neural Networks algorithm) is built on measurements in and around the O2 A-band, which produces the cloud optical thickness and the cloud top height.", "links": [ { diff --git a/datasets/S5P_L2__CO_____1.json b/datasets/S5P_L2__CO_____1.json index 40a2d3eaee..15754cdf26 100644 --- a/datasets/S5P_L2__CO_____1.json +++ b/datasets/S5P_L2__CO_____1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CO_____1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__CO____ data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans.", "links": [ { diff --git a/datasets/S5P_L2__CO_____HiR_1.json b/datasets/S5P_L2__CO_____HiR_1.json index 6e5619cafe..184a2f80eb 100644 --- a/datasets/S5P_L2__CO_____HiR_1.json +++ b/datasets/S5P_L2__CO_____HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CO_____HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__CO_____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans.", "links": [ { diff --git a/datasets/S5P_L2__CO_____HiR_2.json b/datasets/S5P_L2__CO_____HiR_2.json index eb6f4caea7..d999847e61 100644 --- a/datasets/S5P_L2__CO_____HiR_2.json +++ b/datasets/S5P_L2__CO_____HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__CO_____HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__CO_____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans.", "links": [ { diff --git a/datasets/S5P_L2__HCHO___1.json b/datasets/S5P_L2__HCHO___1.json index d029146b9d..b5655bc32b 100644 --- a/datasets/S5P_L2__HCHO___1.json +++ b/datasets/S5P_L2__HCHO___1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__HCHO___1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__HCHO___1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__HCHO___HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__HCHO___HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI HCHO from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover as well as aerosol index are utilized to derive HCHO slant column density (SCD). The air mass factor (AMF) Look-up table has been created with the VLIDORT 2.6 radiative transfer model at the wavelength of 340 nm, and the AMF is used to compute the total column averaging kernels (AK). The background normalization of the slant columns is essential for weak absorbent like formaldehyde to compensate for possible systematic offsets. The main outputs of the DOAS algorithm are the vertical column density (VCD), SCD, AMF, uncertainty, AK, and quality flags.", "links": [ { diff --git a/datasets/S5P_L2__HCHO___HiR_1.json b/datasets/S5P_L2__HCHO___HiR_1.json index 1cdc51ee5b..fb89a9a8a0 100644 --- a/datasets/S5P_L2__HCHO___HiR_1.json +++ b/datasets/S5P_L2__HCHO___HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__HCHO___HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__HCHO___1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__HCHO___HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__HCHO___HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI HCHO from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover as well as aerosol index are utilized to derive HCHO slant column density (SCD). The air mass factor (AMF) Look-up table has been created with the VLIDORT 2.6 radiative transfer model at the wavelength of 340 nm, and the AMF is used to compute the total column averaging kernels (AK). The background normalization of the slant columns is essential for weak absorbent like formaldehyde to compensate for possible systematic offsets. The main outputs of the DOAS algorithm are the vertical column density (VCD), SCD, AMF, uncertainty, AK, and quality flags.", "links": [ { diff --git a/datasets/S5P_L2__HCHO___HiR_2.json b/datasets/S5P_L2__HCHO___HiR_2.json index 5869509ff9..fa56cf05e4 100644 --- a/datasets/S5P_L2__HCHO___HiR_2.json +++ b/datasets/S5P_L2__HCHO___HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__HCHO___HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__HCHO___1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__HCHO___HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__HCHO___HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI HCHO from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover as well as aerosol index are utilized to derive HCHO slant column density (SCD). The air mass factor (AMF) Look-up table has been created with the VLIDORT 2.6 radiative transfer model at the wavelength of 340 nm, and the AMF is used to compute the total column averaging kernels (AK). The background normalization of the slant columns is essential for weak absorbent like formaldehyde to compensate for possible systematic offsets. The main outputs of the DOAS algorithm are the vertical column density (VCD), SCD, AMF, uncertainty, AK, and quality flags.", "links": [ { diff --git a/datasets/S5P_L2__NO2____1.json b/datasets/S5P_L2__NO2____1.json index 3d0bc70f50..fbe39ee04e 100644 --- a/datasets/S5P_L2__NO2____1.json +++ b/datasets/S5P_L2__NO2____1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NO2____1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__NO2___ data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project.", "links": [ { diff --git a/datasets/S5P_L2__NO2____HiR_1.json b/datasets/S5P_L2__NO2____HiR_1.json index e00cc875a3..f1fc54652c 100644 --- a/datasets/S5P_L2__NO2____HiR_1.json +++ b/datasets/S5P_L2__NO2____HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NO2____HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NO2____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project.", "links": [ { diff --git a/datasets/S5P_L2__NO2____HiR_2.json b/datasets/S5P_L2__NO2____HiR_2.json index 92c445c088..3f74aea34d 100644 --- a/datasets/S5P_L2__NO2____HiR_2.json +++ b/datasets/S5P_L2__NO2____HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NO2____HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NO2____1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD3_1.json b/datasets/S5P_L2__NP_BD3_1.json index ecc2e374b5..a1b95f8aed 100644 --- a/datasets/S5P_L2__NP_BD3_1.json +++ b/datasets/S5P_L2__NP_BD3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__NP_BD3 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD3_HiR_1.json b/datasets/S5P_L2__NP_BD3_HiR_1.json index 0f9d2310c9..7559e406c7 100644 --- a/datasets/S5P_L2__NP_BD3_HiR_1.json +++ b/datasets/S5P_L2__NP_BD3_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD3_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD3_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD3_HiR_2.json b/datasets/S5P_L2__NP_BD3_HiR_2.json index 74fd2706a0..aafdab8945 100644 --- a/datasets/S5P_L2__NP_BD3_HiR_2.json +++ b/datasets/S5P_L2__NP_BD3_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD3_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD3_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD6_1.json b/datasets/S5P_L2__NP_BD6_1.json index 5c892fb831..0ef44e8178 100644 --- a/datasets/S5P_L2__NP_BD6_1.json +++ b/datasets/S5P_L2__NP_BD6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__NP_BD6 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD6_HiR_1.json b/datasets/S5P_L2__NP_BD6_HiR_1.json index 9edf37cc4c..7afbc4f98a 100644 --- a/datasets/S5P_L2__NP_BD6_HiR_1.json +++ b/datasets/S5P_L2__NP_BD6_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD6_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD6_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD6_HiR_2.json b/datasets/S5P_L2__NP_BD6_HiR_2.json index 48cfe033a6..dd3507c1dd 100644 --- a/datasets/S5P_L2__NP_BD6_HiR_2.json +++ b/datasets/S5P_L2__NP_BD6_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD6_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD6_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD7_1.json b/datasets/S5P_L2__NP_BD7_1.json index 334995bfa5..f9756b44d0 100644 --- a/datasets/S5P_L2__NP_BD7_1.json +++ b/datasets/S5P_L2__NP_BD7_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD7_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data after August 6th of 2019, please check S5P_L2__NP_BD7 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD7_HiR_1.json b/datasets/S5P_L2__NP_BD7_HiR_1.json index 6c10b4b188..4f0a9cbaef 100644 --- a/datasets/S5P_L2__NP_BD7_HiR_1.json +++ b/datasets/S5P_L2__NP_BD7_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD7_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD7_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__NP_BD7_HiR_2.json b/datasets/S5P_L2__NP_BD7_HiR_2.json index 17fb84f285..1644019769 100644 --- a/datasets/S5P_L2__NP_BD7_HiR_2.json +++ b/datasets/S5P_L2__NP_BD7_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__NP_BD7_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nFor data before August 6th of 2019, please check S5P_L2__NP_BD7_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.", "links": [ { diff --git a/datasets/S5P_L2__O3_TCL_1.json b/datasets/S5P_L2__O3_TCL_1.json index 24c5ea11ea..18c9ae705e 100644 --- a/datasets/S5P_L2__O3_TCL_1.json +++ b/datasets/S5P_L2__O3_TCL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3_TCL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data after July 2nd of 2020, please check S5P_L2__O3_TCL_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover. The products are restricted in the tropical region (-20 degrees to 20 degrees of latitude).\n\nThe main outputs of the Copernicus S5P/TROPOMI tropospheric ozone product include the tropospheric ozone column and corresponding errors, upper tropospheric ozone and corresponding errors, stratospheric ozone column and corresponding errors, and the retrieval quality flags. The data are stored in an enhanced netCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__O3_TCL_2.json b/datasets/S5P_L2__O3_TCL_2.json index f0297fb54c..53985eb082 100644 --- a/datasets/S5P_L2__O3_TCL_2.json +++ b/datasets/S5P_L2__O3_TCL_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3_TCL_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before July 13 of 2020, please check S5P_L2__O3_TCL_1 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nCopernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover. The products are restricted in the tropical region (-20 degrees to 20 degrees of latitude).\n\nThe main outputs of the Copernicus S5P/TROPOMI tropospheric ozone product include the tropospheric ozone column and corresponding errors, upper tropospheric ozone and corresponding errors, stratospheric ozone column and corresponding errors, and the retrieval quality flags. The data are stored in an enhanced netCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__O3_TOT_1.json b/datasets/S5P_L2__O3_TOT_1.json index 83ca95f2d0..10fa075666 100644 --- a/datasets/S5P_L2__O3_TOT_1.json +++ b/datasets/S5P_L2__O3_TOT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3_TOT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__O3_TOT_1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__O3_TOT_HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__O3_TOT_HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThis total column ozone product (S5P_L2__O3_TOT, aka ESA's S5P_L2__O3____) applies the the Direct-fitting algorithm (S5P_TO3_GODFIT), comprising a non-linear least squares inversion by comparing the simulated and measured backscattered radiances. The main products include total vertical column ozone, ozone effective temperature, and the error information in NetCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__O3_TOT_HiR_1.json b/datasets/S5P_L2__O3_TOT_HiR_1.json index 37df9b5e70..92b5f61cd1 100644 --- a/datasets/S5P_L2__O3_TOT_HiR_1.json +++ b/datasets/S5P_L2__O3_TOT_HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3_TOT_HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__O3_TOT_1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__O3_TOT_HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__O3_TOT_HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThis total column ozone product (S5P_L2__O3_TOT, aka ESA's S5P_L2__O3____) applies the the Direct-fitting algorithm (S5P_TO3_GODFIT), comprising a non-linear least squares inversion by comparing the simulated and measured backscattered radiances. The main products include total vertical column ozone, ozone effective temperature, and the error information in NetCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__O3_TOT_HiR_2.json b/datasets/S5P_L2__O3_TOT_HiR_2.json index 3a043997cf..1f6cb395f1 100644 --- a/datasets/S5P_L2__O3_TOT_HiR_2.json +++ b/datasets/S5P_L2__O3_TOT_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3_TOT_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__O3_TOT_1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__O3_TOT_HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__O3_TOT_HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThis total column ozone product (S5P_L2__O3_TOT, aka ESA's S5P_L2__O3____) applies the the Direct-fitting algorithm (S5P_TO3_GODFIT), comprising a non-linear least squares inversion by comparing the simulated and measured backscattered radiances. The main products include total vertical column ozone, ozone effective temperature, and the error information in NetCDF-4 format.", "links": [ { diff --git a/datasets/S5P_L2__O3__PR_HiR_2.json b/datasets/S5P_L2__O3__PR_HiR_2.json index 86706a24c4..02504a457b 100644 --- a/datasets/S5P_L2__O3__PR_HiR_2.json +++ b/datasets/S5P_L2__O3__PR_HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__O3__PR_HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. S5P_L2__O3__PR_HiR data collection contains the high spatial resolution products. \n\nThe Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThis product contains ozone profile with a vertical resolution of 6 km and a horizontal resolution of 30x30km2 observed at about 13:30 local solar time from spectra measured by TROPOMI", "links": [ { diff --git a/datasets/S5P_L2__SO2____1.json b/datasets/S5P_L2__SO2____1.json index 5e3237e96f..07559aa9ef 100644 --- a/datasets/S5P_L2__SO2____1.json +++ b/datasets/S5P_L2__SO2____1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__SO2____1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__SO2____1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__SO2____HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__SO2____HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI SO2 from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover, and geolocation are utilized to derive SO2 slant column density (SCD). A sensitive spectral window of 312 to 326 nm is set as the baseline for the slant column fit with another two spectral windows (325 to 335 nm, 360 to 390 nm) to account for the non-linear effects in those high column amount cases. The SCD is then corrected with the empirical offsets to the systematic biases. The air mass factor (AMF) Look-up table has been created with the LIDORT radiative transfer model. The outputs of the DOAS algorithm are SO2 vertical column density (VCD), SCD, AMF, the DOAS-type averaging kernels (AK), and error estimates.", "links": [ { diff --git a/datasets/S5P_L2__SO2____HiR_1.json b/datasets/S5P_L2__SO2____HiR_1.json index 2e9f4551c0..d403319114 100644 --- a/datasets/S5P_L2__SO2____HiR_1.json +++ b/datasets/S5P_L2__SO2____HiR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__SO2____HiR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__SO2____1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__SO2____HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__SO2____HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI SO2 from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover, and geolocation are utilized to derive SO2 slant column density (SCD). A sensitive spectral window of 312 to 326 nm is set as the baseline for the slant column fit with another two spectral windows (325 to 335 nm, 360 to 390 nm) to account for the non-linear effects in those high column amount cases. The SCD is then corrected with the empirical offsets to the systematic biases. The air mass factor (AMF) Look-up table has been created with the LIDORT radiative transfer model. The outputs of the DOAS algorithm are SO2 vertical column density (VCD), SCD, AMF, the DOAS-type averaging kernels (AK), and error estimates.", "links": [ { diff --git a/datasets/S5P_L2__SO2____HiR_2.json b/datasets/S5P_L2__SO2____HiR_2.json index 5d149807e0..cbed546ff2 100644 --- a/datasets/S5P_L2__SO2____HiR_2.json +++ b/datasets/S5P_L2__SO2____HiR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "S5P_L2__SO2____HiR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.\nFor data before August 6th of 2019, please check S5P_L2__SO2____1 data collection.\nFor data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__SO2____HiR_1 data collection.\nFor data after July 13th of 2020, please check S5P_L2__SO2____HiR_2 data collection.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.\n\nThe retrieval algorithm for Sentinel-5P TROPOMI SO2 from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover, and geolocation are utilized to derive SO2 slant column density (SCD). A sensitive spectral window of 312 to 326 nm is set as the baseline for the slant column fit with another two spectral windows (325 to 335 nm, 360 to 390 nm) to account for the non-linear effects in those high column amount cases. The SCD is then corrected with the empirical offsets to the systematic biases. The air mass factor (AMF) Look-up table has been created with the LIDORT radiative transfer model. The outputs of the DOAS algorithm are SO2 vertical column density (VCD), SCD, AMF, the DOAS-type averaging kernels (AK), and error estimates.", "links": [ { diff --git a/datasets/SAB_Mapping_0.json b/datasets/SAB_Mapping_0.json index 8dcf4dbae0..dde581ed6c 100644 --- a/datasets/SAB_Mapping_0.json +++ b/datasets/SAB_Mapping_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAB_Mapping_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of the South Atlantic Bight (SAB) near the Georgia and South Carolina coasts during 2005.", "links": [ { diff --git a/datasets/SAGE1_AERO_PRF_1.json b/datasets/SAGE1_AERO_PRF_1.json index 9aa316e947..3cb28bed0c 100644 --- a/datasets/SAGE1_AERO_PRF_1.json +++ b/datasets/SAGE1_AERO_PRF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAGE1_AERO_PRF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stratospheric Aerosol and Gas Experiment I - Aerosol Profile - HDF - Altitude profile of aerosol extinction properties at 1000 and 450 nm.", "links": [ { diff --git a/datasets/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0.json b/datasets/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0.json index 85a9650dc5..7cbbb09c13 100644 --- a/datasets/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0.json +++ b/datasets/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0 is the Stratospheric Aerosol and Gas Experiment (SAGE) II Version 7.0 Aerosol, O3, NO2 and H2O Profiles data set in the SAGE II native binary format. It contains aerosol extinction, ozone, nitrogen dioxide, and water vapor profiles. Data collection for this data set is complete. \r\n\r\nOver the long 21-year mission, the spacecraft experienced episodic anomalies in the power system. These anomalies were usually followed by a period where the occultation events were of limited duration. These so-called short events may have had an insufficient number of exoatmospheric scans of the solar disk precluding an accurate determination of the solar limb darkening curve and the scan mirror relative reflectivity. In version 7.0, these events, a total of 4900, were dropped so that the data users no longer needed to filter out those events. Further, there were approximately 150 events that did not complete processing in earlier versions that were successfully processed in this version. The net result was that there were more usable profiles in v7.0 than in previous versions. \r\n\r\nSAGE II was a payload installed aboard the Earth Radiation Budget Satellite (ERBS), which was launched on October 5, 1984, from NASA Space Shuttle Flight 41-G. The SAGE II instrument was a multi-channel spectral radiometer that measured the attenuation of solar radiation at seven wavelengths as they passed through the Earth's atmosphere during the spacecraft's sunrise and sunset events. In one day\u2019s time, the ERBS spacecraft encountered approximately fifteen sunrise and fifteen sunset events. The SAGE II instrument captured solar radiation data for each event. The data span was a vertical distance from about 140 kilometers to the horizon or a cloud top. The ground-track slew distance during data capture varied directly with the duration of the event. Event duration varied with the beta angle of the event - the larger the absolute beta angle, the longer the event. SAGE II continued the SAGE measurements of stratospheric ozone from 1984-2005. After nearly 21 years, the SAGE II Instrument on the ERBS platform was powered off on 22 August, 2005.", "links": [ { diff --git a/datasets/SAILDRONE_ARCTIC_1.0.json b/datasets/SAILDRONE_ARCTIC_1.0.json index a5d240ad26..c432573960 100644 --- a/datasets/SAILDRONE_ARCTIC_1.0.json +++ b/datasets/SAILDRONE_ARCTIC_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAILDRONE_ARCTIC_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2019 Saildrone Arctic campaign featured six Saildrone USVs (jointly funded by NOAA and NASA) deployed during a 150-day cruise in the Bering and Chukchi Seas, spanning from 14 May 2019 to 11 October 2019. The primary mission objective for 2019 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, which could lead to significant improvements in modeling of diurnal warming and understanding of the marginal ice zones. Additionally, these new data will provide additional Arctic SST observations to benefit SST algorithm development and validation, and for studies of air- sea-ice interactions. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2019_Arctic_Cruise_Report.pdf

\r\nDuring the Arctic campaign, NASA-funded Saildrones SD-1036 and SD-1037 undertook transects in the Chukchi Sea, approaching the sea ice edge to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.

\r\nThe Saildrone Arctic 2019 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies. \r\n", "links": [ { diff --git a/datasets/SAILDRONE_ARCTIC_2021_1.json b/datasets/SAILDRONE_ARCTIC_2021_1.json index 6caa71f864..d88ce56a4a 100644 --- a/datasets/SAILDRONE_ARCTIC_2021_1.json +++ b/datasets/SAILDRONE_ARCTIC_2021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAILDRONE_ARCTIC_2021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Saildrone Arctic 2021 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2021 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 76-day cruise in the Bering and Chukchi Seas, spanning from 6 July 2021 to 20 September 2021. The primary mission objective for 2021 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, with special emphasis on better understanding the spatial/temporal scales of air-sea covariance in the Chukchi Sea, which was accomplished by running a series of parallel tracks using the two Saildrones at varying horizontal offsets. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/2021_Saildrone_Arctic_Cruise_Report.pdf

\r\nDuring the Arctic campaign, Saildrones SD-1057 and SD-1058 ran transects in the Chukchi Sea, approaching the sea ice edge (up to 50 km away) to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents.\\\r\nThe Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\r\n

\r\nThe Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\r\n", "links": [ { diff --git a/datasets/SAILDRONE_ARCTIC_2022_1.json b/datasets/SAILDRONE_ARCTIC_2022_1.json index 450e06b11a..0b5b2e566e 100644 --- a/datasets/SAILDRONE_ARCTIC_2022_1.json +++ b/datasets/SAILDRONE_ARCTIC_2022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAILDRONE_ARCTIC_2022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2022 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 60-day cruise in the Bering and Chukchi Seas, spanning from 18 June 2022 to 17 August 2022. The primary mission objective for 2022 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, specifically in collaboration with the Distributed Biological Observatory (DBO; https://www.pmel.noaa.gov/dbo/; https://dbo.cbl.umces.edu/). Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2022_Arctic_Cruise_Report.pdf

\r\nDuring the Arctic campaign, Saildrones SD-1041 and SD-1046 undertook distinct trajectories to cover designated areas. SD-1041 traversed repeat transects from Point Hope, AK southwestward to near the International Date Line, following DBO line #3 (https://dbo.cbl.umces.edu/images/Frey_DBOmap_IceEdge2022.png). In contrast, SD-1046 ventured northward to DBO line #4 and, upon sea ice retreat, proceeded further north to DBO line #5. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.\r\n

\r\nThe Saildrone Arctic 2022 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.", "links": [ { diff --git a/datasets/SAILDRONE_ATOMIC_1.0.json b/datasets/SAILDRONE_ATOMIC_1.0.json index a0dd2f70a2..53f1f1d74b 100644 --- a/datasets/SAILDRONE_ATOMIC_1.0.json +++ b/datasets/SAILDRONE_ATOMIC_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAILDRONE_ATOMIC_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone ATOMIC (Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) campaign involved the deployment of a fleet of saildrones, jointly funded by NASA and NOAA, in the Atlantic waters offshore of Barbados over a 45 day period from 17 January to 2 March 2020. The goal was to understand the Ocean-Atmosphere interaction particularly over the mesoscale ocean eddies in that region. The saildrones were equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone ATOMIC dataset is comprised of two data files for each of the three NASA-funded saildrones deployed, one for the surface observations and one for the ADCP measuements. The surface data files contain saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) spanning the entire cruise at 1 minute temporal resolution. The ADCP files for each saildrone are at 5 minute resolution for the duration of the deployments. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.", "links": [ { diff --git a/datasets/SAILDRONE_BAJA_SURFACE_1.0.json b/datasets/SAILDRONE_BAJA_SURFACE_1.0.json index 4bb90dab2c..781a528716 100644 --- a/datasets/SAILDRONE_BAJA_SURFACE_1.0.json +++ b/datasets/SAILDRONE_BAJA_SURFACE_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAILDRONE_BAJA_SURFACE_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. \r\nThe saildrone Baja campaign was a 60-day cruise from San Francisco Bay, down along the US/Mexico coast to Guadalupe Island and back again over the period 11 April 2018 to 11 June 2018. Repeat surveys were taken around NDBC moored buoys, and during the final week of the cruise a targeted front was sampled. Scientific objectives included studies of upwelling and frontal region dynamics, air-sea interactions, and diurnal warming effects, while its validation objectives included establishing the utility of data from the saildrone platform for assessment of satellite data accuracy and model assimilation. During the Baja campaign, the single deployed saildrone was equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface.\r\nThis Saildrone Baja dataset is comprised of one data file with the saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution. A second file contains the ADCP current vector data that is depth-resolved to 100m at 2m intervals and binned temporally at 5 minute resolution. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.", "links": [ { diff --git a/datasets/SAM2_AERO_PRF_NAT_1.json b/datasets/SAM2_AERO_PRF_NAT_1.json index 3e39e454b0..52be2aff2e 100644 --- a/datasets/SAM2_AERO_PRF_NAT_1.json +++ b/datasets/SAM2_AERO_PRF_NAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAM2_AERO_PRF_NAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAM2_AERO_PRF_NAT data are Stratospheric Aerosol Measurement (SAM) II - Aerosol Profiles in Native (NAT) Format which measure solar irradiance attenuated by aerosol particles in the Arctic and Antarctic stratosphere.The Stratospheric Aerosol Measurement (SAM) II experiment flew aboard the Nimbus 7 spacecraft and provided vertical profiles of aerosol extinction in both the Arctic and Antarctic polar regions. The SAM II data coverage began on October 29, 1978 and extended through December 18, 1993, until SAM II was no longer able to acquire the sun. The data coverage for the Antarctic region extends through December 18, 1993, and has one data gap for the period of time from mid-January through the end of October 1993. The data coverage for the Arctic region extends through January 7, 1991, and contains data gaps beginning in 1988 that increase in size each year due to an orbit degradation associated with the Nimbus-7 spacecraft.", "links": [ { diff --git a/datasets/SAMMI2AE_2.json b/datasets/SAMMI2AE_2.json index 4be76bbf50..897f472cac 100644 --- a/datasets/SAMMI2AE_2.json +++ b/datasets/SAMMI2AE_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMI2AE_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 Aerosol Product.It contains Aerosol optical depth and particle type, with associated atmospheric data for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMMI2LS_2.json b/datasets/SAMMI2LS_2.json index e6f2cbf486..6ae580b4e5 100644 --- a/datasets/SAMMI2LS_2.json +++ b/datasets/SAMMI2LS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMI2LS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level 2 Land Surface product contains information on land directional reflectance properties,albedos(spectral & PAR integrated),FPAR,asssociated radiation parameters & terrain-referenced geometric parameters for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMMI2ST_2.json b/datasets/SAMMI2ST_2.json index a8ab40b24c..090c09250a 100644 --- a/datasets/SAMMI2ST_2.json +++ b/datasets/SAMMI2ST_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMI2ST_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Level 2 TOA/Cloud Stereo Product. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMMIB2E_3.json b/datasets/SAMMIB2E_3.json index 02de9e5479..542c86bb1c 100644 --- a/datasets/SAMMIB2E_3.json +++ b/datasets/SAMMIB2E_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMIB2E_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Ellipsoid-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMMIB2T_3.json b/datasets/SAMMIB2T_3.json index 31474431e8..ed7c14fd8e 100644 --- a/datasets/SAMMIB2T_3.json +++ b/datasets/SAMMIB2T_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMIB2T_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains Terrain-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMMIGEO_2.json b/datasets/SAMMIGEO_2.json index ca121df7ff..f12202e9f1 100644 --- a/datasets/SAMMIGEO_2.json +++ b/datasets/SAMMIGEO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMMIGEO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid for the SAMUM_2006 theme.", "links": [ { diff --git a/datasets/SAMSN7L1RAD_CDROM_001.json b/datasets/SAMSN7L1RAD_CDROM_001.json index 34fdb2f26b..dbc405c0c2 100644 --- a/datasets/SAMSN7L1RAD_CDROM_001.json +++ b/datasets/SAMSN7L1RAD_CDROM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMSN7L1RAD_CDROM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAMSN7L1RAD_CDROM is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a resolution of 100 km in the horizontal by 10 km in the vertical at the limb. This product contains radiances in a daily 2.5 degree latitude x 10 degree longitude grid format, gridded temperature profiles at 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01 and 0.003 hPa, as well as the calibration, apriori and reformatted copies of the original tapes. The data for this product are available from 22 October 1978 through June 9 1983, with a few additional raw radiances to 16 April 1984. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.\n\nThis product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a set of 53 CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the a-priori temperature profile, monthly mean retrieved temperature profile, pre-launch calibration, housekeeping and instrument subsystem status files. CD-ROMs 2-5 contain the gridded temperature data. CD-ROMs 6-22 contain the radiances from the C-series and G-series tapes, and CD-ROMs 34-53 contain the raw radiance values from the R-series tape. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.", "links": [ { diff --git a/datasets/SAMSN7L1RAT_001.json b/datasets/SAMSN7L1RAT_001.json index 73c14cffb8..e7e6a52c48 100644 --- a/datasets/SAMSN7L1RAT_001.json +++ b/datasets/SAMSN7L1RAT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMSN7L1RAT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAMSN7L1RAT is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a vertical resolution of 10 km. The instrument scanned the vertical from about 15 km to 140 km. The data were recovered from the original magnetic tapes, and are now stored online as orbit files in their original proprietary binary format each with about 14 orbits per day.\n\nThe data for this product are available from 26 October 1978 through June 9 1983. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University. This product was subsequently used to create the SAMS/Nimbus-7 Level 1 Radiance Data from CD-ROM product (SAMSN7L1RAD_CDROM), a set of 53 CD-ROMs.", "links": [ { diff --git a/datasets/SAMSN7L3GRIDT_001.json b/datasets/SAMSN7L3GRIDT_001.json index 2fa6044b6a..34f7c53785 100644 --- a/datasets/SAMSN7L3GRIDT_001.json +++ b/datasets/SAMSN7L3GRIDT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMSN7L3GRIDT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAMSN7L3GRIDT is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Gridded Retrieval Temperature Data Product. The Earth's surface is divided into 2.5 deg latitude by 10 deg longitude grids that extend from 50 deg South to 67.5 deg North. The data are stored in two different record types. The first contains temperatures at all 62 retrieved pressure levels between 246 and 0.0012 mbar, and the second contains temperature and error values at 10 standard pressure levels: 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01 and 0.003 mbar. The data were recovered from the original magnetic tapes, and are now stored online as daily files in their original proprietary binary format.\n\nThe data for this product are available from 24 December 1978 through 9 June 1983. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00016 (old ID 78-098A-02B).", "links": [ { diff --git a/datasets/SAMSN7L3ZMTG_001.json b/datasets/SAMSN7L3ZMTG_001.json index 54e9869cdc..c328daf54e 100644 --- a/datasets/SAMSN7L3ZMTG_001.json +++ b/datasets/SAMSN7L3ZMTG_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAMSN7L3ZMTG_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.\n\nThe data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).", "links": [ { diff --git a/datasets/SAOCOM.data.products_NA.json b/datasets/SAOCOM.data.products_NA.json index 473fbeacf3..ae91ba5f8d 100644 --- a/datasets/SAOCOM.data.products_NA.json +++ b/datasets/SAOCOM.data.products_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAOCOM.data.products_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection provides access to the SAOCOM products acquired in the ASI Zone of Exclusivity, that correspond mainly to the European territory plus the international waters in front of North Africa and the Middle East, archived and catalogued in the ASI/CONAE dissemination system.", "links": [ { diff --git a/datasets/SAR_IMM_1P_10.0.json b/datasets/SAR_IMM_1P_10.0.json index 2a71c77cf5..848e40eeed 100644 --- a/datasets/SAR_IMM_1P_10.0.json +++ b/datasets/SAR_IMM_1P_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAR_IMM_1P_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ERS Medium Resolution strip-line product is generated from the Image Mode Level 0 Product. Strip-line image products contain image data for an entire segment, up to a maximum size of 10 minutes per product for IM mode. The processor concatenates together several sub-images called "slices" that were processed separately on a dataset-by-dataset basis in order to form the entire strip-line image. The product is processed to an approximately 150 m x 150 m resolution and has a radiometric resolution that is good enough for ice applications. This product has a lower spatial resolution than the SAR_IMP_1P and SAR_IMS_1P products. The SAR IM L0 full mission data archive has been bulk processed to Level 1 (SAR_IMM_1P) in Envisat format with the PF-ERS processor version 6.01. Product Characteristics: - Pixel size: 5 m (ground range \u2013 across track) x 75 m (azimuth \u2013 along track) - Scene area: 100 km (range) x at least 102.5 km - Scene Size: 1300 pixels (range) x at least 1350 lines (azimuth) - Pixel depth: 16 bits unsigned integer - Total product volume: at least 3.5 MB - Projection: Ground-range - Number of looks: 8 (azimuth) x 7 (range)", "links": [ { diff --git a/datasets/SAR_IMP_1P_8.0.json b/datasets/SAR_IMP_1P_8.0.json index 98c4aeec7f..2cf289cb90 100644 --- a/datasets/SAR_IMP_1P_8.0.json +++ b/datasets/SAR_IMP_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAR_IMP_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SAR Precision product is a multi-look (speckle-reduced), ground range image acquired in Image Mode. This product type is most applicable to users interested in remote sensing applications, but is also suitable for calibration purposes. The products are calibrated and corrected for the SAR antenna pattern and range-spreading loss. Radar backscatter can be derived from the products for geophysical modelling, but no correction is applied for terrain-induced radiometric effects. The images are not geocoded, and terrain distortion (foreshortening and layover) has not been removed. The numbering sequence relates to the satellite position and therefore differs between Ascending and Descending scenes. Product characteristics: - Pixel size: 12.5 m (range - across track) x 12.5 m (azimuth - along track) - Scene area: 100 km (range) x at least 102.5 km (azimuth) - Scene size: 8000 pixels range x at least 8200 lines (azimuth) - Pixel depth: 16 bits unsigned integer - Total product volume: 125 MBs - Projection: Ground-range - Number of looks: 3", "links": [ { diff --git a/datasets/SAR_IMS_1P_8.0.json b/datasets/SAR_IMS_1P_8.0.json index 41f150dc6d..e3aebad778 100644 --- a/datasets/SAR_IMS_1P_8.0.json +++ b/datasets/SAR_IMS_1P_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAR_IMS_1P_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SAR SLC product is a single look complex acquired in Image Mode. It is a digital image, with slant range and phase preserved, generated from raw SAR data using up-to-date auxiliary parameters. The products are intended for use in SAR quality assessment, calibration and interferometric applications. A minimum number of corrections and interpolations are performed on the data. Absolute calibration parameters (when available) are provided in the product annotation. Product characteristics: - Pixel size: 8 m (range - across track) x 4 m (azimuth - along track \u2013 varying slightly depending on acquisition Pulse Repetition Frequency) - Scene area: 100 km (range) x at least 102.5 km (azimuth) - Scene size: 5000 samples (range) x at least 30000 lines (azimuth) - Pixel depth: 32 bits signed integer (16 bits I, 16 bits Q) - Total product volume: 575 MB - Projection: Slant range - Number of looks: 1", "links": [ { diff --git a/datasets/SAR_IM_0P_9.0.json b/datasets/SAR_IM_0P_9.0.json index f92f398f6e..53c721badc 100644 --- a/datasets/SAR_IM_0P_9.0.json +++ b/datasets/SAR_IM_0P_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAR_IM_0P_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This SAR Level 0 product is acquired in Image Mode. The products consist of the SAR telemetry data and are supplied as standard scenes. It also contains all the required auxiliary data necessary for data processing. The product serves two main purposes: For testing ERS SAR processors independently from the HDDR system For users interested in full SAR data processing. Product characteristics: - Scene area: 100 km (range - across track) x full segment length (azimuth - along track) - Scene size: 5616 samples (range) x full segment length (azimuth) - Pixel depth: 10 bits signed integer (5 bits I, 5 bits Q) - Projection: Slant range", "links": [ { diff --git a/datasets/SAR_Methane_Ebullition_AK_1790_1.json b/datasets/SAR_Methane_Ebullition_AK_1790_1.json index c8f92dd556..6fab9bd8f9 100644 --- a/datasets/SAR_Methane_Ebullition_AK_1790_1.json +++ b/datasets/SAR_Methane_Ebullition_AK_1790_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAR_Methane_Ebullition_AK_1790_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Synthetic Aperture Radar (SAR) estimates of lake-source methane ebullition flux in mg CH4/m2/d for thousands of lakes in five regions across Alaska. The study regions include the Atqasuk, Barrow Peninsula, Fairbanks, northern Seward Peninsula, and Toolik. L-band SAR backscatter values for early winter lake ice scenes were collected from 2007 to 2010 over 5,143 lakes using the Phased Array type L-band Synthetic Aperture Radar (PALSAR) instrument on the Advanced Land Observing Satellite (ALOS-1) satellite. The backscatter data were combined with field measurements of methane ebullition from 48 study lakes across the five regions to obtain a volumetric flux estimate for each lake. Mean methane gas-fractions from each region were applied to the SAR-based volumetric fluxes to obtain an estimate of methane ebullition mass flux per lake. The data files contain lake perimeters and the lake-specific attributes of lake area, SAR backscatter values and standard errors, volumetric flux with standard errors, mean percent of methane from gas samples, and methane ebullition mass flux.", "links": [ { diff --git a/datasets/SASSIE_L1_SWIFT_V1_1.json b/datasets/SASSIE_L1_SWIFT_V1_1.json index 40bfa20b6b..922dc686b0 100644 --- a/datasets/SASSIE_L1_SWIFT_V1_1.json +++ b/datasets/SASSIE_L1_SWIFT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L1_SWIFT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Surface Wave Instrument Float with Tracking (SWIFT) drifter is a passive Lagrangian wave-following sensor platform. During the SASSIE deployment, five SWIFT drifters were deployed in September 2022, collecting measurements of salinity, sea surface temperature, waves, and meteorological data. SWIFT drifter buoys contain GPS, a pulse-coherent Doppler velocity profiler, an autonomous meteorological station, and a digital video recorder. Level 1 data are available as compressed files containing graphics of the measurements alongside MATLAB and NetCDF files. ", "links": [ { diff --git a/datasets/SASSIE_L1_WAVEGLIDER_V1_1.json b/datasets/SASSIE_L1_WAVEGLIDER_V1_1.json index f38d463e15..e12f4d9534 100644 --- a/datasets/SASSIE_L1_WAVEGLIDER_V1_1.json +++ b/datasets/SASSIE_L1_WAVEGLIDER_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L1_WAVEGLIDER_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. A waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During the SASSIE deployment, four wavegliders were deployed near Prudhoe Bay on 12-14 August 2022. The wavegliders collect measurements of ocean surface salinity, temperature, currents, waves, and meteorological data. Custom integrated Casting CTDs provide additional profiles of salinity and temperature to a depth of 150m below the surface. L1 data are available as a compressed file containing graphics of the measurements alongside MATLAB data files. ", "links": [ { diff --git a/datasets/SASSIE_L2_ALTO_ALAMO_FLOATS_V1_1.json b/datasets/SASSIE_L2_ALTO_ALAMO_FLOATS_V1_1.json index f7dfdcd56c..bead39c507 100644 --- a/datasets/SASSIE_L2_ALTO_ALAMO_FLOATS_V1_1.json +++ b/datasets/SASSIE_L2_ALTO_ALAMO_FLOATS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_ALTO_ALAMO_FLOATS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains temperature and salinity measurements collected by ALTO and Air Launched Autonomous Micro Observer (ALAMO) profiling floats deployed in the Beaufort Sea. ALTO floats had ice-avoidance firmware, meaning that they stopped surfacing and transmitting data once surface temperatures dropped to near-freezing values (indicating the presence of sea ice). They will hopefully reappear in summer 2023 to report data from the previous ice-covered season. ALAMO floats did not have ice-avoidance, in order to ensure that they reported data as long as possible during ice freeze-up. As a result, they will likely not survive over the winter. Future versions if this dataset may include data collected after Fall 2022. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V1_1.json b/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V1_1.json index 2fbcd69ede..e1726522d1 100644 --- a/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V1_1.json +++ b/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_DRIFTER_UPTEMPO_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains ocean temperature and salinity data collected by surface drifting buoys (called UpTempO or Hydrobuoys, interchangeably) deployed in the Beaufort Sea. Each buoy has a different configuration of sensors, and records to a maximum of 60 m depth. Drifters were left at sea after the completion of the field deployment and are recording data into March 2023. Future versions of this dataset will provide updated data with final quality control and additional parameters. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V2p_2p.json b/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V2p_2p.json index 539782e430..2cb50f6ba1 100644 --- a/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V2p_2p.json +++ b/datasets/SASSIE_L2_DRIFTER_UPTEMPO_V2p_2p.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_DRIFTER_UPTEMPO_V2p_2p", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains ocean temperature and salinity data collected by surface drifting buoys (called UpTempO or Hydrobuoys, interchangeably) deployed in the Beaufort Sea. Each buoy has a different configuration of sensors, and records to a maximum of 60 m depth. Drifters were left at sea after the completion of the field deployment and are recording data into March 2023. Version 2p data has major quality control performed. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_JET_SSP_V1_1.json b/datasets/SASSIE_L2_JET_SSP_V1_1.json index 5caad0626b..fb7e157639 100644 --- a/datasets/SASSIE_L2_JET_SSP_V1_1.json +++ b/datasets/SASSIE_L2_JET_SSP_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_JET_SSP_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains near-surface air pressure, temperature, and winds, as well as ocean temperature and salinity measurements collected using a Jet Surface Salinity Profiler (Jet-SSP). The Jet-SSP is a remotely operated kayak containing various instrumentation. It moved along various horizontal trajectories each deployment, traveling up to 5 kts as it collected data. Data are available in NetCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_PALS_V1_1.json b/datasets/SASSIE_L2_PALS_V1_1.json index fa2e38ca33..fb8b376259 100644 --- a/datasets/SASSIE_L2_PALS_V1_1.json +++ b/datasets/SASSIE_L2_PALS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_PALS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Passive-Active L-Band System (PALS) is an airborne microwave radiometer that senses ocean temperature and surface wind speed. The brightness temperature data is obtained at 1.4GHz using the PALS conical scanner, with the raw data sampled at 1ms and gridded over an approximate 2x2km grid. Several quality control steps were done to remove any scan dependent biases, radio frequency interference, wind-speed dependencies. Calibration of the sensor was done with respect to special aircraft maneuvers as well as in-situ samples. Brightness temperature were converted to salinity via the Klein-Swift salinity retrieval model. Data is available in netCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SBAND_ML_V1_1.json b/datasets/SASSIE_L2_SBAND_ML_V1_1.json index 0b29c1102e..ced7c37306 100644 --- a/datasets/SASSIE_L2_SBAND_ML_V1_1.json +++ b/datasets/SASSIE_L2_SBAND_ML_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SBAND_ML_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Ice concentration rankings ranging from 0 to 3 determined by VGG19 Machine Learning Model. L2 summary data are available in NetCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_ADCP_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_ADCP_V1_1.json index e70e297937..0bb7e23baf 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_ADCP_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_ADCP_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_ADCP_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains measurements of shipboard ocean current speed vertical shear from an acoustic doppler current profiler (ADCP) during the Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1_1.json index af40b1739b..47b867a96c 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_CASTAWAY_CTD_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains profiles of upper ocean temperature, salinity, and density taken with a Shipboard CastAway Conductivity-Temperature-Depth instrument (CastAway-CTD). A total of 254 profiles were taken over the sampling period at various locations, typically every 30 minutes while the ship was underway, with a mean depth of 45m. Data are available in NetCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_DELTA_18O_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_DELTA_18O_V1_1.json index e18390f856..a0d6f4ea81 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_DELTA_18O_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_DELTA_18O_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_DELTA_18O_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. This dataset contains delta-18O measurements from sea water and ice. Delta-18O is the ratio of stable isotopes oxygen-18 (18O) and oxygen-16. Water samples were collected from either a GoFlo bottle lowered from the side of the ship, or the outflow of the Salinity Snake. Ice samples (dimension ice_obs) were collected during two ice stations where augur tailings were collected and melted. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_METEOROLOGY_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_METEOROLOGY_V1_1.json index 1ac22b66e5..49f2cdb765 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_METEOROLOGY_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_METEOROLOGY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_METEOROLOGY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. This dataset contains shipboard meteorology and air-sea flux measurements. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1.json index 0541611db7..d50bff0086 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. This dataset contains salinity and temperature measurements collected by a shipboard salinity snake. The salinity snake system consisted of a ship-mounted boom with a length of 10m to provide sampling of undisturbed water at a depth of 1-2cm. Salinity data are delayed by 40s, which is the average residence time in the hose, pump, and shipboard system before being analyzed. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_TSG_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_TSG_V1_1.json index 55183dc58d..abf676bb2b 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_TSG_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_TSG_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_TSG_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. This dataset contains in-situ ocean temperature and salinity at 4m depth measured via a ship thermosalinograph (TSG). The TSG system consisted of a SBE21 SeaCAT TSG, a SBE38 temperature sensor, and a debubbler. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SASSIE_L2_SHIPBOARD_UCTD_V1_1.json b/datasets/SASSIE_L2_SHIPBOARD_UCTD_V1_1.json index cebd8d12c7..b6785ee972 100644 --- a/datasets/SASSIE_L2_SHIPBOARD_UCTD_V1_1.json +++ b/datasets/SASSIE_L2_SHIPBOARD_UCTD_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SHIPBOARD_UCTD_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains in-situ profiles of upper ocean temperature and salinity taken with a Shipboard Underway Conductivity-Temperature-Depth instrument (uCTD). A total of 2,246 profiles were taken over the sampling period, with mean depth of 100 m and mean horizontal spacing between profiles of ~800 m. Profiling with the uCTD typically occurred as the ship was moving at 1-3 m/s. For higher sea ice concentrations (10-30 %), the ship stopped every ~30 minutes to collect a profile. The measurements have been gridded onto a uniformly spaced 0.1 dbar grid from the sea surface to 200 dbar seawater pressure, and collected into a single netCDF file, where each observation in the time dimension corresponds to a single cast.", "links": [ { diff --git a/datasets/SASSIE_L2_SWIFT_V1_1.json b/datasets/SASSIE_L2_SWIFT_V1_1.json index 694f6880f5..01b72dfe38 100644 --- a/datasets/SASSIE_L2_SWIFT_V1_1.json +++ b/datasets/SASSIE_L2_SWIFT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_SWIFT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. The Surface Wave Instrument Float with Tracking (SWIFT) drifter is a passive Lagrangian wave-following sensor platform. During the SASSIE deployment, five SWIFT drifters were deployed in September 2022, collecting measurements of salinity, sea surface temperature, waves, and meteorological data. SWIFT drifter buoys contain GPS, a pulse-coherent Doppler velocity profiler, an autonomous meteorological station, and a digital video recorder. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_UNDER_ICE_FLOAT_V1_1.json b/datasets/SASSIE_L2_UNDER_ICE_FLOAT_V1_1.json index 768c08693f..406d0ac8c9 100644 --- a/datasets/SASSIE_L2_UNDER_ICE_FLOAT_V1_1.json +++ b/datasets/SASSIE_L2_UNDER_ICE_FLOAT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_UNDER_ICE_FLOAT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains ocean temperature, salinity, and acoustic range measurements collected by an autonomous under ice float deployed in the Beaufort Sea from September 10, 2022 to October 22, 2022. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L2_WAVEGLIDERS_V1_1.json b/datasets/SASSIE_L2_WAVEGLIDERS_V1_1.json index b4fec7dea3..53082d73a7 100644 --- a/datasets/SASSIE_L2_WAVEGLIDERS_V1_1.json +++ b/datasets/SASSIE_L2_WAVEGLIDERS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L2_WAVEGLIDERS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. A waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During the SASSIE deployment, four wavegliders were deployed near Prudhoe Bay on 12-14 August 2022. The wavegliders collect measurements of ocean surface salinity, temperature, currents, waves, and meteorological data. Custom integrated Casting CTDs provide additional profiles of salinity and temperature to a depth of 150m below the surface. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SASSIE_L3_SHIPBOARD_SBAND_V1_1.json b/datasets/SASSIE_L3_SHIPBOARD_SBAND_V1_1.json index 60be54cde5..fec778c5f8 100644 --- a/datasets/SASSIE_L3_SHIPBOARD_SBAND_V1_1.json +++ b/datasets/SASSIE_L3_SHIPBOARD_SBAND_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L3_SHIPBOARD_SBAND_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Images were then georeferenced based on range, heading, image orientation, and ship's GPS position. L3 images are available in GeoTIFF format.", "links": [ { diff --git a/datasets/SASSIE_L4_SHIPBOARD_SBAND_V1_1.json b/datasets/SASSIE_L4_SHIPBOARD_SBAND_V1_1.json index 9f9097c82a..7b73cb129d 100644 --- a/datasets/SASSIE_L4_SHIPBOARD_SBAND_V1_1.json +++ b/datasets/SASSIE_L4_SHIPBOARD_SBAND_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SASSIE_L4_SHIPBOARD_SBAND_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Images were then georeferenced based on range, heading, image orientation, and ship's GPS position. L4 images are available in GeoTIFF format and contain additional processing to classify pixels as sea ice, not sea ice, or not data.", "links": [ { diff --git a/datasets/SAV_Plymouth_Bay_0.json b/datasets/SAV_Plymouth_Bay_0.json index c5c9320f60..848a8616f4 100644 --- a/datasets/SAV_Plymouth_Bay_0.json +++ b/datasets/SAV_Plymouth_Bay_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAV_Plymouth_Bay_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Compact Hydrographic Airborne Rapid Total Survey (CHARTS) is jointly operated and maintained by the U.S. Army Corps of Engineers and the U.S. Naval Oceanographic Office. This system was flown by JALBTCX over Buttermilk and Plymouth Bays in Massachusetts, USA during Sept. 2010 to study Submersed aquatic vegetation species discrimination using an airborne hyperspectral/lidar system. A large, collaborative ground truth sampling campaign was undertaken. Components included bathymetric surveys, laboratory and diver reflectivity measurements of sediment and SAV, camera and diver SAV surveys, water column IOPs, and pigment sampling.", "links": [ { diff --git a/datasets/SAWB_JRA_713_1.json b/datasets/SAWB_JRA_713_1.json index 557561454e..6c81c7c648 100644 --- a/datasets/SAWB_JRA_713_1.json +++ b/datasets/SAWB_JRA_713_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAWB_JRA_713_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRA, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRA worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.", "links": [ { diff --git a/datasets/SAWB_JRB_714_1.json b/datasets/SAWB_JRB_714_1.json index 930dae3fef..5481d33b88 100644 --- a/datasets/SAWB_JRB_714_1.json +++ b/datasets/SAWB_JRB_714_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAWB_JRB_714_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRB, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRB worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.", "links": [ { diff --git a/datasets/SAZ_Chlorophyll_1.json b/datasets/SAZ_Chlorophyll_1.json index 3c5829adbd..f0eabd1449 100644 --- a/datasets/SAZ_Chlorophyll_1.json +++ b/datasets/SAZ_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SAZ_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 6, 1997-1998 - the SAZ (Subantarctic Zone) cruise. Samples were collected in March of 1998. \n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/SBD_Bibliography_1.json b/datasets/SBD_Bibliography_1.json index 1315263e2a..09cae3333c 100644 --- a/datasets/SBD_Bibliography_1.json +++ b/datasets/SBD_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBD_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This bibliography contains references to diseases, health, clinical biochemistry and pathology of captive and wild sea birds. The definition of a sea bird is broad and includes all species that feed within the marine environment and others which are related but inhabit other aquatic environments e.g. cormorants. The bibliography is comprehensive but not exhaustive. The compilers would appreciate lists of missed and new items for inclusion. These should be sent to the data officer at the Australian Antarctic Data Centre at the contact details listed below.", "links": [ { diff --git a/datasets/SBI_0.json b/datasets/SBI_0.json index f1bbb3c3b6..870749e70e 100644 --- a/datasets/SBI_0.json +++ b/datasets/SBI_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBI_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Western Arctic Shelf-Basin Interactions (SBI) experiment contains measurements made in the Chukchi and Beaufort seas off the coast of northern Alaska. SBI at the University of Maryland", "links": [ { diff --git a/datasets/SBUV2N09L2_1.json b/datasets/SBUV2N09L2_1.json index a19175dcad..249689dc03 100644 --- a/datasets/SBUV2N09L2_1.json +++ b/datasets/SBUV2N09L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N09L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-9 Level-2 daily product (SBUV2N09L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N09L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from February 1985 through January 1998. The SBUV2N09L2 data product was used as input in creating the SBUV2N09L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N09L3zm_1.json b/datasets/SBUV2N09L3zm_1.json index 6cce47cf3c..d9b4319316 100644 --- a/datasets/SBUV2N09L3zm_1.json +++ b/datasets/SBUV2N09L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N09L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-9 Level-3 monthly zonal mean (MZM) product (SBUV2N17L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 156 months of data from February 1985 through January 1998. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N09O3_008.json b/datasets/SBUV2N09O3_008.json index bac2b2a6a4..92d4469a38 100644 --- a/datasets/SBUV2N09O3_008.json +++ b/datasets/SBUV2N09O3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N09O3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 8 SBUV/2 NOAA-9 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-9 SBUV/2 v8 data are available in two time periods from 1985-02-02 to 1989-12-31 (ascending orbits) and again from 1992-01-01 to 1998-02-19 (descending orbits) due to the drift of the NOAA-9 satellite. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format.\n\nThe SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.", "links": [ { diff --git a/datasets/SBUV2N11L2_1.json b/datasets/SBUV2N11L2_1.json index 55c1201fed..6e91c31c5b 100644 --- a/datasets/SBUV2N11L2_1.json +++ b/datasets/SBUV2N11L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N11L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-11 Level-2 daily product (SBUV2N11L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N11L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from January 1989 through March 2001. The SBUV2N11L2 data product was used as input in creating the SBUV2N11L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N11L3zm_1.json b/datasets/SBUV2N11L3zm_1.json index e52eb81266..02d7bc3ebf 100644 --- a/datasets/SBUV2N11L3zm_1.json +++ b/datasets/SBUV2N11L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N11L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-11 Level-3 monthly zonal mean (MZM) product (SBUV2N11L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 147 months of data from January 1989 through March 2001. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N11O3_008.json b/datasets/SBUV2N11O3_008.json index fa8fa4b834..d66539572f 100644 --- a/datasets/SBUV2N11O3_008.json +++ b/datasets/SBUV2N11O3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N11O3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 8 SBUV/2 NOAA-11 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-11 SBUV/2 v8 data are available from 1988-12-01 to 2001-03-27. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format.\n\nThe SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.", "links": [ { diff --git a/datasets/SBUV2N14L2_1.json b/datasets/SBUV2N14L2_1.json index f4e81223e5..5601239dde 100644 --- a/datasets/SBUV2N14L2_1.json +++ b/datasets/SBUV2N14L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N14L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-14 Level-2 daily product (SBUV2N14L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N14L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from March 1995 through September 2006. The SBUV2N14L2 data product was used as input in creating the SBUV2N14L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N14L3zm_1.json b/datasets/SBUV2N14L3zm_1.json index 2a28f23fd1..c85c802149 100644 --- a/datasets/SBUV2N14L3zm_1.json +++ b/datasets/SBUV2N14L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N14L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-14 Level-3 monthly zonal mean (MZM) product (SBUV2N14L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 139 months of data from March 1995 through September 2006. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N16L2_1.json b/datasets/SBUV2N16L2_1.json index bdfe26daca..2618cbc28d 100644 --- a/datasets/SBUV2N16L2_1.json +++ b/datasets/SBUV2N16L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N16L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-16 Level-2 daily product (SBUV2N16L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N16L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from October 2000 through July 2013. The SBUV2N16L2 data product was used as input in creating the SBUV2N16L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N16L3zm_1.json b/datasets/SBUV2N16L3zm_1.json index 1f1deda7cb..1941dc6316 100644 --- a/datasets/SBUV2N16L3zm_1.json +++ b/datasets/SBUV2N16L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N16L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-16 Level-3 monthly zonal mean (MZM) product (SBUV2N16L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 154 months of data from October 2000 through July 2013. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N16O3_008.json b/datasets/SBUV2N16O3_008.json index 065ef40cd9..d1727ce75a 100644 --- a/datasets/SBUV2N16O3_008.json +++ b/datasets/SBUV2N16O3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N16O3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 8 SBUV/2 NOAA-16 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-16 SBUV/2 v8 data are available from 2000-10-03 to 2003-12-31. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format.\n\nThe SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.", "links": [ { diff --git a/datasets/SBUV2N17L2_1.json b/datasets/SBUV2N17L2_1.json index 8748092a39..06c576c68b 100644 --- a/datasets/SBUV2N17L2_1.json +++ b/datasets/SBUV2N17L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N17L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-17 Level-2 daily product (SBUV2N17L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N17L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from July 2002 through April 2013. The SBUV2N17L2 data product was used as input in creating the SBUV2N17L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N17L3zm_1.json b/datasets/SBUV2N17L3zm_1.json index d6c7fb7116..db1aaf6a47 100644 --- a/datasets/SBUV2N17L3zm_1.json +++ b/datasets/SBUV2N17L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N17L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-17 Level-3 monthly zonal mean (MZM) product (SBUV2N17L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 126 months of data from August 2002 through January 2013. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N18L2_1.json b/datasets/SBUV2N18L2_1.json index e02e8cc4d9..1c4002fab9 100644 --- a/datasets/SBUV2N18L2_1.json +++ b/datasets/SBUV2N18L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N18L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-18 Level-2 daily product (SBUV2N18L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N18L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from June 2005 through December 2012. The SBUV2N18L2 data product was used as input in creating the SBUV2N18L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N18L3zm_1.json b/datasets/SBUV2N18L3zm_1.json index 3cf0b7edcf..980bec201e 100644 --- a/datasets/SBUV2N18L3zm_1.json +++ b/datasets/SBUV2N18L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N18L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-18 Level-3 monthly zonal mean (MZM) product (SBUV2N18L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 90 months of data from July 2005 through December 2012. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUV2N19L2_1.json b/datasets/SBUV2N19L2_1.json index 24b6242a8d..650b0e1edc 100644 --- a/datasets/SBUV2N19L2_1.json +++ b/datasets/SBUV2N19L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N19L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-19 Level-2 daily product (SBUV2N19L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUV2N19L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from February 2009 through July 2013. The SBUV2N19L2 data product was used as input in creating the SBUV2N19L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUV2N19L3zm_1.json b/datasets/SBUV2N19L3zm_1.json index f0cfe98dd9..12f9eaeabd 100644 --- a/datasets/SBUV2N19L3zm_1.json +++ b/datasets/SBUV2N19L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUV2N19L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from NOAA-19 Level-3 monthly zonal mean (MZM) product (SBUV2N19L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 53 months of data from March 2009 through July 2013. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUVN07L2_1.json b/datasets/SBUVN07L2_1.json index c765efd16e..4e94626da2 100644 --- a/datasets/SBUVN07L2_1.json +++ b/datasets/SBUVN07L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUVN07L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from Nimbus-7 Level-2 daily product (SBUVN07L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.\n\nThe SBUVN07L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from November 1978 through May 1990. The SBUVN07L2 data product was used as input in creating the SBUVN07L3zm monthly zonal mean data product.", "links": [ { diff --git a/datasets/SBUVN07L3zm_1.json b/datasets/SBUVN07L3zm_1.json index 2fbe704d1d..bd0e17c646 100644 --- a/datasets/SBUVN07L3zm_1.json +++ b/datasets/SBUVN07L3zm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUVN07L3zm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Backscattered Ultraviolet (SBUV) from Nimbus-7 Level-3 monthly zonal mean (MZM) product (SBUVN07L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 139 months of data from November 1978 through May 1990. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). \n\nThe MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions:\n\n 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees).\n 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone).\n\nNOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm.\n\nThe zonal means computed for each month are screened according to the following statistical criteria:\n\n 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month.\n 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band.\n 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).", "links": [ { diff --git a/datasets/SBUVN7O3_008.json b/datasets/SBUVN7O3_008.json index 82357efb49..b083099713 100644 --- a/datasets/SBUVN7O3_008.json +++ b/datasets/SBUVN7O3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SBUVN7O3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 8 SBUV Nimbus-7 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The Nimbus-7 SBUV v8 data are available from 1978-10-31 to 1990-06-21. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format.\n\nThe SBUV measures incoming solar irradiance and radiance backscattered by the atmosphere at 12 wavelengths in the UV range [0.25-0.34 micrometer] with a spectral band pass of 0.001 micrometer. The wavelength channels used for ozone retrievals for SBUV were: 256, 273, 283, 288, 292, 298, 302, 306, 312, 318, 331, and 340 nm. Radiation at these wavelengths are absorbed by ozone, such that the difference between the incoming and outgoing radiation can be related to the amount of ozone in the atmosphere.\n\nThe SBUV consists of a double Ebert-Fastie spectrometer and a filter photometer similar to the BUV on Nimbus 4. The bandwidth of the photometer is about 3 nm and the spectral resolution for SBUV monochromator is 1.1 nm.", "links": [ { diff --git a/datasets/SCAMSN6IM_001.json b/datasets/SCAMSN6IM_001.json index 3b08e20e81..2969b316fc 100644 --- a/datasets/SCAMSN6IM_001.json +++ b/datasets/SCAMSN6IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAMSN6IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SCAMSN6IM data product consists of images of brightness temperatures, water vapor and temperature on 70 mm film strips from the Nimbus-6 Scanning Microwave Spectrometer. Each display contains eight vertical strips of data from one orbit. All strips have the same geographic coverage, but each represents a different parameter. The first three are brightness temperatures for channels 2 (31.65 GHz) and 3 (52.85 GHz) and their differences. The next two represent retrieved water vapor and liquid water from clouds or precipitation over the oceans, respectively. The remaining three strips on the right represent inverted mean temperatures for atmospheric layers 1000-500 mbar, 500-250 mbar, and 250-100 mbar, respectively. The first five parameters are displayed in 18-step gray levels, the values of which can be found in a table in each of the first five volumes of \"The Nimbus 6 Data Catalog.\" The last three parameters are displayed by contour bands (labeled on the side) that are spaced 4 K apart. Spatial resolution on the ground for the parameters varies from 145 km at nadir to 330 km at the scan extremes. The images are saved as TIFF digital files. About 3-5 months of images are archived into a ZIP file. Additional information can be found in section 2.4.1 of \"The Nimbus 6 User's Guide.\"\n\nThe SCAMS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 NEMS experiment. SCAMS continuously monitored emitted microwave radiation at frequencies of 22.235, 31.65, 52.85, 53.85 and 55.45 GHz. The three channels near the 5.0-mm oxygen absorption band were used primarily to deduce atmospheric temperature profiles. The two channels near 10 mm permitted water vapor and cloud water content over calm oceans to be estimated separately. The instrument, a Dicke-superheterodyne type, scanned +/- 45 degrees normal to the orbital plane with a 10 degree field of view. The three oxygen channels shared common signal and reference antennas. Both water vapor channels had their own signals and reference antennas. The absolute rms accuracy of the oxygen channels was better than 2 Kelvin and that of the water vapor channels better than 1 Kelvin.\n\nThe SCAMS Principal Investigator was Prof. David H. Staelin from MIT. The Nimbus-6 SCAMS images are available from June 15, 1975 (day of year 166) through May 31, 1976 (day of year 152).\n\nThis product was previously available from the NSSDC with the identifier ESAD-00200 (old ID 75-052A-10B).", "links": [ { diff --git a/datasets/SCAMSN6L2_001.json b/datasets/SCAMSN6L2_001.json index 0fc820ed51..9aed0d6fea 100644 --- a/datasets/SCAMSN6L2_001.json +++ b/datasets/SCAMSN6L2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAMSN6L2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-6 Scanning Microwave Spectrometer (SCAMS) Level 2 data product contains water vapor and temperature profiles, as well as antenna and brightness temperatures. SCAMS was designed to map tropospheric temperature profiles, water vapor abundance, and cloud water content to be used for weather prediction even in the presence of clouds, which block conventional satellite infrared sensors. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, also referred to as a binary TAP file, and contain one orbit of measurements. \n\nThe SCAMS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 NEMS experiment. SCAMS continuously monitored emitted microwave radiation at frequencies of 22.235, 31.65, 52.85, 53.85 and 55.45 GHz. The three channels near the 5.0-mm oxygen absorption band were used primarily to deduce atmospheric temperature profiles. The two channels near 10 mm permitted water vapor and cloud water content over calm oceans to be estimated separately. The instrument, a Dicke-superheterodyne type, scanned +/- 45 degrees normal to the orbital plane with a 10 degree field of view. The three oxygen channels shared common signal and reference antennas. Both water vapor channels had their own signals and reference antennas. The absolute rms accuracy of the oxygen channels was better than 2 Kelvin and that of the water vapor channels better than 1 Kelvin. \nThe SCAMS Principal Investigator was Prof. David H. Staelin from MIT. The Nimbus-6 SCAMS data are available from June 15, 1975 (day of year 166) through May 31, 1976 (day of year 152).\n\nThis product was previously available from the NSSDC with the identifier ESAD-00093 (old ID 75-052A-10A).", "links": [ { diff --git a/datasets/SCAR-B_916_1.json b/datasets/SCAR-B_916_1.json index 9e8d6f6caf..ac4e0605d0 100644 --- a/datasets/SCAR-B_916_1.json +++ b/datasets/SCAR-B_916_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR-B_916_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological data, reanalysis data, remote sensing images, and data on atmospheric composition collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment. The SCAR-B examined the effects of biomass burning on atmospheric processes with four primary goals: (1) improving techniques for remote sensing of these process from space, (2) obtain measurements of the rates of emissions of trace gases and particles from biomass burning, (3) observe the influence of atmospheric processes on the emission products was to obtain measurements of the rates of emissions of trace gases and particles from biomass burning, and to observe the influence of atmospheric processes on these emission products, and (4) characterize the physical and radiative properties of smoke particles from biomass burning. SCAR-B was conducted during biomass burning in the cerrado ( dry savannah) and Amazonia rainforest to understand the influence of land cover type on smoke, clouds, and radiation.Selected archived data and images from SCAR-B are described in the Data Description section table. Extensive background information on SCAR-B is provided following the Data Description.The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. ", "links": [ { diff --git a/datasets/SCARB_ER2_MAS_1.json b/datasets/SCARB_ER2_MAS_1.json index 464a7553f5..2ab2441ef8 100644 --- a/datasets/SCARB_ER2_MAS_1.json +++ b/datasets/SCARB_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCARB_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCARB_ER2_MAS data are Smoke, Clouds and Radiation Brazil (SCARB) NASA ER2 Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS) Data.Smoke/Sulfates, Clouds and Radiation - Brazil (SCAR-B) data include physical and chemical components of the Earth's surface, the atmosphere and the radiation field collected in Brazil with an emphasis in biomass burning. SCAR-B, the third SCAR experiment, was completed in September 1995, studied the effects of biomass burning on atmospheric processes and aids in the preparation of new techniques for remote sensing of these processes from space.The objectives for the SCAR mission are: to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The MODIS Airbourne Simulator (MAS) is a modified Daedalus Wildfire scanning spectrometer which flies on a NASA ER-2 and provides spectral information similar to that provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), launched on Terra (EOS AM-1) in 1999 and Aqua (EOS PM-1) in 2002. The MAS spectrometer acquires high spatial resolution imagery in the wavelength range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range, and the digitizer can be configured to collect data from any 12 of these bands. The digitizer was configured with four 10-bit channels and seven 8-bit channels. The MAS spectrometer was mated to a scanner subassembly which collected image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. The data granules were written using the self documenting file storage format provided through the netCDF interface routines included in the HDF libraries.", "links": [ { diff --git a/datasets/SCAR_A_ER2_MAS_1.json b/datasets/SCAR_A_ER2_MAS_1.json index 9601cb75f5..6bff8acde3 100644 --- a/datasets/SCAR_A_ER2_MAS_1.json +++ b/datasets/SCAR_A_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR_A_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCAR_A_ER2_MAS data are Sulfates, Clouds and Radiation America (SCARA) NASA ER2 Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS) Data in Hierarchical Data Format (HDF).Smoke/Sulfates, Clouds and Radiation - America (SCAR-A) data include physical and chemical components of the Earth's surface, the atmosphere and the radiation field collected in the eastern part of the United States with an emphasis in air pollution.The primary objective of the SCAR-A experiment was to help scientists characterize the the relationship between sulfate particles and clouds' reflective properties. Sulfate aerosols are believed to provide condensation nuclei, resulting in smaller, more numerous droplets within a cloud. SCAR-A was the first in a series of experiments. It was was followed by the SCAR-C experiment conducted over California in 1994. A third experiment, SCAR-B was conducted in Brazil during August and September 1995. The MODIS Airbourne Simulator (MAS) is a modified Daedalus Wildfire scanning spectrometer which flies on a NASA ER-2 and provides spectral information similar to that provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), launched on Terra (EOS AM-1) in 1999 and Aqua (EOS PM-1) in 2002. The MAS spectrometer acquires high spatial resolution imagery in the wavelength range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range, and the digitizer can be configured to collect data from any 12 of these bands. The digitizer was configured with four 10-bit channels and seven 8-bit channels. The MAS spectrometer was mated to a scanner subassembly which collected image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. The data granules were written using the self documenting file storage format provided through the netCDF interface routines included in the HDF libraries.", "links": [ { diff --git a/datasets/SCAR_B_AERONET_1.json b/datasets/SCAR_B_AERONET_1.json index 5d5284d8f2..bb76f1692a 100644 --- a/datasets/SCAR_B_AERONET_1.json +++ b/datasets/SCAR_B_AERONET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR_B_AERONET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCAR_B_AERONET data are Smoke, Clouds and Radiation Brazil (SCARB) Aerosol Robotic Network (AERONET) data for aerosol characterization.Smoke/Sulfates, Clouds and Radiation - Brazil (SCAR-B) data include physical and chemical components of the Earth's surface, the atmosphere and the radiation field collected in Brazil with an emphasis in biomass burning.The objectives for the SCAR mission are: to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes.AERONET (AErosol RObotic NETwork) is an optical ground based aerosol monitoring network and data archive supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally distributed near real time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. The data undergo preliminary processing (real time data), reprocessing (final calibration ~6 mo. after data collection), quality assurance, archiving and distribution from NASA's Goddard Space Flight Center master archive and several identical data bases maintained globally. The data provide algorithm validation of satellite aerosol retrievals and as well as characterization of aerosol properties that are unavailable from satellite sensors.", "links": [ { diff --git a/datasets/SCAR_B_G8_FIRE_1.json b/datasets/SCAR_B_G8_FIRE_1.json index c18600eedc..e59590e28a 100644 --- a/datasets/SCAR_B_G8_FIRE_1.json +++ b/datasets/SCAR_B_G8_FIRE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR_B_G8_FIRE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCAR_B_G8_FIRE data are Smoke/Sulfates, Clouds and Radiation Experiment in Brazil, GOES-8 ABBA Diurnal Fire Product (1995 Fire Season) data.Smoke/Sulfates, Clouds and Radiation - Brazil (SCAR-B) data include physical and chemical components of the Earth's surface, the atmosphere and the radiation field collected in Brazil with an emphasis in biomass burning. SCAR-B, the third SCAR experiment, was completed in September 1995, studied the effects of biomass burning on atmospheric processes and aids in the preparation of new techniques for remote sensing of these processes from space.The objectives for the SCAR mission are: to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes.The Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin-Madison has produced diurnal GOES-8 derived fire products for the 1995 fire season (June-October 1995) with version 5.5 of the GOES-8 Automated Biomass Burning Algorithm (ABBA). The diurnal fire products were produced for 1145, 1445, 1745, and 2045 UTC coinciding with peak burning hours.The GOES-8 Automated Biomass Burning Algorithm (ABBA) fire products are derived from Geostationary Operational Environmental Satellite (GOES)-8 imager radiances from bands 1 (visible), 2 (3.9 micron), and 4 (11 micron).", "links": [ { diff --git a/datasets/SCAR_B_UWC131A_1.json b/datasets/SCAR_B_UWC131A_1.json index 8ac9732f38..3692ae20ff 100644 --- a/datasets/SCAR_B_UWC131A_1.json +++ b/datasets/SCAR_B_UWC131A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR_B_UWC131A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCAR_B_UWC131A data are Smoke/Sulfates, Clouds and Radiation Experiment in Brazil data from instruments on board the University of Washington C131A aircraft in Native format.Smoke/Sulfates, Clouds and Radiation - Brazil (SCAR-B) data include physical and chemical components of the Earth's surface, the atmosphere and the radiation field collected in Brazil with an emphasis in biomass burning.The objectives for the SCAR mission are: to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes.From 17 August to 20 September 1995, the University of Washington's (UW) Cloud and Aerosol Research Group, with its Convair C-131A research aircraft, participated in an intensive field study of smoke emissions from various types of biomass burning over a large area of Brazil. This included 29 flights to collect measurements and photographs.", "links": [ { diff --git a/datasets/SCAR_EGBAMM_RAATD_2018_Filtered_1.json b/datasets/SCAR_EGBAMM_RAATD_2018_Filtered_1.json index 04ba0ec2b1..1d538eac95 100644 --- a/datasets/SCAR_EGBAMM_RAATD_2018_Filtered_1.json +++ b/datasets/SCAR_EGBAMM_RAATD_2018_Filtered_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCAR_EGBAMM_RAATD_2018_Filtered_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research (SCAR) project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. The RAATD project team consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets constitute the compiled tracking data from a large number of research groups that have worked in the Antarctic since the 1990s.\n\nThis metadata record pertains to the \"filtered\" version of the data files. These files contain position estimates that have been processed using a state-space model in order to estimate locations at regular time intervals. For technical details of the filtering process, consult the data paper. The filtering code can be found in the https://github.com/SCAR/RAATD repository.\n\nThis data set comprises one metadata csv file that describes all deployments, along with data files (3 files for each of 17 species). For each species there is:\n- an RDS file that contains the fitted filter model object and model predictions (this file is RDS format that can be read by the R statistical software package)\n- a PDF file that shows the quality control results for each individual model\n- a CSV file containing the interpolated position estimates\n\nFor details of the file contents and formats, consult the data paper.\n\nThe data are also available in a standardized version (see https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised) that contain position estimates as provided by the original data collectors (generally, raw Argos or GPS locations, or estimated GLS locations) without state-space filtering.", "links": [ { diff --git a/datasets/SCATSAT1_ESDR_ANCILLARY_L2_V1.1_1.1.json b/datasets/SCATSAT1_ESDR_ANCILLARY_L2_V1.1_1.1.json index b62ac08842..f352a47463 100644 --- a/datasets/SCATSAT1_ESDR_ANCILLARY_L2_V1.1_1.1.json +++ b/datasets/SCATSAT1_ESDR_ANCILLARY_L2_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCATSAT1_ESDR_ANCILLARY_L2_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the first science quality release (post-provisional after v1.0) of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the SCATSAT-1 Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These ancillary files include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 SCATSAT-1 sampled locations at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. \r\n

\r\nThe dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) improved variable metadata, 2) removed the GlobCurrent stokes drift variables, and 3) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/SCATSAT1_ESDR_L2_WIND_STRESS_V1.1_1.1.json b/datasets/SCATSAT1_ESDR_L2_WIND_STRESS_V1.1_1.1.json index 3a93bf65ed..91b00f57d7 100644 --- a/datasets/SCATSAT1_ESDR_L2_WIND_STRESS_V1.1_1.1.json +++ b/datasets/SCATSAT1_ESDR_L2_WIND_STRESS_V1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCATSAT1_ESDR_L2_WIND_STRESS_V1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard ScatSat-1, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from ScatSat-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.

The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).", "links": [ { diff --git a/datasets/SCIAMACHYLevel1_2.0.json b/datasets/SCIAMACHYLevel1_2.0.json index 55073c81a6..3a1fdd367a 100644 --- a/datasets/SCIAMACHYLevel1_2.0.json +++ b/datasets/SCIAMACHYLevel1_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCIAMACHYLevel1_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Envisat SCIAMACHY Level 1b Geo-located atmospheric spectra V.10 dataset is generated from the full mission reprocessing campaign completed in 2023 under the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ . \rThis data product contains SCIAMACHY geo-located (ir)radiance spectra for Nadir, Limb, and Occultation measurements (Level 1), accompanied by supplementary monitoring and calibration measurements, along with instrumental parameters detailing the operational status and configuration throughout the Envisat satellite lifetime (2002-2012).\r \rAdditionally, calibrated lunar measurements, including individual readings and averaged disk measurements, have been integrated into the Level 1b product.\rThe Level 1b product represents the lowest level of SCIAMACHY data made available to the users. The measurements undergo correction for instrument degradation applying a scan mirror model and m-factors. However, spectra are partially calibrated and require a further step to apply specific calibrations with the SCIAMACHY Calibration and Extraction Tool [_$$SciaL1c$$ https://earth.esa.int/eogateway/tools/scial1c-command-line-tool ]. \rFor many aspects, the SCIAMACHY Level 1b version 10 product marks a significant improvement with respect to previous mission datasets, supplanting the Level 1b dataset version 8.0X with product type SCI_NL__1P. Users are strongly encouraged to make use of the new datasets for optimal results.\r\rThe new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \rPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .\r\rPlease refer to the _$$README$$ https://earth.esa.int/documents/d/earth-online/rmf_0013_sci_____1p_l1v10 file for essential guidance before using the data.", "links": [ { diff --git a/datasets/SCIAMACHYLevel2LimbOzone_3.0.json b/datasets/SCIAMACHYLevel2LimbOzone_3.0.json index d910d39735..50a11784ac 100644 --- a/datasets/SCIAMACHYLevel2LimbOzone_3.0.json +++ b/datasets/SCIAMACHYLevel2LimbOzone_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCIAMACHYLevel2LimbOzone_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Envisat SCIAMACHY Ozone stratospheric profiles dataset has been extracted from the previous baseline (v6.01) of the SCIAMACHY Level 2 data. The dataset is generated in the framework of the full mission reprocessing campaign completed in 2023 under the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ .\rFor optimal results, users are strongly encouraged to make use of these specific ozone limb profiles rather than the ones contained in the _$$SCIAMACHY Level 2 dataset version 7.1$$ https://earth.esa.int/eogateway/catalog/envisat-sciamachy-total-column-densities-and-stratospheric-profiles-sci_ol__2p- .\r\rThe new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \rPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .\r\rPlease refer to the _$$README$$ https://earth.esa.int/eogateway/documents/20142/37627/ENVI-GSOP-EOGD-QD-16-0132.pdf file (L2 v6.01) for essential guidance before using the data.", "links": [ { diff --git a/datasets/SCIAMACHYLevel2_2.0.json b/datasets/SCIAMACHYLevel2_2.0.json index 76c8b22c74..4c0fc4aed6 100644 --- a/datasets/SCIAMACHYLevel2_2.0.json +++ b/datasets/SCIAMACHYLevel2_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCIAMACHYLevel2_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Envisat SCIAMACHY Level 2 Total column densities and stratospheric profiles v7.1 dataset is generated from the full mission reprocessing campaign completed in 2023 under the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ . \rIt provides atmospheric columnar distributions and stratospheric profiles for various trace gases based on the Level 1b version 10 products.\r\rThis SCIAMACHY Level 2 dataset contains total column densities of O3, NO2, OClO, H2O, SO2, BrO, CO, HCHO, CHOCHO and CH4 retrieved from Nadir measurements. Additionally, cloud parameters (fractional coverage, top height, optical thickness) and an aerosol absorption indicator are enclosed. Stratospheric profiles of O3, NO2, and BrO are derived from limb measurements, along with flagging information for different cloud-types. Tropospheric NO2 and BrO columns are retrieved combining limb and nadir measurements. \r\rThis SCIAMACHY Level 2 dataset version 7.1 replaces the previous version 6.01. Users are strongly encouraged to make use of the new datasets for optimal results.\r\rFor limb O3 profiles, a separate product derived from the previous Version 6 processor is provided distinctly -> _$$SCIAMACHY Level 2 - Limb Ozone [SCI_LIMBO3]$$ https://earth.esa.int/eogateway/catalog/envisat-sciamachy-ozone-stratospheric-profiles-sci_limbo3 . This is because the V7.1 limb ozone data is unsuitable for long-term change studies due to its divergent behavior from earlier processor versions, particularly from 2009 onwards. This divergence stems from residual deficiencies in the Level 1, resulting in a vertical oscillating pattern in the drift and bias profiles. In contrast, Version 6 limb ozone data does not exhibit these oscillations in bias and drift. Further details on this issue can be found in the _$$latest README$$ https://earth.esa.int/documents/d/earth-online/rmf_0014_sci_____2p_l2v7-1 file.\rThe new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \rPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .\r\rPlease refer to the _$$README$$ https://earth.esa.int/documents/d/earth-online/rmf_0014_sci_____2p_l2v7-1 file for essential guidance before using the data.", "links": [ { diff --git a/datasets/SCI_NL__1P_6.0.json b/datasets/SCI_NL__1P_6.0.json index 7bf717e10a..edeb07a436 100644 --- a/datasets/SCI_NL__1P_6.0.json +++ b/datasets/SCI_NL__1P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCI_NL__1P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data product covers geo-located, radiometrically and spectrally calibrated limb and nadir radiance spectra for Nadir, Limb, and Occultation measurements with additional monitoring and calibration measurements. The Level 1b product is the lowest level of SCIAMACHY data delivered to the users. The instrument Instantaneous Field of View (IFoV) is approximately 0.045 deg (scan direction) x 1.8 deg (flight direction). This corresponds to a ground pixel size of 25 Km x 0.6 km at the sub-satellite point (nadir) and of 103 km x 2.6 km at the Earth's horizon (limb). Nadir measurements have a maximum swath width of 960 km (in scan direction) and a typical footprint of 30 km (along track) x 60 km (across track). Limb measurements have a tangent height range spanning from 0 to 100 km with 3 km vertical resolution. Azimuth scans are performed for constant elevation angle, typically 34 elevation steps per limb scan. Maximum azimuth range is +/- 44 deg relative to S/C velocity (Note that the azimuth range is adjusted to observe the same atmospheric volume as for nadir measurements within five minutes). The radiometric resolution is 16 bits, with a spectral resolution of 0.24 nm to 1.5 nm, depending on the spectral range. The Sun normalized radiometric accuracy is 2 to 3% in unpolarized light, and 3 to 4% in polarized light. The relative radiometric accuracy is less than 1% and the spectral accuracy spans form 0.005 nm to 0.035 nm. Individual measurements from dedicated monitoring states include: Sun over diffuser Subsolar calibration Spectral lamp measurements White light source measurements Elevation mirror monitoring (Sun/Moon) ADC calibration Level 1b products are corrected for degradation applying a scan mirror model and m-factors. The latest Level 1b dataset is version 8.0X.", "links": [ { diff --git a/datasets/SCI_OL__2P_6.0.json b/datasets/SCI_OL__2P_6.0.json index f03b47b43e..d8128978c2 100644 --- a/datasets/SCI_OL__2P_6.0.json +++ b/datasets/SCI_OL__2P_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCI_OL__2P_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data product provides global column distributions and stratospheric profiles of various trace gases. Total column densities of O3, NO2, OClO, H2O, SO2, BrO, CO, HCHO, CHOCHO and CH4 are retrieved from Nadir measurements. Additional cloud parameters (fractional cloud coverage, cloud-top height, cloud optical thickness) and an aerosol absorption indicator are enclosed. Stratospheric profiles of O3, NO2, and BrO are derived from limb measurements, also with flagging information for cloud-types. Tropospheric NO2 columns are retrieved combining limb and nadir measurements. The latest Level 2 dataset is version 6.01.", "links": [ { diff --git a/datasets/SCMRN5L1RAD_001.json b/datasets/SCMRN5L1RAD_001.json index d52ab119d9..467253a397 100644 --- a/datasets/SCMRN5L1RAD_001.json +++ b/datasets/SCMRN5L1RAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCMRN5L1RAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCMRN5L1RAD is the Nimbus-5 Surface Composition Mapping Radiometer (SCMR) Level 1 Calibrated and Geolocated Radiances data product. SCMR measured (1) terrestrial radiation in the 8.3 to 9.3 micron and 10.2 to 11.2 micron intervals and (2) reflected solar radiation in the 0.8 to 1.1 micron range. Surface composition and sea surface temperatures could be obtained from these measurements.\nThe SCMR had an instantaneous field of view (FOV) of 0.6 mrad, equivalent to a ground resolution of 660 m at nadir. The scan mirror rotated at 10 rps to provide scan lines 800 km wide across the spacecraft track. Data are available from December 11, 1972 through December 30, 1972. A modified version of this instrument, the Heat Capacity Mapping Radiometer, was flown on the Heat Capacity Mapping Mission (HCMM) in 1978.", "links": [ { diff --git a/datasets/SCOAPE_Ground_Data_1.json b/datasets/SCOAPE_Ground_Data_1.json index 3f71f16a67..2ef91c81e8 100644 --- a/datasets/SCOAPE_Ground_Data_1.json +++ b/datasets/SCOAPE_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCOAPE_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCOAPE_Ground_Data is the ground site data collected during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The ground site was located at the Louisiana Universities Marine Consortium (LUMCON; Cocodrie, LA). This collection features NO2 volume mixing ratios from the Teledyne API T500U and boundary layer height information from the UH Vaisala CL31 Ceilometer. Data collection for this product is complete.\r\n\r\nThe Outer Continental Shelf Lands Act (OCSLA) requires the US Department of Interior Bureau of Ocean Energy Management (BOEM) to ensure compliance with the US National Ambient Air Quality Standard (NAAQS) so that Outer Continental Shelf (OCS) oil and natural gas (ONG) exploration, development, and production do not significantly impact the air quality of any US state. In 2017, BOEM and NASA entered into an interagency agreement to begin a study to scope out the feasibility of BOEM personnel using a suite of NASA and non-NASA resources to assess how pollutants from ONG exploration, development, and production activities affect air quality. An important activity of this interagency agreement was SCOAPE, a field deployment that took place in May 2019, that aimed to assess the capability of satellite observations for monitoring offshore air quality. The outcomes of the study are documented in two BOEM reports (Duncan, 2020; Thompson, 2020).\r\n\r\nTo address BOEM\u2019s goals, the SCOAPE science team conducted surface-based remote sensing and in-situ measurements, which enabled a systematic assessment of the application of satellite observations, primarily NO2, for monitoring air quality. The SCOAPE field measurements consisted of onshore ground sites, including in the vicinity of LUMCON, as well as those from University of Southern Mississippi\u2019s Research Vessel (R/V) Point Sur, which cruised in the Gulf of Mexico from 10-18 May 2019. Based on the 2014 and 2017 BOEM emissions inventories as well as daily air quality and meteorological forecasts, the cruise track was designed to sample both areas with large oil drilling platforms and areas with dense small natural gas facilities. The R/V Point Sur was instrumented to carry out both remote sensing and in-situ measurements of NO2 and O3 along with in-situ CH4, CO2, CO, and VOC tracers which allowed detailed characterization of airmass type and emissions. In addition, there were also measurements of multi-wavelength AOD and black carbon as well as planetary boundary layer structure and meteorological variables, including surface temperature, humidity, and winds. A ship-based spectrometer instrument provided remotely-sensed total column amounts of NO2 and O3 for direct comparison with satellite measurements. Ozonesondes and radiosondes were also launched 1-3 times daily from the R/V Point Sur to provide O3 and meteorological vertical profile observations. The ground-based observations, primarily at LUMCON, included spectrometer-measured column NO2 and O3, in-situ NO2, VOCs, and planetary boundary layer structure. A NO2sonde was also mounted on a vehicle with the goal to detect pollution onshore from offshore ONG activities during onshore flow; data were collected along coastal Louisiana from Burns Point Park to Grand Isle to the tip of the Mississippi River delta. The in-situ measurements were reported in ICARTT files or Excel files. The remote sensing data are in either HDF or netCDF files.", "links": [ { diff --git a/datasets/SCOAPE_Pandora_Data_1.json b/datasets/SCOAPE_Pandora_Data_1.json index 3af2b70d92..ec5032ff09 100644 --- a/datasets/SCOAPE_Pandora_Data_1.json +++ b/datasets/SCOAPE_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCOAPE_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCOAPE_Pandora_Data is the column NO2 and ozone data collected by Pandora spectrometers during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). Pandora instruments were located on the University of Southern Mississippi\u2019s Research Vessel (R/V) Point Sur and at the Louisiana Universities Marine Consortium (LUMCON; Cocodrie, LA). Data collection for this product is complete.\r\n\r\nThe Outer Continental Shelf Lands Act (OCSLA) requires the US Department of Interior Bureau of Ocean Energy Management (BOEM) to ensure compliance with the US National Ambient Air Quality Standard (NAAQS) so that Outer Continental Shelf (OCS) oil and natural gas (ONG) exploration, development, and production do not significantly impact the air quality of any US state. In 2017, BOEM and NASA entered into an interagency agreement to begin a study to scope out the feasibility of BOEM personnel using a suite of NASA and non-NASA resources to assess how pollutants from ONG exploration, development, and production activities affect air quality. An important activity of this interagency agreement was SCOAPE, a field deployment that took place in May 2019, that aimed to assess the capability of satellite observations for monitoring offshore air quality. The outcomes of the study are documented in two BOEM reports (Duncan, 2020; Thompson, 2020).\r\n\r\nTo address BOEM\u2019s goals, the SCOAPE science team conducted surface-based remote sensing and in-situ measurements, which enabled a systematic assessment of the application of satellite observations, primarily NO2, for monitoring air quality. The SCOAPE field measurements consisted of onshore ground sites, including in the vicinity of LUMCON, as well as those from University of Southern Mississippi\u2019s R/V Point Sur, which cruised in the Gulf of Mexico from 10-18 May 2019. Based on the 2014 and 2017 BOEM emissions inventories as well as daily air quality and meteorological forecasts, the cruise track was designed to sample both areas with large oil drilling platforms and areas with dense small natural gas facilities. The R/V Point Sur was instrumented to carry out both remote sensing and in-situ measurements of NO2 and O3 along with in-situ CH4, CO2, CO, and VOC tracers which allowed detailed characterization of airmass type and emissions. In addition, there were also measurements of multi-wavelength AOD and black carbon as well as planetary boundary layer structure and meteorological variables, including surface temperature, humidity, and winds. A ship-based spectrometer instrument provided remotely-sensed total column amounts of NO2 and O3 for direct comparison with satellite measurements. Ozonesondes and radiosondes were also launched 1-3 times daily from the R/V Point Sur to provide O3 and meteorological vertical profile observations. The ground-based observations, primarily at LUMCON, included spectrometer-measured column NO2 and O3, in-situ NO2, VOCs, and planetary boundary layer structure. A NO2sonde was also mounted on a vehicle with the goal to detect pollution onshore from offshore ONG activities during onshore flow; data were collected along coastal Louisiana from Burns Point Park to Grand Isle to the tip of the Mississippi River delta. The in-situ measurements were reported in ICARTT files or Excel files. The remote sensing data are in either HDF or netCDF files.", "links": [ { diff --git a/datasets/SCOAPE_RVPointSur_Data_1.json b/datasets/SCOAPE_RVPointSur_Data_1.json index 4f26edb0e9..829afff2aa 100644 --- a/datasets/SCOAPE_RVPointSur_Data_1.json +++ b/datasets/SCOAPE_RVPointSur_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCOAPE_RVPointSur_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCOAPE_RVPointSur_Data is the data collected from instruments onboard the University of Southern Mississippi\u2019s Research Vessel (R/V) Point Sur during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). Data was collected by sun photometers, ceilometers, aethalometers, anemometers, and pyranometers. Data collection for this product is complete.\r\n\r\nThe Outer Continental Shelf Lands Act (OCSLA) requires the US Department of Interior Bureau of Ocean Energy Management (BOEM) to ensure compliance with the US National Ambient Air Quality Standard (NAAQS) so that Outer Continental Shelf (OCS) oil and natural gas (ONG) exploration, development, and production do not significantly impact the air quality of any US state. In 2017, BOEM and NASA entered into an interagency agreement to begin a study to scope out the feasibility of BOEM personnel using a suite of NASA and non-NASA resources to assess how pollutants from ONG exploration, development, and production activities affect air quality. An important activity of this interagency agreement was SCOAPE, a field deployment that took place in May 2019, that aimed to assess the capability of satellite observations for monitoring offshore air quality. The outcomes of the study are documented in two BOEM reports (Duncan, 2020; Thompson, 2020).\r\n\r\nTo address BOEM\u2019s goals, the SCOAPE science team conducted surface-based remote sensing and in-situ measurements, which enabled a systematic assessment of the application of satellite observations, primarily NO2, for monitoring air quality. The SCOAPE field measurements consisted of onshore ground sites, including in the vicinity of the Louisiana Universities Marine Consortium (LUMCON; Cocodrie, LA), as well as those from University of Southern Mississippi\u2019s R/V Point Sur, which cruised in the Gulf of Mexico from 10-18 May 2019. Based on the 2014 and 2017 BOEM emissions inventories as well as daily air quality and meteorological forecasts, the cruise track was designed to sample both areas with large oil drilling platforms and areas with dense small natural gas facilities. The R/V Point Sur was instrumented to carry out both remote sensing and in-situ measurements of NO2 and O3 along with in-situ CH4, CO2, CO, and VOC tracers which allowed detailed characterization of airmass type and emissions. In addition, there were also measurements of multi-wavelength AOD and black carbon as well as planetary boundary layer structure and meteorological variables, including surface temperature, humidity, and winds. A ship-based spectrometer instrument provided remotely-sensed total column amounts of NO2 and O3 for direct comparison with satellite measurements. Ozonesondes and radiosondes were also launched 1-3 times daily from the R/V Point Sur to provide O3 and meteorological vertical profile observations. The ground-based observations, primarily at LUMCON, included spectrometer-measured column NO2 and O3, in-situ NO2, VOCs, and planetary boundary layer structure. A NO2sonde was also mounted on a vehicle with the goal to detect pollution onshore from offshore ONG activities during onshore flow; data were collected along coastal Louisiana from Burns Point Park to Grand Isle to the tip of the Mississippi River delta. The in-situ measurements were reported in ICARTT files or Excel files. The remote sensing data are in either HDF or netCDF files.", "links": [ { diff --git a/datasets/SCOAPE_Sondes_Data_1.json b/datasets/SCOAPE_Sondes_Data_1.json index 86ea7d663e..f1b25eac38 100644 --- a/datasets/SCOAPE_Sondes_Data_1.json +++ b/datasets/SCOAPE_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCOAPE_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCOAPE_Sondes_Data is the NO2-sonde and ozonesonde data collected during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). Data were collected by KNMI NO2-sondes and ozonesondes. Data collection for this product is complete.\r\n\r\nThe Outer Continental Shelf Lands Act (OCSLA) requires the US Department of Interior Bureau of Ocean Energy Management (BOEM) to ensure compliance with the US National Ambient Air Quality Standard (NAAQS) so that Outer Continental Shelf (OCS) oil and natural gas (ONG) exploration, development, and production do not significantly impact the air quality of any US state. In 2017, BOEM and NASA entered into an interagency agreement to begin a study to scope out the feasibility of BOEM personnel using a suite of NASA and non-NASA resources to assess how pollutants from ONG exploration, development, and production activities affect air quality. An important activity of this interagency agreement was SCOAPE, a field deployment that took place in May 2019, that aimed to assess the capability of satellite observations for monitoring offshore air quality. The outcomes of the study are documented in two BOEM reports (Duncan, 2020; Thompson, 2020).\r\n\r\nTo address BOEM\u2019s goals, the SCOAPE science team conducted surface-based remote sensing and in-situ measurements, which enabled a systematic assessment of the application of satellite observations, primarily NO2, for monitoring air quality. The SCOAPE field measurements consisted of onshore ground sites, including in the vicinity of the Louisiana Universities Marine Consortium (LUMCON; Cocodrie, LA), as well as those from University of Southern Mississippi\u2019s Research Vessel (R/V) Point Sur, which cruised in the Gulf of Mexico from 10-18 May 2019. Based on the 2014 and 2017 BOEM emissions inventories as well as daily air quality and meteorological forecasts, the cruise track was designed to sample both areas with large oil drilling platforms and areas with dense small natural gas facilities. The R/V Point Sur was instrumented to carry out both remote sensing and in-situ measurements of NO2 and O3 along with in-situ CH4, CO2, CO, and VOC tracers which allowed detailed characterization of airmass type and emissions. In addition, there were also measurements of multi-wavelength AOD and black carbon as well as planetary boundary layer structure and meteorological variables, including surface temperature, humidity, and winds. A ship-based spectrometer instrument provided remotely-sensed total column amounts of NO2 and O3 for direct comparison with satellite measurements. Ozonesondes and radiosondes were also launched 1-3 times daily from the R/V Point Sur to provide O3 and meteorological vertical profile observations. The ground-based observations, primarily at LUMCON, included spectrometer-measured column NO2 and O3, in-situ NO2, VOCs, and planetary boundary layer structure. A NO2sonde was also mounted on a vehicle with the goal to detect pollution onshore from offshore ONG activities during onshore flow; data were collected along coastal Louisiana from Burns Point Park to Grand Isle to the tip of the Mississippi River delta. The in-situ measurements were reported in ICARTT files or Excel files. The remote sensing data are in either HDF or netCDF files.", "links": [ { diff --git a/datasets/SCRN4L1RAD_001.json b/datasets/SCRN4L1RAD_001.json index e0b469f7f3..eb773fba2b 100644 --- a/datasets/SCRN4L1RAD_001.json +++ b/datasets/SCRN4L1RAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCRN4L1RAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCRN4L1RAD is the Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 6 channels from 2.3 to 15 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud). The radiances were used to obtain the temperatures of six successive 10-km layers of the atmosphere from earth or cloudtop level to 60-km height. The data were recovered from the original 9-track tapes, and are now stored online as daily files in their original proprietary binary format with about 14 orbits per day.\n\nSpatial coverage is near global from latitude -80 to +80 degrees. The data are available from 27 July 1970 (day of year 208) to 20 February 1973 (day of year 51). The channel 1 temperature monitoring system failed on June 15, 1970, thereby reducing the accuracy of the SCR data. Channels 3 and 4 became noisy and unusable on April 18, 1972. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University. \n\nThis product was previously available from the NSSDC with the identifier ESAD-00096 (old ID 78-098A-10E).", "links": [ { diff --git a/datasets/SCRN4L1RAD_CDROM_001.json b/datasets/SCRN4L1RAD_CDROM_001.json index 548ba67b57..3ac49703c8 100644 --- a/datasets/SCRN4L1RAD_CDROM_001.json +++ b/datasets/SCRN4L1RAD_CDROM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCRN4L1RAD_CDROM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCRN4L1RAD_CDROM is the gridded Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km. The CD-ROM contains corrected radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as the original orbit format. The data for this product are available from 27 July 1970 to 2 November 1972. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University.\n\nThis product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a single CD-ROM in ASCII files of hexadecimal characters, and are available in a single gzipped Unix tar archive file. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.", "links": [ { diff --git a/datasets/SCRN5L1RAD_001.json b/datasets/SCRN5L1RAD_001.json index d784d01455..0c05754129 100644 --- a/datasets/SCRN5L1RAD_001.json +++ b/datasets/SCRN5L1RAD_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCRN5L1RAD_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCRN5L1RAD is the Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 16 channels from 2.3 to 133 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud). The radiances were used to obtain the global temperature structure of the atmosphere up to 50 km altitude, the distribution of water vapor, and the density of ice particles in cirrus clouds. The data were recovered from the original 9-track tapes, and are now stored online as daily files in their original proprietary binary format with about 14 orbits per day.\n\nSpatial coverage is near global from latitude -80 to +80 degrees. The data are available from 13 December 1972 (day of year 347) to 26 December 1974 (day of year 360). The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00250 (old ID 72-097A-02A).", "links": [ { diff --git a/datasets/SCRN5L1RAD_CDROM_001.json b/datasets/SCRN5L1RAD_CDROM_001.json index d785c53d63..296dafb09f 100644 --- a/datasets/SCRN5L1RAD_CDROM_001.json +++ b/datasets/SCRN5L1RAD_CDROM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SCRN5L1RAD_CDROM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCRN5L1RAD_CDROM is the gridded Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km. This product contains corrected and uncorrected radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as the original orbit format and reformatted copies of the original tapes. This was the follow-on to the SCR experiment flown on Nimbus-4. The data for this product are available from 13 December 1972 to 20 April 1978. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University.\n\nThis product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a set of 28 CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the gridded radiance data and a few original tape data files, the subsequent CD-ROMs contain the remaining compressed copies of the original data tapes. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.", "links": [ { diff --git a/datasets/SEAC4RS_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_Aerosol_AircraftInSitu_DC8_Data_1.json index 09d06bc83f..9a689e373e 100644 --- a/datasets/SEAC4RS_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Aerosol_AircraftInSitu_DC8_Data are in-situ aerosol data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data_1.json b/datasets/SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data_1.json index 9a23b0a830..13bd60ef08 100644 --- a/datasets/SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Aerosol_AircraftInSitu_Learjet_Data are in-situ aerosol data collected onboard the Learjet aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data_1.json b/datasets/SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data_1.json index 7574cd466e..ed46dbfe22 100644 --- a/datasets/SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data_1.json +++ b/datasets/SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_AircraftRemoteSensing_DIAL_DC8_Data are remotely sensed data collected by the Differential Absorption Lidar (DIAL) onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data_1.json b/datasets/SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data_1.json index 7463728ec8..e0b3c21bd4 100644 --- a/datasets/SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data_1.json +++ b/datasets/SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_AircraftRemoteSensing_RSP_ER2_Data are remotely sensed data collected by the Research Scanning Polarimeter (RSP) onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Analysis_Data_1.json b/datasets/SEAC4RS_Analysis_Data_1.json index 9e07702e01..c69b5b84ce 100644 --- a/datasets/SEAC4RS_Analysis_Data_1.json +++ b/datasets/SEAC4RS_Analysis_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Analysis_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Analysis_Data are ancillary analysis data utilized as part of the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data from GOES-EAST and GOES-WEST relating to overshooting tops are featured in this product. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_Cloud_AircraftInSitu_DC8_Data_1.json index 596e134f23..eca3ef6d38 100644 --- a/datasets/SEAC4RS_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Cloud_AircraftInSitu_DC8_Data are in-situ cloud data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Cloud_AircraftInSitu_ER2_Data_1.json b/datasets/SEAC4RS_Cloud_AircraftInSitu_ER2_Data_1.json index cc93d4fcf3..d12c8ac0c9 100644 --- a/datasets/SEAC4RS_Cloud_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SEAC4RS_Cloud_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Cloud_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Cloud_AircraftInSitu_ER2_Data are in-situ trace gas data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Cloud_AircraftInSitu_Learjet_Data_1.json b/datasets/SEAC4RS_Cloud_AircraftInSitu_Learjet_Data_1.json index 50c613fe6a..0f3fdadfcd 100644 --- a/datasets/SEAC4RS_Cloud_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/SEAC4RS_Cloud_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Cloud_AircraftInSitu_Learjet_Data are in-situ cloud data collected onboard the Learjet aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Merge_Data_1.json b/datasets/SEAC4RS_Merge_Data_1.json index 5634703c87..fcbff13d63 100644 --- a/datasets/SEAC4RS_Merge_Data_1.json +++ b/datasets/SEAC4RS_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Merge_Data are pre-generated merge data files collected during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. This product contains merged data products collected from instruments onboard the DC-8 and ER-2 aircrafts. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_MetNav_AircraftInSitu_DC8_Data_1.json index b90cb43551..712f48122c 100644 --- a/datasets/SEAC4RS_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_MetNav_AircraftInSitu_DC8_Data are in-situ meteorological and navigation data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_MetNav_AircraftInSitu_ER2_Data_1.json b/datasets/SEAC4RS_MetNav_AircraftInSitu_ER2_Data_1.json index 52d48b628e..3dc7213d4c 100644 --- a/datasets/SEAC4RS_MetNav_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SEAC4RS_MetNav_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_MetNav_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_MetNav_AircraftInSitu_ER2_Data are in-situ meteorological and navigational data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_MetNav_AircraftInSitu_Learjet_Data_1.json b/datasets/SEAC4RS_MetNav_AircraftInSitu_Learjet_Data_1.json index 8ae44751c1..daab7edbbf 100644 --- a/datasets/SEAC4RS_MetNav_AircraftInSitu_Learjet_Data_1.json +++ b/datasets/SEAC4RS_MetNav_AircraftInSitu_Learjet_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_MetNav_AircraftInSitu_Learjet_Data are in-situ meteorological and navigational data collected onboard the Learjet aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nThe Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for upper troposphere and lower stratosphere (UT/LS) chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 operated in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Miscellaneous_DC8_Data_1.json b/datasets/SEAC4RS_Miscellaneous_DC8_Data_1.json index 7ca6bb06f2..b0dd3d1389 100644 --- a/datasets/SEAC4RS_Miscellaneous_DC8_Data_1.json +++ b/datasets/SEAC4RS_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Miscellaneous_AircraftInSitu_DC8_Data are miscellaneous ancillary data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data from the Goddard Earth Observing System Model (GEOS-5) are featured in this product. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Miscellaneous_ER2_Data_1.json b/datasets/SEAC4RS_Miscellaneous_ER2_Data_1.json index 2b15f944f5..f50522244a 100644 --- a/datasets/SEAC4RS_Miscellaneous_ER2_Data_1.json +++ b/datasets/SEAC4RS_Miscellaneous_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Miscellaneous_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Miscellaneous_ER2_Data are miscellaneous ancillary data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Radiation_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_Radiation_AircraftInSitu_DC8_Data_1.json index 972318d2e3..efca881851 100644 --- a/datasets/SEAC4RS_Radiation_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_Radiation_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Radiation_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Radiation_AircraftInSitu_DC8_Data are in-situ radiation data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Radiation_AircraftInSitu_ER2_Data_1.json b/datasets/SEAC4RS_Radiation_AircraftInSitu_ER2_Data_1.json index 897d4c3be2..1875f63347 100644 --- a/datasets/SEAC4RS_Radiation_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SEAC4RS_Radiation_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Radiation_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Radiation_AircraftInSitu_ER2_Data are in-situ radiation data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nThe Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for upper troposphere and lower stratosphere (UT/LS) chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 operated in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_Sondes_Data_1.json b/datasets/SEAC4RS_Sondes_Data_1.json index 8f5b299379..d7ab1026bc 100644 --- a/datasets/SEAC4RS_Sondes_Data_1.json +++ b/datasets/SEAC4RS_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_Sondes_Data are data collected via ozonesonde and radiosonde launches at ground locations in Houston, TX, Idabel, OK, Ellington, TX, Boulder, CO, Smith Point, TX, Socorro, NM, St. Louis, MO, Tallahassee, FL, and Huntsville, AL during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nThe Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for upper troposphere and lower stratosphere (UT/LS) chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 operated in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1.json index bd0528085b..525f089882 100644 --- a/datasets/SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_TraceGas_AircraftInSitu_DC8_Data are in-situ trace gas data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1.json b/datasets/SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1.json index e45d1d82da..c9afa2365e 100644 --- a/datasets/SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_TraceGas_AircraftInSitu_ER2_Data are in-situ trace gas data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAC4RS_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/SEAC4RS_jValue_AircraftInSitu_DC8_Data_1.json index 63a71a828e..d69505a8d7 100644 --- a/datasets/SEAC4RS_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SEAC4RS_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAC4RS_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEAC4RS_jValue_AircraftInSitu_DC8_Data are in-situ photolysis rate (j value) data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.\r\n\r\nStudies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.\r\n\r\nThe airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.", "links": [ { diff --git a/datasets/SEAGLIDER_GUAM_2019_V1.json b/datasets/SEAGLIDER_GUAM_2019_V1.json index 56cbadbc72..f4958f0f5c 100644 --- a/datasets/SEAGLIDER_GUAM_2019_V1.json +++ b/datasets/SEAGLIDER_GUAM_2019_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAGLIDER_GUAM_2019_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14\u00b0N 144\u00b0E). The Seaglider is an autonomous profiler measuring salinity and temperature in the upper ocean. The three gliders sampled in an adaptive formation to capture the patchiness of the rain and the corresponding oceanic response in real time. The location was chosen because of the likelihood of intense tropical rain events and the availability of a NEXRAD (S-band) rain radar at the Guam Airport. Spacing between gliders varies from 1 to 60 km. Data samples are gridded by profile and on regular depth bins from 0 to 1000 m. The time interval between profiles was about 3 hours, and they are typically about 1.5 km apart. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated). All Seaglider data files are in netCDF format with standards compliant metadata. The project was led by a team from the Applied Physics Laboratory at the University of Washington.", "links": [ { diff --git a/datasets/SEAHAWK_VALIDATION_0.json b/datasets/SEAHAWK_VALIDATION_0.json index edde850cb7..6594d6db16 100644 --- a/datasets/SEAHAWK_VALIDATION_0.json +++ b/datasets/SEAHAWK_VALIDATION_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEAHAWK_VALIDATION_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite validation work related to the SeaHawk Ocean Color CubeSat mission. This is a partnership between NASA, UNCW, UGA, ACC Clyde Space and Cloudland instruments. The project was funded by the Gordon and Betty Moore Foundation (grant number 11171) for years 2022-2025.", "links": [ { diff --git a/datasets/SEASAT_SAR_L1_HDF5_1.json b/datasets/SEASAT_SAR_L1_HDF5_1.json index 2e0f342fc4..afdfc6c0ec 100644 --- a/datasets/SEASAT_SAR_L1_HDF5_1.json +++ b/datasets/SEASAT_SAR_L1_HDF5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEASAT_SAR_L1_HDF5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEASAT Image Level 1", "links": [ { diff --git a/datasets/SEASAT_SAR_L1_TIFF_1.json b/datasets/SEASAT_SAR_L1_TIFF_1.json index 9afae4a295..4dce43e4b5 100644 --- a/datasets/SEASAT_SAR_L1_TIFF_1.json +++ b/datasets/SEASAT_SAR_L1_TIFF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEASAT_SAR_L1_TIFF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SEASAT Image GeoTIFF", "links": [ { diff --git a/datasets/SEA_0.json b/datasets/SEA_0.json index fbc6394509..0d5363f1de 100644 --- a/datasets/SEA_0.json +++ b/datasets/SEA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the northwest Atlantic and northeast Pacific oceans between 2008 and 2009.", "links": [ { diff --git a/datasets/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205_2205.json b/datasets/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205_2205.json index c2847a2b87..3d9a5bc805 100644 --- a/datasets/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205_2205.json +++ b/datasets/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205_2205.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205_2205", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded Sea Surface Height Anomalies (SSHA) above a mean sea surface, on a 1/6th degree grid every 5 days. It contains the fully corrected heights, with a delay of up to 3 months. The gridded data are derived from the along-track SSHA data of TOPEX/Poseidon, Jason-1, Jason-2, Jason-3 and Jason-CS (Sentinel-6) as reference data from the level 2 along-track data found at https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_CYCLES_V51, plus ERS-1, ERS-2, Envisat, SARAL-AltiKa, CryoSat-2, Sentinel-3A, Sentinel-3B, depending on the date, from the RADS database. The date given in the grid files is the center of the 5-day window. The grids were produced from altimeter data using Kriging interpolation, which gives best linear prediction based upon prior knowledge of covariance.", "links": [ { diff --git a/datasets/SEED_0.json b/datasets/SEED_0.json index 649849b0fc..d680c56bf4 100644 --- a/datasets/SEED_0.json +++ b/datasets/SEED_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEED_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the northern region of the Gulf of Mexico between 2004 and 2005.", "links": [ { diff --git a/datasets/SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection_1.json b/datasets/SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection_1.json index f7ad6a7d2b..b3d43d7737 100644 --- a/datasets/SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection_1.json +++ b/datasets/SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. This dataset is composed of co-registered optical and SAR images time series for the detection of flood events.", "links": [ { diff --git a/datasets/SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification_1.json b/datasets/SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification_1.json index b118200119..9a03102c9e 100644 --- a/datasets/SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification_1.json +++ b/datasets/SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SEN12TS dataset contains Sentinel-1, Sentinel-2, and labeled land cover image triplets over six agro-ecologically diverse areas of interest: California, Iowa, Catalonia, Ethiopia, Uganda, and Sumatra. Using the Descartes Labs geospatial analytics platform, 246,400 triplets are produced at 10m resolution over 31,398 256-by-256-pixel unique spatial tiles for a total size of 1.69 TB. The image triplets include radiometric terrain corrected synthetic aperture radar (SAR) backscatter measurements; interferometric synthetic aperture radar (InSAR) coherence and phase layers; local incidence angle and ground slope values; multispectral optical imagery; and decameter-resolution land cover data. Moreover, sensed imagery is available in timeseries: Within an image triplet, radar-derived imagery is collected at four timesteps 12 days apart. For the same spatial extent, up to 16 image triplets are available across the calendar year of 2020.

The SEN12TS documentation demonstrates two initial use cases for the dataset. The first transforms radar imagery into enhanced vegetation indices by means of a generative adversarial network, and the second tests combinations of input imagery for cropland classification.", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_GRD_FULL_1.json b/datasets/SENTINEL-1A_DP_GRD_FULL_1.json index 5968741b91..98d7709156 100644 --- a/datasets/SENTINEL-1A_DP_GRD_FULL_1.json +++ b/datasets/SENTINEL-1A_DP_GRD_FULL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_GRD_FULL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground range detected full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_GRD_HIGH_1.json b/datasets/SENTINEL-1A_DP_GRD_HIGH_1.json index b73a175cc4..a909e0fd4d 100644 --- a/datasets/SENTINEL-1A_DP_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1A_DP_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground projected high and full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_GRD_MEDIUM_1.json b/datasets/SENTINEL-1A_DP_GRD_MEDIUM_1.json index fb9c38c980..58830ea43e 100644 --- a/datasets/SENTINEL-1A_DP_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1A_DP_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground projected medium resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_META_GRD_FULL_1.json b/datasets/SENTINEL-1A_DP_META_GRD_FULL_1.json index 807845927b..af84dc8438 100644 --- a/datasets/SENTINEL-1A_DP_META_GRD_FULL_1.json +++ b/datasets/SENTINEL-1A_DP_META_GRD_FULL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_META_GRD_FULL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground range detected full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_META_GRD_HIGH_1.json b/datasets/SENTINEL-1A_DP_META_GRD_HIGH_1.json index c39be601b7..68a3d26d0e 100644 --- a/datasets/SENTINEL-1A_DP_META_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1A_DP_META_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_META_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground projected high and full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1A_DP_META_GRD_MEDIUM_1.json b/datasets/SENTINEL-1A_DP_META_GRD_MEDIUM_1.json index c4f36b43f9..b629eda9bd 100644 --- a/datasets/SENTINEL-1A_DP_META_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1A_DP_META_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_DP_META_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Dual-pol ground projected medium resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1A_META_OCN_1.json b/datasets/SENTINEL-1A_META_OCN_1.json index 1cec1114f2..432556d993 100644 --- a/datasets/SENTINEL-1A_META_OCN_1.json +++ b/datasets/SENTINEL-1A_META_OCN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_META_OCN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Metadata for OCN product", "links": [ { diff --git a/datasets/SENTINEL-1A_META_RAW_1.json b/datasets/SENTINEL-1A_META_RAW_1.json index 277f7b4263..e73f5c67b7 100644 --- a/datasets/SENTINEL-1A_META_RAW_1.json +++ b/datasets/SENTINEL-1A_META_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_META_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for Sentinel-1A level zero product", "links": [ { diff --git a/datasets/SENTINEL-1A_META_SLC_1.json b/datasets/SENTINEL-1A_META_SLC_1.json index e81e9ec136..f8062747e9 100644 --- a/datasets/SENTINEL-1A_META_SLC_1.json +++ b/datasets/SENTINEL-1A_META_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_META_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for Sentinel-1A slant-range product", "links": [ { diff --git a/datasets/SENTINEL-1A_OCN_1.json b/datasets/SENTINEL-1A_OCN_1.json index 6ad2e32cec..59c742c6ad 100644 --- a/datasets/SENTINEL-1A_OCN_1.json +++ b/datasets/SENTINEL-1A_OCN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_OCN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SENTINEL-1A Level 2 Ocean wind, wave and current data", "links": [ { diff --git a/datasets/SENTINEL-1A_RAW_1.json b/datasets/SENTINEL-1A_RAW_1.json index 7ecb1e5855..acd4f480b7 100644 --- a/datasets/SENTINEL-1A_RAW_1.json +++ b/datasets/SENTINEL-1A_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A level zero product", "links": [ { diff --git a/datasets/SENTINEL-1A_SLC_1.json b/datasets/SENTINEL-1A_SLC_1.json index 534eaeca7d..ddef741b3d 100644 --- a/datasets/SENTINEL-1A_SLC_1.json +++ b/datasets/SENTINEL-1A_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A slant-range product", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_GRD_FULL_1.json b/datasets/SENTINEL-1A_SP_GRD_FULL_1.json index 88f00c4ed5..704881b389 100644 --- a/datasets/SENTINEL-1A_SP_GRD_FULL_1.json +++ b/datasets/SENTINEL-1A_SP_GRD_FULL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_GRD_FULL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground range detected full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_GRD_HIGH_1.json b/datasets/SENTINEL-1A_SP_GRD_HIGH_1.json index 44dd096e23..c3d3b99663 100644 --- a/datasets/SENTINEL-1A_SP_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1A_SP_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground projected high and full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_GRD_MEDIUM_1.json b/datasets/SENTINEL-1A_SP_GRD_MEDIUM_1.json index f122eb1056..fd72714e38 100644 --- a/datasets/SENTINEL-1A_SP_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1A_SP_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground projected medium resolution images", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_META_GRD_FULL_1.json b/datasets/SENTINEL-1A_SP_META_GRD_FULL_1.json index 467a716026..68872633a3 100644 --- a/datasets/SENTINEL-1A_SP_META_GRD_FULL_1.json +++ b/datasets/SENTINEL-1A_SP_META_GRD_FULL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_META_GRD_FULL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground range detected full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_META_GRD_HIGH_1.json b/datasets/SENTINEL-1A_SP_META_GRD_HIGH_1.json index 406fbfd939..3a93c1161a 100644 --- a/datasets/SENTINEL-1A_SP_META_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1A_SP_META_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_META_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground projected high and full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1A_SP_META_GRD_MEDIUM_1.json b/datasets/SENTINEL-1A_SP_META_GRD_MEDIUM_1.json index 2fb006c9ab..89072c75fd 100644 --- a/datasets/SENTINEL-1A_SP_META_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1A_SP_META_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1A_SP_META_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1A Single-pol ground projected medium resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1B_DP_GRD_HIGH_1.json b/datasets/SENTINEL-1B_DP_GRD_HIGH_1.json index 2fdbc70ffe..c4e2707491 100644 --- a/datasets/SENTINEL-1B_DP_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1B_DP_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_DP_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Dual-pol ground projected high and full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1B_DP_GRD_MEDIUM_1.json b/datasets/SENTINEL-1B_DP_GRD_MEDIUM_1.json index de5811715b..6c082df301 100644 --- a/datasets/SENTINEL-1B_DP_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1B_DP_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_DP_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Dual-pol ground projected medium resolution images", "links": [ { diff --git a/datasets/SENTINEL-1B_DP_META_GRD_HIGH_1.json b/datasets/SENTINEL-1B_DP_META_GRD_HIGH_1.json index a8d18d5606..78f0c50741 100644 --- a/datasets/SENTINEL-1B_DP_META_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1B_DP_META_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_DP_META_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Dual-pol ground projected high and full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1B_DP_META_GRD_MEDIUM_1.json b/datasets/SENTINEL-1B_DP_META_GRD_MEDIUM_1.json index e71fed4bda..967e826d8c 100644 --- a/datasets/SENTINEL-1B_DP_META_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1B_DP_META_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_DP_META_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Dual-pol ground projected medium resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1B_META_OCN_1.json b/datasets/SENTINEL-1B_META_OCN_1.json index 04676a37ee..3ef598b3f9 100644 --- a/datasets/SENTINEL-1B_META_OCN_1.json +++ b/datasets/SENTINEL-1B_META_OCN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_META_OCN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Metadata for OCN product", "links": [ { diff --git a/datasets/SENTINEL-1B_META_RAW_1.json b/datasets/SENTINEL-1B_META_RAW_1.json index da7f7c2a62..a15e57d29e 100644 --- a/datasets/SENTINEL-1B_META_RAW_1.json +++ b/datasets/SENTINEL-1B_META_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_META_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for Sentinel-1B level zero product", "links": [ { diff --git a/datasets/SENTINEL-1B_META_SLC_1.json b/datasets/SENTINEL-1B_META_SLC_1.json index 23120ac78c..b7c97459bd 100644 --- a/datasets/SENTINEL-1B_META_SLC_1.json +++ b/datasets/SENTINEL-1B_META_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_META_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for Sentinel-1B slant-range product", "links": [ { diff --git a/datasets/SENTINEL-1B_OCN_1.json b/datasets/SENTINEL-1B_OCN_1.json index ce470cc0b6..63118be3c3 100644 --- a/datasets/SENTINEL-1B_OCN_1.json +++ b/datasets/SENTINEL-1B_OCN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_OCN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Level 2 Ocean wind, wave and current data", "links": [ { diff --git a/datasets/SENTINEL-1B_RAW_1.json b/datasets/SENTINEL-1B_RAW_1.json index 1e9783501a..177a3733b7 100644 --- a/datasets/SENTINEL-1B_RAW_1.json +++ b/datasets/SENTINEL-1B_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B level zero product", "links": [ { diff --git a/datasets/SENTINEL-1B_SLC_1.json b/datasets/SENTINEL-1B_SLC_1.json index 8abab45867..867176f3bf 100644 --- a/datasets/SENTINEL-1B_SLC_1.json +++ b/datasets/SENTINEL-1B_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B slant-range product", "links": [ { diff --git a/datasets/SENTINEL-1B_SP_GRD_HIGH_1.json b/datasets/SENTINEL-1B_SP_GRD_HIGH_1.json index f0b17a3bc0..9b7575f6ff 100644 --- a/datasets/SENTINEL-1B_SP_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1B_SP_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_SP_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Single-pol ground projected high and full resolution images", "links": [ { diff --git a/datasets/SENTINEL-1B_SP_GRD_MEDIUM_1.json b/datasets/SENTINEL-1B_SP_GRD_MEDIUM_1.json index e26a53855e..838db9a7f3 100644 --- a/datasets/SENTINEL-1B_SP_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1B_SP_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_SP_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Single-pol ground projected medium resolution images", "links": [ { diff --git a/datasets/SENTINEL-1B_SP_META_GRD_HIGH_1.json b/datasets/SENTINEL-1B_SP_META_GRD_HIGH_1.json index 4ac6cf2776..b940f6d129 100644 --- a/datasets/SENTINEL-1B_SP_META_GRD_HIGH_1.json +++ b/datasets/SENTINEL-1B_SP_META_GRD_HIGH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_SP_META_GRD_HIGH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Single-pol ground projected high and full resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1B_SP_META_GRD_MEDIUM_1.json b/datasets/SENTINEL-1B_SP_META_GRD_MEDIUM_1.json index cc43e880c6..fb7ec152e0 100644 --- a/datasets/SENTINEL-1B_SP_META_GRD_MEDIUM_1.json +++ b/datasets/SENTINEL-1B_SP_META_GRD_MEDIUM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1B_SP_META_GRD_MEDIUM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1B Single-pol ground projected medium resolution metadata", "links": [ { diff --git a/datasets/SENTINEL-1_BURSTS_1.json b/datasets/SENTINEL-1_BURSTS_1.json index 8903b63af0..37b57c2e89 100644 --- a/datasets/SENTINEL-1_BURSTS_1.json +++ b/datasets/SENTINEL-1_BURSTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_BURSTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 Interferometric Wide (IW) and Extra Wide (EW) swath modes are collected using a form of ScanSAR imaging called Terrain Observation with Progressive Scans SAR (TOPSAR). With TOPSAR data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths. Sentinel-1 Single Look Complex (SLC) products contain one image per sub-swath and one per polarization channel. Each sub-swath image consists of a series of overlapping bursts, where each burst has been processed as a separate SLC image. The Sentinel-1 Single Look Complex (SLC) Bursts collection identifies each burst from an individual IW or EW SLC product. The granule metadata describes the burst and provides links to a service which extracts the burst image from the SLC product and returns a GeoTIFF file. A link is also provided to the same service to extract the supplemental metadata files from the SLC product and return an XML file. The granules in the collection are generated for the life of the Sentinel-1 mission and include both Sentinel-1A and Sentinel-1B SLC products from both the IW and EW mode.", "links": [ { diff --git a/datasets/SENTINEL-1_INTERFEROGRAMS_1.json b/datasets/SENTINEL-1_INTERFEROGRAMS_1.json index 08f32206f7..bcc0e3192d 100644 --- a/datasets/SENTINEL-1_INTERFEROGRAMS_1.json +++ b/datasets/SENTINEL-1_INTERFEROGRAMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_INTERFEROGRAMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 SLC interferometric products generated by JPL using ISCE v2.0.0, delivered by ASF", "links": [ { diff --git a/datasets/SENTINEL-1_INTERFEROGRAMS_AMPLITUDE_1.json b/datasets/SENTINEL-1_INTERFEROGRAMS_AMPLITUDE_1.json index 55209a5119..a4d1504a8b 100644 --- a/datasets/SENTINEL-1_INTERFEROGRAMS_AMPLITUDE_1.json +++ b/datasets/SENTINEL-1_INTERFEROGRAMS_AMPLITUDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_INTERFEROGRAMS_AMPLITUDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 SLC interferometric products generated by JPL using ISCE v2.0.0, delivered by ASF", "links": [ { diff --git a/datasets/SENTINEL-1_INTERFEROGRAMS_COHERENCE_1.json b/datasets/SENTINEL-1_INTERFEROGRAMS_COHERENCE_1.json index 10704d30de..536886cd5c 100644 --- a/datasets/SENTINEL-1_INTERFEROGRAMS_COHERENCE_1.json +++ b/datasets/SENTINEL-1_INTERFEROGRAMS_COHERENCE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_INTERFEROGRAMS_COHERENCE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 SLC interferometric products generated by JPL using ISCE v2.0.0, delivered by ASF", "links": [ { diff --git a/datasets/SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS_1.json b/datasets/SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS_1.json index 67168b0dd8..dd6da7588b 100644 --- a/datasets/SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS_1.json +++ b/datasets/SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_INTERFEROGRAMS_CONNECTED_COMPONENTS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 SLC interferometric products generated by JPL using ISCE v2.0.0, delivered by ASF", "links": [ { diff --git a/datasets/SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE_1.json b/datasets/SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE_1.json index 6e38f98478..5ccf2bd57e 100644 --- a/datasets/SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE_1.json +++ b/datasets/SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SENTINEL-1_INTERFEROGRAMS_UNWRAPPED_PHASE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 SLC interferometric products generated by JPL using ISCE v2.0.0, delivered by ASF", "links": [ { diff --git a/datasets/SEVIRI_IO_SST-OSISAF-L3C-v1.0_1.0.json b/datasets/SEVIRI_IO_SST-OSISAF-L3C-v1.0_1.0.json index 6178a0eb1a..8396daba0a 100644 --- a/datasets/SEVIRI_IO_SST-OSISAF-L3C-v1.0_1.0.json +++ b/datasets/SEVIRI_IO_SST-OSISAF-L3C-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEVIRI_IO_SST-OSISAF-L3C-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 101.5E-18.5W. Level-3C SST, in the NetCDF format recommended by Group for High Resolution Sea Surface Temperature (GHRSST), is identical to Level-2P GHRSST products, 3 refers to gridded products and C to the fact that hourly products result from compositing 15 minutes (MSG) or 30 minutes (GOES-E) data.\r\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), OSI SAF is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating all 15-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json b/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json index 34e4d97aba..e883d94b46 100644 --- a/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json +++ b/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEVIRI_SST-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). \r\n\r\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\r\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\r\ntime from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de\r\nMeteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved\r\nfrom the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm.\r\nAtmospheric profiles of water vapor and temperature from a numerical weather prediction model,\r\ntogether with a radiatiave transfer model, are used to correct the multispectral algorithm for\r\nregional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is\r\nprocessed at full satellite resolution. The operational products are then produced by remapping\r\nover a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15\r\nminute SST data available in one hour time, and the priority being given to the value the closest in\r\ntime to the product nominal hour. The product format is compliant with the GHRSST Data\r\nSpecification (GDS) version 2.", "links": [ { diff --git a/datasets/SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0.json b/datasets/SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0.json index ea835a3b2c..27bf164e08 100644 --- a/datasets/SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0.json +++ b/datasets/SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm and the cloud mask (CM) from OSI SAF. Atmospheric profiles of water vapor and temperature from a numerical weather prediction (NWP) model, OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15-minute slot is processed at full satellite resolution. The products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all available 15-minute SST data into hourly files with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/SFC_NITROGEN_DIOXIDE_CONC_1.json b/datasets/SFC_NITROGEN_DIOXIDE_CONC_1.json index 06acfa8f4a..2cc32b4d7f 100644 --- a/datasets/SFC_NITROGEN_DIOXIDE_CONC_1.json +++ b/datasets/SFC_NITROGEN_DIOXIDE_CONC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SFC_NITROGEN_DIOXIDE_CONC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nitrogen Dioxide Surface-Level Annual Average Concentrations Product (SFC_NITROGEN_DIOXIDE_CONC) contains estimated global NO2 surface values derived using a Land Use Regression (LUR) model (based on 5220 NO2 monitors in 58 countries and land use variables) for the years 2010-2012. NO2 column densities from the Ozone Monitoring Instrument and MERRA-2 scale the concentrations to other years between 1990 and 2020. This product is part of NASA's Health and Air Quality Applied Sciences Team (HAQAST) effort.\n\nThe data are global over land and span the latitude range between 60 south and 75 north, gridded at 0.0083 degree resolution (array size is 43080 x 16200). Data variables include surface NO2, as well as latitude and longitude values. The data are written to files using the new version 4 netCDF format. The average file size is about 150 Megabytes.", "links": [ { diff --git a/datasets/SFMBON_0.json b/datasets/SFMBON_0.json index c96adb1d4c..53612fcb50 100644 --- a/datasets/SFMBON_0.json +++ b/datasets/SFMBON_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SFMBON_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The South Florida Marine Biodiversity Observation Network (SFMBON) build on the foundations laid by the present Sanctuaries MBON demonstration. A close partnership with NOAA AOML and the FKNMS has focused on periodic MBON surveys of the Florida Keys since 2014. The South Florida MBON seeks to integrate ground and satellite observations related to biodiversity to inform ecosystem-based management in and around the Florida Keys National Marine Sanctuary (FKNMS).", "links": [ { diff --git a/datasets/SFP_0.json b/datasets/SFP_0.json index 8a76db3cb4..6ee4dd5462 100644 --- a/datasets/SFP_0.json +++ b/datasets/SFP_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SFP_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "South Florida Program (SFP)", "links": [ { diff --git a/datasets/SGER_0.json b/datasets/SGER_0.json index 93a389ba57..b1077b08fa 100644 --- a/datasets/SGER_0.json +++ b/datasets/SGER_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SGER_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements sponsored by the NSF Small Grant for Exploratory Research (SGER) program to study the Chesapeake Bay in 2003.", "links": [ { diff --git a/datasets/SHIFT_AVIRISNG_L2A_refl_2376_1.json b/datasets/SHIFT_AVIRISNG_L2A_refl_2376_1.json index d81ed6ea93..5bc8c5ae8e 100644 --- a/datasets/SHIFT_AVIRISNG_L2A_refl_2376_1.json +++ b/datasets/SHIFT_AVIRISNG_L2A_refl_2376_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_AVIRISNG_L2A_refl_2376_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 2A (L2A) unrectified surface reflectance images from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. The AVIRIS-NG L2A data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format.", "links": [ { diff --git a/datasets/SHIFT_AVNG_Canopy_WaterContent_2242_1.json b/datasets/SHIFT_AVNG_Canopy_WaterContent_2242_1.json index 65507d73c3..d542f567ac 100644 --- a/datasets/SHIFT_AVNG_Canopy_WaterContent_2242_1.json +++ b/datasets/SHIFT_AVNG_Canopy_WaterContent_2242_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_AVNG_Canopy_WaterContent_2242_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides per-pixel vegetation canopy water content (CWC) derived from surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument from 2022-02-24 to 2022-05-17. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated as well as atmospherically corrected, and are provided at approximately 5-meter spatial resolution. These data include mosaics of several flight lines that were flown on a weekly basis covering a 640-square-mile (1,656-square-kilometer) study area, which stretches from Los Padres National Forest in the east to the Central California coast and into the coastal ocean in the west, including Dangermond Preserve and UCSB's Sedgwick Reserve. These data will help track changes in vegetation characteristics from late winter through early summer by providing insights into health and resilience of ecosystems as California's climate grows drier. In particular, temporal and spatial patterns of CWC will indicate drought stress and increased wildfire risk. The CWC files in this publication were processed by applying a simple fitting of spectral absorption features of liquid water and are distributed in cloud-optimized GeoTIFF (COG) format.", "links": [ { diff --git a/datasets/SHIFT_AVNG_FullRes_QkLook_2189_1.json b/datasets/SHIFT_AVNG_FullRes_QkLook_2189_1.json index a535e25b3a..c087f0427b 100644 --- a/datasets/SHIFT_AVNG_FullRes_QkLook_2189_1.json +++ b/datasets/SHIFT_AVNG_FullRes_QkLook_2189_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_AVNG_FullRes_QkLook_2189_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds full-resolution 3-band (true color) imagery acquired by NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures radiance at approximately 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The images in this dataset are true color (RGB) images from the wavelengths centered at approximately 808, 658, and 563 nm, subset from the full spectrum collected by AVIRIS-NG. The spatial resolution matches the native observed resolution (variable depending on the flightline, generally finer than 5 m and down to 2 m). There are two files for each flight line, one in PNG and one in georeferenced cloud-optimized GeoTIFF format; the GeoTIFF contains radiance floating point values while the PNG has been scaled and converted to integers.", "links": [ { diff --git a/datasets/SHIFT_AVNG_L1A_RDN_unrec_2184_1.json b/datasets/SHIFT_AVNG_L1A_RDN_unrec_2184_1.json index 0bed97ebaa..f858491452 100644 --- a/datasets/SHIFT_AVNG_L1A_RDN_unrec_2184_1.json +++ b/datasets/SHIFT_AVNG_L1A_RDN_unrec_2184_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_AVNG_L1A_RDN_unrec_2184_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Level 1A (L1A) unrectified surface radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, four file types are included: unrectified measured surface radiance image (rdn) files, geometric lookup table (glt), gridded files of input geometry (igm), and parameters relating to the geometry of observation and illumination rendered using the geometric lookup table (obs). The AVIRIS-NG L1B data are provided in ENVI binary format, which includes a flat binary file (bin) accompanied by a header (hdr) file holding metadata in text format.", "links": [ { diff --git a/datasets/SHIFT_DriedGround_Leaf_Reflec_2244_1.json b/datasets/SHIFT_DriedGround_Leaf_Reflec_2244_1.json index 4c592444ea..f6de9daf40 100644 --- a/datasets/SHIFT_DriedGround_Leaf_Reflec_2244_1.json +++ b/datasets/SHIFT_DriedGround_Leaf_Reflec_2244_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_DriedGround_Leaf_Reflec_2244_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides full-spectrum (350-2500 nm) reflectance measurements of dried ground leaf samples from meadow, shrub, and tree sites. Samples were collected during the period of February 23, 2022 to September 27th, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. The primary goal of the SHIFT campaign was to collect a repeated dense time series of airborne Visible to ShortWave Infrared (VSWIR) airborne imaging spectroscopy data with coincident field measurements in both inland terrestrial and coastal aquatic areas. Reflectance measurements were collected using a ASD FieldSpec 3 spectrometer following Serbin et al. (2014) and Wang et al. (2020). After sample collection, each leaf sample was divided into two portions: one portion with ~10 g fresh weight was oven dried and another portion with ~5 g fresh weight was flash frozen. Both samples were ground and homogenized (20-mesh, 833 micrometers). The oven dried samples were then re-dried in oven at 70 degrees C for 24 h and the flash frozen samples were dried using a Virtis Model 24DX48 specimen freeze dryer before the reflectance measurement. Data are in a comma-separated values (.csv) format.", "links": [ { diff --git a/datasets/SHIFT_Field_Survey_Metadata_2295_1.json b/datasets/SHIFT_Field_Survey_Metadata_2295_1.json index fd7a7df15c..9ac928a496 100644 --- a/datasets/SHIFT_Field_Survey_Metadata_2295_1.json +++ b/datasets/SHIFT_Field_Survey_Metadata_2295_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_Field_Survey_Metadata_2295_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains vegetation plot locations, descriptions, fractional cover, and sample identifier information from surveys conducted as part of the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Surveys took place from 2022-02-23 to 2022-09-27 at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. This project collected field data contemporaneously with weekly flights of the NASA Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Plot information includes: plot tree subform, species lists, plot description, plot samples characterization, and plot location and contextual information. Related data packages contain additional biogeochemical, reflectance, and foliar data. Survey data and metadata are presented in comma-separated values (*.csv) format along with survey plot polygons in GeoJSON (*.geojson) format.", "links": [ { diff --git a/datasets/SHIFT_Foliar_Chemical_Analysis_2337_1.json b/datasets/SHIFT_Foliar_Chemical_Analysis_2337_1.json index b2d6b084be..87a56b43a4 100644 --- a/datasets/SHIFT_Foliar_Chemical_Analysis_2337_1.json +++ b/datasets/SHIFT_Foliar_Chemical_Analysis_2337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_Foliar_Chemical_Analysis_2337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds laboratory foliar chemical analyses results for field samples collected during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. Leaf samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve during the period of 2022-02-23 to 2022-09-27 and dried for later analysis. This project collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Sixteen chemical traits from two different lab analyses are provided. (a) Elemental analysis: foliar nitrogen (%), phosphorus (%), magnesium (%), potassium (%), calcium (%), sulfur (%), boron (ppm), iron (ppm), manganese (ppm), copper (ppm), zinc (ppm), aluminum (ppm), and sodium (ppm). (b) AnkomFiber analysis: foliar hemicellulose and bound protein (%), cellulose (%), and lignin (%). Related data packages contain additional plot-level characterization, biogeochemical, reflectance, and foliar data. These data are provided in comma separated values (CSV) format.", "links": [ { diff --git a/datasets/SHIFT_HyTES_L1_2022_2245_1.json b/datasets/SHIFT_HyTES_L1_2022_2245_1.json index 579fe55730..71923dedd9 100644 --- a/datasets/SHIFT_HyTES_L1_2022_2245_1.json +++ b/datasets/SHIFT_HyTES_L1_2022_2245_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_HyTES_L1_2022_2245_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds Level 1 (L1) brightness temperature data collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022. The SHIFT campaign generated precise, high-frequency data on plant communities for nearly 1,656 square kilometers across Santa Barbara County, California, US, and the nearby ocean. HyTES is a compact image spectrometer that acquires data in 256 spectral bands between 7.5 and 12 micrometers; it was deployed on a Twin Otter aircraft. The SHIFT campaign sought to demonstrate the joint use of both VSWIR and thermal infrared (TIR) data. TIR data are used to measure land surface temperature (LST), which informs models of water flux from land surface through processes such as evapotranspiration. LST is sensitive to solar heat gains and local cooling effects due to evaporative cooling. The HyTES instrument measures TIR radiances that can be used to derive LST, emissivity and Level 3 products such as latent heat flux and detection of air pollution sources. The HyTES data are provided in HDF5 format and include 91 flight scenes. The data are not projected, but georeferencing information for each pixel are provided in the HDF5 and a separate ENVI file for each flight scene. In addition, the flight scene boundaries and an overlay image are provided in Keyhole Markup Language (KML) along with a quicklook image and spectral response data.", "links": [ { diff --git a/datasets/SHIFT_HyTES_L2_2022_2246_1.json b/datasets/SHIFT_HyTES_L2_2022_2246_1.json index 8df1e69eca..a4a45003bb 100644 --- a/datasets/SHIFT_HyTES_L2_2022_2246_1.json +++ b/datasets/SHIFT_HyTES_L2_2022_2246_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_HyTES_L2_2022_2246_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset holds Level 2 (L2) data for surface emissivity and land surface temperature (LST) collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022. The SHIFT campaign generated precise, high-frequency data on plant communities for nearly 1,656 square kilometers across Santa Barbara County, California, US, and the nearby ocean. HyTES is a compact image spectrometer that acquires data in 256 spectral bands between 7.5 and 12 micrometers; it was deployed on a Twin Otter aircraft. The SHIFT campaign sought to demonstrate the joint use of both VSWIR and thermal infrared (TIR) data. TIR data are used to measure land surface temperature (LST), which informs models of water flux from land surface through processes such as evapotranspiration. LST is sensitive to solar heat gains and local cooling effects due to evaporative cooling. The HyTES instrument measures TIR radiances that can be used to derive LST, emissivity and Level 3 products such as latent heat flux and detection of air pollution sources. The HyTES data are provided in HDF5 format and include 91 flight scenes. In addition, overlay images for emissivity and LST are provided in compressed Keyhole Markup Language (KMZ) along with a quicklook image.", "links": [ { diff --git a/datasets/SHIFT_Leaf_Traits_Chl_SB_CA_2233_1.json b/datasets/SHIFT_Leaf_Traits_Chl_SB_CA_2233_1.json index b56e295e79..d9ba6223ee 100644 --- a/datasets/SHIFT_Leaf_Traits_Chl_SB_CA_2233_1.json +++ b/datasets/SHIFT_Leaf_Traits_Chl_SB_CA_2233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_Leaf_Traits_Chl_SB_CA_2233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides leaf images and measurements of leaf traits (area, wet weight, dry weight, leaf mass per area, leaf water content) and leaf pigments (chlorophyll) and species information as sampled from meadow, shrub, and tree from Santa Barbara California, USA. Samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt March Reserve during the period of February 23, 2022 to September 27, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. The associated data package contains image scans used for the leaf area calculations as well as python processing code used to calculate the area. A comma-separated value (CSV) formatted file includes plot-level leaf area (cm2), wet weight (g), leaf mass area (LMA, g leaf dry mass per meter square), leaf water content (LWC, (wet weight - dry weight/wet weight, %)), chlorophyll fluorescence ratio (CFR), and chlorophyll content (CHL).", "links": [ { diff --git a/datasets/SHIFT_Plot_Level_Photos_2334_1.json b/datasets/SHIFT_Plot_Level_Photos_2334_1.json index a8b1ce789d..85815a29b1 100644 --- a/datasets/SHIFT_Plot_Level_Photos_2334_1.json +++ b/datasets/SHIFT_Plot_Level_Photos_2334_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_Plot_Level_Photos_2334_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains photographs of the plots where field vegetation sampling was conducted during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Sampling occurred at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. Photographs were taken from 2022-02-23 to 2022-09-18. This project collected field data contemporaneously with weekly flights of Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over the study areas. Related SHIFT data packages contain additional biogeochemical, reflectance, and foliar data.", "links": [ { diff --git a/datasets/SHIFT_RamsesTriOS_Rrs_SB_CA_2234_1.json b/datasets/SHIFT_RamsesTriOS_Rrs_SB_CA_2234_1.json index f501b91831..1d69485ab7 100644 --- a/datasets/SHIFT_RamsesTriOS_Rrs_SB_CA_2234_1.json +++ b/datasets/SHIFT_RamsesTriOS_Rrs_SB_CA_2234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SHIFT_RamsesTriOS_Rrs_SB_CA_2234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides calculated remote sensing reflectance (Rrs) from measurements collected with a Ramses TriOS radiometer deployed on the Santa Barbara Museum of Natural History Sea Center at Stearns Wharf, Santa Barbara, California, U.S. All measurements were taken over a fixed position at (34.41037665, -119.68557147). Three sensors are used to collect solar downwelling irradiance (Ed), sky radiance (Ls) and water-leaving radiance (Lw). These data have been processed to Rrs at 10 second intervals and are either concurrent or taken within 2.5 hours of SHIFT campaign flights. The data collected by the three Ramses TriOS sensors for eight days during the period 2022-04-05 to 2022-05-29 are also included. The data were translated from the proprietary format output by the Ramses TriOS instrument and saved in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/SIC_187_coregistration_1.json b/datasets/SIC_187_coregistration_1.json index 983908ea32..becc6b2248 100644 --- a/datasets/SIC_187_coregistration_1.json +++ b/datasets/SIC_187_coregistration_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIC_187_coregistration_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The co-registration of the SPOT image (31 March 1991, SIC 187) was done as part of the Heard Island AAS2939 project (Lucieer et al.). This SPOT image was co-registered to the IKONOS Jan. 2004 image (metadata ID: SIC_266_267_georectification). All processing was done in ENVI 4.4 (https://esriaustralia.com.au/envi).", "links": [ { diff --git a/datasets/SIC_266_267_georectification_1.json b/datasets/SIC_266_267_georectification_1.json index d4b57936a6..b7d0b62cd2 100644 --- a/datasets/SIC_266_267_georectification_1.json +++ b/datasets/SIC_266_267_georectification_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIC_266_267_georectification_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOTE - this record has been superseded by \"SIC_266_267_ortho\", which describes a more recent orthorectification of the same two images.\n\nTwo IKONOS 2004 images (SIC 266 and 267) were chosen as the base images for the AAS2939 change detection project. The reason for choosing these images where that they were the earliest very high resolution (VHR) image with the least amount of cloud cover for which DGPS GCPs are available and identified on the image by Jenny Scott. The importance of having a base image to register all other images to is to allow change detection between images.\nThe 2004 IKONOS image was resampled to a 0.6m resolution, in order to match the resolution of the Quickbird images (for future rectification and change detection).", "links": [ { diff --git a/datasets/SIC_462_georectification_1.json b/datasets/SIC_462_georectification_1.json index 9d16f46b54..fe7aa860ee 100644 --- a/datasets/SIC_462_georectification_1.json +++ b/datasets/SIC_462_georectification_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIC_462_georectification_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthorectification of the QuickBird image (15 December 2006, SIC 463) was done as part of the Heard Island AAS2939 project (Lucieer et al.). This QuickBird image was orthorectified and co-registered to the IKONOS Jan. 2004 image (metadata ID: SIC_266_267_georectification). All processing was done in ENVI 4.4 (https://esriaustralia.com.au/envi).", "links": [ { diff --git a/datasets/SIF_PAR_fPAR_US_Midwest_2018_1813_1.json b/datasets/SIF_PAR_fPAR_US_Midwest_2018_1813_1.json index 8c871c43bc..28771d3025 100644 --- a/datasets/SIF_PAR_fPAR_US_Midwest_2018_1813_1.json +++ b/datasets/SIF_PAR_fPAR_US_Midwest_2018_1813_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIF_PAR_fPAR_US_Midwest_2018_1813_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29. SIF of a specific vegetation type (i.e., corn, soybean, grass/pasture, forest) was expressed as the product of photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by the canopy (fPAR), and canopy SIF yield (SIFyield) for each vegetation type. Uncertainty of each variable was also calculated and is provided. These components of the SIF model were derived using a TROPOspheric Monitoring Instrument (TROPOMI) dataset, the USDA National Agricultural Statistics Service Cropland Data Layer, and the MODIS MCD15A2H 8-day 500 m fPAR product. These data could be used to improve estimates of vegetation productivity and vegetation stress.", "links": [ { diff --git a/datasets/SIF_SCIAMACHY_GOME2_Harmonized_2317_2.json b/datasets/SIF_SCIAMACHY_GOME2_Harmonized_2317_2.json index 2ef79455ca..58dfb34ea0 100644 --- a/datasets/SIF_SCIAMACHY_GOME2_Harmonized_2317_2.json +++ b/datasets/SIF_SCIAMACHY_GOME2_Harmonized_2317_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIF_SCIAMACHY_GOME2_Harmonized_2317_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global solar-induced chlorophyll fluorescence (SIF) estimates at a 0.05-degree resolution (approximately 5 km at the equator) for each month from January 2003 through December 2017. SIF data (740 nm) was retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) instruments onboard the MetOp-A satellite. The data were downscaled to 0.05 degrees using the Random Forest algorithm and predictors from Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, and then harmonized with the cumulative distribution function (CDF) matching technique. The uncertainty of the harmonized SIF estimates was also quantified and provided. Validation of the harmonized product showed that it retained high spatial and temporal consistency with the original SCIAMACHY and GOME-2 SIF retrievals and had good correlations with independent airborne and ground-based SIF measurements. The dataset can inform on the synergy between satellite SIF and photosynthesis and research on drought, yield estimation, and land degradation evaluation. The data are provided in netCDF format.", "links": [ { diff --git a/datasets/SIMBAD_DESCHAMPS_LOA_0.json b/datasets/SIMBAD_DESCHAMPS_LOA_0.json index 802bc35f25..380b398bc1 100644 --- a/datasets/SIMBAD_DESCHAMPS_LOA_0.json +++ b/datasets/SIMBAD_DESCHAMPS_LOA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIMBAD_DESCHAMPS_LOA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made by Laboratoire dOptique Atmospherique (LOA) PI Pierr-Yves Deschamps using the SIMBAD radiometer during 1996 to 2001.", "links": [ { diff --git a/datasets/SIO-Pier_0.json b/datasets/SIO-Pier_0.json index 32be19ab02..05096f4f4a 100644 --- a/datasets/SIO-Pier_0.json +++ b/datasets/SIO-Pier_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIO-Pier_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made from the Scripps Ocean Institute pier in 2007.", "links": [ { diff --git a/datasets/SIPEX_ASPECT_1.json b/datasets/SIPEX_ASPECT_1.json index 50e253b435..4b75d077bd 100644 --- a/datasets/SIPEX_ASPECT_1.json +++ b/datasets/SIPEX_ASPECT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_ASPECT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASPeCt is an expert group on multi-disciplinary Antarctic sea ice zone research within the SCAR Physical Sciences program. Established in 1996, ASPeCt has the key objective of improving our understanding of the Antarctic sea ice zone through focussed and ongoing field programs, remote sensing and numerical modelling. The program is designed to complement, and contribute to, other international science programs in Antarctica as well as existing and proposed research programs within national Antarctic programs. ASPeCt also includes a component of data rescue of valuable historical sea ice zone information.\n\nThe overall aim of ASPeCt is to understand and model the role of Antarctic sea ice in the coupled atmosphere-ice-ocean system. This requires an understanding of key processes, and the determination of physical, chemical, and biological properties of the sea ice zone. These are addressed by objectives which are:\n\n1) To establish the distribution of the basic physical properties of sea ice that are important to air-sea interaction and to biological processes within the Antarctic sea-ice zone (ice and snow cover thickness distributions; structural, chemical and thermal properties of the snow and ice; upper ocean hydrography; floe size and lead distribution). These data are required to derive forcing and validation fields for climate models and to determine factors controlling the biology and ecology of the sea ice-associated biota.\n\n2) To understand the key sea-ice zone processes necessary for improved parameterization of these processes in coupled models.\n\nThese ASPeCt measurements were taken onboard the Aurora Australis during the SIPEX voyage in the 2007-2008 summer season.", "links": [ { diff --git a/datasets/SIPEX_II_ASPECT_1.json b/datasets/SIPEX_II_ASPECT_1.json index 22d611e691..62da28be77 100644 --- a/datasets/SIPEX_II_ASPECT_1.json +++ b/datasets/SIPEX_II_ASPECT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_ASPECT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains observations of ice conditions taken from the bridge of the RV Aurora Australis during SIPEX 2012, following the Scientific Committee on Antarctic Research/CliC Antarctic Sea Ice Processes and Climate [ASPeCt] protocols. See aspect.antarctica.gov.au \n\nObservations include total and partial concentration, ice type, thickness, floe size, topography, and snow cover in each of three primary ice categories; open water characteristics, and weather summary.\n\nThe dataset is comprised of the scanned pages of a single logbook, which holds hourly observations taken by observers while the ship was moving through sea-ice zone.\n\nThe following persons assisted in the collection of these data:\nDr R. Massom, AAD, Member of observation team\nMr A. Steer, AAD, Member of observation team\nProf S. Warren, UW(Seattle), USA, Member of observation team\nDr J. Hutchings, IARC, UAF, USA, Member of observation team\nDr T. Toyota, Inst Low Temp Science, Japan, Member of observation team\nDr T. Tamura, NIPR, Japan, Member of EM observation team\nDr G. Dieckmann, AWI, Germany, Member of observation team\nDr E. Maksym, WHOI, USA, Member of observation team\nMr R. Stevens, IMAS, Trainee on observation team\nDr J. Melbourne-Thomas, ACE CRC, Trainee on observation team\nDr A. Giles, ACE CRC, Trainee on observation team\nMs M. Zhia, IMAS, Trainee on observation team\nMs J. Jansens, IMAS, Trainee on observation team\nMr R. Humphries, Univ Wollengong, Trainee on observation team\nMr C. Sampson, Univ Utah, USA, Trainee on observation team\nMr Olivier Lecomte, Univ Catholique, Louvain-la-Neuve, Belgium, Trainee on observation team\nMr D. Lubbers, Univ Utah, USA, Trainee on observation team\nMs M. Zatko, UW(Seattle), USA, Trainee on observation team\nMs C. Gionfriddo, Uni Melbourne, Trainee on observation team\nMr K. Nakata, EES, Japan, Trainee on observation team", "links": [ { diff --git a/datasets/SIPEX_II_AUV_1.json b/datasets/SIPEX_II_AUV_1.json index ce27f7d4ef..ce169c7b77 100644 --- a/datasets/SIPEX_II_AUV_1.json +++ b/datasets/SIPEX_II_AUV_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_AUV_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. \n\nThe AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. \n\nThe AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice.\n\nWe conducted 4 missions beneath sea-ice during the SIPEX-II voyage.\n\nThe current status of the data is that is in un-processed and unavailable until final processing is completed in 2013.\n\nPersons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions.\n\nThe files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as:\n\nfloe-2-20120926.mat\nfloe-3-20121003.mat\nfloe-4-20121006.mat\nfloe-6-20121013.mat\n\nThese can be accessed using the Seabed_plot routines (MATLAB) in this folder.\n\nThere is a readme file provided called what-is-this.txt\n\n\nAlso included is the video footage taken from the AUV using a GoPro HD Hero. \nVideo Codec: avc1\nResolution: 1920x1080 pixels\nFrame Rate: 29.970030 f/s\nAudio Codec: mp4a\nAudio Bitrate: 1536 kb/s\n\nFinally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from.", "links": [ { diff --git a/datasets/SIPEX_II_Aerosols_1.json b/datasets/SIPEX_II_Aerosols_1.json index fda53f0486..8457947abe 100644 --- a/datasets/SIPEX_II_Aerosols_1.json +++ b/datasets/SIPEX_II_Aerosols_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Aerosols_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The current dataset includes total aerosol count from two different Condensation Particle Counters (CPCs). The two CPCs measure total aerosol number in two different size ranges:\n-\tTSI Model 3025A measures particles with diameters larger than 3 nm (files are in the 3025_3nm folder)\n-\tTSI Model 3772 measures particles with diameters larger than 10 nm (files are in the 3772_10nm folder)\nThe two CPCs are measuring from the same sample air and as such, the difference between the two measurements gives a measurement of total aerosol concentration in the 3-10 nm size range, known as the nucleation mode. \n\nInstrument setup:\nThe instruments are setup inside an insulated shipping container mounted on the hatch covers directly aft of the forecastle. A 100 L pump is used to pull sample air from a 3 m high mast located on the starboard side of the forecastle. The air is pulled through 17 m of 50 mm antistatic (copper coil) polyurethane tubing and 2 m of 50 mm stainless steel pipe for connection and extensions. A 1 m length of one quarter inch stainless steel tubing penetrates into the container and directly through the wall of the polyurethane tubing for sampling off the primary flow to the CPCs. The inserted stainless steel tubing is oriented in such a way that sampled aerosol experience minimal turns to avoid sample loss. Approximately 1.7 m of flexible conductive tubing extends to a Y-piece which directs flow into each CPC. Butanol contaminated exhaust from the CPCs is pushed out of the container by two 10 LPM pumps. \n\nData Processing:\nRaw data is calibrated for each instrument's recorded flow rate, and an inlet efficiency to correct for losses in the long inlet. Data is then resampled to minute time resolution, and filtered for logged events, wind directions which sampled ship exhaust, and outliers in the dataset. This produced a dataset which represented the sampling of clean Antarctic background atmosphere.\n\nThe dataset includes both aerosol number concentrations from each instrument giving total number of particles above 3 nm and 10 nm respectively, as well as the different between these values, which gives a measure of newly formed particles in the nucleation mode between 3-10 nm (New Particle Formation, NPF). Associated uncertainties are included in the dataset.", "links": [ { diff --git a/datasets/SIPEX_II_Albedo_1.json b/datasets/SIPEX_II_Albedo_1.json index cf4bbb7dd8..94c8000cd1 100644 --- a/datasets/SIPEX_II_Albedo_1.json +++ b/datasets/SIPEX_II_Albedo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Albedo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'.\n\nMATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. \n\nAlso included is some photography of frost flowers and other examples of ice that was observed.", "links": [ { diff --git a/datasets/SIPEX_II_Boundary_Layer_Met_1.json b/datasets/SIPEX_II_Boundary_Layer_Met_1.json index 4adba0de36..5517a47f66 100644 --- a/datasets/SIPEX_II_Boundary_Layer_Met_1.json +++ b/datasets/SIPEX_II_Boundary_Layer_Met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Boundary_Layer_Met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Note - these data should be used with caution. The chief investigator for the dataset has indicated that a better quality dataset exists, but the AADC have been unable to attain it for archive.\n\n\nMatlab files containing raw data collected using the program \"HC2S3snowwind.CR1\" running on Campbell Scientific CR1000 dataloggers. Datalogger \"C\" was used during all ice stations. On the 8th of October a second mast and logger (\"A\") were installed on what became the final day of Ice Station 4, and both loggers were deployed at stations 6 and 7, with \"C\" containing the longer records for each station as it was always installed first and (conditions permitting) left out longer. \n\nThe sensors on these masts consist of:\nRM Young \"Wind Sentry\" Vane and Anemometer set (on top of each mast), no serial numbers\nRotronics HC2S3 temperature and relative humidity sensors with standard polyethylene filters \n\nUpper sensor, mast \"C\": s/n 60837541\nLower sensor, mast \"C\": s/n 60837536\nUpper sensor, mast \"A\": s/n 60837468\nLower sensor, mast \"A\": s/n 60834204\n\nRM Young \"Wind Sentry\" anemometers (without vane) at 3 additional elevations on each mast\nWenglor YHO3NCT8 photoelectric sensors at 4 heights on each mast. The upper sensor and the third sensor from the top were oriented facing up, while the others faced down. The upper three sensors were purchased in 2012, from a batch of these sensors manufactured in a new Eastern European factory while the lowest sensor on each mast came from a lot purchased in 2007, manufactured in Wenglor's German factory and extensively tested for use in snow.\n\nData contained in these .mat files includes the following variables, with units:\n\nTextdates: CSI formatted dates, UTC except for station 2, which was (accidentally) UTC+12\nDatenm: Matlab \"datenumber\", all UTC except for station 2, which is also UTC+12 hours.\nBattvolt: battery voltage\nWptemp: temperature of the Wiring Panel thermister, degrees C\nTemp 1: air temperature above approximately 50cm, ventilated HC2S3 rotronics sensor, degrees C\nRH1: relative humidity (WRT water) above approximately 50cm, ventilated HC2S3 rotronics sensor, %\nTemp 2: air temperature above approximately 200cm, ventilated HC2S3 rotronics sensor, degrees C\nRH2: relative humidity (WRT water) above 197cm, ventilated HC2S3 rotronics sensor, %\nSnow1: snow particles per 10second interval at approximately 10cm\nSnow2: snow particles per 10second interval at approximately 50cm\nSnow3: snow particles per 10second interval at approximately 100cm\nSnow4: snow particles per 10second interval at approximately 200cm\nWind1: average speed (m/s) at approximately 250cm during 10s interval \nWind1max: maximum speed at approximately 250cm during 10s interval\nWind2: average speed (m/s) at approximately 100cm during 10s interval\nWind2max: maximum speed at approximately 100cm during 10s interval\nWind3: average speed (m/s) at approximately 120cm during 10s interval\nWind3max: maximum speed at approximately 120cm during 10s interval\nWind4: average speed (m/s) at approximately 50cm during 10s interval\nWind4max: maximum speed at approximately 50cm during 10s interval\nWindDir: wind direction at approximately 250cm, degrees, relative to mast orientation (needs correction to true)\n\nMeasurement heights varied by ice station and by mast being used.", "links": [ { diff --git a/datasets/SIPEX_II_Buoys_1.json b/datasets/SIPEX_II_Buoys_1.json index 272b877869..10585e8766 100644 --- a/datasets/SIPEX_II_Buoys_1.json +++ b/datasets/SIPEX_II_Buoys_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Buoys_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In situ Lagrangian drifter positions were collected from nine expendable sea-ice buoys. Positions were collected by GPS receivers aboard each buoy and relayed via the CLS Argos satellite data system.\n\nThe scientific proposal for this project was based on the deployment of two meso-scale buoy arrays over the continental shelf break in the SIPEX 2012 experimental region. Resolving of ice motion over the continental shelf and the shelf break is expected to provide crucial information on sea-ice deformation and ice strength. However, due to the unfavourable cruise track and also due to operational issues with helicopter support, it was not possible to deploy any of the meso-scale buoy arrays. Instead buoys were deployed to resolve ice deformation within the wider SIPEX 2012 region. \n\nPosition data are available hourly from most buoys. CLS Argos transmitted data suffer from a data transmission blackspot just prior to local none, when there will be no data available.\n\nData processing will be carried out as described in Heil et al. [2008]\nThe dataset is build from ASCII files for each buoy with time stamps and observed latitude and longitude. The format (by column [C] for each file is as following:\n \nC1: Program ID\nC2: Buoy ID\nC3: Year\nC4: Month\nC5: Day\nC6: Hour\nC7: Minute\nC8: Second\nC9: Day-of-year\nC10: Lat (degN)\nC11: Lon (degE)", "links": [ { diff --git a/datasets/SIPEX_II_CO2_Flux_1.json b/datasets/SIPEX_II_CO2_Flux_1.json index c091645b13..a3c4f2bb1c 100644 --- a/datasets/SIPEX_II_CO2_Flux_1.json +++ b/datasets/SIPEX_II_CO2_Flux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_CO2_Flux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the ice stations, measurements of the air CO2, concentration for CO2 flux between sea ice and atmosphere were made with the chamber technique. Air-sea ice CO2 fluxes were measured over the sea ice with semi-automated chambers. Sample air from the chamber is passed through Teflon tubes connected to non-dispersive infrared (NDIR) analyzer (Model 800, LICOR Inc., USA) that was connected to a system controller and data logger (Model 10x, Campbell Scientific Inc., USA), that controls the opening/closing of the chambers as well. During the observation period, the CO2 flux was measured under three different conditions or surface types: (1) a chamber was installed above snow; (2) over the bare ice after removing the snow; (3) slush layer after removing the snow and slush crystals. The CO2 concentration in the chamber was measured every 5 s during experiments lasting 20 minutes for each chamber. A one hour cycle of measurements therefore consist of three 20 minute periods from each chamber (i.e. surface type). \n\nData available: excel files containing sampling station name for each spreadsheet, dates, sampling time and air CO2 concentration as output voltage from NDIR (to indicated as ppm we need to calculate, but, not yet done this process) in the air and chamber for CO2 flux measurement.\n\nAlso see the record - SIPEX_II_Gas_Flux", "links": [ { diff --git a/datasets/SIPEX_II_CO2_Incubations_1.json b/datasets/SIPEX_II_CO2_Incubations_1.json index 8f8c1c2ba3..51c3719636 100644 --- a/datasets/SIPEX_II_CO2_Incubations_1.json +++ b/datasets/SIPEX_II_CO2_Incubations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_CO2_Incubations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pulse Amplitude Modulation (WaterPAM, Walz) was used to measure the response of the sea ice brine microalgae to CO2 stress. All data was reported in WinControl software and follows standard formats. Three incubation experiments were carried out at SIPEX stations 4 (expt 1) 7 (expt 3) and 8 (expt 4). \nFile nomenclature\nTO: time zero\nTR1,2,3 refers to times 2,3 and 4 respectively\nIn expt 4 the coding refers to hours since beginning of experiment\n\nEach file contains data in the same columns: Important results include\nColumn E: F\nColumn F: Fm\nColumn G: Fv/Fm\nColumn H: rETR\nColumn I: PAR", "links": [ { diff --git a/datasets/SIPEX_II_CTD_1.json b/datasets/SIPEX_II_CTD_1.json index 93156aa972..0e3390610d 100644 --- a/datasets/SIPEX_II_CTD_1.json +++ b/datasets/SIPEX_II_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multiple CTD (conductivity, temperature, depth) casts were deployed during the SIPEX II AAD Marine Science voyage in September-November 2012. The system uses a descending rosette capable of holding up to 24 CTD bottles. During this voyage the CTD rosette also housed two krill traps (using controllable lights) and two GoPro cameras contained in pressurised, waterproof containers that were used to monitor the krill traps and view objects both on the sea bed and in the water column.\n\nSome functions of the GoPro cameras could be controlled from within the ship using the same transmission cable used by the CTD system. These functions included being able to change the focus setting of the cameras or start/stop recording. More information about the krill traps and cameras is contained in the SIPEX II Bottom Krill dataset.\n\nWhen a bottle is 'fired' from the ship it briefly opens, draws in water samples and closes again. It is not reopened until it is brought on board the ship. Bottles are opened at different depths to obtain samples from these depths. The depths vary from cast to cast and so are recorded in the CTD Log sheets (contained in this dataset as PDF files).\n\nOnly raw data is contained in this dataset. The raw data was used by a variety of experiments during the SIPEX II voyage to produce results applicable to each experiment.\n\nThanks go to the P and O crew of the RV Aurora Australis for their assistance during CTD operations.", "links": [ { diff --git a/datasets/SIPEX_II_Chlorophyll_a_1.json b/datasets/SIPEX_II_Chlorophyll_a_1.json index e05b17857a..89814e08f3 100644 --- a/datasets/SIPEX_II_Chlorophyll_a_1.json +++ b/datasets/SIPEX_II_Chlorophyll_a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Chlorophyll_a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll data was used to measure growth rates of sea ice algae in CO2 incubations. Sea ice brine microalgae was collected from sackholes. Replicate samples were incubated in ambient air (~0.04% CO2), 0.1% CO2, 1.0% CO2 and 2.0% CO2 concentrations. AT the end of the incubations the 50 ml samples were filtered through a 25 mm GF/F filter using vacuum filtration. The filters were placed in 15 ml plastic falcon tubes containing 10 ml of methanol, covered in aluminium foil and kept in the dark at 4 degrees C for 12 hours. Chl a concentration was measured using a 10AU Turner fluorometer following the acidification method of Strickland and Parsons (1972). \nData in spread sheet shows the extracted chl + phaeophytin, phaeophytin and chlorophyll concentrations (micro grams l-1) for each of the three experiments.\n\nData were collected at SIPEX Ice Stations 1-8 and SIPEX CTD stations 2-5", "links": [ { diff --git a/datasets/SIPEX_II_DNA_Sequences_1.json b/datasets/SIPEX_II_DNA_Sequences_1.json index 1154e1d476..e4ab40d027 100644 --- a/datasets/SIPEX_II_DNA_Sequences_1.json +++ b/datasets/SIPEX_II_DNA_Sequences_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_DNA_Sequences_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Purpose of experiments:\n\nSequence data obtained to determine community structure of pack sea-ice microbial communities and whether it is effected by exposures to elevated CO2 levels.\n\nSummary of Methods:\n\nCells in sea-ice brines were filtered onto 0.2 micron filters and material extracted using the MoBio Water DNA extraction kit. The DNA was analysed by Research and Testing Laboratories Inc. (Lubbock, Texas, USA) via 454 pyrosequencing. The bacteria were analysed using primers set 10F-519R, which targets 16S rRNA genes. 16S rRNA genes associated with chloroplast and mitochondria are included in this dataset but represent a minority of sequences in most samples. Eukaryotes were analysed using primers set 550F-1055R, which targets 18S rRNA genes. The 454 pyrosequencing analysis with the Titanium GS FLX+ kit used generates on average 3000 reads incorporating custom pyrotags for later stages of the data analysis. The specific steps used for subsequent data analysis are described in the attached PDF file (Data_Analysis_Methodology.PDF). This output was further refined by first determining consensus sequences at the 98% similarity level using Weizhong Li\u2019s online software site CD-HIT (http://weizhongli-lab.org/cd-hit/) Reference: Niu B, Fu L, Sun S, Li W. 2010. Artificial and natural duplicates in pyrosequencing reads of metagenomic data. BMC Bioinformatics 1:187 doi:10.1186/1471-2105-11-187. The consensus sequences were then checked for errors, manually curated, and aligned against closest matching sequences obtained from the NCBI database (www.ncbi.nlm.nih.gov) to finally obtained a list of consensus operational taxonomic entities and the number of reads obtained for each samples analysed.\n\nFile: SIPEXII_DNA_Sample_information.xlsx provides sampling and analysis information for the detailed results in the other two files\nFile: SCIPEXII__sea_ice_bacteria_OTUs.xlsx contains information on the number of 16S rRNA reads in bacteria Phylum/Class and OTUs\nFile: SCIPEXII_sea_ice_brines_eukaryote_community_OTU_data.xlsx contains information on the number of 16S rRNA reads in eukaryotic microbes: Phylum/Order/Closest taxon and OTUs", "links": [ { diff --git a/datasets/SIPEX_II_Diurnal_Snow_1.json b/datasets/SIPEX_II_Diurnal_Snow_1.json index 1055197482..84bd018478 100644 --- a/datasets/SIPEX_II_Diurnal_Snow_1.json +++ b/datasets/SIPEX_II_Diurnal_Snow_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Diurnal_Snow_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Motivation:\n \nOne of the characteristics of this voyage is that we have long ice stations which last for a few days. Taking this opportunity, we decided to examine the diurnal change of snow properties at the fixed snow pit site. Since this measurement was not included in the original plan, Time interval was a bit variable from 3 hours to 5 hours depending on the progress of the other work.\n\nObservation items:\n \nSnow thickness, Temperature profile (every 3 cm), Grain size, Grain shape, Snow density, Hardness, Salinity\n\nInstruments:\n \nFolding scales, Spatula, Thermometer, Snow sampler, Magnifying glass, Salinometer\n \nInformation pertaining to the dataset:\n\nTime - recorded in local time\n\nHs - snow depth in cm\n\nCloud measurements - oktas\n\nWater level - distance between snow surface and surface seawater in cm\n\nDepth - depth of the individual layer referenced to snow/ice interface (upper column) or snow surface (lower column) in cm\n\nTa - air temperature in degrees celsius\n\nDH, FC, PP, DF, RG stand for Depth hoar, Faceted crystals, Precipitation particles, Decomposing and fragemented precipitation particles, Rounded grains - according to \"The International Classification for Seasonal Snow on the Ground\" (Colbeck et al., 1990).\n\nWeight - g\n\nMid-depth - cm", "links": [ { diff --git a/datasets/SIPEX_II_Fluid_Permeability_1.json b/datasets/SIPEX_II_Fluid_Permeability_1.json index 55be71895a..192a2158a2 100644 --- a/datasets/SIPEX_II_Fluid_Permeability_1.json +++ b/datasets/SIPEX_II_Fluid_Permeability_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Fluid_Permeability_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 9 cm diameter Kovacs corer was used to drill holes partially through the ice. The core was removed, creating a pressure head in the hole. Packers made of ABS tubing wrapped with foam to create a tight seal were inserted into the holes to block the horizontal component of flow. A \"Levelogger\", which is a pressure transducer for monitoring well-water, created by Solinst, was then inserted into each hole to record the change in water level over time. Each Levelogger was fitted into a plastic holder to keep it upright during measurement, which is a requirement for accurate data from the device. The temperature at the bottom of the core was measured immediately after removal, and the bottom 2 centimetres were removed for melting and subsequent measurement of salinity. The measurements of salinity and temperature enable calculation of the brine volume fraction. Solinst Levelogger software was then used to compensate for local barometric changes recorded using a Solinst Barologger. Following each measurement an auger was used to drill through the bottom of each hole to measure the ice thickness and freeboard in each hole.\n\nA full core was taken at each worksite for crystallographic study, immediately adjacent to where permeability measurements were taken. A temperature profile was taken on each of these cores immediately after extraction. Cores were then moved to a -20 degree C cold room for further processing. A thin vertical section, approximately 3mm thick, was taken from each of the cores stored for analysis. These sections were placed between a pair of cross polarised plates and photographed. Each photo was labelled with the core and date it was taken, and was photographed with a meter stick for scale.\n\nAfter the thin sections were photographed, the remaining samples were melted to measure salinity. Some of the melted sea ice was saved for later O18 analysis to distinguish samples containing snow-ice.\n\nRecorded values required to determine permeability are contained within the Master_Core_List.xls Excel spreadsheet, found in the Permeability worksheet. This worksheet is generated directly from notebook data, and contains the date, start and end time for each permeability record, the core number assigned, the depth of the partial sackhole, the levelogger serial number used, the station (site), the temperature 2cm from the bottom of the removed core, the bulk salinity from the bottom 2cm of the removed core, as well as the measured freeboard and thickness at each site. This worksheet also contains a column to indicate which crystal structure the crystallographic core taken from this site and depth had, as well as which crystallographic core this came from. Finally, the worksheet contains notes, and a column to indicate whether we believe this data is somehow bad. Please see the notes section for reasons why a data point was determined invalid. Typically was due to heavy rafting beneath the flow or too quick an influx of water to properly measure. \n\nAll permeability data can be found both in the original binary .xle format used by Solinst levelogger software, as well as exported into comma separated value (CSV) files. These files are located in the datalogger_data directory. Binary files are contained in the raw folder, organised into sub folders by station number. The CSV files are located in the csv folder, again organised into sub folders by station number.\n\nPhotos of the crystallography cores can be found in the crystallography folder, separated into subfolders labelled with the site and core number. Each photo also contains a tag indicating the core number, site taken, date, and what depth range this covers. Tags may not contain a depth range for cores less than 1 meter. Please see the meter stick in each photo for scale.\n\nScans of the original notebooks from which the Permeability worksheet were created are provided in the scanned_notebook directory.", "links": [ { diff --git a/datasets/SIPEX_II_Gas_Flux_1.json b/datasets/SIPEX_II_Gas_Flux_1.json index 3af88759e2..ca93265ecd 100644 --- a/datasets/SIPEX_II_Gas_Flux_1.json +++ b/datasets/SIPEX_II_Gas_Flux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Gas_Flux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gas Flux over Sea Ice\n-------------\n\nWe observed amount of gas exchange between sea ice and atmosphere. At the ice station, semi-automated chambers developed in Japan, were used for measurement of air-sea ice CO2 flux. These chambers could be used to examine spatial variability and also temporal variability of gas flux over sea ice. Samples were also taken from the snow and ice in order to measure CH4 and VOC, however these analyses will be conducted post-voyage. This metadata record will be updated in future to reflect the analysis.\n\nThe chambers are designed to be placed over a snow and sea ice. When the lid is closed, CO2 concentration was measured. The opening and closing functions of the chambers are automated and were set to a 30 minutes interval. CO2 concentration (as voltage) were recorded in the data logger (CR10X, Campbell Scientific Inc.) and downloaded after the experiments. Raw data are contained in the excel files. \n\nDuring the CO2 flux measurement, we collected the snow, sea ice, brine/slush and under-ice water. Snow and sea ice samples were melted after sampling in PVDF film bags (like Tedlar bags in order to avoid gas exchange with ambient air) in 4C temperature and treated for analysis. \nA chemical analysis for carbonate systems and VOC (water), salinity, nutrient, pigment and oxygen isotopic ratio samples will take place in Japan after the voyage for analysis. \nDuring the cruise, to examine ice growth processes, we made sea ice thin-section to classify the ice cores into granular ice, columnar ice or mixed granular and columnar ice (Eicken and Lange, 1989).\n\nThe CO2 data are contained in Excel spreadsheets. These use Japanese column headings.\n\nCalcium Carbonate (CACO3.6H20) as Ikaite in Sea Ice and Snow\n-----------\nAt each listed ice station we collected sea-ice cores using a Kovacs 9cm ice corer. Cores were sectioned into 10-20cm and melted at 4 degrees C, filtered and dried for later analysis of Calcium Carbonate in a home laboratory using an ICP, which produces text file outputs (included).\n\nAlso included is a spreadsheet listing the cores, and the calcium carbonate measurements.", "links": [ { diff --git a/datasets/SIPEX_II_Halocarbons_1.json b/datasets/SIPEX_II_Halocarbons_1.json index c535775d4b..1b482facb0 100644 --- a/datasets/SIPEX_II_Halocarbons_1.json +++ b/datasets/SIPEX_II_Halocarbons_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Halocarbons_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The current data set contains in-situ halocarbon atmospheric measurements of CH3CCl3, CH2Br2, CHBr3, CHCl3, C2Cl4 made using a Gas Chromatograph - Electron Capture Detector (GC-ECD) known as the micro- DIRAC.\n\nInstrument description and setup details:\nGC-ECD\nInstrument Description:\n\"micro-Dirac: an autonomous instrument for halocarbon measurements\"\nB. Gostlow, A. D. Robinson, N. R. P. Harris, L. M. O'Brien, D. E. Oram, G. P. Mills, H. M. Newton, S. E. Yong, and J. A Pyle Atmos. Meas. Tech., 3, 507-521, 2010\n\nInstrument Setup:\nThis instrument is sampling from a weather protected inlet positioned ~2 m off the front port side of the Monkey Deck of the Aurora Australis, directly above the bridge. The end of the Teflon sample line is bare (with an inserted glass wool filter) and contained within the \"Ned Kelly\", a large (~30 cm diameter) stainless steel can which protects against rain, snow, sea spray and major impacts. The quarter inch teflon sample line runs 60m (2 x 30m with 1 join) down to the GC-ECD which was located in a modified shipping container located on the foredeck. Ultra high purity He (99.995% purity) and N2 (99.998% purity) were fed ~ 6m into the instrument container via stainless steel 1/16\" tubing from a smaller adjacent container containing a number of other gas cylinders (primarily He, N2 and H2). A cylinder of calibrated air is located within the instrument container to perform regular calibrations. \n\nInlet flow rate of 1 L/min; sampling rate - about 20 samples per day.\nMethod description: a sample volume of 20 ml is passed through the adsorbent tube which quantitatively traps the compounds of interest. The tube is mounted across a 6 port 2 position valco valve. The adsorbent bed is then purged with helium to remove oxygen and the valco valve is switched to the 'inject' position. The tube is heated to 180 C, releasing the compounds of interest into a carrier flow of helium onto the separation column. The column is heated first isothermally at 35 C for 5 minutes before heating at 8 C/min to 145 C. The target compounds are separated according to boiling point and pass through the ECD. The chromatogram takes ~30 minutes to complete. After the bromoform peak has appeared the column is cooled back to 35 C, the valco valve is switched to the 'load' position and the next sample can be injected.", "links": [ { diff --git a/datasets/SIPEX_II_Ice_ADCP_1.json b/datasets/SIPEX_II_Ice_ADCP_1.json index d759db3d1a..4a8f609819 100644 --- a/datasets/SIPEX_II_Ice_ADCP_1.json +++ b/datasets/SIPEX_II_Ice_ADCP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_ADCP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 600KHz Teledyne RDI Workhorse Sentinel ADCP was deployed through a 10inch auger hole, flush with the base of the ice, looking downwards. At ice stations 2, 3, and 4 the deployment locations was Ridge site 1, the ridge site closest to the ship.\n\nAt ice station 7 there were 4 different deployment locations:\n- Transducer Hole A, by active ridge on 6th October 2012;\n- Trace Metal / Bio Site;\n- 100m Core site of ice-physics transect;\n- Transducer Hole A, re-drilled on 7th October 2012.\n\nLength of deployment varies from stations to station and was limited by AUV operations, when our ADCP was switched off. \n\nFiles contain the data collected in raw format. This format can be read by Teledyne WinSC software. Data files are stored in folders by ice station (see below).", "links": [ { diff --git a/datasets/SIPEX_II_Ice_CTD_1.json b/datasets/SIPEX_II_Ice_CTD_1.json index e525c7adc0..43a2f50ae1 100644 --- a/datasets/SIPEX_II_Ice_CTD_1.json +++ b/datasets/SIPEX_II_Ice_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CTD casts were taken through holes in the ice floe at various locations during ice stations 3, 4, 6 and 7. Two Seabird 37M microcats were used. One microcat did not log time, whereas the other did. An Idronaut Ocean Seven 304 CTD (manufactured in Italy) was used during ice stations 7 and 8.\n\nCSV files are provided. A single file represents a set of casts at a single location. The files are organised in columns as:\nColumn 1: Temperature (C)\nColumn 2: Conductivity\nColumn 3: Pressure\nColumn 4: Salinity (ppt)\nColumn 5: Date (DD MMM YYYY), UTC\nColumn 6: Time (HH:MM:SS), UTC\n\nFor the Seabird 37M (2006 model) belonging to Dr Hutchings, time on the microcat is set to UTC, to the second.\nFor the AWI Seabird 37M (1999 model), time is not output. This microcat dribbled data to a laptop at 1Hz.\n\nIce Station 3: \nA microcat was placed at about 7m below the surface (5m below the ice) at Ridge site 1. Salinity sensor was iced up on this cast\n\nIce Station 4: \nCast 1: 100m cast through the ROV hole on Oct 6th 10:30 UTC. \nCast 2: 10m cast at the trace gas site, on Oct 8th 06 UTC. \nCast 3: 100m cast at the trace gas site, on Oct 8th 09:30 UTC.\n\nIce Station 6:\nCast 1: 100m at ridge site 1 , on Oct 13th 03 UTC. \nCast 2: 10m casts at Trace Gas site, on Oct 13th 04:30 UTC. F\nNote that salinity sensor was iced on 10m cast at trace gas site.\nCast 3: Deployment at 7m depth at ridge site 1, on Oct 13th 06UTC.\nCast 4: 100m cast at ridge site 1, on Oct 14th 23 UTC. \nNote that microcat stopped recording at about 65m in downcast.\n\nIce Station 7:\n- CTD casts with Seabird 37M microcat:\nCast 1: 100m cast, Transducer Hole A, at active ridge. 20th Oct 03:00Z. Power failed 60m into downcast.\nCast 2: 30m cast, Y-axis 50m core hole. 20th Oct 05:15Z\nCast 3: 40m cast followed by 100m cast. Y-axis 100m ADCP hole. 21st Oct 00:00Z. Power failed at 60m.\nCast 4: 15m casts. Y-axis 50m core hole. 21st Oct 05:15Z\nCast 5: ROV Hole. With Polly's pinger. 21 Oct 09:30Z. Power failure at 86m.\n- CTD casts with Gerhard Dieckman's Seabird microcat. Note this microcat does not output time, but dribbles 1Hz data.\nCast 6: Transponder Hole near new ridge. 23rd Oct 06:30Z. \nCast 7: Trace Metal / Bio site. 23rd Oct 07:30Z. \nCast 8: At ROV Hole\n\nIce Station 8:\nSynoptic (3 hourly) CTD casts\nRoster of CTD casts is contained in file 'CTD_time.xls'. This table is pasted below. Please note that the names of excel files containing the raw data are presented in this table.\n\n\nFilenames:\nIce Station 3: \nFilename: 20121004/20121004_IceStation3_microcat_all.dat.\n\nIce Station 4: \nCast 1: Filename: 20121006_IceStation4_microcat_cast1.dat\nCast 2: Filename: 20121008_IceStation4_microcat_cast2_gerhard.dat\nCast 3: Filename: 20121008_IceStation4_microcat_cast3_gerhard.dat\n\nIce Station 6:\nCast 1: Filename: 20121013_IceStation6_microcat_cast1_ridge.dat\nCast 2: Filename: 20121013_IceStation6_microcat_cast2_gerhard.dat\nCast 3: Filename: 20121013_IceStation6_gerhardCat_ridge_052700.dat\nCast 4: Filename: 20121014_IceStation6_microcat_ridge.dat\n\nIce Station 7:\n CTD casts with Seabird 37M microcat:\nCast 1: Filename: 20121020_IceStation7_microcat_transponder_newRidge.dat\nCast 2: Filename: 20121020_IceStation7_microcat_50m.dat\nCast 3: Filename: 20121021_Station7_100m.dat\nCast 4: Filename: 20121021_Station7_50m.dat\nCast 5: Filename: 20121021_Station7_ROVhole_plusPolly2_tryagain.dat\n CTD casts with the AWI Seabird microcat:\nCast 6: Filename: 20121023_gerhardCat.dat\nCast 7: Filename: 20121023_gerhardCat_hole2.dat\nCast 8: Filename: CTD_jenny_20121023.xls\n\nIce Station 8:\nSynoptic (3 hourly) CTD casts: The data files are:\nCTD_jenny_20121023.xls\nCTD_jenny_20121028.xls\nCTD_jenny_20121030.xls\nCTD_jenny_20121031.xls\nCTD_jenny_20121101(1).xls\nCTD_jenny_20121101(2).xls\nCTD_jenny_20121102.xls\nCTD_jenny_20121103.xls\nCTD_jenny_20121104.xls", "links": [ { diff --git a/datasets/SIPEX_II_Ice_Conductivity_1.json b/datasets/SIPEX_II_Ice_Conductivity_1.json index 2b1df8ba33..f2d50d6ad0 100644 --- a/datasets/SIPEX_II_Ice_Conductivity_1.json +++ b/datasets/SIPEX_II_Ice_Conductivity_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_Conductivity_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DC Electrical:\n\nIn order to relate the fluid permeability to the electrical properties of sea ice, we also took measurements of the vertical component of the DC electrical conductivity tensor of sea ice. Cores extending to the bottom of an ice floe were taken and laid out holder. With the exception of sites 7 and 8 where we encountered a slush layer below the hard ice and could not core down to the ocean. The core bottom was determined at sites 7 and 8 to be the ice slush interface. Immediately upon extraction, holes that fit our thermistor probes were drilled every ten centimetres and a temperature profile was taken. Subsequently, slightly larger holes were drilled which fit our electrical probes (stainless steel nails). An AEMC Earth Resistivity Meter was then used to measure the resistance over 10 cm sections of the core (usually offset by 5 cm so that the measured temperature was in the centre of the section where electrical resistance was measured). \n\nThe cores used in resistance measurements were taken very close to where the crystallographic cores were taken. In almost all cases the cores extracted for electrical measurements were also used for crystallographic analysis, so that there was an exact match of electrical properties with crystal structure. In such cases the DC electrical cores were then moved to a -20 degree C cold room for further processing immediately after measurements in the field. \n\nA thin vertical section, approximately 3mm thick, was taken from each of the cores stored for analysis. These sections were placed between a pair of cross polarized plates and photographed. Each photo was labelled with the core and date it was taken, and was photographed with a meter stick alongside for scale.\n\nAfter the thin sections were photographed, the remaining samples were melted to measure salinity. Some of the melted sea ice was saved for later O18 analysis to distinguish samples containing snow ice from those containing marine granular ice. The temperature and salinities we are then used to calculate brine volume fractions along the 10 cm sections of the core. \n\nThe DC conductivity data collected can be found in the Electrical tab of the Master_Core_List.xls Excel file. The raw data can be found in the scans of our field note books located in the folder named notebooks. In the spread sheet the measured resistances of the 10 cm sections, temperatures, salinities and corresponding brine volume fractions are listed per core. \n\nFor each core the supporting crystallography core number can be found in the crystallography column of the spread sheet. The photos of the crystallography cores can be found in the crystallography folder, separated into subfolders labelled with the site and core number, Each photo also contains a tag indicating the core number , site taken , date, and what depth range this covers. Tags may not contain a depth range for cores less than 1 meter. Please see the meter stick in each photo for scale.", "links": [ { diff --git a/datasets/SIPEX_II_Ice_Floe_Survey_1.json b/datasets/SIPEX_II_Ice_Floe_Survey_1.json index 041f38c7ec..bc77b190ff 100644 --- a/datasets/SIPEX_II_Ice_Floe_Survey_1.json +++ b/datasets/SIPEX_II_Ice_Floe_Survey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_Floe_Survey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected to provide a spatial context for activities on SIPEX-2.\nPlease see the document 'SIPEX II ice floe surveying report' for more detail.\nFiles generated and stored in this dataset will be familiar to users of Trimble and Leica GPS equipment, and the UNAVCO 'teqc' utility. Please refer to the relevant documentation from Leica, Trimble and UNAVCO. \n\nTotal station data is extracted to comma separated point lists with either .csv of .asc extensions. The point code list is named 'totalstation.codelist.txt'. It also forms an appendix of the surveying report.", "links": [ { diff --git a/datasets/SIPEX_II_Ice_Snowpits_1.json b/datasets/SIPEX_II_Ice_Snowpits_1.json index c2b848850c..fa694d8cd2 100644 --- a/datasets/SIPEX_II_Ice_Snowpits_1.json +++ b/datasets/SIPEX_II_Ice_Snowpits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_Snowpits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains routine measurements of snow and ice thickness, and snow-ice interface temperature, at 1m intervals along standard transects; snow property characterisation in snow pits measured at 0m, 50m and 100m along the transects; and sea ice cores acquired at various locations both along the transects and elsewhere on ice station floes during the 2012 SIPEX 2 marine science voyage. \n\nIce temperature information is acquired from the cores, which are taken on-board for further analysis. The latter includes thin-section analysis of sea-ice stratigraphy and crystallography at -20C within the freezer lab on-board the ship. The cores are then cut up into 5cm sections and melted for analysis of salinity and stable oxygen isotopes.\n\nObservation items:\nSnow: \n- Thickness\n- Temperature profile (every 3 cm)\n- Snow-ice interface temperature at 1m intervals along the 100m transects\n- Grain size\n- Grain shape\n- Density\n- Hardness\n- Salinity\n- Stable oxygen isotope\nIce:\n- Thickness\n- Freeboard\n- Draft\n- Temperature\n- Salinity\n- Stable oxygen isotope\n- Crystallography and texture\n- Density\n\nInstruments:\n\nSnow: \nFolding scales, Spatula, Thermometer, Snow sampler, Magnifying glass, Salinometer, Temperature and thickness probes, scales\n\nIce:\nDrills, corers, ice-thickness tape measures, thermometer, salinometer, band-saw, cross-polarising filter, scales\n\nThe data are recorded in log books (scanned copies are included in this dataset) and have been transferred into the standard AAD sea-ice database templates (in excel format) for each station.", "links": [ { diff --git a/datasets/SIPEX_II_Ice_Station_CTD_1.json b/datasets/SIPEX_II_Ice_Station_CTD_1.json index 93a3402a79..b6be3e7c75 100644 --- a/datasets/SIPEX_II_Ice_Station_CTD_1.json +++ b/datasets/SIPEX_II_Ice_Station_CTD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_Station_CTD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We deployed CTD sensors on five of the SIPEX 2 ice stations for collecting temperature and salinity of the water column under the sea ice. \n\nThis dataset contains the raw data as outputted from the CTD in Excel format, in English.\n\nThe dates that the CTD were deployed are in the file names (i.e. 20121023 is October 23, 2012).", "links": [ { diff --git a/datasets/SIPEX_II_Ice_Station_EM_1.json b/datasets/SIPEX_II_Ice_Station_EM_1.json index 47bd3ce598..c84c297981 100644 --- a/datasets/SIPEX_II_Ice_Station_EM_1.json +++ b/datasets/SIPEX_II_Ice_Station_EM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ice_Station_EM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We observed total thickness (snow thickness + ice thickness) of sea-ice floes along 100m transects using an electromagnetic (EM) sensor.\n\nThe data were read from the EM and written by hand into a log book as we moved along the transect. They were then transferred into an Excel spreadsheet.\n\nThe parameters included are:\n- distance along transect\n- conductivity (vertical)\n- conductivity (horizontal)\n- total thickness (derived from vertical and horizontal conductivities)", "links": [ { diff --git a/datasets/SIPEX_II_Impurities_1.json b/datasets/SIPEX_II_Impurities_1.json index 47000fed8d..2c2726f6c9 100644 --- a/datasets/SIPEX_II_Impurities_1.json +++ b/datasets/SIPEX_II_Impurities_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Impurities_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Particulate impurities in snow such as dust and soot can absorb sunlight. This is important for snow albedo in the Arctic, but probably not in the Antarctic. Snow was collected in plastic bags through the full depth of the snowpack over first-year sea ice at several locations: one location each on Ice Stations 1, 2, 3, 4, 7, upwind of the ship. The snow was melted and the meltwater filtered. \n\nThe filters will be analysed for light-absorbing impurities in a laboratory spectrophotometer in Seattle. Samples of size ~1 kg were collected at Ice Station 1. The filters were blank, so at Ice Stations 2 and 3 larger samples of size 2-4 kg were collected. The filters were still blank, so at Ice Stations 4 and 7 larger samples of size ~8 kg were collected; these do show a slight darkening visible by eye. No snow samples were collected at Stations 5 and 6. \n\nLocal (ship) time is UTC+10; Sun time is UTC+8. Filters are labelled with prefix 'AO' for Antarctic Ocean.\n\nThis dataset currently contains snow density (except for at one ice station) and volumes of melt water that were filtered. No further analysis has been carried out on these samples at this point, however at a later stage the filters will be analysed for spectral absorption and converted to a mixing ratio of black carbon in the snow.", "links": [ { diff --git a/datasets/SIPEX_II_InSitu_Ice_Physics_1.json b/datasets/SIPEX_II_InSitu_Ice_Physics_1.json index 1658724cf5..a243f344e7 100644 --- a/datasets/SIPEX_II_InSitu_Ice_Physics_1.json +++ b/datasets/SIPEX_II_InSitu_Ice_Physics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_InSitu_Ice_Physics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in situ measurements of ice thickness, snow thickness, and freeboard along transects on the ice-station floes from the SIPEX2012. Ice cores were collected and snow pits were measured at the 0m, 50m and 100m mark along each transect, where possible. Ice temperature measurements are taken in the field as soon as the ice core sections have been recovered from the core hole. Additionally, ice cores were taken for density analysis at a few of the ice-core sites for independent verification of ice density. In addition, electromagnetic [EM] induction measurements of total ice and snow thickness were conducted along the transect where possible.\n\nIce core were transferred -20oC freezer for thin-section analysis for sea-ice stratigraphy and crystallography. The cores are then cut up into suitable short sections, generally about 5cm long, to be melted for analysis of salinity and stable oxygen isotopes. The latter will occur after the end of this cruise.\n\nThere is a data file for each ice station, containing a spreadsheet with the data. The spreadsheet contains information about how to interpret the data. \nAlso included are the scanned field notes containing the hand-written (raw) data collected in the field.\n\nAmong many, many volunteers, whose help is gratefully acknowledged here, the following persons were involved in data collection along the transect:\n\nMr Olivier Lecomte, Univ Catholique, Louvain-la-Neuve, Belgium, Member of observation team, olivier.lecomte@uclouvain.be\nDr T. Toyota, Inst Low Temp Science, Japan, Member of observation team, toyota@lowtem.hokudai.ac.jp\nDr A. Giles, ACE CRC, Member of observation team, barry.giles@utas.edu.au\nDr T. Tamura, NIPR, Japan, Member of EM observation team; tamura.takeshi@nipr.ac.jp\nMr K. Nakata, EES, Japan, Member of EM observation team; kazuki-nakata@ees.hokudai.ac.jp\n\nData were collected on the following dates:\nIce Station 2: 27 - 28 September 2012\nIce Station 3: 03 - 04 October 2012\nIce Station 4: 06 - 08 October 2012\nIce Station 6: 13 - 14 October 2012\nIce Station 7: 19 - 23 October 2012\nIce Station 8: 29 October - 04 November 2012", "links": [ { diff --git a/datasets/SIPEX_II_Iron_1.json b/datasets/SIPEX_II_Iron_1.json index d22c4a826c..d0e65045b4 100644 --- a/datasets/SIPEX_II_Iron_1.json +++ b/datasets/SIPEX_II_Iron_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Iron_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples for dissolved trace metal measurements were collected from the surface (15m) down to the 1000m using an autonomous intelligent rosette system (General Ocanics, USA) specially adapted for trace metal work and deployed on a Dyneema rope. The rosette was equipped with 12x10-L Niskin-1010X bottles specially modified for trace metal water sampling. This system has been successfully deployed on the RSV Aurora Australis during voyages au0703 and au0806. Care was taken to avoid any contamination from the ship and the operating personnel. Water samplers were processed aboard under an ISO class 5 trace-metal-clean laminar flow bench in to a trace-metal-clean laboratory container on the ship's trawl deck. All transfer tubes, filtering devices and sample containers were rinsed liberally with sample before final collection. Samples were then drawn through C-Flex tubing (Cole Parmer) and filtered in-line through 0.2 micron pore-size acid-washed capsules (Pall Supor membrane, Acropak 200). \n\nRegular sampling depths were as follows: 1000m, 750m, 500m, 300m, 200m, 150m, 125m, 100m, 75m, 50m, 30m, 15m. \n\nSamples were analysed within a minute of filtration. Iron(II) was detected with the luminol method combining the experimental set-up of Hansard et al. (2009) with the chemistry as described by Croot and Laan (2002). Samples were not acidified prior to analysis and were pumped directly into the flow cell without an injection valve. Care was taken to maintain a stable light field during measurements as the luminol reagent was found to be extremely sensitive to changes in light intensity.\n\nPhotons from the reaction of luminol with iron(II) were counted with a Hamamatsu photomultiplier tube housed in a light-tight box. The signal was recorded using FloZ software (GlobalFIA) and the data for each run is stored in a separate file. There is a folder for each profile that contains all the files (automatically generated by the software), which are numbered. The file numbers (e.g. sample1, sample2,...) correspond to the runs as noted in the lab book (see scans). \n\nP.L. Croot, P. Laan (2002). Analytica Chimica Acta 466: 261-273. \nS.P. Hansard et al. (2009). Deep-Sea Res. I 56: 1117-1129.", "links": [ { diff --git a/datasets/SIPEX_II_Krill_Camera_1.json b/datasets/SIPEX_II_Krill_Camera_1.json index 8b4c7e52af..f6eaa42a35 100644 --- a/datasets/SIPEX_II_Krill_Camera_1.json +++ b/datasets/SIPEX_II_Krill_Camera_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Krill_Camera_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The intention of the Deep Krill Camera and Trap System was to monitor and capture krill found during deep CTD operations.\n\nTwo traps were installed on the CTD in place of Niskin Bottles. At pre-determined depths an internal light was illuminated and the traps were opened. After a set period of time a second trigger signal was sent to the traps, closing the entry point, encapsulating any Krill that were inside.\n\nThe Krill Camera system was installed onto the CTD rosette. It consisted of a high-definition video camera (a GoPro Hero 2) within a pressure housing, flanked by two LED light sources. The power for this system was supplied via a rechargeable battery pack also mounted to the CTD.\n\nThe camera system was remotely controlled from the surface via the CTD communications link. At specific depths the lights and camera were activated, recording the water column and ocean floor by adjusting focus length for fixed durations in an attempt to document Krill at lower depths. \n\nAn additional camera was introduced into the system, mounted to allow video capture of the Krill Trap Operation. This camera was set to record at the beginning of the operations and left running for the duration of the deployment.\n\nVideo data from the Krill camera is in MTS format, which can be opened with VLC Media Player.\nTrap footage is recorded in MP4 format, which can be opened with Quicktime or VLC Media Player.\n\nTrap triggering and camera operation data was recorded manually by Rob King.", "links": [ { diff --git a/datasets/SIPEX_II_Mercury_Air_1.json b/datasets/SIPEX_II_Mercury_Air_1.json index e4f66a42c6..ca9eaf9b58 100644 --- a/datasets/SIPEX_II_Mercury_Air_1.json +++ b/datasets/SIPEX_II_Mercury_Air_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Mercury_Air_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Instrument description:\nGaseous elemental mercury (GEM) was measured at five minute intervals. GEM was collected and analysed on two parallel gold traps. While GEM was collected on one gold trap, the mercury on the other traps was simultaneously being thermally desorbed and detected by a cold vapour atomic fluorescence spectrometer. The Tekran was calibrated approximately every 24 - 48 hours using an internal Hg permeation source. The internal calibration source was checked prior to shipping the instrument to Australia using an external Hg source. The internal calibration source will be verified upon return of the instrument. \n\nInstrument Setup:\nThis instrument was sampling from a weather protected inlet positioned ~3 m off the front port side of the Monkey Deck of the Aurora Australis, directly above the bridge. The 35m heated Teflon sample line end and filter is contained within the \"Ned Kelly\", a large (~30 cm diameter) stainless steel can which protects against rain, snow, sea spray and major impacts. This sample line ran 25m down to the Tekran instrument which was located in a the Met-Lab. Ar (99.999% purity) was fed into the MetLab via quarter inch Teflon tubing from the oxygen store on the Monkey deck. \nA 2D R.M. Young (model 5305-AQ) anemometer was also deployed at the same elevation on the aft side of the sample inlet. The anemometer was oriented with zero degrees pointed directly forward of the ship. Mean Wind speed and direction were captured using Campbell Scientific CR1000 datalogger at five-minute intervals.\n\nThe files included in this dataset are the raw outputs from the Tekran 2537. They include headers, though not always at the top of the file, because headers are only written when the instrument is started or after recalibration. Also included are scanned log books containing meteorological observations, maintenance notes, and when adjustments were made to the sample line (which alters anemometer data).", "links": [ { diff --git a/datasets/SIPEX_II_Ozone_1.json b/datasets/SIPEX_II_Ozone_1.json index c137fcb004..99984afcfb 100644 --- a/datasets/SIPEX_II_Ozone_1.json +++ b/datasets/SIPEX_II_Ozone_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Ozone_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in-situ atmospheric ozone mixing ratios observed during SIPEX 2. \n\nOzone Monitor\nInstrument Description: Commercial dual cell ultraviolet ozone analyser: Thermoelectron Model 49C. Calibration to a traceable ozone standard prior to and after the voyage. Ozone loss in inlet and on filter quantified and negligible.\n\n\nInstrument Setup:\nThis instrument is sampling from its own Teflon sample air inlet secured to the front port side railing of the Monkey Deck. Air samples are drawn through a 30m quarter inch Teflon tube then through an inline particle filter before being entering the instrument located in the Met-Lab. Each week, a 30 minute instrument zero is performed by inserting an inline scrubber which catalyses ozone destruction. In the current position, wind from the aft of the ship will blow ship exhaust over the inlet, causing fluctuating low ozone values. Use the 2D anemometer and mercury measurements made on \"Ned Kelly\" in the mercury data file to filter for wind direction versus heading, also the mercury data itself is indicative of sampling ship emissions.\n\nThe files included are in csv format. Files are named as per the date they were created. Data continued to log to the most recent file until data collection stopped. There is a \"Long\" and a \"Normal\" file for each set. The \"Long\" contains instrument parameters logged every hour, and the \"Normal\" contains minute average ozone concentrations.", "links": [ { diff --git a/datasets/SIPEX_II_Passive_Microwave_1.json b/datasets/SIPEX_II_Passive_Microwave_1.json index 527c9cb99f..fd43e19644 100644 --- a/datasets/SIPEX_II_Passive_Microwave_1.json +++ b/datasets/SIPEX_II_Passive_Microwave_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Passive_Microwave_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains sea ice surface brightness temperatures using a portable passive-microwave radiometer operating at 36Ghz-H,V mounted to the undercarriage of a Squirrel helicopter during SIPEX 2, 2012.\n\nThis radiometer is the same sensor as satellite passive-microwave radiometer AMSR-E and AMSR2.\n\nOur passive-microwave radiometer is launched on the same helicopter as Jan Lieser's (RAPPAL), so please see the \"SIPEX-2 RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system)\" metadata file for details of the aircraft. The RAPPLS dataset also contains track (GPS position) and altitude data, which can be used in conjunction with this dataset.\n\nThe CSV files in this dataset are the raw files as output by the sensor. These raw data files show only the relevant parameters (time and brightness temperatures).", "links": [ { diff --git a/datasets/SIPEX_II_Sea_Ice_Draft_Maps_1.json b/datasets/SIPEX_II_Sea_Ice_Draft_Maps_1.json index 56d3546358..56083dfb6b 100644 --- a/datasets/SIPEX_II_Sea_Ice_Draft_Maps_1.json +++ b/datasets/SIPEX_II_Sea_Ice_Draft_Maps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Sea_Ice_Draft_Maps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected on the SIPEX II voyage of the Aurora Australis in 2012.\n\nThese data are floe-scale maps of Antarctic sea ice draft (m). These were collected using a multibeam instrument attached to an autonomous underwater vehicle (AUV). This AUV was the WHOI 'SeaBED-class' vehicle named 'Jaguar'. Details on the deployment and processing of this data can be found in Williams, Maksym and Wilkinson et al., 2014 (Nature Geoscience).\n\nData are provided for SIPEX-II stations 3, 4 and 6.\n\nStation 3: October 3 2012, located at 121.03E 64.95S\n\nStation 4: October 9 2012, located at 120.87E 65.13S\n\nStation 6: October 12 2012, located at 120.02E 65.25S\n\nData are provided on grids with 50cm horizontal spatial resolution. For each station, the mean and variance of the sea ice draft, along with the number of observations in each grid cell, are provided. Data are provided in ESRI ASCII grid format and comma-separated (CSV) text files. CSV files do not include grid cells with no observations.", "links": [ { diff --git a/datasets/SIPEX_II_Stable_Isotopes_1.json b/datasets/SIPEX_II_Stable_Isotopes_1.json index 0ac20f0585..73a21e59e3 100644 --- a/datasets/SIPEX_II_Stable_Isotopes_1.json +++ b/datasets/SIPEX_II_Stable_Isotopes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Stable_Isotopes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Overview of the project and objectives:\n\nTo investigate whether the Nitrogen - Silicon - Carbon biogeochemical system functions in the Antarctic Marginal Ice Zone and shows spatial variability possibly induced by varying availability of Fe and other parameters in the region. This toolbox is part of project 4051 - samples were taken (1) on the same sea-ice site or very close than the one used for Trace Metal sampling; (2) via Trace Metal Rosette TMR; (3) via Conductivity Temperature and Depth CTD Rosette. It is also part of project 4073 since some intercalibration studies were conducted in collaboration with the primary production team. \n\nThree main tools were used which can be either independently or intricately studied. For this reason the complete set of sampling done for this stable isotope toolbox is summarized in one excel file which is duplicated and attached to three child metadata records. Same reasoning for raw data acquired on boar and on field information. \n\nThis parent metadata record has thus three child metadata records. Each of the child metadata files explain individually the different approaches which were treated together by the same team to resolve the main question of sea-ice biogeochemical system functioning via the use of stable isotope ratio tools. The details of each are in the respective metadata records.\n\nThe data are attached to this metadata record.\n\nMETADATA FILES are:\n\n- 13C, 15N, 30Si in-situ incubation experiments during SIPEX 2\n- Nitrogen and oxygen isotopic composition of nitrate during SIPEX 2\n- Delta13C signal of brassicasterol and cholesterol in the Antarctic Sea-ice / Is there particulate barium in sea-ice?", "links": [ { diff --git a/datasets/SIPEX_II_Stable_Isotopes_Si_N_C_1.json b/datasets/SIPEX_II_Stable_Isotopes_Si_N_C_1.json index 89aec0ec49..555c102d49 100644 --- a/datasets/SIPEX_II_Stable_Isotopes_Si_N_C_1.json +++ b/datasets/SIPEX_II_Stable_Isotopes_Si_N_C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Stable_Isotopes_Si_N_C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Overview of the project and objectives:\n\nAssessing the contribution of the different N substrates to the primary production process, such as the biogenic silica production and dissolution in the Antarctic sea-ice provides a means to understand the biogeochemical system functioning. In such a semi closed-type system, assess incorporation rates of HCO3-, NO3-, NH4+, SiOH4, BSi dissolution, nitrification, C-release in close-by ice-cores (3 ice-cores dedicated to (i) 13C-assimilation + 15NH4+ uptake rate, (ii) 13C-assimilation + 15NO3- uptake rate and nitrification, (iii) Biogenic silica production and dissolution via 30Si isotope tool) will allow improving the knowledge of system functioning. This is also closely linked to the thematic of iron availability since these experiments are done close to / on the Trace Metal site allowing us to hopefully propose a relatively complete image of biogeochemical activity and relationship with trace metals on this site.\n\nMethodology and sampling strategy:\n\nMost of the time we worked close to / directly on the Trace Metal site following precautions concerning TM sampling (clean suits etc.). When we worked close to the TM site, precautions were not such important because we don't need the same drastic precautions for our own sampling. We work together because we want to propose a set of data which helps to characterize the system of functioning in close relation with TM availability (for that, sampling location have to be as close as possible). 14C and 13C-incubation experiment intercalibration work were conducted on the Biosite (different place than TM site except for station 7)\n\nIncubation experiment samples are analyzed via (1) Elemental Analyzer - Isotope Ratio Mass Spectrometer (EA-IRMS) for carbon and nitrogen (VUB, Brussels, Belgium); (2) High Resolution Inductively Coupled Mass Spectrometer (HR-ICPMS) for silicon (RMCA, Brussels, Belgium).", "links": [ { diff --git a/datasets/SIPEX_II_Stable_Isotopes_Sterols_1.json b/datasets/SIPEX_II_Stable_Isotopes_Sterols_1.json index 719c34fa37..5d24b5b48b 100644 --- a/datasets/SIPEX_II_Stable_Isotopes_Sterols_1.json +++ b/datasets/SIPEX_II_Stable_Isotopes_Sterols_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Stable_Isotopes_Sterols_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Overview of the project and objectives:\n\nSea-ice phytoplankton is significantly enriched in 13C (delta 13C-POC) compared to pelagic phytoplankton in adjacent open waters because of carbon limitation in the brine pockets and due to physiological properties such as the presence of Carbon Concentrating Mechanisms (CCM) and/or the uptake of bicarbonate (HCO3-). Melting of sea-ice with release of sea-ice phytoplankton occurs during the growth season, so these isotopically heavy particles, if sinking out of the surface waters, can be expected to be found deeper in the water column. One hypothesis is that the natural carbon isotopic signal of brassicasterol (phytosterol, mainly diatom indicator) in the south Antarctic Bottom Water (AABW), a water mass which is influenced by the Seasonal Ice Zone (SIZ), is enriched compared to northern deep waters signal due to an enhanced contribution of sea-ice diatoms. The objective of this dataset acquisition is to gain information on the delta 13C signal of brassicasterol in sea-ice diatoms and further estimate the contribution of sea-ice algae release in the Southern Ocean biological pump.\n\nIn the course of the expedition, a second choice has been done to look at the presence of particulate barium in the sea-ice. In the open ocean, presence of particulate barium in the mesopelagic layer is an indicator of remineralisation process. The main idea is that marine snow composed of detritical organic matter (aggregates, faecal pellets, etc.) provides micro-environment favorable for precipitation of excess Barium or Baxs (total particulate Ba minus the lithogenic part; mainly constituted of barite crystals, BaSO4): is there such Baxs components in the sea-ice? \n\nMethodology and sampling strategy:\n\nSampling strategy follows ice stations deployment via Bio ice-core type. Most of the time we worked close to / directly on the Trace Metal site following precautions concerning TM sampling (clean suits etc.). When we worked close to the TM site, precautions were not such important because we don't need the same drastic precautions for our own sampling. We work together because we want to propose a set of data which helps to characterize the system of functioning in close relation with TM availability (for that, sampling location have to be as close as possible). Ice melted from ice-core sections (see attached files for more details) is filtered on precombusted GF-F filters (0.7 microns porosity) and filters are stored at -20 degrees C. For particulate Barium sampling, same protocol but filtration on PC filters 0.4 microns, dry over night and store at ambient temperature.\n\nAt home laboratory (VUB, Brussels, Belgium), sterols samples are analysed via Gas Chromatography - Mass Spectrometer (GC-MS) and Gas Chromatography-combustion column-Isotope Ratio Mass Spectrometer (GC-c-IRMS) after chemical treatment. Barium sample are analysed via Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES).", "links": [ { diff --git a/datasets/SIPEX_II_Trajectories_1.json b/datasets/SIPEX_II_Trajectories_1.json index 225f22d2d3..cd01869447 100644 --- a/datasets/SIPEX_II_Trajectories_1.json +++ b/datasets/SIPEX_II_Trajectories_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Trajectories_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The current data set contains:\nHysplit back-trajectories and IDL reader\n\nTrajectories were launched from the SIPEX II ship location every hour at 10m, 500m, 1000m, 1500m, 2000m, 2500m, 3000m, 3500m, 4000m. Three different meteorological reanalyses datasets (ECMWF, GDAS and NCEP were used to generate these 10 day air parcel back-trajectories.", "links": [ { diff --git a/datasets/SIPEX_II_Transmissometer_1.json b/datasets/SIPEX_II_Transmissometer_1.json index 19e8a55a99..1c43df581a 100644 --- a/datasets/SIPEX_II_Transmissometer_1.json +++ b/datasets/SIPEX_II_Transmissometer_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_II_Transmissometer_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Ffilter samples of known volume of sea water for \n\n- PIC (Particulate Inorganic Carbon)\n- POC (Particulate Organic Carbon)\n- BGSi (BioGenic Silicon)\n\nThe dataset also contains transmissometer data.\n\nThe transmissometer is an attempt at developing a correlation between the PIC filter samples and the transmissometer readings. This is development of methods.\n\nThe data collection times are logged in the file and filter log sheets.", "links": [ { diff --git a/datasets/SIPEX_IceSampling_1.json b/datasets/SIPEX_IceSampling_1.json index a3811e8ef0..d7e91eb5c1 100644 --- a/datasets/SIPEX_IceSampling_1.json +++ b/datasets/SIPEX_IceSampling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_IceSampling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ice samples were collected by means of ice coring with 9 and 13 cm diameter ice corers on a total of 15 stations in the 115-130 degrees E sector off East Antarctica in September and early October 2007 during the Sea Ice Physics and Ecosystem eXperiment (SIPEX).\n\nIce temperature profiles were recorded for one core at each station. Ice cores were cut into 10 cm sections and analysed for ice texture, delta 18O composition, inorganic nutrients (phosphate, silicate, nitrate, nitrite, ammonium), dissolved organic carbon/nitrogen, absorbance spectra of coloured dissolved organic matter (CDOM), particulate organic carbon/nitrogen, persistent organic pollutants (POP), chlorophyll a and pheopigment concentration, photosynthetic parameters measured with a Fast Rate Repetition Fluorometer (FRRF) and a Pulse Amplitude Modulated Fluorometer (PAM) as well as algal species composition and biomass and species composition and biomass estimates of ice-associated metazoans.\n\nIce texture analysis from ice cores taken at the \"biology main site\" was carried out onboard. On the main sites we collected in total 11.8 m of ice cores for textural, salinity and O18 analysis. 28 % of the collected ice was congelation ice while the remaining was largely frazil ice (except for some possible snow ice layers that will be detected later using O18 data). Bulk salinity of the ice cores (n=13) ranged from 4.1 to 9.4, with an average of 6.1. A second, trace metal clean, coring site, was sampled for iron-biogeochemical work.", "links": [ { diff --git a/datasets/SIPEX_LiDAR_sea_ice_1.json b/datasets/SIPEX_LiDAR_sea_ice_1.json index a0d2351697..fd65cdd2d6 100644 --- a/datasets/SIPEX_LiDAR_sea_ice_1.json +++ b/datasets/SIPEX_LiDAR_sea_ice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_LiDAR_sea_ice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the airborne scanning LiDAR of a suite of different instruments deployed during the Sea Ice Physics and Ecosystems eXperiment (SIPEX) in 2007. Surveys have been flown over sea ice between 110-130\ndegrees E as part of the Australian Antarctic science project 2901.\n\nPublic Summary for project 2901\nThis research will contribute to a large multi-disciplinary study of the physics and biology of the Antarctic sea ice zone in early Spring 2007. The physical characteristics of the sea ice will be directly measured using satellite-tracked drifting buoys, ice core analysis and drilled measurements, with detailed measurements of snow cover thickness and properties. Aircraft-based instrumentation will be used to expand our survey area beyond the ship's track and for remote sampling. The data collected will provide valuable ground-truthing for existing and future satellite missions and improve our understanding of the role of sea ice in the climate system.\n\nProject objectives:\n(i) to quantify the spatial variability in sea ice and snow cover properties over scales of metres to hundreds of kilometres in the region of 110-130 degrees E, in order to improve the accuracy of sea ice thickness estimates from satellite altimetry and polarimetric synthetic aperture radar (SAR) data.\n(ii) To determine the drift characteristics, and internal stress, of sea ice in the region 110-130 degrees E.\n(iii) To investigate the relationships between the physical sea ice environment and the structure of Southern Ocean ecosystems (joint with AAS Proposal 2767).", "links": [ { diff --git a/datasets/SIPEX_Ocean_1.json b/datasets/SIPEX_Ocean_1.json index f181d124ef..6b29ca059a 100644 --- a/datasets/SIPEX_Ocean_1.json +++ b/datasets/SIPEX_Ocean_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_Ocean_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We report on the late winter oceanography observed beneath Antarctic sea ice offshore from the Sabrina and BANZARE coast of Wilkes Land, East Antarctica (115- 125 E) in September-October 2007 during the Sea Ice Physics and Ecosystem eXperiment (SIPEX) research voyage. A pilot program using specifically designed 'through-ice' Conductivity-Temperature-Depth (CTD) and acoustic Doppler current profiling (ADCP) systems was conducted to opportunistically measure water mass properties and ocean currents during major ice stations.\n\nThis project involved two independent sub-ice observation platforms: A winch-driven Conductivity-Temperature-Depth system for measuring basic water mass properties and an acoustic Doppler current profiling (ADCP)/GPS system for measuring ocean currents and ice drift. Hereafter these are referred to as the CTD and ADCP systems respectively.\n\nThe CTD system comprised of an Falmouth Scientific Institute (FSI) CTD instrument, a tripod and over 1000m of polyethylene rope on a winch/drum\nattached to a metal sled.", "links": [ { diff --git a/datasets/SIPEX_krill_1.json b/datasets/SIPEX_krill_1.json index a2e0b52190..ec21f6d616 100644 --- a/datasets/SIPEX_krill_1.json +++ b/datasets/SIPEX_krill_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIPEX_krill_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This work was completed as part of the SIPEX - Sea Ice Physics and Ecosystem eXperiment - voyage. September/October 2007. The work formed part of AAS (ASAC) projects 2337 and 2767.\n\nAspects of krill (Euphausia superba), growth and condition during late winter-early spring off East Antarctica (110 - 130 degrees E) were investigated. We assessed diet and condition of larval and postlarval krill collected from open water and below the ice. Condition was assessed using lipid content, growth rates and digestive gland size; feeding history was assessed using fatty acid profiles and stomach content analysis; and a starvation study investigated the response of krill to long-term food deprivation. Potential food items were analysed for lipid and fatty acid composition. Fatty acid profiles and stomach content analysis revealed winter/early spring feeding strategies of both larval and adult krill.\n\nThis work was completed as part of AAS (ASAC) project #2337", "links": [ { diff --git a/datasets/SIR-C_PRECISION.json b/datasets/SIR-C_PRECISION.json index f8b4cfaf3a..2f2c44fd3a 100644 --- a/datasets/SIR-C_PRECISION.json +++ b/datasets/SIR-C_PRECISION.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIR-C_PRECISION", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) is a joint project of the National Aeronautics and Space Administration (NASA), the German Space Agency, Deutsche Agentur fur Raumfahrtangfelegenheiten (DARA), and the Italian Space Agency, Agenzia Spaziale Italiana (ASI). An imaging radar system launched aboard the NASA Space Shuttle twice in 1994, SIR-C/X-SAR's unique contributions to Earth observation and monitoring are its capability to measure, from space, the radar signature of the surface at three different wavelengths and to make measurements for different polarizations at two of those wavelengths. The SIR-C image data help scientists understand the physics behind some of the phenomena seen in radar images at just one wavelength/polarization, such as those produced by SeaSAT. Investigators on the SIR-C/X-SAR Science team use the radar image data to make measurements of vegetation type, extent and deforestation, soil moisture content, ocean dynamics, wave and surface wind speeds and directions, volcanism and tectonic activity, and soil erosion and desertification. The SIR-C provides multi-frequency, multi-polarization radar data.The SIR-C instrument is composed of several subsystems: an antenna array, a transmitter, receivers, a data-handling subsystem, and a ground SAR processor. The data are processed into images with selectable resolution from 10 to 200 meters. The width of the area mapped by the radar varies from 15 to 90 kilometers, depending on how the radar is operated and on the direction in which the antenna beams are pointing. Data from SIR-C/X-SAR are used to develop automatic techniques for extracting information from radar image data.\n", "links": [ { diff --git a/datasets/SIRSN3L1_001.json b/datasets/SIRSN3L1_001.json index e78d02e056..5da2bbf1c9 100644 --- a/datasets/SIRSN3L1_001.json +++ b/datasets/SIRSN3L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIRSN3L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SIRSN3L1 is the Nimbus-3 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day.\n\nThe Nimbus-3 SIRS only viewed the nadir of the subsatellite track. Spatial coverage is near global from about latitude -80 to +80 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A).", "links": [ { diff --git a/datasets/SIRSN4L1_001.json b/datasets/SIRSN4L1_001.json index ce45057e13..e659758b3b 100644 --- a/datasets/SIRSN4L1_001.json +++ b/datasets/SIRSN4L1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIRSN4L1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SIRSN4L1 is the Nimbus-4 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day.\n\nThe Nimbus-4 SIRS used a scan mirror to observe 12.5 deg to either side of the subsatellite track. Spatial coverage is near global from latitude -85 to +85 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A).", "links": [ { diff --git a/datasets/SISTER_Workflow_V004_2335_4.json b/datasets/SISTER_Workflow_V004_2335_4.json index e9d0ce8ba4..631223170b 100644 --- a/datasets/SISTER_Workflow_V004_2335_4.json +++ b/datasets/SISTER_Workflow_V004_2335_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SISTER_Workflow_V004_2335_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission.", "links": [ { diff --git a/datasets/SIZEX-89-SAR.json b/datasets/SIZEX-89-SAR.json index b77397fbc1..dbe5de18f8 100644 --- a/datasets/SIZEX-89-SAR.json +++ b/datasets/SIZEX-89-SAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SIZEX-89-SAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction.", "links": [ { diff --git a/datasets/SLAR.json b/datasets/SLAR.json index cd9744c67c..abcc2f8dd9 100644 --- a/datasets/SLAR.json +++ b/datasets/SLAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SLAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Side-Looking Airborne Radar (SLAR) imagery is available from the U.S. Geological Survey (USGS) for selected project areas in the conterminous United States and Alaska. Data are X-band synthetic aperture radar (horizontally transmitted, horizontally received) with the exception of some test sites. Coverage was contracted on a yearly basis. \n\nThe USGS SLAR images most often consist of contact strip images and 1:250,000-scale, map-controlled mosaics. Greater than half of the available SLAR image strips are distributed on 8-mm cassettes, while some image strips are distributed on CD-ROM. In addition, ancillary products such as indexes (on paper, film, or microfiche) and custom photographic products may also be available. Due to the geographically non-searchable nature of the SLAR inventory, customer assistance may be obtained to determine availability of SLAR data over the user's area of interest. Customer knowledge of USGS 1:250,000-scale map names is beneficial in expediting orders. A scale of 1:50,000 only applies to Alaska coverage.\n", "links": [ { diff --git a/datasets/SLOPE_GPP_CONUS_1786_1.json b/datasets/SLOPE_GPP_CONUS_1786_1.json index 05da182c81..485e40dbbb 100644 --- a/datasets/SLOPE_GPP_CONUS_1786_1.json +++ b/datasets/SLOPE_GPP_CONUS_1786_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SLOPE_GPP_CONUS_1786_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1.", "links": [ { diff --git a/datasets/SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0.json b/datasets/SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0.json index 798e5c7359..f178781075 100644 --- a/datasets/SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0.json +++ b/datasets/SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.

\r\n\r\nThe dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5) CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval.

\r\n\r\nThis JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.", "links": [ { diff --git a/datasets/SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0.json b/datasets/SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0.json index 1180e50158..6c0f4339ac 100644 --- a/datasets/SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0.json +++ b/datasets/SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the PI-produced JPL SMAP-SSS V5.0, level 2B NRT CAP, validated sea surface salinity (SSS) and extreme winds orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory available in near real-time with a latency of about 6 hours. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. The JPL SMAP-SSS L2B CAP NRT product includes data for a range of parameters: derived SMAP sea surface salinity, SSS uncertainty and wind speed/direction data for extreme winds, brightness temperatures for each radiometer polarization, ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day). Data begins on April 1,2015 and is ongoing, with a 6 hour latency in processing and availability. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_JPL_L2B_SSS_CAP_V5_5.0.json b/datasets/SMAP_JPL_L2B_SSS_CAP_V5_5.0.json index 3fc9c47f52..47f2239abb 100644 --- a/datasets/SMAP_JPL_L2B_SSS_CAP_V5_5.0.json +++ b/datasets/SMAP_JPL_L2B_SSS_CAP_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_JPL_L2B_SSS_CAP_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the PI-produced JPL SMAP-SSS V5.0, level 2B CAP, validated sea surface salinity (SSS) and extreme winds orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. The JPL SMAP-SSS L2B CAP product includes data for a range of parameters: derived SMAP sea surface salinity, SSS uncertainty and wind speed/direction data for extreme winds, brightness temperatures for each radiometer polarization, ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day). Data begins on April 1,2015 and is ongoing, with a 3 day latency in processing and availability. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5_5.0.json b/datasets/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5_5.0.json index f7a252a9b1..594f790e5b 100644 --- a/datasets/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the PI-produced JPL SMAP-SSS V5.0 CAP, 8-day running mean, level 3 mapped, sea surface salinity (SSS) product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. Daily data files for this L3 product are based on SSS averages spanning an 8-day moving time window. Associated file variables include: derived SSS with associated uncertainties and wind speed data from SMAP, ancillary ice concentration and HYCOM surface salinity data. SMAP data begins on April 1, 2015 and is ongoing, with a 7-day latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with an approximate spatial resolution of 60km. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_JPL_L3_SSS_CAP_MONTHLY_V5_5.0.json b/datasets/SMAP_JPL_L3_SSS_CAP_MONTHLY_V5_5.0.json index 222b8e97f2..1ff0d77eef 100644 --- a/datasets/SMAP_JPL_L3_SSS_CAP_MONTHLY_V5_5.0.json +++ b/datasets/SMAP_JPL_L3_SSS_CAP_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_JPL_L3_SSS_CAP_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the PI-produced JPL SMAP-SSS V5.0 CAP, level 3, monthly mapped sea surface salinity (SSS) product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. L3 monthly product file variables include: derived SSS with associated uncertainties and wind speed from SMAP and ancillary surface salinity from HYCOM. SMAP data begins on April 1, 2015 and is ongoing, with a 1 month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with an approximate spatial resolution of 60km. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_L1_L3_ANC_GEOS_1.json b/datasets/SMAP_L1_L3_ANC_GEOS_1.json index 712d20b58d..d627ea7c10 100644 --- a/datasets/SMAP_L1_L3_ANC_GEOS_1.json +++ b/datasets/SMAP_L1_L3_ANC_GEOS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L1_L3_ANC_GEOS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.", "links": [ { diff --git a/datasets/SMAP_L1_L3_ANC_NOAA_1.json b/datasets/SMAP_L1_L3_ANC_NOAA_1.json index 45406e4078..cfbb1ea772 100644 --- a/datasets/SMAP_L1_L3_ANC_NOAA_1.json +++ b/datasets/SMAP_L1_L3_ANC_NOAA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L1_L3_ANC_NOAA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains six dynamic data sets originally produced by NOAA or NOAA-affiliated organizations. \n1) NCEP Geophysical Forecast System modeled data provided in 6-hour time steps \n2) Daily Reynolds Sea Surface Temperature data \n3) Snow Cover data from NOAA Interactive Multisensor Snow and Ice Mapping System \n4) NOAA Solar Radio Flux \n5) GPS-derived total electron content used to compute the Faraday rotation correction for the SMAP radar \n6) Instantaneous wave height measures", "links": [ { diff --git a/datasets/SMAP_L1_L3_ANC_SATELLITE_1.json b/datasets/SMAP_L1_L3_ANC_SATELLITE_1.json index d7e5ecd59a..0ee94aa2e9 100644 --- a/datasets/SMAP_L1_L3_ANC_SATELLITE_1.json +++ b/datasets/SMAP_L1_L3_ANC_SATELLITE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L1_L3_ANC_SATELLITE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains two dynamic data sets describing 1) the attitude and 2) the trajectory of the SMAP satellite. The data files are generated using quaternions from the SMAP spacecraft and inputs from earth receiving stations, respectively.", "links": [ { diff --git a/datasets/SMAP_L1_L3_ANC_STATIC_1.json b/datasets/SMAP_L1_L3_ANC_STATIC_1.json index 87af44999a..5fc8d576d1 100644 --- a/datasets/SMAP_L1_L3_ANC_STATIC_1.json +++ b/datasets/SMAP_L1_L3_ANC_STATIC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L1_L3_ANC_STATIC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains more than 50 data sets. These data sets contain the inputs necessary to create SMAP products from raw instrument counts, such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, soils information (primarily sand and clay fraction), vegetation parameters, and surface roughness parameters.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_BPLUT_1.json b/datasets/SMAP_L4_C_ANC_BPLUT_1.json index 1379f1b044..58b0504dcb 100644 --- a/datasets/SMAP_L4_C_ANC_BPLUT_1.json +++ b/datasets/SMAP_L4_C_ANC_BPLUT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_BPLUT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains biophysical characteristics (biome parameters) used to estimate carbon fluxes and soil organic carbon in the SMAP L4 Carbon algorithm. Biophysical characteristics were established from previous studies and the parameters defined for the MODIS MOD17 operation GPP algorithm. This data set was refined through regional and global comparisons and calibration of prototype SMAP L4 Carbon calculations.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json b/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json index 5dbdb796ca..1fb3e12049 100644 --- a/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json +++ b/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_FPAR_CLIM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains a static climatology data set. The climatology data is derived from MODIS Fractional Photosynthetically Active Radiation (FPAR) models and represents a global 8-day average.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MDL_LOG_1.json b/datasets/SMAP_L4_C_ANC_MDL_LOG_1.json index c570c8ff98..b6e035c817 100644 --- a/datasets/SMAP_L4_C_ANC_MDL_LOG_1.json +++ b/datasets/SMAP_L4_C_ANC_MDL_LOG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MDL_LOG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains SMAP L4 Carbon model log files, including model outputs.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MDL_RIP_1.json b/datasets/SMAP_L4_C_ANC_MDL_RIP_1.json index c45eacd73f..461e838d20 100644 --- a/datasets/SMAP_L4_C_ANC_MDL_RIP_1.json +++ b/datasets/SMAP_L4_C_ANC_MDL_RIP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MDL_RIP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MET_1.json b/datasets/SMAP_L4_C_ANC_MET_1.json index f43b67528f..20c500107d 100644 --- a/datasets/SMAP_L4_C_ANC_MET_1.json +++ b/datasets/SMAP_L4_C_ANC_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains dynamic surface meteorological forcing data. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm. The forcing data is processed from hourly GEOS-5 files into daily values. Daily files are processed every eight days.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MET_LOG_1.json b/datasets/SMAP_L4_C_ANC_MET_LOG_1.json index bf79e41825..c059d3cb7b 100644 --- a/datasets/SMAP_L4_C_ANC_MET_LOG_1.json +++ b/datasets/SMAP_L4_C_ANC_MET_LOG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MET_LOG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MET_RIP_1.json b/datasets/SMAP_L4_C_ANC_MET_RIP_1.json index 4cf37df3e2..68631fe17b 100644 --- a/datasets/SMAP_L4_C_ANC_MET_RIP_1.json +++ b/datasets/SMAP_L4_C_ANC_MET_RIP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MET_RIP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains meteorological model configurations, including model inputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MOD_LOG_1.json b/datasets/SMAP_L4_C_ANC_MOD_LOG_1.json index 9161de7cec..597752e7d0 100644 --- a/datasets/SMAP_L4_C_ANC_MOD_LOG_1.json +++ b/datasets/SMAP_L4_C_ANC_MOD_LOG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MOD_LOG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model log files, including model outputs.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_MOD_RIP_1.json b/datasets/SMAP_L4_C_ANC_MOD_RIP_1.json index 2c218c02b7..96372a2e2e 100644 --- a/datasets/SMAP_L4_C_ANC_MOD_RIP_1.json +++ b/datasets/SMAP_L4_C_ANC_MOD_RIP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_MOD_RIP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model configurations, including model inputs.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_PARAM_1.json b/datasets/SMAP_L4_C_ANC_PARAM_1.json index 7f8388ec2f..5bca001039 100644 --- a/datasets/SMAP_L4_C_ANC_PARAM_1.json +++ b/datasets/SMAP_L4_C_ANC_PARAM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_PARAM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains assorted static ancillary parameters, such as reference grids and land cover classifications, also referred to as Plant Function Type (PFT) maps.", "links": [ { diff --git a/datasets/SMAP_L4_C_ANC_SOC_RST_1.json b/datasets/SMAP_L4_C_ANC_SOC_RST_1.json index 357ee61f1d..dd9e954758 100644 --- a/datasets/SMAP_L4_C_ANC_SOC_RST_1.json +++ b/datasets/SMAP_L4_C_ANC_SOC_RST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_C_ANC_SOC_RST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains the yearly soil organic carbon (SOC) restart file. This file contains the area density of SOC at the start of the year, which is used to calculate daily SOC based on defined deposition and decay rates.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_CAT_TILE_1.json b/datasets/SMAP_L4_SM_ANC_CAT_TILE_1.json index bd2760ba54..3de3e4ee07 100644 --- a/datasets/SMAP_L4_SM_ANC_CAT_TILE_1.json +++ b/datasets/SMAP_L4_SM_ANC_CAT_TILE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_CAT_TILE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains tile information for the NASA Land Data Assimilation System (LDAS) Catchment model, including center-of-mass latitude/longitude, minimum/maximum latitude/longitude, and the land area fraction of tiles.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_CLIM_1.json b/datasets/SMAP_L4_SM_ANC_CLIM_1.json index 62bec52b2b..5953e71890 100644 --- a/datasets/SMAP_L4_SM_ANC_CLIM_1.json +++ b/datasets/SMAP_L4_SM_ANC_CLIM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_CLIM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains a static soil moisture climatology data set. Specifically, this data set includes root zone and profile soil moisture climatology files for percentile conversion and post-processing of Land Data Assimilation Systems (LDAS) output.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_LOG_1.json b/datasets/SMAP_L4_SM_ANC_LOG_1.json index eeec48dfdc..0034708dc5 100644 --- a/datasets/SMAP_L4_SM_ANC_LOG_1.json +++ b/datasets/SMAP_L4_SM_ANC_LOG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_LOG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product includes Land Data Assimilation Systems (LDAS) Catchment model log files, including model outputs.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_PARAM_1.json b/datasets/SMAP_L4_SM_ANC_PARAM_1.json index a55814e3d3..9aeeb2a5c4 100644 --- a/datasets/SMAP_L4_SM_ANC_PARAM_1.json +++ b/datasets/SMAP_L4_SM_ANC_PARAM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_PARAM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains three dynamic Land Data Assimilation Systems (LDAS) data sets. These data sets include Brightness Temperature (TB) scaling parameters; catchment model parameters such as topographic statistics, soil texture, and soil hydraulic parameters; and LDAS L-band microwave radiative transfer model parameters.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_RIP_1.json b/datasets/SMAP_L4_SM_ANC_RIP_1.json index 6f252cd9d7..f0d3faada6 100644 --- a/datasets/SMAP_L4_SM_ANC_RIP_1.json +++ b/datasets/SMAP_L4_SM_ANC_RIP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_RIP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains Land Data Assimilation Systems (LDAS) model configurations, including model inputs.", "links": [ { diff --git a/datasets/SMAP_L4_SM_ANC_RST_1.json b/datasets/SMAP_L4_SM_ANC_RST_1.json index 2dfff782cb..c6bcc6311a 100644 --- a/datasets/SMAP_L4_SM_ANC_RST_1.json +++ b/datasets/SMAP_L4_SM_ANC_RST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_L4_SM_ANC_RST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ancillary SMAP product contains static restart files for the Land Data Assimilation Systems (LDAS) Catchment model. This product includes prognostic variables for both the catchment model and perturbations model.", "links": [ { diff --git a/datasets/SMAP_RSS_L2_SSS_NRT_V5_5.0.json b/datasets/SMAP_RSS_L2_SSS_NRT_V5_5.0.json index 3cfd061322..f40d552a98 100644 --- a/datasets/SMAP_RSS_L2_SSS_NRT_V5_5.0.json +++ b/datasets/SMAP_RSS_L2_SSS_NRT_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L2_SSS_NRT_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMAP-SSS level 2C near real-time (NRT) V5.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle. \r\n

\r\nThe dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. Data from July 28, 2022 to present, is available with a latency of about 6 hours. The observations are global, provided on a 0.25° fixed Earth grid with an approximate spatial resolution of 70 km. The major differences to the standard version 5.0 data products are: (1) the NRT version of the L1B SMAP antenna temperatures is used, (2) the latest 6-hourly 0.25° wind speed and direction are used for the ancillary wind speed and direction input, (3) the CMC SST from 2 days earlier is used for the ancillary SST input, (4) the sea-ice mask of the 3-day aggregate RSS AMSR-2 Air-Sea Essential Climate Variables (AS-ECV) data set from 2-days earlier is used for the sea-ice flag, (5) no correction for sea-ice contamination is performed, it is recommended to use only SMAP data that are classified to be within sea-ice zone 0 for open ocean scene and no sea-ice contamination.\r\n

\r\nThis RSS SMAP-SSS V5.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.", "links": [ { diff --git a/datasets/SMAP_RSS_L2_SSS_NRT_V6_6.0.json b/datasets/SMAP_RSS_L2_SSS_NRT_V6_6.0.json index 8263b8ab98..84a7798b00 100644 --- a/datasets/SMAP_RSS_L2_SSS_NRT_V6_6.0.json +++ b/datasets/SMAP_RSS_L2_SSS_NRT_V6_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L2_SSS_NRT_V6_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SMAP-SSS level 2C near real-time (NRT) V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle. \r\n

\r\nThe dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. The observations are global, provided on a 0.25° fixed Earth grid with an approximate spatial resolution of 70 km. The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 5 hours l\r\natency.\r\n

\r\nThis RSS SMAP-SSS V6.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.", "links": [ { diff --git a/datasets/SMAP_RSS_L2_SSS_V6_6.0.json b/datasets/SMAP_RSS_L2_SSS_V6_6.0.json index a7b320f63c..f96f1f45e1 100644 --- a/datasets/SMAP_RSS_L2_SSS_V6_6.0.json +++ b/datasets/SMAP_RSS_L2_SSS_V6_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L2_SSS_V6_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SMAP level 2C sea surface salinity V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015\r\nwith a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.\r\n

\r\nThe major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag.\r\nThe SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. \r\nEach data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 4 days l\r\natency.\r\nData begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json index 895929c253..70521cf930 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 4.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json index 05b3ab6b0e..4e0559024f 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015\r\nwith a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.\r\n

\r\nThe evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower\r\nTA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format.\r\nObservations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0.json index 8fe7508378..ec1abfd7a4 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 5.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fifth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json index 800b724cc5..e75b0aefea 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015\r\nwith a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.\r\n

\r\nThe major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag.\r\nThe RSS SMAP 8-Day running mean product is based on SSS averages spanning an 8-day moving time window, it includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. \r\nEach data file is available in netCDF-4 file format with about 7-day latency (after the end of the averaging period).\r\nData begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json index e078fd9f7b..31b6e66449 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 4.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3.json b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3.json index 0039d1cd88..58c2cd1138 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.\r\n

\r\nThe evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0.json index b6b67b63d2..5b9674bc4b 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The version 5.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.", "links": [ { diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0.json index e21545633f..800458ccde 100644 --- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0.json +++ b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.\r\n

\r\nThe major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.", "links": [ { diff --git a/datasets/SMERGE_RZSM0_40CM_2.0.json b/datasets/SMERGE_RZSM0_40CM_2.0.json index 49587271e2..656f7a2c07 100644 --- a/datasets/SMERGE_RZSM0_40CM_2.0.json +++ b/datasets/SMERGE_RZSM0_40CM_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMERGE_RZSM0_40CM_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative. The data have a 0.125 degree resolution at a daily time-step, covering the entire continental United States and spanning nearly four decades (January 1979 to May 2019).\n\nThis data product contains root-zone soil moisture of 0 - 40 cm layer, Climate Change Initiative (CCI) derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag.\n\nThis data product is the recommended replacement for the AMSR-E/Aqua root zone soil moisture L3 1 day 25 km x 25 km descending and 2-Layer Palmer Water Balance Model V001 product (LPRM_AMSRE_D_RZSM3), which will be removed from archive on June 27, 2022. Smerge provides a better root zone soil moisture estimation because it has higher data quality and longer temporal coverage. \n\n", "links": [ { diff --git a/datasets/SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0.json b/datasets/SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0.json index 61364c95a1..435077dd90 100644 --- a/datasets/SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0.json +++ b/datasets/SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Icebreaker Oden\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX).", "links": [ { diff --git a/datasets/SMHI_IPY_ACEX-2004-Seismic.json b/datasets/SMHI_IPY_ACEX-2004-Seismic.json index 86b08a6b13..5ef747fd7e 100644 --- a/datasets/SMHI_IPY_ACEX-2004-Seismic.json +++ b/datasets/SMHI_IPY_ACEX-2004-Seismic.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMHI_IPY_ACEX-2004-Seismic", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX).", "links": [ { diff --git a/datasets/SMHI_IPY_ACEX-2004-Sites_1.0.json b/datasets/SMHI_IPY_ACEX-2004-Sites_1.0.json index 8cebbe6733..0df2000d6f 100644 --- a/datasets/SMHI_IPY_ACEX-2004-Sites_1.0.json +++ b/datasets/SMHI_IPY_ACEX-2004-Sites_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMHI_IPY_ACEX-2004-Sites_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX).", "links": [ { diff --git a/datasets/SMHI_IPY_AGAVE2007-track_1.0.json b/datasets/SMHI_IPY_AGAVE2007-track_1.0.json index a3ee546a29..1e55b4375c 100644 --- a/datasets/SMHI_IPY_AGAVE2007-track_1.0.json +++ b/datasets/SMHI_IPY_AGAVE2007-track_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMHI_IPY_AGAVE2007-track_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Icebreaker Oden\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007.", "links": [ { diff --git a/datasets/SMHI_IPY_ALIS.json b/datasets/SMHI_IPY_ALIS.json index 93f75bde4c..63943cccde 100644 --- a/datasets/SMHI_IPY_ALIS.json +++ b/datasets/SMHI_IPY_ALIS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMHI_IPY_ALIS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50\u00c3\u009750 km. Each station is equipped with an imager having a high-resolution monochrome 1024\u00c3\u00971024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable.", "links": [ { diff --git a/datasets/SMMRN7IM_001.json b/datasets/SMMRN7IM_001.json index d5cb61df45..cadceb4735 100644 --- a/datasets/SMMRN7IM_001.json +++ b/datasets/SMMRN7IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMMRN7IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17\" x 15\" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection. Sea ice fraction, sea ice and ocean surface temperature, sea ice concentration, including brightness temperature parameters are available as both 3-day and 1-month averages in north and south polar stereographic projections. Images may contain between one and three measured parameters. These SMMR images are available from 30 October 1978 through 2 November 1983. The principal investigator for the SMMR experiment was Dr. Per Gloersen from NASA GSFC.\n \nThese products were previously available from the NSSDC under the ids ESAD-00007, ESAD-00056, ESAD-00123, ESAD-00124, ESAD-00162, ESAD-00172, ESAD-00173, ESAD-00176 ESAD-00177, ESAD-00178, and ESAD-00241 (old ids 78-098A-08I-S).", "links": [ { diff --git a/datasets/SMMR_ALW_PRABHAKARA_1.json b/datasets/SMMR_ALW_PRABHAKARA_1.json index 02bfba9bea..2bed974db2 100644 --- a/datasets/SMMR_ALW_PRABHAKARA_1.json +++ b/datasets/SMMR_ALW_PRABHAKARA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMMR_ALW_PRABHAKARA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMMR_ALW_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Atmospheric Liquid Water (ALW) data by Prabhakara.The Prabhakara Scanning Multichannel Microwave Radiometer (SMMR) Atmospheric Liquid Water (ALW) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Distributed Active Archive Center (DAAC). Each ALW file contains one month of 3 degree by 5 degree gridded mean liquid water. Each element of data is in units of mg/cm2. The data spans the period from February 1979 to May 1984.", "links": [ { diff --git a/datasets/SMMR_IWV_PRABHAKARA_1.json b/datasets/SMMR_IWV_PRABHAKARA_1.json index db06969aad..d06d8aa4df 100644 --- a/datasets/SMMR_IWV_PRABHAKARA_1.json +++ b/datasets/SMMR_IWV_PRABHAKARA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMMR_IWV_PRABHAKARA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMMR_IWV_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Integrated Water Vapor (IWV) data by Prabhakara.The Scanning Multichannel Microwave Radiometer (SMMR) Prabhakara integrated atmospheric water vapor (IWV) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Research Center Distributed Active Archive Center (DAAC). Each IWV file contains one month of 3 degree by 5 degree gridded mean water vapor. A scale factor of 0.1 must be applied to convert the data into units of g/cm2. The data spans the period from October 1979 to September 1983.", "links": [ { diff --git a/datasets/SMODE_L1_ASIT_KABODS_V1_1.json b/datasets/SMODE_L1_ASIT_KABODS_V1_1.json index 049fe39dfe..f71ad66243 100644 --- a/datasets/SMODE_L1_ASIT_KABODS_V1_1.json +++ b/datasets/SMODE_L1_ASIT_KABODS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_ASIT_KABODS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes tower-based Ka-band ocean surface backscatter measurements (cross section, incidence angle, radial velocity from radar, pulse-pair correlation) located offshore of Martha\u2019s Vineyard (41\u00b019.5\u2032N, 70\u00b034\u2032W), Massachusetts (USA) over a period of three months, from October 2019 to January 2020. Data from the Ka-band radar are collected at multiple distances from the tower (up to ~32 m) at several incidence angles and at sub-second resolution. The measurements are provided as hourly files in netCDF format. \r\n
\r\n
\r\nKa-band backscatter data are often utilized to derived ocean surface vector winds. The instrument used for this dataset was a Ka-Band Ocean continuous wave Doppler Scatterometer (KaBODS) built by the University of Massachusetts, Amherst, which was installed on the Woods Hole Oceanographic Institution Air-Sea Interaction Tower (ASIT). The tower is located in 15 m deep water and extends 76 feet into the marine atmosphere. Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The measurements provided the opportunity to develop Ka-band backscatter models as well as study backscattering mechanisms under different wind, wave, and weather conditions in order to support operation of the airborne Ka-band Doppler scatterometer used during the main S-MODE intensive observation periods.\r\n", "links": [ { diff --git a/datasets/SMODE_L1_ASIT_SLOPEFIELDS_V1_1.json b/datasets/SMODE_L1_ASIT_SLOPEFIELDS_V1_1.json index 25018193aa..fac9809e76 100644 --- a/datasets/SMODE_L1_ASIT_SLOPEFIELDS_V1_1.json +++ b/datasets/SMODE_L1_ASIT_SLOPEFIELDS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_ASIT_SLOPEFIELDS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These wave slope data from polarimetry described below are considered preliminary and should not be used for any purpose without consulting Chris Zappa (zappa@ldeo.columbia.edu).\r\n
\r\n
\r\nThis data set includes tower-based measurements of ocean wave slope fields from visible-band polarimetry using a Polaris Pyxis Mono VIS polarimetric camera. The data here include wave slope fields at 30 frames per second temporal resolution and mm-scale spatial resolution over a ~2 m x 2 m area of ocean surface observed off the Air-Sea Interaction Tower (ASIT; 41\u00b020.1950'N, 70\u00b033.3865'W). Measurements were taken over the period from October 2019 through January 2020. Surface slopes are along two dimensions: along-look and cross-look orientations of the camera. Data was acquired for 10 minutes per hour, 8 hours per day, and each data file (netCDF-4) captures one of the 10-minute segments. Note that data files are large, 142 GB each. \r\n
\r\n
\r\nData were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The polarimetric slope sensing (PSS) technique of Zappa et al. [2008] allows one to reconstruct the water surface slope field by measuring the polarization state of reflected light at each image pixel, allowing for surface resolutions of order 1 mm with no in-water measurement component. From these data one is able to compute water surface slope variance, wave directional spreading, and the near-surface current profile. ", "links": [ { diff --git a/datasets/SMODE_L1_DOPPLERSCATT_V1_1.json b/datasets/SMODE_L1_DOPPLERSCATT_V1_1.json index 5a51cc98dd..8e2e6f1041 100644 --- a/datasets/SMODE_L1_DOPPLERSCATT_V1_1.json +++ b/datasets/SMODE_L1_DOPPLERSCATT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_DOPPLERSCATT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 1 data includes geolocated physical measurements for a measurement footprint, which are the basis for the DopplerScatt L2 surface winds and currents estimates. Data are available in netCDF format and are ordered by measurement acquisition time and radar range, and are not on a geospatial grid. ", "links": [ { diff --git a/datasets/SMODE_L1_MASS_DOPPVIS_V1_1.json b/datasets/SMODE_L1_MASS_DOPPVIS_V1_1.json index 89c938070b..3b20a7ae2b 100644 --- a/datasets/SMODE_L1_MASS_DOPPVIS_V1_1.json +++ b/datasets/SMODE_L1_MASS_DOPPVIS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_MASS_DOPPVIS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available as GZIP flightlines containing individual TIFF images.", "links": [ { diff --git a/datasets/SMODE_L1_MASS_HYPERSPECTRAL_V1_1.json b/datasets/SMODE_L1_MASS_HYPERSPECTRAL_V1_1.json index e05b1e31d5..981a74e24f 100644 --- a/datasets/SMODE_L1_MASS_HYPERSPECTRAL_V1_1.json +++ b/datasets/SMODE_L1_MASS_HYPERSPECTRAL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_MASS_HYPERSPECTRAL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a hyperspectral camera operating in the visible to near-IR range (400-990 nm). Hyperspectral data are used by S-MODE to provide visible imagery of the kinematics of whitecaps and ocean color measurements. Level 1 data are available as zip files containing data in ENVI format and text files containing location and timing information.", "links": [ { diff --git a/datasets/SMODE_L1_MASS_LIDAR_V1_1.json b/datasets/SMODE_L1_MASS_LIDAR_V1_1.json index 7b26ab07da..85363c014d 100644 --- a/datasets/SMODE_L1_MASS_LIDAR_V1_1.json +++ b/datasets/SMODE_L1_MASS_LIDAR_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_MASS_LIDAR_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30\u00b0 cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.", "links": [ { diff --git a/datasets/SMODE_L1_MASS_LWIR_V1_1.json b/datasets/SMODE_L1_MASS_LWIR_V1_1.json index b8799d1bb2..39dedfdd3e 100644 --- a/datasets/SMODE_L1_MASS_LWIR_V1_1.json +++ b/datasets/SMODE_L1_MASS_LWIR_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_MASS_LWIR_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available.\r\n
\r\nThis dataset contains airborne longwave infrared (LWIR) imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a FLIR SC6700 camera with 13mm lens was mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the flight track. The camera was synchronized to a coupled GPS/IMU system with images collected at 50hz. Raw images were calibrated for lens distortion, vignetting, and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to the WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent resolution. Level 1 images are available in TIFF format.", "links": [ { diff --git a/datasets/SMODE_L1_MASS_VISIBLE_V1_1.json b/datasets/SMODE_L1_MASS_VISIBLE_V1_1.json index b68cce53f8..be122d6756 100644 --- a/datasets/SMODE_L1_MASS_VISIBLE_V1_1.json +++ b/datasets/SMODE_L1_MASS_VISIBLE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_MASS_VISIBLE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.", "links": [ { diff --git a/datasets/SMODE_L1_PRISM_V1_1.json b/datasets/SMODE_L1_PRISM_V1_1.json index ca2b3b907d..fc174a646f 100644 --- a/datasets/SMODE_L1_PRISM_V1_1.json +++ b/datasets/SMODE_L1_PRISM_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_PRISM_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains PRISM data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which will produce high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and will be used to provide accurate atmospheric correction of the ocean color measurements. Level 1 data is available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L1_SAILDRONES_V1_1.json b/datasets/SMODE_L1_SAILDRONES_V1_1.json index dd23c09f1b..ef7749cde2 100644 --- a/datasets/SMODE_L1_SAILDRONES_V1_1.json +++ b/datasets/SMODE_L1_SAILDRONES_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L1_SAILDRONES_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples are available in their raw 1 Hz sampling frequency as well as 5 minute averages, the latter available with navigation data. Other measurements are available as raw files (1Hz or 20 Hz where applicable), as well as 1 minute averages. L1 data are available as a zip file. ", "links": [ { diff --git a/datasets/SMODE_L2_APEX_FLOAT_V1_1.json b/datasets/SMODE_L2_APEX_FLOAT_V1_1.json index 5d74a34ee5..dfe48e41be 100644 --- a/datasets/SMODE_L2_APEX_FLOAT_V1_1.json +++ b/datasets/SMODE_L2_APEX_FLOAT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_APEX_FLOAT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains APEX float in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. Data was collected approximately 300 km offshore of San Francisco, during Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVO) APEX floats measure subsurface properties including temperature and salinity. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1.json b/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1.json index e643c4cf59..7829e731d9 100644 --- a/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1.json +++ b/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V1 data have been cross-calibrated against SIO-DopVis leading to the 'dopvis_2021' current geophysical model function. It is expected that additional DopVis data will lead to a reprocessing of this data set and it should be regarded as provisional, to be refined after future S-MODE deployments. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2_2.json b/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2_2.json index def2e6afed..a328e7b53f 100644 --- a/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2_2.json +++ b/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V2 data have been cross-calibrated against ADCPs, surface drifters, and the SIO-DopVis instrument collected during the Pilot and IOP1 campaigns. Additional DopVis data collected during IOP1 and IOP2, in addition to IOP2 ADCP and surface drifter data will lead to a reprocessing of this dataset, and it should be regarded as provisional. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_DRIFTER_POSITIONS_V1_1.json b/datasets/SMODE_L2_DRIFTER_POSITIONS_V1_1.json index 02e31dabf0..c1a7621f31 100644 --- a/datasets/SMODE_L2_DRIFTER_POSITIONS_V1_1.json +++ b/datasets/SMODE_L2_DRIFTER_POSITIONS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_DRIFTER_POSITIONS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in-situ position data from surface drifters from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Drifting buoys were deployed from the research vessels and configured to nominally report positions every five minutes. Drifters deployed were a mixture of CARTHE and Microstar types. CARTHE drifters are drogued at 40 cm depth and measure the average horizontal velocity of currents in the upper 60 cm of the ocean (Novelli et al., 2017). Microstar drifters are drogued at 1 m depth and measure the average horizontal velocity of ocean currents between 0.4 m and 1.6 m depth (Ohlmann et al., 2005). See the S-MODE Data Submission Report sections 2.3.2.2, 2.4.2.2 and 2.5.2.3 for more information. Tracking and telemetry of the drifters is done by Pacific Gyre, Inc. The data are available in netCDF format with a dimension of time. \n

\n Novelli, G., C. M. Guigand, C. Cousin, E. H. Ryan, N. J. M. Laxague, H. Dai, B. K. Haus, and T. M. \u00d6zg\u00f6kmen, 2017: A Biodegradable Surface Drifter for Ocean Sampling on a Massive Scale. J. Atmos. Oceanic Technol., 34, 2509\u20132532, https://doi.org/10.1175/JTECH-D-17-0055.1.\n

\nOhlmann, J. C., P. F. White, A. L. Sybrandy, and P. P. Niiler, 2005: GPS\u2013Cellular Drifter Technology for Coastal Ocean Observing Systems. J. Atmos. Oceanic Technol., 22, 1381\u20131388, https://doi.org/10.1175/JTECH1786.1.\n

\nWestbrook, E., Bingham, F. M., Brodnitz, S., Farrar, J. T., Rodriguez, E., & Zappa, C., (2024). Submesoscale Ocean Dynamics Experiment (S-MODE) Data Submission Report. Technical Report. Woods Hole Oceanographic Institution, WHOI-2024-03, https://doi.org/10.1575/1912/69362 ", "links": [ { diff --git a/datasets/SMODE_L2_LAGRANGIAN_FLOATS_V1_1.json b/datasets/SMODE_L2_LAGRANGIAN_FLOATS_V1_1.json index a39b378dea..79229c7545 100644 --- a/datasets/SMODE_L2_LAGRANGIAN_FLOATS_V1_1.json +++ b/datasets/SMODE_L2_LAGRANGIAN_FLOATS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_LAGRANGIAN_FLOATS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity. ", "links": [ { diff --git a/datasets/SMODE_L2_MOSES_LWIR_SST_V1_1.json b/datasets/SMODE_L2_MOSES_LWIR_SST_V1_1.json index e7c79e3378..5a445f65bf 100644 --- a/datasets/SMODE_L2_MOSES_LWIR_SST_V1_1.json +++ b/datasets/SMODE_L2_MOSES_LWIR_SST_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_MOSES_LWIR_SST_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_PRISM_CHLA_V1_1.json b/datasets/SMODE_L2_PRISM_CHLA_V1_1.json index 50f8e27833..4cc3de04ea 100644 --- a/datasets/SMODE_L2_PRISM_CHLA_V1_1.json +++ b/datasets/SMODE_L2_PRISM_CHLA_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_PRISM_CHLA_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimated chlorophyll-a and particulate organic carbon concentration data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2 chlorophyll-a data are available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L2_SAILDRONES_V1_1.json b/datasets/SMODE_L2_SAILDRONES_V1_1.json index 3ce16a7646..a6dcb64418 100644 --- a/datasets/SMODE_L2_SAILDRONES_V1_1.json +++ b/datasets/SMODE_L2_SAILDRONES_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SAILDRONES_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Saildrone in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples originally measured at 1 Hz frequency are averaged into 5 minute bins, along with navigation data. Non-ADCP data from IOP1 contain additional bio-optical measurements. All data are available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L2_SEAGLIDERS_V1_1.json b/datasets/SMODE_L2_SEAGLIDERS_V1_1.json index a696ee2cb8..ad1f4bb2dc 100644 --- a/datasets/SMODE_L2_SEAGLIDERS_V1_1.json +++ b/datasets/SMODE_L2_SEAGLIDERS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SEAGLIDERS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while\r\ncollecting profiles of oceanic variables. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json index f67baefa13..f3f3c89df5 100644 --- a/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_ADCP_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard Acoustic Doppler Current Profiler (ADCP) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign and two intensive operating periods (IOPs) conducted approximately 300 km offshore of San Francisco during Fall 2021, 2022, and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The ADCP was mounted to the bottom of the hulls of the research vessels deployed during each campaign, measuring horizontal and vertical currents, as well as acoustic backscatter from approximately 3 m to 50 m depth along the ship\u2019s track. The data are available in netCDF format with dimensions of time and depth. ", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_BIO_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_BIO_V1_1.json index 8bcf09932a..0be55f970d 100644 --- a/datasets/SMODE_L2_SHIPBOARD_BIO_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_BIO_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_BIO_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_BOTTLES_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_BOTTLES_V1_1.json index 1a5d865c30..ff69e373cf 100644 --- a/datasets/SMODE_L2_SHIPBOARD_BOTTLES_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_BOTTLES_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_BOTTLES_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in-situ seawater samples taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Water samples collected in Niskin bottles mounted on the ship\u2019s rosette sampler were taken of chlorophyll (\u00b5g/L), phaeopigments (\u00b5g/L), and nutrient concentrations (\u00b5M or \u00b5mol/L) of particulate organic carbon, particulate organic nitrogen, silicate, nitrate, nitrite, and phosphate. Samples analyzed with fluorometry contain chlorophyll concentrations in \u00b5g/L and phaeopigment concentrations in \u00b5g/L. Samples analyzed with elemental analysis contain POC molarity in \u00b5M and PON molarity in \u00b5M. Samples analyzed via ion analysis contain silicate concentrations in \u00b5M, total nitrate+nitrite in \u00b5M, phosphate in \u00b5M, nitrite in \u00b5M, and nitrate in \u00b5M. These data are mainly used by S-MODE for validating the PRISM-derived products and calibrating the in-situ sensors on the autonomous platforms. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_CTD_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_CTD_V1_1.json index 504cc49d5f..e7f75e0b71 100644 --- a/datasets/SMODE_L2_SHIPBOARD_CTD_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_CTD_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_CTD_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The shipboard CTD rosette is cast from the R/V Oceanus where it records ocean temperature, conductivity, and pressure as it descends to depth and then returns to the surface. IOP1 and IOP2 measurements also contain biological data. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1_1.json index 5d4540e3b4..f8494a2f79 100644 --- a/datasets/SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard radiometer measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Air-Sea Interaction METeorology (ASIMET) sensors mounted onboard the R/V Oceanus record shortwave and longwave radiation fluxes. These are used by S-MODE to compare with DopplerScatt retrievals. Data are available in netCDF format. ", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1_1.json index e7bf5c4efc..38911ce038 100644 --- a/datasets/SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Sounding profiles were collected using shipboard Windsond S1H3-S radiosondes launched from the R/V Oceanus cruise OC2108A, to a maximum elevation of at least 5 km above ground level (ABL). These measurements are used to understand the vertical structure of atmospheric temperature, winds, and moisture. The original 1Hz observations were gridded onto a uniform 20 m vertical grid. The data are available in netCDF format with dimensions of altitude and profile number. ", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_SUNA_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_SUNA_V1_1.json index 1930ada5d3..5a1bd663d9 100644 --- a/datasets/SMODE_L2_SHIPBOARD_SUNA_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_SUNA_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_SUNA_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_TSG_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_TSG_V1_1.json index 4a7798f030..f836db0cb4 100644 --- a/datasets/SMODE_L2_SHIPBOARD_TSG_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_TSG_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_TSG_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The TSG instrument measures the temperature and conductivity of seawater passing through a port in the hull of the ship. TSG data is calibrated using water samples compared to standard seawater and a laboratory salinometer onboard the ship. This dataset also contains chlorophyll and meteorology measurements including air temperature, barometric pressure, wind speed and direction, relative humidity, and radiative fluxes. Data are available in netCDF format, with separate dimensions for time, time of bio-optics measurements, and time of radiometer measurements.", "links": [ { diff --git a/datasets/SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1_1.json index 92e1ec87be..c1483fc1e0 100644 --- a/datasets/SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1_1.json +++ b/datasets/SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. The level 2 data herein combines measurements from both the UCDT and EcoCDT into a single dataset, where for each variable, all profiles are binned onto a 5m vertical grid and merged into a 2-D matrix. Additional computed variables include backscatter baseline signal and backscatter spike signal. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_SLOCUM_GLIDERS_V1_1.json b/datasets/SMODE_L2_SLOCUM_GLIDERS_V1_1.json index cb9a4d448e..7c66bdd216 100644 --- a/datasets/SMODE_L2_SLOCUM_GLIDERS_V1_1.json +++ b/datasets/SMODE_L2_SLOCUM_GLIDERS_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_SLOCUM_GLIDERS_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Slocum glider in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVOCEANO) Slocum gliders measure subsurface properties including temperature and salinity by profiling to a depth of 1000m at a fixed location every 4 hours. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1.json b/datasets/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1.json index 535479fa1b..fae545e864 100644 --- a/datasets/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1.json +++ b/datasets/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains waveglider observations from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Three wave gliders were deployed as part of the S-MODE pilot campaign, equipped with a suite of sensors including sonic anemometers, shortwave and longwave radiometers, CTD profilers, and ADCPs. All wave gliders include an IMU that records platform orientation and motion at 20Hz. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L2a_PRISM_REFL_V1_1.json b/datasets/SMODE_L2a_PRISM_REFL_V1_1.json index e71c0fecb0..a66a034a70 100644 --- a/datasets/SMODE_L2a_PRISM_REFL_V1_1.json +++ b/datasets/SMODE_L2a_PRISM_REFL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L2a_PRISM_REFL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains orthocorrected and atmospherically corrected water-leaving reflectance data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2a reflectance data is available in ENVI format. ", "links": [ { diff --git a/datasets/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1.json b/datasets/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1.json index 453117bfac..435ada26a8 100644 --- a/datasets/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1.json +++ b/datasets/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while\r\ncollecting profiles of oceanic variables. Data are available in netCDF format.", "links": [ { diff --git a/datasets/SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1_1.json b/datasets/SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1_1.json index d644fab638..bb4e0dccea 100644 --- a/datasets/SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1_1.json +++ b/datasets/SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. Level 3 data are available in netCDF format with dimensions of profile number and depth.", "links": [ { diff --git a/datasets/SMODE_L4_NCOM_V1_1.json b/datasets/SMODE_L4_NCOM_V1_1.json index 58ce279e55..6457075d15 100644 --- a/datasets/SMODE_L4_NCOM_V1_1.json +++ b/datasets/SMODE_L4_NCOM_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMODE_L4_NCOM_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format. ", "links": [ { diff --git a/datasets/SMOS_Open_V7_5.0.json b/datasets/SMOS_Open_V7_5.0.json index c5745ef5e3..6935aed2fe 100644 --- a/datasets/SMOS_Open_V7_5.0.json +++ b/datasets/SMOS_Open_V7_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SMOS_Open_V7_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 SMOS data products are designed for scientific and operational users who need to work with calibrated MIRAS instrument measurements, while Level 2 SMOS data products are designed for scientific and operational users who need to work with geo-located soil moisture and sea surface salinity estimation as retrieved from Level 1 dataset. Products from the operational pipeline in the SMOS Data Processing Ground Segment (DPGS) https://earth.esa.int/eogateway/missions/smos/description, located at the European Space Astronomy Centre (ESAC), have File Class ""OPER"", while reprocessed data is tagged as ""REPR"". For an optimal exploitation of the current SMOS L1 and L2 data set please consult the read-me-first notes. The Level 1A product comprises all calibrated visibilities between receivers (i.e. the interferometric measurements from the sensor including the redundant visibilities), combined per integration time of 1.2s (snapshot). The snapshots are consolidated in a pole-to-pole product file (50 minutes of sensing time) with a maximum size of about 215MB per half orbit (29 half orbits per day). The Level 1B product comprises the result of the image reconstruction algorithm applied to the L1A data. As a result, the reconstructed image at L1B is simply the difference between the sensed scene by the sensor and the artificial scene. The brightness temperature image is available in its Fourier component in the antenna polarisation reference frame top of the atmosphere. Images are combined per integration time of 1.2 seconds (snapshot). The removal of foreign sources (Galactic, Direct Sun, Moon) is also included in the reconstruction. Snapshot consolidation is as per L1A, with a maximum product size of about 115MB per half orbit. ESA provides the Artificial Scene Library (ASL) to add the artificial scene in L1B for any user that wants to start from L1B products and derive the sensed scene. The Level 1C product contains multi-angular brightness temperatures in antenna frame (X-pol, Y-pol, T3 and T4) at the top of the atmosphere, geo-located in an equal-area grid system (ISEA 4H9 - Icosahedral Snyder Equal Area projection). Two L1C products are available: Land for soil moisture retrieval and Sea for sea surface salinity retrieval. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 350MB per half orbit (29 half orbits per day). Spatial resolution is in the range of 30-50 km. For each L1C product there is also a corresponding Browse product containing brightness temperatures interpolated for an incidence angle of 42.5\u00b0. The Level 2 Soil Moisture (SM) product comprises soil moisture measurements geo-located in an equal-area grid system ISEA 4H9. The product contains not only the retrieved soil moisture, but also a series of ancillary data derived from the processing (nadir optical thickness, surface temperature, roughness parameter, dielectric constant and brightness temperature retrieved at top of atmosphere and on the surface) with the corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 7MB (25MB uncompressed data) per half orbit (29 half orbits per day). The Level 2 Ocean Salinity (OS) product comprises sea surface salinity measurements geo-located in an equal-area grid system ISEA 4H9. The product contains one single swath-based sea surface salinity retrieved with and without Land-Sea contamination correction, SSS anomaly based on WOA-2009 referred to Land-Sea corrected sea surface salinity, brightness temperature at the top of the atmosphere and at the sea surface with their corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 10MB (25MB uncompressed data) per half orbit (29 half orbits per day). The following Science data products, belonging to the latest processing baseline, are openly available to all users: MIR_SC_F1B/MIR_SC_D1B: Level 1B product, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SCLF1C/MIR_SCLD1C: Level 1C product over Land, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SCSF1C/MIR_SCSD1C: Level 1C product over Sea, FULL/DUAL polarisation mode, in Earth Explorer format MIR_BWLF1C/MIR_BWLD1C: Level 1C Browse product over Land, FULL/DUAL polarisation mode, in Earth Explorer format MIR_BWSF1C/MIR_BWSD1C: Level 1C Browse product over Sea, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SMUDP2: Level 2 Soil Moisture product, in Earth Explorer and NetCDF format MIR_OSUDP2: Level 2 Sea Surface Salinity product, in Earth Explorer and NetCDF format Access to the following Science data products is restricted to SMOS CalVal users: MIR_SC_F1A/MIR_SC_D1A: Level 1A product, FULL/DUAL polarisation mode, in Earth Explorer format. For an optimal exploitation of the current SMOS L1 and L2 data set please consult the read-me-first notes available in the Resources section below.", "links": [ { diff --git a/datasets/SNDR13CHRP1AQCal_2.json b/datasets/SNDR13CHRP1AQCal_2.json index 006e93d25e..3fffbcefde 100644 --- a/datasets/SNDR13CHRP1AQCal_2.json +++ b/datasets/SNDR13CHRP1AQCal_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13CHRP1AQCal_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the \"standard\". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1AQCal, only contains CHIRP data derived from the AIRS instrument on EOS-AQUA that is not present in the main CHIRP product, SNDR13CHRP1, and therefore starts on Sept. 1, 2016 and will continue until the AIRS end of mission.", "links": [ { diff --git a/datasets/SNDR13CHRP1J1Cal_2.json b/datasets/SNDR13CHRP1J1Cal_2.json index 6a24bf38a5..8bfe6bf74d 100644 --- a/datasets/SNDR13CHRP1J1Cal_2.json +++ b/datasets/SNDR13CHRP1J1Cal_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13CHRP1J1Cal_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the \"standard\". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1J1Cal, contains CHIRP data derived from the JPSS-1 (NOAA-20) CrIS instrument that is not present in the main CHIRP product, SNDR13CHRP1, which include JPSS-1 data from February 17, 2018 through August 31, 2018.", "links": [ { diff --git a/datasets/SNDR13CHRP1SNCal_2.json b/datasets/SNDR13CHRP1SNCal_2.json index 9ed30eb038..5e5360ed6e 100644 --- a/datasets/SNDR13CHRP1SNCal_2.json +++ b/datasets/SNDR13CHRP1SNCal_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13CHRP1SNCal_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the \"standard\". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1SNCal, contains CHIRP data derived from the CrIS instrument on the SNPP platform that is not present in the main CHIRP product, SNDR13CHRP1, and therefore covers the date ranges of November 2, 2015 (when CrIS started producing high-spectral resolution data which is required for CHIRP) through August 31, 2016 when the main CHIRP product, SNDR13CHRP1, switches to using CrIS on JPSS-1. ", "links": [ { diff --git a/datasets/SNDR13CHRP1_2.json b/datasets/SNDR13CHRP1_2.json index c71598f46c..02d4345152 100644 --- a/datasets/SNDR13CHRP1_2.json +++ b/datasets/SNDR13CHRP1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13CHRP1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the \"standard\". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1, is the primary CHIRP product which provides a full radiance record from 2002 onwards starting with AIRS from September 1, 2002 to August 30, 2016, switching to the SNPP CrIS instrument on September 1, 2016. The SNDR13CHRP1 product then switches to using JPSS-1 CrIS radiances starting September 1, 2018. This selection of time periods provides the best match to times when the microwave sounders on each of these platforms exhibited good performance and avoids long outages (such as SNPP CrIS in early Spring 2019). CHIRP is available for AIRS, CrIS-SNPP, and CrIS-JPSS-1 for time periods not used in the product distributed here, and are named SNDR13CHRP1AQCal, SNDR13CHRP1SNCal, and SNDR13CHRP1J1Cal respectively. ", "links": [ { diff --git a/datasets/SNDR13IML3SSDFCNSAT_2.json b/datasets/SNDR13IML3SSDFCNSAT_2.json index 21afb693e2..7f840a93ef 100644 --- a/datasets/SNDR13IML3SSDFCNSAT_2.json +++ b/datasets/SNDR13IML3SSDFCNSAT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13IML3SSDFCNSAT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. \n\nThe Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a \u00bc x \u00bc degree latitude/longitude grid covering the continental United States (CONUS). \n\nThe SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n", "links": [ { diff --git a/datasets/SNDR13IML3SSDFCVPD_2.json b/datasets/SNDR13IML3SSDFCVPD_2.json index 1768b47b89..893d47b678 100644 --- a/datasets/SNDR13IML3SSDFCVPD_2.json +++ b/datasets/SNDR13IML3SSDFCVPD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDR13IML3SSDFCVPD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a \u00bc x \u00bc degree latitude/longitude grid covering the continental United States (CONUS). \n\nThe SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.", "links": [ { diff --git a/datasets/SNDRAQIL2CCPCCR_2.json b/datasets/SNDRAQIL2CCPCCR_2.json index 55c7ffefd6..cf5559d767 100644 --- a/datasets/SNDRAQIL2CCPCCR_2.json +++ b/datasets/SNDRAQIL2CCPCCR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL2CCPCCR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. \n \n\nCloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed if there were no clouds in the FOV.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRAQIL2CCPRET_2.json b/datasets/SNDRAQIL2CCPRET_2.json index 5a56d171d3..0aaecd41d3 100644 --- a/datasets/SNDRAQIL2CCPRET_2.json +++ b/datasets/SNDRAQIL2CCPRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL2CCPRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. \n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere\nprofiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nAn AIRS level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRAQIL2CPS_2.1.json b/datasets/SNDRAQIL2CPS_2.1.json index b92dce028c..d45b51cb54 100644 --- a/datasets/SNDRAQIL2CPS_2.1.json +++ b/datasets/SNDRAQIL2CPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL2CPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere\nprofiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nAn AIRS level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. ", "links": [ { diff --git a/datasets/SNDRAQIL2JSFRET_2.json b/datasets/SNDRAQIL2JSFRET_2.json index 8f08499e70..7a4ae7aab3 100644 --- a/datasets/SNDRAQIL2JSFRET_2.json +++ b/datasets/SNDRAQIL2JSFRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL2JSFRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Single Footprint Retrieval Algorithm (JoSFRA) Level-2 geophysical parameters include estimates of atmospheric temperature and water vapor profiles, cloud properties, and surface temperature. These are retrieved from infrared spectra observed by the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite. AIRS is a grating spectrometer aboard Aqua, the second Earth Observing System (EOS) polar-orbiting platform. AIRS is co-boresited with the Advanced Microwave Sounding Unit (AMSU) also on Aqua. Horizontal resolutions are 50 km for AMSU and 13.5 km for AIRS. The JoSFRA algorithm uses an optimal-estimation scheme and retrieves geophysical quantities from AIRS thermal infrared spectra at their native horizontal resolution. Cloud observations from MODIS are used in the forward model without recourse to a cloud-cleared state. JOSFRA retrievals provide improved spatial resolution (13.5 km vs 50 km for cloud-cleared retrievals) and information content quantification, making them well-suited for process studies. JoSFRA retrievals are particularly useful in cases where high resolution (finer than 45 km) is needed or is beneficial, such as regions of strong horizontal gradients in water vapor. Use of JoSFRA retrievals is recommended under medium to low cloud cover. \n \n AIRS observations provide near-global coverage twice daily (around 1:30 am and pm local time) since August 30, 2002. An AIRS granule includes 6 minutes of data, 90 AIRS (30 AMSU) footprints across the orbit track by 135 AIRS (45 AMSU) footprints along track. Each day includes 240 granules, with an orbit repeat cycle of approximately 16 days. \n \n For the initial release of Version 2 JoSFRA, a limited test data set is provided. Future releases will expand the dataset. The initial dataset includes full global coverage data for two 5-day periods: January 13-17, 2011 and July 13-17, 2011, the Marine ARM GPCI Investigation of Clouds (MAGIC) (Lewis and Teixeira, EOS, 2015) test campaign in the Pacific Ocean with all 6-minute granules that overlap the box bounded by 20-35 degrees North latitude and 120-160 West longitude, June 1, 2012 \u2013 September 30, 2013, select granules from the years 2002-2007 where correlative data were available. The locations include Dept. of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites at the North Slope of Alaska (NSA), Southern Great Plains (SGP), and Tropical Western Pacific (TWP), as well as scientific field campaigns.", "links": [ { diff --git a/datasets/SNDRAQIL2PLEVCPS_2.1.json b/datasets/SNDRAQIL2PLEVCPS_2.1.json index 7743224b03..646392b798 100644 --- a/datasets/SNDRAQIL2PLEVCPS_2.1.json +++ b/datasets/SNDRAQIL2PLEVCPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL2PLEVCPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) instrument aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n\n", "links": [ { diff --git a/datasets/SNDRAQIL3CDCCP_2.json b/datasets/SNDRAQIL3CDCCP_2.json index c13ebed806..684e379e7f 100644 --- a/datasets/SNDRAQIL3CDCCP_2.json +++ b/datasets/SNDRAQIL3CDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3CDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRAQIL3CMCCP_2.json b/datasets/SNDRAQIL3CMCCP_2.json index 900a43388d..274c6a69a4 100644 --- a/datasets/SNDRAQIL3CMCCP_2.json +++ b/datasets/SNDRAQIL3CMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3CMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. \n\n", "links": [ { diff --git a/datasets/SNDRAQIL3SDCCP_2.json b/datasets/SNDRAQIL3SDCCP_2.json index 309f7c334c..4cff36242f 100644 --- a/datasets/SNDRAQIL3SDCCP_2.json +++ b/datasets/SNDRAQIL3SDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3SDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: Users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRAQIL3SMCCP_2.json b/datasets/SNDRAQIL3SMCCP_2.json index b9ea1bc659..bd13c10d1f 100644 --- a/datasets/SNDRAQIL3SMCCP_2.json +++ b/datasets/SNDRAQIL3SMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3SMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: Users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected. \n\n", "links": [ { diff --git a/datasets/SNDRAQIL3SSDFCNSAT_2.json b/datasets/SNDRAQIL3SSDFCNSAT_2.json index 057bb3ae33..007e2257fe 100644 --- a/datasets/SNDRAQIL3SSDFCNSAT_2.json +++ b/datasets/SNDRAQIL3SSDFCNSAT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3SSDFCNSAT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.\n\nThe Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a \u00bc x \u00bc degree latitude/longitude grid covering the continental United States (CONUS). \n\nThe SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n", "links": [ { diff --git a/datasets/SNDRAQIL3SSDFCVPD_2.json b/datasets/SNDRAQIL3SSDFCVPD_2.json index 5fc634e3d4..c44c66c5be 100644 --- a/datasets/SNDRAQIL3SSDFCVPD_2.json +++ b/datasets/SNDRAQIL3SSDFCVPD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIL3SSDFCVPD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.\n\nThe Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a \u00bc x \u00bc degree latitude/longitude grid covering the continental United States (CONUS).\n\nThe SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n", "links": [ { diff --git a/datasets/SNDRAQIML1BCALSUBSUM_2.json b/datasets/SNDRAQIML1BCALSUBSUM_2.json index 8993fb799c..691c32d034 100644 --- a/datasets/SNDRAQIML1BCALSUBSUM_2.json +++ b/datasets/SNDRAQIML1BCALSUBSUM_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML1BCALSUBSUM_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. AIRS/Aqua Level-1C calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. Infrared temperature sounders generate a large amount of Level-1B spectral data. For example, the AIRS instrument with 2378 channels, its visible light component and AMSU with 15 channels create 3x240 files each day, for a total of over 500 MB of data. \n\nThe purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to:\n 1. Facilitate a quick evaluation of the absolute calibration of the instruments.\n 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions.\n 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends.\n 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets.\n\nThe output files are constructed from Level-1B or Level-1C IR and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. The \u201csummary\u201d product includes a large set of cases of interest, including all identified spectra that match selection criteria detailed below for clear, special cloud classes, etc. These amount to about 10% of all spectra. But for each selected case only brightness temperatures (BTs) for selected key channels are saved.", "links": [ { diff --git a/datasets/SNDRAQIML1CCALSUBRND_2.json b/datasets/SNDRAQIML1CCALSUBRND_2.json index 0e64a5f9eb..a93af1af0f 100644 --- a/datasets/SNDRAQIML1CCALSUBRND_2.json +++ b/datasets/SNDRAQIML1CCALSUBRND_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML1CCALSUBRND_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. AIRS/Aqua Level-1C calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. Infrared temperature sounders generate a large amount of Level-1B spectral data. For example, the AIRS instrument with 2378 channels, its visible light component and AMSU with 15 channels create 3x240 files each day, for a total of over 500 MB of data. \n\nThe purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to:\n 1. Facilitate a quick evaluation of the absolute calibration of the instruments.\n 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions.\n 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends.\n 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets.\nThe output files are constructed from Level-1B or Level-1C IR and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. ", "links": [ { diff --git a/datasets/SNDRAQIML2CCPCCR_2.json b/datasets/SNDRAQIML2CCPCCR_2.json index 2ceb6a5523..1622eb2103 100644 --- a/datasets/SNDRAQIML2CCPCCR_2.json +++ b/datasets/SNDRAQIML2CCPCCR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML2CCPCCR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. \n \n\nCloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRAQIML2CCPRET_2.json b/datasets/SNDRAQIML2CCPRET_2.json index 8473950d4a..b444f6b7c1 100644 --- a/datasets/SNDRAQIML2CCPRET_2.json +++ b/datasets/SNDRAQIML2CCPRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML2CCPRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. \n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. \n\n", "links": [ { diff --git a/datasets/SNDRAQIML2CPS_2.1.json b/datasets/SNDRAQIML2CPS_2.1.json index 082c965853..82e25c9974 100644 --- a/datasets/SNDRAQIML2CPS_2.1.json +++ b/datasets/SNDRAQIML2CPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML2CPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day", "links": [ { diff --git a/datasets/SNDRAQIML2PLEVCPS_2.1.json b/datasets/SNDRAQIML2PLEVCPS_2.1.json index 748be920ca..60800ad88d 100644 --- a/datasets/SNDRAQIML2PLEVCPS_2.1.json +++ b/datasets/SNDRAQIML2PLEVCPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML2PLEVCPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder)/AMSU (Advanced Microwave Sounding Unit) instruments aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm\n\nAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. \n\nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.", "links": [ { diff --git a/datasets/SNDRAQIML3CDCCP_2.json b/datasets/SNDRAQIML3CDCCP_2.json index e414762f9c..3e50a84c5d 100644 --- a/datasets/SNDRAQIML3CDCCP_2.json +++ b/datasets/SNDRAQIML3CDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML3CDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRAQIML3CMCCP_2.json b/datasets/SNDRAQIML3CMCCP_2.json index 1a3ad66dd5..9be751279a 100644 --- a/datasets/SNDRAQIML3CMCCP_2.json +++ b/datasets/SNDRAQIML3CMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML3CMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\nWARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 48.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/48.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov", "links": [ { diff --git a/datasets/SNDRAQIML3SDCCP_2.json b/datasets/SNDRAQIML3SDCCP_2.json index 38872c334a..c536dac174 100644 --- a/datasets/SNDRAQIML3SDCCP_2.json +++ b/datasets/SNDRAQIML3SDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML3SDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: Users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRAQIML3SMCCP_2.json b/datasets/SNDRAQIML3SMCCP_2.json index 220417570a..f8705370a5 100644 --- a/datasets/SNDRAQIML3SMCCP_2.json +++ b/datasets/SNDRAQIML3SMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRAQIML3SMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRJ1ATMSL1B_2.json b/datasets/SNDRJ1ATMSL1B_2.json index 063c519fd8..252ba9fe45 100644 --- a/datasets/SNDRJ1ATMSL1B_2.json +++ b/datasets/SNDRJ1ATMSL1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ATMSL1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration).\n \nThe ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with “through-the-antenna” radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the \"integrate-while-scan\" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse.\n\nSince the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries.\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNDRJ1ATMSL1B_3.json b/datasets/SNDRJ1ATMSL1B_3.json index 58d63a124c..6cd4391371 100644 --- a/datasets/SNDRJ1ATMSL1B_3.json +++ b/datasets/SNDRJ1ATMSL1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ATMSL1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration).\n \nThe ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with “through-the-antenna” radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the \"integrate-while-scan\" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse.\n\nSince the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries.\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNDRJ1ATMSMAP_1.json b/datasets/SNDRJ1ATMSMAP_1.json index 49dc53c7a7..8857e86a7a 100644 --- a/datasets/SNDRJ1ATMSMAP_1.json +++ b/datasets/SNDRJ1ATMSMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ATMSMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration). \n\nThe ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with \"through-the-antenna\" radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the \"integrate-while-scan\" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse.\n\nSince the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds.. Data products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format. ", "links": [ { diff --git a/datasets/SNDRJ1CrISL1BIMGC_2.json b/datasets/SNDRJ1CrISL1BIMGC_2.json index 613ae4dae9..c0a33a620f 100644 --- a/datasets/SNDRJ1CrISL1BIMGC_2.json +++ b/datasets/SNDRJ1CrISL1BIMGC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1CrISL1BIMGC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. \n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG_COL product contains the colocation indices of the VIIRS pixels within each CrIS footprint.", "links": [ { diff --git a/datasets/SNDRJ1CrISL1BIMG_2.json b/datasets/SNDRJ1CrISL1BIMG_2.json index 111c68ac55..a80128af81 100644 --- a/datasets/SNDRJ1CrISL1BIMG_2.json +++ b/datasets/SNDRJ1CrISL1BIMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1CrISL1BIMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. /\n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.", "links": [ { diff --git a/datasets/SNDRJ1CrISL1BPCARED_3.0.json b/datasets/SNDRJ1CrISL1BPCARED_3.0.json index 2bff127b6d..ca4e8373b6 100644 --- a/datasets/SNDRJ1CrISL1BPCARED_3.0.json +++ b/datasets/SNDRJ1CrISL1BPCARED_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1CrISL1BPCARED_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This sample data collection contains L1B radiance values that are compressed and denoised via Principal Component Analysis (PCA). Additionally it contains a new Rapid Event Detection (RED) product, which for multiple spectral regions identify where rare signals are observed, providing an efficient and useful way to locate and study interesting phenomena. Some of the potential uses for these RED components are the detection of atmospheric gases where fires and volcanoes occur. PCA/RED might be a desirable alternative to the existing L1B product because (1) it is many times smaller in data volume, (2) about 80% of the random noise is removed, and (3) it comes with the new RED component. \n\nThe Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). \n\n\n", "links": [ { diff --git a/datasets/SNDRJ1CrISL1B_2.json b/datasets/SNDRJ1CrISL1B_2.json index 2d2ca1121f..bbd203559a 100644 --- a/datasets/SNDRJ1CrISL1B_2.json +++ b/datasets/SNDRJ1CrISL1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1CrISL1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. \n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nIf you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.", "links": [ { diff --git a/datasets/SNDRJ1CrISL1B_3.json b/datasets/SNDRJ1CrISL1B_3.json index 4b101218ed..02dd504ff1 100644 --- a/datasets/SNDRJ1CrISL1B_3.json +++ b/datasets/SNDRJ1CrISL1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1CrISL1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. \n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nIf you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.", "links": [ { diff --git a/datasets/SNDRJ1IML2CCPCCR_2.json b/datasets/SNDRJ1IML2CCPCCR_2.json index 57c2568d4a..bb18775489 100644 --- a/datasets/SNDRJ1IML2CCPCCR_2.json +++ b/datasets/SNDRJ1IML2CCPCCR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML2CCPCCR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard. \n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \n The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.", "links": [ { diff --git a/datasets/SNDRJ1IML2CCPRET_2.json b/datasets/SNDRJ1IML2CCPRET_2.json index ba446a4051..0c41269145 100644 --- a/datasets/SNDRJ1IML2CCPRET_2.json +++ b/datasets/SNDRJ1IML2CCPRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML2CCPRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRJ1IML2CPS_2.1.json b/datasets/SNDRJ1IML2CPS_2.1.json index c633587ade..fd39543fdd 100644 --- a/datasets/SNDRJ1IML2CPS_2.1.json +++ b/datasets/SNDRJ1IML2CPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML2CPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRJ1IML2PLEVCPS_2.1.json b/datasets/SNDRJ1IML2PLEVCPS_2.1.json index 52b8fbd9a1..b238436df1 100644 --- a/datasets/SNDRJ1IML2PLEVCPS_2.1.json +++ b/datasets/SNDRJ1IML2PLEVCPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML2PLEVCPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data from the JPSS-1 (Joint Polar Satellite System). They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm \n\nAn level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. \n\nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. ", "links": [ { diff --git a/datasets/SNDRJ1IML3CDCCP_2.json b/datasets/SNDRJ1IML3CDCCP_2.json index 99359164b9..53fb30b81e 100644 --- a/datasets/SNDRJ1IML3CDCCP_2.json +++ b/datasets/SNDRJ1IML3CDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML3CDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRJ1IML3CMCCP_2.json b/datasets/SNDRJ1IML3CMCCP_2.json index c6a0edd5f7..238b9091ea 100644 --- a/datasets/SNDRJ1IML3CMCCP_2.json +++ b/datasets/SNDRJ1IML3CMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML3CMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRJ1IML3SDCCP_2.json b/datasets/SNDRJ1IML3SDCCP_2.json index 93310bcea1..fc1d9d92d2 100644 --- a/datasets/SNDRJ1IML3SDCCP_2.json +++ b/datasets/SNDRJ1IML3SDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML3SDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRJ1IML3SMCCP_2.json b/datasets/SNDRJ1IML3SMCCP_2.json index 47ecf19e11..61b270626c 100644 --- a/datasets/SNDRJ1IML3SMCCP_2.json +++ b/datasets/SNDRJ1IML3SMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1IML3SMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRJ1ML2RMSSUP_3.json b/datasets/SNDRJ1ML2RMSSUP_3.json index 49d6dd30e6..5f578d3a19 100644 --- a/datasets/SNDRJ1ML2RMSSUP_3.json +++ b/datasets/SNDRJ1ML2RMSSUP_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ML2RMSSUP_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nA level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRJ1ML2RMS_3.json b/datasets/SNDRJ1ML2RMS_3.json index b22e2c5016..b1fc7ba966 100644 --- a/datasets/SNDRJ1ML2RMS_3.json +++ b/datasets/SNDRJ1ML2RMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ML2RMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nA level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRJ1ML3DRMS_3.json b/datasets/SNDRJ1ML3DRMS_3.json index a34d0794ce..331fd80384 100644 --- a/datasets/SNDRJ1ML3DRMS_3.json +++ b/datasets/SNDRJ1ML3DRMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ML3DRMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n\n", "links": [ { diff --git a/datasets/SNDRJ1ML3MRMS_3.json b/datasets/SNDRJ1ML3MRMS_3.json index cdcce34527..2dad307a07 100644 --- a/datasets/SNDRJ1ML3MRMS_3.json +++ b/datasets/SNDRJ1ML3MRMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ1ML3MRMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n\n", "links": [ { diff --git a/datasets/SNDRJ2CrISL1BIMGC_3.0.json b/datasets/SNDRJ2CrISL1BIMGC_3.0.json index 0e3d928fe5..a43368c425 100644 --- a/datasets/SNDRJ2CrISL1BIMGC_3.0.json +++ b/datasets/SNDRJ2CrISL1BIMGC_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ2CrISL1BIMGC_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). \n\nThe IMG product supplements the CrIS Level 1B (L1B) hyperspectral radiance product by providing collocated high-spatial resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) imager located on the same platform. VIIRS radiance and cloud mask values are grouped and aggregated for every CrIS field of view (FOV) and made available in a format intended for use alongside the CrIS L1B data. The collocated VIIRS level 1 / cloud mask statistical summary (collection name SNDRJ2CrISL1BIMG) is the main product and consists of collocated CrIS field of views with the VIIRS cloud mask and radiances/reflectances. This can be thought of as a match-up between CrIS and VIIRS. The supplementary product, array indices for collocated VIIRS observations (SNDRJ2CrISL1BIMGC), product additionally makes available CrIS and VIIRS data array index values that result from the collocation process that is performed as part of producing IMG. These index values can be leveraged by end users to further augment CrIS data by extracting collocated observations from any additional VIIRS data products that aren\u2019t already present in IMG.\n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG_COL product contains the colocation indices of the VIIRS pixels within each CrIS footprint.", "links": [ { diff --git a/datasets/SNDRJ2CrISL1BIMG_3.0.json b/datasets/SNDRJ2CrISL1BIMG_3.0.json index edcb57d707..3a12a11d0b 100644 --- a/datasets/SNDRJ2CrISL1BIMG_3.0.json +++ b/datasets/SNDRJ2CrISL1BIMG_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ2CrISL1BIMG_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). \n\nThe IMG product supplements the CrIS Level 1B (L1B) hyperspectral radiance product by providing collocated high-spatial resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) imager located on the same platform. VIIRS radiance and cloud mask values are grouped and aggregated for every CrIS field of view (FOV) and made available in a format intended for use alongside the CrIS L1B data. The collocated VIIRS level 1 / cloud mask statistical summary is the main product and consists of collocated CrIS field of views with the VIIRS cloud mask and radiances/reflectances. This can be thought of as a match-up between CrIS and VIIRS. The supplementary product, array indices for collocated VIIRS observations (collection name SNDRJ2CrISL1BIMGC), consists of array indices for collocated VIIRS observations and provides the collocated indices of the VIIRS pixels within each CrIS footprint. \n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.", "links": [ { diff --git a/datasets/SNDRJ2CrISL1B_3.json b/datasets/SNDRJ2CrISL1B_3.json index a82f65800f..99465030da 100644 --- a/datasets/SNDRJ2CrISL1B_3.json +++ b/datasets/SNDRJ2CrISL1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRJ2CrISL1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of creating this product is to allow users to begin working with data from the CrIS instrument on-board the recently launched NOAA-21 / JPSS-2 (J2) satellite. The J2 beta product was generated using an updated version of the software that is used to generate the Version 3 SNPP and JPSS-1 (J1) products, which are currently available from GES DISC. Changes were made to allow J2 processing, but the underlying algorithm theoretical basis remains the same. It should be noted that work on refining the J2 calibration parameters is ongoing, and the parameters used in this release are preliminary. A future Version 4 set of products is planned for release after J2 has reached validated status, which will include optimized J2 parameters and will use a single software version for all three instruments incorporating various algorithm improvements. \n\nThe Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). The JPSS-2 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. \n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nThe FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nAn issue has been observed in J2 CrIS data that impacts calibration of observations for a period of several minutes during each orbit. The issue occurs in the southern hemisphere during the descending part of the orbit, near a satellite solar zenith angle of -118 degrees. There is significant variability in affected latitudes throughout the year. The issue is indicated by non-zero quality flags in the Level 1B product. The root cause is believed to be variation in calibration views due to rapid instrument temperature changes after the J2 satellite passes from the Earth's shadow into direct sunlight. Potential solutions are being evaluated for implementation in a future product version. \n\nIf you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.", "links": [ { diff --git a/datasets/SNDRSNATMSMAP_1.json b/datasets/SNDRSNATMSMAP_1.json index 77d92c5ac8..906493bd1e 100644 --- a/datasets/SNDRSNATMSMAP_1.json +++ b/datasets/SNDRSNATMSMAP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNATMSMAP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (S-NPP).\n\nThe ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8.3 seconds.\n \nData products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format. ", "links": [ { diff --git a/datasets/SNDRSNCrISL1BIMGC_2.json b/datasets/SNDRSNCrISL1BIMGC_2.json index dd16ff1b67..b7d0362a55 100644 --- a/datasets/SNDRSNCrISL1BIMGC_2.json +++ b/datasets/SNDRSNCrISL1BIMGC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNCrISL1BIMGC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). \n\nThe NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG_COL product contains the colocation indices of the VIIRS pixels within each CrIS footprint.", "links": [ { diff --git a/datasets/SNDRSNCrISL1BIMG_2.json b/datasets/SNDRSNCrISL1BIMG_2.json index c7fb02a4d6..e5d44d96ec 100644 --- a/datasets/SNDRSNCrISL1BIMG_2.json +++ b/datasets/SNDRSNCrISL1BIMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNCrISL1BIMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). \n\nThe NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint..", "links": [ { diff --git a/datasets/SNDRSNIL1BCALSUBRNDN_2.json b/datasets/SNDRSNIL1BCALSUBRNDN_2.json index 81ea682ffd..5c10723595 100644 --- a/datasets/SNDRSNIL1BCALSUBRNDN_2.json +++ b/datasets/SNDRSNIL1BCALSUBRNDN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIL1BCALSUBRNDN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. Infrared temperature sounders generate a large amount of Level-1B spectral data. \n\nThe purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to:\n 1. Facilitate a quick evaluation of the absolute calibration of the instruments.\n 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions.\n 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends.\n 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets.\n\nThe output files are constructed from Level-1B and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. \n\nThe S-NPP CrIS summary subset contains Level-1B BTs for all selection types but only for selected channels, while the S-NPP CrIS random calibration subset contains full Level-1B spectra for only the randomly selected observations. \n", "links": [ { diff --git a/datasets/SNDRSNIL1BCALSUBSUMN_2.json b/datasets/SNDRSNIL1BCALSUBSUMN_2.json index b6b70fcae7..472bba8fc1 100644 --- a/datasets/SNDRSNIL1BCALSUBSUMN_2.json +++ b/datasets/SNDRSNIL1BCALSUBSUMN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIL1BCALSUBSUMN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. Infrared temperature sounders generate a large amount of Level-1B spectral data. \n\nThe purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to:\n 1. Facilitate a quick evaluation of the absolute calibration of the instruments.\n 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions.\n 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends.\n 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets.\n\nThe \u201csummary\u201d product includes a large set of cases of interest, including all identified spectra that match selection criteria detailed below for clear, special cloud classes, etc. These amount to about 10% of all spectra. But for each selected case only brightness temperatures (BTs) for selected key channels are saved.\n\nThe output files are constructed from Level-1B and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. \n\nThe S-NPP CrIS summary subset contains Level-1B BTs for all selection types but only for selected channels, while the S-NPP CrIS random calibration subset contains full Level-1B spectra for only the randomly selected observations. \n", "links": [ { diff --git a/datasets/SNDRSNIL2ESPNH3_1.json b/datasets/SNDRSNIL2ESPNH3_1.json index 65f642dfdf..aff50d5858 100644 --- a/datasets/SNDRSNIL2ESPNH3_1.json +++ b/datasets/SNDRSNIL2ESPNH3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIL2ESPNH3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine the ammonia products generated by the ESSPA (Earth System Science Profiling Algorithm) algorithm from the Cross-track Infrared Sounder (CrIS) instruments. The CrIS instrument used for this product is deployed on board the Suomi National Polar-orbiting Partnership (SNPP) platform and uses the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions.\n\nThe NH3 L2 ammonia algorithm is based on an AER (Atmospheric and Environmental Research, Inc.) program initially developed to process TES (Tropospheric Emissions Spectrometer) trace gas products. This version runs within the ESSPA software framework: it uses the AER OSS forward model and an optimal estimation approach with a Levenberg-Marquardt algorithm. Temperature and water profiles are obtained from the CLIMCAPS Field of Regard (FOR) products, as are initial guesses for surface temperature and emissivity. The algorithm consists of a two-step sequential retrieval the first step retrieves surface temperature and emissivity and the second step an ammonia profile. The algorithm produces ammonia retrievals on every field of view (FOV) in each FOR.\n\nA level 2 granule has been set as 6 minutes of data, 30 footprints crosstrack by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRSNIML2CCPCCRN_2.json b/datasets/SNDRSNIML2CCPCCRN_2.json index e874335dfc..a7612d9255 100644 --- a/datasets/SNDRSNIML2CCPCCRN_2.json +++ b/datasets/SNDRSNIML2CCPCCRN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CCPCCRN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard. \n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \n The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.", "links": [ { diff --git a/datasets/SNDRSNIML2CCPCCR_2.json b/datasets/SNDRSNIML2CCPCCR_2.json index c9e2e95bb8..88f7b3d610 100644 --- a/datasets/SNDRSNIML2CCPCCR_2.json +++ b/datasets/SNDRSNIML2CCPCCR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CCPCCR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 NSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard. \n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \n The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.", "links": [ { diff --git a/datasets/SNDRSNIML2CCPRETN_2.json b/datasets/SNDRSNIML2CCPRETN_2.json index 56a6006659..594ecdf055 100644 --- a/datasets/SNDRSNIML2CCPRETN_2.json +++ b/datasets/SNDRSNIML2CCPRETN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CCPRETN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML2CCPRET_2.json b/datasets/SNDRSNIML2CCPRET_2.json index 3abb72cf25..7b740a3d43 100644 --- a/datasets/SNDRSNIML2CCPRET_2.json +++ b/datasets/SNDRSNIML2CCPRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CCPRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\n", "links": [ { diff --git a/datasets/SNDRSNIML2CHTCCRN_1.json b/datasets/SNDRSNIML2CHTCCRN_1.json index 8f72523499..68e80a3465 100644 --- a/datasets/SNDRSNIML2CHTCCRN_1.json +++ b/datasets/SNDRSNIML2CHTCCRN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CHTCCRN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. \n\nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRI2CCR contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPCCRN contains CRIMSS data processed with an analogous algorithm. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRSNIML2CHTRETN_1.json b/datasets/SNDRSNIML2CHTRETN_1.json index bcd32c80c8..32d74715b2 100644 --- a/datasets/SNDRSNIML2CHTRETN_1.json +++ b/datasets/SNDRSNIML2CHTRETN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CHTRETN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX2SUP contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPRETN contains CRIMSS data processed with an analogous algorithm. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.", "links": [ { diff --git a/datasets/SNDRSNIML2CPSN_2.1.json b/datasets/SNDRSNIML2CPSN_2.1.json index e0b49b7081..23eb6154d9 100644 --- a/datasets/SNDRSNIML2CPSN_2.1.json +++ b/datasets/SNDRSNIML2CPSN_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CPSN_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\n", "links": [ { diff --git a/datasets/SNDRSNIML2CPS_2.1.json b/datasets/SNDRSNIML2CPS_2.1.json index 653e49871f..79ae399e85 100644 --- a/datasets/SNDRSNIML2CPS_2.1.json +++ b/datasets/SNDRSNIML2CPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2CPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n\nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nA level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n \nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\n", "links": [ { diff --git a/datasets/SNDRSNIML2PLEVCPSN_2.1.json b/datasets/SNDRSNIML2PLEVCPSN_2.1.json index 0122140225..328b9824a7 100644 --- a/datasets/SNDRSNIML2PLEVCPSN_2.1.json +++ b/datasets/SNDRSNIML2PLEVCPSN_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2PLEVCPSN_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using Normal Spectral Resolution data from the Suomi-NPP satellite. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm \n\nAn level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. \n\nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n\n", "links": [ { diff --git a/datasets/SNDRSNIML2PLEVCPS_2.1.json b/datasets/SNDRSNIML2PLEVCPS_2.1.json index b9bd8241b9..1b57ae6e0d 100644 --- a/datasets/SNDRSNIML2PLEVCPS_2.1.json +++ b/datasets/SNDRSNIML2PLEVCPS_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2PLEVCPS_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using full spectral resolution inputs. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm\n\nAn level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. \n\nThe CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. \n", "links": [ { diff --git a/datasets/SNDRSNIML2SFSPRET_2.json b/datasets/SNDRSNIML2SFSPRET_2.json index e94afc0798..fe6fb5982a 100644 --- a/datasets/SNDRSNIML2SFSPRET_2.json +++ b/datasets/SNDRSNIML2SFSPRET_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2SFSPRET_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 standard product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This standard product provides retrievals on a reduced vertical profile (11 levels for water profiles and up to 27 for other profile variables). A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML2SFSPSUP_2.json b/datasets/SNDRSNIML2SFSPSUP_2.json index c2645b31a8..d65288586b 100644 --- a/datasets/SNDRSNIML2SFSPSUP_2.json +++ b/datasets/SNDRSNIML2SFSPSUP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML2SFSPSUP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 support product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This support product provides height vertical sampling (up to 98 levels) and also includes more detailed Empirical Orthogonal Function (EOF) information like averaging kernels. A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3CDCCPN_2.json b/datasets/SNDRSNIML3CDCCPN_2.json index a7ca4baa40..5ebe9db883 100644 --- a/datasets/SNDRSNIML3CDCCPN_2.json +++ b/datasets/SNDRSNIML3CDCCPN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CDCCPN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable and applying comprehensive QC methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3CDCCP_2.json b/datasets/SNDRSNIML3CDCCP_2.json index 5acb1621a5..84884eede3 100644 --- a/datasets/SNDRSNIML3CDCCP_2.json +++ b/datasets/SNDRSNIML3CDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3CDCHTN_1.json b/datasets/SNDRSNIML3CDCHTN_1.json index c39092d51f..22deb5f042 100644 --- a/datasets/SNDRSNIML3CDCHTN_1.json +++ b/datasets/SNDRSNIML3CDCHTN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CDCHTN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. \n \nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection with the TqJ suffix (TqJoint) from AIRX3SPD contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML3CDCCPN contains CRIMSS data processed with an analogous algorithm.", "links": [ { diff --git a/datasets/SNDRSNIML3CMCCPN_2.json b/datasets/SNDRSNIML3CMCCPN_2.json index 0a8e3ecda8..3f39920201 100644 --- a/datasets/SNDRSNIML3CMCCPN_2.json +++ b/datasets/SNDRSNIML3CMCCPN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CMCCPN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable and applying comprehensive QC methodology.\n\nComprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n\nWARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 48.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/48.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nIf you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.", "links": [ { diff --git a/datasets/SNDRSNIML3CMCCP_2.json b/datasets/SNDRSNIML3CMCCP_2.json index 4a6eef7d90..ce89eb175e 100644 --- a/datasets/SNDRSNIML3CMCCP_2.json +++ b/datasets/SNDRSNIML3CMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables.\n", "links": [ { diff --git a/datasets/SNDRSNIML3CMCHTN_1.json b/datasets/SNDRSNIML3CMCHTN_1.json index cffb716cb3..6557884b55 100644 --- a/datasets/SNDRSNIML3CMCHTN_1.json +++ b/datasets/SNDRSNIML3CMCHTN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3CMCHTN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThe monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology to form a level-2 daily gridded product. The daily level-3 gridded products are averaged to create the monthly average. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. \n \nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection with the TqJ suffix (TqJoint) from AIRX3STM contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML3CMCCPN contains CRIMSS data processed with an analogous algorithm.", "links": [ { diff --git a/datasets/SNDRSNIML3SDCCPN_2.json b/datasets/SNDRSNIML3SDCCPN_2.json index bb750f9cfc..cc44ba6797 100644 --- a/datasets/SNDRSNIML3SDCCPN_2.json +++ b/datasets/SNDRSNIML3SDCCPN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SDCCPN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable and applying specific QC methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\nWARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 48.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/48.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov", "links": [ { diff --git a/datasets/SNDRSNIML3SDCCP_2.json b/datasets/SNDRSNIML3SDCCP_2.json index c32deeadb0..a4a72a3b75 100644 --- a/datasets/SNDRSNIML3SDCCP_2.json +++ b/datasets/SNDRSNIML3SDCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SDCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3SDCHTN_1.json b/datasets/SNDRSNIML3SDCHTN_1.json index ee8b7e921e..df65ac8757 100644 --- a/datasets/SNDRSNIML3SDCHTN_1.json +++ b/datasets/SNDRSNIML3SDCHTN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SDCHTN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying profile level specific quality control (QC). Specific QC is defined per retrieved geophysical parameter at a given level within a profile. It accepts profile level data from the top of the atmosphere down to the level where the QC algorithm determines that the retrieval is good. Below this level, the data is rejected. Specific QC is designed to maximize the yield of each variable. This is the same methodology used by the AIRS Version 6 processing system.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX3SPD contains similar meteorological information to this CHART data collection.", "links": [ { diff --git a/datasets/SNDRSNIML3SDSFSP_2.json b/datasets/SNDRSNIML3SDSFSP_2.json index 29a0ba6640..11b50f36c2 100644 --- a/datasets/SNDRSNIML3SDSFSP_2.json +++ b/datasets/SNDRSNIML3SDSFSP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SDSFSP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SIFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP L3 products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including atmospheric temperature profiles, water vapor, ozone profiles, clouds, and surface properties. \n\nThis daily one half degree latitude by one half degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use). The Level-3 products maximize the yield of each individual variable and level by collecting all cases where the corresponding Level 2 quality control from the top of the atmosphere down to the level where the QC determines that the value is\u00a00\u00a0(best) or 1 (good). Below this level, the data is rejected. This gives the greatest possible yield for any given level and variable. \n\n", "links": [ { diff --git a/datasets/SNDRSNIML3SMCCPN_2.json b/datasets/SNDRSNIML3SMCCPN_2.json index 9457808ce4..e6fb2aa7dc 100644 --- a/datasets/SNDRSNIML3SMCCPN_2.json +++ b/datasets/SNDRSNIML3SMCCPN_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SMCCPN_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n\nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable and applying specific QC methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3SMCCP_2.json b/datasets/SNDRSNIML3SMCCP_2.json index 7fee0839fe..bd607bf37d 100644 --- a/datasets/SNDRSNIML3SMCCP_2.json +++ b/datasets/SNDRSNIML3SMCCP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SMCCP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WARNING: To users of the derived product \u201cco_mmr_midtrop\u201d (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply \u201cco_mmr_midtrop\u201d by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (\u201cmol_lay/co_mol_lay\u201d) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov\n\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid.\n \nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n\n", "links": [ { diff --git a/datasets/SNDRSNIML3SMCHTN_1.json b/datasets/SNDRSNIML3SMCHTN_1.json index 1d84aba74b..d70de583ff 100644 --- a/datasets/SNDRSNIML3SMCHTN_1.json +++ b/datasets/SNDRSNIML3SMCHTN_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SMCHTN_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\n\nThe monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology to form a level-2 daily gridded product. Specific QC is defined per retrieved geophysical parameter at a given level within a profile. It accepts profile level data from the top of the atmosphere down to the level where the QC algorithm determines that the retrieval is good. Below this level, the data is rejected. This is the same methodology used by the AIRS Version 6 processing system. The daily level-3 gridded products are averaged to create the monthly average. \n\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection from AIRX3STM contains similar meteorological information to this CHART data collection.", "links": [ { diff --git a/datasets/SNDRSNIML3SMSFSP_2.json b/datasets/SNDRSNIML3SMSFSP_2.json index 3dd4612ac8..6b44d504ab 100644 --- a/datasets/SNDRSNIML3SMSFSP_2.json +++ b/datasets/SNDRSNIML3SMSFSP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNIML3SMSFSP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SIFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP L3 products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including atmospheric temperature profiles, water vapor, ozone profiles, clouds, and surface properties. \n\nThis monthly one half degree latitude by one half degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use). The Level-3 products maximize the yield of each individual variable and level by collecting all cases where the corresponding Level 2 quality control from the top of the atmosphere down to the level where the QC determines that the value is\u00a00\u00a0(best) or 1 (good). Below this level, the data is rejected. This gives the greatest possible yield for any given level and variable. \n\n\n", "links": [ { diff --git a/datasets/SNDRSNML2RMSSUP_3.json b/datasets/SNDRSNML2RMSSUP_3.json index 81f54fbbd0..2f38c52e75 100644 --- a/datasets/SNDRSNML2RMSSUP_3.json +++ b/datasets/SNDRSNML2RMSSUP_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNML2RMSSUP_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nA level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRSNML2RMS_1.json b/datasets/SNDRSNML2RMS_1.json index 83103eb9ae..2b1f503682 100644 --- a/datasets/SNDRSNML2RMS_1.json +++ b/datasets/SNDRSNML2RMS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNML2RMS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This SNDRSNM2RMS level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm. Different from the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm, the RAMSES II algorithm is a microwave only retrieval algorithm. While the CLIMCAPS uses measurements from both Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite), the RAMSES II only uses observations from ATMS. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz on board the Suomi National Polar-orbiting Partnership (SNPP) platform. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval. A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\nFor the initial release of version 1 RAMSES II, a test data set of 8 months is provided. This includes the months of January, April, July and October of the years 2013 and 2015. The dataset is designed to allow research and comparisons over a full seasonal cycle and comparisons of different phases of the ENSO cycle\n", "links": [ { diff --git a/datasets/SNDRSNML2RMS_3.json b/datasets/SNDRSNML2RMS_3.json index 011b4de75a..bbd641a041 100644 --- a/datasets/SNDRSNML2RMS_3.json +++ b/datasets/SNDRSNML2RMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNML2RMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nA level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n\n", "links": [ { diff --git a/datasets/SNDRSNML3DRMS_3.json b/datasets/SNDRSNML3DRMS_3.json index 0ea3ac28fd..a7268651f4 100644 --- a/datasets/SNDRSNML3DRMS_3.json +++ b/datasets/SNDRSNML3DRMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNML3DRMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n\n", "links": [ { diff --git a/datasets/SNDRSNML3MRMS_3.json b/datasets/SNDRSNML3MRMS_3.json index d93431ff4a..515550f6d8 100644 --- a/datasets/SNDRSNML3MRMS_3.json +++ b/datasets/SNDRSNML3MRMS_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNDRSNML3MRMS_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval\n\nThis monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n\n", "links": [ { diff --git a/datasets/SNEX17_CAR_1.json b/datasets/SNEX17_CAR_1.json index b8c4b77f27..2bbe02e38e 100644 --- a/datasets/SNEX17_CAR_1.json +++ b/datasets/SNEX17_CAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_CAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of the bidirectional reflectance distribution function (BRDF) for two locations in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin approximately 80 miles to the SSE of Grand Mesa.\n\nMeasurements were acquired using the NASA Cloud Absorption Radiometer (CAR), an airborne multi-angular, multi-wavelength scanning radiometer. The CAR instrument measures scattered light in 14 spectral bands between 0.34\u00a0\u03bcm and 2.30\u00a0\u03bcm, which lie in\u00a0the UV, visible, and near-infrared atmospheric windows.\n\nData were obtained for a variety of conditions including\u00a0snow grain size\u00a0(or age),\u00a0snow liquid water content,\u00a0solar zenith angle,\u00a0cloud cover,\u00a0and snowpack thickness. The data set can be used to assess the accuracy of satellite reflectance and albedo products in snow-covered, forested landscapes.", "links": [ { diff --git a/datasets/SNEX17_DGNSS1_1.json b/datasets/SNEX17_DGNSS1_1.json index 6bef458b4a..bd821bc1b9 100644 --- a/datasets/SNEX17_DGNSS1_1.json +++ b/datasets/SNEX17_DGNSS1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_DGNSS1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the coordinates of SnowEx infrastructure in Grand Mesa, Colorado, collected through a differential GNSS real-time kinematic (RTK) survey. The surveys were conducted at 244 stakes along 90 transects, 31 snow pits, 24 time-lapse cameras, and 15 reference poles used to estimate snow depth from camera images. Data files report the name, location, elevation, horizontal and vertical precision, date and time, original easting and northing, and any relevant notes for each survey point.\n\nReadings were collected using a Trimble R8 GNSS base station and two rovers: a Trimble R8 (Hiemstra) and a Trimble R10 (Gelvin). Both rovers were deployed within approximately 6km of the base station and equipped with a GNSS antennae and a base-station radio antennae from which to receive corrections.", "links": [ { diff --git a/datasets/SNEX17_GPR_2.json b/datasets/SNEX17_GPR_2.json index 11ef0c9e49..1b0ea91922 100644 --- a/datasets/SNEX17_GPR_2.json +++ b/datasets/SNEX17_GPR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_GPR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of a ground penetrating radar survey conducted at Grand Mesa, Colorado. Data include the two-way travel time, calculated snow depth, and calculated snow water equivalent. Data were collected between 08 February 2017 and 25 February 2017 as part of the 2017 SnowEx campaign.\n\nThe unprocessed, raw data are also archived at NSIDC (DOI: 10.5067/ZPOLBRHVWG5V).", "links": [ { diff --git a/datasets/SNEX17_GPR_Raw_1.json b/datasets/SNEX17_GPR_Raw_1.json index 28823be81d..1c803b7d54 100644 --- a/datasets/SNEX17_GPR_Raw_1.json +++ b/datasets/SNEX17_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the RAW and UNPROCESSED results of a ground penetrating radar survey conducted as part of the 2017 SnowEx campaign. The processed data can be found here: https://dx.doi.org/10.5067/NPZYNEEUGQUO\n\nData were collected between 08 February 2017 and 25 February 2017 at Grand Mesa, Colorado. Grand Mesa is a snow-covered, forested study site about 40 miles east of Grand Junction, Colorado.", "links": [ { diff --git a/datasets/SNEX17_KT15_TB_1.json b/datasets/SNEX17_KT15_TB_1.json index 86f40b8984..0916d8621e 100644 --- a/datasets/SNEX17_KT15_TB_1.json +++ b/datasets/SNEX17_KT15_TB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_KT15_TB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperature readings. Brightness temperatures were measured using a KT-15 pyrometer flown on a P-3 aircraft over 2017 SnowEx campaign study areas in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa. Data represents the nadir brightness temperature below the aircraft.", "links": [ { diff --git a/datasets/SNEX17_P3V_1.json b/datasets/SNEX17_P3V_1.json index 670e76c98f..70022a4dc9 100644 --- a/datasets/SNEX17_P3V_1.json +++ b/datasets/SNEX17_P3V_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_P3V_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains video footage of two locations in Colorado, USA: Grand Mesa, a snow-covered forested study site about 40 miles east of Grand Junction, and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa. Video footage was captured using a video camera mounted to the belly of a P-3 aircraft during the 2017 SnowEx science flights. The video footage is raw.", "links": [ { diff --git a/datasets/SNEX17_QWIP_ST_1.json b/datasets/SNEX17_QWIP_ST_1.json index 22ff29e106..7f9449ff9f 100644 --- a/datasets/SNEX17_QWIP_ST_1.json +++ b/datasets/SNEX17_QWIP_ST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_QWIP_ST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains infrared camera images collected during the 2017 SnowEx campaign in Grand Mesa and Senator Beck Basin, Colorado. Images were taken using a Quantum Well Infrared Photodetector (QWIP) camera system manufactured by QmagiQ and mounted to the P-3 aircraft, which was flown over the study areas. The QWIP camera records images at a rate of 60 Hz and has a field of view of 11x9 degrees. The QWIP camera can distinguish temperature variations of approximately 0.02\u00b0C.\n\nThis data set contains raw QWIP data, presented as \"counts.\" Data are contained in TIFF image files with dimensions of 320x256 counts.", "links": [ { diff --git a/datasets/SNEX17_SBR_1.json b/datasets/SNEX17_SBR_1.json index 8d732ab2d0..6116b1266a 100644 --- a/datasets/SNEX17_SBR_1.json +++ b/datasets/SNEX17_SBR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SBR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of surface-based radiometer (SBR) brightness temperatures of the snow surface and vegetation at Grand Mesa, CO, USA, acquired during NASA's 2017 SnowEx campaign. Four SBRs were deployed in the field and obtained measurements at 89, 37, 19, and 10.67 GHz in both vertical (V-pol) and horizontal (H-pol) polarizations. Data are available for 37 locations across Grand Mesa collected over five days in February, 2017. They include both 2-4 minute observations and 20 minute, continuous measurements during surface melt, with the latter designed to capture the brightness temperature increase caused by the presence of liquid water in the snow. \n\nThis data set also contains snowfork measurements of snow wetness for 13 sites.", "links": [ { diff --git a/datasets/SNEX17_SD_1.json b/datasets/SNEX17_SD_1.json index 2553837fa4..bbf6e1a278 100644 --- a/datasets/SNEX17_SD_1.json +++ b/datasets/SNEX17_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the SnowEx 2017 campaign, contains in situ snow depth measurements at two locations in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin approximately 80 miles to the SSE of Grand Mesa. Measurements were obtained approximately 3 meters apart along multiple transects, using either a standard, handheld 3 meter long probe or a 1.2 meter long MagnaProbe.", "links": [ { diff --git a/datasets/SNEX17_SD_Perm_1.json b/datasets/SNEX17_SD_Perm_1.json index 5757c7541b..ffe392d172 100644 --- a/datasets/SNEX17_SD_Perm_1.json +++ b/datasets/SNEX17_SD_Perm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SD_Perm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snowpack relative permittivities and densities derived from Ground Penetrating Radar (GPR) surveys and airborne lidar observations of snow depths. The results of the original GPR surveys are published in the SnowEx17 Ground Penetrating Radar, Version 2 data set, while the lidar snow depths were sourced from ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Data are available between 08 Feb 2017 to 25 Feb 2017 from Grand Mesa, a snow-covered, forested area about 40 miles east of Grand Junction. Parameters include two-way travel (TWT) time, lidar-measured snow depth, calculated snow water equivalent (SWE), calculated snow density, and calculated relative permittivity.", "links": [ { diff --git a/datasets/SNEX17_SMP2_1.json b/datasets/SNEX17_SMP2_1.json index 51f756a540..fec0bd16a6 100644 --- a/datasets/SNEX17_SMP2_1.json +++ b/datasets/SNEX17_SMP2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SMP2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of raw penetration force profiles measured at 8 different snow pits located in Senator Beck, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths.", "links": [ { diff --git a/datasets/SNEX17_SMP_1.json b/datasets/SNEX17_SMP_1.json index 9de4f71c1f..dd2a0b648c 100644 --- a/datasets/SNEX17_SMP_1.json +++ b/datasets/SNEX17_SMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw penetration force profiles measured at snow pits and along linear transects at Grand Mesa, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths.", "links": [ { diff --git a/datasets/SNEX17_SSA_1.json b/datasets/SNEX17_SSA_1.json index 9c2fde86c9..8466572271 100644 --- a/datasets/SNEX17_SSA_1.json +++ b/datasets/SNEX17_SSA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SSA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports vertical profiles of snow reflectance, specific surface area (SSA), and optical equivalent diameter (grain size) at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. Reflectance was measured in situ using a 1310\u00a0nm integrating sphere laser device and converted to SSA and optical equivalent diameter.", "links": [ { diff --git a/datasets/SNEX17_SSD_1.json b/datasets/SNEX17_SSD_1.json index 2f880a2aff..547a29b597 100644 --- a/datasets/SNEX17_SSD_1.json +++ b/datasets/SNEX17_SSD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SSD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east. The sensors were positioned to represent three primary vegetation conditions: open-canopy; canopy-edge; and closed-canopy. A total of 10 and 7 sensors recorded usable data at the west and east sites, respectively, from the beginning of the snow season in November 2016 through the end in June 2017.\n\nThese data can be used for a variety of purposes, including: model forcing, calibration, and validation; evaluation of airborne and satellite remote sensing data; to analyze how vegetation affects snow accumulation and ablation.", "links": [ { diff --git a/datasets/SNEX17_SnowPits_1.json b/datasets/SNEX17_SnowPits_1.json index 33a36f9c97..39a6fe089d 100644 --- a/datasets/SNEX17_SnowPits_1.json +++ b/datasets/SNEX17_SnowPits_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SnowPits_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of snow temperature, density, stratigraphy, grain size, wetness, depth, and snow water equivalent (SWE) for snow pits at two study sites in Colorado, USA: Grand Mesa and Senator Beck Basin. Data were collected during the NASA SnowEx 2017 campaign.", "links": [ { diff --git a/datasets/SNEX17_SnowSAR_1.json b/datasets/SNEX17_SnowSAR_1.json index 6dc8d0376e..f9507b4c8d 100644 --- a/datasets/SNEX17_SnowSAR_1.json +++ b/datasets/SNEX17_SnowSAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SnowSAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the SnowEx 2017 campaign, contains multi-look synthetic aperture radar (SAR) amplitude images. The images are stored in netCDF files; browse images are also available. Data were collected using the SnowSAR instrument at the X (9.6 GHz) and Ku (17.25 GHz) bands. The SnowSAR instrument flew on the NP-3C Orion aircraft and captured data from across Colorado, including near the SnowEx 2017 Grand Mesa, Colorado study site and Vail, Colorado. Data were acquired between 16 February 2017 and 22 February 2017 and have a resolution of 1 meter by 1 meter.", "links": [ { diff --git a/datasets/SNEX17_SnowSAR_Raw_1.json b/datasets/SNEX17_SnowSAR_Raw_1.json index 855faee426..318066ca75 100644 --- a/datasets/SNEX17_SnowSAR_Raw_1.json +++ b/datasets/SNEX17_SnowSAR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_SnowSAR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, part of the SnowEx 2017 campaign, contains raw data captured from the SnowSAR instrument. The processed SnowSAR data are also archived at NSIDC (DOI: 10.5067/TWRTXCYBCBB8).\n\nThe SnowSAR instrument flew on the NP-3C Orion aircraft and collected data at the X (9.6 GHz) and Ku (17.25 GHz) bands. Data were captured across Colorado, including near the SnowEx 2017 Grand Mesa, Colorado study site and Vail, Colorado. Data were acquired between 16 February 2017 and 22 February 2017.", "links": [ { diff --git a/datasets/SNEX17_TLI_1.json b/datasets/SNEX17_TLI_1.json index 37491129fe..7116d673b9 100644 --- a/datasets/SNEX17_TLI_1.json +++ b/datasets/SNEX17_TLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_TLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains time-lapse images. Cameras were placed around Grand Mesa, CO at 34 sites and around Senator Beck Basin, CO at one site, coincident with other SnowEx 2017 measurements, including the TLS scans, sonic snow depth arrays, weather stations, and local scale observation sites.", "links": [ { diff --git a/datasets/SNEX17_TLS_PC_BSU_1.json b/datasets/SNEX17_TLS_PC_BSU_1.json index b1c07b0cac..d1f3106879 100644 --- a/datasets/SNEX17_TLS_PC_BSU_1.json +++ b/datasets/SNEX17_TLS_PC_BSU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_TLS_PC_BSU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2016) and snow-on (February 2017) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.", "links": [ { diff --git a/datasets/SNEX17_TLS_PC_BSU_Raw_1.json b/datasets/SNEX17_TLS_PC_BSU_Raw_1.json index 3be89c5a8f..fafd48020f 100644 --- a/datasets/SNEX17_TLS_PC_BSU_Raw_1.json +++ b/datasets/SNEX17_TLS_PC_BSU_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_TLS_PC_BSU_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected in the fall (September 2016) and winter (February 2017). Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i).", "links": [ { diff --git a/datasets/SNEX17_TLS_PC_CRREL_1.json b/datasets/SNEX17_TLS_PC_CRREL_1.json index 15d66e8480..165de95d89 100644 --- a/datasets/SNEX17_TLS_PC_CRREL_1.json +++ b/datasets/SNEX17_TLS_PC_CRREL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_TLS_PC_CRREL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2017 SnowEx campaign. Data were collected in the fall (September and October) and winter (February) seasons. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available. This is unprocessed data which has not been classified by land use (e.g. bare earth, low vegetation, trees).", "links": [ { diff --git a/datasets/SNEX17_UWScat_1.json b/datasets/SNEX17_UWScat_1.json index 6f2feebda3..be93db2644 100644 --- a/datasets/SNEX17_UWScat_1.json +++ b/datasets/SNEX17_UWScat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX17_UWScat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of ground-based scatterometer data acquired during the SnowEx 2017 campaign at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. The data comprise operational parameters used during data acquisition, Mueller matrices for each acquisition, and the nearfield-corrected normalized radar cross section (NRCS) in VV, VH, HV, and HH polarizations. Range profile data are also provided for each scan which report the raw power returned as a function of range from the antenna.", "links": [ { diff --git a/datasets/SNEX20_A19_GSM_1.json b/datasets/SNEX20_A19_GSM_1.json index 0901e5e2e4..7f8da10f2c 100644 --- a/datasets/SNEX20_A19_GSM_1.json +++ b/datasets/SNEX20_A19_GSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_A19_GSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravimetric soil moisture data measured from collected soil samples in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. A total of 77 soil samples were taken at snow pit locations, soil moisture stations, and other areas where both the mobile and stationary COsmic-ray Soil Moisture Observing Systems (COSMOS) operated.", "links": [ { diff --git a/datasets/SNEX20_A19_SD_1.json b/datasets/SNEX20_A19_SD_1.json index 1528763e4d..47a5be9e4b 100644 --- a/datasets/SNEX20_A19_SD_1.json +++ b/datasets/SNEX20_A19_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_A19_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow depth was measured in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. Measurements were taken in the vicinity of snow pit locations and between pit locations using a Magnaprobe or manual depth probes.", "links": [ { diff --git a/datasets/SNEX20_A19_SP_1.json b/datasets/SNEX20_A19_SP_1.json index 91c4fca4c0..aeb0533fb8 100644 --- a/datasets/SNEX20_A19_SP_1.json +++ b/datasets/SNEX20_A19_SP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_A19_SP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow pit measurements obtained as part of the SnowEx 2020 campaign at the Grand Mesa, Colorado, USA site in Autumn 2019. 21 locations were visited for snow pit observations. Some snow pit measurements are incomplete due to shallow or discontinuous snow cover.", "links": [ { diff --git a/datasets/SNEX20_A19_SWE_1.json b/datasets/SNEX20_A19_SWE_1.json index 3836773e3b..21b53489eb 100644 --- a/datasets/SNEX20_A19_SWE_1.json +++ b/datasets/SNEX20_A19_SWE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_A19_SWE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow water equivalent (SWE) measurements of the autumn shallow snow were obtained using SWE samplers (also referred to as SWE tubes) during the NASA SnowEx 2020 campaign (03-06 Nov. 2019) at Grand Mesa, CO. 221 measurements were taken in the vicinity of snow pit locations.", "links": [ { diff --git a/datasets/SNEX20_BR_BSU_SMP_1.json b/datasets/SNEX20_BR_BSU_SMP_1.json index 3bc66b068b..ef5382d2a6 100644 --- a/datasets/SNEX20_BR_BSU_SMP_1.json +++ b/datasets/SNEX20_BR_BSU_SMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BR_BSU_SMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw penetration force profiles from the SnowEx 2020 Time Series campaign in the Boise River Basin in Idaho. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer, from three snow pits. The data files contain force measurements of the snowpack from the top of the snow surface to the ground. Measurements took place approximately weekly between 18 December 2019 and 24 January 2020.", "links": [ { diff --git a/datasets/SNEX20_BSU_CMP_Raw_1.json b/datasets/SNEX20_BSU_CMP_Raw_1.json index ce7e759823..9fa885d535 100644 --- a/datasets/SNEX20_BSU_CMP_Raw_1.json +++ b/datasets/SNEX20_BSU_CMP_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BSU_CMP_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the geolocated, unprocessed, common midpoint (CMP) gathers from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Multi-offset gathers were collected by placing antennas on the snow surface and expanding the antenna separation about a fixed midpoint out to a 2 m offset. CMP gathers were collected in HH and HV polarizations.\nData were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). \nThese data can be used to estimate snow depth, snow density, and snow water equivalent (SWE).", "links": [ { diff --git a/datasets/SNEX20_BSU_CMP_SWE_1.json b/datasets/SNEX20_BSU_CMP_SWE_1.json index c3c26d7644..39de34e980 100644 --- a/datasets/SNEX20_BSU_CMP_SWE_1.json +++ b/datasets/SNEX20_BSU_CMP_SWE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BSU_CMP_SWE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain snow water equivalent (SWE) estimates. SWE is derived from Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR) two-way travel times. \nData were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). \nThe raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.", "links": [ { diff --git a/datasets/SNEX20_BSU_CMP_TT_1.json b/datasets/SNEX20_BSU_CMP_TT_1.json index d69ed45771..41a8165d63 100644 --- a/datasets/SNEX20_BSU_CMP_TT_1.json +++ b/datasets/SNEX20_BSU_CMP_TT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BSU_CMP_TT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the radar two-way travel times from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). \nThe raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.", "links": [ { diff --git a/datasets/SNEX20_BSU_GPR_1.json b/datasets/SNEX20_BSU_GPR_1.json index 386720a3c8..28284338b9 100644 --- a/datasets/SNEX20_BSU_GPR_1.json +++ b/datasets/SNEX20_BSU_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BSU_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow water equivalent (SWE) and snow depth estimates derived from Ground Penetrating Radar (GPR) measurements. GPR measurements were collected during the SnowEx 2020 Grand Mesa Intensive Observation Period (IOP) between 28 January and 04 February 2020. Snow depth was estimated from GPR two-way travel times and average radar velocity; SWE was estimated from snow depth and snow density.\n\nThese data are derived from the SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR Raw, Version 1 data set (DOI: 10.5067/CJNEM8UDNXKA)", "links": [ { diff --git a/datasets/SNEX20_BSU_GPR_Raw_1.json b/datasets/SNEX20_BSU_GPR_Raw_1.json index 143e5ccda1..b730c7e7f4 100644 --- a/datasets/SNEX20_BSU_GPR_Raw_1.json +++ b/datasets/SNEX20_BSU_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_BSU_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the geolocated, unprocessed results of a ground penetrating radar survey conducted as part of the 2020 SnowEx campaign. Data were collected at Grand Mesa, Colorado (a snow-covered, forested study site) between 28 January 2020 and 04 February 2020.", "links": [ { diff --git a/datasets/SNEX20_COCP_GPR_1.json b/datasets/SNEX20_COCP_GPR_1.json index f9fa9de1b5..679e4d9760 100644 --- a/datasets/SNEX20_COCP_GPR_1.json +++ b/datasets/SNEX20_COCP_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_COCP_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 18 December 2019 and 12 March 2020", "links": [ { diff --git a/datasets/SNEX20_COCP_GPR_Raw_1.json b/datasets/SNEX20_COCP_GPR_Raw_1.json index 551ce85729..84d268ce62 100644 --- a/datasets/SNEX20_COCP_GPR_Raw_1.json +++ b/datasets/SNEX20_COCP_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_COCP_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data were collected between 18 December 2019 and 12 March 2020.", "links": [ { diff --git a/datasets/SNEX20_COCP_SPD_1.json b/datasets/SNEX20_COCP_SPD_1.json index b9a094d222..b5c151861f 100644 --- a/datasets/SNEX20_COCP_SPD_1.json +++ b/datasets/SNEX20_COCP_SPD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_COCP_SPD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, lidar-measured snow depth, derived snow water equivalent, derived snow density, and derived relative permittivity. Ground-penetrating radar two-way travel times were sourced from two previously published data sets: SnowEx20 Cameron Pass Ground Penetrating Radar, Version 1 and SnowEx21 Cameron Pass Ground Penetrating Radar, Version 1. The new data presented here represents a recalculation of the snow water equivalent (SWE), snow density, and relative permittivity using terrestrial lidar scan (TLS) data.", "links": [ { diff --git a/datasets/SNEX20_CRSM_1.json b/datasets/SNEX20_CRSM_1.json index c683be83ee..acf4762e33 100644 --- a/datasets/SNEX20_CRSM_1.json +++ b/datasets/SNEX20_CRSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_CRSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the raw and processed data files from a COSMOS Rover soil moisture probe. The COSMOS Rover uses fast neutron counting to estimate soil moisture over a region up to 300m from the vehicle. Data were collected in one-minute intervals over four days (04 November to 07 November 2019) of driving around Grand Mesa, Colorado. Raw data parameters include atmospheric pressure, temperature, and relative humidity. These raw observations were converted to volumetric soil moisture in the processed data files.", "links": [ { diff --git a/datasets/SNEX20_CSSM_1.json b/datasets/SNEX20_CSSM_1.json index 8409f58d2a..e4c73798dc 100644 --- a/datasets/SNEX20_CSSM_1.json +++ b/datasets/SNEX20_CSSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_CSSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data contain raw and processed hourly observations from a Hydroinnova COSMOS Stationary sensor probe. Parameters in the raw files include atmospheric pressure, temperature, and relative humidity. These observations were converted to volumetric soil moisture in the processed data files. Data were collected between 26 August 2019 and 31 May 2020 at Grand Mesa, Colorado and represent a 200 m to 300 m area around the instrument.", "links": [ { diff --git a/datasets/SNEX20_DSM_1.json b/datasets/SNEX20_DSM_1.json index afae74f464..5bdd70c46b 100644 --- a/datasets/SNEX20_DSM_1.json +++ b/datasets/SNEX20_DSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_DSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2020 field campaign. Soil moisture probes were deployed at 10 stations within the Colorado Grand Mesa study area and monitored soil properties at three different depths (5, 10, and 20 cm).", "links": [ { diff --git a/datasets/SNEX20_GIS_REF_1.json b/datasets/SNEX20_GIS_REF_1.json index 4f911ef1e3..2e4b52e1cc 100644 --- a/datasets/SNEX20_GIS_REF_1.json +++ b/datasets/SNEX20_GIS_REF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GIS_REF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains geolocation information of the infrastructure locations for the SnowEx20 Intensive Observation Period (IOP) and Time Series (TS) campaigns. Available scientific infrastructure locations in this data set are tower and sensor locations, aircraft flight lines, planned and actual snow pit locations, and time-lapse camera locations. Additionally, this data set contains areal snow depth and tree density classification matrix over the Grand Mesa, CO study area.", "links": [ { diff --git a/datasets/SNEX20_GM_CSU_GPR_1.json b/datasets/SNEX20_GM_CSU_GPR_1.json index 9e302bbee2..68c498fe32 100644 --- a/datasets/SNEX20_GM_CSU_GPR_1.json +++ b/datasets/SNEX20_GM_CSU_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_CSU_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two-way travel times, snow depth and snow water equivalent (SWE) collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020.\n\nThese data are derived from the SnowEx20 Grand Mesa IOP CSU 1GHz GPR Raw, Version 1 data set (DOI: 10.5067/CT6NS2LIASRS)", "links": [ { diff --git a/datasets/SNEX20_GM_CSU_GPR_RAW_1.json b/datasets/SNEX20_GM_CSU_GPR_RAW_1.json index 9c3ec9987a..3fa805289b 100644 --- a/datasets/SNEX20_GM_CSU_GPR_RAW_1.json +++ b/datasets/SNEX20_GM_CSU_GPR_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_CSU_GPR_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set the raw files collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020.\n\nThe more processed data are published in SnowEx20 Grand Mesa IOP CSU 1GHz GPR, Version 1 (DOI: 10.5067/S5EGFLCIAB18)", "links": [ { diff --git a/datasets/SNEX20_GM_CTSM_1.json b/datasets/SNEX20_GM_CTSM_1.json index 7edd08cf0e..3c4be5c98c 100644 --- a/datasets/SNEX20_GM_CTSM_1.json +++ b/datasets/SNEX20_GM_CTSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_CTSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files. Containers of snow were collected at 6 snow pits in approximately 17 cm intervals. There were approximately 5-8 discrete containers per pit and each container had an ~2 cm overlap with the sample below. A 6-cm section of snow was dissected from each container of snow, which were analyzed in ~2 cm sub-sections. Each sub-section was scanned in three intervals using a micro-CT instrument. The three interval scans comprise multiple slices, and were combined into the reconstructed final scan used for calculating the snow microstructural data.", "links": [ { diff --git a/datasets/SNEX20_GM_Lidar_1.json b/datasets/SNEX20_GM_Lidar_1.json index 5fec0c0063..4031e9021a 100644 --- a/datasets/SNEX20_GM_Lidar_1.json +++ b/datasets/SNEX20_GM_Lidar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_Lidar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains rasterized snow depth maps derived from lidar point cloud data collected from Grand Mesa, Colorado during the SnowEx20 campaign. The subset data file was used as input data to derive SWE and snow density data, which is available as SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density, Version 1.", "links": [ { diff --git a/datasets/SNEX20_GM_SP_1.json b/datasets/SNEX20_GM_SP_1.json index 5fe64830b3..127e2f41e8 100644 --- a/datasets/SNEX20_GM_SP_1.json +++ b/datasets/SNEX20_GM_SP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_SP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow pit observations from the SnowEx 2020 Grand Mesa Intensive Observation Period. Data were collected from 154 snow pits on Grand Mesa, Colorado between 27 January and 12 February 2020. The main parameters for this data set are snow temperature, snow depth, snow density, snow stratigraphy, snow grain size, liquid water content, and snow water equivalent. In addition to data files, this data set also contains site photos from each snow pit.", "links": [ { diff --git a/datasets/SNEX20_GM_SWE_SD_1.json b/datasets/SNEX20_GM_SWE_SD_1.json index 821dcedf48..1f838b9787 100644 --- a/datasets/SNEX20_GM_SWE_SD_1.json +++ b/datasets/SNEX20_GM_SWE_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_GM_SWE_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set (SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1), a lidar-based digital terrain model (ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1), and two GPR data sets (SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1 and Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1). Data was collected during the NASA SnowEx 2020 field campaign in Grand Mesa, Colorado.", "links": [ { diff --git a/datasets/SNEX20_J_UNM_GPR_1.json b/datasets/SNEX20_J_UNM_GPR_1.json index 48cba846ba..9926b3c72b 100644 --- a/datasets/SNEX20_J_UNM_GPR_1.json +++ b/datasets/SNEX20_J_UNM_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_J_UNM_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two-way travel times from a ground penetrating radar survey conducted at Jemez, New Mexico. Data were collected between 12 February 2020 and 04 March 2020 as part of the SnowEx 2020 campaign.", "links": [ { diff --git a/datasets/SNEX20_QSI_DEM_1.json b/datasets/SNEX20_QSI_DEM_1.json index 248fb28fa6..a7a7eee71f 100644 --- a/datasets/SNEX20_QSI_DEM_1.json +++ b/datasets/SNEX20_QSI_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2021 campaign and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 0.5 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside Vegetation Height and Snow Depth data sets.", "links": [ { diff --git a/datasets/SNEX20_QSI_DEM_3m_1.json b/datasets/SNEX20_QSI_DEM_3m_1.json index 5ef2ec6c67..f3df6c744b 100644 --- a/datasets/SNEX20_QSI_DEM_3m_1.json +++ b/datasets/SNEX20_QSI_DEM_3m_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_DEM_3m_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 3.0 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside Vegetation Height and Snow Depth data sets.", "links": [ { diff --git a/datasets/SNEX20_QSI_SD_1.json b/datasets/SNEX20_QSI_SD_1.json index 7702b3348e..976f0ec407 100644 --- a/datasets/SNEX20_QSI_SD_1.json +++ b/datasets/SNEX20_QSI_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside DEM and Vegetation Height data sets.", "links": [ { diff --git a/datasets/SNEX20_QSI_SD_3m_1.json b/datasets/SNEX20_QSI_SD_3m_1.json index 1901db8e25..30b9a9b70f 100644 --- a/datasets/SNEX20_QSI_SD_3m_1.json +++ b/datasets/SNEX20_QSI_SD_3m_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_SD_3m_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside DEM and Vegetation Height data sets.", "links": [ { diff --git a/datasets/SNEX20_QSI_VH_1.json b/datasets/SNEX20_QSI_VH_1.json index ac73993678..10baedcb47 100644 --- a/datasets/SNEX20_QSI_VH_1.json +++ b/datasets/SNEX20_QSI_VH_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_VH_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside DEM and Snow Depth data sets.", "links": [ { diff --git a/datasets/SNEX20_QSI_VH_3m_1.json b/datasets/SNEX20_QSI_VH_3m_1.json index 6b3fd8b1e5..24a00585a0 100644 --- a/datasets/SNEX20_QSI_VH_3m_1.json +++ b/datasets/SNEX20_QSI_VH_3m_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_QSI_VH_3m_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside DEM and Snow Depth data sets.", "links": [ { diff --git a/datasets/SNEX20_SB_GST_1.json b/datasets/SNEX20_SB_GST_1.json index f3f61110ce..06d7c18f19 100644 --- a/datasets/SNEX20_SB_GST_1.json +++ b/datasets/SNEX20_SB_GST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SB_GST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains hourly ground surface temperature measurements collected between 20 October 2019 and 18 July 2020. Data were collected at 7 points along two plant monitoring transects in the Upper Basin of the Senator Beck Study Basin. Temperatures were measured using iButton temperature sensors.", "links": [ { diff --git a/datasets/SNEX20_SD_1.json b/datasets/SNEX20_SD_1.json index 8a7fa8b58e..82b1418314 100644 --- a/datasets/SNEX20_SD_1.json +++ b/datasets/SNEX20_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, collected during the SnowEx 2020 Intensive Operation Period (IOP) in Grand Mesa, Colorado, contains in situ snow depth measurements. Snow depth was measured using one of three instruments - Magnaprobe, Mesa 2, or pit ruler. Pit ruler data were collected from 150 snow pits identified for the Grand Mesa IOP. Magnaprobe and Mesa 2 data were collected along spiral tracks moving outwards from snow pit locations. This data set has a temporal coverage from 28 January to 12 February 2020.", "links": [ { diff --git a/datasets/SNEX20_SD_TLI_1.json b/datasets/SNEX20_SD_TLI_1.json index 5cd10c0910..a7743d5f6d 100644 --- a/datasets/SNEX20_SD_TLI_1.json +++ b/datasets/SNEX20_SD_TLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SD_TLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains snow depth measurements derived from time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. The time-lapse images are archived separately at NSIDC (SNEX20_TLI).", "links": [ { diff --git a/datasets/SNEX20_SMP_1.json b/datasets/SNEX20_SMP_1.json index 90c9a35e46..ac093ee89e 100644 --- a/datasets/SNEX20_SMP_1.json +++ b/datasets/SNEX20_SMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw penetration force profiles from the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths. Data are available from 28 January 2020 through 12 February 2020.", "links": [ { diff --git a/datasets/SNEX20_SSA_1.json b/datasets/SNEX20_SSA_1.json index 2ddd8e3255..9d8aa4d0da 100644 --- a/datasets/SNEX20_SSA_1.json +++ b/datasets/SNEX20_SSA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SSA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains vertical profiles of snow reflectance, specific surface area (SSA), and spherical equivalent snow grain diameter. Data were collected during the SnowEx 2020 Grand Mesa, Colorado Intensive Observation Period between 27 January and 12 February 2020. Reflectance was measured at snow pits using one of two integrating sphere laser devices, IRIS (InfraRed IntegraXng Sphere) or IceCube. SSA and spherical equivalent diameter were then derived from reflectance.", "links": [ { diff --git a/datasets/SNEX20_SWESARR_TB_1.json b/datasets/SNEX20_SWESARR_TB_1.json index 375f18e70a..2314da6192 100644 --- a/datasets/SNEX20_SWESARR_TB_1.json +++ b/datasets/SNEX20_SWESARR_TB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_SWESARR_TB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains airborne microwave brightness temperature observations from the Goddard Space Flight Center SWESARR (Snow\nWater Equivalent Synthetic Aperture Radar and Radiometer) instrument during the winter (10-12 February 2020) NASA SnowEx 2020 campaign at Grand Mesa, CO.\nObservations were made at three frequencies (10.65, 18.7, and 36.5 GHz; referred to as X, K, and Ka bands, respectively), at horizontal polarization with a nominal 45-degree look angle.", "links": [ { diff --git a/datasets/SNEX20_TLI_1.json b/datasets/SNEX20_TLI_1.json index 92431447d8..c5aa68b1af 100644 --- a/datasets/SNEX20_TLI_1.json +++ b/datasets/SNEX20_TLI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_TLI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains sub-daily time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. Snow depth data derived from these time-lapse images will be published separately at NSIDC.", "links": [ { diff --git a/datasets/SNEX20_TLS_PC_BSU_1.json b/datasets/SNEX20_TLS_PC_BSU_1.json index 9c7dcb38ba..e9ca97b054 100644 --- a/datasets/SNEX20_TLS_PC_BSU_1.json +++ b/datasets/SNEX20_TLS_PC_BSU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_TLS_PC_BSU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.", "links": [ { diff --git a/datasets/SNEX20_TLS_PC_BSU_RAW_1.json b/datasets/SNEX20_TLS_PC_BSU_RAW_1.json index 33f08fa92f..b055f1a9f4 100644 --- a/datasets/SNEX20_TLS_PC_BSU_RAW_1.json +++ b/datasets/SNEX20_TLS_PC_BSU_RAW_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_TLS_PC_BSU_RAW_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). A processed version of this data set can be found here: https://doi.org/10.5067/F0M99WK7JW4X.", "links": [ { diff --git a/datasets/SNEX20_TLS_PC_CRREL_1.json b/datasets/SNEX20_TLS_PC_CRREL_1.json index 2bf38e4145..0d680b5175 100644 --- a/datasets/SNEX20_TLS_PC_CRREL_1.json +++ b/datasets/SNEX20_TLS_PC_CRREL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_TLS_PC_CRREL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2020 SnowEx campaign. Data were collected in fall 2019 (September) and winter 2020 (January and February). Each data file contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available.", "links": [ { diff --git a/datasets/SNEX20_TS_SP_2.json b/datasets/SNEX20_TS_SP_2.json index c3e3d1d516..fdafdde0fa 100644 --- a/datasets/SNEX20_TS_SP_2.json +++ b/datasets/SNEX20_TS_SP_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_TS_SP_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations. Also available are photos of the field notes and snow pit sites.", "links": [ { diff --git a/datasets/SNEX20_UNM_GPR_1.json b/datasets/SNEX20_UNM_GPR_1.json index c9b8b51eee..68ff235e81 100644 --- a/datasets/SNEX20_UNM_GPR_1.json +++ b/datasets/SNEX20_UNM_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_UNM_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains two-way travel times, snow depth and now water equivalent from a ground penetrating radar survey conducted at Grand Mesa, Colorado. Data were collected between 28 January 2020 and 06 February 2020 as part of the SnowEx 2020 campaign.", "links": [ { diff --git a/datasets/SNEX20_VPTS_Raw_1.json b/datasets/SNEX20_VPTS_Raw_1.json index 58aeb87184..4dfa2ff29e 100644 --- a/datasets/SNEX20_VPTS_Raw_1.json +++ b/datasets/SNEX20_VPTS_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX20_VPTS_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains an eight-day time series of vertical temperature profile measurements. Measurements were collected using a thermoprobe to a depth of 30 cm at two site locations. These data were collected as part of the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Alongside temperature profiles, this data set contains snow depth measurements, manual temperature measurements, and thermal infrared camera images. These data are published without QA/QC or calibration with manual measurements.", "links": [ { diff --git a/datasets/SNEX21_COCP_GPR_1.json b/datasets/SNEX21_COCP_GPR_1.json index afa664eae2..67639ef3d8 100644 --- a/datasets/SNEX21_COCP_GPR_1.json +++ b/datasets/SNEX21_COCP_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_COCP_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 13 January 2021 and 27 May 2021.", "links": [ { diff --git a/datasets/SNEX21_COCP_GPR_Raw_1.json b/datasets/SNEX21_COCP_GPR_Raw_1.json index 818d3b3a65..30a5e1e45c 100644 --- a/datasets/SNEX21_COCP_GPR_Raw_1.json +++ b/datasets/SNEX21_COCP_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_COCP_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data were collected between 13 January 2021 and 27 May 2021.", "links": [ { diff --git a/datasets/SNEX21_DSM_1.json b/datasets/SNEX21_DSM_1.json index 20c196a14b..f3b10e66c9 100644 --- a/datasets/SNEX21_DSM_1.json +++ b/datasets/SNEX21_DSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_DSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2021 field campaign. Soil moisture probes were deployed at 9 locations within the Montana Prairie Station study area and monitored soil properties at four different depths (5, 10, 20 and 50 cm).", "links": [ { diff --git a/datasets/SNEX21_PS_DSM_1.json b/datasets/SNEX21_PS_DSM_1.json index 0417b739e0..8716043dcf 100644 --- a/datasets/SNEX21_PS_DSM_1.json +++ b/datasets/SNEX21_PS_DSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_PS_DSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of Digital Surface Models (DSMs) derived from UAV-LiDAR acquired during the SnowEx 2021 field campaign at the Central Agricultural Research Center in central Montana (USA). The DSMs are at 0.3 m resolution and consist of one snow-off and seven snow-on flights.", "links": [ { diff --git a/datasets/SNEX21_PS_MET_1.json b/datasets/SNEX21_PS_MET_1.json index ff344e18ee..bc31262c47 100644 --- a/datasets/SNEX21_PS_MET_1.json +++ b/datasets/SNEX21_PS_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_PS_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains observations from a meteorological station installed at the Central Agricultural Research Center in Moccasin Montana as part of the NASA SnowEx 2021 Prairie Snow field campaign. Parameters include: air temperature, wind speed and direction, and barometric pressure. Data are available from 12 November 2020 through 2 March 2021.", "links": [ { diff --git a/datasets/SNEX21_SSR_1.json b/datasets/SNEX21_SSR_1.json index be9ae31d53..99294164bd 100644 --- a/datasets/SNEX21_SSR_1.json +++ b/datasets/SNEX21_SSR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_SSR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides apparent surface spectral reflectance imagery which demonstrates snow albedo and snow optical property evolution across two distinct snow-covered environments in Colorado. Data collection occurred in the spring of 2021 as part of the NASA SnowEx mission. The two study sites (Senator Beck Basin and Grand Mesa) were chosen for their contrasting terrain and vegetation characteristics. Data collection occurred over three days (19 March, 1 April, and 29 April) to produce a time series data set across varying snow conditions.", "links": [ { diff --git a/datasets/SNEX21_TS_SP_1.json b/datasets/SNEX21_TS_SP_1.json index 7acd3d8d62..ac47461315 100644 --- a/datasets/SNEX21_TS_SP_1.json +++ b/datasets/SNEX21_TS_SP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX21_TS_SP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. Between November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States. Five of the unique sites had a single visit to establish baseline conditions, while the remaining 19 sites had 3 or more repeat visits throughout the season, with a median visit count of 11.5. On a weekly interval, a snow pit was dug approximately 1 m away from the previous week\u2019s snow pit. Available measured parameters are: snow depth, snow temperature, snow density, stratigraphy, grain size, manual wetness, liquid water content (LWC), and snow water equivalent (SWE). Also available are photos of the field notes and snow pit sites.", "links": [ { diff --git a/datasets/SNEX23_BCEF_TLS_1.json b/datasets/SNEX23_BCEF_TLS_1.json index 0bbdecb41c..5b6f5c81a7 100644 --- a/datasets/SNEX23_BCEF_TLS_1.json +++ b/datasets/SNEX23_BCEF_TLS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_BCEF_TLS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains digital terrain models (DTMs) derived from terrestrial lidar scans (TLS) collected as part of the SnowEx 2023 campaign. Data were collected at the Bonanza Creek Experimental Forest near Fairbanks, Alaska in October 2022 (snow-off conditions) and March 2023 (snow-on conditions). The DTMs are provided as Geographic Tagged Image (GeoTIFF) files, where each file corresponds to a unique survey site. Unprocessed point cloud data from which these DTMs were derived are available as the SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans Raw, Version 1 (SNEX23_BCEF_TLS_Raw) data set", "links": [ { diff --git a/datasets/SNEX23_BCEF_TLS_Raw_1.json b/datasets/SNEX23_BCEF_TLS_Raw_1.json index 27f995c1bf..1375710392 100644 --- a/datasets/SNEX23_BCEF_TLS_Raw_1.json +++ b/datasets/SNEX23_BCEF_TLS_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_BCEF_TLS_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains unprocessed point cloud data created from terrestrial lidar scans (TLS) collected during the SnowEx 2023 campaign from the Bonanza Creek Experimental Forest near Fairbanks, Alaska. Data were collected in October 2022 (snow-off) and March 2023 (snow-on). Digital terrain models (DTMs) derived from the raw point cloud data are available as the SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans, Version 1 (SNEX23_BCEF_TLS) data set", "links": [ { diff --git a/datasets/SNEX23_CBand_1.json b/datasets/SNEX23_CBand_1.json index 23eafbdcac..d892f532da 100644 --- a/datasets/SNEX23_CBand_1.json +++ b/datasets/SNEX23_CBand_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_CBand_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains C-band radar data collected during the NASA SnowEx 2023 Alaska field campaign between 08 March 2023 to 15 March 2023. Data was acquired from two study areas near Fairbanks, Alaska using a multi-polarization radar affixed to sled-mounted tower. The study sites (Caribou Poker Creek watershed and Farmer\u2019s Loop/Creamer\u2019s Field) are boreal forest and wetland environments. Data was also collected from a school adjacent to Farmer\u2019s Loop, to record data from man-made surfaces (i.e., concrete and cultivated grass.)", "links": [ { diff --git a/datasets/SNEX23_CRREL_GPR_1.json b/datasets/SNEX23_CRREL_GPR_1.json index 2e291193c7..49f9053171 100644 --- a/datasets/SNEX23_CRREL_GPR_1.json +++ b/datasets/SNEX23_CRREL_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_CRREL_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include two-way travel (TWT) time, calculated snow depth, and calculated snow water equivalent (SWE). Raw GPR data are available as SnowEx23 CRREL Ground Penetrating Radar Raw, Version 1.", "links": [ { diff --git a/datasets/SNEX23_CRREL_GPR_Raw_1.json b/datasets/SNEX23_CRREL_GPR_Raw_1.json index 4962b3852b..e3b294f145 100644 --- a/datasets/SNEX23_CRREL_GPR_Raw_1.json +++ b/datasets/SNEX23_CRREL_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_CRREL_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include georeferenced multichannel ground-penetrating radargrams stored within .nc files. PRocessed GPR data are available as SnowEx23 CRREL Ground Penetrating Radar, Version 1.", "links": [ { diff --git a/datasets/SNEX23_CSU_GPR_Raw_1.json b/datasets/SNEX23_CSU_GPR_Raw_1.json index 0c270a5328..fa9b243b81 100644 --- a/datasets/SNEX23_CSU_GPR_Raw_1.json +++ b/datasets/SNEX23_CSU_GPR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_CSU_GPR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the raw results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field, 2) the Bonanza Creek Experimental Forest, and 3) the Caribou/Poker Creek Research Watershed.", "links": [ { diff --git a/datasets/SNEX23_Lidar_1.json b/datasets/SNEX23_Lidar_1.json index 0420f7e78f..f73e581744 100644 --- a/datasets/SNEX23_Lidar_1.json +++ b/datasets/SNEX23_Lidar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_Lidar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). The raw data from which these data are derived are available as SnowEx23 Airborne Lidar Scans Raw, Version 1.", "links": [ { diff --git a/datasets/SNEX23_Lidar_Raw_1.json b/datasets/SNEX23_Lidar_Raw_1.json index 144a2f7e82..4dd62e5766 100644 --- a/datasets/SNEX23_Lidar_Raw_1.json +++ b/datasets/SNEX23_Lidar_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_Lidar_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). Processed data, including digital terrain models, snow depth, and canopy height derived from Point Cloud Digital Terrain Models (PCDTMs) are available as SnowEx23 Airborne Lidar-Derived 0.5M Snow Depth and Canopy Height, Version 1.", "links": [ { diff --git a/datasets/SNEX23_MAR22_SD_1.json b/datasets/SNEX23_MAR22_SD_1.json index 33c8185ebf..8030cfae7d 100644 --- a/datasets/SNEX23_MAR22_SD_1.json +++ b/datasets/SNEX23_MAR22_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_MAR22_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain). Snow depth measurements collected from the study sampling sites during the subsequent field season are available as SnowEx23 Mar23 IOP Snow Depth Measurements, Version 1.", "links": [ { diff --git a/datasets/SNEX23_MAR23_SD_1.json b/datasets/SNEX23_MAR23_SD_1.json index e3a22c9365..65eb9b9c5b 100644 --- a/datasets/SNEX23_MAR23_SD_1.json +++ b/datasets/SNEX23_MAR23_SD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_MAR23_SD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik). Snow depth measurements collected from the study sampling sites during the previous field season are available as SnowEx23 Mar22 IOP Snow Depth Measurements, Version 1.", "links": [ { diff --git a/datasets/SNEX23_MAR23_SP_1.json b/datasets/SNEX23_MAR23_SP_1.json index 8ee028c6a5..0e87b3f966 100644 --- a/datasets/SNEX23_MAR23_SP_1.json +++ b/datasets/SNEX23_MAR23_SP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_MAR23_SP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set presents snow pit measurements collected during the NASA SnowEx March 2023 Intensive Observation Period (IOP) in Alaska, USA to use for calibration and validation with coincident airborne SWESARR and lidar measurements as part of the strategy focused on snow water equivalence (SWE) and snow depth (HS). In total, 170 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions. Three study areas represented boreal forest snow near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creek Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT).", "links": [ { diff --git a/datasets/SNEX23_OCT22_GSR_1.json b/datasets/SNEX23_OCT22_GSR_1.json index 2f592a586d..9b154b42a1 100644 --- a/datasets/SNEX23_OCT22_GSR_1.json +++ b/datasets/SNEX23_OCT22_GSR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_OCT22_GSR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 13 snow pits located at the Upper Kuparuk and Toolik (UKT) study site, an arctic tundra environment in Northern Alaska. The raw imagery from which these data are derived are available as SnowEx23 Oct22 Ground Surface Roughness Imagery, Version 1.", "links": [ { diff --git a/datasets/SNEX23_OCT22_GSR_Raw_1.json b/datasets/SNEX23_OCT22_GSR_Raw_1.json index d1a0f95abd..fb33477f02 100644 --- a/datasets/SNEX23_OCT22_GSR_Raw_1.json +++ b/datasets/SNEX23_OCT22_GSR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_OCT22_GSR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The images were collected from 13 snow pits located at the Upper Kuparuk and Toolik (UKT) study site, an arctic tundra environment in Northern Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as SnowEx23 Oct22 Ground Surface Roughness Reconstruction, Version 1.", "links": [ { diff --git a/datasets/SNEX23_OCT23_GSR_1.json b/datasets/SNEX23_OCT23_GSR_1.json index dd9cdc8810..9585dcbbd6 100644 --- a/datasets/SNEX23_OCT23_GSR_1.json +++ b/datasets/SNEX23_OCT23_GSR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_OCT23_GSR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. The raw imagery from which these data are derived are available as SnowEx23 Oct23 Ground Surface Roughness Imagery, Version 1.", "links": [ { diff --git a/datasets/SNEX23_OCT23_GSR_Raw_1.json b/datasets/SNEX23_OCT23_GSR_Raw_1.json index a6761f6317..263d080637 100644 --- a/datasets/SNEX23_OCT23_GSR_Raw_1.json +++ b/datasets/SNEX23_OCT23_GSR_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_OCT23_GSR_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as SnowEx23 Oct23 Ground Surface Roughness Reconstruction, Version 1.", "links": [ { diff --git a/datasets/SNEX23_SSA_1.json b/datasets/SNEX23_SSA_1.json index 295d3853c8..0af2ab5a4b 100644 --- a/datasets/SNEX23_SSA_1.json +++ b/datasets/SNEX23_SSA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_SSA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains vertical profiles of snow reflectance and specific surface area (SSA) from the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer\u2019s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik), collected as part of the NASA SnowEx 2023 field campaign in March 2023. Reflectance was measured in snow pits using three different integrating sphere laser devices: an A2 Photonic Sensor IceCube (1310 nm), an IRIS (InfraRed Integrating Sphere) system (1310 nm), and an InfraSnow SSA sensor (945 nm). Measured reflectance values were converted to SSA during data processing. It is recommended that data users work with either the IceCube or IRIS data, as the InfraSnow data was collected primarily for testing of the instrument\u2019s capabilities. Snow-off SSA data from these same study sites are available as SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data, Version 1.", "links": [ { diff --git a/datasets/SNEX23_SSA_SO_1.json b/datasets/SNEX23_SSA_SO_1.json index 1c7cfd7902..baca4053db 100644 --- a/datasets/SNEX23_SSA_SO_1.json +++ b/datasets/SNEX23_SSA_SO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_SSA_SO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports vertical profiles of snow reflectance and specific surface area (SSA) from two study sites in Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer\u2019s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik). Reflectance was measured in situ using an A2 Photonic Sensor IceCube (1310 nm). Measured reflectance values were converted to SSA during data processing following the methods of Gallet et al., (2009). Snow-on SSA data from these same study sites were collected in March 2023 and are available as SnowEx23 Laser Snow Microstructure Specific Surface Area Data, Version 1.", "links": [ { diff --git a/datasets/SNEX23_SWE_1.json b/datasets/SNEX23_SWE_1.json index 2447c73e69..0ef4388e50 100644 --- a/datasets/SNEX23_SWE_1.json +++ b/datasets/SNEX23_SWE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_SWE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.", "links": [ { diff --git a/datasets/SNEX23_UW_GPR_1.json b/datasets/SNEX23_UW_GPR_1.json index b0ce77225d..c3a69f0cb3 100644 --- a/datasets/SNEX23_UW_GPR_1.json +++ b/datasets/SNEX23_UW_GPR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX23_UW_GPR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Arctic Coastal Plain (ACP) site and the Upper Kuparuk/Toolik (UKT) site in northern Alaska during the SnowEx23 field campaign. Data include two-way travel time, derived snow depth, and derived snow water equivalent. Data were collected between 8 - 14 March 2023.", "links": [ { diff --git a/datasets/SNEX_HRSI_SD_DEM_CO_1.json b/datasets/SNEX_HRSI_SD_DEM_CO_1.json index 61b4f60b0f..b5cc2102ac 100644 --- a/datasets/SNEX_HRSI_SD_DEM_CO_1.json +++ b/datasets/SNEX_HRSI_SD_DEM_CO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX_HRSI_SD_DEM_CO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa and the Banded Peak Ranch areas of Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pl\u00e9iades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.", "links": [ { diff --git a/datasets/SNEX_MCS_Lidar_1.json b/datasets/SNEX_MCS_Lidar_1.json index 8c5b2f1ae0..41390b5b83 100644 --- a/datasets/SNEX_MCS_Lidar_1.json +++ b/datasets/SNEX_MCS_Lidar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX_MCS_Lidar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.", "links": [ { diff --git a/datasets/SNEX_MCS_Lidar_Raw_1.json b/datasets/SNEX_MCS_Lidar_Raw_1.json index b22c653d10..fd64c39c8a 100644 --- a/datasets/SNEX_MCS_Lidar_Raw_1.json +++ b/datasets/SNEX_MCS_Lidar_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX_MCS_Lidar_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign. \n\nDigital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1.", "links": [ { diff --git a/datasets/SNEX_Met_1.json b/datasets/SNEX_Met_1.json index a02e0c8ad3..a0b430e625 100644 --- a/datasets/SNEX_Met_1.json +++ b/datasets/SNEX_Met_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX_Met_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling. Each station collects a suite of meteorological data at a fixed geographic point, from varying heights above and below the surface elevation. Measured data include: air temperature, relative humidity, long and shortwave solar radiation, barometric pressure, soil moisture and temperature, and derived snow depth. The temporal data coverage varies between each station, but spans October 2016 to August 2022. \n\n\nThe dataset(s) contain air temperature and relative humidity (10ft and 20ft levels), 4-component radiation (shortwave, longwave), barometric pressure, soil-moisture and temperature (three depths), and a snow-depth product. Data coverage varies for each met station, but spans the time period of October 2016 \u2013 August, 2022. The data frequency is hourly and times are in UTC. The data is monotonic (no duplicate or mis-orderd timestamps) and steps have been taken to remove erroneous data. Periods of missing data are filled with NaN values. The scripts used to process raw data into the current format are available on the github page (https://github.com/wrudisill/GrandMesaMetData/blob/main/process_data_initial.py).", "links": [ { diff --git a/datasets/SNEX_Met_Raw_1.json b/datasets/SNEX_Met_Raw_1.json index bda3400690..e45ae08ba9 100644 --- a/datasets/SNEX_Met_Raw_1.json +++ b/datasets/SNEX_Met_Raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNEX_Met_Raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains raw meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling", "links": [ { diff --git a/datasets/SNF_ASP_CVR_140_1.json b/datasets/SNF_ASP_CVR_140_1.json index 18c3bde313..1da3f5f819 100644 --- a/datasets/SNF_ASP_CVR_140_1.json +++ b/datasets/SNF_ASP_CVR_140_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_ASP_CVR_140_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average percent coverage and standard deviation of each canopy stratum from subplots at each aspen site during the SNF study in the Superior National Forest, Minnesota", "links": [ { diff --git a/datasets/SNF_BIOMASS_141_1.json b/datasets/SNF_BIOMASS_141_1.json index 704dfde471..6a7feb1c50 100644 --- a/datasets/SNF_BIOMASS_141_1.json +++ b/datasets/SNF_BIOMASS_141_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_BIOMASS_141_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dimension analysis (diameter at breast high, tree height, depth of crown), estimated leaf area, and total aboveground biomass for sacrificed spruce and aspens in Superior National Forest, MN", "links": [ { diff --git a/datasets/SNF_BIOPHYS_142_1.json b/datasets/SNF_BIOPHYS_142_1.json index 1fd4d25871..71130ccd49 100644 --- a/datasets/SNF_BIOPHYS_142_1.json +++ b/datasets/SNF_BIOPHYS_142_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_BIOPHYS_142_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biophysical parameters (DBH, NPP, biomass, bark area index, LAI, subcanopy LAI) by study site for Aspen and Spruce in the Superior National Forest, MN (SNF)", "links": [ { diff --git a/datasets/SNF_CAN_COMP_143_1.json b/datasets/SNF_CAN_COMP_143_1.json index 527922c014..4d81d314ad 100644 --- a/datasets/SNF_CAN_COMP_143_1.json +++ b/datasets/SNF_CAN_COMP_143_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_CAN_COMP_143_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SNF study site count of the number of trees over 2 meters, broken down by species code; see also SNF Plant Species Codes", "links": [ { diff --git a/datasets/SNF_CJ_SITES_187_1.json b/datasets/SNF_CJ_SITES_187_1.json index a745820524..f9e53ec425 100644 --- a/datasets/SNF_CJ_SITES_187_1.json +++ b/datasets/SNF_CJ_SITES_187_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_CJ_SITES_187_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site characterization parameters (canopy density, litter components, soil characterization: color, moisture, components) for selected sites within the Superior National Forest, MN during 1988-89", "links": [ { diff --git a/datasets/SNF_CJ_VEG_189_1.json b/datasets/SNF_CJ_VEG_189_1.json index 222575a531..10ab9ba23e 100644 --- a/datasets/SNF_CJ_VEG_189_1.json +++ b/datasets/SNF_CJ_VEG_189_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_CJ_VEG_189_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biophysical parameters (DBH, shrub diameter, growth format, frequency) for selected sites within the Superior National Forest, MN, during 1988-89", "links": [ { diff --git a/datasets/SNF_HELO_MMR_144_1.json b/datasets/SNF_HELO_MMR_144_1.json index ba436891cf..f49b168cc0 100644 --- a/datasets/SNF_HELO_MMR_144_1.json +++ b/datasets/SNF_HELO_MMR_144_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_HELO_MMR_144_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy spectral reflectance data collected from the helicopter-mounted MMR in the Superior National Forest, Minnesota, 1983-84", "links": [ { diff --git a/datasets/SNF_LEAFCARY_183_1.json b/datasets/SNF_LEAFCARY_183_1.json index e31316bad9..01f9c535a2 100644 --- a/datasets/SNF_LEAFCARY_183_1.json +++ b/datasets/SNF_LEAFCARY_183_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_LEAFCARY_183_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reflectance and transmittance properties of the leaves, needles, branches, moss, and litter of 8 major overstory tree species and 3 understory shrubs measured by Cary-14 spectrometer in the SNF, MN", "links": [ { diff --git a/datasets/SNF_LEAF_EXP_180_1.json b/datasets/SNF_LEAF_EXP_180_1.json index 3aefb1db9c..82d69d8536 100644 --- a/datasets/SNF_LEAF_EXP_180_1.json +++ b/datasets/SNF_LEAF_EXP_180_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_LEAF_EXP_180_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of green leaf coverage during the spring of 1984 for canopy, canopy, and understory species for two aspen sites in the Superior National Forest, Minnesota", "links": [ { diff --git a/datasets/SNF_LEAF_TMS_184_1.json b/datasets/SNF_LEAF_TMS_184_1.json index c5f23049ec..cefe6e05eb 100644 --- a/datasets/SNF_LEAF_TMS_184_1.json +++ b/datasets/SNF_LEAF_TMS_184_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_LEAF_TMS_184_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reflectance and transmittance properties of canopy components, measured by Cary-14 spectrometer and averaged (weighted average) to Thematic Mapper Simulator (TMS) wavelength bands; see SNF_CAN_SPEC", "links": [ { diff --git a/datasets/SNF_MET_1972_158_1.json b/datasets/SNF_MET_1972_158_1.json index 0fa7a04e75..caa40211af 100644 --- a/datasets/SNF_MET_1972_158_1.json +++ b/datasets/SNF_MET_1972_158_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1972_158_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1972_1990_159_1.json b/datasets/SNF_MET_1972_1990_159_1.json index 425903e6ae..4ef84c1e26 100644 --- a/datasets/SNF_MET_1972_1990_159_1.json +++ b/datasets/SNF_MET_1972_1990_159_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1972_1990_159_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1973_160_1.json b/datasets/SNF_MET_1973_160_1.json index 61ad6a7509..b76ebbd10e 100644 --- a/datasets/SNF_MET_1973_160_1.json +++ b/datasets/SNF_MET_1973_160_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1973_160_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1974_161_1.json b/datasets/SNF_MET_1974_161_1.json index 4ff8771a88..77dea0b4e8 100644 --- a/datasets/SNF_MET_1974_161_1.json +++ b/datasets/SNF_MET_1974_161_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1974_161_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1975_162_1.json b/datasets/SNF_MET_1975_162_1.json index 5f40d60423..5aff1fc019 100644 --- a/datasets/SNF_MET_1975_162_1.json +++ b/datasets/SNF_MET_1975_162_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1975_162_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1976_163_1.json b/datasets/SNF_MET_1976_163_1.json index 001511c41f..48564d1884 100644 --- a/datasets/SNF_MET_1976_163_1.json +++ b/datasets/SNF_MET_1976_163_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1976_163_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1977_164_1.json b/datasets/SNF_MET_1977_164_1.json index fa59404e05..f7bb5df679 100644 --- a/datasets/SNF_MET_1977_164_1.json +++ b/datasets/SNF_MET_1977_164_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1977_164_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1978_165_1.json b/datasets/SNF_MET_1978_165_1.json index 8f3fc8eecd..f69aa1b5f5 100644 --- a/datasets/SNF_MET_1978_165_1.json +++ b/datasets/SNF_MET_1978_165_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1978_165_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1979_166_1.json b/datasets/SNF_MET_1979_166_1.json index 5e0f3f61a8..46b3e6e0f8 100644 --- a/datasets/SNF_MET_1979_166_1.json +++ b/datasets/SNF_MET_1979_166_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1979_166_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1980_167_1.json b/datasets/SNF_MET_1980_167_1.json index b106e2f664..8a0b4d9fbf 100644 --- a/datasets/SNF_MET_1980_167_1.json +++ b/datasets/SNF_MET_1980_167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1980_167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1981_168_1.json b/datasets/SNF_MET_1981_168_1.json index 4b8bada0cf..10f044870a 100644 --- a/datasets/SNF_MET_1981_168_1.json +++ b/datasets/SNF_MET_1981_168_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1981_168_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1982_169_1.json b/datasets/SNF_MET_1982_169_1.json index c3d1e3154a..5d5f2d86e6 100644 --- a/datasets/SNF_MET_1982_169_1.json +++ b/datasets/SNF_MET_1982_169_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1982_169_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1983_170_1.json b/datasets/SNF_MET_1983_170_1.json index bba983ecb3..bff9bfed89 100644 --- a/datasets/SNF_MET_1983_170_1.json +++ b/datasets/SNF_MET_1983_170_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1983_170_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1984_171_1.json b/datasets/SNF_MET_1984_171_1.json index b9183a9174..34d18f3a16 100644 --- a/datasets/SNF_MET_1984_171_1.json +++ b/datasets/SNF_MET_1984_171_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1984_171_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1985_172_1.json b/datasets/SNF_MET_1985_172_1.json index c566ed8a35..547c321777 100644 --- a/datasets/SNF_MET_1985_172_1.json +++ b/datasets/SNF_MET_1985_172_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1985_172_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1986_173_1.json b/datasets/SNF_MET_1986_173_1.json index 120aae4e9b..7cc4eb7f93 100644 --- a/datasets/SNF_MET_1986_173_1.json +++ b/datasets/SNF_MET_1986_173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1986_173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1987_174_1.json b/datasets/SNF_MET_1987_174_1.json index b4f18fd2e6..1192183960 100644 --- a/datasets/SNF_MET_1987_174_1.json +++ b/datasets/SNF_MET_1987_174_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1987_174_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1988_175_1.json b/datasets/SNF_MET_1988_175_1.json index f2d6537641..1eb95337c1 100644 --- a/datasets/SNF_MET_1988_175_1.json +++ b/datasets/SNF_MET_1988_175_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1988_175_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1989_176_1.json b/datasets/SNF_MET_1989_176_1.json index f0acc365a3..45c96e2e44 100644 --- a/datasets/SNF_MET_1989_176_1.json +++ b/datasets/SNF_MET_1989_176_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1989_176_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_1990_177_1.json b/datasets/SNF_MET_1990_177_1.json index b72cb4d21b..bafda5ea56 100644 --- a/datasets/SNF_MET_1990_177_1.json +++ b/datasets/SNF_MET_1990_177_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_1990_177_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily min, max, average temperature (F), precipitation (water equivalent in inches), and daily insolation (Langleys) for the Superior National Forest area as collected by NWS and U. of Minnesota", "links": [ { diff --git a/datasets/SNF_MET_SUMM_178_1.json b/datasets/SNF_MET_SUMM_178_1.json index 9dd3687dbc..8a03266fe5 100644 --- a/datasets/SNF_MET_SUMM_178_1.json +++ b/datasets/SNF_MET_SUMM_178_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_MET_SUMM_178_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averages of daily temperature, precipitation and insolation data collected by the NWS for region including the Superior National Forest", "links": [ { diff --git a/datasets/SNF_NS001_185_1.json b/datasets/SNF_NS001_185_1.json index 952461fe8a..b71cc5690e 100644 --- a/datasets/SNF_NS001_185_1.json +++ b/datasets/SNF_NS001_185_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_NS001_185_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy spectral reflectance data collected from the NASA C-130-mounted NS001 Thematic Mapper Simulator (TMS) over the Superior National Forest, MN on 13JUL1983, 06AUG1983, and 28JUN1984.", "links": [ { diff --git a/datasets/SNF_SAT_INV_186_1.json b/datasets/SNF_SAT_INV_186_1.json index eadb8d2fbd..4fb3afc4d0 100644 --- a/datasets/SNF_SAT_INV_186_1.json +++ b/datasets/SNF_SAT_INV_186_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_SAT_INV_186_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inventory of various satellite image data acquired for the Superior National Forest, MN study including MSS, TM, SPOT, and HRV1-HRV2 over a period from 03JUL1983 to 16AUG1990", "links": [ { diff --git a/datasets/SNF_SITECOMP_179_1.json b/datasets/SNF_SITECOMP_179_1.json index 7351f3ede9..37684fac00 100644 --- a/datasets/SNF_SITECOMP_179_1.json +++ b/datasets/SNF_SITECOMP_179_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_SITECOMP_179_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a combined data set of canopy, subcanopy and understory composition by vegetation species and study site ID", "links": [ { diff --git a/datasets/SNF_SITE_86_188_1.json b/datasets/SNF_SITE_86_188_1.json index 974e9e71d1..0867f969fd 100644 --- a/datasets/SNF_SITE_86_188_1.json +++ b/datasets/SNF_SITE_86_188_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_SITE_86_188_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site characterization parameters (canopy density, litter components, species composition) collected from selected sites in the SNF, MN, during 1986 for validating image classification methodology", "links": [ { diff --git a/datasets/SNF_TAB3_3T_182_1.json b/datasets/SNF_TAB3_3T_182_1.json index 610dfdb157..45b963d474 100644 --- a/datasets/SNF_TAB3_3T_182_1.json +++ b/datasets/SNF_TAB3_3T_182_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_TAB3_3T_182_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented in table format)", "links": [ { diff --git a/datasets/SNF_UND_CVR_181_1.json b/datasets/SNF_UND_CVR_181_1.json index 0a2218eafb..c1baf70f18 100644 --- a/datasets/SNF_UND_CVR_181_1.json +++ b/datasets/SNF_UND_CVR_181_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNF_UND_CVR_181_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented as list format)", "links": [ { diff --git a/datasets/SNOWPETRELSURVEYSCASEY0203_1.json b/datasets/SNOWPETRELSURVEYSCASEY0203_1.json index ed3159bcbb..940e90bd00 100644 --- a/datasets/SNOWPETRELSURVEYSCASEY0203_1.json +++ b/datasets/SNOWPETRELSURVEYSCASEY0203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNOWPETRELSURVEYSCASEY0203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of grid sites used to survey for snow petrels in the Windmill Islands during the 2002-2003 season. Descriptive information relating to each grid site was recorded and a detailed description of data fields is provided in the attached dataset.\n\nSurvey methodology used 200*200 m grid squares in which exhaustive searches were conducted (FO). Search effort for these is provided in the dataset.\n\nThe fields in this dataset are:\n\nSite\nNest\nRegion\nDate\nTime\nIce free area\nUTM Coordinates", "links": [ { diff --git a/datasets/SNPEMAWSON04-05_1.json b/datasets/SNPEMAWSON04-05_1.json index 57bd785a30..7d40557927 100644 --- a/datasets/SNPEMAWSON04-05_1.json +++ b/datasets/SNPEMAWSON04-05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPEMAWSON04-05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is known about the distribution and abundance of Snow petrels at the regional and local scales. This dataset contains the locations of Snow petrel nests, mapped in the Mawson region during the 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile (ESRI software).\nDescriptive information relating to each bird nest was recorded and a detailed description of the data fields is provided in the description of the shapefile (word document). A text file also provides the attribute information (formatted for input into R statistical software).\n\nThis work has been completed as part of ASAC project 2704 (ASAC_2704).\n\nFields recorded.\n\nSpecies\nActivity\nType\nEntrances\nSlope\nRemnants\nLatitude\nLongitude\nDate\nSnow\nEggchick\nCavitysize\nCavitydepth\nDistnn\nSubstrate\nComments\nSitedotID\nAspect\nFirstfred", "links": [ { diff --git a/datasets/SNPPATMSL1B_2.json b/datasets/SNPPATMSL1B_2.json index 070e84748e..c69a0ae296 100644 --- a/datasets/SNPPATMSL1B_2.json +++ b/datasets/SNPPATMSL1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPATMSL1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP).\n\nThe ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries.\n\nThe ATMS (Advanced Technology Microwave Sounder) and CrIS (Crosstrack InfraRed Sounder) instruments are meant to operate together as a system, thus providing coverage of a much broader range of atmospheric conditions. The ATMS-CrIS system is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPPATMSL1B_3.json b/datasets/SNPPATMSL1B_3.json index 91ab7f390a..138d04c266 100644 --- a/datasets/SNPPATMSL1B_3.json +++ b/datasets/SNPPATMSL1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPATMSL1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP).\n\nThe ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries.\n\nThe ATMS (Advanced Technology Microwave Sounder) and CrIS (Crosstrack InfraRed Sounder) instruments are meant to operate together as a system, thus providing coverage of a much broader range of atmospheric conditions. The ATMS-CrIS system is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPPCrISL1BNSR_2.json b/datasets/SNPPCrISL1BNSR_2.json index 3339f108e3..69585d4029 100644 --- a/datasets/SNPPCrISL1BNSR_2.json +++ b/datasets/SNPPCrISL1BNSR_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPCrISL1BNSR_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). \n\nThe NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPPCrISL1BNSR_3.json b/datasets/SNPPCrISL1BNSR_3.json index 5385e34d6c..43c854b43b 100644 --- a/datasets/SNPPCrISL1BNSR_3.json +++ b/datasets/SNPPCrISL1BNSR_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPCrISL1BNSR_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). \n\nThe NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPPCrISL1B_2.json b/datasets/SNPPCrISL1B_2.json index 21c8be9e68..b21e4dd811 100644 --- a/datasets/SNPPCrISL1B_2.json +++ b/datasets/SNPPCrISL1B_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPCrISL1B_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution (NSR), after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution. \n\nThe FSR files have 2,223 channels: 637 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPPCrISL1B_3.json b/datasets/SNPPCrISL1B_3.json index 4992e9f11c..9a8c53fd94 100644 --- a/datasets/SNPPCrISL1B_3.json +++ b/datasets/SNPPCrISL1B_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPPCrISL1B_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution (NSR), after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution. \n\nThe FSR files have 2,223 channels: 637 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.\n\nCrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite).\n\nIf you were redirected to this page from a DOI from\nan older version, please note this is the current\nversion of the product. Please contact the GES DISC\nuser support if you need information about previous\ndata collections.", "links": [ { diff --git a/datasets/SNPP_CrIS_VIIRS750m_IND_1.json b/datasets/SNPP_CrIS_VIIRS750m_IND_1.json index e139c1041e..fb342a5469 100644 --- a/datasets/SNPP_CrIS_VIIRS750m_IND_1.json +++ b/datasets/SNPP_CrIS_VIIRS750m_IND_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SNPP_CrIS_VIIRS750m_IND_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " This dataset includes SNPP VIIRS-CrIS collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).\n\nThe short name for this collections is SNPP_CrIS_VIIRS750m_IND\n\n", "links": [ { diff --git a/datasets/SOAR1999WMB.json b/datasets/SOAR1999WMB.json index 1ba8814bce..5279f3114f 100644 --- a/datasets/SOAR1999WMB.json +++ b/datasets/SOAR1999WMB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOAR1999WMB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An aerogeophysical survey of the western Marie Byrd Land region of\n Antarctica was flown in Dec. 1998-Jan. 1999, measuring surface and\n base of ice elevation by radar and strength of magnetic and gravity\n fields. The coverage area measured about 460 by 360 km, long\n dimension oriented NE, and included the Shirase Coast of the eastern\n Ross Ice Shelf, much of the Edward VII Peninsula, the Sulzberger Ice\n Shelf, and the Ford Ranges. Track spacing was either 5.3 or 10.6 km\n over most of the area. The 60 Mhz radar system usually provided good\n images of the base of the ice for thicknesses less than 1 km but\n rarely imaged thicknesses greater than 1.5 km. Determination of\n gravity anomalies required corrections for acceleration of the\n aircraft as measured by differential carrier-phase GPS navigation,\n filtering to remove wavelengths less than 10 km, which are commonly\n contaminated by aircraft motion, and editing of occasional spikes.\n The gravity anomalies allow estimation of bed topography under\n floating ice and under ice too thick for radar imaging. Magnetic\n anomaly reduction includes a correction for daily variation as\n measured at the base camp. Data formats for all observations include\n files for original flight profiles and grids of edited data at 1.06 km\n node spacing.", "links": [ { diff --git a/datasets/SOAR1_UTIG.json b/datasets/SOAR1_UTIG.json index c61108857f..d783c8b725 100644 --- a/datasets/SOAR1_UTIG.json +++ b/datasets/SOAR1_UTIG.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOAR1_UTIG", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of airborne geophysical data collected\nbetween 1994 and 2000 by the National Science Foundation's Support\nOffice for Aerogeophysical Research (SOAR) at the University of Texas\nInstitute for Geophysics. Meaurements were made using a laser\naltimeter, a radar echo sounder, a gravimeter, and a magnetometer.\nPositioning was accomplished with kinematic, differential\ncarrier-phase GPS. Multiple areas within Antarctica were covered,\nincluding both grid and line surveys. Some areas have reduced data\nproducts (i.e., surface and bed elevations, ice thickness, gravity and\nmagnetic field anomalies).", "links": [ { diff --git a/datasets/SOAR2_UTIG.json b/datasets/SOAR2_UTIG.json index a8d22feb76..90774aa604 100644 --- a/datasets/SOAR2_UTIG.json +++ b/datasets/SOAR2_UTIG.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOAR2_UTIG", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of airborne geophysical data collected during 2000/01 by\n researchers at The University of Texas Institute for Geophysics. Meaurements\n were made using a laser altimeter, a radar echo sounder, a gravimeter, and a\n magnetometer. Positioning was accomplished with kinematic, differential\n carrier-phase GPS. The data, reduced by UTIG, includes: surface and bed\n elevations, ice thickness, gravity and magnetic field anomalies. Two distinct\n surveys in East Antarctica are covered: a grid-based survey of subglacial Lake\n Vostok and its environs, and a 1200 km line-based transect extending from the\n Transantarctic Mountains (near 160E, 77S) toward Dome A (near 95E, 82S).", "links": [ { diff --git a/datasets/SOCCOM_0.json b/datasets/SOCCOM_0.json index ce0e7f61c7..276a093ccf 100644 --- a/datasets/SOCCOM_0.json +++ b/datasets/SOCCOM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOCCOM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOCCOM (Southern Ocean Carbon and Climate Observations and Modeling project) is a NSF project sampling the Southern Ocean and its influence on climate.Additional Data LinksCLIVAR P16S_2014 Pigment AnalysisCLIVAR P16S_2014 POC dataCLIVAR P16S_2014 Supporting Documentation", "links": [ { diff --git a/datasets/SOC_3M_Maps_NE_TidalWetlands_1905_1.json b/datasets/SOC_3M_Maps_NE_TidalWetlands_1905_1.json index 7a5543d0fd..8f6684403b 100644 --- a/datasets/SOC_3M_Maps_NE_TidalWetlands_1905_1.json +++ b/datasets/SOC_3M_Maps_NE_TidalWetlands_1905_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOC_3M_Maps_NE_TidalWetlands_1905_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018. Northeastern U.S. tidal wetlands and bordering areas were harmonized from government agencies [U.S. Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS), National Cooperative Soil Survey (NCSS), USDA-NRCS - Rapid Carbon Assessment (RaCA), U.S. Environmental Protection Agency - National Wetland Condition and Assessment (EPA-NWCA)] and published studies. Point data for carbon stocks (in kg m-2) at four soil depths (0-5, 0-30, 0-100, and 0-200 cm) are included. SOC for the four depths was predicted for eight regional zones using regression models driven by environmental covariates. Two methods were used to estimate parameters for these models, a Random Forest (RF) Ranger method and a Quantile Regression Forest (QRF) model. The distribution of SOC was predicted for tidal wetland cover types mapped by Correll et al. (2019). Predictions and uncertainties are available at a 3 m resolution.", "links": [ { diff --git a/datasets/SOC_Stocks_Great_Plains_1603_1.json b/datasets/SOC_Stocks_Great_Plains_1603_1.json index ec309f3020..e28a8f0419 100644 --- a/datasets/SOC_Stocks_Great_Plains_1603_1.json +++ b/datasets/SOC_Stocks_Great_Plains_1603_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOC_Stocks_Great_Plains_1603_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01.", "links": [ { diff --git a/datasets/SOE_Bibliography_1.json b/datasets/SOE_Bibliography_1.json index 88def35750..9c16e07a06 100644 --- a/datasets/SOE_Bibliography_1.json +++ b/datasets/SOE_Bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_Bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "State of the Environment bibliography, compiled by Ewan McIvor. Contains 51 records.\n\nThe fields in this dataset are:\nauthor\nyear\njournal\ntitle\nvolume\npages\npublisher\nplace of publication\ncopy on file\nURL\nkeywords", "links": [ { diff --git a/datasets/SOE_Elec_Kingston_1.json b/datasets/SOE_Elec_Kingston_1.json index b44c74b8d6..c751dad12b 100644 --- a/datasets/SOE_Elec_Kingston_1.json +++ b/datasets/SOE_Elec_Kingston_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_Elec_Kingston_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nRecords the quantity of electricity, measured in kWh, used in operating the Australian Antarctic Division site and associated facilites including Macquarie 4 Cargo Facility; Mertonvale Circuit Warehouse and Sandfly Warehouse.\n\nTYPE OF INDICATOR\nThere are three types of indicators:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nEffective monitoring of electricity consumption at the Australian Antarctic Division (AAD) provides tangible evidence of appropriate energy management and the achievment of targets under the Commonwealth Government's energy scheme. \n\nTo allow more effective management of electricity, historical data will be used in calculating maximum demand set-points based on seasonal influences.\n\nMaintaining these records provides a database for submitting information to the Whole of Government Energy Reporting (WOGER), an annual requirement for all federal government divisions.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSite: The Australian Antarctic Division, Kingston Tasmania.\nFrequency: Annual report.\nQuarterly reports for assessing performance over the quarter.\nMeasurement Technique: Data are compiled from the monthly electricity accounts.\n\nRESEARCH ISSUES\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 79 - Stormwater outflow composition for the Australian Antarctic Division Headquarters.\nSOE Indicator 80 - Sewer outflow composition and flow rates for the Australian Antarctic Division headquarters.", "links": [ { diff --git a/datasets/SOE_Hydroxyl_1.json b/datasets/SOE_Hydroxyl_1.json index 5aaf4c5f28..ed000207b5 100644 --- a/datasets/SOE_Hydroxyl_1.json +++ b/datasets/SOE_Hydroxyl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_Hydroxyl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE. \n\nSee the metadata record \"Davis_OH_airglow\" for access to these data.\n\nINDICATOR DEFINITION\nMidwinter atmospheric temperatures at ~87km above Davis station, Antarctica, are determined from hydroxyl airglow emissions. The temperature reported is determined over the interval, day-of-year (DOY) 106 to DOY 258.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nOver the last century the concentration of greenhouse gases has risen in the atmosphere. Greenhouse gases result in warming of the lower atmosphere but enhanced cooling of the upper atmosphere. Enhanced cooling rates of the upper atmosphere may provide a more readily measurable indicator of 'global warming'.\n \nMidwinter hydroxyl layer temperatures, give a proxy temperature for an altitude of ~87km (just below the coldest region of the atmosphere in winter). Associated with anthropogenic greenhouse gas increases, this tenuous region of the atmosphere is expected to cool, with the magnitude of the cooling being significantly larger than the warming at ground level. When properly measured and interpreted, this may be the atmospheric region where variations in trends associated with anthropogenic climate change can be most rapidly and conclusively determined.\n\nHydroxyl airglow is emitted from an ~8km wide layer, centred at ~87 km.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Point value at Davis station, Antarctica\n\nFrequency: Winter averages\n\nMeasurement Technique: Hydroxyl airglow rotational temperatures are determined by the standard technique involving the ratios of the intensity of hydroxyl airglow line emissions. The difficulties encountered and methods adopted at Davis are detailed in Greet et al., 1998. Refinement to the absolute temperatures determined by this technique have been published by French et al., 2000.\n\nRESEARCH ISSUES\nThe values continue to be refined, though their relative difference between years is well quantified (see error estimates). The potential exists for determining some values at earlier epochs if instrumental uncertainties can be better quantified. This work is underway.\n\nLINKS TO OTHER INDICATORS\nNoctilucent cloud observations\nPolar statospheric cloud observations\nStratopause region parameters for Davis\nTropospheric and lower stratospheric temperatures\nMonthly averages of daily maximum and minimum temperatures\nMonthly extremes of daily maximum and minimum temperatures\nAtmospheric concentrations of greenhouse gas species\n\nThe fields in this dataset are:\nYear\nTemperature", "links": [ { diff --git a/datasets/SOE_SFU_1.json b/datasets/SOE_SFU_1.json index ab57a177b4..e88192419c 100644 --- a/datasets/SOE_SFU_1.json +++ b/datasets/SOE_SFU_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_SFU_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are no longer archived in the Australian Antarctic Data Centre. These data cover a period of January 1993 to February 2016.\n\nINDICATOR DEFINITION\nThe amount of electricity (kWh) used at Casey, Davis, Mawson and Macquarie Island stations as measured on a monthly basis and reported in the monthly reports from the Station Plant Inspectors to the Kingston (Head Office) Mechanical Supervisor.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nThe amount of electricity used at a station is a reflection of the efficiency of various electrical and systems and the amount of fuel used to generate this electricity.\n\nThe amount of fuel used in Antarctica for electricity generation is proportional to environmental impact due to the emissions.\n\nThe electricity usage of the station provides an indication of the relative need for electrical power compared with the thermal load of the station.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Australian Antarctic stations: Casey (lat 66 deg 16' 54.5& S, long 110 deg 31' 39.4& E), Davis (lat 68 deg 34' 35.8& S, long 77 deg 58' 02.6& E), Mawson (lat 67 deg 36' 09.7& S, long 62 deg 52' 25.7& E) and Macquarie Island (lat 54 deg 37' 59.9& S, long 158 deg 52' 59.9& E).\n\nFrequency: Monthly reports\n\nMeasurement technique: The figures are obtained by direct reading of gauges on the stations on a regular basis. The data are recorded in the Plant Inspectors monthly reports.\n\nRESEARCH ISSUES\nIn the future, it is planned to automate the collection of most of this data.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations.\nSOE Indicator 2 - Highest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 3 - Lowest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 4 - Monthly mean lower stratospheric temperatures above Australian Antarctic Stations\nSOE Indicator 47 - Number and nature of incidents resulting in environmental impact\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 56 - Monthly fuel usage of the generator sets and boilers", "links": [ { diff --git a/datasets/SOE_adelie_demog_1.json b/datasets/SOE_adelie_demog_1.json index 09a0eb2f7f..9af7553f98 100644 --- a/datasets/SOE_adelie_demog_1.json +++ b/datasets/SOE_adelie_demog_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_adelie_demog_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nDemographic parameters for the Adelie penguin at Bechervaise Island near Mawson.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nThe Adelie penguin is a relatively long lived sea bird dependent on krill. It is expected that major changes in the availability of food (krill) to sea birds will be reflected ultimately in recruitment into the breeding population. Causes of changes in the availability of krill relate directly to changes in both the biological and physical environment brought about by man made or natural means. Ageing populations may give an outward appearance of stability in terms of numbers at a breeding colony but such a condition may mask a decline in recruitment. To determine whether there are environmental influences on the population it is necessary to undertake detailed demographic studies.\n\nDemographic studies carried out over many years on animal populations comprising known age cohorts are required to determine those factors responsible for any observed changes in recruitment and/or mortality. Population reconstruction techniques provide estimates of recruitment and mortality and relate these functions to population size and/or population trends. These studies may alert us to possible changes in the ecosystem particularly related to the availability of food to the penguins or changes to the physical environment. The identification of the cause of changes must come from detailed investigations of food availability and the environment carried out at the same time.\n\nAnnual breeding success at Bechervaise Island (eggs laid to chicks fledged) varies enormously from 0 in catastrophic years to above 1 for good seasons. The population at Bechervaise Island near Mawson has been monitored since 1990 as part of the CCAMLR Ecosystem Monitoring Program. Chicks and adults have been tagged annually. The number of breeding pairs has increased slightly between 1990-2001, but changes in the non -breeding population are unknown. Demographic studies based on the return rate of birds tagged as chicks provide information on trends in the overall population and the net rate of recruitment. Since it is intended that this program be undertaken indefinitely it makes this population an excellent subject for monitoring in the context of the SOE.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Restricted to the Mawson region. Similar studies are carried out by other national research programs at Terra Nova Bay (Italy) and on the Antarctic Peninsula (USA).\n\nFrequency: Annual\n\nMeasurement Technique: The Adelie penguin population at Bechervaise Island consists of approximately 1800 breeding pairs. Each breeding season since 1990/91 in excess of 250 chicks have been given implanted electronic identification tags. The return of birds to their natal colony has been detected automatically by the Automated Penguin Monitoring System (APMS)or by checking all birds with a hand held tag reader. Additional and associated biological data as prescribed by CCAMLR (1997 are collected to aid interpretation of demographic and other trends. To detect trends in the population size and in demographic parameters, particularly of recruitment, it will be necessary to maintain an annual tagging program of chicks and recording of all tagged birds.\n\nRESEARCH ISSUES\ncomprehensive analysis of the data collected over the duration of this study is required to determine natural variation and potential anthropogenic influences affecting Adelie penguin population dynamics.\n\nLINKS TO OTHER INDICATORS\nSea-ice extent and concentration.", "links": [ { diff --git a/datasets/SOE_air_pressure_1.json b/datasets/SOE_air_pressure_1.json index a74bb22398..983e9541e1 100644 --- a/datasets/SOE_air_pressure_1.json +++ b/datasets/SOE_air_pressure_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_air_pressure_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly means of three-hourly pressures, reduced to mean sea level, for Australian Antarctic stations Casey, Davis, Mawson, Macquarie Island and Heard Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nMeasurement of the pressure over Antarctica and the Southern Ocean is considered important for monitoring behaviour of pressure systems on a local and global scale, which will help to interpret global climate change.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E), Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E), Atlas Cove, Heard Island (lat 53 degrees 1' 8\" S, long 73 \ndegrees 23' 30\" E) and Spit Bay, Heard Island (lat 53 degrees 6' 30\" S, 73 degrees 43' 21\" E).\n\nFrequency: Monthly.\n\nMeasurement technique: Barometry.\n\nRESEARCH ISSUES\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic Stations\nSOE Indicators 2 - Monthly highest temperatures for Australian Antarctic Stations\nSOE Indicators 3 - Monthly lowest temperatures for Australian Antarctic Stations\nSOE Indicators 4 - Monthly mean lower-stratospheric temperature above Australian Antarctic Stations\nSOE Indicators 5 - Monthly mean mid-tropospheric temperature above Australian Antarctic Stations\nSOE Indicators 38 - Mean sea level\nSOE Indicators 62 - Water levels of Deep Lake, Vestfold Hills\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300005 - Atlas Cove, Heard Island\n300017 - Casey\n300028 - Spit Bay, Heard Island\n\n\nThe fields in this dataset are:\nMean MSL Pressure\nYear\nMonth\nStation\nStation Code\nField\nValue\nEnough Observations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_chlorophyll_1.json b/datasets/SOE_chlorophyll_1.json index deb599880f..ec22a58d7e 100644 --- a/datasets/SOE_chlorophyll_1.json +++ b/datasets/SOE_chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nChlorophyll concentrations from Southern Ocean surface water collected at a depth of 0-7 metres, along cruise tracks of the Aurora Australis, or other ships. Averaged across latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nThe concentration of the photosynthetic pigment chlorophyll a (referred to as chlorophyll) in marine waters is a proven indicator of the biomass of phytoplankton, the organisms that constitute the base of the marine food web. Fluorometry provides an estimate of chlorophyll levels in sea water and thus an estimate of primary productivity in the upper part of the water column.\n\nDifferences in chlorophyll concentrations across latitude bands can be expected to reflect differences in the nutrient regimes of different oceanic water masses, wind-driven mixing and the seasonal variation in phytoplankton biomass. Interannual variation would reflect longer-term changes in the water masses.\n\nThere is some evidence that anthropogenic changes in nutrient availability in the North Sea have caused dramatic alterations to the pattern of Spring-Autumn blooming. If similar changes were to occur in the nutrient availability, circulation, or temperature of the Southern Ocean, the consequences would have a significant impact on Southern Ocean productivity and ecosystems. Monitoring of chlorophyll levels in the Southern Ocean will secure baseline data and potentially provide insight into productivity trends caused by changes in climate and the marine environment.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scales: Average fluorometer meter readings in latitude bands across the Southern Ocean 40-50 deg S, 50-60 deg S, 60 deg S-continent.\n\nFrequency: Monthly averages.\n\nMeasurement Techniques: Underway fluorometry on all marine science cruises of the Aurora Australis and selected other voyages accompanied by collection of temperature and salinity data. This is measured with a fluorometer fitted with a flow-through cell. Water is taken from the ship's clean seawater line, the intake of which is at a depth of around 7 metres, or from surface collection on other ships. \n\nDiscrete 1 - 2 litre samples to be filtered and the filter frozen in liquid nitrogen or at 80 degrees C for analysis by HPLC on return to Australia. These will be used to determine the major classes of phytoplankton present and to back-calibrate the fluorometer. Preferably 2 samples per day.\n\nOccasional samples (every 5 degrees latitude) to be fixed in Lugols iodine for microscopical analysis.\n\nRemote sensing, especially using satellite-mounted colour scanners (SeaWiFS and similar platforms), is advocated for broad-based monitoring of chlorophyll once appropriate algorithms have been developed and proved.\n\nAircraft-mounted systems can be used to provide more detailed information on areas of interest, or to operate when satellite coverage is not available.\n\nRESEARCH ISSUES\nThe development of appropriate algorithms for remote sensing will allow the use of this technology to provide a more complete picture of large-scale areas in the Southern Ocean. This will aid the detection of changes that may occur due to climate or environmental change.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 32 - Fecundity and pup growth in fur seal colonies on Macquarie Island\nSOE Indicator 40 - Average sea surface temperatures in latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent\nSOE Indicator 41 - Average sea surface salinity in latitude bands: 40-50 deg S, 50-60 deg S, 60 deg S-continent", "links": [ { diff --git a/datasets/SOE_effluent_BOD_1.json b/datasets/SOE_effluent_BOD_1.json index 3824a1b564..0ddabf567e 100644 --- a/datasets/SOE_effluent_BOD_1.json +++ b/datasets/SOE_effluent_BOD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_effluent_BOD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nThis indicator is an estimate of the biological oxygen demand (BOD) of effluent discharged into the ocean from the waste treatment plants (WTP) at each continental station. It is tested monthly and reported in the station plumbers' reports to the Building Services Supervisor in Kingston.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nThe stations produce liquid waste, comprising human waste, waste from kitchens and bathrooms, and limited volumes from workshops (contamination of the latter is usually minimal as it is cleaned of oil before discharge into the sewage system). This is treated in the waste treatment plants.\n\nBOD measurements are an indication of the efficiency of the WTP in destroying microorganisms and of the numbers of live microorganisms being released into the ocean as a result of human occupation.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Australian Antarctic continental stations and Macquarie Island station.\n\nFrequency: Monthly reports\n\nMeasurement technique: Samples are tested using the Oxitop Control method.\n\nStation doctors are trained in the Oxitop control method before departure for Antarctica. The Laboratory Manager, Australian Antarctic Division, Kingston, interprets results. Samples are collected, analysed, interpreted and reported monthly.\n\nWaste at Casey is also UV treated before being discharged into the environment. The data given for Casey station are readings after UV treatment.\n\nRESEARCH ISSUES\nThe following would increase knowledge of the potential impacts of the discharge of the wastewater:\n\nWinter and summer values will also be compared to detect any effects resulting from smaller winter populations, and higher summer populations.\n\nMore accurate microbiological tests.\n\nChemical analysis of effluent for phosphate and other nutrients. This would indicate the extent of enrichment that may occur in the adjacent oceans, and allow feedback for example on the types of washing detergent supplied at stations.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 50 - Volume of wastewater discharged from Australian Antarctic Stations\nSOE Indicator 52 - Suspended solids (SS) content of wastewater discharged from Australian Antarctic Stations", "links": [ { diff --git a/datasets/SOE_elephant_seals_4.json b/datasets/SOE_elephant_seals_4.json index fe0133566a..c3e2034fe9 100644 --- a/datasets/SOE_elephant_seals_4.json +++ b/datasets/SOE_elephant_seals_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_elephant_seals_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nCount of all adult females, fully weaned pups and dead pups hauled out on, or close to, the day of maximum cow numbers, set for October 15.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nElephant seals from Macquarie Island are long distance foragers who can utilise the Southern Ocean both west as far as Heard Island and east as far as the Ross Sea. Thus their populations reflect foraging conditions across a vast area.\n\nThe slow decline in their numbers (-2.3% annually from 1988-1993) suggests that their ocean foraging has been more difficult in recent decades. Furthermore, interactions with humans are negligible due to the absence of significant overlap in their diet with commercial fisheries. This suggests that changes in 'natural' ocean conditions may have altered aspects of prey availability. It is clear that seal numbers are changing in response to ocean conditions but at the moment these conditions cannot be specified.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Five beaches on Macquarie Island (lat54 degrees 37' 59.9' S, long 158 degrees 52' 59.9' E): North Head to Aurora Point; Aurora Point to Caroline Cove; Garden Cove to Sandy Bay; Sandy Bay to Waterfall Bay; Waterfall Bay to Hurd Point.\n\nFrequency: Annual census on 15th October\n\nMeasurement Technique: Monitoring the Southern Elephant Seal population on Macquarie island requires a one day whole island adult female census on October 15 and a daily count of cow numbers, fully weaned pups and dead pups on the west and east isthmus beaches throughout October.\n\nDaily cow counts during October, along the isthmus beaches close to the Station, provide data to identify exactly the day of maximum numbers. The isthmus counts are recorded under the long-established (since 1950) harem names. Daily counts allow adjustment to the census totals if the day of maximum numbers of cows ashore happens to fall on either side of October 15. Personnel need to be dispersed around the island by October 15 so that all beaches are counted for seals on that day. This has been achieved successfully for the last 15 years.\n\nOn the day of maximum haul out (around 15th October) the only Elephant seals present are cows, their young pups and adult males. The three classes can be readily distinguished and counted accurately. Lactating pups are not counted, their numbers are provided by the cow count on a 1:1 proportion. The combined count of cows, fully weaned pups and dead pups provides an index of pup production.\n\nThe count of any group is made until there is agreement between counts to better than +/- 5%. Thus there is always a double count as a minimum; the number of counts can reach double figures when a large group is enumerated. The largest single group on Macquarie Island is that at West Razorback with greater than 1,000 cows; Multiple counts are always required there.\n\nRESEARCH ISSUES\nMuch research has been done already to acquire demographic data so that population models can be produced. Thus there will be predicted population sizes for elephant seals on Macquarie Island in 2002 onwards and the annual censuses will allow these predictions to be tested against the actual numbers. The censuses are also a check on the population status of this endangered species.\n\nLINKS TO OTHER INDICATORS", "links": [ { diff --git a/datasets/SOE_fast_ice_thickness_1.json b/datasets/SOE_fast_ice_thickness_1.json index 112c3291ab..3518f4d1b2 100644 --- a/datasets/SOE_fast_ice_thickness_1.json +++ b/datasets/SOE_fast_ice_thickness_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_fast_ice_thickness_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nRegular measurements of the thickness of the fast ice, and of the snow cover that forms on it, are made through drilled holes at several sites near both Mawson and Davis.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nEach season around the end of March, the ocean surface around Antarctica freezes to form sea ice. Close to the coast in some regions (e.g. near Mawson and Davis stations) this ice remains fastened to the land throughout the winter and is called fast ice.\n\nThe thickness and growth rate of fast ice are determined purely by energy exchanges at the air-ice and ice-water interfaces. This contrasts with moving pack ice where deformational processes of rafting and ridging also determine the ice thickness. The maximum thickness that the fast ice reaches, and the date on which it reaches that maximum, represent an integration of the atmospheric and oceanic conditions.\n\nChanges in ice thickness represent changes in either oceanic or atmospheric heat transfer. Thicker fast ice reflects either a decrease in air temperature or decreasing oceanic heat flux. These effects can be extrapolated to encompass large-scale ocean-atmosphere processes and potentially, global climate change.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: At sites near Australian Antarctic continental stations: Davis; Mawson.\n\nFrequency: at least weekly, reported annually\n\nMeasurement Technique: Tape measurements through freshly drilled 5 cm diameter holes in the ice at marked sites.\n\nRESEARCH ISSUES\nTo more effectively analyse the changes in Antarctic fast ice a detailed long-term dataset of sea ice conditions needs to be established. This would provide a baseline for future comparisons and contribute important data for climate modelling and aid the detection of changes that may occur due to climate or environmental change.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations\nSOE Indicator 40 - Average sea surface temperatures in latitude bands 40-50oS, 50-60oS, 60oS-continent\nSOE Indicator 41 - Average sea surface salinity in latitude bands: 40-50oS, 50-60oS, 60oS-continent\nSOE Indicator 42 - Antarctic sea ice extent and concentration\n\nThe fast ice data are also available as a direct download via the url given below. The data are in word documents, and are divided up by year and site (there are three sites (a,b,c) at each station). Snow thickness data have also been included. A pdf document detailing how the observations are collected is also available for download.", "links": [ { diff --git a/datasets/SOE_fur_seals_1.json b/datasets/SOE_fur_seals_1.json index 75366a3bdc..76d2ae2c8c 100644 --- a/datasets/SOE_fur_seals_1.json +++ b/datasets/SOE_fur_seals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_fur_seals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nThe fecundity (pupping rates) of female fur seals and the growth rates of their pups relative to changes in sea surface temperatures (local primary production) in the vicinity of Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nA highly negative correlation has been detected between sea surface temperatures in the vicinity of Macquarie Island and fur seal fecundity and pup growth. A dataset of over ten years has shown that autumn sea-surface temperatures are highly negatively correlated with female fecundity in the following breeding season.\n\nRather than the reproductive success in terms of fecundity and pup growth being seen simply as a correlate of SST and presumably ocean productivity, the measure is much more than this. What the dataset from the Macquarie Island fur seal populations is rather more unique, in that they indicate how environmental variability effects the reproductive success of animals at annual and lifetime scales. This is especially important as we can now show what impacts environmental/climatic phenomena such as the Antarctic Circumpolar Wave, and global warming will have on fur seals, and how changes in the environment may impact on the viability of populations. In this situation, the data clearly suggest that warmer ocean temperatures significantly effect the reproductive success of fur seals. Sustained warmer temperatures would therefore impose demographic constraints on populations.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: SST data are obtained from a 1 degree square just north of the island that represents the region in which most females obtain food throughout their lactation period.\n\nFrequency: Data on the reproductive success of fur seals is to be collected annually.\n\nMeasurement technique: Each breeding season (November-January), the reproductive success of tagged females is monitored, including their pupping success, and the growth rates of their pups.\n\nRESEARCH ISSUES\n\nLINKS TO OTHER INDICATORS", "links": [ { diff --git a/datasets/SOE_generator_boiler_fuel_usage_1.json b/datasets/SOE_generator_boiler_fuel_usage_1.json index ed0250be75..b087144611 100644 --- a/datasets/SOE_generator_boiler_fuel_usage_1.json +++ b/datasets/SOE_generator_boiler_fuel_usage_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_generator_boiler_fuel_usage_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nThe quantity of fuel used by generator sets and boilers at Casey, Davis, Mawson and Macquarie Island stations as measured on a monthly basis and reported in the monthly reports from the Station Plant Inspectors to the Kingston (Head Office) Mechanical Supervisor.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nThe amount of fuel used in Antarctica for power generation and heating is proportional to environmental impact due to the emissions released.\n\nSpecial Antarctic Blend (SAB), a light diesel like fuel, is used at the stations to power the station generator sets, to provide heat through boilers, and to run plant and equipment including the station incinerator and vehicles.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Australian Antarctic stations: Casey (lat 66 deg 16' 54.5& S, long 110 deg 31' 39.4& E), Davis (lat 68 deg 34' 35.8& S, long 77 deg 58' 02.6& E), Mawson (lat 67 deg 36' 09.7& S, long 62 deg 52' 25.7& E) and Macquarie Island (lat 54 deg 37' 59.9& S, long 158 deg 52' 59.9& E).\n\nFrequency: Monthly reports\n\nMeasurement technique: The figures are obtained by direct reading of gauges on the stations on a regular basis. The data are recorded in the Plant Inspectors monthly reports.\n\nRESEARCH ISSUES\nIn the future, it is planned to automate the collection of most of this data.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations.\nSOE Indicator 2 - Highest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 3 - Lowest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 4 - Monthly mean lower stratospheric temperatures above Australian Antarctic Stations\nSOE Indicator 7 - Monthly mean of three-hourly wind speeds (m/s)\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 57 - Monthly total of fuel used by station incinerators\nSOE Indicator 58 - Monthly total of fuel used by station vehicles\nSOE Indicator 59 - Monthly electricity usage\nSOE Indicator 60 - Total helicopter hours\nSOE Indicator 61 - Total potable water consumption\nSOE Indicator 65 - Station footprint for Australian Antarctic stations", "links": [ { diff --git a/datasets/SOE_greenhouse_gas_1.json b/datasets/SOE_greenhouse_gas_1.json index e026c2d3a5..b45ccc0cb8 100644 --- a/datasets/SOE_greenhouse_gas_1.json +++ b/datasets/SOE_greenhouse_gas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_greenhouse_gas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\n Measurement of air samples for values of the primary greenhouse gases (carbon dioxide, methane and nitrous oxide) and associated species (carbon monoxide, hydrogen and isotopes of carbon dioxide) in the Southern Hemisphere atmosphere.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nOver the last century the concentration of greenhouse gases has risen in the atmosphere. The average rise is about half that expected from human activities, predominantly the burning of fossil fuel. Thus observations of the concentration of these gases provides a measure of anthropogenic greenhouse forcing in the atmosphere, and for example, monitors the effectiveness of oceans and terrestrial biomes in removing the excess CO2.\n\nMeasurements of long-lived trace gas levels in Antarctic air generally provide an accurate integration of global exchanges between the surface and the atmosphere. The climate-influencing gases of main interest are gases released as a result of human activity, as well as from (climate-driven) physical, chemical and biological processes in both land and oceans. The Antarctic monitoring, in concert with other global network results, exploits trace gas ratios to identify and locate globally significant exchanges.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: High latitude Southern Hemisphere air samples are collected from AAD sites by BoM personnel at Mawson station, Casey station and Macquarie Island, and by NOAA staff at South Pole. These complement CSIRO supervised sites at Cape Grim, Tasmania and ~7 other globally distributed locations.\n\nFrequency: Typical sites collect ~4 flasks of air per month for subsequent analysis at CSIRO.\n\nMeasurement Technique: Various chemical analysis techniques (Francey et al. 1996).\n\nRESEARCH ISSUES\nFor global trace gas monitoring, improvements are sought in network intercalibration and in increased sampling, e.g. continuous CO2 monitoring, vertical profiles, continental sites. More generally, improved coordination of atmospheric composition modeling, surface flux measurements and atmospheric transport representations are sought to serve new 'multiple-constraint modeling frameworks'.\n\nLINKS TO OTHER INDICATORS\nMonthly averages of daily maximum and minimum temperatures for Australian Antarctic Stations\nMean sea level Average Summer chlorophyll concentrations in the Southern Ocean, from latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent\nAverage sea surface temperatures in latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent\nAntarctic sea ice extent and concentration", "links": [ { diff --git a/datasets/SOE_incinerated_waste_1.json b/datasets/SOE_incinerated_waste_1.json index 1333f8da0a..10e16c6b68 100644 --- a/datasets/SOE_incinerated_waste_1.json +++ b/datasets/SOE_incinerated_waste_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_incinerated_waste_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nThis indicator identifies the total weight of material incinerated, and the weights of the major components on Casey, Davis, Mawson and Macquarie Island stations. The figures are reported monthly, in the station plumbers' reports to the Building Services Supervisor in Kingston, and to the Operations Environment Officer.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nWaste minimization is an important element of Australia's Antarctic program, so the total weight of waste produced, and any trends, provide an important management tool. Approximately 10% of waste is incinerated, so incineration statistics are an important part of this assessment.\n\nA separate aim of Australia's program is reduction in the amount of material incinerated on the stations, either reduction in the amounts of certain materials sent to the stations or by diverting materials from incineration to reuse or recycling. In either case it will be necessary to target individual materials incinerated, as different materials are likely to respond to different management practices. To properly target these materials it is important to know the amounts of each of the materials incinerated, and trends over time.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Australian Antarctic continental stations and Macquarie Island station.\n\nFrequency: Weights are recorded each time materials are incinerated, which is every few days in winter and daily in summer and reported monthly.\n\nMeasurement technique: Weights are recorded for the following categories: (1) food scraps, (2) spoiled fruit and vegetables, (3) wood and wood products (not treated wood), (4) cardboard, (5) paper products (poor quality paper, books and magazines), (6) medical waste, (7) science waste, (8) hydroponics waste, (9) human waste from the field and (10) miscellaneous. In addition, weights of specific materials may be recorded separately, if burnt in unusually large amounts, for example if large amounts of particular types of fruit and vegetables have been spoiled.\n\nRESEARCH ISSUES\nChemical analysis of emissions as a pollution index and also to assess the efficiency of the burn. This information could be used to indicate the need to change the components of burns or to adjust the equipment. It may also highlight the release of toxic materials into the atmosphere which may be overcome by eliminating certain materials from incineration.\n\nA major audit of total waste production, leading to recommendations on how to achieve maximum waste reduction.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 47 - Number and nature of incidents resulting in environmental impact\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 49 - Medical consultations per 1000 person years\nSOE Indicator 53 - Recycled and quarantine waste returned to Australia\nSOE Indicator 57 - Monthly total of fuel used by station incinerators\nSOE Indicator 69 - Resources committed to environmental issues", "links": [ { diff --git a/datasets/SOE_incinerator_fuel_usage_1.json b/datasets/SOE_incinerator_fuel_usage_1.json index 3e1dfd82a4..090df1dba6 100644 --- a/datasets/SOE_incinerator_fuel_usage_1.json +++ b/datasets/SOE_incinerator_fuel_usage_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_incinerator_fuel_usage_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nThe quantity of fuel used for incinerators at Casey, Davis, Mawson and Macquarie Island stations as measured on a monthly basis and reported in the monthly reports from the Station Plant Inspectors to the Kingston (Head Office) Mechanical Supervisor.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\nThe amount of fuel used in Antarctica for waste incineration contributes to environmental impact due to the emissions released.\n\nSpecial Antarctic Blend (SAB), a light diesel like fuel, is used at the stations for the incinerators.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Australian Antarctic stations: Casey (lat 66 deg 16' 54.5& S, long 110 deg 31' 39.4& E), Davis (lat 68 deg 34' 35.8& S, long 77 deg 58' 02.6& E), Mawson (lat 67 deg 36' 09.7& S, long 62 deg 52' 25.7& E) and Macquarie Island (lat 54 deg 37' 59.9& S, long 158 deg 52' 59.9& E).\n\nFrequency: Monthly reports\n\nMeasurement technique: The figures are obtained by direct reading of gauges on the stations on a regular basis. The data are recorded in the Plant Inspectors monthly reports.\n\nRESEARCH ISSUES\nIn the future, it is planned to automate the collection of most of this data.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 47 - Number and nature of incidents resulting in environmental impact\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 53 - Recycled and quarantine waste returned to Australia\nSOE Indicator 54 - Amount of waste incinerated at Australian stations\nSOE Indicator 56 - Monthly fuel usage of the generator sets and boilers\nSOE Indicator 58 - Monthly total of fuel used by station vehicles\nSOE Indicator 60 - Total helicopter hours", "links": [ { diff --git a/datasets/SOE_low_strato_1.json b/datasets/SOE_low_strato_1.json index d979e818e2..dccd2e3189 100644 --- a/datasets/SOE_low_strato_1.json +++ b/datasets/SOE_low_strato_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_low_strato_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly means of daily temperatures at the 100hPa level (lower stratosphere), from radiosonde soundings above Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nGlobal climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. There is interest in climate variability and change not just at the surface, but extending up into the atmosphere. There is evidence of warming in the lower troposphere, but cooling in the lower stratosphere. Ozone depletion processes are also closely linked to stratospheric temperatures.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E) and Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E).\n\nTemporal scale: Monthly.\n\nMeasurement technique: Radiosonde.\n\nRESEARCH ISSUES\n\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nOver recent years satellite data exist, which could be used in conjunction with radiosonde data. Satellite data and radiosonde data from other nations should lead to a greater coverage.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic Stations\nSOE Indicators 2 - Monthly highest air temperatures for Australian Antarctic Stations\nSOE Indicators 3 - Monthly lowest air temperatures for Australian Antarctic Stations\nSOE Indicators 5 - Monthly mean mid-tropospheric temperature above Australian Antarctic stations\nSOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C)\nSOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa)\nSOE Indicators 11 - Atmospheric concentrations of greenhouse gas species\nSOE Indicators 12 - Noctilucent cloud observations at Davis\nSOE Indicators 13 - Polar stratospheric cloud observations at Davis\nSOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km\nSOE Indicators 16 - Extent of summer surface glacial melt (sq km)\nSOE Indicators 42 - Antarctic sea ice extent and concentration\nSOE Indicators 43 - Fast ice thickness at Davis and Mawson\nSOE Indicators 56 - Monthly fuel usage of the generator sets and boilers\nSOE Indicators 59 - Monthly electricity usage\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300017 - Casey\n\nThe fields in this dataset are:\nMean 100hPa Temperature\nYear\nMonth\nStation\nStation Code\nValue\nEnough Observations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_mid_tropo_1.json b/datasets/SOE_mid_tropo_1.json index 0c55f76240..289a9a414d 100644 --- a/datasets/SOE_mid_tropo_1.json +++ b/datasets/SOE_mid_tropo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_mid_tropo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly means of daily temperatures at the 500hPa level (mid-troposphere), from radiosonde soundings above Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nGlobal climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. There is interest in climate variability and change not just at the surface, but extending up into the atmosphere. There is evidence of warming in the lower troposphere, but cooling in the lower stratosphere.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E) and Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E).\n\nTemporal scale: Monthly.\n\nMeasurement technique: Radiosonde.\n\nRESEARCH ISSUES\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nOver recent years satellite data exist, which could be used in conjunction with radiosonde data. Satellite data and radiosonde data from other nations should lead to a greater coverage.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic Stations.\nSOE Indicators 2 - Monthly highest air temperatures for Australian Antarctic Stations\nSOE Indicators 3 - Monthly lowest air temperatures for Australian Antarctic Stations\nSOE Indicators 4 - Monthly mean lower-stratospheric temperature above Australian Antarctic stations\nSOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C)\nSOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa)\nSOE Indicators 11 - Atmospheric concentrations of greenhouse gas species\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300017 - Casey\n\nThe fields in this dataset are:\nMean 500hPa Temperature\nYear\nMonth\nStation\nStation Code\nValue\nEnough Observations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_monthly_max_temp_1.json b/datasets/SOE_monthly_max_temp_1.json index bcb46b24fd..7bbd3edc95 100644 --- a/datasets/SOE_monthly_max_temp_1.json +++ b/datasets/SOE_monthly_max_temp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_monthly_max_temp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly highest temperatures obtained from observed daily maximum temperatures for Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nGlobal climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E) and Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E).\n\nTemporal scale: Monthly.\n\nMeasurement Technique: Thermometry.\n\nRESEARCH ISSUES\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic Stations\nSOE Indicators 3 - Monthly lowest air temperature for Australian Antarctic Stations\nSOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C)\nSOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C)\nSOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C)\nSOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa)\nSOE Indicators 11 - Atmospheric concentrations of greenhouse gas species\nSOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km\nSOE Indicators 16 - Extent of summer surface glacial melt (sq km)\nSOE Indicators 42 - Antarctic sea ice extent and concentration\nSOE Indicators 43 - Fast ice thickness at Davis and Mawson\nSOE Indicators 56 - Monthly fuel usage of the generator sets and boilers\nSOE Indicators 59 - Monthly electricity usage\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300017 - Casey\n\nThe fields in this dataset are:\nTemperature\nHighest Air Temperature\nYear\nMonth\nStation\nStation Code\nField\nValue\nEnough Observations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_monthly_mean_temp_1.json b/datasets/SOE_monthly_mean_temp_1.json index c3cf54903b..c26ce4a56d 100644 --- a/datasets/SOE_monthly_mean_temp_1.json +++ b/datasets/SOE_monthly_mean_temp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_monthly_mean_temp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly means of three-hourly temperatures for Australian Antarctic stations Casey, Davis, Mawson, Macquarie Island and Heard Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nGlobal climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E), Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E), Atlas Cove, Heard Island (lat 53 degrees 1' 8\" S, long 73 degrees 23' 30\" E) and Spit Bay, Heard Island (lat 53 degrees 6' 30\" S, 73 degrees 43' 21\" E).\n\nFrequency: Monthly\n\nMeasurement Technique: Thermometry\n\nRESEARCH ISSUES\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 2 - Highest monthly record of daily maximum air temperatures\nSOE Indicators 3 - Lowest monthly record of daily minimum air temperatures\nSOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C)\nSOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C)\nSOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C)\nSOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa)\nSOE Indicators 11 - Atmospheric concentrations of greenhouse gas species\nSOE Indicators 12 - Noctilucent cloud observations at Davis\nSOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km\nSOE Indicators 16 - Extent of summer surface glacial melt (sq km)\nSOE Indicators 42 - Antarctic sea ice extent and concentration\nSOE Indicators 43 - Fast ice thickness at Davis and Mawson\nSOE Indicators 56 - Monthly fuel usage of the generator sets and boilers\nSOE Indicators 59 - Monthly electricity usage\nSOE Indicators 62 - Water levels of Deep Lake, Vestfold Hills\n\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300005 - Atlas Cove, Heard Island\n300017 - Casey\n300028 - Spit Bay, Heard Island\n\nThe fields for this dataset are:\nTemperature\nMean Air Temperature\nYear\nMonth\nStation\nStation Code\nField\nValue\nEnough Oobservations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_monthly_min_temp_1.json b/datasets/SOE_monthly_min_temp_1.json index c01d39f3c1..a57355a6a8 100644 --- a/datasets/SOE_monthly_min_temp_1.json +++ b/datasets/SOE_monthly_min_temp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_monthly_min_temp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nMonthly lowest temperatures obtained from observed daily minimum temperatures for Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nGlobal climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5\" S, long 110 degrees 31' 39.4\" E), Davis (lat 68 degrees 34' 35.8\" S, long 77 degrees 58' 02.6\" E), Mawson (lat 67 degrees 36' 09.7\" S, long 62 degrees 52' 25.7\" E) and Macquarie Island (lat 54 degrees 37' 59.9\" S, long 158 degrees 52' 59.9\" E).\n\nTemporal scale: Monthly.\n\nMeasurement Technique: Thermometry.\n\nRESEARCH ISSUES\nThere is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.\n\nSome of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.\n\nBefore the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.\n\nLINKS TO OTHER INDICATORS\nSOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic stations\nSOE Indicators 2 - Monthly highest air temperatures for Australian Antarctic stations\nSOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C)\nSOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C)\nSOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C)\nSOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa)\nSOE Indicators 11 - Atmospheric concentrations of greenhouse gas species\nSOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km\nSOE Indicators 16 - Extent of summer surface glacial melt (sq km)\nSOE Indicators 42 - Antarctic sea ice extent and concentration\nSOE Indicators 43 - Fast ice thickness at Davis and Mawson\nSOE Indicators 56 - Monthly fuel usage of the generator sets and boilers\nSOE Indicators 59 - Monthly electricity usage\n\nNote - Station codes in the data are as follows:\n300000 - Davis\n300001 - Mawson\n300004 - Macquarie Island\n300017 - Casey\n\nThe fields in this dataset are:\nTemperature\nLowest Air Temperature\nYear\nMonth\nStation\nStation Code\nField\nValue\nEnough Observations\nNumber Observations", "links": [ { diff --git a/datasets/SOE_ozone_1.json b/datasets/SOE_ozone_1.json index cbd0d73376..fd7f28acd8 100644 --- a/datasets/SOE_ozone_1.json +++ b/datasets/SOE_ozone_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_ozone_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "INDICATOR DEFINITION\nThe monthly means of total column ozone measured at Macquarie Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nOzone filters ultra-violet (UV) radiation from the Sun and affects the dynamics of the stratosphere and overall atmospheric radiation balance. With reduction in stratospheric ozone levels, UV incidence on the surface has serious ramifications for Antarctic and Southern Ocean biological systems.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial Scale: Macquarie Island (lat 54deg 29' 59\"S, long 158deg 57' 08\"E).\n\nFrequency: Annual report on monthly means of daily figures.\n\nMeasurement Technique: Global-scale daily analyses are produced by the Bureau of Meteorology on a 2.5 - degree latitude by 2.5 - degree longitude grid, using satellite data. In situ Dobson spectrophotometer measurements at Macquarie Island in conjunction with major centres in Australia provide benchmark and calibration data of total-column ozone.\n\nMacquarie Island is the only station situated under the Antarctic Ozone hole, providing an important contribution to ozone monitoring activities.\n\nRESEARCH ISSUES\nThe traceable data set may be extended back a few years before 1978 but cannot go much further back with any confidence.\n\nWhen Dobson measurements began seriously in 1955, there was no traceability of instrument standards. Each instrument was calibrated absolutely rather than by the hierarchy of standards, beginning in 1978. In the initial measurement program, ozone measurements were used mainly for day-to-day variations - the idea of using Dobsons for a long-term trend in ozone was not an objective. Consequently the need for properly referenced instruments was not a high priority. The difficult mechanism of applying standards was not in place when measurements started in 1956.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 10 - Daily broad-band ultra-violet radiation observations using biologically effective UVR detectors\nSOE Indicator 13 - Polar stratospheric cloud observations at Davis", "links": [ { diff --git a/datasets/SOE_sea_surface_salinity_1.json b/datasets/SOE_sea_surface_salinity_1.json index d533b7213f..9c9c147a3a 100644 --- a/datasets/SOE_sea_surface_salinity_1.json +++ b/datasets/SOE_sea_surface_salinity_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_sea_surface_salinity_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nMeasurements of sea surface salinity in the Southern Ocean. Measurements are averaged over latitude bands: 40-50 deg S, 50-60 deg S, 60 deg S-continent.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nAustralian and Antarctic climate and marine living resources are sensitive to the distribution of ocean salinity. Sea surface values are relatively easy to monitor, and therefore can be used as a relevant indicator of the state of the ocean environment.\n\nThe information provided by long records of sea surface salinity is needed to detect changes in the Southern Ocean resulting from climate change; to test climate model predictions; to develop an understanding of links between the Ocean and climate variability in Australia; and for sustainable development of marine resources.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Southern Ocean: 40 deg S to the Antarctic continent\n\nFrequency: Monthly averages over summer\n\nMeasurement technique: Measurements of sea surface salinity from Antarctic supply ships.\n\nRESEARCH ISSUES\nSea surface salinity has not been previously used as a spatially averaged environmental indicator. Some experimentation with past data are required to define the most appropriate averaging strategy.\n\nNew technologies like profiling Argo floats need to be exploited to provide better spatial and temporal coverage of salinity in the Southern Ocean.\n\nLINKS TO OTHER INDICATORS\nSea surface temperature\nSea ice extent and concentration\nChlorophyll concentrations\nconcentrations", "links": [ { diff --git a/datasets/SOE_sea_surface_temp_1.json b/datasets/SOE_sea_surface_temp_1.json index 488c8330af..db8b8945b5 100644 --- a/datasets/SOE_sea_surface_temp_1.json +++ b/datasets/SOE_sea_surface_temp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_sea_surface_temp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nMeasurements of sea surface temperature in the Southern Ocean. Measurements are averaged over latitude bands: 40-50 deg S, 50-60 deg S, 60 deg S-continent.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nAustralian and Antarctic climate and marine living resources are sensitive to the distribution of ocean temperature. Sea surface values are relatively easy to monitor, and therefore can be used as a relevant indicator of the state of the ocean environment.\n\nThe information provided by long records of sea surface temperature is needed to detect changes in the Southern Ocean resulting from climate change; to test climate model predictions; to develop an understanding of links between the Ocean and climate variability in Australia; and for sustainable development of marine resources.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Southern Ocean: 40 deg S to the Antarctic continent\n\nFrequency: Monthly averages over summer\n\nMeasurement technique: Measurements of sea surface temperature from Antarctic supply ships. The best spatial coverage of sea surface temperature is provided by satellites, due to extensive cloud cover in the Southern Ocean and biases in the satellite measurement, in situ observations of sea surface temperature are necessary.\n\nRESEARCH ISSUES\nSea surface temperature has not been previously used as a spatially averaged environmental indicator. Some experimentation with past data are required to define the most appropriate averaging strategy.\n\nNew technologies like profiling Argo floats need to be exploited to provide better spatial and temporal coverage of temperature in the Southern Ocean.\n\nLINKS TO OTHER INDICATORS\nSea ice extent and concentration\nChlorophyll concentrations\nSea surface salinity", "links": [ { diff --git a/datasets/SOE_seabird_candidate_sp_AP_1.json b/datasets/SOE_seabird_candidate_sp_AP_1.json index 88079e265b..44a81e420a 100644 --- a/datasets/SOE_seabird_candidate_sp_AP_1.json +++ b/datasets/SOE_seabird_candidate_sp_AP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_seabird_candidate_sp_AP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nBreeding populations of Adelie penguins at Davis, Mawson and Casey (including Shirley Island and Whitney Point).\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1. Describes the CONDITION of important elements of a system;\n2. Show the extent of the major PRESSURES exerted on a system;\n3. Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nThe breeding population of Adelie penguins is related to resource availability (nesting space and food), behavioural mechanisms (immigration/emigration and breeding effort/success) in addition to climate change and human impacts (fisheries, tourism, pollution, disturbance). Monitoring these colonies and interpretation of the data provides information on changes in the Antarctic ecosystem.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Colonies near Australian Stations -\nCasey (lat 66 deg 16' 54.5\" S, long 110 deg 31' 39.4\" E)\nDavis (lat 68 deg 34' 35.8\" S, long 77 deg 58' 02.6\" E)\nMawson (lat 67 deg 36' 09.7\" S, long 62 deg 52' 25.7\" E)\n\nAll colonies on -\nShirley Island (lat 66 deg 16' 55.9\" S, long 110 deg 29' 17.9\" E) and\nWhitney Point (lat 66 deg 15' 08.6\" S, long 110 deg 31' 40.1\" E)\n\nFrequency: Annual surveys at Shirley Island and Whitney Point. Other colonies every 2-3 years, depending on logistical constraints.\n\nMeasurement technique: Each colony is visited and all breeding birds are counted from the ground by two or three personnel performing replicate counts. Supplementary census data are obtained from oblique ground and aerial photographs. All breeding adults in a colony are counted.\n\nConsiderations regarding disturbance associated with census visits are also incorporated into monitoring strategies. The lack of annual census data for some colonies does not reduce the value of these long-term monitoring programmes.\n\nRESEARCH ISSUES\nAdelie Penguin populations throughout East Antarctica have shown sustained, long-term increases for the past 30 or more years; in contrast, populations elsewhere around the Antarctic and on the Antarctic Peninsula have exhibited decreases or no clear long-term trends (Woehler et al. 2001). Greater coverage of colonies throughout the AAT would provide a more accurate estimate of the total annual breeding population in East Antarctica. In addition to basic inventory requirements, data on the population trends would contribute to a better understanding of the role of Adelie penguins in the Antarctic ecosystem, and provide managers with feedback or management strategies.\n\nLINKS TO OTHER INDICATORS", "links": [ { diff --git a/datasets/SOE_seabird_candidate_sp_KP_1.json b/datasets/SOE_seabird_candidate_sp_KP_1.json index ba2e475abf..a6230f4e19 100644 --- a/datasets/SOE_seabird_candidate_sp_KP_1.json +++ b/datasets/SOE_seabird_candidate_sp_KP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_seabird_candidate_sp_KP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nThe size of the breeding population of King Penguins at Heard Island.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1. Describes the CONDITION of important elements of a system;\n2. Show the extent of the major PRESSURES exerted on a system;\n3. Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nThe breeding population of King Penguins is related to resource availability (nesting space and food), behavioural mechanisms (immigration/emigration and breeding effort/success) in addition to climate change and human impacts such as fisheries. Monitoring breeding population and interpretation of the data provides information on changes in the Subantarctic ecosystem. \n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Heard Island (lat 53 deg 06' 00.0\" S, long 73 deg 31' 59.9\" E).\n\nFrequency: 2-3 years. Access to remote colonies and other logistical constraints do not permit annual visits.\n\nMeasurement technique: Each colony is visited and individual birds are counted from the ground by two or three personnel performing replicate counts. Further counts are obtained by oblique ground and aerial photography. All breeding individuals in a colony are counted. Considerations regarding disturbance associated with census visits are also incorporated into monitoring strategies. The lack of annual census data does not reduce the value of these long-term monitoring programmes.\n\nRESEARCH ISSUES\nThe king penguin breeding population at Heard Island has increased at almost 20% per year since the late 1940s; other king penguin populations throughout the Southern Ocean have also increased, but not as rapidly. At present, there is no alternative hypothesis to that previously proposed, that these population increases are sustained by the enhanced availability of myctophids, the principal prey of king penguins (Woehler et al. 2001). \n\nLINKS TO OTHER INDICATORS", "links": [ { diff --git a/datasets/SOE_seabird_candidate_sp_SGP_1.json b/datasets/SOE_seabird_candidate_sp_SGP_1.json index 32f4211346..c19b6e5d4e 100644 --- a/datasets/SOE_seabird_candidate_sp_SGP_1.json +++ b/datasets/SOE_seabird_candidate_sp_SGP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_seabird_candidate_sp_SGP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nThe number of breeding pairs of Southern Giant Petrels at Heard Island, the McDonald Islands, and in colonies near Casey, Davis and Mawson stations.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1. Describes the CONDITION of important elements of a system;\n2. Show the extent of the major PRESSURES exerted on a system;\n3. Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: CONDITION\n\nRATIONALE FOR INDICATOR SELECTION\nThe breeding population of Southern Giant Petrels is related to resource availability (nesting space and food), behavioural mechanisms (immigration/emigration and breeding effort/success) in addition to climate change and human impacts (including fisheries and human disturbance). Monitoring breeding populations and interpretation of the data provides information on changes in the Antarctic and Subantarctic ecosystems.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Colonies near Australian Stations -\nFrazier Islands, Casey (lat 66 degrees 16' 54.5' S, long 110 degrees 31' 39.4' E)\nHawker Island, Davis (lat 68 degrees 34' 35.8' S, long 77 degrees 58' 02.6' E)\nGiganteus Island, Mawson (lat 67 degrees 36' 09.7' S, long 62 degrees 52' 25.7' E)\n\nHeard Island - (lat 53 degrees 06' 00.0' S, long 73 degrees 31' 59.9' E)\n\nMcDonald Islands - (lat 53 degrees 02' 26.2' S, long 72 degrees 36' 00.0' E)\n\nFrequency: Breeding Southern Giant Petrels are easily disturbed. Colonies are visited every 3-5 years to minimise disturbance to breeding birds.\n\nMeasurement technique: All colonies are visited and breeding birds are counted from outside the colonies by two personnel performing replicate counts. All breeding individuals in a colony are counted. No birds are disturbed off their nests.\n\nConsiderations regarding disturbance associated with census visits are also incorporated into monitoring strategies. The lack of annual census data does not reduce the value of these long-term monitoring programmes.\n\nRESEARCH ISSUES\nAll Southern Giant Petrel breeding populations in the AAT and at HIMI decreased following their discovery. Southern Giant Petrels breeding on Indian Ocean islands are highly sensitive to human disturbance. Access to breeding colonies is restricted, as are the types of activities permitted. Disturbance from researchers has been implicated in the observed decreases in these populations (Woehler et al. 2001, Woehler et al. in press). Analyses of the long term AAT data suggest that the breeding populations at Hawker Is and at the Frazier Is have recovered following the restrictions on access and activities permitted on breeding islands.\n\nLINKS TO OTHER INDICATORS", "links": [ { diff --git a/datasets/SOE_ship_fuel_1.json b/datasets/SOE_ship_fuel_1.json index 718fd35b19..e9ac03d470 100644 --- a/datasets/SOE_ship_fuel_1.json +++ b/datasets/SOE_ship_fuel_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_ship_fuel_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nThe quantity of fuel used by ships travelling to Australian Antarctic stations and on Marine Science voyages as measured on a monthly basis and reported in the monthly reports from the Voyage Leaders to the Kingston (Head Office) Logistics Section.\n\nTYPE OF INDICATOR\nThere are three types of indicators used in this report:\n1.Describes the CONDITION of important elements of a system;\n2.Show the extent of the major PRESSURES exerted on a system;\n3.Determine RESPONSES to either condition or changes in the condition of a system.\n\nThis indicator is one of: PRESSURE\n\nRATIONALE FOR INDICATOR SELECTION\n\nThe amount of fuel used on ships travelling to Antarctica and on Marine Science voyages, for propulsion and power generation, is proportional to environmental impact due to the emissions released.\n\nMarine Gas Oil (MGO), is a marine version of normal diesel and is used on the vessels to power the main engines and generator sets, to provide propulsion and general services to the vessels such as power and heating.\n\nIFO 40 (RMC 10) is a light grade fuel oil used by some of the vessels by the Antarctic Division. This fuel is used for the main engines, and in some cases the generators.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nSpatial scale: Southern Ocean.\n\nFrequency: Monthly reports\n\nMeasurement technique: The figures are obtained by sounding the fuel tanks on the ship and/or a reading from the fuel usage meter.\n\nRESEARCH ISSUES\nDepending on the vessels used by the Antarctic division, future collection of this data may be automated.\n\nLINKS TO OTHER INDICATORS\nSOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations.\nSOE Indicator 2 - Highest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 3 - Lowest monthly air temperatures at Australian Antarctic Stations\nSOE Indicator 4 - Monthly mean lower stratospheric temperatures above Australian Antarctic Stations\nSOE Indicator 7 - Monthly mean of three-hourly wind speeds (m/s)\nSOE Indicator 48 - Station and ship person days\nSOE Indicator 57 - Monthly total of fuel used by station incinerators\nSOE Indicator 58 - Monthly total of fuel used by station vehicles\nSOE Indicator 59 - Monthly electricity usage\nSOE Indicator 60 - Total helicopter hours\nSOE Indicator 61 - Total potable water consumption\nSOE Indicator 65 - Station footprint for Australian Antarctic stations", "links": [ { diff --git a/datasets/SOE_wilderness_1.json b/datasets/SOE_wilderness_1.json index 8c4ad9a6d1..6804892150 100644 --- a/datasets/SOE_wilderness_1.json +++ b/datasets/SOE_wilderness_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOE_wilderness_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This indicator is no longer maintained, and is considered OBSOLETE.\n\nINDICATOR DEFINITION\nThis indicator attempts to measure the effect of human activity on the wilderness values of Antarctica and how the area, which may be considered as wilderness, changes in response to human activities.\n\nRATIONALE FOR INDICATOR SELECTION\nThis indicator measures the changes in wilderness directly through well-defined parameters which are therefore capable of measurement using existing datasets and standard geographical information system (GIS) algorithms.\n\nIt is proposed at the outset that all of Antarctica can be considered as wilderness unless it has been modified by human activity, specifically human activity in Antarctica. Global effects of human activity, such as ozone depletion and greenhouse enhancement have been ignored on the basis that they are not unique to Antarctica. It is further proposed that the way that human activities in Antarctica modify Antarctica, and therefore detract from its wilderness values are through sight and sound of these activities, especially the visibility of permanent structures and the audibility of activities. It is possible to map the areas from which structures are visible and the areas from which activities can be heard.\nIn practice, however, it is has been found that areas from which structures are visible generally include those areas from which sounds can be heard. By mapping the structures and their visibility &footprint& it is possible to track the area of wilderness modified from one reporting period to the next.\n\nDESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM\nThe plan is to assemble data on the location and dimensions of structures at the beginning of each reporting period. A visibility analysis would then be carried out using a digital elevation model (DEM). There are a number of issues which need to be considered such as the acquisition of improved elevation data from which the DEM is constructed and the effect this would have on the analysis and if new algorithms were released by the software vendor. The effects of these would have to be considered in the interpretation. It is considered unlikely that either of these would have a profound effect.\n\nThe visibility footprint is calculated from a visibility analysis of all human structures in the Windmill Islands, principally Casey Station but also including the remnants of Wilkes Station, six field huts, route markers and other features. The analysis was carried out using the VISIBILITY process in the ESRI ArcInfo GRID extension. The inputs into the analysis were a GIS data layer (ArcInfo coverage) of 239 points defining the locations and heights of human structures and a digital elevation model (DEM) of the Windmill Islands area. The attached file lists all the features in the human structures data layer grouped into feature types. Points defining the corners of the buildings in the station area were used rather than lines delineating the faces of the buildings as the VISIBILITY process uses the vertices in lines - which in a building are essentially the four corners anyway. Each structure point has a height value ascribed to it. The VISIBILITY process uses a number of parameters to define the extent and limit of visibility, these include the height of the structure, the distance it is visible and the height of the observer. For the purposes of this analysis a uniform value of 1.75 m was used as the observer height. The distance the structure may be visible from will vary according to the feature type. For example, buildings and large radio masts are visible to a range of about 20 km, depending on the weather, whereas route markers may only be visibile to a range of 3 km. To a certain extent this is somewhat arbitrary as binoculars can extend the visibility range considerably but experience in the field has shown these ranges to be about right for average conditions. Consistency is at least as important as precision in this context. The VISIBILITY process corrects for the curvature of the earth and the refraction of light. The DEM was constructed using the TOPOGRID routine in ArcInfo which creates hydrologically correct DEMs and is based on the ANUDEM package developed at the Australian National University. The DEM was constructed from contour data, constrained with a polygon boundary and lakes acquired from an aerial survey conducted by the Australian Antarctic Division 1993-4 with some additional contours added on the lower slopes of Law Dome. The cell size of the DEM is 10 m. A visibility analysis was conducted in 1998 as part of a pilot project on wilderness and aesthetic values of Antarctica. Although some some radio masts were removed and a wind generator erected during the intervening period, a comparison between the two analyses reveals little change. The difference between the two values is consistent with these changes.\n\nA visibility analysis was conducted in 1998 as part of a pilot project on wilderness and aesthetcic values of Antarctica. Although some some radio masts were removed and a wind generator erected during the intervening period, a comparison between the two analyses reveals little change. The difference between the two values is consistent with these changes.\n\nStation Leaders are instructed to notify the Technical Contact for this Indicator if any of the following types of structure are erected, removed, moved or modified on station or in the local area of the station.\n\n-buildings\n-tanks (fuel, water) and bunding\n-masts, mast anchor points, poles\n-anemometer masts\n-memorial crosses\n-route markers\n-depots, if they are marked\n-wind power generators\n-abandoned stations/buildings/aircraft and all their associated infrastructure eg radio masts\n-refuges in the local area of the station\n-radio masts at each refuge\n-radio repeaters\n-automatic weather stations in the local area of the station\n-navigation beacons\n-hydrographic survey beacons\n-any other man-made features in the local area of the station\n\nWhen such changes occur, Station Leaders are asked for the approximate dimensions of new structures or modifications, and these structures are marked for surveying at the next available opportunity.\n\nRESEARCH ISSUES\nThis topic is the subject of a PhD currently being carried out by Rupert Summerson at the University of Melbourne. Current research needs are as follows:\n\n- Distribution of questionnaires, disposable cameras and other material to ANARE expeditioners;\n\n- Assistance with motivation of expeditioners to participate in this research;\n\n- Access to data on human infrastructure, both ANARE and other nations;\n\n- Access to topographic data, both ANARE and other nations eg RAMP;\n\n- Support to engage with other Antarctic nations and the Antarctic tourism industry.\n\nTotal data received from this study to date:\nDate,Latitude,Longitude,Place,Parameter,Value,Unit of Measure\n1998,66 16.9' S,110 31.7' E,Casey,Visibility footprint,597.482,sq km", "links": [ { diff --git a/datasets/SOFIA_Cape_Sable.json b/datasets/SOFIA_Cape_Sable.json index 92e54e3449..efe99c6098 100644 --- a/datasets/SOFIA_Cape_Sable.json +++ b/datasets/SOFIA_Cape_Sable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOFIA_Cape_Sable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cape Sable peninsula is located on the southwestern tip of the Florida peninsula within Everglades National Park (ENP). Lake Ingraham, the largest lake within Cape Sable, is now connected to the Gulf of Mexico and western Florida Bay by canals built in the early 1920?s. Some of these canals breached a natural marl ridge located to the north of Lake Ingraham. These connections altered the landscape of this area allowing for the transport of sediments to and from Lake Ingraham. Saline intrusion into the formerly fresh interior marsh has impacted the local ecology. Earthen dams installed in the 1950?s and 1960?s in canals that breached the marl ridge have repeatedly failed. Sheet pile dams installed in the early 1990?s subsequently failed resulting in the continued alteration of Lake Ingraham and the interior marsh. The Cape Sable Canals Dam Restoration Project, funded by ENP, proposes to restore the two failed dams in Lake Ingraham. The objective of this study was to collect discharge and water quality data over a series of tidal cycles and flow conditions to establish discharge and sediment surrogate relations prior to initiating the Cape Sable Canals Dam Restoration Project. A dry season synoptic sampling event was performed on April 27-30, 2009. (Source: Zucker, M. and Boudreau, C. 2010. Sediment Transport on Cape Sable, Everglades National Park, FL. Joint Federal Interagency Conference. )", "links": [ { diff --git a/datasets/SOFIA_CarbonFlux.json b/datasets/SOFIA_CarbonFlux.json index f75fbe5e8b..0836580ae7 100644 --- a/datasets/SOFIA_CarbonFlux.json +++ b/datasets/SOFIA_CarbonFlux.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOFIA_CarbonFlux", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Greenhouse gas emissions, specifically carbon dioxide (CO2), are commonly linked with increasing global temperatures and rising sea-level. Of particular concern are rates of sea-level rise and carbon cycling including CO2 emissions or \"footprints\" of urban areas and the capacity of plant communities to absorb and release CO2. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the functioning of water, energy and carbon cycles within Greater Everglades ecosystems. However, measurements of carbon and surface-energy cycling are sparse over plant communities within Department of Interior (DOI) managed lands in south Florida. Specifically, the quantity of CO2 absorbed or released annually within subtropical forests and wetlands as well as carbon and energy cycling in response to changes in hydrology, salinity, forest-fires and/or other factors are poorly known. To reduce these uncertainties, eddy-covariance flux stations were constructed by the U.S. Geological Survey and South Florida Water Management District in the Big Cypress National Preserve in 2006. Water, energy and carbon fluxes are empirically measured at these stations. The goals of the project are to (1) quantify key variables of interest to researchers and policy makers such as latent heat flux (the energy equivalent of evapotranspiration), sensible heat flux, incoming solar radiation, net radiation, changes in stored heat energy, albedos, Bowen ratios, net ecosystem production (NEP), gross ecosystem production (GEP), ecosystem respiration; (2) understand variability and linkages within water, energy and carbon-cycles imposed by both natural processes and regional / global stresses; and (3) publish project results in USGS reports and peer-reviewed journal papers. \n\nDefining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the state and functioning of water, energy and carbon cycles within DOI lands. However, measurements of carbon and surface-energy cycling are sparse over plant communities within DOI managed lands in south Florida. This project intends to measure water and surface energy fluxes within the BCNP. We propose to begin carbon cycling measurements in 2012 and 2013, as time and funding permits. Plant communities selected for study included Pine Upland, Marsh, Cypress Swamp, and Dwarf Cypress. These plant communities are spatially extensive within DOI lands and resources.", "links": [ { diff --git a/datasets/SOIL_CO2_Flux_Costa_Rica_1373_1.json b/datasets/SOIL_CO2_Flux_Costa_Rica_1373_1.json index 8ce66ece9c..3f01a57e31 100644 --- a/datasets/SOIL_CO2_Flux_Costa_Rica_1373_1.json +++ b/datasets/SOIL_CO2_Flux_Costa_Rica_1373_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOIL_CO2_Flux_Costa_Rica_1373_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides measurements of soil carbon dioxide (CO2) emission rates, soil moisture, relative humidity (RH), temperature, and litterfall from six types of tree plantations at the La Selva Biological Station, Costa Rica. Soil CO2 flux and related measurements were made 1) hourly during 2-day diel field campaigns and 2) as single daytime measurements during multiple survey campaigns, over the period 2004-2010. All measurements were made at the same sites to compare hourly, monthly, and inter-annual variations. Most of the emissions data represent a single soil CO2 flux measurement, with three to five measurements per plot. Litterfall was collected monthly from 2003-2009 and was sorted into fractions prior to drying.", "links": [ { diff --git a/datasets/SOLVE1_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE1_Aerosol_AircraftInSitu_DC8_Data_1.json index 21021499e0..a9dbc0092e 100644 --- a/datasets/SOLVE1_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE1_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Aerosol_AircraftInSItu_DC8_Data is the in-situ aerosol data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Forward Scattering Spectrometer Probe (FSSP), Passive Cavity Aerosol Spectrometer Probe (PCASP), Condensation Nuclei Counter (CNC), Focused Cavity Aerosol Spectrometer II (FCAS II), and the Nucleation-Mode Aerosol Size Spectrometer II (N-MASS). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Aerosol_AircraftInSitu_ER2_Data_1.json b/datasets/SOLVE1_Aerosol_AircraftInSitu_ER2_Data_1.json index fef4945262..62ef001bee 100644 --- a/datasets/SOLVE1_Aerosol_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SOLVE1_Aerosol_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Aerosol_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Aerosol_AircraftInSItu_ER2_Data is the in-situ aerosol data for the ER-2 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Multiple-Angle Aerosol Spectrometer Probe (MASPR), Condensation Nuclei Counter (CNC), Focused Cavity Aerosol Spectrometer II (FCAS II), and the Nucleation-Mode Aerosol Size Spectrometer II (N-MASS). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data_1.json b/datasets/SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data_1.json index 773a63d88f..747674341e 100644 --- a/datasets/SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data_1.json +++ b/datasets/SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_AircraftRemoteSensing_DC8_HSRL_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE) by the High Spectral Resolution Lidar (HSRL). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_AircraftRemoteSensing_DC8_LASE_Data_1.json b/datasets/SOLVE1_AircraftRemoteSensing_DC8_LASE_Data_1.json index 59da10b21b..a6b71133af 100644 --- a/datasets/SOLVE1_AircraftRemoteSensing_DC8_LASE_Data_1.json +++ b/datasets/SOLVE1_AircraftRemoteSensing_DC8_LASE_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_AircraftRemoteSensing_DC8_LASE_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE) by the Lidar Atmospheric Sensing Experiment (LASE) instrument. Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data_1.json b/datasets/SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data_1.json index d06055f285..e146c8d0fb 100644 --- a/datasets/SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data_1.json +++ b/datasets/SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_AircraftRemoteSensing_DC8_Lidar_Data is the remotely sensed lidar trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by Differential Absorption Lidar (DIAL) and the Airborne Raman Ozone, Temperature, and Aerosol Lidar (AROTAL). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Analysis_DC8_Data_1.json b/datasets/SOLVE1_Analysis_DC8_Data_1.json index 93cce124fe..3956dd5bfa 100644 --- a/datasets/SOLVE1_Analysis_DC8_Data_1.json +++ b/datasets/SOLVE1_Analysis_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Analysis_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Analysis_DC8_Data contains modeled trajectories and meteorological data along the flight path for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Analysis_ER2_Data_1.json b/datasets/SOLVE1_Analysis_ER2_Data_1.json index 667815dd81..7a6316970d 100644 --- a/datasets/SOLVE1_Analysis_ER2_Data_1.json +++ b/datasets/SOLVE1_Analysis_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Analysis_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Analysis_ER2_Data contains modeled trajectories and meteorological data along the flight path for the ER-2 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Ground_Data_1.json b/datasets/SOLVE1_Ground_Data_1.json index 362e47ff11..733466489e 100644 --- a/datasets/SOLVE1_Ground_Data_1.json +++ b/datasets/SOLVE1_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Ground_Data is the ground site data collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as Differential Absorption Lidar (DIAL) and the JPL MkIV Balloon Interferometer (MkIV). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE1_MetNav_AircraftInSitu_DC8_Data_1.json index 00e60122e4..8352c8d6fb 100644 --- a/datasets/SOLVE1_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE1_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_MetNav_AircraftInSItu_DC8_Data is the in-situ meteorological and navigational data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Also featured in this product is water vapor data from the Diode Laser Hygrometer (DLH) and JPL Laser Hygrometer (JLH). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_MetNav_AircraftInSitu_ER2_Data_1.json b/datasets/SOLVE1_MetNav_AircraftInSitu_ER2_Data_1.json index f5ed6b7b03..1f092b10fb 100644 --- a/datasets/SOLVE1_MetNav_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SOLVE1_MetNav_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_MetNav_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_MetNav_AircraftInSItu_ER2_Data is the in-situ meteorological and navigational data for the ER-2 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Also featured in this product is water vapor data from the Harvard Water Vapor (HWV) and JPL Laser Hygrometer (JLH) instruments. Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Miscellaneous_DC8_Data_1.json b/datasets/SOLVE1_Miscellaneous_DC8_Data_1.json index 5aade7407d..c69346052a 100644 --- a/datasets/SOLVE1_Miscellaneous_DC8_Data_1.json +++ b/datasets/SOLVE1_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Miscellaneous_DC8_Data is the supplementary miscellaneous data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Miscellaneous_ER2_Data_1.json b/datasets/SOLVE1_Miscellaneous_ER2_Data_1.json index 7deb493c85..85b1585fbb 100644 --- a/datasets/SOLVE1_Miscellaneous_ER2_Data_1.json +++ b/datasets/SOLVE1_Miscellaneous_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Miscellaneous_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Miscellaneous_ER2_Data is the supplementary miscellaneous data for the ER-2 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Satellite_Data_1.json b/datasets/SOLVE1_Satellite_Data_1.json index 9a18b32105..00e991a207 100644 --- a/datasets/SOLVE1_Satellite_Data_1.json +++ b/datasets/SOLVE1_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Satellite_Data is the supplementary satellite data for the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Halogen Occultation Experiment (HALOE), the Polar Ozone and Aerosol Measurement III (POAM III) satellite, and the Global Ozone Monitoring Experiment (GOME). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_Sondes_Data_1.json b/datasets/SOLVE1_Sondes_Data_1.json index 9175d966d2..5eb699b1a4 100644 --- a/datasets/SOLVE1_Sondes_Data_1.json +++ b/datasets/SOLVE1_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_Sondes_Data is the balloonsonde and ozonesonde data collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by balloon borne frost point hygrometer, the Airborne Chromatograph for Atmospheric Trace Species (ACATS), Lightweight Airborne Chromatograph Experiment (LACE), Submillimeter Limb Sounder (SLS), JPL MkIV Balloon Interferometer (MkIV), High-Altitude Fast-Response CO2 Analyzer (Harvard CO2), and the Balloon Dual-beam UV in situ O3 Photometer (NOAA O3 Classic). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE1_TraceGas_AircraftInSitu_DC8_Data_1.json index 7d85ac38a1..5f391aa837 100644 --- a/datasets/SOLVE1_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE1_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_TraceGas_AircraftInSItu_DC8_Data is the in-situ trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Tropospheric Ozone and Tracers from Commercial Aircraft Platforms (TOTCAP), Langley In Situ Fast-Response Ozone Measurements (FASTOZ), Airborne Tropospheric Hydroxides Sensor (ATHOS), NO and NOy Chemiluminescence Instrument (NO/NOy), Differential Absorption of CO, CH4, N2O Measurements (DACOM), Multiple Axis Resonance Fluorescence Chemical Conversion Detector for ClO and BrO (ClO/BrO), and the Chemical Ionization Mass Spectrometer (CIMS). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_TraceGas_AircraftInSitu_ER2_Data_1.json b/datasets/SOLVE1_TraceGas_AircraftInSitu_ER2_Data_1.json index 6e153b9fc9..72f578d71b 100644 --- a/datasets/SOLVE1_TraceGas_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SOLVE1_TraceGas_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_TraceGas_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_TraceGas_AircraftInSItu_ER2_Data is the in-situ trace gas data for the ER-2 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Harvard Hydroxyl Experiment (HOx), Chlorine Nitrate Experiment (CLONO2), Advanced Whole Air Sampler (AWAS), Airborne Chromatograph for Atmospheric Trace Species (ACATS), Argus Tunable Diode Laser Instrument (ARGUS), NOAA O3 Classic, Aircraft Laser Infrared Absorption Spectrometer (ALIAS), Chemical Ionization Mass Spectrometer (CIMS), and the High-Altitude Fast Response CO2 Analyzer (Harvard CO2). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data_1.json b/datasets/SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data_1.json index bb1d157662..353bade637 100644 --- a/datasets/SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data_1.json +++ b/datasets/SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_TraceGas_AircraftRemoteSensing_DC8_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by the Airborne Submillimeter Radiometer (ASUR) instrument. Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE1_jValue_AircraftInSitu_DC8_Data_1.json index 72ed2985ac..d8bb5b2d59 100644 --- a/datasets/SOLVE1_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE1_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_jValue_AircraftInSitu_DC8_Data is the in-situ photolysis rate data collected by the DC-8 aircraft during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data was collected by the John Hopkins University Applied Physics Laboratory radiative transfer model and the Scanning Actinic Flux Spectroradiometer (SAFS). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE1_jValue_AircraftInSitu_ER2_Data_1.json b/datasets/SOLVE1_jValue_AircraftInSitu_ER2_Data_1.json index bd5335ff87..616580bdb4 100644 --- a/datasets/SOLVE1_jValue_AircraftInSitu_ER2_Data_1.json +++ b/datasets/SOLVE1_jValue_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE1_jValue_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE1_jValue_AircraftInSitu_ER2_Data is the in-situ photolysis rate data collected by the ER-2 aircraft during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data was collected by the John Hopkins University Applied Physics Laboratory radiative transfer model and the Composition and Photo-Dissociative Flux Measurement (CPFM) instrument. Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE2_Aerosol_AircraftInSitu_DC8_Data_1.json index fe2b0c210e..fe4069a89a 100644 --- a/datasets/SOLVE2_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE2_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Aerosol_AircraftInSItu_DC8_Data is the in-situ aerosol data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by instruments such as the TSI Model 3760 Concdensation Nuclei Counter (TSI 3760), TSI 3563 Nephelometer, Cloud, Aerosol, and Precipitation Spectrometer (CAPS), Condensation Nuclei Counter (CNC), Focused Cavity Aerosol Spectrometer II (FCAS II), and the Nucleation-Mode Aerosol Size Spectrometer II (N-MASS). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data_1.json b/datasets/SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data_1.json index 202abd5efa..f3ee129dda 100644 --- a/datasets/SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data_1.json +++ b/datasets/SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_AircraftRemoteSensing_DC8_AATS14_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II) by the Ames 14-Channel Airborne Tracking Sunphotometer (AATS14). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data_1.json b/datasets/SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data_1.json index f8dc1dbe56..9bc004a26b 100644 --- a/datasets/SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data_1.json +++ b/datasets/SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_AircraftRemoteSensing_DC8_HSRL_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II) by the High Spectral Resolution Lidar (HSRL). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data_1.json b/datasets/SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data_1.json index 02333459d0..4417014e40 100644 --- a/datasets/SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data_1.json +++ b/datasets/SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_AircraftRemoteSensing_DC8_Lidar_Data is the remotely sensed lidar trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by Differential Absorption Lidar (DIAL) and the Airborne Raman Ozone, Temperature, and Aerosol Lidar (AROTAL). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Analysis_DC8_Data_1.json b/datasets/SOLVE2_Analysis_DC8_Data_1.json index e8409ddff8..0200ace5fc 100644 --- a/datasets/SOLVE2_Analysis_DC8_Data_1.json +++ b/datasets/SOLVE2_Analysis_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Analysis_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Analysis_DC8_Data contains modeled trajectories and meteorological data along the flight path for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE2_MetNav_AircraftInSitu_DC8_Data_1.json index a56cdf7b08..a96b8ff95e 100644 --- a/datasets/SOLVE2_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE2_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_MetNav_AircraftInSItu_DC8_Data is the in-situ meteorological and navigational data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Also featured in this product is water vapor data from the Diode Laser Hygrometer (DLH). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Miscellaneous_DC8_Data_1.json b/datasets/SOLVE2_Miscellaneous_DC8_Data_1.json index 647d81698b..b832b5a85c 100644 --- a/datasets/SOLVE2_Miscellaneous_DC8_Data_1.json +++ b/datasets/SOLVE2_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Miscellaneous_DC8_Data is the supplementary miscellaneous data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Radiation_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE2_Radiation_AircraftInSitu_DC8_Data_1.json index 58cd6fa15c..0f90492163 100644 --- a/datasets/SOLVE2_Radiation_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE2_Radiation_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Radiation_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Radiation_AircraftInSitu_DC8_Data is the in-situ radiation data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II) by the Direct Irradiance Airborne Spectrometer (DIAS). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Rocket_Data_1.json b/datasets/SOLVE2_Rocket_Data_1.json index c5970b1bdd..269d3b3be3 100644 --- a/datasets/SOLVE2_Rocket_Data_1.json +++ b/datasets/SOLVE2_Rocket_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Rocket_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Rocket_Data is the rocketsonde data collected by falling sphere sounding during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Satellite_Data_1.json b/datasets/SOLVE2_Satellite_Data_1.json index 17d6f5e003..288145f168 100644 --- a/datasets/SOLVE2_Satellite_Data_1.json +++ b/datasets/SOLVE2_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Satellite_Data is the supplementary satellite data for the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by the Polar Ozone and Aerosol Measurement III (POAM III) satellite. Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_Sondes_Data_1.json b/datasets/SOLVE2_Sondes_Data_1.json index fe90088eab..78c196c332 100644 --- a/datasets/SOLVE2_Sondes_Data_1.json +++ b/datasets/SOLVE2_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_Sondes_Data is the balloonsonde and ozonesonde data collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by frost-point hygrometer, ozonesondes, condensation nuclei counters, Optical Particle Counters (OPC), and the JPL MkIV Balloon Interferometer (MkIV). Data collection for this product is complete. \r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/SOLVE2_TraceGas_AircraftInSitu_DC8_Data_1.json index 02c65e8c56..3728cae42e 100644 --- a/datasets/SOLVE2_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SOLVE2_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_TraceGas_AircraftInSItu_DC8_Data is the in-situ trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by instruments such as the Langley In Situ Fast-Response Ozone Measurements (FASTOZ), Differential Absorption of CO, CH4, N2O Measurements (DACOM), and the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data_1.json b/datasets/SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data_1.json index 3cc4c64899..8c6ab7df48 100644 --- a/datasets/SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data_1.json +++ b/datasets/SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOLVE2_TraceGas_AircraftRemoteSensing_DC8_Data is the remotely sensed trace gas data for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by the Gas and Aerosol Measurement Sensor/Langley Airborne A-Band Spectrometer (GAMS/LAABS). Data collection for this product is complete.\r\n\r\nThe SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA\u2019s Earth Science Enterprise (ESE). SOLVE\u2019s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.\r\n\r\nSOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center\u2019s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.", "links": [ { diff --git a/datasets/SONEX_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/SONEX_Aerosol_AircraftInSitu_DC8_Data_1.json index ea9e105f85..50b89ef33a 100644 --- a/datasets/SONEX_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SONEX_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Aerosol_AircraftInSitu_DC8_Data_1 is the in-situ aerosol data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data was collected via in-situ instrumentation, including multiple condensation nuclei counters (CNCs), ACIR (Aerosol/Cloud Particle Impactor/Replicator), FSSP, PCASP, and SAGA. Data collection for this product is complete.\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1.json b/datasets/SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1.json index 3669e30dde..c299e3b008 100644 --- a/datasets/SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1.json +++ b/datasets/SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_AircraftRemoteSensing_DC8_DIAL_Data_1 is the Differential Absorption Lidar (DIAL) remotely sensed data collected onboard the DC-8 aircraft during the SONEX suborbital campaign. Data collection is complete.\r\n\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_Merge_DC8_Data_1.json b/datasets/SONEX_Merge_DC8_Data_1.json index ff379dc5b4..45e0667381 100644 --- a/datasets/SONEX_Merge_DC8_Data_1.json +++ b/datasets/SONEX_Merge_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Merge_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Merge_DC8_Data_1 is all of the project generated merge files for the SONEX suborbital campaign. Types of merges include 1- and 5-minute, aerosol composition-based, H2O2-based, HNO3-based, hydrocarbon-based, NO-based, and PAN-based merges. Data collection for this product is complete.\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/SONEX_MetNav_AircraftInSitu_DC8_Data_1.json index 237aa0c7be..83897586cb 100644 --- a/datasets/SONEX_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SONEX_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_TraceGas_AircraftInSitu_DC8_Data_1 is the in-situ meteorological and navigation data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data collection for this product is complete.\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_Miscellaneous_DC8_Data_1.json b/datasets/SONEX_Miscellaneous_DC8_Data_1.json index 38d9dc39ed..b210b9fd72 100644 --- a/datasets/SONEX_Miscellaneous_DC8_Data_1.json +++ b/datasets/SONEX_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Miscellaneous_Data_1 is the ancillary datasets from the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX). This dataset contains gif and postscript files of datasets to support DC-8 aircraft measurements. Data collection for this product is complete.\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_Model_Data_1.json b/datasets/SONEX_Model_Data_1.json index ee5df53ff8..b7c51ede4a 100644 --- a/datasets/SONEX_Model_Data_1.json +++ b/datasets/SONEX_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Model_Data_1 contains all of the model data used for the SONEX suborbital campaign. This data product contains: CO from surface emissions at 6 km, NOx from aircraft emission at 6 km, NOx from lightning at 6 km, and NOx from surface pollution at 6 km.\r\n\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_Other_Data_1.json b/datasets/SONEX_Other_Data_1.json index acd790a672..ee138c420c 100644 --- a/datasets/SONEX_Other_Data_1.json +++ b/datasets/SONEX_Other_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Other_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Other_Data_1 is the supplementary datasets for the SONEx suborbital campaign. Included in this product are images from the National Lightning Data Network (NLDN) and Optical Transient Detector (OTD).\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_Satellite_Data_1.json b/datasets/SONEX_Satellite_Data_1.json index 3f334344d3..49f9da5c9d 100644 --- a/datasets/SONEX_Satellite_Data_1.json +++ b/datasets/SONEX_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_Satellite_Data_1 is the satellite data used to support the SONEX suborbital campaign. Contained in this data product are ozone measurements from TOMS-EP and various wind measurements from GOES-8. Data collection for this product is complete.\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/SONEX_TraceGas_AircraftInSitu_DC8_Data_1.json index 52bd895f68..74b40eea54 100644 --- a/datasets/SONEX_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SONEX_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_TraceGas_AircraftInSitu_DC8_Data_1 is the in-situ trace gas data collected onboard the DC-8 aircraft during the SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) suborbital campaign. Data was collected from a variety of in-situ instrumentation, including the Whole Air Sampler (WAS), ATHOS, FASTOZ, University of Rhode Island Chemistry Instrument, chemiluminescence, DACOM, LICOR, PANAK, CIMS and SAGA. Data collection for this product is complete.\r\n\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SONEX_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/SONEX_jValue_AircraftInSitu_DC8_Data_1.json index ad902ee3ba..8925fb7837 100644 --- a/datasets/SONEX_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/SONEX_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SONEX_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SONEX_jValue_AircraftInSitu_DC8_Data_1 is the photolysis frequencies (j-values) measured along the DC-8 flight by the Scanning Actinic Flux Spectroradiometers (SAFS). Data collection is complete.\r\n\r\nThe SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum f\u00fcr Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.\r\nIn SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.\r\nThree sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.", "links": [ { diff --git a/datasets/SOR3D_COMBINED_001.json b/datasets/SOR3D_COMBINED_001.json index 46f093edbf..6c84b9f088 100644 --- a/datasets/SOR3D_COMBINED_001.json +++ b/datasets/SOR3D_COMBINED_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3D_COMBINED_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE Combined XPS, SOLSTICE, and SIM Solar Spectral Irradiance 24-Hour Means product consists of daily averages of the solar spetra from 0.1 to 2412 nm. The SORCE instruments make measurements during each daytime orbit portion, 15 orbits per day. This product combines data from the XPS, SOLSTICE and SIM instruments and merges them into a daily averaged solar spectra. The spectral resolution of SIM varies between 1-34 nm, SOLSTICE is 1 nm, and XPS is 7 nm.\n\nThe SORCE combined data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), min wavelength, max wavelength, instrument mode, input data version, spectral irradiance, irradiance uncertainty, and a data quality flag. Each row represents a separate day and wavelength.", "links": [ { diff --git a/datasets/SOR3SIMD_027.json b/datasets/SOR3SIMD_027.json index 123ea3b73c..a5976c86ac 100644 --- a/datasets/SOR3SIMD_027.json +++ b/datasets/SOR3SIMD_027.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SIMD_027", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 027 is the final version of this data product, and supersedes all previous versions.\n\nThe SORCE SIM Solar Spectral Irradiance (SSI) data product SOR3SIMD is constructed using measurements from the SIM instruments, which are combined into merged daily solar spectra over the spectral range from 240 to 2416 nm at a spectral resolution ranging from 1 to 27 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SIM absolute uncertainty is about 2%.\n\nAll of the SOR3SIMD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3SIMD_TAV_002.json b/datasets/SOR3SIMD_TAV_002.json index ce08f2ca2f..c24bf75fc7 100644 --- a/datasets/SOR3SIMD_TAV_002.json +++ b/datasets/SOR3SIMD_TAV_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SIMD_TAV_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 002 is the most recent version of this data product, and supersedes all previous versions.\n\nThe SORCE SIM Level 3 TSIS-Adjusted Values Solar Spectral Irradiance 24-Hour Means data product (SOR3SIMD_TAV) uses the temporal overlap of the Solar Radiation and Climate Experiment (SORCE) and the Total and Spectral Solar Irradiance Sensor (TSIS-1) Spectral Irradiance Monitor (SIM) instruments to create an alternate SORCE-SIM irradiance calibration, known as the TSIS1 Adjusted Values (TAV). The SORCE-SIM Solar Spectral Irradiance (SSI) data products are provided on a fixed wavelength scale which varies in spectral resolution from 1-34 nm over the spectral range from 240 to 2401 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The TAV data is on the SORCE-SIM wavelength scale, with the exception that the longest TAV wavelength is 2401.4 nm.\n\nAll of the SOR3SIMD_TAV data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3SOLD_HIGH_018.json b/datasets/SOR3SOLD_HIGH_018.json index 0acd09f5e0..d309e28229 100644 --- a/datasets/SOR3SOLD_HIGH_018.json +++ b/datasets/SOR3SOLD_HIGH_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLD_HIGH_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SORCE SOLSTICE FUV and MUV Level 3 Solar Spectral Irradiance 0.1nm Res 24-Hour Means data product (SOR3SOLD_HIGH) is constructed using measurements from the SOLSTICE FUV and MUV instrument, which is combined into merged daily solar spectra over the spectral range from 115 to 310 nm at a high spectral resolution of 0.1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%.\n\nThe SOR3SOLD_HIGH data are stored in netCDF files containing a full year of data. Each file contains variables with the date, Julian day, wavelength, irradiance value, irradiance uncertainty, and irradiance repeatability.", "links": [ { diff --git a/datasets/SOR3SOLD_MGII_018.json b/datasets/SOR3SOLD_MGII_018.json index 76e08a4eda..307703d292 100644 --- a/datasets/SOR3SOLD_MGII_018.json +++ b/datasets/SOR3SOLD_MGII_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLD_MGII_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE SOLSTICE Level 3 MgII Core to Wing Ratio 24 Hour Means product consists of daily averages of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. This product has solar spectra averaged for a day. The spectral resolution of SOLSTICE is 0.1 nm, allowing the Mg-II doublet to be fully resolved and modeled with Gaussians. The Mg-II core-to-wing ratio is used as a measurement of solar activity.\n\nThe Mg-II data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), the core/wing ratio, and the absolute uncertainty. The rows are arranged with one daily average measurment, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3SOLFUVD_018.json b/datasets/SOR3SOLFUVD_018.json index 177084f42c..3d7eebfa50 100644 --- a/datasets/SOR3SOLFUVD_018.json +++ b/datasets/SOR3SOLFUVD_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLFUVD_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 018 is the final version of this data product, and supersedes all previous versions.\n\nThe SORCE SOLSTICE Far-UV Solar Spectral Irradiance (SSI) data product SOR3SOLFUVD is constructed using measurements from the SOLSTICE FUV instrument, which are combined into merged daily solar spectra over the spectral range from 115 to 180 nm at a spectral resolution of 1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%.\n\nAll of the SOR3SOLFUVD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SOLSTICE FUV wavelength range, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3SOLMUVD_018.json b/datasets/SOR3SOLMUVD_018.json index c1a85e9d77..bfcbea687d 100644 --- a/datasets/SOR3SOLMUVD_018.json +++ b/datasets/SOR3SOLMUVD_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLMUVD_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 018 is the final version of this data product, and supersedes all previous versions.\n\nThe SORCE SOLSTICE Mid-UV Solar Spectral Irradiance (SSI) data product SOR3SOLMUVD is constructed using measurements from the SOLSTICE MUV instrument, which are combined into merged daily solar spectra over the spectral range from 180 to 310 nm at a spectral resolution of 1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%.\n\nAll of the SOR3SOLMUVD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SOLSTICE FUV wavelength range, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3SOLS_LA_018.json b/datasets/SOR3SOLS_LA_018.json index 30b82029bd..300069d340 100644 --- a/datasets/SOR3SOLS_LA_018.json +++ b/datasets/SOR3SOLS_LA_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLS_LA_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE SOLSTICE Level 3 Lyman-alpha Irradiance As-Measured Cadence product consists of all measurements of the Lyman-alpha irradiance from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. A complete solar spectrum is made in about 30 minutes, a quick scan mode in about 5 minutes. The spectral resolution of SOLSTICE is 0.1 nm.\n\nThe Lyman-alpha data are stored in netCDF files containing a full year of. Each days measurements are in a separate netCDF group. Each group contains variables for irradiance, uncertainty, repeatability, long x-ray flux, short x-ray flux, spacecraft height, latitude, longitude, time, wavelength and target zenith angle, with overr 3000 measurements.", "links": [ { diff --git a/datasets/SOR3SOLS_MGII_018.json b/datasets/SOR3SOLS_MGII_018.json index 4d134eb93b..c95a8df9c2 100644 --- a/datasets/SOR3SOLS_MGII_018.json +++ b/datasets/SOR3SOLS_MGII_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3SOLS_MGII_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE SOLSTICE Level 3 MgII Core to Wing Ratio As-Measured Cadence product consists of all measurements of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. A complete solar spectrum is made in about 30 minutes, a quick scan mode in about 5 minutes. The spectral resolution of SOLSTICE is 0.1 nm, allowing the Mg-II doublet to be fully resolved and modeled with Gaussians. The Mg-II core-to-wing ratio is used as a measurement of solar activity.\n\nThe Mg-II data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), the core/wing ratio, and the absolute uncertainty. The rows are arranged with one daily average measurment, repeating for each day for the length of the measurement period.", "links": [ { diff --git a/datasets/SOR3TSI6_019.json b/datasets/SOR3TSI6_019.json index c684fb4e6d..e26cad9dab 100644 --- a/datasets/SOR3TSI6_019.json +++ b/datasets/SOR3TSI6_019.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3TSI6_019", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOR3TSI6 Version 019 is the final version of this data product, and supersedes all previous versions.\n\nThe Total Solar Irradiance (TSI) data set SOR3TSI6 contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at 6-hour intervals. The data are normalized to one astronomical unit (1 AU).\n\nThe TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 100 parts per million (ppm, 1 ppm=0.0001% at 1-sigma) and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission. Updates to Level 3 TSI data occur monthly in order to reduce repeated delivery of data.", "links": [ { diff --git a/datasets/SOR3TSID_019.json b/datasets/SOR3TSID_019.json index 82991f016f..56ac2f9232 100644 --- a/datasets/SOR3TSID_019.json +++ b/datasets/SOR3TSID_019.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3TSID_019", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SOR3TSID Version 019 is the final version of this data product, and supersedes all previous versions.\n\nThe Total Solar Irradiance (TSI) data set SOR3TSID contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at daily intervals. The data are normalized to one astronomical unit (1 AU).\n\nThe TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 100 parts per million (ppm, 1 ppm=0.0001% at 1-sigma) and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission. Updates to Level 3 TSI data occur monthly in order to reduce repeated delivery of data.", "links": [ { diff --git a/datasets/SOR3XPS6_012.json b/datasets/SOR3XPS6_012.json index 780420f924..0d8776a44c 100644 --- a/datasets/SOR3XPS6_012.json +++ b/datasets/SOR3XPS6_012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3XPS6_012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) 6-Hour Data Product SOR3XPS6 contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below:\n\n* Diode 1: 0.1 - 7.0 nm\n* Diode 2: 0.1 - 7.0 nm\n* Diode 3: 17.0 - 23.0 nm\n* Diode 6: 0.1 - 11.0 nm\n* Diode 7: 0.1 - 7.0 nm\n* Diode 9: 0.1 - 7.0 nm\n* Diode 11: 121.0 - 122.0 nm\n\nThe data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-30%, photometer dependent, and the long-term repeatability is 1% per year. The SOR3XPS6 data are arranged in a single tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, average measurement date, standard deviation of measurement date, diode number, minimum wavelength, maximum wavelength, instrument mode, data version number, median irradiance, mean irradiance, absolute uncertainty, measurement precision, calculation precision, degradation model, degradation version, and number of data points. The rows are arranged with data for each 6 hour time increment, repeating for each diode wavelength.", "links": [ { diff --git a/datasets/SOR3XPSD_012.json b/datasets/SOR3XPSD_012.json index 30f58d9949..6695103844 100644 --- a/datasets/SOR3XPSD_012.json +++ b/datasets/SOR3XPSD_012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR3XPSD_012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) Daily Data Product SOR3XPSD contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below:\n\n* Diode 1: 0.1 - 7.0 nm\n* Diode 2: 0.1 - 7.0 nm\n* Diode 3: 17.0 - 23.0 nm\n* Diode 6: 0.1 - 11.0 nm\n* Diode 7: 0.1 - 7.0 nm\n* Diode 9: 0.1 - 7.0 nm\n* Diode 11: 121.0 - 122.0 nm\n\nThe data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-30%, photometer dependent, and the long-term repeatability is 1% per year. The SOR3XPSD data are arranged in a single tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, average measurement date, standard deviation of measurement date, diode number, minimum wavelength, maximum wavelength, instrument mode, data version number, median irradiance, mean irradiance, absolute uncertainty, measurement precision, calculation precision, degradation model, degradation version, and number of data points. The rows are arranged with data for each day, repeating for each diode wavelength.", "links": [ { diff --git a/datasets/SOR4XPS5_012.json b/datasets/SOR4XPS5_012.json index 90824ba6e9..0f5b5f2fd7 100644 --- a/datasets/SOR4XPS5_012.json +++ b/datasets/SOR4XPS5_012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR4XPS5_012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 5-Minute product (SOR4XPS5) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm about every 5 minutes. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products.\n\nThe SOR4XPS5 data are stored in netCDF files containing one year of data. Data variables stored in each file include:\nMODELFLUX: Array of solar irradiances [W/m2/nm] in 0.1 nm bins from 0.1 to 39.9 nm\nERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy)\nERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision)\nDATE: Year and day-of-year of observation (YYDDD)\nTIME: Seconds of day for middle of observation\nXPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum\nXPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum\nXPS_FLARE: Flare scale factor for each XPS-1,-2\nGOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum\nGOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum\nGOES_FLARE: Flare scale factor for each GOES XRS-B integration\nFMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A\nFMINDEX: Index into flare model table\nFMWEIGHT: Weight parameter for the flare model\n", "links": [ { diff --git a/datasets/SOR4XPSD_HIGH_012.json b/datasets/SOR4XPSD_HIGH_012.json index e91df410f7..7542f08c2a 100644 --- a/datasets/SOR4XPSD_HIGH_012.json +++ b/datasets/SOR4XPSD_HIGH_012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR4XPSD_HIGH_012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 24-Hour Means product (SOR4XPSD_HIGH) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm (high res) and averaged over a day. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products.\n\nThe SOR4XPSD_HIGH data are stored in netCDF files containing data for the entire mission. Data variables stored in the file include:\nMODELFLUX: Array of solar irradiances [W/m2/nm] in 0.1 nm bins from 0.1 to 39.9 nm\nERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy)\nERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision)\nDATE: Year and day-of-year of observation (YYDDD)\nTIME: Seconds of day for middle of observation\nXPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum\nXPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum\nXPS_FLARE: Flare scale factor for each XPS-1,-2\nGOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum\nGOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum\nGOES_FLARE: Flare scale factor for each GOES XRS-B integration\nFMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A\nFMINDEX: Index into flare model table\nFMWEIGHT: Weight parameter for the flare model\n", "links": [ { diff --git a/datasets/SOR4XPSD_LOW_012.json b/datasets/SOR4XPSD_LOW_012.json index 6341ead4c1..8ea34707f0 100644 --- a/datasets/SOR4XPSD_LOW_012.json +++ b/datasets/SOR4XPSD_LOW_012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SOR4XPSD_LOW_012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SORCE XPS Level 4 Solar Spectral Irradiance 1.0nm Res 24-Hour Means product (SOR4XPSD_LOW) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 1.0 nm (low res) and averaged over a day. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products.\n\nThe SOR4XPS5 data are stored in a single netCDF files containing data for the entire mission. Data variables stored in the file include:\nMODELFLUX: Array of solar irradiances [W/m2/nm] in 1.0 nm bins from 0 to 40 nm\nERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy)\nERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision)\nDATE: Year and day-of-year of observation (YYDDD)\nTIME: Seconds of day for middle of observation\nXPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum\nXPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum\nXPS_FLARE: Flare scale factor for each XPS-1,-2\nGOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum\nGOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum\nGOES_FLARE: Flare scale factor for each GOES XRS-B integration\nFMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A\nFMINDEX: Index into flare model table\nFMWEIGHT: Weight parameter for the flare model\n", "links": [ { diff --git a/datasets/SORTIE_0.json b/datasets/SORTIE_0.json index 9df2ac8b52..2a5b4261f8 100644 --- a/datasets/SORTIE_0.json +++ b/datasets/SORTIE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SORTIE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the SORTIE (Spectral Ocean Radiance Transfer Investigation and Experiment) program between 2007 and 2009.", "links": [ { diff --git a/datasets/SPACE_PHOTOS.json b/datasets/SPACE_PHOTOS.json index 8a4dd48536..4629bad58a 100644 --- a/datasets/SPACE_PHOTOS.json +++ b/datasets/SPACE_PHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPACE_PHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gemini photography was acquired between March 23, 1965 and November 15, 1966. The images were collected as part of the Synoptic Terrain Photography and the Synoptic Weather Photography experiments during Gemini Missions III through XII. Hand-held cameras were used to obtain photographs of geologic, oceanic, and meteorologic targets. The Gemini archive consists primarily of 70-mm black and white (B/W), color, and color-infrared (CIR) film. All Gemini photographs are distributed by the USGS Earth Resources Observation and Science (EROS) Center as digital products only.\n\nSkylab photography was acquired between May 22, 1973 and February 8, 1974 during three manned flights. The Skylab Earth Resources Experiment Package used two photographic remote sensing systems. The Multispectral Photographic Camera (S190A), was a six-camera array, in which each camera used 70-mm film and a six-inch focal length lens. The acquired film ranges from narrow-band B/W to broad-band color and CIR. The Earth Terrain Camera (S190B) consisted of a single high-resolution camera which used five-inch film and an 18-inch focal length lens. The acquired film includes B/W, black and white infrared (BIR), color, and CIR. All Skylab photographs are distributed by the USGS EDC as digital products only.\n\nShuttle Large Format Camera (LFC) images were acquired from the Space Shuttle Challenger Mission on October 5-13, 1984. The LFC was mounted in the cargo bay, and was operated via signals from ground controllers. The archived imagery includes 9 x 18 inch B/W, natural color, and CIR film. Shuttle LFC photographs are distributed by the USGS EDC as digital products only.", "links": [ { diff --git a/datasets/SPANBR.json b/datasets/SPANBR.json index a32185e4f7..91782826bb 100644 --- a/datasets/SPANBR.json +++ b/datasets/SPANBR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPANBR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A network of 9 automatic sunphotometers operates in Brazil. Direct sun\nand sky radiances are acquired every hour by a weather resistant Cimel\nspectral radiometer in the wavelengths of 340, 440, 670,870, 940, and\n1020 nm and transmitted automatically through the NOAA data collection\nsystem geostationary link for near real-time processing into spectral\naerosol optical thickness, wavelength exponent and precipitable\nwater. Evaluation of the atmospheric effects of biomass burning\nemissions from June-November are among the primary targets of the\nmeasurements.\n\nftp://ftp.pmel.noaa.gov", "links": [ { diff --git a/datasets/SPL1AP_002.json b/datasets/SPL1AP_002.json index d9d35ad737..e25db6a98e 100644 --- a/datasets/SPL1AP_002.json +++ b/datasets/SPL1AP_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1AP_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

\n
    \n
  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
    • \n
  • The complex cross-correlations of the fullband channel
    • \n
  • The 16 subband channels for both vertical and horizontal polarizations
    • \n
", "links": [ { diff --git a/datasets/SPL1A_001_1.json b/datasets/SPL1A_001_1.json index 02cadd59ed..42c026cc94 100644 --- a/datasets/SPL1A_001_1.json +++ b/datasets/SPL1A_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Product", "links": [ { diff --git a/datasets/SPL1A_002_2.json b/datasets/SPL1A_002_2.json index 93c6cf85b0..a36d12a8ff 100644 --- a/datasets/SPL1A_002_2.json +++ b/datasets/SPL1A_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Product Version 2", "links": [ { diff --git a/datasets/SPL1A_METADATA_001_1.json b/datasets/SPL1A_METADATA_001_1.json index 36ff25b624..2c16e862b4 100644 --- a/datasets/SPL1A_METADATA_001_1.json +++ b/datasets/SPL1A_METADATA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_METADATA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Product Metadata", "links": [ { diff --git a/datasets/SPL1A_METADATA_002_2.json b/datasets/SPL1A_METADATA_002_2.json index 58b97dc271..eb1d02375e 100644 --- a/datasets/SPL1A_METADATA_002_2.json +++ b/datasets/SPL1A_METADATA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_METADATA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Product Metadata Version 2", "links": [ { diff --git a/datasets/SPL1A_QA_001_1.json b/datasets/SPL1A_QA_001_1.json index 9745b689d2..81927efaa1 100644 --- a/datasets/SPL1A_QA_001_1.json +++ b/datasets/SPL1A_QA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_QA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Data Quality Information", "links": [ { diff --git a/datasets/SPL1A_QA_002_2.json b/datasets/SPL1A_QA_002_2.json index bfc85f434b..f7d4b8395c 100644 --- a/datasets/SPL1A_QA_002_2.json +++ b/datasets/SPL1A_QA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_QA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Data Quality Information Version 2", "links": [ { diff --git a/datasets/SPL1A_RO_001_1.json b/datasets/SPL1A_RO_001_1.json index 56b6ce200b..cdca77ad7c 100644 --- a/datasets/SPL1A_RO_001_1.json +++ b/datasets/SPL1A_RO_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Version 1", "links": [ { diff --git a/datasets/SPL1A_RO_002_2.json b/datasets/SPL1A_RO_002_2.json index 5461f879ef..021f7bb95b 100644 --- a/datasets/SPL1A_RO_002_2.json +++ b/datasets/SPL1A_RO_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Version 2", "links": [ { diff --git a/datasets/SPL1A_RO_003_3.json b/datasets/SPL1A_RO_003_3.json index 23d98e9432..b1ff57d66f 100644 --- a/datasets/SPL1A_RO_003_3.json +++ b/datasets/SPL1A_RO_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Version 3", "links": [ { diff --git a/datasets/SPL1A_RO_METADATA_001_1.json b/datasets/SPL1A_RO_METADATA_001_1.json index 2ed52bfa63..5ad3e0d1ba 100644 --- a/datasets/SPL1A_RO_METADATA_001_1.json +++ b/datasets/SPL1A_RO_METADATA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_METADATA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Metadata Version 1", "links": [ { diff --git a/datasets/SPL1A_RO_METADATA_002_2.json b/datasets/SPL1A_RO_METADATA_002_2.json index f6196e5696..2c4ff198a6 100644 --- a/datasets/SPL1A_RO_METADATA_002_2.json +++ b/datasets/SPL1A_RO_METADATA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_METADATA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Metadata Version 2", "links": [ { diff --git a/datasets/SPL1A_RO_METADATA_003_3.json b/datasets/SPL1A_RO_METADATA_003_3.json index e8c03016c3..cb052845e1 100644 --- a/datasets/SPL1A_RO_METADATA_003_3.json +++ b/datasets/SPL1A_RO_METADATA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_METADATA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Product Metadata Version 3", "links": [ { diff --git a/datasets/SPL1A_RO_QA_001_1.json b/datasets/SPL1A_RO_QA_001_1.json index b6e35c1117..578d526659 100644 --- a/datasets/SPL1A_RO_QA_001_1.json +++ b/datasets/SPL1A_RO_QA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_QA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Data Quality Information Version 1", "links": [ { diff --git a/datasets/SPL1A_RO_QA_002_2.json b/datasets/SPL1A_RO_QA_002_2.json index 300f32052e..d8d96bbaf4 100644 --- a/datasets/SPL1A_RO_QA_002_2.json +++ b/datasets/SPL1A_RO_QA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_QA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Data Quality Information Version 2", "links": [ { diff --git a/datasets/SPL1A_RO_QA_003_3.json b/datasets/SPL1A_RO_QA_003_3.json index e834fa9e7a..f3fe9faf9e 100644 --- a/datasets/SPL1A_RO_QA_003_3.json +++ b/datasets/SPL1A_RO_QA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1A_RO_QA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1A Radar Receive Only Data Quality Information Version 3", "links": [ { diff --git a/datasets/SPL1BTB_006.json b/datasets/SPL1BTB_006.json index 2e0bd87d8e..e6bae3cf37 100644 --- a/datasets/SPL1BTB_006.json +++ b/datasets/SPL1BTB_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1BTB_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.", "links": [ { diff --git a/datasets/SPL1BTB_NRT_105.json b/datasets/SPL1BTB_NRT_105.json index 6249c8626a..24f5bf59f4 100644 --- a/datasets/SPL1BTB_NRT_105.json +++ b/datasets/SPL1BTB_NRT_105.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1BTB_NRT_105", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4\u00b0S to 86.4\u00b0N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20).", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_001_1.json b/datasets/SPL1B_SO_LoRes_001_1.json index e3d3eed83e..4dfd5d518d 100644 --- a/datasets/SPL1B_SO_LoRes_001_1.json +++ b/datasets/SPL1B_SO_LoRes_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_002_2.json b/datasets/SPL1B_SO_LoRes_002_2.json index 8dfcb9aead..dde459eef3 100644 --- a/datasets/SPL1B_SO_LoRes_002_2.json +++ b/datasets/SPL1B_SO_LoRes_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product Version 2", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_003_3.json b/datasets/SPL1B_SO_LoRes_003_3.json index 6eb54ba38a..a187ce02f4 100644 --- a/datasets/SPL1B_SO_LoRes_003_3.json +++ b/datasets/SPL1B_SO_LoRes_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product Version 3", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_METADATA_001_1.json b/datasets/SPL1B_SO_LoRes_METADATA_001_1.json index cfa8b27be9..2ac2eee448 100644 --- a/datasets/SPL1B_SO_LoRes_METADATA_001_1.json +++ b/datasets/SPL1B_SO_LoRes_METADATA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_METADATA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product Metadata", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_METADATA_002_2.json b/datasets/SPL1B_SO_LoRes_METADATA_002_2.json index 8c288c2ab8..7b3b6205e5 100644 --- a/datasets/SPL1B_SO_LoRes_METADATA_002_2.json +++ b/datasets/SPL1B_SO_LoRes_METADATA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_METADATA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product Metadata Version 2", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_METADATA_003_3.json b/datasets/SPL1B_SO_LoRes_METADATA_003_3.json index d7230412f9..932086bf87 100644 --- a/datasets/SPL1B_SO_LoRes_METADATA_003_3.json +++ b/datasets/SPL1B_SO_LoRes_METADATA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_METADATA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Product Metadata Version 3", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_QA_001_1.json b/datasets/SPL1B_SO_LoRes_QA_001_1.json index 0009af466a..da02922569 100644 --- a/datasets/SPL1B_SO_LoRes_QA_001_1.json +++ b/datasets/SPL1B_SO_LoRes_QA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_QA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Data Quality Info", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_QA_002_2.json b/datasets/SPL1B_SO_LoRes_QA_002_2.json index a400b25192..729d2c6b0b 100644 --- a/datasets/SPL1B_SO_LoRes_QA_002_2.json +++ b/datasets/SPL1B_SO_LoRes_QA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_QA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 2", "links": [ { diff --git a/datasets/SPL1B_SO_LoRes_QA_003_3.json b/datasets/SPL1B_SO_LoRes_QA_003_3.json index bf464fdfc0..3b31cef3af 100644 --- a/datasets/SPL1B_SO_LoRes_QA_003_3.json +++ b/datasets/SPL1B_SO_LoRes_QA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1B_SO_LoRes_QA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 3", "links": [ { diff --git a/datasets/SPL1CTB_006.json b/datasets/SPL1CTB_006.json index 6dd10d45f4..94300361eb 100644 --- a/datasets/SPL1CTB_006.json +++ b/datasets/SPL1CTB_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1CTB_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.", "links": [ { diff --git a/datasets/SPL1CTB_E_004.json b/datasets/SPL1CTB_E_004.json index bcf67031c9..d095a2f396 100644 --- a/datasets/SPL1CTB_E_004.json +++ b/datasets/SPL1CTB_E_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1CTB_E_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_001_1.json b/datasets/SPL1C_S0_HiRes_001_1.json index 7b123091b6..30617abe8b 100644 --- a/datasets/SPL1C_S0_HiRes_001_1.json +++ b/datasets/SPL1C_S0_HiRes_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_002_2.json b/datasets/SPL1C_S0_HiRes_002_2.json index 27fa64f6b6..c6f8f9c1ac 100644 --- a/datasets/SPL1C_S0_HiRes_002_2.json +++ b/datasets/SPL1C_S0_HiRes_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product Version 2", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_003_3.json b/datasets/SPL1C_S0_HiRes_003_3.json index 097819dab1..3d0262c85b 100644 --- a/datasets/SPL1C_S0_HiRes_003_3.json +++ b/datasets/SPL1C_S0_HiRes_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product Version 3", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_METADATA_001_1.json b/datasets/SPL1C_S0_HiRes_METADATA_001_1.json index 249765bd73..479cb7bc2d 100644 --- a/datasets/SPL1C_S0_HiRes_METADATA_001_1.json +++ b/datasets/SPL1C_S0_HiRes_METADATA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_METADATA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product Metadata", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_METADATA_002_2.json b/datasets/SPL1C_S0_HiRes_METADATA_002_2.json index fa06b7d313..1c10adb155 100644 --- a/datasets/SPL1C_S0_HiRes_METADATA_002_2.json +++ b/datasets/SPL1C_S0_HiRes_METADATA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_METADATA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product Metadata Version 2", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_METADATA_003_3.json b/datasets/SPL1C_S0_HiRes_METADATA_003_3.json index 58764bbaba..37d14bec36 100644 --- a/datasets/SPL1C_S0_HiRes_METADATA_003_3.json +++ b/datasets/SPL1C_S0_HiRes_METADATA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_METADATA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Product Metadata Version 3", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_QA_001_1.json b/datasets/SPL1C_S0_HiRes_QA_001_1.json index f5711c2c03..e837611551 100644 --- a/datasets/SPL1C_S0_HiRes_QA_001_1.json +++ b/datasets/SPL1C_S0_HiRes_QA_001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_QA_001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Data Quality Info", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_QA_002_2.json b/datasets/SPL1C_S0_HiRes_QA_002_2.json index 0f6221c499..a796394004 100644 --- a/datasets/SPL1C_S0_HiRes_QA_002_2.json +++ b/datasets/SPL1C_S0_HiRes_QA_002_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_QA_002_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Data Quality Info Version 2", "links": [ { diff --git a/datasets/SPL1C_S0_HiRes_QA_003_3.json b/datasets/SPL1C_S0_HiRes_QA_003_3.json index 446cb7060f..25b0f18a5f 100644 --- a/datasets/SPL1C_S0_HiRes_QA_003_3.json +++ b/datasets/SPL1C_S0_HiRes_QA_003_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL1C_S0_HiRes_QA_003_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level 1C Sigma Naught High Res Data Quality Info Version 3", "links": [ { diff --git a/datasets/SPL2SMAP_003.json b/datasets/SPL2SMAP_003.json index ab06b7c1c1..ff8c60a573 100644 --- a/datasets/SPL2SMAP_003.json +++ b/datasets/SPL2SMAP_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMAP_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL2SMAP_S_003.json b/datasets/SPL2SMAP_S_003.json index bccc86718f..bdbb5a2acc 100644 --- a/datasets/SPL2SMAP_S_003.json +++ b/datasets/SPL2SMAP_S_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMAP_S_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "links": [ { diff --git a/datasets/SPL2SMA_003.json b/datasets/SPL2SMA_003.json index 526c8129fd..1dd383907b 100644 --- a/datasets/SPL2SMA_003.json +++ b/datasets/SPL2SMA_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMA_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL2SMP_009.json b/datasets/SPL2SMP_009.json index 92cdaa8372..bb298b4009 100644 --- a/datasets/SPL2SMP_009.json +++ b/datasets/SPL2SMP_009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMP_009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.", "links": [ { diff --git a/datasets/SPL2SMP_E_006.json b/datasets/SPL2SMP_E_006.json index 5912afe0ba..c559cf046a 100644 --- a/datasets/SPL2SMP_E_006.json +++ b/datasets/SPL2SMP_E_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMP_E_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "links": [ { diff --git a/datasets/SPL2SMP_NRT_107.json b/datasets/SPL2SMP_NRT_107.json index 1396d45446..ee5baea0b2 100644 --- a/datasets/SPL2SMP_NRT_107.json +++ b/datasets/SPL2SMP_NRT_107.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL2SMP_NRT_107", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4\u00b0S to 86.4\u00b0N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0).", "links": [ { diff --git a/datasets/SPL3FTA_003.json b/datasets/SPL3FTA_003.json index a77fc2c24f..aef498a2df 100644 --- a/datasets/SPL3FTA_003.json +++ b/datasets/SPL3FTA_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3FTA_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL3FTP_004.json b/datasets/SPL3FTP_004.json index 1479f80395..3eaebe124f 100644 --- a/datasets/SPL3FTP_004.json +++ b/datasets/SPL3FTP_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3FTP_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.", "links": [ { diff --git a/datasets/SPL3FTP_E_004.json b/datasets/SPL3FTP_E_004.json index 788291b4be..ae4c17f395 100644 --- a/datasets/SPL3FTP_E_004.json +++ b/datasets/SPL3FTP_E_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3FTP_E_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.", "links": [ { diff --git a/datasets/SPL3SMAP_003.json b/datasets/SPL3SMAP_003.json index c4951eb5e1..98cf5b9b00 100644 --- a/datasets/SPL3SMAP_003.json +++ b/datasets/SPL3SMAP_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3SMAP_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL3SMA_003.json b/datasets/SPL3SMA_003.json index aa42c72279..eaeacdc1c2 100644 --- a/datasets/SPL3SMA_003.json +++ b/datasets/SPL3SMA_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3SMA_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL3SMP_009.json b/datasets/SPL3SMP_009.json index 8b4270a40f..1940ed6a11 100644 --- a/datasets/SPL3SMP_009.json +++ b/datasets/SPL3SMP_009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3SMP_009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "links": [ { diff --git a/datasets/SPL3SMP_E_006.json b/datasets/SPL3SMP_E_006.json index 4630d44211..e1bf4c69a0 100644 --- a/datasets/SPL3SMP_E_006.json +++ b/datasets/SPL3SMP_E_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL3SMP_E_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.", "links": [ { diff --git a/datasets/SPL4CMDL_007.json b/datasets/SPL4CMDL_007.json index 90647b8051..5d146b4b5f 100644 --- a/datasets/SPL4CMDL_007.json +++ b/datasets/SPL4CMDL_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL4CMDL_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "links": [ { diff --git a/datasets/SPL4SMAU_007.json b/datasets/SPL4SMAU_007.json index b2d2bd5f65..64fd62e99b 100644 --- a/datasets/SPL4SMAU_007.json +++ b/datasets/SPL4SMAU_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL4SMAU_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:\n
    \n
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
    • \n
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
    • \n
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
    • \n
\nFor each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "links": [ { diff --git a/datasets/SPL4SMGP_007.json b/datasets/SPL4SMGP_007.json index 5a2d8f4ad6..2328c211fe 100644 --- a/datasets/SPL4SMGP_007.json +++ b/datasets/SPL4SMGP_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL4SMGP_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:\n* SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)\n* SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)\n* SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).\nFor each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "links": [ { diff --git a/datasets/SPL4SMLM_007.json b/datasets/SPL4SMLM_007.json index 2da5d2ad67..06b54b45f9 100644 --- a/datasets/SPL4SMLM_007.json +++ b/datasets/SPL4SMLM_007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPL4SMLM_007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:\n* SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)\n* SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)\n* SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).\nFor each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "links": [ { diff --git a/datasets/SPOT-6.and.7.ESA.archive_9.0.json b/datasets/SPOT-6.and.7.ESA.archive_9.0.json index f128d9b438..8e7c20bd89 100644 --- a/datasets/SPOT-6.and.7.ESA.archive_9.0.json +++ b/datasets/SPOT-6.and.7.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPOT-6.and.7.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products.\r\rSPOT 6 and 7 Primary and Ortho products can be available in the following modes:\r\rPanchromatic image at 1.5m resolution\rPansharpened colour image at 1.5m resolution\rMultispectral image in 4 spectral bands at 6m resolution\rBundle (1.5m panchromatic image + 6m multispectral image)\rSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. \rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/SPOT1-5_8.0.json b/datasets/SPOT1-5_8.0.json index b0b7041378..f36a48acf3 100644 --- a/datasets/SPOT1-5_8.0.json +++ b/datasets/SPOT1-5_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPOT1-5_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA SPOT1-5 collection is a dataset of SPOT-1 to 5 Panchromatic and Multispectral products that ESA collected over the years. The HRV(IR) sensor onboard SPOT 1-4 provides data at 10 m spatial resolution Panchromatic mode (-1 band) and 20 m (Multispectral mode -3 or 4 bands). The HRG sensor on board of SPOT-5 provides spatial resolution of the imagery to < 3 m in the panchromatic band and to 10 m in the multispectral mode (3 bands). The SWIR band imagery remains at 20 m. The dataset mainly focuses on European and African sites but some American, Asian and Greenland areas are also covered.", "links": [ { diff --git a/datasets/SPOT4-5_Take5.ESAarchive_7.0.json b/datasets/SPOT4-5_Take5.ESAarchive_7.0.json index 0cd9ddd55b..5664e5bb03 100644 --- a/datasets/SPOT4-5_Take5.ESAarchive_7.0.json +++ b/datasets/SPOT4-5_Take5.ESAarchive_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPOT4-5_Take5.ESAarchive_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At the end of SPOT-4 life, the Take5 experiment was launched and the satellite was moved to a lower orbit to obtain a 5 day repeat cycle, same repetition of Sentinel-2. Thanks to this orbit, from 1st of Feb to 19th of June 2013 a time series of images acquired every 5 days with constant angle and over 45 different sites were observed. In analogy to the previous SPOT-4 Take-5 experiment, also SPOT-5 was placed in a 5 days cycle orbit and 145 selected sites were acquired every 5 days under constant angles from 8th of April to 31st of August 2015. With a resolution of 10 m, the following processing levels are available: Level 1A: reflectance at the top of atmosphere (TOA), not orthorectified products Level 1C: data orthorectified reflectance at the top of atmosphere (TOA) Level 2A: data orthorectified surface reflectance after atmospheric correction (BOA), along with clouds mask and their shadow, and mask of water and snow.", "links": [ { diff --git a/datasets/SPOT5_BEAVER_LOEWE_FEATURES_1.json b/datasets/SPOT5_BEAVER_LOEWE_FEATURES_1.json index 33574367e0..3b8b6caef1 100644 --- a/datasets/SPOT5_BEAVER_LOEWE_FEATURES_1.json +++ b/datasets/SPOT5_BEAVER_LOEWE_FEATURES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPOT5_BEAVER_LOEWE_FEATURES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beaver Lake and Loewe Massif Features Mapped from SPOT 5 Imagery.\n\nThe purpose of this Australian Antarctic Data Centre project was to map features on and around Beaver Lake and the Loewe Massif using a rectified SPOT 5 satellite image. The image was captured on 11 January 2004. The features mapped were to be provided as a series of ArcInfo Coverages in Geographicals, conforming to the Feature Catalogue.\n\nFurther information is provided in a downloadable report at the URL given below.\n\nA thumbnail of the image (ID number 167) is available via the Australian Antarctic Data Centre's Satellite Image Catalogue at the URL given below.", "links": [ { diff --git a/datasets/SPOT67fullarchiveandtasking1_9.0.json b/datasets/SPOT67fullarchiveandtasking1_9.0.json index e6cca57448..72335b8e61 100644 --- a/datasets/SPOT67fullarchiveandtasking1_9.0.json +++ b/datasets/SPOT67fullarchiveandtasking1_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPOT67fullarchiveandtasking1_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPOT 6 and 7 satellites ensure data continuity with the no longer operational SPOT 5 satellite and provide an archive of very high resolution optical acquisition as well as the possibility to task the satellites for new acquisitions.\rFollowing the completion of the SPOT 7 mission in March 2023, new acquisition tasking is only available for the SPOT 6 satellite.\rThe ortho-products are automatically generated by the SPOT 6 and 7 ground segment, based on SRTM database or Reference3D when available. The projection available for SPOT 6 and 7 ortho-products is UTM, datum WGS84.\rBands combinations:\r\u2022\tPanchromatic: black&white image at 1.5 m resolution\r\u2022\tPansharpened: 3-bands or 4 bands colour image at 1.5 m resolution\r\u2022\tMultispectral: 4 bands image at 6m resolution \r\u2022\tBundle: 1.5 m panchromatic image and 6 m multispectral image, co-registered.\rGeometric processing levels:\r\u2022\tPrimary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion.\r\u2022\tOrtho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects. Available in MONO acquisition mode only.\rAcquisition modes:\r\u2022\tMono\r\u2022\tStereo\r\u2022\tTristero\r\rTo complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download.\rThe Living Library consist of\r\u2022\tless-than-18-months-old imagery\r\u2022\ta curation of SPOT images with no cloud cover and less than 30\u00b0 incidence angle\r\u2022\tPl\u00e9iades images acquired worldwide with maximum 15% cloud cover and 30\u00b0 Incidence Angle\r\u2022\tPl\u00e9iades Neo premium imagery selection with 2% cloud cover and 30\u00b0 incidence angle\rThese are the available subscription packages (to be consumed withing one year from the activation) \rOneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming\rOneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming\rOneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/SPURS1_ADCP_1.0.json b/datasets/SPURS1_ADCP_1.0.json index 6caec033a5..e88d82c562 100644 --- a/datasets/SPURS1_ADCP_1.0.json +++ b/datasets/SPURS1_ADCP_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_ADCP_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations. Shipborne ADCP data were collected during the 3 US cruises, using the Knorr and Endeavor 300 kHz Workhorse, 75 khz broadband and 75 khz narrowband instruments, and during the Sarmiento cruise using a 76.8 khz broadband ADCP. Corresponding ruise dates were as follows: Knorr: 6 Sept-9 Oct 2012; Sarmiento: 14 Mar-10 Apr 2013, Endeavor: 15 Mar-15 Apr 2013 and 19 Sep-13 Oct 2013. Additionally, lowered ADCP (L-ADCP) measurements were made during the Knorr cruise on every CTD cast and during the Sarmiento cruise. The ADCP data files here (1 per cruise) are for the shipborne ADCP measurements only.", "links": [ { diff --git a/datasets/SPURS1_ARGO_1.0.json b/datasets/SPURS1_ARGO_1.0.json index 3c86269e71..3e0491dab7 100644 --- a/datasets/SPURS1_ARGO_1.0.json +++ b/datasets/SPURS1_ARGO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_ARGO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth profiles from the upper 2000m. Approximately 24 floats were deployed during SPURS-1 within the campaign domain, mainly during the Knorr cruise (6 Sept-9 Oct,2012). These were standard Argo floats with the addition of surface temperature and salinity (STS) sensors and acoustic rain gauges (PAL). Data accessible here only include the standard ARGO profiles, not the STS or PAL data. SPURS-1 ARGO data files are oganized per float and each contain profile trajectory series of conductivity, salinity, temperature, pressure, depth observations.", "links": [ { diff --git a/datasets/SPURS1_CTD_1.0.json b/datasets/SPURS1_CTD_1.0.json index ec124a4930..772ce6170e 100644 --- a/datasets/SPURS1_CTD_1.0.json +++ b/datasets/SPURS1_CTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_CTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. CTD (Conductivity, Temperature, Depth) profilers were deployed at stations on each of the 5 SPURS-1 cruises. These shipboard lowered CTD probes provide continuous conductivity, salinity, and temperature vertical profile observations at fixed sampling locations. There were 100, 52, 17, 22 and 94 CTD casts made during the Knorr, Endeavor-1, Endeavor-2, Sarmiento, and Thalassa cruises respectively. All CTD data were calibrated using shipboard salinometers using IAPSO standard seawater. SPURS-1 shipboard CTD data files (one per cruise) contain the observational data processed to 1 meter bin depth intervals.", "links": [ { diff --git a/datasets/SPURS1_DRIFTER_1.0.json b/datasets/SPURS1_DRIFTER_1.0.json index 7d14f40fa6..d7d5c2b60c 100644 --- a/datasets/SPURS1_DRIFTER_1.0.json +++ b/datasets/SPURS1_DRIFTER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_DRIFTER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Approximately 83 drifters were deployed during the SPURS-1 campaign. A drifter is a passive Lagrangian sensor platform consisting of a surface buoy and tethered subsurface drogue. Drifter buoys contain GPS/ARGOS and satellite data transmitters, with sensors measuring temperature and other properties. For SPURS-1, these were standard Surface Velocity Program (SVP) drifters with salinity sensors added (SVP/S). Data for both US and European drifter deployments during SPURS-1 are available here. For each series, drifter data have been aggregrated within single netCDF data filea with their corresponding drifter-IDs and associaciated near-surface salinity, temperaure georeferenced (GPS and ARGOS) trajectory series data.", "links": [ { diff --git a/datasets/SPURS1_ECOMAPPER_1.0.json b/datasets/SPURS1_ECOMAPPER_1.0.json index 2e3fb48abf..6e82e1f64b 100644 --- a/datasets/SPURS1_ECOMAPPER_1.0.json +++ b/datasets/SPURS1_ECOMAPPER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_ECOMAPPER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Ecomapper or IVER is a portable autonomous underwater vehicle (AUV) capable carrying a range of sensor payloads. For SPURS-1 these included CTD, chlorophyll, oxygen and turbidity sensors. Ecomapper was deployed on two days during the Knorr cruise, 29 and 30 September 2012. The resulting Knorr Ecomapper data files include deployment event information and contain trajectory-depth profile series of chlorophyll, turbidity, oxygen, conductivity, with salinity, and temperature observations from two sensors.", "links": [ { diff --git a/datasets/SPURS1_FLOAT_NEUTRALLYBUOYANT_1.0.json b/datasets/SPURS1_FLOAT_NEUTRALLYBUOYANT_1.0.json index 35ad037234..0ba2e5b334 100644 --- a/datasets/SPURS1_FLOAT_NEUTRALLYBUOYANT_1.0.json +++ b/datasets/SPURS1_FLOAT_NEUTRALLYBUOYANT_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_FLOAT_NEUTRALLYBUOYANT_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Neutrally buoyant floats drift and move through the water column providing continuous temperature and salinity profiles via 2 integrated CTDs and GPS surface position location data. Two floats were deployed during SPURS-1, one during the Knorr cruise in September 2012 another deployed during the April 2013 Endeavor cruise. Recoveries were in April and September 2013 respectively. Neutrally buoyant float trajectory profile data include georeferenced time series of salinity, temperature, and pressure/depth observations.", "links": [ { diff --git a/datasets/SPURS1_METEO_1.0.json b/datasets/SPURS1_METEO_1.0.json index e3d2b6b5bf..d092a50d0b 100644 --- a/datasets/SPURS1_METEO_1.0.json +++ b/datasets/SPURS1_METEO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_METEO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. All US SPURS-1 cruises (Knorr: 6 Sept-9 Oct 2012; Endeavor: 15 Mar-15 Apr 2013 and 19 Sep-13 Oct 2013) were equipped with a ship mast meteorological sensor package. An additional set of sophisticated meteorological sensors, including a direct covariance flux package, was installed on the Knorr. These sensors provided along-track atmospheric pressure, temperature, humidity, IR/visible radiation, rain, wind speed and direct covariance flux measurements. Resulting data files (1 per cruise) contain these georeferenced, SPURS-1 research vessel-based meteorological measurements.", "links": [ { diff --git a/datasets/SPURS1_MOORING_PICO_1.0.json b/datasets/SPURS1_MOORING_PICO_1.0.json index 09ba44e6f2..07abe742bd 100644 --- a/datasets/SPURS1_MOORING_PICO_1.0.json +++ b/datasets/SPURS1_MOORING_PICO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_MOORING_PICO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Two PICO moorings (PICO-1000, PICO-3000) were deployed on the Knorr cruise in September 2012 in the northern and eastern SPURS-1 domain quadrants at N24.74, W37.95 and N24.51, W37.81 respectively. The moorings contained a surface meteorological package and a \"prawler\", a CTD that crawls up and down the mooring line from the near-surface down to about 500m, yielding time series of salinity and temperature profile data at fixed locations. The moorings were recovered on the Endeavor-2 cruise. PICO mooring netCDF files contain georeferenced CTD profile data including salinity, temperature, potential temperature, pressure, depth, meteorological variables, GPS-Lat/Lon, and profile ID.", "links": [ { diff --git a/datasets/SPURS1_MOORING_WHOI_1.0.json b/datasets/SPURS1_MOORING_WHOI_1.0.json index b9a889cd37..c90d5e9f2a 100644 --- a/datasets/SPURS1_MOORING_WHOI_1.0.json +++ b/datasets/SPURS1_MOORING_WHOI_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_MOORING_WHOI_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The SPURS central mooring consisted of a surface meteorological package, surface oceanographic instruments, and subsurface, non-real time oceanographic instruments including CTD, ADCP sensors and current meters providing continuous series of temperature, salinity and current profile observations. Meteorological observations include wind speed, air temperature, precipitation, and radiative flux. The mooring was deployed in 5,535 meters of water at N24:34.867, W38 on 14 September 2012, was serviced on 25 March 2013 and recovered on 30 September 2013. WHOI mooring data files include surface and subsurface time series of sea temperature, skin temperature, salinity, conductivity, wind velocity, air temperature, relative humidity, precipitation rate, barometric pressure, shortwave and longwave radiation, short/longwave flux, heat Flux, wind Speed and direction.", "links": [ { diff --git a/datasets/SPURS1_SEAGLIDER_1.0.json b/datasets/SPURS1_SEAGLIDER_1.0.json index bcd2a21230..08f5779fa6 100644 --- a/datasets/SPURS1_SEAGLIDER_1.0.json +++ b/datasets/SPURS1_SEAGLIDER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_SEAGLIDER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Seaglider is an autonomous profiler measuring salinity and temperature. Three Seagliders were deployed on the Knorr cruise in September 2012. These were retrieved during the first Endeavor cruise, and then redeployed. The Seagliders typically made loops or butterfly patterns around the central SPURS mooring, diving to 1000 m. Seaglider data files contain vertically resolved trajectory series of conductivity, salinity, temperature, pressure, depth observations.", "links": [ { diff --git a/datasets/SPURS1_SEASOAR_1.0.json b/datasets/SPURS1_SEASOAR_1.0.json index c3d9bc5579..a0daf63990 100644 --- a/datasets/SPURS1_SEASOAR_1.0.json +++ b/datasets/SPURS1_SEASOAR_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_SEASOAR_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Seasoar is a towed vehicle equipped with impeller-forced wings that can be rotated on command to allow the vehicle to undulate in the upper ocean. Generally, Seasoar operates between the surface and about 400 meters depth while being towed on faired cable at about eight knots. A typical dive cycle takes about 12 minutes to complete, providing an up and down profile every 3 km. For SPURS-1, a Seasoar was deployed exclusively during the Sarmiento cruise over the period 22 Mar-8 Apr, 2013 and to a maximum depth of 312m. The Seasoar towed sensor system was equipped with dual pumped temperature/conductivity sensors. The Seasoar data in netCDF form here contains a highly processed 1-meter gridded version of the original source dataset, which is comprised of temperature, conductivity, salinity, pressure observations from 1144 casts during 2013 Spring SPURS Cruise.", "links": [ { diff --git a/datasets/SPURS1_TENUSEGLIDER_1.0.json b/datasets/SPURS1_TENUSEGLIDER_1.0.json index e590bc097d..83c3362ea6 100644 --- a/datasets/SPURS1_TENUSEGLIDER_1.0.json +++ b/datasets/SPURS1_TENUSEGLIDER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_TENUSEGLIDER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Tenuse (Slocum) glider is an autonomous undulating profiler measuring salinity and temperature. It was deployed from the Thalassa on 21-August and recovered by the Knorr on 4-October-2012. It made a total of about 1400 profiles during that period (1-2 profiles/hour), going from the surface to 200 m. Resulting trajectory profile data from the Tenuse glider include georeferenced CTD observations on salinity, temperature, pressure, and depth.", "links": [ { diff --git a/datasets/SPURS1_TSG_1.0.json b/datasets/SPURS1_TSG_1.0.json index 95e9940bbf..eaf21c20fc 100644 --- a/datasets/SPURS1_TSG_1.0.json +++ b/datasets/SPURS1_TSG_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_TSG_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. All SPURS-1 vessels were equipped with a thermosalinograph (TSG). A TSG is an automated measurement system that is coupled to a research vessel's water intake and GPS systems to provide continuous, along-track surface temperature and salinity measurements. Each SPURS cruise employed TSGs whose measurements were calibrated against onboard salinometers. TSG data files are one per cruise. Note that Knorr TSG data are contained coupled in the same file as its shipborne meteorological observations.", "links": [ { diff --git a/datasets/SPURS1_UCTD_1.0.json b/datasets/SPURS1_UCTD_1.0.json index 73ddc4a63d..6ccfc78784 100644 --- a/datasets/SPURS1_UCTD_1.0.json +++ b/datasets/SPURS1_UCTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_UCTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. An Underway-CTD (UCTD) was deployed on 2 of the SPURS-1 cruises. An UCTD is a towed CTD instrument providing conductivity, salinity and temperature depth profile observations while underway at up to 20kts. 771 UCTD casts occurred during the Knorr and Endeavor-I cruises (6 Sept-9 Oct 2012 and 15 Mar-15 Apr 2013 respectively) utilizing an Oceanscience instrument. UCTD data files (1 per cruise) each contain the observational data for multiple deployments, binned in 1m depth intervals.", "links": [ { diff --git a/datasets/SPURS1_WAVEGLIDER_1.0.json b/datasets/SPURS1_WAVEGLIDER_1.0.json index 33424d46ed..ed495d5039 100644 --- a/datasets/SPURS1_WAVEGLIDER_1.0.json +++ b/datasets/SPURS1_WAVEGLIDER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS1_WAVEGLIDER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. A Waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During SPURS-1, three wavegliders (ASL2, ASL3 and ASL4) were deployed from the Knorr in September 2012, redeployed in April 2013 (ASL22, ASL32 and ASL42) with final recovery in September. Waveglider trajectories followed a square loop or butterfly pattern around the central SPURS mooring. Sensors included a CTD at the near-surface and another at 6 m depth, a surface current meter, air temperature, atmospheric pressure and wind speed sensors providing continuous along-track observations. NetCDF waveglider data files here contain hour averaged, georeferenced trajectory data for those parameters and depths.", "links": [ { diff --git a/datasets/SPURS2_ADCP_1.0.json b/datasets/SPURS2_ADCP_1.0.json index 5b85127a13..91a406fbcb 100644 --- a/datasets/SPURS2_ADCP_1.0.json +++ b/datasets/SPURS2_ADCP_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_ADCP_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D,SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Shipborne ADCP observations were made during both SPURS-2 R/V Revelle cruises. Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations. The resulting data files available here are for narrowband 75 and 150khz ADCP measurements made during the first cruise, plus narrowband (NB) 75khz and both 75khz and 150khz broadband (BB) ADCP measurements obtained during the second R/V Revelle cruise.", "links": [ { diff --git a/datasets/SPURS2_ARGO_1.0.json b/datasets/SPURS2_ARGO_1.0.json index 42c644add9..b991e63d21 100644 --- a/datasets/SPURS2_ARGO_1.0.json +++ b/datasets/SPURS2_ARGO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_ARGO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth (CTD) profiles from the upper 2000m. Twenty five floats were deployed during SPURS-2 within the campaign spatial domain and time period, yielding approximately 1,893 profiles. These were standard Argo floats with the addition of acoustic rain gauges (PAL) in some cases. SPURS-2 ARGO data files are organized per float and profile with the vertical conductivity, salinity, temperature, pressure, depth observations per the netCDF ARGO file specification with some augmented global metadata attributes.", "links": [ { diff --git a/datasets/SPURS2_CFT_1.0.json b/datasets/SPURS2_CFT_1.0.json index 0a20be687d..c0271ac883 100644 --- a/datasets/SPURS2_CFT_1.0.json +++ b/datasets/SPURS2_CFT_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_CFT_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Controlled Flux Technique (CFT) is a system for measuring the net heat transfer velocity and turbulent kinetic energy (TKE) dissipation at the ocean surface, and is a useful tool for studying the turbulence generated at the ocean surface by the impact of raindrops. CFT was employed during both SPURS-2 Revelle cruises. It involves a laser heating a small patch of water on the ocean surface, and an infrared imaging camera then tracking the resulting thermal decay. This decay is known to be proportional to the dissipation of TKE at the water surface, which in turn can be used to scale the transfer velocity for the net heat flux. SPURS2 CFT data take the form of a series of .raw video files each with corresponding .met text header files containing the associated file metadata. The CFT data was recorded at 15 frames per second (fps) during the first Revelle cruise in 2016, and at 25 fps during the second in 2017. Matlab CFT reader software are provided by UW/APL and distributed here with the CFT data files.", "links": [ { diff --git a/datasets/SPURS2_CTD_1.0.json b/datasets/SPURS2_CTD_1.0.json index 7a4b650f99..90cde58bbb 100644 --- a/datasets/SPURS2_CTD_1.0.json +++ b/datasets/SPURS2_CTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_CTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. CTD (Conductivity, Temperature, Depth) casts were undertaken at stations on each of the two R/V Revelle cruises during SPURS-2. These shipboard lowered CTD probes provide continuous conductivity, salinity, and temperature vertical profile observations at fixed sampling locations. There were a total of 50 and 14 CTD casts made during the first and second R/V Revelle cruises respectively, and the data files available here are for continuous CTD profile data for each of the individual casts deployed. All CTD data were calibrated using shipboard salinometers using IAPSO standard seawater.", "links": [ { diff --git a/datasets/SPURS2_DISDR_1.0.json b/datasets/SPURS2_DISDR_1.0.json index b6a6cd2818..c0477c69a4 100644 --- a/datasets/SPURS2_DISDR_1.0.json +++ b/datasets/SPURS2_DISDR_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_DISDR_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS-2 raindrop ODM-470 disdrometer dataset was collected from the ship during both the 2016 and 2017 cruises. Please see file global attributes and Klepp et al. (2015, 2018) for information on the disdrometer: http://dx.doi.org/10.1016/j.atmosres.2014.12.014, https://doi.org/10.1038/sdata.2018.122 . As explained in the references and global attributes, small drops that cause voltage drops < 0.12 V (i.e. drops with diameters < 0.44 mm) cannot be distinguished from noise by this instrument, and are thus missed. This undercounting of small drops cannot be corrected, and prevents accurate estimation of DSD parameters such as Nw, D0, Dm with any confidence or precision since the minimum detectable drop size is close to the median drop size of tropical oceanic rain (Thompson et al. 2015, https://doi.org/10.1175/JAS-D-14-0206.1). Nonetheless, this dataset provides estimates of drop counts as a function of drop size for the remaining rain drops > 0.44 mm in diameter, and their associated rain rates and liquid water contents. The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. ", "links": [ { diff --git a/datasets/SPURS2_DRIFTER_1.0.json b/datasets/SPURS2_DRIFTER_1.0.json index b8a5396f04..d60d59aa03 100644 --- a/datasets/SPURS2_DRIFTER_1.0.json +++ b/datasets/SPURS2_DRIFTER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_DRIFTER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A drifter is a passive Lagrangian sensor platform consisting of a surface buoy and tethered subsurface drogue. Drifter buoys contain GPS/ARGOS and satellite data transmitters, with sensors measuring temperature and other properties. For SPURS-2, a range of drifters were deployed during both Revelle SPURS-2 cruises. These included: standard Surface Velocity Program (SVP) drifters with salinity sensors added (SVP/S), Surface Contact Salinity drifters, CODE, SADOS, AOML and CARTHE-SUPRACT drifters. For each series, drifter data have been aggregrated within single netCDF data files with their corresponding drifter-IDs and associated near-surface salinity, temperature georeferenced (GPS and ARGOS) trajectory series data.", "links": [ { diff --git a/datasets/SPURS2_FLOAT_NEUTRALLYBUOYANT_1.0.json b/datasets/SPURS2_FLOAT_NEUTRALLYBUOYANT_1.0.json index a27d106dfc..2b7cd4ce0f 100644 --- a/datasets/SPURS2_FLOAT_NEUTRALLYBUOYANT_1.0.json +++ b/datasets/SPURS2_FLOAT_NEUTRALLYBUOYANT_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_FLOAT_NEUTRALLYBUOYANT_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Neutrally buoyant floats (also known as Mixed Layer Floats - MLF) drift and move through the water column providing continuous CTD temperature and salinity profiles and GPS surface position location data. One float was deployed in SPURS-2 during the first Revelle cruise in August 2016 and recovered in December 2016 after 3.5 months about 1800 km east of the central mooring. The MLF data are provided in netCDF file format with standards compliant metadata.", "links": [ { diff --git a/datasets/SPURS2_LADYAMBER_1.0.json b/datasets/SPURS2_LADYAMBER_1.0.json index 969299173b..6df26f72b0 100644 --- a/datasets/SPURS2_LADYAMBER_1.0.json +++ b/datasets/SPURS2_LADYAMBER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_LADYAMBER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Underway physical data from 6 cruises undertaken by the schooner Lady Amber during the SPURS-2 field campaign include along-track meteorological, salinity snake and fixed-hull CTD measurements at 1m and 2 m intake depths. Comparisons with nearby Revelle data facilitate evaluation of uncertainties arising from collecting data from a sailboat, and the characterization of small-scale spatial variability in the ocean and atmosphere. Data files are in netCDF CD/ACDD standards compliant format.", "links": [ { diff --git a/datasets/SPURS2_METEO_1.0.json b/datasets/SPURS2_METEO_1.0.json index 61805cef71..341c932487 100644 --- a/datasets/SPURS2_METEO_1.0.json +++ b/datasets/SPURS2_METEO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_METEO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A ship mast meteorological sensor package with an additional set of sophisticated sensors, including a direct covariance flux package was set up on both SPURS-2 Revelle cruises. These provided georeferenced, along-track atmospheric pressure, temperature, humidity, IR/visible radiation, rain, and wind speed and air-sea flux measurements. Resulting data are packaged in netCDF files (one per cruise) with standards compliant metadata.", "links": [ { diff --git a/datasets/SPURS2_MOORING_CENTRAL_1.0.json b/datasets/SPURS2_MOORING_CENTRAL_1.0.json index e56aefd616..ebe909285d 100644 --- a/datasets/SPURS2_MOORING_CENTRAL_1.0.json +++ b/datasets/SPURS2_MOORING_CENTRAL_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_MOORING_CENTRAL_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and countercurrent. The SPURS central mooring consisted of a surface meteorological package, surface oceanographic instruments, and subsurface, non-real time oceanographic instruments including CTD, ADCP sensors and point current meters providing continuous series of temperature, salinity and current profile data. Meteorological observations included wind speed, air temperature, precipitation, and radiative flux. The mooring was deployed in 4769 m depth of water on 24 August 2016, at N10:03.0481, W125:01.939, and was recovered on November 11, 2017. WHOI mooring netCDF data files include surface and subsurface time series of sea temperature, skin temperature, salinity, conductivity, wind velocity, air temperature, relative humidity, precipitation rate, barometric pressure, shortwave and longwave radiation, short/longwave flux, heat Flux, wind Speed and direction.", "links": [ { diff --git a/datasets/SPURS2_MOORING_PICO_1.0.json b/datasets/SPURS2_MOORING_PICO_1.0.json index 2c0c2213cd..efca9b11c6 100644 --- a/datasets/SPURS2_MOORING_PICO_1.0.json +++ b/datasets/SPURS2_MOORING_PICO_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_MOORING_PICO_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Two PICO moorings (PMEL 9N and 11N) were deployed on the Revelle cruise in September 2016 in northern and southern domain quadrants at 9deg2.830N, 124deg59.833W and N10:59.0498, W124:57.531 respectively. These moorings contained a surface meteorological package and a \"prawler\", a CTD that crawls up and down the mooring line from 4-450m, yielding time series of salinity and temperature profile data at fixed locations (nominally 8 profiles per day). The moorings were recovered on the second Revelle cruise (Oct. 22 & Nov. 2, 2017). PICO mooring netCDF files contain georeferenced CTD profile data including salinity, temperature, potential temperature, pressure, depth, surface meteorological package data, GPS-Lat/Lon, and profile ID.", "links": [ { diff --git a/datasets/SPURS2_PALS_1.0.json b/datasets/SPURS2_PALS_1.0.json index a20b503faa..060e462015 100644 --- a/datasets/SPURS2_PALS_1.0.json +++ b/datasets/SPURS2_PALS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_PALS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth (CTD) profiles from the upper 2000m. Four of the Twenty five floats deployed during SPURS-2 within the campaign spatial domain and time period were additionally equipped with acoustic rain gauges (PAL - Passive Acoustic Listeners). SPURS-2 ARGO-PAL data files are in netCDF/CF-compliant data format and organized per float. Float identifiers associated with ARGO CTD data are referenced in the metadata of the related PAL files.", "links": [ { diff --git a/datasets/SPURS2_RAINRADAR_1.0.json b/datasets/SPURS2_RAINRADAR_1.0.json index 04bf8b0183..46a19df2e0 100644 --- a/datasets/SPURS2_RAINRADAR_1.0.json +++ b/datasets/SPURS2_RAINRADAR_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_RAINRADAR_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The SEA-POL rain radar instrument was employed over the period 22 Oct.-10 Nov. 2017 during the second SPURS-2 R/V Revelle cruise. SEA-POL (seagoing-polarimetric radar) is a C-band, Doppler polarimetric radar system providing 240-degree sector coverage centered on the ships bow via its 1-degree beam width antenna. SEA-POL was used primarily to map rainfall in SPURS-2. The resulting dataset is a series of gridded netCDF data files for a 20 day period at 5-20 minute intervals comprised of rain rate and rain accumulation fields.", "links": [ { diff --git a/datasets/SPURS2_RAWINSONDE_1.0.json b/datasets/SPURS2_RAWINSONDE_1.0.json index 9d00bd9e27..0e43452b03 100644 --- a/datasets/SPURS2_RAWINSONDE_1.0.json +++ b/datasets/SPURS2_RAWINSONDE_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_RAWINSONDE_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A Rawinsonde is a helium balloon carrying meteorological instruments and a radar target, enabling the velocity of atmospheric parameters to be measured. During the first Revelle cruise, rawinsondes were launched every 6-hours, providing a total of 85 profiles of temperature, humidity, wind speed and direction through the marine atmospheric boundary layer within the SPURS-2 domain. Similarly, during the second Revelle cruise, rawinsondes were deployed four-times daily within the study area over the 3-week period. SPURS2 rawinsonde data are available as netCDF, CF-compliant data files.", "links": [ { diff --git a/datasets/SPURS2_SAILDRONE_1.0.json b/datasets/SPURS2_SAILDRONE_1.0.json index 77866bf75f..45cdda2e6b 100644 --- a/datasets/SPURS2_SAILDRONE_1.0.json +++ b/datasets/SPURS2_SAILDRONE_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_SAILDRONE_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Two saildrones were deployed over a month period during the second SPURS-2 R/V Revelle cruise in 2017. Saildrone is a state-of-the-art, remotely guided, wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months. It is equipped with a suite of instruments and sensors providing high quality, georeferenced, near real-time, multi-parameter surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. Saildrone data files are in netCDF format and CF/ACDD/NCEI compliant. They contain the saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution.", "links": [ { diff --git a/datasets/SPURS2_SALINITYSNAKE_1.0.json b/datasets/SPURS2_SALINITYSNAKE_1.0.json index c7d53caaab..dbb5c2824f 100644 --- a/datasets/SPURS2_SALINITYSNAKE_1.0.json +++ b/datasets/SPURS2_SALINITYSNAKE_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_SALINITYSNAKE_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Salinity Snake (SS) measures sea surface salinity in the top 1 - 2 cm of the water column, which is the radiometric depth of L-Band satellite radiometers such as on Aquarius/SAC-D, SMAP and SMOS satellites that measure salinity remotely. The SS consists of four key components: a 10m boom mast, a hose, which is deployed from this boom, a powerful self-priming peristaltic pump which transports a constant stream of a seawater/air emulsion, and a shipboard apparatus, which filters, de-bubbles, sterilizes and analyses the salinity of the water. The SS was deployed during both SPURS-2 Revelle cruises. SS data series are provided in netCDF file format, one per cruise.", "links": [ { diff --git a/datasets/SPURS2_SEAGLIDER_1.0.json b/datasets/SPURS2_SEAGLIDER_1.0.json index b9460c864e..ef2c145145 100644 --- a/datasets/SPURS2_SEAGLIDER_1.0.json +++ b/datasets/SPURS2_SEAGLIDER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_SEAGLIDER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Seaglider is an autonomous profiler measuring salinity and temperature. A total of five Seagliders were deployed over the two SPURS2 cruises. Three Seagliders were deployed on the first Revelle cruise in August 2016, recovered by the Lady Amber after 7 months and redeployed, to be retrieved finally during the second cruise in November 2017. One of the Seagliders was deployed alongside and tracked the Lagrangian array across the study region, diving to depths of 1000m. All Seaglider data files are in netCDF format with standards compliant metadata.", "links": [ { diff --git a/datasets/SPURS2_SSP_1.0.json b/datasets/SPURS2_SSP_1.0.json index 1dda398f2b..425589a32c 100644 --- a/datasets/SPURS2_SSP_1.0.json +++ b/datasets/SPURS2_SSP_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_SSP_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The towed Surface Salinity Profiler (SSP) platform is a converted paddleboard with a keel and surfboard outrigger that is tethered to the ship and skims the sea surface beyond the ships wake. Below the paddleboard are salinity and temperature sensors at depths of 10, 30, 50 and 100cm, and microstructure sensors that measure turbulence. The SSP was deployed 19 times throughout the first SPURS-2 cruise, totaling over 200 hours of measurements, and a further 15 times during the 2017 cruise. SSP deployment is most informative when there is a rain event leading to near-surface ocean stratification. The SSP then measures how the ocean changes over the periods before, during, and after rain, and how rainwater mixes into the ocean during recovery. All SSP data files are in netCDF format with standards compliant metadata.", "links": [ { diff --git a/datasets/SPURS2_UCTD_1.0.json b/datasets/SPURS2_UCTD_1.0.json index 2a7215e355..ef29dcb636 100644 --- a/datasets/SPURS2_UCTD_1.0.json +++ b/datasets/SPURS2_UCTD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_UCTD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. An underway-CTD (uCTD) is a towed profiling CTD instrument providing salinity and temperature observations from the surface to 500m while underway at up to 12 kts. A total of 262 and 501 uCTD casts were performed during the first and second Revelle cruises respectively. uCTD data files (1 per cruise) are in netCDF format and each contain the observational data for multiple deployments, binned in 6 or 8m depth intervals.", "links": [ { diff --git a/datasets/SPURS2_UNDERWAY_pCO2_DIC_pH_1.0.json b/datasets/SPURS2_UNDERWAY_pCO2_DIC_pH_1.0.json index a14d75a490..dd189b7895 100644 --- a/datasets/SPURS2_UNDERWAY_pCO2_DIC_pH_1.0.json +++ b/datasets/SPURS2_UNDERWAY_pCO2_DIC_pH_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_UNDERWAY_pCO2_DIC_pH_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. During both Revelle cruises, continuous measurements of the partial pressure of CO2 (pCO2), dissolved inorganic carbon (DIC), and pH at surface (0m) and 5m depths were made on water pumped continuously from the Salinity Snake and the ship's intake port. In addition to these measurements, observational data from the salinity snake and thermosalinograph also include water temperature and salinity time series at the same depths. The temporal resolution of the observations range from 3 seconds (pH) to 3 minutes (DIC). All pCO2 and associated underway data comprising this dataset are in netCDF file format with standards compliant metadata. Due to issues with the quality of the 2016 underway data, only the data file for the 2017 cruise is available.", "links": [ { diff --git a/datasets/SPURS2_USPS_1.0.json b/datasets/SPURS2_USPS_1.0.json index 92e3bfd17a..a4afde16e2 100644 --- a/datasets/SPURS2_USPS_1.0.json +++ b/datasets/SPURS2_USPS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_USPS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Underway surface profiling systems (USPS) are automated measurement systems coupled to a research vessels water intake and GPS systems. They provide continuous, along-track surface temperature and salinity measurements at depths of 2, 3 and 5 m using through-hull ports in the bow of the ship. Both SPURS-2 cruises had USPS and associated thermosalinograph (TSG) instrumentation, with measurements calibrated against onboard salinometers. There is one USPS netCDF containing the complete series for each of the 2 cruises.", "links": [ { diff --git a/datasets/SPURS2_WAMOS_1.0.json b/datasets/SPURS2_WAMOS_1.0.json index 9aee2b6c0c..5b5bfa2f06 100644 --- a/datasets/SPURS2_WAMOS_1.0.json +++ b/datasets/SPURS2_WAMOS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_WAMOS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The WaMoS wave radar instrument was available during the second R/V Revelle cruise of SPURS-2. WaMoS is a radar-based wave and surface current monitoring system providing wave field imagery and station time series or along track data series for key wave parameter in near near-real time. The single resulting SPURS-2 WaMos data file contains along track wave measurement from the R/V Revelle over the duration of this cruise (5 Oct. to 16 Nov. 2017) for the following essential wave field parameters: wave period, wave length, and wave direction, as well as surface current speed and direction.", "links": [ { diff --git a/datasets/SPURS2_WAVEGLIDER_1.0.json b/datasets/SPURS2_WAVEGLIDER_1.0.json index cdd1f6d13e..fff1e93deb 100644 --- a/datasets/SPURS2_WAVEGLIDER_1.0.json +++ b/datasets/SPURS2_WAVEGLIDER_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_WAVEGLIDER_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A Waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. For SPURS-2, sensors included a CTD at the near-surface and another at 6 m depth, providing continuous salinity and temperature observations plus air temperature and wind measurements. Three wavegliders (ASL22, 32, 42) were deployed from the Revelle in August 2016 and again in November 2017 before final retrieval at the conclusion on the second cruise. Waveglider trajectories followed a 20x20km square loop around the moorings and a butterfly pattern around the neutrally-buoyant float. NetCDF waveglider data files here (one per platform) contain hour averaged, georeferenced trajectory data for those parameters and depths.", "links": [ { diff --git a/datasets/SPURS2_XBAND_1.0.json b/datasets/SPURS2_XBAND_1.0.json index a3e9d083fa..13f553f65d 100644 --- a/datasets/SPURS2_XBAND_1.0.json +++ b/datasets/SPURS2_XBAND_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_XBAND_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58\u201365, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.", "links": [ { diff --git a/datasets/SPURS2_XBAND_IMG_1.0.json b/datasets/SPURS2_XBAND_IMG_1.0.json index fefd7c2343..72c2556723 100644 --- a/datasets/SPURS2_XBAND_IMG_1.0.json +++ b/datasets/SPURS2_XBAND_IMG_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_XBAND_IMG_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58\u201365, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.", "links": [ { diff --git a/datasets/SPURS2_XBT_1.0.json b/datasets/SPURS2_XBT_1.0.json index 4e53fe4d3d..e7ee629a16 100644 --- a/datasets/SPURS2_XBT_1.0.json +++ b/datasets/SPURS2_XBT_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SPURS2_XBT_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Expendable bathythermograph (XBT) casts were undertaken at stations during both of the SPURS-2 R/V Revelle cruises. Launched off the side of the ship, XBT probes provide vertical profile measurements of the water column at fixed locations. There were a total of 25 and 11 XBT deployments made during the first and second R/V Revelle cruises respectively. There is one XBT data file per cruise, each containing the temperature profile data from all instrument deployments undertaken during that cruise.", "links": [ { diff --git a/datasets/SRDB_V5_1827_5.json b/datasets/SRDB_V5_1827_5.json index 4cb047b5c2..fe5fd9c567 100644 --- a/datasets/SRDB_V5_1827_5.json +++ b/datasets/SRDB_V5_1827_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRDB_V5_1827_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science.", "links": [ { diff --git a/datasets/SRE4_SAB_gammaclones_1.json b/datasets/SRE4_SAB_gammaclones_1.json index e1796ab835..af8940ed31 100644 --- a/datasets/SRE4_SAB_gammaclones_1.json +++ b/datasets/SRE4_SAB_gammaclones_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_SAB_gammaclones_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A clone library was created from DNA extracted from an SAB-treated sample from the SRE4 in situ biodegradation experiment. The clone libary was created using one universal primer and one primer designed to be specific for the gammaproteobacteria. Sequences of approximately 600 bp were obtained.\n\nThe samples used in this experiment were collected from O'Brien Bay, near Casey Station in the Windmill Islands.\n\nGammaproteobacteria clone library\n\nClone library created from SRE4 T2 SAB sample using primers 10F (GAG TTT GAT CCT GGC TCA G ) and GAMR (GGT AAG GTT CTT CGC GTT GCA T).\n\nClones sequenced on a CEQ8000 Genetic Analysis system (Beckman-Coulter) and alignments were done in BioEdit v 5.0.9.\n\nText file SRE4gammaclonesalign is a text version of BioEdit file SRE4gammaclones.\n\nThis work was completed as part of ASAC project 2672 (ASAC_2672).", "links": [ { diff --git a/datasets/SRE4_desulfobaculaDGGE_1.json b/datasets/SRE4_desulfobaculaDGGE_1.json index 966aaac559..00d7a88269 100644 --- a/datasets/SRE4_desulfobaculaDGGE_1.json +++ b/datasets/SRE4_desulfobaculaDGGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_desulfobaculaDGGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65.\n\nSamples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Desulfobacula group.\n\nSamples A,B,C,D,E,F,G,H,I are all initial samples collected different days\nSamples beginning T0 are predeployment samples, the next number refers to the batch.\nSamples beginning T2 are 1 year samples with:\nC = control \nS = SAB \nL = lubricant\nU = used lubricant\nB = biodegradable lubricant\n\nPCR conditions were as follows:\nPrimers: 764F: ACAATGGTAAATGAGGGCA\n1392RC: CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCCACGGGCGG TGTGTAC\n\n50 ul (micro litre) reactions with Advantage II taq (Clontech) following manufacturer's recommendations with 20 pmol (pico mol) each primer and 20 ng (nano gram) template DNA.\n\nCycling: 94C 5 minutes \n10 cycles of:\n94C 1 minutes \n65C 1 minutes (-1C per cycle) \n72C 2 minutes \n\n\n20 cycles of:\n94C 1 minutes \n55C 1 minutes \n72C 2 minutes \n72C 30 minutes \n\nDGGE carried out using the D-Code system (BioRad).\nGel: 8% acrylamide 30 - 65% denaturant with 2 cm stacking gel (15% acrylamide) \n1 x TAE, 60 degrees C, 70V 16 hours \nThe gels were pre-run for 20 minutes then half reaction volume was loaded and the lanes flushed out after 15 minutes.\nGels were stained with SYBRGold.\nImages were captured using Storm Phosphorimager and ImageQuant v5.2 software(.gel files).\n\nSamples were only compared within a gel. Band pattern results are in the file desulfodgge.xls. For each comparison made there is a separate sheet in this file (see below). The first column in each sheet is the band position (or band name) and the remaining columns are samples with the first row being the sample name. '0' '1' indicate the band was 'absent' or 'present'.\n\nComparison Image files (.gel and .tif) results sheets\nBackground variation 140704f; 140704b 140704f and 140704b\npredeployment batches 180604f; 180406b 180604f and 180604b\neffect of setup 150704 150704\nimmediate effect of oil 250604f; 250604b 250604f and 250604b\n1 year samples (T2) 040804f; 040804b 040804f and 040804b\n\nThis work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201).", "links": [ { diff --git a/datasets/SRE4_gammaproteobacteriaDGGE_1.json b/datasets/SRE4_gammaproteobacteriaDGGE_1.json index ff21b2c79a..9b1acb27c6 100644 --- a/datasets/SRE4_gammaproteobacteriaDGGE_1.json +++ b/datasets/SRE4_gammaproteobacteriaDGGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_gammaproteobacteriaDGGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65.\n\nSamples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Gammaproteobacteria.\n\nSamples used were from Time2 (1 year)\nInitial: T-1C; T-1E\nControl: T2C\nSAB treatment: T2S\n\nPCR conditions: \nPrimers: GAMFC: CGC CCG CCG CGC CCC GCG CCC GGC CCG CCG CCC CCG CCC GGG\nTTA ATC GGA ATT ACT GG\nGAMR: GGT AAG GTT CTT CGC GTT GCA T\n\n50 ul (micro litre) reactions with HotStar (qiagen) mix, 5ul Q solution, 10 pmol (pico mol) each primer and 20 ng (nano gram) template DNA\n\ncycling: 94C 15 minutes\n35 cycles of:\n\n94C 1 minutes\n55C 1 minutes\n72C 1 minutes\n72C 20 minutes\n\nDGGE was performed using D-Code system (BioRad).\nGel: 8% acryloamide, 30 - 65% denaturant with 2 cm stacking gel\n1 x TAE, 60 degrees C, 80V 16 hours\nGel was pre-run for 20 minutes and lanes were flushed out after 15 minutes.\nGel was stained with Sybrgold. \nImage captured using Storm Phosphorimager and ImageQuant v5.2 software (.gel files). The image files are called 151105#2.gel and 151105.tif\n\nBand pattern results are in gammadgge.xls. The first column is the band position (or band name) and the remaining columns are samples with the first row being the sample name. The numbers indicates how many times the band appeared for that sample out of 2 DGGE runs.\n\nThis work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201).", "links": [ { diff --git a/datasets/SRE4_hydrocarbondegrading_MPN_2001_1.json b/datasets/SRE4_hydrocarbondegrading_MPN_2001_1.json index 1107b7b608..c2b17be73b 100644 --- a/datasets/SRE4_hydrocarbondegrading_MPN_2001_1.json +++ b/datasets/SRE4_hydrocarbondegrading_MPN_2001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_hydrocarbondegrading_MPN_2001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Most probable number counts of bacteria in sediment samples from the Sediment Recruitment Experiment 4. Samples were taken immediately after the collection of sediment, after the addition of the oils to the sediment and after five weeks in-situ incubation.\n\nSediment was treated by the addition of oil (four different types: synthetic lubricant, used synthetic lubricant, biodegradable lubricant and special Antarctic blend diesel) and the number of bacteria able to degrade components of Special Antarctic Blend diesel (SAB) was determined using an MPN method on a marine mineral medium with Special Antarctic Blend diesel as sole carbon source.\n\nData are presented as the most-probable-number of bacteria per gram of wet sediment.\n\nSee also the metadata record 'SRE4_hydrocarbondegrading_MPN_2006'.\n\nThis work was completed as part of ASAC project 1228.\n\nThe download file contains a readme which provides further information about the dataset, as well as an excel and csv copy of the data.", "links": [ { diff --git a/datasets/SRE4_hydrocarbondegrading_MPN_2006_1.json b/datasets/SRE4_hydrocarbondegrading_MPN_2006_1.json index d24125bf19..f597f707a5 100644 --- a/datasets/SRE4_hydrocarbondegrading_MPN_2006_1.json +++ b/datasets/SRE4_hydrocarbondegrading_MPN_2006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_hydrocarbondegrading_MPN_2006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Most probable number counts of bacteria in sediment samples from the Sediment Recruitment Experiment 4. Samples were taken immediately after the collection of sediment, after the addition of the oils to the sediment and after five years in situ incubation in O'Brien Bay near Casey station in the Windmill Islands region of Antarctica.\n\nSediment was treated by the addition of oil (four different types: synthetic lubricant, used synthetic lubricant, biodegradable lubricant and special antarctic blend diesel) and the number of bacteria able to degrade components of Special Antarctic Blend diesel (SAB) was determined using an MPN method on a marine mineral medium with Special Antarctic Blend diesel as sole carbon source.\n\nThe total number of aerobic heterotrophic bacteria present was also estimated for the control and SAB treatments using marine medium 2216 from Bacto.\n\nData are presented as the most-probable-number of bacteria per gram of wet sediment.\n\nSee also the metadata record 'SRE4_hydrocarbondegrading_MPN_2001'.\n\nThis work was completed as part of ASAC projects 2201 and 2672 (ASAC_2201, ASAC_2672).\n\nMore information about the dataset is presented on the summary worksheet of the download file - this information is copied below:\n\n4 -tube Most probable number counts were carried out on sediment samples from the SRE4 experiment collected in Dec 2006.\n\nSample names consist of: treatment:block: replicate where\n B = biodegradable oil\n C = control\n L = lubricant\n S = SAB diesel\n U = Used lubricant\n\nBlocks 3, 11, 18 and 20 were sampled in this season.\n3 replicates (A,B,C) were carried out for each sample.\nMost probable number was calculated using the MPN Calculator available from: http:/members.ync.net/mcuriale/mpn/index.html\n\nTotal heterotrophs were estimated for control and SAB treatments only.\nUsing Difco marine medium 2216 in 96-well titre trays.\n1:10 serial dilutions were performed in a total of 200 microlitres of medium (as indicated, some samples were done with 1:5 serial dilutions).\nPlates were incubated at 4 degrees C for 7 days.\nPlates were scored manually with visually turbid wells being positive. \n\nNumbers of SAB-degrading bacteria were estimated for all treatments.\nSAB-degrading bacteria were estimated using an artificial seawater broth (see reference below) and 5 microlitres of SAB as carbon source.\n1:5 serial dilutions were made in a total of 200ul of medium.\nPlates were incubated at 4 degrees C for 4 weeks. At this time 40 microlitres of INT solution were added and incubated for another two days (INT = 2.25 g/l of iodonitrotetrazolium chloride).\nPlates were scored manually, with the presence of a red precipitate or red colour being positive. \n\nNumbers of alkane-degrading bacteria were estimated for control and SAB treatments only. \nn-alkane-degrading bacteria were estimated using an artificial seawater broth (see reference below) and 3 microlitres of a 1:1 mix of hexadecane and nonane as carbon source.\n1:5 serial dilutions were made in a total of 200 microlitres of medium.\nPlates were incubated at 4 degrees C for 4 weeks. At this time 40 microlitres of INT solution were added and incubated for another two days (INT = 2.25 g/l of iodonitrotetrazolium chloride).\nPlates were scored manually, with the presence of a red precipitate or red colour being positive.", "links": [ { diff --git a/datasets/SRE4_tRFLP_all_1.json b/datasets/SRE4_tRFLP_all_1.json index 086d2e4897..2c154d6fe3 100644 --- a/datasets/SRE4_tRFLP_all_1.json +++ b/datasets/SRE4_tRFLP_all_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRE4_tRFLP_all_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The diversity of the microbial communities in each treatment in the SRE4 in situ biodegradation experiment were examined using terminal restriction fragment length polymorphism of the 16S rRNA gene. The SRE4 experiment was a field experiment that looked at the effect of synthetic lubricant, used synthetic lubricant, a biodegradable lubricant and special Antarctic blend (SAB) diesel on the marine benthic environment. Samples were collected at 5 weeks, 54 weeks, 65 weeks, 2 years and 5 years after deployment of the experimentally contaminated sediment.\n\nThe samples used in this experiment were collected from O'Brien Bay, near Casey Station in the Windmill Islands. \n\nAn information sheet is provided at the beginning of the excel workbook giving method information in detail.\n\nThe dataset consists of list of t-RFs (fragment lengths) for each sample and also the statistical analysis for significant differences between treatment groups. This work was completed as part of ASAC project 2672 (ASAC_2672).", "links": [ { diff --git a/datasets/SRTMGL1N_003.json b/datasets/SRTMGL1N_003.json index 4ea2706659..c43ed4439e 100644 --- a/datasets/SRTMGL1N_003.json +++ b/datasets/SRTMGL1N_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL1N_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) version SRTM, which includes the global 1 arc second (~30 meter) product.\n\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days.\n\nEach SRTMGL1 data tile contains a mosaic and blending of elevations generated by averaging all \"data takes\" that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL1.HGT). The primary goal of creating the Version 3 data was to eliminate voids that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL1N (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1N.003) product (such as N37W105.SRTMGL1N.NUM).\n\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \n\nImprovements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\n\n\n", "links": [ { diff --git a/datasets/SRTMGL1_003.json b/datasets/SRTMGL1_003.json index 41d38183cc..cfa6a99907 100644 --- a/datasets/SRTMGL1_003.json +++ b/datasets/SRTMGL1_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL1_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) version SRTM, which includes the global 1 arc second (~30 meter) product.\n\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days.\n\nEach SRTMGL1 data tile contains a mosaic and blending of elevations generated by averaging all \"data takes\" that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL1.HGT). The primary goal of creating the Version 3 data was to eliminate voids that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding (SRTMGL1N) (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1N.003) product (such as N37W105.SRTMGL1N.NUM).\n\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass. \n\n", "links": [ { diff --git a/datasets/SRTMGL1_NC_003.json b/datasets/SRTMGL1_NC_003.json index 2c32a6f2b6..89f86e9328 100644 --- a/datasets/SRTMGL1_NC_003.json +++ b/datasets/SRTMGL1_NC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL1_NC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) version SRTM, which includes the global 1 arc second (~30 meter) product. SRTMGL1_NC offers the data product in NetCDF. \r\n\r\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days. \r\n\r\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 North (N) and 56\u00b0 South (S) latitude. This accounts for about 80% of Earth\u2019s total landmass.\r\n\r\nImprovements/Changes from Previous Versions \r\n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\r\n", "links": [ { diff --git a/datasets/SRTMGL1_NUMNC_003.json b/datasets/SRTMGL1_NUMNC_003.json index c8e9905d30..b1a093b35c 100644 --- a/datasets/SRTMGL1_NUMNC_003.json +++ b/datasets/SRTMGL1_NUMNC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL1_NUMNC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) version SRTM, which includes the global 1 arc second (~30 meter) product. SRTMGL1_NUMNC is used along with the SRTMGL1_NC data product and offers the number count in NetCDF. \r\n\r\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days. \r\n\r\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 North (N) and 56\u00b0 South (S) latitude. This accounts for about 80% of Earth\u2019s total landmass.\r\n\r\nImprovements/Changes from Previous Versions \r\n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\r\n", "links": [ { diff --git a/datasets/SRTMGL30_002.json b/datasets/SRTMGL30_002.json index fcbdb7e296..3c23063c3b 100644 --- a/datasets/SRTMGL30_002.json +++ b/datasets/SRTMGL30_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL30_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSURES) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 30 arc second (~1,000 meter) product. \r\n\r\nThe NASA SRTM product with sample spacing of 3 arc second (~90 meter) generated by a 3 X 3 averaging of the 1 arc second data are then 10 X 10 averaged to produce thirty 30 arc second (~1,000 meter) data to correspond with Global 30 Arc Second Elevation (GTOPO30). (See the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.1.4.)\r\n\r\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\r\n\r\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. \r\n\r\n", "links": [ { diff --git a/datasets/SRTMGL3N_003.json b/datasets/SRTMGL3N_003.json index 2196c16e2e..a36702fe96 100644 --- a/datasets/SRTMGL3N_003.json +++ b/datasets/SRTMGL3N_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL3N_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) number product. \n\nAncillary one-byte (0 to 255) \u201cNUM\u201d (number) files were produced for NASA SRTM Version 3. These files have names corresponding to the elevation files, except with the extension \u201c.NUM\u201d (such as N37W105.NUM). The elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.HGT). The separate NUM file indicates the source of each DEM pixel; the number of ASTER scenes used (up to 100), if ASTER; and the number of SRTM data takes (up to 24), if SRTM. The NUM file for both 3 arc second products (whether sampled or averaged) references the 3 x 3 center pixel. Note that NUMs less than 6 are water and those greater than 10 are land. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf).\n\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass.\n\nImprovements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\n", "links": [ { diff --git a/datasets/SRTMGL3S_003.json b/datasets/SRTMGL3S_003.json index a504d48808..c7ec7fe9b8 100644 --- a/datasets/SRTMGL3S_003.json +++ b/datasets/SRTMGL3S_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL3S_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) sub-sampled product. The 3 arc second data was derived from the 1 arc second using sampling methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf)\n\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTMGL3 data were sub-sampled from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL3S.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (http://dx.doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM).\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. \n\nImprovements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "links": [ { diff --git a/datasets/SRTMGL3_003.json b/datasets/SRTMGL3_003.json index 95239d3d3d..3e45406f63 100644 --- a/datasets/SRTMGL3_003.json +++ b/datasets/SRTMGL3_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL3_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf).\n \nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTMGL3 data were generated from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL3.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM).\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ", "links": [ { diff --git a/datasets/SRTMGL3_NC_003.json b/datasets/SRTMGL3_NC_003.json index e9e6f2d1b3..c717bdaadf 100644 --- a/datasets/SRTMGL3_NC_003.json +++ b/datasets/SRTMGL3_NC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL3_NC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) SRTM, which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. SRTMGL3_NC offers the data product in NetCDF.\r\n\r\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days. \r\n\r\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 North (N) and 56\u00b0 South (S) latitude. This accounts for about 80% of Earth\u2019s total landmass.\r\n\r\nImprovements/Changes from Previous Versions \r\n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\r\n", "links": [ { diff --git a/datasets/SRTMGL3_NUMNC_003.json b/datasets/SRTMGL3_NUMNC_003.json index fd22353196..cac512e53f 100644 --- a/datasets/SRTMGL3_NUMNC_003.json +++ b/datasets/SRTMGL3_NUMNC_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGL3_NUMNC_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) SRTM, which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. SRTMGL3_NUMNC is used along with the SRTMGL3_NC data product and offers the number count in NetCDF.\n\nNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days. \n\nSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 North (N) and 56\u00b0 South (S) latitude. This accounts for about 80% of Earth\u2019s total landmass.\n\nImprovements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).\n", "links": [ { diff --git a/datasets/SRTMGLOBAL1N.json b/datasets/SRTMGLOBAL1N.json index fd4fe5c52f..51b9d97c89 100644 --- a/datasets/SRTMGLOBAL1N.json +++ b/datasets/SRTMGLOBAL1N.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMGLOBAL1N", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations.\n\nThe radars used during the SRTM mission were actually developed and flown on two Endeavour missions in 1994. The C-band Spaceborne Imaging Radar and the X-Band Synthetic Aperture Radar (X-SAR) hardware were used on board the space shuttle in April and October 1994 to gather data about Earth's environment. The technology was modified for the SRTM mission to collect interferometric radar, which compared two radar images or signals taken at slightly different angles. This mission used single-pass interferometry, which acquired two signals at the same time by using two different radar antennas. An antenna located on board the space shuttle collected one data set and the other data set was collected by an antenna located at the end of a 60-meter mast that extended from the shuttle. Differences between the two signals allowed for the calculation of surface elevation.\n\nEndeavour orbited Earth 16 times each day during the 11-day mission, completing 176 orbits. SRTM successfully collected radar data over 80% of the Earth's land surface between 60\u00b0 north and 56\u00b0 south latitude with data points posted every 1 arc-second (approximately 30 meters).\n \n\nTwo resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive:\n\n1 arc-second (approximately 30-meter) high resolution elevation data offer worldwide coverage of void filled data at a resolution of 1 arc-second (30 meters) and provide open distribution of this high-resolution global data set. Some tiles may still contain voids. The SRTM 1 Arc-Second Global (30 meters) data set will be released in phases starting September 24, 2014. Users should check the coverage map in EarthExplorer to verify if their area of interest is available.\n\n3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60\u00b0 north and 56\u00b0 south latitude.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/SRTMIMGM_003.json b/datasets/SRTMIMGM_003.json index e675a455a8..32a4f809ae 100644 --- a/datasets/SRTMIMGM_003.json +++ b/datasets/SRTMIMGM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMIMGM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.)\n\nThe combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information.\n\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. \n\nImprovements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "links": [ { diff --git a/datasets/SRTMIMGR_003.json b/datasets/SRTMIMGR_003.json index 197d04d520..2487d5d41d 100644 --- a/datasets/SRTMIMGR_003.json +++ b/datasets/SRTMIMGR_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMIMGR_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1)\n\nThe SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile. Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set.\n\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. \n\n Improvements/Changes from Previous Versions \n* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "links": [ { diff --git a/datasets/SRTMSWBD_003.json b/datasets/SRTMSWBD_003.json index 0ac1232330..4015961860 100644 --- a/datasets/SRTMSWBD_003.json +++ b/datasets/SRTMSWBD_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SRTMSWBD_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats.\n\nThe NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.\n\nThe SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ", "links": [ { diff --git a/datasets/SSBUVIRR_008.json b/datasets/SSBUVIRR_008.json index 41db864d2f..186643adfc 100644 --- a/datasets/SSBUVIRR_008.json +++ b/datasets/SSBUVIRR_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SSBUVIRR_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions:\n\nFlight #1: 1989 October 19, 20, 21\nFlight #2: 1990 October 7, 8, 9\nFlight #3: 1991 August 3, 4, 5, 6\nFlight #4: 1992 March 29, 30\nFlight #5: 1993 April 9, 11, 13, 15, 16\nFlight #6: 1994 March 14, 15, 17\nFlight #7: 1994 November 5, 7, 10, 13\nFlight #8: 1996 January 12, 16, 18\n\nThe Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22.", "links": [ { diff --git a/datasets/SSBUVO3_008.json b/datasets/SSBUVO3_008.json index 7f3f73fce2..20c44d3160 100644 --- a/datasets/SSBUVO3_008.json +++ b/datasets/SSBUVO3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SSBUVO3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods:\n\nFlight #1: 1989 October 19, 20, 21.\nFlight #2: 1990 October 7, 8, 9.\nFlight #3: 1991 August 3, 4, 5, 6.\nFlight #4: 1992 March 29, 31.\nFlight #5: 1993 April 9, 11, 13, 15, 16.\nFlight #6: 1994 March 14, 15, 17.\nFlight #7: 1994 November 5, 7, 10, 13.\nFlight #8: 1996 January 12, 16, 18.\n\nSSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb.", "links": [ { diff --git a/datasets/SSDP_HAZARD_EARTHQUAKE.json b/datasets/SSDP_HAZARD_EARTHQUAKE.json index 041f7b8e80..0ab052e1a4 100644 --- a/datasets/SSDP_HAZARD_EARTHQUAKE.json +++ b/datasets/SSDP_HAZARD_EARTHQUAKE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SSDP_HAZARD_EARTHQUAKE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detailed maps bring a greater resolution to the number and locations of active\nfaults. Preparing maps at a higher resolution requires extensive field study,\nand with a GIS, information, such as tract and parcel data, utility corridors,\nand flood hazard zones, can be incorporated to help decision makers in locating\nremediation facilities.\n\nAfter the Sylmar earthquake in 1972, building codes were strengthened, and the\nAlquist-Priolo Special Studies Zone Act was passed. Its purpose is to mitigate\nthe hazard of fault rupture by prohibiting the location of most human occupancy\nstructures across the traces of active faults. Earthquake fault zones are\nregulatory zones that encompass surface traces of active faults with a\npotential for future surface fault rupture. The zones are generally established\nabout 500 feet on either side of the surface trace of active faults.\n\nActive faults and strips of state-mandated zoning along faults (Alquist-Priolo\nzones) riddle the Salton Sea Basin. The primary fault, the San Andreas, steps\nfrom the northeast side of the Salton Sea across the southern end, along a\nseries of poorly understood faults, to the Brawley and Imperial fault systems.\nThis stepover region has not had a historic ground-rupturing earthquake.\nAlquist-Priolo zones could not be defined because the faults are not\nwell-located. Faults parallel to, and splaying from, the San Andreas are also\ncapable of major earthquakes.\n\nInitial plans for remediation facilities take into account the generalized\ninformation (at 1:750,000 scale) on active faults, and the fault maps do not\nprovide information on strong ground shaking. The shaking can damage facilities\nthat lie far from an earthquake epicenter and far from active faults.\nInformation on near-surface materials is required to estimate the\nground-shaking hazards.", "links": [ { diff --git a/datasets/SSEC-AMRC-AIRCRAFT.json b/datasets/SSEC-AMRC-AIRCRAFT.json index 84d527e505..256ef43968 100644 --- a/datasets/SSEC-AMRC-AIRCRAFT.json +++ b/datasets/SSEC-AMRC-AIRCRAFT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SSEC-AMRC-AIRCRAFT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMRC has been archiving the Aircraft data since the 2000's in the ftp archive. Products used to be made in real-time, but data collection has ended starting 31 August, 2015.", "links": [ { diff --git a/datasets/SSFR_irradiance_841_1.json b/datasets/SSFR_irradiance_841_1.json index 034b2ec698..6ffa2bbcbf 100644 --- a/datasets/SSFR_irradiance_841_1.json +++ b/datasets/SSFR_irradiance_841_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SSFR_irradiance_841_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Spectral Flux Radiometer (SSFR) was deployed on the University of Washington CV-580 during the dry season component of the Southern African Regional Science Initiative, August 1 - September 20, 2000. The SSFR made simultaneous measurements of both downwelling and upwelling net solar spectral irradiance at varying flight levels. Data have been provided for twenty flights in netcdf format for the period August 17 - September 16, 2000.For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project.", "links": [ { diff --git a/datasets/STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1.json b/datasets/STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1.json index 5a039254bc..f2f83fd47a 100644 --- a/datasets/STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1.json +++ b/datasets/STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1.json b/datasets/STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1.json index ff225f23fe..5d7561bdf5 100644 --- a/datasets/STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1.json +++ b/datasets/STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the High Spectral Resolution Lidar-2 (HSRL-2) as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json b/datasets/STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json index 2abd9cadcb..8fde94d168 100644 --- a/datasets/STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json +++ b/datasets/STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1.json b/datasets/STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1.json index ec6d2893c6..239c6af753 100644 --- a/datasets/STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1.json +++ b/datasets/STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the High Altitude Lidar Observatory (HALO) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_Chiwaukee-Prairie_Data_1.json b/datasets/STAQS_Chiwaukee-Prairie_Data_1.json index 54f7a04948..d06bccfcfe 100644 --- a/datasets/STAQS_Chiwaukee-Prairie_Data_1.json +++ b/datasets/STAQS_Chiwaukee-Prairie_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_Chiwaukee-Prairie_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_Chiwaukee-Prairie_Data is the data collected at the Chiwaukee Prairie site during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_Drone_Data_1.json b/datasets/STAQS_Drone_Data_1.json index 30781dc4ba..ef2cd62e9f 100644 --- a/datasets/STAQS_Drone_Data_1.json +++ b/datasets/STAQS_Drone_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_Drone_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_Drone_Data is the PM 2.5 data collected by the BlueHalo E900 UAV during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_Ground_Data_1.json b/datasets/STAQS_Ground_Data_1.json index 46c8982ca0..20f733415a 100644 --- a/datasets/STAQS_Ground_Data_1.json +++ b/datasets/STAQS_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_Ground_Data is the ground site data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_INSTEP_Data_1.json b/datasets/STAQS_INSTEP_Data_1.json index 3e4c06eee9..13c4789666 100644 --- a/datasets/STAQS_INSTEP_Data_1.json +++ b/datasets/STAQS_INSTEP_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_INSTEP_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_INSTEP_Data is the trace gas data collected by the Inexpensive Network Sensor Technology Exploring Pollution (INSTEP) instrument during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_SeaRey_Data_1.json b/datasets/STAQS_SeaRey_Data_1.json index 43bcbec0d7..12a4b4ba28 100644 --- a/datasets/STAQS_SeaRey_Data_1.json +++ b/datasets/STAQS_SeaRey_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_SeaRey_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_SeaRey_Data is the data collected onboard the Progressive Aerodyne SeaRey aircraft during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STAQS_Sondes_Data_1.json b/datasets/STAQS_Sondes_Data_1.json index c204fdd41a..f06f2f618f 100644 --- a/datasets/STAQS_Sondes_Data_1.json +++ b/datasets/STAQS_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STAQS_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STAQS_Sondes_Data is the balloonsonde and ozonesonde data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.\n\nLaunched in April 2023, NASA\u2019s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA\u2019s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center\u2019s (LaRC\u2019s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer \u2013 Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.", "links": [ { diff --git a/datasets/STRAT_Aerosol_AircraftInSitu_ER2_Data_1.json b/datasets/STRAT_Aerosol_AircraftInSitu_ER2_Data_1.json index e3ed2046a5..cf9053c404 100644 --- a/datasets/STRAT_Aerosol_AircraftInSitu_ER2_Data_1.json +++ b/datasets/STRAT_Aerosol_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Aerosol_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Aerosol_AircraftInSitu_ER2_Data is the in-situ trace aerosol data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the Condensation Nuclei Counter (CNC) and Focused Cavity Aerosol Spectrometer (FCAS) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_Analysis_ER2_Data_1.json b/datasets/STRAT_Analysis_ER2_Data_1.json index ae4db4cd3c..c8d058e0cc 100644 --- a/datasets/STRAT_Analysis_ER2_Data_1.json +++ b/datasets/STRAT_Analysis_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Analysis_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Analysis_ER2_Data is the modeled trajectories and meteorological data along the flight path for the ER-2 aircraft collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_Ground_Data_1.json b/datasets/STRAT_Ground_Data_1.json index 79e6d65e42..bdecec2bc2 100644 --- a/datasets/STRAT_Ground_Data_1.json +++ b/datasets/STRAT_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Ground_Data is the ground site data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the JPL ozone lidar at Mauna Loa and Table Mountain, the Composition and Photo-Dissociative Flux Measurement (CPFM), and the Airborne Raman Ozone, Temperature, and Aerosol Lidar (AROTAL) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_MetNav_AircraftInSitu_ER2_Data_1.json b/datasets/STRAT_MetNav_AircraftInSitu_ER2_Data_1.json index a567101b41..3c794f5a23 100644 --- a/datasets/STRAT_MetNav_AircraftInSitu_ER2_Data_1.json +++ b/datasets/STRAT_MetNav_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_MetNav_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_MetNav_AircraftInSitu_ER2_Data is the in-situ meteorological and navigational data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the Meteorological Measurement System (MMS), ER-2 Nav Recorder (NavRec), Microwave Temperature Profiler (MTP), and the Composition and Photo-Dissociative Flux Measurement (CPFM) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_Model_Data_1.json b/datasets/STRAT_Model_Data_1.json index 8fe4b22ec2..0585607a1a 100644 --- a/datasets/STRAT_Model_Data_1.json +++ b/datasets/STRAT_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Model_Data is the model data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_Satellite_Data_1.json b/datasets/STRAT_Satellite_Data_1.json index 011d25962d..4c5fa8ef00 100644 --- a/datasets/STRAT_Satellite_Data_1.json +++ b/datasets/STRAT_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Satellite_Data is the supplementary satellite data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Satellite images from the GOES-7 and GOES-9 satellites are featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_Sondes_Data_1.json b/datasets/STRAT_Sondes_Data_1.json index eaf8c38665..7a9c97da6c 100644 --- a/datasets/STRAT_Sondes_Data_1.json +++ b/datasets/STRAT_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_Sondes_Data is the balloonsonde and ozonesonde data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_TraceGas_AircraftInSitu_ER2_Data_1.json b/datasets/STRAT_TraceGas_AircraftInSitu_ER2_Data_1.json index 2c642d5ec2..e1bd612f05 100644 --- a/datasets/STRAT_TraceGas_AircraftInSitu_ER2_Data_1.json +++ b/datasets/STRAT_TraceGas_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_TraceGas_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_TraceGas_AircraftInSitu_ER2_Data is the in-situ trace gas data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the High-Altitude Fast-Response CO2 Analyzer (Harvard CO2), Advanced Whole Air Sampler (AWAS), Airborne Chromatograph For Atmospheric Trace Species (ACATS), NOAA NOy Instrument, Harvard Hydroxyl Experiment (HOx), Airborne Tunable Laser Absorption Spectrometer (ATLAS), and the Dual-Beam UV-Absorption Ozone Photometer (NOAA O3 Classic) are featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STRAT_jValue_AircraftInSitu_ER2_Data_1.json b/datasets/STRAT_jValue_AircraftInSitu_ER2_Data_1.json index 3790a395c1..dd0361604b 100644 --- a/datasets/STRAT_jValue_AircraftInSitu_ER2_Data_1.json +++ b/datasets/STRAT_jValue_AircraftInSitu_ER2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STRAT_jValue_AircraftInSitu_ER2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STRAT_jValue_AircraftInSitu_ER2_Data is the photolysis frequencies (j-values) collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the Composition and Photo-Dissociative Flux Measurement (CPFM) is featured in this collection. Data collection for this product is complete.\r\n\r\nThe STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. \r\n\r\nTo accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission. ", "links": [ { diff --git a/datasets/STS-59_BROWSE_GRD_1.json b/datasets/STS-59_BROWSE_GRD_1.json index 85d8f8d436..99f6c184e2 100644 --- a/datasets/STS-59_BROWSE_GRD_1.json +++ b/datasets/STS-59_BROWSE_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_BROWSE_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Browse for STS-59 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-59_BROWSE_SLC_1.json b/datasets/STS-59_BROWSE_SLC_1.json index f467585b3e..4ab0fefb5b 100644 --- a/datasets/STS-59_BROWSE_SLC_1.json +++ b/datasets/STS-59_BROWSE_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_BROWSE_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Browse for STS-59 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/STS-59_GRD_1.json b/datasets/STS-59_GRD_1.json index 91160fe4d8..f43cc5784e 100644 --- a/datasets/STS-59_GRD_1.json +++ b/datasets/STS-59_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STS-59 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-59_META_GRD_1.json b/datasets/STS-59_META_GRD_1.json index b4586d012b..db1c6883cb 100644 --- a/datasets/STS-59_META_GRD_1.json +++ b/datasets/STS-59_META_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_META_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for STS-59 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-59_META_SLC_1.json b/datasets/STS-59_META_SLC_1.json index 3fa67acd70..a45f12ec52 100644 --- a/datasets/STS-59_META_SLC_1.json +++ b/datasets/STS-59_META_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_META_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for STS-59 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/STS-59_SLC_1.json b/datasets/STS-59_SLC_1.json index 5bff5716a6..53196130da 100644 --- a/datasets/STS-59_SLC_1.json +++ b/datasets/STS-59_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-59_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STS-59 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/STS-68_BROWSE_GRD_1.json b/datasets/STS-68_BROWSE_GRD_1.json index b92529145b..126860bb9b 100644 --- a/datasets/STS-68_BROWSE_GRD_1.json +++ b/datasets/STS-68_BROWSE_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_BROWSE_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Browse for STS-68 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-68_BROWSE_SLC_1.json b/datasets/STS-68_BROWSE_SLC_1.json index 4dcbe7642b..d1efcbe03f 100644 --- a/datasets/STS-68_BROWSE_SLC_1.json +++ b/datasets/STS-68_BROWSE_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_BROWSE_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Browse for STS-68 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/STS-68_GRD_1.json b/datasets/STS-68_GRD_1.json index f188feb98e..d5c1457fd2 100644 --- a/datasets/STS-68_GRD_1.json +++ b/datasets/STS-68_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STS-68 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-68_META_GRD_1.json b/datasets/STS-68_META_GRD_1.json index 6acde8daf7..327c63d7cf 100644 --- a/datasets/STS-68_META_GRD_1.json +++ b/datasets/STS-68_META_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_META_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for STS-68 SIR-C Ground Range Product", "links": [ { diff --git a/datasets/STS-68_META_SLC_1.json b/datasets/STS-68_META_SLC_1.json index f6120537f0..a80ab478c5 100644 --- a/datasets/STS-68_META_SLC_1.json +++ b/datasets/STS-68_META_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_META_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Metadata for STS-68 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/STS-68_SLC_1.json b/datasets/STS-68_SLC_1.json index 253ff0cf40..392c3205f5 100644 --- a/datasets/STS-68_SLC_1.json +++ b/datasets/STS-68_SLC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "STS-68_SLC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "STS-68 SIR-C Slant Range Product", "links": [ { diff --git a/datasets/SUCCESS_UTAH_PDL_1.json b/datasets/SUCCESS_UTAH_PDL_1.json index fe21e6d1c7..0cf11e48f6 100644 --- a/datasets/SUCCESS_UTAH_PDL_1.json +++ b/datasets/SUCCESS_UTAH_PDL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SUCCESS_UTAH_PDL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SUCCESS_UTAH_PDL data set contains ground-based measurements made by the University of Utah Polarization Diversity LIDAR at the CART site during the April-May 1996 SUCCESS Mission.SUbsonic aircraft: Contrail & Clouds Effects Special Study (SUCCESS) is a NASA field program using scientifically instrumented aircraft and ground based measurements to investigate the effects of subsonic aircraft on contrails, cirrus clouds and atmospheric chemistry. The experiment is cosponsored by NASA's Subsonic Assessment Program and the Radiation Sciences Program which are part of the overall Aeronautics and Mission to Planet Earth Programs, respectively. SUCCESS has well over a hundred direct participants from several NASA Centers, other agencies, universities and private research companies.", "links": [ { diff --git a/datasets/SUPERYACHT_SCIENCE_0.json b/datasets/SUPERYACHT_SCIENCE_0.json index 14a04344e4..440db4a4f1 100644 --- a/datasets/SUPERYACHT_SCIENCE_0.json +++ b/datasets/SUPERYACHT_SCIENCE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SUPERYACHT_SCIENCE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean-colour sensors mounted on satellites can view the entire ocean over a period of a few days. However, to calibrate these sensors and validate the data, satellite observations must be compared with accurate and reliable in situ measurements, collected at the ocean surface. There are many regions of the ocean where these in situ measurements are rarely collected. Ocean-colour sensors can be mounted on research vessels and ships of opportunity. In this project, an ocean-colour sensor (Seabird HyperSAS Solar Tracker) was mounted on a yacht that visits remote regions of the planet where few observations have been collected. This project aims to process this data to a level for use by the scientific community for scientific applications, such as satellite validation.", "links": [ { diff --git a/datasets/SV08LC_1.json b/datasets/SV08LC_1.json index 5be10f717d..e1f346ab77 100644 --- a/datasets/SV08LC_1.json +++ b/datasets/SV08LC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08LC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).", "links": [ { diff --git a/datasets/SV08PLBK_1.json b/datasets/SV08PLBK_1.json index 0d229141da..08fff583c7 100644 --- a/datasets/SV08PLBK_1.json +++ b/datasets/SV08PLBK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08PLBK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).", "links": [ { diff --git a/datasets/SV08PLTB_1.json b/datasets/SV08PLTB_1.json index dd3f47575d..93ed71bf97 100644 --- a/datasets/SV08PLTB_1.json +++ b/datasets/SV08PLTB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08PLTB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).", "links": [ { diff --git a/datasets/SV08SM_1.json b/datasets/SV08SM_1.json index 6d24524120..50433c6f4d 100644 --- a/datasets/SV08SM_1.json +++ b/datasets/SV08SM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08SM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes several parameters that were obtained from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).", "links": [ { diff --git a/datasets/SV08SR_1.json b/datasets/SV08SR_1.json index d670bdf8a0..918f26669b 100644 --- a/datasets/SV08SR_1.json +++ b/datasets/SV08SR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08SR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface roughness data for this data set were collected at several field sites as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.", "links": [ { diff --git a/datasets/SV08ST_1.json b/datasets/SV08ST_1.json index 4ccdeda430..3e87bc42f5 100644 --- a/datasets/SV08ST_1.json +++ b/datasets/SV08ST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08ST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.", "links": [ { diff --git a/datasets/SV08VWC_1.json b/datasets/SV08VWC_1.json index 2a6403b8fe..c01049bf48 100644 --- a/datasets/SV08VWC_1.json +++ b/datasets/SV08VWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08VWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.", "links": [ { diff --git a/datasets/SV08V_1.json b/datasets/SV08V_1.json index 3da5060b08..844d4e57cd 100644 --- a/datasets/SV08V_1.json +++ b/datasets/SV08V_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV08V_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes in situ vegetation data collected during the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.", "links": [ { diff --git a/datasets/SV12CSMA_1.json b/datasets/SV12CSMA_1.json index 73462142f4..cb457b7554 100644 --- a/datasets/SV12CSMA_1.json +++ b/datasets/SV12CSMA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12CSMA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ soil moisture data collected with coring devices at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12CST_1.json b/datasets/SV12CST_1.json index d3c4c23bfb..6fe33d28e1 100644 --- a/datasets/SV12CST_1.json +++ b/datasets/SV12CST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12CST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ soil texture data collected with coring devices at several sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12LC_1.json b/datasets/SV12LC_1.json index eea7c06832..c50aab9c57 100644 --- a/datasets/SV12LC_1.json +++ b/datasets/SV12LC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12LC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Syst\u00e8me Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.", "links": [ { diff --git a/datasets/SV12PLBK_1.json b/datasets/SV12PLBK_1.json index 20213383f4..e1c92fe307 100644 --- a/datasets/SV12PLBK_1.json +++ b/datasets/SV12PLBK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12PLBK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft instrument as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12PLSM_1.json b/datasets/SV12PLSM_1.json index 46a7b81229..cbd0b677d1 100644 --- a/datasets/SV12PLSM_1.json +++ b/datasets/SV12PLSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12PLSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil moisture data obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.", "links": [ { diff --git a/datasets/SV12PLTB_1.json b/datasets/SV12PLTB_1.json index e644f97990..b1e2291226 100644 --- a/datasets/SV12PLTB_1.json +++ b/datasets/SV12PLTB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12PLTB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.", "links": [ { diff --git a/datasets/SV12PSMA_1.json b/datasets/SV12PSMA_1.json index f5336e3366..580bac1a10 100644 --- a/datasets/SV12PSMA_1.json +++ b/datasets/SV12PSMA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12PSMA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ soil moisture data collected at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12PSMF_1.json b/datasets/SV12PSMF_1.json index 0e19d1d8b5..c5817ff663 100644 --- a/datasets/SV12PSMF_1.json +++ b/datasets/SV12PSMF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12PSMF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ soil moisture data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12SRA_1.json b/datasets/SV12SRA_1.json index b812f28ea1..f1c0e64574 100644 --- a/datasets/SV12SRA_1.json +++ b/datasets/SV12SRA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12SRA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface roughness data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12SRF_1.json b/datasets/SV12SRF_1.json index 70f304f346..139320bd07 100644 --- a/datasets/SV12SRF_1.json +++ b/datasets/SV12SRF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12SRF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface roughness data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12STM_1.json b/datasets/SV12STM_1.json index 57742743f4..7efbc3fd27 100644 --- a/datasets/SV12STM_1.json +++ b/datasets/SV12STM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12STM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). The soil texture classification map provides information about vegetation present in the study area.", "links": [ { diff --git a/datasets/SV12UBK_1.json b/datasets/SV12UBK_1.json index a5fc0a9e50..69538eb310 100644 --- a/datasets/SV12UBK_1.json +++ b/datasets/SV12UBK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12UBK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains backscatter data obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12VA_1.json b/datasets/SV12VA_1.json index a6f396fe2a..ccf2427acc 100644 --- a/datasets/SV12VA_1.json +++ b/datasets/SV12VA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12VA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12VF_1.json b/datasets/SV12VF_1.json index 077415b82b..81afe56750 100644 --- a/datasets/SV12VF_1.json +++ b/datasets/SV12VF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12VF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ vegetation data collected at several forest sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).", "links": [ { diff --git a/datasets/SV12VWC_1.json b/datasets/SV12VWC_1.json index dbaea10aa6..880b3d84be 100644 --- a/datasets/SV12VWC_1.json +++ b/datasets/SV12VWC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV12VWC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.", "links": [ { diff --git a/datasets/SV15PLSM_1.json b/datasets/SV15PLSM_1.json index 53baa39e0b..f5ee667a90 100644 --- a/datasets/SV15PLSM_1.json +++ b/datasets/SV15PLSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV15PLSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil moisture data derived from the brightness temperatures measured by the Passive Active L-band System (PALS) microwave aircraft instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15).", "links": [ { diff --git a/datasets/SV15PLTB_1.json b/datasets/SV15PLTB_1.json index ad6c2b5a4d..45ae694d71 100644 --- a/datasets/SV15PLTB_1.json +++ b/datasets/SV15PLTB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV15PLTB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX15, the Soil Moisture Active Passive Validation Experiment 2015.", "links": [ { diff --git a/datasets/SV15PSM_1.json b/datasets/SV15PSM_1.json index 62fe0bbe7d..55c0de5b4b 100644 --- a/datasets/SV15PSM_1.json +++ b/datasets/SV15PSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV15PSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ gravimetric and volumetric soil moisture, bulk density, and\u00a0soil temperature measurements collected for the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). Sampling was performed at field\u00a0sites approximately 1 m apart.", "links": [ { diff --git a/datasets/SV16I_PLTBSM_1.json b/datasets/SV16I_PLTBSM_1.json index c474cfe3ad..45714074e9 100644 --- a/datasets/SV16I_PLTBSM_1.json +++ b/datasets/SV16I_PLTBSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16I_PLTBSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a study area in South Fork, Iowa, United States. A total of 20 soil stations were distributed throughout this same area. \n\nThe soil characteristics included in this data set are volumetric soil moisture, vertically and horizontally polarized brightness temperature, effective soil temperature, effective vegetation temperature, vegetation water content, land cover classification, sand and clay fraction, and volumetric soil moisture uncertainty estimates.", "links": [ { diff --git a/datasets/SV16I_PNET_1.json b/datasets/SV16I_PNET_1.json index dd34f9ab44..418f8a2c0e 100644 --- a/datasets/SV16I_PNET_1.json +++ b/datasets/SV16I_PNET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16I_PNET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of soil moisture, soil temperature and precipitation measurements recorded in 2016 by the permanent soil moisture network; SMAPVEX16-Iowa. The sites were spread out over the experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA.\n\nThe data file contains the soil moisture, soil temperature and\u00a0precipitation measurements for each station located at the site.", "links": [ { diff --git a/datasets/SV16I_TNET_1.json b/datasets/SV16I_TNET_1.json index dfe377b7d8..a92ecfe67e 100644 --- a/datasets/SV16I_TNET_1.json +++ b/datasets/SV16I_TNET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16I_TNET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of soil moisture and temperature measurements recorded by the temporary soil moisture network deployed to SMAPVEX16-Iowa for the summer season of 2016.\u00a0The sites were spread out over an experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA. The data file contains the soil moisture and temperature measurements for each station located at the site.", "links": [ { diff --git a/datasets/SV16I_TPSM_1.json b/datasets/SV16I_TPSM_1.json index 101a3d18d4..cc7f21f79a 100644 --- a/datasets/SV16I_TPSM_1.json +++ b/datasets/SV16I_TPSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16I_TPSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of calibrated soil moisture sensor measurements recorded by manual sampling teams at the field sites of SMAPVEX16-Iowa during two intensive observation periods in June and August of 2016. The sites were located throughout an experiment domain of about 30 km by 40 km approximately 30 km north of Ames, Iowa.", "links": [ { diff --git a/datasets/SV16M_CRS_1.json b/datasets/SV16M_CRS_1.json index 27ea2be7e8..618cdb0601 100644 --- a/datasets/SV16M_CRS_1.json +++ b/datasets/SV16M_CRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_CRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains CropScan observations (solar irradiance and incidence angle) collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_CSM_1.json b/datasets/SV16M_CSM_1.json index befac2334d..d354dab04b 100644 --- a/datasets/SV16M_CSM_1.json +++ b/datasets/SV16M_CSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_CSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ measurements of soil moisture and bulk density collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_LAI_1.json b/datasets/SV16M_LAI_1.json index 4ca8451152..2f8dd857c2 100644 --- a/datasets/SV16M_LAI_1.json +++ b/datasets/SV16M_LAI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_LAI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ Leaf Area Index (LAI) data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_LC_1.json b/datasets/SV16M_LC_1.json index 607a6d6f0a..81871d435b 100644 --- a/datasets/SV16M_LC_1.json +++ b/datasets/SV16M_LC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_LC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_MET_1.json b/datasets/SV16M_MET_1.json index e44ef6a58d..acf66de236 100644 --- a/datasets/SV16M_MET_1.json +++ b/datasets/SV16M_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains meteorological data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_PLTBSM_1.json b/datasets/SV16M_PLTBSM_1.json index 35930ffe9a..30fba030d9 100644 --- a/datasets/SV16M_PLTBSM_1.json +++ b/datasets/SV16M_PLTBSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_PLTBSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a 26 km x 48 km domain in Manitoba, Canada. Nine permanent soil stations were distributed throughout this same area. \n\nThe soil characteristics included in this data set are volumetric soil moisture, vertically and horizontally polarized brightness temperature, effective soil temperature, effective vegetation temperature, vegetation water content, land cover classification, sand and clay fraction, and volumetric soil moisture uncertainty estimates.", "links": [ { diff --git a/datasets/SV16M_PSM_1.json b/datasets/SV16M_PSM_1.json index 97659ff50f..439e16fdb3 100644 --- a/datasets/SV16M_PSM_1.json +++ b/datasets/SV16M_PSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_PSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ measurements of soil moisture, soil and vegetation temperature, and real dielectric constant collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_SDB_1.json b/datasets/SV16M_SDB_1.json index 73629b2693..d2f46f4613 100644 --- a/datasets/SV16M_SDB_1.json +++ b/datasets/SV16M_SDB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_SDB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains detailed soil survey data used for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign. Data are provided in a relational geodatabase.", "links": [ { diff --git a/datasets/SV16M_SR_1.json b/datasets/SV16M_SR_1.json index 8b62c9742f..d62812368b 100644 --- a/datasets/SV16M_SR_1.json +++ b/datasets/SV16M_SR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_SR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains surface soil roughness data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_SSM_1.json b/datasets/SV16M_SSM_1.json index 144e7d70e7..33084154a7 100644 --- a/datasets/SV16M_SSM_1.json +++ b/datasets/SV16M_SSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_SSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil moisture, temperature, and precipitation data collected at temporary soil stations and the Real-time In-Situ Soil Monitoring for Agriculture (RISMA) station network for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_ST_1.json b/datasets/SV16M_ST_1.json index 4a35dc9f8c..e2c5f70853 100644 --- a/datasets/SV16M_ST_1.json +++ b/datasets/SV16M_ST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_ST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains soil texture data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_TB_1.json b/datasets/SV16M_TB_1.json index d12257b7cb..9b76ae402c 100644 --- a/datasets/SV16M_TB_1.json +++ b/datasets/SV16M_TB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_TB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains brightness temperatures obtained by in situ L-band radiometers. The data were collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV16M_V_1.json b/datasets/SV16M_V_1.json index d878a6531b..4f5a469fca 100644 --- a/datasets/SV16M_V_1.json +++ b/datasets/SV16M_V_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV16M_V_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in situ measurements of crop density, height, and biomass collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.", "links": [ { diff --git a/datasets/SV19MA_DEM_1.json b/datasets/SV19MA_DEM_1.json index f5f3585db9..ade58f1869 100644 --- a/datasets/SV19MA_DEM_1.json +++ b/datasets/SV19MA_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during \"leaf-off\u201d and \"leaf-on\" conditions.", "links": [ { diff --git a/datasets/SV19MA_DSM_1.json b/datasets/SV19MA_DSM_1.json index a56c980430..fddc99d687 100644 --- a/datasets/SV19MA_DSM_1.json +++ b/datasets/SV19MA_DSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_DSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize \u2018leaf-on\u2019 conditions. DSM data represents the highest elevation of features on the Earth\u2019s surface, which may include bare-earth, vegetation, and human-made objects.", "links": [ { diff --git a/datasets/SV19MA_LID_1.json b/datasets/SV19MA_LID_1.json index 1050ea8243..bdc355a2c5 100644 --- a/datasets/SV19MA_LID_1.json +++ b/datasets/SV19MA_LID_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_LID_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off\u201d and \"leaf-on\" conditions.", "links": [ { diff --git a/datasets/SV19MA_SAR_1.json b/datasets/SV19MA_SAR_1.json index abd0d8dbaf..821a8d09b1 100644 --- a/datasets/SV19MA_SAR_1.json +++ b/datasets/SV19MA_SAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_SAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.", "links": [ { diff --git a/datasets/SV19MA_TNET_1.json b/datasets/SV19MA_TNET_1.json index 4d39d80903..e76f6b238f 100644 --- a/datasets/SV19MA_TNET_1.json +++ b/datasets/SV19MA_TNET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_TNET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of ground-based, soil moisture, soil temperature, and air temperature measurements recorded by twenty-five temporary stations located in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The stations were installed across an area of approximately 23 km by 36 km in May 2019 and operated through 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future.", "links": [ { diff --git a/datasets/SV19MA_VOD_1.json b/datasets/SV19MA_VOD_1.json index 406c1e723b..c8791e30b4 100644 --- a/datasets/SV19MA_VOD_1.json +++ b/datasets/SV19MA_VOD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MA_VOD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the SMAPVEX19-22 campaign, an L-band radiometer was deployed on top of a tower at Harvard Forest,Massachusetts, looking down at a stand of red oak forest. The radiometer collected data in V-polarization from late April to mid October 2019. Over 4 days in early July 2019, the water potential and L-band complex dielectric constant of canopy leaves were measured at various times of day. Other instruments were installed within the radiometer's field of view to measure soil moisture and temperature, air temperature, tree xylem apparent dielectric permittivity at 70 MHz, tree xylem water potential, and canopy wetness. The goal of this experiment was to study the sensitivity of L-band vegetation optical depth (VOD) to changing vegetation water potential over a growing season.", "links": [ { diff --git a/datasets/SV19MB_DEM_1.json b/datasets/SV19MB_DEM_1.json index 53e26593ef..d7a560659a 100644 --- a/datasets/SV19MB_DEM_1.json +++ b/datasets/SV19MB_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MB_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during \"leaf-off\" and \"leaf-on\" conditions.", "links": [ { diff --git a/datasets/SV19MB_DSM_1.json b/datasets/SV19MB_DSM_1.json index 543c7eba11..e6cf95e363 100644 --- a/datasets/SV19MB_DSM_1.json +++ b/datasets/SV19MB_DSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MB_DSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize \u2018leaf-on\u2019 conditions. DSM data represents the highest elevation of features on the Earth\u2019s surface, which may include bare-earth, vegetation, and human-made objects.", "links": [ { diff --git a/datasets/SV19MB_LID_1.json b/datasets/SV19MB_LID_1.json index eaeb17ce82..10ea4e1f5e 100644 --- a/datasets/SV19MB_LID_1.json +++ b/datasets/SV19MB_LID_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MB_LID_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off\" and \"leaf-on\" conditions.", "links": [ { diff --git a/datasets/SV19MB_SAR_1.json b/datasets/SV19MB_SAR_1.json index 541f95ca2a..611fcf47e1 100644 --- a/datasets/SV19MB_SAR_1.json +++ b/datasets/SV19MB_SAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MB_SAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.", "links": [ { diff --git a/datasets/SV19MB_TNET_1.json b/datasets/SV19MB_TNET_1.json index 55d4744689..a3f4e3309e 100644 --- a/datasets/SV19MB_TNET_1.json +++ b/datasets/SV19MB_TNET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SV19MB_TNET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data consist of soil moisture sensor, soil temperature and air temperature measurements recorded by 25 temporary stations in the Millbrook (MB) experiment location during SMAPVEX19-22. The stations were spread out over the experiment domain of about 30 by 40 km located around Millbrook, NY. The stations were installed in May 2019 and are to continue operation until 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future.", "links": [ { diff --git a/datasets/SWDB_L2_004.json b/datasets/SWDB_L2_004.json index 9d4c89edcf..d122996da9 100644 --- a/datasets/SWDB_L2_004.json +++ b/datasets/SWDB_L2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue (SWDB) Level 2 Product contains data corresponding to a single SeaWiFS swath using Deep Blue algorithm. There are about 15 Level 2 data files produced per day. Each contains retrieved aerosol properties averaged to a resolution of 3x3 SeaWiFS pixels (13.5x13.5 km at the center of the swath given 4.5km SeaWiFS pixels). \n\n The primary data parameters are aerosol optical thickness, and Angstrom exponent.", "links": [ { diff --git a/datasets/SWDB_L305_004.json b/datasets/SWDB_L305_004.json index 12c110a5c0..019fae7249 100644 --- a/datasets/SWDB_L305_004.json +++ b/datasets/SWDB_L305_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L305_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue (SWDB) Level 3 daily global gridded (0.5 x 0.5 deg) data is derived from SeaWiFS Deep Blue Level 2 data. In most cases, each data field represents the arithmetic mean of all cells whose latitude and longitude places it within the bounds of each grid element. Furthermore, only cells measured on the day of interest are included in this calculation. The local date based on the longitude of the cell is calculated from the time of measurement. If the local date equals the day of interest, the cell is included in the L3 data processing.\n\n The primary data parameters are aerosol optical thickness, and Angstrom exponent.", "links": [ { diff --git a/datasets/SWDB_L310_004.json b/datasets/SWDB_L310_004.json index 3eeede4ef5..b5b5821926 100644 --- a/datasets/SWDB_L310_004.json +++ b/datasets/SWDB_L310_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L310_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue Level 3 daily global gridded (1.0 x 1.0 deg) data is derived from SeaWiFS Deep Blue Level 2 data. In most cases, each data field represents the arithmetic mean of all cells whose latitude and longitude places it within the bounds of each grid element. Furthermore, only cells measured on the day of interest are included in this calculation. The local date based on the longitude of the cell is calculated from the time of measurement. If the local date equals the day of interest, the cell is included in the L3 data processing.\n\n The primary data parameters are aerosol optical thickness, and Angstrom exponent.", "links": [ { diff --git a/datasets/SWDB_L3M05_004.json b/datasets/SWDB_L3M05_004.json index 03c21d6fe6..2c56d5de78 100644 --- a/datasets/SWDB_L3M05_004.json +++ b/datasets/SWDB_L3M05_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L3M05_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue Level 3 monthly product contains monthly global gridded (0.5 x 0.5 deg) Version 4 data derived from SeaWiFS Deep Blue Level 3 daily gridded data at the same spatial resolution. \n\n The primary data parameters are aerosol optical thickness, and Angstrom exponent.", "links": [ { diff --git a/datasets/SWDB_L3M10_004.json b/datasets/SWDB_L3M10_004.json index ebfcae6d70..31c1902641 100644 --- a/datasets/SWDB_L3M10_004.json +++ b/datasets/SWDB_L3M10_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L3M10_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This SeaWiFS Deep Blue Level 3 monthly product contains monthly global gridded (1 x 1 deg) data derived from SeaWiFS Deep Blue Level 3 daily gridded data at the same resolution. \n\n The primary data parameters are aerosol optical thickness, and Angstrom exponent.", "links": [ { diff --git a/datasets/SWDB_L3MC05_004.json b/datasets/SWDB_L3MC05_004.json index 9822a6ed33..a6b055de49 100644 --- a/datasets/SWDB_L3MC05_004.json +++ b/datasets/SWDB_L3MC05_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L3MC05_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue Level 3 monthly climatology product contains monthly global climatology gridded (0.5 x 0.5 deg) data derived from SeaWiFS Deep Blue Level 3 monthly gridded data. \n\n The primary data parameters are aerosol optical thickness.", "links": [ { diff --git a/datasets/SWDB_L3MC10_004.json b/datasets/SWDB_L3MC10_004.json index 4482567688..dfa8754c61 100644 --- a/datasets/SWDB_L3MC10_004.json +++ b/datasets/SWDB_L3MC10_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWDB_L3MC10_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS Deep Blue Level 3 Monthly Climatology Product contains monthly global climatology gridded (1 x 1 deg) data derived from SeaWiFS Deep Blue Level 3 monthly gridded data. \n\n The primary data parameters are aerosol optical thickness.", "links": [ { diff --git a/datasets/SWFL_0.json b/datasets/SWFL_0.json index cde13b6228..ff180dbad0 100644 --- a/datasets/SWFL_0.json +++ b/datasets/SWFL_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWFL_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near southwest Florida in 2010 and 2011.", "links": [ { diff --git a/datasets/SWOT_ATTD_RECONST_2.0_2.0.json b/datasets/SWOT_ATTD_RECONST_2.0_2.0.json index b09924e8bf..274f657be6 100644 --- a/datasets/SWOT_ATTD_RECONST_2.0_2.0.json +++ b/datasets/SWOT_ATTD_RECONST_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_ATTD_RECONST_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite attitude reconstructed from combination of onboard gyro and star tracker data. Daily 26-hour files centered at 12:00:00 (TAI) provide quaternions to represent the rotation between the spacecraft body-fixed KaRIn Metering Structure Reference Frame and the inertial Geocentric Celestial Reference Frame. Available in netCDF-4 file format with latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_L1B_HR_SLC_1.1_1.1.json b/datasets/SWOT_L1B_HR_SLC_1.1_1.1.json index aca337356b..4fed1f8f8d 100644 --- a/datasets/SWOT_L1B_HR_SLC_1.1_1.1.json +++ b/datasets/SWOT_L1B_HR_SLC_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1B_HR_SLC_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L1B_HR_SLC_2.0_2.0.json b/datasets/SWOT_L1B_HR_SLC_2.0_2.0.json index b7e2a5764d..f803dc950c 100644 --- a/datasets/SWOT_L1B_HR_SLC_2.0_2.0.json +++ b/datasets/SWOT_L1B_HR_SLC_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1B_HR_SLC_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.
\r\nPlease note that this collection contains SWOT Version C science data products.", "links": [ { diff --git a/datasets/SWOT_L1B_LR_INTF_1.0_1.0.json b/datasets/SWOT_L1B_LR_INTF_1.0_1.0.json index 897c02a06b..5b0cbfd8fe 100644 --- a/datasets/SWOT_L1B_LR_INTF_1.0_1.0.json +++ b/datasets/SWOT_L1B_LR_INTF_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1B_LR_INTF_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Interferograms for each of the 9 Doppler beams formed and spatially averaged (low rate) by the On Board Processor, corrected on the ground for phase biases (inherent to the processing applied on board). The geometry of the measurements is also reported for use in subsequent processing. Gridded; full swath for each half orbit. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L1B_LR_INTF_1.1_1.1.json b/datasets/SWOT_L1B_LR_INTF_1.1_1.1.json index 0c39d810bb..348719f32c 100644 --- a/datasets/SWOT_L1B_LR_INTF_1.1_1.1.json +++ b/datasets/SWOT_L1B_LR_INTF_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1B_LR_INTF_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Interferograms for each of the 9 Doppler beams formed and spatially averaged (low rate) by the On Board Processor, corrected on the ground for phase biases (inherent to the processing applied on board). The geometry of the measurements is also reported for use in subsequent processing. Gridded; full swath for each half orbit. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L1B_LR_INTF_2.0_2.0.json b/datasets/SWOT_L1B_LR_INTF_2.0_2.0.json index 0329cd0b86..8698d34ea5 100644 --- a/datasets/SWOT_L1B_LR_INTF_2.0_2.0.json +++ b/datasets/SWOT_L1B_LR_INTF_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1B_LR_INTF_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Interferograms for each of the 9 Doppler beams formed and spatially averaged (low rate) by the On Board Processor, corrected on the ground for phase biases (inherent to the processing applied on board). The geometry of the measurements is also reported for use in subsequent processing. Gridded; full swath for each half orbit. Available in netCDF-4 file format.
\r\nPlease note that this collection contains SWOT Version C science data products.", "links": [ { diff --git a/datasets/SWOT_L1_DORIS_RINEX_1.0_1.0.json b/datasets/SWOT_L1_DORIS_RINEX_1.0_1.0.json index 5962293b3f..ca173fa8cd 100644 --- a/datasets/SWOT_L1_DORIS_RINEX_1.0_1.0.json +++ b/datasets/SWOT_L1_DORIS_RINEX_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1_DORIS_RINEX_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tracking data measurements from the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) payload receiver onboard SWOT. The tracking data are generated using signals from DORIS ground beacons and are used to perform precise orbit determination of the SWOT spacecraft. They are also used to compute the precise orbit ephemeris\r\n(POE), and the medium-accuracy orbit ephemeris (MOE) used for SWOT data processing. Distributed as one file per day in RINEX file format, available with latency of < 2 days.", "links": [ { diff --git a/datasets/SWOT_L1_GPSP_RINEX_1.0_1.0.json b/datasets/SWOT_L1_GPSP_RINEX_1.0_1.0.json index f70dc5fab0..ac59a9518a 100644 --- a/datasets/SWOT_L1_GPSP_RINEX_1.0_1.0.json +++ b/datasets/SWOT_L1_GPSP_RINEX_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L1_GPSP_RINEX_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS tracking data measurements from the GPS Payload (GPSP) receiver onboard SWOT. The tracking data are generated by the GPSP using signals from the GPS constellation of satellites and are used to perform precise orbit determination of the SWOT spacecraft. They are also used to compute the precise orbit ephemeris (POE), and the medium-accuracy orbit ephemeris (MOE) used for SWOT data processing. Distributed as one RINEX file per data downlink regardless of temporal coverage, available with latency of < 2 days.", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeAvg_2.0_2.0.json b/datasets/SWOT_L2_HR_LakeAvg_2.0_2.0.json index 8b21d8c537..932c15cd52 100644 --- a/datasets/SWOT_L2_HR_LakeAvg_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_LakeAvg_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeAvg_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cycle average and aggregation of lake pass data within predefined hydrological basins. Basin for each cycle. Available in Shapefile file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeSP_1.1_1.1.json b/datasets/SWOT_L2_HR_LakeSP_1.1_1.1.json index 06782e852f..1b34e07a7e 100644 --- a/datasets/SWOT_L2_HR_LakeSP_1.1_1.1.json +++ b/datasets/SWOT_L2_HR_LakeSP_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeSP_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shapefiles of lakes identified in prior lake database and detected features not in the prior river or lake databases. Lake attributes include water surface elevation, area, derived storage change. Full swath covering individual continents for each half orbit. Available in Shapefile file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeSP_2.0_2.0.json b/datasets/SWOT_L2_HR_LakeSP_2.0_2.0.json index 16d204ca3b..8230ebe7eb 100644 --- a/datasets/SWOT_L2_HR_LakeSP_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_LakeSP_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeSP_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Lake Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a PLD-oriented feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis dataset is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeSP_obs_2.0_2.0.json b/datasets/SWOT_L2_HR_LakeSP_obs_2.0_2.0.json index 69fd26d20e..1687738db2 100644 --- a/datasets/SWOT_L2_HR_LakeSP_obs_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_LakeSP_obs_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeSP_obs_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Lake Single-Pass Vector Obs Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains observation-oriented feature datasets of lakes identified in the prior lake database (PLD).", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeSP_prior_2.0_2.0.json b/datasets/SWOT_L2_HR_LakeSP_prior_2.0_2.0.json index a131399654..1d02daf11d 100644 --- a/datasets/SWOT_L2_HR_LakeSP_prior_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_LakeSP_prior_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeSP_prior_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Lake Single-Pass Vector Prior Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of lakes identified in the PLD.", "links": [ { diff --git a/datasets/SWOT_L2_HR_LakeSP_unassigned_2.0_2.0.json b/datasets/SWOT_L2_HR_LakeSP_unassigned_2.0_2.0.json index 98ade33aef..a3282ac143 100644 --- a/datasets/SWOT_L2_HR_LakeSP_unassigned_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_LakeSP_unassigned_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_LakeSP_unassigned_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Lake Single-Pass Vector Unassigned Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of unassigned water features that were not identified in the PLD or PRD.", "links": [ { diff --git a/datasets/SWOT_L2_HR_PIXCVec_1.1_1.1.json b/datasets/SWOT_L2_HR_PIXCVec_1.1_1.1.json index da7dfd9a27..4e2c9670c3 100644 --- a/datasets/SWOT_L2_HR_PIXCVec_1.1_1.1.json +++ b/datasets/SWOT_L2_HR_PIXCVec_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_PIXCVec_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Auxiliary information for pixel cloud product indicating to which water bodies the pixels are assigned in river and lake products. Also includes height-constrained pixel geolocation after reach- or lake-scale averaging. Point cloud over tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_PIXCVec_2.0_2.0.json b/datasets/SWOT_L2_HR_PIXCVec_2.0_2.0.json index 337497c909..2c0517eefe 100644 --- a/datasets/SWOT_L2_HR_PIXCVec_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_PIXCVec_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_PIXCVec_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Auxiliary information for pixel cloud product indicating to which water bodies the pixels are assigned in river and lake products. Also includes height-constrained pixel geolocation after reach- or lake-scale averaging. Point cloud over tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.
\r\nPlease note that this collection contains SWOT Version C science data products.", "links": [ { diff --git a/datasets/SWOT_L2_HR_PIXC_1.1_1.1.json b/datasets/SWOT_L2_HR_PIXC_1.1_1.1.json index 4313115184..25657631f3 100644 --- a/datasets/SWOT_L2_HR_PIXC_1.1_1.1.json +++ b/datasets/SWOT_L2_HR_PIXC_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_PIXC_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Point cloud of water mask pixels (\u201cpixel cloud\u201d) with geolocated heights, backscatter, geophysical fields, and flags. Point cloud over tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_PIXC_2.0_2.0.json b/datasets/SWOT_L2_HR_PIXC_2.0_2.0.json index ea6d5827e0..55bedf0acc 100644 --- a/datasets/SWOT_L2_HR_PIXC_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_PIXC_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_PIXC_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Point cloud of water mask pixels (\u201cpixel cloud\u201d) with geolocated heights, backscatter, geophysical fields, and flags. Point cloud over tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.
\r\nPlease note that this collection contains SWOT Version C science data products.", "links": [ { diff --git a/datasets/SWOT_L2_HR_Raster_1.1_1.1.json b/datasets/SWOT_L2_HR_Raster_1.1_1.1.json index 14da8ebf9b..151c9344f6 100644 --- a/datasets/SWOT_L2_HR_Raster_1.1_1.1.json +++ b/datasets/SWOT_L2_HR_Raster_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_Raster_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rasterized water surface elevation and inundation extent in geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Provides rasters with water surface elevation, area, water fraction, backscatter, geophysical information. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. Gridded scene (approx 128x128 km2, georeferenced); full swath. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_Raster_100m_2.0_2.0.json b/datasets/SWOT_L2_HR_Raster_100m_2.0_2.0.json index 3970cbb18d..bfffdc423c 100644 --- a/datasets/SWOT_L2_HR_Raster_100m_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_Raster_100m_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_Raster_100m_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Water Mask Raster Image 100m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.\\r\\n
\r\nWater surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 100 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_HR_Raster_2.0_2.0.json b/datasets/SWOT_L2_HR_Raster_2.0_2.0.json index b11fbcf680..913d2445af 100644 --- a/datasets/SWOT_L2_HR_Raster_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_Raster_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_Raster_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis dataset is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_HR_Raster_250m_2.0_2.0.json b/datasets/SWOT_L2_HR_Raster_250m_2.0_2.0.json index 5b13ec439a..1b89028b37 100644 --- a/datasets/SWOT_L2_HR_Raster_250m_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_Raster_250m_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_Raster_250m_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Water Mask Raster Image 250m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.\\r\\n
\r\nWater surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 250 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_HR_RiverAvg_2.0_2.0.json b/datasets/SWOT_L2_HR_RiverAvg_2.0_2.0.json index bbca05673b..18db8b6c25 100644 --- a/datasets/SWOT_L2_HR_RiverAvg_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_RiverAvg_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_RiverAvg_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cycle average and aggregation of river reach pass data within predefined hydrological basins. Basin for each cycle. Available in Shapefile file format.
\r\nPlease note that this collection contains SWOT Version C science data products.", "links": [ { diff --git a/datasets/SWOT_L2_HR_RiverSP_1.1_1.1.json b/datasets/SWOT_L2_HR_RiverSP_1.1_1.1.json index cbdbb8271f..9582686305 100644 --- a/datasets/SWOT_L2_HR_RiverSP_1.1_1.1.json +++ b/datasets/SWOT_L2_HR_RiverSP_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_RiverSP_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Shapefiles of river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in prior river database. Reach attributes include water surface elevation, slope, width, derived discharge. Full swath covering individual continents for each half orbit. Available in Shapefile file format.", "links": [ { diff --git a/datasets/SWOT_L2_HR_RiverSP_2.0_2.0.json b/datasets/SWOT_L2_HR_RiverSP_2.0_2.0.json index fd06a395c3..08f3f1dbc6 100644 --- a/datasets/SWOT_L2_HR_RiverSP_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_RiverSP_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_RiverSP_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 River Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis dataset is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_reach_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json b/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json index b1c6fc9fa0..38f4ee25e1 100644 --- a/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_RiverSP_node_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 River Single-Pass Vector Node Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0 It contains only river nodes.\r\n", "links": [ { diff --git a/datasets/SWOT_L2_HR_RiverSP_reach_2.0_2.0.json b/datasets/SWOT_L2_HR_RiverSP_reach_2.0_2.0.json index 14aac0487f..f8f19c4613 100644 --- a/datasets/SWOT_L2_HR_RiverSP_reach_2.0_2.0.json +++ b/datasets/SWOT_L2_HR_RiverSP_reach_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_HR_RiverSP_reach_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 River Single-Pass Vector Reach Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nWater surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0 It contains only river reaches.\r\n", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_1.0_1.0.json b/datasets/SWOT_L2_LR_SSH_1.0_1.0.json index 16c731a39c..1397820353 100644 --- a/datasets/SWOT_L2_LR_SSH_1.0_1.0.json +++ b/datasets/SWOT_L2_LR_SSH_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface height data product with data from the KaRIn swath spanning 60 km on both sides of nadir with a nadir gap. Product provides sea surface height, sea surface height anomaly, wind speed, significant waveheight, on a geographically fixed, swath-aligned 2x2 km2 grid, as well as sea surface height on a 250x250 m2 native grid. Gridded; full swath for each half orbit. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_1.1_1.1.json b/datasets/SWOT_L2_LR_SSH_1.1_1.1.json index e4d4cde5ed..b4e6ca623b 100644 --- a/datasets/SWOT_L2_LR_SSH_1.1_1.1.json +++ b/datasets/SWOT_L2_LR_SSH_1.1_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_1.1_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface height data product with data from the KaRIn swath spanning 60 km on both sides of nadir with a nadir gap. Product provides sea surface height, sea surface height anomaly, wind speed, significant waveheight, on a geographically fixed, swath-aligned 2x2 km2 grid, as well as sea surface height on a 250x250 m2 native grid. Gridded; full swath for each half orbit. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_2.0_2.0.json b/datasets/SWOT_L2_LR_SSH_2.0_2.0.json index 58899d024c..48e6a3cb6c 100644 --- a/datasets/SWOT_L2_LR_SSH_2.0_2.0.json +++ b/datasets/SWOT_L2_LR_SSH_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nThe L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis dataset is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Basic_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_WindWave_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Expert_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Unsmoothed_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_BASIC_2.0_2.0.json b/datasets/SWOT_L2_LR_SSH_BASIC_2.0_2.0.json index 5190d1f2d1..68f947f760 100644 --- a/datasets/SWOT_L2_LR_SSH_BASIC_2.0_2.0.json +++ b/datasets/SWOT_L2_LR_SSH_BASIC_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_BASIC_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Basic Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nThe L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Basic\" file from each L2 SSH product, which contains a limited set of variables and is aimed at the general user.", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_EXPERT_2.0_2.0.json b/datasets/SWOT_L2_LR_SSH_EXPERT_2.0_2.0.json index 4d5f084e78..c1848426d0 100644 --- a/datasets/SWOT_L2_LR_SSH_EXPERT_2.0_2.0.json +++ b/datasets/SWOT_L2_LR_SSH_EXPERT_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_EXPERT_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Expert Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nThe L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Expert\" file from each L2 SSH product, which contain all related variables and is intended for expert users.", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0.json b/datasets/SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0.json index dcb42f80c8..d3974a5e77 100644 --- a/datasets/SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0.json +++ b/datasets/SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Unsmoothed Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nThe L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Unsmoothed\" file from each L2 SSH product, which includes all related variables on the finer resolution \"native\" grid with minimal smoothing applied.", "links": [ { diff --git a/datasets/SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0.json b/datasets/SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0.json index 1863815abc..42de2a5ed2 100644 --- a/datasets/SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0.json +++ b/datasets/SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Windwave Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
\r\nThe L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
\r\nPlease note that this collection contains SWOT Version C science data products.
\r\nThis collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Windwave\" file from each L2 SSH product, which includes significant wave height (SWH), normalized radar cross section (NRCS or backscatter cross section or sigma0), wind speed derived from sigma0 and SWH, model information on wind and waves, and quality flags.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json index 2b4e387de4..e67308b958 100644 --- a/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_GDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.\r\n
This collection is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_SSHA_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_GDR_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_SGDR_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_NALT_GDR_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_GDR_GDR_2.0_2.0.json index 69e6626236..2c078fca2b 100644 --- a/datasets/SWOT_L2_NALT_GDR_GDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_GDR_GDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_GDR_GDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_GDR_SGDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_GDR_SGDR_2.0_2.0.json index 2673d4ef23..ca7e2890a5 100644 --- a/datasets/SWOT_L2_NALT_GDR_SGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_GDR_SGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_GDR_SGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_GDR_SSHA_2.0_2.0.json b/datasets/SWOT_L2_NALT_GDR_SSHA_2.0_2.0.json index 5f5e0e64fe..707a8d5f62 100644 --- a/datasets/SWOT_L2_NALT_GDR_SSHA_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_GDR_SSHA_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_GDR_SSHA_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_IGDR_1.0_1.0.json index faf50b28bc..9982f053fa 100644 --- a/datasets/SWOT_L2_NALT_IGDR_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Interim Geophysical Data Record (IGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe IGDR dataset consists of discrete measurements along the nadir track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using the Medium-accuracy (preliminary) Orbit Ephemeris (MOE) and preliminary values for certain auxiliary data. The IGDR data are distributed as one file per half orbit in netCDF-4 file format with a nominal latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json index b075c33b38..095ced2ffc 100644 --- a/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Interim Geophysical Data Record (IGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe IGDR dataset consists of discrete measurements along the nadir track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using the Medium-accuracy (preliminary) Orbit Ephemeris (MOE) and preliminary values for certain auxiliary data. The IGDR data are distributed as one file per half orbit in netCDF-4 file format with a nominal latency of < 1.5 days.\r\n
This collection is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_SSHA_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_GDR_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_SGDR_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_GDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_IGDR_GDR_1.0_1.0.json index a4d8de66f4..3bee0eb8ac 100644 --- a/datasets/SWOT_L2_NALT_IGDR_GDR_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_GDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_GDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json index 7814d0e8fd..3a137a1aba 100644 --- a/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_GDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json index 38565621d4..c13343d77b 100644 --- a/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_SGDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_SGDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_SGDR_2.0_2.0.json index f44caecbf8..970184fcc0 100644 --- a/datasets/SWOT_L2_NALT_IGDR_SGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_SGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_SGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json b/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json index 91b6cf565f..0d8f5a7ccd 100644 --- a/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_SSHA_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_IGDR_SSHA_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_SSHA_2.0_2.0.json index e7d7f1ad5a..aafb0e986b 100644 --- a/datasets/SWOT_L2_NALT_IGDR_SSHA_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_IGDR_SSHA_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_IGDR_SSHA_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR, using preliminary values for some auxiliary data. Uses Medium-accuracy (preliminary) Orbit Ephemeris (MOE). Available with latency of < 1.5 days. Discrete measurements at nadir for each half orbit, along the ground track. Available in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json index c64089329e..1ab0b18e9c 100644 --- a/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Operational Geophysical Data Record (OGDR) with Waveforms Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe OGDR dataset consists of discrete measurements along the nadir track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using the onboard DORIS orbit ephemeris, with predicted values for certain auxiliary data and no GIM ionosphere model values. The OGDR data are distributed as one file per data downlink in netCDF-4 file format with a nominal latency of < 7 hours.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_OGDR_2.0_2.0.json index fa1fa7387f..27014d46bc 100644 --- a/datasets/SWOT_L2_NALT_OGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Nadir Altimeter Operational Geophysical Data Record (OGDR) with Waveforms Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe OGDR dataset consists of discrete measurements along the nadir track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using the onboard DORIS orbit ephemeris, with predicted values for certain auxiliary data and no GIM ionosphere model values. The OGDR data are distributed as one file per data downlink in netCDF-4 file format with a nominal latency of < 7 hours.\r\n
This collection is the parent collection to the following sub-collections:
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_SSHA_2.0
\r\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_GDR_2.0
", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_GDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_OGDR_GDR_1.0_1.0.json index ac1d822eaf..e92c993ff6 100644 --- a/datasets/SWOT_L2_NALT_OGDR_GDR_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_GDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_GDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR using predicted values for some auxiliary data, and does not have GIM ionosphere model values. Uses the onboard DORIS orbit ephemeris. Available with latency of < 7 hours. Discrete measurements at nadir for each data downlink, along the ground track. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_OGDR_GDR_2.0_2.0.json index 31dcc64382..302206136b 100644 --- a/datasets/SWOT_L2_NALT_OGDR_GDR_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_GDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_GDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR using predicted values for some auxiliary data, and does not have GIM ionosphere model values. Uses the onboard DORIS orbit ephemeris. Available with latency of < 7 hours. Discrete measurements at nadir for each data downlink, along the ground track. Available in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_SSHA_1.0_1.0.json b/datasets/SWOT_L2_NALT_OGDR_SSHA_1.0_1.0.json index cef46f96b6..6a640895d0 100644 --- a/datasets/SWOT_L2_NALT_OGDR_SSHA_1.0_1.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_SSHA_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_SSHA_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR using predicted values for some auxiliary data, and does not have GIM ionosphere model values. Uses the onboard DORIS orbit ephemeris. Available with latency of < 7 hours. Discrete measurements at nadir for each data downlink, along the ground track. Available in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json b/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json index 20df4f2eed..69c934d935 100644 --- a/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json +++ b/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_NALT_OGDR_SSHA_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Same as L2_NALT_GDR using predicted values for some auxiliary data, and does not have GIM ionosphere model values. Uses the onboard DORIS orbit ephemeris. Available with latency of < 7 hours. Discrete measurements at nadir for each data downlink, along the ground track. Available in netCDF-4 file format.\r\n
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_2.0 ", "links": [ { diff --git a/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json b/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json index 630fd418b7..8b6ff12f3f 100644 --- a/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json +++ b/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_RAD_GDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Radiometer Brightness Temperatures and Troposphere Geophysical Data Record (GDR) dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides atmospheric water vapor and liquid water content from the Advanced Microwave Radiometer (AMR), a Jason-class radiometer that measures sea surface brightness temperatures at three microwave frequencies (18.7, 23.8 and 34 GHz). Brightness temperatures are processed to estimate the wet troposphere content, atmospheric attenuation to backscatter, cloud liquid water, water vapor content, and wind speed coincident with each range measurement from the nadir altimeter and applied to correct for altimeter range delays caused by atmospheric effects. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022 and aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. This radiometer dataset consists of discrete measurements along two tracks located approximately 30-km to the left and right of the satellite nadir. The data were processed using the Precise Orbit Ephemeris (POE) and analyzed calibrations. The data are available with latency of < 90 days and distributed in netCDF-4 file format.", "links": [ { diff --git a/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json b/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json index 3c00025525..b367b60d27 100644 --- a/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json +++ b/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_RAD_IGDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Radiometer Brightness Temperatures and Troposphere Interim Geophysical Data Record (IGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides atmospheric water vapor and liquid water content from the Advanced Microwave Radiometer (AMR), a Jason-class radiometer that measures sea surface brightness temperatures at three microwave frequencies (18.7, 23.8 and 34 GHz). Brightness temperatures are processed to estimate the wet troposphere content, atmospheric attenuation to backscatter, cloud liquid water, water vapor content, and wind speed coincident with each range measurement from the nadir altimeter and applied to correct for altimeter range delays caused by atmospheric effects. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe interim radiometer dataset consists of discrete measurements along two tracks located approximately 30-km to the left and right of the satellite nadir. The data were processed using the Medium-accuracy (preliminary) Orbit Ephemeris (MOE) with preliminary calibrations applied. They are distributed as one file per half-orbit in netCDF4 file format with a nominal latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_L2_RAD_IGDR_2.0_2.0.json b/datasets/SWOT_L2_RAD_IGDR_2.0_2.0.json index b6690d32ec..b61af21870 100644 --- a/datasets/SWOT_L2_RAD_IGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_RAD_IGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_RAD_IGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Radiometer Brightness Temperatures and Troposphere Interim Geophysical Data Record (IGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides atmospheric water vapor and liquid water content from the Advanced Microwave Radiometer (AMR), a Jason-class radiometer that measures sea surface brightness temperatures at three microwave frequencies (18.7, 23.8 and 34 GHz). Brightness temperatures are processed to estimate the wet troposphere content, atmospheric attenuation to backscatter, cloud liquid water, water vapor content, and wind speed coincident with each range measurement from the nadir altimeter and applied to correct for altimeter range delays caused by atmospheric effects. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe interim radiometer dataset consists of discrete measurements along two tracks located approximately 30-km to the left and right of the satellite nadir. The data were processed using the Medium-accuracy (preliminary) Orbit Ephemeris (MOE) with preliminary calibrations applied. They are distributed as one file per half-orbit in netCDF4 file format with a nominal latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_L2_RAD_OGDR_1.0_1.0.json b/datasets/SWOT_L2_RAD_OGDR_1.0_1.0.json index 90bdcb2bec..46eb024b61 100644 --- a/datasets/SWOT_L2_RAD_OGDR_1.0_1.0.json +++ b/datasets/SWOT_L2_RAD_OGDR_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_RAD_OGDR_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Radiometer Brightness Temperatures and Troposphere Operational Geophysical Data Record (OGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides atmospheric water vapor and liquid water content from the Advanced Microwave Radiometer (AMR), a Jason-class radiometer that measures sea surface brightness temperatures at three microwave frequencies (18.7, 23.8 and 34 GHz). Brightness temperatures are processed to estimate the wet troposphere content, atmospheric attenuation to backscatter, cloud liquid water, water vapor content, and wind speed coincident with each range measurement from the nadir altimeter and applied to correct for altimeter range delays caused by atmospheric effects. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022 and aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe operational radiometer dataset consists of discrete measurements along two tracks located approximately 30-km to the left and right of the satellite nadir. They were processed using the onboard DORIS orbit ephemeris and preliminary calibrations. They are distributed as one file per data downlink in netCDF-4 file format with a nominal latency of < 7 hours.", "links": [ { diff --git a/datasets/SWOT_L2_RAD_OGDR_2.0_2.0.json b/datasets/SWOT_L2_RAD_OGDR_2.0_2.0.json index b4c16771c0..33f7eee699 100644 --- a/datasets/SWOT_L2_RAD_OGDR_2.0_2.0.json +++ b/datasets/SWOT_L2_RAD_OGDR_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L2_RAD_OGDR_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Level 2 Radiometer Brightness Temperatures and Troposphere Operational Geophysical Data Record (OGDR) Version 1.0 dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides atmospheric water vapor and liquid water content from the Advanced Microwave Radiometer (AMR), a Jason-class radiometer that measures sea surface brightness temperatures at three microwave frequencies (18.7, 23.8 and 34 GHz). Brightness temperatures are processed to estimate the wet troposphere content, atmospheric attenuation to backscatter, cloud liquid water, water vapor content, and wind speed coincident with each range measurement from the nadir altimeter and applied to correct for altimeter range delays caused by atmospheric effects. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022 and aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally.\r\nThe operational radiometer dataset consists of discrete measurements along two tracks located approximately 30-km to the left and right of the satellite nadir. They were processed using the onboard DORIS orbit ephemeris and preliminary calibrations. They are distributed as one file per data downlink in netCDF-4 file format with a nominal latency of < 7 hours.", "links": [ { diff --git a/datasets/SWOT_L3_LR_SSH_1.0_1.0.json b/datasets/SWOT_L3_LR_SSH_1.0_1.0.json index 0fb2ae4ff0..bedc128755 100644 --- a/datasets/SWOT_L3_LR_SSH_1.0_1.0.json +++ b/datasets/SWOT_L3_LR_SSH_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L3_LR_SSH_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This SWOT_L3_LR_SSH product provides ocean topography measurements obtained from the SWOT KaRIn and Nadir altimeter instruments, merged into a single variable. The dataset includes measurements from KaRIn swaths on both sides of the image, while the measurements from the Nadir altimeter are located in the central columns. In the areas between the Nadir track and the two KaRIn swaths, as well as on the outer edges of each swath (restricted to cross-track distances ranging from 10 to 60 km), default values are expected. This is a cross-calibrated product from multiple missions that contains only the ocean topography content necessary for thematic research (e.g., oceanography, geodesy) and related applications. This product is designed to be simple and ready-to-use, and can be combined with other altimetry missions. The SWOT_L3_LR_SSH product is a research-orientated extension of the L2_LR_SSH product, distributed by the SWOT project (NASA/JPL and CNES). This L3 product is managed by the SWOT Science Team project DESMOS.", "links": [ { diff --git a/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_1.json b/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_1.json index acd957478d..222ad26f0d 100644 --- a/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_1.json +++ b/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_L4_DAWG_SOS_DISCHARGE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SWOT Sword of Science River Discharge Products dataset from the Surface Water and Ocean Topography (SWOT) mission and produced by the Discharge Algorithm Working Group (DAWG), provides estimates of river discharge derived from the SWOT Level 2 River Single-Pass Vector Data Product, and includes both unconstrained and gauge constrained estimates that leverage in-situ measurements. The SWOT mission is implemented jointly by NASA and Centre National D'Etudes Spatiales (CNES) to provide valuable data and information about the world's oceans and its terrestrial surface water such as lakes, rivers, and wetlands.\r\nSword of Science data products are generated from the open-source SWOT Confluence program and contain river discharge parameter estimates as well as discharge time series for both river reaches and river nodes, following the SWOT River Database (SWORD) structure. Granules from both constrained and unconstrained branches are composed of prior information (e.g., mean annual flow predicted by global hydrological models) and the resulting discharge estimates. \r\nPriors and results files for both constrained and unconstrained branches are available in netCDF format. Users are encouraged to reference the SWOT Confluence documentation and notebook tutorials for full documentation of the data structure and variables available.\r\nDevelopment of the SWOT Confluence program as well as the Sword of Science data products was funded by NASA\u2019s Advanced Information Systems Technology (AIST) program.", "links": [ { diff --git a/datasets/SWOT_MOE_1.0_1.0.json b/datasets/SWOT_MOE_1.0_1.0.json index dbe5907287..9d527618ab 100644 --- a/datasets/SWOT_MOE_1.0_1.0.json +++ b/datasets/SWOT_MOE_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_MOE_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Medium-accuracy Orbit Ephemeris (MOE) providing position and velocity vectors of satellite center of mass used in forward stream processing. MOE products are organized into daily files, spanning 26 hours and centered at 12:00:00 (TAI) of each day (i.e., from day D-1 23:00 to day D+1 01:00 TAI time). Available in netCDF-4 file format with latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_POE_2.0_2.0.json b/datasets/SWOT_POE_2.0_2.0.json index ada259876f..73dc43f16b 100644 --- a/datasets/SWOT_POE_2.0_2.0.json +++ b/datasets/SWOT_POE_2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_POE_2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise Orbit Ephemeris (POE) providing position and velocity vectors of satellite center of mass used in the first SWOT reprocessing. POE products are organized into daily files, spanning 26 hours and centered at 12:00:00 (TAI) of each day (i.e., from day D-1 23:00 to day D+1 01:00 TAI time). Available in netCDF-4 file format with latency of < 35 days.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_BPR_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_BPR_V1_1.0.json index 3eada61c22..b33ee5f758 100644 --- a/datasets/SWOT_PRELAUNCH_L2_BPR_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_BPR_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_BPR_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the bottom pressure measurements collected during the 2019-2020 SWOT prelaunch field campaign conducted around the SWOT crossover location in the California Currents, 300km west of Monterey, California, USA. The Paroscienti\ufb01c Digiquartz pressure sensor was used. The data are recorded on a 15-second interval.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_GLIDER_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_GLIDER_V1_1.0.json index 1386d83015..fe45d8bc71 100644 --- a/datasets/SWOT_PRELAUNCH_L2_GLIDER_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_GLIDER_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_GLIDER_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Conductivity, Temperature, and Depth measurements carried by a Slocum glider. The measurements were collected during the 2019-2020 SWOT prelaunch field campaign conducted near the SWOT crossover location in the California Currents, 300km west of Monterey, California, USA. It has 883 CTD profiles with glider diving depths varying between 500 m and 1000 m. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_GPS_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_GPS_V1_1.0.json index a262720351..91f4c2e311 100644 --- a/datasets/SWOT_PRELAUNCH_L2_GPS_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_GPS_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_GPS_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the 1Hz time series of the sea surface height measured by a surface buoy equipped with a Global Position System (GPS). The GPS-mooring was deployed by the SWOT prelaunch field campaign conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The GPS measurements represent the total sea surface height including the Inverted barometer component. The same mooring also carries fixed-depth CTD sensors https://doi.org/10.5067/SWTPR-CTD11. They were used together with atmospheric pressure and bottom pressure measurements to close the sea surface equation (Wang et al., 2022). The campaign also deployed another two CTD moorings, a slocum glider, and a Pressure Inverted Echo Sounder (PIES). Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_PIES_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_PIES_V1_1.0.json index 173aa17ef7..b1a19aebc3 100644 --- a/datasets/SWOT_PRELAUNCH_L2_PIES_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_PIES_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_PIES_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the in-situ measurements from a Pressure-sensing Inverted Echo Sounder (PIES) deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The campaign also deployed three CTD moorings, a slocum glider, and another bottom pressure recorder. The PIES measurements include bottom pressure and the round-trip travel time from the IES, which can be used to derive equivalent steric height through regression. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0.json index 1ccc43f85e..cfd1c0cdaa 100644 --- a/datasets/SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the conductivity, temperature and depth (CTD) profiles from a Prawler profiler mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_SIOCTD_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_SIOCTD_V1_1.0.json index 6753378663..c1c96a5935 100644 --- a/datasets/SWOT_PRELAUNCH_L2_SIOCTD_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_SIOCTD_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_SIOCTD_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the conductivity, temperature and depth (CTD) measurements from the fixed-depth CTD sensors mounted on a full-depth mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. These fixed-depth CTDs are below 500 m while the upper part of the mooring has a WireWalker (WW) profiler. The CTD data from WW is available here https://doi.org/10.5067/SWTPR-WW001. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0.json index a8d3f24ed6..17bf7fd206 100644 --- a/datasets/SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the conductivity, temperature and depth (CTD) measurements from the fixed-depth CTD sensors mounted on a full-depth mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. These fixed-depth CTDs cover the full depth from the ocean surface to the bottom. The surface buoy is equipped with a Global Positioning System (GPS) https://doi.org/10.5067/SWTPR-GPS01. There is also an adjacent bottom pressure recorder https://doi.org/10.5067/SWTPR-BPR01. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_PRELAUNCH_L2_WW_V1_1.0.json b/datasets/SWOT_PRELAUNCH_L2_WW_V1_1.0.json index eae3f8d9f3..a6f55d690b 100644 --- a/datasets/SWOT_PRELAUNCH_L2_WW_V1_1.0.json +++ b/datasets/SWOT_PRELAUNCH_L2_WW_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_PRELAUNCH_L2_WW_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the conductivity, temperature and depth (CTD) measurements from the CTD sensors on a WireWalker profiler on a full-depth mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The WW samples the upper 500 m of the water column, while the deep ocean below 500 m are measured by fixed-depth CTDs https://doi.org/10.5067/SWTPR-CTD01. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder. Details can be found in the user guide and the journal reference given in the documentation section.", "links": [ { diff --git a/datasets/SWOT_SAT_COM_1.0_1.0.json b/datasets/SWOT_SAT_COM_1.0_1.0.json index 06fdcef186..18f1a14a75 100644 --- a/datasets/SWOT_SAT_COM_1.0_1.0.json +++ b/datasets/SWOT_SAT_COM_1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SAT_COM_1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite center of mass position with respect to its reference point. The SAT_COM product provides the X/Y/Z coordinates of satellite center of mass in the KaRIn metering and structure reference frame (KMSF) with associated information of the origin of the variation. This is a historical product and the most recently available file provides a complete history since launch. Available in netCDF-4 file format with latency of < 1.5 days.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1_1.json b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1_1.json index 7b624fdbfe..8f4e1f7b86 100644 --- a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) SSH data from KaRIn. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1_1.json b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1_1.json index 5100d0f47a..d26211e265 100644 --- a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_SCIENCE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) SSH data from KaRIn. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json index ca3070560f..86416e5c91 100644 --- a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height data product that resembles data which will be collected by KaRIn. Swaths span 60 km on both sides of nadir with a nadir gap. Product provides sea surface height, sea surface height anomaly, wind speed, significant waveheight, on a geographically fixed, swath-aligned 2x2 km2 grid, as well as sea surface height on a 250x250 m2 native grid. SSH data produced by GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1_1.json b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1_1.json index f47f209ded..41abf041bf 100644 --- a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height data product that resembles data which will be collected by KaRIn. Swaths span 60 km on both sides of nadir with a nadir gap. Product provides sea surface height, sea surface height anomaly, wind speed, significant waveheight, on a geographically fixed, swath-aligned 2x2 km2 grid, as well as sea surface height on a 250x250 m2 native grid. SSH data produced by GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1_1.json b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1_1.json index 341f37e5d1..24b9a843b9 100644 --- a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1_1.json b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1_1.json index 658178b677..7e227ff6c7 100644 --- a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1_1.json b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1_1.json index 7f474b3f20..a8668d233d 100644 --- a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS). SSH data from GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1_1.json b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1_1.json index 4ac05fd574..2d60caa668 100644 --- a/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1_1.json +++ b/datasets/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_SCIENCE_V1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS). SSH data from GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0.json b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0.json index 95c574fe2e..e01ae704a9 100644 --- a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0.json +++ b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a simulated lake product to be provided by the Surface Water and Ocean Topography (SWOT) mission with a focus on the North America continent. The product is derived from the high-rate (HR) measurements produced by the SWOT main instrument, a Ka-band Radar Interferometer. These data are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product consists of three shapefiles: 1) an observation-oriented shapefile of lakes identified in the Prior Lake Database (PLD); 2) a PLD-oriented shapefile of lakes identified in the PLD; 3) a shapefile of unassigned features that have not been identified as a lake in the PLD nor as a river in the Prior River Database (PRD). Lake attributes include water surface elevation, area, and uncertainty estimates. The identified lake shapes inherit the SWOT swath width that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0.json b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0.json index 6f669eec46..e40909f0d4 100644 --- a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0.json +++ b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. This product is complementary to the L2_HR_PIXC_V1 product. It provides a less noisy, height-constrained geolocation (latitude, longitude, and height) of the L2_HR_PIXC_V1 pixels. In addition, this product provides an identifier associated with each pixel. The identifier contains the information of the river and/or lake features pulled from the Prior River Database (PRD) or in the Prior Lake Database (PLD). Note that this is a simulated SWOT product and not suited for any scientific exploration.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0.json b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0.json index c1cae90222..23bfeba730 100644 --- a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0.json +++ b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes simulated water surface elevations that resemble the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. The primary product contains the following: 1. Geolocated elevations (latitude, longitude, and height) 2. Classification mask (water/land flags, and water fraction) 3. Surface areas (projected pixel area on the ground) 4. Relevant data needed to compute and aggregate height and area uncertainties. Additional information includes: 1. Meta data (global instrument parameters) 2. Time varying parameters (TVP), which include sensor position, velocity, altitude, and time 3. Noise power estimates 4. Quality flags 5. Interferogram measurements (power and phase) and range and azimuth indices 6. Geophysical and crossover-calibration correction values. These additional fields are provided to improve the utility of the product and to facilitate generation of downstream products. Note that this is a simulated SWOT product and not suited for any scientific exploration.", "links": [ { diff --git a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0.json b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0.json index c491c77fd4..f54caa6854 100644 --- a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0.json +++ b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. This is a derived product through resampling the upstream dataset L2_HR_PIXC_V1 and L2_HR_PIXCVEC_V1 onto a uniform grid over the North America continent. A uniform grid is superimposed onto the pixel cloud from the source products, and all pixel-cloud samples within each grid cell are aggregated to produce a single value per raster cell. The raster data are produced geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Note that this is a simulated SWOT product and not suited for any scientific exploration. ", "links": [ { diff --git a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0.json b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0.json index 29648a61ca..fa7aba11b4 100644 --- a/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0.json +++ b/datasets/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.", "links": [ { diff --git a/datasets/Sahel_Water_Bodies_1269_1.json b/datasets/Sahel_Water_Bodies_1269_1.json index d4142058ca..7b3cae8040 100644 --- a/datasets/Sahel_Water_Bodies_1269_1.json +++ b/datasets/Sahel_Water_Bodies_1269_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sahel_Water_Bodies_1269_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides an estimate of the spatial and temporal extent of surface water at 250-m resolution over nine years (2003-2011) for the African Sahel region (10-20 degrees N) using imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS). Water bodies were identified by a spectral analysis of MODIS vegetation indices with the aim to improve existing regional to global mapping products. This data set can be used to enhance the understanding of Earth system processes, and to support global change studies, agricultural planning, and disease prevention. These data provide a gridded (250-m) estimate of the number of years (during 2003-2011) that a pixel was covered by water. The data are presented in a single netCDF (*.nc) file.", "links": [ { diff --git a/datasets/Salt_Marsh_Biomass_CONUS_2348_1.json b/datasets/Salt_Marsh_Biomass_CONUS_2348_1.json index 5cb76ce05e..910a4eae3d 100644 --- a/datasets/Salt_Marsh_Biomass_CONUS_2348_1.json +++ b/datasets/Salt_Marsh_Biomass_CONUS_2348_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Salt_Marsh_Biomass_CONUS_2348_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.", "links": [ { diff --git a/datasets/San_Diego_Coastal_Project_0.json b/datasets/San_Diego_Coastal_Project_0.json index 1fa7fc4dc3..c2edbcec3e 100644 --- a/datasets/San_Diego_Coastal_Project_0.json +++ b/datasets/San_Diego_Coastal_Project_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "San_Diego_Coastal_Project_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements near the Southern Californias coast made under the San Diego Coastal Project between 2004 and 2006.", "links": [ { diff --git a/datasets/Sargassum_GOM_0.json b/datasets/Sargassum_GOM_0.json index 28c6ca293f..0aecd3f638 100644 --- a/datasets/Sargassum_GOM_0.json +++ b/datasets/Sargassum_GOM_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sargassum_GOM_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made under the Linking habitat to recruitment: evaluating the importance of pelagic Sargassum to fisheries management in the Gulf of Mexico, in the Northern Gulf of Mexico. Collaboration with USF and USM.", "links": [ { diff --git a/datasets/Saskatchewan_Soils_125m_SSA_1346_2.json b/datasets/Saskatchewan_Soils_125m_SSA_1346_2.json index 0e30aa83c0..1442839d8e 100644 --- a/datasets/Saskatchewan_Soils_125m_SSA_1346_2.json +++ b/datasets/Saskatchewan_Soils_125m_SSA_1346_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Saskatchewan_Soils_125m_SSA_1346_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides soil descriptions for forested areas in the BOREAS southern study area (SSA) in central Saskatchewan, Canada provided by Agriculture Canada. The data contain soil code, modifiers, extent, and soil names for the primary, secondary, and tertiary soil units within each polygon.", "links": [ { diff --git a/datasets/Sat_ActiveLayer_Thickness_Maps_1760_1.json b/datasets/Sat_ActiveLayer_Thickness_Maps_1760_1.json index dffd9a0d75..27189ddfc3 100644 --- a/datasets/Sat_ActiveLayer_Thickness_Maps_1760_1.json +++ b/datasets/Sat_ActiveLayer_Thickness_Maps_1760_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sat_ActiveLayer_Thickness_Maps_1760_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided.", "links": [ { diff --git a/datasets/SatelliteDerived_Forest_Mexico_2320_1.json b/datasets/SatelliteDerived_Forest_Mexico_2320_1.json index 76c7984da2..15eabb8cb3 100644 --- a/datasets/SatelliteDerived_Forest_Mexico_2320_1.json +++ b/datasets/SatelliteDerived_Forest_Mexico_2320_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SatelliteDerived_Forest_Mexico_2320_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.", "links": [ { diff --git a/datasets/Scambos_PLR1441432.json b/datasets/Scambos_PLR1441432.json index 4683ced420..10b252aff7 100644 --- a/datasets/Scambos_PLR1441432.json +++ b/datasets/Scambos_PLR1441432.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Scambos_PLR1441432", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group.", "links": [ { diff --git a/datasets/SciSat-1.Ace.FTS.and.Maestro_4.0.json b/datasets/SciSat-1.Ace.FTS.and.Maestro_4.0.json index 12d9270278..cb40f7231f 100644 --- a/datasets/SciSat-1.Ace.FTS.and.Maestro_4.0.json +++ b/datasets/SciSat-1.Ace.FTS.and.Maestro_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SciSat-1.Ace.FTS.and.Maestro_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SCISAT-1 data aim at monitoring and analysing the chemical processes that control the distribution of ozone in the upper troposphere and stratosphere. It provides acquisitions from the 2 instruments MAESTRO and ACE-FTS. \u2022 MAESTRO: Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation. Dual-channel optical spectrometer in the spectral region of 285-1030 nm. The objective is to measure ozone, nitrogen dioxide and aerosol/cloud extinction (solar occultation measurements of atmospheric attenuation during satellite sunrise and sunset with the primary objective of assessing the stratospheric ozone budget). Solar occultation spectra are being used for retrieving vertical profiles of temperature and pressure, aerosols, and trace gases (O3, NO2, H2O, OClO, and BrO) involved in middle atmosphere ozone distribution. The use of two overlapping spectrometers (280 - 550 nm, 500 - 1030 nm) improves the stray-light performance. The spectral resolution is about 1-2 nm. \u2022 ACE-FTS: Fourier Transform Spectrometer The objective is to measure the vertical distribution of atmospheric trace gases, in particular of the regional polar O3 budget, as well as pressure and temperature (derived from CO2 lines). The instrument is an adapted version of the classical sweeping Michelson interferometer, using an optimized optical layout. The ACE-FTS measurements are recorded every 2 s. This corresponds to a measurement spacing of 2-6 km which decreases at lower altitudes due to refraction. The typical altitude spacing changes with the orbital beta angle. For historical reasons, the retrieved results are interpolated onto a 1 km "grid" using a piecewise quadratic method. For ACE-FTS version 1.0, the results were reported only on the interpolated grid (every 1 km from 0.5 to 149.5 km). For versions 2.2, both the "retrieval" grid and the "1 km" grid profiles are available. SCISAT-1 collection provides ACE-FTS and MAESTRO Level 2 Data. As of today, ACE-FTS products are available in version 4.1, while MAESTRO products are available in version 3.13.", "links": [ { diff --git a/datasets/Scotia_Prince_ferry_0.json b/datasets/Scotia_Prince_ferry_0.json index a6a1a5a35f..58196e47b1 100644 --- a/datasets/Scotia_Prince_ferry_0.json +++ b/datasets/Scotia_Prince_ferry_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Scotia_Prince_ferry_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although the ferry that data were collected from no longer operates, longstanding data collection methods continue. The Scotia Prince ferry dataset has been reorganized and added to the GNATS experiment dataset (Gulf of Maine North Atlantic Time Series, 10.5067/SeaBASS/GNATS/DATA001). Please refer to that dataset to find data that were originally listed here.", "links": [ { diff --git a/datasets/Scotts_Fuel_1.json b/datasets/Scotts_Fuel_1.json index 7f5ebe75bb..ee75637baf 100644 --- a/datasets/Scotts_Fuel_1.json +++ b/datasets/Scotts_Fuel_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Scotts_Fuel_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a direct result of the 1989-90 trip as part of ASAC 245, a sample of petrol used by Scott on his ill-fated expedition to the South Pole was obtained. This petrol sample was supplied by the late Garth Varcoe of the New Zealand Antarctic Division following a discussion ensuing from a lecture given whilst on the Icebird when stuck in the ice off Davis. This sample is of intense historical interest and the results of the studies are in the download file. The material in the file reports the studies on the composition of the petrol which was left by the remaining members of Scott's group when they departed their base at Evans Head. The aim of this work was to identify the source of the fuel. A later study will attempt to comment on its suitability as a fuel for use under Antarctic conditions.\n\nThere are five files on the CD.\na)a poster presented at the Australian Organic Geochemistry Conference held in Leura, NSW in February of this year,\nb)a brief description highlighting some salient points of the poster; presented orally,\nc)an abstract of this work included in the conference proceedings,\nd)the conference proceedings and\ne)manuscript of a full paper submitted for publication in the Journal of Organic Geochemistry, including a table of data\n\nGeochemical analyses of the fuel used for the motor driven sledges used by the explorer Robert Falcon Scott for his 1911/1912 quest to the South Pole indicates that it is a straight run gasoline. The presence of bicadinanes, oleanane and other oleanoid angiosperm markers indicate that the feedstock oil was likely to be sourced from terrestrial source rocks of Tertiary age in the South East Asian region. The overall chemical composition of the fuel in its present state indicates that it may have been too heavy for usage in polar regions.", "links": [ { diff --git a/datasets/Sea2Space_0.json b/datasets/Sea2Space_0.json index dc9ad6a088..5b94214ecb 100644 --- a/datasets/Sea2Space_0.json +++ b/datasets/Sea2Space_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sea2Space_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Sea2Space (Sea to Space Particle Investigation) project aboard the RV Falkor, supported by the Schmidt Ocean Institute, in the central and northeast Pacific.", "links": [ { diff --git a/datasets/SeaSat.ESA.archive_6.0.json b/datasets/SeaSat.ESA.archive_6.0.json index c9a1be1f4d..6cc612bf57 100644 --- a/datasets/SeaSat.ESA.archive_6.0.json +++ b/datasets/SeaSat.ESA.archive_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaSat.ESA.archive_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection gives access to the complete SEASAT dataset acquired by ESA and mainly covers Europe. The dataset comprises some of the first ever SAR data recorded for scientific purposes, reprocessed with the most recent processor. The Level-1 products are available as: \u2022\tSAR Ellipsoid Geocoded Precision Image \u2022\tSAR Precision Image \u2022\tSAR Single Look Complex Image European Space Agency, Seasat SAR Precision Image. Version 1.0. https://doi.org/10.5270/SE1-99j66hv European Space Agency, Seasat SAR Single Look Complex. Version 1.0. https://doi.org/10.5270/SE1-4uij92n European Space Agency, Seasat SAR Ellipsoid Geocoded Precision Image . Version 1.0. https://doi.org/10.5270/SE1-ungwqxv", "links": [ { diff --git a/datasets/SeaWiFS_L0_1.json b/datasets/SeaWiFS_L0_1.json index 898bcb9368..da5fcdfe3b 100644 --- a/datasets/SeaWiFS_L0_1.json +++ b/datasets/SeaWiFS_L0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L1_GAC_2.json b/datasets/SeaWiFS_L1_GAC_2.json index a31a155d03..d84b6ad10d 100644 --- a/datasets/SeaWiFS_L1_GAC_2.json +++ b/datasets/SeaWiFS_L1_GAC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L1_GAC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L1_MLAC_2.json b/datasets/SeaWiFS_L1_MLAC_2.json index ad290e9c8b..d1b5241966 100644 --- a/datasets/SeaWiFS_L1_MLAC_2.json +++ b/datasets/SeaWiFS_L1_MLAC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L1_MLAC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_GAC_IOP_2022.0.json b/datasets/SeaWiFS_L2_GAC_IOP_2022.0.json index 645487b561..277381319a 100644 --- a/datasets/SeaWiFS_L2_GAC_IOP_2022.0.json +++ b/datasets/SeaWiFS_L2_GAC_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_GAC_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_GAC_IOP_R2022.0.json b/datasets/SeaWiFS_L2_GAC_IOP_R2022.0.json index 54bc345086..9870f3e4bd 100644 --- a/datasets/SeaWiFS_L2_GAC_IOP_R2022.0.json +++ b/datasets/SeaWiFS_L2_GAC_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_GAC_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_GAC_OC_2022.0.json b/datasets/SeaWiFS_L2_GAC_OC_2022.0.json index 5065798abe..169dbf81c9 100644 --- a/datasets/SeaWiFS_L2_GAC_OC_2022.0.json +++ b/datasets/SeaWiFS_L2_GAC_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_GAC_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_GAC_OC_R2022.0.json b/datasets/SeaWiFS_L2_GAC_OC_R2022.0.json index 01d7877b9c..9a59e0e9ba 100644 --- a/datasets/SeaWiFS_L2_GAC_OC_R2022.0.json +++ b/datasets/SeaWiFS_L2_GAC_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_GAC_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_LAND_2022.0.json b/datasets/SeaWiFS_L2_LAND_2022.0.json index 76a61e37d5..d0c47e0016 100644 --- a/datasets/SeaWiFS_L2_LAND_2022.0.json +++ b/datasets/SeaWiFS_L2_LAND_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_LAND_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_MLAC_IOP_2022.0.json b/datasets/SeaWiFS_L2_MLAC_IOP_2022.0.json index 30861bd105..abcf6f4f0b 100644 --- a/datasets/SeaWiFS_L2_MLAC_IOP_2022.0.json +++ b/datasets/SeaWiFS_L2_MLAC_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_MLAC_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_MLAC_IOP_R2022.0.json b/datasets/SeaWiFS_L2_MLAC_IOP_R2022.0.json index 4e5129e59a..fa04c90e43 100644 --- a/datasets/SeaWiFS_L2_MLAC_IOP_R2022.0.json +++ b/datasets/SeaWiFS_L2_MLAC_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_MLAC_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_MLAC_OC_2022.0.json b/datasets/SeaWiFS_L2_MLAC_OC_2022.0.json index 3cfc67b75f..f70b5d989d 100644 --- a/datasets/SeaWiFS_L2_MLAC_OC_2022.0.json +++ b/datasets/SeaWiFS_L2_MLAC_OC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_MLAC_OC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L2_MLAC_OC_R2022.0.json b/datasets/SeaWiFS_L2_MLAC_OC_R2022.0.json index e0768dcf8e..fc5d99f454 100644 --- a/datasets/SeaWiFS_L2_MLAC_OC_R2022.0.json +++ b/datasets/SeaWiFS_L2_MLAC_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L2_MLAC_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_CHL_2022.0.json b/datasets/SeaWiFS_L3b_CHL_2022.0.json index ce96c306fc..88323afab4 100644 --- a/datasets/SeaWiFS_L3b_CHL_2022.0.json +++ b/datasets/SeaWiFS_L3b_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_CHL_R2022.0.json b/datasets/SeaWiFS_L3b_CHL_R2022.0.json index fd8a804f7d..f415828ec5 100644 --- a/datasets/SeaWiFS_L3b_CHL_R2022.0.json +++ b/datasets/SeaWiFS_L3b_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_GSM_2022.0.json b/datasets/SeaWiFS_L3b_GSM_2022.0.json index b91d05760f..f364b26725 100644 --- a/datasets/SeaWiFS_L3b_GSM_2022.0.json +++ b/datasets/SeaWiFS_L3b_GSM_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_GSM_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_IOP_2022.0.json b/datasets/SeaWiFS_L3b_IOP_2022.0.json index 963ece199b..f789244cdf 100644 --- a/datasets/SeaWiFS_L3b_IOP_2022.0.json +++ b/datasets/SeaWiFS_L3b_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_IOP_R2022.0.json b/datasets/SeaWiFS_L3b_IOP_R2022.0.json index 98c2b4c2ae..b9c74b28c4 100644 --- a/datasets/SeaWiFS_L3b_IOP_R2022.0.json +++ b/datasets/SeaWiFS_L3b_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_KD_2022.0.json b/datasets/SeaWiFS_L3b_KD_2022.0.json index e4b84c6ce2..3b6214e582 100644 --- a/datasets/SeaWiFS_L3b_KD_2022.0.json +++ b/datasets/SeaWiFS_L3b_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_KD_R2022.0.json b/datasets/SeaWiFS_L3b_KD_R2022.0.json index 9ec03f5a9e..bc63f247bf 100644 --- a/datasets/SeaWiFS_L3b_KD_R2022.0.json +++ b/datasets/SeaWiFS_L3b_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_LAND_2022.0.json b/datasets/SeaWiFS_L3b_LAND_2022.0.json index c6b97f02da..81c82c058b 100644 --- a/datasets/SeaWiFS_L3b_LAND_2022.0.json +++ b/datasets/SeaWiFS_L3b_LAND_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_LAND_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_PAR_2022.0.json b/datasets/SeaWiFS_L3b_PAR_2022.0.json index 86cd0d7b86..cac4873e58 100644 --- a/datasets/SeaWiFS_L3b_PAR_2022.0.json +++ b/datasets/SeaWiFS_L3b_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_PAR_R2022.0.json b/datasets/SeaWiFS_L3b_PAR_R2022.0.json index 04e796e594..cfdb643e38 100644 --- a/datasets/SeaWiFS_L3b_PAR_R2022.0.json +++ b/datasets/SeaWiFS_L3b_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_PIC_2022.0.json b/datasets/SeaWiFS_L3b_PIC_2022.0.json index d41029c423..d82e48585d 100644 --- a/datasets/SeaWiFS_L3b_PIC_2022.0.json +++ b/datasets/SeaWiFS_L3b_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_PIC_R2022.0.json b/datasets/SeaWiFS_L3b_PIC_R2022.0.json index fc51ee4845..460dc5d21f 100644 --- a/datasets/SeaWiFS_L3b_PIC_R2022.0.json +++ b/datasets/SeaWiFS_L3b_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_POC_2022.0.json b/datasets/SeaWiFS_L3b_POC_2022.0.json index dded9d1d4f..b748609633 100644 --- a/datasets/SeaWiFS_L3b_POC_2022.0.json +++ b/datasets/SeaWiFS_L3b_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_POC_R2022.0.json b/datasets/SeaWiFS_L3b_POC_R2022.0.json index 0dd7e57a98..d1b3eb0a6b 100644 --- a/datasets/SeaWiFS_L3b_POC_R2022.0.json +++ b/datasets/SeaWiFS_L3b_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_QAA_2022.0.json b/datasets/SeaWiFS_L3b_QAA_2022.0.json index c4971c68f6..e323dcc92d 100644 --- a/datasets/SeaWiFS_L3b_QAA_2022.0.json +++ b/datasets/SeaWiFS_L3b_QAA_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_QAA_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_RRS_2022.0.json b/datasets/SeaWiFS_L3b_RRS_2022.0.json index b138f33e40..f243d72311 100644 --- a/datasets/SeaWiFS_L3b_RRS_2022.0.json +++ b/datasets/SeaWiFS_L3b_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_RRS_R2022.0.json b/datasets/SeaWiFS_L3b_RRS_R2022.0.json index d7650b100f..2057ea4d6c 100644 --- a/datasets/SeaWiFS_L3b_RRS_R2022.0.json +++ b/datasets/SeaWiFS_L3b_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3b_ZLEE_2022.0.json b/datasets/SeaWiFS_L3b_ZLEE_2022.0.json index 155c21cea9..c45524f95b 100644 --- a/datasets/SeaWiFS_L3b_ZLEE_2022.0.json +++ b/datasets/SeaWiFS_L3b_ZLEE_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3b_ZLEE_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_CHL_2022.0.json b/datasets/SeaWiFS_L3m_CHL_2022.0.json index 3c8c96306b..57d78d6cac 100644 --- a/datasets/SeaWiFS_L3m_CHL_2022.0.json +++ b/datasets/SeaWiFS_L3m_CHL_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_CHL_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_CHL_R2022.0.json b/datasets/SeaWiFS_L3m_CHL_R2022.0.json index 98486fc8de..5c467d9d0b 100644 --- a/datasets/SeaWiFS_L3m_CHL_R2022.0.json +++ b/datasets/SeaWiFS_L3m_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_GSM_2022.0.json b/datasets/SeaWiFS_L3m_GSM_2022.0.json index 379350b91d..fcb40c1fa6 100644 --- a/datasets/SeaWiFS_L3m_GSM_2022.0.json +++ b/datasets/SeaWiFS_L3m_GSM_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_GSM_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_IOP_2022.0.json b/datasets/SeaWiFS_L3m_IOP_2022.0.json index 341126978a..ec03d6ab7f 100644 --- a/datasets/SeaWiFS_L3m_IOP_2022.0.json +++ b/datasets/SeaWiFS_L3m_IOP_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_IOP_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_IOP_R2022.0.json b/datasets/SeaWiFS_L3m_IOP_R2022.0.json index f1dc8f5a8b..03515b9f80 100644 --- a/datasets/SeaWiFS_L3m_IOP_R2022.0.json +++ b/datasets/SeaWiFS_L3m_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_KD_2022.0.json b/datasets/SeaWiFS_L3m_KD_2022.0.json index 637fbf2e5c..59e95346f7 100644 --- a/datasets/SeaWiFS_L3m_KD_2022.0.json +++ b/datasets/SeaWiFS_L3m_KD_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_KD_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_KD_R2022.0.json b/datasets/SeaWiFS_L3m_KD_R2022.0.json index ca1a580f80..085a765835 100644 --- a/datasets/SeaWiFS_L3m_KD_R2022.0.json +++ b/datasets/SeaWiFS_L3m_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_LAND_2022.0.json b/datasets/SeaWiFS_L3m_LAND_2022.0.json index 95eeb4d644..8d0ae7d44d 100644 --- a/datasets/SeaWiFS_L3m_LAND_2022.0.json +++ b/datasets/SeaWiFS_L3m_LAND_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_LAND_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_PAR_2022.0.json b/datasets/SeaWiFS_L3m_PAR_2022.0.json index 1913de1223..ddb1f77216 100644 --- a/datasets/SeaWiFS_L3m_PAR_2022.0.json +++ b/datasets/SeaWiFS_L3m_PAR_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_PAR_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_PAR_R2022.0.json b/datasets/SeaWiFS_L3m_PAR_R2022.0.json index b632c35210..a5d8dd8b15 100644 --- a/datasets/SeaWiFS_L3m_PAR_R2022.0.json +++ b/datasets/SeaWiFS_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_PIC_2022.0.json b/datasets/SeaWiFS_L3m_PIC_2022.0.json index 395059c945..c0d6050495 100644 --- a/datasets/SeaWiFS_L3m_PIC_2022.0.json +++ b/datasets/SeaWiFS_L3m_PIC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_PIC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_PIC_R2022.0.json b/datasets/SeaWiFS_L3m_PIC_R2022.0.json index ac32d87af5..0dabce4da0 100644 --- a/datasets/SeaWiFS_L3m_PIC_R2022.0.json +++ b/datasets/SeaWiFS_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_POC_2022.0.json b/datasets/SeaWiFS_L3m_POC_2022.0.json index a38630f166..15c4b2419a 100644 --- a/datasets/SeaWiFS_L3m_POC_2022.0.json +++ b/datasets/SeaWiFS_L3m_POC_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_POC_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_POC_R2022.0.json b/datasets/SeaWiFS_L3m_POC_R2022.0.json index 2dcfefcabd..db9094231b 100644 --- a/datasets/SeaWiFS_L3m_POC_R2022.0.json +++ b/datasets/SeaWiFS_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_QAA_2022.0.json b/datasets/SeaWiFS_L3m_QAA_2022.0.json index a6cf012125..badb8e0863 100644 --- a/datasets/SeaWiFS_L3m_QAA_2022.0.json +++ b/datasets/SeaWiFS_L3m_QAA_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_QAA_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_RRS_2022.0.json b/datasets/SeaWiFS_L3m_RRS_2022.0.json index 2c147fad1f..2a47991a20 100644 --- a/datasets/SeaWiFS_L3m_RRS_2022.0.json +++ b/datasets/SeaWiFS_L3m_RRS_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_RRS_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_RRS_R2022.0.json b/datasets/SeaWiFS_L3m_RRS_R2022.0.json index 7fc017ca1b..657cf9a909 100644 --- a/datasets/SeaWiFS_L3m_RRS_R2022.0.json +++ b/datasets/SeaWiFS_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L3m_ZLEE_2022.0.json b/datasets/SeaWiFS_L3m_ZLEE_2022.0.json index 4544d8959b..9d4d4d229a 100644 --- a/datasets/SeaWiFS_L3m_ZLEE_2022.0.json +++ b/datasets/SeaWiFS_L3m_ZLEE_2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L3m_ZLEE_2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L4b_GSM_R2022.0.json b/datasets/SeaWiFS_L4b_GSM_R2022.0.json index e699e62210..09770cd1b0 100644 --- a/datasets/SeaWiFS_L4b_GSM_R2022.0.json +++ b/datasets/SeaWiFS_L4b_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L4b_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/SeaWiFS_L4m_GSM_R2022.0.json b/datasets/SeaWiFS_L4m_GSM_R2022.0.json index 96553cc6ce..6267c08769 100644 --- a/datasets/SeaWiFS_L4m_GSM_R2022.0.json +++ b/datasets/SeaWiFS_L4m_GSM_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SeaWiFS_L4m_GSM_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.", "links": [ { diff --git a/datasets/Sea_of_Japan_0.json b/datasets/Sea_of_Japan_0.json index 90c2b65000..7e9fcea5eb 100644 --- a/datasets/Sea_of_Japan_0.json +++ b/datasets/Sea_of_Japan_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sea_of_Japan_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Sea of Japan made between 1999 and 2000.", "links": [ { diff --git a/datasets/Seabirds_AAT_1.json b/datasets/Seabirds_AAT_1.json index 5ce020447f..062d13efd6 100644 --- a/datasets/Seabirds_AAT_1.json +++ b/datasets/Seabirds_AAT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Seabirds_AAT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution and abundance of breeding seabirds in the AAT.\n\nThis dataset comprises a broad range of component datasets derived from ground surveys aerial photography and oblique photography.\n\nAerial and oblique photography has been used to obtain supplementary information on distribution and abundance of seabirds in the region.\n\nRecent surveys, 2000/01 onwards, have made use of GPS for more precise geographic information on seabird nests and colonies.\n\nAt present there are a number of child metadata records attached to this record. See the link above for details.", "links": [ { diff --git a/datasets/Seabirds_HIMI_1.json b/datasets/Seabirds_HIMI_1.json index ca1f7fdab2..dbbee16efb 100644 --- a/datasets/Seabirds_HIMI_1.json +++ b/datasets/Seabirds_HIMI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Seabirds_HIMI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution and abundance of breeding seabirds at Heard I and the McDonald Is.\n\nThis dataset comprises a broad range of component datasets derived from ground surveys aerial photography and oblique photography. Since the data have also been derived from old station logs for the 1947-54 period, and from published and unpublished records for the 1947-present day period.\n\nAerial and oblique photography has been used to obtain supplementary information on distribution and abundance of seabirds in the region.\n\nRecent surveys, 2000/01 onwards, have made use of GPS for more precise geographic information on seabird nests and colonies.\n\nAt present there are a number of child metadata records attached to this record. See the link above for details.", "links": [ { diff --git a/datasets/Seagrass_Mapping_Florida_0.json b/datasets/Seagrass_Mapping_Florida_0.json index 35e4d92339..3c696dbd42 100644 --- a/datasets/Seagrass_Mapping_Florida_0.json +++ b/datasets/Seagrass_Mapping_Florida_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Seagrass_Mapping_Florida_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality measurements taken near the Big Bend Seagrasses Aquatic Preserve in Florida.", "links": [ { diff --git a/datasets/Searcher_0.json b/datasets/Searcher_0.json index d5a537f557..0e00ad5598 100644 --- a/datasets/Searcher_0.json +++ b/datasets/Searcher_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Searcher_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Baltic Sea in 1999.", "links": [ { diff --git a/datasets/Seasonality_Tundra_Vegetation_1606_1.json b/datasets/Seasonality_Tundra_Vegetation_1606_1.json index db19d682ee..d5191a366d 100644 --- a/datasets/Seasonality_Tundra_Vegetation_1606_1.json +++ b/datasets/Seasonality_Tundra_Vegetation_1606_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Seasonality_Tundra_Vegetation_1606_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers.", "links": [ { diff --git a/datasets/Secret_0.json b/datasets/Secret_0.json index db553949c2..0ec0cceb22 100644 --- a/datasets/Secret_0.json +++ b/datasets/Secret_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Secret_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements spanning from the California coast to Hawaii in the mid-Pacific Ocean from 1998 to 2006.", "links": [ { diff --git a/datasets/Semantic Segmentation of Crop Type in Ghana_1.json b/datasets/Semantic Segmentation of Crop Type in Ghana_1.json index 5f0299ed49..318987ceb5 100644 --- a/datasets/Semantic Segmentation of Crop Type in Ghana_1.json +++ b/datasets/Semantic Segmentation of Crop Type in Ghana_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Semantic Segmentation of Crop Type in Ghana_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms.\n\n\nThe dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0.", "links": [ { diff --git a/datasets/Semantic Segmentation of Crop Type in South Sudan_1.json b/datasets/Semantic Segmentation of Crop Type in South Sudan_1.json index 365d376047..8e66644b12 100644 --- a/datasets/Semantic Segmentation of Crop Type in South Sudan_1.json +++ b/datasets/Semantic Segmentation of Crop Type in South Sudan_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Semantic Segmentation of Crop Type in South Sudan_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms.\n\n\nThe dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0.", "links": [ { diff --git a/datasets/Semi-Arid_Tree_Carbon_50cm_2117_1.json b/datasets/Semi-Arid_Tree_Carbon_50cm_2117_1.json index 1c32127e83..5409fe4ad2 100644 --- a/datasets/Semi-Arid_Tree_Carbon_50cm_2117_1.json +++ b/datasets/Semi-Arid_Tree_Carbon_50cm_2117_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Semi-Arid_Tree_Carbon_50cm_2117_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides allometrically-estimated carbon stocks of 9,947,310,221 tree crowns derived from 50-cm resolution satellite images within the 0 to 1000 mm/year precipitation zone of Africa north of the equator and south of the Sahara Desert. These data are presented in GeoPackage (*.gpkg) format and are summarized in Cloud-Optimized GeoTIFF (COG) format. An interactive viewer application developed to display these carbon estimates at the individual tree level across the study area is available at: https://trees.pgc.umn.edu/app. The analysis utilized 326,523 Maxar multispectral satellite images collected between 2002 to 2021 for the early dry season months of November to March to identify tree crowns. Metadata from satellite image processing across the study area are presented in Shapefile (*.shp) format. Additionally, field measurements from destructive harvests used to derive allometry equations are contained in comma-separated values (*.csv) files. These data demonstrate a new tool for studying discrete semi-arid carbon stocks at the tree level with immediate applications provided by the viewer application. Uncertainty of carbon estimates are +/- 19.8%.", "links": [ { diff --git a/datasets/Sentinel-1_Burst_Map_1.json b/datasets/Sentinel-1_Burst_Map_1.json index 3611f82ac1..b203170f98 100644 --- a/datasets/Sentinel-1_Burst_Map_1.json +++ b/datasets/Sentinel-1_Burst_Map_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sentinel-1_Burst_Map_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-1 performs systematic acquisition of bursts in both IW and EW modes. The bursts overlap almost perfectly between different passes and are always located at the same place. With the deployment of the SAR processor S1-IPF 3.4, a new element has been added to the products annotations: the Burst ID, which should help the end user to identify a burst area of interest and facilitate searches. The Burst ID map is a complementary auxiliary product. The maps have a validity that covers the entire time span of the mission and they are global, i.e., they include as well information where no SAR data is acquired. Each granule contains information about burst and sub-swath IDs, relative orbit and burst polygon, and should allow for an easier link between a certain burst ID in a product and its corresponding geographic location.", "links": [ { diff --git a/datasets/Sentinel-2 Cloud Cover Segmentation Dataset_1.json b/datasets/Sentinel-2 Cloud Cover Segmentation Dataset_1.json index 9fd713c2ec..3d34426156 100644 --- a/datasets/Sentinel-2 Cloud Cover Segmentation Dataset_1.json +++ b/datasets/Sentinel-2 Cloud Cover Segmentation Dataset_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sentinel-2 Cloud Cover Segmentation Dataset_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In many uses of multispectral satellite imagery, clouds obscure what we really care about - for example,\ntracking wildfires, mapping deforestation, or monitoring crop health. Being able to more accurately remove\nclouds from satellite images filters out interference, unlocking the potential of a vast range of use cases.\nWith this goal in mind, this training dataset was generated as part of [crowdsourcing competition](https://www.drivendata.org/competitions/83/cloud-cover/), and later\non was validated using a team of expert annotators. The dataset consists of Sentinel-2 satellite imagery \nand corresponding cloudy labels stored as GeoTiffs. There are 22,728 chips in the training data, \ncollected between 2018 and 2020.", "links": [ { diff --git a/datasets/Seward_Peninsula_Veg_Maps_1363_1.json b/datasets/Seward_Peninsula_Veg_Maps_1363_1.json index 46dff91efd..6ace23ed00 100644 --- a/datasets/Seward_Peninsula_Veg_Maps_1363_1.json +++ b/datasets/Seward_Peninsula_Veg_Maps_1363_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Seward_Peninsula_Veg_Maps_1363_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.", "links": [ { diff --git a/datasets/Shrub_Biomass_Toolik_Lake_AK_1573_1.json b/datasets/Shrub_Biomass_Toolik_Lake_AK_1573_1.json index 9250855dff..ac359fb792 100644 --- a/datasets/Shrub_Biomass_Toolik_Lake_AK_1573_1.json +++ b/datasets/Shrub_Biomass_Toolik_Lake_AK_1573_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Shrub_Biomass_Toolik_Lake_AK_1573_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates for aboveground shrub biomass and uncertainty at high spatial resolution (0.80-m) across three research areas near Toolik Lake, Alaska. The estimates for August of 2013 were generated and mapped using Random Forest modeling with input variables of optimized LiDAR-derived canopy volume and height, mean NDVI from 4-band RGB color and near-IR orthophotographs, and harvested biomass data. Uncertainty in the final shrub biomass maps was quantified by producing separate maps showing the coefficient of variation (CV) of the Random Forest map estimates. Shrub biomass was harvested at Toolik Lake in 2014 and used to optimize inputs and validate the final model and these biomass data are also provided.", "links": [ { diff --git a/datasets/SiB3_Carbon_Flux_909_1.json b/datasets/SiB3_Carbon_Flux_909_1.json index fd61911624..55bed52541 100644 --- a/datasets/SiB3_Carbon_Flux_909_1.json +++ b/datasets/SiB3_Carbon_Flux_909_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SiB3_Carbon_Flux_909_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Simple Biosphere Model, Version 3 (SiB3) was used to produce a global data set of hourly carbon fluxes between the atmosphere and the terrestrial biosphere for the years 1998-2006. This data set represents the global net ecosystem exchange (NEE) of carbon between the atmosphere and the terrestrial biosphere; specifically, the flux of CO2 between the planetary boundary layer (PBL) and the surface vegetation layer. Following atmospheric convention, flux is defined as positive into the atmosphere and negative into the surface vegetation.The data reported are 9 years of estimated hourly carbon flux for 14637 land points. Units are moles C/m2/sec.Data are provided in two NetCDF formats: The NetCDF format provided by the investigators -- format designed specifically to minimize disk storage volume that excludes water grid cells, and A CF Compliant NetCDF format -- generated by the ORNL DAAC that includes both land and water grid cells.The investigator provided NetCDF formatted files can be processed using the provided FORTRAN code (sib_process_flux.f90) and the land mask (sib_mask.nc) into hourly, daily-mean, or monthly-mean fluxes on a global 1x1 degree Cartesian grid. The monthly-mean SiB3 fluxes were compared to TransCom flux data available for years 2000-2005 (Gurney et al., 2008) as a means of evaluating overall behavior of the model. In general, SiB3 fluxes are within the error bars of the TransCom results.The CF compliant NetCDF format files have been processed by the ORNL DAAC and the hourly and summarized daily-mean and monthly-mean flux data files are provided. GeoTIFF format files:In addition, the CF convention NetCDF files were converted to GeoTIFF image files by the ORNL DAAC and are included with the data set. Companion file:Additional information about the data formats, methodology, and data quality is found in the companion file: SiB3_carbon_flux_readme.pdfAccess to GeoTIFF format files via WCS Interface:The ORNL DAAC also provides access to the GeoTIFF files via a Web Coverage Service Interface (WCS). The OpenGIS Web Coverage Service Interface Standard (WCS) defines a standard interface and operations that enables interoperable access to geospatial coverages.These data are a carbon cycle reanalysis, which may be thought of as analogous to NCEP meteorological reanalysis products. Carbon fluxes have been used by a large community of atmospheric transport modelers to create reanalysis of CO2 concentrations and the results have been evaluated against observations. In addition, the reanalyzed flux and CO2 fields are important for designing future observing strategies for the global carbon cycle.", "links": [ { diff --git a/datasets/SiB4_Global_HalfDegree_Daily_1849_1.json b/datasets/SiB4_Global_HalfDegree_Daily_1849_1.json index 8e6bf50ef0..2805f9238f 100644 --- a/datasets/SiB4_Global_HalfDegree_Daily_1849_1.json +++ b/datasets/SiB4_Global_HalfDegree_Daily_1849_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SiB4_Global_HalfDegree_Daily_1849_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global daily output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Daily output includes carbon, carbonyl sulfide, and energy fluxes; solar-induced fluorescence; carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.", "links": [ { diff --git a/datasets/SiB4_Global_HalfDegree_Hourly_1847_1.json b/datasets/SiB4_Global_HalfDegree_Hourly_1847_1.json index cfe207af1c..3171cdcf6c 100644 --- a/datasets/SiB4_Global_HalfDegree_Hourly_1847_1.json +++ b/datasets/SiB4_Global_HalfDegree_Hourly_1847_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SiB4_Global_HalfDegree_Hourly_1847_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global hourly output predicted from the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Hourly output includes carbon fluxes, carbonyl sulfide (COS) fluxes, gross primary production, ecosystem respiration, solar-induced fluorescence (SIF), top-layer soil temperature and moisture, vegetation stress, photosynthetically active radiation (PAR), leaf and canopy-level carbon-dioxide partial pressures, and canopy conductance. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.", "links": [ { diff --git a/datasets/SiB4_Global_HalfDegree_Monthly_1848_1.json b/datasets/SiB4_Global_HalfDegree_Monthly_1848_1.json index cbc3b05329..837126c446 100644 --- a/datasets/SiB4_Global_HalfDegree_Monthly_1848_1.json +++ b/datasets/SiB4_Global_HalfDegree_Monthly_1848_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SiB4_Global_HalfDegree_Monthly_1848_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.", "links": [ { diff --git a/datasets/Siberian_Biomass_Wildfire_1321_1.json b/datasets/Siberian_Biomass_Wildfire_1321_1.json index a427642419..14a8468ee4 100644 --- a/datasets/Siberian_Biomass_Wildfire_1321_1.json +++ b/datasets/Siberian_Biomass_Wildfire_1321_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Siberian_Biomass_Wildfire_1321_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia.", "links": [ { diff --git a/datasets/Siberian_Larch_Stand_Age_1364_1.json b/datasets/Siberian_Larch_Stand_Age_1364_1.json index 3e3a7aaacc..e5554e40e8 100644 --- a/datasets/Siberian_Larch_Stand_Age_1364_1.json +++ b/datasets/Siberian_Larch_Stand_Age_1364_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Siberian_Larch_Stand_Age_1364_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map.", "links": [ { diff --git a/datasets/Skelton_Aeromag_Data.json b/datasets/Skelton_Aeromag_Data.json index 9b812e75af..b23e703056 100644 --- a/datasets/Skelton_Aeromag_Data.json +++ b/datasets/Skelton_Aeromag_Data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Skelton_Aeromag_Data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Transantarctic Mountains (TAM) rift-flank uplift has developed along the\nancestral margin of the East Antarctic craton, and forms the boundary between\nthe craton and the thinned lithosphere of the West Antarctic rift system.\nGeodynamic processes associated with the exceptionally large-magnitude uplift\nof the mountain belt remain poorly constrained, but may involve interaction of\nrift-related mechanical and thermal processes and the inherited mechanical\nelements of the cratonic lithosphere. The Transantarctic Mountain\nAerogeophysical Research Activities (TAMARA) program proposes to document the\nregional structural architecture of a key segment of the Transantarctic\nMountains in the region around the Royal Society Range where the rift flank is\noffset along a transverse accommodation zone.", "links": [ { diff --git a/datasets/SkySat.Full.Archive.and.New.Tasking_9.0.json b/datasets/SkySat.Full.Archive.and.New.Tasking_9.0.json index 046b4e517e..8e75043b71 100644 --- a/datasets/SkySat.Full.Archive.and.New.Tasking_9.0.json +++ b/datasets/SkySat.Full.Archive.and.New.Tasking_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SkySat.Full.Archive.and.New.Tasking_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SkySat Level 1 Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of the Planet imagery offer.\rThe SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent to the imaging process. Rational Polynomial Coefficients (RPCs) are provided to enable orthorectification by the user.\r\u2022\tBasic Panchromatic Scene product \u2013 unorthorectified, radiometrically corrected, panchromatic (PAN) imagery.\r\u2022\tBasic Panchromatic DN Scene product \u2013 unorthorectified, panchromatic (PAN) imagery.\r\u2022\tBasic L1A Panchromatic DN Scene product \u2013 unorthorectified, pre-super resolution, panchromatic (PAN) imagery.\r\u2022\tBasic Analytic Scene product \u2013 unorthorectified, radiometrically corrected, 4-band multispectral (BGR-NIR) imagery.\r\u2022\tBasic Analytic DN Scene product \u2013 unorthorectified, 4-band multispectral (BGR-NIR) imagery.\rBasic Scene Product Components and Format\rProduct Components and Format\t\u2022\tImage File (GeoTIFF format)\r\u2022\tMetadata File (JSON format)\r\u2022\tRational Polynomial Coefficients (Text File)\r\u2022\tUDM File (GeoTIFF format)\rImage Configurations\t\u2022\t1-band Panchromatic/Panchromatic DN Image (PAN)\r\u2022\t4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR)\rGround Sampling Distance (nadir)\t\u2022\tSkySat-1 & -2: 0.86 m (PAN), 1.0 m (MS)\r\u2022\tSkySat-3 to -15: 0.65 m (PAN), 0.8 m (MS). 0.72 m (PAN) and 1.0 m (MS) for data acquired prior to 30/06/2020\r\u2022\tSkySat-16 to -21: 0.57 m (PAN), 0.75 m (MS)\rGeolocation Accuracy\t<50 m RMSE\r\rThe SkySat Ortho Scene product is sensor- and geometrically-corrected (using DEMs with a post spacing of 30 \u2013 90 m) and is projected to a cartographic map projection; the accuracy of the product varies from region-to-region based on available GCPs.\r\u2022\tOrtho Panchromatic Scene product \u2013 orthorectified, radiometrically corrected, panchromatic (PAN) imagery.\r\u2022\tOrtho Panchromatic DN Scene product \u2013 orthorectified, panchromatic (PAN), uncalibrated digital number imagery.\r\u2022\tOrtho Analytic Scene product \u2013 orthorectified, 4-band multispectral (BGR-NIR) imagery. Radiometric corrections are applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance.\r\u2022\tOrtho Analytic DN Scene product \u2013 orthorectified, 4-band multispectral (BGR-NIR), uncalibrated digital number imagery. Radiometric corrections are applied to correct for any sensor artifacts.\r\u2022\tOrtho Pansharpened Multispectral Scene product \u2013 orthorectified, pansharpened, 4-band (BGR-NIR) imagery.\r\u2022\tOrtho Visual Scene product \u2013 orthorectified, pansharpened, colour-corrected (using a colour curve) 3-band (RGB) imagery.\rOrtho Scene Product Components and Format\rProduct Components and Format\t\u2022\tImage File (GeoTIFF format)\r\u2022\tMetadata File (JSON format)\r\u2022\tRational Polynomial Coefficients (Text File)\r\u2022\tUDM File (GeoTIFF format)\rImage Configurations\t\u2022\t1-band Panchromatic/Panchromatic DN Image (PAN)\r\u2022\t4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR)\r\u2022\t4-band Pansharpened Multispectral Image (Blue, Green, Red, NIR)\r\u2022\t3-band Pansharpened (Visual) Image (Red, Green, Blue)\rOrthorectified Pixel Size\t50 cm\rProjection\tUTM WGS84\rGeolocation Accuracy\t<10 m RMSE\r\rThe SkySat Ortho Collect product is created by composing SkySat Ortho Scene products along an imaging strip into segments typically unifying ~60 individual SkySat Ortho Scenes, resulting in an image with a footprint of approximately 20 km x 5.9 km. The products may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/SkySatESAarchive_8.0.json b/datasets/SkySatESAarchive_8.0.json index e4fbf3a1c8..ed0e41c12a 100644 --- a/datasets/SkySatESAarchive_8.0.json +++ b/datasets/SkySatESAarchive_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SkySatESAarchive_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products.\r\rTwo different product types are offered, Ground Sampling Distance at nadir up to 65 cm for panchromatic and up to 0.8m for multi-spectral.\r\rEO-SIP Product Type\tProduct Description\tContent\rSSC_DEF_SC\tBasic and Ortho scene\t\rLevel 1B 4-bands Analytic /DN Basic scene\rLevel 1B 4-bands Panchromatic /DN Basic scene\rLevel 1A 1-band Panchromatic DN Pre Sup resolution Basic scene\rLevel 3B 3-bands Visual Ortho Scene\rLevel 3B 4-bands Pansharpened Multispectral Ortho Scene\rLevel 3B 4-bands Analytic/DN/SR Ortho Scene\rLevel 3B 1-band Panchromatic /DN Ortho Scene\rSSC_DEF_CO\tOrtho Collect\t\rVisual 3-band Pansharpened Image\rMultispectral 4-band Pansharpened Image\rMultispectral 4-band Analytic/DN/SR Image (B, G, R, N)\r1-band Panchromatic Image\r \r\rThe Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process:\rAnalytic - unorthorectified, radiometrically corrected, multispectral BGRN\rAnalytic DN - unorthorectified, multispectral BGRN\rPanchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN)\rPanchromatic DN - unorthorectified, panchromatic (PAN)\rL1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN)\rThe Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection:\rVisual - orthorectified, pansharpened, and colour-corrected (using a colour curve) 3-band RGB Imagery\rPansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery\rAnalytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product.\rAnalytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance.\rAnalytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts\rPanchromatic - orthorectified, radiometrically correct, panchromatic (PAN)\rPanchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product\rThe Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes.\rSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service.\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Smallholder Cashew Plantations in Benin_1.json b/datasets/Smallholder Cashew Plantations in Benin_1.json index 7dfbf7c40c..d30f42395f 100644 --- a/datasets/Smallholder Cashew Plantations in Benin_1.json +++ b/datasets/Smallholder Cashew Plantations in Benin_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Smallholder Cashew Plantations in Benin_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pl\u00e9iades imagery.", "links": [ { diff --git a/datasets/SnowMeltDuration_PMicrowave_1843_1.1.json b/datasets/SnowMeltDuration_PMicrowave_1843_1.1.json index 8b45992c16..67bdcbd7a8 100644 --- a/datasets/SnowMeltDuration_PMicrowave_1843_1.1.json +++ b/datasets/SnowMeltDuration_PMicrowave_1843_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SnowMeltDuration_PMicrowave_1843_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.", "links": [ { diff --git a/datasets/Snow_Cover_Extent_and_Depth_1757_1.json b/datasets/Snow_Cover_Extent_and_Depth_1757_1.json index 5d31e4ee3b..880f1a286e 100644 --- a/datasets/Snow_Cover_Extent_and_Depth_1757_1.json +++ b/datasets/Snow_Cover_Extent_and_Depth_1757_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Snow_Cover_Extent_and_Depth_1757_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.", "links": [ { diff --git a/datasets/Snow_Depth_Data_Images_1656_1.json b/datasets/Snow_Depth_Data_Images_1656_1.json index 54777531fd..e1054fd12e 100644 --- a/datasets/Snow_Depth_Data_Images_1656_1.json +++ b/datasets/Snow_Depth_Data_Images_1656_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Snow_Depth_Data_Images_1656_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek.", "links": [ { diff --git a/datasets/Snow_Wildlife_Tracks_AK_WA_2188_1.json b/datasets/Snow_Wildlife_Tracks_AK_WA_2188_1.json index a26f59fd9f..4887768d9c 100644 --- a/datasets/Snow_Wildlife_Tracks_AK_WA_2188_1.json +++ b/datasets/Snow_Wildlife_Tracks_AK_WA_2188_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Snow_Wildlife_Tracks_AK_WA_2188_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Snowmelt_timing_maps_V2_1712_2.json b/datasets/Snowmelt_timing_maps_V2_1712_2.json index be0255ff93..f1fec860d0 100644 --- a/datasets/Snowmelt_timing_maps_V2_1712_2.json +++ b/datasets/Snowmelt_timing_maps_V2_1712_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Snowmelt_timing_maps_V2_1712_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018.", "links": [ { diff --git a/datasets/Snowpack_Dall_Sheep_Track_1583_1.json b/datasets/Snowpack_Dall_Sheep_Track_1583_1.json index 903cdba8c2..eef60062d8 100644 --- a/datasets/Snowpack_Dall_Sheep_Track_1583_1.json +++ b/datasets/Snowpack_Dall_Sheep_Track_1583_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Snowpack_Dall_Sheep_Track_1583_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided.", "links": [ { diff --git a/datasets/SoilResp_HeterotrophicResp_1928_1.json b/datasets/SoilResp_HeterotrophicResp_1928_1.json index 49b7211620..42742faed3 100644 --- a/datasets/SoilResp_HeterotrophicResp_1928_1.json +++ b/datasets/SoilResp_HeterotrophicResp_1928_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SoilResp_HeterotrophicResp_1928_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016.", "links": [ { diff --git a/datasets/SoilSCAPE_1339_1.json b/datasets/SoilSCAPE_1339_1.json index acffaccabd..da22426f74 100644 --- a/datasets/SoilSCAPE_1339_1.json +++ b/datasets/SoilSCAPE_1339_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SoilSCAPE_1339_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales.", "links": [ { diff --git a/datasets/SoilSCAPE_V2_2049_2.json b/datasets/SoilSCAPE_V2_2049_2.json index 288a6af9a7..86265c34f3 100644 --- a/datasets/SoilSCAPE_V2_2049_2.json +++ b/datasets/SoilSCAPE_V2_2049_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SoilSCAPE_V2_2049_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format.", "links": [ { diff --git a/datasets/Soil_ActiveLayer_Properties_AK_2315_1.json b/datasets/Soil_ActiveLayer_Properties_AK_2315_1.json index 7dd76d532d..30266a4929 100644 --- a/datasets/Soil_ActiveLayer_Properties_AK_2315_1.json +++ b/datasets/Soil_ActiveLayer_Properties_AK_2315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_ActiveLayer_Properties_AK_2315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Soil_Carbon_Flux_Maps_1683_1.json b/datasets/Soil_Carbon_Flux_Maps_1683_1.json index c246092a70..247a51721c 100644 --- a/datasets/Soil_Carbon_Flux_Maps_1683_1.json +++ b/datasets/Soil_Carbon_Flux_Maps_1683_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_Carbon_Flux_Maps_1683_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100.", "links": [ { diff --git a/datasets/Soil_Moisture_Alaska_Alberta_2123_1.json b/datasets/Soil_Moisture_Alaska_Alberta_2123_1.json index 46471c3830..deadafad02 100644 --- a/datasets/Soil_Moisture_Alaska_Alberta_2123_1.json +++ b/datasets/Soil_Moisture_Alaska_Alberta_2123_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_Moisture_Alaska_Alberta_2123_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes hourly in-situ soil moisture measurements from data loggers in predominantly organic soils (very low bulk density) at two locations: 1) along the Sag River in Alaska, U.S., and 2) near Red Earth Creek in Alberta, Canada. The dataset also provides soil moisture probe periods, temperature probe readings, as well as calibration coefficients and soil profile measurements used to create per probe calibrations for derived volumetric moisture content. The Campbell Scientific CR200 data loggers used CS625 water content reflectometers and temperature probe 109. Further details to the derivation of the calibrations are provided in a supplementary document. The purpose of the dataset is to provide field measurements that can be used for calibration/validation for satellite-based soil moisture retrieval algorithms. With some interruptions, the dataset exists from July 2017 to July 2021. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Soil_Sensors_1.json b/datasets/Soil_Sensors_1.json index e5d4439ba1..2fc30b7cd5 100644 --- a/datasets/Soil_Sensors_1.json +++ b/datasets/Soil_Sensors_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_Sensors_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data are collected for the purposes of monitoring on-ground works at Australian Antarctic stations associated with the remediation of petroleum hydrocarbon contaminated soil. Output datasets consist of soil oxygen (%), soil temperature (C), soil moisture content (VWC - Volumetric Water Content %), and aeration manifold pressure as measured by buried sensors (O2, T C, VWC) or manifold instruments (pressure). Sensor types are either:\n\nAD590 (temperature C)\nAD592 (temperature C)\nFigaro KE25 (% oxygen)\nVegetronix VH400 (Volumetric Water Content %)\n26PCD (Pressure, kPa)\n\nSensors are attached via instrument cables to Datataker dt80 series loggers, which are housed in waterproof containers mounted on buildings, or inside buildings at Australian Antarctic stations. \n\nAt the Macquarie Island isthmus, oxygen sensors are attached to buried groundwater monitoring wells (screened PVC tubes, known as mini-piezometers). Pressure sensors are attached to air distribution manifolds (part of an in-situ aeration distribution network), and temperature sensors are buried in the soil profile. Sensor nomenclature is as follows:\nFF0807/1/O2 (Fuel Farm, 2008 installation, mini-piezometer number 07, Sensor 1, Oxygen sensor)\nMPH_PS_3 (Main Power House, pressure sensor number 03)\n\nBiopiles consist of excavated soil placed in temporary, geo-engineered liner cells. Soil oxygen, soil temperature, and soil moisture content are typically measured at 50 cm height intervals from within the soil piles. Temperature and moisture are also typically measured from within the subgrade and liner materials - common nomenclature for sensor names are as follows:\nBP1/0.5SS_G11/O2 (Biopile 1, buried 0.5 m in soil profile, location G11, Oxygen sensor)\nBP1/AGM_G1/T(Biopile 1, Above GeoMembrane, Location G1, Temperature sensor)\nBP6/AGCL_N1/M (Biopile 6, Above Geosynthetic Clay Liner, Location N1, Moisture sensor)\nBP6/IGCL_N9/M (Biopile 6, Inside Geosynthetic Clay Liner, Location N9, Moisture sensor)\nEXT/-30SS_E1/M (External soil location, 30 cm below sediment surface, Sensor 1, Moisture sensor)\n\nPermeable Reactive Barrier (PRB's) are permeable gates emplaced within the regolith to treat hydrocarbon contaminated groundwater/meltwater and prevent offsite migration of contaminants (primarily hydrocarbons). The barriers have undergone several design iterations, but have consisted of staged (3 sections) permeable reactive or non-reactive filter media (Granular Activated Carbon, Silica sand, Zeolite, MaxBac (TM), Zeopro (TM), Zero Valent Iron), which are placed in buried galvanised shipping cages. The original PRB (installed 2005/06) is named \"PRB\", the second smaller PRB (named the Upper PRB or \"UPRB\" due to its higher elevation in the ) was installed in 2010/11 to treat contaminated groundwater around the MPH settling tank bund and protected the area cleaned as part of the MPH excavation. From this date, the original PRB has also been referred to as the \"lower PRB\".\n\nSensor nomenclature is as follows:\nC_MP9/700/T (MiniPiezometer 9, 700 mm below ground surface, Temperature sensor)\nC_CG3_3/600/02 (Cage 3,Section 3, 600 mm below ground surface, Oxygen sensor)\n\nThese data are downloaded from the sensors to the Australian Antarctic Division on a daily basis. Data are collected by the sensors every 5-20 minutes.\n\nAs of 2013-03-04, the following personnel have been involved in the project:\n\nGreg Hince (AAD) - Project Manager, Field Remediation (11/12-ongoing). Principle Contact\nIan Snape (AAD) - Project Principal (Macquarie Island and Casey Station), Macquarie Island 2008 field team.\nGeoff Stevens (University of Melbourne) - Project Principal - Casey Lower PRB installation\nBen Raymond (AAD) - Calibration and Installation of sensors for Macquarie Island 08/09 field season, maintenance of database and remote troubleshooting of dataloggers.\nTim Spedding (ex AAD) - Field Project Manager (08/09-10/11), Macquarie Island 2008 field team\nDan Wilkins (AAD) - Datalogger management and system design (2009 onwards), Casey station sensor installation 10/11 and 11/12. \nJohn Rayner (ex AAD) - System design - Oxygen sensors. Macquarie Island 2008 field team. Installation of lower PRB (Casey) in 05/06.\nLauren Wise (AAD) - Field maintenance and system operation (Macquarie Island, 10/11 and 12/13)\nRebecca McWatters (AAD)- Casey Station sensors installation 10/11, 11/12, 12/13\nSusan Ferguson (ex AAD) - Macquarie Island 2008 field team, Macquarie Island system maintenance 2009.\nBrett Quinton (ex AAD) - Macquarie Island system maintenance 2009\nCharles Sutherland (AAD contractor/expeditioner) - Macquarie Island system maintenance 12/13 field season\nRobby Kilpatrick (AAD contractor/expeditioner) - Calibration and Installation of sensors for Macquarie Island 11/12 field season\nKathryn Mumford (AAS Project Co-investigator, University of Melbourne) - Installation of lower PRB (Casey) in 05/06. \nTom Statham (University of Melbourne, PhD student) - System installation, Casey 10/11\nWarren Nichols - Oxygen sensor modifications (resin encasement)\nRebecca Miller (AAD contractor/expeditioner) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season\nDan Jones (Queens University, Canada) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season\nVarious members of AAD Telecommunications Team (on ground troubleshooting and maintenance)", "links": [ { diff --git a/datasets/Soil_Temp_Moisture_Alaska_1869_1.json b/datasets/Soil_Temp_Moisture_Alaska_1869_1.json index ec26e5a21b..cd42ed76b8 100644 --- a/datasets/Soil_Temp_Moisture_Alaska_1869_1.json +++ b/datasets/Soil_Temp_Moisture_Alaska_1869_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_Temp_Moisture_Alaska_1869_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format.", "links": [ { diff --git a/datasets/Soil_Temperature_Profiles_AK_1767_1.json b/datasets/Soil_Temperature_Profiles_AK_1767_1.json index 70743ac1f0..6ea639af9e 100644 --- a/datasets/Soil_Temperature_Profiles_AK_1767_1.json +++ b/datasets/Soil_Temperature_Profiles_AK_1767_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Soil_Temperature_Profiles_AK_1767_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska.", "links": [ { diff --git a/datasets/Sonoma_County_Forest_AGB_1764_1.json b/datasets/Sonoma_County_Forest_AGB_1764_1.json index b638f5ae45..8bb392fbe5 100644 --- a/datasets/Sonoma_County_Forest_AGB_1764_1.json +++ b/datasets/Sonoma_County_Forest_AGB_1764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Sonoma_County_Forest_AGB_1764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated.", "links": [ { diff --git a/datasets/South Africa Crop Type Competition_1.json b/datasets/South Africa Crop Type Competition_1.json index cb20539b20..886e377990 100644 --- a/datasets/South Africa Crop Type Competition_1.json +++ b/datasets/South Africa Crop Type Competition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "South Africa Crop Type Competition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites.", "links": [ { diff --git a/datasets/Southern_Boreal_Plot_Attribute_1740_1.json b/datasets/Southern_Boreal_Plot_Attribute_1740_1.json index 7879dc65f6..3f9dbf2c76 100644 --- a/datasets/Southern_Boreal_Plot_Attribute_1740_1.json +++ b/datasets/Southern_Boreal_Plot_Attribute_1740_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Southern_Boreal_Plot_Attribute_1740_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques.", "links": [ { diff --git a/datasets/Southern_Ocean_Drifter_0.json b/datasets/Southern_Ocean_Drifter_0.json index 0f819df5de..bbc60d67ca 100644 --- a/datasets/Southern_Ocean_Drifter_0.json +++ b/datasets/Southern_Ocean_Drifter_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Southern_Ocean_Drifter_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken by a drifter in the Southern Pacific Ocean in 1996.", "links": [ { diff --git a/datasets/Spire.live.and.historical.data_8.0.json b/datasets/Spire.live.and.historical.data_8.0.json index 741a35e240..3dc4328631 100644 --- a/datasets/Spire.live.and.historical.data_8.0.json +++ b/datasets/Spire.live.and.historical.data_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Spire.live.and.historical.data_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height.\rThe following products can be requested:\r\rGNSS Polarimetric Radio Occultation (STRATOS)\rNovel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal\u2019s bending angle. Various levels of the products are provided. \r \rGNSS Reflectometry (STRATOS)\rGNSS Reflectometry (GNSS-R) is a technique to measure Earth\u2019s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. \rHistorical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present.\r\rName\tTemporal coverage\tSpatial coverage\tDescription\tData format and content\tApplication\rPolarimetric Radio Occultation (PRO) measurements\t15-May-2023 to 30-November-2023\tGlobal\tPRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. \tleoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file\tPRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor.\r\t\t\t\tproObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal.\t\r\t\t\t\th(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (\u201cH\u201d + \u201cV\u201d) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information.\t\r\t\t\t\tpolPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation.\t\r\t\t\t\tpatmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the \u201ccombined\u201d excess phase delay (catmPhs)\t\rNear-Nadir GNSS Reflectometry (NN GNSS-R) measurements \t25-January-2024 to 24-July-2024\tGlobal\tTracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs).\tgbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites.\tNN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc.\r\t\t\t\tgbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites.\t\r\t\t\t\tgbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground.\t\r\t\t\t\tgbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties.\t\rGrazing angle GNSS Reflectometry (GA GNSS-R) measurements\t25-January-2024 to 24-July-2024\tGlobal\tTracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations.\tgrzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations.\tGA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays.\r\t\t\t\tgrzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification.\t\r\t\t\t\tgrzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides).\t\r\rAdditionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: \rOther STRATOS measurements: profiles of the Earth\u2019s atmosphere and ionosphere, from December 2018\rADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018\rAIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016\r\rThe products are available as part of the Spire provision with worldwide coverage.\rAll details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 .", "links": [ { diff --git a/datasets/Stream_GIS_USGS.json b/datasets/Stream_GIS_USGS.json index 32163ac61c..a7f28418a3 100644 --- a/datasets/Stream_GIS_USGS.json +++ b/datasets/Stream_GIS_USGS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Stream_GIS_USGS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a 1:2,000,000 coverage of streams for the conterminous United States.\nThis coverage was intended for use as a background display for the National\nWater Summary program.\n\nThe stream layer was extracted from the 1:2,000,000 Digital Line Graph files.\nOriginally, each state was stored as a separate coverage. In this version, the\nindividual state coverages all have been appended.\n\n[Summary provided by EPA]", "links": [ { diff --git a/datasets/Surface_Oligo_Med_Sea_0.json b/datasets/Surface_Oligo_Med_Sea_0.json index fd939a0b0b..49b3d06b56 100644 --- a/datasets/Surface_Oligo_Med_Sea_0.json +++ b/datasets/Surface_Oligo_Med_Sea_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Surface_Oligo_Med_Sea_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the west-central Mediterranean Sea of surface oligotrophic water in 2008.", "links": [ { diff --git a/datasets/Survey_1980_81_Ingrid_Christenson_1.json b/datasets/Survey_1980_81_Ingrid_Christenson_1.json index e7438126f0..f7e7fc52f4 100644 --- a/datasets/Survey_1980_81_Ingrid_Christenson_1.json +++ b/datasets/Survey_1980_81_Ingrid_Christenson_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_1980_81_Ingrid_Christenson_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on field season on Ingrid Christenson coast summer 1980-81. Program aims: Helicopter Geophysical (gravity) Glaciological Survey; Palaeomagnetism, Vertical Air Photography.\n\nSee the report for more details.", "links": [ { diff --git a/datasets/Survey_1988_89_Mawson_npcms_1.json b/datasets/Survey_1988_89_Mawson_npcms_1.json index 2eebfcacf4..6b21a27754 100644 --- a/datasets/Survey_1988_89_Mawson_npcms_1.json +++ b/datasets/Survey_1988_89_Mawson_npcms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_1988_89_Mawson_npcms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field season report of these programs: 1988/89 Summer Season surveying and mapping North Prince Charles Mountains; ...mapping program Northern PCM's - Mawson Doppler Translocation Support; ....mapping program Voyage 6 stopover Davis. Includes maps and mapsheet layouts.\n\nSee the report for full details on the program.\n\nContents are:\nIntroduction\nPreparation\nVoytage to Antarctica\n1988/89 Summer Season Surveying and Mapping Program, Northern Prince Charles Mountains\n1988/89 Summer Season Surveying and Mapping Program, Voyage 6 Stopover, Davis\nPerformance of Equipment\nStation Marking\nField Camping\nClimatic Conditions\nConclusion\nAppendices\n", "links": [ { diff --git a/datasets/Survey_1989_90_Casey_airfield_1.json b/datasets/Survey_1989_90_Casey_airfield_1.json index b082b0fa89..7ee387ea89 100644 --- a/datasets/Survey_1989_90_Casey_airfield_1.json +++ b/datasets/Survey_1989_90_Casey_airfield_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_1989_90_Casey_airfield_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report on surveys with major tasks: Casey Airfield Survey; Casey Engineering Surveys; Tunnel Terrestrial Photography. Includes tables, diagrams and colour photographic prints.\n\nThe aims of the 1989/90 summer season surveying and mapping program at Casey are as set out in priority order below:\n\n1) Provide surveying support as and when required to the RAAF ground contingent charged with the responsibility of preparing an ice runway for the proposed RAAF C130 Hercules sorties in mid-February 1990.\n\n2) Carry out the following engineering surveys for the Australian Construction Services:\n - Detail survey of proposed helipad site approximately centred on 2040E, 7135N on Casey Master Plan Issue No. 9.\n - Detail survey of proposed helipad site approximately centred on 2254E, 7132N on Casey Master Plan Issue No. 9.\n - Old-New Casey link road movement monitoring survey.\n - Hydrographic survey of the melt water lake at 1900E, 6900N on Casey Master Plan Issue No. 9.\n - Hydrographic survey of the melt water lake at 2000E, 7200N on Casey Master Plan Issue No. 9.\n\n3) Observe horizontal and vertical angles and EDM distances which will enable the strengthening of the geodetic control network in the Bailey and Clark Peninsula areas.\n\n4) Carry out a topographic survey of the Bailey Peninsula area which will enable the preparation of the Casey Management Plan.", "links": [ { diff --git a/datasets/Survey_1989_90_Lambert_1.json b/datasets/Survey_1989_90_Lambert_1.json index 68803cc29c..b8bf7d9022 100644 --- a/datasets/Survey_1989_90_Lambert_1.json +++ b/datasets/Survey_1989_90_Lambert_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_1989_90_Lambert_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report by survey staff on the Lambert Glacier Basin Traverse in summer of 1989/90. Includes original photographic prints.\n\nThe Lambert Glacier Basin Traverse was one of the projects included in the 1989/90 summer programme of the Australian National Antarctic Research Expedition (ANARE) that involved significant survey involvement. The project is an important part of the on-going research programme of the Glaciology section, Antarctic Division.\n\nThe Lambert Glacier is the largest glacier on Earth. Lying in MacRobertson Land of the Australian Antarctic Territory it drains an area almost half the size of Australia. Recent programs by the Antarctic Division have investigated the glacier itself, however to achieve the overall objective of establishing the Mass Budget of the Lambert system and all its related mechanisms, a study of the catchment was necessary. To this end the first of a series of glaciological traverses was undertaken in the 1989/1990 summer season to make various measurements including ice movement, ice thickness, gravity, magnetometer and snow accumulation.\n\nThe over snow traverse was effected using three specially built D7H tractors hauling a series of sleds for transport and manned by a party of six. In two and a half months the party, often as two separate units, travelled eight hundred kilometres into the interior of MacRobertson Land along the 2500 metre contour in temperatures ranging between -15 and -38 degrees centigrade.\n\nThe first priorities were to set up and accurately position ice movement stations to establish rates of flow into the glacier, and to depot fuel to facilitate further traversing over the next few years. Geodetic measurements were effected using four WM102 dual frequency GPS receivers and two MX1502 Transit receivers in a survey network carefully planned to overcome a series of anticipated problems, many being peculiar to operations in polar regions.", "links": [ { diff --git a/datasets/Survey_1989_90_mawson_1.json b/datasets/Survey_1989_90_mawson_1.json index d0f041c9f4..a6ad8c0a5a 100644 --- a/datasets/Survey_1989_90_mawson_1.json +++ b/datasets/Survey_1989_90_mawson_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_1989_90_mawson_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Report of reconnaissance of selected areas of blue ice in the Mawson hinterland - regarding possible future runway sites suitable for C130 Hercules type aircraft. Program 22 - 26 February 1990. Includes colour print copies, diagrams, slopes of blue ice areas and maps.\n\nAim - To carry out a preliminary reconnaissance of selected area of blue ice in the Mawson hinterland in order to determine which, if any, of these areas may be suitable for further detailed investigation as possible future runway sites capable of handling C130 Hercules type aircraft.\n\nPersonnel - Mr P. Murphy, Surveyor Class 1, Mr J. Hyslop, Surveyor Class 1, Mr N. Peters, Technical Officer Grade 2, Mr P. Malcolm, Glaciology, Mr R. Kiernan, Glaciology.\n\nTime Frame - 22-26 February, 1990 (approx).\n\nMr Phil Barnaart carried out a reconnaissance of possible blue ice runway sites in 1988 related to the proposed operation of Russian aircraft. He prepared a reconnaissance report.\n\nMore information available in the download file.", "links": [ { diff --git a/datasets/Survey_compilation_macquarie_island_1992_1996_1.json b/datasets/Survey_compilation_macquarie_island_1992_1996_1.json index 2521831617..dfe7178cc6 100644 --- a/datasets/Survey_compilation_macquarie_island_1992_1996_1.json +++ b/datasets/Survey_compilation_macquarie_island_1992_1996_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_compilation_macquarie_island_1992_1996_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report compiles the survey work on Macquarie Island for the summer seasons between November 1992 and February 1996.\n\nIt includes survey of ground control points throughout the island, at the station and levelling at the tide gauge site in Garden Bay adjacent to Macquarie Island station.\n\nThe survey control data has been included in the Australian Antarctic Data Centre survey control database.", "links": [ { diff --git a/datasets/Survey_report_Macquarie-Island_2011_12_V6_1.json b/datasets/Survey_report_Macquarie-Island_2011_12_V6_1.json index e484336dcd..001ddba29e 100644 --- a/datasets/Survey_report_Macquarie-Island_2011_12_V6_1.json +++ b/datasets/Survey_report_Macquarie-Island_2011_12_V6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Survey_report_Macquarie-Island_2011_12_V6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from the survey report:\n\nThis report details the survey work carried out on Macquarie Island in April 2012 on re-supply Voyage 6 by Australian Antarctic Division Mapping Officer, Henk Brolsma and UTAS final year survey student, David Cromarty.\n\nThe first task of the survey team was to calibrate the tide gauges and second to bring the location of the station area buildings up to date.\n\nSecond priority was to survey the natural surface level of; a) The isthmus for erosion studies and b) Wireless Hill to prepare an accurate DEM for the hill.\n\nProject Brief\nThe survey-mapping brief lists the following tasks in order of priority:\n1. Calibrate the tide gauges in Garden Bay using the GPS in the float technique, 2. Level between the tide gauge bench marks and the tide gauge reference points, 3. Survey location of new buildings, 4. Survey new tourist walkway and 5. Survey natural surface profiles of the isthmus.\n\nSee the report for full details.", "links": [ { diff --git a/datasets/SwAO_0.json b/datasets/SwAO_0.json index d2ff633b55..be84af5900 100644 --- a/datasets/SwAO_0.json +++ b/datasets/SwAO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "SwAO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the southwest Atlantic Ocean spanning 1995 to 2004.", "links": [ { diff --git a/datasets/Swarm.Core_3.0.json b/datasets/Swarm.Core_3.0.json index d7c2b50c1b..9b0140fdb8 100644 --- a/datasets/Swarm.Core_3.0.json +++ b/datasets/Swarm.Core_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Core_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spherical harmonic model of the main (core) field and its temporal variation", "links": [ { diff --git a/datasets/Swarm.Geodesy_Gravity_3.0.json b/datasets/Swarm.Geodesy_Gravity_3.0.json index 8416d68152..5101d7d7fc 100644 --- a/datasets/Swarm.Geodesy_Gravity_3.0.json +++ b/datasets/Swarm.Geodesy_Gravity_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Geodesy_Gravity_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly gravity field of the Earth, non-gravitational accelerations", "links": [ { diff --git a/datasets/Swarm.Ionosphere_Magnetosphere_3.0.json b/datasets/Swarm.Ionosphere_Magnetosphere_3.0.json index f2267c3945..c3f298d345 100644 --- a/datasets/Swarm.Ionosphere_Magnetosphere_3.0.json +++ b/datasets/Swarm.Ionosphere_Magnetosphere_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Ionosphere_Magnetosphere_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spherical harmonic model of the large-scale magnetospheric field and its Earth-induced counterpart, spherical harmonic model of the daily geomagnetic variation at middle latitudes and low latitudes ,Ionospheric bubble index, ionospheric total electron content, ionosphericfield-aligned currents, dayside ionospheric equatorial electric field, ionospheric plasma density and plasma irregularities", "links": [ { diff --git a/datasets/Swarm.L1B_4.0.json b/datasets/Swarm.L1B_4.0.json index 6a608d27f7..985cd2c0e6 100644 --- a/datasets/Swarm.L1B_4.0.json +++ b/datasets/Swarm.L1B_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.L1B_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level 1b products of the Swarm mission contains time-series of quality-screen, calibrated, and corrected measurements given in physical, SI units in geo-localized reference frames. Level 1b products are provided individually for each of the three satellites Swarm A, Swarm B, and Swarm C on a daily basis.", "links": [ { diff --git a/datasets/Swarm.L2.daily_3.0.json b/datasets/Swarm.L2.daily_3.0.json index 6e46733979..3018b66e06 100644 --- a/datasets/Swarm.L2.daily_3.0.json +++ b/datasets/Swarm.L2.daily_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.L2.daily_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swarm Level 1b data products are the corrected and formatted output from each of the three Swarm satellites. By a complex assimilation of these individual satellite measurements into one set of products for the satellite constellation, the Swarm Level 2 Processor ensures a very significant improvement of the quality of the final scientific data products.", "links": [ { diff --git a/datasets/Swarm.L2.longterm_3.0.json b/datasets/Swarm.L2.longterm_3.0.json index 60be35a7f0..6da93c5f1e 100644 --- a/datasets/Swarm.L2.longterm_3.0.json +++ b/datasets/Swarm.L2.longterm_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.L2.longterm_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swarm Level 2 Long Term data products are the corrected and formatted output from each of the three Swarm satellites. By a complex assimilation of these individual satellite measurements into one set of products for the satellite constellation, the Swarm Level 2 Processor ensures a very significant improvement of the quality of the final scientific data products.", "links": [ { diff --git a/datasets/Swarm.Lithosphere_3.0.json b/datasets/Swarm.Lithosphere_3.0.json index 427ddd519b..02aa04be40 100644 --- a/datasets/Swarm.Lithosphere_3.0.json +++ b/datasets/Swarm.Lithosphere_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Lithosphere_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spherical harmonic model of the lithospheric field", "links": [ { diff --git a/datasets/Swarm.Mantle_3.0.json b/datasets/Swarm.Mantle_3.0.json index f36d690a4c..a3c286b363 100644 --- a/datasets/Swarm.Mantle_3.0.json +++ b/datasets/Swarm.Mantle_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Mantle_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Model of mantle conductivity, core-mantle interaction", "links": [ { diff --git a/datasets/Swarm.Oceans_3.0.json b/datasets/Swarm.Oceans_3.0.json index 91ccf3cc77..6a6748951a 100644 --- a/datasets/Swarm.Oceans_3.0.json +++ b/datasets/Swarm.Oceans_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Oceans_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceans tides, induced magnetic field", "links": [ { diff --git a/datasets/Swarm.Space.Weather_3.0.json b/datasets/Swarm.Space.Weather_3.0.json index d62a3b7c53..3f16151f60 100644 --- a/datasets/Swarm.Space.Weather_3.0.json +++ b/datasets/Swarm.Space.Weather_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Space.Weather_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Environmental conditions in Earth's magnetosphere, ionosphere and thermosphere due to the solar activity that drive the Sun-Earth interactions", "links": [ { diff --git a/datasets/Swarm.Thermosphere_4.0.json b/datasets/Swarm.Thermosphere_4.0.json index 0c708b755d..bc585c126e 100644 --- a/datasets/Swarm.Thermosphere_4.0.json +++ b/datasets/Swarm.Thermosphere_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Swarm.Thermosphere_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Neutral thermospheric density", "links": [ { diff --git a/datasets/TAHOE_0.json b/datasets/TAHOE_0.json index f8adcbe8ef..bae4877986 100644 --- a/datasets/TAHOE_0.json +++ b/datasets/TAHOE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TAHOE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Lake Tahoe in Northern California during 2001.", "links": [ { diff --git a/datasets/TAO_0.json b/datasets/TAO_0.json index f52bfd8b3e..b5c977cfa7 100644 --- a/datasets/TAO_0.json +++ b/datasets/TAO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TAO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the ships visiting the TOA (Tropical Atmosphere Ocean) buoy array.", "links": [ { diff --git a/datasets/TAO_Moorings_0.json b/datasets/TAO_Moorings_0.json index 2c6abb5e06..8adc82d85a 100644 --- a/datasets/TAO_Moorings_0.json +++ b/datasets/TAO_Moorings_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TAO_Moorings_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mooring data from the TAO buoy array between 1992 and 2002.", "links": [ { diff --git a/datasets/TARA-EUROPA_0.json b/datasets/TARA-EUROPA_0.json index f5f46bcd42..f4b4a33e4a 100644 --- a/datasets/TARA-EUROPA_0.json +++ b/datasets/TARA-EUROPA_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARA-EUROPA_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For two consecutive years (2023-2024), the schooner Tara is participating in the study of coastal ecosystems all along the European coast. The sampling of Tara Europa is part of the TREC expedition Traversing European Coastlines, led by the European Molecular Biology Laboratory (EMBL) in collaboration with the Tara OceanS consortium, the Tara Ocean Foundation and more than 70 scientific institutions. Its objective is to study the land-sea interface, where biodiversity meets numerous pollutions.", "links": [ { diff --git a/datasets/TARA_PACIFIC_expedition_0.json b/datasets/TARA_PACIFIC_expedition_0.json index d8e336be83..16325fd65d 100644 --- a/datasets/TARA_PACIFIC_expedition_0.json +++ b/datasets/TARA_PACIFIC_expedition_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARA_PACIFIC_expedition_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data measured during the Tara Oceans Pacific Expedition, 2016-2018", "links": [ { diff --git a/datasets/TARFOX_UWC131A_1.json b/datasets/TARFOX_UWC131A_1.json index 684360d4f3..d1af364107 100644 --- a/datasets/TARFOX_UWC131A_1.json +++ b/datasets/TARFOX_UWC131A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARFOX_UWC131A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TARFOX_UWC131A is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - University of Washington instrumented C-131A aircraft data set. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.", "links": [ { diff --git a/datasets/TARFOX_UWC131A_SUNP_1.json b/datasets/TARFOX_UWC131A_SUNP_1.json index 119134817f..0c11f96389 100644 --- a/datasets/TARFOX_UWC131A_SUNP_1.json +++ b/datasets/TARFOX_UWC131A_SUNP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARFOX_UWC131A_SUNP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TARFOX_UWC131A_SUNP Data Set was collected from the 6-channel Sun Photometer flown on the University of Washington C-131A aircraft during the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) mission. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.", "links": [ { diff --git a/datasets/TARFOX_WALLOPS_MET_1.json b/datasets/TARFOX_WALLOPS_MET_1.json index f576e94d72..c13f55495f 100644 --- a/datasets/TARFOX_WALLOPS_MET_1.json +++ b/datasets/TARFOX_WALLOPS_MET_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARFOX_WALLOPS_MET_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TARFOX_WALLOPS_MET is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Surface Meteorological data set Wallops ground station.The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.", "links": [ { diff --git a/datasets/TARFOX_WALLOPS_SMPS_1.json b/datasets/TARFOX_WALLOPS_SMPS_1.json index c32ce03d59..cb6eff2ab7 100644 --- a/datasets/TARFOX_WALLOPS_SMPS_1.json +++ b/datasets/TARFOX_WALLOPS_SMPS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARFOX_WALLOPS_SMPS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TARFOX_WALLOPS_SMPS is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Scanning Mobility Particle Sizer (SMPS) data set from Wallops ground station. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.", "links": [ { diff --git a/datasets/TARFOX_WALLOPS_SONDE_1.json b/datasets/TARFOX_WALLOPS_SONDE_1.json index 988ed20bac..73bfc6baae 100644 --- a/datasets/TARFOX_WALLOPS_SONDE_1.json +++ b/datasets/TARFOX_WALLOPS_SONDE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TARFOX_WALLOPS_SONDE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TARFOX_WALLOPS_SONDE is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Vaisala radiosonde data set from balloons launched at Wallops ground station. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.", "links": [ { diff --git a/datasets/TCTE3TSI6_004.json b/datasets/TCTE3TSI6_004.json index 9934f31d9b..e2dde50714 100644 --- a/datasets/TCTE3TSI6_004.json +++ b/datasets/TCTE3TSI6_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TCTE3TSI6_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TCTE3TSI6 Version 004 is the final version of this data product, and supersedes all previous versions.\n\nThe Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSI6 contains 6-hour averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum. The data are normalized to one astronomical unit (1 AU).\n\nThe TCTE/TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 350 parts per million (ppm, 1 ppm=0.0001% at 1-sigma), and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size of these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission in an ASCII column formatted file.\n\nEarly in the mission, between Dec 2013 and May 2014, TCTE acquired daily measurements to establish good overlap with the SORCE TIM. From May 2014 to Dec 2014, the TCTE measurements were reduced to weekly, which greatly subsample the true solar variability, and thus have little value for solar research. Beginning in Jan 2015, daily obervations were resumed. The mission ended June 30, 2019.\n", "links": [ { diff --git a/datasets/TCTE3TSID_004.json b/datasets/TCTE3TSID_004.json index 3d229f6f46..a5678c9e6f 100644 --- a/datasets/TCTE3TSID_004.json +++ b/datasets/TCTE3TSID_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TCTE3TSID_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TCTE3TSID Version 004 is the final version of this data product, and supersedes all previous versions.\n\nThe Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSID contains daily averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum. The data are normalized to one astronomical unit (1 AU).\n\nThe TCTE/TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 350 parts per million (ppm, 1 ppm=0.0001% at 1-sigma), and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size of these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission in an ASCII column formatted file.\n\nEarly in the mission, between Dec 2013 and May 2014, TCTE acquired daily measurements to establish good overlap with the SORCE TIM. From May 2014 to Dec 2014, the TCTE measurements were reduced to weekly, which greatly subsample the true solar variability, and thus have little value for solar research. Beginning in Jan 2015, daily obervations were resumed. The mission ended June 30, 2019.\n", "links": [ { diff --git a/datasets/TDPforAtmosphere_4.0.json b/datasets/TDPforAtmosphere_4.0.json index 09638b8108..46fa5f6a9b 100644 --- a/datasets/TDPforAtmosphere_4.0.json +++ b/datasets/TDPforAtmosphere_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforAtmosphere_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Atmospheric Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing Total Column Water Vapour (TCWV), Cloud Liquid Water Path (LWP), Atmospheric Attenuation of the altimeter backscattering coefficient at Ku-band (AttKu), and Wet Tropospheric Correction (WTC), retrieved from observations of the Microwave Radiometer (MWR) instruments flown on-board the ERS-1, and ERS-2, and Envisat satellites.\r\rCompared to existing datasets, the Atmospheric TDP demonstrates notable improvements in several aspects:\r\rImproved temporal coverage, especially for ERS-2\rImproved L0 -> 1 processing\rTwo different corrections are provided based on a neural network retrieval or on a 1D-VAR approach\r\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TDPforInlandWater_4.0.json b/datasets/TDPforInlandWater_4.0.json index f8b2a6e5d4..535ab53282 100644 --- a/datasets/TDPforInlandWater_4.0.json +++ b/datasets/TDPforInlandWater_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforInlandWater_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Inland Waters Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing improved Water Surface Height (WSH) data record from the ERS-1, ERS-2 and Envisat missions estimated using the ICE1 retracking range for its better performance on the hydro targets.\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TDPforLandice_4.0.json b/datasets/TDPforLandice_4.0.json index fa060cdacc..d8159c9791 100644 --- a/datasets/TDPforLandice_4.0.json +++ b/datasets/TDPforLandice_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforLandice_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Land Ice Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing estimates of ice sheet surface elevation and associated uncertainties.\rThe collection covers data for three different missions: ERS-1, ERS-2 and Envisat, and based on Level 1 data coming from previous reprocessing (ERS REAPER and the Envisat V3.0) but taking into account the improvements made at Level 0/Level 1 in the frame of FDR4ALT (_$$ALT FDR$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry).\rThe Land Ice TDP focuses specifically on the ice sheets of Greenland and Antarctica, providing these data in different files.\rFor many aspects, the Land Ice Level 2 and Level 2+ processing is very innovative:\r\rImproved relocation approach correcting for topographic effects within the beam footprint to identify the Point of Closest Approach \rHomogeneous timeseries of surface elevation measurements at regular along-track reference nodes.\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TDPforOceanCoastalTopography_4.0.json b/datasets/TDPforOceanCoastalTopography_4.0.json index 7514b741af..3a3a625a29 100644 --- a/datasets/TDPforOceanCoastalTopography_4.0.json +++ b/datasets/TDPforOceanCoastalTopography_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforOceanCoastalTopography_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Ocean and Coastal Topography Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing improved sea surface height anomaly data records both at 1 Hz and 20 Hz resolution to address climate and/or coastal areas studies. The collection covers data for the ERS-1, ERS-2 and Envisat missions. Note that a dedicated processing to coastal zones has been applied for coastal distances below 200 km.\rCompared to existing datasets, the Ocean and Coastal Topography TDP demonstrates notable improvements in several aspects:\r\rUp-to-date orbit and geophysical corrections applied\rAdaptive retracker for Envisat\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TDPforOceanWaves_4.0.json b/datasets/TDPforOceanWaves_4.0.json index 92a6fe257c..853f62ccb2 100644 --- a/datasets/TDPforOceanWaves_4.0.json +++ b/datasets/TDPforOceanWaves_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforOceanWaves_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Ocean Waves Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing Significant Wave Height estimates for the ERS-1, ERS-2 and Envisat missions.\rCompared to existing datasets, the Ocean Waves TDP demonstrates notable improvements in several aspects:\r\rGreat improvements for Envisat due to noise reduction from Adaptive retracker and High-Frequency Adjustment (HFA)\rAll variables are given at 5 Hz\rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TDPforSeaice_4.0.json b/datasets/TDPforSeaice_4.0.json index 35b088ef70..c6bf52b9cc 100644 --- a/datasets/TDPforSeaice_4.0.json +++ b/datasets/TDPforSeaice_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TDPforSeaice_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the Sea Ice Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing the sea ice related geophysical parameters, along with associated uncertainties: snow depth, radar and sea-ice freeboard, sea ice thickness and concentration.\rThe collection covers data for the ERS-1, ERS-2 and Envisat missions, and bases on Level 1 data coming from previous reprocessing (ERS REAPER and the Envisat V3.0) but taking into account the improvements made at Level 0/Level 1 in the frame of FDR4ALT (_$$ALT FDR$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry).\rThe Sea Ice TDP provides data from the northern or southern hemisphere in two files corresponding to the Arctic and Antarctic regions respectively for the winter periods only, i.e., October to June for the Arctic, and May to November for the Antarctic.\rFor many aspects, the Sea Ice TDP is very innovative: \r\rFirst time series of sea-ice thickness estimates for ERS\rHomogeneous calibration, allowing the first Arctic radar freeboard time series from ERS-1 (1991) to CryoSat-2 (2021)\rUncertainties estimated along-track with a bottom-up approach based on dominant sources \rERS pulse blurring error corrected using literature procedure [Peacock, 2004] \rThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\rInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\rPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\rThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "links": [ { diff --git a/datasets/TELLUS_GIA_L3_0.5-DEG_V1.0_1.0.json b/datasets/TELLUS_GIA_L3_0.5-DEG_V1.0_1.0.json index 255badef08..959ff218f1 100644 --- a/datasets/TELLUS_GIA_L3_0.5-DEG_V1.0_1.0.json +++ b/datasets/TELLUS_GIA_L3_0.5-DEG_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GIA_L3_0.5-DEG_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 0.5-degree global grid compatible with the JPL Mascon solution, provided in netCDF format.", "links": [ { diff --git a/datasets/TELLUS_GIA_L3_1-DEG_V1.0_1.0.json b/datasets/TELLUS_GIA_L3_1-DEG_V1.0_1.0.json index 00a9932063..e7ddbbd815 100644 --- a/datasets/TELLUS_GIA_L3_1-DEG_V1.0_1.0.json +++ b/datasets/TELLUS_GIA_L3_1-DEG_V1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GIA_L3_1-DEG_V1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 1.0-degree global grid in netCDF format.", "links": [ { diff --git a/datasets/TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY_3.3.json b/datasets/TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY_3.3.json index e6729e91bc..8fea075727 100644 --- a/datasets/TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY_3.3.json +++ b/datasets/TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY_3.3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY_3.3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format.", "links": [ { diff --git a/datasets/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4_RL06.3Mv04.json b/datasets/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4_RL06.3Mv04.json index 7e54736534..cb9da19dae 100644 --- a/datasets/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4_RL06.3Mv04.json +++ b/datasets/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4_RL06.3Mv04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4_RL06.3Mv04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). A Coastal Resolution Improvement (CRI) filter has been applied to this data set to reduce signal leakage errors across coastlines. For most land hydrology, oceanographic as well as land-ice applications this is the recommend data set for the analysis of surface mass changes. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions.\n

\nThe complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals. In a post-processing step, the CRI filter is applied to those mixed land/ocean Mascons to separate land and ocean mass. The land mask used to perform this separation is provided in the same directory as this dataset, as are uncertainty values, and the gridded mascon-ID number to enable further analysis. Since the individual mascons act as an inherent smoother on the gravity field, a set of optional gain factors (for continental hydrology applications) that can be applied to the solution to study mass change signals at sub-mascon resolution is also provided within the same data directory as the Mascon data. For use-case examples and further background on the gain factors, please see Wiese, Landerer & Watkins, 2016, https://doi.org/10.1002/2016WR019344.\n

\nThis RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03 (DOI, 10.5067/TEMSC-3JC63). For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. For a detailed description of the CRI filter implementation, please see Wiese et al., 2016, doi:10.1002/2016WR019344.", "links": [ { diff --git a/datasets/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4_RL06.3Mv04.json b/datasets/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4_RL06.3Mv04.json index f87d5a545b..9c37c2946e 100644 --- a/datasets/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4_RL06.3Mv04.json +++ b/datasets/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4_RL06.3Mv04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4_RL06.3Mv04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions.\n

\nThe complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines.\n

\nThis RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_CSR_RL06_LND_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_CSR_RL06_LND_v04_RL06v04.json index e4d3abe468..0c0271c48c 100644 --- a/datasets/TELLUS_GRAC_L3_CSR_RL06_LND_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_CSR_RL06_LND_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_CSR_RL06_LND_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_CSR_RL06_OCN_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_CSR_RL06_OCN_v04_RL06v04.json index 66218bd45c..797b9f2717 100644 --- a/datasets/TELLUS_GRAC_L3_CSR_RL06_OCN_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_CSR_RL06_OCN_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_CSR_RL06_OCN_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_GFZ_RL06_LND_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_GFZ_RL06_LND_v04_RL06v04.json index 2b2421aa53..da562ed4ad 100644 --- a/datasets/TELLUS_GRAC_L3_GFZ_RL06_LND_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_GFZ_RL06_LND_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_GFZ_RL06_LND_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04.json index a4e05ba4d3..53c9b4a339 100644 --- a/datasets/TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04.json index 5da67ce412..93e54269fe 100644 --- a/datasets/TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04.json b/datasets/TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04.json index fedc81ba2d..76dc313b22 100644 --- a/datasets/TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04.json +++ b/datasets/TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04.json index 676f08d688..9b0ff92a1e 100644 --- a/datasets/TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.

\n\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04.json index ba38060823..01a8b2513f 100644 --- a/datasets/TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. \n

\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04.json index 47befcf114..e9e55157b1 100644 --- a/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. \n

\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json index 617147bfdd..243e7ed911 100644 --- a/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. \n

\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json index 1b496c5e94..7429b4a6a4 100644 --- a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.

\r\n\r\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json index 3c5230ea71..ffecf7620a 100644 --- a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json +++ b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.

\r\n\r\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.", "links": [ { diff --git a/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json b/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json index 45e05da61c..c43000a580 100644 --- a/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json +++ b/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPEST_STPH8_L1_TSDR_V10.0_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the TEMPEST (Temporal Experiment for Storms and Tropical Systems) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. TEMPEST swath width is 1400 kilometers and resolution at nadir is 25 km for the 87 GHz channel and 13 km for the 180 GHz channels. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.\n

\nThe TEMPEST instrument is a microwave radiometer deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission, with the primary objective of tropical cyclone intensity tracking. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. A successful mission will demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data. TEMPEST was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.", "links": [ { diff --git a/datasets/TEMPO_CLDO4_L2_V03.json b/datasets/TEMPO_CLDO4_L2_V03.json index ba7f987a6f..be74bd084a 100644 --- a/datasets/TEMPO_CLDO4_L2_V03.json +++ b/datasets/TEMPO_CLDO4_L2_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_CLDO4_L2_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "O2-O2 cloud Level 2 files provide cloud information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on effective cloud fraction (ECF), cloud optical centroid pressure (OCP), ancillary data, processing quality flags, etc. The ECF is derived from reflectance at 466 nm. The OCP is derived from O2-O2 slant column density. The cloud retrieval uses Look Up Tables (LUTs) of reflectance and air mass factors, GEOS-CF forecast meteorology, and GLER surface albedo. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_CLDO4_L3_V03.json b/datasets/TEMPO_CLDO4_L3_V03.json index 6599f489b5..2ce83ddc41 100644 --- a/datasets/TEMPO_CLDO4_L3_V03.json +++ b/datasets/TEMPO_CLDO4_L3_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_CLDO4_L3_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "O2-O2 cloud Level 3 files provide cloud information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on effective cloud fraction, cloud optical centroid pressure, and ancillary data. The re-gridding algorithm uses an area-weighted approach. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_DRK_L1_V02.json b/datasets/TEMPO_DRK_L1_V02.json index ebce97da30..00f0fb5d5e 100644 --- a/datasets/TEMPO_DRK_L1_V02.json +++ b/datasets/TEMPO_DRK_L1_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_DRK_L1_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 dark files provide the processed dark currents, corresponding to either solar irradiance measurements or radiance measurements. Each file includes the measured dark currents for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on dark current rates of all frames and their average for the UV and visible bands, pixel quality flags and other ancillary information. The product is produced using the image processing of L0-1b processor. Please refer to the ATBD for details.\r\nThese data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters.\r\nBecause the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status.\r\nThe TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov.", "links": [ { diff --git a/datasets/TEMPO_DRK_L1_V03.json b/datasets/TEMPO_DRK_L1_V03.json index cb785c4be4..d50f2a6cc3 100644 --- a/datasets/TEMPO_DRK_L1_V03.json +++ b/datasets/TEMPO_DRK_L1_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_DRK_L1_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 dark files provide the processed dark currents, corresponding to either solar irradiance measurements or radiance measurements. Each file includes the measured dark currents for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on dark current rates of all frames and their average for the UV and visible bands, pixel quality flags and other ancillary information. The product is produced using the image processing of L0-1b processor. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_HCHO_L2_V03.json b/datasets/TEMPO_HCHO_L2_V03.json index 8225efdc43..7ad281b782 100644 --- a/datasets/TEMPO_HCHO_L2_V03.json +++ b/datasets/TEMPO_HCHO_L2_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_HCHO_L2_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Formaldehyde Level 2 files provide trace gas information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on vertical columns, ancillary data used in air mass factor calculations and reference sector corrections, and retrieval quality flags. The retrieval uses a three-step approach: (1) spectral fitting of slant columns, (2) air mass factor calculation and derivation of vertical columns, and (3) reference sector corrections. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_HCHO_L3_V03.json b/datasets/TEMPO_HCHO_L3_V03.json index 23f329a4e3..8a0c39bff0 100644 --- a/datasets/TEMPO_HCHO_L3_V03.json +++ b/datasets/TEMPO_HCHO_L3_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_HCHO_L3_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Formaldehyde Level 3 files provide trace gas information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on formaldehyde vertical columns, ancillary data used in air mass factor calculations and reference sector or de-striping corrections, and retrieval quality flags. The re-gridding algorithm uses an area-weighted approach. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_IRRR_L1_V02.json b/datasets/TEMPO_IRRR_L1_V02.json index 78bac5bd12..60428f00e1 100644 --- a/datasets/TEMPO_IRRR_L1_V02.json +++ b/datasets/TEMPO_IRRR_L1_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_IRRR_L1_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 reference irradiance files provide solar irradiance measured using the reference solar diffuser. Each file includes the measured solar irradiance for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated solar irradiance for the UV and visible bands, corresponding noise, parameterized wavelength grid, solar viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, and (2) Additional wavelength calibration to improve wavelength registration. Please refer to the ATBD for details.\r\nThese data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters.\r\nBecause the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status.\r\nThe TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov.", "links": [ { diff --git a/datasets/TEMPO_IRRR_L1_V03.json b/datasets/TEMPO_IRRR_L1_V03.json index 244860f31c..ab582dc930 100644 --- a/datasets/TEMPO_IRRR_L1_V03.json +++ b/datasets/TEMPO_IRRR_L1_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_IRRR_L1_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 reference irradiance files provide solar irradiance measured using the reference solar diffuser. Each file includes the measured solar irradiance for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated solar irradiance for the UV and visible bands, corresponding noise, parameterized wavelength grid, solar viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, and (2) Additional wavelength calibration to improve wavelength registration. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_IRR_L1_V02.json b/datasets/TEMPO_IRR_L1_V02.json index 491be315be..c4953e7c53 100644 --- a/datasets/TEMPO_IRR_L1_V02.json +++ b/datasets/TEMPO_IRR_L1_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_IRR_L1_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 irradiance files provide solar irradiance measured using the working solar diffuser. Each file includes the measured solar irradiance for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated solar irradiance for the UV and visible bands, corresponding noise, parameterized wavelength grid, solar viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, and (2) Additional wavelength calibration to improve wavelength registration. Please refer to the ATBD for details.\r\nThese data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters.\r\nBecause the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status.\r\nThe TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov.", "links": [ { diff --git a/datasets/TEMPO_IRR_L1_V03.json b/datasets/TEMPO_IRR_L1_V03.json index fc062841dc..3f28956241 100644 --- a/datasets/TEMPO_IRR_L1_V03.json +++ b/datasets/TEMPO_IRR_L1_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_IRR_L1_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 irradiance files provide solar irradiance measured using the working solar diffuser. Each file includes the measured solar irradiance for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated solar irradiance for the UV and visible bands, corresponding noise, parameterized wavelength grid, solar viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, and (2) Additional wavelength calibration to improve wavelength registration. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_NO2_L2_V03.json b/datasets/TEMPO_NO2_L2_V03.json index 2f3189212e..390b4fef95 100644 --- a/datasets/TEMPO_NO2_L2_V03.json +++ b/datasets/TEMPO_NO2_L2_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_NO2_L2_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nitrogen dioxide Level 2 files provide trace gas information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on tropospheric, stratospheric and total nitrogen dioxide vertical columns, ancillary data used in air mass factor and stratospheric/tropospheric separation calculations, and retrieval quality flags. The retrieval uses a three-step approach: (1) spectral fitting of slant columns, (2) air mass factor calculation and derivation of vertical columns, and (3) stratospheric/tropospheric separation. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_NO2_L3_V03.json b/datasets/TEMPO_NO2_L3_V03.json index fab2aafa0c..dc0707d28c 100644 --- a/datasets/TEMPO_NO2_L3_V03.json +++ b/datasets/TEMPO_NO2_L3_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_NO2_L3_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nitrogen dioxide Level 3 files provide trace gas information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on tropospheric, stratospheric and total nitrogen dioxide vertical columns, ancillary data used in air mass factor and stratospheric/tropospheric separation calculations, and retrieval quality flags. The re-gridding algorithm uses an area-weighted approach. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_O3TOT_L2_V03.json b/datasets/TEMPO_O3TOT_L2_V03.json index 973ddd5750..98f2374eda 100644 --- a/datasets/TEMPO_O3TOT_L2_V03.json +++ b/datasets/TEMPO_O3TOT_L2_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_O3TOT_L2_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total ozone Level 2 files provide ozone information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on total column ozone and some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, effective cloud fraction, effective cloud pressure, radiative cloud fraction, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The retrieval is based on the OMI TOMS V8.5 algorithm adapted for TEMPO. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_O3TOT_L3_V03.json b/datasets/TEMPO_O3TOT_L3_V03.json index 31d46df0e1..b0ad1e6f1c 100644 --- a/datasets/TEMPO_O3TOT_L3_V03.json +++ b/datasets/TEMPO_O3TOT_L3_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_O3TOT_L3_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total ozone Level 3 files provide ozone information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on total column ozone and some auxiliary derived and ancillary input parameters including effective cloud fraction, effective cloud pressure, radiative cloud fraction, SO2 index, and terrain pressure. The re-gridding algorithm uses an area-weighted approach. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_RADT_L1_V03.json b/datasets/TEMPO_RADT_L1_V03.json index 89006e5e47..78dcae605f 100644 --- a/datasets/TEMPO_RADT_L1_V03.json +++ b/datasets/TEMPO_RADT_L1_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_RADT_L1_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 twilight radiance files provide radiance measured during twilight hours to capture city lights at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically calibrated and geolocated radiances for the UV and visible bands, corresponding noise, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes image processing steps to produce radiometrically calibrated radiances with nominal navigation. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMPO_RAD_L1_V02.json b/datasets/TEMPO_RAD_L1_V02.json index df55701644..4d4d8a0fb4 100644 --- a/datasets/TEMPO_RAD_L1_V02.json +++ b/datasets/TEMPO_RAD_L1_V02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_RAD_L1_V02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 radiance files provide radiance information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging and polarization correction. Please refer to the ATBD for details.\r\nThese data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters.\r\nBecause the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status.\r\nThe TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov.", "links": [ { diff --git a/datasets/TEMPO_RAD_L1_V03.json b/datasets/TEMPO_RAD_L1_V03.json index 5e9db16270..f00c3e6f3f 100644 --- a/datasets/TEMPO_RAD_L1_V03.json +++ b/datasets/TEMPO_RAD_L1_V03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMPO_RAD_L1_V03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1 radiance files provide radiance information at TEMPO\u2019s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging. These data reached provisional validation on December 9, 2024.", "links": [ { diff --git a/datasets/TEMR_RSFCE.json b/datasets/TEMR_RSFCE.json index c07f36e293..ba04155b39 100644 --- a/datasets/TEMR_RSFCE.json +++ b/datasets/TEMR_RSFCE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TEMR_RSFCE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrometeorological data on the conditions of the environment are\n held by the Russian State Fund of data. This dataset was created by\n Computer Centre North Administration for hydrometeorology in 1990 and\n containes air temperature from 68 stations in Arhangelsk, Vologda\n regions and Komi ASSR in Russia. Data is currently stored on magnetic\n tape (800 bit/inch).", "links": [ { diff --git a/datasets/TG02_Balloon_VOC_1110_1.json b/datasets/TG02_Balloon_VOC_1110_1.json index 197c67341a..c4abfe866a 100644 --- a/datasets/TG02_Balloon_VOC_1110_1.json +++ b/datasets/TG02_Balloon_VOC_1110_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG02_Balloon_VOC_1110_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000. The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest. Two other sites were also located in Rondonia: at a forest reserve (Rebio Jaru) and a pasture (Fazenda Nossa Senhora Aparecida). The BVOCs measured included isoprene, alpha and beta pinene, camphene, sabinene, myrcene, limonene, and other monoterpenes. Approximately 24 to 40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. There is one comma-delimited data file with this data set.", "links": [ { diff --git a/datasets/TG03_AERONET_AOT_1128_1.json b/datasets/TG03_AERONET_AOT_1128_1.json index a52c3b6ed6..a1761d7326 100644 --- a/datasets/TG03_AERONET_AOT_1128_1.json +++ b/datasets/TG03_AERONET_AOT_1128_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG03_AERONET_AOT_1128_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes aerosol optical thickness measurements from the CIMEL sunphotometer for 22 sites in Brazil during the period from 1993-2005. The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (cloud-screened and quality-assured). Data provided here are Level 2.0. There are 22 comma-delimited data files with this data set and one companion text file which contains the latitude, longitude, and elevation of the 22 sites.", "links": [ { diff --git a/datasets/TG03_Aeronet_Solar_Flux_1137_1.json b/datasets/TG03_Aeronet_Solar_Flux_1137_1.json index ed56df125f..71f364fca0 100644 --- a/datasets/TG03_Aeronet_Solar_Flux_1137_1.json +++ b/datasets/TG03_Aeronet_Solar_Flux_1137_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG03_Aeronet_Solar_Flux_1137_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes solar surface irradiance from Kipp and Zonen CM-21 pyranometers, both total unfiltered and filtered (RG695), and photosynthetically active radiation (PAR) from Skye-Probetech SKE-510 PAR sensors. Measurements were made at six sites acrosss the Brazilian Amazon during the period from 1999 to 2004. These sites were co-located with AERONET (AErosol RObotic NETwork) program sites. There are 17 comma-delimited data files (.csv) with this data set. The AERONET program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of those properties. The network imposes standardization of instruments, calibration, and processing. ", "links": [ { diff --git a/datasets/TG05_CASA_1199_1.json b/datasets/TG05_CASA_1199_1.json index 2a3c0667bd..fe6785c907 100644 --- a/datasets/TG05_CASA_1199_1.json +++ b/datasets/TG05_CASA_1199_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG05_CASA_1199_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides maps produced from model output data from the National Aeronautics and Space Administration-Carnegie Ames Stanford Approach (NASA-CASA) model and other modeling approaches. The maps include estimated annual Net Primary Production (ANPP), leaf (live) biomass carbon, wood (live) biomass carbon, fine root (live) biomass carbon, metabolic leaf litter (dead) carbon, structural leaf litter (dead) carbon, woody detritus (dead) carbon, and slow soil carbon, gridded at half-degree spatial resolution for the years 1982-1998, and 2001 (NPP data) for Brazil. Maps are provided at one-degree resolution for monthly soil emissions and soil uptake of N2O, NO, CO, and CH4. In addition, there are maps in 8-km resolution for soil texture, soil carbon, soil pH, soil maximum plant available water (paw), and net primary productivity (NPP).There are three files with this data set in tar.gz format. The files are in half-degree, one-degree, and 8-km resolution. When expanded, the half degree and one degree files contain 83 map files in GeoTIFF (.tif) format. The third file (8-km resolution) contains the soil and productivity maps. When expanded, this file contains 22 files in GeoTIFF (.tif) format.", "links": [ { diff --git a/datasets/TG06_Vertical_Profiles_1175_1.json b/datasets/TG06_Vertical_Profiles_1175_1.json index 97a0b535e1..83a0e8d069 100644 --- a/datasets/TG06_Vertical_Profiles_1175_1.json +++ b/datasets/TG06_Vertical_Profiles_1175_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG06_Vertical_Profiles_1175_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), hydrogen (H2), nitrous oxide (N2O), and sulfur hexafluoride (SF6) collected from December 2000-November 2005 as vertical profiles above three sites in Brazil: Fortaleza, Santarem, and Manaus. At Santarem, ascending profiles were made above the Tapajos National Forest, near the km 67 Tower Site. At Manaus, ascending profiles were made above the K34 flux tower (aka, ZF2 km 34 tower) to the northwest of the city of Manaus. Descending profiles were flown nearby, but at locations upwind of population centers to avoid possible pollution. Fortaleza samples were collected off the coast, over the Atlantic Ocean to sample background air before it flows over the Amazon Basin.Air samples were collected as discrete samples aboard light aircraft and shipped to laboratories for analysis relative to internationally accepted calibration standards.There are three comma-delimited (.csv) data files with this data set.", "links": [ { diff --git a/datasets/TG07_Autochamber_Soil_CO2_Flux_Km67_927_1.json b/datasets/TG07_Autochamber_Soil_CO2_Flux_Km67_927_1.json index 19b7fc3f28..125cbf8564 100644 --- a/datasets/TG07_Autochamber_Soil_CO2_Flux_Km67_927_1.json +++ b/datasets/TG07_Autochamber_Soil_CO2_Flux_Km67_927_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Autochamber_Soil_CO2_Flux_Km67_927_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of the soil-atmosphere flux of CO2 were made at the km 67 flux tower site in the Tapajos National Forest, Santarem, Para, Brazil. Eight chambers were set up to measure trace gas exchange between the soil and atmosphere about 5 times a day (during daylight and night) at this undisturbed forest site from April 2001 to April 2003. CO2 soil efflux data are reported in one ASCII comma separated file.The automated chamber system consisted of 8 automatically opening and closing aluminum chambers with an infrared gas analyzer. The chambers were installed in a 0.5 ha area close to the flux tower on patches of ground without apparent photosynthetic vegetation. Each chamber was sequentially closed, sampled, and re-opened 5 times per day (closed 7% of the day). The maximum daily average flux was 4.3 and the minimum was 1.3 umol CO2 m-2 s-1.", "links": [ { diff --git a/datasets/TG07_DBH_Cauaxi_1063_1.json b/datasets/TG07_DBH_Cauaxi_1063_1.json index cf8bd1c13a..73ad08adf0 100644 --- a/datasets/TG07_DBH_Cauaxi_1063_1.json +++ b/datasets/TG07_DBH_Cauaxi_1063_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_DBH_Cauaxi_1063_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy measurements in an undisturbed eastern Amazon forest (Cauxi, Para, Brazil. See Figure 1) were derived from a one-time event in 2000 using a hand-held laser range finder, and diameter at breast height (DBH) was determined manually. Parameters reported include: Crown Width, Crown Depth, Tree Height, and DBH. There is one comma-delimited ASCII data file with this data set. In addition, these manually derived measurements were compared to the IKONOS satellite data of crown dimensions that were acquired on 2 November 2000, from an orbital altitude of 680 km. The data from a 600 x 600 m block of undisturbed forest, including the 50 ha area surveyed in the field, were analyzed in a combined image processing and geographic information system environment.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: Only the general location for this study was identified -- Cauaxi, Para, Brazil. The tree measurement data are of limited use because coordinates for the study site, coordinates of the beginning and end of the transects, and coordinates of the measured trees were not provided. Also, the area that the IKONOS image captured was not provided and the IKONOS image is not available due to restricted distribution.", "links": [ { diff --git a/datasets/TG07_FFT_Survey_Km83_923_1.json b/datasets/TG07_FFT_Survey_Km83_923_1.json index dded16a0bd..7e39609a60 100644 --- a/datasets/TG07_FFT_Survey_Km83_923_1.json +++ b/datasets/TG07_FFT_Survey_Km83_923_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_FFT_Survey_Km83_923_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A field inventory of trees was conducted in March of 1997 in a logging concession at the Tapajos National Forest, south of Santarem, Para, Brazil. The inventory was conducted by the foresters and technicians of the Tropical Forest Foundation (FFT) and included all trees with diameter at breast height greater than or equal to 35 cm. Four blocks of approximately 100 ha each within the 3,200 ha concession were inventoried. Within each block, parallel trails 50 m apart were established, and the location of each tree measured was recorded to the nearest meter using an orthogonal coordinate system based on these trails. Field data for each tree includes: identification number, ground position, diameter, common name, scientific name and qualitative estimates of bole and canopy quality. Data are provided in one ASCII comma separated file. These data were used to calculate above-ground live biomass as described in Keller et al. (2001), but biomass data are not included in this data set.", "links": [ { diff --git a/datasets/TG07_Fallen_Standing_Necromass_998_1.json b/datasets/TG07_Fallen_Standing_Necromass_998_1.json index 4781020c1d..6eba22e2e7 100644 --- a/datasets/TG07_Fallen_Standing_Necromass_998_1.json +++ b/datasets/TG07_Fallen_Standing_Necromass_998_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Fallen_Standing_Necromass_998_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the characterization of fallen necromass as the volume and density of coarse woody debris (CWD), and standing necromass as the volume and density of standing dead trees. Measurements were made in undisturbed and logged forest areas of the Tapajos National Forest, and Cauaxi Forest, Para, Brazil, and Juruena Forest, Mato Grosso, Brazil from 2002-2004. Fallen and standing necromass were classified into one of five categories according to its state of decomposition. There are two comma-delimited ASCII data files with this data set: two files contain the sampling information, decomposition state, and DBH measurements. There are also two files provided as companion data files which provide sampling transect descriptions. ", "links": [ { diff --git a/datasets/TG07_Litter_Decomposition_925_1.json b/datasets/TG07_Litter_Decomposition_925_1.json index b295036d94..08f056e710 100644 --- a/datasets/TG07_Litter_Decomposition_925_1.json +++ b/datasets/TG07_Litter_Decomposition_925_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Litter_Decomposition_925_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of this study was to determine the effects of soil phosphorus (P) status on litter decomposition rates using two factors: soil texture (with associated differences in soil P pools) and fertilization, in a fully factorial design. Mass and nutrient pools in litter from a single species, Marupa (Simaruba amara (Aubl.)), were measured quarterly between March 2000 and February 2001 in an undisturbed mature forest within the Tapajos National Forest at the kilometer 83 site. Reported here are litter mass and nutrients (carbon, nitrogen and phosphorus) as both raw values (grams for mass, percent for carbon and nitrogen and mg per kg for phosphorus), and as proportion of the initial mass and nutrient pool. Data are provided in a comma separated ASCII format.", "links": [ { diff --git a/datasets/TG07_Manual_Flux_Km67_1026_1.json b/datasets/TG07_Manual_Flux_Km67_1026_1.json index bff8768b11..9d062e5f55 100644 --- a/datasets/TG07_Manual_Flux_Km67_1026_1.json +++ b/datasets/TG07_Manual_Flux_Km67_1026_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Manual_Flux_Km67_1026_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace gas fluxes of carbon dioxide, methane, nitrous oxide, and nitric oxide (CO2, CH4, N2O, and NO) from surface soil were measured manually in an undisturbed forest at the Tapajos National Forest Seca-Floresta Site, which is within the footprint of the km 67 eddy flux tower. Measurements were made in January 2000 through April 2004, approximately twice per month. On each sampling date, up to four sets of 30-m lines were established off the existing transects at the Seca-Floresta site. Along each line eight chambers were installed for gas collection. In addition soil samples were collected for analysis of soil moisture as water-filled pore space (WFPS). There is one comma-delimited ASCII file with this data set.", "links": [ { diff --git a/datasets/TG07_Root_Mortality_Experiment_924_1.json b/datasets/TG07_Root_Mortality_Experiment_924_1.json index 885cfbc3b1..ef36379a36 100644 --- a/datasets/TG07_Root_Mortality_Experiment_924_1.json +++ b/datasets/TG07_Root_Mortality_Experiment_924_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Root_Mortality_Experiment_924_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of an experiment that tested the short-term effects of root mortality on the soil-atmosphere fluxes of nitrous oxide, nitric oxide, methane, and carbon dioxide in a tropical evergreen forest. Weekly trace gas fluxes are provided for treatment and control plots on sand and clay tropical forest soils in two comma separated ASCII files.The study site in the Tapajos National Forest (TNF) is near km 83 on the Santarem-Cuiaba Highway south of Santarem, Para, Brazil. Root mortality was induced by isolating blocks of land to 1 m depth using trenching and root exclusion screening. Gas fluxes were measured weekly for ten weeks following the trenching treatment and monthly for the remainder of the year. Monthly data are not included at this time.", "links": [ { diff --git a/datasets/TG07_Root_Mortality_Longterm_1116_1.json b/datasets/TG07_Root_Mortality_Longterm_1116_1.json index 7a96d1e926..f9bd7fab87 100644 --- a/datasets/TG07_Root_Mortality_Longterm_1116_1.json +++ b/datasets/TG07_Root_Mortality_Longterm_1116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Root_Mortality_Longterm_1116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports measurements of trace gas fluxes of methane (CH4), nitric oxide (N2O), nitrous oxide (NO), carbon dioxide (CO2) from soils at a study site in the Tapajos National Forest (TNF), near the km 83 on the Santarem-Cuiaba Highway south of Santarem, Para, Brazil. Data for root mass and carbon content, soil nitrogen (N), nitrification, and moisture content are also provided. There are five comma-delimited data files with this data set.The research was conducted to test the effects of root mortality on the soil-atmosphere trace-gas fluxes over the course of one year. Root mortality was induced by isolating blocks of land to 1 m depth using trenching and root exclusion screening. Gas fluxes were measured weekly for ten weeks following the trenching treatment and monthly for the remainder of the year.Note: The related data set LBA-ECO TG-07 Soil Trace Gas Flux and Root Mortality, Tapajos National Forest contains the same flux data that were measured weekly for ten weeks following the trenching treatment. This data set also provides the monthly data for the remainder of the year. ", "links": [ { diff --git a/datasets/TG07_STM_GLAS_836_1.json b/datasets/TG07_STM_GLAS_836_1.json index 0914014181..c80e4818d1 100644 --- a/datasets/TG07_STM_GLAS_836_1.json +++ b/datasets/TG07_STM_GLAS_836_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_STM_GLAS_836_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of a GLAS (the Geoscience Laser Altimeter System) forest structure validation survey conducted in Santarem and Sao Jorge, Para during November 2004 (Lefsky et al., 2005). DBH, total height, commercial height, canopy width and canopy class description were measured for 11 primary forest sites in Santarem along two 75m transects per GLAS measurement. For 10 secondary forest sites in Sao Jorge, the number of stems 0-2cm, 2-5cm, 5-10cm, and greater than 10cm were measured. For all stems greater than 10cm the DBH was measured, and for all sites, the maximum height was recorded. The basal area was calculated for all trees with DBH greater than 10cm within our transects, and biomass was calculated using the Brown, 1997 formula.Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage.", "links": [ { diff --git a/datasets/TG07_Soil-Atmosphere_Flux_Km83_926_1.json b/datasets/TG07_Soil-Atmosphere_Flux_Km83_926_1.json index 00a341cbb2..0c8e3029e4 100644 --- a/datasets/TG07_Soil-Atmosphere_Flux_Km83_926_1.json +++ b/datasets/TG07_Soil-Atmosphere_Flux_Km83_926_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Soil-Atmosphere_Flux_Km83_926_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Trace gas fluxes of carbon dioxide, methane, nitrous oxide, and nitric oxide were measured manually at undisturbed and logged forest sites in the Tapajos National Forest, near Santarem, Para, Brazil. Manual measurements were made approximately weekly at both the undisturbed and logged sites. Fluxes from clay and sand soils were completed at the undisturbed sites. Fluxes were measured at the deck (patio), skid trail, clearing and forest at the logged sites. Soil moisture is reported as daily average water-filled pore space (WFPS) for the undisturbed forest clay and sand soils. Data are reported in three ASCII comma separated files.", "links": [ { diff --git a/datasets/TG07_Soil_Nutrients_1085_1.json b/datasets/TG07_Soil_Nutrients_1085_1.json index 93f4d966eb..70a9510ebc 100644 --- a/datasets/TG07_Soil_Nutrients_1085_1.json +++ b/datasets/TG07_Soil_Nutrients_1085_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Soil_Nutrients_1085_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports phosphorus (P), carbon (C), and nitrogen (N) nutrient pool concentrations for forest soils and roots and P pool concentrations for forest floor litter, soil solutions, and microbial extracts. Soils samples were also extracted using the Hedley sequential fractionation method and the extracts analyzed for P. Nutrient pool concentrations are presented on an areal basis of 1 hectare to a depth of 10 cm, as calculated from soil bulk densities and respective pool biomass quantities. There is one comma-delimited ASCII file with this data set. These measurements were made during a soil P addition fertilization experiment conducted at the km 83 site, Tapajos National Forest, Para, Brazil. Control and fertilized plots were established in both sandy loam and clay soils. Soil cores were collected every 4 months from August 1999 through April 2000 (McGroddy et al. 2008). ", "links": [ { diff --git a/datasets/TG07_Trace_Gas_Profiles_1107_1.json b/datasets/TG07_Trace_Gas_Profiles_1107_1.json index e4931f2488..71a7d771b9 100644 --- a/datasets/TG07_Trace_Gas_Profiles_1107_1.json +++ b/datasets/TG07_Trace_Gas_Profiles_1107_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG07_Trace_Gas_Profiles_1107_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides concentrations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from air samples collected at several heights on towers at three locations in upland old growth forests in the Brazilian Amazon during the wet and dry seasons of 2004 and 2005. Towers are located in the Caxiuana National Forest, in the state of Amazonas; the Manaus, Para, site in the Cuieiras Reserve; and the Sinop site, located north of that city in the state of Mato Grosso. Two sampling campaigns were conducted at each location. Samples were collected from each height 3-5 times on several nights and at least once during well-mixed daytime conditions during each campaign for a total of 75 profiles on 19 dates. There is one comma-delimited ASCII file with this data set. ", "links": [ { diff --git a/datasets/TG08_Soil_Gas_Fertilization_1105_1.json b/datasets/TG08_Soil_Gas_Fertilization_1105_1.json index 2b45a14c74..af62854c3b 100644 --- a/datasets/TG08_Soil_Gas_Fertilization_1105_1.json +++ b/datasets/TG08_Soil_Gas_Fertilization_1105_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG08_Soil_Gas_Fertilization_1105_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides nitric oxide (NO), nitrous oxide (N2O), carbon dioxide (CO2) flux measurements, nitrogen (N) and phosphorus (P) pools, net N mineralization and nitrification rates, and measurements of soil moisture, in response to nitrogen and phosphorus soil fertilization treatments. The research was conducted in a mature moist tropical forest and an 11-year pasture at Nova Vida in Rondonia, in the Brazilian Amazon, in 1998 and 1999. There is one comma-delimited ASCII data file with this data set.", "links": [ { diff --git a/datasets/TG08_Soil_Gas_Wetting_1101_1.json b/datasets/TG08_Soil_Gas_Wetting_1101_1.json index de8fe16e56..ee3220a5cc 100644 --- a/datasets/TG08_Soil_Gas_Wetting_1101_1.json +++ b/datasets/TG08_Soil_Gas_Wetting_1101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG08_Soil_Gas_Wetting_1101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes the results of measurements of the soil gas fluxes of nitric oxide (NO), nitrous oxide (N2O), and carbon dioxide (CO2), soil moisture, soil temperature, and soil pools of ammonium and nitrate in response to a simulated rain event. Study sites were soils in mature forests and pastures of two ages (11 and 26 yrs old). The study took place during the dry season in August 1998 at Fazenda Nova Vida, Rondonia in the Brazilian Amazon. There is one comma-delimited ASCII file with this data set. This study investigated how changes in soil moisture (i.e., rains at the end of the dry season) affected the fluxes of NO, N20 and CO2 from forest and pasture soils in the southwestern Brazilian Amazon (Garcia-Montiel, et al., 2003). The main objectives were to measure the short-term dynamics of soil emissions of NO, N20, and CO2 in forest and pasture soils associated with soil wetting after prolonged dryness; and quantify the contribution of the pulses of N oxide fluxes resulting from soil wetting to dry season and annual fluxes. ", "links": [ { diff --git a/datasets/TG09_N2O_Soils_1013_1.json b/datasets/TG09_N2O_Soils_1013_1.json index 52e51e1c6e..deb6cfd445 100644 --- a/datasets/TG09_N2O_Soils_1013_1.json +++ b/datasets/TG09_N2O_Soils_1013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG09_N2O_Soils_1013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the results of carbon, nitrogen, and oxygen isotopic analyses of soil, soil water, and N2O soil gas samples; total soil carbon and nitrogen concentrations; and soil texture and bulk density. Samples were collected from the km 83 Logged Forest Tower Site and the km 67 Seca-Floresta Site in the Tapajos National Forest (TNF) near Santarem, Para, Brazil. Soil samples were collected in July of 2000 and soil gas samples were collected in 2001 and 2002. Soil and gas samples were collected from various soil types at each site and from several depths in specially constructed pits. There is one comma-delimited ASCII data file with this data set. ", "links": [ { diff --git a/datasets/TG10_TROFFEE_1195_1.json b/datasets/TG10_TROFFEE_1195_1.json index 792fe6209c..122c01aae8 100644 --- a/datasets/TG10_TROFFEE_1195_1.json +++ b/datasets/TG10_TROFFEE_1195_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TG10_TROFFEE_1195_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides derived emission factors (EFs), reported in grams of compound emitted per kilogram of dry fuel (g/kg), for PM10 (particulate matter up to 10 micrometers in size), O3, CO2, CO, NO, NO2, HONO, HCN, NH3, OCS, DMS, CH4, and up to 48 non-methane organic compounds (NMOC) from the Tropical Forest and Fire Emissions Experiment (TROFFEE). TROFFEE used laboratory measurements followed by airborne and ground based field campaigns in Mato Grosso, Para, and Amazonas, Brazil during the 2004 Amazon dry season to quantify the emissions from pristine tropical forest and several plantations as well as the emissions, fuel consumption, and fire ecology of tropical deforestation fires. EFs were determined for 19 tropical deforestation fires in August and September, 2004. The combined output of these fires created a massive megaplume more than 500-km wide and covered a large area in Brazil, Bolivia, and Paraguay for about one month. For the megaplume, the EFs (reported in grams of compound emitted per kilogram of dry fuel (g/kg)) represented the effective emissions factor measured downwind from the source.There are two comma-delimited data files (.csv) and one text file (.txt) with this data set. The text file contains information regarding the fuel/fire sources, latitude and longitudes (also provided in the data files).", "links": [ { diff --git a/datasets/THAILAND_0.json b/datasets/THAILAND_0.json index cfc41ea0f0..90fcd80f87 100644 --- a/datasets/THAILAND_0.json +++ b/datasets/THAILAND_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THAILAND_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Gulf of Thailand and the Andaman Sea in 2003.", "links": [ { diff --git a/datasets/THIRN4IMCH67_001.json b/datasets/THIRN4IMCH67_001.json index 053b4713a9..0a5f1432a3 100644 --- a/datasets/THIRN4IMCH67_001.json +++ b/datasets/THIRN4IMCH67_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN4IMCH67_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN4IMCH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 microns on 70 mm photofacsimile film strips. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution of 22.6 km at nadir decreases as the horizontal distance from the subsatellite track increases. Each film strip is gridded with geographic coordinates and is identified by orbit number, time, and an indication of whether it is daytime (D) or nighttime (N). The images are saved as JPEG 2000 digital files. About 1 week of images are archived into a TAR file. Additional information can be found in section 3.4.1 of \"The Nimbus IV User's Guide\".\n\nThe THIR instrument was designed to detect emitted thermal radiation in both the 10.5- to 12.5-micrometer region (IR window) and the 6.5- to 7.0-micrometer region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from April 18, 1970 until April 8, 1971.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00194 (old ID 70-025A-02B).", "links": [ { diff --git a/datasets/THIRN4L1CH115_001.json b/datasets/THIRN4L1CH115_001.json index eb973390d0..f40920c80a 100644 --- a/datasets/THIRN4L1CH115_001.json +++ b/datasets/THIRN4L1CH115_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN4L1CH115_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN4L1CH115 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-4 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-4 replaced the measurements made by the HRIR and MRIR instruments flown on previous Nimbus satellites. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night.\n\nThe THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-4 THIR data are available from April 13, 1970 (day of year 103) through April 1, 1971 (day of year 91). The THIRN4L1CH67 product contains the 6.7 micron channel data.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00004 (old ID 70-025A-02D).", "links": [ { diff --git a/datasets/THIRN4L1CH67_001.json b/datasets/THIRN4L1CH67_001.json index 57c979346e..7921596ae0 100644 --- a/datasets/THIRN4L1CH67_001.json +++ b/datasets/THIRN4L1CH67_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN4L1CH67_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN4L1CH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-4 satellite was successfully launched on April 8, 1970. The THIR experiment on Nimbus-4 replaced the measurements made the HRIR and MRIR instruments flown on previous Nimbus satellites. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night.\n\nThe THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-4 THIR data are available from April 14, 1970 (day of year 104) through March 25, 1971 (day of year 84). The THIRN4L1CH115 product contains the 11.5 micron channel data.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00071 (old ID 70-025A-02E).", "links": [ { diff --git a/datasets/THIRN5L1CH115_001.json b/datasets/THIRN5L1CH115_001.json index 4666946805..be20818c44 100644 --- a/datasets/THIRN5L1CH115_001.json +++ b/datasets/THIRN5L1CH115_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN5L1CH115_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN5L1CH115 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-5 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-5 THIR data are available from December 19, 1972 (day of year 354) through March 1, 1975 (day of year 60). The THIRN5L1CH67 product contains the 6.7 micron channel data.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00020 (old ID 72-097A-08C).", "links": [ { diff --git a/datasets/THIRN5L1CH67_001.json b/datasets/THIRN5L1CH67_001.json index 5f6e7a8188..ac97c13788 100644 --- a/datasets/THIRN5L1CH67_001.json +++ b/datasets/THIRN5L1CH67_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN5L1CH67_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN5L1CH67 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-5 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night.\n\nThe THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-5 THIR data are available from December 19, 1972 (day of year 354) through August 26, 1974 (day of year 238). The THIRN5L1CH115 product contains the 11.5 micron channel data.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00167 (old ID 72-097A-08D).", "links": [ { diff --git a/datasets/THIRN6IM_001.json b/datasets/THIRN6IM_001.json index 92c56c4380..7d37efd380 100644 --- a/datasets/THIRN6IM_001.json +++ b/datasets/THIRN6IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN6IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The THIRN6IM data product consists of daily montages of brightness temperatures on 70 mm photofacsimile film strips from the Nimbus-6 Temperature-Humidity Infrared Radiometer measured at 6.7 and 11.5 microns. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution of 22.6 km for 6.7 microns and 8.2 km for 11.5 microns at nadir decreases as the horizontal distance from the subsatellite track increases. Each film strip is gridded with geographic coordinates and is identified by orbit number, time, and an indication of whether it is daytime (D) or nighttime (N). The images are saved as JPEG 2000 digital files. About 1 week of images are archived into a TAR file. Additional information can be found in section 2.4.1 of \"The Nimbus 6 User's Guide.\"\n\nThe Nimbus 6 Temperature-Humidity Infrared Radiometer (THIR) was designed to detect emitted thermal radiation in both the 10.5- to 12.5-micron region (IR window) and the 6.5- to 7.0-micron region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from June 18, 1975 until May 6, 1976.", "links": [ { diff --git a/datasets/THIRN6L1CH115_001.json b/datasets/THIRN6L1CH115_001.json index 9b417b3e57..81122ea4d1 100644 --- a/datasets/THIRN6L1CH115_001.json +++ b/datasets/THIRN6L1CH115_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN6L1CH115_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN6L1CH115 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-6 satellite was successfully launched on June 18, 1975. The THIR experiment on Nimbus-6 continued the measurements made by its predecessors flown on Nimbus-4 and Nimbus-5. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:\n\n1) The 11.5 micron channel provides both day and night cloud top or surface temperatures, with a resolution at nadir of 8 km, and operates day and night.\n2) The 6.7 micron channel gives information on the water vapor content of the upper troposphere and stratosphere and the location of jet streams and frontal systems, with a resolution at nadir of 22 km, and operates mostly at night.\n\nThe THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00125 (old ID 75-052A-12C).", "links": [ { diff --git a/datasets/THIRN6L1CH67_001.json b/datasets/THIRN6L1CH67_001.json index 2f70d0eeed..56f540416b 100644 --- a/datasets/THIRN6L1CH67_001.json +++ b/datasets/THIRN6L1CH67_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN6L1CH67_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN6L1CH67 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron (water vapor) channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe Nimbus-6 satellite was successfully launched on June 18, 1975. The Temperature-Humidity Infrared Radiometer (THIR) experiment on Nimbus-6 continues the measurements made by its predecessors flown on Nimbus-4 and Nimbus-5. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:\n\n1) The 11.5-12.5 micron channel provides both day and night cloud top or surface temperatures, with a resolution at nadir of 8 km, and operates day and night.\n2) The 6.7 micron channel gives information on the water vapor content of the upper troposphere and stratosphere and the location of jet streams and frontal systems, with a resolution at nadir of 22 km, and operates mostly at night.\n\nThe THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00164 (old ID 75-052A-12D).", "links": [ { diff --git a/datasets/THIRN7IM_001.json b/datasets/THIRN7IM_001.json index 7a44eae3f5..0950aee9ad 100644 --- a/datasets/THIRN7IM_001.json +++ b/datasets/THIRN7IM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN7IM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN7IM is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 and 11.5 microns. Each montage contains either a daytime or nighttime assembly of up to 14 individual swaths. Each swath corresponds to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution is 22.6 km for 6.7 microns and 8.2 km for 11.5 microns at nadir and decreases as the horizontal distance from the subsatellite track increases. Below each swath is information describing the orbit number, and the equator crossing longitude and time.\n\nThe THIR instrument was designed to detect emitted thermal radiation in both the 10.5 to 12.5 micron region (IR window) and the 6.5 to 7.0 micron region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from Oct. 30, 1978 to May 9, 1985 when the instrument was turned off to conserve power. Additional information can be found in section 9 of \"The Nimbus 7 User's Guide,\" and the \"Nimbus 7 Temperature-Humidity Infrared Radiometer (THIR) Data User's Guide.\"\n\nThis product was previously available from the NSSDC as two products with the identifiers ESAD-00174 and ESAD-00239 (old ID 78-098A-10A and 78-098A-10B).", "links": [ { diff --git a/datasets/THIRN7L1BCLT_001.json b/datasets/THIRN7L1BCLT_001.json index d70704429a..5c16938b73 100644 --- a/datasets/THIRN7L1BCLT_001.json +++ b/datasets/THIRN7L1BCLT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN7L1BCLT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN7L1BCLT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Cloud Data for SBUV/TOMS (BCLT) product and contains total cloud amounts; radiances at three cloud altitudes: low (below 2km), middle (2 to 7 km depending on latitude), and height (above the middle cloud layer); cirrus and deep convective clouds; and mean and RMS deviations of cloud and surface radiances. Data are averaged orbit by orbit onto each of the Nimbus 7 TOMS IFOV, which vary from 50 km to 50 km at nadir to 200 km x 200 km at the edges, as well as SBUV 180 km x 180 km IFOVs. The BCLT product includes improved cloud estimation compared to the earlier CLT product.\n\nEach file contains one day of data (~14 orbits per day). Spatial coverage is global. The data are available from October 31, 1978 (day of year 304) through October 27, 1984 (day of year 301).\n\nThis product was previously available from the NSSDC with the identifier ESAD-00196 (old ID 78-098A-10E)", "links": [ { diff --git a/datasets/THIRN7L1CLDT_001.json b/datasets/THIRN7L1CLDT_001.json index dd2bb1768a..7ff0979dff 100644 --- a/datasets/THIRN7L1CLDT_001.json +++ b/datasets/THIRN7L1CLDT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THIRN7L1CLDT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THIRN7L1CLDT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Calibrated Located Radiation Data (CLDT) at 6.7 and 11.5 microns product and contains radiances expressed in units of W/m2/sr measured in the 10.5 - 12.5 (11.5) micron and 6.5 - 7.0 (6.7) micron channels. The data, originally written on IBM 360 machines, were recovered from magnetic 9-track tapes. The data are archived in their original proprietary format.\n\nThe Nimbus-7 satellite was successfully launched on October 28, 1978. The Temperature-Humidity Infrared Radiometer (THIR) experiment on Nimbus-7 is basically identical to its predecessors flown on Nimbus-4, -5 and -6, except that the data were digitized on board. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 6.7 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 20 km and operates mostly at night.\n\nThe THIR Principal Investigator was Dr. Larry L. Stowe from NOAA NESDIS. The Nimbus-7 THIR data are available from October 30, 1979 (day of year 303) through May 13, 1985 (day of year 133).\n\nThis product was previously available from the NSSDC with the identifier ESAD-00107 (old ID 78-098A-10C).", "links": [ { diff --git a/datasets/THORPEX_ER2_MAS_1.json b/datasets/THORPEX_ER2_MAS_1.json index 6123e960d1..1cea44f3e4 100644 --- a/datasets/THORPEX_ER2_MAS_1.json +++ b/datasets/THORPEX_ER2_MAS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "THORPEX_ER2_MAS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "THORPEX_ER2_MAS data are THe Observing-system Research and Predictability EXperiment (THORPEX) ER_2 MODIS Airborne Simulator (MAS) Data in HDF covering Hawaii and the Pacific Ocean.THe Observing-system Research and predictability experiment (THORpex) is a ten-year international research program where the primary objective is to accelerate improvements in short range weather predictions and warnings over the Northern Hemisphere. The fifth in an ongoing series of ER-2 field experiments, THORpex is the primary over-water validation experiment for the GIFTS (Geosynchronous Imaging Fourier Transform Spectrometer) satellite. The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995. The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees.", "links": [ { diff --git a/datasets/TIROS2L1FMRT_001.json b/datasets/TIROS2L1FMRT_001.json index 4d5f160f87..b89ee6f510 100644 --- a/datasets/TIROS2L1FMRT_001.json +++ b/datasets/TIROS2L1FMRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS2L1FMRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TIROS-2 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe TIROS-2 satellite was successfully launched on November 23, 1960. The Medium-Resolution Scanning Radiometer experiment successfully returned data for about five months, becoming the first radiometer to make meteorlogical measurements from space. Three follow-on instruments were flown on TIROS-3, -4 and -7. Initially, all channels performed normally. However, channels 1 and 4 gradually deteriorated and by January 1961 were useless. The signal to noise ratio of channels 3 and 5 was extremely low, and the output was highly questionable. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics:\n\nChannel 1: 6.0 to 6.5 microns - water vapor absorption\nChannel 2: 8.0 to 12.0 microns - atmospheric window\nChannel 3: 0.2 to 6.0 microns - reflected solar radiation\nChannel 4: 8.0 to 30 microns - terrestial radiation\nChannel 5: 0.55 to 0.75 microns - response to the TV system\n\nThe Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00113 (old ID 60-016A-02A).", "links": [ { diff --git a/datasets/TIROS3L1FMRT_001.json b/datasets/TIROS3L1FMRT_001.json index bb4562a18f..160160c632 100644 --- a/datasets/TIROS3L1FMRT_001.json +++ b/datasets/TIROS3L1FMRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS3L1FMRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TIROS-3 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1, 2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe TIROS-3 satellite was successfully launched on July 12, 1961. The Medium-Resolution Scanning Radiometer experiment returned data for about two and a half months. A previous instrument flew on TIROS-2 and two follow-on instruments were flown on TIROS-4 and -7. Response characteristics of all channels degraded rapidly after launch. The greatest uncertainty in the radiation measurements is due to the apparent shift in the zero radiation level. Data are usable for channels 1, 2, 3, 4, and 5 up to orbits 118, 875, 875, 130 and 300, respectively. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics:\n\nChannel 1: 6.0 to 6.5 microns - water vapor absorption\nChannel 2: 8.0 to 12.0 microns - atmospheric window\nChannel 3: 0.2 to 6.0 microns - reflected solar radiation\nChannel 4: 8.0 to 30 - terrestial radiation\nChannel 5: 0.55 to 0.75 microns - response to the TV system\n\nThe Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00141 (old id 61-017A-03A).", "links": [ { diff --git a/datasets/TIROS3L1ORT_001.json b/datasets/TIROS3L1ORT_001.json index 997dcb61c9..b5672b4c66 100644 --- a/datasets/TIROS3L1ORT_001.json +++ b/datasets/TIROS3L1ORT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS3L1ORT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their original text format.\n\nThe TIROS-3 satellite was successfully launched on July 12, 1961. The Low-Resolution Omnidirectional Radiometer experiment returned data for about three months. Two follow-on instruments were flown on TIROS-4 and -7, while a similar instrument flew on Explorer-7.\n\nThe Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00187 (old id 61-017A-01A).", "links": [ { diff --git a/datasets/TIROS4L1FMRT_001.json b/datasets/TIROS4L1FMRT_001.json index bb4a399305..5629573363 100644 --- a/datasets/TIROS4L1FMRT_001.json +++ b/datasets/TIROS4L1FMRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS4L1FMRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TIROS-4 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe TIROS-4 satellite was successfully launched on February 8, 1962. The Medium-Resolution Scanning Radiometer experiment successfully returned data for about five months, continuing the measurements made by its predecessors flown on TIROS-2, and -3. A follow-on instrument was flown on TIROS-7. Degradation of the instrument after launch results in a departure of the data from the pre-launch calibration. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics:\n\nChannel 1: 6.0 to 6.5 microns - water vapor absorption\nChannel 2: 8.0 to 12.0 microns - atmospheric window\nChannel 3: 0.2 to 6.0 microns - reflected solar radiation\nChannel 4: unused - transmitted redundant time reference signals\nChannel 5: 0.55 to 0.75 microns - response to the TV system\n\nThe Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00139 (old ID 62-002A-03A).", "links": [ { diff --git a/datasets/TIROS4L1ORR_001.json b/datasets/TIROS4L1ORR_001.json index e428583f36..d1602f7a36 100644 --- a/datasets/TIROS4L1ORR_001.json +++ b/datasets/TIROS4L1ORR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS4L1ORR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Radiance Data product contains the longwave radiation values in Langleys/min derived from the black and white sensors. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Radiance (ORR) tapes. The data are archived in their text format.\n\nThe TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7.\n\nThe Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00152 (old id 62-002A-01B).", "links": [ { diff --git a/datasets/TIROS4L1ORT_001.json b/datasets/TIROS4L1ORT_001.json index 9de3fdb68e..bf60988150 100644 --- a/datasets/TIROS4L1ORT_001.json +++ b/datasets/TIROS4L1ORT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS4L1ORT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their text format.\n\nThe TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7.\n\nThe Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00252 (old id 62-002A-01A).", "links": [ { diff --git a/datasets/TIROS7L1FMRT_001.json b/datasets/TIROS7L1FMRT_001.json index a194294d0c..dbf71fdf62 100644 --- a/datasets/TIROS7L1FMRT_001.json +++ b/datasets/TIROS7L1FMRT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TIROS7L1FMRT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TIROS-7 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1,2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file.\n\nThe TIROS-7 satellite was successfully launched on June 19, 1963. The Medium-Resolution Scanning Radiometer experiment successfully returned data for two years, continuing the measurements made by its predecessors flown on TIROS-2, -3 and -4. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics:\n\nChannel 1: 14.8 to 15.5 microns - carbon dioxide absorption\nChannel 2: 8.0 to 12.0 microns - atmospheric window\nChannel 3: 0.2 to 6.0 microns - reflected solar radiation\nChannel 4: 8.0 to 30 microns - thermal radiation from the earth and atmosphere\nChannel 5: 0.55 to 0.75 microns - response to the TV system\n\nThe Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00217 (old ID 63-024A-02A).", "links": [ { diff --git a/datasets/TL1BL_5.json b/datasets/TL1BL_5.json index f73eebfc79..207a3740ef 100644 --- a/datasets/TL1BL_5.json +++ b/datasets/TL1BL_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL1BL_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL1BL_5 is the Tropospheric Emission Spectrometer (TES)/Aura L1B Spectra Limb Version 5 data product. TES Level 1B Spectra data contaisn radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESR). The geolocation, quality and some engineering data were also provided with this data product. \r\rTES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. Each L1B data file contained data from a single TES orbit starting from the South Pole Apex. A Nadir sequence within the TES Global Survey was two low resolution scans over the same ground locations, thus pointing directly to the surface of the earth. The Nadir standard product consisted of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. The Global Survey Limb observations, however, used a repeating sequence of filter wheel positions and were pointed at various off-nadir angles into the atmosphere. Special Observations were only scheduled during the 9 or 10 orbit gaps in the Global Surveys and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement.", "links": [ { diff --git a/datasets/TL1BN_6.json b/datasets/TL1BN_6.json index 896552be3b..005f2e1fb9 100644 --- a/datasets/TL1BN_6.json +++ b/datasets/TL1BN_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL1BN_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL1BN_6 is the Tropospheric Emission Spectrometer (TES)/Aura L1B Spectra Nadir Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 1B Spectra data contains radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESR). The geolocation, quality and some engineering data were also provided. Each L1B data file contains spectra data composed of the Global Survey Nadir observations formed a single TES orbit starting from the South Pole Apex. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement.", "links": [ { diff --git a/datasets/TL1BSOL_6.json b/datasets/TL1BSOL_6.json index 599e1f9149..8946e8b92d 100644 --- a/datasets/TL1BSOL_6.json +++ b/datasets/TL1BSOL_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL1BSOL_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL1BSOL_6 is the Tropospheric Emission Spectrometer (TES)/Aura L1B Spectra Special Observation Low Resolution Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 1B Spectra data contains radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESR). The geolocation, quality and some engineering data were also provided. Each L1B data file contains spectra data composed of the Global Survey Nadir observations from a single TES orbit starting from the South Pole Apex. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe Global Survey Limb observations use a repeating sequence of filter wheel positions. Special Observations can only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and are conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement.", "links": [ { diff --git a/datasets/TL2ANCS_7.json b/datasets/TL2ANCS_7.json index a7a4d4ef24..e7a1c8e8b3 100644 --- a/datasets/TL2ANCS_7.json +++ b/datasets/TL2ANCS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ANCS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ANCS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ancillary Special Observation Product Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2ANCS_8.json b/datasets/TL2ANCS_8.json index 1d79f8dc45..c6475c628a 100644 --- a/datasets/TL2ANCS_8.json +++ b/datasets/TL2ANCS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ANCS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ANCS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ancillary Special Observation Product Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2ANC_7.json b/datasets/TL2ANC_7.json index 7dd3cb4a09..b599c66792 100644 --- a/datasets/TL2ANC_7.json +++ b/datasets/TL2ANC_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ANC_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ANC_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ancillary Version 7 product . TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. For this product, the geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. \r\rA global survey consisted of a maximum of 16 consecutive orbits. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. \r\rEach TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. The organization of data within the Swath object was based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation could have contained estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC).", "links": [ { diff --git a/datasets/TL2ANC_8.json b/datasets/TL2ANC_8.json index 52d62e681b..dbccf6aded 100644 --- a/datasets/TL2ANC_8.json +++ b/datasets/TL2ANC_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ANC_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ANC_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ancillary Version 8 product . TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. For this product, the geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. \r\rA global survey consisted of a maximum of 16 consecutive orbits. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. \r\rEach TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. The organization of data within the Swath object was based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation could have contained estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC).", "links": [ { diff --git a/datasets/TL2ATMLN_006.json b/datasets/TL2ATMLN_006.json index 1cd1e055c6..4c3d1deec7 100644 --- a/datasets/TL2ATMLN_006.json +++ b/datasets/TL2ATMLN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ATMLN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2ATMLN_7.json b/datasets/TL2ATMLN_7.json index ebd9ff204c..b67aabadbc 100644 --- a/datasets/TL2ATMLN_7.json +++ b/datasets/TL2ATMLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ATMLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ATMLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperature Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2ATMTL_006.json b/datasets/TL2ATMTL_006.json index 99cf550779..927041ee65 100644 --- a/datasets/TL2ATMTL_006.json +++ b/datasets/TL2ATMTL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ATMTL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2ATMTN_7.json b/datasets/TL2ATMTN_7.json index ada534e1ba..8a1b5a2df3 100644 --- a/datasets/TL2ATMTN_7.json +++ b/datasets/TL2ATMTN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ATMTN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ATMTN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Limb sequence within the TES Global Survey involved three high-resolution scans over the same limb locations. The Limb standard product consisted of four files, where each file was composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. \r\rEach limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object was based on a superset of Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2ATMTN_8.json b/datasets/TL2ATMTN_8.json index ff228d9b3e..030a9b42da 100644 --- a/datasets/TL2ATMTN_8.json +++ b/datasets/TL2ATMTN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2ATMTN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2ATMTN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Limb sequence within the TES Global Survey involved three high-resolution scans over the same limb locations. The Limb standard product consisted of four files, where each file was composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. \r\rEach limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object was based on a superset of Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CH4LN_006.json b/datasets/TL2CH4LN_006.json index 25b5bf2c4f..d55f569444 100644 --- a/datasets/TL2CH4LN_006.json +++ b/datasets/TL2CH4LN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4LN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2CH4LN_7.json b/datasets/TL2CH4LN_7.json index 598d6d9bbd..acad3d9794 100644 --- a/datasets/TL2CH4LN_7.json +++ b/datasets/TL2CH4LN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4LN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CH4LN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methane Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that was common to both nadir and limb files.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CH4NS_7.json b/datasets/TL2CH4NS_7.json index f2f0934f31..f24ed4d7be 100644 --- a/datasets/TL2CH4NS_7.json +++ b/datasets/TL2CH4NS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4NS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CH4NS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methane Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CH4NS_8.json b/datasets/TL2CH4NS_8.json index c8557d9613..47a5c83a4a 100644 --- a/datasets/TL2CH4NS_8.json +++ b/datasets/TL2CH4NS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4NS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CH4NS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methane Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CH4N_7.json b/datasets/TL2CH4N_7.json index 0034a696a1..715af7edc2 100644 --- a/datasets/TL2CH4N_7.json +++ b/datasets/TL2CH4N_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4N_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CH4N_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methane Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CH4N_8.json b/datasets/TL2CH4N_8.json index d35eb020f2..9f218d01e4 100644 --- a/datasets/TL2CH4N_8.json +++ b/datasets/TL2CH4N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CH4N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CH4N_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methane Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CO2LN_006.json b/datasets/TL2CO2LN_006.json index 6cab953359..422202822d 100644 --- a/datasets/TL2CO2LN_006.json +++ b/datasets/TL2CO2LN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2LN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2CO2LN_7.json b/datasets/TL2CO2LN_7.json index 8d922f447a..a03d752c3c 100644 --- a/datasets/TL2CO2LN_7.json +++ b/datasets/TL2CO2LN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2LN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CO2LN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Dioxide Lite Nadir Version 7 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits.\r\rA Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix.\r\rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported.\r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CO2NS_7.json b/datasets/TL2CO2NS_7.json index e8778fc692..afd03b3738 100644 --- a/datasets/TL2CO2NS_7.json +++ b/datasets/TL2CO2NS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2NS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CO2NS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Dioxide Nadir Special Observation Version 7 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. Nadir and limb observations are in separate L2 files, and a single ancillary file is composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CO2NS_8.json b/datasets/TL2CO2NS_8.json index ebfa8a8918..175ca3404f 100644 --- a/datasets/TL2CO2NS_8.json +++ b/datasets/TL2CO2NS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2NS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CO2NS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Dioxide Nadir Special Observation Version 8 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CO2N_7.json b/datasets/TL2CO2N_7.json index 03bdb48d83..4d62702958 100644 --- a/datasets/TL2CO2N_7.json +++ b/datasets/TL2CO2N_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2N_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CO2N_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Dioxide Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CO2N_8.json b/datasets/TL2CO2N_8.json index 0d5865c0be..d8bb113332 100644 --- a/datasets/TL2CO2N_8.json +++ b/datasets/TL2CO2N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CO2N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CO2N_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Dioxide Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2COLN_006.json b/datasets/TL2COLN_006.json index 6ae26b4e30..40adb4e79d 100644 --- a/datasets/TL2COLN_006.json +++ b/datasets/TL2COLN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2COLN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2COLN_7.json b/datasets/TL2COLN_7.json index bc6cf6c988..65541f1b8a 100644 --- a/datasets/TL2COLN_7.json +++ b/datasets/TL2COLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2COLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2COLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Monoxide Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CONS_7.json b/datasets/TL2CONS_7.json index c0503fea8e..1153f847b7 100644 --- a/datasets/TL2CONS_7.json +++ b/datasets/TL2CONS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CONS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CONS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Monoxide Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CONS_8.json b/datasets/TL2CONS_8.json index cd56bac5c9..1e740e2236 100644 --- a/datasets/TL2CONS_8.json +++ b/datasets/TL2CONS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CONS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CONS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Monoxide Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g. surface characteristics for nadir observations) were also provided. Level 2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported.\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CON_7.json b/datasets/TL2CON_7.json index 11df812051..b22f845144 100644 --- a/datasets/TL2CON_7.json +++ b/datasets/TL2CON_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CON_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CON_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Monoxide Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2CON_8.json b/datasets/TL2CON_8.json index ebf61b67a2..6f5c0407df 100644 --- a/datasets/TL2CON_8.json +++ b/datasets/TL2CON_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2CON_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2CON_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbon Monoxide Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2FORLN_006.json b/datasets/TL2FORLN_006.json index 7b8ee0e5c9..d3b807bc2c 100644 --- a/datasets/TL2FORLN_006.json +++ b/datasets/TL2FORLN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORLN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2FORLN_7.json b/datasets/TL2FORLN_7.json index 92f02744ca..6aafb9d639 100644 --- a/datasets/TL2FORLN_7.json +++ b/datasets/TL2FORLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2FORLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Formic Acid Lite Nadir Version 7 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits.\r\rA Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix.\r\rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported.\r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2FORNS_7.json b/datasets/TL2FORNS_7.json index 4c804c8046..68ee0df061 100644 --- a/datasets/TL2FORNS_7.json +++ b/datasets/TL2FORNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2FORNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Formic Acid Nadir Special Observation Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2FORNS_8.json b/datasets/TL2FORNS_8.json index cd414893d7..5419f247a0 100644 --- a/datasets/TL2FORNS_8.json +++ b/datasets/TL2FORNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2FORNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Formic Acid Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2FORN_7.json b/datasets/TL2FORN_7.json index 1afd333dd7..a3209d12cc 100644 --- a/datasets/TL2FORN_7.json +++ b/datasets/TL2FORN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2FORN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Formic Acid Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3.\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2FORN_8.json b/datasets/TL2FORN_8.json index 93f4563095..adeae9bffb 100644 --- a/datasets/TL2FORN_8.json +++ b/datasets/TL2FORN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2FORN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2FORN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Formic Acid Nadir Version 8 data product.TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2OLN_6.json b/datasets/TL2H2OLN_6.json index 4661c43a59..d50005c01f 100644 --- a/datasets/TL2H2OLN_6.json +++ b/datasets/TL2H2OLN_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2OLN_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2OLN_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 H2O Lite Nadir Version 6 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and some other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters.\r\rL2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. Nadir and limb observations are in separate L2 files, and a single ancillary file is composed of data that are common to both nadir and limb files.\r\rA Nadir sequence with in the TES Global Survey is two low resolution scans over the same ground locations. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix.\r\rA Limb sequence within the TES Global Survey is three high-resolution scans over the same limb locations. The Limb standard product will consist of four files, where each file will be composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations use a repeating sequence of filter wheel positions.\r\rSpecial Observations can only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and are conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement. See http://tes.jpl.nasa.gov/instrument/special observations/ for details.\r\rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals are performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities.\r\rEach TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also missing or bad retrievals are not reported. Each limb observation Limb 1, Limb 2 and Limb 3, are processed independently. Thus each limb standard product consists of three sets where each set consist of 1,152 observations. For TES, the swath object represents one of these sets. Thus each limb standard product consists of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3.\r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 87 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 88 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2OLN_7.json b/datasets/TL2H2OLN_7.json index af491cacc7..32a5fe2dce 100644 --- a/datasets/TL2H2OLN_7.json +++ b/datasets/TL2H2OLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2OLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2OLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Water Vapor Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2OLS_006.json b/datasets/TL2H2OLS_006.json index 560c2c25c8..e2f08bf9aa 100644 --- a/datasets/TL2H2OLS_006.json +++ b/datasets/TL2H2OLS_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2OLS_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2H2OL_006.json b/datasets/TL2H2OL_006.json index d2411505a5..90bab04d0a 100644 --- a/datasets/TL2H2OL_006.json +++ b/datasets/TL2H2OL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2OL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2H2ONS_7.json b/datasets/TL2H2ONS_7.json index 24431a9a65..c2c3ca824d 100644 --- a/datasets/TL2H2ONS_7.json +++ b/datasets/TL2H2ONS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2ONS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2ONS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Water Vapor Nadir Special Observation Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2ONS_8.json b/datasets/TL2H2ONS_8.json index 3e94d36059..ce5f4d22f2 100644 --- a/datasets/TL2H2ONS_8.json +++ b/datasets/TL2H2ONS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2ONS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2ONS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Water Vapor Nadir Special Observation Version 8 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2ON_7.json b/datasets/TL2H2ON_7.json index 58986f7a48..a6796f159b 100644 --- a/datasets/TL2H2ON_7.json +++ b/datasets/TL2H2ON_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2ON_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2ON_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Water Vapor Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2H2ON_8.json b/datasets/TL2H2ON_8.json index 71beb1fa1b..d825fbffe0 100644 --- a/datasets/TL2H2ON_8.json +++ b/datasets/TL2H2ON_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2H2ON_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2H2ON_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Water Vapor Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HCNNS_8.json b/datasets/TL2HCNNS_8.json index 162cc17b1b..796856cb40 100644 --- a/datasets/TL2HCNNS_8.json +++ b/datasets/TL2HCNNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HCNNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HCNNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Hydrogen Cyanide Nadir Special Observation Version 8 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits.\rA Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix.\r\rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported.\r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HCNN_8.json b/datasets/TL2HCNN_8.json index 1645b09a39..4081b30223 100644 --- a/datasets/TL2HCNN_8.json +++ b/datasets/TL2HCNN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HCNN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HCNN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Hydrogen Cyanide Nadir Version 8 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits.\rA Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix.\rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported.\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site:\rhttps://eosweb.larc.nasa.gov/project/TES/DPS\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDOLN_6.json b/datasets/TL2HDOLN_6.json index 328b69ef15..ddc84dabaa 100644 --- a/datasets/TL2HDOLN_6.json +++ b/datasets/TL2HDOLN_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDOLN_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDOLN_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 HDO Lite Nadir Version 6 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and some other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. Nadir and limb observations are in separate L2 files, and a single ancillary file is composed of data that are common to both nadir and limb files.\r\rA Nadir sequence with in the TES Global Survey is two low resolution scans over the same ground locations. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Limb sequence within the TES Global Survey is three high-resolution scans over the same limb locations. The Limb standard product will consist of four files, where each file will be composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations use a repeating sequence of filter wheel positions.\r\rSpecial Observations can only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and are conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement. \rA Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals are performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities.\r\rEach TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also missing or bad retrievals are not reported. Each limb observation Limb 1, Limb 2 and Limb 3, are processed independently. Thus each limb standard product consists of three sets where each set consist of 1,152 observations. For TES, the swath object represents one of these sets. Thus each limb standard product consists of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3.\r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 87 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 88 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDOLN_7.json b/datasets/TL2HDOLN_7.json index 3783f65713..c030705fc3 100644 --- a/datasets/TL2HDOLN_7.json +++ b/datasets/TL2HDOLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDOLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDOLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Deuterium Oxide Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDOLS_006.json b/datasets/TL2HDOLS_006.json index 3dc66c31ed..efd8bda07b 100644 --- a/datasets/TL2HDOLS_006.json +++ b/datasets/TL2HDOLS_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDOLS_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2HDOL_006.json b/datasets/TL2HDOL_006.json index 66a3905205..d63669213d 100644 --- a/datasets/TL2HDOL_006.json +++ b/datasets/TL2HDOL_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDOL_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2HDONS_7.json b/datasets/TL2HDONS_7.json index 84320683e8..f051fe82dc 100644 --- a/datasets/TL2HDONS_7.json +++ b/datasets/TL2HDONS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDONS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDONS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Deuterium Oxide Nadir Special Observation Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDONS_8.json b/datasets/TL2HDONS_8.json index f60f21eb36..cf9ed55904 100644 --- a/datasets/TL2HDONS_8.json +++ b/datasets/TL2HDONS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDONS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDONS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Deuterium Oxide Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDON_7.json b/datasets/TL2HDON_7.json index 353c0d7304..16e6b816cb 100644 --- a/datasets/TL2HDON_7.json +++ b/datasets/TL2HDON_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDON_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDON_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Deuterium Oxide Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HDON_8.json b/datasets/TL2HDON_8.json index 00b7177a2f..69d26258f2 100644 --- a/datasets/TL2HDON_8.json +++ b/datasets/TL2HDON_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HDON_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2HDON_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Deuterium Oxide Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2HNO3L_006.json b/datasets/TL2HNO3L_006.json index 33b062b471..1f7a8d5dc4 100644 --- a/datasets/TL2HNO3L_006.json +++ b/datasets/TL2HNO3L_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HNO3L_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2HNO3S_006.json b/datasets/TL2HNO3S_006.json index 6919d62210..cec75ef420 100644 --- a/datasets/TL2HNO3S_006.json +++ b/datasets/TL2HNO3S_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2HNO3S_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2IRKNS_7.json b/datasets/TL2IRKNS_7.json index 94886cb493..00886b0c52 100644 --- a/datasets/TL2IRKNS_7.json +++ b/datasets/TL2IRKNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2IRKNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2IRKNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Instantaneous Radiative Kernel Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. Using TES radiances, Jacobians and ozone profiles with hemispherical integration, it was possible to compute the TOA (top-of-atmosphere) flux from the infrared ozone band (in W/m2), instantaneous radiative kernels (IRK) (in W/m2/ppb), and logarithmic instantaneous radiative forcing kernels (LIRK) (in W/m2) for ozone. The IRK provided unique information for questions of chemistry-climate coupling since this was a direct measure of the radiative role of ozone, which explicitly accounted for more dominant radiative processes such as clouds and water vapor. These products can be compared to climate model predictions of the same quantities.\r\rTES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2IRKNS_8.json b/datasets/TL2IRKNS_8.json index 3425841aeb..0eee868624 100644 --- a/datasets/TL2IRKNS_8.json +++ b/datasets/TL2IRKNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2IRKNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2IRKNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Instantaneous Radiative Kernel Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. Using TES radiances, Jacobians and ozone profiles with hemispherical integration, it was possible to compute the TOA (top-of-atmosphere) flux from the infrared ozone band (in W/m2), instantaneous radiative kernels (IRK) (in W/m2/ppb), and logarithmic instantaneous radiative forcing kernels (LIRK) (in W/m2) for ozone. The IRK provided unique information for questions of chemistry-climate coupling since this was a direct measure of the radiative role of ozone, which explicitly accounted for more dominant radiative processes such as clouds and water vapor. These products can be compared to climate model predictions of the same quantities.\r\rTES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2IRKN_7.json b/datasets/TL2IRKN_7.json index ba38af80e1..ccd443635b 100644 --- a/datasets/TL2IRKN_7.json +++ b/datasets/TL2IRKN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2IRKN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2IRKN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. Using TES radiances, Jacobians and ozone profiles with hemispherical integration, made it possible to compute the TOA (top-of-atmosphere) flux from the infrared ozone band (in W/m2), instantaneous radiative kernels (IRK) (in W/m2/ppb), and logarithmic instantaneous radiative forcing kernels (LIRK) (in W/m2) for ozone. The IRK provided unique information for questions of chemistry-climate coupling since this is a direct measure of the radiative role of ozone which explicitly accounted for more dominant radiative processes such as clouds and water vapor. These products can be compared to climate model predictions of the same quantities.\r\rTES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2IRKN_8.json b/datasets/TL2IRKN_8.json index bbd66430b9..a6299cf53e 100644 --- a/datasets/TL2IRKN_8.json +++ b/datasets/TL2IRKN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2IRKN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2IRKN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. Using TES radiances, Jacobians and ozone profiles with hemispherical integration, made it possible to compute the TOA (top-of-atmosphere) flux from the infrared ozone band (in W/m2), instantaneous radiative kernels (IRK) (in W/m2/ppb), and logarithmic instantaneous radiative forcing kernels (LIRK) (in W/m2) for ozone. The IRK provided unique information for questions of chemistry-climate coupling since this is a direct measure of the radiative role of ozone which explicitly accounted for more dominant radiative processes such as clouds and water vapor. These products can be compared to climate model predictions of the same quantities.\r\rTES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2MTLLN_6.json b/datasets/TL2MTLLN_6.json index 346fff0950..444c12d61f 100644 --- a/datasets/TL2MTLLN_6.json +++ b/datasets/TL2MTLLN_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLLN_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2MTLLN_7.json b/datasets/TL2MTLLN_7.json index 7fb6c9f4d5..96dcadb8f3 100644 --- a/datasets/TL2MTLLN_7.json +++ b/datasets/TL2MTLLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2MTLLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methanol Lite Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. The TES Lite products were intended to simplify TES data usage including data/model and data/data comparisons. This product can be used for science analysis as each data product is fully characterized. The TES Lite products were also meant to facilitate use of TES data by end users by (1) aggregating product results by month (no averaging is applied), (2) reducing data dimensionality to the retrieved pressure levels, which results in a minimal reduction of information but reduces data sizes by 1/3 to 1/10, (3) applying known corrections quantified through validation campaigns (4) combining data from ancillary files and multiple TES product files that are needed for science analysis (particularly for CH4 and HDO), and (5) removing fields that are not typically used. For example, the HDO product also includes the H2O product; it contains the recommended bias correction for HDO, results are mapped to 18 pressures, and the averaging kernel and error covariances are packed together from the H2O, HDO, and ancillary individual product files into full matrices for easier use by modelers and for science analysis. The products include the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid to support cross-comparison between products and models. NH3 and CH4 contain Representative Tropospheric volume mixing ratio (RTVMR) fields (Payne et al. , 2009) that map the full profile to levels that are most representative of the atmosphere based on the altitude dependent sensitivity of the estimate.\r\rTES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2MTLNS_7.json b/datasets/TL2MTLNS_7.json index db1b211ed6..74c2d2a7a1 100644 --- a/datasets/TL2MTLNS_7.json +++ b/datasets/TL2MTLNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2MTLNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methanol Nadir Special Observation Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2MTLNS_8.json b/datasets/TL2MTLNS_8.json index 01c8f44555..ab07f7277c 100644 --- a/datasets/TL2MTLNS_8.json +++ b/datasets/TL2MTLNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2MTLNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methanol Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could have potentially contained estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2MTLN_7.json b/datasets/TL2MTLN_7.json index 56ee54902d..0b5493d01e 100644 --- a/datasets/TL2MTLN_7.json +++ b/datasets/TL2MTLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2MTLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methanol Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that were common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2MTLN_8.json b/datasets/TL2MTLN_8.json index 88230f654a..1ddc5eff19 100644 --- a/datasets/TL2MTLN_8.json +++ b/datasets/TL2MTLN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2MTLN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2MTLN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Methanol Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2N2ONS_7.json b/datasets/TL2N2ONS_7.json index 17a10ff5f6..1086733dd9 100644 --- a/datasets/TL2N2ONS_7.json +++ b/datasets/TL2N2ONS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2N2ONS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2N2ONS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Nitrous Oxide Nadir Special Observation Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2N2ONS_8.json b/datasets/TL2N2ONS_8.json index fb8323188c..e3f52be845 100644 --- a/datasets/TL2N2ONS_8.json +++ b/datasets/TL2N2ONS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2N2ONS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2N2ONS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Nitrous Oxide Nadir Special Observation Version 8 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TTES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2N2ON_7.json b/datasets/TL2N2ON_7.json index c74e0e76bd..1d3d0ffcc0 100644 --- a/datasets/TL2N2ON_7.json +++ b/datasets/TL2N2ON_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2N2ON_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2N2ON_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Nitrous Oxide Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2N2ON_8.json b/datasets/TL2N2ON_8.json index c423248f69..e1e895230e 100644 --- a/datasets/TL2N2ON_8.json +++ b/datasets/TL2N2ON_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2N2ON_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2N2ON_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Nitrous Oxide Nadir Version 8 data product. It consists of information for one molecular species, Nitrous Oxide, for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NH3LN_006.json b/datasets/TL2NH3LN_006.json index 228690db8d..5583660747 100644 --- a/datasets/TL2NH3LN_006.json +++ b/datasets/TL2NH3LN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3LN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2NH3LN_7.json b/datasets/TL2NH3LN_7.json index a09a670b6d..c77832e167 100644 --- a/datasets/TL2NH3LN_7.json +++ b/datasets/TL2NH3LN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3LN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2NH3LN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ammonia Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NH3NS_7.json b/datasets/TL2NH3NS_7.json index de9cc7e56d..eeaa751170 100644 --- a/datasets/TL2NH3NS_7.json +++ b/datasets/TL2NH3NS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3NS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2NH3NS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ammonia Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rEach limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NH3NS_8.json b/datasets/TL2NH3NS_8.json index e319319a47..39cee31c98 100644 --- a/datasets/TL2NH3NS_8.json +++ b/datasets/TL2NH3NS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3NS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2NH3NS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ammonia Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NH3N_7.json b/datasets/TL2NH3N_7.json index a9a7e1a5c1..f8b1c18955 100644 --- a/datasets/TL2NH3N_7.json +++ b/datasets/TL2NH3N_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3N_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2NH3N_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ammonia Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NH3N_8.json b/datasets/TL2NH3N_8.json index d2a9d963dc..75fffc7bf9 100644 --- a/datasets/TL2NH3N_8.json +++ b/datasets/TL2NH3N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NH3N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2NH3N_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ammonia Nadir Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could have potentially contained estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NO2L_6.json b/datasets/TL2NO2L_6.json index 92a6648700..9efd066a25 100644 --- a/datasets/TL2NO2L_6.json +++ b/datasets/TL2NO2L_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NO2L_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L2 NO2 data consist of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. Nadir and limb observations are in separate L2 files, and a single ancillary file is composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Limb sequence within the TES Global Survey is three high-resolution scans over the same limb locations. The Limb standard product will consist of four files, where each file will be composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations use a repeating sequence of filter wheel positions. Special Observations can only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and are conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement. See http://tes.jpl.nasa.gov/instrument/specialobservations/ for details. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals are performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. Each limb observation Limb 1, Limb 2 and Limb 3, are processed independently. Thus each limb standard product consists of three sets where each set consist of 1,152 observations. For TES, the swath object represents one of these sets. Thus each limb standard product consists of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: https://eosweb.larc.nasa.gov/project/tes/DPS To minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2NO2S_6.json b/datasets/TL2NO2S_6.json index bc1a3ec2f2..350a49a616 100644 --- a/datasets/TL2NO2S_6.json +++ b/datasets/TL2NO2S_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2NO2S_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L2 NO2 data consist of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. Nadir and limb observations are in separate L2 files, and a single ancillary file is composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Limb sequence within the TES Global Survey is three high-resolution scans over the same limb locations. The Limb standard product will consist of four files, where each file will be composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations use a repeating sequence of filter wheel positions. Special Observations can only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and are conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depends on the science requirement. See http://tes.jpl.nasa.gov/instrument/specialobservations/ for details. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals are performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. Each limb observation Limb 1, Limb 2 and Limb 3, are processed independently. Thus each limb standard product consists of three sets where each set consist of 1,152 observations. For TES, the swath object represents one of these sets. Thus each limb standard product consists of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: https://eosweb.larc.nasa.gov/project/tes/DPS To minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivities, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3LN_006.json b/datasets/TL2O3LN_006.json index e047c3e13c..74fa459ff9 100644 --- a/datasets/TL2O3LN_006.json +++ b/datasets/TL2O3LN_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3LN_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid.", "links": [ { diff --git a/datasets/TL2O3LN_7.json b/datasets/TL2O3LN_7.json index a185bb1ae3..397fde609a 100644 --- a/datasets/TL2O3LN_7.json +++ b/datasets/TL2O3LN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3LN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3LN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ozone Lite Nadir Version 7 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits.\r\n\r\nA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \r\n\r\nThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\n\r\nTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3LS_6.json b/datasets/TL2O3LS_6.json index 8a1812e369..99cdf3c374 100644 --- a/datasets/TL2O3LS_6.json +++ b/datasets/TL2O3LS_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3LS_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3LS_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 O3 Limb Special Observation Version 6 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3L_006.json b/datasets/TL2O3L_006.json index b39d623bb1..5605a7404d 100644 --- a/datasets/TL2O3L_006.json +++ b/datasets/TL2O3L_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3L_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2O3NS_7.json b/datasets/TL2O3NS_7.json index 0fed25d42e..57e2d06b84 100644 --- a/datasets/TL2O3NS_7.json +++ b/datasets/TL2O3NS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3NS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3NS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ozone Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consisted of four files, where each file was composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations used a single set of filter mix. A Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consisted of four files, where each file was composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations were a repeating sequence of filter wheel positions. \r\rSpecial Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation wa the input for retrievals of species volume mixing ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3NS_8.json b/datasets/TL2O3NS_8.json index f8cac499c0..f06ba0313b 100644 --- a/datasets/TL2O3NS_8.json +++ b/datasets/TL2O3NS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3NS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3NS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ozone Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3N_7.json b/datasets/TL2O3N_7.json index 164c2fd34b..baeda6b16e 100644 --- a/datasets/TL2O3N_7.json +++ b/datasets/TL2O3N_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3N_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3N_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ozone Nadir Version 7 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2O3N_8.json b/datasets/TL2O3N_8.json index 04e7d31f30..c18de3f788 100644 --- a/datasets/TL2O3N_8.json +++ b/datasets/TL2O3N_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2O3N_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2O3N_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Ozone Nadir Version 8 data product. It consists of information for one molecular species for an entire Global Survey or Special Observation. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2OCSLN_7.json b/datasets/TL2OCSLN_7.json index 6e1aa4954b..5dd0e9daf1 100644 --- a/datasets/TL2OCSLN_7.json +++ b/datasets/TL2OCSLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2OCSLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2OCSLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Carbonyl Sulfide Lite Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2OCSNS_7.json b/datasets/TL2OCSNS_7.json index 1119fa9303..cd0ff425ca 100644 --- a/datasets/TL2OCSNS_7.json +++ b/datasets/TL2OCSNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2OCSNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2OCSNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2OCSNS_8.json b/datasets/TL2OCSNS_8.json index 174329ee1d..2b8de8f37c 100644 --- a/datasets/TL2OCSNS_8.json +++ b/datasets/TL2OCSNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2OCSNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2OCSNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2OCSN_7.json b/datasets/TL2OCSN_7.json index 94afbe67f0..7fb24a212f 100644 --- a/datasets/TL2OCSN_7.json +++ b/datasets/TL2OCSN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2OCSN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2OCSN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors. \r\rTES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2OCSN_8.json b/datasets/TL2OCSN_8.json index de6066d038..ee2ecd46fa 100644 --- a/datasets/TL2OCSN_8.json +++ b/datasets/TL2OCSN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2OCSN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2OCSN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors. \r\rTES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir observations, which point directly to the surface of the Earth are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2PANLN_7.json b/datasets/TL2PANLN_7.json index c9ebf73c4a..d7c9c206de 100644 --- a/datasets/TL2PANLN_7.json +++ b/datasets/TL2PANLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2PANLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2PANLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. It contains atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2PANNS_7.json b/datasets/TL2PANNS_7.json index ac66eccabc..1a90ce3901 100644 --- a/datasets/TL2PANNS_7.json +++ b/datasets/TL2PANNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2PANNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2PANNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. It contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. \r\rA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \r\rThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2PANNS_8.json b/datasets/TL2PANNS_8.json index a15202955d..04e15ab385 100644 --- a/datasets/TL2PANNS_8.json +++ b/datasets/TL2PANNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2PANNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2PANNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. TES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2PANN_7.json b/datasets/TL2PANN_7.json index 7559eb6df6..61f0e19b95 100644 --- a/datasets/TL2PANN_7.json +++ b/datasets/TL2PANN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2PANN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2PANN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. It consisted of information for one molecular species for an entire Global Survey or Special Observation. It contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.\r\rTES Level 2 data contained retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were in separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2PANN_8.json b/datasets/TL2PANN_8.json index c1aa7bed74..48279afb2b 100644 --- a/datasets/TL2PANN_8.json +++ b/datasets/TL2PANN_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2PANN_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2PANN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. \nA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \nThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\nTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2RHLN_7.json b/datasets/TL2RHLN_7.json index 67075a1cbc..d4da6f7f17 100644 --- a/datasets/TL2RHLN_7.json +++ b/datasets/TL2RHLN_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2RHLN_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2RHLN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. It contains atmospheric vertical profile estimates and associated errors including the mapping matrix to relate the reduced-size retrieval vectors, covariances, and averaging kernels back to the TES forward model pressure grid. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also provided. L2 modeled spectra are evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compares observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. L2 standard product files include information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consists of a maximum of 16 consecutive orbits. \nA nadir sequence within the TES Global Survey is a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations currently only use a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals are performed. Each observation is the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reports information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object is bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product can have a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals are not reported. \nThe organization of data within the Swath object is based on a superset of the UARS pressure levels used to report concentrations of trace atmospheric gases. The reporting grid is the same pressure grid used for modeling. There are 67 reporting levels from 1211.53 hPa, which allows for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products will report values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation can potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels are not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value will be applied.\nTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUMS_7.json b/datasets/TL2SUMS_7.json index 0aaf178d1b..a998ccdd17 100644 --- a/datasets/TL2SUMS_7.json +++ b/datasets/TL2SUMS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUMS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUMS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. The satellite flew at an altitude of 705 km in an orbit that took it near Earth's North and South Poles. Each orbit, the spacecraft advanced 22\u00b0 westward and, after 233 orbits (16 days) it was back to its starting point. This product contains atmospheric vertical profile estimates, along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2SUMS_8.json b/datasets/TL2SUMS_8.json index 0ca1dc7756..0984a90f7a 100644 --- a/datasets/TL2SUMS_8.json +++ b/datasets/TL2SUMS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUMS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUMS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits, over which 3,200 retrievals were performed. \r\rA nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Global Survey consists of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species volume mixing ratios (VMRs), temperature profiles, surface temperature and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object wa bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging is employed. Also, missing or bad retrievals were not reported. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels that was used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUM_7.json b/datasets/TL2SUM_7.json index 18e0cbf30f..a2283f5841 100644 --- a/datasets/TL2SUM_7.json +++ b/datasets/TL2SUM_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUM_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUM_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Summary Profiles Version 7 data product. It contains atmospheric vertical profile estimates, along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, and priori constraint vectors.TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUM_8.json b/datasets/TL2SUM_8.json index a3ab99d5b2..7755cd26f4 100644 --- a/datasets/TL2SUM_8.json +++ b/datasets/TL2SUM_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUM_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUM_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Summary Profiles Version 8 data product. It contains atmospheric vertical profile estimates, along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, and priori constraint vectors.TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUPS_7.json b/datasets/TL2SUPS_7.json index e321847c32..9433a7a4ac 100644 --- a/datasets/TL2SUPS_7.json +++ b/datasets/TL2SUPS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUPS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUPS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Supplemental Profiles Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUPS_8.json b/datasets/TL2SUPS_8.json index fb36087a1a..39a3b7d008 100644 --- a/datasets/TL2SUPS_8.json +++ b/datasets/TL2SUPS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUPS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUPS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Supplemental Profiles Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. \r\rNadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2SUP_7.json b/datasets/TL2SUP_7.json index d81b33f5c6..94cebc6e71 100644 --- a/datasets/TL2SUP_7.json +++ b/datasets/TL2SUP_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUP_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUP_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Supplemental Profiles Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. The satellite flew at an altitude of 705 km in an orbit that took it near Earth's North and South Poles. After each orbit, the spacecraft advanced 22\u00b0 westward. After 233 orbits (16 days) it was then back to its starting point. This product contains input data to the TES radiance forward model. These were profiles generated from climatology databases to be used in the forward model calculation but are not retrieved parameters. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. \r\rLevel 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits, over which 3,200 retrievals were performed.", "links": [ { diff --git a/datasets/TL2SUP_8.json b/datasets/TL2SUP_8.json index 8bc5159d93..f349530ddc 100644 --- a/datasets/TL2SUP_8.json +++ b/datasets/TL2SUP_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2SUP_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2SUP_8 is the the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Supplemental Profiles Version 8 data product. It contains input data to the TES radiance forward model. These were profiles generated from climatology databases to be used in the forward model calculation but are not retrieved parameters. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updates the atmospheric parameters. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations.\u201d\r\rA Limb sequence within the TES Global Survey was three high-resolution scans over the same limb locations. The Limb standard product consists of four files, where each file is composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. \r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied. Also, missing or bad retrievals were not reported. \r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2TLS_006.json b/datasets/TL2TLS_006.json index 2b49f4ceb3..4ee6a99f25 100644 --- a/datasets/TL2TLS_006.json +++ b/datasets/TL2TLS_006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2TLS_006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TL2TNS_7.json b/datasets/TL2TNS_7.json index 0827cab30c..fa44dbc4eb 100644 --- a/datasets/TL2TNS_7.json +++ b/datasets/TL2TNS_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2TNS_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2TNS_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Special Observation Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and priori constraint vectors. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL2TNS_8.json b/datasets/TL2TNS_8.json index c3ef37216c..475bb99eb7 100644 --- a/datasets/TL2TNS_8.json +++ b/datasets/TL2TNS_8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL2TNS_8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL2TNS_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Special Observation Version 8 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product contains atmospheric vertical profile estimates and associated errors (diagonals and covariance matrices), along with retrieved surface temperature, cloud effective optical depth, column estimates, quality flags, averaging kernels and priori constraint vectors. TES Level 2 data contain retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits.\r\rNadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. \r\rA Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 3,200 retrievals were performed. Each observation was the input for retrievals of species Volume Mixing Ratios (VMRs), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Further, observations were occasionally scheduled on non-global survey days. In general they were measurements made for validation purposes or with highly focused science objectives. Those non-global survey measurements were referred to as \u201cspecial observations\u201d\r\rThe organization of data within the Swath object was based on a superset of the Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus in the Standard Product files, each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was applied.\r\rTo minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) was collected into a separate standard product, termed the TES L2 Ancillary Data product (Short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.", "links": [ { diff --git a/datasets/TL3ATD_5.json b/datasets/TL3ATD_5.json index 2fba8d9f30..4039ff6224 100644 --- a/datasets/TL3ATD_5.json +++ b/datasets/TL3ATD_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3ATD_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3ATD_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Daily Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3ATD_6.json b/datasets/TL3ATD_6.json index b039da7599..75a89d6468 100644 --- a/datasets/TL3ATD_6.json +++ b/datasets/TL3ATD_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3ATD_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3ATD_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Daily Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3ATM_004.json b/datasets/TL3ATM_004.json index 8a094516ab..2a44996447 100644 --- a/datasets/TL3ATM_004.json +++ b/datasets/TL3ATM_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3ATM_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L3 ATD data consist of daily atmospheric temperature and VMR for the atmospheric species. Data are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolates the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF - EOS-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms 'Daily' and 'Monthly' representing the approximate time coverage of the L3 products. However the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey will not be split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represent a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products are 'daily' and 'monthly'. L3 data is provided at uniform grids in latitude and longitude and at selected pressure levels. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: https://eosweb.larc.nasa.gov/project/tes/DPS", "links": [ { diff --git a/datasets/TL3ATM_5.json b/datasets/TL3ATM_5.json index d8d2f67c62..7100d5f052 100644 --- a/datasets/TL3ATM_5.json +++ b/datasets/TL3ATM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3ATM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3ATM_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consists of monthly atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3ATM_6.json b/datasets/TL3ATM_6.json index 1670f2f946..562c049e48 100644 --- a/datasets/TL3ATM_6.json +++ b/datasets/TL3ATM_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3ATM_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3ATM_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consists of monthly atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3CH4D_5.json b/datasets/TL3CH4D_5.json index be7277c794..8fa978412e 100644 --- a/datasets/TL3CH4D_5.json +++ b/datasets/TL3CH4D_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CH4D_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3CH4D_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Methane Daily Gridded Version 5 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3CH4D_6.json b/datasets/TL3CH4D_6.json index a9a81755ea..900e56d925 100644 --- a/datasets/TL3CH4D_6.json +++ b/datasets/TL3CH4D_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CH4D_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3CH4D_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Methane Daily Gridded Version 6 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3CH4M_004.json b/datasets/TL3CH4M_004.json index eeae402d99..9504189abc 100644 --- a/datasets/TL3CH4M_004.json +++ b/datasets/TL3CH4M_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CH4M_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L3 CH4 data consist of monthly averages of atmospheric Methane for atmospheric species. Data are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolates the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF - EOS-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms 'Daily' and 'Monthly' representing the approximate time coverage of the L3 products. However the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey will not be split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represent a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products are 'daily' and 'monthly'. L3 data is provided at uniform grids in latitude and longitude and at selected pressure levels. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: http://eosweb.larc.nasa.gov/project/tes/DPS", "links": [ { diff --git a/datasets/TL3CH4M_5.json b/datasets/TL3CH4M_5.json index 07c575022f..3d1b79db5c 100644 --- a/datasets/TL3CH4M_5.json +++ b/datasets/TL3CH4M_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CH4M_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3CH4M_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Methane Monthly Gridded Version 5 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, methane, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3CH4M_6.json b/datasets/TL3CH4M_6.json index 67c30afa77..89ac55e6e3 100644 --- a/datasets/TL3CH4M_6.json +++ b/datasets/TL3CH4M_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CH4M_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3CH4M_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Methane Monthly Gridded Version 6 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, methane, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3CO2LM_3.json b/datasets/TL3CO2LM_3.json index e10ba50378..c0b9312fd8 100644 --- a/datasets/TL3CO2LM_3.json +++ b/datasets/TL3CO2LM_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CO2LM_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L3 CO2 data consist of daily atmospheric temperature and VMR for the atmospheric species. Data are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolates the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF - EOS-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey will not be split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represent a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products are daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: http://eosweb.larc.nasa.gov/project/tes/DPS", "links": [ { diff --git a/datasets/TL3CO2M_3.json b/datasets/TL3CO2M_3.json index ea88a2376a..4b46390b92 100644 --- a/datasets/TL3CO2M_3.json +++ b/datasets/TL3CO2M_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3CO2M_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TES Aura L3 CO2 data consist of daily atmospheric temperature and VMR for the atmospheric species. Data are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolates the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF - EOS-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey will not be split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represent a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products are daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS) which is available from the LaRC ASDC site: http://eosweb.larc.nasa.gov/project/tes/DPS", "links": [ { diff --git a/datasets/TL3COD_5.json b/datasets/TL3COD_5.json index 4e5d09f2fb..b131c00316 100644 --- a/datasets/TL3COD_5.json +++ b/datasets/TL3COD_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3COD_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3COD_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level3 Carbon Monoxide Daily Gridded Version 5 data product. It consists of daily atmospheric temperature and VMR for the atmospheric species, carbon monoxide, which are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3COD_6.json b/datasets/TL3COD_6.json index d6cd10bdfe..74d56042f8 100644 --- a/datasets/TL3COD_6.json +++ b/datasets/TL3COD_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3COD_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3COD_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level3 Carbon Monoxide Daily Gridded Version 6 data product. It consists of daily atmospheric temperature and VMR for the atmospheric species, carbon monoxide, which are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3COM_003.json b/datasets/TL3COM_003.json index 2fd5a3e8d7..f55a025848 100644 --- a/datasets/TL3COM_003.json +++ b/datasets/TL3COM_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3COM_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly averages of atmospheric temperature and VMR for atmospheric species are provided at 2 deg. lat. X 4 deg. long. spatial grids and at a subset of TES standard pressure levels. Algorithms for deriving TES L3 data will be provided in the data files.", "links": [ { diff --git a/datasets/TL3COM_5.json b/datasets/TL3COM_5.json index cd1b3ac626..bfdf42102d 100644 --- a/datasets/TL3COM_5.json +++ b/datasets/TL3COM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3COM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3COM_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Carbon Monoxide Monthly Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consists of monthly atmospheric temperature and volume mixing ratio (VMR) for the atmospheric carbon monoxide species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3H2OD_5.json b/datasets/TL3H2OD_5.json index bfb53e939c..f2e72c85f5 100644 --- a/datasets/TL3H2OD_5.json +++ b/datasets/TL3H2OD_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3H2OD_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3H2OD_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Daily Gridded Version 5 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the water vapor atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3H2OD_6.json b/datasets/TL3H2OD_6.json index 4c4cf00783..779b3f7d91 100644 --- a/datasets/TL3H2OD_6.json +++ b/datasets/TL3H2OD_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3H2OD_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3H2OD_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Daily Gridded Version 6 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the water vapor atmospheric species, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3H2OM_4.json b/datasets/TL3H2OM_4.json index aa51a6d846..6d1835f604 100644 --- a/datasets/TL3H2OM_4.json +++ b/datasets/TL3H2OM_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3H2OM_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3H2OM_4 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Monthly Gridded Version 4 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of monthly atmospheric temperature and volume mixing ratios (VMRs) for the Water Vapor atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels.\r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3H2OM_5.json b/datasets/TL3H2OM_5.json index dc50cb7134..f129a777b4 100644 --- a/datasets/TL3H2OM_5.json +++ b/datasets/TL3H2OM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3H2OM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3H2OM_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Monthly Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of monthly atmospheric temperature and volume mixing ratios (VMRs) for the Water Vapor atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels.\r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3H2OM_6.json b/datasets/TL3H2OM_6.json index 2f109a8079..8e72d94fea 100644 --- a/datasets/TL3H2OM_6.json +++ b/datasets/TL3H2OM_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3H2OM_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3H2OM_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Monthly Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of monthly atmospheric temperature and volume mixing ratios (VMRs) for the Water Vapor atmospheric species, which are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels.\r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3HDOD_5.json b/datasets/TL3HDOD_5.json index 25f61c8655..8df37a9da1 100644 --- a/datasets/TL3HDOD_5.json +++ b/datasets/TL3HDOD_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HDOD_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HDOD_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 (L3) 3 Deuterium Oxide Daily Gridded Version 5 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the deuterium oxide atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3HDOD_6.json b/datasets/TL3HDOD_6.json index f57551be66..a141b9492b 100644 --- a/datasets/TL3HDOD_6.json +++ b/datasets/TL3HDOD_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HDOD_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HDOD_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 (L3) 3 Deuterium Oxide Daily Gridded Version 6 data product. It consists of daily atmospheric temperature and volume mixing ratio (VMR) for the deuterium oxide atmospheric species, which are provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rThe TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.", "links": [ { diff --git a/datasets/TL3HDOM_4.json b/datasets/TL3HDOM_4.json index ba5727fdae..c8402524d4 100644 --- a/datasets/TL3HDOM_4.json +++ b/datasets/TL3HDOM_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HDOM_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HDOM_4 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Deuterium Oxide Monthly Gridded Version 4 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. \r\rThe L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may have been present. Specific to L3 processing were the terms Daily and Monthly, which represented the approximate time coverage of the L3 products. However, the input data granules to the L3 process were completed Global Surveys; in other words a Global Survey were not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3HDOM_5.json b/datasets/TL3HDOM_5.json index 4ffb461e96..dd47fbfcaf 100644 --- a/datasets/TL3HDOM_5.json +++ b/datasets/TL3HDOM_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HDOM_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HDOM_5 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Deuterium Oxide Monthly Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. \r\rThe L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may have been present. Specific to L3 processing were the terms Daily and Monthly, which represented the approximate time coverage of the L3 products. However, the input data granules to the L3 process were completed Global Surveys; in other words a Global Survey were not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3HDOM_6.json b/datasets/TL3HDOM_6.json index c45ff168f7..79542b143d 100644 --- a/datasets/TL3HDOM_6.json +++ b/datasets/TL3HDOM_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HDOM_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HDOM_6 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Deuterium Oxide Monthly Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. \r\rThe L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may have been present. Specific to L3 processing were the terms Daily and Monthly, which represented the approximate time coverage of the L3 products. However, the input data granules to the L3 process were completed Global Surveys; in other words a Global Survey were not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3HNOD_4.json b/datasets/TL3HNOD_4.json index b5aaf22828..0d8602a92c 100644 --- a/datasets/TL3HNOD_4.json +++ b/datasets/TL3HNOD_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HNOD_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HNOD_4 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 4 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. The satellite flew at an altitude of 705 km in an orbit that took it near Earth's North and South Poles. Each orbit, the spacecraft advanced 22\u00b0 westward and, after 233 orbits (16 days) it was back to its starting point. TES/Aura L3 HNO3 Daily Gridded V004 measures daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which are provided at 2 degrees latitude by 4 degrees longitude spatial grids and at a subset of TES standard pressure levels. Algorithms for deriving TES Level 3 data will be provided in the data files.", "links": [ { diff --git a/datasets/TL3HNOM_4.json b/datasets/TL3HNOM_4.json index ee4d4950ec..08ac9005c1 100644 --- a/datasets/TL3HNOM_4.json +++ b/datasets/TL3HNOM_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3HNOM_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3HNOM_4 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 4 data product. It consists of monthly averages of atmospheric temperature and VMR for atmospheric species are provided at 2 deg. lat. X 4 deg. long. spatial grids and at a subset of TES standard pressure levels. Algorithms for deriving TES L3 data will be provided in the data files.", "links": [ { diff --git a/datasets/TL3O3D_5.json b/datasets/TL3O3D_5.json index ce9c1f392e..b904adb3cb 100644 --- a/datasets/TL3O3D_5.json +++ b/datasets/TL3O3D_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3O3D_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3O3D_5 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Ozone Daily Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file is produced for each different atmospheric species. \r\rTES obtains data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are completed Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceeded the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3O3D_6.json b/datasets/TL3O3D_6.json index a8465aee6e..f241c6b13d 100644 --- a/datasets/TL3O3D_6.json +++ b/datasets/TL3O3D_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3O3D_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3O3D_6 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Ozone Daily Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file is produced for each different atmospheric species. \r\rTES obtains data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are completed Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceeded the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3O3M_4.json b/datasets/TL3O3M_4.json index fdcd5bd964..8adcfabee1 100644 --- a/datasets/TL3O3M_4.json +++ b/datasets/TL3O3M_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3O3M_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3O3M_4 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Ozone (O3) Monthly Gridded Version 4 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, ozone, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product may have contained, in separate folders, limb data, nadir data, or both folders could have been present. \r\rSpecific to L3 processing were the terms Daily and Monthly, representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process were complete Global Surveys; in other words a Global Survey was not split in relation to time when they were input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3O3M_5.json b/datasets/TL3O3M_5.json index 260e5d32f8..6b266114ff 100644 --- a/datasets/TL3O3M_5.json +++ b/datasets/TL3O3M_5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3O3M_5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3O3M_5 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Ozone (O3) Monthly Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, ozone, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product may have contained, in separate folders, limb data, nadir data, or both folders could have been present. \r\rSpecific to L3 processing were the terms Daily and Monthly, representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process were complete Global Surveys; in other words a Global Survey was not split in relation to time when they were input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TL3O3M_6.json b/datasets/TL3O3M_6.json index 0020bbdefb..3ae25f6459 100644 --- a/datasets/TL3O3M_6.json +++ b/datasets/TL3O3M_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TL3O3M_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TL3O3M_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Ozone (O3) Monthly Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, ozone, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product may have contained, in separate folders, limb data, nadir data, or both folders could have been present. \r\rSpecific to L3 processing were the terms Daily and Monthly, representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process were complete Global Surveys; in other words a Global Survey was not split in relation to time when they were input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).", "links": [ { diff --git a/datasets/TLS_Lidar_BlueFlux_Mangroves_2311_1.json b/datasets/TLS_Lidar_BlueFlux_Mangroves_2311_1.json index 6185195549..2af72ec0da 100644 --- a/datasets/TLS_Lidar_BlueFlux_Mangroves_2311_1.json +++ b/datasets/TLS_Lidar_BlueFlux_Mangroves_2311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TLS_Lidar_BlueFlux_Mangroves_2311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS \"Blueflux\" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (*.las) format.", "links": [ { diff --git a/datasets/TMI-REMSS-L2P-v4_4.0.json b/datasets/TMI-REMSS-L2P-v4_4.0.json index 25ea9d498e..7ea9a2d8a1 100644 --- a/datasets/TMI-REMSS-L2P-v4_4.0.json +++ b/datasets/TMI-REMSS-L2P-v4_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TMI-REMSS-L2P-v4_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GDS2 Version -The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems has produced a Version-4 TMI ocean SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"", "links": [ { diff --git a/datasets/TMI-REMSS-L3U-v7.1a_7.1a.json b/datasets/TMI-REMSS-L3U-v7.1a_7.1a.json index ae49b428bd..87ad64a825 100644 --- a/datasets/TMI-REMSS-L3U-v7.1a_7.1a.json +++ b/datasets/TMI-REMSS-L3U-v7.1a_7.1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TMI-REMSS-L3U-v7.1a_7.1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, sea surface temperature (SST) and surface wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial processing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems (REMSS) has produced a Version-7.1a TMI SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"", "links": [ { diff --git a/datasets/TML2COS_2.json b/datasets/TML2COS_2.json index 5a36054113..bfe4e4106b 100644 --- a/datasets/TML2COS_2.json +++ b/datasets/TML2COS_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TML2COS_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TML2COS_2 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 2 data product. It consists of atmospheric vertical profile estimates and associated errors derived using TES and MLS spectral radiance measurements taken at nearest time and locations. Also provided are calculated total vertical column, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TML2CO_2.json b/datasets/TML2CO_2.json index c0993dbd83..9f178525cc 100644 --- a/datasets/TML2CO_2.json +++ b/datasets/TML2CO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TML2CO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TML2CO_2 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Limb Version 2 data product. It consists of atmospheric vertical profile estimates and associated errors derived using TES and MLS spectral radiance measurements taken at nearest time and locations. Also provided are calculated total vertical column, averaging kernels and a priori constraint vectors.", "links": [ { diff --git a/datasets/TM_MOSAICS.json b/datasets/TM_MOSAICS.json index 32e7c3c253..da348a1e56 100644 --- a/datasets/TM_MOSAICS.json +++ b/datasets/TM_MOSAICS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TM_MOSAICS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mosaic data products, which are also available for Tri-Decadal Global Landsat Orthorectified TM and ETM+ Pan-sharpened data, and may be searched and downloaded through EarthExplorer.\n\nGround control points are fixed, and images have been registered to the Universal Transverse Mercator (UTM) map projection and coordinate system and the World Geodetic System 1984 (WGS84) datum. All image bands have been individually resampled, using a nearest neighbor algorithm. Positional accuracy on the final image product has a Root Mean Square Error of better than 100 meters (MSS) and 50 meters (TM and ETM+). The Landsat data were acquired and processed through a National Aeronautics and Space Administration (NASA) contract with Earth Satellite Corporation, Rockville, Maryland, and are part of NASA's Scientific Data Purchase program.\n\nWhen possible, data were collected when vegetation was at peak greenness. Peak greenness was determined from global 1-kilometer Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data. When peak greenness data were not available, images from other times of the year were substituted.\n", "links": [ { diff --git a/datasets/TNE_8A_acidification_microbes_1.json b/datasets/TNE_8A_acidification_microbes_1.json index 1e87a54196..57bdf5b372 100644 --- a/datasets/TNE_8A_acidification_microbes_1.json +++ b/datasets/TNE_8A_acidification_microbes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TNE_8A_acidification_microbes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three experiments were performed at Davis Station, East Antarctica 77 degrees 58' E, 68 degrees 35' S to determine the effects of ocean acidification on natural assemblages of Antarctica marine microbes (bacteria, viruses, phytoplankton and protozoa). Incubation tanks (minicosms) were filled on the 30/12/08, 20/01/09 and 09/02/09 with sea water that was filtered through 200 microns mesh to remove metazoan grazers. The pH of the contents of each tank was then adjusted by adding calculated amounts to CO2 saturated sea water to achieve and maintain CO2 concenrtations that encompassed atmospheric concenrtations from pre-industrial to post-2100. As 6 tanks were available the 3 x current CO2 treatment was duplicated to indicate the variance among replicate tanks. Instead, responses were analysed to determine trends among concentrations. The microbial communities were incubated for 10, 12 and 10 days, in experiments 1, 2 and 3 respectively. Chemical and biological parameters were measured every second day to determine concentrations of macronutrients, particulate and dissolved organic carbon, pigment composition, dissolved oxygen, concentrations of phytoplankton, protozoa, bacteria (and viruses) using flow cytometry, light and electron microscopy, lipids, rates of primary, bacterial production and microzooplankton grazing.\n \nThese data have been collected as part of ASAC project 40 (ASAC_40), and Terrestrial Nearshore Ecosystems project 8A.\n\nThe excel spreadsheet contains:\n\nSeparate sheets reporting the results from each of the 3 experiments run at Davis Station in the 2008/09 summer. \nAbbreviations are as follows:\n\nNutrients: NO3 =nitrate, PO4 = Phosphate, Si = silicate\n\nPrimary production and respiration were determined from oxygen microelectrodes: net photosynthesis from oxygen increase during exposure to light and respiration determined from net decrease in oxygen in the absence of light.\n\nPhotosynthetic parameters were also measure using 14C bicarbonate as a trace for Carbon uptake, these being: maximum photosynthetic rate) Pmax, Photosynthetic efficiency (Alpha) and saturating light intensity (Ek).\n\nFlow cytometry was used to count 7 microbial parameters: pico phytoplankton (Picos) nanophytoplankton in two regions (Nano R2 and Nano R3).\n\nCryptophytes, high DNA bacteria (HDNA_bact) and low DNA bacteria (LDNA_bact).\n\nMicroscope cell counts identified a range of taxa/groups that comprised greater than 1% of the total phytoplankton abundance: unidentified nanoplankton (UNAN), small pennate diatoms (Pennate less than 10 microns) and other taxa as specified.\n\nOrganic material measurements including: Particulate organic carbon (POC), Particulate organic nitrogen (PON) particulate carbon to nitrogen ratio (C:N), Dissolved organic carbon (DOC)\n\nIntermittent measurements were also made of rates of herbivory and bacterivory and rates of phytoplankton and bacterial growth in 3 of the 6 tanks.\n\nPhotosynthetic pigments were measured and are given only for experiment 1 so far (other to come later): Beta-Beta carotene (BB carotene), Chlorophylls c1 (Chl c1), c2 (Chl c2), c3 Chl c3), a (Chl a), b (Chl b), Chlorophyllide a (Chlidea), diadinoxanthin (Ddx), Diatoxanthin (dtx), Chl a epimer (epi), Fucoxanthin (Fuc), 19'-hexanoyloxyfucoxathin (Hex), Methyl Chlorophyllide a (MeChlidea), Magnesium divinyl pheaoporphyrin monomethyl ester (MgDVP), Phaeophytin (Phaeo), Violaxanthin (viola) and total pigment concentration. CHEMTAX will also be performed using these pigments to study CO2-induced changes in phytoplankton community structure.", "links": [ { diff --git a/datasets/TOL2O3S_2.json b/datasets/TOL2O3S_2.json index be507dfe18..9c531121f2 100644 --- a/datasets/TOL2O3S_2.json +++ b/datasets/TOL2O3S_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOL2O3S_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOL2O3S_2 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 (L2) Atmospheric Temperatures Limb Version 2 data product. It was derived from TES nadir and Ozone Monitoring Instrument (OMI) hyper-spectral measurements from the Aura satellite to jointly estimate an atmospheric ozone (O3) profile with extended vertical range compared to profiles retrieved from the individual measurement. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rTES and OMI stand-alone O3 profile retrievals were largely complementary, with TES infrared measurements being largely sensitive to lower to middle troposphere while OMI total column O3 in the upper troposphere and lower stratosphere. TES nadir and OMI locations were paired within 6-8 min and within 220 km. The paired radiance measurements of the two instruments in each location were optimally combined to retrieve a single O3 profile along with other trace gases whose signal interfered with that from O3. This combined O3 profile was a vertical resolution and vertical range that was an improvement over the two stand-alone products, especially in the upper troposphere/lower stratosphere. This Aura TES-OMI O3 product, using TES and OMI processing results, provided a unique data set for studying tropospheric transport of air pollutants and troposphere-stratospheric exchange processes.", "links": [ { diff --git a/datasets/TOL2O3S_3.json b/datasets/TOL2O3S_3.json index c5475dc9b8..4beb84b552 100644 --- a/datasets/TOL2O3S_3.json +++ b/datasets/TOL2O3S_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOL2O3S_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOL2O3S_3 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 (L2) Atmospheric Temperatures Limb Version 3 data product. It was derived from TES nadir and Ozone Monitoring Instrument (OMI) hyper-spectral measurements from the Aura satellite to jointly estimate an atmospheric ozone (O3) profile with extended vertical range compared to profiles retrieved from the individual measurement. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. \r\rTES and OMI stand-alone O3 profile retrievals were largely complementary, with TES infrared measurements being largely sensitive to lower to middle troposphere while OMI total column O3 in the upper troposphere and lower stratosphere. TES nadir and OMI locations were paired within 6-8 min and within 220 km. The paired radiance measurements of the two instruments in each location were optimally combined to retrieve a single O3 profile along with other trace gases whose signal interfered with that from O3. This combined O3 profile was a vertical resolution and vertical range that was an improvement over the two stand-alone products, especially in the upper troposphere/lower stratosphere. This Aura TES-OMI O3 product, using TES and OMI processing results, provided a unique data set for studying tropospheric transport of air pollutants and troposphere-stratospheric exchange processes.", "links": [ { diff --git a/datasets/TOL2O3_2.json b/datasets/TOL2O3_2.json index cb638b407f..8370f884c2 100644 --- a/datasets/TOL2O3_2.json +++ b/datasets/TOL2O3_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOL2O3_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOL2O3_2 is the Tropospheric Emission Spectrometer (TES)/Ozone Monitoring Instrument (OMI) Level 2 Ozone (O3) Nadir Version 2 data product. It was derived from TES Nadir and OMI hyper-spectral measurements from the Aura satellite and jointly estimated an atmospheric ozone (O3) profile with extended vertical range compared to profiles retrieved from the individual measurement. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete.\r\rTES and OMI stand-alone O3 profile retrievals were largely complementary, with TES infrared (IR) measurements being largely sensitive to the lower to middle troposphere while OMI total column O3 in the upper troposphere and lower stratosphere. TES nadir, which point directly to the surface of the Earth, and OMI locations were paired within 6-8 min and within 220 km. The paired radiance measurements of the two instruments in each location were optimally combined to retrieve a single O3 profile along with other trace gases whose signal interfered with that from O3. This combined O3 profile had a vertical resolution and vertical range that was an improvement over the two stand-alone products, especially in the upper troposphere/lower stratosphere. This Aura TES-OMI O3 product used TES and OMI processing results and provided a unique data set for studying tropospheric transport of air pollutants and troposphere-stratospheric exchange processes.", "links": [ { diff --git a/datasets/TOL2O3_3.json b/datasets/TOL2O3_3.json index 79c6c302a5..1e4f4ac76b 100644 --- a/datasets/TOL2O3_3.json +++ b/datasets/TOL2O3_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOL2O3_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOL2O3_3 is the Tropospheric Emission Spectrometer (TES)/Ozone Monitoring Instrument (OMI) Level 2 Ozone (O3) Nadir Version 3 data product. It was derived from TES Nadir and OMI hyper-spectral measurements from the Aura satellite and jointly estimated an atmospheric ozone (O3) profile with extended vertical range compared to profiles retrieved from the individual measurement. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete.\r\rTES and OMI stand-alone O3 profile retrievals were largely complementary, with TES infrared (IR) measurements being largely sensitive to the lower to middle troposphere while OMI total column O3 in the upper troposphere and lower stratosphere. TES nadir, which point directly to the surface of the Earth, and OMI locations were paired within 6-8 min and within 220 km. The paired radiance measurements of the two instruments in each location were optimally combined to retrieve a single O3 profile along with other trace gases whose signal interfereed with that from O3. This combined O3 profile had a vertical resolution and vertical range that was an improvement over the two stand-alone products, especially in the upper troposphere/lower stratosphere. This Aura TES-OMI O3 product used TES and OMI processing results and provided a unique data set for studying tropospheric transport of air pollutants and troposphere-stratospheric exchange processes.", "links": [ { diff --git a/datasets/TOLNet_CCNY_Data_1.json b/datasets/TOLNet_CCNY_Data_1.json index e6e69888c6..3af9da89f3 100644 --- a/datasets/TOLNet_CCNY_Data_1.json +++ b/datasets/TOLNet_CCNY_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_CCNY_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_CCNY_Data is the lidar data collected by the New York Tropospheric Ozone Lidar System (NYTOLS) at the City College of New York (CCNY) as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_CSL_Data_1.json b/datasets/TOLNet_CSL_Data_1.json index d4437b7532..d677c7c009 100644 --- a/datasets/TOLNet_CSL_Data_1.json +++ b/datasets/TOLNet_CSL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_CSL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_CSL_Data is the lidar data collected by the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_ECCC_Data_1.json b/datasets/TOLNet_ECCC_Data_1.json index 1afd47a2aa..0c61288313 100644 --- a/datasets/TOLNet_ECCC_Data_1.json +++ b/datasets/TOLNet_ECCC_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_ECCC_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_ECCC_Data is the lidar data collected by the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of six Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. \r\nThe remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_GSFC_Data_1.json b/datasets/TOLNet_GSFC_Data_1.json index 7ec3ebdad8..d02e2cbb9b 100644 --- a/datasets/TOLNet_GSFC_Data_1.json +++ b/datasets/TOLNet_GSFC_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_GSFC_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_GSFC_Data is the lidar data collected by the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC) as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_JPL_Data_1.json b/datasets/TOLNet_JPL_Data_1.json index 73c4879469..c72c0a184e 100644 --- a/datasets/TOLNet_JPL_Data_1.json +++ b/datasets/TOLNet_JPL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_JPL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_JPL_Data is the lidar data collected by the Table Mountain Facility (TMF) tropospheric ozone lidar system at the NASA Jet Propulsion Laboratory (JPL) as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_LaRC_Data_1.json b/datasets/TOLNet_LaRC_Data_1.json index 98a4ad61dd..65327fa215 100644 --- a/datasets/TOLNet_LaRC_Data_1.json +++ b/datasets/TOLNet_LaRC_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_LaRC_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_LaRC_Data is the lidar data collected by the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC) as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOLNet_UAH_Data_1.json b/datasets/TOLNet_UAH_Data_1.json index ed75696dbb..da1beda49c 100644 --- a/datasets/TOLNet_UAH_Data_1.json +++ b/datasets/TOLNet_UAH_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOLNet_UAH_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOLNet_UAH_Data is the lidar data collected by the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).", "links": [ { diff --git a/datasets/TOMSEPAER_2.json b/datasets/TOMSEPAER_2.json index 708611fd6e..bdfa604189 100644 --- a/datasets/TOMSEPAER_2.json +++ b/datasets/TOMSEPAER_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPAER_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 32-year record of satellite near-UV observations by three sensors. \n\nThe TOMS Earth Probe version 2 Level-2 orbital data product consists of cloud fraction, cloud optical depth, normalized radiance, reflectivity, residue, and UV aerosol index at approximately 40x40 km resolution (at nadir). This product also contains ancillary variables for ocean corrected surface albedo and terrain pressure.\n\nTotal Ozone Mapping Spectrometer (TOMS) instruments have been successfully flown in orbit aboard the Nimbus-7(Nov. 1978 - May 1993), Meteor-3 (Aug. 1991 - Dec. 1994), Earth Probe (June 1996 - December 2005), and ADEOS (Sep. 1996 - June 1997) satellites.\n\nThese Level-2 data are stored in the Hierarchical Data Format 5 (HDF5) and are available from the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).", "links": [ { diff --git a/datasets/TOMSEPL2_008.json b/datasets/TOMSEPL2_008.json index 8f67d0adf2..1c564e9ad7 100644 --- a/datasets/TOMSEPL2_008.json +++ b/datasets/TOMSEPL2_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL2_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags. These data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5).\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3_008.json b/datasets/TOMSEPL3_008.json index cbb6dfd965..0294257537 100644 --- a/datasets/TOMSEPL3_008.json +++ b/datasets/TOMSEPL3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS).\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3daer_008.json b/datasets/TOMSEPL3daer_008.json index b6e9b386c6..f5ce79ec12 100644 --- a/datasets/TOMSEPL3daer_008.json +++ b/datasets/TOMSEPL3daer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3daer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3dery_008.json b/datasets/TOMSEPL3dery_008.json index 3b81f1dbe5..4c3744a309 100644 --- a/datasets/TOMSEPL3dery_008.json +++ b/datasets/TOMSEPL3dery_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3dery_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3dref_008.json b/datasets/TOMSEPL3dref_008.json index 0a2ea5d59b..3c4f35040b 100644 --- a/datasets/TOMSEPL3dref_008.json +++ b/datasets/TOMSEPL3dref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3dref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3dtoz_008.json b/datasets/TOMSEPL3dtoz_008.json index 823acff23a..d01dbc3cab 100644 --- a/datasets/TOMSEPL3dtoz_008.json +++ b/datasets/TOMSEPL3dtoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3dtoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3maer_008.json b/datasets/TOMSEPL3maer_008.json index 67fc474ac6..b265356c2c 100644 --- a/datasets/TOMSEPL3maer_008.json +++ b/datasets/TOMSEPL3maer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3maer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3mery_008.json b/datasets/TOMSEPL3mery_008.json index d600350c9c..51942939ed 100644 --- a/datasets/TOMSEPL3mery_008.json +++ b/datasets/TOMSEPL3mery_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3mery_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3mref_008.json b/datasets/TOMSEPL3mref_008.json index d966c79075..4a782b612c 100644 --- a/datasets/TOMSEPL3mref_008.json +++ b/datasets/TOMSEPL3mref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3mref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3mtoz_008.json b/datasets/TOMSEPL3mtoz_008.json index ed8197a39f..a642bad521 100644 --- a/datasets/TOMSEPL3mtoz_008.json +++ b/datasets/TOMSEPL3mtoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3mtoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3zaer_008.json b/datasets/TOMSEPL3zaer_008.json index 7caa6997a6..1374cd48a5 100644 --- a/datasets/TOMSEPL3zaer_008.json +++ b/datasets/TOMSEPL3zaer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3zaer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3zref_008.json b/datasets/TOMSEPL3zref_008.json index 7e00432761..06f8faa88a 100644 --- a/datasets/TOMSEPL3zref_008.json +++ b/datasets/TOMSEPL3zref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3zref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPL3ztoz_008.json b/datasets/TOMSEPL3ztoz_008.json index bddddc3cf1..7bdaac918b 100644 --- a/datasets/TOMSEPL3ztoz_008.json +++ b/datasets/TOMSEPL3ztoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPL3ztoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSEPOVP_008.json b/datasets/TOMSEPOVP_008.json index 9ae7252a59..92d2fac2ba 100644 --- a/datasets/TOMSEPOVP_008.json +++ b/datasets/TOMSEPOVP_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSEPOVP_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format.\n\nTOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSM3L3_008.json b/datasets/TOMSM3L3_008.json index 8a6243e5ac..d8098a81c8 100644 --- a/datasets/TOMSM3L3_008.json +++ b/datasets/TOMSM3L3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSM3L3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS).\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSM3L3dref_008.json b/datasets/TOMSM3L3dref_008.json index e66c283a67..ce53f25526 100644 --- a/datasets/TOMSM3L3dref_008.json +++ b/datasets/TOMSM3L3dref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSM3L3dref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSM3L3dtoz_008.json b/datasets/TOMSM3L3dtoz_008.json index c9447d8cf5..25a121d276 100644 --- a/datasets/TOMSM3L3dtoz_008.json +++ b/datasets/TOMSM3L3dtoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSM3L3dtoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSM3OVP_008.json b/datasets/TOMSM3OVP_008.json index 0509772a9b..7bf4b1b3e1 100644 --- a/datasets/TOMSM3OVP_008.json +++ b/datasets/TOMSM3OVP_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSM3OVP_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format.\n\nTOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7AER_2.json b/datasets/TOMSN7AER_2.json index 2da22ad052..0025640d33 100644 --- a/datasets/TOMSN7AER_2.json +++ b/datasets/TOMSN7AER_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7AER_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 40-year record of satellite near-UV observations by three sensors. \n\nThe TOMS Nimbus 7 version 2 Level-2 orbital data product consists of cloud fraction, cloud optical depth, normalized radiance, reflectivity, residue, and UV aerosol index at approximately 50x50 km resolution (at nadir). This product also contains ancillary variables for ocean corrected surface albedo and terrain pressure.\n\nTotal Ozone Mapping Spectrometer (TOMS) instruments have been successfully flown in orbit aboard the Nimbus-7(Nov. 1978 - May 1993), Meteor-3 (Aug. 1991 - Dec. 1994), Earth Probe (June 1996 - December 2005), and ADEOS (Sep. 1996 - June 1997) satellites.\n\nThese Level-2 data are stored in the Hierarchical Data Format 5 (HDF5) and are available from the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).", "links": [ { diff --git a/datasets/TOMSN7L2_008.json b/datasets/TOMSN7L2_008.json index 2b86cd4bce..4d5d3795dc 100644 --- a/datasets/TOMSN7L2_008.json +++ b/datasets/TOMSN7L2_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L2_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags. These data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5).\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3_008.json b/datasets/TOMSN7L3_008.json index 26780cacc3..e0a132db7a 100644 --- a/datasets/TOMSN7L3_008.json +++ b/datasets/TOMSN7L3_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS).\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3daer_008.json b/datasets/TOMSN7L3daer_008.json index 1a570c1ca5..50c409dc5f 100644 --- a/datasets/TOMSN7L3daer_008.json +++ b/datasets/TOMSN7L3daer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3daer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3dery_008.json b/datasets/TOMSN7L3dery_008.json index f9a1a87d54..4b5805b4d8 100644 --- a/datasets/TOMSN7L3dery_008.json +++ b/datasets/TOMSN7L3dery_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3dery_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3dref_008.json b/datasets/TOMSN7L3dref_008.json index cd50d24cc0..0a485e06e9 100644 --- a/datasets/TOMSN7L3dref_008.json +++ b/datasets/TOMSN7L3dref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3dref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3dtoz_008.json b/datasets/TOMSN7L3dtoz_008.json index ecc88d2baf..b02e4d2e43 100644 --- a/datasets/TOMSN7L3dtoz_008.json +++ b/datasets/TOMSN7L3dtoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3dtoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3maer_008.json b/datasets/TOMSN7L3maer_008.json index 49694a65c2..cf4dd86d71 100644 --- a/datasets/TOMSN7L3maer_008.json +++ b/datasets/TOMSN7L3maer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3maer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3mery_008.json b/datasets/TOMSN7L3mery_008.json index 4f11743329..f18c24f257 100644 --- a/datasets/TOMSN7L3mery_008.json +++ b/datasets/TOMSN7L3mery_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3mery_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3mref_008.json b/datasets/TOMSN7L3mref_008.json index e99a2de253..fe2376d450 100644 --- a/datasets/TOMSN7L3mref_008.json +++ b/datasets/TOMSN7L3mref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3mref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3mtoz_008.json b/datasets/TOMSN7L3mtoz_008.json index aaf0e2f499..7787c22f8c 100644 --- a/datasets/TOMSN7L3mtoz_008.json +++ b/datasets/TOMSN7L3mtoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3mtoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3zaer_008.json b/datasets/TOMSN7L3zaer_008.json index c28709acd7..330d05b3e7 100644 --- a/datasets/TOMSN7L3zaer_008.json +++ b/datasets/TOMSN7L3zaer_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3zaer_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3zref_008.json b/datasets/TOMSN7L3zref_008.json index 2eb317c4d1..1e6523996f 100644 --- a/datasets/TOMSN7L3zref_008.json +++ b/datasets/TOMSN7L3zref_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3zref_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7L3ztoz_008.json b/datasets/TOMSN7L3ztoz_008.json index 658a232695..5e0985c2b5 100644 --- a/datasets/TOMSN7L3ztoz_008.json +++ b/datasets/TOMSN7L3ztoz_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7L3ztoz_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.\n\nThe TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7OVP_008.json b/datasets/TOMSN7OVP_008.json index 2f78e13d1a..703b9067c9 100644 --- a/datasets/TOMSN7OVP_008.json +++ b/datasets/TOMSN7OVP_008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7OVP_008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format.\n\nTOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).", "links": [ { diff --git a/datasets/TOMSN7SO2_3.json b/datasets/TOMSN7SO2_3.json index e24ee4e427..edcbb9f246 100644 --- a/datasets/TOMSN7SO2_3.json +++ b/datasets/TOMSN7SO2_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMSN7SO2_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 3 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 3.\nThe goal of this data set is to create and archive a Level 2 SO2 Earth Science Data Record (ESDR) from backscatter Ultraviolet (BUV) measurements from Total Ozone Mapping Spectrometer (TOMS) flown on NASA's Nimbus - 7 satellite in 1978-1993. We apply TOMS ozone team calibration techniques and consistent MEaSUREs SO2 (MS_SO2) algorithm, to obtain the best measurement-based ESDR of volcanic and anthropogenic SO2 masses and emissions. \n\nAs part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, the Goddard Earth Science (GES) Data and Information Data Center (DISC) has released a new SO2 Earth System Data Record (ESDR), TOMSN7SO2, re-processed using new 4 UV wavelength bands MS_SO2 algorithm that spans the full Nimbus 7 data period. TOMSN7SO2 is a Level 2 orbital swath product, which will be used to study the fifteen-year SO2 record from the Nimbus-7 TOMS and to expand the historical database of known volcanic eruptions.\n\n Sulfur Dioxide (SO2) is a short-lived gas primarily produced by volcanoes, power plants, refineries, metal smelting and burning of fossil fuels. Where SO2 remains near the Earth s surface, it is toxic, causes acid rain, and degrades air quality. Where SO2 is lofted into the free troposphere, it forms aerosols that can alter cloud reflectivity and precipitation. In the stratosphere, volcanic SO2 forms sulfate aerosols that can result in climate change.\n", "links": [ { diff --git a/datasets/TOMS_aerosol_823_1.json b/datasets/TOMS_aerosol_823_1.json index 7abd3ded08..28d2acc94a 100644 --- a/datasets/TOMS_aerosol_823_1.json +++ b/datasets/TOMS_aerosol_823_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMS_aerosol_823_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily Aerosol Index (AI) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 12-September 25, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TOMS AI is formed directly from measured TOMS radiances in two channels. It is a measure of how much the wavelength dependence of backscattered UV radiation from an atmosphere containing aerosols (Mie scattering, Rayleigh scattering, and absorption) differs from that of a pure molecular atmosphere (pure Rayleigh scattering). Quantitatively, the AI is defined at http://toms.gsfc.nasa.gov/aerosols/AI_definition/ai_ep_definition.pdf. Positive values represent absorbing aerosols (dust and smoke); negative values represent non-absorbing aerosols. The identification is not perfect because of geophysical reasons (e.g., when aerosols are too low to the ground).The data from TOMS records have been used increasingly to understand the behavior of aerosols within the atmosphere. The TOMS is the first instrument to allow observation of aerosols as the particles cross the land/sea boundary. Using these data it is possible to observe a wide range of phenomena such as desert dust storms, forest fires, and biomass burning.The TOMS AI data are a daily gridded Level-3 product (ASCII .dat format) that covers the area of 40 deg. S to the Equator and 40 deg. W to 80 deg. E. There is also a JPEG image of each data file.", "links": [ { diff --git a/datasets/TOMS_ozone_824_1.json b/datasets/TOMS_ozone_824_1.json index 1f7e94ba01..3b966fec57 100644 --- a/datasets/TOMS_ozone_824_1.json +++ b/datasets/TOMS_ozone_824_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOMS_ozone_824_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tropical Tropospheric Ozone (TTO) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 8-September 29, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TTO measurement is derived from TOMS total ozone (Thompson and Hudson, 1999; Thompson et al., 2001) using the modified-residual method to separate stratospheric ozone from tropospheric ozone. The tropospheric ozone column thickness is reported in Dobson Units (DU).EP TOMS is currently the only NASA spacecraft on orbit specializing in ozone retrieval. EP TOMS was launched in 1996 into an orbit 500 km rather than the 950 km that was originally planned. The Earth Probe satellite was boosted to 740 km in 1997 when the ADEOS satellite failed. The lower orbit of EP TOMS decreased the size of the footprint of each measurement, which increased the resolution and also increased the ability to make measurements over cloudless scenes. This orbit was chosen to improve the ability of the TOMS instrument to make measurements of UV-absorbing aerosols in the troposphere and enhanced the capability of converting the TOMS aerosol measurements into geophysical quantities such as optical depth. Tropospheric aerosols play a major role in the Earth's climate and the capability to measure them from a TOMS instrument had recently been developed using data from Nimbus-7 and Meteor-3 TOMS.The TOMS Tropospheric Ozone data are 9-day averaged, gridded (1-degree by 2-degree) ASCII products. There is also a GIF image of each data file. ", "links": [ { diff --git a/datasets/TOPEX_ALTSDR_A.json b/datasets/TOPEX_ALTSDR_A.json index 7932f3b561..3f7d0cdbe8 100644 --- a/datasets/TOPEX_ALTSDR_A.json +++ b/datasets/TOPEX_ALTSDR_A.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOPEX_ALTSDR_A", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sensory Data Record (SDR) is similar to the GDR product except that it also contains waveforms, which are required for retracking. This is an expert level product. If you do not need the waveforms then the GDR should suit your needs.", "links": [ { diff --git a/datasets/TOPEX_POSEIDON_GDR_F_F.json b/datasets/TOPEX_POSEIDON_GDR_F_F.json index 450a47e04c..47f81da334 100644 --- a/datasets/TOPEX_POSEIDON_GDR_F_F.json +++ b/datasets/TOPEX_POSEIDON_GDR_F_F.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOPEX_POSEIDON_GDR_F_F", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TOPEX/POSEIDON Geophysical Data Record (GDR) contains global coverage altimeter data. The objective of the TOPEX/POSEIDON mission, launched in August 1992, is to determine ocean topography with a sea surface height measurement precision of 3 cm and a sealevel measurement accuracy of 13 cm. The dataset contains measurements from two altimeters, a NASA dual frequency (Ku and C band) instrument similar to the Geosat altimeter, and a French space agency (CNES) instrument which is a proof-of-concept solid-state altimeter (Ku band). It also contains Sea Surface Height (SSH), significant wave height, ionospheric correction, tides and other geophysical corrections. It is emphasized that this product is considered research data because of the form and content of the data. The data consist entirely of files comprising headers and data records which contain over a hundred parameters for each second. It is swath data and there are no images. Analysis software is the responsibility of the user. Calculation of sea surface height anomalies from the altimeter range and environmental corrections is the responsibility of the user. The data are arranged in 10 day cycles that are separated into 254 passes, each about 56 minutes.", "links": [ { diff --git a/datasets/TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1.json index 23cd5e57f3..46e4c65b53 100644 --- a/datasets/TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Aerosol_AircraftInSitu_DC8_Data_1 is the in situ collected onboard the DC-8 aircraft during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data from the Multiple-Angle Spectrometer Probe (MASP), 2D-C Aerosol Probe, and FSSP Aerosol Size distributions are featured in this data product. Data collection is complete.\r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1.json b/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1.json index 55078c061a..4726c4d2c2 100644 --- a/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1.json +++ b/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_AircraftRemoteSensing_DC8_DIAL_Data_1 is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data collection is complete.\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1.json b/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1.json index d9d221c078..1affd823d0 100644 --- a/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1.json +++ b/datasets/TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_AircraftRemoteSensing_DC8_Lidar_Data_1 is the remotely sensed Raman Lidar data collected onboard the DC-8 aircraft during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Methane and water vapor data are featured in this dataset. Data collection is complete.\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_Analysis_DC8_Data_1.json b/datasets/TOTE-VOTE_Analysis_DC8_Data_1.json index 8e83ec1cbe..5abc8c2cc9 100644 --- a/datasets/TOTE-VOTE_Analysis_DC8_Data_1.json +++ b/datasets/TOTE-VOTE_Analysis_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Analysis_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Analysis_DC8_Data_1 is the modeled meteorological data along the flight path for the DC-8 aircraft collected during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data collection for this product is complete.\r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_Ground_Data_1.json b/datasets/TOTE-VOTE_Ground_Data_1.json index cd25b600a1..75824b09ea 100644 --- a/datasets/TOTE-VOTE_Ground_Data_1.json +++ b/datasets/TOTE-VOTE_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Ground_Data_1 is the ground site data collected as part of the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data featured in the product includes data from the NASA GSFC Stratospheric Ozone Lidar Trailer Experiment (STROZ-LITE) at Mauna Loa, and the JPL Table Mountain Facility, Mauna Loa Lidar. Data collection for this product is complete. \r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1.json index 3a9e799317..bd8ca74bc0 100644 --- a/datasets/TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1 features the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) Campaign. Instruments included in this dataset include the Microwave Temperature Profiler (MTP), DC-8 Data Acquisition and Distribution System (DADS) and Diode Laser Hygrometer (DLH). Data collection is complete.\r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_Miscellaneous_DC8_Data_1.json b/datasets/TOTE-VOTE_Miscellaneous_DC8_Data_1.json index bf5a29e87d..04b38470c4 100644 --- a/datasets/TOTE-VOTE_Miscellaneous_DC8_Data_1.json +++ b/datasets/TOTE-VOTE_Miscellaneous_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Miscellaneous_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Analysis_DC8_Data_1 is the ancillary datasets from the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. This dataset contains postscript files of datasets to support DC-8 aircraft measurements. \r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_Satellite_Data_1.json b/datasets/TOTE-VOTE_Satellite_Data_1.json index a7933e571c..beb043e6c0 100644 --- a/datasets/TOTE-VOTE_Satellite_Data_1.json +++ b/datasets/TOTE-VOTE_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Satellite_Data_1 is the supplementary satellite data for the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data in this product includes GOES-7 infrared imagery and GOES-9 water vapor imagery. Data collection for this product is complete. \r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_Sondes_Data_1.json b/datasets/TOTE-VOTE_Sondes_Data_1.json index db3aba4cc2..93027fbbe7 100644 --- a/datasets/TOTE-VOTE_Sondes_Data_1.json +++ b/datasets/TOTE-VOTE_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_Sondes_Data_1 is the radiosonde data collected during the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data collection for this product is complete.\r\n\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1.json index fa38e140e8..f486add6d2 100644 --- a/datasets/TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTE-VOTE_TraceGas_AircraftInSitu_DC8_Data_1 is the in situ trace gas data collected onboard the DC-8 aircraft as part of the Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign. Data collected by the DACOM, LICOR, and chemiluminescence are featured in this product. Data collection is completed.\r\nThe Tropical Ozone Transport Experiment \u2013 Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.\r\n\r\nVarious instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP\u2019s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.", "links": [ { diff --git a/datasets/TOTO_0.json b/datasets/TOTO_0.json index c8e1aebb47..b19ab40362 100644 --- a/datasets/TOTO_0.json +++ b/datasets/TOTO_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOTO_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tongue of the Ocean (TOTO) experiment data was collected around the Bahamas and West Florida Shelf.", "links": [ { diff --git a/datasets/TOVSA5ND_01.json b/datasets/TOVSA5ND_01.json index c9ffe20f64..da0fae2e14 100644 --- a/datasets/TOVSA5ND_01.json +++ b/datasets/TOVSA5ND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSA5ND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSA5ND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contains 5-day averages.", "links": [ { diff --git a/datasets/TOVSA5NF_01.json b/datasets/TOVSA5NF_01.json index d5386ad6f3..fd7d10e716 100644 --- a/datasets/TOVSA5NF_01.json +++ b/datasets/TOVSA5NF_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSA5NF_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSA5NF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the\ndifference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. This collection contains a 5 day average.", "links": [ { diff --git a/datasets/TOVSA5NG_01.json b/datasets/TOVSA5NG_01.json index 41c6423644..9e767b3162 100644 --- a/datasets/TOVSA5NG_01.json +++ b/datasets/TOVSA5NG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSA5NG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSA5NG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. These data products are 5 day averages.", "links": [ { diff --git a/datasets/TOVSA5NH_01.json b/datasets/TOVSA5NH_01.json index 174124960b..2596a3aca1 100644 --- a/datasets/TOVSA5NH_01.json +++ b/datasets/TOVSA5NH_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSA5NH_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSA5NH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contain 5-day averages.", "links": [ { diff --git a/datasets/TOVSA5TN_01.json b/datasets/TOVSA5TN_01.json index 8703e62885..bd08f974ff 100644 --- a/datasets/TOVSA5TN_01.json +++ b/datasets/TOVSA5TN_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSA5TN_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSA5TN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contains 5-day averages.", "links": [ { diff --git a/datasets/TOVSADND_01.json b/datasets/TOVSADND_01.json index 781f3d33f3..118682de3a 100644 --- a/datasets/TOVSADND_01.json +++ b/datasets/TOVSADND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSADND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSADND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSADNF_01.json b/datasets/TOVSADNF_01.json index d9d83d1313..2c1f7e9a64 100644 --- a/datasets/TOVSADNF_01.json +++ b/datasets/TOVSADNF_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSADNF_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSADNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSADNG_01.json b/datasets/TOVSADNG_01.json index f611dc11f0..3250ed9583 100644 --- a/datasets/TOVSADNG_01.json +++ b/datasets/TOVSADNG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSADNG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSADNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSADNH_01.json b/datasets/TOVSADNH_01.json index 05ccdbe578..5abf2e43e2 100644 --- a/datasets/TOVSADNH_01.json +++ b/datasets/TOVSADNH_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSADNH_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSADNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSADTN_01.json b/datasets/TOVSADTN_01.json index f6377ea51e..9c19419a28 100644 --- a/datasets/TOVSADTN_01.json +++ b/datasets/TOVSADTN_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSADTN_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSADTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNA_02.json b/datasets/TOVSAMNA_02.json index 32583e9687..bc154043ab 100644 --- a/datasets/TOVSAMNA_02.json +++ b/datasets/TOVSAMNA_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNA_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNA) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-6 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNC_02.json b/datasets/TOVSAMNC_02.json index e82227cbcd..60b04d67b8 100644 --- a/datasets/TOVSAMNC_02.json +++ b/datasets/TOVSAMNC_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNC_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNC) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-7 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMND_01.json b/datasets/TOVSAMND_01.json index bcd9e3987f..385006f6b1 100644 --- a/datasets/TOVSAMND_01.json +++ b/datasets/TOVSAMND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMND_02.json b/datasets/TOVSAMND_02.json index 563dec9c4c..4f5d781708 100644 --- a/datasets/TOVSAMND_02.json +++ b/datasets/TOVSAMND_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMND_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNE_02.json b/datasets/TOVSAMNE_02.json index 465fb9776b..dff905f6dc 100644 --- a/datasets/TOVSAMNE_02.json +++ b/datasets/TOVSAMNE_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNE_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNE) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-8 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNF_01.json b/datasets/TOVSAMNF_01.json index ea69ad22d0..3c9b6545d3 100644 --- a/datasets/TOVSAMNF_01.json +++ b/datasets/TOVSAMNF_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNF_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNF_02.json b/datasets/TOVSAMNF_02.json index 9a0aba12fb..0425282eef 100644 --- a/datasets/TOVSAMNF_02.json +++ b/datasets/TOVSAMNF_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNF_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNG_01.json b/datasets/TOVSAMNG_01.json index 4a06fc2324..9a24e79589 100644 --- a/datasets/TOVSAMNG_01.json +++ b/datasets/TOVSAMNG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are 6 level 3 data product files, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNG_02.json b/datasets/TOVSAMNG_02.json index 73ec04b1d3..f671941a52 100644 --- a/datasets/TOVSAMNG_02.json +++ b/datasets/TOVSAMNG_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNG_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNH_01.json b/datasets/TOVSAMNH_01.json index eb476fd63b..c64bb0819b 100644 --- a/datasets/TOVSAMNH_01.json +++ b/datasets/TOVSAMNH_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNH_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMNH_02.json b/datasets/TOVSAMNH_02.json index b7844b49fc..af814803fa 100644 --- a/datasets/TOVSAMNH_02.json +++ b/datasets/TOVSAMNH_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNH_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. For this NOAA-11 satellite the downlink of data was turned off in mid-1994 then reactivated in 1997. Therefore there is a gap in the monthly data products.", "links": [ { diff --git a/datasets/TOVSAMNJ_02.json b/datasets/TOVSAMNJ_02.json index b19354dc65..e4543b5a07 100644 --- a/datasets/TOVSAMNJ_02.json +++ b/datasets/TOVSAMNJ_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMNJ_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMNJ) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-14 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMTN_01.json b/datasets/TOVSAMTN_01.json index 1dd96c7d4b..2c15360ca0 100644 --- a/datasets/TOVSAMTN_01.json +++ b/datasets/TOVSAMTN_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMTN_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROSN and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThere are 6 level 3 data product files, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSAMTN_02.json b/datasets/TOVSAMTN_02.json index ed0517101b..5d0209b2c4 100644 --- a/datasets/TOVSAMTN_02.json +++ b/datasets/TOVSAMTN_02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSAMTN_02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloud top height, total ozone overburden and precipitation estimates.\n\nThe Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.\n\nThe retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).\n\nThese Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.", "links": [ { diff --git a/datasets/TOVSB5ND_01.json b/datasets/TOVSB5ND_01.json index dc9fb2b917..b9bbf038ec 100644 --- a/datasets/TOVSB5ND_01.json +++ b/datasets/TOVSB5ND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSB5ND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 5 days and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-12 satellite.", "links": [ { diff --git a/datasets/TOVSB5NG_01.json b/datasets/TOVSB5NG_01.json index a5204356f6..5db3730bd7 100644 --- a/datasets/TOVSB5NG_01.json +++ b/datasets/TOVSB5NG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSB5NG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 5 days and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.", "links": [ { diff --git a/datasets/TOVSBDND_01.json b/datasets/TOVSBDND_01.json index a6b134d427..7be2a45846 100644 --- a/datasets/TOVSBDND_01.json +++ b/datasets/TOVSBDND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSBDND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 day and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-12 satellite.", "links": [ { diff --git a/datasets/TOVSBDNG_01.json b/datasets/TOVSBDNG_01.json index a77adea831..c70f4e727f 100644 --- a/datasets/TOVSBDNG_01.json +++ b/datasets/TOVSBDNG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSBDNG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 day and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.", "links": [ { diff --git a/datasets/TOVSBMND_01.json b/datasets/TOVSBMND_01.json index cc728b78d7..d894ec0764 100644 --- a/datasets/TOVSBMND_01.json +++ b/datasets/TOVSBMND_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSBMND_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 month and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-12 satellite.", "links": [ { diff --git a/datasets/TOVSBMNG_01.json b/datasets/TOVSBMNG_01.json index 512eb14320..bc703e423c 100644 --- a/datasets/TOVSBMNG_01.json +++ b/datasets/TOVSBMNG_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVSBMNG_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 month and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.", "links": [ { diff --git a/datasets/TOVS_717_1.json b/datasets/TOVS_717_1.json index ed65bdc239..aad59e044b 100644 --- a/datasets/TOVS_717_1.json +++ b/datasets/TOVS_717_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TOVS_717_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA's TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) is a suite of three sensors: the Microwave Sounding Unit (MSU), the High resolution Infrared Radiation Sounder (HIRS), and the Stratospheric Sounding Unit (SSU) aboard the NOAA series of polar-orbiting operational meteorological satellites. TOVS-derived data provide a means to investigate long-term climate change and interannual variability and study local and periodic phenomena such as El Nino and stratospheric warmings. A set of the derived meteorological parameters for southern Africa have been selected by SAFARI 2000.", "links": [ { diff --git a/datasets/TRACE-A_920_1.json b/datasets/TRACE-A_920_1.json index 340ea3be8b..1e9061944b 100644 --- a/datasets/TRACE-A_920_1.json +++ b/datasets/TRACE-A_920_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_920_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains atmospheric chemistry and meteorological data from the NASA Transport and Atmospheric Chemistry near the Equator-Atlantic (TRACE-A) field study. The NASA TRACE-A study took place in August 1992 to determine the cause and source of high concentrations of ozone that accumulate over the Atlantic ocean between southern Africa and South America during the months of August through October. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but have now been archived individually. ", "links": [ { diff --git a/datasets/TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data_1.json b/datasets/TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data_1.json index fe1ece5484..5769f47cff 100644 --- a/datasets/TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data_1.json +++ b/datasets/TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_Brazil_Data_1.json b/datasets/TRACE-A_Brazil_Data_1.json index f74901ceee..ad217e995e 100644 --- a/datasets/TRACE-A_Brazil_Data_1.json +++ b/datasets/TRACE-A_Brazil_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_Brazil_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_Brazil_Data is the aircraft and rawinsonde data collected in Brazil during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR) is featured in this collection. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_Merge_Data_1.json b/datasets/TRACE-A_Merge_Data_1.json index d709f8cf2c..c69ac2ccf7 100644 --- a/datasets/TRACE-A_Merge_Data_1.json +++ b/datasets/TRACE-A_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_Merge_Data is merge data files created from data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-A_MetNav_AircraftInSitu_DC8_Data_1.json index 85f96c79ec..aa510a53a3 100644 --- a/datasets/TRACE-A_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-A_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.\r\n\r\nTRACE-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_Satellite_Data_1.json b/datasets/TRACE-A_Satellite_Data_1.json index 0cbd2d40f0..b972f57e2e 100644 --- a/datasets/TRACE-A_Satellite_Data_1.json +++ b/datasets/TRACE-A_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_Satellite_Data is the supplementary satellite data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the NOAA 10, 11, and 12 satellites and the Total Ozone Mapping Spectrometer (TOMS) satellite instrument are featured in this collection. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_Sondes_Data_1.json b/datasets/TRACE-A_Sondes_Data_1.json index c360d29a76..7e6395d306 100644 --- a/datasets/TRACE-A_Sondes_Data_1.json +++ b/datasets/TRACE-A_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-A_TraceGas_AircraftInSitu_DC8_Data_1.json index f64a995c1e..9198188b10 100644 --- a/datasets/TRACE-A_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-A_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-A_Trajectory_Data_1.json b/datasets/TRACE-A_Trajectory_Data_1.json index 4c288df72b..3d4ab0280f 100644 --- a/datasets/TRACE-A_Trajectory_Data_1.json +++ b/datasets/TRACE-A_Trajectory_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-A_Trajectory_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-A_Trajectory_Data is the kinematic trajectory data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe TRACE-A mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October.\u202fNASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal.\u202f \r\n\r\nThe NASA DC-8 aircraft\u202fand\u202fozonesondes\u202fwere\u202futilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few\u202finstruments on the DC-8 include the Differential Absorption Lidar (DIAL),\u202fthe Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified\u202fLicor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone,\u202fand ozone column. The Laser-Induced Fluorescence instrument\u202fcollected measurements on\u202fNxOy\u202fin the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified\u202fLicor\u202frecorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2.\u202fOzonesondes\u202fplayed a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\u202f", "links": [ { diff --git a/datasets/TRACE-P_Aerosol_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-P_Aerosol_AircraftInSitu_DC8_Data_1.json index 7f1263dc7c..f6f1818996 100644 --- a/datasets/TRACE-P_Aerosol_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-P_Aerosol_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Aerosol_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Aerosol_AircraftInSitu_P3B_Data_1.json b/datasets/TRACE-P_Aerosol_AircraftInSitu_P3B_Data_1.json index 73e49ce66a..dfd0cd9822 100644 --- a/datasets/TRACE-P_Aerosol_AircraftInSitu_P3B_Data_1.json +++ b/datasets/TRACE-P_Aerosol_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Aerosol_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data_1.json b/datasets/TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data_1.json index f586ac6293..6f70f10fb6 100644 --- a/datasets/TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data_1.json +++ b/datasets/TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Cloud_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-P_Cloud_AircraftInSitu_DC8_Data_1.json index a4d451116d..7aa8d8e484 100644 --- a/datasets/TRACE-P_Cloud_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-P_Cloud_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Cloud_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Ground_Data_1.json b/datasets/TRACE-P_Ground_Data_1.json index cf1a9a4336..6ace75ac65 100644 --- a/datasets/TRACE-P_Ground_Data_1.json +++ b/datasets/TRACE-P_Ground_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Ground_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Ground_Data is the ground site data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Merge_Data_1.json b/datasets/TRACE-P_Merge_Data_1.json index 0a393a3a37..f7e17b666c 100644 --- a/datasets/TRACE-P_Merge_Data_1.json +++ b/datasets/TRACE-P_Merge_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Merge_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Merge_Data is the merge data files created from data collected during during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_MetNav_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-P_MetNav_AircraftInSitu_DC8_Data_1.json index 8cab83b7e9..e0d42f890f 100644 --- a/datasets/TRACE-P_MetNav_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-P_MetNav_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_MetNav_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Diode Laser Hygrometer (DLH) instrument is featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_MetNav_Aircraft_InSitu_P3B_Data_1.json b/datasets/TRACE-P_MetNav_Aircraft_InSitu_P3B_Data_1.json index f56fedc1cf..676c74c49c 100644 --- a/datasets/TRACE-P_MetNav_Aircraft_InSitu_P3B_Data_1.json +++ b/datasets/TRACE-P_MetNav_Aircraft_InSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_MetNav_Aircraft_InSitu_P3B_Data is the in situ meteorology and navigation data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the P-3B Turbulent Air Motion Measurement System (TAMMS) is featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Model_Data_1.json b/datasets/TRACE-P_Model_Data_1.json index cfaa698baf..4d0fd42335 100644 --- a/datasets/TRACE-P_Model_Data_1.json +++ b/datasets/TRACE-P_Model_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Model_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Model_Data is the model data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Satellite_Data_1.json b/datasets/TRACE-P_Satellite_Data_1.json index 62783649a5..6fa196e04e 100644 --- a/datasets/TRACE-P_Satellite_Data_1.json +++ b/datasets/TRACE-P_Satellite_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Satellite_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Satellite_Data is the supplementary satellite data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Multi-Angle Imaging SpectroRadiometer (MISR) and the Measurements of Pollution in the Troposphere (MOPITT) satellite instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Sondes_Data_1.json b/datasets/TRACE-P_Sondes_Data_1.json index 17a99a57e5..f2fdc9122a 100644 --- a/datasets/TRACE-P_Sondes_Data_1.json +++ b/datasets/TRACE-P_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_TraceGas_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-P_TraceGas_AircraftInSitu_DC8_Data_1.json index efd01edb18..c602b7b6a6 100644 --- a/datasets/TRACE-P_TraceGas_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-P_TraceGas_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_TraceGas_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_TraceGas_InSitu_P3B_Data_1.json b/datasets/TRACE-P_TraceGas_InSitu_P3B_Data_1.json index 8a5c347764..1c3810a91e 100644 --- a/datasets/TRACE-P_TraceGas_InSitu_P3B_Data_1.json +++ b/datasets/TRACE-P_TraceGas_InSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_TraceGas_InSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Trajectory_DC8_Data_1.json b/datasets/TRACE-P_Trajectory_DC8_Data_1.json index ce6e1280af..f28d00c9f9 100644 --- a/datasets/TRACE-P_Trajectory_DC8_Data_1.json +++ b/datasets/TRACE-P_Trajectory_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Trajectory_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_Trajectory_P3B_Data_1.json b/datasets/TRACE-P_Trajectory_P3B_Data_1.json index d6f08bcfeb..7725c3c596 100644 --- a/datasets/TRACE-P_Trajectory_P3B_Data_1.json +++ b/datasets/TRACE-P_Trajectory_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_Trajectory_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_jValue_AircraftInSitu_DC8_Data_1.json b/datasets/TRACE-P_jValue_AircraftInSitu_DC8_Data_1.json index fd3407b9e8..e816d3aea8 100644 --- a/datasets/TRACE-P_jValue_AircraftInSitu_DC8_Data_1.json +++ b/datasets/TRACE-P_jValue_AircraftInSitu_DC8_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_jValue_AircraftInSitu_DC8_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) measured along the DC-8 flight during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACE-P_jValue_AircraftInSitu_P3B_Data_1.json b/datasets/TRACE-P_jValue_AircraftInSitu_P3B_Data_1.json index 6611b3e9d5..20fd5c2305 100644 --- a/datasets/TRACE-P_jValue_AircraftInSitu_P3B_Data_1.json +++ b/datasets/TRACE-P_jValue_AircraftInSitu_P3B_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACE-P_jValue_AircraftInSitu_P3B_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACE-P_jValue_AircraftInSitu_P3B_Data is the photolysis frequencies (j-values) measured along the P-3B flight aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.\r\n\r\nThe NASA TRACE-P mission was a part of NASA\u2019s Global Tropospheric Experiment (GTE) \u2013 an assemblage of missions conducted from 1983-2001 with various research goals and objectives.\u202fTRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities.\u202fTRACE-P deployed\u202fits payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging\u202ffrom Asia\u202fto the western Pacific.\u202fAlong with this, TRACE-P had the objective\u202fstudying\u202fthe chemical evolution of the air as it moved away from Asia.\u202f \r\n\r\nIn order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation.\u202fTRACE-P also relied on ground sites,\u202fand\u202fsatellites\u202fto collect data. The DC-8 aircraft was equipped with 19 instruments in total\u202fwhile the P-3B\u202fboasted\u202f21 total instruments.\u202fSome instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various\u202fwavelengths\u202fincluding\u202faerosol\u202fscattering\u202f(450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O.\u202fDIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm),\u202ftropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere.\u202fThe P-3B aircraft was equipped with various instruments for TRACE-P, some of which include\u202fthe MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the\u202fCondensation particle counter and Pulse Height Analysis (PHA). The\u202fMSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN.\u202fFinally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes,\u202fSeaWiFS\u202fcloud imagery, 8-day exposure to TOMS aerosols, and\u202fSeaWiFS\u202faerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\u202f\u202f ", "links": [ { diff --git a/datasets/TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data_1.json b/datasets/TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data_1.json index 1d681d6ba4..eb1c28be4d 100644 --- a/datasets/TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data_1.json +++ b/datasets/TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data is the remotely sensed GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) data collected onboard the Johnson Space Center (JSC) Gulfstream V (G-V) aircraft during the TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) field study. Data collection is complete.\n\nThe TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; and provide observations for evaluating air quality models and satellite observations.\n\nThe primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.\n\nThe airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.", "links": [ { diff --git a/datasets/TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data_1.json b/datasets/TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data_1.json index ede04fd23f..0dfd450f93 100644 --- a/datasets/TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data_1.json +++ b/datasets/TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data is the remotely sensed High Spectral Resolution Lidar-2 (HSRL-2) data collected onboard the Johnson Space Center (JSC) Gulfstream V (G-V) aircraft during the TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) field study. Data collection is complete.\n\nThe TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; and provide observations for evaluating air quality models and satellite observations.\n\nThe primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.\n\nThe airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.", "links": [ { diff --git a/datasets/TRACERAQ_Ground_LaPorte_Data_1.json b/datasets/TRACERAQ_Ground_LaPorte_Data_1.json index 705eda93f4..fa0e6d3472 100644 --- a/datasets/TRACERAQ_Ground_LaPorte_Data_1.json +++ b/datasets/TRACERAQ_Ground_LaPorte_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACERAQ_Ground_LaPorte_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACERAQ_Ground_LaPorte_Data is the data collected at the LaPorte ground site during the TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) field study. Data collection is complete.\n\nThe TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; and provide observations for evaluating air quality models and satellite observations.\n\nThe primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.\n\nThe airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.", "links": [ { diff --git a/datasets/TRACERAQ_Pandora_Data_1.json b/datasets/TRACERAQ_Pandora_Data_1.json index ca9b02e778..fa21618486 100644 --- a/datasets/TRACERAQ_Pandora_Data_1.json +++ b/datasets/TRACERAQ_Pandora_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACERAQ_Pandora_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACERAQ_Pandora_Data is the Pandora spectrometer data collected at various ground sites during the TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) field study. Data collection is complete.\n\nThe TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; and provide observations for evaluating air quality models and satellite observations.\n\nThe primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.\n\nThe airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.", "links": [ { diff --git a/datasets/TRACERAQ_Sondes_Data_1.json b/datasets/TRACERAQ_Sondes_Data_1.json index 5cf8beb891..c9ee590e1a 100644 --- a/datasets/TRACERAQ_Sondes_Data_1.json +++ b/datasets/TRACERAQ_Sondes_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRACERAQ_Sondes_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRACERAQ_Sondes_Data is the ozonesonde and radiosonde data launched at the University of Houston and LaPorte ground sites during the TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) field study. Data collection is complete.\n\nThe TRacking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; and provide observations for evaluating air quality models and satellite observations.\n\nThe primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas. A second deployment occurred in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.\n\nThe airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE (GEOstationary Coastal and Air Pollution Events) Airborne Simulator) for making measurements of column nitrogen dioxide (NO2) and formaldehyde (HCHO) as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure ozone (O3) and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.", "links": [ { diff --git a/datasets/TRATLEQ1_0.json b/datasets/TRATLEQ1_0.json index 95132d4116..b05b37f3c4 100644 --- a/datasets/TRATLEQ1_0.json +++ b/datasets/TRATLEQ1_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRATLEQ1_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRATLEQ1 was an interdisciplinary cruise focusing on upwelling in the tropical Atlantic, its physical forcing, its importance for biological production and plankton communities, associated chemical cycles, as well as on the current system setting the background conditions for the downward carbon export. This cruise represents the first physical, chemical, biogeochemical and biological measurement program covering a whole equatorial section from the eastern to the western boundary and from the surface to the bottom. TRATLEQ I is a contribution to the GEOMAR research program OCEANS, to the EU projects TRIATLAS, the Make Our Planet Great Again project by R. Kiko and to the BMBF cooperative project BANINO in the frame of the BMBF SPACES program.", "links": [ { diff --git a/datasets/TRENZ_2010_1.json b/datasets/TRENZ_2010_1.json index 359aa4bfff..e01a727452 100644 --- a/datasets/TRENZ_2010_1.json +++ b/datasets/TRENZ_2010_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRENZ_2010_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbon and nitrogen stable isotope data for a range of benthic invertebrates, macroalgae, phytoplankton, sea ice algae and fish from shallow marine coastal region around Davis Station.\n\nPart of the TRENZ program (The TRophic Ecology of the antarctic Nearshore Zone: local and global constraints on patterns and processes), and AAS project 2948.", "links": [ { diff --git a/datasets/TRMM_1A01_7.json b/datasets/TRMM_1A01_7.json index d537cffc5c..55c5c99023 100644 --- a/datasets/TRMM_1A01_7.json +++ b/datasets/TRMM_1A01_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1A01_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-1A Product file, \"1A01\", is a concatenation of Header record, Spacecraft Attitude packets, VIRS Housekeeping Data packets, VIRS Science Data packets, QACs, and an MDUL.\nAs such, it is reversible to Level 0. The header record contains information pertaining to orbit times, orbit number, times of the first scan, and number of scans, among other things. The Level 0 data contain the actual channel data expressed as \"sensor counts\". A Level 1A file contains data for a single orbit and has a file size of about 31 MB (uncompressed). There are 16 files of VIRS 1A01 data produced per day.\n\nThe Visible and Infrared Scanner (VIRS) is a five-channel visible/infrared radiometer, which builds on the heritage of theAdvanced Very High Resolution Radiometer (AVHRR) instrument flown aboard the NOAA series of Polar-Orbiting Operational EnvironmentalSatellites (POES). The VIRS detects radiation at 1 visible, 2 near infrared and 2 thermal infrared wavelengths, allowing determination of cloud coverage, cloud top height and temperature, and precipitation indices. The central wavelengths for the VIRS channels are 0.63, 1.60,3.75, 10.8, and 12.0 microns. All channels are in operation during the daytime, but only channels 3, 4 and 5 operate during the nighttime.\n\nSpatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.", "links": [ { diff --git a/datasets/TRMM_1A11_7.json b/datasets/TRMM_1A11_7.json index 52e038da8e..d6ae8ebd09 100644 --- a/datasets/TRMM_1A11_7.json +++ b/datasets/TRMM_1A11_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1A11_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-1A Product file, \"1A11\", is a concatenation of Header record, Spacecraft Attitude packets, TMI Housekeeping packets, TMI Science Data packets, QACs and an MDUL. As such, it is reversible to Level 0. The header record contains information pertaining to orbit times, orbit number, times of the first scan, and number of scans, among other things. The Level 0 data contain the actual channel data expressed as\"sensor counts\". A Level 1A file contains data for a single orbit and has a file size of about 31 MB (uncompressed). \n\nSpatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.", "links": [ { diff --git a/datasets/TRMM_1B01_7.json b/datasets/TRMM_1B01_7.json index eaba60a995..21f7f34fcd 100644 --- a/datasets/TRMM_1B01_7.json +++ b/datasets/TRMM_1B01_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1B01_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This TRMM Visible and Infrared Scanner (VIRS) Level 1B Calibrated Radiance Product (1B01) contains calibrated radiances and auxiliary geolocation information from the five channels of the VIRS instrument, for each pixel of each scan. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the VIRS measurements. A file contains a single orbit of data with a file size of about 95 MB. The EOSDIS \"swath\" structure is used to accommodate the actual geophysical data arrays. There are 16 files of VIRS 1B01 data produced per day.\n\nFor channels 1 and 2, Level 1B radiances are derived from the Level 1A (1A01) sensor counts by computing calibration parameters (gain and offset) derived from the counts registered during space and solar and/or lunar views. New calibration parameters are produced every one to four weeks. Channels 3, 4, and 5 are calibrated using the internal blackbody and the space view. These calibration parameters, together with a quadratic term determined pre-launch, are used to generate a counts vs. radiance curve for each band, which is then used to convert the earth-view pixel counts to spectral radiances.\n\nGeolocation and channel data are written out for each pixel along the scan, whereas the time stamp, scan status (containing scan quality information), navigation, calibration coefficients, and solar/satellite geometry are specified on a per-scan basis. There are in general 18026 scans along the orbit pre-boost and 18223 post-boost, with each scan consisting of 261 pixels. The scan width is about 720 km pre-boost and 833 km post-boost.\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 720 km; Horizontal Resolution: 2.2 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 833 km; Horizontal Resolution: 2.4 km\n\t", "links": [ { diff --git a/datasets/TRMM_1B11_7.json b/datasets/TRMM_1B11_7.json index 34c102bf56..d563896973 100644 --- a/datasets/TRMM_1B11_7.json +++ b/datasets/TRMM_1B11_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1B11_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " The new version of these data is in GPM-like format (consistent with the GPM Microwave Imager data format), and can be found under the name GPM_1BTMI (search by keyword GPM_1BTMI).\n This dataset contains TRMM Micrwave Imager (TMI) L1B calibrated radiances in terms of Brightness Temperatures.\n\nThe TMI calibration algorithm (1B11) converts the radiometer counts to antenna temperatures by applying a linear relationship of the form Ta = c1 + c2 x count. The coefficients are provided by the instrument contractor. Antenna temperatures are corrected for cross-polarization and spill over to produce brightness temperatures (Tb), but no antenna beam pattern correction or sample to pixel averaging are performed. Temperatures are provided at 104 scan positions for the low frequency channels and 208 scan positions at 85 GHz. There are four samples per pixel (3 -dB beamwidth) at 10 GHz, two samples at 19, 22, and 37 GHz, and one sample per pixel for the 85 GHz.\nData Flow Description\n\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km\n", "links": [ { diff --git a/datasets/TRMM_1B21_7.json b/datasets/TRMM_1B21_7.json index 1f6774f71a..8d70ab6792 100644 --- a/datasets/TRMM_1B21_7.json +++ b/datasets/TRMM_1B21_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1B21_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation.\n \nThe 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. Periodically the input-output characteristics are measured using an internal calibration loop for the IF unit and later receiver stages. To make an absolute calibration, an Active Radar Calibrator (ARC) is placed at Kansai Branch of CRL and overall system gain of the PR is being measured every 2 months. Using the transfer function based on the above internal and external calibrations, the PR received power is obtained. Note that the value assumes that the signal follows the Rayleigh fading, so if the fading characteristics of a scatter is different, a small bias error may occur (within 1 or 2 dB).\n\t\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km", "links": [ { diff --git a/datasets/TRMM_1B51_7.json b/datasets/TRMM_1B51_7.json index eb17f7aac1..e55bf63199 100644 --- a/datasets/TRMM_1B51_7.json +++ b/datasets/TRMM_1B51_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1B51_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM_1B51 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation. The GV radar FOV is defined as a base scan (i.e., the lowest level sweep). Each product file has the Existence data of one site (not one radar) for one month. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_1C21_7.json b/datasets/TRMM_1C21_7.json index f6911c286c..73d17ba480 100644 --- a/datasets/TRMM_1C21_7.json +++ b/datasets/TRMM_1C21_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1C21_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation.\n \nThe 1C21 calculates the effective radar reflectivity factor at 13.8 GHz without any propagation loss (due to rain or any other atmospheric gas) correction (Zm). Therefore, the Zm value can be calculated just by applying a radar equation for volume scatter with PR system parameters. The noise-equivalent Zm is about 21 dBZ. Through the subtraction of the system noise, the Zm value as small as 16 or 18 dBZ are still usable although the data quality is marginal. In 1C21, all echoes stored in 1B21 are converted to \"dBZ\" unit. This is not relevant for \"non-rain\" echo; however, this policy is adopted so that the 1B21 and 1C21 product format should be as close as possible except for the following points: \n- Radar quantity is Zm in dBZ unit instead of received power (dBm).\n- Data at echo-free range bins judged in 1B21 are replaced with a dummy value.\n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n", "links": [ { diff --git a/datasets/TRMM_1C51UW_7.json b/datasets/TRMM_1C51UW_7.json index 8a747a4f57..3e858b6290 100644 --- a/datasets/TRMM_1C51UW_7.json +++ b/datasets/TRMM_1C51UW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1C51UW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\nFiles are in \"Universal Format\", described in BAMS, Vol 61, No 11, November 1980, pp. 1401-1404.\n\nThe purpose of the 1C51UW algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm. Optimum performance is time consuming and requires an analyst to select different sets of parameters on a per volume scan basis, and on occasions performing several iterations while adjusting one or more of the parameters.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_1C51_7.json b/datasets/TRMM_1C51_7.json index 3686f53fd2..d8b9cf0704 100644 --- a/datasets/TRMM_1C51_7.json +++ b/datasets/TRMM_1C51_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_1C51_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the 1C51 algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm. Optimum performance is time consuming and requires an analyst to select different sets of parameters on a per volume scan basis, and on occasions performing several iterations while adjusting one or more of the parameters.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A12_7.json b/datasets/TRMM_2A12_7.json index 33e1014f6f..436907d3b3 100644 --- a/datasets/TRMM_2A12_7.json +++ b/datasets/TRMM_2A12_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A12_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format and can be found under the name GPM_2AGPROFTRMMTMI_CLIM.\n\nThis dataset, 2A12, \u201dTMI Profiling\u201d, generates surface rainfall and vertical hydrometeor profiles on a pixel by pixel basis from the TRMM Microwave Imager (TMI) brightness temperature data using the Goddard Profiling algorithm GPROF2010. Because the vertical information comes from a radiometer, it is not written out in independent vertical layers like the TRMM Precipitation Radar. Instead, the output is referenced to one of 100 typical structures for each hydrometeor or heating profile. These vertical structures are referenced as clusters in the output structure. Vertical hydrometeor profiles can be reconstructed to 28 layers by knowing the cluster number (i.e. shape) of the profile and a scale factor that is written for each pixel.\n\nThis product contains hydrometeor profiles of cloud liquid water, precipitation water, cloud ice water, precipitation ice, rainfall type, and latent heating in 28 atmospheric layers. \n\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km\n", "links": [ { diff --git a/datasets/TRMM_2A21_7.json b/datasets/TRMM_2A21_7.json index f6e0d71cac..7a303b3e75 100644 --- a/datasets/TRMM_2A21_7.json +++ b/datasets/TRMM_2A21_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A21_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR.\n\nThis is the sigma zero algorithm, which inputs the PR power (1B21) and computes estimates of the path attenuation and its reliability by using the surface as a reference target. It also computes the spatial and temporal statistics of the surface scattering cross section and classifies the cross sections into land/ocean and rain/no rain categories.\nChanges in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001:\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n", "links": [ { diff --git a/datasets/TRMM_2A23_7.json b/datasets/TRMM_2A23_7.json index e21842e16b..33160c73b7 100644 --- a/datasets/TRMM_2A23_7.json +++ b/datasets/TRMM_2A23_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A23_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR.\n\nThe TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning single-frequency radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation.\n\nPR 2A23 produces a rain/no-rain flag. Its main objectives are (1) to detect bright band (BB), (2) to classify rain type, and (3) to detect warm rain.\n\n2A23 uses two different methods for classifying rain type: (1) vertical profile method (V-method) and (2) horizontal pattern method (H-method). Both methods classify rain into three categories: stratiform, convective, and other. To make the results user-friendly, 2A23 outputs a unified rain type. Further information about 2A23 can be found in Awaka et al. (1998).\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n", "links": [ { diff --git a/datasets/TRMM_2A25_7.json b/datasets/TRMM_2A25_7.json index 2014e1a0f7..2edbddb758 100644 --- a/datasets/TRMM_2A25_7.json +++ b/datasets/TRMM_2A25_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A25_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR.\n\nThe TRMM 2A25 data are estimates of the three-dimensional distribution of rain from the TRMM Precipitation Radar. The average rainfall rate between two pre-defined altitudes is calculated for each beam position. Other output data include parameters of Z-R relationships, integrated rain rate of each beam, range bin numbers of rain layer boundaries, and many intermediate parameters. Iguchi and Meneghini (1994) describe this algorithm.\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n\t", "links": [ { diff --git a/datasets/TRMM_2A52_7.json b/datasets/TRMM_2A52_7.json index f3ab3ae0f1..740d249d89 100644 --- a/datasets/TRMM_2A52_7.json +++ b/datasets/TRMM_2A52_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A52_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM_2A52 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation. The GV radar FOV is defined as a base scan (i.e., the lowest level sweep). Each product file has the Existence data of one site (not one radar) for one month. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A53UW_7.json b/datasets/TRMM_2A53UW_7.json index 2010583f60..11e36a5854 100644 --- a/datasets/TRMM_2A53UW_7.json +++ b/datasets/TRMM_2A53UW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A53UW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\n\nInstantaneous rain rate cartesian grid based on baseUW and 2A54UW. Units are mm/hr. Min range is 17 km, max range is 150 km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A53_7.json b/datasets/TRMM_2A53_7.json index 9db215e86a..63dd26fba7 100644 --- a/datasets/TRMM_2A53_7.json +++ b/datasets/TRMM_2A53_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A53_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "'Radar Site Rain Map', is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A54UW_7.json b/datasets/TRMM_2A54UW_7.json index cd565fb506..e7dea9e749 100644 --- a/datasets/TRMM_2A54UW_7.json +++ b/datasets/TRMM_2A54UW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A54UW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\n\nInstantaneous convective-stratiform cartesian grid based on baseUW. Values are 0 (no echo), 1 (stratiform), and 2 (convective). Min range is 17 km, max range is 150 km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A54_7.json b/datasets/TRMM_2A54_7.json index d0c032b43b..ba181792b6 100644 --- a/datasets/TRMM_2A54_7.json +++ b/datasets/TRMM_2A54_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A54_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "'Radar Site Convective/Stratiform Map', is an instantaneous map in Cartesian coordinates with a 2 km resolution. At single radar sites, the map covers an area of 300 km x 300 km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km. The map identifies the surface precipitation as convective or stratiform. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A55UW_7.json b/datasets/TRMM_2A55UW_7.json index 2596512939..d2f21a78c1 100644 --- a/datasets/TRMM_2A55UW_7.json +++ b/datasets/TRMM_2A55UW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A55UW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\n\nMultiple-level cartesian grid containing output of NCAR SPRINT interpolation of CZ and VR fields of 1C51UW. Horizontal extent is 150x150km and resolution is 2km. Vertical range is 2-15km and resolution is 1km. Unlike other products, 2A55UW is not created for every volume but only for volumes of interest. These are defined as volumes within +/-15 minutes of overpass times and volumes with significant rainfall (areal rainfall rate greater or equal 0.15mm/hr).\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A55_7.json b/datasets/TRMM_2A55_7.json index 8713e11aa4..3dbb4aa8da 100644 --- a/datasets/TRMM_2A55_7.json +++ b/datasets/TRMM_2A55_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A55_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "'Radar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site. It has a vertical resolution of 1.5km and a height range up to 19.5 km. The second field has an array of vertical profiles based on the first field, and the third field has an array of the Contoured Frequency by Altitude Diagram (CFAD) data based on the first and second field. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2A56_7.json b/datasets/TRMM_2A56_7.json index 4e27d5d5a7..8949ae130e 100644 --- a/datasets/TRMM_2A56_7.json +++ b/datasets/TRMM_2A56_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2A56_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The program rgmin generates 1-minute hourly rain rates from discrete tipping bucket rain gauge data by applying an interpolation algorithm. The interpolating routine is based on the cubic spline routine published in the book \"Numerical Recipes\". The mathematical theory underlying this algorithm is presented in section 3.3 of the book along with a copy of the source code. The mathematics involved will not be described in any detail, rather the spline will be described in context with its application to the broader algorithm being applied in rgmin. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_2B31_7.json b/datasets/TRMM_2B31_7.json index 1cbf9e7328..12abc4e894 100644 --- a/datasets/TRMM_2B31_7.json +++ b/datasets/TRMM_2B31_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_2B31_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM combined algorithm (2B31) combines data from the TMI and PR to produce the best rain estimate for TRMM. This combined rainfall product is derived from vertical hydrometeor profiles using data from the PR radar and TMI. It also includes computed correlation-corrected, mass-weighted, mean drop diameter and its standard deviation, and latent heating data.\n\nPre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km \n\nPost-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n\t", "links": [ { diff --git a/datasets/TRMM_3A11_7.json b/datasets/TRMM_3A11_7.json index 2c395d67e6..124883c308 100644 --- a/datasets/TRMM_3A11_7.json +++ b/datasets/TRMM_3A11_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A11_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\".\n\nThe TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box.\n\nTMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit.", "links": [ { diff --git a/datasets/TRMM_3A12_7.json b/datasets/TRMM_3A12_7.json index 330d766f09..4ebe9ff9af 100644 --- a/datasets/TRMM_3A12_7.json +++ b/datasets/TRMM_3A12_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A12_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\".\n\nThis product contains global monthly means of surface precipitation rate, rain rate, convective surface precipitation rate and 28 vertical layers of hydrometeor contents (cloud liquid water, rain water, cloud ice liquid water, snow liquid water, graupel liquid water and latent heating) on 0.5 x 0.5 degree grids.", "links": [ { diff --git a/datasets/TRMM_3A25_7.json b/datasets/TRMM_3A25_7.json index 6ba7b9a2e7..fcdb9b9002 100644 --- a/datasets/TRMM_3A25_7.json +++ b/datasets/TRMM_3A25_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A25_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR.\n\nThis product consists of monthly statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.5 degrees x 0.5 degrees) horizontal resolution. The low resolution grids are in the Planetary Grid 1 structure and include 1) mean and standard deviation of the rain rate, reflectivity, path-integrated attenuation (PIA), storm height, Xi, bright band height and the NUBF (Non-Uniform Beam Filling) correction; 2) rain fractions; 3) histograms of the storm height, bright-band height, snow-ice layer, reflectivity, rain rate, path-attenuation and NUBF correction; 4) correlation coefficients. The high resolution grids are in the Planetary Grid 2 structure and contain mean rain rate along with standard deviation and rain fractions.\n\t", "links": [ { diff --git a/datasets/TRMM_3A26_7.json b/datasets/TRMM_3A26_7.json index 9d6cf84164..6d8d384f49 100644 --- a/datasets/TRMM_3A26_7.json +++ b/datasets/TRMM_3A26_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A26_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR.\n\nThis dataset contains distributions of monthly surface rainfall. These data were derived from rain rate statistics and include the estimated values of the probability distribution function of the space-time rain rates at four levels (2 km, 4 km, 6 km, and path-averaged) and the mean, standard deviation, and probability of rain derived from these distributions. Three different rain rate estimates are used as input to the algorithm: (1) the standard Z-R (or 0th-order estimate having no attenuation correction); (2) the Hitschfield-Bordan (H-B); and (3) the rain rates taken from 2A25.\n\t", "links": [ { diff --git a/datasets/TRMM_3A53_7.json b/datasets/TRMM_3A53_7.json index 849922cfb5..271dbff542 100644 --- a/datasets/TRMM_3A53_7.json +++ b/datasets/TRMM_3A53_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A53_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the 5-day accumulation of the 2A53 product, 'Radar Site Rain Map', which originally is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_3A54UW_7.json b/datasets/TRMM_3A54UW_7.json index 558e547e91..ea5dd10996 100644 --- a/datasets/TRMM_3A54UW_7.json +++ b/datasets/TRMM_3A54UW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A54UW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\n\nMonthly rain accumulation cartesian grid based on 2A53UW. Units are mm. Min range is 17 km, max range is 150 km. Horizontal extent is 150x150km and resolution is 2km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_3A54_7.json b/datasets/TRMM_3A54_7.json index cb019baf9c..c7fc9103e3 100644 --- a/datasets/TRMM_3A54_7.json +++ b/datasets/TRMM_3A54_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A54_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 3A54 product, 'Site Rainfall Map', is a map of monthly surface rain totals derived from the instantaneous rain rate maps (2A53). The map is in Cartesian coordinates with a 2 km horizontal resolution and covers an area of 300km x 300km at single radar sites while the covered area varies for multiple radar sites - 724 km x 568 km at Texas site and 512 km x 704 km at Florida site. This monthly rainfall map is not a simple accumulation of the instantaneous maps as gaps in the data must be factored into the calculation. \n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_3A55_7.json b/datasets/TRMM_3A55_7.json index 3237b8bd69..7802c8ba15 100644 --- a/datasets/TRMM_3A55_7.json +++ b/datasets/TRMM_3A55_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3A55_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 3A55, 'Monthly 3-D Structure', provides radar site monthly 3-D structure information obtained from 2A55. \nThe 2A55 'Radar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site. It has a vertical resolution of 1.5km and a height range up to 19.5 km. The second field has an array of vertical profiles based on the first field, and the third field has an array of the Contoured Frequency by Altitude Diagram (CFAD) data based on the first and second field.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_3B31_7.json b/datasets/TRMM_3B31_7.json index 27e33413a2..0e80d4aaf9 100644 --- a/datasets/TRMM_3B31_7.json +++ b/datasets/TRMM_3B31_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B31_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a combined rainfall product. 3B31 uses the high quality retrievals done for the narrow swath in 2B31 to calibrate the wide swath retrievals generated in 2A12. For each 0.5 degree box and each vertical layer, an adjustment ratio is calculated for the swath overlap region for one month. Only TMI pixels with 2A12 pixelStatus equal to zero are included in monthly averages, which effectively removes sea ice.\n\t", "links": [ { diff --git a/datasets/TRMM_3B40RT_7.json b/datasets/TRMM_3B40RT_7.json index 5b1c129fe9..2527c7ff9f 100644 --- a/datasets/TRMM_3B40RT_7.json +++ b/datasets/TRMM_3B40RT_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B40RT_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B40RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).\n\n These data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to e.g. research quality product 3B42. This particular dataset is an intermediate high-quality (HQ) estimate from merged Microwave precipitation estimates.\n\nData files start with a header consisteing of a 2880-byte header record containing ASCII characters. The header line makes the file nearly self-documenting, in particular spelling out the\nvariable and version names, and giving the units of the variables. Immediately after the header follow the data fields. All fields are 1440x720 grid boxes (0-360\ufffdE,90\ufffdN-S). The first grid box center is at (0.125\ufffdE,89.875\ufffdN). The grid increments most rapidly to the east. Grid boxes without valid data are filled with the (2-byte integer) \"missing\" value -31999. Estimates are only computed for the band 70\ufffdN-S. This binary data sets are in IEEE ?big-endian? floating-point format.\n\nFiles are produced every 3 hours on synoptic observation hours (00, 03, ..., 21 UTC) as an accumulation of all HQ swath data observed within +/-90 minutes of the nominal file time. I.e. Each file is a snapshot considered to represent the three-hour period centered on the \"nominal\" file time. So, e.g., 00 UTC nominally represents the period from 2230 UTC of the previous day to 0130 UTC of the current day.", "links": [ { diff --git a/datasets/TRMM_3B41RT_7.json b/datasets/TRMM_3B41RT_7.json index 80c70f332d..5c3eacf7ed 100644 --- a/datasets/TRMM_3B41RT_7.json +++ b/datasets/TRMM_3B41RT_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B41RT_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B41RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).\n\nThese data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product 3B42. This particular dataset is an intermediate variable (VAR) rainrate IR estimate.\n\nData files start with a header consisting of a 2880-byte record containing ASCII characters. The header line makes the file nearly self-documenting, in particular spelling out the variable and version names, and giving the units of the variables. \n\nImmediately after the header follow 3 data fields, \"precip\", \"error\",\"# pixels\", with byte count correspondingly 1382400,1382400,691200. First two are 2-byte integers, and the third is 1-byte. All fields are 1440x480 grid boxes (0-360E,60N-S). The first grid box center is at (0.125E,59.875N). The grid increments most rapidly to the east. Grid boxes without valid data are filled with the (2-byte integer) \"missing\" value -31999. Valid estimates are only provided in the band 50N-S. This binary data sets are in IEEE \"big-endian\" floating-point format. \n\n\n", "links": [ { diff --git a/datasets/TRMM_3B42RT_7.json b/datasets/TRMM_3B42RT_7.json index 00690f696a..45470330ec 100644 --- a/datasets/TRMM_3B42RT_7.json +++ b/datasets/TRMM_3B42RT_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B42RT_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B42RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).\n\nThese data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product.\n\nEach file is a snapshot considered to represent the three-hour period centered on the \"nominal\" file time. So, e.g., 00 UTC nominally represents the period from 2230 UTC of the previous day to 0130 UTC of the current day. Estimates outside the band 50 degree N-S are considered highly experimental. \n\nGES DISC initially receives these data from the Precipitation Processing System (PPS) in binary format. However, before archiving, the data are scaled to real numbers, and re-arranged to a standard grid so that the first grid cell is at 180W, 60S. Thus formatted, data are stored into CF-1.6 compliant netCDF-4 files and archived. This format is machine-independent, self-explanatory, provides extremely efficient seamless compression, and gives various options for previewing the data without downloading it.\n\nApart from these technical differences, all other science content details remain the same, and users are strongly encouraged to read the provider's documentation that is linked to from here.", "links": [ { diff --git a/datasets/TRMM_3B42RT_Daily_7.json b/datasets/TRMM_3B42RT_Daily_7.json index 698535dc09..c69ac2157c 100644 --- a/datasets/TRMM_3B42RT_Daily_7.json +++ b/datasets/TRMM_3B42RT_Daily_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B42RT_Daily_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06).\n\nThis daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. \n\nCounts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available.\n\nEfforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. \n\nThe latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product.\n\nThe information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format.\n\n\n\nThe following describes the derivation in more details.\n\nThe daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have \n\nPdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf]\nPdaily_cnt = SUM{1[Pi valid]}\n\nwhere:\nPdaily - Daily accumulation (mm)\nPi - 3-hourly input, in (mm/hr)\nNf - Number of 3-hourly files per day, Nf=8\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\n\nOn occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which\n Pdaily_cnt less than Nf.\n\n\nThere are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied:\n\nPerr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_daily\t- Magnitude of the daily accumulated error power, (mm)\nNcnt_err\t- The counts for the error variable\n\nThus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate:\n\nrms_err = { (Perr_daily/3) ^2 / Ncnt_err }^0.5\t(mm/hr)\n\n\nThis estimate assumes that the error given in the 3-hourly files is representative of the error of the rainfall rate (mm/hr) within the 3-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n\n\n\n", "links": [ { diff --git a/datasets/TRMM_3B42_7.json b/datasets/TRMM_3B42_7.json index c32c89b84c..2185976b65 100644 --- a/datasets/TRMM_3B42_7.json +++ b/datasets/TRMM_3B42_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B42_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B42) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-HH/06).\n\nThis dataset was the output from the TMPA (TRMM Multi-satellite Precipitation Analysis) Algorithm. It provides precipitation estimates in the TRMM regions that have the (nearly-zero) bias of the \u201dTRMM Combined Instrument\u201d precipitation estimate and the dense sampling of high-quality microwave data with fill-in using microwave-calibrated infrared estimates. The granule temporal coverage is 3 hours.\n", "links": [ { diff --git a/datasets/TRMM_3B42_Daily_7.json b/datasets/TRMM_3B42_Daily_7.json index 45277d0b6a..5a1f4a6724 100644 --- a/datasets/TRMM_3B42_Daily_7.json +++ b/datasets/TRMM_3B42_Daily_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B42_Daily_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B42_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDF/DAY/06).\n\nThis daily accumulated precipitation product is generated from the research-quality 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. \n\nCounts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available.\n\nEfforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format.\n\nThe information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format.\n\n\n\nThe following describes the derivation in more details.\n\nThe daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have \n\nPdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf]\nPdaily_cnt = SUM{1[Pi valid]}\n\nwhere:\nPdaily - Daily accumulation (mm)\nPi - 3-hourly input, in (mm/hr)\nNf - Number of 3-hourly files per day, Nf=8\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\n\nOn occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which\n Pdaily_cnt less than Nf.\n\n\nThere are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied:\n\nPerr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_daily\t- Magnitude of the daily accumulated error power, (mm)\nNcnt_err\t- The counts for the error variable\n\nThus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, accumulates first within the 3-hour period of the source data, and then continues to accumulate during the day. These values, however, can easily be converted to root mean square error estimate of the rainfall rate:\n\nrms_err = { (Perr_daily/3) ^2 / Ncnt_err }^0.5\t(mm/hr)\n\n\nThis estimate assumes that the error given in the 3-hourly files is representative of the error of the rainfall rate (mm/hr) within the 3-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n\n\n", "links": [ { diff --git a/datasets/TRMM_3B43_7.json b/datasets/TRMM_3B43_7.json index 84f99f42ed..0016dc3bd8 100644 --- a/datasets/TRMM_3B43_7.json +++ b/datasets/TRMM_3B43_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3B43_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMPA (3B43) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-MONTH/06).\n\nThe 3B43 dataset is the monthly version of the 3B42 dataset.\n\nThis product was created using TRMM-adjusted merged microwave-infrared precipitation rate (in mm/hr) and root-mean-square (RMS) precipitation-error estimates.\nIt provides a \"best\" precipitation estimate in a latitude band covering 50o N to 50o S, an expansion of the TRMM region, from all global data sources, namely high-quality microwave data, infrared data, and analyses of rain gauges. The granule size is one month.", "links": [ { diff --git a/datasets/TRMM_3G25_7.json b/datasets/TRMM_3G25_7.json index 6668ea40a5..4356925167 100644 --- a/datasets/TRMM_3G25_7.json +++ b/datasets/TRMM_3G25_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3G25_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3G25, \"Gridded Orbital Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from Precipitation Radar (PR) rain. The granule size is one orbit.", "links": [ { diff --git a/datasets/TRMM_3G31_7.json b/datasets/TRMM_3G31_7.json index 1ad3edc1d7..185a0fac2f 100644 --- a/datasets/TRMM_3G31_7.json +++ b/datasets/TRMM_3G31_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3G31_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3G31, Gridded Orbital Convective Stratiform Heating from Combined, produces 0.5 degree x 0.5 degree orbital apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The granule size is one orbit.", "links": [ { diff --git a/datasets/TRMM_3H25_7.json b/datasets/TRMM_3H25_7.json index bc5f3f3956..060c170631 100644 --- a/datasets/TRMM_3H25_7.json +++ b/datasets/TRMM_3H25_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3H25_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3H25, \"Monthly Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from PR rain", "links": [ { diff --git a/datasets/TRMM_3H31_7.json b/datasets/TRMM_3H31_7.json index 5cb66f6d5f..7985056ce6 100644 --- a/datasets/TRMM_3H31_7.json +++ b/datasets/TRMM_3H31_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_3H31_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "3H31, \"Monthly Convective Stratiform Heating from Combined\", produces 0.5 deg x 0.5 deg monthly apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The PI is Dr. Wei-Kuo Tao. The granule size is one month.", "links": [ { diff --git a/datasets/TRMM_CSH_6.json b/datasets/TRMM_CSH_6.json index 5f81ce79fb..bf7c913ddd 100644 --- a/datasets/TRMM_CSH_6.json +++ b/datasets/TRMM_CSH_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_CSH_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a discontinued TRMM product from the old version '6' suite that is in a state of permament preservation. The most recent replacement can be found by following this DOI: 10.5067/GPM/PRTMI/TRMM/CSH/3B-MONTH/07. The latter should also be better cross-calibrated with the continuation GPM combined Dual-Frequency Precipitation Radar and GPM Microwave Imager latent heating product: 10.5067/GPM/DPRGMI/CSH/3B-MONTH/07.\n\n The version '6' dataset is output from the old Goddard Convective-Stratiform Heating (CSH) algorithm. The dataset contains global 0.5 x 0.5 monthly latent heating profiles from surface convective rain rate and surface stratiform rain rate.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_KuPR_DU2_NA.json b/datasets/TRMM_GPMFormat_PR_KuPR_DU2_NA.json index 00dd1312a5..c9d77fd858 100644 --- a/datasets/TRMM_GPMFormat_PR_KuPR_DU2_NA.json +++ b/datasets/TRMM_GPMFormat_PR_KuPR_DU2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_KuPR_DU2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR KuPR Environment Auxiliary dataset is the new (GPM-formated) TRMM product. It replaces the old GPM DU2. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.In the current algorithm formulation, only the analysis data such as analysis data, must be integrated from an external source during combined algorithm processing. Analysis data are required to produce initial estimations of environmental parameters such as total precipitable water, TPWanal, cloud liquid water path, CLWPanal, surface skin temperature, Tsfcanal, and 10m altitude wind speed, U10manal. The current algorithm design requires space-time interpolation of these data from the Japanese Meteorological agency's (JMA) global analysis (GANAL) during standard algorithm processing. The data are interpolated to the DPR footprint/range bin locations and overpass times in the Vertical Profile Submodule (VER) of the Level 2 Radar Algorithm and then output. For near real-time processing, the JMA forecast fields, but if these fields are not received in time for any reason, the climate value data are substituted for the JMA analysis/forecast data in the VER processing.Main parameters: Air temperature, Air pressure, Water vapor, Cloud liquid waterSwath width: 245 kmResolution: 5 km(horizontal), 125m(vertical)The generation unit is orbit. The current version of the product is Version 6.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_KuPR_L1B_DUB_NA.json b/datasets/TRMM_GPMFormat_PR_KuPR_L1B_DUB_NA.json index 73d6beced3..7b028d784d 100644 --- a/datasets/TRMM_GPMFormat_PR_KuPR_L1B_DUB_NA.json +++ b/datasets/TRMM_GPMFormat_PR_KuPR_L1B_DUB_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_KuPR_L1B_DUB_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR/KuPR L1B Received Power dataset is the new (GPM-formated) TRMM product and replaces the old TRMM_1B21,1C21. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. The provided format is HDF5. The Sampling resolution are 5 km (horizontal) and 250 m (vertical). The current version of the product is Version 5A. The generation unit is one orbit.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_KuPR_L2_DU2_NA.json b/datasets/TRMM_GPMFormat_PR_KuPR_L2_DU2_NA.json index e0f93352ba..ab21e5274e 100644 --- a/datasets/TRMM_GPMFormat_PR_KuPR_L2_DU2_NA.json +++ b/datasets/TRMM_GPMFormat_PR_KuPR_L2_DU2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_KuPR_L2_DU2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR/KuPR L2 Precipitation dataset is the new (GPM-formated) TRMM product and replaces the old TRMM_2A21, 2A23 and 2A25 datasets. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.TRMM 2A21 dataset computes the path-integrated attenuation (PIA) using the surface reference technique (SRT). TRMM 2A23 dataset detects of bright band (BB) and determines of the height of BB, the strength of BB, and the width (i.e. thickness) of BB when BB exists, and classificates of rain type into the three categories. TRMM 2A25 dataset corrects for the rain attenuation in measured radar reflectivity (Zm) and to estimate the instantaneous three-dimensional distribution of rain from the TRMM Precipitation Radar data.The provided format is HDF5. The Sampling resolution are 5km (horizontal) and 125m (vertical). The current version of the product is Version 6. The generation unit is one orbit.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L2_SLP_NA.json b/datasets/TRMM_GPMFormat_PR_L2_SLP_NA.json index 59be40548f..4a78bf5637 100644 --- a/datasets/TRMM_GPMFormat_PR_L2_SLP_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L2_SLP_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L2_SLP_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L2 Spectral Latent Heating Profiles dataset is the new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u00e2\u0080\u0094convective, shallow stratiform, and anvil rain (deep stratiform with a melting level) were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The provided format is HDF5. The Sampling resolution are 5 km(horizontal) and 250 m (vertical). The current version of the product is Version 6. The generation unit is one orbit.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L3_D3D_1day_0.1deg_NA.json b/datasets/TRMM_GPMFormat_PR_L3_D3D_1day_0.1deg_NA.json index 419629e538..f78776d6eb 100644 --- a/datasets/TRMM_GPMFormat_PR_L3_D3D_1day_0.1deg_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L3_D3D_1day_0.1deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L3_D3D_1day_0.1deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L3 Precipitaion (1-Day,0.1deg) dataset is the new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. The closest ancestor was TRMM 3A25 which was a monthly radar statistic. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset calculates various daily statistics from the level 2 PR. Four types of statistics are calculated: Probabilities of occurrence (count values), Means and standard deviationsThe provided format is TEXT. The Sampling resolution is 0.1degree grid (Daily TEXT). The current version of the product is Version 6. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L3_D3M_1month_0.25deg_NA.json b/datasets/TRMM_GPMFormat_PR_L3_D3M_1month_0.25deg_NA.json index b5bfd3fd5e..2fd10e134d 100644 --- a/datasets/TRMM_GPMFormat_PR_L3_D3M_1month_0.25deg_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L3_D3M_1month_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L3_D3M_1month_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L3 Precipitaion (1-Month,0.25deg) dataset is the new (GPM-formated) TRMM product. This is the new (GPM-formated) TRMM product. It replaces the old TRMM 3A25, 3A26. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset calculates various daily statistics from the level 2 PR. Four types of statistics are calculated: Probabilities of occurrence (count values), Means and standard deviations, Histograms, and Correlation coefficients.The provided format is HDF5. The Sampling resolution is 0.25 degree grid (Monthly HDF). The current version of the product is Version 6. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L3_D3Q_1day_0.25deg_NA.json b/datasets/TRMM_GPMFormat_PR_L3_D3Q_1day_0.25deg_NA.json index fa7b997a29..5401f8d76a 100644 --- a/datasets/TRMM_GPMFormat_PR_L3_D3Q_1day_0.25deg_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L3_D3Q_1day_0.25deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L3_D3Q_1day_0.25deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L3 Precipitaion (1-Day,0.25deg) dataset is the new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. The closest ancestor was TRMM 3A25 which was a monthly radar statistics.It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset calculates various daily statistics from the level 2 PR. Four types of statistics are calculated: Probabilities of occurrence (count values), Means and standard deviationsThe provided format is HDF5. The Sampling resolution is 0.25 degree grid (Daily HDF). The current version of the product is Version 6. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L3_SLG_0.5deg_NA.json b/datasets/TRMM_GPMFormat_PR_L3_SLG_0.5deg_NA.json index 4247d77ba4..14ef219db0 100644 --- a/datasets/TRMM_GPMFormat_PR_L3_SLG_0.5deg_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L3_SLG_0.5deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L3_SLG_0.5deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L3 Gridded Orbital Spectral Latent Heating (0.5deg) dataset is the new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u00e2\u0080\u0094convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)\u00e2\u0080\u0094were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The provided format is HDF5. The sampling resolution are 0.5degree grid. The current version of the product is Version 6. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_GPMFormat_PR_L3_SLM_1month_0.5deg_NA.json b/datasets/TRMM_GPMFormat_PR_L3_SLM_1month_0.5deg_NA.json index 1922f94c47..cb94bd0d34 100644 --- a/datasets/TRMM_GPMFormat_PR_L3_SLM_1month_0.5deg_NA.json +++ b/datasets/TRMM_GPMFormat_PR_L3_SLM_1month_0.5deg_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_GPMFormat_PR_L3_SLM_1month_0.5deg_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM_GPMFormat/PR L3 Spectral Latent Heating (1-Month,0.5deg) dataset is the new (GPM-formated) TRMM product. It replaces the old TRMM 3H25. It is obtained from the Precipitation Radar (PR) sensor onboard Tropical Rainfall Measuring Mission (TRMM) Core Satellite and produced by the Japan Aerospace Exploration Agency (JAXA).The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.This dataset uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types\u00e2\u0080\u0094convective, shallow stratiform, and anvil rain (deep stratiform with a melting level) were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The provided format is HDF5. The Sampling resolution are 0.5degree grid. The statistical period is 1 month. The generation unit is global. The current version of the product is Version 6.", "links": [ { diff --git a/datasets/TRMM_PR_L1_1B21_NA.json b/datasets/TRMM_PR_L1_1B21_NA.json index 970d30f1a3..c4d1d62475 100644 --- a/datasets/TRMM_PR_L1_1B21_NA.json +++ b/datasets/TRMM_PR_L1_1B21_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L1_1B21_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L1 Received Power is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.In PR Level 1 processing, Level 0 transmitted from NASA data is checked whether it is in an observation mode, and then three types of products are processed which are 1A21, 1B21, and 1C21. Moreover received power, noise level, Z factor including rain attenuation is calculated. However, 1A21 is actually processed within a same routine as 1B21, so 1A21 itself is not singly output. In 1B21 processing, the radar video signal digital count value is converted into a received power value as well as a noise level value in accordance with the algorithm (calibration of received power based on temperature calibration as well as transfer function) created based on the radiometric model of the precipitation radar. Longitude and latitude information of ground surface is added to convert this value into radar reflectivity factor (Z factor) including rain attenuation. Also, rain/no rain is determined for each angle bin, a flag is set up, and the rain height is calculated. Moreover, influence of surface clutter, which is mixed from antenna main lobe and side lobe, is evaluated, and the evaluation result is reflected to the calculation of surface range bin number and the determination of rain/no rain.The provided format is HDF4. The Sampling resolution are 5km (horizontal) and 250m (range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L1_1C21_NA.json b/datasets/TRMM_PR_L1_1C21_NA.json index 25aa21f4f3..da2db52647 100644 --- a/datasets/TRMM_PR_L1_1C21_NA.json +++ b/datasets/TRMM_PR_L1_1C21_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L1_1C21_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L1 Reflectiveities is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.In PR Level 1 processing, Level 0 transmitted from NASA data is checked whether it is in an observation mode, and then three types of products are processed which are 1A21, 1B21, and 1C21. Moreover received power, noise level, Z factor including rain attenuation is calculated. However, 1A21 is actually processed within a same routine as 1B21, so 1A21 itself is not singly output. In 1C21 processing, the dummy radar reflectivity factor (Z factor: Zm) including rain attenuation during a rainfall is calculated using the radar equation, from the already calibrated received power value and noise level value calculated in the 1B21 processing.The provided format is HDF4. The Sampling resolution are 5km (horizontal) and 250m (range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L2_2A21_NA.json b/datasets/TRMM_PR_L2_2A21_NA.json index 3b10207de7..7a208c5ef3 100644 --- a/datasets/TRMM_PR_L2_2A21_NA.json +++ b/datasets/TRMM_PR_L2_2A21_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L2_2A21_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L2 Surface Cross Section is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.Based on the 1B-21 radar received power, time and spatial average of ground surface scattering coefficient (Scattering Radar Cross Section of the surface) are calculated. If it's raining, Path Integrated Attenuation (PIA) is calculated based on the surface reference data of no rain area. This PIA is used to calculate rainfall profile in 2A-25 as reference data using Surface Reference Technique (SRT).The provided format is HDF4. The Sampling resolution are 5 km (horizontal) and 250 m (range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L2_2A23_NA.json b/datasets/TRMM_PR_L2_2A23_NA.json index a30744f556..0e03c23a66 100644 --- a/datasets/TRMM_PR_L2_2A23_NA.json +++ b/datasets/TRMM_PR_L2_2A23_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L2_2A23_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L2 Qualitative is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Rain/no rain flag, the rain type, The height of rainfall are calculated from TRMM/PR 1C21 etc.data.The provided format is HDF4. The Sampling resolution are 5km(horizontal) and 250m(range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L2_2A25_NA.json b/datasets/TRMM_PR_L2_2A25_NA.json index 4778b38787..f468f6ad56 100644 --- a/datasets/TRMM_PR_L2_2A25_NA.json +++ b/datasets/TRMM_PR_L2_2A25_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L2_2A25_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L2 Rainfall Profile is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Vertical profile of rain rate (R) calculated from TRMM/PR 1C21, 2A21 and 2A23.The provided format is HDF4. The Sampling resolution are 5 km (horizontal) and 250 m (range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L2_2H25_NA.json b/datasets/TRMM_PR_L2_2H25_NA.json index c92c410e67..b495fc7845 100644 --- a/datasets/TRMM_PR_L2_2H25_NA.json +++ b/datasets/TRMM_PR_L2_2H25_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L2_2H25_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L2 Spectral Latent Heating is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Latent heating, Q1-QR, and Q2 profiles derived from TRMM/PR 2A25. Note:2H25 product is defined almost as standard product.The provided format is HDF4. The Sampling resolution are 5 km (horizontal) and 125 m / 250 m (range). The current version of the product is Version 7. The generation unit is orbit.", "links": [ { diff --git a/datasets/TRMM_PR_L3_3A25_NA.json b/datasets/TRMM_PR_L3_3A25_NA.json index 4772791cc3..3877c945f2 100644 --- a/datasets/TRMM_PR_L3_3A25_NA.json +++ b/datasets/TRMM_PR_L3_3A25_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L3_3A25_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L3 Monthly Rainfall is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes.Monthly average of rain parameter at the height of 2, 4, 6, (10, 15) km in lon./lat. 5deg x 5deg and 0.5deg x 0.5deg region using TRMM/PR 1C21, 2A21, 2A23 and 2A25. (* 10,15km data are available only for 5deg x 5deg gridded region.The provided format is HDF4. The statistical period is 1 month. The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_PR_L3_3A26_NA.json b/datasets/TRMM_PR_L3_3A26_NA.json index 40446cd551..45e12349ee 100644 --- a/datasets/TRMM_PR_L3_3A26_NA.json +++ b/datasets/TRMM_PR_L3_3A26_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L3_3A26_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L3 Monthly Surface Rain is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Monthly rainfall, rain rate averages, rain rate standard deviation and probability distribution function in 5deg x 5deg grid at three layers in the height of 2 km, 4 km and 6 km.The provided format is HDF4. The statistical period is 1 month. The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_PR_L3_3G25_NA.json b/datasets/TRMM_PR_L3_3G25_NA.json index 612a3f53ee..909bcb40c6 100644 --- a/datasets/TRMM_PR_L3_3G25_NA.json +++ b/datasets/TRMM_PR_L3_3G25_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L3_3G25_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L3 Gridded Orbital Spectral Latent Heating is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Latent heating, Q1-QR, and Q2 profiles derived from TRMM/PR 2H25. The spatial coverage is one orbit with a single grid cell being 0.5deg x 0.5deg.The provided format is HDF4. The statistical period is 1 month. The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_PR_L3_3H25_NA.json b/datasets/TRMM_PR_L3_3H25_NA.json index 17c64892cd..6cccfce429 100644 --- a/datasets/TRMM_PR_L3_3H25_NA.json +++ b/datasets/TRMM_PR_L3_3H25_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_PR_L3_3H25_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TRMM/PR L3 Monthly Spectral Latent Heating is obtained from the PR sensor onboard TRMM and produced by the Japan Aerospace Exploration Agency (JAXA). The Precipitation Radar (PR) is the primary instrument onboard TRMM. The most innovative of the five TRMM instruments, the PR is the first quantitative rain radar instrument to be flown in space. The major objectives of the PR instrument are as follows:a. Provides a 3-dimensional rainfall structureb. Achieves quantitative measurements of the rain rates over both land and ocean When properly combined with TMI measurements, the Precipitation Radar (PR) data is instrumental in obtaining the height profile of the precipitation content, from which the profile of latent heat release from the Earth can be estimated. The rain rate is estimated from the radar reflectivity factor when the rain rate is small by applying conventional algorithms used for ground-based radar. For large rain rates, a rain attenuation correction is made using the total-path attenuation of land or sea surface echoes. Latent heating, Q1-QR, and Q2 profiles derived from TRMM/PR 2H25. The spatial coverage is global with a single grid cell being 0.5deg x 0.5deg.The provided format is HDF4. The statistical period is 1 month. The current version of the product is Version 7. The generation unit is global.", "links": [ { diff --git a/datasets/TRMM_TMPA_LandSeaMask_2.json b/datasets/TRMM_TMPA_LandSeaMask_2.json index ecfbd807b3..ddeba1171d 100644 --- a/datasets/TRMM_TMPA_LandSeaMask_2.json +++ b/datasets/TRMM_TMPA_LandSeaMask_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_TMPA_LandSeaMask_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.\n\nThis land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Global Change Data Center, restructured this TMPA land sea mask to match the TMPA grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions.\n\nUsers should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data.", "links": [ { diff --git a/datasets/TRMM_baseUW_7.json b/datasets/TRMM_baseUW_7.json index 771413562c..55754c9b2e 100644 --- a/datasets/TRMM_baseUW_7.json +++ b/datasets/TRMM_baseUW_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_baseUW_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is part of the University of Washington TRMM Ground Validation products.\n\nData contains single level, cartesian grids containing output of NCAR SPRINT interpolation of lowest sweep of 1C51UW. Horizontal extent is 150x150km and horizontal resolution is 2km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create this product) and \"z_spacing\" has no meaning.\n\nA key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products.\n\nThe four primary GV sites are:\n\n+ Darwin, Australia; \n+ Houston, Texas; \n+ Kwajalein, Republic of the Marshall Islands;\n+ Melbourne, Florida. \n\nA significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n", "links": [ { diff --git a/datasets/TRMM_precip_718_1.json b/datasets/TRMM_precip_718_1.json index d6c2905b69..2cf4a04fdf 100644 --- a/datasets/TRMM_precip_718_1.json +++ b/datasets/TRMM_precip_718_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRMM_precip_718_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the Tropical Rainfall Measuring Mission (TRMM) and TRMM Product 3B-43 is to provide a monthly, best-estimate precipitation rate and root-mean-square (RMS) precipitation error. These gridded estimates are on a one-calendar month temporal resolution and a 1-degree by 1-degree spatial resolution for the global band extending from 40 degrees south to 40 degrees north in latitude.", "links": [ { diff --git a/datasets/TROPICS01ANTTL1A_1.0.json b/datasets/TROPICS01ANTTL1A_1.0.json index 5d34703b07..a382497b75 100644 --- a/datasets/TROPICS01ANTTL1A_1.0.json +++ b/datasets/TROPICS01ANTTL1A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01ANTTL1A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS01BRTTL1B_1.0.json b/datasets/TROPICS01BRTTL1B_1.0.json index df77776502..37bed75641 100644 --- a/datasets/TROPICS01BRTTL1B_1.0.json +++ b/datasets/TROPICS01BRTTL1B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01BRTTL1B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS01MIRSL2B_1.0.json b/datasets/TROPICS01MIRSL2B_1.0.json index 2859c81764..bddb07d196 100644 --- a/datasets/TROPICS01MIRSL2B_1.0.json +++ b/datasets/TROPICS01MIRSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01MIRSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the Pathfinder satellite, as the full version of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS01PRPSL2B_1.0.json b/datasets/TROPICS01PRPSL2B_1.0.json index 45332402e8..0cb6890878 100644 --- a/datasets/TROPICS01PRPSL2B_1.0.json +++ b/datasets/TROPICS01PRPSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01PRPSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the Pathfinder satellite, as the full version of the Level 1a geolocated antenna temperatures (radiance) in units of kelvins that are timestamped to UTC and are reported at native spatial resolutions. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS01TCIEL2B_1.0.json b/datasets/TROPICS01TCIEL2B_1.0.json index cc72b997dd..a1368bdbf3 100644 --- a/datasets/TROPICS01TCIEL2B_1.0.json +++ b/datasets/TROPICS01TCIEL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01TCIEL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThe TROPICS Tropical Cyclone Intensity Estimate algorithm (TCIE), developed at the University of Wisconsin/CIMSS that uses native microwave brightness temperatures, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). The TROPICS TCIE uses the brightness temperature perturbation of two temperature sounding channels (Ch. 6 and Ch. 7) and one channel from the moisture sounding channel (Ch. 1) along with ancillary information from the TC working best track file and the CIMSS ARCHER algorithm (eye size information) to estimate the TC intensity. This validated TCIE data release starts in June 2023 for the constellation CubeSats, and August 2021 for the TROPICS-01/Pathfinder.", "links": [ { diff --git a/datasets/TROPICS01URADL2A_1.0.json b/datasets/TROPICS01URADL2A_1.0.json index c8900e4611..130a659862 100644 --- a/datasets/TROPICS01URADL2A_1.0.json +++ b/datasets/TROPICS01URADL2A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS01URADL2A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the Pathfinder satellite, as the provisional version of the Level 2A geolocated brightness temperature that are reported at native spatial resolutions. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS03ANTTL1A_1.0.json b/datasets/TROPICS03ANTTL1A_1.0.json index 5cc4e84ea2..02d2b51a6f 100644 --- a/datasets/TROPICS03ANTTL1A_1.0.json +++ b/datasets/TROPICS03ANTTL1A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03ANTTL1A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS03BRTTL1B_1.0.json b/datasets/TROPICS03BRTTL1B_1.0.json index 4c5010a1bb..92ae46304e 100644 --- a/datasets/TROPICS03BRTTL1B_1.0.json +++ b/datasets/TROPICS03BRTTL1B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03BRTTL1B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS03MIRSL2B_1.0.json b/datasets/TROPICS03MIRSL2B_1.0.json index 1eea737eb6..e964e9c0f2 100644 --- a/datasets/TROPICS03MIRSL2B_1.0.json +++ b/datasets/TROPICS03MIRSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03MIRSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. \n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS03 satellite, as the Validated Stage-1 release of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS03PRPSL2B_1.0.json b/datasets/TROPICS03PRPSL2B_1.0.json index 1770925480..ca68640fb9 100644 --- a/datasets/TROPICS03PRPSL2B_1.0.json +++ b/datasets/TROPICS03PRPSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03PRPSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. \n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS03 satellite, as the Validated Stage-1 version of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS03TCIEL2B_1.0.json b/datasets/TROPICS03TCIEL2B_1.0.json index cdcff1d2c1..bd91a136f3 100644 --- a/datasets/TROPICS03TCIEL2B_1.0.json +++ b/datasets/TROPICS03TCIEL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03TCIEL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThe TROPICS Tropical Cyclone Intensity Estimate algorithm (TCIE), developed at the University of Wisconsin/CIMSS that uses native microwave brightness temperatures, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). The TROPICS TCIE uses the brightness temperature perturbation of two temperature sounding channels (Ch. 6 and Ch. 7) and one channel from the moisture sounding channel (Ch. 1) along with ancillary information from the TC working best track file and the CIMSS ARCHER algorithm (eye size information) to estimate the TC intensity. This validated TCIE data release starts in June 2023 for the constellation CubeSats, and August 2021 for the TROPICS-01/Pathfinder.", "links": [ { diff --git a/datasets/TROPICS03URADL2A_1.0.json b/datasets/TROPICS03URADL2A_1.0.json index 8f503e7812..ef956f9af0 100644 --- a/datasets/TROPICS03URADL2A_1.0.json +++ b/datasets/TROPICS03URADL2A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS03URADL2A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS03 satellite, as the Validated Stage-1 version of the Level 2A geolocated brightness temperature with the water vapor sounding channels (Ch. 9 to 12) converted from their native G-band resolution to the temperature sounding channel (F-band) native resolution (i.e., all measurements at the same unified larger resolution). This product is used in the Atmospheric Vertical Temperature Profile (AVTP) retrievals to gain the benefit of averaging the G-band channels (i.e., noise reduction) while maintain the F-band (AVTP) spatial resolution. The conversion uses the Backus-Gilbert technique. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS05ANTTL1A_0.2.json b/datasets/TROPICS05ANTTL1A_0.2.json index e94ef15edb..8f16d8d0e1 100644 --- a/datasets/TROPICS05ANTTL1A_0.2.json +++ b/datasets/TROPICS05ANTTL1A_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS05ANTTL1A_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS05BRTTL1B_0.2.json b/datasets/TROPICS05BRTTL1B_0.2.json index 12a29709d2..d5e8109b61 100644 --- a/datasets/TROPICS05BRTTL1B_0.2.json +++ b/datasets/TROPICS05BRTTL1B_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS05BRTTL1B_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS05PRPSL2B_0.2.json b/datasets/TROPICS05PRPSL2B_0.2.json index c0cbf98ce9..eed0f35e23 100644 --- a/datasets/TROPICS05PRPSL2B_0.2.json +++ b/datasets/TROPICS05PRPSL2B_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS05PRPSL2B_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. \n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS05 satellite and is the Provisional version of the Level 2B retrievals of instantaneous surface rain rate in units of mm/hr at G-band spatial resolution. The algorithm was adapted from the NASA Goddard Precipitation Retrieval and Profiling Scheme (PRPS). Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS05TCIEL2B_1.0.json b/datasets/TROPICS05TCIEL2B_1.0.json index d3c1f3892d..a7635f0326 100644 --- a/datasets/TROPICS05TCIEL2B_1.0.json +++ b/datasets/TROPICS05TCIEL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS05TCIEL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThe TROPICS Tropical Cyclone Intensity Estimate algorithm (TCIE), developed at the University of Wisconsin/CIMSS that uses native microwave brightness temperatures, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). The TROPICS TCIE uses the brightness temperature perturbation of two temperature sounding channels (Ch. 6 and Ch. 7) and one channel from the moisture sounding channel (Ch. 1) along with ancillary information from the TC working best track file and the CIMSS ARCHER algorithm (eye size information) to estimate the TC intensity. This validated TCIE data release starts in June 2023 for the constellation CubeSats, and August 2021 for the TROPICS-01/Pathfinder.", "links": [ { diff --git a/datasets/TROPICS05URADL2A_0.2.json b/datasets/TROPICS05URADL2A_0.2.json index aea28a9285..a9d7a73a40 100644 --- a/datasets/TROPICS05URADL2A_0.2.json +++ b/datasets/TROPICS05URADL2A_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS05URADL2A_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS05 satellite, as the Provisional version of the Level 2A geolocated brightness temperature with the water vapor sounding channels (Ch. 9 to 12) converted from their native G-band resolution to the temperature sounding channel (F-band) native resolution (i.e., all measurements at the same unified larger resolution). This product is used in the Atmospheric Vertical Temperature Profile (AVTP) retrievals to gain the benefit of averaging the G-band channels (i.e., noise reduction) while maintain the F-band (AVTP) spatial resolution. The conversion uses the Backus-Gilbert technique. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS06ANTTL1A_1.0.json b/datasets/TROPICS06ANTTL1A_1.0.json index b91ee36741..273230dbde 100644 --- a/datasets/TROPICS06ANTTL1A_1.0.json +++ b/datasets/TROPICS06ANTTL1A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06ANTTL1A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS06BRTTL1B_1.0.json b/datasets/TROPICS06BRTTL1B_1.0.json index 678cc34301..37169ea311 100644 --- a/datasets/TROPICS06BRTTL1B_1.0.json +++ b/datasets/TROPICS06BRTTL1B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06BRTTL1B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS06MIRSL2B_1.0.json b/datasets/TROPICS06MIRSL2B_1.0.json index 31e604a983..3a81090f36 100644 --- a/datasets/TROPICS06MIRSL2B_1.0.json +++ b/datasets/TROPICS06MIRSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06MIRSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. \n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS06 satellite, as the Validated Stage-1 release of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS06PRPSL2B_1.0.json b/datasets/TROPICS06PRPSL2B_1.0.json index 38edf9b308..a5957ccad3 100644 --- a/datasets/TROPICS06PRPSL2B_1.0.json +++ b/datasets/TROPICS06PRPSL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06PRPSL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. \n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS06 satellite, as the Validated Stage-1 version of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data. ", "links": [ { diff --git a/datasets/TROPICS06TCIEL2B_1.0.json b/datasets/TROPICS06TCIEL2B_1.0.json index 9e7e0aced0..dfe57bb5a2 100644 --- a/datasets/TROPICS06TCIEL2B_1.0.json +++ b/datasets/TROPICS06TCIEL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06TCIEL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThe TROPICS Tropical Cyclone Intensity Estimate algorithm (TCIE), developed at the University of Wisconsin/CIMSS that uses native microwave brightness temperatures, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). The TROPICS TCIE uses the brightness temperature perturbation of two temperature sounding channels (Ch. 6 and Ch. 7) and one channel from the moisture sounding channel (Ch. 1) along with ancillary information from the TC working best track file and the CIMSS ARCHER algorithm (eye size information) to estimate the TC intensity. This validated TCIE data release starts in June 2023 for the constellation CubeSats, and August 2021 for the TROPICS-01/Pathfinder.", "links": [ { diff --git a/datasets/TROPICS06URADL2A_1.0.json b/datasets/TROPICS06URADL2A_1.0.json index ba3b8ede1d..e15f88ed72 100644 --- a/datasets/TROPICS06URADL2A_1.0.json +++ b/datasets/TROPICS06URADL2A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS06URADL2A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS06 satellite, as the Validated Stage-1 version of the Level 2A geolocated brightness temperature with the water vapor sounding channels (Ch. 9 to 12) converted from their native G-band resolution to the temperature sounding channel (F-band) native resolution (i.e., all measurements at the same unified larger resolution). This product is used in the Atmospheric Vertical Temperature Profile (AVTP) retrievals to gain the benefit of averaging the G-band channels (i.e., noise reduction) while maintain the F-band (AVTP) spatial resolution. The conversion uses the Backus-Gilbert technique. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS07ANTTL1A_0.2.json b/datasets/TROPICS07ANTTL1A_0.2.json index d0612c33e2..9f2f079804 100644 --- a/datasets/TROPICS07ANTTL1A_0.2.json +++ b/datasets/TROPICS07ANTTL1A_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS07ANTTL1A_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS07BRTTL1B_0.2.json b/datasets/TROPICS07BRTTL1B_0.2.json index 939e5f1ba9..80bb89a8d0 100644 --- a/datasets/TROPICS07BRTTL1B_0.2.json +++ b/datasets/TROPICS07BRTTL1B_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS07BRTTL1B_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nEach TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPICS07TCIEL2B_1.0.json b/datasets/TROPICS07TCIEL2B_1.0.json index f8b4b6694a..4f56e5c16d 100644 --- a/datasets/TROPICS07TCIEL2B_1.0.json +++ b/datasets/TROPICS07TCIEL2B_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS07TCIEL2B_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThe TROPICS Tropical Cyclone Intensity Estimate algorithm (TCIE), developed at the University of Wisconsin/CIMSS that uses native microwave brightness temperatures, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). The TROPICS TCIE uses the brightness temperature perturbation of two temperature sounding channels (Ch. 6 and Ch. 7) and one channel from the moisture sounding channel (Ch. 1) along with ancillary information from the TC working best track file and the CIMSS ARCHER algorithm (eye size information) to estimate the TC intensity. This validated TCIE data release starts in June 2023 for the constellation CubeSats, and August 2021 for the TROPICS-01/Pathfinder.", "links": [ { diff --git a/datasets/TROPICS07URADL2A_0.2.json b/datasets/TROPICS07URADL2A_0.2.json index 50dbe6d2f1..af5439ce84 100644 --- a/datasets/TROPICS07URADL2A_0.2.json +++ b/datasets/TROPICS07URADL2A_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPICS07URADL2A_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.\n\nEach SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.\n\nThis dataset is from the TROPICS07 satellite, as the Provisional version of the Level 2A geolocated brightness temperature with the water vapor sounding channels (Ch. 9 to 12) converted from their native G-band resolution to the temperature sounding channel (F-band) native resolution (i.e., all measurements at the same unified larger resolution). This product is used in the Atmospheric Vertical Temperature Profile (AVTP) retrievals to gain the benefit of averaging the G-band channels (i.e., noise reduction) while maintain the F-band (AVTP) spatial resolution. The conversion uses the Backus-Gilbert technique. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.", "links": [ { diff --git a/datasets/TROPOMAER_1.json b/datasets/TROPOMAER_1.json index 8b7ee8ca69..0cf101bc15 100644 --- a/datasets/TROPOMAER_1.json +++ b/datasets/TROPOMAER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPOMAER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 32-year record of satellite near-UV observations by three sensors. \n\nThe Corpenicus Sentinel-5P TROPOMI Near UV (version 1) Aerosol Optical Depth and Single Scattering Albedo data product consists of aerosol absorption optical depth, aerosol total optical depth, aerosol layer height, aerosol UV index, and aerosol single scattering albedo at approximately 7.5kmx3km. This product also contains ancillary data for ocean corrected surface albedo and terrain pressure.\n\nData since July 19, 2022 (orbit 24688) has been processed with the most recent calibrated L1B radiance data (version 2.0.1). Data prior to July 19, 2022, data was processed with version 2.0.0 L1B radiances. Soon, the entire record will be reprocessed using version 2.0.1 L1B data. Any upgrades will be posted here. \n\nThese Level-2 data are stored in the NetCDF-4 format and are available from the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).", "links": [ { diff --git a/datasets/TROPOMI_MINDS_NO2_1.1.json b/datasets/TROPOMI_MINDS_NO2_1.1.json index 75189298d4..f277128591 100644 --- a/datasets/TROPOMI_MINDS_NO2_1.1.json +++ b/datasets/TROPOMI_MINDS_NO2_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPOMI_MINDS_NO2_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2011), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.", "links": [ { diff --git a/datasets/TROPOMI_SIF_Arctic_Ocean_2378_1.json b/datasets/TROPOMI_SIF_Arctic_Ocean_2378_1.json index 2eb00eaf22..c8e785c8f6 100644 --- a/datasets/TROPOMI_SIF_Arctic_Ocean_2378_1.json +++ b/datasets/TROPOMI_SIF_Arctic_Ocean_2378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TROPOMI_SIF_Arctic_Ocean_2378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides solar-induced chlorophyll fluorescence (SIF) estimates over the Arctic Ocean at a 0.05-degree resolution for each month from January 2004 through December 2020. Red SIF data from TROPOspheric Monitoring Instrument (TROPOMI) (2018 to 2021) were extended over the study period using a random forest machine learning model trained using TROPOMI SIF climatological records. These data are useful for monitoring the physiological responses of phytoplankton to ongoing climate change over this ocean region. The data are provided in cloud optimized GeoTIFF format.", "links": [ { diff --git a/datasets/TRPSCRAERNH42H2D_1.json b/datasets/TRPSCRAERNH42H2D_1.json index f014f79589..cfc17c51a0 100644 --- a/datasets/TRPSCRAERNH42H2D_1.json +++ b/datasets/TRPSCRAERNH42H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNH42H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NH4 2-Hourly 2-dimensional Product contains surface concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERNH46H3D_1.json b/datasets/TRPSCRAERNH46H3D_1.json index e635f51f47..11d76fc17e 100644 --- a/datasets/TRPSCRAERNH46H3D_1.json +++ b/datasets/TRPSCRAERNH46H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNH46H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NH4 6-Hourly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERNH4M3D_1.json b/datasets/TRPSCRAERNH4M3D_1.json index f6b1e10965..233633c275 100644 --- a/datasets/TRPSCRAERNH4M3D_1.json +++ b/datasets/TRPSCRAERNH4M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNH4M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NH4 Monthly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERNO32H2D_1.json b/datasets/TRPSCRAERNO32H2D_1.json index d36c291f1f..c9c218ade6 100644 --- a/datasets/TRPSCRAERNO32H2D_1.json +++ b/datasets/TRPSCRAERNO32H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNO32H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NO3 2-Hourly 2-dimensional Product contains surface concentrations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERNO36H3D_1.json b/datasets/TRPSCRAERNO36H3D_1.json index 1143936635..87ae80db97 100644 --- a/datasets/TRPSCRAERNO36H3D_1.json +++ b/datasets/TRPSCRAERNO36H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNO36H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NO3 6-Hourly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERNO3M3D_1.json b/datasets/TRPSCRAERNO3M3D_1.json index 77c7ed21d3..824013645c 100644 --- a/datasets/TRPSCRAERNO3M3D_1.json +++ b/datasets/TRPSCRAERNO3M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERNO3M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol NO3 Monthly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERSO42H2D_1.json b/datasets/TRPSCRAERSO42H2D_1.json index 92ac79eec1..98ef8e5833 100644 --- a/datasets/TRPSCRAERSO42H2D_1.json +++ b/datasets/TRPSCRAERSO42H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERSO42H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol SO4 2-Hourly 3-dimensional Product contains surface concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERSO46H3D_1.json b/datasets/TRPSCRAERSO46H3D_1.json index ae6b214879..423d8ecf01 100644 --- a/datasets/TRPSCRAERSO46H3D_1.json +++ b/datasets/TRPSCRAERSO46H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERSO46H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol SO4 6-Hourly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRAERSO4M3D_1.json b/datasets/TRPSCRAERSO4M3D_1.json index 028042e503..49caa41809 100644 --- a/datasets/TRPSCRAERSO4M3D_1.json +++ b/datasets/TRPSCRAERSO4M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRAERSO4M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Aerosol SO4 Monthly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCH2O2H2D_1.json b/datasets/TRPSCRCH2O2H2D_1.json index d55bc7e42e..76e744060f 100644 --- a/datasets/TRPSCRCH2O2H2D_1.json +++ b/datasets/TRPSCRCH2O2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCH2O2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CH2O 2-Hourly 2-dimensional Product contains surface concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCH2O6H3D_1.json b/datasets/TRPSCRCH2O6H3D_1.json index 13ff7739aa..ebb96f9f24 100644 --- a/datasets/TRPSCRCH2O6H3D_1.json +++ b/datasets/TRPSCRCH2O6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCH2O6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CH2O 6-Hourly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCH2OM3D_1.json b/datasets/TRPSCRCH2OM3D_1.json index 64ced87bde..2db1efbcb8 100644 --- a/datasets/TRPSCRCH2OM3D_1.json +++ b/datasets/TRPSCRCH2OM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCH2OM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CH2O Monthly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCO2H2D_1.json b/datasets/TRPSCRCO2H2D_1.json index f84bf64ae3..ae51732521 100644 --- a/datasets/TRPSCRCO2H2D_1.json +++ b/datasets/TRPSCRCO2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCO2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CO 2-Hourly 2-dimensional Product contains surface concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCO6H3D_1.json b/datasets/TRPSCRCO6H3D_1.json index 29d7e1f918..68c54eeb8a 100644 --- a/datasets/TRPSCRCO6H3D_1.json +++ b/datasets/TRPSCRCO6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCO6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CO 6-Hourly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCOM3D_1.json b/datasets/TRPSCRCOM3D_1.json index ec4923c0d5..c801623554 100644 --- a/datasets/TRPSCRCOM3D_1.json +++ b/datasets/TRPSCRCOM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCOM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CO Monthly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCOS6H3D_1.json b/datasets/TRPSCRCOS6H3D_1.json index 5148770825..61206ef81a 100644 --- a/datasets/TRPSCRCOS6H3D_1.json +++ b/datasets/TRPSCRCOS6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCOS6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CO Spread 6-Hourly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRCOSM3D_1.json b/datasets/TRPSCRCOSM3D_1.json index 75e305414a..07cb79596a 100644 --- a/datasets/TRPSCRCOSM3D_1.json +++ b/datasets/TRPSCRCOSM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRCOSM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis CO Spread Monthly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRECOAM2D_1.json b/datasets/TRPSCRECOAM2D_1.json index 17cf7cdd7d..0e1f4a2d11 100644 --- a/datasets/TRPSCRECOAM2D_1.json +++ b/datasets/TRPSCRECOAM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRECOAM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Anthropogenic CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRECOBM2D_1.json b/datasets/TRPSCRECOBM2D_1.json index 4b4b8098d5..7e2de1f9fd 100644 --- a/datasets/TRPSCRECOBM2D_1.json +++ b/datasets/TRPSCRECOBM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRECOBM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Biomass Burning CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRECOTM2D_1.json b/datasets/TRPSCRECOTM2D_1.json index 0ca6fdf8ee..3ad789ef1c 100644 --- a/datasets/TRPSCRECOTM2D_1.json +++ b/datasets/TRPSCRECOTM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRECOTM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Total CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRENOXAM2D_1.json b/datasets/TRPSCRENOXAM2D_1.json index a285f70bd1..9ed0967731 100644 --- a/datasets/TRPSCRENOXAM2D_1.json +++ b/datasets/TRPSCRENOXAM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRENOXAM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Anthropogenic NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRENOXBM2D_1.json b/datasets/TRPSCRENOXBM2D_1.json index 7f26edd286..006acba5a7 100644 --- a/datasets/TRPSCRENOXBM2D_1.json +++ b/datasets/TRPSCRENOXBM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRENOXBM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Biomass Burning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRENOXLM2D_1.json b/datasets/TRPSCRENOXLM2D_1.json index f0548de73a..9bd523b80f 100644 --- a/datasets/TRPSCRENOXLM2D_1.json +++ b/datasets/TRPSCRENOXLM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRENOXLM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Lightning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from lightning strikes. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRENOXSM2D_1.json b/datasets/TRPSCRENOXSM2D_1.json index 72f73d9915..4dc2fb1eab 100644 --- a/datasets/TRPSCRENOXSM2D_1.json +++ b/datasets/TRPSCRENOXSM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRENOXSM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Soil NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from surface soil sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRENOXTM2D_1.json b/datasets/TRPSCRENOXTM2D_1.json index a1d8b3a936..8dd048d8e3 100644 --- a/datasets/TRPSCRENOXTM2D_1.json +++ b/datasets/TRPSCRENOXTM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRENOXTM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Total NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRESO2TM2D_1.json b/datasets/TRPSCRESO2TM2D_1.json index be77be906d..6337d887f2 100644 --- a/datasets/TRPSCRESO2TM2D_1.json +++ b/datasets/TRPSCRESO2TM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRESO2TM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Total SO2 emissions Monthly 2-dimensional Product contains sulfur dioxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRHNO32H2D_1.json b/datasets/TRPSCRHNO32H2D_1.json index 39073ee3ea..4c5f995489 100644 --- a/datasets/TRPSCRHNO32H2D_1.json +++ b/datasets/TRPSCRHNO32H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRHNO32H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis HNO3 2-Hourly 2-dimensional Product contains surface concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRHNO36H3D_1.json b/datasets/TRPSCRHNO36H3D_1.json index 51b498a3aa..20e49fd28d 100644 --- a/datasets/TRPSCRHNO36H3D_1.json +++ b/datasets/TRPSCRHNO36H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRHNO36H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis HNO3 6-Hourly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRHNO3M3D_1.json b/datasets/TRPSCRHNO3M3D_1.json index eb739cf8de..27e562ddd2 100644 --- a/datasets/TRPSCRHNO3M3D_1.json +++ b/datasets/TRPSCRHNO3M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRHNO3M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis HNO3 Monthly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO22H2D_1.json b/datasets/TRPSCRNO22H2D_1.json index 0ed2fa0429..b8b0e196f4 100644 --- a/datasets/TRPSCRNO22H2D_1.json +++ b/datasets/TRPSCRNO22H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO22H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO2 2-Hourly 2-dimensional Product contains surface concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO26H3D_1.json b/datasets/TRPSCRNO26H3D_1.json index 05e80c75cc..6edb84b2dd 100644 --- a/datasets/TRPSCRNO26H3D_1.json +++ b/datasets/TRPSCRNO26H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO26H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO2 6-Hourly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO2H2D_1.json b/datasets/TRPSCRNO2H2D_1.json index 6c61fe69af..67bd5aa4e4 100644 --- a/datasets/TRPSCRNO2H2D_1.json +++ b/datasets/TRPSCRNO2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO 2-Hourly 2-dimensional Product contains surface concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO2M3D_1.json b/datasets/TRPSCRNO2M3D_1.json index 1553bfe23c..cea298e477 100644 --- a/datasets/TRPSCRNO2M3D_1.json +++ b/datasets/TRPSCRNO2M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO2M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO2 Monthly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO2S6H3D_1.json b/datasets/TRPSCRNO2S6H3D_1.json index 93fc571ba8..3ff05b6941 100644 --- a/datasets/TRPSCRNO2S6H3D_1.json +++ b/datasets/TRPSCRNO2S6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO2S6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO2 Spread 6-Hourly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO2SM3D_1.json b/datasets/TRPSCRNO2SM3D_1.json index b001771f7c..e22ae69ba0 100644 --- a/datasets/TRPSCRNO2SM3D_1.json +++ b/datasets/TRPSCRNO2SM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO2SM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO2 Spread Monthly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNO6H3D_1.json b/datasets/TRPSCRNO6H3D_1.json index c55d3b869d..b91c6090a7 100644 --- a/datasets/TRPSCRNO6H3D_1.json +++ b/datasets/TRPSCRNO6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNO6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO 6-Hourly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRNOM3D_1.json b/datasets/TRPSCRNOM3D_1.json index 4087a7eb7a..7ac8a9b021 100644 --- a/datasets/TRPSCRNOM3D_1.json +++ b/datasets/TRPSCRNOM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRNOM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis NO Monthly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO32H2D_1.json b/datasets/TRPSCRO32H2D_1.json index 51db02bb8c..db71c1c2c9 100644 --- a/datasets/TRPSCRO32H2D_1.json +++ b/datasets/TRPSCRO32H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO32H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface O3 2-Hourly 2-dimensional Product contains surface concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO36H3D_1.json b/datasets/TRPSCRO36H3D_1.json index 09aedcdf6f..7a40417572 100644 --- a/datasets/TRPSCRO36H3D_1.json +++ b/datasets/TRPSCRO36H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO36H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 6-Hourly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO3I6H3D_1.json b/datasets/TRPSCRO3I6H3D_1.json index 5b3bd59784..615c6bf862 100644 --- a/datasets/TRPSCRO3I6H3D_1.json +++ b/datasets/TRPSCRO3I6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO3I6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 Increment 6-Hourly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO3IM3D_1.json b/datasets/TRPSCRO3IM3D_1.json index 29115d3d18..04362a085c 100644 --- a/datasets/TRPSCRO3IM3D_1.json +++ b/datasets/TRPSCRO3IM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO3IM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 Increment Monthly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO3M3D_1.json b/datasets/TRPSCRO3M3D_1.json index 97d9b79517..7deffe4480 100644 --- a/datasets/TRPSCRO3M3D_1.json +++ b/datasets/TRPSCRO3M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO3M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 Monthly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO3S6H3D_1.json b/datasets/TRPSCRO3S6H3D_1.json index df1188523d..676abf5f8d 100644 --- a/datasets/TRPSCRO3S6H3D_1.json +++ b/datasets/TRPSCRO3S6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO3S6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 Spread 6-Hourly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRO3SM3D_1.json b/datasets/TRPSCRO3SM3D_1.json index adeb5b8e61..65f3f53bb4 100644 --- a/datasets/TRPSCRO3SM3D_1.json +++ b/datasets/TRPSCRO3SM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRO3SM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis O3 Spread Monthly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCROH2H2D_1.json b/datasets/TRPSCROH2H2D_1.json index 2eff5a97c6..60df7902f0 100644 --- a/datasets/TRPSCROH2H2D_1.json +++ b/datasets/TRPSCROH2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCROH2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis OH 2-Hourly 2-dimensional Product contains surface concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCROH6H3D_1.json b/datasets/TRPSCROH6H3D_1.json index 37f6c8f39e..96a56f6db6 100644 --- a/datasets/TRPSCROH6H3D_1.json +++ b/datasets/TRPSCROH6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCROH6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis OH 6-Hourly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCROHM3D_1.json b/datasets/TRPSCROHM3D_1.json index 80e195363b..279b401113 100644 --- a/datasets/TRPSCROHM3D_1.json +++ b/datasets/TRPSCROHM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCROHM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis OH Monthly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPAN2H2D_1.json b/datasets/TRPSCRPAN2H2D_1.json index d7a7112d1a..396946db96 100644 --- a/datasets/TRPSCRPAN2H2D_1.json +++ b/datasets/TRPSCRPAN2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPAN2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis PAN 2-Hourly 2-dimensional Product contains surface concentrations of peryoxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPAN6H3D_1.json b/datasets/TRPSCRPAN6H3D_1.json index 3dbb30eabb..f6014092ea 100644 --- a/datasets/TRPSCRPAN6H3D_1.json +++ b/datasets/TRPSCRPAN6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPAN6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis PAN 6-Hourly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPANM3D_1.json b/datasets/TRPSCRPANM3D_1.json index 9d2705a0db..8af47c2ec2 100644 --- a/datasets/TRPSCRPANM3D_1.json +++ b/datasets/TRPSCRPANM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPANM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis PAN Monthly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPS2H2D_1.json b/datasets/TRPSCRPS2H2D_1.json index a86d56afb8..378aa151c5 100644 --- a/datasets/TRPSCRPS2H2D_1.json +++ b/datasets/TRPSCRPS2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPS2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Pressure 2-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPS6H2D_1.json b/datasets/TRPSCRPS6H2D_1.json index a07dcb9941..a98dbf3119 100644 --- a/datasets/TRPSCRPS6H2D_1.json +++ b/datasets/TRPSCRPS6H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPS6H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis PS 6-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRPSM2D_1.json b/datasets/TRPSCRPSM2D_1.json index c707fefcba..5664e77dca 100644 --- a/datasets/TRPSCRPSM2D_1.json +++ b/datasets/TRPSCRPSM2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRPSM2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis PS Monthly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRQ2H2D_1.json b/datasets/TRPSCRQ2H2D_1.json index 71dd8b8b8e..8e9054418e 100644 --- a/datasets/TRPSCRQ2H2D_1.json +++ b/datasets/TRPSCRQ2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRQ2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Specific Humidity 2-Hourly 2-dimensional Product contains surface specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRQ6H3D_1.json b/datasets/TRPSCRQ6H3D_1.json index b2acac04d5..0221776ca5 100644 --- a/datasets/TRPSCRQ6H3D_1.json +++ b/datasets/TRPSCRQ6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRQ6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Specific Humidity 6-Hourly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRQM3D_1.json b/datasets/TRPSCRQM3D_1.json index 3d004a5abd..a41a7d9e70 100644 --- a/datasets/TRPSCRQM3D_1.json +++ b/datasets/TRPSCRQM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRQM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Specific Humidity Monthly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRSO22H2D_1.json b/datasets/TRPSCRSO22H2D_1.json index 58d7bebb53..cee6dfffd4 100644 --- a/datasets/TRPSCRSO22H2D_1.json +++ b/datasets/TRPSCRSO22H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRSO22H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis SO2 2-Hourly 2-dimensional Product contains surface concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRSO26H3D_1.json b/datasets/TRPSCRSO26H3D_1.json index 577b8a1f5c..e0018daabf 100644 --- a/datasets/TRPSCRSO26H3D_1.json +++ b/datasets/TRPSCRSO26H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRSO26H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis SO2 6-Hourly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRSO2M3D_1.json b/datasets/TRPSCRSO2M3D_1.json index 9058a73ceb..9a1b21fc7e 100644 --- a/datasets/TRPSCRSO2M3D_1.json +++ b/datasets/TRPSCRSO2M3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRSO2M3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis SO2 Monthly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRT2H2D_1.json b/datasets/TRPSCRT2H2D_1.json index fabbecb1df..5020777324 100644 --- a/datasets/TRPSCRT2H2D_1.json +++ b/datasets/TRPSCRT2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRT2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Temperature 2-Hourly 2-dimensional Product contains surface temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRT6H3D_1.json b/datasets/TRPSCRT6H3D_1.json index 2affb03bb2..2a0cbef59a 100644 --- a/datasets/TRPSCRT6H3D_1.json +++ b/datasets/TRPSCRT6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRT6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Temperature 6-Hourly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRTM3D_1.json b/datasets/TRPSCRTM3D_1.json index 8a7ea12ce7..f8ebc5113e 100644 --- a/datasets/TRPSCRTM3D_1.json +++ b/datasets/TRPSCRTM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRTM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Temperature Monthly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRU2H2D_1.json b/datasets/TRPSCRU2H2D_1.json index 562281a769..091043fb8b 100644 --- a/datasets/TRPSCRU2H2D_1.json +++ b/datasets/TRPSCRU2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRU2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Zonal Wind 2-Hourly 2-dimensional Product contains surface zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRU6H3D_1.json b/datasets/TRPSCRU6H3D_1.json index b1af5b33c5..097d4d72a8 100644 --- a/datasets/TRPSCRU6H3D_1.json +++ b/datasets/TRPSCRU6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRU6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Zonal Wind 6-Hourly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRUM3D_1.json b/datasets/TRPSCRUM3D_1.json index fc0358930e..ce48dad7b2 100644 --- a/datasets/TRPSCRUM3D_1.json +++ b/datasets/TRPSCRUM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRUM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Zonal Wind Monthly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRV2H2D_1.json b/datasets/TRPSCRV2H2D_1.json index aebdfa393c..27ad32a4df 100644 --- a/datasets/TRPSCRV2H2D_1.json +++ b/datasets/TRPSCRV2H2D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRV2H2D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Surface Meridional Wind 2-Hourly 2-dimensional Product contains surface meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRV6H3D_1.json b/datasets/TRPSCRV6H3D_1.json index a8cc097765..5a85f9ae52 100644 --- a/datasets/TRPSCRV6H3D_1.json +++ b/datasets/TRPSCRV6H3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRV6H3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Meridional Wind 6-Hourly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSCRVM3D_1.json b/datasets/TRPSCRVM3D_1.json index 5fabcc55ab..9896e3ca83 100644 --- a/datasets/TRPSCRVM3D_1.json +++ b/datasets/TRPSCRVM3D_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSCRVM3D_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS Chemical Reanalysis Meridional Wind Monthly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.\n\nThe data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGBEI_1.json b/datasets/TRPSDL2ALLCRSMGBEI_1.json index 2bee42b588..8dae51f984 100644 --- a/datasets/TRPSDL2ALLCRSMGBEI_1.json +++ b/datasets/TRPSDL2ALLCRSMGBEI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGBEI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Beijing Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGDEL_1.json b/datasets/TRPSDL2ALLCRSMGDEL_1.json index df231da221..03a88343b1 100644 --- a/datasets/TRPSDL2ALLCRSMGDEL_1.json +++ b/datasets/TRPSDL2ALLCRSMGDEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGDEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Delhi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGKAR_1.json b/datasets/TRPSDL2ALLCRSMGKAR_1.json index 6160de68f2..e2af58b332 100644 --- a/datasets/TRPSDL2ALLCRSMGKAR_1.json +++ b/datasets/TRPSDL2ALLCRSMGKAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGKAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Karachi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGLAG_1.json b/datasets/TRPSDL2ALLCRSMGLAG_1.json index ff79e1ca71..16b371938b 100644 --- a/datasets/TRPSDL2ALLCRSMGLAG_1.json +++ b/datasets/TRPSDL2ALLCRSMGLAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGLAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Lagos Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGLOS_1.json b/datasets/TRPSDL2ALLCRSMGLOS_1.json index 2610b9ce3f..2e2ca4f8c2 100644 --- a/datasets/TRPSDL2ALLCRSMGLOS_1.json +++ b/datasets/TRPSDL2ALLCRSMGLOS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGLOS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGMEX_1.json b/datasets/TRPSDL2ALLCRSMGMEX_1.json index f3890317a4..cc22d160da 100644 --- a/datasets/TRPSDL2ALLCRSMGMEX_1.json +++ b/datasets/TRPSDL2ALLCRSMGMEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGMEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Mexico City Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGSAO_1.json b/datasets/TRPSDL2ALLCRSMGSAO_1.json index 59a5fdd535..c0f2d74f4e 100644 --- a/datasets/TRPSDL2ALLCRSMGSAO_1.json +++ b/datasets/TRPSDL2ALLCRSMGSAO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGSAO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2ALLCRSMGTOK_1.json b/datasets/TRPSDL2ALLCRSMGTOK_1.json index adb0311e03..c218b6ae8d 100644 --- a/datasets/TRPSDL2ALLCRSMGTOK_1.json +++ b/datasets/TRPSDL2ALLCRSMGTOK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2ALLCRSMGTOK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Tokyo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2CH4AIRSFS_1.json b/datasets/TRPSDL2CH4AIRSFS_1.json index 12e3051c06..ddfc005174 100644 --- a/datasets/TRPSDL2CH4AIRSFS_1.json +++ b/datasets/TRPSDL2CH4AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2CH4AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2CH4CRS1FS_1.json b/datasets/TRPSDL2CH4CRS1FS_1.json index 27ffafd8d8..a33a3e04ab 100644 --- a/datasets/TRPSDL2CH4CRS1FS_1.json +++ b/datasets/TRPSDL2CH4CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2CH4CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2CH4CRSAUS_1.json b/datasets/TRPSDL2CH4CRSAUS_1.json index 1259329e13..a3415081d9 100644 --- a/datasets/TRPSDL2CH4CRSAUS_1.json +++ b/datasets/TRPSDL2CH4CRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2CH4CRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Methane for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2CH4CRSFS_1.json b/datasets/TRPSDL2CH4CRSFS_1.json index a29088e5f2..92145115aa 100644 --- a/datasets/TRPSDL2CH4CRSFS_1.json +++ b/datasets/TRPSDL2CH4CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2CH4CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COAIRSFS_1.json b/datasets/TRPSDL2COAIRSFS_1.json index 1d08c8ce87..0e8219d5c5 100644 --- a/datasets/TRPSDL2COAIRSFS_1.json +++ b/datasets/TRPSDL2COAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRS1FS_1.json b/datasets/TRPSDL2COCRS1FS_1.json index c1b0b7e681..2d788b41f7 100644 --- a/datasets/TRPSDL2COCRS1FS_1.json +++ b/datasets/TRPSDL2COCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSAUS_1.json b/datasets/TRPSDL2COCRSAUS_1.json index 08e4382d54..00da410800 100644 --- a/datasets/TRPSDL2COCRSAUS_1.json +++ b/datasets/TRPSDL2COCRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSFS_1.json b/datasets/TRPSDL2COCRSFS_1.json index e2338abef0..8335e7c0a4 100644 --- a/datasets/TRPSDL2COCRSFS_1.json +++ b/datasets/TRPSDL2COCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSTS1_1.json b/datasets/TRPSDL2COCRSTS1_1.json index 4ea160c018..0cf43624ba 100644 --- a/datasets/TRPSDL2COCRSTS1_1.json +++ b/datasets/TRPSDL2COCRSTS1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSTS1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS1, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSTS2_1.json b/datasets/TRPSDL2COCRSTS2_1.json index 39c7b1db8c..536bc19823 100644 --- a/datasets/TRPSDL2COCRSTS2_1.json +++ b/datasets/TRPSDL2COCRSTS2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSTS2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS2, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSWCFHI_1.json b/datasets/TRPSDL2COCRSWCFHI_1.json index 08c347cbf8..3e695a0794 100644 --- a/datasets/TRPSDL2COCRSWCFHI_1.json +++ b/datasets/TRPSDL2COCRSWCFHI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSWCFHI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2COCRSWCF_1.json b/datasets/TRPSDL2COCRSWCF_1.json index b82c4cc07a..e934f34531 100644 --- a/datasets/TRPSDL2COCRSWCF_1.json +++ b/datasets/TRPSDL2COCRSWCF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2COCRSWCF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2H2OAIRSFS_1.json b/datasets/TRPSDL2H2OAIRSFS_1.json index 966ef37122..8371f4c767 100644 --- a/datasets/TRPSDL2H2OAIRSFS_1.json +++ b/datasets/TRPSDL2H2OAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2H2OAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2H2OCRS1FS_1.json b/datasets/TRPSDL2H2OCRS1FS_1.json index 62ee35d185..b559b9771c 100644 --- a/datasets/TRPSDL2H2OCRS1FS_1.json +++ b/datasets/TRPSDL2H2OCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2H2OCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2H2OCRSAUS_1.json b/datasets/TRPSDL2H2OCRSAUS_1.json index e24d24e808..31e98bee8f 100644 --- a/datasets/TRPSDL2H2OCRSAUS_1.json +++ b/datasets/TRPSDL2H2OCRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2H2OCRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Water for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2H2OCRSFS_1.json b/datasets/TRPSDL2H2OCRSFS_1.json index 76747deeab..376497c0ad 100644 --- a/datasets/TRPSDL2H2OCRSFS_1.json +++ b/datasets/TRPSDL2H2OCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2H2OCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2HDOAIRSFS_1.json b/datasets/TRPSDL2HDOAIRSFS_1.json index 4b898c3a06..053ffd61c6 100644 --- a/datasets/TRPSDL2HDOAIRSFS_1.json +++ b/datasets/TRPSDL2HDOAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2HDOAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2HDOCRS1FS_1.json b/datasets/TRPSDL2HDOCRS1FS_1.json index 13cf0e8e7f..488468bda7 100644 --- a/datasets/TRPSDL2HDOCRS1FS_1.json +++ b/datasets/TRPSDL2HDOCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2HDOCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2HDOCRSAUS_1.json b/datasets/TRPSDL2HDOCRSAUS_1.json index dacd7e2258..065c1735b3 100644 --- a/datasets/TRPSDL2HDOCRSAUS_1.json +++ b/datasets/TRPSDL2HDOCRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2HDOCRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2HDOCRSFS_1.json b/datasets/TRPSDL2HDOCRSFS_1.json index d9174c8fbe..33e8eb1f86 100644 --- a/datasets/TRPSDL2HDOCRSFS_1.json +++ b/datasets/TRPSDL2HDOCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2HDOCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3AIRSFS_1.json b/datasets/TRPSDL2NH3AIRSFS_1.json index 29630944fa..a38ffb0223 100644 --- a/datasets/TRPSDL2NH3AIRSFS_1.json +++ b/datasets/TRPSDL2NH3AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3CRS1FS_1.json b/datasets/TRPSDL2NH3CRS1FS_1.json index 5a279b5744..97d4205283 100644 --- a/datasets/TRPSDL2NH3CRS1FS_1.json +++ b/datasets/TRPSDL2NH3CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3CRSAUS_1.json b/datasets/TRPSDL2NH3CRSAUS_1.json index 3ec8318939..61af69cde5 100644 --- a/datasets/TRPSDL2NH3CRSAUS_1.json +++ b/datasets/TRPSDL2NH3CRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3CRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3CRSFS_1.json b/datasets/TRPSDL2NH3CRSFS_1.json index 8e2c52c188..73f99ae08a 100644 --- a/datasets/TRPSDL2NH3CRSFS_1.json +++ b/datasets/TRPSDL2NH3CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3CRSWCFHI_1.json b/datasets/TRPSDL2NH3CRSWCFHI_1.json index e3cabdb9a7..b1f36724ab 100644 --- a/datasets/TRPSDL2NH3CRSWCFHI_1.json +++ b/datasets/TRPSDL2NH3CRSWCFHI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3CRSWCFHI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2NH3CRSWCF_1.json b/datasets/TRPSDL2NH3CRSWCF_1.json index c5b718cd77..aae5008f3c 100644 --- a/datasets/TRPSDL2NH3CRSWCF_1.json +++ b/datasets/TRPSDL2NH3CRSWCF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2NH3CRSWCF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3AIRSFS_1.json b/datasets/TRPSDL2O3AIRSFS_1.json index 1a38ecbad9..5d8a6729c3 100644 --- a/datasets/TRPSDL2O3AIRSFS_1.json +++ b/datasets/TRPSDL2O3AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3AIRSOMIFS_1.json b/datasets/TRPSDL2O3AIRSOMIFS_1.json index 7a8ba641a0..a65801a76b 100644 --- a/datasets/TRPSDL2O3AIRSOMIFS_1.json +++ b/datasets/TRPSDL2O3AIRSOMIFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3AIRSOMIFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3CRS1FS_1.json b/datasets/TRPSDL2O3CRS1FS_1.json index f5c5437ad5..9b2e24d168 100644 --- a/datasets/TRPSDL2O3CRS1FS_1.json +++ b/datasets/TRPSDL2O3CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3CRSAUS_1.json b/datasets/TRPSDL2O3CRSAUS_1.json index 5ff153414e..88b87bea1f 100644 --- a/datasets/TRPSDL2O3CRSAUS_1.json +++ b/datasets/TRPSDL2O3CRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3CRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3CRSFS_1.json b/datasets/TRPSDL2O3CRSFS_1.json index 1b40029eb4..6f194cb617 100644 --- a/datasets/TRPSDL2O3CRSFS_1.json +++ b/datasets/TRPSDL2O3CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3CRSWCFHI_1.json b/datasets/TRPSDL2O3CRSWCFHI_1.json index 8af9ca98db..245dc98bc6 100644 --- a/datasets/TRPSDL2O3CRSWCFHI_1.json +++ b/datasets/TRPSDL2O3CRSWCFHI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3CRSWCFHI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3CRSWCF_1.json b/datasets/TRPSDL2O3CRSWCF_1.json index 0b5676d55e..6d35e06a4b 100644 --- a/datasets/TRPSDL2O3CRSWCF_1.json +++ b/datasets/TRPSDL2O3CRSWCF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3CRSWCF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2O3OMIFS_1.json b/datasets/TRPSDL2O3OMIFS_1.json index fa77730e9f..b01146652f 100644 --- a/datasets/TRPSDL2O3OMIFS_1.json +++ b/datasets/TRPSDL2O3OMIFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2O3OMIFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2PANCRS1FS_1.json b/datasets/TRPSDL2PANCRS1FS_1.json index b8a44cc2fb..456faf6695 100644 --- a/datasets/TRPSDL2PANCRS1FS_1.json +++ b/datasets/TRPSDL2PANCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2PANCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2PANCRSAUS_1.json b/datasets/TRPSDL2PANCRSAUS_1.json index eb08f5c6b2..6b8a25d65e 100644 --- a/datasets/TRPSDL2PANCRSAUS_1.json +++ b/datasets/TRPSDL2PANCRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2PANCRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2PANCRSFS_1.json b/datasets/TRPSDL2PANCRSFS_1.json index 63b7233ad8..408264b956 100644 --- a/datasets/TRPSDL2PANCRSFS_1.json +++ b/datasets/TRPSDL2PANCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2PANCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2PANCRSWCFHI_1.json b/datasets/TRPSDL2PANCRSWCFHI_1.json index 7373a3d93e..5b99e59fae 100644 --- a/datasets/TRPSDL2PANCRSWCFHI_1.json +++ b/datasets/TRPSDL2PANCRSWCFHI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2PANCRSWCFHI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2PANCRSWCF_1.json b/datasets/TRPSDL2PANCRSWCF_1.json index 9ba3d63db7..3344c52d42 100644 --- a/datasets/TRPSDL2PANCRSWCF_1.json +++ b/datasets/TRPSDL2PANCRSWCF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2PANCRSWCF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2TATMAIRSFS_1.json b/datasets/TRPSDL2TATMAIRSFS_1.json index cb5f24f743..9d031c5026 100644 --- a/datasets/TRPSDL2TATMAIRSFS_1.json +++ b/datasets/TRPSDL2TATMAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2TATMAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (AIRS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2TATMCRS1FS_1.json b/datasets/TRPSDL2TATMCRS1FS_1.json index 04edfa4371..d684e8dabf 100644 --- a/datasets/TRPSDL2TATMCRS1FS_1.json +++ b/datasets/TRPSDL2TATMCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2TATMCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2TATMCRSAUS_1.json b/datasets/TRPSDL2TATMCRSAUS_1.json index 3cedc5cb96..d1314a9080 100644 --- a/datasets/TRPSDL2TATMCRSAUS_1.json +++ b/datasets/TRPSDL2TATMCRSAUS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2TATMCRSAUS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Atmospheric Temperature for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSDL2TATMCRSFS_1.json b/datasets/TRPSDL2TATMCRSFS_1.json index ca46e0824e..f22fca07d2 100644 --- a/datasets/TRPSDL2TATMCRSFS_1.json +++ b/datasets/TRPSDL2TATMCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSDL2TATMCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGBEI_1.json b/datasets/TRPSYL2ALLCRSMGBEI_1.json index 35d56ae30f..a2933132e4 100644 --- a/datasets/TRPSYL2ALLCRSMGBEI_1.json +++ b/datasets/TRPSYL2ALLCRSMGBEI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGBEI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Beijing Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGDEL_1.json b/datasets/TRPSYL2ALLCRSMGDEL_1.json index 774920b656..326a0162d9 100644 --- a/datasets/TRPSYL2ALLCRSMGDEL_1.json +++ b/datasets/TRPSYL2ALLCRSMGDEL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGDEL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Delhi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGKAR_1.json b/datasets/TRPSYL2ALLCRSMGKAR_1.json index 5db3904d56..e9ee5028ed 100644 --- a/datasets/TRPSYL2ALLCRSMGKAR_1.json +++ b/datasets/TRPSYL2ALLCRSMGKAR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGKAR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Karachi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGLAG_1.json b/datasets/TRPSYL2ALLCRSMGLAG_1.json index 858938a485..67442faa90 100644 --- a/datasets/TRPSYL2ALLCRSMGLAG_1.json +++ b/datasets/TRPSYL2ALLCRSMGLAG_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGLAG_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Lagos Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGLOS_1.json b/datasets/TRPSYL2ALLCRSMGLOS_1.json index 28cdbc309e..2704f3fbf6 100644 --- a/datasets/TRPSYL2ALLCRSMGLOS_1.json +++ b/datasets/TRPSYL2ALLCRSMGLOS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGLOS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGMEX_1.json b/datasets/TRPSYL2ALLCRSMGMEX_1.json index d0e5a8b0d4..71dd8fa8c1 100644 --- a/datasets/TRPSYL2ALLCRSMGMEX_1.json +++ b/datasets/TRPSYL2ALLCRSMGMEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGMEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Mexico City Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGSAO_1.json b/datasets/TRPSYL2ALLCRSMGSAO_1.json index 897df3190d..3147a0886a 100644 --- a/datasets/TRPSYL2ALLCRSMGSAO_1.json +++ b/datasets/TRPSYL2ALLCRSMGSAO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGSAO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2ALLCRSMGTOK_1.json b/datasets/TRPSYL2ALLCRSMGTOK_1.json index d6e2c1f3a0..6eb8fb6678 100644 --- a/datasets/TRPSYL2ALLCRSMGTOK_1.json +++ b/datasets/TRPSYL2ALLCRSMGTOK_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2ALLCRSMGTOK_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 for Tokyo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2CH4AIRSFS_1.json b/datasets/TRPSYL2CH4AIRSFS_1.json index a130db9708..9eb1b951c3 100644 --- a/datasets/TRPSYL2CH4AIRSFS_1.json +++ b/datasets/TRPSYL2CH4AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2CH4AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2CH4AIRSORS_1.json b/datasets/TRPSYL2CH4AIRSORS_1.json index 2b883b1895..4328f6c716 100644 --- a/datasets/TRPSYL2CH4AIRSORS_1.json +++ b/datasets/TRPSYL2CH4AIRSORS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2CH4AIRSORS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Auqa satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2CH4CRS1FS_1.json b/datasets/TRPSYL2CH4CRS1FS_1.json index fb5bb6dd32..381e13c6f9 100644 --- a/datasets/TRPSYL2CH4CRS1FS_1.json +++ b/datasets/TRPSYL2CH4CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2CH4CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2CH4CRSFS_1.json b/datasets/TRPSYL2CH4CRSFS_1.json index a33a721410..1d738bbb0a 100644 --- a/datasets/TRPSYL2CH4CRSFS_1.json +++ b/datasets/TRPSYL2CH4CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2CH4CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2CH4CRSRS_1.json b/datasets/TRPSYL2CH4CRSRS_1.json index 1d6ab703f9..3c0076fb63 100644 --- a/datasets/TRPSYL2CH4CRSRS_1.json +++ b/datasets/TRPSYL2CH4CRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2CH4CRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for CH4. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2COAIRSFS_1.json b/datasets/TRPSYL2COAIRSFS_1.json index 8ebb80dff6..d6fd96a3b9 100644 --- a/datasets/TRPSYL2COAIRSFS_1.json +++ b/datasets/TRPSYL2COAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2COAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2COAIRSORS_1.json b/datasets/TRPSYL2COAIRSORS_1.json index a33c1c0d7b..5c03603de2 100644 --- a/datasets/TRPSYL2COAIRSORS_1.json +++ b/datasets/TRPSYL2COAIRSORS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2COAIRSORS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2COCRS1FS_1.json b/datasets/TRPSYL2COCRS1FS_1.json index 1bfe1f9aee..85cb24f9fd 100644 --- a/datasets/TRPSYL2COCRS1FS_1.json +++ b/datasets/TRPSYL2COCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2COCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2COCRSFS_1.json b/datasets/TRPSYL2COCRSFS_1.json index 11b37c86e6..b6e40e1942 100644 --- a/datasets/TRPSYL2COCRSFS_1.json +++ b/datasets/TRPSYL2COCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2COCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2COCRSRS_1.json b/datasets/TRPSYL2COCRSRS_1.json index 61a6930922..6aa7e64b1a 100644 --- a/datasets/TRPSYL2COCRSRS_1.json +++ b/datasets/TRPSYL2COCRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2COCRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2HDOAIRSFS_1.json b/datasets/TRPSYL2HDOAIRSFS_1.json index 4f813a1326..119548aef0 100644 --- a/datasets/TRPSYL2HDOAIRSFS_1.json +++ b/datasets/TRPSYL2HDOAIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2HDOAIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2HDOAIRSORS_1.json b/datasets/TRPSYL2HDOAIRSORS_1.json index afe8d6a95e..93c6be6aee 100644 --- a/datasets/TRPSYL2HDOAIRSORS_1.json +++ b/datasets/TRPSYL2HDOAIRSORS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2HDOAIRSORS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2HDOCRS1FS_1.json b/datasets/TRPSYL2HDOCRS1FS_1.json index 97f3ddf174..90acaa6e24 100644 --- a/datasets/TRPSYL2HDOCRS1FS_1.json +++ b/datasets/TRPSYL2HDOCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2HDOCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2HDOCRSFS_1.json b/datasets/TRPSYL2HDOCRSFS_1.json index a4b32c18a2..0e9ef5928c 100644 --- a/datasets/TRPSYL2HDOCRSFS_1.json +++ b/datasets/TRPSYL2HDOCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2HDOCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2HDOCRSRS_1.json b/datasets/TRPSYL2HDOCRSRS_1.json index 6184c4e12b..a21b35a4f3 100644 --- a/datasets/TRPSYL2HDOCRSRS_1.json +++ b/datasets/TRPSYL2HDOCRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2HDOCRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for HDO. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2NH3AIRSFS_1.json b/datasets/TRPSYL2NH3AIRSFS_1.json index 6c56a962cf..2b061ae5a1 100644 --- a/datasets/TRPSYL2NH3AIRSFS_1.json +++ b/datasets/TRPSYL2NH3AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2NH3AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2NH3CRS1FS_1.json b/datasets/TRPSYL2NH3CRS1FS_1.json index efaa95476e..debaa68bbb 100644 --- a/datasets/TRPSYL2NH3CRS1FS_1.json +++ b/datasets/TRPSYL2NH3CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2NH3CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2NH3CRSFS_1.json b/datasets/TRPSYL2NH3CRSFS_1.json index 148abd9d1a..45cb453e10 100644 --- a/datasets/TRPSYL2NH3CRSFS_1.json +++ b/datasets/TRPSYL2NH3CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2NH3CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2NH3CRSRS_1.json b/datasets/TRPSYL2NH3CRSRS_1.json index 2b348adc9e..884109af35 100644 --- a/datasets/TRPSYL2NH3CRSRS_1.json +++ b/datasets/TRPSYL2NH3CRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2NH3CRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ammonia for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3AIRSFS_1.json b/datasets/TRPSYL2O3AIRSFS_1.json index 5296a22c9e..c6b45a2474 100644 --- a/datasets/TRPSYL2O3AIRSFS_1.json +++ b/datasets/TRPSYL2O3AIRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3AIRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3AIRSOMIFS_1.json b/datasets/TRPSYL2O3AIRSOMIFS_1.json index becd2aa698..097771356b 100644 --- a/datasets/TRPSYL2O3AIRSOMIFS_1.json +++ b/datasets/TRPSYL2O3AIRSOMIFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3AIRSOMIFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3AIRSORS_1.json b/datasets/TRPSYL2O3AIRSORS_1.json index bc23c61717..604abe3f0f 100644 --- a/datasets/TRPSYL2O3AIRSORS_1.json +++ b/datasets/TRPSYL2O3AIRSORS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3AIRSORS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS AIRS-Aqua L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3CRS1FS_1.json b/datasets/TRPSYL2O3CRS1FS_1.json index b445e168ac..f63e60413d 100644 --- a/datasets/TRPSYL2O3CRS1FS_1.json +++ b/datasets/TRPSYL2O3CRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3CRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3CRSFS_1.json b/datasets/TRPSYL2O3CRSFS_1.json index 548b606b7d..0cde87c5c9 100644 --- a/datasets/TRPSYL2O3CRSFS_1.json +++ b/datasets/TRPSYL2O3CRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3CRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3CRSRS_1.json b/datasets/TRPSYL2O3CRSRS_1.json index 5717157f99..45d22d3e97 100644 --- a/datasets/TRPSYL2O3CRSRS_1.json +++ b/datasets/TRPSYL2O3CRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3CRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2O3OMIFS_1.json b/datasets/TRPSYL2O3OMIFS_1.json index af9f250c1a..5526c68fc5 100644 --- a/datasets/TRPSYL2O3OMIFS_1.json +++ b/datasets/TRPSYL2O3OMIFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2O3OMIFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2PANCRS1FS_1.json b/datasets/TRPSYL2PANCRS1FS_1.json index 7ca965f7aa..91e1994af5 100644 --- a/datasets/TRPSYL2PANCRS1FS_1.json +++ b/datasets/TRPSYL2PANCRS1FS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2PANCRS1FS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2PANCRSFS_1.json b/datasets/TRPSYL2PANCRSFS_1.json index 23a97690ad..d06dc5bb5a 100644 --- a/datasets/TRPSYL2PANCRSFS_1.json +++ b/datasets/TRPSYL2PANCRSFS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2PANCRSFS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TRPSYL2PANCRSRS_1.json b/datasets/TRPSYL2PANCRSRS_1.json index 27d91c4d56..2ef0228b87 100644 --- a/datasets/TRPSYL2PANCRSRS_1.json +++ b/datasets/TRPSYL2PANCRSRS_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TRPSYL2PANCRSRS_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).\n\nThe data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.", "links": [ { diff --git a/datasets/TSIS_SSI_L3_12HR_12.json b/datasets/TSIS_SSI_L3_12HR_12.json index aa3751294a..e17fd07ba6 100644 --- a/datasets/TSIS_SSI_L3_12HR_12.json +++ b/datasets/TSIS_SSI_L3_12HR_12.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TSIS_SSI_L3_12HR_12", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 12 is the current release of this data product, and supercedes all previous versions.\n\nThe TSIS SIM Level 3 Solar Spectral Irradiance (SSI) 12-Hour Means data product (TSIS_SSI_L3_12HR) uses measurements from the Spectal Irradiance Monitor (SIM) instrument, and averages them over a 12-hour period. TSIS-1 was launched on December 15, 2017 and mounted on the International Space Station (ISS) on December 30, 2017. Solar spectra are measured over the spectral range from 200 to 2400 nm at a spectral resolution ranging from 2 nm (<0.28 microns) to 45 nm (>0.4 microns). Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SIM absolute uncertainty is about 0.2%.\n\nAll of the data from this product are arranged into a single file in a tabular ASCII text format which can be easily read into a spreadsheet application. New data are appended to the file on a daily basis. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the mission.", "links": [ { diff --git a/datasets/TSIS_SSI_L3_24HR_12.json b/datasets/TSIS_SSI_L3_24HR_12.json index d65cafba3f..7221d24e5d 100644 --- a/datasets/TSIS_SSI_L3_24HR_12.json +++ b/datasets/TSIS_SSI_L3_24HR_12.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TSIS_SSI_L3_24HR_12", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 12 is the current release of this data product, and supercedes all previous versions.\n\nThe TSIS SIM Level 3 Solar Spectral Irradiance (SSI) 24-Hour Means data product (TSIS_SSI_L3_24HR) uses measurements from the Spectral Irradiance Monitor (SIM) instrument, and averages them over a 24-hour period. TSIS-1 was launched on December 15, 2017 and mounted on the International Space Station (ISS) on December 30, 2017. Solar spectra are measured over the spectral range from 200 to 2400 nm at a spectral resolution ranging from 2 nm (<0.28 microns) to 45 nm (>0.4 microns). Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SIM absolute uncertainty is about 0.2%.\n\nAll of the data from this product are arranged into a single file in a tabular ASCII text format which can be easily read into a spreadsheet application. New data are appended to the file on a daily basis. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the mission.", "links": [ { diff --git a/datasets/TSIS_TSI_L3_06HR_04.json b/datasets/TSIS_TSI_L3_06HR_04.json index 2cffaa5359..34ed073c4a 100644 --- a/datasets/TSIS_TSI_L3_06HR_04.json +++ b/datasets/TSIS_TSI_L3_06HR_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TSIS_TSI_L3_06HR_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 04 is the current release of this data product, and supercedes all previous versions.\n\nThe TSIS TIM Level 3 Total Solar Irradiance (TSI) 6-Hour Means data product (TSIS_TSI_L3_06HR) uses measurements from the Total Irradiance Monitor (TIM) instrument over the entire spectrum, and averages them over a 6-hour period. TSIS-1 was launched on December 15, 2017 and mounted on the International Space Station (ISS) on December 30, 2017. TIM instrument has long-term repeatability with estimated uncertainties less than 0.014 W/m^2/yr (10 ppm/yr). Accuracy is currently reported as 0.48 W/m^2 (350 ppm), but expected to decrease as calibrations are refined and incorporated. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun.\n\nAll of the data from this product are arranged into a single file in a tabular ASCII text format which can be easily read into a spreadsheet application. New data are appended to the file on a daily basis. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full TIM wavelength range, repeating for each day for the length of the mission.", "links": [ { diff --git a/datasets/TSIS_TSI_L3_24HR_04.json b/datasets/TSIS_TSI_L3_24HR_04.json index 1ffa18f3ab..6c16ba0972 100644 --- a/datasets/TSIS_TSI_L3_24HR_04.json +++ b/datasets/TSIS_TSI_L3_24HR_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TSIS_TSI_L3_24HR_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Version 04 is the current release of this data product, and supercedes all previous versions.\n\nThe TSIS TIM Level 3 Total Solar Irradiance (TSI) 24-Hour Means data product (TSIS_TSI_L3_24HR) uses measurements from the Total Irradiance Monitor (TIM) instrument over the entire spectrum, and averages them over a 24-hour period. TSIS-1 was launched on December 15, 2017 and mounted on the International Space Station (ISS) on December 30, 2017. TIM instrument has long-term repeatability with estimated uncertainties less than 0.014 W/m^2/yr (10 ppm/yr). Accuracy is currently reported as 0.48 W/m^2 (350 ppm), but expected to decrease as calibrations are refined and incorporated. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun.\n\nAll of the data from this product are arranged into a single file in a tabular ASCII text format which can be easily read into a spreadsheet application. New data are appended to the file on a daily basis. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full TIM wavelength range, repeating for each day for the length of the mission.", "links": [ { diff --git a/datasets/TVcleanup_2002-2004_1.json b/datasets/TVcleanup_2002-2004_1.json index ac18f35f5a..275d5f33df 100644 --- a/datasets/TVcleanup_2002-2004_1.json +++ b/datasets/TVcleanup_2002-2004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TVcleanup_2002-2004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical assessment data for selected heavy metals determined for the abandoned waste disposal site at Thala Valley, Casey Station, over two seasons (pre- and during cleanup).\n\n2002-03 summer: assessment of site, method testing \n* samples TV001 to TV073 \n* spatial data determined using electronic theodolite \n* total metals by XRF (mg/kg dry soil) \n* leachable metals by TCLP/flame-AAS (mg/L extract) - independent analysis of leachates by ICP-MS (CSL) \n* total metals data for Thala Valley tip fines standard (mg/kg dry soil) - ICP-MS of total digest and XRF (UTas SES-CODES), ICP-OES of aqua regia digest (DPIWE AST) 2003-04 summer: site assessment (limited), site validation after remediation, classification of excavated waste for disposal \n* site samples (TVS001 to TVS038) \n* spatial data determined using theodolite \n* waste container samples for excavated waste (TVC****) \n* total metals by XRF (mg/kg dry soil) - independent analysis of sample sub-set: ICP-OES of aqua regia digests (mg/kg) \n* leachable metals by TCLP/flame-AAS (mg/L extract) - independent analysis by ICP-OES of sub-set of leachates, mg/L (DPIWE-AST) - independent TCLP/ICP-OES of sub-set of soil samples, mg/L (DPIWE-AST)", "links": [ { diff --git a/datasets/TVcleanup_2002-2004_field_lab_books_1.json b/datasets/TVcleanup_2002-2004_field_lab_books_1.json index 6fd897e1ef..1bf14e567e 100644 --- a/datasets/TVcleanup_2002-2004_field_lab_books_1.json +++ b/datasets/TVcleanup_2002-2004_field_lab_books_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TVcleanup_2002-2004_field_lab_books_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are the scanned electronic copies of field and lab books used at Casey Station between 2002 and 2004 as part of the Thala Valley clean up.", "links": [ { diff --git a/datasets/T_microcosm_results_1.json b/datasets/T_microcosm_results_1.json index ca8ad892f5..bf49a534c3 100644 --- a/datasets/T_microcosm_results_1.json +++ b/datasets/T_microcosm_results_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "T_microcosm_results_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geochemical, microbial and 14C data on remediation of petroleum hydrocarbons in Antarctica.\n\nThis record is part of ASAC project 1163 (ASAC_1163).\n\nMicrocosm study using Old Casey petroleum hydrocarbon contaminated sediment investigating the effect of temperature on biodegradation. The experiment was performed over a temperature range of -2 to 42 degrees. The experiment was run over 40 days. Degradation was traced by radiometric methods and total aliphatic hydrocarbons were measured by gas chromatography.\nRadiometric data in file radiometric_99.xls, Gas Chromatography data in file\ngc_99.xls.\n\nThe radiometric spreadsheet is divided up as follows:\nCALCULATIONS is how much nutrients, water, radioactivity was added to the sediment.\nSUMMARY is what went into each microcosm flask.\n%DPMVRST are recovery graphs of individual flasks.\nAVERAGE RECOVERY and FIRST ORDER are graphs.\n-2A,-2B etc are the raw data for each flask. The number refers to the temperature, the letter refers to the replicate.\n\nThe gc_99 spreadsheet is divided up as follows:\nMicroN2B, MicroN2C etc is the raw gas chromatography data, peak areas, heights, amount extracted. This spreadsheet uses the same codes as the radiometric spreadsheet in the metadata record entitled: 'Mineralisation results using 14C octadecane at a range of water, nutrient levels and freeze thaw cycles'.\nSUMMARY is the actual hydrocarbon concentrations in ug/g and weathering indices.\nGRAPHS-ABS are the graphs of absolute concentrations.\nGRAPHS-RATIO are the graphs of weathering indices.\n\nThe fields in this dataset are:\nDays - Since experiment started.\nHours - Since experiment started.\nWeight of NaOH (g)\nCount (dpm) - measurement from radiation counter - disintegrations per minute.\nDiscarded dpm's - counts 'lost' from the system. Used in a 'mass balance'.\nVolume NaOH (ml)\ndpm in trap/dresschel bottle\nAbsolute dpm's\n% dpm recovered\nmillimole octadecane mineralised\n\nRetention Time\nArea\n% Area\nHeight of peak\nAmount\nInt Type\nUnits\nPeak Type\nCodes", "links": [ { diff --git a/datasets/Tags_heard_1949_2.json b/datasets/Tags_heard_1949_2.json index 3fe52eadbc..8bf9e8359a 100644 --- a/datasets/Tags_heard_1949_2.json +++ b/datasets/Tags_heard_1949_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tags_heard_1949_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At Heard Island, Southern Indian Ocean, seals were branded between 1949-1953. Seal length was measured in feet and inches. Recaptures of seals were made up until 1955, and growth and age-specific survival was calculated.", "links": [ { diff --git a/datasets/Taiga_Tundra_Tree_Cover_1218_1.json b/datasets/Taiga_Tundra_Tree_Cover_1218_1.json index 095ff629a9..70ba5bcd32 100644 --- a/datasets/Taiga_Tundra_Tree_Cover_1218_1.json +++ b/datasets/Taiga_Tundra_Tree_Cover_1218_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Taiga_Tundra_Tree_Cover_1218_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a map of selected areas with defined tree canopy cover over the circumpolar taiga-tundra ecotone (TTE). Canopy cover was derived from the 500-meter MODIS Vegetation Continuous Fields (VCF) product as averaged over six years from 2000-2005 and processed as described in Ranson et al. (2011). This process identified patches of low tree canopy cover which are indicative of the transition from forest to tundra and differentiate the circumpolar taiga-tundra ecotone for the 2000-2005 period. The TTE is the Earth's longest vegetation transition zone and stretches for more than 13,400 km around Arctic North America, Scandinavia, and Eurasia. In Eurasia, the map extends from 60 degrees N to 70 degrees N, and in North America from 50 degrees N to 70 degrees N, excluding Baffin Island in northeastern Canada and the Aleutian Peninsula in southwestern Alaska. Note that for this product, taiga is being used one and the same as boreal forest.This circumpolar TTE area was classified according to VCF tree canopy cover.", "links": [ { diff --git a/datasets/Tampa_Bay_0.json b/datasets/Tampa_Bay_0.json index 235bd28d14..85d726500b 100644 --- a/datasets/Tampa_Bay_0.json +++ b/datasets/Tampa_Bay_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tampa_Bay_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in Tampa Bay in Florida between 2008 and 2012.", "links": [ { diff --git a/datasets/Tansat_3.0.json b/datasets/Tansat_3.0.json index 67d039d3b0..66fb19a9b9 100644 --- a/datasets/Tansat_3.0.json +++ b/datasets/Tansat_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tansat_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Carbon-dioxide Grating Spectrometer (ACGS) instrument is pushbroom spectrometer operating in NIR and SWIR bands which allows the measuring of CO2 mole fraction. The available ACGS products have a temporal coverage between March 2017 and January 2020 (not all days included in the time frame): \u2022\tL1A DS: Sample Dark Calibration sample product \u2022\tL1A GL: Sample Glint Sample products \u2022\tL1A LS: Sample Lamp Calibration sample product \u2022\tL1A ND: Principal-Plane Nadir Sample product \u2022\tL1A ZS: Sample Z-Axis Solar Calibration Sample \u2022\tL1B CAL DS: Sample Dark Calibration product \u2022\tL1B CAL LS: Sample Lamp Calibration product \u2022\tL1B CAL ZS: Sample Z-Axis Solar Calibration product \u2022\tL1B SCI GL: Sample Glint Science product \u2022\tL1B SCI ND: Principal-Plane Nadir Science product The Cloud Aerosol Polarization Imager (CAPI) is a pushbroom radiometer in VIS, NIR and SWIR bands for the observation of aerosols and clouds optical properties. The CAPI products are available in a time range from July 2019 and January 2020 (not all days included in the time frame): \u2022\tL1A ND: Principal-Plane Nadir product \u2022\tL1B ND 1000M : Principal-Plane Nadir products at 1000m resolution (1375nm, 1640nm) \u2022\tL1B ND 250M : Principal-Plane Nadir products at 250m resolution (380nm, 670nm, 870nm) \u2022\tL1B ND GEOQK: Principal-Plane Nadir georeferenced at 250m resolution \u2022\tL1B ND GEO1K: Principal-Plane Nadir georeferenced at 1000m resolution \u2022\tL1B ND OBC: Principal-Plane Nadir on-board calibrator product \u2022\tL2 ND CLM: Principal-Plane Nadir cloud flag product", "links": [ { diff --git a/datasets/Tara_Mediterranean_0.json b/datasets/Tara_Mediterranean_0.json index bf59191a02..450f7df3ec 100644 --- a/datasets/Tara_Mediterranean_0.json +++ b/datasets/Tara_Mediterranean_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tara_Mediterranean_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Over the past decades, the proliferation of plastics has rapidly become a global problem affecting all oceans. With 80% of plastics in the sea originating from land, this pollution highlights the interactions between our daily lives and the ocean, and reinforces the need for a transition to an economy that is more respectful of the planet. During the Tara Mediterranean expedition in 2014, the schooner crisscrossed the Mare nostrum to study the interaction of plastics with plankton, and biodiversity in general. First edifying observation: of the 2000 samples taken during the expedition from 350 different sites, all contained plastic fragments.", "links": [ { diff --git a/datasets/Tara_Oceans_Polar_Circle_0.json b/datasets/Tara_Oceans_Polar_Circle_0.json index 0cc0beeada..b23a24f734 100644 --- a/datasets/Tara_Oceans_Polar_Circle_0.json +++ b/datasets/Tara_Oceans_Polar_Circle_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tara_Oceans_Polar_Circle_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tara Oceans Polar Circle 2013", "links": [ { diff --git a/datasets/Tara_Oceans_expedition_0.json b/datasets/Tara_Oceans_expedition_0.json index 36152f238b..9c4f17cd8b 100644 --- a/datasets/Tara_Oceans_expedition_0.json +++ b/datasets/Tara_Oceans_expedition_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tara_Oceans_expedition_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tara Oceans expedition, a 2.5-year long and 57,000 mile long trajectory, was conceived to provide a snapshot of the distribution of planktonic organisms in the world ocean, providing 'A global-scale study of morphological, genetic, and functional biodiversity of plankton organisms in relation to the changing physico-chemical parameters of the oceans' (Karsenti et al., 2011). The expedition took place from September 2009 to March 2012, spanned the majority of the world's oceans, and included, besides a large array of biological sampling, hydrographic measurements, optical measurements of surface hyper-spectral particulate absorption and attenuation, hyper-spectral reflectance and HPLC pigments.E. Karsenti, S.G. Acinas, P. Bork, C. Bowler, C. De Vargas, J. Raes, and 22 co-authors, A holistic approach to marine eco-systems biology, PLoS Biol. 9, e1001177, (2011), doi:10.1371/journal.pbio.1001177.", "links": [ { diff --git a/datasets/TerraSAR-X_8.0.json b/datasets/TerraSAR-X_8.0.json index ed8494923e..ce13c0f2ce 100644 --- a/datasets/TerraSAR-X_8.0.json +++ b/datasets/TerraSAR-X_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TerraSAR-X_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TerraSAR-X ESA archive collection consists of TerraSAR-X and TanDEM-X products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. TerraSAR-X/TanDEM-X Image Products can be acquired in 6 image modes with flexible resolutions (from 0.25m to 40m) and scene sizes. Thanks to different polarimetric combinations and processing levels the delivered imagery can be tailored specifically to meet the requirements of the application. The following list delineates the characteristics of the SAR imaging modes that are disseminated under ESA Third Party Missions (TPM). \u2022 StripMap (SM): Resolution 3 m, Scene size 30x50 km2 (up to 30x1650 km2) \u2022 SpotLight (SL): Resolution 2 m, Scene size 10x10 km2 \u2022 Staring SpotLight (ST): Resolution 0.25m, Scene size 4x3.7 km2 \u2022 High Resolution SpotLight (HS): Resolution 1 m, Scene size 10x5 km2 \u2022 ScanSAR (SC): Resolution 18 m, Scene size 100x150 km2 (up to 100x1650 km2) \u2022 Wide ScanSAR (WS): Resolution 40 m, Scene size 270x200 km2 (up to 270x1500 km2) The following list briefly delineates the available processing levels for the TerraSAR-X dataset: \u2022 SSC (Single Look Slant Range Complex) in DLR-defined COSAR binary format \u2022 MGD (Multi Look Ground Range Detected) in GeoTiff format \u2022 GEC (Geocoded Ellipsoid Corrected) in GeoTiff format \u2022 EEC (Enhanced Ellipsoid Corrected in GeoTiff format", "links": [ { diff --git a/datasets/TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0.json b/datasets/TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0.json index 3a6eef0277..bdc7a1f8d7 100644 --- a/datasets/TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0.json +++ b/datasets/TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TerraSAR-X/TanDEM-X full archive and new tasking products can be acquired in six image modes with flexible resolutions (from 0.25 m to 40 m) and scene sizes and are provided in different packages: Staring SpotLight (basic, Interferometric pack, and Maritime pack) High Resolution SpotLight (basic, Interferometric pack, and Maritime pack) SpotLight (basic, Interferometric pack, and Maritime pack) StripMap (basic, Interferometric pack, and Maritime pack) ScanSAR (basic and Maritime pack) Wide ScanSAR (basic and Maritime pack) Product Overview: >> Product: SAR-ST \u2022 Instrument mode: Staring SpotLight \u2022 Available resolutions (up to): 0.25 m \u2022 Scene size: 4x3.7 km2 >> Product: SAR-HS \u2022 Instrument mode: High Resolution SpotLight \u2022 Available resolutions (up to): 1 m \u2022 Scene size: 10x5 km2 >> Product: SAR-SL \u2022 Instrument mode: SpotLight \u2022 Available resolutions (up to): 2 m \u2022 Scene size: 10x10 km2 >> Product: SAR-SM \u2022 Instrument mode: StripMap \u2022 Available resolutions (up to): 3 m \u2022 Scene size: 30x50 km2 (up to 30x1650) \u2022 Basic products (SAR-SM) are intended as the products delivered as a standard scene. The available processing levels are: SSC (Single Look Slant Range Complex) in DLR-defined COSAR binary format, MGD (Multi Look Ground Range Detected) in GeoTiff format, GEC (Geocoded Ellipsoid Corrected) in GeoTiff format, EEC (Enhanced Ellipsoid Corrected) in GeoTiff format. >> Product: SAR-SC \u2022 Instrument mode: ScanSAR \u2022 Available resolutions (up to): 18 m \u2022 Scene size: 100x150 km2 (up to 100x1650) >> Product: SAR-WS \u2022 Instrument mode: Wide ScanSAR \u2022 Available resolutions (up to): 40 m \u2022 Scene size: 270x200 km2 (up to 270x1500) >> Available processing levels: \u2022 SSC (Single Look Slant Range Complex): azimuth - slant range (time domain) \u2022 MGD (Multi Look Ground Range Detected): azimuth - ground range (without terrain correction) \u2022 GEC (Geocoded Ellipsoid Corrected): map geometry with ellipsoidal corrections only (no terrain correction performed) \u2022 EEC (Enhanced Ellipsoid Corrected): map geometry with terrain correction, using a DEM >> Format: \u2022 SSC: DLR-defined COSAR binary \u2022 MGD: GeoTiff \u2022 GEC: GeoTiff \u2022 EEC: GeoTiff >> Spatial coverage: Worldwide >> Interferometry package: \u2022 InSAR-ST, InSAR-HS, InSAR-SL, InSAR-SM \u2022 Only SSC \u2022 At least five ordered scenes within six months from first order \u2022 N/A for SAR-SC and SAR-WS >> Maritime Monitoring package: \u2022 MmSAR-ST, MmSAR-HS, MmSAR-SL, MmSAR-SM, MmSAR-SC, MmSAR-WS \u2022 Only SSC, MGD, GEC \u2022 At least 75% of the scene area is water \u2022 More than five ordered scenes in three months The following WorldDEM products can be requested: Product: WorldDEMcore Description: WorldDEMcore is output of interferometric processing of StripMap data pairs without any post-processing Product: WorldDEMTM Description: WorldDEMTM is produced based on WorldDEMcore, representing the surface of the Earth (including buildings, infrastructure and vegetation). Hydrological consistency is ensured Product: WorldDEM DTM Description: In additional editing steps, WorldDEMTMis transformed into a Digital Terrain Model (DTM) representing bare Earth elevation Product: WorldDEM Bundle Description: Includes WorldDEMTM, WorldDEM DTM, and Quality Layers The main specifications of the WorldDEM products are: - Horizontal Coordinate Reference System: World Geodetic System 1984 (WGS84-G1150) - Vertical Coordinate Reference System: Earth Gravitational Model 2008 (EGM2008) - Absolute Horizontal Accuracy: <6 m - Vertical Accuracy: 2 m Relative, 4 m Absolute - Quality Layers (including water body mask) can be requested as an option with the WorldDEM and WorldDEM DTM - Auxiliary Layers are delivered together with the WorldDEMcore product The products are available as part of the Airbus provision from TerraSAR-X and Tandem-X missions with worldwide coverage: the TerraSAR-X/TanDEM-X Catalogue (https://terrasar-x-archive.terrasar.com/) can be accessed to discover and check the basic product data readiness; using the WorldDEM database viewers (https://worlddem-database.terrasar.com/ ). All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/TSX-TDX-Terms-Of-Applicability.pdf/265d10ac-6900-45de-8d31-ccfe3dd8d6e6) available in Resources section.", "links": [ { diff --git a/datasets/Thermokarst_Circumpolar_Map_1332_1.json b/datasets/Thermokarst_Circumpolar_Map_1332_1.json index ca8fd65485..84661bb889 100644 --- a/datasets/Thermokarst_Circumpolar_Map_1332_1.json +++ b/datasets/Thermokarst_Circumpolar_Map_1332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Thermokarst_Circumpolar_Map_1332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the distribution of thermokarst landscapes in the boreal and tundra ecoregions within the northern circumpolar permafrost zones. This dataset provides an areal estimate of wetland, lake, and hillslope thermokarst landscapes as of 2015. Estimates of soil organic carbon (SOC) content associated with thermokarst and non-thermokarst landscapes were based on available circumpolar 0 to 3 meter SOC storage data.", "links": [ { diff --git a/datasets/ThreeRivers_0.json b/datasets/ThreeRivers_0.json index e1c4ea31db..8d99883d45 100644 --- a/datasets/ThreeRivers_0.json +++ b/datasets/ThreeRivers_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ThreeRivers_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA_3Rivers_NNX11AF22G cruise consisted of watershed sampling of the Kennebec, Androscoggin, St. John Rivers in Maine, USA and New Brunswick, Canada. Water samples were collected from a nominal depth of 0.5 m and then processed as described by the accompanying documentation. Each station was visited monthly from early spring, as soon as accessible, to late fall, November. Stations were accessed by land and samples were collected by foot in the deepest water available, usually 1 to 1.5 meters in depth.", "links": [ { diff --git a/datasets/Tidal_Marsh_Biomass_US_V1-1_1879_1.1.json b/datasets/Tidal_Marsh_Biomass_US_V1-1_1879_1.1.json index 99f019aa45..37408e0cb7 100644 --- a/datasets/Tidal_Marsh_Biomass_US_V1-1_1879_1.1.json +++ b/datasets/Tidal_Marsh_Biomass_US_V1-1_1879_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tidal_Marsh_Biomass_US_V1-1_1879_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.", "links": [ { diff --git a/datasets/Tidal_Marsh_Vegetation_US_1608_1.json b/datasets/Tidal_Marsh_Vegetation_US_1608_1.json index 5b90310382..376399f255 100644 --- a/datasets/Tidal_Marsh_Vegetation_US_1608_1.json +++ b/datasets/Tidal_Marsh_Vegetation_US_1608_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tidal_Marsh_Vegetation_US_1608_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water. Using this high-resolution map, the percent of each class within Landsat pixel extents was calculated to produce a 30m fraction of green vegetation map for each region.", "links": [ { diff --git a/datasets/Tidal_Wetland_Estuaries_Data_1742_1.json b/datasets/Tidal_Wetland_Estuaries_Data_1742_1.json index 03c1c3d54c..01ce16b242 100644 --- a/datasets/Tidal_Wetland_Estuaries_Data_1742_1.json +++ b/datasets/Tidal_Wetland_Estuaries_Data_1742_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tidal_Wetland_Estuaries_Data_1742_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface temperature, ocean dissolved inorganic phosphorus, estuary latitude, longitude, depth, perimeter, salinity, and estuary volume, river flow, carbon, nitrogen, and phosphorus river flux, sediment organic carbon content, windspeed, mean temperature, daily and mean precipitation, frost days, and the population within each catchment. Estuaries were also classified to one of six typological categories. Coastal locations were determined by natural environmental and political divisions within the US. The data were used to investigate how tidal wetland soil organic carbon density is distributed across the continental US among various coastal locations, estuarine typologies, vegetation types, water regimes, and management regimes, and to identify whether SOC density is correlated with different environmental variables. The analytical results are not included with this dataset.", "links": [ { diff --git a/datasets/Tidal_Wetland_GPP_CONUS_1792_1.json b/datasets/Tidal_Wetland_GPP_CONUS_1792_1.json index 5438b5d57f..32befc3840 100644 --- a/datasets/Tidal_Wetland_GPP_CONUS_1792_1.json +++ b/datasets/Tidal_Wetland_GPP_CONUS_1792_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tidal_Wetland_GPP_CONUS_1792_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or \"blue carbon,\" is a valued resource that is increasingly important for restoration and conservation purposes.", "links": [ { diff --git a/datasets/Tidal_Wetland_Soil_Carbon_1612_1.json b/datasets/Tidal_Wetland_Soil_Carbon_1612_1.json index 81a5a65123..eda05036c3 100644 --- a/datasets/Tidal_Wetland_Soil_Carbon_1612_1.json +++ b/datasets/Tidal_Wetland_Soil_Carbon_1612_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tidal_Wetland_Soil_Carbon_1612_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data. All 30 x 30-meter C-CAP pixels were extracted that are coded as estuarine emergent, scrub/shrub, or forested in either 2006 or 2010. A soil database for model fitting and validation was compiled from 49 different studies with spatially explicit empirical depth profile data and associated metadata, totaling 1,959 soil cores from 18 of the 22 coastal states. Reported estimates of carbon stocks were derived with modeling approaches that included (1) applying a single average carbon stock value from the compiled soil core data, (2) applying models fit using the empirical data and applied spatially using soil, vegetation and salinity maps, (3) relying on independently generated soil carbon maps from The United States Department of Agriculture (USDA)'s Soil Survey Geographic Database (SSURGO), and the NWI that intersected with mapped tidal wetlands, and (4) using a version of SSURGO bias-corrected for bulk density. Comparisons of uncertainty, precision, and accuracy among these four approaches are also provided.", "links": [ { diff --git a/datasets/TillageErosion_SOCRedistribute_1944_1.json b/datasets/TillageErosion_SOCRedistribute_1944_1.json index 4344fd4927..c670a85279 100644 --- a/datasets/TillageErosion_SOCRedistribute_1944_1.json +++ b/datasets/TillageErosion_SOCRedistribute_1944_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TillageErosion_SOCRedistribute_1944_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains model predictions of soil erosion and soil organic carbon (SOC) redistribution caused by agricultural practices such as tillage erosion. Soil erosion diminishes agricultural productivity by driving the loss of SOC. This model addresses a growing need to predict soil organic carbon transport, loss, and deposition. The model was applied to three sites containing paired prairie grassland and field plots in Iowa, and predicts SOC redistribution between 1859 to 2019. The model was developed by incorporating a SOC mixing model with a landscape evolution model that simulates tillage erosion.", "links": [ { diff --git a/datasets/Tokyo_Bay_0.json b/datasets/Tokyo_Bay_0.json index e35f35956f..dbc3faf90a 100644 --- a/datasets/Tokyo_Bay_0.json +++ b/datasets/Tokyo_Bay_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tokyo_Bay_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the Tokyo Bay between 1982 and 1984.", "links": [ { diff --git a/datasets/Toolik_Lake_Area_Veg_Maps_1380_1.json b/datasets/Toolik_Lake_Area_Veg_Maps_1380_1.json index 2fb91f03f3..ead6646518 100644 --- a/datasets/Toolik_Lake_Area_Veg_Maps_1380_1.json +++ b/datasets/Toolik_Lake_Area_Veg_Maps_1380_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Toolik_Lake_Area_Veg_Maps_1380_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.", "links": [ { diff --git a/datasets/Toolik_Lake_Veg_Plots_1333_1.json b/datasets/Toolik_Lake_Veg_Plots_1333_1.json index 5fe34f1b1b..54fa6eb70c 100644 --- a/datasets/Toolik_Lake_Veg_Plots_1333_1.json +++ b/datasets/Toolik_Lake_Veg_Plots_1333_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Toolik_Lake_Veg_Plots_1333_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental, soil, and vegetation data collected in August 1989 from 81 study plots at the Toolik Lake research site, located in the southern Arctic Foothills of the Brooks Range, Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 26 communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the Toolik Lake region and across Alaska.", "links": [ { diff --git a/datasets/TopSoil_Erosion_MidWest_US_1774_1.json b/datasets/TopSoil_Erosion_MidWest_US_1774_1.json index 48abba9043..f8d5e5e873 100644 --- a/datasets/TopSoil_Erosion_MidWest_US_1774_1.json +++ b/datasets/TopSoil_Erosion_MidWest_US_1774_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TopSoil_Erosion_MidWest_US_1774_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site.The relationship between topsoil loss and topographic curvature was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.", "links": [ { diff --git a/datasets/TowerBased_PhotoSpec_SIF_SK_CA_1887_1.json b/datasets/TowerBased_PhotoSpec_SIF_SK_CA_1887_1.json index 1a45d2b9c5..19e78a24d4 100644 --- a/datasets/TowerBased_PhotoSpec_SIF_SK_CA_1887_1.json +++ b/datasets/TowerBased_PhotoSpec_SIF_SK_CA_1887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TowerBased_PhotoSpec_SIF_SK_CA_1887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes daily averaged solar-induced chlorophyll fluorescence (SIF) in the red (680-686 nm) and far-red (745-758 nm) wavelength ranges, relative SIF (SIF/Intensity), chlorophyll-carotenoid index (CCI), photochemical reflectance index (PRI), near-infrared vegetation index (NIRv), and normalized difference vegetation index (NDVI) for both black spruce (Picea mariana) and larch (Larix laricina) targets. The study site (Southern Old Black Spruce, SOBS Fluxnet ID CA-Obs) is located near the southern limit of the boreal forest ecotone in Saskatchewan, Canada. Data were collected for the spring transition in both 2019 and 2020 using PhotoSpec. Species-specific averages were calculated over each 30-minute period, then averaged again to report daily averages of SIF relative and reflectance measurements for both black spruce and larch.", "links": [ { diff --git a/datasets/Tree_Canopy_Cover_Mexico_2137_1.json b/datasets/Tree_Canopy_Cover_Mexico_2137_1.json index 39d0b516f4..1fc214a7b8 100644 --- a/datasets/Tree_Canopy_Cover_Mexico_2137_1.json +++ b/datasets/Tree_Canopy_Cover_Mexico_2137_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tree_Canopy_Cover_Mexico_2137_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022). The hierarchical U-Net framework first developed a U-Net model for very high-resolution aerial images (NAIP) using training labels derived from previous work based on an interactive image segmentation tool and iterative updates with expert knowledge (Basu et al., 2015). The developed NAIP U-Net model and NAIP data produced 1-m NAIP TC across all lower 48 CONUS states. A Landsat U-Net model was developed for multi-year and large-scale TC mapping based on the very high-resolution NAIP TC made in the earlier stage. The Landsat U-Net model developed was adopted over the CONUS for testing its transferability, validation, and improvement across Mexico. This dataset provides national-scale percent tree cover estimates over Mexico and can be helpful for studies of carbon cycling, land cover and land use change, etc. The team has been working on improving temporal stability of the product and will update the product once the next version is ready to be shared.", "links": [ { diff --git a/datasets/Tree_Mortality_Western_US_1512_1.1.json b/datasets/Tree_Mortality_Western_US_1512_1.1.json index ded420488d..18821af90c 100644 --- a/datasets/Tree_Mortality_Western_US_1512_1.1.json +++ b/datasets/Tree_Mortality_Western_US_1512_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tree_Mortality_Western_US_1512_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.", "links": [ { diff --git a/datasets/TropForest_6.0.json b/datasets/TropForest_6.0.json index 0e3d0ccfdc..a1ce95616e 100644 --- a/datasets/TropForest_6.0.json +++ b/datasets/TropForest_6.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TropForest_6.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of the ESA TropForest project was to create a harmonised geo-database of ready-to-use satellite imagery to support 2010 global forest assessment performed by the Joint Research Centre (JRC) of the European Commission and by the Food and Agriculture Organization (FAO). Assessments for year 2010 were essential for building realistic deforestation benchmark rates at global to regional levels. To reach this objective, the project aimed to create a harmonised ortho-rectified/pre-processed imagery geo-database based on satellite data acquisitions (ALOS AVNIR-2, GEOSAT-1 SLIM6, KOMPSAT-2 MSC) performed during year 2009 and 2010, for the Tropical Latin America (excluding Mexico) and for the Tropical South and Southeast Asia (excluding China), resulting in 1971 sites located at 1 deg x 1 deg geographical lat/long intersections. The project finally delivered 1866 sites (94.7% of target) due to cloud coverages too high for missing sites", "links": [ { diff --git a/datasets/Tropical Cyclone Wind Estimation Competition_1.json b/datasets/Tropical Cyclone Wind Estimation Competition_1.json index e4e77e732f..4f5b44e68f 100644 --- a/datasets/Tropical Cyclone Wind Estimation Competition_1.json +++ b/datasets/Tropical Cyclone Wind Estimation Competition_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tropical Cyclone Wind Estimation Competition_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of tropical storms in the Atlantic and East Pacific Oceans from 2000 to 2019 with corresponding maximum sustained surface wind speed. This dataset is split into training and test categories for the purpose of a competition [Read more about the [competition](https://www.drivendata.org/competitions/72/predict-wind-speeds/)].", "links": [ { diff --git a/datasets/TundraTransect_VegRefl_Soil_2232_1.json b/datasets/TundraTransect_VegRefl_Soil_2232_1.json index 8ff87e2038..807740e800 100644 --- a/datasets/TundraTransect_VegRefl_Soil_2232_1.json +++ b/datasets/TundraTransect_VegRefl_Soil_2232_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TundraTransect_VegRefl_Soil_2232_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons.", "links": [ { diff --git a/datasets/TundraVeg_Reflectance_Soil_CO2_1960_1.json b/datasets/TundraVeg_Reflectance_Soil_CO2_1960_1.json index bd5d70f179..202320853b 100644 --- a/datasets/TundraVeg_Reflectance_Soil_CO2_1960_1.json +++ b/datasets/TundraVeg_Reflectance_Soil_CO2_1960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "TundraVeg_Reflectance_Soil_CO2_1960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrared Thermometer, and thaw depth (or active layer depth) was measured using a metal rod graduated in centimeter intervals. At small plots (~15 cm) at Utqiagvik (referred to as Patch plots) chambers were constructed that enclosed an individual patch to determine photosynthetic rate and estimate respiration rate (made by covering the chamber in a dark cloth). Efficiency using PAM fluorometer, ambient yield estimations, and rapid light curve measurements were taken. Chlorophyll concentration was measured with a portable spectrometer configured as a spectrophotometer. At larger plots (approximately 1 m2) which were part of the International Tundra EXperiment (ITEX plots) at Utqiagvik (referred to as Barrow) and Atqasuk, a sub-sample of five control and five warmed plots at each site were fitted with 0.45 m diameter polyvinyl chloride collars for chamber flux measurements. To determine the total fraction of absorbed photosynthetically active radiation (fAPAR), a series of photosynthetically active radiation (PAR) measurements were made using a custom-made light bar consisting of a linear array of GaAsP sensors mounted within an aluminum U-bar under a white plastic diffuser. In addition, a visual estimate was made of the fraction of standing dead vegetation based on percent cover. The data are provided in comma-separated values (*.csv) format. In addition, photographs of plots and instruments are provided.", "links": [ { diff --git a/datasets/Tundra_Fire_Veg_Plots_1547_1.json b/datasets/Tundra_Fire_Veg_Plots_1547_1.json index d7df6e4256..f5f80dba6e 100644 --- a/datasets/Tundra_Fire_Veg_Plots_1547_1.json +++ b/datasets/Tundra_Fire_Veg_Plots_1547_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tundra_Fire_Veg_Plots_1547_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites. The sampling design established a chronosequence of sites that varied in time since last fire to better understand post-fire vegetation successional trajectories. Complete species lists and their cover abundance data are provided for both study areas. Environmental data include the baseline plot descriptive information for vegetation, soils, and site factors. No soil samples were collected.", "links": [ { diff --git a/datasets/Tundra_Greeness_Temp_Trends_1893_1.json b/datasets/Tundra_Greeness_Temp_Trends_1893_1.json index 2c2deadea4..b0f5ace6e2 100644 --- a/datasets/Tundra_Greeness_Temp_Trends_1893_1.json +++ b/datasets/Tundra_Greeness_Temp_Trends_1893_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tundra_Greeness_Temp_Trends_1893_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites.", "links": [ { diff --git a/datasets/Tundra_Leaf_Spectra_2005_1.json b/datasets/Tundra_Leaf_Spectra_2005_1.json index 2e759c6985..4745a8bc34 100644 --- a/datasets/Tundra_Leaf_Spectra_2005_1.json +++ b/datasets/Tundra_Leaf_Spectra_2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Tundra_Leaf_Spectra_2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut off for the FluoWat low pass filter (either 650 or 700 nm). Chlorophyll content of samples was measured using a CCM-300 Chlorophyll Content. The data are provided in comma-separated values (.csv) format.", "links": [ { diff --git a/datasets/Turbid9_0.json b/datasets/Turbid9_0.json index 1575819e46..ff8f01f618 100644 --- a/datasets/Turbid9_0.json +++ b/datasets/Turbid9_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Turbid9_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Chesapeake Bay in 2004.", "links": [ { diff --git a/datasets/Turkish_Seas_0.json b/datasets/Turkish_Seas_0.json index 60e04b1cc0..7e328e56c2 100644 --- a/datasets/Turkish_Seas_0.json +++ b/datasets/Turkish_Seas_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Turkish_Seas_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll-a and pigment measurements made in the seas surrounding Turkey between 1997 and 1999.", "links": [ { diff --git a/datasets/UAEM1LME_002.json b/datasets/UAEM1LME_002.json index e35b33e8d8..ed1a560c77 100644 --- a/datasets/UAEM1LME_002.json +++ b/datasets/UAEM1LME_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEM1LME_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Local Mode Ellipsoid Radiance Data subset for the UAE region V002 contains the ellipsoid projected TOA parameters for the single local mode scene, resampled to WGS84 ellipsoid.", "links": [ { diff --git a/datasets/UAEM1LMT_002.json b/datasets/UAEM1LMT_002.json index 7426f93b38..462dd92404 100644 --- a/datasets/UAEM1LMT_002.json +++ b/datasets/UAEM1LMT_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEM1LMT_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Local Mode Terrain Radiance Data subset for the UAE region V002 contains the terrain-projected TOA radiance for the single local mode scene, resampled at the surface and topographically corrected.", "links": [ { diff --git a/datasets/UAEMIAAE_002.json b/datasets/UAEMIAAE_002.json index 9463b421f6..39a934d00a 100644 --- a/datasets/UAEMIAAE_002.json +++ b/datasets/UAEMIAAE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIAAE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Aerosol parameters subset for the UAE region V002 contains Aerosol optical depth and particle type, with associated atmospheric data.", "links": [ { diff --git a/datasets/UAEMIALS_002.json b/datasets/UAEMIALS_002.json index 146ed54569..0c17ba896c 100644 --- a/datasets/UAEMIALS_002.json +++ b/datasets/UAEMIALS_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIALS_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Land Surface parameters subset for the UAE region V002 contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); associated radiation parameters; and terrain-referenced geometric parameters.", "links": [ { diff --git a/datasets/UAEMIB2E_002.json b/datasets/UAEMIB2E_002.json index 200833adb7..31754b3342 100644 --- a/datasets/UAEMIB2E_002.json +++ b/datasets/UAEMIB2E_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIB2E_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Data subset for the UAE region V002 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected.", "links": [ { diff --git a/datasets/UAEMIB2E_003.json b/datasets/UAEMIB2E_003.json index 34e7277242..febe03a3cc 100644 --- a/datasets/UAEMIB2E_003.json +++ b/datasets/UAEMIB2E_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIB2E_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Data subset for the UAE region V003 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/UAEMIB2T_002.json b/datasets/UAEMIB2T_002.json index ab55697450..10a4a726fc 100644 --- a/datasets/UAEMIB2T_002.json +++ b/datasets/UAEMIB2T_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIB2T_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Data subset for the UAE region V002 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected.", "links": [ { diff --git a/datasets/UAEMIB2T_003.json b/datasets/UAEMIB2T_003.json index b2ad0a25d9..65e3f40310 100644 --- a/datasets/UAEMIB2T_003.json +++ b/datasets/UAEMIB2T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIB2T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Data subset for the UAE region V003 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/UAEMIRCM_004.json b/datasets/UAEMIRCM_004.json index b192e10075..7edf66c33e 100644 --- a/datasets/UAEMIRCM_004.json +++ b/datasets/UAEMIRCM_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMIRCM_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR radiometric camera-by-camera Cloud Mask subset for the UAE region V004 contains the Radiometric camera-by-camera Cloud Mask dataset. It is used to determine whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over ocean. This version of the ESDT is used by MISR PGE 13.", "links": [ { diff --git a/datasets/UAEMITAL_002.json b/datasets/UAEMITAL_002.json index 0a4cbcd98b..a51f502a55 100644 --- a/datasets/UAEMITAL_002.json +++ b/datasets/UAEMITAL_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMITAL_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Albedo parameters subset for the UAE region V002 contains local, restrictive, and expansive albedo, with associated data.", "links": [ { diff --git a/datasets/UAEMITCL_003.json b/datasets/UAEMITCL_003.json index 19e842c1c9..721042be38 100644 --- a/datasets/UAEMITCL_003.json +++ b/datasets/UAEMITCL_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMITCL_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Classifier parameters subset for the UAE region V003 contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.", "links": [ { diff --git a/datasets/UAEMITST_002.json b/datasets/UAEMITST_002.json index 064a0067ec..fb72b9a236 100644 --- a/datasets/UAEMITST_002.json +++ b/datasets/UAEMITST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMITST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Stereo parameters subset for the UAE region V002 contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.", "links": [ { diff --git a/datasets/UAEMRDAE_004.json b/datasets/UAEMRDAE_004.json index 89ed6dc4fb..4070958ea2 100644 --- a/datasets/UAEMRDAE_004.json +++ b/datasets/UAEMRDAE_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMRDAE_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpecrtroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day subset for the UAE region V004 contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aersosol compositional type frequency histogram. This data product is a regional summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/UAEMRDLS_004.json b/datasets/UAEMRDLS_004.json index 928a163442..838daad17c 100644 --- a/datasets/UAEMRDLS_004.json +++ b/datasets/UAEMRDLS_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMRDLS_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a day subset for the UAE region V004 contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters, classified into six vegetated and one non-vegetated types. This data product is a regional summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/UAEMRDRD_004.json b/datasets/UAEMRDRD_004.json index 84b1fe4973..cc9ab15fcc 100644 --- a/datasets/UAEMRDRD_004.json +++ b/datasets/UAEMRDRD_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMRDRD_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a day subset for the UAE region V004 contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications; and a statistical summary of spectral expansive albedos for several sky classifications. This data product is a regional summary of the Level 1 radiance parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/UAEMRMLS_004.json b/datasets/UAEMRMLS_004.json index f82b6d95ad..e1abf2c561 100644 --- a/datasets/UAEMRMLS_004.json +++ b/datasets/UAEMRMLS_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMRMLS_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a month subset for the UAE region V004 contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band(DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters, classified into six vegetated and one non-vegetated types. This data product is a regional summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/UAEMRMRD_005.json b/datasets/UAEMRMRD_005.json index 7aa83167ba..e4febce010 100644 --- a/datasets/UAEMRMRD_005.json +++ b/datasets/UAEMRMRD_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAEMRMRD_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a month subset for the UAE region V005 contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications; and a statistical summary of spectral expansive albedos for several sky classifications. This data product is a regional summary of the Level 1 radiance parameters of interest averaged over a month and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.", "links": [ { diff --git a/datasets/UARCL3AL_009.json b/datasets/UARCL3AL_009.json index ca4d063005..4cc837d271 100644 --- a/datasets/UARCL3AL_009.json +++ b/datasets/UARCL3AL_009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARCL3AL_009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns. CLAES was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, and also investigated the depletion of stratospheric ozone and ozone chemistry. Limb measurements were made in the altitude range between 10 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The CLAES Level 3AL data were processed with the version 9 algorithm, except H2O which is version 7.\n\nThe CLAES level 3AL product consists of 20 granules per day. A data granule is one CLAES species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARCL3AT_009.json b/datasets/UARCL3AT_009.json index 15d8ea0f2c..ccffb41c22 100644 --- a/datasets/UARCL3AT_009.json +++ b/datasets/UARCL3AT_009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARCL3AT_009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns. CLAES was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, and also investigated the depletion of stratospheric ozone and ozone chemistry. Limb measurements were made in the altitude range between 10 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The CLAES Level 3AT data were processed with the version 9 algorithm, except H2O which is version 7.\n\nThe CLAES level 3AL product consists of 20 granules per day. A data granule is one CLAES species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARHA2FN_019.json b/datasets/UARHA2FN_019.json index 9d9ff361f1..fbf013923a 100644 --- a/datasets/UARHA2FN_019.json +++ b/datasets/UARHA2FN_019.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARHA2FN_019", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Halogen Occultation Experiment (HALOE) Level 2 data product consists of daily vertical profiles of temperature, aerosol extinciton and pressure, as well as concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 and CO2 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns. HALOE was flown on NASA's Upper Atmosphere Research Satellite (UARS) to investigate upper atmosphere chemistry, dynamics and radiative processes, particlarly stratospheric ozone destruction and chlorofluromethane impact on ozone. HALOE is a solar occultation experiment which typically views 15 sunrise and sunset events each day. Limb measurements were made in the altitude range between 3 km and 130 km (depending on channel) with less than 1 km resolution. For a given day, the profiles were in two distinct latitude bands, for the sunset and sunrise events. Coverage across the full range of latitudes between 80S and 80N was achieved on a time period ranging from about two to six weeks, depending on the time of year. The HALOE Level 2 data were processed with the version 19 algorithm.\n\nThe HALOE level 2 product consists of a single granule or file per day. A data granule contains all measured HALOE species and subtypes, and ancillary information. Aerosol extinction coefficients are measured in units of 1/km, chemical species are given in mixing ratios, and temperature is in units of degrees Kelvin. HALOE level 2 data are on 491 instrument resolution altitude levels. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARHA3AT_019.json b/datasets/UARHA3AT_019.json index 86ab783020..9ca5026ede 100644 --- a/datasets/UARHA3AT_019.json +++ b/datasets/UARHA3AT_019.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARHA3AT_019", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Halogen Occultation Experiment (HALOE) Level 3AT data product consists of daily vertical profiles of temperature, aerosol extinction and concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns. HALOE was flown on NASA's Upper Atmosphere Research Satellite (UARS) to investigate upper atmosphere chemistry, dynamics and radiative processes, particlarly stratospheric ozone destruction and chlorofluromethane impact on ozone. HALOE is a solar occultation experiment which typically views 15 sunrise and sunset events each day. Limb measurements were made in the altitude range between 15 km and 60 - 130 km (depending on channel) with about 2.5 km resolution. For a given day, the profiles were in two distinct latitude bands, for the sunset and sunrise events. Coverage across the full range of latitudes between 80S and 80N was achieved on a time period ranging from about two to six weeks, depending on the time of year. The HALOE Level 3AT data were processed with the version 19 algorithm.\n\nThe HALOE level 3AT product consists of 13 granules per day. A data granule is one HALOE species or subtype per day. HALOE data are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... 54\n\nEach of the 13 HALOE granules is accompanied by 2 additional parameter files, designated as level 3TP. The first parameter file, subtype LAT, contains the latitude values for each pressure level of each event for the day. The second parameter file, subtype LON, contains the longitude values. The level 3AT files only contain the latitude/longitude location corresponding to the 30 km retrieval point. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARHR3AL_011.json b/datasets/UARHR3AL_011.json index d011657cda..55c8b7ea8d 100644 --- a/datasets/UARHR3AL_011.json +++ b/datasets/UARHR3AL_011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARHR3AL_011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Resolution Doppler Imager (HRDI) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm. HRDI was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure winds and other parameters in the mesosphere and lower thermosphere by primarily observing the Doppler shift of emitted light, and in the stratosphere by observing the Doppler shift of atmospheric absorption features. Measurements were made in the mesosphere between 50 and 115 km, and in the stratosphere between 10 and 40 km at about 2.5 km resolution. Data were collected between latitude 40S and 76N and 76S and 40N, alternating each satellite yaw cycle of about 36 days. The HRDI Level 3AL data were processed with the version 11 algorithm.\n\nThe HRDI level 3AL product consists of 8 granules per day. A data granule is one HRDI species or subtype per day.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5*i for i<=12\nz(i) = 60 + (i-12)*3 for 13 <= i <= 32\nz(i) = 120 + (i-32)*5 for 33 <= i <= 88\n\nas well as the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARHR3AT_011.json b/datasets/UARHR3AT_011.json index 5aa55d7835..6d6c09acb8 100644 --- a/datasets/UARHR3AT_011.json +++ b/datasets/UARHR3AT_011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARHR3AT_011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Resolution Doppler Imager (HRDI) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm. HRDI was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure winds and other parameters in the mesosphere and lower thermosphere by primarily observing the Doppler shift of emitted light, and in the stratosphere by observing the Doppler shift of atmospheric absorption features. Measurements were made in the mesosphere between 50 and 115 km, and in the stratosphere between 10 and 40 km at about 2.5 km resolution. Data were collected between latitude 40S and 76N and 76S and 40N, alternating each satellite yaw cycle of about 36 days. The HRDI Level 3AT data were processed with the version 11 algorithm.\n\nThe HRDI level 3AT product consists of 8 granules per day. A data granule is one HRDI species or subtype per day.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5*i for i<=12\nz(i) = 60 + (i-12)*3 for 13 <= i <= 32\nz(i) = 120 + (i-32)*5 for 33 <= i <= 88\n\nas well as the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARIS3AL_010.json b/datasets/UARIS3AL_010.json index 80723e0f78..cd9d05c7b7 100644 --- a/datasets/UARIS3AL_010.json +++ b/datasets/UARIS3AL_010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARIS3AL_010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns. ISAMS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the global temperature and composition profiles in the stratosphere and mesosphere. Limb measurements were made in the altitude range between 15 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The ISAMS Level 3AL data were processed with the version 10 algorithm, except H2O which is version 9.\n\nThe ISAMS level 3AL product consists of 10 granules per day. A data granule is one ISAMS species or subtype per day.\n\nData are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nEach of the 10 ISAMS granules is accompanied by its own additional parameter file, designated as level 3LP. The parameter file, contains the additional ancillary and quality information not found in the 3AL file. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARIS3AT_010.json b/datasets/UARIS3AT_010.json index 165bdda6b1..38c4a48fd1 100644 --- a/datasets/UARIS3AT_010.json +++ b/datasets/UARIS3AT_010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARIS3AT_010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns. ISAMS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the global temperature and composition profiles in the stratosphere and mesosphere. Limb measurements were made in the altitude range between 15 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The ISAMS Level 3AT data were processed with the version 10 algorithm, except H2O which is version 9.\n\nThe ISAMS level 3AT product consists of 10 granules per day. A data granule is one ISAMS species or subtype per day.\n\nData are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nEach of the 10 ISAMS granules is accompanied by its own additional parameter file, designated as level 3TP. The parameter file, contains the additional ancillary and quality information not found in the 3AT file. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARML3AL_005.json b/datasets/UARML3AL_005.json index a466f8907d..3ea66c3e96 100644 --- a/datasets/UARML3AL_005.json +++ b/datasets/UARML3AL_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARML3AL_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Limb Sounder (MLS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2. The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz. MLS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, relationship between chlorine monoxide and ozone destruction. Limb measurements were made in the altitude range between 10 and 85 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The MLS Level 3AL data were processed with the version 5 algorithm, except SO2 which remained version 4. Note: H2O and O3 at 183 GHz data are available only through April 15, 1993 when the 183 GHz radiometer failed.\n\nThe MLS level 3AL product consists of 9 granules per day. A data granule is one MLS species or subtype per day.\n\nData are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nEach of the 9 MLS granules is accompanied by an additional parameter file, designated as level 3LP. The parameter file, contains the additional ancillary and quality information, as well as total column amounts not found in the 3AL files. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARML3AT_005.json b/datasets/UARML3AT_005.json index b0f74431da..6d89c8132a 100644 --- a/datasets/UARML3AT_005.json +++ b/datasets/UARML3AT_005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARML3AT_005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Limb Sounder (MLS) Level 3AT data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2, as well as upper tropospheric humidity (UTH). The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz. MLS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, relationship between chlorine monoxide and ozone destruction. Limb measurements were made in the altitude range between 10 and 85 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The MLS Level 3AT data were processed with the version 5 algorithm, except H2O and HNO3 which are version 6, and SO2 and UTH which remained version 4. Note: H2O and O3 at 183 GHz data are available only through April 15, 1993 when the 183 GHz radiometer failed.\n\nThe MLS level 3AT product consists of 10 granules per day. A data granule is one MLS species or subtype per day.\n\nData are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nEach of the 10 MLS granules is accompanied by an additional parameter file, designated as level 3TP. The parameter file, contains the additional ancillary and quality information, as well as total column amounts not found in the 3AT files. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARPE2AXIS1_001.json b/datasets/UARPE2AXIS1_001.json index 493353996f..3e363b1115 100644 --- a/datasets/UARPE2AXIS1_001.json +++ b/datasets/UARPE2AXIS1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2AXIS1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 1 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS1 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe PEM AXIS1 X-ray data cover roughly the energy range from 2 keV to 300 keV. There are eight AXIS1 pixels mounted in the AXIS1 housing, each viewing different directions. The AXIS1 pixels are aligned on the spacecraft to project about 45 degrees toward the +x-axis direction from the Earthward pointing direction (+z-axis). Each pixel of AXIS1 is staggered about the 45 degree direction in an every-other fashion. Pixel 1 is closest to the center line of the spacecraft and Pixel 8 is furtherest away; however, their ground projection is dependent on the spacecraft orientation.\n\nThere is one data file per day for the PEM AXIS1 product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the AXIS1 product ranges between -80 and +80 degrees latitude. The AXIS1 data files are written in network binary format. For more information please review the PEM AXIS1 data format guide.", "links": [ { diff --git a/datasets/UARPE2AXIS2_001.json b/datasets/UARPE2AXIS2_001.json index 63a646f38d..39ea8cdc50 100644 --- a/datasets/UARPE2AXIS2_001.json +++ b/datasets/UARPE2AXIS2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2AXIS2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 2 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS2 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe PEM AXIS2 X-ray data cover roughly the energy range from 2 keV to 300 keV. There are eight AXIS2 pixels mounted in the AXIS2 housing, each viewing different directions. The AXIS2 pixels are aligned on the spacecraft to project about 45 degrees toward the +x axis direction from the Earthward pointing direction (+z axis). Each pixel of AXIS2 is staggered about the 45 degree direction in an every-other fashion. Pixel 1 is closest to the center line of the spacecraft and Pixel 8 is furtherest away; however, their ground projection is dependent on the spacecraft orientation.\n\nThere is one data file per day for the PEM AXIS2 product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the AXIS2 product ranges between -80 and +80 degrees latitude. The AXIS2 data files are written in network binary format. For more information please review the PEM AXIS2 data format guide.", "links": [ { diff --git a/datasets/UARPE2HEPSA_002.json b/datasets/UARPE2HEPSA_002.json index fc0ed9e625..4693f6a056 100644 --- a/datasets/UARPE2HEPSA_002.json +++ b/datasets/UARPE2HEPSA_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2HEPSA_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) A daily product contains electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith UARS boom. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe PEM HEPS electron data covers roughly the energy range from 35 keV to 5 MeV. There are four telescopes mounted in different directions. These measure -15 deg, +15 deg, +45 deg, and +90 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The HEPS electron units accumulate a spectrum in 4.086 seconds. There are two HEPS units in these data product files (labeled HEPS1 and HEPS2), each containing two telescopes (labeled telescope 1 and telescope 2). Each telescope contains a stack of solid state crystals. The signals from each of the crystals are combined by energy processing electronics of the instrument to yield two logical crystals or detectors, called the DE and EE detectors. The DE detector has the rough energy range from 35 keV to 300 keV and the EE detector rough energy range is from 300 keV to 5 MeV.\n\nThere is one data file per day for the PEM HEPSA product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the HEPSA product ranges between -57 and +57 degrees latitude. The HEPSA data files are written in network binary format. For more information please review the PEM HEPSA data format guide.", "links": [ { diff --git a/datasets/UARPE2HEPSB_001.json b/datasets/UARPE2HEPSB_001.json index 2a71242382..d97c788972 100644 --- a/datasets/UARPE2HEPSB_001.json +++ b/datasets/UARPE2HEPSB_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2HEPSB_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) B daily product contains the electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the nadir UARS boom, and the proton high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith boom. There are no HEPS proton measuring instruments on the nadir boom. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe PEM HEPS nadir boom electron data covers the rough range from 35 keV to 5 MeV. There are two telescopes mounted in different directions. These measure -165 and +165 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The HEPS electron units accumulate a spectrum in 16.384 sec. The PEM HEPS zenith boom proton data covers the rough range from 70 keV to 150 MeV for mounting angles less than 30 degrees and 500 keV to 150 MeV for mounting angles greater than 30 deg. There are six telescopes mounted in four different directions. These measure -15 deg, +15 deg, +45 deg, and +90 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The PEM HEPS proton units accumulate a spectrum in 16.384 sec. The nadir HEPS electron unit in this file (labeled HEPS3) contains two telescopes (labeled telescope 1 and telescope 2). Each telescope contains a stack of solid state crystals. The signals from each of the crystals are combined by energy processing electronics of the instrument to yield two logical crystals or detectors, called the DE and EE detectors. The DE detector has the rough energy range from 35 keV to 300 keV and the EE detector rough energy range is from 300 keV to 5 MeV. These designations are used in the description of HEPS electron data.\n\nThere is one data file per day for the PEM HEPSB product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the HEPSB product ranges between -57 and +57 degrees latitude. The HEPSB data files are written in network binary format. For more information please review the PEM HEPSB data format guide.", "links": [ { diff --git a/datasets/UARPE2MEPS_001.json b/datasets/UARPE2MEPS_001.json index db2345c50f..0c2ba158da 100644 --- a/datasets/UARPE2MEPS_001.json +++ b/datasets/UARPE2MEPS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2MEPS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) level 2 Medium-Energy Particle Spectrometer (MEPS) daily product contains the electron and proton high-resolution spectral data converted to number intensity units from the MEPS sensors mounted on both the zenith and nadir UARS booms. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe PEM MEPS data covers roughly the energy range from 1 eV - 5 eV to 32 keV, where the lower energy cutoff is determined by internal instrument protection potentials. There are five analyzers mounted in different directions on the zenith boom, each of which contains an electron and ion sensor. These analyzers are mounted at -23.7 deg, +6.3 deg, +21.3 deg, +36.3 deg, and +66.3 degrees with respect to the spacecraft -z axis and along the spacecraft +y axis. There are three analyzers mounted in different directions on the nadir boom, each of which contain only an electron sensor. These analyzers are mounted at -158.7 deg, +156.3 deg, and +126.3 deg, with respect to the spacecraft -z axis and along the spacecraft +y axis. All MEPS analyzers accumulate a spectrum in 2.046 sec.\n\nThere is one data file per day for the PEM MEPS product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the MEPS product ranges between -57 and +57 degrees latitude. The MEPS data files are written in network binary format. For more information please review the PEM MEPS data format guide.", "links": [ { diff --git a/datasets/UARPE2VMAGAC_001.json b/datasets/UARPE2VMAGAC_001.json index 65a7b81e4f..f5382e4de2 100644 --- a/datasets/UARPE2VMAGAC_001.json +++ b/datasets/UARPE2VMAGAC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2VMAGAC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) AC daily product contains the Vector Magnetic Field AC component. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe VMAG DC magnetic field measurements are limited to frequencies less than 2.5 Hz by an 18 db/octave antialiasing filter. Higher frequencies are measured with a peak detector and are the Vector Magnetic Field AC component. The AC value is derived from each vector field component where the sensor output is pass band filtered from 2.5 to 50 Hz. The resultant signal is then half wave rectified and passed to an RC circuit with a time constant of 4.7 s. The voltage across the RC circuit is the peak detector output which therefore represents the peak positive amplitude of the 2.5-50 Hz filtered sensor output occurring during the previous 5 seconds; however, the peak detector is determined at a rate of about every second (the VMAG AC data has been adjusted for this time offset). The X and Z peak detectors have full scale of 1oo nT peak to peak (= 5 Volts) and the Y peak detector has a full scale of 10 nT peak to peak (= 5 Volts). The analog 0 to 5 Volts peak detector outputs are sent directly to the PEM central electronics package where they are digitized into a 265 binary word. This is the raw value in the data. No in-flight calibration of the AC peak detection electronics is provided. The VMAG unit is located on the zenith boom of UARS.\n\nThere is one data file per day for the PEM VMAG AC product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the VMAG AC product ranges between -57 and +57 degrees latitude. The VMAG AC data files are written in network binary format. For more information please review the PEM VMAG AC data format guide.", "links": [ { diff --git a/datasets/UARPE2VMAGDC_001.json b/datasets/UARPE2VMAGDC_001.json index 084a37b500..7642e977e5 100644 --- a/datasets/UARPE2VMAGDC_001.json +++ b/datasets/UARPE2VMAGDC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE2VMAGDC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) DC daily product contains the Vector Magnetic Field component, UARS Aspect Magnetometers component, and UARS Torquer Bars component data converted to nanoTesla and milliAmp units. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.\n\nThe VMAG DC data range is from -65000 nT to +65000 nT, with 2 nT resolution. Measurements were from the three vector components, X, Y, and Z with respect to the UARS spacecraft orientation. The VMAG sensor consists of three orthogonal ring-core fluxgate sensors, surrounded by a thermal sphere. Stray magnetic fields from the spacecraft can be accounted for by use of the UARS torquer bar currents. The VMAG unit is located on the zenith boom of UARS. The UARS aspect magnetometers and torquer bars are located on the body of the UARS spacecraft.\n\nThere is one data file per day for the PEM VMAG DC product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the VMAG DC product ranges between -57 and +57 degrees latitude. The VMAG DC data files are written in network binary format. For more information please review the PEM VMAG DC data format guide.", "links": [ { diff --git a/datasets/UARPE3AT_004.json b/datasets/UARPE3AT_004.json index 121cef900b..8e5039a1cc 100644 --- a/datasets/UARPE3AT_004.json +++ b/datasets/UARPE3AT_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE3AT_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) Level 3AT data product consists of daily, 65.536 second interval time-ordered, vertical profiles of electron, proton and x-ray energy deposition rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG). PEM was flown on NASA's Upper Atmosphere Research Satellite (UARS) to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM electron and proton data are processed with the version 4 algorithm, while the x-ray data remained version 3.\n\nThe PEM level 3AT data consist of 18 granules per day. A data granule is defined as one PEM subtype per day. The x-ray pixel subtypes are grouped together in a single tar file for each day, the electron and proton are available as individual files per day.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5 * i, i <= 12\nz(i) = 60 + (i - 12) * 3, 13 <= i <= 32\nz(i) = 120 + (i - 32) * 10, 33 <= i <= 50\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARPE3TP_004.json b/datasets/UARPE3TP_004.json index 5f9824d01f..f2ceae1f68 100644 --- a/datasets/UARPE3TP_004.json +++ b/datasets/UARPE3TP_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARPE3TP_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Environment Monitor (PEM) Level 3TP data product consists of daily, 65.536 second and 2.048 interval time-ordered, vertical profiles of electron and proton rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG). PEM was flown on NASA's Upper Atmosphere Research Satellite (UARS) to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM Level 3TP data are processed with the version 4 algorithm.\n\nThe PEM level 3TP data are a special data product, providing higher temporal resolution than the level 3AT data, with the HEPS and MEPS instrument data in separate files. The HEPS profiles are made every 65.536 seconds, and the MEPS profiles are made every 2.048 seconds.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5 * i, i <= 12\nz(i) = 60 + (i - 12) * 3, 13 <= i <= 32\nz(i) = 120 + (i - 32) * 10, 33 <= i <= 50\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARSO3BS_018.json b/datasets/UARSO3BS_018.json index 36b502ee9e..2586649730 100644 --- a/datasets/UARSO3BS_018.json +++ b/datasets/UARSO3BS_018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARSO3BS_018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar-Stellar Irradiance Comparison Experiment (SOLSTICE) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument was a three-channel ultraviolet spectrometer that measured the magnitude of solar and spectral irradiances in the wavelength range from 119 to 420 nm. The design of the instrument allowed for observations of both the sun and bright blue stars, whose spectra is assumed to be constant. SOLSTICE was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the full disk solar irradiance with high precision and accuracy to follow short-term (minutes to hours), intermediate-term (days to weeks), and long-term (11 year sunspot, and 22 year solar magnetic field cycles) variations in the solar output. The SORCE Level 3BS data were processed with the version 18 algorithm.\n\nThe SOLSTICE Level 3BS data consists of 1 granule per day. The data are normalized to an earth-sun distance of 1 AU, and instrument degradation have been applied to the data. Included with the primary solar spectral irradiance values are daily average integrated intensities of Lyman alpha (121.6 nm), O-I (130.4 nm), C-IV (154.8 nm) and C-I (156.1 and 165.6 nm), and core-wing ratios of Mg-II (280.0 nm) and Ca-II (393.3 nm), as well as Carrington latitude and longitudes. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARSU3BS_022.json b/datasets/UARSU3BS_022.json index 379bd50872..af00382047 100644 --- a/datasets/UARSU3BS_022.json +++ b/datasets/UARSU3BS_022.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARSU3BS_022", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Solar Ultraviolet Spectral Irradiance Monitor (SUSIM) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument consisted of two identical double-dispersion scanning spectrometers, seven detectors (5 photodiodes, 2 photon counters), and solar ultraviolet calibration sources measuring in the wavelength range from 110 to 410 nm. One spectrometer measured the solar disk while the other was for calibration using high-precision deuterium lamps calibrated by NIST. SUSIM was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the full disk solar irradiance with high precision and accuracy to follow short-term (minutes to hours), intermediate-term (days to weeks), and long-term (11 year sunspot, and 22 year solar magnetic field cycles) variations in the solar output. The SUSIM Level 3BS data were processed with the version 22 algorithm.\n\nThe SUSIM Level 3BS data consists of 1 granule per day. The data are normalized to an earth-sun distance of 1 AU, and instrument degradation have been applied to the data. Included with the primary solar spectral irradiance values are the following solar indices: Lyman alpha, O-I (130 nm), C-II (134 nm) and C-IV (155 nm), Al Edge, and core-wing ratios of Mg-I and Mg-II. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARWI3AL_011.json b/datasets/UARWI3AL_011.json index 26f94604ff..e9a32261e8 100644 --- a/datasets/UARWI3AL_011.json +++ b/datasets/UARWI3AL_011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARWI3AL_011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Wind Imaging Interferometer (WINDII) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm. WINDII was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure wind, temperature and emission rates in the mesosphere and thermosphere between 80 and 300 km at about 5 km resolution. Data were collected between latitude 40S and 72N and 72S and 40N, alternating each satellite yaw cycle of about 36 days. The WINDII Level 3AL data were processed with the version 11 algorithm.\n\nThe WINDII level 3AL product consists of 3 granules per day. A data granule is one WINDII species or subtype per day.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5*i for 1 <= i <= 12\nz(i) = 60 + (i-12)*3 for 13 <= i <= 32\nz(i) = 120 + (i-32)*5 for 33 <= i <= 88\n\nEach of the 3 WINDII granules is accompanied by an additional parameter file, designated as level 3LP. The parameter file, contains additional ancillary and quality information not found in the 3AL files. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARWI3AT_011.json b/datasets/UARWI3AT_011.json index 0fd4a81fa7..a8033c470c 100644 --- a/datasets/UARWI3AT_011.json +++ b/datasets/UARWI3AT_011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARWI3AT_011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Wind Imaging Interferometer (WINDII) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm. WINDII was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure wind, temperature and emission rates in the mesosphere and thermosphere between 80 and 300 km at about 5 km resolution. Data were collected between latitude 40S and 72N and 72S and 40N, alternating each satellite yaw cycle of about 36 days. The WINDII Level 3AT data were processed with the version 11 algorithm.\n\nThe WINDII level 3AT product consists of 3 granules per day. A data granule is one WINDII species or subtype per day.\n\nData are on the UARS standard altitude levels (in km) given by:\n\nz(i) = 5*i for 1 <= i <= 12\nz(i) = 60 + (i-12)*3 for 13 <= i <= 32\nz(i) = 120 + (i-32)*5 for 33 <= i <= 88\n\nEach of the 3 WINDII granules is accompanied by an additional parameter file, designated as level 3TP. The parameter file, contains additional ancillary and quality information not found in the 3AT files. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARZCNMC_001.json b/datasets/UARZCNMC_001.json index 950752d8f5..88456bae7d 100644 --- a/datasets/UARZCNMC_001.json +++ b/datasets/UARZCNMC_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARZCNMC_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UARS Correlative assimilation data from NOAA's National Meteorological Center (NMC) consists of daily model runs at 12 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments. The NMC data product includes temperature (Kelvin), humidity (%), geopotential height (m), and zonal and meridional wind components (m/s).\n\nGeopotential height and atmospheric temperature data are derived from two analysis systems: 1) tropospheric fields from 1000 to 100 mb, and 2) stratospheric analyses from 70 to 0.4 mb. The tropospheric fields are the 12 GMT gridded fields which are part of the Global Daily Assimilation System (GDAS), where data from radiosondes, aircraft, satellites, ships, bouys, or any other conventional means are assimilated and merged into meteorological fields (heights, temperature, winds). The stratospheric analyses are 12 GMT operational analyses at the 70 - 0.4 mb pressure levels produced from satellite temperature retrievals and RAOBS via a modified Cressman analysis. Tropospheric temperature analyses use combined NOAA-10 and NOAA-11 data. Moisture (northern hemisphere only) and Winds data are obtained from the NMC GDAS.\n\nThe gridded fields are on the standard 65 x 65 NMC polar stereographic grid oriented 80W (grid increment 381 km at 60N), and 100E (grid increment 381 km at 60S); Pole at (33,33). The NMC uses 18 standard pressure levels for data at 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 10, 5, 2, 1, 0.4 millibars. Height and temperature data are produced at all 18 pressure levels from 1000 mb to 0.4 mb. Moisture data are only produced in the northern hemisphere for the 6 lowest altitude pressure levels. Wind data are produced for the 12 lowest altitude levels in the northern hemisphere, and at the 4 levels 1000, 500, 300, and 250 mb in the southern hemisphere.\n\nThere are four data files representing each subtype per day. Included in the NMC correlative data set will be a geographical data richness file. This file is used by the NMC access routines, and indicates the radiosonde coverage for each point on the NMC grid. The data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UARZCUKM_001.json b/datasets/UARZCUKM_001.json index e50651af38..c24247e070 100644 --- a/datasets/UARZCUKM_001.json +++ b/datasets/UARZCUKM_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UARZCUKM_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UARS Correlative assimilation data from the U.K. Meteorological Office (UKMO) consists of daily model runs at 12:00 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments. The UKMO product includes temperature (Kelvin), geopotential height (m), zonal and meridional wind components (m/s), and vertical velocity (Pa/s). Note: vertical velocity (omega) data are available from 26 August 1992 forward.\n\nThe numerical Unified Model used in the assimilation system is a global primitive equation model, with a split-explicit time integration scheme. It incorporates a comprehensive range of physical parameterization schemes. It uses a hybrid vertical coordinate system, with terrain-following model levels at low levels, gradually changing to pressure levels in the stratosphere. The input to the assimilation system are from the World Weather Watch network of surface and upper air observations and satellite data.\n\nThe UKMO correlative product consists of a single granule per day containing all the geophysical parameters. Data coverage is global for where the horizontal resolution is 2.5 degrees latitude by 3.75 degrees longitude, using a staggered grid system. For parameters other than wind components, each horizontal field consists of 73 rows of 96 points, starting at 90N, 0E. The wind data are staggered by half a grid, starting at 88.75N, 1.875E (with 72 rows).\n\nThe UKMO correlative assimilation data are on the UARS standard pressure levels (in mbars) given by:\n\nP(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ...\n\nThe data files are available in a binary record oriented format.", "links": [ { diff --git a/datasets/UAVSAR_INSAR_AMP_1.json b/datasets/UAVSAR_INSAR_AMP_1.json index ff8a8c2e8a..d64bda42c6 100644 --- a/datasets/UAVSAR_INSAR_AMP_1.json +++ b/datasets/UAVSAR_INSAR_AMP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_AMP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Amplitude Scene", "links": [ { diff --git a/datasets/UAVSAR_INSAR_AMP_GRD_1.json b/datasets/UAVSAR_INSAR_AMP_GRD_1.json index ccfcce88ea..d44ce28739 100644 --- a/datasets/UAVSAR_INSAR_AMP_GRD_1.json +++ b/datasets/UAVSAR_INSAR_AMP_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_AMP_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Ground Projected Amplitude Scene", "links": [ { diff --git a/datasets/UAVSAR_INSAR_DEM_1.json b/datasets/UAVSAR_INSAR_DEM_1.json index 757201f6a4..c06eb7b31c 100644 --- a/datasets/UAVSAR_INSAR_DEM_1.json +++ b/datasets/UAVSAR_INSAR_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Scene DEM TIFF", "links": [ { diff --git a/datasets/UAVSAR_INSAR_INT_1.json b/datasets/UAVSAR_INSAR_INT_1.json index f57bb2e8a9..5de9cb63ae 100644 --- a/datasets/UAVSAR_INSAR_INT_1.json +++ b/datasets/UAVSAR_INSAR_INT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_INT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Scene", "links": [ { diff --git a/datasets/UAVSAR_INSAR_INT_GRD_1.json b/datasets/UAVSAR_INSAR_INT_GRD_1.json index 99e28c5687..0e31fd581a 100644 --- a/datasets/UAVSAR_INSAR_INT_GRD_1.json +++ b/datasets/UAVSAR_INSAR_INT_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_INT_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Ground Projected Scene", "links": [ { diff --git a/datasets/UAVSAR_INSAR_KMZ_1.json b/datasets/UAVSAR_INSAR_KMZ_1.json index 0d592f59e6..5e113bdd73 100644 --- a/datasets/UAVSAR_INSAR_KMZ_1.json +++ b/datasets/UAVSAR_INSAR_KMZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_KMZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Scene KMZ", "links": [ { diff --git a/datasets/UAVSAR_INSAR_META_1.json b/datasets/UAVSAR_INSAR_META_1.json index 6c07079a9e..81e8917d7f 100644 --- a/datasets/UAVSAR_INSAR_META_1.json +++ b/datasets/UAVSAR_INSAR_META_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_INSAR_META_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR Repeat Pass Interferometry Scene Metadata", "links": [ { diff --git a/datasets/UAVSAR_POL_DEM_1.json b/datasets/UAVSAR_POL_DEM_1.json index 52ed696bf2..2fe941dde8 100644 --- a/datasets/UAVSAR_POL_DEM_1.json +++ b/datasets/UAVSAR_POL_DEM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_DEM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene DEM TIFF", "links": [ { diff --git a/datasets/UAVSAR_POL_INC_1.json b/datasets/UAVSAR_POL_INC_1.json index c8223c66b9..020bf4fd88 100644 --- a/datasets/UAVSAR_POL_INC_1.json +++ b/datasets/UAVSAR_POL_INC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_INC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Incidence Angle", "links": [ { diff --git a/datasets/UAVSAR_POL_KMZ_1.json b/datasets/UAVSAR_POL_KMZ_1.json index 5d5eb6990e..da20edae5e 100644 --- a/datasets/UAVSAR_POL_KMZ_1.json +++ b/datasets/UAVSAR_POL_KMZ_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_KMZ_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene KMZ", "links": [ { diff --git a/datasets/UAVSAR_POL_META_1.json b/datasets/UAVSAR_POL_META_1.json index f4fca0739e..208e82b4ae 100644 --- a/datasets/UAVSAR_POL_META_1.json +++ b/datasets/UAVSAR_POL_META_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_META_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Metadata", "links": [ { diff --git a/datasets/UAVSAR_POL_ML_CMPLX_GRD_1.json b/datasets/UAVSAR_POL_ML_CMPLX_GRD_1.json index 70301a5a5d..945a4fefef 100644 --- a/datasets/UAVSAR_POL_ML_CMPLX_GRD_1.json +++ b/datasets/UAVSAR_POL_ML_CMPLX_GRD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_ML_CMPLX_GRD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Projected", "links": [ { diff --git a/datasets/UAVSAR_POL_ML_CMPLX_GRD_3X3_1.json b/datasets/UAVSAR_POL_ML_CMPLX_GRD_3X3_1.json index 6ce6f19041..337d3538a8 100644 --- a/datasets/UAVSAR_POL_ML_CMPLX_GRD_3X3_1.json +++ b/datasets/UAVSAR_POL_ML_CMPLX_GRD_3X3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_ML_CMPLX_GRD_3X3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Projected Multilook 3x3", "links": [ { diff --git a/datasets/UAVSAR_POL_ML_CMPLX_GRD_5X5_1.json b/datasets/UAVSAR_POL_ML_CMPLX_GRD_5X5_1.json index 88f34a9f80..1bee6156ca 100644 --- a/datasets/UAVSAR_POL_ML_CMPLX_GRD_5X5_1.json +++ b/datasets/UAVSAR_POL_ML_CMPLX_GRD_5X5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_ML_CMPLX_GRD_5X5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Projected Multilook 5x5", "links": [ { diff --git a/datasets/UAVSAR_POL_ML_CMPLX_SLANT_1.json b/datasets/UAVSAR_POL_ML_CMPLX_SLANT_1.json index 50af6865cb..cab602af1c 100644 --- a/datasets/UAVSAR_POL_ML_CMPLX_SLANT_1.json +++ b/datasets/UAVSAR_POL_ML_CMPLX_SLANT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_ML_CMPLX_SLANT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Complex", "links": [ { diff --git a/datasets/UAVSAR_POL_PAULI_1.json b/datasets/UAVSAR_POL_PAULI_1.json index ece2ad64fd..56659fe735 100644 --- a/datasets/UAVSAR_POL_PAULI_1.json +++ b/datasets/UAVSAR_POL_PAULI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_PAULI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Pauli Decomposition", "links": [ { diff --git a/datasets/UAVSAR_POL_SLOPE_1.json b/datasets/UAVSAR_POL_SLOPE_1.json index d44c9551b1..9f8cc3de77 100644 --- a/datasets/UAVSAR_POL_SLOPE_1.json +++ b/datasets/UAVSAR_POL_SLOPE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_SLOPE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Slope", "links": [ { diff --git a/datasets/UAVSAR_POL_STOKES_1.json b/datasets/UAVSAR_POL_STOKES_1.json index ab272ceed8..742f1fc2ab 100644 --- a/datasets/UAVSAR_POL_STOKES_1.json +++ b/datasets/UAVSAR_POL_STOKES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAVSAR_POL_STOKES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UAVSAR PolSAR Scene Stokes", "links": [ { diff --git a/datasets/UAV_Imagery_BigLakeTrail_1834_1.json b/datasets/UAV_Imagery_BigLakeTrail_1834_1.json index 0461a4efcb..fcfdb16c29 100644 --- a/datasets/UAV_Imagery_BigLakeTrail_1834_1.json +++ b/datasets/UAV_Imagery_BigLakeTrail_1834_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UAV_Imagery_BigLakeTrail_1834_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides multispectral reflectance imagery (green at 550 nm, red at 660 nm, red edge at 735 nm, and near-infrared at 790 nm), normalized difference vegetation index (NDVI), and digital surface and terrain models for a 0.5 km2 area surrounding Big Trail Lake (BTL) in the Goldstream Creek Valley north of Fairbanks, Alaska. These high spatial resolution maps (13 cm x 13 cm) were generated by unmanned aerial vehicle (UAV) imagery collected on 2019-08-04. Raw images (n=908) were combined into mosaic layers that incorporated ground control points with centimeter accuracy. These layers were then used to generate vegetation, water body, and elevation maps and then combined with in situ measurements of methane flux to improve upscaling models of greenhouse gas emissions.", "links": [ { diff --git a/datasets/UCLA_DEALIASED_SASS_L3_1.json b/datasets/UCLA_DEALIASED_SASS_L3_1.json index 03ad85b1fd..8aa7771aee 100644 --- a/datasets/UCLA_DEALIASED_SASS_L3_1.json +++ b/datasets/UCLA_DEALIASED_SASS_L3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UCLA_DEALIASED_SASS_L3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains dealiased ocean wind vector components (zonal and meridional) derived from the Seasat-A Scatterometer (SASS) provided on a global 1x1 degree grid. Dealiasing of the SASS data was achieved manually using ship observations in a joint effort between JPL, UCLA and AES. This data set underwent restoration in 1997. Data are provided in ASCII text files at six hour intervals.", "links": [ { diff --git a/datasets/UKASSEL_GLOBAL_IRRIGATED_AREA.json b/datasets/UKASSEL_GLOBAL_IRRIGATED_AREA.json index eb124738b3..59b09b0b10 100644 --- a/datasets/UKASSEL_GLOBAL_IRRIGATED_AREA.json +++ b/datasets/UKASSEL_GLOBAL_IRRIGATED_AREA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UKASSEL_GLOBAL_IRRIGATED_AREA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the purpose of global modeling of water use and crop\nproduction, a digital global map of irrigated areas was developed. The\nmap depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell\nthat was equipped for irrigation in 1995. It was derived by\ncombininginformation from large-scale maps with outlines of irrigated\nareas (one or more countries per map), FAO data on total irrigated\narea per country in 1995 and national data on total irrigated area per\ncounty, drainage basin or federal state. In the documentation of the\nmap, the data and map sources as well as the map generation process is\ndescribed, and the data uncertainty is discussed.\n\n\"http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf\"\n\nWe plan to improve this map in the future. Therefore, comments,\ninformation and data that might contribute to this effort are highly\nwelcome.", "links": [ { diff --git a/datasets/UM0405_26_aerosol_optical.json b/datasets/UM0405_26_aerosol_optical.json index c443a26bf7..7b1884ddc2 100644 --- a/datasets/UM0405_26_aerosol_optical.json +++ b/datasets/UM0405_26_aerosol_optical.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UM0405_26_aerosol_optical", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition.", "links": [ { diff --git a/datasets/UM0506_26_aerosol_optical.json b/datasets/UM0506_26_aerosol_optical.json index 84eb7bbbfb..1257088586 100644 --- a/datasets/UM0506_26_aerosol_optical.json +++ b/datasets/UM0506_26_aerosol_optical.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UM0506_26_aerosol_optical", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition.", "links": [ { diff --git a/datasets/UM0708_25_multi-frequency_acoustic.json b/datasets/UM0708_25_multi-frequency_acoustic.json index 5d7e97cb27..c30093bcf8 100644 --- a/datasets/UM0708_25_multi-frequency_acoustic.json +++ b/datasets/UM0708_25_multi-frequency_acoustic.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UM0708_25_multi-frequency_acoustic", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations.", "links": [ { diff --git a/datasets/UM0809_33_nano.json b/datasets/UM0809_33_nano.json index 7fcbef7958..47268f8909 100644 --- a/datasets/UM0809_33_nano.json +++ b/datasets/UM0809_33_nano.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UM0809_33_nano", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, \u2160-10, \u2161-7) off L\u00fctzow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000http://biows.ac.jp/~plankton/um0809-1a.png", "links": [ { diff --git a/datasets/UMD_GEOL388_0.json b/datasets/UMD_GEOL388_0.json index 3e468d20cf..df7603f76c 100644 --- a/datasets/UMD_GEOL388_0.json +++ b/datasets/UMD_GEOL388_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UMD_GEOL388_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the Atlantic Ocean made by the University of Maryland between New England, Bermuda, and Brazil in 2003.", "links": [ { diff --git a/datasets/UNEP_GRID_SF_AFRICA_third version.json b/datasets/UNEP_GRID_SF_AFRICA_third version.json index d14f58ee5c..0ebbf0551a 100644 --- a/datasets/UNEP_GRID_SF_AFRICA_third version.json +++ b/datasets/UNEP_GRID_SF_AFRICA_third version.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UNEP_GRID_SF_AFRICA_third version", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The African administrative boundaries and population database is part\n of an ongoing effort to improve global, spatially referenced\n demographic data holdings. Such databases are useful for a variety of\n applications including strategic-level agricultural research and\n applications in the analysis of the human dimensions of global change\n \n This documentation describes the third version of a database of\n administrative units with associated population figures for\n Africa. The first version was compiled for UNEP's Global\n Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while\n the second version represented an update and expansion of this first\n product (Deichmann 1994, WRI 1995). The work discussed in the\n following paragraphs is also related to NCGIA activities to produce a\n global database of subnational population estimates (Tobler et\n al. 1995), and an improved database for the Asian continent (Deichmann\n 1996a). The new version for Africa provides considerably more detail:\n more than 4700 administrative units, compared to about 800 in the\n first and 2200 in the second version. In addition, for each of these\n units a population estimate was compiled for 1960, 70, 80 and 90 which\n provides an indication of past population dynamics in Africa.", "links": [ { diff --git a/datasets/UNEP_GRID_SF_ASIA.json b/datasets/UNEP_GRID_SF_ASIA.json index e93caa2e6f..e4260ef300 100644 --- a/datasets/UNEP_GRID_SF_ASIA.json +++ b/datasets/UNEP_GRID_SF_ASIA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UNEP_GRID_SF_ASIA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Asian administrative boundaries and population database is part of\n an ongoing effort to improve global, spatially referenced demographic\n data holdings. Such databases are useful for a variety of applications\n including strategic-level agricultural research and applications in\n the analysis of the human dimensions of global change.\n \n This project (which has been carried out as a cooperative activity\n between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has\n pooled available data sets, many of which had been assembled for the\n global demography project. All data were checked, international\n boundaries and coastlines were replaced with a standard template, the\n attribute database was redesigned, and new, more reliable population\n estimates for subnational units were produced for all countries. From\n the resulting data sets, raster surfaces representing population\n distribution and population density were created in collaboration\n between NCGIA and GRID-Geneva.", "links": [ { diff --git a/datasets/UNEP_GRID_SF_GLOBAL.json b/datasets/UNEP_GRID_SF_GLOBAL.json index f652e11e87..3228dfde26 100644 --- a/datasets/UNEP_GRID_SF_GLOBAL.json +++ b/datasets/UNEP_GRID_SF_GLOBAL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UNEP_GRID_SF_GLOBAL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Population databases are forming the backbone of many important\n studies modelling the complex interactions between population growth\n and environmental degradation, predicting the effects of global\n climate change on humans, and assessing the risks of various hazards\n such as floods, air pollution and radiation. Detailed information on\n population size, growth and distribution (along with many other\n environmental parameters) is of fundamental importance to such\n efforts. This database includes rural population distributions,\n population distrbution for cities and gridded global population\n distributions.\n \n This project has provided a population database depicting the\n worldwide distribution of population in a 1X1 latitude/longitude grid\n system. The database is unique, firstly, in that it makes use of the\n most recent data available (1990). Secondly, it offers true\n apportionment for each grid cell that is, if a cell contains\n populations from two different countries, each is assigned a\n percentage of the grid cell area, rather than artificially assigning\n the whole cell to one or the other country (this is especially\n important for European countries). Thirdly, the database gives the\n percentage of a country's total population accounted for in each\n cell. So if a country's total in a given year around 1990 (1989 or\n 1991, for example) is known, then population in each cell can be\n calculated by using the percentage given in the database with the\n assumption that the growth rate in each cell of the country is the\n same. And lastly, this dataset is easy to be updated for each country\n as new national population figures become available.", "links": [ { diff --git a/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json b/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json index e5004d62fe..efda53f40a 100644 --- a/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json +++ b/datasets/UNEP_GRID_SF_LATINAMERICA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UNEP_GRID_SF_LATINAMERICA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Latin America population database is part of an ongoing effort to\n improve global, spatially referenced demographic data holdings. Such\n databases are useful for a variety of applications including\n strategic-level agricultural research and applications in the analysis\n of the human dimensions of global change.\n \n This documentation describes the Latin American Population Database, a\n collaborative effort between the International Center for Tropical\n Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID,\n Sioux Falls) and the World Resources Institute (WRI). This work is\n intended to provide a population database that compliments previous\n work carried out for Asia and Africa. This data set is more detailed\n than the Africa and Asia data sets. Population estimates for 1960,\n 1970, 1980, 1990 and 2000 are also provided. The work discussed in the\n following paragraphs is also related to NCGIA activities to produce a\n global database of subnational population estimates (Tobler et\n al. 1995), and an improved database for the Asian continent (Deichmann\n 1996a).", "links": [ { diff --git a/datasets/UNEP_SDG14_2022_0.json b/datasets/UNEP_SDG14_2022_0.json index 60b9b6bdeb..ab14e0f0a0 100644 --- a/datasets/UNEP_SDG14_2022_0.json +++ b/datasets/UNEP_SDG14_2022_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UNEP_SDG14_2022_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Validation campaign in support of the United Nations Environment - Sustainable Development Goal 14.1.1a of 2022: Index of coastal eutrophication in Latin America. This dataset contains validation data for ocean color satellite data products and collects nutrient data on eutrophication. The data will be used to evaluate the effectiveness of the satellite-derived indicators and to develop more specific, level 2 satellite data indicators for the member countries in the future.", "links": [ { diff --git a/datasets/USAP-0231006_1.json b/datasets/USAP-0231006_1.json index b3b7e46716..ba1d55a7bb 100644 --- a/datasets/USAP-0231006_1.json +++ b/datasets/USAP-0231006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0231006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic notothenioid fishes exhibit two adaptive traits to survive in frigid temperatures. The first of these is the production of anti-freeze proteins in their blood and tissues. The second is a system-wide ability to perform cellular and physiological functions at extremely cold temperatures.The proposal goals are to show how Antarctic fishes use these characteristics to avoid freezing, and which additional genes are turned on, or suppressed in order for these fishes to maintain normal physiological function in extreme cold temperatures. Progressively colder habitats are encountered in the high latitude McMurdo Sound and Ross Shelf region, along with somewhat milder near?shore water environments in the Western Antarctic Peninsula (WAP). By quantifying the extent of ice crystals invading and lodging in the spleen, the percentage of McMurdo Sound fish during austral summer (Oct-Feb) will be compared to the WAP intertidal fish during austral winter (Jul-Sep) to demonstrate their capability and extent of freeze avoidance. Resistance to ice entry in surface epithelia (e.g. skin, gill and intestinal lining) is another expression of the adaptation of these fish to otherwise lethally freezing conditions.\r\n\r\n\r\n\r\nThe adaptive nature of a uniquely characteristic polar genome will be explored by the study of the transcriptome (the set of expressed RNA transcripts that constitutes the precursor to set of proteins expressed by an entire genome). Three notothenioid species (E.maclovinus, D. Mawsoni and C. aceratus) will be analysed to document evolutionary genetic changes (both gain and loss) shaped by life under extreme chronic cold. A differential gene expression (DGE) study will be carried out on these different species to evaluate evolutionary modification of tissue-wide response to heat challenges. The transcriptomes and other sequencing libraries will contribute to de novo ice-fish genome sequencing efforts.", "links": [ { diff --git a/datasets/USAP-0424589.json b/datasets/USAP-0424589.json index b99137d5e5..b3d38c5644 100644 --- a/datasets/USAP-0424589.json +++ b/datasets/USAP-0424589.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0424589", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award is for the continuation of the Center for Remote Sensing of Ice Sheets (CReSIS), an NSF Science and Technology Center (STC) established in June 2005 to study present and probable future contributions of the Greenland and Antarctic ice sheets to sea-level rise. The Center's vision is to understand and predict the role of polar ice sheets in sea level change. In particular, the Center's mission is to develop technologies, to conduct field investigations, to compile data to understand why many outlet glaciers and ice streams are changing rapidly, and to develop models that explain and predict ice sheet response to climate change. The Center's mission is also to educate and train a diverse population of graduate and undergraduate students in Center-related disciplines and to encourage K-12 students to pursue careers in science, technology, engineering and mathematics (STEM-fields). The long-term goals are to perform a four-dimensional characterization (space and time) of rapidly changing ice-sheet regions, develop diagnostic and predictive ice-sheet models, and contribute to future assessments of sea level change in a warming climate. In the first five years, significant progress was made in developing, testing and optimizing innovative sensors and platforms and completing a major aircraft campaign, which included sounding the channel under Jakobshavn Isbr. In the second five years, research will focus on the interpretation of integrated data from a suite of sensors to understand the physical processes causing changes and the subsequent development and validation of models. Information about CReSIS can be found at http://www.cresis.ku.edu. \n\nThe intellectual merits of the STC are the multidisciplinary research it enables its faculty, staff and students to pursue, as well as the broad education and training opportunities it provides to students at all levels. During the first phase, the Center provided scientists and engineers with a collaborative research environment and the opportunity to interact, enabling the development of high-sensitivity radars integrated with several airborne platforms and innovative seismic instruments. Also, the Center successfully collected data on ice thickness and bed conditions, key variables in the study of ice dynamics and the development of models, for three major fast-flowing glaciers in Greenland. During the second phase, the Center will collect additional data over targeted sites in areas undergoing rapid changes; process, analyze and interpret collected data; and develop advanced process-oriented and ice sheet models to predict future behavior. The Center will continue to provide a rich environment for multidisciplinary education and mentoring for undergraduate students, graduate students, and postdoctoral fellows, as well as for conducting K-12 education and public outreach.\n\nThe broader impacts of the Center stem from addressing a global environmental problem with critical societal implications, providing a forum for citizens and policymakers to become informed about climate change issues, training the next generation of scientists and engineers to serve the nation, encouraging underrepresented students to pursue careers in STEM-related fields, and transferring new technologies to industry. Students involved in the Center find an intellectually stimulating atmosphere where collaboration between disciplines is the norm and exposure to a wide variety of methodologies and scientific issues enriches their educational experience. The next generation of researchers should reflect the diversity of our society; the Center will therefore continue its work with ECSU to conduct outreach and educational programs that attract minority students to careers in science and technology. The Center has also established a new partnership with ADMI that supports faculty and student exchanges at the national level and provides expanded opportunities for students and faculty to be involved in Center-related research and education activities. These, and other collaborations, will provide broader opportunities to encourage underrepresented students to pursue STEM careers. \n\nAs lead institution, The University of Kansas (KU) provides overall direction and management, as well as expertise in radar and remote sensing, Uninhabited Aerial Vehicles (UAVs), and modeling and interpretation of data. Five partner institutions and a DOE laboratory play critical roles in the STC. The Pennsylvania State University (PSU) continues to participate in technology development for seismic measurements, field activities, and modeling. The Center of Excellence in Remote Sensing, Education and Research (CERSER) at Elizabeth City State University (ECSU) contributes its expertise to analyzing satellite data and generating high-level data products. ECSU also brings to the Center their extensive experience in mentoring and educating traditionally under-represented students. ADMI, the Association of Computer and Information Science/Engineering Departments at Minority Institutions, expands the program's reach to underrepresented groups at the national level. Indiana University (IU) provides world-class expertise in CI and high-performance computing to address challenges in data management, processing, distribution and archival, as well as high-performance modeling requirements. The University of Washington (UW) provides expertise in satellite observations of ice sheets and process-oriented interpretation and model development. Los Alamos National Laboratory (LANL) contributes in the area of ice sheet modeling. All partner institutions are actively involved in the analysis and interpretation of observational and numerical data sets.", "links": [ { diff --git a/datasets/USAP-0732711.json b/datasets/USAP-0732711.json index 63643c6c6c..531c093f7b 100644 --- a/datasets/USAP-0732711.json +++ b/datasets/USAP-0732711.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0732711", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A profound transformation in ecosystem structure and function is occurring in coastal waters of the western Weddell Sea, with the collapse of the Larsen B ice shelf. This transformation appears to be yielding a redistribution of energy flow between chemoautotrophic and photosynthetic production, and to be causing the rapid demise of the extraordinary seep ecosystem discovered beneath the ice shelf. This event provides an ideal opportunity to examine fundamental aspects of ecosystem transition associated with climate change. We propose to test the following hypotheses to elucidate the transfor mations occurring in marine ecosystems as a consequence of the Larsen B collapse: (1) The biogeographic isolation and sub-ice shelf setting of the Larsen B seep has led to novel habitat characteristics, chemoautotrophically dependent taxa and functional adaptations. (2) Benthic communities beneath the former Larsen B ice shelf are fundamentally different from assemblages at similar depths in the Weddell sea-ice zone, and resemble oligotrophic deep-sea communities. Larsen B assemblages are undergoing rapid change. (3) The previously dark, oligotrophic waters of the Larsen B embayment now support a thriving phototrophic community, with production rates and phytoplankton composition similar to other productive areas of the Weddell Sea. To document rapid changes occurring in the Larsen B ecosystem, we will use a remotely operated vehicle, shipboard samplers, and moored sediment traps. We will characterize microbial, macrofaunal and megafaunal components of the seep community; evaluate patterns of surface productivity, export flux, and benthic faunal composition in areas previously covered by the ice shelf, and compare these areas to the open sea-ice zone. These changes will be placed within the geological, glaciological and climatological context that led to ice-shelf retreat, through companion research projects funded in concert with this effort. Together these projects will help predict the likely consequences of ice-shelf collapse to marine ecosystems in other regions of Antarctica vulnerable to climate change. The research features international collaborators from Argentina, Belgium, Canada, Germany, Spain and the United Kingdom. The broader impacts include participation of a science writer; broadcast of science segments by members of the Jim Lehrer News Hour (Public Broadcasting System); material for summer courses in environmental change; mentoring of graduate students and postdoctoral fellows; and showcasing scientific activities and findings to students and public through podcasts.", "links": [ { diff --git a/datasets/USAP-0732917_1.json b/datasets/USAP-0732917_1.json index 5d60592596..96d90d2d84 100644 --- a/datasets/USAP-0732917_1.json +++ b/datasets/USAP-0732917_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0732917_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A profound transformation in ecosystem structure and function is occurring in coastal waters of the western Weddell Sea, with the collapse of the Larsen B ice shelf. This transformation appears to be yielding a redistribution of energy flow between chemoautotrophic and photosynthetic production, and to be causing the rapid demise of the extraordinary seep ecosystem discovered beneath the ice shelf. This event provides an ideal opportunity to examine fundamental aspects of ecosystem transition associated with climate change. We propose to test the following hypotheses to elucidate the transformations occurring in marine ecosystems as a consequence of the Larsen B collapse: (1) The biogeographic isolation and sub-ice shelf setting of the Larsen B seep has led to novel habitat characteristics, chemoautotrophically dependent taxa and functional adaptations. (2) Benthic communities beneath the former Larsen B ice shelf are fundamentally different from assemblages at similar depths in the Weddell sea-ice zone, and resemble oligotrophic deep-sea communities. Larsen B assemblages are undergoing rapid change. (3) The previously dark, oligotrophic waters of the Larsen B embayment now support a thriving phototrophic community, with production rates and phytoplankton composition similar to other productive areas of the Weddell Sea. To document rapid changes occurring in the Larsen B ecosystem, we will use a remotely operated vehicle, shipboard samplers, and moored sediment traps. We will characterize microbial, macrofaunal and megafaunal components of the seep community; evaluate patterns of surface productivity, export flux, and benthic faunal composition in areas previously covered by the ice shelf, and compare these areas to the open sea-ice zone. These changes will be placed within the geological, glaciological and climatological context that led to ice-shelf retreat, through companion research projects funded in concert with this effort. Together these projects will help predict the likely consequences of ice-shelf collapse to marine ecosystems in other regions of Antarctica vulnerable to climate change. The research features international collaborators from Argentina, Belgium, Canada, Germany, Spain and the United Kingdom. The broader impacts include participation of a science writer; broadcast of science segments by members of the Jim Lehrer News Hour (Public Broadcasting System); material for summer courses in environmental change; mentoring of graduate students and postdoctoral fellows; and showcasing scientific activities and findings to students and public through podcasts.", "links": [ { diff --git a/datasets/USAP-0944266.json b/datasets/USAP-0944266.json index 544553ddb6..e3523865f1 100644 --- a/datasets/USAP-0944266.json +++ b/datasets/USAP-0944266.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0944266", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports renewal of funding of the WAIS Divide Science Coordination Office (SCO). The Science Coordination Office (SCO) was established to represent the research community and facilitates the project by working with support organizations responsible for logistics, drilling, and core curation. During the last five years, 26 projects have been individually funded to work on this effort and 1,511 m of the total 3,470 m of ice at the site has been collected. This proposal seeks funding to continue the SCO and related field operations needed to complete the WAIS Divide ice core project. Tasks for the SCO during the second five years include planning and oversight of logistics, drilling, and core curation; coordinating research activities in the field; assisting in curation of the core in the field; allocating samples to individual projects; coordinating the sampling effort; collecting, archiving, and distributing data and other information about the project; hosting an annual science meeting; and facilitating collaborative efforts among the research groups. The intellectual merit of the WAIS Divide project is to better predict how human-caused increases in greenhouse gases will alter climate requires an improved understanding of how previous natural changes in greenhouse gases influenced climate in the past. Information on previous climate changes is used to validate the physics and results of climate models that are used to predict future climate. Antarctic ice cores are the only source of samples of the paleo-atmosphere that can be used to determine previous concentrations of carbon dioxide. Ice cores also contain records of other components of the climate system such as the paleo air and ocean temperature, atmospheric loading of aerosols, and indicators of atmospheric transport. The WAIS Divide ice core project has been designed to obtain the best possible record of greenhouse gases during the last glacial cycle (last ~100,000 years). The site was selected because it has the best balance of high annual snowfall (23 cm of ice equivalent/year), low dust Antarctic ice that does not compromise the carbon dioxide record, and favorable glaciology. The main science objectives of the project are to investigate climate forcing by greenhouse gases, initiation of climate changes, stability of the West Antarctic Ice Sheet, and cryobiology in the ice core. The project has numerous broader impacts. An established provider of educational material (Teachers' Domain) will develop and distribute web-based resources related to the project and climate change for use in K-12 classrooms. These resources will consist of video and interactive graphics that explain how and why ice cores are collected, and what they tell us about future climate change. Members of the national media will be included in the field team and the SCO will assist in presenting information to the general public. Video of the project will be collected and made available for general use. Finally, an opportunity will be created for cryosphere students and early career scientists to participate in field activities and core analysis. An ice core archive will be available for future projects and scientific discoveries from the project can be used by policy makers to make informed decisions.", "links": [ { diff --git a/datasets/USAP-0944348.json b/datasets/USAP-0944348.json index 4fb28cd646..4ec8a6cc2c 100644 --- a/datasets/USAP-0944348.json +++ b/datasets/USAP-0944348.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-0944348", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports renewal of funding of the WAIS Divide Science Coordination Office (SCO). The Science Coordination Office (SCO) was established to represent the research community and facilitates the project by working with support organizations responsible for logistics, drilling, and core curation. During the last five years, 26 projects have been individually funded to work on this effort and 1,511 m of the total 3,470 m of ice at the site has been collected. This proposal seeks funding to continue the SCO and related field operations needed to complete the WAIS Divide ice core project. Tasks for the SCO during the second five years include planning and oversight of logistics, drilling, and core curation; coordinating research activities in the field; assisting in curation of the core in the field; allocating samples to individual projects; coordinating the sampling effort; collecting, archiving, and distributing data and other information about the project; hosting an annual science meeting; and facilitating collaborative efforts among the research groups. The intellectual merit of the WAIS Divide project is to better predict how human-caused increases in greenhouse gases will alter climate requires an improved understanding of how previous natural changes in greenhouse gases influenced climate in the past. Information on previous climate changes is used to validate the physics and results of climate models that are used to predict future climate. Antarctic ice cores are the only source of samples of the paleo-atmosphere that can be used to determine previous concentrations of carbon dioxide. Ice cores also contain records of other components of the climate system such as the paleo air and ocean temperature, atmospheric loading of aerosols, and indicators of atmospheric transport. The WAIS Divide ice core project has been designed to obtain the best possible record of greenhouse gases during the last glacial cycle (last ~100,000 years). The site was selected because it has the best balance of high annual snowfall (23 cm of ice equivalent/year), low dust Antarctic ice that does not compromise the carbon dioxide record, and favorable glaciology. The main science objectives of the project are to investigate climate forcing by greenhouse gases, initiation of climate changes, stability of the West Antarctic Ice Sheet, and cryobiology in the ice core. The project has numerous broader impacts. An established provider of educational material (Teachers' Domain) will develop and distribute web-based resources related to the project and climate change for use in K-12 classrooms. These resources will consist of video and interactive graphics that explain how and why ice cores are collected, and what they tell us about future climate change. Members of the national media will be included in the field team and the SCO will assist in presenting information to the general public. Video of the project will be collected and made available for general use. Finally, an opportunity will be created for cryosphere students and early career scientists to participate in field activities and core analysis. An ice core archive will be available for future projects and scientific discoveries from the project can be used by policy makers to make informed decisions.", "links": [ { diff --git a/datasets/USAP-1043471.json b/datasets/USAP-1043471.json index 96e733f8b1..0b4bded7fb 100644 --- a/datasets/USAP-1043471.json +++ b/datasets/USAP-1043471.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1043471", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception.", "links": [ { diff --git a/datasets/USAP-1043623_1.json b/datasets/USAP-1043623_1.json index c29d5052ff..dfe2c55bfa 100644 --- a/datasets/USAP-1043623_1.json +++ b/datasets/USAP-1043623_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1043623_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. \n\n\n\nAir-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes.\n\n\n\nA stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards.", "links": [ { diff --git a/datasets/USAP-1056396_1.json b/datasets/USAP-1056396_1.json index 95c3688a3a..6dd0a01e7d 100644 --- a/datasets/USAP-1056396_1.json +++ b/datasets/USAP-1056396_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1056396_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project supported an integrated research and education program in the fields of polar biology and environmental microbiology, focusing on single-celled eukaryotes (protists) in high latitude ice-covered Antarctic lakes systems. Protists play important roles in energy flow and material cycling, and act as both primary producers (fixing inorganic carbon by photosynthesis) and consumers (preying on bacteria by phagotrophic digestion). The McMurdo Dry Valleys (MDV) located in Victoria Land, Antarctica, harbor microbial communities which are isolated in the unique aquatic ecosystem of perennially ice-capped lakes. The project studied: (1) the impact of permanent biogeochemical gradients on protist trophic strategy, (2) the effect of major abiotic drivers (light and nutrients) on the distribution of two key mixotrophic and photoautotrophic protist species, and (3) the effect of episodic nutrient pulses on mixotroph communities in high latitude (ultraoligotrophic) MDV lakes versus low latitude (eutrophic) watersheds. \n\nSampling dates: February 4 \u2013 April 10, 2008; November 11- 28, 2012; December 12, 2012 \n\nSampling locations/depths: \nEast Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m \nWest Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m \nLake Fryxell/5m, 7m, 9m, 11m, 12m, 15m \nLake Vanda/10m, 20m, 30m, 40m, 50m, 60m, 70m, 75m, 80m\n\nTwo kinds of metadata from this project are available:\n\n1) DNA sequence data \u2013 DNA was extracted from filtered lake water (1-2L) collected from sampling locations and dates reported above. Environmental DNA was PCR-amplified using primers specific for the following genes: 16S rRNA, 18S rRNA, rbcL, cbbM, nifJ, psbA. Genes were sequenced on an Applied Biosystems DNA analyzer or an Illumina MiSeq or HiSeq instruments. All DNA sequences from this project are available via GenBank. \n\n2) Limnological metadata - Limnological data was collected from sampling locations and dates reported above. Data includes PAR, conductivity, temperature, Chlorophyll a, and macronutrients and is available via the McMurdo Dry Valleys LTER Data Center.", "links": [ { diff --git a/datasets/USAP-1141939.json b/datasets/USAP-1141939.json index 3e0a81eeca..20e2c63bdd 100644 --- a/datasets/USAP-1141939.json +++ b/datasets/USAP-1141939.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1141939", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic clouds constitute an important parameter of the surface radiation budget and thus play a significant role in Antarctic climate and climate change. The variability in, and long term trends of, cloud optical and microphysical properties are therefore fundamental in parameterizing the mixed phase (water-snow-ice) coastal Antarctic stratiform clouds experienced around the continent.\n\nUsing a spectoradiometer that covers the wavelength range of 350 to 2200nm, the downwelled spectral irradiance at the earth surface (Ross Island) will be used to retrieve the optical depth, thermodynamic phase, liquid water droplet effective radius, and ice-cloud effective particle size of overhead clouds, at hourly intervals and for an austral summer season (Oct-March). Based on the very limited data sets that exist for the maritime Antarctic, expectations are that Ross Island (Lat 78 S) should exhibit clouds with:\n\na) An abundance of supercooled liquid water, and related mixed-phase cloud processes\n\nb) Cloud nucleation from year round biogenic and oceanic sources, in an otherwise pristine environment\n\nc) Simple cloud geometries of predominantly stratiform cloud decks\n\nIncreased understanding of the cloud properties in the region of the main USAP base, McMurdo station is also relevant to operational weather forecasting relevant to aviation. A range of educational and outreach activities are associate with the project, including provision of workshops for high school teachers will be carried out.", "links": [ { diff --git a/datasets/USAP-1142084_1.json b/datasets/USAP-1142084_1.json index cef17553d0..cf63f25406 100644 --- a/datasets/USAP-1142084_1.json +++ b/datasets/USAP-1142084_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1142084_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected GPS tracks and stomach temperature records from Blackbrowed Albatross from a breeding colony at \"Canon des Sourcils Noirs\" on Kerguelen Island for the purpose of analyzing their flight patterns with regard to foraging events. We found that most birds regurgitated their stomach temperature pill transmitters early on in their trip. The GPS tracks do show their overall foraging flight patterns and include events that are characteristic of olfactory foraging such as upwind turns and zigzagging flight.", "links": [ { diff --git a/datasets/USAP-1149085_1.json b/datasets/USAP-1149085_1.json index 436a8b8433..e68cf3b1da 100644 --- a/datasets/USAP-1149085_1.json +++ b/datasets/USAP-1149085_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1149085_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CAREER award supports a project to develop physically based bounds on the amount ice sheets can contribute to sea level rise in the coming centuries. To simulate these limits, a three-dimensional discrete element model will be developed and applied to simulate regions of interest in the Greenland and Antarctic ice sheets. These regions will include Helheim Glacier, Jakobshavn Isbr\u00e4e, Pine Island Glacier and sections of the Larsen Ice Shelf. In the discrete element model the ice will be discretized into distinct blocks or boulders of ice that interact through inelastic collisions, frictional forces and bonds. The spectrum of best to worst case scenarios will be examined by varying the strength and number of bonds between neighboring blocks of ice. The worst case scenario corresponds to completely disarticulated ice that behaves in a manner akin to a granular material while the best case scenario corresponds to completely intact ice with no preexisting flaws or fractures. Results from the discrete element model will be compared with those from analogous continuum models that incorporate a plastic yield stress into the more traditional viscous flow approximations used to simulate ice sheets. This will be done to assess if a fracture permitting plastic rheology can be efficiently incorporated into large-scale ice sheet models to simulate the evolution of ice sheets over the coming centuries. This award will also support to forge a partnership with two science teachers in the Ypsilanti school district in southeastern Michigan. The Ypsilanti school district is a low income, resource- poor region with a population that consists of ~70% underrepresented minorities and ~69% of students qualify for a free or reduced cost lunch. The cornerstone of the proposed partnership is the development of lesson plans and content associated with a hands-on ice sheet dynamics activity for 6th and 7th grade science students. The activity will be designed so that it integrates into existing classroom lesson plans and is aligned with State of Michigan Science Technology, Engineering and Math (STEM) curriculum goals. The aim of this program is to not only influence the elementary school students, but also to educate the teachers to extend the impact of the partnership beyond the duration of this study. Graduate students will be mentored and engaged in outreach activities and assist in supervising undergraduate students. Undergraduates will play a key role in developing an experimental, analogue ice dynamics lab designed to illustrate how ice sheets and glaciers flow and allow experimental validation of the proposed research activities. The research program advances ice sheet modeling infrastructure by distributing results through the community based Community Ice Sheet Model.", "links": [ { diff --git a/datasets/USAP-1245766_1.json b/datasets/USAP-1245766_1.json index 24eb248a63..b9d34f4ba7 100644 --- a/datasets/USAP-1245766_1.json +++ b/datasets/USAP-1245766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1245766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Western Antarctic Peninsula is experiencing climate change at one of the fastest rates of anywhere around the globe. Accelerated climate change is likely to affect the many benthic marine invertebrates that live within narrow temperature windows along the Antarctic Continental Shelf in presently unidentified ways. At present however, there are few data on the physiological consequences of climate change on the sensitive larval stages of cold-water corals, and none on species living in thermal extremes such as polar waters. This project will collect the larvae of the non-seasonal, brooding scleractinian Flabellum impensum to be used in a month-long climate change experiment at Palmer Station. Multidisciplinary techniques will be used to examine larval development and cellular stress using a combination of electron microscopy, flow cytometry, and Inductively Coupled Plasma Mass Spectometry. Data from this project will form the first systematic study of the larval stages of polar cold-water corals, and how these stages are affected by temperature stress at the cellular and developmental level. \n\n\n\nCold-water corals have been shown to be important ecosystem engineers, providing habitat for thousands of associated species, including many that are of commercial importance. Understanding how the larvae of these corals react to warming trends seen today in our oceans will allow researchers to predict future changes in important benthic communities around the globe. Associated education and outreach include: 1) Increasing student participation in polar research by involving postdoctoral and undergraduate students in the field and research program; ii) promotion of K-12 teaching and learning programs by providing information via a research website, Twitter, and in-school talks in the local area; iii) making the data collected available to the wider research community via peer reviewed published literature and iv) reaching a larger public audience through such venues as interviews in the popular media, You Tube and other popular media outlets, and local talks to the general public.", "links": [ { diff --git a/datasets/USAP-1246045_1.json b/datasets/USAP-1246045_1.json index 791a033859..7e4d00c923 100644 --- a/datasets/USAP-1246045_1.json +++ b/datasets/USAP-1246045_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1246045_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supported a project that investigated a number of questions regarding the measurement and development of crystal orientation fabrics in ice sheets, and the relation of crystal orientation fabric to the development of stratigraphic disturbances in ice.\n\nInterpretation of thin-section fabric measurements requires accurate understanding of uncertainty and other statistical aspects of the measurements. To this end, we developed novel mathematically-justified uncertainty estimates for fabric parameters derived from thin sections. These estimates were applied to thin-section data collected at the WAIS Divide ice-core, showing that uncertainty of fabric eigenvalues derived from ice cores can be larger than usually assumed. We also examined the use of parameterized c-axis orientation-distribution functions (PODFs). We introduced the Bingham distribution to glaciology as a PODF. We developed maximum-likelihood methods of fitting PODFs to thin-section data, and used these methods to compare previously proposed PODFs and the Bingham distribution to thin-section data from the WAIS and Siple Dome ice cores.\n\nTo gain more accurate estimates of crystal orientation fabric from ice cores, we developed a method to accurately infer ice fabric leveraging both thin-section measurements and measurements of borehole sonic velocities in such a way that retains the strengths of both methods while reducing their weaknesses. We applied this technique to data from the WAIS and NEEM ice cores. Sonic velocity measurements sample large volumes of ice, and thus do not suffer from the sampling error of thin-section c-axis measurements. However, sonic-velocity measurements are often subject to large amounts of low spatial-frequency error. This error resulted in the sonic velocity measurements taken at WAIS and NEEM being of limited utility in isolation. Using our technique, we corrected this error to provide a spatially-continuous, and accurate record of fabric.\n\nWe conducted the first theoretical examination of the stability of coupled anisotropic ice flow and crystal orientation fabric development. We developed an analytical coupled anisotropic ice flow and crystal fabric evolution model. Using this model, we showed that anisotropic ice flow coupled to fabric evolution can be unstable in both simple shear and pure shear. In particular, we showed that in our model, shear bands leading to layer offsets can occur in pure shear. This has important implications for understanding the development of smaller-scale folding and other stratigraphic disturbances commonly observed in ice sheets. We also showed that plane flow in simple shear and pure shear is susceptible to fabric perturbations leading to a nonzero out-of-plane velocity component. This shows that two-dimensional models previously applied to anisotropic ice flow are insufficient to capture the full dynamics of the coupled system.\n\nThe results of this work are useful for a number of areas in glaciology. The work on uncertainty and measurement of crystal orientation fabric is not only important for studying the development of fabric in ice sheets, but also allows for improved inference of past climate from crystal fabric. Our work in the dynamics of coupled ice flow and fabric is an important step forward in understanding the development of stratigraphic disturbances in ice sheets, which are a key difficulty in constructing accurate ice-core depth-age relationships, especially in deep, old ice. To our knowledge, it is the first analytical coupled model, and the first examination of stability of the coupled system. \n\nThis work was communicated to the glaciology community by a number of talks and poster sessions, and will be published in upcoming papers. We communicated this work to the public with outreach events at the Seattle Science Center and general public lectures at Bellevue College. Code developed during the project is archived on github at \n\t\thttps://github.com/mjhay/ \nThe NEEM Sonic Model section of the github repository contains Python 2.7 code that takes in crystal-fabric eigenvalues inferred from thin section of an ice core, and sonic velocities (P,Sv,S) measured in the borehole, and produces a new and improved set of eigenvalues as a function of core depth. \n\nThe Stochastic_fabric section of the github repository contains scripts written in the Julia programming language and in the Python language, relating to stochastic models of ice sheet fabric. This includes a method of solving stochastic differential equations resulting from forcing a fabric evolution model with a velocity gradient with stochastic noise. Additional utilities are provided for maximum-likelihood fits of parameterized orientation distribution functions to thin section data, and bootstrap and analytical estimates of thin-section fabric uncertainty.", "links": [ { diff --git a/datasets/USAP-1341464_1.json b/datasets/USAP-1341464_1.json index f1bcd40769..65d6dc632e 100644 --- a/datasets/USAP-1341464_1.json +++ b/datasets/USAP-1341464_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1341464_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The rise in atmospheric carbon dioxide concentrations and associated climate changes make understanding the role of the ocean in large scale carbon cycle a priority. Geologic samples allow exploration of potential mechanisms for carbon dioxide drawdown during glacial periods through the use of geochemical proxies. Nitrogen and silicon isotope signatures from fossil diatoms (microscopic plants) are used to investigate changes in the physical supply and biological demand for nutrients (like nitrogen and silicon and carbon) in the Southern Ocean. The project will evaluate the use the nitrogen and silicon isotope proxies through a series of laboratory experiments and Southern Ocean field sampling. The results will provide quantification of real relationships between nitrogen and silicon isotopes and nutrient usage in the Southern Ocean and allow exploration of the role of other factors, including biological diversity, ice cover, and mixing, in altering the chemical signatures recorded by diatoms. Seafloor sediment samples will be used to evaluate how well the signal created in the water column is recorded by fossil diatoms buried in the seafloor. Improving the nutrient isotope proxies will allow for a more quantitative understanding of the role of polar biology in regulating natural variation in atmospheric carbon dioxide. The project will also result in the training of a graduate student and development of outreach materials targeting a broad popular audience.\n\nThis project seeks to test the fidelity of the diatom nitrogen and silicon isotope proxies, two commonly used paleoceanographic tools for investigating the role of the Southern Ocean biological pump in regulating atmospheric CO2 concentrations on glacial-interglacial timescales. Existing ground-truthing data, including culture experiments, surface sediment data and downcore reconstructions, all suggest that nutrient utilization is the primary driver of isotopic variation in the Southern Ocean. However, strong contribution of interspecific variation is implied by recent culture results. Moreover, field and laboratory studies present some contradictory results in terms of the relative importance of interspecific variation and of inferred post-depositional alteration of the nutrient isotope signals. Here, a first order test of the N and Si diatom nutrient isotope paleo-proxies, involving water column dissolved and particulate sampling and laboratory culturing of field-isolates, is proposed. Southern Ocean water, biomass, live diatoms and fossil diatom sampling will be conducted to investigate species and assemblage related variability in diatom nitrogen and silicon isotopes and their relationship to surface nutrient fields and early diagenesis. Access to fresh materials produced in an analogous environmental context to the sediments of primary interest is critical for making robust paleoceanographic reconstructions. Field sampling will occur along 175\u00b0W, transecting the Antarctic Circumpolar Current from the subtropics to the marginal ice edge. Collection of water, sinking/suspended particles and multi-core samples from 13 stations and 3 shipboard incubation experiments will be used to test four proposed hypotheses that together evaluate the significance of existing culture results and seek to allow the best use of diatom nutrient isotope proxies in evaluating the biological pump.", "links": [ { diff --git a/datasets/USAP-1341612_1.json b/datasets/USAP-1341612_1.json index 77beb06d84..0ce58b77b7 100644 --- a/datasets/USAP-1341612_1.json +++ b/datasets/USAP-1341612_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1341612_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Agglutinated foraminifera (forams for short) are early-evolving, single-celled organisms. These \"living fossils\" construct protective shells using sediment grains held together by adhesive substances that they secrete. During shell construction, agglutinated forams display amazing properties of selection - for example, some species build their shells of clear quartz grains, while other species use only grains of a specific size. Understanding how these single cells assemble complex structures may contribute to nanotechnology by enabling people to use forams as \"cellular machines\" to aid in the construction of nano-devices. This project will analyze the genomes of at least six key foram species, and then \"mine\" these genomes for technologically useful products and processes. The project will focus initially on the adhesive materials forams secrete, which may have wide application in biomedicine and biotechnology. Furthermore, the work will further develop a molecular toolkit which could open up new avenues of research on the physiology, ecology, and population dynamics of this important group of Antarctic organisms. The project will also further the NSF goals of making scientific discoveries available to the general public and of training new generations of scientists. Educational experiences related to the \"thrill of scientific exploration and discovery\" for students and the general public will be provided through freely-available short films and a traveling art/science exhibition. The project will also provide hands-on research opportunities for undergraduate students.\n\n\n\nExplorers Cove, situated on the western shore of McMurdo Sound, harbors a unique population of foraminiferan taxa at depths accessible by scuba diving that otherwise are primarily found in the deep sea. The project will use next-generation DNA sequencing and microdissection methods to obtain and analyze nuclear and mitochondrial genomes from crown members of two species each from three distinct, early-evolving foraminiferal groups. It will also use next generation sequencing methods to characterize the in-situ prokaryotic assemblages (microbiomes) of one of these groups and compare them to reference sediment microbiomes. The phyogenomic studies of the targeted Antarctic genera will help fill significant gaps in our current understanding of early foram evolution. Furthermore, comparative genomic analyses of these six species are expected to yield a better understanding of the physiology of single-chambered agglutinated forams, especially the bioadhesive proteins and regulatory factors involved in shell composition and morphogenesis. Additionally, the molecular basis of cold adaptation in forams will be examined, particularly with respect to key proteins.", "links": [ { diff --git a/datasets/USAP-1341680_1.json b/datasets/USAP-1341680_1.json index 93d52484e1..d85f20f44c 100644 --- a/datasets/USAP-1341680_1.json +++ b/datasets/USAP-1341680_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1341680_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intellectual Merit: This project will yield new information on the long term Antarctic climate and landscape evolution from measurements of cosmogenic nuclides in quartz sand from two unique permafrost cores collected in Beacon Valley, Antarctica. The two cores have already been drilled in ice-cemented, sand-rich permafrost at 5.5 and 30.6 meters depth, and are currently in cold storage at the University of Washington. The cores are believed to record the monotonic accumulation of sand that has been blown into lower Beacon Valley and inflated the surface over time. The rate of accumulation and any hiatus in the accumulation are believed to reflect in part the advance and retreat of the Taylor Glacier. Preliminary measurements of cosmogenically-produced beryllium (10Be) and aluminum (26Al) in quartz sand in the 5.5-meter depth core reveal that it has been accreting at a rate of 2.5 meters/Myr for the past million years. Furthermore, prior to that time, lower Beacon Valley was most likely covered (shielded from the atmosphere thereby having no or very low production of cosmogenic nuclides in quartz) by Taylor Glacier from 1 to 3.5 Myr BP. These preliminary measurements also suggest that the 30.6 meter core may provide a record of over 10 million years. The emphasis is the full characterization of the core and analysis of cosmogenic nuclides (including cosmogenic neon) in the 30.6 meter permafrost core to develop a burial history of the sands and potentially a record the waxing and waning of the Taylor Glacier. This will allow new tests of our current understanding of surface dynamics and climate history in the McMurdo Dry Valleys (MDV) based on the dated stratigraphy of eolian sand that has been accumulating and inflating the surface for millions of years. This is a new process of surface inflation whose extent has not been well documented, and holds the potential to develop a continuous history of surface burial and glacial expansion. This project will provide a new proxy for understanding the climatic history of the Dry Valleys and will test models for the evolution of permafrost in Beacon Valley.\n\n\n\nBroader impacts: \n\n\n\nThe landscape history of the McMurdo Dry Valleys is important because geological deposits there comprise the richest terrestrial record available from Antarctica. By testing the current age model for these deposits, we will improve understanding of Antarctica?s role in global climate change. This project will train one graduate and one undergraduate student in geochemistry, geochronology, and glacial and periglacial geology. They will participate substantively in the research and are expected to develop their own original ideas. Results from this work will be incorporated into undergraduate and graduate teaching curricula, will be published in the peer reviewed literature, and the data will be made public.", "links": [ { diff --git a/datasets/USAP-1341712.json b/datasets/USAP-1341712.json index 909499caf7..64acb7fd84 100644 --- a/datasets/USAP-1341712.json +++ b/datasets/USAP-1341712.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1341712", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Many of the natural processes that modify the landscape inhabited by humans occur over very long timescales, making them difficult to observe. Exceptions include rare catastrophic events such as earthquakes, volcanic eruptions, and floods that occur on short timescales. Many significant processes that affect the land and landscape that we inhabit operate on time scales imperceptible to humans. One of these processes is wind transport of sand, with related impacts to exposed rock surfaces and man-made objects, including buildings, windshields, solar panels and wind-farm turbine blades. The goal of this project is to gain an understanding of wind erosion processes over long timescales, in the Antarctic Dry Valleys, a cold desert environment where there were no competing processes (such as rain and vegetation) that might mask the effects. The main objective is recovery of rock samples that were deployed in 1983/1984 at 11 locations in the Antarctic Dry Valleys, along with measurements on the rock samples and characterization of the sites. In the late 1980s and early 1990s some of these samples were returned and indicated more time was needed to accumulate information about the timescales and impacts of the wind erosion processes. This project will allow collection of the remaining samples from this experiment after 30 to 31 years of exposure. The field work will be carried out during the 2014/15 Austral summer. The results will allow direct measurement of the abrasion rate and hence the volumes and timescales of sand transport; this will conclude the longest direct examination of such processes ever conducted. Appropriate scaling of the results may be applied to buildings, vegetation (crops), and other aspects of human presence in sandy and windy locations, in order to better determine the impact of these processes and possible mitigation of the impacts. The project is a collaborative effort between a small business, Malin Space Scien ce Systems (MSSS), and the University of Washington (UW). MSSS will highlight this Antarctic research on its web site, by developing thematic presentations describing our research and providing a broad range of visual materials. The public will be engaged through daily updates on a website and through links to material prepared for viewing in Google Earth. UW students will be involved in the laboratory work and in the interpretation of the results.\n\n\nTechnical Description of Project:\n\nThe goal of this project is to study the role of wind abrasion by entrained particles in the evolution of the McMurdo Dry Valleys in the Transantarctic Mountains. During the 1983 to 1984 field seasons, over 5000 rock targets were installed at five heights facing the 4 cardinal directions at 10 locations (with an additional site containing fewer targets) to study rates of physical weathering due primarily to eolian abrasion. In addition, rock cubes and cylinders were deployed at each site to examine effects of chemical weathering. The initial examination of sam ples returned after 1, 5, and 10 years of exposure, showed average contemporary abrasion rates consistent with those determined by cosmogenic isotope studies, but further stress that average should not be interpreted as meaning uniform. The samples will be characterized using mass measurements wtih 0.01 mg precision balances, digital microphotography to compare the evolution of their surface features and textures, SEM imaging to examine the micro textures of abraded rock surfaces, and optical microscopy of thin sections of a few samples to examine the consequences of particle impacts extending below the abraded surfaces. As much as 60-80% of the abrasion measured in samples from 1984-1994 appears to have occurred during a few brief hours in 1984. This is consistent with theoretical models that suggest abrasion scales as the 5th power of wind velocity. The field work will allow return of multiple samples after three decades of exposure, which will provide a statistical sampling (beyond what is acquired by studying a single sample), and will yield the mass loss data in light of complementary environmental and sand kinetic energy flux data from other sources (e.g. LTER meteorology stations). This study promises to improve insights into one of the principal active geomorphic process in the Dry Valleys, an important cold desert environment, and the solid empirical database will provide general constraints on eolian abrasion under natural conditions.", "links": [ { diff --git a/datasets/USAP-1341717_1.json b/datasets/USAP-1341717_1.json index f28a72b9de..d925310b6a 100644 --- a/datasets/USAP-1341717_1.json +++ b/datasets/USAP-1341717_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1341717_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The one place on Earth consistently showing increases in sea ice area, duration, and concentration is the Ross Sea in Antarctica. Satellite imagery shows about half of the Ross Sea increases are associated with changes in the austral fall, when the new sea ice is forming. The most pronounced changes are also located near polynyas, which are areas of open ocean surrounded by sea ice. To understand the processes driving the sea ice increase, and to determine if the increase in sea ice area is also accompanied by a change in ice thickness, this project will conduct an oceanographic cruise to the polynyas of the Ross Sea in April and May, 2017, which is the austral fall. The team will deploy state of the art research tools including unmanned airborne systems (UASs, commonly called drones), autonomous underwater vehicles (AUVs), and remotely operated underwater vehicles (ROVs). Using these tools and others, the team will study atmospheric, oceanic, and sea ice properties and processes concurrently. A change in sea ice production will necessarily change the ocean water below, which may have significant consequences for global ocean circulation patterns, a topic of international importance. All the involved institutions will be training students, and all share the goal of expanding climate literacy in the US, emphasizing the role high latitudes play in the Earth's dynamic climate.\n\n\n\nThe main goal of the project is to improve estimates of sea ice production and water mass transformation in the Ross Sea. The team will fully capture the spatial and temporal changes in air-ice-ocean interactions when they are initiated in the austral fall, and then track the changes into the winter and spring using ice buoys, and airborne mapping with the newly commissioned IcePod instrument system, which is deployed on the US Antarctic Program's LC-130 fleet. The oceanographic cruise will include stations in and outside of both the Terra Nova Bay and Ross Ice Shelf polynyas. Measurements to be made include air-sea boundary layer fluxes of heat, freshwater, and trace gases, radiation, and meteorology in the air; ice formation processes, ice thickness, snow depth, mass balance, and ice drift within the sea ice zone; and temperature, salinity, and momentum in the ocean below. Following collection of the field data, the team will improve both model parameterizations of air-sea-ice interactions and remote sensing algorithms. Model parameterizations are needed to determine if sea-ice production has increased in crucial areas, and if so, why (e.g., stronger winds or fresher oceans). The remote sensing validation will facilitate change detection over wider areas and verify model predictions over time. Accordingly this project will contribute to the international Southern Ocean Observing System (SOOS) goal of measuring essential climate variables continuously to monitor the state of the ocean and ice cover into the future.", "links": [ { diff --git a/datasets/USAP-1419979_1.json b/datasets/USAP-1419979_1.json index 158e845ecd..18c083af0a 100644 --- a/datasets/USAP-1419979_1.json +++ b/datasets/USAP-1419979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1419979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The PIs have designed and built a new type of rapid access ice drill (RAID) for use in Antarctica. This community tool has the ability to rapidly drill through ice up to 3300 m thick and then collect samples of the ice, ice-sheet bed interface, and bedrock substrate below. This drilling technology will provide a new way to obtain in situ measurements and samples for interdisciplinary studies in geology, glaciology, paleoclimatology, microbiology, and astrophysics. The RAID drilling platform will give the scientific community access to records of geologic and climatic change on a variety of timescales, from the billion-year rock record to million-year ice and climate histories. Development of this platform will enable scientists to address critical questions about the deep interface between the Antarctic ice sheets and the substrate below. Phase I was for design and work with the research community to develop detailed science requirements for the drill. This proposal, Phase II, constructed, assembled and tested the RAID drilling platform at a site near McMurdo (Minna Bluff) where 700-m thick ice sits on bedrock.", "links": [ { diff --git a/datasets/USAP-1443260.json b/datasets/USAP-1443260.json index 509bc07877..6f6625289e 100644 --- a/datasets/USAP-1443260.json +++ b/datasets/USAP-1443260.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1443260", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Previous work has shown that the Allan Hills Blue Ice Area preserves a continuous climate record that extends back at least 400k years along an ice-flow line. Two kilometers to the east of this flow line, the oldest ice on Earth (~1 million years old) has been found only 120 m below the surface. Meteorites collected in the area are reported to be as old as 1.8 million years, suggesting still older ice may be present. Combined, these data suggest that the Allan Hills area could contain a continuous, well-resolved environmental record spanning at least the last million years. The area has been selected as an upcoming target for the Intermediate Depth Ice Core Drill by the US Ice Core Working Group. The project goal of this project is to select a core site to extract a continuous record of million-year-old ice. Ice-penetrating radar surveys will be used to track outcropping dated radar-detected layers throughout the region. The maps of ice-thickness and isochronous layers will be used to select a potential drill site. Ice cores provide a robust reconstruction of past climate and extending this record beyond 800k years will open new opportunities to study the Earth climate system. The data collected will also be used to investigate bedrock and ice flow conditions that are favorable to the preservation of old ice, which may allow targeted investigation of other blue ice areas in Antarctica. Results from this study will ensure the successful future collection of the oldest, continuous ice core climate record thereby advancing scientific discovery and innovation. The study will also enhance research partnerships and infrastructure by extension of the framework for an \"Ice Climate Park\" in the Allan Hills at which any interested US or foreign investigator could study continuous climate archives for the past 1+ Ma through the collection of highly accessible, large volume samples from developed ice age and flow maps. UMaine's state-of-th e-art cyber-infrastructure will provide the global community of scientists with fast access to project result. Work will be presented to the public through outreach programs including, but not limited to, school visits, on-site tours, and media releases. Lastly, the project will provide Antarctic fieldwork and research experience for a graduate student and support the career development of two early career scientists.", "links": [ { diff --git a/datasets/USAP-1443470_1.json b/datasets/USAP-1443470_1.json index a808bc7add..4a0d8e5008 100644 --- a/datasets/USAP-1443470_1.json +++ b/datasets/USAP-1443470_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1443470_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the past, Earth's climate underwent dramatic changes that influenced physical, chemical, geological, and biological processes on a global scale. Such changes left an imprint in Earth's atmosphere, as shown by the variability in abundances of trace gases like carbon dioxide and methane. In return, changes in the atmospheric trace gas composition affected Earth's climate. Studying compositional variations of the past atmosphere helps us understand the history of interactions between global biogeochemical cycles and Earth?s climate. The most reliable information on past atmospheric composition comes from analysis of air entrapped in polar ice cores. This project aims to generate ice-core records of relatively short-lived, very-low-abundance trace gases to determine the range of past variability in their atmospheric levels and investigate the changes in global biogeochemical cycles that caused this variability. This project measures three such gases: carbonyl sulfide, methyl chloride, and methyl bromide. Changes in carbonyl sulfide can indicate changes in primary productivity and photosynthetic update of carbon dioxide. Changes in methyl chloride and methyl bromide significantly impact natural variability in stratospheric ozone. In addition, the processes that control atmospheric levels of methyl chloride and methyl bromide are shared with those controlling levels of atmospheric methane. The measurements will be made in the new ice core from the South Pole, which is expected to provide a 40,000-year record.\n\n\n\nThe primary focus of this project is to develop high-quality trace gas records for the entire Holocene period (the past 11,000 years), with additional, more exploratory measurements from the last glacial period including the period from 29,000-36,000 years ago when there were large changes in atmospheric methane. Due to the cold temperatures of the South Pole ice, the proposed carbonyl sulfide measurements are expected to provide a direct measure of the past atmospheric variability of this gas without the large hydrolysis corrections that are necessary for interpretation of measurements from ice cores in warmer settings. Furthermore, we will test the expectation that contemporaneous measurements from the last glacial period in the deep West Antarctic Ice Sheet Divide ice core will not require hydrolysis loss corrections. With respect to methyl chloride, we aim to verify and improve the existing Holocene atmospheric history from the Taylor Dome ice core in Antarctica. The higher resolution of our measurements compared with those from Taylor Dome will allow us to derive a more statistically significant relationship between methyl chloride and methane. With respect to methyl bromide, we plan to extend the existing 2,000-year database to 11,000 years. Together, the methyl bromide and methyl chloride records will provide strong measurement-based constraints on the natural variability of stratospheric halogens during the Holocene period. In addition, the methyl bromide record will provide insight into the correlation between methyl chloride and methane during the Holocene period due to common sources and sinks.", "links": [ { diff --git a/datasets/USAP-1443637_1.json b/datasets/USAP-1443637_1.json index 7ced973d9c..71613cf34c 100644 --- a/datasets/USAP-1443637_1.json +++ b/datasets/USAP-1443637_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1443637_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We studied the molecular evolution and physiology of two types of ion channels: voltage gated potassium channels and transient receptor potential (TRP) channels. We also studied the molecular evolution and expression of water-passing channels, the aquaporins, to determine if these show signs of evolutionary change in notothenioids. \n\nWe noted apparent amino acid substitutions at a number of sites in a muscle-expressing\npotassium channel (Kv1.3). We were surprised to find that although the AAs at these sites\nappeared highly conserved in teleosts and even in tetrapods, reverting them singly, in pairs,\nor all together back to the ancestral condition had no effect on the biophysical properties of\nthe channels that we measured (voltage-sensitivity; rate of activation) at room temperature\nas well as over a range of temperatures down to 4oC.\n\nThe results for the TRP channels and aquaporins can be accessed in their publications. York and Zakon (2022) in Genome Biology and Evolution, and two forthcoming papers.", "links": [ { diff --git a/datasets/USAP-1444167_1.json b/datasets/USAP-1444167_1.json index 725be08756..788a3160d5 100644 --- a/datasets/USAP-1444167_1.json +++ b/datasets/USAP-1444167_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1444167_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic fish and their early developmental stages are an important component of the food web that sustains life in the cold Southern Ocean (SO) that surrounds Antarctica. They feed on smaller organisms and in turn are eaten by larger animals, including seals and killer whales. Little is known about how rising ocean temperatures will impact the development of Antarctic fish embryos and their growth after hatching. This project will address this gap by assessing the effects of elevated temperatures on embryo viability, on the rate of embryo development, and on the gene \"toolkits\" that respond to temperature stress. One of the two species to be studied does not produce red blood cells, a defect that may make its embryos particularly vulnerable to heat. The outcomes of this research will provide the public and policymakers with \"real world\" data that are necessary to inform decisions and design strategies to cope with changes in the Earth's climate, particularly with respect to protecting life in the SO. The project will also further the NSF goals of training new generations of scientists, including providing scientific training for undergraduate and graduate students, and of making scientific discoveries available to the general public. This includes the unique educational opportunity for undergraduates to participate in research in Antarctica and engaging the public in several ways, including the development of professionally-produced educational videos with bi-lingual \nclosed captioning. \nSince the onset of cooling of the SO about 40 million years ago, evolution of Antarctic marine organisms has been driven by the development of cold temperatures. Because body temperatures of Antarctic fishes fall in a narrow range determined by their habitat (-1.9 to +2.0 C), they are particularly attractive models for understanding how organismal physiology and biochemistry have been shaped to maintain life in a cooling environment. Yet these fishes are now threatened by rapid warming of the SO. The long-term objective of this project is to understand the capacities of Antarctic fishes to acclimatize and/or adapt to oceanic warming through analysis of their underlying genetic \"toolkits.\" This objective will be accomplished through three Specific Aims: 1) assessing the effects of elevated temperatures on gene expression during development of embryos; 2) examining the effects of elevated temperatures on embryonic morphology and on the temporal and spatial patterns of gene expression; and 3) evaluating the evolutionary mechanisms that have led to the loss of the red blood cell genetic program by the white-blooded fishes. Aims 1 and 2 will be investigated by acclimating experimental embryos of both red-blooded and white-blooded fish to elevated temperatures. Differential gene expression will be examined through the use of high throughput RNA sequencing. The temporal and spatial patterns of gene expression in the context of embryonic morphology (Aim 2) will be determined by microscopic analysis of embryos \"stained\" with (hybridized to) differentially expressed gene probes revealed by Aim 1; other key developmental marker genes will also be used. The genetic lesions resulting from loss of red blood cells by the white-blooded fishes (Aim 3) will be examined by comparing genes and genomes in the two fish groups.", "links": [ { diff --git a/datasets/USAP-1542778.json b/datasets/USAP-1542778.json index 31c0070315..2477e81515 100644 --- a/datasets/USAP-1542778.json +++ b/datasets/USAP-1542778.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1542778", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a three-year effort to study physical properties of the South Pole ice core to help provide a high-time-resolution history of trace gases and other paleoclimatic indicators from an especially cold site with high preservation potential for important signals. The physical-properties studies include visual inspection to identify any flow disturbances and for identifying annual layers and other features, and combined bubble, grain and ice crystal orientation studies to better understand the processes occurring in the ice that affect the climate record and the ice-sheet behavior. Success of these efforts will provide necessary support for dating and quality control to others studying the ice core, as well as determining the climate history of the site, flow state, and key physical processes in ice.\n\nThe intellectual merits of the project include better understanding of physical processes, paleoclimatic reconstruction, dating of the ice, and quality assurance. Visual inspection of the core will help identify evidence of flow disturbances that would disrupt the integrity of the climate record and will reveal volcanic horizons and other features of interest. Annual layer counting will be conducted to help estimate accumulation rate over time as recorded in the ice core. Measurements of C-axis fabric, grain size and shapes, and bubble characteristics will provide information about processes occurring in the ice sheet as well as the history of ice flow, current flow state and how the ice is flowing and how easily it will flow in the future. Analysis of this data in conjunction with microCT data will help to reveal grain-scale processes. The broader impacts of the project include support for an early-career, post-doctoral researcher, and improved paleoclimatic data of societal relevance. The results will be incorporated into the active program of education and outreach which have educated many students, members of the public and policy makers through the sharing of information and educational materials about all aspects of ice core science and paleoclimate.", "links": [ { diff --git a/datasets/USAP-1543383_1.json b/datasets/USAP-1543383_1.json index 47a7df80f3..e95abec2bb 100644 --- a/datasets/USAP-1543383_1.json +++ b/datasets/USAP-1543383_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1543383_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "microRNAs (miRNAs) are key post-transcriptional regulators of gene expression that modulate development and physiology in temperate animals. Although miRNAs act by binding to messenger RNAs (mRNAs), a process that is strongly sensitive to temperature, miRNAs have yet not been studied in Antarctic animals, including Notothenioid fish, which dominate the Southern Ocean. This project will compare miRNA regulation in 1) Antarctic vs. temperate fish to learn the roles of miRNA regulation in adaptation to constant cold; and in 2) bottom-dwelling, dense-boned, red-blooded Nototheniods vs. high buoyancy, osteopenic, white-blooded icefish to understand miRNA regulation in specialized organs after the evolution of the loss of hemoglobin genes and red blood cells, the origin of enlarged heart and vasculature, and the evolution of increased buoyancy, which arose by decreased bone mineralization and increased lipid deposition. Aim 1 is to test the hypothesis that Antarctic fish evolved miRNA-related genome specializations in response to constant cold. The project will compare four Antarctic Notothenioid species to two temperate Notothenioids and two temperate laboratory species to test the hypotheses that (a) Antarctic fish evolved miRNA genome repertoires by loss of ancestral genes and/or gain of new genes, (b) express miRNAs that are involved in cold tolerance, and (c) respond to temperature change by changing miRNA gene expression. Aim 2 is to test the hypothesis that the evolution of icefish from red-blooded bottom-dwelling ancestors was accompanied by an altered miRNA genomic repertoire, sequence, and/or expression. The project will test the hypotheses that (a) miRNAs in icefish evolved in sequence and/or in expression in icefish specializations, including head kidney (origin of red blood cells); heart (changes in vascular system), cranium and pectoral girdle (reduced bone mineral density); and skeletal muscle (lipid deposition), and (b) miRNAs that evolved in icefish specializations had ancestral functions related to their derived roles in icefish, as determined by functional tests of zebrafish orthologs of icefish miRNAs in developing zebrafish. The program will isolate, sequence, and determine the expression of miRNAs and mRNAs using high-throughput transcriptomics and novel software. Results will show how the microRNA system evolves in vertebrate animals pushed to physiological extremes and provide insights into the prospects of key species in the most rapidly warming part of the globe.", "links": [ { diff --git a/datasets/USAP-1543454_1.json b/datasets/USAP-1543454_1.json index 6771af1bda..31dfe18651 100644 --- a/datasets/USAP-1543454_1.json +++ b/datasets/USAP-1543454_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1543454_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic ice core tephra records tend to be dominated by proximal volcanism and infrequently contain tephra from distal volcanoes within and off of the continent. Tephra layers in East Antarctic ice cores are largely derived from Northern Victoria Land volcanoes. For example, 43 out of 55 tephra layers in Talos Dome ice core are from local volcanoes. West Antarctic ice cores are dominated by tephra from Marie Byrd Land volcanoes. Thirty-six out of the 52 tephra layers in WAIS are from Mt. Berlin or Mt.Takahe. It would be expected that the majority of the tephra layers found in cores on and adjacent to the Antarctic Peninsula and Weddell Sea should be from Sub-Antarctic islands (e.g., South Sandwich and South Shetland Islands). Unfortunately, these records are poorly characterized, making correlations to the source volcanoes very unlikely.\n\nThe South Pole ice core (SPICEcore) is uniquely situated to capture the volcanic records from all of these regions of the continent, as well as sub-tropical eruptions with significant global climate signatures. Twelve visible tephra layers have been characterized in SPICEcore and represent tephra produced by volcanoes from the Sub-Antarctic Islands (6), Marie Byrd Land (5), and one from an unknown sub-tropical eruption, likely from South America. Three of these tephra layers correlate to other ice core tephra providing important \u201cpinning points\u201d for timescale calibrations, recently published (Winski et al, 2019). Two tephra layers from Marie Byrd Land correlate to WAIS Divide ice core tephra (15.226ka and 44.864ka), and one tephra eruptive from the South Sandwich Island can be correlated EPICA Dome C, Vostok, and RICE (3.559ka). An additional eight cryptotephra have been characterized, and one layer geochemically correlates with the 1257 C.E. eruption of Samalas volcano in Indonesia.\n\nSPICEcore does not have a tephra record dominated by one volcanic region. Instead, it contains more of the tephra layers derived from off-continent volcanic sources. The far-travelled tephra layers from non-Antarctic sources improve our understanding of tephra transport to the interior of Antarctica. The location in the middle of the continent along with the longer transport distances from the local volcanoes has allowed for a unique tephra record to be produced that begins to link more of future ice core records together.\n\n", "links": [ { diff --git a/datasets/USAP-1543498_1.json b/datasets/USAP-1543498_1.json index 3ae8b05685..fb96ff2c2a 100644 --- a/datasets/USAP-1543498_1.json +++ b/datasets/USAP-1543498_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1543498_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Ad\u00e9lie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and \"NestCheck\" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Ad\u00e9lie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region.", "links": [ { diff --git a/datasets/USAP-1544526_1.json b/datasets/USAP-1544526_1.json index 6ea3dd8009..878e723f1f 100644 --- a/datasets/USAP-1544526_1.json +++ b/datasets/USAP-1544526_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1544526_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students.\n\nLittle is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms.", "links": [ { diff --git a/datasets/USAP-1643534_1.json b/datasets/USAP-1643534_1.json index f752709e1e..649ee7e533 100644 --- a/datasets/USAP-1643534_1.json +++ b/datasets/USAP-1643534_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1643534_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project seeks to make detailed measurements of the oxygen content of the surface ocean along the Western Antarctic Peninsula. Detailed maps of changes in net oxygen content will be combined with measurements of the surface water chemistry and phytoplankton distributions. The project will determine the extent to which on-shore or offshore phytoplankton blooms along the peninsula are likely to lead to different amounts of carbon being exported to the deeper ocean. The project will analyze oxygen in relation to argon that will allow determination of the physical and biological contributions to surface ocean oxygen dynamics. These assessments will be combined with spatial and temporal distributions of nutrients (iron and macronutrients) and irradiances. This will allow the investigators to unravel the complex interplay between ice dynamics, iron and physical mixing dynamics as they relate to Net Community Production (NCP) in the region. NCP measurements will be normalized to Particulate Organic Carbon (POC) and be used to help identify area of \"High Biomass and Low NCP\" and those with \"Low Biomass and High NCP\" as a function of microbial plankton community composition. The team will use machine learning methods- including decision tree assemblages and genetic programming- to identify plankton groups key to facilitating biological carbon fluxes. Decomposing the oxygen signal along the West Antarctic Peninsula will also help elucidate biotic and abiotic drivers of the O2 saturation to further contextualize the growing inventory of oxygen measurements (e.g. by Argo floats) throughout the global oceans.", "links": [ { diff --git a/datasets/USAP-1643722_1.json b/datasets/USAP-1643722_1.json index b70e94a413..f0adb9c5b7 100644 --- a/datasets/USAP-1643722_1.json +++ b/datasets/USAP-1643722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1643722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. \r\n\r\nMethane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project.", "links": [ { diff --git a/datasets/USAP-1643864_1.json b/datasets/USAP-1643864_1.json index 18f9e75a9b..694268bf6a 100644 --- a/datasets/USAP-1643864_1.json +++ b/datasets/USAP-1643864_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1643864_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises new photographs and measurements of a WAIS Divide vertical thin section, WDC-06A 420 VTS, previously prepared and measured by J. Fitzpatrick, D. E. Voigt, and R. Alley (dataset DOI: 10.7265/N5W093VM; http://www.usap-dc.org/view/dataset/609605) as part of a larger study of the WAIS Divide ice core (Fitzpatrick, J. et al, 2014, Physical properties of the WAIS Divide ice core, Journal of Glaciology, 60, 224, 1181-1198. (doi:10.3189/2014JoG14J100). These images were taken as a design test of our new automated lightweight c-axis analyzer, dubbed ALPACA, which implements the ice fabric analysis functionality of the Wilen system used by Fitzpatrick et al. in an easily-portable, field-deployable form factor.", "links": [ { diff --git a/datasets/USAP-1644004_1.json b/datasets/USAP-1644004_1.json index e5c4f16662..570f064e30 100644 --- a/datasets/USAP-1644004_1.json +++ b/datasets/USAP-1644004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1644004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This research project is a multidisciplinary effort that brings together a diverse team of scientists from multiple institutions together to understand the foraging behavior and physiology of leopard seals and their role in the Southern Ocean food web. The project will examine the physiology and behavior of leopard seals to in an effort to determine their ability to respond to potential changes in their habitat and foraging areas. Using satellite tracking devices the team will examine the movement and diving behavior of leopard seals and couple this information with measurements of their physiological capacity. The project will determine whether leopard seals- who feed on diverse range of prey- are built differently than their deep diving relatives the Weddell and elephant seal who feed on fish and squid. The team will also determine whether leopard seals are operating at or near their physiological capability to determine how much, if any, ?reserve capacity? they might have to forage and live in changing environments. A better understanding of their home ranges, movement patterns, and general behavior will also be informative to help in managing human-leopard seal interactions. The highly visual nature of the data and analysis for this project lends itself to public and educational display and outreach, particularly as they relate to the changing Antarctic habitats. The project will use the research results to educate the public on the unique physiological and ecological adaptations to extreme environments seen in diving marine mammals, including adaptations to exercise under low oxygen conditions and energy utilization, which affect and dictate the lifestyle of these exceptional organisms. The results of the project will also contribute to the broader understanding that may enhance the aims of managing marine living resources.\n\nThe leopard seal is an apex predator in the Antarctic ecosystem. This project seeks to better understand the ability of the leopard seal to cope with a changing environment. The project will first examine the foraging behavior and habitat utilization of leopard seals using satellite telemetry. Specifically, satellite telemetry tags will be used to obtain dive profiles and movement data for individuals across multiple years. Diet and trophic level positions across multiple temporal scales will then be determined from physiological samples (e.g., blood, vibrissae, blubber fatty acids, stable isotopes, fecal matter). Oceanographic data will be integrated with these measures to develop habitat models that will be used to assess habitat type, habitat utilization, habitat preference, and home range areas for individual animals. Diet composition for individual seals will be evaluated to determine whether specific animals are generalists or specialists. Second, the team will investigate the physiological adaptations that allow leopard seals to be apex predators and determine to what extent leopard seals are working at or near their physiological limit. Diving behavior and physiology of leopard seals will be evaluated (for instance the aerobic dive limit for individual animals and skeletal muscle adaptations will be determined for diving under hypoxic conditions). Data from time-depth recorders will be used to determine foraging strategies for individual seals, and these diving characteristics will be related to physiological variables (e.g., blood volume, muscle oxygen stores) to better understand the link between foraging behavior and physiology. The team will compare myoglobin storage in swimming muscles associated with both forelimb and hind limb propulsion and the use of anaerobic versus aerobic metabolic systems while foraging.", "links": [ { diff --git a/datasets/USAP-1644073_1.json b/datasets/USAP-1644073_1.json index bdc837c148..6005d6de69 100644 --- a/datasets/USAP-1644073_1.json +++ b/datasets/USAP-1644073_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1644073_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phytoplankton blooms in the coastal waters of the Ross Sea, Antarctica are typically dominated by either diatoms or Phaeocystis Antarctica (a flagellated algae that often can form large colonies in a gelatinous matrix). The project seeks to determine if an association of bacterial populations with Phaeocystis antarctica colonies can directly supply Phaeocystis with Vitamin B12, which can be an important co-limiting micronutrient in the Ross Sea. The supply of an essential vitamin coupled with the ability to grow at lower iron concentrations may put Phaeocystis at a competitive advantage over diatoms. Because Phaeocystis cells can fix more carbon than diatoms and Phaeocystis are not grazed as efficiently as diatoms, the project will help in refining understanding of carbon dynamics in the region as well as the basis of the food web webs. Such understanding also has the potential to help refine predictive ecological models for the region. The project will conduct public outreach activities and will contribute to undergraduate and graduate research. Engagement of underrepresented students will occur during summer student internships. A collaboration with Italian Antarctic researchers, who have been studying the Terra Nova Bay ecosystem since the 1980s, aims to enhance the project and promote international scientific collaborations. \n\n\n\nThe study will test whether a mutualistic symbioses between attached bacteria and Phaeocystis provides colonial cells a mechanism for alleviating chronic Vitamin B12 co-limitation effects thereby conferring them with a competitive advantage over diatom communities. The use of drifters in a time series study will provide the opportunity to track in both space and time a developing algal bloom in Terra Nova Bay and to determine community structure and the physiological nutrient status of microbial populations. A combination of flow cytometry, proteomics, metatranscriptomics, radioisotopic and stable isotopic labeling experiments will determine carbon and nutrient uptake rates and the role of bacteria in mitigating potential vitamin B12 and iron limitation. Membrane inlet and proton transfer reaction mass spectrometry will also be used to estimate net community production and release of volatile organic carbon compounds that are climatically active. Understanding how environmental parameters can influence microbial community dynamics in Antarctic coastal waters will advance an understanding of how changes in ocean stratification and chemistry could impact the biogeochemistry and food web dynamics of Southern Ocean ecosystems.", "links": [ { diff --git a/datasets/USAP-1644197_1.json b/datasets/USAP-1644197_1.json index b900719415..66608c0d2f 100644 --- a/datasets/USAP-1644197_1.json +++ b/datasets/USAP-1644197_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1644197_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Glacier ice loss from Antarctica has the potential to lead to a significant rise in global sea level. One line of evidence for accelerated glacier ice loss has been an increase in the rate at which the land has been rising across the Antarctic Peninsula as measured by GPS receivers. However, GPS observations of uplift are limited to the last two decades. One goal of this study is to determine how these newly observed rates of uplift compare to average rates of uplift across the Antarctic Peninsula over a longer time interval. Researchers reconstructed past sea levels using the age and elevation of ancient beaches now stranded above sea level on the low-lying coastal hills of the Antarctica Peninsula and determined the rate of uplift over the last 5,000 years. The researchers analyzed the structure of the beaches using ground-penetrating radar and the characteristics of beach sediments to understand how sea-level rise and past climate changes are recorded in beach deposits. We found that unlike most views of how sea level changed across Antarctica over the last 5,000 years, its history is complex with periods of increasing rates of sea-level fall as well as short periods of potential sea-level rise. We attribute these oscillations in the nature of sea-level change across the Antarctic Peninsula to changes in the ice sheet over the last 5,000 years. These changes in sea level also suggest our understanding of the Earth structure beneath the Antarctic Peninsula need to be revised. The beach deposits themselves also record periods of climate change as reflected in the size and shape of their cobbles. This project has lead to the training of five graduate students, three undergraduate students, and outreach talks to k-12 schools in three communities.", "links": [ { diff --git a/datasets/USAP-1644234_1.json b/datasets/USAP-1644234_1.json index 9faf6d4110..1519c80a10 100644 --- a/datasets/USAP-1644234_1.json +++ b/datasets/USAP-1644234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1644234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. \n\nTechnical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. \n\n\n\nThe cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions.\n\nThe research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica.", "links": [ { diff --git a/datasets/USAP-1656344_1.json b/datasets/USAP-1656344_1.json index bf48b216a6..1105c93a9c 100644 --- a/datasets/USAP-1656344_1.json +++ b/datasets/USAP-1656344_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1656344_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the \"omics\" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). \n\n\n\nThe critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare.", "links": [ { diff --git a/datasets/USAP-1744755_1.json b/datasets/USAP-1744755_1.json index c1015164bc..26e90bfb62 100644 --- a/datasets/USAP-1744755_1.json +++ b/datasets/USAP-1744755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1744755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. ", "links": [ { diff --git a/datasets/USAP-1744828_1.json b/datasets/USAP-1744828_1.json index 3aa593ed12..660de5cc36 100644 --- a/datasets/USAP-1744828_1.json +++ b/datasets/USAP-1744828_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1744828_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This proposal is directed toward an investigation of the coupling phenomena between the solar wind and the Earth's magnetosphere and ionosphere, particularly on the day side of the Earth and observed simultaneously at high latitudes in both northern and southern hemispheres. Through past NSF support, several magnetometers have been deployed in Antarctica, Greenland, and Svalbard, while new collaborations have been developed with the Polar Research Institute of China (PRIC) to further increase coverage through data sharing. This project will expand the existing Virginia Tech-PRIC partnership to include New Jersey Institute of Technology, University of New Hampshire, and the Technical University of Denmark and (1) construct two new stations to be deployed by PRIC along a chain from Zhongshan station to Dome A to complete a conjugate area array, (2) integrate data from all stations into a common format, and (3) address two focused science questions. Both instrument deployment and data processing efforts are motivated by a large number of solar wind-magnetosphere-ionosphere (SWMI) coupling science questions; this project will address two questions pertaining to Ultra Low Frequency (ULF) waves: (1) What is the global ULF response to Hot Flow Anomalies (HFA) and how is it affected by asymmetries in the SWMI system? (2) How do dawn-dusk and north-south asymmetries in the coupled SWMI system affect global ULF wave properties during periods with large, steady east-west Interplanetary Magnetic field (IMF By)? This proposal requires fieldwork in the Antarctic, but all fieldwork will be conducted by PRIC. ", "links": [ { diff --git a/datasets/USAP-1744989_1.json b/datasets/USAP-1744989_1.json index 443bfabcae..0d71977afc 100644 --- a/datasets/USAP-1744989_1.json +++ b/datasets/USAP-1744989_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1744989_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public.", "links": [ { diff --git a/datasets/USAP-1745116_1.json b/datasets/USAP-1745116_1.json index 7400fd5c2f..26cb8cdd56 100644 --- a/datasets/USAP-1745116_1.json +++ b/datasets/USAP-1745116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1745116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow or firn aquifers are areas of subsurface meltwater storage that form in glaciated regions experiencing intense summer surface melting and high snowfall. Aquifers can induce hydrofracturing, and thereby accelerate flow or trigger ice-shelf instability leading to increased ice-sheet mass loss. Widespread aquifers have recently been discovered in Greenland. These have been modelled and mapped using new satellite and airborne remote-sensing techniques. In Antarctica, a series of catastrophic break-ups at the Wilkins Ice Shelf on the Antarctic Peninsula that was previously attributed to effects of surface melting and brine infiltration is now recognized as being consistent with a firn aquifer--possibly stimulated by long-period ocean swell--that enhanced ice-shelf hydrofracture. This project will verify inferences (from the same mapping approach used in Greenland) that such aquifers are indeed present in Antarctica. The team will survey two high-probability sites: the Wilkins Ice Shelf, and the southern George VI Ice Shelf.

This two-year study will characterize the firn at the two field sites, drill shallow (~60 m maximum) ice cores, examine snow pits (~2 m), and install two AMIGOS (Automated Met-Ice-Geophysics Observing System) stations that include weather, GPS, and firn temperature sensors that will collect and transmit measurements for at least a year before retrieval. Ground-penetrating radar survey in areas surrounding the field sites will track aquifer extent and depth variations. Ice and microwave model studies will be combined with the field-observed properties to further explore the range of firn aquifers and related upper-snow-layer conditions. This study will provide valuable experience for three early-career scientists. An outreach effort through field blogging, social media posts, K-12 presentations, and public lectures is planned to engage the public in the team's Antarctic scientific exploration and discovery.\r\n\r\n\r\n

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1745137_1.json b/datasets/USAP-1745137_1.json index bc95e2af34..b9bb46873c 100644 --- a/datasets/USAP-1745137_1.json +++ b/datasets/USAP-1745137_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1745137_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth's geologic record shows that the great ice sheets have contributed to rates of sea-level rise that have been much higher than those observed today. That said, some sectors of the current Antarctic ice sheet are losing mass at large and accelerating rates. One of the primary challenges for placing these recent and ongoing changes in the context of geologically historic rates, and for making projections decades to centuries into the future, is the difficulty of observing conditions and processes beneath the ice sheet. Whereas satellite observations allow tracking of the ice-surface velocity and elevation on the scale of glacier catchments to ice sheets, airborne ice-penetrating radar has been the only approach for assessing conditions on this scale beneath the ice. These radar observations have been made since the late 1960s, but, because many different instruments have been used, it is difficult to track change in subglacial conditions through time. This project will develop the technical tools and approaches required to cross-compare among these measurements and thus open up opportunities for tracking and understanding changes in the critical subglacial environment. Intertwined with the research and student training on this project will be an outreach education effort to provide middle school and high school students with improved resources and enhanced exposure to geophysical, glaciological, and remote-sensing topics through partnership with the National Science Olympiad.\r\n\r\n\r\n\r\nThe radar sounding of ice sheets is a powerful tool for glaciological science with broad applicability across a wide range of cryosphere problems and processes. Radar sounding data have been collected with extensive spatial and temporal coverage across the West Antarctic Ice Sheet, including areas where multiple surveys provide observations that span decades in time or entire cross-catchment ice-sheet sectors. However, one major obstacle to realizing the scientific potential of existing radar sounding observations in Antarctica is the lack of analysis approaches specifically developed for cross-instrument interpretation. Radar is also spatially limited and often has gaps of many tens of kilometers between data points. Further work is needed to investigate ways of extrapolating radar information beyond the flight lines. This project aims to directly address these barriers to full utilization of the collective Antarctic radar sounding record by developing a suite of processing and interpretation techniques to enable the synthesis of radar sounding data sets collected with systems that range from incoherent to coherent, single-channel to swath-imaging, and digital to optically-recorded radar sounders. This includes a geostatistical analysis of ice sheet and radar datasets to make probabilistic predictions of conditions at the bed. The approaches will be assessed for two target regions: the Amundsen Sea Embayment and the Siple Coast. All pre- and post-processed sounding data produced by this project will be publically hosted for use by the wider research community.\r\n\r\n\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1753101_1.json b/datasets/USAP-1753101_1.json index 041189fea4..06b154059b 100644 --- a/datasets/USAP-1753101_1.json +++ b/datasets/USAP-1753101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1753101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic krill are essential in the Southern Ocean as they support vast numbers of marine mammals, seabirds and fishes, some of which feed almost exclusively on krill. Antarctic krill also constitute a target species for industrial fisheries in the Southern Ocean. The success of Antarctic krill populations is largely determined by the ability of their young to survive the long, dark winter, where food is extremely scarce. To survive the long-dark winter, young Antarctic krill must have a high-quality diet in autumn. However, warming in certain parts of Antarctica is changing the dynamics and quality of the polar food web, resulting in a shift in the type of food available to young krill in autumn. It is not yet clear how these dynamic changes are affecting the ability of krill to survive the winter. This project aims to fill an important gap in current knowledge on an understudied stage of the Antarctic krill life cycle, the 1-year old juveniles. The results derived from this work will contribute to the development of improved bioenergetic, population and ecosystem models, and will advance current scientific understanding of this critical Antarctic species. This CAREER projects core education and outreach objectives seek to enhance education and increase diversity within STEM fields. An undergraduate course will be developed that will integrate undergraduate research and writing in way that promotes authentic scientific inquiry and analysis of original research data by the students, and that enhances their communication skills. A graduate course will be developed that will promote students skills in communicating their own research to a non-scientific audience. Graduate students will be supported through the proposed study and will gain valuable research experience. Traditionally underserved undergraduate students will be recruited to conduct independent research under the umbrella of the larger project. Throughout each field season, the research team will maintain a weekly blog that will include short videos, photographs and text highlighting the research, as well as their experiences living and working in Antarctica. The aim of the blog will be to engage the public and increase awareness and understanding of Antarctic ecosystems and the impact of warming, and of the scientific process of research and discovery.\n\n\n\nIn this 5-year CAREER project, the investigator will use a combination of empirical and theoretical techniques to assess the effects of diet on 1-year old krill in autumn-winter. The research is centered on four hypotheses: (H1) autumn diet affects 1-year old krill physiology and condition at the onset of winter; (H2) autumn diet has an effect on winter physiology and condition of 1-year old krill under variable winter food conditions; (H3) the rate of change in physiology and condition of 1-year old krill from autumn to winter is dependent on autumn diet; and (H4) the winter energy budget of 1-year old krill will vary between years and will be dependent on autumn diet. Long-term feeding experiments and in situ sampling will be used to measure changes in the physiology and condition of krill in relation to their diet and feeding environment. Empirically-derived data will be used to develop theoretical models of growth rates and energy budgets to determine how diet will influence the overwinter survival of 1-year old krill. The research will be integrated with an education and outreach plan to (1) develop engaging undergraduate and graduate courses, (2) train and develop young scientists for careers in polar research, and (3) engage the public and increase their awareness and understanding.\n\n\n\nThis award reflects NSFs statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1823135_1.json b/datasets/USAP-1823135_1.json index b5bcb59668..2ee434daa6 100644 --- a/datasets/USAP-1823135_1.json +++ b/datasets/USAP-1823135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1823135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This research will take advantage of the greater number of Antarctic weather observations collected as part of the World Meteorological Organization's \"Year of Polar Prediction\". Researchers will use these additional observations to study new ways of incorporating data into existing weather prediction models. The primary goal of this research is to improve the accuracy of weather forecasts in Antarctica. This work is important, as the harsh weather in Antarctica greatly impacts scientific research and the support of this research. Being able to accurately predict changing weather increases the safety and efficiency of Antarctic field science and operations. The proposed effort seeks to advance goals of the World Meteorological Organization's Polar Prediction Project and its Year of Polar Prediction-Southern Hemisphere (YOPP-SH) effort. Researchers will investigate and demonstrate the forecast impact of enhanced atmospheric observations obtained from YOPP-SH's Special Observing Period on polar numerical weather prediction. This will be done by using the Antarctic Mesoscale Prediction System (AMPS). AMPS is the primary numerical weather prediction capability for the United States Antarctic Program (USAP). Modeling experimentation will assess the impact of Special Observing Period data on Antarctic forecasts and will serve as a vehicle for testing new data assimilation approaches for AMPS. The primary goal for this work is improved forecasting and numerical weather prediction tools. Outcomes will include quantification of the value of enhanced southern hemisphere atmospheric observations. This work will also help improve AMPS and its ability to support the USAP. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1844793_1.json b/datasets/USAP-1844793_1.json index 692bde1f5f..5752ba3605 100644 --- a/datasets/USAP-1844793_1.json +++ b/datasets/USAP-1844793_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1844793_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will test the hypothesis that physical and thermal properties of Antarctic firn--partially compacted granular snow in an intermediate stage between snow and glacier ice--can be remotely measured from space. Although these properties, such as internal temperature, density, grain size, and layer thickness, are highly relevant to studies of Antarctic climate, ice-sheet dynamics, and mass balance, their measurement currently relies on sparse in-situ surveys under challenging weather conditions. Sensors on polar-orbiting satellites can observe the entire Antarctic every few days during their years-long lifetime. Consequently, the approaches developed in this study, when coupled with the advancing technologies of small and low-cost CubeSats, aim to contribute to Antarctic science and lead to cost-effective, convenient, and accurate long-term analyses of the Antarctic system while reducing the human footprint on the continent. Moreover, the project will be solely based on publicly-available datasets; thus, while contributing to interdisciplinary undergraduate and graduate research and education at the grantee's institution, the project will also encourage engagement of citizen scientists through its website.\n\nThe overarching goal of this project is to characterize Antarctic firn layers in terms of their thickness, physical temperature, density, and grain size through multi-frequency microwave radiometer measurements from space. Electromagnetic penetration depth changes with frequency in ice; thus, multi-frequency radiometers are able to profile firn layer properties versus depth. To achieve its objective, the project will utilize the Global Precipitation Measurement (GPM) satellite constellation as a single multi-frequency microwave radiometer system with 11 frequency channels observing the Antarctic Ice Sheet. Archived in-situ measurements of Antarctic firn density, grain size, temperature, and layer thickness will be collected and separated into training and test datasets. Microwave emissions simulated using the training data will be compared to GPM constellation measurements to evaluate and improve state-of-the-art forward microwave emission models. Based on these models, the project will develop numerical retrieval algorithms for the thermal and physical properties of Antarctic firn. Results of retrievals will be validated using the test dataset, and uncertainty and error analyses will be conducted. Lastly, changes in the thermal and physical characteristics of Antarctic firn will be examined through long-term retrieval studies exploiting GPM constellation measurements.", "links": [ { diff --git a/datasets/USAP-1846837_1.json b/datasets/USAP-1846837_1.json index ad8db212bd..cf289d7c29 100644 --- a/datasets/USAP-1846837_1.json +++ b/datasets/USAP-1846837_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1846837_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coastal Antarctic is undergoing great environmental change. Physical changes in the environment, such as altered sea ice duration and extent, have a direct impact on the phytoplankton and bacteria species which form the base of the marine foodweb. Photosynthetic phytoplankton are the ocean's primary producers, transforming (fixing) CO2 into organic carbon molecules and providing a source of food for zooplankton and larger predators. When phytoplankton are consumed by zooplankton, or killed by viral attack, they release large amounts of organic carbon and nutrients into the environment. Heterotrophic bacteria must eat other things, and function as \"master recyclers\", consuming these materials and converting them to bacterial biomass which can feed larger organisms such as protists. Some protists are heterotrophs, but others are mixotrophs, able to grow by photosynthesis or heterotrophy. Previous work suggests that by killing and eating bacteria, protists and viruses may regulate bacterial populations, but how these processes are regulated in Antarctic waters is poorly understood. This project will use experiments to determine the rate at which Antarctic protists consume bacteria, and field studies to identify the major bacterial taxa involved in carbon uptake and recycling. In addition, this project will use new sequencing technology to obtain completed genomes for many Antarctic marine bacteria. To place this work in an ecosystem context this project will use microbial diversity data to inform rates associated with key microbial processes within the PALMER ecosystem model. This project addresses critical unknowns regarding the ecological role of heterotrophic marine bacteria in the coastal Antarctic and the top-down controls on bacterial populations. Previous work suggests that at certain times of the year grazing by heterotrophic and mixotrophic protists may meet or exceed bacterial production rates. Similarly, in more temperate waters bacteriophages (viruses) are thought to contribute significantly to bacterial mortality during the spring and summer. These different top-down controls have implications for carbon flow through the marine foodweb, because protists are grazed more efficiently by higher trophic levels than are bacteria. This project uses a combination of grazing experiments and field observations to assess the temporal dynamics of mortality due to temperate bacteriophage and protists. Although many heterotrophic bacterial strains observed in the coastal Antarctic are taxonomically similar to strains from other regions, recent work suggest that they are phylogenetically and genetically distinct. To better understand the ecological function and evolutionary trajectories of key Antarctic marine bacteria, their genomes will be isolated and sequenced. Then, these genomes will be used to improve the predictions of the paprica metabolic inference pipeline, and our understanding of the relationship between heterotrophic bacteria and their major predators in the Antarctic marine environment. Finally, the research team will modify the Regional Test-Bed Model model to enable microbial diversity data to be used to optimize the starting conditions of key parameters, and to constrain the model's data assimilation methods.", "links": [ { diff --git a/datasets/USAP-1847173_1.json b/datasets/USAP-1847173_1.json index d2c4ddd86c..9bfc30e473 100644 --- a/datasets/USAP-1847173_1.json +++ b/datasets/USAP-1847173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1847173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Iceberg calving is a complex natural fracture process and a dominant cause of mass loss from the floating ice shelves on the margins of the Antarctic ice sheet. There is concern that rapid changes at these ice shelves can destabilize parts of the ice sheet and accelerate their contribution to sea-level rise. The goal of this project is to understand and simulate the fracture mechanics of calving and to develop physically-consistent calving schemes for ice-sheet models. This would enable more reliable estimation of Antarctic mass loss by reducing the uncertainty in projections. The research plan is integrated with an education and outreach plan that aims to (1) enhance computational modeling skills of engineering and Earth science students through a cross-college course and a high-performance computing workshop and (2) increase participation and diversity in engineering and sciences by providing interdisciplinary research opportunities to undergraduates and by deploying new cyberlearning tools to engage local K-12 students in the Metro Nashville Public Schools in computational science and engineering, and glaciology.\n\n\n\nThis project aims to provide fundamental understanding of iceberg calving by advancing the frontiers in computational fracture mechanics and nonlinear continuum mechanics and translating it to glaciology. The project investigates crevasse propagation using poro-damage mechanics models for hydrofracture that are consistent with nonlinear viscous ice rheology, along with the thermodynamics of refreezing in narrow crevasses at meter length scales. It will develop a fracture-physics based scheme to better represent calving in ice-sheet models using a multiscale method. The effort will also address research questions related to calving behavior of floating ice shelves and glaciers, with the goal of enabling more reliable prediction of calving fronts in whole-Antarctic ice-sheet simulations over decadal-to-millennial time scales.\n\n\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1848887_1.json b/datasets/USAP-1848887_1.json index f00879e683..6b6eda2634 100644 --- a/datasets/USAP-1848887_1.json +++ b/datasets/USAP-1848887_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1848887_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Undersea forests of seaweeds dominate the shallow waters of the central and northern coast of the western Antarctic Peninsula and provide critical structural habitat and carbon resources (food) for a host of marine organisms. Most of the seaweeds are chemically defended against herbivores yet support very high densities of herbivorous shrimp-like grazers (crustaceans, primarily amphipods) which greatly benefit their hosts by consuming filamentous and microscopic algae that otherwise overgrow the seaweeds. The amphipods benefit from the association with the chemically defended seaweeds by gaining an associational refuge from fish predation. The project builds on recent work that has demonstrated that several species of amphipods that are key members of crustacean assemblages associated with the seaweeds suffer significant mortality when chronically exposed to increased seawater acidity (reduced pH) and elevated temperatures representative of near-future oceans. By simulating these environmental conditions in the laboratory at Palmer Station, Antarctica, the investigators will test the overall hypothesis that ocean acidification will play a significant role in structuring crustacean assemblages associated with seaweeds. Broader impacts include expanding fundamental knowledge of the impacts of global climate change by focusing on a geographic region of the earth uniquely susceptible to climate change. This project will also further the NSF goals of training new generations of scientists and of making scientific discoveries available to the general public. This includes training graduate students and early career scientists with an emphasis on diversity, presentations to K-12 groups and the general public, and a variety of social media-based outreach programs.\n\nThe project will compare population and assemblage-wide impacts of natural (ambient) and carbon dioxide enriched seawater on assemblages of seaweed-associated crustacean grazers. Based on prior results, it is likely that some species will be relative \"winners\" and some will be relative \"losers\" under the changed conditions. The project will then aim to carry out measurements of growth, calcification, mineralogy, the incidence of molts, and biochemical and energetic body composition for two key amphipod \"winners\" and two key amphipod \"losers\". These measurements will allow an assessment of what factors drive species-specific enhanced or diminished performance under conditions of ocean acidification and sea surface warming. The project will expand on what little is known about prospective impacts of changing conditions on benthic marine Crustacea, in Antarctica, a taxonomic group that faces the additional physiological stressor of molting. The project is likely to provide additional insight on the indirect regulation of the seaweeds that comprise Antarctic undersea forests that provide key architectural components of the coastal marine ecosystem.", "links": [ { diff --git a/datasets/USAP-1933764_1.json b/datasets/USAP-1933764_1.json index 16dbfc7022..e87f830dde 100644 --- a/datasets/USAP-1933764_1.json +++ b/datasets/USAP-1933764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1933764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project uses repeat, very high-resolution (~0.5 m pixel width and length) satellite images acquired by the WorldView satellites, to estimate rates of iceberg melting in key coastal regions around Antarctica. The satellite images are used to construct maps of iceberg surface elevation change over time, which are converted to estimates of area-averaged submarine melt rates. Where ocean temperature observations are available, the melt rates are compared to these data to determine if variations in ocean temperature can explain observed iceberg melt variability. The iceberg melt rates are also compared to glacier frontal ablation rates (flow towards the terminus minus changes in terminus position over time) and integrated into a numerical ice flow model in order to assess the importance of submarine melting on recent changes in terminus position, ice flow, and dynamic mass loss. Overall, the analysis will yield insights into the effects of changes in ocean forcing on the submarine melting of Antarctic ice shelves and icebergs. The project does not require field work in Antarctica.", "links": [ { diff --git a/datasets/USAP-1935635_1.json b/datasets/USAP-1935635_1.json index e88fb25590..f3c06817ff 100644 --- a/datasets/USAP-1935635_1.json +++ b/datasets/USAP-1935635_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1935635_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the genomic changes underlying adaptations to polar environments is critical for predicting how ecological changes will affect life in these fragile environments. Accomplishing these goals requires looking in detail at genome-scale data across a wide array of organisms in a phylogenetic framework. This study combines multifaceted computational and functional approaches that involves analyzing in the genic evolution of invertebrate organisms, known as the bryozoans or ectoprocts. In addition, the commonality of our results in other taxa will be tested by comparing the results to those produced from the previous and newly proposed workshops. Specific aims of this study include: 1) identifying genes involved in adaptation to Antarctic marine environments using transcriptomic and genomic data from bryozoans to test for positively selected genes in a phylogenetic framework, 2) experimentally testing identified candidate enzymes (especially those involved in calcium signaling, glycolysis, the citric acid cycle, and the cytoskeleton) for evidence of cold adaption, and 3) conducting computational workshops aimed at training scientists in techniques for the identification of genetic adaptations to polar and other disparate environments. The proposed work provides critical insights into the molecular rules of life in rapidly changing Antarctic environments, and provides important information for understanding how Antarctic taxa will respond to future environmental conditions. ", "links": [ { diff --git a/datasets/USAP-1937546_1.json b/datasets/USAP-1937546_1.json index 516d7bdcf4..244aac184b 100644 --- a/datasets/USAP-1937546_1.json +++ b/datasets/USAP-1937546_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1937546_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microbial communities are of more than just a scientific curiosity. Microbes represent the single largest source of evolutionary and biochemical diversity on the planet. They are the major agents for cycling carbon, nitrogen, phosphorus, and other elements through the ecosystem. Despite their importance in ecosystem function, microbes are still generally overlooked in food web models and nutrient cycles. Moreover, microbes do not live in isolation: their growth and metabolism are influenced by complex interactions with other microorganisms. This project will focus on the ecology, activity and roles of microbial communities in Antarctic Lake ecosystems. The team will characterize the genetic underpinnings of microbial interactions and the influence of environmental gradients (e.g. light, nutrients, oxygen, sulfur) and seasons (e.g. summer vs. winter) on microbial networks in Lake Fryxell and Lake Bonney in the Taylor Valley within the McMurdo Dry Valley region. Finally, the project furthers the NSF goals of training new generations of scientists by including undergraduate and graduate students, a postdoctoral researcher and a middle school teacher in both lab and field research activities. This partnership will involve a number of other outreach training activities, including visits to classrooms and community events, participation in social media platforms, and webinars.\r\n\r\nPart II: Technical description: Ecosystem function in the extreme Antarctic Dry Valleys ecosystem is dependent on complex biogeochemical interactions between physiochemical environmental factors (e.g. light, nutrients, oxygen, sulfur), time of year (e.g. summer vs. winter) and microbes. Microbial network complexity can vary in relation to specific abiotic factors, which has important implications on the fragility and resilience of ecosystems under threat of environmental change. This project will evaluate the influence of biogeochemical factors on microbial interactions and network complexity in two Antarctic ice-covered lakes. The study will be structured by three main objectives: 1) infer positive and negative interactions from rich spatial and temporal datasets and investigate the influence of biogeochemical gradients on microbial network complexity using a variety of molecular approaches; 2) directly observe interactions among microbial eukaryotes and their partners using flow cytometry, single-cell sorting and microscopy; and 3) develop metabolic models of specific interactions using metagenomics. Outcomes from amplicon sequencing, meta-omics, and single-cell genomic approaches will be integrated to map specific microbial network complexity and define the role of interactions and metabolic activity onto trends in limnological biogeochemistry in different seasons. These studies will be essential to determine the relationship between network complexity and future climate conditions. Undergraduate researchers will be recruited from both an REU program with a track record of attracting underrepresented minorities and two minority-serving institutions. To further increase polar literacy training and educational impacts, the field team will include a teacher as part of a collaboration with the successful NSF-funded PolarTREC program and participation in activities designed for public outreach.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.\r\n", "links": [ { diff --git a/datasets/USAP-1943550_1.json b/datasets/USAP-1943550_1.json index 6807e3f03f..ed3c45750c 100644 --- a/datasets/USAP-1943550_1.json +++ b/datasets/USAP-1943550_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1943550_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will identify behavioral and physiological variability in foraging Emperor Penguins that can be directly linked to individual success in the marine environment using an optimal foraging theory framework during two critical life history stages. First, this project will investigate the foraging energetics, ecology, and habitat use of Emperor Penguins at Cape Crozier using fine-scale movement and video data loggers during late chick-rearing, an energetically demanding life history phase. Specifically, this study will 1) Estimate the foraging efficiency and examine its relationship to foraging behavior and diet using an optimal foraging theory framework to identify what environmental or physiological constraints influence foraging behavior; 2) Investigate the inter- and intra-individual behavioral variability exhibited by emperor penguins, which is essential to predict how resilient they will be to climate change; and 3) Integrate penguin foraging efficiency data with environmental data to identify important habitat. Next the researchers will study the ecology and habitat preference after the molt and through early reproduction using satellite-linked data loggers. The researchers will: 1) Investigate the inter- and intra-individual behavioral variability exhibited by Emperor Penguins during the three-month post-molt and early winter foraging trips; and 2) Integrate penguin behavioral data with environmental data to identify which environmental features are indicative of habitat preference when penguins are not constrained to returning to the colony to feed a chick. These fine- and coarse-scale data will be combined with climate predictions to create predictive habitat models. The education objectives of this CAREER project are designed to inspire, engage, and train the next generation of scientists using the data and video generated while investigating Emperor Penguins in the Antarctic ecosystem. This includes development of two courses (general education and advanced techniques), training of undergraduate and graduate students, and a collaboration with the NSF funded \u201cPolar Literacy: A model for youth engagement and learning\u201d program to develop afterschool and camp curriculum that target underserved and underrepresented groups.\n\n", "links": [ { diff --git a/datasets/USAP-1945127_1.json b/datasets/USAP-1945127_1.json index c1aaad9279..ebfe69ff45 100644 --- a/datasets/USAP-1945127_1.json +++ b/datasets/USAP-1945127_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1945127_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Freshwater discharges from melting high-latitude continental ice glacial reserves strongly control salt budgets, circulation and associated ocean water mass formation arising from polar ice shelves. These are different in nature than freshwater inputs associated with riverine coastal inputs. The PI proposes an observational deployment to measure a specific, previously-identified example of a coastal freshwater-driven current, the Antarctic Peninsula Coastal Current (APCC).\r\n\r\nThe research component of this CAREER project aims to improve understanding of the dynamics of freshwater discharge around the Antarctic continent. Associated research questions pertain to the i) controls on the cross- and along-shelf spreading of fresh, buoyant coastal currents, ii) the role of distributed coastal freshwater sources (as opposed to 'point' source river outflow sources typical of lower latitudes), and iii) the contribution of these coastal currents to water mass transformation and heat transfer on the continental shelf. An educational CAREER program component leverages a series of field experiences and research outputs including data, model outputs, and theory, to bring polar science to the classroom and the general public, as well as training a new polar scientist. This combined strategy will allow the investigator to lay the foundation for a successful academic career as a researcher and teacher at the University of Delaware. The project will also provide the opportunity to train a PhD student. Informal outreach efforts will include giving public lectures at University of Deleware's sponsored events, including Coast Day, a summer event that attracts 8000-10000 people, and remote lectures from the field using an existing outreach network. This proposal requires fieldwork in the Antarctic.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-1947094_1.json b/datasets/USAP-1947094_1.json index 1d8dc081ad..2adfc1d8e5 100644 --- a/datasets/USAP-1947094_1.json +++ b/datasets/USAP-1947094_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1947094_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The research supported by this grant centers on the evolution of fossil amphibians (temnospondyls) from the Early Triassic, a crucial time interval in the evolution of life on Earth following the end-Permian mass extinction, specifically based on fossil material from Antarctica, a high-latitude paleoenvironment that may have served as a refuge for tetrapods across the extinction event. Previous records of temnospondyls, mostly reported several decades ago, are highly fragmentary, and their original interpretations are considered dubious or demonstrably erroneous by contemporary workers. The Antarctic record of temnospondyls is of great import in understanding the biotic recovery in terrestrial environments for several reasons. Firstly, temnospondyls, like amphibians today, were highly speciose in the Triassic but were also some of the most susceptible to environmental perturbations and instability. Therefore, temnospondyls provide key insights into the paleoenvironmental conditions, either in place of or alongside other lines of data. Secondly, the record of temnospondyls from the Early Triassic is quite rich, but it is also restricted to a few densely sampled regions, such as the Karoo Basin of South Africa. In order to ascertain whether observed patterns such as an unusual abundance of small-bodied taxa or a lack of faunal overlap between different depositional basins (endemism) are real or merely artifactual, study of additional, less sampled regions takes on great import. Recent collection of substantial new temnospondyl material from several horizons in the Triassic exposure of Antarctica provides the requisite data to begin to address these questions. Finally, correlating the Triassic rocks of Antarctica with those of adjacent regions is largely reliant on comparisons of faunal assemblages. In particular, the middle Fremouw Formation, one of the horizons from which new temnospondyl material was collected, remains of uncertain relation and age due to the paucity of described material. ", "links": [ { diff --git a/datasets/USAP-1947562_1.json b/datasets/USAP-1947562_1.json index a656d94579..337b8064d3 100644 --- a/datasets/USAP-1947562_1.json +++ b/datasets/USAP-1947562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1947562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Responses of the carbon balance of terrestrial ecosystems to warming will feed back to the pace of climate change, but the size and direction of this feedback are poorly constrained. Least known are the effects of warming on carbon losses from soil, and clarifying the major microbial controls is an important research frontier. This study uses a series of experiments and observations to investigate microbial, including autotrophic taxa, and plant controls of net ecosystem productivity in response to warming in intact ecosystems. Field warming is achieved using open-top chambers paired with control plots, arrayed along a productivity gradient. Along this gradient incoming and outgoing carbon fluxes will be measured at the ecosystem-level. The goal is to tie warming-induced shifts in net ecosystem carbon balance to warming effects on soil microbes and plants. The field study will be supplemented with lab temperature incubations. Because soil microbes dominate biogeochemical cycles in Antarctica, a major focus of this study is to determine warming responses of bacteria, fungi and archaea. This is achieved using a cutting-edge stable isotope technique, quantitative stable isotope probing (qSIP) developed by the proposing research team, that can identify the taxa that are active and involved in processing new carbon. This technique can identify individual microbial taxa that are actively participating in biogeochemical cycling of nutrients (through combined use of 18O-water and 13C-bicarbonate) and thus can be distinguished from those that are simply present (cold-preserved). The study further assesses photosynthetic uptake of carbon by the vegetation and their sensitivity to warming. Results will advance research in climate change, plant and soil microbial ecology, and ecosystem modeling.", "links": [ { diff --git a/datasets/USAP-1947646_1.json b/datasets/USAP-1947646_1.json index 92860e4340..1690a0f964 100644 --- a/datasets/USAP-1947646_1.json +++ b/datasets/USAP-1947646_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1947646_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Presently, Antarctica's glaciers are melting as Earth's atmosphere and the Southern Ocean warm. Not much is known about how Antarctica's ice sheets might respond to ongoing and future warming, but such knowledge is important because Antarctica's ice sheets might raise global sea levels significantly with continued melting. Over time, mud accumulates on the sea floor around Antarctica that is composed of the skeletons and debris of microscopic marine organisms and sediment from the adjacent continent. As this mud is deposited, it creates a record of past environmental and ecological changes, including ocean depth, glacier advance and retreat, ocean temperature, ocean circulation, marine ecosystems, ocean chemistry, and continental weathering. Scientists interested in understanding how Antarctica's glaciers and ice sheets might respond to ongoing warming can use a variety of physical, biological, and chemical analyses of these mud archives to determine how long ago the mud was deposited and how the ice sheets, oceans, and marine ecosystems responded during intervals in the past when Earth's climate was warmer. In this project, researchers from the University of South Florida, University of Massachusetts, and Northern Illinois University will reconstruct the depth, ocean temperature, weathering and nutrient input, and marine ecosystems in the central Ross Sea from ~17 to 13 million years ago, when the warm Miocene Climate Optimum transitioned to a cooler interval with more extensive ice sheets. Record will be generated from new sediments recovered during the International Ocean Discovery Program (IODP) Expedition 374 and legacy sequences recovered in the 1970s during the Deep Sea Drilling Program. Results will be integrated into ice sheet and climate models to improve the accuracy of predictions.", "links": [ { diff --git a/datasets/USAP-1951603_1.json b/datasets/USAP-1951603_1.json index 2fc3ce35af..ed71481992 100644 --- a/datasets/USAP-1951603_1.json +++ b/datasets/USAP-1951603_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1951603_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Antarctic Meteorological Research and Data Center (AMRDC) project will create an Antarctic meteorological observational data repository and archive system based on an open source platform to manage data from submission to end-user retrieval. The new archival system will host both currently available datasets and campaign meteorological datasets deposited by other Antarctic investigators. Both real-time meteorological data and archive data from the repository (e.g. Antarctic composite satellite imagery, AWS observations, etc.) will be accessible on a newly constructed website. The project will engage undergraduate and graduate students in order to provide them with meaningful experiences that can translate to any science, technology, engineering, and mathematics (STEM) career path. Project participants and students will be involved in case studies, climatology reporting and development of whitepapers on related topics. The outcomes of this project revolve around data, and the students, researchers, and decision makers who all use and rely on Antarctic meteorological data. The AMRDC will not only be a resource for users, but it will also provide investigators a repository to place campaign datasets and meet NSF standards and requirements. This project also aims to give students Antarctic field experiences who are considering a career in science, technology, engineering and mathematics (STEM).", "links": [ { diff --git a/datasets/USAP-1954241_1.json b/datasets/USAP-1954241_1.json index 7ff9aaa6cf..f91419499b 100644 --- a/datasets/USAP-1954241_1.json +++ b/datasets/USAP-1954241_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-1954241_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The frequency and severity of hypoxic events are increasing in marine and freshwater environments worldwide with climate warming, threatening the health of aquatic ecosystems and the viability of fish populations. The Southern Ocean surrounding Antarctica has historically been a stable, icy-cold, and oxygen-rich environment, but is now warming at an unprecedented rate and faster than all other regions in the Southern hemisphere. Evolution at sub-zero temperatures has equipped Antarctic fishes with traits allowing them to thrive in frigid waters, but has diminished their resilience to warming. Presently little is known about the ability of Antarctic fishes to withstand hypoxic conditions that often accompany warming. This research will investigate the hypoxia tolerance of four species of Antarctic fishes, including two species of icefishes that lack the oxygen-carrying protein, hemoglobin, which may compromise their ability to oxygenate tissues under hypoxic conditions. The hypoxia tolerance of Antarctic fish species will be compared to that of a related fish species inhabiting coastal regions of South America. Physiological and biochemical responses to hypoxia will be evaluated and compared amongst the five species to bolster our predictions of the capacity of Antarctic fishes to cope with a changing environment. This research will provide training opportunities for undergraduate and graduate students, and a postdoctoral research fellow. A year-long seminar series hosted by the Aquarium of the Pacific will feature female scientists who work in Antarctica to inspire youth in the greater Los Angeles area to pursue careers in science.", "links": [ { diff --git a/datasets/USAP-2019719_1.json b/datasets/USAP-2019719_1.json index 31ab5800a5..777852e67a 100644 --- a/datasets/USAP-2019719_1.json +++ b/datasets/USAP-2019719_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2019719_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cores drilled through the Antarctic ice sheet provide a remarkable window on the evolution of Earth\u2019s climate and unique samples of the ancient atmosphere. The clear link between greenhouse gases and climate revealed by ice cores underpins much of the scientific understanding of climate change. Unfortunately, the existing data do not extend far enough back in time to reveal key features of climates warmer than today. COLDEX, the Center for Oldest Ice Exploration, will solve this problem by exploring Antarctica for sites to collect the oldest possible record of past climate recorded in the ice sheet. COLDEX will provide critical information for understanding how Earth\u2019s near-future climate may evolve and why climate varies over geologic time. New technologies will be developed for exploration and analysis that will have a long legacy for future research. An archive of old ice will stimulate new research for the next generations of polar scientists. COLDEX programs will galvanize that next generation of polar researchers, bring new results to other scientific disciplines and the public, and help to create a more inclusive and diverse scientific community.\n\nKnowledge of Earth\u2019s climate history is grounded in the geologic record. This knowledge is gained by measuring chemical, biological and physical properties of geologic materials that reflect elements of climate. Ice cores retrieved from polar ice sheets play a central role in this science and provide the best evidence for a strong link between atmospheric carbon dioxide and climate on geologic timescales. The goal of COLDEX is to extend the ice-core record of past climate to at least 1.5 million years by drilling and analyzing a continuous ice core in East Antarctica, and to much older times using discontinuous ice sections at the base and margin of the ice sheet. COLDEX will develop and deploy novel radar and melt-probe tools to rapidly explore the ice, use ice-sheet models to constrain where old ice is preserved, conduct ice coring, develop new analytical systems, and produce novel paleoclimate records from locations across East Antarctica. The search for Earth\u2019s oldest ice also provides a compelling narrative for disseminating information about past and future climate change and polar science to students, teachers, the media, policy makers and the public. COLDEX will engage and incorporate these groups through targeted professional development workshops, undergraduate research experiences, a comprehensive communication program, annual scientific meetings, scholarships, and broad collaboration nationally and internationally. COLDEX will provide a focal point for efforts to increase diversity in polar science by providing field, laboratory, mentoring and networking experiences for students and early career scientists from groups underrepresented in STEM, and by continuous engagement of the entire COLDEX community in developing a more inclusive scientific culture.", "links": [ { diff --git a/datasets/USAP-2032463_1.json b/datasets/USAP-2032463_1.json index 036cfde47b..a79cb2662e 100644 --- a/datasets/USAP-2032463_1.json +++ b/datasets/USAP-2032463_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2032463_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Overview
\nIt is proposed that laser cutting technology can be used to rapidly extract high quality ice samples from borehole walls. The technology applies to both existing boreholes and newly drilled ones, even enabling scientists to obtain samples using non\u2010coring mechanical drills. Since the instrumentation is highly portable, a field team of three persons might take no longer than a few days in the field to extract ice, and samples from a critical time period could be extracted from multiple locations in a single field season.\n\nThis pilot program will investigate and validate the technology of laser sampling. It is beneficial to use fiber optics to convey light in borehole instrumentation rather than attempting to package a complete laser system for travel down a borehole, so the cutting laser and wavelength (1.07Pm) are chosen with such engineering in mind. The primary scientific goals of the program are to: 1) determine optimum cutting conditions in terms of laser power and operating conditions, 2) quantifying the effects of residual meltwater that remain in the cut slot after a cut so that re-cutting needs can be predicted or mitigated, 3) designing and testing mechanical structures to retract samples from blocks of ice once cut, and 4) analyzing the composition and crystal structure of ice near a cut slot to determine the impacted volume (if any) of ice and temperatures where scientific readings might be affected by the sampling process.\n

\nIntellectual Merits
\nThe collection of deep ice from the Polar Ice Sheets involves large amounts of time, effort, and expense. Often, the most important information is held in very small volumes of core, and while replicate coring can supplement this core, there is often a need to retrieve additional ice samples based on recent scientific findings or borehole logging at a site. In addition, there is currently no easy method of extracting ice from boreholes drilled by non\u2010coring mechanical drills, which are often much faster, lighter, and less expensive to operate. There are numerous specific projects that could immediately benefit from laser sampling including sampling ice overlaying buried impact craters and bolides, filling critical gaps in the chemical record in damaged core sections from Siple Dome, obtaining oldest ice cores from brittle sections near the surface of the Allan Hills blue ice area, where coring drills apply stresses that may fracture the ice, and replacing core whose value has degraded due to time and depressurization. This program builds on a prior engineering advances in optical fiber\u2010based logging technology, developed previously for Siple Dome borehole logging.\n

\nBroader Impact
\nLaser sampling would advance numerous fields interfaced with glaciology and ice core studies. These include climate and paleoenvironmental science, volcanology, and human history where large volumes of ice are crucial to extract ultra\u2010high resolution records of natural and anthropogenic emissions. Potentially the principle of laser sampling could be used to directly sample and study ice on other planets or their satellites.\nThis program encompasses a broad base of theoretical, experimental, and design work, which makes it ideal for training postdoctoral scientists, graduate students, and advanced undergraduates. The program will include a research opportunity for one or more middle school teachers through a Research Experience for Teachers program with one of the local school districts of the Twin Cities area. The teacher(s) will assist the investigators in the analysis of scattered laser light in glacier ice, and will set up a small experiment at various visible wavelengths to measure scattering constants. These experiments have been chosen because they can easily translate into classroom demonstrations and hands\u2010on activities using eye-safe visible- light LED sources and large samples of artificial ice. The teacher(s) will also produce a lesson plan on basic optics, glacial ice, or polar science as a deliverable.\nThis proposal does not involve field work.", "links": [ { diff --git a/datasets/USAP-2046240_1.json b/datasets/USAP-2046240_1.json index f5db6e990f..30489ddff3 100644 --- a/datasets/USAP-2046240_1.json +++ b/datasets/USAP-2046240_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2046240_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rapid and persistent climate warming in the Western Antarctic Peninsula is likely resulting in intensified snow-algae growth and an extended bloom season in coastal areas. Similarly, deposition of light absorbing particles (LAPs) onto Antarctica cryosphere surfaces, such as black carbon from intensifying Southern Hemisphere wildfire seasons, and dust from the expansion of ice-free regions in the Antarctic Peninsula, may be increasing. The presence of snow algae blooms and LAPs enhance the absorption of solar radiation by snow and ice surfaces. This positive feedback creates a measurable radiative forcing, which can have immediate local and long-term regional impacts on albedo, snow melt and downstream ecosystems. This project will investigate the spatial and temporal distribution of snow algae, black carbon and dust across the Western Antarctica Peninsula region, their response to climate warming, and their role in regional snow and ice melt. Data will be collected across multiple spatial scales from in situ field measurements and sample collection to imagery from ground-based photos and high resolution multi-spectral satellite sensors. Ground measurements will inform development and application of novel algorithms to map algal bloom extent through time using 0.5-3m spatial resolution multi-spectral satellite imagery. Results will be used to improve snow algae parameterization in a new version of the Snow Ice Aerosol Radiation model (SNICARv3) that includes bio-albedo feedbacks, eventually informing models of ice-free area expansion through incorporation of SNICARv3 in the Community Earth System Model. Citizen scientists will be mentored and engaged in the research through an active partnership with the International Association of Antarctic Tour Operators that frequently visits the region. The cruise ship association will facilitate sampling to develop a unique snow algae observing network to validate remote sensing algorithms that map snow algae with high-resolution multi-spectral satellite imagery from space. These time-series will inform instantaneous and interannual radiative forcing calculations to assess impacts of snow algae and LAPs on regional snow melt. Quantifying the spatio-temporal growing season of snow algae and impacts from black carbon and dust will increase our ability to model their impact on snow melt, regional climate warming and ice-free expansion in the Antarctic Peninsula region.", "links": [ { diff --git a/datasets/USAP-2046437_1.json b/datasets/USAP-2046437_1.json index a27bb5ece6..e445acba2b 100644 --- a/datasets/USAP-2046437_1.json +++ b/datasets/USAP-2046437_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2046437_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polar ecosystems currently experience significant impacts due to global changes. Measurable negative effects on polar wildlife have already occurred, such as population decreases of numerous seabird species, including the complete loss of colonies of one of the most emblematic species of the Antarctic, the emperor penguin. These existing impacts on polar species are alarming, especially because many polar species still remain poorly studied due to technical and logistical challenges imposed by the harsh environment and extreme remoteness. Developing technologies and tools for monitoring such wildlife populations is, therefore, a matter of urgency. This project aims to help close major knowledge gaps about the emperor penguin, in particular about their adaptive capability to a changing environment, by the development of next-generation tools to remotely study entire colonies. Specifically, the main goal of this project is to implement and test an autonomous unmanned ground vehicle equipped with Radio-frequency identification (RFID) antennas and wireless mesh communication data-loggers to: 1) identify RFID-tagged emperor penguins during breeding to studying population dynamics without human presence; and 2) receive GPS-TDR datasets from VHF-GPS-TDR data-loggers without human presence to study animal behavior and distribution at sea. The autonomous vehicles navigation through the colony will be aided by an existing remote penguin observatory (SPOT). Properly implemented, this technology can be used to study of the life history of individual penguins, and therefore gather data for behavioral and population dynamic studies. The education objectives of this CAREER project are designed to increase the interest in a STEM education for the next generation of scientists by combining the charisma of the emperor penguin with robotics research. Within this project, a new class on ecosystem robotics will be developed and taught, Robotics boot-camps will allow undergraduate students to remotely participate in Antarctic field trips, and an annual curriculum will be developed that allows K-12 students to follow the life of the emperor penguin during the breeding cycle, powered by real-time data obtained using the unmanned ground vehicle as well as the existing emperor penguin observatory. ", "links": [ { diff --git a/datasets/USAP-2046800_1.json b/datasets/USAP-2046800_1.json index 2b5bdfb8bd..dd00017588 100644 --- a/datasets/USAP-2046800_1.json +++ b/datasets/USAP-2046800_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2046800_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Due to persistent cold temperatures, geographical isolation, and resulting evolutionary distinctness of Southern Ocean fauna, the study of Antarctic reducing habitats has the potential to fundamentally alter our understanding of the biologic processes that inhibit greenhouse gas emissions from our oceans. Marine methane, a greenhouse gas 25x as potent as carbon dioxide for warming our atmosphere, is currently a minor component of atmospheric forcing due to the microbial oxidation of methane within the oceans. Based on studies of persistent deep-sea seeps at mid- and northern latitudes we have learned that bacteria and archaea create a \u2018sediment filter\u2019 that oxidizes methane prior to its release. As increasing global temperatures have and will continue to alter the rate and variance of methane release, the ability of the microbial filter to respond to fluctuations in methane cycles is a critical yet unexplored avenue of research. Antarctica contains vast reservoirs of methane, equivalent to all of the permafrost in the Arctic, and yet we know almost nothing about the fauna that may mitigate its release, as until recently, we had not discovered an active methane seep. In 2012, a methane seep was discovered in the Ross Sea, Antarctica that formed in 2011 providing the first opportunity to study an active Antarctic methane-fueled habitat and simultaneously the impact of microbial succession on the oxidation of methane, a critical ecosystem service. Previous work has shown that after 5 years of seepage, the community was at an early stage of succession and unable to mitigate the release of methane from the seafloor. In addition, additional areas of seepage had begun nearby. This research aims to quantify the community trajectory of these seeps in relation to their role in the Antarctic Ecosystem, from greenhouse gas mitigation through supporting the food web. Through the application of genomic and transcriptomic approaches, taxa involved in methane cycling and genes activated by the addition of methane will be identified and contrasted with those from other geographical locations. These comparisons will elucidate how taxa have evolved and adapted to the polar environment. This research uses a \u2018genome to ecosystem\u2019 approach to advance our understanding of organismal and systems ecology in Antarctica. By quantifying the trajectory of community succession following the onset of methane emission, the research will decipher temporal shifts in biodiversity/ecosystem function relationships. Phylogenomic approaches focusing on taxa involved in methane cycling will advance the burgeoning field of microbial biogeography on a continent where earth\u2019s history may have had a profound yet unquantified impact on microbial evolution. Further, the research will empirically quantify the role of chemosynthesis as a form of export production from seeps and in non-seep habitats in the nearshore Ross Sea benthos, informing our understanding of Antarctic carbon cycling. ", "links": [ { diff --git a/datasets/USAP-2055455_1.json b/datasets/USAP-2055455_1.json index f0399d3f1b..258fa01822 100644 --- a/datasets/USAP-2055455_1.json +++ b/datasets/USAP-2055455_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2055455_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Part 1: Non-technical description: It is well known that the Southern Ocean plays an important role in global carbon cycling and also receives a disproportionately large influence of climate change. The role of marine viruses on ocean productivity is largely understudied, especially in this global region. This team proposes to use combination of genomics, flow cytometry, and network modeling to test the hypothesis that viral biogeography, infection networks, and viral impacts on microbial metabolism can explain variations in net community production (NCP) and carbon cycling in the Southern Ocean. The project includes the training of a postdoctoral scholar, graduate students and undergraduate students. It also includes the development of a new Polar Sci ReachOut program in partnership with the University of Michigan Museum of Natural History especially targeted to middle-school students and teachers and the general public. The team will also produce a Science for Tomorrow (SFT) program for use in middle schools in metro-Detroit communities and lead a summer Research Experience for Teachers (RET) fellows. Part 2: Technical description: The study will leverage hundreds of existing samples collected for microbes and viruses from the Antarctic Circumpolar Expedition (ACE). These samples provide the first contiguous survey of viral diversity and microbial communities around Antarctica. Viral networks are being studied in the context of biogeochemical data to model community networks and predict net community production (NCP), which will provide a way to evaluate the role of viruses in Southern Ocean carbon cycling. Using cutting edge molecular and flow cytometry approaches, this project addresses the following questions: 1) How/why are Southern Ocean viral populations distributed across environmental gradients? 2a) Do viruses interfere with \"keystone\" metabolic pathways and biogeochemical processes of microbial communities in the Southern Ocean? 2b) Does nutrient availability or other environmental variables drive changes in virus-microbe infection networks in the Southern Ocean? Results will be used to develop and evaluate generative models of NCP predictions that incorporate the importance of viral traits and virus-host interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-2130663_1.json b/datasets/USAP-2130663_1.json index df6d81487b..54c30ed83c 100644 --- a/datasets/USAP-2130663_1.json +++ b/datasets/USAP-2130663_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2130663_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation.\n\nThis workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-2132641_1.json b/datasets/USAP-2132641_1.json index 6569a88e0a..90389cd6f7 100644 --- a/datasets/USAP-2132641_1.json +++ b/datasets/USAP-2132641_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2132641_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nematode worms are abundant and ubiquitous in marine sediment habitats worldwide, performing key functions such as nutrient cycling and sediment stability. However, study of this phylum suffers from a perpetual and severe taxonomic deficit, with less than 5,000 formally described marine species. Fauna from the Southern Ocean are especially poorly studied due to limited sampling and the general inaccessibility of the Antarctic benthos. This study is providing the first large-scale molecular-based investigation from marine nematodes in the Eastern Antarctic continental shelf, providing an important comparative dataset for the existing body of historical (morphological) taxonomic studies. This project uses a combination of classical taxonomy (microscopy) and modern -omics tools to achieve three overarching aims: 1) determine if molecular data supports high biodiversity and endemism of benthic meiofauna in Antarctic benthic ecosystems; 2) determine the proportion of marine nematode species that have a deep-sea versus shallow-water evolutionary origin on the Antarctic shelf, and assess patterns of cryptic speciation in the Southern Ocean; and 3) determine the most important drivers of the host-associated microbiome in Antarctic marine nematodes. This project is designed to rapidly advance knowledge of the evolutionary origins of Antarctic meiofauna, provide insight on population-level patterns within key indicator genera, and elucidate the potential ecological and environmental factors which may influence microbiome patterns. Broader Impacts activities include an intensive cruise- and land-based outreach program focusing on social media engagement and digital outreach products, raising awareness of Antarctic marine ecosystems and understudied microbial-animal relationships. The diverse research team includes female scientists, first-generation college students, and Latinx trainees.\n", "links": [ { diff --git a/datasets/USAP-2133684_1.json b/datasets/USAP-2133684_1.json index cc9bdc764a..387e1a08cd 100644 --- a/datasets/USAP-2133684_1.json +++ b/datasets/USAP-2133684_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2133684_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not all of Antarctica is covered in ice. In fact, soils are common to many parts of Antarctica, and these soils are often unlike any others found on Earth. Antarctic soils harbor unique microorganisms able to cope with the extremely cold and dry conditions common to much of the continent. For decades, microbiologists have been drawn to the unique soils in Antarctica, yet critical knowledge gaps remain. Most notably, it is unclear what properties allow certain microbes to thrive in Antarctic soils. By using a range of methods, this project is developing comprehensive model that discovers the unique genomic features of soils diversity, distributions, and adaptations that allow Antarctic soil microbes to thrive in extreme environments. The proposed work will be relevant to researchers in many fields, including engineers seeking to develop new biotechnologies, ecologists studying the contributions of these microbial communities to the functioning of Antarctic ecosystems, microbiologists studying novel microbial adaptations to extreme environmental conditions, and even astrobiologists studying the potential for life on Mars. More generally, the proposed research presents an opportunity to advance our current understanding of the microbial life found in one of the more distinctive microbial habitats on Earth, a habitat that is inaccessible to many scientists and a habitat that is increasingly under threat from climate change.\n\nThe research project explores the microbial diversity in Antarctic soils and links specific features to different soil types and environmental conditions. The overarching questions include: What microbial taxa are found in a variety of Antarctic environments? What are the environmental preferences of specific taxa or lineages? What are the genomic and phenotypic traits of microorganisms that allow them to persist in extreme environments and determine biogeographical differneces? This project will analyze archived soils collected from across Antarctica by a network of international collaborators, with samples selected to span broad gradients in soil and site conditions. The project uses cultivation-independent, high-throughput genomic analysis methods and cultivation-dependent approaches to analyze bacterial and fungal communities in soil samples. The results will be used to predict the distributions of specific taxa and lineages, obtain genomic information for the more ubiquitous and abundant taxa, and quantify growth responses in vitro across gradients in temperature, moisture, and salinity. This integration of ecological, environmental, genomic, and trait-based information will provide a comprehensive understanding of microbial life in Antarctic soils. This project will also help facilitate new collaborations between scientists across the globe while providing undergraduate students with ''hands-on'' research experiences that introduce the next generation of scientists to the field of Antarctic biology.\n\nThis award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-2141555_1.json b/datasets/USAP-2141555_1.json index dfdcd6bfe1..e7032a3861 100644 --- a/datasets/USAP-2141555_1.json +++ b/datasets/USAP-2141555_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2141555_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ross Sea, Antarctica, is one of the last large intact marine ecosystems left in the world, yet is facing increasing pressure from commercial fisheries and environmental change. It is the most productive stretch of the Southern Ocean, supporting an array of marine life, including Antarctic toothfish the regions top fish predator. While a commercial fishery for toothfish continues to grow in the Ross Sea, fundamental knowledge gaps remain regarding toothfish ecology and the impacts of toothfish fishing on the broader Ross Sea ecosystem. Recognizing the global value of the Ross Sea, a large (>2 million km2) marine protected area was adopted by the multi-national Commission for the Conservation of Antarctic Marine Living Resources in 2016. This research will fill a critical gap in the knowledge of Antarctic toothfish and deepen understanding of biological-physical interactions for fish ecology, while contributing to knowledge of impacts of fishing and environmental change on the Ross Sea system. This work will further provide innovative tools for studying connectivity among geographically distinct fish populations and for synthesizing and assessing the efficacy of a large-scale marine protected area. In developing an integrated research and education program in engaged scholarship, this project seeks to train the next generation of scholars to engage across the science-policy-public interface, engage with Southern Ocean stakeholders throughout the research process, and to deepen the publics appreciation of the Antarctic. A major research priority among Ross Sea scientists is to better understand the life history of the Antarctic toothfish and test the efficacy of the Ross Sea Marine Protected Area (MPA) in protecting against the impacts of overfishing and climate change. Like growth rings of a tree, fish ear bones, called otoliths, develop annual layers of calcium carbonate that incorporates elements from their environment. Otoliths offer information on the fishs growth and the surrounding ocean conditions. Hypothesizing that much of the Antarctic toothfish life cycle is structured by ocean circulation, this research employs a multi-disciplinary approach combining age and growth work with otolith chemistry testing, while also utilizing GIS mapping. The project will measure life history parameters as well as trace elements and stable isotopes in otoliths in three distinct sets collected over the last four decades in the Ross Sea. The information will be used to quantify the transport pathways Antarctic toothfish use across their life history, and across time, in the Ross Sea. The project will assess if toothfish populations from the Ross Sea are connected more widely across the Antarctic. By comparing life history and otolith chemistry data across time, the researchers will assess change in life history parameters and spatial dynamics and seek to infer if these changes are driven by fishing or climate change. Spatially mapping of these data will allow an assessment of the efficacy of the Ross Sea MPA in protecting toothfish and where further protections might be needed. This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-2142491_1.json b/datasets/USAP-2142491_1.json index 98d065605b..a4241097ad 100644 --- a/datasets/USAP-2142491_1.json +++ b/datasets/USAP-2142491_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2142491_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aims of this CAREER proposal are to gain a greater understanding of the role of sympagic algae in Antarctic marine ecosystems with the goal to better parameterize their role in biogeochemical and ecosystem processes across dynamic environments. Specifically, this proposal will apply a laboratory-scale, ice-tank system that recreates the seasonal cycle of sea ice in the laboratory for the purpose of studying sympagic microbes to study the following questions:\n1.1 \tStarting with a late autumn, mixed phytoplankton community, how do different algal species specialize to sea ice, seawater and flooded snow/ice habitats over winter?\n1.2 \tWhat are the relationships between different methods of measuring primary production (fluorescence, O2 production, CO2 drawdown) in sea ice? Does this differ from seawater?\n1.3 \tDoes the presence of sea-ice algae influence the physical structure of sea ice?\n1.4\tHow does the release of compatible solutes from algae during ice melt influence the dissolved organic pool?\nIn addition, I propose to integrate educational activities with my research goals. This includes development of an educational program at the university and K-12 level on Antarctic Sciences that develops critical thinking and quantitative skills, encourages STEM participation from underrepresented groups and establishes an interactive network of Antarctic researchers to broaden research opportunities.\n", "links": [ { diff --git a/datasets/USAP-2149070_1.json b/datasets/USAP-2149070_1.json index 359d54c99f..61f8d24d8b 100644 --- a/datasets/USAP-2149070_1.json +++ b/datasets/USAP-2149070_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2149070_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This proposal represents collaborative research to explore manganese (Mn) limitation in Antarctic diatoms by two early career investigators. Diatoms are central players in the Southern Ocean carbon cycle, where the micronutrient chemistry is fundamentally different from other oceans. The Southern Ocean is characterized by widespread low Mn, coupled with high zinc (Zn). High Zn levels are potentially toxic to diatoms as Zn can competitively inhibit Mn uptake and metabolism, compromising the ability of building critical cellular components, thus impacting the biological pump. Using culture experiments with a matrix of micronutrient treatments (Mn, Zn, Fe) and irradiances, and using physiological and transcriptomic approaches, along with biochemical principles, the Principal Investigators will address the central hypothesis (that diatoms from the Southern Ocean possess unique physiological mechanisms to adapt to low Mn/high Zn) by quantifying rates of uptake and transporter binding constants. The transcriptomics approach will help to identify candidate genes that may provide Antarctic diatoms physiological mechanisms in low Mn/high Zn environment. The project does not require fieldwork but instead would make use of culture experiments with 4 diatom species (3 Antarctic, and 1 temperate). The proposed approach will also enable the goal of developing biomarker(s) for assessing Mn stress or Zn toxicity and results from the physiological experiments will help parameterize models of micronutrient limitation in the Southern Ocean.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "links": [ { diff --git a/datasets/USAP-2232891_1.json b/datasets/USAP-2232891_1.json index 6782199779..4a4e673618 100644 --- a/datasets/USAP-2232891_1.json +++ b/datasets/USAP-2232891_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2232891_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic animals face tremendous threats as Antarctic ice sheets melt and temperatures rise. About 34 million years ago, when Antarctica began to cool, most species of fish became locally extinct. A group called the notothenioids, however, survived due to the evolution of antifreeze. The group eventually split into over 120 species. Why did this group of Antarctic fishes evolve into so many species? One possible reason why a single population splits into two species relates to sex genes and sex chromosomes. Diverging species often have either different sex determining genes (genes that specify whether an individual\u2019s gonads become ovaries or testes) or have different sex chromosomes (chromosomes that differ between males and females within a species, like the human X and Y chromosomes). We know the sex chromosomes of only a few notothenioid species and know the genetic basis for sex determination in none of them. \nThe aims of this research are to: 1) identify sex chromosomes in species representing every major group of Antarctic notothenioid fish; 2) discover possible sex determining genes in every major group of Antarctic notothenioid fish; and 3) find sex chromosomes and possible sex determining genes in two groups of temperate, warmer water, notothenioid fish. These warmer water fish include groups that never experienced the frigid Southern Ocean and groups that had ancestors inhabiting Antarctic oceans that later adjusted to warmer waters. This project will help explain the mechanisms that led to the division of a group of species threatened by climate change. This information is critical to conserve declining populations of Antarctic notothenioids, which are major food sources for other Antarctic species such as bird and seals. \nThe project will offer a diverse group of undergraduates the opportunity to develop a permanent exhibit at the Eugene Science Center Museum. The exhibit will describe the Antarctic environment and explain its rapid climate change. It will also introduce the continent\u2019s bizarre fishes that live below the freezing point of water. The project will collaborate with the university\u2019s Science and Comics Initiative and students in the English Department\u2019s Comics Studies Minor to prepare short graphic novels explaining Antarctic biogeography, icefish specialties, and the science of this project as it develops.", "links": [ { diff --git a/datasets/USAP-2240780_1.json b/datasets/USAP-2240780_1.json index ee53108ecc..aafad31dff 100644 --- a/datasets/USAP-2240780_1.json +++ b/datasets/USAP-2240780_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2240780_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mixotrophic microorganisms that are capable of both photosynthetic and heterotrophic forms of metabolism are key contributors to primary productivity and organic carbon remineralization in the Southern Ocean. However, uncertainties in their grazing behavior and physiology prevent an accurate understanding of microbial food web dynamics and biogeochemical cycling in the Antarctic ecosystem. Polar mixotrophs have evolved to withstand extreme seasonality, including variable light, sea ice, temperature, and micronutrient concentrations. In particular, the Southern Ocean appears to be the only region of the world\u2019s ocean where the bioessential trace metals iron (Fe) and manganese (Mn) are low enough to inhibit photosynthetic growth. The molecular physiology of mixotrophs experiencing Fe and Mn growth limitation has not yet been examined, and we lack an understanding of how seasonal changes in the availability of these micronutrients influence mixotrophic growth dynamics. We aim to examine whether grazing affords mixotrophs an ecological advantage in the Fe and Mn-deficient Southern Ocean, and to characterize the shifts in metabolic processes that occur during transitions in micronutrient conditions. Culture studies will directly measure growth responses, grazing behavior, and changes in elemental stoichiometry in response to Fe and Mn limitation. Transcriptomic analyses will reveal the metabolic underpinnings of trophic behavior and micronutrient stress responses, with implications for key biogeochemical processes such as carbon fixation, remineralization, and nutrient cycling. ", "links": [ { diff --git a/datasets/USAP-2324998_1.json b/datasets/USAP-2324998_1.json index d62d99ee8b..3d467ff7c9 100644 --- a/datasets/USAP-2324998_1.json +++ b/datasets/USAP-2324998_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-2324998_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Part I: Nontechnical description The ecologically important notothenioid fish of the Southern Ocean surrounding Antarctica will be studied to address questions central to polar, evolutionary, and adaptational biology. The rapid diversification of the notothenioids into >120 species following a period of Antarctic glaciation and cooling of the Southern Ocean is thought to have been facilitated by key evolutionary innovations, including antifreeze glycoproteins to prevent freezing and bone reduction to increase buoyancy. In this project, a large dataset of genomic sequences will be used to evaluate the genetic mechanisms that underlie the broad pattern of novel trait evolution in these fish, including traits relevant to human diseases (e.g., bone density, renal function, and anemia). The team will develop new STEM-based research and teaching modules for undergraduate education at Northeastern University. The work will provide specific research training to scholars at all levels, including a post-doctoral researcher, a graduate student, undergraduate students, and high school students. The team will also contribute to public outreach, including, in part, the develop of teaching videos in molecular evolutionary biology and accompanying educational supplements. \n\n\n\n Part II: Technical description The researchers will leverage their comprehensive notothenioid phylogenomic dataset comprising >250,000 protein-coding exons and conserved non-coding elements across 44 ingroup and 2 outgroup species to analyze the genetic origins of three iconic notothenioid traits: (1) loss of erythrocytes by the icefish clade in a cold, stable and highly-oxygenated marine environment. (2) reduction in bone mass and retention of juvenile skeletal characteristics as buoyancy mechanisms to facilitate foraging. And (3) loss of kidney glomeruli to retain energetically expensive antifreeze glycoproteins. The team will first track patterns of change in erythroid-related genes throughout the notothenioid phylogeny. They will then examine whether repetitive evolution of a pedomorphic skeleton in notothenioids is based on parallel or divergent evolution of genetic regulators of heterochrony. Third, they will determine whether there is mutational bias in the mechanisms of loss and re-emergence of kidney glomeruli. Finally, identified genetic mechanisms of evolutionary change will be validated by experimental testing using functional genomic strategies in the zebrafish model system.", "links": [ { diff --git a/datasets/USAP-9615281_1.json b/datasets/USAP-9615281_1.json index 8b805215cc..de6a42bc03 100644 --- a/datasets/USAP-9615281_1.json +++ b/datasets/USAP-9615281_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-9615281_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS.", "links": [ { diff --git a/datasets/USAP-9725024_1.json b/datasets/USAP-9725024_1.json index 2755d37449..c676e68d1c 100644 --- a/datasets/USAP-9725024_1.json +++ b/datasets/USAP-9725024_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USAP-9725024_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will study the dynamics of Circumpolar Deep Water intruding on the continental shelf of the West Antarctic coast, and the effect of this intrusion on the production of cold, dense bottom water, and melting at the base of floating glaciers and ice tongues. It will concentrate on the Amundsen Sea shelf, specifically in the region of the Pine Island Glacier, the Thwaites Glacier, and the Getz Ice Shelf. Circumpolar Deep Water (CDW) is a relatively warm water mass (warmer than +1.0 deg Celsius) which is normally confined to the outer edge of the continental shelf by an oceanic front separating this water mass from colder and saltier shelf waters. In the Amundsen Sea however, the deeper parts of the continental shelf are filled with nearly undiluted CDW, which is mixed upward, delivering significant amounts of heat to the base of the floating glacier tongues and the ice shelf. The melt rate beneath the Pine Island Glacier averages ten meters of ice per year with local annual rates reaching twenty meters. By comparison, melt rates beneath the Ross Ice Shelf are typically twenty to forty centimeters of ice per year. In addition, both the Pine Island and the Thwaites Glacier are extremely fast-moving, and have a significant effect on the regional ice mass balance of West Antarctica. This project therefore has an important connection to antarctic glaciology, particularly in assessing the combined effect of global change on the antarctic environment. The particular objectives of the project are (1) to delineate the frontal structure on the continental shelf sufficiently to define quantitatively the major routes of CDW inflow, meltwater outflow, and the westward evolution of CDW influence; (2) to use the obtained data set to validate a three-dimensional model of sub-ice ocean circulation that is currently under construction, and (3) to refine the estiamtes of in situ melting on the mass balance of the antarctic ice sheet. The observational program will be carried out from the research vessel Nathaniel B. Palmer in February and March, 1999.", "links": [ { diff --git a/datasets/USARC_AERIAL_PHOTOS.json b/datasets/USARC_AERIAL_PHOTOS.json index 9ba32a037c..3e0665bc17 100644 --- a/datasets/USARC_AERIAL_PHOTOS.json +++ b/datasets/USARC_AERIAL_PHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USARC_AERIAL_PHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight\nindexes of the Antarctic.\n\nThere are over 500,000 photographs in the collection.\n\nMost photographs are 9\" x 9\" black and white images taken with three Fairchild\ncameras each with a metrogon lense resulting in trimetrogon photography (left\noblique, vertical and right oblique photographs).\n\nSpecial-purpose photographs showing sites of specific scientific interest\n\"vertical and handheld oblique as well as photographs taken from helicopters\"\nare also on file.\n\nSome color photographs are also available. Line indexes to identify coverage\nare available for most aerial photographic missions.\n\nContact prints in either matte or glossy finish are available for inspection or\nstereoscopic viewing. Special feature options, such as ice and rock\nenhancements, may be special ordered.", "links": [ { diff --git a/datasets/USArray_Ground_Temperature_1680_1.1.json b/datasets/USArray_Ground_Temperature_1680_1.1.json index 0b0e5acb41..f64dc5d903 100644 --- a/datasets/USArray_Ground_Temperature_1680_1.1.json +++ b/datasets/USArray_Ground_Temperature_1680_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USArray_Ground_Temperature_1680_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location.", "links": [ { diff --git a/datasets/USDA0113.json b/datasets/USDA0113.json index 401bb2a559..13b253099f 100644 --- a/datasets/USDA0113.json +++ b/datasets/USDA0113.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USDA0113", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis for 400 domestic wells for selected constituents. Reconnaissance of\nGround Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and\nHaywood counties, Tennessee.\n\nCollection Organization: USDA-CSREES/USGS - University of Tennessee; Institute\nof Agriculture\n\nCollection Methodology: Samples collected by UTAES staff, trained volunteers,\nand USGS Personnel - USGS conducted field and laboratory analysis.\n\nCollection Frequency: One-time.\n\nUpdate Characteristics: N/A\n\nSTATISTICAL INFORMATION: 400 wells; 20 parameters per sample.\nLANGUAGE: English\nACCESS/AVAILABILITY:\nData Center: U.S. Geological Survey\nDissemination Media: USGS Data Base\nAccess Instructions: Contact the data center.", "links": [ { diff --git a/datasets/USDA0114.json b/datasets/USDA0114.json index 262811395d..baf01ca429 100644 --- a/datasets/USDA0114.json +++ b/datasets/USDA0114.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USDA0114", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis for 200 domestic wells and springs for selected constituents. \nReconnaissance of Ground Water Quality in Bedford and Coffee Counties, TN.\n\nCollection Organization: USDA-CSREES/USGS - University of Tennessee; Institute\nof Agriculture\n\nCollection Methodology: Samples collected by UTAES staff, trained volunteers,\nand USGS Personnel - USGS conducted field and laboratory analysis.\n\nCollection Frequency: One-time.\n\nUpdate Characteristics: N/A\n\nSTATISTICAL INFORMATION:\n200 wells/springs; 7 parameters per sample.\n\nLANGUAGE: English\n\nACCESS/AVAILABILITY:\nData Center: U.S. Geological Survey\nMedia: USGS Data Base\nAccess Instructions: Contact the data center.", "links": [ { diff --git a/datasets/USDA0115.json b/datasets/USDA0115.json index 4b8f5869e8..9d9a1b90ba 100644 --- a/datasets/USDA0115.json +++ b/datasets/USDA0115.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USDA0115", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of 150 wells for selected constituents, reconnaissance of Ground Water\nQuality in Tennessee.\n\nCollection Organization: USDA-CSREES - University of Tennessee; Institute of\nAgriculture\n\nCollection Methodology: Samples collected by USGS staff. USGS conducted field\nand laboratory analysis at their national lab.\n\nCollection Frequency: One-time.\n\nUpdate Characteristics: N/A\n\nSTATISTICAL INFORMATION:\n150 wells on farmsteads across Tennessee; 7 parameters per\nwell.\n\nLANGUAGE: English\n\nACCESS/AVAILABILITY:\nData Center: U.S. Geological Survey\nMedia: USGS Data Base.\nAccess Instructions: Contact the data center.", "links": [ { diff --git a/datasets/USGS-DDS-058_1.0.json b/datasets/USGS-DDS-058_1.0.json index 13781938ce..420f6afd06 100644 --- a/datasets/USGS-DDS-058_1.0.json +++ b/datasets/USGS-DDS-058_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-058_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The safe disposal of high-level radioactive wastes is one of the most pressing\nenvironmental issues of modern times. At present, most of these materials are\nbeing stored under temporary conditions at many of the individual nuclear power\nplants where they were produced. In recognition of the need for permanent\nwaste storage, Yucca Mountain in southwestern Nevada has been investigated by\nFederal agencies since the 1970's as one of the Nation's potential geologic\ndisposal sites. In 1987, Congress selected Yucca Mountain for an expanded and\nmore detailed site characterization effort, and a broad multidisciplinary\nprogram of studies was developed by the U.S. Department of Energy to further\nevaluate the suitability of the mountain as a safe and permanent underground\ndisposal facility. The scope and objectives of the many kinds of \ninvestigations to be pursued were guided in large measure by regulations\ngoverning the siting of geologic repositories for high-level radioactive\nwastes that were issued by the U.S. Nuclear Regulatory Commission (Code of\nFederal Regulations 10CFR60) and supplmented by further requirements set forth\nby the U.S. Department of Energy (Code of Federal Regulations 10CFR960).\n\nAs an integral part of the planned site-characterization program, the U.S.\nGeological Survey began a series of detailed geologic, geophysical, and\nrelated investigations designed to characterize the tectonic setting, fault\nbehavior, and seismicity of the Yucca Mountain area. A broad goal was to\nprovide essential data for assessing the possible risks posed by future\nseismic and fault activity in the area that may affect the design and\nlong-term performance, and the safe operation, of the potential surface and\nsubsurface repository facilities.\n\nThe results of 13 of the many studies undertaken to increase understanding of\nthe tectonic environment of Yucca Mountain and the adjacent area are presented\nin this report.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-066_1.0.json b/datasets/USGS-DDS-066_1.0.json index 85a1c90df2..9220a3ea05 100644 --- a/datasets/USGS-DDS-066_1.0.json +++ b/datasets/USGS-DDS-066_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-066_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To obtain subsurface geologic information about the alluvium in the Big\nThompson River valley, S -wave refraction data were collected along three roads\nthat cross the valley. The traveltimes were processed to estimate velocities\nand thicknesses for a layered-earth model; from these models, three cross\nsections of the river valley were constructed. The river valleys are covered by\na layer of soil, which is 0.2 to 1.5 m thick. Beneath the soil, there is one\nlayer of alluvium at some locations and two layers at other locations. For the\ntwo westernmost cross sections, the total thickness of the alluvium ranges from\nabout 6 to 10 m near the center of the valley and from about 2 to 6 m near the\nsides of the valley. The easternmost cross section is somewhat more complex\nthan the other two, because it is near the confluence of the Big Thompson and\nthe Little Thompson Rivers. In this cross section, the thickness of the\nalluvium ranges from about 8 to 10 m in the southern half of the valley and\nfrom about 3 to 13 m in the northern half. In all three cross sections, the\nalluvium overlies bedrock, which is the upper transition member of the Pierre\nShale.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-067.json b/datasets/USGS-DDS-067.json index af13e3d782..968c2c5a02 100644 --- a/datasets/USGS-DDS-067.json +++ b/datasets/USGS-DDS-067.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-067", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1997, the U. S. Geological Survey published USGS Bulletin 2146, comprising\n12 chapters dealing with geologic, geochemical, and assessment issues related\nto deep gas resources (Dyman and others, 1997). A primary goal of that report\nwas to provide geology-based information that might aid in future improvements\nto technology for deep gas exploration and development. Chapters of this report\n represent a continuation of that work. The current work is funded by the U. S.\nDepartment of Energy, National Energy Technology Laboratory, Morgantown, W. Va.\n(contract No. DE-AT26-98FT40032), Gas Technology Institute (GTI), Chicago,\nIll. (contract No. 5094-210-3366 through a Cooperative Research and Development\nAgreement with Advanced Resources International, Arlington, Va.), and the U.\nS. Geological Survey, Denver, Colo.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-11.json b/datasets/USGS-DDS-11.json index 0250e0e851..1313aef59d 100644 --- a/datasets/USGS-DDS-11.json +++ b/datasets/USGS-DDS-11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conversion of the geologic map of the U.S. to a digital format was undertaken\nto facilitate the presentation and analysis of earth-science data. Digital\nmaps can be displayed at any scale or projection, whereas a paper map has a\nfixed scale and projection. However, the geology on this disc is not intended\nto be used at any scale finer than 1:2,500,000.\n\nThis CD-ROM contains a digital version of the Geologic Map of the United\nStates, originally published at a scale of 1:2,500,000 (King and Beikman,\n1974b). It excludes Alaska and Hawaii. In addition to the graphical formats,\nthe map key is included in ASCII text.\n\nA geographic information system (GIS) allows combining and overlaying of layers\nfor analysis of spatial relations not readily apparent in the standard paper\npublication. This disc contains only geology. However, digital data on\ngeology, geophysics, and geochemistry can be combined to create useful\nderivative products-- for example, see Phillips and others (1993).\n\nThis CD-ROM contains a copy of the text and figures from Professional Paper 901\nby King and Beikman (1974a). This text describes the historical background of\nthe map, details of the compilation process, and limitations to interpretation.\n The digital version of the text can be searched for keywords or phrases.\n\nFor DOS users, the CD-ROM contains menu-driven analytical software, in which\nthe user selects from an array of topics. The CD-ROM also contains MAPPER\ndisplay software, a user-friendly package that displays the interactive vector\nmap. The raster image of the geologic map can be displayed with VIEWLBL.\n\nFor other types of computer users, the map must be converted from one of the\nfollowing formats included on the CD-ROM:\n\nARC/INFO 6.1.1 Export\nDigital Line Graph (DLG) Optional\nDrawing Exchange File (DXF)\nMap Overlay Statistical System (MOSS)", "links": [ { diff --git a/datasets/USGS-DDS-18-A_1.0.json b/datasets/USGS-DDS-18-A_1.0.json index 85864991b6..98a876d163 100644 --- a/datasets/USGS-DDS-18-A_1.0.json +++ b/datasets/USGS-DDS-18-A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-18-A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an online version of a CD-ROM publication. It is intended for use only\non DOS-based computer systems. The files must be downloaded onto your computer\nbefore they can be used. The files are presented here in two forms: as the\noriginal folders that were published on the CD-ROM and as a large zip file that\nyou can use to download the entire product in one step.\n\nThis publication contains National Uranium Resource Evaluation (NURE) data for\nthe conterminous United States. The data has been compressed and requires\nGSSEARCH software for access. GSSEARCH, supplied below, runs only under DOS. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-19.json b/datasets/USGS-DDS-19.json index 3bcca3c6c0..9980d216e6 100644 --- a/datasets/USGS-DDS-19.json +++ b/datasets/USGS-DDS-19.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-19", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PROJECT OVERVIEW\n\nConversion of the information from the original folio to a computerized \ndigital format was undertaken to facilitate the presentation and analysis of\nearth-science data. Digital maps can be displayed at any scale or \nprojection, whereas a paper map has a fixed scale and projection. However, \nmost of the maps on this disc are not intended to be used at any scale more \ndetailed than 1:500,000. \n\nA geographic information system (GIS) allows combining and overlaying of \nlayers for analysis of spatial relations not readily apparent in the \nstandard paper publication. Digital information on geology, geophysics, and\ngeochemistry can be combined to create useful derivative products.\n\nHISTORY OF THE MAPS\n\nIn 1986 and 1987, the U.S. Geological Survey (USGS), the Dirección \nGeneral de Geología, Minas e Hidrocarburos, and the Universidad de Costa \nRica conducted a mineral-resource assessment of the Republic of Costa Rica. \nThe results were published as a large 80- by 50-cm color folio (U.S.\nGeological Survey and others, 1987). The 75-page document consists of maps \nas well as descriptive and interpretive text in English and Spanish covering\nphysiographic, geologic, geochemical, geophysical, and mineral site themes\nas well as a mineral-resource assessment. The following maps are present in\nthe original folio:\n\n 1) Physiographic base map at a scale of 1:500,000 with hypsography, \n place names, and drainage.\n 2) Geologic map at a scale of 1:500,000.\n 3) Regional geophysical maps, including gravity, aeromagnetic, and \n seismicity maps at various scales.\n 4) Mineral sites map at a scale of 1:500,000 showing mines, prospects, \n and occurrences.\n 5) Volcanological framework of the Tilarán region important for \n epithermal gold deposits at a scale of 1:100,000.\n 6) Rock sample locations, mining areas, and vein locations for several \n parts of the country.\n 7) Permissive areas delineated for selected mineral deposit types.\n 8) Digital elevation model.\n\nThis CD-ROM contains most of the above maps; it lacks items 1 and 8 and \nthe seismicity and aeromagnetic maps of item 3. The linework was digitized \nfrom preliminary and printed maps with GSMAP (Selner and Taylor, 1987), a \nUSGS-authored program for map editing and publishing. Conversion from GSMAP\nto ARC/INFO was accomplished through the use of the GSMARC program (Green \nand Selner, 1988). The arcs and polygons were tagged using Alacarte \n(Wentworth and Fitzgibbon, 1991). \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-27_1.json b/datasets/USGS-DDS-27_1.json index 6b73dd2709..9b66fbc320 100644 --- a/datasets/USGS-DDS-27_1.json +++ b/datasets/USGS-DDS-27_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-27_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data set is to provide paleoclimate researchers with a tool\nfor estimating the average seasonal variation in sea-ice concentration in the\nmodern polar oceans and for estimating the modern monthly sea-ice concentration\nat any given polar oceanic location. It is expected that these data will be\ncompared with paleoclimate data derived from geological proxy measures such as\nfaunal census analyses and stable-isotope analyses. The results can then be\nused to constrain general circulation models of climate change.\n\nThis data set represents the results of calculations carried out on\nsea-ice-concentration data from the SMMR and SSM/I instruments. The original\ndata were obtained from the National Snow and Ice Data Center (NSIDC). The data\nset also contains the source code of the programs that made the calculations. \nThe objective was to derive monthly averages for the whole 13.25-year series\n(1978-1991) and to derive a composite series of monthly averages representing\nthe variation of an average year. The resulting file set contains monthly\nimages for each of the polar regions for each year, yielding 160 files for each\npole, and composite monthly averages in which the years are combined, yielding\n12 more files. Averaging the images in this way tends to reduce the number of\ngrid cells that lack valid data; the composite averages are designed to\nsuppress interannual variability.\n\nAlso included in the data set are programs that can retrieve seasonal\nice-concentration profiles at user-specified locations. These nongraphical\ndata retrieval programs are provided in versions for UNIX, extended DOS, and\nMacintosh computers. Graphical browse utilities are included for the same\ncomputing platforms but require more sophisticated display systems.\n\nThe data contained in this data set are derived from the Scanning Multichannel\nMicrowave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data\nproduced by the National Snow and Ice Data Center (NSIDC) at the University of\nColorado in cooperation with the U.S. National Aeronautics and Space\nAdministration (NASA) and the U.S. National Oceanic and Atmospheric\nAdministration (NOAA). The basic data come from satellites of the U.S. Air\nForce Defense Meteorological Satellite Program. NSIDC distributes three\ncollections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7\nSMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the\nSMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two\nseparate volumes, covering the periods from July 9 of 1987 to December 31 of\n1989, and from January 1 of 1990 through December 31 of 1991, respectively. The\nNASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the\npresent data set. SMMR images were collected every 2 to 3 days, whereas SSM/I\ndata are provided in daily ice-concentration grids. Apart from a number of\nsmall gaps (5 or fewer days) in the record, the only long period for which no\ndata are available is December 3 of 1987 through January 12 of 1988, inclusive.\n\nAs ancillary data, the ETOPO5 global gridded elevation and bathymetry data\n(Edwards, 1989) were interpolated to the resolution of the NSIDC data; the\ninterpolated topographic data are included.\n\nThe images are provided in three formats: Hierarchical Data Format (HDF), a\nflexible scientific data format developed at the National Center for\nSupercomputing Applications; Graphics Interchange Format (GIF); and Macintosh\nPICT format. The ice- concentration grids are distributed by NSIDC in HDF\nformat.", "links": [ { diff --git a/datasets/USGS-DDS-3.json b/datasets/USGS-DDS-3.json index 49d54efafe..44f859e529 100644 --- a/datasets/USGS-DDS-3.json +++ b/datasets/USGS-DDS-3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes sea floor characteristics for the Western \nMassachusetts Bay. This data set was created using sidescan-sonar imagery, \nphotography, and sediment samples.", "links": [ { diff --git a/datasets/USGS-DDS-33_1.0.json b/datasets/USGS-DDS-33_1.0.json index 83e22ef9c1..6233336396 100644 --- a/datasets/USGS-DDS-33_1.0.json +++ b/datasets/USGS-DDS-33_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-33_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Upper Cretaceous Sussex \"B\" sandstone was deposited as a probable\ntransgressive-marine sand-ridge complex in a mid-shelf position. The \"B\"\nsandstone is bounded by upper and basal disconformities and encased in\nmudstones and low-porosity and low-permeability sandstones of the Cody Shale.\nReservoir characteristics are controlled primarily by depositional and\ndiagenetic heterogeneity at megascopic (field), macroscopic (well), and\nmicroscopic (rock sample) levels. To simplify, this means production of oil is\ncontrolled by stacking and interbedding of sandstone and mudstone beds and by\ngeochemical changes through time that affect flow of fluids through the rock.\n\nMore than 24.8 million barrels of oil (MMBO) have been produced from the Sussex\n\"B\" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest\noil production, porosity, and permeability, the thickest reservoir sandstone\nintervals, and best lateral continuity of the primary reservoir facies are all\nlocated parallel and proximal to field axes. Decrease in reservoir quality west\nof the axes is due to greater heterogeneity from interbedding of low- and\nmoderate-depositional-energy facies, with associated drop in porosity and\npermeability. Decrease in production east of the axes results primarily from a\ncombination of seaward thinning of the primary reservoir facies and\nnon-deposition of sand ridges.\n\nThe House Creek field has two axis orientations; these are related to\ndepositional patterns of the four sand ridges. Deposition of the \"B\" sandstone\nbegan in the southeastern corner of the field with sand ridge 1; axis\norientation is about north 20 degrees west. Later-deposited sand ridges 2\nthrough 4 are located west and north of sand ridge 1; their axis orientations\nare approximately north 32 degrees west. Progressive northward deposition of\nlater sand ridges is probably concurrent with uplift of the northeast-trending\nBelle Fourche arch. Movement along the arch and of lineaments may have caused\ntopographic highs that localized Sussex and Shannon deposition proximal to the\narch. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS-74_2.0.json b/datasets/USGS-DDS-74_2.0.json index d4525c0875..87e31e5807 100644 --- a/datasets/USGS-DDS-74_2.0.json +++ b/datasets/USGS-DDS-74_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-74_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42\u00fd 22.6' N, 70\u00fd 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42\u00fd 9.8' N, 70\u00fd 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002).\n\nThe long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation.\n\nThis data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set .\n\n[Summary provided by the USGS.]\n", "links": [ { diff --git a/datasets/USGS-DDS-79.json b/datasets/USGS-DDS-79.json index e34d80b129..a268f891d0 100644 --- a/datasets/USGS-DDS-79.json +++ b/datasets/USGS-DDS-79.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS-79", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Louisiana contains 25 percent of the vegetated wetlands and 40 percent of the\ntidal wetlands in the 48 conterminous States. These critical natural systems\nare being lost. Louisiana leads the Nation in coastal erosion and wetland loss\nas a result of a complex combination of natural processes (e.g. storms,\nsea-level rise, subsidence) and manmade alterations to the Mississippi River\nand the wetlands over the past 200 years. Erosion of several of the barrier\nislands, which lie offshore of the estuaries and wetlands and buffer and\nprotect these important ecosystems from the open marine environment, exceeds 20\nmeters/year. The average rate of shoreline erosion is over 10 meters/year.\nWithin the past 100 years, Louisiana's barrier islands have decreased in area\nby more than 40 percent, and some islands have lost more than 75 percent of\ntheir land area. If these loss rates continue, several of the barriers are\nexpected to erode completely within the next three decades. Their disappearance\nwill contribute to further loss and deterioration of wetlands and back-barrier\nestuaries and increase the risk to infrastructure.\n\nCoastal wetland environments, which include associated bays and estuaries,\nsupport a rich harvest of renewable natural resources with an estimated annual\nvalue of over $1 billion. More than 30 percent of the Nation's fisheries come\nfrom these wetlands, as well as 25 percent of oil and gas coming through the\nwetlands. Louisiana also has the highest rate of wetland loss: 80 percent of\nthe Nation's total loss of wetlands has occurred in this State. The rate of\nwetland loss in the Mississippi River delta plain is estimated to be about 70\nsquare kilometers/year -- the equivalent of a football field every 20 minutes.\nIf these rates continue, an estimated 4,000 square kilometers of wetlands will\nbe lost in the next 50 years. Losses of this magnitude have direct implications\non the Nation's energy supplies, economic security, and environmental\nintegrity.\n\nOver the past two decades, the USGS, working in partnership with other\nscientists in universities and State agencies, has led the research effort to\ndocument barrier erosion and wetland loss and understand the natural and\nmanmade causes responsible. Some products resulting from this research,\nincluded in this DVD, are providing the baseline data and information being\nused for Federal-State wetlands restoration programs underway and being\nplanned. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-DDS_30_P-10_cells.json b/datasets/USGS-DDS_30_P-10_cells.json index b27c2b3c99..a05336f4c8 100644 --- a/datasets/USGS-DDS_30_P-10_cells.json +++ b/datasets/USGS-DDS_30_P-10_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS_30_P-10_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 10 (San Joaquin Basin) are listed\nhere by play number, type, and name:\n\nNumber Type Name\n1001 conventional Pliocene Non-associated Gas\n1002 conventional Southeast Stable Shelf\n1003 conventional Lower Bakersfield Arch\n1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks.\n1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks\n1006 conventional Northeast Shelf of Neogene Basin\n1007 conventional Northern Area Non-associated Gas\n1008 conventional Tejon Platform\n1009 conventional South End Thrust Salient\n1010 conventional East Central Basin and Slope North of Bakersfield Arch\n1011 conventional Deep Overpressured Fractured Rocks of West Side\n Fold and Overthrust Belt", "links": [ { diff --git a/datasets/USGS-DDS_30_P10_conventional.json b/datasets/USGS-DDS_30_P10_conventional.json index 95b00d255c..90c270e062 100644 --- a/datasets/USGS-DDS_30_P10_conventional.json +++ b/datasets/USGS-DDS_30_P10_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DDS_30_P10_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. ", "links": [ { diff --git a/datasets/USGS-DS-91_1.1.json b/datasets/USGS-DS-91_1.1.json index 14e722d542..5f4f778104 100644 --- a/datasets/USGS-DS-91_1.1.json +++ b/datasets/USGS-DS-91_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-DS-91_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS presents an updated model of the Juan de Fuca slab beneath southern\nBritish Columbia, Washington, Oregon, and northern California, and use this\nmodel to separate earthquakes occurring above and below the slab surface. The\nmodel is based on depth contours previously published by Fl\u00fcck and others\n(1997). Our model attempts to rectify a number of shortcomings in the original\nmodel and to update it with new work. The most significant improvements include\n(1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation\nof the slab surface to its full northern and southern edges, (3) extension of\nthe slab surface from 50-km depth down to 110-km beneath the Cascade arc\nvolcanoes, and (4) revision of the slab shape based on new seismic-reflection\nand seismic-refraction studies. We have used this surface to sort earthquakes\nand present some general observations and interpretations of seismicity\npatterns revealed by our analysis. In addition, we provide files of earthquakes\nabove and below the slab surface and a 3-D animation or fly-through showing a\nshaded-relief map with plate boundaries, the slab surface, and hypocenters for\nuse as a visualization tool. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS-OFR-92-299_1.0.json b/datasets/USGS-OFR-92-299_1.0.json index 2c2258e957..f3acbadb1e 100644 --- a/datasets/USGS-OFR-92-299_1.0.json +++ b/datasets/USGS-OFR-92-299_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-OFR-92-299_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information about and data from the USGS Open-File Report 92-299 (Molecular\nand isotopic analyses of the hydrocarbon gases within gas hydrate-bearing rock\nunits of the Prudhoe Bay-Kuparuk River area in northern Alaska) are available\nOn-line via the World Wide Web:\n\n\"http://pubs.usgs.gov/of/of92-299//\"\n\nor \"http://pubs.usgs.gov/of/1992/of92-299/\"\n\n The following information about the data set was provided by the data center\ncontact:\n\n The objective of this study was to document the molecular and isotopic\ncomposition of the gas trapped within the gas hydrate-bearing stratigraphic\nintervals overlying the Prudhoe Bay and Kuparuk River oil fields. To reach\nthis objective, we have analyzed cuttings gas and free gas samples collected\nfrom 10 drilling-production wells in the Prudhoe Bay and Kuparuk River fields.\n\n The dataset includes a report documenting the materials, the procedures used\nto analyze them, and the results. Results are given in tabular form as\nspreadsheets showing headspace, headspace/free gas, and blended headspace\nanalyses. Gas characteristics analyzed include nitrogen, carbon dioxide,\nmethane, ethane, ethene, propane, propene, isobutane, n-butane, isopentane,\nn-pentane, stable carbon isotope composition of the methane, ethane, and carbon\ndioxide fractions, and deuterium isotope composition of the methane fraction.\n\n Methane is the most abundant hydrocarbon gas within the gas hydrate- bearing\nrock units of the Prudhoe Bay-Kuparuk River area in the North Slope of Alaska.\nIsotopic analysis indicates that both microbial and thermogenic processes have\ncontributed to the formation of this methane. The thermogenic component\nprobably migrated into the rock units from greater depths, since vitrinite\nreflectance measurements show that the units never endured temperatures within\nthe thermogenic range. Approximately 50 to 70 percent of the methane within\nthe gas hydrate units is thermogenic in origin.\n\n This is U.S. Geological Survey Open-File Report 92-299\n\n This report is preliminary and has not been reviewed for conformity with U.S.\nGeological Survey editorial standards or with the North American Stratigraphic\nCode. Any use of trade, product, or firm names is for descriptive purposes\nonly and does not imply endorsement by the U.S. Government.", "links": [ { diff --git a/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json b/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json index 23b308a9bf..421eedbbc2 100644 --- a/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json +++ b/datasets/USGS-PRISM-PACIFIC-OSTRACODES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS-PRISM-PACIFIC-OSTRACODES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the Pliocene Research, Interpretation, and Synoptic\nMapping (PRISM) Project. \n\nThis data set describes marine ostracode species and related sample and\nstratigraphic information produced as part of the USGS PRISM Project (Pliocene\nResearch, Interpretation, and Synoptic Mapping). The general goals of PRISM\nare to reconstruct global climate during a period of extreme warmth about 3\nmillion years ago and to determine the causes of the warmth and the subsequent\nclimatic change towards colder climates about 2.5 million years ago. To do\nthis, PRISM has been studying Pliocene deposits and their microfaunas and, by\ncomparison with modern assemblages, estimating past boundary conditions such as\nocean temperatures. To obtain more reliable estimates of past environments in\npaleoclimate studies, the use of ecologically sensitive species requires extensive modern datasets on living species with limited environmental tolerances. Thus, much of the data generated by PRISM consists of species\ncounts from modern samples that form a \"coretop\" dataset applicable not only to\nPRISM Pliocene assemblages but also to Quaternary assemblages as well.\n\n This situation was especially true for ostracodes, a group of Crustacea that\nincludes many species that have limited range of water temperatures required\nfor survival, reproduction, or both. Fossil assemblages of ostracodes can\ntherefore yield information on past bottom water conditions on continental\nshelves in the mixed ocean layer above the thermocline and they are especially\nuseful where planktic foraminifers are rare or absent. However comprehensive\ndatasets with quantitative ostracode data were not available for application to\nregional paleoceanographic studies. Further, because of the endemic nature of\nostracodes living on continental shelves, separate modern datasets needed to be\ndeveloped for regions of the Pacific, Atlantic and Arctic Oceans. The data\ncontained in the files in this folder come from the western North Pacific\nOcean, mainly the seas around Japan. These regions encompass subtropical to\ncold temperate and subfrigid marine climate zones and include faunas from the\nmajor Western North Pacific water masses such as the Oyashio and Kuroshio\ncurrent systems.\n\n The ostracode data sets were developed in collaboration with Prof. Noriyuki\nIkeya, Institute of Geosciences, Shizuoka University, Shizuoka, Japan, Prof.\nIkeya's students, and other Japanese colleagues, with support from the USGS\nGlobal Change and Climate History Program and grants from the National Science\nFoundation (NSF grant INT: LTV-9013402) and the Japanese Society for the\nPromotion of Science (JSPS grant EPAR- 093). Most of the faunal slides are\nhoused at Shizuoka University.\n\n Separate PRISM ostracode data sets contain modern and Pliocene species data\nfrom continental shelves of the Arctic and Atlantic Oceans and from deep sea\nenvironments.\n\n Among the various types of quantitative analyses used to evaluate the\nostracode data, the Squared Chord Distance (SCD) coefficient of dissimilarity\nwas found to be useful in identifying modern analog assemblages for fossil\nassemblages on the basis of the proportions of shared species between two\nsamples.\n\n The ostracode data and analyses of them are discussed in detail in the\nfollowing published scientific papers:\n\n Ikeya, Noriyuki and Cronin, Thomas. M., 1993, Quantitative analysis of\nOstracoda and water masses around Japan: Application to Pliocene and\nPleistocene paleoceanography: Micropaleontology, v. 39, p. 263-281.\n\n Cronin, T.M., Kitamura, A., Ikeya, N., Watanabe, M., and Kamiya, T.,\nin press. Late Pliocene climate change 3.4-2.3 Ma: Paleoceanographic\nrecord from the Yabuta Formation, Sea of Japan: Palaeogeography,\nPalaeoclimatology, Palaeoecology.", "links": [ { diff --git a/datasets/USGSPHOTOS.json b/datasets/USGSPHOTOS.json index dc02b0f6eb..07ea72a632 100644 --- a/datasets/USGSPHOTOS.json +++ b/datasets/USGSPHOTOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGSPHOTOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) Aerial Photography data set includes over 2.5 million film transparencies. Beginning in 1937, photographs were acquired for mapping purposes at different altitudes using various focal lengths and film types. The resultant black-and-white photographs contain less than 5 percent cloud cover and were acquired under rigid quality control and project specifications (e.g., stereo coverage, continuous area coverage of map or administrative units). Prior to the initiation of the National High Altitude Photography (NHAP) program in 1980, the USGS photography collection was one of the major sources of aerial photographs used for mapping the United States. Since 1980, the USGS has acquired photographs over project areas that require photographs at a larger scale than the photographs in the NHAP and National Aerial Photography Program collections.", "links": [ { diff --git a/datasets/USGS_ALASKA_RADIOCARBON.json b/datasets/USGS_ALASKA_RADIOCARBON.json index 458e91d42b..62c71d8ce5 100644 --- a/datasets/USGS_ALASKA_RADIOCARBON.json +++ b/datasets/USGS_ALASKA_RADIOCARBON.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ALASKA_RADIOCARBON", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data base contains published radiocarbon dates with entries consisting of\nlaboratory and reference numbers. The data set is subdivided into two segments\nincluding RCFILE which contains the radiocarbon dates and author citation; and\nRCBIB which is a complete bibliography of all published dates. The RCFILE can\nbe sorted by date, author citation, latitude and longitude, geographic region,\nand quadrangle. The RCFILE is run using the software program 'Nutshell.' The\ncombined size of the two files is 1,092,908 bytes. There are 3,609 radiocarbon\nage determinations (published ages with a reference). The following is a\nbreakdown of the number of age determinations by geographic region: Northern\n997, East-Central 417, West-Central 332, Southern 769, Southwestern 603,\nSoutheastern 448, Offshore 35, and General 8.", "links": [ { diff --git a/datasets/USGS_ARSENIC_H2O.json b/datasets/USGS_ARSENIC_H2O.json index eeb6cf58d1..960b38fc2d 100644 --- a/datasets/USGS_ARSENIC_H2O.json +++ b/datasets/USGS_ARSENIC_H2O.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ARSENIC_H2O", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "[From Arsenic in ground water of the United States, \"http://water.usgs.gov/nawqa/trace/arsenic/\"\n\nArsenic is a naturally occurring element in the environment. Arsenic in ground water is largely the result of minerals dissolving naturally from weathered rocks and soils. Several types of cancer have been linked to arsenic in water. The US Environmental Protection Agency is currently reviewing the maximum contaminant level of arsenic permitted in drinking water, and will likely lower it, as recommended last year by the National Research Council.\n\nThe USGS has developed a map that shows where and to what extent arsenic occurs in ground water across the country. Highest concentrations were found throughout the West and in parts of the Midwest and Northeast.", "links": [ { diff --git a/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json b/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json index 2181c31428..3b37e26eb4 100644 --- a/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json +++ b/datasets/USGS_ASC_MarineEcoregionsLayer_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ASC_MarineEcoregionsLayer_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: To better understand of how and why marine ecosystems vary, we developed a map of \"Large Marine Ecosystems\" (LME) for the area surrounding Alaska. These LMEs were constructed using the best information available on bathymetry, currents, temperature, and primary productivity.", "links": [ { diff --git a/datasets/USGS_ASTER_HydrothermalAlterationMaps.json b/datasets/USGS_ASTER_HydrothermalAlterationMaps.json index b05301cf0d..5dfb296269 100644 --- a/datasets/USGS_ASTER_HydrothermalAlterationMaps.json +++ b/datasets/USGS_ASTER_HydrothermalAlterationMaps.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ASTER_HydrothermalAlterationMaps", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and Interactive Data Language (IDL) logical operator algorithms were used to map hydrothermally altered rocks in the central and southern parts of the Basin and Range province of the United States. The hydrothermally altered rocks mapped in this study include (1) hydrothermal silica-rich rocks (hydrous quartz, chalcedony, opal, and amorphous silica), (2) propylitic rocks (calcite-dolomite and epidote-chlorite mapped as separate mineral groups), (3) argillic rocks (alunite-pyrophyllite-kaolinite), and (4) phyllic rocks (sericite-muscovite). A series of hydrothermal alteration maps, which identify the potential locations of hydrothermal silica-rich, propylitic, argillic, and phyllic rocks on Landsat Thematic Mapper (TM) band 7 orthorectified images, and shape files of hydrothermal alteration units are provided.\n", "links": [ { diff --git a/datasets/USGS_BIO_KATRINA.json b/datasets/USGS_BIO_KATRINA.json index 7863e6fdc9..9e4abaafca 100644 --- a/datasets/USGS_BIO_KATRINA.json +++ b/datasets/USGS_BIO_KATRINA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_BIO_KATRINA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This website provides information regarding the emergency response and rescue\nefforts provided by USGS personnel from the National Wetlands Research Center\nand USGS Louisiana Water Science Center to the population and area impacted by\nHurricane Katrina. This website also chronicles the activities by the USGS to\nprovide geospatial technology to aid in locating stranded hurricane victims.\nImpacts to the biological resources affected by Hurricane Katrina are also\nbeing assessed. Information on these resources can be accessed from this\nwebsite.", "links": [ { diff --git a/datasets/USGS_BISON.json b/datasets/USGS_BISON.json index c7136a15fa..17431235a2 100644 --- a/datasets/USGS_BISON.json +++ b/datasets/USGS_BISON.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_BISON", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Biodiversity Information Serving Our Nation (BISON) project is an online mapping information system consisting of a large collection of species occurrence datasets (e.g., plants and animals) found in the United States, with relevant geospatial layers. Species occurrences are records of organisms at a particular time and location that are often collected as part of biological field studies and taxonomic collections. These data serve as a foundation for biodiversity and conservation research.", "links": [ { diff --git a/datasets/USGS_BRD_SageSTEP.json b/datasets/USGS_BRD_SageSTEP.json index 7284038833..5198878c2c 100644 --- a/datasets/USGS_BRD_SageSTEP.json +++ b/datasets/USGS_BRD_SageSTEP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_BRD_SageSTEP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To study the effects of land management options, two experiments will be conducted across a regional network of sites in sagebrush communities. Using this regional network of sites will allow us to understand the thresholds between healthy and unhealthy sagebrush communities over a broad range of conditions across the Great Basin. Management treatment effects on plants, potential for wildfire, soils and nutrients, water runoff/erosion, and birds and insects will be documented. Additionally, an economic analysis will be conducted to assist managers in selecting optimal management strategies, and citizens\u2019 and managers\u2019 views about the treatments will be explored. The first experiment is focused on cheatgrass invasion (Cheatgrass Network), and the second experiment is focused on woodland encroachment (Woodland Network). \n\nCheatgrass Network: For this experiment, sites will be located in sagebrush communities threatened by cheatgrass invasion, and we will study the effects of four land management options: control (no management action), prescribed fire, mechanical thinning of sagebrush by mowing, and herbicide application (to thin old, unproductive sagebrush plants and encourage growth of young sagebrush and native understory grasses). An additional herbicide application to control cheatgrass will be applied within portions of treated areas. The objective is to address the question of what amount of native perennial bunchgrasses needs to be present in the understory of a sagebrush community in order for managers to improve land health without having to conduct expensive restoration, such as reseeding of native grasses. \n\nWoodland Network: For this experiment, sites will be located in sagebrush communities threatened by woodland encroachment, and we will study the effects of no management action (control), prescribed fire, and mechanical removal of trees (chainsaw cutting). The objective is to address the question of what amount of the native sagebrush/bunchgrass community there needs to be in order for managers to improve land health without having to conduct expensive restoration.", "links": [ { diff --git a/datasets/USGS_BioData.json b/datasets/USGS_BioData.json index 0c7b23a012..ab15c4b95c 100644 --- a/datasets/USGS_BioData.json +++ b/datasets/USGS_BioData.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_BioData", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) BioData Retrieval system provides access to aquatic bioassessment data (biological community and physical habitat data) collected by USGS scientists from stream ecosystems across the nation. USGS scientists collect fish-, aquatic macroinvertebrate-, and algae-community samples and conduct stream physical habitat surveys as part of its fundamental mission to describe and understand the Earth. The publicly available BioData Retrieval system disseminates data from over 15,000 fish, aquatic macroinvertebrate, and algae community samples. Additionally, the system serves data from over 5000 physical data sets (samples), such as reach habitat and light availability, that were collected to support the community sample analyses. The system contains sample data that were collected and processed since 1991 using the protocols of the National Water-Quality Assessment (NAWQA). As of 2010, the system has added data collected by USGS scientists using the procedures and protocols of the U.S. Environmental Protection Agency National Rivers and Streams Assessment program (NRSA).", "links": [ { diff --git a/datasets/USGS_Bulletin_2064-A_1.0.json b/datasets/USGS_Bulletin_2064-A_1.0.json index 0ebb0e68b7..44ec358bdf 100644 --- a/datasets/USGS_Bulletin_2064-A_1.0.json +++ b/datasets/USGS_Bulletin_2064-A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Bulletin_2064-A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a geologic GIS database of the terranes\nof the Hailey 1x2 quadrangle and the western part of the Idaho Falls 1x2\nquadrangle in south-central Idaho for use in spatial analysis.\n\nThe paper version of Map Showing Geologic Terranes of the Hailey 1x2 Quadrangle\nand the western part of the Idaho Falls 1x2 Quadrangle, south-central Idaho was\ncompiled by Ron Worl and Kate Johnson in 1995. The plate was compiled on a\n1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort\nCollins Colorado digitized this map under contract for N.Shock. G.Green edited\nand prepared the digital version for publication as a geographic information\nsystem database. The digital geologic map database can be queried in many ways\nto produce a variety of geologic maps.", "links": [ { diff --git a/datasets/USGS_Bulletin_2064-C_1.0.json b/datasets/USGS_Bulletin_2064-C_1.0.json index 14feeb1b19..5a176a6b2c 100644 --- a/datasets/USGS_Bulletin_2064-C_1.0.json +++ b/datasets/USGS_Bulletin_2064-C_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Bulletin_2064-C_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a geologic GIS database of the Geologic\nmap of outcrop areas of sedimentary units in the eastern part of the Hailey 1\ndeg. x 2 deg. Quadrangle and part of the southern part of the Challis 1 deg. x\n2 deg. Quadrangle, south-central Idaho for use in spatial analysis. \n\nThe paper version of the Geologic map of outcrop areas of sedimentary units in\nthe eastern part of the Hailey 1 deg. x 2 deg. Quadrangle and part of the\nsouthern part of the Challis 1 deg. x 2 deg. Quadrangle, south-central Idaho\nwas compiled by Paul Link and others in 1995. The plate was compiled on a\n1:100,000 scale topographic base map. TechniGraphic System, Inc. of Fort\nCollins Colorado digitized this map under contract for N.Shock. G.Green edited\nand prepared the digital version for publication as a GIS database. The\ndigital geologic map database can be queried in many ways to produce a variety\nof geologic maps.", "links": [ { diff --git a/datasets/USGS_CLUES.json b/datasets/USGS_CLUES.json index f7414b73a4..819beb00d5 100644 --- a/datasets/USGS_CLUES.json +++ b/datasets/USGS_CLUES.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_CLUES", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation changes caused by climatic variations and/or land use may have large impacts on forests, agriculture, rangelands, natural ecosystems, and endangered species. Climate modeling studies indicate that vegetation cover, in turn, has a strong influence on regional climates, and this must be better understood before models can estimate future environmental conditions. To address these issues, this project investigates vegetational response to climatic change, and vegetation-land surface impacts on climate change. The project involves calibration of the modern relations between the range limits of plant species and climatic variables, relations that are then used: 1) to estimate past climatic fluctuations from paleobotanical data for a number of time periods within the late Quaternary; 2) to 'validate' climate model simulations of past climates; 3) to explore the potential influences of land cover changes on climate change; and 4) to estimate the potential future ranges of plant species under a number of future climate scenarios. Methodologies and data developed by this project are being used as part of the national global change assessment of potential impacts of future climate changes.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_CORE_RESEARCH_CENTER.json b/datasets/USGS_CORE_RESEARCH_CENTER.json index 616fe9d828..1442ef2b51 100644 --- a/datasets/USGS_CORE_RESEARCH_CENTER.json +++ b/datasets/USGS_CORE_RESEARCH_CENTER.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_CORE_RESEARCH_CENTER", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Descriptive data of core samples housed within the Core Research Center. The\ndatabase contains information about drill hole locations, intervals of core\navailability, formation names, and geologic ages. CORE information sets also\nindicate availability of non-automated information including analyses,\nphotographs, cuttings, and thin sections.", "links": [ { diff --git a/datasets/USGS_CT_NATTEN.json b/datasets/USGS_CT_NATTEN.json index 5b47166e59..4ede84269e 100644 --- a/datasets/USGS_CT_NATTEN.json +++ b/datasets/USGS_CT_NATTEN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_CT_NATTEN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this project is to estimate the rate of nitrogen loss in\nselected reaches of the Connecticut River. In-stream loss of nitrogen may\ninfluence the total nitrogen loads being input to Long Island Sound (LIS);\ntherefore, an improved understanding of nitrogen attenuation is needed to plan\neffective strategies for meeting the goals of the LIS Total Maximum Daily Load\n(TMDL) allocation plan approved by the U.S. Environmental Protection Agency\n(USEPA) in 2001. The TMDL plan was instituted to reduce the problem of chronic\nseasonal hypoxia (low dissolved oxygen) that results from excessive nitrogen\nloading in Long Island Sound.\n\nTwo study methods were used to measure nitrogen loss in selected study reaches\nof the Connecticut River during 2005: a mass-balance study to observe in-stream\nchanges in total nitrogen, and a dissolved nitrogen gas study to measure\ndenitrification. For the mass-balance study, samples were collected from all\nmajor tributaries and at the upstream and downstream ends of two 30- to 40-mile\nstudy reaches, and were analyzed for total nitrogen (including ammonia,\nnitrite, nitrate, and organic nitrogen). Streamflow data (from USGS gaging\nstations or manual measurements) were also taken at the time of sampling so\nthat the mass flux of nitrogen could be computed at each site. To assess the\neffects of different hydrologic conditions and water temperatures on nitrogen\nattenuation in the Connecticut River, the study reaches were sampled two times\nin the spring and summer. The calculations of nitrogen mass flux entering and\nexiting each study reach will indicate when and where nitrogen removal\nprocesses are significant.\n\nThe study of dissolved nitrogen gas was performed on a 6-mile sub-reach of the\nConnecticut River during a period of late summer when warm temperatures and\nlow-flow conditions are most conducive to observing measurable rates of\ndenitrification. Denitrification is estimated by measuring the downstream\nchange in dissolved nitrogen after compensating for gas exchange with the\natmosphere and dilution from inflows. Gas exchange is computed from the\ndownstream concentration changes of SF6 gas and Bromide, which are injected at\nthe head of the study reach.\n\nThe data from this study will be useful for verifying predictions of nitrogen\ninputs, transport, and loss from water-quality models such as the New England\nSPARROW model and the RivR-N model. The results will assist state resource\nmanagers in the development of nitrogen reduction strategies for the\nConnecticut River Watershed, including the selection of sources in which to\ntarget these strategies. Results of the study will be presented in a journal\npaper in 2007. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_CascadeRange_HydrothermalMonitoring.json b/datasets/USGS_CascadeRange_HydrothermalMonitoring.json index a4ab49a7f8..b12aacb1c3 100644 --- a/datasets/USGS_CascadeRange_HydrothermalMonitoring.json +++ b/datasets/USGS_CascadeRange_HydrothermalMonitoring.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_CascadeRange_HydrothermalMonitoring", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Traditionally, most measurement and sampling of hydrothermal fluids has been on a highly intermittent basis. Such intermittent data, with sampling frequencies typically >1 year, are not well-suited for comparison with continuous seismic and geodetic monitoring data. Further, when volcanic unrest becomes evident from other geophysical observations, baseline hydrothermal observations are sometimes non-existent, and are often limited to the season when weather conditions are most amenable to field work. The preponderance of field-season, daytime data means that there is limited information on seasonal or diurnal variability.\n\nBeginning in the summer of 2009, motivated by the dramatic hydrothermal anomalies associated with volcanic unrest at South Sister volcano (Wicks and others, 2002; Evans and others, 2004), the USGS made a concerted effort to develop hourly hydrothermal records in the Cascade Range. The 25 selected monitoring sites show evidence of magmatic influence in the form of high 3He/4He ratios and (or) large fluxes of magmatic CO2 or heat. The monitoring sites can be grouped into three broad categories (Fig. 1): (1) sites with continuous pressure-temperature-conductivity monitoring and intermittent liquid sampling and discharge measurements; (2) sites with continuous temperature monitoring and intermittent gas sampling; and (3) sites that lack hourly data, but where the USGS has carried out intermittent flux measurements over a period of several decades.\n\nFor most sites, correlations have been developed to convert pressure-temperature-conductivity data into a flux of heat or (more often) to the flux of a solute species of interest. We relate (1) specific electrical conductance to lab-measured concentrations of dissolved constituents and (2) pressure (depth of water) to field-measured discharge. The metadata includes descriptions of the sites and methods and plots of the calculated fluxes. The workbook files contain all of the data and correlations upon which those fluxes are based.\n\nPart of the database compilation is a list of relevant references for each area. These lists include all references cited in the metadata.", "links": [ { diff --git a/datasets/USGS_DDS-27_1.json b/datasets/USGS_DDS-27_1.json index 2f840aa078..4c00b58df3 100644 --- a/datasets/USGS_DDS-27_1.json +++ b/datasets/USGS_DDS-27_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-27_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the\nmodern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change.\n\nThis data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original\ndata were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. \nThe objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing\nthe variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each\npole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of\ngrid cells that lack valid data; the composite averages are designed to suppress interannual variability.\n\nAlso included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical\ndata retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems.\n\nThe data contained in this data set are derived from the Scanning Multichannel\nMicrowave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data\nproduced by the National Snow and Ice Data Center (NSIDC) at the University of\nColorado in cooperation with the U.S. National Aeronautics and Space\nAdministration (NASA) and the U.S. National Oceanic and Atmospheric\nAdministration (NOAA). The basic data come from satellites of the U.S. Air\nForce Defense Meteorological Satellite Program. NSIDC distributes three\ncollections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7\nSMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the\nSMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two\nseparate volumes, covering the periods from July 9 of 1987 to December 31 of\n1989, and from January 1 of 1990 through December 31 of 1991, respectively. The\nNASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the\npresent data set. SMMR images were collected every 2 to 3 days, whereas SSM/I\ndata are provided in daily ice-concentration grids. Apart from a number of\nsmall gaps (5 or fewer days) in the record, the only long period for which no\ndata are available is December 3 of 1987 through January 12 of 1988, inclusive.\n\nAs ancillary data, the ETOPO5 global gridded elevation and bathymetry data\n(Edwards, 1989) were interpolated to the resolution of the NSIDC data; the\ninterpolated topographic data are included.\n\nThe images are provided in three formats: Hierarchical Data Format (HDF), a\nflexible scientific data format developed at the National Center for\nSupercomputing Applications; Graphics Interchange Format (GIF); and Macintosh\nPICT format. The ice- concentration grids are distributed by NSIDC in HDF\nformat.", "links": [ { diff --git a/datasets/USGS_DDS-46.json b/datasets/USGS_DDS-46.json index 471ee384f4..96f67511da 100644 --- a/datasets/USGS_DDS-46.json +++ b/datasets/USGS_DDS-46.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-46", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conversion of the Venezuela maps to a computerized digital format was\nundertaken for the following reasons:\n\n1) The digital format facilitates the presentation and analysis of\nearth-science data. Digital maps can be displayed at any scale or projection,\nwhereas a paper map has a fixed scale and projection. However, the maps on this\ndisc are not intended to be used at any scale more detailed than 1:500,000.\n\nA geographic information system (GIS) allows combining and overlaying of layers\nfor analysis of spatial relations not readily apparent in the standard paper\npublication. Digital data on geology, geophysics, and geochemistry can be\ncombined to create useful derivative products.\n\n2) The digital format was used to facilitate publication in both paper and\nelectronic form. For the Rio Caura paper map publication (Brooks and others,\n1995), digital images were sent to the Gerber plotter, a vector-to-film\nprocessor. The other 1:500,000-scale MF maps were reproduced photographically\nfrom electrostatic plotter output on clear mylar. The published digital\nformats include this CD-ROM and ARC/INFO Export files to be located on the\nWorld Wide Web on the Internet.\n\n The data in this CD-ROM are based on a mineral resource assessment of the\nVenezuelan Guayana Shield, conducted between 1987 and 1991 by the U.S.\nGeological Survey and Corporacion Venezolana de Guayana, Tecnica Minera, (USGS,\n1993). The Venezuelan Shield occupies about 415,000 sq km in the south and\neast part of Venezuela. The study area is bounded on the north by the Rio\nOrinoco. It includes all of the Territorio Federal Amazonas, Estado Bolivar,\nand part of Estado Delta Amacuro. The original resource assessment publication\nUSGS Bulletin 2062 consists of 121 pages of text and figures as well as eight\nfull-color maps:\n\n Geographic\n Geologic and tectonic\n Bouguer gravity\n Two mineral-occurrence maps\n Side-looking airborne radar image\n Two permissive domain maps\n\nThe side-looking airborne radar image and the Bouguer gravity map are not\nincluded in this CD-ROM. The geology layer from the 1993 Bulletin was revised\nand published as a series of MF and I maps.", "links": [ { diff --git a/datasets/USGS_DDS-55_EF_1.0.json b/datasets/USGS_DDS-55_EF_1.0.json index 44f6790d97..3afa21a54e 100644 --- a/datasets/USGS_DDS-55_EF_1.0.json +++ b/datasets/USGS_DDS-55_EF_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-55_EF_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Accurate base maps are a prerequisite for any geological study, regardless of the objectives. Land-based studies commonly utilize aerial photographs, USGS 7.5-minute quadrangle maps, and satellite images as base maps. Until now, studies that involve the ocean floor have been at a disadvantage due to an almost complete lack of accurate marine base maps. Many base maps of the sea floor have been constructed over the past century but with a wide range in navigational and depth accuracies.\n\nOnly in the past few years has marine surveying technology advanced far enough to produce navigational accuracy of 1 meter and depth resolutions of 50 centimeters. The Pacific Seafloor Mapping Project, U.S. Geological Survey, Western Coastal and Marine Geology Program, Menlo Park, California, U.S.A. in cooperation with the Ocean Mapping Group, University of New Brunswick, Canada is using this new technology to systematically map the ocean floor and lakes. This type of marine surveying, called Multibeam surveying, collects high-resolution bathymetry and backscatter data that can be used for a variety of basemaps, GIS coverages, and scientific visualization methods.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DDS-55_WF.json b/datasets/USGS_DDS-55_WF.json index c1aae8e4d7..45492aede3 100644 --- a/datasets/USGS_DDS-55_WF.json +++ b/datasets/USGS_DDS-55_WF.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-55_WF", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data and information are intended for science researchers, students from\nelementary through college, policy makers, and general public. Pacific Seafloor\nMapping Project Test cruise.\n\nBathymetry and seafloor backscatter data for the Flower Gardens National Marine\nSanctuary are provided in TIFF image format.\n\nThis data set contains data, metadata, and formal metadata associated with a\nmarine data collection activity referred to by the USGS/CMG Activity ID:\nA-1-97-GM\n\nSimilar information is available for over 1500 other USGS/CMG-related\nActivities. If known, available are Activity-specific navigation, gravity,\nmagnetic, bathymetry, seismic, and sampling data; track maps; and equipment\ninformation; as well as summary overviews, crew lists, and information about\nanalog materials. Primary access to the USGS/CMG Information Bank's digital\ndata, analog data, and metadata is provided through\n\"http://walrus.wr.usgs.gov/infobank/programs/html/main/activities.html\" \nThis\npage accommodates a variety of search approaches (e.g., by ship, by region, by\nscientist, by equipment type, etc.).\n\nPlease recognize the U.S. Geological Survey (USGS) as the source of this\ninformation. Physical materials are under controlled on-site access. Some USGS\ninformation accessed through this means may be preliminary in nature and\npresented without the approval of the Director of the USGS. This information is\nprovided with the understanding that it is not guaranteed to be correct or\ncomplete and conclusions drawn from such information are the responsibility of\nthe user. This information is not intended for navigational purposes. Any use\nof trade, firm, or product names is for descriptive purposes only and does not\nimply endorsement by the U.S. Government.", "links": [ { diff --git a/datasets/USGS_DDS-66_1.0.json b/datasets/USGS_DDS-66_1.0.json index c50eecd289..ed137a2fda 100644 --- a/datasets/USGS_DDS-66_1.0.json +++ b/datasets/USGS_DDS-66_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-66_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To obtain subsurface geologic information about the alluvium in the Big\nThompson River Valley, S-wave refraction data were collected along three roads\nthat cross the valley. The refraction data were used to estimate velocities and\nthickness for a layered-earth model from these models, three cross sections of\nthe river valley were constructed. These cross sections show the thickness and\ngross stratigraphy of the alluvium.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DDS-68.json b/datasets/USGS_DDS-68.json index 52493f8945..2e8f1b38f1 100644 --- a/datasets/USGS_DDS-68.json +++ b/datasets/USGS_DDS-68.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-68", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastal Changes Due to Sea-Level Rise:\n\nOne of the most important applied problems in coastal geology today is\ndetermining the physical response of the coastline to sea-level rise.\nPredicting shoreline retreat, beach loss, cliff retreat, and land loss rates is\ncritical to planning coastal zone management strategies and assessing\nbiological impacts due to habitat change or destruction. Presently, long-term\n(>50 years) coastal planning and decision-making has been done piecemeal, if at\nall, for the nation's shoreline (National Research Council, 1990; 1995).\nConsequently, facilities are being located and entire communities are being\ndeveloped without adequate consideration of the potential costs of protecting\nor relocating them from sea-level rise related erosion, flooding and storm\ndamage.\n\nRecent estimates of future sea-level rise based on climate modeling (Wigley and\nRaper, 1992) suggest an increase in global eustatic sea-level of between 15 and\n95 cm by 2100, with a \"best estimate\" of 50 cm (IPCC, 1995). This is more than\ndouble the rate of eustatic rise for the past century (Douglas, 1997; Peltier\nand Jiang, 1997).\n\nThe prediction of coastal evolution is not straightforward. There is no\nstandard methodology, and even the kinds of data required to make such\npredictions are the subject of much scientific debate. A number of predictive\napproaches have been used (National Research Council, 1990), including: 1.\nextrapolation of historical data (for example, coastal erosion rates); 2.\nstatic inundation modeling; 3. application of a simple geometric model (for\nexample, the Bruun Rule); 4. application of a sediment dynamics/budget model;\nor 5. Monte Carlo (probabilistic) simulation based on parameterized physical\nforcing variables. Each of these approaches, however, has its shortcomings or\ncan be shown to be invalid for certain applications (National Research Council,\n1990). Similarly, the types of input data required vary widely, and for a given\napproach (for example, sediment budget), existing data may be indeterminate or\nmay simply not exist (Klein and Nicholls, 1999). Furthermore, human\nmanipulation of the coast in the form of beach nourishment, construction of\nseawalls, groins, and jetties, as well as coastal development itself, may\ndictate Federal, State and local priorities for coastal management without\nproper regard for geologic processes. Thus, the long-term decision to renourish\nor otherwise engineer a coastline may be the primary determining factor in how\nthat coastal segment evolves.\n\nVariables Affecting Coastal Vulnerability:\n\nWe use here a fairly simple classification of the relative vulnerability of\ndifferent U.S. coastal environments to future rises in sea-level. This approach\ncombines the coastal system's susceptibility to change with its natural ability\nto adapt to changing environmental conditions, and yields a relative measure of\nthe system's natural vulnerability to the effects of sea-level rise (Klein and\nNicholls, 1999). The vulnerability classification is based upon the relative\ncontributions and interactions of six variables:\n\n1. Tidal range, which contributes to inundation hazards.\n\n2. Wave height, which is linked to inundation hazards.\n\n3. Coastal slope (steepness or flatness of the coastal region), which is linked\nto the susceptibility of a coast to inundation by flooding and to the rapidity\nof shoreline retreat.\n\n4. Shoreline erosion rates, which indicate how the given section of shoreline\nhas been eroding.\n\n5. Geomorphology, which indicates the relative erodibility of a given section\nof shoreline.\n\n6. Historical rates of relative sea-level rise, which correspond to how the\nglobal (eustatic) sea-level rise and local tectonic processes (land motion such\nas uplift or subsidence) have affected a section of shoreline.\n\nThe input data for this database of coastal vulnerability have been assembled\nusing the original, and sometimes variable, horizontal resolution, which then\nwas resampled to a 3-minute grid cell. A data set for each risk variable is\nthen linked to each grid point. For mapping purposes, data stored in the\n3-minute grid is transferred to a 1:2,000,000 vector shoreline with each\nsegment of shoreline lying within a single grid cell.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DDS-72.json b/datasets/USGS_DDS-72.json index ab31d85177..e830d505de 100644 --- a/datasets/USGS_DDS-72.json +++ b/datasets/USGS_DDS-72.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS-72", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are intended for science researchers, students, policy makers, and\nthe general public. The data can be used with geographic information systems\n(GIS) or other software to display bathymetry and backscatter data of Crater\nLake, Oregon.\n\nThese data include high-resolution bathymetry and calibrated acoustic\nbackscatter in XYZ ASCII and ArcInfo GRID format generated from the 2000\nmultibeam sonar survey of Crater Lake, Oregon.\n\nInformation for USGS Coastal and Marine Geology related activities are online\nat \"http://walrus.wr.usgs.gov/infobank/s/s100or/html/s-1-00-or.meta.html\"\n\n These data not intended for navigational purposes. Please recognize the U.S.\nGeological Survey (USGS) as the source of this information. USGS-authored or\nproduced data and information are in the public domain. Although these data\nhave been used by the U.S. Geological Survey, U.S. Department of the Interior,\nthese data and information are provided with the understanding that they are\nnot guaranteed to be usable, timely, accurate, or complete. Users are cautioned\nto consider carefully the provisional nature of these data and information\nbefore using them for decisions that concern personal or public safety or the\nconduct of business that involves substantial monetary or operational\nconsequences. Conclusions drawn from, or actions undertaken on the basis of,\nsuch data and information are the sole responsibility of the user. Neither the\nU.S. Government nor any agency thereof, nor any of their employees,\ncontractors, or subcontractors, make any warranty, express or implied, nor\nassume any legal liability or responsibility for the accuracy, completeness, or\nusefulness of any data, software, information, apparatus, product, or process\ndisclosed, nor represent that its use would not infringe on privately owned\nrights. Trade, firm, or product names and other references to non-USGS products\nand services are provided for information only and do not constitute\nendorsement or warranty, express or implied, by the USGS, USDOI, or U.S.\nGovernment, as to their suitability, content, usefulness, functioning,\ncompleteness, or accuracy.", "links": [ { diff --git a/datasets/USGS_DDS_10_1.json b/datasets/USGS_DDS_10_1.json index 3ed0759417..e9f30b00a7 100644 --- a/datasets/USGS_DDS_10_1.json +++ b/datasets/USGS_DDS_10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data set is to provide paleoclimate researchers with a tool\nfor estimating the average seasonal variation in sea-surface temperature (SST)\nthroughout the modern world ocean and for estimating the modern monthly and\nweekly sea-surface temperature at any given oceanic location. It is expected\nthat these data will be compared with temperature estimates derived from\ngeological proxy measures such as faunal census analyses and stable isotopic\nanalyses. The results can then be used to constrain general circulation models\nof climate change.\n\nThe data contained in this data set are derived from the NOAA Advanced Very\nHigh Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR\nMCSST), which are obtainable from the Distributed Active Archive Center at the\nJet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain\nweekly images of SST from October 1981 through December 1990 in nine regions of\nthe world ocean: North Atlantic, Eastern North Atlantic, South Atlantic,\nAgulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and\nNorthwest Pacific.\n\nThis data set represents the results of calculations carried out on the NOAA\ndata and also contains the source code of the programs that made the\ncalculations. The objective was to derive the average sea-surface temperature\nof each month and week throughout the whole 10-year series, meaning, for\nexample, that data from January of each year would be averaged together. The\nresult is 12 monthly and 52 weekly images for each of the oceanic regions. \nAveraging the images in this way tends to reduce the number of grid cells that\nlack valid data and to suppress interannual variability.\n\nAs ancillary data, the ETOPO5 global gridded elevation and bathymetry data\n(Edwards, 1989) were interpolated to the resolution of the SST data; the\ninterpolated topographic data are included.\n\nThe images are provided in three formats: a modified form of run-length\nencoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format.\n\nAlso included in the data set are programs that can retrieve seasonal\ntemperature profiles at user-specified locations and that can decompress the\ndata files. These nongraphical SST retrieval programs are provided in versions\nfor UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are\nincluded for users of UNIX with the X Window System, 80386- based PC's, and\nMacintosh computers.", "links": [ { diff --git a/datasets/USGS_DDS_P12_cells.json b/datasets/USGS_DDS_P12_cells.json index 19a1b79846..11aac51c53 100644 --- a/datasets/USGS_DDS_P12_cells.json +++ b/datasets/USGS_DDS_P12_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P12_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 12 (Santa Maria Basin) are listed\nhere by play number, type, and name:\n\n Number Type Name\n 1201 conventional Anticlinal Trends - Onshore\n 1202 conventional Basin Margin\n 1204 conventional Diagenetic\n 1211 conventional Anticlinal Trends - Offshore State Waters", "links": [ { diff --git a/datasets/USGS_DDS_P12_conventional.json b/datasets/USGS_DDS_P12_conventional.json index 71a82714c7..2f35c91c49 100644 --- a/datasets/USGS_DDS_P12_conventional.json +++ b/datasets/USGS_DDS_P12_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P12_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 12 (Santa Maria Basin)\nare listed here by play number and name:\n\n Number Name\n 1201 Anticlinal Trends - Onshore\n 1202 Basin Margin\n 1204 Diagenetic\n 1211 Anticlinal Trends - Offshore State Waters", "links": [ { diff --git a/datasets/USGS_DDS_P13_cells.json b/datasets/USGS_DDS_P13_cells.json index ac55a331de..5303c69f68 100644 --- a/datasets/USGS_DDS_P13_cells.json +++ b/datasets/USGS_DDS_P13_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P13_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 13 (Ventura Basin) are listed here\nby play number, type, and name:\n\n Number Type Name\n 1301 conventional Paleogene - Onshore\n 1302 conventional Neogene - Onshore\n 1304 conventional Cretaceous\n 1311 conventional Paleogene - Offshore State Waters\n 1312 conventional Neogene - Offshore State Waters", "links": [ { diff --git a/datasets/USGS_DDS_P13_conventional.json b/datasets/USGS_DDS_P13_conventional.json index c3cace99c4..f6cd8f96ff 100644 --- a/datasets/USGS_DDS_P13_conventional.json +++ b/datasets/USGS_DDS_P13_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P13_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 13 (Ventura Basin) are\nlisted here by play number and name:\n\n Number Name\n 1301 Paleogene - Onshore\n 1302 Neogene - Onshore\n 1304 Cretaceous\n 1311 Paleogene - Offshore State Waters\n 1312 Neogene - Offshore State Waters", "links": [ { diff --git a/datasets/USGS_DDS_P14_cells.json b/datasets/USGS_DDS_P14_cells.json index be3def0ec9..1880a80c20 100644 --- a/datasets/USGS_DDS_P14_cells.json +++ b/datasets/USGS_DDS_P14_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P14_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 14 (Los Angeles Basin) are listed\nhere by play number, type, and name:\n\n Number Type Name\n 1401 conventional Santa Monica Fault System and Las Cienegas\n Fault and Block\n 1402 conventional Southwestern Shelf and Adjacent Offshore\n State Lands\n 1403 conventional Newport-Inglewood Deformation Zone and\n Southwestern Flank of Central Syncline\n 1404 conventional Whittier Fault Zone and Fullerton Embayment\n 1405 conventional Northern Shelf and Northern Flank of\n Central Syncline\n 1406 conventional Anaheim Nose\n 1407 conventional Chino Marginal Basin, Puente and San Jose\n Hills, and San Gabriel Valley Marginal Basin", "links": [ { diff --git a/datasets/USGS_DDS_P14_conventional.json b/datasets/USGS_DDS_P14_conventional.json index ac86bc0b78..e899e7bc61 100644 --- a/datasets/USGS_DDS_P14_conventional.json +++ b/datasets/USGS_DDS_P14_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P14_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 14 (Los Angeles Basin)\nare listed here by play number and name:\n\n Number Name\n 1401 Santa Monica Fault System and Las Cienegas Fault and Block\n 1402 Southwestern Shelf and Adjacent Offshore State Lands\n 1403 Newport-Inglewood Deformation Zone and Southwestern Flank\n of Central Syncline\n 1404 Whittier Fault Zone and Fullerton Embayment\n 1405 Northern Shelf and Northern Flank of Central Syncline\n 1406 Anaheim Nose\n 1407 Chino Marginal Basin, Puente and San Jose Hills, and\n San Gabriel Valley Marginal Basin", "links": [ { diff --git a/datasets/USGS_DDS_P15_cells.json b/datasets/USGS_DDS_P15_cells.json index 2fbb77cc22..359dbe3de4 100644 --- a/datasets/USGS_DDS_P15_cells.json +++ b/datasets/USGS_DDS_P15_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P15_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 15 (San Diego - Oceanside) are\nlisted here by play number, type, and name.", "links": [ { diff --git a/datasets/USGS_DDS_P16_cells.json b/datasets/USGS_DDS_P16_cells.json index 8aaa14866c..e46fcb1a2f 100644 --- a/datasets/USGS_DDS_P16_cells.json +++ b/datasets/USGS_DDS_P16_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P16_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\n\nOil and gas plays within province 16 (Salton Trough) are listed here\nby play number, type, and name.", "links": [ { diff --git a/datasets/USGS_DDS_P17_cells.json b/datasets/USGS_DDS_P17_cells.json index f622ac8ea5..170d5ddaed 100644 --- a/datasets/USGS_DDS_P17_cells.json +++ b/datasets/USGS_DDS_P17_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P17_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 17 (Idaho - Snake River Downwarp)\nare listed here by play number, type, and name:\n\n Number Type Name\n 1701 conventional Miocene Lacustrine (Lake Bruneau)\n 1702 conventional Pliocene Lacustrine (Lake Idaho)\n 1703 conventional Pre-Miocene\n 1704 conventional Older Tertiary", "links": [ { diff --git a/datasets/USGS_DDS_P17_conventional.json b/datasets/USGS_DDS_P17_conventional.json index 89c913038b..613a6b72ac 100644 --- a/datasets/USGS_DDS_P17_conventional.json +++ b/datasets/USGS_DDS_P17_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P17_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 17 (Idaho - Snake River\nDownwarp) are listed here by play number and name:\n\n Number Name\n 1701 Miocene Lacustrine (Lake Bruneau)\n 1702 Pliocene Lacustrine (Lake Idaho)\n 1703 Pre-Miocene\n 1704 Older Tertiary", "links": [ { diff --git a/datasets/USGS_DDS_P18_cells.json b/datasets/USGS_DDS_P18_cells.json index c741179afc..9889467c74 100644 --- a/datasets/USGS_DDS_P18_cells.json +++ b/datasets/USGS_DDS_P18_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P18_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 18 (Western Great Basin) are listed\nhere by play number, type, and name:\n\n Number Type Name\n 1801 conventional Hornbrook Basin-Modoc Plateau\n 1802 conventional Eastern Oregon Neogene Basins\n 1803 conventional Permian-Triassic Source Rocks Northwestern\n Nevada and East Central and Eastern Oregon\n 1804 conventional Cretaceous Source Rocks, Northwestern Nevada\n 1805 conventional Neogene Source Rocks, Northwestern Nevada\n and Eastern California", "links": [ { diff --git a/datasets/USGS_DDS_P18_conventional.json b/datasets/USGS_DDS_P18_conventional.json index b5dcfb032e..f0b10c7c67 100644 --- a/datasets/USGS_DDS_P18_conventional.json +++ b/datasets/USGS_DDS_P18_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P18_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 18 (Western Great\nBasin) are listed here by play number and name:\n\n Number Name\n 1801 Hornbrook Basin-Modoc Plateau\n 1802 Eastern Oregon Neogene Basins\n 1803 Permian-Triassic Source Rocks Northwestern Nevada\n and East Central and Eastern Oregon\n 1804 Cretaceous Source Rocks, Northwestern Nevada\n 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California", "links": [ { diff --git a/datasets/USGS_DDS_P19_cells.json b/datasets/USGS_DDS_P19_cells.json index dd145ab97d..91e5790e1d 100644 --- a/datasets/USGS_DDS_P19_cells.json +++ b/datasets/USGS_DDS_P19_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P19_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 19 (Eastern Great Basin) are listed\nhere by play number, type, and name:\n\n Number Type Name\n 1901 conventional Unconformity \"A\"\n 1902 conventional Late Paleozoic\n 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass\n and Equivalents\n 1905 conventional Younger Tertiary Basins\n 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt\n 1907 conventional Sevier Frontal Zone", "links": [ { diff --git a/datasets/USGS_DDS_P19_conventional.json b/datasets/USGS_DDS_P19_conventional.json index f18d216190..fe63d187e0 100644 --- a/datasets/USGS_DDS_P19_conventional.json +++ b/datasets/USGS_DDS_P19_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P19_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 19 (Eastern Great\nBasin) are listed here by play number and name:\n\n Number Name\n 1901 Unconformity \"A\"\n 1902 Late Paleozoic\n 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents\n 1905 Younger Tertiary Basins\n 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt\n 1907 Sevier Frontal Zone", "links": [ { diff --git a/datasets/USGS_DDS_P20_cells.json b/datasets/USGS_DDS_P20_cells.json index d2ca9babab..d571cd87ff 100644 --- a/datasets/USGS_DDS_P20_cells.json +++ b/datasets/USGS_DDS_P20_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P20_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 20 (Uinta - Piceance Basin) are\nlisted here by play number, type, and name:\n\n Number Type Name\n 2001 conventional Piceance Tertiary Conventional\n 2002 conventional Uinta Tertiary Oil and Gas\n 2003 conventional Upper Cretaceous Conventional\n 2004 conventional Cretaceous Dakota to Jurassic\n 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates\n 2007 continuous Tight Gas Piceance Mesaverde Williams Fork\n 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil\n 2010 continuous Tight Gas Piceance Mesaverde Iles\n 2014 conventional Basin Margin Subthrusts\n 2015 continuous Tight Gas Uinta Tertiary East\n 2016 continuous Tight Gas Uinta Tertiary West\n 2018 continuous Basin Flank Uinta Mesaverde\n 2020 continuous Deep Synclinal Uinta Mesaverde\n 2050 coalbed gas Uinta Basin - Book Cliffs\n 2051 coalbed gas Uinta Basin - Sego\n 2052 coalbed gas Uinta Basin - Emery\n 2053 coalbed gas Piceance Basin - White River Dome\n 2054 coalbed gas Piceance Basin - Western Basin Margin\n 2055 coalbed gas Piceance Basin - Grand Hogback\n 2056 coalbed gas Piceance Basin - Divide Creek Anticline\n 2057 coalbed gas Piceance Basin - Igneous Intrusion", "links": [ { diff --git a/datasets/USGS_DDS_P20_continuous.json b/datasets/USGS_DDS_P20_continuous.json index 49b965b6c3..dea77d73d3 100644 --- a/datasets/USGS_DDS_P20_continuous.json +++ b/datasets/USGS_DDS_P20_continuous.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P20_continuous", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the play map is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nContinuous oil and gas plays within province 20 (Uinta - Piceance\nBasin) are listed here by play number and name:\n\n Number Name\n 2007 Tight Gas Piceance Mesaverde Williams Fork\n 2009 Cretaceous Self-Sourced Fractured Shales Oil\n 2010 Tight Gas Piceance Mesaverde Iles\n 2015 Tight Gas Uinta Tertiary East\n 2016 Tight Gas Uinta Tertiary West\n 2018 Basin Flank Uinta Mesaverde\n 2020 Deep Synclinal Uinta Mesaverde\n 2050 Uinta Basin - Book Cliffs\n 2051 Uinta Basin - Sego\n 2052 Uinta Basin - Emery\n 2053 Piceance Basin - White River Dome\n 2054 Piceance Basin - Western Basin Margin\n 2055 Piceance Basin - Grand Hogback\n 2056 Piceance Basin - Divide Creek Anticline\n 2057 Piceance Basin - Igneous Intrusion", "links": [ { diff --git a/datasets/USGS_DDS_P20_conventional.json b/datasets/USGS_DDS_P20_conventional.json index 6f3b8dde6e..f15807e553 100644 --- a/datasets/USGS_DDS_P20_conventional.json +++ b/datasets/USGS_DDS_P20_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P20_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 20 (Uinta - Piceance\nBasin) are listed here by play number and name:\n\n Number Name\n 2001 Piceance Tertiary Conventional\n 2002 Uinta Tertiary Oil and Gas\n 2003 Upper Cretaceous Conventional\n 2004 Cretaceous Dakota to Jurassic\n 2005 Permian-Pennsylvanian Sandstones and Carbonates\n 2014 Basin Margin Subthrusts", "links": [ { diff --git a/datasets/USGS_DDS_P2_cells.json b/datasets/USGS_DDS_P2_cells.json index a2ed79856b..76f5874870 100644 --- a/datasets/USGS_DDS_P2_cells.json +++ b/datasets/USGS_DDS_P2_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P2_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\n\nOil and gas plays within province 2 (Central Alaska) are listed here\nby play number, type, and name:\n\n Number Type Name\n 201 conventional Central Alaska Cenozoic Gas\n 202 conventional Central Alaska Mesozoic Gas\n 203 conventional Central Alaska Paleozoic Oil\n 204 conventional Kandik Pre-Mid-Cretaceous Strata\n 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata", "links": [ { diff --git a/datasets/USGS_DDS_P2_conventional.json b/datasets/USGS_DDS_P2_conventional.json index 2a142b23a0..e91c90ddda 100644 --- a/datasets/USGS_DDS_P2_conventional.json +++ b/datasets/USGS_DDS_P2_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DDS_P2_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 2 (Central Alaska) are\nlisted here by play number and name:\n\n Number Name\n 201 Central Alaska Cenozoic Gas\n 202 Central Alaska Mesozoic Gas\n 203 Central Alaska Paleozoic Oil\n 204 Kandik Pre-Mid-Cretaceous Strata\n 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata", "links": [ { diff --git a/datasets/USGS_DOQ.json b/datasets/USGS_DOQ.json index 9deeac406f..4e68d56a29 100644 --- a/datasets/USGS_DOQ.json +++ b/datasets/USGS_DOQ.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DOQ", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map.\n\nDOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution.\n\nThe USGS produces three types of DOQs:\n\n1. 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files.\n\n2. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files.\n\n3. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS-845_PierScoutDatabase_1.0.json b/datasets/USGS_DS-845_PierScoutDatabase_1.0.json index fe7799b4e0..fad21bc151 100644 --- a/datasets/USGS_DS-845_PierScoutDatabase_1.0.json +++ b/datasets/USGS_DS-845_PierScoutDatabase_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS-845_PierScoutDatabase_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field.", "links": [ { diff --git a/datasets/USGS_DS_2006_171.json b/datasets/USGS_DS_2006_171.json index a9e62e861e..ca171aa20d 100644 --- a/datasets/USGS_DS_2006_171.json +++ b/datasets/USGS_DS_2006_171.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_171", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database release, USGS Data Series 171, contains data collected during\nfour Japan-USA collaborative cruises that characterize the seafloor around the\nHawaiian Islands. The Japan Agency for Marine-Earth Science and Technology\n(JAMSTEC) sponsored cruises in 1998, 1999, 2001, and 2002, to build a greater\nunderstanding of the deep marine geology around the Hawaiian Islands. During\nthese cruises, scientists surveyed over 600,000 square kilometers of the\nseafloor with a hull-mounted multibeam seafloor-mapping sonar system (SEA BEAM\u00ae\n2112), observed the seafloor and collected samples using robotic and manned\nsubmersible dives, collected dredge and piston-core samples, and performed\nsingle-channel seismic surveys. To date, 32 research papers have been published\ndescribing results from these cruises. For a list of these articles see the\nbibliography.\n\nThis digital database was compiled with ESRI ArcInfo version 7.2.2 and\nArcGIS 9.0. The GIS files contain multibeam bathymetry, and acoustic\nbackscatter data in ESRI grid format, and dive, seafloor sampling, and siesmic\nlocation data in ESRI shapefile format; ArcInfo-compatible GIS software is\ntherefore required to use the files of this database. Metadata for the GIS\nfiles are available as text files. The GIS files were also symbolized and used\nto create Portable Document Format (PDF) files that are ready to be printed.\nAdobe Reader or other software that can translate PDFs is necessary to print\nthese files.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_177.json b/datasets/USGS_DS_2006_177.json index e7f410bd2b..9fcd095a61 100644 --- a/datasets/USGS_DS_2006_177.json +++ b/datasets/USGS_DS_2006_177.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_177", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this map is to show the location of and evidence for recent\nmovement on active fault traces within the Hayward Fault Zone, California. The\nmapped traces represent the integration of the following three different types\nof data: (1) geomorphic expression, (2) creep (aseismic fault slip),and (3)\ntrench exposures. This publication is a major revision of an earlier map\n(Lienkaemper, 1992), which both brings up to date the evidence for faulting and\nmakes it available formatted both as a digital database for use within a\ngeographic information system (GIS) and for broader public access interactively\nusing widely available viewing software. The pamphlet describes in detail the\ntypes of scientific observations used to make the map, gives references\npertaining to the fault and the evidence of faulting, and provides guidance for\nuse of and limitations of the map.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_180_1.0.json b/datasets/USGS_DS_2006_180_1.0.json index 0b25cc4b37..7eaedda961 100644 --- a/datasets/USGS_DS_2006_180_1.0.json +++ b/datasets/USGS_DS_2006_180_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_180_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At the request of the Washington Department of Ecology (WDOE), the US\nGeological Survey (USGS) collected bathymetry data in Capital Lake, Olympia,\nWash., on September 21, 2004. The data are to be used to calculate sediment\ninfilling rates within the lake as well as for developing the bottom boundary\nconditions for numerical models of water quality, sediment transport, and\nmorphological change. In addition, the USGS collected sediment samples in\nCapitol Lake in February, 2005, to help characterize bottom sediment for\nnumerical model calculations and substrate assessment.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_184_1.0.json b/datasets/USGS_DS_2006_184_1.0.json index c5ae02b15e..ad37fc0c68 100644 --- a/datasets/USGS_DS_2006_184_1.0.json +++ b/datasets/USGS_DS_2006_184_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_184_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This digital map database has been prepared by R.W. Tabor from the published\nGeologic map of the Chelan 30-Minute Quadrangle, Washington. Together with the\naccompanying text files as PDF, it provides information on the geologic\nstructure and stratigraphy of the area covered. The database delineates map\nunits that are identified by general age and lithology following the\nstratigraphic nomenclature of the U.S. Geological Survey. The authors mapped\nmost of the bedrock geology at 1:100,000 scale, but compiled Quaternary units\nat 1:24,000 scale. The Quaternary contacts and structural data have been much\nsimplified for the 1:100,000-scale map and database. The spatial resolution\n(scale) of the database is 1:100,000 or smaller.\n\nThis database depicts the distribution of geologic materials and structures at\na regional (1:100,000) scale. The report is intended to provide geologic\ninformation for the regional study of materials properties, earthquake shaking,\nlandslide potential, mineral hazards, seismic velocity, and earthquake faults.\nIn addition, the report contains information and interpretations about the\nregional geologic history and framework. However, the regional scale of this\nreport does not provide sufficient detail for site development purposes.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_190.json b/datasets/USGS_DS_2006_190.json index aa8230b54e..c42cef693d 100644 --- a/datasets/USGS_DS_2006_190.json +++ b/datasets/USGS_DS_2006_190.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_190", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "More than 1,200 water-level measurements from 1957 to 2005 in the Rainier Mesa\narea of the Nevada Test Site were quality assured and analyzed. Water levels\nwere measured from 50 discrete intervals within 18 boreholes and from 4 tunnel\nsites. An interpretive database was constructed that describes water-level\nconditions for each water level measured in the Rainier Mesa area. Multiple\nattributes were assigned to each water-level measurement in the database to\ndescribe the hydrologic conditions at the time of measurement. General quality,\ntemporal variability, regional significance, and hydrologic conditions are\nattributed for each water-level measurement. The database also includes\nhydrograph narratives that describe the water-level history of each well.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_199_1.0.json b/datasets/USGS_DS_2006_199_1.0.json index 2144dedd25..23d7baa788 100644 --- a/datasets/USGS_DS_2006_199_1.0.json +++ b/datasets/USGS_DS_2006_199_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_199_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital geologic map and GIS database of Venezuela captures GIS compatible\ngeologic and hydrologic data from the \"Geologic Shaded Relief Map of\nVenezuela,\" which was released online as U.S. Geological Survey Open-File\nReport 2005-1038. Digital datasets and corresponding metadata files are stored\nin ESRI geodatabase format; accessible via ArcGIS 9.X. Feature classes in the\ngeodatabase include geologic unit polygons, open water polygons, coincident\ngeologic unit linework (contacts, faults, etc.) and non-coincident geologic\nunit linework (folds, drainage networks, etc.). Geologic unit polygon data were\nattributed for age, name, and lithologic type following the L\u00e9xico\nEstratigr\u00e1fico de Venezuela. All digital datasets were captured from source\ndata at 1:750,000. Although users may view and analyze data at varying scales,\nthe authors make no guarantee as to the accuracy of the data at scales larger\nthan 1:750,000.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_203.json b/datasets/USGS_DS_2006_203.json index b2325712bd..ea614ab0a2 100644 --- a/datasets/USGS_DS_2006_203.json +++ b/datasets/USGS_DS_2006_203.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_203", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In June of 1997, the U.S. Geological Survey, in cooperation with Coastal\nCarolina University, conducted a geophysical survey of the shallow geologic\nframework of the continental shelf offshore of central South Carolina from the\nIsle of Palms to Bull Island. Data were collected as part of the USGS Coastal\nChange and Transport (CCT) Project. This report serves as an archive of\nunprocessed digital boomer seismic reflection data, trackline maps, navigation\nfiles, GIS information, observers' logbooks, Field Activity Collection System\n(FACS) logs, and formal FGDC metadata. Filtered and gained digital images of\nthe seismic profiles are also provided. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_216.json b/datasets/USGS_DS_2006_216.json index 5ba5ebb518..d9b5e0f0f9 100644 --- a/datasets/USGS_DS_2006_216.json +++ b/datasets/USGS_DS_2006_216.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_216", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Base-flow yields at approximately 50 percent of the annual mean ground-water\nrecharge rate were estimated for watersheds in the Berkeley County area, W.Va.\nThese base-flow yields were determined from two sets of discharge measurements\nmade July 25-28, 2005, and May 4, 2006. Two sections of channel along Opequon\nCreek had net flow losses that are expressed as negative base-flow watershed\nyields; these and other base-flow watershed yields in the eastern half of the\nstudy area ranged from -940 to 2,280 gallons per day per acre ((gal/d)/acre)\nand averaged 395 (gal/d)/acre. The base-flow yields for watersheds in the\nwestern half of the study area ranged from 275 to 482 (gal/d)/acre and averaged\n376 (gal/d)/acre.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_220.json b/datasets/USGS_DS_2006_220.json index 859845596c..7138ec9064 100644 --- a/datasets/USGS_DS_2006_220.json +++ b/datasets/USGS_DS_2006_220.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_220", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pressure transducers and high-water marks were used to document the inland\nwater levels related to storm surge generated by Hurricane Rita in southwestern\nLouisiana and southeastern Texas. On September 22-23, 2005, an experimental\nmonitoring network consisting of 47 pressure transducers (sensors) was deployed\nat 33 sites over an area of about 4,000 square miles to record the timing,\nextent, and magnitude of inland hurricane storm surge and coastal flooding.\nSensors were programmed to record date and time, temperature, and barometric or\nwater pressure. Water pressure was corrected for changes in barometric pressure\nand salinity. Elevation surveys using global-positioning systems and\ndifferential levels were used to relate all storm-surge water-level data,\nreference marks, benchmarks, sensor measuring points, and high-water marks to\nthe North American Vertical Datum of 1988 (NAVD 88). The resulting data\nindicated that storm-surge water levels over 14 feet above NAVD 88 occurred at\nthree locations and rates of water-level rise greater than 5 feet per hour\noccurred at three locations near the Louisiana coast.\n\nQuality-assurance measures were used to assess the variability and accuracy of\nthe water-level data recorded by the sensors. Water-level data from sensors\nwere similar to data from co-located sensors, permanent U.S. Geological Survey\nstreamgages, and water-surface elevations performed by field staff. Water-level\ndata from sensors at selected locations were compared to corresponding\nhigh-water mark elevations. In general, the water-level data from sensors were\nsimilar to elevations of high quality high-water marks, while reporting\nconsistently higher than elevations of lesser quality high-water marks.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_221.json b/datasets/USGS_DS_2006_221.json index 20de5e03d0..1ca5d32788 100644 --- a/datasets/USGS_DS_2006_221.json +++ b/datasets/USGS_DS_2006_221.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_221", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report describes the processing and results of land-cover and impervious\nsurface derivation for parts of three metropolitan areas being studied as part\nof the U.S. Geological Survey's (USGS) National Water-Quality Assessment\n(NAWQA) Program Effects of Urbanization on Stream Ecosystems (EUSE). The data\nwere derived primarily from Landsat-7 Enhanced Thematic Mapper Plus (ETM+)\nsatellite imagery from the period 1999-2002, and are provided as 30-meter\nresolution raster datasets. Data were produced to a standard consistent with\ndata being produced as part of the USGS National Land Cover Database 2001\n(NLCD01) Program, and were derived in cooperation with, and assistance from,\nNLCD01 personnel. The data were intended as surrogates for NLCD01 data because\nof the EUSE Program's time-critical need for updated land-cover for parts of\nthe United States that would not be available in time from the NLCD01 Program.\nSix datasets are described in this report: separate land-cover (15-class\ncategorical data) and imperviousness (0-100 percent continuous data) raster\ndatasets for parts of the general Denver, Colorado area (South Platte River\nBasin), Dallas-Fort Worth, Texas area (Trinity River Basin), and\nMilwaukee-Green Bay, Wisconsin area (Western Lake Michigan Drainages).\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_224.json b/datasets/USGS_DS_2006_224.json index 988e8c9970..cb1124858e 100644 --- a/datasets/USGS_DS_2006_224.json +++ b/datasets/USGS_DS_2006_224.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_224", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An airborne high-resolution magnetic and coincidental horizontal magnetic\ngraviometer survey was completed over the Taylor Mountains area in southwest\nAlaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the\nUnited States Geological Survey (USGS). First tests and calibration flights\nwere completed by April 7, 2004, and data acquisition was initiated on April\n17, 2004. The final data acquisition and final test/calibrations flight was\ncompleted on May 31, 2004. Data acquired during the survey totaled 8,971.15\nline-miles.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2006_234_1.0.json b/datasets/USGS_DS_2006_234_1.0.json index a8913b365c..2284b92330 100644 --- a/datasets/USGS_DS_2006_234_1.0.json +++ b/datasets/USGS_DS_2006_234_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2006_234_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Magnetic anomalies are due to variations in the Earth's magnetic field caused\nby the uneven distribution of magnetic minerals (primarily magnetite) in the\nrocks that make up the upper part of the Earth's crust. The features and\npatterns of the magnetic anomalies can be used to delineate details of\nsubsurface geology, including the locations of buried faults and\nmagnetite-bearing rocks and the depth to the base of sedimentary basins. This\ninformation is valuable for mineral exploration, geologic mapping, and\nenvironmental studies.\n\nThe Nevada magnetic map is constructed from grids that combine information (see\ndata processing details) collected in 82 separate magnetic surveys conducted\nbetween 1947 and 2004. The data from these surveys are of varying quality. The\ndesign and specifications (terrain clearance, sampling rates, line spacing, and\nreduction procedures) varied from survey to survey depending on the purpose of\nthe project and the technology of that time.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_119.json b/datasets/USGS_DS_2007_119.json index 79f3402dea..08121e9bfd 100644 --- a/datasets/USGS_DS_2007_119.json +++ b/datasets/USGS_DS_2007_119.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_119", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In March of 2004, the U.S. Geological Survey conducted a geophysical survey in\nthe Withlacoochee River of west-central Florida. This report serves as an\narchive of unprocessed digital boomer seismic reflection data, trackline maps,\nnavigation files, GIS information, Field Activity Collection System (FACS)\nlogs, observer's logbook, and FGDC metadata. Filtered and gained digital images\nof the seismic profiles are also provided.\n\nThe archived trace data are in standard Society of Exploration Geophysicists\n(SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed\nwith commercial or public domain software such as Seismic Unix (SU). Example SU\nprocessing scripts and USGS software for viewing the SEG-Y files (Zihlman,\n1992) are also provided.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_242.json b/datasets/USGS_DS_2007_242.json index e324ca56d9..d691e314e7 100644 --- a/datasets/USGS_DS_2007_242.json +++ b/datasets/USGS_DS_2007_242.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_242", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In August of 2005, the U.S. Geological Survey conducted geophysical surveys\noffshore of Port Fourchon and Timbalier Bay, Louisiana, and in nearby\nwaterbodies. This report serves as an archive of unprocessed digital chirp\nseismic reflection data, trackline maps, navigation files, GIS information,\nField Activity Collection System (FACS) logs, observer's logbook, and formal\nFGDC metadata. Filtered and gained digital images of the seismic profiles are\nalso provided.\n\nThe archived trace data are in standard Society of Exploration Geophysicists\n(SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed\nwith commercial or public domain software such as Seismic Unix (SU). Example SU\nprocessing scripts and USGS software for viewing the SEG-Y files (Zihlman,\n1992) are also provided.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_244.json b/datasets/USGS_DS_2007_244.json index 125be4edb5..0e5056bb0a 100644 --- a/datasets/USGS_DS_2007_244.json +++ b/datasets/USGS_DS_2007_244.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_244", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "North-central and northeast Nevada contains numerous large plutons and smaller\nstocks but also contains many small, shallowly emplaced intrusive bodies,\nincluding dikes, sills, and intrusive lava dome complexes. Decades of geologic\ninvestigations in the study area demonstrate that many ore deposits,\nrepresenting diverse ore deposit types, are spatially, and probably temporally\nand genetically, associated with these igneous intrusions. However, despite the\nnumber and importance of igneous intrusions in the study area, no synthesis of\ngeochemical data available for these rocks has been completed. This report\npresents a synthesis of geochemical data for these rocks. The product\nrepresents the first phases of an effort to evaluate the\ntime-space-compositional evolution of Mesozoic and Cenozoic magmatism in the\nstudy area and identify genetic associations between magmatism and mineralizing\nprocesses in this region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_246_1.0.json b/datasets/USGS_DS_2007_246_1.0.json index 4cbdd9dd91..cd4aee02da 100644 --- a/datasets/USGS_DS_2007_246_1.0.json +++ b/datasets/USGS_DS_2007_246_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_246_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 1990 and 1991, a series of research flows were released from Glen Canyon\nDam. Data collected at the streamflow-gaging station on the Colorado River\nabove National Canyon near Supai from that period have been compiled and\nentered into the U.S. Geological Survey database. The data consist of\nmeasurements of suspended-sediment concentration and sand sizes in suspension,\nsand sizes of streambed sediment, and velocity of the Colorado River above\nNational Canyon near Supai streamflow-gaging site. Velocity and sediment data\nare available upon request from the Arizona Water Science Center and from the\nU.S. Geological Survey water-quality database\n(http://waterdata.usgs.gov/az/nwis/qw).\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_250.json b/datasets/USGS_DS_2007_250.json index 05225a8700..6c021f3841 100644 --- a/datasets/USGS_DS_2007_250.json +++ b/datasets/USGS_DS_2007_250.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_250", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "North-central and northeast Nevada contains numerous large plutons and smaller\nstocks but also contains many small, shallowly emplaced intrusive bodies,\nincluding dikes, sills, and intrusive lava dome complexes. Decades of geologic\ninvestigations in the study area demonstrate that many ore deposits,\nrepresenting diverse ore deposit types, are spatially, and probably temporally\nand genetically, associated with these igneous intrusions. However, despite the\nnumber and importance of igneous instrusions in the study area, no synthesis of\ngeochemical data available for these rocks has been completed. This report\npresents a synthesis of composition and age data for these rocks. The product\nrepresents the first phases of an effort to evaluate the\ntime-space-compositional evolution of Mesozoic and Cenozoic magmatism in the\nstudy area and identify genetic associations between magmatism and mineralizing\nprocesses in this region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_DS_2007_254.json b/datasets/USGS_DS_2007_254.json index dd9f91537e..66149794e2 100644 --- a/datasets/USGS_DS_2007_254.json +++ b/datasets/USGS_DS_2007_254.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_DS_2007_254", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In May of 2006, the U.S. Geological Survey conducted geophysical surveys\noffshore of Siesta Key, Florida. This report serves as an archive of\nunprocessed digital chirp seismic reflection data, trackline maps, navigation\nfiles, GIS information, Field Activity Collection System (FACS) logs,\nobserver's logbook, and formal FGDC metadata. Gained digital images of the\nseismic profiles are also provided.\n\nThe archived trace data are in standard Society of Exploration Geophysicists\n(SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed\nwith commercial or public domain software such as Seismic Unix (SU). Example SU\nprocessing scripts and USGS software for viewing the SEG-Y files (Zihlman,\n1992) are also provided.", "links": [ { diff --git a/datasets/USGS_EDC_EO1_ALI.json b/datasets/USGS_EDC_EO1_ALI.json index 678905952f..c12a55e786 100644 --- a/datasets/USGS_EDC_EO1_ALI.json +++ b/datasets/USGS_EDC_EO1_ALI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_EDC_EO1_ALI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Advanced Land Imager (ALI) provides image data from ten spectral bands (band designations). The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band. The standard scene width is 37 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information).\n \nFor Advanced Land Imager (ALI) data, the following levels of correction are available:\n \nLevel 1R radiometrically corrected with no geometric correction applied. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) and are distributed on CD-ROM, DVD, and File Transfer Protocol (FTP).\n \nLevel 1Gs is geometrically corrected and will be provided as a single \"stitched\" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP).\n \nLevel 1Gst is terrain corrected and will be provided as a single \"stitched\" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). \n \n[Source: USGS/EDC Homepage]", "links": [ { diff --git a/datasets/USGS_EDC_EO1_Hyperion.json b/datasets/USGS_EDC_EO1_Hyperion.json index ecbbed13cb..7136451acb 100644 --- a/datasets/USGS_EDC_EO1_Hyperion.json +++ b/datasets/USGS_EDC_EO1_Hyperion.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_EDC_EO1_Hyperion", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Earth-Observing One (EO-1) satellite was decommissioned March 2017. The EO-1 satellite was launched on November 21, 2000 with the NASA's New Millennium Program (NMP). The NMP was an advanced-technology development program created a new generation of technologies and mission concepts into future Earth and space science missions. Information of the EO-1 mission can be found on the EOPortal.\u00a0All EO-1 ALI and Hyperion historical data will continue to be available through\u00a0EarthExplorer\u00a0for the foreseeable future.\u00a0\r\nEO-1 Product Description\r\n\r\nThe Earth Observing-1 (EO-1) satellite was launched November 21, 2000 as a one-year technology demonstration/validation mission. After the initial technology mission was completed, NASA and the USGS agreed to the continuation of the EO-1 program as an Extended Mission. The EO-1 Extended Mission is chartered to collect and distribute Hyperion hyperspectral and Advanced Land Imager (ALI) multispectral products according to customer tasking requests.\r\nHyperion Instrument on board the EO-1 spacecraft\r\n\r\nHyperion\u00a0collects 220 unique spectral channels ranging from 0.357 to 2.576 micrometers with a 10-nm bandwidth. The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for all bands. The standard scene width is 7.7 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information).\r\n\r\nAll Hyperion and Advanced Land Imager (ALI) data in the archive will be attempted to be processed to the Level 1Gst level of correction. If the scene fails the Level 1Gst processing level, it will be removed from the archive and will become unavailable. As of June 15th, 2009, not all of the EO-1 data has been processed; please continue to check back if the scene of your interest is not available. We will be making attempts to process the failed scene as time and workload permits; however there are no guarantees that all of the EO-1 scenes will be able to be processed.", "links": [ { diff --git a/datasets/USGS_EDC_IFSAR.json b/datasets/USGS_EDC_IFSAR.json index 0e20ab6841..1054ec2284 100644 --- a/datasets/USGS_EDC_IFSAR.json +++ b/datasets/USGS_EDC_IFSAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_EDC_IFSAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) National Geospatial Program (NGP) developed the Alaska Mapping Initiative (AMI) to collaborate with the State and other Federal partners to acquire 3-dimensional elevation data to improve statewide topographic maps for Alaska. AMI coordinates Federal activities through the Alaska Mapping Executive Committee (AMEC) and State efforts through Alaska's Statewide Digital Mapping Initiative (SDMI) to ensure a unified approach for consistent data acquisition and enhancement of elevation data products. AMI attained interferometric synthetic aperture radar (IFSAR) to generate digital elevation model (DEM) data. This radar mapping technology is an effective tool for collecting data in challenging circumstances such as cloud cover, extreme weather conditions, rugged terrain, and remote locations. Airborne IFSAR data were flown over South Central Alaska in the summer of 2010 and over Northwest Alaska in 2012.", "links": [ { diff --git a/datasets/USGS_EDC_NRCS.json b/datasets/USGS_EDC_NRCS.json index 3112e4ac57..ae78c03d05 100644 --- a/datasets/USGS_EDC_NRCS.json +++ b/datasets/USGS_EDC_NRCS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_EDC_NRCS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The unique landscape of South Dakota, known for its diverse wetlands and large areas of native prairie, provides critical habitat for many of the nation\u2019s migratory birds, including grassland birds.", "links": [ { diff --git a/datasets/USGS_FORT_Mesa_Verda_NP_veg.json b/datasets/USGS_FORT_Mesa_Verda_NP_veg.json index 14602ad152..b20d4ebac7 100644 --- a/datasets/USGS_FORT_Mesa_Verda_NP_veg.json +++ b/datasets/USGS_FORT_Mesa_Verda_NP_veg.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_FORT_Mesa_Verda_NP_veg", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mesa Verde National Park Vegetation Map Database was developed as a primary product in the Mesa Verde National Park Vegetation Classification, Distribution, and Mapping project. The map database maps vegetation at three levels of thematic organization at the park: the base, group, and management map classes. Most of the base map classes represent plant communities identified to National Vegetation Classification associations. The associated report, Vegetation Classification and Distribution Mapping Report: Mesa Verde National Park, describes in detail the methods used to develop the map database and map classes. The project was sponsored by the USA-National Vegetation Mapping Program and the National Park Service (NPS) Southern Colorado Plateau Network and the work was executed by a multi-agency and organizational team. The vegetation map database covers the park and an approximately 1 kilometer buffer around the park boundary.", "links": [ { diff --git a/datasets/USGS_FORT_WY_WindTurbines2012.json b/datasets/USGS_FORT_WY_WindTurbines2012.json index 1e507ab7a2..ce325ce2e4 100644 --- a/datasets/USGS_FORT_WY_WindTurbines2012.json +++ b/datasets/USGS_FORT_WY_WindTurbines2012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_FORT_WY_WindTurbines2012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data represent locations of wind turbines found within Wyoming as of August 2012. We assigned each wind turbine to a wind farm and, in these data, provide information about each turbine\u2019s potential megawatt output, rotor diameter, hub height, rotor height, the status of the land ownership where the turbine exists, the county each turbine is located in, wind farm power capacity, the number of units currently associated with each wind farm, the wind turbine manufacturer and model, the wind farm developer, the owner of the wind farm, the current purchaser of power from the wind farm, the year the wind farm went online, and the status of its operation. Some of the attributes are estimates based on the information we found via the American Wind Energy Association and other on-line reports. The locations are derived from National Agriculture Imagery Program (2009 and 2012) true color aerial photographs and have a positional accuracy of approximately +/-5 meters. These data will provide a planning tool for wildlife- and habitat-related projects underway at the U.S. Geological Survey\u2019s Fort Collins Science Center and other government and non-government organizations. Specifically, we will use these data to support quantifying disturbances of the landscape as related to wind energy as well as to quantify indirect disturbances to flora and fauna. This data set represents an update to a previous version by O\u2019Donnell and Fancher (2010).", "links": [ { diff --git a/datasets/USGS_FRESC_Columbia_Basin_sagebrush_1.0.json b/datasets/USGS_FRESC_Columbia_Basin_sagebrush_1.0.json index 9c16aeca70..6b6a3b8743 100644 --- a/datasets/USGS_FRESC_Columbia_Basin_sagebrush_1.0.json +++ b/datasets/USGS_FRESC_Columbia_Basin_sagebrush_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_FRESC_Columbia_Basin_sagebrush_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A new regional dataset was produced using decision tree classifier and other techniques to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. Results of the validation will be presented in the final report and are not available at this time. Mapping area models were mosaicked to create the Columbia Basin Regional Dataset (Idaho, Oregon and Washington), which was subsequently combined with the Southwest Regional Gap Landcover Dataset to create the final seamless 8 state regional landcover map. The final map contains 126 Landcover classes (103 NatureServe Ecological Systems, 7 NLCD and 16 non-native vegetation classes) and has a minimum mapping unit (MMU) of approximately 1 acre.", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Algeria.json b/datasets/USGS_GEOGLAM_Algeria.json index 57d5f205f1..64c69ad70a 100644 --- a/datasets/USGS_GEOGLAM_Algeria.json +++ b/datasets/USGS_GEOGLAM_Algeria.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Algeria", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. \n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Argentina.json b/datasets/USGS_GEOGLAM_Argentina.json index 3ba0463d52..d702eaceb6 100644 --- a/datasets/USGS_GEOGLAM_Argentina.json +++ b/datasets/USGS_GEOGLAM_Argentina.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Argentina", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. \n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Australia.json b/datasets/USGS_GEOGLAM_Australia.json index 323709a083..021e2a36c8 100644 --- a/datasets/USGS_GEOGLAM_Australia.json +++ b/datasets/USGS_GEOGLAM_Australia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Australia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. \n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Ethiopia.json b/datasets/USGS_GEOGLAM_Ethiopia.json index a1ce0351d7..b60c72a684 100644 --- a/datasets/USGS_GEOGLAM_Ethiopia.json +++ b/datasets/USGS_GEOGLAM_Ethiopia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Ethiopia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification.\n \n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Pakistan.json b/datasets/USGS_GEOGLAM_Pakistan.json index b12534aaf0..4ce4edd8b4 100644 --- a/datasets/USGS_GEOGLAM_Pakistan.json +++ b/datasets/USGS_GEOGLAM_Pakistan.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Pakistan", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. \n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Russia.json b/datasets/USGS_GEOGLAM_Russia.json index 499e642247..86720f368b 100644 --- a/datasets/USGS_GEOGLAM_Russia.json +++ b/datasets/USGS_GEOGLAM_Russia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Russia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organized communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this over arching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilisation of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification.\n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Uganda.json b/datasets/USGS_GEOGLAM_Uganda.json index 9f9805034a..1a7166991e 100644 --- a/datasets/USGS_GEOGLAM_Uganda.json +++ b/datasets/USGS_GEOGLAM_Uganda.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Uganda", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this over arching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilisation of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification.\n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GEOGLAM_Ukraine.json b/datasets/USGS_GEOGLAM_Ukraine.json index ee4493598d..e1ce994616 100644 --- a/datasets/USGS_GEOGLAM_Ukraine.json +++ b/datasets/USGS_GEOGLAM_Ukraine.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GEOGLAM_Ukraine", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of GEO is to fulfill a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of coordinating existing institutions, organized communities, space agencies, insitu monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. \n\n(Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)", "links": [ { diff --git a/datasets/USGS_GFOI_Argentina.json b/datasets/USGS_GFOI_Argentina.json index ed4fc330f2..c6c923791f 100644 --- a/datasets/USGS_GFOI_Argentina.json +++ b/datasets/USGS_GFOI_Argentina.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Argentina", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_BANGLADESH.json b/datasets/USGS_GFOI_BANGLADESH.json index f882f454ad..ed256bc0bd 100644 --- a/datasets/USGS_GFOI_BANGLADESH.json +++ b/datasets/USGS_GFOI_BANGLADESH.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_BANGLADESH", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Belize.json b/datasets/USGS_GFOI_Belize.json index 43f92345d5..2679e3d53c 100644 --- a/datasets/USGS_GFOI_Belize.json +++ b/datasets/USGS_GFOI_Belize.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Belize", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Bolivia.json b/datasets/USGS_GFOI_Bolivia.json index 3fd4d42cda..4c928f6ac9 100644 --- a/datasets/USGS_GFOI_Bolivia.json +++ b/datasets/USGS_GFOI_Bolivia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Bolivia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Borneo_Island.json b/datasets/USGS_GFOI_Borneo_Island.json index 021482b22a..524a013a62 100644 --- a/datasets/USGS_GFOI_Borneo_Island.json +++ b/datasets/USGS_GFOI_Borneo_Island.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Borneo_Island", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Forest Carbon Tracking Task (GEO FCT) has been established to support countries wanting to establish national forest-change, carbon estimation and reporting systems. It will facilitate access to long-term satellite, airborne and in situ data, provide the associated analysis and prediction tools, and create the appropriate framework and technical standards for a global network of national forest carbon tracking systems. The task follows the guidelines set out by the United Nations Framework Convention on Climate Change (UNFCCC). Its outputs will be available to support interested countries in their efforts to implement the Convention. The task is being carried out by a partnership of GEO member governments, key UN bodies, space agencies, the science community and the private sector.", "links": [ { diff --git a/datasets/USGS_GFOI_Brazil.json b/datasets/USGS_GFOI_Brazil.json index 1f189805d3..9df7174178 100644 --- a/datasets/USGS_GFOI_Brazil.json +++ b/datasets/USGS_GFOI_Brazil.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Brazil", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Burma.json b/datasets/USGS_GFOI_Burma.json index 0398518488..0aa61c6fd1 100644 --- a/datasets/USGS_GFOI_Burma.json +++ b/datasets/USGS_GFOI_Burma.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Burma", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Cambodia.json b/datasets/USGS_GFOI_Cambodia.json index 97e142f4be..ae781a5c42 100644 --- a/datasets/USGS_GFOI_Cambodia.json +++ b/datasets/USGS_GFOI_Cambodia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Cambodia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Cameroon.json b/datasets/USGS_GFOI_Cameroon.json index 0fea65a5a6..91ed3541e6 100644 --- a/datasets/USGS_GFOI_Cameroon.json +++ b/datasets/USGS_GFOI_Cameroon.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Cameroon", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Colombia.json b/datasets/USGS_GFOI_Colombia.json index 9e8013cf4b..47348cc65f 100644 --- a/datasets/USGS_GFOI_Colombia.json +++ b/datasets/USGS_GFOI_Colombia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Colombia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring sy\nstems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Costa_Rica.json b/datasets/USGS_GFOI_Costa_Rica.json index 9420de8535..c69c867976 100644 --- a/datasets/USGS_GFOI_Costa_Rica.json +++ b/datasets/USGS_GFOI_Costa_Rica.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Costa_Rica", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Democratic_Republic_of_Congo.json b/datasets/USGS_GFOI_Democratic_Republic_of_Congo.json index 8061bb64da..68199c77a1 100644 --- a/datasets/USGS_GFOI_Democratic_Republic_of_Congo.json +++ b/datasets/USGS_GFOI_Democratic_Republic_of_Congo.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Democratic_Republic_of_Congo", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Ecuador.json b/datasets/USGS_GFOI_Ecuador.json index f7a169e12d..3616bfd4d9 100644 --- a/datasets/USGS_GFOI_Ecuador.json +++ b/datasets/USGS_GFOI_Ecuador.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Ecuador", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Fiji.json b/datasets/USGS_GFOI_Fiji.json index 0442007a9c..babe2beb7d 100644 --- a/datasets/USGS_GFOI_Fiji.json +++ b/datasets/USGS_GFOI_Fiji.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Fiji", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_GABON.json b/datasets/USGS_GFOI_GABON.json index edcb0e8c23..e4501f60ff 100644 --- a/datasets/USGS_GFOI_GABON.json +++ b/datasets/USGS_GFOI_GABON.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_GABON", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Guatemala.json b/datasets/USGS_GFOI_Guatemala.json index 82da09d6d1..d582d9cadf 100644 --- a/datasets/USGS_GFOI_Guatemala.json +++ b/datasets/USGS_GFOI_Guatemala.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Guatemala", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Guyana.json b/datasets/USGS_GFOI_Guyana.json index 36e90d8a8b..b2fd19020c 100644 --- a/datasets/USGS_GFOI_Guyana.json +++ b/datasets/USGS_GFOI_Guyana.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Guyana", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Honduras.json b/datasets/USGS_GFOI_Honduras.json index a75a739cc4..8ff381dd8e 100644 --- a/datasets/USGS_GFOI_Honduras.json +++ b/datasets/USGS_GFOI_Honduras.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Honduras", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Laos.json b/datasets/USGS_GFOI_Laos.json index 7e22ce9d87..e26d8a9a0b 100644 --- a/datasets/USGS_GFOI_Laos.json +++ b/datasets/USGS_GFOI_Laos.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Laos", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Malawi.json b/datasets/USGS_GFOI_Malawi.json index e50b645c99..131983b364 100644 --- a/datasets/USGS_GFOI_Malawi.json +++ b/datasets/USGS_GFOI_Malawi.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Malawi", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Mexico.json b/datasets/USGS_GFOI_Mexico.json index 6bbf59c156..d6e9767e04 100644 --- a/datasets/USGS_GFOI_Mexico.json +++ b/datasets/USGS_GFOI_Mexico.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Mexico", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Nepal.json b/datasets/USGS_GFOI_Nepal.json index 97d3d72a6f..2c228a46c9 100644 --- a/datasets/USGS_GFOI_Nepal.json +++ b/datasets/USGS_GFOI_Nepal.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Nepal", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Nicaragua.json b/datasets/USGS_GFOI_Nicaragua.json index a7c5b07c7f..5a8082ef58 100644 --- a/datasets/USGS_GFOI_Nicaragua.json +++ b/datasets/USGS_GFOI_Nicaragua.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Nicaragua", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Panama.json b/datasets/USGS_GFOI_Panama.json index 55f0b51610..ee5b923aee 100644 --- a/datasets/USGS_GFOI_Panama.json +++ b/datasets/USGS_GFOI_Panama.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Panama", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Peru.json b/datasets/USGS_GFOI_Peru.json index 7887e25288..0cd436a551 100644 --- a/datasets/USGS_GFOI_Peru.json +++ b/datasets/USGS_GFOI_Peru.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Peru", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).\n", "links": [ { diff --git a/datasets/USGS_GFOI_Philippines.json b/datasets/USGS_GFOI_Philippines.json index 2ad350c4f0..c57847a24b 100644 --- a/datasets/USGS_GFOI_Philippines.json +++ b/datasets/USGS_GFOI_Philippines.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Philippines", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_REP_CONGO.json b/datasets/USGS_GFOI_REP_CONGO.json index c37ccc568e..b4f4319889 100644 --- a/datasets/USGS_GFOI_REP_CONGO.json +++ b/datasets/USGS_GFOI_REP_CONGO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_REP_CONGO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Sumatra.json b/datasets/USGS_GFOI_Sumatra.json index a73112fba8..a157202b4c 100644 --- a/datasets/USGS_GFOI_Sumatra.json +++ b/datasets/USGS_GFOI_Sumatra.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Sumatra", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Tanzania.json b/datasets/USGS_GFOI_Tanzania.json index 7a3ed0b10b..c4a05995c2 100644 --- a/datasets/USGS_GFOI_Tanzania.json +++ b/datasets/USGS_GFOI_Tanzania.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Tanzania", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Tasmania_Island.json b/datasets/USGS_GFOI_Tasmania_Island.json index 2619d0521f..fb4977ce67 100644 --- a/datasets/USGS_GFOI_Tasmania_Island.json +++ b/datasets/USGS_GFOI_Tasmania_Island.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Tasmania_Island", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Forest Carbon Tracking Task (GEO FCT) has been established to support countries wanting to establish national forest-change, carbon estimation and reporting systems. It will facilitate access to long-term satellite, airborne and in situ data, provide the associated analysis and prediction tools, and create the appropriate framework and technical standards for a global network of national forest carbon tracking systems. The task follows the guidelines set out by the United Nations Framework Convention on Climate Change (UNFCCC). Its outputs will be available to support interested countries in their efforts to implement the Convention. The task is being carried out by a partnership of GEO member governments, key UN bodies, space agencies, the science community and the private sector.", "links": [ { diff --git a/datasets/USGS_GFOI_Thailand.json b/datasets/USGS_GFOI_Thailand.json index 37c6f09729..b4c0bfef75 100644 --- a/datasets/USGS_GFOI_Thailand.json +++ b/datasets/USGS_GFOI_Thailand.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Thailand", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Vietnam.json b/datasets/USGS_GFOI_Vietnam.json index 1ccff6ee62..da881f5a16 100644 --- a/datasets/USGS_GFOI_Vietnam.json +++ b/datasets/USGS_GFOI_Vietnam.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Vietnam", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GFOI_Zambia.json b/datasets/USGS_GFOI_Zambia.json index 1847338472..1026bc3be5 100644 --- a/datasets/USGS_GFOI_Zambia.json +++ b/datasets/USGS_GFOI_Zambia.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GFOI_Zambia", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).", "links": [ { diff --git a/datasets/USGS_GLCC_1.2.json b/datasets/USGS_GLCC_1.2.json index 96b8fe0882..0426e07ff7 100644 --- a/datasets/USGS_GLCC_1.2.json +++ b/datasets/USGS_GLCC_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GLCC_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community. \n\nThe project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program.\n\nFunding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme. \n\nThe data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set.\n \n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_GLOBAL_CRUST.json b/datasets/USGS_GLOBAL_CRUST.json index 165104b826..1490beee07 100644 --- a/datasets/USGS_GLOBAL_CRUST.json +++ b/datasets/USGS_GLOBAL_CRUST.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GLOBAL_CRUST", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1988, work was started on a global database intended to characterize the Earth's crust. Today, this database has over 10,000 entries, covering a large portion of the Earth's surface. The primary data source is from published literature detailing the results of seismic refraction profiles, although some unpublished results have been used as well, especially in Russia and China. From these seismic profiles, we extract a 1-D seismic velocity model (Vp and Vs if available) for a specific latitude and longitude. The 1-D model includes the thickness and seismic velocity for each crustal layer as well as annotations of sedimentary layers, velocity gradients, and the moho depth. Other crustal parameters are added to each point to create a complete image of the Earth's crust.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_GLSC_GreatLakesCopepods.json b/datasets/USGS_GLSC_GreatLakesCopepods.json index 6d3ef5e7df..65209adb93 100644 --- a/datasets/USGS_GLSC_GreatLakesCopepods.json +++ b/datasets/USGS_GLSC_GreatLakesCopepods.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_GLSC_GreatLakesCopepods", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We intend that this website provide individuals interested in copepod and branchiuran crustaceans of the Great Lakes with the best taxonomic information currently available, a brief introduction to the known distributions and ecology of the various species, and some of the most relevant literature.\n\nThis product reflects our belief that the taxonomy of even \"difficult\" groups can be made understandable, interesting, and informative, especially in this digital age.\n\nMost of the information on the copepod fauna of the Great Lakes can be accessed directly from the Main Menu. To access information on identification nuances, distribution, life history, ecology, and synonymies for each species, there are two routes available. You can go to the Species List of Major Groups and Distribution Within the Great Lakes and in the table click on the name of the species in which you are interested, or you can click on the species name within the key when you reach the end of the identification process. Photographs, drawings, and text can be printed by placing the cursor over the object or page of interest, right-clicking, and then selecting the appropriate option from the drop down menu.", "links": [ { diff --git a/datasets/USGS_IndianapolisMetroStreams.json b/datasets/USGS_IndianapolisMetroStreams.json index 4fd87c82cc..67de89c61c 100644 --- a/datasets/USGS_IndianapolisMetroStreams.json +++ b/datasets/USGS_IndianapolisMetroStreams.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_IndianapolisMetroStreams", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aquatic-biology and sediment-chemistry data were collected at seven sites on the White River and at six tributary sites in the Indianapolis metropolitan area of Indiana during the period 2009 through 2012. Data collected included benthic-invertebrate and fish-community information and concentrations of metals, insecticides, herbicides, and semivolatile organic compounds adsorbed to streambed sediments. A total of 120 benthic-invertebrate samples were collected, of which 16 were replicate samples. A total of 26 fish-community samples were collected in 2010 and 2012. Thirty streambed-sediment chemistry samples were collected in 2009 and 2011, of which four were concurrent duplicate samples.", "links": [ { diff --git a/datasets/USGS_JECAM_Canada_South_Nation.json b/datasets/USGS_JECAM_Canada_South_Nation.json index bb0d61a314..be5a28737a 100644 --- a/datasets/USGS_JECAM_Canada_South_Nation.json +++ b/datasets/USGS_JECAM_Canada_South_Nation.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_JECAM_Canada_South_Nation", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Joint Experiment for Crop Assessment and Monitoring\n\nThe overarching goal of JECAM is to reach a convergence of approaches, develop monitoring and reporting protocols and best practices for a variety of global agricultural systems. JECAM will enable the global agricultural monitoring community to compare results based on disparate sources of data, using various methods, over a variety of global cropping systems. It is intended that the JECAM experiments will facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring. The JECAM initiative is developed in the framework of GEO Global Agricultural Monitoring (GEOSS Task AG0703 a) and Agricultural Risk Management (GEOSS Task AG0703 b).", "links": [ { diff --git a/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json b/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json index 3d6f307c69..a133f8780f 100644 --- a/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json +++ b/datasets/USGS_KATRINA_COASTAL_IMPACT_LIDAR.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_KATRINA_COASTAL_IMPACT_LIDAR", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In a cooperative research program, the USGS, NASA and the US Army Corps of\n Engineers (USACE) are using airborne laser mapping systems to survey coastal\n areas before and after hurricanes. As the aircraft flies along the coast, a\n laser altimeter (lidar) scans a several hundred meter wide swath of the earth's\n surface acquiring an estimate of ground elevation approximately every square\n meter. The elevation data from different flights can be compared to determine\n the patterns and magnitudes of coastal change (erosion, overwash, etc.) and the\n loss (or gain) of buildings and infrastructure. Results come from two lidar\n systems, the USACE's Compact Hydrographic Airborne Rapid Total Survey (CHARTS)\n and NASA's Experimental Advanced Airborne Research Lidar (EAARL).\n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_Katrina_Coastal_Impact.json b/datasets/USGS_Katrina_Coastal_Impact.json index 566badb5c0..4f92bf960f 100644 --- a/datasets/USGS_Katrina_Coastal_Impact.json +++ b/datasets/USGS_Katrina_Coastal_Impact.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Katrina_Coastal_Impact", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hurricane Katrina made landfall as a category 4 storm in Plaquemines Parish, LA on August 29, 2005. The U.S. Geological Survey (USGS), NASA, the U.S. Army\r\n Corps of Engineers, and the University of New Orleans are cooperating in a\r\n research project investigating coastal change that occurred as a result of\r\n Hurricane Katrina. Aerial video, still photography, and laser altimetry surveys of post-storm\r\n beach conditions were collected August 31 and September 1, 2005 for comparison\r\n with earlier data. The comparisons will show the nature, magnitude, and spatial\r\n variability of coastal changes such as beach erosion, overwash deposition, and\r\n island breaching. These data will also be used to further refine predictive\r\n models of coastal impacts from severe storms. The data are being made available\r\n to local, state, and federal agencies for purposes of disaster recovery and\r\n erosion mitigation.\r\n \r\n ", "links": [ { diff --git a/datasets/USGS_MAP_MF-2336_1.0.json b/datasets/USGS_MAP_MF-2336_1.0.json index a3e60e2154..2a94882ef8 100644 --- a/datasets/USGS_MAP_MF-2336_1.0.json +++ b/datasets/USGS_MAP_MF-2336_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2336_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:100,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the California\nState Geologist (Hart and Bryant, 1997). Similarly, the database cannot be\nused to identify or delineate landslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (ceghmf.ps, ceghmf.pdf, ceghmf.txt), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:100,000 or smaller.\n\nThis is the pre-release version of the report. The accompanying text file\nmf2336.rev contains version numbers for each part of the data set.\n\nThis report consists of a set of geologic map database files (Arc/ Info\ncoverages) and supporting text and plotfiles. In addition, the report includes\ntwo sets of plotfiles (PostScript and PDF format) that will generate map sheets\nand pamphlets similar to a traditional USGS Miscellaneous Field Studies Report.\n These files are described in the explanatory pamphlets (ceghdesc and ceghdb).\nThe base map layer used in the preparation of the geologic map plotfiles was\ndownloaded from the web (www.gisdatadepot.com) as Digital Raster Graphic files\nof scale-stable versions of the USGS 1:100,000 topographic maps and coverted to\nTIFF images which were then converted to GRIDs. These grids contain no\ndatabase information other than position, and are included for reference only. \nThe base maps used were the Cape Mendocino (1989 edition), Eureka (1987\nedition), Garberville (1979 edition), Hayfork (1978 edition) 1:100,000\ntopographic maps, which all have a 50-meter contour interval. The bathymetry\nmaps were converted from the Coast and Geodetic Survey hydrographic chart 1308\nN-12, 1969.", "links": [ { diff --git a/datasets/USGS_MAP_MF-2349_1.0.json b/datasets/USGS_MAP_MF-2349_1.0.json index f4b790e9bd..4e5f7f0b2d 100644 --- a/datasets/USGS_MAP_MF-2349_1.0.json +++ b/datasets/USGS_MAP_MF-2349_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2349_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a scale of 1:24,000. The report is intended to\nprovide geologic information for the regional study of materials properties,\nearthquake shaking, landslide potential, mineral hazards, seismic velocity, and\nearthquake faults. In addition, the report contains new information and\ninterpretations about the regional geologic history and framework. However,\nthe regional scale of this report does not provide sufficient detail for site\ndevelopment purposes. In addition, this map does not take the place of\nfault-rupture hazard zones designated by the California State Geologist (Hart\nand Bryant, 1997). Similarly, the database cannot be used to accurately\nidentify or delineate landslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (skmf.txt, skmf.pdf, or skmf.ps), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:24,000 or smaller.\n\nThis report consists of a set of geologic map database files (Arc/Info\ncoverages) and supporting text and plotfiles. In addition, the report includes\ntwo sets of plotfiles (PostScript and PDF format) that will generate map sheets\nand pamphlets similar to a traditional USGS Miscellaneous Field Studies Report.\n These files are described below:\n\n>ARC/INFO Resultant Description of Coverage\n>export file Coverage\n>----------- ----------- --------------------------------\n>sk-geol.e00 sk-geol/ Polygon and line coverage showing\n> faults, depositional contacts, and\n> rock units in the map area.\n>\n>sk-strc.e00 sk-strc/ Point and line coverage showing\n> strike and dip information and fold axes.\n>\n>sk-lnds.e00 sk-lnds/ Point and line coverage showing arrows\n> indicating landslide directions as well\n> as the locations of wells and springs\n> not included in the topographic base map.\n\nASCII text files, including explanatory text, ARC/INFO key files, PostScript\nand PDF plot files, and a ARC Macro Language file for conversion of ARC export\nfiles into ARC coverages:\n\n>skmf.ps A PostScript plot file of the pamphlet\n> containing detailed unit descriptions\n> and geological information, a description\n> of the digital files associated with the\n> publication, plus references cited.\n>\n>skmf.pdf A PDF version of mamf.ps.\n>\n>skmf.txt A text-only file containing an unformatted\n> version of skmf.ps.\n>\n>import.aml ASCII text file in ARC Macro Language to\n> convert ARC export files to ARC coverages\n> in ARC/INFO.\n>\n>skmap.ps A PostScript plottable file containing\n> an image of the geologic map and base\n> maps at a scale of 1:24,000, along with\n> a simple map key.\n>\n>skmap.pdf A PDF file containing an image of the\n> geologic map and base maps at a scale\n> of 1:24,000, along with a simple map key.\n\nBase maps\n\nBase Map layers used in the preparation of the geologic map plotfiles were\nderived from published digital maps (Aitken, 1997) obtained from the U.S.\nGeological Survey Geologic Division Website for the Western Region\n(http://wrgis.wr.usgs.gov). Please see the website for more detailed\ninformation about the original databases. Because the base map digital files\nare already available at the website mentioned above, they are not included in\nthe digital database package.", "links": [ { diff --git a/datasets/USGS_MAP_MF-2352_Version 1.0.json b/datasets/USGS_MAP_MF-2352_Version 1.0.json index acd0960b31..5fa4f15512 100644 --- a/datasets/USGS_MAP_MF-2352_Version 1.0.json +++ b/datasets/USGS_MAP_MF-2352_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2352_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this mapping was to determine the bedrock geology that would\ncontrol or impact ground-water flow from the Espanola basin into the Santo\nDomingo basin. As it is a multi-purpose geologic map, it is suitable as the\ngeologic layer for any variety of interdisciplinary investigations\nincorporating geology as a theme.\n\nThis digital geologic map summarizes all available geologic information for the\nTetilla Peak quadrangle located immediately southwest of Santa Fe, New Mexico. \nThe geologic map consists of new polygon (geologic map units) and line\n(contact, fault, fold axis, dike, flow contact, hachure) data, as well as point\ndata (locations for structural measurements, geochemical and geochronologic\ndata, geophysical soundings, and water wells). The map database has been\ngenerated at 1:24,000 scale, and provides significant new geologic information\nfor an area of the southern Cerros del Rio volcanic field, which sits astride\nthe boundary of the Espanola and Santo Domingo basins of the Rio Grande rift.\n\nThe quadrangle includes the west part of the village of La Cienega along its\neastern border and includes the southeasternmost part of the Cochiti Pueblo\nreservation along its northwest side. The central part of the quadrangle\nconsists of Santa Fe National Forest and Bureau of Land Management lands, and\nparts of several Spanish-era land grants.\n\nInterstate 25 cuts through the southern half of the quadrangle between Santa Fe\nand Santo Domingo Pueblo. Canada de Santa Fe, a major river tributary to the\nRio Grande, cuts through the quadrangle, but there is no dirt or paved road\nalong the canyon bottom.\n\nA small abandoned uranium mine (the La Bajada mine) is found in the bottom of\nthe Canada de Santa Fe about 3 km east of the La Bajada fault zone; it has been\npartially reclaimed.\n\nThe surface geology of the Tetilla Peak quadrangle consists predominantly of a\nthin (1-2 m generally, locally as thick as 10? m) layer of windblown surficial\ndeposits that has been reworked colluvially. Locally, landslide, fluvial, and\npediment deposits are also important. These colluvial deposits mantle the\nprincipal bedrocks units, which are (from most to least common): (1) basalts,\nbasanites, andesite, and trachyte of the Pliocene (2.7-2.2 Ma) Cerros del Rio\nvolcanic field; (2) unconsolidated deposits of the Santa Fe Group, mainly along\nthe western border, in the hanging wall of the La Bajada fault zone, but\nlocally extending 2-3 km east under the Cerros del Rio volcanic field; (3)\nolder Tertiary volcanic and sedimentary rocks (Abiquiu?, Espinaso, and Galisteo\nFormations); (4) intrusive rocks of the Cerrillos intrusive center that are\nroughly coeval with the Espinaso volcanic rocks; and (5) Mesozoic sedimentary\nrocks ranging in age from the Upper Triassic Chinle Formation to the Upper\nCretaceous Mancos Shale.\n\nGEOSPATIAL DATAFILES AND OTHER FILES INCLUDED IN THIS DATA SET:\n Map political location: Santa Fe and Sandoval Counties, New Mexico\n Compilation scale: 1:24,000\n Geology mapped: 1996-1998\n >tepk_geol: geologic units, faults, dikes, volcanic flow boundaries\n >tepk_struct: bearing and attitude measurements of structural\nfeatures\n >tepk_bed: attitude measurements of geologic units\n >tepk_chem: geochemical and geochronologic data by sample\n >tepk_amt: audio-magneto-telluric (AMT) geophysical sample data\n >tepk_wells: water well locations\n >tepk_marker: cartographic decorations (bar and ball symbol, etc.)\n >color524.shd: ArcInfo shadeset used to color geology polygons\n >geoscamp1.mrk: ArcInfo markerset used to plot geologic symbols\n >geoscamp1.lin: ArcInfo lineset used to plot geologic line symbols\n >tepk_base.tif,.tfw: 1:24,000-scale topographic base", "links": [ { diff --git a/datasets/USGS_MAP_MF-2354_Version 1.0.json b/datasets/USGS_MAP_MF-2354_Version 1.0.json index 59af9ab03d..16e0c1b7f1 100644 --- a/datasets/USGS_MAP_MF-2354_Version 1.0.json +++ b/datasets/USGS_MAP_MF-2354_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2354_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Chewelah 30' X 60' quadrangle has been jointly\nprepared by the U.S. Geological Survey Mineral Resource Program, the\nSouthern California Areal Mapping Project (SCAMP), and the Washington\nDivision of Geology and Earth Resources, as part of an ongoing effort to\nutilize a Geographical Information System (GIS) format to create regional\ndigital geologic databases. These regional databases are being developed\nas contributions to the National Geologic Map Data Base of the National\nCooperative Geologic Mapping Program of the USGS.\n\nThe digital geologic map database for the Chewelah 30' X 60' quadrangle\nhas been created as a general-purpose data set that is applicable to\nother land-related investigations in the earth and biological sciences.\nFor example, it can be used for mineral resource evaluation studies,\nanimal and plant habitat studies, and soil studies in the Colville and\nKaniksu National Forests. The database is not suitable for site-specific\ngeologic evaluations.\n\nThis data set maps and describes the geology of the Chewelah 30' X 60'\nquadrangle, Washington and Idaho. Created using Environmental Systems\nResearch Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage containing geologic contacts and\nunits, (2) a point coverage containing site-specific geologic structural\ndata, (3) two coverages derived from 1:100,000 Digital Line Graphs (DLG);\none of which represents topographic data, and the other, cultural data,\n(4) two line coverages that contain cross-section lines and unit-label\nleaders, respectively, and (5) attribute tables for geologic units\n(polygons), contacts (arcs), and site-specific data (points). In\naddition, the data set includes the following graphic and text products:\n(1) A PostScript graphic plot-file containing the geologic map,\ntopography, cultural data, and two cross sections, and on a separate\nsheet, a Correlation of Map Units (CMU) diagram, an abbreviated\nDescription of Map Units (DMU), modal diagrams for granitic rocks, an\nindex map, a regional geologic and structure map, and a key for point and\nline symbols; (2) PDF files of the Readme text-file and expanded\nDescription of Map Units (DMU), and (3) this metadata file.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. The map was compiled from geologic maps of eight 1:48,000\n15' quadrangle blocks, each of which was made by mosaicing and reducing\nthe four constituent 7.5' quadrangles. These 15' quadrangle blocks were\nmapped chiefly at 1:24,000 scale, but the detail of the mapping was\ngoverned by the intention that it was to be compiled at 1:48,000 scale.\nThe compilation at 1:100,000 scale entailed necessary simplification in\nsome areas and combining of some geologic units. Overall, however,\ndespite a greater than two times reduction in scale, most geologic detail\nfound on the 1:48,000 maps is retained on the 1:100,000 map. Geologic\ncontacts across boundaries of the eight constituent quadrangles required\nminor adjustments, but none significant at the final 1:100,000 scale.\n\nThe geologic map was compiled on a base-stable cronoflex copy of the\nChewelah 30' X 60' topographic base and then scribed. The scribe guide\nwas used to make a 0.007 mil-thick blackline clear-film, which was\nscanned at 1200 DPI by Optronics Specialty Company, Northridge,\nCalifornia. This image was converted to vector and polygon GIS layers\nand minimally attributed by Optronics Specialty Company. Minor\nhand-digitized additions were made at the USGS. Lines, points, and\npolygons were subsequently edited at the USGS by using standard\nARC/INFO commands. Digitizing and editing artifacts significant enough\nto display at a scale of 1:100,000 were corrected. Within the database,\ngeologic contacts are represented as lines (arcs), geologic units as\npolygons, and site-specific data as points. Polygon, arc, and point\nattribute tables (.pat, .aat, and .pat, respectively) uniquely identify\neach geologic datum.\n\nData package contents:\n>chew_geo.e00 Contacts, faults, geologic unit labels\n>chew_pts.e00 Attitudes and their dip values. Dip values plotted\n> as annotation.\n>chew_xs.e00 lines of cross sections\n>chew_ldr.e00 unit label leaders\n>chew_hyps.e00 Topography\n>chew_trans.e00 Roads, cultural information\n>lines.rel.e00 Line dictionary\n>points.rel.e00 Point dictionary\n>scamp2.shd.e00 SCAMP shade set", "links": [ { diff --git a/datasets/USGS_MAP_MF-2356_1.0.json b/datasets/USGS_MAP_MF-2356_1.0.json index a95b58998d..43f94527b4 100644 --- a/datasets/USGS_MAP_MF-2356_1.0.json +++ b/datasets/USGS_MAP_MF-2356_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2356_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To provide a digital geologic map database of the quadrangle that improves understanding of the regional geologic framework and its influence on the regional groundwater flow system. \n\nThis digital geologic map compilation presents new polygon (i.e., geologic map unit contacts), line (i.e., fault, fold axis, and structure contour), and point\n(i.e., structural attitude, contact elevations) vector data for the Jasper 7 1/2' quadrangle in northern Arkansas. The map database, which is at 1:24,000-scale resolution, provides geologic coverage of an area of current hydrogeologic, tectonic, and stratigraphic interest. The Jasper quadrangle is located in northern Newton and southern Boone Counties about 20 km south of the town of Harrison. The map area is underlain by sedimentary rocks of Ordovician, Mississippian, and Pennsylvanian age that were mildly deformed by a\nseries of normal and strike-slip faults and folds. The area is representative of the stratigraphic and structural setting of the southern Ozark Dome. The\nJasper quadrangle map provides new geologic information for better understanding groundwater flow paths in and adjacent to the Buffalo River watershed.\n\nThe current map database incorporates geologic data from: (1) early geologic mapping (1906) by Purdue and Miser and (2) more recent field mapping (1995-1998) by M. R. Hudson. Buffalo National River, under the auspices of the National Park Service, occupies the central part of the map area.\n\n>FILES INCLUDED WITH THIS DATA SET:\n>jsp24k: geology polygon coverage\n>jsppnt: strike/dip point locations and data\n>jspcontrol: field elevation control points\n>jspcontour: structure contours on the top of the Boone Formation\n>geoscamp1.lin: geologic line symbols\n>geoscamp1.mrk: geologic marker symbols\n>fnt037: font used with geoscamp1.mrk\n>wpgcmykg.shd: shadeset used to color polygons in jsp24k coverage\n>fnt027: font containing geologic age symbols", "links": [ { diff --git a/datasets/USGS_MAP_MF-2359_1.0.json b/datasets/USGS_MAP_MF-2359_1.0.json index d9bcf3b0e7..138f2037b8 100644 --- a/datasets/USGS_MAP_MF-2359_1.0.json +++ b/datasets/USGS_MAP_MF-2359_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF-2359_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To update earlier small-scale geologic mapping, and to provide sufficient\ngeologic information for land-use decisions.\n\n1:24,000-scale geologic mapping in the Clifton 7.5' quadrangle, in support of\nthe USGS Colorado River/I-70 Corridor Cooperative Geologic Mapping Project,\nprovides interpretations of the Quaternary stratigraphy and geologic hazards in\nthis area of the Grand Valley.\n\nThe Clifton 1:24,000 quadrangle is in Mesa County in western Colorado. Because\nthe map area is dominated by various surficial deposits, the map depicts 16\ndifferent Quaternary units. Five prominent river terraces are present in the\nquadrangle containing gravels deposited by the Colorado River. The map area\ncontains a large landslide deposit on the southern slopes of Mount Garfield.\nThe landslide developed in the Mancos Shale and contains large blocks of the\noverlying Mesaverde Group. In addition, the landslide is a source of debris\nflows that have closed I-70 in the past. The major bedrock unit in the\nquadrangle is the Mancos Shale of Upper Cretaceous age.\n\nThe map is accompanied by text containing unit descriptions, and sections on\ngeologic hazards (including landslides, piping, gullying, expansive soils, and\nflooding), and economic geology (including sand and gravel). A table indicates\nwhat map units are susceptible to a given hazard. Approximately 20 references\nare cited at the end of the report.\n\nMap political location: Mesa County, Colorado\n\nCompilation scale: 1:24,000\n\nGeology mapped in 1996 to 1998.\n\nCompilation completed March 1999.\n\nDATASETS INCLUDED IN THIS GEOSPATIAL DATABASE:\n> clifpoly: geology polygons, contacts, and other linear features\n> clifline: line of cross-section A-A'\n> clifpnt: point features - bedding attitudes, drillholes", "links": [ { diff --git a/datasets/USGS_MAP_MF2329_1.0.json b/datasets/USGS_MAP_MF2329_1.0.json index e65c99025f..30c0d4c97f 100644 --- a/datasets/USGS_MAP_MF2329_1.0.json +++ b/datasets/USGS_MAP_MF2329_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MAP_MF2329_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are intended for geographic display and analysis at the national\nlevel, and for large regional areas. It is not intended for hazard evaluation\nor other site-specific work, and should not be used for such. It can be used\nto determine where debris flow processes may be a problem and where additional\ninformation and investigation are warranted. Although the digital form of the\ndata removes the constraint imposed by the scale of a paper map, the detail and\naccuracy inherent in map scale are also present in the digital data. The fact\nthat this database was edited at a scale of 1:2,500,000 means that higher\nresolution information is not present in the data. Plotting at scales larger\nthan 1:2,500,000 will not yield greater real detail, and it may reveal\nfine-scale irregularities below the intended resolution of the database. \nSimilarly, where this database is used in combination with other data of higher\nresolution, the resolution of the combined output will be limited by the lower\nresolution of these data. No responsibility is assumed by the U.S. Geological\nSurvey in the use of these data.\n\nDebris flows, debris avalanches, mud flows and lahars are fast-moving\nlandslides that occur in a wide variety of environments throughout the world. \nThey are particularly dangerous to life and property because they move quickly,\ndestroy objects in their paths, and can strike with little warning. The\npurpose of this map is to show where debris flows have occurred in the\nconterminous United States and where these slope movements might be expected in\nthe future.", "links": [ { diff --git a/datasets/USGS_MASSBAY.json b/datasets/USGS_MASSBAY.json index 7ceda04a17..53d3a12dfd 100644 --- a/datasets/USGS_MASSBAY.json +++ b/datasets/USGS_MASSBAY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MASSBAY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the circulation of water in Massachusetts and Cape Cod Bays is of\ncritical importance for determining how nutrients, sediment, contaminants and\nother water-borne materials are transported. Numerical circulation models\nrepresent a powerful tool to build understanding of transport processes in\nthese bays, as well as for synthesis, scenario testing and prediction. The U.S.\nGeological Survey has developed a three-dimensional model of circulation in\nMassachusetts Bay driven by tides, wind, river runoff, surface heating and\ncooling and remote forcing from the Gulf of Maine. The circulation calculated\nfrom this model was used as input to the HydroQual water quality model.\n\nThe USGS is currently using the model in a Regional Marine Research Program in\nthe Gulf of Maine funded study of sources, transport and nutrient environment\nof red tide populations in the western gulf. Together with investigators from\nWHOI and UNH, this work seeks to characterize the physical transport mechanisms\nthat influence the distribution and fate of toxic Alexandrium cells in this\nregion, and the processes by which cells are transported to Massachusetts Bay.\nThe ability of the regional model to represent the movement of fresh water from\nthe Kennebec and Androscoggin rivers will be determined.\n\nOver the next three years, the USGS will be developing a regional sediment\ntransport model by interfacing existing surface wave, bottom boundary layer and\nsediment erosion models into the current hydrodynamic model. Current and\nsuspended sediment data from the long-term mooring (as well as other sites)\nwill be used for calibration and verification.\n\nWhen the new outfall comes online, additional hydrodynamic model runs in\nMassachusetts Bay will be performed to test the ability of the model to\nsimulate the effects of the relocated effluent discharge.", "links": [ { diff --git a/datasets/USGS_MF-2323_1.0.json b/datasets/USGS_MF-2323_1.0.json index e175dcee90..393aaea57d 100644 --- a/datasets/USGS_MF-2323_1.0.json +++ b/datasets/USGS_MF-2323_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MF-2323_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this map is to show the differences between the extents of late\nPleistocene pluvial lakes and older, larger lakes caused by much higher\neffective moisture during past glacial-pluvial episodes. \n\nDuring the Pliocene to middle Pleistocene, pluvial lakes in the western Great\nBasin repeatedly rose to levels much higher than those of the well-documented\nlate Pleistocene pluvial lakes, and some presently isolated basins were\nconnected. Sedimentologic, geomorphic, and chronologic evidence at sites shown\non the map indicates that Lakes Lahontan and Columbus-Rennie were as much as 70\nm higher in the early-middle Pleistocene than during their late Pleistocene\nhigh stands. Lake Lahontan at its 1400-m shoreline level would submerge\npresent-day Reno, Carson City, and Battle Mountain, and would flood other\nnow-dry basins. To the east, Lakes Jonathan (new name), Diamond, Newark, and\nHubbs also reached high stands during the early-middle(?) Pleistocene that were\n25-40 m above their late Pleistocene shorelines; at these very high levels, the\nlakes became temporarily or permanently tributary to the Humboldt River and\nhence to Lake Lahontan. Such a temporary connection could have permitted fish\nto migrate from the Humboldt River southward into the presently isolated Newark\nValley and from Lake Lahontan into Fairview Valley. The timing of drainage\nintegration also provides suggested maximum ages for fish to populate the\nbasins of Lake Diamond and Lake Jonathan. Reconstructing and dating these lake\nlevels also has important implications for paleoclimate, tectonics, and\ndrainage evolution in the western Great Basin. For example, shorelines in\nseveral basins form a stair-step sequence downward with time from the highest\nlevels, thought to have formed at about 650 ka, to the lowest, formed during\nthe late Pleistocene. This descending sequence indicates progressive drying of\npluvial periods, possibly caused by uplift of the Sierra Nevada and other\nwestern ranges relative to the western Great Basin. However, these effects\ncannot account for the extremely high lake levels during the early middle\nPleistocene; rather, these high levels were probably due to a combination of\nincreased effective moisture and changes in the size of the Lahontan drainage\nbasin.", "links": [ { diff --git a/datasets/USGS_MOJAVE_CLIM.json b/datasets/USGS_MOJAVE_CLIM.json index 002770870a..43c789606d 100644 --- a/datasets/USGS_MOJAVE_CLIM.json +++ b/datasets/USGS_MOJAVE_CLIM.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_MOJAVE_CLIM", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Climate History of the Mojava Desert Region provides an overview of regional climate variations including precipitation and temperature information.\n\nTo evaluate climate variation, weather data was compiled from 48 long-term weather stations across the Mojave Desert. The stations are in western Arizona,\neastern California, southern Nevada, and southwest Utah.\n\nThe primary data set consists of about 1.2 and 1.8 million daily observations of precipitation and temperature, respectively. These data were collected\nmainly at weather stations staffed by volunteers (NOAA, 1986). Some of the raw data were purchased in electronic form from EarthInfo, Inc. who obtained it\nfrom the National Climate Data Center (NCDC), Asheville, North Carolina. These data do not contain the entire record of a particular station, as the available electronic record typically begins in 1948. To evaluate climate variation, the longest possible record is necessary. Thus, the USGS obtained from the NCDC the complete National Weather Service reports on microfiche for the four-state region of the Mojave Desert. These data were entered into the computer\nmanually, producing a record of precipitation beginning in 1892.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_Map-MF-2377_1.0.json b/datasets/USGS_Map-MF-2377_1.0.json index 81ddc9abe6..a1044df053 100644 --- a/datasets/USGS_Map-MF-2377_1.0.json +++ b/datasets/USGS_Map-MF-2377_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map-MF-2377_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map data was compiled for the purpose of comparing multiple Animas River\nWatershed Abandoned Mine Lands Project datasets such as geophysical, biologic,\nremote sensing, and geochemical datasets in a geologic context.\n\nThis dataset represents geology compiled for the upper Animas River Watershed\nnear Silverton, Colorado. The source data used are derived from 1:24,000,\n1:20,000, 1:48,000 and 1:250,000-scale geologic maps by geologists who have\nworked in this area since the early 1960's.\n\nThis product consists of seven vector coverages. These separate coverages\ninclude the geology, faults, veins, andesite dikes, dacite dikes, rhyolite\ndikes, and San Juan Caldera topographic margin.", "links": [ { diff --git a/datasets/USGS_Map-MF-2387_1.0.json b/datasets/USGS_Map-MF-2387_1.0.json index 69b0ceb3f5..c1dd028370 100644 --- a/datasets/USGS_Map-MF-2387_1.0.json +++ b/datasets/USGS_Map-MF-2387_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map-MF-2387_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geologic map of Hidden Hills and vicinity covers part of the Arizona Strip\nnorth of Grand Canyon and several large tributary canyons that make up the\nwestern part of Arizona's Grand Canyon. The map is part of a cooperative U.S.\nGeological Survey, National Park Service, and Bureau of Land Management project\nto provide geologic information for areas within the newly established Grand\nCanyon-Parashant National Monument. This map fills in one of the remaining\nareas where uniform quality geologic mapping was needed. The geologic\ninformation will be useful for future resource management studies for federal,\nstate, and private agencies.\n\nThis digital map database is compiled from unpublished data and new mapping by\nthe authors and represents the general distribution of surficial and bedrock\ngeology in the mapped area. Together with the accompanying pamphlet, it\nprovides current information on the geologic structure and stratigraphy of the\narea. The database delineates map units that are identified by age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution of the\ndatabase to 1:31,680 or smaller.", "links": [ { diff --git a/datasets/USGS_Map-MF-2388_1.0.json b/datasets/USGS_Map-MF-2388_1.0.json index 4e15d85fbf..a8ef126e7d 100644 --- a/datasets/USGS_Map-MF-2388_1.0.json +++ b/datasets/USGS_Map-MF-2388_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map-MF-2388_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The report may be used for land-use planning (e.g., selecting land-fill sites,\ngreenbelts, avoiding geologic hazards), for finding aggregate resources\n(crushed rock, sand, and gravel), and for study of geomorphology and Quaternary\ngeology. The report identifies geologic hazards (e.g., landslides, swelling\nsoils, heaving bedrock, and flooding) if they are known to be located in, or\ncharacteristic of, mapped units. Surficial deposits in the quadrangle are\nevidence of depositional events of the Quaternary Period (the most recent 1.8\nmillion years). Some events such as floods are familiar to persons living in\nthe area, while others preceded human occupation. The latter include\nglaciation, probable large earthquakes, protracted drought, and widespread\ndeposition of sand and silt by wind. At least twice in the past 200,000 years\n(most recently from about 30,000 to 12,000 years ago) global cooling caused\nglaciers to form on Pikes Peak and in the high parts of the Sangre de Cristo\nMountains. Some glaciers advanced down valleys, deeply eroded the bedrock, and\ndeposited moraines (map units tbk, tbg, tbj, tbi) and deposited outwash (ggq,\ngge), in the Wet Mountain Valley. On the plains (east part of map area), eolian\nsand (es), stabilized dune sand (ed), and loess (elb) are present and in places\ncontain buried paleosols, which indicate sand dune deposition alternating with\nperiods of stabilized landscape during which soils developed.\n\nFifty-three types of surficial geologic deposits and residual materials of\nQuaternary age are described in a pamphlet and located on a map of the greater\nPueblo area, in part of the Front Range, in the Wet and Sangre de Cristo\nMountains, and on the plains east of Colorado Springs and Pueblo. Deposits\nformed by landslides, wind, and glaciers, as well as colluvium, residuum,\nalluvium, and others are described in terms of predominant grain size, mineral\nor rock composition (e.g., gypsiferous, calcareous, granitic, andesitic),\nthickness, and other physical characteristics. Origins and ages of the deposits\nand geologic hazards related to them are noted. Many lines drawn between units\non our map were placed by generalizing contacts on published maps. However, in\n1997-1999 we mapped new boundaries as well. The map was projected to the UTM\nprojection. This large map area extends from near Salida (on the west edge),\neastward about 107 mi (172 km), and from Antero Reservoir and Woodland Park on\nthe north edge to near Colorado City at the south edge (68 mi; 109 km). \n\nCompilation scale: 1:250,000. Map is available in digital and print-on-demand\npaper formats. Deposits are described in terms of predominant grain size,\nmineralogic and lithologic composition, general thickness, and geologic\nhazards, if any, and relevant geologic historical information and paleosoil\ninformation, if any. Fifty-three map units of deposits include alluvium,\ncolluvium, residuum, eolian deposits, periglacial/disintegrated deposits,\ntills, landslide units, glaciofluvial units, and a diamicton. A bedrock map\nunit depicts large areas of mostly bare bedrock.\n\nThe physical properties of materials were compiled from published soil and\ngeologic maps and reports, our field observations, and from earth science\njournal articles. Selected deposits in the field were checked for conformity to\ndescriptions of map units by the Quaternary geologist who compiled the\nsurficial geologic map units.\n\n>puebpoly: polygon coverage containing geologic unit contacts and labels.\n>puebline: arc coverage containing faults.\n>puebpnt: point coverage containing point locations of decorative\n> bar-and-ball symbols for faults.\n>geol_sfo.lin: This lineset file defines geologic line types in the\n> geologically themed coverages.\n>geoscamp2.mrk: This markerset file defines the geologic markers in the\n> geologically themed coverages.\n>color524.shd: This shadeset file defines the cmyk values of colors\n> assigned to polygons in the geologically themed coverages.", "links": [ { diff --git a/datasets/USGS_Map_MF-2326_1.0.json b/datasets/USGS_Map_MF-2326_1.0.json index 83a587178f..605efe865e 100644 --- a/datasets/USGS_Map_MF-2326_1.0.json +++ b/datasets/USGS_Map_MF-2326_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2326_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map has been prepared to provide the first detailed view of the Palisade\n1:24,000-scale quadrangle. Previous geologic mapping that encompassed the map\narea was at scales of 1:100,000 and 1: 250,000. The Palisade area is an\nimportant agricultural region of Colorado, fruit orchards were first\nestablished in the area in the late 19th century. In addition, the Palisade\nquadrangle is undergoing rapid growth, as is the rest of the Grand Valley.\nBecause of this rapid growth, the recognition of geologic hazards is important.\nThe map depicts many surficial units associated with geologic hazards. The map\nis accompanied by a separate leaflet containing a section on geologic hazards\n(including landslides, piping, gullying, expansive soils, and flooding). A\ntable indicates what map units are susceptible to a given hazard. The map will\nbe of interest to town and county officials, land- use planners, as well as the\ngeneral public.\n\nThe Palisade 1:24,000 quadrangle is in Mesa County in western Colorado. Because\nthe map area is dominated by various surficial deposits, the map depicts 22\ndifferent Quaternary units. Two prominent river terraces are present in the\nquadrangle containing gravels deposited by the Colorado River. The map area\ncontains many mass movement deposits. Extensive landslide deposits are present\nalong the eastern part of the quadrangle. These massive landslides originate on\nthe flanks of Grand Mesa, in the Green River and Wasatch Formations, and flow\nwest onto the Palisade quadrangle. In addition, large areas of the eastern and\nsouthern parts of the map are covered by extensive pediment surfaces. These\npediment surfaces are underlain by debris flow deposits also originating from\nGrand Mesa. Material in these deposits consists of mainly subangular basalt\ncobbles and boulders and indicate that these debris flow deposits have traveled\nas much as 10 km from their source area. The pediment surfaces have been\ndivided into 5 age classes based on their height above surrounding drainages. \n\nTwo common bedrock units in the map area are the Mancos Shale and the Mesaverde\nGroup both of Upper Cretaceous age. The Mancos shale is common in low lying\nareas near the western map border. The Mesaverde Group forms prominent\nsandstone cliffs in the north-central map area.\n\nThe map is accompanied by a separate pamphlet containing unit descriptions, a\nsection on geologic hazards (including landslides, piping, gullying, expansive\nsoils, and flooding), and a section on economic geology (including sand and\ngravel, and coal). A table indicates what map units are susceptible to a given\nhazard. Approximately twenty references are cited at the end of the report.\n\nMap political location: Mesa County, Colorado\n\nCompilation scale: 1:24,000\n\nGeology mapped in 1996 and 1997", "links": [ { diff --git a/datasets/USGS_Map_MF-2330.json b/datasets/USGS_Map_MF-2330.json index 8f9c3c9103..30aab75e49 100644 --- a/datasets/USGS_Map_MF-2330.json +++ b/datasets/USGS_Map_MF-2330.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2330", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Maps Provide an overview of coal production from the Appalachian basin, by\ncounty.\n\nThis report on Appalachian basin coal production consists of four maps and\nassociated graphs and tables, with links to the basic data that were used to\nconstruct the maps. Plate 1 shows the time (year) of maximum coal production,\nby county. For illustration purposes, the years of maximum production are\ngrouped into decadal units. Plate 2 shows the amount of coal produced (tons)\nduring the year of maximum coal production for each county. Plate 3\nillustrates the cumulative coal production (tons) for each county since about\nthe beginning of the 20th century. Plate 4 shows 1996 annual production by\ncounty. During the current (third) cycle of coal production in the Appalachian\nbasin, only seven major coal-producing counties (those with more than 500\nmillion tons cumulative production), including Greene County, Pa.; Boone,\nKanawha, Logan, Mingo, and Monongalia Counties, W. Va.; and Pike County, KY.,\nexhibit a general increase in coal production. Other major coal-producing\ncounties have either declined to a small percentage of their maximum production\nor are annually maintaining a moderate level of production. In general, the\nareas with current high coal production have large blocks of coal that are\nsuitable for mining underground with highly efficient longwall methods, or are\noccupied by very large scale, relatively low cost surface mining operations. \nThe estimated cumulative production for combined bituminous and anthracite coal\nis about 100 billion tons or less for the Appalachian basin. In general, it is\nanticipated that the remaining resources will be progressively of lower\nquality, will cost more to mine, and will become economical only as new\ntechnologies for extraction, beneficiation, and consumption are developed, and\nthen only if prices for coal increase.", "links": [ { diff --git a/datasets/USGS_Map_MF-2331_1.0.json b/datasets/USGS_Map_MF-2331_1.0.json index ac4ce733df..8e35c93e46 100644 --- a/datasets/USGS_Map_MF-2331_1.0.json +++ b/datasets/USGS_Map_MF-2331_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2331_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To update and reinterpret earlier geologic mapping, and to provide sufficient\ngeologic information for land-use decisions.\n\nNew 1:24,000-scale geologic mapping in the Silt 7.5' quadrangle, in support of\nthe USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project,\nprovides new interpretations of the stratigraphy, structure, and geologic\nhazards in the area of the southwest flank of the White River uplift, the Grand\nHogback, and the eastern Piceance Basin.\n\nThe Wasatch Formation was subdivided into three formal members, the Shire,\nMolina, and Atwell Gulch Members. Also a sandstone unit within the Shire\nMember was broken out. The Mesaverde Group consists of the upper Williams Fork\nFormation and the lower Iles Formation. Members for the Iles Formation consist\nof the Rollins Sandstone, the Cozzette Sandstone, and the Corcoran Sandstone\nMembers. The Cozzette and Corcoran Sandstone Members were mapped as a combined\nunit. Only the upper part of the Upper Member of the Mancos Shale is exposed\nin the quadrangle.\n\nFrom the southwestern corner of the map area toward the northwest, the\nunfaulted early Eocene to Paleocene Wasatch Formation and underlying Mesaverde\nGroup gradually increase in dip to form the Grand Hogback monocline that\nreaches 45-75 degree dips to the southwest (section A-A'). The shallow west-\nnorthwest-trending Rifle syncline separates the northern part of the quadrangle\nfrom the southern part along the Colorado River.\n\nGeologic hazards in the map area include erosion, expansive soils, and\nflooding. Erosion includes mass wasting, gullying, and piping. Mass wasting\ninvolves any rock or surficial material that moves downslope under the\ninfluence of gravity, such as landslides, debris flows, or rock falls, and is\ngenerally more prevalent on steeper slopes. Locally, where the Grand Hogback\nis dipping greater than 60 degrees and the Wasatch Formation has been eroded,\nleaving sandstone slabs of the Mesa Verde Group unsupported over vertical\ndistances as great as 500 m, the upper part of the unit has collapsed in\nlandslides, probably by a process of beam-buckle failure. In the source area\nof these landslides strata are overturned and dip shallowly to the northeast. \nLandslide deposits now armor Pleistocene pediment surfaces and extend at least\n1 km into Cactus Valley. Gullying and piping generally occur on more gentle\nslopes. Expansive soils and expansive bedrock are those unconsolidated\nmaterials or rocks that swell when wet and shrink when dry. Most floods are\nrestricted to low-lying areas.\n\nSeveral gas-producing wells extract methane from coals from the upper part of\nthe Iles Formation.\n\nMap political location: Garfield County, Colorado\nCompilation scale: 1:24,000\nGeology mapped in 1992 to 1996.\nCompilation completed March 1997.", "links": [ { diff --git a/datasets/USGS_Map_MF-2337_1.0.json b/datasets/USGS_Map_MF-2337_1.0.json index 0396591800..a9afbdb4a8 100644 --- a/datasets/USGS_Map_MF-2337_1.0.json +++ b/datasets/USGS_Map_MF-2337_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2337_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:62,500) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the California\nState Geologist (Hart and Bryant, 1997). Similarly, the database cannot be\nused to identify or delineate landslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (mageo.txt, mageo.pdf, or mageo.ps), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:62,500 or smaller.\n\nThe databases in this report were compiled in ARC/INFO, a commercial Geographic\nInformation System (Environmental Systems Research Institute, Redlands,\nCalifornia), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and\nWentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files\nare in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector\ndata) format. Coverages are stored in uncompressed ARC export format (ARC/INFO\nversion 7.x). ARC/INFO export files (files with the .e00 extension) can be\nconverted into ARC/INFO coverages in ARC/INFO (see below) and can be read by\nsome other Geographic Information Systems, such as MapInfo via ArcLink and\nESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from\nESRI's web site: http://www.esri.com ). The digital compilation was done in\nversion 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE\n(Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon,\n1991).\n\nThe geologic map information was digitized from stable originals of the\ngeologic maps at 1:62,500 scale. The author manuscripts (pen on mylar) were\nscanned using a Altek monochrome scanner with a resolution of 800 dots per\ninch. The scanned images were vectorized and transformed from scanner\ncoordinates to projection coordinates with digital tics placed by hand at\nquadrangle corners. The scanned lines were edited interactively by hand using\nALACARTE, color boundaries were tagged as appropriate, and scanning artifacts\nvisible at 1:24,000 were removed.\n\nRevisions:\n8/31/99 This is the pre-release version of the report. There have been no\nrevisions to any part of the report.\n\nData Revision List\n> File Report Version Last Update\n> Last Updated\n>\n> mamap.ps 1.0\n> maexpl.ps 1.0\n> mageo.ps 1.0\n> mamap.pdf 1.0\n> maexpl.pdf 1.0\n> mageo.pdf 1.0\n> ma-geol.e00 1.0\n> ma-strc.e00 1.0\n> ma-blks.e00 1.0\n> ma-altr.e00 1.0\n> ma-quad.e00 1.0\n> ma-corr.e00 1.0\n> ma-so.e00 1.0\n> ma-terr.e00 1.0\n> mageo.txt 1.0\n> mafig1.tif 1.0\n> mafig2.tif 1.0\n> madb.ps 1.0\n> madb.pdf 1.0\n> madb.txt 1.0\n> import.aml 1.0\n> mageol.met 1.0\n\nReviews_Applied_to_Data:\n\nThis report has undergone two scientific peer reviews, one digital database\nreview, one review for conformity with geologic names policy, and review of the\nplotfiles for conformity with USGS map standards.\n\nRelated_Spatial_and_Tabular_Data_Sets:\n\nThis report consists of a set of geologic map database files (Arc/Info\ncoverages) and supporting text and plotfiles. In addition, the report includes\ntwo sets of plotfiles (PostScript and PDF format) that will generate map sheets\nand pamphlets similar to a traditional USGS Miscellaneous Field Studies Report.\n These files are described below:\n\n> ARC/INFO Resultant Description of Coverage\n> export file Coverage\n> ----------- ----------- --------------------------------\n> ma-geol.e00 ma-geol/ Polygon and line coverage showing faults,\n> depositional contacts, and rock units\n> in the map area.\n>\n> ma-strc.e00 ma-strc/ Point and line coverage showing strike and dip\n> information and fold axes.\n>\n> ma-blks.e00 ma-blks/ Point coverage showing location of high-grade\n> blocks in Franciscan rock units.\n>\n> ma-altr.e00 ma-altr/ Polygon coverage showing areas of hydrothermal\n> alteration.\n>\n> ma-quad.e00 ma-quad/ Line coverage showing index map of quadrangles\n> in the map area. Lines and annotation only.\n>\n> ma-corr.e00 ma-corr/ Polygon and line coverage of the correlation\n> table for the units in this map database.\n> This database is not geospatial.\n>\n> ma-so.e00 ma-so/ Line coverage showing sources of data index\n> map for this map database.\n>\n> ma-terr.e00 ma-terr/ Polygon and line coverage of the index map of\n> tectonostratigraphic terranes in the map area.\n> (Terranes are described in mageo.txt,\n> mageo.ps, or mageo.pdf).\n\nASCII text files, including explanatory text, ARC/INFO key files, PostScript\nand PDF plot files, and a ARC Macro Language file for conversion of ARC export\nfiles into ARC coverages:\n\n> mageo.ps A PostScript plot file of a report containing\n> detailed unit descriptions and geological\n> information, plus sources of data and references\n> cited, with two figures.\n>\n> mageo.pdf A PDF version of mageo.ps.\n>\n> mageo.txt A text-only file containing an unformatted\n> version of mageo.ps without figures.\n>\n> mafig1.tif A TIFF file of Figure 1 from mageo.ps\n>\n> mafig2.tif A TIFF file of Figure 2 from mageo.ps\n>\n> madb.ps A PostScript plot file of a pamphlet containing\n> detailed information about the contents and\n> availability of this report.\n>\n> madb.pdf A PDF version of madb.ps.\n>\n> madb.txt A text-only file containing an unformatted\n> version of madb.ps.\n>\n> import.aml ASCII text file in ARC Macro Language to convert\n> ARC export files to ARC coverages in ARC/INFO.\n>\n> mamap.ps A PostScript plottable file containing an image\n> of the geologic map and base maps at a scale of\n> 1:62,500, along with a simple map key.\n>\n> maexpl.ps A PostScript plot file containing an image of\n> the explanation sheet, including terrane map,\n> index maps, correlation chart, and unit\n> descriptions.\n>\n> mamap.pdf A PDF file containing an image of the geologic\n> map and base maps at a scale of 1:62,500, along\n> with a simple map key.\n>\n> maexpl.pdf A PDF file containing an image of the\n> explanation sheet, including terrane map, index\n> maps, correlation chart, and unit descriptions.\n\nBase maps\n\nBase Map layers used in the preparation of the geologic map plotfiles were\nderived from published digital maps (Aitken, 1997) obtained from the U.S.\nGeological Survey Geologic Division Website for the Western Region\n(http://wrgis.wr.usgs.gov). Please see the website for more detailed\ninformation about the original databases. Because the base map digital files\nare already available at the website mentioned above, they are not included in\nthe digital database package.\n", "links": [ { diff --git a/datasets/USGS_Map_MF-2341_1.0.json b/datasets/USGS_Map_MF-2341_1.0.json index 3220741556..68492bf28e 100644 --- a/datasets/USGS_Map_MF-2341_1.0.json +++ b/datasets/USGS_Map_MF-2341_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2341_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "New 1:24,000-scale geologic map of the Rifle Falls 7.5' quadrangle, in support of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping Project, provides new interpretations of the stratigraphy, structure, and geologic hazards in the area of the southwest flank of the White River uplift.\n\nBedrock strata include the Upper Cretaceous Iles Formation through Ordovician and Cambrian units. The Iles Formation includes the Cozzette Sandstone and Corcoran Sandstone Members, which are undivided. The Mancos Shale is divided into three members, an upper member, the Niobrara Member, and a lower member. The Lower Cretaceous Dakota Sandstone, the Upper Jurassic Morrison Formation, and the Entrada Sandstone are present. Below the Upper Jurassic Entrada Sandstone, the easternmost limit of the Lower Jurassic and Upper Triassic Glen Canyon Sandstone is recognized. Both the Upper Triassic Chinle Formation and the Lower Triassic(?) and Permian State Bridge Formation are present. The Pennsylvanian and Permian Maroon Formation is divided into two members, the Schoolhouse Member and a lower member. All the exposures of the Middle Pennsylvanian Eagle Evaporite intruded into the Middle Pennsylvanian Eagle Valley Formation, which includes locally mappable limestone beds. The Middle and Lower Pennsylvanian Belden Formation and the Lower Mississippian Leadville Limestone are present. The Upper Devonian Chaffee Group is divided into the Dyer Dolomite, which is broken into the Coffee Pot Member and the Broken Rib Member, and the Parting Formation. Ordovician through Cambrian units are undivided.\n\nThe southwest flank of the White River uplift is a late Laramide structure that is represented by the steeply southwest-dipping Grand Hogback, which is only present in the southwestern corner of the map area, and less steeply southwest-dipping older strata that flatten to nearly horizontal attitudes in the northern part of the map area. Between these two is a large-offset, mid-Tertiary(?) Rifle Falls normal fault, that dips southward placing Leadville Limestone adjacent to Eagle Valley and Maroon Formations. Diapiric Eagle Valley Evaporite intruded close to the fault on the down-thrown side and presumably was injected into older strata on the upthrown block creating a blister-like, steeply north-dipping sequence of Mississippian and older strata. Also, removal of evaporite by either flow or dissolution from under younger parts of the strata create structural benches, folds, and sink holes on either side of the normal fault. A prominent dipslope of the Morrison-Dakota-Mancos part of the section forms large slide blocks that form distinctly different styles of compressive deformation called the Elk Park fold and fault complex at different parts of the toe of the slide.\n\nThe major geologic hazard in the area consist of large landslides both associated with dip-slope slide blocks and the steep slopes of the Eagle Valley Formation and Belden Formation in the northern part of the map. Significant uranium and vanadium deposits were mined prior to 1980.", "links": [ { diff --git a/datasets/USGS_Map_MF-2342_1.0.json b/datasets/USGS_Map_MF-2342_1.0.json index 09bb544c9a..05c6539646 100644 --- a/datasets/USGS_Map_MF-2342_1.0.json +++ b/datasets/USGS_Map_MF-2342_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2342_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:24,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the California\nState Geologist (Hart and Bryant, 1997). Similarly, the database cannot be\nused to identify or delineate landslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (oakmf.ps, oakmf.pdf, oakmf.txt), it provides current\ninformation on the geologic structure and stratigraphy of the area covered.\nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:24,000 or smaller.\n\nThe databases in this report were compiled in ARC/INFO, a commercial Geographic\nInformation System (Environmental Systems Research Institute, Redlands,\nCalifornia), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and\nWentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files\nare in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/INFO vector\ndata) format. Coverages are stored in uncompressed ARC export format (ARC/INFO\nversion 7.x). ARC/INFO export files (files with the .e00 extension) can be\nconverted into ARC/INFO coverages in ARC/INFO (see below) and can be read by\nsome other Geographic Information Systems, such as MapInfo via ArcLink and\nESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from\nESRI's web site: http://www.esri.com ). The digital compilation was done in\nversion 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE\n(Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon,\n1991). The geologic map information was digitized from stable originals of the\ngeologic maps at 1:62,500 and 1:24,000 scale. The author manuscripts (pen on\nmylar) were scanned using a Altek monochrome scanner with a resolution of 800\ndots per inch. The scanned images were vectorized and transformed from scanner\ncoordinates to projection coordinates with digital tics placed by hand at\nquadrangle corners. The scanned lines were edited interactively by hand using\nALACARTE, color boundaries were tagged as appropriate, and scanning artifacts\nvisible at 1:24,000 were removed.", "links": [ { diff --git a/datasets/USGS_Map_MF-2343_1.0.json b/datasets/USGS_Map_MF-2343_1.0.json index ab1d7317c9..b5ad7c138a 100644 --- a/datasets/USGS_Map_MF-2343_1.0.json +++ b/datasets/USGS_Map_MF-2343_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2343_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geologic map of the upper Parashant Canyon area covers part of the Colorado\n Plateau and several large tributary canyons that make up the western part of\n Arizona's Grand Canyon. The map is part of a cooperative U.S. Geological\n Survey and National Park Service project to provide geologic information for\n areas within the newly established Grand Canyon/Parashant Canyon National\n Monument. Most of the Grand Canyon and parts of the adjacent plateaus have been\n geologically mapped; this map fills in one of the remaining areas where uniform\n quality geologic mapping was needed. The geologic information presented may be\n useful in future related studies as to land use management, range management,\n and flood control programs for federal and state agencies, and private\n concerns.\n \n This digital map database is compiled from unpublished data and new mapping by\n the authors, represents the general distribution of surficial and bedrock\n geology in the mapped area. Together with the accompanying pamphlet, it\n provides current information on the geologic structure and stratigraphy of the\n area. The database dilineate map units that are identified by age and\n lithology following the stratigraphic nomenclature of the U.S. Geological\n Survey. The scale of the source maps limits the spatial resolution of the\n database to 1:31,680 or smaller.\n \n This report consists of a set of geologic map database files (ARC/ INFO\n coverages) and supporting text and plot files. In addition, the report\n includes two sets of plot files (Post Script and PDF format) that will generate\n map sheets and pamphlets similar to a traditional USGS Miscellaneous Field\n Studies Report. These files are described in the explanatory pamphlets\n (para.eps, para.pdf, or para.txt). The base layer used in the preparation of\n the geologic map plot files was derived from four Digital Raster Graphic\n versions of standard USGS 7.5' quadrangles. These raster images where\n converted to Grid format in ARC/INFO, trimmed and seamed together, then\n converted to a GeoTIFF image. The resultant TIFF image was combined with\n geologic data to produce the final map image in Illustrator 8.0.", "links": [ { diff --git a/datasets/USGS_Map_MF-2347_1.0.json b/datasets/USGS_Map_MF-2347_1.0.json index 9470c31466..4f2afa3704 100644 --- a/datasets/USGS_Map_MF-2347_1.0.json +++ b/datasets/USGS_Map_MF-2347_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2347_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map and descriptions offer information that may be used for: land-use\nplanning (e.g. selecting land fill sites, greenbelts, avoiding geologic\nhazards), for finding aggregate resources (crushed rock, sand, and gravel), for\nstudy of geomorphology and Quaternary geology. Geologic hazards (e.g.,\nlandslides, swelling soils, heaving bedrock, and flooding) known to be located\nin, or characteristic of some mapped units, were identified.\n\nSurficial deposits in the quadrangle partially record depositional events of\nthe Quaternary Period (the most recent 1.8 million years). Some events such as\nfloods are familiar to persons living in the area, while other recorded events\nare pre-historical. The latter include glaciation, probable large earthquakes,\nprotracted drought, and widespread deposition of sand and silt by wind. At\nleast twice in the past 200,000 years (most recently about 30,000 to 12,000\nyears ago) global cooling caused glaciers to form along the Continental Divide.\nThe glaciers advanced down valleys in the Front Range, deeply eroded the\nbedrock, and deposited moraines (map units tbg, tbj) and outwash (ggq, gge). On\nthe plains (east part of map), eolian sand (es), stabilized dune sand (ed), and\nloess (elb) are present and in places contain buried paleosols. These deposits\nindicate that periods of sand dune deposition alternated with periods of\nstabilized dunes and soil formation.\n\nThirty-nine types of surficial geologic deposits and residual materials of\nQuaternary age are described and mapped in the greater Denver area, in part of\nthe Front Range, and in the piedmont and plains east of Denver, Boulder, and\nCastle Rock. Descriptions appear in the pamphlet that accompanies the map.\nLandslide deposits, colluvium, residuum, alluvium, and other deposits or\nmaterials are described in terms of predominant grain size, mineral or rock\ncomposition (e.g., gypsiferous, calcareous, granitic, andesitic), thickness of\ndeposits, and other physical characteristics. Origins and ages of the deposits\nand geologic hazards related to them are noted. Many lines between geologic\nunits on our map were placed by generalizing contacts on published maps.\nHowever, in 1997-1999 we mapped new boundaries, as well. The map was projected\nto the UTM projection. This large map area extends from the Continental Divide\nnear Winter Park and Fairplay ( on the west edge), eastward about 107 mi (172\nkm); and extends from Boulder on the north edge to Woodland Park at the south\nedge (68 mi; 109 km).\n\nCompilation scale: 1:250,000. Map is available in digital and print-on-demand\npaper formats. Deposits are described in terms of predominant grain size,\nmineralogic and lithologic composition, general thickness, and geologic\nhazards, if any, relevant geologic historical information and paleosoil\ninformation, if any. Thirty- nine map units of deposits include 5 alluvium\ntypes, 15 colluvia, 6 residua, 3 types of eolian deposits, 2\nperiglacial/disintegrated deposits, 3 tills, 2 landslide units, 2 glaciofluvial\nunits, and 1 diamicton. An additional map unit depicts large areas of mostly\nbare bedrock. \n\nThe physical properties of the surficial materials were compiled from published\nsoil and geologic maps and reports, our field observations, and from earth\nscience journal articles. Selected deposits in the field were checked for\nconformity to descriptions of map units by the Quaternary geologist who\ncompiled the surficial geologic map units.\n\nFILES INCLUDED IN THIS DATA SET:\n>denvpoly: polygon coverage containing geologic unit contacts and labels.\n>denvline: arc coverage containing faults.\n>geol_sfo.lin: This lineset file defines geologic line types in the\n> geologically themed coverages.\n>geoscamp2.mrk: This markerset file defines the geologic markers in the\n> geologically themed coverages.\n>color524.shd: This shadeset file defines the cmyk values of colors\n> assigned to polygons in the geologically themed coverages.", "links": [ { diff --git a/datasets/USGS_Map_MF-2361_1.0.json b/datasets/USGS_Map_MF-2361_1.0.json index 5e0b9229c3..aecca38686 100644 --- a/datasets/USGS_Map_MF-2361_1.0.json +++ b/datasets/USGS_Map_MF-2361_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2361_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map was funded by the National Cooperative Geologic Program as part of the\ngeologic mapping studies conducted along the I-70 urban corridor. This\ncorridor is experiencing rapid urban growth and geologic mapping is needed to\naid in land-use planning in order to address, avoid, and mitigate known and\npotential geologic hazards.\n\nThe Eagle quadrangle covers an area that straddles the Eagle River and\nInterstate 70 (I-70) and it includes the town of Eagle, Colo., which is located\nin the southwestern part of the quadrangle, just south of I-70 and the Eagle\nRiver, about 37 km west of Vail, Colo. The map area is part of the I-70 urban\ncorridor, which is experiencing rapid and escalating urban growth. Geologic\nmapping along this corridor is needed for ongoing land-use planning. A variety\nof rocks and deposits characterize the map area and areas nearby. Sedimentary\nrocks present in the map area range in age from Pennsylvanian rocks, which were\ndeposited in the ancestral Eagle basin during the formation of the ancestral\nRocky Mountains, to Late Cretaceous rocks that were deposited just prior to the\nformation of the present Rocky Mountains. The Pennsylvanian rocks in the map\narea include a thick sequence of evaporitic rocks (Eagle Valley Evaporite). \nThese evaporitic rocks are commonly complexly folded throughout the southern\npart of the quadrangle where they are exposed. In general, in the central and\nnorthern parts of the quadrangle, the sedimentary rocks overlying the evaporite\ndip gently to moderately northward. Consequently, the youngest sedimentary\nrocks (Late Cretaceous rocks) are exposed dipping gently to the north in the\nnorthern part of the quadrangle; landslide complexes are widespread along the\nnortherly dipping, dip slopes in shaly rocks of the Cretaceous sequence in the\nnortheastern part of the map area. During the Early Miocene, basaltic\nvolcanism formed extensive basaltic flows that mantled the previously deformed\nand eroded sedimentary rocks. Erosional remnants of the basaltic flows are\npreserved in the southeastern, west-central, and north-central parts of the map\narea. Some of these basaltic flows are faulted and downdropped in a manner\nthat suggests they were downdropped in areas where large volumes of the\nunderlying evaporitic rocks were removed from the subsurface, beneath the\nbasaltic rocks, by dissolution or flowage of the evaporite in the subsurface. \nQuaternary and late Tertiary(?) surficial deposits in the map area consist\nmainly of Quaternary alluvium and colluvium, late and middle Pleistocene\nterrace gravels of the Eagle River, Miocene(?) gravel remnants of the ancestral\nEagle River and its tributaries, and Pleistocene to recent mass movement\ndeposits that include landslides and debris flows. Potential geologic hazards\nin the map area include landslides, debris flows, rockfalls, local flooding,\nground subsidence, and expansive and corrosive soils.\n\nMap political location: Eagle County, Colorado\nCompilation scale: 1:24,000\nGeology mapped in 1997.\n\nGEOSPATIAL DATA FILES INCLUDED IN THIS DATA SET:\n eaglepy: polygon coverage containing geologic unit contacts and labels.\n eagleln: arc coverage containing fold axes and other line entities.\n eaglept: point coverage containing bedding attitude measurements\n and other point entities.\n eaglepit: polygon coverage containing gravel pits.", "links": [ { diff --git a/datasets/USGS_Map_MF-2363_1.0.json b/datasets/USGS_Map_MF-2363_1.0.json index ab768abab8..82b9af674d 100644 --- a/datasets/USGS_Map_MF-2363_1.0.json +++ b/datasets/USGS_Map_MF-2363_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2363_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To update and reinterpret earlier geologic mapping, and provide sufficient\ngeologic information for land-use decisions for private land and for areas\nmanaged by the Bureau of Land Management and the National Park Service. Use of\nthese data at scales greater than 1:24,000 would be inappropriate because\nmapping was performed at that scale.\n\nThis 1:24,000-scale geologic map of the Grand Junction 7.5' quadrangle, in\nsupport of the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping\nProject, provides new interpretations of the stratigraphy, structure, and\ngeologic hazards in the area of the junction of the Colorado River and the\nGunnison River. Bedrock strata include the Upper Cretaceous Mancos Shale\nthrough the Lower Jurassic Wingate Sandstone units. Below the Mancos Shale,\nwhich floors the Grand Valley, the Upper and Lower(?)Cretaceous Dakota\nFormation and the Lower Cretaceous Burro Canyon Formation hold up much of the\nresistant northeast- dipping monocline along the northeast side of the\nUncompahgre uplift. The impressive sequence of Jurassic strata below include\nthe Brushy Basin, Salt Wash, and Tidwell Members of the Upper Jurassic Morrison\nFormation, the Middle Jurassic Wanakah Formation and informal \"board beds\" unit\nand Slick Rock Member of the Entrada Formation, and the Lower Jurassic Kayenta\nFormation and Wingate Sandstone. The Upper Triassic Chinle Formation and Early\nProterozoic meta-igneous gneiss and migmatitic meta- sedimentary rocks, which\nare exposed in the Colorado National Monument quadrangle to the west, do not\ncrop out here. The monoclinal dip slope of the northeastern margin of the\nUncompahgre uplift is apparently a Laramide structural feature. Unlike the\nsouthwest-dipping, high-angle reverse faults in the Proterozoic basement and\ns-shaped fault- propagation folds in the overlying strata found in the Colorado\nNational Monument 7.5' quadrangle along the front of the uplift to the west,\nthe monocline in the map area is unbroken except at two localities. One\nlocality displays a small asymmetrical graben that drops strata to the\nsouthwest. This faulted character of the structure dies out to the northwest\ninto an asymmetric fault-propagation fold that also drops strata to the\nsouthwest. Probably both parts of this structure are underlain by a\nnortheast-dipping high-angle reverse fault. The other locality displays a\nsecond similar asymmetric fold. No evidence of post-Laramide tilting or uplift\nexists here, but the antecedent Unaweep Canyon, only 30 km to the\nsouth-southwest of the map area, provides clear evidence of Late Cenozoic, if\nnot Pleistocene, uplift. The major geologic hazards in the area include large\nlandslides associated with the dip-slope-underlain, smectite-rich Brushy Basin\nMember of the Morrison Formation and overlying Dakota and Burro Canyon\nFormations. Active landslides affect the southern bank of the Colorado River\nwhere undercutting by the river and smectitic clays in the Mancos trigger\nlandslides. The Wanakah, Morrison, and Dakota Formations and the Mancos Shale\ncreate a significant hazard to houses and other structures by containing\nexpansive smectitic clay. In addition to seasonal spring floods associated\nwith the Colorado and Gunnison Rivers, a serious flash flood hazard associated\nwith sudden summer thunderstorms threatens the intermittent washes that drain\nthe dip slope of the monocline.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in\nARC/INFO format, this metadata file may include some ARC/INFO-specific\nterminology.\n\nGeospatial data files of this data set:\n> gj24k: geology polygons, contacts, faults\n> gjpnt: point data representing bedding attitudes\n> gjline: line representing location of the cross section,\n> and fold axes\n\nSymbolsets used for plotting in ArcInfo:\n> wpgcmykg.shd: shadeset\n> geol_sfo.lin: lineset\n> geoscamp1.mrk: markerset", "links": [ { diff --git a/datasets/USGS_Map_MF-2364_1.0.json b/datasets/USGS_Map_MF-2364_1.0.json index bdbb96b0b7..400178d94d 100644 --- a/datasets/USGS_Map_MF-2364_1.0.json +++ b/datasets/USGS_Map_MF-2364_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2364_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This geologic map is part of a cooperative project between the U.S. Geological Survey and the Kaibab National Forest Service to provide geologic information for the Paradine Plains Cactus (Pediocactus pardinei Benson, 1957) Conservation Assessment and Strategy conducted by the Kaibab National Forest, Williams, Arizona. The map area includes part of House Rock Valley and part of the Kaibab Plateau, sub-physiographic provinces of the Colorado Plateau. This part of the Colorado Plateau was not previously mapped in adequate geologic detail. This map completes one of several remaining areas where uniform quality geologic mapping was needed. The geologic information in this report may be useful to future biological studies, land management, range management, and flood control programs for all federal, state, and private agencies.\n\nThe map area is in the North Kaibab Ranger District of the Kaibab National Forest and the Arizona Strip Field Office of the Bureau of Land Management (BLM). The nearest settlement is Jacob Lake about 8 km (5 mi) west of the map area (fig. 1). Elevations range from about 2,305 m (7,560 ft) on the Kaibab Plateau in the northwest corner of the map area to about 1,555 m (5,100 ft) in House Rock Valley in the east-central edge of the map area. Primary vehicle access is by U.S. Highway 89A in the northern part of the map area. Four-wheel-drive roads access most of the map area. Dirt roads are not passable in winter snow conditions.\n\nThe Bureau of Land Management Arizona Strip Field Office in St. George, Utah, manages the public lands, and the North Kaibab Ranger District in Fredonia, Arizona manages the U.S. National Forest system land. Other lands include one quarter of a section belonging to the State of Arizona, about 0.7 of a section of private land, and about 1.5 sections within the BLM-administered Paria Canyon-Vermilion Cliffs Wilderness Area (U.S. Department of the Interior, 1993). The private land is in House Rock Valley near State Highway 89A.\n\nLower elevations within upper House Rock Valley support a sparse growth of cactus, grass, and a variety of desert shrubs. Sagebrush, grass, cactus, cliffrose bush, pinion pine trees, juniper trees, ponderosa pine, and oak trees thrive at elevations above 1,830 m (6,000 ft).\n\nSurface runoff in the map area drains eastward toward the Colorado River through House Rock Valley and into Marble Canyon of the Colorado River at Mile 17 (17 miles downstream from Lees Ferry, Arizona).", "links": [ { diff --git a/datasets/USGS_Map_MF-2366_1.0.json b/datasets/USGS_Map_MF-2366_1.0.json index a69351b35e..3f6128cf5c 100644 --- a/datasets/USGS_Map_MF-2366_1.0.json +++ b/datasets/USGS_Map_MF-2366_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2366_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This geologic map is part of a cooperative project between the U.S. Geological\nSurvey and the Kaibab National Forest Service to provide geologic information\nfor the Paradine Plains Cactus (Pediocactus pardinei B,W. Benson) Conservation\nAssessment and Strategy conducted by the Kaibab National Forest, Williams,\nArizona. The map area includes part of House Rock Valley and part of the\nKaibab Plateau, sub- physiographic provinces of the Colorado Plateau. This\npart of the Colorado Plateau was not previously mapped in adequate geologic\ndetail. This map completes one of several remaining areas where uniform\nquality geologic mapping was needed. The geologic information in this report\nmay be useful to land management, range management, and flood control programs\nfor all federal and state agencies, and private affairs.\n\nThis digital map database is compiled from unpublished data and new mapping by\nthe authors, represents the general distribution of surficial and bedrock\ngeology in the mapped area. Together with the accompanying pamphlet, it\nprovides current information on the geologic structure and stratigraphy of the\narea. The database delineate map units thatare identified by age and lithology\nfollowing the stratigraphic nomenclature of the U.S. Geological Survey. The\nscale of the source maps limits the spatial resolution of the database to\n1:24,000 or smaller.\n\nThis report consists of a set of geologic map database files (ARC/ INFO\ncoverages) and supporting text and plot files. In addition, the report\nincludes two sets of plot files (Post Script and PDF format) that will generate\nmap sheets and pamphlets similar to a traditional USGS Miscellaneous Field\nStudies Report. These files are described in the explanatory pamphlets\n(canegeo.doc, canegeo. pdf, or canegeo.txt). The base layer used in the\npreparation of the geologic map plot files was derived from a Digital Raster\nGraphic of a standard USGS 7.5' quadrangle. This raster image was converted to\nGrid format in ARC/INFO, trimmed and rotated, then converted to a GeoTIFF\nimage. The resultant TIFF image was combined with geologic data to produce the\nfinal map layout in Illustrator 8.0.", "links": [ { diff --git a/datasets/USGS_Map_MF-2367_1.0.json b/datasets/USGS_Map_MF-2367_1.0.json index 272755bc2b..e2737aeb9f 100644 --- a/datasets/USGS_Map_MF-2367_1.0.json +++ b/datasets/USGS_Map_MF-2367_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2367_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the House Rock Spring area. Together with\nthe accompanying text, it provides current information on the geologic\nstructure and stratigraphy of the area covered. The database delineates map\nunits that are identified by general age, lithology, and geomorphology\nfollowing the spatial resolution (scale) of the database to 1:24,000. The\ncontent and character of the database, as well as three methods of obtaining\nthe database, are described below.\n\nThis digital map database is compiled from unpublished data and new mapping by\nthe authors, represents the general distribution of surficial and bedrock\ngeology in the mapped area. Together with the accompanying pamphlet, it\nprovides current information on the geologic structure and stratigraphy of the\narea. The database delineate map units that are identified by age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution of the\ndatabase to 1:24,000 or smaller.\n\nThis report consists of a set of geologic map database files (ARC/ INFO\ncoverages) and supporting text and plot files. In addition, the report\nincludes two sets of plot files(Post Script and PDF format) that will generate\nmap sheets and pamphlets similar to a traditional USGS Miscellaneous Field\nStudies Report. These files are described in the explanatory pamphlets\n(hrsgeo.doc, hrsgeo. pdf, or hrsgeo.txt). The base layer used in the\npreparation of the geologic map plot files was derived from a Digital Raster\nGraphic version of a standard USGS 7.5' quadrangle. This raster image was\nconverted to Grid format in ARC/INFO, trimmed and converted to a GeoTIFF image.\n The resultant TIFF image was combined with geologic data to produce the final\nmap image in Illustrator 8.0.", "links": [ { diff --git a/datasets/USGS_Map_MF-2368_1.0.json b/datasets/USGS_Map_MF-2368_1.0.json index 36f3f2641a..f65e31336b 100644 --- a/datasets/USGS_Map_MF-2368_1.0.json +++ b/datasets/USGS_Map_MF-2368_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2368_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geologic map of the Uinkaret volcanic field area covers part of the\nColorado Plateau and several large tributary canyons that make up the western\npart of Arizona's Grand Canyon. The map is part of a cooperative U.S.\nGeological Survey and National Park Service project to provide geologic\ninformation for areas within the newly established Grand Canyon/Parashant\nCanyon National Monument. Most of the Grand Canyon and parts of the adjacent\nplateaus have been geologically mapped; this map fills in one of the remaining\nareas where uniform quality geologic mapping was needed. The geologic\ninformation presented may be useful in future related studies as to land use\nmanagement, range management, and flood control programs for federal and state\nagencies, and private concerns.\n\nThis digital map database is compiled from unpublished data and new mapping by\nthe authors, represents the general distribution of surficial and bedrock\ngeology in the mapped area. Together with the accompanying pamphlet, it\nprovides current information on the geologic structure and stratigraphy of the\narea. The database dilineate map units that are identified by age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution of the\ndatabase to 1:31,680 or smaller.\n\nThis report consists of a set of geologic map database files (ARC/ INFO\ncoverages) and supporting text and plot files. In addition, the report\nincludes two sets of plot files (Post Script and PDF format) that will generate\nmap sheets and pamphlets similar to a traditional USGS Miscellaneous Field\nStudies Report. These files are described in the explanatory pamphlets\n(uink.eps, uink.pdf, or uink.txt). The base layer used in the preparation of\nthe geologic map plot files was derived from four Digital Raster Graphic\nversions of standard USGS 7.5' quadrangles. These raster images where\nconverted to Grid format in ARC/INFO, trimmed and seamed together, then\nconverted to a GeoTIFF image. The resultant TIFF image was combined with\ngeologic data to produce the final map image in Illustrator 9.0.", "links": [ { diff --git a/datasets/USGS_Map_MF-2369_1.0.json b/datasets/USGS_Map_MF-2369_1.0.json index 77cff92956..d1cb2efdaf 100644 --- a/datasets/USGS_Map_MF-2369_1.0.json +++ b/datasets/USGS_Map_MF-2369_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2369_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map was funded by and is a product of the National Cooperative Geologic\nMapping Program. This corridor is experiencing rapid urban growth. Geologic\nmapping is needed to aid in land development planning in order to address,\navoid, or mitigate known and potential geologic hazards.\n\nThis new 1:24,000-scale geologic map of the Vail West 7.5' quadrangle, as part\nof the USGS Western Colorado I-70 Corridor Cooperative Geologic Mapping\nProject, provides new interpretations of the stratigraphy, structure, and\ngeologic hazards in the area on the southwest flank of the Gore Range.\n\nBedrock strata include Miocene tuffaceous sedimentary rocks, Mesozoic and upper\nPaleozoic sedimentary rocks, and undivided Early(?) Proterozoic metasedimentary\nand igneous rocks. Tuffaceous rocks are found in fault-tilted blocks. Only\nsmall outliers of the Dakota Sandstone, Morrison Formation, Entrada Sandstone,\nand Chinle Formation exist above the redbeds of the Permian-Pennsylvanian\nMaroon Formation and Pennsylvanian Minturn Formation, which were derived during\nerosion of the Ancestral Front Range east of the Gore fault zone. In the\nsouthwestern area of the map, the proximal Minturn facies change to distal\nEagle Valley Formation and the Eagle Valley Evaporite basin facies. The Jacque\nMountain Limestone Member, previously defined as the top of the Minturn\nFormation, cannot be traced to the facies change to the southwest. Abundant\nsurficial deposits include Pinedale and Bull Lake Tills, periglacial deposits,\nearth-flow deposits, common diamicton deposits, common Quaternary landslide\ndeposits, and an extensive, possibly late Pliocene landslide deposit. \nLandscaping has so extensively modified the land surface in the town of Vail\nthat a modified land-surface unit was created to represent the surface unit.\n\nLaramide movement renewed activity along the Gore fault zone, producing a\nseries of northwest-trending open anticlines and synclines in Paleozoic and\nMesozoic strata, parallel to the trend of the fault zone. Tertiary\ndown-to-the-northeast normal faults are evident and are parallel to similar\nfaults in both the Gore Range and the Blue River valley to the northeast;\npresumably these are related to extensional deformation that occurred during\nformation of the northern end of the Rio Grande rift system in Colorado.\n\nIn the southwestern part of the map area, a diapiric(?) exposure of the Eagle\nValley Evaporite exists and chaotic faults and folds suggest extensive\ndissolution and collapse of overlying bedrock, indicating the presence of a\ngeologic hazard. Quaternary landslides are common and indicate that landslide\nhazards are widespread in the area, particularly where old slide deposits are\ndisturbed by construction. The late Pliocene(?) landslide that consists largely\nof a smectitic upper Morrison Formation matrix and boulders of Dakota Sandstone\nis readily reactivated. Debris flows are likely to invade low-standing areas\nwithin the towns of Vail and West Vail where tributaries of Gore Creek issue\nfrom the mountains on the north side of the valley.\n\nDATASETS INCLUDED IN THIS GEOSPATIAL DATABASE:\n > vwpoly: geologic polygons, contacts, faults, marker beds, and\n intra-unit scarps\n > vwline: fold axes, concealed linear features, limits of abundant\n chert fragments in the Maroon Formation, and cross-section lines\n > vwpoint: bedding and foliation attitudes, and miscellaneous point data", "links": [ { diff --git a/datasets/USGS_Map_MF-2371.json b/datasets/USGS_Map_MF-2371.json index e56a16910f..9c18dadf32 100644 --- a/datasets/USGS_Map_MF-2371.json +++ b/datasets/USGS_Map_MF-2371.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2371", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:24,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the Oregon State\nGeologist. Similarly, the database cannot be used to identify or delineate\nlandslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits of the Silver lake 7.5 minute quadrangle. The\ndatabase delineates map units that are identified by general age and lithology\nfollowing the stratigraphic nomenclature of the U.S. Geological Survey. The\nscale of the source maps limits the spatial resolution (scale) of the database\nto 1:24,000 or smaller.", "links": [ { diff --git a/datasets/USGS_Map_MF-2372_1.0.json b/datasets/USGS_Map_MF-2372_1.0.json index 39c85a72fa..c7105cdba4 100644 --- a/datasets/USGS_Map_MF-2372_1.0.json +++ b/datasets/USGS_Map_MF-2372_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2372_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These maps (maps A and B) were prepared in support of a regional\nthree-dimensional ground-water model currently being constructed by the U.S.\nGeological Survey (USGS) for the DVRFS. The maps identify regional geologic\nstructures whose possible hydrologic significance merits their inclusion in the\nHFM for the DVRFS.\n\nThe locations of principal faults and structural zones that may influence\nground-water flow were compiled in support of a three-dimensional ground-water\nmodel for the Death Valley regional flow system (DVRFS), which covers 80,000\nsquare km in southwestern Nevada and southeastern California. Faults include\nNeogene extensional and strike-slip faults and pre-Tertiary thrust faults.\nEmphasis was given to characteristics of faults and deformed zones that may\nhave a high potential for influencing hydraulic conductivity. These include: \n(1) faulting that results in the juxtaposition of stratigraphic units with\ncontrasting hydrologic properties, which may cause ground-water discharge and\nother perturbations in the flow system; (2) special physical characteristics of\nthe fault zones, such as brecciation and fracturing, that may cause specific\nparts of the zone to act either as conduits or as barriers to fluid flow; (3)\nthe presence of a variety of lithologies whose physical and deformational\ncharacteristics may serve to impede or enhance flow in fault zones; (4)\norientation of a fault with respect to the present-day stress field, possibly\ninfluencing hydraulic conductivity along the fault zone; and (5) faults that\nhave been active in late Pleistocene or Holocene time and areas of contemporary\nseismicity, which may be associated with enhanced permeabilities.\n\nThe faults shown on maps A (Structural Framework, Neogene Basins, and\nPotentiometric Surface) and B (Structural Framework, Earthquake Epicenters, and\nPotential Zones of Enhanced Hydraulic Conductivity) are largely from Workman\nand others (in press), and fit one or more of the following criteria: (1)\nfaults that are more than 10 km in map length; (2) faults with more than 500 m\nof displacement; and (3) faults in sets that define a significant structural\nfabric that characterizes a particular domain of the DVRFS. The following fault\ntypes are shown: Neogene normal, Neogene strike-slip, Neogene low-angle\nnormal, pre-Tertiary thrust, and structural boundaries of Miocene calderas. We\nhave highlighted faults that have late Pleistocene to Holocene displacement\n(Piety, 1996). Areas of thick Neogene basin-fill deposits (thicknesses 1-2 km,\n2-3 km, and >3 km) are shown on map A, based on gravity anomalies and\ndepth-to-basement modeling by Blakely and others (1999). We have interpreted\nthe positions of faults in the subsurface, generally following the\ninterpretations of Blakely and others (1999). Where geophysical constraints\nare not present, the faults beneath late Tertiary and Quaternary cover have\nbeen extended based on geologic reasoning. Nearly all of these concealed faults\nare shown with continuous solid lines on maps A and B, in order to provide\ncontinuous structures for incorporation into the hydrogeologic framework model\n(HFM). Map A also shows the potentiometric surface, regional springs (25-35\ndegrees Celsius, D'Agnese and others, 1997), and cold springs (Turner and\nothers, 1996).\n\nA composite base map is included based upon published 83-m DEM data from USGS\n1:250,000-scale quadrangles, as well as road lines and political boundaries\nfrom published USGS 1:100,000-scale DLG data. The 1:100,000-scale data were\ngeneralized to 1:250,000 scale for inclusion with the 1:250,000-scale database.\n\nAdditional coverages include a ground-water model area coverage, and text\nlabels for structural features. Files necessary for printing the map are also\nincluded such as text fonts, linesets, shadesets, projection files, and AML\nfiles. These files are all explained in the included README.txt file.", "links": [ { diff --git a/datasets/USGS_Map_MF-2373_1.0.json b/datasets/USGS_Map_MF-2373_1.0.json index c094784cfa..1ea208cd5b 100644 --- a/datasets/USGS_Map_MF-2373_1.0.json +++ b/datasets/USGS_Map_MF-2373_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2373_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:24,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the California\nState Geologist (Hart and Bryant, 1997). Similarly, the database cannot be\nsubstituted for comprehensive maps that systematically identify and classify\nlandslide hazards.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (scvmf.ps, scvmf.pdf, scvmf.txt), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:24,000 or smaller.", "links": [ { diff --git a/datasets/USGS_Map_MF-2381-A_1.0.json b/datasets/USGS_Map_MF-2381-A_1.0.json index 1cee981ff9..12cd30eb8e 100644 --- a/datasets/USGS_Map_MF-2381-A_1.0.json +++ b/datasets/USGS_Map_MF-2381-A_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2381-A_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This digital geologic and tectonic database of the Death Valley ground-water\nmodel area, as well as its accompanying geophysical maps, are compiled at\n1:250,000 scale. The map compilation presents new polygon, line, and point\nvector data for the Death Valley region. The map area is enclosed within a 3\ndegree X 3 degree area along the border of southern Nevada and southeastern\nCalifornia. In addition to the Death Valley National Park and Death\nValley-Furnace Creek fault systems, the map area includes the Nevada Test Site,\nthe southwest Nevada volcanic field, the southern end of the Walker Lane (from\nsouthern Esmeralda County, Nevada, to the Las Vegas Valley shear zone and\nStateline fault system in Clark County, Nevada), the eastern California shear\nzone (in the Cottonwood and Panamint Mountains), the eastern end of the Garlock\nfault zone (Avawatz Mountains), and the southern basin and range (central Nye\nand western Lincoln Counties, Nevada). This geologic map improves on previous\ngeologic mapping in the area by providing new and updated Quaternary and\nbedrock geology, new interpretation of mapped faults and regional structures,\nnew geophysical interpretations of faults beneath the basins, and improved GIS\ncoverages. The basic geologic database has tectonic interpretations imbedded\nwithin it through attributing of structure lines and unit polygons which\nemphasize significant and through-going structures and units. An emphasis has\nbeen put on features which have important impacts on ground-water flow.\nConcurrent publications to this one include a new isostatic gravity map (Ponce\nand others, 2001), a new aeromagnetic map (Ponce and Blakely, 2001), and\ncontour map of depth to basement based on inversion of gravity data (Blakely\nand Ponce, 2001).\n\nThis map compilation was completed in support of the Death Valley Ground-Water\nBasin regional flow model funded by the Department of Energy in conjunction\nwith the U. S. Geological Survey and National Park Service. The proposed model\nis intended to address issues concerning the availability of water in Death\nValley National Park and surrounding counties of Nevada and California and the\nmigration of contaminants off of the Nevada Test Site and Yucca Mountain\nhigh-level waste repository. The geologic compilation and tectonic\ninterpretations contained within this database will serve as the basic\nframework for the flow model. The database also represents a synthesis of many\nsources of data compiled over many years in this geologically and tectonically\nsignificant area.", "links": [ { diff --git a/datasets/USGS_Map_MF-2381-C_1.0.json b/datasets/USGS_Map_MF-2381-C_1.0.json index 346acec655..1cde50dac5 100644 --- a/datasets/USGS_Map_MF-2381-C_1.0.json +++ b/datasets/USGS_Map_MF-2381-C_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2381-C_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An isostatic gravity map of the Death Valley groundwater model area was\nprepared from over 40,0000 gravity stations as part of an interagency effort by\nthe U.S. Geological Survey and the U.S. Department of Energy to help\ncharacterize the geology and hydrology of southwest Nevada and parts of\nCalifornia.\n\nThis dataset was completed in support of the Death Valley Ground-Water Basin\nregional flow model funded by the U.S. Department of Energy in conjunction with\nthe U. S. Geological Survey and U.S. National Park Service. The proposed model\nis intended to address issues concerning the availability of water in Death\nValley National Park and surrounding counties of Nevada and California and the\nmigration of contaminants out of the Nevada Test Site and Yucca Mountain\nhigh-level waste repository.", "links": [ { diff --git a/datasets/USGS_Map_MF-2381-D_1.0.json b/datasets/USGS_Map_MF-2381-D_1.0.json index 7c9a670fef..299c8bb9f7 100644 --- a/datasets/USGS_Map_MF-2381-D_1.0.json +++ b/datasets/USGS_Map_MF-2381-D_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2381-D_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An aeromagnetic map of the Death Valley groundwater model area was prepared\nfrom published aeromagnetic surveys as part of an interagency effort by the\nU.S. Geological Survey and the U.S. Department of Energy to help characterize\nthe geology and hydrology of southwest Nevada and parts of California.\n\nThis dataset was completed in support of the Death Valley Ground-Water Basin\nregional flow model funded by the U.S. Department of Energy in conjunction with\nthe U. S. Geological Survey and U.S. National Park Service. The proposed model\nis intended to address issues concerning the availability of water in Death\nValley National Park and surrounding counties of Nevada and California and the\nmigration of contaminants off of the Nevada Test Site and Yucca Mountain\nhigh-level waste repository.", "links": [ { diff --git a/datasets/USGS_Map_MF-2381-E_1.0.json b/datasets/USGS_Map_MF-2381-E_1.0.json index c7f2387432..745887fe2f 100644 --- a/datasets/USGS_Map_MF-2381-E_1.0.json +++ b/datasets/USGS_Map_MF-2381-E_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2381-E_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A depth to basement map of the Death Valley groundwater model area was prepared\nusing over 40,0000 gravity stations as part of an interagency effort by the\nU.S. Geological Survey and the U.S. Department of Energy to help characterize\nthe geology and hydrology of southwest Nevada and parts of California.\n\nThis dataset was completed in support of the Death Valley Ground-Water Basin\nregional flow model funded by the U.S. Department of Energy in conjunction with\nthe U. S. Geological Survey and U.S. National Park Service. The proposed model\nis intended to address issues concerning the availability of water in Death\nValley National Park and surrounding counties of Nevada and California and the\nmigration of contaminants off of the Nevada Test Site and Yucca Mountain\nhigh-level waste repository.", "links": [ { diff --git a/datasets/USGS_Map_MF-2385_1.0.json b/datasets/USGS_Map_MF-2385_1.0.json index 1bc4cf4374..73e4325209 100644 --- a/datasets/USGS_Map_MF-2385_1.0.json +++ b/datasets/USGS_Map_MF-2385_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Map_MF-2385_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mitigation is superior to post-disaster response in reducing the billions of\ndollars in losses resulting from U.S. natural disasters, and information that\npredicts the varying likelihood of geologic hazards can help public agencies\nimproves the necessary decision making on land use and zoning. Accordingly,\nthis map was created to increase the resistance of one urban area, metropolitan\nOakland, California, to land sliding. Prepared in a geographic information\nsystem from a statistical model, the map estimates the relative likelihood of\nlocal slopes to fail by two processes common to this area of diverse geology,\nterrain, and land use.\n\nMap data that predict the varying likelihood of land sliding can help public\nagencies make informed decisions on land use and zoning. This map, prepared in\na geographic information system from a statistical model, estimates the\nrelative likelihood of local slopes to fail by two processes common to an area\nof diverse geology, terrain, and land use centered on metropolitan Oakland. \nThe model combines the following spatial data: (1) 120 bedrock and surficial\ngeologic-map units, (2) ground slope calculated from a 30-m digital elevation\nmodel, (3) an inventory of 6,714 old landslide deposits (not distinguished by\nage or type of movement and excluding debris flows), and (4) the locations of\n1,192 post-1970 landslides that damaged the built environment. The resulting\nindex of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is\ncomputed as a continuous variable over a large area (872 km2) at a\ncomparatively fine (30 m) resolution. This new model complements landslide\ninventories by estimating susceptibility between existing landslide deposits,\nand improves upon prior susceptibility maps by quantifying the degree of\nsusceptibility within those deposits.\n\nSusceptibility is defined for each geologic-map unit as the spatial frequency\n(areal percentage) of terrain occupied by old landslide deposits, adjusted\nlocally by steepness of the topography. Susceptibility of terrain between the\nold landslide deposits is read directly from a slope histogram for each\ngeologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each\none-degree slope interval that coincides with the deposits.\n\nSusceptibility within landslide deposits (0.00 to 1.33) is this same percentage\nraised by a multiplier (1.33) derived from the comparative frequency of recent\nfailures within and outside the old deposits. Positive results from two\nevaluations of the model encourage its extension to the 10-county San Francisco\nBay region and elsewhere. A similar map could be prepared for any area where\nthe three basic constituents, a geologic map, a landslide inventory, and a\nslope map, are available in digital form. Added predictive power of the new\nsusceptibility model may reside in attributes that remain to be explored-among\nthem seismic shaking, distance to nearest road, and terrain elevation, aspect,\nrelief, and curvature.", "links": [ { diff --git a/datasets/USGS_NAWQA_HG_DEP.json b/datasets/USGS_NAWQA_HG_DEP.json index d2c038afb5..4962ba8855 100644 --- a/datasets/USGS_NAWQA_HG_DEP.json +++ b/datasets/USGS_NAWQA_HG_DEP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NAWQA_HG_DEP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Atmospheric deposition has been found to be the dominant source of mercury (Hg)\nin New England's aquatic environment (Krabbenhoft and others, 1999; Northeast\nStates for Coordinated Air Use Management (NESCAUM) and others, 1998). Little\nis known about atmospheric mercury deposition in urban areas because most\natmospheric monitoring to date has been done in rural areas. Preliminary water,\nsediment, and fish tissue data, collected by U.S. Geological Survey's New\nEngland Coastal Basins (NECB) study as part of the National Water Quality\nAssessment (NAWQA) program, shows elevated concentrations of mercury in the\nBoston metropolitan area. The NECB Mercury Deposition Network is a four-site,\n2-year data collection effort by the USGS to help define the levels of mercury\nin precipitation and identify how atmospheric mercury may be contributing to\nmercury in the aquatic ecosystem.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_NEIC_NEARRT.json b/datasets/USGS_NEIC_NEARRT.json index 1c368f1a32..bc10c827d3 100644 --- a/datasets/USGS_NEIC_NEARRT.json +++ b/datasets/USGS_NEIC_NEARRT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NEIC_NEARRT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Earthquake Information Center (NEIC of the\nU.S. Geological Survey provides current earthquake information and\ndata including interactive earthquake maps, near real time earthquake\ndata, fast moment and broadband solutions, and lists of earthquakes\nfor the past 3 weeks.\n\nCurrent earthquake information and data are located at:\nhttp://earthquake.usgs.gov/\n\nNear real time earthquake data is located at:\nhttp://earthquake.usgs.gov/\n\nArchives of past earthquakes can be found at:\nhttp://earthquake.usgs.gov/earthquakes/eqinthenews/", "links": [ { diff --git a/datasets/USGS_NHD_CATCH.json b/datasets/USGS_NHD_CATCH.json index c87411d84b..8c1e1ea1e3 100644 --- a/datasets/USGS_NHD_CATCH.json +++ b/datasets/USGS_NHD_CATCH.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NHD_CATCH", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Topographically-based catchments will be delineated for all stream-reach\nsegments of the National Hydrography Dataset (NHD) within the entire\nconterminous United States. The NHD is a digital hydrographic dataset produced\nby the USGS, in cooperation with the U.S. Environmental Protection Agency\n(USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an\ninitial scale of 1:100,000. This effort is being supported by the USEPA and\nUSGS and is intended to benefit a wide variety of water-quality and stream-flow\nstudies across the nation.\n\nThe catchment-delineation technique is the same as that developed for use in\nthe New England SPARROW model. The New England SPARROW model was the first to\nutilize the detail of the National Hydrography Dataset (NHD) as the underlying\nstream-reach network. Final products for this project will be the completion of\nNHD catchment delineations for the conterminous United States, which will be\npart of the NHDPlus project to be completed and made available in 2006.", "links": [ { diff --git a/datasets/USGS_NPS_AcadiaAccuracy_Final.json b/datasets/USGS_NPS_AcadiaAccuracy_Final.json index a9066f3474..0bd5003a71 100644 --- a/datasets/USGS_NPS_AcadiaAccuracy_Final.json +++ b/datasets/USGS_NPS_AcadiaAccuracy_Final.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NPS_AcadiaAccuracy_Final", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database.", "links": [ { diff --git a/datasets/USGS_NPS_AcadiaFieldPlots_Final.json b/datasets/USGS_NPS_AcadiaFieldPlots_Final.json index 701355f92a..85311bb4c8 100644 --- a/datasets/USGS_NPS_AcadiaFieldPlots_Final.json +++ b/datasets/USGS_NPS_AcadiaFieldPlots_Final.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NPS_AcadiaFieldPlots_Final", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database.\n", "links": [ { diff --git a/datasets/USGS_NPS_AcadiaParkBoundary_Final.json b/datasets/USGS_NPS_AcadiaParkBoundary_Final.json index b6a41fb8ae..0e72f0b2a9 100644 --- a/datasets/USGS_NPS_AcadiaParkBoundary_Final.json +++ b/datasets/USGS_NPS_AcadiaParkBoundary_Final.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NPS_AcadiaParkBoundary_Final", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983.\n", "links": [ { diff --git a/datasets/USGS_NPS_AcadiaSpatialVeg_Final.json b/datasets/USGS_NPS_AcadiaSpatialVeg_Final.json index 65ebb06383..38bf61c270 100644 --- a/datasets/USGS_NPS_AcadiaSpatialVeg_Final.json +++ b/datasets/USGS_NPS_AcadiaSpatialVeg_Final.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NPS_AcadiaSpatialVeg_Final", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%.\n\n\n", "links": [ { diff --git a/datasets/USGS_NSHMP.json b/datasets/USGS_NSHMP.json index 5bdf0ed4e3..5d7528e413 100644 --- a/datasets/USGS_NSHMP.json +++ b/datasets/USGS_NSHMP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NSHMP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Seismic Hazard Mapping Project (NSHMP) provides online maps. The hazard maps depict probabilistic ground motions and spectral response with 10%, 5%, and 2% probabilities of exceedance (PE) in 50 years. These maps correspond to return times of approximately 500, 1000, and 2500 years, respectively. The maps are based on the assumption that earthquake occurrence is Poissonian, so that the probability of occurrence is time-independent. The maps cover all of the U.S. including Hawaii and Alaska along with\nother pertinent information related to earthquake hazards.", "links": [ { diff --git a/datasets/USGS_NWRC_LA_LandChange_1932-2010.json b/datasets/USGS_NWRC_LA_LandChange_1932-2010.json index a7733d2d98..b444fd5d8a 100644 --- a/datasets/USGS_NWRC_LA_LandChange_1932-2010.json +++ b/datasets/USGS_NWRC_LA_LandChange_1932-2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_NWRC_LA_LandChange_1932-2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The analyses of landscape change presented in this dataset use historical surveys, aerial data, and satellite data to track landscape changes in coastal Louisiana. Persistent loss and gain data are presented for 1932-2010. The U.S. Geological Survey (USGS) analyzed landscape changes in coastal Louisiana by determining land and water classifications for 17 datasets. These datasets include survey data from 1932, aerial data from 1956, and Landsat Multispectral Scanner System (MSS) and Thematic Mapper (TM) data from the 1970s to 2010.", "links": [ { diff --git a/datasets/USGS_OF99-535_1.0.json b/datasets/USGS_OF99-535_1.0.json index 28f027c820..7bf3638c6d 100644 --- a/datasets/USGS_OF99-535_1.0.json +++ b/datasets/USGS_OF99-535_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OF99-535_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the USGS Global Change Research effort, the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project has documented the characteristics of middle Pliocene climate on a global scale. The middle Pliocene was selected for detailed study because it spans the transition from relatively warm global climates when glaciers were absent or greatly reduced in the Northern Hemisphere to the generally cooler climates of the Pleistocene with expanded Northern Hemisphere ice sheets and prominent glacial-interglacial cycles.\n \n The purpose of this report is to document and explain the PRISM2 mid Pliocene reconstruction. The PRISM2 reconstruction consists of a series of 28 global scale data sets (Table 1) on a 2\u00b0 latitude by 2\u00b0 longitude grid. As such, it is the most complete and detailed global reconstruction of climate and environmental conditions older than the last glacial.\n \n PRISM2 evolved from a series of studies that summarized conditions at a large number of marine and terrestrial sites and areas (eg. Cronin and Dowsett, 1991; Poore and Sloan, 1996). The first global reconstruction of mid Pliocene climate (PRISM1) was based upon 64 marine sites and 74 terrestrial sites and included data sets representing annual vegetation and land ice, monthly sea surface temperature (SST) and sea-ice, sea level and topography on a 2\u00b0x2\u00b0 grid (Dowsett et al. (1996) and Thompson and Fleming (1996)). The current reconstruction (PRISM2) is a revision of PRISM1 that incorporates several important differences:\n \n 1) Additional sites were added to the marine portion of the reconstruction to improve previous coverage. Sites from the Mediterranean Sea and Indian Ocean are incorporated for the first time in PRISM2.\n \n 2) All Pliocene sea surface temperature (SST) estimates were recalculated based upon a new core top calibration to the Reynolds and Smith (1995) adjusted optimum interpolation (AOI) SST data set. This reduced some of the problems previously encountered when different fossil groups were calibrated to different modern climatologies (Climate / Long Range Investigation Mapping and Predictions [CLIMAP], Goddard Institute for Space Sciences [GISS], Advanced Very High Resolution Radiometer [AVHRR], etc.).\n \n 3) PRISM2 uses a +25m rise in sea level for the Pliocene (PRISM1 used +35m), in keeping with much new data that has become available.\n \n 4) Although the change in global ice volume between PRISM1 and PRISM2 is minor, PRISM2 uses model results from Prentice (personal communication) to guide the areal and topographic distribution of Antarctic ice. This results in a more realistic Antarctic ice configuration in tune with the +25m sea level rise.", "links": [ { diff --git a/datasets/USGS_OFR-03-13.json b/datasets/USGS_OFR-03-13.json index bed1cfb5ac..9b2d4154e6 100644 --- a/datasets/USGS_OFR-03-13.json +++ b/datasets/USGS_OFR-03-13.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR-03-13", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract\n\nThe Cascadia Tsunami Deposit Database contains data on the location and\nsedimentological properties of tsunami deposits found along the Cascadia\nmargin. Data have been compiled from 52 studies, documenting 59 sites from\nnorthern California to Vancouver Island, British Columbia that contain known or\npotential tsunami deposits. Bibliographical references are provided for all\nsites included in the database. Cascadia tsunami deposits are usually seen as\nanomalous sand layers in coastal marsh or lake sediments. The studies cited in\nthe database use numerous criteria based on sedimentary characteristics to\ndistinguish tsunami deposits from sand layers deposited by other processes,\nsuch as river flooding and storm surges. Several studies cited in the database\ncontain evidence for more than one tsunami at a site. Data categories include\nage, thickness, layering, grainsize, and other sedimentological characteristics\nof Cascadia tsunami deposits. The database documents the variability observed\nin tsunami deposits found along the Cascadia margin.", "links": [ { diff --git a/datasets/USGS_OFR-97-792.json b/datasets/USGS_OFR-97-792.json index f23765137d..afd4115640 100644 --- a/datasets/USGS_OFR-97-792.json +++ b/datasets/USGS_OFR-97-792.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR-97-792", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Devils Hole is a tectonically formed cave developed in the discharge zone of a\nregional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls\nof this subaqueous cavern are coated with dense vein calcite which provides an\nideal material for precise uranium-series dating via thermal ionization mass\nspectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein\ncalcite from which we obtained an approximately 500,000-year-long continuous\nrecord of paleotemperature and other climatic proxies. Data from this core were\nrecently used by Winograd and others (1997) to discuss the length and stability\nof the last four interglaciations. These data are given in table 1\n(http://pubs.usgs.gov/of/1997/ofr97-792/)\n\nThese records have provided information that has posed several challenges to\nthe orbital theory of the causation of the Pleistocene glaciations, suggested\ninsights regarding the duration of current Holocene climate, provided a new\nchronology for the Vostok, Antarctica, ice core paleotemperature record, and\nyielded insights on the age of the groundwater in the principal aquifer of\nsouthern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/)\n\nCarbon and oxygen stable isotopic ratios were measured on 285 samples cut at\nregular intervals inward from the free face of the core (as reported in\nWinograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284\nsamples because a sample taken at 114.28 mm was eliminated when post-1994\nreanalysis of its delta 18O value indicated an error in the earlier\ndetermination. Carbon isotopic ratios are reported in per mill relative to\nVPDB, defined by assigning a delta 13C of +1.95 per mill to the reference\nmaterial NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW\nreference water on a scale normalized such that SLAP reference water is -55.5\nper mill relative to VSMOW reference water. The oxygen isotopic fractionation\nfactors employed in this determination are those listed in Coplen and others\n(1983). The delta 18O value of the isotopic reference material NBS 19 on this\nscale is +28.65 per mill. The \u00b1 1 sd (standard deviation) error for the delta\n18O and delta 13C analyses is \u00b10.07 and 0.05 per mill, respectively.\n\nAges were estimated by linear interpolation between age control points taken at\nkey intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age\nestimates in Table 1 are based on the original 21 control points (see Table 2\nin Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for\nthe recently obtained TIMS age of 143.8\u00b10.9 ka (2 sd analytical error) at 51.5\nmm (Winograd, et al., 1997). The later sample was taken specifically for\nadditional control in a critical portion of the core. Errors in the ages vary\nbut are bounded by the errors in the appropriate control points. (See Table 2\nin Ludwig, et al., 1992.)", "links": [ { diff --git a/datasets/USGS_OFR00-45_1.0.json b/datasets/USGS_OFR00-45_1.0.json index 1ef51dadf2..cc06b4e02e 100644 --- a/datasets/USGS_OFR00-45_1.0.json +++ b/datasets/USGS_OFR00-45_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-45_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Our mapping study was funded by the USGS Toxic Substances Hydrology Program and\nwas undertaken for the following reasons: 1) to ascertain whether the area\nmight have a greater number of mappable lithologic units than shown on Barton's\n(1997) map, and to verify the stratigraphically higher formations shown on the\nmap; 2) to have sufficient data to draw geologic cross- sections through the\nMirror Lake research site; 3) to gather more data on brittle fracture\ndistribution and orientation; and 4) to assess the degree to which the\nsubsurface lithologies, ductile structures, and fractures observed at the two\nMirror Lake well fields correlate with the geology of the surrounding region.\n\nThe bedrock geology of the Hubbard Brook Experimental Forest, Grafton County,\nNew Hampshire is described in this report of new field investigation. The\ndatabase includes contacts of bedrock geologic units, faults, folds, and other\nstructural geologic information, as well as the base maps on which the mapped\ngeological features are registered. This report supersedes Barton (1997).\n\nData were originally collected in UTM coordinates, zone 19, NAD 1927, and\nreprojected to geographic coordinates (Lat/Long), NAD 1983. The database is\naccompanied by two large format color maps, a readme.txt file, and a\nexplanatory pamphlet.", "links": [ { diff --git a/datasets/USGS_OFR00-462.json b/datasets/USGS_OFR00-462.json index b5f20dfdf7..546e3f09f8 100644 --- a/datasets/USGS_OFR00-462.json +++ b/datasets/USGS_OFR00-462.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-462", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In November 1999, the U. S. Geological Survey, in cooperation with\nCoastal Carolina University, began a program to produce geologic maps\nof the nearshore regime off northern South Carolina, utilizing high\nresolution sidescan sonar, interferometric (direct phase methods)\nswath bathymetry, and seismic subbottom profiling systems. The study\nareas extends from the ~7m isobath to about 10km offshore (water\ndepths <12m). The goals of the investigation are to determine regional\nscale sand resource availability needed for planned beach nourishment\nprograms, to investigate the roles that the inner shelf morphology and\ngeologic framework play in the evolution of this coastal region, and\nto provide baseline geologic maps for use in proposed biologic habitat\nstudies.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS MGNM 00014 cruise. The coverage is the\nnearshore of central South Carolina. The seismic-reflection data are\nstored as SEG-Y standard format that can be read and manipulated by\nmost seismic-processing software. Much of the information specific to\nthe data are contained in the headers of the SEG-Y format files. The\nfile system format is ISO 9660 which can be read with DOS, Unix, and\nMAC operating systems with the appropriate CD-ROM driver software\ninstalled.", "links": [ { diff --git a/datasets/USGS_OFR00-463.json b/datasets/USGS_OFR00-463.json index ffe364a164..60ce05d6e7 100644 --- a/datasets/USGS_OFR00-463.json +++ b/datasets/USGS_OFR00-463.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-463", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In November 1999, the U. S. Geological Survey, in cooperation with\nCoastal Carolina University, began a program to produce geologic maps\nof the nearshore regime off northern South Carolina, utilizing high\nresolution sidescan sonar, interferometric (direct phase methods)\nswath bathymetry, and seismic subbottom profiling systems. The study\nareas extends from the ~7m isobath to about 10km offshore (water\ndepths <12m). The goals of the investigation are to determine regional\nscale sand resource availability needed for planned beach nourishment\nprograms, to investigate the roles that the inner shelf morphology and\ngeologic framework play in the evolution of this coastal region, and\nto provide baseline geologic maps for use in proposed biologic habitat\nstudies.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS MGNM 00014 cruise. The coverage is the\nnearshore of central South Carolina. The seismic-reflection data are\nstored as SEG-Y standard format that can be read and manipulated by\nmost seismic-processing software. Much of the information specific to\nthe data are contained in the headers of the SEG-Y format files. The\nfile system format is ISO 9660 which can be read with DOS, Unix, and\nMAC operating systems with the appropriate CD-ROM driver software\ninstalled.", "links": [ { diff --git a/datasets/USGS_OFR00-467.json b/datasets/USGS_OFR00-467.json index a09ab4d650..ac17cefd38 100644 --- a/datasets/USGS_OFR00-467.json +++ b/datasets/USGS_OFR00-467.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-467", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the USGS Woods Hole Field Center in Cooperation with the\nU.S. Army Corps of Engineers, began a program designed to map the\nseafloor offshore of the New York-New Jersey metropolitan area; the\nmost heavily populated, and one of the most impacted coastal regions\nof the United States. The ultimate goal of this program is to provide\nan overall synthesis of the sea floor environment, including surficial\nsediment texture, subsurface geometry, and anthropogenic impact (e.g.\nocean dumping, trawling, channel dredging), through the use and\nanalysis of sidescan-sonar and subbottom mapping techniques. This\nregional synthesis will support a wide range of management decisions\nand will provide a basis for further process-oriented investigations.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS DIAN 96040 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR00-494.json b/datasets/USGS_OFR00-494.json index b44ec19771..fe4b7463d9 100644 --- a/datasets/USGS_OFR00-494.json +++ b/datasets/USGS_OFR00-494.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-494", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine seismic reflection data are used to image and map sedimentary\nand structural features of the seafloor and subsurface. These data\nare useful in mapping faults (such as the San Andreas and Hayward\nFaults) where they pass under the waters of the San Francisco Bay, and\nin assessing other submarine geologic characteristics and features.\nParticularattention was devoted to investigating the offshore\nconfluence of the San Andreas and San Gregorio fault zones. These\ndata were collected under the auspices of the auspices of the Central\nCalifornia/San Francisco Bay Earthquake Hazards Project of the Western\nCoastal and Marine Geology Program. Further information concerning the\nobjectives and efforts of this project may be found at:\n\"http://walrus.wr.usgs.gov/earthquakes/cencal/\"\n\nThis report consists of two-dimensional marine seismic reflection\nprofile data from the San Francisco Bay area. These data were\nacquired between 1993 and 1997 with the Research Vessels David\nJohnston and Robert Gray. The data are available in a variety of\nformats, including binary, postscript and GIF image. Binary data are\nin Society of Exploration Geologists (SEG) SEG-Y format and may be\ndownloaded for further processing or display. Reference maps and GIF\nimages othe profiles may be viewed with your Web browser. Seismic\nreflection profiles are acquired by means of an acoustic source\n(usually generated electromagnetically or with compressed air), and a\nhydrophone or hydrophone array. Both elements are typically towed in\nthe waterbehind a survey vessel. The sound source emits a short\nacoustic pulse, which propogates through the water and sediment\ncolumns. The acoustic energy is reflected at density boundaries (such\nas the seafloor or sediment layers beneath the seafloor), and detected\nat the hydrophone. As the vessel moves, this process is repeated at\nintervals ranging between 0.5 and 20 meters depending on the source\ntype. In this way a two-dimensional image of the geologic structure\nbeneath the ship track is constructed.", "links": [ { diff --git a/datasets/USGS_OFR00-495_1.0.json b/datasets/USGS_OFR00-495_1.0.json index 2ddcd4f7dc..90e2cec6b5 100644 --- a/datasets/USGS_OFR00-495_1.0.json +++ b/datasets/USGS_OFR00-495_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00-495_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the combination of geology data (geologic units, faults, folds, and dikes) from 6 1;100,000 scale digital coverages in eastern Washington (Chewelah, Colville, Omak, Oroville, Nespelem, Republic). The data was converted to an Arc grid in ArcView using the Spatial Analyst extension.", "links": [ { diff --git a/datasets/USGS_OFR0047.json b/datasets/USGS_OFR0047.json index 79c48074f2..5ec8d7ba00 100644 --- a/datasets/USGS_OFR0047.json +++ b/datasets/USGS_OFR0047.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\n spatial database of coal-bearing regions in China. This data set will be\n utilized in energy research\n and cartographic projects.\n \n The data set covers of coal-bearing regions, coal fields, structural\n sedimentary basins, major coal mine production, and other commodities in China.\n \n Procedures_Used:\n \n The coal-bearing regions were digitized from the Energy Mineral Resource Map of\n China and Adjacent Seas, published in 1992 by the Geological Publishing House,\n Chinese Institute of Geology and Mineral Resources Information and Institute of\n Mineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR0047_coal_bearing.json b/datasets/USGS_OFR0047_coal_bearing.json index 0a8252e354..8a52baae7f 100644 --- a/datasets/USGS_OFR0047_coal_bearing.json +++ b/datasets/USGS_OFR0047_coal_bearing.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047_coal_bearing", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\nspatial database of coal-bearing regions in China. This data set will be\nutilized in energy research and cartographic projects.\n\nThis dataset is a collection of coal-bearing regions located in The Peoples\nRepublic of China. Included in this dataset are the age and name of the\ncoal-bearing regions.\n\nProcedures_Used:\n\nThe coal-bearing regions were digitized from the Energy Mineral Resource Map of\nChina and Adjacent Seas, published in 1992 by the Geological Publishing House,\nChinese Institute of Geology and Mineral Resources Information and Institute of\nMineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR0047_coal_type.json b/datasets/USGS_OFR0047_coal_type.json index 54b29401a6..ba0f4a8dbb 100644 --- a/datasets/USGS_OFR0047_coal_type.json +++ b/datasets/USGS_OFR0047_coal_type.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047_coal_type", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists\na spatial database of coal fields in China. This data set will be\nutilized in energy research and cartographic projects.\n\nThis dataset is a collection of coal field locations in The Peoples\nRepublic of China. Included in this dataset is the rank of the coal in\neach field.\n\nProcedures_Used:\n\nThe coal types were digitized from the Energy Mineral Resource Map of\nChina and Adjacent Seas, published in 1992 by the Geological\nPublishing House, Chinese Institute of Geology and Mineral Resources\nInformation and Institute of Mineral Deposits of Chinese Academy of\nGeological Sciences, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR0047_commodities.json b/datasets/USGS_OFR0047_commodities.json index 582c84ac8e..d2865d5ac7 100644 --- a/datasets/USGS_OFR0047_commodities.json +++ b/datasets/USGS_OFR0047_commodities.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047_commodities", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\nspatial database of commodities in China. This data set will be utilized in\nenergy research and cartographic projects.\n\nThis dataset is a collection of commodities (coal gas, oil shale, and\nquaternary peat) located in The Peoples Republic of China. Included in this\ndataset is the type of the commodities.\n\nProcedures_Used:\n\nThe commodities were digitized from the Energy Mineral Resource Map of China\nand Adjacent Seas, published in 1992 by the Geological Publishing House,\nChinese Institute of Geology and Mineral Resources Information and Institute of\nMineral Deposits of Chinese Academy of Geological Sciences, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR0047_mines.json b/datasets/USGS_OFR0047_mines.json index 26f967ad69..1ba2b04134 100644 --- a/datasets/USGS_OFR0047_mines.json +++ b/datasets/USGS_OFR0047_mines.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047_mines", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\nspatial database of major coal mines and their production in China. This data\nset will be utilized in energy research and cartographic projects. \n\nThis dataset is a collection of major coal mine production located in The\nPeoples Republic of China. Included in this dataset are the locations of major\ncoal mines and the approximate annual amount of coal mined annually in millions\nof metric tons.\n\nProcedures_Used:\nThe major coal mine production points were digitized from the Coalfield\nPrediction Map of China, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR0047_sed_basins.json b/datasets/USGS_OFR0047_sed_basins.json index e268ff5043..fdec3cc2e4 100644 --- a/datasets/USGS_OFR0047_sed_basins.json +++ b/datasets/USGS_OFR0047_sed_basins.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR0047_sed_basins", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\nspatial database of structural sedimentary basins in China. This data set will\nbe utilized in energy research and cartographic projects.\n\nThis dataset is a collection of structural sedimentary basin locations in The\nPeoples Republic of China. Included in this dataset are the age and name of\neach structural sedimentary basin.\n\nProcedures_Used:\nThe structural sedimentary basins were digitized from the Energy Mineral\nResource Map of China and Adjacent Seas, published in 1992 by the Geological\nPublishing House, Chinese Institute of Geology and Mineral Resources\nInformation and Institute of Mineral Deposits of Chinese Academy of Geological\nSciences, utilizing ARC/INFO.", "links": [ { diff --git a/datasets/USGS_OFR00503cellarbndry.json b/datasets/USGS_OFR00503cellarbndry.json index 1c8f38963e..0b36d684b8 100644 --- a/datasets/USGS_OFR00503cellarbndry.json +++ b/datasets/USGS_OFR00503cellarbndry.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00503cellarbndry", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this project is to map the surficial geology of the sea\nfloor of Historic Area Remediation Site (HARS) and changes in\nsurficial characteristics over time. This GIS project presents\nmultibeam and other data in a digital format for analysis and display\nby scientists, policy makers, managers and the general public.\n\nThis data set includes the boundaries of the Cellar Dirt Disposal\nsite, located offshore of New York and New Jersey.", "links": [ { diff --git a/datasets/USGS_OFR00503dredgesite.json b/datasets/USGS_OFR00503dredgesite.json index eb0eefb7a5..ff68b8b557 100644 --- a/datasets/USGS_OFR00503dredgesite.json +++ b/datasets/USGS_OFR00503dredgesite.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00503dredgesite", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this project is to map the surficial geology of the sea\nfloor of Historic Area Remediation Site (HARS) and changes in\nsurficial characteristics over time. This GIS project presents\nmultibeam and other data in a digital format for analysis and display\nby scientists, policy makers, managers and the general public.\n\nThis data set includes the location and volume of material placed on\nthe sea floor in the Historic Area Remediation Site between November\n1996 and April 2000, extracted from records maintained by the\nU.S. Army Corps of Engineers. These data are maintained in a system\ncalled DAN-NY (Disposal Analysis System NY). DAN-NY includes data\nfrom Inspector Logs (data recorded by inspectors on the barges), and\nmore recently data acquired by NYDISS (New York Disposal Surveillance\nSystem). The NYDISS automatically records the location of the barge\nwhen placement begins and ends. For material placed between November\n1996 and November 1998, the plotted locations are from Inspector Logs.\nFor the material placed between November 1998 and April 2000, the\nplacement location was determined by NYDISS. This Open-File Report\nutilizes the location (latitude and longitude), date of placement, and\nvolume of material in the scow from these data bases.", "links": [ { diff --git a/datasets/USGS_OFR00503epabndry.json b/datasets/USGS_OFR00503epabndry.json index 28661f2352..b8e5027d4a 100644 --- a/datasets/USGS_OFR00503epabndry.json +++ b/datasets/USGS_OFR00503epabndry.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR00503epabndry", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this project is to map the surficial geology of the sea\nfloor of Historic Area Remediation Site (HARS) and changes in\nsurficial characteristics over time. This GIS project presents\nmultibeam and other data in a digital format for analysis and display\nby scientists, policy makers, managers and the general public.\n\nThis project presents maps of the sea floor in GIS format of the\nHistoric Area Remedition Site (HARS), located offshore of New York and\nNew Jersey. The data were collected with a multibeam sea floor mapping\nsystem on surveys conducted November 23 - December 3, 1996, October 26\n- November 11, 1998, and April 6 - 30, 2000. The maps show sea floor\ntopography, shaded relief, and backscatter intensity (a measure of sea\nfloor texture and roughness) at a spatial resolution of 3 m/pixel, and\nlocations of dredged material placed on the sea floor. The sea floor\nof the HARS, approximately 9 square nautical miles in area, is being\nremediated by placing at least a one-meter of clean dredged material\non top of the existing surface sediments that exhibit varying degrees\ndegradation resulting from previous disposal of dredged and other\nmaterial. Comparison of the topography and backscatter intensity from\nthe three surveys show changes in topography and surficial sediment\nproperties resulting from placement of dredged material in 1996 and\n1997 prior to designation of the HARS, as well as placement of\nmaterial for remediation of the HARS. This study is carried out\ncooperatively by the U.S. Geological Survey and the U.S. Army Corps of\nEngineers.", "links": [ { diff --git a/datasets/USGS_OFR01-122_Version 1.0, April 20, 2001.json b/datasets/USGS_OFR01-122_Version 1.0, April 20, 2001.json index e76a73aa4d..0d3e3d7811 100644 --- a/datasets/USGS_OFR01-122_Version 1.0, April 20, 2001.json +++ b/datasets/USGS_OFR01-122_Version 1.0, April 20, 2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-122_Version 1.0, April 20, 2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "U.S. Geological Survey scientists desired to have the data presented in the\nSource in a digital format to use in GIS and spreadsheet software programs for\naggregate models and aggregate assessment.\n\nThe data set marpits1 is an ArcInfo coverage of point features representing pit\nlocations and attribution data captured from an atlas of map sheets and pit\ndata sheets titled \"A Materials Inventory of Maricopa County [Arizona]\" by the\nArizona Highway Department (AHD), now named the Arizona Department of\nTransportation (ADOT), hereafter referred to as the 'Source'.\n\nPit locations were represented by point symbols in the Source map sheets. \nPoints were digitized from the Source map sheets. Selected attribute data were\ncollected from the Source pit data and map sheets. In the Source introduction\nit states:\n \"The pit location maps show the location of all\n pits bearing Materials Services serial numbers.\n Other sources are not shown. The plotted locations\n are as close as possible to the true location\n as the scale of the map will allow.\"\n\nThe point attribute data, captured from the Source pit data sheets are\n\n \"designed to show test results (sieve analysis,\n plasticity index, and abrasion) for the usable\n material within each ADOT pit.\"\n\nThe digital editor and digital compilers of the GIS data set made certain\nadjustments to the data to make them complete and usable in a GIS. These\nadjustments include adding points locations for records in the accompanying\nSource pit data sheets where no point representation existed on the Source map\nsheets, adding attribution data to the furthest extent possible for points on\nthe Source map sheets without entries in the accompanying Source pit data\nsheets, appending a letter to the pit number of repeated (duplicate) pit\nnumbers to make them unique and correspond one-to-one with a record in the\nSource pit data sheets, and adding a '-999' to represent 'No data' or 'No\nobservation' for blank entries in the pit data sheets. Table 3 in the\nOpen-File Report text describes the actions taken to insure data consistency\nand uniqueness of the individual points. An accompanying ArcInfo arc coverage\ncalled marbase of the generalized Maricopa County boundary, and generalized\nmajor roadways and generalized major hydrography of Maricopa County has been\nincluded to give a general reference of pit location in proximity to natural\nand man-made features.", "links": [ { diff --git a/datasets/USGS_OFR01-123.json b/datasets/USGS_OFR01-123.json index 8756b3ec42..3cd3fb5a55 100644 --- a/datasets/USGS_OFR01-123.json +++ b/datasets/USGS_OFR01-123.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-123", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study was completed as part of an ongoing project in the field of\nnatural gas hydrate research. Natural gas hydrates are an ice-like\ncrystalline combination of water and gas, most commonly methane.\nThe data included in this report were collected in an effort to understand\na site where we believe large quantities of methane, approximately 4% of\nthe present atmospheric total, was released from seafloor sediments. This\nsite is known as the Blake Ridge collapse structure, located 300 km off\nthe South Carolina coast at approximately 2600 m of water depth.\n\nThis CD-ROM contains copies of the navigation and deep-towed chirp\nsubbottom data collected aboard the R/V Cape Hatteras on cruises 92023 and\n95023 in 1992 and 1995 respectively. This CD-ROM is (Compact Disc-Read\nOnly Memory UDF (Universal Disc Format) CD-ROM Standard (ISO 9660\nequivalent). The HTML documentation is written utilizing some HTML 4.0\nenhancements. The disk should be viewable by all WWW browsers but may not\nproperly format on some older WWW browsers. Also, some links to USGS\ncollaborators and other agencies are available on this CD-ROM. These links\nare only accessible if access to the Internet is available during browsing\nof the CD-ROM.\n\nOn cruise 92023, 58 km of deep-towed chirp data were recorded on 4 lines\nand broken into a total of 8 files. 78 square kilometers of sidescan\nmosaic and approximately 1000 km of air gun single channel seismic\nreflection data were recorded as well but are not achived on this report.\nOn cruise 95023, 100km of deep- towed chirp data were recorded on 5 lines\nand broken into 18 files. 152 square kilometers of sidescan mosaic and\n244.3 km of GI gun single channel seismic reflection were also recorded\nbut are not archived on this report.\n\nThe archived Chirp subbottom data are in standard Society of Exploration\nGeologists (SEG) SEG-Y format (Barry and others, 1975) and may be\ndownloaded for processing with software such as Seismic Unix or SIOSEIS.\nThe subbottom data were recorded on the ISIS data acquisition system in\nQMIPS format. Chirp subbottom channel extracted from raw QMIPS format\nsonar files and converted to 16-bit Int. SEG-Y format using the program\nQMIPSTOSEGY. Even though the data are in SEG-Y format, it is not the\nconventional time series data (e.g. voltages or pressures), but rather\ninstantaneous amplitude or envelope detected and therefore all of the\namplitudes are positive (though not simply rectified).\n\nSeismic reflection profiles are acquired by means of an acoustic source\n(usually generated electromagnetically or with compressed air), and a\nhydrophone or hydrophone array. Both elements are typically towed in the\nwater behind a survey vessel, or some cases, mounted on side of the hull.\nThe sound source emits a short acoustic pulse, which propagates through\nthe water and sediment columns. The acoustic energy is reflected at\ndensity boundaries (such as the seafloor or sediment layers beneath the\nseafloor), and detected at the hydrophone. As the vessel moves, this\nprocess is repeated at intervals ranging between 0.5 and 20 meters\ndepending on the source type. In this way, a two-dimensional image of the\ngeologic structure beneath the ship track is constructed. For more\ninformation concerning seismic reflection profiling at the USGS Woods Hole\n\"http://woodshole.er.usgs.gov/operations/sfmapping/\"", "links": [ { diff --git a/datasets/USGS_OFR01-131_Version 1.0.json b/datasets/USGS_OFR01-131_Version 1.0.json index 5eaec62d69..9f92e73613 100644 --- a/datasets/USGS_OFR01-131_Version 1.0.json +++ b/datasets/USGS_OFR01-131_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-131_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the San Bernardino North 7.5' quadrangle was prepared under\nthe U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) a\npart of an ongoing effort to develop a regional geologic framework of southern\nCalifornia, and to utilize a Geographical Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThe digital geologic map database for the San Bernardino North 7.5' quadrangle\nhas been created as a general-purpose data set that is applicable to other\nland-related investigations in the earth and biological sciences. For example,\nitcan be used for groundwater studies in the San Bernardino basin, and for\nmineral resource evaluation studies, animal and plant habitat studies, and soil\nstudies in the San Bernardino National Forest. The database is not suitable\nfor site-specific geologic evaluations.\n\nThis data set maps and describes the geology of the San Bernardino North 7.5'\nquadrangle, San Bernardino County, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage containing geologic contacts and units, (2)\nattribute tables for geologic units (polygons), contacts (arcs), and\nsite-specific data (points). In addition, the data set includes the following\ngraphic and text products: (1) A PostScript graphic plot-file containing the\ngeologic map, topography, cultural data, a Correlation of Map Units (CMU)\ndiagram, a Description of Map Units (DMU), an index map, a regional geologic\nand structure map, and a key for point and line symbols; (2) PDF files of this\nReadme (including the metadata file as an appendix), Description of Map Units\n(DMU), and the graphic produced by the PostScript plot file.\n\nThe geologic map covers a part of the southwestern San Bernardino Mountains and\nthe northwestern San Bernardino basin. Granitic and metamorphic rocks underlie\nmost of the mountain area, and a complex array of Quaternary deposits fill the\nbasin. These two areas are separated by strands of the seismically active San\nAndreas Fault. Bedrock units in the San Bernardino Mountains are dominate by\nlarge Cretaceous and Jurassic granitic bodies, ranging in composition from\nonzogranite to monzodiorite, and include lesser Triassic monzonite. The younger\nof these granitic rocks intrude a complex assemblage of gneiss, marble, and\ngranitic rock of probable early Mesozoic age; the relationship between these\nmetemorphic rocks and the Triassic rocks is unknown. Spanning the Pleistocene\nin age, large and small alluvial bodies emerge from the San Bernardino\nMountains, and and fill the San Bernardino basin. In the southwestern part of\nthe quadrangle, Cajon Wash carries sediments from both the San Bernardino and\nSan Gabriel Mountains, and Lytle Creek heads in the eastern San Gabriel\nMountains. Limite bedrock areas showing through the Quaternary sediments of the\nbasin consist exclusively of Mesozoic Pelona Schist locally intruded by\nTertairy dikes. Youthful-appearing fault scarps discontinuously mark the\ntraces of the San Andreas Fault along the southern edge of the San Bernardino\nMountains. Unnamed Tertiary sedimentary rocks are bounded by two strands of\nthe fault between Badger Canyon and the east edge of the quadrangle. Young and\nold high-angle faults cut bedrock units within the San Bernardino Mountains,\nand the buried, seismically active San Jacinto Fault traverses the southwestern\npart of the quadrangle.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. This digital Open-File map superceeds an older analog Open-File\nmap of the quadrangle, and includes extensive new data on the Quaternary\ndeposits, and revises some fault and bedrock distribution within the San\nBernardino Mountains. The digital map was compiled on a base-stable cronoflex\ncopy of the San Bernardino North 7.5' topographic base and then scribed. This\nscribe guide was used to make a 0.007 mil blackline clear-film, which was\nscanned at 1200 DPI by Optronics Specialty Company, Northridge, California;\nminor hand-digitized additions were made at the USGS. Lines, points, and\npolygonswere subsequently edited at the USGS using standard ARC/INFO commands. \nDigitizing and editing artifacts significant enough to display at a scale of\n1:24,000 were corrected. Within the database, geologic contacts are\nrepresented as lines (arcs), geologic units as polygons, and site-specific data\nas points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat,\nrespectively) uniquely identify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR01-132_Version 1.0.json b/datasets/USGS_OFR01-132_Version 1.0.json index 3d62c8f74e..81f2fd57cb 100644 --- a/datasets/USGS_OFR01-132_Version 1.0.json +++ b/datasets/USGS_OFR01-132_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-132_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Fifteenmile Valley 7.5' quadrangle was prepared under the\nU.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as\npart of an ongoing effort to develop a regional geologic framework of southern\nCalifornia, and to utilize a Geographical Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThe digital geologic map database for the Fifteenmile Valley 7.5' quadrangle\nhas been created as a general-purpose data set that is applicable to other\nland-related investigations in the earth and biological sciences. For example,\nit can be used for mineral resource evaluation studies, animal and plant\nhabitat studies, and soil studies in the San Bernardino National Forest. The\ndatabase is not suitable for site-specific geologic evaluations.\n\nThis data set maps and describes the geology of the Fifteenmile Valley 7.5'\nquadrangle, San Bernardino County, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage containing geologic contacts and units, (2)\nattribute tables for geologic units (polygons), contacts (arcs), and\nsite-specific data (points). In addition, the data set includes the following\ngraphic and text products: (1) A PostScript graphic plot-file containing the\ngeologic map, topography, cultural data, a Correlation of Map Units (CMU)\ndiagram, a Descriptionof Map Units (DMU), an index map, a regional geologic and\nstructure map, and a key for point and line symbols; (2) PDF files of this\nReadme (including the metadata file as an appendix), Description of Map Units\n(DMU), and a screen graphic of the plot produced by the PostScript plot file.\n\nThe geologic map covers the northernmost part of the San Bernardino Mountain\nand the southern Granite Mountains. These two bedrock areas are separated by\nthe wide, alluviated Fifteenmile Valley. Bedrock units in the San Bernardino\nMountains are dominated by large Cretaceous granitic bodies ranging in\ncomposition from monzogranite to gabbro, and include lesser Triassic monzonite.\n The Granite Mountains are underlain chiefly by large Triassic monzonite\nbodies, and in the western part, by Cretaceous and possibly Jurassic\nmonzogranite to monzodiorite. Spanning the Pleistocene in age, large alluvial\nfans, flank the north side of the San Bernardino Mountains, and are dominated\nby debris flow deposits. The central part of Fifteenmile Valley is covered by\nfine grained alluvial material deposited by streams flowing into Rabbit Lake\nand an unnamed dry lake in the northwestern part of the quadrangle. Young,\nsouth dipping reverse faults, some with moderately to well eroded fault scarps,\ndiscontinuously flank the northern edge of the San Bernardino Mountains. Young\nand old high-angle faults are mapped within both the San Bernardino and Granite\nMountains.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. The map was compiled on a base-stable cronoflex copy of the\nFifteenmile Valley 7.5' topographic base and then scribed. This scribe guide\nwas used to make a0.007 mil blackline clear-film, which was scanned at 1200 DPI\nby Optronics Specialty Company, Northridge, California; minor hand-digitized\nadditions were madeat the USGS. Lines, points, and polygons were subsequently\nedited at the USGS using standard ARC/INFO commands. Digitizing and editing\nartifacts significan enough to display at a scale of 1:24,000 were corrected. \nWithin the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc,\nand point attribute tables (.pat, .aat, and .pat, respectively) uniquely\nidentify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR01-142_1.json b/datasets/USGS_OFR01-142_1.json index d13ca9ed4e..d0fe5b06ca 100644 --- a/datasets/USGS_OFR01-142_1.json +++ b/datasets/USGS_OFR01-142_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-142_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a spatial database that delineates mining-related features in areas of\nhistoric and active phosphate mining in the core of the southeastern Idaho\nphosphate resource area. The data has varying degrees of accuracy and\nattribution detail. The breakdown of areas by type of activity at active mines\nis detailed; however, the disturbed areas at many of the closed or inactive\nmines are not subdivided into specific categories detailing the type of\nactivity that occurred.\n\nNineteen phosphate mine sites are included in the study. A total of 5,728 hc\n(14,154 ac), or more than 57 km2 (22 mi2), of phosphate mining-related surface\ndisturbance are documented in the spatial coverage of the core of the southeast\nIdaho phosphate resource area. The study includes 4 active phosphate minebsDry\nValley, Enoch Valley, Rasmussen Ridge, and Smoky Canyobnand 15 historic\nphosphate minebsBallard, Champ, Conda, Diamond Gulch, Gay, Georgetown Canyon,\nHenry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon,\nRattlesnake Canyon, Waterloo, and Wooley Valley. Spatial data on the inactive\nhistoric mines is relatively up-to-date; however, spatially described areas for\nactive mines are based on digital maps prepared in early 1999. The inactive Gay\nmine has the largest total area of disturbance: 1,917 hc (4,736 ac) or about 19\nkm2 (7.4 mi2). It encompasses over three times the disturbance area of the next\nlargest mine, the Conda mine with 607 hc (1,504 ac), and it is nearly four\ntimes the area of the Smoky Canyon mine, the largest of the active mines with\n497 hc (1,228 ac).\n\nThe wide range of phosphate mining-related surface disturbance features\n(approximately 80) were reduced to 13 types or features used in this studbyadit\nand pit, backfilled mine pit, facilities, mine pit, ore stockpile, railroad,\nroad, sediment catchment, tailings or tailings pond, topsoil stockpile, water\nreservoir, and disturbed land (undifferentiated). In summary, the spatial\ncoverage includes polygons totaling 1,114 hc (2,753 ac) of mine pits, 272 hc\n(671 ac) of backfilled mine pits, 1,570 hc (3,880 ac) of waste dumps, 26 hc (64\nac) of ore stockpiles, and 44 hc (110 ac) of tailings or tailings ponds. Areas\nof undifferentiated phosphate mining-related land disturbances, called bed\nland,b site-specific studies to delineate distinct mine features will allow\nmodification of this preliminary spatial database.", "links": [ { diff --git a/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json b/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json index 3a23d45f69..0293409128 100644 --- a/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json +++ b/datasets/USGS_OFR01-153_Version 1.0, February 13, 2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-153_Version 1.0, February 13, 2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heavy or high-density minerals in the 63-250-um (micron) size fraction (very\nfine and fine sand) were analyzed from beach and offshore sites to determine\nthe areal and temporal mineralogic distributions and the relation of those\ndistributions to the deposit affected by effluent discharged from the Los\nAngeles County Sanitation District sewage system.\n\nHeavy or high-density minerals in the 63-250-_m (micron) size fraction (very\nfine and fine sand) were analyzed from 36 beach and offshore sites (38 samples)\nof the Palos Verdes margin to determine the areal and temporal mineralogic\ndistributions and the relation of those distributions to the deposit affected\nby material discharged from the Los Angeles County Sanitation District sewage\nsystem (Lee, 1994) (Figure 1). Data presented here were tabulated for a report\nto the Department of Justice (Wong, 1994). The results of the data analysis are\ndiscussed in Wong (in press).", "links": [ { diff --git a/datasets/USGS_OFR01-157.json b/datasets/USGS_OFR01-157.json index 4abbf070ec..c0a732191b 100644 --- a/datasets/USGS_OFR01-157.json +++ b/datasets/USGS_OFR01-157.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-157", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beginning in 1995, the USGS, in cooperation with the U.S Army Corps of\nEngineers (USACE), New York District, began a program to generate\nreconnaissance maps of the sea floor offshore of the New York-New\nJersey metropolitan area, one of the most populated coastal regions of\nthe United States. The goal of this mapping program is to provide a\nregional synthesis of the sea-floor environment, including a\ndescription of sedimentary environments, sediment texture, seafloor\nmorphology, and geologic history to aid in understanding the impacts\nof anthropogenic activities, such as ocean dumping. This mapping\neffort differs from previous studies of this area by obtaining\ndigital, sidescan sonar images that cover 100 percent of the sea\nfloor. This investigation was motivated by the need to develop an\nenvironmentally acceptable solution for the disposal of dredged\nmaterial from the New York - New Jersey Port, by the need to identify\npotential sources of sand for renourishment of the southern shore of\nLong island, and by the opportunity to develop a better understanding\nof the transport and long-term fate of contaminants by investigations\nof the present distribution of materials discharged into the New York\nBight over the last 100+ years (Schwab and others, 1997).\n\nThis DVD-ROM contains copies of the navigation and field Water Gun\nsubbottom data collected aboard the R/V Seaward Explorer, from 7-25\nMay, 1995. The coverage is in the New York Bight area. This DVD-ROM\n(Digital Versatile Disc-Read Only Memory) has been produced in\naccordance with the UDF (Universal Disc Format) DVD-ROM Standard (ISO\n9660 equivalent) and is therefore capable of being read on any\ncomputing platform that has appropriate DVD-ROM driver software\ninstalled. Access to the data and information contained on this\nDVD-ROM was developed using the HyperText Markup Language (HTML)\nutilized by the World Wide Web (WWW) project. Development of the\nDVD-ROM documentation and user interface in HTML allows a user to\naccess the information by using a variety of WWW information browsers\nto facilitate browsing and locating information and data. To access\nthe information contained on this disk with a WWW client browser, open\nthe file'index.htm' at the top level directory of this DVD-ROM with\nyour selected browser. The HTML documentation is written utilizing\nsome HTML 4.0 enhancements. The disk should be viewable by all WWW\nbrowsers but may not properly format on some older WWW browsers. Also,\nsome links to USGS collaborators and other agencies are available on\nthis DVD-ROM. These links are only accessible if access to the\nInternet is available during browsing of the DVD-ROM.", "links": [ { diff --git a/datasets/USGS_OFR01-173_Version 1.0.json b/datasets/USGS_OFR01-173_Version 1.0.json index 36e5245c52..fb88d752ea 100644 --- a/datasets/USGS_OFR01-173_Version 1.0.json +++ b/datasets/USGS_OFR01-173_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-173_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Devore 7.5' quadrangle was prepared under the U.S.\nGeological Survey Southern California Areal Mapping Project (SCAMP) as part of\nan ongoing effort to develop a regional geologic framework of southern\nCalifornia, and to utilize a Geographical Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThe digital geologic map database for the Devore 7.5' quadrangle has been\ncreated as a general-purpose data set that is applicable to other land-related\ninvestigations in the earth and biological sciences. For example, it can be\nused for groundwater studies in the San Bernardino basin, and for mineral\nresource evaluation studies, animal and plant habitat studies, and soil studies\nin the San Bernardino National Forest. The database is not suitable for\nsite-specific geologic evaluations.\n\nThis data set maps and describes the geology of the Devore 7.5' quadrangle, San\nBernardino County, California. Created using Environmental Systems Research\nInstitute's ARC/INFO software, the data base consists of the following items:\n(1) a map coverage containing geologic contacts and units, (2) attribute tables\nfor geologic units (polygons), contacts (arcs), and site-specific data\n(points). In addition, the data set includes the following graphic and text\nproducts: (1) A PostScript graphic plot-file containing the geologic map,\ntopography, cultural data, a Correlation of Map Units (CMU) diagram, a\nDescription of Map Units (DMU), an index map, a regional geologic and structure\nmap, and a key for point and line symbols; (2) PDF files of this Readme\n(including the metadata file as an appendix), Description of Map Units (DMU),\nand the graphic produced by the PostScript plot file.\n\nThe Devore quadrangle straddles part of the boundary between two major\nphysiographic provinces of California, the Transverse Ranges Province to the\nnorth and the Peninsular Ranges Province to the south. The north half of the\nquadrangle includes the eastern San Gabriel Mountains and a small part of the\nwestern San Bernardino Mountains, both within the east-central part of the\nTransverse Ranges Province. South of the Cucamonga and San Andreas Fault zones,\nthe extensive alluviated area in the south half of the quadrangle lies within\nthe upper Santa Ana River Valley, and represents the northernmost part of the\nPeninsular Ranges Province.\n\nThere are numerous active faults within the quadrangle, including right-lateral\nstrike-slip faults of the San Andreas Fault system, which dominate the younger\nstructural elements, and separate the San Gabriel from the San Bernardino\nMountains. The active San Jacinto Fault zone projects toward the quadrangle\nfrom the southeast, but its location is poorly constrained not only within the\nquadrangle, but for at least several kilometers to the southeast. As a result,\nthe interrelation between it, the Glen Helen Fault, and the probable\neasternmost part of the San Gabriel Fault is intrepretive. Thrust faults of\nthe Cucamonga Fault zone along the south margin of the San Gabriel Mountains,\nrepresent the rejuvinated eastern end of a major old fault zone that bounds the\nsouth side of the western and central Transverse Ranges (Morton and Matti,\n1993). Rejuvenation of this old fault zone, including the Cucamonga Fault\nzone, is apparently in response to compression in the eastern San Gabriel\nMountains resulting from initiation of right-lateral slip on the San Jacinto\nFault zone in the Peninsular Ranges.The structural grain within the San Gabriel\nMountains, as defined by basement rocks, is generally east striking. Within the\nDevore quadrangle, these basement rocks include a Paleozoic (?) schist,\nquartzite, and marble metasedimentary sequence, which occurs as discontinuous\nlenses and septa within Cretaceous granitic rocks. Most of the granitic rocks\nare of tonalitic composition, and much of them are mylonitic. South of the\ngranitic rocks is a complex assemblage of Proterozoic (?) metamorphic rocks, at\nleast part of which is metasedimentary. The assemblage was metamorphosed to\nupper amphibolite and lower granulite grade, and subsequently remetamorphosed\nto a lower metamorphic grade. It is also intensely deformed by mylonitization\nwhich is characterized by an east striking, north dipping foliation, and by a\npronounced lineation that plunges shallowly east and west.\n\nEast of Lytle Creek and west of the San Andreas Fault zone, the predominant\nbasement lithology is Mesozoic Pelona Schist, which consists mostly of\ngreenschist grade metabasalt and metagraywacke. Intruding the Pelona Schist,\nbetween Lytle Creek and Cajon Canyon, is the granodiorite of Telegraph Peak of\nOligocene age (May and Walker, 1989). East of the San Andreas Fault in the San\nBernardino Mountains, basement rocks consist of amphibolite grade gneiss and\nschist intermixed with concordant and discordant tonalitic rock and pegmatite. \nTertiary conglomerate and sandstone occur in the Cucamonga Fault zone and in a\nzone 200 to 700 m wide between strands of the San Andreas Fault zone and\nlocalized thrust faults northeast of the San Andreas. Most of the conglomerate\nand sandstone within the Cucamonga Fault zone is overturned forming the north\nlimb of an overturned syncline. Clasts in the conglomerate are not derived\nfrom any of the basement rocks in the eastern San Gabriel Mountains. Clasts in\nthe conglomerate and sandstone northeast of the San Andreas Fault zone do not\nappear to be locally derived either. The south half of the quadrangle is\ndominated by the large symmetrical alluvial-fan emanating from the canyon of\nLytle Creek, and by the complex braided stream sediments of Lytle Creek and\nCajon Wash.\n\nThe San Andreas Fault is restricted to a relatively narrow zone marked by a\npronounced scarp that is especially well exposed near the east margin of the\nquadrangle. Two poorly exposed, closely spaced, north-dipping thrust faults\nnortheast of the San Andreas Fault have dips that appear to range from 55? to\nnear horizontal. These hallower dips probably are the result of rotation of\ninitially steeper fault surfaces by downhill surface creep. Between the San\nAndreas and Glen Helen Fault zones, there are several faults that have north\nfacing scarps, the largest of which are the east striking Peters Fault and the\nnorthwest striking Tokay Hill Fault. The Tokay Hill Fault is at least in part\na reverse fault. Scarps along both faults are youthful appearing.\n\nThe Glen Helen Fault zone along the west side of Cajon Creek, is well defined\nby a pronounced scarp from the area north of Interstate 15, south through Glen\nHelen Regional Park; an elongate sag pond is located within the park.\n\nThe large fault zone along Meyers Canyon, between Penstock and Lower Lytle\nRidges, is probably the eastward extension of the San Gabriel Fault zone that\nis deformed into a northwest orientation due to compression in the eastern San\nGabriel Mountains (Morton and Matti, 1993). At the south end of Sycamore Flat,\nthis fault zone consists of three discreet faults distributed over a width of\n300 m. About 2.5 km northwest of Sycamore Flats, it consists of a 300 m wide\nshear zone. At the north end of Penstock Ridge, the fault zone has bifurcated\ninto four strands, which at the northwest corner of the quadrangle are\ndistributed over a width of about one kilometer. From the northern part of\nSycamore Flat, for a distance of nearly 5 km northwestward, a northeast dipping\nreverse fault is located along the east side of the probable San Gabriel Fault\nzone. This youthful reverse fault has locally placed the Oligocene\ngranodiorite of Telegraph Peak over detritus derived from the granodiorite.\n\nThe Lytle Creek Fault, which is commonly considered the western splay of the\nSan Jacinto Fault zone, is located on the west side of Lytle Creek. Lateral\ndisplacement on the Lytle Creek Fault has offset parts of the old Lytle Creek\nchannel; this offset gravel-filled channel is best seen at Texas Hill, near the\nmouth of Lytle Creek, where the gravel was hydraulic mined for gold in the\n1890s.\n\nThe Cucamonga Fault zone consists of a one kilometer wide zone of northward\ndip-ping thrust faults. Most splays of this fault zone dip north 25 to 35.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. This digital Open-File map supercedes an older analog Open-File\nmap of the quadrangle, and includes extensive new data on the Quaternary\ndeposits, and revises some fault and bedrock distribution within the San\nGabriel Mountains. The digital map was compiled on a base-stable cronoflex\ncopy of the Devore 7.5 deg. topographic base and then scribed. This scribe\nguide was used to make a 0.007 mil blackline clear-film, from which lines and\npoint were hand digitized. Lines, points, and polygons were subsequently\nedited at the USGS using standard ARC/INFO commands. Digitizing and editing\nartifacts significant enough to display at a scale of 1:24,000 were corrected. \nWithin the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc,\nand point attribute tables (.pat, .aat, and .pat, respectively) uniquely\nidentify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR01-227_1.0.json b/datasets/USGS_OFR01-227_1.0.json index a4607f2e1b..0110b582e5 100644 --- a/datasets/USGS_OFR01-227_1.0.json +++ b/datasets/USGS_OFR01-227_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-227_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geology was researched and compiled for use in studies of ecosystem health,\nenvironmental impact, soils, groundwater, land use, tectonics, crustal genesis,\nsedimentary provenance, and any others that could benefit from geographically\nreferenced geological data.\n\nThe Washington DC Area geologic map database (DCDB) provides geologic map\ninformation of areas to the NW, W, and SW of Washington, DC to various\nprofessionals and private citizens who have uses for geologic data. Digital,\ngeographically referenced, geologic data is more versatile than traditional\nhard copy maps, and facilitates the examination of relationships between\nnumerous aspects of the geology and other types of data such as: land-use data,\nvegetation characteristics, surface water flow and chemistry, and various types\nof remotely sensed images. The DCDB was created by combining Arc/Info\ncoverages, designing a Microsoft (MS) Access database, and populating this\ndatabase. Proposed improvements to the DCDB include the addition of more\ngeochemical, structural, and hydrologic data. \n\nData are provided in several common GIS formats and MS Access database files. \nThe geologic data themes included are bedrock, surficial, faults and fold axes,\nneat line, structural data, and sinkholes; the base themes are political\nboundaries, roads, elevation contours, and hydrography.\n\nData were originally collected in UTM coordinates, zone 18, NAD 1927, and\nprojected to geographic coordinates (Lat/Long), NAD 1983. The data base is\naccompanied by large format color maps, a readme.txt file, and a explanatory\nPDF pamphlet.", "links": [ { diff --git a/datasets/USGS_OFR01-290_1.0.json b/datasets/USGS_OFR01-290_1.0.json index 613cab1426..de93bca5af 100644 --- a/datasets/USGS_OFR01-290_1.0.json +++ b/datasets/USGS_OFR01-290_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-290_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:24,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, mineral\nand energy resources, seismic velocity, and earthquake faults. In addition,\nthe report contains new information and interpretations about the regional\ngeologic history and framework. However, the regional scale of this report\ndoes not provide sufficient detail for site development purposes. In addition,\nthis map does not take the place of fault-rupture hazard zones designated by\nthe California State Geologist (Hart and Bryant, 1997). Similarly, the database\ncannot be substituted for comprehensive maps that systematically identify and\nclassify landslide hazards.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (srm_expl.txt, srm_expl.pdf), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:24,000 or smaller.\n\nThe databases in this report were compiled in ARC/INFO, a commercial Geographic\nInformation System (Environmental Systems Research Institute, Redlands,\nCalifornia), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and\nWentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files\nare COVERAGE (ARC/INFO vector data) format. Coverages are stored in\nuncompressed ARC export format (ARC/INFO version 8.0. 2). ARC/INFO export\nfiles (files with the .e00 extension) can be converted into ARC/INFO coverages\nin ARC/INFO (see below) and can be read by some other Geographic Information\nSystems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for\nWindows 3.1 to 3.11 is available for free from ESRI's web site:\nhttp://www.esri.com.) The digital compilation was done in version 7.2.1 of\nARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and\nWentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The\ngeologic map information was digitized from stable originals of the geologic\nmaps at 1:24,000 scale. The author manuscripts (pen on mylar) were scanned\nusing a Altek monochrome scanner with a resolution of 800 dots per inch. The\nscanned images were vectorized and transformed from scanner coordinates to\nprojection coordinates with digital tics placed by hand at quadrangle corners. \nThe scanned lines were edited interactively by hand using ALACARTE, color\nboundaries were tagged as appropriate, and some scanning artifacts visible at\n1:24,000 were removed.\n\nThis report consists of a set of geologic map database files (Arc/Info\ncoverages) and supporting text and plotfiles. In addition, the report includes\ntwo sets of plotfiles (PostScript and PDF format) that will generate map sheets\nand pamphlets similar to a traditional USGS Open-File Report. These files are\ndescribed in the explanatory pamphlets (srm.ps, srm.pdf, and srm.txt). The\nbase map layers used in the preparation of the geologic map plotfiles were\nscanned from a scale-stable version of the USGS 1:24,000 topographic maps of\nthe San Rafael Mtn. (1959, photorevised 1988) 7.5-minute quadrangle. The map\nhas a 40 foot contour interval.", "links": [ { diff --git a/datasets/USGS_OFR01-293_Version 1.0.json b/datasets/USGS_OFR01-293_Version 1.0.json index 1129b19e13..eef292b359 100644 --- a/datasets/USGS_OFR01-293_Version 1.0.json +++ b/datasets/USGS_OFR01-293_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-293_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Telegraph 7.5' quadrangle was prepared under the U.S. \nGeological Survey Southern California Areal Mapping Project (SCAMP) and the\nCalifornia Division of Mines as part of an ongoing effort to develop a regional\ngeologic framework of southern California, and to utilize a Geographical\nInformation System (GIS) format to create regional digital geologic databases. \nThese regional databases are being developed as contributions to the National\nGeologic Map Database of the National Cooperative Geologic Mapping Program of\nthe USGS.\n\nThe digital geologic map database for the Telegraph 7.5' quadrangle has been\ncreated as a general-purpose data set that is applicable to other land-related\ninvestigations in the earth and biological sciences. For example, the U.S.\nForest Service, San Bernardino National Forest, may use the map and database as\na basic geologic data source for soil studies, mineral resource evaluations,\nroad building, biological surveys, and general forest management. The database\nis not suitable for site-specific geologic evaluations at scales greater than\n1:24,000 (1 in = 2,000 ft).\n\nThis data set maps and describes the geology of the Telegraph 7.5' quadrangle,\nSan Bernardino County, California. Created using Environmental Systems Research\nInstitute's ARC/INFO software, the data base consists of the following items:\n(1) a double precision map coverage containing geologic contacts and units, (2)\na coverage containing site-specific structural data, (3) a coverage containing\ngeologic-unit label leaders and their associated attribute tables for geologic\nunits (polygons), contacts (arcs), and site-specific data (points). In\naddition, the data set includes the following graphic and text products: (1) A\nPostScript graphic plot-file containing the geologic map, topography, cultural\ndata, a Correlation of Map Units (CMU) diagram, a Description of Map Units\n(DMU), an index map, a regional geologic and structure map, and a key for point\nand line symbols; (2) PDF files of this Readme (including the metadata file as\nan appendix), Description of Map Units (DMU), and the graphic produced by the\nPostScript plot file.\n\nThe Telegraph Peak quadrangle is located in the eastern San Gabriel Mountains\npart of the Transverse Ranges Province of southern California. The generally\neast-striking structural grain characteristic of the crystalline rocks of much\nof the San Gabriel Mountains is apparent, but not well developed in the\nTelegraph Peak quadrangle. Here, the east-striking structural grain is\nsomewhat masked by the northwest-striking grain associated with the San Andreas\nFault zone.\n\nFaults within the quadrangle include northwest-striking, right-lateral\nstrike-slip faults of the San Andreas system. The active San Andreas Fault,\nlocated in the northern part of the quadrangle, dominates the younger\nstructural elements. North of the San Andreas Fault is the inactive Cajon\nValley Fault that was probably an early strand of the San Andreas system. It\nwas active during deposition of the middle Miocene Cajon Valley Formation. \nSouth of the San Andreas, the Punchbowl Fault, which is probably a\nlong-abandoned segment of the San Andreas Fault (Matti and Morton, 1993), has a\nsinuous trace apparently due to compression in the eastern San Gabriel\nMountains that post-dates displacement on the fault. The Punchbowl Fault\nseparates two major subdivisions of the Mesozoic Pelona Schist and is\nleft-laterally offset by a northeast-striking fault in the northwestern part of\nthe quadrangle. Within the Punchbowl Fault zone is a thin layer of highly\ndeformed basement rock, which is clearly not part of the Pelona Schist. To the\nsoutheast, in the Devore quadrangle, this included basement rock attains a\nthickness of several hundred feet. Along strike to the northwest, Tertiary\nsedimentary rocks are included within the fault zone. South of the Punchbowl\nFault are several arcuate (in plan) faults that are part of an antiformal\nschuppen-like fault complex of the eastern San Gabriel Mountains. Most of\nthese arcuate faults are reactivated and deformed older faults, and probably\ninclude the eastern part of the San Gabriel Fault.\n\nThe Vincent Thrust of late Cretaceous or early Tertiary age separates the\nPelona Schist in the lower plate from a heterogeneous basement complex in the\nupper plate. Immediately above the Vincent Thrust is a variable thickness of\nmylonitic rock generally interpreted as a product of displacement on the\nthrust. The upper plate includes two Paleozoic units, a schist and gneiss\nsequence and a schist, quartzite, and marble metasedimentary sequence. Both\nsequences are thrust over the Mesozoic Pelona Schist along the Vincent Thrust,\nand intruded by Tertiary (late Oligocene) granitic rocks, granodiorite of\nTelegraph Peak, that also intrude the Vincent Thrust. The Pelona Schist\nconsists mostly of greenschist to amphibolite metamorphic grade meta-basalt\n(greenschist and amphibolite) and meta-graywacke (siliceous and white mica\nschist), with minor impure quartzite and marble, in which all primary\nstructures have been destroyed and all layering transposed. Cretaceous\ngranitic rocks, chiefly tonalite, intrude the schist and gneiss sequence, but\nnot the Pelona Schist or the Vincent Thrust.\n\nNorth of the San Andreas Fault, bedrock units consist of undifferentiated\nCretaceous tonalite, here informally named tonalite of Circle Mountain, with\nsome included small boldies of gneiss and marble. These basement rocks are the\nwestward continuation of rocks of the San Bernardino Mountains and not rocks of\nthe San Gabriel Mountains south of the San Andreas Fault. Also north of the\nSan Andreas Fault are the Oligocene Vaqueros Formation, middle Miocene Cajon\nValley Formation, and Pliocene rocks of Phelan Peak. The latter two formations\nare divided into several conglomerate and arkosic sandstone subunits. In the\nnortheastern corner of the quadrangle, the rocks of Phelan Peak are\nunconformably overlain by the Quaternary Harold Formation and Shoemaker Gravel.\n Quaternary units ranging from early Pleistocene to recent are mapped, and\nrepresent alluvial fan, landslide, talus, and wash environments.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. This digital Open-File map supercedes an older analog Open-File\nmap of the quadrangle, and includes extensive new data on the Quaternary\ndeposits, and revises some fault and bedrock distribution within the San\nGabriel Mountains. The digital map was compiled on a base-stable cronoflex\ncopy of the Telegraph 7.5' topographic base and then scribed. This scribe\nguide was used to make a 0.007 mil blackline clear-film, from which lines and\npoint were hand digitized. Lines, points, and polygons were subsequently\nedited at the USGS using standard ARC/INFO commands. Digitizing and editing\nartifacts significant enough to display at a scale of 1:24,000 were corrected. \nWithin the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc,\nand point attribute tables (.pat, .aat, and .pat, respectively) uniquely\nidentify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR01-308_Version 1.0.json b/datasets/USGS_OFR01-308_Version 1.0.json index d5674f1bd9..6df22c92dc 100644 --- a/datasets/USGS_OFR01-308_Version 1.0.json +++ b/datasets/USGS_OFR01-308_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-308_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is intended for scientific research of beach morphology and\nvolume changes.\n\nBiannual beach profiles were collected at 42 Oahu and 36 Maui Locations between\nAugust 1994 and August 1999. Surveys were conducted at approximately\nsummer-winter intervals and extend from landward of the active beach to about\n-4 meters water depth. Profile data on this CDROM are presented in both\nMicrosoft EXCEL 97/98 & 5.0/95 Workbook (.xls) format and comma separated value\n(.csv) format. Graphical representation of the surveys (x vs. z and x vs. y)\nare presented in EXCEL format only. Site descriptions, including beach\nlocation, directions to site, GPS information, and a description of Reference\nPoints used, are available in both EXCEL and ADOBE ACROBAT .pdf format.\n\nCross-shore beach profile data were collected as a component of the Hawaii\nCoastal Erosion Study, a cooperative effort by U.S. Geological Survey and\nUniversity of Hawaii in order to document seasonal and longer-term variations\nin beach volume and behavior. The overall objectives of the Hawaii Coastal\nErosion Study are to document the recent history of shoreline change in Hawaii\nand to determine the primary factor(s) responsible for coastal erosion in\nlow-latitude environments for the purpose of predicting future changes and to\nprovide quality scientific data that is useful to other scientists, planners,\nengineers, and coastal managers.\n\nThe overall strategy consists of first quantifying the magnitude and location\nof serious erosion problems followed by close monitoring of coastal change in\ncritical areas. Bi-annual beach profiles have been collected at over 40\ncritical beach sites on the islands of Oahu and Maui. Once sufficient\nbackground information is analyzed and key problems are defined, field sites\nwill be selected for detailed process- oriented studies (both physical and\nbiological) to gain an understanding of the complex relationships between reef\ncarbonate production, sediment dispersal, and the interaction of man-made\nstructures with sediment movement along the shore.\n\nInformation derived from this project will be used to develop general\nguidelines for sediment production, transport, and deposition of low- latitude\ncoasts. Planned major products include a comprehensive atlas of coastal\nhazards, journal articles and reports presenting results of our studies, and a\n\"living\" database of shoreline history and changes based on results of the\nbeach profile monitoring and softcopy photogrammetric analysis.", "links": [ { diff --git a/datasets/USGS_OFR01-30_1.0.json b/datasets/USGS_OFR01-30_1.0.json index 34d6c6e993..0942600cf1 100644 --- a/datasets/USGS_OFR01-30_1.0.json +++ b/datasets/USGS_OFR01-30_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-30_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Porcupine Wash quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Porcupine Wash data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Porcupine Wash database has been prepared in cooperation with the National Park Service as part of an ongoing project to\nprovide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials.\n\nThe digital geologic map database for the Porcupine Wash quadrangle has been created as a general-purpose data set that is applicable to land-related\ninvestigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Porcupine Wash database\nhas been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic\nactivity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for\nbuilding a hydrogeologic framework for Pinto Basin.\n\nThis data set maps and describes the geology of the Porcupine Wash 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in\nJoshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the Hexie Mountains, Cottonwood\nMountains, northern Eagle Mountains, and south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks,\nMesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface\npreserved in remnants in the Eagle and Cottonwood Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion\nsurface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle and Hexie Mountains, each in turn overlain by\nsuccessively younger residual and alluvial deposits.\n\nThe Tertiary erosion surface is deformed and broken by\nnorth-northwest-trending, high-angle, dip-slip faults and an east-west trending system of high-angle dip- and left-slip faults. East-west trending faults are\nyounger than and perhaps in part coeval with faults of the northwest-trending set.\n\nThe Porcupine Wash database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a scanned topographic base at a scale of 1:24,000, and (5) attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data\n(points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document\nfile (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting\nof a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf\nfiles of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the\nmarginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information.\n\nMap nomenclature and symbols\n\nWithin the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have\nattempted to adhere to the stratigraphic nomenclature of the U.S. Geological\nSurvey and the North American Stratigraphic Code, but the database has not\nreceived a formal editorial review of geologic names.\n\nSpecial symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Prpgd?) and unit name where unit\nassignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which\nthey apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of\ndatabase, the question mark must be included as part of the unit symbol.\n\nGeologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic\nappears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their\ncorresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols.", "links": [ { diff --git a/datasets/USGS_OFR01-311_Version 1.0.json b/datasets/USGS_OFR01-311_Version 1.0.json index b8a970c991..3671d7e084 100644 --- a/datasets/USGS_OFR01-311_Version 1.0.json +++ b/datasets/USGS_OFR01-311_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-311_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Cucamonga Peak 7.5' quadrangle has been prepared under\nthe U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as\npart of an ongoing effort to develop a regional geologic framework of southern\nCalifornia, andto utilize a Geographical Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThe digital geologic map database for the Cucamonga Peak 7.5' quadrangle has\nbeen created as a general-purpose data set that is applicable to other\nland-related investigations in the earth and biological sciences. For example,\nthe U.S. Forest Service and the San Bernardino National Forest may use the map\nand data base as a basic geologic data source for soil studies, mineral\nresource evaluations, road building, biologicalsurveys, and general forest\nmanagement. The Cucamonga Peak database is not suitablefor site-specific\ngeologic evaluations at scales greater than 1:24,000 (1in = 2,000 ft.).\n\nThis data set maps and describes the geology of the Cucamonga Peak 7.5'\nquadrangle, San Bernardino County, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the database consists of the\nfollowing items: (1) a map coverage containing geologic contacts and units, (2)\na coverage containing site-specific structural data, (3) a coverage containing\ngeologic-unit label leaders and their associated attribute tables for geologic\nunits (polygons), contacts (arcs), and site-specific data (points). In\naddition, the data set includes the following graphic and text products: (1) A\nPostScript graphic plot-file containing the geologic map, topography, cultural\ndata, a Correlation of Map Units (CMU) diagram, a Description of Map Units\n(DMU), an index map, a regional geologic and structure map, and a key for point\nand line symbols; (2) PDF files of this Readme (including the metadata file as\nan appendix) and the graphic produced by the PostScript plot file.\n\nThe Cucamonga Peak quadrangle includes part of the boundary between two major\nphysiographic provinces of California, the Transverse Ranges Province to the\nnorth and the Peninsular Ranges Province to the south. The north part of the\nquadrangle isin the eastern San Gabriel Mountains, and the southern part\nincludes an extensive quaternary alluvial-fan complex flanking the upper Santa\nAna River valley, the northernmost part of the Peninsular Ranges Province.\n\nThrust faults of the active Cucamonga Fault zone along the the south margin of\nthe San Gabriel Mountains are the rejuvenated eastern terminus of a major old\nfault zone that bounds the south side of the western and central Transverse\nRanges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including\nthe Cucamonga Fault zone, is apparently in response to compression in the\neastern San Gabriel Mountains resulting from initiation of right-lateral slip\non the San Jacinto Fault zone in the Peninsular Ranges. Within the northern\npart of the quadrangle are several arcuate-in-plan faults that are part of an\nantiformal, schuppen-like fault complex of the eastern San Gabriel Mountains. \nMost of these arcuate faults are reactivated and deformed older faults that\nprobably include the eastern part of the San Gabriel Fault.\n\nThe structural grain within the San Gabriel Mountains, as defined by basement\nrocks, is generally east striking. Within the Cucamonga Peak quadrangle, these\nbasement rocks include a Paleozoic schist and gneiss sequence which occurs as\nlarge, continuous and discontinuous bodies intruded by Cretaceous granitic\nrocks. Most of the granitic rocks are of tonalitic composition, and many are\nmylonitic. South of the granitic rocks is a comple assemblage of\nProterozoic(?) metamorphic rocks, at least part of which is metasedimentary. \nThis assemblage is intruded by Cretaceous tonalite on its north side, and by\ncharnockitic rocks near the center of the mass. The charnockitic rocks are in\ncontact with no other Cretaceous granitic rocks. Consequently, their relative\nposition in the intrusive sequence is unknown. The Proterozoic(?) assemblage\nwas metamorphosed to upper amphibolite and lower granulite grade, and\nsubsequently to a lower metamorphic grade. It is also intensely deformed by\nmylonitization characterized by an east-striking, north-dipping foliation, and\nby a pronounced subhorizontal lineation that plunges shallowly east and west.\n\nThe southern half of the quadrangle is dominated by extensive, symmetrical\nalluvial-fan complexes, particularly two emanating from Day and Deer Canyons. \nOther Quaternary units ranging from early Pleistocene to recent are mapped, and\nrepresent alluvial-fan, landslide, talus, and wash environments.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. This digital Open-File map supercedes an older analog Open-File\nmap of the quadrangle, and includes extensive new data on the Quaternary\ndeposits, and revises some fault and bedrock distribution within the San\nGabriel Mountains. The digital map was compiled on a base-stable cronoflex\ncopy of the Cucamonga Peak 7.5' topographic base and then scribed. This scribe\nguide was used to make a 0.007 mil blackline clear-film, from which lines and\npoint were hand digitized. Lines, points, and polygons were subsequently\nedited at the USGS using standard ARC/INFO commands. Digitizing and editing\nartifacts significant enough to display at a scale of 1:24,000 were corrected. \nWithin the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc,\nand point attribute tables (.pat, .aat, and .pat, respectively) uniquely\nidentify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR01-318.json b/datasets/USGS_OFR01-318.json index eb94537042..b09965569c 100644 --- a/datasets/USGS_OFR01-318.json +++ b/datasets/USGS_OFR01-318.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\n spatial database of major coal mines and their production in China. This data\n set will be utilized in energy research and cartographic projects.\n \n This data set consists of several databases on coal mining activities in China:\n \n The ESRI ArcView shapefiles are the coverages of political boundaries,\n counties, provinces, cities, urban areas, airfields, roads, railroads,\n electrical power network, river networks, ecoregions, population density,\n dental fluorosis prevalence rates, elevated flouride sources, ore deposits, oil\n and gas fields, coal-bearing age units, coal fields, coal mine production and\n rank, and geology (scale 1:10,000,000).", "links": [ { diff --git a/datasets/USGS_OFR01-318_fl_pollution.json b/datasets/USGS_OFR01-318_fl_pollution.json index de9fff2b01..9b1419050f 100644 --- a/datasets/USGS_OFR01-318_fl_pollution.json +++ b/datasets/USGS_OFR01-318_fl_pollution.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318_fl_pollution", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To describe areal extents of environmental pollution sources of fluorosis\n affected areas in China. This dataset will be used to provide support for the\n assessment and analysis of coal quality and potential inventories and risks to\n human health in China.\n \n Map representing environmental pollution sources of fluorsis affected areas in\n the People's Republic of China. This coverage was developed as part of the\n World Coal Quality Inventory (WoCQI) project by the Energy Resources Team, U.S.\n Geological Survey.", "links": [ { diff --git a/datasets/USGS_OFR01-318_fluorosis.json b/datasets/USGS_OFR01-318_fluorosis.json index 3c39323622..7ae3d6a1c7 100644 --- a/datasets/USGS_OFR01-318_fluorosis.json +++ b/datasets/USGS_OFR01-318_fluorosis.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318_fluorosis", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To describe areal extents and rates of endemic flourosis in China. This\n dataset will be used to provide support for the assessment and analysis of coal\n quality and potential inventories and risks to human health in China.\n \n Map representing prevalence rates of endemic fluorosis in the People's Republic\n of China. This coverage was developed as part of the World Coal Quality\n Inventory (WoCQI) project by the Energy Resources Team, U.S. Geological\n Survey.", "links": [ { diff --git a/datasets/USGS_OFR01-318_power_lines.json b/datasets/USGS_OFR01-318_power_lines.json index 67f2d30d46..97eea86402 100644 --- a/datasets/USGS_OFR01-318_power_lines.json +++ b/datasets/USGS_OFR01-318_power_lines.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318_power_lines", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\n spatial database of the electrical power line network in China. This data set\n will be utilized in energy research and cartographic projects.\n \n This dataset contains the distribution, type, and power handling capacity of\n power line networks in the People's Republic of China.", "links": [ { diff --git a/datasets/USGS_OFR01-318_power_stations.json b/datasets/USGS_OFR01-318_power_stations.json index 2b8e05f8ac..46ce0cc9a9 100644 --- a/datasets/USGS_OFR01-318_power_stations.json +++ b/datasets/USGS_OFR01-318_power_stations.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318_power_stations", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists\n a spatial database of electrical power stations in China. This data\n set will be utilized in energy research and cartographic projects.\n \n This dataset contains locations, type, and power generation capacity\n of powerplants/powerstations in the People's Republic of China.", "links": [ { diff --git a/datasets/USGS_OFR01-318coal_mines.json b/datasets/USGS_OFR01-318coal_mines.json index 1321721e39..e6ada1a907 100644 --- a/datasets/USGS_OFR01-318coal_mines.json +++ b/datasets/USGS_OFR01-318coal_mines.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-318coal_mines", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this dataset is to give geologists and other scientists a\n spatial database of major coal mines and their production in China. This data\n set will be utilized in energy research and cartographic projects.\n \n This dataset is a collection of major coal mine locations and production data\n for The Peoples Republic of China. Included in this dataset are the locations\n of major coal mines, Coal Mining Administrations, and the approximate annual\n amount of coal and type mined annually in millions of metric tons. \n \n Procedures_Used:\n The major coal mine production points were digitized utilizing\n ARC/VIEW from page-size maps found in the U.S. Environmental\n Protection Agency Report ERP 430-R-96-005.\n Revisions: none\n Reviews_Applied_to_Data: None", "links": [ { diff --git a/datasets/USGS_OFR01-31_Version 1.0.json b/datasets/USGS_OFR01-31_Version 1.0.json index 9183c80aa7..d8df53d996 100644 --- a/datasets/USGS_OFR01-31_Version 1.0.json +++ b/datasets/USGS_OFR01-31_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-31_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Conejo Well quadrangle has been prepared by the Southern\nCalifornia Areal Mapping Project (SCAMP), a cooperative project sponsored\njointly by the U.S. Geological Survey and the California Division of Mines and\nGeology. The Conejo Well data set represents part of an ongoing effort to\ncreate a regional GIS geologic database for southern California. This regional\ndigital database, in turn, is being developed as a contribution to the National\nGeologic Map Database of the National Cooperative Geologic Mapping Program of\nthe USGS. The Conejo Well database has been prepared in cooperation with the\nNational Park Service as part of an ongoing project to provide Joshua Tree\nNational Park with a geologic map base for use in managing Park resources and\ndeveloping interpretive materials.\n\nThe digital geologic map database for the Conejo Well quadrangle has been\ncreated as a general-purpose data set that is applicable to land-related\ninvestigations in the earth and biological sciences. Along with geologic map\ndatabases in preparation for adjoining quadrangles, the Conejo Well database\nhas been generated to further our understanding of bedrock and surficial\nprocesses at work in the region and to document evidence for seismotectonic\nactivity in the eastern Transverse Ranges. The database is designed to serve as\na base layer suitable for ecosystem and mineral resource assessment and for\nbuilding a hydrogeologic framework for Pinto Basin.\n\nThis data set maps and describes the geology of the Conejo Well 7.5 minute\nquadrangle, Riverside County, southern California. The quadrangle, situated in\nJoshua Tree National Park in the eastern Transverse Ranges physiographic and\nstructural province, encompasses part of the northern Eagle Mountains and part\nof the south flank of Pinto Basin. It is underlain by a basement terrane\ncomprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic\nand Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a\nwidespread Tertiary erosion surface preserved in remnants in the Eagle\nMountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene\nbasalt overlies the erosion surface. A sequence of at least three Quaternary\npediments is planed into the north piedmont of the Eagle Mountains, each in\nturn overlain by successively younger residual and alluvial deposits.\n\nThe Tertiary erosion surface is deformed and broken by\nnorth-northwest-trending, high-angle, dip-slip faults in the Eagle Mountains\nand an east-west trending system of high-angle dip- and left-slip faults. In\nand adjacent to the Conejo Well quadrangle, faults of the northwest-trending\nset displace Miocene sedimentary rocks and basalt deposited on the Tertiary\nerosion surface and Pliocene and (or) Pleistocene deposits that accumulated on\nthe oldest pediment. Faults of this system appear to be overlain by\nPleistocene deposits that accumulated on younger pediments. East-west trending\nfaults are younger than and perhaps in part coeval with faults of the\nnorthwest-trending set.\n\nThe Conejo Well database was created using ARCVIEW and ARC/INFO, which are\ngeographical information system (GIS) software products of Environmental\nSystems Research Institute (ESRI). The database consists of the following\nitems: (1) a map coverage showing faults and geologic contacts and units, (2) a\nseparate coverage showing dikes, (3) a coverage showing structural data, (4) a\npoint coverage containing line ornamentation, and (5) a scanned topographic\nbase at a scale of 1:24,000. The coverages include attribute tables for\ngeologic units (polygons and regions), contacts (arcs), and site-specific data\n(points). The database, accompanied by a pamphlet file and this metadata file,\nalso includes the following graphic and text products: (1) A portable document\nfile (.pdf) containing a navigable graphic of the geologic map on a 1:24,000\ntopographic base. The map is accompanied by a marginal explanation consisting\nof a Description of Map and Database Units (DMU), a Correlation of Map and\nDatabase Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf\nfiles of the DMU and CMU, individually. (3) A PostScript graphic-file\ncontaining the geologic map on a 1:24,000 topographic base accompanied by the\nmarginal explanation. (4) A pamphlet that describes the database and how to\naccess it. Within the database, geologic contacts , faults, and dikes are\nrepresented as lines (arcs), geologic units as polygons and regions, and\nsite-specific data as points. Polygon, arc, and point attribute tables (.pat,\n.aat, and .pat, respectively) uniquely identify each geologic datum and link it\nto other tables (.rel) that provide more detailed geologic information.\n\nMap nomenclature and symbols\n\nWithin the geologic map database, map units are identified by standard geologic\nmap criteria such as formation-name, age, and lithology. The authors have\nattempted to adhere to the stratigraphic nomenclature of the U.S. Geological\nSurvey and the North American Stratigraphic Code, but the database has not\nreceived a formal editorial review of geologic names.\n\nSpecial symbols are associated with some map units. Question marks have been\nadded to the unit symbol (e.g., QTs?, Jmi?) and unit name where unit\nassignment based on interpretation of aerial photographs is uncertain. Question\nmarks are plotted as part of the map unit symbol for those polygons to which\nthey apply, but they are not shown in the CMU or DMU unless all polygons of a\ngiven unit are queried. To locate queried map-unit polygons in a search of\ndatabase, the question mark must be included as part of the unit symbol. In\nsome polygons, multiple units crop out in individual domains that are too small\nor too intricately intermingled to distinguish at 1:24,000, or for which\nrelations are not well documented. For these polygons, unit symbols are\ncombined using plus (+) signs (e.g., Qyaos + Qyas2) in the LABL and PLABL\nitems.\n\nGeologic map unit labels entered in database items LABL and PLABL contain\nsubstitute characters for conventional stratigraphic age symbols: Proterozoic\nappears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL\nand as '^' in PLABL. The substitute characters in PLABL invoke their\ncorresponding symbols from the GeoAge font group to generate map unit labels\nwith conventional stratigraphic symbols.", "links": [ { diff --git a/datasets/USGS_OFR01-321_Version 1.0.json b/datasets/USGS_OFR01-321_Version 1.0.json index 779cbe93f8..694e97d8a8 100644 --- a/datasets/USGS_OFR01-321_Version 1.0.json +++ b/datasets/USGS_OFR01-321_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-321_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was developed to provide a geologic map GIS of the east slope of\nIron Mountain, Sweet Grass County, Montana for use in future spatial analysis\nby a variety of users. These data can be printed in a variety of ways to\ndisplay various geologic features or used for digital analysis and modeling. \nThis database is not meant to be used or displayed at any scale larger than\n1:3077 (for example, 1:2000 or 1:1500).\n\nThe digital geologic map of the east slope of Iron Mountain, Sweet Grass\nCounty, Montana was prepared from preliminary digital datasets digitized by\nOptronics Specialty Co., Inc. from a paper copy of plate 10 from UGSG Bulletin\n1015-D (Howland, 1955).The files were prepared and transformed to the Montana\nState Plane South projection by Helen Z. Kayser (Information Systems Support,\nInc.). Further editing and attributing was performed by Lorre A. Moyer in 2001.\nThe resulting spatial digital database can be queried in many ways to produce a\nvariety of derivative geologic maps.\n\nThis GIS consists of two major ArcInfo datasets: one line and polygon file\n(ironmtn) containing geologic contacts and structures (lines) and geologic map\nrock units (polygons), and one point file (ironmtnp) containing structural\ndata.", "links": [ { diff --git a/datasets/USGS_OFR01-411_1.json b/datasets/USGS_OFR01-411_1.json index 1c13936ab4..583bf58420 100644 --- a/datasets/USGS_OFR01-411_1.json +++ b/datasets/USGS_OFR01-411_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01-411_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical Composition of Samples Collected from Waste Rock Dumps and Other\nMining-Related Features at Selected Phosphate Mines in Southeastern Idaho,\nWestern Wyoming, and Northern Utah\n\nThe sampling effort was undertaken as a reconnaissance and does not constitute\na characterization of mine wastes. Twenty-five samples were collected from\nwaste rock dumps, 2 from stockpiles, and 1 each from slag, tailings, mill\nshale, and an outcrop. All samples were analyzed for a suite of major, minor,\nand trace elements.\n\nThis text file contains chemical analyses for 31 samples collected from various\nphosphate mine sites in southeastern Idaho (25), northern Utah (2), and western\nWyoming (4).", "links": [ { diff --git a/datasets/USGS_OFR01139_Version 1.0.json b/datasets/USGS_OFR01139_Version 1.0.json index 27ed404579..a67be5479b 100644 --- a/datasets/USGS_OFR01139_Version 1.0.json +++ b/datasets/USGS_OFR01139_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR01139_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report was prepared to document the chemical composition of sediments and\nsoils in the Coeur d'Alene (CdA) drainage basin in northern Idaho. These\ncompositions are of interest because of the potential for human and wildlife\nhealth impacts from high metal contents of some sediments and soils from over\n100 years of mining activity.\n\nThis report presents the results of over 1100 geochemical analyses of samples\nof soil and sediment from the Coeur d'Alene (CdA) drainage basin in northern\nIdaho. The location (in 3 dimensions) and a lithological description of each\nsample is included with the laboratory analytical data. Methods of sample\nlocation, collection, preparation, digestion and geochemical analysis are\ndescribed. Five different laboratories contributed geochemical data for this\nreport and the quality control procedures used by each laboratory are\ndescribed. Comparison of the analytical accuracy and precision of each\nlaboratory is given by comparing analyses of standard reference materials and\nof splits of CdA samples.\n\nThese geochemical data are presented in seven MS Excel tables and seven dBase4\ntables. The seven dBase4 files allow users to more easily import these\ngeochemical data into a GIS. Only one of these seven tables includes geospatial\ndata AppendixB. However, in AppendixB there is a Site ID column that will allow\nusers to link or join the matching Site Id columns in the six associated\nlithologic and geochemical tables.\n\nDue to format constraints of dBase4, the column names (headers) had to be\nmodified to a maximum of only ten ASCII characters. As a result, some of the\ndBase4 column header names can be rather cryptic. To assist dBase files users,\nthis ten digit dBase4 column name is also found directly under the more\ndescriptive column names found in the MS Excel tables packaged with this\nreport. Additional formatting requirements such as changing the below detection\nlimit symbol (<) to a negative symbol (-) were used to accurately display the\ndata the dBase4 format.\n\nThis dataset consists of the following MS Excel 2000 spreadsheets and\nequivalent dBase4 files:\nAppendixB.xls, AppendixB.dbf: sample location and site description\nAppendixC.xls, AppendixC.dbf: lithologic descriptions of samples\nAppendixD.xls, AppendixD.dbf: USGS EDXRF Data\nAppendixE.xls, AppendixE.dbf: EWU 4-acid ICP-MS, ICP-AES, and FAA Data\nAppendixF.xls, AppendixF.dbf: CHEMEX nitric/aqua regia ICP-AES Data\nAppendixG.xls, AppendixG.dbf: XRAL 4-acid ICP-AES Data\nAppendixH.xls, AppendixH.dbf: ACZ microwave assisted nitric acid ICP-AES Data", "links": [ { diff --git a/datasets/USGS_OFR_00-376_1.0.json b/datasets/USGS_OFR_00-376_1.0.json index acc48837db..3bbbbd6a64 100644 --- a/datasets/USGS_OFR_00-376_1.0.json +++ b/datasets/USGS_OFR_00-376_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00-376_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:100,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the Oregon State\nGeologist. Similarly, the database cannot be used to identify or delineate\nlandslides in the region.\n\nThis digital map database, largely compiled from new mapping by the authors,\nrepresents the general distribution of bedrock and surficial deposits of the\nRoseburg 30 x 60 minute quadrangle along the southeastern margin of the Oregon\nCoast Range and its tectonic boundary with Mesozoic terranes of the Klamath\nMountains. Together with the accompanying text files as PDF (rb_geol.pdf), it\nprovides current information on the geologic structure and stratigraphy of the\narea covered. The database delineates map units that are identified by general\nage and lithology following the stratigraphic nomenclature of the U.S. \nGeological Survey. The scale of the source maps is 1:24,000, but the\nQuaternary contacts and structural data have been much simplified for the\n1:100,000-scale map and database. The spatial resolution (scale) of the\ndatabase is 1:100,000 or smaller.", "links": [ { diff --git a/datasets/USGS_OFR_00135_Version 1.0.json b/datasets/USGS_OFR_00135_Version 1.0.json index cff4adb4d3..c6bd4b0ad5 100644 --- a/datasets/USGS_OFR_00135_Version 1.0.json +++ b/datasets/USGS_OFR_00135_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00135_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was developed to provide geologic map GIS of the Coeur d'Alene\n1:100,000 quadrangle for use in future spatial analysis by a variety of users. \nThese data can be printed in a variety of ways to display various geologic\nfeatures or used for digital analysis and modeling. This database is not meant\nto be used or displayed at any scale larger than 1:100,000 (e.g. 1:62,500 or\n1:24,000).\n\nThe digital geologic map of the Coeur d'Alene 1:100,000 quadrangle was compiled\nfrom preliminary digital datasets [Athol, Coeur d'Alene, Kellogg, Kingston,\nLakeview, Lane, and Spirit Lake 15-minute quadrangles] prepared by the Idaho\nGeological Survey from A. B. Griggs (unpublished field maps), supplemented by\nGriggs (1973) and by digital data from Bookstrom and others (1999) and Derkey\nand others (1996). The digital geologic map database can be queried in many\nways to produce a variety of derivative geologic maps.\n\n This GIS consists of two major Arc/Info data sets: one line and polygon file\n(cda100k) containing geologic contacts and structures (lines) and geologic map\nrock units (polygons), and one point file (cda100kp) containing structural\ndata.", "links": [ { diff --git a/datasets/USGS_OFR_00142.json b/datasets/USGS_OFR_00142.json index 549af3cd1c..f31a0492c7 100644 --- a/datasets/USGS_OFR_00142.json +++ b/datasets/USGS_OFR_00142.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00142", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will generate reconnaissance maps of the sea floor\noffshore of the New York - New Jersey metropolitan area -- the most\nheavily populated, and one of the most impacted coastal regions of the\nUnited States. The surveys will provide an overall synthesis of the\nsea floor environment, including seabed texture and bed forms,\nQuaternary strata geometry, and anthropogenic impact (e.g., ocean\ndumping, trawling, channel dredging). The goal of this project is to\nsurvey the offshore area, the harbor, and the southern shore of Long\nIsland, providing a regional synthesis to support a wide range of\nmanagement decisions and a basis for further process-oriented\ninvestigations. The project is conducted cooperatively with the\nU.S. Army Corps of Engineer (USACE).\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS Diane G 97032 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00145_Version 1.0.json b/datasets/USGS_OFR_00145_Version 1.0.json index 66a2cf98e5..713698456b 100644 --- a/datasets/USGS_OFR_00145_Version 1.0.json +++ b/datasets/USGS_OFR_00145_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00145_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Butler Peak quadrangle has been prepared by the Southern\nCalifornia Areal Mapping Project (SCAMP), a cooperative project sponsored\njointly by the U.S. Geological Survey and the California Division of Mines and\nGeology, as part of an ongoing effort to utilize a Geographical Information\nSystem (GIS) format to create a regional digital geologic database for southern\nCalifornia. This regional database is being developed as a contribution to the\nNational Geologic Map Data Base of the National Cooperative Geologic Mapping\nProgram of the USGS. Development of the data set for the Butler Peak quadrangle\nhas also been supported by the U.S. Forest Service, San Bernardino National\nForest.\n\nThe digital geologic map database for the Butler Peak quadrangle has been\ncreated as a general-purpose data set that is applicable to other land-related\ninvestigations in the earth and biological sciences. For example, the U.S. \nForest Service, San Bernardino National Forest, is using the database as part\nof a study of an endangered plant species that shows preference for particular\nrock type environments. The Butler Peak database is not suitable for\nsite-specific geologic evaluations at scales greater than 1:24,000 (1 in =\n2,000 ft).\n\nThis data set maps and describes the geology of the Butler Peak 7.5'\nquadrangle, San Bernardino County, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage showing geologic contacts and units,(2) a\nscanned topographic base at a scale of 1:24,000, and (3) attribute tables for\ngeologic units (polygons), contacts (arcs), and site-specific data (points). \nIn addition, the data set includes the following graphic and text products: (1)\nA PostScript graphic plot-file containing the geologic map on a 1:24,000\ntopographic base accompanied by a Description of Map Units (DMU), a Correlation\nof Map Units (CMU), and a key to point and line symbols; (2) PDF files of the\nDMU and CMU, and of this Readme, and (3) this metadata file.\n\nThe geologic map data base contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. The map was created by transferring lines from the aerial\nphotographs to a 1:24,000 mylar orthophoto-quadrangle and then to a base-stable\ntopographic map. This map was then scribed, and a .007 mil, right-reading,\nblack line clear film made by contact photographic processes.The black line was\nscanned and auto-vectorized by Optronics Specialty Company, Northridge, CA. \nThe non-attributed scan was imported into ARC/INFO, where the database was\nbuilt. Within the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc, and\npoint attribute tables (.pat, .aat, and .pat, respectively) uniquely identify\neach geologic datum and link it to other tables (.rel) that provide more\ndetailed geologic information.", "links": [ { diff --git a/datasets/USGS_OFR_0014_version 1.0.json b/datasets/USGS_OFR_0014_version 1.0.json index 00eb23ee69..b53cbcacec 100644 --- a/datasets/USGS_OFR_0014_version 1.0.json +++ b/datasets/USGS_OFR_0014_version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0014_version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide mineral resource data for the region of\nnortheast WA for use in future spatial analysis by a variety of users.\n\nThis database is not meant to be used or displayed at any scale larger than\n1:24,000 \n\nThis report is a tabular presentation of mineral activities for mining and\nexploration in Washington during 1985 to 1997. The data may be incomplete as it\ndepended on published data or data volunteered by operators.", "links": [ { diff --git a/datasets/USGS_OFR_00152.json b/datasets/USGS_OFR_00152.json index 5a03e7b094..a73fa2a890 100644 --- a/datasets/USGS_OFR_00152.json +++ b/datasets/USGS_OFR_00152.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00152", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will generate reconnaissance maps of the sea floor\noffshore of the New York - New Jersey metropolitan area -- the most\nheavily populated, and one of the most impacted coastal regions of the\nUnited States. The surveys will provide an overall synthesis of the\nsea floor environment, including seabed texture and bed forms,\nQuaternary strata geometry, and anthropogenic impact (e.g., ocean\ndumping, trawling, channel dredging). The goal of this project is to\nsurvey the offshore area, the harbor, and the southern shore of Long\nIsland, providing a regional synthesis to support a wide range of\nmanagement decisions and a basis for further process-oriented\ninvestigations. The project is conducted cooperatively with the\nU.S. Army Corps of Engineer (USACE).\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS DIAN 97032 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00153.json b/datasets/USGS_OFR_00153.json index 5d1cb5cadc..315a29f648 100644 --- a/datasets/USGS_OFR_00153.json +++ b/datasets/USGS_OFR_00153.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00153", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In November 1999, the U. S. Geological Survey, in cooperation with\nCoastal Carolina University, began a program to produce geologic maps\nof the nearshore regime off northern South Carolina, utilizing high\nresolution sidescan sonar, interferometric (direct phase methods)\nswath bathymetry, and seismic subbottom profiling systems. The study\nareas extends from the ~7m isobath to about 10km offshore (water\ndepths <12m). The goals of the investigation are to determine regional\nscale sand resource availability needed for planned beach nourishment\nprograms, to investigate the roles that the inner shelf morphology and\ngeologic framework play in the evolution of this coastal region, and\nto provide baseline geologic maps for use in proposed biologic habitat\nstudies.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS ATSV 99044 cruise. The coverage is the\nnearshore of the northern South Carolina. The seismic-reflection data\nare stored as SEG-Y standard format that can be read and manipulated\nby most seismic-processing software. Much of the information specific\nto the data are contained in the headers of the SEG-Y format\nfiles. The file system format is ISO 9660 which can be read with DOS,\nUnix, and MAC operating systems with the appropriate CD-ROM driver\nsoftware installed.", "links": [ { diff --git a/datasets/USGS_OFR_00175_Version 1.0.json b/datasets/USGS_OFR_00175_Version 1.0.json index 1a26a39a23..83310ec378 100644 --- a/datasets/USGS_OFR_00175_Version 1.0.json +++ b/datasets/USGS_OFR_00175_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00175_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Cougar Buttes quadrangle has been prepared by the\nSouthern California Areal Mapping Project (SCAMP), a cooperative project\nsponsored jointly by the U.S. Geological Survey and the California Division of\nMines and Geology, as part of an ongoing effort to utilize a Geographical\nInformation System (GIS) format to create a regional digital geologic database\nfor southern California. This regional database is being developed as a\ncontribution to the National Geologic Map Data Base of the National Cooperative\nGeologic Mapping Program of the USGS. Development of the data set for the\nCougar Buttes quadrangle has also been supported by the Mojave Water Agency and\nU.S. Forest Service, San Bernardino National Forest.\n\nThe digital geologic map database for the Cougar Buttes quadrangle has been\ncreated as a general-purpose data set that is applicable to other land-related\ninvestigations in the earth and biological sciences. In cooperation with the\nWater Resources Division of the U.S. Geological Survey, we have used our\nmapping in the Cougar Buttes and adjoining quadrangles together with well log\ndata to develop a hydrogeologic framework for the basin. In an effort to\nunderstand surficial processes and to provide a base suitable for ecosystem\nassessment, we have differentiated surficial veneers on piedmont and pediment\nsurfaces and distinguished the various substrates found beneath these veneers. \nCurrently, the geologic database for the Cougar Buttes quadrangle is being\napplied in groundwater investigations in the Lucerne Valley basin (USGS, Water\nResources Division), in biological species studies of the Cushenbury Canyon\narea (U.S. Forest Service, San Bernardino National Forest), and in the study of\nsoils on various Quaternary landscape surfaces on the north piedmont of the San\nBernardino Mountains (University of New Mexico). The Cougar Buttes database is\nnot suitable for site-specific geologic evaluations at scales greater than\n1:24,000 (1 in = 2,000 ft).\n\n\nThis data set maps and describes the geology of the Cougar Buttes 7.5'\nquadrangle, San Bernardino County, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage showing geologic contacts and units, (2) a\nseparate coverage layer showing structural data, (3) a scanned topographic base\nat a scale of 1:24,000, and (4) attribute tables for geologic units (polygons),\ncontacts (arcs), and site-specific data (points). The data base is accompanied\nby a readme file and this metadata file. In addition, the data set includes the\nfollowing graphic and text products: (1) A portable document file (.pdf)\ncontaining a browse-graphic of the geologic map on a 1:24,000 topographic base.\nThe map is accompanied by a marginal explanation consisting of a Description of\nMap Units (DMU), a Correlation of Map Units (CMU), and a key to point and line\nsymbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A\nPostScript graphic plot-file containing the geologic map on a 1:24,000\ntopographic base accompanied by the marginal explanation. (4) A pamphlet that\nsummarizes the late Cenozoic geology of the Cougar Buttes quadrangle.\n\nThe geologic map data base contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs, including low-altitude color and black-and-white photographs and\nhigh-altitude infrared photographs. The map was created by transferring lines\nfrom the aerial photographs to a 1:24,000 topographic base via a mylar\northophoto-quadrangle or by using a PG-2 plotter. The map was then scribed,\nscanned, and imported into ARC/INFO, where the database was built. Within the\ndatabase, geologic contacts are represented as lines (arcs), geologic units as\npolygons, and site-specific data as points. Polygon, arc, and point attribute\ntables (.pat, .aat, and .pat, respectively) uniquely identify each geologic\ndatum and link it to other tables (.rel) that provide more detailed geologic\ninformation.", "links": [ { diff --git a/datasets/USGS_OFR_00177.json b/datasets/USGS_OFR_00177.json index 121c980a18..4b96c33ef4 100644 --- a/datasets/USGS_OFR_00177.json +++ b/datasets/USGS_OFR_00177.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00177", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1999, the USGS began developing a cooperative mapping program in\nNorth Carolina, with collaborators at the North Carolina Geological\nSurvey (NCGS), and academic institutions. The goal of the program is\nto develop a refined understanding of the regional geological\nframework and non-living resources of the North Carolina coastal area,\nincluding the emerged and submerged portions of the Coastal Plain. The\nUSGS Coastal and Marine Geology Program is focusing on nearshore\nmorphologic evolution (using LIDAR), short-term shoreline change (with\nSWASH), and with the present cruise, collecting data on the geologic\nframework of the shoreface and inner continental shelf. The goal of\nthe inner shelf mapping program is to provide a regional synthesis of\nthe seafloor environment, including a description of sedimentary\nenvironments, sediment texture, seafloor morphology, and shallow\nstratigraphy to aid in understanding the long- and short-term\nevolution of the coastal system, the form and distribution of sand and\ngravel resources, and to provide a basis for sediment dynamics\nstudies.", "links": [ { diff --git a/datasets/USGS_OFR_00192_Version 1.0.json b/datasets/USGS_OFR_00192_Version 1.0.json index bf08af2465..d9fb9aa40a 100644 --- a/datasets/USGS_OFR_00192_Version 1.0.json +++ b/datasets/USGS_OFR_00192_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00192_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital representation of geologic mapping facilitates the presentation and\nanalysis of earth-science data. Digital maps may be displayed at any scale or\nprojection, however the geologic data in this coverage is not intended for use\nat a scale larger than 1:250,000.\n\nThis data set represents reconnaissance geologic mapping of the Christian\nquadrangle, Alaska. It is used to create the mapsheet in USGS OFR 00-192,\nwhich shows bedrock and surficial deposits of the 1:250,000 scale Christian\nquadrangle in northern Alaska.", "links": [ { diff --git a/datasets/USGS_OFR_00222_1.0.json b/datasets/USGS_OFR_00222_1.0.json index e5cb5ab7ab..c87160feb9 100644 --- a/datasets/USGS_OFR_00222_1.0.json +++ b/datasets/USGS_OFR_00222_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00222_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This geologic map database for the El Mirage Lake area describes geologic materials for the dry lake, parts of the adjacent Shadow Mountains and Adobe Mountain, and much of the piedmont extending south from the lake upward toward the San Gabriel Mountains. This area lies within the western Mojave Desert of San Bernardino and Los Angeles Counties, southeastern California (see Fig. 1). The area is traversed by a few paved highways that service the community of El Mirage, and by numerous dirt roads that lead to outlying properties. An off-highway vehicle area established by the Bureau of Land Management encompasses the dry lake and much of the land north and east of the lake. The physiography of the area consists of the dry lake, flanking mud and sand flats and alluvial piedmonts, and a few sharp craggy mountains.\n\nThis digital geologic map database, intended for use at 1:24,000-scale, describes and portrays the rock units and surficial deposits of the El Mirage Lake area. The map database was prepared to aid in a water-resource assessment of the area by providing surface geologic information with which deepergroundwater-bearing units may be understood. The area mapped covers the Shadow Mountains SE and parts of the Shadow Mountains, Adobe Mountain, and El Mirage 7.5-minute quadrangles (see Fig. 2). The map includes detailed geology of surface and bedrock deposits, which represent a significant update from previous bedrock geologic maps by Dibblee (1960) and Troxel and Gunderson (1970), and the surficial geologic map of Ponti and Burke (1980); it incorporates a fringe of the detailed bedrock mapping in the Shadow Mountains by Martin (1992). The map data were assembled as a digital database using ARC/INFO to enable wider applications than traditional paper-product geologic maps and to provide for efficient meshing with other digital data bases prepared by the U.S. Geological Survey's Southern California Areal Mapping Project. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_00241.json b/datasets/USGS_OFR_00241.json index bb4dafe93c..cf89feed40 100644 --- a/datasets/USGS_OFR_00241.json +++ b/datasets/USGS_OFR_00241.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00241", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the USGS Woods Hole Field Center in Cooperation with the\nU.S. Army Corps of Engineers, began a program designed to map the\nseafloor offshore of the New York-New Jersey metropolitan area; the\nmost heavily populated, and one of the most impacted coastal regions\nof the United States. The ultimate goal of this program is to provide\nan overall synthesis of the sea floor environment, including surficial\nsediment texture, subsurface geometry, and anthropogenic impact (e.g.\nocean dumping, trawling, channel dredging), through the use and\nanalysis of sidescan-sonar and subbottom mapping techniques. This\nregional synthesis will support a wide range of management decisions\nand will provide a basis for further process-oriented investigations.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS DIAN 97011 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00242.json b/datasets/USGS_OFR_00242.json index c01325c32f..eb1493b332 100644 --- a/datasets/USGS_OFR_00242.json +++ b/datasets/USGS_OFR_00242.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00242", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the USGS Woods Hole Field Center in Cooperation with the\nU.S. Army Corps of Engineers, began a program designed to map the\nseafloor offshore of the New York-New Jersey metropolitan area; the\nmost heavily populated, and one of the most impacted coastal regions\nof the United States. The ultimate goal of this program is to provide\nan overall synthesis of the sea floor environment, including surficial\nsediment texture, subsurface geometry, and anthropogenic impact (e.g.\nocean dumping, trawling, channel dredging), through the use and\nanalysis of sidescan-sonar and subbottom mapping techniques. This\nregional synthesis will support a wide range of management decisions\nand will provide a basis for further process-oriented investigations.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS DIAN 97011 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00273.json b/datasets/USGS_OFR_00273.json index 3bfad1904d..f280ceb87e 100644 --- a/datasets/USGS_OFR_00273.json +++ b/datasets/USGS_OFR_00273.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00273", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the USGS Woods Hole Field Center, in Cooperation with the\nU.S. Army Corps of Engineers, began a program designed to map the\nseafloor offshore of the New York-New Jersey metropolitan area, the\nmost heavily populated, and one of the most impacted coastal regions\nof the United States. The ultimate goal of this program is to provide\nan overall synthesis of the sea floor environment, including surficial\nsediment texture, subsurface geometry, and anthropogenic impact (e.g.\nocean dumping, trawling, channel dredging). Sidescan sonar and\nhigh-resoluion seismic reflection profiling have been used to map the\nregion north of about 42o10' and west of about 73o15'. The Hudson\nShelf Valley, a shallow topographic feature that cuts across the shelf\nfrom offshore of New York to the shelf edge, was mapped using\nmultibeam. This regional synthesis will support a wide range of\nmanagement decisions and will provide a basis for further\nprocess-oriented investigations.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS MGNM 99023 cruise. The coverage is the\nnearshore of Southern Long Island and the Hudson Shelf Valley. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00304.json b/datasets/USGS_OFR_00304.json index fd7ecae93a..313a2969a6 100644 --- a/datasets/USGS_OFR_00304.json +++ b/datasets/USGS_OFR_00304.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Open File Report (00-304) consists of over 30 digital data sets representing GIS layers assessing the benthic communities and spatial seafloor structures of the Long Island Sound. These data sets are made available as a USGS Open File Report and correspond to research arctiles published in a thematic section of the Journal of Coastal Research (Vol. 16 (3), 2000).", "links": [ { diff --git a/datasets/USGS_OFR_00304_BENTHOS.json b/datasets/USGS_OFR_00304_BENTHOS.json index cea5a67d7e..23c7504c1d 100644 --- a/datasets/USGS_OFR_00304_BENTHOS.json +++ b/datasets/USGS_OFR_00304_BENTHOS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_BENTHOS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS layer, which focuses on benthic communities, was developed as\npart of a cooperative project between the University of New Haven, the\nConnecticut DEP, and the U.S. Geological Survey. Benthic communities\nare an integral component of the ecology of Long Island Sound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThis GIS layer provides the location where samples from Pellegrino and\nHubbard were summarized to provide detailed analysis of 35 common\nspecies found in Long Island Sound benthic communities.", "links": [ { diff --git a/datasets/USGS_OFR_00304_BUZAS.json b/datasets/USGS_OFR_00304_BUZAS.json index 408ab903b8..11228cffc5 100644 --- a/datasets/USGS_OFR_00304_BUZAS.json +++ b/datasets/USGS_OFR_00304_BUZAS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_BUZAS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this layer is to disseminate a digital version of the\nlocation of samples collected and analyzed by M. A. Buzas in 1965.\n\nThis GIS layer contains a point overlay showing the the distribution\nof benthic foraminiferal samples collected in 1965 by M. A. Buzas in\nLong Island Sound.\n\nSediment samples were washed on a 0.062 mm sieve to separate the\nforaminifera from the silt and clay. Foraminifera were picked from the\nfraction retained on the sieve and individually identified and counted\nwith a binocular microscope using reflected light.\n\nThe foraminifera data and navigation were entered into a flat-file\ndatabase (Excel) and inported into Mapview for graphical analysis. The\nfiles were subsequently exported into MIDMIF files, and converted into\nshape files with the Arc utility MIFSHAPE.EXE.", "links": [ { diff --git a/datasets/USGS_OFR_00304_CPERFLOC.json b/datasets/USGS_OFR_00304_CPERFLOC.json index 3b1589fe72..b72aa0d9e2 100644 --- a/datasets/USGS_OFR_00304_CPERFLOC.json +++ b/datasets/USGS_OFR_00304_CPERFLOC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_CPERFLOC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this layer is to disseminate a digital version of the\nlocation of samples containing Clostridium perfringens, and\nconcentrations of Clostridium perfringens in those samples.\n\nThis GIS layer contains a point layer showing the location of\nsurficial sediment samples in Long Island Sound containing Clostridium\nperfringens and the concentration of Clostridium perfringens in those\nsamples.\n\nGrab samples were frozen at sea, and freeze-dried in the lab. Analyses\nfor Clostridium perfringens were performed by Biological Analytical\nLabs of North Kingston, RI, according to methods described by Emerson\nand Cabelli (1982) and Bisson and Cabelli (1979). Data consisting of\nstation navigation and Clostridium perfringens concentrations in the\nsurficial sediments were inmported as text files.", "links": [ { diff --git a/datasets/USGS_OFR_00304_CST27.json b/datasets/USGS_OFR_00304_CST27.json index 63ae510b45..be4710400c 100644 --- a/datasets/USGS_OFR_00304_CST27.json +++ b/datasets/USGS_OFR_00304_CST27.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_CST27", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer provides a medium resolution coastline for the Long\nIsland Sound Study Area in OFR 00-304.\n\nNOAA's Medium Resolution Digital Vector Shoreline is a high quality,\nGIS-ready, general-use digital vector data set created by the\nStrategic Environmental Assessments (SEA) Division of NOAA's Office of\nOcean esources Conservation and Assessment. The coastlines are\ncompiled from the NOAA coast charts.\n\nThe specified section of NOAA's medium resolution shoreline was\ndownloaded from their website. That file was clipped to include the\nare of interest for the Long Island Sound studies.\n\nNOAA's Medium Resolution Digital Vector Shoreline was compiled from\nhundreds of NOAA coast charts and comproses over 75,000 nautical miles\nof coastline. The portion contained here is part of the EC80_04 -\nChincoteague Inlet Virginia to Block Island Sound Rhode Island data\nlayer, which is part of the Atlantic East-Coast Section.", "links": [ { diff --git a/datasets/USGS_OFR_00304_FRANZ.json b/datasets/USGS_OFR_00304_FRANZ.json index e64318921a..e40897c4ec 100644 --- a/datasets/USGS_OFR_00304_FRANZ.json +++ b/datasets/USGS_OFR_00304_FRANZ.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_FRANZ", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS layer, which focuses on benthic communities, was developed as\npart of a cooperative project between the University of New Haven, the\nConnecticut DEP, and the U.S. Geological Survey. Benthic communities\nare an integral component of the ecology of Long Island Sound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThis GIS layer provides the location where samples were taken in a\nsurvey conducted by D. Franz (1976).", "links": [ { diff --git a/datasets/USGS_OFR_00304_GRAVITY.json b/datasets/USGS_OFR_00304_GRAVITY.json index f70f6f431c..eedaa7c91c 100644 --- a/datasets/USGS_OFR_00304_GRAVITY.json +++ b/datasets/USGS_OFR_00304_GRAVITY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_GRAVITY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose is to disseminate the only existing free-air gravity\ninformation in digital form to the research community, and to\nfacilitate modern geophysical and environmental studies of the Long\nIsland and Block Island Sounds.\n\nThis GIS layer contains an interpretive layer represented by contour\nlines (2-mgal intervals) of the free-air gravity of Long Island and\nBlock Island Sounds.", "links": [ { diff --git a/datasets/USGS_OFR_00304_LISGRABS.json b/datasets/USGS_OFR_00304_LISGRABS.json index 1dadd39bd9..afe83590af 100644 --- a/datasets/USGS_OFR_00304_LISGRABS.json +++ b/datasets/USGS_OFR_00304_LISGRABS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_LISGRABS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this datalayer is to disseminate a digital version of\nthe map showing the locations of surficial samples used in the\nanalysis of metal distributions in Long Island Sound.\n\nThis GIS layer contains a point overlay showing the location of\nsurficial samples used in the analysis of metal distributions in Long\nIsland Sound. Attribute information containing the chemical analysis\nvalues are also included in the data layer.", "links": [ { diff --git a/datasets/USGS_OFR_00304_LISTEX.json b/datasets/USGS_OFR_00304_LISTEX.json index 302883e7e5..3f26665dcb 100644 --- a/datasets/USGS_OFR_00304_LISTEX.json +++ b/datasets/USGS_OFR_00304_LISTEX.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_LISTEX", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose is to disseminate a digital version of a regional map\nshowing the distribution of surficial sediments in Long Island\nSound. Grain size is the most basic attribute of sediment texture, and\ntexture controls many benthic ecological and chemical processes.\n\nThis GIS layer contains an computer generated model of the\ndistribution of surficial sediments in Long Island Sound.", "links": [ { diff --git a/datasets/USGS_OFR_00304_LISTOC.json b/datasets/USGS_OFR_00304_LISTOC.json index 7d1a63f620..052b5f6eb8 100644 --- a/datasets/USGS_OFR_00304_LISTOC.json +++ b/datasets/USGS_OFR_00304_LISTOC.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_LISTOC", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this layer is to disseminate a digital version of the\nregional total organic carbon distribution in Long Island Sound.\n\nThis GIS layer contains a polygon overlay showing the distribution of\nTotal Organic Carbon (TOC) in the sediments of Long Island Sound.\n\nThese data, which represent the only regional total organic carbon\nstudy of Long Island Sound, were originally published in USGS\nOpen-File Report 98-502.", "links": [ { diff --git a/datasets/USGS_OFR_00304_MARINET.json b/datasets/USGS_OFR_00304_MARINET.json index 7c5426f749..be69c23635 100644 --- a/datasets/USGS_OFR_00304_MARINET.json +++ b/datasets/USGS_OFR_00304_MARINET.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_MARINET", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose is to disseminate a digital version of a regional map\nshowing the marine transgressive surface in Long Island Sound.\n\nThis GIS layer contains an interpretive layer represented by contour\nlines showing the marine transgressive surface in Long Island Sound.", "links": [ { diff --git a/datasets/USGS_OFR_00304_MCCALL.json b/datasets/USGS_OFR_00304_MCCALL.json index 3650002496..854d29fcc7 100644 --- a/datasets/USGS_OFR_00304_MCCALL.json +++ b/datasets/USGS_OFR_00304_MCCALL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_MCCALL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS layer, which focuses on benthic communities, was developed as\npart of a cooperative project between the University of New Haven, the\nConnecticut DEP, and the U.S. Geological Survey. Benthic communities\nare an integral component of the ecology of Long Island\nSound. Understanding the role that spatial heterogeneity plays in the\ndynamic of benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThis GIS layer provides the location where samples were taken in a\nsurvey conducted by P.L. McCall (1975).", "links": [ { diff --git a/datasets/USGS_OFR_00304_MOSAREA.json b/datasets/USGS_OFR_00304_MOSAREA.json index 24df1329cd..68d7181328 100644 --- a/datasets/USGS_OFR_00304_MOSAREA.json +++ b/datasets/USGS_OFR_00304_MOSAREA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_MOSAREA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS layer, which focuses on benthic communities, was developed as\npart of a cooperative project between the University of New Haven, the\nConnecticut DEP, and the U.S. Geological Survey. Benthic communities\nare an integral component of the ecology of Long Island Sound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThis GIS layer shows the extent of the area covered by the sidescan\nsonar mosaic from the study area off New London, CT.", "links": [ { diff --git a/datasets/USGS_OFR_00304_NLBENTHS.json b/datasets/USGS_OFR_00304_NLBENTHS.json index e022f48cdc..adcfaa9a8e 100644 --- a/datasets/USGS_OFR_00304_NLBENTHS.json +++ b/datasets/USGS_OFR_00304_NLBENTHS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_NLBENTHS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer, which focuses on benthic communities, was developed\nas part of a cooperative project between the University of New Haven,\nthe Connecticut DEP, and the U.S. Geological Survey. Benthic\ncommunities are an integral component of the ecology of Long Island\nSound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThe original studies were conducted to describe the benthic\ncommunities in Long Island Sound; the corresponding data layer is\npresented to show the available species richness data in eastern Long\nIsland Sound.\n\nThis data layer depicts benthic communities found in the New London\nsidescan sonar mosaic study area.", "links": [ { diff --git a/datasets/USGS_OFR_00304_NLMOSINT.json b/datasets/USGS_OFR_00304_NLMOSINT.json index dd71795586..c363034c8e 100644 --- a/datasets/USGS_OFR_00304_NLMOSINT.json +++ b/datasets/USGS_OFR_00304_NLMOSINT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_NLMOSINT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer, which focuses on benthic communities, was developed\nas part of a cooperative project between the University of New Haven,\nthe Connecticut DEP, and the U.S. Geological Survey. Benthic\ncommunities are an integral component of the ecology of Long Island\nSound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nMapping was performed on a sidescan sonar survey. This survey was\nprocessed at 3,479-scale utilizing the U.S.G.S. Mini Image Processing\nsystem (MIPS) in an Equatorial Mercator Projection. Processing\nincluded bottom, ratio, and radiometric corrections; sectioning the\nsurvey area; \"Geoming\" individual map sections; \"stenciling\" and\n\"mosaicing\"; and building the final image. The shading convention for\nthis mosaic is that dark tones are interpreted as fine sediment (fine\nsand, silt and clay); and light tones are interpreted as coarse\nsediment. Rough and \"grainy\" patches are interpreted as glacial drift\nor bedrock outcrops.The image files contained here have been modified,\nusing Arc/Info software, from the three original TIFFs delivered by\nUniversity of Rhode Island. The images were converted to grids,\ngeo-referenced, and individually reclassified in a manner similar to\nlinear stretching to account for variations in gray scales among the\nthree sections of the mosaic. The grids were then converted back to\nTIFF format with world files in Latitude/Longitude decimal degrees (no\nprojection). Pixel size is approximately 0.8 meters.\n\nThe original studies were conducted to describe the benthic\ncommunities in Long Island Sound; the corresponding data layer is\npresented to show the extent of the sidescan sonar mosaic off New\nLondon, in eastern Long Island Sound, and the distribution of habitats\non the mosaic.\n\nThis data layer is an interpretation of the sidescan sonar mosaic from\nthe study area off New London, CT.", "links": [ { diff --git a/datasets/USGS_OFR_00304_PARKER.json b/datasets/USGS_OFR_00304_PARKER.json index 4edcf7f3bf..a8dec0b505 100644 --- a/datasets/USGS_OFR_00304_PARKER.json +++ b/datasets/USGS_OFR_00304_PARKER.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_PARKER", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this layer is to disseminate a digital version of the\nlocation of samples collected and analyzed by F. L. Parker in 1952 in\nLong Island Sound.\n\nThis GIS layer contains a point overlay showing the the distribution\nof benthic foraminiferal samples collected in 1952 by F. L. Parker.", "links": [ { diff --git a/datasets/USGS_OFR_00304_PELLEGRI.json b/datasets/USGS_OFR_00304_PELLEGRI.json index 145e776637..9e023ffd4c 100644 --- a/datasets/USGS_OFR_00304_PELLEGRI.json +++ b/datasets/USGS_OFR_00304_PELLEGRI.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_PELLEGRI", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer, which focuses on benthic communities, was developed\nas part of a cooperative project between the University of New Haven,\nthe Connecticut DEP, and the U.S. Geological Survey. Benthic\ncommunities are an integral component of the ecology of Long Island\nSound.\n\nUnderstanding the role that spatial heterogeneity plays in the dynamic\nof benthic landscapes may be a key to developing a better\nunderstanding of the estuarine ecology and the impacts of human\nactivity. The purpose of providing this data layer is to help\nestablish a regional framework for developing a more extensive GIS for\nbenthic communities in Long Island Sound that can be used for\neducation, research, and environmental management.\n\nThis data layer provides the location where samples were taken in a\nsurvey conducted by P. Pellegrino and W. Hubbard (1983). Sediment\nsamples were collected by grab sampler and were wet sieved to remove\nthe mud fraction. Coarse fractions were stored in formalin until\nindividual species specimens could be identified and counted with a\nbinocular microscope under reflected light.", "links": [ { diff --git a/datasets/USGS_OFR_00304_REIDETAL.json b/datasets/USGS_OFR_00304_REIDETAL.json index 2b05eb56d2..bd9df8e5ad 100644 --- a/datasets/USGS_OFR_00304_REIDETAL.json +++ b/datasets/USGS_OFR_00304_REIDETAL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_REIDETAL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management.\n\nThis GIS layer provides the location where samples were taken in a survey conducted by R.N. Reid, et al (1979).\n", "links": [ { diff --git a/datasets/USGS_OFR_00304_SANDERS.json b/datasets/USGS_OFR_00304_SANDERS.json index 5d2534c206..9316663704 100644 --- a/datasets/USGS_OFR_00304_SANDERS.json +++ b/datasets/USGS_OFR_00304_SANDERS.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_SANDERS", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data layer, which focuses on benthic communities, was developed as part of a cooperative project between the University of New Haven, the Connecticut DEP, and the U.S. Geological Survey. Benthic communities are an integral component of the ecology of Long Island Sound. Understanding the role that spatial heterogeneity plays in the dynamic of benthic landscapes may be a key to developing a better understanding of the estuarine ecology and the impacts of human activity. The purpose of providing this data layer is to help establish a regional framework for developing a more extensive GIS for benthic communities in Long Island Sound that can be used for education, research, and environmental management.\n\nThis GIS layer provides the location where samples were taken in a survey conducted by H.L. Sanders (1956). \n", "links": [ { diff --git a/datasets/USGS_OFR_00304_TOCPNT1.json b/datasets/USGS_OFR_00304_TOCPNT1.json index 08946abc49..d6a4359b5a 100644 --- a/datasets/USGS_OFR_00304_TOCPNT1.json +++ b/datasets/USGS_OFR_00304_TOCPNT1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00304_TOCPNT1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data layer is to disseminate a digital version of\nthe map showing the locations of surficial total organic carbon\nsampling stations in Long Island Sound.\n\nThis GIS layer contains a point overlay showing the location of\nsamples with Total Organic Carbon (TOC). This layer shows the\ndistribution of samples used in the creation of the TOC polygon layer,\nlistoc.", "links": [ { diff --git a/datasets/USGS_OFR_00351_1.0.json b/datasets/USGS_OFR_00351_1.0.json index c527fc8a5d..808279ce62 100644 --- a/datasets/USGS_OFR_00351_1.0.json +++ b/datasets/USGS_OFR_00351_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00351_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:24,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the Oregon State\nGeologist. Similarly, the database cannot be used to identify or delineate\nlandslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits of the Salem East and Turner 7.5 minute\nquadrangles. A previously published adjacent geologic map and database by\nTolan, Beeson, and Wheeler (1999) contains a text file (geol.txt or geol.ps),\nit provides current information on the geologic structure and stratigraphy of\nthe area covered. The database delineates map units that are identified by\ngeneral age and lithology following the stratigraphic nomenclature of the U.S.\nGeological Survey. The scale of the source maps limits the spatial resolution\n(scale) of the database to 1:24,000 or smaller.\n\nThe Salem East and Turner 7.5-minute quadrangles are situated in the center of\nthe Willamette Valley near the western margin of the Columbia River Basalt\nGroup (CRBG) distribution. The terrain within the area is of low to moderate\nrelief, ranging from about 150 to almost 1,100-ft elevation. Mill Creek flows\nnorthward from the Stayton basin (Turner quadrangle) to the northern Willamette\nValley (Salem East quadrangle) through a low that dissects the Columbia River\nbasalt that forms the Salem Hills on the west and the Waldo Hills to the east.\nApproximately eight flows of CRBG form a thickness of up to 700 in these two\nquadrangles. The Ginkgo intracanyon flow that extends from east to west through\nthe south half of the Turner quadrangle is exposed in the hills along the\nsoutheast part of the quadrangle.\n\nThe major emphasis of this study was to identify and map CRBG units within the\nSalem East and Turner Quadrangles and to utilize this detailed CRBG\nstratigraphy to identify and characterize structural features. Water well logs\nwere used to provide better subsurface stratigraphic control. Three other\nquadrangles (Scotts Mills, Silverton, and Stayton NE) in the Willamette Valley\nhave been mapped in this way (Tolan and Beeson, 1999).\n\nThe databases in this report were compiled in ARC/INFO, a commercial Geographic\nInformation System (Environmental Systems Research Institute, Redlands,\nCalifornia), with version 3.0 of the menu interface ALACARTE (Fitzgibbon and\nWentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files\nare in either GRID (ARC/ INFO raster data) format or COVERAGE (ARC/INFO vector\ndata) format. Coverages are stored in uncompressed ARC export format (ARC/INFO\nversion 7.x). ARC/INFO export files (files with the .e00 extension) can be\nconverted into ARC/INFO coverages in ARC/INFO (see below) and can be read by\nsome other Geographic Information Systems, such as MapInfo via ArcLink and\nESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from\nESRI's web site: \"http://www.esri.com\"). The digital compilation was done in\nversion 7.1.1 of ARC/INFO with version 3.0 of the menu interface ALACARTE\n(Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon,\n1991). The geologic map information was digitized from stable originals of the\ngeologic maps at 1:24,000 scale. The author manuscripts (pen on mylar and pen\non paper) were scanned using a Anatek rasterizing color scanner with a\nresolution of 600 and 400 dots per inch. The scanned images were vectorized\nand transformed from scanner coordinates to projection coordinates with digital\ntics placed by hand at quadrangle corners. The scanned lines were edited\ninteractively by hand using ALACARTE, color boundaries were tagged as\nappropriate, and scanning artifacts visible at 1:24,000 were removed.", "links": [ { diff --git a/datasets/USGS_OFR_00356_Version 1.0.json b/datasets/USGS_OFR_00356_Version 1.0.json index 9b49a055a3..ea004535ad 100644 --- a/datasets/USGS_OFR_00356_Version 1.0.json +++ b/datasets/USGS_OFR_00356_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00356_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital representation of geologic mapping facilitates the presentation and\nanalysis of earth-science data. Digital maps may be displayed at any scale or\nprojection, however, the geologic data in this coverage is not intended for use\nat a larger scale.\n\nThis data set represents reconnaissance geologic mapping of the Wildcat Lake\n7.5' Quadrangle, Kitsap and Mason Counties, Washington. It is used to create\nthe map sheet in USGS OFR 00-356 , which shows bedrock, surficial, and\nstructural geology of the Wildcat Lake Quadrangle.\n\nThis data was hand digitized in ARC/Info from an unfolded paper 1:24,000 scale\ncompilation map. The arcs and polygons were attributed. For the purposes of\ndistribution, the coverage has been converted to an interchange format file\nusing the ARC/Info export command.", "links": [ { diff --git a/datasets/USGS_OFR_00359_Version 1.0.json b/datasets/USGS_OFR_00359_Version 1.0.json index 3d6672a8af..0b424f07a8 100644 --- a/datasets/USGS_OFR_00359_Version 1.0.json +++ b/datasets/USGS_OFR_00359_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00359_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Apache Canyon quadrangle has been prepared by the\nSouthern California Areal Mapping Project (SCAMP), a cooperative project\nsponsored jointly by the U.S. Geological Survey and the California Division of\nMines and Geology, as part of an ongoing effort to utilize a Geographical\nInformation System (GIS) format to create a regional digital geologic database\nfor southern California. This regional database is being developed as a\ncontribution to the National Geologic Map Data Base of the National Cooperative\nGeologic Mapping Program of the USGS.\n\nThe digital geologic map database for the Apache Canyon quadrangle has been\ncreated as a general-purpose data set that is applicable to other land-related\ninvestigations in the earth and biological sciences. The Apache Canyon\ndatabase is not suitable for site-specific geologic evaluations at scales\ngreater than 1:24,000 (1 in = 2,000 ft).\n\nThis data set maps and describes the geology of the Apache Canyon 7.5'\nquadrangle, Ventura and Kern Counties, California. Created using Environmental\nSystems Research Institute's ARC/INFO software, the data base consists of the\nfollowing items: (1) a map coverage showing geologic contacts, faults and\nunits, (2) a separate coverage layer showing structural data, (3) an additional\npoint coverage which contains bedding data, (4) a point coverage containing\nsample localities, (5) a scanned topographic base at a scale of 1:24,000, and\n(6) attribute tables for geologic units (polygons), contacts (arcs), and\nsite-specific data (points). The data base is accompanied by a readme file and\nthis metadata file. In addition, the data set includes the following graphic\nand text products: (1) A jpg file (.jpg) containing a browse-graphic of the\ngeologic map on a 1:24,000 topographic base. The map is accompanied by a\nmarginal explanation consisting of a List of Map Units, a Correlation of Map\nUnits, and a key to point and line symbols. (2) A .pdf file of a geologic\nexplanation pamphlet that includes a Description of Map Units. (3) Two\npostScript graphic plot-files: one containing the geologic map on a 1:24,000\ntopographic base and the other, three accompanying structural cross sections.\n\nThe geologic map database contains original U.S. Geological Survey data\ngenerated by detailed field observation and by interpretation of aerial\nphotographs. The map was created by transferring lines and point data from the\naerial photographs to a 1:24,000 topographic base by using a PG-2 plotter. The\nmap was scribed, scanned, and imported into ARC/INFO, where the database was\nbuilt. Within the database, geologic contacts are represented as lines (arcs),\ngeologic units as polygons, and site-specific data as points. Polygon, arc, and\npoint attribute tables (.pat, .aat, and .pat, respectively) uniquely identify\neach geologic datum and link it to other tables (.rel) that provide more\ndetailed geologic information.", "links": [ { diff --git a/datasets/USGS_OFR_00366.json b/datasets/USGS_OFR_00366.json index 60f3469663..2d372078cd 100644 --- a/datasets/USGS_OFR_00366.json +++ b/datasets/USGS_OFR_00366.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00366", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the USGS Woods Hole Field Center, in cooperation with the\nU.S. Army Corps of Engineers, began a program designed to map the\nseafloor offshore of the New York-New Jersey metropolitan area; the\nmost heavily populated, and one of the most impacted coastal regions\nof the United States. The ultimate goal of this program is to provide\nan overall synthesis of the sea floor environment, including surficial\nsediment texture, subsurface geometry, and anthropogenic impact\n(e.g. ocean dumping, trawling, channel dredging), through the use and\nanalysis of sidescan-sonar and subbottom mapping techniques. This\nregional synthesis will support a wide range of management decisions\nand will provide a basis for further process-oriented investigations.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS DIAN 97011 cruise. The coverage is the\nnearshore of Long Island, NY in the vicinity of Fire Island. The\nseismic-reflection data are stored as SEG-Y standard format that can\nbe read and manipulated by most seismic-processing software. Much of\nthe information specific to the data are contained in the headers of\nthe SEG-Y format files. The file system format is ISO 9660 which can\nbe read with DOS, Unix, and MAC operating systems with the appropriate\n\nCD-ROM driver software installed.", "links": [ { diff --git a/datasets/USGS_OFR_00396.json b/datasets/USGS_OFR_00396.json index d63f7936fb..1bbdbaac23 100644 --- a/datasets/USGS_OFR_00396.json +++ b/datasets/USGS_OFR_00396.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00396", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beginning in 1995, the USGS, in cooperation with the U.S Army Corps of\nEngineers (USACE), New York District, began a program to generate\nreconnaissance maps of the sea floor offshore of the New York-New\nJersey metropolitan area, one of the most populated coastal regions of\nthe United States. The goal of this mapping program is to provide a\nregional synthesis of the sea-floor environment, including a\ndescription of sedimentary environments, sediment texture, seafloor\nmorphology, and geologic history to aid in understanding the impacts\nof anthropogenic activities, such as ocean dumping. This mapping\neffort differs from previous studies of this area by obtaining\ndigital, sidescan sonar images that cover 100 percent of the sea\nfloor. This investigation was motivated by the need to develop an\nenvironmentally acceptable solution for the disposal of dredged\nmaterial from the New York - New Jersey Port, by the need to identify\npotential sources of sand for renourishment of the southern shore of\nLong Island, and by the opportunity to develop a better understanding\nof the transport and long-term fate of contaminants by investigations\nof the present distribution of materials discharged into the New York\nBight over the last 100+ years (Schwab and others, 1997).\n\nThis DVD-ROM contains copies of the navigation and field Water Gun\nsubbottom data collected aboard the R/V Seaward Explorer, from 1 May -\n9 June, 1996. This DVD-ROM (Digital Versatile Disc-Read Only Memory)\nhas been produced in accordance with the UDF (Universal Disc Format)\nDVD-ROM Standard (ISO 9660 equivalent) and is therefore capable of\nbeing read on any computing platform that has appropriate DVD-ROM\ndriver software installed. Access to the data and information\ncontained on this DVD-ROM was developed using the HyperText Markup\nLanguage (HTML) utilized by the World Wide Web (WWWW). Development of\nthe DVD-ROM documentation and user interface in HTML allows a user to\naccess the information by using a variety of WWW browsers to\nfacilitate browsing and locating information and data. To access the\ninformation contained on this disk with a WWW client browser, open the\nfile'index.htm' at the top level directory of this DVD-ROM with your\nselected browser. The HTML documentation is written utilizing some\nHTML 4.0 enhancements. The disk should be viewable by all WWW browsers\nbut may not properly format on some older WWW browsers. Also, some\nlinks to USGS collaborators and other agencies are available on this\nDVD-ROM. These links are only accessible if access to the Internet is\navailable during browsing of the DVD-ROM.", "links": [ { diff --git a/datasets/USGS_OFR_0040.json b/datasets/USGS_OFR_0040.json index b50a068851..3a0d96eaf0 100644 --- a/datasets/USGS_OFR_0040.json +++ b/datasets/USGS_OFR_0040.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0040", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In November 1999, the U. S. Geological Survey, in cooperation with\nCoastal Carolina University, began a program to produce geologic maps\nof the nearshore regime off northern South Carolina, utilizing high\nresolution sidescan sonar, interferometric (direct phase methods)\nswath bathymetry, and seismic subbottom profiling systems. The study\nareas extends from the ~7m isobath to about 10km offshore (water\ndepths <12m). The goals of the investigation are to determine regional\nscale sand resource availability needed for planned beach nourishment\nprograms, to investigate the roles that the inner shelf morphology and\ngeologic framework play in the evolution of this coastal region, and\nto provide baseline geologic maps for use in proposed biologic habitat\nstudies.\n\nThis CD-ROM contains digital high resolution seismic reflection data\ncollected during the USGS ATSV 99044 cruise. The coverage is the\nnearshore of the northern South Carolina. The seismic-reflection data\nare stored as SEG-Y standard format that can be read and manipulated\nby most seismic-processing software. Much of the information specific\nto the data are contained in the headers of the SEG-Y format\nfiles. The file system format is ISO 9660 which can be read with DOS,\nUnix, and MAC operating systems with the appropriate CD-ROM driver\nsoftware installed.", "links": [ { diff --git a/datasets/USGS_OFR_00409_Digital Version 1.0.json b/datasets/USGS_OFR_00409_Digital Version 1.0.json index a9ddc9f910..3278445553 100644 --- a/datasets/USGS_OFR_00409_Digital Version 1.0.json +++ b/datasets/USGS_OFR_00409_Digital Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_00409_Digital Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database was developed to provide a GIS of the geologic map of the State\nof Arizona for use at a scale of 1:500,000 or smaller. This GIS is intended for\nuse in future spatial analysis by a variety of users. The geologic unit\ndescriptions for this map may be updated to reflect more current description of\nstructures and the geochronology of the map units.\n\nThis database is not meant to be used or displayed at any scale larger than\n1:500,000 (e.g., 1:100,000 or 1:24,000) \n\nThe Geologic Map of Arizona was compiled at a scale of 1:500,000 by Eldred D.\nWilson, Richard T. Moore and John R. Cooper, in 1969 and reprinted in 1977,\n1981, and 1983.\n\nComparison of an acetate copy of the 1983 map with existing paper copies of\nearlier maps shows some updating of the original by 1983. This 1983 acetate was\nscanned and vectorized by Optronics Specialty Co., Inc. in 1998, and put into\nan Arc/Info geographic information system (GIS). The digital geologic map\ndatabase can be queried in many ways to produce a variety of derivative\ngeologic maps.\n\nThis GIS database consists of 4 Arc/Info data sets: one line and polygon file\n(azgeol) containing geologic contacts and structures (lines) and geologic map\nrock units (polygons), one line file (azfold) containing the folds and crater\nboundaries, one point file (azptfeat) containing geologic features, cinder\ncones and diatremes. one point file (azptdec) containing decorations, and", "links": [ { diff --git a/datasets/USGS_OFR_011_version 1.0.json b/datasets/USGS_OFR_011_version 1.0.json index 6767af7663..60f294a91a 100644 --- a/datasets/USGS_OFR_011_version 1.0.json +++ b/datasets/USGS_OFR_011_version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_011_version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was developed as part of a larger effort by the U.S. Geological\nSurvey to provide plottable locations of aggregate producers for National Atlas\nand for aggregate research.\n\nThis data set contains latitudes, longitudes, and other descriptive data for\naggregate producers in New Mexico that are believed to have been active during\nthe period 1997-1999. The data in this compilation were derived from U.S.\nGeological Survey files, U.S. Bureau of Land Management files, contact with\nproducers, and reports from the New Mexico Bureau of Mines and Mineral\nResources, the New Mexico Bureau of Mine Inspection, and the New Mexico Mining\nand Mineral Division. This dataset includes 2 tables: Table 1 contains crushed\nstone operations and table 2 contains sand and gravel operations.\n\nThis data set consists of one Excel 97 spreadsheet file (NMsandg2.xls) which\ncontains information about Sand Gravel operations in New Mexico and one Excel\n97 spreadsheet file NMcstn1.xls) which contains information about Crushed Stone\noperations in New Mexico. The files are also included in DIF format under the\nsame filenames, but with the .DIF extension.", "links": [ { diff --git a/datasets/USGS_OFR_02-266.json b/datasets/USGS_OFR_02-266.json index 6f3325152c..bc3c7ad5ba 100644 --- a/datasets/USGS_OFR_02-266.json +++ b/datasets/USGS_OFR_02-266.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_02-266", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report presents the data for the Vostok - Devils Hole chronology, termed\nV-DH chronology, for the Antarctic Vostok ice core record. This depth - age\nrelation is based on a join between the Vostok deuterium profile (delD) and the\nstable oxygen isotope ratio (del18O) record of paleotemperature from a calcitic\ncore at Devils Hole, Nevada, using the algorithm developed by Landwehr and\nWinograd (2001). Both the control points defining the V-DH chronology and the\nnumeric values for the chronology are given. In addition, a plausible\nchronology for a deformed bottom portion of the Vostok core developed with this\nalgorithm is presented. Landwehr and Winograd (2001) demonstrated the broader\nutility of their algorithm by applying it to another appropriate Antarctic\npaleotemperature record, the Antarctic Dome Fuji ice core del18O record.\nControl points for this chronology are also presented in this report but deemed\npreliminary because, to date, investigators have published only the visual\ntrace and not the numeric values for the Dome Fuji del18O record. The total\nuncertainty that can be associated with the assigned ages is also given.", "links": [ { diff --git a/datasets/USGS_OFR_02005.json b/datasets/USGS_OFR_02005.json index 745311516c..48326f5ac7 100644 --- a/datasets/USGS_OFR_02005.json +++ b/datasets/USGS_OFR_02005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_02005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Our objective was to map the region between the 50 to 150-m isobaths south\nfrom the eastern edge of De Soto Canyon as far as Steamboat Lumps using a\nstate-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg\nSimrad EM1002 MBES, the latest generation of high-resolution mapping systems.\nThe EM1002 produces both geodetically accurate georeferenced bathymetry and\ncoregistered, calibrated, acoustic backscatter. Acoustic backscatter is the\nintensity of an acoustic pulse that is backscattered off the seafloor back to\nthe transducer. The signal can give an indication of the type of material\nexposed on the ocean floor (i.e. rock vs. mud). These data should prove\nextremely useful in relating dominant species groups (which display highly\nspecific biotope affinities) to the geomorphology (e.g., reef flattop, forereef\ncrest, reef wall, reef base, circum-reef talus zone, circum-reef,\nhigh-reflectivity sediment apron, etc.).\n\nThese data are intended for science researchers, students, policy makers, and\nthe general public. The data can be used with geographic information systems\n(GIS) or other software to display bathymetry and backscatter data of the West\nFlorida Shelf, Gulf of Mexico.\n\nThis report provides multibeam bathymetry and acoustic backscatter data, along\nwith images for parts of the sea floor. These data were obtained through a\nmultibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are\nprovided in ASCII and ArcInfo GRID formats.\n\nInformation for USGS Coastal and Marine Geology related activities are online\nat \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", "links": [ { diff --git a/datasets/USGS_OFR_02006.json b/datasets/USGS_OFR_02006.json index 9c611c3d75..0b74ab3fbe 100644 --- a/datasets/USGS_OFR_02006.json +++ b/datasets/USGS_OFR_02006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_02006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Our objective was to map as large an area of the outer shelf deep reefs off\nAlabama-Mississippi as the project budget allowed using a state-of-the-art\nmultibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest\ngeneration of high-resolution multibeam mapping systems (HRMBS). The EM1002\nproduces both accurate georeferenced bathymetry and coregistered, calibrated,\nacoustic backscatter. These data should prove extremely useful in relating\ndominant species groups (which display highly specific biotope affinities) to\nthe geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base,\ncircumreef talus zone, circum-reef, high-reflectivity sediment apron). The\nmapping is the first phase of a two-phase study of the Pinnacles area. The\nsecond year of this study (FY01) will concentrate on measuring the currents in\nand around the reefs as well as continued census of the fish populations. \nThese data are intended for science researchers, students, policy\nmakers, and the general public. The data can be used with geographic\ninformation systems (GIS) or other software to display bathymetry and\nbackscatter data of the West Florida Shelf, Gulf of Mexico.\n\nThis report provides multibeam bathymetry and acoustic backscatter data, along\nwith images for parts of the sea floor. These data were obtained through a\nmultibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data\nare provided in ASCII and ArcInfo GRID formats.\n\nInformation for USGS Coastal and Marine Geology related activities are online\nat\n\"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", "links": [ { diff --git a/datasets/USGS_OFR_0205.json b/datasets/USGS_OFR_0205.json index 7d15138bc0..adc466a070 100644 --- a/datasets/USGS_OFR_0205.json +++ b/datasets/USGS_OFR_0205.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0205", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective was to map the region between the 50 to 150-m isobaths south\nfrom the eastern edge of De Soto Canyon as far as Steamboat Lumps using a\nstate-of-the-art multibeam mapping system (MBES). The cruise used a Kongsberg\nSimrad EM1002 MBES, the latest generation of high-resolution mapping systems.\nThe EM1002 produces both geodetically accurate georeferenced bathymetry and\ncoregistered, calibrated, acoustic backscatter. Acoustic backscatter is the\nintensity of an acoustic pulse that is backscattered off the seafloor back to\nthe transducer. The signal can give an indication of the type of material\nexposed on the ocean floor (i.e. rock vs. mud). These data should prove\nextremely useful in relating dominant species groups (which display highly\nspecific biotope affinities) to the geomorphology (e.g., reef flattop, forereef\ncrest, reef wall, reef base, circum-reef talus zone, circum-reef,\nhigh-reflectivity sediment apron, etc.).\n\nThese data are intended for science researchers, students, policy makers, and\nthe general public. The data can be used with geographic information systems\n(GIS) or other software to display bathymetry and backscatter data of the West\nFlorida Shelf, Gulf of Mexico.\n\nThis report provides multibeam bathymetry and acoustic backscatter data, along\nwith images for parts of the sea floor. These data were obtained through a\nmultibeam sonar survey of the West Florida Shelf, Gulf of Mexico. Data are\nprovided in ASCII and ArcInfo GRID formats. \n\nInformation for USGS Coastal and Marine Geology related activities are online\nat \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", "links": [ { diff --git a/datasets/USGS_OFR_0206.json b/datasets/USGS_OFR_0206.json index 5fe294abcf..044dd136fc 100644 --- a/datasets/USGS_OFR_0206.json +++ b/datasets/USGS_OFR_0206.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0206", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective was to map as large an area of the outer shelf deep reefs off\nAlabama-Mississippi as the project budget allowed using a state-of-the-art\nmultibeam mapping system. The cruise used a Kongsberg Simrad EM1002, the latest\ngeneration of high-resolution multibeam mapping systems (HRMBS). The EM1002\nproduces both accurate georeferenced bathymetry and coregistered, calibrated,\nacoustic backscatter. These data should prove extremely useful in relating\ndominant species groups (which display highly specific biotope affinities) to\nthe geomorphology (e.g., reef flattop, forereef crest, reef wall, reef base,\ncircumreef talus zone, circum-reef, high-reflectivity sediment apron). The\nmapping is the first phase of a two-phase study of the Pinnacles area. The\nsecond year of this study concentrated on measuring the currents in and around\nthe reefs as well as continued census of the fish populations.\n\nThese data are intended for science researchers, students, policy makers, and\nthe general public. The data can be used with geographic information systems\n(GIS) or other software to display bathymetry and backscatter data of the West\nFlorida Shelf, Gulf of Mexico.\n\nThis report provides multibeam bathymetry and acoustic backscatter data, along\nwith images for parts of the sea floor. These data were obtained through a\nmultibeam sonar survey of the Pinnacles region, northern Gulf of Mexico. Data\nare provided in ASCII and ArcInfo GRID formats.\n\nInformation for USGS Coastal and Marine Geology related activities are online\nat: \"http://walrus.wr.usgs.gov/infobank/m/m201gm/html/m-2-01-gm.meta.html\"", "links": [ { diff --git a/datasets/USGS_OFR_02110_1.0.json b/datasets/USGS_OFR_02110_1.0.json index 15dbf1df60..be56b7ef06 100644 --- a/datasets/USGS_OFR_02110_1.0.json +++ b/datasets/USGS_OFR_02110_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_02110_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was compiled due to interest in Afghanistan and anticipated\ncontinuing interest as post-war aid and reconstruction begin.\n\nThis data set contains latitudes, longitudes, commodity, and limited geologic\ndata for metallic and nonmetallic mines, deposits, and mineral occurrences of\nAfghanistan. The data in this compilation were derived from published\nliterature and data files of members of the USGS National Industrial Minerals\nproject. This data set consists of one table with 17 fields and over 1000\nsites.\n\nThis data set consists of one Excel 98 spreadsheet file, OF02110.xls. Data\nfields include location, deposit, commodity, and geologic data for mineral\ndeposits, mines and occurrences.", "links": [ { diff --git a/datasets/USGS_OFR_0221_Version 1.0.json b/datasets/USGS_OFR_0221_Version 1.0.json index 2dde497ab9..07091dd744 100644 --- a/datasets/USGS_OFR_0221_Version 1.0.json +++ b/datasets/USGS_OFR_0221_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0221_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Corona South 7.5' quadrangle was prepared under the U.S.\nGeological Survey Southern California Areal Mapping Project (SCAMP) as part of\nan ongoing effort to develop a regional geologic framework of southern\nCalifornia, and to utilize a Geographic Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThis data set maps and describes the geology of the Corona South 7.5'\nquadrangle, Riverside and Orange Counties, California. Created using\nEnvironmental Systems Research Institute's ARC/INFO software, the data base\nconsists of the following items: (1) a map coverage containing geologic\ncontacts and units, (2) a coverage containing structural data, (3) a coverage\ncontaining geologic unit annotation and leaders, and (4) attribute tables for\ngeologic units (polygons), contacts (arcs), and site-specific data (points). \nIn addition, the data set includes the following graphic and text products: (1)\na postscript graphic plot-file containing the geologic map, topography,\ncultural data, a Correlation of Map Units (CMU) diagram, a Description of Map\nUnits (DMU), and a key for point and line symbols, and (2) PDF files of the\nReadme (including the metadata file as an appendix), and the graphic produced\nby the Postscript plot file.\n\nThe Corona South quadrangle is located near the northern end of the Peninsular\nRanges Province. Diagonally crossing the quadrangle is the northern end of the\nElsinore Fault zone, a major active right-lateral strike-slip fault zone of the\nSan Andreas Fault system. East of the fault zone is the Perris block and to the\nwest the Santa Ana Mountains block. Basement in the Perris block part of the\nquadrangle is almost entirely Cretaceous volcanic rocks and granitic rocks of\nthe Cretaceous Peninsular Ranges batholith. Three small exposures of very low\nmetamorphic grade siliceous rocks correlated on the basis of lithology with\nMesozoic age rocks are located near the eastern edge of the quadrangle. \nExposures of batholithic rocks is restricted to mostly granodiorite of the\nCajalco pluton that underlies extensive areas to the east and north. There are\nlimited amounts of undifferentiated granitic rock and one small body of gabbro.\n The most extensive basement rocks are volcanic shallow intrusives and\nextrusives of the Estelle Mountain volcanics. The volcanics, predominantly\nlatite and rhyolite, are quarried as a source of crushed rock.\n\nWest of the Elsinore Fault zone is a thick section of Bedford Canyon Formation\nof Jurassic age. This unit consists of incipiently metamorphosed marine\nsedimentary rocks consisting of argillite, slate, graywacke, impure quartzite,\nand small pods of limestone. Bedding and other primary sedimentary structures\nare commonly preserved and tight folds are common. Incipiently developed\ntransposed layering, S1, is locally well developed. Included within the\nsiliceous rocks are small outcrops of fossiliferous limestone than contain a\nfauna indicating the limestone formed in a so-called black smoker environment.\nUnconformably overlying and intruding the Bedford Canyon Formation is the\nSantiago Peak Volcanics of Cretaceous age. These volcanics consist of basaltic\nandesite, andesite, dacite, rhyolite, breccia and volcanoclastic rocks. Much\nof the unit has been hydrothermally altered; the alteration was contemporaneous\nwith the volcanism. A minor occurrence of serpentine and associated\nsilica-carbonate rock occurs in association with the volcanics.\n\nSedimentary rocks of late Cretaceous and Paleogene age and a few Neogene age\nrocks occur within the Elsinore Fault zone. Marine sandstone of the middle\nMiocene Topanga Formation occurs within the fault zone southeast of Corona.\nUnderlying the Topanga Formation is the nonmarine undivided Sespe and Vaqueros\nFormation that are predominantly sandstone. Sandstone, siltstone, and\nconglomerate of the marine and nonmarine Paleocene Silverado Formation extends\nessentially along the entire length of the fault zone in the quadrangle. Clay\nbeds in the Silverado Formation have been an important source of clay. In the\nnorthwest corner of the quadrangle is a thick, faulted, sedimentary section\nthat ranges in age from Cretaceous to early Pliocene-Miocene.\n\nEmanating from the Santa Ana Mountains is an extensive alluvial fan complex\nthat underlies Corona and the surrounding valleys. This fan complex includes\nboth Pleistocene and Holocene age deposits.\n\nThe Elsinore Fault zone at the base of the Santa Ana Mountains splays in the\nnorthwestern part of the quadrangle; beyond the quadrangle boundary the name\nElsinore Fault is generally not used. The southern splay takes a more western\ntrend and to the west of the quadrangle is termed the Whittier Fault, a major\nactive fault. The eastern splay continues on strike along the east side of the\nChino (Puente) Hills north of the quadrangle where it is termed the Chino\nFault. The Chino Fault appears to have very limited displacement.\n\nThe geologic map data base contains original U.S. Geological Survey data\ngenerated by detailed field observation recorded on 1:24,000 scale aerial\nphotographs. The map was created by transferring lines from the aerial\nphotographs to a 1:24,000 scale topographic base. The map was digitized and\nlines, points, and polygons were subsequently edited using standard ARC/INFO\ncommands. Digitizing and editing artifacts significant enough to display at a\nscale of 1:24,000 were corrected. Within the database, geologic contacts are\nrepresented as lines (arcs), geologic units are polygons, and site-specific\ndata as points. Polygon, arc, and point attribute tables (.pat, .aat, and\n.pat, respectively) uniquely identify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR_0222_Version 1.0.json b/datasets/USGS_OFR_0222_Version 1.0.json index e09449d1e7..199984e126 100644 --- a/datasets/USGS_OFR_0222_Version 1.0.json +++ b/datasets/USGS_OFR_0222_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_0222_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set for the Corona North 7.5' quadrangle was prepared under the U.S.\nGeological Survey Southern California Areal Mapping Project (SCAMP) as part of\nan ongoing effort to develop a regional geologic framework of southern\nCalifornia, and to utilize a Geographic Information System (GIS) format to\ncreate regional digital geologic databases. These regional databases are being\ndeveloped as contributions to the National Geologic Map Database of the\nNational Cooperative Geologic Mapping Program of the USGS.\n\nThis data set maps and describes the geology of the Corona North 7.5'\nquadrangle, Riverside and San Bernardino Counties, California. Created using\nEnvironmental Systems Research Institute's ARC/INFO software, the data base\nconsists of the following items: (1) a map coverage containing geologic\ncontacts and units, (2) a coverage containing structural data, (3) a coverage\ncontaining geologic unit annotation and leaders, and (4) attribute tables for\ngeologic units (polygons), contacts (arcs), and site-specific data (points). \nIn addition, the data set includes the following graphic and text products: (1)\na postscript graphic plot-file containing the geologic map, topography,\ncultural data, a Correlation of Map Units (CMU) diagram, a Description of Map\nUnits (DMU), and a key for point and line symbols, and (2) PDF files of the\nReadme (including the metadata file as an appendix), and the graphic produced\nby the Postscript plot file.\n\nThe Corona North quadrangle is located near the northern end of the Peninsular\nRanges Province. All but the southeastern tip of the quadrangle is within the\nPerris block, a relatively stable, rectangular in plan area located between the\nElsinore and San Jacinto fault zones. The southeastern tip of the quadrangle\nis barely within the Elsinore fault zone.\n\nThe quadrangle is underlain by Cretaceous plutonic rocks that are part of the\ncomposite Peninsular Ranges batholith. These rocks are exposed in a\ntriangular-shaped area bounded on the north by the Santa Ana River and on the\nsouth by Temescal Wash, a major tributary of the Santa Ana River. A variety of\nmostly silicic granitic rocks occur in the quadrangle, and are mainly of\nmonzogranite and granodioritic composition, but range in composition from\nmicropegmatitic granite to gabbro. Most rock units are massive and contain\nvarying amounts of meso- and melanocratic equant-shaped inclusions. The most\nwidespread granitic rock is monzogranite of the Cajalco pluton, a large pluton\nthat extends some distance south of the quadrangle. North of Corona is a body\nof micropegmatite that appears to be unique in the batholith rocks.\n\nDiagonally bisecting the quadrangle is the Santa Ana River. North of the Santa\nAna River alluvial deposits are dominated by the distal parts of alluvial fans\nemanating from the San Gabriel Mountains north of the quadrangle. Widespread\nareas of the fan deposits are covered by a thin layer of wind blown sand.\n\nAlluvial deposits in the triangular-shaped area between the Santa Ana River and\nTemescal Wash are quite varied, but consist principally of locally derived\nolder alluvial fan deposits. These deposits rest on remnants of older, early\nQuaternary or late Tertiary age, nonmarine sedimentary deposits that were\nderived from both local sources and sources as far away as the San Bernardino\nMountains. These deposits in part were deposited by an ancestral Santa Ana\nRiver. Older are a few scattered remnants of late Tertiary (Pliocene) marine\nsandstone that include some conglomerate lenses. Clasts in the conglomerate\ninclude siliceous volcanic rocks exotic to this part of southern California.\nThis sandstone was deposited as the southeastern-most part of the Los Angeles\nsedimentary marine basin and was deposited along a rocky shoreline developed in\nthe granitic rocks, much like the present day shoreline at Monterey,\nCalifornia. Most of the sandstone and granitic paleoshoreline features have\nbeen removed by quarrying and grading in the area of Porphyry north to Highway\n91. Excellent exposures in highway road cuts still remain on the north side of\nHighway 91 just east of the 91-15 interchange and on the east side of U.S. 15\njust north of the interchange.\n\nSouth of Temescal Wash is a series of both younger and older alluvial fan\ndeposits emanating from the Santa Ana Mountains to the southeast. In the\nimmediate southwest corner of the quadrangle is a small exposure of sandstone\nand pebble conglomerate of the Sycamore Canyon member of the Puente Formation\nof early Pliocene and Miocene age and sandstone and conglomerate of undivided\nSespe and Vaqueros Formations of early Miocene, Oligocene, and late Eocene age.\n\nThe geologic map data base contains original U.S. Geological Survey data\ngenerated by detailed field observation recorded on 1:24,000 scale aerial\nphotographs. The map was created by transferring lines from the aerial\nphotographs to a 1:24,000 scale topographic base. The map was digitized and\nlines, points, and polygons were subsequently edited using standard ARC/INFO\ncommands. Digitizing and editing artifacts significant enough to display at a\nscale of 1:24,000 were corrected. Within the database, geologic contacts are\nrepresented as lines (arcs), geologic units are polygons, and site-specific\ndata as points. Polygon, arc, and point attribute tables (.pat, .aat, and\n.pat, respectively) uniquely identify each geologic datum.", "links": [ { diff --git a/datasets/USGS_OFR_2001_0497_1.0.json b/datasets/USGS_OFR_2001_0497_1.0.json index 89daabc316..b9bff42326 100644 --- a/datasets/USGS_OFR_2001_0497_1.0.json +++ b/datasets/USGS_OFR_2001_0497_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2001_0497_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data release contains mineral resource data for metallic and nonmetallic\nmineral sites in the State of Wyoming. Along with resource data is additional\ndata, such as mineralized areas and mining districts; mine, prospect and\ncommodity information; claim density by section; county boundaries;\nquadrangles; and simplified geology. All the data are provided in both\nspreadsheet format (Microsoft Excel) and in formats for two commonly used\nGeographic Information Systems (GIS) software packages (MapInfo and ESRI's\nArcView). Not only does GIS software allow the data to be shown as layers in\nmap views that can be displayed with various geographic and geologic data,\nbut the data can be queried and analyzed relative to data in any of the layers.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2001_164.json b/datasets/USGS_OFR_2001_164.json index c0c532dfa1..6c3ca21fe7 100644 --- a/datasets/USGS_OFR_2001_164.json +++ b/datasets/USGS_OFR_2001_164.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2001_164", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The two most important factors influencing the level of earthquake ground\nmotion at a site are the magnitude and distance of the earthquake. The map\navailable here shows the influence of a third important factor, the site\neffect: conditions at a particular location can increase (amplify) or decrease\nthe level of shaking that is otherwise expected for a given magnitude and\ndistance. Combining information about site effects with where and how often\nearthquakes of various magnitudes are likely to occur should provide improved\nassessments of seismic hazard.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2002_002.json b/datasets/USGS_OFR_2002_002.json index 68909f668b..b578281518 100644 --- a/datasets/USGS_OFR_2002_002.json +++ b/datasets/USGS_OFR_2002_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2002_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since 1980 the Coastal and Marine Geology Program of the U.S. Geological\nSurvey and Connecticut Department of Environmental Protection have conducted a\njoint program of cooperative geologic research in Long Island Sound and its\nvicinity. As part of this program, a highly successful regional-scale study of\ntheSeismic reflection acquisition illustration geologic framework was\ncompleted. Reconnaissance high-resolution seismic reflection data were\ncollected and used to establish the basic stratigraphy within the Sound and to\nmap the major geologic units (Needell and Lewis, 1984; Lewis and Needell, 1987;\nNeedell and others, 1987); field verification of the geologic interpretations\nof the seismic profiles was primarily accomplished with vibratory cores\n(Williams, 1981; Thomas, 1985; Neff and others, 1989). These interpretations\nwere in turn used to produce basin-wide syntheses of the late Quaternary\ndepositional history (Lewis and Stone, 1991; Stone and others, 1998; Lewis and\nDiGiacomo-Cohen, 2000).\n\nUnfortunately, the original seismic records and core logs were generated only\nin analog form. These unique paper documents, which are still under demand for\nindustrial applications and academic research, are fragile and have become\nragged from frequent use. The purpose of this report is to preserve these data\nby converting the seismic profiles, core descriptions, and ancillary reports\ninto digital form, and to organize these files into a product that can be more\nreadily accessed and disseminated. \n\nNot all of the existing high-resolution seismic-reflection surveys, collected\nin Long Island Sound through cooperatives with the U.S. Geological Survey and\nthe Connecticut Department of Environmental Protection, have been incorporated\ninto this report. These surveys, whose records are still in need of\npreprocessing and annotation, generally cover smaller areas along the\nConnecticut coast and were originally intended to provide additional detail to\nthe larger, more regional data sets presented herein. The digital release of\nthe omitted data sets is planned as part of a future product.", "links": [ { diff --git a/datasets/USGS_OFR_2002_206.json b/datasets/USGS_OFR_2002_206.json index 3e65ab2db4..a208f5ab05 100644 --- a/datasets/USGS_OFR_2002_206.json +++ b/datasets/USGS_OFR_2002_206.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2002_206", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The story of Lake Pontchartrain and its surrounding Basin is a fascinating\nsaga. Created at the end of the last Ice Age, this estuary is much more than\njust a magnificent natural resource. It has provided humans with sources of\nfood, as well as a means of communication, transportation and commerce. These\nand a host of other benefits have supported the growth of New Orleans and the\nsurrounding communities.\n\nToday, the 1632 km2 (630 mi2) Lake Pontchartrain is the centerpiece of the\n12,173 km2 (4,700 mi2) Pontchartrain drainage basin or watershed. The Basin\nencompasses land in 16 Louisiana parishes and 4 Mississippi counties. This vast\necological system includes lakes, rivers, bayous, forest, swamps and marshes.\nIt is habitat for countless species of fish, birds, mammals, reptiles and\nplants. It is also the most densely populated portion of Louisiana with almost\n1.5 million people residing immediately around the Lake.\n\nThe history of the environmental quality of the Pontchartrain Basin\ndemonstrates that no resource should be taken for granted or exploited. As the\npopulation grew in the Twentieth Century, use and, unfortunately, abuse of this\nnationally important estuary also grew. By the second half of the Twentieth\nCentury, Pontchartrain's environmental quality had deteriorated to a point that\nmany believed unrecoverable.\n\nResponsibility and stewardship are necessary for natural resource protection,\nrestoration and preservation. Recognizing these needs, area citizens began the\nSAVE OUR LAKE movement that led to the creation of the Lake Pontchartrain Basin\nFoundation in 1989. The Foundation's mission is to coordinate the overall\nrestoration and preservation of the entire Lake Pontchartrain Basin ecosystem.\nThe Environmental Atlas of the Lake Pontchartrain Basin will become one of our\ntools to help accomplish that mission.\n\nThe Environmental Atlas of the Lake Pontchartrain Basin is more than a summary\nof Pontchartrain's ecology. It presents information about geology, land cover,\ntypes of shorelines, biological resources, flow patterns, significant storms,\ngrowth trends and more. This Atlas is more of a directory to the Basin's\nenvironment. Hopefully, it will become an easily understandable reference for\nstudents and the public as well as a readily used source for professionals.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_080.json b/datasets/USGS_OFR_2003_080.json index 435ed0113d..fbb38fff58 100644 --- a/datasets/USGS_OFR_2003_080.json +++ b/datasets/USGS_OFR_2003_080.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_080", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aeromagnetic anomalies are due to variations in the Earth's magnetic field\ncaused by the uneven distribution of magnetic minerals (primarily magnetite) in\nthe rocks that make up the upper part of the Earth's crust. The features and\npatterns of the aeromagnetic anomalies can be used to delineate details of\nsubsurface geology including the locations of buried faults, magnetite-bearing\nrocks, and the thickness of surficial sedimentary rocks (which are generally\nnon-magnetic). This information is valuable for mineral exploration, geologic\nmapping, and environmental studies.\n\nThe New Jersey aeromagnetic map in this report is constructed from grids that\ncombine aeromagnetic data (see data processing details) collected in eight\nseparate aeromagnetic surveys flown between 1950 and 1979. The data from these\nsurveys are of varying quality. The design and specifications (terrain\nclearance, flight line separation, flight direction, analog/digital recording,\nnavigation, and reduction procedures) may vary between surveys depending on the\npurpose of the project and the technology of that time. All of the pre-1976\ndata are available only on hand-contoured analog maps and had to be digitized.\nThese maps were digitized along flight-line/contour-line intersections, which\nis considered to be the most accurate method of recovering the original data.\nDigitized data are available as USGS Open File Report 99-557. All surveys have\nbeen continued to 304.8 meters (1000 feet) above ground and then blended or\nmerged together. The merging of grids and production of images were created\nusing a PC version of Geosoft/OASIS montaj software. An index map and data\ntable gives an overview of the original surveys and summarizes the\nspecifications of the surveys. The resulting grid has a data interval of 500 m\nand can be downloaded. A color-shaded relief image of the grid is shown on the\nopening page of this web report.\n\nThis grid is an interim product. Considerable editing of digital flight line\ndata was undertaken for survey 3144 to reduce leveling inconsistencies between\nadjacent flight lines, most notably in the southern part of the state. Anomaly\nresolution is only fair in the northern portion of this survey, which was flown\nat one-mile flight line separation, where the source rocks are at or near the\nsurface. In these areas of this survey where the anomalies run roughly\nparallel to the flight lines, the gridding process produces a 'string of\npearls' effect. Improved resolution can only be rectified by new surveys with\nmore closely spaced flight lines. Heavy strike filtering in the direction of\nthe flight lines was necessary to reduce flight line striping for two digital\nsurveys (5004 and 6027). Where local high-resolution surveys were not\navailable, in either digital or digitized format, we used aeromagnetic data\ncollected by the National Uranium Resource Evaluation (NURE) program of the\nU.S. Department of Energy, which are available in digital format and together\ncover the entire state. However, because magnetic surveying was not the\nprimary objective in the design of the NURE surveys, these data are subject to\ncertain limitations. Although the NURE surveys were flown at elevations close\nto the reduction datum level, the spacing between flight lines generally ranged\nfrom 4.8 to 9.6 km (3 to 6 mile). In some areas of the U.S., detailed NURE\nsurveys were flown with a finer line spacing, usually at a 0.4 km (0.25 mile)\ninterval. In New Jersey, the NURE program flew the Reading Prong (5004) at\nthis interval.\n\nThis New Jersey aeromagnetic compilation is one part of a national digital\ncompilation by the U.S. Geological Survey. Certain characteristics are common\nto all of the State compilations. Whereas surveys are typically flown either\nat a constant elevation above sea level or draped to a constant mean terrain\nclearance, the standard selected for this national compilation is a survey\nelevation of 305 m (1000 ft) above mean terrain. All of the surveys used in\nthe New Jersey compilation were flown at either 122 m (400 ft) or 152 m (500\nft) above terrain. To conform to the national standard, the entire State grid\nwas analytically continued upward to 305 m (1000 ft) above ground (Hildenbrand,\n1983).\n\nThis aeromagnetic compilation supercedes a prior report (Snyder, 1992)\nreleasing the same data as three separate grids on 5.25\" floppies. The same\ndata have since been reprocessed to produce better results.\n\nThis project was supported by the Mineral Resource and Geologic Mapping\nPrograms of the USGS. Thanks to USGS colleagues Pat Hill and Robert Kucks for\ntheir assistance in preparing this report.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_090_1.0.json b/datasets/USGS_OFR_2003_090_1.0.json index 5210449090..d6a16b349a 100644 --- a/datasets/USGS_OFR_2003_090_1.0.json +++ b/datasets/USGS_OFR_2003_090_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_090_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data release contains mineral resource data for metallic and nonmetallic\nmineral sites in the State of Colorado. Along with the resource data, there is\nadditional data, such as mineralized areas and mining districts; mine, prospect\nand commodity information; claim density by section; county boundaries;\nquadrangles; and simplified geology. All the geographic data are provided in\nformats for two commonly used Geographic Information Systems (GIS) software\npackages (MapInfo and ESRI?s ArcView). Not only does GIS software allow the\ndata to be shown as layers in ?map? views that can be displayed with various\ngeographic and geologic data, but the data can be queried and analyzed relative\nto data in any of the layers. Free shareware, ArcExplorer, is provided with\nthis report so users may display the data in ?map? views and query the various\ndatasets (Appendix A) without requiring a GIS program such as Arc/Info1,\nArcView1, or MapInfo1. Additional data, such as original and unedited mine and\nprospect files, bibliography and references, and text are provided in\nappropriate formats such as in spreadsheets (Microsoft Excel), or documents\n(text, WordPerfect, or Microsoft Word).\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_095_1.1.json b/datasets/USGS_OFR_2003_095_1.1.json index b77643e87b..5b5f8c9dcd 100644 --- a/datasets/USGS_OFR_2003_095_1.1.json +++ b/datasets/USGS_OFR_2003_095_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_095_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows faults and folds in the state of Oregon that exhibit evidence of\nQuaternary deformation, and includes data on timing of most recent movement,\nsense of movement, slip rate, and continuity of surface expression. The primary\npurpose of this compilation is for use in earthquake-hazard evaluations.\nPaleoseismic studies, which evaluate the history of surface faulting or\ndeformation along structures with evidence of Quaternary movement, provide a\nlong-term perspective that augments the short historic records of seismicity in\nmany regions. Published or publicly available data are the primary sources of\ndata used to compile this report.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_096_1.0.json b/datasets/USGS_OFR_2003_096_1.0.json index 3f55b482ac..fd0b9bb4c8 100644 --- a/datasets/USGS_OFR_2003_096_1.0.json +++ b/datasets/USGS_OFR_2003_096_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_096_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set maps and describes the geology of the Valjean Hills 7.5' quadrangle, San Bernardino County, California.", "links": [ { diff --git a/datasets/USGS_OFR_2003_102_1.0.json b/datasets/USGS_OFR_2003_102_1.0.json index 286486ee0f..d77b16561a 100644 --- a/datasets/USGS_OFR_2003_102_1.0.json +++ b/datasets/USGS_OFR_2003_102_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_102_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geologic Map and Digital Database of the Romoland 7.5' Quadrangle,\nRiverside County, California report contains a digital geologic map database of\nthe Romoland 7.5' quadrangle, Riverside County, California that includes:\n\n1. ARC/INFO version 7.2.1 coverages of the various elements of the geologic\nmap.\n\n2. A Postscript file to plot the geologic map on a topographic base, and\ncontaining a Correlation of Map Units diagram (CMU), a Description of Map Units\n(DMU), and an index map.\n\n3. Portable Document Format (.pdf) files of:\na. This Readme; includes in Appendix I, data contained in rom_met.txt\nb. The same graphic as plotted in 2 above. Test plots have not produced precise\n1:24,000-scale map sheets. Adobe Acrobat page size setting influences map\nscale.\n\nThe Correlation of Map Units and Description of Map Units is in the editorial\nformat of USGS Geologic Investigations Series (I-series) maps but has not been\nedited to comply with I-map standards. Within the geologic map data package,\nmap units are identified by standard geologic map criteria such as\nformationname, age, and lithology. Where known, grain size is indicated on the\nmap by a subscripted letter or letters following the unit symbols as follows:\nlg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay;\ne.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous.\nMultiple letters are used for more specific identification or for mixed units,\ne.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a\ncompound symbol; e.g., Qyf2sc.\n\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_103_1.0.json b/datasets/USGS_OFR_2003_103_1.0.json index 9d369e37b4..c03dc5e80b 100644 --- a/datasets/USGS_OFR_2003_103_1.0.json +++ b/datasets/USGS_OFR_2003_103_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_103_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geologic Map and Digital Database of the Bachelor Mountain 7.5' Quadrangle,\nRiverside County, California contains a digital geologic map database of the\nBachelor Mountain 7.5 - quadrangle, Riverside County, California that includes:\n\n1. ARC/INFO (Environmental Systems Research Institute, http://www.esri.com)\nversion 7.2.1 coverages of the various elements of the geologic map.\n\n2. A Postscript file to plot the geologic map on a topographic base, and\ncontaining a Correlation of Map Units diagram (CMU), a Description of Map Units\n(DMU), and an index map.\n\n3. Portable Document Format (.pdf) files of:\na. This Readme; includes in Appendix I, data contained in bch_met.txt\nb. The same graphic as plotted in 2 above. Test plots have not produced precise\n1:24,000-\nscale map sheets. Adobe Acrobat page size setting influences map scale.\n\nThe Correlation of Map Units and Description of Map Units is in the editorial\nformat of USGS Geologic Investigations Series (I-series) maps but has not been\nedited to comply with I-map standards. Within the geologic map data package,\nmap units are identified by standard geologic map criteria such as\nformationname, age, and lithology. Where known, grain size is indicated on the\nmap by a subscripted letter or letters following the unit symbols as follows:\nlg, large boulders; b, boulder; g, gravel; a, arenaceous; s, silt; c, clay;\ne.g. Qyfa is a predominantly young alluvial fan deposit that is arenaceous.\nMultiple letters are used for more specific identification or for mixed units,\ne.g., Qfysa is a silty sand. In some cases, mixed units are indicated by a\ncompound symbol; e.g., Qyf2sc.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_108_1.0.json b/datasets/USGS_OFR_2003_108_1.0.json index f242a5e06e..d7811141a8 100644 --- a/datasets/USGS_OFR_2003_108_1.0.json +++ b/datasets/USGS_OFR_2003_108_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_108_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastal vulnerability index (CVI) was used to map the relative vulnerability\nof the coast to future sea-level rise within Gulf Islands National Seashore\n(GUIS) in Mississippi and Florida. The CVI ranks the following in terms of\ntheir physical contribution to sea-level rise-related coastal change:\ngeomorphology, regional coastal slope, rate of relative sea-level rise,\nshoreline change rates, mean tidal range and mean wave height. The rankings for\neach variable were combined and an index value calculated for 1-minute grid\ncells covering the park. The CVI highlights those regions where the physical\neffects of sea-level rise might be the greatest. This approach combines the\ncoastal system's susceptibility to change with its natural ability to adapt to\nchanging environmental conditions, yielding a quantitative, although relative,\nmeasure of the park's natural vulnerability to the effects of sea-level rise.\nThe Gulf Islands in Mississippi and Florida consist of stable and washover\ndominated portions of barrier beach backed by wetland and marsh. The areas\nlikely to be most vulnerable to sea-level rise are those with the highest\noccurrence of overwash, the highest rates of shoreline change, the gentlest\nregional coastal slope, and the highest rates of relative sea-level rise. The\nCVI provides an objective technique for evaluation and long-term planning by\nscientists and park managers.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_120.json b/datasets/USGS_OFR_2003_120.json index 90607d6ecf..d8aa37e391 100644 --- a/datasets/USGS_OFR_2003_120.json +++ b/datasets/USGS_OFR_2003_120.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_120", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetry and selected perspective views of 6 reef and coastal areas in\nNorthern Lake Michigan involves applying state of the art laser technology and\nderivative imagery to map the detailed morphology and of principal lake trout\nspawning sites on reefs in Northern Lake Michigan and to provide a geologic\ninterpretation. One objective was to identify the presence of ideal spawning\nsubstrate: shallow, \"clean\" gravel/cobble substrate, adjacent to deeper water.\nThis study is a pilot collaborative effort with the US Army Corps of Engineers\nSHOALS (Scanning Hydrographic Operational Airborne Lidar Survey) program. The\nhigh-definition maps are integrated with known and developing data on\nfisheries, as well as limited substrate sedimentology information and\nunderlying Paleozoic carbonate rocks.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_135.json b/datasets/USGS_OFR_2003_135.json index d63cc8beda..ba31f819a6 100644 --- a/datasets/USGS_OFR_2003_135.json +++ b/datasets/USGS_OFR_2003_135.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_135", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS is creating an integrated national database for digital state geologic\nmaps that includes stratigraphic, age, and lithologic information. The majority\nof the conterminous 48 states have digital geologic base maps available, often\nat scales of 1:500,000. This product is a prototype, and is intended to\ndemonstrate the types of derivative maps that will be possible with the\nnational integrated database. This database permits the creation of a number of\ntypes of maps via simple or sophisticated queries, maps that may be useful in a\nnumber of areas, including mineral-resource assessment, environmental\nassessment, and regional tectonic evolution.\n\nThis database is distributed with three main parts: a Microsoft Access 2000\ndatabase containing geologic map attribute data, an Arc/Info (Environmental\nSystems Research Institute, Redlands, California) Export format file containing\npoints representing designation of stratigraphic regions for the Geologic Map\nof Utah, and an ArcView 3.2 (Environmental Systems Research Institute,\nRedlands, California) project containing scripts and dialogs for performing a\nseries of generalization and mineral resource queries.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_150.json b/datasets/USGS_OFR_2003_150.json index 07e9e52a74..c6a78e9cbc 100644 --- a/datasets/USGS_OFR_2003_150.json +++ b/datasets/USGS_OFR_2003_150.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_150", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of the September, 2002 Geophysical Surveys of Bear Lake,\nUtah-Idaho operations, preliminarily reported here, were (1) to compile a\ndetailed bathymetric map of the lake using swath-mapping techniques, in order\nto provide baseline data for a variety of applications and studies, and (2) to\ncomplete a sidescan-sonar survey of the lake, providing a nearly complete\nacoustic image of the lake floor. Limited amounts of subbottom\nacoustic-reflection data (chirp) were also collected, along with samples of\nlake-floor sediments representative of different kinds of backscatter patterns.\nThese surveys followed an earlier subbottom acoustic-reflection survey (1997),\nusing boomer and 3.5 kHz systems (S. M. Colman, unpublished data).\n\nPast seismic-reflection work has indicated that faults secondary to the\neast-side master fault cut the lake floor. These faults were among the primary\ntargets of the sidescan-sonar survey. Preliminary interpretation of the data\nsuggests that the morphology of the fault scarps on the lake floor are too\nsubtle to be imaged by the sidescan-sonar system. However, some segments of the\nEast Bear Lake fault at the foot of the steep eastern margin of the lake, are\nvisible in the sidescan-sonar images. The other main targets of the\nsidescan-sonar survey were possible springs discharging at the lake floor.\nDischarge from such springs may be necessary to explain the chemistry and\nmineralogy of the lake sediments. A number of structures that appear to be\nrelated to spring discharge were observed in the sidescan-sonar images, and\nsediments at some of these features were sampled. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_225_1.0.json b/datasets/USGS_OFR_2003_225_1.0.json index 80d9655c20..9ab030355e 100644 --- a/datasets/USGS_OFR_2003_225_1.0.json +++ b/datasets/USGS_OFR_2003_225_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_225_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This geographic information system (GIS) data layer shows the dominant\nlithology and geochemical, termed lithogeochemical, character of near-surface\nbedrock in the New England region covering the states of Connecticut, Maine,\nMassachusetts, New Hampshire, Rhode Island, and Vermont. The bedrock units in\nthe map are generalized into groups based on their lithological composition\nand, for granites, geochemistry. Geologic provinces are defined as\ntime-stratigraphic groups that share common features of age of formation,\ngeologic setting, tectonic history, and lithology.\n\nThis data set incorporates data from digital maps of two NAWQA study areas, the\nNew England Coastal Basin (NECB) and the Connecticut, Housatonic, and Thames\nRiver Basins (CONN) areas and extends data to cover the states of Connecticut,\nMaine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The result is a\nregional dataset for the lithogeochemical characterization of New England (the\nlayer named NE_LITH). Polygons in the final coverage are attributed according\nto state, drainage area, geologic province, general rock type, lithogeochemical\ncharacteristics, and specific bedrock map unit. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_230_1.1.json b/datasets/USGS_OFR_2003_230_1.1.json index 83ce769a95..688acc7cca 100644 --- a/datasets/USGS_OFR_2003_230_1.1.json +++ b/datasets/USGS_OFR_2003_230_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_230_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Digital depth horizon compilations of the Alaskan North Slope and adjacent\narctic regions file report contains data that has been digitized and combined\nto create four detailed depth horizon grids spanning the Alaskan North Slope\nand adjacent offshore areas. These map horizon compilations were created to aid\nin petroleum system modeling and related studies.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_235.json b/datasets/USGS_OFR_2003_235.json index 3c5b262b95..823011fc95 100644 --- a/datasets/USGS_OFR_2003_235.json +++ b/datasets/USGS_OFR_2003_235.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_235", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) Woods Hole Field Center (WHFC), in\ncooperation with the USGS Water Resources Division conducted high-resolution\nseismic-reflection surveys along the nearshore areas of outer Cape Cod,\nMassachusetts from Chatham to Provincetown, Massachusetts.\n\nThe objectives of this investigation were to determine the stratigraphy of the\nnearshore in relation to the Quaternary stratigraphy of outer Cape Cod by\ncorrelating units between the nearshore and onshore and to define the geologic\nframework of the region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_236_1.0.json b/datasets/USGS_OFR_2003_236_1.0.json index 028196df7c..5f47de334e 100644 --- a/datasets/USGS_OFR_2003_236_1.0.json +++ b/datasets/USGS_OFR_2003_236_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_236_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Geochronological Data Base (NGDB) was established by the United\nStates Geological Survey (USGS) to collect and organize published isotopic\n(also known as radiometric) ages of rocks in the United States. The NGDB\n(originally known as the Radioactive Age Data Base, RADB) was started in 1974.\nA committee appointed by the Director of the USGS was given the mission to\ninvestigate the feasibility of compiling the published radiometric ages for the\nUnited States into a computerized data bank for ready access by the user\ncommunity. A successful pilot program, which was conducted in 1975 and 1976 for\nthe State of Wyoming, led to a decision to proceed with the compilation of the\nentire United States.\n\nFor each dated rock sample reported in published literature, a record\ncontaining information on sample location, rock description, analytical data,\nage, interpretation, and literature citation was constructed and included in\nthe NGDB. The NGDB was originally constructed and maintained on a mainframe\ncomputer, and later converted to a Helix Express relational database maintained\non an Apple Macintosh desktop computer. The NGDB and a program to search the\ndata files were published and distributed on Compact Disc-Read Only Memory\n(CD-ROM) in standard ISO 9660 format as USGS Digital Data Series DDS-14\n(Zartman and others, 1995). As of May 1994, the NGDB consisted of more than\n18,000 records containing over 30,000 individual ages, which is believed to\nrepresent approximately one-half the number of ages published for the United\nStates through 1991.\n\nBecause the organizational unit responsible for maintaining the database was\nabolished in 1996, and because we wanted to provide the data in more usable\nformats, we have reformatted the data, checked and edited the information in\nsome records, and provided this online version of the NGDB.\n\nThis report describes the changes made to the data and formats, and provides\ninstructions for the use of the database in geographic information system (GIS)\napplications. The data are provided in *.mdb (Microsoft Access), *.xls\n(Microsoft Excel), and *.txt (tab-separated value) formats. We also provide a\nsingle non-relational file that contains a subset of the data for ease of use.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_241_1.0.json b/datasets/USGS_OFR_2003_241_1.0.json index 2146d21075..762bc16aa6 100644 --- a/datasets/USGS_OFR_2003_241_1.0.json +++ b/datasets/USGS_OFR_2003_241_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_241_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Contaminated Sediments Database for Long Island Sound and the New York\nBight provides a compilation of published and unpublished sediment texture and\ncontaminant data. This report provides maps of several of the contaminants in\nthe database as well as references and a section on using the data to assess\nthe environmental status of these coastal areas. The database contains\ninformation collected between 1956-1997; providing an historical foundation for\nfuture contaminant studies in the region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_247_1.0.json b/datasets/USGS_OFR_2003_247_1.0.json index 586a9053fd..9c3b03e668 100644 --- a/datasets/USGS_OFR_2003_247_1.0.json +++ b/datasets/USGS_OFR_2003_247_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_247_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report consists of a compilation of twelve digital geologic maps provided\nin ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps\nconsisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000\nscale county maps. This publication presents a digital composite of these data\nintact and without modification across quadrangle boundaries to resolve\ngeologic unit discontinuities. An ESRI ArcView shapefile formatted version and\nAdobe Acrobat (pdf) plot file of the compiled digital map are also provided.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_265.json b/datasets/USGS_OFR_2003_265.json index 41d90944c1..ad81421630 100644 --- a/datasets/USGS_OFR_2003_265.json +++ b/datasets/USGS_OFR_2003_265.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_265", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An experimental water release from the Glen Canyon Dam into the Colorado River\nabove Grand Canyon was conducted in September 2000 by the U.S. Bureau of\nReclamation. The U.S. Geological Survey (USGS) conducted sidescan sonar surveys\nbetween Glen Canyon Dam (mile -15) and Diamond Creek (mile 220), Arizona (mile\ndesignations after Stevens, 1998) to determine the sediment characteristics of\nthe Colorado River bed before and after the release. The first survey\n(R3-00-GC, 28 Aug to 5 Sep 2000) was conducted before the release when the\nriver was at its Low Summer Steady Flow (LSSF) of 8,000 cfs. The second survey\n(R4-00-GC, 10 to 18 Sep 2000) was conducted immediately after the September\n2000 experimental release when the average daily flow was as high as 30,800 cfs\nas measured below Glen Canyon Dam (Figure 2). Riverbed sediment properties\ninterpreted from the sidescan sonar images include sediment type and sandwaves;\noverall changes in these properties between the two surveys were calculated.\n\nSidescan sonar data from the USGS surveys were processed for segments of the\nColorado River from Glen Canyon Dam (mile -15) to Phantom Ranch (mile 87.7,\nFigure 3). The surveys targeted pools between rapids that are part of the Grand\nCanyon Monitoring and Research Center (GCMRC http://www.gcmrc.gov/) physical\nsciences study.\n\nMaps interpreted from the sidescan sonar images show the distribution of\nsediment types (bedrock, boulders, pebbles or cobbles, and sand) and the extent\nof sandwaves for each of the pre- and post-flow surveys. The changes between\nthe two surveys were calculated with spatial arithmetric and had properties of\nfining, coarsening, erosion, deposition, and the appearance or disappearance of\nsandwaves.\n\nThis report describes GIS spatial data files for this project and provides\nexamples of the data from the Colorado River near mile 2 below the confluence\nof the Paria and Colorado Rivers. The complete data set includes sidescan sonar\nimages and interpreted map files for each of the pre- and post-flow surveys and\nthe changes between the segments of rivers.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_267.json b/datasets/USGS_OFR_2003_267.json index 8f03963d81..b35b0f410e 100644 --- a/datasets/USGS_OFR_2003_267.json +++ b/datasets/USGS_OFR_2003_267.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_267", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alaska Volcano Observatory (AVO), a cooperative program of the U.S.\nGeological Survey, the Geophysical Institute of the University of Alaska\nFairbanks, and the Alaska Division of Geological and Geophysical Surveys, has\nmaintained seismic monitoring networks at historically active volcanoes in\nAlaska since 1988 (Power and others, 1993; Jolly and others, 1996; Jolly and\nothers, 2001; Dixon and others, 2002). The primary objectives of this program\nare the seismic monitoring of active, potentially hazardous, Alaskan volcanoes\nand the investigation of seismic processes associated with active volcanism.\nThis catalog presents the basic seismic data and changes in the seismic\nmonitoring program for the period January 1, 2002 through December 31, 2002.\nAppendix G contains a list of publications pertaining to seismicity of Alaskan\nvolcanoes based on these and previously recorded data. The AVO seismic network\nwas used to monitor twenty-four volcanoes in real time in 2002. These include\nMount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine\nVolcano, Katmai Volcanic Group (Snowy Mountain, Mount Griggs, Mount Katmai,\nNovarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater,\nMount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin\nVolcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Great\nSitkin Volcano, and Kanaga Volcano (Figure 1). Monitoring highlights in 2002\ninclude an earthquake swarm at Great Sitkin Volcano in May-June; an earthquake\nswarm near Snowy Mountain in July-September; low frequency (1-3 Hz) tremor and\nlong-period events at Mount Veniaminof in September-October and in December;\nand continuing volcanogenic seismic swarms at Shishaldin Volcano throughout the\nyear. Instrumentation and data acquisition highlights in 2002 were the\ninstallation of a subnetwork on Okmok Volcano, the establishment of telemetry\nfor the Mount Veniaminof subnetwork, and the change in the data acquisition\nsystem to an EARTHWORM detection system. AVO located 7430 earthquakes during\n2002 in the vicinity of the monitored volcanoes. This catalog includes: (1) a\ndescription of instruments deployed in the field and their locations; (2) a\ndescription of earthquake detection, recording, analysis, and data archival\nsystems; (3) a description of velocity models used for earthquake locations;\n(4) a summary of earthquakes located in 2002; and (5) an accompanying UNIX\ntar-file with a summary of earthquake origin times, hypocenters, magnitudes,\nand location quality statistics; daily station usage statistics; and all\nHYPOELLIPSE files used to determine the earthquake locations in 2002.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2003_85_1.0.json b/datasets/USGS_OFR_2003_85_1.0.json index 425bd6cd17..29d63193ca 100644 --- a/datasets/USGS_OFR_2003_85_1.0.json +++ b/datasets/USGS_OFR_2003_85_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2003_85_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nearshore benthic habitat of the Santa Barbara coast and Channel Islands\nsupports diverse marine life that is commercially, recreationally, and\nintrinsically valuable. Some of these resources are known to be endangered\nincluding a variety of rockfish and the white abalone. Agencies of the state of\nCalifornia and the United States have been mandated to preserve and enhance\nthese resources. Data from sidescan sonar, bathymetry, video and dive\nobservations, and physical samples are consolidated in a geographic information\nsystem (GIS). The GIS provides researchers and policymakers a view of the\nrelationship among data sets to assist scienctific research and to help with\neconomic and social policy-making decisions regarding this protected\nenvironment.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1007_1.0.json b/datasets/USGS_OFR_2004_1007_1.0.json index 0f4f6cc5a6..fcf709c817 100644 --- a/datasets/USGS_OFR_2004_1007_1.0.json +++ b/datasets/USGS_OFR_2004_1007_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1007_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landscape features in the Mojave National Preserve are a product of ongoing\nprocesses involving tectonic forces, weathering, and erosion. Long-term\nclimatic cycles (wet and dry periods) have left a decipherable record preserved\nas landform features and sedimentary deposits. This website provides and\nintroduction to climate-driven desert processes influencing landscape features\nincluding stream channels, alluvial fans, playas (dry lakebeds), dunes, and\nmountain landscapes. Bedrock characteristics, and the geometry of past and\nongoing faulting, fracturing, volcanism, and landscape uplift and subsidence\ninfluence the character of processes happening at the surface.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1008_1.0.json b/datasets/USGS_OFR_2004_1008_1.0.json index 61809b2cc2..96a142dd7d 100644 --- a/datasets/USGS_OFR_2004_1008_1.0.json +++ b/datasets/USGS_OFR_2004_1008_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1008_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study of geophysical terranes within and surrounding the Great Basin of\nthe western United States integrates geophysical and geologic data to\nprovide new insights on basement composition and structure at local,\nintermediate, and regional scales. Potential field (gravity and magnetic)\nstudies are particularly useful to define the location, depth, and extent of\nburied basement sources and fundamental structural or compositional boundaries.\nThey especially serve in imaging the subsurface in areas of extensive Cenozoic\ncover or where surface outcrops may be detached from the deeper crust.\nIdentifying buried compositional or structural boundaries has applications, for\nexample, in tectonic and earthquake hazard studies as they may reflect unmapped\nor buried faults. In many places, such features act as guides or barriers to\nfluid or magma flow or form favorable environments for mineralization and are\ntherefore important to mineral, groundwater, and geothermal studies. This work\nserves in assessing the potential for undiscovered mineral deposits and\nprovides important long-term land-use planning information. The primary\ncomponent of this report is a set of geophysical maps with anomalies that are\nlabeled and keyed to tables containing information on the anomaly and its\nsource. Maps and data tables are provided in a variety of formats (tab\ndelimited text, Microsoft Excel, PDF, and ArcGIS) for readers to review and\ndownload. The PDF formatted product allows the user to easily move between\nfeatures on the maps and their entries in the tables, and vice-versa.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1009.json b/datasets/USGS_OFR_2004_1009.json index 02f25872e2..56f7c2a8f7 100644 --- a/datasets/USGS_OFR_2004_1009.json +++ b/datasets/USGS_OFR_2004_1009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ugashik-Mount Peulik volcanic center, 550 km southwest of Anchorage on the\nAlaska Peninsula, consists of the late Quaternary 5-km-wide Ugashik caldera and\nthe stratovolcano Mount Peulik built on the north flank of Ugashik. The center\nhas been the site of explosive volcanism including a caldera-forming eruption\nand post-caldera dome-destructive activity. Mount Peulik has been formed\nentirely in Holocene time and erupted in 1814 and 1845. A large lava dome\noccupies the summit crater, which is breached to the west. A smaller dome is\nperched high on the southeast flank of the cone. Pyroclastic-flow deposits form\naprons below both domes. One or more sector-collapse events occurred early in\nthe formation of Mount Peulik volcano resulting in a large area of\ndebris-avalanche deposits on the volcano's northwest flank. \n\nThe Ugashik-Mount Peulik center is a calcalkaline suite of basalt, andesite,\ndacite, and rhyolite, ranging in SiO2 content from 51 to 72 percent. The\nUgashik-Mount Peulik magmas appear to be co-genetic in a broad sense and their\ncompositional variation has probably resulted from a combination of fractional\ncrystallization and magma-mixing. \n\nThe most likely scenario for a future eruption is that one or more of the\nsummit domes on Mount Peulik are destroyed as new magma rises to the surface.\nDebris avalanches and pyroclastic flows may then move down the west and, less\nlikely, east flanks of the volcano for distances of 10 km or more. A new lava\ndome or series of domes would be expected to form either during or within some\nfew years after the explosive disruption of the previous dome. This cycle of\ndome disruption, pyroclastic flow generation, and new dome formation could be\nrepeated several times in a single eruption. \n\nThe volcano poses little direct threat to human population as the area is\nsparsely populated. The most serious hazard is the effect of airborne volcanic\nash on aircraft since Mount Peulik sits astride heavily traveled air routes\nconnecting the U.S. and Europe to Asia. Activity of the type described could\nproduce eruption columns to heights of 15 km and result in significant amounts\nof ash 250-300 km downwind. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1010_1.0.json b/datasets/USGS_OFR_2004_1010_1.0.json index 2b5e614b77..303380268e 100644 --- a/datasets/USGS_OFR_2004_1010_1.0.json +++ b/datasets/USGS_OFR_2004_1010_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1010_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Workshops in 2001 and 2002 were convened to determine critical issues in the\ndevelopment of tsunami inundation maps for the Puget Sound region. The Tsunami\nInundation Mapping Effort (TIME) is conducted under the multi-agency National\nTsunami Hazard Mitigation Program (NTHMP). The Puget Sound Tsunami/Landslide\nWorkshop in 2001 focused on integrated tsunami research involving a wide range\nof research studies and tsunami hazard mitigation issues. The 2002 Puget Sound\nTsunami Sources workshop (Gonz\u00e1lez et al., 2003) made specific recommendations\nfor tsunami source modeling and improving our state of knowledge for sources in\nthe Puget Sound region. One of the recommendations stated in Gonz\u00e1lez et al.\n(2003) is \"Develop methods to assess the sensitivity of coastal areas to\ntsunami inundation, based on multiple simulations that reflect the possible\nrange of variations in the source parameters.\" Tsunami inundation models rely\nheavily on the imposed initial conditions which, for an earthquake source, is\nthe coseismic vertical displacement field. For example, Koshimura et al. (2002)\nuse the geologic uplift observations (Buknam et al., 1992) to constrain the\nslip distribution for the event that occurred 1100 years ago, resulting in an\naverage slip of 3.7 m and a magnitude of 7.6. Walsh et al. (2003) develop a\ntsunami inundation map for Elliot Bay based on a M 7.3 earthquake and the\ngeologic uplift observations from the 1100 y.b.p. event as in Koshimura et al.\n(2002), though they use a constant fault dip of 60\u00b0 rather than different dips\nfor deep and shallow segments. The objective of this report is to examine how\ncoseismic vertical displacement from a smaller M 6.5 Seattle Fault earthquake\n(as in Hartzell et al., 2002) is affected by structural heterogeneity and\ndifferent slip distribution patterns.\n\nThe three-dimensional crustal structure of the Puget Sound region has recently\nbeen defined using shallow seismic reflection data (Pratt et al., 1997; Johnson\net al., 1999) and reflection and wide-angle recordings from the large-scale\nSHIPS experiments (e.g., Brocher et al., 2001; ten Brink et al., 2002). The\npresence of a deep sedimentary basin (Seattle Basin) adjacent to the Seattle\nFault has led to the question of whether structural heterogeneity has an effect\non our estimate of vertical displacement for earthquake scenarios in the\nregion. We use a three-dimensional elastic finite-element model (Yoshioka et\nal., 1989) to calculate vertical displacements from rupture on a two-segment\n(deep and shallow) Seattle fault using a heterogeneous crustal structure.\nSimilar studies by Geist and Yoshioka (1996) and Masterlark et al. (2001) used\nthree-dimensional, finite-element models (FEM) to study the effect of\nstructural heterogeneity on coseismic displacement fields. Results for the\nPuget Sound study are compared to calculations using a homogeneous structure as\nassumed with conventional elastic dislocation solutions. Effects of slip\ndistribution patterns on vertical displacement is computed using the stochastic\nsource model adopted for tsunami studies by Geist (2002). Finally, we examine\nan alternate model for shallow faulting proposed by ten Brink et al. (2002) and\nBrocher et al. (submitted) and its effect on the vertical displacement field.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1011_1.0.json b/datasets/USGS_OFR_2004_1011_1.0.json index 8c0821530b..3b89f6ba54 100644 --- a/datasets/USGS_OFR_2004_1011_1.0.json +++ b/datasets/USGS_OFR_2004_1011_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1011_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These maps present preliminary assessments of the probability of debris-flow\nactivity and estimates of peak discharges that can potentially be generated by\ndebris flows issuing from basins burned by the Cedar and Paradise Fires of\nOctober 2003 in southern California in response to 25-year, 10-year, and 2-year\nrecurrence, 1-hour duration rain storms. The probability maps are based on the\napplication of a logistic multiple regression model that describes the percent\nchance of debris-flow production from an individual basin as a function of\nburned extent, soil properties, basin gradients, and storm rainfall. The\npeak-discharge maps are based on application of a multiple-regression model\nthat can be used to estimate debris-flow peak discharge at a basin outlet as a\nfunction of basin gradient, burn extent, and storm rainfall. Probabilities of\ndebris-flow occurrence for the Cedar Fire range between 0 and 98% and estimates\nof debris-flow peak discharges range between 893 and 5,987 ft3/s (25 to 170\nm3/s). Basins burned by the Paradise Fire show probabilities for debris-flow\noccurrence between 2 and 98%, and peak discharge estimates between 1,814 and\n5,980 ft3/s (51 and 169 m3/s). These maps are intended to identify those basins\nthat are most prone to the largest debris-flow events and provide critical\ninformation for the preliminary design of mitigation measures and for the\nplanning of evacuation timing and routes.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1013_1.0.json b/datasets/USGS_OFR_2004_1013_1.0.json index 6713fb792c..0f3a103f79 100644 --- a/datasets/USGS_OFR_2004_1013_1.0.json +++ b/datasets/USGS_OFR_2004_1013_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1013_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "South Carolina's Grand Strand is a heavily populated coastal region that\nsupports a large tourism industry. Like most densely developed coastal\ncommunities, the potential for property damage and lost revenues associated\nwith coastal erosion and vulnerability to severe storms is of great concern. In\nresponse to these concerns, the U.S. Geological Survey (USGS) and the South\nCarolina Sea Grant Consortium have chosen to focus upon the Grand Strand (the\narcuate strand of beaches between the North Carolina Border and Winyah Bay, SC)\nand adjacent Long Bay as a portion of Phase II of the South Carolina/Georgia\nCoastal Erosion Study (SC/GCES). \n\nPhase I of the SC/GCES (1994 - 1999) focused upon critical areas of erosion\nalong the central portion of the South Carolina coastline. Research conducted\nduring Phase I began to identify how physical processes, inlet-beach\ninteraction, framework geology and shoreline geometry combine to control\npatterns of erosion along the central South Carolina coast. Phase II of SC/GCES\n(1999 - present) was designed to gain a further understanding of the factors\naffecting shoreline change within northern South Carolina and Georgia. Specific\ngoals of the Phase II study include: 1) quantifying historic shoreline change\nand identifying erosional hotspots; 2) mapping geologic framework and\ndetermining its role in the area's coastal evolution; and 3) calculating a\nsediment budget and identifying transport mechanisms within the study area.\n\nIn November 1999, to address the second goal of Phase II of the SC/GCES, the\nUSGS, Coastal Carolina University (CCU) and Scripps Institution of Oceanography\n(SIO) began a program to systematically map the geologic framework within the\nSouth Carolina segment of Long Bay. Data sources used to produce these maps\ninclude high-resolution sidescan-sonar, interferometric sonar swath bathymetry\nand sub-bottom profiling. Surface sediment samples, vibracores and video data\nprovide groundtruth for the geophysical data. The goals of the program include\ndetermining regional-scale sand-resource availability (needed for ongoing beach\nnourishment projects) and investigating the role that inner-shelf morphology\nand geologic framework play in the evolution of this portion of coastal South\nCarolina.\n\nThis report presents preliminary maps generated through integrated\ninterpretation of geophysical data, which detail the geometries of Cretaceous\nand Tertiary continental shelf deposits, show the location and extent of\npaleochannel incisions, and define a regional transgressive unconformity and\noverlying bodies of reworked sediment. Defining the shallow sub-surface\ngeologic framework will provide a base for future process-oriented studies and\nprovide insight into coastal evolution.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1014.json b/datasets/USGS_OFR_2004_1014.json index fa8652d03b..6e09b8b13a 100644 --- a/datasets/USGS_OFR_2004_1014.json +++ b/datasets/USGS_OFR_2004_1014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recently, concerns about declining stocks of endangered anadromous salmonids in\nthe Columbia River basin raised the issue of restoration of riverine functions\nin this and other Columbia and Snake River reservoirs (ISG, 2000; Dauble and\nothers, 2003). One option for restoration of riverine functions includes\nlowering water levels within selected reservoirs such as the John Day\nReservoir. Questions about how much sediment has been trapped by this dam\nwarranted a detailed study of the floor of the reservoir to assess changes that\nhad occurred since impoundment. High-resolution geophysical mapping techniques\nwere employed to provide, to our knowledge, the first detailed view of the\nfloor of the reservoir since its formation. This geophysical \"road map\" in\nconcert with bottom video images, some sediment samples, and historical data\ncollected prior to creation of the reservoir were incorporated into a GIS. The\nsubsequent text summarizes the techniques used in this study. It also provides\na preliminary analysis of the results and a background for the GIS that\naccompanies this report.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1020_1.0.json b/datasets/USGS_OFR_2004_1020_1.0.json index 79a4e2b9a4..345f6a5570 100644 --- a/datasets/USGS_OFR_2004_1020_1.0.json +++ b/datasets/USGS_OFR_2004_1020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastal vulnerability index (CVI) was used to map relative vulnerability of\nthe coast to future sea-level rise within Assateague Island National Seashore\n(ASIS) in Maryland and Virginia. The CVI ranks the following in terms of their\nphysical contribution to sea-level rise-related coastal change: geomorphology,\nregional coastal slope, rate of relative sea-level rise, shoreline change\nrates, mean tidal range and mean wave height. Rankings for each variable were\ncombined and an index value calculated for 1-minute grid cells covering the\npark. The CVI highlights those regions where the physical effects of sea-level\nrise might be the greatest. This approach combines the coastal system's\nsusceptibility to change with its natural ability to adapt to changing\nenvironmental conditions, yielding a quantitative, although relative, measure\nof the park's natural vulnerability to the effects of sea-level rise. The CVI\nprovides an objective technique for evaluation and long-term planning by\nscientists and park managers. Assateague Island consists of stable and washover\ndominated portions of barrier beach backed by wetland and marsh. The areas\nwithin Assateague that are likely to be most vulnerable to sea-level rise are\nthose with the highest occurrence of overwash and the highest rates of\nshoreline change.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1021_1.0.json b/datasets/USGS_OFR_2004_1021_1.0.json index 024f751b11..6f2f6b83f1 100644 --- a/datasets/USGS_OFR_2004_1021_1.0.json +++ b/datasets/USGS_OFR_2004_1021_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1021_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastal vulnerability index (CVI) was used to map the relative vulnerability\nof the coast to future sea-level rise within Olympic National Park (OLYM),\nWashington. The CVI scores the following in terms of their physical\ncontribution to sea-level rise-related coastal change: geomorphology, regional\ncoastal slope, rate of relative sea-level rise, shoreline change rates, mean\ntidal range and mean wave height. The rankings for each variable were combined\nand an index value calculated for 1-minute grid cells covering the park. The\nCVI highlights those regions where the physical effects of sea-level rise might\nbe the greatest. This approach combines the coastal system's susceptibility to\nchange with its natural ability to adapt to changing environmental conditions,\nyielding a quantitative, although relative, measure of the park's natural\nvulnerability to the effects of sea-level rise. The CVI provides an objective\ntechnique for evaluation and long-term planning by scientists and park\nmanagers. The Olympic National Park coast consists of rocky headlands, pocket\nbeaches, glacial-fluvial features, and sand and gravel beaches. The Olympic\ncoastline that is most vulnerable to sea-level rise are beaches in gently\nsloping areas.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1026.json b/datasets/USGS_OFR_2004_1026.json index 5d712b67ef..92921a9042 100644 --- a/datasets/USGS_OFR_2004_1026.json +++ b/datasets/USGS_OFR_2004_1026.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1026", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This online publication portrays regional data for pH, alkalinity, and specific\nconductance for stream waters and a multi-element geochemical dataset for\nstream sediments collected in the New England states of Connecticut, Maine,\nMassachusetts, New Hampshire, Rhode Island, and Vermont. A series of\ninterpolation grid maps portray the chemistry of the stream waters and\nsediments in relation to bedrock geology, lithology, drainage basins, and urban\nareas. A series of box plots portray the statistical variation of the chemical\ndata grouped by lithology and other features.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1038.json b/datasets/USGS_OFR_2004_1038.json index 9094b01881..66df0d97f1 100644 --- a/datasets/USGS_OFR_2004_1038.json +++ b/datasets/USGS_OFR_2004_1038.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1038", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The significant mineral deposit inventory supports the U.S. Geological Survey\nHeadwaters project, which will provide Federal land management agencies with\nbasic geologic and mineral resource information that can be used to manage\nnear-term mineral resource development activity. The Headwaters study is\nfocused on areas in Idaho lying north of the Snake River plain and in western\nMontana where a preponderance of the lands are managed by the U.S. Forest\nService. The scope of this mineral resource inventory embraces a broader\ngeographic area that includes all of Idaho, the western half of Montana and\nsmall portions of extreme eastern Oregon and Washington. \n\nThis inventory covers only significant mineral deposits. Significant deposits\nare those deposits where a mineral or natural material endowment occurs in such\na high concentration that it is reasonable to expect that recovery was or\ncould, in the future, be economically viable. Minimum endowments proposed by\nLong (personal communication) for 46 commodities have been used in this\ncompilation. For deposits of other commodities where minimum endowments have\nnot been established a default deposit size minimum of one million metric tons\nof ore has been utilized. A significant status has also been applied to\ndeposits where a commodity or material is of a highly unusual nature. \n\nData collection was limited to deposit attributes that reflect directly on the\nendowed size and location of a deposit, and ancillary information that can be\nused in assessing regional mineral resource potential. The data are organized\nin topical information categories that include name, location, deposit\nclassification, discovery date, production and resources, surface area,\ndevelopment status, and source of new information. Data were extracted from a\ndiverse array of sources that includes scientific, technical, and trade\npublications of public and private institutions, organizations, and\nassociations that follow and report on scientific, business, and environmental\nissues in the minerals industry; company financial reports, news releases, and\ntechnical reports available at company web sites; mineral information databases\nmaintained by Federal and state agencies involved with monitoring and\nregulating mining activities and compiling mining industry statistics; and oral\ncommunications with individual mining company personnel and with staff of\nFederal and state regulatory agencies. Several formatting conventions are used\nto indicate what the relative accuracy of the numerical data is believed to be.\n\nA total of 256 significant deposit sites are identified by location and\ndeposit-type. Production and resource figures are given in both English and\nmetric units and the approximate surface areas associated with three aspects of\ndeposit development are expressed in acres. Of the 256 sites, 208 have some\nhistory of past or present production, of which 23 are currently producing and\nmining could resume at 7 others on short notice with a rise in commodity\nprices. Within the 208 sites are 34 placer districts and two zeolite operations\nwherein mining activity on a small scale occurs intermittently. There are 166\nsites where the presence of a significant resource has been recognized, of\nwhich 49 have no prior history of development. Due to the presence of a\nsignificant resource, these 166 sites are candidates for consideration when\naddressing issues associated with management of near-term mineral development.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1039.json b/datasets/USGS_OFR_2004_1039.json index bfbe4447a4..359dd7433f 100644 --- a/datasets/USGS_OFR_2004_1039.json +++ b/datasets/USGS_OFR_2004_1039.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1039", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Western Idaho Suture Zone (WISZ) represents the boundary between crust\noverlying Proterozoic North American lithosphere and Late Paleozoic and\nMesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed\nplutons constituted of both arc and sialic components intrude the WISZ and in\nplaces are thrust over the accreted terranes. Pronounced variations in Sr, Nd,\nand O isotope ratios and in major and trace element composition occur across\nthe suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to\neast increase in initial 87Sr/86Sr ratios, traceable for over 300 km from\neastern Washington near Clarkston, east along the Clearwater River thorough a\nbend to the south of about 110\u00b0 from Orofino Creek to Harpster, and extending\nsouth-southwest to near Ola, Idaho, where Columbia River basalts conceal its\nextension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and\nbiotite from Jurassic and Early Cretaceous plutons in the accreted terranes are\nhighly discordant within about 10 km of the WISZ, exhibiting patterns of\nthermal loss caused by deformation, subsequent batholith intrusion, and rapid\nrise of the continental margin. Major crustal movements within the WISZ\ncommenced after about 135 Ma, but much of the displacement may have been\nlargely vertical, during and following emplacement of batholith-scale silicic\nmagmas. Deformation continued until at least 85 Ma and probably until 74 Ma,\nprogressing from south to north.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1049_1.0.json b/datasets/USGS_OFR_2004_1049_1.0.json index abdb4fb7bc..9bf1e1b96f 100644 --- a/datasets/USGS_OFR_2004_1049_1.0.json +++ b/datasets/USGS_OFR_2004_1049_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1049_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report presents a series of maps that describe the bathymetry and late\nQuaternary geology of the San Pedro shelf area as interpreted from\nseismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some\nof the seismic-reflection profiles were collected with Uniboom and 120-kJ\nsparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973\n(K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining\nseismic-reflection profiles were collected in 2000 using Geopulse boomer and\nminisparker during USGS cruise A-1-00-SC. The report consists of seven\noversized sheets:\n\n1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz\ntracklines used to map faults and folds on San Pedro Shelf.\n\n2. Maps of multibeam shaded bathymetric relief with faults and folds,\nand bathymetric contours.\n\n3. Isopach map of unconsolidated sediment, seismic-reflection\nprofile across the San Pedro shelf, seismic-reflection profile\nacross San Gabriel paleo-valley.\n\n4. Seismic-reflection profiles across the Palos Verdes Fault Zone.\n\n5. Geologic map and samples of Uniboom and 120-kJ sparker\nseismic-reflection profiles used to make the map.\n\n6. Map showing thickness of uppermost (Holocene?) sediment layer.\n\n7. Map of San Gabriel Canyon paleo-valley and associated\ndrainage basins.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1054.json b/datasets/USGS_OFR_2004_1054.json index 5db24347fb..089f8da867 100644 --- a/datasets/USGS_OFR_2004_1054.json +++ b/datasets/USGS_OFR_2004_1054.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1054", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Bluegill landslide, located in south-central Idaho, is part of a larger\nlandslide complex that forms an area in the Salmon Falls Creek drainage named\nSinking Canyon. The landslide is on public property administered by the U.S.\nBureau of Land Management (BLM). As part of ongoing efforts to address possible\npublic safety concerns, the BLM requested that the U.S. Geological Survey\n(USGS) conduct a preliminary hazard assessment of the landslide, examine\npossible mitigation options, and identify alternatives for further study and\nmonitoring of the landslide. This report presents the findings of that\nassessment based on a field reconnaissance of the landslide on September 24,\n2003, a review of data and information provided by BLM and researchers from\nIdaho State University, and information collected from other sources.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1058.json b/datasets/USGS_OFR_2004_1058.json index 61f693b54e..fd31b5e32c 100644 --- a/datasets/USGS_OFR_2004_1058.json +++ b/datasets/USGS_OFR_2004_1058.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1058", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alaska Volcano Observatory (AVO) tracks activity at the more than 40\nhistorically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24\nof these volcanoes are monitored with short-period seismometer networks. AVO's\nmonitoring program also includes daily analysis of satellite imagery supported\nby occasional over flights and compilation of pilot reports, observations of\nlocal residents, and observations of mariners. In 2002, AVO responded to\neruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska\n- Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and\nGreat Sitkin volcanoes.\n\nIn addition to responding to eruptive activity at Alaskan volcanoes, AVO also\ndisseminated information on behalf of the Kamchatkan Volcanic Eruption Response\nTeam (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy,\nBezymianny, Karymsky, and Chikurachki.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1064.json b/datasets/USGS_OFR_2004_1064.json index 97166a4a4b..4076b31681 100644 --- a/datasets/USGS_OFR_2004_1064.json +++ b/datasets/USGS_OFR_2004_1064.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1064", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastal vulnerability index (CVI) was used to map the relative vulnerability\nof the coast to future sea-level rise within Cape Hatteras National Seashore\n(CAHA) in North Carolina. The CVI ranks the following in terms of their\nphysical contribution to sea-level rise-related coastal change: geomorphology,\nregional coastal slope, rate of relative sea-level rise, historical shoreline\nchange rates, mean tidal range, and mean significant wave height. The rankings\nfor each variable were combined and an index value was calculated for 1-minute\ngrid cells covering the park. The CVI highlights those regions where the\nphysical effects of sea-level rise might be the greatest. This approach\ncombines the coastal system's susceptibility to change with its natural ability\nto adapt to changing environmental conditions, yielding a quantitative,\nalthough relative, measure of the park's natural vulnerability to the effects\nof sea-level rise. The CVI provides an objective technique for evaluation and\nlong-term planning by scientists and park managers. Cape Hatteras National\nSeashore consists of stable and washover dominated segments of barrier beach\nbacked by wetland and marsh. The areas within Cape Hatteras that are likely to\nbe most vulnerable to sea-level rise are those with the highest occurrence of\noverwash and the highest rates of shoreline change.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1067.json b/datasets/USGS_OFR_2004_1067.json index 9b0cbc8217..e96684cd53 100644 --- a/datasets/USGS_OFR_2004_1067.json +++ b/datasets/USGS_OFR_2004_1067.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1067", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) compiled a database of aggregate sites and geotechnical sample data for six counties - Ada, Boise, Canyon, Elmore, Gem, and Owyhee - in southwest Idaho as part of a series of studies in support of the Bureau of Land Management (BLM) planning process. Emphasis is placed on sand and gravel sites in deposits of the Boise River, Snake River, and other fluvial systems and in Neogene lacustrine deposits. Data were collected primarily from unpublished Idaho Transportation Department (ITD) records and BLM site descriptions, published Army Corps of Engineers (ACE) records, and USGS sampling data. The results of this study provides important information needed by land-use planners and resource managers, particularly in the BLM, to anticipate and plan for demand and development of sand and gravel and other mineral material resources on public lands in response to the urban growth in southwestern Idaho.\n\nThe aggregate database combines two data sets - site information and geotechnical sample data - into an integrated spatial database with 82 unique fields. The material source site data set includes information on 680 sites, and the geotechnical data set consists of selected information from 2,723 laboratory analyses of samples collected from many, but not all, of the sites. The 680 aggregate sites are divided into six classes: sand & gravel (614); rock quarry (43); cinder quarry (9); placer tailings (8); talus (4); and mine waste rock (2). Most importantly, the aggregate database includes detailed location information allowing individual sites to be located at least within a section and most often within a small parcel of a section. Additional information includes, but is not limited to: lithology-mineralogy or geologic formation (if known); surface ownership; size; production; permitting; agency; and number of samples. Geotechnical data include: lab number and test date; field parameters including sample location, type of material, and size; and the results of geotechnical analyses - gradation (grain size distribution), Los Angeles (LA) Degradation, sand equivalent, absorption, density, and several other tests. Ninety-five percent of the 2,723 geotechnical sample records include gradation data, and 72 percent of the samples have sand equivalent data. However, LA Degradation, absorption, and bulk density data are reported only in about 30 percent of the sample records.\n\nLarge volumes of geotechnical data reside in a variety of accessible but little-used archives maintained by local and county highway districts, state transportation bureaus, and federal engineering, construction and transportation agencies. Integration of good quality geotechnical lithogeochemical information, particularly in digital form suitable for geospatial analysis, can produce profoundly superior databases that may allow more accurate and reliable \"expert\" decision making and improved land use planning. The database that accompanies this report, structured for direct import into geographic information system (GIS) software, is the first step toward producing such an integrated geologic-geotechnical spatial database. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1069.json b/datasets/USGS_OFR_2004_1069.json index b5cd6c70fa..4b21c5f627 100644 --- a/datasets/USGS_OFR_2004_1069.json +++ b/datasets/USGS_OFR_2004_1069.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1069", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in\nsouth-central Alaska, began in April 1966. At three long-term sites in the\nresearch basin, the measurements included snow depth, snow density, heights of\nthe glacier surface and stratigraphic summer surfaces on stakes, and\nidentification of the surface materials. Calculations of the mass balance of\nthe surface strata-snow, new firn, superimposed ice, and old firn and ice mass\nat each site were based on these measurements. Calculations of fixed-date\nannual mass balances for each hydrologic year (October 1 to September 30), as\nwell as net balances and the dates of minimum net balance measured between\ntime-transgressive summer surfaces on the glacier, were made on the basis of\nthe strata balances augmented by air temperature and precipitation recorded in\nthe basin. From 1966 through 1995, the average annual balance at site A (590\nmeters altitude) was -4.06 meters water equivalent; at site B (1,070 meters\naltitude), was -0.90 meters water equivalent; and at site C (1,290 meters\naltitude), was +1.45 meters water equivalent.\n\nGeodetic determination of displacements of the mass balance stake, and glacier\nsurface altitudes was added to the data set in 1975 to detect the glacier\nmotion responses to variable climate and mass balance conditions. The average\nsurface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters\nper year at site B, and 37.2 meters per year at site C. The average surface\naltitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters\nat site C; the glacier surface altitudes rose and fell over a range of 19.4\nmeters at site A, 14.1 meters at site B, and 13.2 meters at site C. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1074.json b/datasets/USGS_OFR_2004_1074.json index c09a81dfc7..667dd1c3e8 100644 --- a/datasets/USGS_OFR_2004_1074.json +++ b/datasets/USGS_OFR_2004_1074.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1074", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Severe flooding occurred on June 4, 2002, in the Indian Creek Basin in Linn\nCounty, Iowa, following thunderstorm activity over east-central Iowa. The rain\ngage at Cedar Rapids, Iowa, recorded a 24-hour rainfall of 4.76 inches at 6:00\np.m. on June 4th. Radar indications estimated as much as 6 inches of rain fell\nin the headwaters of the Indian Creek Basin. Peak discharges on Indian Creek of\n12,500 cubic feet per second at County Home Road north of Marion, Iowa, and\n24,300 cubic feet per second at East Post Road in southeast Cedar Rapids, were\ndetermined for the flood. The recurrence interval for these peak discharges\nboth exceed the theoretical 500-year flood as computed using flood-estimation\nequations developed by the U.S. Geological Survey. Information about the basin\nand flood history, the 2002 thunderstorms and associated flooding, and a\nprofile of high-water marks are presented for selected reaches along Indian and\nDry Creeks.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1075.json b/datasets/USGS_OFR_2004_1075.json index 412a4a037e..92f0c88a4f 100644 --- a/datasets/USGS_OFR_2004_1075.json +++ b/datasets/USGS_OFR_2004_1075.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1075", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following geographic information system (GIS) data layers provide a digital\nformat for the map plate in Bulletin 1979 (Robinson et al., 1991), Bedrock\nGeology and Mineral Resources of the Knoxville 1 degree x 2 degree Quadrangle,\nTennessee,\nNorth Carolina, and South Carolina. This open-file report is meant to\nsupplement Bulletin 1979. The Knoxville 1 degree x 2 degree quadrangle spans\nthe Southern\nBlue Ridge physiographic province at its widest point from eastern Tennessee\nacross western North Carolina to the northwest corner of South Carolina. The\nquadrangle also contains small parts of the Valley and Ridge province in\nTennessee and the Piedmont province in North and South Carolina. The bedrock\ngeology for the coverage area is provided as a polygon coverage with bedrock\nunit information included. Mineral resources and geologic faults are provided\nas point and line files, respectively, to overlay the geology coverage. \nDetailed geologic information is provided in the attribute tables for these\nfiles, and .avl legend files are provided.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1083.json b/datasets/USGS_OFR_2004_1083.json index a2c78cdfd3..abf7fddf99 100644 --- a/datasets/USGS_OFR_2004_1083.json +++ b/datasets/USGS_OFR_2004_1083.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1083", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report present a cross-section and map views of earthquakes that occurred\nfrom 1984 to 2000 in the vicinity of the Hayward and Calaveras faults in the\nSan Francisco Bay region, California. These earthquakes came from a catalog of\nevents relocated using the double-difference technique, which provides superior\nrelative locations of nearby events. As a result, structures such as fault\nsurfaces and alignments of events along these surfaces are more sharply defined\nthan in previous catalogs.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1086_1.2.json b/datasets/USGS_OFR_2004_1086_1.2.json index 96ab80cdd4..a8ade31831 100644 --- a/datasets/USGS_OFR_2004_1086_1.2.json +++ b/datasets/USGS_OFR_2004_1086_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1086_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Catalog of Significant Historical Earthquakes in the Central United States\nuse Modified Mercalli intensity assignments to estimate source locations and\nmoment magnitude M for eighteen 19th-century and twenty early- 20th-century\nearthquakes in the central United States (CUS) for which estimates of M are\notherwise not available. We use these estimates, and locations and M estimated\nelsewhere, to compile a catelog of significant historical earthquakes in the\nCUS. The 1811-1812 New Madrid earthquakes apparently dominated CUS seismicity\nin the first two decades of the 19th century. M5-6 earthquakes occurred in the\nNew Madrid Seismic Zone in 1843 and 1878, but none have occurred since 1878.\nThere has been persistent seismic activity in the Illinois Basin in southern\nIllinois and Indiana, with M > 5.0 earthquakes in 1895, 1909, 1917, 1968, and\n1987. Four other M > 5.0 CUS historical earthquakes have occurred: in Kansas in\n1867, in Nebraska in 1877, in Oklahoma in 1882, and in Kentucky in 1980.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1096.json b/datasets/USGS_OFR_2004_1096.json index ed33ce5fbe..8fe136ac57 100644 --- a/datasets/USGS_OFR_2004_1096.json +++ b/datasets/USGS_OFR_2004_1096.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1096", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The past two decades have been a period of unrest for the Long Valley caldera\nof eastern California. The unrest began in 1978 and continued through late\n1999 and included recurring swarms of moderate earthquakes, as well as\nuplifting of the Resurgent Dome, which has totaled approximately 80 cm. It is\nbelieved that the seismicity is accompanied by magmatic intrusion beneath both\nthe Resurgent Dome at a depth of about 7 km; 10 km and the South Moat Seismic\nZone (SMSZ) at a depth of about 15 km (Sorey and others, 2003). Seismic\nsurveys within the caldera's topographic boundary have indicated the seismicity\nbeneath the northwest section of the caldera is associated with fluid injection\ninto narrow conduits and fractures (Stroujkova and Malin, 2000). Like the\ndominant regional structural trend, these conduits run in a northwest-southeast\ndirection and are only expressed at the surface by a slight topographic relief\nof about 3 m. Merged aeromagnetic data (Roberts and Jachens, 1999) over the\ncaldera show a magnetic low in the west and a high in the east (Figure 3). The\nwestern part has been modeled to relate to altered, low-magnetization (about\n2.5 km thick) Bishop Tuff beneath the Resurgent Dome, indicating hydrothermal\nalteration in the west, whereas the high in the east represents the unaltered\nBishop Tuff (Williams and others, 1977). The ground magnetic survey was\nconducted to locate magnetic lows that might indicate altered zones reflecting\nconduits for hydrothermal fluid flow in the northwest portion of the caldera.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1192.json b/datasets/USGS_OFR_2004_1192.json index 9bbdb3cb6a..e66e100b0c 100644 --- a/datasets/USGS_OFR_2004_1192.json +++ b/datasets/USGS_OFR_2004_1192.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1192", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since the California Gold Rush of 1849, sediment deposition, erosion, and the\nbathymetry of South San Francisco Bay have been altered by both natural\nprocesses and human activities. Historical hydrographic surveys can be used to\nassess how this system has evolved over the past 150 years. The National Ocean\nService (NOS) (formerly the United States Coast and Geodetic Survey (USCGS),\ncollected five hydrographic surveys of South San Francisco Bay from 1858 to\n1983. Analysis of these surveys enables us to reconstruct the surface of the\nbay floor for each time period and quantify spatial and temporal changes in\ndeposition, erosion, and bathymetry.\n\nThe creation of accurate bathymetric models involves many steps. Sounding data\nwas obtained from the original USCGS and NOS hydrographic sheets and were\nsupplemented with hand drawn depth contours. Shorelines and marsh areas were\nobtained from topographic sheets. The digitized soundings and shorelines were\nentered into a Geographic Information System (GIS), and georeferenced to a\ncommon horizontal datum. Using surface modeling software, bathymetric grids\nwith a horizontal resolution of 50 m were developed for each of the five\nhydrographic surveys. Prior to conducting analyses of sediment deposition and\nerosion, we converted all of the grids to a common vertical datum and made\nadjustments to correct for land subsidence that occurred from 1934 to 1967.\nDeposition and erosion that occurred during consecutive periods was then\ncomputed by differencing the corrected grids. From these maps of deposition and\nerosion, we calculated volumes and rates of net sediment change in the bay. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1194_1.0.json b/datasets/USGS_OFR_2004_1194_1.0.json index c345236beb..99159bea9b 100644 --- a/datasets/USGS_OFR_2004_1194_1.0.json +++ b/datasets/USGS_OFR_2004_1194_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1194_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In conjunction with integrated mapping of the Oligocene central San Juan\ncaldera cluster, southwestern Colorado (USGS I-Map 2799, in press), all modern\nchemical analyses of volcanic rocks for this area determined in laboratories of\nthe U.S. Geological Survey have been re-evaluated in terms of the stratigraphic\nsequence as presently understood. These include approximately 700 unpublished\nanalyses made between 1986 and 2003, as well as all USGS analyses published\nsince 1965 when the widespread presence of regional welded ash-flow tuffs\nerupted from large calderas was first recognized. All the analyses are assigned\nunit identifiers consistent with those used for the new geologic map; quite a\nfew of these differ from those used on sample submittal forms and in prior USGS\npublications. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1196.json b/datasets/USGS_OFR_2004_1196.json index 20dfb8a6f6..53f55a9ce0 100644 --- a/datasets/USGS_OFR_2004_1196.json +++ b/datasets/USGS_OFR_2004_1196.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1196", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastal vulnerability index (CVI) was used to map the relative vulnerability\nof the coast to future sea-level rise within Cumberland Island National\nSeashore in Georgia. The CVI ranks the following in terms of their physical\ncontribution to sea-level rise-related coastal change: geomorphology, regional\ncoastal slope, rate of relative sea-level rise, historical shoreline change\nrates, mean tidal range and mean significant wave height. The rankings for each\ninput variable were combined and an index value calculated for 1-minute grid\ncells covering the park. The CVI highlights those regions where the physical\neffects of sea-level rise might be the greatest. This approach combines the\ncoastal system's susceptibility to change with its natural ability to adapt to\nchanging environmental conditions, yielding a quantitative, although relative,\nmeasure of the park's natural vulnerability to the effects of sea-level rise.\nThe CVI provides an objective technique for evaluation and long-term planning\nby scientists and park managers. Cumberland Island National Seashore consists\nof stable to washover-dominated portions of barrier beach backed by wetland,\nmarsh, mudflat and tidal creek. The areas within Cumberland that are likely to\nbe most vulnerable to sea-level rise are those with the lowest foredune ridge\nand highest rates of shoreline erosion.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1201.json b/datasets/USGS_OFR_2004_1201.json index 8488d9b093..d46f4f6f54 100644 --- a/datasets/USGS_OFR_2004_1201.json +++ b/datasets/USGS_OFR_2004_1201.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1201", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cattlemans detention basin, South Lake Tahoe, California is designed to capture\nand reduce urban runoff and pollutants originating from developed areas before\nentering Cold Creek, which is tributary to Trout Creek and to Lake Tahoe. The\neffectiveness of the basin in reducing sediment and nutrient loads currently is\nbeing assessed with a five-year study. Hydraulic conductivity of the alluvium\nnear the detention basin is needed to estimate ground-water flow and subsurface\nnutrient transport. Hydraulic conductivity was estimated using slug tests in 27\nmonitoring wells that surround the detention basin. For each test, water was\npoured rapidly into a well, changes in water-level were monitored, and the\nobserved changes were analyzed using the Bouwer and Rice method. Each well was\ntested one to four times. A total of 24 wells were tested more than once. Of\nthe 24 wells, the differences among the tests were within 10 percent of the\naverage. Estimated hydraulic conductivities of basin alluvium range from 0.5 to\n70 feet per day with an average of 17.8 feet per day. This range is consistent\nwith the sandy alluvial deposits observed in the area of Cattlemans detention\nbasin.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1208.json b/datasets/USGS_OFR_2004_1208.json index cd050b5c26..3cfc39cdda 100644 --- a/datasets/USGS_OFR_2004_1208.json +++ b/datasets/USGS_OFR_2004_1208.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1208", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Samples of creek bed sediment collected near seal-coated parking lots in\nAustin, Texas, by the City of Austin during 2001-02 had unusually elevated\nconcentrations of polycyclic aromatic hydrocarbons (PAHs). To investigate the\npossibility that PAHs from seal-coated parking lots might be transported to\nurban creeks, the U.S. Geological Survey, in cooperation with the City of\nAustin, sampled runoff and scrapings from four test plots and 13 urban parking\nlots. The surfaces sampled comprise coal-tar-emulsion-sealed,\nasphalt-emulsion-sealed, unsealed asphalt, and unsealed concrete. Particulates\nand filtered water in runoff and surface scrapings were analyzed for PAHs. In\naddition, particulates in runoff were analyzed for major and trace elements.\nSamples of all three media from coal-tar-sealed parking lots had concentrations\nof PAHs higher than those from any other types of surface. The average total\nPAH concentrations in particulates in runoff from parking lots in use were\n3,500,000, 620,000, and 54,000 micrograms per kilogram from coal-tar-sealed,\nasphalt-sealed, and unsealed (asphalt and concrete combined) lots,\nrespectively. The probable effect concentration sediment quality guideline is\n22,800 micrograms per kilogram. The average total PAH (sum of detected PAHs)\nconcentration in filtered water from parking lots in use was 8.6 micrograms per\nliter for coal-tar-sealed lots; the one sample analyzed from an asphalt-sealed\nlot had a concentration of 5.1 micrograms per liter and the one sample analyzed\nfrom an unsealed asphalt lot was 0.24 microgram per liter. The average total\nPAH concentration in scrapings was 23,000,000, 820,000, and 14,000 micrograms\nper kilogram from coal-tar-sealed, asphalt-sealed, and unsealed asphalt lots,\nrespectively. Concentrations were similar for runoff and scrapings from the\ntest plots. Concentrations of lead and zinc in particulates in runoff\nfrequently exceeded the probable effect concentrations, but trace element\nconcentrations showed no consistent variation with parking lot surface type.\n\n[Summary provided by USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1211.json b/datasets/USGS_OFR_2004_1211.json index 19461b0b61..1a4f3c26e1 100644 --- a/datasets/USGS_OFR_2004_1211.json +++ b/datasets/USGS_OFR_2004_1211.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1211", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lead-rich sediments, containing at least 1000 ppm of lead (Pb), and derived\nmainly from discarded mill tailings in the Coeur d'Alene mining region, cover\nabout 60 km2 of the 80-km2 floor of the main stem of the Coeur d'Alene River\nvalley, in north Idaho. Although mill tailings have not been discarded directly\ninto tributary streams since 1968, frequent floods continue to re-mobilize\nsediment from large secondary sources, previously deposited on the bed, banks,\nalluvial terraces, and natural levees of the river. Thus, lead-rich sediments\n(also enriched in iron, manganese, zinc, copper, arsenic, cadmium, antimony and\nmercury) continue to be deposited on the floodplain. This is hazardous to the\nhealth of resident and visiting human and wildlife populations, attracted by\nthe river and its lateral lakes and wetlands.\n\nThis report documents and compares depositional rates and lead concentrations\nof lead-rich sediments deposited on the bed, banks, natural levees, and flood\nbasins of the main stem of the Coeur d'Alene River during several\ntime-stratigraphic intervals. These intervals are defined by their\nstratigraphic positions relative to the base of the section of lead-rich\nsediments, the 1980 Mt. St. Helens volcanic-ash layer, and the sedimentary\nsurface at the time of sampling. Four important intervals represent sediment\ndeposition during the following time spans (younger to older): 1. Baseline,\nfrom 1980 to about 1993 (after tailings disposal to streams ended, but before\nany major removals of lead-rich sediments); 2. Early post-tailings-release,\nfrom about 1968 to 1980; 3. Historic floodplain-contamination, from about 1903\nto 1968; and 4. Background, before the 1893 flood (the first major flood after\nlarge-scale mining and milling began upstream in 1886).\n\nMedians of baseline depositional rates and lead concentrations in levee\nsediments vary laterally, from 6.4 cm/10y and 3300 ppm Pb on riverbanks and\nlevee fore-slopes to 2.8 cm/10y and 3800 ppm Pb on levee back-slope uplands. In\nlateral flood basins, baseline medians increase with water depth, from 2.2\ncm/10y and 1900 ppm Pb in lateral marshes, to 2.9 cm/10y and 2100 ppm Pb in\nlittoral margins of lateral lakes, and 4.0 cm/10y and 4400 ppm Pb on limnetic\nbottoms of lateral lakes. \n\nThe median of lead concentrations in baseline sediments is 82 percent of the\nmedian for early post-tailings-release sediments, with a 69-percent probability\nthat the two data sets represent statistically different populations. By\ncontrast, the median of lead concentrations in baseline sediments is 57 percent\nof the corresponding median for historic-interval sediments, and these two data\nsets definitely represent statistically different populations. The\narea-weighted average of medians of lead concentrations in baseline sediments\nof all depositional settings is 2900 ppm Pb, which is 1.6 times the 1800 ppm Pb\nthat can be lethal to waterfowl. It also is 2.9 times the 1000-ppm-Pb threshold\nfor removal of contaminated soil from residential yards in the Coeur d'Alene\nmining region, and 111 times the 26-ppm median of background lead\nconcentrations in pre-industrial floodplain sediments.\n\nDuring episodes of high discharge, lead-rich sediments will continue to be\nmobilized from large secondary sources on the bed, banks, and natural levees of\nthe river, and will continue to be deposited on the floodplain during frequent\nfloods. Floodplain deposition of lead-rich sediments will continue for\ncenturies unless major secondary sources are removed or stabilized. It is\ntherefore important to design, sequence, implement, and maintain remediation in\nways that will limit recontamination. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1214.json b/datasets/USGS_OFR_2004_1214.json index c2a86f0d00..2bee4ef69a 100644 --- a/datasets/USGS_OFR_2004_1214.json +++ b/datasets/USGS_OFR_2004_1214.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1214", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field and laboratory studies were conducted to determine the effects of\npesticide mixtures on Chinook salmon under various environmental conditions in\nsurface waters of the northern Central Valley of California. This project was a\ncollaborative effort between the U.S. Geological Survey (USGS) and the\nUniversity of California. The project focused on understanding the\nenvironmental factors that influence the toxicity of pesticides to juvenile\nsalmon and their prey. During the periods January through March 2001 and\nJanuary through May 2002, water samples were collected at eight surface water\nsites in the northern Central Valley of California and analyzed by the USGS for\ndissolved pesticide and dissolved organic carbon concentrations. Water samples\nwere also collected by the USGS at the same sites for aquatic toxicity testing\nby the Aquatic Toxicity Laboratory at the University of California Davis;\nhowever, presentation of the results of these toxicity tests is beyond the\nscope of this report. Samples were collected to characterize dissolved\npesticide and dissolved organic carbon concentrations, and aquatic toxicity,\nassociated with winter storm runoff concurrent with winter run Chinook salmon\nout-migration. Sites were selected that represented the primary habitat of\njuvenile Chinook salmon and included major tributaries within the Sacramento\nand San Joaquin River Basins and the Sacramento San Joaquin Delta. Water\nsamples were collected daily for a period of seven days during two winter storm\nevents in each year. Additional samples were collected weekly during January\nthrough April or May in both years. Concentrations of 31 currently used\npesticides were measured in filtered water samples using solid-phase extraction\nand gas chromatography-mass spectrometry at the U.S. Geological Survey's\norganic chemistry laboratory in Sacramento, California. Dissolved organic\ncarbon concentrations were analyzed in filtered water samples using a Shimadzu\nTOC-5000A total organic carbon analyzer.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1220.json b/datasets/USGS_OFR_2004_1220.json index 5f1d02538f..fde5f77d43 100644 --- a/datasets/USGS_OFR_2004_1220.json +++ b/datasets/USGS_OFR_2004_1220.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1220", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Anadromous fish populations historically have found healthy habitat in Jordan\nCreek, Juneau, Alaska. Concern regarding potential degradation to the habitat\nby urban development within the Mendenhall Valley led to a cooperative study\namong the City and Borough of Juneau, Alaska Department of Environmental\nConservation, and the U.S. Geological Survey, that assessed current hydrologic,\nwater-quality, and physical-habitat conditions of the stream corridor.\n\nPeriods of no streamflow were not uncommon at the Jordan Creek below Egan Drive\nnear Auke Bay stream gaging station. Additional flow measurements indicate that\nperiods of no flow are more frequent downstream of the gaging station. Although\nperiods of no flow typically were in March and April, streamflow measurements\ncollected prior to 1999 indicate similar periods in January, suggesting that no\nflow conditions may occur at any time during the winter months. This dewatering\nin the lower reaches likely limits fish rearing and spawning habitat as well as\nlimiting the migration of juvenile salmon out to the ocean during some years.\n\nDissolved-oxygen concentrations may not be suitable for fish survival during\nsome winter periods in the Jordan Creek watershed. Dissolved-oxygen\nconcentrations were measured as low as 2.8 mg/L at the gaging station and were\nmeasured as low as 0.85 mg/L in a tributary to Jordan Creek. Intermittent\nmeasurements of pH and dissolved-oxygen concentrations in the mid-reaches of\nJordan Creek were all within acceptable limits for fish survival, however, few\nmeasurements of these parameters were made during winter-low-flow conditions.\nOne set of water quality samples was collected at six different sites in the\nJordan Creek watershed and analyzed for major ions and dissolved nutrients.\nMajor-ion chemistry showed Jordan Creek is calcium bicarbonate type water with\nlittle variation between sampling sites. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1221.json b/datasets/USGS_OFR_2004_1221.json index c100141345..31d445fb33 100644 --- a/datasets/USGS_OFR_2004_1221.json +++ b/datasets/USGS_OFR_2004_1221.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1221", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey in cooperation with the University of New Hampshire\nand the University of New Brunswick mapped the nearshore regions off Los\nAngeles and San Diego, California using multibeam echosounders. Multibeam\nbathymetry and co-registered, corrected acoustic backscatter were collected in\nwater depths ranging from about 3 to 900 m offshore Los Angeles and in water\ndepths ranging from about 17 to 1230 m offshore San Diego. Continuous, 16-m\nspatial resolution, GIS ready format data of the entire Los Angeles Margin and\nSan Diego Margin are available online as separate USGS Open-File Reports.\n\nFor ongoing research, the USGS has processed sub-regions within these datasets\nat finer resolutions. The resolution of each sub-region was determined by the\ndensity of soundings within the region. This Open-File Report contains the\nfiner resolution multibeam bathymetry and acoustic backscatter data that the\nUSGS, Western Region, Coastal and Marine Geology Team has processed into GIS\nready formats as of April 2004. The data are available in ArcInfo GRID and XYZ\nformats. See the Los Angeles or San Diego maps for the sub-region locations.\n\nThese datasets in their present form were not originally intended for\npublication. The bathymetry and backscatter have data-collection and processing\nartifacts. These data are being made public to fulfill a Freedom of Information\nAct request. Care must be taken not to confuse artifacts with real seafloor\nmorphology and acoustic backscatter.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1228.json b/datasets/USGS_OFR_2004_1228.json index 40f548b0c5..753588c690 100644 --- a/datasets/USGS_OFR_2004_1228.json +++ b/datasets/USGS_OFR_2004_1228.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1228", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pulley Ridge is a series of drowned barrier islands that extend over 100 km in\n60-100 m water depths. This drowned ridge is located on the Florida Platform in\nthe southeastern Gulf of Mexico about 250 km west of Cape Sable, Florida\n(Halley and others, 2003). This barrier island chain formed during the initial\nstage of the Holocene marine transgression approximately 7000 years before\npresent. These islands were then submerged and left abandoned near the outer\nedge of the Florida Platform. The southern portion of Pulley Ridge, the focus\nof this study, hosts zooxanthellate scleractinian corals, green, red and brown\nmacro algae, and a mix of deep and typically shallow-water tropical fishes.\nThis largely photosynthetic community is unique in that it thrives with only 5%\nof the light typically associated with shallow-water reefs with similar fauna.\n\nSeveral factors help to account for the existence of this unique deep-water\ncommunity. First, the underlying drowned barrier island provides both elevated\ntopography and lithified substrate for the establishment of the hardbottom\ncommunity. Second, the region is dominated by the west edge of the Loop\nCurrent, which brings relatively clear and warm water to this area. Third, the\nridge's position on the continental shelf places it within the thermocline\nwhich provides nutrients to the reef during upwelling (Halley and others,\n2003).\n\nThis report presents the still photographs acquired during the April 2003\ncruise aboard the Florida Institute of Oceanography's research vessel\nSuncoaster. These data are just one part of a multi-year study which includes\nthe acquisition of sidescan-sonar imagery, high-resolution bathymetry,\nhigh-resolution seismic-reflection profiles, bottom video imagery, and bottom\nsamples.\n\n[Summary provided by the USGS.]\n", "links": [ { diff --git a/datasets/USGS_OFR_2004_1234.json b/datasets/USGS_OFR_2004_1234.json index cbcff681d6..d61a6f0068 100644 --- a/datasets/USGS_OFR_2004_1234.json +++ b/datasets/USGS_OFR_2004_1234.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1234", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Alaska Volcano Observatory (AVO), a cooperative program of the U.S.\nGeological Survey, the Geophysical Institute of the University of Alaska\nFairbanks, and the Alaska Division of Geological and Geophysical Surveys, has\nmaintained seismic monitoring networks at historically active volcanoes in\nAlaska since 1988. The primary objectives of this program are the near real\ntime seismic monitoring of active, potentially hazardous, Alaskan volcanoes and\nthe investigation of seismic processes associated with active volcanism. This\ncatalog presents the calculated earthquake hypocenter and phase arrival data,\nand changes in the seismic monitoring program for the period January 1 through\nDecember 31, 2003.\n\nThe AVO seismograph network was used to monitor the seismic activity at\ntwenty-seven volcanoes within Alaska in 2003. These include Mount Wrangell,\nMount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai\nvolcanic cluster (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta,\nTrident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount\nVeniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano,\nFisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Okmok Caldera,\nGreat Sitkin Volcano, Kanaga Volcano, Tanaga Volcano, and Mount Gareloi.\nMonitoring highlights in 2003 include: continuing elevated seismicity at Mount\nVeniaminof in January-April (volcanic unrest began in August 2002),\nvolcanogenic seismic swarms at Shishaldin Volcano throughout the year, and\nlow-level tremor at Okmok Caldera throughout the year. Instrumentation and data\nacquisition highlights in 2003 were the installation of subnetworks on Tanaga\nand Gareloi Islands, the installation of broadband installations on Akutan\nVolcano and Okmok Caldera, and the establishment of telemetry for the Okmok\nCaldera subnetwork. AVO located 3911 earthquakes in 2003.\n\nThis catalog includes: (1) a description of instruments deployed in the field\nand their locations; (2) a description of earthquake detection, recording,\nanalysis, and data archival systems; (3) a description of velocity models used\nfor earthquake locations; (4) a summary of earthquakes located in 2003; and (5)\nan accompanying UNIX tar-file with a summary of earthquake origin times,\nhypocenters, magnitudes, phase arrival times, and location quality statistics;\ndaily station usage statistics; and all HYPOELLIPSE files used to determine the\nearthquake locations in 2003. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1235.json b/datasets/USGS_OFR_2004_1235.json index d7033737a9..b71d122202 100644 --- a/datasets/USGS_OFR_2004_1235.json +++ b/datasets/USGS_OFR_2004_1235.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1235", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The distribution of sedimentary environments presents the limited domain of\ndeposits from \"River Input\", the flood tide wedge of \"Atlantic Sediment\", and\nthe extensive region of indigenous, recycled \"Coastal Erosion Sediment\" in the\nChesapeake Bay littoral environment. Studies by Miller (1982, 1983, 1987) along\nselected reaches of the tidewater Potomac River showed that bluff retreat in\nthe littoral environment could be measured and modeled at as much as 0.5 to 1.0\nm/yr. During the September 18, 2003 Hurricane Isabel storm surge of nearly 3 m,\nas much as 8 to 10 m of coastal erosion was measured near some of Miller's\nsites.\n\nStorm-driven coastal erosion is the most extensive source of Holocene sediment\nin the modern Bay. Although massive amounts were eroded from the terraces and\nuplands during lowered sea level and cold climates, presently most sediment\neroded and transported from terrace and upland source areas has been stored on\nslopes and alluvial bottoms of the Coastal Plain landscapes that surround the\nChesapeake.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1249.json b/datasets/USGS_OFR_2004_1249.json index 3866b6e94a..f94426bf5e 100644 --- a/datasets/USGS_OFR_2004_1249.json +++ b/datasets/USGS_OFR_2004_1249.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1249", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on forest vegetation in western Oregon were assembled for 2323 ecological\nsurvey plots. All data were from fixed-radius plots with the standardized\ndesign of the Current Vegetation Survey (CVS) initiated in the early 1990s. For\neach site, the database includes: 1) live tree density and basal area of common\ntree species, 2) total live tree density, basal area, estimated biomass, and\nestimated leaf area; 3) age of the oldest overstory tree examined, 4)\ngeographic coordinates, 5) elevation, 6) interpolated climate variables, and 7)\nother site variables. The data are ideal for ecoregional analyses of existing\nvegetation.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1252.json b/datasets/USGS_OFR_2004_1252.json index ea252f3f05..a722a1bead 100644 --- a/datasets/USGS_OFR_2004_1252.json +++ b/datasets/USGS_OFR_2004_1252.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1252", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This publication contains a a series of files for Northeast Asia geodynamics,\nmineral deposit location, and metallogenic belt maps descriptions of map units\nand metallogenic belts, and stratigraphic columns. This region includes Eastern\nSiberia, Russian Far East, Mongolia, Northeast China, South Korea, and Japan.\nThe files include: (1) a geodynamics map at a scale of 1:5,000,000; (2)\npage-size stratigraphic columns for major terranes; (3) a generalized\ngeodynamics map at a scale of 1:15,000,000; (4) a mineral deposit location map\nat a scale of 1:7,500,000; (5) metallogenic belt maps at a scale of\n1:15,000,000; (6) detailed descriptions of geologic units with references; (7)\ndetailed descriptions of metallogenic belts with references; and (8) summary\nmineral deposit and metallogenic belt tables. The purpose of this publication\nis to provide high-quality, digital graphic files for maps and figures, and\nWord files for explanations, descriptions, and references to customers and\nusers.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1260.json b/datasets/USGS_OFR_2004_1260.json index 0bd497b8ed..4f2d843790 100644 --- a/datasets/USGS_OFR_2004_1260.json +++ b/datasets/USGS_OFR_2004_1260.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1260", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coal-bed methane exploration and production have begun within the Tongue River\nwatershed in southeastern Montana. The development of coal-bed methane requires\nproduction of large volumes of ground water, some of which may be discharged to\nstreams, potentially increasing stream discharge and sediment load. Changes in\nstream discharge or sediment load may result in changes to channel morphology\nthrough changes in erosion and vegetation. These changes might be subtle and\ndifficult to detect without baseline data that indicate stream-channel\nconditions before extensive coal-bed methane development began. In order to\nprovide this baseline channel-morphology data, the U.S. Geological Survey, in\ncooperation with the Bureau of Land Management, collected channel-morphology\ndata in 2001-02 to document baseline conditions for several reaches along the\nTongue River and selected tributaries.\n\nThis report presents channel-morphology data for five sites on the mainstem\nTongue River and four sites on its tributaries. Bankfull, water-surface, and\nthalweg elevations, channel sections, and streambed-particle sizes were\nmeasured along reaches near streamflow-gaging stations. At each site, the\nchannel was classified using methods described by Rosgen. For six sites,\nbankfull discharge was determined from the stage- discharge relation at the\ngage for the stage corresponding to the bankfull elevation. For three sites,\nthe step-backwater computer model HEC-RAS was used to estimate bankfull\ndischarge. Recurrence intervals for the bankfull discharge also were estimated\nfor eight of the nine sites. Channel-morphology data for each site are\npresented in maps, tables, graphs, and photographs.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1265.json b/datasets/USGS_OFR_2004_1265.json index e0f96f565d..01c4c1fd9c 100644 --- a/datasets/USGS_OFR_2004_1265.json +++ b/datasets/USGS_OFR_2004_1265.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1265", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A hydrologic analysis was made at three canal sites and four tidal sites along\nthe St. Lucie River Estuary in southeastern Florida from 1998 to 2001. The data\nincluded for analysis are stage, 15-minute flow, salinity, water temperature,\nturbidity, and suspended-solids concentration. During the period of record, the\nestuary experienced a drought, major storm events, and high-water discharge\nfrom Lake Okeechobee.\n\nFlow mainly occurred through the South Fork of the St. Lucie River; however,\nwhen flow increased through control structures along the C-23 and C-24 Canals,\nthe North Fork was a larger than usual contributor of total freshwater inflow\nto the estuary. At one tidal site (Steele Point), the majority of flow was\nsouthward toward the St. Lucie Inlet; at a second tidal site (Indian River\nBridge), the majority of flow was northward into the Indian River Lagoon.\n\nLarge-volume stormwater discharge events greatly affected the St. Lucie River\nEstuary. Increased discharge typically was accompanied by salinity decreases\nthat resulted in water becoming and remaining fresh throughout the estuary\nuntil the discharge events ended. Salinity in the estuary usually returned to\nprestorm levels within a few days after the events. Turbidity decreased and\nsalinity began to increase almost immediately when the gates at the control\nstructures closed. Salinity ranged from less than 1 to greater than 35 parts\nper thousand during the period of record (1998-2001), and typically varied by\nseveral parts per thousand during a tidal cycle.\n\nSuspended-solids concentrations were observed at one canal site (S-80) and two\ntidal sites (Speedy Point and Steele Point) during a discharge event in April\nand May 2000. Results suggest that most deposition of suspended-solids\nconcentration occurs between S-80 and Speedy Point. The turbidity data\ncollected also support this interpretation. The ratio of inorganic to organic\nsuspended-solids concentration observed at S-80, Speedy Point, and Steele Point\nduring the discharge event indicates that most flocculation of suspended-solids\nconcentration occurs between Speedy Point and Steele Point. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1269_1.0.json b/datasets/USGS_OFR_2004_1269_1.0.json index 47b09175c2..414b07709a 100644 --- a/datasets/USGS_OFR_2004_1269_1.0.json +++ b/datasets/USGS_OFR_2004_1269_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1269_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The December 22, 2003, San Simeon, California, (M6.5) earthquake caused damage\nto houses, road surfaces, and underground utilities in Oceano, California. The\ncommunity of Oceano is approximately 50 miles (80 km) from the earthquake\nepicenter. Damage at this distance from a M6.5 earthquake is unusual. To\nunderstand the causes of this damage, the U.S. Geological Survey conducted\nextensive subsurface exploration and monitoring of aftershocks in the months\nafter the earthquake. The investigation included 37 seismic cone penetration\ntests, 5 soil borings, and aftershock monitoring from January 28 to March 7,\n2004.\n\nThe USGS investigation identified two earthquake hazards in Oceano that explain\nthe San Simeon earthquake damage?site amplification and liquefaction. Site\namplification is a phenomenon observed in many earthquakes where the strength\nof the shaking increases abnormally in areas where the seismic-wave velocity of\nshallow geologic layers is low. As a result, earthquake shaking is felt more\nstrongly than in surrounding areas without similar geologic conditions. Site\namplification in Oceano is indicated by the physical properties of the geologic\nlayers beneath Oceano and was confirmed by monitoring aftershocks.\n\nLiquefaction, which is also commonly observed during earthquakes, is a\nphenomenon where saturated sands lose their strength during an earthquake and\nbecome fluid-like and mobile. As a result, the ground may undergo large\npermanent displacements that can damage underground utilities and well-built\nsurface structures. The type of displacement of major concern associated with\nliquefaction is lateral spreading because it involves displacement of large\nblocks of ground down gentle slopes or towards stream channels. The USGS\ninvestigation indicates that the shallow geologic units beneath Oceano are very\nsusceptible to liquefaction. They include young sand dunes and clean sandy\nartificial fill that was used to bury and convert marshes into developable\nlots. Most of the 2003 damage was caused by lateral spreading in two separate\nareas, one near Norswing Drive and the other near Juanita Avenue. The areas\ncoincided with areas with the highest liquefaction potential found in Oceano.\n\nAreas with site amplification conditions similar to those in Oceano are\nparticularly vulnerable to earthquakes. Site amplification may cause shaking\nfrom distant earthquakes, which normally would not cause damage, to increase\nlocally to damaging levels. The vulnerability in Oceano is compounded by the\nwidespread distribution of highly liquefiable soils that will reliquefy when\nground shaking is amplified as it was during the San Simeon earthquake. The\nexperience in Oceano can be expected to repeat because the region has many\nactive faults capable of generating large earthquakes. In addition,\nliquefaction and lateral spreading will be more extensive for moderate-size\nearthquakes that are closer to Oceano than was the 2003 San Simeon earthquake.\n\nSite amplification and liquefaction can be mitigated. Shaking is typically\nmitigated in California by adopting and enforcing up-to-date building codes.\nAlthough not a guarantee of safety, application of these codes ensures that the\nbest practice is used in construction. Building codes, however, do not always\nrequire the upgrading of older structures to new code requirements.\nConsequently, many older structures may not be as resistant to earthquake\nshaking as new ones. For older structures, retrofitting is required to bring\nthem up to code. Seismic provisions in codes also generally do not apply to\nnonstructural elements such as drywall, heating systems, and shelving.\nFrequently, nonstructural damage dominates the earthquake loss.\n\nMitigation of potential liquefaction in Oceano presently is voluntary for\nexisting buildings, but required by San Luis Obispo County for new\nconstruction. Multiple mitigation procedures are available to individual\nproperty owners. These procedures typically involve either changing the\nphysical state of the underlying sands so they cannot liquefy or building a\nfoundation that can resist the permanent displacement of the ground. Lateral\nspreading, which is the major threat to underground utilities, is particularly\nchallenging to mitigate because typically large areas are involved and sizeable\nvolumes of soil must be prevented from moving. Procedures to prevent spreading\ncommonly require subsurface barrier walls. Prevention of lateral spreading may\nalso require community rather than individual efforts because of the scale and\ncost of these mitigation measures.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1287_1.0.json b/datasets/USGS_OFR_2004_1287_1.0.json index 38e567cfe7..8957016e52 100644 --- a/datasets/USGS_OFR_2004_1287_1.0.json +++ b/datasets/USGS_OFR_2004_1287_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1287_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution measurements of currents, temperature, salinity and turbidity\nwere made over the course of three months off West Maui in the summer and early\nfall of 2003 to better understand coastal dynamics in coral reef habitats.\nMeasurements were made through the emplacement of a series of bottom-mounted\ninstruments in water depths less than 11 m. The studies were conducted in\nsupport of the U.S. Geological Survey (USGS) Coastal and Marine Geology\nProgram's Coral Reef Project. The purpose of these measurements was to collect\nhydrographic data to better constrain the variability in currents and water\ncolumn properties such as water temperature, salinity and turbidity in the\nvicinity of nearshore coral reef systems over the course of a summer and early\nfall when coral larvae spawn. These measurements support the ongoing process\nstudies being conducted under the Coral Reef Project; the ultimate goal is to\nbetter understand the transport mechanisms of sediment, larvae, pollutants and\nother particles in coral reef settings. This report, the third in a series of\nthree, describes data acquisition, processing and analysis. Previous reports\nprovided data and results on: Long-term measurements of currents, temperature,\nsalinity and turbidity off Kahana (PART I), and The spatial structure of\ncurrents, temperature, salinity and suspended sediment along West Maui (PART\nII).\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1297.json b/datasets/USGS_OFR_2004_1297.json index 7c8f81813e..5c20da52b7 100644 --- a/datasets/USGS_OFR_2004_1297.json +++ b/datasets/USGS_OFR_2004_1297.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1297", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map has 2 mGal gravity contours over a topographic base at a scale of\n1:100,000. It covers the southern portion of San Francisco Bay, most of the\nSanta Clara Valley, and the surrounding mountains. It is a companion to U.S.\nGeological Survey Open-File Report 03-360, Shaded Relief Aeromagnetic Map of\nthe Santa Clara Valley and Vicinity, California by Carter W. Roberts and Robert\nC. Jachens.\n\n[Summary provided by USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1303_1.0.json b/datasets/USGS_OFR_2004_1303_1.0.json index 0e0c71aa1b..e7f991fba5 100644 --- a/datasets/USGS_OFR_2004_1303_1.0.json +++ b/datasets/USGS_OFR_2004_1303_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1303_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Open-File Report provides digital data (shapefiles and .e00 files) for the\nbedrock geology in the Port Wing, Solon Springs, and parts of the Duluth and\nSandstone quadrangles in Wisconsin. A Miscellaneous Investigations Series map\n(I map) is currently in review with analogous data in paper format.\n\nThis map portrays the geology of part of the Midcontinent rift system (MRS)\nalong the southern extension of the Lake Superior syncline in northern\nWisconsin. The map area contains the St. Croix horst, a rift graben filled\nwith Mesoproterozoic rocks of the Keweenawan Supergroup that was subsequently\ninverted. The horst exposes about 15 - 20 km of strata that record the opening\nof the Midcontinent rift, its subsequent transition to a thermal subsidence\nbasin, and eventual inversion. About 3 km of underlying Mesoproterozoic\nstrata, including the Gogebic iron range, and about 10 km of Neoarchean rocks,\nexposed in the southernmost part of the map area lie to the southeast of the\nhorst.\n\nThe nearly flat-lying continental red beds of the Oronto and Bayfield Groups,\nthe youngest strata of the Keweenawan Supergroup, overlie the volcanic rocks.\n\nA wealth of geologic data exists for the area as a result of many individual\nstudies over the last hundred years, but much has remained unpublished in\ntheses, dissertations, and other reports of limited availability. This map has\nincorporated most of that data (see list of data sources) and includes results\nof our investigations conducted from 1992 to 2000. Our studies were designed\nto fill gaps in existing data and reconcile conflicting interpretations on some\naspects of the geology of the region.\n\nThe purpose of this map is to complete digital coverage of quadrangles with\nsignificant exposure of rocks of the Midcontinent rift in Wisconsin and\nMichigan at a scale of 1:100,000. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1322_1.0.json b/datasets/USGS_OFR_2004_1322_1.0.json index d570467b7e..8f041305f2 100644 --- a/datasets/USGS_OFR_2004_1322_1.0.json +++ b/datasets/USGS_OFR_2004_1322_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1322_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Digital Shaded-Relief Map of Venezuela is a composite of more than 20 tiles of 90 meter (3 arc second) pixel resolution elevation data, captured during the Shuttle Radar Topography Mission (SRTM) in February 2000. The SRTM, a joint project between the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA), provides the most accurate and comprehensive international digital elevation dataset ever assembled. The 10-day flight mission aboard the U.S. Space Shuttle Endeavour obtained elevation data for about 80% of the world's landmass at 3-5 meter pixel resolution through the use of synthetic aperture radar (SAR) technology. SAR is desirable because it acquires data along continuous swaths, maintaining data consistency across large areas, independent of cloud cover. Swaths were captured at an altitude of 230 km, and are approximately 225 km wide with varying lengths.\n\nRendering of the shaded-relief image required editing of the raw elevation data to remove numerous holes and anomalously high and low values inherent in the dataset. Customized ArcInfo Arc Macro Language (AML) scripts were written to interpolate areas of null values and generalize irregular elevation spikes and wells. Coastlines and major water bodies used as a clipping mask were extracted from 1:500,000-scale geologic maps of Venezuela (Bellizzia and others, 1976). The shaded-relief image was rendered with an illumination azimuth of 315\u00ef\u00bf\u00bd and an altitude of 65\u00ef\u00bf\u00bd. A vertical exaggeration of 2X was applied to the image to enhance land-surface features. Image post-processing techniques were accomplished using conventional desktop imaging software.\n\n[Summary provided by the USGS.]\n", "links": [ { diff --git a/datasets/USGS_OFR_2004_1335.json b/datasets/USGS_OFR_2004_1335.json index dc1811c652..807483cd1c 100644 --- a/datasets/USGS_OFR_2004_1335.json +++ b/datasets/USGS_OFR_2004_1335.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1335", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A digital map of soil parameters for the international Ambos Nogales watershed \nwas prepared to use as input for selected soils-erosion models. The Ambos\nNogales watershed in southern Arizona and northern Sonora, Mexico, contains the\nNogales wash, a tributary of the Upper Santa Cruz River. The watershed covers\nan area of 235 km squared just under half of which is in Mexico. Preliminary\ninvestigations of potential erosion revealed a discrepancy in soils data and\nmapping across the United States-Mexican border due to issues including\ndifferent mapping resolutions, incompatible formatting, and varying\nnomenclature and classification systems. To prepare a digital soils map\nappropriate for input to a soils-erosion model, the historical analog soils\nmaps for Nogales, Ariz., were scanned and merged with the larger-scale digital\nsoils data available for Nogales, Sonora, Mexico using a geographic information\nsystem.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1345_1.json b/datasets/USGS_OFR_2004_1345_1.json index 4cdc44579e..731be832bc 100644 --- a/datasets/USGS_OFR_2004_1345_1.json +++ b/datasets/USGS_OFR_2004_1345_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1345_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The raster grid, model1, represents the elevation of the surface of the Climax\nand Gold Meadows Stocks. The elevation was generated by inverse modeling of\nthe pseudogravity anomaly.\n\nThis raster grid was created to model depth to the granitoid body that crops\nout at the Climax and Gold Meadows Stocks. Because granitic bodies may have\nhydrologic properties different from those of rocks they intrude, knowledge of\ntheir three-dimensional distribution in the subsurface is important for\nanalyzing the southward flow of ground water into Yucca flat.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2004_1352.json b/datasets/USGS_OFR_2004_1352.json index 15cbd5d79e..f8095d0aab 100644 --- a/datasets/USGS_OFR_2004_1352.json +++ b/datasets/USGS_OFR_2004_1352.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2004_1352", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are digital facsimiles of the original 1984 Engineering Aspects of\nKarst map by Davies and others. This data set was converted from a printed map\nto a digital GIS coverage to provide users with a citable national scale karst\ndata set to use for graphic and demonstration purposes until new, improved data\nare developed. These data may be used freely with proper citation. Because it\nhas been converted to GIS format, these data can be easily projected, displayed\nand queried for multiple uses in GIS. The karst polygons of the original map\nwere scanned from the stable base negatives of the original, vectorized, edited\nand then attributed with unit descriptions. All of these processes potentially\nintroduce small errors and distortions to the geography. The original map was\nproduced at a scale of 1:7,500,000; this coverage is not as accurate, and\nshould be used for broad-scale purposes only. It is not intended for any\nsite-specific studies.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1038_1.0.json b/datasets/USGS_OFR_2005_1038_1.0.json index 0abb66bcf8..054357188c 100644 --- a/datasets/USGS_OFR_2005_1038_1.0.json +++ b/datasets/USGS_OFR_2005_1038_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1038_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geologic shaded relief map of Venezuela was created by direct digitization\nof geologic and hydrologic data north of the Orinoco River from a 1:500,000\nscale paper map set. These data were integrated with a digital geologic map of\nthe Venezuela Guayana Shield, also derived from 1:500,000 scale paper maps.\nFault type information portrayed on the map, including unlabeled fault types,\nare as depicted in the original data sources. Geologic polygons were attributed\nfor age, name, and lithologic type following the Lexico Estratigrafico de\nVenezuela. Significant revisions to the geology of the Cordillera de la Costa\nwere incorporated based on new, detailed (1:25,000 scale) geologic mapping.\nGeologic polygons and fold and fault lines were draped over a shaded relief\nimage produced by processing 90 m (3-arc second) radar interferometric data\nobtained by the space shuttle radar topography mission (SRTM). Values for\nnull-data areas inherent in the SRTM data set were filled by interpolation\nbased on surrounding data cells. The digital elevation model data was\nhill-shaded using an illumination direction of 315 degrees at an angle of 65\ndegrees above the horizon to produce the shaded relief image. The map\nprojection used is equidistant conic, with latitudes 4 and 9 degrees north as\nstandard parallels, and longitude 66 degrees west as the central meridian.\n\nThe data contained in this map compilation primarily was derived from 1:500,000\nscale maps and arranged for presentation and use at the scale of 1:750,000.\nUsers may zoom in to view greater detail at larger scale; however, the authors\nmake no guarantee of the accuracy of the map representation at scales larger\nthan 1:750,000.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1063.json b/datasets/USGS_OFR_2005_1063.json index 65c7201a92..65748e9dea 100644 --- a/datasets/USGS_OFR_2005_1063.json +++ b/datasets/USGS_OFR_2005_1063.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1063", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2001, a cooperative monitoring effort between the U.S. Geological Survey\n(USGS), the Burlington Northern Santa Fe Railway (BNSF), BNSF's geotechnical\nconsultant, Shannon and Wilson, Inc., and the Washington Department of\nTransportation was begun to determine whether near-real-time monitoring of\nrainfall and shallow subsurface hydrologic conditions could be used to\nanticipate landslide activity on the bluffs. Monitoring currently occurs at two\nsites-one near Edmonds, Washington, and the other near Everett, Washington.\nDuring initial planning, the USGS proposed to evaluate the monitoring results\nat the end of 3 years. This report summarizes site conditions, methods, system\nreliability, data, and scientific results, and identifies possible future\ndirections for development of monitoring and early warning of impending\nlandslide activity.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1067_1.0.json b/datasets/USGS_OFR_2005_1067_1.0.json index 9fd0d1bc99..565a979b16 100644 --- a/datasets/USGS_OFR_2005_1067_1.0.json +++ b/datasets/USGS_OFR_2005_1067_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1067_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On January 10, 2005, a landslide struck the community of La Conchita in Ventura\nCounty, California, destroying or seriously damaging 36 houses and killing 10\npeople. This was not the first destructive landslide to damage this community,\nnor is it likely to be the last. This open file report describes the field\nobservations and provides a description of the La Conchita area and its\nlandslide history, a comparison of the 1995 and 2005 landslides, and a\ndiscussion.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1069.json b/datasets/USGS_OFR_2005_1069.json index 4c55bd3d74..a6fddf919c 100644 --- a/datasets/USGS_OFR_2005_1069.json +++ b/datasets/USGS_OFR_2005_1069.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1069", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collaborative project between the U.S. Geological Survey's Coastal and Marine\n Geology Program and the National Park Service (NPS) has been developed to\n create an inventory of geologic resources for National Park Service lands on\n the Big Island of Hawai?i. The NPS Geologic Resources Inventories are\n recognized as essential for the effective management, interpretation, and\n understanding of vital park resources. In general, there are three principal\n components of the inventories: geologic bibliographies, digital geologic maps,\n and geologic reports. The geologic reports are specific to each individual park\n and include information on the geologic features and processes that are\n important to the management of park resources, including ecological, cultural\n and recreational resources. This report summarizes a component of the geologic\n inventory concerned specifically with characterizing the coastal geomorphology\n of the beach system within Kaloko-Honokohau National Historical Park (NHP) and\n describes an analysis that utilizes georeferenced and orthorectified aerial\n photography to understand the spatial and temporal trends in shoreline change\n from 1950 to 2002. In addition, spatial patterns of beach change were examined\n and a beach stability map was developed. Both the shoreline change rates and\n the beach stability map are designed to help Park personnel effectively manage\n the valuable park resources within the context of understanding natural changes\n to the KAHO beach system.\n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1070_1.0.json b/datasets/USGS_OFR_2005_1070_1.0.json index cc4c4c3534..7d350edbba 100644 --- a/datasets/USGS_OFR_2005_1070_1.0.json +++ b/datasets/USGS_OFR_2005_1070_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1070_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The detailed high-resolution map layer provided here documents habitat\ncharacterization of a critical coral reef in Hawai'i. Integration of the aerial\nimagery, SHOALS bathymetry, and field observations made it possible to create\ndetailed thematic maps reaching depths of 35 m (120 ft). This depth range\nencompasses the base of the Moloka'i forereef, and is deeper than can be mapped\nwith standard optical remote sensing instruments. These maps can be used as\nstand-alone or in a GIS to provide useful information to scientists, managers\nand the general public.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1132_1.0.json b/datasets/USGS_OFR_2005_1132_1.0.json index 223ffc74ee..52c41e8a21 100644 --- a/datasets/USGS_OFR_2005_1132_1.0.json +++ b/datasets/USGS_OFR_2005_1132_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1132_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution aeromagnetic surveys of the Amargosa Desert region, California\nand Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide\nrange of mid- and upper-crustal lithologies. In most cases, these anomalies can\nbe interpreted in terms of exposed rocks and sedimentary deposits. More\ndifficult to explain are linear magnetic anomalies situated over lithologies\nthat typically have very low magnetizations. Aeromagnetic anomalies are\nobserved, for example, over thick sections of Quaternary alluvial deposits and\nspring deposits associated with past or modern ground-water discharge in Ash\nMeadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically\nconsidered nonmagnetic. To help determine the source of these aeromagnetic\nanomalies, we conducted ground-magnetic studies at five areas: near Death\nValley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring,\nand near Travertine Springs. Depth-to-source calculations show that the sources\nof these anomalies lie within the Tertiary and Quaternary sedimentary section.\nWe conclude that they are caused by discrete volcanic units lying above the\npre-Tertiary basement. At Death Valley Junction and Travertine Springs, these\nconcealed volcanic units are probably part of the Miocene Death Valley volcanic\nfield exposed in the nearby Greenwater Range and Black Mountains. The linear\nnature of the aeromagnetic anomalies suggests that these concealed volcanic\nrocks are bounded and offset by near-surface faults. \n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1135_1.0.json b/datasets/USGS_OFR_2005_1135_1.0.json index 1c8f4c39ba..3a594049c8 100644 --- a/datasets/USGS_OFR_2005_1135_1.0.json +++ b/datasets/USGS_OFR_2005_1135_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1135_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This website presents Modified Mercalli Intensity maps for the great San\nFrancisco earthquake of April 18, 1906. These new maps combine two important\ndevelopments. First, we have re-evaluated and relocated the damage and shaking\nreports compiled by Lawson (1908). These reports yield intensity estimates for\nmore than 600 sites and constitute the largest set of intensities ever compiled\nfor a single earthquake. Second, we use the recent ShakeMap methodology to map\nthese intensities. The resulting MMI intensity maps are remarkably detailed and\neloquently depict the enormous power and damage potential of this great\nearthquake.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1144.json b/datasets/USGS_OFR_2005_1144.json index 0c7259ddd6..cfbf8a007e 100644 --- a/datasets/USGS_OFR_2005_1144.json +++ b/datasets/USGS_OFR_2005_1144.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1144", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reflectance of huminite in 19 cuttings samples was determined in support of\nongoing investigations into the coal bed methane potential of subsurface\nPaleocene and Upper Cretaceous coals of South Texas. Coal cuttings were\nobtained from the Core Research Center of the Bureau of Economic Geology, The\nUniversity of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs,\ncompletion cards, and scout tickets were used to select potentially\ncoal-bearing sample suites and to identify specific sample depths. Reflectance\nmeasurements indicate coals of subbituminous rank are present in a wider area\nin South Texas than previously recognized.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1148_1.0.json b/datasets/USGS_OFR_2005_1148_1.0.json index 0c6a3d4674..f9da6ab188 100644 --- a/datasets/USGS_OFR_2005_1148_1.0.json +++ b/datasets/USGS_OFR_2005_1148_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1148_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recent construction for Interstate Highway 99 (I?99) exposed pyrite and\nassociated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative\nweathering along a 40-60-m deep roadcut through a 270-m long section of the\nOrdovician Bald Eagle Formation at Skytop, near State College, Centre County,\nPennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and\nlimestone in Blair and Centre Counties were prospected in the past; however,\nthese deposits generally were not viable as commercial mines. The pyritic\nsandstone from the roadcut was crushed and used locally as road base and fill\nfor adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps\nand runoff from the exposed cut and crushed sandstone raised concerns about\nsurface- and ground-water contamination and prompted a halt in road\nconstruction and the beginning of costly remediation. Mineralized sandstones\nfrom the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt.\nPb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the\ncut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net\nacidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t).\nPrimary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. %\nFe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale\nveinlets and disseminated grains in sandstone, as needles, and in a locally\nmassive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and\nNi-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or\nless) explain the primary source of trace metals in the rock and in associated\nsecondary minerals and seepage. Wet/dry cycles associated with intermittent\nrainfall promoted oxidative weathering and dissolution of primary sulfides and\ntheir oxidation products. Resulting sulfate solutions evaporated during dry\nperiods to form intermittent ?blooms? of soluble, yellow and white efflorescent\nsulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and\nother surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor\nZn. They readily dissolved in deionized water in the laboratory to form\nsolutions with pH <2.5, consistent with field observations. In addition to\nelevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at\nthe base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and\nNi. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester\nmetals and acidity between rainfall events. Episodic salt dissolution then\ncontributes pulses of contamination including acid to surface runoff and ground\nwater. The Skytop experience highlights the need to understand dynamic\ninteractions of mineralogy and hydrology in order to avoid potentially negative\nenvironmental impacts associated with excavation in sulfidic rocks.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1153_1.0.json b/datasets/USGS_OFR_2005_1153_1.0.json index 95b0eaf9e3..b9c428043f 100644 --- a/datasets/USGS_OFR_2005_1153_1.0.json +++ b/datasets/USGS_OFR_2005_1153_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1153_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) in cooperation with the Minerals Management\nService (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel\nand northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V\nMaurice Ewing. The survey was directed and funded by the Minerals Management\nService, which is interested in maps of hard bottom habitats, particularly\nnatural outcrops, that support reef communities in areas affected by oil and\ngas activity. The maps are also useful to biologists studying fish that use the\nplatforms and the sea floor beneath them as habitat.\n\nThe survey collected bathymetry and corrected, co-registered acoustic\nbackscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was\nmounted on the hull of the R/V Maurice Ewing. Three main regions were mapped\nduring the survey including: (1) the Eastern Santa Barbara Channel adjacent to\nan area previously mapped with multibeam-sonar by the Monterey Bay Aquarium\nResearch Institute (see the MBARI Santa Barbara Basin Multibeam Survey web\npage), (2) the Footprint area south of Anacapa Island, which has been studied\nextensively by rockfish biologists and is considered a good site for a marine\nprotected area, and (3) part of the submarine canyons along the continental\nslope south of Port Hueneme. These data will be used to support a number of new\nand ongoing projects including, habitat mapping, shelf and slope processes, and\noffshore hazards and resources.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1164_1.0.json b/datasets/USGS_OFR_2005_1164_1.0.json index 5429425eb6..6bd50535a2 100644 --- a/datasets/USGS_OFR_2005_1164_1.0.json +++ b/datasets/USGS_OFR_2005_1164_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1164_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A National Volcano Early Warning System NVEWS is being formulated by the\nConsortium of U.S. Volcano Observatories (CUSVO) to establish a proactive,\nfully integrated, national-scale monitoring effort that ensures the most\nthreatening volcanoes in the United States are properly monitored in advance of\nthe onset of unrest and at levels commensurate with the threats posed. Volcanic\nthreat is the combination of hazards (the destructive natural phenomena\nproduced by a volcano) and exposure (people and property at risk from the\nhazards).\n\nThe United States has abundant volcanoes, and over the past 25 years the Nation\nhas experienced a diverse range of the destructive phenomena that volcanoes can\nproduce. Hazardous volcanic activity will continue to occur, and because of\nincreasing population, increasing development, and expanding national and\ninternational air traffic over volcanic regions the exposure of human life\nand enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit\nprecursory unrest that if detected and analyzed in time allows eruptions to be\nanticipated and communities at risk to be forewarned with reliable information\nin sufficient time to implement response plans and mitigation measures.\n\nIn the 25 years since the cataclysmic eruption of Mount St. Helens, scientific\nand technological advances in volcanology have been used to develop and test\nmodels of volcanic behavior and to make reliable forecasts of expected activity\na reality. Until now, these technologies and methods have been applied on an ad\nhoc basis to volcanoes showing signs of activity. However, waiting to deploy a\nrobust, modern monitoring effort until a hazardous volcano awakens and an\nunrest crisis begins is socially and scientifically unsatisfactory because it\nforces scientists, civil authorities, citizens, and businesses into playing\ncatch up with the volcano, trying to get instruments and civil-defense\nmeasures in place before the unrest escalates and the situation worsens.\nInevitably, this manner of response results in our missing crucial early stages\nof the volcanic unrest and hampers our ability to accurately forecast events.\nRestless volcanoes do not always progress to eruption; nevertheless, monitoring\nis necessary in such cases to minimize either over-reacting, which costs money,\nor under-reacting, which may cost lives.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1176.json b/datasets/USGS_OFR_2005_1176.json index 7b91d04853..67ad507e2d 100644 --- a/datasets/USGS_OFR_2005_1176.json +++ b/datasets/USGS_OFR_2005_1176.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1176", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Androscoggin River flooded the town of Canton, Maine in December 2003,\nresulting in damage to and (or) evacuation of 44 homes. Streamflow records at\nthe U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS\nstation identification number 01054500) and Auburn (01059000) were used to\nestimate the peak streamflow for the Androscoggin in the town of Canton for\nthis flood (December 18-19, 2003). The estimated peak flood streamflow at\nCanton was approximately 39,800 ft3/s, corresponding to an estimated recurrence\ninterval of 4.4 years; however, an ice jam downstream from Canton Point on\nDecember 18-19 obstructed river flow resulting in a high-water elevation\ncommensurate with an open-water flood approximately equal to a 15-year event.\nThe high water-surface elevations attained during the December 18-19 flood\nevent in Canton were higher than the expected open-water flood water-surface\nelevations; this verified the assumption that the water-surface elevation was\naugmented due to the downstream ice jam.\n\nThe change in slope of the riverbed from upstream of Canton to the impoundment\nat the downstream corporate limits, and the river bend near Stevens Island are\nprincipal factors in ice-jam formation near Canton. The U.S. Army Corps of\nEngineers Ice Jam Database indicates five ice-jam-related floods (including\nDecember 2003) for the town of Canton: March 13, 1936; January 1978; March 12,\n1987; January 29, 1996; and December 18-19, 2003. There have been more\nice-jam-related flood events in Canton than these five documented events, but\nthe exact number and nature of ice jams in Canton cannot be determined without\nfurther research.", "links": [ { diff --git a/datasets/USGS_OFR_2005_1201.json b/datasets/USGS_OFR_2005_1201.json index 3ab9b3675d..a906e42b21 100644 --- a/datasets/USGS_OFR_2005_1201.json +++ b/datasets/USGS_OFR_2005_1201.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1201", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water-use data were compiled for the 78 municipios of the Commonwealth of\nPuerto Rico for 2000. Five offstream categories were considered: public-supply\nwater withdrawals, domestic self-supplied water use, industrial self-supplied\nwithdrawals, crop irrigation water use, and thermoelectric power fresh water\nuse. Two additional categories also were considered: power generation instream\nuse and public wastewater treatment return-flows. Fresh water withdrawals for\noffstream use from surface- and ground-water sources in Puerto Rico were\nestimated at 617 million gallons per day. The largest amount of fresh water\nwithdrawn was by public-supply water facilities and was estimated at 540\nmillion gallons per day. Fresh surface- and ground-water withdrawals by\ndomestic self-supplied users was estimated at 2 million gallons per day and the\nindustrial self-supplied withdrawals were estimated at 9.5 million gallons per\nday. Withdrawals for crop irrigation purposes were estimated at 64 million\ngallons per day, or approximately 10 percent of all offstream fresh water\nwithdrawals. Saline instream surface-water withdrawals for cooling purposes by\nthermoelectric power facilities was estimated at 2,191 million gallons per day,\nand instream fresh water withdrawals by hydroelectric facilities at 171 million\ngallons per day. Total discharge from public wastewater treatment facilities\nwas estimated at 211 million gallons per day.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1203_1.0.json b/datasets/USGS_OFR_2005_1203_1.0.json index 4ad61d2144..5fac1aac79 100644 --- a/datasets/USGS_OFR_2005_1203_1.0.json +++ b/datasets/USGS_OFR_2005_1203_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1203_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of an ongoing study to derive records of past environmental change from\nlake sediments in the western United States, a set of three cores was collected\nfrom Bear Lake, Utah, in 1996. The three cores, BL96-1, -2, and -3, form an\neast-west profile and are located in about 50, 40 , and 30 m of water,\nrespectively. The cores range in length from 4 m to 5 m, but because sediments\nthin markedly to the west (Colman, 2005) the maximum age of sediments\npenetrated increases from east to west. Together the cores provide a record\nfrom the last glacial period through the Holocene. This report presents\nmagnetic property data acquired from these cores.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1253_1.0.json b/datasets/USGS_OFR_2005_1253_1.0.json index 22e0dfe75d..00b4a69d62 100644 --- a/datasets/USGS_OFR_2005_1253_1.0.json +++ b/datasets/USGS_OFR_2005_1253_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1253_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report contains major- and trace-element concentration data for soil\nsamples collected from 265 sites along two continental-scale transects in North\nAmerica. One of the transects extends from northern Manitoba to the United\nStates-Mexico border near El Paso, Tex. and consists of 105 sites. The other\ntransect approximately follows the 38th parallel from the Pacific coast of the\nUnited States near San Francisco, Calif., to the Atlantic coast along the\nMaryland shore and consists of 160 sites. Sampling sites were defined by first\ndividing each transect into approximately 40-km segments. For each segment, a\n1-km-wide latitudinal strip was randomly selected; within each strip, a\npotential sample site was selected from the most representative landscape\nwithin the most common soil type. At one in four sites, duplicate samples were\ncollected 10 meters apart to estimate local spatial variability. At each site,\nup to four separate soil samples were collected as follows: (1) material from\n0-5 cm depth; (2) O horizon, if present; (3) a composite of the A horizon; and\n(4) C horizon. Each sample collected was analyzed for total major- and\ntrace-element composition by the following methods: (1) inductively coupled\nplasma-mass spectrometry (ICP-MS) and inductively coupled plasma-atomic\nemission spectrometry (ICPAES) for aluminum, antimony, arsenic, barium,\nberyllium, bismuth, cadmium, calcium, cerium, cesium, chromium, cobalt, copper,\ngallium, indium, iron, lanthanum, lead, lithium, magnesium, manganese,\nmolybdenum, nickel, niobium, phosphorus, potassium, rubidium, scandium, silver,\nsodium, strontium, sulfur, tellurium, thallium, thorium, tin, titanium,\n\ntungsten, uranium, vanadium, yttrium, and zinc; (2) cold vapor- atomic\nabsorption spectrometry for mercury; (3) hydride generation-atomic absorption\nspectrometry for antimony and selenium; (4) coulometric titration for carbonate\ncarbon; and (5) combustion for total carbon and total sulfur.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1307.json b/datasets/USGS_OFR_2005_1307.json index fd45051c4a..71350352bc 100644 --- a/datasets/USGS_OFR_2005_1307.json +++ b/datasets/USGS_OFR_2005_1307.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1307", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 3.5-year study was conducted to determine the signifcance of atmospheric\ndeposition to the pesticide concentrations in runoff. Both wet and dry\natmospheric depostion were collected at six sites in the central San Joaquin\nValley, California. Wet deposition samples were collected during individual\nrain events and dry deposition samples were collected for periods ranging from\nthree weeks to four months. Each sample was analyzed for 41 currently used\npesticides and 23 transformation products, including the oxygen analogs of nine\norganophosphorus (OP) insecticides. Ten compounds in rainfall and 19 in dry\ndeposition were detected in at least 50% of the samples.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1315.json b/datasets/USGS_OFR_2005_1315.json index 9fcde524da..cbf8d3f258 100644 --- a/datasets/USGS_OFR_2005_1315.json +++ b/datasets/USGS_OFR_2005_1315.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1315", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered\nduring the year. The seismic summary is offered without interpretation as a\nsource of preliminary data. It is complete in the sense that most data for\nevents of Me1.5 routinely gathered by the Observatory are included.\n\nThe HVO summaries have been published in various forms since 1956. Summaries\nprior to 1974 were issued quarterly, but cost, convenience of preparation and\ndistribution, and the large quantities of data dictated an annual publication\nbeginning with Summary 74 for the year 1974. Summary 86 (the introduction of\nCUSP at HVO) includes a description of the seismic instrumentation,\ncalibration, and processing used in recent years. Beginning with 2004,\nsummaries will simply be identified by the year, rather than Summary number.\nThe present summary includes background information on the seismic network and\nprocessing to allow use of the data and to provide an understanding of how they\nwere gathered.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1317.json b/datasets/USGS_OFR_2005_1317.json index 784bb64004..1e53cddadf 100644 --- a/datasets/USGS_OFR_2005_1317.json +++ b/datasets/USGS_OFR_2005_1317.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1317", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report summarizes and documents empirical compressional wave velocity (Vp)\nversus depth relationships for several important rock types in northern\nCalifornia used in constructing the new USGS Bay Area Velocity Model 05.0.0\n(http://www.sf06simulation.org/). These rock types include the Jurassic and\nCretaceous Franciscan Complex (metagraywacke and greenstones), serpentinites,\nCretaceous Salinian and Sierra granites and granodiorites, Jurassic and\nCretaceous Great Valley Sequence, and older Cenozoic sedimentary rocks\n(including the La Honda basin). Similar relations for less volumetrically\nimportant rocks are also developed for andesites, basalts, gabbros, and Sonoma\nVolcanics. For each rock type I summarize and plot the data used to develop the\nvelocity versus depth relationships. These plots document the existing\nconstraints on the proposed relationships. This report also presents a new\nempirical Vp versus depth relation derived from hundreds of measurements in\nUSGS 30-m vertical seismic profiles (VSPs) for Holocene and Plio-Quaternary\ndeposits in the San Francisco Bay area. For the upper 40 m (0.04 km) these\nmainly Holocene deposits, can be approximated by Vp (km/s) = 0.7 + 42.968z -\n575.8z2 + 2931.6z3 - 3977.6z4, where z is depth in km. In addition, this report\nprovides tables summarizing these VSP observations for the various types of\nHolocene and Plio-Quaternary deposits. In USGS Bay Area Velocity Model 05.0.0\nthese compressional wave velocity (Vp) versus depth relationships are converted\nto shear wave velocity (Vs) versus depth relationships using recently proposed\nempirical Vs versus Vp relations. Density is calculated from Vp using Gardner's\nrule and relations for crystalline rocks proposed by Christensen and Mooney\n(1995). Vs is then used to calculate intrinsic attenuation coefficients for\nshear and compressional waves, Qs and Qp, respectively.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1319.json b/datasets/USGS_OFR_2005_1319.json index a323ae1dcc..3fd7d98f72 100644 --- a/datasets/USGS_OFR_2005_1319.json +++ b/datasets/USGS_OFR_2005_1319.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1319", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overall goal of our research on Bear Lake is to create records of past\nclimate change for the region, including changes in precipitation (rain and\nsnow) patterns during the last 10,000 years and longer. As part of the project,\nwe are attempting to determine how the size of Bear Lake has varied in the past\nin order to assess the possibility of future flooding and drought. We also seek\nto understand human influences on sediment deposition, chemistry, and life in\nthe lake.\n\nEvidence of past conditions comes from sediments deposited in the lake, so\nreconstructions of past conditions require accurate dating of the sediments.\nThe study includes the upper Bear River watershed as well as Bear Lake. The\nBear River is the largest river in the Great Basin and the source of the\nmajority of water flowing into the Great Salt Lake. In this region, wet periods\nmay produce flooding along the course of the Bear River and around Great Salt\nLake, while dry periods, or droughts, may affect water availability for\necosystems, as well as for agricultural, industrial, and residential use.\n\nDiatoms are one of the most sensitive indicators of environments in many lakes.\nIn addition to species compositions and abundances (Moser and Kimball, 2005),\ntotal diatom productivity commonly varies considerably with changes in\nlimnological conditions. Biogenic silica preserved in sediments is an index of\ntotal diatom productivity and, thus, is an indirect proxy for paleolimnology\n(for example, Colman and others, 1995; Johnson and others, 2001). In this\npaper, we present the results of biogenic silica analyses of two cores taken in\nBear Lake, Utah, and discuss preliminary paleolimnologic conclusions based on\nthese data.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1329.json b/datasets/USGS_OFR_2005_1329.json index c2bd9bec1b..531587d40a 100644 --- a/datasets/USGS_OFR_2005_1329.json +++ b/datasets/USGS_OFR_2005_1329.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1329", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A ground-water reconnaissance study of the Bijou Creek watershed in South Lake\nTahoe, California was done during the summer and early fall of 2003. This study\nprovides basic hydrologic data for a region in the Lake Tahoe Basin in which a\ncontinuing loss of lake clarity is occurring in the nearshore zone of Lake\nTahoe. Wells, springs, and a surface-water site were located and basic\nhydrologic data were collected. Water levels were measured and water samples\nwere collected and analyzed for nutrients. Measurements of water temperature,\nspecific conductance, and pH were made at all ground-water sites where possible\nand at one surface-water site.\n\nOrganic nitrogen plus ammonia, ammonia, and biologically-available iron\nconcentrations generally were greater in the ground water in the Bijou Creek\nwatershed than those observed in ground water elsewhere in the Lake Tahoe\nBasin. Nitrate concentrations were similar in the two groups. Phosphorus and\northophosphate concentrations generally were lower in the ground water of the\nBijou Creek watershed compared to ground water from elsewhere in the Lake Tahoe\nBasin. Specific conductance and pH of ground water were similar between the\nBijou Creek watershed and the Lake Tahoe Basin, but the temperature of ground\nwater was generally greater in the Bijou Creek watershed.\n\nNitrate concentrations appeared to increase over time at one of two long-term\nground-water sites. Orthophosphate concentration decreased while specific\nconductance increased at one of the two sites, but no trend was detected at the\nother site for either parameter. No trends were detected for phosphorus,\nbiologically-available iron, water temperature, or pH at either of the\nlong-term sites. Trends in ammonia and organic nitrogen plus ammonia\nconcentrations were not evaluated because a majority of the values were below\nthe method detection limits.\n\nThere were no obvious spatial distribution patterns for nutrient concentrations\nor field parameters in the Bijou Creek watershed. The altitude of the\nground-water table above sea level generally increased with increasing distance\nfrom Lake Tahoe. The altitude of the ground-water table was greater than the\naltitude of the surface of Lake Tahoe except at one ground-water site which is\ninfluenced by a cone of depression around a nearby production well. Ground\nwater in the Bijou Creek watershed discharges to Lake Tahoe and may contribute\nto the higher than normal turbidity in the area.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1333.json b/datasets/USGS_OFR_2005_1333.json index 8948e5df06..c85a2edb7e 100644 --- a/datasets/USGS_OFR_2005_1333.json +++ b/datasets/USGS_OFR_2005_1333.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1333", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study, completed by the U.S. Geological Survey (USGS) in cooperation with\nthe Pennsylvania Department of Conservation and Natural Resources, Bureau of\nTopographic and Geologic Survey (T&GS), provides estimates of ground-water\nrecharge for watersheds throughout Pennsylvania computed by use of two\nautomated streamflow-hydrograph-analysis methods--PART and RORA. The PART\ncomputer program uses a hydrograph-separation technique to divide the\nstreamflow hydrograph into components of direct runoff and base flow. Base flow\ncan be a useful approximation of recharge if losses and interbasin transfers of\nground water are minimal. The RORA computer program uses a recession-curve\ndisplacement technique to estimate ground-water recharge from each storm period\nindicated on the streamflow hydrograph.\n\nRecharge estimates were made using streamflow records collected during\n1885-2001 from 197 active and inactive streamflow-gaging stations in\nPennsylvania where streamflow is relatively unaffected by regulation. Estimates\nof mean-annual recharge in Pennsylvania computed by the use of PART ranged from\n5.8 to 26.6 inches; estimates from RORA ranged from 7.7 to 29.3 inches.\nEstimates from the RORA program were about 2 inches greater than those derived\nfrom the PART program.\n\nMean-monthly recharge was computed from the RORA program and was reported as a\npercentage of mean-annual recharge. On the basis of this analysis, the major\nground-water recharge period in Pennsylvania typically is November through May;\nthe greatest monthly recharge typically occurs in March.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1339_1.0.json b/datasets/USGS_OFR_2005_1339_1.0.json index 9de764f24d..e8f5922666 100644 --- a/datasets/USGS_OFR_2005_1339_1.0.json +++ b/datasets/USGS_OFR_2005_1339_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1339_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of gravity anomalies in Cave, Dry Lake, and Delamar valleys in\neast-central Nevada defines the overall shape of their basins, provides\nestimates of the depth to pre-Cenozoic basement rocks, and identifies buried\nfaults beneath the sedimentary cover. In all cases, the basins are asymmetric\nin their cross section and in their placement beneath the valley, reflecting\nthe extensional tectonism that initiated during Miocene time in this area.\nAbsolute values of basin depths are estimated using a density-depth profile\ncalibrated by deep oil and gas wells that encountered basement rocks in Cave\nValley. The basin beneath southern Cave Valley extends down to -6.0 km, that of\nDry Lake Valley extends to -8.2 km, and that of Delamar Valley extends to -6.4\nkm. The ranges surrounding Dry Lake and Delamar valleys are dominated by\nvolcanic units that may produce lower-density basin infill, which in turn,\nwould make the maximum depth estimates somewhat less. Dry Lake Valley is\ncharacterized by a slot-like graben in its center, whereas the deep portions of\nCave and Delamar valleys are more bowl-shaped. Significant portions of the\nbasins are shallow (<1 km deep), as are the transitions between each of these\nvalleys. A seismic reflection image across southern Cave and Muleshoe valleys\nconfirms the basin shapes inferred from gravity analysis. The architecture of\nthese basins inferred from gravity will aid in interpreting the hydrogeologic\nframework of Cave, Dry Lake, and Delamar valleys by placing estimates on the\nvolume and connectivity of potential unconsolidated alluvial aquifers and by\nidentifying faults buried beneath basin deposits.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1402.json b/datasets/USGS_OFR_2005_1402.json index 2f18e40dc6..252eb50146 100644 --- a/datasets/USGS_OFR_2005_1402.json +++ b/datasets/USGS_OFR_2005_1402.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1402", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Interpretation of reprocessed data from a regional grid of 25 public-domain 2-D\nseismic profiles in the National Petroleum Reserve in Alaska has enabled an\nanalysis of subsurface geologic relations throughout that region. Notable\nresults include interpretations of the geometry of the Mississippian Umiat and\nMeade basins, and depositional patterns in the thick succession of younger\nstrata that were influenced by major structural features such as the Barrow\narch and the Brooks Range.\n\nPre-Mississippian low-grade metamorphic rocks and subordinate granites of the\nFranklinian sequence are the basement rocks of the region. The top of the\nFranklinian is imaged as one of the highest amplitude, most continuous\nreflections.\n\nThe sedimentary succession includes (1) the Mississippian to Triassic\nEllesmerian sequence (consisting of the Endicott, Lisburne and Sadlerochit\ngroups, and the Shublik Formation and Sag River Sandstone; (2) the Beaufortian\nsequence, comprising the Jurassic to Lower Cretaceous Kingak Shale and the\noverlying Lower Cretaceous pebble shale unit; and (3) the Cretaceous to\nPaleocene Brookian sequence, which includes the Hue Shale and the Torok,\nNanushuk, Seabee, Tuluvak, Schrader Bluff, and Prince Creek formations.\n\nStratigraphic horizons that were mapped seismically include the tops of the\nFranklinian basement, the Endicott, Lisburne, and Sadlerochit groups, the\nShublik Formation, the Sag River Sandstone, the Lower Cretaceous unconformity\n(LCU), and the gamma-ray zone of the Hue Shale. Distinguishing criteria were\nestablished for the seismic-reflection characteristics for each of these\nhorizons, and the results were used in the correlation of units across the\nbasins and onto the bordering margins. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1405.json b/datasets/USGS_OFR_2005_1405.json index 27dbf04ce8..ee47214b47 100644 --- a/datasets/USGS_OFR_2005_1405.json +++ b/datasets/USGS_OFR_2005_1405.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1405", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landforms in Seattle, Washington, that were created primarily by landsliding\nwere mapped using LIDAR-derived imagery. These landforms include landslides\n(primarily landslide complexes), headscarps, and denuded slopes. Over 93\npercent of about 1,300 reported historical landslides are located within the\nLIDAR-mapped landform boundaries. The spatial densities of reported historical\nlandslides within the LIDAR-mapped landforms provide the relative\nsusceptibilities of the landforms to past landslide activity. Because the\nlandforms were primarily created by prehistoric landslides, the spatial\ndensities also provide reasonable estimates of future landslide susceptibility.\nThe mapped landforms and susceptibilities provide useful tools for landslide\nhazard reduction in Seattle.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1407.json b/datasets/USGS_OFR_2005_1407.json index b17598e4f1..2479d589f6 100644 --- a/datasets/USGS_OFR_2005_1407.json +++ b/datasets/USGS_OFR_2005_1407.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1407", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data contain information on the results of single-well aquifer tests,\nlineament analysis, and a bedrock geologic map compilation for Jefferson\nCounty, West Virginia. Efforts have been initiated by management agencies of\nJefferson County in cooperation with the U.S. Geological Survey to further the\nunderstanding of the spatial distribution of fractures in the carbonate regions\nand their correlation with aquifer properties. This report presents\ntransmissivity values from 181 single-well aquifer tests and a map of\nfracture-traces determined from aerial photos and field investigations.\nTransmissivity values were compared to geologic factors possibly affecting\ntheir magnitude.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2005_1450.json b/datasets/USGS_OFR_2005_1450.json index ae7f0c18b1..55d8a89618 100644 --- a/datasets/USGS_OFR_2005_1450.json +++ b/datasets/USGS_OFR_2005_1450.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2005_1450", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An assessment by the U.S. Geological Survey (USGS), Nevada Bureau of Mines and\nGeology (NBMG), and University of Nevada, Las Vegas (UNLV) is in progress of\nknown and undiscovered mineral resources of selected areas administered by the\nBureau of Land Management (BLM) in Clark and Nye Counties, Nevada. The purpose\nof this work is to provide the BLM with information for use in their long-term\nplanning process in southern Nevada so that they can make better-informed\ndecisions.\n\nExisting information about the areas, including geology, geophysics,\ngeochemistry, and mineral-deposit information is being compiled, and field\nexaminations of selected areas and mineral occurrences have been conducted.\nThis information will be used to determine the geologic setting, metallogenic\ncharacteristics, and mineral potential of the areas.\n\nTwenty-five Areas of Critical Environmental Concern (ACECs) have been\nidentified by BLM as the object of this study. They range from tiny (less than\none square km) to large (more than 1,000 square km). This report includes\ngeochemical data for rock samples collected by the USGS and NBMG in these ACECs\nand nearby areas. Samples have been analyzed from the Big Dune, Ash Meadows,\nArden, Desert Tortoise Conservation Center, Coyote Springs Valley, Mormon Mesa,\nVirgin Mountains, Gold Butte A and B, Whitney Pockets, Rainbow Gardens, River\nMountains, and Piute-Eldorado Valley ACECs.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1032.json b/datasets/USGS_OFR_2006_1032.json index b2a4cf12f0..e16577a665 100644 --- a/datasets/USGS_OFR_2006_1032.json +++ b/datasets/USGS_OFR_2006_1032.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1032", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2001, the U.S. Geological Survey Landslide Hazards Program provided funding\nfor seven State geological surveys to report on the status of landslide\ninvestigation strategies in each of their States, and to suggest improved ways\nto approach the tracking of landslides, their effects, losses associated with\nthe landslides, and hazard mitigation strategies. Each State was to provide a\ndraft report suggesting innovative ways to track landslides, and to participate\nin subsequent workshops. A workshop was convened in June 2003 in Lincoln, Neb.,\nto discuss the results and future strategies on how best to incorporate the\nseven pilot projects into one methodology that all of the 50 States could\nadopt. The seven individual reports produced by the State surveys are published\nhere to put forth a forum for discussion of the varying methods of tracking\nlandslides.\n\nThis pilot study, conducted by seven State geological surveys, examines the\nfeasibility of collecting accurate and reliable information on economic losses\nassociated with landslides. Each State survey examined the availability,\ndistribution, and inherent uncertainties of economic loss data in their study\nareas. Their results provide the basis for identifying the most fruitful\nmethods of collecting landslide loss data nationally, using methods that are\nconsistent and provide common goals. These results can enhance and establish\nthe future directions of scientific investigation priorities by convincingly\ndocumenting landslide risks and consequences that are universal throughout the\n50 States. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1038.json b/datasets/USGS_OFR_2006_1038.json index b6c3b4129c..ad9736ac92 100644 --- a/datasets/USGS_OFR_2006_1038.json +++ b/datasets/USGS_OFR_2006_1038.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1038", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map shows the physical characteristics, geology and mineral resources for \nAfghanistan.", "links": [ { diff --git a/datasets/USGS_OFR_2006_1042.json b/datasets/USGS_OFR_2006_1042.json index 57a2b59f1d..ed60c0cf0e 100644 --- a/datasets/USGS_OFR_2006_1042.json +++ b/datasets/USGS_OFR_2006_1042.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1042", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity and magnetic data were collected in the vicinity of Virgin Valley to\nhelp better characterize the buried sedimentary Mesquite and Mormon basins.\nDetailed gravity measurements were made over the buried saddle between the\nMesquite and Mormon basins, discovered by earlier gravity studies, in order to\ncalculate the depth to pre-Cenozoic basement. The purpose of this study was to\nprovide estimates of sedimentary fill in this area prior to drilling a water\nwell on Mormon Mesa. The calculated depth-to-basement results in an estimate of\nabout 1.5 km of alluvial fill in this area. Additional gravity data were\ncollected to help better define the shape and magnitude of the anomaly\nassociated with the Mesquite Basin. Testing of an experimental towed\nmagnetometer was also carried out, which showed very good correlation with an\nexisting aeromagnetic survey.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1051.json b/datasets/USGS_OFR_2006_1051.json index f28f2ce266..22ca40722c 100644 --- a/datasets/USGS_OFR_2006_1051.json +++ b/datasets/USGS_OFR_2006_1051.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1051", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These maps, and the tables that accompany them, are a compilation of isotopic\nage determinations of rocks and minerals in four 1:100,000-scale quadrangles in\nthe northern and central Front Range, Colorado. Phanerozoic (primarily Tertiary\nand Cretaceous) age data are shown on one map; Proterozoic data are on the\nother (sheet 1). A sample location map (sheet 2) is included for ease of\nmatching specific localities and data in the tables to the maps. Several\nrecords in the tables were not included in the maps because either there were\nambiguous dates or lack of location precluded accurate plotting.\n\nTo illustrate the geological setting for the samples, the plutonic rocks are\nshown on the maps. The boundaries of the plutons are from the Geologic Map of\nColorado with a few modifications. For ease of reference, we labeled each of\nthe larger (and some of the smaller) plutons with a generally accepted name\nfrom the literature. As a convenience in using the data, we have informally\nnamed some plutons based on geographic features on or near those plutons. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1070.json b/datasets/USGS_OFR_2006_1070.json index 2b007e4bb0..a44c0e062a 100644 --- a/datasets/USGS_OFR_2006_1070.json +++ b/datasets/USGS_OFR_2006_1070.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1070", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Kuskokwim mineral belt of Bundtzen and Miller (1997) forms an important\nmetallogenic region in southwestern Alaska that has yielded more than 3.22\nmillion ounces of gold and 400,000 ounces of silver. Precious-metal and related\ndeposits in this region associated with Late Cretaceous to early Tertiary\nigneous complexes extend into the Taylor Mountains 1:250,000-scale quadrangle.\nThe U.S. Geological Survey is conducting geologic mapping and a mineral\nresource assessment of this area that will provide a better understanding of\nthe geologic framework, regional geochemistry, and may provide targets for\nmineral exploration and development. During the 2004 field season 137 rock\nsamples were collected for a variety of purposes.\n\nAll samples were analyzed for a suite of 42 trace-elements to provide data for\nuse in geochemical exploration as well as some baseline data. Selected samples\nwere analyzed by additional methods; 104 targeted geochemical exploration\nsamples were analyzed for gold, arsenic, and mercury; 21 of these samples were\nalso analyzed to obtain concentrations of 10 loosely bound metals; 33 rock\nsamples were analyzed for major element oxides to support the regional mapping\nprogram, of which 28 sedimentary rock samples were also analyzed for total\ncarbon, and carbonate carbon.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1081.json b/datasets/USGS_OFR_2006_1081.json index 2a2091ed1d..3b945d4087 100644 --- a/datasets/USGS_OFR_2006_1081.json +++ b/datasets/USGS_OFR_2006_1081.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1081", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In April 2004, more than 40 hours of georeferenced submarine digital video was\ncollected in water depths of 15-370 m in Glacier Bay to (1) ground-truth\nexisting geophysical data (bathymetry and acoustic reflectance), (2) examine\nand record geologic characteristics of the sea floor, and (3) investigate the\nrelation between substrate types and benthic communities, and (4) construct\npredictive maps of sea floor geomorphology and habitat distribution. Common\nsubstrates observed include rock, boulders, cobbles, rippled sand, bioturbated\nmud, and extensive beds of living horse mussels and scallops. Four principal\nsea-floor geomorphic types are distinguished by using video observations. Their\ndistribution in lower and central Glacier Bay is predicted using a supervised,\nhierarchical decision-tree statistical classification of geophysical data.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1085.json b/datasets/USGS_OFR_2006_1085.json index ecc81a7aae..5d63e407ec 100644 --- a/datasets/USGS_OFR_2006_1085.json +++ b/datasets/USGS_OFR_2006_1085.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1085", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution measurements of waves, currents, water levels, temperature,\nsalinity and turbidity were made in Hanalei Bay, northern Kauai, Hawaii, during\nthe summer of 2005 to better understand coastal circulation and sediment\ndynamics in coral reef habitats. A series of bottom-mounted instrument packages\nwere deployed in water depths of 10 m or less to collect long-term,\nhigh-resolution measurements of waves, currents, water levels, temperature,\nsalinity and turbidity. These data were supplemented with a series of vertical\ninstrument casts to characterize the vertical and spatial variability in water\ncolumn properties within the bay. The purpose of these measurements was to\ncollect hydrographic data to learn how waves, currents and water column\nproperties vary spatially and temporally in an embayment that hosts a nearshore\ncoral reef ecosystem adjacent to a major river drainage. These measurements\nsupport the ongoing process studies being conducted as part of the U.S.\nGeological Survey (USGS) Coastal and Marine Geology Program's Coral Reef\nProject; the ultimate goal is to better understand the transport mechanisms of\nsediment, larvae, pollutants and other particles in coral reef settings. This\nreport, the first part in a series, describes data acquisition, processing and\nanalysis.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1091.json b/datasets/USGS_OFR_2006_1091.json index 8f1f439734..4b1813f89b 100644 --- a/datasets/USGS_OFR_2006_1091.json +++ b/datasets/USGS_OFR_2006_1091.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1091", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples were collected in streams and springs in the karst terrane of the\nSinking Creek Basin in 2004 as part of study in cooperation with the Kentucky\nDepartment of Agriculture. A total of 48 water samples were collected at 7\nsites (4 springs, 2 streams, and 1 karst window) from April through November\n2004. The karst terrane of the Sinking Creek Basin (also known as Boiling\nSpring Basin) encompasses about 125 square miles in Breckinridge County and\nportions of Meade and Hardin Counties in Kentucky.\n\nFourteen pesticides were detected of the 52 pesticides analyzed in the stream\nand spring samples. Of the 14 detected pesticides, 12 were herbicides and 2\nwere insecticides. The most commonly detected pesticides; atrazine, simazine,\nmetolachlor, and acetochlor-were those most heavily used on crops during the\nstudy. Atrazine was detected in 100 percent of all samples; simazine,\nmetolachlor, and acetochlor were detected in more than 35 percent of all\nsamples. The pesticide-transformation compound, deethylatrazine, was detected\nin 98 percent of the samples. Only one nonagricultural herbicide, prometon, was\ndetected in more than 30 percent of the samples. Malathion, the most commonly\ndetected insecticide, was found in 4 percent of the samples, which was followed\nby carbofuran (2 percent).\n\nMost of the pesticides were present in low concentrations; however, atrazine\nwas found in springs exceeding the U.S. Environmental Protection Agency's\n(USEPA) standards for drinking water. Atrazine exceeded the USEPA's maximum\ncontaminant level 2 times in 48 detections.\n\nConcentrations of nitrate greater than 10 milligrams per liter (mg/L) were not\nfound in water samples from any of the sites. Concentrations of nitrite plus\nnitrate ranged from 0.21 to 3.9 mg/L at the seven sites. The median\nconcentration of nitrite plus nitrate for all sites sampled was 1.5 mg/L.\nConcentrations of nitrite plus nitrate generally were higher in the springs\nthan in the main stem of Sinking Creek.\n\nForty-two percent of the concentrations of total phosphorus at all seven sites\nexceeded the USEPA's recommended maximum concentration of 0.1 mg/L. The median\nconcentration of total phosphorus for all sites sampled was 0.09 mg/L. The\nhighest median concentrations of total phosphorus were found in the springs.\nMedian concentrations of orthophosphate followed the same pattern as\nconcentrations of total phosphorus in the springs. Concentrations of\northophosphate ranged from 0.006 to 0.192 mg/L.\n\nConcentrations of suspended sediment generally were low throughout the basin;\nthe median concentration of suspended sediment for all sites sampled was 23\nmg/L. The highest concentration of suspended sediment (1,486 mg/L) was measured\nfollowing a storm event at Sinking Creek near Lodiburg, Ky.", "links": [ { diff --git a/datasets/USGS_OFR_2006_1096.json b/datasets/USGS_OFR_2006_1096.json index c7beaca696..a177c8d6c6 100644 --- a/datasets/USGS_OFR_2006_1096.json +++ b/datasets/USGS_OFR_2006_1096.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1096", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary purpose of the USGS National Assessment of Coastal Change Project\nis to provide accurate representations of pre-storm ground conditions for areas\nthat are designated high priority because they have dense populations or\nvaluable resources that are at risk from storm waves. A secondary purpose of\nthe project is to develop a geomorphic (land feature) coastal classification\nthat, with only minor modification, can be applied to most coastal regions in\nthe United States.\n\nA Coastal Classification Map describing local geomorphic features is the first\nstep toward determining the hazard vulnerability of an area. The Coastal\nClassification Maps of the National Assessment of Coastal Change Project\npresent ground conditions such as beach width, dune elevations, overwash\npotential, and density of development. In order to complete a\nhazard-vulnerability assessment, that information must be integrated with other\ninformation, such as prior storm impacts and beach stability. The Coastal\nClassification Maps provide much of the basic information for such an\nassessment and represent a critical component of a storm-impact forecasting\ncapability. \n\nThe map above shows the areas covered by this web site. Click on any of the\nlocation names or outlines to view the Coastal Classification Map for that\narea.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1110.json b/datasets/USGS_OFR_2006_1110.json index 69419121d6..51a5536288 100644 --- a/datasets/USGS_OFR_2006_1110.json +++ b/datasets/USGS_OFR_2006_1110.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1110", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) conducted geophysical studies in support of\nthe resource appraisal of the Crump Geyser Known Geothermal Resource Area\n(KGRA). This area was designated as a KGRA by the USGS, and this designation\nbecame effective on December 24, 1970. The land classification standards for a\nKGRA were established by the Geothermal Steam Act of 1970 (Public Law 91-581).\nFederal lands so classified required competitive leasing for the development of\ngeothermal resources.\n\nThe author presented an administrative report of USGS geophysical studies\nentitled \"Geophysical background of the Crump Geyser area, Oregon, KGRA\" to a\nUSGS resource committee on June 17, 1975. This report, which essentially was a\ndescription of geophysical data and a preliminary interpretation without\ndiscussion of resource appraisal, is in Appendix 1. Reduction of sheets or\nplates in the original administrative report to page-size figures, which are\nlisted and appended to the back of the text in Appendix 1, did not seem to\nsignificantly degrade legibility. Bold print in the text indicates where minor\nchanges were made. A colored page-size index and tectonic map, which also show\nregional geology not shown in figure 2, was substituted for original figure 1.\nDetailed descriptions for the geologic units referenced in the text and shown\non figures 1 and 2 were separately defined by Walker and Repenning (1965) and\npresumably were discussed in other reports to the committee. Heavy dashed lines\non figures 1 and 2 indicate the approximate KGRA boundary.\n\nOne of the principal results of the geophysical studies was to obtain a gravity\nmap (Appendix 1, fig. 10; Plouff, and Conradi, 1975, pl. 9), which reflects the\nfault-bounded steepness of the west edge of sediments and locates the maximum\nthickness of valley sediments at about 10 kilometers south of Crump Geyser.\nBased on the indicated regional-gravity profile and density-contrast\nassumptions for the two-dimensional profile, the maximum sediment thickness was\nestimated at 820 meters. A three-dimensional gravity model would have yielded a\ngreater thickness. Audiomagnotelluric measurements were not made as far south\nas the location of the gravity low, as determined in the field, due to a lack\nof communication at that time. A boat was borrowed to collect gravity\nmeasurements along the edge of Crump Lake, but the attempt was curtailed by\nharsh, snowy weather on May 21, 1975, which shortly followed days of hot\ntemperature.\n\nMost of the geophysical data and illustrations in Appendix 1 have been\npublished (Gregory and Martinez, 1975; Plouff, 1975; and Plouff and Conradi,\n1975), and Donald Plouff (1986) discussed a gravity interpretation of Warner\nValley at the Fall 1986 American Geophysical Union meeting in San Francisco.\nFurther interpretation of possible subsurface geologic sources of geophysical\nanomalies was not discussed in Appendix 1. For example, how were apparent\nresistivity lows (Appendix 1, figs. 3-6) centered near Crump Geyser affected by\na well and other manmade electrically conductive or magnetic objects? What is\nthe geologic significance of the 15-milligal eastward decrease across Warner\nValley? The explanation that the two-dimensional gravity model (Appendix 1,\nfig. 14) was based on an inverse iterative method suggested by Bott (1960) was\nnot included. Inasmuch as there was no local subsurface rock density\ndistribution information to further constrain the gravity model, the\nthree-dimensional methodology suggested by Plouff (1976) was not attempted.\n\nInasmuch as the associated publication by Plouff (1975), which released the\ngravity data, is difficult to obtain and not in digital format, that report is\nreproduced in Appendix 2. Two figures of the publication are appended to the\nback of the text. A later formula for the theoretical value of gravity for the\ngiven latitudes at sea level (International Association of Geodesy, 1971)\nshould be used to re-compute gravity anomalies. To merge the observed-gravity\nvalues printed in that report with later measurements, an empirically\ndetermined constant gravity datum shift should be applied. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1129_WIPP_NM_1.0 Online.json b/datasets/USGS_OFR_2006_1129_WIPP_NM_1.0 Online.json index 597848d882..24d2bfd47b 100644 --- a/datasets/USGS_OFR_2006_1129_WIPP_NM_1.0 Online.json +++ b/datasets/USGS_OFR_2006_1129_WIPP_NM_1.0 Online.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1129_WIPP_NM_1.0 Online", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S.Geological Survey Open-File Report consists of the results of a series\nof aquifer tests (shut-in test, flow test, bailing test, slug test, swabbing\ntest and pressure-pulse test)performed by the U.S. Geological Survey on\ngeologic units of Permian age at the Waste Isoliation Pilot Plant site between\nFebruary 1979 and July 1980 in wells H-1, H-2 complex (H2-2A, H-2B, and H-2C),\nand H-3. The tested geologic units included the Magenta Dolomite and Culebra\nDolomite Members of the Rustler Formation, and the contact zone between the\nRustler and Salado Formations. Selected information on the tested formations,\ntest dates, pre-test static water levels, test configurations, and raw data\ncollected during these tests are tabulated in this report. \n \n[Summary taken in large part from U.S. Geological Survey Open-File Report \nabstract]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1136.json b/datasets/USGS_OFR_2006_1136.json index bfb7755a7d..327482c727 100644 --- a/datasets/USGS_OFR_2006_1136.json +++ b/datasets/USGS_OFR_2006_1136.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1136", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a USGS Open-File-Report for the preliminary release of aeromagnetic\ndata collected in the Dillingham Area of Southwest Alaska and associated\ncontractor reports.\n\nAn airborne high-resolution magnetic survey was completed over the Dillingham\nand Nushagak Bay and Naknek area in southwestern Alaska. The flying was\nundertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological\nSurvey (USGS). First tests and calibration flights were completed by August\n26th, 2005 and data acquisition was initiated on September 1st, 2005. The final\ndata acquisition flight was completed on October 22nd, 2005. A total of 8,630\nline-miles of data were acquired during the survey.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1247.json b/datasets/USGS_OFR_2006_1247.json index b6181bae5a..39bfbf285a 100644 --- a/datasets/USGS_OFR_2006_1247.json +++ b/datasets/USGS_OFR_2006_1247.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1247", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report consists of high-resolution chirp seismic reflection profiledata\nfrom the northern Gulf of Lions, Spain. These data were acquired in2004 using\nthe Research Vessel Oceanus (USGS Cruise ID: O-1-04-MS). Thedata are available\nin binary and JPEG image formats. Binary data arein Society of Exploration\nGeologists (SEG) SEG-Y format and may bedownloaded for further processing or\ndisplay. Reference maps andJPEG images of the profiles may be viewed with your\nWeb browser.\n\nMarine seismic reflection data are used to image and mapsedimentary and\nstructural features of the seafloor and subsurface.These data were acquired\nacross the shelf and canyon area of the Gulfof Lions, Spain as part of a\nmultinational effort to characterize thegeologic framework and sedimentary\nenvironment of the region.The specific objective of this seismic survey is to\nprovide seismicreflection images of the depositional geometry of the upper 50\nmeters ofsubbottom stratigraphy in order to better understand the mechanisms\nofsediment transport and deposition. These chirp seismic profiles\nprovidehigh-quality images with approximately 20 cm of verticalresolution and\nup to 80 m of subbottom penetration.\n\nChirp seismic reflection profiles are acquired by means of anacoustic source\nand a hydrophone array, both contained in a single unittowed in the water\nbehind a survey vessel. The sound source emits ashort (30 ms) swept-frequency\n(500 to 7200 Hz)acoustic pulse,which propagates through the water and sediment\ncolumns. The acousticenergy is reflected at density boundaries (such as the\nseafloor orsediment layers beneath the seafloor), and detected by the\nhydrophonearray, and digitally recorded by the onboard PC-based acquisition\nsystem.As the vessel moves, this process is repeated multiple times per\nsecond,producing a two-dimensional image of the shallow geologic\nstructurebeneath the ship track.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1274.json b/datasets/USGS_OFR_2006_1274.json index 9674af81e0..5ace0ab1be 100644 --- a/datasets/USGS_OFR_2006_1274.json +++ b/datasets/USGS_OFR_2006_1274.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1274", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report includes three posters with analyses of net land area changes in\ncoastal Louisiana after the 2005 hurricanes (Katrina and Rita). The first\nposter presents a basic analysis of net changes from 2004 to 2005; the second\npresents net changes within marsh communities from 2004 to 2005; and the third\npresents net changes from 2004 to 2005 within the historical perspective of\nchange in coastal Louisiana from 1956 to 2004. The purpose of this analysis was\nto provide preliminary information on land area changes shortly after\nHurricanes Katrina and Rita and to serve as a regional baseline for monitoring\nwetland recovery following the 2005 hurricane season. Estimation of permanent\nlosses cannot be made until several growing seasons have passed and the\ntransitory impacts of the hurricanes are minimized, but this preliminary\nanalysis indicates an approximate 217-mi2 (562.03-km2) decrease in\nland/increase in water across coastal Louisiana.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1280.json b/datasets/USGS_OFR_2006_1280.json index 146b4cb234..adc45d5679 100644 --- a/datasets/USGS_OFR_2006_1280.json +++ b/datasets/USGS_OFR_2006_1280.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1280", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Great Basin physiographic province in the Western United States contains a\ndiverse assortment of world-class ore deposits. It currently (2006) is the\nworld's second leading producer of gold, contains large silver and base metal\n(Cu, Zn, Pb, Mo, W) deposits, a variety of other important metallic (Fe, Ni,\nBe, REE's, Hg, PGE) and industrial mineral (diatomite, barite, perlite,\nkaolinite, gallium) resources, as well as petroleum and geothermal energy\nresources. Ore deposits are most numerous and largest in size in linear mineral\nbelts with complex geology.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1299_1.0.json b/datasets/USGS_OFR_2006_1299_1.0.json index a18ab7972f..8c62bf7459 100644 --- a/datasets/USGS_OFR_2006_1299_1.0.json +++ b/datasets/USGS_OFR_2006_1299_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1299_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A three-dimensional inversion of gravity data from the Rainier Mesa area and\nsurrounding regions reveals a topographically complex pre-Cenozoic basement\nsurface. This model of the depth to pre-Cenozoic basement rocks is intended for\nuse in a 3D hydrogeologic model being constructed for the Rainier Mesa area.\nPrior to this study, our knowledge of the depth to pre-Cenozoic basement rocks\nwas based on a regional model, applicable to general studies of the greater\nNevada Test Site area but inappropriate for higher resolution modeling of\nground-water flow across the Rainier Mesa area. The new model incorporates\nseveral changes that lead to significant improvements over the previous\nregional view. First, the addition of constraining wells, encountering old\nvolcanic rocks lying above but near pre-Cenozoic basement, prevents modeled\nbasement from being too shallow. Second, an extensive literature and well data\nsearch has led to an increased understanding of the change of rock density with\ndepth in the vicinity of Rainier Mesa. The third, and most important change,\nrelates to the application of several depth-density relationships in the study\narea instead of a single generalized relationship, thereby improving the\noverall model fit. In general, the pre-Cenozoic basement surface deepens in the\nwestern part of the study area, delineating collapses within the Silent Canyon\nand Timber Mountain caldera complexes, and shallows in the east in the Eleana\nRange and Yucca Flat regions, where basement crops out. In the Rainier Mesa\nstudy area, basement is generally shallow (< 1 km). The new model identifies\npreviously unrecognized structures within the pre-Cenozoic basement that may\ninfluence ground-water flow, such as a shallow basement ridge related to an\ninferred fault extending northward from Rainier Mesa into Kawich Valley.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1396_1.0.json b/datasets/USGS_OFR_2006_1396_1.0.json index 9ffb8e43f8..81fd88fe5a 100644 --- a/datasets/USGS_OFR_2006_1396_1.0.json +++ b/datasets/USGS_OFR_2006_1396_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1396_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity and seismic data from Tule Desert, Meadow Valley Wash, and California\nWash, Nevada, provide insight into the subsurface geometry of these three\nbasins that lie adjacent to rapidly developing areas of Clark County, Nevada.\nEach of the basins is the product of Tertiary extension accommodated with the\ngeneral form of north-south oriented, asymmetrically-faulted half-grabens.\nGeophysical inversion of gravity observations indicates that Tule Desert and\nMeadow Valley Wash basins are segmented into subbasins by shallow, buried\nbasement highs. In this study, basement refers to pre-Cenozoic bedrock units\nthat underlie basins filled with Cenozoic sedimentary and volcanic units. In\nTule Desert, a small, buried basement high inferred from gravity data appears\nto be a horst whose placement is consistent with seismic reflection and\nmagnetotelluric observations. Meadow Valley Wash consists of three subbasins\nseparated by basement highs at structural zones that accommodated different\nstyles of extension of the adjacent subbasins, an interpretation consistent\nwith geologic mapping of fault traces oblique to the predominant north-south\nfault orientation of Tertiary extension in this area. California Wash is a\nsingle structural basin. The three seismic reflection lines analyzed in this\nstudy image the sedimentary basin fill, and they allow identification of faults\nthat offset basin deposits and underlying basement. The degree of faulting and\nfolding of the basin-fill deposits increases with depth. Pre-Cenozoic units are\nobserved in some of the seismic reflection lines, but their reflections are\ngenerally of poor quality or are absent. Factors that degrade seismic reflector\nquality in this area are rough land topography due to erosion, deformed\nsedimentary units at the land surface, rock layers that dip out of the plane of\nthe seismic profile, and the presence of volcanic units that obscure underlying\nreflectors. Geophysical methods illustrate that basin geometry is more\ncomplicated than would be inferred from extrapolation of surface topography and\ngeology, and these methods aid in defining a three-dimensional framework to\nunderstand groundwater storage and flow in southern Nevada.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2006_1397_1.0.1.json b/datasets/USGS_OFR_2006_1397_1.0.1.json index 2d2731b14e..ca13b459e2 100644 --- a/datasets/USGS_OFR_2006_1397_1.0.1.json +++ b/datasets/USGS_OFR_2006_1397_1.0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2006_1397_1.0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Scenic Drive landslide in La Honda, San Mateo County, California began\nmovement during the El Ni\u00f1o winter of 1997-98. Recurrent motion occurred during\nthe mild El Nino winter of 2004-2005 and again during the winter of 2005-06.\nThis report documents the changing geometry and motion of the Scenic Drive\nlandslide in 2005-2006, and it documents changes and persistent features that\nwe interpret to reflect underlying structural control of the landslide. We have\nalso compared the displacement history to near-real time rainfall history at a\ncontinuously recording gauge for the period October 2004-November 2006.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1006.json b/datasets/USGS_OFR_2007_1006.json index 2d66f8dd7f..b53595e4e2 100644 --- a/datasets/USGS_OFR_2007_1006.json +++ b/datasets/USGS_OFR_2007_1006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASTER data and logical operators were successfully used to map phyllic and\nargillic-altered rocks in the southeastern part of Afghanistan. Hyperion data\nwere used to correct ASTER band 5 and ASTER data were georegistered to\northorectified Landsat TM data. Logical operator algorithms produced argillic\nand phyllic byte ASTER images that were converted to vector data and overlain\non ASTER and Landsat TM images.\n\nAlteration and fault patterns indicated that two areas, the Argandab igneous\ncomplex, and the Katawaz basin may contain potential polymetallic vein and\nporphyry copper deposits. ASTER alteration mapping in the Chagai Hills\nindicates less extensive phyllic and argillic-altered rocks than mapped in the\nArgandab igneous complex and the Katawaz basin and patterns of alteration are\ninconclusive to predict potential deposit types.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1011_1.0.json b/datasets/USGS_OFR_2007_1011_1.0.json index 2e397159c9..73264e2e8e 100644 --- a/datasets/USGS_OFR_2007_1011_1.0.json +++ b/datasets/USGS_OFR_2007_1011_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1011_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Southern California Coastal Water Research Project (SCCWRP) is developing a\nhydrodynamic model of the SGR estuary, which is part of the comprehensive\nwater-quality model of the SGR estuary and watershed investigated by SCCWRP and\nother local agencies. The hydrodynamic model will help understanding of 1) the\nexchange processes between the estuary and coastal ocean; 2) the circulation\npatterns in the estuary; 3) upstream natural runoff and the cooling discharge\nfrom PGS.\n\nLike all models, the SGR hydrodynamic model is only useful after it is fully\ncalibrated and validated. In May 2005, SCCWRP requested the assistance of the\nU.S. geological Survey (USGS) Coastal and Marine Geology team (CMG) in\ncollecting data on the hydrodynamic conditions in the estuary during the summer\ndry season. The summer was chosen for field data collection as this was assumed\nto be the season with the greatest potential for chronic degraded water quality\ndue to low river flow and high thermal stratification within the estuary (due\nto both higher average air temperature and PGS output). Water quality can be\ndegraded in winter as well, when higher river discharge events bring large\nvolumes of water from the Los Angeles basin into the estuary. The objectives of\nthis project were to 1) collect hydrodynamic data along the SGR estuary; 2)\nstudy exchange processes within the estuary through analysis of the\nhydrodynamic data; and 3) provide field data for model calibration and\nvalidation. As the data only exist for the summer season, the results herein\nonly apply to summer conditions.", "links": [ { diff --git a/datasets/USGS_OFR_2007_1029_1.0.json b/datasets/USGS_OFR_2007_1029_1.0.json index 7a38430ec0..16ac53b034 100644 --- a/datasets/USGS_OFR_2007_1029_1.0.json +++ b/datasets/USGS_OFR_2007_1029_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1029_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2005, the U.S. Agency for International Development and the U.S. Trade and\nDevelopment Agency contracted with the U.S. Geological Survey to perform\nassessments of the natural resources within Afghanistan. The assessments\nconcentrate on the resources that are related to the economic development of\nthat country. Therefore, assessments were initiated in oil and gas, coal,\nmineral resources, water resources, and earthquake hazards. All of these\nassessments require geologic, structural, and topographic information\nthroughout the country at a finer scale and better accuracy than that provided\nby the existing maps, which were published in the 1970's by the Russians and\nGermans. The very rugged terrain in Afghanistan, the large scale of these\nassessments, and the terrorist threat in Afghanistan indicated that the best\napproach to provide the preliminary assessments was to use remotely sensed,\nsatellite image data, although this may also apply to subsequent phases of the\nassessments. Therefore, the first step in the assessment process was to produce\nsatellite image mosaics of Afghanistan that would be useful for these\nassessments. This report discusses the production of the Landsat false-color\nimage database produced for these assessments, which was produced from the\ncalibrated Landsat ETM+ image mosaics described by Davis (2006).\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1054.json b/datasets/USGS_OFR_2007_1054.json index a5d92922fe..2825a520e8 100644 --- a/datasets/USGS_OFR_2007_1054.json +++ b/datasets/USGS_OFR_2007_1054.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1054", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dead wood has become an increasingly important conservation issue in managed forests, as awareness of its function in providing wildlife habitat and in basic ecological processes has dramatically increased over the last several decades. The Decayed Wood Advisor (DecAID) is the most comprehensive tool currently available to inform dead-wood management. This report highlights the advantages of using DecAID to evaluate and manage dead-wood resources.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1055.json b/datasets/USGS_OFR_2007_1055.json index 2baea85c61..bc1d80d38e 100644 --- a/datasets/USGS_OFR_2007_1055.json +++ b/datasets/USGS_OFR_2007_1055.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1055", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS reports chemical and isotopic analyses of 345 water samples collected\nfrom the Osage-Skiatook Petroleum Environmental Research (OSPER) project. Water\nsamples were collected as part of an ongoing multi-year USGS investigation to\nstudy the transport, fate, natural attenuation, and ecosystem impacts of\ninorganic salts and organic compounds present in produced water releases at two\noil and gas production sites from an aging petroleum field located in Osage\nCounty, in northeast Oklahoma. The water samples were collected primarily from\nmonitoring wells and surface waters at the two research sites, OSPER A (legacy\nsite) and OSPER B (active site), during the period March, 2001 to February,\n2005. The data include produced water samples taken from seven active oil\nwells, one coal-bed methane well and two domestic groundwater wells in the\nvicinity of the OSPER sites.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1073_1.0.json b/datasets/USGS_OFR_2007_1073_1.0.json index 0c25ef1a29..4dd267f070 100644 --- a/datasets/USGS_OFR_2007_1073_1.0.json +++ b/datasets/USGS_OFR_2007_1073_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1073_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hawaiian Volcano Observatory (HVO) summary presents seismic data gathered\nduring the year. The seismic summary is offered without interpretation as a\nsource of preliminary data. It is complete in the sense that most data for\nevents of M≥1.5 routinely gathered by the Observatory are included.\n\nThe HVO summaries have been published in various forms since 1956. Summaries\nprior to 1974 were issued quarterly, but cost, convenience of preparation and\ndistribution, and the large quantities of data dictated an annual publication\nbeginning with Summary 74 for the year 1974. Summary 86 (the introduction of\nCUSP at HVO) includes a description of the seismic instrumentation,\ncalibration, and processing used in recent years. Beginning with 2004,\nsummaries are simply identified by the year, rather than Summary number. The\npresent summary includes background information on the seismic network and\nprocessing to allow use of the data and to provide an understanding of how they\nwere gathered.\n\nA report by Klein and Koyanagi (1980) tabulates instrumentation, calibration,\nand recording history of each seismic station in the network. It is designed as\na reference for users of seismograms and phase data and includes and augments\nthe information in the station table in this summary. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1084.json b/datasets/USGS_OFR_2007_1084.json index c3df24fa3c..59095860de 100644 --- a/datasets/USGS_OFR_2007_1084.json +++ b/datasets/USGS_OFR_2007_1084.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1084", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heavy oil and natural bitumen are oils set apart by their high viscosity (resistance to flow) and high density (low API gravity). These attributes reflect the invariable presence of up to 50 weight percent asphaltenes, very high molecular weight hydrocarbon molecules incorporating many heteroatoms in their lattices. Almost all heavy oil and natural bitumen are alteration products of conventional oil. Total resources of heavy oil in known accumulations are 3,396 billion barrels of original oil in place, of which 30 billion barrels are included as prospective additional oil. The total natural bitumen resource in known accumulations amounts to 5,505 billion barrels of oil originally in place, which includes 993 billion barrels as prospective additional oil. This resource is distributed in 192 basins containing heavy oil and 89 basins with natural bitumen. Of the nine basic Klemme basin types, some with subdivisions, the most prolific by far for known heavy oil and natural bitumen volumes are continental multicyclic basins, either basins on the craton margin or closed basins along convergent plate margins. The former includes 47 percent of the natural bitumen, the latter 47 percent of the heavy oil and 46 percent of the natural bitumen. Little if any heavy oil occurs in fore-arc basins, and natural bitumen does not occur in either fore-arc or delta basins.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1108.json b/datasets/USGS_OFR_2007_1108.json index dd87bace20..9be266be55 100644 --- a/datasets/USGS_OFR_2007_1108.json +++ b/datasets/USGS_OFR_2007_1108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ample geologic evidence indicates early Holocene and Pleistocene debris flows from the south side of the Santa Catalina Mountains north of Tucson, Arizona, but few records document historical events. On July 31, 2006, an unusual set of atmospheric conditions aligned to produce record floods and an unprecedented number of debris flows in the Santa Catalinas. During the week prior to the event, an upper-level area of low pressure centered near Albuquerque, New Mexico generated widespread heavy rainfall in southern Arizona. After midnight on July 31, a strong complex of thunderstorms developed over central Arizona in a deformation zone that formed on the back side of the upper-level low. High atmospheric moisture (2.00\" of precipitable water) coupled with cooling aloft spawned a mesoscale thunderstorm complex that moved southeast into the Tucson basin. A 15-20 knot low-level southwesterly wind developed with a significant upslope component over the south face of the Santa Catalina Mountains advecting moist and unstable air into the merging storms. National Weather Service radar indicated that a swath of 3-6\" of rainfall occurred over the lower and middle elevations of the southern Santa Catalina Mountains. This intense rain falling on saturated soil triggered over 250 hill slope failures and debris flows throughout the mountain range. Sabino Canyon, a heavily used recreation area administered by the U.S. Forest Service, was the epicenter of mass wasting, where at least 18 debris flows removed structures, destroyed the roadway in multiple locations, and closed public access for months. The debris flows were followed by stream flow floods which eclipsed the record discharge in the 75-year gaging record of Sabino Creek. In five canyons adjacent to Sabino Canyon, debris flows approached or excited the mountain front, compromising flow conveyance structures and flooding some homes.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1115_1.0.json b/datasets/USGS_OFR_2007_1115_1.0.json index 0707ce9ce3..862ce677fd 100644 --- a/datasets/USGS_OFR_2007_1115_1.0.json +++ b/datasets/USGS_OFR_2007_1115_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1115_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Great Basin physiographic province covers a large part of the western United States and contains one of the world's leading gold-producing areas, the Carlin Trend. In the Great Basin, many sedimentary-rock-hosted disseminated gold deposits occur along such linear mineral-occurrence trends. The distribution and genesis of these deposits is not fully understood, but most models indicate that regional tectonic structures play an important role in their spatial distribution. Over 100 magnetotelluric (MT) soundings were acquired between 1994 and 2001 by the U.S. Geological Survey to investigate crustal structures that may underlie the linear trends in north-central Nevada. MT sounding data were used to map changes in electrical resistivity as a function of depth that are related to subsurface lithologic and structural variations. Two-dimensional (2-D) resistivity modeling of the MT data reveals primarily northerly and northeasterly trending narrow 2-D conductors (1 to 30 ohm-m) extending to mid-crustal depths (5-20 km) that are interpreted to be major crustal fault zones. There are also a few westerly and northwesterly trending 2-D conductors. However, the great majority of the inferred crustal fault zones mapped using MT are perpendicular or oblique to the generally accepted trends. The correlation of strike of three crustal fault zones with the strike of the Carlin and Getchell trends and the Alligator Ridge district suggests they may have been the root fluid flow pathways that fed faults and fracture networks at shallower levels where gold precipitated in favorable host rocks. The abundant northeasterly crustal structures that do not correlate with the major trends may be structures that are open to fluid flow at the present time.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1122.json b/datasets/USGS_OFR_2007_1122.json index 838aeda38d..4599446f31 100644 --- a/datasets/USGS_OFR_2007_1122.json +++ b/datasets/USGS_OFR_2007_1122.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1122", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From May 13-17, 2006, central and southern New Hampshire experienced severe flooding caused by as much as 14 inches of rainfall in the region. As a result of the flood damage, a presidential disaster declaration was made on May 25, 2006, for seven counties-Rockingham, Hillsborough, Strafford, Merrimack, Belknap, Carroll, and Grafton. Following the flooding, the U.S. Geological Survey, in a cooperative investigation with the Federal Emergency Management Agency, determined the peak stages, peak discharges, and recurrence-interval estimates of the May 2006 flood at 65 streamgages in the counties where the disaster declaration was made. Data from flood-insurance studies published by the Federal Emergency Management Agency also were compiled for each streamgage location for comparison purposes.\n\nThe peak discharges during the May 2006 flood were the largest ever recorded at 14 long-term (more than 10 years of record) streamgages in New Hampshire. In addition, peak discharges equaled or exceeded a 100-year recurrence interval at 14 streamgages and equaled or exceeded a 50-year recurrence interval at 22 streamgages. The most severe flooding occurred in Rockingham, Strafford, Merrimack, and eastern and northern Hillsborough Counties.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1133.json b/datasets/USGS_OFR_2007_1133.json index b47225c1ec..6e7bbf4045 100644 --- a/datasets/USGS_OFR_2007_1133.json +++ b/datasets/USGS_OFR_2007_1133.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1133", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastal cliff retreat, the landward migration of the cliff face,\nis a chronic problem along many rocky coastlines in the United\nStates. As coastal populations continue to grow and community\ninfrastructures are threatened by erosion, there is increased demand\nfor accurate information regarding trends and rates of coastal cliff\nretreat. There is also a need for a comprehensive analysis of cliff\nretreat that is consistent from one coastal region to another. To meet\nthese national needs, the U.S. Geological Survey is conducting an\nanalysis of historical coastal cliff retreat along open-ocean rocky\ncoastlines of the conterminous United States and parts of Hawaii,\nAlaska, and the Great Lakes. One purpose of this work is to develop\nstandard repeatable methods for mapping and analyzing coastal cliff\nretreat so that periodic updates of coastal erosion can be made\nnationally that are systematic and internally consistent. This report\non the California Coast is an accompaniment to a report on long-term\nsandy shoreline change for California. This report summarizes the\nmethods of analysis, interprets the results, and provides explanations\nregarding long-term rates of cliff retreat. Neither detailed\nbackground information on the National Assessment of Shoreline Change\nProject nor detailed descriptions of the geology and geomorphology of\nthe California coastline are presented in this report. The reader is\nreferred to the shoreline change report (Hapke et al., 2006) for this\ntype of background information. Cliff retreat evaluations are based on\ncomparing one historical cliff edge digitized from maps, with a recent\ncliff edge interpreted from lidar (Light Detection and Ranging)\ntopographic surveys. The historical cliff edges are from a period\nranging from 1920-1930, whereas the lidar cliff edges are from either\n1998 or 2002. Long-term (~70-year) rates of retreat are calculated\nusing the two cliff edges. The rates of retreat presented in this\nreport represent conditions from the 1930s to 1998, and are not\nintended for predicting future cliff edge positions or rates of\nretreat. Due to the geomorphology of much of California's rocky coast\n(high-relief, steep slopes with no defined cliff edge) as well as to\ngaps in both the historical maps and lidar data, we were able to\nderive two cliff edges and therefore calculate cliff retreat rates for\na total of 353 km. The average rate of coastal cliff retreat for the\nState of California was -0.3\u00b10.2 m/yr, based on rates averaged from\n17,653 individual transects measured throughout all areas of\nCalifornia's rocky coastline. The average amount of cliff retreat was\n17.7 m over the 70-year time period of our analysis. Retreat rates\nwere generally lowest in Southern California where coastal engineering\nprojects have greatly altered the natural coastal system. California\npermits shoreline stabilization structures where homes, buildings or\nother community infrastructure are imminently threatened by\nerosion. While seawalls and/or riprap revetments have been constructed\nin all three sections of California, a larger proportion of the\nSouthern California coast has been protected by engineering works,\ndue, in part, to the larger population pressures in this area.\n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1146.json b/datasets/USGS_OFR_2007_1146.json index 58480c961b..b495c1f0c0 100644 --- a/datasets/USGS_OFR_2007_1146.json +++ b/datasets/USGS_OFR_2007_1146.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1146", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Large amounts of rain fell on southern Maine from the afternoon of April 15, 2007, to the afternoon of April 16, 2007, causing substantial damage to houses, roads, and culverts. This report provides an estimate of the peak flows on two rivers in southern Maine - the Mousam River and the Little Ossipee River because of their severe flooding. The April 2007 estimated peak flow of 9,230 ft per second at the Mousam River near West Kennebunk had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 25 years to greater than 500 years. The April 2007 estimated peak flow of 8,220 ft per second at the Little Ossipee River near South Limington had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 50 years to greater than 500 years.\n\n[Summary provided by the USGS.]\n", "links": [ { diff --git a/datasets/USGS_OFR_2007_1152.json b/datasets/USGS_OFR_2007_1152.json index b8ecff12a9..5bcd2ed253 100644 --- a/datasets/USGS_OFR_2007_1152.json +++ b/datasets/USGS_OFR_2007_1152.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1152", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In support of earthquake hazards and ground motion studies by researchers at the Utah Geological Survey, University of Utah, Utah State University, Brigham Young University, and San Diego State University, the U.S. Geological Survey Geologic Hazards Team Intermountain West Project conducted three high-resolution seismic imaging investigations along the Wasatch Front between September 2003 and September 2005. These three investigations include: (1) a proof-of-concept P-wave minivib reflection imaging profile in south-central Salt Lake Valley, (2) a series of seven deep (as deep as 400 m) S-wave reflection/refraction soundings using an S-wave minivib in both Salt Lake and Utah Valleys, and (3) an S-wave (and P-wave) investigation to 30 m at four sites in Utah Valley and at two previously investigated S-wave (Vs) minivib sites. In addition, we present results from a previously unpublished downhole S-wave investigation conducted at four sites in Utah Valley.\n\nThe locations for each of these investigations are shown in figure 1. Coordinates for the investigation sites are listed in Table 1. With the exception of the P-wave common mid-point (CMP) reflection profile, whose end points are listed, these coordinates are for the midpoint of each velocity sounding. Vs30 and Vs100, also shown in Table 1, are defined as the average shear-wave velocities to depths of 30 and 100 m, respectively, and details of their calculation can be found in Stephenson and others (2005). The information from these studies will be incorporated into components of the urban hazards maps along the Wasatch Front being developed by the U.S. Geological Survey, Utah Geological Survey, and numerous collaborating research institutions.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1159_2007-1159.json b/datasets/USGS_OFR_2007_1159_2007-1159.json index 969685a52d..d09b405a65 100644 --- a/datasets/USGS_OFR_2007_1159_2007-1159.json +++ b/datasets/USGS_OFR_2007_1159_2007-1159.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1159_2007-1159", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Concern over flooding along rivers in the Prairie Pothole Region\nhas stimulated interest in developing spatially distributed hydrologic\nmodels to simulate the effects of wetland water storage on peak river\nflows. Such models require spatial data on the storage volume and\ninterception area of existing and restorable wetlands in the watershed\nof interest. In most cases, information on these model inputs is\nlacking because resolution of existing topographic maps is inadequate\nto estimate volume and areas of existing and restorable\nwetlands. Consequently, most studies have relied on wetland area to\nvolume or interception area relationships to estimate wetland basin\nstorage characteristics by using available surface area data obtained\nas a product from remotely sensed data (e.g., National Wetlands\nInventory). Though application of areal input data to estimate volume\nand interception areas is widely used, a drawback is that there is\nlittle information available to provide guidance regarding the\napplication, limitations, and biases associated with such\napproaches. Another limitation of previous modeling efforts is that\nwater stored by wetlands within a watershed is treated as a simple\nlump storage component that is filled prior to routing overflow to a\npour point or gaging station. This approach does not account for\ndynamic wetland processes that influence water stored in prairie\nwetlands. Further, most models have not considered the influence of\nhuman-induced hydrologic changes, such as land use, that greatly\ninfluence quantity of surface water inputs and, ultimately, the rate\nthat a wetland basin fills and spills.\n \n The goals of this study were to (1) develop and improve\nmethodologies for estimating and spatially depicting wetland storage\nvolumes and interceptions areas and (2) develop models and approaches\nfor estimating/simulating the water storage capacity of potentially\nrestorable and existing wetlands under various restoration, land use,\nand climatic scenarios. To address these goals, we developed models\nand approaches to spatially represent storage volumes and interception\nareas of existing and potentially restorable wetlands in the upper\nMustinka subbasin within Grant County, Minn. We then developed and\napplied a model to simulate wetland water storage increases that would\nresult from restoring 25 and 50 percent of the farmed and drained\nwetlands in the upper Mustinka subbasin. The model simulations were\nperformed during the growing season (May October) for relatively wet\n(1993; 0.67 m of precipitation) and dry (1987; 0.32 m of\nprecipitation) years. Results from the simulations indicated that the\n25 percent restoration scenario would increase water storage by 2732\npercent and that a 50 percent scenario would increase storage by\n5363 percent. Additionally, we estimated that wetlands in the\nsubbasin have potential to store 11.5720.98 percent of the total\nprecipitation that fell over the entire subbasin area (52,758 ha). Our\nsimulation results indicated that there is considerable potential to\nenhance water storage in the subbasin; however, evaluation and\ncalibration of the model is necessary before simulation results can be\napplied to management and planning decisions.\n \n In this report we present guidance for the development and\napplication of models (e.g., surface area-volume predictive models,\nhydrology simulation model) to simulate wetland water storage to\nprovide a basis from which to understand and predict the effects of\nnatural or human-induced hydrologic alterations. In developing these\napproaches, we tried to use simple and widely available input data to\nsimulate wetland hydrology and predict wetland water storage for a\nspecific precipitation event or a series of events. Further, the\nhydrology simulation model accounted for land use and soil type, which\ninfluence surface water inputs to wetlands. Although information\npresented in this report is specific to the Mustinka subbasin, the\napproaches and methods developed should be applicable to other regions\nin the Prairie Pothole Region.\n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1161.json b/datasets/USGS_OFR_2007_1161.json index 581f29eacc..d9cc78f8d4 100644 --- a/datasets/USGS_OFR_2007_1161.json +++ b/datasets/USGS_OFR_2007_1161.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1161", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An historical analysis of images and documents shows that the Mississippi-Alabama (MS-AL) barrier islands are undergoing rapid land loss and translocation. The barrier island chain formed and grew at a time when there was a surplus of sand in the alongshore sediment transport system, a condition that no longer prevails. The islands, except Cat, display alternating wide and narrow segments. Wide segments generally were products of low rates of inlet migration and spit elongation that resulted in well-defined ridges and swales formed by wave refraction along the inlet margins. In contrast, rapid rates of inlet migration and spit elongation under conditions of surplus sand produced low, narrow, straight barrier segments.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1169.json b/datasets/USGS_OFR_2007_1169.json index 67724b8898..7eb6497b88 100644 --- a/datasets/USGS_OFR_2007_1169.json +++ b/datasets/USGS_OFR_2007_1169.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1169", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1190.json b/datasets/USGS_OFR_2007_1190.json index 73279d5929..8e3b5a5c55 100644 --- a/datasets/USGS_OFR_2007_1190.json +++ b/datasets/USGS_OFR_2007_1190.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1190", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin and these were investigated to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. Cooperative studies described herein have established 1,447 new gravity stations in the region, providing a detailed description of density variations in the middle to upper crust. All previously available gravity data for the study area were evaluated to determine their reliability, prior to combining with our recent results and calculating an up-to-date isostatic residual gravity map of the area. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill in the major valleys of the study area. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a much improved view of subsurface shapes of these basins and provides insights useful for the development of hydrogeologic models for the region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1202.json b/datasets/USGS_OFR_2007_1202.json index 23763a1913..d778879588 100644 --- a/datasets/USGS_OFR_2007_1202.json +++ b/datasets/USGS_OFR_2007_1202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indonesia is an archipelago of more than 17,000 islands that stretches astride the equator for about 5,200 km in southeast Asia (figure 1) and includes major Cenozoic volcano-plutonic arcs, active volcanoes, and various related onshore and offshore basins. These magmatic arcs have extensive Cu and Au mineralization that has generated much exploration and mining in the last 50 years. Although Au and Ag have been mined in Indonesia for over 1000 years (van Leeuwen, 1994), it was not until the middle of the nineteenth century that the Dutch explored and developed major Sn and minor Au, Ag, Ni, bauxite, and coal resources. The metallogeny of Indonesia includes Au-rich porphyry Cu, porphyry Mo, skarn Cu-Au, sedimentary-rock hosted Au, epithermal Au, laterite Ni, and diamond deposits. For example, the Grasberg deposit in Papua has the world's largest gold reserves and the third-largest copper reserves (Sillitoe, 1994).\n\nCoal mining in Indonesia also has had a long history beginning with the initial production in 1849 in the Mahakam coal field near Pengaron, East Kalimantan; in 1891 in the Ombilin area, Sumatra, (van Leeuwen, 1994); and in South Sumatra in 1919 at the Bukit Asam mine (Soehandojo, 1989). Total production from deposits in Sumatra and Kalimantan, from the 19thth century to World War II, amounted to 40 million metric tons (Mt). After World War II, production declined due to various factors including politics and a boom in the world-wide oil economy. Active exploration and increased mining began again in the 1980's mainly through a change in Indonesian government policy of collaboration with foreign companies and the global oil crises (Prijono, 1989).\n\nThis recent coal revival (van Leeuwen, 1994) has lead Indonesia to become the largest exporter of thermal (steam) coal and the second largest combined thermal and metallurgical (coking) coal exporter in the world market (Fairhead and others, 2006). The exported coal is desirable as it is low sulfur and ash (generally <1 and < 10 wt.%, respectively). Coal mining for both local use and for export has a very strong future in Indonesia although, at present, there are concerns about the strong need for a major revision in mining laws and foreign investment policies (Wahju, 2004; United States Embassy Jakarta, 2004). The World Coal Quality Inventory (WoCQI) program of the U.S. Geological Survey (Tewalt and others, 2005) is a cooperative project with about 50 countries (out of 70 coal-producing countries world-wide). The WoCQI initiative has collected and published extensive coal quality data from the world's largest coal producers and consumers. The important aspects of the WoCQI program are; (1) samples from active mines are collected, (2) the data have a high degree of internal consistency with a broad array of coal quality parameters, and (3) the data are linked to GIS and available through the world-wide-web. The coal quality parameters include proximate and ultimate analysis, sulfur forms, major-, minor-, and trace-element concentrations and various technological tests. This report contains geochemical data from a selected group of Indonesian coal samples from a range of coal types, localities, and ages collected for the WoCQI program.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1208.json b/datasets/USGS_OFR_2007_1208.json index 1c76a55268..e824164513 100644 --- a/datasets/USGS_OFR_2007_1208.json +++ b/datasets/USGS_OFR_2007_1208.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1208", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cenozoic basins of interior Alaska are poorly understood, but may host undiscovered hydrocarbon resources in sufficient quantities to serve remote villages and for possible export. Purported oil seeps and the regional occurrence of potential hydrocarbon source and reservoir rocks fuel an exploration interest in the 46,000 km2 Yukon Flats basin. Whether hydrocarbon source rocks are present in the pre-Cenozoic basement beneath Yukon Flats is difficult to determine because vegetation and surficial deposits obscure the bedrock geology, only limited seismic data are available, and no deep boreholes have been drilled. Analysis of regional potential field data (aeromagnetics and gravity) is valuable, therefore, for preliminary characterization of basement lithology and structure.\n\nWe present our analysis as a red-green-blue composite spectral map consisting of: (1) reduced-to-the-pole magnetics (red), (2) magnetic potential (green), and (3) basement gravity (blue). The color and texture patterns on this composite map highlight domains with common geophysical characteristics and, by inference, lithology. The observed patterns yield the primary conclusion that much of the basin is underlain by Devonian to Jurassic oceanic rocks related to the Angayucham and Tozitna terranes (JDat). These rocks are part of a lithologically diverse assemblage of brittlely deformed, generally low-grade metamorphic rocks of oceanic affinity; such rocks probably have little or no potential for hydrocarbon generation.\n\nThe JDat geophysical signature extends from the Tintina fault system northward to the Brooks Range. Along the eastern edge of the basin, JDat appears to overlie moderately dense and non-magnetic Proterozoic(?) and Paleozoic continental margin rocks. The western edge of the JDat in subsurface is difficult to distinguish due to the presence of magnetic granites similar to those exposed in the Ruby geanticline. In the southern portion of the basin, geophysical patterns indicate the possibility of overthrusting of Cenozoic sediments and underlying JDat by Paleozoic and Proterozoic rocks of the Schwatka sequence. These structural hypotheses provide the basis for an overthrust play within the Cenozoic section just south of the basin.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1217.json b/datasets/USGS_OFR_2007_1217.json index 962179e338..420c478cd8 100644 --- a/datasets/USGS_OFR_2007_1217.json +++ b/datasets/USGS_OFR_2007_1217.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1217", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Beach in San Francisco, California, contains a persistent erosional section in the shadow of the San Francisco ebb tidal delta and south of Sloat Boulevard that threatens valuable public infrastructure as well as the safe recreational use of the beach. Coastal managers have been discussing potential mediation measures for over a decade, with little scientific research available to aid in decision making. The United States Geological Survey (USGS) initiated the Ocean Beach Coastal Processes Study in April 2004 to provide the scientific knowledge necessary for coastal managers to make informed management decisions. This study integrates a wide range of field data collection and numerical modeling techniques to document nearshore sediment transport processes at the mouth of San Francisco Bay, with emphasis on how these processes relate to erosion at Ocean Beach. The Ocean Beach Coastal Processes Study is the first comprehensive study of coastal processes at the mouth of San Francisco Bay.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1238.json b/datasets/USGS_OFR_2007_1238.json index 8c0aa0ac82..75ad8e93d8 100644 --- a/datasets/USGS_OFR_2007_1238.json +++ b/datasets/USGS_OFR_2007_1238.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1238", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monthly values of ground-water recharge, for current land-use and land-cover conditions, to the Yakima River Basin aquifer system, Washington, during water years 1960-2001 were previously estimated. Monthly estimates are spatially related to a Geographic Information System raster dataset with a grid cell size of 500 ft on a side. These estimates of monthly recharge are provided in 42 ASCII files, 1 file for each water year. The grid with its metadata and 42 files provide potential users easy access to the information.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1248.json b/datasets/USGS_OFR_2007_1248.json index 945badf9c7..2e521d8221 100644 --- a/datasets/USGS_OFR_2007_1248.json +++ b/datasets/USGS_OFR_2007_1248.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1248", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report contains digital data, image files, and text files describing data formats and survey procedures for aeromagnetic data collected during a survey covering the southwestern portion of Taos County west of the Town of Taos, New Mexico, in October, 2006.\n\nSeveral derivative products from these data are also presented as grids and images, including reduced-to-pole data and data continued to a reference surface. Images are presented in various formats and are intended to be used as input to geographic information systems, standard graphics software, or map plotting packages.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1264.json b/datasets/USGS_OFR_2007_1264.json index b1728d81ae..c59e86218f 100644 --- a/datasets/USGS_OFR_2007_1264.json +++ b/datasets/USGS_OFR_2007_1264.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1264", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The most recent episode in the ongoing Pu OO-Kupaianaha eruption of Kilauea Volcano is currently producing lava flows north of the east rift zone. Although they pose no immediate threat to communities, changes in flow behavior could conceivably cause future flows to advance downrift and impact communities thus far unaffected. This report reviews lava flow hazards in the Puna District and discusses the potential hazards posed by the recent change in activity. Members of the public are advised to increase their general awareness of these hazards and stay up-to-date on current conditions.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1269.json b/datasets/USGS_OFR_2007_1269.json index 0acd71cd08..b2865dc95d 100644 --- a/datasets/USGS_OFR_2007_1269.json +++ b/datasets/USGS_OFR_2007_1269.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1269", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1270.json b/datasets/USGS_OFR_2007_1270.json index 27c719bbb3..a1282feb8a 100644 --- a/datasets/USGS_OFR_2007_1270.json +++ b/datasets/USGS_OFR_2007_1270.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1270", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The County of Santa Cruz Department of Public Works and the County of Santa Cruz Redevelopment Agency requested the U.S. Geological Survey (USGS) Western Coastal and Marine Geology Team (WCMG) to provide baseline geologic and oceanographic information on the coast and inner shelf at Pleasure Point, Santa Cruz County, California. The rationale for this proposed work is a need to better understand the environmental consequences of a proposed bluff stabilization project on the beach, the near shore and the surf at Pleasure Point, Santa Cruz County, California. To meet these information needs, the USGS-WCMG Team collected baseline scientific information on the morphology and waves at Pleasure Point. This study provided high-resolution topography of the coastal bluffs and bathymetry of the inner shelf off East Cliff Drive between 32nd Avenue and 41st Avenue. The spatial and temporal variation in waves and their breaking patterns at the study site were documented. Although this project did not actively investigate the impacts of the proposed bluff stabilization project, these data provide the baseline information required for future studies directed toward predicting the impacts of stabilization on the sea cliffs, beach and near shore sediment profiles, natural rock reef structures, and offshore habitats and resources. They also provide a basis for calculating potential changes to wave transformations into the shore at Pleasure Point.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1305.json b/datasets/USGS_OFR_2007_1305.json index 7efcf3c708..215671ee7f 100644 --- a/datasets/USGS_OFR_2007_1305.json +++ b/datasets/USGS_OFR_2007_1305.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1305", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nearshore bathymetry, substrate type, and circulation patterns in Westcott Bay, San Juan Islands, Washington, were mapped using two acoustic sonar systems, video and direct sampling of seafloor sediments. The goal of the project was to characterize nearshore habitat and conditions influencing eelgrass (Z. marina) where extensive loss has occurred since 1995. A principal hypothesis for the loss of eelgrass is a recent decrease in light availability for eelgrass growth due to increase in turbidity associated with either an increase in fine sedimentation or biological productivity within the bay. To explore sources for this fine sediment and turbidity, a dual-frequency Biosonics sonar operating at 200 and 430 kHz was used to map seafloor depth, morphology and vegetation along 69 linear kilometers of the bay. The higher frequency 430 kHz system also provided information on particulate concentrations in the water column. A boat-mounted 600 kHz RDI Acoustic Doppler Current Profiler (ADCP) was used to map current velocity and direction and water column backscatter intensity along another 29 km, with select measurements made to characterize variations in circulation with tides. An underwater video camera was deployed to ground-truth acoustic data. Seventy one sediment samples were collected to quantify sediment grain size distributions across Westcott Bay. Sediment samples were analyzed for grain size at the Western Coastal and Marine Geology Team sediment laboratory in Menlo Park, Calif. These data reveal that the seafloor near the entrance to Westcott Bay is rocky with a complex morphology and covered with dense and diverse benthic vegetation. Current velocities were also measured to be highest at the entrance and along a deep channel extending 1 km into the bay. The substrate is increasingly comprised of finer sediments with distance into Westcott Bay where current velocities are lower. This report describes the data collected and preliminary findings of USGS Cruise B-6-07-PS conducted between May 31, 2007 and June 5, 2007.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1306_1.0.json b/datasets/USGS_OFR_2007_1306_1.0.json index ded58ae10e..540ec1a6a8 100644 --- a/datasets/USGS_OFR_2007_1306_1.0.json +++ b/datasets/USGS_OFR_2007_1306_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1306_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Newark Valley area, eastern Nevada is one of thirteen major ground-water basins investigated by the BARCAS (Basin and Range Carbonate Aquifer Study) Project. Gravity data are being used to help characterize the geophysical framework of the region. Although gravity coverage was extensive over parts of the BARCAS study area, data were sparse for a number of the valleys, including the northern part of Newark Valley. We addressed this lack of data by establishing seventy new gravity stations in and around Newark Valley. All available gravity data were then evaluated to determine their reliability, prior to calculating an isostatic residual gravity map to be used for subsequent analyses. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a view of subsurface shape of the basin and will provide information useful for the development of hydrogeologic models for the region.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1308.json b/datasets/USGS_OFR_2007_1308.json index 19932164f0..16e96bb9ba 100644 --- a/datasets/USGS_OFR_2007_1308.json +++ b/datasets/USGS_OFR_2007_1308.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1308", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Many agricultural and forested areas in proximity to National Wildlife Refuges (NWR) are under increasing economic pressure for commercial or residential development. The upper portion of the Little Blackwater River watershed - a 27 square mile area within largely low-lying Dorchester County, Maryland, on the eastern shore of the Chesapeake Bay - is important to the U.S. Fish and Wildlife Service (USFWS) because it flows toward the Blackwater National Wildlife Refuge (BNWR), and developmental impacts of areas upstream from the BNWR are unknown.\n \n One of the primary concerns for the Refuge is how storm-water runoff may affect living resources downstream. The Egypt Road project (fig. 1), for which approximately 600 residential units have been approved, has the potential to markedly change the land use and land cover on the west bank of the Little Blackwater River. In an effort to limit anticipated impacts, the Maryland Department of Natural Resources (Maryland DNR) recently decided to purchase some of the lands previously slated for development. Local topography, a high water table (typically 1 foot or less below the land surface), and hydric soils present a challenge for the best management of storm-water flow from developed surfaces.\n \n A spatial data coordination group was formed by the Dorchester County Soil and Conservation District to collect data to aid decision makers in watershed management and on the possible impacts of development on this watershed. Determination of stream flow combined with land cover and impervious-surface baselines will allow linking of hydrologic and geologic factors that influence the land surface. This baseline information will help planners, refuge managers, and developers discuss issues and formulate best management practices to mitigate development impacts on the refuge.\n \n In consultation with the Eastern Region Geospatial Information Office, the dataset selected to be that baseline land cover source was the June-July 2005 National Agricultural Imagery Program (NAIP) 1-meter resolution orthoimagery of Maryland. This publicly available, statewide dataset provided imagery corresponding to the closest in time to the installation of a U.S. Geological Survey (USGS) Water Resources Discipline gaging station on the Little Blackwater River. It also captures land cover status just before major residential development occurs. This document describes the process used to create a database of impervious surfaces for the Little Blackwater watershed.\n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1309.json b/datasets/USGS_OFR_2007_1309.json index 38bee5856a..1b9d44c751 100644 --- a/datasets/USGS_OFR_2007_1309.json +++ b/datasets/USGS_OFR_2007_1309.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1309", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Many agricultural and forested areas in proximity to National Wildlife Refuges (NWR) are under increasing economic pressure to develop lands for commercial or residential development. The upper portion of the Little Blackwater River watershed - a 27 square mile area within largely low-lying Dorchester County, Maryland, on the eastern shore of the Chesapeake Bay - is important to the U.S. Fish and Wildlife Service (USFWS) because it flows toward the Blackwater National Wildlife Refuge (BNWR), and developmental impacts of areas upstream from the BNWR are unknown.\n\nOne of the primary concerns for the refuge is how storm-water runoff may affect living resources downstream. The Egypt Road project (fig. 1), for which approximately 600 residential units have been approved, has the potential to markedly change the land use and land cover on the west bank of the Little Blackwater River. In an effort to limit anticipated impacts, the Maryland Department of Natural Resources (Maryland DNR) recently decided to purchase some of the lands previously slated for development. Local topography, a high water table (typically 1 foot or less below the land surface), and hydric soils present a challenge for the best management of storm-water flow from developed surfaces.\n\nA spatial data coordination group was formed by the Dorchester County Soil and Conservation District to collect data to aid decision makers in watershed management and on the possible impacts of development on this watershed. Determination of streamflow combined with land cover and impervious-surface baselines will allow linking of hydrologic and geologic factors that influence the land surface. This baseline information will help planners, refuge managers, and developers discuss issues and formulate best management practices to mitigate development impacts on the refuge.\n\nIn consultation with the Eastern Region Geospatial Information Office, the dataset selected to be that baseline land cover source was the June-July 2005 National Agricultural Imagery Program (NAIP) 1-meter resolution orthoimagery of Maryland. This publicly available, statewide dataset provided imagery corresponding to the closest in time to the installation of a U.S. Geological Survey (USGS) Water Resources Discipline gaging station on the Little Blackwater River. It also captures land cover status just before major residential development occurs. This document describes the process used to create a land use database for the Little Blackwater watershed.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1367.json b/datasets/USGS_OFR_2007_1367.json index 90865544ac..9d466fa27f 100644 --- a/datasets/USGS_OFR_2007_1367.json +++ b/datasets/USGS_OFR_2007_1367.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1367", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose is to update with six additional years of data, our creep data archive on San Francisco Bay region active faults for use by the scientific research community. Earlier data (1979-2001) were reported in Galehouse (2002) and were analyzed and described in detail in a summary report (Galehouse and Lienkaemper, 2003). A complete analysis of our earlier results obtained on the Hayward fault was presented in Lienkaemper, Galehouse and Simpson (2001). Jon Galehouse of San Francisco State University (SFSU) and many student research assistants measured creep (aseismic slip) rates on these faults from 1979 until his retirement from the project in 2001. The creep measurement project, which was initiated by Galehouse, has continued through the Geosciences Department at SFSU from 2001-2006 under the direction of Co-P.I's Karen Grove and John Caskey (Grove and Caskey, 2005), and by Caskey since 2006. Forrest McFarland has managed most of the technical and logistical project operations as well as data processing and compilation since 2001. We plan to publish detailed analyses of these updated creep data in future publications.\n\nWe maintain a project web site (http://funnel.sfsu.edu/creep/) that includes the following information: project description, project personnel, creep characteristics and measurement, map of creep measurement sites, creep measurement site information, and data plots for each measurement site. Our most current, annually updated results are therefore accessible to the scientific community and to the general public. Information about the project can currently be requested by the public by an email link (fltcreep@sfsu.edu) found on our project website \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1372.json b/datasets/USGS_OFR_2007_1372.json index 445f6db196..94f0af6275 100644 --- a/datasets/USGS_OFR_2007_1372.json +++ b/datasets/USGS_OFR_2007_1372.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1372", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water-quality and streamflow data from 34 sites in nontidal parts of the Chesapeake Bay watershed are presented to document annual nutrient and sediment loads and trends for 1985 through 2006, as part of an annual evaluation of water-quality conditions by the U.S. EPA Chesapeake Bay Program. This study presents the results of trends analysis for streamflow, loads, and concentrations. Annual mean flow to the bay for 2006 (78,650 cubic feet per second) was approximately 1 percent above the long-term annual mean flow from 1937 to 2005. Total freshwater flow entering the bay for the summer season (July-August-September) was the only season classified as 'wet' in 2006. For the period 1985 through 2006, streamflow was significantly increasing at two of the 34 sites. Observed (bias-corrected) concentration summaries indicate higher ranges in concentrations of total nitrogen in the northern major river basins (Pennsylvania, Maryland, and northern Virginia) than in the southern basins in Virginia. Results indicate almost half of the monitoring sites in the northern basins exhibited significant downward bias-corrected concentration trends in total nitrogen over time; results were similar for total phosphorus and sediment. Generally, loads for all constituents at the nine River Input Monitoring Program (RIM) sites, which comprise 78 percent of the streamflow entering the bay, were lower in 2006 than in 2005. The loads for total nitrogen are below the long-term average loads at eight of the nine RIM sites and total phosphorus and sediment loads are also below the long-term average at seven RIM sites. Combined annual mean total nitrogen flow-weighted concentrations from the nine RIM sites indicated an upward tendency in 2006; in contrast, total phosphorus and sediment indicated a downward tendency.\n\nFrom 1990 to 2006 for the 9 RIM sites, the mean concentrations of total nitrogen, total phosphorus, and sediment were 3.49, 0.195, and 116 milligrams per liter, respectively. Flow-weighted concentrations for phosphorus and sediment were lowest in the Susquehanna River at Conowingo, Md., most likely because of the trapping efficiency of three large reservoirs upstream from the sampling point.\n\nFor all 34 sites and all constituents, trends in concentrations (not adjusted for flow) showed 12 statistically significant upward trends and 59 statistically significant downward trends for the period 1985 through 2006. When trends in concentrations are adjusted for flow, they can be used as indicators of human activity and effectiveness of management actions. The flow-adjusted trends indicated significant downward trends at approximately 74, 68, and 32 percent of the sites for total nitrogen, total phosphorus, and sediment, respectively. This may indicate that management actions are having some effect in reducing nutrients and sediments.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1392.json b/datasets/USGS_OFR_2007_1392.json index 17930025c2..ec867c7112 100644 --- a/datasets/USGS_OFR_2007_1392.json +++ b/datasets/USGS_OFR_2007_1392.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1392", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coastal erosion on Northern Gulf of Mexico barrier islands is an ongoing issue that was exacerbated by the storm seasons of 2004 and 2005 when several hurricanes made landfall in the Gulf of Mexico. Two units of the Gulf Islands National Seashore (GUIS), located on Santa Rosa Island, a barrier island off the Panhandle coast of Florida, were highly impacted during the hurricanes of 2004 (Ivan) and 2005 (Cindy, Dennis, Katrina and Rita). In addition to the loss of or damage to natural and cultural resources within the park, damage to park infrastructure, including park access roads and utilities, occurred in areas experiencing rapid shoreline retreat. The main park road was located as close as 50 m to the pre-storm (2001) shoreline and was still under repair from damage incurred during Hurricane Ivan when the 2005 hurricanes struck. A new General Management Plan is under development for the Gulf Islands National Seashore. This plan, like the existing General Management Plan, strives to incorporate natural barrier island processes, and will guide future efforts to provide access to units of Gulf Islands National Seashore on Santa Rosa Island.\n\nTo assess changes in island geomorphology and provide data for park management, the National Park Service and the U.S. Geological Survey are currently analyzing shoreline change to better understand long-term (100+ years) shoreline change trends as well as short-term shoreline impact and recovery to severe storm events. Results show that over an ~140-year period from the late 1800s to May 2004, the average shoreline erosion rates in the Fort Pickens and Santa Rosa units of GUIS were -0.7m/yr and -0.1 m/yr, respectively. Areas of historic erosion, reaching a maximum rate of -1.3 m/yr, correspond to areas that experienced overwash and road damage during the 2004 hurricane season.. The shoreline eroded as much as ~60 m during Hurricane Ivan, and as much as ~88 m over the course of the 2005 storm season. The shoreline erosion rates in the areas where the park road was heavily damaged were as high as -70.2 m/yr over the 2004-2005 time period. Additional post-storm monitoring of these sections of the island, to assess whether erosion rates stabilize, will help to parks to determine the best long-term management strategy for the park infrastructure.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1405.json b/datasets/USGS_OFR_2007_1405.json index a9fab41866..c71fc64ba1 100644 --- a/datasets/USGS_OFR_2007_1405.json +++ b/datasets/USGS_OFR_2007_1405.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1405", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The San Luis Valley region population is growing. Water shortfalls could have serious consequences. Future growth and land management in the region depend on accurate assessment and protection of the region's ground-water resources. An important issue in managing the ground-water resources is a better understanding of the hydrogeology of the Santa Fe Group and the nature of the sedimentary deposits that fill the Rio Grande rift, which contain the principal ground-water aquifers. The shallow unconfined aquifer and the deeper confined Santa Fe Group aquifer in the San Luis Basin are the main sources of municipal water for the region.\n\nThe U.S. Geological Survey (USGS) is conducting a series of multidisciplinary studies of the San Luis Basin located in southern Colorado. Detailed geologic mapping, high-resolution airborne magnetic surveys, gravity surveys, an electromagnetic survey (called magnetotellurics, or MT), and hydrologic and lithologic data are being used to better understand the aquifers. The MT survey primary goal is to map changes in electrical resistively with depth that are related to differences in rock types. These various rock types help control the properties of aquifers. This report does not include any data interpretation. Its purpose is to release the MT data acquired at 24 stations. Two of the stations were collected near Santa Fe, New Mexico, near deep wildcat wells. Well logs from those wells will help tie future interpretations of this data with geologic units from the Santa Fe Group sediments to Precambrian basement.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1410.json b/datasets/USGS_OFR_2007_1410.json index a0ed1bf9f4..45d85d882d 100644 --- a/datasets/USGS_OFR_2007_1410.json +++ b/datasets/USGS_OFR_2007_1410.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1410", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flagstaff is becoming warmer and drier. Estimated average-daily temperatures of the Flagstaff area are 2.3-degrees warmer since 1970 and annual precipitation at Flagstaff has been below average for nine of 11 years since 1996. Rising temperatures in the area parallel those of global-surface temperatures, particularly the rapid rise since the early 1970s.\n\nOngoing drought since 1996 is strongly affecting winter, spring, and fall precipitation. Winter moisture has been below average in 11 of the past 12 years, spring was below average in eight of the past 11 years, while fall was below normal in nine of the past 12 years. The precipitation decrease of the three seasons is 44 percent since 1996. In contrast, summer-monsoon related rainfall is unaffected by the ongoing drought. Although summer rainfall tends to be more abundant and dependable than the other seasons, cool season moisture is more important hydrologically. This means that aspects of Flagstaff's environment that require cool-season moisture, particularly the ponderosa pine forest, are increasingly stressed. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2007_1435.json b/datasets/USGS_OFR_2007_1435.json index 94b8607f7b..b729432f85 100644 --- a/datasets/USGS_OFR_2007_1435.json +++ b/datasets/USGS_OFR_2007_1435.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2007_1435", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report summarizes the findings of a study conducted as a pilot for part of a park-wide monitoring program being developed for the Ozark National Scenic Riverways (ONSR) of southeastern Missouri. The objective was to evaluate using crayfish (Orconectes spp.) and Asian clam (Corbicula fluminea) for monitoring concentrations of metals associated with lead-zinc mining. Lead-zinc mining presently (2007) occurs near the ONSR and additional mining has been proposed. Three composite samples of each type (crayfish and Asian clam), each comprising ten animals of approximately the same size, were collected during late summer and early fall of 2005 from five sites on the Current River and Jacks Fork within the ONSR and from one site on the Eleven Point River and the Big River, which are outside the ONSR. The Big River has been contaminated by mine tailings from historical lead zinc mining. Samples were analyzed by inductively coupled plasma mass spectrometry for lead, zinc, cadmium, cobalt, and nickel concentrations. All five metals were detected in all samples; concentrations were greatest in samples of both types from the Big River, and lowest in samples from sites within the ONSR. Concentrations of zinc and cadmium typically were greater in Asian clams than in crayfish, but differences were less evident for the other metals. In addition, differences among sites were small for cobalt in Asian clams and for zinc in crayfish, indicating that these metals are internally regulated to some extent. Consequently, both sample types are recommended for monitoring. Concentrations of metals in crayfish and Asian clams were consistent with those reported by other studies and programs that sampled streams in southeast Missouri.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1005.json b/datasets/USGS_OFR_2008_1005.json index 393e2beb4e..b29f672c34 100644 --- a/datasets/USGS_OFR_2008_1005.json +++ b/datasets/USGS_OFR_2008_1005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A recently compiled mosaic of a LIDAR-based digital elevation model (DEM) is presented with geomorphic analysis of new macro-topographic details. The geologic framework of the surficial and near surface late Cenozoic deposits of the central uplands, Pocomoke River valley, and the Atlantic Coast includes Cenozoic to recent sediments from fluvial, estuarine, and littoral depositional environments. Extensive Pleistocene (cold climate) sandy dune fields are deposited over much of the terraced landscape. The macro details from the LIDAR image reveal 2 meter-scale resolution of details of the shapes of individual dunes, and fields of translocated sand sheets. Most terrace surfaces are overprinted with circular to elliptical rimmed basins that represent complex histories of ephemeral ponds that were formed, drained, and overprinted by younger basins. The terrains of composite ephemeral ponds and the dune fields are inter-shingled at their margins indicating contemporaneous erosion, deposition, and re-arrangement and possible internal deformation of the surficial deposits. The aggregate of these landform details and their deposits are interpreted as the products of arid, cold climate processes that were common to the mid-Atlantic region during the Last Glacial Maximum.\n\nIn the Pocomoke valley and its larger tributaries, erosional remnants of sandy flood plains with anastomosing channels indicate the dynamics of former hydrology and sediment load of the watershed that prevailed at the end of the Pleistocene. As the climate warmed and precipitation increased during the transition from late Pleistocene to Holocene, dune fields were stabilized by vegetation, and the stream discharge increased. The increased discharge and greater local relief of streams graded to lower sea levels stimulated down cutting and created the deeply incised valleys out onto the continental shelf. These incised valleys have been filling with fluvial to intertidal deposits that record the rising sea level and warmer, more humid climate in the mid-Atlantic region throughout the Holocene. Thus, the geomorphic details provided by the new LIDAR DEM actually record the response of the landscape to abrupt climate change.\n\nHolocene trends and land-use patterns from Colonial to modern times can also be interpreted from the local macro- scale details of the landscape. Beyond the obvious utility of these data for land-use planning and assessments of resources and hazards, the new map presents new details on the impact of climate changes on a mid-latitude, outer Coastal plain landscape. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1086.json b/datasets/USGS_OFR_2008_1086.json index f299bde22a..20d0a20007 100644 --- a/datasets/USGS_OFR_2008_1086.json +++ b/datasets/USGS_OFR_2008_1086.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1086", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples were collected from 27 wells from August through November 2006 to characterize ground-water quality in the Mohawk River Basin. The Mohawk River Basin covers 3,500 square miles in central New York; most of the basin is underlain by sedimentary bedrock, including shale, sandstone, and carbonates. Sand and gravel form the most productive aquifers in the basin. Samples were collected from 13 sand and gravel wells and 14 bedrock wells, including production and domestic wells. The samples were collected and processed through standard U.S. Geological Survey procedures and were analyzed for 226 physical properties and constituents, including physical properties, major ions, nutrients, trace elements, radon-222, pesticides, volatile organic compounds, and bacteria.\n\nMany constituents were not detected in any sample, but concentrations of some constituents exceeded current or proposed Federal or New York State drinking-water quality standards, including color (1 sample), pH (2 samples), sodium (11 samples), chloride (2 samples), fluoride (1 sample), sulfate (1 sample), aluminum (2 samples), arsenic (2 samples), iron (10 samples), manganese (10 samples), radon-222 (12 samples), and bacteria (6 samples). Dissolved oxygen concentrations were greater in samples from sand and gravel wells (median 5.6 milligrams per liter [mg/L]) than from bedrock wells (median 0.2 mg/L). The pH was typically neutral or slightly basic (median 7.3); the median water temperature was 11°C. The ions with the highest concentrations were bicarbonate (median 276 mg/L), calcium (median 58.9 mg/L), and sodium (median 41.9 mg/L). Ground water in the basin is generally very hard (180 mg/L as CaCO3 or greater), especially in the Mohawk Valley and areas with carbonate bedrock. Nitrate-plus-nitrite concentrations were generally higher samples from sand and gravel wells (median concentration 0.28 mg/L as N) than in samples from bedrock wells (median < 0.06 mg/L as N), although no concentrations exceeded established State or Federal drinking-water standards of 10 mg/L as N for nitrate and 1 mg/L as N for nitrite. Ammonia concentrations were higher in samples from bedrock wells (median 0.349 mg/L as N) than in those from samples from sand and gravel wells (median 0.006 mg/L as N). The trace elements with the highest concentrations were strontium (median 549 micrograms per liter [¼g/L]), iron (median 143 ¼g/L), boron (median 35 ¼g/L), and manganese (median 31.1 ¼g/L). Concentrations of several trace elements, including boron, copper, iron, manganese, and strontium, were higher in samples from bedrock wells than those from sand and gravel wells. The highest radon-222 activities were in samples from bedrock wells (maximum 1,360 pCi/L); 44 percent of all samples exceeded a proposed U.S. Environmental Protection Agency drinking water standard of 300 pCi/L. Nine pesticides and pesticide degradates were detected in six samples at concentrations of 0.42 ¼g/L or less; all were herbicides or their degradates, and most were degradates of alachlor, atrazine, and metolachlor. Six volatile organic compounds were detected in four samples at concentrations of 0.8 ¼g/L or less, including four trihalomethanes, tetrachloroethene, and toluene; most detections were in sand and gravel wells and none of the concentrations exceeded drinking water standards. Coliform bacteria were detected in six samples but fecal coliform bacteria, including Escherichia coli, were not detected in any sample.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1088.json b/datasets/USGS_OFR_2008_1088.json index 457d2adf3a..d408ac9813 100644 --- a/datasets/USGS_OFR_2008_1088.json +++ b/datasets/USGS_OFR_2008_1088.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1088", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Interior River Lowlands ecoregion encompasses 93,200 square kilometers (km2) across southern and western Illinois, southwest Indiana, east-central Missouri, and fractions of northwest Kentucky and southeast Iowa. The ecoregion includes the confluence areas of the Mississippi, Missouri, Ohio, Illinois, and Wabash Rivers, and their tributaries.\n\nThis ecoregion was formed in non-resident, non-calcareous sedimentary rock (U.S. Environmental Protection Agency, 2006). The unstratified soil deposits present north of the White River in Indiana are evidence that pre-Wisconsinan ice once covered much of the Interior River Lowlands. The geomorphic characteristics of this area also include terraced valleys filled with alluvium as well as outwash, acolian, and lacustrine deposits.\n\nHistorically, agricultural land use has been a vital economic resource for this region. The drained alluvial soils are farmed for feed grains and soybeans, whereas the valley uplands also are used for forage crops, pasture, woodlots, mixed farming, and livestock (USEPA, 2006). This ecoregion provides a key component of national energy resources as it contains the second largest coal reserve in the United States, and the largest reserve of bituminous coal (Varanka and Shaver, 2007). One of the primary reasons for change in the ecoregion is urbanization.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1100.json b/datasets/USGS_OFR_2008_1100.json index 99f3fa4349..9e52aa9ac1 100644 --- a/datasets/USGS_OFR_2008_1100.json +++ b/datasets/USGS_OFR_2008_1100.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1100", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This publication describes soil moisture modeling in the Mojave Desert. It provides a general background on the process of pedogenesis, or soil development, which is a major factor affecting soil moisture properties. Soil texture changes with pedogenesis, which, in turn, affects soil moisture. Soil moisture is vital to plant survival, and therefore to the survival of all desert organisms associated with plants. Developing soil moisture models provides valuable information that can be used in predicting the impacts of disturbance, an area's ability to recover from disturbance, and in making land management decisions.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1119.json b/datasets/USGS_OFR_2008_1119.json index 53608f692c..46dd72fc46 100644 --- a/datasets/USGS_OFR_2008_1119.json +++ b/datasets/USGS_OFR_2008_1119.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1119", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A study conducted in 2006 by the U.S. Geological Survey collected 57 surface rock samples from nine types of intrusive rock in the Iron Hill carbonatite complex. This intrusive complex, located in Gunnison County of southwestern Colorado, is known for its classic carbonatite-alkaline igneous geology and petrology. The Iron Hill complex is also noteworthy for its diverse mineral resources, including enrichments in titanium, rare earth elements, thorium, niobium (columbium), and vanadium. This study was performed to reexamine the chemistry and metallic content of the major rock units of the Iron Hill complex by using modern analytical techniques, while providing a broader suite of elements than the earlier published studies. The report contains the geochemical analyses of the samples in tabular and digital spreadsheet format, providing the analytical results for 55 major and trace elements.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1121_1.0.json b/datasets/USGS_OFR_2008_1121_1.0.json index e499b80575..069b1d68a0 100644 --- a/datasets/USGS_OFR_2008_1121_1.0.json +++ b/datasets/USGS_OFR_2008_1121_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1121_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Modified Mercalli Intensity maps for the Hayward earthquake of October 21, 1868. To construct the Modified Mercalli Intensity (MMI) ShakeMap for the 1868 Hayward earthquake, we started with two sets of damage descriptions and felt reports. The first set of 100 sites was compiled by A.A. Bullock in the Lawson (1908) report on the 1906 San Francisco earthquake. The second set of 45 sites was compiled by Toppozada et al. (1981) from an extensive search of newspaper archives. We supplemented these two sets of reports with new observations from 30 sites using surveys of cemetery damage, reports of damage to historic adobe structures, pioneer narratives, and reports from newspapers that Toppozada et al. (1981) did not retrieve.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1130_1.0.json b/datasets/USGS_OFR_2008_1130_1.0.json index 324569a8fb..ff8507a9ba 100644 --- a/datasets/USGS_OFR_2008_1130_1.0.json +++ b/datasets/USGS_OFR_2008_1130_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1130_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling and analysis of eruptive products at Mount St. Helens is an integral part of volcano monitoring efforts conducted by the U.S. Geological Survey's Cascades Volcano Observatory (CVO). The objective of our eruption sampling program is to enable petrological assessments of pre-eruptive magmatic conditions, critical for ascertaining mechanisms for eruption triggering and forecasting potential changes in eruption behavior. This report provides a catalog of near-vent lithic debris and new dome-lava collected during 34 intra-crater sampling forays throughout the October 2004 to October 2007 (2004-7) eruptive interval at Mount St. Helens. In addition, we present comprehensive bulk-rock geochemistry for a time-series of representative (2004-7) eruption products. This data, along with that in a companion report on Mount St. Helens 2004 to 2006 tephra by Rowe and others (2008), are presented in support of the contents of the U.S. Geological Survey Professional Paper 1750 (Sherrod and others, eds., 2008). Readers are referred to appropriate chapters in USGS Professional Paper 1750 for detailed narratives of eruptive activity during this time period and for interpretations of sample characteristics and geochemical data. The suite of rock samples related to the 2004-7 eruption of Mount St. Helens and presented in this catalog are archived at the David A. Johnson Cascades Volcano Observatory, Vancouver, Wash.\n\nThe Mount St. Helens 2004-7 Dome Sample Catalogue with major- and trace-element geochemistry is tabulated in 3 worksheets of the accompanying Microsoft Excel file, of2008-1130.xls. Table 1 provides location and sampling information. Table 2 presents sample descriptions. In table 3, bulk-rock major and trace-element geochemistry is listed for 44 eruption-related samples with intra-laboratory replicate analyses of 19 dacite lava samples.\n\nA brief overview of the collection methods and lithology of dome samples is given below as an aid to deciphering the dome sample catalog. This is followed by an explanation of the categories of sample information (column headers) in Tables 1 and 2. A summary of the analytical methods used to obtain the geochemical data in this report introduces the presentation of major- and trace-element geochemistry of 2004-7 Mount St. Helens dome samples in table 3. Intra-laboratory results for the USGS AGV-2 standard are presented (tables 4 and 5), which demonstrate the compatibility of chemical data from different sources. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1131_1.0.json b/datasets/USGS_OFR_2008_1131_1.0.json index 8c34262702..45c46459c3 100644 --- a/datasets/USGS_OFR_2008_1131_1.0.json +++ b/datasets/USGS_OFR_2008_1131_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1131_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This open-file report presents a catalog of information about 135 ash samples along with geochemical analyses of bulk ash, glass and individual mineral grains from tephra deposited as a result of volcanic activity at Mount St. Helens, Washington, from October 1, 2004 until August 15, 2005. This data, in conjunction with that in a companion report on 2004-2007 Mount St. Helens dome samples by Thornber and others (2008a) are presented in support of the contents of the U.S. Geological Survey Professional Paper 1750 (Sherrod and others, ed., 2008). Readers are referred to appropriate chapters in USGS Professional Paper 1750 for detailed narratives of eruptive activity during this time period and for interpretations of sample characteristics and geochemical data presented here. All ash samples reported herein are currently archived at the David A. Johnston Cascades Volcano Observatory in Vancouver, Washington.\n\nThe Mount St. Helens 2004-2005 Tephra Sample Catalogue along with bulk, glass and mineral geochemistry are tabulated in 6 worksheets of the accompanying Microsoft Excel file, of2008-1131.xls. Samples in all tables are organized by collection date. Table 1 is a detailed catalog of sample information for tephra deposited downwind of Mount St. Helens between October 1, 2004 and August 18, 2005. Table 2 provides major- and trace-element analyses of 8 bulk tephra samples collected throughout that interval. Major-element compositions of 82 groundmass glass fragments, 420 feldspar grains, and 213 mafic (clinopyroxene, amphibole, hypersthene, and olivine) mineral grains from 12 ash samples collected between October 1, 2004 and March 8, 2005 are presented in tables 3 through 5. In addition, trace-element abundances of 198 feldspars from 11 ash samples (same samples as major-element analyses) are provided in table 6. Additional mineral and bulk ash analyses from 2004 and 2005 ash samples are published in chapters 30 (oxide thermometry; Pallister and others, 2008), 32 (amphibole major elements; Thornber and others, 2008b) and 37 (210Pb; 210Pb/226Pa; Reagan and others, 2008) of U.S. Geological Survey Professional Paper 1750 (Sherrod and others, 2008).\n\nA brief overview of sample collection methods is given below as an aid to deciphering the tephra sample catalog. This is followed by an explanation of the categories of sample information (column headers) in table 1. A summary of the analytical methods used to obtain the geochemical data in this report introduces the presentation of major and trace-element geochemistry of Mount St. Helens 2004-2005 tephra samples in tables 2-6. Rhyolite glass standard analyses are reported (Appendix 1) to demonstrate the accuracy and precision of similar glass analyses presented herein. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1132.json b/datasets/USGS_OFR_2008_1132.json index ed29a78496..00fce54dc4 100644 --- a/datasets/USGS_OFR_2008_1132.json +++ b/datasets/USGS_OFR_2008_1132.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1132", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the summer of 2007, the U.S. Geological Survey (USGS) began an exploration geochemical research study over the Pebble porphyry copper-gold-molydenum (Cu-Au-Mo) deposit in southwest Alaska. The Pebble deposit is extremely large and is almost entirely concealed by tundra, glacial deposits, and post-Cretaceous volcanic and volcaniclastic rocks. The deposit is presently being explored by Northern Dynasty Minerals, Ltd., and Anglo-American LLC. The USGS undertakes unbiased, broad-scale mineral resource assessments of government lands to provide Congress and citizens with information on national mineral endowment. Research on known deposits is also done to refine and better constrain methods and deposit models for the mineral resource assessments. The Pebble deposit was chosen for this study because it is concealed by surficial cover rocks, it is relatively undisturbed (except for exploration company drill holes), it is a large mineral system, and it is fairly well constrained at depth by the drill hole geology and geochemistry. The goals of the USGS study are (1) to determine whether the concealed deposit can be detected with surface samples, (2) to better understand the processes of metal migration from the deposit to the surface, and (3) to test and develop methods for assessing mineral resources in similar concealed terrains.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1169_1.0.json b/datasets/USGS_OFR_2008_1169_1.0.json index 33eb83a4cf..94c817880d 100644 --- a/datasets/USGS_OFR_2008_1169_1.0.json +++ b/datasets/USGS_OFR_2008_1169_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1169_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Presented in this report are 27 digital elevation model (DEM) datasets for the crater area of Mount St. Helens. These datasets include pre-eruption baseline data collected in 2000, incremental model subsets collected during the 2004\u201307 dome building eruption, and associated shaded-relief image datasets. Each dataset was collected photogrammetrically with digital softcopy methods employing a combination of manual collection and iterative compilation of x,y,z coordinate triplets utilizing autocorrelation techniques. DEM data points collected using autocorrelation methods were rigorously edited in stereo and manually corrected to ensure conformity with the ground surface. Data were first collected as a triangulated irregular network (TIN) then interpolated to a grid format. DEM data are based on aerotriangulated photogrammetric solutions for aerial photograph strips flown at a nominal scale of 1:12,000 using a combination of surveyed ground control and photograph-identified control points. The 2000 DEM is based on aerotriangulation of four strips totaling 31 photographs. Subsequent DEMs collected during the course of the eruption are based on aerotriangulation of single aerial photograph strips consisting of between three and seven 1:12,000-scale photographs (two to six stereo pairs). Most datasets were based on three or four stereo pairs. Photogrammetric errors associated with each dataset are presented along with ground control used in the photogrammetric aerotriangulation. The temporal increase in area of deformation in the crater as a result of dome growth, deformation, and translation of glacial ice resulted in continual adoption of new ground control points and abandonment of others during the course of the eruption. Additionally, seasonal snow cover precluded the consistent use of some ground control points.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1270_1.0.json b/datasets/USGS_OFR_2008_1270_1.0.json index d75d0316d0..c6021cf4df 100644 --- a/datasets/USGS_OFR_2008_1270_1.0.json +++ b/datasets/USGS_OFR_2008_1270_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1270_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Maps showing the probability of surface manifestations of liquefaction in the northern Santa Clara Valley were prepared with liquefaction probability curves. The area includes the communities of San Jose, Campbell, Cupertino, Los Altos, Los Gatos Milpitas, Mountain View, Palo Alto, Santa Clara, Saratoga, and Sunnyvale. The probability curves were based on complementary cumulative frequency distributions of the liquefaction potential index (LPI) for surficial geologic units in the study area. LPI values were computed with extensive cone penetration test soundings. Maps were developed for three earthquake scenarios, an M7.8 on the San Andreas Fault comparable to the 1906 event, an M6.7 on the Hayward Fault comparable to the 1868 event, and an M6.9 on the Calaveras Fault. Ground motions were estimated with the Boore and Atkinson (2008) attenuation relation. Liquefaction is predicted for all three events in young Holocene levee deposits along the major creeks. Liquefaction probabilities are highest for the M7.8 earthquake, ranging from 0.33 to 0.37 if a 1.5-m deep water table is assumed, and 0.10 to 0.14 if a 5-m deep water table is assumed. Liquefaction probabilities of the other surficial geologic units are less than 0.05. Probabilities for the scenario earthquakes are generally consistent with observations during historical earthquakes. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1274_1.0.json b/datasets/USGS_OFR_2008_1274_1.0.json index f0ddcc731b..290f8c76f5 100644 --- a/datasets/USGS_OFR_2008_1274_1.0.json +++ b/datasets/USGS_OFR_2008_1274_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1274_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From July 31 to August 1, 2006, an unusual set of atmospheric conditions aligned to produce record floods and an unprecedented number of slope failures and debris flows in southeastern Arizona. During the week leading up to the event, an upper-level low-pressure system centered over New Mexico generated widespread and locally heavy rainfall in southeastern Arizona, culminating in a series of strong, mesoscale convective systems that affected the region in the early morning hours of July 31 and August 1. Rainfall from July 27 through 30 provided sufficient antecedent moisture that the storms of July 31 through August 1 resulted in record streamflow flooding in northeastern Pima County and eastern Pinal County. The rainfall caused at least 623 slope failures in four mountain ranges, including more than 30 near Bowie Mountain in the northern Chiracahua Mountains, and 113 at the southern end of the Huachuca Mountains within and adjacent to Coronado National Memorial.\n\nIn the Santa Catalina Mountains north of Tucson, 435 slope failures spawned debris flows on July 31 that, together with flood runoff, damaged structures and roads, affecting infrastructure within Tucson\u2019s urban boundary. Heavy, localized rainfall in the Galiuro Mountains on August 1, 2006, resulted in at least 45 slope failures and an unknown number of debris flows in Aravaipa Canyon. In the southern Santa Catalina Mountains, the maximum 3-day precipitation measured at a climate station for July 29-31 was 12.04 in., which has a 1,200-year recurrence interval. Other rainfall totals from late July to August 1 in southeastern Arizona also exceeded 1,000-year recurrence intervals. The storms produced floods of record along six watercourses, and these floods had recurrence intervals of 100-500 years. Repeat photography suggests that the spate of slope failures was historically unprecedented, and geologic mapping and cosmogenic dating of ancient debris-flow deposits indicate that debris flows reaching alluvial fans in the Tucson basin are extremely rare events. Although recent watershed changes\u2014particularly the impacts of recent wildland fires\u2014may be important locally, the record number of slope failures and debris flows were related predominantly to extreme precipitation, not other factors such as fire history.\n\nThe large number of slope failures and debris flows in an area with few such occurrences historically underscores the rarity of this type of meteorological event in southeastern Arizona. Most slope failures appeared to be shallow-seated slope failures of colluvium on steep slopes that caused deep scour of chutes and substantial aggradation of channels downstream. In the southern Santa Catalina Mountains, we estimate that 1.5 million tons of sediment were released from slope failures into the channels of ten drainage basins. Thirty-six percent of this sediment (527,000 tons) is gravel-sized or smaller and is likely to be transported by streamflow out of the mountain drainages and into the drainage network of metropolitan Tucson. This sediment poses a potential flood hazard by reducing conveyance in fixed-section flood control structures along Rillito Creek and its major tributaries, although our estimates suggest that deposition may be small if it is distributed widely along the channel, which is expected.\n\nUsing the stochastic debris-flow model LAHARZ, we simulated debris-flow transport from slope failures to the apices of alluvial fans flanking the southern Santa Catalina Mountains. Despite considerable uncertainty in applying coefficients developed from worldwide observations to conditions in the southern Santa Catalina Mountains, we predicted the approximate area of depositional zones for several 2006 debris flows, particularly for Soldier Canyon. Better results could be achieved in some canyons if sediment budgets could be developed to account for alternating transport and deposition zones in channels with abrupt expansions and contractions, such as Rattlesnake Canyon. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1295.json b/datasets/USGS_OFR_2008_1295.json index 42c0455a90..ff94e06728 100644 --- a/datasets/USGS_OFR_2008_1295.json +++ b/datasets/USGS_OFR_2008_1295.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1295", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution measurements of waves, currents, water levels, temperature, salinity and turbidity were made in Hanalei Bay, northern Kaua\u2018i, Hawai\u2018i, during the summer of 2006 to better understand coastal circulation, sediment dynamics, and the potential impact of a river flood in a coral reef-lined embayment during quiescent summer conditions. A series of bottommounted instrument packages were deployed in water depths of 10 m or less to collect long-term, high-resolution measurements of waves, currents, water levels, temperature, salinity, and turbidity. These data were supplemented with a series of profiles through the water column to characterize the vertical and spatial variability in water column properties within the bay. These measurements support the ongoing process studies being conducted as part of the U.S. Geological Survey (USGS) Coastal and Marine Geology Program\u2019s Pacific Coral Reef Project; the ultimate goal is to better understand the transport mechanisms of sediment, larvae, pollutants, and other particles in coral reef settings. Information regarding the USGS study conducted in Hanalei Bay during the 2005 summer is available in Storlazzi and others (2006), Draut and others (2006) and Carr and others (2006). This report, the last part in a series, describes data acquisition, processing, and analysis for the 2006 summer data set. \n\n[Summary provided by the U.S. Geological Survey.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1299.json b/datasets/USGS_OFR_2008_1299.json index 5e51a71ed3..9f2cbf7af0 100644 --- a/datasets/USGS_OFR_2008_1299.json +++ b/datasets/USGS_OFR_2008_1299.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1299", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin, and our continuing studies are intended to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. The current study in Nevada provides additional high-resolution gravity along transects in Dry Lake and Delamar Valleys to supplement data we established previously in Cave and Muleshoe Valleys. We combine all previously available gravity data and calculate an up-to-date isostatic residual gravity map of the study area. Major density contrasts are identified, indicating zones where Cenozoic tectonic activity could have been accommodated. A gravity inversion method is used to calculate depths to pre-Cenozoic basement rock and to estimate maximum alluvial/volcanic fill in the valleys. Average depths of basin fill in the deeper parts of Cave, Muleshoe, Dry Lake, and Delamar Valleys are approximately 4 km, 2 km, 5 km, and 3 km, respectively.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2008_1306_1.0.json b/datasets/USGS_OFR_2008_1306_1.0.json index 1e5d4df7a5..a7789761dc 100644 --- a/datasets/USGS_OFR_2008_1306_1.0.json +++ b/datasets/USGS_OFR_2008_1306_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2008_1306_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2004, the U.S. Geological Survey (USGS), the Geological Survey of Canada (GSC), and the Mexican Geological Survey (Servicio Geologico Mexicano, or SGM) initiated pilot studies in preparation for a soil geochemical survey of North America called the Geochemical Landscapes Project. The purpose of this project is to provide a better understanding of the variability in chemical composition of soils in North America. The data produced by this survey will be used to construct baseline geochemical maps for regions within the continent. Two initial pilot studies were conducted: (1) a continental-scale study involving a north-south and east-west transect across North America and (2) a regional-scale study. The pilot studies were intended to test and refine sample design, sampling protocols, and field logistics for the full continental soils geochemical survey. Smith and others (2005) reported the results from the continental-scale pilot study. The regional-scale California study was designed to represent more detailed, higher resolution geochemical investigations in a region of particular interest that was identified from the low-sample-density continental-scale survey. \n\nA 20,000-km area of northern California (fig. 1), representing a wide variety of topography, climate, and ecoregions, was chosen for the regional-scale pilot study. This study area also contains diverse geology and soil types and supports a wide range of land uses including agriculture in the Sacramento Valley, forested areas in portions of the Sierra Nevada, and urban/suburban centers such as Sacramento, Davis, and Stockton. Also of interest are potential effects on soil geochemistry from historical hard rock and placer gold mining in the foothills of the Sierra Nevada, historical mercury mining in the Coast Range, and mining of base-metal sulfide deposits in the Klamath Mountains to the north. This report presents the major- and trace-element concentrations from the regional-scale soil geochemical survey in northern California.\n\n[Summary provided by the U.S. Geological Survey.]", "links": [ { diff --git a/datasets/USGS_OFR_2010_1172.json b/datasets/USGS_OFR_2010_1172.json index c62cc4b8d2..1d665b26b9 100644 --- a/datasets/USGS_OFR_2010_1172.json +++ b/datasets/USGS_OFR_2010_1172.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2010_1172", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report describes a database of sedimentary characteristics of tsunami deposits derived from published accounts of tsunami deposit investigations conducted shortly after the occurrence of a tsunami. The database contains 228 entries, each entry containing data from up to 71 categories. It includes data from 51 publications covering 15 tsunamis distributed between 16 countries. The database encompasses a wide range of depositional settings including tropical islands, beaches, coastal plains, river banks, agricultural fields, and urban environments. It includes data from both local tsunamis and teletsunamis. The data are valuable for interpreting prehistorical, historical, and modern tsunami deposits, and for the development of criteria to identify tsunami deposits in the geologic record. \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2010_1190_1.0.json b/datasets/USGS_OFR_2010_1190_1.0.json index bef4874cb9..23030f8aa1 100644 --- a/datasets/USGS_OFR_2010_1190_1.0.json +++ b/datasets/USGS_OFR_2010_1190_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2010_1190_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As a result of prolonged and intense periods of rainfall in late May and early June, 2008, along with heavier than normal snowpack the previous winter, record flooding occurred in Iowa in the Iowa River and Cedar River Basins. The storms were part of an exceptionally wet period from May 29 through June 12, when an Iowa statewide average of 9.03 inches of rain fell; the normal statewide average for the same period is 2.45 inches. From May 29 to June 13, the 16-day rainfall totals recorded at rain gages in Iowa Falls and Clutier were 14.00 and 13.83 inches, respectively. Within the Iowa River Basin, peak discharges of 51,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453100 Iowa River at Marengo, Iowa streamflow-gaging station (streamgage) on June 12, and of 39,900 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05453520 Iowa River below Coralville Dam near Coralville, Iowa streamgage on June 15 are the largest floods on record for those sites. A peak discharge of 41,100 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) on June 15 at the 05454500 Iowa River at Iowa City, Iowa streamgage is the fourth highest on record, but is the largest flood since regulation by the Coralville Dam began in 1958.\n\nWithin the Cedar River Basin, the May 30 to June 15, 2008, flood is the largest on record at all six streamgages in Iowa located on the mainstem of the Cedar River and at five streamgages located on the major tributaries. Flood-probability estimates for 10 of these 11 streamgages are less than 1 percent. Peak discharges of 112,000 cubic feet per second (flood-probability estimate of 0.2 to 1 percent) at the 05464000 Cedar River at Waterloo, Iowa streamgage on June 11 and of 140,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05464500 Cedar River at Cedar Rapids, Iowa streamgage on June 13 are the largest floods on record for those sites. Downstream from the confluence of the Iowa and Cedar Rivers, the peak discharge of 188,000 cubic feet per second (flood-probability estimate of less than 0.2 percent) at the 05465500 Iowa River at Wapello, Iowa streamgage on June 14, 2008, is the largest flood on record in the Iowa River and Cedar River Basins since 1903.\n\nHigh-water marks were measured at 88 locations along the Iowa River between State Highway 99 near Oakville and U.S. Highway 69 in Belmond, a distance of 319 river miles. High-water marks were measured at 127 locations along the Cedar River between Fredonia near the mouth (confluence with the Iowa River) and Riverview Drive north of Charles City, a distance of 236 river miles. The high-water marks were used to develop flood profiles for the Iowa and Cedar River.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2010_1198.json b/datasets/USGS_OFR_2010_1198.json index dc75fb2165..16c493b60e 100644 --- a/datasets/USGS_OFR_2010_1198.json +++ b/datasets/USGS_OFR_2010_1198.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2010_1198", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust \"scaling up\" of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005).\n\nAt the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002).\n\nLand-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global deforestation initiative, and elements of the project design are being used in the North American Carbon Program's assessment of forest disturbance.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2010_1223.json b/datasets/USGS_OFR_2010_1223.json index bbc3617449..96ff915c33 100644 --- a/datasets/USGS_OFR_2010_1223.json +++ b/datasets/USGS_OFR_2010_1223.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2010_1223", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Water-Quality Assessment Program of the U.S. Geological Survey has groundwater studies that focus on water-quality conditions in principal aquifers of the United States. The Program specifically focuses on aquifers that are important to public supply, domestic, and other major uses. Estimates for self-supplied domestic withdrawals and the population served for 20 aquifers in the United States for calendar year 2005 are provided in this report. These estimates are based on county-level data for self-supplied domestic groundwater withdrawals and the population served by those withdrawals, as compiled by the National Water Use Information Program, for areas within the extent of the 20 aquifers. In 2005, the total groundwater withdrawals for self-supplied domestic use from the 20 aquifers represented about 63 percent of the total self-supplied domestic groundwater withdrawals in the United States; the population served by the withdrawals represented about 61 percent of the total self-supplied domestic population in the United States.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2010_1330.json b/datasets/USGS_OFR_2010_1330.json index a51050f704..c1c1c85d47 100644 --- a/datasets/USGS_OFR_2010_1330.json +++ b/datasets/USGS_OFR_2010_1330.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2010_1330", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) is studying coastal hazards and coastal change to improve our understanding of coastal ecosystems and to develop better capabilities of predicting future coastal change. One approach to understanding the dynamics of coastal systems is to monitor changes in barrier-island subenvironments through time. This involves examining morphologic and topographic change at temporal scales ranging from millennia to years and spatial scales ranging from tens of kilometers to meters. Of particular interest are the processes that produce those changes and the determination of whether or not those processes are likely to persist into the future. In these analyses of hazards and change, both natural and anthropogenic influences are considered. Quantifying past magnitudes and rates of coastal change and knowing the principal factors that govern those changes are critical to predicting what changes are likely to occur under different scenarios, such as short-term impacts of extreme storms or long-term impacts of sea-level rise. Gulf Islands National Seashore was selected for detailed mapping of barrier-island morphology and topography because the islands offer a diversity of depositional subenvironments and because island areas and positions have changed substantially in historical time. The geomorphologic and subenvironmental maps emphasize the processes that formed the surficial features and also serve as a basis for documenting which subenvironments are relatively stable, such as the vegetated barrier core, and those which are highly dynamic, such as the beach and inactive overwash zones.\n\nThe primary mapping procedures were supervised functions within a Geographic Information System (GIS) that were applied to delineate and classify depositional subenvironments and features, collectively referred to as map units. The delineated boundaries of the map units were exported to create one shapefile, and are differentiated by the field \"Type\" in the associated attribute table. Map units were delineated and classified based on differences in tonal patterns of features in contrast to adjacent features observed on orthophotography. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map unit boundaries.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_2013_1305_1.0.json b/datasets/USGS_OFR_2013_1305_1.0.json index e1a3c915f6..ccc4d3aa65 100644 --- a/datasets/USGS_OFR_2013_1305_1.0.json +++ b/datasets/USGS_OFR_2013_1305_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2013_1305_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report presents a global dataset of site-specific surface-displacement data on faults. We have compiled estimates of successive displacements attributed to individual earthquakes, mainly paleoearthquakes, at sites where two or more events have been documented, as a basis for analyzing inter-event variability in surface displacement on continental faults.\n\nAn earlier version of this composite dataset was used in a recent study relating the variability of surface displacement at a point to the magnitude-frequency distribution of earthquakes on faults, and to hazard from fault rupture (Hecker and others, 2013). The purpose of this follow-on report is to provide potential data users with an updated comprehensive dataset, largely complete through 2010 for studies in English-language publications, as well as in some unpublished reports and abstract volumes.\n\n[Summary provided by the U.S. Geological Survey.]\n", "links": [ { diff --git a/datasets/USGS_OFR_2014-1094_SantaCatalinaBackscatter.json b/datasets/USGS_OFR_2014-1094_SantaCatalinaBackscatter.json index 7bfba129fb..fdc5f447e5 100644 --- a/datasets/USGS_OFR_2014-1094_SantaCatalinaBackscatter.json +++ b/datasets/USGS_OFR_2014-1094_SantaCatalinaBackscatter.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2014-1094_SantaCatalinaBackscatter", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2010 and 2011, the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), collected bathymetry and acoustic-backscatter data from the outer shelf and slope offshore the Oceanside region in southern California. These data were acquired as part of the USGS Marine Geohazards Program. Assessment of the hazards posed by offshore faults, submarine landslides, and tsunamis are facilitated by accurate and detailed bathymetric data. The surveys were conducted using the USGS R/V Parke Snavely outfitted with a 100 kHz Reson 7111 multibeam echosounder. While the surveys were focused on the collection of bathymetric data, the limited acoustic backscatter data are made available. These metadata describe the backscatter data provided in the report. The backscatter from the three separate surveys were not merged and these metadata describe the three separate backscatter images.", "links": [ { diff --git a/datasets/USGS_OFR_2014-1094_SantaCatalinaBathy.json b/datasets/USGS_OFR_2014-1094_SantaCatalinaBathy.json index 6afd7f8678..88e08a96b3 100644 --- a/datasets/USGS_OFR_2014-1094_SantaCatalinaBathy.json +++ b/datasets/USGS_OFR_2014-1094_SantaCatalinaBathy.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2014-1094_SantaCatalinaBathy", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2010 and 2011, the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), collected bathymetry and acoustic-backscatter data from the outer shelf and slope offshore the Oceanside region in southern California. These data were acquired as part of the USGS Marine Geohazards Program. Assessment of the hazards posed by offshore faults, submarine landslides, and tsunamis are facilitated by accurate and detailed bathymetric data. The surveys were conducted using the USGS R/V Parke Snavely outfitted with a 100 kHz Reson 7111 multibeam echosounder. These metadata describe the bathymetry data provided in the report.", "links": [ { diff --git a/datasets/USGS_OFR_2014-5038_FlAquiferTimeSeries.json b/datasets/USGS_OFR_2014-5038_FlAquiferTimeSeries.json index 2062a4a66f..d286048ef5 100644 --- a/datasets/USGS_OFR_2014-5038_FlAquiferTimeSeries.json +++ b/datasets/USGS_OFR_2014-5038_FlAquiferTimeSeries.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_2014-5038_FlAquiferTimeSeries", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In Florida\u2019s karst terrain, where groundwater and surface waters interact, a mapping time \nseries of the potentiometric surface in the Upper Floridan aquifer offers a versatile metric \nfor assessing the hydrologic condition of both the aquifer and overlying streams and wetlands. \nLong-term groundwater monitoring data were used to generate a monthly time series of \npotentiometric surfaces in the Upper Floridan aquifer over a 573-square-mile area of \nwest-central Florida between January 2000 and December 2009. Recorded groundwater elevations \nwere collated for 260 groundwater monitoring wells in the Northern Tampa Bay area, and a \ncontinuous time series of daily observations was created for 197 of the wells by estimating \nmissing daily values through regression relations with other monitoring wells. Kriging was \nused to interpolate the monthly average potentiometric-surface elevation in the Upper Floridan \naquifer over a decade. The mapping time series gives spatial and temporal coherence to \ngroundwater monitoring data collected continuously over the decade by three different \norganizations, but at various frequencies. Further, the mapping time series describes the \npotentiometric surface beneath parts of six regionally important stream watersheds and \n11 municipal well fields that collectively withdraw about 90 million gallons per day from \nthe Upper Floridan aquifer.\n\nMonthly semivariogram models were developed using monthly average groundwater levels at wells. \nKriging was used to interpolate the monthly average potentiometric-surface elevations and to \nquantify the uncertainty in the interpolated elevations. Drawdown of the potentiometric surface \nwithin well fields was likely the cause of a characteristic decrease and then increase in the \nobserved semivariance with increasing lag distance. This characteristic made use of the hole \neffect model appropriate for describing the monthly semivariograms and the interpolated surfaces. \nSpatial variance reflected in the monthly semivariograms decreased markedly between 2002 and \n2003, timing that coincided with decreases in well-field pumping. Cross-validation results suggest \nthat the kriging interpolation may smooth over the drawdown of the potentiometric surface near \nproduction wells.\n\n\nThe groundwater monitoring network of 197 wells yielded an average kriging error in the \npotentiometric-surface elevations of 2 feet or less over approximately 70 percent of the map \narea. Additional data collection within the existing monitoring network of 260 wells and near \nselected well fields could reduce the error in individual months. Reducing the kriging error in \nother areas would require adding new monitoring wells. Potentiometric-surface elevations fluctuated \nby as much as 30 feet over the study period, and the spatially averaged elevation for the entire \nsurface rose by about 2 feet over the decade. Monthly potentiometric-surface elevations describe \nthe lateral groundwater flow patterns in the aquifer and are usable at a variety of spatial scales \nto describe vertical groundwater recharge and discharge conditions for overlying surface-water \nfeatures.\n", "links": [ { diff --git a/datasets/USGS_OFR_94-710.json b/datasets/USGS_OFR_94-710.json index d003d4b94b..09ae19e4a8 100644 --- a/datasets/USGS_OFR_94-710.json +++ b/datasets/USGS_OFR_94-710.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_94-710", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information on USGS OFR 94-710 is available on-line via the World\n Wide Web:\n\n\n \"http://www.data.scec.org/ftp/ca.earthquakes/homestead/\"\n \"http://www.data.scec.org/fault_index/homeval.html\"\n\n The following information on the Homestead Valley earthquake\n aftershock data set was extracted from the Southern California\n Earthquake Center Data Center WWW site\n(\"http://www.data.scec.org/\"):\n\n On March 15, 1979, four moderate earthquakes (ML 4.9, 5.3,\n 4.5, 4.8) occurred in the Homestead Valley area of the Mojave\n Desert. Recently, phase data from portable instruments deployed\n by the U. S. Geological Survey on March 17 - 18, 1979 have\n been merged with those recorded by the Southern California\n Seismic Network (SCSN). The results of this study have been\n published in a U.S.Geological Survey-Open File Report.\n\n\n -homestead.hyp relocated hypocenters with portable data\n -homestead.phase phase data from portable instruments\n (hypoinverse format)\n -hvnetandport.dat SCSN and portable data\n -lnv8z0.mod velocity model used in relocations\n -homestead.sta portable instrument locations", "links": [ { diff --git a/datasets/USGS_OFR_97_745_E.json b/datasets/USGS_OFR_97_745_E.json index 0c790d642d..f6ff19a2c6 100644 --- a/datasets/USGS_OFR_97_745_E.json +++ b/datasets/USGS_OFR_97_745_E.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_97_745_E", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report is a digital database package containing both plotfiles and\nGeographic Information Systems (GIS) databases of maps of potential debris flow\nsources, as well as locations of historic debris flow sources, in the San\nFrancisco Bay Region. The data are provided for both the entire region and each\ncounty within the region, in two formats. The data are provided as ARC/INFO\n(Environmental Systems Research Institute, Redlands, CA) coverages and grids\nfor use in GIS packages, and as PostScript plotfiles of formatted maps similar\nto traditional U.S. Geological Survey map products.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99-11.json b/datasets/USGS_OFR_99-11.json index e9300778fb..d23daa418c 100644 --- a/datasets/USGS_OFR_99-11.json +++ b/datasets/USGS_OFR_99-11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99-11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The color shaded relief map of the conterminous U.S. was created from 15\narc-second digital elevation model (DEM) data. The data set traces its origins\nback to the early 1960's when .01 inch scans of 1:250,000 USGS topographic\nsheets were produced by the Defense Mapping Agency and converted to 3 second\ndata by the USGS National Cartographic Information Center. The 15 second grid\ncell data (Michael Webring, written communication) used in this report dates\nfrom the mid-1980's with occasional local and regional updates. The 3 second\ngrid nodes were averaged with a 6x6 operator and decimated to 15 second grid\ncells which is about the resolution of the original .01 inch data set. The 3\nsecond data is available as 950 separate 1x1 degree quadrangles from the USGS\nEROS Data Center.\n\nAdditional information available at\n\"http://pubs.usgs.gov/of/of99-011/1readme.html\"\n\n[Summary provided by the USGS.]\n\n\n.", "links": [ { diff --git a/datasets/USGS_OFR_99-422_1.0.json b/datasets/USGS_OFR_99-422_1.0.json index 601bf28477..f991dac984 100644 --- a/datasets/USGS_OFR_99-422_1.0.json +++ b/datasets/USGS_OFR_99-422_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99-422_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accompanying directory structure contains a Geographic Information Systems\n (GIS) compilation of geophysical, geological, and tectonic data for the\n Circum-North Pacific. This area includes the Russian Far East, Alaska, the\n Canadian Cordillera, linking continental shelves, and adjacent oceans. This GIS\n compilation extends from 120\u00b0E to 115\u00b0W, and from 40\u00b0N to 80\u00b0N. This area\n encompasses: (1) to the south, the modern Pacific plate boundary of the\n Japan-Kuril and Aleutian subduction zones, the Queen Charlotte transform fault,\n and the Cascadia subduction zone; (2) to the north, the continent-ocean\n transition from the Eurasian and North American continents to the Arctic Ocean;\n (3) to the west, the diffuse Eurasian-North American plate boundary, including\n the probable Okhotsk plate; and (4) to the east, the Alaskan-Canadian\n Cordilleran fold belt. This compilation should be useful for: (1) studying the\n Mesozoic and Cenozoic collisional and accretionary tectonics that assembled\n this continental crust of this region; (2) studying the neotectonics of active\n and passive plate margins in this region; and (3) constructing and interpreting\n geophysical, geologic, and tectonic models of the region.\n \n Geographic Information Systems (GIS) programs provide powerful tools for\n managing and analyzing spatial databases. Geological applications include\n regional tectonics, geophysics, mineral and petroleum exploration, resource\n management, and land-use planning. This CD-ROM contains thematic layers of\n spatial data-sets for geology, gravity field, magnetic field, oceanic plates,\n overlap assemblages, seismology (earthquakes), tectonostratigraphic terranes,\n topography, and volcanoes. The GIS compilation can be viewed, manipulated, and\n plotted with commercial software (ArcView and ArcInfo) or through a freeware\n program (ArcExplorer) that can be downloaded from http://www.esri.com for both\n Unix and Windows computers using the button below.\n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99-463_1.0.json b/datasets/USGS_OFR_99-463_1.0.json index 09f072e62f..be731e5ae9 100644 --- a/datasets/USGS_OFR_99-463_1.0.json +++ b/datasets/USGS_OFR_99-463_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99-463_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These geospatial data sets were produced as part of a regional precipitation\nfrequency analysis for Oklahoma. The data sets consist of surface grids of\nprecipitation depths for seven frequencies (expressed as recurrence intervals\nof 2-, 5-, 10-, 25-, 50-, 100-, and 500-years) and 12 durations (15-, 30-, and\n60-minutes; 1-, 2-, 3-, 6-, 12-, and 24-hours; and 1-, 3-, and 7-days).\nEighty-four depth-duration-frequency surfaces were produced from\nprecipitation-station data. Precipitation-station data from which the surfaces\nwere interpolated and contour lines derived from each surface also are\nincluded. Contour intervals vary from 0.05 to 0.5 inch.\n\nData were used from precipitation gage stations with at least 10 years of\nrecord within Oklahoma and a zone extending about 50 kilometers into bordering\nstates. Three different rain gage networks provided the data (15-minute,\n1-hour, and 1-day). Precipitation annual maxima (depths) were determined from\nthe station data for each duration for 110 15-minute, 141 hourly, and 413 daily\nstations. Statistical methods were used to estimate precipitation depths for\neach duration-frequency at each station. These station depth-duration-frequency\nestimates were interpolated to produce continuous grids with grid-cell spacing\nof 2,000 meters. Contour lines derived from these surfaces (grids) were used to\nproduce the maps in the \"Depth-Duration Frequency of Precipitation for\nOklahoma,\" by R.L. Tortorelli, Alan Rea, and W.H. Asquith, U.S. Geological\nSurvey Water-Resources Investigations Report 99-4232. The geospatial data sets\nare presented in digital form for use with geographic information systems.\nThese geospatial data sets may be used to determine an interpolated value of\ndepth-duration-frequency of precipitation for any point in Oklahoma.\n\n[Summary provided by USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99-77_1.0.json b/datasets/USGS_OFR_99-77_1.0.json index fdd1830214..fa34cbe5b6 100644 --- a/datasets/USGS_OFR_99-77_1.0.json +++ b/datasets/USGS_OFR_99-77_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99-77_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report contains digital data sets describing principal aquifers, surficial\ngeology, and ground-water regions of the conterminous United States. The data\nsource for the principal aquifers and surficial geology data sets is \"The\nNational Atlas of the United States of America\" (U.S. Geological Survey, 1970;\nHunt, 1979). The data source for the ground-water regions data set is\n\"Ground-Water Regions of the United States\" by Heath (1984). Some of the\ndigital lines describing coastlines were modified from U.S. Geological Survey\nboundary information (U.S. Geological Survey, 1990; Lanfear, 1984). Because\nmost of the source materials do not cover Alaska and Hawaii, only the\nconterminous 48 states are included in these data sets.\n\nCompilation of the data sets was supported by the National Water-Quality\nAssessment (NAWQA) Program of the U.S. Geological Survey (USGS). The objectives\nof the NAWQA Program are to: (1) describe current water-quality conditions for\na large part of the Nation's freshwater streams, rivers, and aquifers, (2)\ndescribe how water quality is changing over time, and (3) improve the\nunderstanding of the primary natural and anthropogenic factors that affect\nwater-quality conditions. National analysis of data, based on aggregation of\ncomparable information obtained from across the United States, is a major\ncomponent of the NAWQA Program. The data sets included in this report were\ncreated in support of NAWQA national data analysis activities.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99-78_1.0.json b/datasets/USGS_OFR_99-78_1.0.json index abca2410fd..12a2ff3a59 100644 --- a/datasets/USGS_OFR_99-78_1.0.json +++ b/datasets/USGS_OFR_99-78_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99-78_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report contains digital data sets describing water use, toxic chemical\nreleases, metropolitan areas, and population density of the conterminous United\nStates. The data source for the water-use data is from the U.S. Geological\nSurvey (USGS) (U.S. Geological Survey, 1990, 1998b; Lanfear, 1984). The toxic\nchemical release information is from the U.S. Environmental Protection Agency\n(1997, 1998), and the metropolitan area and population density data sets were\nderived from data provided by the U.S. Bureau of the Census (1995) and the\nConsortium for International Earth Science Information Network (1995). Because\nmost of the source materials do not cover Alaska and Hawaii, only the\nconterminous 48 states are included in these data sets.\n\nCompilation of the data sets was supported by the National Water-Quality\nAssessment (NAWQA) Program of the U.S. Geological Survey. The objectives of the\nNAWQA Program are to: (1) describe current water-quality conditions for a large\npart of the Nation's freshwater streams, rivers, and aquifers, (2) describe how\nwater quality is changing over time, and (3) improve the understanding of the\nprimary natural and anthropogenic factors that affect water-quality conditions.\nNational analysis of data, based on aggregation of comparable information\nobtained from across the United States, is a major component of the NAWQA\nProgram. The data sets included in this report were created for NAWQA national\ndata analysis activities.", "links": [ { diff --git a/datasets/USGS_OFR_99_414_1.0.json b/datasets/USGS_OFR_99_414_1.0.json index a59c8b72b4..6c38840f3d 100644 --- a/datasets/USGS_OFR_99_414_1.0.json +++ b/datasets/USGS_OFR_99_414_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99_414_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide mineral resource data for\nthe region of northeast WA for use in future spatial analysis by a\nvariety of users.\n\nThis database is not meant to be used or displayed at any scale\nlarger than 1:24,000.\n\nPermits to explore for and (or) develop mineral resources on\nFederal lands can be used to indicate locations and types of\nmineral-related activities on national forests. Permits for these\nactivities require filing of a Notice of Intent to conduct mineral\nexploration activities and (or) a Plan of Operation, if\nsignificant land disturbance results. This compilation of notices\nand plans for the Colville, Kaniksu, Okanogan, and Wenatchee\nnational forests between 1967 and 1998 is intended for use in\ncombination with geologic and economic information to predict\nfuture mineral-related activities in the region.\n\nThis dataset consists of one Excel 97 spreadsheet file\n(of99-414.xls) which contains information about permits on\nnational forest lands in northeast Washington State.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99_436.json b/datasets/USGS_OFR_99_436.json index 096dcb7dd7..7b6da090dc 100644 --- a/datasets/USGS_OFR_99_436.json +++ b/datasets/USGS_OFR_99_436.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99_436", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will generate reconnaissance maps of the sea\nfloor offshore of the New York - New Jersey metropolitan\narea -- the most heavily populated, and one of the most\nimpacted coastal regions of the United States. The surveys\nwill provide an overall synthesis of the sea floor\nenvironment, including seabed texture and bed forms,\nQuaternary strata geometry, and anthropogenic impact\n(e.g., ocean dumping, trawling, channel dredging). The\ngoal of this project is to survey the offshore area, the\nharbor, and the southern shore of Long Island, providing a\nregional synthesis to support a wide range of management\ndecisions and a basis for further process-oriented\ninvestigations. The project is conducted cooperatively with\nthe U.S. Army Corps of Engineer (USACE).\n\nThis CD-ROM contains digital high resolution seismic\nreflection data collected during the USGS ALPH 98013\ncruise. The seismic-reflection data are stored as SEG-Y\nstandard format that can be read and manipulated by most\nseismic-processing software. Much of the information\nspecific to the data are contained in the headers of the\nSEG-Y format files. The file system format is ISO 9660\nwhich can be read with DOS, Unix, and MAC operating\nsystems with the appropriate CD-ROM driver software\ninstalled.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_99_438_1.0.json b/datasets/USGS_OFR_99_438_1.0.json index 3aaeba2599..79a50147e6 100644 --- a/datasets/USGS_OFR_99_438_1.0.json +++ b/datasets/USGS_OFR_99_438_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_99_438_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was developed to provide geologic map GIS of the\nIdaho portion of the Thompson Falls 1:100,000 quadrangle for use\nin future spatial analysis by a variety of users.\n\nThis database is not meant to be used or displayed at any scale\nlarger than 1:100,000 (e.g., 1:62,500 or 1:24,000).\n\nThe geology of the Thompson Falls 1:100,000 quadrangle, Idaho was\ncompiled by Reed S. Lewis in 1997 onto a 1:100,000-scale topographic\nbase map for input into an Arc/Info geographic information system\n(GIS). The digital geologic map database can be queried in many ways\nto produce a variety of derivative geologic maps.\n\nThis GIS consists of two major Arc/Info data sets: one line and\npolygon file (tf100k) containing geologic contacts and structures\n(lines) and geologic map rock units (polygons), and one point file\n(tfpnt) containing structural data.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_OFR_Acid_Deposition.json b/datasets/USGS_OFR_Acid_Deposition.json index 61fbf0ac09..b6d0ae410c 100644 --- a/datasets/USGS_OFR_Acid_Deposition.json +++ b/datasets/USGS_OFR_Acid_Deposition.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_Acid_Deposition", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is\na project that studies areas having various susceptibilities to acid deposition\nfrom air pollution which are designated on a three tier ranking in the region\nof the Southern Appalachian Assessment (SAA). The assessment is being conducted\nby Federal agencies that are members of the Southern Appalachian Man and\nBiosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high,\nmedium, and low are assigned on the basis of bedrock compositions and their\nassociated soils, and their capacities to neutralize acid precipitation. The\ndata is available in Arc/Info export format.\n\n[Summary provided by the USGS]", "links": [ { diff --git a/datasets/USGS_OFR_aqbound_1.0.json b/datasets/USGS_OFR_aqbound_1.0.json index d57bc09246..d69e9ace94 100644 --- a/datasets/USGS_OFR_aqbound_1.0.json +++ b/datasets/USGS_OFR_aqbound_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_OFR_aqbound_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digitized aquifer boundaries of the Antlers aquifer\nin southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an\nimportant source of water in an area that underlies about 4,400-square miles of\nall or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall,\nMcCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay,\nconglomerate, and limestone in the outcrop area. The upper part of the Antlers\naquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt,\nand clay. The Antlers aquifer is unconfined where it outcrops in about an\n1,800-square-mile area.\n\nThe data set includes the outcrop area of the Antlers Sandstone in Oklahoma and\nareas where the Antlers is overlain by alluvial and terrace deposits and a few\nsmall thin outcrops of the Goodland Limestone. Most of the aquifer boundary\nlines were extracted from published digital geology data sets. Some of the\nlines were interpolated in areas where the Antlers aquifer is overlain by\nalluvial and terrace deposits near streams and rivers. The interpolated lines\nare very similar to the aquifer boundaries published in a ground-water modeling\nreport for the Antlers aquifer. The maps from which this data set was derived\nwere scanned or digitized from maps published at a scale of 1:250,000.\n\nThis data set is one of four digital map data sets being published together for\nthis aquifer. The four data sets are:\n\n aqbound - aquifer boundaries\n cond - hydraulic conductivity\n recharg - aquifer recharge\n wlelev - water-level elevation contours", "links": [ { diff --git a/datasets/USGS_P-11_cells.json b/datasets/USGS_P-11_cells.json index c65192c495..521a04ae2e 100644 --- a/datasets/USGS_P-11_cells.json +++ b/datasets/USGS_P-11_cells.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_P-11_cells", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of the cell map is to display the exploration maturity,\ntype of production, and distribution of production in quarter-mile\ncells in each of the oil and gas plays and each of the provinces\ndefined for the 1995 U.S. National Oil and Gas Assessment.\n\nCell maps for each oil and gas play were created by the USGS as a\nmethod for illustrating the degree of exploration, type of production,\nand distribution of production in a play or province. Each cell\nrepresents a quarter-mile square of the land surface, and the cells\nare coded to represent whether the wells included within the cell are\npredominantly oil-producing, gas-producing, both oil and\ngas-producing, or dry. The well information was initially retrieved\nfrom the Petroleum Information (PI) Well History Control System\n(WHCS), which is a proprietary, commercial database containing\ninformation for most oil and gas wells in the U.S. Cells were\ndeveloped as a graphic solution to overcome the problem of displaying\nproprietary WHCS data. No proprietary data are displayed or included\nin the cell maps. The data from WHCS were current as of December 1990\nwhen the cell maps were created in 1994.\n\nOil and gas plays within province 11 (Central Coastal) are listed here\nby play number, type, and name:\n\n Number Type Name\n 1101 conventional Point Arena Oil\n 1102 conventional Point Reyes Oil\n 1103 conventional Pescadero Oil\n 1104 conventional La Honda Oil\n 1105 conventional Bitterwater Oil\n 1106 conventional Salinas Oil\n 1107 conventional Western Cuyama Basin\n 1109 conventional Cox Graben", "links": [ { diff --git a/datasets/USGS_P-11_conventional.json b/datasets/USGS_P-11_conventional.json index ee2cf12c10..5e559a9109 100644 --- a/datasets/USGS_P-11_conventional.json +++ b/datasets/USGS_P-11_conventional.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_P-11_conventional", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of these files is to illustrate the geologic boundary of\nthe play as defined for the 1995 U.S. National Assessment. The play\nwas used as the fundamental assessment unit.\n\nThe fundamental geologic unit used in the 1995 National Oil and Gas\nAssessment was the play, which is defined as a set of known or\npostulated oil and or gas accumulations sharing similar geologic,\ngeographic, and temporal properties, such as source rock, migration\npathways, timing, trapping mechanism, and hydrocarbon type. The\ngeographic limit of each play was defined and mapped by the geologist\nresponsible for each province. The play boundaries were defined\ngeologically as the limits of the geologic elements that define the\nplay, such as the limits of the reservoir rock, geologic structures,\nsource rock, and seal lithologies. The only exceptions to this are\nplays that border the Federal-State water boundary. In these cases,\nthe Federal-State water boundary forms part of the play boundary. The\nplay boundaries were defined in the period 1993-1994.\n\nConventional oil and gas plays within province 11 (Central Coastal)\nare listed here by play number and name:\n\n Number Name\n 1101 Point Arena Oil\n 1102 Point Reyes Oil\n 1103 Pescadero Oil\n 1104 La Honda Oil\n 1105 Bitterwater Oil\n 1106 Salinas Oil\n 1107 Western Cuyama Basin\n 1109 Cox Graben", "links": [ { diff --git a/datasets/USGS_P1650-a_1.0.json b/datasets/USGS_P1650-a_1.0.json index e63aa2ce20..fbf9327fa2 100644 --- a/datasets/USGS_P1650-a_1.0.json +++ b/datasets/USGS_P1650-a_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_P1650-a_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This atlas explores the continental-scale relations between the geographic\nranges of woody plant species and climate in North America. A 25-km equal-area\ngrid of modern climatic and bioclimatic parameters was constructed from\ninstrumental weather records. The geographic distributions of selected tree and\nshrub species were digitized, and the presence or absence of each species was\ndetermined for each cell on the 25-km grid, thus providing a basis for\ncomparing climatic data and species' distributions. The relations between\nclimate and plant distributions are explored in graphical and tabular form. The\nresults of this effort are primarily intended for use in biogeographic,\npaleoclimatic, and global-change research.\n\nThese web pages provide access to the text, digital representations of figures,\nand supplemental data files from USGS Professional Paper 1650, chapters A and\nB. A printed set of these volumes can be ordered from the USGS at a cost of\nUS$63.00. To order, please call or write:\n\nUSGS Information Services\nBox 25286\nDenver Federal Center\nDenver, CO 80225\nTel: 303-202-4700; Fax: 303-202-4693 \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_PA_DIGIT_1.0.json b/datasets/USGS_PA_DIGIT_1.0.json index 4f7569306b..0585fd101d 100644 --- a/datasets/USGS_PA_DIGIT_1.0.json +++ b/datasets/USGS_PA_DIGIT_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_PA_DIGIT_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1989, the Pennsylvania Department of Environmental Resources (PaDER), in cooperation with the U.S. Geological Survey, Water Resources Division (USGS published the Pennsylvania (PA) Gazetteer of Streams. This publication contains information related to named streams in Pennsylvania. Drainage basin boundaries are delineated on the 7.5-minute series topographic paper quadrangle maps for PA and parts of the bordering states of New York, Maryland, Ohio, West Virginia, and Delaware. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows, using the hollow name, e.g. \"Smith Hollow\". This was done in an effort to name as many of the 64,000 streams as possible.\n\nIn 1991, work began by USGS to put these drainage basin boundaries into digital form for use in a geographic information system (GIS). Digitizing started with USGS in Lemoyne, PA., but expanded with assistance by PaDER and the Natural Resource Conservation Service (NRCS), formerly the U.S Department of Agriculture, Soil Conservation Service (SCS). USGS performed all editing, attributing, and edgematching. \n\nThere are 878, 7.5-minute quadrangle maps in PA. This documentation applies to only those maps in the Delaware River basin (164). Parts of the Delaware River drainage originate outside the PA border. At this time, no effort is being made by USGS to include those named stream basins.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_PONTCHARTRAIN.json b/datasets/USGS_PONTCHARTRAIN.json index 7d1a4b02b4..041e99ce7a 100644 --- a/datasets/USGS_PONTCHARTRAIN.json +++ b/datasets/USGS_PONTCHARTRAIN.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_PONTCHARTRAIN", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lake Pontchartrain and adjacent lakes in Louisiana form one of the larger\nestuaries in the Gulf Coast region. The estuary drains the Pontchartrain Basin\n(at right), an area of over 12,000 km2 situated on the eastern side of the\nMississippi River delta plain. In Louisiana, nearly one-third of the State\npopulation lives within the 14 parishes of the basin.\n\nOver the past 60 years, rapid growth and development within the basin, along\nwith natural processes, have resulted in significant environmental degradation\nand loss of critical habitat in and around Lake Pontchartrain. Human activities\nassociated with pollutant discharge and surface drainage have greatly affected\nthe water quality in the lake. This change is evident in the bottom sediments,\nwhich record the historic health of the lake. Also, land-altering activities\nsuch as logging, dredging, and flood control in and around the lake, lead to\nshoreline erosion and loss of wetlands.The effects of pollution, shoreline\nerosion and wetland loss on the lake and surrounding areas have become a major\npublic concern.\n\nTo better understand the basin's origin and the processes driving its\ndevelopment and degradation requires a wide-ranging study involving many\norganizations and personnel. When the U.S. Geological Survey began the study of\nLake Pontchartrain in 1994, information on four topics was needed:\n\n-Geologic Framework, or how the various sedimentary layers that make up\nthe basin are put together\n\n-Sediment Characterization, that is, what are the sediments made of, where\ndid they come from, and what kinds of pollutants do they contain\n\n-Shoreline and Wetland Change over time\n\n-what are the processes that control Water Circulation \n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_PWRC_BioEco.json b/datasets/USGS_PWRC_BioEco.json index 6a71f8ad63..f00771c9be 100644 --- a/datasets/USGS_PWRC_BioEco.json +++ b/datasets/USGS_PWRC_BioEco.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_PWRC_BioEco", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The Biomonitoring of Environmental Status and Trends (BEST) program is designed to assess and monitor the effects of environmental contaminants on biological resources, particularly those under the stewardship of the Department of the Interior. BEST examines contaminant issues at national, regional, and local scales, and uses field monitoring techniques and information assessment tools tailored to each scale. As part of this program, the threat of contaminants and other anthropogenic activities to terrestrial vertebrates residing in or near to Atlantic coast estuarine ecosystems is being evaluated by data synthesis and field activities. One of the objectives of this project is to evaluate the relative sensitivity and suitability of various wildlife species for regional contaminant monitoring of estuaries and ecological risk assessment.\n", "links": [ { diff --git a/datasets/USGS_RIMP.json b/datasets/USGS_RIMP.json index a0ce88a54b..f4fac9747a 100644 --- a/datasets/USGS_RIMP.json +++ b/datasets/USGS_RIMP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_RIMP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Chesapeake Bay River Input Monitoring (RIM) Program was established to\nquantify loads and long-term trends in concentrations of nutrients and\nsuspended material entering the tidal part of the Chesapeake Bay Basin from its\nnine major tributaries. These nine rivers account for approximately 93% of the\nstream flow entering Chesapeake Bay from the non-tidal part of its watershed.\nResults of the RIM program are being used by resource managers, policy makers,\nand concerned citizens to help evaluate the effectiveness of strategies aimed\nat reducing nutrients and sediment entering Chesapeake Bay from its\ntributaries.\n\nWater samples are collected upstream of the transition area between the tidal\nand non-tidal regions of the nine rivers (Figure 1). This transition zone\nhistorically has been referred to as the \"Fall Line\" for the many sets of falls\nand rapids that are found at this point on the rivers. Below the Fall Line in\nthe tidal areas of these rivers, tides can transport water, nutrients, and\nsuspended material from downstream, making it difficult to determine the cause\nof any observed changes. Because water-quality samples are collected above the\ninfluence of tides, any observed changes in nutrients or suspended material can\nbe attributed to upstream causes.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_RITA_COASTAL_IMPACT.json b/datasets/USGS_RITA_COASTAL_IMPACT.json index c1b643cabb..7a54f47063 100644 --- a/datasets/USGS_RITA_COASTAL_IMPACT.json +++ b/datasets/USGS_RITA_COASTAL_IMPACT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_RITA_COASTAL_IMPACT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hurricane Rita made landfall on September 24, 2005 near the TX-LA border. The\n U.S. Geological Survey (USGS), NASA, the U.S. Army Corps of Engineers, the\n University of New Orleans, Louisiana State University and the Texas Bureau of\n Economic Geology are cooperating in a research project investigating coastal\n change that is expected as a result of Hurricane Rita.\n \n Aerial video, still photography, and laser altimetry surveys of post-storm\n beach conditions will be collected for comparison with earlier data as soon as\n weather allows. The comparisons will show the nature, magnitude, and spatial\n variability of coastal changes such as beach erosion, overwash deposition, and\n island breaching. These data will also be used to further refine predictive\n models of coastal impacts from severe storms. The data will be made available\n to local, state, and federal agencies for purposes of disaster recovery and\n erosion mitigation. \n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_Report_MF-2332_1.0.json b/datasets/USGS_Report_MF-2332_1.0.json index 91e7f44bda..04dd080afd 100644 --- a/datasets/USGS_Report_MF-2332_1.0.json +++ b/datasets/USGS_Report_MF-2332_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Report_MF-2332_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database and accompanying plot files depict the distribution of geologic\nmaterials and structures at a regional (1:100,000) scale. The report is\nintended to provide geologic information for the regional study of materials\nproperties, earthquake shaking, landslide potential, mineral hazards, seismic\nvelocity, and earthquake faults. In addition, the report contains new\ninformation and interpretations about the regional geologic history and\nframework. However, the regional scale of this report does not provide\nsufficient detail for site development purposes. In addition, this map does\nnot take the place of fault-rupture hazard zones designated by the California\nState Geologist (Hart and Bryant, 1997). Similarly, the database cannot be\nused to identify or delineate landslides in the region.\n\nThis digital map database, compiled from previously published and unpublished\ndata, and new mapping by the authors, represents the general distribution of\nbedrock and surficial deposits in the mapped area. Together with the\naccompanying text file (pamf.ps, pamf.pdf, pamf.txt), it provides current\ninformation on the geologic structure and stratigraphy of the area covered. \nThe database delineates map units that are identified by general age and\nlithology following the stratigraphic nomenclature of the U.S. Geological\nSurvey. The scale of the source maps limits the spatial resolution (scale) of\nthe database to 1:62,500 or smaller.\n\nThe attached text file mf2332.rev contains current revision numbers for all\nparts of this product.\n\nThis report consists of a set of geologic map database files (Arc/ Info\ncoverages) and supporting text and plotfiles. In addition, the report includes\ntwo sets of plotfiles (PostScript and PDF format) that will generate map sheets\nand pamphlets similar to a traditional USGS Miscellaneous Field Studies Report.\n These files are described in the explanatory pamphlets (pamf.ps, pamf.pdf,\npamf.txt). The base map layer used in the preparation of the geologic map\nplotfiles was scanned from a scale-stable version of the USGS 1:100,000\ntopographic map at a resolution of 300 dpi as a monochrome TIFF image. The\nraster data was converted to a GRID in Arc/Info, and combined with geologic\npolygon data to produce the final map image. The base map coverages included\nwith the database publication are vectorized versions of scans of scale-stable\nseperates. These coverages contain no database information other than\nposition, and are included for reference only. In both cases the map digitized\nwas the Palo Alto (1982 version) 1: 100,000 quadrangle, which has a 50-meter\ncontour interval.", "links": [ { diff --git a/datasets/USGS_SESC_CrayfishStatus.json b/datasets/USGS_SESC_CrayfishStatus.json index be299951f3..49a63af5ab 100644 --- a/datasets/USGS_SESC_CrayfishStatus.json +++ b/datasets/USGS_SESC_CrayfishStatus.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_CrayfishStatus", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "About: This website presents the 2007 American Fisheries Society Endangered Species Committee list of freshwater crayfishes of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa.\n\nSummary: This is the second compilation of crayfishes of the United States and Canada prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1996, the number of crayfish taxa in need of conservation attention changed little. This list includes 363 taxa representing 12 genera and 2 families. Approximately 48% of species or subspecies in the study area are imperiled. There are 54 vulnerable, 52 threatened, and 66 endangered extant taxa; 2 taxa are possibly extinct. For some crayfishes, limited natural range (e.g., one locality or one drainage system) precipitates recognition as Endangered or Threatened; but for many others, status assignments continue to be hampered by a paucity of recent distributional information. While progress has been made in this arena, basic ecological and current distributional information are lacking for 60% of the U.S. and Canadian crayfish fauna. Threats highlighted in Taylor et al. (1996) such as habitat loss and introduction of nonindigenous crayfishes continue to persist and are greatly magnified by the limited distribution of many species. Recognition of the potential for rapid decimation of crayfish species, especially those with limited ranges, should provide impetus for proactive efforts toward conservation.\n\nMaps: Each taxon on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. States or provinces with parentheses in text and tables are locations where taxa are known or suspected to be introduced. A variety of sources were used to obtain distributional information, most notably Taylor et al. (1996) and multiple state-specific literature and websites.\n", "links": [ { diff --git a/datasets/USGS_SESC_ExtinctFish.json b/datasets/USGS_SESC_ExtinctFish.json index f0df1c0727..011c68ed54 100644 --- a/datasets/USGS_SESC_ExtinctFish.json +++ b/datasets/USGS_SESC_ExtinctFish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_ExtinctFish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extinction is a natural process in nature and is the opposite of speciation\u2014the evolution of new life forms. Importantly, 90%\u201396% of all species that became extinct over geological time disappeared during the normal give and take of speciation and extinction1. There is widespread evidence that modern rates of extinction in many plants and animals significantly exceed background rates in the fossil record. From 1900 to 2010, 57 species and subspecies of North American freshwater fishes became extinct, and since 1898, three distinct populations of valued fishes were extirpated from the continent2. Intuitively, this number of extinctions seems unnaturally high. Since the first tally of extinct North American fishes in 19893, the number of extinct fishes increased by 25%2,4. From the end of the 19th century to the present, modern extinctions varied by decade but significantly increased after 1950. The post-1950s increase in extinction rates likely corresponds to substantial economic, demographic, and land-use changes that occurred in North America after WWII2.\n\nA meaningful way to evaluate modern extinctions is to compare modern rates of extinction to background rates using data from the fossil record. The mean background extinction rate (from origination to extinction) for freshwater fish species is estimated to be one extinction/3 million years. The modern extinction rate in North American freshwater fishes is conservatively estimated to be 877 times greater than the background rate\u2014for the interval 1900 to 2010. Calculation of modern to background extinction rate (M:BER) is similar to extinctions per million species years (E/MSY) but differs in that actual background extinction rates are used in lieu of one extinction/million years.) M:BER ratios fluctuate by year because total North American fishes increases each year due to descriptions of new species and because extinctions are intermittent (the last one occurred in 2006). The M:BER value for North American freshwater fishes in 2012=863 and will continue to decline annually until the next extinction occurs. During the 20th century, freshwater fishes had the highest extinction rate among all vertebrates worldwide. Low numbers of fish extinctions documented from other continents suggests that extinctions are under-reported in Africa, Eurasia, and South America at this time. It is estimated that future extinctions in North America will increase from 39 currently extinct fish species to between 53 and 86 species by 2050.\n", "links": [ { diff --git a/datasets/USGS_SESC_ImperiledFish.json b/datasets/USGS_SESC_ImperiledFish.json index 49be0a6fd7..af7501acf3 100644 --- a/datasets/USGS_SESC_ImperiledFish.json +++ b/datasets/USGS_SESC_ImperiledFish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_ImperiledFish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions.\n\nSummary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management.\n\nMaps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins.\n\nEach taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past.", "links": [ { diff --git a/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json b/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json index a79a8a84ab..13c63780db 100644 --- a/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json +++ b/datasets/USGS_SESC_ImperiledFreshwaterOrganisms.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_ImperiledFreshwaterOrganisms", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries.\n\nBoth the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America.", "links": [ { diff --git a/datasets/USGS_SESC_SnailStatus.json b/datasets/USGS_SESC_SnailStatus.json index 602a453b19..65c72168f4 100644 --- a/datasets/USGS_SESC_SnailStatus.json +++ b/datasets/USGS_SESC_SnailStatus.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_SnailStatus", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "About: This website presents the 2013 American Fisheries Society Endangered Species Committee list of freshwater snails (Gastropods) of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa.\n\nSummary: This is the first conservation status review for freshwater snails (gastropods) of Canada and the United States by the American Fisheries Society's Endangered Species Freshwater Gastropod Subcommittee. The goals of this contribution are to provide: 1) a current and comprehensive taxonomic authority list for all native freshwater gastropods of Canada and the United States, 2) provincial and state distributions as presently understood, 3) a conservation assessment, and, 4) references on their biology, distribution and conservation. Freshwater gastropods occupy every type of aquatic habitat ranging from subterranean aquifers to brawling montane headwater creeks. Gastropods are ubiquitous invertebrates and frequently dominate aquatic invertebrate biomass. Of the 703 gastropods documented by Johnson et al. (2013), 74% are imperiled or extinct (278 endangered, 102 threatened, 73 vulnerable, and 67 are considered extinct); only 157 species are considered stable. Map queries display species distributions in provinces and states in which they are believed to occur or occurred in the past, but considerable fieldwork is required to determine exact geographic limits of species. We hope this list stimulates a surge in the study of freshwater gastropods.\n\nSupporting Literature: Supporting literature for the North American freshwater gastropods assessment are organized alphabetically by state and province, followed by national, regional, and other general references. This literature compilation is not comprehensive, but offers considerable information for individuals interested in freshwater snails.\n\nRecovery Examples: Although the gastropod fauna of Canada and the United States is beleaguered by multiple forms of habitat loss, the fauna is resilient and capable of remarkable recovery when suitable habitat is available. Three examples of recovery demonstrate the inherent reviving potential of freshwater gastropods. Images of the incredible diversity of freshwater snails are presented in plates and photo gallery.\n\nMaps: Each species on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. Resources used to obtain distributional information include state and regional publications.", "links": [ { diff --git a/datasets/USGS_SESC_SturgeonBiblio_3.json b/datasets/USGS_SESC_SturgeonBiblio_3.json index 852b67c655..5f632673fd 100644 --- a/datasets/USGS_SESC_SturgeonBiblio_3.json +++ b/datasets/USGS_SESC_SturgeonBiblio_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SESC_SturgeonBiblio_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior.\n\nEach reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as \"Atlantic sturgeon\"]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov.", "links": [ { diff --git a/datasets/USGS_SIR-5079_MSRiverFloodMaps.json b/datasets/USGS_SIR-5079_MSRiverFloodMaps.json index e7ff13ea96..7087015d0e 100644 --- a/datasets/USGS_SIR-5079_MSRiverFloodMaps.json +++ b/datasets/USGS_SIR-5079_MSRiverFloodMaps.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SIR-5079_MSRiverFloodMaps", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital flood-inundation maps for a 6.3-mile reach of the Mississippi River in Saint Paul, Minnesota, were developed through a multi-agency effort by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers and in collaboration with the National Weather Service. The inundation maps, which can be accessed through the U.S. Geological Survey Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ and the National Weather Service Advanced Hydrologic Prediction Service site at http://water.weather.gov/ahps/inundation.php , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage at the Mississippi River at Saint Paul (05331000). The National Weather Service forecasted peak-stage information at the streamgage may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.\n\nIn this study, flood profiles were computed for the Mississippi River by means of a one-dimensional step-backwater model. The hydraulic model was calibrated using the most recent stage-discharge relation at the Robert Street location (rating curve number 38.0) of the Mississippi River at Saint Paul (streamgage 05331000), as well as an approximate water-surface elevation-discharge relation at the Mississippi River at South Saint Paul (U.S. Army Corps of Engineers streamgage SSPM5). The model also was verified against observed high-water marks from the recent 2011 flood event and the water-surface profile from existing flood insurance studies. The hydraulic model was then used to determine 25 water-surface profiles for flood stages at 1-foot intervals ranging from approximately bankfull stage to greater than the highest recorded stage at streamgage 05331000. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from high-resolution topography data, to delineate potential areas flooded and to determine the water depths within the inundated areas for each stage at streamgage 05331000.\n\nThe availability of these maps along with information regarding current stage at the U.S. Geological Survey streamgage and forecasted stages from the National Weather Service provides enhanced flood warning and visualization of the potential effects of a forecasted flood for the city of Saint Paul and its residents. The maps also can aid in emergency management planning and response activities, such as evacuations and road closures, as well as for post-flood recovery efforts.", "links": [ { diff --git a/datasets/USGS_SOFIA_75_29_flows.json b/datasets/USGS_SOFIA_75_29_flows.json index 9d6bc98b31..6f73418a3a 100644 --- a/datasets/USGS_SOFIA_75_29_flows.json +++ b/datasets/USGS_SOFIA_75_29_flows.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_75_29_flows", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area.\n \n Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area.", "links": [ { diff --git a/datasets/USGS_SOFIA_75_29_hydro_data.json b/datasets/USGS_SOFIA_75_29_hydro_data.json index 5c5b2509a0..9ebf1a8137 100644 --- a/datasets/USGS_SOFIA_75_29_hydro_data.json +++ b/datasets/USGS_SOFIA_75_29_hydro_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_75_29_hydro_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site.\n \n Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. \n \n Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area.", "links": [ { diff --git a/datasets/USGS_SOFIA_ACME_DB.json b/datasets/USGS_SOFIA_ACME_DB.json index bced5bb863..19ac6de84c 100644 --- a/datasets/USGS_SOFIA_ACME_DB.json +++ b/datasets/USGS_SOFIA_ACME_DB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ACME_DB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Between 1995 and 2008, the Aquatic Mercury Cycling in the Everglades (ACME) project examined in detail the biogeochemical parameters that influence methylmercury (MeHg) production in the Florida Everglades. The interdisciplinary ACME team studied Hg cycling in the Everglades through a process-based, biogeochemical lens (Hurley et al. 1998). In the Everglades, as in most other ecosystems, inorganic mercury is transformed into methylmercury primarily by the action of anaerobic bacteria in surficial sediments and soils. The ACME project has been a collaborative research effort designed to understand the biogeochemical drivers of mercury cycling in the Greater Florida Everglades. The project is led be a team of scientists from the USGS and the Smithsonian Institution, with additional collaborators from the University of Wisconsin, Texas A&M, the SFWMD and FL DEP. ACME\ufffds main objective has been to define the key processes that control the fate and transport of Hg in the Everglades. The study has used a process-oriented, multi-disciplinary approach, focusing on a suite of intensively-studied sites across the trophic gradient of the Water Conservation Areas and Everglades National Park. Since 1995, a core set of sites has been examined in detail through time, including changes in season and in hydrology. The biogeochemical parameters examined focus on those that impact net methylmercury (MeHg) production, and include sulfur, carbon and nutrient biogeochemistry. The study examined Hg and MeHg concentrations, and associated biogeochemical parameters in surface waters, soils, periphyton, emergent plants and biota. The core study sites have been supplemented with survey data across many additional sites in the Greater Everglades Ecosystem. The field study was also supplemented with experimental studies of Hg complexation, photochemistry, and bioavailability. The ACME project has been funded by a variety of agencies including the USGS, NSF, EPA, SFWMD and FL DEP.", "links": [ { diff --git a/datasets/USGS_SOFIA_ASR_04.json b/datasets/USGS_SOFIA_ASR_04.json index a1d5925b82..547c235685 100644 --- a/datasets/USGS_SOFIA_ASR_04.json +++ b/datasets/USGS_SOFIA_ASR_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ASR_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this study are to: (1) inventory and assess the strengths and weaknesses of available hydrogeologic, hydraulic, hydrochemical, well construction, and cycle test information at existing ASR sites, (2) conduct a critical review of the hydrogeology on a site-by-site basis and relate to existing regional hydrogeology frameworks, allowing for the delineation of hydrogeologic factors that may be important to recovery efficiency, (3) identify hydrogeologic, design, and management factors which locally or regionally constrain the efficient storage and recovery of fresh water within the Upper Floridan aquifer, and (4) conduct a comparative analysis of the performance of all ASR sites having adequate data. This five-year study is divided into two phases, the first of which was two years long. The first phase laid the groundwork for data inventory, review, and analysis, and the second phase will allow for collection of additional data as it becomes available, expand the hydrogeologic framework, and perform a more complete comparative analysis of ASR sites. The study is in the second phase.\n \n Aquifer storage and recovery (ASR) has been described as 'the storage of water in a suitable aquifer through a well during times when water is available, and recovery of the water from the same well during times when it is needed'. Water can be stored in aquifers with poor water quality. ASR in south Florida is proposed in the Comprehensive Everglades Restoration Plan (CERP) as a cost-effective water-supply alternative that can help meet needs of agricultural, municipal, and recreational users while providing the water critical for Everglades ecosystem restoration. In CERP, plans have been made to utilize ASR in the Floridan aquifer system on an unprecedented scale. Precedence for ASR in southern Florida has been set with wells having been constructed at over 30 sites, mostly by local municipalities or counties in coastal areas. The Upper Floridan aquifer, the aquifer used at most of these sites, is brackish to saline in south Florida, which can have a large impact on the recovery of the fresh or potable water recharged and stored. Few regional investigations of the Floridan aquifer system hydrogeology in south Florida have been conducted, and the focus of those studies was not on ASR. Lacking a regional ASR framework to aid the decision-making process, ASR well sites in south Florida have been primarily located based on factors such as land availability, source-water quality, and source-water proximity (preexisting surface-water bodies, surficial aquifer system well fields, or water treatment plants). Little effort has been made to link information collected from each site as part of a regional hydrogeologic analysis. Results of this study should help the managers of the CERP program in locating, designing, constructing, and cycle testing ASR wells. These results should help establish a standard cycle testing protocol that can be used to measure the performance of individual CERP wells or clusters of wells.", "links": [ { diff --git a/datasets/USGS_SOFIA_ASR_coordination.json b/datasets/USGS_SOFIA_ASR_coordination.json index 6123ddc7fa..e1c38b6e5d 100644 --- a/datasets/USGS_SOFIA_ASR_coordination.json +++ b/datasets/USGS_SOFIA_ASR_coordination.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ASR_coordination", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The Comprehensive Everglades Restoration Plan (CERP) relies heavily on Aquifer Storage and Recovery (ASR) technology. The CERP includes approximately 333 ASR wells in South Florida with a total capacity of over 1.6 billion gallons per day. Much of the 'new water' in the CERP is derived from storing excess water that was previously discharged to the ocean. However, this new water would not be very useful unless there is a place to store it for use during dry periods. ASR is included in the CERP as one mechanism to provide this storage. Despite construction of some ASR facilities by local utilities, there remains a considerable number of significant technical and engineering-related uncertainties. Key Findings: 1) An analysis was conducted to describe and interpret the lithology of a part of the Upper Floridan aquifer penetrated by the Regional Observation Monitoring Program (ROMP) 29A test corehole in Highlands County, Florida. Information obtained was integrated into a conceptual model that delineates likely CERP ASR storage zones and confining units in the context of sequence stratigraphy. Carbonate sequence stratigraphy correlation strategies appear to reduce risk of miscorrelation of key ground-water flow units and confining units. 2) A hierarchical arrangement of rock unit cycles can be identified; High Frequency Cycle formed of peritidal, subtidal, and deeper subtidal) form High Frequency Sequence, and those can be grouped into Cycle Sequences. There appears to be a spatial relation among wells that penetrate water-bearing rocks having relatively high and low transmissivities. 3) Assuming hydrogeologic conditions observed in the ROMP 29A well are representative of in south-central Florida, the uppermost (Lower Hawthorn-Suwannee) of two likely CERP ASR storage zones does not appear to be viable with respect to the proposed 200 CERP ASR facility planned to be sited northwest of Lake Okeechobee. Insufficient data were available to adequately characterize the lower flow zone contained within the Avon Park Formation.", "links": [ { diff --git a/datasets/USGS_SOFIA_BigCypress_PineIsland_SatMap.json b/datasets/USGS_SOFIA_BigCypress_PineIsland_SatMap.json index a0326c2f93..ae8c509170 100644 --- a/datasets/USGS_SOFIA_BigCypress_PineIsland_SatMap.json +++ b/datasets/USGS_SOFIA_BigCypress_PineIsland_SatMap.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_BigCypress_PineIsland_SatMap", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The map is a composite image of spectral bands 3 (630-690 nanometers, red), 4 (775-900 nanometers, near-infrared), and 5 (1,550-1750 nanometers, middle-infrared) and the new panchromatic band (520-900, green to near-infrared) acquired by the Landsat 7 enhanced thematic mapper (ETM) sensor on January 27, 2000.", "links": [ { diff --git a/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json b/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json index 816d3bf07f..9b056f5447 100644 --- a/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json +++ b/datasets/USGS_SOFIA_Caloos_Franklin_Locks_flow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Caloos_Franklin_Locks_flow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Monitoring stations established thru this project are designed as part of a larger network needed for the Caloosahatchee River and tributaries that should remain in place long-term (~10 years). Data from monitoring stations included in this project will be evaluated during the third year of data collection in order to assess viability and need for changes\n.\nThe objective of this study is to quantify freshwater flows into the tidal reach of the Caloosahatchee River, west of Franklin Locks.", "links": [ { diff --git a/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json b/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json index 4921a53f4e..bdced77ba4 100644 --- a/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json +++ b/datasets/USGS_SOFIA_Caloosahatchee_water_quality.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Caloosahatchee_water_quality", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beginning in September 2011, water-quality surveys were conducted a minimum of six times per year in the tidal Caloosahatchee River and surrounding estuaries. Geo-referenced measurements were made at 5 second intervals during moving boat surveys in order to create high resolution water-quality maps of the study area. Water-quality characteristics measured and recorded include salinity, temperature, dissolved oxygen, pH, and turbidity", "links": [ { diff --git a/datasets/USGS_SOFIA_CarbonFlux.json b/datasets/USGS_SOFIA_CarbonFlux.json index 95713120e5..cfc27ca418 100644 --- a/datasets/USGS_SOFIA_CarbonFlux.json +++ b/datasets/USGS_SOFIA_CarbonFlux.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_CarbonFlux", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Greenhouse gas emissions, specifically carbon dioxide (CO2), are commonly linked with increasing global temperatures and rising sea-level. Of particular concern are rates of sea-level rise and carbon cycling including CO2 emissions or \"footprints\" of urban areas and the capacity of plant communities to absorb and release CO2. Defining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the functioning of water, energy and carbon cycles within Greater Everglades ecosystems. However, measurements of carbon and surface-energy cycling are sparse over plant communities within Department of Interior (DOI) managed lands in south Florida. Specifically, the quantity of CO2 absorbed or released annually within subtropical forests and wetlands as well as carbon and energy cycling in response to changes in hydrology, salinity, forest-fires and/or other factors are poorly known. To reduce these uncertainties, eddy-covariance flux stations were constructed by the U.S. Geological Survey and South Florida Water Management District in the Big Cypress National Preserve in 2006. Water, energy and carbon fluxes are empirically measured at these stations. The goals of the project are to (1) quantify key variables of interest to researchers and policy makers such as latent heat flux (the energy equivalent of evapotranspiration), sensible heat flux, incoming solar radiation, net radiation, changes in stored heat energy, albedos, Bowen ratios, net ecosystem production (NEP), gross ecosystem production (GEP), ecosystem respiration; (2) understand variability and linkages within water, energy and carbon-cycles imposed by both natural processes and regional / global stresses; and (3) publish project results in USGS reports and peer-reviewed journal papers. \n\nDefining and predicting ecosystem response to regional (e.g., freshwater discharge) and global (e.g., sea level rise) environmental change will require empirical baseline data on the state and functioning of water, energy and carbon cycles within DOI lands. However, measurements of carbon and surface-energy cycling are sparse over plant communities within DOI managed lands in south Florida. This project intends to measure water and surface energy fluxes within the BCNP. We propose to begin carbon cycling measurements in 2012 and 2013, as time and funding permits. Plant communities selected for study included Pine Upland, Marsh, Cypress Swamp, and Dwarf Cypress. These plant communities are spatially extensive within DOI lands and resources", "links": [ { diff --git a/datasets/USGS_SOFIA_Ding_Darling_baseline.json b/datasets/USGS_SOFIA_Ding_Darling_baseline.json index 36f7d2a685..e88750731e 100644 --- a/datasets/USGS_SOFIA_Ding_Darling_baseline.json +++ b/datasets/USGS_SOFIA_Ding_Darling_baseline.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Ding_Darling_baseline", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The greater Everglades Restoration program includes a management plan for the C-43 Canal, or Caloosahatchee River. This plan affects the quantity, quality, and timing of freshwater releases at control structure S-79 at Franklin Locks. Freshwater contributions are from Lake Okeechobee, and farming runoff along the canal from Lake Okeechobee to the town of Alva. This study will provide basic information on the effects on the quality of water entering J. N. Ding Darling National Wildlife Refuge as the result of freshwater releases at control structure S-79", "links": [ { diff --git a/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json b/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json index 8f71c332c8..5bfb2ccf90 100644 --- a/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json +++ b/datasets/USGS_SOFIA_EDEN_grid_shapefile_v02.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_EDEN_grid_shapefile_v02", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This shapefile serves as a net (fishnet or grid) to be placed over the South Florida study area to allow for sampling within the 400 meter cells (grid cells or polygons).\nThe origin and extent of the Everglades Depth Estimation Network (EDEN) grid were selected to cover not only existing Airborne Height Finder (AHF) data and current regions of interest for Everglades restoration, but to cover a rectangular area that includes all landscape units (USACE, 2004) and conservation areas in place at the time of its development. This will allow for future expansion of analyses throughout the Greater Everglades region should resources allow and scientific or management questions require it. Combined with the chosen extent, the 400m cell resolution produces a grid that is 675 rows and 375 columns..\n\nThe shapefile contains the 253125 grid cells described above. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. These characteristics were selected to design as robust a grid as possible and to ensure the grid\u2019s long-term utility.", "links": [ { diff --git a/datasets/USGS_SOFIA_EDEN_proj.json b/datasets/USGS_SOFIA_EDEN_proj.json index 7bf16e02a7..c167652ef0 100644 --- a/datasets/USGS_SOFIA_EDEN_proj.json +++ b/datasets/USGS_SOFIA_EDEN_proj.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_EDEN_proj", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), on-line water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to:1) guide large-scale field operations, 2) integrate hydrologic and ecological responses, and 3) support biological and ecological assessments that measure ecosystem responses to the implementation of the comprehensive Everglades Restoration plan (CERP) from the U.S. Army Corps of Engineers in 1999.\n\n \n Research has shown that relatively high-resolution data are needed to explicitly represent variations in the Everglades topography and vegetation that are important for landscape analysis and modeling. The EDEN project will provide a better representation of water depths if elevation variation within each 400-meter grid cell can be taken into account. The EDEN network provides a framework to integrate data collected by other agencies in a common quality-assured database. In addition to real-time network, collaboration among agencies will provide the EDEN project with valuable historic vegetation and water-depth data. This is the first time these data have been compiled and analyzed as a collective set.", "links": [ { diff --git a/datasets/USGS_SOFIA_Eco_hist_db_2008_present_2.json b/datasets/USGS_SOFIA_Eco_hist_db_2008_present_2.json index e857fdfdb2..631b4e6af8 100644 --- a/datasets/USGS_SOFIA_Eco_hist_db_2008_present_2.json +++ b/datasets/USGS_SOFIA_Eco_hist_db_2008_present_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). \n\nTwo general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information.\n\nData are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. \nModern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. \n\nCore data contain basic location information.", "links": [ { diff --git a/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json b/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json index a0be4be612..46afdbe4c3 100644 --- a/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json +++ b/datasets/USGS_SOFIA_Ever_hydr_FB_dynam.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Ever_hydr_FB_dynam", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This interdisciplinary synthesis project is designed to identify and document the interrelation of Everglades\u2019 hydrology and tidal dynamics of Florida Bay on ecosystem response to past environmental changes, both natural and human imposed. The project focuses on integrating historical, hydrological, and ecological findings of scientific investigations within the Southern Inland and Coastal System (SICS), which encompasses the transition zone between the wetlands of Taylor Slough and C-111 canal and nearshore embayments of Florida Bay. In the ecological component, hindcast simulations of historical flow events are being developed for ecological analyses. The Across Trophic Level System Simulation (ATLSS) ecological modeling team is collaborating with the SICS hydrologic modeling team to develop the necessary hydrologic inputs for refined indicator species models.\n \n The interconnected freshwater wetland and coastal marine ecosystems of south Florida have undergone numerous human disturbances, including the introduction of exotic species and the alteration of wetland hydroperiods, landscape characteristics, and drainage patterns through implementation of the extensive canal and road system and the expansion of agricultural activity. In this project, collaborative efforts are focused on documenting the impact of past hydrological and ecological changes along the southern Everglades interface with Florida Bay by reconstructing past hydroperiods and investigating the correlation of human-imposed and natural impacts on hydrological changes with shifts in biotic species. The primary objectives are to identify the historical effects of past management practices, to integrate refined hydrological and ecological modeling efforts at indicator species levels to identify cause-and-effect relationships, and to produce a report that documents findings that link hydrological and ecological changes to management practices, wherever evident.", "links": [ { diff --git a/datasets/USGS_SOFIA_Fbbslmap.json b/datasets/USGS_SOFIA_Fbbslmap.json index c52ad8412a..55ef583b13 100644 --- a/datasets/USGS_SOFIA_Fbbslmap.json +++ b/datasets/USGS_SOFIA_Fbbslmap.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Fbbslmap", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The maps show the bottom salinity for Florida Bay at 5ppt salinity intervals approximately every other month beginning in November 1994 through December 1996.\n \n Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_Fbbtypes.json b/datasets/USGS_SOFIA_Fbbtypes.json index a30f02a63c..92ed27b1b4 100644 --- a/datasets/USGS_SOFIA_Fbbtypes.json +++ b/datasets/USGS_SOFIA_Fbbtypes.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Fbbtypes", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987).\n \n The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated.", "links": [ { diff --git a/datasets/USGS_SOFIA_Fbsaldat.json b/datasets/USGS_SOFIA_Fbsaldat.json index 01317b71ec..70c128d320 100644 --- a/datasets/USGS_SOFIA_Fbsaldat.json +++ b/datasets/USGS_SOFIA_Fbsaldat.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Fbsaldat", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The raw data files contain a point ID, date of collection, salinity values in ppt, and longitude and latitude. For some dates water temperature, time of data collection, and conductivity in millisiemens were recorded. Surface salinity values for Florida Bay are available beginning in November 1994 through November 2001 and bottom salinity values from November 1994 through December 1996. The data are in comma-separated ASCII text files.\n \n Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_FireHydroSoils.json b/datasets/USGS_SOFIA_FireHydroSoils.json index eb9f52846a..4079097436 100644 --- a/datasets/USGS_SOFIA_FireHydroSoils.json +++ b/datasets/USGS_SOFIA_FireHydroSoils.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_FireHydroSoils", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fire in the south Florida landscape has historically been influential in shaping the ecosystem. The link between hydrology, soil formation, and fire is a critical complex component in the persistence of the biotic components of the Everglades (Smith et al 2003, Beckage 2005). As a result, Everglades National Park has been at the forefront of NPS fire policy development since the inception of the park. It was the first to allow prescribed burns and one of the first to develop a fire management plan (Taylor 1981). \nThe occurrence of invasive exotic plants has confounded the fire regime in Everglades National Park by changing the dynamics of how the vegetation burns. This phenomenon has been observed in mangrove forests especially along ecotones with upland vegetation communities. By examining the association between fire, soil, water and vegetation we can begin to understand the ecology and dynamics of these areas.", "links": [ { diff --git a/datasets/USGS_SOFIA_HAED_WCA_Everglades.json b/datasets/USGS_SOFIA_HAED_WCA_Everglades.json index 8c67a7e2ee..e8ef97566f 100644 --- a/datasets/USGS_SOFIA_HAED_WCA_Everglades.json +++ b/datasets/USGS_SOFIA_HAED_WCA_Everglades.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_HAED_WCA_Everglades", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html . The work was performed for Everglades ecosystem restoration purposes.\n \n The data are from regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", "links": [ { diff --git a/datasets/USGS_SOFIA_HAED_okee.json b/datasets/USGS_SOFIA_HAED_okee.json index d6bd1b5d8e..851e701b9c 100644 --- a/datasets/USGS_SOFIA_HAED_okee.json +++ b/datasets/USGS_SOFIA_HAED_okee.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_HAED_okee", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) coordinated the acquisition of high accuracy elevation data (meters) for the Lake Okeechobee Littoral Zone collected on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). The topographic surveys were performed using differential GPS technology and a USGS developed helicopter-based instrument known as the Airborne Height Finder (AHF). The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html\n \n This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", "links": [ { diff --git a/datasets/USGS_SOFIA_HAED_truck.json b/datasets/USGS_SOFIA_HAED_truck.json index e7328efe41..2d3037c5a6 100644 --- a/datasets/USGS_SOFIA_HAED_truck.json +++ b/datasets/USGS_SOFIA_HAED_truck.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_HAED_truck", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. Data were collected in areas near Homestead, Florida. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html\n \n These data are from topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", "links": [ { diff --git a/datasets/USGS_SOFIA_Hg_DOC_fy04.json b/datasets/USGS_SOFIA_Hg_DOC_fy04.json index 1fd26858e4..05f876c58e 100644 --- a/datasets/USGS_SOFIA_Hg_DOC_fy04.json +++ b/datasets/USGS_SOFIA_Hg_DOC_fy04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Hg_DOC_fy04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is designed to more clearly define the factors that control the occurrence, nature, and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). The primary objectives of our research are (1) to more clearly define the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, and (2) to quantify the effects of DOM on the transport and reactivity of Hg, especially with regard to the biological transformation and accumulation of mercury (Hg) in the Everglades. To meet these objectives, we have adopted a combined field/ laboratory approach. In conjunction with other research projects our field efforts are designed (1) to characterize DOM at a variety of field locations chosen to provide information about the influences of hydrology, seasonal factors (wetting and drying events) and source materials (e.g. vegetation, periphyton, peat) on the nature and amount of DOM in the system, and (2) to elucidate the roles of DOM in controlling the reactivity and bioavailability of Hg in the Everglades.\n\nThis research is relevant because of the high natural production of organic carbon in the peat soils and wetlands, the relatively high carbon content of shallow ground water systems in the region, the interactions of organic matter with other chemical species, such as trace metals, divalent cations, mercury, and anthropogenic compounds, the accumulation of organic carbon in corals and carbonate precipitates, and the potential changes in the quality and reactivity of dissolved organic carbon (DOC) resulting from land use and water management practices. Proposed attempts to return the Everglades to more natural flow conditions will result in changes to the current transport of organic matter from the Everglades Agricultural Area and the northern conservation areas to Florida Bay. In addition, the presence of dissolved organic matter is important in the production of drinking water, contributes to pollutant transport, and will influence ASR performance. Finally, interactions of mercury (Hg) with organic matter play important roles in controlling the reactivity, bioavailability and transport of Hg in the Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIA_Hi_res_bathy_FB.json b/datasets/USGS_SOFIA_Hi_res_bathy_FB.json index 3b1c5c1d57..530963289a 100644 --- a/datasets/USGS_SOFIA_Hi_res_bathy_FB.json +++ b/datasets/USGS_SOFIA_Hi_res_bathy_FB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Hi_res_bathy_FB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry.\n\nDetailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay had not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_IMMAGE.json b/datasets/USGS_SOFIA_IMMAGE.json index 490c734158..37b5655fc3 100644 --- a/datasets/USGS_SOFIA_IMMAGE.json +++ b/datasets/USGS_SOFIA_IMMAGE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_IMMAGE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "IMMAGE will develop a coupled GIS-enabled web-based decision support (DS) framework to provide interactive model-based scenarios to evaluate the potential impact of sea level rise on water supply, inland flooding, storm surge, and habitat management in South Florida. The DS framework will be developed to allow scientists, local planners and resource managers to evaluate the impact of sea level rise on:\n1. salt water intrusion into coastal water well fields, 2. the optimal use of canals to impede the inland movement of saline groundwater, 3. urban flooding, 4. the risk to populated areas and natural habitat from catastrophic storm surge, 5. wetland inundation periods and depths, 6. habitat suitability, 7. magnitude and distribution of future population growth, and 8. the impact of forecasted population growth on water demand and protected areas.\n\nThe IMMAGE project will address the need to run the model with changing input parameters by developing a framework of online GIS-based interfaces to four selected models, thereby enhancing their usability and making them available to a broader user community.", "links": [ { diff --git a/datasets/USGS_SOFIA_L-31NSeep_Pilot.json b/datasets/USGS_SOFIA_L-31NSeep_Pilot.json index a3f19c312c..e563aba6f2 100644 --- a/datasets/USGS_SOFIA_L-31NSeep_Pilot.json +++ b/datasets/USGS_SOFIA_L-31NSeep_Pilot.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_L-31NSeep_Pilot", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster are drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool.\n\nThe goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee.", "links": [ { diff --git a/datasets/USGS_SOFIA_L-31N_wells_data.json b/datasets/USGS_SOFIA_L-31N_wells_data.json index 2678d63b61..7866bc590b 100644 --- a/datasets/USGS_SOFIA_L-31N_wells_data.json +++ b/datasets/USGS_SOFIA_L-31N_wells_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_L-31N_wells_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this data acquisition project were to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The USGS on-site geologist provided technical guidance to the drill crew, described the lithology of the core and unconsolidated sediments, and stored the cores and sediment samples for the duration of the project. USGS staff provided and ran gamma ray, fluid conductivity and temperature, EM-induction, 3-arm caliper, full wave form sonic tools, a heat-pulse flow meter, an OBI-40 Mark III digital optical tool, and the Laval video tool.\n\nThe goal of the L-31N Seepage Management Pilot Project is to reduce levee seepage that moves from Everglades National Park to the east. As participants in this pilot project, the South Florida Water Management District, the United States Army Corps of Engineers, and the United States Geological Survey worked together to provide a hydrogeologic characterization of the Surficial aquifer underlying the L-31N Levee in Miami-Dade County, Florida. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the L-31N Seepage Management Project Delivery Team (PDT) installed two clusters of monitor wells and four additional coreholes along the levee to provide the necessary detailed hydrogeologic data to the PDT. The L-31N project was spilt into two seperate projects, a data acquisition project and an interpretive project. The objectives of the data acquisition project was to provide the on-site geologic expertise while the four coreholes and the deepest well in each cluster were drilled and constructed, and to complete the downhole geophysical logging including video and flow meter logging of these boreholes. The objective of the data interpretation project was to provide the PDT with detailed hydrogeologic information in order to understand the movement of water in the Surficial aquifer along the L-31N levee and delineate the lithology and hydrostratigraphy of the rocks and sediments underlying the levee.", "links": [ { diff --git a/datasets/USGS_SOFIA_LOX_NWR_data.json b/datasets/USGS_SOFIA_LOX_NWR_data.json index 19f20a1314..d3fcc3282e 100644 --- a/datasets/USGS_SOFIA_LOX_NWR_data.json +++ b/datasets/USGS_SOFIA_LOX_NWR_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_LOX_NWR_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Loxahatchee National Wildlife Refuge (LOX) is a water-dominated ecosystem that is susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in LOX because of its designation as Outstanding Florida Waters.\n\nLoxahatchee National Wildlife Refuge (LOX) maintains a separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout the units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data, and dependence on surface water depth and season. Collection and analysis of water-quality samples at LOX was done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. The study area was extended into LOX in 2003.", "links": [ { diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement.json b/datasets/USGS_SOFIA_LinkingLandAirManagement.json index 80dc76a8f9..0e0a19546b 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_LinkingLandAirManagement", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The approaches used will be extensions of previous efforts by the lead investigators, whereby we will enhance our abilities to address land management and ecosystem restoration questions. Major changes implemented in this project will include the use of environmental chambers (controlled enclosures or mesocosums) and isotopic tracers to provide a more definitive means addressing specific management questions, such as \"What reductions in toxicity (methylation and bioaccumulation) would be realized if atmospheric mercury emissions were reduced by 75%?\" or, \"Over what time scales could we expect to see improvements to the ecosystem if nutrient and sulfur loading were reduced by implementation of agricultural best management practices and the storm water treatment areas (STA)?\" Results of these geochemical investigations will provide critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem", "links": [ { diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json index 99bd5736b0..3d42240005 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_LinkingLandAirManagement_Task1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA\u2019s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure.", "links": [ { diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json index 8bf6c2c2ca..b19c1c1a08 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_LinkingLandAirManagement_Task2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools.\nThe major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments.\n\nData available for this project include dissolved sulfate and solid sulfur geochemistry and surface and pore water chemistry.", "links": [ { diff --git a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json index 868ef028d3..12d22065ed 100644 --- a/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json +++ b/datasets/USGS_SOFIA_LinkingLandAirManagement_Task3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_LinkingLandAirManagement_Task3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Task (Task 3 of the overall study) focuses on the factors that control the occurrence, nature and reactivity of dissolved organic matter (DOM) in the Florida Everglades, especially with regard to the biological transformation and accumulation of mercury (Hg). Our goal is to provide fundamental information on the nature and reactivity of DOM in the Everglades and to elucidate the mechanisms and pathways by which the DOM influences the chemistry of Hg throughout the system.", "links": [ { diff --git a/datasets/USGS_SOFIA_Mangrove_Sawfish.json b/datasets/USGS_SOFIA_Mangrove_Sawfish.json index 5f967ab060..76ec47ce34 100644 --- a/datasets/USGS_SOFIA_Mangrove_Sawfish.json +++ b/datasets/USGS_SOFIA_Mangrove_Sawfish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_Mangrove_Sawfish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This pilot project has several related goals concerning a specific type of habitat thought to be important for juvenile sawfish habitat: mangrove shorelines. First, we will delineate and classify historic mangrove shorelines. Second, we will map and classify current mangrove shorelines. Third, we will determine amounts of shoreline change. Lastly, we will conduct an analysis to compare sawfish sightings and / or captures with the type of shoreline where those sightings-captures occurred. This will allow us to answer the question: Are juvenile sawfish selecting for a specific type of mangrove shoreline, and if so, what type of mangrove shoreline is it?", "links": [ { diff --git a/datasets/USGS_SOFIA_MeHg_degrad_rates.json b/datasets/USGS_SOFIA_MeHg_degrad_rates.json index 7d110fef8c..9768d73ebf 100644 --- a/datasets/USGS_SOFIA_MeHg_degrad_rates.json +++ b/datasets/USGS_SOFIA_MeHg_degrad_rates.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_MeHg_degrad_rates", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The spreadsheet contains the data for 12 sites for sediment methylmercury degradation potential rate measurements.\n \n High concentrations of methyl-mercury (CH3Hg+), a toxic substance to both animals and humans, recently have been measured in a number of top predators (including panthers and game fish) native to the Florida Everglades. The objective of this research was to provide ecosystem managers with CH3Hg+ degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades. The focus was on better understanding the microbial and geochemical controls regulating CH3Hg+ degradation. At the time of the study, little was known about the specific factors influencing this process in natural systems.", "links": [ { diff --git a/datasets/USGS_SOFIA_SF_CIR_DOQs.json b/datasets/USGS_SOFIA_SF_CIR_DOQs.json index c5c966b702..2dadee6642 100644 --- a/datasets/USGS_SOFIA_SF_CIR_DOQs.json +++ b/datasets/USGS_SOFIA_SF_CIR_DOQs.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_SF_CIR_DOQs", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital orthophoto quadrangles (DOQ's) produced by the USGS for the South Florida Ecosystem Initiative iare color-infrared, 1-meter ground resolution quadrangle images covering 3.75 minutes of latitude by 3.75 minutes of longitude at a map scale of 12,000. Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquadrangle (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75 minutes of latitude by 3.75 minutes of longitude) image cast on the Universal Transverse Mercator projection (UTM) on the North American Datum of 1983 (NAD83). The geographic extent of the DOQ is equivalent to a quarter-quadrangle plus the overedge ranges from a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The radiometric image brightness values are stored as 256 gray levels, ranging from 0 to 255. The standard, uncompressed gray scale DOQ format contains an ASCII header followed by a series of 8-bit image data lines. The keyword-based, ASCII header may vary in the number of data entries. The header is affixed to the beginning of the image and is composed of strings of 80 characters with an asterisk (*) as character 79 and an invisible newline character as character 80. Each keyword string contains information for either identification, display, or registration of the image. Additional strings of blanks are added to the header so that the length of a header line equals the number of bytes in a line of image data. The header line will be equal in length to the length of an image line. If the sum of the byte count of the header is less than the sample count of one DOQ image line, then the remainder of the header is padded with the requisite number of 80 character blank entries, each terminated with an asterisk and newline character.\n\nThe objective of this project was to provide color infrared (CIR) digital orthophoto coverage for the entire south Florida ecosystem area. The main advantage of a digital orthophoto is that it gives a measurable image free of distortion. Therefore, the digital orthophotos for the ecosystem provide multi-use base images for identifying natural and manmade features and for determining their extent and boundaries; the images can also be used for the interpretation and classification of these areas.", "links": [ { diff --git a/datasets/USGS_SOFIA_SnailKites_AppleSnails.json b/datasets/USGS_SOFIA_SnailKites_AppleSnails.json index cd67e36733..b727dcb3e4 100644 --- a/datasets/USGS_SOFIA_SnailKites_AppleSnails.json +++ b/datasets/USGS_SOFIA_SnailKites_AppleSnails.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_SnailKites_AppleSnails", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The endangered snail kite (Rostrhamus sociabilis) is a wetland-dependent raptor feeding almost exclusively on a single species of aquatic snail, the Florida apple snail (Pomacea paludosa). The viability of the kite population is dependent on the hydrologic conditions (both short-term and long-term) that (1) maintain sufficient abundances and densities of apple snails, and (2) provide suitable conditions for snail kite foraging and nesting, which include specific vegetative community compositions. Many wetlands comprising its range are no longer sustained by the natural processes under which they evolved (USFWS 1999, RECOVER 2005), and not necessarily characteristic of the historical ecosystems that once supported the kite population (Bennetts and Kitchens 1999, Martin et al. 2008). Natural resource managers currently lack a fully integrative approach to managing hydrology and vegetative communities with respect to the apple snail and snail kite populations. At this point in time the kite population is approximately 1,218 birds (Cattau et al 2012), down from approximately 4000 birds in 1999. It is imperative to improve our understanding hydrological conditions effecting kite reproduction and recruitment. Water Conservation area 3-A, WCA3A, is one of the 'most critical' wetlands comprising the range of the kite in Florida (see Bennetts and Kitchens 1997, Mooij et al. 2002, Martin et al. 2006, 2008). Snail kite reproduction in WCA3A sharply decreased after 1998 (Martin et al. 2008), and alarmingly, no kites were fledged there in 2001, 2005, 2007, or 2008. Bowling (20098) found that juvenile movement probabilities away (emigrating) from WCA3A were significantly higher for the few kites that did fledge there in recent years (i.e. 2003, 2004, 2006) compared to those that fledged there in the 1990s. The paucity of reproduction in and the high probability of juveniles emigrating from WCA3A are likely indicative of habitat degradation (Bowling 20098, Martin et al. 2008), which may stem, at least in part, from a shift in water management regimes (Zweig and Kitchens 2008). Given the recent demographic trends in snail kite population, the need for a comprehensive conservation strategy is imperative; however, information gaps currently preclude our ability to simultaneously manage the hydrology in WCA3A with respect to vegetation, snails, and kites. While there have been significant efforts in filling critical information gaps regarding snail kite demography (e.g., Martin et al. 2008) and variation in apple snail density to water management issues (e.g., Darby et al. 2002, Karunaratne et al. 2006, Darby et al. 2008), there is surprisingly very little information relevant for management that directly links variation in apple snail density with the demography and behavior of snail kites (but see Bennetts et al. 2006). The U.S. Fish and Wildlife Service (USFWS), the U. S. Army Corps of Engineers, and the Florida Fish and Wildlife Conservation Commission (FWC) have increasingly sought information pertaining to the potential effects of specific hydrological management regimes with respect to the apple snail and snail kite populations, as well as the vegetative communities that support them.", "links": [ { diff --git a/datasets/USGS_SOFIA_YY_Males.json b/datasets/USGS_SOFIA_YY_Males.json index d7a8861b00..a48d8757a6 100644 --- a/datasets/USGS_SOFIA_YY_Males.json +++ b/datasets/USGS_SOFIA_YY_Males.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_YY_Males", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dozens of non-native fish species have established throughout south Florida (including Everglades National Park, Big Cypress National Preserve, Biscayne National Park and various state and private lands). Thus far, research on these species has focused on documenting their distributions, natural history, and physiological tolerances. Research is beginning to emerge on interactions of native species with non-natives, although it is only in the early stages. Research on control of non-native fishes in South Florida is also lacking, although it is potentially the most important and useful to natural resource managers. At present, the only management techniques available to control non-native fishes are physical removal, dewatering or ichthyocides. Unfortunately, all of these methods negatively impact native fauna as well as the targeted non-native fishes and require a great deal of effort (and therefore, funding). Herein, we propose a research program focused on applying a genetic technique common in aquaculture to control of non-native fishes. This proposal focuses on developing a technique (YY supermales) to control a non-native fish in South Florida (African jewelfish Hemichromis letourneuxi). However, the concept can be applied to a wide variety of species, including other fishes (e.g., brown hoplo Hoplosternum littorale), invasive applesnails (Pomacea spp.), the Australian red claw crayfish (Cherax spp.) and the green mussel (Perna veridis).", "links": [ { diff --git a/datasets/USGS_SOFIA_aerial-photos.json b/datasets/USGS_SOFIA_aerial-photos.json index 83804387d7..29bf1394a5 100644 --- a/datasets/USGS_SOFIA_aerial-photos.json +++ b/datasets/USGS_SOFIA_aerial-photos.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_aerial-photos", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number.\n \nA foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html", "links": [ { diff --git a/datasets/USGS_SOFIA_analysis_hist_wq.json b/datasets/USGS_SOFIA_analysis_hist_wq.json index 4e64a17207..14547e473b 100644 --- a/datasets/USGS_SOFIA_analysis_hist_wq.json +++ b/datasets/USGS_SOFIA_analysis_hist_wq.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_analysis_hist_wq", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Big Cypress National Preserve (BICY), the Everglades National Park (EVER), and Loxahatchee National Wildlife Refuge (LOX) are water-dominated ecosystems that are susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in BICY, EVER, and LOX because of their designations as Outstanding Florida Waters.\n\nBig Cypress National Preserve (BICY), Everglades National Park (EVER)), and Loxahatchee National Wildlife Refuge (LOX) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY, EVER, and LOX are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two parks has yet to be performed. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality. The initial study area was in BICY and EVER; the study area was extended into LOX in 2003.", "links": [ { diff --git a/datasets/USGS_SOFIA_asr_data_lake_okee.json b/datasets/USGS_SOFIA_asr_data_lake_okee.json index 57a9c94c0c..598fc70a03 100644 --- a/datasets/USGS_SOFIA_asr_data_lake_okee.json +++ b/datasets/USGS_SOFIA_asr_data_lake_okee.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_asr_data_lake_okee", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery.\n\n", "links": [ { diff --git a/datasets/USGS_SOFIA_atlss_prog.json b/datasets/USGS_SOFIA_atlss_prog.json index 895e7690a1..b42f49b663 100644 --- a/datasets/USGS_SOFIA_atlss_prog.json +++ b/datasets/USGS_SOFIA_atlss_prog.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_atlss_prog", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ATLSS (Across Trophic Level System Simulation) program addresses CERP\u2019s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading.\n\nAn essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS\u2019s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions\n\nThere are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds.\n\nModels currently available are:\n\nATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1)\n\nSpatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1)\n\nSpatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17)\n\nGIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1)\n\nLandscape Models: High Resolution Topography (HRT - Version 1.4.8)\n\nVegetation productivity (HTDAM - Version 1.1) High Resolution\n\nHydrology (HRH - Version 1.4.8)", "links": [ { diff --git a/datasets/USGS_SOFIA_avian_ecology_spoonbills.json b/datasets/USGS_SOFIA_avian_ecology_spoonbills.json index 5eb41ed07c..37b219c027 100644 --- a/datasets/USGS_SOFIA_avian_ecology_spoonbills.json +++ b/datasets/USGS_SOFIA_avian_ecology_spoonbills.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_avian_ecology_spoonbills", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objectives of our research are to (1) quantify the changes in spatial distribution and success of nesting spoonbills relative to hydrologic patterns, (2) test hypotheses about the causal mechanisms for observed changes, (3) establish a science-based criteria for nesting distribution and success to be used as a performance measure for hydrologic restoration, and (4) estimate demographic parameters. To meet these objectives, we will use a combined field/modeling approach. Based on previous and concurrent research, hypothesized relationships between hydrology, fish populations, and spoonbill nesting distribution and success will be expressed in a simple, but spatially explicit, conceptual model. Field data will be collected and compared with predicted responses to monitor changes in spoonbill nesting as hydrologic restoration is implemented, and to test the hypothesized mechanisms for observed changes. Variation of hydrologic conditions among years and locations is a virtual certainty; thus we will treat this variation in a quasi-experimental framework where the variation in wet and dry season conditions constitutes a series of \"natural experiments\".\n\nOur project is designed to evaluate the effect of hydrologic restoration on the nesting distribution and success of Roseate Spoonbills (Ajaia ajaia) in Florida Bay and surrounding mangrove estuarine habitats. This project is further designed to test hypotheses about the causal mechanisms of observed changes. The Everglades ecosystem has suffered extensive degradation over the past century, including an 85-90% decrease in the numbers of wading birds. Previous monitoring of Roseate Spoonbills in Florida Bay over the past 50 years has shown that this species responds markedly to changes in hydrology and corresponding changes in prey abundance and availability. Shifts in nesting distribution and declines in nest success have been attributed to declines in prey populations as a direct result of water management. Consequently, the re-establishment of spoonbill colonies in northeast Florida Bay is one change predicted under a conceptual model of the mangrove estuarine transition zone of Florida Bay. Changes in nesting distribution and success will further be used as a performance measure for success of restoration efforts and will be incorporated in a model linking mangrove fish populations and spoonbills to alternative hydrologic scenarios.", "links": [ { diff --git a/datasets/USGS_SOFIA_ba_geologic_data.json b/datasets/USGS_SOFIA_ba_geologic_data.json index 34b7afa26e..d1ec651a2d 100644 --- a/datasets/USGS_SOFIA_ba_geologic_data.json +++ b/datasets/USGS_SOFIA_ba_geologic_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ba_geologic_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report from which the data is taken identifies and characterizes candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using GPR, cyclostratigraphy, borehole geophysical logs, continuously drilled cores, and paleontology. About 60 mi of GPR profiles were acquired and are used to calculate the depth to shallow geologic contacts and hydrogeologic units, image karst features, and produce a qualitative perspective of the porosity distribution within the upper part of the karstic Biscayne aquifer in the Lake Belt area of north-central Miami-Dade County. . Descriptions of lithology, rock fabric, cyclostratigraphy, and depositional environments of 50 test coreholes were linked to geophysical data to provide a more refined hydrogeologic framework for the upper part of the Biscayne aquifer. Interpretation of depositional environments was constrained by analysis of depositional textures and molluscan and benthic foraminiferal paleontology. Digital borehole images were used to help quantify large-scale vuggy porosity. Preliminary heat-pulse flowmeter data were coupled with the digital borehole image data to identify potential ground-water flow zones.\n \n The objectives of this cooperative project were to identify and characterize candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using ground-penetrating radar, cyclostratigraphy, borehole geophysical logs, continuously drilled cores and paleontology. In 1998, the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD), initiated a study to provide a regional-scale hydrogeologic framework of a shallow semiconfining unit within the Biscayne aquifer of southeastern Florida. Initially, the primary objective was to characterize and delineate a low-permeability zone in the upper part of the Biscayne aquifer that spans the base of the Miami Limestone and uppermost part of the Fort Thompson Formation. Delineation of this zone was to aid development of a conceptual hydrogeologic model to be used as input into the SFWMD Lake Belt ground-water model. The approximate area encompassed by the conceptual hydrogeologic model is shown as the study area at http://sofia.usgs.gov/exchange/cunningham/bbwelllocation.html. Subsequent analysis of the preliminary data suggested hydraulic compartmentalization occurred within the Biscayne aquifer, and that there was a need to characterize and delineate ground-water flow zones and relatively low-permeability zones within the upper part of the Biscayne aquifer. Consequently, preliminary results suggested that the historical understanding of the porosity and preferential pathways for Biscayne aquifer ground-water flow required considerable revision.\n \n This project was carried out in cooperation with the South Florida Water Management District (SFWMD).", "links": [ { diff --git a/datasets/USGS_SOFIA_bbcw_geophysical.json b/datasets/USGS_SOFIA_bbcw_geophysical.json index 8b26e8abab..318d3f4885 100644 --- a/datasets/USGS_SOFIA_bbcw_geophysical.json +++ b/datasets/USGS_SOFIA_bbcw_geophysical.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_bbcw_geophysical", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this data acquisition project were to complete the downhole geophysical logging including video and flowmeter logging of two core holes (9A and 11A), which are the deepest wells at monitor well sites 0009AB and 0011AB.\n \n The goal of the Comprehensive Everglades Restoration Plan Biscayne Bay Coastal Wetlands Project (BBCWP) is to rehydrate wetlands and reduce point-source discharge to Biscayne Bay. The BBCWP will replace lost overland flow and partially compensate for the reduction in ground-water seepage by redistributing, through a spreader system, available surface water entering the area from regional canals. The proposed redistribution of freshwater flow across a broad front is expected to restore or enhance freshwater wetlands, tidal wetlands, and near shore bay habitat. A critical component of the BBCWP is the development of a realistic representation of ground-water flow within the karst Biscayne aquifer. Mapping these ground-water flow units is key to the development of models that simulate ground-water flow from the Everglades and urban areas through the coastal wetlands to Biscayne Bay. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the Biscayne Bay Coastal Wetlands Project Delivery Team installed two monitor-well sites and collected the necessary detailed hydrogeologic data. The L-31 North Canal Seepage Management Pilot Project is intended to curtail easterly seepage emanating from within Everglades National Park (ENP). The pilot project is examining various seepage management technologies as well as operational changes that could be implemented to reduce the water losses from ENP. This project is in close proximity to Biscayne Bay so an effort has been made to combine ongoing work efforts at the two project areas. The distribution of seepage into the L-31 North Canal and beneath it is not known with any degree of certainty today. A canal draw down experiment was conducted to provide additional field data that will be utilized to refine seepage estimates in the study area as well as determine aquifer parameters in the study area.\n \n This project was funded by the USGS Florida Integrated Science Center and the South Florida Water Management District (SFWMD).", "links": [ { diff --git a/datasets/USGS_SOFIA_bcunits_pts_point.json b/datasets/USGS_SOFIA_bcunits_pts_point.json index 6e7caccf2b..c78a84d814 100644 --- a/datasets/USGS_SOFIA_bcunits_pts_point.json +++ b/datasets/USGS_SOFIA_bcunits_pts_point.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_bcunits_pts_point", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrogeologic unit depths at 321 selected points, determined from published cross sections and contour maps, were entered into a point data layer. Generalized land-surface elevations were also entered for each point.\n \n Geographic information systems (GIS) have become an important tool in assessing and planning for the protection of natural resources. Most Federal and State natural resource agencies and many County environmental agencies in Florida are currently using GIS to assist in mathematical modeling, resource mapping, and risk assessments. The U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD) and the Broward County Office of Natural Resource Protection (BCONRP), developed a digital spatial data base for Broward County consisting of layers of data that can be used in water-resources investigations. These data layers include manmade features such as municipal boundaries and roads, topographic features, hydrologic features such as canals and lakes, and hydrogeologic features such as aquifer thickness. Computer programs were written for use in developing additional layers of data from existing data bases such as the Florida Department of Environmental Regulation (FDER) underground storage tank data base. This report describes the digital spatial data base that was developed and the five computer programs that can be used to create additional data layers from existing data files or to document existing layers. Most of the data layers cover Broward County east of the conservation areas. Some data layers cover all of Broward and may include parts of Miami-Dade County.", "links": [ { diff --git a/datasets/USGS_SOFIA_bicy_fish_inventory.json b/datasets/USGS_SOFIA_bicy_fish_inventory.json index d6c3991d3f..cf068ddfbd 100644 --- a/datasets/USGS_SOFIA_bicy_fish_inventory.json +++ b/datasets/USGS_SOFIA_bicy_fish_inventory.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_bicy_fish_inventory", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Big Cypress national Preserve Fish Inventory database contains records of the inventory of freshwater fishes of the Big Cypress National Preserve (BICY) conducted by the National Audubon Society's Tavernier Science Center as part of the National Park Service (NPS) Inventory and Monitoring Project. The database includes data from October 2002 through April 2004. The Big Cypress National Preserve Fish Monitoring and Assessment data collections for aquatic animals from BICY were begun in July 2004. The spreadsheet contains worksheets for Raccoon Point, L28, and Bear Island.\n \n Although a major ecosystem of the South Florida area, the Big Cypress National Preserve (BICY), is poorly understood in biological terms. To detect changes in natural and artificial habitats resulting from Comprehensive Everglades Restoration Plan (CERP) restoration programs, baseline data on constituent aquatic communities and their ecology are needed before and after restoration actions. Fishes and aquatic invertebrates serve as indicators of the health of these wetlands. These organisms are also important because they are major prey for many of the characteristic South Florida predatory species, especially alligators and wading birds. This project has several objectives, the foremost of which is to continue a program of aquatic study in BICY begun in 2002. Work will be performed in partnership with National Audubon Society (NAS) and the National Park Service to design and implement a spatially and temporally explicit, quantitative sampling program for aquatic animals in BICY. This program will 1) provide baseline data which may be used to track changes in hydrology as a result of CERP projects 2) document the distribution, composition, and habitat use by native and introduced aquatic animals to evaluate the effects of CERP on BICY aquatic habitats, and 3) provide ecological data for use in the ATLSS fish simulation model used to plan and evaluate restoration actions during CERP (presently, inappropriate data from the Everglades are being used in the model for cells that lie in BICY). The strategy used to accomplish these goals will be to employ techniques used by the co-principal investigators in establishing monitoring programs in the Everglades (since 1977) and the mangrove zone of Florida Bay (since 1989).", "links": [ { diff --git a/datasets/USGS_SOFIA_brwd_biscayne_limit_west_arc.json b/datasets/USGS_SOFIA_brwd_biscayne_limit_west_arc.json index 10a88b5bf4..723fca672a 100644 --- a/datasets/USGS_SOFIA_brwd_biscayne_limit_west_arc.json +++ b/datasets/USGS_SOFIA_brwd_biscayne_limit_west_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_brwd_biscayne_limit_west_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The approximate western and northern limits of the Biscayne aquifer are shown in this map. The limit is drawn where the thickness of very highly permeable limestone or calcareous sandstone is estimated to decrease to less than 10 feet. The sediments in the excluded area are predominantly muddy sands and shell or limestone that are generally not highly permeable.\n \n Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "links": [ { diff --git a/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json b/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json index 8bf9f3e951..ca71b62321 100644 --- a/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json +++ b/datasets/USGS_SOFIA_brwd_config_base_biscayne_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_brwd_config_base_biscayne_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The base of the Biscayne aquifer are shown in this map. The base is drawn on the bottom of highly permeable limestone or sandstone in the Tamiami Formation that is virtually contiguous with overlying rocks of very high permeability in the Fort Thompson Formation, Anastasia Formation, or Tamiami Formation. In general, the Biscayne aquifer is shallow, and the base deepens gradually in west and central Broward county. However, the aquifer thickens, and the base deepens very rapidly in the coastal area to more than 300 feet below sea level.\n \n Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "links": [ { diff --git a/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json b/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json index cc5352f680..66d13a7a15 100644 --- a/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json +++ b/datasets/USGS_SOFIA_brwd_config_base_surficial_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_brwd_config_base_surficial_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map shows the altitude of the base of the surficial aquifer system below sea level. In addition to the test holes drilled in this study, eight others from Parker and others (1955) or from the U.S. Geological Survey files were used to select the base. The contour interval is 20 feet.\n \n Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "links": [ { diff --git a/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json b/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json index 9471b3d5b0..1b1a565959 100644 --- a/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json +++ b/datasets/USGS_SOFIA_brwd_glime_altbase_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_brwd_glime_altbase_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains contours of the base of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet.\n \n Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "links": [ { diff --git a/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json b/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json index 2d06864c6b..976b1c9bba 100644 --- a/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json +++ b/datasets/USGS_SOFIA_brwd_glime_alttop_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_brwd_glime_alttop_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is map contains contours of the top of the highly permeable gray limestone aquifer in the Tamiami Formation. The contour interval is 10 feet.\n \n Provide fundamental background information that is basic for qualitative or quantitative evaluations of the ground water resource and the hydraulic response of the system to natural or artificial stresses.", "links": [ { diff --git a/datasets/USGS_SOFIA_ccsoil.json b/datasets/USGS_SOFIA_ccsoil.json index 5727263058..fb89af3ad8 100644 --- a/datasets/USGS_SOFIA_ccsoil.json +++ b/datasets/USGS_SOFIA_ccsoil.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ccsoil", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets consist of two files, an ARC/INFO shape file with associated files and an ARC/INFO export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale.\n \n Getting geographic information into a form that can be analyzed in a Geographic Information System (GIS) has always been a labor-intensive process. Graphic information was historically captured using variations of manual digitizing techniques. Users either digitized directly from printed materials on digitizing tablets or tables or by a variation of heads-up digitizing from scanned graphics displayed on computer monitors. Data collection involves considerable interaction between the user and a computer to capture and manipulate graphical data into a GIS layers. By using inexpensive image processing software to process and manipulate scanned images before processing these images in the GIS, features can be semi-automatically extracted from the scanned graphics, virtually eliminating the process of manual delineation. Common photo editing techniques combined with GIS expertise can dramatically decrease the time required to collect GIS data layers.\n \n The mentioning of specific software brands or registered trademarks does not constitute a commercial endorsement; their mention is done for clarity only. Mention of software products in the description of graphic processing techniques should be viewed as a use of available tools and not a recommendation for a software product.", "links": [ { diff --git a/datasets/USGS_SOFIA_ch1999cont_arc.json b/datasets/USGS_SOFIA_ch1999cont_arc.json index c348aaaf36..58fdbcf78b 100644 --- a/datasets/USGS_SOFIA_ch1999cont_arc.json +++ b/datasets/USGS_SOFIA_ch1999cont_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ch1999cont_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surficial aquifer system underlies Palm Beach, Martin, and St. Lucie Counties and primarily consists of sand, clay, silt, shell, and limestone of Holocene, Pleistocene, and Pliocene age. Its thickness is variable (decreasing westward and northward) and is estimated to be as much as 400 feet in Palm Beach County (Miller, 1987; Shine and others, 1989), 210 feet in Martin County (Miller, 1980), and 180 feet in St. Lucie County (Miller, 1980).The surficial aquifer system is composed of several stratigraphic units. The maps shows the altitude of the base of the surficial aquifer system in Palm Beach, Martin, and St. Lucie counties in 1997-1998. The contour interval is 40 feet.\n \n Urban development in Palm Beach, Martin, and St. Lucie Counties, Florida, has expanded rapidly in recent decades, resulting in a need for additional freshwater withdrawals from the surficial aquifer system - the primary source of drinking water for this tri-county area. Potable-water demand for urban users is projected to increase 115 percent in Palm Beach County and 89 percent each in Martin and St. Lucie Counties from 1990 to 2010 (South Florida Water Management District, 1998).The increased demand on the coastal well fields, which draw water from the surficial aquifer system, may contribute to saltwater intrusion. There are limited data as to the location or movement of the saltwater interface in the tri-county area, with the exception of previously collected data in the immediate vicinity of the existing coastal well fields. It is possible that the combination of pumpage from the well fields and drainage caused by rivers and canals has a regional effect on the saltwater interface. In October 1996, the U.S.Geological Survey (USGS) entered into a cooperative study with the South Florida Water Management District to determine the present location of the interface between freshwater and oceanic saltwater in the surficial aquifer system along the coast of southeastern Florida. This map report documents the position of the saltwater interface in the surficial aquifer system in 1997- 98 through the evaluation of chloride and geophysical data. This map was developed to delineate the base of the surficial aquifer system in the study area.", "links": [ { diff --git a/datasets/USGS_SOFIA_chron_isotope_geochem_FL_Keys.json b/datasets/USGS_SOFIA_chron_isotope_geochem_FL_Keys.json index 5e799fce96..070c121a43 100644 --- a/datasets/USGS_SOFIA_chron_isotope_geochem_FL_Keys.json +++ b/datasets/USGS_SOFIA_chron_isotope_geochem_FL_Keys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_chron_isotope_geochem_FL_Keys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project involves sampling surface waters and ground waters from Florida Bay, the Keys, and offshore to the barrier reef. Analyses will be done on a variety of isotopic and chemical species that have been used elsewhere to determine ground-water ages, contaminant sources, and geo- chemical reactions. Water Research Discipline researchers will coordinate ground water sampling and analytical work; Geologic Discipline researchers will provide access to wells and back- ground data, handle field logistics, etc.\n \n A significant issue of concern in South Florida is the potential effect of anthropogenic pollutants from the Florida Keys or elsewhere on the water quality and health of offshore marine ecosystems. It has been suggested that certain contaminants (e.g., bacteria, excess nutrients) found in some offshore ground waters may be transported 'in the subsurface to discharge sites beneath Florida Bay or the reef tract, where they may be contributing to declining ecosystem health. But not much is known about the origins of the ground waters underlying the region, how the subsurface flow systems operate, and what is the fate of contaminants emplaced in ground water in the Keys. Ground waters are potential sources, sinks, and carriers of nutrients and other contaminants beneath the Florida Keys and offshore regions to the north and south. This project is designed to provide new data on the sources, flow directions, exchange rates, and chemical characteristics of ground waters underlying the region of Florida Bay, the Keys, and offshore reefs. The results, to be derived in part from analyses of environmental tracers and isotopes, will provide general empirical information about subsurface transport processes and their potential impact on surface water chemistry.", "links": [ { diff --git a/datasets/USGS_SOFIA_coastal_ever_tjslll_04.json b/datasets/USGS_SOFIA_coastal_ever_tjslll_04.json index e5ff76433a..c95aadec32 100644 --- a/datasets/USGS_SOFIA_coastal_ever_tjslll_04.json +++ b/datasets/USGS_SOFIA_coastal_ever_tjslll_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_coastal_ever_tjslll_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration).\n \n The objective of this project is to conduct integrated studies to develop an understanding of how hydrologic parameters, disturbance, sediment, and global change (e.g. sea level) influence ecological systems in coastal wetlands. Hydrological factors studied include surface and groundwater stage and conductivity, surface water flow, nutrient concentration and suspended sediment. Fire, freeze, hurricanes and lightning strikes are among the disturbances that are important in coastal wetlands. Sediment elevation changes in coastal wetlands as a function of plant growth and decomposition, accretion or erosion due to tides and surface water flows, fire (in freshwater peats) and hurricanes. Both positive and negative feedbacks on sediment elevation have been discovered. Sea level has increased almost 30cm in the past century. The influence of continued sea level rise on CERP for restoring coastal areas is unknown at present. These questions have been addressed by the development of an integrated network of sampling and measurement sites where instrumentation is collocated. Many sites have surface and ground water sampling wells, sediment elevations tables and permanent vegetation plots. Transects, with both permanent plots and hydrology sampling wells, have been established across the mangrove - marsh ecotone to examine the influence of hydrology and fires (both partly controllable), freezes and sea level (not manageable) on the position of the ecotone.", "links": [ { diff --git a/datasets/USGS_SOFIA_coastal_grads.json b/datasets/USGS_SOFIA_coastal_grads.json index e103ccf626..8823479add 100644 --- a/datasets/USGS_SOFIA_coastal_grads.json +++ b/datasets/USGS_SOFIA_coastal_grads.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_coastal_grads", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ten monitoring stations will be operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations will include water level, velocity, salinity, and temperature. Three stations (Upstream North River, North River, and West Highway Creek) will also include automatic samplers for the collection of water samples and determination of Total Nutrients (TN and TP). These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects. By combining data collected from the ten monitoring stations and the existing monitoring network, information will be available across 9 generalized coastal gradients or transects. Data collected at all flow sites will be transmitted in near real time (every 1 or 4 hours) by way of satellite telemetry to the automated data processing system (ADAPS) database in the USGS Center for Water and Restoration Studies (CWRS) in Miami and available for CERP purposes. In addition to data from monitoring stations described above, salinity surveys will be performed along these 9 generalized transects, and these will include salinity, temperature, and GPS data from boat-mounted systems. Surveys will be performed regularly on a quarterly basis and twice following hydrologic events, totaling a maximum of 6 surveys per year.\n \n The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities.", "links": [ { diff --git a/datasets/USGS_SOFIA_coastal_grads_salsurveys.json b/datasets/USGS_SOFIA_coastal_grads_salsurveys.json index cb13bea392..042dc44e55 100644 --- a/datasets/USGS_SOFIA_coastal_grads_salsurveys.json +++ b/datasets/USGS_SOFIA_coastal_grads_salsurveys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_coastal_grads_salsurveys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ten monitoring stations were operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations includes water level, velocity, salinity, and temperature. These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects.\n \n The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities.", "links": [ { diff --git a/datasets/USGS_SOFIA_coupled_sw-gw_model.json b/datasets/USGS_SOFIA_coupled_sw-gw_model.json index c3849ceb63..6c5a3efa48 100644 --- a/datasets/USGS_SOFIA_coupled_sw-gw_model.json +++ b/datasets/USGS_SOFIA_coupled_sw-gw_model.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_coupled_sw-gw_model", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development.\n \n The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B).", "links": [ { diff --git a/datasets/USGS_SOFIA_dade_biscayne_limit_west_arc.json b/datasets/USGS_SOFIA_dade_biscayne_limit_west_arc.json index 7b977592db..9696721d83 100644 --- a/datasets/USGS_SOFIA_dade_biscayne_limit_west_arc.json +++ b/datasets/USGS_SOFIA_dade_biscayne_limit_west_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dade_biscayne_limit_west_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the approxiamte western limit of the Biscayne aquifer in Miami-Dade County.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_dade_config_base_biscayne_arc.json b/datasets/USGS_SOFIA_dade_config_base_biscayne_arc.json index 14ae570f36..a8f000a436 100644 --- a/datasets/USGS_SOFIA_dade_config_base_biscayne_arc.json +++ b/datasets/USGS_SOFIA_dade_config_base_biscayne_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dade_config_base_biscayne_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the altitude below sea level of the base of the Biscayne aquifer in Miami-Dade County. The contour interval is 10 feet.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_dade_config_base_glime_arc.json b/datasets/USGS_SOFIA_dade_config_base_glime_arc.json index 0100eee537..6bdc930900 100644 --- a/datasets/USGS_SOFIA_dade_config_base_glime_arc.json +++ b/datasets/USGS_SOFIA_dade_config_base_glime_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dade_config_base_glime_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contours of the altitude below sea level of the base of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. The contour interval is 10 feet.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_dade_config_base_surficial_arc.json b/datasets/USGS_SOFIA_dade_config_base_surficial_arc.json index b41cd9fa12..8da263bfdd 100644 --- a/datasets/USGS_SOFIA_dade_config_base_surficial_arc.json +++ b/datasets/USGS_SOFIA_dade_config_base_surficial_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dade_config_base_surficial_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contours of the base of the surficial aquifer system are shown on this map. The base of the aquifer system occurs at a relatively uniform elevation of 180 to 220 ft. below sea level over most of Dade County. The contour interval is 20 feet.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_dade_config_top_glime_arc.json b/datasets/USGS_SOFIA_dade_config_top_glime_arc.json index 743870bdb8..9d82a3988b 100644 --- a/datasets/USGS_SOFIA_dade_config_top_glime_arc.json +++ b/datasets/USGS_SOFIA_dade_config_top_glime_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dade_config_top_glime_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contours of the elevation of the top of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. Also included are highly permeable beds of coarse, shelly sands (sometimes with sandstone) that are contiguous with limestone above or below or are likely to connect laterally with the limestone. The contour interval is 10 feet.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_dawmet.json b/datasets/USGS_SOFIA_dawmet.json index 84aca34c70..ce9b255d55 100644 --- a/datasets/USGS_SOFIA_dawmet.json +++ b/datasets/USGS_SOFIA_dawmet.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dawmet", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment).\n \n This project is designed to document the terrestrial ecosystem history of south Florida and is collaborating with other projects at the USGS and other agencies on Florida Bay, Biscayne Bay, and the Buttonwood Embankment. The specific goals of the project are 1) document the patterns of floral and faunal change at sites throughout southern Florida over the last 150 years; 2) determine whether changes occurred throughout the entire region or whether they were localized; 3) examine the floral and faunal history of the region over the last few millennia; 4) determine the baseline level of variability in the communities prior to significant human activity in the region; and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. Data generated from this project will be integrated with data from other projects to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters.", "links": [ { diff --git a/datasets/USGS_SOFIA_discharge_tamiami_canal.json b/datasets/USGS_SOFIA_discharge_tamiami_canal.json index 2f88c8a52c..f0434a329b 100644 --- a/datasets/USGS_SOFIA_discharge_tamiami_canal.json +++ b/datasets/USGS_SOFIA_discharge_tamiami_canal.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_discharge_tamiami_canal", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are from the following four stations: Station 02288800 - Tamiami Canal Outlets, Monroe to Carnestown; Station 02288900 - Tamiami Canal Outlets, 40-Mile Bend to Monroe, near Miami, FL; Station 02289040 - Tamiami Canal Outlets, Levee 67A to 40-Mile Bend, near Miami, FL; Station 02289060 - Tamiami Canal Outlets, Levee 30 to Levee 67A, near Miami, FL. The data were compiled from records from 1986 to 1999 in the USGS Ft. Lauderdale, FL office of the Water Resources Discipline in 2000. Each station has numerous individual flow measurements at gages that were used in the calculation of the mean flow for each station. The data were collected by USGS personnel and the gages are maintained and operated by USGS Ft. Lauderdale office personnel.\n \n Canals are a major water-delivery component of the south Florida ecosystem. They interact with surrounding flow systems and waterbodies, either directly through structure discharges and levee overflows or indirectly through levee seepage and leakage, and thereby quantitatively affect wetland hydroperiods as well as estuarine salinities. Knowledge of this flow interaction, as well as timing, extent, and duration of inundation that it contributes to, is needed to identify and eliminate any potential adverse effects of altered flow conditions and transported constituents on vegetation and biota. Comprehensive analytical tools and methods are needed to assess the effects of nutrient and contaminant loads from agricultural and urban run-off entering canals and thereby conveyed into connected wetlands and other adjoining coastal ecosystems. These data from the individual gages were transferred to electronic form to provide a better understanding of the distribution of flow from north to south under the Tamiami Trail to aid in decisions about future changes to flow along the Trail.", "links": [ { diff --git a/datasets/USGS_SOFIA_dk_merc_cycl_bio.json b/datasets/USGS_SOFIA_dk_merc_cycl_bio.json index 07fcfe6a4e..2e44f6367b 100644 --- a/datasets/USGS_SOFIA_dk_merc_cycl_bio.json +++ b/datasets/USGS_SOFIA_dk_merc_cycl_bio.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_dk_merc_cycl_bio", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA\u0092s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure.\n \n Although ecological impacts from phosphorous contamination have become synonymous with water quality in south Florida, especially for Everglades restoration, there are several other contaminants presently entering the Everglades that may be of equal or greater impact, including: pesticides, herbicides, polycyclic aromatic hydrocarbons, and trace metals. This project focuses on mercury, a sparingly soluble trace metal that is principally derived from atmospheric sources and affects the entire south Florida ecosystem. Mercury interacts with another south Florida contaminant, sulfur, that is derived from agricultural runoff, and results in a problem with potentially serious toxicological impacts for all the aquatic food webs (marine and freshwater) in the south Florida ecosystem. The scientific focus of this project is to examine the complex interactions of these contaminants (synergistic and antagonistic), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological and chemical components of this ecosystem. However, it remains uncertain what overall effects will occur as these components react to the perturbations (especially the biological and chemical components) and toward what type of 'new ecosystem' the Everglades will evolve. The approaches used by this study have been purposefully chosen to yield results that should be directly useable by land management and restoration decision makers. Presently, we are addressing several major questions surrounding the mercury research field, and the Everglades Restoration program: (l) What, if any, ecological benefit to the Everglades would be realized if mercury emissions reductions would be enacted, and over what time scales (years or tens of years) would improvements be realized? (2) What is the role of old mercury (previously deposited and residing in soils and sediment) versus new mercury (recent deposition) in fueling the mercury problem? (3) In the present condition, is controlling sulfur or mercury inputs more important for reducing the mercury problem in the Everglades? (4) Does sulfur loading have any additional ecological impacts that have not been realized previously (e.g., toxicity to plant and animals) that may be contributing to an overall decreased ecological health? (5) Commercial fisheries in the Florida Bay are contaminated with mercury, is this mercury derived from Everglades runoff or atmospheric runoff? (6) What is the precise role of carbon (the third member of the 'methylmercury axis of evil', along with sulfur and mercury), and do we have to be concerned with high levels of natural carbon mobilization from agricultural runoff as well? (7) Hundreds of millions of dollars are being, or have been spent, on STA construction to reduce phosphorus loading to the Everglades, however, recently constructed STAs have yielded the highest known concentration of toxic methylmercury; can STA operations be altered to reduce methylmercury production and maintain a high level of phosphorus retention over extended periods of time? The centerpiece of our research continues to be the use of environmental chambers (enclosures or mesocosms), inside which we conduct dosing experiments using sulfate, dissolved organic carbon and mercury isotopic tracers. The goal of the mesocosm experiments is to quantify the in situ ecological response to our chemical dosing, and to also determine the ecosystem recovery time to the doses.", "links": [ { diff --git a/datasets/USGS_SOFIA_eco_assess_risk_toxics.json b/datasets/USGS_SOFIA_eco_assess_risk_toxics.json index 9ba273804a..bc1e9b3eb1 100644 --- a/datasets/USGS_SOFIA_eco_assess_risk_toxics.json +++ b/datasets/USGS_SOFIA_eco_assess_risk_toxics.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eco_assess_risk_toxics", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will be carried out in several locations throughout those areas critical to the South Florida Restoration Initiative. These areas include: 1) Water Conservation Areas 1, 2, and 3 of the Central Everglades, 2) Everglades National Park, 3) Loxahatchee National Wildlife Refuge, 4) Big Cypress National Preserve, 5) multiple Miami Metropolitan area canals and drainages, and 6) restoration related STA\u0092s (STA\u0092s 1-6) adjacent to the Everglades. Specific site selections will be based upon consideration of USACE restoration plans and upon discussions with other place-based and CESI approved projects. The overall objectives are characterize the exposure of wildlife to contaminants within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk, and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates.\n \n Historically, little consideration has been given to environmental chemical stressors/contaminants within the ecosystem restoration efforts for the Greater Everglades Ecosystem. The restoration is primarily guided by determining and restoring the historical relationships between ecosystem function and hydrology. The restoration plan was formulated to restore the natural hydrology and therefore, the resultant landscape patterns, bio-diversity and wildlife abundance. However, additional efforts need to consider the role that chemical contaminants such as pesticides and other inorganic/organic contaminants play in the structure and function of the resultant South Florida ecosystems. Indeed, the current level of agriculture and expanding urbanization and development necessitate that more emphasis be placed on chemical contaminants within this sensitive ecosystem and the current restoration efforts. The primary goal of the proposed project, therefore, is to develop an improved understanding of the exposure/fate (i.e. degradation, metabolism, dissipation, accumulation and transport) and potential ecological effects produced as a result of chemical stressors and their interactions in South Florida freshwater and wetland ecosystems. The overall objectives are to evaluate the risk posed by contaminants to biota within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. The specific objectives of this project are to: 1. Assess current exposure and potential adverse effects for appropriate receptors/species within the South Florida ecosystems with some emphasis on DOI trust species. These efforts will determine whether natural populations are significantly exposed to a variety of chemical stressors/contaminants, such as mercury, chlorinated hydrocarbon pesticides, historic and/or current use agricultural chemicals, and/or mixtures, as well as document lethal and non-lethal adverse effects in multiple health, physiologic and/or endocrine endpoints. 2. Assess exposure and potential adverse effects for appropriate species within South Florida as a function of restoration implementation.", "links": [ { diff --git a/datasets/USGS_SOFIA_eco_hist_db1995-2007_version 7.json b/datasets/USGS_SOFIA_eco_hist_db1995-2007_version 7.json index 199e6c5156..b98bf560af 100644 --- a/datasets/USGS_SOFIA_eco_hist_db1995-2007_version 7.json +++ b/datasets/USGS_SOFIA_eco_hist_db1995-2007_version 7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available.", "links": [ { diff --git a/datasets/USGS_SOFIA_eco_hist_db_version 3.json b/datasets/USGS_SOFIA_eco_hist_db_version 3.json index 46c183685e..92bdf33c7f 100644 --- a/datasets/USGS_SOFIA_eco_hist_db_version 3.json +++ b/datasets/USGS_SOFIA_eco_hist_db_version 3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eco_hist_db_version 3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ecosystem History Access Database contains listings of all sites (modern\n and core), modern monitoring site survey information, and published core data. Two general\n types of data are contained within this database: 1) Modern Field Data and 2) Core data -\n primarily faunal assemblages. \n \n Scientists over the past few decades\n have noticed that the South Florida ecosystem has become increasingly stressed. The purposes\n of the ecosystem history projects (started in 1995) are to determine what south Florida's\n estuaries have looked like over time, how they have changed, and what is the rate and\n frequency of change. To accomplish this, shallow sediment cores are collected within the\n bays, and the faunal and floral remains, sediment geochemistry, and shell biochemistry are\n analyzed. Modern field data are collected from the same region as the cores and serve as\n proxies to allow accurate interpretation of past depositional environments. The USGS South\n Florida Ecosystem History Project is designed to integrate studies from a number of\n researchers compiling data from terrestrial, marine, and freshwater ecosystems within south\n Florida. The project is divided into 3 regions: Biscayne Bay and the Southeast coast,\n Florida Bay and the Southwest coast, and Terrestrial and Freshwater Ecosystems of Southern\n Florida. The purpose of the projects is to provide information about the ecosystem's recent\n history based on analyses of paleontology, geochemistry, hydrology, and sedimentology of\n cores taken from the south Florida region. Data generated from the South Florida Ecosystem\n History project will be integrated to provide biotic reconstructions for the area at\n selected time slices and will be useful in testing ecological models designed to predict\n floral and faunal response to changes in environmental parameters. Biscayne Bay is of\n interest to scientists because of the rapid urbanization that has occurred in the Miami area\n and includes Biscayne National Park. Dredging, propeller scars, and changes in freshwater\n input have altered parts of Biscayne Bay. Currently, the main freshwater input to Biscayne\n Bay is through the canal system, but many scientists believe subsurface springs used to\n introduce fresh groundwater into the Bay ecosystem. Study of the modern environment and core\n sediments from Biscayne Bay will provide important information on past salinity and seagrass\n coverage which will be useful for predicting future change within the Bay. Plant and animal\n communities in the South Florida ecosystem have undergone striking changes over the past few\n decades. In particular, Florida Bay has been plagued by seagrass die-offs, algal blooms, and\n declining sponge and shellfish populations. These alterations in the ecosystem have\n traditionally been attributed to human activities and development in the region. Scientists\n at the U.S. Geological Survey (USGS) are studying the paleoecological changes taking place\n in Florida Bay in hopes of understanding the physical environment to aid in the restoration\n process. As in Biscayne Bay, scientists must first determine which changes are part of the\n natural variation in Florida Bay and which resulted from human activities. To answer this\n question, scientists are studying both modern samples and piston cores that reveal changes\n over the past 150-600 years. These two types of data complement each other by providing\n information about the current state of the Bay, changes that occurred over time, and\n patterns of change. Terrestrial ecosystems of South Florida have undergone numerous human\n disturbances, ranging from alteration of the hydroperiod, fire history, and drainage\n patterns through implementation of the canal system to expansion of the agricultural\n activity to the introduction of exotic species such as Melalueca, Australian pine, and the\n Pepper Tree. Over historical time, dramatic changes in the ecosystem have been documented\n and these changes attributed to various human activities. However, cause-and-effect\n relationships between specific biotic and environmental changes have not been established\n scientifically. One part of the South Florida Ecosystem History group of project is designed\n to document changes in the terrestrial ecosystem quantitatively, to date any changes and\n determine whether they resulted from documented human activities, and to establish the\n baseline level of variability in the South Florida ecosystem to estimate whether the\n observed changes are greater than what would occur naturally. Specific goals of this part of\n the project are to 1) document the patterns of floral and faunal changes at sites throughout\n southern Florida over the last 150 years, 2) determine whether the changes occurred\n throughout the region or whether they were localized, 3) examine the floral and faunal\n history of the region over the last few millennia, 4) determine the baseline level of\n variability in the communities prior to significant human activity in the region, and 5)\n determine whether the fire frequency, extent, and influence can be quantified, and if so,\n document the fire history for sites in the region.", "links": [ { diff --git a/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json b/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json index 476149926d..7d33ca58f4 100644 --- a/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json +++ b/datasets/USGS_SOFIA_eco_hist_swcoast_srs_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eco_hist_swcoast_srs_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project are to document impacts of changes in salinity, water quality, coastal plant and animal communities and other critical ecosystem parameters on a subdecadal-centennial scale in the southwest coastal region (from Whitewater Bay, north to the 10,000 Islands), and to correlate these changes with natural events and resource management practices. Emphasis will be placed on 1) determining the amount, timing and sources of freshwater influx (groundwater vs. runoff) into the coastal ecosystem prior to and since significant anthropogenic alteration of flow; and 2) determining whether the rate of mangrove and brackish marsh migration inland has increased since 20th century water diversion and what role sealevel rise might play in the migration. First, the environmental preferences and distributions of modern fauna and flora are established through analyses of modern samples in south Florida estuaries and coastal systems. Much of these data have already been obtained through project work conducted in Florida Bay and the terrestrial Everglades starting in 1995. These modern data are used as proxies for interpreting the historical data from Pb-210 and C-14 dated sediment cores based on assemblage analysis. On the basis of USGS data obtained from cores in Florida Bay and Biscayne Bay, the temporal span of the cores should be at a minimum the last 150 years; this is in agreement with University of Miami data showing sedimentation rates in Whitewater Bay to be approximately 1cm/year. For the estuarine/coastal ecosystems, a multidisciplinary, multiproxy approach will be utilized on cores from a transect from Whitewater Bay north to 10,000 Islands. Biochemical analyses of shells and chemical analyses of sediments will be used to refine data on salinity and nutrient supply, and isotopic analyses of shells will determine sources of water influx into the system. Nutrient analyses will be conducted to determine historical patterns of nutrient influx. To examine the inland migration of the mangrove/coastal marsh ecotone, transects from the mouth of the Shark and Harney Rivers inland into Shark River slough will be taken. These cores will be evaluated for floral remains, nutrients, charcoal, and if present, faunal remains. This project will provide 1) baseline data for restoration managers and hydrologic modelers on the amount and sources of freshwater influx into the southwest coastal zone and the quality of the water, 2) the relative position of the coastal marsh-mangrove ecotone at different periods in the past, and 3) data to test probabilities of system response to restoration changes.\n \n One of the primary goals of the Central Everglades Restoration Plan (CERP) is to restore the natural flow of water through the terrestrial Everglades and into the coastal zones. Historically, Shark River Slough, which flows through the central portion of the Everglades southwestward, was the primary flow path through the Everglades Ecosystem. However, this flow has been dramatically reduced over the last century as construction of canals, water conservation areas and the Tamiami Trail either retained or diverted flow from Shark River Slough. The reduction in flow and changes in water quality through Shark River have had a profound effect on the freshwater marshes and the associated coastal ecosystems. Additionally, the flow reduction may have shifted the balance of fresh to salt-water inflow along coastal zones, resulting in an acceleration of the rate of inland migration of mangroves into the freshwater marshes. For successful restoration to occur, it is critical to understand how CERP and the natural patterns of freshwater flow, precipitation, and sea level rise will affect the future maintenance of the mangrove-freshwater marsh ecotone and the coastal environment.", "links": [ { diff --git a/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json b/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json index 66726fd0ab..753a706b16 100644 --- a/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json +++ b/datasets/USGS_SOFIA_eden_dem_cm_nov07_nc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eden_dem_cm_nov07_nc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the 1st release of the third version of an Everglades Depth\n Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne\n height finder (AHF) and airboat collected ground surface elevations for the Greater\n Everglades Region. This version includes all data collected and certified by the USGS prior\n to the conclusion of the AHF collection process. It differs from the previous elevation\n model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75)\n is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been\n both refined and reduced to the region where standard error of cross-validation points falls\n below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide\n large-scale field operations, to integrate hydrologic and ecological responses, and to\n support biological and ecological assessments that measure ecosystem responses to\n Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of\n water depths, the EDEN requires a system-wide DEM of the ground surface. This file is a\n modification of the eden dem released in October of 2007 (i.e., eden_em_oct07) in which the\n elevation values have been converted from meters (m) to centimeters(cm) for use by EDEN\n applications software. This file is intended specifically for use in the EDEN applications\n software. Aside from this difference in horizontal units, the following documentation\n applies.\n \n These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network.", "links": [ { diff --git a/datasets/USGS_SOFIA_eden_em_oct07_400m.json b/datasets/USGS_SOFIA_eden_em_oct07_400m.json index d373d76fd0..ec0a179a3c 100644 --- a/datasets/USGS_SOFIA_eden_em_oct07_400m.json +++ b/datasets/USGS_SOFIA_eden_em_oct07_400m.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eden_em_oct07_400m", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface.\n \n These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network.", "links": [ { diff --git a/datasets/USGS_SOFIA_eden_water_surfs.json b/datasets/USGS_SOFIA_eden_water_surfs.json index 8ca43966ef..749e2670f3 100644 --- a/datasets/USGS_SOFIA_eden_water_surfs.json +++ b/datasets/USGS_SOFIA_eden_water_surfs.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_eden_water_surfs", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spatially continuous interpolation of water surface across the greater Everglades is generated for daily mean values of the water level gages for the EDEN network beginning January 1, 2000. Surfaces are recorded as elevations in centimeters relative to the North American Vertical Datum of 1988 (NAVD 88). These surfaces are served on the web as GIS data layers.\n \n Spatially explicit hydrologic information can be critical in understanding and predicting changes in biotic communities in wetland ecosystems. Repeated field measurements, the traditional method of collecting water surface information, is labor intensive and doesn't produce spatially continuous data across large areas. For this reason the EDEN project was started to collect data from over 200 real time stage monitoring gages that automatically record and radio-transmit data. The project integrates existing and new telemetered water level gages into a single network. Combined with a high resolution ground elevation model it generates a daily continuous water surface and water depth for the freshwater greater Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIA_estero_bay_ap_data.json b/datasets/USGS_SOFIA_estero_bay_ap_data.json index db31fec015..81f90ca95d 100644 --- a/datasets/USGS_SOFIA_estero_bay_ap_data.json +++ b/datasets/USGS_SOFIA_estero_bay_ap_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_estero_bay_ap_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data for each of the collection sites are available for fiscal years 2002-2005. The files are available in several formats. Salinity and temperature were collected for all stations. Stage, discharge, and wind speed and direction were also collected at some of the stations.\n \n Estero Bay is a shallow estuary, across which salinity gradients from freshwater to saltwater occur over short land-sea distances. Such gradient compressions can result in a highly variable salinity environment and affect a diverse range of estuarine flora and fauna when even a small change in watershed runoff occurs. Rapid development within the bay's watershed has a changing effect on the amount, timing, and quality of runoff into the bay. Currently there is no information available to assess the effect that these alterations of runoff may have on the bay and its biota, nor to define watershed runoff and loading limits that provide desirable ranges in salinity and water quality at historical, current, and potential locations for seagrass, oysters, and other species of concern. To manage and preserve the Estero Bay ecosystem, it is necessary to: (1) understand the salinity patterns of the bay in relation to freshwater inflow and water exchange with the Gulf of Mexico; (2) describe the mixing and freshwater residence times within the bay; and (3) study the effects on light penetration from increased Total Suspended-Solids (TSS) load and re-suspension. Results from this study will facilitate management decisions geared toward defining flow and sediment loading limits that provide desirable ranges in salinity and water quality by providing necessary hydrological information. To carry out the objectives of the study, a network of monitoring stations will be established and will include: (1) the monitoring of flow, water level, salinity, temperature, Acoustic Backscatter Strength (ABS), and turbidity near the mouth of three of four tributaries flowing into Estero Bay; (2) monitoring of water level, salinity, temperature, turbidity, wind speed and direction, and barometric pressure at one location inside the bay; (3) monitoring of water level, flow, salinity, temperature, and ABS at three of four tidal exchange points with the Gulf of Mexico along the barrier islands; (4) monitoring of water level (depth), salinity and temperature at one open-water location in the Gulf of Mexico.", "links": [ { diff --git a/datasets/USGS_SOFIA_ever_hydro_wq_data.json b/datasets/USGS_SOFIA_ever_hydro_wq_data.json index ef4d412804..f151fa2908 100644 --- a/datasets/USGS_SOFIA_ever_hydro_wq_data.json +++ b/datasets/USGS_SOFIA_ever_hydro_wq_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ever_hydro_wq_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two hydrology and water quality datasets are available for this project. The Northern Everglades Research Site and Sample Information dataset contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information dataset contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483.\n \n For restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", "links": [ { diff --git a/datasets/USGS_SOFIA_ever_isotope_data.json b/datasets/USGS_SOFIA_ever_isotope_data.json index 798089a012..57be422f10 100644 --- a/datasets/USGS_SOFIA_ever_isotope_data.json +++ b/datasets/USGS_SOFIA_ever_isotope_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_ever_isotope_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site.\n \n A first step of the Everglades restoration efforts is \"getting the water right\". However, the underlying goal is actually to re-establish, as much as possible, the \"pre-development\" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major \"long-term\" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. We have generally completed the sample analysis parts of objectives #1-5, and are writing interpretative reports on topics #1-5. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program.\n \n This work started as part of the Aquatic Cycling of Mercury in the E verglades (ACME) project in 1996 and was made a separate project in 2000.", "links": [ { diff --git a/datasets/USGS_SOFIA_exist_core.json b/datasets/USGS_SOFIA_exist_core.json index 019d9078cd..bb0ba2c536 100644 --- a/datasets/USGS_SOFIA_exist_core.json +++ b/datasets/USGS_SOFIA_exist_core.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_exist_core", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ABSTRACT: The proposed work was divided into several phases: (1) collection of existing core samples and slab preparation of core samples, (2) lithologic examination, and (3) report preparation", "links": [ { diff --git a/datasets/USGS_SOFIA_fb-fk_grndwtr_flow.json b/datasets/USGS_SOFIA_fb-fk_grndwtr_flow.json index b727ce6200..ed2d4c35b3 100644 --- a/datasets/USGS_SOFIA_fb-fk_grndwtr_flow.json +++ b/datasets/USGS_SOFIA_fb-fk_grndwtr_flow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fb-fk_grndwtr_flow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The strategy of this study was to use artificial tracers to determine rate and direction of flow. Tracers were injected into well clusters, existing sewage treatment facilities, and sewage disposal wells. In addition to tracer studies groundwaters were collected for contamination analysis so as to provide a baseline against which the effects of population increase and success of future wastewater treatment facilities can be evaluated.\n \n Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", "links": [ { diff --git a/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json b/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json index bb957ba7e3..21121298f6 100644 --- a/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json +++ b/datasets/USGS_SOFIA_fb_1890-1990_data_version 1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fb_1890-1990_data_version 1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The maps of the tracklines are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the Root Mean Square (RMS) error. \n \n Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_fb_bb_pollen_data.json b/datasets/USGS_SOFIA_fb_bb_pollen_data.json index 4ef8db24e8..e37ef5d269 100644 --- a/datasets/USGS_SOFIA_fb_bb_pollen_data.json +++ b/datasets/USGS_SOFIA_fb_bb_pollen_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fb_bb_pollen_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project developed, refined, and utilized a variety of proxies to provide estimates of seasonal, interannual, and decadal salinity history of Florida Bay and Biscayne Bay based on strategically placed sediment cores that aided in the validation and sensitivity testing of hydrologic models and decision making in water management. The datasets contain the pollen information at various depths in the cores.\n \n Terrestrial ecosystems of south Florida have undergone numerous human disturbances, ranging from alteration of hydroperiod, fire history, and drainage patterns from the introduction of the canal system to expansion of agricultural activity to the introduction of exotic species, Over historical time, dramatic changes in the ecosystem have been documented and these changes have been attributed to various human activities. However, the natural variability of the ecosystem was unknown and needed to be determined to assess the true impact of human activity on the modern ecosystem. The project was designed to document historical changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted form documented human activities, and to establish the baseline level of variability on the south Florida ecosystem to estimate whether the observed changes are greater than would occur naturally.", "links": [ { diff --git a/datasets/USGS_SOFIA_field_data_bicy.json b/datasets/USGS_SOFIA_field_data_bicy.json index 4a9719cc42..2e86ee8a36 100644 --- a/datasets/USGS_SOFIA_field_data_bicy.json +++ b/datasets/USGS_SOFIA_field_data_bicy.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_field_data_bicy", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data catagories include site name, date, time, station ID, record #, agency analyzing sample, agency collecting sample, discharge (daily mean), gage height, lab spec condition, field spec condition, total dissolved solids, water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, magnesium, sodium, potassium, chloride, sulfate, calcium, and silica.\n\n \n Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality.\n\nCURRENTNESS REFERENCE: ground condition\n\nSPATIAL DATA ORGANIZATION INFORMATION \nIndirect Spatial Reference: Big Cypress National Preserve\nDirect Spatial Reference: Point\nSDTS Point and Vector Object Type: Point\nPoint and Vector Object Count: 5\n\nSPATIAL REFERENCE INFORMATION - GEODETIC MODEL \nHorizontal Datum Name: North American Datum of 1983\nEllipsoid Name: Geodetic Reference System 80\nSemi-major Axis: 6378137\nDenominator of Flattening Ratio: 298.257\n\nNATIVE: Data are provided as Excel spreadsheets.", "links": [ { diff --git a/datasets/USGS_SOFIA_field_data_br105.json b/datasets/USGS_SOFIA_field_data_br105.json index d0092a39e0..778b81b39f 100644 --- a/datasets/USGS_SOFIA_field_data_br105.json +++ b/datasets/USGS_SOFIA_field_data_br105.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_field_data_br105", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, silica, and carbon.\n \n Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality.", "links": [ { diff --git a/datasets/USGS_SOFIA_field_data_interEver.json b/datasets/USGS_SOFIA_field_data_interEver.json index 8e812d8cf1..87ce83f5a4 100644 --- a/datasets/USGS_SOFIA_field_data_interEver.json +++ b/datasets/USGS_SOFIA_field_data_interEver.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_field_data_interEver", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data catagories include site name, date, time, station ID, medium, record #, agency analyzing sample, agency collecting sample, discharge (daily mean and instantaneous), gage height, lab spec condition, field spec condition, total dissolved solids, air and water temp, pH (lab and field), and amounts of oxygen, nitrogen and nitrogen compounds, phosphorus, carbonates, magnesium, sodium, potassium, chloride, sulfate, calcium, floride, silica, and carbon.\n \n Big Cypress National Preserve (BICY) and Everglades National Park (EVER) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY and EVER are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 \"internal marsh\" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 \"internal\" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two units has yet to be performed. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality.", "links": [ { diff --git a/datasets/USGS_SOFIA_fire_ecol_sfl_04.json b/datasets/USGS_SOFIA_fire_ecol_sfl_04.json index 695c6adce8..2af062f545 100644 --- a/datasets/USGS_SOFIA_fire_ecol_sfl_04.json +++ b/datasets/USGS_SOFIA_fire_ecol_sfl_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fire_ecol_sfl_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The project objective is to determine the importance that season of burning has on the response of vegetation to fire. We have addressed this through the use of experimental prescribed fires at different times of the year. In Big Cypress National Preserve we have established a long-term study of season and frequency of burning in the unlogged hydric pinelands of the Raccoon Point area. This study includes three seasonal treatments: winter (dry season), spring (early wet season) and summer (mid wet season). A shorter study comparing the response to winter and summer burns was carried out in the pine rocklands on Big Pine Key. We are also studying the effect of season of burning on muhly grass (Muhlenbergia filipes), a component of hydric pinelands and often a dominant in short-hydroperiod wetlands known as muhly or marl prairies. We are conducting field and nursery studies to determine how the season of burning effects the rate of recovery of muhly and its ability to tolerate flooding.\n\n \n Prescribed fire constitutes one of the most pervasive management actions influencing the restoration and maintenance of the Greater Everglades Ecosystem. It is generally assumed that lightning-ignited fires were common at the beginning of the rainy season, but there have probably been human-caused fires at other times for the last several thousand years. Since lighting-ignited fire cannot be allowed to operate naturally in South Florida, prescribed (or management-ignited) fire must be used to maintain these habitats. The seasonal occurrence of fire can have an important influence on ecological responses. We have conducted a set of experimental studies to determine the response of vegetation to different seasons of burning. The results of this work will influence the fire management of the publicly owned lands in the Greater Everglades ecosystem.", "links": [ { diff --git a/datasets/USGS_SOFIA_fish_sample.json b/datasets/USGS_SOFIA_fish_sample.json index a8e3003649..e618b95cb4 100644 --- a/datasets/USGS_SOFIA_fish_sample.json +++ b/datasets/USGS_SOFIA_fish_sample.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fish_sample", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study seeks to refine sampling methodology in the forested wetlands, to collect baseline data for aquatic animals to enable comparisons between Comprehensive Everglades Restoration Plan (CERP) and non-CERP impacted wetlands, and to begin studies of food-web structure in cypress and mangrove wetlands.\n \n Forested wetlands, mainly comprised by mangrove and cypress swamps in south Florida, and contiguous marshes formerly functioned as critical feeding and nesting sites for wading birds, populations of which have declined precipitously in coincidence with changes to the hydrology of the region. Human-induced changes have affected the natural variability of environmental conditions through the construction of canals and levees that can either act to drain or flood the wetlands. These changes are hypothesized to have negatively affected the production and availability of fish prey for the birds. A major target of restoration is the reestablishment of the natural hydrological conditions in the wetlands. Another alteration to these systems has been the introduction of more than 10 species of non-native fishes. The Big Cypress Swamp and mangrove ecosystems have been affected by these anthropogenic activities, yet the effects are unclear because of the lack of study. In both ecosystems, there is little quantitative information on the community composition, size-structure, and biomass of fishes and macro-invertebrates because few studies have been carried out there, This is especially true in the forested habitats of those ecosystems. Reasons for lack of study include logistical problems such as access to study areas and difficulties in devising appropriate sampling methods and feasible designs. However, because of the scope of anthropogenic changes in the drainage basins, there can be little doubt that the standing stocks of aquatic animals and habitat use have been affected negatively.", "links": [ { diff --git a/datasets/USGS_SOFIA_fk_gw_seep.json b/datasets/USGS_SOFIA_fk_gw_seep.json index 3250725cdc..c4c3399402 100644 --- a/datasets/USGS_SOFIA_fk_gw_seep.json +++ b/datasets/USGS_SOFIA_fk_gw_seep.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fk_gw_seep", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains information and data collected during the seepage meter (groundwater seepage) experiments along the Florida Keys on both the Florida Bay and Atlantic Ocean sides.\n \n Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", "links": [ { diff --git a/datasets/USGS_SOFIA_fl_coop_map.json b/datasets/USGS_SOFIA_fl_coop_map.json index 6514e40862..bca942af79 100644 --- a/datasets/USGS_SOFIA_fl_coop_map.json +++ b/datasets/USGS_SOFIA_fl_coop_map.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_fl_coop_map", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project was designed to provide the framework for understanding (1) ecosystem variability and change prior to and during human development of South Florida (i.e., the detailed ecosystem history over the last 200 years, differentiating natural variability from man-made change) and (2) the resource distribution (primarily water and phosphate) in the subsurface of Florida (i.e., the detailed geology of constraining and resource units). The overall strategy is is to: 1. Sample modern environments throughout the Greater Everglades Ecosystem to understand the present ecosystem and locate undisturbed shallow sediment cores to analyze ecosystem variability and change over the last few hundred years. 2. Analyze deep cores for sedimentology, diagenesis, biostratigraphy, paleoecology, and chemostratigraphy in transects across the southern Florida Peninsula to better understand the factors controlling ground water movement and to define aquifer characteristics. In order to understand the role of facies relationships and genetic depositional units in determining groundwater flow, the distribution and abundance of micro mollusks, foraminifers, dinocysts, ostracodes, pollen and spores, and charcoal will be analyzed, and strontium isotopes will be used for geochronology.\n \n A multitude of water-related societal issues face southern Florida in the 1990's. These issues include the increasing demands for water for agriculture; business, and the rapidly growing population in the Naples and Miami area (Miami showing the fourth fastest growth rate in the U.S. in the 1980's), the recently mandated restoration of natural sheet flow through the Everglades ecosystem, the effects of runoff from agricultural and urban areas, and the vitality of the important fisheries of Florida Bay and Biscayne Bay. This project provides baselines for ecosystem variability and tracks the change in ecosystems through the last several hundred years to provide critical information for reasonable restoration targets to land planners and managers in southern Florida. In addition, it provides the geologic framework for the aquifers that supply water to the area.", "links": [ { diff --git a/datasets/USGS_SOFIA_flow_murray_solis.json b/datasets/USGS_SOFIA_flow_murray_solis.json index dd9c71edf3..690a6bab97 100644 --- a/datasets/USGS_SOFIA_flow_murray_solis.json +++ b/datasets/USGS_SOFIA_flow_murray_solis.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_flow_murray_solis", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentalization including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South. \n \n The accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA\u0092s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM\u0092s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab.\n\n \n This project was initiated by Mitch Murray in October 1995.", "links": [ { diff --git a/datasets/USGS_SOFIA_flow_velocity.json b/datasets/USGS_SOFIA_flow_velocity.json index 999b667ccc..684aa160a5 100644 --- a/datasets/USGS_SOFIA_flow_velocity.json +++ b/datasets/USGS_SOFIA_flow_velocity.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_flow_velocity", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sheet flow over the Buttonwood Embankment during periods of high flow is an unknown element of the water budget for the Everglades. An ongoing project to estimate the flows over the embankment through modeling will require water-level and water velocity data measured at the embankment to accurately estimate simulated flows over this physical land feature. The actual measurement of water velocities and depths at the embankment would greatly improve the model calibration. Although it is virtually impossible to conventionally measure flow over the entire embankment, water depths and velocities at known points along the embankment, combined with the detailed topography of the embankment being developed in another ongoing project, should allow a much better estimate of the total flow than presently available.\n \n The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity.\n \n This project has been integrated into the TIME project. The project was started by Marvin Franklin.", "links": [ { diff --git a/datasets/USGS_SOFIA_flow_velocity_data.json b/datasets/USGS_SOFIA_flow_velocity_data.json index 07fa9778c6..ad9b2dcb1c 100644 --- a/datasets/USGS_SOFIA_flow_velocity_data.json +++ b/datasets/USGS_SOFIA_flow_velocity_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_flow_velocity_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. These data were collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected. \n \n The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity.", "links": [ { diff --git a/datasets/USGS_SOFIA_freshwater_east_coast.json b/datasets/USGS_SOFIA_freshwater_east_coast.json index 82865f71e8..3b8db97b97 100644 --- a/datasets/USGS_SOFIA_freshwater_east_coast.json +++ b/datasets/USGS_SOFIA_freshwater_east_coast.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_freshwater_east_coast", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Discharges through 10 selected coastal control structures in Broward and Palm Beach Counties and the 16 coastal structures in Miami-Dade County, Fla., Florida, are presently computed using the theoretical discharge-coefficient ratings developed from scale modeling, theoretical discharge coefficients, and some field calibrations whose accuracies for specific sites are unknown. To achieve more accurate discharge-coefficient ratings for the coastal control structures, field discharge measurements were taken with an Acoustic Doppler Current Profiler at each coastal control structure under a variety of flow conditions. These measurements were used to determine computed discharge-coefficient ratings for the coastal control structures under different flow regimes: submerged orifice flow, submerged weir flow, free orifice flow, and free weir flow. Theoretical and computed discharge-coefficient ratings for submerged orifice and weir flows were determined at the coastal control structures, and discharge ratings for free orifice and weir flows were determined at three coastal control structures. The difference between the theoretical and computed discharge-coefficient ratings varied from structure to structure.\n\n \n A system of canals and levees has been constructed over the last century for the purpose of drainage, flood control, and aquifer discharge. Strategically placed control structures allow the water management officials to move water from inland areas during high-rainfall periods and retain water in dry periods. Freshwater discharged to tide through coastal structures not only affects the amount of water available for water supply in the lower east coast and the Everglades, but it also affects the biota in the Intracoastal Waterway and Biscayne Bay. Therefore, it is imperative that there be accurate ratings for these structures to predict the effects of various water restoration alternatives. Although these coastal structures are a pivotal part of the man-made system, the discharge through most of them is computed only from theoretical ratings. Actual field measurements are needed in order to determine if the theoretical ratings are adequate, and to develop more accurate ratings. Stage measurements are made by the South Florida Water Management District (SFWMD) or the USGS at the east coast structures. The flows through the coastal structures in Miami-Dade, Broward, and Palm Beach counties can be computed by developing stage-discharge ratings from field measurements of flow, stage, and structure operations. Although theoretical ratings exist for the structures, no check as to the accuracy of these ratings has been made. In order to develop ratings from field measurements, discharge measurements must be made at the structure simultaneously with water level and structure operation measurements. Difficulties in making accurate discharge measurements arise from the slow flows and non-standard velocity profiles in south Florida canals. The Acoustic Doppler Current Profiler (ADCP), which uses the Doppler shift in acoustic signals to determine water velocity and compute discharge, is ideal for measurements in slow and spatially varying velocity fields. Statistical techniques were used to determine the best-fit ratings for the structures and error analysis of the ratings. The objective of this study was to determine discharge ratings for 10 coastal hydraulic control structures (7 in eastern Broward and 3 in southeastern Palm Beach counties as well as for 16 coastal hydraulic control structures in eastern Miami-Dade county.", "links": [ { diff --git a/datasets/USGS_SOFIA_freshwtr_flow.json b/datasets/USGS_SOFIA_freshwtr_flow.json index 9712b8f7f9..1243cd1009 100644 --- a/datasets/USGS_SOFIA_freshwtr_flow.json +++ b/datasets/USGS_SOFIA_freshwtr_flow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_freshwtr_flow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1995, the U.S. Geological Survey (USGS) began a study to gage several major creeks that discharge freshwater into northeastern Florida Bay. This study provides flow, salinity, and water-level data for model development and calibration and also provides baseline data for other physical, biological and chemical studies being conducted in the area. The monitoring network provides coastal discharge data for the majority of estuarine creeks in northeastern Florida Bay. The timing and distribution of freshwater deliveries to northeastern Florida Bay have been documented since 1996. In 2003 the USGS coastal and estuarine unit also began calculating nutrient loads at selected sites in northeastern Florida Bay and along the southwestern Everglades coast. The larger network has provided discharge information to researchers to develop nutrient budgets and loading (Rudnick, 1999; Sutula and others, 2003; Davis, 2004; and Levesque, 2004).\n \n In South Florida, changes in water-management practices to accommodate a large and rapidly growing urban population along the Atlantic coast, as well as intensive agricultural activities, have resulted in a highly managed hydrologic system. This managed system altered the natural hydrology of the Everglades ecosystem, including Florida Bay. During the last few decades, Florida Bay has experienced seagrass die-offs and algal blooms. Both are signals of ecological deterioration that has been attributed to increases in salinity and nutrient content of bay waters. With plans to restore water levels in the Everglades to more natural conditions, changes also are expected in the amount and timing of freshwater discharge through the major creeks into Florida Bay. Flow through the estuarine creeks through the Buttonwood Embankment and into Florida Bay is naturally controlled by the water level in the Everglades; regional wind patterns; and to a lesser extent, tides. Florida Bay restoration requires an understanding of the linkage between the amount of freshwater flowing into the bay and the salinity and quality of the bay environment. Historically, there has been no accurate quantification of the amount of freshwater being discharged into Florida Bay from the mainland due to the difficulties of accurately gaging flows in shallow, bi-directional, and vertically stratified streams. The project objectives are to determine the quantity, timing and distribution of freshwater flow into Florida Bay and adjacent estuaries, determine baseline hydrologic conditions and provide information on hydrologic change during the restoration process. This project helps determine how freshwater flow affects the health of Florida Bay, a critical component of the CERP, and how changes in water-management practices upstream (Taylor Slough and C-111 basins) directly influence flow and salinity conditions in the estuary.\n \n The project managers for this study include Eduardo Patino (1995-2000), Clinton Hittle (2001-2003), and Mark Zucker (2003 -present).", "links": [ { diff --git a/datasets/USGS_SOFIA_frnkflow.json b/datasets/USGS_SOFIA_frnkflow.json index 3f474b48a3..e7eb6da101 100644 --- a/datasets/USGS_SOFIA_frnkflow.json +++ b/datasets/USGS_SOFIA_frnkflow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_frnkflow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. This data was collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected.\n \n The objective of the project is to make the best possible estimate of the flow velocities and water depths across Buttonwood Embankment during high flow for use as model input for an ongoing project being conducted in the USGS-WRD office in Miami. Estimates will be used to provide management alternatives and will create salinity concentrations suitable for ecologic integrity.", "links": [ { diff --git a/datasets/USGS_SOFIA_gachemca.json b/datasets/USGS_SOFIA_gachemca.json index de59bb03ab..b039605f58 100644 --- a/datasets/USGS_SOFIA_gachemca.json +++ b/datasets/USGS_SOFIA_gachemca.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gachemca", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the following parameters: Lab ID, site ID, lab pH, lab alkalinity, Cl, SO4, Ca, Mg, Na, K, and ion balance for 27 samples collected from 10 sites.\n \n It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", "links": [ { diff --git a/datasets/USGS_SOFIA_gachmdoc.json b/datasets/USGS_SOFIA_gachmdoc.json index f1dd33a680..f7aa6c8971 100644 --- a/datasets/USGS_SOFIA_gachmdoc.json +++ b/datasets/USGS_SOFIA_gachmdoc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gachmdoc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the following parameters: Lab ID, site ID, DOC, specific UV, B, Ba, Fe, H4SiO4, Li, Mn, Sr, and Zn for 27 samples collected from 10 sites.\n \n It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", "links": [ { diff --git a/datasets/USGS_SOFIA_gaines_04.json b/datasets/USGS_SOFIA_gaines_04.json index a7963760ab..0f098ff6f1 100644 --- a/datasets/USGS_SOFIA_gaines_04.json +++ b/datasets/USGS_SOFIA_gaines_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gaines_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project includes models for primary food bases; the functional group of small fishes, upon which many of the wading birds depend, and the main reptile and amphibian functional groups, which constitute much of the diet of the American alligator. In addition, population models for several important species have been developed. These include a model for the snail kite population of Florida, models for the key wading bird species, and a model of the American crocodile population, all focusing on the effects of hydrology.\n \n This project has the goal of developing models for key components of the Everglades landscape as part of the overall Across Trophic Level System Simulation (ATLSS) program. The proposed work has four major objectives: 1. Provide rapid support for CERP by producing output and interpretation of requested runs of ATLSS models. 2. Complete an ATLSS model for the American crocodile that is in the final stage of work. 3. Validate models of the snail kite and the Cape Sable seaside sparrow. 4. Providing field work and habitat quality indices for effects of hydrology on selected small mammal and amphibian species.", "links": [ { diff --git a/datasets/USGS_SOFIA_gaqwfp.json b/datasets/USGS_SOFIA_gaqwfp.json index 6855bbf0f6..4d10731688 100644 --- a/datasets/USGS_SOFIA_gaqwfp.json +++ b/datasets/USGS_SOFIA_gaqwfp.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gaqwfp", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the following parameters: Lab ID, site ID, collection date and time, field pH, field specific conductivity, and water temperature at 10 locations.\n \n It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", "links": [ { diff --git a/datasets/USGS_SOFIA_gaqwssi.json b/datasets/USGS_SOFIA_gaqwssi.json index 5ae8a28131..e5ef57f942 100644 --- a/datasets/USGS_SOFIA_gaqwssi.json +++ b/datasets/USGS_SOFIA_gaqwssi.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gaqwssi", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the following parameters: Lab ID, site ID, site name, latitude/longitude, sampling depth, sample type, subsample type, and method for 27 samples from 10 locations.\n \n It is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylation and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiagency research project.", "links": [ { diff --git a/datasets/USGS_SOFIA_gawlik_wading_birds.json b/datasets/USGS_SOFIA_gawlik_wading_birds.json index 1810bd4c51..d513b03234 100644 --- a/datasets/USGS_SOFIA_gawlik_wading_birds.json +++ b/datasets/USGS_SOFIA_gawlik_wading_birds.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gawlik_wading_birds", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The conceptual model for this study is based on the idea that hydroperiod is a long-term process that primarily influences the abundance, body size, and species composition of the prey community whereas water depth has immediate effects on individual birds by influencing their ability to capture prey. This study seeks to determine through field experiments, the proximate effects of water depth, prey density, prey size, and prey species on wading bird foraging parameters. The species of wading birds examined in this study are those in the ATLSS wading bird model: the Wood Stork, White Ibis, Great Egret, and Great Blue Heron.\n \n The recovery of wading bird populations has been identified as a key component of successful Everglades restoration. Proposed causes for the decline in wading bird numbers have in common the notion that current hydropatterns have altered the availability of prey. Indeed, food availability may be the single most important factor limiting populations of wading birds in the Everglades. In the face of conflicting management scenarios, knowing the relative importance of each component of food availability is a precursor to understanding the effects of specific water management regimes on wading birds. Ongoing modeling efforts in south Florida such as the ATLSS program, integrate such information and provide predictive power for future management decisions. Currently, the biggest information gap limiting the wading bird model of ATLSS is foraging success as a function of prey availability. The South Florida Water Management District (SFWMD) is currently conducting a series of experiments aimed at determining the effects of water management on the use of foraging sites by wading birds. Site-use data are available immediately after each experiment and thus allow for quick analyses and write-up. However, also as part of those experiments, we recorded on film, foraging behavior of wading birds at feeding sites with known prey availabilities.", "links": [ { diff --git a/datasets/USGS_SOFIA_geochem_asr_lo.json b/datasets/USGS_SOFIA_geochem_asr_lo.json index 669cbf5c0e..03d666e561 100644 --- a/datasets/USGS_SOFIA_geochem_asr_lo.json +++ b/datasets/USGS_SOFIA_geochem_asr_lo.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_geochem_asr_lo", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery.\n \n To meet water-supply needs of natural systems as well as existing and future urban and agricultural water demands in South Florida, the U.S. Army Corps of Engineers (Corps) has identified ASR near Lake Okeechobee and in other areas as a critical component needed to provide adequate water storage functions for successful Everglades restoration. Several ASR pilot studies have demonstrated the feasibility of storing and recovering potable water from the brackish Floridan aquifer system on a local scale in south Florida (Muniz and Ziegler, 1995; Pyne, 1995). However, to demonstrate the viability of ASR on a greatly expanded regional scale, as proposed by the Corps, considerably more water-quality information is needed to provide assurance that recovered water is suitable for intended uses. At present, little or no information exists to address the following questions: 1. Will interactions between injected water, aquifer material, and native ground water result in elevated levels of radionuclides or trace elements that would be of concern to human or environmental health? 2. What is the fate of nutrients (C, N, P) from injected surface water that could be stored in the aquifer for prolonged time periods? 3.Would chemically aggressive waters injected into target aquifers cause chemical reactions that would result in clogging, biological fouling, or extensive dissolution of aquifer material? 4. If disinfection of surface water is needed prior to injection, what is the fate of resultant disinfection byproducts in water stored in the aquifer? Geochemical models are used to answer these questions and to evaluate other geochemical processes that may affect water quality during ASR operations. These models require knowledge of the chemical composition of the injected (source) water, the native aquifer (receiving) water, and the aquifer materials. This study will provide the characterization of potential source and receiving water in areas of proposed ASR development that are needed for geochemical modeling. Characterization of aquifer materials will be done as part of a Federally funded study following exploratory drilling and recovery of core material from target zones in the Floridan aquifer system. The results of this study will also determine if seasonal changes in water chemistry will require the removal of undesirable constituents prior to injection.", "links": [ { diff --git a/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json b/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json index cef2eb3b08..a56c6f32e6 100644 --- a/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json +++ b/datasets/USGS_SOFIA_geochem_mon_restore_fy04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_geochem_mon_restore_fy04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continued geochemical monitoring efforts will provide a measure of the progress and effects of restoration on environmental health and water quality, and complement biological monitoring of indicator species. This information is essential for identifying when successful restoration has been accomplished. Additionally, this geochemical monitoring program will serve as a model for developing similar programs for monitoring other coastal and lacustrine environments targeted in future projects. Products include a productivity database for Florida Bay and bimonthly salinity, dissolved oxygen, pH, carbon speciation, and air:sea CO2 gas flux maps of Florida Bay.\n \n The flow of fresh water from the Everglades to Florida Bay and the interaction of Bay water with the Gulf of Mexico and Atlantic Ocean are critical processes that have defined the Florida Bay Ecosystem. Reconstruction of historical changes in the Florida Bay Ecosystem using paleoecological and geochemical data from cores and historical databases indicates that significant changes in water quality and circulation (McIvor et al., 1994; Rudnick et al., 1999; Boyer et al., 1999; Halley and Roulier, 1999; Swart et al., 1999), and biological species composition and ecology (Brewster-Wingard and Ishman, 1999; Fourqurean and Robblee, 1999; Hall et al., 1999; Zieman et al., 1999) have been coincident with alteration of drainage patterns in the Everglades and construction of bridges linking the Keys. Paleoecological data from cores also indicates that changes in the abundance of seagrass and algae in the Bay have been coincident with salinity changes and that significant loss of seagrass on mud banks and basins has occurred over the last several years. Stable isotope data from sediment cores indicate decreased circulation in the Bay coincident with railroad building and early drainage in South Florida. Water management practices in South Florida are already being altered in an effort to restore the Everglades and Florida Bay. Resulting changes in water chemistry will first affect biogeochemical processes, and may, subsequently, result in changes in species distributions (such as seagrass, algae, etc.) in the Bay. An extensive water quality monitoring program for Florida Bay has been in operation for several years. Primary participants include ENP - fixed water quality monitoring stations, NOAA -salinity, chlorophyll, and transmittance bimonthly surveys, SFWMD - northeast Bay and north coast monitoring, and Florida International University (FIU) - nutrient monitoring. These programs have provided detailed information on concentrations of water quality parameters in the Bay. However, in situ monitoring of key biogeochemical processes resulting directly from biological activity has not been undertaken. Monitoring changes in biogeochemical processes is critical to early identification of ecological response to restoration and predicting changes in species distribution within the Bay. Additionally, these processes may directly impact water quality. Calcification, photosynthesis, and respiration directly affect dissolved oxygen, pH, dissolved inorganic carbon and a number of other chemical characteristics of the water column. This information will enable managers to evaluate the progress and success of South Florida restoration efforts.", "links": [ { diff --git a/datasets/USGS_SOFIA_geophys_mon_fy04.json b/datasets/USGS_SOFIA_geophys_mon_fy04.json index ddf4fb325e..725e49b317 100644 --- a/datasets/USGS_SOFIA_geophys_mon_fy04.json +++ b/datasets/USGS_SOFIA_geophys_mon_fy04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_geophys_mon_fy04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water management decisions that impact Everglades restoration efforts require high quality data and reliable hydrologic models. Traditionally these data for hydrologic\n models have been obtained through observation wells. In the Everglades, this approach is\n limited by the difficult access due to water which covers most of the area and to the\n limited number of roads. Airborne geophysical techniques provide a means of accessing large\n parcels of land and developing three-dimensional resistivity models of the area. The overall\n objective of this project is the collection of geophysical data that can be used to develop\n ground-water flow models of the area capable of modeling saltwater intrusion. This objective\n includes mapping of subsurface electrical properties of the aquifer and correlation of\n lateral variation in these properties to aspects of aquifer geometry and water quality that\n are pertinent to hydrologic model development. Completion of combined ground and airborne\n geophysical surveys in Everglades National Park and Big Cypress National Preserve has shown\n the utility of these methods to map saltwater intrusion and provide geological information\n needed to develop ground-water flow models. The strategy that has been used is to interpret\n the HEM data as layered-earth resistivity models that slowly vary from place to place.\n Surface geophysical measurements (time-domain electromagnetic soundings) have been used to\n assist in this interpretation and provide an independent check on the HEM data. Borehole\n data in the form of formation resistivities and water quality sampling have allowed us to\n develop relationships for converting the interpreted resistivity-depth models into estimated\n water quality given as specific conductance (SC) or chloride concentration. This information\n is of great value to hydrologic modelers. These data will be used to develop a ground-water\n flow model which is bounded on the north by the Tamiami Trail, on the south by Florida Bay,\n on the east by the Atlantic coastal ridge, and on the west by the Gulf of\n Mexico.\n \n Completion of a combined ground and airborne geophysical\n study in the southern portion of Everglades National Park has shown the utility of these\n methods to map the extent of saltwater intrusion and provide geological information needed\n to develop ground-water flow models. The same approach should prove equally useful in the\n development of hydrologic models in the region to the west where little subsurface\n information exists. The approach requires three components: ground-based, airborne, and\n borehole electrical geophysical measurements. In combination these measurements can provide\n detailed information on the location of geologic and hydrologic boundaries essential for\n ground-water model development. The mapping of saltwater intrusion in coastal aquifers has\n traditionally relied upon observation wells and collection of water samples. This approach\n may miss important hydrologic features related to saltwater intrusion in areas where access\n is difficult and wells are widely spaced, such as the Everglades. To map saltwater intrusion\n in Everglades National Park, a different approach has been used. We have relied heavily on\n helicopter electromagnetic (HEM) measurements to map lateral variations of electrical\n resistivity, which are directly related to water quality. The HEM data are inverted to\n provide a three-dimensional resistivity model of the subsurface. Borehole geophysical and\n water quality measurements made in a selected set of observations wells are used to\n determine the relation between formation resistivity and specific conductance of pore water.\n Applying this relation to the 3-D HEM resistivity model produces an estimated water-quality\n model. This model provides constraints for variable density, ground-water models of the\n area. Time-domain electromagnetic (TEM) soundings have also be used to map saltwater\n intrusion. Because of the high density of HEM sampling (a measurement point every 10 meters\n along flight lines) models with a cell size of 100 meters on a side are possible, revealing\n features which could not be recognized from either the TEM or the observation wells alone.\n The very detailed resistivity maps show the extent of saltwater intrusion and the effect of\n former and present canals and roadbeds. The TEM survey provides a means of quickly obtaining\n a synoptic picture of saltwater intrusion, which also serves as a baseline for monitoring\n the effects of Everglades restoration activities.", "links": [ { diff --git a/datasets/USGS_SOFIA_german_et_04.json b/datasets/USGS_SOFIA_german_et_04.json index 532d5ec0c1..546507141a 100644 --- a/datasets/USGS_SOFIA_german_et_04.json +++ b/datasets/USGS_SOFIA_german_et_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_german_et_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overall objective is to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives include: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors.; 2) Integration of evapotranspiration estimates into a process-oriented model; 3) Verification and refinement of model using ET measurements at additional sites.\n \n Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The Everglades ET project provides the necessary ET data, and methods of estimating ET throughout the Everglades system, that are required by all flow models.", "links": [ { diff --git a/datasets/USGS_SOFIA_german_et_data.json b/datasets/USGS_SOFIA_german_et_data.json index d23f8cfd4f..947021e71c 100644 --- a/datasets/USGS_SOFIA_german_et_data.json +++ b/datasets/USGS_SOFIA_german_et_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_german_et_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional evaluation of evapotranspiration (Et) in the Florida Everglades began in 1996 with operation of 9 sites at locations selected to represent the sawgrass or cattail marshes, wet prairie, and open-water areas that constitute most of the natural Everglades system. The Bowen-ratio energy-budget method was used to measure Et at 30-minute intervals. Site models were developed to determine Et for intervals when a Bowen ratio could not be accurately determined. Regional models were then developed for determining 30-minute Et at any location as a function of solar intensity and water depth using data from the 9 sites for 1996-97. Five of the original 9 sites continued in operation after 1997 for various periods. Two of these sites were operated continuously until September 2003. Three new sites were installed in the western part of Shark Valley in November 2001 for the purpose of testing regional model transferability. Additionally, an evaporation pan was installed at one site in April 2001 for comparing actual Et determined by the Bowen-ratio site with potential pan evaporation. All data collection ended in September 2003. The dataset contains the meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. Data is available by year for each of the collection sites. The a_read_me file in the Data summary and data files for Everglades Et sites, 1996-2003 describes the format of data files of meteorological and evapotranspiration data. Additionally, tables listing model coefficients and goodness-of-fit statistics for site models for the period 1998-2003 are included, and tables listing a comparison for measured Et and Et estimated from the regional models. This latest data release is different in format from the original release for all data from 1998 on. No changes were made in the 1996-97 data. One change made in reporting format is that Et data from 1998 on are not smoothed by averaging over one or more measurement intervals. With this release data are provided at the measurement interval so that users may use whatever smoothing technique that is appropriate for the intended use. Another change in format for data from 1998 on is that Et sums are provided for \"raw\" and \"edited\" 30-minute periods. The \"raw\" data refer to Et sums that have not been edited from computed results, although the Et sum may be an actual measurement that has passed all input-data screening tests (see WRI 00-4217), or may be a \"gap-filled\" value computed from the Priestley-Taylor site mode that was developed using only data that passed all screening tests. Data in the \"edited\" column have been edited graphically by comparing each value to the pattern of Et defined by the entire set of data during part of a day. The final change in format for data from 1998 on is that a flag indicator is provided to show which 30-minute Et data are measured and which are model derived because the input data did not pass screening criteria.\n \n Everglades restoration efforts will rely heavily upon development of hydrologic flow models that will be used to help guide restoration and management decisions. Any hydrologic model requires an assessment of the water budget, including the amount of water removed from the system by evapotranspiration (ET). ET is a major part of the water budget in the Everglades, being similar in magnitude to rainfall. The overall objective was to develop a process-oriented appraisal of evapotranspiration within the Everglades drainage unit, excluding agricultural and brackish environments. Specific objectives included: 1) Field measurement of evapotranspiration at a variety of sites encompassing a regionally representative range of environmental factors and 2) Verification and refinement of model using ET measurements at additional sites.", "links": [ { diff --git a/datasets/USGS_SOFIA_gfr_bay.json b/datasets/USGS_SOFIA_gfr_bay.json index 55e7b5ac6e..ef5a37826b 100644 --- a/datasets/USGS_SOFIA_gfr_bay.json +++ b/datasets/USGS_SOFIA_gfr_bay.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gfr_bay", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the values for the dyes from the tracer study on the bayside of Key Largo.\n \n Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", "links": [ { diff --git a/datasets/USGS_SOFIA_gfr_ocean.json b/datasets/USGS_SOFIA_gfr_ocean.json index a83bc35631..580b71391c 100644 --- a/datasets/USGS_SOFIA_gfr_ocean.json +++ b/datasets/USGS_SOFIA_gfr_ocean.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gfr_ocean", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the values for the dyes from the tracer study on the oceanside of Key Largo.\n\n \n Treated sewage is injected into the limestone under the Florida Keys via on-site disposal systems (OSDs). There are 25,000 septic tank systems, approximately 5,000 cesspools, and approximately 1000 class 5 injection wells. Depths of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases and both marine grass and sponge mortality is perceived by the local population, NOAA, and EPA to be caused by sewage nutrients leaking from groundwater on both sides of the Florida Keys. Determining the rate and direction of saline groundwater movement beneath the Key, and the Florida Bay was considered critical to understanding the fate and effects of subsurface waste water disposal n the Florida Keys. The objective of this research was to determine the rate, direction of flow, and contamination levels of saline groundwater in the Florida Keys and Florida Bay. Contamination studies are necessary to determine if nutrient and other contaminant levels are rising and to provide a baseline of data for future decision making.", "links": [ { diff --git a/datasets/USGS_SOFIA_gis_tool.json b/datasets/USGS_SOFIA_gis_tool.json index 7a750d4fca..b74e6fe72c 100644 --- a/datasets/USGS_SOFIA_gis_tool.json +++ b/datasets/USGS_SOFIA_gis_tool.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gis_tool", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning.\n \n South Florida\u0092s national parks and wildlife refuges are threatened by accelerated growth of the surrounding built environment which alters the natural hydrology and ecology, and introduces harmful levels of sediment, nutrients and toxins. Department of the Interior (DOI) scientists and land managers are faced with major informational and financial challenges and conflicting stakeholder interests in their efforts to manage and protect resources to fulfill their stewardship responsibilities. The web-based EPM will contribute to improved public understanding and awareness of the importance of protecting South Florida habitats and ecosystem functions, as well as the possible externalities associated with upcoming land use decisions.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_alt_ucu_arc.json b/datasets/USGS_SOFIA_glime_alt_ucu_arc.json index ef4010b166..5fbe451ea4 100644 --- a/datasets/USGS_SOFIA_glime_alt_ucu_arc.json +++ b/datasets/USGS_SOFIA_glime_alt_ucu_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_alt_ucu_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the altitude of the top of the confining unit which ranges from 10 ft above sea level to 50 ft below sea level in much of the study area, and slopes downward to the east and southeast. The contour interval is 25 feet.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_altbase_arc.json b/datasets/USGS_SOFIA_glime_altbase_arc.json index 9a6fb63036..55a4e71fa7 100644 --- a/datasets/USGS_SOFIA_glime_altbase_arc.json +++ b/datasets/USGS_SOFIA_glime_altbase_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_altbase_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The base of the gray limestone aquifer is shallowest in Collier and Hendry Counties and slopes to the southeast and east. The altitude of the base of the aquifer generally ranges from 50 to 160 ft below sea level, but the basal surface can be comparatively irregular in some areas. The map shows the altitude of the base of the gray limestone aquifer in feet below sea level. The contour interval is 50 feet.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_alttop_arc.json b/datasets/USGS_SOFIA_glime_alttop_arc.json index f8ce44684d..1b76101454 100644 --- a/datasets/USGS_SOFIA_glime_alttop_arc.json +++ b/datasets/USGS_SOFIA_glime_alttop_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_alttop_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The top of the gray limestone aquifer is shallowest in Collier and Hendry Counties and slopes to the southeast and east. The altitude of the top of the gray limestone aquifer generally ranges between sea level and 100 ft below sea level in the study area. The map shows the altitude of the top of the gray limestone aquifer in feet bwelow sea level. The contour interval is 50 feet.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_ext_aq_polygon.json b/datasets/USGS_SOFIA_glime_ext_aq_polygon.json index 269d8e7607..7694cf6972 100644 --- a/datasets/USGS_SOFIA_glime_ext_aq_polygon.json +++ b/datasets/USGS_SOFIA_glime_ext_aq_polygon.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_ext_aq_polygon", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leakance, which is the vertical hydraulic conductivity of the confining unit divided by its thickness, can be used to provide an indication of the degree of confinement of the aquifer. For purposes of this discussion, an aquifer is considered to be well confined, or have 'good confinement', if leakance was less than 1.0 x 10-3 1/d. Sites where leakance was determined by aquifer testing to be less than 1.0 x 10-3 1/d or the behavior of the aquifer was described as confined or well confined (tables 8 and 9) are shown in figure 29. These sites are located in southern Hendry County, western Broward County, and central Miami-Dade County and are in areas where the thickness of the confining unit approaches or is more than 50 ft. However, confining bed thickness did not necessarily prove to be a determinant of confinement. The map shows the extent of the gray limestone aquifer in southwestern Florida. Results from 35 new test coreholes and aquifer-test, water-level, and water quality data were combined with existing hydrogeologic data to define the extent, thickness, hydraulic properties, and degree of confinement of the gray limestone aquifer in Southern Florida. The western boundary is not mapped and is set to the western boundary of the study area.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_lim_ucu_arc.json b/datasets/USGS_SOFIA_glime_lim_ucu_arc.json index 2cd9e4c5ab..700a3a00f0 100644 --- a/datasets/USGS_SOFIA_glime_lim_ucu_arc.json +++ b/datasets/USGS_SOFIA_glime_lim_ucu_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_lim_ucu_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The upper confining unit of the gray limestone aqifer in southwestern Florida ranges from 20 to 60 ft in thickness in most of the study area, but is absent to the west and southwest in much of Collier County and most of Monroe County. The upper confining unit exists east of the line in the data set, with two small circular areas depicting areas where the unit is absent (to the north) and present (to the south). The unit is also present to the southwest of the short line in the southwest part of the area. The map shows the limit of the upper confining unit of the gray limestone aquifer in Collier, Hendry, Miami-Dade, and Monroe counties.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_limit_arc.json b/datasets/USGS_SOFIA_glime_limit_arc.json index e5d1263370..24e6458259 100644 --- a/datasets/USGS_SOFIA_glime_limit_arc.json +++ b/datasets/USGS_SOFIA_glime_limit_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_limit_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The maps shows the limit of the gray limestone aquifer in southern Florida. The lower Tamiami aquifer is mapped as being present in most of western and northeastern Hendry County, which are outside of the study area. However, the limestones of the Tamiami Formation, which are included in the lower Tamiami aquifer, thin to the north, and sand and sandstone layers make up most of the thickness of the formation in central Hendry County. The easternmost extent of the gray limestone aquifer corresponds closely to the limits previously delineated by Fish (1988) and Fish and Stewart (1991). In northeastern Broward County, the eastern edge of the aquifer occurs at the transition from highly permeable limestone or contiguous shell sand to a significantly less permeable facies composed of sandy, clayey limestone and quartz sand and sandstone. In northeastern Miami-Dade County, the eastern limit of the aquifer is mapped where the aquifer merges with the Biscayne aquifer and the intervening semiconfining unit wedges out. South of the Tamiami Trail, the eastern boundary occurs at a transition to less-permeable siliciclastic sediments. The northern and western extents of the gray limestone aquifer were not defined in this study.\n \n The purpose of this report is to evaluate the hydrogeologic framework, hydraulic properties, and ground-water flow of the gray limestone aquifer in southern Florida. The report also emphasizes the geologic framework (stratigraphy and structure) and the hydrogeologic framework (aquifers and confining and semiconfining units) above and below the gray limestone aquifer.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_limit_brwd_east_arc.json b/datasets/USGS_SOFIA_glime_limit_brwd_east_arc.json index 30759c0bef..e9356b82c0 100644 --- a/datasets/USGS_SOFIA_glime_limit_brwd_east_arc.json +++ b/datasets/USGS_SOFIA_glime_limit_brwd_east_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_limit_brwd_east_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An investigation of the surficial aquifer system in Broward County, begun in 1981, is part of a regional study of the aquifer system in southeast Florida. Test drilling for lithologic samples, flow measurements taken during drilling, aquifer testing, and analyses of previously available data permitted delineation of the permeability framework (on geologic sections), the aquifers in the system and the generalized transmissivity distribution, and interpretation of the ground-water flow system. In addition to the Biscayne aquifer, a previously undefined aquifer, composed of gray (in places, greenish-gray or tan) limestone of the lower part and locally the middle part of the Tamiami Formation, was found at depth in west Broward County. Although it is less permeable than the Biscayne aquifer, the gray limestone is nevertheless a significant aquifer and a potential source of water. The aquifer is informally and locally named as the gray limestone aquifer. It is defined as that part of the limestone beds (usually gray) and contiguous coarse clastic beds of the lower to middle part of the Tamiami Formation that are highly permeable (having a hydraulic conductivity of about 100 ft/d or more) and at least 10 feet thick. Above and below the gray limestone aquifer in west Broward County and separating it from the Biscayne aquifer and the base of the surficial aquifer system are sediments having relatively low permeability, such as mixtures of sand, clay, silt, shell, and lime mud, and some sediments of moderate to low permeability, such as limestone, sandstone, and claystone. Subsequent drilling has traced the gray limestone aquifer into southwest Palm Beach County where the water contains high dissolved solids and into northwest Dade County where the water generally has low dissolved solids. The aquifer probably extend westward into Collier County, and it likely is the source of water for irrigation and drinking on the Seminole Indian Reservation and sugar cane fields of southeast Hendry County. The map shows the approximate eastern limit of the gray limestone aquifer in Broward County.\n \n Most previous work in southeast Florida had been concentrated in the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or overlying zones. Hence, information concerning the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system were insufficient for present needs. Because of persistent increases in demand from the surficial aquifer system in the highly populated and growing coastal area of southeast Florida and because of attendant concerns for the protection and management of the water supply, the U.S. Geological Survey, in cooperation with the South Florida Water management District, began an investigation to define the extent of the surficial aquifer system and its characteristics on a regional scale. The overall objectives of the regional study are to determine the hydrogeologic framework, the extent and thickness of the surficial aquifer system and the aquifers within it, the areal and vertical water-quality distribution and factors that affect the water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_glime_limit_dade_east_arc.json b/datasets/USGS_SOFIA_glime_limit_dade_east_arc.json index 4ae1c52ba1..76c3b0445a 100644 --- a/datasets/USGS_SOFIA_glime_limit_dade_east_arc.json +++ b/datasets/USGS_SOFIA_glime_limit_dade_east_arc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_glime_limit_dade_east_arc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An aquifer identified by Fish (1988) in Broward County, composed of predominantly gray (in places, greenish-gray or tan) limestone of the lower part and locally the middle part of the Tamiami Formation, was identified at depths of about 70 to 160 ft below land surface in western Dade County. Although it is less permeable than the Biscayne aquifer, the gray limestone aquifer is still significant and is a potential source of water, particularly west of the western limit of the Biscayne aquifer. It is defined as that part of the limestone beds (usually gray) and contiguous, very coarse, elastic beds of the lower to middle part of the Tamiami Formation that are highly permeable (having a hydraulic conductivity of about 100 ft/d or greater) and at least 10 ft thick. Above and below the gray limestone aquifer in western Dade County, and separating it from the Biscayne aquifer and the base of the surficial aquifer system, are sediments having relatively low permeability, such as mixtures of sand, clay, silt, shell, and lime mud, as well as some sediments having moderate to low permeability, such as limestone, sandstone, and claystone. Drilling has identified the gray limestone aquifer in western Broward County and in southwestern Palm Beach County; in these areas, water in the aquifer contains high concentrations of dissolved solids. The aquifer may extend westward into Collier County, and it may be the source of water for irrigation of sugarcane fields in southeastern Hendry County and domestic use on the Seminole Indian Reservation. The map shows the approximate eastern limit of the gray limestone aquifer in Miami-Dade County.\n \n Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system.", "links": [ { diff --git a/datasets/USGS_SOFIA_grndwtr_seepage.json b/datasets/USGS_SOFIA_grndwtr_seepage.json index a807d4dbea..ae30b90c0e 100644 --- a/datasets/USGS_SOFIA_grndwtr_seepage.json +++ b/datasets/USGS_SOFIA_grndwtr_seepage.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_grndwtr_seepage", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project installed seepage meters to measure the volume of groundwater seepage into the overlying marine environment. The water will be analyzed for major nutrient levels. The data from this project include the site and values of seepage flux.\n \n The Florida Keys contain 25,000 septic tank systems, approximately 5000 cesspools, and 1000 class 5 injection wells. Depth of injection wells ranges from 10 to 30 meters. Excessive algal growth, coral diseases, and both marine grass and sponge lortality is perceived to be caused by sewage nutrients leaking from roundwater on both sides of the Florida Keys. Determining the volume and composition of groundwaters seeping into the marine environment from teh sea floor is vital to management decisions on the area. The objective of this study was to determine the volume and composition of groundwaters seeping upward through the rock water interface into Florida Bay and the coral reef tract. Submarine groundwater input into Florida Bay has been ignored by modelers and results show current models are likely to be erroneous. An additional major product will be an improved seepage meter design.", "links": [ { diff --git a/datasets/USGS_SOFIA_gw-sw_wq_everglades.json b/datasets/USGS_SOFIA_gw-sw_wq_everglades.json index 0f994df82d..ee8de24b79 100644 --- a/datasets/USGS_SOFIA_gw-sw_wq_everglades.json +++ b/datasets/USGS_SOFIA_gw-sw_wq_everglades.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gw-sw_wq_everglades", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. Two data sets are available for this project. The Northern Everglades Research Site and Sample Information data set contains a summary of the site locations, data types, and measurement periods in ENR, WCA2A, and WCA2B. The Seepage Meters Site and Sample Information data set contains vertical fluxes across wetland peat surface measured by seepage meters at research sites in ENR, WCA2A, WCA2B, and WCA3A. Additional data can be found in the appendices of the Open-File Reports 00-168 and 00-483.\n \nFor restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", "links": [ { diff --git a/datasets/USGS_SOFIA_gw_flow_trans_TIME.json b/datasets/USGS_SOFIA_gw_flow_trans_TIME.json index d882d63dd2..e6ac730825 100644 --- a/datasets/USGS_SOFIA_gw_flow_trans_TIME.json +++ b/datasets/USGS_SOFIA_gw_flow_trans_TIME.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_gw_flow_trans_TIME", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this project is to develop a numerical groundwater flow model that can be used with the TIME surface water model to quantify and predict flows and salinities in the coastal wetlands of the southern Everglades. Field data will be collected to help formulate the hydrogeologic conceptual model and for calibration of the model to flows, water levels, and salinities. Data collection will consist of monitoring well installation, seepage measurements, spatial characterization of peat thickness, and continuous monitoring of water levels and salinities at selected locations. The SICS model encompasses Taylor Slough and uses a 300-m grid resolution. The larger TIME model encompasses Shark and Taylor Sloughs and uses a 500-m grid resolution. A groundwater model has already been developed and linked with the SICS surface water model. This integrated SICS model simulates flows, stages, and salinities for the 5-year period from 1995 to 2000. Plans for the SICS model are to extend the simulation period through 2002 and complete a linkage to the South Florida Water Management District\u0092s model, called the \"2x2\" model. The SICS model will then be capable of performing detailed restoration scenarios for the Taylor Slough area. A preliminary groundwater model has also been developed for the TIME area, but this groundwater model has not yet been linked with a surface water model. Ray Schaffranek is currently finalizing a 3-month simulation with the TIME surface water model. As part of this project, the groundwater model will be linked with the TIME surface water model, and the simulation period will be extended to cover 2 years. A related CERP (Comprehensive Everglades Restoration Plan) project will extend this simulation period to 7 years and link with the 2x2 to perform Everglade restoration scenarios. This project also involves quantifying surface water and groundwater interactions by using nested monitoring wells and seepage meters. Data from the field studies are used to calibrate and verify the SICS and TIME models.\n \n The interaction between surface water and groundwater can be a potentially significant component of the hydrologic water budget in the Everglades. Recent research has shown that surface water and groundwater interactions also can affect salinities in coastal wetlands. As Everglades restoration is largely dependent upon \"getting the water right\", the U.S. Geological Survey is developing the TIME (Tides and Inflows in the Mangroves of the Everglades) and SICS (Southern Inland and Coastal Systems) models, hydrodynamic surface water models of the southern Everglades. The purpose of the TIME and SICS models is to accurately simulate flows and salinities in the coastal wetlands of the southern Everglades. Once calibrated, these models will be used to evaluate proposed restoration scenarios by feeding hydrologic information into the ATLSS biological models. These biological models are highly sensitive to hydrologic inputs such as flows, stages, and salinities; thus, the TIME and SICS models are expected to play an important role in linking the hydrologic component of the Everglades to the biologic component. In recent years, this project focused on developing a groundwater component for the SICS model, an integrated model of Taylor Slough and northern Florida Bay. The SICS model is now calibrated, operational, and providing important insight into the flow and salinity patterns of the southern coastal Everglades. Hydrologic output from the SICS model is being used in development of ATLSS fish models. The next step with this groundwater project is to extend the methodologies developed as part of the SICS modeling effort to the much larger TIME model.\n\n \n This project is now part of the SICS and TIME model linkages and development in support of Everglades Restoration project.", "links": [ { diff --git a/datasets/USGS_SOFIA_hansen_1890_trackline_map_version 1.json b/datasets/USGS_SOFIA_hansen_1890_trackline_map_version 1.json index e1cc893350..96ed3a0e65 100644 --- a/datasets/USGS_SOFIA_hansen_1890_trackline_map_version 1.json +++ b/datasets/USGS_SOFIA_hansen_1890_trackline_map_version 1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hansen_1890_trackline_map_version 1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the tracklines for historical bathymetric data for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS set to 0.0.\n \n Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Digitizing the historical shoreline and bathymetric data for comparison with modern data provides information on sedimentation rates within the Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_hansen_1990_trackline_map_version 1.json b/datasets/USGS_SOFIA_hansen_1990_trackline_map_version 1.json index 9a450fb071..8f6af9bc07 100644 --- a/datasets/USGS_SOFIA_hansen_1990_trackline_map_version 1.json +++ b/datasets/USGS_SOFIA_hansen_1990_trackline_map_version 1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hansen_1990_trackline_map_version 1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the tracklines for bathymetric data collected between 1995 and 1999 for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS computed from Ashtech PNAV software. The data set is labeled 1990 for easy comparison. The project duration was a decade.\n \n Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay has not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay. The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry.", "links": [ { diff --git a/datasets/USGS_SOFIA_hardness_swp_lnwr.json b/datasets/USGS_SOFIA_hardness_swp_lnwr.json index aa1de5272b..4536820d05 100644 --- a/datasets/USGS_SOFIA_hardness_swp_lnwr.json +++ b/datasets/USGS_SOFIA_hardness_swp_lnwr.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hardness_swp_lnwr", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Alterations to ground-water and surface-water hydrology and water chemistry in South Florida have contributed to increased flows of mineral-rich (hard water) canal water into historically rain-fall driven (soft water) areas of the Everglades. The interior of the A. R. M. Loxahatchee National Wildlife Refuge largely has retained its historic low conductivity or soft water condition due to its relative isolation from canal flows. However, recent sampling by USGS and the Refuge has shown that canal influences on water quality extend several kilometers into the Refuge in some areas, and Refuge managers and scientists are concerned that these influences may increase depending on future changes in water management operations. A survey across existing mineral gradients will be performed to document patterns of vegetation change and their relationship to changes in water hardness and other environmental factors. Laboratory and field experiments will test these correlative relationships to determine the relative importance of increasing water hardness as a cause of observed vegetation changes across canal gradients.\n \n Intrusion of canal waters into the Refuge increases the availability of Phosphorus (P), the primary limiting plant nutrient in the Everglades, as well as concentrations of major mineral ions such as Ca 2+, Mg 2+ and SO4 2-. While the ecological effects of P enrichment on the Everglades is fairly well understood, potential impacts caused by increased mineral concentrations in this soft-water wetland are largely unknown. Understanding the types and magnitude of these impacts is particularly important given that the area of the Refuge exposed to mineral enrichment is much greater than that exposed to P enrichment. The objective of this project is to determine the effects of increased flows of mineral-rich water on the aquatic plant community of the Refuge interior. Slough-wet prairie (SWP) habitats area a major landscape feature in the Refuge and several SWP plant species may be adapted to the soft-water conditions in the Refuge interior. Increased mineral loads to the Refuge may result in a shift towards a more species-poor and spatially homogeneous community, In addition, there is a small amount of evidence to suggest that mineral enrichment may favor the growth and expansion of sawgrass and a consequent decline in the coverage of the SWP habitats.", "links": [ { diff --git a/datasets/USGS_SOFIA_helio_mag_data.json b/datasets/USGS_SOFIA_helio_mag_data.json index dc76466027..57ea447bb4 100644 --- a/datasets/USGS_SOFIA_helio_mag_data.json +++ b/datasets/USGS_SOFIA_helio_mag_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_helio_mag_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These helicopter electromagnetic data were flown over a portion of Everglades National Park and surrounding areas in south Florida 9-14 December 1994. Two versions of the data are provided: the original dataset and the corrected dataset.\n \n This project addressed the question of determining the location of the fresh-water/salt-water interface (FWSWI) in the coastal regions of southern Miami-Dade and Monroe counties, synoptic monitoring of changes in water quality associated with changes in water management practices, and looking for geophysical evidence of subsurface discharges to Florida Bay. This project provided basic information needed to create ground-water models and test various restoration strategies and their impact on ground-water quality.", "links": [ { diff --git a/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json b/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json index f039181c5e..9e7a7cd6f5 100644 --- a/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json +++ b/datasets/USGS_SOFIA_hi_accuracy_elev_collection_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hi_accuracy_elev_collection_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. Data were also collected in the Lake Okeechobee littoral zone. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html. The work was performed for Everglades ecosystem restoration purposes. The project started in 1995 and concluded in 2007.\n \n This project performed regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that were being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", "links": [ { diff --git a/datasets/USGS_SOFIA_high_acc_elev_data.json b/datasets/USGS_SOFIA_high_acc_elev_data.json index 846352fdd6..65657082e0 100644 --- a/datasets/USGS_SOFIA_high_acc_elev_data.json +++ b/datasets/USGS_SOFIA_high_acc_elev_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_high_acc_elev_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) is coordinating the aquisition of high accuracy elevation data. Three formats of the data are available for each data set: .cor files which contain complete lists of Global Positioning System point files, .asc files which are the same as the .cor files but have been reformatted to process into ARC/INFO coverages, and .e00 files which are the ARC/INFO coverages. The files are available in the same 7.5- by 7.5-minute coverages as USGS quadrangles. The elevation data is collected on a 400 by 400 meter grid. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88).\n \n This project is performing regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services are also being rendered to provide vertical reference points for numerous water level gauges.\n \n Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.", "links": [ { diff --git a/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json b/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json index 6e82aa099f..055df5e3b5 100644 --- a/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json +++ b/datasets/USGS_SOFIA_highres_bathy_sfl_est-coast_sys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_highres_bathy_sfl_est-coast_sys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The plan to acquire bathymetric data for the Caloosahatchee Estuary and Estero Bay areas is to employ two methods which have been developed by the U. S. Geological Survey (USGS) and National Aeronautical and Space Administration (NASA). The USGS method is an acoustic based system named System for Accurate Nearshore Depth Surveys (SANDS), and the NASA method is an airborne LIDAR system named Experimental Advanced Airborne Research Lidar (EAARL). The plan is to use the EAARL system to map shallow (less than 1.5 secchi depth) and non-turbid areas in Estero Bay and nearshore areas. The SANDS system will be used in deeper areas and those which are turbid which includes the Caloosahatchee River.\n \n High resolution, GPS based bathymetric surveying is a proven method to map river, lake, and ocean floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day bathymetry of Caloosahatchee Estuary and Estero Bay regions. This information can be used by water management decision-makers to develop of Minimum Flows and Levels (MFL) and better preserve fragile habitats. The areas in and around the Caloosahatchee Estuary and Estero Bay Watershed have undergone dramatic increases in the rate of residential and commercial development as well as population growth during the past 15 years. As a result, a series of initiatives have been proposed to balance development and environmental interests in the region. Several recent initiatives including the development MFL and the Southwest Florida Feasibility Study (SWFFS) necessitate the development of hydrodynamic models of coastal waters in the Caloosahatchee Estuary and Estero Bay areas. One of the important data requirements for these models is the bathymetry. The information available at this time is dated (the last complete bathymetric survey is over 100 years old) and needs to be upgraded with a new survey. In addition, recommendations of the Estero Bay and Watershed Assessment completed in November of 1999 recommended the development of a Bay hydrodynamic and water quality model. Updated river, bay, and coastal bathymetry is required for these efforts. The area for bathymetry collection and interpretation includes Estero Bay, Charlotte Harbor, Pine Island Sound, offshore regions of Sanibel and Captive Islands, and the Caloosahatchee, Loxahatchee and St. Lucie Rivers. In addition, a need for an Estero Bay and Charlotte Harbor estuarine mixing model has been identified by the Southwest Florida Regional Restoration Coordination Team and the Southwest Florida Feasibility Study. In order to create an accurate numerical model, current bathymetric data must be obtained. Bathymetry data is also needed for the creation of a seagrass vision maps (an NEP effort) and to populate the species response models being created as assessment tools for several restoration programs.", "links": [ { diff --git a/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json b/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json index 36ddb3d1fc..deaf3f1805 100644 --- a/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json +++ b/datasets/USGS_SOFIA_hist_salinity_wq_veg_bb_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hist_salinity_wq_veg_bb_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project are to examine in broad context the historical changes in the Biscayne Bay ecosystem at selected sites on a decadal-centennial scale, and to correlate these changes with natural events and anthropogenic alterations in the South Florida region. Specific emphasis will be placed on historical changes to 1) amount, timing, and sources of freshwater influx and the resulting effects on salinity and water quality; 2) shoreline and sub-aquatic vegetation; and 3) the relationship between sea-level change, onshore vegetation, and salinity. In addition, a detailed examination of historical seasonal salinity patterns will be derived from biochemical analyses of molluscs, ostracodes, foraminifera and corals. The corals will allow us to compare marine and estuarine trends, examine the linkage between the two systems, and will provide precise chronological control. Land management agencies (principally SFWMD, ACOE and Biscayne NP) can use the data derived from this project to establish performance criteria for restoring natural flow, and to understand the consequences of altered flow. These data can also be used to forecast potential problems as upstream changes in water delivery are made during restoration.\n \n During the last century, Biscayne Bay has been greatly affected by anthropogenic alteration of the environment through urbanization of the Miami/Dade County area, and alteration of natural flow. The sources, timing, delivery, and quality of freshwater flow into the Bay, and the shoreline and sub-aquatic vegetation have changed. Current restoration goals are attempting to restore natural flow of fresh water into Biscayne and Florida Bays and to restore the natural vegetation, but first we must address of what the was environment prior to significant human alteration in order to establish targets for restoration. This project is designed to examine the natural patterns of temporal change in salinity, water quality, vegetation, and benthic fauna in Biscayne Bay over the last 100-300 years and to examine the causes of change.", "links": [ { diff --git a/datasets/USGS_SOFIA_hlms_physical_data.json b/datasets/USGS_SOFIA_hlms_physical_data.json index d0e275db1a..fb9f513df4 100644 --- a/datasets/USGS_SOFIA_hlms_physical_data.json +++ b/datasets/USGS_SOFIA_hlms_physical_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hlms_physical_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the core number, depth (cm), wet bulk density, dry bulk density, accumulated dry bulk density, dry bulk fines, total % H2O content, % insoluble residue, % loss on ignition, coarse (% dry wt.) > 0.062 mm, fines (% dry wt.) < 0.062 mm, and total Pb-210 activity (dpm/g and error).\n \n The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", "links": [ { diff --git a/datasets/USGS_SOFIA_hlms_radchem_data.json b/datasets/USGS_SOFIA_hlms_radchem_data.json index 4bedbf3333..4d483906ab 100644 --- a/datasets/USGS_SOFIA_hlms_radchem_data.json +++ b/datasets/USGS_SOFIA_hlms_radchem_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hlms_radchem_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The datasets contains the core number, depth (cm), and Ra-226 activity(dpm/g) and Ra-226 error (dpm/g).\n \n The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", "links": [ { diff --git a/datasets/USGS_SOFIA_hlmsclog.json b/datasets/USGS_SOFIA_hlmsclog.json index 64795fb200..a5779aa4cb 100644 --- a/datasets/USGS_SOFIA_hlmsclog.json +++ b/datasets/USGS_SOFIA_hlmsclog.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hlmsclog", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the core number, location, latitude/longitude, date collected, storage location, core surface description, and analyses for cores taken from Rabbitt Key, Cluett Key, Whipray Basin, Bob Allen Key, Rankin Bight, Lake Ingraham, Russell Bank, Johnson Key, Porjoe Key, Trout Creek, Little Madeira Bay, Crocodile Point, Pass Key, and Park Key. \n \n The use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_flow_TT.json b/datasets/USGS_SOFIA_hydro_flow_TT.json index 9fc341670e..4af3cc3766 100644 --- a/datasets/USGS_SOFIA_hydro_flow_TT.json +++ b/datasets/USGS_SOFIA_hydro_flow_TT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_flow_TT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The proposed study capitalizes on field expertise and existing decision support tools to assess the benefits and/or consequences of CERP hydrologic goals and projects on mangrove/marsh habitat for park and refuge lands of the Greater Everglades system. The primary goal of this study is to monitor and model surface water, groundwater, and evapotranspiration fluxes across a major hydrological barrier in south Florida (U.S. Hwy. 41, Tamiami Trail), and across the oligohaline-estuarine gradient of Ten Thousand Islands National Wildlife Refuge (TTINWR). This research will record the rate and stage of water flow under varying climatic conditions (e.g., wet and dry season) across the coastal margin of TTINWR prior to and following implementation of hydrologic restoration outlined for the Picayune Strand Restoration Project (and Southern Golden Gate Estates Hydrologic Restoration). Overall project tasks and objectives include: gaging hydrologic conditions, surveying ground and water elevations, correlating hydroperiod and plant associations, monitoring intra-annual growth response to climate and hydrology, and modeling hydrologic coupling and vegetative succession.\n\nMajor restoration projects have been proposed to restore freshwater flow across the Tamiami Trail (U.S. 41) into coastal marshes and estuaries of the northern Everglades including Big Cypress National Preserve and Ten Thousand Islands National Wildlife Refuge (TTINWR) with little or no understanding of the hydrologic coupling and potential impact to vegetation communities. Monitoring activities and models are needed to assess the hydrologic exchange across the Tamiami Trail and at the estuarine interface within the coastal watersheds of TTINWR. Under the proposed Picayune Strand Restoration Project, plugs and culverts will be installed to shunt more freshwater across the Tamiami Trail north-to-south akin to historic flows which will alter the stage, discharge, timing, and distribution of flow across the marsh/mangrove coastal margin. There is a critical need for current hydrologic and vegetation data to understand current processes and relations controlling hydroperiod, salinity, and plant succession under pre-project conditions and climate in order to build models and to predict how increasing freshwater flow and sea-level rise will impact future habitat quality and distribution. This study will establish a stratified network of gaging stations to monitor continuous water levels and salinity conditions associated with vegetation type and growth response and to produce a hydrodynamic model to predict changes in hydroperiod and salinity under different rates of freshwater inflow, pre- and post-project. Gaging stations will be surveyed to vertical datum to create a digital elevation model of both land and water surface that can be used to calibrate hydroperiod and salinity relations that control vegetation growth and succession. Model applications will be extended to predict vegetation migration and succession under changing freshwater delivery regimes and changing sea-level under projected climate change.\n\nFor additional information about this project, please contact :\n\nKen Krauss\n700 Cajundome Blvd.\nLafayette, LA, 70506\nvoice: 337 266-8882\nfax: 337 266-8592\nemail: kkrauss@usgs.gov", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_mon_joe_bay.json b/datasets/USGS_SOFIA_hydro_mon_joe_bay.json index 71b7ff01c3..af4e59f364 100644 --- a/datasets/USGS_SOFIA_hydro_mon_joe_bay.json +++ b/datasets/USGS_SOFIA_hydro_mon_joe_bay.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_mon_joe_bay", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The datasets contain values collected at 15 minute and hourly intervals for stage (water level), discharge (flow), salinity, and temperature between 1999 and 2006. The stage is measured in feet relative to NAVD 88, the discharge in cubic feet per second (cfs), temperature in degrees Celsius, and salinity in parts per thousand (ppt). The data are referenced to date and time in hours and minutes.\n\nJoe Bay is the primary hydrologic connection between the freshwater Everglades and northeastern Florida Bay. Flow and salinity monitoring by the U.S. Geological Survey (USGS) has determined that Trout Creek is the largest contributor of freshwater flow to northeastern Florida Bay and is connected to Joe Bay (Hittle and others 2001). Sources of freshwater to Joe Bay include Taylor Slough and the C-111 Canal. Hydrologic parameters such as water level, discharge, and salinity observations in conjunction with water quality sampling have been useful in determining contributions of freshwater flow from Taylor Slough and C-111 Canal to Joe Bay (Zucker 2003). Hourly salinity data has been collected at four locations in Joe Bay since May 1999. In 2001, three index velocity stations were installed at Joe Bay 2E, Joe Bay 5C, and Joe Bay 8W. The current monitoring network in Joe Bay can assist with determining the effect upstream restoration efforts have on the timing and distribution of freshwater flows into northeastern Florida Bay.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_mon_net.json b/datasets/USGS_SOFIA_hydro_mon_net.json index 678514bfcd..0f8e7e1500 100644 --- a/datasets/USGS_SOFIA_hydro_mon_net.json +++ b/datasets/USGS_SOFIA_hydro_mon_net.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_mon_net", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of the study include: (1) integration of hydrologic analysis and synthesis with biological studies; (2) separation of water level, stream flow, and salinity time series into the natural (tidal, climate) and anthropogenic components; and, (3) identification of additional areas where application of data mining techniques can address the DOI science needs in South Florida.\n\nNew technologies in environmental monitoring have made it cost effective to acquire tremendous amounts of hydrologic and water-quality data. Although these data are a valuable resource for understanding environmental systems, often there is seldom a thorough analysis of the data. The monitoring network(s) supported by the Comprehensive Everglades Restoration Plan (CERP) records tremendous amounts of data each day and the data base incorporates millions of data points describing the environmental response of the system to changing conditions. To enhance the evaluation of the CERP data base, there is an immediate need to apply new methodologies to systematically analyze the data set to answer critical questions such as relative impacts of controlled freshwater releases, tidal dynamics, and meteorological forcing on streamflow, water level, and salinity. There also is a need to integrate longer-term hydrologic data with shorter-term hydrologic data collected for biological resource studies. This study will be undertaken as a series of pilot studies to demonstrate the efficacy of data mining techniques to evaluate CERP data and address hydrologic issues important to DOI's efforts in South Florida. In addition, preliminary assessment of the complete set of hydrologic data networks for further integration and analysis using data mining techniques will be conducted.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_restoration_impacts_SW_FL.json b/datasets/USGS_SOFIA_hydro_restoration_impacts_SW_FL.json index af4c9b0e14..4d1acf46d6 100644 --- a/datasets/USGS_SOFIA_hydro_restoration_impacts_SW_FL.json +++ b/datasets/USGS_SOFIA_hydro_restoration_impacts_SW_FL.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_restoration_impacts_SW_FL", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project sought to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We described how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determined the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats.\nThe overall strategy was to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json b/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json index a615df3bf8..860e483c33 100644 --- a/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json +++ b/datasets/USGS_SOFIA_hydro_wq_ofr_00-168.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_wq_ofr_00-168", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "At present there are few reliable estimates of hydrologic fluxes between groundwater and surface water in the Everglades. This gap in hydrological investigations not only leaves the water budget of the Everglades uncertain, it also hampers progress in understanding the processes that determine mobility and transformation of contaminants, such as mercury, sulfate and nutrients. The objective of this project is to quantify hydrologic exchange fluxes between groundwater and surface water and its effects on transport of contaminants in the Everglades. The research furthermore relates surface water and ground water interactions to past, present, and proposed management of surface-water levels and flows in the Everglades. The principal research sites are the Everglades Nutrient Removal Project (ENR), Water Conservation Area 2A (WCA-2A), and the freshwater wetlands of Everglades National Park. Results are being used to quantify ground-water exchange with surface flow, and to quantify the enhancement of chemical transformations of contaminants during transport across the interface between surface water and ground water. The datasets available in the appendixes of the OFR provide information on site locations and measurements in the Everglades Nutrient Removal (ENR) area and Water Conservation Area (WCA) 2A.\n\nFor restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json b/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json index 2215e65a1f..b467768b84 100644 --- a/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json +++ b/datasets/USGS_SOFIA_hydro_wq_ofr_00-483.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydro_wq_ofr_00-483", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data in the appendixes of the report are products of an investigation that quantified interactions between ground water and surface water in Taylor Slough in Everglades National Park. In order to define basic hydrologic characteristics of the wetland, depth of wetland peat was mapped and hydraulic conductivity and vertical hydraulic gradients in peat were determined. During specific time periods representing both wet and dry conditions in the area, the distribution of major ions, nutrients, and water stable isotopes throughout the slough were determined. The purpose of the chemical measurements was to identify an environmental tracer that could be used to quantify ground-water discharge. Data available in the appendixes include site locations and hydrologic characteristics of peat and individual tables for data collected on September 22-October 2, 1997; November 10, 1997; November 19-20, 1997; December 11-17, 1997; June 3-6, 1998; July 20-23, 1998; September 20-October 5, 1999; and October 25-28, 1999. \n\nFor restoration of the Everglades to succeed there must be comprehensive knowledge about physical, chemical, and biological processes throughout the system. A key measure of success in the Everglades is the improvement or protection of water quality under changing hydrologic conditions. Although there is a basic understanding of how interactions between groundwater and surface water will affect water budgets under restoration, there is only a rudimentary understanding of how interactions between groundwater and surface water will affect water quality. Only field-oriented research and modeling can determine whether interactions between groundwater and surface water are currently storing pollutants in groundwater, how long those pollutants are likely to be stored in the aquifer, and under what changing management conditions associated with restoration will those pollutants be returned into the surface water system.", "links": [ { diff --git a/datasets/USGS_SOFIA_hydrology_data_zwp.json b/datasets/USGS_SOFIA_hydrology_data_zwp.json index cc9caa61e4..d7b07079ba 100644 --- a/datasets/USGS_SOFIA_hydrology_data_zwp.json +++ b/datasets/USGS_SOFIA_hydrology_data_zwp.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_hydrology_data_zwp", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data were produced by four separate projects: Coastal Gradients of Flow, Salintiy, and Nutrients; Freshwater Flows to Northeastern Florida Bay; Hydrologic Monitoring in Joe Bay; and Southwest Florida Coastal and Wetland Systems Monitoring. Data are available for 43 separate sites.\n\nThe Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. Hydrologic information throughout the Everglades ecosystem is key to the development of restoration strategies and for future evaluation of restoration results. There are significant hydrologic information gaps throughout the Everglades wetlands and estuaries that need to be addressed, particularly along Florida\u0092s southwest coast. Among these gaps are flow, water level, and salinity data.", "links": [ { diff --git a/datasets/USGS_SOFIA_impacts_20thcent.json b/datasets/USGS_SOFIA_impacts_20thcent.json index 516bdcd77d..828a9503b7 100644 --- a/datasets/USGS_SOFIA_impacts_20thcent.json +++ b/datasets/USGS_SOFIA_impacts_20thcent.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_impacts_20thcent", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are available as Arc/Info coverages from USGS Circular 1275. The Landuse coverages are in Florida State Plane Cordinate System, east zone, units feet, zone 3601, datum NAD27. All other coverages are in UTM Coordinate System, unit meters, zone 17, datum NAD27. \n\nSaltwater intrusion into the surficial aquifer is a direct consequence of water-management practices, concurrent agricultural and urban development, and natural drought conditions. An important part of this synthesis is to link water-management practices (canal-discharge), consumptive water use, water levels within the surficial aquifer system, chloride concentrations, ground-water discharge, and Holocene paleohistory of the Florida Bay and Biscayne Bay. For example, a series of water table maps for specific selected 5-year increments have been developed to spatially identify the areal extent where long-term water levels within the surficial aquifer have declined and to compare these changes with movement of the interface. Such changes are also being compared with changes in coastal outflows from major canals to distinquish between long-term declines caused by regional drainage and a large number of municipal pumping centers. Paleontologic data are being used to prepare maps illustrate temporal changes in salinity within the Biscayne Bay over the last 150 years. Salinity changes within the bay are largely attributed to a decrease in ground-water and surface water discharge.\n\nThis is a completed project. The GIS data layers have been updated as of 4/26/2006. The previous layers available from SOFIA have been replaced with the updated layers.", "links": [ { diff --git a/datasets/USGS_SOFIA_int_surf_water_flows_04.json b/datasets/USGS_SOFIA_int_surf_water_flows_04.json index 44bf3c6864..7e101adfd8 100644 --- a/datasets/USGS_SOFIA_int_surf_water_flows_04.json +++ b/datasets/USGS_SOFIA_int_surf_water_flows_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_int_surf_water_flows_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Proposed modified water deliveries to Indian Tribal Lands, Big Cypress National Preserve, and Water Conservation Area 3A require that flow and nutrient loads at critical points in the interior surface water network be measured. Defining the foundation for water levels, flows, and nutrient loads has become an important baseline for Storm Treatment Area 5 and 6 development, recent C-139 Basin flow re-diversions, and future L-28 Interceptor Canal de-compartmentaliztion including flow rerouting into the Big Cypress Preserve. Flow monitoring for the two primary flow routes for both L-28 Interceptor Canal and L-28 is key to developing this network. Data are available for L-28 Interceptor Canal below S-190, L-28 Canal above S-140, and L-28 Interceptor South\n.\n\nThe accurate determination of flow through the interior canal networks south of Lake Okeechobee and the C-139 basin remains critical for water budgets and regional model calibrations as defined by the Everglades Forever Act of 1994 and due to the Comprehensive Everglades Restoration Plan (CERP) initiative to reroute Big Cypress Preserve flows. The implementation of strategically located stream flow gaging points and associated data collection for nutrients has helped define future surface-water flow requirements and has provided valuable baseline flow data prior to the establishment of the recently constructed northern Storm Treatment Areas (STA\u0092s 5 and 6) and the Rotenberger Wildlife Management Area. Generating continuous flow data at selected impact points for interior basins has complemented the existing eastern coastal canal discharge network, and has allowed for more accurately timed surface-water releases while providing flow and nutrient monitoring after recent STA implementation. A unique multi-agency experiment was conducted with much success with the focus on cooperation and development of new instrumentation and acoustic flow-weight auto-sampler protocols. The original data collection and processing was provided by three separate entities at each site with responsibilities originally allocated between the U.S. Geological Survey (USGS), the Seminole Tribe of Florida, and SFWMD. USGS provides calibration, analysis and processing of acoustic velocity meters (AVM\u0092s) and side-looking Doppler systems and stage shaft encoders, SFWMD provides data loggers with real-time flow-weighted algorithms, and radio frequency (RF) telemetry instrumentation. The Seminole Tribe provides auto-sampler service and funds nutrient load analysis through the USGS Ocala Lab.", "links": [ { diff --git a/datasets/USGS_SOFIA_integrating_manatee.json b/datasets/USGS_SOFIA_integrating_manatee.json index 31e7437c8a..b0108eea4b 100644 --- a/datasets/USGS_SOFIA_integrating_manatee.json +++ b/datasets/USGS_SOFIA_integrating_manatee.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_integrating_manatee", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will extend previous studies into ENP, where manatees have not been intensively studied. To ascertain how restoration may affect the distribution and abundance of manatees in the region, an individual-based model has been under development, but completion of that model requires a hydrologic model for the rivers and estuaries affected by the accelerated Picayune Strand restoration. This study will provide integrated regional hydrologic models covering nearly the entire southwest coast below Naples, including portions of Picayune Strand and Big Cypress, providing much needed hydrologic modeling capabilities for evaluating restoration effects on coastal, estuarine, and freshwater ecosystems. This effort will enable us to model manatee response to restoration, and more adequately address science and management needs. Three tasks will be undertaken to develop the necessary components for this regional model: (1) Link the TIME hydrology model and the ATLSS manatee model to assess restoration effects in the Everglades and Picayune Strand, (2) Model changes to manatee thermal refugia due to hydrological restoration, and (3) Design and implement a regional manatee monitoring program using aerial surveys and use robust statistical analysis techniques to estimate manatee distribution and abundance before restoration.\n\nA significant population of the endangered West Indian manatee occurs in southwest Florida, throughout extensive estuarine and coastal areas within the Ten Thousand Islands (TTI; managed primarily by FWS) and Everglades National Park (ENP; managed by NPS). Planned restoration activities for the Everglades and Picayune Strand (an Acceler-8 project which discharges into TTI) may impact manatees by changing availability of freshwater for drinking, the quality and availability of seagrass forage, and the quality and availability of passive thermal basins used for refuge from lethal winter cold fronts. Changes in freshwater availability and forage are expected to result in a shift in manatee distribution, which could necessitate new management actions to reduce human-manatee interactions. Restoration also could negatively impact important passive thermal refugia by increasing cold sheet flow during winter or disrupting haloclines that maintain warm bottom layers of salty water. Recent telemetry and aerial survey studies of manatees in TTI have revealed much about their use of this area: this project will extend the study into ENP.", "links": [ { diff --git a/datasets/USGS_SOFIA_karst_model.json b/datasets/USGS_SOFIA_karst_model.json index 68323b7f65..573902c843 100644 --- a/datasets/USGS_SOFIA_karst_model.json +++ b/datasets/USGS_SOFIA_karst_model.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_karst_model", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project in being undertaken to develop a high-resolution 3-dimensional karst hydrogeologic framework of the Biscayne aquifer between Everglades National Park (ENP) and Biscayne National Park (BNP) using test coreholes, borehole geophysical logging, cyclostratigraphy, hydrostratigraphy, and hydrologic modeling. The development of an expanded conceptual karst hydrogeologic framework in this project will be used to assist development of procedures for numeric simulations to improve the monitoring and assessment of the response of the ground-water system to hydrologic changes caused by CERP-related changes in stage within the Everglades wetlands, including seepage-management pilot project implementation. Specifically, the development of procedures for ground-water modeling of the karst Biscayne aquifer in the area of Northern Shark Slough will help determine the appropriate hydrologic response to rainfall and translate that information into appropriate performance targets for input into the design and operating rules to manage water levels and flow volumes for the two Seepage Management Areas. Mapping of the karstic stratiform ground-water flow passageways in the Biscayne aquifer is recent and limited to a small area of Miami-Dade County adjacent to the Everglades wetlands. Extension of this karst framework between the Everglades wetlands and coastal Biscayne Bay will aid in the simulation of coupled ground-water and surface-water flows to Biscayne Bay. The development of procedures for modeling in the karst Biscayne aquifer will useful to the establishment of minimum flows and levels to the Biscayne Bay and seasonal flow patterns. Also, these improved procedures for simulations will assist in ecologic modeling efforts of Biscayne Bay coastal estuaries.\n\nResearch is needed to determine how planned Comprehensive Everglades Restoration Plan (CERP) seepage control actions within the triple-porosity karstic Biscayne aquifer in the general area of Northeast Shark Slough will affect ground-water flows and recharge between the Everglades wetlands and Biscayne Bay. A fundamental problem in the simulation of karst ground-water flow and solute transport is how best to represent aquifer heterogeneity as defined by the spatial distribution of porosity, permeability, and storage. The triple porosity of the Biscayne aquifer is principally: (1) matrix of interparticle and separate-vug porosity, providing much of the storage and, under dynamic conditions, diffuse-carbonate flow; (2) touching-vug porosity creating stratiform ground-water flow passageways; and (3) less common conduit porosity composed mainly of bedding plane vugs, thin solution pipes, and cavernous vugs. The objectives of this project are to: (1) build on the Lake Belt area hydrogeologic framework (recently completed by the principal investigator), mainly using cyclostratigraphy and digital optical borehole images to map porosity types and develop the triple-porosity karst framework between the Everglades wetlands and Biscayne Bay; and (2) develop procedures for numerical simulation of ground-water flow within the Biscayne aquifer multi-porosity system.", "links": [ { diff --git a/datasets/USGS_SOFIA_kendall_stable_isotopes.json b/datasets/USGS_SOFIA_kendall_stable_isotopes.json index a2cb9433b0..d261431111 100644 --- a/datasets/USGS_SOFIA_kendall_stable_isotopes.json +++ b/datasets/USGS_SOFIA_kendall_stable_isotopes.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_kendall_stable_isotopes", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site.\n\nA first step of the Everglades restoration efforts is \"getting the water right\". However, the underlying goal is actually to re-establish, as much as possible, the \"pre-development\" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major \"long-term\" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program.\n\nThis work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000.", "links": [ { diff --git a/datasets/USGS_SOFIA_kitchens_snail_kite.json b/datasets/USGS_SOFIA_kitchens_snail_kite.json index 1786db8b20..65f281b3ff 100644 --- a/datasets/USGS_SOFIA_kitchens_snail_kite.json +++ b/datasets/USGS_SOFIA_kitchens_snail_kite.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_kitchens_snail_kite", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Life history traits and the population dynamics of the snail kite may vary considerably across space and over time. Understanding the influence of environmental (spatial and temporal) variation on demographic parameters is essential to understanding the population dynamics of a given species. Recognition of information needs for management decisions and conservation strategies has resulted in an increased emphasis on correlations to spatial and temporal environmental variation in relation to demographic studies. The purpose if this study is to provide valid estimates of the demographic parameters of the snail kite, including temporal and spatial variability due to environmental factors. These parameters will be used in a predictive model of the snail kite already developed under the ATLSS Program (Mooij et al. 2002).\n\nThe snail kite (Rostrhamus sociabilis) is an endangered species that resides in the highly fluctuating ecosystem in the central and southern Florida wetlands. Many demographic traits, such as stage-dependent survival, reproduction, and movement of the snail kite vary both temporally and spatially. How these demographic parameters vary as a function of environmental conditions, hydrology in particular, is crucial for understanding how the snail kite will respond to proposed changes in water regulation in South and Central Florida. In particular, these data are needed for testing and improving the existing spatially-explicit, individual-based ATLSS snail kite model, developed by Mooij and Bennetts, which has recently been delivered to Department of Interior and other agencies (Mooij et al. 2002). From these data and the model, projections can be made on snail kite response to any hydrologic scenario. Also, continued estimates will be made of the rate of population growth. Assessing the demographic parameters is critical for identifying and evaluating the effectiveness of management actions and conservation strategies. In addition, new modeling techniques, such as structural modeling are being explored to better understand the effects of hydrology on the snail kite. The objectives of this project are the following: 1. To monitor the status of the snail kite population trends in central and southern Florida. 2. To provide estimates of demographic parameters for the spatially explicit individual-based model in ATLSS. 3. To collaborate with Dr. Wolf Mooij of the Netherlands Institute of Ecology to use snail kite data to validate the snail kite model.", "links": [ { diff --git a/datasets/USGS_SOFIA_la_florida.json b/datasets/USGS_SOFIA_la_florida.json index dec2885888..8e92cd5d44 100644 --- a/datasets/USGS_SOFIA_la_florida.json +++ b/datasets/USGS_SOFIA_la_florida.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_la_florida", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. \nWe propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. \nOur approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management.", "links": [ { diff --git a/datasets/USGS_SOFIA_lake_okee_bathy_data.json b/datasets/USGS_SOFIA_lake_okee_bathy_data.json index d4d78caaa5..d90ce66a95 100644 --- a/datasets/USGS_SOFIA_lake_okee_bathy_data.json +++ b/datasets/USGS_SOFIA_lake_okee_bathy_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_lake_okee_bathy_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data from the bathymetric mapping of Lake Okeechobee are provided in two forms: as raw data files and as elevation contour maps. \n\nHigh resolution acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day lakebed in Lake Okeechobee. This information can be used by water-management decision-makers to better assess the water capacity of the lake at various levels.", "links": [ { diff --git a/datasets/USGS_SOFIA_land_margin_ecosystems.json b/datasets/USGS_SOFIA_land_margin_ecosystems.json index bcaaecad51..801d2764c2 100644 --- a/datasets/USGS_SOFIA_land_margin_ecosystems.json +++ b/datasets/USGS_SOFIA_land_margin_ecosystems.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_land_margin_ecosystems", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration).", "links": [ { diff --git a/datasets/USGS_SOFIA_lbwfbay.json b/datasets/USGS_SOFIA_lbwfbay.json index 5be21c2342..87a5c2b8c1 100644 --- a/datasets/USGS_SOFIA_lbwfbay.json +++ b/datasets/USGS_SOFIA_lbwfbay.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_lbwfbay", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recent negative trends in the Florida Bay ecosystem have been attributed to human activities, however, neither the natural patterns of change, nor the pre-human baseline for the environment have been determined. The major objectives of this project are 1) to determine patterns of faunal and floral change over the last 150-200 years, and 2) to explore associations between biotic changes and anthropogenically-induced changes and/or natural changes in the physical environment. Environmental managers and policy makers responsible for restoring the Everglades ecosystem to a \"natural state\" can use these data to make economical and realistic decisions about restoration goals and to determine interim steps to ameliorate further damage to the ecosystem. The history of the ecosystem during the last 150-200 years is studied by analysis of faunal and floral assemblages from a series of shallow cores taken in Florida Bay. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro-and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay (Ecosystems History: Terrestrial and Fresh Water Ecosystems of Southern Florida Project and Ecosystems History: Biscayne Bay and the southeast coast Project). The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment.\n\n\nThis project is one component of an interdisciplinary study of the ecosystem history in Florida Bay. A number of USGS and other agencies scientist's are examining a series of shallow cores (~1-2 m) collected from Florida Bay. By studying the patterns of change that have occurred in the ecosystem over the last two centuries, we gain insight into the natural processes, including the natural range of variability that exists within any ecosystem. We can then determine the degree to which anthropogenic-induced change has effected the system. This understanding is critical to the restoration effort; otherwise we will be attempting to restore the system to a targeted snapshot in time, without understanding how realistic or obtainable those goals are. The ecosystem history component of the initiative will save time and money by providing realistic, economical, obtainable goals. Our component of this study is to analyze the down-core faunal and floral assemblages, over the last 150-200 years. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro- and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment.", "links": [ { diff --git a/datasets/USGS_SOFIA_levesque_field_params.json b/datasets/USGS_SOFIA_levesque_field_params.json index e534777c1c..bb9d0ee89e 100644 --- a/datasets/USGS_SOFIA_levesque_field_params.json +++ b/datasets/USGS_SOFIA_levesque_field_params.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_levesque_field_params", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997.\n\n\nThe southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the \"River of Grass\". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models.\n\nThis project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project.", "links": [ { diff --git a/datasets/USGS_SOFIA_levesque_flow.json b/datasets/USGS_SOFIA_levesque_flow.json index de89ebb7b9..350c1cc4d2 100644 --- a/datasets/USGS_SOFIA_levesque_flow.json +++ b/datasets/USGS_SOFIA_levesque_flow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_levesque_flow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1996, the U.S. Geological Survey began a 4-year study of the flow and nutrient characteristics of three major streams that drain parts of the Everglades National Park. An upward looking acoustic Doppler current profiler, a water-level sensor, and two specific conductance sensors were installed at each site. Monthly discharge measurements are made with an acoustic Doppler current profiler to develop discharge ratings. Nutrient data are collected monthly at each site. Data collected at the Broad River, Harney River, and Shark River stations include water level, water velocity, specific conductance and temperature, total and dissolved phosphorus species, pH, and dissolved oxygen. These three stations were established in 1997.\n\nThe southwest coast of Florida is part of a wilderness area with unique hydraulic characteristics that has historically been described as the \"River of Grass\". Flat terrain and lack of controlled topographic information has made it difficult to define drainage divides. Low gradients, coupled with tidal effects, create complex conditions under which to measure river flow. It has been almost thirty years since any effort has been made to monitor flow characteristics continuously in the area. Significant technological advancements have occurred during this time and this new technology can be applied to help obtain the information needed to make informed decisions about the future of this unique coastal area. Flow, nutrient concentrations, and nutrient load data will provide part of the basic information needed to understand the hydrologic and water-quality characteristics for a part of the southwest coast of Florida. The analysis of these measurements will help characterize the current conditions for the three sites and explain the relation between upgradient water levels and southwest coastal stream flows, and the possible interaction between southwest coastal waters and the waters of Florida Bay. The data can also be used as input to hydrodynamic and water-quality models.\n\nSUPPLEMENTAL INFORMATION: This project is now part of the Tides and Inflows in the Mangrove Ecotone (TIME) Model Development project.", "links": [ { diff --git a/datasets/USGS_SOFIA_mangrove_modeling_04.json b/datasets/USGS_SOFIA_mangrove_modeling_04.json index 99fa4e1a4e..b3a5339e03 100644 --- a/datasets/USGS_SOFIA_mangrove_modeling_04.json +++ b/datasets/USGS_SOFIA_mangrove_modeling_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_mangrove_modeling_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project provides an integrated suite of vegetation and nutrient resource models of the land-margin ecosystem compatible with and undergirding other restoration models of hydrology and higher trophic levels identified as critical. This modeling project fills the gaps and needs of existing restoration models, ELM and ATLSS, for a vegetation and nutrient dynamics component and complements continuing empirical studies within the land-margin ecosystem of the Everglades restoration program. The proposed work has eight major objectives: 1. Re-measurement and analysis of mangrove permanent plots 10 years after the passage of Hurricane Andrew to verify forest structure models (SELVA-MANGRO) and to re-calibrate output accordingly. 2. Map historic marsh-mangrove ecotone boundaries in selected southwest Florida regions. 3. Survey land/water datums across the intertidal and develop tidal ebb/flow synoptic functions for incorporation into SELVA-MANGRO. 4. Site quality characterization across the mangrove landscape using ground surveys and research studies, aerial photography, and aerial videography. 5. Develop external SELVA-MANGRO model linkages and WEB-based access to SELVA-MANGRO for Everglades restoration evaluations. 6. Verify HYMAN (hydrology), NUMAN (nutrient/organic matter decomposition), and FORMAN (forest structure/primary productivity) unit ecological simulation models with application to Everglades restoration evaluations. 7. Link SALSA (Hydrology BOX model) to HYMAN and FORMAN models to develop a better link between vegetation response and hydrological fluxes to the Everglades system. 8. Conduct field and greenhouse studies on nutrient biogeochemistry and determine the effects of nutrients and hydroperiod on forest biomass allocation and soil formation.\n\nLand-margin ecosystems (mangrove forests, brackish marshes, and coastal lakes) comprise some 40% of Everglades National Park. They support the important detrital foodwebs, fisheries, and wading bird colonies of the coastal zone. These systems are at the receiving end for the water management decisions made upstream which will impact the spatial distribution, timing, and quantity of freshwater flow. Additional factors which are important include disturbance history related to hurricanes and potential effects of projected sea-level rise. This project integrates the suite of spatial simulation models necessary to evaluate the response of land-margin ecosystems to upstream water management. Included are algorithms and databases of critical processes and spatio-temporal relations operating at the landscape, stand-level, and soil interface. These process and modeling studies are critical to the extended applications of the ATLSS and ELM modeling programs into the land-margin ecosystems of the Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIA_mcivor_hydroimpact.json b/datasets/USGS_SOFIA_mcivor_hydroimpact.json index a3e1831b2a..85685be735 100644 --- a/datasets/USGS_SOFIA_mcivor_hydroimpact.json +++ b/datasets/USGS_SOFIA_mcivor_hydroimpact.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_mcivor_hydroimpact", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project seeks to characterize habitat relationships between selected faunal groups and their mangrove environment on the Southwest Florida coast. We are describing how mangrove associated fish species are distributed in fringing forest habitat along a salinity gradient in the tidal portions of the Shark River; the ecology and population dynamics of diamondback terrapins in the Big Sable Creek complex; experimentally determining the preferred habitat of the specialist fish Rivulus marmoratus via field and laboratory experiments; and how the conversion of mangrove forests to intertidal mud flats in the Big Sable Creek complex has affected fish composition and use of those habitats. The overall strategy is to collect robust empirical field data on forage fish distribution and abundance that can serve multiple purposes: as performance measures in restoration assessment; as the beginning of a long-term dataset analogous to three very powerful datasets from other locales in the Greater Everglades Ecosystem: 15-20 yr from freshwater marshes, 10 yr from the mangrove ecotone of Taylor Slough and adjacent tidal creeks, and 10-12 yr from Florida Bay; and contribute to the basic ecological understanding of mangrove-associated fishes.\n\nA primary goal of Everglades restoration is the recreation of water flows and water quality more closely approximating pre-drainage conditions in both freshwater and estuarine ecosystems within Everglades National Park. These estuarine systems include submerged aquatic vegetation (SAV), mangroves (tidal forests), and brackish marshes. Four primary groups of animals are closely associated with, and often dependent upon, one or more of these ecosystems: fish and decapod crustaceans (shrimp, crabs), diamondback terrapins, manatees, and wading birds. This research focuses on fish and decapod crustaceans and diamondback terrapins in mangrove tidal forests and associated creeks. Concern about the fate of mangrove ecosystems derives from their known use as habitat for a wide range of aquatic animal species, especially fishes and decapod crustaceans of forage as well as of commercial and recreational importance. Additionally, in South Florida, mangroves on Cape Sable support a seemingly healthy population of diamondback terrapins, a species at risk in many salt marsh environments on the Gulf and Atlantic coasts. This project is being undertaken to: (1) determine what fish species make routine use of flooded fringing mangrove forests along the tidal portion of the major drainage of the historical Everglades, i.e., Shark River, and to develop empirical relationships that link species composition, density and biomass to environmental variables at those sites; (2) describe the population structure of a species of special concern, the diamondback terrapin, in mangrove tidal creek habitat within the complex of creeks that make up Big Sable Creek on Cape Sable, and secondarily to determine how this population is related to other populations on the Atlantic and Gulf coasts via DNA analysis; (3) experimentally determine via field and lab experiments the preferred habitat of a species of special concern but a common fish along the Shark River salinity gradient, mangrove rivulus; (4) determine the fisheries impact of the hurricane-induced conversion of mangrove forests to intertidal mudflats in the Big Sable Creek complex.", "links": [ { diff --git a/datasets/USGS_SOFIA_mdcsoil.json b/datasets/USGS_SOFIA_mdcsoil.json index 5e3bfd93d5..bcd6c457c3 100644 --- a/datasets/USGS_SOFIA_mdcsoil.json +++ b/datasets/USGS_SOFIA_mdcsoil.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_mdcsoil", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets consist of two files, an ARC/INFO shape file with associated files and an export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale.\n \n Getting geographic information into a form that can be analyzed in a Geographic Information System (GIS) has always been a labor-intensive process. Graphic information was historically captured using variations of manual digitizing techniques. Users either digitized directly from printed materials on digitizing tablets or tables or by a variation of heads-up digitizing from scanned graphics displayed on computer monitors. Data collection involves considerable interaction between the user and a computer to capture and manipulate graphical data into a GIS layers. By using inexpensive image processing software to process and manipulate scanned images before processing these images in the GIS, features can be semi-automatically extracted from the scanned graphics, virtually eliminating the process of manual delineation. Common photo editing techniques combined with GIS expertise can dramatically decrease the time required to collect GIS data layers.\n \n The mentioning of specific software brands or registered trademarks does not constitute a commercial endorsement; their mention is done for clarity only. Mention of software products in the description of graphic processing techniques should be viewed as a use of available tools and not a recommendation for a software product.", "links": [ { diff --git a/datasets/USGS_SOFIA_metholms.json b/datasets/USGS_SOFIA_metholms.json index 39936d802c..c8ebaabf60 100644 --- a/datasets/USGS_SOFIA_metholms.json +++ b/datasets/USGS_SOFIA_metholms.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metholms", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to manage an ecosystem, it is imperative to define the rate at which ecologic, physical and chemical changes have occurred. The lack of historical records documenting ecological changes dictates that other methods are used to measure the rate of change. A common method of \"dating\" change is to measure the decay of naturally occurring radioactive nuclides.\n\nThe use of radioactive isotopes is founded on the known physical property of radioactive material, the half-life. A half-life of an isotope is the amount of time it takes for half of a given number of atoms to \"decay\" to another element. The age of objects that contain radioactive isotopes with known half-lives can be calculated by determining the percent of the remaining radioactive material. To use this method successfully certain other prerequisites must be met. These are: 1. the chemistry of the nuclide (element) is known; 2. Once the nuclide is incorporated into the substrate the only change is radioactive decay, and 3. in order to be useful, it is relatively easy to measure.", "links": [ { diff --git a/datasets/USGS_SOFIA_metish.json b/datasets/USGS_SOFIA_metish.json index bb84e63e78..f1f20b6d07 100644 --- a/datasets/USGS_SOFIA_metish.json +++ b/datasets/USGS_SOFIA_metish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Historical changes in South Florida related to rapid population growth in the early to mid-1900's have led to significant alteration of the natural hydrocycles and water quality of Florida and Biscayne Bays. The Biscayne Bay ecosystem shows increasing signs of distress; declines in fisheries, increased pollution, and dramatic changes in nearshore vegetation. Northern and central Biscayne Bay are strongly affected by the urban development associated with the growth of Miami. Southern Biscayne Bay is influenced by drainage from the Everglades, which has been altered by canals and agricultural activities. Restoration and preservation of Biscayne Bay and Biscayne National Park are dependent on a comprehensive understanding of the linkages between the hydrologic system and the bay ecosystem, and of the natural versus human-induced variability of the ecosystem. In this project modern surface samples were collected from 26 sites in Biscayne Bay. The primary biota analyzed were 1) benthic foraminifera, 2) ostracodes, 3) mollusks, 4) dinoflagellate cysts, 5) pollen and macro-plant material. The distribution of the biota was quantified to determine relationships with environmental conditions. These results were used to interpret historical faunal and floral changes recorded in shallow sediment cores. Water samples, ostracode and foraminiferal shells collected from the modern sediment samples are being analyzed for trace element geochemistry to derive a calibration equation to calculate absolute salinity in down-core samples. Shallow cores (1-2 meters) were collected along a north-south transect within Biscayne Bay for analysis of the downcore faunal and floral assemblages over the last 150 years. Quantitative down-core assemblage diagrams will be drawn up and the various faunal and floral data will be compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment will be made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora; data from Biscayne Bay will supplement and be correlated to onshore data and to data from Florida Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. Living assemblages will be collected twice a year to provide data on habitat distribution, preferred substrates and seasonality of the living biota for interpretation of the down-core assemblages.\n\n\nRecent negative trends have been observed in the ecosystem of Florida Bay, including algal blooms, seagrass die-offs, and declining numbers or shellfish, adversely affecting the fishing and tourist industries. Many theories of cause and effect exist to explain the adverse trends, but these theories have not been scientifically tested. Prior to finalizing plans for ecosystem restoration, the relative roles of human activities versus natural ecosystem variations need to be established. This project addresses this need by focusing on two primary goals. First, to determine the characteristics of the ecosystem prior to significant human alteration, including the natural range of variation in the system; this establishes the baseline for restoration. Second, to establish the extent, range, and timing of changes to the ecosystem over approximately the last 150 years and to determine if these changes correlate to human alteration, meteorological patterns, or a combination of factors. In addition, data on recovery times of certain components of the ecosystem will be obtained allowing biologists to estimate responses to proposed restoration efforts. This project is planned as a five year study, to be completed in 2000. This project is one segment in a group of coordinated USGS projects examining the biota, geochronology, geochemistry, sedimentology, and hydrology of southern Florida, Florida Bay and the surrounding areas. Data are being compiled from terrestrial, marine, and freshwater environments in onshore and offshore sites in order to reconstruct the ecosystem history for the entire region over the last 150 years.", "links": [ { diff --git a/datasets/USGS_SOFIA_metjen.json b/datasets/USGS_SOFIA_metjen.json index e3a12082c8..78dd6797c2 100644 --- a/datasets/USGS_SOFIA_metjen.json +++ b/datasets/USGS_SOFIA_metjen.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metjen", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flows in and through the Everglades wetlands and bordering subtidal embayments are often characterized by very low velocities that are driven or controlled at various scales by wind, gravity, pressure, and vegetative resistance. Little is known about the effect of wind on water movement in these environments, and no focused efforts are currently underway to assess its importance. This project will examine the effect of wind on surface-water flows.\n\nWith insight into the functional relationships and into the scales at which wind forcing data must be collected for model input gained from the field efforts, the treatment of wind forcing in models can be improved. This, in turn, can lead to enhanced understanding of the significance of wind effects on flow, transport, and horizontal mixing in the SICS (Southern inland coastal systems of Dade County) study area.\n\nThis project has been integrated into the TIME project (http://time.er.usgs.gov/)", "links": [ { diff --git a/datasets/USGS_SOFIA_metkotra.json b/datasets/USGS_SOFIA_metkotra.json index 72e822266f..117806ad27 100644 --- a/datasets/USGS_SOFIA_metkotra.json +++ b/datasets/USGS_SOFIA_metkotra.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metkotra", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Human activities have led to the deterioration of the productivity, biodiversity, and stability of the south Florida ecosystem. The fate of anthropogenic contaminants incorporated into the organic-rich sediments is not fully understood. Physical, chemical, and biological processes may remobilize some of the contaminants and reintroduce them into water, atmosphere, and the biological community. Other contaminants may be transformed during diagenesis and remain in surficial materials until the system is disturbed. This project examined the occurrence and cycling of mercury and metals in organic-rich sediments, pore fluids, and plants at selected sites in south Florida.\n\nAn understanding of the relationship between diagenesis, concentration, speciation, and historical variation of elements of environmental significance is essential for planners in developing long-term remediation and management strategies for wetlands of south Florida. A better understanding of the controls on the cycling of these elements is critical for making informed decisions regarding the regulation of water levels and anticipating the effect of water regulation.", "links": [ { diff --git a/datasets/USGS_SOFIA_metlang.json b/datasets/USGS_SOFIA_metlang.json index 9a8708e097..d962aa6dc7 100644 --- a/datasets/USGS_SOFIA_metlang.json +++ b/datasets/USGS_SOFIA_metlang.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metlang", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this project was to quantify the rates of ground water discharge to Biscayne Bay. This was achieved through the collection of field data and the development of two- and three-dimensional numerical models to simulate variable-density ground water flow. As part of this project, the SEAWAT code, which represents variable-density ground water flow, was developed to simulate ground water discharge. Monitoring wells were installed offshore and inland along three transects perpendicular to the shore of Biscayne Bay.\n\nSeveral surveys during the late 19th and early 20th centuries describe the occurrence of large quantities of ground-water flow to Biscayne Bay by way of underground channels or conduits. The construction of the drainage and flood-control network in southeastern Florida began during the early 20th century for the purpose of managing the water resources of the area. This drainage canal network affected the hydrologic pattern in southeastern Florida by replacing sheetflow with canal flow, thereby significantly reducing the altitude of the water table and diminishing ground-water flow to Biscayne Bay. This led to the inland movement of the saltwater interface. In 1960, there was still ground water discharging to the bottom of Biscayne Bay, but no quantification of the amount of ground-water discharges to the bay was made at the time. In 1967, discharges to the bay in the Cutler Ridge area were estimated by assuming Darcian flow and considering the tidal cycle. It was estimated that 210 cubic feet per square foot of flow section area was discharged during a 12.5-hour tidal cycle. The U.S. Army Corps of Engineers (COE) is planning to construct gated spillways and culverts to allow for the restoration of natural sheetflow conditions to Everglades National Park (ENP). These proposed changes may further affect the hydrologic conditions of ENP and other parts of the ecosystem, thus leading to the following questions: (1) Is ground water flowing to Biscayne Bay a significant component of the water budget in south Florida? (2) Would the quantity of ground water flowing to Biscayne Bay be greatly affected by changes in the operation of gates and control structures in canals? (3) How much change in ground-water discharges to Biscayne Bay has occurred due to modifications to the hydrologic system? Quantification of ground water flowing to Biscayne Bay is needed as input to two interagency projects: the South Florida Ecosystem Restoration Program and the Biscayne Bay Feasibility Study. The principal objective of the Biscayne Bay Feasibility Study is to investigate ongoing construction/dredging projects and propose solutions to alleviate adverse factors that affect the bay and to aid in the development of guidelines for future management of the natural resources of Biscayne Bay. The Biscayne Bay Feasibility Study includes the implementation of a surface-water circulation model which will be developed by the Waterway Experimental Station of the COE. Quantification of ground-water discharges to Biscayne Bay is needed as input to the bay water circulation model.", "links": [ { diff --git a/datasets/USGS_SOFIA_metlietz.json b/datasets/USGS_SOFIA_metlietz.json index 2fd67774fa..2b69de4177 100644 --- a/datasets/USGS_SOFIA_metlietz.json +++ b/datasets/USGS_SOFIA_metlietz.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metlietz", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project were threefold: 1) to determine if historical water-quality data collected as grab samples at 0.5 and c1.0 m below the surface near the centroid of flow adequately represent stream cross-sectional chemistry, 2) to develop reliable estimates of nitrogen and phosphorus loads for east coast canals based on statistical models developed from utilizing the techniques of ordinary least squares regression, and 3) to summarize water-quality data and determine temporal trends for water-quality constituents at two sites that are strategic to Biscayne Bay and the south Florida ecosystem. During phase 1 of the project an intensive field sampling and data collection effort was undertaken. Depth-integrated samples were collected by the equal-width-increment method as well as grab samples at each canal. During Phase 2 data analysis was done. Nutrient data were collected upstream of 15 coastal control structures in Miami-Dade County. Samples were collected over a typical hydrologic period during various flow conditions. Sampling began at 5 sites in May 1996 and at 10 sites in October 1996. Constituents collected included ammonia, nitrite, nitrate, orthophosphate, and total phosphorus.\n\nOf major concern in many coastal areas around the Nation is the ecological health of bays and estuaries. A common problem in many of these areas is increased nutrient loads as a result of agricultural, commercial, industrial, and urban processes. Biscayne Bay is a shallow subtropical estuary along the southern coast of Florida. The Biscayne Bay ecosystem provides an aquatic environment that is a habitat to a diverse array of plant and animal communities. Nutrients are essential compounds for the growth and maintenance of all organisms and especially for the productivity of aquatic environments. Nitrogen and phosphorus compounds are especially important to seagrass, macroalgae, and phytoplankton. However, heavy nutrient loads to bays and estuaries can result in conditions conducive to eutrophication and the attendant problems of algal blooms and high phytoplankton productivity. Additionally, reduced light penetration in the water column because of phytoplankton blooms can adversely affect seagrasses, which many commercial and sport fish rely on for their habitat. Providing reliable estimates of nonpoint source nutrient loads to Biscayne Bay is important to the development of nutrient budgets as well as input to eutrophication models. Understanding the effects of these nutrient loads is a necessary initial step in planning restoration of the ecological health of Biscayne Bay. Nutrient data have been collected from the east coast canals for many years by various government agencies. Much of the data collected have been from grab samples at 0.5 or 1.0 meter below the stream surface near the centroid of flow. The degree to which these samples adequately represent nitrogen and phosphorus concentrations within the water column of the canals of south Florida is presently unknown and limits confidence in loading estimates. Furthermore, the relation between discharge and nutrient concentration that occurs in natural uncontrolled streams in other parts of the Nation may not apply to the artificially controlled canals of south Florida. Both of these issues need to be addressed to develop nutrient budgets and to plan effective restoration strategy now and in the future.", "links": [ { diff --git a/datasets/USGS_SOFIA_metorem.json b/datasets/USGS_SOFIA_metorem.json index 53c0137a36..d882a756e0 100644 --- a/datasets/USGS_SOFIA_metorem.json +++ b/datasets/USGS_SOFIA_metorem.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metorem", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project is examining (1) sources of nutrients (nitrogen and phosphorus), sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes in the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Major project objectives are as follows - (1) use isotope and other tracer methods to examine the major sources of nutrients, carbon, and sulfur to the south Florida ecosystem, (2) use geochemical methods to examine the major forms of nutrients, carbon, and sulfur in the sediments, the stabilities of the observed chemical species, and sinks of these elements in the sediments, (3) examine the biogeochemical processes controlling the cycling of nutrients, carbon, and sulfur in the ecosystem, and use geochemical modeling of porewater and sediment chemical data to determine the rates of these recycling processes, (4) develop geochemical sediment budgets for nutrients, carbon, and sulfur on a regional scale, including accumulation rates of these elements in the sediments, fluxes out of the sediments, and sequestration rates, (5) collaborate with mercury projects (USGS ACME team and others) to examine the role of sulfur and sulfate reduction in the production of methyl mercury in wetlands of south Florida, and the bioaccumulation of mercury in fish and other wildlife, (6) develop a geochemical history of the south Florida ecosystem from an examination of changes downcore in the concentration, speciation, and isotopic composition of nutrients, carbon and sulfur; use organic marker compounds and stable isotopes to develop a model of seagrass history in Florida Bay, (7) incorporate information from nutrient studies in overall ecosystem nutrient model, and results from sulfur studies in ecosystem mercury model.\n\nThis project addresses three major areas of interest to land and water managers in south Florida: (1) nutrients and eutrophication of the Everglades, (2) the role of sulfur in the methylation of mercury and its bioaccumulation, and (3) the geochemical history of the south Florida ecosystem. Our nutrient studies are focused on using isotope methods to examine the sources of nutrients to the ecosystem, and on using sediment and porewater geochemical studies to determine the rates of nutrient recycling and nutrient sinks within the sediments. A nutrient sediment budget will be developed for incorporation in the nutrient model for the ecosystem. Results will assist managers in determining the fate of excess nutrients (especially phosphorus) stored in contaminated sediments (e.g. will the excess nutrients be buried, or recycled for movement further south into protected areas). The sediment studies will also provide managers with information relevant to the effectiveness of planned remediation methods. Studies of sulfur within the ecosystem relate directly to the issue of methyl mercury production and bioaccumulation, a serious threat to both wildlife and to the human population. Microbial sulfate reduction in wetlands (an anaerobic process) is the primary driver of mercury methylation. Understanding the source of sulfate to the wetlands of south Florida may be a key to understanding why mercury methylation rates are so high, and on how remediation efforts in the Everglades may impact mercury methylation rates. We are also examining the sulfur geochemistry of sediments on a regional scale, with emphasis on areas that are methyl mercury \"hotspots\". We are emphasizing co-sampling with USGS mercury researchers (ACME team). The geochemical history component of this project will provide information on historical changes in the chemical conditions existing in south Florida wetlands. This will provide wetland managers with baseline information on the water quality goals needed to achieve \"restoration\" of the ecosystem. It will also provide land managers with an estimate of the range of water quality and environmental conditions that have affected the south Florida ecosystem in the past. Geochemical history data in combination with information from paleontologic studies of the USGS paleoecology group and others will also provide insights on how organisms in the south Florida ecosystem have responded to environmental change in the past, and predict how these organisms will likely respond to changes in the ecosystem resulting from restoration efforts.", "links": [ { diff --git a/datasets/USGS_SOFIA_metroys.json b/datasets/USGS_SOFIA_metroys.json index ccaa290d5f..a3c87d40f6 100644 --- a/datasets/USGS_SOFIA_metroys.json +++ b/datasets/USGS_SOFIA_metroys.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metroys", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground-water flow models were developed to calculate a water budget, including seepage losses, for a transect perpendicular to Levee 30. Data required for input to and calibration of the models were obtained from: (1) previous studies conducted in the area, (2) analysis of a geologic core and geophysical logs from a new monitor well drilled along the transect, (3) ground-water-level data from monitor wells along the transect, (4) surface-water-stage data in Water Conservation Area 3B and in the Levee 30 canal, (5) discharge measurement made at several locations under varying conditions in the Levee 30 canal, and (6) vertical seepage fluxes between surfacewater and groundwater in Water Conservation Area 3B obtained from seepage meters.\n\nDetermining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", "links": [ { diff --git a/datasets/USGS_SOFIA_metschaf.json b/datasets/USGS_SOFIA_metschaf.json index bf434b5a3c..833c83bb36 100644 --- a/datasets/USGS_SOFIA_metschaf.json +++ b/datasets/USGS_SOFIA_metschaf.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metschaf", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Significant canal and wetland flow exchanges can potentially occur along the southwest overbank area of canal C-111 between hydraulic control structures S-18C and S-197. This coupled flow system is of particular concern to restoration efforts in that it provides a pathway for fresh water to nearshore embayments in Florida Bay. New construction modifications and operational strategies proposed for C-111 under the Central and Southern Florida \"Restudy\" Project are intended to enhance sheet flow to these subtidal embayments. The objectives of the canal and wetland flow/transport interaction project were to (1) develop numerical techniques and algorithms to facilitate the coupling of existing generic models for improved simulation of canal and wetland interactions, (2) translate recent findings of ongoing process studies within the South Florida Ecosystem Program (SFEP) into new mathematical formulations, empirical expressions, and numerical approximations to enhance generic simulation model capabilities for the south Florida ecosystem, (3) investigate new instrument capabilities and field deployment approaches to collect the refined data needed to identify and quantify the important flow-controlling forces and landscape features for model implementation, (4) integrate process-study findings and the results of physiographic mapping and remote sensing efforts specific to the C-111 basin into a numerical simulation model of the interconnected canal and wetland flow system, and (5) use the resultant model and data to study, evaluate, and demonstrate the significance of driving forces relative to controlling flow exchanges between canal C-111 and its bordering wetlands. Discharge data for Tamiami Canal are also available for water years 1986-1999, 2000, and 2001.\n\nA complex network of canals, levees, and control structures, designed to control flooding and provide a continuous supply of fresh water for household and agricultural use, has altered naturally occurring flow patterns through the Everglades and into Florida Bay. Quantification of dynamic flow conditions within the south Florida ecosystem is vital to assessing implications of the residence time of water, potentially nutrient-enriched (with nitrates or phosphates) or contaminant-laden (with metals or pesticides), that can alter plant life and affect biological communities. Improved numerical techniques are needed not only to more accurately evaluate discrete forces governing flow in the canals and wetlands but also to analyze their complex interaction in order to facilitate coupled representation of transport processes. Flow and transport processes are integrally linked meaning that precise quantification of the fluid dynamics is required to accurately evaluate the transport of waterborne constituents. Robust models that employ highly accurate numerical methods to invoke coupled solution of the most appropriately formulated and representative equations governing flow and transport processes are needed. Through strategic use of a model, cause-and-effect relations between discharge sources, flow magnitudes, transport processes, and changes in vegetation and biota can be systematically investigated. The effects of driving forces on nutrient cycling and contaminant transport can then be quantified, evaluated, and more effectively factored into the development of remedial management plans. A well-developed model can be used to evaluate newly devised plans to improve freshwater deliveries to Florida Bay prior to implementation.\n\nThis project ended in 1999. Related work can be found at http://time.er.usgs.gov/.\n\nFor additional information about this project contact either:\nEric Swain, edswain@usgs.gov, 954 377-5925 or Chris Langevin, langevin@usgs.gov, 954 377-5917", "links": [ { diff --git a/datasets/USGS_SOFIA_metweed.json b/datasets/USGS_SOFIA_metweed.json index 18d765de0a..37f59a70c0 100644 --- a/datasets/USGS_SOFIA_metweed.json +++ b/datasets/USGS_SOFIA_metweed.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_metweed", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this project is to provide to hydrologic modelers a three-dimensional database of the geologic and hydrologic properties of the sediments and rocks of the surficial aquifer system in southwest Florida, in Collier and Monroe Counties. Emphasis will be placed on the geologic framework of the aquifer. Two independent methods are used in this study to estimate the age of the aquifer rocks and sediments. Samples from cores will be examined for fossil dinoflagellate cysts, pollen, mollusks, foraminifers, and ostracodes, and their age determined by correlation to other distant sites that have been dated isotopically. Age also will be estimated by the isotopic composition of strontium in unaltered shells. The ratio of the stable isotopes of strontium in the oceans has varied over geologic time such that, in the last 40 million years, there has been a unique relation between age and isotopic composition. Marine invertebrates incorporate the strontium isotopic ratio of the ocean into their shells as they grow, thereby preserving evidence of their age. Geophysical logs provide a continuous downhole record of the properties of the rocks that form the aquifer. They are especially valuable in providing physical and chemical properties of the corehole where particular intervals of core recovery are poor. Also, they allow extension of hydrologic test data from discrete samples to the rest of the core. Geophysical logs, combined with aquifer water properties and flow measurements, will be used to relate large-scale ground-water circulation to the distribution of hydrologic properties of the aquifer. For example, flowmeter logs can confirm that the most permeable intervals, as inferred from core measurements, coincide with the intervals that conduct the most flow in the vicinity of test wells. Geophysical logs also will indicate which confining units act to separate the aquifer system into discrete aquifers having different water quality and hydraulic head.\n\nRestoration and management of the south Florida ecosystem will be guided by hydrologic models that simulate water flowing through the wetlands and shallow subsurface aquifers beneath them. The restoration of the ecosystem is, essentially, the restoration of the natural hydrologic system. As surface water is re-diverted from manmade canals to its more natural sstate and overland flow, several changes are predicted to occurr. First, because water flowing over land moves more slowly than in canals, overland flow should remain in the wetlnad ecosystem for a longer period each year. Second, as flowing water spreads out over the wetlands, recharge to the shallow aquifers should increase as more of that water infiltrates into the ground. The U.S. Corps of Engineers (USACE) and the South Florida Water Management District (SFWMD) will use hydrologic models to anticipate the consequences of these proposed restoration plans. This reseaerch project is designed to provide essential subsurface data to improve hydrologic models for land and water managers in southwest Florida where subsurface information is lacking. Obtaining hydrogeological data involves core drilling, corehole testing, and rock and sediment analysis. Understanding the geologic history of the sediments and rocks of the aquifer system is necessary to place the hydrologic properties of that system into a geologic framework.", "links": [ { diff --git a/datasets/USGS_SOFIA_mmarvin.json b/datasets/USGS_SOFIA_mmarvin.json index bd25125616..962ef36b6e 100644 --- a/datasets/USGS_SOFIA_mmarvin.json +++ b/datasets/USGS_SOFIA_mmarvin.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_mmarvin", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Methylmercury (MeHg) degradation was investigated along an eutrophication gradient in the Florida Everglades by quantifying 14CH4 and 14CO2 production after incubation of anaerobic sediments with 14C-MeHg. Degradation rate constants (k) were consistently <=0.1 per day, and decreased with sediment depth. Higher k values were observed when shorter incubation times and lower MeHg amendment levels were used, and k increased two-fold as in-situ MeHg concentrations were approached. The average floc layer k was 0.046 +/- 0.023/ d (n=17) for 1-2 day incubations. In-situ degradation rates were estimated to be 0.02 to 0.5 ng MeHg/g dry sed/d, increasing from eutrophied to pristine areas. Nitrate-respiring bacteria did not demethylate MeHg, and NO3- addition partially inhibited degradation in some cases. MeHg degradation rates were not affected by PO4-3 addition. 14CO2 production in all samples indicated that oxidative demethylation (OD) was an important degradation mechanism. OD occurred over five orders of magnitude of applied MeHg concentration, with lowest limits (1-18 ng MeHg/g dry sediment) in the range of in-situ MeHg levels. Sulfate reducers and methanogens were the primary agents of anaerobic OD, although it is suggested that methanogens dominate degradation at in-situ MeHg concentrations. Specific pathways of OD by these two microbial groups are proposed.\n\nThe objective of this research is to provide ecosystem managers with MeHg degradation rate data from a number of study sites that represent a diversity of hydrologic and nutrient regimes common to the Everglades, and to forge a better understanding of the microbial and geochemical controls regulating MeHg degradation in this system.", "links": [ { diff --git a/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json b/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json index 30aaa42f08..ebbe761e4d 100644 --- a/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json +++ b/datasets/USGS_SOFIA_monitor_sav_rs_fb_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_monitor_sav_rs_fb_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This pilot study will focus on Florida Bay, a region that suffered the loss of 40,000 ha of turtle grass in a die-off event that began in 1987, and a small, localized die-off in 1999. These events were well documented and provide a baseline for testing methods of monitoring grass beds remotely. Remote sensing data, including aerial photos and satellite imagery data, and data extracted from sediment cores will be used to examine the long-term sequences of events leading up to seagrass die-off events. The objectives of this pilot study are to develop a methodology for monitoring spatial and temporal changes in sub-aquatic vegetation using remote sensing, satellite imagery, and aerial photography, and to analyze potential causes of seagrass die-off using geographic, geologic and biologic tools. The ultimate goal is to develop a method for forecasting potential sea-grass die-offs and to determine if remediation efforts would be cost-effective. Florida Bay is selected for the pilot study because the thorough documentation of the 1987-1988 die-off event provides a baseline for examining data preceding and succeeding the event. In addition, a small well studied die-off occurred in 1999-2000 at Barnes Key in Florida Bay. A 10-15 km2 portion of Florida Bay that encompasses areas affected by the 1987 and 1999 die-offs will be analyzed for this pilot study. Current remotely sensed data, aerial photos and satellite images from this area will be used to test different platforms, determine detection limits, and to attempt to isolate distinct signals for different types of vegetation. When ground-truthing is completed, archived remotely sensed data and/or aerial photographs can then be used to examine the sequences of events leading up to the die-offs. The remotely sensed data can be compared and compiled with the data collected by seagrass biologists in 1987 and 1999, and to sediment core data collected at the sites of seagrass die-off. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota. The geologic, biologic and remotely sensed data will be integrated and analyzed to determine the patterns of change and sequences of events that occur in healthy seagrass beds and in beds undergoing a die-off. Several remote sensor types will be compared in this study to determine the ideal sensor bands and spatial resolution necessary to detect and monitor the health of seagrass beds. The sensors to be tested include Landsat 7 (30m multi-spectral spatial resolution), ASTER (15 and 30m multi-spectral), Quickbird (2.5m multi-spectral and <1m panchromatic), and large-scale aerial photography (anticipated spatial resolution .25m with visible and near-infrared bands). Imagery with bands in the blue wavelength may help to penetrate water and infrared or near-infrared bands are predicted to perform better for resolving vegetation. It is theorized that through a combination of blue, and infrared bands and higher spatial resolution it will be possible to map the extent of seagrass beds. Although Landsat ETM+ 7 has several bands in desirable wavelengths, this sensor is predicted to be too course of a dataset to resolve individual seagrass beds. Landsat ETM+ may be used to develop an index of chlorophyll values that may be translated into a measure of seagrass health. ASTER\u0092s multiple infrared bands and increased spatial resolution may be successful in distinguishing between the types of vegetation, but these bands are not designed for water penetration. Higher spatial resolution platforms are predicted to have better mapping capabilities. The Quickbird sensor can provide 2.5m spatial resolution with multi-spectral capability. The multi-spectral bands include a blue band for water penetration and a near-infrared band for vegetation detection. Finally, aerial photography flown at low altitude represents the highest spatial resolution (.25m) and can be collected in visible and near-infrared to allow processing of blue and infrared bands. A combination of sensor types to maximize both spatial resolution and spectral signatures may provide the best solution for mapping and monitoring seagrass beds.\n \n Seagrass beds are essential components of any marine ecosystem because they provide feeding grounds, nurseries, and habitats for many forms of marine life, including commercially valuable species; they are important foraging grounds for migratory birds; and they anchor sediments and impede resuspension and coastal erosion during storms. This valuable natural resource has been suffering die-offs around the world in recent years, yet the causes of these die-offs are undetermined. The purpose of this project is to use a number of tools - geographic, geologic, and biologic - to investigate the causes of seagrass die-offs and to develop methods that can be used to monitor the health of seagrass meadows. If we understand the causes of the die-offs and can easily monitor the health of seagrass beds, then resource managers have a tool for forecasting areas of potential die-offs. By integrating remotely sensed data, biological data and core data the long-term (decadalscale) sequences of events leading up to die-off events can be examined. These data can be contrasted to normal seasonal changes that occur in healthy grass beds to establish criteria for identifying areas that may be on the threshold of experiencing a decline. This provides a very powerful predictive tool for resource managers. By examining the causes of die-off and the natural patterns of change in seagrass meadows over biologically significant periods of time we can determine the components of change that may be related to anthropogenic activities versus natural cycles of change. This information would allow resource managers to make informed decisions about the cost-effectiveness of and mechanisms for remediation, if an area of decline was identified via the predictive tool. Once the predictive tools and potential remediation tools have been developed in this pilot study, in well-studied seagrass meadows, the tools can be applied to threatened coastal ecosystems around the country and worldwide.", "links": [ { diff --git a/datasets/USGS_SOFIA_nssmet.json b/datasets/USGS_SOFIA_nssmet.json index 5ef0499f2d..fbbf895c93 100644 --- a/datasets/USGS_SOFIA_nssmet.json +++ b/datasets/USGS_SOFIA_nssmet.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_nssmet", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Methylmercury, a neurotoxin, is found in the game fish of south Florida. Samples of periphyton, the assemblage of microalgae that live in shallow submerged substrates which is home to, and food for, creatures that are the foundation of the food chain, have concentrations of methylmercury that range from non-detectable to tenths of a part per million on a dry weight basis. The report produced from this project presents data for samples of periphyton and water collected in 1995 and 1996 from Water Conservation Areas, the Big Cypress National Preserve, and the Everglades National Park in south Florida. Periphyton samples were analyzed for concentrations of total mercury, methylmercury, nitrogen, phosphorus, organic carbon, and inorganic carbon. Water-column samples collected on the same dates as the periphyton samples were analyzed for concentrations of major ions.\n\nThe goal of this project is to answer the question - How does mercury produced in the aquatic environment enter the food chain and become part of the body burden of animals such as game fish in south Florida?", "links": [ { diff --git a/datasets/USGS_SOFIA_nuts_S_orgmat_04.json b/datasets/USGS_SOFIA_nuts_S_orgmat_04.json index f1141faa25..997c577adb 100644 --- a/datasets/USGS_SOFIA_nuts_S_orgmat_04.json +++ b/datasets/USGS_SOFIA_nuts_S_orgmat_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_nuts_S_orgmat_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The scientific focus of this project is to examine the complex interactions (synergistic and antagonistic) of contaminants (externally derived nutrients, mercury, sulfur, pesticides, herbicides, polycyclic aromatic and aliphatic hydrocarbons, and other metals), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The major objectives of this project are to use an integrated biogeochemical approach to examine: (1) anthropogenic-induced changes in the water chemistry of the Everglades ecosystem, (2) biogeochemical processes within the ecosystem affecting water chemistry, and (3) the predicted impacts of restoration efforts on water chemistry. The project uses a combination of field investigations, experimental approaches (mesocosm experiments in the ecosystem, and controlled laboratory experiments), and modeling to achieve these objectives. Contaminants of concern will include nutrients, sulfur, mercury, organic compounds, and other metals. Protocols for the collection of samples and chemical analyses developed during earlier studies will be employed in these efforts. Integration of the individual tasks within the project is achieved by colocation of field sampling sites, and cooperative planning and execution of laboratory and mesocosm experiments. Results from all tasks within the project are archived within a single database for use in Decision Management GIS systems and ecosystem models.\n\nThis project is an integration of a number of individual but interrelated tasks that address environmental impacts in the south Florida ecosystem using geochemical approaches. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological, and chemical components of this ecosystem. However, it reamins uncertain what overall effects will occur as these components react to the perturbations especially of the biological and chemical components and toward what type of \"new ecosystem\" the Everglades will evolve. Results of these geochemical investigations will provide the critical elements for building ecosystem models and screening-level risk assessment for contaminants in the ecosystem.", "links": [ { diff --git a/datasets/USGS_SOFIA_orem_fb_sed_geochem.json b/datasets/USGS_SOFIA_orem_fb_sed_geochem.json index 9c62b0c06a..c69a0c71f2 100644 --- a/datasets/USGS_SOFIA_orem_fb_sed_geochem.json +++ b/datasets/USGS_SOFIA_orem_fb_sed_geochem.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_orem_fb_sed_geochem", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the sample ID, depth (cm), sediment size, fine sediment fraction (<60m), total C %, organic C %, total N %, total P %, C/N, C/P, and N/P.\n\nThis project is examining (1) sources of nutrients, sulfur, and carbon to wetlands of south Florida, (2) the important role of chemical and biological processes to the wetland sediments (biogeochemical processes) in the cycling of these elements, and (3) the ultimate fate (i.e. sinks) of these elements in the ecosystem. The focus on nutrients and carbon reflects the problem of eutrophication in the northern Everglades, where excess phosphorus from agricultural runoff has dramatically altered the biology of the ecosystem. Results will be used by land and water managers to predict the fate of nutrients (especially phosphorus) in contaminated areas of the Everglades, and to evaluate the long-term effectiveness of buffer wetlands being constructed as nutrient removal areas. Studies of sulfur in the ecosystem are important for understanding the processes involved in mercury methylation in the Everglades. Methyl mercury (a potent neurotoxin) poses a severe health risk to organisms in the south Florida ecosystem and to humans. Sediment studies conducted by this project will also be used to construct a geochemical history of the ecosystem. An understanding of past changes in the geochemical environment of south Florida provides land and water managers with baseline information on what water quality goals for the ecosystem should be, and on how the ecosystem has responded to past environmental change and will likely respond to the changes that will accompany restoration.", "links": [ { diff --git a/datasets/USGS_SOFIA_panther_refuge_hydro.json b/datasets/USGS_SOFIA_panther_refuge_hydro.json index 47bcbebb4a..0769dfda1f 100644 --- a/datasets/USGS_SOFIA_panther_refuge_hydro.json +++ b/datasets/USGS_SOFIA_panther_refuge_hydro.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_panther_refuge_hydro", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project are to 1. Inventory existing hydrologic data available in the vicinity of the Florida Panther National Wildlife Refuge (FPNWR) including all data that can be used for determining past and current conditions. 2. Design and install a hydrologic monitoring network for the FPNWR. The network will include continuous and intermittently monitored ground-water level and surface water stations. The network will be used to monitor hydrologic conditions within the FPNWR and to evaluate the relationship between ground water and surface water. 3. Collect other hydrologic data as needed to assist in determining the hydrologic conditions in the area. Examples of other types of data include stable isotopes, which can be used to determine sources of water in a sample, evapotranspiration data, surface and borehole geophysical data, seepage measurements. 4. Evaluate historical and current data to determine trends and baseline conditions at and in the vicinity of the FPNWR.\n\nThe biologic communities of the Florida Panther National Wildlife Refuge (FPNWR) and surrounding areas have been historically impacted by the changes in hydrology associated with past highway and canal construction and will be impacted by future plans for hydrologic restoration. Currently, little hydrologic data is collected in the vicinity of the FPNWR. Two continuous recording stations located up gradient in Big Cypress National Park (stations A1 and A2) are the nearest wetland stations to the FPNWR. Additional stations are located in the canals near the FPNWR. Information on current hydrologic conditions and a monitoring network are needed in order to determine the impact of the planned Picayune Strand Hydrologic Restoration on the hydrology of the area. These hydrologic changes will have effects on the threatened and endangered species as well as other biologic communities in the FPNWR. There are two components to the hydrology of the area that have an impact on the ecology, surface water, and shallow ground water. The surface water consists of wetlands within and canals bordering the FPNWR. Canals bordering the refuge have a major impact on the hydrology in the area. The FPNWR currently maintains a hydrologic monitoring program of 8 stations (Larry Richardson, verbal communication). These hydrologic monitoring stations have not been surveyed to a vertical datum, which is required to adequately evaluate the data being collected. The survey information is required to determine the relationship between ground water and surface water in the area. Additional information needed to evaluate the hydrology of the area include stage and flow rates in the canals bordering the FPNWR.", "links": [ { diff --git a/datasets/USGS_SOFIA_rice_alligators_04.json b/datasets/USGS_SOFIA_rice_alligators_04.json index fb7cc0a705..4058533f12 100644 --- a/datasets/USGS_SOFIA_rice_alligators_04.json +++ b/datasets/USGS_SOFIA_rice_alligators_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_rice_alligators_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will accomplish several tasks with a combination of field data collection, GIS mapping, and computer simulation. Our main objectives are designed to answer questions critical to restoration success and to provide the tools necessary for evaluation: 1. Develop monitoring methods necessary for evaluation of restoration success in alligator populations. 2. Understand the effects of decompartmentalization and other CERP (Comprehensive Everglades Restoration Plan) projects on restoration of alligator populations. 3. Identify and quantify the extent of aquatic refugia maintained by alligators throughout the system and develop relationships necessary to predict restoration of refugia. 4. Validate and update ecological models for use in prediction of the effects of restoration.\n\nMany important questions concerning the effects of Everglades restoration on alligator populations remain unanswered such as the impacts of decompartmentalization, the role of alligator holes as aquatic refugia, and the effects of hydrology on population growth and condition. Further, the methods for monitoring and evaluating restoration success are not clear or have not been adapted for use during CERP. Also, we need to continue to update and validate restoration tools such as population models for use in alternative selection, performance measure development, and prediction. This project will directly address the questions outlined above, develop monitoring methods, and validate restoration tools for use in CERP. All project tasks have been requested by management agencies in South Florida (NPS, USFWS), listed as critical CERP priority research needs (see USGS Ecological Modeling Workshop at http://sofia.usgs.gov/publications/infosheets/ecoworkshop/ ), and/or highlighted as science objectives for CESI\n", "links": [ { diff --git a/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json b/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json index 0f95dc4a4a..6034176543 100644 --- a/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json +++ b/datasets/USGS_SOFIA_robblee_fb_shrimp_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_robblee_fb_shrimp_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of these activities are broadly: 1) to develop and implement (with other agency members) a program of research to support the restoration of Florida Bay; 2) with other PDT members to develop and evaluate restoration alternatives for Florida Bay and 3) with other committee members to develop performance measures and assess restoration alternatives affecting Florida Bay, Biscayne Bay, Barnes Sound and Manatee Bay and the lower southwest coast mangrove estuaries.\n\nFlorida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980\u0092s - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. The pink shrimp is a species of special interest in each of the above studies because it has been chosen as an indicator species for use in restoration of south Florida estuaries. Empirical and experimental data developed in these studies will be used to support the development of a pink shrimp landscape simulation model and restoration performance measures.", "links": [ { diff --git a/datasets/USGS_SOFIA_robblee_shrimp.json b/datasets/USGS_SOFIA_robblee_shrimp.json index e4cb4dc5f5..cb41731afe 100644 --- a/datasets/USGS_SOFIA_robblee_shrimp.json +++ b/datasets/USGS_SOFIA_robblee_shrimp.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_robblee_shrimp", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Tortugas/Florida Bay pink shrimp simulation model has been identified as a priority need in CERP by the South Florida Water Management District, NOAA, NPS and USGS. This model has been under development through the collaboration of a team of NMFS, USGS and University of Miami (UM) researchers since 1997. To date this project has been funded by NOAA's Coastal Oceans Program, DOI's Critical Ecosystem Studies Initiative and by USGS base funds. The purpose of the model is to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. A series of monitoring or empirical studies either have been completed or are ongoing. NMFS continues to monitor Tortugas pink shrimp harvest and develop the simulation model and has completed pink shrimp salinity/temperature tolerance experiments. USGS is continuing to monitor pink shrimp distribution and abundance in relation to environmental conditions and habitat in Florida Bay and to measure water flow in order to estimate postlarval transport within the Bay. With UM a critical collaborative study to identify and quantify the seasonality and magnitude of pathways of postlarval immigration to Florida Bay is continuing. Statistical studies of these and other data are ongoing relating pink shrimp to salinity, temperature and habitat in Florida Bay.\n\nFlorida Bay lies downstream of the Everglades ecosystem. Perceived deterioration of the Everglades over the last century - and Florida Bay since the mid-1980's - is generally viewed as linked to changes in freshwater flow and water quality associated with water management in South Florida. A pink shrimp simulation model is being developed to assist in designing and refining restoration alternatives by predicting their impact on production of pink shrimp in Florida Bay and on shrimp recruitment from Florida Bay to the Tortugas fishery. The pink shrimp is a good indicator of the health and productivity of the Bay. The effect of salinity and temperature on pink shrimp growth and survivorship and of habitat on juvenile density provide a basis for predicting the abundance of pink shrimp juveniles in Florida Bay and thus the magnitude of recruitment to the Tortugas fishery. A landscape model is needed to express pink shrimp performance measures as functions of spatially complex factors acting across the Bay. Florida Bay is a complex shallow water ecosystem with distinct zones of different physical and biological characteristics (Fourqurean and Robblee 1999) that differ in their potential to support pink shrimp. The influence of upstream water management on pink shrimp recruitment from Florida Bay is expected to express itself principally through changes in salinity and seagrass habitat associated with changes in freshwater inflow. Predictions of the effect of these changes on the Bay's productive capacity require consideration not only of the resulting salinity and seagrass changes but also the resulting change in the area of overlap of these factors favorable to the pink shrimp (Browder and Moore 1981; Browder 1991). Critical long-term databases exist for pink shrimp that are suitable for developing empirical relationships and baselines.", "links": [ { diff --git a/datasets/USGS_SOFIA_rsl30dv.json b/datasets/USGS_SOFIA_rsl30dv.json index 0ff961259d..f54fe892fa 100644 --- a/datasets/USGS_SOFIA_rsl30dv.json +++ b/datasets/USGS_SOFIA_rsl30dv.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_rsl30dv", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily maximum water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and daily mean stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30.\n\nDetermining the volume of water seeping from the water-conservation areas to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", "links": [ { diff --git a/datasets/USGS_SOFIA_rsl30uv.json b/datasets/USGS_SOFIA_rsl30uv.json index a52a7c0cdd..2ad22398e2 100644 --- a/datasets/USGS_SOFIA_rsl30uv.json +++ b/datasets/USGS_SOFIA_rsl30uv.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_rsl30uv", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains hourly readings for water level elevation in feet below mean sea level(feet msl) for 21 groundwater wells and stage in feet msl for 2 surface water stations for 1996 along a transect, approximately 1,000 feet long that is perpendicular to and bisected by Levee 30.\n\nDetermining the volume of water seeping from the water-conservation to the underlying aquifers is important in managing water levels in the conservation areas and freshwater deliveries to Everglades National Park. An accurate water budget to meet the competing natural and anthropogenic needs cannot be determined without this information. From Water Conservation Area 3B, water seeps into the Biscayne aquifer, which is about 80 feet thick directly beneath Levee 30 and thickens to the east, and flows relatively fast (due to high permeability of the aquifer) toward the urban and agricultural areas to the east. Water is also discharged to the canal along the eastern part of Levee 30. The rate of discharge is controlled by structures at the northern and southern ends of the canal. This seepage to the aquifer and canal discharge of water are critical for water-supply wells to the east and for preventing the inland movement of saltwater from the coast. However, lowering of ground-water levels to the east has resulted in higher ground-water seepage and canal discharge, reducing flows to the south in the water-conservation area. As a result, Levees 67A and 67C were constructed to direct water southward toward the central region of Everglades National Park. This water-management scheme has been effective in delivering water to the southwest; however, it reduced the flow to the southeast (northeastern part of Everglades National Park). The altering of historical flow directions and water-level durations has caused significant adverse effects to parts of the Everglades ecosystem. Water managers want to restore predevelopment flow conditions for the Everglades to survive, while also taking into consideration the urban and agricultural needs. The objective of this project was to evaluate approaches for quantifying ground-water seepage beneath Levee 30. The accounting of all significant hydrologic inflows and outflows to the Everglades ecosystem of the south Florida mainland is a key element of the South Florida Ecosystem Program.", "links": [ { diff --git a/datasets/USGS_SOFIA_rtt_sfwmd.json b/datasets/USGS_SOFIA_rtt_sfwmd.json index 109a0b75b3..85a995aaa5 100644 --- a/datasets/USGS_SOFIA_rtt_sfwmd.json +++ b/datasets/USGS_SOFIA_rtt_sfwmd.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_rtt_sfwmd", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This management and coordination effort supports several of the initiatives listed in the DOI science plan. The USGS representative will participate in CERP and RECOVER meetings and share information with USGS Priority Ecosystem Science (PES) staff, represent USGS in the SFWMD Biscayne Bay work group meetings, and assist DOI partners with obtaining and using USGS technical data and information on the Greater Everglades area.\n\nThis project includes support of Comprehensive Everglades Restoration Plan/REstoration COordination and VERification (CERP/RECOVER), assistance with Greater Everglades Priority Ecosystem Science coordination, and USGS liaison with the South Florida Water Management District.", "links": [ { diff --git a/datasets/USGS_SOFIA_rtt_usace.json b/datasets/USGS_SOFIA_rtt_usace.json index bfdb3992e8..52db36c8b4 100644 --- a/datasets/USGS_SOFIA_rtt_usace.json +++ b/datasets/USGS_SOFIA_rtt_usace.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_rtt_usace", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of this project are: 1. support of Comprehensive Everglades Restoration Plan/REstoration COordination and VERification (CERP/RECOVER) by USGS participation in CERP PDT and RECOVER meetings and working with federal, State, and other restoration partners to ensure technology transfer and science synthesis 2. Assistance with Greater Everglades Priority Ecosystem Science (GE PES) coordination 3. USGS liaison with US Army Corps of Engineers (USACE).\n\nThis management and coordination effort supports several of the initiatives listed in the Department of the Interior science plan.", "links": [ { diff --git a/datasets/USGS_SOFIA_smith_hist_photo_archive.json b/datasets/USGS_SOFIA_smith_hist_photo_archive.json index 64eaed50b0..5dd3c33a3d 100644 --- a/datasets/USGS_SOFIA_smith_hist_photo_archive.json +++ b/datasets/USGS_SOFIA_smith_hist_photo_archive.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_smith_hist_photo_archive", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The major products are planned as a series of USGS Open-File Reports, one for each complete, or near complete, set of photos. A photoset is defined as a collection of aerial photos that were taken during a discrete time, generally 30-60 days, with the same scale, film type, and camera. All OFRs will be distributed on CD-ROM and several on DVD. Each report will encompass a photoset with descriptive text sections such as Introduction, Metadata & Procedures, Study Area, and Acknowledgements. All scanned images will be in a downloadable format.\n\nA foundation for Everglades restoration must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies, and pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs, from the mid to late 1920s, and resulted in the production of T-Sheets (Topographic Sheets) for the coasts and shorelines of south Florida. The T-Sheets are remarkably detailed, delineating features such as shorelines, ponds, and waterways, in addition to the position of the boundary between differing vegetation communities. If followed through time changes in the position of these ecotones could potentially be used to judge effects of changes in the landscape of the Everglades ecosystem, providing a standard by which restoration success can be ascertained. The overall objective is to create a digital archive of historical aerial photographs of Everglades national park and surrounding area of the greater Everglades and south Florida. The archive will be in readily available Geographic Information System formats for ease of accessibility. Each set of photos will be broadly disseminated to client agencies, academic institutions and the general public via Open-File Reports and through the Internet.", "links": [ { diff --git a/datasets/USGS_SOFIA_solomet.json b/datasets/USGS_SOFIA_solomet.json index 9d4fd707dd..61460abfc1 100644 --- a/datasets/USGS_SOFIA_solomet.json +++ b/datasets/USGS_SOFIA_solomet.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_solomet", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objective of this investigation is to quantify seepage below Levee L31N. The amount of water lost to the L-31N Canal versus the fraction that flows below the canal will be estimated. A conceptual model is currently being developed for the site based upon results from an on-going stable isotope (oxygen -18 and deuterium) study. Quantification of seepage rates will be based upon a computer model, MODBRANCH, which couples both groundwater and surface water flows. Particular attention will be devoted to model performance under transient conditions caused by fluctuations in the stage of the L-31N Canal and pumping operations of the West Wellfield. In addition, an alternative leakage relationship based on reach transmissivity will be incorporated into MODBRANCH; this relationship is believed to be more suitable for transient conditions. The reach transmissivity relationship will be evaluated in comparison to MODBRANCH's existing leakage relationship, which is based on Darcian flow through the bed of the surface water channel. Modeling results will be used to develop an algorithm for real time estimation of seepage beneath Levee L31N. It is expected that this algorithm will estimate seepage using head differences at monitoring stations in the vicinity of the levee.\n\nPlans to restore historical hydrologic conditions in the northeast section of Everglades National Park (ENP) include the raising of water levels in ENP and water conservation area 3B, which overlie the Biscayne aquifer, an extremely permeable aquifer. The increase in water levels is likely to cause an increase in seepage losses to the east. Quantifying this seepage loss is necessary for water management purposes as well as for models of the Everglades and coastal systems. Levee L-31N has been identified as a critical area for potential water losses. The L-31N study site includes a wetland area within ENP on the west; the L-31N Canal flows from north to south through the longitudinal center of the site, and the eastern portion of the region is a suburban area of Miami which includes a major municipal wellfield, the West Wellfield, and rock mining activities.\n\nThis project was completed in 1999.", "links": [ { diff --git a/datasets/USGS_SOFIA_sus_parts.json b/datasets/USGS_SOFIA_sus_parts.json index 3dea36382c..03211c51cf 100644 --- a/datasets/USGS_SOFIA_sus_parts.json +++ b/datasets/USGS_SOFIA_sus_parts.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_sus_parts", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objectives of the study are: To quantify through detailed field experiments previously unstudied processes in the Everglades, such as rates of fine-particle movement and filtration by vegetation as well as advective solute exchange between surface water and zones of solute storage in relatively stagnant waters (in areas of thick vegetation and in peat pore water). Our study focuses on determining the effects of these processes on chemical reactions of the contaminants as well as overall effects on downstream transport. At least initially, the emphasis will be on improved understanding of factors influencing transport of dissolved and fine particle forms of phosphorus. To apply the new knowledge gained from field measurements first in our own transport models (which are necessarily limited in time and space) and then to encourage application in more widely used water-quality models (e.g. DMSTA, ELM), and water quality models currently in development (e.g. extension of USGS SICS model in Taylor Slough). The goal is more accurate simulation of the effects of restoration on Everglades water quality, thus allowing more reliable use of water-quality models for prediction of the effects of restoration. To guide the use of improved water-quality models to estimate potential rates of transport, storage, and remobilization of phosphorus (and other contaminants) in WCA-2A, Shark and Taylor Sloughs in Everglades National Park, and Loxahatchee Wildlife Refuge, with a goal to predict potential rates of downstream movement of phosphorus in these systems under \"restored\" flows.\n\nA key measure of success in the Everglades restoration is protecting water quality while increasing the quantity of water flowing through the Everglades. The restoration's goal of increasing surface-water flow through the wetlands could have the unintended consequence of transporting contaminants farther into the Everglades than ever before. Thus, the need to augment water delivery will at times inevitably result in using water with higher than desirable total dissolved solids, particulate organic matter, sulfate, nutrients, and mercury. In addition, greater water flows may increase transport of those contaminants farther into the wetlands than ever before. Our investigation seeks a better understanding of the fundamental processes that affect the rates at which contaminants are transported in wetlands, focusing especially on critical unknowns - 1) rates of contaminant transport in association with fine suspended particles, and 2) rates of solute exchange between surface water and storage areas reservoirs in relatively stagnant surface waters (in thick vegetation and subsurface pore water in peat). Our studies are planned to be the definitive experimental investigations of solute and particle transport in the Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json b/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json index 6b6fc99b2c..6bbe99a279 100644 --- a/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json +++ b/datasets/USGS_SOFIA_sw-pore_water_DOC_SUVA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_sw-pore_water_DOC_SUVA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are for dissolved organic carbon (DOC) and specific ultraviolet absorbance (SUVA) for surface water and pore water in the South Florida Water Management District (SFWMD) water conservation areas.\nIt is well recognized that the chemical forms of mercury in the water column and sediments are intimately related to bioaccumulation and body burden. Interactions of mercury and dissolved organic matter may play an important role in controlling the bioavailability and reactivity of mercury. The goal of our research is to provide information about the interactions of mercury and dissolved organic matter that will better define this important, albeit, poorly understood process. Ultimately, this research will lead to a more complete model of mercury behavior in the Everglades. Our research focused on the effect of DOC on the transport and reactivity of mercury in the Everglades through a combined field and laboratory study. The underlying hypothesis of this research is that the chemistry and structural characteristics of organic matter in the Everglades have a controlling influence on mercury cycling processes such as methylamine and volatilization. The South Florida Water Management District, the U.S. Environmental Protection Agency, and the USGS South Florida Ecosystems Initiative have organized an intensive study of surface water chemistry in Southern Florida. In 1994, several onsite-research locations were selected in the Water Conservation Areas of the South Florida Water Management District in conjunction with this multidisciplinary, multiage research project.", "links": [ { diff --git a/datasets/USGS_SOFIA_target_sal_vals.json b/datasets/USGS_SOFIA_target_sal_vals.json index 7e2359f3d7..a41bfec771 100644 --- a/datasets/USGS_SOFIA_target_sal_vals.json +++ b/datasets/USGS_SOFIA_target_sal_vals.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_target_sal_vals", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objective of this project is to provide information to Comprehensive Everglades Restoration Plan (CERP) managers that can be used to establish target salinity values and performance measures for the estuaries and coastal ecosystems. The information provided will consider the contribution of climate, sea level rise, and anthropogenic alteration of salinity values in the estuaries and coastal ecosystems. The four areas of focus for the project are: 1. Refine existing modern analog dataset by completing analyses of modern samples collected between 1996 and 2004 and applying the data to core data compiled in the Synthesis Task 2. Collect new cores (if necessary) within the southern estuaries to fill in information gaps identified by the land management agencies (Everglades National Park (ENP), and Biscayne National Park (BNP) and the Southern Estuaries Subteam of the Regional Evaluation Team (RET) of Restoration Coordination and Verification (RECOVER) 3. Select a few sites in the transition zones to collect cores in a transect moving perpendicular to shore to analyze the rate of sea level rise in the region 4. Work with collaborators to input all of the combined paleoecology data into linear regression models that can hindcast salinity for different parts of the system. \n\nThe importance and application of ecosystem history research to restoration goals has been previously identified. The Department of the Interior (DOI) Science Plan lists as one of three primary restoration activities the need to \"ensure that hydrologic performance targets accurately reflect the natural predrainage hydrology and ecology\". The primary goal of this project is to determine the predrainage and ecology of critical regions within the estuaries and coastal ecosystems of south Florida identified by the groups charged with setting performance measures and targets for these coastal zones.", "links": [ { diff --git a/datasets/USGS_SOFIA_terrapin_mark-recap_data.json b/datasets/USGS_SOFIA_terrapin_mark-recap_data.json index 1b79e97a39..4567380f12 100644 --- a/datasets/USGS_SOFIA_terrapin_mark-recap_data.json +++ b/datasets/USGS_SOFIA_terrapin_mark-recap_data.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_terrapin_mark-recap_data", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2001 a mark-recapture study on mangrove terrapins (Malaclemys terrapin) in the Big Sable Creek (BSC) complex within Everglades National Park was initiated. The summary data for terrapins in BSC were collected over 5 sampling trips in a two-year period (November 2001 - October 2003) and from analysis of individual terrapin capture histories.\n\nStudy objectives were to estimate adult survival probablility, capture probablilty, and abundance of terrapins at this study site. This allowed the establishment of the first baseline assessment for mangrove terrapins in the coastal Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIA_willard_tree_islands_04.json b/datasets/USGS_SOFIA_willard_tree_islands_04.json index c0c6f169de..6d1c2e2b63 100644 --- a/datasets/USGS_SOFIA_willard_tree_islands_04.json +++ b/datasets/USGS_SOFIA_willard_tree_islands_04.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIA_willard_tree_islands_04", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Analysis of 209 pollen assemblages from surface samples in ten vegetation types in the Florida Everglades form the basis to identify wetland sub-environments from the pollen record. This calibration dataset makes it possible to infer past trends in hydrology and disturbance regime based on pollen assemblages preserved in sediment cores. Pollen assemblages from sediment cores collected in different vegetation types throughout the Everglades provide evidence on wetland response to natural fluctuations in climate as well as impacts of human alteration of Everglades hydrology. Sediment cores were located primarily in sawgrass marshes, cattail marshes, tree islands, sawgrass ridges, sloughs, marl prairies, and mangroves. The datasets contain raw data on pollen abundance as well as pollen concentration (pollen grains per gram dry sediment).\n\nEverglades restoration planning requires an understanding of the impact of natural and human-induced environmental change on wetland stability, and this project focuses specifically on three wetland types: tree islands, the sawgrass ridge and slough system, and marl prairies. Tree islands are considered key indicators of the health of the Everglades ecosystem because of their sensitivity to both flooding and drought conditions. Tree islands also act as a sink for nutrients in the ecosystem and may play an important role in regulating nutrient dynamics. Although management strategies to restore and even create tree islands are being formulated, the published data on their age, developmental history, geochemistry, and response to hydrologic alterations is limited. To address these issues, this project integrates floral and geochemical data with geologic and vegetational mapping activities to establish the timing of tree-island formation and impacts of both flooding and droughts on tree islands throughout the Everglades.", "links": [ { diff --git a/datasets/USGS_SOFIF_Fbbtypes.json b/datasets/USGS_SOFIF_Fbbtypes.json index a35b33de2c..9a05354210 100644 --- a/datasets/USGS_SOFIF_Fbbtypes.json +++ b/datasets/USGS_SOFIF_Fbbtypes.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOFIF_Fbbtypes", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987).\n\nThe purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated.", "links": [ { diff --git a/datasets/USGS_SOIL_CHEMISTRY.json b/datasets/USGS_SOIL_CHEMISTRY.json index 9d51d55677..44f2f2d2e1 100644 --- a/datasets/USGS_SOIL_CHEMISTRY.json +++ b/datasets/USGS_SOIL_CHEMISTRY.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_SOIL_CHEMISTRY", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following abstract was taken from the the Chemical Analyses of Soils and\nOther Surficial Materials of the Conterminous United States Metadata, written\nby David B. Smith, Research Geologist, U.S. Geological Survey, Denver,\nColorado. This metadata may be viewed in HTML at\n\"http://minerals.usgs.gov/\".\n\nABSTRACT\nThis data set contains geochemical data from soils and other regoliths\ncollected and analyzed by Hans Shacklette and colleagues beginning in 1958 and\ncontinuing until about 1976. The samples were collected at a depth of about 20\ncm from sites that, insofar as possible, had surficial materials that were very\nlittle altered from their natural condition and that supported native plants. \nThe sample material at most sites could be termed \"soil\" because it was a\nmixture of disintegrated rock an organic matter. Some of the sampled deposits,\nhowever, were not soils as defined above, but were other regolith types. These\nincluded desert sands, sand dunes, some loess deposits, and beach and alluvial\ndeposits that contained little or no visible organic material. The samples\nwere chemically analyzed by a variety of techniques in the U.S. Geological\nSurvey laboratories in Denver, CO.\n\nDATA\nThe data set contains 1,323 samples for a sampling density of approximately one\nsample per 6,000 square kilometers. The data set is currently the only\nnational geochemical data set collected and analyzed according to standardized\nprotocols. The data are most appropriately used to provide information on\nbackground concentrations of elements in soil. \n\nANALYSIS METHODS\nThe data was acquired using various chemical analysis methods. In summary the\nmethods used were: 1)Emission spectrography for Al, Ba, Be, B, Ca, Ce, Cr, Co,\nCu, Ga, Fe, La, Pb, Mg, Mn, Mo, Nd, Ni, Nb, P, K, Sc, Na, Sr, Ti, V, Yb, Y, Zn,\nand Zr; 2)EDTA titration for Ca; 3)Colorimetric methods for P and Zn, 4)Flame\nphotometry for K; 5)Flame atomic absorption for Hg, Li, Mg, Na, Rb, and Zn;\n6)Flameless atomic absorption for Hg; 7)X-ray fluorescence spectrometry for Ca,\nGe, Fe, K, Se, Ag, S, and Ti; 8)Combustion for total carbon; and 9)Neutron\nactivation analysis for U and Th.\n\nTHE DATA\nThe data file is an ArcVie Shapefile and has been compressed using the WinZip\nprogram. The usere will need to uncompress the file with WinZip or compatible\nsoftware before attempting to import the file into ArcView or ArcInfo. \nShapefiles can only be de-compressed with programs that recognize multi-file\narchives.\n\nThe shapefiles are designed for use with Arc/Info and ArcView, which are\nGIS/Mapping software marketed by ESRI. By visiting\n\"http://www.esri.com/software/arcexplorer/index.html\" you may download ESRI's\nfree Arc Explorer software for viewing shape files on Windows 95, 98, or NT.", "links": [ { diff --git a/datasets/USGS_Sherman_QUAD_1.0.json b/datasets/USGS_Sherman_QUAD_1.0.json index 7a31c77c5c..f87ac6ea42 100644 --- a/datasets/USGS_Sherman_QUAD_1.0.json +++ b/datasets/USGS_Sherman_QUAD_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_Sherman_QUAD_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for use in a regional ground-water model of the Lake\nTexoma watershed for a project by the U.S. Environmental Protection Agency,\nNational Risk Management Research Laboratory located in Ada, Oklahoma, titled\n\"Development of protocols and decision support tools for assessing watershed\nsystem assimilative capacity, in support of risked-based ecosystem management\nand restoration practices.\"\n\nAlthough this data set was created for use in a specific project, it may be\nused to make geologic maps, and determine approximate areas and locations of\nvarious geologic units.\n\nThis digital data set contains geologic formations for the 1:250,000-scale\nSherman quadrangle, Texas and Oklahoma. The original data are from the Bureau\nof Economic Geology publication, \"Geologic Atlas of Texas, Sherman sheet\", by\nJ.H. McGowen, T.F. Hentz, D.E. Owen, M.K. Pieper, C.A. Shelby, and V.E. Barnes,\n1967, revised 1991.\n\nAdditional geology data sets are available for Oklahoma at URL\n\"http://ok.water.usgs.gov/gis/geology/index.html\". The source maps for three\ncounties in the Oklahoma panhandle are at a scale of 1:125,000. The source maps\nfor the rest of Oklahoma are at a scale of 1:250,000.\n\nThe original geology source map was published in the Transverse Mercator\nProjection, Zone 14. This data set was projected to an Albers Equal Area\nprojection (Synder, 1987), cast on the North American Datum of 1983.\n\nThis electronic report was subjected to the same review standard that applies\nto all U.S. Geological Survey reports. Reviewers were asked to check the\ntopological consistency, tolerances, attribute frequencies and statistics,\nprojection, and geographic extent. Reviewers were given digital data sets and\npaper plots for checking against the source maps to verify the linework and\nattributes. The reviewers were asked to check the metadata and accompanying\nfiles for completeness and accuracy.", "links": [ { diff --git a/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json b/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json index 42b3a0152e..a06b62e00b 100644 --- a/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json +++ b/datasets/USGS_TamiamiFlowMonitoring_2007-2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_TamiamiFlowMonitoring_2007-2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "he construction of U.S. Highway 41 (Tamiami Trail), the Southern Golden Gate Estates development, and the Barron River Canal has altered the flow of freshwater to the Ten Thousand Islands estuary of Southwest Florida. Two restoration projects, the Picayune Strand Restoration Project and the Tamiami Trail Culverts Project, both associated with the Comprehensive Everglades Restoration Plan, were initiated to address this issue. Quantifying the flow of freshwater to the estuary is essential to assessing the effectiveness of these projects.\n\nThe U.S. Geological Survey conducted a study between March 2006 and September 2010 to quantify the freshwater flowing under theTamiami Trail between County Road 92 and State Road 29 in southwest Florida, excluding the Faka Union Canal (which is monitored by South Florida Water Management District). The study period was after the completion of the Tamiami Trail Culverts Project and prior to most of the construction related to the Picayune Restoration Project. The section of the Tamiami Trail that was studied contains too many structures (35 bridges and 16 culverts) to cost-effectively measure each structure on a continuous basis, so the area was divided into seven subbasins. One bridge within each of the subbasins was instrumented with an acoustic Doppler velocity meter. The index velocity method was used to compute discharge at the seven instrumented bridges. Periodic discharge measurements were made at all structures, using acoustic Doppler current profilers at bridges and acoustic Doppler velocity meters at culverts. Continuous daily mean values of discharge for the uninstrumented structures were calculated on the basis of relations between the measured discharge at the uninstrumented stations and the discharge and stage at the instrumented bridge. Estimates of daily mean discharge are available beginning in 2006 or 2007 through September 2010 for all structures. Subbasin comparison is limited to water years 2008?2010.\n\nThe Faka Union Canal contributed more than half (on average 60 percent) of the flow under the Tamiami Trail between State Road 29 and County Road 92 during water years 2008?2010. During water years 2008?2010, an average 9 percent of the flow through the study area came from west of the Faka Union Canal and an average 31 percent came from east of the Faka Union Canal. Flow data provided by this study serve as baseline information about the seasonal and spatial distribution of freshwater flow under the Tamiami Trail between County Road 92 and State Road 29, and study results provide data to evaluate restoration efforts.", "links": [ { diff --git a/datasets/USGS_VOLCANO.json b/datasets/USGS_VOLCANO.json index 0b34d1fd30..03f8bca72c 100644 --- a/datasets/USGS_VOLCANO.json +++ b/datasets/USGS_VOLCANO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_VOLCANO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data shows the location of all known volcanoes in the world.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_WHFC_SUPERDIF3.json b/datasets/USGS_WHFC_SUPERDIF3.json index 9ebadbefbe..7445ac0f1b 100644 --- a/datasets/USGS_WHFC_SUPERDIF3.json +++ b/datasets/USGS_WHFC_SUPERDIF3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHFC_SUPERDIF3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time-series oceanographic data for the coast of Massachusetts collected by the\nUSGS or used in conjunction with USGS projects. These data are stored as NetCDF\nfiles using conventions developed by NOAA's Pacific Marine Environmental\nLaboratory (PMEL) lab to be compatible with their EPIC system. The hourly data\nis available online through the USGS Coastal Marine Time Series Browser\n\"http://stellwagen.er.usgs.gov/\". Variables include current, temperature,\npressure, conductivity, light transmission (beam attenuation) and others.\n\nAvailable data sets for the coast of Massachusetts:\n\n* Buzzards Bay (Jul 1982 - Oct 1985)\n* Cape Cod Bay (Feb 1986 - Apr 1986)\n* Cape Cod Misc (Jul-Aug 1980)\n* Long Term Observations (MWRA) (Jan 1990 - Present)\n* Massachusetts Bay Circulation Experiment (Sep 1990 - Jun 1991)\n* Massachusetts Bay Internal Wave Experiment (Aug-Sep 1998)\n* Stellwagen Bank (Feb 1994 - Apr 1995)\n* Western Massachusetts Bay (Jan-May 1987)", "links": [ { diff --git a/datasets/USGS_WHFC_SUPERDIF4.json b/datasets/USGS_WHFC_SUPERDIF4.json index 05fcaeae11..06c56e59c3 100644 --- a/datasets/USGS_WHFC_SUPERDIF4.json +++ b/datasets/USGS_WHFC_SUPERDIF4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHFC_SUPERDIF4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time-series oceanographic data for the New Jersey outer continental shelf (Middle Atlantic Bight) collected by the USGS or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. The hourly data is available online through the USGS Coastal Marine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include current, temperature, pressure, conductivity, light transmission (beam attenuation) and others.\n\nAvailable data sets for the Middle Atlantic Bight:\n\n* Deep Water Dump Site 106 (Sep 1989 - Jul 1990)\n* Hudson Shelf Valley (Dec 1999 - Apr 2000)\n* Middle Atlantic Bight (Dec 1975 - Oct 1980)\n* New England Continental Slope (Nov 1982 - Nov 1984)", "links": [ { diff --git a/datasets/USGS_WHFC_SUPERDIF6.json b/datasets/USGS_WHFC_SUPERDIF6.json index a1fbb363aa..132e3120b8 100644 --- a/datasets/USGS_WHFC_SUPERDIF6.json +++ b/datasets/USGS_WHFC_SUPERDIF6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHFC_SUPERDIF6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time-series oceanographic data for the Gulf of Mexico (Alabama coast) collected\nby the USGS or used in conjunction with USGS projects. These data are stored as\nNetCDF files using conventions developed by NOAA's Pacific Marine Environmental\nLaboratory (PMEL) lab to be compatible with their EPIC system. The hourly data\nis available online through the USGS Coastal Marine Time Series Browser\n\"http://stellwagen.er.usgs.gov/\". Variables include current, temperature,\npressure, conductivity, light transmission (beam attenuation) and others.\n\nAvailable data sets for the Gulf of Mexico:\n\nChandeleur Islands (Jul - Nov 2010)\n* Deep Reef (May 2001)\n* Lake Ponchartrain (Mar-Jul 1995)\n* Mobile Bay (Apr-Jul 1990; May 1991 - May 1992)", "links": [ { diff --git a/datasets/USGS_WHFC_SUPERDIF7.json b/datasets/USGS_WHFC_SUPERDIF7.json index 1ea4a55413..40ea075d37 100644 --- a/datasets/USGS_WHFC_SUPERDIF7.json +++ b/datasets/USGS_WHFC_SUPERDIF7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHFC_SUPERDIF7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time-series oceanographic data for the coast of California collected by the\nUSGS or used in conjunction with USGS projects. These data are stored as NetCDF\nfiles using conventions developed by NOAA's Pacific Marine Environmental\nLaboratory (PMEL) lab to be compatible with their EPIC system. The hourly data\nis available online through the USGS Coastal Marine Time Series Browser\n\"http://stellwagen.er.usgs.gov/\". Variables include current, temperature,\npressure, conductivity, light transmission (beam attenuation) and others.\n\nAvailable data sets for California:\n\n* California Area Monitoring Program (CAMP) (May-Jun 1987, Dec 1988 - Feb 1989)\n* Farallones (May 1989 - Aug 1990; Nov 1997 -Nov 1998)\n* Monterey Bay National Marine Sanctuary (May 1985 - Aug 1998)\n* Monterey Canyon (Aug 1993 - May 1995)\n* Orange County, CA (Jun 2001 - Jan 2003)\n* Palos Verdes Shelf (May 1992 - Mar 1993)\n* Southern California (Nov 1997 - Mar 2000)\n* Sediment Transport on Shelves and Slopes (STRESS) (Dec 1988 - May 1989; Nov\n1990 - Mar 1991)", "links": [ { diff --git a/datasets/USGS_WHFC_SUPERDIF8.json b/datasets/USGS_WHFC_SUPERDIF8.json index 5284770eee..a2f0939c83 100644 --- a/datasets/USGS_WHFC_SUPERDIF8.json +++ b/datasets/USGS_WHFC_SUPERDIF8.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHFC_SUPERDIF8", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Time-series oceanographic data for the Pacific Ocean in the vicinity of\nJohnston Atoll, collected by the USGS or used in conjunction with USGS\nprojects. These data are stored as NetCDF files using conventions developed by\nNOAA's Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with\ntheir EPIC system. The hourly data is available online through the USGS Coastal\nMarine Time Series Browser \"http://stellwagen.er.usgs.gov/\". Variables include\ncurrent, temperature, pressure, conductivity, light transmission (beam\nattenuation) and others.\n\nAvailable data sets for Hawaii:\n\n* Mamala Bay (Jun 1996 - Aug 1997)\n* Molokai (Jan-Apr 2001; Nov 2001 - Feb 2002)", "links": [ { diff --git a/datasets/USGS_WHSC_MassBay_89-06_3.0.json b/datasets/USGS_WHSC_MassBay_89-06_3.0.json index 67ee9c3d14..dbd056aa19 100644 --- a/datasets/USGS_WHSC_MassBay_89-06_3.0.json +++ b/datasets/USGS_WHSC_MassBay_89-06_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WHSC_MassBay_89-06_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42\ufffd 22.6' N., 70&\ufffd 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42\ufffd 9.8' N., 70\ufffd 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. \nThis research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard.", "links": [ { diff --git a/datasets/USGS_WILMA_COASTAL_IMPACT.json b/datasets/USGS_WILMA_COASTAL_IMPACT.json index f623bdcd6d..ec4681d66d 100644 --- a/datasets/USGS_WILMA_COASTAL_IMPACT.json +++ b/datasets/USGS_WILMA_COASTAL_IMPACT.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WILMA_COASTAL_IMPACT", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hurricane Wilma made landfall as a category 2 storm south of\n Fort Meyers, Florida on October 24, 2005. The U.S. Geological Survey (USGS),\n NASA, and the U.S. Army Corps of Engineers are cooperating in a research\n project investigating coastal change that might result from Hurricane Wilma.\n \n Pre-landfall vulnerability estimates for west Florida's barrier islands falling\n within the cone of uncertainty for Wilma's path are available. These maps\n highlight the extreme vulnerability of the West-Florida coastline to a direct\n hit from a storm of Wilma's predicted magnitude.\n \n Aerial video, still photography, and laser altimetry surveys of post-storm\n beach conditions will be collected for comparison with earlier data as soon as\n weather allows. The comparisons will show the nature, magnitude, and spatial\n variability of coastal changes such as beach erosion, overwash deposition, and\n island breaching. These data will also be used to further refine predictive\n models of coastal impacts from severe storms. The data will be made available\n to local, state, and federal agencies for purposes of disaster recovery and\n erosion mitigation.\n \n [Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_WRD_NWIS-W.json b/datasets/USGS_WRD_NWIS-W.json index 8b94066da5..8521471c4c 100644 --- a/datasets/USGS_WRD_NWIS-W.json +++ b/datasets/USGS_WRD_NWIS-W.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_WRD_NWIS-W", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Water Information System database (NWIS)provide access to\nwater-resources data collected at approximately 1.5 million sites in all 50\nStates, the District of Columbia, and Puerto Rico. Online access to this data\nis organized around these categories:\n- Surface Water\n-Ground Water\n-Water Quality\n\nThe USGS investigates the occurrence, quantity, quality, distribution, and\nmovement of surface and underground waters and disseminates the data to the\npublic, State and local governments, public and private utilities, and other\nFederal agencies involved with managing our water resources.\n\n\n[Summary adapted from: \"http://waterdata.usgs.gov/usa/nwis/\"]", "links": [ { diff --git a/datasets/USGS_YosemiteRockFalls.json b/datasets/USGS_YosemiteRockFalls.json index d49e9382fc..6559e692c8 100644 --- a/datasets/USGS_YosemiteRockFalls.json +++ b/datasets/USGS_YosemiteRockFalls.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_YosemiteRockFalls", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inventories of rock falls and other types of landslides are valuable tools for improving understanding of these events. For example, detailed information on rock falls is critical for identifying mechanisms that trigger rock falls, for quantifying the susceptibility of different cliffs to rock falls, and for developing magnitude-frequency relations. Further, inventories can assist in quantifying the relative hazard and risk posed by these events over both short and long time scales.\n\nThis report describes and presents the accompanying rock fall inventory database for Yosemite National Park, California. The inventory database documents 925 events spanning the period 1857\u20132011. Rock falls, rock slides, and other forms of slope movement represent a serious natural hazard in Yosemite National Park. Rock-fall hazard and risk are particularly relevant in Yosemite Valley, where glacially steepened granitic cliffs approach 1 km in height and where the majority of the approximately 4 million yearly visitors to the park congregate. In addition to damaging roads, trails, and other facilities, rock falls and other slope movement events have killed 15 people and injured at least 85 people in the park since the first documented rock fall in 1857.\n\nThe accompanying report describes each of the organizational categories in the database, including event location, type of slope movement, date, volume, relative size, probable trigger, impact to humans, narrative description, references, and environmental conditions. The inventory database itself is contained in a Microsoft Excel spreadsheet (Yosemite_rock_fall_database_1857-2011.xlsx). Narrative descriptions of events are contained in the database, but are also provided in a more readable Adobe portable document format (pdf) file (Yosemite_rock_fall_database_narratives_1857-2011.pdf) available for download separate from the database.\n", "links": [ { diff --git a/datasets/USGS_ag_chem_1.0.json b/datasets/USGS_ag_chem_1.0.json index 233dddb598..edd1fe299c 100644 --- a/datasets/USGS_ag_chem_1.0.json +++ b/datasets/USGS_ag_chem_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ag_chem_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage contains estimates of agricultural-chemical use in counties in\nthe conterminous United States as reported in the 1987 Census of Agriculture\n(U.S. Department of Commerce, 1989a). Agricultural-chemical use data are\nreported as either acres on which used, tons, or as a percentage of county\narea. Agricultural-chemical use estimates were generated from surveys of all\nfarms where $1,000 or more of agricultural products were sold, or normally\nwould have been sold, during the census year.\n\nMost of the attributes summarized represent 1987 data, but some information\nfrom the 1982 Census of Agriculture also was included.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).", "links": [ { diff --git a/datasets/USGS_ag_stock_1.0.json b/datasets/USGS_ag_stock_1.0.json index 69d029b9e1..f7a77578e3 100644 --- a/datasets/USGS_ag_stock_1.0.json +++ b/datasets/USGS_ag_stock_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ag_stock_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The livestock holdings estimates in this coverage are intend for use in\nestimating regional livestock holdings, and in producing visual displays and\nmapping relative amounts of agricultural livestock holdings across broad\nregions of the United States.\n\nThis coverage contains estimates of livestock holdings in counties in the\nconterminous United States as reported in the 1987 Census of Agriculture (U.S.\nDepartment of Commerce, 1989a). Livestock holdings data are reported as either\na number (for example, number of milk cows), number of farms, or in thousands\nof dollars. Livestock holdings estimates were generated from surveys of all\nfarms where $1,000 or more of agricultural products were sold, or normally\nwould have been sold, during the census year.\n\nMost of the attributes summarized represent 1987 data, but some information for\nthe 1982 Census of Agriculture also was included.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).\n\nLivestock Census of Agriculture Counties United States\n\nProcedures_Used: CENSUS DATA\nAn automated procedure was developed for processing the raw census data into\nARC/INFO coverage attributes. The procedure is summarized below: 1) copy\ncounty2m coverage to coverage representing type of census data (i.e. ag_expn or\nag_land), 2) run agadd.aml for each item added to the coverage, giving coverage\nname and attribute field number as arguments.\n\nThe agadd.aml program runs a fortran program to extract field data from the raw\ncensus data files, and then processes that raw data finally adding it as a\ncolumn of attribute data to the county coverage. Other programs were developed\nto calculate summary statistics of the census attribute data, and to make\ngraphics representing attribute values across the United States. COUNTY\n\nBOUNDARIES\n\nThis series of maps was published as part of the National Atlas of the United\nStates (U.S.Geological Survey, 1970). The maps for the conterminou United\nStates were digitized in 15 sheets and published in the Digital Lin Graph (DLG)\nformat as described by Domeratz and others (1983).\n\nEach sheet was prepared by reading the DLG files of the political and water\nbodies layers, converting them to ARC/INFO, extracting the county boundaries\nand the coastline, respectively, and joining the two layers. FIPS codes were\nassigned to all polygons by using available sources and were checked manually.\n\nBoundaries with adjacent sheets of the 15-sheet set were edgematched manually,\narbitrarily choosing one of the sheets as the \"correct\" border. Edgematching\noperations adjusted the linework as far as was necessary so that the coverages\nwould fit to a tolerance of 100 meters. The coverage (referred to herein as\nVersion 1.0) was stored as 49 separate coverages (48 States and the District of\nColumbia) because the ARC/INFO software in use at the time could not process\nthe entire coverage. Individual States could be joined by specifying a\ntolerance of 100 meters.\n\nFrom time to time, adjustments were made to the State coverages to reflect\nchanges in U.S. counties. It is believed the accuracy of these adjustments is\ncomparable to the original linework.\n\nFor Version 2.0, all State coverages were rejoined and manually edited to\nproduce a perfect edgematch between all States. For States on the original map\nsheet boundaries, this adjustment averaged less than 20 meters and in no case\nwas more than 100 meters. The whole coverage was CLEANed to a tolerance of 20\nmeters, which resulted in few, if any, effect on small offshore islands. The\ncoverage also was checked to ensure that it represented current U.S. counties\nor county equivalents.\n\nThe coverage in Version 1.0 stopped at the coastline. There was no attempt to\ndepict offshore areas. This created some problems when the coverage was used to\nassign county codes to sampling stations located near the coast. To help in\nthis matter, Version 2.0 includes offshore extensions of the county polygons.\nThe (water) boundaries of many of these polygons are arbitrary.\n\nThe Canadian Great Lakes features are another new addition to Version 2. They\nwere added to improve the utility of the coverage for visual displays Although\nthe Canadian Great Lakes are logically represented by a single polygon,\npractical considerations -- the inability of some software to plo polygons with\na large number of vertices -- made it necessary to separate them into four\npolygons. The dividing lines are located in narrow channel to minimize\ninterference with plotting patterns. Canadian islands within the Great Lakes\nalso were included. \n\nAll ticks were relocated to places that are easily visible on maps of the\nUnited States, to help in registering maps that may not otherwise have adequate\nregistration information.\n\nTo expedite accessing parts of the coverage, certain items have been indexed\nwith the procedure, INDEX_COUNTY.AML. See Section 3 above. A spatial index also\nwas created.\n\nWhen using this coverage to clip or intersect other coverages, a tolerance as\nlow as 2 meters can be used.\n\nThe processing used to derive this coverage moved boundaries from their\npositions on the original maps. In cases of conflicting lines, preference was\ngiven to forming the correct topology. Strictly speaking, this coverage is not\nidentical to the source materials. These changes were unavoidable in producing\na continuous coverage of the conterminous United States. \nRevisions: COUNTY POLYGON DATA\n\nRevision 1.0, 12/17/90. This revision represents numerous corrections and minor\nmodifications made to this set of coverages from its construction in 1985\nthrough the revision date. \n\nRevision 2.0, 3/18/91. Major reworking of the coverage, combining all\nState coverages.\n\nReviews_Applied_to_Data: The Census of Agriculture data processing\nprocedure and attribute data have been peer reviewed in 1993 by\nLeonard Orzol and Barbara Ruddy, both hydrologist with the USGS.\n\nThe county boundaries in this coverage have received no formal review. They\nhave, however, been used in numerous applications where serious error would\nhave been obvious. Some State coverages were corrected following such use. The\noffshore polygon extensions and the Canadian Great Lakes polygons have had no\nreview.\n\nRelated_Spatial_and_Tabular_Data_Sets: This coverage is part of series of\n1:2,000,000-scale base maps covering the United States. Layers in this set\ninclude:\n\nCOUNTY -- County boundaries. STATE -- State boundaries (formed from COUNTY).\nWATERBOD -- Water Bodies. STREAM -- Streams. HUC -- Hydrologic cataloging units\n(basins).", "links": [ { diff --git a/datasets/USGS_arapbase_Version 1.0, July 22, 1998.json b/datasets/USGS_arapbase_Version 1.0, July 22, 1998.json index cc6c2891a9..c4b4173c05 100644 --- a/datasets/USGS_arapbase_Version 1.0, July 22, 1998.json +++ b/datasets/USGS_arapbase_Version 1.0, July 22, 1998.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_arapbase_Version 1.0, July 22, 1998", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to display the altitude of the base of the upper\nArapahoe aquifer as depicted in Robson and others (1998).\n\n This digital geospatial data set consists of structure contours on the base\nof the upper member of the Arapahoe aquifer. The U.S. Geological Survey\ndeveloped this data set as part of a project described in the report,\n\"Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the Western\nMargin of the Denver Basin, Colorado\" (Robson and others, 1998).", "links": [ { diff --git a/datasets/USGS_benchmark_1.0.json b/datasets/USGS_benchmark_1.0.json index f6d7e6ae65..8662b78ee4 100644 --- a/datasets/USGS_benchmark_1.0.json +++ b/datasets/USGS_benchmark_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_benchmark_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage was created for the 1990-91 National Water Summary.\n\nThe coverage shows locations of NASQAN benchmark stations.\n\nProcedures_Used: The point coverage was created from data taken from U.S.\nGeological Survey computer files.", "links": [ { diff --git a/datasets/USGS_cir89_Version 1.0.json b/datasets/USGS_cir89_Version 1.0.json index 7f4c293ddb..4de2ba96f8 100644 --- a/datasets/USGS_cir89_Version 1.0.json +++ b/datasets/USGS_cir89_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_cir89_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set was created to determine phreatophyte boundaries used in the\nreport, \"Ground-water discharge determined from estimates of\nevapotranspiration, Death Valley regional flow system, Nevada and California\".\n\n The raster-based, color-infrared composite was derived from Landsat Thematic\nMapper imagery data acquired during June 1989 for the Sarcobatus Flat area of\nthe Death Valley regional flow system. The image is a single-channel,\nparallelepiped classification that when displayed using a 256-color color table\nshows a simulation of a color-infrared composite. The data set was used in\ndetermining phreatophyte boundaries for a ground-water evapotranspiration\nstudy.\n\n The raster-based, color-infrared composite (CIR) was derived from Landsat\nThematic Mapper (TM) imagery data acquired during June 1989 for the Death\nValley regional flow system, Nevada and California.\n\n The image is a single-channel, parallelepiped classification that when\ndisplayed using a 256-color color table shows a simulation of a color-infrared\ncomposite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the\nclassification process. The wavelengths of these channels correspond to those\nused for a CIR composite. The data range of each channel is divided into eight\ndivisions. The 512 possible combinations are then reduced to 256. A color table\nof red, green, and blue values is created for display of the image. Sixteen\npossible color values exist for each color. These values are scaled between 0\nand 255. The image is reduced from more than 16 million colors to 256 colors.\n\nReviews\n\nThe CIR image for 1989 was checked for consistency and accuracy during the data\nprocessing. Two external reviews were done. The reviewers were asked to check\nmetadata and other documentation files for completeness and accuracy. Reviewers\nalso were asked to check the topological consistency, tolerances, projections,\nand geographic extent.\n\n The Landsat Entity-identification number is LT5040034008917210.", "links": [ { diff --git a/datasets/USGS_cira92_Version 1.0.json b/datasets/USGS_cira92_Version 1.0.json index 02192c3f3a..20e316e3ae 100644 --- a/datasets/USGS_cira92_Version 1.0.json +++ b/datasets/USGS_cira92_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_cira92_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to determine phreatophyte boundaries for use in\nthe report, \"Ground-water discharge determined from estimates of\nevapotranspiration, Death Valley regional flow system, Nevada and California\".\n\n The raster-based, color-infrared composite was derived from Landsat Thematic\nMapper imagery data acquired during June 1992 for the Death Valley regional\nflow system. The image is a single-channel, parallelepiped classification that\nwhen displayed using a 256-color color table shows a simulation of a\ncolor-infrared composite. The data set was used in determining phreatophyte\nboundaries for a ground-water evapotranspiration study.\n\n The raster-based, color-infrared composite (CIR) was derived from Landsat\nThematic Mapper (TM) imagery data acquired during June 1992 for the Death\nValley ground-water flow system, Nevada and California.\n\n The image is a single-channel, parallelepiped classification that when\ndisplayed using a 256-color color table shows a simulation of a color-infrared\ncomposite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the\nclassification process. The wavelengths of these channels correspond to those\nused for a CIR composite. The data range of each channel is divided into eight\ndivisions. The 512 possible combinations are then reduced to 256. A color table\nof red, green, and blue values is created for display of the image. Sixteen\npossible color values exist for each color. These values are scaled between 0\nand 255. The image is reduced from more than 16 million colors to 256 colors.", "links": [ { diff --git a/datasets/USGS_cont1992.json b/datasets/USGS_cont1992.json index 6c0adb4c99..98d43551cf 100644 --- a/datasets/USGS_cont1992.json +++ b/datasets/USGS_cont1992.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_cont1992", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of digital water-table contours for the Mojave River\nBasin. The U.S. Geological Survey, in cooperation with the Mojave Water\nAgency, constructed a water-table map of the Mojave River ground-water basin\nfor ground-water levels measured in November 1992. Water-level data were\ncollected from approximately 300 wells to construct the contours. The\nwater-table contours were digitized from the paper map which was published at a\nscale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above\nsea level.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_cont1994.json b/datasets/USGS_cont1994.json index 3f36599d02..5b28e52c43 100644 --- a/datasets/USGS_cont1994.json +++ b/datasets/USGS_cont1994.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_cont1994", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of digital water-table contours for the Morongo Basin. \nThe U.S. Geological Survey constructed a water-table map of the Morongo\nground-water basin for ground-water levels measured during the period\nJanuary-October 1994. Water-level data were collected from 248 wells to\nconstruct the contours. The water-table contours were digitized from the paper\nmap which was published at a scale of 1:125,000. The contour interval ranges\nfrom 3,400 to 1,500 feet above sea level.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_cont1996.json b/datasets/USGS_cont1996.json index da493ce684..1d9875d050 100644 --- a/datasets/USGS_cont1996.json +++ b/datasets/USGS_cont1996.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_cont1996", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of digital water-table contours for the Mojave River,\nthe Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey\nconstructed a water-table map of the Mojave River, the Morongo and the Fort\nIrwin Ground-Water Basins for ground-water levels measured during the period\nJanuary-September 1996. Water-level data were collected from 632 wells to\nconstruct the contours. The water-table contours were digitized from the paper\nmap which was published at a scale of 1:175,512. The contour interval ranges\nfrom 3,400 to 1,550 feet above sea level.\n\n[Summary provided by the USGS.]", "links": [ { diff --git a/datasets/USGS_erf1_Version 1.2, August 01, 1999.json b/datasets/USGS_erf1_Version 1.2, August 01, 1999.json index faca47171e..ea8e41a7e4 100644 --- a/datasets/USGS_erf1_Version 1.2, August 01, 1999.json +++ b/datasets/USGS_erf1_Version 1.2, August 01, 1999.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_erf1_Version 1.2, August 01, 1999", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ERF1 was designed to be a digital data base of river reaches capable of\nsupporting regional and national water-quality and river-flow modeling and\ntransport investigations in the water-resources community. \n\nERF1 has been recently used at the U.S. Geological Survey to support\ninterpretations of stream water-quality monitoring network data (see Alexander\nand others, 1996; Smith and others, 1995). In these analyses, the reach network\nhas been used to determine flow pathways between the sources of point and\nnonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and\ndownstream water-quality monitoring locations in support of predictive\nwater-quality models of stream nutrient transport.\n\nThe digital data set ERF1 includes enhancements to the U.S. Environmental\nProtection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity\nof the digital reach traces and to quantify the time of travel of river reaches\nand reservoirs [see U.S.EPA (1996) for a description of the original RF1].\n\nAny use of trade, product, or firm names is for descriptive", "links": [ { diff --git a/datasets/USGS_erfi-2_2.0, November 19, 2001.json b/datasets/USGS_erfi-2_2.0, November 19, 2001.json index 2de898e8c7..00a10437eb 100644 --- a/datasets/USGS_erfi-2_2.0, November 19, 2001.json +++ b/datasets/USGS_erfi-2_2.0, November 19, 2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_erfi-2_2.0, November 19, 2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report describes the process of enhancements to the stream reach network,\nERF1, which is an enhanced version of EPA's RF1. The U.S. Environmental\nProtection Agency's reach file (RF1) is a database of interconnected stream\nsegments or \"reaches\" that comprise the surface water drainage system for the\nUnited States. A variety of attributes have been assigned to each reach in\nsupport of spatial analysis and mapping applications. ERF1-2 was designed to be\na digital database of river reaches capable of supporting regional and national\nwater-quality and river-flow modeling by the water-resources community. ERF1,\non which ERF1-2 is based, is used at the U.S. Geological Survey to support\nnational-level water-quality monitoring modeling with the SPARROW model (see\nAlexander and others, 2000; Smith and others, 1997). In the current and earlier\nanalyses, the reach network is used to determine flow pathways between the\nsources of point and nonpoint pollutants (e.g., fertilizer use, municipal\nwastewater discharges) and downstream water-quality monitoring locations in\nsupport of predictive water- quality models of stream nutrient transport. \nAcknowledgements\n\nThe authors would like to thank Richard Smith, a co-developer of the SPARROW\napproach, Kristine Verdin, and Stephen Char, all of the U.S. Geological Survey,\nfor providing technical assistance. The reviewers of this report, Dave Stewart,\nand Mike Wieczorek, are also acknowledged for their significant contributions.\n\nThe digital segmented network based on watershed boundaries, ERF1-2, includes\nenhancements to the U.S. Environmental Protection Agency's River Reach File 1\n(RF1) (USEPA, 1996; DeWald and others, 1985) to support national and\nregional-scale surface water-quality modeling. Alexander and others (1999)\ndeveloped ERF1, which assessed the hydrologic integrity of the digital reach\ntraces and calculated the mean water time-of-travel in river reaches and\nreservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced\nRegressions (of nutrient transport) On Watershed) modeling. Within the context\nof a Geographic Information System, SPARROW estimates the proportion of\nwatersheds in the conterminous U.S. with outflow concentrations of several\nnutrients, including total nitrogen and total phosphorus, (Smith, R.A.,\nSchwarz, G.E., and Alexander, R.B., 1997). This version of the network expands\non ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and\ntotal drainage area derived from 1-kilometer (km) elevation data for North\nAmerica. Previous estimates of the water time-of-travel were recomputed for\nreaches with water- quality monitoring sites that included two reaches. The\nmean flow and velocity estimates for these split reaches are based on previous\nestimation methods (Alexander et al., 1999) and are unchanged in ERF1-2.\nDrainage area calculations provide data used to estimate the contribution of a\ngiven nutrient to the outflow. Data estimates depend on the accuracy of node\nconnectivity. Reaches split at water- quality or pesticide-monitoring sites\nindicate the source point for estimating the contribution and transport of\nnutrients and their loads throughout the watersheds. The ERF1-2 coverage\nextends the earlier ERF1 coverage by providing digital-elevation-model\n(DEM-based estimates of reach drainage area founded on the 1-kilometer data for\nNorth America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster\ngrid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder,\n1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson,\n1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed\nboundaries are maintained in a raster (grid cell) format as well as a vector\n(polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and\nthe grid, ERF1-2WS are available at:\n\"http://water.usgs.gov/orh/nrwww/sparrow_section5_nolan.pdf\". Any use of\ntrade, product, or firm names is for descriptive purposes only and does not\nimply endorsement by the U.S. Government. \n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in ArcInfo\nformat, this metadata file may include some ArcInfo-specific terminology.", "links": [ { diff --git a/datasets/USGS_etsite_Version 1.0.json b/datasets/USGS_etsite_Version 1.0.json index 08b0291cd2..598459e9ee 100644 --- a/datasets/USGS_etsite_Version 1.0.json +++ b/datasets/USGS_etsite_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_etsite_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital data set was created to display site locations at which\nmicrometeorological data were collected in Ash Meadows and Oasis Valley, Nev.\n\nThe digital data set provides locations and general descriptions of sites\ninstrumented to collect micrometeorological data from which mean annual ET\nrates were computed. Sites are located in Ash Meadows and Oasis Valley, Nevada.\nData were collected December 1993 through present.\n\nIntroduction\n\nThe digital data set was created in cooperation with the U.S. Department of\nEnergy. The data set was created as part of a study to refine current estimates\nof ground-water discharge from the major discharge areas of the Death Valley\nregional flow system. This digital data set provides locations and general\ndescriptions of sites instrumented during recent studies of evapotranspiration\nin Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993\nthrough 2001.\n\nReviews\n\nThe digital data set has gone through a multi-level, quality-control process to\nensure that the data are a reasonable representation of source points.\nReviewers were asked to check metadata and other documentation files for\ncompleteness and accuracy. Reviewers also were asked to check the topological\nconsistency, tolerances, projections, and geographic extent.\n\nNotes\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government. Although the data set has\nbeen used by the U.S. Geological Survey, U.S. Department of the Interior, no\nwarranty expressed or implied is made by the U.S. Geological Survey as to the\naccuracy of the data and related materials.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in non-proprietary form, as well as in\nArcInfo format, this metadata file may include some ArcInfo-specific\nterminology.\n\nUsers should exercise caution and judgment in applying these data, and be aware\nthat errors may be present in any or all of the digital image data. If errors \nare encountered in this data set, it will be appreciated if the user would pass\nthis information to the Metadata_Contact.", "links": [ { diff --git a/datasets/USGS_etunit_Version 1.0.json b/datasets/USGS_etunit_Version 1.0.json index 18ec47c91f..a1b10119f9 100644 --- a/datasets/USGS_etunit_Version 1.0.json +++ b/datasets/USGS_etunit_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_etunit_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set was created to delineate the aerial extent and quantify acreage of\nthe different ET units found within the many major discharge areas of the Death\nValley regional flow system.\n\nThe raster-based classification of evapotranspiration (ET) units is for nine\nmajor discharge areas in the Death Valley regional flow system. The ET units\ndelineate general areas of similar vegetation and soil-moisture conditions.\nClassifications were derived from Landsat Thematic Mapper imagery data acquired\nJune 13, 1992; Sept. 1, 1992; and June 21, 1989.\n\nIntroduction\n\nThe raster-based classification of ET units within the major discharge areas of\nthe Death Valley regional flow system determined from Landsat Thematic Mapper\n(TM) imagery data acquired June 13, 1992, Sept. 1, 1992; and June 21, 1989.\nBackground information on classification procedures can be found in American\nSociety of Photogrammetry (1983). Except for Sarcobatus Flat, all discharge\nareas were classified using the 1992 TM imagery. An accurate classification of\nSarcobatus Flat could not be attained from 1992 TM imagery because of extensive\ncloud cover over the area. Instead, Sarcobatus Flat was classified from TM data\nacquired June 21, 1989.\n\nReviews\n\nThe final classification of ET units within each major discharge area was\nchecked for consistency and accuracy during data processing. Two external\nreviews were done. The reviewers were asked to check metadata and other\ndocumentation files for completeness and accuracy. Reviewers also were asked to\ncheck the topological consistency, tolerances, projections, and geographic\nextent.", "links": [ { diff --git a/datasets/USGS_gpwa_utm27f_met.json b/datasets/USGS_gpwa_utm27f_met.json index 80f2726e23..f3121aaca8 100644 --- a/datasets/USGS_gpwa_utm27f_met.json +++ b/datasets/USGS_gpwa_utm27f_met.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_gpwa_utm27f_met", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This coverage is intended as a data layer representing the spatial\ndistribution of mean annual precipitation in Ohio for the years 1931-80.\nInformation contained in this coverage has been used to obtain values of mean\nannual precipitation at basin centroid locations.\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in ArcInfo\nformat, this metadata file may include some ArcInfo-specific terminology.\n\nThis is a Triangulated Irregular Network (TIN) of mean annual precipitation for\nthe period 1931-80 for Ohio.\n\nA 1:1,100,000 scale (approximate) paper isoline map of mean annual\nprecipitation from Harstine (1991) was digitized as arcs directly into an\nAlbers equal-area projection. The arc coverage was projected to the State Plane\nCoordinate system, zone 5001, and then converted to a TIN by means of the\n\"arctin\" command.", "links": [ { diff --git a/datasets/USGS_ha24_hum.json b/datasets/USGS_ha24_hum.json index 483cb82404..8135a103d7 100644 --- a/datasets/USGS_ha24_hum.json +++ b/datasets/USGS_ha24_hum.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ha24_hum", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to display the topographic and administrative\nhydrographic area boundaries for the Humboldt River Basin at 1:24,000-scale.\n\nThis data set contains the topographic and administrative hydrographic area\nboundaries for the Humboldt River Basin at 1:24,000-scale.\n\nIntroduction\n\nThe hydrographic area (HA) boundaries for the State of Nevada were delineated\non 1:250,000-scale maps, in cooperation with the U.S. Geological Survey (USGS),\nand then redrawn and published at 1:500,000-scale (Cardinalli and others,\n1968). This 1:500,000-scale map is the current reference for HAs in Nevada and\nis used as a guide in delineating the HAs at 1:24,000-scale.\n\nThis data set contains the topographic and administrative HA boundaries for the\nHumboldt River Basin. The Humboldt River Basin HAs were delineated and\ndigitized from 1993 to 1998 using 1:24,000-scale USGS topographic maps.\n\nReviews\n\nThe digital data in this data base has gone through a rigorous, multi-level,\nquality-control process that ensures the data set is a fair representation of\nthe source map. If errors are encountered in this data set, it will be\nappreciated if the user would pass this information to the Metadata_Contact.\n\nTwo reviews were done. The reviewers were asked to check metadata and other\ndocumentation files for completeness and accuracy. Reviewers were asked to\ncheck the topological consistency, tolerances, projections, and geographic\nextent.\n\nNotes\n\nIt should be noted that, although the boundary lines between hydrographic areas\ngenerally coincide with true topographic basin divides, some of the lines are\narbitrary divisions that have no basis in topography, but are administrative\nand specified by Nevada Division of Water Resources.\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in non-proprietary form, as well as in\nARC/INFO format, this metadata file may include some ARC/INFO-specific\nterminology.", "links": [ { diff --git a/datasets/USGS_herbicide2_1.0.json b/datasets/USGS_herbicide2_1.0.json index 8a3d2fba8c..586fbc1cd9 100644 --- a/datasets/USGS_herbicide2_1.0.json +++ b/datasets/USGS_herbicide2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_herbicide2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The herbicide-use estimates in this coverage are intended for use as a means\nfor estimating regional herbicide use, and for producing maps showing relative\nrates of herbicide use across broad regions of the United States.\n\nThis coverage contains estimates of herbicide use for the twenty-first through\nthe fortieth most-used herbicides in the conterminous United States as reported\nin Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are\nreported for each county polygon as acres treated, pounds of active ingredient\nused, and pounds used per square mile. The herbicide-use estimates provided by\nGianessi and Puffer (1991) list acres treated and pounds of active ingredient\napplied for a given crop in each county for which use has been estimated.\nCropping data are from the 1987 Census of Agriculture, and are subject to\noccasional suppressions of acreage estimates at the county level due to\nproblems of confidentiality and census disclosure rules. The herbicide-use\nestimates included in this coverage are totals of use on all crops treated in a\ngiven county.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).\n\n[Summary provided by USGS]", "links": [ { diff --git a/datasets/USGS_herbicide3_1.0.json b/datasets/USGS_herbicide3_1.0.json index 1a1d983b7d..f3d3d0af0d 100644 --- a/datasets/USGS_herbicide3_1.0.json +++ b/datasets/USGS_herbicide3_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_herbicide3_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The herbicide-use estimates in this coverage are intended for use as a means\nfor estimating regional herbicide use, and for producing maps showing relative\nrates of herbicide use across broad regions of the United States.\n\nThis coverage contains estimates of herbicide use for the forty-first through\nthe sixtieth most-used herbicides in the conterminous United States as reported\nin Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are\nreported for each county polygon as acres treated, pounds of active ingredient\nused, and pounds used per square mile.\n\nThe herbicide-use estimates provided by Gianessi and Puffer (1991) list acres\ntreated and pounds of active ingredient applied for a given crop in each county\nfor which use has been estimated. Cropping data are from the 1987 Census of\nAgriculture, and are subject to occasional suppressions of acreage estimates at\nthe county level due to problems of confidentiality and census disclosure\nrules. The herbicide-use estimates included in this coverage are totals of use\non all crops treated in a given county.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).\n\n[Summary provided by USGS]", "links": [ { diff --git a/datasets/USGS_herbicide4_1.0.json b/datasets/USGS_herbicide4_1.0.json index 4ca21b55f8..254fceaa0c 100644 --- a/datasets/USGS_herbicide4_1.0.json +++ b/datasets/USGS_herbicide4_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_herbicide4_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The herbicide-use estimates in this coverage are intended for use as a\nmeans for estimating regional herbicide use, and for producing maps\nshowing relative rates of herbicide use across broad regions of the\nUnited States.\n\nThis coverage contains estimates of herbicide use for the sixty-first through\nthe eightieth most-used herbicides in the conterminous United States as\nreported in Gianessi and Puffer (1991). Herbicide-use estimates in this\ncoverage are reported for each county polygon as acres treated, pounds of\nactive ingredient used, and pounds used per square mile.\n\nThe herbicide-use estimates provided by Gianessi and Puffer (1991) list acres\ntreated and pounds of active ingredient applied for a given crop in each county\nfor which use has been estimated. Cropping data are from the 1987 Census of\nAgriculture, and are subject to occasional suppressions of acreage estimates at\nthe county level due to problems of confidentiality and census disclosure\nrules. The herbicide-use estimates included in this coverage are totals of use\non all crops treated in a given county.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).\n\n[Summary provided by USGS]", "links": [ { diff --git a/datasets/USGS_herbicidel_01_1.0.json b/datasets/USGS_herbicidel_01_1.0.json index e31a1c47c0..9b08a20ead 100644 --- a/datasets/USGS_herbicidel_01_1.0.json +++ b/datasets/USGS_herbicidel_01_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_herbicidel_01_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The herbicide-use estimates in this coverage are intended for use as a means\nfor estimating regional herbicide use, and for producing maps showing relative\nrates of herbicide use across broad regions of the United States.\n\nThis coverage contains estimates of herbicide use for the 20 most-used\nherbicides in the conterminous United States as reported in Gianessi and Puffer\n(1991). Herbicide-use estimates in this coverage are reported for each county\npolygon as acres treated, pounds of active ingredient used, and pounds used per\nsquare mile. The herbicide-use estimates provided by Gianessi and Puffer (1991)\nlist acres treated and pounds of active ingredient applied for a given crop in\neach county for which use has been estimated. Cropping data are from the 1987\nCensus of Agriculture, and are subject to occasional suppressions of acreage\nestimates at the county level due to problems of confidentiality and census\ndisclosure rules. The herbicide-use estimates included in this coverage are\ntotals of use on all crops treated in a given county.\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in\nthe National Atlas of the United States (1970).\n\nHerbicides Herbicide use Counties United States\n\nProcedures_Used: HERBICIDE-USE DATA\n\nAn automated procedure was developed to process the raw herbicide-use data into\nARC/INFO coverage attributes. The procedure is summarized below:\n\n(1) copy county2m coverage to coverage called herbicide%#%, and (2) run the AML\nherbadd.aml for each herbicide to be added.\n\nThe herbadd.aml program runs a fortran program to total estimates of herbicide\nuse on all crops by county, then processes these data, finally adding them as\nthree columns of attribute data to the county coverage. Other programs were\ndeveloped to calculate summary statistics of the herbicide-attribute data and\nto produce maps that show attribute values across the United States.\n\nCOUNTY BOUNDARIES\n\nThis series of maps was published as part of the National Atlas of the United\nStates (U.S.Geological Survey, 1970). The maps for the conterminous United\nStates were digitized in 15 sheets and published in the Digital Line Graph\n(DLG) format as described by Domeratz and others (1983).\n\nEach sheet was prepared by reading the DLG files of the political and\nwater-bodies layers, converting them to ARC/INFO; extracting the county\nboundaries and the coastline, respectively; and joining the two layers. FIPS\ncodes were assigned to all polygons by using available sources and were checked\nmanually.\n\nBoundaries with adjacent sheets of the 15-sheet set were edgematched manually;\none of the sheets was chosen arbitrarily as the \"correct\" border. Edgematching\noperations were used to adjust the linework as far as was necessary so that the\ncoverages would fit to a tolerance of 100 meters (328.1 feet). The coverage\n(referred to herein as Version 1.0) was stored as 49 separate coverages (48\nStates and the District of Columbia) because the ARC/INFO software in use at\nthe time could not process the entire coverage. Individual States could be\njoined by specifying a tolerance of 100 meters.\n\nFrom time to time, adjustments were made to the State coverages to reflect\nchanges in counties. The accuracy of these adjustments is believed to be\ncomparable to that of the original linework.\n\nFor Version 2.0, all State coverages were rejoined and manually edited to\nproduce a perfect edgematch between all States. For States on the original\nmap-sheet boundaries, this adjustment averaged less than 20 meters and in no\ncase was more than 100 meters. The whole coverage was Cleaned to a tolerance of\n20 meters (65.6 feet), which resulted in few, if any, effects on small offshore\nislands. The coverage also was checked to ensure that it represented current\ncounties or county equivalents.\n\nThe coverage in Version 1.0 ended at the coastline. No attempt was made to\ndepict offshore areas. This created problems when the coverage was used to\nassign county codes to sampling stations located near the coast. To help in\nthis matter, Version 2.0 includes offshore extensions of the county polygons.\nThe (water) boundaries of many of these polygons are arbitrary.\n\nThe Canadian Great Lakes features are another new addition to Version 2.0. They\nwere added to improve the utility of the coverage for visual displays. Although\nthe Canadian Great Lakes are represented logically by a single polygon,\npractical considerations--the inability of some software to plot polygons with\na large number of vertices--made it necessary to separate them into four\npolygons. The dividing lines are located in narrow channels to minimize\ninterference with plotting patterns. Canadian islands within the Great Lakes\nalso were included.\n\nAll tick marks were relocated to places that are easily visible on maps of the\nUnited States, to help in registering maps that otherwise may not have adequate\nregistration information.\n\nTo expedite accessing parts of the coverage, certain items have been indexed\nwith the procedure INDEX_COUNTY.AML. See Section 3 above. A spatial index also\nwas created.\n\nWhen this coverage is used to clip or intersect other coverages, a tolerance as\nlow as 2 meters (6.6 feet) can be used.\n\nThe processing used to derive this coverage moved boundaries from their\npositions on the original maps. In cases of conflicting lines, preference was\ngiven to forming the correct topology. Strictly speaking, this coverage is not\nidentical to the source materials. These changes were unavoidable in producing\na continuous coverage of the conterminous United States.", "links": [ { diff --git a/datasets/USGS_hgmr_Version 1.json b/datasets/USGS_hgmr_Version 1.json index 8a66422999..85252ed7d6 100644 --- a/datasets/USGS_hgmr_Version 1.json +++ b/datasets/USGS_hgmr_Version 1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_hgmr_Version 1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was used to compare base-flow and ground-water nitrate loads to\nassess the significance of ground-water discharge as a source of nitrate load\nto non tidal streams in the Chesapeake Bay watershed.\n\nGeneralized lithology (rock type) and physiography based on geologic formations\nwere used to characterize hydrgeomorphic regions (HGMR) within the Chesapeake\nBay watershed. These HGMRs were used in conjunction with existing data to\nassess the significance of ground-water discharge as a source of nitrate load\nto non tidal streams in the Chesapeake Bay watershed (Bachman and others,\n1998). This work is part of the U.S. Geological Survey's (USGS) Chesapeake Bay\ninitiative to develop an understanding and provide scientific information for\nthe restoration of the Chesapeake Bay and its watershed (Phillips and Caughron,\n1997).\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Geological Survey.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in non proprietary form, as well as in\nARC/INFO format, this metadata file may include some ARC/INFO-specific\nterminology. \n\nThe HGMR data set is the result of combining digital data sets of physiography\nand rock type from numerous sources.", "links": [ { diff --git a/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json b/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json index 546eed3183..e392c8eb84 100644 --- a/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json +++ b/datasets/USGS_hydmain_hum_Version 1.0, (September, 2001).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_hydmain_hum_Version 1.0, (September, 2001)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was created as a layer of a geographic information system (GIS)\nto calculate river miles on the Humboldt River. The currentness and accuracy of\nthe digital orthophoto quadrangle (DOQ) source exceeded that of other available\ndata.\n\nThis data set contains the main stem of the Humboldt River as defined by\nHumboldt Project personnel of the U.S. Geological Survey Nevada District, 2001.\nThe data set was digitized on screen using digital orthophoto quadrangles from\n1994.\n\nReviews\n\nThe digital data in this data set has gone through a rigorous, multi-level,\nquality-control process that ensures the data set is a fair representation of\nthe source map. If errors are found in this data set, it will be appreciated if\nthe user would pass this information to the Metadata_Contact.\n\nTwo formal reviews were done. The reviewers were asked to check metadata and\nother documentation files for completeness and accuracy. Reviewers were asked\nto check the topological consistency, tolerances, projections, and geographic\nextent.\n\nNotes\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in ArcInfo\nformat, this metadata file may include some ArcInfo-specific terminology.", "links": [ { diff --git a/datasets/USGS_landfills_1.1.json b/datasets/USGS_landfills_1.1.json index c5de03f75f..cb6694507f 100644 --- a/datasets/USGS_landfills_1.1.json +++ b/datasets/USGS_landfills_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_landfills_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a point coverage of landfills shown in the 1986 National Water Summary\nReport (U.S. Geological Survey, 1987).\n", "links": [ { diff --git a/datasets/USGS_landuse_1.json b/datasets/USGS_landuse_1.json index a7aa440cd1..5696e20ef0 100644 --- a/datasets/USGS_landuse_1.json +++ b/datasets/USGS_landuse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_landuse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The intended use of this coverage was for the state sections of the 1990-91\nNational Water Summary on surface-water quality. Each state report contains a\nmap of the state's major land uses and, where possible, discusses the influence\nof land use on water quality in the state. \n\nThis is a polygon coverage of major land uses in the United States. The source\nof the coverage is the map of major land uses in the 1970 National Atlas of the\nUnited States, pages 158-159, which was adapted from U.S. Department of\nAgriculture, \"Major Land Uses in the United States,\" by Francis J. Marschner,\nrevised by James R. Anderson, 1967.", "links": [ { diff --git a/datasets/USGS_lfhbase_Version 1.0, July 09, 1998.json b/datasets/USGS_lfhbase_Version 1.0, July 09, 1998.json index 3e15319c1e..802d7d1d1f 100644 --- a/datasets/USGS_lfhbase_Version 1.0, July 09, 1998.json +++ b/datasets/USGS_lfhbase_Version 1.0, July 09, 1998.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_lfhbase_Version 1.0, July 09, 1998", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to display the altitude of the base of the\nLaramie-Fox Hills aquifer and the Arapahoe aquifer as depicted in the plates in\nRobson and others, (1988).\n\nThis digital geospatial data set consists of structure contours of the base of\nthe Laramie-Fox Hills aquifer and the base of the Arapahoe aquifer along the\nFront Range of Colorado. The U.S. Geological Survey developed this data set as\npart of a project described in the report, \"Structure, Outcrop, and Subcrop of\nthe Bedrock Aquifers Along the Western Margin of the Denver Basin, Colorado\"\n(Robson and others, 1998).", "links": [ { diff --git a/datasets/USGS_lfhtop_Version 1.0 (July 20, 1998).json b/datasets/USGS_lfhtop_Version 1.0 (July 20, 1998).json index b027482867..aedd52c744 100644 --- a/datasets/USGS_lfhtop_Version 1.0 (July 20, 1998).json +++ b/datasets/USGS_lfhtop_Version 1.0 (July 20, 1998).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_lfhtop_Version 1.0 (July 20, 1998)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to display maps of the altitude of the top of the\nLaramie-Fox Hills aquifer (Robson and others, 1998).\n\nThis digital geospatial data set consists of structure contours of the top of\nthe Laramie-Fox Hills aquifer along the Front Range of Colorado. The U.S.\nGeological Survey developed this data set as part of a project described in the\nreport, \"Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the\nWestern Margin of the Denver Basin, Colorado\" (Robson and others, 1998).", "links": [ { diff --git a/datasets/USGS_manure_1.0.0.json b/datasets/USGS_manure_1.0.0.json index 555b423946..5a3d4e51ba 100644 --- a/datasets/USGS_manure_1.0.0.json +++ b/datasets/USGS_manure_1.0.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_manure_1.0.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These estimates are intended for large-scale ground- and surface-water\nanalyses of nutrient sources or changes in these sources. These data on\nnutrients in manure can be compared to fertilizer inputs of nutrients.\n\nThis data set contains county estimates of nitrogen and phosphorus content of\nanimal wastes produced annually for the years 1982, 1987, and 1992. The\nestimates are based on animal populations for those years from the 1992 Census\nof Agriculture (U.S. Bureau of the Census, 1995) and methods for estimating the\nnutrient content of manure from the Soil Conservation Service (1992).\n\nThe data set includes several components..\n\n1. Spatial component - generalized county boundaries in ARC/INFO format/1/,\nincluding nine INFO lookup tables containing animal counts and nutrient\nestimates keyed to the county polygons using county code. (The county lines\nwere not used in the nutrient computations and are provided for displaying the\ndata as a courtesy to the user.) The data is organized by 5-digit state/county\nFIPS (Federal Information Processing Standards) code. Another INFO table lists\nthe county names that correspond to the FIPS codes.\n\n2. Tabular component - Nine tab-delimited ASCII lookup tables of animal counts\nand nutrient estimates organized by 5-digit state/county FIPS (Federal\nInformation Processing Standards) code. Another table lists the county names\nthat correspond to the FIPS codes.\n\nThe amount of nitrogen and phosphorus present in manure (in kilograms) has been\ncalculated for each county of the United States. The procedure is identical to\nthat of Smith and others (1997), which covered the year 1987. Nutrient\nestimates for the years 1982 and 1987 were computed again for the data set\nhere, and the results were checked against the results computed previously by\nAlexander (written commun., 1992) for those years to ensure that they were\nidentical.\n\nLimitations:\n\nThe estimates are county level and are based on estimates of the nutrient\ncontent of animal manure produced per 1,000 pounds of animal weight on a daily\nbasis.\n\nOne important limitation of the animal population numbers from the Census of\nAgriculture is that for some counties and animal classes, no data are reported.\nThis limitation reportedly is the result of restrictions on including animal\npopulation data for counties where animal production is dominated or limited to\none business or farm. These data therefore are considered trade secrets and may\nnot be included in the county-based data. This limitation on population data at\nthe county level results in discrepancies when county-based data are summed and\ncompared to national animal population totals. At the present we have no way of\nestimating animal populations for those counties with missing data and further\nhave no way of determining which counties are missing data. Therefore, the\nanimal manure, nitrogen and phosphorus estimates for some counties are an\nunderestimate of the total nutrient form animal manure in those counties.", "links": [ { diff --git a/datasets/USGS_map-2653_1.0.json b/datasets/USGS_map-2653_1.0.json index 7f0066bea2..cbea2f2b0c 100644 --- a/datasets/USGS_map-2653_1.0.json +++ b/datasets/USGS_map-2653_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_map-2653_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this geologic map and database is to support and be part of a\nthree-dimensional geologic framework study of south-central Missouri. The\nframework will be used to assess environmental impacts of lead and zinc mining\nin the Mark Twain National Forest on the hydrologic system of the Ozark\nNational Scenic Riverways.\n\nThe geology of the Eminence 7 1/2-minute quadrangle , Shannon County, Missouri\nwas mapped from 1996 through 1997 as part of the Midcontinent Karst Systems and\nGeologic Mapping Project, Eastern Earth Surface Processes Team. The map\nsupports the production of a geologic framework that will be used in\nhydrogeologic investigations related to potential lead and zinc mining in the\nMark Twain National Forest adjacent to the Ozark National Scenic Riverways\n(National Park Service). Digital geologic coverages will be used by other\nfederal and state agencies in hydrogeologic analyses of the Ozark karst system\nand in ecological models.\n\nBedrock, Quaternary , residual units, faults, and structural data are each\nstored in separate coverages. See readme.txt file for explanation of\norganization.", "links": [ { diff --git a/datasets/USGS_mapi-1300_Version 1.0.json b/datasets/USGS_mapi-1300_Version 1.0.json index af8f7b574f..6f7e59c487 100644 --- a/datasets/USGS_mapi-1300_Version 1.0.json +++ b/datasets/USGS_mapi-1300_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-1300_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide geologic map GIS of the Choteau\n1:250,000 quadrangle for use in future spatial analysis by a variety of users. \nThese data can be printed in a variety of ways to display various geologic\nfeatures or used for digital analysis and modeling. This database is not meant\nto be used or displayed at any scale larger than 1:250,000 (e.g. 1:100,000 or\n1:24,000).\n\nThe geologic and structure map of Choteau 1 x 2 degree quadrangle (Mudge and\nothers, 1982) was originally converted to a digital format by Jeff Silkwood\n(U.S. Forest Service and completed by the U.S. Geological Survey staff and\ncontractor at the Spokane Field Office (WA) in 2000 for input into a geographic\ninformation system (GIS). The resulting digital geologic map (GIS) database\ncan be queried in many ways to produce a variey of geologic maps. Digital base\nmap data files (topography, roads, towns, rivers and lakes, etc.) are not\nincluded: they may be obtained from a variety of commercial and government\nsources. This database is not meant to be used or displayed at any scale\nlarger than 1:250,000 (e.g. 1:100,000 or 1:24,000. The digital geologic map\ngraphics and plot files (chot250k.gra/.hp/.eps and chot-map.pdf) that are\nprovided in the digital package are representations of the digital database. \nThey are not designed to be cartographic products.\n\nThis GIS consists of two major Arc/Info datasets, a line and polygon file\n(chot250k) containing geologic contact and structures (lines) and geologic map\nrock units (polygons), and a point file (chot250kp) containing structural point\ndata for plunging folds.", "links": [ { diff --git a/datasets/USGS_mapi-1509A_version 1.0.json b/datasets/USGS_mapi-1509A_version 1.0.json index 1fb0c2e9d5..804e5dd3dd 100644 --- a/datasets/USGS_mapi-1509A_version 1.0.json +++ b/datasets/USGS_mapi-1509A_version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-1509A_version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a geologic map GIS of the Wallace 1x2\ndegree quadrangle for use in future spatial analysis by a variety of users.\n\nThis database is not meant to be used or displayed at any scale larger than\n1:250,000 (e.g., 1:100,000 or 1:24,000)\n\nThis dataset was digitized by the U.S. Geological Survey EROS Data Center and\nU.S. Geological Survey Spokane Field Office for input into an Arc/Info\ngeographic information system (GIS) The digital geologic map database can be\nqueried in many ways to produce a variety of derivative geologic maps.\n\nThis GIS consists of two major and Arc/Info datasets: one line and polygon file\n(wal250k) containing geologic contacts and structures (lines) and geologic map\nrock units (polygons), and one point file (wal250bc) containing breccia\noutcrops.", "links": [ { diff --git a/datasets/USGS_mapi-1803_1.0.json b/datasets/USGS_mapi-1803_1.0.json index ddb504ea61..9fa89a22ea 100644 --- a/datasets/USGS_mapi-1803_1.0.json +++ b/datasets/USGS_mapi-1803_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-1803_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS database was prepared to provide digital geologic coverage for the\nDillon 1 degree by 2 degree quadrangle of southwest Montana and east-central\nIdaho.\n\nThe digital ARC/INFO databases included in this website provide a GIS database\nfor the geologic map of the Dillon 1 degree by 2 degree quadrangle of southwest\nMontana and east-central Idaho. The geologic map was orginally published as\nU.S. Geological Survey Miscellaneous Investigations Series Map I-1803-H. This\nwebsite directory contains ARC/INFO format files that can be used to query or\ndisplay the geology of USGS Map I-1803-H with GIS software.\n(\"http://pubs.usgs.gov/imap/1993/i-1803-h/\")", "links": [ { diff --git a/datasets/USGS_mapi-1819_1.0.json b/datasets/USGS_mapi-1819_1.0.json index 61ec4bc57c..8e9a6f0a0c 100644 --- a/datasets/USGS_mapi-1819_1.0.json +++ b/datasets/USGS_mapi-1819_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-1819_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a geologic map GIS database of Challis\n1x2 Quadrangle, Idaho for use in spatial analysis.\n\nThe paper version of The geology of the Challis 1 x 2 quadrangle, was compiled\nby Fred Fisher, Dave McIntyre and Kate Johnson in 1992. The geology was\ncompiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc.\nof Fort Collins Colorado digitized this map under contract for N.Shock. G.Green\nedited and prepared the Challis digital version for publication as a geographic\ninformation system database. The digital geologic map database can be queried\nin many ways to produce a variety of geologic maps.", "links": [ { diff --git a/datasets/USGS_mapi-2267.json b/datasets/USGS_mapi-2267.json index 37b6ade30e..83ce630f6b 100644 --- a/datasets/USGS_mapi-2267.json +++ b/datasets/USGS_mapi-2267.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2267", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide geologic map GIS of the Kalispell\n1:250,000 quadrangle for use in the future spatial analysis by a variety of\nusers. \n\nThis database is not meant to be used or displayed at any scale larger than\n1:250,000 (e.g., 1:100,000 or 1:24,000).\n\nThis dataset was digitized by the U.S. Geological Survey EROS Data Center and\nU.S. Geological Survey Spokane Field Office for input into an Arc/Info\ngeographic information system (GIS). The digital geologic map database can be\nqueried in many ways to produce a variety of derivative geologic maps.\n\nThis GIS dataset consists of one major Arc/Info dataset: a line and polygon\nfile (kal250k) that contains geologic contacts and structures (lines) and\ngeologic map rock units (polygons).", "links": [ { diff --git a/datasets/USGS_mapi-2395_1.0.json b/datasets/USGS_mapi-2395_1.0.json index ec052acf80..474671325d 100644 --- a/datasets/USGS_mapi-2395_1.0.json +++ b/datasets/USGS_mapi-2395_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2395_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a geologic map GIS database of Challis\nNational Forest, Idaho for use in spatial analysis by a variety of users.\n\nThe paper version of the Geologic Map of the eastern part of the Challis\nNational Forest and vicinity, Idaho was compiled by Anna Wilson and Betty Skipp\nin 1994. The geology was compiled on a 1:250,000 scale topographic base map. \nTechniGraphic System, Inc. of Fort Collins Colorado digitized this map under\ncontract for N.Shock. G.Green edited and prepared the digital version for\npublication as a GIS database. The digital geologic map database can be\nqueried in many ways to produce a variety of geologic maps.", "links": [ { diff --git a/datasets/USGS_mapi-2494_1.0.json b/datasets/USGS_mapi-2494_1.0.json index 2d05c70da1..fded22df5f 100644 --- a/datasets/USGS_mapi-2494_1.0.json +++ b/datasets/USGS_mapi-2494_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2494_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files in this directory are those that were used to create the Generalized Thermal Maturity Map of Alaska (USGS Miscellaneous Investigations Map I-2494), published in 1996. These files are necessary for importing the map in digital form into a Geographical Information System. Output files in several formats also are included in this directory; these can be used any time a digital version of the complete map is needed. \n The map is based, in large part, on the vitrinite-reflectance (VR) and\nconodont color-alteration-index (CAI) data in USGS Open-File Report 92-409, an\nupdated version of which also is included on this CD-ROM.\n\nAlaska is a complex amalgamation of tectonic blocks with diverse histories. Sedimentary basins that are formed on these blocks both before amalgamation and as a result of collisions between them record the tectonic history of this complex region. Thermal-maturity data-indicators of maximum burial temperatures-provide important constraints both on basin evolution and on terrane amalgamation. To help elucidate these relations, and to provide constraints for hydrocarbon assessments, the U.S. Geological survey (USGS) has compiled thermal-maturity data from Alaska for many decades. This report is a digital release of our current understanding of thermal-maturity patters in Alaska.\n\nThe 10 ARC/INFO coverages used to construct the map, together with the directory \"INFO\" (needed by ARC/INFO to support the coverages), are found in the \"coverages\" subdirectory. These coverages can be used by any GIS capable of importing files in ARC/INFO format.\n\nExport version of these coverages are found in the subdirectory \"export files.\" These files can be imported into ARC/INFO with the \"import\" command.\n\nShapefile versions of 9 of the ARC/INFO coverages are found in the subdirectory \"shapefiles.\" Each shapefile actually consists of three files, with extensions .shp, .shx., and .dbf; all are needed for importation into a GIS supporting the shapefile format.\n\nInset figures and text, as well as the map title, headers, and latitude-longitude ticks, were created as a separate file. This file is available in Adobe Illustrator 6.0 format (insets.ill) and in Encapsulated PostScript format (insets.eps) in the subdirectory \"insetfigures.\" \n\nThe subdirectory \"miscfiles\" contains many important files needed to use these coverages in ARC/INFO or another GIS.\n", "links": [ { diff --git a/datasets/USGS_mapi-2634_2.0.json b/datasets/USGS_mapi-2634_2.0.json index fe446d92db..73b8b7ab34 100644 --- a/datasets/USGS_mapi-2634_2.0.json +++ b/datasets/USGS_mapi-2634_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2634_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The geology of the Sedan quadrangle was mapped as part of a regional study of\nthe western Crazy Mountains Basin. It was digitized for ease of production of\nthe printed version and for greater distribution for analytical use.\n\nThis quadrangle lies 6.4 km (4 mi) northeast of Bozeman, Mont., in southwestern\nMontana. Metamorphic, sedimentary, and volcanic rocks of Precambrian to\nTertiary age are exposed in the Bridger Range and southwestern margin of the\nCrazy Mountains Basin in a crustal cross section and a structural triangle\nzone. Surface geology records Precambrian extension, Late Paleocene\neast-vergent contraction, including backthrusts, and Holocene basin-range\nextension.\n\nA preliminary map was published as a U.S. Geological Survey Open-File Report in\n1971. The geologic data was interpreted 1965-93, the interpretation being\ninformed by data from two wells in addition to the original field work. The\ndigital files for the map were released in November 1998. The map-on-demand\nedition, released in January 2000, includes supplemental figures,three cross\nsections, and interpretive text.\n\nUsers should be aware that of the many faults mapped, the only active one is\nthe range front fault on the west side of the Bridger Range.\n\nThe dataset for the Sedan quadrangle consists of 10 coverages:\ngeo_net, geo_pnt, stru_net, stru_pnt, data_net, data_pnt, pnt_sym,\npnt_graphic, stpnt_graphic, and dvalues. The three coverages\npnt_graphic, stpnt_graphic, and dvalues are not \"true\" ARC/INFO\ncoverages. They contain the graphic representations of symbols used\non the geologic map:\n\n>pnt_sym = pnt_graphic,\n>stru_pnt and geo_pnt = stpnt_graphic, and\n>dvalues = annotation for stru_pnt and geo_pnt.", "links": [ { diff --git a/datasets/USGS_mapi-2645_version 1.0.json b/datasets/USGS_mapi-2645_version 1.0.json index 10267d1dea..d3345b2810 100644 --- a/datasets/USGS_mapi-2645_version 1.0.json +++ b/datasets/USGS_mapi-2645_version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2645_version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database was developed to improve upon previous mapping in the central\nMarysvale volcanic field and compile older mapping at a consistent scale. This\narea is an important mining district, and a regional understanding of the\ngeology and mineral deposits will assist in understanding genesis of deposits\nand in exploration for new deposits. The area is also an important part of the\ntransition zone between the Colorado Plateau to the east and the Great Basin to\nthe west. This tectonically significant province may hold keys to the style\nand mechanisms of continent-scale deformation in the Western United States.\n\nThe geologic map of the central Marysvale volcanic field, southwestern Utah,\nshows the geology at 1:100,000 scale of the heart of one of the largest\nCenozoic volcanic fields in the Western United States. The map shows the area\nof 38 degrees 15' to 38 degrees 42'30\" N., and 112 degrees to 112 degrees\n37'30\" W. The Marysvale field occurs mostly in the High Plateaus, a\nsubprovince of the Colorado Plateau and structurally a transition zone between\nthe complexly deformed Great Basin to the west and the stable, little-deformed\nmain part of the Colorado Plateau to the east. The western part of the field\nis in the Great Basin proper. The volcanic rocks and their source intrusions\nin the volcanic field range in age from about 31 Ma (Oligocene) to about 0.5 Ma\n(Pleistocene). These rocks overlie sedimentary rocks exposed in the mapped\narea that range in age from Ordovician to early Cenozoic. The area has been\ndeformed by thrust faults and folds formed during the late Mesozoic to early\nCenozoic Sevier deformational event, and later by mostly normal faults and\nfolds of the Miocene to Quaternary basin-range episode. The map revises and\nupdates knowledge gained during a long-term U.S. Geological Survey\ninvestigation of the volcanic field, done in part because of its extensive\nhistory of mining. The investigation also was done to provide framework\ngeologic knowledge suitable for defining geologic and hydrologic hazards, for\nlocating hydrologic and mineral resources, and for an understanding of geologic\nprocesses in the area. A previous geologic map (Cunningham and others, 1983,\nU.S. Geological Survey Miscellaneous Investigations Series I-1430-A) covered\nthe same area as this map but was published at 1:50,000 scale and is obsolete\ndue to new data. This new geologic map of the central Marysvale field, here\npublished as U.S. Geological Survey Geologic Investigations Series I-2645-A, is\naccompanied by gravity and aeromagnetic maps of the same area and the same\nscale (Campbell and others, 1999, U.S. Geological Survey Geologic\nInvestigations Series I-2645-B).", "links": [ { diff --git a/datasets/USGS_mapi-2690.json b/datasets/USGS_mapi-2690.json index 0756b733e2..dbb613773f 100644 --- a/datasets/USGS_mapi-2690.json +++ b/datasets/USGS_mapi-2690.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2690", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map forms part of the Montana State Geological Map.\n\nThe Ennis 1:100,000 quadrangle lies within both the Laramide (Late Cretaceous\nto early Tertiary) foreland province of southwestern Montana and the\nnortheastern margin of the middle to late Tertiary Basin and Range province.\n\nThe oldest rocks in the quadrangle are Archean high-grade gneiss, and granitic\nto ultramafic intrusive rocks that are as old as about 3.0 Ga. The gneiss\nincludes a supracrustal assemblage of quartz-feldspar gneiss, amphibolite,\nquartzite, and biotite schist and gneiss. The basement rocks are overlain by a\nplatform sequence of sedimentary rocks as old as Cambrian Flathead Quartzite\nand as young as Upper Cretaceous Livingston Group sandstones, shales, and\nvolcanic rocks.\n\nThe Archean crystalline rocks crop out in the cores of large basement uplifts,\nmost notably the \"Madison-Gravelly arch\" that includes parts of the present\nTobacco Root Mountains and the Gravelly, Madison, and Gallatin Ranges. These\nbasement uplifts or blocks were thrust westward during the Laramide orogeny\nover rocks as young as Upper Cretaceous. The thrusts are now exposed in the\nquadrangle along the western flanks of the Gravelly and Madison Ranges (the\nGreenhorn thrust and the Hilgard fault system, respectively). Simultaneous with\nthe west-directed thrusting, northwest-striking, northeast-side-up reverse\nfaults formed a parallel set across southwestern Montana; the largest of these\nis the Spanish Peaks fault, which cuts prominently across the Ennis quadrangle.\n\nBeginning in late Eocene time, extensive volcanism of the Absorka Volcanic\nSupergroup covered large parts of the area; large remnants of the volcanic\nfield remain in the eastern part of the quadrangle. The volcanism was\nconcurrent with, and followed by, middle Tertiary extension. During this time,\nthe axial zone of the \"Madison-Gravelly arch,\" a large Laramide uplift,\ncollapsed, forming the Madison Valley, structurally a complex down-to-the-east\nhalf graben. Basin deposits as thick as 4,500 m filled the graben.\n\nPleistocene glaciers sculpted the high peaks of the mountain ranges and formed\nthe present rugged topography.\n\nCompilation scale is 1:100,000. Geology mapped between 1988 and 1995.\nCompilation completed 1997.\nReview and revision completed 1997.\nArchive files prepared 1998-02.", "links": [ { diff --git a/datasets/USGS_mapi-2691_1.0.json b/datasets/USGS_mapi-2691_1.0.json index 2fea2f90f3..38ad5c57a0 100644 --- a/datasets/USGS_mapi-2691_1.0.json +++ b/datasets/USGS_mapi-2691_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2691_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital representation of geologic mapping facilitates the presentation and\nanalysis of earth-science data. Digital maps may be displayed at any scale or\nprojection, however the geologic data in this coverage is not intended for use\nat a scale larger that 1:24,000.\n\nData set describes the geology of Paleozoic through Quaternary units in the\nAlligator Ridge area, which hosts disseminated gold deposits. These digital\nfiles were used to create the 1:24,000-scale geologic map of the Buck Mountain\nEast and Mooney Basin Summit Quadrangles and parts of the Sunshine Well NE and\nLong Valley Slough Quadrangles, White Pine County, east-central Nevada.", "links": [ { diff --git a/datasets/USGS_mapi-2737.json b/datasets/USGS_mapi-2737.json index 683fe1aa4d..4af951b669 100644 --- a/datasets/USGS_mapi-2737.json +++ b/datasets/USGS_mapi-2737.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2737", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map is an educational tool with which to inform the public about the\nexistence and the broad, regional nature of earthquake hazard in the Northeast.\nThe data were created digitally in order to ease and speed production and\npublication of the map. Text on the map cautions against using the map for\nscientific or engineering purposes, or to estimate hazard in small areas or at\nsingle sites. Entries in Lineage under Data_Quality_Information explain the\nreasons for this caution (see also Wheeler, 2000; reference in Lineage).\n\n The earthquake catalog was constructed in such a way that it should not be\nutilized in scientific, engineering, or hazards use (Wheeler, 2000; reference\nin Lineage). Accordingly, the catalog is not being published separately, in\norder to minimize the potential for misuse. It is available only as part of the\ndigital files from which the entire map was made.\n\nThe data are those used to make a large-format, colored map of earthquakes in\nthe northeastern United States and adjacent parts of Canada and the Atlantic\nOcean (Wheeler, 2000; Wheeler and others, 2001; references in\nData_Quality_Information, Lineage). The map shows the locations of 1,069 known\nearthquakes of magnitude 3.0 or larger, and is designed for a non-technical\naudience. Colored circles represent earthquake locations, colored and sized by\nmagnitude. Short descriptions, colonial-era woodcuts, newspaper headlines, and\nphotographs summarize the dates, times of day, damage, and other effects of\nnotable earthquakes. The base map shows color-coded elevation, shaded to\nemphasize relief.\n\nThis metadata record describes the data on earthquakes and topography. Other\ndata, such as for roads and urban areas, were obtained elsewhere and we lack\nmetadata for them. Instead, this field cites the sources of these data that\nwere obtained elsewhere.", "links": [ { diff --git a/datasets/USGS_mapi-2740_1.0.json b/datasets/USGS_mapi-2740_1.0.json index e93ca721b4..5c7359f4e7 100644 --- a/datasets/USGS_mapi-2740_1.0.json +++ b/datasets/USGS_mapi-2740_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-2740_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To update the interpretation and increase the scale of geologic mapping,\nprovide a geologic map for the public to use at Colorado National Monument, and\nto provide sufficient geologic information for land-use and land-management\ndecisions.\n\nNew 1:24,000-scale geologic mapping in the Colorado National Monument\nQuadrangle and adjacent areas, in support of the USGS Western Colorado I-70\nCorridor Cooperative Geologic Mapping Project, provides new interpretations of\nand data for the stratigraphy, structure, geologic hazards in the area from the\nColorado River in Grand Valley onto the Uncompahgre Plateau. The plateau drops\nabruptly along northwest-trending structures toward the northeast 800 m to the\nRedlands area and the Colorado River in Grand Valley.\n\nIn addition to common alluvial and colluvial deposits, surficial deposits\ninclude Holocene and late Pleistocene charcoal-bearing valley-fill deposits,\nlate to middle Pleistocene river-gravel terrace deposits, Holocene to middle\nPleistocene younger, intermediate, and old fan-alluvium deposits, late to\nmiddle Pleistocene local gravel deposits, Holocene to late Pleistocene\nrock-fall deposits, Holocene to middle Pleistocene young and old landslide\ndeposits, Holocene to late Pleistocene sheetwash deposits and eolian deposits,\nand Holocene Cienga-type deposits.\n\nOnly the lowest part of the Upper Cretaceous Mancos Shale is exposed in the map\narea near the Colorado River. The Upper and Lower Cretaceous Dakota Formation\nand the Lower Cretaceous Burro Canyon Formation form resistant dipslopes in the\nGrand Valley and a prominent ridge on the plateau. Less resistant strata of\nthe Upper Jurassic Morrison Formation consisting of the Brushy Basin, Salt\nWash, and Tidwell Members form slopes on the plateau and low areas below the\nmountain front of the plateau. The Middle Jurassic Wanakah Formation\nnomenclature replaces the previously used Summerville Formation. Because an\nupper part of the Middle Jurassic Entrada Formation is not obviously correlated\nwith strata found elsewhere, it is therefore not formally named; however, the\nlower rounded cliff former Slickrock Member is clearly present. The Lower\nJurassic silica-cemented Kayenta Formation forms the cap rock for the Lower\nJurassic carbonate-cemented Wingate Sandstone, which forms the impressive\ncliffs of the monument. The Upper Triassic Chinle Formation was deposited on\nthe eroded and weathered Middle Proterozoic meta-igneous gneiss, pegmatite\ndikes, and migmatitic gneiss.\n\nStructurally the area is deceptively challenging. Nearly flat-lying strata on\nthe plateau are folded by northwest-trending fault-propagation folds into at\nleast two S-shaped folds along the mountain front of the plateau. Strata under\nGrand Valley dip at about 6 degrees to the northeast. In the absence of local\nevidence, the uplifted plateau is attributed to Laramide deformation by dated\nanalogous structures elsewhere in the Colorado Plateau. The major exposed\nfault records high-angle reverse relationships in the basement rocks but\ndissipates strain as a triangular zone of distributed microfractures and\ncataclastic flow into overlying Mesozoic strata that absorb the fault strain,\nleaving only folds. Evidence for younger, probably late Pliocene or early\nPleistocene, uplift does exist at the antecedent Unaweep Canyon south and east\nof the map area. To what degree this younger deformation affected the map area\nis unknown.\n\nSeveral geologic hazards affect the area. Middle and late Pleistocene\nlandslides involving the smectite-bearing Brushy Basin Member of the Morrison\nFormation are extensive on the plateau and common in the Redlands below the\nplateau. Expansive clay in the Brushy Basin and other strata create foundation\nstability problems for roads and homes. Flash floods create a serious hazard to\npeople on foot in narrow canyons in the monument and to homes close to water\ncourses downstream from narrow restrictions close to the monument boundary.\n\nMap political location: Mesa County, Colorado\nCompilation scale: 1:24,000\nGeology mapped in 1998.\n\nGeospatial data files included in this data set:\ncnmpoly: geologic units\ncnmline: faults, fold axes, dikes, and other line features\ncnmpoint: strike and dip measurements and other point features\ncnmsym: cartographic decorations-strike/dip symbols, leaders, line\n decorations, etc.\ncnmtext: text labels for map units\ncnmborder: neatline of map\ncnmboundary: boundary of Colorado National Monument\ncnmhydro: hydrologic features\ncnmhypso: elevation contours\ncnmrailroads: railroads\ncnmroads: roads", "links": [ { diff --git a/datasets/USGS_mapi-797Scan_Version 1.0.json b/datasets/USGS_mapi-797Scan_Version 1.0.json index 25b3c9a8e0..c0676421bb 100644 --- a/datasets/USGS_mapi-797Scan_Version 1.0.json +++ b/datasets/USGS_mapi-797Scan_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-797Scan_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This set of images was developed to provide georeferenced digital images of\nthe 1:12000-scale geologic map (Page and Nokleberg, 1974) These images can be\nused in conjunction with the vector data files now available as part of the\nI-797 dataset. This database is not meant to be used or displayed at any scale\nlarger than 1:12000 (for example, 1:2000).\n\nThis collection of four georeferenced MrSID files and one TIFF file provides\nraster images of the five map sheets comprising the Geologic map of the\nStillwater Complex, Montana by Page and Nokleberg (1974). Paper copies of the\nfour geologic map sheets and the explanation were scanned, and the geologic map\nsheets were georeferenced to the Montana State Plane South coordinate system.\n\nEach georeferenced MrSID image consists of a package of three files with the\nextensions: sid, .sdw (MrSID world file), and .aux (ArcInfo 8.1 georeferencing\ninformation).\n\nThe MrSID and TIFF files are listed below:\n\n i797origs1.sid/.sdw/.aux - Sheet 1 - east end of the Stillwater Complex\n\n i797origs2.sid/.sdw/.aux - Sheet 2 - east central part of the Stillwater\nComplex\n\n i797origs3.sid/.sdw/.aux - Sheet 3 - west central part of the Stillwater\nComplex\n\n i797origs4.sid/.sdw/.aux - Sheet 4 - west end of the Stillwater Complex\n\n i797origs5.tif - Sheet 5 - explanation of map symbols, correlation\n of map units and map unit descriptions used on Sheets 1 through 4.", "links": [ { diff --git a/datasets/USGS_mapi-797Topo_Version 1.0.json b/datasets/USGS_mapi-797Topo_Version 1.0.json index df0374da0f..0f786c8407 100644 --- a/datasets/USGS_mapi-797Topo_Version 1.0.json +++ b/datasets/USGS_mapi-797Topo_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-797Topo_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This image was prepared for archival purposes and is not meant to be used or\ndisplayed at any scale larger than 1:12000 (for example. 1:2000).\n\nFour film positives of topography [which was prepared in 1943 and subsequently\nused by Page and Nokleberg (1974) for a base map for the geology of the\nStillwater Complex, Montana] were scanned, and the resulting TIFF images were\nthen geoferenced, rectified, and spliced together to create i797base.tif.", "links": [ { diff --git a/datasets/USGS_mapi-797_Version 1.0.json b/datasets/USGS_mapi-797_Version 1.0.json index c50b482547..6d68d37b6b 100644 --- a/datasets/USGS_mapi-797_Version 1.0.json +++ b/datasets/USGS_mapi-797_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mapi-797_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was developed to provide a spatial database of the 1:12,000 scale\ngeologic map of the Stillwater Complex for use in future spatial analysis. \nThese data can be printed in a variety of ways to display various geologic\nfeatures or used for digital analysis and modeling. This database is not meant\nto be used or displayed at any scale larger than 1:12000. The digital geologic\nmap graphics and plot files (i797.gra/ps and i797-map.pdf) that are provided in\nthe digital package are representations of the digital database. They are not\ndesigned to be cartographic products.\n\nThis report provides a digital version of the Geologic map of the Stillwater\nComplex, Montana originally published by N. Page and W. Nokleberg (1974). \nPaper copies of the four geologic map sheets from the original report were\nscanned and initially attributed by Optronics Specialty Company (Northridge,\nCA) and remitted to the U.S. Geological Survey for further attribution and\npublication of the geospatial digital files. The resulting digital geologic\ndataset can be queried in a geographic information system (GIS) in many ways to\nproduce a variety of geological maps.\n\nThis GIS dataset consists of two Arc/Info datasets. The first is a line and\npolygon file (i797) containing geologic contacts and structures (lines) and\ngeologic map rock units (polygons). A second file contains structural point\ndata (i797p). Since the topographic base map for the original publication is no\nlonger readily available, a georeferenced image (tiff) of the original basemap\nis also included.", "links": [ { diff --git a/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json b/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json index a3b5cb9a23..d468c92510 100644 --- a/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json +++ b/datasets/USGS_mdnet_Version 1.3 (July 06, 2001).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mdnet_Version 1.3 (July 06, 2001)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset MDNET was created to provide locations of Maryland ground-water\nobservation wells for use within a Geographic Information System.\n\nMDNET is a point coverage that represents the locations and names of a network\nof observation wells for the State of Maryland. Additional information on water\nconditions at these sites can be found in the Ground-Water Site Inventory\nSystem (GWSI) database, which is maintained by the U.S. Geological Survey. Site\ninformation can be accessed on the internet at URL:\n\"http://waterdata.usgs.gov/nwis/\".\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in\nARC/INFO format, this metadata file may include some ARC/INFO-specific\nterminology.", "links": [ { diff --git a/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json b/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json index 22dcf5ce24..1ad605926e 100644 --- a/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json +++ b/datasets/USGS_mdwu_98_Version 1.3, July 06, 2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_mdwu_98_Version 1.3, July 06, 2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset MDWU98 was created to provide the locations of MDE permitted\nground-water withdrawal sites in Maryland for use within a Geographic\nInformation System.\n\nMDWU98 is a point coverage that represents the locations of wells for the State\nof Maryland that are permitted to withdraw 10,000 gallons or more per day by\nthe Maryland Department of the Environment (MDE). Each site has the permit\nnumber, permit amount, reported withdrawal, aquifer code, and type of use.\nInformation contained in the dataset comes from the U.S.Geological Survey\nsite-specific water-use database (SWUDS).\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.\n\nAlthough this Federal Geographic Data Committee-compliant metadata file is\nintended to document the data set in nonproprietary form, as well as in\nARC/INFO format, this metadata file may include some ARC/INFO-specific\nterminology.", "links": [ { diff --git a/datasets/USGS_msavi_92_Version 1.0.json b/datasets/USGS_msavi_92_Version 1.0.json index 6b03670a0a..8ba35b1fb5 100644 --- a/datasets/USGS_msavi_92_Version 1.0.json +++ b/datasets/USGS_msavi_92_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_msavi_92_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set was created to determine areas of regional plant-cover\ninformation for use in the report \"Ground-water discharge determined from\nestimates of evapotranspiration, Death Valley regional flow system, Nevada and\nCalifornia.\"\n\nThe raster-based Modified Soil Adjusted Vegetation Index was derived from\nLandsat Thematic Mapper imagery data acquired during June 1992 for the Death\nValley regional flow system. The index has been shown to increase the dynamic\nrange of the vegetation signal while further minimizing the soil background\ninfluences, resulting in greater vegetation sensitivity as defined by a\n\"vegetation signal\" to \"soil noise\" ratio. The data set was used in determining\nphreatophyte boundaries for a ground-water evapotranspiration study and\nrelative differences in vegetation density between discharge areas.\n\nIntroduction\n\nThe raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived\nfrom Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for\nthe Death Valley regional flow system. Background and formulas of the MSAVI are\ndetailed in Qi and others (1994).\n\nThe MSAVI has been shown to increase the dynamic range of the vegetation signal\nwhile further minimizing the soil background influences, resulting in greater\nvegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\"\nratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI\nincludes a constant soil adjustment factor L. The MSAVI uses the Normalized\nDifference Vegetation Index (NDVI) and Weighted Difference Vegetation Index\n(WDVI) to compute the L value in the SAVI for each picture element or pixel.\nThis is referred to as a self-adjusting L function in Qi and others (1994, p.\n123). The slope of the soil line used in the equations was 1.06. This was used\nby Qi and others (1994, p. 123) and was determined to be an acceptable value\nfor this study.\n\nReviews\n\nThe MSAVI image for 1992 was checked for consistency and accuracy during the\ndata processing. Two external reviews were done. The reviewers were asked to\ncheck metadata and other documentation files for completeness and accuracy.\nReviewers also were asked to check the topological consistency, tolerances,\nprojections, and geographic extent.", "links": [ { diff --git a/datasets/USGS_msavi_Version 1.0.json b/datasets/USGS_msavi_Version 1.0.json index 51752aacb3..174b5a5c3a 100644 --- a/datasets/USGS_msavi_Version 1.0.json +++ b/datasets/USGS_msavi_Version 1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_msavi_Version 1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set was created to determine areas of regional plant-cover information\nfor use in the report, \"Ground-water discharge determined from estimates of\nevapotranspiration, Death Valley regional flow system, Nevada and California.\"\n\nThe raster-based Modified Soil Adjusted Vegetation Index was derived from\nLandsat Thematic Mapper imagery data acquired during June 1989 for Sarcobatus\nFlat. The index has been shown to increase the dynamic range of the vegetation\nsignal while further minimizing the soil background influences, resulting in\ngreater vegetation sensitivity as defined by a \"vegetation signal\" to \"soil\nnoise\" ratio. The data set was used in determining phreatophyte boundaries for\na ground-water evapotranspiration study and relative differences in vegetation\ndensity between discharge areas. \n\nIntroduction\n\nThe raster-based Modified Soil Adjusted Vegetation Index (MSAVI) was derived\nfrom Landsat Thematic Mapper (TM) imagery data acquired during June 1989 for\nthe Sarcobatus Flat area of the Death Valley regional flow system. Background\nand formulas of the MSAVI are detailed in Qi and others (1994). \n\nThe MSAVI has been shown to increase the dynamic range of the vegetation signal\nwhile further minimizing the soil background influences, resulting in greater\nvegetation sensitivity as defined by a \"vegetation signal\" to \"soil noise\"\nratio. The MSAVI is a type of Soil Adjusted Vegetation Index (SAVI). The SAVI\nincludes a constant soil adjustment factor L. The MSAVI uses the Normalized\nDifference Vegetation Index (NDVI) and Weighted Difference Vegetation Index\n(WDVI) to compute the L value in the SAVI for each picture element or pixel.\nThis is referred to as a self-adjusting L function in Qi and others (1994, p.\n123). The slope of the soil line used in the equations was 1.06. This was used\nby Qi and others (1994, p. 123) and was determined to be an acceptable value\nfor this study.\n\nReviews\n\nThe MSAVI image for 1989 was checked for consistency and accuracy during the\ndata processing. Two external reviews were done. The reviewers were asked to\ncheck metadata and other documentation files for completeness and accuracy.\nReviewers also were asked to check the topological consistency, tolerances,\nprojections, and geographic extent.", "links": [ { diff --git a/datasets/USGS_nit85_1.0.json b/datasets/USGS_nit85_1.0.json index fa73e043b2..ec3a47c198 100644 --- a/datasets/USGS_nit85_1.0.json +++ b/datasets/USGS_nit85_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_nit85_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NITROGEN-FERTILIZER SALES DATA Estimates of nitrogen-fertilizer sales by\ncounty were generated by the U.S. Environmental Protection Agency (1990) and by\nJerald Fletcher (West Virginia University, written commun., 1992) by using the\nfollowing procedure: (1) compiling annual State fertilizer-sales data reported\nas tonnages to the National Fertilizer and Environmental Research Center of the\nTVA; (2) calculating the ratio of expenditures for commercial fertilizers by\ncounty to expenditures for commercial fertilizers by States from the 1987\nCensus of Agriculture (U.S. Department of Commerce, 1989a); and (3) computing\nannual county-level nitrogen-fertilizer sales, in tons, by multiplying\nestimates of annual States sales by the ratio of county expenditures to States\nexpenditures. In some counties no fertilizer sales were reported, but some\nfertiliz use was reported in the Census Data. Although fertilizer expenditures\nestimates (in $1,000) represent the 1987 growing year, the nitrogen-fertilizer\nsales estimates (tons) generally reflect 1985 amounts. Estimates of\nnitrogen-fertilizer sales by county were constructed fro a combination of data\nreported to State regulatory agencies and from data in the 1987 Census of\nAgriculture. Fertilizer-sales data submitted annually to State regulatory\nagencies by fertilizer dealers reflect total sales without regard to the land\nuse for which it was bought, or the State (or county) in which the fertilizer\nwas actually used. In the Census of Agriculture sampling and statistics were\nused to account for non responding farm operations (U.S. Department of\nCommerce, 1989b) Thus, the information that describes county-level fertilizer\nsales is subject to sampling variability as well as reporting and coverage\nerrors. Census disclosure rules also prevent the publication of information\nthat would reveal the operation of individual farms. COUNTY BOUNDARIES The\noriginal files for this map were provided in 15 sections. Boundaries near the\nedges of sections have been adjusted in edgematching. Polygons that extend\ninto the water (an ocean or the Great Lakes) should be considered arbitrary.\nOriginating_Center: (required) Group: Reference End_Group Group: Summary\n\nThe nitrogen-fertilizer sales estimates in this coverage are intended for use\nin estimating regional fertilizer sales, and in producing visual displays and\nmapping relative rates of fertilizer sales across broad regions of the United\nStates.\n\nThis coverage contains estimates of nitrogen-fertilizer sales for the\nconterminous United States in 1985 as reported by the U.S. Environmental\nProtection Agency (1990) and by Jerald Fletcher (West Virginia University,\nwritten commun., 1992). Nitrogen-fertilizer sales estimates in this coverage\nare reported for each county polygon in tons of actual nutrient sold (inorganic\nnitrogen, phosphate, and potash) as distinct from total tons of fertilizer\nproduct. \n\nThe data are summarized for fertilizer years (i.e. the 1987 fertilizer year\nruns from July 1, 1986 to June 30, 1987).\n\nThe polygons representing county boundaries in the conterminous United States,\nas well as lakes, estuaries, and other nonland-area features were derived from\nthe Digital Line Graph (DLG) file representing the 1:2,000,000-scale map in the\nNational Atlas of the United States (1970).", "links": [ { diff --git a/datasets/USGS_ofr00-265-geol_Version 1.0, May 9, 2000.json b/datasets/USGS_ofr00-265-geol_Version 1.0, May 9, 2000.json index daa50ae4b4..9cd7a3d04e 100644 --- a/datasets/USGS_ofr00-265-geol_Version 1.0, May 9, 2000.json +++ b/datasets/USGS_ofr00-265-geol_Version 1.0, May 9, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00-265-geol_Version 1.0, May 9, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for analysis of the ground-water system of the study\narea.\n\nThis geospatial data set describes bedrock geology of the Turkey Creek drainage\nbasin in Jefferson County, Colorado. It was digitized from maps of fault\nlocations and geologic map units based on age and lithology. Created for use in\nthe Jefferson County Mountain Ground-Water Resources Study, it is to be used at\na scale no more detailed than 1:50,000. \n\nThe source materials for the generation of this data set consist of bedrock\ngeology mapped on U.S. Geological Survey (USGS) topographic quadrangles at a\nscale of 1:24,000 by the USGS. The source materials were converted to digital\nformat, topologically developed, and attributed on a quadrangle-by- quadrangle\nbasis before being combined into one data set.\n\nThe procedures for converting the materials to digital format differed for each\nquadrangle and are summarized as follows:\n\nConifer\n\nThe original camara-ready transparency of the map publication, Reconnaissance\nGeologic Map of the Conifer Quadrangle, Jefferson County, Colorado, was\nobtained from the USGS. A film-positive was made from this transparency. To\nsimplify the linework, this film-positive was then traced by hand onto mylar.\nThe mylar was then digitally scanned at 300 dots per inch (dpi) and stored as a\nTIFF image. Using Arc/INFO software from Environmental Systems Research\nInstitute, the image was georeferenced to real-world coordinates and converted\ninto an Arc/INFO raster data set format known as a grid, which was then\nvectorized into an Arc/INFO vector data set format known as a coverage. A\nquadrangle boundary outline that was generated from quadrangle boundary\ncoordinates and then projected into real-world coordinates was added to the\ncoverage, which was then converted to a coverage with polygon topology. Line\nfeatures in the coverage were attributed according to fault type\nclassification, and the polygon features were attributed according to bedrock\ngeologic map unit and fault zone classification. \n\nEvergreen\n\nAn incomplete collection of the original pre-press mylar separates for the map\npublication, Geologic Map of the Evergreen Quadrangle, Jefferson County,\nColorado, was obtained from the USGS. Mylar separates of Quaternary geologic\ncontacts and faults were identified and digitally scanned at 300 dpi into TIFF\nimages. All other geologic contacts in the area of interest were traced onto\nmylar from a paper print of the map publication. Furthermore, an enclosing\npolygon outline outside of the area of interest was drawn on the mylar so that\nthe traced contacts would form polygon features. The mylar was then digitally\nscanned at 300 dpi into a TIFF image. All the images were then georeferenced to\nreal-world coordinates, converted into grids, and vectorized into three\nseparate coverages, one for each of the two mylar sources, and one for the\ntraced source. These coverages were then combined into one coverage. One of the\nauthors of the map publication provided updated nomenclature for Precambrian\nmap units (Bruce Bryant, U.S. Geological Survey, oral communication, 1998) so\nthat the nomenclature would match that of adjacent quadrangles. The line\nfeatures in the coverage were attributed according to fault type, and polygon\nfeatures were attributed according to geologic map unit and fault zone\nclassification.\n\nIndian Hills\n\nA paper print of the map publication, Geologic Map of the Indian Hills\nQuadrangle, Jefferson County, Colorado, was obtained from the USGS. For the\narea of interest on the quadrangle, two mylars were hand-traced from this paper\nprint. One mylar consisted of geologic contacts and an enclosing polygon\noutline outside of the area of interest that was drawn so that the contacts\nwould form polygon features. The other mylar consisted of fault traces. The two\nmylars were then digitally scanned at 300 dpi into TIFF images. These images\nwere georeferenced to real-world coordinates and converted into grids which\nwere then vectorized into coverages. The coverages were then combined into one\ncoverage. One of the authors of the map publication provided updated nomencla-\nture for Precambrian map units (Bruce Bryant, U.S. Geological Survey, oral\ncommunication, 1998) so that the nomenclature would match that of adjacent\nquadrangles. Line features in the coverage were attributed according to fault\ntype, and polygon features were attributed according to geologic map unit and\nfault zone classification.\n\nMeridian Hill\n\nA paper photocopy of preliminary geologic mapping consisting of faults and\ngeologic contacts for the Meridian Hill Quadrangle, Clear Creek, Jefferson, and\nPark Counties, was obtained from the USGS. For the area of interest on this\nquadrangle, all the linework was traced onto mylar. Furthermore, an enclosing\npolygon outline outside of the area of interest was drawn on the mylar so that\nthe traced contacts would form polygon features. The mylar was then digitally\nscanned at 300 dpi into a TIFF image. The image was georeferenced to real-world\ncoordinates and converted into a grid which was then vectorized into a\ncoverage. Line features in the coverage were then attributed according to fault\ntype, and the polygon features were attributed according to geologic map unit\nand fault zone classification.\n\nMorrison\n\nThe original pre-press mylar separates for the map publication Geologic Map of\nthe Morrison Quadrangle, Jefferson County, Colorado, were obtained from the\nUSGS. The mylar separate of geologic contacts was digitally scanned at 300 dpi\ninto a TIFF image. This image was georeferenced to real-world coordinates and\nconverted into a grid which was then vectorized into a coverage. The fault\nlinework was digitized into a coverage from another mylar separate of the same\npublication that had too many other themes on it and was therefore too\ndifficult to scan and vectorize. The fault coverage was then transformed to\nreal-world coordinates. The coverages were then combined into one coverage. An\nenclosing polygon outline outside of the area of interest was digitized into\nthe coverage so that the geologic contacts would form polygon features. Line\nfeatures in the coverage were attributed according to fault type, and polygons\nwere attributed according to geologic map unit and fault zone classification.\n\nOnce the polygon and vector topology was developed for each quadrangle, the\nindividual coverages were combined into one coverage. No edgematching was\nperformed. A study-area outline of the Turkey Creek Watershed was delineated in\nArc/INFO with USGS Digital Elevation Model data sets. A 500-meter buffer\npolygon of this outline was used to clip the geology coverage.\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government.", "links": [ { diff --git a/datasets/USGS_ofr00-96_wlc80_97_1.0.json b/datasets/USGS_ofr00-96_wlc80_97_1.0.json index 0de8df585e..cf4a82c7d2 100644 --- a/datasets/USGS_ofr00-96_wlc80_97_1.0.json +++ b/datasets/USGS_ofr00-96_wlc80_97_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00-96_wlc80_97_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to document the original map (McGuire, V.L. and\nFischer, B.C., 1999) produced by the High Plains Water-Level Monitoring Project\nand to make available the data on this map for use with geographic information\nsystems.\n\nThis data set consists of digital water-level-change contours for the High\nPlains aquifer in the central United States, 1980 to 1997. The High Plains\naquifer extends from south of 32 degrees to almost 44 degrees north latitude\nand from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer\nunderlies about 174,000 square miles in parts of Colorado, Kansas, Nebraska,\nNew Mexico, Oklahoma, South Dakota, Texas, and Wyoming.\n\nThis digital data set was created from 5,233 wells measured in both 1980 and\n1997. The water-level-change contours were drawn manually on mylar at a scale\nof 1:1,000,000. The contours then were converted to a digital map.\n\nIntroduction --\n\nThe information provided in this introduction is found in U.S. Geological\nSurvey Professional Paper 1400-B (Gutentag and others, 1984). This data set\nconsists of digital water-level-change contours for the High Plains aquifer in\nthe United States, 1980 to 1997. The High Plains aquifer, which underlies about\n174,000 square miles in parts of eight states, is the principal water source in\none of the nation's major agricultural areas. In 1980, about 170,000 wells\npumped water from the aquifer to irrigate about 13 million acres.\n\nThe High Plains aquifer is a regional water-table aquifer consisting mostly of\nnear-surface sand-and-gravel deposits. In 1980, the maximum saturated thickness\nof the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic\nconductivity and specific yield of the aquifer depend on sediment types, which\nvary significantly both horizontally and vertically. Hydraulic conductivity\nranged from less than 25 to greater than 300 feet per day and averaged 60 feet\nper day. Specific yields ranged from less than 10 to 30 percent and averaged\nabout 15 percent.\n\nThe High Plains aquifer boundaries were determined by erosional extent of\nassociated geologic units and by hydraulic and physiographic boundaries where\nthe High Plains aquifer extends eastward from the Great Plains physiographic\nprovince (Fenneman, 1931). In most of the area, the erosional extent of the\nhydraulically connected Tertiary and Quaternary deposits were used as the\naquifer boundary. In eastern Nebraska, streams and physiographic boundaries\nwere used as the aquifer boundary.\n\nReviews Applied to Data --\n\nThis electronic report was subjected to the same review standards that apply to\nall U.S. Geological Survey reports. Reviewers were asked to check the\ntopological consistency, tolerances, attribute frequencies and statistics,\nprojection, and geographic extent. Reviewers were given digital data sets for\nchecking against the source maps to verify the linework and attributes. The\nreviewers checked the metadata files for completeness and accuracy.", "links": [ { diff --git a/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json b/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json index a80ed4a8d7..ae1fba702b 100644 --- a/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json +++ b/datasets/USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_ddwdscon_Version 1.0, March 27, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents geologic structure contours for the top of the\nDeadwood Formation, Black Hills, South Dakota.", "links": [ { diff --git a/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json b/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json index 73bd5d34a6..2d906c109e 100644 --- a/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json +++ b/datasets/USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_hydrogeo_Version 1.0, April 27, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents surficial hydrogeology for the Black Hills of South\nDakota.\n\nThis electronic report was subjected to the same review standard that applies\nto all U.S. Geological Survey reports. Reviewers were asked to check the\ntopological consistency, tolerances, attribute frequencies and statistics,\nprojection, and geographic extent. The reviewers checked the metadata and\na_readme.1st files for completeness and accuracy.", "links": [ { diff --git a/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json b/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json index 929d90b131..e37558b8b2 100644 --- a/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json +++ b/datasets/USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_inkrscon_Version 1.0, March 17, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents geologic structure contours for the top of the Inyan\nKara Group, Black Hills, South Dakota.", "links": [ { diff --git a/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json index 71cb685a41..15b162de1b 100644 --- a/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_mnktscon_Version 1.0, March 21, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents geologic structure contours for the top of the\nMinnekahta Limestone, Black Hills, South Dakota.", "links": [ { diff --git a/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json index a3a30885c2..4c1ff456a6 100644 --- a/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_mnlsscon_Version 1.0, March 21, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents geologic structure contours for the Minnelusa\nFormation, Black Hills, South Dakota.", "links": [ { diff --git a/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json b/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json index f7f0bd9942..c7c30f548a 100644 --- a/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json +++ b/datasets/USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr00471_mnlssurf_Version 1.0, March 21, 2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created as part of the Black Hills Hydrology Study (BHHS).\nThe BHHS is a long-term investigation that was initiated in 1990 as a\ncooperative effort between the U.S. Geological Survey (USGS), the South Dakota\nDepartment of Environment and Natural Resources (DENR), and the West Dakota\nWater Development District. West Dakota represents various local and county\ncooperators. The purpose of the study is to assess the quantity, quality, and\ndistribution of surface and ground water in the Black Hills area of western\nSouth Dakota (Driscoll, 1992). The study area includes parts of Butte, Custer,\nFall River, Lawrence, Meade, and Pennington counties in South Dakota.\n\nThis data set represents geologic structure contours for the Minnelusa\nFormation, Black Hills, South Dakota.", "links": [ { diff --git a/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json b/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json index a45cd35e4b..d923fce6dc 100644 --- a/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json +++ b/datasets/USGS_ofr02-007_lithogeo_1.0, February, 2002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr02-007_lithogeo_1.0, February, 2002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The lithogeochemical data layer was compiled to provide the NECB NAWQA study\narea with digital geologic information that could be used in the analysis of\nsurface- and ground-water quality. Goals of the NAWQA program are to describe\nthe status and trends of a large representative part of the Nation's surface-\nand ground-water resources and to identify the natural and human factors that\naffect the quality of these resources (Leahy and others, 1990). The data layer\npresented here was intended to characterize the bedrock units in the study area\nin terms of mineralogic and chemical parameters relevant to water quality, such\nthat the geologic data could be used in GIS to plan NAWQA study-unit\nactivities, and to analyze and interpret water-quality and ecosystem\nconditions.\n\nThis geographic information system (GIS) data layer shows the generalized\nlithologic and geochemical, termed lithogeochemical, character of near-surface\nbedrock in the New England Coastal Basins (NECB) study area of the U.S.\nGeological Survey's National Water Quality Assessment (NAWQA) Program. The area\nencompasses 23,000 square miles in western and central Maine, eastern\nMassachusetts, most of Rhode Island, eastern New Hampshire and a small part of\neastern Connecticut. The NECB study area includes the Kennebec, Androscogginn,\nSaco, Merrimack, Charles, and Blackstone River Basins, as well as all of Cape\nCod. Bedrock units in the NECB study area are classified into 38\nlithogeochemical units based on the relative reactivity of their constituent\nminerals to dissolution and the presence of carbonate or sulfide minerals. The\n38 lithogeochemical units are generalized into 7 major groups: (1)\ncarbonate-bearing metasedimentary rocks; (2)primarily noncalcareous, clastic\nsedimentary rocks with restricted deposition in discrete fault-bounded\nsedimentary basins of Mississipian or younger age; (3) primarily noncalcareous,\nclastic sedimentary rocks at or above biotite-grade of regional metamorphism;\n(4) mafic igneous rocks and their metamorphic equivalents; (5) ultramafic\nrocks; (6) felsic igneous rocks and their metamorphic equivalents; and (7)\nunconsolidated and poorly consolidated sediments.\n\nThe classification scheme used was first developed as part of the USGS's study\nof the Connecticut, Housatonic, and Thames River Basins (CONN), an adjacent\nNAWQA study area (Robinson and others, 1999). The classification scheme is\nbased on geochemical principles, previous studies of the relations among\nwater-quality and ecosystem characteristics and rock type, and the regional\ngeology of New England. The classification scheme and data set are intended to\nprovide a general, flexible framework for classifying and mapping bedrock units\nin the study area for all types of water-quality analysis. The data set is a\nlithologic map that has been coded to reflect the potential influence of\ngeology on water quality. The classification scheme provides flexibility\nbecause the user can reclassify the 38 lithogeochemical units into other groups\nfor other types of data analysis.\n\nThe bedrock units in this study area have been mapped defined by time-\nstratigraphic and other geologic criteria which may not be directly relevant to\nwater quality. Bedrock units depicted on the State geologic maps are\ninconsistent across state boundaries in some areas (See\nData_Quality_Information section of this document for explanation on how these\ndiscrepancies were addressed with the classification scheme). Thus, a\nstudy-area-wide coding scheme was developed to classify the geologic map units\naccording to mineralogical and chemical characteristics that are relevant for\nwater-quality investigations.\n\nBedrock units were classified for water-quality purposes according to the\nchemical composition and relative susceptibility to weathering of their\nconstituent minerals. Although weathering rates may vary, the relative\nstability of different minerals during weathering in moist climates is\ngenerally consistent (Robinson, 1997). However, the degree to which a rock\nweathers reflects the proportions of its constituent mineral as well as many\nother factors such as degree of induration and relative amount of mineral\nsurfaces exposed to water through primary and secondary porosity. Thus,\nalthough largely based on the relative stability of rock constituent minerals,\nthe classification scheme to group bedrock units according to effects on water\nquality is more complex than mineral- stability sequences. Most common\nrock-forming minerals are only sparingly soluble, so that small amounts of\nhighly reactive minerals can have large effects of water quality (Robinson,\n1997). For example, carbonate minerals are more rapidly weathered and tend to\nproduce higher solute concentrations in natural waters than other rock types. \nIn contrast, granites, schists and quartzites, which are rich in\nalkali-feldspar, muscovites, and quartz, produce low solute concentrations\nbecause they react to a lesser degree and at slower rates than other rock types\nin humid temperate climates (Robinson, 1997). The lithogeochemical\nclassification scheme used in this data set incorporates the relative stability\nof minerals classifications criteria such as used in previous studies, and the\ncharacteristics of bedrock geology specific to the study area (such as the\npresence of a discrete fault bounded sedimentary basins of Mississipian or\nyounger age). Further description of the lithogeochemical classification\nscheme and the expected water- quality and ecosystem characteristics associated\nwith each lithogeochemical unit is explained in Robinson (1997).\n\nThirty-eight lithogeochemical units have been defined for the NECB study area\nbased on the mineral and textural properties of the bedrock unit's constituent\nminerals, presence of carbonate and sulfide minerals and for some of the\ngranitic units, relative age. The classification scheme used descriptions from\nState geologic maps (Osberg and others, 1985; Lyons and others, 1997; Zen and\nothers, 1985;Hermes and others, 1994; and Rogers, 1985) of the lithology,\nmineralogy, and weathering characteristics of the bedrock units. For example,\n\"rusty-weathering\" serves as an indicator of sulfidic-bearing bedrock units\n(Robinson, 1997). Carbonate and sulfide minerals predominate in the\nclassification scheme because these highly reactive minerals have a\ndisproportionately large effect on water chemistry compared to other minerals\ncommonly found in the rocks of this region. In the Maine data set, information\nabout metamorphic grade was also used to classify bedrock units. A digital data\nlayer of generalized regional metamorphic zones (Guidotti, 1985, shown in\nOsberg and others,1985), was obtained from the Maine Geological Survey. This\nlayer was intersected with the digital bedrock geology to determine the\nregional metamorphic grade of each polygon in the bedrock geology data layer.\nPolygons lying within two metamorphic zones were split at the metamorphic-zone\nboundary. Metamorphic grade and geochemical composition of the protolith\n(pre-metamorphism source rock) were used to classify polygons into\nlithogeochemical units. For example, bedrock units with protoliths of\n\"limestone and(or) dolostone\" were classified as \"limestone, dolomite, and\ncarbonate-rich clastic sediments\" (lithogeochemical unit \"11u\") in areas of\nnone or weak regional metamorphism and as \"marble, may include some\ncalc-silicate rock\" (lithogeochemical unit \"12u\") in areas of greenschist\nfacies or high grade metamorphism.\n\nThe 38 lithogeochemical units defined for the NECB study area result from the\ncombination of a lithology code (numeric) with a modifier code (alphabetic).\nThere are 17 lithology codes that represent the influences on water chemistry\nof lithology, metamorphic grade, and geologic setting. Each bedrock unit is\nassigned one of 17 lithology codes based on the description of the bedrock unit\nfrom the State bedrock geologic maps. There are 13 modifier codes used to\nidentify minor amounts of carbonate and(or) sulfide minerals, and subdivide\ngranitic units into subgroups based on their chemical and mineral\ncharacteristics and relative age. A description of the 38 lithogoechemical\nunits in the NECB study area and their potental effects on water quality can be\nfound in the Supplemental_Information section of this document.\n\nThe 38 lithogeochemical units are generalized into 7 major groups that share\nsimilarities in overall geochemistry and lithology: (1) carbonate-bearing\nmetasedimentary rocks; (2) primarily noncalcareous, clastic sedimentary rocks\ndeposited in fault-bounded sedimentary basins of Mississipian or younger age;\n(3) primarily noncalcareous, clastic sedimentary rocks at or above\nbiotite-grade of regional metamorphism; (4) mafic igneous rocks and their\nmetamorphic equivalents; (5) ultramafic rocks; (6) felsic igneous rocks and\ntheir metamorphic equivalents; and (7) unconsolidated and poorly consolidated\nsediments. Major group 7 encompasses areas in the south-coastal part of the\nNECB study area where the bedrock is overlain by thick glacial sediments at the\nsurface. These surficial glacial deposits are the primary aquifer for these\nareas. An example of how this data set has been used in study design strategies\nand in analyzing water-quality characteristic by lithogeochemical units and\nmajor groups is provided in Ayotte and others (1999).\n\nThe bedrock units shown on the individual State maps for the NECB were\nclassified according to a lithogeochemical scheme modified from Robinson and\nothers (1999). Specifically, the modification included the subdivision of\ngranitic bedrock units into additional lithogeochemical units with modifying\nattributes to indicate relative age. However, this modification to the\nclassification system is evident in the lithogeochemical units. Thus, the CONN\nand the NECB data set can be readily merged together to create a larger\nregional product with these difference being more frequent when the data set is\nviewed with the lithogeochemical units showing and less frequent when the data\nset is viewed with the major groups showing. Overall, the bedrock units in the\ntwo study units are classified in a consistent manner to a create regional\nproduct that can be used to evaluate the influences of bedrock geology on\nwater-quality characteristics.\n\nQuality Assurance procedures: The scientific content of this digital data set\nunderwent technical review by two USGS scientists who have knowledge of the\nregional geology,and GIS and spatial-data production. The data set was\nevaluated on positional accuracy, contextual accuracy, attribute accuracy, and\ntopological consistency.", "links": [ { diff --git a/datasets/USGS_ofr02-338_depth2wt.json b/datasets/USGS_ofr02-338_depth2wt.json index 0ed2f17091..fd07afc110 100644 --- a/datasets/USGS_ofr02-338_depth2wt.json +++ b/datasets/USGS_ofr02-338_depth2wt.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr02-338_depth2wt", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was created by the U.S. Geological Survey (USGS) in the\ndevelopment of the USGS Front Range Infrastructure Resources Project. This\ndataset was used in the creation of 1:50,000-scale hydrogeologic contour maps.\n\nThe U.S. Geological Survey developed this dataset as part of the Colorado Front\nRange Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to\nprovide information on the availability of those hydrogeologic resources that\nare either critical to maintaining infrastructure along the northern Front\nRange or that may become less available because of urban expansion in the\nnorthern Front Range. This dataset extends from the Boulder-Jefferson County\nline on the south, to the middle of Larimer and Weld Counties on the North. On\nthe west, this dataset is bounded by the approximate mountain front of the\nFront Range of the Rocky Mountains; on the east, by an arbitrary north-south\nline extending through a point about 6.5 kilometers east of Greeley. This\ndigital geospatial dataset consists of depth-to-water (unsaturated-thickness)\ncontours that were generated from hydrogeologic data with Geographic\nInformation System (GIS) software.", "links": [ { diff --git a/datasets/USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998.json b/datasets/USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998.json index 1df45c7f73..21c001a27c 100644 --- a/datasets/USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998.json +++ b/datasets/USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr02-338_studyarea_Version 1.0, June 22, 1998", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to display the outline of the study area as depicted\nin (Robson and others, 1998).\n\nThis digital geospatial data set consists of outlines of the study area in the\nreport \"Structure, Outcrop, and Subcrop of the Bedrock Aquifers Along the\nWestern Margin of the Denver Basin, Colorado\" (Robson and others, 1998).", "links": [ { diff --git a/datasets/USGS_ofr96-443_cond_1.0.json b/datasets/USGS_ofr96-443_cond_1.0.json index b658b6a8e6..4dda134255 100644 --- a/datasets/USGS_ofr96-443_cond_1.0.json +++ b/datasets/USGS_ofr96-443_cond_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-443_cond_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data set that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digitized polygons of a constant hydraulic\nconductivity value for the Antlers aquifer in southeastern Oklahoma. The Early\nCretaceous-age Antlers Sandstone is an important source of water in an area\nthat underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter,\nChoctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The\nAntlers aquifer consists of sand, clay, conglomerate, and limestone in the\noutcrop area. The upper part of the Antlers aquifer consists of beds of sand,\npoorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is\nunconfined where it outcrops in about an 1,800-square-mile area. \n\nThe hydraulic conductivity polygons were developed from the hydraulic\nconductivity value used as input into a ground-water flow model and from\npublished digital data sets of the surficial geology of the Antlers Sandstone\nexcept in areas overlain by alluvial and terrace deposits near streams. Some of\nthe lines were interpolated where the Antlers aquifer is overlain by alluvial\nand terrace deposits. The interpolated lines are very similar to the aquifer\nboundaries shown on maps published in a ground-water modeling report for the\nAntlers aquifer. The constant hydraulic conductivity value used as input to the\nground-water flow model was estimated as 5.74 feet per day.\n\nGround-water flow models are numerical representations that simplify and\naggregate natural systems. Models are not unique; different combinations of\naquifer characteristics may produce similar results. Therefore, values of\nhydraulic conductivity used in the model and presented in this data set are not\nprecise, but are within a reasonable range when compared to independently\ncollected data.", "links": [ { diff --git a/datasets/USGS_ofr96-444_cond_1.0.json b/datasets/USGS_ofr96-444_cond_1.0.json index 16c43f13b7..eff73673db 100644 --- a/datasets/USGS_ofr96-444_cond_1.0.json +++ b/datasets/USGS_ofr96-444_cond_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-444_cond_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digitized polygons of constant hydraulic conductivity\nvalues for the Vamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada\naquifer is an important source of water that underlies about 2,320-square miles\nof parts of Osage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole\nCounties. Approximately 75 percent of the water withdrawn from the Vamoosa-Ada\naquifer is for municipal use. Rural domestic use and water for stock animals\naccount for most of the remaining water withdrawn. The Vamoosa-Ada aquifer is\ndefined in a ground-water report as consisting principally of the rocks of the\nLate Pennsylvanian-age Vamoosa Formation and overlying Ada Group. The\nVamoosa-Ada aquifer consists of a complex sequence of fine- to very\nfine-grained sandstone, siltstone, shale, and conglomerate interbedded with\nvery thin limestones. The water-yielding capabilities of the aquifer are\ngenerally controlled by lateral and vertical distribution of the sandstone beds\nand their physical characteristics. The Vamoosa-Ada aquifer is unconfined where\nit outcrops in about an 1,700-square-mile area.\n\nThe hydraulic conductivity of the Vamoosa-Ada aquifer was computed as 3 feet\nper day in a ground-water report. Most of the hydraulic conductivity polygons\nwere extracted from published digital geology data sets. The lines in the\ndigital geology data sets were scanned or digitized from maps published at a\nscale of 1:250,000 and represent geologic contacts. Some of the lines in the\ndata set were interpolated in areas where the Vamoosa-Ada aquifer is overlain\nby alluvial and terrace deposits near streams and rivers.", "links": [ { diff --git a/datasets/USGS_ofr96-444_wlelev_1.0.json b/datasets/USGS_ofr96-444_wlelev_1.0.json index 89fa260d3f..30cf3f3f93 100644 --- a/datasets/USGS_ofr96-444_wlelev_1.0.json +++ b/datasets/USGS_ofr96-444_wlelev_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-444_wlelev_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digitized water-level elevation contours for the\nVamoosa-Ada aquifer in east-central Oklahoma. The Vamoosa-Ada aquifer is an\nimportant source of water that underlies about 2,320-square miles of parts of\nOsage, Pawnee, Payne, Creek, Lincoln, Okfuskee, and Seminole Counties.\nApproximately 75 percent of the water withdrawn from the Vamoosa-Ada aquifer is\nfor municipal use. Rural domestic use and water for stock animals account for\nmost of the remaining water withdrawn. The Vamoosa-Ada aquifer is defined in a\nground-water report as consisting principally of the rocks of the Late\nPennsylvanian-age Vamoosa Formation and overlying Ada Group. The Vamoosa-Ada\naquifer consists of a complex sequence of fine- to very fine-grained sandstone,\nsiltstone, shale, and conglomerate interbedded with very thin limestones. The\nwater-yielding capabilities of the aquifer are generally controlled by lateral\nand vertical distribution of the sandstone beds and their physical\ncharacteristics. The Vamoosa-Ada aquifer is unconfined where it outcrops in\nabout an 1,700-square-mile area.\n\nThe water-level elevation contours were digitized from a mylar map, at a scale\nof 1:250,000, used to publish a plate in a ground-water report about the\nVamoosa-Ada aquifer. The water-level elevation contours in this data set extend\nwest of the aquifer outcrop to areas where Vanoss Group rocks overlie the Ada\nGroup. The data set also includes a water-level elevation contour for a terrace\ndeposit east of the aquifer outcrop near the North Canadian River. Water-level\nelevations range from 800 to 1,000 feet above sea level for the Vamoosa-Ada\naquifer.", "links": [ { diff --git a/datasets/USGS_ofr96-445_aqbound_1.0.json b/datasets/USGS_ofr96-445_aqbound_1.0.json index 77e7f977da..6caab3940d 100644 --- a/datasets/USGS_ofr96-445_aqbound_1.0.json +++ b/datasets/USGS_ofr96-445_aqbound_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-445_aqbound_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data set that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digital aquifer boundaries for the alluvial and\nterrace deposits along the Cimarron River from Freedom to Guthrie in\nnorthwestern Oklahoma. Ground water in 1,305 square miles of Quaternary-age\nalluvial and terrace deposits along the Cimarron River from Freedom to Guthrie\nis an important source of water for irrigation, industrial, municipal, stock,\nand domestic supplies. Alluvial and terrace deposits are composed of\ninterfingering lenses of clay, sandy clay, and cross-bedded poorly sorted sand\nand gravel. The aquifer is composed of hydraulically connected alluvial and\nterrace deposits that uncomfortably overlie the Permian-age Formations.\n\nThe aquifer boundaries along geological contacts were extracted from published\ndigital geology data sets. Additional boundaries defining the geographic limits\nof the aquifer and areas of less than 5 feet saturated thickness were digitized\nfrom a mylar map, at a scale of 1:250,000. The maps were published at a scale\nof 1:900,000.", "links": [ { diff --git a/datasets/USGS_ofr96-445_cond_1.0.json b/datasets/USGS_ofr96-445_cond_1.0.json index 6cc70b3887..861ad9356e 100644 --- a/datasets/USGS_ofr96-445_cond_1.0.json +++ b/datasets/USGS_ofr96-445_cond_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-445_cond_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data set that could be used in ground-water vulnerability analysis. \n\nThis data set consists of digital polygons of constant hydraulic conductivity\nvalues for the alluvial and terrace deposits along the Cimarron River from\nFreedom to Guthrie in northwestern Oklahoma. Ground water in 1,305 square miles\nof Quaternary-age alluvial and terrace deposits along the the Cimarron River\nfrom Freedom to Guthrie is an important source of water for irrigation,\nindustrial, municipal, stock, and domestic supplies. Alluvial and terrace\ndeposits are composed of interfingering lenses of clay, sandy clay, and\ncross-bedded poorly sorted sand and gravel. The aquifer is composed of\nhydraulically connected alluvial and terrace deposits that uncomfortably\noverlie the Permian-age Formations.\n\nThe hydraulic-conductivity values for alluvial and terrace deposits used in\nthis data set were published in a steady-state ground-water flow modeling\nreport. The aquifer boundaries along geological contacts were extracted from\npublished digital geology data sets. Boundaries defining the geographic limits\nof the aquifer were digitized from a mylar map, at a scale of 1:250,000. The\nmaps were published at a scale of 1:900,000. The hydraulic conductivity values\nare 104.5 feet per day for the alluvial deposits and 47.5 feet per day for the\nterrace deposits.\n\nGround-water flow models are numerical representations that simplify and\naggregate natural systems. Models are not unique; different combinations of\naquifer characteristics may produce similar results. Therefore, values of\nhydraulic conductivity used in the model and presented in this data set are not\nprecise, but are within a reasonable range when compared to independently\ncollected data.", "links": [ { diff --git a/datasets/USGS_ofr96-445_wlelev_1.0.json b/datasets/USGS_ofr96-445_wlelev_1.0.json index 6bd42dd4cc..75c0c3359a 100644 --- a/datasets/USGS_ofr96-445_wlelev_1.0.json +++ b/datasets/USGS_ofr96-445_wlelev_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-445_wlelev_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data set that could be used in ground-water vulnerability analysis. \n\nThis data set consists of digital water-level elevation contours for the\nalluvial and terrace deposits along the Cimarron River in northwestern Oklahoma\nduring 1985-86. Ground water in 1,305 square miles of Quaternary-age alluvial\nand terrace deposits along the the Cimarron River from Freedom to Guthrie is an\nimportant source of water for irrigation, industrial, municipal, stock, and\ndomestic supplies. Alluvial and terrace deposits are composed of interfingering\nlenses of clay, sandy clay, and cross-bedded poorly sorted sand and gravel. The\naquifer is composed of hydraulically connected alluvial and terrace deposits\nthat unconformably overlie the Permian-age Formations.\n\nWater-level elevations measured in 1985 and 1986 ranged from 1,650 feet to 950\nfeet above sea level. Regional ground-water flow is generally southeast to\nsouthwest towards the Cimarron River, except where the flow direction is\naffected by perennial tributaries. The water-level elevation contours were\ndigitized from a mylar map at a scale of 1:250,000. The maps were published at\na scale of 1:900,000.", "links": [ { diff --git a/datasets/USGS_ofr96-446_aqbound_1.0.json b/datasets/USGS_ofr96-446_aqbound_1.0.json index 5b0513536f..2a27780d37 100644 --- a/datasets/USGS_ofr96-446_aqbound_1.0.json +++ b/datasets/USGS_ofr96-446_aqbound_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-446_aqbound_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digital aquifer boundaries for the alluvial and\nterrace deposits along the Beaver-North Canadian River from the panhandle to\nCanton Lake in northwestern Oklahoma. Ground water in 830 square miles of the\nQuaternary-age alluvial and terrace aquifer is an important source of water for\nirrigation, industrial, municipal, stock, and domestic supplies. The aquifer\nconsists of poorly sorted, fine to coarse, unconsolidated quartz sand with\nminor amounts of clay, silt, and basal gravel. The hydraulically connected\nalluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala\nFormation and Permian-age formations.\n\nThe aquifer boundaries established in a ground-water flow model for the aquifer\ninclude areas: 1) where the terrace deposits pinch out against relatively\nimpermeable Permian-age formations; 2) where the alluvium has been deposited\nagainst relatively impermeable Permian-age formations; 3) where the alluvial\nand terrace deposits have been eroded and underlying Permian-age formations are\nexposed at the surface; 4) where the aquifer extends beyond the geographic\nlimit of the study area; and 5) where the aquifer has little or no saturated\nthickness.\n\nThe lines in the data set representing aquifer boundaries along geological\ncontacts were extracted from a published digital surficial geology data set\nbased on a scale of 1:250,000. The geographic limits of the aquifer and areas\nof little or no saturated thickness were digitized from a folded paper map, at\na scale of 1:250,000 from a ground-water modeling report.", "links": [ { diff --git a/datasets/USGS_ofr96-446_cond_1.0.json b/datasets/USGS_ofr96-446_cond_1.0.json index 1b7efad8c9..4abcad5b1e 100644 --- a/datasets/USGS_ofr96-446_cond_1.0.json +++ b/datasets/USGS_ofr96-446_cond_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-446_cond_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digital hydraulic conductivity values for the\nalluvial and terrace deposits along the Beaver-North Canadian River from the\npanhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square\nmiles of the Quaternary-age alluvial and terrace aquifer is an important source\nof water for irrigation, industrial, municipal, stock, and domestic supplies.\nThe aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz\nsand with minor amounts of clay, silt, and basal gravel. The hydraulically\nconnected alluvial and terrace deposits unconformably overlie the Tertiary-age\nOgallala Formation and Permian-age formations.\n\nSix zones of ranges of hydraulic conductivity values for the alluvial and\nterrace deposits reported in a ground-water modeling report are used in this\ndata set. The hydraulic conductivity values range from 0 to 160 feet per day,\nand average 59 feet per day.\n\nThe features in the data set representing aquifer boundaries along geological\ncontacts were extracted from a published digital surficial geology data set\nbased on a scale of 1:250,000. The geographic limits of the aquifer and zones\nrepresenting ranges of hydraulic conductivity values were digitized from folded\npaper maps, at a scale of 1:250,000 from a ground-water modeling report.\n\nGround-water flow models are numerical representations that simplify and\naggregate natural systems. Models are not unique; different combinations of\naquifer characteristics may produce similar results. Therefore, values of\nhydraulic conductivity used in the model and presented in this data set are not\nprecise, but are within a reasonable range when compared to independently\ncollected data.", "links": [ { diff --git a/datasets/USGS_ofr96-446_recharg_1.0.json b/datasets/USGS_ofr96-446_recharg_1.0.json index 6f08a2aa4a..3690787e13 100644 --- a/datasets/USGS_ofr96-446_recharg_1.0.json +++ b/datasets/USGS_ofr96-446_recharg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofr96-446_recharg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created for a project to develop data sets to support\nground-water vulnerability analysis. The objective was to create and document a\ndigital geospatial data set from a published report or map, or existing digital\ngeospatial data sets that could be used in ground-water vulnerability analysis.\n\nThis data set consists of digital polygons of a constant recharge value for the\nalluvial and terrace deposits along the Beaver-North Canadian River from the\npanhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square\nmiles of the Quaternary-age alluvial and terrace aquifer is an important source\nof water for irrigation, industrial, municipal, stock, and domestic supplies.\nThe aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz\nsand with minor amounts of clay, silt, and basal gravel. The hydraulically\nconnected alluvial and terrace deposits unconformably overlie the Tertiary-age\nOgallala Formation and Permian-age formations.\n\nA recharge rate of 1 inch per year was estimated in the ground-water modeling\nreport for the alluvial and terrace deposits and used in this data set. The\nrecharge rate was estimated using a base-flow method and a\nmonthly-water-balance method.\n\nThe features in the data set representing boundaries along geological contacts\nwere extracted from a published digital surficial geology data set based on a\nscale of 1:250,000. The geographic limits of the aquifer were digitized from a\nfolded paper map, at a scale of 1:250,000 in the ground-water modeling report.\n\nGround-water flow models are numerical representations that simplify and\naggregate natural systems. Models are not unique; different combinations of\naquifer characteristics may produce similar results. Therefore, values of\nrecharge used in the model and presented in this data set are not precise, but\nare within a reasonable range when compared to independently collected data.", "links": [ { diff --git a/datasets/USGS_ofroo-300_SATTK9697_1.0.json b/datasets/USGS_ofroo-300_SATTK9697_1.0.json index 7b6d49826b..f786340092 100644 --- a/datasets/USGS_ofroo-300_SATTK9697_1.0.json +++ b/datasets/USGS_ofroo-300_SATTK9697_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USGS_ofroo-300_SATTK9697_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was created to document the original map (McGuire and Fischer,\n1999) produced by the High Plains Water-level Monitoring project and make\navailable the data on this map for use with geographic information systems.\n\nThis digital data set consists of saturated thickness contours for the High\nPlains aquifer in Central United States, 1996-97. The High Plains aquifer\nextends from south of 32 degrees to almost 44 degrees north latitude and from\n96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies\nabout 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico,\nOklahoma, South Dakota, Texas, and Wyoming.\n\nThis data set was based on 10,085 water-level measurements, 49 stream\nelevations, (March 1997) and 10,036 water-level elevations from wells (1,370\nfrom 1996 and 8,666 from 1997) and the base of aquifer value for each\nmeasurement location. The saturated thickness at each measurement location was\ndetermined by subtracting the water-level elevation from the base of aquifer at\nthat location.\n\n\n Introduction --\n\nThe information provided in this introduction is found in U.S. Geological\nSurvey Professional Paper 1400-B (Gutentag and others,1984). This data set\nconsists of saturated thickness contours for the High Plains aquifer in Central\nUnited States, 1996-97 (modified from Weeks and Gutentag, 1981; Cederstrand and\nBecker, 1999). The High Plains aquifer, which underlies about 174,000 square\nmiles in parts of eight states, is the principal water source in one of the\nnation's major agricultural areas. In 1980, about 170,000 wells pumped water\nfrom the aquifer to irrigate about 13 million acres.\n\nThe High Plains aquifer is a regional water-table aquifer consisting mostly of\nnear-surface sand and gravel deposits. In 1980, the maximum saturated thickness\nof the aquifer was about 1,000 feet and averaged about 200 feet. Hydraulic\nconductivity and specific yield of the aquifer depend on sediment types, which\nvary significantly both horizontally and vertically. Hydraulic conductivity\nranged from less than 25 to greater than 300 feet per day and averaged 60 feet\nper day. Specific yields ranged from less than 10 to 30 percent and averaged\nabout 15 percent.\n\nThe High Plains aquifer boundaries were determined by erosional extent of\nassociated geologic units and by hydraulic and physiographic boundaries where\nthe High Plains aquifer extends eastward from the Great Plains physiographic\nprovince (Fenneman, 1931). In most of the area, the erosional extent of the\nhydraulically connected Tertiary and Quaternary deposits were used as the\naquifer boundary. In eastern Nebraska, streams and physiographic boundaries\nwere used as the aquifer boundary.\n\n Reviews Applied to Data --\n\nThis electronic report was subjected to the same review standard that applies\nto all U.S. Geological Survey reports. Reviewers were asked to check the\ntopological consistency, tolerances, attribute frequencies and statistics,\nprojection, and geographic extent. Reviewers were given digital data sets for\nchecking against the source maps to verify the linework and attributes. The\nreviewers checked the metadata files for completeness and accuracy.", "links": [ { diff --git a/datasets/USM_pCO2_0.json b/datasets/USM_pCO2_0.json index 7418661f9a..946c8fc021 100644 --- a/datasets/USM_pCO2_0.json +++ b/datasets/USM_pCO2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "USM_pCO2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of pCO2 taken by the University of Southern Mississippi in the Gulf of Mexico near the Louisiana coast in 2005 and 2006", "links": [ { diff --git a/datasets/US_FOREST_FRAGMENTATION.json b/datasets/US_FOREST_FRAGMENTATION.json index 6eff511be1..f64f281438 100644 --- a/datasets/US_FOREST_FRAGMENTATION.json +++ b/datasets/US_FOREST_FRAGMENTATION.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "US_FOREST_FRAGMENTATION", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Land Cover Data (NLCD) was reclassified into three categories: forest,\nother natural (e.g., grassland and wetland), and anthropogenic use (e.g.,\nagricultural and urban). Three new grids were created, one for each edge type\n(forest, forest, forest natural, and forest anthropogenic). The values in these\ngrids were calculated as the number of edges with the appropriate type in the\nwindow divided by the total number of forest edges, regardless of neighbor.\nThese grids represented forest connectivity (forest forest edges), naturally\ncaused forest fragmentation (forest natural edges), and human-caused forest\nfragmentation (forest anthropogenic edges).\n\nIn the map, forest connectivity is displayed in green, natural fragmentation in\nblue, and human fragmentation in red. Pure green identifies areas where most or\nall forest edges are shared by another forest pixel. Pure red areas are where\nforest edges are largely shared with human land use. Pure blue areas show where\nmost or all forest edges are shared with another natural land cover type.\nDifferent mixes of the three edge types can produce other colors. Two common\nexamples in the map are yellow and cyan. Yellow identifies areas with roughly\nequal amounts of forest connectivity and anthropogenic fragmentation. Cyan is\nwhere forest connectivity and natural fragmentation are approximately equal.\nBlack represents areas with no forest in the window, and white represents\nignored areas, mostly water, as well as state boundaries.\n\nWith few exceptions, forest fragmentation by other natural land cover types is\nconfined to the western United States, while most human-caused forest\nfragmentation is in the East and Midwest. The yellow and red areas around\nYellowstone in northwest Wyoming are a result of the wildfires in 1988. The\nburned areas are classified as \"transitional\" in the NLCD, which are treated as\nanthropogenic use. The Mississippi River valley was largely forested at one\ntime but has been almost entirely converted to agricultural use, resulting in a\ndisplay of black and red.\n\nLas Vegas, Nevada, is visible as a patch of red in the Mojave Desert due to an\n\"urban forest\" effect from trees planted by residents. Riparian corridors are\nhighly visible in arid and developed areas, especially the West and Midwest. In\narid areas, climate often confines trees to riparian zones that are displayed\nin shades of blue. In the intensely farmed Midwest, intact and restored\nriparian vegetation is depicted in yellow or red. Southern Atlantic coastal\nplain riparian zones are wider; forest is better connected and is shown in\ngreen.", "links": [ { diff --git a/datasets/US_MODIS_NDVI_1299_3.json b/datasets/US_MODIS_NDVI_1299_3.json index 8ff83be771..9b79036f2f 100644 --- a/datasets/US_MODIS_NDVI_1299_3.json +++ b/datasets/US_MODIS_NDVI_1299_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "US_MODIS_NDVI_1299_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels.", "links": [ { diff --git a/datasets/US_MODIS_Veg_Parameters_1539_1.json b/datasets/US_MODIS_Veg_Parameters_1539_1.json index 7646b0fc88..c3ef856c1a 100644 --- a/datasets/US_MODIS_Veg_Parameters_1539_1.json +++ b/datasets/US_MODIS_Veg_Parameters_1539_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "US_MODIS_Veg_Parameters_1539_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products.", "links": [ { diff --git a/datasets/UTC_1990countyboundaries.json b/datasets/UTC_1990countyboundaries.json index 97c259d05b..ca4dbbbea8 100644 --- a/datasets/UTC_1990countyboundaries.json +++ b/datasets/UTC_1990countyboundaries.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_1990countyboundaries", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set portrays the 1990 State and county boundaries of the United\nStates, Puerto Rico, and the U.S. Virgin Islands. The data set was created by\nextracting county polygon features from the individual 1:2,000,000-scale State\nboundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey.\n These files were then merged into a single file and the boundaries were\nmodified to what they were in 1990. This is a revised version of the March\n2000 data set.", "links": [ { diff --git a/datasets/UTC_TNgeologicmaps.json b/datasets/UTC_TNgeologicmaps.json index 11916323d3..4dcd2540ce 100644 --- a/datasets/UTC_TNgeologicmaps.json +++ b/datasets/UTC_TNgeologicmaps.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_TNgeologicmaps", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a digital representation of the printed 1:250,000 geologic\nmaps from the Tennessee Department of Environment and Conservation, Division of\nGeology. The coverage was designed primarily to provide a more detailed\ngeologic base than the 1:2,500,000 King and Beikman (1974). 1:24,000 scale\ncoverage of the state is available for about 40 percent of the state. Formation\nnames and geologic unit codes used in the coverage are from the Tennessee\nDivision of Geology published maps and may not conform to USGS nomenclature.\nThe Tennessee Division of Geology can be contacted at (615) 532-1500.", "links": [ { diff --git a/datasets/UTC_TRIfacilities.json b/datasets/UTC_TRIfacilities.json index d82d91a14d..342452ef73 100644 --- a/datasets/UTC_TRIfacilities.json +++ b/datasets/UTC_TRIfacilities.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_TRIfacilities", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of the U.S. Environmental Protection Agency (USEPA)\nEnvirofacts point data set which includes facilities included in the the Toxic\nRelease Inventory. Information on total pounds of volatile organic compounds\nreleased in 1995 (from USEPA's Toxic Release Inventory CD-ROM) has been\nincluded. This data set is designed to locate or plot manufacturing facilities\nincluded in the Toxic Release Inventory and display or analysis of volatile\norganic compounds releases in pounds per year. The following are the volatile\norganic compounds (VOC's) selected to calculate the total releases at each\nfacility. Not all of these chemicals actually appear in the TRI data set, but\nthis list was used to select releases to sum for each facility.\n\n\n CAS-ID Chemical name\n> ---------- ----------------------------\n> 1 630-20-6 1,1,1,2-Tetrachloroethane\n> 2 71-55-6 1,1,1-Trichloroethane\n> 3 79-34-5 1,1,2,2-Tetrachloroethane\n> 4 76-13-1 1,1,2-Trichloro-1,2,2-trifluoroethane\n> 5 79-00-5 1,1,2-Trichloroethane\n> 6 75-34-3 1,1-Dichloroethane\n> 7 75-35-4 1,1-Dichloroethene\n> 8 563-58-6 1,1-Dichloropropene\n> 9 87-61-6 1,2,3-Trichlorobenzene\n> 10 96-18-4 1,2,3-Trichloropropane\n> 11 120-82-1 1,2,4-Trichlorobenzene\n> 12 95-63-6 1,2,4-Trimethylbenzene\n> 13 96-12-8 1,2-Dibromo-3-chloropropane\n> 14 106-93-4 1,2-Dibromoethane\n> 15 95-50-1 1,2-Dichlorobenzene\n> 16 107-06-2 1,2-Dichloroethane\n> 17 78-87-5 1,2-Dichloropropane\n> 18 108-67-8 1,3,5-Trimethylbenzene\n> 19 541-73-1 1,3-Dichlorobenzene\n> 20 142-28-9 1,3-Dichloropropane\n> 21 106-46-7 1,4-Dichlorobenzene\n> 22 95-49-8 1-Chloro-2-methylbenzene\n> 23 106-43-4 1-Chloro-4-methylbenzene\n> 24 594-20-7 2,2-Dichloropropane\n> 25 71-43-2 Benzene\n> 26 108-86-1 Bromobenzene\n> 27 74-97-5 Bromochloromethane\n> 28 75-27-4 Bromodichloromethane\n> 29 74-83-9 Bromomethane\n> 30 108-90-7 Chlorobenzene\n> 31 75-00-3 Chloroethane\n> 32 75-01-4 Chloroethene\n> 33 74-87-3 Chloromethane\n> 34 124-48-1 Dibromochloromethane\n> 35 74-95-3 Dibromomethane\n> 36 75-71-8 Dichlorodifluoromethane\n> 37 75-09-2 Dichloromethane\n> 38 1330-20-7 Dimethylbenzenes\n> 39 100-42-5 Ethenylbenzene\n> 40 100-41-4 Ethylbenzene\n> 41 87-68-3 Hexachlorobutadiene\n> 42 98-82-8 Isopropylbenzene\n> 43 1634-04-4 Methyl tert-butyl ether\n> 44 108-88-3 Methylbenzene\n> 45 91-20-3 Naphthalene\n> 46 127-18-4 Tetrachloroethene\n> 47 56-23-5 Tetrachloromethane\n> 48 75-25-2 Tribromomethane\n> 49 79-01-6 Trichloroethene\n> 50 75-69-4 Trichlorofluoromethane\n> 51 67-66-3 Trichloromethane\n> 52 156-59-2 cis-1,2-Dichloroethene\n> 53 10061-01-5 cis-1,3-Dichloropropene\n> 54 104-51-8 n-Butylbenzene\n> 55 103-65-1 n-Propylbenzene\n> 56 99-87-6 p-Isopropyltoluene\n> 57 135-98-8 sec-Butylbenzene\n> 58 98-06-6 tert-Butylbenzene\n> 59 156-60-5 trans-1,2-Dichloroethene\n> 60 10061-02-6 trans-1,3-Dichloropropene\n\nAny use of trade, product, or firm names is for descriptive purposes only and\ndoes not imply endorsement by the U.S. Government. Although this Federal\nGeographic Data Committee-compliant metadata file is intended to document the\ndata set in nonproprietary form, as well as in ARC/INFO format, this metadata\nfile may include some ARC/INFO-specific terminology.", "links": [ { diff --git a/datasets/UTC_USdams.json b/datasets/UTC_USdams.json index b6cccabaf5..750552e359 100644 --- a/datasets/UTC_USdams.json +++ b/datasets/UTC_USdams.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_USdams", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The data set was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre- feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 75,187 dams in the U.S. Army Corps of Engineers National Inventory of Dams. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. In the online, interactive National Atlas of the United States, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1173 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (240 dams). The dams in this file were selected from the National Inventory of Dams (NID). First, a subset of the attributes contained in the NID was selected based on input from the Army Corps of Engineers. Using an ArcView query, the dams with a height of 50 feet or more were selected, along with the dams with a normal storage capacity of 5,000 acre-feet or more, and those with a maximum storage capacity of 25,000 acre-feet or more. (The International Committee on Large Dams considers dams over 50 feet to be large dams. The USGS Water Resources Division considers large reservoirs to be those with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.) The resulting data set was converted to an ArcView shape file using the \"Convert to Shapefile\" command. 33 dams that fell outside the 50 States were deleted (1 in Guam, 1 in the Trust Territories, and 31 in Puerto Rico), and 78 dams without coordinates were also deleted. Several misspelled county names were corrected, and the entries in the FIPS_cnty (County FIPS) field were cleaned up. For all dams with a valid county name but no County FIPS, the FIPS code was added based on the listed county name. If two county names were given, the FIPS code used was for the first one listed, or for the county in the listed State. Where the county name was invalid or missing, the county was determined by comparing the dam location to the National Atlas counties file. If the dam fell on a State line, the county name and FIPS code used were those appropriate for the listed State. The shape file was converted to an Arc/Info coverage and then converted to NAD 83 for display purposes. The result was then converted back to shapefile format.", "links": [ { diff --git a/datasets/UTC_hydrography.json b/datasets/UTC_hydrography.json index 10c473c2c7..9df32ef278 100644 --- a/datasets/UTC_hydrography.json +++ b/datasets/UTC_hydrography.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_hydrography", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set portrays the polygon and line water features of the United States,\nPuerto Rico, and the U.S. Virgin Islands. The file was produced by joining the\nindividual State hydrographic layers from the 1:2,000,000- scale Digital Line\nGraph (DLG) data produced by the USGS. This is a revised version of the March\n1999 data set.\n\nThese data are intended for geographic display and analysis at the national\nlevel, and for large regional areas. The data should be displayed and analyzed\nat scales appropriate for 1:2,000,000-scale data. No responsibility is assumed\nby the U.S. Geological Survey in the use of these data.", "links": [ { diff --git a/datasets/UTC_landpolygonfeatures.json b/datasets/UTC_landpolygonfeatures.json index d013c42686..ec72dcab43 100644 --- a/datasets/UTC_landpolygonfeatures.json +++ b/datasets/UTC_landpolygonfeatures.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_landpolygonfeatures", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of federally owned land polygon features of the United\nStates. The data set was created by extracting federal land polygon features\nfrom the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG)\nfiles produced by the U.S. Geological Survey. These files were then appended\ninto a single coverage. This is a revised version of the June 1998 data set.\nThere may be private in holdings within the boundaries of Federal Lands in this\ndata set. \n\nThese data are intended for geographic display and analysis at the national\nlevel, and for large regional areas. The data should be displayed and analyzed\nat scales appropriate for 1:2,000,000-scale data. No responsibility is assumed\nby the U.S. Geological Survey in the use of these data.", "links": [ { diff --git a/datasets/UTC_majorgeologicunits.json b/datasets/UTC_majorgeologicunits.json index 4bea4cb6e3..5fb2882427 100644 --- a/datasets/UTC_majorgeologicunits.json +++ b/datasets/UTC_majorgeologicunits.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UTC_majorgeologicunits", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains boundaries and tags for major geologic units in the\nconterminous United States. In addition to the polygons representing the areal\nextent of geologic units, it identifies boundaries of metamorphic provinces,\nmajor faults, calderas, impact structures, and the limits of continental\nglaciation. The data depict the geology of the bedrock that lies at or near\nthe land surface, but not the distribution of surficial materials such as\nsoils, alluvium, and glacial deposits. The data are generalized from a\ncompilation prepared for use in the Geologic Map of North America, to be\npublished in hard copy by the Geological Society of America and released as a\ndigital file by the U.S. Geological Survey. These data have been prepared with\na degree of detail appropriate for viewing at a scale of 1:7,500,000. Because\nof the degree of generalization required (generalization based on compilation\nscale), the data are intended primarily for display and for regional and\nnational analysis, rather than for more detailed analysis in specific areas. No\nresponsibility is assumed by the U.S. Geological Survey in the use of these\ndata.", "links": [ { diff --git a/datasets/UV_DMS_MINICOSM_1.json b/datasets/UV_DMS_MINICOSM_1.json index 28b829080f..7a647d2f0a 100644 --- a/datasets/UV_DMS_MINICOSM_1.json +++ b/datasets/UV_DMS_MINICOSM_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UV_DMS_MINICOSM_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dimethylsulfide and its precursors and derivatives constitute a major sulfate aerosol source. This dataset incorporates the potential for increased UV radiation effects due to stratospheric ozone depletion over spring and summer in Antarctica, using large-scale incubation systems and 13-14 day incubation periods. Surface seawater (200 micron filtered) from the Davis coastal embayment was incubated during four experiments over the 2002-03 Antarctic Summer.\n\nThe data incorporates seawater measurements of DMS, DMSP and DMSO over a temporal progression during each incubation experiment. Six polyethylene tanks of varying PAR and UV irradiances were incubated. Water was collected stored and analysed by gas chromatography according to a specific sampling protocol, employed by all investigators associated with the project. \n\nThe data are organised according to analysis day, with each days calibration data displayed at the top of each sheet. The sample code is followed by GC run number and then the raw count data from the GC. This is calculated to nanomoles DMS, DMSP or DMSO.\n\nSample Codes: Codes for temporal data follow format X.XXXX\n1st X gives experiment number, 1 to 4.\n2nd X gives sampling day, 0, 0.5, 1, 2, 4, 7, 14 (will result in digit code for day no. less than 10\n3rd X gives tank number relating to irradiance level(one to six)\n4th and 5th X is replicate number, (01, 02, 03, DMS), (04, 05,06, DMSP total), (07, 08, 09, DMSP dissolved), (10, 11, 12, DMSO total).\n\nThe fields in this dataset are:\n\nSample Code\nRun Number from the GC\nCounts - GC generated raw data\nLog Counts - logarithmic conversion of the count data\nLog -c - logarithmic conversion minus the y-intercept determined by calibration of the GC.\n(log -c)/m - log -c divided by m, determined by calibration of the GC.\nngS anti log - nanograms of Sulfur\nNaOH - NaOH adjustment\nngS/L - adjustment per litre\nnM-DMSP/L - nanoMol's DMSP per litre\nnm-DMS/L - nanoMol's DMS per litre\n\nSeptember 2013 Update: DMSO was analysed in these experiments according to an adaptation of the sodium borohydride (NaBH4) reduction method of Andreae (1980). The method has since been superseded and the data here probably displays inaccuracies as a result of the analytical method used. This DMSO data should be treated with caution.", "links": [ { diff --git a/datasets/UV_Davis_1.json b/datasets/UV_Davis_1.json index 313bb35307..64499e9e27 100644 --- a/datasets/UV_Davis_1.json +++ b/datasets/UV_Davis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "UV_Davis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data provides the date, time and 10 min cumulative erythemal irradiance at Davis Station for downwelling solar radiation and the irradiance at depth beneath neutral density screen (ND-polythene) and 3.3, 5.5, and 9.0 mm borosilicate glass. These light treatments simulated water column depths of 1.0, 2.0, 3.0, and 3.6 m depth (calculated using Beer's Law and the average UV transmittance of Antarctic seawater at 4 ice-edge sites). A no erythemal UV control (transmittance greater than 375 nm) was also used in which samples were incubated beneath UV-stabilised polycarbonate.\n\nThe fields in this dataset are:\ndate\nDavis solar time\ndownwell\nwnd\nw3.3\nw5.5\nw9", "links": [ { diff --git a/datasets/Umiat_Veg_Plots_1370_1.json b/datasets/Umiat_Veg_Plots_1370_1.json index 99a124c257..d8a5591b55 100644 --- a/datasets/Umiat_Veg_Plots_1370_1.json +++ b/datasets/Umiat_Veg_Plots_1370_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Umiat_Veg_Plots_1370_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides vegetation cover and plot data collected during the periods of July and August, 1951, from 51 stands (areas of homogeneous vegetation communities) in the the Umiat region of Alaska, on the Colville River. The Umiat area is within the Northern Foothills section of the Arctic Foothills province on the slope north of the Brooks Range. Data include vegetation species, percent cover classes, soil moisture, topographic position, slope, aspect, and plot shape and size.", "links": [ { diff --git a/datasets/Unalaska_Veg_Plots_1375_1.json b/datasets/Unalaska_Veg_Plots_1375_1.json index 970e03963f..7cb65e3a18 100644 --- a/datasets/Unalaska_Veg_Plots_1375_1.json +++ b/datasets/Unalaska_Veg_Plots_1375_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Unalaska_Veg_Plots_1375_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected during August 2007 from 69 study plots at the Unalaska Island research site, and one plot on Amaknak Island. The study sites are within the eastern Aleutian Islands, Alaska, USA. Data includes the plot information for vegetation, soils, and site characteristics for the study plots subjectively located in 11 plant communities that occur in six broad habitat types. Specific attributes include: dominant vegetation species and cover, soil chemistry, moisture, organic matter, topography, and elevation. Cover-abundance was estimated for all vascular plants, bryophytes, and macrolichens according to the nine-point ordinal scale of Westhoff and van der Maarel (1973).", "links": [ { diff --git a/datasets/Uncertainty_US_Coastal_GHG_1650_1.json b/datasets/Uncertainty_US_Coastal_GHG_1650_1.json index af706ac164..e4e83cabbe 100644 --- a/datasets/Uncertainty_US_Coastal_GHG_1650_1.json +++ b/datasets/Uncertainty_US_Coastal_GHG_1650_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Uncertainty_US_Coastal_GHG_1650_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review. Uncertainty in carbon flux was estimated from 10,000 iterations of a Monte Carlo analysis. In addition to the uncertainty analysis, this dataset also provides a probabilistic map of the extent of tidal elevation, as well as the geospatial files used to create that surface, and a land cover and land cover change map of the coastal zone from 2006 to 2011 with accompanying estimated median soil, biomass, methane, and total CO2 equivalent annual fluxes, each with reported 95% confidence intervals, at 30 m resolution. Land cover was quantified using the Coastal Change Analysis Program (C-CAP), a Landsat-based land cover mapping product.", "links": [ { diff --git a/datasets/Understory_Veg_Biomass_Alaska_2340_1.json b/datasets/Understory_Veg_Biomass_Alaska_2340_1.json index 046dfa5984..f44443e9fd 100644 --- a/datasets/Understory_Veg_Biomass_Alaska_2340_1.json +++ b/datasets/Understory_Veg_Biomass_Alaska_2340_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Understory_Veg_Biomass_Alaska_2340_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected. Vascular vegetation was clipped from 50 cm x 50 cm plots, and non-vascular vegetation was clipped from 25 cm x 25 cm plots. All harvested vegetation was sorted by functional group or by species where identification was possible. The sorted vegetation was dried and then weighed to determine biomass. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma separated values (CSV) format.", "links": [ { diff --git a/datasets/VATECH_VAdust.json b/datasets/VATECH_VAdust.json index 86de9eeea7..77bafb9aac 100644 --- a/datasets/VATECH_VAdust.json +++ b/datasets/VATECH_VAdust.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VATECH_VAdust", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dust samples taken annually for five years from 55 sites in southern Nevada and\n California provide an unparalleled source of information on modern rates of\n dust deposition, grain size, and mineralogical and chemical composition. The\n relations of modern dust to climatic factors, type and lithology of dust\n source, and regional wind patterns shed new light on the processes of dust\n entrainment and deposition. The average silt-plus-clay flux in southern Nevada\n and southeastern California ranges from 4.3 to 15.7 g/m2/yr, but in\n southwestern California the average flux is as high is 30 g/m2/yr. These rates\n are generally less than those of previous studies in the arid southwestern\n United States, probably due to differences in measurement techniques (other\n studies mostly used traps at lower heights and did not exclude bird- derived\n sediment).\n \n The climatic factors that affect dust flux interact with each other and with\n the factors of source type, source lithology, geographic area, and human\n disturbance. For example, average dust flux increases with mean annual\n temperature but is only weakly related to decreases in mean annual\n precipitation, because the prevailing winds bring dust to relatively wet areas.\n In contrast, annual dust flux mostly reflects changes in annual precipitation\n rather than temperature. Although playa and alluvial sources emit about the\n same amount of dust per unit area, the volume of dust from the more extensive\n alluvial sources is much larger. In addition, playa and alluvial sources\n respond differently to annual changes in precipitation. Most playas emit dust\n that is richer in soluble salts and carbonate than that from alluvial sources\n (except carbonate-rich alluvial fans), but the dust-deposition rates do not\n reflect this trend: salt flux tends to be larger in mountain ranges, and gypsum\n flux parallels carbonate flux. Gypsum dust may be produced by the interaction\n of carbonate dust and anthropogenic sulfates. Cultivated areas generally yield\n about 20 percent more dust than uncultivated areas. The dust flux in an arid\n urbanizing area may be as much as twice that before disturbance, but decreases\n when construction stops.\n \n The mineralogic and major-oxide composition of the dust samples indicate that\n sand and some silt is locally derived and deposited, whereas clay and some silt\n from different sources can be far-travelled. Dust deposited in the Transverse\n Ranges of California by the Santa Ana winds appears to be mainly derived from\n sources to the north and east.\n \n The sampling design for this study was not statistically based; rather, sites\n were chosen to provide data on dust influx at soil-study sites and to answer\n specific questions about the relations of dust to local source lithology and\n type, distance from source, and climate. Some sites were chosen for their\n proximity to potential dust sources of different lithologic composition (for\n example, playas versus granitic, calcic, or mafic alluvial fans). Other sites\n were placed along transects crossing topographic barriers downwind from a dust\n source. These transects include sites east of Tonopah (43-46) crossing the\n rhyolitic Kawich Range, sites downwind of northern (40, 35, 36) and central\n Death Valley (38, 39, 11-14) crossing the mixed-lithology Grapevine and Funeral\n Mountains, respectively, and sites downwind of Desert Dry Lake crossing the\n calcareous Sheep Range (47-50) north of Las Vegas. In addition, some sites were\n chosen for their proximity to weather stations. Specific locations for dust\n traps were chosen on the basis of the above criteria plus accessibility,\n absence of dirt roads or other artificially disturbed areas upwind, and\n inconspicuousness. The last factor is important because the sites are not\n protected or monitored; hence, most sites are at least 0.5 mile from a road or\n trail. Despite these precautions, dust traps are sometimes tampered with, often\n violently. This is a particular problem in areas close to population centers,\n and most of these sites (52-55 near Los Angeles and 17-19 and 22 near Las\n Vegas) have been abandoned. A few other sites, mostly those that appeared to be\n greatly influenced by nearby farming (20, 21, and 41), were eliminated in 1989.\n Dust traps were also generally placed in flat, relatively open areas to\n mitigate wind-eddy effects created by tall vegetation or topographic\n irregularities.\n \n The 55 sites established in 1984 and 1985 were sampled annually through 1989 in\n order to establish an adequate statistical basis to calculate annual dust flux.\n Sampling continues at 37 of these sites (many sites now have two or more dust\n traps) every two or three years as opportunity and funding permit.", "links": [ { diff --git a/datasets/VBEMI2AE_002.json b/datasets/VBEMI2AE_002.json index d815d96cff..61cea3b87e 100644 --- a/datasets/VBEMI2AE_002.json +++ b/datasets/VBEMI2AE_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMI2AE_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Aerosol Product subset for the VBBE region V002 contains Aerosol optical depth and particle type, with associated atmospheric data.", "links": [ { diff --git a/datasets/VBEMI2LS_002.json b/datasets/VBEMI2LS_002.json index ef483feba0..5e44f358ee 100644 --- a/datasets/VBEMI2LS_002.json +++ b/datasets/VBEMI2LS_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMI2LS_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 Land Surface Product subset for the VBBE region V002 contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); asssociated radiation parameters; and terrain-referenced geometric parameters.", "links": [ { diff --git a/datasets/VBEMI2ST_002.json b/datasets/VBEMI2ST_002.json index 9bb36f1796..67aafac575 100644 --- a/datasets/VBEMI2ST_002.json +++ b/datasets/VBEMI2ST_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMI2ST_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Stereo Product subset for the VBBE region V002 contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.", "links": [ { diff --git a/datasets/VBEMIB2E_003.json b/datasets/VBEMIB2E_003.json index 8ffb8e7de8..ff98855dfe 100644 --- a/datasets/VBEMIB2E_003.json +++ b/datasets/VBEMIB2E_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMIB2E_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Product subset for the VBBE region V003 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/VBEMIB2T_003.json b/datasets/VBEMIB2T_003.json index 5581f6dba1..bfb81217eb 100644 --- a/datasets/VBEMIB2T_003.json +++ b/datasets/VBEMIB2T_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMIB2T_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Product subset for the VBBE region V003 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.", "links": [ { diff --git a/datasets/VBEMIGEO_002.json b/datasets/VBEMIGEO_002.json index eb5414ef68..033278ce42 100644 --- a/datasets/VBEMIGEO_002.json +++ b/datasets/VBEMIGEO_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VBEMIGEO_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters subset for the VBBE region V003 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid.", "links": [ { diff --git a/datasets/VCF5KYR_001.json b/datasets/VCF5KYR_001.json index decd1223bc..f18b7681bc 100644 --- a/datasets/VCF5KYR_001.json +++ b/datasets/VCF5KYR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VCF5KYR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Continuous Fields (VCF) Version 1 data product (VCF5KYR) provides global fractional vegetation cover at 0.05 degree (5,600 meter) spatial resolution at yearly intervals from 1982 to 2016. The VCF5KYR data product is derived from a bagged linear model algorithm using Long Term Data Record Version 4 (LTDR V4) data compiled from Advanced Very High Resolution Radiometer (AVHRR) observations. Fractional vegetation cover (FVC) is the ratio of the area of the vertical projection of green vegetation above ground to the total area, capturing the horizontal distribution and density of vegetation on the Earth\u2019s surface. FVC is a primary means for measuring global forest cover change and is a key parameter for a variety of environmental and climate-related applications, including carbon land surface models and biomass measurements. The three bands included in each VCF5KYR Version 1 GeoTIFF are: percent of tree cover, non-tree vegetation, and bare ground. A water mask was applied with all pure water pixels (defined as \u2265 95% water coverage) set to zero.\r\n\r\nData from years 1994 and 2000 were excluded due to lack of data in the LTDR V4.", "links": [ { diff --git a/datasets/VHMap_1.json b/datasets/VHMap_1.json index 41b7e521de..9ee4d93772 100644 --- a/datasets/VHMap_1.json +++ b/datasets/VHMap_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VHMap_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vestfold Hills are mostly late Archaean-Palaeoproterozoic high grade gneisses that were accreted, metamorphosed and deformed during a short interval of about 50 m.y. spanning the Archaean-Proterozoic boundary. The gneisses are described relative to a simple structural framework dominated by two regional high-grade deformation episodes (D1 and D2), according to whether gneisses were affected by D1, or intruded in the D1 to pre- or syn-D2 interval.\n\nThe oldest gneisses are volumetrically minor metasedimentary (Taynaya and Chelnok Paragneisses) and metabasic (Tryne Mafic Gneiss) units that were intruded by pre-D1 felsic magmas (Mossel Gneiss protolith). These units were folded and interleaved during a regional high-grade deformation event (D1-M1), and subsequently intruded by regionally extensive and compositionally diverse magmas gneisses and Crooked Lake group protolith). Pre-D1 gneisses and Crooked Lake Group intrusives were then deformed and metamorphosed in a second regional high-grade deformation event (D2-M2), closely followed by regional open warping.\n\nSubsequent geological activity in the Vestfold Hills was dominated by ocalised deformation and dyke emplacement. At least 8 major dyke suites and many localised deformation episodes have been defined based on structural, geochemical and cross-cutting criteria.", "links": [ { diff --git a/datasets/VH_bathy_99_1.json b/datasets/VH_bathy_99_1.json index 470a38ddbc..e24aae439a 100644 --- a/datasets/VH_bathy_99_1.json +++ b/datasets/VH_bathy_99_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VH_bathy_99_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains 102 depth measurements of the water column in Long and Tryne fjords, which are in the northern Vestfold Hills, Prydz Bay, Antarctica. Sea ice thickness and snow thickness were recorded simultaneously. The motivation for this project has been to yield a description of the pupping and moulting habitat of Weddell seals. This information will assist the interpretation of 25+ years of data on seal distribution within that area.\n\nOur data were collected between 7th and 13th December 1999. The measurement sites were chosen according to geographical features; their exact location was determined by GPS with an accuracy of about 25m. At each site a 5cm diameter hole was drilled through the sea ice and a weighted measurement tape was lowered through the ice-hole to the bottom. Water depths were measured to the nearest centimetre; ice and snow thicknesses were measured to the nearest millimetre. A minimum depth of less than 3m was found in a narrow channel between small islands immediately west of Shirokaya Bay. The maximum depth of the water column was 222m in the middle basin of Long Fjord. The tidal range for the measured days was less than 0.5m, with tidal corrections applied to the raw data. Water samples were taken in Breid Basin and the middle basin of Long Fjord. These and water samples taken in Snezhnyy Bay [pers. comm. J. Laybourn-Parry, 1999] show aerobic and relatively fresh water for all upper basins. This indicates that even the far basins of both fjords are well mixed despite the drainage of large volumes meltwater from the Antarctic plateau into the fjords.\n\nSee related URL for data and a spatial summary of the data.\n\nSee Entry: long_tryne_bathy for an interpolation of bathymetry made using the Topogrid command within the ArcInfo GIS software, version 8.0.2. Coastline and spot height (heights above sea level) data, extracted from the Australian Antarctic Data Centre's Vestfold Hills topographic GIS dataset (see Entry: vest_hills_gis), was also used as input data to optimise the interpolation close to the coastline.\n\nThe fields in this dataset are:\nday\nweighpoint\nlat(dd)\nlong(dd)\nice (cm)\nfreeboard(cm)\nsnow(cm)\ndepth(m)", "links": [ { diff --git a/datasets/VIIRSJ1_L1_2.json b/datasets/VIIRSJ1_L1_2.json index 108e00c7e0..e453c5a0c4 100644 --- a/datasets/VIIRSJ1_L1_2.json +++ b/datasets/VIIRSJ1_L1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L1_GEO_2.json b/datasets/VIIRSJ1_L1_GEO_2.json index 431f05997f..7eb5c7efec 100644 --- a/datasets/VIIRSJ1_L1_GEO_2.json +++ b/datasets/VIIRSJ1_L1_GEO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L1_GEO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L1_OBC_2.json b/datasets/VIIRSJ1_L1_OBC_2.json index 74b64a0504..2523f363dc 100644 --- a/datasets/VIIRSJ1_L1_OBC_2.json +++ b/datasets/VIIRSJ1_L1_OBC_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L1_OBC_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Geolocation Onboard Calibrator (OBC)-IP file contains solar diffuser observations, the associated gain state and HAM side information, and all engineering and housekeeping data, including unscaled data from the Solar Diffuser Stability Monitor (SDSM)/VIIRS Earth View Radiometric Calibration Unit and the Solar Diffuser GEO angles.", "links": [ { diff --git a/datasets/VIIRSJ1_L2_IOP_NRT_R2022.0.json b/datasets/VIIRSJ1_L2_IOP_NRT_R2022.0.json index 98109ea52c..310c19320c 100644 --- a/datasets/VIIRSJ1_L2_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L2_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L2_IOP_R2022.0.json b/datasets/VIIRSJ1_L2_IOP_R2022.0.json index fa243bb9c4..eb0629d7df 100644 --- a/datasets/VIIRSJ1_L2_IOP_R2022.0.json +++ b/datasets/VIIRSJ1_L2_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L2_OC_NRT_R2022.0.json b/datasets/VIIRSJ1_L2_OC_NRT_R2022.0.json index 0e6feb5adc..e691490e51 100644 --- a/datasets/VIIRSJ1_L2_OC_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L2_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L2_OC_R2022.0.json b/datasets/VIIRSJ1_L2_OC_R2022.0.json index 937ae3e115..4ff2a19f40 100644 --- a/datasets/VIIRSJ1_L2_OC_R2022.0.json +++ b/datasets/VIIRSJ1_L2_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L2_SST3_2024.0.json b/datasets/VIIRSJ1_L2_SST3_2024.0.json index 364d75c380..4964ade17c 100644 --- a/datasets/VIIRSJ1_L2_SST3_2024.0.json +++ b/datasets/VIIRSJ1_L2_SST3_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_SST3_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L2_SST3_NRT_2024.0.json b/datasets/VIIRSJ1_L2_SST3_NRT_2024.0.json index 21719b8413..aba31dd85c 100644 --- a/datasets/VIIRSJ1_L2_SST3_NRT_2024.0.json +++ b/datasets/VIIRSJ1_L2_SST3_NRT_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_SST3_NRT_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L2_SST_2024.0.json b/datasets/VIIRSJ1_L2_SST_2024.0.json index 30181f04d4..5630221dff 100644 --- a/datasets/VIIRSJ1_L2_SST_2024.0.json +++ b/datasets/VIIRSJ1_L2_SST_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_SST_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L2_SST_NRT_2024.0.json b/datasets/VIIRSJ1_L2_SST_NRT_2024.0.json index 49b941bcd0..163bf45851 100644 --- a/datasets/VIIRSJ1_L2_SST_NRT_2024.0.json +++ b/datasets/VIIRSJ1_L2_SST_NRT_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L2_SST_NRT_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3b_CHL_NRT_R2022.0.json b/datasets/VIIRSJ1_L3b_CHL_NRT_R2022.0.json index 598c566738..1db2ce414a 100644 --- a/datasets/VIIRSJ1_L3b_CHL_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3b_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3b_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3b_CHL_R2022.0.json b/datasets/VIIRSJ1_L3b_CHL_R2022.0.json index 1d07dfb22d..27a98fed27 100644 --- a/datasets/VIIRSJ1_L3b_CHL_R2022.0.json +++ b/datasets/VIIRSJ1_L3b_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3b_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3b_IOP_NRT_R2022.0.json b/datasets/VIIRSJ1_L3b_IOP_NRT_R2022.0.json index a0aa475e8f..293983560e 100644 --- a/datasets/VIIRSJ1_L3b_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3b_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3b_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3b_IOP_R2022.0.json b/datasets/VIIRSJ1_L3b_IOP_R2022.0.json index 933e91d6b6..2691ee7e0d 100644 --- a/datasets/VIIRSJ1_L3b_IOP_R2022.0.json +++ b/datasets/VIIRSJ1_L3b_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3b_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3b_KD_NRT_R2022.0.json b/datasets/VIIRSJ1_L3b_KD_NRT_R2022.0.json index 7e92c42578..07b4a2c819 100644 --- a/datasets/VIIRSJ1_L3b_KD_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3b_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3b_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. 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Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_CHL_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_CHL_NRT_R2022.0.json index 997e1f1698..96db09f1d6 100644 --- a/datasets/VIIRSJ1_L3m_CHL_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_CHL_R2022.0.json b/datasets/VIIRSJ1_L3m_CHL_R2022.0.json index d09932332a..216a6eb602 100644 --- a/datasets/VIIRSJ1_L3m_CHL_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). 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S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_KD_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_KD_NRT_R2022.0.json index c9e47e7df9..c7833e8eb9 100644 --- a/datasets/VIIRSJ1_L3m_KD_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_KD_R2022.0.json b/datasets/VIIRSJ1_L3m_KD_R2022.0.json index 8bfeae8832..9aa898dea6 100644 --- a/datasets/VIIRSJ1_L3m_KD_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_LAND_R2022.0.json b/datasets/VIIRSJ1_L3m_LAND_R2022.0.json index 494e1c6848..24c2c0d0b9 100644 --- a/datasets/VIIRSJ1_L3m_LAND_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_NSST_2024.0.json b/datasets/VIIRSJ1_L3m_NSST_2024.0.json index 1a77cb8424..b82427428c 100644 --- a/datasets/VIIRSJ1_L3m_NSST_2024.0.json +++ b/datasets/VIIRSJ1_L3m_NSST_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_NSST_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_NSST_NRT_2024.0.json b/datasets/VIIRSJ1_L3m_NSST_NRT_2024.0.json index 250bef3dc8..643f571c80 100644 --- a/datasets/VIIRSJ1_L3m_NSST_NRT_2024.0.json +++ b/datasets/VIIRSJ1_L3m_NSST_NRT_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_NSST_NRT_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_PAR_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_PAR_NRT_R2022.0.json index fa25525316..db58158484 100644 --- a/datasets/VIIRSJ1_L3m_PAR_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_PAR_R2022.0.json b/datasets/VIIRSJ1_L3m_PAR_R2022.0.json index 46874707f8..d6fd189d54 100644 --- a/datasets/VIIRSJ1_L3m_PAR_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_PIC_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_PIC_NRT_R2022.0.json index e495b5ae80..b601593573 100644 --- a/datasets/VIIRSJ1_L3m_PIC_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_PIC_R2022.0.json b/datasets/VIIRSJ1_L3m_PIC_R2022.0.json index b005ea34a9..1f7dbc0126 100644 --- a/datasets/VIIRSJ1_L3m_PIC_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_POC_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_POC_NRT_R2022.0.json index 1b8b748b2b..3bb4b1679d 100644 --- a/datasets/VIIRSJ1_L3m_POC_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_POC_R2022.0.json b/datasets/VIIRSJ1_L3m_POC_R2022.0.json index a5bd7e0cfa..990aac0d30 100644 --- a/datasets/VIIRSJ1_L3m_POC_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_RRS_NRT_R2022.0.json b/datasets/VIIRSJ1_L3m_RRS_NRT_R2022.0.json index 7a9d08b667..2cf1f61a60 100644 --- a/datasets/VIIRSJ1_L3m_RRS_NRT_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_RRS_R2022.0.json b/datasets/VIIRSJ1_L3m_RRS_R2022.0.json index 17653b9bb5..13cd076624 100644 --- a/datasets/VIIRSJ1_L3m_RRS_R2022.0.json +++ b/datasets/VIIRSJ1_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_SST3_2024.0.json b/datasets/VIIRSJ1_L3m_SST3_2024.0.json index 8a98f53c79..ed05069f0b 100644 --- a/datasets/VIIRSJ1_L3m_SST3_2024.0.json +++ b/datasets/VIIRSJ1_L3m_SST3_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_SST3_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_SST3_NRT_2024.0.json b/datasets/VIIRSJ1_L3m_SST3_NRT_2024.0.json index 43a53b73ff..d6196c0048 100644 --- a/datasets/VIIRSJ1_L3m_SST3_NRT_2024.0.json +++ b/datasets/VIIRSJ1_L3m_SST3_NRT_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_SST3_NRT_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_SST_2024.0.json b/datasets/VIIRSJ1_L3m_SST_2024.0.json index 83b513bf45..c821581914 100644 --- a/datasets/VIIRSJ1_L3m_SST_2024.0.json +++ b/datasets/VIIRSJ1_L3m_SST_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_SST_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ1_L3m_SST_NRT_2024.0.json b/datasets/VIIRSJ1_L3m_SST_NRT_2024.0.json index d756863d56..c97708560a 100644 --- a/datasets/VIIRSJ1_L3m_SST_NRT_2024.0.json +++ b/datasets/VIIRSJ1_L3m_SST_NRT_2024.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ1_L3m_SST_NRT_2024.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L1_1.json b/datasets/VIIRSJ2_L1_1.json index 7b6822f9a3..4b6c322dda 100644 --- a/datasets/VIIRSJ2_L1_1.json +++ b/datasets/VIIRSJ2_L1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L1_GEO_1.json b/datasets/VIIRSJ2_L1_GEO_1.json index 93ef79d4ae..3465ecc999 100644 --- a/datasets/VIIRSJ2_L1_GEO_1.json +++ b/datasets/VIIRSJ2_L1_GEO_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L1_GEO_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L1_OBC_1.json b/datasets/VIIRSJ2_L1_OBC_1.json index fc7f4b9658..8fd77ec4f4 100644 --- a/datasets/VIIRSJ2_L1_OBC_1.json +++ b/datasets/VIIRSJ2_L1_OBC_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L1_OBC_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Geolocation Onboard Calibrator (OBC)-IP file contains solar diffuser observations, the associated gain state and HAM side information, and all engineering and housekeeping data, including unscaled data from the Solar Diffuser Stability Monitor (SDSM)/VIIRS Earth View Radiometric Calibration Unit and the Solar Diffuser GEO angles.", "links": [ { diff --git a/datasets/VIIRSJ2_L2_IOP_NRT_R2022.0.json b/datasets/VIIRSJ2_L2_IOP_NRT_R2022.0.json index 67db6708d3..eb0df3213a 100644 --- a/datasets/VIIRSJ2_L2_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L2_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L2_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L2_IOP_R2022.0.json b/datasets/VIIRSJ2_L2_IOP_R2022.0.json index 17fed84ef3..d42ae42f0d 100644 --- a/datasets/VIIRSJ2_L2_IOP_R2022.0.json +++ b/datasets/VIIRSJ2_L2_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L2_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L2_LAND_R2022.0.json b/datasets/VIIRSJ2_L2_LAND_R2022.0.json index 9ab1e80b52..1369e57e42 100644 --- a/datasets/VIIRSJ2_L2_LAND_R2022.0.json +++ b/datasets/VIIRSJ2_L2_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L2_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L2_OC_NRT_R2022.0.json b/datasets/VIIRSJ2_L2_OC_NRT_R2022.0.json index 40dce5e3ff..6e51550b08 100644 --- a/datasets/VIIRSJ2_L2_OC_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L2_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L2_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L2_OC_R2022.0.json b/datasets/VIIRSJ2_L2_OC_R2022.0.json index 2be754d0d6..6ee1740f88 100644 --- a/datasets/VIIRSJ2_L2_OC_R2022.0.json +++ b/datasets/VIIRSJ2_L2_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L2_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L3b_CHL_NRT_R2022.0.json b/datasets/VIIRSJ2_L3b_CHL_NRT_R2022.0.json index 17542e3e1d..40d5b247d7 100644 --- a/datasets/VIIRSJ2_L3b_CHL_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L3b_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3b_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. 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There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_PAR_NRT_R2022.0.json b/datasets/VIIRSJ2_L3m_PAR_NRT_R2022.0.json index d9e8c0cab5..ce50dd3912 100644 --- a/datasets/VIIRSJ2_L3m_PAR_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_PAR_R2022.0.json b/datasets/VIIRSJ2_L3m_PAR_R2022.0.json index 31941bd46f..2d013f519f 100644 --- a/datasets/VIIRSJ2_L3m_PAR_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_PIC_NRT_R2022.0.json b/datasets/VIIRSJ2_L3m_PIC_NRT_R2022.0.json index afe0446ab3..26b626b0c9 100644 --- a/datasets/VIIRSJ2_L3m_PIC_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_PIC_R2022.0.json b/datasets/VIIRSJ2_L3m_PIC_R2022.0.json index b0c80ebcfd..a93fe17294 100644 --- a/datasets/VIIRSJ2_L3m_PIC_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_POC_NRT_R2022.0.json b/datasets/VIIRSJ2_L3m_POC_NRT_R2022.0.json index 5e384315e4..a4e070fc53 100644 --- a/datasets/VIIRSJ2_L3m_POC_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_POC_R2022.0.json b/datasets/VIIRSJ2_L3m_POC_R2022.0.json index 2471adf564..59bd8ea724 100644 --- a/datasets/VIIRSJ2_L3m_POC_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_RRS_NRT_R2022.0.json b/datasets/VIIRSJ2_L3m_RRS_NRT_R2022.0.json index f50f11d92c..189670e685 100644 --- a/datasets/VIIRSJ2_L3m_RRS_NRT_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSJ2_L3m_RRS_R2022.0.json b/datasets/VIIRSJ2_L3m_RRS_R2022.0.json index 24a71144f0..836abab58f 100644 --- a/datasets/VIIRSJ2_L3m_RRS_R2022.0.json +++ b/datasets/VIIRSJ2_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSJ2_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L1_2.json b/datasets/VIIRSN_L1_2.json index 174a26c372..9c34469614 100644 --- a/datasets/VIIRSN_L1_2.json +++ b/datasets/VIIRSN_L1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L1_GEO_2.json b/datasets/VIIRSN_L1_GEO_2.json index 474fe84e7e..bd97fe4733 100644 --- a/datasets/VIIRSN_L1_GEO_2.json +++ b/datasets/VIIRSN_L1_GEO_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L1_GEO_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB).", "links": [ { diff --git a/datasets/VIIRSN_L2_IOP_NRT_R2022.0.json b/datasets/VIIRSN_L2_IOP_NRT_R2022.0.json index 105ea65c87..35820e416b 100644 --- a/datasets/VIIRSN_L2_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSN_L2_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L2_IOP_R2022.0.json b/datasets/VIIRSN_L2_IOP_R2022.0.json index 64e4cad4c6..87665b6a37 100644 --- a/datasets/VIIRSN_L2_IOP_R2022.0.json +++ b/datasets/VIIRSN_L2_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L2_LAND_2014.json b/datasets/VIIRSN_L2_LAND_2014.json index 3dc62316ee..e804bdb263 100644 --- a/datasets/VIIRSN_L2_LAND_2014.json +++ b/datasets/VIIRSN_L2_LAND_2014.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_LAND_2014", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Product Title: Visible Infrared Imager-Radiometer Suite NPP Level-2\tThe Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinaryinstrument that is being flown on the Joint Polar Satellite System (JPSS) series ofspacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) thatlaunched in October 2011. JPSS is a multi-platform, multi-agency program thatconsolidates the polar orbiting spacecraft of NASA and the National Oceanic andAtmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series,and VIIRS is the successor to MODIS for Earth science data product generation. VIIRShas 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolutionbands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).\t", "links": [ { diff --git a/datasets/VIIRSN_L2_OC_NRT_R2022.0.json b/datasets/VIIRSN_L2_OC_NRT_R2022.0.json index 73db305a89..922eabc27d 100644 --- a/datasets/VIIRSN_L2_OC_NRT_R2022.0.json +++ b/datasets/VIIRSN_L2_OC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_OC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L2_OC_R2022.0.json b/datasets/VIIRSN_L2_OC_R2022.0.json index 7a4d2d45fd..ff5fbba9fb 100644 --- a/datasets/VIIRSN_L2_OC_R2022.0.json +++ b/datasets/VIIRSN_L2_OC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_OC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L2_SST3_2016.2.json b/datasets/VIIRSN_L2_SST3_2016.2.json index f6a158e2bb..31b9848abb 100644 --- a/datasets/VIIRSN_L2_SST3_2016.2.json +++ b/datasets/VIIRSN_L2_SST3_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_SST3_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L2_SST3_NRT_2016.2.json b/datasets/VIIRSN_L2_SST3_NRT_2016.2.json index 00d6288f8e..e1135c28ab 100644 --- a/datasets/VIIRSN_L2_SST3_NRT_2016.2.json +++ b/datasets/VIIRSN_L2_SST3_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_SST3_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L2_SST_2016.2.json b/datasets/VIIRSN_L2_SST_2016.2.json index c36e02415b..2d6902b163 100644 --- a/datasets/VIIRSN_L2_SST_2016.2.json +++ b/datasets/VIIRSN_L2_SST_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_SST_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L2_SST_NRT_2016.2.json b/datasets/VIIRSN_L2_SST_NRT_2016.2.json index c780410099..4e4c70eebd 100644 --- a/datasets/VIIRSN_L2_SST_NRT_2016.2.json +++ b/datasets/VIIRSN_L2_SST_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L2_SST_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_CHL_NRT_R2022.0.json b/datasets/VIIRSN_L3b_CHL_NRT_R2022.0.json index 80ad4d34f4..d4524aeec2 100644 --- a/datasets/VIIRSN_L3b_CHL_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_CHL_R2022.0.json b/datasets/VIIRSN_L3b_CHL_R2022.0.json index 515db91664..08458e28cb 100644 --- a/datasets/VIIRSN_L3b_CHL_R2022.0.json +++ b/datasets/VIIRSN_L3b_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_IOP_NRT_R2022.0.json b/datasets/VIIRSN_L3b_IOP_NRT_R2022.0.json index 1cf83a8059..dcdc63a8b6 100644 --- a/datasets/VIIRSN_L3b_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_IOP_R2022.0.json b/datasets/VIIRSN_L3b_IOP_R2022.0.json index b340d2e0ae..5a71328def 100644 --- a/datasets/VIIRSN_L3b_IOP_R2022.0.json +++ b/datasets/VIIRSN_L3b_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_KD_NRT_R2022.0.json b/datasets/VIIRSN_L3b_KD_NRT_R2022.0.json index ab59357639..6f4f8a568b 100644 --- a/datasets/VIIRSN_L3b_KD_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_KD_R2022.0.json b/datasets/VIIRSN_L3b_KD_R2022.0.json index c8806fab3b..764faa1935 100644 --- a/datasets/VIIRSN_L3b_KD_R2022.0.json +++ b/datasets/VIIRSN_L3b_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_LAND_R2022.0.json b/datasets/VIIRSN_L3b_LAND_R2022.0.json index fd2ee74916..78fde55987 100644 --- a/datasets/VIIRSN_L3b_LAND_R2022.0.json +++ b/datasets/VIIRSN_L3b_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_NSST_2016.2.json b/datasets/VIIRSN_L3b_NSST_2016.2.json index 9c48349f85..9486514e00 100644 --- a/datasets/VIIRSN_L3b_NSST_2016.2.json +++ b/datasets/VIIRSN_L3b_NSST_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_NSST_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_NSST_NRT_2016.2.json b/datasets/VIIRSN_L3b_NSST_NRT_2016.2.json index 902a134daf..ea9f1eb5e6 100644 --- a/datasets/VIIRSN_L3b_NSST_NRT_2016.2.json +++ b/datasets/VIIRSN_L3b_NSST_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_NSST_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_PAR_NRT_R2022.0.json b/datasets/VIIRSN_L3b_PAR_NRT_R2022.0.json index 03b2073df4..02a5b2130e 100644 --- a/datasets/VIIRSN_L3b_PAR_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_PAR_R2022.0.json b/datasets/VIIRSN_L3b_PAR_R2022.0.json index 1a08ec53f2..7a8c86dee1 100644 --- a/datasets/VIIRSN_L3b_PAR_R2022.0.json +++ b/datasets/VIIRSN_L3b_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_PIC_NRT_R2022.0.json b/datasets/VIIRSN_L3b_PIC_NRT_R2022.0.json index b38f17e8d0..57605ebe7a 100644 --- a/datasets/VIIRSN_L3b_PIC_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_PIC_R2022.0.json b/datasets/VIIRSN_L3b_PIC_R2022.0.json index 687101aae0..02ea82f79c 100644 --- a/datasets/VIIRSN_L3b_PIC_R2022.0.json +++ b/datasets/VIIRSN_L3b_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_POC_NRT_R2022.0.json b/datasets/VIIRSN_L3b_POC_NRT_R2022.0.json index 8a51783079..666615d833 100644 --- a/datasets/VIIRSN_L3b_POC_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_POC_R2022.0.json b/datasets/VIIRSN_L3b_POC_R2022.0.json index 99f5f27a60..1f00bbf02f 100644 --- a/datasets/VIIRSN_L3b_POC_R2022.0.json +++ b/datasets/VIIRSN_L3b_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_RRS_NRT_R2022.0.json b/datasets/VIIRSN_L3b_RRS_NRT_R2022.0.json index 7c8256628b..44d71b7639 100644 --- a/datasets/VIIRSN_L3b_RRS_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3b_RRS_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_RRS_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_RRS_R2022.0.json b/datasets/VIIRSN_L3b_RRS_R2022.0.json index b7b447e593..cd98342388 100644 --- a/datasets/VIIRSN_L3b_RRS_R2022.0.json +++ b/datasets/VIIRSN_L3b_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_SST3_2016.2.json b/datasets/VIIRSN_L3b_SST3_2016.2.json index 4ee10d4734..589cc942e4 100644 --- a/datasets/VIIRSN_L3b_SST3_2016.2.json +++ b/datasets/VIIRSN_L3b_SST3_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_SST3_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_SST3_NRT_2016.2.json b/datasets/VIIRSN_L3b_SST3_NRT_2016.2.json index 383a300cf4..9db72367cc 100644 --- a/datasets/VIIRSN_L3b_SST3_NRT_2016.2.json +++ b/datasets/VIIRSN_L3b_SST3_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_SST3_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3b_SST_2016.2.json b/datasets/VIIRSN_L3b_SST_2016.2.json index ac09bfea48..3c54f7f9d7 100644 --- a/datasets/VIIRSN_L3b_SST_2016.2.json +++ b/datasets/VIIRSN_L3b_SST_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_SST_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3b_SST_NRT_2016.2.json b/datasets/VIIRSN_L3b_SST_NRT_2016.2.json index c323d9c194..0c992da03e 100644 --- a/datasets/VIIRSN_L3b_SST_NRT_2016.2.json +++ b/datasets/VIIRSN_L3b_SST_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3b_SST_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_CHL_NRT_R2022.0.json b/datasets/VIIRSN_L3m_CHL_NRT_R2022.0.json index 3377f8f8ab..0b24e9f81d 100644 --- a/datasets/VIIRSN_L3m_CHL_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_CHL_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_CHL_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_CHL_R2022.0.json b/datasets/VIIRSN_L3m_CHL_R2022.0.json index d51f759c89..64fcc47d74 100644 --- a/datasets/VIIRSN_L3m_CHL_R2022.0.json +++ b/datasets/VIIRSN_L3m_CHL_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_CHL_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_IOP_NRT_R2022.0.json b/datasets/VIIRSN_L3m_IOP_NRT_R2022.0.json index 0c7419b138..7e1125dd3c 100644 --- a/datasets/VIIRSN_L3m_IOP_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_IOP_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_IOP_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_IOP_R2022.0.json b/datasets/VIIRSN_L3m_IOP_R2022.0.json index 5658e7cd66..5e296479dd 100644 --- a/datasets/VIIRSN_L3m_IOP_R2022.0.json +++ b/datasets/VIIRSN_L3m_IOP_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_IOP_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_KD_NRT_R2022.0.json b/datasets/VIIRSN_L3m_KD_NRT_R2022.0.json index 51f525a840..fba9cea0b4 100644 --- a/datasets/VIIRSN_L3m_KD_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_KD_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_KD_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_KD_R2022.0.json b/datasets/VIIRSN_L3m_KD_R2022.0.json index 7566615cf7..faaa806a6b 100644 --- a/datasets/VIIRSN_L3m_KD_R2022.0.json +++ b/datasets/VIIRSN_L3m_KD_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_KD_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_LAND_R2022.0.json b/datasets/VIIRSN_L3m_LAND_R2022.0.json index c72de4326f..a2cbda8c4e 100644 --- a/datasets/VIIRSN_L3m_LAND_R2022.0.json +++ b/datasets/VIIRSN_L3m_LAND_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_LAND_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_NSST_2016.2.json b/datasets/VIIRSN_L3m_NSST_2016.2.json index c90392f72f..86d5811190 100644 --- a/datasets/VIIRSN_L3m_NSST_2016.2.json +++ b/datasets/VIIRSN_L3m_NSST_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_NSST_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_NSST_NRT_2016.2.json b/datasets/VIIRSN_L3m_NSST_NRT_2016.2.json index a9f8855695..cdb12b08ac 100644 --- a/datasets/VIIRSN_L3m_NSST_NRT_2016.2.json +++ b/datasets/VIIRSN_L3m_NSST_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_NSST_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_PAR_NRT_R2022.0.json b/datasets/VIIRSN_L3m_PAR_NRT_R2022.0.json index c2640c6b2c..a35108dfd3 100644 --- a/datasets/VIIRSN_L3m_PAR_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_PAR_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_PAR_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_PAR_R2022.0.json b/datasets/VIIRSN_L3m_PAR_R2022.0.json index 7d95e6946a..b5f32f8173 100644 --- a/datasets/VIIRSN_L3m_PAR_R2022.0.json +++ b/datasets/VIIRSN_L3m_PAR_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_PAR_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_PIC_NRT_R2022.0.json b/datasets/VIIRSN_L3m_PIC_NRT_R2022.0.json index 2c46ca9757..ad2a9d127c 100644 --- a/datasets/VIIRSN_L3m_PIC_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_PIC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_PIC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_PIC_R2022.0.json b/datasets/VIIRSN_L3m_PIC_R2022.0.json index 94a45be893..1df5e2ec40 100644 --- a/datasets/VIIRSN_L3m_PIC_R2022.0.json +++ b/datasets/VIIRSN_L3m_PIC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_PIC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_POC_NRT_R2022.0.json b/datasets/VIIRSN_L3m_POC_NRT_R2022.0.json index 2b4ce95337..df0d6a73bb 100644 --- a/datasets/VIIRSN_L3m_POC_NRT_R2022.0.json +++ b/datasets/VIIRSN_L3m_POC_NRT_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_POC_NRT_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_POC_R2022.0.json b/datasets/VIIRSN_L3m_POC_R2022.0.json index 35ced40896..10110e1cab 100644 --- a/datasets/VIIRSN_L3m_POC_R2022.0.json +++ b/datasets/VIIRSN_L3m_POC_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_POC_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_RRS_R2022.0.json b/datasets/VIIRSN_L3m_RRS_R2022.0.json index fbd7fbce41..12e203812e 100644 --- a/datasets/VIIRSN_L3m_RRS_R2022.0.json +++ b/datasets/VIIRSN_L3m_RRS_R2022.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_RRS_R2022.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_SST3_2016.2.json b/datasets/VIIRSN_L3m_SST3_2016.2.json index fcfb14d84c..defd829353 100644 --- a/datasets/VIIRSN_L3m_SST3_2016.2.json +++ b/datasets/VIIRSN_L3m_SST3_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_SST3_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_SST3_NRT_2016.2.json b/datasets/VIIRSN_L3m_SST3_NRT_2016.2.json index fa337dc3d4..96acbdd4d5 100644 --- a/datasets/VIIRSN_L3m_SST3_NRT_2016.2.json +++ b/datasets/VIIRSN_L3m_SST3_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_SST3_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRSN_L3m_SST_2016.2.json b/datasets/VIIRSN_L3m_SST_2016.2.json index ce0638aee4..5151a3c8fa 100644 --- a/datasets/VIIRSN_L3m_SST_2016.2.json +++ b/datasets/VIIRSN_L3m_SST_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_SST_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "links": [ { diff --git a/datasets/VIIRSN_L3m_SST_NRT_2016.2.json b/datasets/VIIRSN_L3m_SST_NRT_2016.2.json index 729e7fc181..0501a973cc 100644 --- a/datasets/VIIRSN_L3m_SST_NRT_2016.2.json +++ b/datasets/VIIRSN_L3m_SST_NRT_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRSN_L3m_SST_NRT_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "links": [ { diff --git a/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json b/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json index 9d58d9961d..51261ac6f8 100644 --- a/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json +++ b/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_N20-NAVO-L2P-v3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series.

\r\n \r\nVIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 20 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.

\r\n\r\nThe product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N21 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N21-NAVO-L2P-v3.0) datasets. It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N20-VIIRS-L2P-ACSPO-v2.80). \r\n", "links": [ { diff --git a/datasets/VIIRS_N20-STAR-L2P-v2.80_2.80.json b/datasets/VIIRS_N20-STAR-L2P-v2.80_2.80.json index b48565ffad..d49d804138 100644 --- a/datasets/VIIRS_N20-STAR-L2P-v2.80_2.80.json +++ b/datasets/VIIRS_N20-STAR-L2P-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_N20-STAR-L2P-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask. ", "links": [ { diff --git a/datasets/VIIRS_N20-STAR-L3U-v2.80_2.80.json b/datasets/VIIRS_N20-STAR-L3U-v2.80_2.80.json index 8ef9db1df5..87d9e6d92b 100644 --- a/datasets/VIIRS_N20-STAR-L3U-v2.80_2.80.json +++ b/datasets/VIIRS_N20-STAR-L3U-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_N20-STAR-L3U-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L2P-v2.80. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.", "links": [ { diff --git a/datasets/VIIRS_N21-NAVO-L2P-v3.0_3.0.json b/datasets/VIIRS_N21-NAVO-L2P-v3.0_3.0.json index 7006eacfd7..009aa10540 100644 --- a/datasets/VIIRS_N21-NAVO-L2P-v3.0_3.0.json +++ b/datasets/VIIRS_N21-NAVO-L2P-v3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_N21-NAVO-L2P-v3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series.

\r\n \r\nVIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 21 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.

\r\n\r\nThe product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N20 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-NAVO-L2P-v3.0). It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N21-VIIRS-L2P-ACSPO-v2.80). \r\n\r\n", "links": [ { diff --git a/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json b/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json index c5805cbe6b..2058f0ae6d 100644 --- a/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json +++ b/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-JPL-L2P-v2016.2_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first. JPSS is a multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). Suomi-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data. VIIRS has 22 spectral bands ranging from 412 nm to 12 micron . There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375 m), and one day-night band (DNB). VIIRS uses on-board pixel aggregation to reduce the growth in size of pixels away from nadir. Two SST products are contained in these files. The first is a skin SST produced separately for day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST products from heritage and current NASA sensors. At night, a second triple channel SST product is generated using the 3.7 , 11 and 12 micron IR channels, identified as SST_triple. Due to the sun glint in the 3.7 micron SST_triple can only be used at night. VIIRS L2P SST data have a 750 spatial resolution at nadir and are stored in ~288 five minute granules per day. Full global coverage is obtained each day. The production of VIIRS NASA L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS were responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of VIIRS ocean products. JPL acquires VIIRS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. In mid-August, 2018, the RSMAS involvement in the VIIRS SST project ceased, and the subsequent fields are not maintained.The R2016.2 supersedes the previous v2016.0 datasets which can be found at https://doi.org/10.5067/GHVRS-2PN16", "links": [ { diff --git a/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json b/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json index 8b009cf4fd..605bda0647 100644 --- a/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json +++ b/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-NAVO-L2P-v1.0_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011.\r\nThe VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/VIIRS_NPP-NAVO-L2P-v2.0_2.0.json b/datasets/VIIRS_NPP-NAVO-L2P-v2.0_2.0.json index c5175c7193..0aeedd58ea 100644 --- a/datasets/VIIRS_NPP-NAVO-L2P-v2.0_2.0.json +++ b/datasets/VIIRS_NPP-NAVO-L2P-v2.0_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-NAVO-L2P-v2.0_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011.\r\nThe VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json b/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json index e1c3552a65..883c8e0994 100644 --- a/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json +++ b/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-NAVO-L2P-v3.0_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth.\nThe VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json b/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json index 66d52b79fc..b376306016 100644 --- a/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json +++ b/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-STAR-L2P-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.", "links": [ { diff --git a/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json b/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json index 42cb9d6835..a88ff480f1 100644 --- a/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json +++ b/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_NPP-STAR-L3U-v2.80_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO NPP/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L2P-v2.80 . The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.", "links": [ { diff --git a/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json b/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json index 84d477956e..ab2cd622d3 100644 --- a/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json +++ b/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the North Atlantic Region (NAR) based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/VIIRS_Val_FLKeys_0.json b/datasets/VIIRS_Val_FLKeys_0.json index 38f17a1daf..53256c7d7b 100644 --- a/datasets/VIIRS_Val_FLKeys_0.json +++ b/datasets/VIIRS_Val_FLKeys_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_Val_FLKeys_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Florida Keys as part of efforts to Validate the VIIRS instrument.", "links": [ { diff --git a/datasets/VIIRS_Validation_0.json b/datasets/VIIRS_Validation_0.json index b4ae60e462..6871c96963 100644 --- a/datasets/VIIRS_Validation_0.json +++ b/datasets/VIIRS_Validation_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIIRS_Validation_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIIRS Validation measurements.", "links": [ { diff --git a/datasets/VIMS_2005_0.json b/datasets/VIMS_2005_0.json index 4f50d0ab62..62578449a7 100644 --- a/datasets/VIMS_2005_0.json +++ b/datasets/VIMS_2005_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIMS_2005_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the York River by the Virginia Institute of Marine Science.", "links": [ { diff --git a/datasets/VIP01_004.json b/datasets/VIP01_004.json index 15ccc6460d..2da056c798 100644 --- a/datasets/VIP01_004.json +++ b/datasets/VIP01_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIP01_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", "links": [ { diff --git a/datasets/VIP07_004.json b/datasets/VIP07_004.json index c2d2bcf070..cc8dcb342d 100644 --- a/datasets/VIP07_004.json +++ b/datasets/VIP07_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIP07_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIP07 VI data product is a composite of seven daily images with 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP07 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", "links": [ { diff --git a/datasets/VIP15_004.json b/datasets/VIP15_004.json index ce9454c475..3a2083732b 100644 --- a/datasets/VIP15_004.json +++ b/datasets/VIP15_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIP15_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.\n\nThe VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year.", "links": [ { diff --git a/datasets/VIP30_004.json b/datasets/VIP30_004.json index 332e218d88..2c2eb00616 100644 --- a/datasets/VIP30_004.json +++ b/datasets/VIP30_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIP30_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS.\n\nThe VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", "links": [ { diff --git a/datasets/VIPPHEN_EVI2_004.json b/datasets/VIPPHEN_EVI2_004.json index 386e15e41e..1fb4e89084 100644 --- a/datasets/VIPPHEN_EVI2_004.json +++ b/datasets/VIPPHEN_EVI2_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIPPHEN_EVI2_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS.\n\nThe VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", "links": [ { diff --git a/datasets/VIPPHEN_NDVI_004.json b/datasets/VIPPHEN_NDVI_004.json index 2e361474f4..e02f9f4cb9 100644 --- a/datasets/VIPPHEN_NDVI_004.json +++ b/datasets/VIPPHEN_NDVI_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIPPHEN_NDVI_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 \u2013 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. \n\nThe VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data. ", "links": [ { diff --git a/datasets/VIRGAS_MetNav_AircraftInSitu_Data_1.json b/datasets/VIRGAS_MetNav_AircraftInSitu_Data_1.json index 213403b32a..1d5ad7662a 100644 --- a/datasets/VIRGAS_MetNav_AircraftInSitu_Data_1.json +++ b/datasets/VIRGAS_MetNav_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIRGAS_MetNav_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIRGAS_MetNav_AircraftInSitu_Data are the meteorology and navigational data collected during the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) sub-orbital campaign. Data from the Meteorological Measurement System (MMS) are featured in this data product and data collection is complete.\r\n\r\nConducted in October 2015, the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) field campaign had a primary objective to test instrument capability and readiness for deployment in the investigation of major volcanic eruptions. VIRGAS aimed to enable researchers to assess the impact of these volcanic eruptions on stratospheric aerosols and the ozone layer. As sulfur dioxide is a characteristic component of volcanic emissions, the LIF SO2 instrument was of critical importance to VIRGAS. VIRGAS was conducted in one deployment consisting of six science flights based from Houston, TX. The current available data products are from the NOAA LASER-Induced Fluorescence (LIF SO2) instrument, the NOAA Unmanned Aircraft System O3 Photometer (UASO3), and NASA\u2019s Meteorological Measurement System (MMS). The ASDC houses data including 1 Hz SO2 data from seven flights, 1 Hz O3 data from ten flights, and 1 Hz and 20 Hz data for temperature, pressure, and 3-D winds from 5 flights.\r\n\r\nVIRGAS was led by Dr. Karen Rosenlof and Dr. Ru-Shan Gao of the NOAA Chemical Sciences Laboratory (NOAA CSL), as well as by Dr. Paul Newman of NASA Godard Space Flight Center\u2019s Earth Sciences Division. Other participants include researchers from NASA Ames Research Center, the Bay Area Environmental Research Institute (BAERI), and the University of Miami.", "links": [ { diff --git a/datasets/VIRGAS_TraceGas_AircraftInSitu_Data_1.json b/datasets/VIRGAS_TraceGas_AircraftInSitu_Data_1.json index 4cf528bd34..8dff36308f 100644 --- a/datasets/VIRGAS_TraceGas_AircraftInSitu_Data_1.json +++ b/datasets/VIRGAS_TraceGas_AircraftInSitu_Data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VIRGAS_TraceGas_AircraftInSitu_Data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIRGAS_TraceGas_AircraftInSitu_Data are the in-situ trace gas data collected during the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) sub-orbital campaign. Data from the whole air sampler, NOAA UASO3 and Laser Induced Fluorescence - SO2 (LIF-SO2) are featured in this data product. Data collection is complete.\r\n\r\nConducted in October 2015, the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) field campaign had a primary objective to test instrument capability and readiness for deployment in the investigation of major volcanic eruptions. VIRGAS aimed to enable researchers to assess the impact of these volcanic eruptions on stratospheric aerosols and the ozone layer. As sulfur dioxide is a characteristic component of volcanic emissions, the LIF SO2 instrument was of critical importance to VIRGAS. VIRGAS was conducted in one deployment consisting of six science flights based from Houston, TX. The current available data products are from the NOAA LASER-Induced Fluorescence (LIF SO2) instrument, the NOAA Unmanned Aircraft System O3 Photometer (UASO3), and NASA\u2019s Meteorological Measurement System (MMS). The ASDC houses data including 1 Hz SO2 data from seven flights, 1 Hz O3 data from ten flights, and 1 Hz and 20 Hz data for temperature, pressure, and 3-D winds from 5 flights.\r\n\r\nVIRGAS was led by Dr. Karen Rosenlof and Dr. Ru-Shan Gao of the NOAA Chemical Sciences Laboratory (NOAA CSL), as well as by Dr. Paul Newman of NASA Godard Space Flight Center\u2019s Earth Sciences Division. Other participants include researchers from NASA Ames Research Center, the Bay Area Environmental Research Institute (BAERI), and the University of Miami.", "links": [ { diff --git a/datasets/VISSRGOES1IMIR_001.json b/datasets/VISSRGOES1IMIR_001.json index c78ad44ad8..5149a8da24 100644 --- a/datasets/VISSRGOES1IMIR_001.json +++ b/datasets/VISSRGOES1IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES1IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-1 satellite was parked over the equator at longitude 115W on Dec 18, 1975 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).", "links": [ { diff --git a/datasets/VISSRGOES1IMVIS_001.json b/datasets/VISSRGOES1IMVIS_001.json index 194442ee7e..6f0a2acae1 100644 --- a/datasets/VISSRGOES1IMVIS_001.json +++ b/datasets/VISSRGOES1IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES1IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-1 satellite was parked over the equator at longitude 115W on Dec 18, 1975 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).", "links": [ { diff --git a/datasets/VISSRGOES2IMIR_001.json b/datasets/VISSRGOES2IMIR_001.json index 1f59b1c5ec..4261899ad5 100644 --- a/datasets/VISSRGOES2IMIR_001.json +++ b/datasets/VISSRGOES2IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES2IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-2 satellite was parked over the equator at longitude 75W from 1977 through 1978 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00028 (old ID 77-048A-01C).", "links": [ { diff --git a/datasets/VISSRGOES2IMVIS_001.json b/datasets/VISSRGOES2IMVIS_001.json index 869443a58a..feb6b54a64 100644 --- a/datasets/VISSRGOES2IMVIS_001.json +++ b/datasets/VISSRGOES2IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES2IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-2 satellite was parked over the equator at longitude 75W from 1977 through 1978 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00087 (old ID 77-048A-01B).", "links": [ { diff --git a/datasets/VISSRGOES3IMIR_001.json b/datasets/VISSRGOES3IMIR_001.json index 38513b4135..da7d702a03 100644 --- a/datasets/VISSRGOES3IMIR_001.json +++ b/datasets/VISSRGOES3IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES3IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES3IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-3 satellite was parked over the equator at longitude 135W from 1978 through 1981 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00105 (old ID 75-100A-01C).", "links": [ { diff --git a/datasets/VISSRGOES3IMVIS_001.json b/datasets/VISSRGOES3IMVIS_001.json index cc771ad886..19245e94e7 100644 --- a/datasets/VISSRGOES3IMVIS_001.json +++ b/datasets/VISSRGOES3IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRGOES3IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRGOES3IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe GOES-3 satellite was parked over the equator at longitude 135W from 1978 through 1981 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).", "links": [ { diff --git a/datasets/VISSRSMS1IMIR_001.json b/datasets/VISSRSMS1IMIR_001.json index 43b60214ba..99e64ff964 100644 --- a/datasets/VISSRSMS1IMIR_001.json +++ b/datasets/VISSRSMS1IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS1IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS-1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00068 (old ID 74-033A-01C).", "links": [ { diff --git a/datasets/VISSRSMS1IMVIS_001.json b/datasets/VISSRSMS1IMVIS_001.json index 9ca55ae92f..293b9407b8 100644 --- a/datasets/VISSRSMS1IMVIS_001.json +++ b/datasets/VISSRSMS1IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS1IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS-1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00040 (old ID 74-033A-01B).", "links": [ { diff --git a/datasets/VISSRSMS1L1AOIPS_001.json b/datasets/VISSRSMS1L1AOIPS_001.json index 77bde0b721..9fc99bd821 100644 --- a/datasets/VISSRSMS1L1AOIPS_001.json +++ b/datasets/VISSRSMS1L1AOIPS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS1L1AOIPS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS1L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the first Synchronous Meteorological Satellite (SMS-1). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information (blank before 1974/10/29), and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes.\n\nThe data were used to make 70mm film negatives and 9.5\u201d positive prints on a Dicomed Image Recording System. Data for this product are available from 1974/07/01 through 1979/04/19 (with gaps plus no data between 1975/08/20 and 1979/02/17). The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS 1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00018 (old ID 74-033A-01D).", "links": [ { diff --git a/datasets/VISSRSMS1L1EHT_001.json b/datasets/VISSRSMS1L1EHT_001.json index 3770a5573a..9d2357a6af 100644 --- a/datasets/VISSRSMS1L1EHT_001.json +++ b/datasets/VISSRSMS1L1EHT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS1L1EHT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS1L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the first Synchronous Meteorological Satellite (SMS-1). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. A data file is structured with a header, followed by an IR scan line and then 8 visible scan lines (although some files only contain IR scans). Visible scans are at full resolution of 15288 pixels and a file will contain several hundred scan lines. IR scans are at 3822 pixels and up to a hundred scan lines. A full scan of the Earth was made every 20 minutes.\n\nData for this product are only available for 9 days: 1974/08/23 (IR only), 1974/08/27 (IR only), 1974/08/31, 1974/09/01, 1974/09/02, 1974/09/05, 1974/09/24 (IR only), 1975/01/10, and 1975/02/17. The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS 1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00126 (old ID 74-033A-01A).", "links": [ { diff --git a/datasets/VISSRSMS2IMIR_001.json b/datasets/VISSRSMS2IMIR_001.json index 453fa335c2..660e5a00de 100644 --- a/datasets/VISSRSMS2IMIR_001.json +++ b/datasets/VISSRSMS2IMIR_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS2IMIR_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00038 (old ID 75-011A-04C).", "links": [ { diff --git a/datasets/VISSRSMS2IMVIS_001.json b/datasets/VISSRSMS2IMVIS_001.json index fb6efc1399..255115ad9a 100644 --- a/datasets/VISSRSMS2IMVIS_001.json +++ b/datasets/VISSRSMS2IMVIS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS2IMVIS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification.\n\nThe SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00202 (old ID 75-011A-04B).", "links": [ { diff --git a/datasets/VISSRSMS2L1AOIPS_001.json b/datasets/VISSRSMS2L1AOIPS_001.json index 2411a8a344..a36ef38798 100644 --- a/datasets/VISSRSMS2L1AOIPS_001.json +++ b/datasets/VISSRSMS2L1AOIPS_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS2L1AOIPS_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS2L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the second Synchronous Meteorological Satellite (SMS-2). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information, and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes.\n\nThe data were used to make 70mm film negatives and 9.5\u201d positive prints on a Dicomed Image Recording System. Data for this product are available from 1975/04/27 through 1980/08/22 (with gaps plus no data between 1975/07/31 and 1979/05/10). The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00095 (old ID 75-011A-04D).", "links": [ { diff --git a/datasets/VISSRSMS2L1EHT_001.json b/datasets/VISSRSMS2L1EHT_001.json index d89247ecea..5375d8a641 100644 --- a/datasets/VISSRSMS2L1EHT_001.json +++ b/datasets/VISSRSMS2L1EHT_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VISSRSMS2L1EHT_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VISSRSMS2L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the second Synchronous Meteorological Satellite (SMS-2). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. A data file is structured with a header, followed by an IR scan line and then 8 visible scan lines (although some files only contain IR scans). Visible scans are at full resolution of 15288 pixels and a file will contain several hundred scan lines. IR scans are at 3822 pixels and up to a hundred scan lines. A full scan of the Earth was made every 20 minutes.\n\nData for this product are only available for 5 days: 1975/02/17, 1975/04/24, 1975/04/25, 1975/05/06, and 1975/08/28. The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.\n\nThis product was previously available from the NSSDC with the identifier ESAD-00039 (old ID 75-011A-04A).", "links": [ { diff --git a/datasets/VJ101_NRT_2.1.json b/datasets/VJ101_NRT_2.1.json index 465f390fd2..20dacdc795 100644 --- a/datasets/VJ101_NRT_2.1.json +++ b/datasets/VJ101_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ101_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) JPSS1/VIIRS Raw Radiances in Counts 6-Min L1A Swath (VJ101_NRT) data product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted.\r\n\r\nFor more information download VIIRS Level 1 Product User's Guide at:\r\nhttps://oceancolor.gsfc.nasa.gov/docs/format/VIIRS_Level-1_DataProductUsersGuide.pdf", "links": [ { diff --git a/datasets/VJ101_NRT_2.json b/datasets/VJ101_NRT_2.json index 0dcdbdc66f..6fddda5317 100644 --- a/datasets/VJ101_NRT_2.json +++ b/datasets/VJ101_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ101_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) JPSS1/VIIRS Raw Radiances in Counts 6-Min L1A Swath (VJ101_NRT) data product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted.\n\nFor more information download VIIRS Level 1 Product User's Guide at:\nhttps://oceancolor.gsfc.nasa.gov/docs/format/VIIRS_Level-1_DataProductUsersGuide.pdf", "links": [ { diff --git a/datasets/VJ102DNB_2.1.json b/datasets/VJ102DNB_2.1.json index df361fb817..5db8c990e3 100644 --- a/datasets/VJ102DNB_2.1.json +++ b/datasets/VJ102DNB_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102DNB_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ102DNB is platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB\u2019s spectral wavelength ranges from 0.5 \u00b5m to 0.9 \u00b5m. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath.", "links": [ { diff --git a/datasets/VJ102DNB_NRT_2.1.json b/datasets/VJ102DNB_NRT_2.1.json index e330aba095..dafa2d9e0b 100644 --- a/datasets/VJ102DNB_NRT_2.1.json +++ b/datasets/VJ102DNB_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102DNB_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Day/Night Band 6-Min L1B Swath 750m Near Real Time (NRT) product, short-name VJ102DNB_NRT is one of the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the Joint Polar Satellite System - 1 (JPSS1) satellite overpass. The Day/Night band (DNB) is a panchromatic channel covering the wavelengths from 500 nm to 900 nm, and sensitive to visible and near-infrared from daylight down to the low-level radiation observed at night.\r\n\r\nThe VIIRS DNB is much improved from previous products due in large part to its complicated continuous on-board calibration. In addition, new-moon Earth observations are used to estimate and remove stray light. These corrections are a first of its kind to provide on-orbit radiometric calibration. The corrections made to the DNB data are provided by the NASA VIIRS Characterization Support Team and are likely to continue to evolve given this new methodology.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath.\r\n\r\nAs the DNB is sensitive to nighttime radiation over the full lunar cycle, the incoming solar and lunar radiation must be properly modeled to calculate the reflectance. However, the DNB is sensitive to more sources of radiation than just the sun and moon.", "links": [ { diff --git a/datasets/VJ102DNB_NRT_2.json b/datasets/VJ102DNB_NRT_2.json index 2d3e733032..50d0ccdb73 100644 --- a/datasets/VJ102DNB_NRT_2.json +++ b/datasets/VJ102DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Level 1 and Level 2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the Joint Polar Satellite System - 1 (JPSS1) satellite overpass. The Day/Night band (DNB) is a panchromatic channel covering the wavelengths from 500 nm to 900 nm, and sensitive to visible and near-infrared from daylight down to the low-level radiation observed at night.\n\nThe VIIRS DNB is much improved from previous products due in large part to its complicated continuous on-board calibration. In addition, new-moon Earth observations are used to estimate and remove stray light. These corrections are a first of its kind to provide on-orbit radiometric calibration. The corrections made to the DNB data are provided by the NASA VIIRS Characterization Support Team and are likely to continue to evolve given this new methodology.\n\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath.\n\nAs the DNB is sensitive to nighttime radiation over the full lunar cycle, the incoming solar and lunar radiation must be properly modeled to calculate the reflectance. However, the DNB is sensitive to more sources of radiation than just the sun and moon.", "links": [ { diff --git a/datasets/VJ102GDC_NRT_2.1.json b/datasets/VJ102GDC_NRT_2.1.json index 9e138a441a..e8d408cc94 100644 --- a/datasets/VJ102GDC_NRT_2.1.json +++ b/datasets/VJ102GDC_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102GDC_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Moderate-Resolution Dual Gain Bands Calibrated Radiance 6-Min L1B Swath 750m Near Real Time (NRT) product, short-name VJ102GDC_NRT contains unaggregated, calibrated TOA radiances for those VIIRS sub-pixel samples that are aggregated along-scan during post-calibration ground processing. In other words, this file contains the calibrated M1 - M5, M7 and M13 dual gain band data from the nadir and near-nadir zones that would otherwise be discarded following post-calibration aggregation/Earth View Radiometric Calibration Unit.\r\n\r\nThe Level-1 and Level-2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 micron to 11.45 micron. There are also 7 dual-gain VIIRS bands. The dual gain moderate resolution bands (M1 to M5, M7 and M13) have 6304 samples and the other moderate resolution bands have 3200.\r\n\r\nFor additional information, see the Algorithm Theoretical Basis Document (ATBD) for the L1B product (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/NASARevisedJPSSVIIRSRadCalATBD2014.pdf). The document describes how VIIRS operates in space and provides the equations implemented by the L1B software to generate the MODIS Level-1 intermediate products. It is a summary document that presents the formulae and error budges used to transform VIIRS digital counts to radiance and reflectance.", "links": [ { diff --git a/datasets/VJ102GDC_NRT_2.json b/datasets/VJ102GDC_NRT_2.json index 74d092644a..8db0d120e9 100644 --- a/datasets/VJ102GDC_NRT_2.json +++ b/datasets/VJ102GDC_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102GDC_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-1 and Level-2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 µm to 11.45 µm. There are also 7 dual-gain VIIRS bands. The dual gain moderate resolution bands (M1 to M5, M7 and M13) have 6304 samples and the other moderate resolution bands have 3200.\n\nThe Near Real Time (NRT) VIIRS/JPSS1 Moderate-Resolution Dual Gain Bands Calibrated Radiance 6-Min L1B Swath 750m product (VJ102GDC_NRT) contains unaggregated, calibrated TOA radiances for those VIIRS sub-pixel samples that are aggregated along-scan during post-calibration ground processing. In other words, this file contains the calibrated M1 – M5, M7 and M13 dual gain band data from the nadir and near-nadir zones that would otherwise be discarded following post-calibration aggregation/Earth View Radiometric Calibration Unit.\n\nFor additional information, see the Operational Algorithm Description (OAD) Document for the L1B product at http://npp.gsfc.nasa.gov/sciencedocs/2015-08/474-00090_OAD-VIIRS-CAL-GEO-SDR_H.pdf. The document describes how VIIRS operates in space and provides the equations implemented by the L1B software to generate the MODIS Level-1 intermediate products. It is a summary document that presents the formulae and error budges used to transform VIIRS digital counts to radiance and reflectance.", "links": [ { diff --git a/datasets/VJ102IMG_2.1.json b/datasets/VJ102IMG_2.1.json index 60ac497b38..f562bcb8e9 100644 --- a/datasets/VJ102IMG_2.1.json +++ b/datasets/VJ102IMG_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102IMG_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ102IMG product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB). Ranging in wavelengths from 0.6 \u00b5m to 12.4 \u00b5m, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes.", "links": [ { diff --git a/datasets/VJ102IMG_NRT_2.1.json b/datasets/VJ102IMG_NRT_2.1.json index 9cfedbb5df..2df85674ad 100644 --- a/datasets/VJ102IMG_NRT_2.1.json +++ b/datasets/VJ102IMG_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102IMG_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Imagery Resolution 6-Min L1B Swath 375m Near REal Time (NRT), short-name VJ102IMG_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 micrometer to 11.45 micrometer. The VJ102IMG product is comprised of 5 bands that are sensitive to visible, near-infrared (NIR), and thermal wavelengths. The spatial resolution of the instrument at viewing nadir is approximately 375 m for the Imagery bands, and 750m for the DNB and the Moderate-resolution Bands.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375m for the Imagery bands.", "links": [ { diff --git a/datasets/VJ102IMG_NRT_2.json b/datasets/VJ102IMG_NRT_2.json index 1716b7cf42..b17c8b9168 100644 --- a/datasets/VJ102IMG_NRT_2.json +++ b/datasets/VJ102IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Level 1 and Level 2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 micrometer to 11.45 micrometer. The VJ102IMG product is comprised of 5 bands that are sensitive to visible, near-infrared (NIR), and thermal wavelengths. The spatial resolution of the instrument at viewing nadir is approximately 375 m for the Imagery bands, and 750m for the DNB and the Moderate-resolution Bands.\n\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375m for the Imagery bands.", "links": [ { diff --git a/datasets/VJ102MOD_2.1.json b/datasets/VJ102MOD_2.1.json index 5917e8e828..2e96a0fb26 100644 --- a/datasets/VJ102MOD_2.1.json +++ b/datasets/VJ102MOD_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102MOD_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Moderate Resolution 6-Min L1B Swath 750 m, short-name VJ102MOD is VIIRS Level-1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 \u00b5m to 12.49 \u00b5m, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. ", "links": [ { diff --git a/datasets/VJ102MOD_NRT_2.1.json b/datasets/VJ102MOD_NRT_2.1.json index e92922a4a0..ba1ba6385a 100644 --- a/datasets/VJ102MOD_NRT_2.1.json +++ b/datasets/VJ102MOD_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102MOD_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Moderate Resolution 6-Min L1B Swath 750m Near Real Time (NRT), short-name VJ102MOD_NRT is one of VIIRS Level 1 and Level 2 swath products that is generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VJ102MOD_NRT is VIIRS L1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 micron to 12.49 micron, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. \r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375 m for the Imagery bands.", "links": [ { diff --git a/datasets/VJ102MOD_NRT_2.json b/datasets/VJ102MOD_NRT_2.json index 5e60073d8a..365b9c2320 100644 --- a/datasets/VJ102MOD_NRT_2.json +++ b/datasets/VJ102MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ102MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Level 1 and Level 2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the JPSS1 satellite overpass. The VIIRS sensor has 16 moderate-resolution channels (M-bands) that have 16 detectors (16 rows of pixels per scan), that span the wavelengths from 0.412 micrometer to 12.1 micrometer. The VJ102MOD product is comprised of 16 bands that are sensitive to visible, near-infrared (NIR), and thermal wavelengths.\n\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375 m for the Imagery bands.", "links": [ { diff --git a/datasets/VJ103DNB_2.1.json b/datasets/VJ103DNB_2.1.json index f128953c23..e8e8a01724 100644 --- a/datasets/VJ103DNB_2.1.json +++ b/datasets/VJ103DNB_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103DNB_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VJ103DNB_NRT_2.1.json b/datasets/VJ103DNB_NRT_2.1.json index 604bd71080..80f3aabfa5 100644 --- a/datasets/VJ103DNB_NRT_2.1.json +++ b/datasets/VJ103DNB_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103DNB_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/VJ1 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Near Real Time (NRT) product, short-name VJ103DNB_NRT includes the geolocation fields that are calculated for VIIRS day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03DNB Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar and lunar zenith and azimuth angles, lunar phase angle and illumination fraction, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS day/night band products, particularly those produced by the Land team. ", "links": [ { diff --git a/datasets/VJ103DNB_NRT_2.json b/datasets/VJ103DNB_NRT_2.json index 8ff666f8af..0128a72aa1 100644 --- a/datasets/VJ103DNB_NRT_2.json +++ b/datasets/VJ103DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/VJ1 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m (VJ103DNB_NRT) product includes the geolocation fields that are calculated for VIIRS day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03DNB Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar and lunar zenith and azimuth angles, lunar phase angle and illumination fraction, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS day/night band products, particularly those produced by the Land team. ", "links": [ { diff --git a/datasets/VJ103IMG_2.1.json b/datasets/VJ103IMG_2.1.json index e55ea27d98..b5332a42d9 100644 --- a/datasets/VJ103IMG_2.1.json +++ b/datasets/VJ103IMG_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103IMG_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m product, short-name VJ103IMG, contains the derived line- of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VJ103IMG_NRT_2.1.json b/datasets/VJ103IMG_NRT_2.1.json index 72fff0bff6..2f7de353f9 100644 --- a/datasets/VJ103IMG_NRT_2.1.json +++ b/datasets/VJ103IMG_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103IMG_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m Near Real Time (NRT) product, short-name VJ103IMG_NRT includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG_NRT Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VJ103IMG_NRT_2.json b/datasets/VJ103IMG_NRT_2.json index a88f83c768..6713b096c8 100644 --- a/datasets/VJ103IMG_NRT_2.json +++ b/datasets/VJ103IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m (VJ103IMG_NRT) product includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG_NRT Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VJ103MODLL_021.json b/datasets/VJ103MODLL_021.json index efba048b4a..9fd50563b8 100644 --- a/datasets/VJ103MODLL_021.json +++ b/datasets/VJ103MODLL_021.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ103MODLL_021", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). \r\n\r\nProvided in the VJ103MODLL product are layers for height, latitude, and longitude. \r\n\r\nThese Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.002) swath product for accurate geolocation information.", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). \r\n\r\nProvided in the VJ103MODLL product are layers for height, latitude, and longitude. \r\n\r\nThese Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.001) swath product for accurate geolocation information.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2018-01-05T00:00:00Z", null ] ] @@ -109,9 +109,17 @@ ] }, "assets": { + "gov/VIIRS/VJ103MODLL": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ103MODLL.021/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314612-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1855860844-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -125,34 +133,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ103MODLL_021": { - "href": "s3://lp-prod-protected/VJ103MODLL.021", - "title": "lp_prod_protected_VJ103MODLL_021", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ103MODLL_021": { - "href": "s3://lp-prod-public/VJ103MODLL.021", - "title": "lp_prod_public_VJ103MODLL_021", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314612-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ103MOD_2.1.json b/datasets/VJ103MOD_2.1.json index b4c7500473..949ebbfd3a 100644 --- a/datasets/VJ103MOD_2.1.json +++ b/datasets/VJ103MOD_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103MOD_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VJ103MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VJ103MOD_NRT_2.1.json b/datasets/VJ103MOD_NRT_2.1.json index a3bdf2323f..bb7a99bd90 100644 --- a/datasets/VJ103MOD_NRT_2.1.json +++ b/datasets/VJ103MOD_NRT_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103MOD_NRT_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Near Real Time (NRT) product, short-name VJ103MOD_NRT includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VJ103MOD_NRT_2.json b/datasets/VJ103MOD_NRT_2.json index 2270ebbd41..e296756fbb 100644 --- a/datasets/VJ103MOD_NRT_2.json +++ b/datasets/VJ103MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ103MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m (VJ103MOD_NRT) product includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VJ109A1_002.json b/datasets/VJ109A1_002.json index 43d31a4153..f558530e55 100644 --- a/datasets/VJ109A1_002.json +++ b/datasets/VJ109A1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109A1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) surface reflectance (VJ109A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. \n\n", "links": [ { diff --git a/datasets/VJ109CMG_002.json b/datasets/VJ109CMG_002.json index ad78b29c4f..3b1ac51ae2 100644 --- a/datasets/VJ109CMG_002.json +++ b/datasets/VJ109CMG_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109CMG_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance Climate Modeling Grid (VJ109CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. \n\n", "links": [ { diff --git a/datasets/VJ109CMG_NRT_2.json b/datasets/VJ109CMG_NRT_2.json index c97b70fc36..b72583e90f 100644 --- a/datasets/VJ109CMG_NRT_2.json +++ b/datasets/VJ109CMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109CMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VJ109CMG_NRT is a Near Real Time (NRT) daily surface reflectance Climate Modeling Grid Version 2 product which provides an estimate of land surface reflectance from the NOAA-20 (previously called JPSS1) Visible Infrared Imager Radiometer Suite (VIIRS) sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping.\r\n\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product, aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf\r\n\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html", "links": [ { diff --git a/datasets/VJ109GA_002.json b/datasets/VJ109GA_002.json index e4b0aaad64..45b2871f52 100644 --- a/datasets/VJ109GA_002.json +++ b/datasets/VJ109GA_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ109GA_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. \r\n\r\nThe inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. \r\n\r\nThe inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2018-01-05T00:00:00Z", null ] ] @@ -110,33 +110,33 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_NOAA20_SurfaceReflectance_BandsM5-M4-M3.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download VIIRS_NOAA20_SurfaceReflectance_BandsM5-M4-M3.jpg", + "title": "Download BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ109GA.002/VJ109GA.A2024211.h18v04.002.2024212074053/BROWSE.VJ109GA.A2024211.h18v04.002.2024212074053.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_NOAA20_SurfaceReflectance_BandsM5-M4-M3.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", + "gov/VIIRS/VJ109GA": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ109GA.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", "roles": [ - "thumbnail" + "data" ] }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1851388634-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -149,34 +149,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ109GA_002": { - "href": "s3://lp-prod-protected/VJ109GA.002", - "title": "lp_prod_protected_VJ109GA_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ109GA_002": { - "href": "s3://lp-prod-public/VJ109GA.002", - "title": "lp_prod_public_VJ109GA_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841524-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ109GA_NRT_2.json b/datasets/VJ109GA_NRT_2.json index b4ff8b82e4..a5c7fed6b1 100644 --- a/datasets/VJ109GA_NRT_2.json +++ b/datasets/VJ109GA_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109GA_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VJ109GA_NRT is a Near Real Time (NRT) VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid product. The VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. VJ109GA is a Level-2G surface reflectance product produced on a 10km x 10km grid. The VNP09GA surface reflectance product is composed of all available surface reflectance observations for a given day over a set of tiles with global coverage. The tile numbering scheme and boundaries are the same as for MODIS. The first set of observations for each data set and grid cell are projected onto a two-dimensional grid and stored as 10km square tiles at 500m and 1 km resolution.\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product , aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html", "links": [ { diff --git a/datasets/VJ109H1_002.json b/datasets/VJ109H1_002.json index 0ed15d7ad8..a579a1a140 100644 --- a/datasets/VJ109H1_002.json +++ b/datasets/VJ109H1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109H1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VJ109H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. \r\n\r\n", "links": [ { diff --git a/datasets/VJ109_2.json b/datasets/VJ109_2.json index 94d366c13e..5b0e956a85 100644 --- a/datasets/VJ109_2.json +++ b/datasets/VJ109_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VJ109, are estimates of surface reflectance in each of the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VJ109 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. ", "links": [ { diff --git a/datasets/VJ109_NRT_2.json b/datasets/VJ109_NRT_2.json index 83bdab308d..a2c20eab25 100644 --- a/datasets/VJ109_NRT_2.json +++ b/datasets/VJ109_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ109_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VJ109_NRT is a Near Real Time (NRT) Visible Infrared Imager Radiometer Suite (VIIRS) 375 m, 750 m Atmospherically Corrected Surface Reflectance product. The VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 Level-2 surface reflectance product contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. \r\n\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product, aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf\r\n\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html", "links": [ { diff --git a/datasets/VJ110A1F_2.json b/datasets/VJ110A1F_2.json index 0933aeb4aa..ad21053df1 100644 --- a/datasets/VJ110A1F_2.json +++ b/datasets/VJ110A1F_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ110A1F_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-1 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace \u2018cloud-covered\u2019 pixels with \u2018cloud-free pixels\u2019 for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.", "links": [ { diff --git a/datasets/VJ110A1_2.json b/datasets/VJ110A1_2.json index 69a10e3e30..e7ef1a33f5 100644 --- a/datasets/VJ110A1_2.json +++ b/datasets/VJ110A1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ110A1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.", "links": [ { diff --git a/datasets/VJ110C1_2.json b/datasets/VJ110C1_2.json index 0082ade385..de15eef494 100644 --- a/datasets/VJ110C1_2.json +++ b/datasets/VJ110C1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ110C1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05\u00b0 (approx. 5 km at the equator) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/JPSS1 Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/UAJGR7WVWDDI).", "links": [ { diff --git a/datasets/VJ110_2.json b/datasets/VJ110_2.json index f0e7544d47..952c5a305c 100644 --- a/datasets/VJ110_2.json +++ b/datasets/VJ110_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ110_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.", "links": [ { diff --git a/datasets/VJ110_NRT_2.json b/datasets/VJ110_NRT_2.json index 09e4fb2645..3bf9f861ae 100644 --- a/datasets/VJ110_NRT_2.json +++ b/datasets/VJ110_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ110_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VJ110_NRT is Near Real Time (NRT) VIIRS/JPSS1 Snow Cover 6-Min L2 Swath 375m data set which reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens. The VNP10_NRT product is provided in NETCDF format.\r\n\r\nMore information can be find from VIIRS Land website at:\r\nhttps://viirsland.gsfc.nasa.gov/Products/NASA/SnowESDR.html", "links": [ { diff --git a/datasets/VJ113A1_002.json b/datasets/VJ113A1_002.json index b1ed3fafd9..27ab63631a 100644 --- a/datasets/VJ113A1_002.json +++ b/datasets/VJ113A1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ113A1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ113 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VJ113A1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VJ113A2_002.json b/datasets/VJ113A2_002.json index 907c87beab..5e3b982c6d 100644 --- a/datasets/VJ113A2_002.json +++ b/datasets/VJ113A2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ113A2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VJ113A2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VJ113A3_002.json b/datasets/VJ113A3_002.json index 2c78d6f414..30461f2553 100644 --- a/datasets/VJ113A3_002.json +++ b/datasets/VJ113A3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ113A3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VJ113A3 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VJ113C1_002.json b/datasets/VJ113C1_002.json index 5ca06d700a..f8c953a804 100644 --- a/datasets/VJ113C1_002.json +++ b/datasets/VJ113C1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ113C1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VJ113C2_002.json b/datasets/VJ113C2_002.json index 76e65f038f..10e415743e 100644 --- a/datasets/VJ113C2_002.json +++ b/datasets/VJ113C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ113C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VJ114A1_002.json b/datasets/VJ114A1_002.json index 48ffd30adc..e17e387f96 100644 --- a/datasets/VJ114A1_002.json +++ b/datasets/VJ114A1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ114A1_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\r\n\r\nThe VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\r\n\r\nThe VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -114,25 +114,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114A1.002/VJ114A1.A2024210.h27v04.002.2024211072626/BROWSE.VJ114A1.A2024210.h27v04.002.2024211072626.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.01/BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ114A1.A2024210.h27v04.002.2024211072626.1.jpg", + "title": "Download BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114A1.002/VJ114A1.A2024210.h27v04.002.2024211072626/BROWSE.VJ114A1.A2024210.h27v04.002.2024211072626.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.01/BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ114A1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ114A1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1856130843-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ114A1_002": { - "href": "s3://lp-prod-protected/VJ114A1.002", - "title": "lp_prod_protected_VJ114A1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ114A1_002": { - "href": "s3://lp-prod-public/VJ114A1.002", - "title": "lp_prod_public_VJ114A1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310874-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ114IMGTDL_NRT_2.json b/datasets/VJ114IMGTDL_NRT_2.json index e09508af8f..01e08301ad 100644 --- a/datasets/VJ114IMGTDL_NRT_2.json +++ b/datasets/VJ114IMGTDL_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ114IMGTDL_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Near real-time (NRT) NOAA-20 (formally JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on the instrument's 375 m nominal resolution data. Compared to other coarser resolution (\u22651km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.\n\nVJ114IMGTDL_NRT are available in the following formats: TXT, SHP, KML, and WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.", "links": [ { diff --git a/datasets/VJ114IMGT_NRT_2.json b/datasets/VJ114IMGT_NRT_2.json index 881528f874..38d1cf871c 100644 --- a/datasets/VJ114IMGT_NRT_2.json +++ b/datasets/VJ114IMGT_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ114IMGT_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Near real-time (NRT) NOAA-20 (formally JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on the instrument's 375 m nominal resolution data. Compared to other coarser resolution (\u22651km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.\r\n\r\nVJ114IMGTDL_NRT are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. For the HDF version see: VJ114IMG_NRT \r\n\r\nRecommended reading: VIIRS 375m Active Fire Algorithm User Guide (https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf) (updated December 2015).\r\n", "links": [ { diff --git a/datasets/VJ114IMG_002.json b/datasets/VJ114IMG_002.json index 67331d600d..92f22f7a76 100644 --- a/datasets/VJ114IMG_002.json +++ b/datasets/VJ114IMG_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ114IMG_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as thermal anomalies. \r\n\r\nThe VJ114IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes.\r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products.\r\n\r\nUse of the VJ103MODLL data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS).\r\n\r\n", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n \r\nThe VJ114IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.002) data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS).\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2018-01-05T00:00:00Z", null ] ] @@ -82,13 +82,13 @@ "license": "proprietary", "keywords": [ "EARTH SCIENCE", + "LAND SURFACE", + "SURFACE THERMAL PROPERTIES", + "LAND SURFACE TEMPERATURE", "BIOSPHERE", "ECOLOGICAL DYNAMICS", "FIRE ECOLOGY", - "FIRE OCCURRENCE", - "LAND SURFACE", - "SURFACE THERMAL PROPERTIES", - "LAND SURFACE TEMPERATURE" + "FIRE OCCURRENCE" ], "providers": [ { @@ -114,24 +114,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114IMG.002/VJ114IMG.A2024212.0654.002.2024212131840/BROWSE.VJ114IMG.A2024212.0654.002.2024212131840.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ114IMG.A2024212.0654.002.2024212131840.1.jpg", + "title": "Download BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114IMG.002/VJ114IMG.A2024212.0654.002.2024212131840/BROWSE.VJ114IMG.A2024212.0654.002.2024212131840.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ114IMG": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ114IMG.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2734197957-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1856231986-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ114IMG_002": { - "href": "s3://lp-prod-protected/VJ114IMG.002", - "title": "lp_prod_protected_VJ114IMG_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ114IMG_002": { - "href": "s3://lp-prod-public/VJ114IMG.002", - "title": "lp_prod_public_VJ114IMG_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2734197957-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ114IMG_NRT_2.json b/datasets/VJ114IMG_NRT_2.json index e0c4a19b76..af71f84207 100644 --- a/datasets/VJ114IMG_NRT_2.json +++ b/datasets/VJ114IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ114IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VJ114IMG_NRT is a Near Real Time (NRT) NOAA-20 (formally JPSS-1) /VIIRS 375 m active fire detection data product. Compared to other coarser resolution (\u22651km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.\r\n\r\n\r\nThe algorithm uses all five 375 m VIIRS channels to detect fires and separate land, water, and cloud pixels in the image. Additional 750 m channels complement the available VIIRS multispectral data. Those channels are used as input to the baseline active fire detection product, which provides continuity to the EOS/MODIS 1 km Fire and Thermal Anomalies product.\r\n\r\n\r\nThe VIIRS 375 m fire detection data is a Level 2 product based on the input Science Data Record (SDR) Level 1 swath format. The NRT product is currently available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). The data are formatted as NetCDF4 files. Complementary ASCII files containing the short list of fire pixels detected are also available through LANCE FIRMS processing systems.\r\n\r\n\r\nFor more information read VIIRS 375 m Active Fire Algorithm User Guide at https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf \r\n\r\nand\r\n\r\nSchroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. doi:10.1016/j.rse.2013.12.008 PDF from UMD\r\n\r\nor\r\n\r\nvisit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VJ114_002.json b/datasets/VJ114_002.json index 5fb8e25507..340cdabc1f 100644 --- a/datasets/VJ114_002.json +++ b/datasets/VJ114_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ114_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n\r\nThe VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.021) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS).\r\n", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (Vj114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n\r\nThe VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vJ103modll.001) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS).\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2018-01-05T00:00:00Z", null ] ] @@ -114,24 +114,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114.002/VJ114.A2024212.0148.002.2024212083835/BROWSE.VJ114.A2024212.0148.002.2024212083835.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ114.A2024212.0148.002.2024212083835.1.jpg", + "title": "Download BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ114.002/VJ114.A2024212.0148.002.2024212083835/BROWSE.VJ114.A2024212.0148.002.2024212083835.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ114": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ114.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310869-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1856094637-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ114_002": { - "href": "s3://lp-prod-protected/VJ114.002", - "title": "lp_prod_protected_VJ114_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ114_002": { - "href": "s3://lp-prod-public/VJ114.002", - "title": "lp_prod_public_VJ114_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310869-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ114_NRT_2.json b/datasets/VJ114_NRT_2.json index 7594be6ee2..d33238f05c 100644 --- a/datasets/VJ114_NRT_2.json +++ b/datasets/VJ114_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ114_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product, short-name VJ114_NRT is based on the MODIS Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics.\r\n\r\nThe VJ114_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation.\r\n\r\nThe product provides day and nighttime active fire detection over land and water (from gas flares). The VJ114 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. \r\n\r\nFor more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VJ115A2H_002.json b/datasets/VJ115A2H_002.json index 5382b6bef5..de5bec85de 100644 --- a/datasets/VJ115A2H_002.json +++ b/datasets/VJ115A2H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ115A2H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VJ115A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NOAA-20 satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VJ115A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VJ115A2H granule: LAI and FPAR.", "links": [ { diff --git a/datasets/VJ121A1D_002.json b/datasets/VJ121A1D_002.json index 6386255d65..2ea79d3d5d 100644 --- a/datasets/VJ121A1D_002.json +++ b/datasets/VJ121A1D_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ121A1D_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). \r\n\r\nThe VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule.\r\n", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,25 +112,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ121A1D.002/VJ121A1D.A2024211.h10v05.002.2024212072632/BROWSE.VJ121A1D.A2024211.h10v05.002.2024212072632.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ121A1D.A2024211.h10v05.002.2024212072632.1.jpg", + "title": "Download BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ121A1D.002/VJ121A1D.A2024211.h10v05.002.2024212072632/BROWSE.VJ121A1D.A2024211.h10v05.002.2024212072632.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ121A1D": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ121A1D.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1860372473-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ121A1D_002": { - "href": "s3://lp-prod-protected/VJ121A1D.002", - "title": "lp_prod_protected_VJ121A1D_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ121A1D_002": { - "href": "s3://lp-prod-public/VJ121A1D.002", - "title": "lp_prod_public_VJ121A1D_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310887-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ121A1N_002.json b/datasets/VJ121A1N_002.json index d6f0496696..0e85cc25bc 100644 --- a/datasets/VJ121A1N_002.json +++ b/datasets/VJ121A1N_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ121A1N_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nThe VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule.\r\n", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,25 +112,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ121A1N.002/VJ121A1N.A2024188.h11v04.002.2024189064949/BROWSE.VJ121A1N.A2024188.h11v04.002.2024189064949.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ121A1N.A2024188.h11v04.002.2024189064949.1.jpg", + "title": "Download BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ121A1N.002/VJ121A1N.A2024188.h11v04.002.2024189064949/BROWSE.VJ121A1N.A2024188.h11v04.002.2024189064949.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ121A1N": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ121A1N.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1860425836-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ121A1N_002": { - "href": "s3://lp-prod-protected/VJ121A1N.002", - "title": "lp_prod_protected_VJ121A1N_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ121A1N_002": { - "href": "s3://lp-prod-public/VJ121A1N.002", - "title": "lp_prod_public_VJ121A1N_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310892-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ121A2_002.json b/datasets/VJ121A2_002.json index e91d71c5e9..1869f5e562 100644 --- a/datasets/VJ121A2_002.json +++ b/datasets/VJ121A2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121A2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VJ121A2) combines the daily (VJ121A1D) (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and (VJ121A1N) (http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product.\r\n\r\nThe VJ121A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file.\r\n\r\nThe VJ121A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nThe VJ121A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121A2 granule.\r\n", "links": [ { diff --git a/datasets/VJ121C1_002.json b/datasets/VJ121C1_002.json index cb1e900b75..a1c4cf55b6 100644 --- a/datasets/VJ121C1_002.json +++ b/datasets/VJ121C1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121C1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VJ121C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).", "links": [ { diff --git a/datasets/VJ121C2_002.json b/datasets/VJ121C2_002.json index 93f703bbb8..a485b0feeb 100644 --- a/datasets/VJ121C2_002.json +++ b/datasets/VJ121C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ121C2) combines the daily (VJ121A1D) (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and (VJ121A1N) (http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf.\r\n", "links": [ { diff --git a/datasets/VJ121C3_002.json b/datasets/VJ121C3_002.json index 4dcab49ac5..64463642cd 100644 --- a/datasets/VJ121C3_002.json +++ b/datasets/VJ121C3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121C3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VJ121C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VJ121C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VJ121A1D (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and VJ121A1N (http://doi.org/10.5067/VIIRS/VJ121A1N.002) daily acquisitions from the monthly period. Unlike the VJ121A1 data sets where the daytime and nighttime acquisitions are separate products, the VJ121C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n", "links": [ { diff --git a/datasets/VJ121IMG_NRT_2.json b/datasets/VJ121IMG_NRT_2.json index ef60abe87c..543ef7cefd 100644 --- a/datasets/VJ121IMG_NRT_2.json +++ b/datasets/VJ121IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 375m product with short-name VJ121IMG_NRT, is the same product but at 375m spatial resolution. The VJ121 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at:\r\n \r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf\r\n\r\nand user guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf", "links": [ { diff --git a/datasets/VJ121_002.json b/datasets/VJ121_002.json index f24c3a583a..7b1fb80d5f 100644 --- a/datasets/VJ121_002.json +++ b/datasets/VJ121_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ121_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.\r\n\r\nThe VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nProvided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule.\r\n", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.\r\n\r\nThe VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf).\r\n\r\nProvided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,32 +112,32 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_NOAA20_Land_Surface_Temp_Day.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download VIIRS_NOAA20_Land_Surface_Temp_Day.jpg", + "title": "Download BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ121.002/VJ121.A2018011.1942.002.2022255145406/BROWSE.VJ121.A2018011.1942.002.2022255145406.h5images.Raster_Image_0.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_NOAA20_Land_Surface_Temp_Day.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", + "gov/VIIRS/VJ121": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ121.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", "roles": [ - "thumbnail" + "data" ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310883-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1860279988-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -151,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ121_002": { - "href": "s3://lp-prod-protected/VJ121.002", - "title": "lp_prod_protected_VJ121_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ121_002": { - "href": "s3://lp-prod-public/VJ121.002", - "title": "lp_prod_public_VJ121_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310883-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ121_NRT_2.json b/datasets/VJ121_NRT_2.json index 724772012d..cd58fd7406 100644 --- a/datasets/VJ121_NRT_2.json +++ b/datasets/VJ121_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ121_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 750m product (VJ121_NRT) uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for the three VIIRS thermal infrared bands M14 (8.55 micrometer), M15 (10.76 micrometer), and M16 (12 micrometer) at a spatial resolution of 750 m at nadir. The VJ!21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at:\r\n \r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf\r\n\r\nand user guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf", "links": [ { diff --git a/datasets/VJ128C2_002.json b/datasets/VJ128C2_002.json index 362bd965a6..834ff1e997 100644 --- a/datasets/VJ128C2_002.json +++ b/datasets/VJ128C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ128C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VJ128C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\r\n\r\nThe VJ128C2 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the VIIRS satellite (VJ109H1).\r\n\r\nThe VJ128C2 data product consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, and water storage capacity. \r\n", "links": [ { diff --git a/datasets/VJ128C3_002.json b/datasets/VJ128C3_002.json index f5346be611..c17f13555b 100644 --- a/datasets/VJ128C3_002.json +++ b/datasets/VJ128C3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ128C3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Monthly Level 3 (L3) Global (VJ128C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\r\n\r\nThe VJ128C3 data product is a composite of the 8-day area classifications from VJ128C2 which is converted to provide monthly elevation and water storage. The Lake Temperature and Evaporation Model (LTEM) with input from VIIRS Land Surface Temperature and Emissivity (VJ121A2) and meteorological data from Global Land Data Assimilation System (GLDAS) are used to produce monthly evaporation rates and volume losses.\r\n\r\nThe VJ128C3 data product provides a monthly time series that consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, water storage capacity, evaporation rate, and evaporation volume. \r\n", "links": [ { diff --git a/datasets/VJ129P1D_2.json b/datasets/VJ129P1D_2.json index b43ae985cd..f12050c28c 100644 --- a/datasets/VJ129P1D_2.json +++ b/datasets/VJ129P1D_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ129P1D_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI).\n\nVIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.", "links": [ { diff --git a/datasets/VJ129_2.json b/datasets/VJ129_2.json index 6d567d7556..8f3205d899 100644 --- a/datasets/VJ129_2.json +++ b/datasets/VJ129_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ129_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.", "links": [ { diff --git a/datasets/VJ129_NRT_2.json b/datasets/VJ129_NRT_2.json index 74032c7389..e28f9ee0ee 100644 --- a/datasets/VJ129_NRT_2.json +++ b/datasets/VJ129_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ129_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imager Radiometer Suite (VIIRS) Sea Ice Extent 6-Min L2 Swath 375m is Near Real Time(NRT) (short name VJ129_NRT) product reports the location of sea ice cover derived from radiance data acquired by VIIRS. Following the approach used by MODIS, the algorithm assumes that sea ice is snow covered and can be detected using the Normalized Difference Snow Index (NDSI). The VIIRS instrument flies on board the NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) satellite.\r\n\r\nFor more information, consult product users guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VIIRS%20C2%20Sea%20Ice%20Cover%20Product%20User%20Guide%20v3.pdf\r\n\r\nAnd product Algorithm Theoretical Basis Document (ATBD):\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VIIRS_SeaIceCover_ATBD_V2.pdf", "links": [ { diff --git a/datasets/VJ130P1D_2.json b/datasets/VJ130P1D_2.json index 6d08d0e37f..60b064a0df 100644 --- a/datasets/VJ130P1D_2.json +++ b/datasets/VJ130P1D_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ130P1D_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n\nVIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.", "links": [ { diff --git a/datasets/VJ130P1N_2.json b/datasets/VJ130P1N_2.json index ea609a08da..70d8b19d4b 100644 --- a/datasets/VJ130P1N_2.json +++ b/datasets/VJ130P1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ130P1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n\nVIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.", "links": [ { diff --git a/datasets/VJ130_2.json b/datasets/VJ130_2.json index e0b7d60c11..8d99014b1e 100644 --- a/datasets/VJ130_2.json +++ b/datasets/VJ130_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ130_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.", "links": [ { diff --git a/datasets/VJ130_NRT_2.json b/datasets/VJ130_NRT_2.json index 2a34f1e361..0591f9b4f8 100644 --- a/datasets/VJ130_NRT_2.json +++ b/datasets/VJ130_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ130_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imager Radiometer Suite (VIIRS) Ice Surface Temperature 6-Min L2 Swath 750m is Near Real Time(NRT) (short name VJ130_NRT) product provides surface temperatures retrieved at VIIRS moderate resolution for Arctic and Antarctic Sea Ice, for both day and night. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) satellite.", "links": [ { diff --git a/datasets/VJ143DNBA1_002.json b/datasets/VJ143DNBA1_002.json index 09e9708672..84cd6d7915 100644 --- a/datasets/VJ143DNBA1_002.json +++ b/datasets/VJ143DNBA1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143DNBA1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ`143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VJ143DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format.", "links": [ { diff --git a/datasets/VJ143DNBA2_002.json b/datasets/VJ143DNBA2_002.json index 556fc61aa5..830923252c 100644 --- a/datasets/VJ143DNBA2_002.json +++ b/datasets/VJ143DNBA2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143DNBA2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nThe VJ143DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.\r\n\r\n", "links": [ { diff --git a/datasets/VJ143DNBA3_002.json b/datasets/VJ143DNBA3_002.json index b41c0392b8..33583d1154 100644 --- a/datasets/VJ143DNBA3_002.json +++ b/datasets/VJ143DNBA3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143DNBA3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \n\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). \n\nThe VJ143DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format.", "links": [ { diff --git a/datasets/VJ143DNBA4_002.json b/datasets/VJ143DNBA4_002.json index 36c91c7a4d..1ef7782f60 100644 --- a/datasets/VJ143DNBA4_002.json +++ b/datasets/VJ143DNBA4_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143DNBA4_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite.\n\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VJ143DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format.\n\n", "links": [ { diff --git a/datasets/VJ143IA1N_2.json b/datasets/VJ143IA1N_2.json index 9ef9733f8b..35e2b35b65 100644 --- a/datasets/VJ143IA1N_2.json +++ b/datasets/VJ143IA1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143IA1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VJ143IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days).\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VJ143IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format.", "links": [ { diff --git a/datasets/VJ143IA1_002.json b/datasets/VJ143IA1_002.json index 7299f4a009..66b9825bae 100644 --- a/datasets/VJ143IA1_002.json +++ b/datasets/VJ143IA1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143IA1_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.\r\n", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VJ143IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. \r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA1.002/VJ143IA1.A2024293.h11v05.002.2024301065026/BROWSE.VJ143IA1.A2024293.h11v05.002.2024301065026.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143IA1.A2024293.h11v05.002.2024301065026.1.jpg", + "title": "Download BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA1.002/VJ143IA1.A2024293.h11v05.002.2024301065026/BROWSE.VJ143IA1.A2024293.h11v05.002.2024301065026.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143IA1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143IA1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310914-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1877114381-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143IA1_002": { - "href": "s3://lp-prod-protected/VJ143IA1.002", - "title": "lp_prod_protected_VJ143IA1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143IA1_002": { - "href": "s3://lp-prod-public/VJ143IA1.002", - "title": "lp_prod_public_VJ143IA1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143IA2N_2.json b/datasets/VJ143IA2N_2.json index 2a1b167ddb..90b2e33acf 100644 --- a/datasets/VJ143IA2N_2.json +++ b/datasets/VJ143IA2N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143IA2N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product).\r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VJ143IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.", "links": [ { diff --git a/datasets/VJ143IA2_002.json b/datasets/VJ143IA2_002.json index ca94d0d06c..1baaa2441b 100644 --- a/datasets/VJ143IA2_002.json +++ b/datasets/VJ143IA2_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143IA2_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. \r\n", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VJ143_ATBD_V2.pdf).\r\n\r\nThe VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. \r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -111,9 +111,17 @@ ] }, "assets": { + "gov/VIIRS/Vj143IA2": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/Vj143IA2.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310918-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1877802065-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -127,34 +135,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143IA2_002": { - "href": "s3://lp-prod-protected/VJ143IA2.002", - "title": "lp_prod_protected_VJ143IA2_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143IA2_002": { - "href": "s3://lp-prod-public/VJ143IA2.002", - "title": "lp_prod_public_VJ143IA2_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143IA3N_2.json b/datasets/VJ143IA3N_2.json index 37a2cf9822..193e2735e5 100644 --- a/datasets/VJ143IA3N_2.json +++ b/datasets/VJ143IA3N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143IA3N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3.", "links": [ { diff --git a/datasets/VJ143IA3_002.json b/datasets/VJ143IA3_002.json index 672ef4127a..5166f2f374 100644 --- a/datasets/VJ143IA3_002.json +++ b/datasets/VJ143IA3_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143IA3_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA3.002/VJ143IA3.A2024294.h24v06.002.2024302065408/BROWSE.VJ143IA3.A2024294.h24v06.002.2024302065408.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143IA3.A2024294.h24v06.002.2024302065408.1.jpg", + "title": "Download BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA3.002/VJ143IA3.A2024294.h24v06.002.2024302065408/BROWSE.VJ143IA3.A2024294.h24v06.002.2024302065408.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143IA3": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143IA3.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310922-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878583618-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143IA3_002": { - "href": "s3://lp-prod-protected/VJ143IA3.002", - "title": "lp_prod_protected_VJ143IA3_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143IA3_002": { - "href": "s3://lp-prod-public/VJ143IA3.002", - "title": "lp_prod_public_VJ143IA3_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143IA4N_2.json b/datasets/VJ143IA4N_2.json index 84ea00fe82..16f780bf45 100644 --- a/datasets/VJ143IA4N_2.json +++ b/datasets/VJ143IA4N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143IA4N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA4N product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product.\r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VJ143IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3.", "links": [ { diff --git a/datasets/VJ143IA4_002.json b/datasets/VJ143IA4_002.json index fa0e12310b..f8c8dc4419 100644 --- a/datasets/VJ143IA4_002.json +++ b/datasets/VJ143IA4_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143IA4_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA4.002/VJ143IA4.A2024294.h12v04.002.2024302063644/BROWSE.VJ143IA4.A2024294.h12v04.002.2024302063644.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143IA4.A2024294.h12v04.002.2024302063644.1.jpg", + "title": "Download BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143IA4.002/VJ143IA4.A2024294.h12v04.002.2024302063644/BROWSE.VJ143IA4.A2024294.h12v04.002.2024302063644.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143IA4": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143IA4.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310926-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878591259-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143IA4_002": { - "href": "s3://lp-prod-protected/VJ143IA4.002", - "title": "lp_prod_protected_VJ143IA4_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143IA4_002": { - "href": "s3://lp-prod-public/VJ143IA4.002", - "title": "lp_prod_public_VJ143IA4_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143MA1N_2.json b/datasets/VJ143MA1N_2.json index 92c25d3a36..97281bee8e 100644 --- a/datasets/VJ143MA1N_2.json +++ b/datasets/VJ143MA1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143MA1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VJ143MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VJ143MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11.", "links": [ { diff --git a/datasets/VJ143MA1_002.json b/datasets/VJ143MA1_002.json index 85cef50cc1..0f76284b12 100644 --- a/datasets/VJ143MA1_002.json +++ b/datasets/VJ143MA1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143MA1_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. ", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA1.002/VJ143MA1.A2024292.h09v05.002.2024300071228/BROWSE.VJ143MA1.A2024292.h09v05.002.2024300071228.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143MA1.A2024292.h09v05.002.2024300071228.1.jpg", + "title": "Download BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA1.002/VJ143MA1.A2024292.h09v05.002.2024300071228/BROWSE.VJ143MA1.A2024292.h09v05.002.2024300071228.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143MA1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143MA1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310930-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878597692-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143MA1_002": { - "href": "s3://lp-prod-protected/VJ143MA1.002", - "title": "lp_prod_protected_VJ143MA1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143MA1_002": { - "href": "s3://lp-prod-public/VJ143MA1.002", - "title": "lp_prod_public_VJ143MA1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143MA2N_2.json b/datasets/VJ143MA2N_2.json index 4c6de3c85d..255633c7e1 100644 --- a/datasets/VJ143MA2N_2.json +++ b/datasets/VJ143MA2N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143MA2N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VJ143MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.", "links": [ { diff --git a/datasets/VJ143MA2_002.json b/datasets/VJ143MA2_002.json index 6518ccbac8..94426fbdad 100644 --- a/datasets/VJ143MA2_002.json +++ b/datasets/VJ143MA2_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143MA2_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name.", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) \r\n(https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3) (https://doi.org/10.5067/VIIRS/VJ143MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VJ143MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -111,9 +111,17 @@ ] }, "assets": { + "gov/VIIRS/VJ143MA2": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143MA2.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310934-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878599437-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -127,34 +135,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143MA2_002": { - "href": "s3://lp-prod-protected/VJ143MA2.002", - "title": "lp_prod_protected_VJ143MA2_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143MA2_002": { - "href": "s3://lp-prod-public/VJ143MA2.002", - "title": "lp_prod_public_VJ143MA2_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143MA3N_2.json b/datasets/VJ143MA3N_2.json index 69471480f8..3ed83ef497 100644 --- a/datasets/VJ143MA3N_2.json +++ b/datasets/VJ143MA3N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143MA3N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VJ143MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS).", "links": [ { diff --git a/datasets/VJ143MA3_002.json b/datasets/VJ143MA3_002.json index 2fbc98cfe8..d08e219d8f 100644 --- a/datasets/VJ143MA3_002.json +++ b/datasets/VJ143MA3_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143MA3_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format.", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) (https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VJ143MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA3.002/VJ143MA3.A2024294.h19v11.002.2024302064612/BROWSE.VJ143MA3.A2024294.h19v11.002.2024302064612.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143MA3.A2024294.h19v11.002.2024302064612.1.jpg", + "title": "Download BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA3.002/VJ143MA3.A2024294.h19v11.002.2024302064612/BROWSE.VJ143MA3.A2024294.h19v11.002.2024302064612.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143MA3": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143MA3.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310938-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878630884-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143MA3_002": { - "href": "s3://lp-prod-protected/VJ143MA3.002", - "title": "lp_prod_protected_VJ143MA3_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143MA3_002": { - "href": "s3://lp-prod-public/VJ143MA3.002", - "title": "lp_prod_public_VJ143MA3_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ143MA4N_2.json b/datasets/VJ143MA4N_2.json index dd3b6ce569..27a78c69d9 100644 --- a/datasets/VJ143MA4N_2.json +++ b/datasets/VJ143MA4N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ143MA4N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product.\r\n\r\nThe algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VJ143MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11.", "links": [ { diff --git a/datasets/VJ143MA4_002.json b/datasets/VJ143MA4_002.json index c29fca1107..37420f780d 100644 --- a/datasets/VJ143MA4_002.json +++ b/datasets/VJ143MA4_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VJ143MA4_002", - "stac_version": "1.0.0", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.", + "stac_version": "1.1.0", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2018-01-01T00:00:00Z", + "2017-01-01T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA4.002/VJ143MA4.A2024294.h11v05.002.2024302063644/BROWSE.VJ143MA4.A2024294.h11v05.002.2024302063644.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VJ143MA4.A2024294.h11v05.002.2024302063644.1.jpg", + "title": "Download BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ143MA4.002/VJ143MA4.A2024294.h11v05.002.2024302063644/BROWSE.VJ143MA4.A2024294.h11v05.002.2024302063644.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VJ143MA4": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VJ143MA4.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310943-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1878635633-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VJ143MA4_002": { - "href": "s3://lp-prod-protected/VJ143MA4.002", - "title": "lp_prod_protected_VJ143MA4_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VJ143MA4_002": { - "href": "s3://lp-prod-public/VJ143MA4.002", - "title": "lp_prod_public_VJ143MA4_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VJ146A1G_NRT_2.json b/datasets/VJ146A1G_NRT_2.json index ffdaa2b97e..76efb6cec7 100644 --- a/datasets/VJ146A1G_NRT_2.json +++ b/datasets/VJ146A1G_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ146A1G_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VJ146A1G_NRT, is same as VJ146A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.", "links": [ { diff --git a/datasets/VJ146A1_NRT_2.json b/datasets/VJ146A1_NRT_2.json index cea37be548..49e29bfcfb 100644 --- a/datasets/VJ146A1_NRT_2.json +++ b/datasets/VJ146A1_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ146A1_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first of two Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) based Near Real Time (NRT) datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/JPSS1 Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT. Known by its short-name, VJ146A1_NRT, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.", "links": [ { diff --git a/datasets/VJ201_NRT_2.json b/datasets/VJ201_NRT_2.json index beffece8ea..ac55b2e3aa 100644 --- a/datasets/VJ201_NRT_2.json +++ b/datasets/VJ201_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ201_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Raw Radiances in Counts 6-Min L1A Swath, short-name VJ201_NRT data product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted.\r\n\r\nFor more information download VIIRS Level 1 Product User's Guide at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf", "links": [ { diff --git a/datasets/VJ202DNB_2.json b/datasets/VJ202DNB_2.json index a372f93f16..8dfcce68ac 100644 --- a/datasets/VJ202DNB_2.json +++ b/datasets/VJ202DNB_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202DNB_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ202DNB, of the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB\u2019s spectral wavelength ranges from 0.5 micrometer to 0.9 micrometer. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. \r\n\r\nThe J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). \r\n\r\nFor more information and for users guide, visit:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202DNB", "links": [ { diff --git a/datasets/VJ202DNB_NRT_2.json b/datasets/VJ202DNB_NRT_2.json index 4d3c4f82a7..b82ab3da7b 100644 --- a/datasets/VJ202DNB_NRT_2.json +++ b/datasets/VJ202DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS Level 1 and Level 2 swath (VJ202DNB_NRT) product is single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB's spectral wavelength ranges from 0.5 \u00b5m to 0.9 \u00b5m. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. The DNB attempts to maintain a nearly constant 750-m resolution over the entire 3060 km orbital swath by resorting to an on-board aggregation method, which also renders the calibration of the DNB a challenge. Stray-light and other sources of noise (lunar illuminance, twilight, clouds, noisy scan-edges, etc.) affect the DNB quality, and warrant correction.\r\n\r\nFor more information download VIIRS Level 1 Product User's Guide at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf", "links": [ { diff --git a/datasets/VJ202GDC_NRT_2.json b/datasets/VJ202GDC_NRT_2.json index f86bf753a0..23870a3f46 100644 --- a/datasets/VJ202GDC_NRT_2.json +++ b/datasets/VJ202GDC_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202GDC_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Moderate-Resolution Dual Gain Bands Calibrated Radiance 6-Min L1B Swath 750m product (VJ202GDC_NRT) contains unaggregated, calibrated TOA radiances for those VIIRS sub-pixel samples that are aggregated along-scan during post-calibration ground processing. In other words, this file contains the calibrated M1 \u2013 M5, M7 and M13 dual gain band data from the nadir and near-nadir zones that would otherwise be discarded following post-calibration aggregation/Earth View Radiometric Calibration Unit.\r\n\r\nThe Level-1 and Level-2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during theJPSS-2/NOAA-21 satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 µm to 11.45 µm. There are also 7 dual-gain VIIRS bands. The dual gain moderate resolution bands (M1 to M5, M7 and M13) have 6304 samples and the other moderate resolution bands have 3200.\r\n\r\nFor additional information, see the Operational Algorithm Description (OAD) Document for the L1B product at http://npp.gsfc.nasa.gov/sciencedocs/2015-08/474-00090_OAD-VIIRS-CAL-GEO-SDR_H.pdf. The document describes how VIIRS operates in space and provides the equations implemented by the L1B software to generate the MODIS Level-1 intermediate products. It is a summary document that presents the formulae and error budges used to transform VIIRS digital counts to radiance and reflectance.", "links": [ { diff --git a/datasets/VJ202IMG_2.json b/datasets/VJ202IMG_2.json index 7091c99a56..70437c6e67 100644 --- a/datasets/VJ202IMG_2.json +++ b/datasets/VJ202IMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202IMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ202IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB). Each of the I-bands has 32 detectors in the along-track direction with 32 rows of pixels per scan that offer a resolution that is twice finer than that of the moderate (M) and Day-Night bands (DNB). Ranging in wavelengths from 0.6 \u00b5m to 12.4 \u00b5m, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. \r\n\r\nThe J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information and documents, visit LAADS product page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202IMG", "links": [ { diff --git a/datasets/VJ202IMG_NRT_2.json b/datasets/VJ202IMG_NRT_2.json index 858ed802ce..328367a928 100644 --- a/datasets/VJ202IMG_NRT_2.json +++ b/datasets/VJ202IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Imagery Resolution 6-Min L1B Swath 375m Near Real Time (NRT), short-name VJ202IMG_NRT is platform-derived NASA VIIRS L1B calibrated radiances product that comprises five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB). Each of the I-bands has 32 detectors in the along-track direction with 32 rows of pixels per scan that offer a resolution that is twice finer than that of the moderate (M) and Day-Night bands (DNB). Ranging in wavelengths from 0.6 \u00b5m to 12.4 \u00b5m, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. The image dimensions of the 375-m swath product measure 6464 lines by 6400 pixels.\r\n\r\nThe J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information download VIIRS Level 1 Product User's Guide at: \r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf", "links": [ { diff --git a/datasets/VJ202MOD_2.json b/datasets/VJ202MOD_2.json index ebf8409be4..50daf63354 100644 --- a/datasets/VJ202MOD_2.json +++ b/datasets/VJ202MOD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202MOD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Moderate Resolution 6-Min L1B Swath 750m, short-name VJ202MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 \u00b5m to 12.49 \u00b5m, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata.\r\n\r\nThe J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information and documentation, visit LAADS product page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202MOD\r\n\r\n", "links": [ { diff --git a/datasets/VJ202MOD_NRT_2.json b/datasets/VJ202MOD_NRT_2.json index 44bef26e0b..10024d0a91 100644 --- a/datasets/VJ202MOD_NRT_2.json +++ b/datasets/VJ202MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ202MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Moderate Resolution 6-Min L1B Swath 750m, short-name VJ202MOD_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA VIIRS L1B calibrated radiances product that comprises of sixteen moderate-resolution or M-bands with a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 \u00b5m to 12.49 \u00b5m, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. The image dimensions of the 750-m swath product measure 3232 lines by 3200 pixels.\r\n\r\nThe J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information download VIIRS Level 1 Product User's Guide at: \r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf", "links": [ { diff --git a/datasets/VJ203DNB_2.json b/datasets/VJ203DNB_2.json index a53717a4ab..9894ddcedf 100644 --- a/datasets/VJ203DNB_2.json +++ b/datasets/VJ203DNB_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203DNB_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203DNB product is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.\r\n\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. For more information and documents, visit LAADS product page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203DNB", "links": [ { diff --git a/datasets/VJ203DNB_NRT_2.json b/datasets/VJ203DNB_NRT_2.json index 0b39d05642..f09c9d4c98 100644 --- a/datasets/VJ203DNB_NRT_2.json +++ b/datasets/VJ203DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m, short-name VJ203DNB_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA VIIRS L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.\r\n\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.", "links": [ { diff --git a/datasets/VJ203IMG_2.json b/datasets/VJ203IMG_2.json index 18511d83a5..f2497655c4 100644 --- a/datasets/VJ203IMG_2.json +++ b/datasets/VJ203IMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203IMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375 m, short-name VJ203IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-derived NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product and contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203IMG provides a fundamental input to derive a number of VIIRS I-band higher-level products.\r\n\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. For more information and documents, visit LAADS product page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203IMG", "links": [ { diff --git a/datasets/VJ203IMG_NRT_2.json b/datasets/VJ203IMG_NRT_2.json index 90b1922af0..da20ddf652 100644 --- a/datasets/VJ203IMG_NRT_2.json +++ b/datasets/VJ203IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath, short-name VJ203IMG_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-derived NASA VIIRS L1 terrain-corrected geolocation product and contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203IMG provides a fundamental input to derive a number of VIIRS I-band higher-level products.\r\n\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.", "links": [ { diff --git a/datasets/VJ203MOD_2.json b/datasets/VJ203MOD_2.json index 78210ee73d..36c075c9fb 100644 --- a/datasets/VJ203MOD_2.json +++ b/datasets/VJ203MOD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203MOD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203MOD provides a fundamental input to derive a number of VIIRS M-band higher-level products.\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. For more information and documents, visit LAADS product page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203MOD", "links": [ { diff --git a/datasets/VJ203MOD_NRT_2.json b/datasets/VJ203MOD_NRT_2.json index eadca81041..23c204f00e 100644 --- a/datasets/VJ203MOD_NRT_2.json +++ b/datasets/VJ203MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ203MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS/JPSS2 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath, short-name VJ203MOD_NRT) is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA VIIRS L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203MOD provides a fundamental input to derive a number of VIIRS M-band higher-level products.\r\n\r\n\r\nThe J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.", "links": [ { diff --git a/datasets/VJ214IMGTDL_NRT_1.json b/datasets/VJ214IMGTDL_NRT_1.json index 97658e1457..fe1a4707ab 100644 --- a/datasets/VJ214IMGTDL_NRT_1.json +++ b/datasets/VJ214IMGTDL_NRT_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ214IMGTDL_NRT_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Near real-time (NRT) NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on that instrument's 375 m nominal resolution data. Compared to other coarser resolution (\u22651km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline N21/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.\n\nVJ214IMGTDL_NRT are available through NASA FIRMS in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.", "links": [ { diff --git a/datasets/VJ214IMG_NRT_2.json b/datasets/VJ214IMG_NRT_2.json index aa666e24d7..cfc8eab6a5 100644 --- a/datasets/VJ214IMG_NRT_2.json +++ b/datasets/VJ214IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ214IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Active Fires 6-Min L2 Swath 375m NRT with short-name VNP14IMG_NRT is a Near Real Time (NRT) active fire detection data product (Schroeder 2014). The product is built on the EOS/MODIS fire product heritage [Kaufman et al., 1998; Giglio et al., 2003], using a multi-spectral contextual algorithm to identify sub-pixel fire activity and other thermal anomalies in the Level 1 (swath) input data. The algorithm uses all five 375 m VIIRS channels to detect fires and separate land, water, and cloud pixels in the image. Additional 750 m channels complement the available VIIRS multispectral data. Those channels are used as input to the baseline active fire detection product, which provides continuity to the EOS/MODIS 1 km Fire and Thermal Anomalies product.
\r\nThe VIIRS 375 m fire detection data is a 6-min Level 2 swath product based on the input Science Data Record (SDR) Level 1 swath format. The NRT product is currently available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). The data are formatted as NetCDF4 files.\r\n\r\n\r\nFor more information read VIIRS 375 m Active Fire Algorithm User Guide at https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf \r\n\r\nand\r\n\r\nSchroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. doi:10.1016/j.rse.2013.12.008 PDF from UMD\r\n\r\nor\r\n\r\nvisit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VJ214_NRT_2.json b/datasets/VJ214_NRT_2.json index 69057438db..338ed45d58 100644 --- a/datasets/VJ214_NRT_2.json +++ b/datasets/VJ214_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VJ214_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS2 Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product, short-name VJ214_NRT is based on the MODIS Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics.\r\n\r\nThe VJ214_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation.\r\n\r\nThe product provides day and nighttime active fire detection over land and water (from gas flares). The VJ214 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. \r\n\r\nFor more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VMS_Bathy_Processing_1.json b/datasets/VMS_Bathy_Processing_1.json index fddc4e8a74..f56f1f07bf 100644 --- a/datasets/VMS_Bathy_Processing_1.json +++ b/datasets/VMS_Bathy_Processing_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VMS_Bathy_Processing_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office.\n\nThe dataset is titled HI483A because the processing was done on a 2010/11 voyage to Mawson and HI 483 was going to be a RAN survey at Mawson. The RAN survey wasn't feasible because of sea ice.\n\nThe data processed (12KHz EDO 323HP echo sounder data) was collected on the following voyages:\n2006/07 V2, V4, V6\n2007/08 SIP, V3, V6\n2008/09 V0, V1, V2, V3, V5\n2009/10 V0, V1, V2, V3, V4, V5, V7\n2010/11 Trials, V1, V2, V3, VE2, VMS\n\nAll voyage data sets were processed in the following manner. As the Aurora Australis sails from either Hobart, Tasmania or Fremantle, Western Australia all the shallow water data files containing depths less then 200m around these ports were not processed and deleted. If the sea floor image was too hard to determine during the voyage either parts of day lines were not processed or the whole line deleted depending on the quality of the data. This is evident with some day *.CSV files containing a second or third file, these files had the same file name and were given a end character of _2 or _3. Unfortunately the program Echoview is meant to allow the user to span gaps when processing a line but more often than not, this was not the case. So if there was a requirement to a have gap in the daily file then usually a second file was created. Regularly throughout all voyages files were observed that had no GPS data associated with the depths. Any raw files without GPS data could not be processed, all these files have been deleted. Occasionally corrupt files were experienced, and these corrupt files have also been deleted. When the Aurora Australis was at anchor off an Antarctic Station these files too were deleted. With the various problems with the raw data files, no voyage has complete sounding data for the whole voyage. Some voyages have large sections of data missing, but unfortunately this data was not able to processed due to one of the above factors.\n\nAll soundings were processed utilising the spheroid, WGS84 and only geographic co-ordinates have been determined. UTM grid co-ordinates were not calculated during the processing stages due to software limitations. Grid co-ordinates were not calculated for the final HTF files.\n\nScripts were developed to apply depth water corrections, tide offsets if shallower than 200m of water and the layback of the sounder with respect to the Ashtech GPS.\n\nThe processing of the data from 2007/08 V3, 2007/08 V6 and 2010/11 V3 was incomplete. Complete processing of the data from these voyages was done as part of HI513 which is described by the metadata record with ID AAD_voyage_soundings_HI513.\n\nThe data has not been through the verification process for use in charts.", "links": [ { diff --git a/datasets/VMS_Benthic_Photography_1.json b/datasets/VMS_Benthic_Photography_1.json index 146814af70..a3b565ad05 100644 --- a/datasets/VMS_Benthic_Photography_1.json +++ b/datasets/VMS_Benthic_Photography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VMS_Benthic_Photography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geoscience Australia and the Australian Antarctic Division conducted a benthic community survey using underwater still photographs on the shelf around the Mertz Glacier region. The purpose of the work was to collect high resolution still photographs of the seafloor across the shelf to address three main objectives:\n1.\nto investigate benthic community composition in the area previously covered by the Mertz Glacier tongue and to the east, an area previously covered by fast ice\n2.\nto investigate benthic community composition (or lack thereof) in areas of known iceberg scours\n3.\nto investigate the lateral extent of cold water coral communities in canyons along the shelf break.\nBenthic photos were captured using a Canon EOS 20D SLR 8 megapixel stills camera fitted with a Canon EF 35mm f1.4 L USM lens in a 2500m rated flat port anodised aluminium housing. Two Canon 580EX Speedlight strobes were housed in 6000m rated stainless steel housings with hemispherical acrylic domes. The camera and strobes were powered with a 28V 2.5Ah cyclone SLA battery pack fitted in the camera housing and connected using Brantner Wetconn series underwater connectors. The results were obtained with 100 ASA and a flash compensation value of +2/3 of a stop. The focus was set manually to 7m and the image was typically exposed at f2.8 and a shutter speed of 1/60 sec. The interval between photos was set to 10 or 15 seconds.\nThe camera was fitted to either the CTD frame or the beam trawl frame and lowered to approximately 4-5 m from the bottom. Two laser pointers, set 50 cm apart, were used for scale. The camera was deployed at 93 stations, 7 using the beam trawl frame and 86 using the CTD frame.\nThe stations were named by:\n1.\nCamera deployment frame (e.g. CTD or beam trawl, BT)\n2.\nFrame sequence number (e.g. CTD53)\n3.\nInstrument (e.g. camera = CAM)\n4.\nSequence of camera deployments through the survey overall (e.g. first deployment = CAM01, second deployment = CAM02 etc).\nFor example, BT5_CAM16 is the sixteenth camera deployment of the survey overall, and was the fifth deployment using the beam trawl frame.\nFrom the 93 stations, there were 75 successful camera deployments. There were no photos captured at 9 stations. This was due to the camera or strobes malfunctioning, the camera being too far from the bottom, or the camera or strobes being in the mud at the bottom. The photos at a further 9 stations are considered poor due to the camera being out of focus, the camera being a little too far from the bottom or because very few photos were captured of the bottom.\nThe benthic photo will be used to document the fauna and communities associated with representative habitats in the study area. The post-cruise analysis of the benthic photos will involve recording seabed geology and biology (class or order, and whatever is significant for the habitat) for each image", "links": [ { diff --git a/datasets/VMS_FRRF_1.json b/datasets/VMS_FRRF_1.json index 9bc315e2c4..5d8611f433 100644 --- a/datasets/VMS_FRRF_1.json +++ b/datasets/VMS_FRRF_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VMS_FRRF_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FRRF deployments were conducted at 22 sites in conjunction with ship stop times when the CTD was deployed. See event log for locations. Some underway FRRF sampling was conducted on the return voyage.\n\nThis work was conducted as part of the VMS (Voyage Marine Science) voyage of the Aurora Australis in the 2010-2011 season.\n\nA report providing further details about the FRRF work is available as part of the download file. The download file also contains a word document (also included in the download file for metadata record ASAC_1307) explaining the data columns in the excel spreadsheets.", "links": [ { diff --git a/datasets/VMS_Genomics_1.json b/datasets/VMS_Genomics_1.json index e4ba272f06..628a46e437 100644 --- a/datasets/VMS_Genomics_1.json +++ b/datasets/VMS_Genomics_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VMS_Genomics_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis:\n\nFor each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect.\nSamples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage.", "links": [ { diff --git a/datasets/VNP01_NRT_2.json b/datasets/VNP01_NRT_2.json index 3b8bab903e..45930b6dfa 100644 --- a/datasets/VNP01_NRT_2.json +++ b/datasets/VNP01_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP01_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIIRS/NPP Raw Radiances in Counts 6-Min L1A Swath - NRT product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted. The shortname for this product is VNP01_NRT.\r\n\r\nFor more information download VIIRS Level 1 Product User's Guide at https://oceancolor.gsfc.nasa.gov/docs/format/VIIRS_Level-1_DataProductUsersGuide.pdf\r\n\r\nfile_naming_convention = VNP01_NRT.AYYYYDDD.HHMM.CCC.nc\r\n\r\n AYYYYDDD = Acquisition Year and Day of Year\r\n HHMM = Acquisition Hour and Minute\r\n CCC = Collection number\r\n nc = NetCDF5", "links": [ { diff --git a/datasets/VNP02DNB_2.json b/datasets/VNP02DNB_2.json index 094d789d5e..35b756f090 100644 --- a/datasets/VNP02DNB_2.json +++ b/datasets/VNP02DNB_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02DNB_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m product, short-name VNP02DNB, is a panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB\u2019s spectral wavelength ranges from 0.5 \u00b5m to 0.9 \u00b5m. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight.\r\nMore information is available at product page at:\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP02DNB/", "links": [ { diff --git a/datasets/VNP02DNB_NRT_2.json b/datasets/VNP02DNB_NRT_2.json index 12925b06d5..8bf08c9a40 100644 --- a/datasets/VNP02DNB_NRT_2.json +++ b/datasets/VNP02DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Day/Night Band 6-Min L1B Swath SDR 750m Near Real Time (NRT) product, short-name VNP02DNB_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the S-NPP satellite overpass. The Day/Night band (DNB) is a panchromatic channel covering the wavelengths from 500 nm to 900 nm, and sensitive to visible and near-infrared from daylight down to the low-level radiation observed at night.\r\n\r\nThe VIIRS DNB is much improved from previous products due in large part to its complicated continuous on-board calibration. In addition, new-moon Earth observations are used to estimate and remove stray light. These corrections are a first of its kind to provide on-orbit radiometric calibration. The corrections made to the DNB data are provided by the NASA VIIRS Characterization Support Team and are likely to continue to evolve given this new methodology.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath.\r\n\r\nAs the DNB is sensitive to nighttime radiation over the full lunar cycle, the incoming solar and lunar radiation must be properly modeled to calculate the reflectance. However, the DNB is sensitive to more sources of radiation than just the sun and moon.", "links": [ { diff --git a/datasets/VNP02GDC_NRT_2.json b/datasets/VNP02GDC_NRT_2.json index 835e348fd2..250d1daea0 100644 --- a/datasets/VNP02GDC_NRT_2.json +++ b/datasets/VNP02GDC_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02GDC_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NPP/VIIRS Moderate-Resolution Dual Gain Bands Calibrated Radiance 6-Min L1B Swath 750m Near Real Time (NRT) product, short-name VNP02GDC, contains unaggregated, calibrated TOA radiances for those VIIRS sub-pixel samples that are aggregated along-scan during post-calibration ground processing. In other words, this file contains the calibrated M1- M5, M7 and M13 dual gain band data from the nadir and near-nadir zones that would otherwise be discarded following post-calibration aggregation/Earth View Radiometric Calibration Unit.\r\n\r\nThe VIIRS Level-1 and Level-2 swath products are generated from the processing of 6 minutes of VIIRS data acquired during the NPP satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 micron to 11.45 micron. There are also 7 dual-gain VIIRS bands. The dual gain moderate resolution bands (M1 to M5, M7 and M13) have 6304 samples and the other moderate resolution bands have 3200.\r\n\r\nFor additional information, see the Algorithm Theoretical Basis Document (ATBD) for the L1B product (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/NASARevisedJPSSVIIRSRadCalATBD2014.pdf). The document describes how VIIRS operates in space and provides the equations implemented by the L1B software to generate the MODIS Level-1 intermediate products. It is a summary document the presents the formulae and error budges used to transform VIIRS digital counts to radiance and reflectance.\r\n", "links": [ { diff --git a/datasets/VNP02IMG_2.json b/datasets/VNP02IMG_2.json index 867d0a049f..5b828173f8 100644 --- a/datasets/VNP02IMG_2.json +++ b/datasets/VNP02IMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02IMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375 m product, short-name VNP02IMG is derived from the five image-resolution or I-bands, which have a 375-meter resolution at nadir. Ranging in wavelengths from 0.6 \u00b5m to 12.4 \u00b5m, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. The image dimensions of the 375-m swath product measure 6464 lines by 6400 pixels.\r\n", "links": [ { diff --git a/datasets/VNP02IMG_NRT_2.json b/datasets/VNP02IMG_NRT_2.json index 1305efd66a..a525cd854f 100644 --- a/datasets/VNP02IMG_NRT_2.json +++ b/datasets/VNP02IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375m Near REal Time (NRT), short-name VNP02IMG_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the S-NPP satellite overpass. The VIIRS sensor has 5 high-resolution imagery channels (I-bands) that have 32 detectors (32 rows of pixels per scan), with twice the resolution of the M-bands and the DNB, that span the wavelengths from 0.640 micrometer to 11.45 micrometer. The VNP02IMG product is comprised of 5 bands that are sensitive to visible, near-infrared (NIR), and thermal wavelengths. The spatial resolution of the instrument at viewing nadir is approximately 375 m for the Imagery bands, and 750m for the DNB and the Moderate-resolution Bands.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375 m for the Imagery bands.", "links": [ { diff --git a/datasets/VNP02MOD_2.json b/datasets/VNP02MOD_2.json index 8a0f24d49e..d0e03aba6b 100644 --- a/datasets/VNP02MOD_2.json +++ b/datasets/VNP02MOD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02MOD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VNP02MOD | VIIRS/NPP Moderate Resolution 6-Min L1B Swath 750m, short-name VNP02MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VNP02MOD_NRT_2.json b/datasets/VNP02MOD_NRT_2.json index 5efc8ff55b..cf3d66e151 100644 --- a/datasets/VNP02MOD_NRT_2.json +++ b/datasets/VNP02MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP02MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Moderate Resolution Bands L1B 6-Min Swath 750m Near Real Time (NRT) product, short-name VNP02MOD_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the S-NPP satellite overpass. The VIIRS sensor has 16 moderate-resolution channels (M-bands) that have 16 detectors (16 rows of pixels per scan), that span the wavelengths from 0.412 micrometer to 12.1 micrometer. The VNP02MOD product is comprised of 16 bands that are sensitive to visible, near-infrared (NIR), and thermal wavelengths.\r\n\r\nThe spatial resolution of the instrument at viewing nadir is approximately 750 m for the Moderate-resolution and Day/Night Bands, and 375m for the Imagery bands.\r\n\r\n", "links": [ { diff --git a/datasets/VNP03DNB_2.json b/datasets/VNP03DNB_2.json index a664d0004b..0364cad20c 100644 --- a/datasets/VNP03DNB_2.json +++ b/datasets/VNP03DNB_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03DNB_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Day/Night Band Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VNP03DNB includes the geolocation fields that are calculated for Visible-Infrared Imaging-Radiometer Suite (VIIRS) day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.\r\n\r\n", "links": [ { diff --git a/datasets/VNP03DNB_NRT_2.json b/datasets/VNP03DNB_NRT_2.json index 0c7425fb3e..1c57139992 100644 --- a/datasets/VNP03DNB_NRT_2.json +++ b/datasets/VNP03DNB_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03DNB_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Day/Night Band Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m Near Real Time (NRT) product, short-name VNP03DNB_NRT includes the geolocation fields that are calculated for VIIRS day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03DNB Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar and lunar zenith and azimuth angles, lunar phase angle and illumination fraction, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS day/night band products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VNP03IMG_2.json b/datasets/VNP03IMG_2.json index 3bf4a03c90..52b6e27496 100644 --- a/datasets/VNP03IMG_2.json +++ b/datasets/VNP03IMG_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03IMG_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m, short-name VNP03IMG, product contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VNP03IMG_NRT_2.json b/datasets/VNP03IMG_NRT_2.json index 735e943546..79597ee3b4 100644 --- a/datasets/VNP03IMG_NRT_2.json +++ b/datasets/VNP03IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m Near Real Time (NRT) product, short-name VNP03IMG includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team.", "links": [ { diff --git a/datasets/VNP03MODLL_001.json b/datasets/VNP03MODLL_001.json index 6aa943a714..de42a598cf 100644 --- a/datasets/VNP03MODLL_001.json +++ b/datasets/VNP03MODLL_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03MODLL_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 1 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). \r\n\r\nProvided in the VNP03MODLL product are layers for height, latitude, and longitude. \r\n\r\nThese Science Data Sets (SDS) layers are used in conjunction with the (VNP14) (https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information.", "links": [ { diff --git a/datasets/VNP03MODLL_002.json b/datasets/VNP03MODLL_002.json index 9b27f32ea0..d245a5d880 100644 --- a/datasets/VNP03MODLL_002.json +++ b/datasets/VNP03MODLL_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP03MODLL_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth's geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the VNP14 swath product for accurate geolocation information.", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). \r\n\r\nProvided in the VNP03MODLL product are layers for height, latitude, and longitude. \r\n\r\nThese Science Data Sets (SDS) layers are used in conjunction with the (VNP14) (https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -109,9 +109,17 @@ ] }, "assets": { + "gov/VIIRS/VNP03MODLL": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP03MODLL.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545310947-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1796747704-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -125,34 +133,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP03MODLL_002": { - "href": "s3://lp-prod-protected/VNP03MODLL.002", - "title": "lp_prod_protected_VNP03MODLL_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP03MODLL_002": { - "href": "s3://lp-prod-public/VNP03MODLL.002", - "title": "lp_prod_public_VNP03MODLL_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310947-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP03MOD_2.json b/datasets/VNP03MOD_2.json index 71c760e2ed..20b1021890 100644 --- a/datasets/VNP03MOD_2.json +++ b/datasets/VNP03MOD_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03MOD_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750 m product, short-name VNP03MOD, contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform\u2019s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.", "links": [ { diff --git a/datasets/VNP03MOD_NRT_2.json b/datasets/VNP03MOD_NRT_2.json index afe0d34102..4e2df79794 100644 --- a/datasets/VNP03MOD_NRT_2.json +++ b/datasets/VNP03MOD_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP03MOD_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation L1 6-Min Swath 750m Near Real Time (NRT) product, short-name VNP03MOD_NRT includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team.\r\n\r\n", "links": [ { diff --git a/datasets/VNP09A1_001.json b/datasets/VNP09A1_001.json index e5fefdbdfc..e7f73e012d 100644 --- a/datasets/VNP09A1_001.json +++ b/datasets/VNP09A1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09A1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09A1_002.json b/datasets/VNP09A1_002.json index 622c594901..9a51c34d15 100644 --- a/datasets/VNP09A1_002.json +++ b/datasets/VNP09A1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09A1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) surface reflectance (VNP09A1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09CMG_001.json b/datasets/VNP09CMG_001.json index 242d5b616b..2c55329792 100644 --- a/datasets/VNP09CMG_001.json +++ b/datasets/VNP09CMG_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09CMG_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 1 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09CMG_002.json b/datasets/VNP09CMG_002.json index b8119ff661..41069bd731 100644 --- a/datasets/VNP09CMG_002.json +++ b/datasets/VNP09CMG_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09CMG_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09CMG_NRT_2.json b/datasets/VNP09CMG_NRT_2.json index e347f42843..ad48dbf0d7 100644 --- a/datasets/VNP09CMG_NRT_2.json +++ b/datasets/VNP09CMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09CMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TheVNP09CMG_NRT is a Near Real Time (NRT) daily surface reflectance Climate Modeling Grid Version 2 product which provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping.\r\n\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf\r\n\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html", "links": [ { diff --git a/datasets/VNP09GA_001.json b/datasets/VNP09GA_001.json index 72d30d1c2c..6666f5a167 100644 --- a/datasets/VNP09GA_001.json +++ b/datasets/VNP09GA_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09GA_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 1 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. \r\n\r\nThe inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", "links": [ { diff --git a/datasets/VNP09GA_002.json b/datasets/VNP09GA_002.json index 9ce490efd2..8bc537b726 100644 --- a/datasets/VNP09GA_002.json +++ b/datasets/VNP09GA_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP09GA_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. \r\n\r\nThe inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. \r\n\r\nThe inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -110,33 +110,33 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_SNPP_SurfaceReflectance_BandsM5-M4-M3.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download VIIRS_SNPP_SurfaceReflectance_BandsM5-M4-M3.jpg", + "title": "Download BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP09GA.002/VNP09GA.A2024205.h13v09.002.2024206084626/BROWSE.VNP09GA.A2024205.h13v09.002.2024206084626.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP09GA.A2019182.h19v04.001.2019183070609.1.jpg?_ga=2.79669204.116070394.1561987039-1109527761.1561753117", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_SNPP_SurfaceReflectance_BandsM5-M4-M3.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", + "gov/VIIRS/VNP09GA": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP09GA.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", "roles": [ - "thumbnail" + "data" ] }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1791082413-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -149,34 +149,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP09GA_002": { - "href": "s3://lp-prod-protected/VNP09GA.002", - "title": "lp_prod_protected_VNP09GA_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP09GA_002": { - "href": "s3://lp-prod-public/VNP09GA.002", - "title": "lp_prod_public_VNP09GA_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841556-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP09GA_NRT_2.json b/datasets/VNP09GA_NRT_2.json index 57f43d0643..1007abb21f 100644 --- a/datasets/VNP09GA_NRT_2.json +++ b/datasets/VNP09GA_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09GA_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VNP09GA_NRT is a Near Real Time (NRT) S-NPP/VIIRS 500m and 1km Daily Level 2G Surface Reflectance product. The NPP/ VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. VNP09GA is a Level-2G surface reflectance product produced on a 10km x 10km grid. The VNP09GA surface reflectance product is composed of all available surface reflectance observations for a given day over a set of tiles with global coverage. The tile numbering scheme and boundaries are the same as for MODIS. The first set of observations for each data set and grid cell are projected onto a two-dimensional grid and stored as 10km square tiles at 500m and 1 km resolution.\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v1.1.pdf\r\n\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html", "links": [ { diff --git a/datasets/VNP09H1_001.json b/datasets/VNP09H1_001.json index 0f05c0cf5a..14169c4f8f 100644 --- a/datasets/VNP09H1_001.json +++ b/datasets/VNP09H1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09H1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VNP09H1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09H1_002.json b/datasets/VNP09H1_002.json index 8b1f5602be..07f9abe605 100644 --- a/datasets/VNP09H1_002.json +++ b/datasets/VNP09H1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09H1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VNP09H1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. \r\n\r\n", "links": [ { diff --git a/datasets/VNP09_2.json b/datasets/VNP09_2.json index 8781a023a3..eccce35f42 100644 --- a/datasets/VNP09_2.json +++ b/datasets/VNP09_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VNP09, are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. ", "links": [ { diff --git a/datasets/VNP09_NRT_2.json b/datasets/VNP09_NRT_2.json index ade5e4ceaf..55cdc0619f 100644 --- a/datasets/VNP09_NRT_2.json +++ b/datasets/VNP09_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP09_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VNP09_NRT is a Near Real Time (NRT) S-NPP/VIIRS 375 m, 750 m Atmospherically Corrected Surface Reflectance product. The NPP/VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 Level-2 surface reflectance product contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products.\r\n\r\n\r\nSurface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).\r\n\r\n\r\nAll surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.\r\n\r\n\r\nFor more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v1.3.pdf\r\n\r\nor \r\n\r\nvisit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html", "links": [ { diff --git a/datasets/VNP10A1F_1.json b/datasets/VNP10A1F_1.json index 5366255518..b5a4aa95e1 100644 --- a/datasets/VNP10A1F_1.json +++ b/datasets/VNP10A1F_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10A1F_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 1 snow cover product. A cloud-gap-filled algorithm is utilized to replace \u2018cloud-covered\u2019 pixels with \u2018cloud-free pixels\u2019 for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.", "links": [ { diff --git a/datasets/VNP10A1F_2.json b/datasets/VNP10A1F_2.json index 4211f52fa4..02545efc1a 100644 --- a/datasets/VNP10A1F_2.json +++ b/datasets/VNP10A1F_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10A1F_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace \u2018cloud-covered\u2019 pixels with \u2018cloud-free pixels\u2019 for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.", "links": [ { diff --git a/datasets/VNP10A1_1.json b/datasets/VNP10A1_1.json index 29d1d14d77..b1d6ddb1a6 100644 --- a/datasets/VNP10A1_1.json +++ b/datasets/VNP10A1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10A1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.", "links": [ { diff --git a/datasets/VNP10A1_2.json b/datasets/VNP10A1_2.json index 53aa57e513..3c2f7f6a1a 100644 --- a/datasets/VNP10A1_2.json +++ b/datasets/VNP10A1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10A1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.", "links": [ { diff --git a/datasets/VNP10C1_2.json b/datasets/VNP10C1_2.json index e65f209dbd..3f6ad4629f 100644 --- a/datasets/VNP10C1_2.json +++ b/datasets/VNP10C1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10C1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05\u00b0 (approx. 5 km) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/45VDCKJBXWEE).", "links": [ { diff --git a/datasets/VNP10_1.json b/datasets/VNP10_1.json index b31b8b16c4..730f1706f4 100644 --- a/datasets/VNP10_1.json +++ b/datasets/VNP10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.", "links": [ { diff --git a/datasets/VNP10_2.json b/datasets/VNP10_2.json index 728e825fcb..172b92198f 100644 --- a/datasets/VNP10_2.json +++ b/datasets/VNP10_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.", "links": [ { diff --git a/datasets/VNP10_NRT_2.json b/datasets/VNP10_NRT_2.json index 1ee5538b60..6bff55e17b 100644 --- a/datasets/VNP10_NRT_2.json +++ b/datasets/VNP10_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP10_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VNP10_NRT is Near Real Time (NRT) VIIRS/NPP Snow Cover 6-Min L2 Swath 375m data set which reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens. The VNP10_NRT product is provided in NETCDF format.\r\n\r\nMore information can be find from VIIRS Land website at:\r\nhttps://viirsland.gsfc.nasa.gov/Products/NASA/SnowESDR.html", "links": [ { diff --git a/datasets/VNP13A1_001.json b/datasets/VNP13A1_001.json index c7a91a4de9..b1c5fc8ab9 100644 --- a/datasets/VNP13A1_001.json +++ b/datasets/VNP13A1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI),the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A1_002.json b/datasets/VNP13A1_002.json index 001482e0e4..8875729acf 100644 --- a/datasets/VNP13A1_002.json +++ b/datasets/VNP13A1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A2_001.json b/datasets/VNP13A2_001.json index 571c7aaa16..66f0433139 100644 --- a/datasets/VNP13A2_001.json +++ b/datasets/VNP13A2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A2_002.json b/datasets/VNP13A2_002.json index 246d2c220f..a261599c2e 100644 --- a/datasets/VNP13A2_002.json +++ b/datasets/VNP13A2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A3_001.json b/datasets/VNP13A3_001.json index d0a79da1ce..8c183c7d7a 100644 --- a/datasets/VNP13A3_001.json +++ b/datasets/VNP13A3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A3_002.json b/datasets/VNP13A3_002.json index 381454d5ad..cd40bc95c4 100644 --- a/datasets/VNP13A3_002.json +++ b/datasets/VNP13A3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13A4N_2.json b/datasets/VNP13A4N_2.json index f6e671e989..008a620847 100644 --- a/datasets/VNP13A4N_2.json +++ b/datasets/VNP13A4N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13A4N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS Near Real Time (NRT) Vegetation Indices 8-Day L3 Global 500m SIN Grid data, short-name VNP13A4N are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion.\n\nNote: This is a near real-time product only. Standard historical data and imagery for VNP13A4N (8-Day 500m) are not available. The only 500m standard Vegetation Indices product available is a 16-Day composite (VNP13A1). So, users can either use VNP13A1, use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/), or access the science quality VNP09A1 data and create the VI product of their own.", "links": [ { diff --git a/datasets/VNP13C1_001.json b/datasets/VNP13C1_001.json index 7b331770a4..3016d0c893 100644 --- a/datasets/VNP13C1_001.json +++ b/datasets/VNP13C1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13C1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13C1_002.json b/datasets/VNP13C1_002.json index 9c8c860238..c66c9bff52 100644 --- a/datasets/VNP13C1_002.json +++ b/datasets/VNP13C1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13C1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13C2_001.json b/datasets/VNP13C2_001.json index 2a29cef00a..8460f8ca27 100644 --- a/datasets/VNP13C2_001.json +++ b/datasets/VNP13C2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13C2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP13C2_002.json b/datasets/VNP13C2_002.json index 8595faf8b9..201068cffe 100644 --- a/datasets/VNP13C2_002.json +++ b/datasets/VNP13C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP13C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. \r\n\r\nAlong with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI.", "links": [ { diff --git a/datasets/VNP14A1_001.json b/datasets/VNP14A1_001.json index 507585fed9..2e67cac5ef 100644 --- a/datasets/VNP14A1_001.json +++ b/datasets/VNP14A1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP14A1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\r\n\r\nThe VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", "links": [ { diff --git a/datasets/VNP14A1_002.json b/datasets/VNP14A1_002.json index d5fab9dc9d..789a97b1d8 100644 --- a/datasets/VNP14A1_002.json +++ b/datasets/VNP14A1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP14A1_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\r\n\r\nThe VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", + "stac_version": "1.1.0", + "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\r\n\r\nThe VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -114,25 +114,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14A1.002/VNP14A1.A2012019.h20v09.002.2023122183153/BROWSE.VNP14A1.A2012019.h20v09.002.2023122183153.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.01/BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP14A1.A2012019.h20v09.002.2023122183153.1.jpg", + "title": "Download BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14A1.002/VNP14A1.A2012019.h20v09.002.2023122183153/BROWSE.VNP14A1.A2012019.h20v09.002.2023122183153.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.01/BROWSE.VNP14A1.A2019181.h11v08.001.2019182082815.1.jpg?_ga=2.108480450.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP14A1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP14A1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1796968944-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP14A1_002": { - "href": "s3://lp-prod-protected/VNP14A1.002", - "title": "lp_prod_protected_VNP14A1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP14A1_002": { - "href": "s3://lp-prod-public/VNP14A1.002", - "title": "lp_prod_public_VNP14A1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314541-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP14IMGTDL_NRT_2.json b/datasets/VNP14IMGTDL_NRT_2.json index bcf579ee68..e4f8997e3f 100644 --- a/datasets/VNP14IMGTDL_NRT_2.json +++ b/datasets/VNP14IMGTDL_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP14IMGTDL_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Near real-time (NRT) Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on that instrument's 375 m nominal resolution data. Compared to other coarser resolution (\u22651km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.\n\nVNP14IMGTDL_NRT are available through NASA FIRMS in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.", "links": [ { diff --git a/datasets/VNP14IMG_002.json b/datasets/VNP14IMG_002.json index 21b6ad9052..3d3d398468 100644 --- a/datasets/VNP14IMG_002.json +++ b/datasets/VNP14IMG_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP14IMG_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This Level 2 product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events. Due to its higher spatial resolution, the VNP14IMG active fire product provides greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters in comparison to the VNP14 fire data product.\r\n\r\nThe VNP14IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VNP03MODLL) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS).\r\n", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the Suomi National Polar Orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well asermal anomalies. \r\n identifying th\r\nThe VNP14IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS).\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -82,13 +82,13 @@ "license": "proprietary", "keywords": [ "EARTH SCIENCE", + "LAND SURFACE", + "SURFACE THERMAL PROPERTIES", + "LAND SURFACE TEMPERATURE", "BIOSPHERE", "ECOLOGICAL DYNAMICS", "FIRE ECOLOGY", - "FIRE OCCURRENCE", - "LAND SURFACE", - "SURFACE THERMAL PROPERTIES", - "LAND SURFACE TEMPERATURE" + "FIRE OCCURRENCE" ], "providers": [ { @@ -114,24 +114,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14IMG.002/VNP14IMG.A2012019.0506.002.2024002130147/BROWSE.VNP14IMG.A2012019.0506.002.2024002130147.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP14IMG.A2012019.0506.002.2024002130147.1.jpg", + "title": "Download BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14IMG.002/VNP14IMG.A2012019.0506.002.2024002130147/BROWSE.VNP14IMG.A2012019.0506.002.2024002130147.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP14IMG": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP14IMG.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2734202914-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1797068115-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP14IMG_002": { - "href": "s3://lp-prod-protected/VNP14IMG.002", - "title": "lp_prod_protected_VNP14IMG_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP14IMG_002": { - "href": "s3://lp-prod-public/VNP14IMG.002", - "title": "lp_prod_public_VNP14IMG_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2734202914-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP14IMG_NRT_2.json b/datasets/VNP14IMG_NRT_2.json index ae1f95a70c..d3334bc53f 100644 --- a/datasets/VNP14IMG_NRT_2.json +++ b/datasets/VNP14IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP14IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VNP14IMG_NRT is a Near Real Time (NRT) S-NPP/VIIRS 375 m active fire detection data product (Schroeder 2014). The product is built on the EOS/MODIS fire product heritage [Kaufman et al., 1998; Giglio et al., 2003], using a multi-spectral contextual algorithm to identify sub-pixel fire activity and other thermal anomalies in the Level 1 (swath) input data. The algorithm uses all five 375 m VIIRS channels to detect fires and separate land, water, and cloud pixels in the image. Additional 750 m channels complement the available VIIRS multispectral data. Those channels are used as input to the baseline active fire detection product, which provides continuity to the EOS/MODIS 1 km Fire and Thermal Anomalies product.\r\n\r\nThe VIIRS 375 m fire detection data is a Level 2 product based on the input Science Data Record (SDR) Level 1 swath format. The NRT product is currently available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). The data are formatted as NetCDF4 files. Complementary ASCII files containing the short list of fire pixels detected are also available through LANCE FIRMS processing systems.\r\n\r\nFor more information read VIIRS 375 m Active Fire Algorithm User Guide at https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf \r\n\r\nand\r\n\r\nSchroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. doi:10.1016/j.rse.2013.12.008 PDF from UMD\r\n\r\nor\r\n\r\nvisit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VNP14_001.json b/datasets/VNP14_001.json index 945ebc427a..2a2c8a85c2 100644 --- a/datasets/VNP14_001.json +++ b/datasets/VNP14_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP14_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 1 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n\r\nThe VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS).\r\n", "links": [ { diff --git a/datasets/VNP14_002.json b/datasets/VNP14_002.json index 3aff5415f8..7c3b04e93b 100644 --- a/datasets/VNP14_002.json +++ b/datasets/VNP14_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP14_002", - "stac_version": "1.0.0", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n\r\nThe VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS).\r\n", + "stac_version": "1.1.0", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. \r\n\r\nThe VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. \r\n\r\nEach swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products.\r\n\r\nUse of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS).\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -114,24 +114,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14.002/VNP14.A2024206.0124.002.2024206084953/BROWSE.VNP14.A2024206.0124.002.2024206084953.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP14.A2024206.0124.002.2024206084953.1.jpg", + "title": "Download BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP14.002/VNP14.A2024206.0124.002.2024206084953/BROWSE.VNP14.A2024206.0124.002.2024206084953.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.06.03/BROWSE.VNP14.A2019152.0924.001.2019152151125.1.jpg?_ga=2.49817702.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP14": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP14.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314536-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1796848062-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -145,34 +153,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP14_002": { - "href": "s3://lp-prod-protected/VNP14.002", - "title": "lp_prod_protected_VNP14_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP14_002": { - "href": "s3://lp-prod-public/VNP14.002", - "title": "lp_prod_public_VNP14_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314536-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP14_NRT_2.json b/datasets/VNP14_NRT_2.json index 842feeb137..729b1122a7 100644 --- a/datasets/VNP14_NRT_2.json +++ b/datasets/VNP14_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP14_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product, short-name VNP14_NRT is based on the MODIS Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics.\r\n\r\nThe VNP14_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation.\r\n\r\nThe product provides day and nighttime active fire detection over land and water (from gas flares). The VNP14 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. \r\n\r\nFor more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/", "links": [ { diff --git a/datasets/VNP15A2H_001.json b/datasets/VNP15A2H_001.json index 558cbd1d46..39e46403cc 100644 --- a/datasets/VNP15A2H_001.json +++ b/datasets/VNP15A2H_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP15A2H_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 1 data product provides information about the vegetative canopy layer at 500 meter resolution (VNP15A2H). The VIIRS sensor is located aboard the NOAA/NASA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR.", "links": [ { diff --git a/datasets/VNP15A2H_002.json b/datasets/VNP15A2H_002.json index 18623fe024..7b24743b08 100644 --- a/datasets/VNP15A2H_002.json +++ b/datasets/VNP15A2H_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP15A2H_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VNP15A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission.\r\n\r\nThe VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR.", "links": [ { diff --git a/datasets/VNP21A1D_001.json b/datasets/VNP21A1D_001.json index eb27921ca4..cfbf636d06 100644 --- a/datasets/VNP21A1D_001.json +++ b/datasets/VNP21A1D_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21A1D_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule.\r\n", "links": [ { diff --git a/datasets/VNP21A1D_002.json b/datasets/VNP21A1D_002.json index a467e366a5..7d2c112358 100644 --- a/datasets/VNP21A1D_002.json +++ b/datasets/VNP21A1D_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP21A1D_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule.\r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,25 +112,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP21A1D.002/VNP21A1D.A2012019.h23v05.002.2023131142739/BROWSE.VNP21A1D.A2012019.h23v05.002.2023131142739.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP21A1D.A2012019.h23v05.002.2023131142739.1.jpg", + "title": "Download BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP21A1D.002/VNP21A1D.A2012019.h23v05.002.2023131142739/BROWSE.VNP21A1D.A2012019.h23v05.002.2023131142739.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1D.A2019120.h17v05.001.2019150004150.1.jpg?_ga=2.101614658.2140299264.1561987470-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP21A1D": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP21A1D.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1801683103-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP21A1D_002": { - "href": "s3://lp-prod-protected/VNP21A1D.002", - "title": "lp_prod_protected_VNP21A1D_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP21A1D_002": { - "href": "s3://lp-prod-public/VNP21A1D.002", - "title": "lp_prod_public_VNP21A1D_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314555-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP21A1N_001.json b/datasets/VNP21A1N_001.json index 64940ae1d2..6030587b29 100644 --- a/datasets/VNP21A1N_001.json +++ b/datasets/VNP21A1N_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21A1N_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 1 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule.\r\n", "links": [ { diff --git a/datasets/VNP21A1N_002.json b/datasets/VNP21A1N_002.json index aeb8012b90..698287785c 100644 --- a/datasets/VNP21A1N_002.json +++ b/datasets/VNP21A1N_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP21A1N_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nThe VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule.\r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,25 +112,33 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP21A1N.002/VNP21A1N.A2024205.h10v04.002.2024206084315/BROWSE.VNP21A1N.A2024205.h10v04.002.2024206084315.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP21A1N.A2024205.h10v04.002.2024206084315.1.jpg", + "title": "Download BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP21A1N.002/VNP21A1N.A2024205.h10v04.002.2024206084315/BROWSE.VNP21A1N.A2024205.h10v04.002.2024206084315.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21A1N.A2019120.h17v05.001.2019150003254.1.jpg?_ga=2.257795400.2140299264.1561987470-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP21A1N": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP21A1N.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://appeears.earthdatacloud.nasa.gov/", + "href": "https://search.earthdata.nasa.gov/search?q=C1801750721-LPDAAC_ECS", "title": "Direct Download [1]", - "description": "The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and efficient way to perform data access and transformation processes.", + "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" ] @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP21A1N_002": { - "href": "s3://lp-prod-protected/VNP21A1N.002", - "title": "lp_prod_protected_VNP21A1N_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP21A1N_002": { - "href": "s3://lp-prod-public/VNP21A1N.002", - "title": "lp_prod_public_VNP21A1N_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314559-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP21A2_001.json b/datasets/VNP21A2_001.json index 47724c74b4..080cefc218 100644 --- a/datasets/VNP21A2_001.json +++ b/datasets/VNP21A2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21A2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.001) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.001) products over an 8-day compositing period into a single product.\r\n\r\nThe VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file.\r\n\r\nThe VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule.\r\n", "links": [ { diff --git a/datasets/VNP21A2_002.json b/datasets/VNP21A2_002.json index 71d0ecaf54..4dcdde82d4 100644 --- a/datasets/VNP21A2_002.json +++ b/datasets/VNP21A2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21A2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product.\r\n\r\nThe VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file.\r\n\r\nThe VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nThe VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule.\r\n", "links": [ { diff --git a/datasets/VNP21C1_002.json b/datasets/VNP21C1_002.json index 28fa316fea..534c335752 100644 --- a/datasets/VNP21C1_002.json +++ b/datasets/VNP21C1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21C1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VNP21C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n", "links": [ { diff --git a/datasets/VNP21C2_002.json b/datasets/VNP21C2_002.json index 6fb1ee0ce2..32411f7f3c 100644 --- a/datasets/VNP21C2_002.json +++ b/datasets/VNP21C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VNP21C2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n", "links": [ { diff --git a/datasets/VNP21C3_002.json b/datasets/VNP21C3_002.json index 02cbe4eb86..2a0a27e93a 100644 --- a/datasets/VNP21C3_002.json +++ b/datasets/VNP21C3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21C3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VNP21C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VNP21C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VNP21A1D (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and VNP21A1N (http://doi.org/10.5067/VIIRS/VNP21A1N.002) daily acquisitions from the monthly period. Unlike the VNP21A1 data sets where the daytime and nighttime acquisitions are separate products, the VNP21C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).", "links": [ { diff --git a/datasets/VNP21IMG_NRT_2.json b/datasets/VNP21IMG_NRT_2.json index 57c1c860b9..27fdf074ba 100644 --- a/datasets/VNP21IMG_NRT_2.json +++ b/datasets/VNP21IMG_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21IMG_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 375m product with short-name VNP21IMG_NRT, is the same product but at 375m spatial resolution. The VNP21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at:\r\n \r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf\r\n\r\nand user guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf", "links": [ { diff --git a/datasets/VNP21_001.json b/datasets/VNP21_001.json index 47fa963c7e..05e0a41c43 100644 --- a/datasets/VNP21_001.json +++ b/datasets/VNP21_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 1 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.\r\n\r\nThe VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). \r\n\r\nProvided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule.\r\n", "links": [ { diff --git a/datasets/VNP21_002.json b/datasets/VNP21_002.json index 3dc7c16141..93314eaae6 100644 --- a/datasets/VNP21_002.json +++ b/datasets/VNP21_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP21_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.\r\n\r\nThe VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).\r\n\r\nProvided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule.\r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.\r\n\r\nThe VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf).\r\n\r\nProvided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule.\r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,32 +112,32 @@ }, "assets": { "browse": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_SNPP_Land_Surface_Temp_Day.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", "type": "image/jpeg", - "title": "Download VIIRS_SNPP_Land_Surface_Temp_Day.jpg", + "title": "Download BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", "roles": [ "browse" ] }, - "thumbnail_0": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP21.002/VNP21.A2024205.2036.002.2024213181314/BROWSE.VNP21.A2024205.2036.002.2024213181314.1.jpg", - "title": "Thumbnail [0]", + "thumbnail": { + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.05.30/BROWSE.VNP21.A2019120.0730.001.2019149100035.1.jpg?_ga=2.88916184.2140299264.1561987470-1109527761.1561753117", + "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, - "thumbnail_1": { - "href": "https://worldview.earthdata.nasa.gov/images/layers/previews/geographic/VIIRS_SNPP_Land_Surface_Temp_Day.jpg", - "title": "Thumbnail [1]", - "description": "The URL for viewing a Worldview visualization. The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 600 global, full-resolution satellite imagery layers and then download the underlying data.", + "gov/VIIRS/VNP21": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP21.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", "roles": [ - "thumbnail" + "data" ] }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314550-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1801679785-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -151,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP21_002": { - "href": "s3://lp-prod-protected/VNP21.002", - "title": "lp_prod_protected_VNP21_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP21_002": { - "href": "s3://lp-prod-public/VNP21.002", - "title": "lp_prod_public_VNP21_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314550-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP21_NRT_2.json b/datasets/VNP21_NRT_2.json index 229713ad38..6337cec9c5 100644 --- a/datasets/VNP21_NRT_2.json +++ b/datasets/VNP21_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP21_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) VIIRSLand Surface Temperature and Emissivity 6-Min L2 Swath 750m product (VNP21_NRT) uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for the three VIIRS thermal infrared bands M14 (8.55 micrometer), M15 (10.76 micrometer), and M16 (12 micrometer) at a spatial resolution of 750 m at nadir. The VNP21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at:\r\n \r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf\r\n\r\nand user guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf", "links": [ { diff --git a/datasets/VNP22C2_001.json b/datasets/VNP22C2_001.json index 8bcc23102d..a81a39be99 100644 --- a/datasets/VNP22C2_001.json +++ b/datasets/VNP22C2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP22C2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year.\r\n \r\nProvided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. ", "links": [ { diff --git a/datasets/VNP22C2_002.json b/datasets/VNP22C2_002.json index 0df55d4f44..6cbe7a0587 100644 --- a/datasets/VNP22C2_002.json +++ b/datasets/VNP22C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP22C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year.\r\n \r\nProvided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. ", "links": [ { diff --git a/datasets/VNP22Q2_001.json b/datasets/VNP22Q2_001.json index ffd8135d65..abff15b74b 100644 --- a/datasets/VNP22Q2_001.json +++ b/datasets/VNP22Q2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP22Q2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year.\r\n\r\nProvided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. ", "links": [ { diff --git a/datasets/VNP22Q2_002.json b/datasets/VNP22Q2_002.json index f5a2416495..b65f3cc99f 100644 --- a/datasets/VNP22Q2_002.json +++ b/datasets/VNP22Q2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP22Q2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year.\r\n\r\nProvided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. ", "links": [ { diff --git a/datasets/VNP28C2_002.json b/datasets/VNP28C2_002.json index 6bec9f0669..a3a90a9cf2 100644 --- a/datasets/VNP28C2_002.json +++ b/datasets/VNP28C2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP28C2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VNP28C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\r\n\r\nThe VNP28C2 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the VIIRS satellite (VNP09H1).\r\n\r\nThe VNP28C2 data product consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, and water storage capacity. \r\n", "links": [ { diff --git a/datasets/VNP28C3_002.json b/datasets/VNP28C3_002.json index 802f4698f6..50478681e3 100644 --- a/datasets/VNP28C3_002.json +++ b/datasets/VNP28C3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP28C3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Product Monthly Level 3 (L3) Global (VNP28C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.\r\n\r\nThe VNP28C3 data product is a composite of the 8-day area classifications from VNP28C2 which is converted to provide monthly elevation and water storage. The Lake Temperature and Evaporation Model (LTEM) with input from VIIRS Land Surface Temperature and Emissivity (VNP21A2) and meteorological data from Global Land Data Assimilation System (GLDAS) are used to produce monthly evaporation rates and volume losses.\r\n\r\nThe VNP28C3 data product provides a monthly time series that consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, water storage capacity, evaporation rate, and evaporation volume. ", "links": [ { diff --git a/datasets/VNP29P1D_2.json b/datasets/VNP29P1D_2.json index 1c7e261c27..7d212f2e26 100644 --- a/datasets/VNP29P1D_2.json +++ b/datasets/VNP29P1D_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP29P1D_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI).\n\nVIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/VNP29_1.json b/datasets/VNP29_1.json index 384e3b1fd1..0feba4d381 100644 --- a/datasets/VNP29_1.json +++ b/datasets/VNP29_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP29_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, Sea Ice is detected using the Normalized Difference Snow Index. The VIIRS instrument flies onboard the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/VNP29_2.json b/datasets/VNP29_2.json index 85162e8c32..6815bc8f4a 100644 --- a/datasets/VNP29_2.json +++ b/datasets/VNP29_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP29_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, Sea Ice is detected using the Normalized Difference Snow Index.", "links": [ { diff --git a/datasets/VNP29_NRT_2.json b/datasets/VNP29_NRT_2.json index c4614fd12c..fb753c48c8 100644 --- a/datasets/VNP29_NRT_2.json +++ b/datasets/VNP29_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP29_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imager Radiometer Suite (VIIRS) Sea Ice Extent 6-Min L2 Swath 375m is Near Real Time(NRT) (short name VNP29_NRT) data set reports the location of sea ice cover derived from radiance data acquired by VIIRS. Following the approach used by MODIS, the algorithm assumes that sea ice is snow covered and can be detected using the Normalized Difference Snow Index (NDSI). The VIIRS instrument flies on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite.\r\n\r\nFor more information, consult product users guide at:\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VIIRS%20C2%20Sea%20Ice%20Cover%20Product%20User%20Guide%20v3.pdf\r\n\r\nAnd product Algorithm Theoretical Basis Document (ATBD):\r\n\r\nhttps://viirsland.gsfc.nasa.gov/PDF/VIIRS_SeaIceCover_ATBD_V2.pdf", "links": [ { diff --git a/datasets/VNP30P1D_2.json b/datasets/VNP30P1D_2.json index 33eb97a5aa..32b00ae6b5 100644 --- a/datasets/VNP30P1D_2.json +++ b/datasets/VNP30P1D_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP30P1D_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set estimates of sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n\nVIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/VNP30P1N_2.json b/datasets/VNP30P1N_2.json index 361e984e6b..c057b15f96 100644 --- a/datasets/VNP30P1N_2.json +++ b/datasets/VNP30P1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP30P1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n\nVIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/VNP30_1.json b/datasets/VNP30_1.json index 80f991ec36..720036c687 100644 --- a/datasets/VNP30_1.json +++ b/datasets/VNP30_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP30_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n\nVIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.", "links": [ { diff --git a/datasets/VNP30_2.json b/datasets/VNP30_2.json index 9c63e718c0..6936050743 100644 --- a/datasets/VNP30_2.json +++ b/datasets/VNP30_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP30_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.", "links": [ { diff --git a/datasets/VNP30_NRT_2.json b/datasets/VNP30_NRT_2.json index 9f27203406..399bcb8194 100644 --- a/datasets/VNP30_NRT_2.json +++ b/datasets/VNP30_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP30_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Visible Infrared Imager Radiometer Suite (VIIRS) Ice Surface Temperature 6-Min L2 Swath 750m is Near Real Time(NRT) (short name VNP30_NRT) product provides surface temperatures retrieved at VIIRS moderate resolution for Arctic and Antarctic Sea Ice, for both day and night. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite.", "links": [ { diff --git a/datasets/VNP43C1_001.json b/datasets/VNP43C1_001.json index 904dc071c8..bcd3c65223 100644 --- a/datasets/VNP43C1_001.json +++ b/datasets/VNP43C1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43C1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C1) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C1 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model that best describes the anisotropy of each pixel, which is used to produce the VNP43C3 Albedo and VNP43C4 Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. \r\n\r\nThe VNP43C1 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format.\r\n", "links": [ { diff --git a/datasets/VNP43C2_001.json b/datasets/VNP43C2_001.json index 0f9ea3492e..7bab49a3b1 100644 --- a/datasets/VNP43C2_001.json +++ b/datasets/VNP43C2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43C2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the VNP43C3 Albedo and VNP43C4 Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. \r\n\r\nThe VNP43C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs.\r\n", "links": [ { diff --git a/datasets/VNP43C3_001.json b/datasets/VNP43C3_001.json index 7474a7d1fe..e628962c58 100644 --- a/datasets/VNP43C3_001.json +++ b/datasets/VNP43C3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43C3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C3) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43C1 to compute white-sky albedos and the black-sky albedos at local solar noon for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. \r\n\r\nThe VNP43C3 product includes 26 layers containing white-sky albedos and the black-sky albedos for the VIIRS DNB, moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and NIR broadbands. Along with the albedo data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format.\r\n", "links": [ { diff --git a/datasets/VNP43C4_001.json b/datasets/VNP43C4_001.json index f694b77ea1..7eb9a45d18 100644 --- a/datasets/VNP43C4_001.json +++ b/datasets/VNP43C4_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43C4_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C4) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43C1 to compute NBAR values for the VIIRS Day/Night band (DNB), and moderate resolution bands M1 through M5, M7, M8, M10, and M11. The algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. \r\n\r\nThe VNP43C3 product includes 10 layers containing NBAR values for VIIRS DNB and moderate resolution bands M1 through M5, M7, M8, M10, and M11. Along with the NBAR data for the 10 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format.\r\n", "links": [ { diff --git a/datasets/VNP43D01_001.json b/datasets/VNP43D01_001.json index cc9633d856..cd93d0655f 100644 --- a/datasets/VNP43D01_001.json +++ b/datasets/VNP43D01_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D01_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VNP43D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1(https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D01 is the BRDF isotropic parameter for VIIRS band M1 (0.412 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.", "links": [ { diff --git a/datasets/VNP43D02_001.json b/datasets/VNP43D02_001.json index c22ad96920..57dfeafc3f 100644 --- a/datasets/VNP43D02_001.json +++ b/datasets/VNP43D02_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D02_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VNP43D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D02 is the BRDF volumetric parameter for VIIRS band M1 (0.412 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.", "links": [ { diff --git a/datasets/VNP43D03_001.json b/datasets/VNP43D03_001.json index 7ffd99249d..106738ea3f 100644 --- a/datasets/VNP43D03_001.json +++ b/datasets/VNP43D03_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D03_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VNP43D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.", "links": [ { diff --git a/datasets/VNP43D04_001.json b/datasets/VNP43D04_001.json index cfb9af9fbc..718254f5f9 100644 --- a/datasets/VNP43D04_001.json +++ b/datasets/VNP43D04_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D04_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VNP43D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D04 is the BRDF isotropic parameter for VIIRS band M2 (0.445 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2.\r\n", "links": [ { diff --git a/datasets/VNP43D05_001.json b/datasets/VNP43D05_001.json index ed58919c69..22d77dfeeb 100644 --- a/datasets/VNP43D05_001.json +++ b/datasets/VNP43D05_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D05_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VNP43D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D05 is the BRDF volumetric parameter for VIIRS band M2 (0.445 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D06_001.json b/datasets/VNP43D06_001.json index 0ad932335d..ae873a8a35 100644 --- a/datasets/VNP43D06_001.json +++ b/datasets/VNP43D06_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D06_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VNP43D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D06 is the BRDF geometric parameter for VIIRS band M2 (0.445 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2.\r\n", "links": [ { diff --git a/datasets/VNP43D07_001.json b/datasets/VNP43D07_001.json index 428f90a806..643f5e3287 100644 --- a/datasets/VNP43D07_001.json +++ b/datasets/VNP43D07_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D07_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VNP43D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D07 is the BRDF isotropic parameter for VIIRS band M3 (0.488 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D08_001.json b/datasets/VNP43D08_001.json index 224a65ff1d..67100d638a 100644 --- a/datasets/VNP43D08_001.json +++ b/datasets/VNP43D08_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D08_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VNP43D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document [ATBD).\r\n\r\nVNP43D08 is the BRDF volumetric parameter for VIIRS band M3 (0.488 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3.", "links": [ { diff --git a/datasets/VNP43D09_001.json b/datasets/VNP43D09_001.json index 4754676b90..74aac2d47b 100644 --- a/datasets/VNP43D09_001.json +++ b/datasets/VNP43D09_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D09_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VNP43D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D09 is the BRDF geometric parameter for VIIRS band M3 (0.488 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3.\r\n", "links": [ { diff --git a/datasets/VNP43D10_001.json b/datasets/VNP43D10_001.json index 10d7fefeab..cd1f6f3c1b 100644 --- a/datasets/VNP43D10_001.json +++ b/datasets/VNP43D10_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D10_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VNP43D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D10 is the BRDF isotropic parameter for VIIRS band M4 (0.555 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D11_001.json b/datasets/VNP43D11_001.json index 8e7f85fcbc..6b729356b8 100644 --- a/datasets/VNP43D11_001.json +++ b/datasets/VNP43D11_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D11_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VNP43D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D11 is the BRDF volumetric parameter for VIIRS band M4 (0.555 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4.", "links": [ { diff --git a/datasets/VNP43D12_001.json b/datasets/VNP43D12_001.json index b03f3b821a..1c88926c22 100644 --- a/datasets/VNP43D12_001.json +++ b/datasets/VNP43D12_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D12_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VNP43D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D12 is the BRDF geometric parameter for VIIRS band M4 (0.555 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D13_001.json b/datasets/VNP43D13_001.json index b9795b91b4..3141fb61bb 100644 --- a/datasets/VNP43D13_001.json +++ b/datasets/VNP43D13_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D13_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VNP43D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D13 is the BRDF isotropic parameter for VIIRS band M5 (0.672 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D14_001.json b/datasets/VNP43D14_001.json index e6b13d37a4..9218e65efa 100644 --- a/datasets/VNP43D14_001.json +++ b/datasets/VNP43D14_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D14_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VNP43D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D14 is the BRDF volumetric parameter for VIIRS band M5 (0.672 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5.", "links": [ { diff --git a/datasets/VNP43D15_001.json b/datasets/VNP43D15_001.json index 88f68c8ea0..a3840c850c 100644 --- a/datasets/VNP43D15_001.json +++ b/datasets/VNP43D15_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D15_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VNP43D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D15 is the BRDF geometric parameter for VIIRS band M5 (0.672 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5.", "links": [ { diff --git a/datasets/VNP43D16_001.json b/datasets/VNP43D16_001.json index a1a6d386e1..c79ddb5613 100644 --- a/datasets/VNP43D16_001.json +++ b/datasets/VNP43D16_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D16_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VNP43D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D16 is the BRDF isotropic parameter for VIIRS band M7 (0.865 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D17_001.json b/datasets/VNP43D17_001.json index 9eb15e744e..8cc6536d88 100644 --- a/datasets/VNP43D17_001.json +++ b/datasets/VNP43D17_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D17_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VNP43D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D17 is the BRDF volumetric parameter for VIIRS band M7 (0.865 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7.\r\n", "links": [ { diff --git a/datasets/VNP43D18_001.json b/datasets/VNP43D18_001.json index 7a05419040..c4241430c8 100644 --- a/datasets/VNP43D18_001.json +++ b/datasets/VNP43D18_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D18_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VNP43D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D18 is the BRDF geometric parameter for VIIRS band M7 (0.865 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D19_001.json b/datasets/VNP43D19_001.json index 1c5b44bdb4..048ef81ae0 100644 --- a/datasets/VNP43D19_001.json +++ b/datasets/VNP43D19_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D19_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VNP43D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D19 is the BRDF isotropic parameter for VIIRS band M8 (1.240 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D20_001.json b/datasets/VNP43D20_001.json index 609d466371..c7e02c03ae 100644 --- a/datasets/VNP43D20_001.json +++ b/datasets/VNP43D20_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D20_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VNP43D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D20 is the BRDF volumetric parameter for VIIRS band M8 (1.240 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D21_001.json b/datasets/VNP43D21_001.json index d53e16ef10..76cfab712f 100644 --- a/datasets/VNP43D21_001.json +++ b/datasets/VNP43D21_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D21_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VNP43D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D21 is the BRDF geometric parameter for VIIRS band M8 (1.240 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8.\r\n", "links": [ { diff --git a/datasets/VNP43D22_001.json b/datasets/VNP43D22_001.json index b12f4dc2a3..5cec751826 100644 --- a/datasets/VNP43D22_001.json +++ b/datasets/VNP43D22_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D22_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.", "links": [ { diff --git a/datasets/VNP43D23_001.json b/datasets/VNP43D23_001.json index 69ce566998..f2b9a0137b 100644 --- a/datasets/VNP43D23_001.json +++ b/datasets/VNP43D23_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D23_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VNP43D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D23 is the BRDF volumetric parameter for VIIRS band M10 (1.61 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D24_001.json b/datasets/VNP43D24_001.json index adef3feba5..be5508a68c 100644 --- a/datasets/VNP43D24_001.json +++ b/datasets/VNP43D24_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D24_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VNP43D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D24 is the BRDF geometric parameter for VIIRS band M10 (1.61 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.\r\n", "links": [ { diff --git a/datasets/VNP43D25_001.json b/datasets/VNP43D25_001.json index 834d5429fa..a27244fd6b 100644 --- a/datasets/VNP43D25_001.json +++ b/datasets/VNP43D25_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D25_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VNP43D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D25 is the BRDF isotropic parameter for VIIRS band M11 (2.25 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.", "links": [ { diff --git a/datasets/VNP43D26_001.json b/datasets/VNP43D26_001.json index c6bba35db7..2046639bd7 100644 --- a/datasets/VNP43D26_001.json +++ b/datasets/VNP43D26_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D26_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VNP43D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document [ATBD).\r\n\r\nVNP43D26 is the BRDF volumetric parameter for VIIRS band M11 (2.25 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.", "links": [ { diff --git a/datasets/VNP43D27_001.json b/datasets/VNP43D27_001.json index fd32e5bbf0..2a85fd10ec 100644 --- a/datasets/VNP43D27_001.json +++ b/datasets/VNP43D27_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D27_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VNP43D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D27 is the BRDF geometric parameter for VIIRS band M11 (2.25 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.\r\n", "links": [ { diff --git a/datasets/VNP43D28_001.json b/datasets/VNP43D28_001.json index 301125ea39..c29c6f2774 100644 --- a/datasets/VNP43D28_001.json +++ b/datasets/VNP43D28_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D28_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VNP43D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D28 is the BRDF isotropic parameter for the VIIRS visible broadband (0.64 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.\r\n", "links": [ { diff --git a/datasets/VNP43D29_001.json b/datasets/VNP43D29_001.json index 21228be2b1..bd8e796ac1 100644 --- a/datasets/VNP43D29_001.json +++ b/datasets/VNP43D29_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D29_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VNP43D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D29 is the BRDF volumetric parameter for the VIIRS visible broadband (0.64 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D30_001.json b/datasets/VNP43D30_001.json index b9a3b77eff..452772d538 100644 --- a/datasets/VNP43D30_001.json +++ b/datasets/VNP43D30_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D30_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VNP43D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D30 is the BRDF geometric parameter for the VIIRS visible broadband (0.64 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.", "links": [ { diff --git a/datasets/VNP43D31_001.json b/datasets/VNP43D31_001.json index cd2d4d83c5..b8d464d162 100644 --- a/datasets/VNP43D31_001.json +++ b/datasets/VNP43D31_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D31_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 NIR product (VNP43D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D31 is the BRDF isotropic parameter for the VIIRS NIR broadband (0.865 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband.\r\n", "links": [ { diff --git a/datasets/VNP43D32_001.json b/datasets/VNP43D32_001.json index a8793d964b..498efa82f1 100644 --- a/datasets/VNP43D32_001.json +++ b/datasets/VNP43D32_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D32_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 NIR product (VNP43D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D32 is the BRDF volumetric parameter for the VIIRS NIR broadband (0.865 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D33_001.json b/datasets/VNP43D33_001.json index 67260764f9..4546a668c1 100644 --- a/datasets/VNP43D33_001.json +++ b/datasets/VNP43D33_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D33_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 NIR product (VNP43D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D33 is the BRDF geometric parameter for the VIIRS NIR broadband (0.865 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D34_001.json b/datasets/VNP43D34_001.json index 3218ec7552..f99d6c97bf 100644 --- a/datasets/VNP43D34_001.json +++ b/datasets/VNP43D34_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D34_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VNP43D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D34 is the BRDF isotropic parameter for the VIIRS shortwave broadband (1.61 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband.\r\n", "links": [ { diff --git a/datasets/VNP43D35_001.json b/datasets/VNP43D35_001.json index 5345d41d9e..efc1938323 100644 --- a/datasets/VNP43D35_001.json +++ b/datasets/VNP43D35_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D35_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VNP43D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D35 is the BRDF volumetric parameter for VIIRS shortwave broadband (1.61 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband.", "links": [ { diff --git a/datasets/VNP43D36_001.json b/datasets/VNP43D36_001.json index 4795dbc459..2de295fbbf 100644 --- a/datasets/VNP43D36_001.json +++ b/datasets/VNP43D36_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D36_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VNP43D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D36 is the BRDF geometric parameter for VIIRS shortwave broadband (1.61 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband.\r\n", "links": [ { diff --git a/datasets/VNP43D37_001.json b/datasets/VNP43D37_001.json index 27a1ec620f..3e0c04000a 100644 --- a/datasets/VNP43D37_001.json +++ b/datasets/VNP43D37_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D37_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VNP43D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D37 is the BRDF isotropic parameter for the VIIRS DNB (0.7 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB.", "links": [ { diff --git a/datasets/VNP43D38_001.json b/datasets/VNP43D38_001.json index d4bc20d9de..1276475587 100644 --- a/datasets/VNP43D38_001.json +++ b/datasets/VNP43D38_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D38_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VNP43D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D38 is the BRDF volumetric parameter for the VIIRS DNB (0.7 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D39_001.json b/datasets/VNP43D39_001.json index 111d5362f2..b53653b7f4 100644 --- a/datasets/VNP43D39_001.json +++ b/datasets/VNP43D39_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D39_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day-Night Band (DNB) product (VNP43D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D39 is the BRDF geometric parameter for the VIIRS DNB (0.7 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D40_001.json b/datasets/VNP43D40_001.json index 342bd2b6f4..16f3774bac 100644 --- a/datasets/VNP43D40_001.json +++ b/datasets/VNP43D40_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D40_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VNP43D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D40 consists of BRDF/Albedo quality information representing the overall quality of each pixel for VIIRS moderate resolution bands M1 through M5, M7, M8, M10, M11, and DNB.", "links": [ { diff --git a/datasets/VNP43D41_001.json b/datasets/VNP43D41_001.json index 40653fb262..5c8002c852 100644 --- a/datasets/VNP43D41_001.json +++ b/datasets/VNP43D41_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D41_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VNP43D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D41 contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period.\r\n", "links": [ { diff --git a/datasets/VNP43D42_001.json b/datasets/VNP43D42_001.json index 1d0a429a6e..03eafeaded 100644 --- a/datasets/VNP43D42_001.json +++ b/datasets/VNP43D42_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D42_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VNP43D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D42 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M1.", "links": [ { diff --git a/datasets/VNP43D43_001.json b/datasets/VNP43D43_001.json index 7c11488dcf..ed4c77ecae 100644 --- a/datasets/VNP43D43_001.json +++ b/datasets/VNP43D43_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D43_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VNP43D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D43 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M2.", "links": [ { diff --git a/datasets/VNP43D44_001.json b/datasets/VNP43D44_001.json index 319676d067..116320cfdf 100644 --- a/datasets/VNP43D44_001.json +++ b/datasets/VNP43D44_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D44_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VNP43D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D44 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M3.", "links": [ { diff --git a/datasets/VNP43D45_001.json b/datasets/VNP43D45_001.json index a6d8027b64..2b560da07c 100644 --- a/datasets/VNP43D45_001.json +++ b/datasets/VNP43D45_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D45_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VNP43D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D45 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M4.", "links": [ { diff --git a/datasets/VNP43D46_001.json b/datasets/VNP43D46_001.json index 2764e124c7..92139ae9cd 100644 --- a/datasets/VNP43D46_001.json +++ b/datasets/VNP43D46_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D46_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VNP43D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the[VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D46 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M5.", "links": [ { diff --git a/datasets/VNP43D47_001.json b/datasets/VNP43D47_001.json index a7e1f59889..ff25bb14f2 100644 --- a/datasets/VNP43D47_001.json +++ b/datasets/VNP43D47_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D47_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VNP43D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D47 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M7.", "links": [ { diff --git a/datasets/VNP43D48_001.json b/datasets/VNP43D48_001.json index 6aaa8fe53d..afb57f5f25 100644 --- a/datasets/VNP43D48_001.json +++ b/datasets/VNP43D48_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D48_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VNP43D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D48 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M8.", "links": [ { diff --git a/datasets/VNP43D49_001.json b/datasets/VNP43D49_001.json index 87bcb80d0a..2583c0d1fc 100644 --- a/datasets/VNP43D49_001.json +++ b/datasets/VNP43D49_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D49_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VNP43D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D49 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M10.", "links": [ { diff --git a/datasets/VNP43D50_001.json b/datasets/VNP43D50_001.json index defc84d7f9..e69fa53480 100644 --- a/datasets/VNP43D50_001.json +++ b/datasets/VNP43D50_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D50_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VNP43D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D50 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M11.", "links": [ { diff --git a/datasets/VNP43D51_001.json b/datasets/VNP43D51_001.json index 40fde40b23..e94689d8cf 100644 --- a/datasets/VNP43D51_001.json +++ b/datasets/VNP43D51_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D51_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VNP43D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D51 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS DNB.", "links": [ { diff --git a/datasets/VNP43D52_001.json b/datasets/VNP43D52_001.json index 35c8a0d9c8..4b29d5844c 100644 --- a/datasets/VNP43D52_001.json +++ b/datasets/VNP43D52_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D52_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D52 contains the snow status quality layer, which identifies each pixel as either \u201cSnow-free Albedo Retrieved\u201d or \u201cSnow Albedo Retrieved\u201d for the acquisition period.\r\n", "links": [ { diff --git a/datasets/VNP43D53_001.json b/datasets/VNP43D53_001.json index c2ca5d5906..b8bd7d395d 100644 --- a/datasets/VNP43D53_001.json +++ b/datasets/VNP43D53_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D53_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VNP43D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). \r\n\r\nVNP43D53 contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. \r\n", "links": [ { diff --git a/datasets/VNP43D54_001.json b/datasets/VNP43D54_001.json index b0dc77daf3..3e018c14ef 100644 --- a/datasets/VNP43D54_001.json +++ b/datasets/VNP43D54_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D54_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VNP43D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D54 is the BSA for VIIRS band M1 (0.412 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D55_001.json b/datasets/VNP43D55_001.json index 1ce2788478..d8ee6b7b25 100644 --- a/datasets/VNP43D55_001.json +++ b/datasets/VNP43D55_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D55_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VNP43D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D55 is the BSA for VIIRS band M2 (0.445 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D56_001.json b/datasets/VNP43D56_001.json index 74a474c205..b32dccf181 100644 --- a/datasets/VNP43D56_001.json +++ b/datasets/VNP43D56_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D56_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VNP43D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D56 is the BSA for VIIRS band M3 (0.488 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D57_001.json b/datasets/VNP43D57_001.json index 247a42c1f1..843c84eeee 100644 --- a/datasets/VNP43D57_001.json +++ b/datasets/VNP43D57_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D57_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VNP43D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D57 is the BSA for VIIRS band M4 (0.555 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D58_001.json b/datasets/VNP43D58_001.json index 92bd0e6b1d..6baf840f13 100644 --- a/datasets/VNP43D58_001.json +++ b/datasets/VNP43D58_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D58_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VNP43D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D58 is the BSA for VIIRS band M5 (0.672 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D59_001.json b/datasets/VNP43D59_001.json index b6f87ff15c..58cd7fdbe4 100644 --- a/datasets/VNP43D59_001.json +++ b/datasets/VNP43D59_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D59_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VNP43D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D59 is the BSA for VIIRS band M7 (0.865 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D60_001.json b/datasets/VNP43D60_001.json index e4fe40d0df..d4b9da407d 100644 --- a/datasets/VNP43D60_001.json +++ b/datasets/VNP43D60_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D60_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VNP43D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D60 is the BSA for VIIRS band M8 (1.240 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D61_001.json b/datasets/VNP43D61_001.json index e147373450..d7ed65021a 100644 --- a/datasets/VNP43D61_001.json +++ b/datasets/VNP43D61_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D61_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VNP43D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D61 is the BSA for VIIRS band M10 (1.61 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D62_001.json b/datasets/VNP43D62_001.json index b97649dbad..1e58d1eda3 100644 --- a/datasets/VNP43D62_001.json +++ b/datasets/VNP43D62_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D62_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VNP43D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D62 is the BSA for VIIRS band M11 (2.25 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D63_001.json b/datasets/VNP43D63_001.json index 1dfbd91c58..47d55526ab 100644 --- a/datasets/VNP43D63_001.json +++ b/datasets/VNP43D63_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D63_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VNP43D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D63 is the BSA for the VIIRS visible broadband (0.64 \u03bcm).\r\n\r\n\r\n", "links": [ { diff --git a/datasets/VNP43D64_001.json b/datasets/VNP43D64_001.json index 02b1b61828..a91c7ce008 100644 --- a/datasets/VNP43D64_001.json +++ b/datasets/VNP43D64_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D64_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VNP43D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D64 is the BSA for the VIIRS NIR broadband (0.865 \u03bcm).\r\n", "links": [ { diff --git a/datasets/VNP43D65_001.json b/datasets/VNP43D65_001.json index df186c6750..0a25c999a7 100644 --- a/datasets/VNP43D65_001.json +++ b/datasets/VNP43D65_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D65_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VNP43D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D65 is the BSA for the VIIRS shortwave broadband (1.61 \u03bcm).", "links": [ { diff --git a/datasets/VNP43D66_001.json b/datasets/VNP43D66_001.json index 81cf0965d1..417c5c21cf 100644 --- a/datasets/VNP43D66_001.json +++ b/datasets/VNP43D66_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D66_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for DNB (VNP43D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D66 is the BSA for the VIIRS DNB (0.7 \u03bcm).", "links": [ { diff --git a/datasets/VNP43D67_001.json b/datasets/VNP43D67_001.json index 983e6a3442..c2a5bd4125 100644 --- a/datasets/VNP43D67_001.json +++ b/datasets/VNP43D67_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D67_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VNP43D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D67 is the WSA for VIIRS band M1 (0.412 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D68_001.json b/datasets/VNP43D68_001.json index 732076c322..ed2ab12108 100644 --- a/datasets/VNP43D68_001.json +++ b/datasets/VNP43D68_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D68_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VNP43D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D68 is the WSA for VIIRS band M2 (0.445 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D69_001.json b/datasets/VNP43D69_001.json index 67f51db8eb..9fcb94feee 100644 --- a/datasets/VNP43D69_001.json +++ b/datasets/VNP43D69_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D69_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VNP43D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D69 is the WSA for VIIRS band M3 (0.488 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D70_001.json b/datasets/VNP43D70_001.json index f1f14d07d1..475acec38b 100644 --- a/datasets/VNP43D70_001.json +++ b/datasets/VNP43D70_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D70_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VNP43D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D70 is the WSA for VIIRS band M4 (0.555 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D71_001.json b/datasets/VNP43D71_001.json index 599519d879..b3993269c4 100644 --- a/datasets/VNP43D71_001.json +++ b/datasets/VNP43D71_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D71_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VNP43D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D71 is the WSA for VIIRS band M5 (0.672 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D72_001.json b/datasets/VNP43D72_001.json index b07bd2f725..13ec34cd84 100644 --- a/datasets/VNP43D72_001.json +++ b/datasets/VNP43D72_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D72_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VNP43D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D72 is the WSA for VIIRS band M7 (0.865 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D73_001.json b/datasets/VNP43D73_001.json index d64d440283..0e0b5c858f 100644 --- a/datasets/VNP43D73_001.json +++ b/datasets/VNP43D73_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D73_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VNP43D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D73 is the WSA for VIIRS band M8 (1.240 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D74_001.json b/datasets/VNP43D74_001.json index 7ef185e3ad..fa8c45bb90 100644 --- a/datasets/VNP43D74_001.json +++ b/datasets/VNP43D74_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D74_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VNP43D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D74 is the WSA for VIIRS band M10 (1.61 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D75_001.json b/datasets/VNP43D75_001.json index 14e1a18b72..edf8a85d35 100644 --- a/datasets/VNP43D75_001.json +++ b/datasets/VNP43D75_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D75_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VNP43D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D75 is the WSA for VIIRS band M11 (2.25 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D76_001.json b/datasets/VNP43D76_001.json index 3de622361e..56b10add16 100644 --- a/datasets/VNP43D76_001.json +++ b/datasets/VNP43D76_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D76_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VNP43D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D76 is the WSA for the VIIRS visible broadband (0.64 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D77_001.json b/datasets/VNP43D77_001.json index 01690b795f..bb56566e27 100644 --- a/datasets/VNP43D77_001.json +++ b/datasets/VNP43D77_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D77_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VNP43D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D77 is the WSA for the VIIRS NIR broadband (0.865 \u03bcm).", "links": [ { diff --git a/datasets/VNP43D78_001.json b/datasets/VNP43D78_001.json index 27f8153b82..8e7821d3fa 100644 --- a/datasets/VNP43D78_001.json +++ b/datasets/VNP43D78_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D78_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VNP43D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.\r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D78 is the WSA for the VIIRS shortwave broadband (1.61 \u03bcm).", "links": [ { diff --git a/datasets/VNP43D79_001.json b/datasets/VNP43D79_001.json index f29d12bd27..2bbed89942 100644 --- a/datasets/VNP43D79_001.json +++ b/datasets/VNP43D79_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D79_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for DNB (VNP43D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D79 is the WSA for the VIIRS DNB (0.7 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D80_001.json b/datasets/VNP43D80_001.json index 15c44f53e5..449e7292bc 100644 --- a/datasets/VNP43D80_001.json +++ b/datasets/VNP43D80_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D80_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VNP43D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D80 is the NBAR for VIIRS band M1 (0.412 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D81_001.json b/datasets/VNP43D81_001.json index 80919806d3..e3310601eb 100644 --- a/datasets/VNP43D81_001.json +++ b/datasets/VNP43D81_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D81_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VNP43D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D81 is the NBAR for VIIRS band M2 (0.445 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D82_001.json b/datasets/VNP43D82_001.json index 5d99a32cf5..746d6b5ab0 100644 --- a/datasets/VNP43D82_001.json +++ b/datasets/VNP43D82_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D82_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VNP43D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D82 is the NBAR for VIIRS band M3 (0.488 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D83_001.json b/datasets/VNP43D83_001.json index 223cbe72a2..a21fc8ee72 100644 --- a/datasets/VNP43D83_001.json +++ b/datasets/VNP43D83_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D83_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VNP43D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D83 is the NBAR for VIIRS band M4 (0.555 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D84_001.json b/datasets/VNP43D84_001.json index 5dccb00cc7..a6b139f686 100644 --- a/datasets/VNP43D84_001.json +++ b/datasets/VNP43D84_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D84_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/ NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VNP43D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D84 is the NBAR for VIIRS band M5 (0.672 \u03bcm). \r\n\r\n", "links": [ { diff --git a/datasets/VNP43D85_001.json b/datasets/VNP43D85_001.json index 32025fe300..110b191400 100644 --- a/datasets/VNP43D85_001.json +++ b/datasets/VNP43D85_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D85_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VNP43D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D85 is the NBAR for VIIRS band M7 (0.865 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D86_001.json b/datasets/VNP43D86_001.json index de554c890b..2cdb4f39e3 100644 --- a/datasets/VNP43D86_001.json +++ b/datasets/VNP43D86_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D86_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VNP43D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D86 is the NBAR for VIIRS band M8 (1.240 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D87_001.json b/datasets/VNP43D87_001.json index 5c6a3f7141..7b1fe144cf 100644 --- a/datasets/VNP43D87_001.json +++ b/datasets/VNP43D87_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D87_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VNP43D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D87 is the NBAR for VIIRS band M10 (1.61 \u03bcm). ", "links": [ { diff --git a/datasets/VNP43D88_001.json b/datasets/VNP43D88_001.json index 94b8a96bca..2241718e7e 100644 --- a/datasets/VNP43D88_001.json +++ b/datasets/VNP43D88_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D88_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VNP43D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D88 is the NBAR for VIIRS band M11 (2.25 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43D89_001.json b/datasets/VNP43D89_001.json index b354f6fa8d..7e6270d841 100644 --- a/datasets/VNP43D89_001.json +++ b/datasets/VNP43D89_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43D89_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for DNB (VNP43D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D89 is the NBAR for VIIRS DNB (0.7 \u03bcm). \r\n", "links": [ { diff --git a/datasets/VNP43DNBA1_001.json b/datasets/VNP43DNBA1_001.json index 1e1e1a7bda..64654e687a 100644 --- a/datasets/VNP43DNBA1_001.json +++ b/datasets/VNP43DNBA1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format.", "links": [ { diff --git a/datasets/VNP43DNBA1_002.json b/datasets/VNP43DNBA1_002.json index 19542ec1b6..62482684e2 100644 --- a/datasets/VNP43DNBA1_002.json +++ b/datasets/VNP43DNBA1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VNP43DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format.", "links": [ { diff --git a/datasets/VNP43DNBA2_001.json b/datasets/VNP43DNBA2_001.json index 259ad04bd3..1e38ec9b69 100644 --- a/datasets/VNP43DNBA2_001.json +++ b/datasets/VNP43DNBA2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43DNBA2_002.json b/datasets/VNP43DNBA2_002.json index 3810ce351b..ce77fc6cc5 100644 --- a/datasets/VNP43DNBA2_002.json +++ b/datasets/VNP43DNBA2_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA2_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VNP43DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.\n\n", "links": [ { diff --git a/datasets/VNP43DNBA3_001.json b/datasets/VNP43DNBA3_001.json index 305e28126a..8cd2de50af 100644 --- a/datasets/VNP43DNBA3_001.json +++ b/datasets/VNP43DNBA3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format.", "links": [ { diff --git a/datasets/VNP43DNBA3_002.json b/datasets/VNP43DNBA3_002.json index b979174a3b..682b961cc3 100644 --- a/datasets/VNP43DNBA3_002.json +++ b/datasets/VNP43DNBA3_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA3_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VNP43DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format.", "links": [ { diff --git a/datasets/VNP43DNBA4_001.json b/datasets/VNP43DNBA4_001.json index 6825386d59..51f22e5ea8 100644 --- a/datasets/VNP43DNBA4_001.json +++ b/datasets/VNP43DNBA4_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA4_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNB4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format.\r\n\r\n", "links": [ { diff --git a/datasets/VNP43DNBA4_002.json b/datasets/VNP43DNBA4_002.json index 0fdf21e1dd..bf4914e757 100644 --- a/datasets/VNP43DNBA4_002.json +++ b/datasets/VNP43DNBA4_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43DNBA4_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format.\n\n", "links": [ { diff --git a/datasets/VNP43IA1N_2.json b/datasets/VNP43IA1N_2.json index f1c381dede..980fcb9427 100644 --- a/datasets/VNP43IA1N_2.json +++ b/datasets/VNP43IA1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VNP43IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days).\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VNP43IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format.", "links": [ { diff --git a/datasets/VNP43IA1_001.json b/datasets/VNP43IA1_001.json index f93f8577b0..cfe22dd77b 100644 --- a/datasets/VNP43IA1_001.json +++ b/datasets/VNP43IA1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 1 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. \r\n", "links": [ { diff --git a/datasets/VNP43IA1_002.json b/datasets/VNP43IA1_002.json index a7028df738..2136c5fab9 100644 --- a/datasets/VNP43IA1_002.json +++ b/datasets/VNP43IA1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43IA1_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. \r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. \r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA1.002/VNP43IA1.A2024287.h18v04.002.2024295223529/BROWSE.VNP43IA1.A2024287.h18v04.002.2024295223529.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43IA1.A2024287.h18v04.002.2024295223529.1.jpg", + "title": "Download BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA1.002/VNP43IA1.A2024287.h18v04.002.2024295223529/BROWSE.VNP43IA1.A2024287.h18v04.002.2024295223529.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA1.A2019175.h12v11.001.2019183070621.1.jpg?_ga=2.141902834.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43IA1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43IA1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314578-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1847586293-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43IA1_002": { - "href": "s3://lp-prod-protected/VNP43IA1.002", - "title": "lp_prod_protected_VNP43IA1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43IA1_002": { - "href": "s3://lp-prod-public/VNP43IA1.002", - "title": "lp_prod_public_VNP43IA1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43IA2N_2.json b/datasets/VNP43IA2N_2.json index 238299eaa2..1c44834210 100644 --- a/datasets/VNP43IA2N_2.json +++ b/datasets/VNP43IA2N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA2N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/JPSS1 Level 3 16-Day BRDF/Albedo - 500m Near Real Time (NRT), short-name VNP43IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product).\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VNP43IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.", "links": [ { diff --git a/datasets/VNP43IA2_001.json b/datasets/VNP43IA2_001.json index dad6facdd2..57ab753403 100644 --- a/datasets/VNP43IA2_001.json +++ b/datasets/VNP43IA2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. \r\n", "links": [ { diff --git a/datasets/VNP43IA2_002.json b/datasets/VNP43IA2_002.json index 950decf8d2..4d4d8ce010 100644 --- a/datasets/VNP43IA2_002.json +++ b/datasets/VNP43IA2_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43IA2_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. \r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. VNP43IA2 provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. \r\n", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -111,9 +111,17 @@ ] }, "assets": { + "gov/VIIRS/VNP43IA2": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43IA2.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314582-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1847649141-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -127,34 +135,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43IA2_002": { - "href": "s3://lp-prod-protected/VNP43IA2.002", - "title": "lp_prod_protected_VNP43IA2_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43IA2_002": { - "href": "s3://lp-prod-public/VNP43IA2.002", - "title": "lp_prod_public_VNP43IA2_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43IA3N_2.json b/datasets/VNP43IA3N_2.json index 77bbb7638f..1ca2282ac8 100644 --- a/datasets/VNP43IA3N_2.json +++ b/datasets/VNP43IA3N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA3N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3.", "links": [ { diff --git a/datasets/VNP43IA3_001.json b/datasets/VNP43IA3_001.json index 7d05e48bec..9af0601016 100644 --- a/datasets/VNP43IA3_001.json +++ b/datasets/VNP43IA3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { diff --git a/datasets/VNP43IA3_002.json b/datasets/VNP43IA3_002.json index b7885d5d9f..6479763c73 100644 --- a/datasets/VNP43IA3_002.json +++ b/datasets/VNP43IA3_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43IA3_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA3.002/VNP43IA3.A2024286.h28v05.002.2024295210809/BROWSE.VNP43IA3.A2024286.h28v05.002.2024295210809.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43IA3.A2024286.h28v05.002.2024295210809.1.jpg", + "title": "Download BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA3.002/VNP43IA3.A2024286.h28v05.002.2024295210809/BROWSE.VNP43IA3.A2024286.h28v05.002.2024295210809.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA3.A2019175.h18v04.001.2019183075449.1.jpg?_ga=2.75253586.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43IA3": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43IA3.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314588-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1847699003-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43IA3_002": { - "href": "s3://lp-prod-protected/VNP43IA3.002", - "title": "lp_prod_protected_VNP43IA3_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43IA3_002": { - "href": "s3://lp-prod-public/VNP43IA3.002", - "title": "lp_prod_public_VNP43IA3_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43IA4N_2.json b/datasets/VNP43IA4N_2.json index 7b726a87fe..c916d9cfb5 100644 --- a/datasets/VNP43IA4N_2.json +++ b/datasets/VNP43IA4N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA4N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA4N product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VNP43IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3.", "links": [ { diff --git a/datasets/VNP43IA4_001.json b/datasets/VNP43IA4_001.json index d27d8d1340..f4e414f974 100644 --- a/datasets/VNP43IA4_001.json +++ b/datasets/VNP43IA4_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43IA4_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", "links": [ { diff --git a/datasets/VNP43IA4_002.json b/datasets/VNP43IA4_002.json index 86c3a8c7f8..8a169176e6 100644 --- a/datasets/VNP43IA4_002.json +++ b/datasets/VNP43IA4_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43IA4_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA4.002/VNP43IA4.A2024287.h28v06.002.2024295224155/BROWSE.VNP43IA4.A2024287.h28v06.002.2024295224155.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43IA4.A2024287.h28v06.002.2024295224155.1.jpg", + "title": "Download BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43IA4.002/VNP43IA4.A2024287.h28v06.002.2024295224155/BROWSE.VNP43IA4.A2024287.h28v06.002.2024295224155.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43IA4.A2019175.h18v05.001.2019183082319.1.jpg?_ga=2.176448485.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43IA4": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43IA4.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314592-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1847707918-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43IA4_002": { - "href": "s3://lp-prod-protected/VNP43IA4.002", - "title": "lp_prod_protected_VNP43IA4_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43IA4_002": { - "href": "s3://lp-prod-public/VNP43IA4.002", - "title": "lp_prod_public_VNP43IA4_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43MA1N_2.json b/datasets/VNP43MA1N_2.json index 668aef317d..80c3a1be10 100644 --- a/datasets/VNP43MA1N_2.json +++ b/datasets/VNP43MA1N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA1N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VNP43MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VNP43MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11.", "links": [ { diff --git a/datasets/VNP43MA1_001.json b/datasets/VNP43MA1_001.json index f8570791dc..7df6ee9073 100644 --- a/datasets/VNP43MA1_001.json +++ b/datasets/VNP43MA1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { diff --git a/datasets/VNP43MA1_002.json b/datasets/VNP43MA1_002.json index ba70295415..0caf8d20c9 100644 --- a/datasets/VNP43MA1_002.json +++ b/datasets/VNP43MA1_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43MA1_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format.", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA1.002/VNP43MA1.A2024287.h31v11.002.2024295220755/BROWSE.VNP43MA1.A2024287.h31v11.002.2024295220755.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43MA1.A2024287.h31v11.002.2024295220755.1.jpg", + "title": "Download BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA1.002/VNP43MA1.A2024287.h31v11.002.2024295220755/BROWSE.VNP43MA1.A2024287.h31v11.002.2024295220755.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA1.A2019175.h20v09.001.2019183074528.1.jpg?_ga=2.150366198.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43MA1": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43MA1.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314596-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1848645495-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43MA1_002": { - "href": "s3://lp-prod-protected/VNP43MA1.002", - "title": "lp_prod_protected_VNP43MA1_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43MA1_002": { - "href": "s3://lp-prod-public/VNP43MA1.002", - "title": "lp_prod_public_VNP43MA1_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43MA2N_2.json b/datasets/VNP43MA2N_2.json index 8c0cc15e16..d0c256dcba 100644 --- a/datasets/VNP43MA2N_2.json +++ b/datasets/VNP43MA2N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA2N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VNP43MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.", "links": [ { diff --git a/datasets/VNP43MA2_001.json b/datasets/VNP43MA2_001.json index 4ccedc14e2..ac7984dcb8 100644 --- a/datasets/VNP43MA2_001.json +++ b/datasets/VNP43MA2_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA2_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) \r\n(https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "links": [ { diff --git a/datasets/VNP43MA2_002.json b/datasets/VNP43MA2_002.json index ed9ca9f4b1..4d5b4aec13 100644 --- a/datasets/VNP43MA2_002.json +++ b/datasets/VNP43MA2_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43MA2_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name.", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) \r\n(https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -111,9 +111,17 @@ ] }, "assets": { + "gov/VIIRS/VNP43MA2": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43MA2.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314601-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1848652493-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -127,34 +135,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43MA2_002": { - "href": "s3://lp-prod-protected/VNP43MA2.002", - "title": "lp_prod_protected_VNP43MA2_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43MA2_002": { - "href": "s3://lp-prod-public/VNP43MA2.002", - "title": "lp_prod_public_VNP43MA2_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43MA3N_2.json b/datasets/VNP43MA3N_2.json index caccf7cb82..bb01aca5d4 100644 --- a/datasets/VNP43MA3N_2.json +++ b/datasets/VNP43MA3N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA3N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf\r\n\r\nThe VNP43MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS).", "links": [ { diff --git a/datasets/VNP43MA3_001.json b/datasets/VNP43MA3_001.json index 79be184707..87b5b1a3cb 100644 --- a/datasets/VNP43MA3_001.json +++ b/datasets/VNP43MA3_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA3_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).\r\n\r\nThe VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { diff --git a/datasets/VNP43MA3_002.json b/datasets/VNP43MA3_002.json index 328f62786f..82fa1636c4 100644 --- a/datasets/VNP43MA3_002.json +++ b/datasets/VNP43MA3_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43MA3_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format.", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. \r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf).\r\n\r\nThe VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA3.002/VNP43MA3.A2024287.h29v09.002.2024295222253/BROWSE.VNP43MA3.A2024287.h29v09.002.2024295222253.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43MA3.A2024287.h29v09.002.2024295222253.1.jpg", + "title": "Download BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA3.002/VNP43MA3.A2024287.h29v09.002.2024295222253/BROWSE.VNP43MA3.A2024287.h29v09.002.2024295222253.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA3.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.83716054.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43MA3": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43MA3.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314605-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1848653739-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43MA3_002": { - "href": "s3://lp-prod-protected/VNP43MA3.002", - "title": "lp_prod_protected_VNP43MA3_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43MA3_002": { - "href": "s3://lp-prod-public/VNP43MA3.002", - "title": "lp_prod_public_VNP43MA3_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP43MA4N_2.json b/datasets/VNP43MA4N_2.json index 1dffafac04..7d1431fdc8 100644 --- a/datasets/VNP43MA4N_2.json +++ b/datasets/VNP43MA4N_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA4N_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf.\r\n\r\nThe VNP43MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11.", "links": [ { diff --git a/datasets/VNP43MA4_001.json b/datasets/VNP43MA4_001.json index ff89981ffd..0ca2f40065 100644 --- a/datasets/VNP43MA4_001.json +++ b/datasets/VNP43MA4_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP43MA4_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { diff --git a/datasets/VNP43MA4_002.json b/datasets/VNP43MA4_002.json index fe5dc1ce3a..aab24232fa 100644 --- a/datasets/VNP43MA4_002.json +++ b/datasets/VNP43MA4_002.json @@ -1,8 +1,8 @@ { "type": "Collection", "id": "VNP43MA4_002", - "stac_version": "1.0.0", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.\r\n", + "stac_version": "1.1.0", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\r\n\r\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nThe VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.\r\n\r\nProduct Maturity\r\n\r\nValidation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "links": [ { "rel": "license", @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2012-01-17T00:00:00Z", + "2012-01-19T00:00:00Z", null ] ] @@ -112,24 +112,32 @@ }, "assets": { "browse": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA4.002/VNP43MA4.A2024287.h25v07.002.2024295222238/BROWSE.VNP43MA4.A2024287.h25v07.002.2024295222238.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "type": "image/jpeg", - "title": "Download BROWSE.VNP43MA4.A2024287.h25v07.002.2024295222238.1.jpg", + "title": "Download BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "roles": [ "browse" ] }, "thumbnail": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP43MA4.002/VNP43MA4.A2024287.h25v07.002.2024295222238/BROWSE.VNP43MA4.A2024287.h25v07.002.2024295222238.1.jpg", + "href": "https://e4ftl01.cr.usgs.gov/WORKING/BRWS/Browse.001/2019.07.02/BROWSE.VNP43MA4.A2019175.h19v04.001.2019183072445.1.jpg?_ga=2.79669076.116070394.1561987039-1109527761.1561753117", "title": "Thumbnail", "description": "Browse image for Earthdata Search", "roles": [ "thumbnail" ] }, + "gov/VIIRS/VNP43MA4": { + "href": "https://e4ftl01.cr.usgs.gov/VIIRS/VNP43MA4.002/", + "title": "Direct Download [0]", + "description": "LP DAAC Data Pool provides direct access to available products via HTTPS.", + "roles": [ + "data" + ] + }, "nasa": { - "href": "https://search.earthdata.nasa.gov/search?q=C2545314608-LPCLOUD", - "title": "Direct Download", + "href": "https://search.earthdata.nasa.gov/search?q=C1848671901-LPDAAC_ECS", + "title": "Direct Download [1]", "description": "Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data.", "roles": [ "data" @@ -143,34 +151,6 @@ "metadata" ] }, - "s3_lp_prod_protected_VNP43MA4_002": { - "href": "s3://lp-prod-protected/VNP43MA4.002", - "title": "lp_prod_protected_VNP43MA4_002", - "roles": [ - "data" - ] - }, - "s3_lp_prod_public_VNP43MA4_002": { - "href": "s3://lp-prod-public/VNP43MA4.002", - "title": "lp_prod_public_VNP43MA4_002", - "roles": [ - "data" - ] - }, - "s3_credentials": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials", - "title": "S3 credentials API endpoint", - "roles": [ - "metadata" - ] - }, - "s3_credentials_documentation": { - "href": "https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME", - "title": "S3 credentials API endpoint documentation", - "roles": [ - "metadata" - ] - }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.xml", "type": "application/xml", diff --git a/datasets/VNP46A1G_NRT_2.json b/datasets/VNP46A1G_NRT_2.json index b67a4f60ed..82f0bd0d01 100644 --- a/datasets/VNP46A1G_NRT_2.json +++ b/datasets/VNP46A1G_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A1G_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Near Real Time (NRT) Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1G_NRT, is same as VNP46A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.", "links": [ { diff --git a/datasets/VNP46A1_1.json b/datasets/VNP46A1_1.json index a6bc964cb6..85d1d380fc 100644 --- a/datasets/VNP46A1_1.json +++ b/datasets/VNP46A1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first of two VIIRS DNB-based datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night. Known by its short-name, VNP46A1, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.", "links": [ { diff --git a/datasets/VNP46A1_2.json b/datasets/VNP46A1_2.json index 06691cede6..4b0dc943aa 100644 --- a/datasets/VNP46A1_2.json +++ b/datasets/VNP46A1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. \n\nThe current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at:\nhttps://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf\n", "links": [ { diff --git a/datasets/VNP46A1_NRT_2.json b/datasets/VNP46A1_NRT_2.json index 77370e131d..dff27e6a0d 100644 --- a/datasets/VNP46A1_NRT_2.json +++ b/datasets/VNP46A1_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A1_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first of two Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) based Near Real Time (NRT) datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1_NRT, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.", "links": [ { diff --git a/datasets/VNP46A2_1.json b/datasets/VNP46A2_1.json index 44f736824d..b69caf2aa5 100644 --- a/datasets/VNP46A2_1.json +++ b/datasets/VNP46A2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format\u2013Earth Observing System (HDF-EOS5) format. ", "links": [ { diff --git a/datasets/VNP46A2_NRT_2.json b/datasets/VNP46A2_NRT_2.json index 3dd1690888..10d1154951 100644 --- a/datasets/VNP46A2_NRT_2.json +++ b/datasets/VNP46A2_NRT_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A2_NRT_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format\u2013Earth Observing System (HDF-EOS5) format. ", "links": [ { diff --git a/datasets/VNP46A3_1.json b/datasets/VNP46A3_1.json index 85a778c9e0..79b8d304e8 100644 --- a/datasets/VNP46A3_1.json +++ b/datasets/VNP46A3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 500m Linear Lat Lon Grid, with short-name VNP46A3, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A3 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This description pertains to the SNPP VIIRS Monthly Lunar BRDF-adjusted NTL collection, whose record starts from January 1st 2012.", "links": [ { diff --git a/datasets/VNP46A4_1.json b/datasets/VNP46A4_1.json index 6dad0854ff..a9d36958d3 100644 --- a/datasets/VNP46A4_1.json +++ b/datasets/VNP46A4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP46A4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This yearly Lunar BRDF-Adjusted NTL collection record starts from January 1st 2012.", "links": [ { diff --git a/datasets/VNP64A1_001.json b/datasets/VNP64A1_001.json index a39fe4be85..273f390992 100644 --- a/datasets/VNP64A1_001.json +++ b/datasets/VNP64A1_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP64A1_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. \n\nVNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask was released on October 22, 2024. Users are encouraged to use the improved V002 burned area product.\n\nThe data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.\n\nNotification: VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas. Users are encouraged to use the improved Version 2 data.\n\n", "links": [ { diff --git a/datasets/VNP64A1_002.json b/datasets/VNP64A1_002.json index 9f1a7918ae..db66fae7e1 100644 --- a/datasets/VNP64A1_002.json +++ b/datasets/VNP64A1_002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VNP64A1_002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. \n\nThe data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.\n", "links": [ { diff --git a/datasets/VOLPE_0.json b/datasets/VOLPE_0.json index 6d6c50a6ab..c204360139 100644 --- a/datasets/VOLPE_0.json +++ b/datasets/VOLPE_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VOLPE_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made off the San Diego, Californian coast in 1999.", "links": [ { diff --git a/datasets/VPRM_North_America_Parameters_1349_1.json b/datasets/VPRM_North_America_Parameters_1349_1.json index 015a43e32a..d3300eff99 100644 --- a/datasets/VPRM_North_America_Parameters_1349_1.json +++ b/datasets/VPRM_North_America_Parameters_1349_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VPRM_North_America_Parameters_1349_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0). Observed eddy covariance data from the 65 tower sites, locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture were used as input data to optimize the VPRM parameters for the 65 sites. The data are provided by individual site, plant functional types (PFTs), and all sites together, and as monthly, annual, and all available data. The data are for the conterminous USA, Alaska, and Canada for the period 2000 to 2006.", "links": [ { diff --git a/datasets/VT_GOCE_Data_5.0.json b/datasets/VT_GOCE_Data_5.0.json index 39ca356206..aa72ee0543 100644 --- a/datasets/VT_GOCE_Data_5.0.json +++ b/datasets/VT_GOCE_Data_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "VT_GOCE_Data_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains the VT GOCE software and associated data set needed to run the software that is used for GOCE data visualisation.", "links": [ { diff --git a/datasets/Veg_Soil_Tundra_Burned_Area_2119_1.json b/datasets/Veg_Soil_Tundra_Burned_Area_2119_1.json index 91ad99dfef..01b436b0b3 100644 --- a/datasets/Veg_Soil_Tundra_Burned_Area_2119_1.json +++ b/datasets/Veg_Soil_Tundra_Burned_Area_2119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Veg_Soil_Tundra_Burned_Area_2119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017.", "links": [ { diff --git a/datasets/Vegetation_Maps_Toolik_Lake_1690_1.json b/datasets/Vegetation_Maps_Toolik_Lake_1690_1.json index 4a39311f20..2360c93dfd 100644 --- a/datasets/Vegetation_Maps_Toolik_Lake_1690_1.json +++ b/datasets/Vegetation_Maps_Toolik_Lake_1690_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vegetation_Maps_Toolik_Lake_1690_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps.", "links": [ { diff --git a/datasets/Vegetation_Photos_Toolik_Lake_1718_1.json b/datasets/Vegetation_Photos_Toolik_Lake_1718_1.json index 64e394ecf2..54b9ed5184 100644 --- a/datasets/Vegetation_Photos_Toolik_Lake_1718_1.json +++ b/datasets/Vegetation_Photos_Toolik_Lake_1718_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vegetation_Photos_Toolik_Lake_1718_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains 731 ground-based nadir vegetation community and ground surface photographs of selected field plots taken as ground reference data for vegetation classification studies at three areas near Toolik Lake, Alaska during the summers of 2014 and 2015. The largest area, 'Toolik', (approximately 6 km2) covers research areas near Toolik Field Station at Toolik Lake, including Arctic LTER installations. The other two areas are each roughly 3 km2: the 'Pipeline' area: a stretch of the Trans-Alaska Pipeline, and the 'Imnavait' area: along Imnavait Creek roughly 10 km east of Toolik Lake.", "links": [ { diff --git a/datasets/Vegetation_greenness_trend_1576_1.json b/datasets/Vegetation_greenness_trend_1576_1.json index a612a2c93e..8caf832ecb 100644 --- a/datasets/Vegetation_greenness_trend_1576_1.json +++ b/datasets/Vegetation_greenness_trend_1576_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vegetation_greenness_trend_1576_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the \"trend\" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the \"trend_sig\" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend.", "links": [ { diff --git a/datasets/Vermont_HighRes_LandCover_2072_1.json b/datasets/Vermont_HighRes_LandCover_2072_1.json index a219a157b8..f0c483114a 100644 --- a/datasets/Vermont_HighRes_LandCover_2072_1.json +++ b/datasets/Vermont_HighRes_LandCover_2072_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vermont_HighRes_LandCover_2072_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (*.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.", "links": [ { diff --git a/datasets/Vertebrate_Biology_MI_1990_1.json b/datasets/Vertebrate_Biology_MI_1990_1.json index a0ea78d581..ac2aa25942 100644 --- a/datasets/Vertebrate_Biology_MI_1990_1.json +++ b/datasets/Vertebrate_Biology_MI_1990_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vertebrate_Biology_MI_1990_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a scanned copy of the annual report on vertebrate biology at Macquarie Island, 1990, by Rupert Woods.\n\nThe scanned report contains information on:\n\n - Elephant seal census\n - Elephant seal tagging program (1984-1985, 1987-1991)\n - Freeze branding\n - Weaner weights\n - Anaesthetics\n - Gastric lavage\n - Opthalmology problems\n - Penguin crush (mass deaths of King Penguins)\n - PTTs and TDTRs\n - Toxoplasmosis\n - Morbilivirus\n - DNA samples (elephant seals and fur seals)\n - Anaesthesia and surgery of birds\n - Details of a neo-natal longfinned pilot whale washed ashore\n - Fur seals (census, euthanasia, injuries, net entanglements)\n - Letters\n - Abandoned elephant seal pup\n - Drift cards\n - Killer whale attack", "links": [ { diff --git a/datasets/Vestfold_Hills_Limnological_Sulphur_1.json b/datasets/Vestfold_Hills_Limnological_Sulphur_1.json index 9e451dd803..c3ee75ac06 100644 --- a/datasets/Vestfold_Hills_Limnological_Sulphur_1.json +++ b/datasets/Vestfold_Hills_Limnological_Sulphur_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vestfold_Hills_Limnological_Sulphur_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a scanned copy of the report, 'Aspects of the biological sulphur cycle in limnological ecosystems in the Vestfold Hills, Antarctica' by P.P. Deprez and P.D. Franzmann.\n\nTaken from the document:\n\nThis document is a report of the work carried out by two wintering biologists at Davis in 1984. It encompasses work completed between January 1984 and October 30 1984. It is not a publication in the scientific sense. It was written quickly, in the first two weeks of November, 1984 and was not revised. It was edited by Harry Burton in December, 1984.\n\nIt includes:\n\n1) Determination of sulphate reduction rates by radiometric methods in the sediments of Burton Lake, Ace Lake, Watts Lake, Shield Lake and Ellis Fjord.\n2) Measurement of reduced sulphur compounds in Antarctic Lakes by gas chromatography with dual flame photometric detection.\n3) Chemical measurements and parameters of some Antarctic lakes.\n4) Collection and preliminary investigation of Antarctic micro-organisms.\n5) Other bits.", "links": [ { diff --git a/datasets/Vestfold_Lake_Areas_1.json b/datasets/Vestfold_Lake_Areas_1.json index 237f3ac375..117e9f40f5 100644 --- a/datasets/Vestfold_Lake_Areas_1.json +++ b/datasets/Vestfold_Lake_Areas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vestfold_Lake_Areas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains lake areas and perimeters given in metres of the lakes found within the Vestfold Hills near Davis Station Antarctica.\nThe data are held in an excel spreadsheet.\nThe area of the lakes is given in square metres (and perimeters in metres).\nThe last two columns are the areas in square km, and then hectares.\n\nThe fields in this dataset are:\nlake\nnumber\narea\nperimeter\ndevelopment of coastline", "links": [ { diff --git a/datasets/Vision-1.full.archive.and.tasking_8.0.json b/datasets/Vision-1.full.archive.and.tasking_8.0.json index d2ddf2f6b3..5c6ac95ee1 100644 --- a/datasets/Vision-1.full.archive.and.tasking_8.0.json +++ b/datasets/Vision-1.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vision-1.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vision-1 provides very high resolution optical products, with 87cm resolution in Panchromatic mode and 3.48m in Multispectral Mode.\r\rProducts are in DIMAP format; the image is in GeoTiff format with 16 bit encoding; the applied geographical projection is WGS84 UTM.\r\rSpectral band combination options:\r\rPanchromatic (PAN): includes data contained within a single high resolution black and white band, with product pixel size of 0.87m\rMultispectral (MS4): includes four multispectral (colour) bands: Blue, Green, Red and Near Infrared. The product pixel size is 3.48m\rBundle (BUN): provides both the 4-band multispectral, and the panchromatic data from the same acquisition in a single, non-merged product. Data is provided as 16-bit GeoTiffs with pixel sizes of 3.48m and 0.87m for MS and PAN data respectively\rPansharpened (PSH): single higher resolution 0.87 colour product obtained by the combination of the visual coloured information of the multispectral data with the details provided in the panchromatic data.\rTwo different geometric processing levels are:\r\rProjected (level 2): The product is mapped onto the Earth cartographic system using a standard reference datum and projection system at a constant terrestrial altitude, relative to the reference ellipsoid. By default, the map projection system is WGS84/UTM. The image is georeferenced without the application of a Digital Elevation Model (DEM) and supplied with the RPC model file. Pansharpened are not available as projected product\rStandard Ortho (level 3): georeferenced image in Earth geometry, including the application of a Airbus World DEM for Ortho and GCPs (using Airbus Intelligence One Atlas BaseMap as reference). The orthorectification procedure eliminates the perspective effect on the ground (excluding buildings) to restore the geometry of a vertical shot.\rOnly the basic radiometric processing is available providing the radiance value.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/Vulcan_V3_Annual_Emissions_1741_1.json b/datasets/Vulcan_V3_Annual_Emissions_1741_1.json index 6a3698b1e6..175e0095a8 100644 --- a/datasets/Vulcan_V3_Annual_Emissions_1741_1.json +++ b/datasets/Vulcan_V3_Annual_Emissions_1741_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vulcan_V3_Annual_Emissions_1741_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vulcan version 3.0 annual dataset provides estimates of annual carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the State of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Data are gridded annually on a 1-km grid for the years 2010 to 2015. These data are annual sums of hourly estimates. Also provided are estimates of the upper 95% confidence interval and the lower 95% confidence interval boundaries for each emission estimate. For each uncertainty level, there are 10 individual sector files and one total file. These data are designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications.", "links": [ { diff --git a/datasets/Vulcan_V3_Hourly_Emissions_1810_1.json b/datasets/Vulcan_V3_Hourly_Emissions_1810_1.json index c05e195a3c..361431db52 100644 --- a/datasets/Vulcan_V3_Hourly_Emissions_1810_1.json +++ b/datasets/Vulcan_V3_Hourly_Emissions_1810_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Vulcan_V3_Hourly_Emissions_1810_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vulcan version 3.0 hourly dataset quantifies hourly emissions at a 1-km resolution for the 2010-2015 time period. Estimates are provided of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the state of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Files for hourly total emissions are also available. Data are represented in space on a 1 km x 1 km grid as hourly totals for 2010-2015. This dataset provides the first bottom-up U.S.-wide FFCO2 emissions data product at 1 km2/hourly for multiple years and is designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications.", "links": [ { diff --git a/datasets/WACS2_0.json b/datasets/WACS2_0.json index 8e789305cd..17e49a79e0 100644 --- a/datasets/WACS2_0.json +++ b/datasets/WACS2_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WACS2_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea spray aerosol (SSA) impacts the Earth\u2019s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models.Sea spray aerosol (SSA) impacts the Earth\u2019s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models.", "links": [ { diff --git a/datasets/WAF_DEALIASED_SASS_L2_1.json b/datasets/WAF_DEALIASED_SASS_L2_1.json index 590378c07a..a129407eac 100644 --- a/datasets/WAF_DEALIASED_SASS_L2_1.json +++ b/datasets/WAF_DEALIASED_SASS_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WAF_DEALIASED_SASS_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich.", "links": [ { diff --git a/datasets/WARd0002_108.json b/datasets/WARd0002_108.json index 19ec0d1d21..760088a07f 100644 --- a/datasets/WARd0002_108.json +++ b/datasets/WARd0002_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0002_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Administration division of Poland created on a basis of digitization\nwith manual generalisation proper for specific scales. Projection\nAlbers; points and polygons; ARC/INFO and SINUS systems", "links": [ { diff --git a/datasets/WARd0004_108.json b/datasets/WARd0004_108.json index b80d081e02..255a7299a3 100644 --- a/datasets/WARd0004_108.json +++ b/datasets/WARd0004_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0004_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land use map of Poland acquisited form interpreted Landsat TM, MSS\nimages by digitization. 24 classes of land use grouped in subjects\n(agriculture, grass lands, settlements and communication areas,\nforests, surface waters, industry, not used areas). Vector and raster\nformat; projection Albers; ARC/INFO and SINUS systems", "links": [ { diff --git a/datasets/WARd0005_108.json b/datasets/WARd0005_108.json index 9ad85c5cbb..6d03324ca1 100644 --- a/datasets/WARd0005_108.json +++ b/datasets/WARd0005_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0005_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geomorphological forms of Poland created within Central Scientific\nProgramme 10.4/1989. Digitized from the map of relief types in Poland; Scale\n1:1 000 000.", "links": [ { diff --git a/datasets/WARd0006_108.json b/datasets/WARd0006_108.json index 2f8f07e1b2..1fa3d9648b 100644 --- a/datasets/WARd0006_108.json +++ b/datasets/WARd0006_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0006_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Borders of hunting units digitized from the maps prepared by Polish\nHunting Association within Central Scientific Programme 10.4/1989.", "links": [ { diff --git a/datasets/WARd0010_108.json b/datasets/WARd0010_108.json index f4f5a4a976..503f13a085 100644 --- a/datasets/WARd0010_108.json +++ b/datasets/WARd0010_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0010_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Four-level hydrographic division of Poland prepared in accordance to a\nnew scheme of catchment division elaborated by the Institute of\nMeteorology and Water Management (IMGW). Scanned from the\n\"Hydrological Atlas of Poland\".", "links": [ { diff --git a/datasets/WARd0011_108.json b/datasets/WARd0011_108.json index 37c86c8590..44292cbc1c 100644 --- a/datasets/WARd0011_108.json +++ b/datasets/WARd0011_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0011_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecological hazards digitized from the map of protected landscape.", "links": [ { diff --git a/datasets/WARd0012_108.json b/datasets/WARd0012_108.json index 06c6d3e067..02b631678a 100644 --- a/datasets/WARd0012_108.json +++ b/datasets/WARd0012_108.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WARd0012_108", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Main cities in Poland digitized from the Review Map of Poland", "links": [ { diff --git a/datasets/WATVP_D3_VIIRS_SNPP_1.json b/datasets/WATVP_D3_VIIRS_SNPP_1.json index 5a134e0b7c..9660d49a8b 100644 --- a/datasets/WATVP_D3_VIIRS_SNPP_1.json +++ b/datasets/WATVP_D3_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WATVP_D3_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_D3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (daily) output files for further aggregation purposes.", "links": [ { diff --git a/datasets/WATVP_L2_VIIRS_SNPP_1.json b/datasets/WATVP_L2_VIIRS_SNPP_1.json index fdc038d200..c77f3b42fc 100644 --- a/datasets/WATVP_L2_VIIRS_SNPP_1.json +++ b/datasets/WATVP_L2_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WATVP_L2_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Suomi NPP VIIRS Water Vapor Products provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and CrIS plus ATMS water vapor soundings to continue the depiction of global moisture at high spatial resolution started with MODIS on the Terra and Aqua platforms. While MODIS has two water vapor channels within the 6.5 μm H2O absorption band and four channels within the 15 micrometer CO2 absorption band, VIIRS has no channels in either IR absorption band. The VNPWATVP algorithm is similar to the MODIS MOD07 synthetic regression algorithm. It uses the three VIIRS longwave IR window bands in a regression relation and adds the NUCAPS (CrIS+ATMS) water vapor product to compensate for the absence of VIIRS water vapor channels. The Level-2 6-minute granule and 750 m spatial resolution VIIRS TPW product file includes the collocated NUCAPS background TPW, the VIIRS-only TPW, and VIIRS+NUCAPS TPW retrievals with quality flags.", "links": [ { diff --git a/datasets/WATVP_M3_VIIRS_SNPP_1.json b/datasets/WATVP_M3_VIIRS_SNPP_1.json index 465360a8a5..1af7eaba09 100644 --- a/datasets/WATVP_M3_VIIRS_SNPP_1.json +++ b/datasets/WATVP_M3_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WATVP_M3_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/SNPP Water Vapor Level-3 monthly 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_M3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (monthly) output files for further aggregation purposes.", "links": [ { diff --git a/datasets/WAVeTrends_1738_1.json b/datasets/WAVeTrends_1738_1.json index 843bbb6b89..95049306ab 100644 --- a/datasets/WAVeTrends_1738_1.json +++ b/datasets/WAVeTrends_1738_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WAVeTrends_1738_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WAVeTrends dataset is a 0.05 degree (5.55 km) vegetation change product, spanning the West African Sudano-Sahel region. It provides pixel-wise information on concurrent woody and herbaceous vegetation trends over a 32-year period (1982-2013). Change in woody vegetation was derived using long-term rain use efficiency (RUE) sensitivity, i.e., the per-pixel comparison of the difference of mean RUE between the first and last decades of the 32-year time series. Herbaceous vegetation change was defined by short-term RUE sensitivity, i.e., comparing the slope of the RUE relationship (productivity vs. precipitation) between both decades using per-pixel Analysis of Covariance (ANCOVA). Categorical vegetation change was then determined for each pixel using the direction of the change and a significance level of p<0.05. The use of RUE (the amount of biomass produced per unit of precipitation) for vegetation trend analysis in savanna regions relies on the assumption that rainfall is a significant positive driver of net production in drylands. Testing of this long-term productivity-rainfall relationship revealed that the assumption was not always met, therefore, validity flags are included for each pixel location.", "links": [ { diff --git a/datasets/WBD_Copepods_1.json b/datasets/WBD_Copepods_1.json index 2f15f37f1e..cdeb3d5657 100644 --- a/datasets/WBD_Copepods_1.json +++ b/datasets/WBD_Copepods_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WBD_Copepods_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees.\n\nFrom the CD booklet:\n\nThis CD-ROM covers The Calanoid Copepods of the Family Aetideidae of the World Ocean and was made by Dr Elena L. Markhaseva. This work contains a comprehensive and fully up-to-date account of Aetideidae, including extensive species descriptions (with more than 2600 illustrations). A new and highly detailed illustrated key provides easy access to the information on the taxa. An interactive glossary of terms forms part of this unique program that provides information on 165 species (including 2 subspecies) and 34 higher taxa. Principal literature references are also included.\n\nThe Aetideidae are an important family of the Calanoida that form an order of the Copepoda (Crustacea). Most calanoids are free living and marine, though some groups are restricted to freshwater. The majority of species are planktonic, but some live on, or close to, the ocean floor. They represent the dominant group within the organisms that make up the marine zooplankton, and form a very important link in the food chain of the aquatic environment.\n\nThe family Aetideidae occurs in all vertical ranges of the marine pelagic zone and also inhabits the hyperbenthic environment. In order to obtain comprehensive information on the geographical distribution of the presented cosmopolitan calanoid family, the present program was based on material from numerous localities in the world's oceans.\n\nThe work was performed primarily at the Zoological Institute of the Russian Academy of Sciences (St Petersburg), using the museum's collections.\n\nThe Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world.", "links": [ { diff --git a/datasets/WBD_Euphausiids_1.json b/datasets/WBD_Euphausiids_1.json index 2873558611..d1f3976131 100644 --- a/datasets/WBD_Euphausiids_1.json +++ b/datasets/WBD_Euphausiids_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WBD_Euphausiids_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees.\n\nFrom the CD booklet:\n\nEuphausiids of the World Ocean is a CD-ROM monograph on the order of Euphausiacea by Edward Brinton, Mark D. Ohman, Annie W Townsend, Margaret D. Knight and Amy L. Bridgeman of the Scripps Institution of Oceanography.\n\nThis CD-ROM summarises information concerning Euphausiid taxonomy, morphology, biogeographic distributions, and larval development. An illustrated multiple-entry taxonomic key and a pictorial key facilitate the identification of the 86 species covered. Species recognition is further aided by an interactive glossary and a wealth of illustrations, photos and movies.\n\nThis taxonomic reference work concerns a completely revised monograph on the order Euphausiaceae. Detailed information on all 86 recognised species and higher taxa is given, including full systematic descriptions, taxonomy, morphology, biogeographic distributions, references and colour photographs and drawings. Some information on euphausiid behaviour is included, mostly in the form of short videos of swimming, bioluminescence, and schooling.\n\nThe database also holds unique information on larval development for most of the species. An explanation of the probable evolutionary progression of euphausiid genera is given. Two types of fully illustrated computer-assisted identification keys (a multiple-entry key expert system and a picture key) provide quick and easy access to the species information. The distribution information is stored in an interactive geographic information system, allowing for fast geographic searches and comparisons of distribution patterns. All text is hyperlinked; an illustrated glossary explains 104 terms and a reference database contains all principal literature references for the euphausiids.\n\nThis CD-ROM is the definitive information source on Euphausiids for marine biologists, environmentalists and students. It should be present in any marine biology library.\n\nThe Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world.", "links": [ { diff --git a/datasets/WBD_Macrobenthos_North_Sea_1.json b/datasets/WBD_Macrobenthos_North_Sea_1.json index fda71e4e5f..b04b140611 100644 --- a/datasets/WBD_Macrobenthos_North_Sea_1.json +++ b/datasets/WBD_Macrobenthos_North_Sea_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WBD_Macrobenthos_North_Sea_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees. \n\nFrom the CD booklet:\n\nThe Macrobenthos of the North Sea - Keys to Mollusca, Brachiopoda, Ploychaeta, Nemertina, Sipuncula, Platyhelminthes and miscellaneous groups was constructed by Mario de Kluijver, Sarita Ingaluso, Rykel de Bruyne, Andre; van Nieuwenhuijzen and Huub Veldhuijzen van Zanten.\n\nThe Macrobenthos of the North Sea project was initiated by the Expert Center for Taxonomic Identification (ETI) and the Zoological Museum at the University of Amsterdam. It provides users with a complete and up-to-date catalogue of macrobenthic species, including reliable stadardised identification guides. In a series of CD-ROMs the biological diversity in the shallow-water macrobenthos of the North Sea (down to 100m) is documented.\n\nVolume 1 of this series contains detailed information on 525 species of the phylum Mollusca and the phylum Brachiopoda, including fully illustrated picture keys, glossaries of used terms and interactive distribution maps of the phylum Mollusca.\n\nVolume 2 of this series contains detailed information on 651 species of Polychaeta, Nemertina, Sipuncula, Platyhelminthes and miscellaneous groups (Priapulida, Echiura and Pogonphora), including fully illustrated picture keys, glossaries of used terms.\n\nThe Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world.", "links": [ { diff --git a/datasets/WBD_Marine_Mammals_1.json b/datasets/WBD_Marine_Mammals_1.json index b72580d5ec..48107ec99c 100644 --- a/datasets/WBD_Marine_Mammals_1.json +++ b/datasets/WBD_Marine_Mammals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WBD_Marine_Mammals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees.\n\nFrom the CD booklet:\n\nThis CD-ROM title covers Marine Mammals of the World and is based on the Food and Agricultural Organization of the United Nations (FAO) Species Identification Guide, supplemented with updated information and full colour pictures. This product comprises a detailed and up-to-date overview of all species in this group. Many people contributed to this CD-ROM; an extensive list is provided in the program itself. Dr T.A. Jefferson, Dr S. Leatherwood and Dr M.A. Webber are the authors of the book and reviewed the CD-ROM contents; Dr P.A. Folkens kindly provided his original drawings. Drs M. Brugman, A. van Hertum and Ing. G. Gijswijt served as technical editors. Dr P. van Bree of the Zoological Museum Amsterdam updated the distribution data. Dr R. Leewis of Thalassa Picture Services contacted numerous authors for high-quality photographic, video, and audio materials.\n\nThe primary purpose of the Species Identification and Data Program is to provide basic tools necessary for the management of fishery resources. In order to manage a fishery resource, either through a biological population or community approach, information such as growth parameters, habitat preference, reproduction, and feeding types is required for each species. A problem for data collection occurs because a wide diversity of species are encountered in fisheries, especially in the tropics. If a species is not identified correctly, then biological information attributed to it can be meaningless or, worse, misleading. This program produces documents that allow proper identification and provide biological and fisheries information for each species. Furthermore, the Species Identification and Data Program is the only program of its kind that provides this vital service for fisheries on a global basis. This service is provided mostly through the production of three types of documents, each with identification tools and with different levels of coverage of biological and fisheries information: 1) Global Soecies Catalogues for specific resource groups (for example, shrimps, sharks, and snappers) with extensive information, 2) Identification Guides with intermediate level information for larger regions (for example, the western Indian Ocean or FAO Fishing Area 51), and 3) Field Guides with abbreviated information for a country or group of countries (for example, Mozambique and the northern coast of South America).\n\nThis is a worldwide guide to the identification of marine mammals and those cetaceans, seals, and sireneans also found in fresh water. The 119 species include a variety of taxa: baleen whales, toothed whales, dolphins, porpoises, seals, sea lions, sireneans, marine otters, and the polar bear. In total, 121 distribution maps and more than 1000 illustrations were included to fully describe the species and identification characters. Where available, videos and sounds were added as well.\n\nThe Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world.", "links": [ { diff --git a/datasets/WBD_Planarians_1.json b/datasets/WBD_Planarians_1.json index 149b8be735..c4ec087b85 100644 --- a/datasets/WBD_Planarians_1.json +++ b/datasets/WBD_Planarians_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WBD_Planarians_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record has been created to describe a commercial CD product of the Expert Center for Taxonomic Identification. This CD has in no way been produced by the Australian Antarctic Division (AAD), and the metadata record is only intended as a reference for AAD employees.\n\nFrom the CD booklet:\n\nThis CD-ROM volume is dedicated to Marine Planarians of the World and is based upon work by Dr Ronald Sluys of the Expert Center for Taxonomic Identification. It comprises a complete guide to all species in this Turbellarian group. It is based on the treatise 'A Monograph on Marine Triclads'.\n\nThe introduction section of the program describes the general characteristics of Turbellarian flatworms and provides the user with details about the morphology, ecology and anatomy of the Maricola.\n\nThe higher taxa section gives information on 58 higher taxa, which are described in a similar way to the species. In total, 404 drawings and pictures have been added to the CD-ROM, giving a complete and detailed overview of all taxa described.\n\nThe Expert Center for Taxonomic Identification (ETI) is a Non-governmental Organisation (NGO) in operational relation with UNESCO, dedicated to the production of scientific and educational computer software for the preservation of knowledge concerning the worlds plants and animals. ETI is supported by The Netherlands Government, the University of Amsterdam, UNESCO, and other international organisations. It cooperates with major scientific institutes around the world.", "links": [ { diff --git a/datasets/WCMC_149.json b/datasets/WCMC_149.json index 12afa90b40..5301d103c9 100644 --- a/datasets/WCMC_149.json +++ b/datasets/WCMC_149.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WCMC_149", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This sourcebook of biodiversity data was released in part as a\n contribution to the First Conference of the Parties to the\n Convention on Biological Diversity (Bahamas, 28 November - 9\n December) in anticipation that it will provide information of\n interest and relevance.\n \n An extended introduction to many theoretical and applied aspects\n of biological diversity was provided in Global Biodiversity:\n status of the Earth's living resources (WCMC, 1992; funded\n largely by the UK Overseas Development Administration). That\n document, which benefitted from collaboration with many\n organisations and individual scientists, was produced at the time\n of the United Nations Conference on Environment and Development\n held in 1992 in Rio de Janeiro. The purpose of the book was to\n provide conceptual background and baseline data both to\n practitioners in the biodiversity field, and to all concerned\n persons who needed a guide into that complex and highly topical\n area.\n \n Given the grounding previously provided in Global Biodiversity,\n the present volume concentrates on data rather than text and\n provides an illustrative set of data tables, in part revised and\n expanded from the earlier volume. The choice of data to be\n included and the manner of presentation have been determined with\n the likely end-users borne strongly in mind. With this aim, most\n data are presented in standardised tables by country, so that\n they are immediately available to users working at a national\n level but can also be placed easily in regional and global\n contexts. Overall, they give a good indication of the global\n availability of information on many aspects of biodiversity,\n drawing attention to some of the gaps that exist and to the\n regional imbalances in the distribution of biodiversity and the\n resources that have been devoted to its assessment and study.", "links": [ { diff --git a/datasets/WCMC_155.json b/datasets/WCMC_155.json index a28fb0183a..916fa574ca 100644 --- a/datasets/WCMC_155.json +++ b/datasets/WCMC_155.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WCMC_155", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WCMC has provided services to the Convention on International Trade in\n Endangered Species CITES since 1980, computerising the trade records\n of species listed in the CITES Appendices, as reported by the Parties.\n This computer database is the largest of its kind, currently holding\n over 2 million records on trade in wildlife species and their\n derivative products. The information spans from 1975, when a mere 148\n trade records were reported, to the present and is constantly being\n updated as further annual reports are received from CITES Parties.\n Since 1986 more than 200,000 trade records have been reported\n annually. In addition to the trade records, the database holds some\n 29,000 scientific names and synonyms. \"http://www.cites.org/\".\n \n The annual report data arrive in many different formats, ranging from\n copies of permits, hand-written or printed reports, to computer tapes,\n diskettes and electronic mail. The information is entered into the\n database, either manually or by direct electronic transfer, and\n customised translation programmes written in Perl at WCMC now enable\n automatic loading of most reports received on magnetic media. Now that\n WCMC is connected to the Internet, it is expected that many countries\n will be able to submit their information directly via the network,\n this process having already been successfully carried out by the\n Management Authority of Brazil. In order to investigate the further\n potential of this type of data collection WCMC has devised a\n questionnaire that the CITES Secretariat is circulating to all Party\n States.\n \n At the beginning of 1993 the trade data were transferred from a WANG\n computer to an Ingres relational database held on a Sunsparc 10/30. A\n large suite of custom-built programs allow sophisticated control,\n maintenance and manipulation of the data; for example, information on\n a species Appendix-listing is linked to the taxonomic file thus\n possible errors at the data input stage are reduced to a minimum.\n Current work at WCMC is linking the trade data with species\n distribution information and with the Centre's Geographical\n Information Systems (GIS), known as the Biodiversity Map Library, to\n ensure that the information, so laboriously collected, can be used in\n the best way to promote species conservation. Further links with\n information on national and international legislation may be possible\n in the future.\n \n In addition to input and maintenance of the trade data, WCMC collects\n information on protected areas, habitats and species of conservation\n concern, and can therefore provide comprehensive analyses and reports.\n The trade outputs usually comprise one of three standard formats:\n \n Gross/Net Trade Tabulation\n - will provide gross or net import/export data for a specified\n year(s), country, species and/or product, thus allowing yearly\n trends to be monitored.\n \n Comparative Tabulation\n - produces data from corresponding importing and exporting\n countries for a specific year, species, product, etc., thus\n allowing a comparison of the reporting between the two Parties\n and a chance to identify any potentially illegal trade.\n \n Annual Report\n - format will provide a complete printout of all CITES trade\n for a particular year reported by a specific CITES Party. Where\n Parties are unable to produce their own annual report, WCMC can\n produce one based on that country's returned permits.\n \n Regular requests for information from the database are made by the\n CITES Secretariat, Management and Scientific Authorities, the TRAFFIC\n Network, WWF, IUCN, Universities, NGO's, researchers, journalists and\n teachers, etc. With permission from the CITES Secretariat, WCMC can\n provide data in any of the above formats although a fee is charged to\n cover the production costs of the work.\n \n WCMC have carried out detailed analyses of the status and trade data\n have included the following:\n * selected species listed in Appendix II\n * Green and Hawksbill turtles\n * world trade in raw and worked ivory\n * Asian monitor lizards\n * South East Asian pythons\n * Crocodile farming and ranching\n \n LANGUAGE:\n English\n \n STATISTICAL INFORMATION:\n \n ACCESS AND DISTRIBUTION:\n \n WCMC makes information available through published media, through\n provision of datasets, and through the provision of either standard or\n customised reports. WCMC is committed to the principle of the free\n exchange of data with other institutions and users. In so far as is\n practical, it places its data in the public domain and encourages\n their wide distribution. However, costs may be incurred in accessing\n and distributing datasets, and where analysis and assessment provide\n an added-value service. Such costs are passed on to the user.", "links": [ { diff --git a/datasets/WCMC_157.json b/datasets/WCMC_157.json index f624005180..24ec86fce4 100644 --- a/datasets/WCMC_157.json +++ b/datasets/WCMC_157.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WCMC_157", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Availability of Biodiversity Information for East Africa\n \n Launched in 1992 at the Conference Conservation of Biodiversity in\n Africa, Nairobi, the project represents a survey of the sources and types\n of information held on biodiversity for Kenya, Tanzania and Uganda by\n organisations both within and outside East Africa. The project came about\n in response to the need for a systematic review of data holdings for the\n region in support of conservation and sustainable development. This need\n has been subsequently underscored by provisions in the Convention on\n Biological Diversity calling for contracting parties to develop national\n strategies, plans or programmes, assisted by such baseline information.\n \n The study was a collaborative venture between the IUCN Regional Office\n for Eastern Africa (EARO), the World Conservation Monitoring Centre\n (WCMC), and key national institutions in each of the three countries.\n Funding was provided by The European Commission (B7-5040 Contract\n 92/11) and through a contract with the Food and Agriculture Organization\n of the United Nations, executing agency for a GEF/UNDP project entitled\n Institutional Support for the Protection of East African Biodiversity\n (UNO/RAF/006/GEF).\n \n Information for the study was collected using a standard questionnaire\n design, administered by means of face to face interviews for the majority\n of the 100 institutions surveyed within East Africa, by mailing the\n questionnaire to more than 1000 institutions outside the region, and by\n posting the questionnaire on \"News\", a global electronic bulletin board,\n with the potential of reaching another 1 million+ subscribers.\n \n A total of 350 questionnaires were completed and returned, the results\n of which were used in the production of the following outputs:\n \n The creation of a \"data sources\" database (metadatabase) of the\n sources and types of biodiversity information held for the region\n \n The production of a printed report including (1) summary\n information and analysis of results in terms of taxonomic and\n geographic coverage of biodiversity information; and (2) a catalogue\n of questionnaire entries\n \n Presentation of catalogue entries in Folio Views text-retrieval\n software, with accompanying User's Guide\n \n Important follow-up to this study includes forthcoming publication and\n distribution of hard-copy and electronic outputs, maintenance and\n updating of the database, ongoing provision of training in information\n collection and database use, and general support for biodiversity\n initiatives by promoting networking between institutions and accessibility\n to information. Consideration is being given to extending the study to\n other regions of Africa and elsewhere, and the experience gained from\n this initiative is being used in support of larger institutional capacity\n building projects currently being undertaken at WCMC.\n \n \n LANGUAGE:\n English\n \n STATISTICAL INFORMATION:\n \n ACCESS AND DISTRIBUTION:\n \n WCMC makes information available through published media, through provision\n of datasets, and through the provision of either standard or customised reports.\n WCMC is committed to the principle of the free exchange of data with other\n institutions and users. In so far as is practical, it places its data in the\n public domain and encourages their wide distribution. However, costs may be\n incurred in accessing and distributing datasets, and where analysis and\n assessment provide an added-value service. Such costs are passed on to the\n user.", "links": [ { diff --git a/datasets/WCMC_158.json b/datasets/WCMC_158.json index 71e893facd..2c572f1d8d 100644 --- a/datasets/WCMC_158.json +++ b/datasets/WCMC_158.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WCMC_158", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the species activities, WCMC maintains nomenclature,\n distribution and conservation status information on some 18,000 animal\n species and subspecies worldwide. WCMC stores animal information in a\n series of Foxpro database files which include data on single country\n endemics, globally threatened species, and species included on various\n International Conventions. These animal data are currently being\n converted from Foxpro to A-Rev. For further details of structure see\n Plant Species Database description.\n \n The basic data elements on species conservation include scientific and\n common names, distribution by country and conservation status.\n Additional information on population size, trends and habitat are\n sought wherever possible. For species subject to wildlife trade,\n information on levels of trade, impact on wild populations, protection\n and management measures are important.\n \n WCMC publishes the Red List of Threatened Animals in collaboration\n with IUCN and the Species Survival Commission. All the data are held\n on computer, including nomenclature, common names, distribution,\n conservation status, threats, etc. The Centre is actively seeking\n collaboration to prepare digital distribution maps for threatened\n animals and plants, but this work is at an early stage.\n \n LANGUAGE:\n English\n \n STATISTICAL INFORMATION:\n \n ACCESS AND DISTRIBUTION:\n \n WCMC makes information available through published media, through\n provision of datasets, and through the provision of either standard or\n customised reports. WCMC is committed to the principle of the free\n exchange of data with other institutions and users. In so far as is\n practical, it places its data in the public domain and encourages\n their wide distribution. However, costs may be incurred in accessing\n and distributing datasets, and where analysis and assessment provide\n an added-value service. Such costs are passed on to the user.", "links": [ { diff --git a/datasets/WC_LSMEM_SOILM_025_001.json b/datasets/WC_LSMEM_SOILM_025_001.json index 9f9769f972..cf89243e14 100644 --- a/datasets/WC_LSMEM_SOILM_025_001.json +++ b/datasets/WC_LSMEM_SOILM_025_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WC_LSMEM_SOILM_025_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSR-E/Aqua surface soil moisture (LSMEM) L3 1 day 0.25 degree x 0.25 degree V001 is a global, 10-year (2002-2011) data set. It is created from soil moisture retrieved from passive microwave brightness temperatures measured by the 10.65 and 36.5 GHz radiometers on the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the NASA Aqua satellite. The retrieval algorithm is based on Princeton's Land Surface Microwave Emission Model (LSMEM), a physically-based radiative transfer model, and serves as the core algorithm in the estimation procedure. To retrieve surface soil moisture, two unknowns, the soil moisture and the effective vegetation optical depth, are simultaneously solved from two radiative transfer equations in LSMEM, one for the 10.65 GHz horizontally-polarized brightness temperature and the other for the 10.65 GHz vertically-polarized brightness temperature. The land surface temperature required in the estimation procedure is estimated from the 36.5 GHz vertically-polarized brightness temperature, using a regression relationship. This soil moisture product does not include areas covered by snow, so the snow model is not described. Also, the atmosphere is assumed to have constant brightness temperatures; therefore, the atmosphere model is also not described.", "links": [ { diff --git a/datasets/WC_MULTISEN_PREC_025_001.json b/datasets/WC_MULTISEN_PREC_025_001.json index 8d9b5c2258..26b2899a1b 100644 --- a/datasets/WC_MULTISEN_PREC_025_001.json +++ b/datasets/WC_MULTISEN_PREC_025_001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WC_MULTISEN_PREC_025_001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TMI/TRMM precipitation and uncertainty (TMPA) L3 3 hour 0.25 degree x 0.25 degree V001 provides estimates of accumulated precipitation from the Tropical Rainfall Measuring Mission (TRMM) and Other Data Precipitation Data Set (TRMM 3B42; Huffman et al., 2007), along with estimates of the uncertainty in the TRMM 3B42 made by Bytheway and Kummerow (2013). The data set covers both ocean and land from 50 degree North to 50 degree South.", "links": [ { diff --git a/datasets/WC_PM_ET_050_1.json b/datasets/WC_PM_ET_050_1.json index 5a79964385..e6f7a63bd0 100644 --- a/datasets/WC_PM_ET_050_1.json +++ b/datasets/WC_PM_ET_050_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WC_PM_ET_050_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SRB/GEWEX evapotranspiration (Penman-Monteith) L4 3 hour 0.5 degree x 0.5 degree V1 is a global, 24-year (1984-2007), satellite-derived evapotranspiration over land data set. It is based on the Penman-Monteith model. Evapotranspiration provides the critical link between the water and energy cycles within the Earth system. Better representation of the spatial distribution and temporal development of surface evapotranspiration is needed not only to improve the description of water vapor exchanges for global water budget estimation but also to advance our understanding of the climate system.\n\nInput data sets include (1) vegetation index data, i.e., Leaf Area Index (LAI), derived from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the EOS-Terra and EOS-Aqua satellites; (2) meteorology data from the latest version of the Princeton University global forcing data sets and from the Variable Infiltration Capacity (VIC) land surface model output; and (3) radiative data from the NASA Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget Project.", "links": [ { diff --git a/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json b/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json index 437348ab72..6d5f19caf2 100644 --- a/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json +++ b/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WENTZ_NIMBUS-7_SMMR_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains three parameters: ocean near-surface wind speed, columnar water vapor, and columnar liquid water. Product is produced by Frank Wentz at Remote Sensing Systems using data obtained from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR). Observations within 100 km of land are excluded; ice flags are also utilized. Data is obtained from all 10 individual SMMR channels, which closely correspond to the SMM/I channels and utilizing the same processing algorithms that were used to produce similar products derived from SSM/I observations (see PO.DAAC products 33 and 34).", "links": [ { diff --git a/datasets/WENTZ_SASS_SIGMA0_L2_1.json b/datasets/WENTZ_SASS_SIGMA0_L2_1.json index 2718d03247..0b2bf19363 100644 --- a/datasets/WENTZ_SASS_SIGMA0_L2_1.json +++ b/datasets/WENTZ_SASS_SIGMA0_L2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WENTZ_SASS_SIGMA0_L2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality.", "links": [ { diff --git a/datasets/WHITECAPS_0.json b/datasets/WHITECAPS_0.json index 1721cbf74c..a100ecb9b3 100644 --- a/datasets/WHITECAPS_0.json +++ b/datasets/WHITECAPS_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WHITECAPS_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The influence of whitecaps on ocean color and aerosol remote sensing from space were invistigated onboard the R/V Melville (MV1102) from Cape Town, South Africa to Valparaiso, Chile from February 2, 2011 to March 14, 2011. Satellite imagery has revealed relatively large amounts of aerosols and particulate organic and inorganic carbon in the Southern oceans, but it is not clear whether this is real or the result of not taking into account properly whitecap effects in the retrieval algorithms. By measuring whitecap optical properties and profiles of marine reflectance and backscattering and absorption coefficients, a bulk whitecap reflectance model will be developed. The measurements will allow comparisons of the aerosol optical thickness and marine reflectance one should retrieve (i.e., in the absence of whitecaps and bubbles) with the satellite-derived estimates. The parameters/variables that will be measured include whitecap coverage, surface reflectance, aerosol optical thickness, in situ profiles of marine reflectance, backscattering and attenuation coefficients, and particle size distribution, and absorption and backscattering coefficients and HPLC pigments from water samples. The backscattering and absorption measurements from water samples will characterize conditions without whitecaps. Cruise information can be found in the R2R repository: https://www.rvdata.us/search/cruise/MV1102.", "links": [ { diff --git a/datasets/WILKS_2018_Chatham_sedimenttraps_specieslist_3.json b/datasets/WILKS_2018_Chatham_sedimenttraps_specieslist_3.json index 120205c2cc..6c52e871c1 100644 --- a/datasets/WILKS_2018_Chatham_sedimenttraps_specieslist_3.json +++ b/datasets/WILKS_2018_Chatham_sedimenttraps_specieslist_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WILKS_2018_Chatham_sedimenttraps_specieslist_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This spreadsheet contains species lists and counts from four sediment trap records. The sediment traps were deployed for ~1 year north and south of the Chatham Rise, New Zealand, between 1996 and 1997. Sheets 1a and 1b refer to North Chatham Rise (NCR). 1a = the 300m trap. 1b = the 1000m trap. Sheets 2a and 2b are for the South Chatham Rise traps (SCR). 2a= 300m, 2b= 1000m. \n\nCounting was undertaken on 1/16th splits. Material was cleaned of organics before diatom counting under light microscopy. Coccolith counting on uncleaned material was only undertaken at the 300m traps. Radiolarians and silicoflagellates were counted but not identified. Diatoms and coccoliths were counted along non-overlapping transects until 300 specimens had been counted per sample, or until 10 transects had been made.\n\nThis dataset includes counts of diatom, coccolithophores, radiolarians and silicoflagellates for four sediment trap records- North Chatham Rise (NCR) and South Chatham Rise (SCR) at two trap depths each (300 m and 1000 m). It is intended as supplementary material to Wilks et al. 2018 (submitted) \"Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific.\"\n\nNumbers are raw count per sample cup. Authorities are given. Coordinates of traps given in degrees, minutes and seconds.", "links": [ { diff --git a/datasets/WIND_3DP.json b/datasets/WIND_3DP.json index 73e0db3a73..c797f28421 100644 --- a/datasets/WIND_3DP.json +++ b/datasets/WIND_3DP.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WIND_3DP", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main purpose of the Wind spacecraft is to measure the incoming\n solar wind, magnetic fields and particles, although early on it will\n also observe the Earth's foreshock region. Wind, together with\n Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative\n scientific satellite project designated the International Solar\n Terrestrial Physics (ISTP) program which aims at gaining improved\n understanding of the physics of solar terrestrial relations.\n \n This experiment is designed to measure the full three-dimensional\n distribution of suprathermal electrons and ions at energies from a few\n eV to over several hundred keV on the WIND spacecraft. Its high\n sensitivity, wide dynamic range, and good energy and angular\n resolution make it especially capable of detecting and characterizing\n the numerous populations of particles that are present in\n interplanetary space at energies above the bulk of the solar wind\n particles and below the energies typical of most cosmic rays.\n \n Data consists of ion moments, energy spectra, electron spectra,\n electron and ion omni directional energy spectra. Data are available\n from SSL at University of California, Berkeley\n (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC\n CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/)", "links": [ { diff --git a/datasets/WIR_98_4105.json b/datasets/WIR_98_4105.json index 5d409d2815..662df36573 100644 --- a/datasets/WIR_98_4105.json +++ b/datasets/WIR_98_4105.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WIR_98_4105", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In September 1996, a water-quality study was done by the U.S. Geological\nSurvey, in coordination with the U.S. Forest Service, in headwater streams of\nSteamboat Creek, a tributary to the North Umpqua River Basin in southwestern\nOregon. Field measurements were made in and surface-water and bottom-sediment\nsamples were collected from three tributaries of Steamboat Creek-Singe Creek,\nCity Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream\nfrom where the three tributaries flow into Steamboat Creek. Water samples\ncollected in Singe Creek had larger concentrations of most major-ion\nconstituents and smaller concentrations of most nutrient constitu ents than was\nobserved in the other three creeks. City Creek, Horse Heaven Creek, and\nSteamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek\nhad primarily a calcium sulfate water; the calcium sulfate water detected in\nSinge Creek, along with the smallest observed alkalinity and pH values,\nsuggests that Singe Creek may be receiving naturally occurring acidic water. Of\nthe 18 trace elements analyzed in filtered water samples, only 6 were\ndetected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the\ntrace elements were detected in Singe Creek, at concentrations generally larger\nthan those observed in the other three creeks. Of the detected trace elements,\nonly iron and zinc have chronic toxicity criteria established by the U.S.\nEnvironmental Protection Agency (USEPA) for the protection of aquatic life;\nnone exceeded the USEPA criterion. Bottom-sediment concentrations of antimony,\narsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest\nin City Creek. In City Creek and Horse Heaven Creek, concentrations for 11\nconstituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven\nCreek only), mercury, selenium, silver, zinc, and organic carbon (City Creek\nonly)--exceeded concentrations considered to be enriched in streams of the\nnearby Willamette River Basin, whereas in Steamboat Creek only two trace\nelements--antimony and nickel--exceeded Willamette River enriched\nconcentrations. Bottom-sediment concentrations for six of these constituents in\nCity Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and\nzinc--also exceeded interim Canadian threshold effect level (TEL)\nconcentrations established for the protection of aquatic life, whereas only\nfour constituents between Singe Creek and Steamboat Creek--arsenic, chromium,\ncopper (Singe Creek only), and nickel--exceeded the TEL concentrations.\n\nThe data set checked for the concentrations of major ions, nutrients, and trace\nelements in water and bottom sediments collected in the four tributaries during\nthe low-flow conditions of September 9-13, 1996. Stream-water chemistry results\nwere contrasted, and trace-element concentrations were compared with U.S.\nEnvironmental Protection Agency chronic aquatic life toxicity criteria. \nBottom-sediment trace-element results were also contrasted and compared with\nconcentrations considered to be enriched in streams of the nearby Willamette\nRiver Basin and to interim Canadian threshold level (TEL) concentrations\nestablished for the protection of aquatic life.\n\nThe area of study was Headwater streams of Steamboat Creek, a tributary to the\nnorth Umpqua River Basin in southwestern Oregon\n\nField measurements and surface-water and bottom-sediment samples at each of the\nfour sites included streamflow, stream temperature, specific conductance,\ndissolved oxygen, pH, alkalinity, major ions in filtered water (8\nconstituents), low-level concentrations of trace elements in filtered water (18\nelements), and trace elements and carbon in bottom sediment (47 elements). \n\nStream temperature, specific conductance, dissolved oxygen, and pH were\nmeasured using a calibrated Hydrolab multiparameter unit. Because stream widths\nwere less than 8 feet, field measurements were made only near the center of\nflow at 1 foot or less below water surface. The Hydrolab unit was calibrated\nat each site before and after sampling. Stream temperatures were recorded to\nthe nearest 0.1 degree Centigrade; specific conductance to the nearest 1\nmicrosiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the\nnearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units.\nMeasurements of streamflow were made in accordance with standard USGS\nprocedures (Rantz and others, 1982). Alkalinity measurements were made on\nfiltered water samples using an incremental titration method (Shelton, 1994),\nand results were reported to the nearest 1 milligram per liter as calcium\ncarbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth\nacid-rinsed polyethylene bottles from a minimum of eight verticals in the cross\nsection, suing an equal-width-increment method described by Edwards and Glysson\n(1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter.\nSample and compositing containers were prerinsed with native water prior to\nsample collection. Water samples were collected using clean procedures as\noutlined by Horowitz and others (1994). Samples were chilled on ice from time\nof sample collection until analysis, except when samples were processed. \nProcessing of the field samples was accomplished either in the mobile field\nlaboratory or in an area suitably clean for carrying out the filtering and\npreservation procedures. Samples for major ions, nutrients, and trace elements\nin filtered water (operationally defined as dissolved) were passed through 0.45\nmicrometer pore-size capsule filters into polyethylene bottles using procedures\noutlined by Horowitz and others (1994). Filtered-water trace-element samples\nwere preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of\nsample; nutrient samples were placed in dark brown polyethylene bottles and\nwere chilled for preservation. All chemical samples were shipped to the USGS\nNational Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis\naccording to methods outlined by Fishman (1993).\n\nThe information for this metadata was taken from the Online Publications of the\nOregon District at http://oregon.usgs.gov/pubs_dir/online_list.html .", "links": [ { diff --git a/datasets/WISPMAWSON04-05_1.json b/datasets/WISPMAWSON04-05_1.json index 5bd2c575cf..1ac265adba 100644 --- a/datasets/WISPMAWSON04-05_1.json +++ b/datasets/WISPMAWSON04-05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WISPMAWSON04-05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software).\n\nThis work has been completed as part of ASAC project 2704 (ASAC_2704).\n\nFields recorded\n\nSpecies\nActivity\nType\nEntrances\nSlope\nRemnants\nLatitude\nLongitude\nDate\nSnow\nEggchick\nCavitysize\nCavitydepth\nDistnn\nSubstrate\nComments\nSitedotID\nAspect\nFirstfred", "links": [ { diff --git a/datasets/WLDAS_NOAHMP001_DA1_D1.0.json b/datasets/WLDAS_NOAHMP001_DA1_D1.0.json index 7da41f7d29..38979f4377 100644 --- a/datasets/WLDAS_NOAHMP001_DA1_D1.0.json +++ b/datasets/WLDAS_NOAHMP001_DA1_D1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WLDAS_NOAHMP001_DA1_D1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders\u2019 needs in the region. WLDAS uses NASA\u2019s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables. ", "links": [ { diff --git a/datasets/WOCE91_Chlorophyll_1.json b/datasets/WOCE91_Chlorophyll_1.json index efb0a56941..212decb47e 100644 --- a/datasets/WOCE91_Chlorophyll_1.json +++ b/datasets/WOCE91_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WOCE91_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chloropyll a data were collected along the WOCE transect on voyage 1 of the Aurora Australis, during October of 1991.\n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/WOES_Chlorophyll_1.json b/datasets/WOES_Chlorophyll_1.json index 73375ddef1..666e4f2833 100644 --- a/datasets/WOES_Chlorophyll_1.json +++ b/datasets/WOES_Chlorophyll_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WOES_Chlorophyll_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 7, 1992-1993 - the WOES (Wildlife Oceanography Ecosystem Survey) cruise. Samples were collected from March-May of 1993. \n\nThese data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms).", "links": [ { diff --git a/datasets/WOV_PRYDZ_BIRD_COMMUNITIES_1.json b/datasets/WOV_PRYDZ_BIRD_COMMUNITIES_1.json index b3a616f3da..f7df627ff9 100644 --- a/datasets/WOV_PRYDZ_BIRD_COMMUNITIES_1.json +++ b/datasets/WOV_PRYDZ_BIRD_COMMUNITIES_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WOV_PRYDZ_BIRD_COMMUNITIES_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observations of seabirds at sea have been made by observers on Australian Antarctic ships in the Prydz Bay region during most seasons since 1980/81. Approximately 32000 observations of 26 main species were made from the 1980/81 to 2001/02 seasons. These observations provide a two-decade history of seabird activity in this region of the Antarctic. This project used clustering techniques to identify the communities within the seabird populations.\n\nSummary of results\n\nWe found three distinct communities of seabirds within Prydz Bay. The first comprised all nine species of seabird which breed in the Prydz Bay area (emperor and Adelie penguins, snow, Cape, and Antarctic petrels, southern giant petrels, southern fulmars, Wilson's storm petrel, and south polar skuas). The second comprised those species which breed in sub-Antarctic or temperate regions and forage in Prydz Bay in the summer months (including many species of albatrosses and shearwaters). There was an overlap of these two communities which had a broad mix of species.\n\nThe spatial and temporal ranges of these communities is given in this data set.\n\nThe raw data for this dataset was generated through ASAC project 2208 - Distribution and abundance of seabirds in the Southern Indian Ocean, 1980/81+ (ASAC_2208_seabirds).\n\nThe fields in this dataset are:\n\nYear\nMonth\nDay\nHour\nMinute\nVoyage\nLatitude\nLongitude\nAssemblage\nConstancy\nFidelity", "links": [ { diff --git a/datasets/WRF_STILT_Footprints_Boston_1572_1.json b/datasets/WRF_STILT_Footprints_Boston_1572_1.json index 9045f393b7..866e23a130 100644 --- a/datasets/WRF_STILT_Footprints_Boston_1572_1.json +++ b/datasets/WRF_STILT_Footprints_Boston_1572_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WRF_STILT_Footprints_Boston_1572_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) footprint data products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. The data are gridded footprints on a 1-km grid congruent with the ACES emissions inventory. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio, quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume.", "links": [ { diff --git a/datasets/WRF_STILT_Particles_Boston_1596_1.json b/datasets/WRF_STILT_Particles_Boston_1596_1.json index 7e75353cd4..efeb7d2805 100644 --- a/datasets/WRF_STILT_Particles_Boston_1596_1.json +++ b/datasets/WRF_STILT_Particles_Boston_1596_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WRF_STILT_Particles_Boston_1596_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data and footprint products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. Meteorological fields from version 3.6.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio (ppm) per surface flux (umol m-2 s-1), quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. Footprints are provided for the two receptors at two temporal and spatial scales: three days of surface influence over the whole North American coverage area at 1-degree resolution and 24 hours of surface influence within a smaller region close to the measurement locations ('near field') at 0.1-degree resolution.", "links": [ { diff --git a/datasets/WRIR_01_4005.json b/datasets/WRIR_01_4005.json index ea398d485d..97ca87d4b2 100644 --- a/datasets/WRIR_01_4005.json +++ b/datasets/WRIR_01_4005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WRIR_01_4005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Excessive total dissolved gas pressure can cause gas-bubble trauma in fish\ndownstream from dams on the Columbia River. In cooperation with the U.S. Army\nCorps of Engineers, the U.S. Geological Survey collected data on total\ndissolved gas pressure, barometric pressure, water temperature, and probe depth\nat eight stations on the lower Columbia River from the John Day forebay (river\nmile 215.6) to Camas (river mile 121.7) in water year 2000 (October 1, 1999, to\nSeptember 30, 2000). These data are in the databases of the U.S. Geological\nSurvey and the U.S. Army Corps of Engineers. Methods of data collection,\nreview, and processing, and quality-assurance data are presented in this\nreport.\n\nThe purpose of TDG monitoring is to provide USACE with (1) real-time data for\nmanaging streamflows and TDG levels upstream and downstream from its project\ndams in the lower Columbia River and (2) reviewed and corrected TDG data to\nevaluate conditions in relation to water-quality criteria and to develop a TDG\ndata base model for modeling the effect of various management scenarios of\nstream flow and spill on TDG levels.\n\nInstrumentation at each fixed station consisted of a TDG probe, an electronic\nbarometer, a data-collection platform (DCP), and a power supply. The TDG probe\nwas manufactured by Hydrolab Corporation. The probe had individual sensors for\nTDG, temperature, and probe depth (unvented sensor). The TDG sensor consisted\nof a cylindrical framework wound with a length of Silastic (dimethyl silicon)\ntubing. The tubing was tied off at one end and the other end was connected to a\npressure transducer. After the TDG pressure in the river equilibrated with the\ngas pressure inside the tubing (about 15 to 20 minutes), the pressure\ntransducer produced a measure of the TDG presure in the River. The\nwater-temperature sensor was a thermocouple. The barometer was contained in\nthe display unit of the Model TBO-L, a total dissolved gas meter manufactured\nby Common Sensing, Inc. More information abou the TDG probe is provided by\nTanner, D. Q. And Johnston and M.W. 2001.\n\nThe fixed station monitors were calibrated every 2 weeks from March 10 to\nSeptember 15, 2000, and every three weeks for the remainder of the year, at\nwhich time Warrendale and Bonneville forebay were the only sites in operation. \nThe general procedure was to check the operation of the TDG probe in the field\nwithout disturbing it, replace the field probe with one that had just been\ncalibrated in the laboratory, and then check the operation of the newly\ndeployed field probe. The details of the laboratory calibration procedure are\noutlined in Tanner and Johnston, 2001.\n\nInformation for this metadata was obtained from the Technical Reports of the\nOregon District available at http://oregon.usgs.gov/pubs_dir/online_list.html .", "links": [ { diff --git a/datasets/WRIR_97_4268.json b/datasets/WRIR_97_4268.json index 7cc326d522..45cbd94f54 100644 --- a/datasets/WRIR_97_4268.json +++ b/datasets/WRIR_97_4268.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WRIR_97_4268", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water quality samples were collected at sites in 16 randomly selected agricultural and 4 urban subbasins as part of Phase III of the Willamette River Basin Water Quality Study in Oregon during 1996. Ninety-five samples were collected and analyzed for suspended sediment, conventional constituents (temperature, dissolved oxygen, pH, specific conductance, nutrients, biochemical oxygen demand, and bacteria) and a suite of 86 dissolved pesticides. The data were collected to characterize the distribution of dissolved pesticide concentrations in small streams (drainage areas 2.6? 13 square miles) throughout the basin, to document exceedances of water quality standards and guidelines, and to identify the relative importance of several upstream land use categories (urban, agricultural, percent agricultural land, percent of land in grass seed crops, crop diversity) and seasonality in affecting these distributions. A total of 36 pesticides (29 herbicides and 7 insecticides) were detected basinwide. The five most frequently detected compounds were the herbicides atrazine (99% of samples), desethylatrazine (93%), simazine (85%), metolachlor (85%), and diuron (73%). Fifteen compounds were detected in 12?35% of samples, and 16 compounds were detected in 1?9% of samples. Water quality standards or criteria were exceeded more frequently for conventional constituents than for pesticides. State of Oregon water quality standards were exceeded at all but one site for the indicator bacteria E. coli, 3 sites for nitrate, 10 sites for water temperature, 4 sites for dissolved oxygen, and 1 site for pH. Pesticide concentrations, which were usually less than 1 part per billion, exceeded State of Oregon or U.S. Environmental Protection Agency aquatic life toxicity criteria only for chlorpyrifos, in three samples from one site; such criteria have been established for only two other detected pesticides. However, a large number of unusually high concentrations (1?90 parts per billion) were detected, indicating that pesticides in the runoff sampled in these small streams were more highly concentrated than in the larger streams sampled in previous studies. These pulses could have had short term toxicological implications for the affected streams; however, additional toxicological assessment of the detected pesticides was limited because of a lack of available information on the response of aquatic life to the observed pesticide concentrations. Six pesticides, including atrazine, diuron, and metolachlor, had significantly higher (p<0.08 for metolachlor, p<0.05 for the other five) median concentrations at agricultural sites than at urban sites. Five other compounds ?carbaryl, diazinon, dichlobenil, prometon, and tebuthiuron?had significantly higher (p<0.05) concentrations at the urban sites than at the agricultural sites. Atrazine, metolachlor, and diuron also had significantly higher median concentrations at southern agricultural sites (dominated by grass seed crops) than northern agricultural sites. Other compounds that had higher median concentrations in the south included 2,4-D and metribuzin, which are both used on grass seed crops, and triclopyr, bromacil, and pronamide. A cluster analysis of the data grouped sites according to their pesticide detections in a manner that was almost identical to a grouping made solely on the basis of their upstream land use patterns (urban, agricultural, crop diversity, percentage of basin in agricultural production). In this way inferences about pesticide associations with different land uses could be drawn, illustrating the strength of these broad land use categories in determining the types of pesticides that can be expected to occur. Among the associations observed were pesticides that occurred at a group of agricultural sites, but which have primarily noncropland uses such as vegetation control along rights-of-way. Also, the amount of forested land in a basin was negatively associated with pesticide occurrence", "links": [ { diff --git a/datasets/WRIR_99_4196.json b/datasets/WRIR_99_4196.json index 567cb840ce..7eafc8dd44 100644 --- a/datasets/WRIR_99_4196.json +++ b/datasets/WRIR_99_4196.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WRIR_99_4196", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ten sites on small South Umpqua River tributaries were sampled for inorganic constituents in water and streambed sediment. In aqueous samples, high concentrations (concentrations exceeding U.S. Environmental Protection Agency criterion continuous concentration for the protection of aquatic life) of zinc, copper, and cadmium were detected in Middle Creek at Silver Butte, and the concentration of zinc was high at Middle Creek near Riddle. Similar patterns of trace-element occurrence were observed in streambed-sediment samples.The dissolved aqueous load of zinc carried by Middle Creek along the stretch between the upper site (Middle Creek at Silver Butte) and the lower site (Middle Creek near Riddle) decreased by about 0.3 pounds per day. Removal of zinc from solution between the upper and lower sites on Middle Creek evidently was occurring at the time of sampling. However, zinc that leaves the aqueous phase is not necessarily permanently lost from solution. For example, zinc solubility is pH-dependent, and a shift between solid and aqueous phases towards release of zinc to solution in Middle Creek could occur with a perturbation in stream-water pH. Thus, at least two potentially significant sources of zinc may exist in Middle Creek: (1) the upstream source(s) producing the observed high aqueous zinc concentrations and (2) the streambed sediment itself (zinc-bearing solid phases and/or adsorbed zinc). Similar behavior may be exhibited by copper and cadmium because these trace elements also were present at high concentrations in streambed sediment in the Middle Creek Basin.", "links": [ { diff --git a/datasets/WUS_UCLA_SR_1.json b/datasets/WUS_UCLA_SR_1.json index b021913ed2..5b5972a8a8 100644 --- a/datasets/WUS_UCLA_SR_1.json +++ b/datasets/WUS_UCLA_SR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WUS_UCLA_SR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S. and over several decades.", "links": [ { diff --git a/datasets/WV01_Pan_L1B_1.json b/datasets/WV01_Pan_L1B_1.json index 3dce1fc001..6f9084aef8 100644 --- a/datasets/WV01_Pan_L1B_1.json +++ b/datasets/WV01_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV01_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present. Data have a spatial resolution of 0.5 meters at nadir and a temporal resolution of approximately 1.7 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV02_MSI_L1B_1.json b/datasets/WV02_MSI_L1B_1.json index 646747c969..df2fd0a6e5 100644 --- a/datasets/WV02_MSI_L1B_1.json +++ b/datasets/WV02_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV02_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-2 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV02_MSI_L2A_1.json b/datasets/WV02_MSI_L2A_1.json index a1daa776a1..b1f915f8b6 100644 --- a/datasets/WV02_MSI_L2A_1.json +++ b/datasets/WV02_MSI_L2A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV02_MSI_L2A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-2 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. These level 2A data have been processed and undergone radiometric correction, sensor correction, projected to a plane using a map projection and datum, and has a coarse DEM applied. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV02_Pan_L1B_1.json b/datasets/WV02_Pan_L1B_1.json index be73efc77f..6123bbf024 100644 --- a/datasets/WV02_Pan_L1B_1.json +++ b/datasets/WV02_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV02_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-2 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV03_MSI_L1B_1.json b/datasets/WV03_MSI_L1B_1.json index 3a0e8c0831..7854714229 100644 --- a/datasets/WV03_MSI_L1B_1.json +++ b/datasets/WV03_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV03_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-3 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This satellite imagery is in a range of wavebands with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. The imagery has a spatial resolution of 1.24m at nadir and a temporal resolution of less than one day. The data are provided in National Imagery Transmission Format (NITF). This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV03_MSI_L2A_1.json b/datasets/WV03_MSI_L2A_1.json index 35b5458ae6..463515a1c3 100644 --- a/datasets/WV03_MSI_L2A_1.json +++ b/datasets/WV03_MSI_L2A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV03_MSI_L2A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-3 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This satellite imagery is in a range of wavebands with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. The imagery has a spatial resolution of 1.24m at nadir and a temporal resolution of less than one day. The data are provided in National Imagery Transmission Format (NITF). These level 2A data have been processed and undergone radiometric correction, sensor correction, projected to a plane using a map projection and datum, and has a coarse DEM applied. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV03_Pan_L1B_1.json b/datasets/WV03_Pan_L1B_1.json index ece9733e50..7bc87b110b 100644 --- a/datasets/WV03_Pan_L1B_1.json +++ b/datasets/WV03_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV03_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-3 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This imagery has a spatial resolution of 0.31m at nadir and a temporal resolution of less than one day. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV03_SWIR_L1B_1.json b/datasets/WV03_SWIR_L1B_1.json index c4bfe3a765..2cad09d42b 100644 --- a/datasets/WV03_SWIR_L1B_1.json +++ b/datasets/WV03_SWIR_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV03_SWIR_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-3 Level 1B Shortwave Infrared 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This data product includes 8 shortwave infrared bands. The spatial resolution is 3.7m at nadir and the temporal resolution is less than one day. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV04_MSI_L1B_1.json b/datasets/WV04_MSI_L1B_1.json index 37a6fec462..cc7b8db6e2 100644 --- a/datasets/WV04_MSI_L1B_1.json +++ b/datasets/WV04_MSI_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV04_MSI_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV04_Pan_L1B_1.json b/datasets/WV04_Pan_L1B_1.json index 946caab006..4796fd3b98 100644 --- a/datasets/WV04_Pan_L1B_1.json +++ b/datasets/WV04_Pan_L1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV04_Pan_L1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019. This data product includes panchromatic imagery with a spatial resolution of 0.31m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program.", "links": [ { diff --git a/datasets/WV_LCC_SC_FSCA_1.json b/datasets/WV_LCC_SC_FSCA_1.json index 1d27e5244d..aa6c796616 100644 --- a/datasets/WV_LCC_SC_FSCA_1.json +++ b/datasets/WV_LCC_SC_FSCA_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WV_LCC_SC_FSCA_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license.", "links": [ { diff --git a/datasets/WYGISC_HYDRO100K.json b/datasets/WYGISC_HYDRO100K.json index c518b49590..49b1afa411 100644 --- a/datasets/WYGISC_HYDRO100K.json +++ b/datasets/WYGISC_HYDRO100K.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WYGISC_HYDRO100K", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data layer was to provide a base layer of water features at\n a statewide level for riparian/aquatic species distribution modeling for the\n Wyoming Gap Analysis project. However the data may also be used for a variety\n of other natural resources management/biological studies at the appropriate\n scale.\n \n Hydrographic features for Wyoming at 1:100,000-scale, including perennial and\n intermittent designations and Strahler stream order attributes for streams.\n Does not include man-made ditches, canals and aqueducts. The data was\n originally produced by USGS, a Digital Line Graph (DLG) product, though this\n product was enhanced (edgematched, some linework and attributes corrected,\n stream order attribute added).\n \n A subset of this dataset is also available for distribution, including only\n major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce\n the size of this subset, the line segments were dissolved to remove unncessary\n segments.\n \n Both datasets are available in Arc export file and shapefile format for\n download\n \n Statewide and tiled data: there is one export file, which when imported into\n ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and\n line (streams) topology and two feature attribute files (.PAT and .AAT) along\n with three additional attribute files containing descriptive information. In\n shapefile format, there will be two shapefiles (polygons and lines separated),\n with all attribute files in Dbase format.", "links": [ { diff --git a/datasets/WYGISC_HYDRO24K.json b/datasets/WYGISC_HYDRO24K.json index 747284784f..94f001f868 100644 --- a/datasets/WYGISC_HYDRO24K.json +++ b/datasets/WYGISC_HYDRO24K.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WYGISC_HYDRO24K", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data layer is to provide a base layer of\n hydrography at the watershed scale for GIS display and analysis.\n \n The hydrography described by this metadata, including streams, lakes,\n reservoirs and\" ditches, came from three different sources, all at\n 1:24,000-scale:\"\n \n -USGS Digital Line Graphs\n -USFS Cartographic Feature File\n -digitized by Wyoming Water Resources Center off of paper topographic maps", "links": [ { diff --git a/datasets/WYGISC_LANDUSE.json b/datasets/WYGISC_LANDUSE.json index ddb8817f0a..654ec59a14 100644 --- a/datasets/WYGISC_LANDUSE.json +++ b/datasets/WYGISC_LANDUSE.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WYGISC_LANDUSE", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data layer is to provide a digital layer showing\n areas of agriculture and agricultural chemical use in Wyoming. This layer\n was designed to be applied in the Wyoming Ground-Water Vulnerability\n Mapping Project.\n \n This dataset represents croplands of Wyoming as interpreted from\n 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial\n photography. The photos, which were taken in 1980-1982, were interpreted\n and land use designations were hand-drawn onto plots produced at the same\n scale as the photos, using a light table. The plots were then digitized\n as polygons into ARC/INFO 7.0.2. Valid polygons include\n irrigated croplands, non-irrigated croplands, urban lands, golf-courses,\n and non-agricultural lands. Golf courses boundaries, which have changed\n recently, were later updated with 1994 NAPP photos.", "links": [ { diff --git a/datasets/WaterBalance_Daily_Historical_GRIDMET_1.5.json b/datasets/WaterBalance_Daily_Historical_GRIDMET_1.5.json index 446de2480d..6697605932 100644 --- a/datasets/WaterBalance_Daily_Historical_GRIDMET_1.5.json +++ b/datasets/WaterBalance_Daily_Historical_GRIDMET_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WaterBalance_Daily_Historical_GRIDMET_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format.", "links": [ { diff --git a/datasets/WaterBalance_Monthly_Historical_GRIDMET_1.5.json b/datasets/WaterBalance_Monthly_Historical_GRIDMET_1.5.json index eb72de47fa..e19aaad81b 100644 --- a/datasets/WaterBalance_Monthly_Historical_GRIDMET_1.5.json +++ b/datasets/WaterBalance_Monthly_Historical_GRIDMET_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WaterBalance_Monthly_Historical_GRIDMET_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format.", "links": [ { diff --git a/datasets/WebbRosenzweig_548_1.json b/datasets/WebbRosenzweig_548_1.json index b7c696ad37..f121d0a46e 100644 --- a/datasets/WebbRosenzweig_548_1.json +++ b/datasets/WebbRosenzweig_548_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WebbRosenzweig_548_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A standardized global data set of soil horizon thicknesses and textures (particle size distributions).", "links": [ { diff --git a/datasets/Wed_distrib_1.json b/datasets/Wed_distrib_1.json index 2b6f19ca78..d482abd4db 100644 --- a/datasets/Wed_distrib_1.json +++ b/datasets/Wed_distrib_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wed_distrib_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The information in the dataset is the location of Weddell seals with pups at the Vestfold Hills. Resolution of locations is 0.63 sq. kilometres because the data are recorded as x-y coordinates of a grid-square map (see metadata record 'Weddell seal reporting grid of the Vestfold Hills, Antarctica', Data Set ID 'Wed-map'). The source of the data are the Australian Antarctic Division TAGS database, Report No. 11 (pups per annum/season - sorted by X and Y). The data consists of counts of tagged pups per grid square summed over 24 years, and also counts of years that each grid square was occupied by one or more pup. The data are expressed as number, percentage of total number, and ratio of number of pups to number of years occupied for each grid square. The total number of pups is 3 795 pups. The total number of years is 24 years. Temporal coverage is 1973-1999 excluding years 1975, 1976, and 1997 because seals were recorded in few grid squares in those years (2, 6, and 0 grid squares respectively). For the other 24 years, the average (+/- standard deviation) number of grid squares occupied per year is 21.5 +/- 6.5 grid squares per year. The average (+/- standard deviation) number of pups born and tagged per year is 158.1 +/- 31.0 pups per year. This and other spatial data for Weddell seals at the Vestfold Hills has been published in ANARE Research Notes 19 (Green et al. 1995) and also in Sam Lake's masters thesis (1995). \n\nThe fields in this dataset are:\n\nGridsquare\nCount\nTotal\nCount/Year\nCount%\nTotal%", "links": [ { diff --git a/datasets/West_Florida_Shelf_0.json b/datasets/West_Florida_Shelf_0.json index 70b278375d..ec7ed57717 100644 --- a/datasets/West_Florida_Shelf_0.json +++ b/datasets/West_Florida_Shelf_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "West_Florida_Shelf_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made along the West Florida Shelf between 2005 and 2008.", "links": [ { diff --git a/datasets/West_Soil_Carbon_1238_1.json b/datasets/West_Soil_Carbon_1238_1.json index 6c2c60c254..3b38980385 100644 --- a/datasets/West_Soil_Carbon_1238_1.json +++ b/datasets/West_Soil_Carbon_1238_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "West_Soil_Carbon_1238_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a soil map with estimates of soil carbon (C) in g C/m2 for 20-cm layers from the surface to one meter depth for the conterminous United States.STATSGO v.1 (State Soil Geographic Database, Soil Survey Staff, 1994) data were used to estimate by 20-cm intervals to a 1-m depth the mean soil carbon for each of the STATSGO-delineated soil map units. These map units are the polygons represented in the provided Shapefile data product. ", "links": [ { diff --git a/datasets/Western USA Live Fuel Moisture_1.json b/datasets/Western USA Live Fuel Moisture_1.json index 7bdd0a9121..f8737b5e17 100644 --- a/datasets/Western USA Live Fuel Moisture_1.json +++ b/datasets/Western USA Live Fuel Moisture_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Western USA Live Fuel Moisture_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contains manually collected live fuel moisture measurements in the western United States and remotely-sensed variables. Live fuel moisture represents the mass of water in live vegetation elements like leaves, needles, and twigs divided by its oven-dried mass. It is represented in percentages. Higher the live fuel moisture, wetter the vegetation elements, and vice versa. Live fuel moisture measurements were collected by the United States Forest Service and are available from the [National Fuel Moisture Database](https://www.wfas.net/index.php/national-fuel-moisture-database-moisture-drought-103). Each row of the data corresponds to one unique ground measurement of live fuel moisture (column named \"percent(t)\") matched with various remotely-sensed observables that may be used to predict live fuel moisture. The live fuel moisture is sampled for representative species within a 5-acre plot (or 20,000 m2) centered at the location described by the columns \"latitude\" and \"longitude\" on the day described by the column \"date\". All other columns represent remotely-sensed observables from satellites (e.g., Sentinel-1 and Landsat-8) or maps (e.g., soil texture). Temporally varying remotely-sensed observables are interpolated to 15-day periods and are provided for the date closest to the day of ground-measurement as well as for 6 fortnights preceding the day of live fuel moisture measurement. The time series of satellite data may allow for greater predictability of live fuel moisture.", "links": [ { diff --git a/datasets/Western_Gulf_of_Maine_0.json b/datasets/Western_Gulf_of_Maine_0.json index cea45b0ec0..20fb659b54 100644 --- a/datasets/Western_Gulf_of_Maine_0.json +++ b/datasets/Western_Gulf_of_Maine_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Western_Gulf_of_Maine_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observations from the Western Gulf of Maine", "links": [ { diff --git a/datasets/Wetland_Soil_CarbonStocks_WA_2249_1.json b/datasets/Wetland_Soil_CarbonStocks_WA_2249_1.json index dc4624f7d7..83e9b78279 100644 --- a/datasets/Wetland_Soil_CarbonStocks_WA_2249_1.json +++ b/datasets/Wetland_Soil_CarbonStocks_WA_2249_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wetland_Soil_CarbonStocks_WA_2249_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (*.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (*.csv) file.", "links": [ { diff --git a/datasets/Wetland_VegClassification_PAD_2069_1.json b/datasets/Wetland_VegClassification_PAD_2069_1.json index 1aad3ea3b7..cd3d688f74 100644 --- a/datasets/Wetland_VegClassification_PAD_2069_1.json +++ b/datasets/Wetland_VegClassification_PAD_2069_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wetland_VegClassification_PAD_2069_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats.", "links": [ { diff --git a/datasets/WhitePhenoregions_799_1.json b/datasets/WhitePhenoregions_799_1.json index a61e9282b3..d167e9ccfe 100644 --- a/datasets/WhitePhenoregions_799_1.json +++ b/datasets/WhitePhenoregions_799_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WhitePhenoregions_799_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e., monitoring land surface phenology. The user is referred to White et al. [2005] for further details. Using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity.This dataset contains material providing users with direct access to data used to construct the figures in White et al. [2005]. Users are referred to this reference for additional information. Data files include ASCII and binary versions of the image files for the 500 elemental phenoregions and the 136 final monitoring phenoregions (shown in figure below) and a corresponding .jpg map. Also included are the classification data in tabular ACSII format for each of the 500 elemental phenoregions.Selected monitoring phenoregions. Phenoregions with fewer than 100 pixels or dominated by crop, urban or barren landcover removed. The 136 remaining phenoregions are those passing the screening factors in Table 1 and are shown with normalized rankings by landcover. (From White et al., 2005)", "links": [ { diff --git a/datasets/WhiteSpruce_Leaf_Traits_Alaska_2124_1.json b/datasets/WhiteSpruce_Leaf_Traits_Alaska_2124_1.json index ba523bcbbb..472b658601 100644 --- a/datasets/WhiteSpruce_Leaf_Traits_Alaska_2124_1.json +++ b/datasets/WhiteSpruce_Leaf_Traits_Alaska_2124_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WhiteSpruce_Leaf_Traits_Alaska_2124_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format.", "links": [ { diff --git a/datasets/Wildfire_Effects_Spruce_Field_1595_1.json b/datasets/Wildfire_Effects_Spruce_Field_1595_1.json index 2fc62cb74b..8dfa186abf 100644 --- a/datasets/Wildfire_Effects_Spruce_Field_1595_1.json +++ b/datasets/Wildfire_Effects_Spruce_Field_1595_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfire_Effects_Spruce_Field_1595_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015.", "links": [ { diff --git a/datasets/Wildfire_Impacts_Boreal_Ecosys_2359_1.json b/datasets/Wildfire_Impacts_Boreal_Ecosys_2359_1.json index 249453dab5..6274d240a2 100644 --- a/datasets/Wildfire_Impacts_Boreal_Ecosys_2359_1.json +++ b/datasets/Wildfire_Impacts_Boreal_Ecosys_2359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfire_Impacts_Boreal_Ecosys_2359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts.", "links": [ { diff --git a/datasets/Wildfires_2014_NWT_Canada_1307_1.json b/datasets/Wildfires_2014_NWT_Canada_1307_1.json index 978521df98..7873467393 100644 --- a/datasets/Wildfires_2014_NWT_Canada_1307_1.json +++ b/datasets/Wildfires_2014_NWT_Canada_1307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfires_2014_NWT_Canada_1307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites.", "links": [ { diff --git a/datasets/Wildfires_Date_of_Burning_1559_1.1.json b/datasets/Wildfires_Date_of_Burning_1559_1.1.json index 6651a63401..d887be1d1a 100644 --- a/datasets/Wildfires_Date_of_Burning_1559_1.1.json +++ b/datasets/Wildfires_Date_of_Burning_1559_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfires_Date_of_Burning_1559_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.", "links": [ { diff --git a/datasets/Wildfires_NWT_Canada_1548_1.json b/datasets/Wildfires_NWT_Canada_1548_1.json index cb2bd1a80c..c34f763523 100644 --- a/datasets/Wildfires_NWT_Canada_1548_1.json +++ b/datasets/Wildfires_NWT_Canada_1548_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfires_NWT_Canada_1548_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites.", "links": [ { diff --git a/datasets/Wildfires_NWT_Canada_2018_1703_1.json b/datasets/Wildfires_NWT_Canada_2018_1703_1.json index b17626395b..6d13dcd99a 100644 --- a/datasets/Wildfires_NWT_Canada_2018_1703_1.json +++ b/datasets/Wildfires_NWT_Canada_2018_1703_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfires_NWT_Canada_2018_1703_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas.", "links": [ { diff --git a/datasets/Wildfires_NWT_Canada_2019_1900_1.json b/datasets/Wildfires_NWT_Canada_2019_1900_1.json index 1f81627f19..347ab15603 100644 --- a/datasets/Wildfires_NWT_Canada_2019_1900_1.json +++ b/datasets/Wildfires_NWT_Canada_2019_1900_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wildfires_NWT_Canada_2019_1900_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas.", "links": [ { diff --git a/datasets/Willow_Veg_Plots_1368_1.json b/datasets/Willow_Veg_Plots_1368_1.json index c4ab973178..8bf77eb40e 100644 --- a/datasets/Willow_Veg_Plots_1368_1.json +++ b/datasets/Willow_Veg_Plots_1368_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Willow_Veg_Plots_1368_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the region and across Alaska.", "links": [ { diff --git a/datasets/WindSat-REMSS-L3U-v7.0.1a_7.0.1a.json b/datasets/WindSat-REMSS-L3U-v7.0.1a_7.0.1a.json index 51b4637b18..e6d9cb3848 100644 --- a/datasets/WindSat-REMSS-L3U-v7.0.1a_7.0.1a.json +++ b/datasets/WindSat-REMSS-L3U-v7.0.1a_7.0.1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WindSat-REMSS-L3U-v7.0.1a_7.0.1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7.0.1a\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v7.0.1a\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter \"a\" refers to the file incompliance with GHRSST format.", "links": [ { diff --git a/datasets/Wolves_Denning_Pups_Climate_1846_1.json b/datasets/Wolves_Denning_Pups_Climate_1846_1.json index 07a90efcba..ca63750ce5 100644 --- a/datasets/Wolves_Denning_Pups_Climate_1846_1.json +++ b/datasets/Wolves_Denning_Pups_Climate_1846_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Wolves_Denning_Pups_Climate_1846_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range.", "links": [ { diff --git a/datasets/WorldView-1.full.archive.and.tasking_8.0.json b/datasets/WorldView-1.full.archive.and.tasking_8.0.json index b55f089095..b998e2b2bf 100644 --- a/datasets/WorldView-1.full.archive.and.tasking_8.0.json +++ b/datasets/WorldView-1.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView-1.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WorldView-1 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rIn particular, WorldView-1 offers archive and tasking panchromatic products up to 0.50 m GSD resolution.\r\rBand Combination\tData Processing Level\tResolution\rPanchromatic\tStandard(2A)/View Ready STANDARD (OR2A)\t50 cm, 30 cm HD\rView Ready Stereo\t50 cm\rMap-Ready (Ortho) 1:12.000 Orthorectified\t50 cm, 30 cm HD\r \r\rNative 50 cm resolution products are processed with MAXAR HD Technology to generate the 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/WorldView-2.European.Cities_10.0.json b/datasets/WorldView-2.European.Cities_10.0.json index b0f380c522..3d084a3f07 100644 --- a/datasets/WorldView-2.European.Cities_10.0.json +++ b/datasets/WorldView-2.European.Cities_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView-2.European.Cities_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015.", "links": [ { diff --git a/datasets/WorldView-2.full.archive.and.tasking_8.0.json b/datasets/WorldView-2.full.archive.and.tasking_8.0.json index 54ab64ba63..9746ef0d3e 100644 --- a/datasets/WorldView-2.full.archive.and.tasking_8.0.json +++ b/datasets/WorldView-2.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView-2.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WorldView-2 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rIn particular, WorldView-2 offers archive and tasking panchromatic products up to 0.46 m GSD resolution, and 4-Bands/8-Bands Multispectral products up to 1.84 m GSD resolution.\r\rBand Combination\tData Processing Level\tResolution\rPanchromatic and 4-bands\tStandard (2A)/View Ready Standard (OR2A)\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap-Ready (Ortho) 1:12.000 Orthorectified\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\r8-bands\tStandard(2A)/View Ready Standard (OR2A)\t30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap-Ready (Ortho) 1:12.000 Orthorectified\t30 cm, 40 cm, 50/60 cm\r \r\r4-Bands being an optional from:\r\r4-Band Multispectral (BLUE, GREEN, RED, NIR1)\r4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1)\r4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1)\r3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED)\r3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1).\r8-Bands being an optional from:\r\r8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2)\r8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2).\rNative 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels, improves the visual clarity and allows to obtain an aesthetically refined imagery with precise edges and well reconstructed details.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/WorldView-3.full.archive.and.tasking_8.0.json b/datasets/WorldView-3.full.archive.and.tasking_8.0.json index 1cf01773f8..78762531ff 100644 --- a/datasets/WorldView-3.full.archive.and.tasking_8.0.json +++ b/datasets/WorldView-3.full.archive.and.tasking_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView-3.full.archive.and.tasking_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WorldView-3 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\r\rIn particular, WorldView-3 offers archive and tasking panchromatic products up to 0.31m GSD resolution, 4-Bands/8-Bands products up to 1.24 m GSD resolution, and SWIR products up to 3.70 m GSD resolution.\r\rBand Combination\tData Processing Level\tResolution\rHigh Res Optical: Panchromatic and 4-bands\tStandard(2A)/View Ready Standard (OR2A)\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap Ready (Ortho) 1:12.000 Orthorectified\t15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm\rHigh Res Optical: 8-bands\tStandard(2A)/View Ready Standard (OR2A)\t30 cm, 40 cm, 50/60 cm\rView Ready Stereo\t30 cm, 40 cm, 50/60 cm\rMap Ready (Ortho) 1:12.000 Orthorectified\t30 cm, 40 cm, 50/60 cm\rHigh Res Optical: SWIR\tStandard(2A)/View Ready Standard (OR2A)\t3.7 m or 7.5 m (depending on the collection date)\rMap Ready (Ortho) 1:12.000 Orthorectified\r \r\r4-Bands being an optional from:\r\r4-Band Multispectral (BLUE, GREEN, RED, NIR1)\r4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1)\r4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1)\r3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED)\r3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1)\r8-Bands being an optional from:\r\r8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2)\r8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2)\rNative 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details.\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/WorldView-4.full.archive_7.0.json b/datasets/WorldView-4.full.archive_7.0.json index 0f498bd020..f274efd792 100644 --- a/datasets/WorldView-4.full.archive_7.0.json +++ b/datasets/WorldView-4.full.archive_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView-4.full.archive_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "WorldView-4 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-4 offers archive panchromatic products up to 0.31m GSD resolution, and 4-Bands Multispectral products up to 1.24m GSD resolution Band Combination: Panchromatic and 4-bands Data Processing Level: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m The options for 4-Bands are the following: \u2022 4-Band Multispectral (BLUE, GREEN, RED, NIR1) \u2022 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) \u2022 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) \u2022 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) \u2022 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) The list of available archived data can be retrieved using the Image Library (https://www.euspaceimaging.com/image-library/) catalogue.", "links": [ { diff --git a/datasets/WorldView.ESA.archive_9.0.json b/datasets/WorldView.ESA.archive_9.0.json index 70c87c90c2..42c9e32076 100644 --- a/datasets/WorldView.ESA.archive_9.0.json +++ b/datasets/WorldView.ESA.archive_9.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "WorldView.ESA.archive_9.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldView ESA archive is composed of products acquired by WorldView-1, -2, -3 and -4 satellites and requested by ESA supported projects over their areas of interest around the world\r\rPanchromatic, 4-Bands, 8-Bands and SWIR products are part of the offer, with the resolution at Nadir depicted in the table.\r\rBand Combination\tMission\tGSD Resolution at Nadir\tGSD Resolution (20\u00b0 off nadir)\rPanchromatic\tWV-1\t50 cm\t55 cm\rWV-2\t46 cm\t52 cm\rWV-3\t31 cm\t34 cm\rWV-4\t31 cm\t34 cm\r4-Bands\tWV-2\t1.84 m\t2.4 m\rWV-3\t1.24 m\t1.38 m\rWV-4\t1.24 m\t1.38 m\r8-Bands\tWV-2\t1.84 m\t2.4 m\rWV-3\t1.24 m\t1.38 m\rSWIR\tWV-3\t3.70 m\t4.10 m\r\rThe 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR). The 8-Bands being an option from Multispectral (COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2) and Bundle (PAN, COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2).\rThe processing levels are:\r\rStandard (2A): normalised for topographic relief\rView Ready Standard: ready for orthorectification (RPB files embedded)\rView Ready Stereo: collected in-track for stereo viewing and manipulation (not available for SWIR)\rMap Scale (Ortho) 1:12,000 Orthorectified: additional processing unnecessary\rSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/WorldView/ available on the Third Party Missions Dissemination Service.\rThe following table summarises the offered product types\r\rEO-SIP Product Type\tBand Combination\tProcessing Level\tMissions\rWV6_PAN_2A\tPanchromatic (PAN)\tStandard/View Ready Standard\tWorldView-1 and 4\rWV6_PAN_OR\tPanchromatic (PAN)\tView Ready Stereo\tWorldView-1 and 4\rWV6_PAN_MP\tPanchromatic (PAN)\tMap Scale Ortho\tWorldView-1 and 4\rWV1_PAN__2A\tPanchromatic (PAN)\tStandard/View Ready Standard\tWorldView-2 and 3\rWV1_PAN__OR\tPanchromatic (PAN)\tView Ready Stereo\tWorldView-2 and 3\rWV1_PAN__MP\tPanchromatic (PAN)\tMap Scale Ortho\tWorldView-2 and 3\rWV1_4B__2A\t4-Band (4B)\tStandard/View Ready Standard\tWorldView-2, 3 and 4\rWV1_4B__OR\t4-Band (4B)\tView Ready Stereo\tWorldView-2, 3 and 4\rWV1_4B__MP\t4-Band (4B)\tMap Scale Ortho\tWorldView-2, 3 and 4\rWV1_8B_2A\t8-Band (8B)\tStandard/View Ready Standard\tWorldView-2 and 3\rWV1_8B_OR\t8-Band (8B)\tView Ready Stereo\tWorldView-2 and 3\rWV1_8B_MP\t8-Band (8B)\tMap Scale Ortho\tWorldView-2 and 3\rWV1_S8B__2A\tSWIR\tStandard/View Ready Standard\tWorldView-3\rWV1_S8B__MP\tSWIR\tMap Scale Ortho\tWorldView-3\r\rAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "links": [ { diff --git a/datasets/XAERDT_L2_ABI_G16_1.json b/datasets/XAERDT_L2_ABI_G16_1.json index 50ddcea580..b561c99b15 100644 --- a/datasets/XAERDT_L2_ABI_G16_1.json +++ b/datasets/XAERDT_L2_ABI_G16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_ABI_G16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite \u2013 GOES-16 has been serving in the operational GOES-East position (near -75\u00b0W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022.\r\n\r\nThe XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\nThe XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_ABI_G17_1.json b/datasets/XAERDT_L2_ABI_G17_1.json index 6b2e9b70e0..1ee8a462a1 100644 --- a/datasets/XAERDT_L2_ABI_G17_1.json +++ b/datasets/XAERDT_L2_ABI_G17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_ABI_G17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite \u2013 GOES-17 served in the operational GOES-West position (near -137\u00b0W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022.\r\n\r\nThe XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\nThe XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_AHI_H08_1.json b/datasets/XAERDT_L2_AHI_H08_1.json index e675debf72..2d4938efdf 100644 --- a/datasets/XAERDT_L2_AHI_H08_1.json +++ b/datasets/XAERDT_L2_AHI_H08_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_AHI_H08_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7\u00b0E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument.\r\n\r\nThe XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\nThe XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_AHI_H09_1.json b/datasets/XAERDT_L2_AHI_H09_1.json index f02fa4504c..64605a592c 100644 --- a/datasets/XAERDT_L2_AHI_H09_1.json +++ b/datasets/XAERDT_L2_AHI_H09_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_AHI_H09_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7\u00b0E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022.\r\n\r\nThe XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\n\r\nThe XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\n\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_MODIS_Aqua_1.json b/datasets/XAERDT_L2_MODIS_Aqua_1.json index 0d1fd47b2a..ce156fb03d 100644 --- a/datasets/XAERDT_L2_MODIS_Aqua_1.json +++ b/datasets/XAERDT_L2_MODIS_Aqua_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_MODIS_Aqua_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Aqua is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Aqua/MODIS L2 collection record spans from January 2019 through December 2022.\r\n\r\n\r\nThe XAERDT_L2_MODIS_Aqua product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\n\r\nThe XAERDT_L2_MODIS_Aqua product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Aqua\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_MODIS_Terra_1.json b/datasets/XAERDT_L2_MODIS_Terra_1.json index 084cdbb3a2..6a2680709f 100644 --- a/datasets/XAERDT_L2_MODIS_Terra_1.json +++ b/datasets/XAERDT_L2_MODIS_Terra_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_MODIS_Terra_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Terra is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Terra/MODIS L2 collection record spans from January 2019 through December 2022.\r\n\r\n\r\nThe XAERDT_L2_MODIS_Terra product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\n\r\nThe XAERDT_L2_MODIS_Terra product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Terra\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_VIIRS_NOAA20_1.json b/datasets/XAERDT_L2_VIIRS_NOAA20_1.json index 2087142033..49b24ea720 100644 --- a/datasets/XAERDT_L2_VIIRS_NOAA20_1.json +++ b/datasets/XAERDT_L2_VIIRS_NOAA20_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_VIIRS_NOAA20_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VIIRS/NOAA20 L2 Dark Target Aerosol 6-Min L2 Swath 6 km product, short-name XAERDT_L2_VIIRS_NOAA20 is provided at 6-km spatial resolution (at-nadir) and a 6-minute cadence that typically yields about 130 granules over the daylit hours of a 24-hour period. The NOAA20/VIIRS L2 collection record spans from January 2019 through December 2022.\r\n\r\nThe XAERDT_L2_VIIRS_NOAA20 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\nThe XAERDT_L2_VIIRS_NOAA20 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_VIIRS_NOAA20\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/XAERDT_L2_VIIRS_SNPP_1.json b/datasets/XAERDT_L2_VIIRS_SNPP_1.json index 8cb2929b53..be7cc63925 100644 --- a/datasets/XAERDT_L2_VIIRS_SNPP_1.json +++ b/datasets/XAERDT_L2_VIIRS_SNPP_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "XAERDT_L2_VIIRS_SNPP_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SNPP/VIIRS L2 Dark Target Aerosol 6-Min L2 Swath 6 km product, short-name XAERDT_L2_VIIRS_SNPP is provided at 6-km spatial resolution (at-nadir) and a 6-minute cadence that typically yields about 130 granules over the daylit hours of a 24-hour period. The SNPP/VIIRS L2 collection record spans from January 2019 through December 2022.\r\n\r\nThe XAERDT_L2_VIIRS_SNPP product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.\r\n\r\nThe XAERDT_L2_VIIRS_SNPP product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.\r\n\r\nFor more information consult LAADS product description page at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_VIIRS_SNPP\r\n\r\nOr, Dark Target aerosol team Page at: \r\nhttps://darktarget.gsfc.nasa.gov/", "links": [ { diff --git a/datasets/Xingu_Albedo_Radiation_1622_1.json b/datasets/Xingu_Albedo_Radiation_1622_1.json index 0922a162bc..7cb0da8b2a 100644 --- a/datasets/Xingu_Albedo_Radiation_1622_1.json +++ b/datasets/Xingu_Albedo_Radiation_1622_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Xingu_Albedo_Radiation_1622_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides daily average land surface net radiation (Rnet) as an 8-day time series at approximately 0.5 km resolution for the upper Xingu River Basin in Mato Grosso, Brazil, from 2000-02-18 to 2012-11-16. The parameters needed to calculate Rnet, including albedo, downward shortwave radiation (RSnet), upward longwave radiation (RLnet[up]) and downward longwave radiation (RLnet[down]) were derived from MODIS products (MOD43A3, MOD11A2, MOD08E3) and local weather station data. Parameters were estimated under all sky conditions. These parameters are also provided for the Xingu Basin but at varying spatial and temporal resolutions.", "links": [ { diff --git a/datasets/YKDelta_EnvChange_InfoExchange_1894_1.json b/datasets/YKDelta_EnvChange_InfoExchange_1894_1.json index 1f9485f50a..71a7ba5245 100644 --- a/datasets/YKDelta_EnvChange_InfoExchange_1894_1.json +++ b/datasets/YKDelta_EnvChange_InfoExchange_1894_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "YKDelta_EnvChange_InfoExchange_1894_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018. Yup'ik participants represented several YKD villages that occupy very different biophysical environments, and they have lifelong perspectives of environmental conditions and change that predate the era of Earth-observing satellites by many decades. Nearly 16 hours of discussion and testimonials from YKD elders were recorded during the meeting. The booklet is structured according to the environmental change processes that were discussed (e.g., coastal flooding, permafrost thaw, shrub expansion, climate change) and includes narrative summaries, quotations from participants, graphical illustrations, and examples of the field- and remote-sensing-based scientific findings, and map products developed as part of the larger ABoVE project.", "links": [ { diff --git a/datasets/Young_Russian_Forest_Map_1330_1.json b/datasets/Young_Russian_Forest_Map_1330_1.json index 161963e371..8ed38e6e88 100644 --- a/datasets/Young_Russian_Forest_Map_1330_1.json +++ b/datasets/Young_Russian_Forest_Map_1330_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Young_Russian_Forest_Map_1330_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the distribution of young forests (forests less than 27 years of age) and their estimated stand ages across the full extent of Russia at 500-m resolution for the year 2012. The distribution of young forests was modeled with MODIS 500-m records for 12- to 27-year-old forests and augmented with the 0- to 11-year-old forest distribution as aggregated from 30 m resolution contemporary Landsat imagery.", "links": [ { diff --git a/datasets/ZZZ302.json b/datasets/ZZZ302.json index 73de3990c1..3af8929dde 100644 --- a/datasets/ZZZ302.json +++ b/datasets/ZZZ302.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ZZZ302", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multispectral imagery of the state of Alabama is available from the\n Geological Survey of Alabama for the time period of 1972-1984.\n Imagery from the Landsat multispectral scanner (MSS) is available as\n prints or transparencies for all bands (with selected color composites\n avaliable) at an approximate scale of 1:1,000,000. MSS data is\n collected in four spectral bands ranging from 0.5 to 1.1 micrometer\n with a ground resolution of about 80m.\n \n Images available from Skylab 3 and 4 include 9 x 9 prints and\n transparencies at 1:750,000 (skylab 3) and 1:500,000 (skylab 4).\n These images were taken in 1973 and are along three tracks; northeast\n from New Orleans, LA to South Carolina, northeast from Pensacola, FL\n to Columbus, GA, and from Pearl River, Jackson MI to Pensacola, FL.\n The multispectral photographic facility onboard Skylab provided\n imagery in several wavelength bands ranging from 0.5 to 0.9\n Micrometers. This camera system provided ground resolution of\n approximately 40 m in visible wavelengths to 75 m in the infrared.\n \n A variety of high and low altitude aircraft imagery of Alabama is also\n available from the Geological Survey of Alabama. Microfiche images of\n MSS/TM imagery of North America since 1986 (landsat browse imagery)\n are also available. Similar imagery for other locations and time\n periods is available from the Eros Data Center.", "links": [ { diff --git a/datasets/ZinkeSoil_221_1.json b/datasets/ZinkeSoil_221_1.json index e3a290bb7d..28fc7f028c 100644 --- a/datasets/ZinkeSoil_221_1.json +++ b/datasets/ZinkeSoil_221_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ZinkeSoil_221_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A compilation of worldwide soil carbon and nitrogen data for more than 3500 soil profiles.", "links": [ { diff --git a/datasets/Zinke_soil_683_1.json b/datasets/Zinke_soil_683_1.json index 862079d6a4..aa02b735be 100644 --- a/datasets/Zinke_soil_683_1.json +++ b/datasets/Zinke_soil_683_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Zinke_soil_683_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The point data are available in three formats: a comma-delimited ASCII file (*.csv), an ESRI shapefile, and an ESRI export file (*.e00).The data for the global data set (Zinke et al. 1986) were obtained from soil surveys conducted by Zinke in 1965-1984 and from soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this database from soil survey literature. When carbon content was known but bulk densities were absent from soil samples reported in the literature, densities were estimated by regression analysis on the basis of the relationship between organic carbon content and measured bulk density in 1800 soil profiles for which bulk densities were known.Further information can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/Zinke_soil/comp/zinke_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/ZoblerSoilDerived_540_1.json b/datasets/ZoblerSoilDerived_540_1.json index d94d2a1f92..5e57bc8246 100644 --- a/datasets/ZoblerSoilDerived_540_1.json +++ b/datasets/ZoblerSoilDerived_540_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ZoblerSoilDerived_540_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution.", "links": [ { diff --git a/datasets/ZoblerSoil_418_1.json b/datasets/ZoblerSoil_418_1.json index 3f06cd32c8..2ecd9f3260 100644 --- a/datasets/ZoblerSoil_418_1.json +++ b/datasets/ZoblerSoil_418_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ZoblerSoil_418_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global digital data base of soil properties is available at 1 degree longitude resolution. For each land cell, the data base includes major and associated soil units, surface texture, and slope; phase and miscellaneous land units are included where available. The data base was compiled as part of an effort to improve modeling of the hydrologic cycle in the GISS Genreal Circulation Model.", "links": [ { diff --git a/datasets/Zobler_Soil_649_1.json b/datasets/Zobler_Soil_649_1.json index 8f952bbf9a..dcdb02d3d9 100644 --- a/datasets/Zobler_Soil_649_1.json +++ b/datasets/Zobler_Soil_649_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "Zobler_Soil_649_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A SAFARI 2000 data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands.", "links": [ { diff --git a/datasets/ZonalFlux_0.json b/datasets/ZonalFlux_0.json index bbccf9af31..32ff95bc1a 100644 --- a/datasets/ZonalFlux_0.json +++ b/datasets/ZonalFlux_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ZonalFlux_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken in the western equatorial Pacific Ocean in 1996.", "links": [ { diff --git a/datasets/a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0.json b/datasets/a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0.json index 743bb33b76..01f8a7d133 100644 --- a/datasets/a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0.json +++ b/datasets/a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.", "links": [ { diff --git a/datasets/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0.json b/datasets/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0.json index 71c47b0102..403d53c673 100644 --- a/datasets/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0.json +++ b/datasets/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Sch\u00fcrholt, K., Kowalski, J., L\u00f6we, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics", "links": [ { diff --git a/datasets/a0782135bcd04d77a1dae4aa71fba47c_NA.json b/datasets/a0782135bcd04d77a1dae4aa71fba47c_NA.json index 582f5071a2..75ab4a53a5 100644 --- a/datasets/a0782135bcd04d77a1dae4aa71fba47c_NA.json +++ b/datasets/a0782135bcd04d77a1dae4aa71fba47c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a0782135bcd04d77a1dae4aa71fba47c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", "links": [ { diff --git a/datasets/a0d9764a3068439b997c42928ef739d2_NA.json b/datasets/a0d9764a3068439b997c42928ef739d2_NA.json index 3d8c68569a..260b852892 100644 --- a/datasets/a0d9764a3068439b997c42928ef739d2_NA.json +++ b/datasets/a0d9764a3068439b997c42928ef739d2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a0d9764a3068439b997c42928ef739d2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH).", "links": [ { diff --git a/datasets/a13994c5-8d10-4627-90b8-60077ab5de40_NA.json b/datasets/a13994c5-8d10-4627-90b8-60077ab5de40_NA.json index 4e22f94031..94465c2b17 100644 --- a/datasets/a13994c5-8d10-4627-90b8-60077ab5de40_NA.json +++ b/datasets/a13994c5-8d10-4627-90b8-60077ab5de40_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a13994c5-8d10-4627-90b8-60077ab5de40_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The EnMAP HSI L0 Quicklooks collection contains the VNIR and SWIR quicklook images as well as the quality masks for haze, cloud, or snow; based on the latest atmospheric correction methodology of the land processor. It allows users to get an overview which L0 data has been acquired and archived since the operational start of the EnMAP mission and which data is potentially available for on-demand processing into higher level products with specific processing parameters via the EOWEB-GeoPortal. The database is constantly updated with newly acquired L0 data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth\u2019s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making. For more information, please see the mission website: https://www.enmap.org/mission/", "links": [ { diff --git a/datasets/a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA.json b/datasets/a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA.json index 3b93e3db9e..f146a51e59 100644 --- a/datasets/a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA.json +++ b/datasets/a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/a6efcb0868664248b9cb212aba44313d_NA.json b/datasets/a6efcb0868664248b9cb212aba44313d_NA.json index e55ad6e4db..5fd0fdd1be 100644 --- a/datasets/a6efcb0868664248b9cb212aba44313d_NA.json +++ b/datasets/a6efcb0868664248b9cb212aba44313d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a6efcb0868664248b9cb212aba44313d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json b/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json index 0d4ea6f604..8e69f4ecb2 100644 --- a/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json +++ b/datasets/a78f0eb5-a146-4129-9066-519378e22fd8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a78f0eb5-a146-4129-9066-519378e22fd8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Protected Areas of Africa were provided by the International Union for the Conservation of Nature and Natural Resources (IUCN) - World Conservation Monitoring Center (WCMC) of Gland, Switzerland and Cambridge, UK, to UNEP/GRID-Geneva for digitization into computer form in 1986. The map was digitized in ARC/INFO and subsequently rasterized to a two-minute cell size in the ELAS software format. Today, the same data set resides at GRID on an IBM mainframe computer, but it has not been updated since the initial work was carried out.*\n\nThe Protected Areas of Africa data set shows a series of 11 different types of parks, reserves and other unique areas which had some degree of protected status. The various types of Protected Areas are all shown as squares of varying size on a background map of Africa, with four square sizes which are proportional to the actual size of each area, and with center points approximately equal to the actual central location of each Protected Area. Thus, this data set is perhaps most useful for showing the general distribution of African Protected Areas by type, circa 1986. \n\nThere are two versions of the Protected Areas of Africa data set (two data files) at UNEP/GRID-Geneva. Because there is significant overlap between the Protected Area squares, the first shows the various squares superimposed with the size of Protected Areas used as the criterion for which take precedence over others. The second version shows Protected Areas ranked by importance; that is, squares take precedence according to the order in which they appear in the legend, with the more highly ranked Protected Areas overlaying others of lower rank. Following is the legend which applies to the Protected Areas of Africa (categories were formulated by IUCN-WCMC): \n\nValues Category of Protected Area\n------ --------------------------\n\n 1 Scientific Reserve\n 2 National Park\n 3 National Monument\n 4 Wildlife Sanctuary\n 5 Protected Landscape\n 6 Resource Reserve\n 7 Anthropological Reserve\n 8 Multiple Use Management Area\n 9 Biosphere Reserve\n 10 World Heritage Site\n 11 Unclassified\n\n\nThe Protected Areas data set from IUCN-WCMC covers the entire African continent at a spatial resolution of two minutes (120 seconds) of latitude/longitude, or approximately 3.7 kilometers. The data file consists of 2191 rows (lines/records) by 2161 columns (elements/pixels/ samples). Its upper-left or northwest corner origin is 38 degrees, 0 minutes and 45 seconds North latitude (38d 00' 45\" N), and -20 degrees, 1 minute and 15 seconds West longitude (-20d 01' 15\" W); and it extends to -35 degrees, 1 minute and 15 seconds South latitude (-35d 01' 15\" S), and 52 degrees, 0 minutes and 45 seconds East longitude (52d 00' 45\" E) at its terminal point in the lower-right or southeast corner. The data file comprises 4.74 Megabytes. \nThe source of the Protected Area data is, as mentioned above, the\nInternational Union for the Conservation of Nature and Natural\nResources (IUCN's) World Conservation Monitoring Center (WCMC) in\nCambridge, UK. There is no published reference for this data set.\n\n* - Another more recent version of Protected Areas for Africa, with actual protected area boundaries, exists at UNEP/GRID-Nairobi.\n", "links": [ { diff --git a/datasets/a7b87a912c494c03b4d2fa5ab8479d1c_NA.json b/datasets/a7b87a912c494c03b4d2fa5ab8479d1c_NA.json index f0b68acd4c..b7b70d0c9f 100644 --- a/datasets/a7b87a912c494c03b4d2fa5ab8479d1c_NA.json +++ b/datasets/a7b87a912c494c03b4d2fa5ab8479d1c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a7b87a912c494c03b4d2fa5ab8479d1c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from satellite (ERS\u00e2\u0080\u00901, ERS\u00e2\u0080\u00902, Envisat and Cryosat) radar altimetry. The ice mask is based on the GEUS/GST land/ice/ocean mask provided as part of national mapping projects, and based on 1980\u00e2\u0080\u0099s aerial photography. The data from ERS and Envisat are based on a 5\u00e2\u0080\u0090year running average, using combined algorithms of repeat\u00e2\u0080\u0090track (RT), along\u00e2\u0080\u0090track (AT) or cross\u00e2\u0080\u0090over (XO) algorithms, and include propagated error estimates. It is important to note that different processing algorithms were applied to the ERS\u00e2\u0080\u00901, ERS\u00e2\u0080\u00902, Envisat and CryoSat data; for details see the Product User Guide (PUG), available on the CCI website and in the documentation section here. For ERS\u00e2\u0080\u00901, the radar data were processed using a cross\u00e2\u0080\u0090over algorithm (XO) only. For ERS\u00e2\u0080\u00902 data and Envisat data in repeat mode, a combination of RT and XO algorithms was applied, followed by filtering. For across\u00e2\u0080\u0090mission (i.e. ERS\u00e2\u0080\u00902\u00e2\u0080\u0090Envisat) combinations, and for Envisat operating in a drifting orbit, an AT and XO combination was applied (the difference between RT and AT algorithms is that AT use reference tracks and searches for data in the vicinity of this track). For CryoSat data a binning/gridding and plane fit method has been applied, following by weak filtering (0.05 degree resolution).", "links": [ { diff --git a/datasets/a7e11745933a4f37b5aa1d4b23d71a83_NA.json b/datasets/a7e11745933a4f37b5aa1d4b23d71a83_NA.json index 2222696825..f1fc7682f5 100644 --- a/datasets/a7e11745933a4f37b5aa1d4b23d71a83_NA.json +++ b/datasets/a7e11745933a4f37b5aa1d4b23d71a83_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a7e11745933a4f37b5aa1d4b23d71a83_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. Data are available for the period 1995-2002.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA.json b/datasets/a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA.json index 031811ad5a..b2e23511f2 100644 --- a/datasets/a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA.json +++ b/datasets/a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/a8b8191d62504acdb218d4767b446280_NA.json b/datasets/a8b8191d62504acdb218d4767b446280_NA.json index a034ac06e4..a2bd250505 100644 --- a/datasets/a8b8191d62504acdb218d4767b446280_NA.json +++ b/datasets/a8b8191d62504acdb218d4767b446280_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "a8b8191d62504acdb218d4767b446280_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the Swansea University (SU) algorithm, version 4.3. Data are available for the period 1995-2003.For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/aa09603e91b44f3cb1573c9dd415e8a8_NA.json b/datasets/aa09603e91b44f3cb1573c9dd415e8a8_NA.json index ac1b0bd123..bbe604c44c 100644 --- a/datasets/aa09603e91b44f3cb1573c9dd415e8a8_NA.json +++ b/datasets/aa09603e91b44f3cb1573c9dd415e8a8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aa09603e91b44f3cb1573c9dd415e8a8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.The Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. The data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. For further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "links": [ { diff --git a/datasets/aa8268e2ca0e48d98aee372795722253_NA.json b/datasets/aa8268e2ca0e48d98aee372795722253_NA.json index 1f8f81c9f7..bbc797745e 100644 --- a/datasets/aa8268e2ca0e48d98aee372795722253_NA.json +++ b/datasets/aa8268e2ca0e48d98aee372795722253_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aa8268e2ca0e48d98aee372795722253_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/aad_ais_gz_modis_slope_break_1.json b/datasets/aad_ais_gz_modis_slope_break_1.json index d5a79033ac..e40710724d 100644 --- a/datasets/aad_ais_gz_modis_slope_break_1.json +++ b/datasets/aad_ais_gz_modis_slope_break_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aad_ais_gz_modis_slope_break_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Grounding Zone of the Amery Ice Shelf, East Antarctica defined by break of surface slope as determined through interpretation of MODIS images. It defines the landward edge of the grounding zone and therefore the maximum extent of the ice shelf.\n\nThe MODIS data from the 250 m Channel 2 were processed to a reflectance product and remapped to a Polar Stereographic Projection. The image contrast was stretched so that subtle variations in reflectance could be perceived. The variation in reflectance was used as an indicator of variation in slope. The break of slope of the snow surface was picked interactively on an image display at a frequency sufficient to define the shape of the grounding zone margin. The series of points are provided as a Point shapefile file as well as a set of arcs connecting the points. The point positions are given in geographic coordinates.\n\nThis work was completed as part of ASAC projects 2224 and 3067 (ASAC_2224, ASAC_3067).", "links": [ { diff --git a/datasets/aad_ctd_database_1.json b/datasets/aad_ctd_database_1.json index 950ff8e2df..ace58cbdd3 100644 --- a/datasets/aad_ctd_database_1.json +++ b/datasets/aad_ctd_database_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aad_ctd_database_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microsoft Access database containing a compilation of CTD data collected in the Southern Ocean from Australian Antarctic Division (AAD), Antarctic Climate and Ecosystems Co-operative Research Centre (ACE CRC) and Hydrographic Atlas of the Southern Ocean (SOA) data sources. This SOA data contains discrete CTD (Conductivity, Temperature and Depth) station data along with a 1 x 1 degree gridded CTD data set interpolated in space and time.\n\nParameters include pressure, temperature, salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, and silicate). \n\nOcean Tools software developed by AAD is available in conjunction with this database to manipulate, extract and visualise data (including station map, transect selection, xy plots, vertical cross sections, geostrophic velocity/transport calculations).\n\nThe download file contains an access database of the compiled CTD data, a word document containing further information about the structure of the database and the data (AAD CTD Data.doc), and a folder of the original source data, including readmes providing reference details, and specific information.", "links": [ { diff --git a/datasets/aae157df-5b91-4a49-b00b-d81729a566d7_NA.json b/datasets/aae157df-5b91-4a49-b00b-d81729a566d7_NA.json index 198c0f2a98..7cd205a17f 100644 --- a/datasets/aae157df-5b91-4a49-b00b-d81729a566d7_NA.json +++ b/datasets/aae157df-5b91-4a49-b00b-d81729a566d7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aae157df-5b91-4a49-b00b-d81729a566d7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in High Resolution Spotlight mode. High Resolution Spotlight imaging allows for a spatial resolution of up to 1 m at a scene size of 10 km (across swath) x 5 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to:\t https://www.dlr.de/content/de/missionen/terrasar-x.html", "links": [ { diff --git a/datasets/aae643e1a7614c24b6b604dea82cad93_NA.json b/datasets/aae643e1a7614c24b6b604dea82cad93_NA.json index 492596875a..750dd3af8f 100644 --- a/datasets/aae643e1a7614c24b6b604dea82cad93_NA.json +++ b/datasets/aae643e1a7614c24b6b604dea82cad93_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aae643e1a7614c24b6b604dea82cad93_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "links": [ { diff --git a/datasets/aamhcpex_1.json b/datasets/aamhcpex_1.json index a5000a802a..5761b54d8d 100644 --- a/datasets/aamhcpex_1.json +++ b/datasets/aamhcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aamhcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format.", "links": [ { diff --git a/datasets/ab90030e26c54ba495b1cbec51e137e1_NA.json b/datasets/ab90030e26c54ba495b1cbec51e137e1_NA.json index 5a73e2b3ba..6acda8ab61 100644 --- a/datasets/ab90030e26c54ba495b1cbec51e137e1_NA.json +++ b/datasets/ab90030e26c54ba495b1cbec51e137e1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ab90030e26c54ba495b1cbec51e137e1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31. Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/above-and-below-ground-herbivore-communities-along-elevation_1.0.json b/datasets/above-and-below-ground-herbivore-communities-along-elevation_1.0.json index 38abe9915c..69f7cdb101 100644 --- a/datasets/above-and-below-ground-herbivore-communities-along-elevation_1.0.json +++ b/datasets/above-and-below-ground-herbivore-communities-along-elevation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "above-and-below-ground-herbivore-communities-along-elevation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.", "links": [ { diff --git a/datasets/accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0.json b/datasets/accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0.json index 6d7ad76e0b..8d3f257b78 100644 --- a/datasets/accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0.json +++ b/datasets/accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport. A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the \"helicopter\" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based. Road transport was investigated using network analysis, based on the data set \"Forest access roads 2013\" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account. Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the \"helicopter\" category or transport with trucks under 26 tons). The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php) Input data used: - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)", "links": [ { diff --git a/datasets/accum-measurements-domec-traverse-1982_1.json b/datasets/accum-measurements-domec-traverse-1982_1.json index fa21befef2..8fdf070a40 100644 --- a/datasets/accum-measurements-domec-traverse-1982_1.json +++ b/datasets/accum-measurements-domec-traverse-1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "accum-measurements-domec-traverse-1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/accumulation-movement-markers-mirny-domec_1.json b/datasets/accumulation-movement-markers-mirny-domec_1.json index efff6acafb..22471ca140 100644 --- a/datasets/accumulation-movement-markers-mirny-domec_1.json +++ b/datasets/accumulation-movement-markers-mirny-domec_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "accumulation-movement-markers-mirny-domec_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detailed notes about each of the markers used for movement (and accumulation) measurements along the Mirny-Dome C traverse line. Includes processing notes from the JMR position analysis.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/accumulation_lawdome_1960_1.json b/datasets/accumulation_lawdome_1960_1.json index effa17d7ec..d8361eb36b 100644 --- a/datasets/accumulation_lawdome_1960_1.json +++ b/datasets/accumulation_lawdome_1960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "accumulation_lawdome_1960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960.\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/aces1am_1.json b/datasets/aces1am_1.json index 55df8a8057..ca2c263082 100644 --- a/datasets/aces1am_1.json +++ b/datasets/aces1am_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1am_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). ", "links": [ { diff --git a/datasets/aces1cont_1.json b/datasets/aces1cont_1.json index 861ff23405..7b4992e786 100644 --- a/datasets/aces1cont_1.json +++ b/datasets/aces1cont_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1cont_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.", "links": [ { diff --git a/datasets/aces1efm_1.json b/datasets/aces1efm_1.json index 18f8e496f7..bf7db7009c 100644 --- a/datasets/aces1efm_1.json +++ b/datasets/aces1efm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1efm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments.", "links": [ { diff --git a/datasets/aces1log_1.json b/datasets/aces1log_1.json index cfb980cf06..249c885744 100644 --- a/datasets/aces1log_1.json +++ b/datasets/aces1log_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1log_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight.", "links": [ { diff --git a/datasets/aces1time_1.json b/datasets/aces1time_1.json index 34c180a7fc..39193412e6 100644 --- a/datasets/aces1time_1.json +++ b/datasets/aces1time_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1time_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform.", "links": [ { diff --git a/datasets/aces1trig_1.json b/datasets/aces1trig_1.json index 28726cbf28..b495826070 100644 --- a/datasets/aces1trig_1.json +++ b/datasets/aces1trig_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aces1trig_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued.", "links": [ { diff --git a/datasets/acoustic_charts_v6_1994_95_1.json b/datasets/acoustic_charts_v6_1994_95_1.json index ae6a77a71b..7e67dd79e9 100644 --- a/datasets/acoustic_charts_v6_1994_95_1.json +++ b/datasets/acoustic_charts_v6_1994_95_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "acoustic_charts_v6_1994_95_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. \nBANGSS is an acronym for Big ANtarctic Geological and Seismic Survey.\nThe voyage began on 6 February 1995 and finished on 12 April 1995.\n\nEach chart is labelled with information about when and where the data was collected: date, time, latitude and longitude.\n\nThe charts provide a profile of the sea floor and have a time axis with numbers in the following format.\nthe first two digits are the day\nthe next two digits are the month\nthe next five digits are the time (UTC)\nthe last ten digits are the maximum value on the depth axis \neg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres \n \nSee a Related URL for a link to information about the voyage including the voyage report.", "links": [ { diff --git a/datasets/acoustic_doppler_current_profiler_data_-_2010.json b/datasets/acoustic_doppler_current_profiler_data_-_2010.json index f0454e520e..1b15b995e7 100644 --- a/datasets/acoustic_doppler_current_profiler_data_-_2010.json +++ b/datasets/acoustic_doppler_current_profiler_data_-_2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "acoustic_doppler_current_profiler_data_-_2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable \"readme\" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

\"Bad\" data are marked with the flag value 999.99.", "links": [ { diff --git a/datasets/acoustic_doppler_current_profiler_data_-_2011.json b/datasets/acoustic_doppler_current_profiler_data_-_2011.json index f28e4fb636..7c5a3b6352 100644 --- a/datasets/acoustic_doppler_current_profiler_data_-_2011.json +++ b/datasets/acoustic_doppler_current_profiler_data_-_2011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "acoustic_doppler_current_profiler_data_-_2011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable \"readme\" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

\"Bad\" data are marked with the flag value 999.99.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_atqasuk_alaska_2010.json b/datasets/active_layer_arcss_grid_atqasuk_alaska_2010.json index e4b3bf6091..c6d9dbb7bc 100644 --- a/datasets/active_layer_arcss_grid_atqasuk_alaska_2010.json +++ b/datasets/active_layer_arcss_grid_atqasuk_alaska_2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_atqasuk_alaska_2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_atqasuk_alaska_2011.json b/datasets/active_layer_arcss_grid_atqasuk_alaska_2011.json index b149062dcb..16c87e51c1 100644 --- a/datasets/active_layer_arcss_grid_atqasuk_alaska_2011.json +++ b/datasets/active_layer_arcss_grid_atqasuk_alaska_2011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_atqasuk_alaska_2011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_atqasuk_alaska_2012.json b/datasets/active_layer_arcss_grid_atqasuk_alaska_2012.json index 79bec01bb5..674533aced 100644 --- a/datasets/active_layer_arcss_grid_atqasuk_alaska_2012.json +++ b/datasets/active_layer_arcss_grid_atqasuk_alaska_2012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_atqasuk_alaska_2012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_barrow_alaska_2010.json b/datasets/active_layer_arcss_grid_barrow_alaska_2010.json index 9e2048ad4c..435d75d015 100644 --- a/datasets/active_layer_arcss_grid_barrow_alaska_2010.json +++ b/datasets/active_layer_arcss_grid_barrow_alaska_2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_barrow_alaska_2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_barrow_alaska_2011.json b/datasets/active_layer_arcss_grid_barrow_alaska_2011.json index d6abd9549a..4b72650c4f 100644 --- a/datasets/active_layer_arcss_grid_barrow_alaska_2011.json +++ b/datasets/active_layer_arcss_grid_barrow_alaska_2011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_barrow_alaska_2011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass.", "links": [ { diff --git a/datasets/active_layer_arcss_grid_barrow_alaska_2012.json b/datasets/active_layer_arcss_grid_barrow_alaska_2012.json index 7127de2d61..1b0e01b7fe 100644 --- a/datasets/active_layer_arcss_grid_barrow_alaska_2012.json +++ b/datasets/active_layer_arcss_grid_barrow_alaska_2012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_arcss_grid_barrow_alaska_2012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass.", "links": [ { diff --git a/datasets/active_layer_nims_grid_atqasuk_alaska_2011.json b/datasets/active_layer_nims_grid_atqasuk_alaska_2011.json index 1bad109cac..2ad9ebbe8f 100644 --- a/datasets/active_layer_nims_grid_atqasuk_alaska_2011.json +++ b/datasets/active_layer_nims_grid_atqasuk_alaska_2011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_nims_grid_atqasuk_alaska_2011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SEL\u0092s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass.\n", "links": [ { diff --git a/datasets/active_layer_nims_grid_atqasuk_alaska_2012.json b/datasets/active_layer_nims_grid_atqasuk_alaska_2012.json index 319138ecdc..aa613d3be1 100644 --- a/datasets/active_layer_nims_grid_atqasuk_alaska_2012.json +++ b/datasets/active_layer_nims_grid_atqasuk_alaska_2012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_nims_grid_atqasuk_alaska_2012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass.", "links": [ { diff --git a/datasets/active_layer_nims_grid_barrow_alaska_2011.json b/datasets/active_layer_nims_grid_barrow_alaska_2011.json index 51d141a583..bbf56ad106 100644 --- a/datasets/active_layer_nims_grid_barrow_alaska_2011.json +++ b/datasets/active_layer_nims_grid_barrow_alaska_2011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_nims_grid_barrow_alaska_2011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass.", "links": [ { diff --git a/datasets/active_layer_nims_grid_barrow_alaska_2012.json b/datasets/active_layer_nims_grid_barrow_alaska_2012.json index a4c0a802e1..0570a063da 100644 --- a/datasets/active_layer_nims_grid_barrow_alaska_2012.json +++ b/datasets/active_layer_nims_grid_barrow_alaska_2012.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "active_layer_nims_grid_barrow_alaska_2012", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL\u0092s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass.", "links": [ { diff --git a/datasets/ada968fd392d49fbbb07ac84eeb23ac6_NA.json b/datasets/ada968fd392d49fbbb07ac84eeb23ac6_NA.json index 6ab7032d3f..1e8626a649 100644 --- a/datasets/ada968fd392d49fbbb07ac84eeb23ac6_NA.json +++ b/datasets/ada968fd392d49fbbb07ac84eeb23ac6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ada968fd392d49fbbb07ac84eeb23ac6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway.", "links": [ { diff --git a/datasets/adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table.json b/datasets/adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table.json index 300250a72d..1358325215 100644 --- a/datasets/adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table.json +++ b/datasets/adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover.", "links": [ { diff --git a/datasets/adcp_2.json b/datasets/adcp_2.json index c5bf5940ae..df9cdd8f49 100644 --- a/datasets/adcp_2.json +++ b/datasets/adcp_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adcp_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic Doppler current profiler (ADCP) measurements from a hull mounted 150 kHz narrow band ADCP unit were collected in the Southern Ocean from 1994 to 1999, on the following cruises: au9404, au9501, au9604, au9601, au9701, au9706, au9807 and au9901.\n\nThe fields in this dataset are:\nCurrents\nbottom depth\ncruise number\nship speed\ntime\nvelocity\nGPS", "links": [ { diff --git a/datasets/add104f4c4454b629dbc7648efaa1b50_NA.json b/datasets/add104f4c4454b629dbc7648efaa1b50_NA.json index f524d6d329..3d6f47fd3e 100644 --- a/datasets/add104f4c4454b629dbc7648efaa1b50_NA.json +++ b/datasets/add104f4c4454b629dbc7648efaa1b50_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "add104f4c4454b629dbc7648efaa1b50_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR (544.6 GHz) instrument. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-MZM-SMR_ODIN-544_6_GHz-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for ODIN/SMR at 544.6GHz in 2008.", "links": [ { diff --git a/datasets/adpe-aat-census_1.json b/datasets/adpe-aat-census_1.json index ac777bdbb5..d0f1e852a7 100644 --- a/datasets/adpe-aat-census_1.json +++ b/datasets/adpe-aat-census_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adpe-aat-census_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E).\n\nThe census records have been collected and compiled from a literature search.", "links": [ { diff --git a/datasets/adu_birp.json b/datasets/adu_birp.json index 494b5d810f..8920a8e5f7 100644 --- a/datasets/adu_birp.json +++ b/datasets/adu_birp.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adu_birp", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BIRP is a joint project of BirdLife South Africa (BLSA), and the Animal Demography Unit (ADU), based at the University of Cape Town (UCT). The basic purpose of BIRP is to compile a comprehensive catalogue of the species of birds which occur and breed in South Africa\u2019s many protected areas. A database of this kind will help to identify the species which are as yet not adequately protected and will also provide the managers of protected areas with information useful in setting management policies.", "links": [ { diff --git a/datasets/adu_cwac.json b/datasets/adu_cwac.json index a97937d5ca..cc7060517e 100644 --- a/datasets/adu_cwac.json +++ b/datasets/adu_cwac.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adu_cwac", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Coordinated Waterbird Counts (CWAC) project was launched in 1992. The objective of CWAC is to monitor South Africa's waterbird populations and the conditions of the wetlands which are important for waterbirds. This is being done by means of a programme of regular mid-summer and mid-winter censuses at a large number of South African wetlands. Regular six-monthly counts are conducted; however, we do encourage counters to survey their wetlands on a more regular basis as this provides better data. CWAC currently monitors over 400 wetlands around the country on a regular basis, and furthermore curates waterbird data for close to 600 wetlands.", "links": [ { diff --git a/datasets/adu_safring.json b/datasets/adu_safring.json index 9c03907eb5..fde7ffd5fd 100644 --- a/datasets/adu_safring.json +++ b/datasets/adu_safring.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "adu_safring", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). \n\nThe Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species.", "links": [ { diff --git a/datasets/aerial_casa_2010_11_1.json b/datasets/aerial_casa_2010_11_1.json index 3e9fab78e4..bc1c9af13e 100644 --- a/datasets/aerial_casa_2010_11_1.json +++ b/datasets/aerial_casa_2010_11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_casa_2010_11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. \n\nPhotographs were taken for various projects or needs:\nWhales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). \n\nThe photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS.\n\nVertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit.\n\nThe set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre.\n\nData extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude.\n\nThe images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011).", "links": [ { diff --git a/datasets/aerial_mosaics_macquarie_2017_2.json b/datasets/aerial_mosaics_macquarie_2017_2.json index a00ee78a11..d29526f889 100644 --- a/datasets/aerial_mosaics_macquarie_2017_2.json +++ b/datasets/aerial_mosaics_macquarie_2017_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_mosaics_macquarie_2017_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. \nThe photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees.\nHugin software was used to create the mosaics.\nThe photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west).\nThe vertical mosaic was produced at the request of the Building Services Supervisor at the station.", "links": [ { diff --git a/datasets/aerial_photo_sea_ice_1.json b/datasets/aerial_photo_sea_ice_1.json index 91dd05852e..08e223a7dc 100644 --- a/datasets/aerial_photo_sea_ice_1.json +++ b/datasets/aerial_photo_sea_ice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photo_sea_ice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages:\nAustralian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX).\n\nVoyage dates:\nARISE: 10 Sep 2003 to 31 Oct 2003\nISPOL: 6 Nov 2004 to 19 Jan 2005\nSIPEX: 29 Aug 2007 to 16 Oct 2007 \nSIPEX II: 25 Sep 2012 to 6 Nov 2012\n\nThe child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs.\n", "links": [ { diff --git a/datasets/aerial_photo_sea_ice_ARISE_1.json b/datasets/aerial_photo_sea_ice_ARISE_1.json index b45ea0abaf..1ebfac60b9 100644 --- a/datasets/aerial_photo_sea_ice_ARISE_1.json +++ b/datasets/aerial_photo_sea_ice_ARISE_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photo_sea_ice_ARISE_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003.\n\nThe Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs.\n\nThe ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis.\n\nThe collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. \n\nSelect ARISE from the Aerial Photography Series picklist.\n\nPreview images of the photos may be viewed using this search.\n\nDigital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue.\n\nThe flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles.\n", "links": [ { diff --git a/datasets/aerial_photo_sea_ice_ISPOL_1.json b/datasets/aerial_photo_sea_ice_ISPOL_1.json index 7fb03b6f07..3424f23cc5 100644 --- a/datasets/aerial_photo_sea_ice_ISPOL_1.json +++ b/datasets/aerial_photo_sea_ice_ISPOL_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photo_sea_ice_ISPOL_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the voyage, Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05, 6 Nov 2004 to 19 Jan 2005.\n \nFlights were conducted around the edges of a triangular array of drifting buoys each transmitting GPS location. Flights throughout the experiment show changes in the surface properties (floe size, extent of surface flooding) with time.\n\nSee the metadata record 'AAD buoy data collected during ISPOL 2004, Western Weddell Sea' for more information on the ISPOL project.\n\nThe Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs.\n\nThe ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis.\n\nThe collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. \n\nSelect ISPOL from the Aerial Photography Series picklist.\n\nPreview images of the photos may be viewed using this search.\n\nDigital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue.\n\nThe flight lines and photo centres for ISPOL are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles.\n", "links": [ { diff --git a/datasets/aerial_photo_sea_ice_SIPEX_1.json b/datasets/aerial_photo_sea_ice_SIPEX_1.json index c5a60a2f70..cb44d6956d 100644 --- a/datasets/aerial_photo_sea_ice_SIPEX_1.json +++ b/datasets/aerial_photo_sea_ice_SIPEX_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photo_sea_ice_SIPEX_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX).\n\nVoyage dates:\nSIPEX: 29 Aug 2007 to 16 Oct 2007 \n\nThe Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs.\n\nSome of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations.\n\nThe metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012.", "links": [ { diff --git a/datasets/aerial_photo_sea_ice_shapefiles_1.json b/datasets/aerial_photo_sea_ice_shapefiles_1.json index 13158c35df..37f467d222 100644 --- a/datasets/aerial_photo_sea_ice_shapefiles_1.json +++ b/datasets/aerial_photo_sea_ice_shapefiles_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photo_sea_ice_shapefiles_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages:\nAustralian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05.\n\nVoyage dates:\nARISE: 10 Sep 2003 to 31 Oct 2003\nISPOL: 6 Nov 2004 to 19 Jan 2005 \n\nThe ARISE and ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis.\n\nDigital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue.\n", "links": [ { diff --git a/datasets/aerial_photographs_from_columbia_glacier_1976-2010.json b/datasets/aerial_photographs_from_columbia_glacier_1976-2010.json index db622f1629..b12718bec3 100644 --- a/datasets/aerial_photographs_from_columbia_glacier_1976-2010.json +++ b/datasets/aerial_photographs_from_columbia_glacier_1976-2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_photographs_from_columbia_glacier_1976-2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

", "links": [ { diff --git a/datasets/aerial_rpa_nov2016_1.json b/datasets/aerial_rpa_nov2016_1.json index 56a4d409eb..f44154ec15 100644 --- a/datasets/aerial_rpa_nov2016_1.json +++ b/datasets/aerial_rpa_nov2016_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_rpa_nov2016_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. \n\nThe following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): \n1 A report prepared by Doug Thost, the chief RPA pilot; \n2 The aerial photographs of Davis and Heidemann Valley; and \n3 Some panoramas created from aerial photographs taken at Davis. \n\nThe AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. \n\nSee the report for further details.", "links": [ { diff --git a/datasets/aerial_surveys_vestfold_2017-18_1.json b/datasets/aerial_surveys_vestfold_2017-18_1.json index 533d8a2559..5016b522e3 100644 --- a/datasets/aerial_surveys_vestfold_2017-18_1.json +++ b/datasets/aerial_surveys_vestfold_2017-18_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerial_surveys_vestfold_2017-18_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station.\nThe first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level.\nThe second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level.\nThe third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states \"As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality\nsurvey overall.\"\n\nThe spatial extents given in this metadata record are for the second survey.\n\nFor each survey there is zip file with a report and the following products generated from the survey data:\n(i) an orthophoto;\n(ii) a Digital Surface Model (DSM); and \n(iii) contours generated from the DSM.\nThe products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. \nThere is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre.\n\nGround control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox.\nSome permanent survey marks were used as an independent check of the overall accuracy of the DSM.", "links": [ { diff --git a/datasets/aerosol-data-davos-wolfgang_1.0.json b/datasets/aerosol-data-davos-wolfgang_1.0.json index fba05c58b5..b83c4ec6be 100644 --- a/datasets/aerosol-data-davos-wolfgang_1.0.json +++ b/datasets/aerosol-data-davos-wolfgang_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerosol-data-davos-wolfgang_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.", "links": [ { diff --git a/datasets/aerosol-data-weissfluhjoch_1.0.json b/datasets/aerosol-data-weissfluhjoch_1.0.json index b97cd2511e..04c46c1889 100644 --- a/datasets/aerosol-data-weissfluhjoch_1.0.json +++ b/datasets/aerosol-data-weissfluhjoch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerosol-data-weissfluhjoch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.", "links": [ { diff --git a/datasets/aerosol_properties_725_1.json b/datasets/aerosol_properties_725_1.json index 58a56d942a..7d438f1480 100644 --- a/datasets/aerosol_properties_725_1.json +++ b/datasets/aerosol_properties_725_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aerosol_properties_725_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAFARI 2000 provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003).", "links": [ { diff --git a/datasets/aes5davg_236_1.json b/datasets/aes5davg_236_1.json index 6fa89d9e0f..3ef6d48c14 100644 --- a/datasets/aes5davg_236_1.json +++ b/datasets/aes5davg_236_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aes5davg_236_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba.", "links": [ { diff --git a/datasets/aes_upl1_238_1.json b/datasets/aes_upl1_238_1.json index 3d2857e5f6..2e62538cfd 100644 --- a/datasets/aes_upl1_238_1.json +++ b/datasets/aes_upl1_238_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aes_upl1_238_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.", "links": [ { diff --git a/datasets/aes_upl2_239_1.json b/datasets/aes_upl2_239_1.json index 751f280e96..aca490fff0 100644 --- a/datasets/aes_upl2_239_1.json +++ b/datasets/aes_upl2_239_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aes_upl2_239_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.", "links": [ { diff --git a/datasets/af60720c1e404a9e9d2c145d2b2ead4e_NA.json b/datasets/af60720c1e404a9e9d2c145d2b2ead4e_NA.json index 0b8df58b25..05e1379c0d 100644 --- a/datasets/af60720c1e404a9e9d2c145d2b2ead4e_NA.json +++ b/datasets/af60720c1e404a9e9d2c145d2b2ead4e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "af60720c1e404a9e9d2c145d2b2ead4e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR instrument and JAXA\u00e2\u0080\u0099s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", "links": [ { diff --git a/datasets/afforestation-stillberg_1.0.json b/datasets/afforestation-stillberg_1.0.json index 4fb7fd04d8..bcc3845efd 100644 --- a/datasets/afforestation-stillberg_1.0.json +++ b/datasets/afforestation-stillberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afforestation-stillberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Long-term monitoring of the large-scale high-elevation afforestation has generated data about tree growth, survival, and vitality. In addition, detailed characteristics of the microsite conditions of the research were conducted. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone. # Experiment description The Stillberg afforestation experiment was established in 1975 by planting 92,000 seedlings of *Larix decidua*, *Pinus cembra* and *Pinus mugo* ssp. *uncinata* in the alpine treeline ecotone. The afforestation site is located on a northeast-facing slope with steep, topographically highly structured terrain and covers elevations from 2075 to 2230 m a.s.l. The afforestation site was divided into 4052 square plots of 3.5 \u00d7 3.5 m, arranged in a regular species-alternating pattern over the whole area. Each plot contained 25 trees of one species (1350 plots per species), and the seedlings were systematically planted 70 cm apart. The trees have been monitored since 1975. Specifically, tree mortality was assessed annually from 1975 until 1995 and has been documented every ten years since then, with surveys in 2005 and 2015 (the next survey is due in 2025). Height of the surviving trees was measured in 1975, 1979, 1982, 1985, 1990, 1995, 2005, and 2015. In 1995, 2005, and 2015, drivers of tree vitality were assessed for a subset of trees per plot. Additionally, an extensive set of environmental parameters characterizing microsite conditions of the afforestation area were recorded before and after the planting of the trees. # Data description The five datasets from the afforestation experiment comprise ecological and environmental data from the main afforestation experiment in five datasets with accompanying metadata (Stillberg_afforestation_all_metadata.xlsx). All data and metadata files are bundled in a ZIP-file (Stillberg_afforestation_v1.zip). In particular, a first dataset contains environmental data characterising microsite conditions of the 4000 plots with regard to soil, topography, vegetation and microclimatic conditions (Stillberg_afforestation_plot_data_v1.csv; Stillberg_afforestation_plot_metadata_v1.csv. In each plot, the natural tree regeneration was assessed by counting seedings of several tree species in 2005 and 2015 (Stillberg_afforestation_regeneration_data_v1.csv; Stillberg_afforestation_regeneration_metadata_v1.csv). Furthermore, specific information about each of the 92\u2019000 planted trees of the tree species is available (Stillberg_afforestation_tree_parameter_data_v1.csv; Stillberg_afforestation_tree_parameter_metadata_v1.csv). Survival data for each of the 92\u2019000 individual trees can be found in a separate dataset (Stillberg_afforestation_tree_survival_data_v1.csv; Stillberg_afforestation_tree_survival_metadata_v1.csv). Tree growth and vitality parameters are available for all trees from 1995, and for subsets of trees for 2005 and 2015 (Stillberg_afforestation_tree_measurements_data_v1.csv; Stillberg_afforestation_tree_measurements_metadata_v1.csv).", "links": [ { diff --git a/datasets/afm06ihd_240_1.json b/datasets/afm06ihd_240_1.json index 1610a38f2b..f645127487 100644 --- a/datasets/afm06ihd_240_1.json +++ b/datasets/afm06ihd_240_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm06ihd_240_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains AFM-06 hourly inversion height measurements.", "links": [ { diff --git a/datasets/afm06ptd_241_1.json b/datasets/afm06ptd_241_1.json index 6dbfcfa888..ce9f3da0fc 100644 --- a/datasets/afm06ptd_241_1.json +++ b/datasets/afm06ptd_241_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm06ptd_241_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area.", "links": [ { diff --git a/datasets/afm06pwd_242_1.json b/datasets/afm06pwd_242_1.json index 55e0cd41d9..1db1882c0e 100644 --- a/datasets/afm06pwd_242_1.json +++ b/datasets/afm06pwd_242_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm06pwd_242_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the AFM-06 wind profiler data near the Old Jack Pine site in the Southern Study Area.", "links": [ { diff --git a/datasets/afm06smd_243_1.json b/datasets/afm06smd_243_1.json index 89a0574440..c896e15bbd 100644 --- a/datasets/afm06smd_243_1.json +++ b/datasets/afm06smd_243_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm06smd_243_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the AFM-06 surface meteorological data near the Old Jack Pine site in the Southern Study Area.", "links": [ { diff --git a/datasets/afm11afr_244_1.json b/datasets/afm11afr_244_1.json index a8046ba869..311851474a 100644 --- a/datasets/afm11afr_244_1.json +++ b/datasets/afm11afr_244_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm11afr_244_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reports from the BOREAS AFM-11 team regarding quality control and sampling analysis of data collected by other AFM personnel using the Electra, LongEZ, and Twin Otter aircraft. These reports are stored in Adobe Acrobat (.PDF) format.", "links": [ { diff --git a/datasets/afm13afr_245_1.json b/datasets/afm13afr_245_1.json index 1e697ea049..c7106e07f3 100644 --- a/datasets/afm13afr_245_1.json +++ b/datasets/afm13afr_245_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm13afr_245_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not technically a data set, this is a report of the cross-comparison of data collected by various flux aircraft. Reports of these analyses are available as TIF and ASCII files. ", "links": [ { diff --git a/datasets/afm2as94_494_1.json b/datasets/afm2as94_494_1.json index 31ecf3aa4b..9e04ec9e43 100644 --- a/datasets/afm2as94_494_1.json +++ b/datasets/afm2as94_494_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm2as94_494_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Parameters include wind direction, wind speed, west wind component (u), south wind component (v), static pressure, air dry bulb temperature, potential temperature, dewpoint, temperature, water vapor mixing ratio, and CO2 concentration.", "links": [ { diff --git a/datasets/afm3as94_496_1.json b/datasets/afm3as94_496_1.json index 0e8e65a2ab..9d330e5818 100644 --- a/datasets/afm3as94_496_1.json +++ b/datasets/afm3as94_496_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm3as94_496_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration over the NSA, SSA, and the transect during BOREAS IFCs 1, 2, and 3 during 1994.", "links": [ { diff --git a/datasets/afm3mw94_495_1.json b/datasets/afm3mw94_495_1.json index 79cd8f3e70..cad71478e2 100644 --- a/datasets/afm3mw94_495_1.json +++ b/datasets/afm3mw94_495_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm3mw94_495_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA.", "links": [ { diff --git a/datasets/afm4toas_498_1.json b/datasets/afm4toas_498_1.json index 7014315d9d..cd7c6ae680 100644 --- a/datasets/afm4toas_498_1.json +++ b/datasets/afm4toas_498_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm4toas_498_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements include concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction at both the NSA and the SSA in 1994 and 1996.", "links": [ { diff --git a/datasets/afm4tofx_497_1.json b/datasets/afm4tofx_497_1.json index accb076e03..15c4e04233 100644 --- a/datasets/afm4tofx_497_1.json +++ b/datasets/afm4tofx_497_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm4tofx_497_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction.", "links": [ { diff --git a/datasets/afm6gifs_433_1.json b/datasets/afm6gifs_433_1.json index 9108571df0..dc79d680a0 100644 --- a/datasets/afm6gifs_433_1.json +++ b/datasets/afm6gifs_433_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "afm6gifs_433_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994.", "links": [ { diff --git a/datasets/african_woody_savanna_850_1.json b/datasets/african_woody_savanna_850_1.json index 57314515f6..ac66a77132 100644 --- a/datasets/african_woody_savanna_850_1.json +++ b/datasets/african_woody_savanna_850_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "african_woody_savanna_850_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf).", "links": [ { diff --git a/datasets/agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0.json b/datasets/agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0.json index 93e4337544..f323b82b0c 100644 --- a/datasets/agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0.json +++ b/datasets/agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Supplementary material for the publication \" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis\" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions.", "links": [ { diff --git a/datasets/air_methane_lawdome_1.json b/datasets/air_methane_lawdome_1.json index 17647d22f5..8e67e93b90 100644 --- a/datasets/air_methane_lawdome_1.json +++ b/datasets/air_methane_lawdome_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "air_methane_lawdome_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This work was completed as part of ASAC project 757 (ASAC_757).\n \nThis file comprises three main records compiled for publication in the following: \n\nV. Morgan, M. Delmotte, T. van Ommen, J. Jouzel, J. Chappellaz, S. Woon, V. Masson-Delmotte and D. Raynaud. Relative Timing of Deglacial Climate Events in Antarctica and Greenland, Science, 13 September 2002, Vol 297 (5588), pp. 1862-1864, DOI: 10.1126/science.1074257.\nSupporting Material - http://www.sciencemag.org/cgi/content/full/sci;297/5588/1862/DC1\n\nLaw Dome is a small (200 km in diameter) ice sheet located at the edge of the Indian Ocean sector of East Antarctica. The core site, near the summit of Law Dome (66 degrees 46'S, 112 degrees 48'E), is characterised by a high rate of accumulation (late Holocene average, 0.68 m ice equivalent per year) that results in an ice core with a highly tapered time scale in which the Holocene represents some 93% of the ice thickness of 1200 m. However, the full Law Dome isotopic record generally matches the long records from Vostok and Byrd to at least 80 ka, indicating that the record is continuous and undisturbed over this period. The Law Dome record is suited to gas-synchronisation studies because the high accumulation rate and consequent rapid burial give a small age difference (Delta age) between trapped air and the older enclosing ice.\n\nDerivation of an age scale for the Law Dome core, is based upon a Dansgaard- Johnsen flow model (S1) matched to the observed layer thinning (S2). Continuously sampled seasonal cycles down to ~1/3 ice-thickness (~1ky) and spot measurements of seasonal layers to ~85% ice-thickness (~4 ky) constrain the ice-flow model through this period in which mean accumulation is assumed to be free of large trends. Chronological control in the lower portion of the ice-sheet prior to 4 ky is through ties to other records. For the period of discussion, namely 10 ky to 17 ky, ties at 9.6 ky, 11.0 ky, 11.6 ky, 12.5 ky, 12.8 ky, 14.3 ky and 16.3 ky, are obtained by matching air composition changes with those of GRIP. The 9.6 ky tie is obtained by matching to d18O of air in GRIP (S3) and GISP2 (S4) data, and the remainder synchronise with the Byrd and GRIP CH4 records (S5). Dust concentration data also provide additional constraints on the 16.3 ky tie. Beyond 16.3 ky control is by a tie at 32 ky (based on both dust and d18Oice matched to the Byrd ice core (S6) on the GRIP timescale (S5)). The mean temporal resolution of the LD isotope data is ~24y through this period.\n\nThe air-composition age-ties require Delta age computations for sequencing events within the LD record and for synchronisation of the chronology with GRIP. The high accumulation at DSS results in a particularly small Delta age value. The modern difference between ice-age and gas-age is 60 plus or minus 2 years for methane (S7). Note that at such low Delta age values, the diffusive mixing time from the free atmosphere down to seal-off depth becomes significant and must be accounted for; in the case of CH4 this is ~8 years (S7).\n\nThe absolute chronology derived for the LD record has contributions from both the LD and GRIP Delta age errors, but the relative timing between the LD CH4 and water isotope (d18Oice) signals is only uncertain to within the small errors associated with LD Delta age.\n\nWhile the present-day trapping age at LD is small, lower temperatures and accumulation rates during the deglaciation lead to longer trapping times. To estimate Delta age under past conditions, we use a model (S8) to compute trapping age from accumulation and temperature (this model agrees with precise experimentally determined present day values). Since we have no direct indicators for palaeoaccumulation and palaeotemperature, we adopt two scenarios that use alternative estimation methods.\n\nEstimation of palaeotemperature from the isotope data in both scenarios is by application of a calibration slope, \"Beta ppt/degrees C\". For the young chronology, which has minimum Delta age, the commonly applied spatial slope of Beta=0.67 ppt/degrees C is used, giving relatively warm temperatures. The default chronology uses a long-term temporal calibration (S9) for Law Dome, Beta=0.44 ppt/degrees C. This estimate, which is seasonally derived, gives greater temperature sensitivity for isotopic changes than the spatial slope. The use of this lower value for Beta is supported by direct comparisons between annual averages in d18O and temperature at the site and elsewhere on Law Dome. Over several years to a few decades, these yield coefficients of typically ~0.33 ppt/degrees C. We adopt the value 0.44 ppt/degrees C as a conservative choice, based on a longer-term calibration and because the incorporation of seasonal sea-ice variations may better capture glacial-to- Holocene environmental shifts.\n\nEstimation of palaeoaccumulation for the young chronology is via the commonly applied method (see, e.g. S5) that scales modern accumulation-rate using the derivative of saturation vapour-pressure versus temperature relationship (also using Beta=0.67 ppt/degrees C). This method explicitly assumes no non-thermodynamic changes to moisture transport during climate variations (such as, e.g., atmospheric circulation changes) that may be important at this near-coastal location. Our alternative palaeoaccumulation estimate used for the default chronology assumes that the flow-model is correct and infers accumulation from the known age-intervals between the gas ties. This leads to considerably larger changes in accumulation which may nonetheless be understandable given the distinctively high Holocene precipitation regime that prevails at Law Dome. In addition, dust concentration data show a larger LGM to Holocene decrease at LD than Vostok. If relative flux changes at the two sites are similar, then the exaggerated dilution at LD is consistent with a large interglacial accumulation shift.\n\nTrapped gas measurements were made in France: CH4 measurements at LGGE, Grenoble and d18Oair measurements at LSCE, Saclay. Both analyses were conducted using a wet extraction procedure to release the air of the ice and followed by an injection into a gas chromatograph (CH4 measurement) or by a mass spectrometer isotopic analysis (d18Oair measurements). Both analyses were conducted using established procedures (S10,S11). The methane analytical uncertainty is plus or minus 20 ppbv with values were obtained on a single measurement (in which the sample was exhausted) and are presented on the LGGE scale which differs slightly from the NOAA scale but is well calibrated against it: LGGE = 1.02*NOAA (S12). The d18Oair values arise from means of duplicate measurements (except for one point with an obvious experimental problem, 1127.492 m depth). The analytical precision for d18Oair is around 0.05 ppt with a mean reproducibility of about 0.1 ppt.\n\nd18Oice measurements were made in Hobart and have an analytical precision of approximately 0.1 ppt. The results are expressed using the conventional reference of VSMOW (Vienna Standard Mean Ocean Water).\n\nSupporting References and Notes\nS1. W. Dansgaard, S. J. Johnsen, J. Glaciol., 8, 215 (1969).\nS2. V. Morgan et al., J. Glaciol., 43, 3 (1997).\nS3. M. Cross, (Compiler) Greenland summit ice cores CD-ROM. Boulder, CO: National Snow and Ice Data Center in association with the World Data Center for Paleoclimatology at NOAA-NGDC, and the Institute of Arctic and Alpine Research (1997).\nS4. M. Bender et al., Nature 372, 663-666 (1994).\nS5. T. Blunier, et al., Nature 394, 739 (1998).\nS6. S. J. Johnsen, W. Dansgaard, H. B. Clausen, C. C. Langway, Nature, 235, 429 (1972).\nS7. D. M. Etheridge et al., J. Geophys. Res., 101, 4115 (1996).\nS8. J.-M. Barnola, P. Pimienta, D. Raynaud, Y. S. Korotkevich, Tellus Ser. B, 43, 83 (1991).\nS9. T. D. van Ommen, V. Morgan, J. Geophys. Res., 102, 9351 (1997).\nS10. J. Chappellaz, et al., J. Geophys. Res., 102, 15987, (1997).\nS11. B. Malaize, Analyse isotopique de l'oxygene de l'air piege dans les glaces de l'Antarctique et du Groenland: correlation inter-hemispheriques et effet Dole, PhD thesis, University Paris 6, (1998).\nS12. T. Sowers et al, J. Geophys. Res., 102, 26527, (1997).", "links": [ { diff --git a/datasets/air_sea_gas_exchange_xdeg_1208_1.json b/datasets/air_sea_gas_exchange_xdeg_1208_1.json index d6cde0885f..0cb7ee8a05 100644 --- a/datasets/air_sea_gas_exchange_xdeg_1208_1.json +++ b/datasets/air_sea_gas_exchange_xdeg_1208_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "air_sea_gas_exchange_xdeg_1208_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period.-Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000.-Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50 degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line. ", "links": [ { diff --git a/datasets/air_temperature_observations_in_the_arctic_1979-2004.json b/datasets/air_temperature_observations_in_the_arctic_1979-2004.json index 1465a2d300..ff92da577d 100644 --- a/datasets/air_temperature_observations_in_the_arctic_1979-2004.json +++ b/datasets/air_temperature_observations_in_the_arctic_1979-2004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "air_temperature_observations_in_the_arctic_1979-2004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004.", "links": [ { diff --git a/datasets/airmoss_chamela_mexico.json b/datasets/airmoss_chamela_mexico.json index 0a17ec3d2a..e4c5386297 100644 --- a/datasets/airmoss_chamela_mexico.json +++ b/datasets/airmoss_chamela_mexico.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "airmoss_chamela_mexico", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "North American ecosystems are critical components of the global carbon cycle, exchanging large amounts of carbon dioxide and other gases with the atmosphere. Net ecosystem exchange (NEE) quantifies these carbon fluxes, but current continental-scale estimates contain high levels of uncertainty. Root-zone soil moisture (RZSM) and its spatial and temporal hetergeneity influence NEE and contribute as much as 60-80 percent to the uncertainty. Energy and CO2 Fluxes have been monitored from 1997 to 2007 using Bowen Ratio technique, and since spring of 2004 with eddy covariance.", "links": [ { diff --git a/datasets/airscm3b_448_1.json b/datasets/airscm3b_448_1.json index 85d993bde1..dfc56d7bb4 100644 --- a/datasets/airscm3b_448_1.json +++ b/datasets/airscm3b_448_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "airscm3b_448_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).", "links": [ { diff --git a/datasets/airscpex_1.json b/datasets/airscpex_1.json index 21db9e509f..f3c1c420a5 100644 --- a/datasets/airscpex_1.json +++ b/datasets/airscpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "airscpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Infrared Sounder (AIRS) CPEX dataset contains products obtained from the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions from May through June 2017. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 11, 2017 through July 16, 2017 and are available in HDF-4 format.", "links": [ { diff --git a/datasets/airssy3b_507_1.json b/datasets/airssy3b_507_1.json index 0bfc1c6995..2a4f29d11e 100644 --- a/datasets/airssy3b_507_1.json +++ b/datasets/airssy3b_507_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "airssy3b_507_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high resolution images that represent the radar backscatter of the imaged surface atdifferent frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). The level-3b AIRSAR SY data are the JPL synoptic product and contain 3 of the 12 total frequency and polarization combinations that are possible.", "links": [ { diff --git a/datasets/airsunp_61_1.json b/datasets/airsunp_61_1.json index 1745ad3628..af55965adf 100644 --- a/datasets/airsunp_61_1.json +++ b/datasets/airsunp_61_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "airsunp_61_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne sunphotometer data collected on 8/13-15/90 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance", "links": [ { diff --git a/datasets/ais_1970_log_1.json b/datasets/ais_1970_log_1.json index 4eb41559cc..d45c9fca49 100644 --- a/datasets/ais_1970_log_1.json +++ b/datasets/ais_1970_log_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ais_1970_log_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division carried out a traverse to the Amery Ice Shelf in the summer of 1970. A daily log of the activities carried out was maintained, noting what the traverse team did, and the problems they dealt with along the traverse.\n\nRecords for this work have been archived at the Australian Antarctic Division. \n\nLogbook(s): \nGlaciology Amery Ice Shelf Traverse Summer 1970 - The daily log from the traverse.", "links": [ { diff --git a/datasets/alaska_census_regional_database.json b/datasets/alaska_census_regional_database.json index 26a345bb63..9226fcfe96 100644 --- a/datasets/alaska_census_regional_database.json +++ b/datasets/alaska_census_regional_database.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alaska_census_regional_database", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1970-2000 decennial census results by 27 census areas conformed to 2000 Census geography. Dataset consists of 611 variables covering demography, employment, education, income, mobility, and housing.", "links": [ { diff --git a/datasets/alaskan_air_ground_snow_and_soil_temperatures__1998-2005.json b/datasets/alaskan_air_ground_snow_and_soil_temperatures__1998-2005.json index 2aee825dcb..04cfafce9c 100644 --- a/datasets/alaskan_air_ground_snow_and_soil_temperatures__1998-2005.json +++ b/datasets/alaskan_air_ground_snow_and_soil_temperatures__1998-2005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alaskan_air_ground_snow_and_soil_temperatures__1998-2005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains air and ground temperature measurements collected from three different regions, each with multiple sites. The regions sampled are North Slope, Council, and Ivotuk. Early measurements were taken as part of the Land-Atmosphere-Ice Interactions - Arctic Transitions in the Land-Atmosphere System (LAII-ATLAS) program. The research project was funded by the Arctic System Sciences (ARCSS) Program, grant numbers OPP-9721347, OPP-9870635, and OPP-9732126", "links": [ { diff --git a/datasets/albedo_line_snow_depths.json b/datasets/albedo_line_snow_depths.json index 5a0fee7903..c67e39fce9 100644 --- a/datasets/albedo_line_snow_depths.json +++ b/datasets/albedo_line_snow_depths.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "albedo_line_snow_depths", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed. Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2", "links": [ { diff --git a/datasets/ali_etm_tandem_821_1.json b/datasets/ali_etm_tandem_821_1.json index 06f630ceb1..bce88e52d5 100644 --- a/datasets/ali_etm_tandem_821_1.json +++ b/datasets/ali_etm_tandem_821_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ali_etm_tandem_821_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite. ALI is a technology validation testbed that employs novel wide-angle optics and a highly integrated multispectral and panchromatic spectroradiometer.The tandem pair was produced to evaluate the differences between ALI and ETM+ and determine if technology similar to that of the ALI is suitable for future land imaging that will continue the observations begun by the Landsat satellites in 1972.The ALI and ETM+ images are false color composites combining shortwave infrared, near infrared, and visible wavelengths, displayed as red, green, and blue, respectively. Dense vegetation appears green. The similarity of the images demonstrates the ability of the ALI to produce data comparable to ETM+. Several SAFARI 2000 field campaigns conducted in KNP provided ground-based data needed to evaluate measurements from the satellite sensors.Each band is stored as an individual binary file. A metadata file accompanies each set of ALI and ETM+ band files to document the path and row number, sample and line counts, band file names, and sun azimuth and elevation angles. There is also a calibration parameter file that was used for 1R processing.", "links": [ { diff --git a/datasets/allADCP_GB.json b/datasets/allADCP_GB.json index f2da2a8b3c..dcd00857f9 100644 --- a/datasets/allADCP_GB.json +++ b/datasets/allADCP_GB.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "allADCP_GB", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Acoustic Doppler Current Profiler (ADCP) observations, were\n collected from the R/V Seward Johnson on two cruises to the Georges\n Bank region, April-June 1995. Three different ADCP units were used:\n two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The\n broadband 150 kHz unit was used at anchor stations with data reported\n at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz\n units collected data in the along track mode with data reported at\n five minute intervals. For each time interval, the u and v components\n of currents are reported at uniform depth intervals throughout the\n water column.\n \n Ship cruise dates\n R/V Seward Johnson 9506 1995 04 25 1995 05 02\n R/V Seward Johnson 9508 1995 06 06 1995 06 16", "links": [ { diff --git a/datasets/alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0.json b/datasets/alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0.json index 968ddddf9e..302eb3d466 100644 --- a/datasets/alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0.json +++ b/datasets/alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flavescence dor\u00e9e (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dor\u00e9e phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees.", "links": [ { diff --git a/datasets/alos-prism-l1c_8.0.json b/datasets/alos-prism-l1c_8.0.json index 53fa83fd90..2d395ab34f 100644 --- a/datasets/alos-prism-l1c_8.0.json +++ b/datasets/alos-prism-l1c_8.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alos-prism-l1c_8.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East. The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: \u2022\tTime window: from 2006-08-01 to 2011-03-31 \u2022\tOrbits: from 2768 to 27604 \u2022\tPath (corresponds to JAXA track number): from 1 to 665 \u2022\tRow (corresponds to JAXA scene centre frame number): from 310 to 6790. The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors). The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width. The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention.", "links": [ { diff --git a/datasets/alos.prism.l1c.european.coverage.cloud.free_12.0.json b/datasets/alos.prism.l1c.european.coverage.cloud.free_12.0.json index 7264ec05af..eb3972b1b2 100644 --- a/datasets/alos.prism.l1c.european.coverage.cloud.free_12.0.json +++ b/datasets/alos.prism.l1c.european.coverage.cloud.free_12.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alos.prism.l1c.european.coverage.cloud.free_12.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection is composed of a subset of ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C products from the _$$ALOS PRISM L1C collection$$ https://earth.esa.int/eogateway/catalog/alos-prism-l1c (DOI: 10.57780/AL1-ff3877f) which have been chosen so as to provide a cloud-free coverage over Europe. 70% of the scenes contained within the collection have a cloud cover percentage of 0%, while the remaining 30% of the scenes have a cloud cover percentage of no more than 20%.\rThe collection is composed of PSM_OB1_1C EO-SIP products, with the PRISM sensor operating in OB1 mode with three views (Nadir, Forward and Backward) at 35 km width.", "links": [ { diff --git a/datasets/alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0.json b/datasets/alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0.json index 959ee5e60b..3a878501b9 100644 --- a/datasets/alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0.json +++ b/datasets/alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the simulation dataset from _\"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland\"_, M. Bavay, T. Gr\u00fcnewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013 A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graub\u00fcnden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models. The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095. Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.", "links": [ { diff --git a/datasets/als-based-snow-depth_1.0.json b/datasets/als-based-snow-depth_1.0.json index a620259442..da00efa743 100644 --- a/datasets/als-based-snow-depth_1.0.json +++ b/datasets/als-based-snow-depth_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "als-based-snow-depth_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA\u2019s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa. All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset. __Paper Citation:__ > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198\u2013 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._", "links": [ { diff --git a/datasets/amanda_bay_sat_1.json b/datasets/amanda_bay_sat_1.json index 035d57b71b..c7aa74259d 100644 --- a/datasets/amanda_bay_sat_1.json +++ b/datasets/amanda_bay_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amanda_bay_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Amanda Bay, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:100 000, and was produced from Landsat 4 TM imagery (124-108, 124-109). It is projected on a Transverse Mercator projection, and shows traverses/routes/foot track charts, glaciers/ice shelves, penguin colonies, stations/bases, runways/helipads, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/amazon_precip_228_1.json b/datasets/amazon_precip_228_1.json index f89cf5e86a..2c17213259 100644 --- a/datasets/amazon_precip_228_1.json +++ b/datasets/amazon_precip_228_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amazon_precip_228_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The precipitation data is 0.2 degree gridded monthly precipitation data based upon monthly rain data from Peru and Bolivia and daily rain data from Brazil. The extent of the data ranges from 5.2N and -20.0S to -49.4W to -79.6W", "links": [ { diff --git a/datasets/ames_sunphotometer_643_1.json b/datasets/ames_sunphotometer_643_1.json index 1e7571e87b..9dc5c88d23 100644 --- a/datasets/ames_sunphotometer_643_1.json +++ b/datasets/ames_sunphotometer_643_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ames_sunphotometer_643_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA Ames Airborne Tracking 14-channel Sunphotometer (AATS-14) was operated successfully aboard the University of Washington CV-580 for 24 data flights during the dry-season airborne campaign from August 13 to September 25, 2000. Flights originated from Pietersburg, South Africa; Kasane, Botswana; and Walvis Bay, Namibia. The AATS-14 instrument measures the transmission of the direct solar beam at 14 discrete wavelengths (350-1558 nm) from which we derived spectral aerosol optical depths (AOD) and columnar water vapor (CWV).", "links": [ { diff --git a/datasets/amount_of_dead_wood-214_1.0.json b/datasets/amount_of_dead_wood-214_1.0.json index fdd59e78b5..4ccb36a20b 100644 --- a/datasets/amount_of_dead_wood-214_1.0.json +++ b/datasets/amount_of_dead_wood-214_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amount_of_dead_wood-214_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/amphibian-and-landscape-data-swiss-lowlands_1.0.json b/datasets/amphibian-and-landscape-data-swiss-lowlands_1.0.json index 53b638eec4..c01c5d36f0 100644 --- a/datasets/amphibian-and-landscape-data-swiss-lowlands_1.0.json +++ b/datasets/amphibian-and-landscape-data-swiss-lowlands_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amphibian-and-landscape-data-swiss-lowlands_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Z\u00fcrich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban \"green\" and \"grey\" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.", "links": [ { diff --git a/datasets/amphibian-data-aargau_1.0.json b/datasets/amphibian-data-aargau_1.0.json index a3c7116d20..664de7b981 100644 --- a/datasets/amphibian-data-aargau_1.0.json +++ b/datasets/amphibian-data-aargau_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amphibian-data-aargau_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width \u22656m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each. The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau. The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.", "links": [ { diff --git a/datasets/amprimpacts_1.json b/datasets/amprimpacts_1.json index 7032b28210..5b4dd3ed57 100644 --- a/datasets/amprimpacts_1.json +++ b/datasets/amprimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA\u2019s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. ", "links": [ { diff --git a/datasets/amprtbcp_2.json b/datasets/amprtbcp_2.json index 5b7c173db2..3c70510d0a 100644 --- a/datasets/amprtbcp_2.json +++ b/datasets/amprtbcp_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbcp_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation/Electrification Experiment (CaPE). AMPR data werecollected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of July 21, 1991 - Aug. 16, 1991. CaPE took place in centralFlorida between 43 N - 25.5 N latitude and 86 W - 69 W longitude.", "links": [ { diff --git a/datasets/amprtbcx1_2.json b/datasets/amprtbcx1_2.json index d0b20e82ed..974dfbc746 100644 --- a/datasets/amprtbcx1_2.json +++ b/datasets/amprtbcx1_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbcx1_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1) conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25.5N - 43N latitude and 70W - 83W longitude.", "links": [ { diff --git a/datasets/amprtbcx2_2.json b/datasets/amprtbcx2_2.json index 75bd831d49..e86e64c290 100644 --- a/datasets/amprtbcx2_2.json +++ b/datasets/amprtbcx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbcx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2). AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of August 23 - August 30, 1995. The geographic domain of the CAMEX-2 region was between 25.5 N - 43 N latitude and 83 W - 70 W longitude.", "links": [ { diff --git a/datasets/amprtbcx3_1.json b/datasets/amprtbcx3_1.json index 0e1df8fe31..e249c9a3a8 100644 --- a/datasets/amprtbcx3_1.json +++ b/datasets/amprtbcx3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbcx3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Third Convection and Moisture Experiment (CAMEX-3). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of August 8, 1998 - September 27, 1998. The purpose of the CAMEX-3 mission was to study hurricanes over land and ocean in the U.S. Gulf of Mexico, Caribbean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and rain gauge sites.", "links": [ { diff --git a/datasets/amprtbf3a_1.json b/datasets/amprtbf3a_1.json index d50d5fd432..7d30ec518b 100644 --- a/datasets/amprtbf3a_1.json +++ b/datasets/amprtbf3a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbf3a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First ISCCP Regional Experiment-III Arctic Cloud Experiment (FIRE-III/ACE). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of May 18, 1998 through June 6, 1998. The FIRE-III/ACE mission studied sea-ice melting, sea-ice drift, and other sea-ice properties. The experiment was focused on the Arctic Ocean in and near the Beaufort Sea off the northern coast of Alaska, in coordination with the ice-bound research ship, Sheba.", "links": [ { diff --git a/datasets/amprtbjax_2.json b/datasets/amprtbjax_2.json index d4128a2b9a..66c30268b8 100644 --- a/datasets/amprtbjax_2.json +++ b/datasets/amprtbjax_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbjax_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed in Jacksonville, FL for the initial AMPR instrument validation. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 10 October 1990 through 19 October 1990. The purpose of the Jacksonville mission was to study convection over the land and ocean for validation, along with clear (dry) vertical columns of atmosphere over the ocean for calibration.", "links": [ { diff --git a/datasets/amprtbkwj_1.json b/datasets/amprtbkwj_1.json index b57306abb4..6fc6816bee 100644 --- a/datasets/amprtbkwj_1.json +++ b/datasets/amprtbkwj_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbkwj_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the First Kwajelein Experiment (KWAJEX), which provided Ground Validation for instruments onboard the Tropical Rain Measurement Mission (TRMM). AMPR brightness temperature data were collected at four microwave frequencies suited to study rain cloud systems (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 30 July - 14 September 1999.", "links": [ { diff --git a/datasets/amprtblba_1.json b/datasets/amprtblba_1.json index 2e0807e77d..a4808bfdb3 100644 --- a/datasets/amprtblba_1.json +++ b/datasets/amprtblba_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtblba_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Tropical Rainfall Measuring Mission - Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA); the second of three TRMM ground validation missions. AMPR data were collected at four distinct microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the time period of January 23 through February 26, 1999. The geographic domain of the TRMM-LBA region was wholly within Brazilian Amazon Basin between 16 S to 6N latitude and 76W to 49 W longitude. The TRMM-LBA mission was to study convection over humid tropical land regions within the range of research-quality radar, lightning, radiosonde and raingage sites located in the Amazon Basin (Rondonia, Brazil).", "links": [ { diff --git a/datasets/amprtbta_1.json b/datasets/amprtbta_1.json index fa11d9a88d..1dacd4a8c3 100644 --- a/datasets/amprtbta_1.json +++ b/datasets/amprtbta_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbta_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Texas-Florida Underflights (TEFLUN-A); the first of three TRMM ground validation missions. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 15 April 1998 through 04 May 1998. The TEFLUN-A mission studied convection over sub-tropical land and ocean regions within the range of research-quality radar, lightning, radiosonde, and raingage sites in Florida and Texas.", "links": [ { diff --git a/datasets/amprtbtc_2.json b/datasets/amprtbtc_2.json index c41a50c0f1..d8be4694d6 100644 --- a/datasets/amprtbtc_2.json +++ b/datasets/amprtbtc_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amprtbtc_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Precipitation Radiometer (AMPR) data set was part of the atmospheric measurements collected during the intensive observation period of the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA COARE). AMPR brightness temperature data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) and for the period Jan. 12, 1993 - Feb. 25, 1993. The TOGA COARE geographic domain pertinent to the AMPR data set was from the equator to 21 S latitude and 145 E - 161 E longitude.", "links": [ { diff --git a/datasets/ams_cs93_403_1.json b/datasets/ams_cs93_403_1.json index 81b0d892f0..cc95c43b79 100644 --- a/datasets/ams_cs93_403_1.json +++ b/datasets/ams_cs93_403_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ams_cs93_403_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data from 1993 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. ", "links": [ { diff --git a/datasets/ams_cs94_404_1.json b/datasets/ams_cs94_404_1.json index 647ff9fb70..df0c54a73b 100644 --- a/datasets/ams_cs94_404_1.json +++ b/datasets/ams_cs94_404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ams_cs94_404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. ", "links": [ { diff --git a/datasets/ams_cs95_405_1.json b/datasets/ams_cs95_405_1.json index 038d278893..6bfb10cbbd 100644 --- a/datasets/ams_cs95_405_1.json +++ b/datasets/ams_cs95_405_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ams_cs95_405_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. ", "links": [ { diff --git a/datasets/ams_cs96_406_1.json b/datasets/ams_cs96_406_1.json index 32b58a6ccb..6dfa856d08 100644 --- a/datasets/ams_cs96_406_1.json +++ b/datasets/ams_cs96_406_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ams_cs96_406_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. ", "links": [ { diff --git a/datasets/amsua15sp_1.json b/datasets/amsua15sp_1.json index 483cd54272..b91de09412 100644 --- a/datasets/amsua15sp_1.json +++ b/datasets/amsua15sp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amsua15sp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit.", "links": [ { diff --git a/datasets/amsua16sp_1.json b/datasets/amsua16sp_1.json index b83f9e918d..726db6c4ee 100644 --- a/datasets/amsua16sp_1.json +++ b/datasets/amsua16sp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amsua16sp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit.", "links": [ { diff --git a/datasets/amsua17sp_1.json b/datasets/amsua17sp_1.json index ce94aece6a..49c35e3fed 100644 --- a/datasets/amsua17sp_1.json +++ b/datasets/amsua17sp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "amsua17sp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster.", "links": [ { diff --git a/datasets/anezet-analysing-net-zero-transformations_1.0.json b/datasets/anezet-analysing-net-zero-transformations_1.0.json index 940aa9cfa5..6f9e085f0c 100644 --- a/datasets/anezet-analysing-net-zero-transformations_1.0.json +++ b/datasets/anezet-analysing-net-zero-transformations_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "anezet-analysing-net-zero-transformations_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We have analysed past transformations in Switzerland in four environmental domains, with the aim to draw conclusions for current challenges, such as the net\u2010zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research.", "links": [ { diff --git a/datasets/angle-of-repose-of-snow_1.0.json b/datasets/angle-of-repose-of-snow_1.0.json index 4ffb479be6..f05a218ba3 100644 --- a/datasets/angle-of-repose-of-snow_1.0.json +++ b/datasets/angle-of-repose-of-snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "angle-of-repose-of-snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Angle of repose experiments were performed with different snow types at temperatures between -2 and -40\u00b0C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed. This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape. The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.", "links": [ { diff --git a/datasets/ant_dist_1.json b/datasets/ant_dist_1.json index d0fb5a2245..3d34ea7b71 100644 --- a/datasets/ant_dist_1.json +++ b/datasets/ant_dist_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ant_dist_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spreadsheet of distances between Antarctic locations (eg. Mawson Station, Prince Edward Island) and world locations (eg. Melbourne, Santiago).", "links": [ { diff --git a/datasets/ant_seafloor_geomorph_1.json b/datasets/ant_seafloor_geomorph_1.json index 66f261716d..4e0e824604 100644 --- a/datasets/ant_seafloor_geomorph_1.json +++ b/datasets/ant_seafloor_geomorph_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ant_seafloor_geomorph_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Publicly available bathymetry and geophysical data can be used to map geomorphic features of the Antarctic continental margin and adjoining ocean basins at scales of 1:1-5 million. These data can also be used to map likely locations for some Vulnerable Marine Ecosystems. Seamounts over a certain size are readily identified and submarine canyons and mid ocean ridge central valleys which harbour hydrothermal vents can be located. Geomorphic features and their properties can be related to major habitat characteristics such as sea floor type (hard versus soft), ice keel scouring, sediment deposition or erosion and current regimes. Where more detailed data are available, shelf geomorphology can be shown to provide a guide to the distribution in the area of the shelf benthic communities recognised by Gutt (2007). The geomorphic mapping method presented here provides a layer to add to benthic bioregionalistion using readily available data.\n\nAn AADC maintained copy of these data are publicly available for download from the provided URL. The master copy of these data are attached to the metadata record held at Geoscience Australia (see the provided URL).", "links": [ { diff --git a/datasets/antarctic_biodiversity_db_1.json b/datasets/antarctic_biodiversity_db_1.json index 9e1fabf512..ac3ed89dbe 100644 --- a/datasets/antarctic_biodiversity_db_1.json +++ b/datasets/antarctic_biodiversity_db_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "antarctic_biodiversity_db_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The biodiversity database is planned to be a reference on Antarctic and subantarctic flora and fauna collated by the Regional Sensitivity to Climate Change (RiSCC) group and developed by the Australian Antarctic Data Centre.\n\nSearches are available in the following areas:\n\nTaxonomy\nProtection and convention measures (protected species)\nObservations\nScientific Bibliographies", "links": [ { diff --git a/datasets/antarctic_circumpolar_current_fronts_1.json b/datasets/antarctic_circumpolar_current_fronts_1.json index f6f206144a..5f25cabb6d 100644 --- a/datasets/antarctic_circumpolar_current_fronts_1.json +++ b/datasets/antarctic_circumpolar_current_fronts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "antarctic_circumpolar_current_fronts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This line shapefile represents the following features of the Antarctic Circumpolar Current:\nSubtropical Front (STF);\nSubantarctic Front (SAF);\nSouthern Antarctic Circumpolar Current Front (sACCf);\nPolar Front (PF);\nSouthern Boundary of the Antarctic Circumpolar Current \n\nas described in \n\nAlejandro H. Orsi, Thomas Whitworth III, and Worth D. Nowlin Jr (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research 42 (5), 641-673.\n\nThe shapefile was created from data provided by lead author Alejandro Orsi to the Australian Antarctic Data Centre in August 2001. The data in the files from Alejandro Orsi was also combined in a csv file. \nThe data available for download includes the original data, the shapefile and the csv file.", "links": [ { diff --git a/datasets/antarctic_single_frames.json b/datasets/antarctic_single_frames.json index 6a2e5054f6..e2ac6d2494 100644 --- a/datasets/antarctic_single_frames.json +++ b/datasets/antarctic_single_frames.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "antarctic_single_frames", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic Single Frame Records are a collection of aerial photographs over Antarctica from the United States Antarctic Resource Center (USARC) and the British Antarctic Survey (BAS) dating from 1946 to 2000. The Antarctic Single Frame Records collection includes black-and-white, natural color and color infrared images with a photographic scale ranging from 1:1,000 to 1:64,000.\n", "links": [ { diff --git a/datasets/anthropogenic-change-and-net-n-mineralization_1.0.json b/datasets/anthropogenic-change-and-net-n-mineralization_1.0.json index b39764ea9c..dedfb341d4 100644 --- a/datasets/anthropogenic-change-and-net-n-mineralization_1.0.json +++ b/datasets/anthropogenic-change-and-net-n-mineralization_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "anthropogenic-change-and-net-n-mineralization_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. Paper Citation: Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Sch\u00fctz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology Please cite this paper together with the citation for the datafile. We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth\u2019s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).", "links": [ { diff --git a/datasets/aoci0bil_281_1.json b/datasets/aoci0bil_281_1.json index 21c921959e..11a0b7b745 100644 --- a/datasets/aoci0bil_281_1.json +++ b/datasets/aoci0bil_281_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aoci0bil_281_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. ", "links": [ { diff --git a/datasets/apr3cpex_1.json b/datasets/apr3cpex_1.json index 619f25134c..e517d60019 100644 --- a/datasets/apr3cpex_1.json +++ b/datasets/apr3cpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "apr3cpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format.", "links": [ { diff --git a/datasets/apr3cpexaw_1.json b/datasets/apr3cpexaw_1.json index 73277a9968..d0e6dd5af8 100644 --- a/datasets/apr3cpexaw_1.json +++ b/datasets/apr3cpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "apr3cpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format.", "links": [ { diff --git a/datasets/apr3cpexcv_1.json b/datasets/apr3cpexcv_1.json index 152c2f31eb..c43baa7762 100644 --- a/datasets/apr3cpexcv_1.json +++ b/datasets/apr3cpexcv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "apr3cpexcv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross-section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX \u2013 Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through September 30, 2022, in netCDF-4 format, with associated browse imagery in JPG format. \r\n", "links": [ { diff --git a/datasets/apuimpacts_1.json b/datasets/apuimpacts_1.json index 0d1d8251a1..6446a063ed 100644 --- a/datasets/apuimpacts_1.json +++ b/datasets/apuimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "apuimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Autonomous Parsivel Unit (APU) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format from January 15, 2020 through February 29, 2020.", "links": [ { diff --git a/datasets/area_of_shrub_forest-123_1.0.json b/datasets/area_of_shrub_forest-123_1.0.json index 72eb7284d9..8bb1acf789 100644 --- a/datasets/area_of_shrub_forest-123_1.0.json +++ b/datasets/area_of_shrub_forest-123_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "area_of_shrub_forest-123_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "All plots classified as shrub forest according to the NFI forest definition. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/arthropod-biomass-abundance-species-richness-trends-limpach_1.0.json b/datasets/arthropod-biomass-abundance-species-richness-trends-limpach_1.0.json index 54dcb131df..a2b6e46172 100644 --- a/datasets/arthropod-biomass-abundance-species-richness-trends-limpach_1.0.json +++ b/datasets/arthropod-biomass-abundance-species-richness-trends-limpach_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "arthropod-biomass-abundance-species-richness-trends-limpach_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recent publications about declines in arthropod biomass, abundance and species diversity raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58\u2019255 specimens of 1\u2019343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics. Data are provided on request to contact person against bilateral agreement.", "links": [ { diff --git a/datasets/asas.json b/datasets/asas.json index 1f49704f35..85504641c5 100644 --- a/datasets/asas.json +++ b/datasets/asas.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asas", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle.\n\nThe ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.).\n\nData in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994.", "links": [ { diff --git a/datasets/asas_l1b_562_1.json b/datasets/asas_l1b_562_1.json index 8f40e0934e..d8e75864ba 100644 --- a/datasets/asas_l1b_562_1.json +++ b/datasets/asas_l1b_562_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asas_l1b_562_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude. The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.", "links": [ { diff --git a/datasets/asasrefl_287_1.json b/datasets/asasrefl_287_1.json index b8f6ad142d..caab5f0889 100644 --- a/datasets/asasrefl_287_1.json +++ b/datasets/asasrefl_287_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asasrefl_287_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS.", "links": [ { diff --git a/datasets/ascatcpex_1.json b/datasets/ascatcpex_1.json index 29f8bef635..d50ed2708f 100644 --- a/datasets/ascatcpex_1.json +++ b/datasets/ascatcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ascatcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.", "links": [ { diff --git a/datasets/asosimpacts_1.json b/datasets/asosimpacts_1.json index 8e8e7e2859..cc79e2d476 100644 --- a/datasets/asosimpacts_1.json +++ b/datasets/asosimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asosimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Automated Surface Observing Systems (ASOS) IMPACTS dataset consists of a variety of ground-based observations during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This ASOS dataset consists of 176 stations within the IMPACTS domain. Each station provides observations of surface temperature, dew point, precipitation, wind direction, wind speed, wind gust, sea level pressure, and the observed weather code. The ASOS data are available from December 29, 2019, through March 1, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/aspas_asmas_aat_3.json b/datasets/aspas_asmas_aat_3.json index eb63f67183..44f31cadb6 100644 --- a/datasets/aspas_asmas_aat_3.json +++ b/datasets/aspas_asmas_aat_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aspas_asmas_aat_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This record describes GIS polygon data (a shapefile) representing the boundaries of Antarctic Specially Protected Areas (ASPAs) and an Antarctic Specially Managed Area (ASMA) in the Australian Antarctic Territory for which Australia was the proponent or co-proponent. Also included is the boundary of ASPA 168 for which China was the proponent. \nThe following is a list of the ASPAs and ASMA:\nASPA 101 Taylor Rookery\nASPA 102 Rookery Islands\nASPA 103 Ardery Island and Odbert Island\nASPA 135 North-east Bailey Peninsula\nASPA 136 Clark Peninsula\nASPA 143 Marine Plain\nASPA 160 Frazier Islands\nASPA 162 Mawson's Huts\nASPA 164 Scullin and Murray Monoliths\nASPA 167 Hawker Island\nASPA 168 Mt Harding\nASPA 169 Amanda Bay\nASPA 174 Stornes\nASMA 6 Larsemann Hills\n\nThe data is available from a link in this metadata record and also, as a separate shapefile for each ASPA or ASMA, from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database (see related url).\nGIS data representing the boundaries of other ASPAs and ASMAs is also available from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database.", "links": [ { diff --git a/datasets/asrb-dav_1.0.json b/datasets/asrb-dav_1.0.json index f8e99aa02e..48830c57a5 100644 --- a/datasets/asrb-dav_1.0.json +++ b/datasets/asrb-dav_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asrb-dav_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Incoming and outgoing shortwave and longwave 2 min radiation measurements in Davos Dorf, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.", "links": [ { diff --git a/datasets/asrb-vf_1.0.json b/datasets/asrb-vf_1.0.json index f893397d71..b72aaeeb11 100644 --- a/datasets/asrb-vf_1.0.json +++ b/datasets/asrb-vf_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asrb-vf_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153\u2013366 cm over the period 1934\u20132012. The measurement site is located in an almost flat part of a southeast oriented slope. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.", "links": [ { diff --git a/datasets/asrb-wfj_1.0.json b/datasets/asrb-wfj_1.0.json index 8e5b1e5d64..275651a0f8 100644 --- a/datasets/asrb-wfj_1.0.json +++ b/datasets/asrb-wfj_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "asrb-wfj_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.", "links": [ { diff --git a/datasets/aster_1.json b/datasets/aster_1.json index c62d0ed025..a50e5bfb5a 100644 --- a/datasets/aster_1.json +++ b/datasets/aster_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aster_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Advanced Spaceborne Thermal Emission and Reflection Radiometer.\n\nLevel 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling.\n\nThere are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. \n\nSearch the Satellite Image Catalogue for more information using the link included.", "links": [ { diff --git a/datasets/aster_global_dem.json b/datasets/aster_global_dem.json index 2c91fb48b2..7612f755ff 100644 --- a/datasets/aster_global_dem.json +++ b/datasets/aster_global_dem.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aster_global_dem", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras. Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m). \n\nThe methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive. Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels. All cloud-screened DEMS are stacked and residual bad values and outliers are removed. Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (\u00b0) x 1\u00b0 tiles.\n\nThe ASTER GDEM covers land surfaces between 83\u00b0N and 83\u00b0S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30\u2013m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid.", "links": [ { diff --git a/datasets/atlas_buildings_gis_1.json b/datasets/atlas_buildings_gis_1.json index b6e7c70cc2..c58a9ac6f3 100644 --- a/datasets/atlas_buildings_gis_1.json +++ b/datasets/atlas_buildings_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atlas_buildings_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Alistair Grinbergs (Heritage Officer) was on Heard island in January and February 2000) as part of the 2000 ANARE, to make an assessment of the heritage value of the old ANARE station ruins. This GPS survey data of the corners of buildings and other artefacts will form part of the record of the station site, together with drawings and other measurements.\n\nThe assessment will be used to formulate a conservation management plan for the site.", "links": [ { diff --git a/datasets/atlas_cove_photos_1.json b/datasets/atlas_cove_photos_1.json index 0d77a3c7fa..c579f5e0e3 100644 --- a/datasets/atlas_cove_photos_1.json +++ b/datasets/atlas_cove_photos_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atlas_cove_photos_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Photographs and photo locations of the historic Australian National Antarctic Research Expedition (ANARE) base at Atlas Cove on Heard Island.\n\nThe station was established 11 December 1947 and was closed down on 9 March 1955.\n\nPhotos were taken in March of 2008 by Kerry Steinberner during a visit to Heard Island. The map used to locate the images is described in the following metadata record:\n\nTopographic Survey at Atlas Cove, Heard Island, November 2000 [atlas_survey2000_gis]\n\nThe images include shots of the remains of ANARE buildings, vehicles, tanks, debris, fences, artefacts and flora.\n\nThe dataset includes a copy of the images, an excel spreadsheet cataloguing the images, and shapefiles showing the image locations.", "links": [ { diff --git a/datasets/atlas_photocontrol_gis_1.json b/datasets/atlas_photocontrol_gis_1.json index 0a3102b7b4..2ba8d000dd 100644 --- a/datasets/atlas_photocontrol_gis_1.json +++ b/datasets/atlas_photocontrol_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atlas_photocontrol_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dave Gardner was on Heard Island in January and February 2000 as part of the 2000 ANARE. Opportunistic use was made of the the differential gps system to take accurate locations of 16 points identified from the 1987 aerial photography, so that they could be used as reference points for merging the photographs into an accurate photo mosaic.\n\nAround the station and to the NE it was easy to identify features from the photographs with confidence. To the west of the station the topography and features of the azorella wallows had changed significant and it was not possible to identify features with confidence.", "links": [ { diff --git a/datasets/atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0.json b/datasets/atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0.json index 047bf414a2..ea72710295 100644 --- a/datasets/atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0.json +++ b/datasets/atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade. Thanks to a large spatio-temporal coverage, high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the \u201cTeleo\u201d barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature). This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.", "links": [ { diff --git a/datasets/atmos_co2_by_erosion_xdeg_1019_1.json b/datasets/atmos_co2_by_erosion_xdeg_1019_1.json index 12ea4aaf87..10cdf651ed 100644 --- a/datasets/atmos_co2_by_erosion_xdeg_1019_1.json +++ b/datasets/atmos_co2_by_erosion_xdeg_1019_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atmos_co2_by_erosion_xdeg_1019_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Continental Atmospheric CO2 Consumption data set represents gridded estimates for the riverine export of carbon and of sediments based on empirical models. All data exist for the overall continental area in a spatial resolution of 0.5 x 0.5 degree longitude/ latitude. The units are tC/km2/yr for all carbon species, and t/km2/yr for sediment fluxes. There are two data files (*.zip) with this data set which describe the following: dissolved organic carbon (DOC) export, particulate organic carbon (POC) export, bicarbonate export, export of bicarbonate being of atmospheric origin (also called atmospheric CO2 consumption by rock weathering), and sediment export.", "links": [ { diff --git a/datasets/atree-forest-owner-clearances-offsetting_1.0.json b/datasets/atree-forest-owner-clearances-offsetting_1.0.json index 08a895ca81..72502e8851 100644 --- a/datasets/atree-forest-owner-clearances-offsetting_1.0.json +++ b/datasets/atree-forest-owner-clearances-offsetting_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atree-forest-owner-clearances-offsetting_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In April 2020, about 1700 forest owners of the plateau region of the Canton of Berne were invited to participate in a survey (virtually all of them received a conventional paper-pencil questionnaire) about their willingness to provide forest nature conservation measures in their forest to compensate forest clearances that cannot be compensated by afforestation. The questionnaire contained a survey experiment (conjoint analysis) that offered a choice between two options and the status quo in 9 decision-making situations. Of the 607 completed questionnaires that were returned the survey experiment was completed by about 400.", "links": [ { diff --git a/datasets/atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0.json b/datasets/atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0.json index 39f4f9ce0e..5457e14774 100644 --- a/datasets/atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0.json +++ b/datasets/atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest owners of the Canton of Lucerne were survey about their willingness to employ different forest management measures to provicde climate regulation services by forests. Of the nearly 3000 forest owners that received an invitation to a online-survey and the 900 forest owners that received a paper and pencil survey, 1055 valid responses were received. The questionnaire contained a survey experiment in which 9 choice situations were presented to the respondents in which they had the choice between two options and the status quo. This survey experiment part of the survey was completed by 990 respondents.", "links": [ { diff --git a/datasets/atree-q-methodology-forest-clearances-offsetting_1.0.json b/datasets/atree-q-methodology-forest-clearances-offsetting_1.0.json index 228a716285..588293f564 100644 --- a/datasets/atree-q-methodology-forest-clearances-offsetting_1.0.json +++ b/datasets/atree-q-methodology-forest-clearances-offsetting_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atree-q-methodology-forest-clearances-offsetting_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In Novdember 2019 about 19 experts on forest surface protection and forest clearances were invited to a workshop in order to discuss policy design and implementation problems regarding the offsetting of forest clearances. In Switzerland such offsetting can be provided under certain circumstances by implementing forest nature conservation measures in the forest instead of providing in-kind compensation, i.e. reafforestation on agricultural land. The workshop included the sorting of 34 statements \u2013 that were elaborated beforehand, partially also with help of the participants \u2013 according to the \"Q-methodology\" survey technique (participants arrange given statements about a certain subject into boxes that are normally distributed over a \"agree - do not agree\" answer scale). The participants included representatives from cantonal and national forest administrations, nature conservation NGOs, forest NGOs, spatial planning NGOs, private counseling enterprises as well as national, cantonal and regional forest owner organizations. The data allows a factor analytical differentiation of actors into groups with distinct positions towards forest clearance compensation as well as a positioning of these groups relative to each statement.", "links": [ { diff --git a/datasets/atree-social-network-analysis-carbon-sequestration-lucerne_1.0.json b/datasets/atree-social-network-analysis-carbon-sequestration-lucerne_1.0.json index 4589eedbb4..63d703c3b8 100644 --- a/datasets/atree-social-network-analysis-carbon-sequestration-lucerne_1.0.json +++ b/datasets/atree-social-network-analysis-carbon-sequestration-lucerne_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atree-social-network-analysis-carbon-sequestration-lucerne_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the \"actor coalition framework\" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations.", "links": [ { diff --git a/datasets/atrs.json b/datasets/atrs.json index 8634506ef0..5eaefcb70d 100644 --- a/datasets/atrs.json +++ b/datasets/atrs.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "atrs", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Developmental airborne coherent radar sounding data collected over a variety of\n sounding targets in Antarctica, including a full gridded survey of subglacial\n Lake Vostok and its environs. This was an instrument development award, so the\n data are not of \"production\" quality. Receiver sensitivity documents are\n provided with the data.\n \n The data resides in 6, DLT 4 tapes (~30 Gb each).", "links": [ { diff --git a/datasets/au0103_1.json b/datasets/au0103_1.json index 74af258444..92d6eb514a 100644 --- a/datasets/au0103_1.json +++ b/datasets/au0103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along CLIVAR Southern Ocean meridional repeat transect SR3 between Tasmania and Antarctica from October to December 2001. A total of 135 CTD vertical profile stations were taken, more than half to within 20 m of the bottom. Over 2200 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, CFC's, CCl4, dissolved inorganic carbon, alkalinity, 13C, DMS/DMSP/DMSO, halocarbons, barium, barite, ammonia, del30Si, dissolved and particulate organic carbon, particulate silica, 15N-nitrate, 18O, 234Th, 230Th, 231Pa, primary productivity and biological parameters, using a 24 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Two sediment trap moorings were serviced, and a third mooring was deployed at a new location. A summary of all CTD data and data quality is presented in the data report.\n\nThis work was completed as part of ASAC project 1335.", "links": [ { diff --git a/datasets/au0106_1.json b/datasets/au0106_1.json index 39dd24bdac..d7f81fb6a7 100644 --- a/datasets/au0106_1.json +++ b/datasets/au0106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements conducted on voyage 6 of the Aurora Australis of the 2000-2001 season. These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data.\n\nThese data were collected by Mark Rosenberg.\n\nThis metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise.\n\nBasic information about voyage 6:\nThe voyage will complete a range of Marine Science activities off the Mawson Coast, and off the Amery Ice Shelf before calling at Davis to retrieve summer personnel and helicopters prior to returning to Hobart. Science equipment calibration will be undertaken at Mawson. (Marine Science activities were interrupted when the Aurora Australis was required to provide assistance in the Polar Bird's attempt to reach Casey, complete the station resupply and return to open water.)\n\nLeader: Dr Graham Hosie\nDeputy Leader: Mr Andrew McEldowney\n\nSee the readme files in the downloads for more information.", "links": [ { diff --git a/datasets/au0201_1.json b/datasets/au0201_1.json index 4c4fa47f22..6e69164453 100644 --- a/datasets/au0201_1.json +++ b/datasets/au0201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements conducted on voyage 7 of the Aurora Australis of the 2002-2003 season. These data are ADCP (Acoustic Doppler Current Profiler) data.\n\nThese data were collected/collated by Mark Rosenberg.\n\nFinal ADCP data for voyage au0201 (SAZ mooring turnaround and\niceberg B9B experiment), Aurora Australis Voyage 1 2002/2003, 17th Oct 2002 to\n18th Nov 2002.\n\n* The complete ADCP data for cruise au0201 are in the file:\n au020101.cny (ascii format)\n a0201dop.mat (matlab format)\n\n* The \"on station\" ADCP data (specifically, the data for\nwhich the ship speed was less than or equal to 0.35 m/s)\nare in the files:\n au0201_slow35.cny (ascii format)\n a0201dop_slow35.mat (matlab format)\n\n* The file bindep.dat shows the water depths (in metres) that\ncorrespond to the centre of each vertical bin.\n\n* The data are 30 minute averages. Each 30 minute averageing\nperiod starts from the time indicated.\n(so, e.g., an ensemble with time 120000 is the average from\n120000 to 123000).\n\n* ADCP currents are absolute - i.e. ship's motion has\nbeen subtracted out.\n\n* Note that the top few bins can have bad data from water dragged\nalong by the ship. \n\n* Beware of data when the ship is underway - it's often suspect.\n\n\n\nMATLAB VECTORS AND MATRICES:\n============================\n\nheader info\n-----------\nfor header info: column number corresponds to 30 minute average number\nbotd = mean bottom depth (m) over the 30 minute period\ncnav = GPS info: don't worry about it\ncruise = cruise number\ndate = ddmmyy (UTC)\nibcover = a bottom track parameter: don't worry about it\nicover = percentage of 30 minute averageing period covered\n by acceptable 3 minute ensembles\nlastgd = deepest accepted bin in this profile\nlat = mean latitude over the 30 minute period (decimal degrees)\nlon = mean longitude over the 30 minute period (decimal degrees)\nnbins = no. of bins logged (=60)\nshipspeed = scalar resultant of shipu and shipv\nshipu = ship's E/W velocity over the ground over 30 minute period \n (m/s, +ve east)\nshipv = ship's N/S velocity over the ground over 30 minute period \n (m/s, +ve north)\ntime = hhmmss, time (UTC) at start of 30 minute averageing period\ndectime = time in decimal days from start of year 2002 (e.g. midday on\n January 2nd = 1.5000)\n\n\nadcp data\n---------\nfor adcp data matrices: row number corresponds to bin number\n column number corresponds to 30 min. average no.\nbindep = depth (m) to centre of each bin in the profile (will be the\n same for all profiles)\nipcok = percentage of the profile period for which there was\n good data in this bin (N.B. data=NaN when ipcok=0)\nqc = a quality control value for each bin - see below\nspeed = scalar resultant of u and v\nu = east/west current (m/s, +ve east)\nv = north/south current (m/s, +ve north)\n\n\n\nASCII FORMAT FILE:\n==================\n\n* The file starts with a 3 line header.\n\n* Then comes each 30 min. ensemble, as follows:\n\nFirst, a 1 line header, containing\n\ndate (UTC) (dd-mmm-yyyy)\ntime (UTC) (hh:mm:ss)\n% of 30 min average covered by acceptable 3 min. ensembles\ndeepest accepted bin in the profile\nship's E/W velocity over the ground over the 30min (m/s)\nship's N/S velocity over the ground over the 30min (m/s)\nP= GPS position-derived velocity (D=direct GPS vel.; B=bottom track vel.)\nmean longitude over the 30 min.\nmean latitude over the 30 min.\n% of interfix period for which there was bottom depth information\nmean bottom depth over the 30 min.\n0\n0\n\nNext, the data, from the shallowest bin to the deepest bin:\n\nfor each bin, there's 4 parameters:\n\nu = east/west current (m/s, +ve east)\nv = north/south current (m/s, +ve north)\nqc = quality control value - see below\nipcok = percentage of the profile period for which there was\n good data in this bin\n\nNote that the data are written left to right across each line, then\nonto the next line, etc. (so 4 bins on a full line)\n\n\nquality control value:\n----------------------\nqc = %good / (Verr+0.05)\n\nwhere:\n%good = percent good pings after logging system screening\nVerr = RMS error velocity (m/s).\n\nPossible range of qc is 0-20, with an expected range of 0-10;\nvalues of 0-4 indicate very poor data; values above 8 indicate\nvery good data.", "links": [ { diff --git a/datasets/au0207_1.json b/datasets/au0207_1.json index 0db24f1e6d..390e70b05c 100644 --- a/datasets/au0207_1.json +++ b/datasets/au0207_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0207_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements conducted on voyage 7 of the Aurora Australis of the 2001-2002 season. These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data.\n\nThese data were collected by Mark Rosenberg.\n\nThis metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise.\n\nBasic information about voyage 7:\nSubject to ice conditions, the voyage will undertake a range of Marine Science activities in the Prydz Bay area and will retrieve summer personnel, helicopters and limited RTA cargo from Davis station.\n\nLeader: Mr Rob Easther\nDeputy Leader: Ms Gerry Nash\nSee the readme files in the downloads for more information.", "links": [ { diff --git a/datasets/au0304_1.json b/datasets/au0304_1.json index 3896dc4592..019621fcea 100644 --- a/datasets/au0304_1.json +++ b/datasets/au0304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the South Indian Ocean sector during the southern summer of 2002/2003 on Aurora Australis voyage au0304, V4 2002/2003. A total of 64 vertical CTD stations were taken, in a krill survey area in the vicinity of Mawson, and approximately following WOCE I08 meridional transect passing up the western flank of the Kerguelen Plateau and then continuing south across the Princess Elizabeth Trough to the Antarctic continental shelf. Over 1050 Niskin bottle samples were collected using a SeaBird 24 bottle rosette sampler, with samples collected for the analysis of salinity, dissolved oxygen, nutrients, and biological parameters. Full-depth current profile data were collected by either 1 or 2 lowered acoustic Doppler profilers (LADCP) attached to the CTD rosette package. Near surface current data were also collected using a ship mounted ADCP. An array of 8 moorings comprising current meters and thermosalinographs were deployed along the western flank of the Kerguelen Plateau, for the Deep Western Boundary Current Experiment. Ship's underway data, (including bathymetry, met. sensors and sea surface salinity/temperature/fluorescence) are included in the cruise data set; an offset correction was applied to the underway sea surface salinity and temperature data, derived from comparison with near surface CTD data.\n\nA summary of all data and important data quality information is presented in the data report.\n\nNote that LADCP data are not included here.\n\nThis work was completed as part of ASAC projects 1250 and 2312.\n\nModels of climate change project a decrease in the global ocean overturning circulation, significantly impacting climate and ocean ecosystems. The Deep Western Boundary Current experiment commenced on this voyage aims to measure the northward transport of Antarctic Bottom Water east of the Kerguelen Plateau so that future change in this component of the global thermohaline circulation can be detected.", "links": [ { diff --git a/datasets/au0403_1.json b/datasets/au0403_1.json index 73372b2140..9c1487e556 100644 --- a/datasets/au0403_1.json +++ b/datasets/au0403_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0403_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the Southern Ocean Indian sector during the southern summer of 2004/2005 on Aurora Australis voyage au0403, V3 2004/2005. Data were collected during a complete occupation of CLIVAR meridional section I9S; and then along a transect up the northeastern flank of the Kerguelen Plateau, south across the Princess Elizabeth Trough and onward to the Antarctic continental shelf. A total of 115 CTD vertical profile stations were taken, most to within 30 m of the bottom. Over 2450 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, CFCs, dissolved inorganic carbon, alkalinity, oxygen-18, methane, selenium and biological parameters, using a 24 bottle rosette sampler. Full depth current profiles were collected by a lowered acoustic Doppler profiler (LADCP) attached to the rosette package, while near surface current data were collected by a ship mounted ADCP. An array of 8 current meter and thermosalinograph moorings, deployed 2 years earlier on cruise au0304, were recovered from the vicinity of the Kerguelen Plateau.\n\nShip's underway data (including bathymetry, met. sensors and sea surface parameters) are included in the cruise data set; an offset correction was applied to the underway sea surface salinity and temperature data, derived from comparison with near surface CTD data.\n\nA summary of all data and important data quality information is presented in the data report. LADCP data are not included in this data set.\n\nThis work was completed as part of ASAC projects 2312 and 2572.", "links": [ { diff --git a/datasets/au0404_1.json b/datasets/au0404_1.json index 6428a31721..668dcca45f 100644 --- a/datasets/au0404_1.json +++ b/datasets/au0404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au0404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic data were collected on Aurora Australis Voyage 4 2003/2004, from December 2003 to February 2004, and a calibrated data set was created. The oceanographic program on the voyage was a part of the cruise-determining fish survey in the vicinity of Heard Island. A total of 42 CTD vertical profile stations were taken, most to within 5 m of the bottom. Over 450 Niskin bottle samples were collected and analysed on board, for calibration of the CTD conductivity sensors. Nutrient samples were also collected, but not analysed. Near surface current data were collected using a ship mounted ADCP. Data from the array of ship's underway sensors are included in the data set.\n\nThe data report describes the processing/calibration of the CTD and ADCP data, and gives important details concerning data quality. An offset correction was derived for the underway sea surface temperature and salinity data, by comparison with near surface CTD data.\n\nThese data form part of the overall dataset for ASAC project 2388 (ASAC_2388).", "links": [ { diff --git a/datasets/au1121_1.json b/datasets/au1121_1.json index 04f68b1a6d..a4325048c2 100644 --- a/datasets/au1121_1.json +++ b/datasets/au1121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au1121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard Aurora Australis cruise au1121, voyage \"Marine Science\" (i.e. voyage 2.1) 2010/2011, from 4th January to 6th February 2011.\n\nThe cruise commenced with a full north to south occupation of the CLIVAR/WOCE meridional repeat section SR3, followed by work around the Antarctic continental margin in the region of the Adelie Depression and the former Mertz Glacier ice tongue.\n\nA total of 149 CTD vertical profile stations were taken on the cruise, most to within 15 metres of the bottom. Over 2000 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite and silicate), oxygen-18, dissolved inorganic carbon (i.e. TCO2), alkalinity, pH, helium, tritium, and biological parameters, using a 24 bottle rosette sampler. Upper water column current profile data were collected by a ship mounted ADCP. Meteorological and water property data were collected by the array of ship's underway sensors. An array of 3 bottom mounted ADCP moorings were deployed near the Adelie Depression, for recovery in the 2012/13 season.\n\nUnderway data were also collected on this voyage, and are linked to this metadata record at the provided URL. A detailed readme is available as part of the download.\n\nFinally, ADCP (Acoustic Doppler Current Profiler) data are also linked, and are in Matlab format.", "links": [ { diff --git a/datasets/au1203_1.json b/datasets/au1203_1.json index b7e0030506..284f871cdb 100644 --- a/datasets/au1203_1.json +++ b/datasets/au1203_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au1203_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard Aurora Australis cruise au1203, voyage 3 2011/2012, from 5th January to 12th February 2012. The cruise commenced with opportunistic CTD's in the region of the Adelie Depression and the former Mertz Glacier ice tongue, followed by a full south to north occupation of the CLIVAR/WOCE meridional section I9S. A total of 95 CTD vertical profile stations were taken on the cruise, most to within 15 metres of the bottom. Over 1500 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite and silicate), dissolved inorganic carbon (i.e. TCO2), alkalinity, pH, barium (dissolved), and biological parameters, using a 24 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package, while upper water column current profile data were collected by a ship mounted ADCP. Meteorological and water property data were collected by the array of ship's underway sensors. An array of 5 current meter moorings was recovered from the Antarctic continental slope at the south end of the I9S transect.", "links": [ { diff --git a/datasets/au1402_2.json b/datasets/au1402_2.json index 6a1653710b..4c0babc162 100644 --- a/datasets/au1402_2.json +++ b/datasets/au1402_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au1402_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard Aurora Australis cruise au1402, voyage 2 2014/2015, from 5th December 2014 to 25th January 2015. The cruise commenced with a Casey resupply, followed by work around the Dalton Polynya/Moscow University Iceshelf/Totten Glacier system, and then around the Mertz Glacier region. A total of 141 CTD vertical profile stations were taken on the cruise, most to within 11 metres of the bottom. Over 1000 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite and silicate), dissolved inorganic carbon (i.e. TCO2), alkalinity, helium, 18O, and biological parameters, using a 24 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package, and bottom video footage was collected by a camera system (also mounted to the CTD package) for most casts. Upper water column current profile data were collected by a ship mounted ADCP. An underway CTD system (P.I. Alex Orsi, Texas A and M University) was used to collected measurements from the aft of the ship along several small transects around the Dalton Polynya. Meteorological and water property data were collected by the array of ship's underway sensors. 10 'Argo equivalent' floats were also deployed in both the Totten and Mertz regions, for an ice float pilot study.\n\nSix oceanographic moorings were recovered from around the Dalton Polynya, three Australian and three US (for the US moorings: P.I.'s Alex Orsi, Texas A and M University, Amy Leventer, Colgate University, and Eugene Domack, University of South Florida). Three temporary acoustic sound source moorings were also deployed then recovered in the same area, in support of an autonomous glider deployment (P.I. Craig Lee, University of Washington). Three oceanographic moorings were recovered from the Mertz region, two Australian and one French (P.I. Marie-Noelle Houssais, Universite Pierre et Marie Curie, for the French mooring).\n\nThe data set here includes the CTD and Niskin bottle data, in both text and matlab format. The included README file gives full details on file formats.", "links": [ { diff --git a/datasets/au9005_1.json b/datasets/au9005_1.json index b1418dc7d7..d1c4c60b7c 100644 --- a/datasets/au9005_1.json +++ b/datasets/au9005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted between Tasmania and Heard Island, and then around Heard and McDonald Islands from May to July 1990. A total of 96 CTD (conductivity, temperature and depth) vertical profile stations were taken, most to near bottom. No Niskin bottle water data are available unfortunately (for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic carbon, alkalinity, carbon isotopes, primary productivity, and biological parameters. Measurement and data processing techniques are summarised, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au9006_1.json b/datasets/au9006_1.json index 8af885ecae..d2213b2a63 100644 --- a/datasets/au9006_1.json +++ b/datasets/au9006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted from Tasmania to Antarctica, and then primarily in the Prydz Bay region, from January 1991 to March 1991. A total of 159 CTD (conductivity, temperature and depth) vertical profile stations were taken, most to near bottom. Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, helium, tritium, dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, dimethyl sulphide/dimethyl sulphoniopropionate, iodate/iodide, oxygen 18, primary productivity, and biological parameters, using a 24 bottle rosette sampler. Unfortunately, only salinity data from the bottle samples is available due to poor data quality of the nutrient and dissolved oxygen data. CTD salinity data have been calibrated against bottle samples, and are accurate to approximately 0.005 (PSS78). Measurement and data processing techniques are described, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation\nniskin bottle number", "links": [ { diff --git a/datasets/au91_9201_1.json b/datasets/au91_9201_1.json index b8afeaf2b8..6524bc85aa 100644 --- a/datasets/au91_9201_1.json +++ b/datasets/au91_9201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au91_9201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica from September to October 1991. A total of 36 CTD vertical profile stations were taken. Over 600 Niskin bottle water samples were collected for measurements of parameters including salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, and biological parameters, using a 24 bottle rosette sampler.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nfluorescence\nphotosynthetically active radiation\nCTD pressure (dbar)\nCTD temperature (C) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au9206_1.json b/datasets/au9206_1.json index f2d5080b26..bb321a28f4 100644 --- a/datasets/au9206_1.json +++ b/datasets/au9206_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9206_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the Southern Ocean around Heard and McDonald Islands, and in the Prydz Bay region, from January 1992 to March 1992. A total of 168 CTD (conductivity, temperature and depth) vertical profile stations were taken, most to near bottom. Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, helium, tritium, dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, dimethyl sulphide/dimethyl sulphoniopropionate, iodate/iodide, oxygen 18, primary productivity, and biological parameters, using a 24 bottle rosette sampler.\n\nCTD salinity data have been calibrated against bottle samples, although the calibration quality varies over the cruise. CTD salinity accuracies can be summarised as follows:\n\nStations 1-26: no bottle samples; conductivity calibration from later stations applied; accuracy therefore unknown.\n\nStations 27-102: accuracy approximately 0.005 (PSS78).\n\nStations 83-93: residuals a bit lower than surrounding stations: data uncertainty may be slightly increased.\n\nStations 103-111: no bottle samples; conductivity calibration from surrounding stations applied; accuracy therefore unknown.\n\nStations 112-168: significant increase in data scatter; accuracy approximately 0.010 (PSS78).\n\nThe bottle data file contains salinities and nutrients. Dissolved oxygen data exist only as titration values recorded on the laboratory analysis sheets. The nutrient data show a fair amount of scatter, particularly when looking at the nitrate vs phosphate ratios. These data should be used with caution.\n\nMeasurement and data processing techniques are described, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation\nniskin bottle number", "links": [ { diff --git a/datasets/au9301_1.json b/datasets/au9301_1.json index 4610ee9e8f..af569a5872 100644 --- a/datasets/au9301_1.json +++ b/datasets/au9301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted north of Heard Island from August 1993 to October 1993. A total of 58 CTD (conductivity, temperature and depth) vertical profile stations were taken, most to near bottom. Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, helium, tritium, dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, dimethyl sulphide/dimethyl sulphoniopropionate, iodate/iodide, oxygen 18, primary productivity, and biological parameters, using a 24 bottle rosette sampler.\n\nCTD salinity data have been calibrated against bottle samples. Upcast CTD burst data were no longer available, however comparison of bottle samples to CTD data from the equivalent downcast pressures gives a salinity accuracy of approximately 0.005 (PSS78). The bottle data file contains salinities and dissolved oxygens. Nutrient data exist only in files output from the laboratory analysis program 'DAPA', and are contained on floppy disks held by Andrew Forbes (CSIRO).\n\nMeasurement and data processing techniques are described, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation\nniskin bottle number", "links": [ { diff --git a/datasets/au9309_9391_1.json b/datasets/au9309_9391_1.json index fa1e348375..8a1eff048b 100644 --- a/datasets/au9309_9391_1.json +++ b/datasets/au9309_9391_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9309_9391_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along WOCE Southern Ocean meridional sections SR3 and P11 between Tasmania and Antarctica, from March to May, 1993. A total of 128 CTD vertical profile stations were taken, most to near bottom. Over 2500 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, and silicate), dissolved inorganic carbon, carbon isotopes, barium, and biological parameters, using 24 and 12 bottle rosette samplers. The data report describes measurement and data processing techniques, and a summary of the data are presented in graphical and tabular form. \n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nfluorescence\nphotosynthetically active radiation\nCTD pressure (dbar)\nCTD temperature (C) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au9404_1.json b/datasets/au9404_1.json index 869068ab75..f69b1f50f5 100644 --- a/datasets/au9404_1.json +++ b/datasets/au9404_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9404_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica, and along the part of WOCE Southern ocean zonal section S4 lying between approximately 110 and 162 deg.E, from December 1994 to February 1995. An array of 4 current meter moorings at approximately 51 deg.S in the vicinity of the SR3 line was successfully recovered. A total of 107 CTD vertical profile stations were taken, most to near bottom. Over 2380 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, helium, tritium, dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, dimethyl sulphide/dimethyl sulphoniopropionate, iodate/iodide, oxygen 18, primary productivity, and biological parameters, using a 24 bottle rosette sampler. Measurement and data processing techniques are described, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nfluorescence\nphotosynthetically active radiation\nCTD pressure (dbar)\nCTD temperature (C) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au9407_1.json b/datasets/au9407_1.json index cbe5364581..643b6e4714 100644 --- a/datasets/au9407_1.json +++ b/datasets/au9407_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9407_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in January 1994 (on voyage 7 of the 1993/1994 summer season) along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica, and along a northward section lying between 82 and 86 deg.E and crossing the Princess Elizabeth Trough. Additional measurements were made at mooring locations, and at a time series station. A total of 102 CTD vertical profile stations were taken, most to near bottom. Over 2000 Niskin water bottle samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic and organic carbon, carbon 13, dimethyl sulphide/dimethyl sulphoniopropionate, iodate/iodide, and biological parameters, using a 24 bottle rosette sampler. Measurement and data processing techniques are described, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nfluorescence\nphotosynthetically active radiation\nCTD pressure (dbar)\nCTD temperature (C) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au9501_1.json b/datasets/au9501_1.json index 2353729900..1be4fb2a4e 100644 --- a/datasets/au9501_1.json +++ b/datasets/au9501_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9501_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica, and around the boundary of a square-plan test volume south of the Antarctic Divergence, from July to September 1995 on voyage 1 of the 1995/1996 summer season. A total of 208 CTD vertical profile stations were taken, 64 of those to near bottom, and the remaining 144 to a depth of 500 m. Over 2300 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved organic and inorganic carbon, iodate/iodide, primary productivity, and biological parameters, using both a 24 and 12 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Measurement and data processing techniques are summarised, and a summary of the data are presented in graphical and tabular form. \n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nfluorescence\nphotosynthetically active radiation\nCTD pressure (dbar)\nCTD temperature (C) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au9601_1.json b/datasets/au9601_1.json index 8134d4b760..4b471fa559 100644 --- a/datasets/au9601_1.json +++ b/datasets/au9601_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9601_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along WOCE Southern Ocean meridional section SR3 between Tasmania and Antarctica from August to September 1996. A total of 71 CTD vertical profile stations were taken, most to near bottom. Over 1500 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic carbon, alkalinity, carbon isotopes, primary productivity, and biological parameters, using a 24 bottle rosette sampler. Measurement and data processing techniques are summarised, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au9604_1.json b/datasets/au9604_1.json index 36f917080d..f376ac1fc3 100644 --- a/datasets/au9604_1.json +++ b/datasets/au9604_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9604_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted along a series of meridional and zonal sections along the Antarctic continental shelf and slope region between 80 and 150 deg.E, from January to March 1996 during the BROKE cruise of the Aurora Australis. A total of 147 CTD vertical profile stations were taken, most to near bottom. Over 2450 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), chlorofluorocarbons, oxygen 18, primary productivity, and biological parameters, using a 24 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Measurement and data processing techniques are summarised, and a summary of the data are presented in graphical and tabular form.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au96_9705_1.json b/datasets/au96_9705_1.json index b9e6939c28..b073593a87 100644 --- a/datasets/au96_9705_1.json +++ b/datasets/au96_9705_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au96_9705_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "10 full depth CTD casts were taken in the vicinity of Mawson and Casey as part of the geoscience work on Aurora Australis cruise au9705, January to March 1997. A 12 bottle rosette sampler was used to collect salinity samples only, for calibration of CTD data.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ntemperature (C) (ITS-90)\nsalinity (PSS78)\ndensity-1000 (kg.m-3)\nspecific volume anomaly x 108\ngeopotential anomaly\ndissolved oxygen (mmol.l-1)\nnumber of data points used in the 2 dbar averaging bin\nstandard deviation of temperature values in the 2 dbar bin\nstandard deviation of conductivity values in the 2 dbar bin\nstation number\nCTD pressure (dbar)\nCTD temperature (oC) (ITS-90)\nreversing thermometer temperature (C)\nCTD conductivity (mS.cm-1)\nCTD salinity (PSS78)\nbottle salinity (PSS78)\nbottle quality flag (-1=rejected, 0=suspect, 1=good)\nniskin bottle number", "links": [ { diff --git a/datasets/au97_9801_1.json b/datasets/au97_9801_1.json index 6b10744e93..ce622bb395 100644 --- a/datasets/au97_9801_1.json +++ b/datasets/au97_9801_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au97_9801_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the Subantarctic Zone south of Tasmania in September 1997. 5 sediment trap moorings were deployed, and a total of 10 CTD vertical profiles were taken. Over 90 Niskin bottle water samples were collected for the measurement of salinity and nutrients (phosphate, nitrate+nitrite, silicate).\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au97_9806_1.json b/datasets/au97_9806_1.json index 39aa7acee3..f7ec73590a 100644 --- a/datasets/au97_9806_1.json +++ b/datasets/au97_9806_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au97_9806_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted in the Subantarctic Zone south of Tasmania in March 1998. A total of 97 CTD vertical profiles were taken. Over 800 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, N2O isotopes, pH, oxygen-18, barium, nitrogen-15, arsenic, ammonia, DMS/P, bacteria, silicon-32, particulate silicon, productivity, ETS, pigments, species counts, cytometry, particulate organic carbon and nitrogen, urea, copper and iron, using a 24 bottle rosette sampler. Four sediment trap moorings were recovered, and two were redeployed.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au97_9807_1.json b/datasets/au97_9807_1.json index e277bdb41a..6c6cab01e9 100644 --- a/datasets/au97_9807_1.json +++ b/datasets/au97_9807_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au97_9807_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were conducted on a cruise of the Aurora Australis to the Southern Ocean in April and May of 1998. A total of 97 CTD vertical profiles were taken. Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, N2O isotopes, pH, oxygen-18, barium, nitrogen-15, arsenic, ammonia, DMS/P, bacteria, silicon-32, particulate silicon, productivity, ETS, pigments, species counts, cytometry, particulate organic carbon and nitrogen, urea, copper and iron, using a 24 bottle rosette sampler.\n \nThese data have been recovered by the AADC - as such this is a generic metadata record.\n\nThe fields in this dataset are:\noceanography\nship\nstation number\ndate\nstart time\nbottom time\nfinish time\ncruise\nstart position\nbottom position\nfinish position\nmaximum position\nbottom depth\npressure\ntemperature (T-90)\nsalinity\nsigma-T\nspecific volume anomaly\ngeopotential anomaly\ndissolved oxygen\nfluorescence\nphotosynthetically active radiation", "links": [ { diff --git a/datasets/au9901_1.json b/datasets/au9901_1.json index 402b9312cd..8757e4ad48 100644 --- a/datasets/au9901_1.json +++ b/datasets/au9901_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "au9901_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements conducted on voyage 1 of the Aurora Australis of the 1999-2000 season. These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data.\n\nThese data were collected by Mark Rosenberg.\n\nThis metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise.\n\nBasic information about voyage 1:\nPolynya study off Mertz Glacier at about 145 deg E. The vessel departed from Port Arthur for the polynya study site without returning to Hobart. The voyage also deployed moorings and delivered biologists (for seal and penguin programs) and a small quantity of essential supplies and mail to Macquarie Island.\n\nLeader: Dr Ian Allison\nDeputy Leader: Dr Tony Worby\nCargo Supervisor: Dr Vicky Lytle\n\nSee the readme files in the downloads for more information.", "links": [ { diff --git a/datasets/auslig_m7.json b/datasets/auslig_m7.json index 291a7bfbf7..5b79b75459 100644 --- a/datasets/auslig_m7.json +++ b/datasets/auslig_m7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "auslig_m7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The M7 data represent the highest points in each 30 minute by 30\n minute grid square for Australia.\n \n see: 'http://www.ga.gov.au/'\n \n The following text was abstracted from Bruce Gittings' Digital\n Elevation Data Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'.\n The catalogue is a comprehensive source of information on digital\n elevation data and should be retrieved in its entirety for additional\n information.\n \n Australian Data is available from the Australian Survey and Land\n Information Group (AUSLIG). There are three products; M7 are Critical\n Aeronautical Heights which represent the highest point in each 30'x30'\n quad, M8 are Spot heights (ie. an irregular grid) and M9 represents an\n 18\" (~500m) grid at 1:250,000 scale (gridded from M8 using an\n Hutchinson Algorithm). Both M8 and M9 have incomplete coverage of the\n country.\n \n The 500m grid covers 30% of Australia (Southern New South Wales,\n Victoria, parts of Northern Queensland and selected cities). The size\n of the 1:250 000 scale files is 60,501 points each x 63 files =\n 38,176,131 elevation points. Costs: License 1:250 000 AU$1000 / File\n 1:100 000 AU$250.\n \n NB. 1996: 100m and 200m DEMs covering all of Australia are also\n now available. Prices are as follows (in US Dollars):\n \n Per km2 Total Cost Copyright Restrictions\n ------- ---------- ----------------------\n 100m DEM $0.0028 $21,433 One-time license fee\n 200m DEM $0.0017 $13,089 \" \"\n \n URL: 'http://www.auslig.gov.au/'", "links": [ { diff --git a/datasets/automated-avalanche-release-area-pra-delineation-davos_1.0.json b/datasets/automated-avalanche-release-area-pra-delineation-davos_1.0.json index 4aff15c4d9..e306c97d82 100644 --- a/datasets/automated-avalanche-release-area-pra-delineation-davos_1.0.json +++ b/datasets/automated-avalanche-release-area-pra-delineation-davos_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "automated-avalanche-release-area-pra-delineation-davos_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the output and reference data published in the paper \"Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping\" Yves B\u00fchler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences. Abstract: Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations. In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 \u2013 30 years return period) and extreme (100 \u2013 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.", "links": [ { diff --git a/datasets/automatic-classification-of-avalanches_1.0.json b/datasets/automatic-classification-of-avalanches_1.0.json index 5d53f29f25..7d84b8228d 100644 --- a/datasets/automatic-classification-of-avalanches_1.0.json +++ b/datasets/automatic-classification-of-avalanches_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "automatic-classification-of-avalanches_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.", "links": [ { diff --git a/datasets/avalanche-accidents-in-switzerland-since-1970-71_1.0.json b/datasets/avalanche-accidents-in-switzerland-since-1970-71_1.0.json index ea3054806c..e5b1742a0b 100644 --- a/datasets/avalanche-accidents-in-switzerland-since-1970-71_1.0.json +++ b/datasets/avalanche-accidents-in-switzerland-since-1970-71_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avalanche-accidents-in-switzerland-since-1970-71_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**. This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one person involved (caught). The data set commences on 01/10/1970. After the completion of a hydrological year, the new data is added. The following information is provided: * avalanche identifier * date of the accident * accuracy of the date in range of days before and after * canton * municipality * start zone point latitude * start zone point longitude * start zone point accuracy (in meters) * start zone point elevation (in meteres above sea level) * slope aspect (main orientation of start zone) * slope inclination (in degree, steepest point within start zone) * forecasted avalanche danger level 1 (first danger) * forecasted avalanche danger level 2 (second danger) * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast) * number of dead persons * number of caught persons * number of fully buried persons * activity/location of the accident party at the time of the incident", "links": [ { diff --git a/datasets/avalanche-fatalities-european-alps-1969-2015_1.0.json b/datasets/avalanche-fatalities-european-alps-1969-2015_1.0.json index e3b4444964..006c2f00de 100644 --- a/datasets/avalanche-fatalities-european-alps-1969-2015_1.0.json +++ b/datasets/avalanche-fatalities-european-alps-1969-2015_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avalanche-fatalities-european-alps-1969-2015_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities. Data complementing Figure 2 in: _\"Avalanche fatalities in the European Alps: long-term trends and statistics\"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pav\u0161ek, M., Valt, M., and Darms, G. Data description: please refer to section 2 (Data and Methods) in the mentioned publication", "links": [ { diff --git a/datasets/avalanche-fatalities-per-calendar-year-since-1936_1.0.json b/datasets/avalanche-fatalities-per-calendar-year-since-1936_1.0.json index 234fc79230..d8f38a14a3 100644 --- a/datasets/avalanche-fatalities-per-calendar-year-since-1936_1.0.json +++ b/datasets/avalanche-fatalities-per-calendar-year-since-1936_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avalanche-fatalities-per-calendar-year-since-1936_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset. If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936] The following information is contained (by column and column title): - year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definitions for these four categories, as described in the guidelines to the avalanche accident database are: __tour:__ activities include back-country ski, snowboard or snow-shoe touring __offpiste:__ access from ski area, generally from the top of a skilift with short hiking distances __transportation.corridors__ (Techel et al., 2016): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) __buildings__ (Techel et al., 2016): people inside or just outside buildings, and workers on high alpine building sites", "links": [ { diff --git a/datasets/avalanche-fatalities-switzerland-1936_1.0.json b/datasets/avalanche-fatalities-switzerland-1936_1.0.json index eb6be4359f..cd5864853d 100644 --- a/datasets/avalanche-fatalities-switzerland-1936_1.0.json +++ b/datasets/avalanche-fatalities-switzerland-1936_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avalanche-fatalities-switzerland-1936_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset. The following information is contained (by column and column title): - hydrological year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definition for these four categories as described in the guidelines to the avalanche accident database: **tour**: activities include back-country ski, snowboard or snow-shoe touring **offpiste**: access from ski area, generally from the top of a skilift with short hiking distances **transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) **buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people inside or just outside buildings, and workers on high alpine building sites", "links": [ { diff --git a/datasets/avalanche-prediction-snowpack-simulations_1.0.json b/datasets/avalanche-prediction-snowpack-simulations_1.0.json index f4e69dbde0..1c2cba565f 100644 --- a/datasets/avalanche-prediction-snowpack-simulations_1.0.json +++ b/datasets/avalanche-prediction-snowpack-simulations_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avalanche-prediction-snowpack-simulations_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contained in this repository was used in the analysis by Mayer et al. (2023): Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.", "links": [ { diff --git a/datasets/avapsimpacts_1.json b/datasets/avapsimpacts_1.json index d549ab7f1a..85b88b24c2 100644 --- a/datasets/avapsimpacts_1.json +++ b/datasets/avapsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avapsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde\u2019s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format.", "links": [ { diff --git a/datasets/avhrr_822_1.json b/datasets/avhrr_822_1.json index 1a6241578b..be1edc09d8 100644 --- a/datasets/avhrr_822_1.json +++ b/datasets/avhrr_822_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrr_822_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images. These AVHRR data contain four main sets of data:1.5 km daily site extracts of SAFARI core sites (2000)1.5 km 15-day composites of SAFARI core sites (1998-2000)1.5 km 15-day composites of the southern African region (Mar, Sept 2000)6 km 15-day composites of the southern African region (1998-2000)The primary data layers for site extracts and regional composites are fire pixel counts and maximum NDVI. The fire product is different for the daily and for the composited products (see readme file) and a fire product is not included in the 1.5 km regional data set. NDVI composite-associated data layers for the regional data sets include land surface temperature, reflectance, solar zenith angle, view zenith angle, and relative azimuth angle. NDVI composite-associated data layers for the site extracts include these same variables as well as brightness temperature, fire mask composite, latitude, and longitude. The data are stored in binary image format files. There is a metadata file for each site and date/compositing period, in ASCII format.", "links": [ { diff --git a/datasets/avhrr_albedo_1995_xdeg_928_1.json b/datasets/avhrr_albedo_1995_xdeg_928_1.json index c70fdc734c..99aa67aa94 100644 --- a/datasets/avhrr_albedo_1995_xdeg_928_1.json +++ b/datasets/avhrr_albedo_1995_xdeg_928_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrr_albedo_1995_xdeg_928_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree. Black-sky albedo (direct beam contribution) and white-sky (Completely diffuse contribution) can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon. ", "links": [ { diff --git a/datasets/avhrrl3b_481_1.json b/datasets/avhrrl3b_481_1.json index 76c3425827..1a421f8028 100644 --- a/datasets/avhrrl3b_481_1.json +++ b/datasets/avhrrl3b_481_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrrl3b_481_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery. These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996. Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region. ", "links": [ { diff --git a/datasets/avhrrl4b_438_1.json b/datasets/avhrrl4b_438_1.json index 0c36a2d0a2..53fdcc3b77 100644 --- a/datasets/avhrrl4b_438_1.json +++ b/datasets/avhrrl4b_438_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrrl4b_438_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images. Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. ", "links": [ { diff --git a/datasets/avhrrl4c_439_1.json b/datasets/avhrrl4c_439_1.json index 4cdec3429b..2856463ec7 100644 --- a/datasets/avhrrl4c_439_1.json +++ b/datasets/avhrrl4c_439_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrrl4c_439_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images. Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region.", "links": [ { diff --git a/datasets/avhrrlc1_434_1.json b/datasets/avhrrlc1_434_1.json index bbb05e50ee..a12c2be904 100644 --- a/datasets/avhrrlc1_434_1.json +++ b/datasets/avhrrlc1_434_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "avhrrlc1_434_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This regional land cover data set was developed as part of a multitemporal 1-km AVHRR land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada (Steyaert et al., 1997).", "links": [ { diff --git a/datasets/aws_gis_1.json b/datasets/aws_gis_1.json index 43af716895..217812bddf 100644 --- a/datasets/aws_gis_1.json +++ b/datasets/aws_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "aws_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This layer is a point dataset in the Geographical Information System (GIS). Point data represents Australian Antarctic Automatic Weather Stations. The operating dates for all stations is attached in the attribute table. The dataset was compiled in August 2003 from the The Australian Antarctic automatic weather station dataset http://aws.acecrc.org.au/datapage.html", "links": [ { diff --git a/datasets/b017235a8e544d6fbad21387ebfbf0d8_NA.json b/datasets/b017235a8e544d6fbad21387ebfbf0d8_NA.json index 56ec523a6d..8afc4cea20 100644 --- a/datasets/b017235a8e544d6fbad21387ebfbf0d8_NA.json +++ b/datasets/b017235a8e544d6fbad21387ebfbf0d8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b017235a8e544d6fbad21387ebfbf0d8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.2) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016)The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065", "links": [ { diff --git a/datasets/b03b3887ad2f4d5481e7a39344239ab2_NA.json b/datasets/b03b3887ad2f4d5481e7a39344239ab2_NA.json index d4362983a8..35c5bd7e4b 100644 --- a/datasets/b03b3887ad2f4d5481e7a39344239ab2_NA.json +++ b/datasets/b03b3887ad2f4d5481e7a39344239ab2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b03b3887ad2f4d5481e7a39344239ab2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the Swansea University (SU) algorithm, version 4.3. It covers the period from 2002 - 2012.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/b0ec72a28b6a4829a33ed9adc215d5bc_NA.json b/datasets/b0ec72a28b6a4829a33ed9adc215d5bc_NA.json index fd5edc258a..aa1a2e12ec 100644 --- a/datasets/b0ec72a28b6a4829a33ed9adc215d5bc_NA.json +++ b/datasets/b0ec72a28b6a4829a33ed9adc215d5bc_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b0ec72a28b6a4829a33ed9adc215d5bc_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "links": [ { diff --git a/datasets/b1bd715112ca43ab948226d11d72b85e_NA.json b/datasets/b1bd715112ca43ab948226d11d72b85e_NA.json index aa05d8d8ca..5a1704a48c 100644 --- a/datasets/b1bd715112ca43ab948226d11d72b85e_NA.json +++ b/datasets/b1bd715112ca43ab948226d11d72b85e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b1bd715112ca43ab948226d11d72b85e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide.", "links": [ { diff --git a/datasets/b1f1ac03077b4aa784c5a413a2210bf5_NA.json b/datasets/b1f1ac03077b4aa784c5a413a2210bf5_NA.json index 8515f618c1..099b878fab 100644 --- a/datasets/b1f1ac03077b4aa784c5a413a2210bf5_NA.json +++ b/datasets/b1f1ac03077b4aa784c5a413a2210bf5_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b1f1ac03077b4aa784c5a413a2210bf5_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "links": [ { diff --git a/datasets/b25d4a6174de4ac78000d034f500a268_NA.json b/datasets/b25d4a6174de4ac78000d034f500a268_NA.json index 1309bf46fc..50c3acc743 100644 --- a/datasets/b25d4a6174de4ac78000d034f500a268_NA.json +++ b/datasets/b25d4a6174de4ac78000d034f500a268_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b25d4a6174de4ac78000d034f500a268_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).Case A: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/b382ebe6679d44b8b0e68ea4ef4b701c_NA.json b/datasets/b382ebe6679d44b8b0e68ea4ef4b701c_NA.json index b66e1a5814..517cb99bcb 100644 --- a/datasets/b382ebe6679d44b8b0e68ea4ef4b701c_NA.json +++ b/datasets/b382ebe6679d44b8b0e68ea4ef4b701c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b382ebe6679d44b8b0e68ea4ef4b701c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).", "links": [ { diff --git a/datasets/b431fbecf73c4442ad5d7bcf80929b03_NA.json b/datasets/b431fbecf73c4442ad5d7bcf80929b03_NA.json index b3e99576e8..9dba290c10 100644 --- a/datasets/b431fbecf73c4442ad5d7bcf80929b03_NA.json +++ b/datasets/b431fbecf73c4442ad5d7bcf80929b03_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b431fbecf73c4442ad5d7bcf80929b03_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc\u00e2\u0080\u009d contains monthly zonal mean data for GOMOS in 2008.", "links": [ { diff --git a/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json b/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json index 357c6562e7..78a275f19a 100644 --- a/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json +++ b/datasets/b480d7c8-3694-4772-8294-941f3d3ede9f_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b480d7c8-3694-4772-8294-941f3d3ede9f_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The European Remote Sensing Forest/Non-forest Digital Map was originally prepared for the European Space Agency (ESA) as a contribution to the World Forest Watch project of the International Space Year (ISY), 1992. The actual production of the map was carried out by a consortium of four companies, GAF mbH (Munich FRG), the Swedish Space Corporation (Kiruna), SCOT Conseil (France) and the National Land Survey of Finland (Helsinki). It is based entirely on the digital classification of NOAA/AVHRR-HRPT* one-kilometer resolution multispectral data, approximately 70 scenes from the summer periods only of 1990 to 1992.\n\nAs such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was \"economically\" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map:\n\n - Satellite data selection (minimal cloud cover)/acquisition;\n - Data pre-processing for a) geometric correction and b) cloud masking;\n - Data subset stratification into homogeneous spectral zones;\n - Data subset classification (Bayesian maximum likelihood);\n - Accuracy assessment (using classified Landsat MSS);\n - Mosaicking of classified data subsets;\n - Merging of final results and overlays;\n - Cartographic preparation.\n\nThe producers of the digital map used only data from AVHRR channels 1, 2 and 3 with \"maximal geometric and radiometric resolution\"; that is, the central 1200 to 1600 pixels of any given scan line, to map European forest areas greater than one square kilometer. Because the AVHRR sensor is not capable of distinguishing among different European forest types, many broad classes (Boreal, Central European and Mediterranean) are grouped together as \"forest\" in the digital map.\n\n* - the National Oceanic and Atmospheric Administration (NOAA) /\n satellite's Advanced Very High Resolution Radiometer (AVHRR)\n sensor - and High Resolution Picture Transmission (HRPT) data.\n\n\nFor the 32 Landsat scenes compared with the NOAA/AVHRR forest/non-forest classification, the overall accuracy (percentage of pixels \"correctly\" classified) was calculated as 82.5%, and the surface area accuracy (degree of agreement in areal extent between the NOAA/AVHRR results and the Landsat MSS used as \"ground truth\") was found to be 93.8%.\n\nFormat of the Original ESA/ESTEC-Provided Data Set\n\nThe European Forest/Non-Forest Digital Map was provided to GRID on a single 150-Mb data cartridge, as a total of seven ARC/INFO-format data files for separate parts of the continent as follows: Northwest; North; Central; Southwest and Southeast Europe; the Commonwealth of Independent States (CIS, up to the Ural Mountains only); and North Africa. A total of 53 countries are included altogether. Within this original digital map, data are coded by country and category (i.e. forest, non-forest or water), but \"overall\" selections of one category or another are rendered difficult because the codes are in combination (i.e. country + category). Also, the large size of the seven individual ARC/INFO coverages all but prohibits working with the digital data for the entire pan-European area.\n\nExplanation of the Data Processing done by GRID\n\nGRID's objective in data processing of the European Forest/Non-forest Digital Map was to create a single seamless product covering most of the continent, for forestry and GIS studies at a pan-European level. The assemblage of the seven original individual coverages prepared for ESA/ESTEC into a single entity proved impractical due to both hardware and software limitations; thus, the seventh and largest portion for the Commonwealth of Independent States (CIS) was left out of the overall assemblage. Even so, it was still necessary to generalize the data somewhat, given the total number of polygons (>100000) and arcs (>170000) in the remaining six original coverages.\n\nThus, the following methodology was followed to reduce the amount of data and assemble the six coverages into a single product (all data processing was done using commands in the ARC/INFO software):\n\n- Polygon elimination based on area - After several experiments, polygons with an area smaller than four square kilometers (sq. km.) were eliminated. This minimum area proved to be a good compromise between original forest patterns and number of polygons eliminated (total of 70%). The equivalent of four sq. km. at a central latitude within each of the six original coverages was calculated, and this value was used in the 'ELIMINATE' command. It would have been more accurate to perform the 'ELIMINATEs' with the data in an equal-area projection, but for practical reasons (space and time) they were not.\n\n- Assembling six coverages into one - The six coverages were put together using the 'MAPJOIN' command. The software limitation of a maximum 10000 arcs per polygon was circumvented by splitting the outer polygon of Europe into three separate parts.\n\n - Editing errors produced by step (2) - The 'MAPJOIN' command puts adjacent coverages together and recreates topology using an assigned distance known as the \"fuzzy tolerance\" factor. Any reasonable factor forces some lines to converge, creating dangling arcs and new polygons without IDs. As a result, interactive editing of the new coverage was necessary to delete dangling arcs, and to assign proper polygon IDs.\n \n- Update of the topology - After the modifications made in step (3), it was necessary to re-create the polygon topology using 'CLEAN'.\n\n- Addition of INFO item 'classes' - A new numeric item (format 3 3 I) was added in the polygon attribute table (.PAT) to contain the following values: 1) Forest; 2) Non-forest; and 3) Water. This item allows a user to select e.g. all of the European forested area polygons, as opposed to just those within a single country, in one simple INFO command.\n\nThe European Forest/Non-forest data set is available from GRID as one ARC/INFO 'EXPORT'-format data file in the Geographic Projection, which covers an area from 20 to 80 degrees North latitude, and -30 degrees West to 60 degrees East longitude. The single data file \"EURO_FOR.E00\" comprises 77.25 Mb., but after being 'IMPORTed' to the equivalent ARC/INFO coverage, is reduced to 19.7 Mb in size.\n\nThere is also the separate, original (non-generalized) data file which covers the CIS area alone; this additional 'EXPORT'-format data file \"CIS.E00\" comprises 68.262 Mb. Users who would prefer to have other original portions of the European Forest/Non-forest Digital Map listed above, as opposed to the GRID version documented herein, are requested to contact ESA/ESTEC at the address listed below.\n\nReference and Source\n\nThe source of the data set is the ESA/ESTEC ISY Office*, as modified by UNEP/GRID-Geneva. The proper reference to the data set is \"ESA, 1992, Remote sensing forest map of Europe (brochure), ESA/ESTEC, 18 pages.\" ESA/ESTEC also provides a paper entitled \"Digital data set of the remote sensing forest map of Europe; guidelines for data handling (as prepared by GAF-Munich in April 1993)\", which contains much useful information about their original digital data product and the seven individual data files they distribute as one entity. In addition, ESA/ESTEC distributes a paper map of the original product having the same name as above, at a scale of 1:6 000 000 (the paper map uses the Lambert Azimuthal Equal-Area projection).\n\n\n* - the European Space Agency/European Space Research and Technology Centre - the International Space Year; P. O. Box 299; 2200 AG Noordwijk; The Netherlands (Mr. K. Pseiner; fax = 01719-17400).\n", "links": [ { diff --git a/datasets/b54d5f1c08594879a05929ce09951c56_NA.json b/datasets/b54d5f1c08594879a05929ce09951c56_NA.json index 807b65c516..1f8afe0fc1 100644 --- a/datasets/b54d5f1c08594879a05929ce09951c56_NA.json +++ b/datasets/b54d5f1c08594879a05929ce09951c56_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b54d5f1c08594879a05929ce09951c56_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/b673f41b-d934-49e4-af6b-44bbdf164367_NA.json b/datasets/b673f41b-d934-49e4-af6b-44bbdf164367_NA.json index f314ade640..052ad2fbd1 100644 --- a/datasets/b673f41b-d934-49e4-af6b-44bbdf164367_NA.json +++ b/datasets/b673f41b-d934-49e4-af6b-44bbdf164367_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "b673f41b-d934-49e4-af6b-44bbdf164367_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)\" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between \u201339.5\u00b0C and +87\u00b0C with a radiometric resolution of 0.5\u00b0C. A value of \u201340\u00b0C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/", "links": [ { diff --git a/datasets/bark-and-wood-boring-insects-in-pines_1.0.json b/datasets/bark-and-wood-boring-insects-in-pines_1.0.json index 2043e9575b..0047d72c22 100644 --- a/datasets/bark-and-wood-boring-insects-in-pines_1.0.json +++ b/datasets/bark-and-wood-boring-insects-in-pines_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bark-and-wood-boring-insects-in-pines_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack.", "links": [ { diff --git a/datasets/baro-levelling-to-domec_1.json b/datasets/baro-levelling-to-domec_1.json index bb8c348306..907c8d5404 100644 --- a/datasets/baro-levelling-to-domec_1.json +++ b/datasets/baro-levelling-to-domec_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "baro-levelling-to-domec_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Record of barometric leveling measurements taken during the traverse from Pioneerskaya to Dome C (year currently unknown).\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/baro_pressure_1968_1.json b/datasets/baro_pressure_1968_1.json index 7f0d8a5c33..3ea671a43d 100644 --- a/datasets/baro_pressure_1968_1.json +++ b/datasets/baro_pressure_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "baro_pressure_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements taken of barometric pressure and air temperature during traverse across Law Dome and Wilkes Land in 1968.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/basal_area-92_1.0.json b/datasets/basal_area-92_1.0.json index 0da5c385cb..95f7e73479 100644 --- a/datasets/basal_area-92_1.0.json +++ b/datasets/basal_area-92_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "basal_area-92_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sum of the stem cross-section areas of all living trees and shrubs starting at 12 cm dbh (standing and lying) at a height of 1.3 m (dbh measurement height). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/basal_area_of_dead_wood-171_1.0.json b/datasets/basal_area_of_dead_wood-171_1.0.json index 1dfded00e1..b639bdc16d 100644 --- a/datasets/basal_area_of_dead_wood-171_1.0.json +++ b/datasets/basal_area_of_dead_wood-171_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "basal_area_of_dead_wood-171_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sum of the stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/basal_area_of_dead_wood_nfi1-247_1.0.json b/datasets/basal_area_of_dead_wood_nfi1-247_1.0.json index 8c306f8a7a..1d013a3bf3 100644 --- a/datasets/basal_area_of_dead_wood_nfi1-247_1.0.json +++ b/datasets/basal_area_of_dead_wood_nfi1-247_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "basal_area_of_dead_wood_nfi1-247_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sum of stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height) recorded according to the NFI1 method. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/base-cation-dynamics-in-an-oriental-beech-forest_1.0.json b/datasets/base-cation-dynamics-in-an-oriental-beech-forest_1.0.json index a9e244a4ae..4f15af8db4 100644 --- a/datasets/base-cation-dynamics-in-an-oriental-beech-forest_1.0.json +++ b/datasets/base-cation-dynamics-in-an-oriental-beech-forest_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "base-cation-dynamics-in-an-oriental-beech-forest_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap. In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.", "links": [ { diff --git a/datasets/basin_border_670_1.json b/datasets/basin_border_670_1.json index 2d4ffcf6a4..72c3a77de2 100644 --- a/datasets/basin_border_670_1.json +++ b/datasets/basin_border_670_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "basin_border_670_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is an expanded version of the Costa et al. (2000) data set and consists of a single grid with values of 1 for cells within the basins and 0 for cells outside. The resolution of the data set is 5 x 5 min (approximately 9 x 9 km). The area of this data set is consistent with the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data file is in ASCII GRID format.", "links": [ { diff --git a/datasets/bathy_proposedMPAs_eastantarctica_1.json b/datasets/bathy_proposedMPAs_eastantarctica_1.json index de59dcf88b..35e01e85e7 100644 --- a/datasets/bathy_proposedMPAs_eastantarctica_1.json +++ b/datasets/bathy_proposedMPAs_eastantarctica_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bathy_proposedMPAs_eastantarctica_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division (AAD) has developed a proposal for the establishment of seven Marine Protected Areas (MPAs) located around east Antarctica for the purposes of marine ecosystem conservation. As seafloor morphology is a key component of marine ecosystems, this bathymetry compilation for the proposed MPAs was produced to support the AAD proposal. All bathymetry data available to Geoscience Australia at the time of compilation were used. This included multibeam and singlebeam acoustic data which were verified and processed to ensure the data were as accurate as possible. Processing included sound velocity corrections, navigation verification and the rejection of erroneous data points. Once processed, the data were gridded to 100m resolution and projected into suitable WGS84 UTM zones. The gridded data was exported into several formats to facilitate ease of use. The formats include xyz files, ESRI rasters, geoTIFs, CARISTM image files and soundings.\n\nThe data and the technical report are available for download from URLs below.", "links": [ { diff --git a/datasets/bats-and-nocturnal-insects-in-urban-green-areas_1.0.json b/datasets/bats-and-nocturnal-insects-in-urban-green-areas_1.0.json index 9bcb9d93f0..8eee991986 100644 --- a/datasets/bats-and-nocturnal-insects-in-urban-green-areas_1.0.json +++ b/datasets/bats-and-nocturnal-insects-in-urban-green-areas_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bats-and-nocturnal-insects-in-urban-green-areas_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Animal biodiversity in cities is generally expected to be uniformly reduced, but recent studies show that this is modulated by the composition and configuration of Urban Green Areas (UGAs). UGAs represent a heterogeneous network of vegetated spaces in urban settings that have repeatedly shown to support a significant part of native diurnal animal biodiversity. However, nocturnal taxa have so far been understudied, constraining our understanding of the role of UGAs on maintaining ecological connectivity and enhancing overall biodiversity. We present a well-replicated multi-city study on the factors driving bat and nocturnal insect biodiversity in three European cities. To achieve this, we sampled bats with ultrasound recorders and flying insects with light traps during the summer of 2018. Results showed a greater abundance and diversity of bats and nocturnal insects in the city of Zurich, followed by Antwerp and Paris. We identified artificial lighting in the UGA to lower bat diversity by probably filtering out light-sensitive species. We also found a negative correlation between both bat activity and diversity and insect abundance, suggesting a top-down control. An in-depth analysis of the Zurich data revealed divergent responses of the nocturnal fauna to landscape variables, while pointing out a bottom-up control of insect diversity on bats. Thus, to effectively preserve biodiversity in urban environments, UGAs management decisions should take into account the combined ecological needs of bats and nocturnal insects and consider the specific spatial topology of UGAs in each city.", "links": [ { diff --git a/datasets/bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA.json b/datasets/bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA.json index 66b140e79c..9dcf2ed194 100644 --- a/datasets/bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA.json +++ b/datasets/bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in Staring Spotlight mode. Staring Spotlight imaging allows for a spatial resolution of up to 25 cm. The scene size varies depending on the incidence angle. As an example, 4 km (across swath) x 3.7 km (in orbit direction) can be achieved at 60\u00b0. TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.\t\t\tFor more information concerning the TerraSAR-X mission, the reader is referred to:\t\t\thttps://www.dlr.de/content/de/missionen/terrasar-x.html", "links": [ { diff --git a/datasets/bds_dragonfly.json b/datasets/bds_dragonfly.json index 442b3f894e..449d1e98b1 100644 --- a/datasets/bds_dragonfly.json +++ b/datasets/bds_dragonfly.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bds_dragonfly", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dragonflies are among the most ancient of living creatures. Fossil\nrecords, clearly recognisable as dragonflies, go back to Carboniferous\ntimes which means that they date back almost 300 million years,\npredating pterodactyls by 100 million years and birds by some 150\nmillion. It would he tragic if, after surviving such an unimaginable\nnumber of years, it should be our generation that witnesses the\ndecline of these fascinating and beautiful insects.\n\nThe British Dragonfly Society maintains a checklist of British and\nIrish dragonflies.\nThis checklist includes all British and Irish species including\nmigrants, vagrants and species now believed extinct in the British\nIsles. The species name provides a link to a photograph where available.\n\nInformation was obtained from\n\"http://www.british-dragonflies.org.uk/content/uk-species\".", "links": [ { diff --git a/datasets/beaver_sat_1.json b/datasets/beaver_sat_1.json index 9d12e23787..8674f4ba3c 100644 --- a/datasets/beaver_sat_1.json +++ b/datasets/beaver_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "beaver_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Double-sided satellite image and topographic map of Beaver Lake, Antarctica. These maps were produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. Both maps are at a scale of 1:100 000. The satellite image map was produced from SPOT 1 and LANDSAT 5 TM scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases and gives some historical text information. The map has both geographical and UTM co-ordinates. Contours on the topographic map were derived from Russian maps (values have not been verified.) This map is also projected on a transverse mercator projection, and shows traverses/routes/foot track charts, bases/stations, glaciers/ice shelves, survey marks, and gives some historical text information.", "links": [ { diff --git a/datasets/bech_nest_locations_1.json b/datasets/bech_nest_locations_1.json index b7a4cac1c6..5138cb2df4 100644 --- a/datasets/bech_nest_locations_1.json +++ b/datasets/bech_nest_locations_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bech_nest_locations_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour.\n\nThe dataset contains three files - an image file and two zip files.\n\nThe image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations.\n\nThe zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island.\n\nThe zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations.", "links": [ { diff --git a/datasets/beech_stress_thresholds_1.0.json b/datasets/beech_stress_thresholds_1.0.json index 0e2b30a570..601a889107 100644 --- a/datasets/beech_stress_thresholds_1.0.json +++ b/datasets/beech_stress_thresholds_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "beech_stress_thresholds_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the data presented in the figures 1-6 in Walthert et al. (2020): From the comfort zone to crown dieback: sequence of physiological stress thresholds in mature European beech trees across progressive drought. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2020.141792. A detailed methodical description of the data can be found in the Material and Methods section of the paper. Drought responses of mature trees are still poorly understood making it difficult to predict species distributions under a warmer climate. Using mature European beech (Fagus sylvatica L.), a widespread and economically important tree species in Europe, we aimed at developing an empirical stress-level scheme to describe its physiological response to drought. We analysed effects of decreasing soil and leaf water potential on soil water uptake, stem radius, native embolism, early defoliation and crown dieback with comprehensive measurements from overall nine hydrologically distinct beech stands across Switzerland, including records from the exceptional 2018 drought and the 2019/2020 post-drought period. Based on the observed responses to decreasing water potential we derived the following five stress levels: I (predawn leaf water potential >-0.4 MPa): no detectable hydraulic limitations; II (-0.4 to -1.3): persistent stem shrinkage begins and growth ceases; III (-1.3 to -2.1): onset of native embolism and defoliation; IV (-2.1 to -2.8): onset of crown dieback; V (<-2.8): transpiration ceases and crown dieback is >20%. Our scheme provides, for the first time, quantitative thresholds regarding the physiological downregulation of mature European beech trees under drought and therefore synthesises relevant and fundamental information for process-based species distribution models. Moreover, our study revealed that European beech is drought vulnerable, because it still transpires considerably at high levels of embolism and because defoliation occurs rather as a result of embolism than preventing embolism. During the 2018 drought, an exposure to the stress levels III-V of only one month was long enough to trigger substantial crown dieback in beech trees on shallow soils. On deep soils with a high water holding capacity, in contrast, water reserves in deep soil layers prevented drought stress in beech trees. This emphasises the importance to include local data on soil water availability when predicting the future distribution of European beech.", "links": [ { diff --git a/datasets/bender2020_1.0.json b/datasets/bender2020_1.0.json index 28e10560b6..5112197fa4 100644 --- a/datasets/bender2020_1.0.json +++ b/datasets/bender2020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bender2020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100. This includes: * meteorological forcing, * climate change timeries and * simulation files together with * snow depth * ground temperature __Please refer to the following publication for further details which should be cited when using this dataset:__ __Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.__", "links": [ { diff --git a/datasets/beryllium_10be_isotopes_lawdome_1.json b/datasets/beryllium_10be_isotopes_lawdome_1.json index 7c7fc1f7dc..26b84c0cb9 100644 --- a/datasets/beryllium_10be_isotopes_lawdome_1.json +++ b/datasets/beryllium_10be_isotopes_lawdome_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "beryllium_10be_isotopes_lawdome_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output.\n\n3 x 3\" PICO firn cores were drilled immediately in front of snow pit.\n\nThe 3 pico cores were sampled at 14cm intervals and the sections combined resulting in 16 samples. Some length was lost during transit, especially in the top cores. It was assumed that the lost length was from the breaks in the core as the ends rubbed against each other during transport, and was evenly lost from each break, using the field notes to help. The bottom of each core was assumed to be the lengths as measured in the field.\n\nThe samples were placed in a melting jar with carrier and left to melt overnight. ~10mL of the samples were retained for water isotope analysis. The samples were filtered and pumped onto cation columns.", "links": [ { diff --git a/datasets/beryllium_7be_isotopes_lawdome_1.json b/datasets/beryllium_7be_isotopes_lawdome_1.json index 55c9d482e7..40d19970cb 100644 --- a/datasets/beryllium_7be_isotopes_lawdome_1.json +++ b/datasets/beryllium_7be_isotopes_lawdome_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "beryllium_7be_isotopes_lawdome_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output.\n\nA ~1.4 x 1 x 1 m pit was dug on Law Dome. The wall was flattened using a ~60 cm level, handsaw and paint scrappers. A significant sastrugi could be seen in the top right of the wall. Sampling was started on the left of the wall to avoid this where possible. Wearing plastic gloves to avoid contaminating the samples, the top surface was levelled to the lowest point, and some of the snow collected as sample P1-1. It was around 4 cm at its highest point. A 10 cm x 10 cm grid was drawn into the wall, covering 80 cm x 80 cm. The top 10 cm layer was sawn out of the wall using a hand saw, cutting into the wall by at least 20 cm along the horizontal 10 cm below the top surface, then the back 20 cm from the front surface, and finally chopping the large block into smaller blocks.\n\nThe extra six blocks were discarded, and the two samples were put into zip lock bags as P2-1 and P2-2. The back of the sampling area was cleared back to allow easier access for the next layer. This was repeated for seven more layers, finishing with P9.\n\nOne block from each level was used for density measurements. The samples from each level were combined into a melting jar and carrier added. For some samples, not all the blocks fitted at once, so a portion of the blocks were melted (with the carrier) in the oven at 60 degrees C. The samples were allowed melt completely overnight. ~10mL of the samples were retained for water isotopes . The samples were filtered though 41 microns and the 0.45 microns and pumped onto cation columns.", "links": [ { diff --git a/datasets/bet_1.0.json b/datasets/bet_1.0.json index bc47d6b4a0..d63f76c34e 100644 --- a/datasets/bet_1.0.json +++ b/datasets/bet_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bet_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).", "links": [ { diff --git a/datasets/bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA.json b/datasets/bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA.json index 5f2df4c91a..a80947cef3 100644 --- a/datasets/bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA.json +++ b/datasets/bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.", "links": [ { diff --git a/datasets/bf535053562141c6bb7ad831f5998d77_NA.json b/datasets/bf535053562141c6bb7ad831f5998d77_NA.json index 059722fce8..2f2d340cbc 100644 --- a/datasets/bf535053562141c6bb7ad831f5998d77_NA.json +++ b/datasets/bf535053562141c6bb7ad831f5998d77_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bf535053562141c6bb7ad831f5998d77_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA\u00e2\u0080\u0099s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude.", "links": [ { diff --git a/datasets/bf5eae2a052848aab2abf93d96e7e9aa_NA.json b/datasets/bf5eae2a052848aab2abf93d96e7e9aa_NA.json index 9f4349f9d3..7aa4866e3f 100644 --- a/datasets/bf5eae2a052848aab2abf93d96e7e9aa_NA.json +++ b/datasets/bf5eae2a052848aab2abf93d96e7e9aa_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bf5eae2a052848aab2abf93d96e7e9aa_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. In 2002, it also contains data from the AATSR instrument on the ENVISAT satellite. A separate AATSR product covering the period 2002-2012 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA.json b/datasets/bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA.json index 921bd7a5b1..97f4572894 100644 --- a/datasets/bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA.json +++ b/datasets/bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/bhc1_bhc2_1982_1.json b/datasets/bhc1_bhc2_1982_1.json index ebdcf384d0..20b96a52f2 100644 --- a/datasets/bhc1_bhc2_1982_1.json +++ b/datasets/bhc1_bhc2_1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhc1_bhc2_1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Results for the temperature and orientation recorded from the BHC1 and BHC2 ice core boreholes in 1982-83.\n\nA hard copy of this document has been archived in the Australian Antarctic Division records section.", "links": [ { diff --git a/datasets/bhc1_bhc2_analysis_notes_1.json b/datasets/bhc1_bhc2_analysis_notes_1.json index ba9b849229..d956d2ac56 100644 --- a/datasets/bhc1_bhc2_analysis_notes_1.json +++ b/datasets/bhc1_bhc2_analysis_notes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhc1_bhc2_analysis_notes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of notes and results from the analysis of the BHC1 and BHC2 ice cores, collected from Law Done in 1981-82. Contains notes on how the cores were collected and sampled, along with notes on oxygen isotopes, ice crystal structure, microfractures, etc.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/bhc1_bhc2_bhq_logs_notes_1.json b/datasets/bhc1_bhc2_bhq_logs_notes_1.json index 2d1d13604b..7c7abbcd1f 100644 --- a/datasets/bhc1_bhc2_bhq_logs_notes_1.json +++ b/datasets/bhc1_bhc2_bhq_logs_notes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhc1_bhc2_bhq_logs_notes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data and analysis of data from BHC1, BHC2 and BHQ boreholes on Law Dome. Includes caliper data, temperature, and inclination/azimuth.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/bhd_inclinometer_temp_1977_1.json b/datasets/bhd_inclinometer_temp_1977_1.json index 0feee91b9c..53bc467d28 100644 --- a/datasets/bhd_inclinometer_temp_1977_1.json +++ b/datasets/bhd_inclinometer_temp_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhd_inclinometer_temp_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Recorded logs of the inclinometer and temperature readings taken from the BHD ice core borehole in 1977.\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/bhq_ice_core_logbooks_1.json b/datasets/bhq_ice_core_logbooks_1.json index 4f5fb6fb39..c64504cb90 100644 --- a/datasets/bhq_ice_core_logbooks_1.json +++ b/datasets/bhq_ice_core_logbooks_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhq_ice_core_logbooks_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of 3 books recording the details of the ice cores drilled at site BHQ on Law Dome in 1977. Includes limited stratigraphy information on some core segments.\n\nA hard copy of this document has been archived in the Australian Antarctic Division records section.", "links": [ { diff --git a/datasets/bhq_temp_1977_1.json b/datasets/bhq_temp_1977_1.json index 4f045360a7..6a38b8f02e 100644 --- a/datasets/bhq_temp_1977_1.json +++ b/datasets/bhq_temp_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bhq_temp_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Results for the temperatures recorded from the BHQ ice core borehole in 1977.\n\nA hard copy of this document has been archived in the Australian Antarctic Division records section.", "links": [ { diff --git a/datasets/billmark_828_1.json b/datasets/billmark_828_1.json index 0023d51c59..99bae75e17 100644 --- a/datasets/billmark_828_1.json +++ b/datasets/billmark_828_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "billmark_828_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Southern African Regional Science Initiative (SAFARI 2000) was conducted in part to investigate the impacts of the large-scale transport and deposition of increasingly anthropogenic emissions on southern African biogeochemical cycling. Aerosol samples from the Mongu site in eastern Zambia were collected and analyzed to identify chemical biomarkers during the SAFARI 2000 dry season field campaign. Total suspended particulate aerosol samples were collected diurnally for a period of two weeks during August and September of 2000.These data include bulk organic carbon, nitrogen and sulfur stable isotopic measurements of total suspended particulate aerosols and gas chromatography/mass spectrometry (GC/MS) analysis of fatty acids extracted from collected aerosols. These data were used to chemically describe temporal variability in aerosol compositions.", "links": [ { diff --git a/datasets/bioclim_plus_1.0.json b/datasets/bioclim_plus_1.0.json index 2a87024d26..11910cba57 100644 --- a/datasets/bioclim_plus_1.0.json +++ b/datasets/bioclim_plus_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bioclim_plus_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world\u2019s ecosystems and associated services to societies.", "links": [ { diff --git a/datasets/biodiversity-integration_1.0.json b/datasets/biodiversity-integration_1.0.json index 0b8f4d2242..a449f257b3 100644 --- a/datasets/biodiversity-integration_1.0.json +++ b/datasets/biodiversity-integration_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biodiversity-integration_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## Introduction The ZIP file contains all data and code to replicate the analyses reported in the following paper. Reber, U., Fischer, M., Ingold, K., Kienast, F., Hersperger, A. M., Gr\u00fctter, R., & Benz, R. (2022). Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics. *Policy Sciences*. [https://doi.org/10.1007/s11077-022-09456-4](https://doi.org/10.1007/s11077-022-09456-4) If you use any of the material included in this repository, please refer to the paper. If you use (parts of) the text corpus, please also refer to the sources used for its compilation listed below. The content of the texts may not be changed. ## Data folder The data folder contains the following files. * _corpus.parquet_: Text corpus of Swiss policy documents * _dict_de.csv_: Biodiversity dictionary (German) * _dict_fr.csv_: Biodiversity dictionary (French) * _dict_it.csv_: Biodiversity dictionary (Italian) * _topic_labels.csv_: labels/codes for policy sectors * _topics.csv_: labels/codes for policy sectors The corpus and the dictionary were compiled by the authors specifically for this project. The labels/codes for policy sectors are based on the [coding scheme](http://ws-old.parlament.ch/affairs/topics) of the Swiss Parliament. ### Text corpus The text corpus consists of 439,984 Swiss policy documents in German, French, and Italian from 1999 to 2018. The corpus was compiled from the following source between 2020-10-01 and 2021-01-31. * Transcripts and parliamentary businesses (e.g. questions, motions, parliamentary initiatives) via the [Web Services (WS)](https://www.parlament.ch/de/%C3%BCber-das-parlament/fakten-und-zahlen/open-data-web-services) provided by the Swiss Parliament * The official compilation of federal legislation (\"Amtliche Sammlung\", AS) via [opendata.swiss](https://opendata.swiss/de/dataset/official-compilation-of-federal-legislation-bs-as-1947-2018) provided by the Swiss Federal Archives (SFA) * The federal gazette (\"Bundesblatt\") via [fedlex.admin.ch](https://www.fedlex.admin.ch/de/fga/index) * Decisions of federal courts via [entscheidsuche.ch (ES)](https://entscheidsuche.ch/) The corpus is stored in a single data frame to use with R saved as [PARQUET](https://parquet.apache.org/) file (corpus.parquet). The data frame has the following structure. * _text_id_: Unique identifier for each text (source information as prefix, e.g. \"t_\") * _doc_type_: Document type (see coding scheme below) * _branch_: Government branche (1 legislative, 2 executive, 3 judicative) * _stage_: Stage of policy process (1 drafting, 2 introduction, 3 interpretation) * _year_: Year of publication * _topic_: Policy sector (coding scheme in separate file in data folder) * _lang_: Language (de, fr, it) * _text_: Text The following list contains the coding scheme for the doc_type variable. * 101: Federal gazette // Draft for public consultation (\"Vernehmlassungsverfahren\") * 102: Federal gazette // Explanation of draft for parliament (\"Botschaft\") * 103: Federal gazette // Strategy, action plan * 104: Federal gazette // Federal council decree (\"Bundesratsbeschluss\") * 105: Federal gazette // (Simple) Federal decree (\"(Einfacher) Bundesbeschluss\") * 106: Federal gazette // General decree (\"Allgemeinverf\u00fcgung\") * 107: Federal gazette // Treaty (\"\u00dcbereinkommen\") * 108: Federal gazette // Treaty (\"Abkommen\") * 109: Federal gazette // Draft for parliament (\"Entwurf\") * 110: Federal gazette // Report (\"Bericht\") * 111: Federal gazette // Report of parliamentary comission (\"Bericht\") * 112: Federal gazette // Report of federal council (\"Bericht\") * 201: Parl. businesses // Submitted text * 202: Parl. businesses // Reason text * 203: Parl. businesses // Federal council response * 204: Parl. businesses // Initial situation * 205: Parl. businesses // Proceedings * 301: Parl. transcripts // Speech of MP * 302: Parl. transcripts // Speech of federal council * 401: Federal legislation // Legal text of the official compilation (law, ordinances, etc.) * 501: Court decisions // Federal Supreme Court * 502: Court decisions // Federal Criminal Court * 503: Court decisions // Federal Administrative Court ## Code folder The code folder contains all R code for the analyses. The files are numbered chronologically. * _1_classifier_training.R_: Training of classifiers for classification of policy sectors * _2_classifier_application.R_: Classification of documents in corpus * _3_dictionary_application.R_: Biodiversity indexing of documents in corpus * _4_stm_truncation.R_: Truncation of indexed documents to keep only relevant parts * _5_stm_translation.R_: Translation of FR and IT documents to DE * _6_stm_model.R_: Preprocesssing and structural topic model * _7_plots.R_: Plots and numbers as included in the paper The code/functions folder contains custom functions used in the scripts, e.g. to support topic model interpretation. Package versions and setup details are noted in the code files. ## Contact Please direct any questions to Ueli Reber (ueli.reber@eawag.ch).", "links": [ { diff --git a/datasets/biofuel_emissions_753_1.json b/datasets/biofuel_emissions_753_1.json index d62bfb9229..5209aae59f 100644 --- a/datasets/biofuel_emissions_753_1.json +++ b/datasets/biofuel_emissions_753_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biofuel_emissions_753_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Domestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively. As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.", "links": [ { diff --git a/datasets/biogas-aus-hofdunger-in-der-schweiz_1.0.json b/datasets/biogas-aus-hofdunger-in-der-schweiz_1.0.json index 2a2deda1dd..41c4294db8 100644 --- a/datasets/biogas-aus-hofdunger-in-der-schweiz_1.0.json +++ b/datasets/biogas-aus-hofdunger-in-der-schweiz_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biogas-aus-hofdunger-in-der-schweiz_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ziel dieses Whitepapers ist es, Entscheidungstr\u00e4gern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verf\u00fcgung zu stellen, um die optimale Nutzung von Bioenergie aus Hofd\u00fcnger in der Schweizer Energiewende zu f\u00f6rdern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums f\u00fcr Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erw\u00e4hnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.", "links": [ { diff --git a/datasets/biogas-from-animal-manure-in-switzerland_1.0.json b/datasets/biogas-from-animal-manure-in-switzerland_1.0.json index eb05939517..8ea16c9861 100644 --- a/datasets/biogas-from-animal-manure-in-switzerland_1.0.json +++ b/datasets/biogas-from-animal-manure-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biogas-from-animal-manure-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.", "links": [ { diff --git a/datasets/biomass_above_ground_of_live_trees-19_1.0.json b/datasets/biomass_above_ground_of_live_trees-19_1.0.json index 6e6bb09392..3f99934181 100644 --- a/datasets/biomass_above_ground_of_live_trees-19_1.0.json +++ b/datasets/biomass_above_ground_of_live_trees-19_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_above_ground_of_live_trees-19_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of the aboveground parts of living trees and shrubs starting at 12 cm dbh. This consists of the tree parts: stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomass_allocation_703_1.json b/datasets/biomass_allocation_703_1.json index bda41947e2..2ee5bf69c5 100644 --- a/datasets/biomass_allocation_703_1.json +++ b/datasets/biomass_allocation_703_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_allocation_703_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious", "links": [ { diff --git a/datasets/biomass_of_live_trees-18_1.0.json b/datasets/biomass_of_live_trees-18_1.0.json index d9ec87ba5d..740608fccb 100644 --- a/datasets/biomass_of_live_trees-18_1.0.json +++ b/datasets/biomass_of_live_trees-18_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_of_live_trees-18_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of living trees and shrubs starting at 12 cm dbh. This consists of the tree parts: roots, stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomass_of_lying_dead_trees-70_1.0.json b/datasets/biomass_of_lying_dead_trees-70_1.0.json index cfd59b79cf..a95555efd0 100644 --- a/datasets/biomass_of_lying_dead_trees-70_1.0.json +++ b/datasets/biomass_of_lying_dead_trees-70_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_of_lying_dead_trees-70_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of dead, lying trees and shrubs starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition of the stem, the branch coarse wood. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomass_of_lying_dead_wood_lis-72_1.0.json b/datasets/biomass_of_lying_dead_wood_lis-72_1.0.json index 3864a555d6..3372fe23a0 100644 --- a/datasets/biomass_of_lying_dead_wood_lis-72_1.0.json +++ b/datasets/biomass_of_lying_dead_wood_lis-72_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_of_lying_dead_wood_lis-72_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of lying deadwood starting at 7 cm in diameter that does not fulfil the criteria for a tally tree (measurement location of dbh not identifiable or the dbh is less than 12cm). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomass_of_standing_dead_trees-69_1.0.json b/datasets/biomass_of_standing_dead_trees-69_1.0.json index 53b1b48c60..045b6d12d3 100644 --- a/datasets/biomass_of_standing_dead_trees-69_1.0.json +++ b/datasets/biomass_of_standing_dead_trees-69_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_of_standing_dead_trees-69_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of dead, standing trees and shrubs starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition, the branch coarse wood. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomass_of_total_dead_wood-71_1.0.json b/datasets/biomass_of_total_dead_wood-71_1.0.json index 666787c45e..7f1a9748fb 100644 --- a/datasets/biomass_of_total_dead_wood-71_1.0.json +++ b/datasets/biomass_of_total_dead_wood-71_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomass_of_total_dead_wood-71_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of all deadwood. This consists of the standing dead trees and shrubs starting at 12cm dbh and the lying deadwood starting at 7cm in diameter. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/biomdens_450_1.json b/datasets/biomdens_450_1.json index 23b6117808..8b1255ed90 100644 --- a/datasets/biomdens_450_1.json +++ b/datasets/biomdens_450_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomdens_450_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area. It is derived from a Landsat-5 TM image collected on 02-Sep-1994. The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA.", "links": [ { diff --git a/datasets/biomebg2_296_1.json b/datasets/biomebg2_296_1.json index e4f77e7eff..edffdc2a4b 100644 --- a/datasets/biomebg2_296_1.json +++ b/datasets/biomebg2_296_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomebg2_296_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution. Mode", "links": [ { diff --git a/datasets/biomebgc_295_1.json b/datasets/biomebgc_295_1.json index a9c2c59a9a..f78c51eb1d 100644 --- a/datasets/biomebgc_295_1.json +++ b/datasets/biomebgc_295_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "biomebgc_295_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales. BIOME-BGC is used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.", "links": [ { diff --git a/datasets/block_invertebrates_1.json b/datasets/block_invertebrates_1.json index fca2a7f8b2..855266875a 100644 --- a/datasets/block_invertebrates_1.json +++ b/datasets/block_invertebrates_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "block_invertebrates_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset was compiled from papers entered into Block's bibliography of invertebrate occurrences in the Antarctic and sub-Antarctic. The dataset provides a comprehensive list of all terrestrial invertebrates recorded from the Antarctic and sub-Antarctic (at that time). Data were entered into an Excel spreadsheet, which contains approximately 3500 entries.\n\nThis dataset forms part of the work completed for Australian Antarctic Science (AAS) project 1146 (ASAC_1146) and the RiSCC program, AAS project 1015 (ASAC_1015).\n\nPapers from the Block Bibliography are available as a separate collection in the Australian Antarctic Division Library.\n\nThis dataset has also been incorporated into the biodiversity database, which can be found at the provided URL.", "links": [ { diff --git a/datasets/bluegreen-ecological-network-data_1.0.json b/datasets/bluegreen-ecological-network-data_1.0.json index aae02df245..ecdcc297cb 100644 --- a/datasets/bluegreen-ecological-network-data_1.0.json +++ b/datasets/bluegreen-ecological-network-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bluegreen-ecological-network-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).", "links": [ { diff --git a/datasets/bole_wood_mass_of_live_trees-50_1.0.json b/datasets/bole_wood_mass_of_live_trees-50_1.0.json index 051f0a22ed..1b1017a7e3 100644 --- a/datasets/bole_wood_mass_of_live_trees-50_1.0.json +++ b/datasets/bole_wood_mass_of_live_trees-50_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bole_wood_mass_of_live_trees-50_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of the stemwood with bark of the living trees and shrubs starting at 12 cm dbh. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0.json b/datasets/book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0.json index 6d50daab56..a039e7af03 100644 --- a/datasets/book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0.json +++ b/datasets/book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Book of abstracts from the virtual conference \"From Plans to Land Change: Dynamics of Urban Regions\" Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning\u2019s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models. This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field. The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/boreas_aeshrday_235_2.json b/datasets/boreas_aeshrday_235_2.json index 8591df1676..d49efc2be6 100644 --- a/datasets/boreas_aeshrday_235_2.json +++ b/datasets/boreas_aeshrday_235_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "boreas_aeshrday_235_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount.", "links": [ { diff --git a/datasets/box_hill_ice_compression_1.json b/datasets/box_hill_ice_compression_1.json index 8688fd26cd..baa393a7bc 100644 --- a/datasets/box_hill_ice_compression_1.json +++ b/datasets/box_hill_ice_compression_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "box_hill_ice_compression_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of ice compression tests were carried out by Jo Jacka in 1977, and again by J.S.Birch in 1979-82, all aimed at determining how ice reacted under different circumstances. For each series of experiments, five different \"Box Hill\" rigs were set up, and kept at -10C (1977) or -30 (1979) for the duration of the experiments. The experiments in 1977 came to an early end when the cold room being used failed.\n\nThe setup and method for each experiment, along with the results, were recorded in log books and have been archived at the Australian Antarctic Division.\n\nLogbook(s):\nGlaciology Box Hill Compression Rig Experiments, Book 1 - Initial notes on setup, and recordings of early results for the 1977 experiments.\n\nGlaciology Box Hill Compression Rig Experiments, Book 2 - More results from 1977.\n\nGlaciology Ice Compression Logbook - Setup and results for the 1979 experiments.", "links": [ { diff --git a/datasets/bratts_penguin_gis_1.json b/datasets/bratts_penguin_gis_1.json index d6bc7e3d7d..490b6a98f6 100644 --- a/datasets/bratts_penguin_gis_1.json +++ b/datasets/bratts_penguin_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bratts_penguin_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler).\nThe penguin colonies were traced, then digitised (John Cox), and saved as DXF-files.\nUsing the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands.\n\nUpdate May 2015 - \nThis dataset has been rename from \"Brattstrand Bluff penguin GIS dataset\" to \"Islands NE of Brattstrand Bluff penguin GIS dataset\" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east.\nThe Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record:\nThe DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced.\nFour photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands.\nA shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.", "links": [ { diff --git a/datasets/brdglsc0001.json b/datasets/brdglsc0001.json index c80bc90161..1c840a16bd 100644 --- a/datasets/brdglsc0001.json +++ b/datasets/brdglsc0001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdglsc0001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Great Lakes Commercial Fishing Database contains commercial fishing data\nfrom the United States. The states of Illinois, Indiana, Michigan, Minnesota,\nNew York, Ohio, Pennsylvania, and Wisconsin gather monthly fishing reports and\nforward them to the Great Lakes Center. The database provides the fisherman's\nname, information about the vessel, the estimated weight and estimated dollar\nvalue.\n\nMethodology:\nThe database is not a scientific one. The data is reported by individual\nlicensed fishermen to each state juridsdiction. The states gather monthly\nfishing reports and forward them to the Great Lakes Science Center. GLSC then\ncompiles all of the information into a database each year and produces an\nannual summary that is called the NOAA report. It is sent to the National\nMarine Fishery Service (NMFS) and is included with commercial fishing data from\nthe entire United States into a publication.", "links": [ { diff --git a/datasets/brdlsc0007.json b/datasets/brdlsc0007.json index 00ee991f46..e149541f87 100644 --- a/datasets/brdlsc0007.json +++ b/datasets/brdlsc0007.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdlsc0007", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An evaluation of adaptive cluster sampling was based on a simulation\nexperiment where samples were drawn from an enumeration of three\nspecies of waterfowl wintering in central Florida. The initial\nsamples were taken either by simple random sampling or with\nprobability proportional to available habitat. Efficiency of adaptive\ncluster sampling relative to simple random sampling was highest when\n1) the within-network variance was close to the population variance,\nand 2) the final sampling fraction was close to the initial sampling\nfraction. The within-network variance is determined by the spatial\ndistribution of the population, quadrat size, and the condition that\ndetermined when to adapt sampling. The final sampling fraction\ndepends on the previous factors as well as the size and selection of\nthe initial sample. Some combinations of these factors led to\nincreased precision compared to simple random sampling and some did\nnot.\n\nGeographic Description:\nCentral Florida (5,000 km2). The study region extended 100 km east\nand 50 km north from the southwest corner at 0438000, 3056000\n(Universal Transverse Mercator coordinates; zone 17). 1.5.2 Bounding\nRectangle Coordinates\n\nMethodology:\nAn effort was made to count every individual duck of the three\nwaterfowl species in a 5,000 km2 area of central Florida by making\nsystematic flights over the entire study region. Two biologists\ncounted waterfowl from separate helicopters (Bell Jet Rangers) during\n13-15 December, 1992 and used the LORAN-C and GPS systems to determine\nflock locations Field.", "links": [ { diff --git a/datasets/brdpier0004.json b/datasets/brdpier0004.json index add0232417..9520e0c4eb 100644 --- a/datasets/brdpier0004.json +++ b/datasets/brdpier0004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdpier0004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Relative abundance, breeding ecology, annual survival, home range, and\nforaging ecology of the Akiapolaau (Hemignathus munroi), an endangered Hawaiian\nhoneycreeper, were studied on the island of Hawaii. The species is a\nspecialist; Akiapolaau used koa (Acacia koa) for foraging much more than\nexpected based on koa availability, and most Akiapolaau occurred in old-growth\nkoa and ohia (Metrosideros polymorpha forests. Male Akiapolaau most often\nforaged on the trunks and large branches of koa, whereas females used small\nbranches and twigs. The longer bill of males is apparently adapted to the\ngreater bark thickness of larger branches. Lichen-covered and dead branches\nwere preferred feeding sites. Akiapolaau showed serial monogamy and had a\nrelatively low reproductive rate of 0.86 young per par per year, with a long\nparental dependency period. Home range sizes averaged 10.7 ha and did not\ndiffer between males and females. Annual survival for adults was 0.71. Avian\ndiseases appear to restrict Akiapolaau to higher elevation forests where\nmosquitos are rare. Protection of remaining old-growth koa and ohia forests\nabove the mosquito zone are critical to the survival of the species.\n\n Geographic Description:\n Akiapolaau were studied at five study sites on Hawaii: Keauhou\nRanch (19.52, -155.33; 1,740 m elevation), Kilauea Forest (19.52,\n-155.32 1,630 m), Hamakua (19.78 -155.33; 1,770 m), Kau Forest (19.22,\n-155.65; 1,750 m), and a Dry Forest site (19.82, -155.55 1,865-2,800\nm). 1.5.2 Bounding Rectangle Coordinates\n\n Methodology:\n 25-162 stations were surveyed each month at each study site using the\nvariable circular-plot method with 8-minute counts (Scott et al. 1986) to\nobtain indices of Akiapolaau abundance.", "links": [ { diff --git a/datasets/brdpier0006.json b/datasets/brdpier0006.json index 8d88d5874a..3e6d6f1e90 100644 --- a/datasets/brdpier0006.json +++ b/datasets/brdpier0006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdpier0006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Populations of the endangered Akepa (Loxops coccineus coccineus) and Hawaii\nCreeper (Oreomystis mana) at four sites on the island of Hawaii. Mean monthly\ndensity (+/-) of Akepa was 5.74 +/- 0.87, 1.35 +/-0.41, 0.96 +/- 0.13, and 0.76\n+/- 0.12 Akepa/ha at Kau Forest, Hamakua, Keauhou Ranch, and Kilauea Forest\nstudy areas, respectively. Hawaii creepers were found at densities of 1.68 +/-\n0.53, 1.79 +/- 0.42, 0.48 +/- 0.06, and 0.54 +/- 0.08 birds /ha, respectively ,\nat the four study areas. Highest capture rates and numbers of birds counted\nfrom stations occurred from August through November and February through March.\n Hatching-year birds were captured from May through December for Akepa and\nApril through December for Hawaii Creeper. Annual survival for adults at\nKeauhou Ranch was 0.70 +/- 0.27 SE fro 61 Akepa and 0.73 +/- 0.12 SE for 49\nHawaii Creepers. Lowest rates of mortality and emigration occurred between May\nand August. Both species appeared to defend Type-B territories typical of\ncardueline finches, retained mates for more than one year, and showed strong\nphilopatry. Home ranges for Hawaii Creepers ( x = 7.48 ha) were larger than\nthose for Akepa (x = 3.94 ha). No difference was found between home range\nsizes of males and females for either species.\n\n Geographic Description:\n Hawaii Creepers and Akepa were studied at four sites on the island of\nHawaii. The Keauhou Ranch study area (19.50, 155.33; 1800 m elevation) had a\ndiscontinuous canopy dominated by ohia and naio (Myoporum sandwicense) and had\na long history of grazing by cattle and logging for koa and ohia. A 16-ha grid\nmarked at 50-m intervals was established at this wet (ca 2000 mm annual\nrainfall) forest site. The 16-ha Kilauea Forest study area (19 .52, 155.32;\n1600-1650 m) was in a relatively pristine, closed-canopy, wet forest dominated\nby 20-30m tall koa and 15-25 m tall ohia trees, and was approximately 1.8 km NW\nof the Keauhou Ranch study area. This site was described in detail by\nMueller-Dombois et al. (1981).. The 50-ha Hamakua study area near mor\ncontinuous canopy and an almost complete lack of native understory plants\nbecause of intensive grazing by cattle The 50-ha Kau Forest study area (19.22,\n155.65; 1750 m) had a closed canopy of ohia and a largely ungrazed understory\nof kolea (Myrsine lessertiana), olapa (Cheirodendron trigynum), kawau (Ilex\nanomala), and native ferns. 1.5.2 Bounding Rectangle Coordinates\n\n Methodology:\n Estimated densities of Hawaii Creepers and Akepa at each of the four study\nareas by the variable circular-plot method (Reynolds et al. 1980, Ramsey and\nScott 1979) during eight min count periods as described in Ralph (1981).", "links": [ { diff --git a/datasets/brdpier0008.json b/datasets/brdpier0008.json index 3d6bcaab47..0dfa9748f2 100644 --- a/datasets/brdpier0008.json +++ b/datasets/brdpier0008.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdpier0008", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Methods to determine the age and sex of 'Oma'o (Myadestes obscurus) were\ndeveloped on the basis of 66 museum speciments and 149 live 'Oma'o captured in\nmist nets on the island of Hawaii. 'Oma'o in juvenile plumage are heavily\nspotted with scalloped greater coverts and tertials and are easily\ndistinguished from adults. Birds in their first prebasic plumage usually\nretain one or more scallped wing coverts or tertials. Wing lengths of adult\nand immature male 'Oma'o were significantly longer than those of females, but\nonly 80% of adult specimens were accurately sexed by wing length.\n\n Geographic Description:\n Island of Hawaii, Keauhou Ranch (19.50, -155.33; 1800 m elevation) and\nKilauea Forest (19.52, -155.32; 1600-1650 m). 1.5.2 Bounding Rectangle\nCoordinates\n\n Methodology:\n Recorded plumage characteristics and exteral measurements of 55 'Oma'o\nspecimens at the Bernice P. Bishop Museum and 11 'Oma'o specimens loaned by the\nAmerican Museum of Natural History. 'Oma'op juvenal plumage are dark and below\nand are easily distinguished from adults. The feathers of the breast, belly,\nand flanks are buffy white in the center but are broadly bordered with blackish\nbrown, giving the feathers a scalloped pattern (Berger 1981, Pratt 1982).", "links": [ { diff --git a/datasets/brdpier0009.json b/datasets/brdpier0009.json index a2373f6886..cf371f5f13 100644 --- a/datasets/brdpier0009.json +++ b/datasets/brdpier0009.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdpier0009", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The feral house cat (Felis catus), Hawaiian Short-eared Owl or Pueo (Asio\nflammeus sandwichensis), and Common Barn Owl (Tyto alba) are important\npredators of birds and introduced rodents in Hawai'i. Cat scats from the\nisland of Hawai'i (n=87), Pueo pellets from Hawai'i, Kaua'i, and Kaho'olawe\n(n=36), and Barn Owl pellets from Hawai'i, O'ahu and Kaho'olawe (n=301) were\nexamined to determine the incidence of rodent, bird and insect remains in the\ndiets of these predators. Rodents were the main prey of cats, Pueo, and Barn\nOwls, but the incidence of bird remains in diets of all three predator species\nwas high relative to studies conducted elsewhere in the world.\n\n Geographic Description:\n All cat scats were collected in dry mamane (Sophora chrysophylla)-naio\n(Myoporum sandwichensis) forests on the western and eastern slopes of Mauna Kea\nabove 2,000 m elevation. Pueo pellets were collected in dry forests on Mauna\nKea (n=13), from Kaumana Gulch on Kaho'olawe (n=21), and from the Alakai Swamp\non Kaua'i (n=2). Barn Owl pellets were collected at roosts and nests at\nKakalau Forest National Wildlife Refuge on Hawai'i (n=207), near the Pu'u La'au\ncabin on Mauna Kea (n= 73), on O'ahu (n=19), at Ahupi Beach on Kaho'olawe\n(n=1). Acumulations of Barn Owl pellets were found below roosting sites,\nwhereas single Pueo pellets were found below tall trees or on open ground\n(Mauna Kea), or on cliff faces on Kaho'olawe. On Kaua'i, Pueo pellets were\nfound in an open bog near the remains of a recent Pacific Golden Plover kill.\n1.5.2 Bounding Rectangle Coordinates\n\n Methodology:\n Determined predator diets from analysis of 87 cat scats, 36 Pueo pellets,\nand\n301 Barn Owl pellets. All cat scats were collected in dry mamane-naio forests.\n Size, appearance, and consistency were used to determine the source of scats\nand pellets. Cat scats were smaller than pellets and had tapered ends with\nfewer bones distributed through them. Pueo pellets were smaller than Barn Owl\npellets and had a uniformly cylindrical shape. They fit Mikkola's (1983)\ndescription as \"elongated, roughly cylindrical dark gray and formed from a\ntightly-massed conglomeration of fur or feathers with a central core of mammal\nand bird bones.\"", "links": [ { diff --git a/datasets/brdwerc0002.json b/datasets/brdwerc0002.json index 9d40541101..434a7645c9 100644 --- a/datasets/brdwerc0002.json +++ b/datasets/brdwerc0002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brdwerc0002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The larger Sierra Nevada Global Change Research Program (SNGCRP) seeks to\nunderstand past, present, and possible future changes in Sierran forest\nstructure, composition, and dynamics resulting from changing management\npractices and anticipated global climate change. Within the larger program,\nthis project (\"Comparison of the sedimentary record of fire with the tree-ring\nrecord within and near giant sequoia groves, Sierra Nevada, California\") will\nuse high precision carbon dating of charcoal and pollen in sediment cores in\norder to (1) develop a 10,000-year record of fire history in the southern and\ncentral Sierra Nevada, calibrated against multi-millennial, annual-resolution\nfire histories from tree rings at the same sites, and (2) develop detailed\ndescriptions of changes in forest composition over the last few millennia, to\nbe compared with climate and fire histories developed by other SNGCRP projects.\nThis work will provide data for calibration and testing of fire spread and\nforest dynamics models currently being developed by other global change\nresearch projects, and will provide baseline data on past disturbance regimes,\ntheir variability, and consequent forest response. These objectives will be\nachieved by analyzing four sediment cores. The cores have already been\ncollected from meadows adjacent to sites with multi-millennial,\nannual-resolution fire histories developed from giant sequoia tree rings: Giant\nForest (Sequoia National Park), Mountain Home Grove (Mountain Home State\nForest), Mariposa Grove (Yosemite National Park), and Big Stump Grove (Kings\nCanyon National Park).", "links": [ { diff --git a/datasets/breeding_success_BI_1.json b/datasets/breeding_success_BI_1.json index 8ebe42a23e..523651a878 100644 --- a/datasets/breeding_success_BI_1.json +++ b/datasets/breeding_success_BI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "breeding_success_BI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest.\n\nBreeding Success = the number of chicks raised to fledging per nest with eggs\n\nBreeding success is calculated from four different whole island counts:\n1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count'\n\n2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count'\n\n3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count'\n\n4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count'\n\nEach colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success.\n\nIncubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January.\n\nWhole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks.\n\nThis work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.\n\nThe fields in this dataset are:\n\nYear\nBreeding success\nOccupied nests", "links": [ { diff --git a/datasets/brok_5k_gis_1.json b/datasets/brok_5k_gis_1.json index 08d0cf9ac1..a3483aee19 100644 --- a/datasets/brok_5k_gis_1.json +++ b/datasets/brok_5k_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brok_5k_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Broknes Peninsula, Larsemann Hills, 1:5000 GIS dataset.\nThis dataset has been superseded by the datasets described by the metadata records:\n'Larsemann Hills - Mapping from aerial photography captured February 1998' and\n'Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000'.\n\nThese data have been archived as they have been superseded.", "links": [ { diff --git a/datasets/broknes_lake_catchments_gis_1.json b/datasets/broknes_lake_catchments_gis_1.json index 40481b3844..a6fd5662b0 100644 --- a/datasets/broknes_lake_catchments_gis_1.json +++ b/datasets/broknes_lake_catchments_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "broknes_lake_catchments_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Catchment boundaries of the the lakes on Broknes, Larsemann Hills.\nThese catchments were generated using the FLOWDIRECTION and BASINS routines in the GRID module of ArcInfo GIS.", "links": [ { diff --git a/datasets/bromwich_0337948_1.json b/datasets/bromwich_0337948_1.json index 1e70eab52b..a6014f9b2e 100644 --- a/datasets/bromwich_0337948_1.json +++ b/datasets/bromwich_0337948_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bromwich_0337948_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades.\n \n Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it.\n \n MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html\n \n Please reference the following publication if you use the data in a publication:\n Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708.", "links": [ { diff --git a/datasets/brownbay_bathy_dem_1.json b/datasets/brownbay_bathy_dem_1.json index e3e135ab20..9ff5ec0fce 100644 --- a/datasets/brownbay_bathy_dem_1.json +++ b/datasets/brownbay_bathy_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "brownbay_bathy_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. \n\nThe data are stored in a UTM zone 49 projection.\n\nThey were created by interpolation of point data using Kriging.\n\nThe input point data comprised soundings and terrestrial contour vertices.\n\nTHE DATA ARE NOT FOR NAVIGATION PURPOSES.", "links": [ { diff --git a/datasets/bryophyte-observer-bias_1.0.json b/datasets/bryophyte-observer-bias_1.0.json index 93d6956cc1..e76a8b0034 100644 --- a/datasets/bryophyte-observer-bias_1.0.json +++ b/datasets/bryophyte-observer-bias_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bryophyte-observer-bias_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relev\u00e9s conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relev\u00e9s as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise. Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, K\u00fcchler H, Pichon NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of bryophyte species richness. _Journal of Vegetation Science_. (submitted)", "links": [ { diff --git a/datasets/bunger_east_sat_1.json b/datasets/bunger_east_sat_1.json index b6f1ce239f..a52e8cb0b4 100644 --- a/datasets/bunger_east_sat_1.json +++ b/datasets/bunger_east_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bunger_east_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Bunger Hills East/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992. The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/bunger_geology_gis_1.json b/datasets/bunger_geology_gis_1.json index 8ca495346c..ccb57743f5 100644 --- a/datasets/bunger_geology_gis_1.json +++ b/datasets/bunger_geology_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bunger_geology_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset.\nFor additional information, see the published map 'Bunger Hills - Denman Glacier Bedrock Geology', published in 1994, and available at the provided URL.", "links": [ { diff --git a/datasets/bunger_hills_contours_1.json b/datasets/bunger_hills_contours_1.json index a669290d1d..84be6dfb99 100644 --- a/datasets/bunger_hills_contours_1.json +++ b/datasets/bunger_hills_contours_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bunger_hills_contours_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fifty metre interval contours were derived for cartographic purposes from a Digital Elevation Model (DEM) of the Bunger Hills created using SPOT 5 HRS satellite imagery acquired 17 January 2007. \nThe DEM is described by the metadata record 'Bunger Hills SPOT5 DEM (Digital Elevation Model)' with Entry ID bunger_hills_spot5_dem_gis.\nThe DEM was referenced to Mean Sea Level using Earth Gravitational Model 1996 (EGM96).\nEstimated accuracies of the DEM (confidence level 90%):\nplanimetric - 15 to 30 metres\nvertical - 10 metres to 20 metres for slope less than or equal to 20 per cent\nThe DEM should be viewed before using the contours as it has some No Data areas.\n\nThe contours were created in ArcGIS 10.3 using the following procedure:\n1 The DEM was resampled using the Resample tool to a cell size of 50 metres using the bilinear technique; \n2 The DEM resulting from step 1 was used as an input to the Focal Statistics tool which was used to calculate, for each cell, the mean elevation of a three cell by three cell square neighbourhood;\n3 The Contour tool was used to create 50 metre interval contours from the DEM resulting from step 2;\n4 The contours resulting from step 3 were smoothed using the Smooth Line tool with the Paek algorithm and a smoothing tolerance of 50 metres; \n5 The contours resulting from step 4 were converted to single part features using the Multipart to Singlepart tool; \n6 A topology was created for the contours resulting from step 5 and used to identify contours touching and editing was carried out to correct these errors.", "links": [ { diff --git a/datasets/bunger_hills_spot5_dem_gis_1.json b/datasets/bunger_hills_spot5_dem_gis_1.json index 18a9ec6026..a3174393ac 100644 --- a/datasets/bunger_hills_spot5_dem_gis_1.json +++ b/datasets/bunger_hills_spot5_dem_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bunger_hills_spot5_dem_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A digital elevation Model (DEM) of the Bunger Hills with a five metre grid interval,and held in UTM Zone 47, WGS 84 horizontal coordinates and EGM 96 elevation datum. Heights are referenced to Ellipsoid EGM96. The DEM was produced by SPOT image and conforms to standard SPOT image specifications.\n\nSee PDF document SPOT DEM Product Description Version 1.2 January 1, 2005. SPOT DEM accuracies.\n\nThe DEM accuracy specifications below are valid for a full square degree and solely apply to DEMs generated from HRS imagery and not to DEMs derived from external sources. The SPOT DEM absolute horizontal and vertical accuracies depend on the dimensions of the area of interest or on the availability of Reference3D on this area:\n\nAbsolute planimetric accuracy:\nCircular error with respect to WGS84 (confidence level 90%) 15m to 30m\n\nAbsolute elevation accuracy:\nLinear error with respect to EGM96 (confidence level 90%)\nflat or rolling terrain (slope - 20%) 10m to 20m\n\nSPOT DEM products may have variable planimetric and elevation performances: Small areas: One to a few HRS stereopairs HRS intrinsic accuracies 30m @90% planimetric accuracies 20m@90% elevation accuracy\n\nThe absolute planimetric accuracy of 15m can be achieved if excellent-quality ground control points (better than 10m) are used. \nNOTE: No ground control points were used in the DEM.\n\nDEM layer corrections\nSPOT DEM production systematically includes:\nAutomatic filtering to eliminate correlation artifacts Flattening of non-running water bodies (rivers etc excluded) exceeding 0.5km2", "links": [ { diff --git a/datasets/bunger_west_sat_1.json b/datasets/bunger_west_sat_1.json index a29f348242..451d3032d9 100644 --- a/datasets/bunger_west_sat_1.json +++ b/datasets/bunger_west_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bunger_west_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Bunger Hills West/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992. The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/burning_emissions_752_1.json b/datasets/burning_emissions_752_1.json index b485b4e22c..874c69220f 100644 --- a/datasets/burning_emissions_752_1.json +++ b/datasets/burning_emissions_752_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "burning_emissions_752_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biomass burning is a major source for gaseous and particulate atmospheric pollution over southern Africa and globally. The purpose of this study was to quantify biomass burning emissions in an attempt to better understand and predict associated environmental impacts. Sixty biomass burning experiments were carried out November 2000-January 2001 in three regions of southern Africa that are representative of major regional ecosystem types: Etosha National Park (Namibia), Kruger National Park (South Africa), and woodland sites in Zambia and Malawi.", "links": [ { diff --git a/datasets/bvoc_flux_759_1.json b/datasets/bvoc_flux_759_1.json index d8901cb6b7..4478cc16c4 100644 --- a/datasets/bvoc_flux_759_1.json +++ b/datasets/bvoc_flux_759_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "bvoc_flux_759_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogenic volatile organic compound (BVOC) emissions were measured in a Colophospermum mopane woodland near Maun, Botswana, and in a Combretum-Acacia savanna in Kruger National Park, 13 km from Skukuza, Republic of South Africa (RSA) during the 2001 wet season campaign of SAFARI 2000. In addition, relaxed eddy accumulation (REA) measurements of BVOC fluxes were made on flux towers at these sites, where net CO2 emissions were also measured simultaneously.", "links": [ { diff --git a/datasets/c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1.json b/datasets/c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1.json index 64bb95cfca..0afdd90069 100644 --- a/datasets/c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1.json +++ b/datasets/c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for August.", "links": [ { diff --git a/datasets/c0b9f42f-640a-44e0-9080-7e80081942c9_NA.json b/datasets/c0b9f42f-640a-44e0-9080-7e80081942c9_NA.json index 1b3436d35d..c08d6aff73 100644 --- a/datasets/c0b9f42f-640a-44e0-9080-7e80081942c9_NA.json +++ b/datasets/c0b9f42f-640a-44e0-9080-7e80081942c9_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c0b9f42f-640a-44e0-9080-7e80081942c9_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps.", "links": [ { diff --git a/datasets/c183044b88734442b6d37f5c4f6b0092_NA.json b/datasets/c183044b88734442b6d37f5c4f6b0092_NA.json index 793ed1fea4..652fb8261e 100644 --- a/datasets/c183044b88734442b6d37f5c4f6b0092_NA.json +++ b/datasets/c183044b88734442b6d37f5c4f6b0092_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c183044b88734442b6d37f5c4f6b0092_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite. A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json b/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json index 113397dc4e..28411e7ef9 100644 --- a/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json +++ b/datasets/c241e665-5175-4c26-b0cd-f0dfee32afdb.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c241e665-5175-4c26-b0cd-f0dfee32afdb", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes earthqakes events with magnitudes higher than 5.0 as reported by the Advanced national Seismic System (ANSS) Catalogue over the period 1970 - March 2011.\n\nUNEP/GRID-Europe processed the intensity buffer of each event following a methodology developped in GRAVITY I and II (http://www.grid.unep.ch/product/publication/download/ew_gravity1.pdf and http://www.grid.unep.ch/product/publication/download/ew_gravity2.pdf).\n\nCredit: Earthquakes events (USGS/ANSS), Intensity buffers UNEP/GRID-Europe.\n\nAttributes descriptions:\nEV_ID: Event ID\nISO3YEAR: Country and year\nISO3: Country ISO3\nID_NAT: Event ID and ISO3\nID_CAT: ANSS ID\nYEAR: Year\nSTART_DATE: Year, Month and Day (YYYYMMDD)\nMAG: Earthquake magnitude\nDEPTH: Earthquake depth (kilometer)\nRADIUS_M: Buffer radius following Gravity I and II methodology (meter)\nLATITUDE: Latitude (decimal degrees)\n", "links": [ { diff --git a/datasets/c2af8764c84744de87a69db7fecf7af9_NA.json b/datasets/c2af8764c84744de87a69db7fecf7af9_NA.json index 59d82132ef..773fef4433 100644 --- a/datasets/c2af8764c84744de87a69db7fecf7af9_NA.json +++ b/datasets/c2af8764c84744de87a69db7fecf7af9_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c2af8764c84744de87a69db7fecf7af9_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "links": [ { diff --git a/datasets/c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc.json b/datasets/c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc.json index e42f368a67..230b44702e 100644 --- a/datasets/c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc.json +++ b/datasets/c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for January.", "links": [ { diff --git a/datasets/c4_percent_1deg_932_1.json b/datasets/c4_percent_1deg_932_1.json index f3e6210e8a..0e6db50446 100644 --- a/datasets/c4_percent_1deg_932_1.json +++ b/datasets/c4_percent_1deg_932_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4_percent_1deg_932_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling.The C4 percentage is determined from data sets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.", "links": [ { diff --git a/datasets/c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA.json b/datasets/c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA.json index 0f401c9ca9..5822fe8141 100644 --- a/datasets/c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA.json +++ b/datasets/c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational H2O total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV/VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total H2O column is retrieved from GOME solar backscattered measurements in the red wavelength region (614-683.2 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/c4aaero_1.json b/datasets/c4aaero_1.json index 1e0e1a3ffc..f373ffa47a 100644 --- a/datasets/c4aaero_1.json +++ b/datasets/c4aaero_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4aaero_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Aerosonde dataset contains temperature, humidity, and atmospheric pressure measurements collected to study the boundary layer below levels where traditional hurricane reconnasissance aircaft fly. The Aerosonde is an unmanned aerial vehicle with a wingspan of 2.9 meters (~9 feet) weighing approximately 14 kg (~31 lbs). Carrying a payload of air pressure, temperature and humidity probes, the aircraft can fly at altitudes from near the surface to 21,000 feet at speeds of 50-95 mph for periods of up to 30 hours. Controlled by dual computers and navigated by GPS, the Aerosonde is designed to economically collect meteorological data over a wide area.", "links": [ { diff --git a/datasets/c4dcm_1.json b/datasets/c4dcm_1.json index ffe05f7b63..2857000294 100644 --- a/datasets/c4dcm_1.json +++ b/datasets/c4dcm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dcm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Microphysics dataset consists of particle size distributions from three instruments, the 2D-P (two dimensional precipitation probe), the 2D-C (two dimensional cloud probe) and the FSSP (Forward Scattering Spectrometer Probe). These three instruments yield precipitation, hydrometeor and aerosol sizes ranging from 0.3-6400 micrometers. Data is in the form of images and ascii tables.", "links": [ { diff --git a/datasets/c4dcstar_1a.json b/datasets/c4dcstar_1a.json index 7c73fa2bab..adda560a18 100644 --- a/datasets/c4dcstar_1a.json +++ b/datasets/c4dcstar_1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dcstar_1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Conically-Scanning Two-Look Airborne Radiometer (C-STAR) dataset was collected by the Conically-Scanning Two-look Airborne Radiometer (C-STAR), which was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). C-STAR data were collected at 37GHz (in the microwave part of the electromagnetic spectrum) for the period of 8 Aug 2001 through 24 Sept 2001. The CAMEX-4 missions studied hurricanes over land and ocean in the U.S., Gulf of Mexico, Caribean, and Western Atlantic Ocean, and made use of multiple aircraft and research-quality radar, lightning, and radiosonde sites.", "links": [ { diff --git a/datasets/c4dcvi_1.json b/datasets/c4dcvi_1.json index 421a65f357..7d7633569b 100644 --- a/datasets/c4dcvi_1.json +++ b/datasets/c4dcvi_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dcvi_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Forward and NADIR Video dataset consists of DVDs which capture the forward and nadir views from the NASA DC-8 aircraft during CAMEX-4 flights. These videos contain timestamps and the recorded voice channels of the scientists and mission managers aboard the aircraft during flights studying storm conditions.", "links": [ { diff --git a/datasets/c4dd8d_1.json b/datasets/c4dd8d_1.json index 7d11b6f93f..17e0b6de9e 100644 --- a/datasets/c4dd8d_1.json +++ b/datasets/c4dd8d_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dd8d_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Dropsonde System dataset was collected by the DC-8 Dropsonde System (D8D) uses dropwindsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temperature, humidity, windspeed/direction, pressure, and location in 3 dimensional space during the sonde's descent once each half second. Measurements are transmitted to the aircraft from the time of release until impact with the ocean's surface.", "links": [ { diff --git a/datasets/c4dicats_1.json b/datasets/c4dicats_1.json index 7f8cea7328..7c20c16f92 100644 --- a/datasets/c4dicats_1.json +++ b/datasets/c4dicats_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dicats_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Information Collection and Transmission System dataset was collected by the Information Collection and Transmission System (ICATS), which is designed to: 1) Interface and process avionics and environmental paramaters from the Navigational Management System, GPS, Central Air Data Computer, Embedded GPS/INS, and analog voltage sources from aircraft and experimenters, 2) Furnish engineering unit values of selected parameters and computed functions for real-time video display, and archive ASCII data at experimenter stations, 3) Archive engineering unit values of 'Appendix A' (to the ICATS document included with dataset documentation) on data storage for post flight retrieval.", "links": [ { diff --git a/datasets/c4djlh_1.json b/datasets/c4djlh_1.json index 726f75ea8f..f01d4fff18 100644 --- a/datasets/c4djlh_1.json +++ b/datasets/c4djlh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4djlh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 JPL Laser Hygrometer dataset contains water vapor volume and mixing ratio concentractions collected during the CAMEX-4 campaign to study tropical cyclones. The Laser Hygrometer measures in situ water vapor content using a tuneable laser emitting at 1.37 microns. Absorption at that wavelength is a function of water vapor content, thus measuring the amount of absorption in an open path beyond the aircraft boundary layer, a value of water vapor pressure is made. The maximum sampling rate is 8 Hz, but the instrument is normally configured through the software for a 1Hz sampling rate.", "links": [ { diff --git a/datasets/c4dlase_1.json b/datasets/c4dlase_1.json index d203b8becd..40345f5077 100644 --- a/datasets/c4dlase_1.json +++ b/datasets/c4dlase_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dlase_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 LIDAR Atmospheric Sensing Experiment (LASE) dataset was collected by the LASE instrument, which is an airborne DIAL (Differential Absorption Lidar) system used to measure water vapor, aerosols, and clouds throughout the troposphere. LASE operates by locking to a strong water vapor line and electronically tuning to any spectral position on the absorption line to choose the suitable absorption cross-section for optimum measurements over a range of water vapor concentrations in the atmosphere. During CAMEX-4, LASE operated from the NASA DC-8 using strong and weak water vapor lines in both the nadir and zenith modes, thereby simultaneously acquiring data below and above the aircraft.", "links": [ { diff --git a/datasets/c4dlip_1.json b/datasets/c4dlip_1.json index 3108200bae..ec6c9f5b76 100644 --- a/datasets/c4dlip_1.json +++ b/datasets/c4dlip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dlip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Lightning Instrument Package (LIP) dataset was collected by the ER-2 Lightning Instrument Package (LIP), which allows the vector components of the electric field (i.e, Ex, Ey, Ez) to be readily obtained, and thus, greatly improves our knowledge of the electrical structure of storms overflown during the CAMEX-4 campaign. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (i.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.", "links": [ { diff --git a/datasets/c4dmms_1.json b/datasets/c4dmms_1.json index b24fe174fe..1b645a3931 100644 --- a/datasets/c4dmms_1.json +++ b/datasets/c4dmms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dmms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Meteorological Measurement System (MMS) was collected by the MMS, which consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the Earth, and a data acquisition system to sample, process, and record the measured quantities. The MMS data was collected during the CAMEX-4 campaign to study physical properties of atmospheric temperature.", "links": [ { diff --git a/datasets/c4dmtp_1.json b/datasets/c4dmtp_1.json index ba247ae434..7adf71be85 100644 --- a/datasets/c4dmtp_1.json +++ b/datasets/c4dmtp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dmtp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Microwave Temperature Profiler (MTP) dataset was collected by the MTP, which is a passive microwave radiometer used during the CAMEX-4 campaign to collect data measurements of thermal emmission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperature versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path. These data were collected from August 16 - September 25, 2002 from Jacksonville Naval Air Station, Florida.", "links": [ { diff --git a/datasets/c4dnevzor_1.json b/datasets/c4dnevzor_1.json index 7365b6dae1..fc1bdea325 100644 --- a/datasets/c4dnevzor_1.json +++ b/datasets/c4dnevzor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dnevzor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Nevzorov Total Condensed Water Content Sensor dataset was collected by the Nevzorov total condensed water content sensor which was used to measure the total water content of air sampled during the CAMEX-4 campaign. The Nevzorov water vapor probe flew aboard the NASA DC-8 aircraft during CAMEX-4 to study tropical storms and hurricanes. Nevzorov is a so-called hot-wire device, where two resistors are heated to evaporate all hydrometeors that touch their surfaces during the flight. Due to their shape, they are able to catch small droplets or droplets and ice crystals. The amount of energy necessary to evaporate particles is a direct measure of the liquid water content of the hydrometeor (liquid or frozen) and also gives an indication of the water vapor present.", "links": [ { diff --git a/datasets/c4dpr2_1.json b/datasets/c4dpr2_1.json index 9f49ab2f5e..7521e7ba95 100644 --- a/datasets/c4dpr2_1.json +++ b/datasets/c4dpr2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dpr2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 2nd Generation Precipitation Radar dataset was collected by the Second Generation Precipitation Radar (PR-2), which is a dual-frequency, Doppler, dual-polarization radar system that includes digital, real-time pulse compression, extremely compact RF electronics, and a large deployable dual-frequency cylindrical parabolic antenna subsystem. The PR-2 Doppler radar was used during the CAMEX-4 campaign to collect data for studying tropical storms and cyclones.", "links": [ { diff --git a/datasets/c4dvid_1.json b/datasets/c4dvid_1.json index 6349ec3687..9a502efe1d 100644 --- a/datasets/c4dvid_1.json +++ b/datasets/c4dvid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4dvid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 DC-8 Forward and NADIR Video dataset consists of DVDs which capture the forward and nadir views from the NASA DC-8 aircraft during CAMEX-4 flights. These videos contain timestamps and the recorded voice channels of the scientists and mission managers aboard the aircraft during flights studying storm conditions. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/c4eampr_1.json b/datasets/c4eampr_1.json index 70cbc629ba..2225860a14 100644 --- a/datasets/c4eampr_1.json +++ b/datasets/c4eampr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4eampr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 AMPR Brightness Temperature (TB) dataset was collected by the Advanced Microwave Precipitation Radiometer (AMPR), which was deployed during the Fourth Convection and Moisture Experiment (CAMEX-4). AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz) for the period of 26 August 2001 through 24 September 2001. The purpose of the CAMEX-4 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Caribbean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, and radiosonde sites.", "links": [ { diff --git a/datasets/c4eedop_1.json b/datasets/c4eedop_1.json index 4509010304..4c175687a9 100644 --- a/datasets/c4eedop_1.json +++ b/datasets/c4eedop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4eedop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 ER-2 Doppler Radar dataset was collected by the ER-2 Doppler radar (EDOP), which is an X-band (9.6 GHz) Doppler radar mounted in the nose of ER-2. The instrument has two fixed antennas, one pointing at nadir and the second pointing approximately 33 degrees ahead of nadir. The beam width of the antenna is 3 degrees in the vertical and horizontal directions which, for a 20 km altitude, yields a nadir footprint at the surface of 1 km.", "links": [ { diff --git a/datasets/c4eehad_1.json b/datasets/c4eehad_1.json index 2b4da1818b..9d8f9526d4 100644 --- a/datasets/c4eehad_1.json +++ b/datasets/c4eehad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4eehad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 ER-2 High Altitude Dropsonde dataset was collected by the ER-2 High Altitude Dropsonde System (EHAD), which used dropwinsondes fitted with Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temp, humidity, windspeed/direction, pressure) and location in 3 dimensional space during the sonde's descent once each half second. Measurements was transmitted to the aircraft from the time of release until impact with the ocean's surface.", "links": [ { diff --git a/datasets/c4ehamsr_1.json b/datasets/c4ehamsr_1.json index ec5ef5805d..5d6f4c322e 100644 --- a/datasets/c4ehamsr_1.json +++ b/datasets/c4ehamsr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4ehamsr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 High Altitude MMIC Sounding Radiometer (HAMSR) dataset was collected by the High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), which is a microwave atmospheric sounder recently developed by JPL under the NASA Instrument Incubator Program. Operating with 25 spectral channels in the 50-190 HGz region, it provides measurements that can be used to infer the 3-D distribution of temperature, water vapor, and liquid water in the atmosphere, even in the presence of clouds. HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft.", "links": [ { diff --git a/datasets/c4elip_1.json b/datasets/c4elip_1.json index 17e5fe4cb8..3e55d29de2 100644 --- a/datasets/c4elip_1.json +++ b/datasets/c4elip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4elip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 ER-2 Lightning Instrument Package (LIP) dataset was collected by the ER-2 LIP, which allows the vector components of the electric field (i.e, Ex, Ey, Ez ) to be readily obtained, and thus greatly improves our knowledge of the electrical structure of storms overflown. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (i.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.", "links": [ { diff --git a/datasets/c4emas_1.json b/datasets/c4emas_1.json index f252d23ddc..d85b33dcc8 100644 --- a/datasets/c4emas_1.json +++ b/datasets/c4emas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4emas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. This dataset has visible and infrared imagery calibrated to at-sensor radiance. Included are many associated browse files including the flight line tracks, and also text files of nadir brightness temperature and radiance for selected bands.", "links": [ { diff --git a/datasets/c4emtp_1.json b/datasets/c4emtp_1.json index b854d2ed40..6cec95a83d 100644 --- a/datasets/c4emtp_1.json +++ b/datasets/c4emtp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4emtp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 ER-2 Microwave Temperature Profiler dataset was collected by the Microwave Temperature Profiler (MTP), which is a passive microwave radiometer which measures the thermal emission from oxygen molecules in the atmosphere for a selection of elevation angles. Current observing frequencies are 56.6 and 58.8 GHz. Measured brightness temperature versus elevation angle is converted to air temperature versus altitude using a statistical retrieval procedure. An altitude temperature profile is produced every three km along the flight path. Data were collected from the Jacksonville Naval Air Station, Florida during the CAMEX-4 campaign spanning from August 26 - September 26. 2001.", "links": [ { diff --git a/datasets/c4enav_1.json b/datasets/c4enav_1.json index 52bec7bfdb..e13b925618 100644 --- a/datasets/c4enav_1.json +++ b/datasets/c4enav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4enav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) these files contain outside meteorological parameters such as wind speed and direction and temperature. These ascii text files was recorded every second for the length of the sortie. Additionally, graphical representations of these measured parameters are shown in .gif files.", "links": [ { diff --git a/datasets/c4enlh_1.json b/datasets/c4enlh_1.json index 8c21b319ab..cc6b8ce797 100644 --- a/datasets/c4enlh_1.json +++ b/datasets/c4enlh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4enlh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NOAA Lyman-Alpha Hygrometer dataset was collected by the NOAA Lyman-alpha Total Water Hygrometer, which was flown during the fourth field campaign in the CAMEX series (CAMEX-4). CAMEX-4 ran from 16 August to 24 September 2001 and was based out of Jacksonville Naval Air Station, Florida, and included missions in the Gulf of Mexico, Caribbean and Western Atlantic. The experiment focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using both NASA-funded aircraft and surface remote sensing instrumentation.", "links": [ { diff --git a/datasets/c4eo3p_1.json b/datasets/c4eo3p_1.json index fae68fe71e..f77b385e25 100644 --- a/datasets/c4eo3p_1.json +++ b/datasets/c4eo3p_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4eo3p_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Dual-Beam UV-Absorption Ozone Photometer dataset was measured by using a photometer consisting of a mercury lamp, two sample chambers that could be peridically scrubbed of ozone, and two detectors that measured the 254-nm radiation transmitted through the chamber from the lamp. The ozone number density was calculated using the ozone absorption cross-section at 254nm and the Beer-Lambert Law. The one second data collection rate at the minimum detectable concentration of ozone (one sigma) was 1.5 x 1010 molecules/cm3.", "links": [ { diff --git a/datasets/c4gandros_1.json b/datasets/c4gandros_1.json index 695ef4f2fa..cc8fa1011a 100644 --- a/datasets/c4gandros_1.json +++ b/datasets/c4gandros_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gandros_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Andros Lisland Rawinsonde and Radiosondes dataset was collected by using numerous rawinsondes (radiosondes), which were launched from Andros Island in support of CAMEX-4. These sondes provided atmospheric soundings of temperature, pressure, relative humidity, wind, and altitude.", "links": [ { diff --git a/datasets/c4gmipclo_1.json b/datasets/c4gmipclo_1.json index 826dce6c4a..0984d3e751 100644 --- a/datasets/c4gmipclo_1.json +++ b/datasets/c4gmipclo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipclo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 MIPS Ceilometer dataset was collected by the University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS), which is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel Microwave Profiling Radiometer (MPR), Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles.The ceilometer gathered backscatter power and up to three cloud base heights.", "links": [ { diff --git a/datasets/c4gmipfm_1.json b/datasets/c4gmipfm_1.json index c7386e7411..0b159f74f8 100644 --- a/datasets/c4gmipfm_1.json +++ b/datasets/c4gmipfm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipfm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from the Electric Field Mills which yield information about the atmospheric electrical fields above the instruments.", "links": [ { diff --git a/datasets/c4gmipmpr_1.json b/datasets/c4gmipmpr_1.json index 2e2c093f61..584a5bb647 100644 --- a/datasets/c4gmipmpr_1.json +++ b/datasets/c4gmipmpr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipmpr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. The 12 channel microwave profiling radiometer provides profiles of temperature, water vapor and liquid water and integrated values of water vapor and liquid water from the surface to 10km every ~ 15 minutes. Cloud base temperature is also measured.", "links": [ { diff --git a/datasets/c4gmipsod_1.json b/datasets/c4gmipsod_1.json index 17fd19eb63..bd71256ded 100644 --- a/datasets/c4gmipsod_1.json +++ b/datasets/c4gmipsod_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipsod_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of cdf and mom files tarred together for a day. The 'cdf' file collects 15 minute average 3-D wind profiles from the Doppler Sodar starting at the beginning of each day. The 'mom' file contains data from each beam about radial velocity and backscattter intensity. Each horizontal beam is approximately 7 seconds apart; vertical beams are approximately 21 seconds apart.", "links": [ { diff --git a/datasets/c4gmipss1_1.json b/datasets/c4gmipss1_1.json index c1b097f9c9..be6a9d6f34 100644 --- a/datasets/c4gmipss1_1.json +++ b/datasets/c4gmipss1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipss1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station One containing multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer. Information, collected at 1Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.", "links": [ { diff --git a/datasets/c4gmipss2_1.json b/datasets/c4gmipss2_1.json index c62a3f52e5..9da9fb84cb 100644 --- a/datasets/c4gmipss2_1.json +++ b/datasets/c4gmipss2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipss2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS) is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset consists of data from Surface Station Two which contained multiple instruments including an anemometer, rain gauge, thermometer, pyranometer and barometer. Information, collected at 0.5 Hz, includes windspeed and direction as well as precipitation, temperature/humidity, solar radiation, and atmospheric pressure.", "links": [ { diff --git a/datasets/c4gmipwp_1.json b/datasets/c4gmipwp_1.json index b78e209237..c4c93f3964 100644 --- a/datasets/c4gmipwp_1.json +++ b/datasets/c4gmipwp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmipwp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 MIPS 915 MHZ Doppler Wind Profiler dataset was collected by the University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS), which is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel microwave profiling radiometer, Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles. Additionally, radial velocity and backscatter intensity data are contained in the dataset.", "links": [ { diff --git a/datasets/c4gmisrep_1.json b/datasets/c4gmisrep_1.json index 4a037c84bd..59e729cbea 100644 --- a/datasets/c4gmisrep_1.json +++ b/datasets/c4gmisrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gmisrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Convection And Moisture EXperiment (CAMEX)-4 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.", "links": [ { diff --git a/datasets/c4gnpol_1.json b/datasets/c4gnpol_1.json index 5950c38cb7..22759c94b5 100644 --- a/datasets/c4gnpol_1.json +++ b/datasets/c4gnpol_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gnpol_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NASA Portable S-Band Multiparameter WX Research Radar dataset was collected by the NASA Portable S-band Multiparameter Weather Research Radar (NPOL), which is a Doppler S-band radar that when used continuously during an operation provides a full volume scan every ten minutes. Scans may be either 300km long range scans or 150km range for most high resolution data scans. Products available include real time PPI scans of reflectivities and velocities, and near real time displays of other radar products, including RHI's, CAPPI's, and Polarimetric products. Browse imagery is available for PPI reflectivities.", "links": [ { diff --git a/datasets/c4gsmart_1.json b/datasets/c4gsmart_1.json index 92162249c4..a01f9ae1a3 100644 --- a/datasets/c4gsmart_1.json +++ b/datasets/c4gsmart_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gsmart_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Shared Mobile Atmospheric Research and Teching Radars dataset was collected by the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), which is a portable 5 cm Doppler radar. All equipment (e.g., antenna, power generator, processors, and readout computers) are truck mounted to provide maximum transportability. Originally located in the Florida Keys during CAMEX-4, the radar was moved to the Venice Florida area for landfall of TS Gabrielle on September 14, 2001.", "links": [ { diff --git a/datasets/c4gtoga_1.json b/datasets/c4gtoga_1.json index 21e67757f5..b037747e4b 100644 --- a/datasets/c4gtoga_1.json +++ b/datasets/c4gtoga_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gtoga_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TOGA radar dataset consists of browse and radar data collected from the TOGA radar during the CAMEX-4 experiment. TOGA is a C-band linear polarized doppler radar using 500KW of radiated power. Products available include real time PPI scans of reflectivities and Doppler velocities.", "links": [ { diff --git a/datasets/c4gxpow_1.json b/datasets/c4gxpow_1.json index ce498196b4..02aadb09dd 100644 --- a/datasets/c4gxpow_1.json +++ b/datasets/c4gxpow_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4gxpow_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 Mobile X-Band Polarimetric Weather Radar dataset was collected by the Mobile X-band Polarimetric Weather Radar on Wheels (X-POW), which is a Doppler scanning radar operating at 9.3 GHz with horizontal and vertical polarization. The X-POW was used for detection and detailing of surface rainfall rate and precipitation classification fields, as well as for 3D precipitation microphysical retrievals including water/frozen hydrometeor contents and drop size distribution profiles. The X-POW was located in the Florida Keys during the CAMEX-4 field experiment.", "links": [ { diff --git a/datasets/c4p3cp_1.json b/datasets/c4p3cp_1.json index 1a0a08e180..d7d921190d 100644 --- a/datasets/c4p3cp_1.json +++ b/datasets/c4p3cp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4p3cp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NOAA WP-3D Cloud Physics dataset used the NOAA WP-3D Orion aircraft, which has multiple meteorological and microphysical sensors. These include, for example, cloud particle imagers and temperature and dewpoint probes. CAMEX-4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. This dataset includes navigation data as well as the meteorological and microphysical data. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/c4p3flt_1.json b/datasets/c4p3flt_1.json index ee68128d24..32a564e95e 100644 --- a/datasets/c4p3flt_1.json +++ b/datasets/c4p3flt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4p3flt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NOAA WP-3D Flight Level Data dataset used the NOAA WP-3D Orion aircraft, which collects numerous in-situ meteorological measurements along with navigation and aircraft state parameters during each flight. CAMEX-4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. The WP-3D data are encoded on 8mm tapes in what is called the 'AOC Standard Tape Format'. Examples of meteorological data include total temperature, dew point, liquid water content and dynamic pressure (from several sensors). Aircraft parameters include angle of attack, airspeed, and slip angle. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/c4p3rad_1.json b/datasets/c4p3rad_1.json index ef03bc8916..9027f6f138 100644 --- a/datasets/c4p3rad_1.json +++ b/datasets/c4p3rad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4p3rad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NPAA WP-3D Radar dataset used the NOAA WP-3D Orion aircraft, which has two separate research radars to collect meteorological data. One is mounted on the lower fuselage (C-band), and the other is located in the tail (X-band). CAMEX-4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. Data from these radars consist of reflectivity in range and azimuth coordinates collected either in the horizontal (lower fuselage) or vertical (tail radar) planes. Doppler radial velocity is also collected by the tail radar. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/c4p3vid_1.json b/datasets/c4p3vid_1.json index 34127b0196..5671707311 100644 --- a/datasets/c4p3vid_1.json +++ b/datasets/c4p3vid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4p3vid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 NOAA WP-3D Video dataset was collected during the fourth field campaign in the CAMEX series (CAMEX-4), which ran from 16 August to 25 September, 2001 and was based out of Jacksonville Naval Air Station, Florida. An important addition to CAMEX-4 was the participation of the NOAA weather reconnaissance WP-3D that collected radar, video and microphysical data.The NOAA WP-3D Videos were created giving a forward, left, right and downward views relative to the aircraft. Each view is a separate tape. All are recoreded in SVHS format in compressed time mode. That means that the video shows time passing at a rate approximately 12.5 times that of normal speed (e.g. 1 minute real time takes 5 seconds on the video). For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/c4sg8_1.json b/datasets/c4sg8_1.json index cbea3eb9af..b43b1e3b99 100644 --- a/datasets/c4sg8_1.json +++ b/datasets/c4sg8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c4sg8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-4 GOES-8 Products dataset was collected during the CAMEX-4 field campaign, which ocused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files are available in McIDAS format, and browse imagery is also available.", "links": [ { diff --git a/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json b/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json index 9b97c151dd..926522dfcb 100644 --- a/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json +++ b/datasets/c5064da0-ce61-47fc-b17f-c837bd2847be.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c5064da0-ce61-47fc-b17f-c837bd2847be", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes an estimate of flood events. \n\nIt is based on two sources:\n1) A GIS modeling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value. \n\n2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO).\n\nThis product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). \nIt was modeled using global data.\n\nCredit: GIS processing UNEP/GRID-Europe, with key support from USGS EROS Data Center, Dartmouth Flood Observatory 2008.", "links": [ { diff --git a/datasets/c65ce27928f34ebd92224c451c2a8bed_NA.json b/datasets/c65ce27928f34ebd92224c451c2a8bed_NA.json index 08d2e56e98..56a906e575 100644 --- a/datasets/c65ce27928f34ebd92224c451c2a8bed_NA.json +++ b/datasets/c65ce27928f34ebd92224c451c2a8bed_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c65ce27928f34ebd92224c451c2a8bed_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements. These data cover the period between 09/1991 and 12/2010.The Version 1.1 data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20", "links": [ { diff --git a/datasets/c88_data_1.json b/datasets/c88_data_1.json index bd2ce95f83..dbd5041376 100644 --- a/datasets/c88_data_1.json +++ b/datasets/c88_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "c88_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from fish captured by Erwin, Casey 1988. Includes fish size, weight, sex, reproductive stage data as well as quantitative stomach contents data and qualitative position data. Approximate locations where fish were caught are provided in the database. Additionally an approximate image map is also provided as a visual reference. These data are stored in an Access Database.\n\nAdditionally, another Microsoft Access database containing data from this cruise, plus several others is available for download from the URL given below. The Entry ID's of the other metadata records also related to this data are:\n\nAADC-00038 \nAADC-00068 \nAADC-00073 \nAADC-00075 \nAADC-00080 \nAADC-00082\nc88_data\n\nThe fields in this dataset are:\n \nCruises\nDate\nLocation\nLatitude\nLongitude\nSpecies\nGear\nLength\nWeight\nSex\nGonad\nEye\nOtolith\nStomach\nLifestage\nFamily", "links": [ { diff --git a/datasets/calibgas_500_1.json b/datasets/calibgas_500_1.json index 885090fc0c..2ab76622bb 100644 --- a/datasets/calibgas_500_1.json +++ b/datasets/calibgas_500_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "calibgas_500_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to improve the comparability of trace gas measurements made by various science teams, the BOReal Ecosystem-Atmosphere Study (BOREAS) obtained several cylinders of carbon dioxide (CO2) and methane (CH4) that were used as calibration standards.", "links": [ { diff --git a/datasets/canopychem_422_1.json b/datasets/canopychem_422_1.json index 13ec08d1f3..d729f8ef2d 100644 --- a/datasets/canopychem_422_1.json +++ b/datasets/canopychem_422_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "canopychem_422_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The nitrogen and chlorophyll concentrations of constructed Douglas-fir and bigleaf maple seedling canopies were determined. Canopy reflectance spectra were measured before foliage samples were collected.", "links": [ { diff --git a/datasets/canopyspec_423_1.json b/datasets/canopyspec_423_1.json index 4f0d285412..5c4ec60d29 100644 --- a/datasets/canopyspec_423_1.json +++ b/datasets/canopyspec_423_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "canopyspec_423_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reflectance spectra of Douglas-fir and bigleaf maple seedling canopies were measured. Canopies varied in fertilizer treatment and leaf area density respectively.", "links": [ { diff --git a/datasets/capeden_management_gis_1.json b/datasets/capeden_management_gis_1.json index 25216cb724..406fef7734 100644 --- a/datasets/capeden_management_gis_1.json +++ b/datasets/capeden_management_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "capeden_management_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is comprised of the boundary of the Visual Protection Zone at Cape Denison, Antarctica.\nThe data were created for the Management Plan for Historic Site and Monument No 77 and Antarctic Specially Managed Area (ASMA) No 3 produced by the Australian Antarctic Division in 2004.\nThe data are formatted according to the SCAR Feature Catalogue and are available for download (see Related URLS).\n", "links": [ { diff --git a/datasets/capeden_sat_ikonos_1.json b/datasets/capeden_sat_ikonos_1.json index f14935a8a0..cac75037dc 100644 --- a/datasets/capeden_sat_ikonos_1.json +++ b/datasets/capeden_sat_ikonos_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "capeden_sat_ikonos_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following was done by a contractor for the Australian Antarctic Division:\n\nA satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001.\nThe multispectral component of the mosaic was then \n(i) pan sharpened to increase the resolution from 4 metres to 1 metre; and \n(ii) georeferenced.\n\nSee the Quality section for details about the satellite images and the georeferencing.\n\nThe georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details.\n\nThree satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details.", "links": [ { diff --git a/datasets/carabid-beetles-in-forests_2.0.json b/datasets/carabid-beetles-in-forests_2.0.json index acd048f884..8161bd21d3 100644 --- a/datasets/carabid-beetles-in-forests_2.0.json +++ b/datasets/carabid-beetles-in-forests_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "carabid-beetles-in-forests_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types. Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG. Data are provided on request to contact person against bilateral agreement.", "links": [ { diff --git a/datasets/casey_alk_clones_1.json b/datasets/casey_alk_clones_1.json index 62bf5f3460..19520b8bf0 100644 --- a/datasets/casey_alk_clones_1.json +++ b/datasets/casey_alk_clones_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_alk_clones_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of 67 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with word-processing as well as sequence analysis software. \n\nThe clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. \n\nThe library was created from a marine sediment sample that was part of the SRE4 marine biodegradation experiment in O'Brien bay near Casey station. The sample used was from the sediment exposed to Special Antarctic Blend diesel 5-weeks after the time of deployment. \n\nThese data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments.", "links": [ { diff --git a/datasets/casey_aws_1.json b/datasets/casey_aws_1.json index a080aa4e96..e5666c9bd2 100644 --- a/datasets/casey_aws_1.json +++ b/datasets/casey_aws_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_aws_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The automatic weather stations at the Australian stations (Casey, Davis, Macquarie Island, and Mawson) were installed by the Bureau of Meteorology. They collect information on the following (in the following units):\n\ndate\n\nTime hh:mm\n\nwind speed knots\n\nwind direction degrees\n\nair temperature degrees celsius\n\nrelative humidity percent\n\nair pressure hPa\n\nTimes are in UT.\n\nMeasurements are made at 4 metres.\n\nThe fields in this dataset are:\ndate\ntime (hh:mm)\nwind speed (knots)\nwind direction (degrees)\nair temperature (degrees celsius)\nrelative humidity (percent)\nair pressure (hPa)\n\nMore current data are provided at the AWS data page at the provided URL.\n\nA download file is available from the provided URL which provides information about the locations where wind measurements at Casey have been made. The information was provided to David Smith of the Australian Antarctic Data Centre by Phil Smart of the Hobart office of the Bureau of Meteorology in February 2009. David added the coordinates and the information about their origin.", "links": [ { diff --git a/datasets/casey_biopiles_DSM_2013_1.json b/datasets/casey_biopiles_DSM_2013_1.json index 9962919d10..745634735e 100644 --- a/datasets/casey_biopiles_DSM_2013_1.json +++ b/datasets/casey_biopiles_DSM_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_biopiles_DSM_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Digital Surface Model (DSM) was created by Dr Arko Lucieer of TerraLuma (http://www.terraluma.net/) and the University of Tasmania for the Terrestrial and Nearshore Ecosystems research group at the Australian Antarctic Division (TNE/AAD).\nAn orthophoto was also created. See the metadata record 'Orthophoto of the biopiles and nearby area at Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 10 February 2013' with ID 'casey_biopiles_ortho_2013'.\n\nThe products were requested for Australian Antarctic Science Project 4036: \nRemediation of petroleum contaminants in the Antarctic and subantarctic.\nThe products were created from digital photos taken on the 10th February, 2013, with a Canon EOS 550D from a Mikrokopter Oktokopter piloted by Arko Lucieer and Zybnek Malenovsky. \nThe products were georeferenced to ground control points surveyed using differential GPS by Dr Daniel Wilkins of TNE/AAD.\nRaw photo metadata: ISO-400, Focal Length 20mm, f/6.3 Exposure Time 1/1250 sec.\nHorizontal Datum: ITRF2000.", "links": [ { diff --git a/datasets/casey_ice_coring_1979_1.json b/datasets/casey_ice_coring_1979_1.json index 70008c7588..a40046d6d1 100644 --- a/datasets/casey_ice_coring_1979_1.json +++ b/datasets/casey_ice_coring_1979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_ice_coring_1979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A handwritten copy of the 1979 report on ice core drilling on Law Dome (final draft?) Includes detailed notes on methods and equipment, as well as data for inclination, temperature and diameters of boreholes for several sites (SGF, SGP, SGB, BHQ), and results of measurements from S2.", "links": [ { diff --git a/datasets/casey_traverse_1970_1.json b/datasets/casey_traverse_1970_1.json index 44a3a06fd7..08cd70e4ad 100644 --- a/datasets/casey_traverse_1970_1.json +++ b/datasets/casey_traverse_1970_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_traverse_1970_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1970, several traverses on Law Dome extensively recorded barometric pressure, air temperature, magnetic field and gravity as they travelled. These results are recorded in two log books.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/casey_vostok_radiosonde_1.json b/datasets/casey_vostok_radiosonde_1.json index 6b63c25ea1..e50322f222 100644 --- a/datasets/casey_vostok_radiosonde_1.json +++ b/datasets/casey_vostok_radiosonde_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_vostok_radiosonde_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected (elevation, temperature, pressure, relative humidity, and plots of data for several flights over Casey-Vostok line in 1984.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/casey_wilkes_con_1.json b/datasets/casey_wilkes_con_1.json index e793441194..13de228100 100644 --- a/datasets/casey_wilkes_con_1.json +++ b/datasets/casey_wilkes_con_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casey_wilkes_con_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes maps produced from the Australian Antarctic Data Centre GIS for use in environmental management of the 'old' Casey station tip site and the abandoned Wilkes station site: a map of the Windmill Islands showing the locations of Casey and Wilkes, contour maps of Casey and Wilkes and a map showing the water flow directions at Casey.\nThe maps were used for locating contaminated areas and identifying the processes involved in contamination spread.\nAlso included in the dataset is the GIS topographic and derived data used to create the maps.", "links": [ { diff --git a/datasets/caseybathy_gis_1.json b/datasets/caseybathy_gis_1.json index dd42693061..8b194d54b8 100644 --- a/datasets/caseybathy_gis_1.json +++ b/datasets/caseybathy_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "caseybathy_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetric Contours and height range polygons of approaches to Casey Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings.", "links": [ { diff --git a/datasets/casfair1_gis_1.json b/datasets/casfair1_gis_1.json index 1ca5bfb93f..cdd76ae66d 100644 --- a/datasets/casfair1_gis_1.json +++ b/datasets/casfair1_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casfair1_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 161 V5/500 6610/1 scale 1:10 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.", "links": [ { diff --git a/datasets/casfair2_gis_1.json b/datasets/casfair2_gis_1.json index bb96227f16..e75a8286a3 100644 --- a/datasets/casfair2_gis_1.json +++ b/datasets/casfair2_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casfair2_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 161 V5/500 6610/2 scale 1:25 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.", "links": [ { diff --git a/datasets/casfair3_1.json b/datasets/casfair3_1.json index 84b1a4f3f8..24aa7aaf0b 100644 --- a/datasets/casfair3_1.json +++ b/datasets/casfair3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "casfair3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 189 V5/584 6610/1 scale 1:25 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.", "links": [ { diff --git a/datasets/catchment-biodiversity-vaud-edna_1.0.json b/datasets/catchment-biodiversity-vaud-edna_1.0.json index 3059060ba8..40b0ab2e44 100644 --- a/datasets/catchment-biodiversity-vaud-edna_1.0.json +++ b/datasets/catchment-biodiversity-vaud-edna_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "catchment-biodiversity-vaud-edna_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.", "links": [ { diff --git a/datasets/causal-effect-of-lup_1.0.json b/datasets/causal-effect-of-lup_1.0.json index 3abf20546b..f29a65daca 100644 --- a/datasets/causal-effect-of-lup_1.0.json +++ b/datasets/causal-effect-of-lup_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "causal-effect-of-lup_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China. Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020. Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020. XZQDM2 is villages' unique administrative ID; Area is the land area of village i; Dis2water is the Euclidean distance from village i to the nearest waterbody; Dis2coastl is the Euclidean distance from village i to the nearest coastline; Dis2city is the the Euclidean distance from village i to the city center; Dis2county is the the Euclidean distance from village i to the nearest county center; Elevation is the the average elevation within village i; Dis2road is the the Euclidean distance from village i to the nearest road; Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t; Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones; Intensity is the percentage of land that was assigned to the development-permitted zones in village i; Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020; BuLE is the dependent variable, representing built-up land expansion in village i in year t; Town is town' unique administrative ID. Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.", "links": [ { diff --git a/datasets/causal-effect-of-mfoz_1.0.json b/datasets/causal-effect-of-mfoz_1.0.json index 1df594b2d6..ce9545c036 100644 --- a/datasets/causal-effect-of-mfoz_1.0.json +++ b/datasets/causal-effect-of-mfoz_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "causal-effect-of-mfoz_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China? Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013\u20132015, 2013\u20132018 and 2013\u20132020). Data: Data.xlsx contains the variables of 954 towns in Fujian Province. Town_ID is the town unique ID; County_ID is the county unique ID; City_ID is the city unique ID; MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded); Builtup_13_15 is the built-up land expansion from 2013 to 2015; Builtup_13_18 is the built-up land expansion from 2013 to 2018; Builtup_13_20 is the built-up land expansion from 2013 to 2020; Dis2water is the Euclidean distance from the town to the nearest waterbody; Slope is the the average slope within the town; GDP is the average GDP in 2010 within the town; Pop is the average population in 2010 within the town; Road is the average population in 2010 within the town; Dis2city is the Euclidean distance from the town to the nearest prefectural city centre; Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010. Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013\u20132015, 2013\u20132018 and 2013\u20132020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.", "links": [ { diff --git a/datasets/cb54bd70826842a9acf658ebabe4a104_NA.json b/datasets/cb54bd70826842a9acf658ebabe4a104_NA.json index e7ba5091d2..ee08f9c828 100644 --- a/datasets/cb54bd70826842a9acf658ebabe4a104_NA.json +++ b/datasets/cb54bd70826842a9acf658ebabe4a104_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cb54bd70826842a9acf658ebabe4a104_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for SCIAMACHY in 2008.", "links": [ { diff --git a/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json b/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json index 4cabb35fe4..2db52b25e8 100644 --- a/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json +++ b/datasets/cc4d85ee-6c72-4249-8775-a96e359457ad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cc4d85ee-6c72-4249-8775-a96e359457ad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Assessment of Human Induced Soil Degradation (GLASOD) was conducted by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, as commissioned by the United Nations Environment Programme (UNEP). ISRIC produced a 1:10 million scale wall chart in 1990 and subsequently produced a digital data set. In essence, the GLASOD database contains information on soil degradation within map units as reported by numerous soil experts around the world through a questionnaire. It includes the type, degree, extent, cause and rate of soil degradation. From these data, the GRID-Nairobi center produced digital and hardcopy maps and made area calculations.\n \nThe GLASOD database includes a topographic basemap or global template of continental coastlines, islands and lakes, which GRID-Nairobi extracted from the digital version of GLASOD's 1:10 million wall map. All of the boundaries that defined oceans and lakes were selected to create a new ARC/INFO coverage, which was subsequently used as a basemap for all the maps in UNEP's World Atlas of Desertification (see reference below).\n \nThe global boundaries template contains 306 polygons of four types, which are coded in the data set as follows: 1) Oceans; 2) Lakes; 3) Continents; and 4) Islands. It is available from GRID as a single ARC/INFO 'EXPORT'-format file comprising 1.7 Mb when uncompressed. While the original projection ISRIC used for the GLASOD wall map was the Mercator to display the various continents with as little distortion as possible, it is distributed by GRID in either the Van der Grinten (a variation of Mercator) or the Geographic projection.\n \nThe sources of the global boundaries template are ISRIC and UNEP/GRID, and the proper references are as follows:\n \nOldeman, L. R., Hakkeling, R. T. A. and W. G. Sombroek. October 1990. \"World Map of the Status of Human-Induced Soil Degradation; Explanatory Note\". (The) Global Assessment of Soil Degradation, ISRIC and UNEP in cooperation with the Winand Staring Centre, ISSS, FAO and ITC; 27 pages.\n \nDeichmann, Uwe and Lars Eklundh. July 1991. \"Global digital data sets for land degradation studies: a GIS approach\". GRID Case Study Series No. 4; UNEP/GEMS & GRID; Nairobi, Kenya; 103 pages (mostly pp. 29-32). An additional reference is UNEP's 1992 World Atlas of Desertification (Edward Arnold, London, UK, 69 pages - see pages vii to ix).\n", "links": [ { diff --git a/datasets/ccamlr_subareas_gis_1.json b/datasets/ccamlr_subareas_gis_1.json index c3e0c8f7be..7f053b3495 100644 --- a/datasets/ccamlr_subareas_gis_1.json +++ b/datasets/ccamlr_subareas_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ccamlr_subareas_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CCAMLR (Commission for the Conservation of Antarctic Marine Living Resources) Statistical Reporting Subareas.\nGIS data representing the boundary (line) and centroid (point with the area name as an attribute) of each area.\nThe southern boundary of the areas adjacent to Antarctica is the coastline of Antarctica. The coastline has not been included with this data.\n\nThis dataset is no longer maintained by the Australian Antarctic Data Centre as the CCAMLR Statistical Reporting Subarea boundaries are now available from CCAMLR's Online GIS (see the Related URL).", "links": [ { diff --git a/datasets/ccbeb356a88847058159049678fe5c35_NA.json b/datasets/ccbeb356a88847058159049678fe5c35_NA.json index 5c1f0949b8..77431803be 100644 --- a/datasets/ccbeb356a88847058159049678fe5c35_NA.json +++ b/datasets/ccbeb356a88847058159049678fe5c35_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ccbeb356a88847058159049678fe5c35_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for ACE in 2008.", "links": [ { diff --git a/datasets/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0.json b/datasets/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0.json index daedb5d6ab..81630868db 100644 --- a/datasets/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0.json +++ b/datasets/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "__Cloud Condensation Nuclei (CCN) data:__ A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/). __Hygroscopicity data:__ The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying K\u00f6hler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/). __Predicted cloud droplet numbers:__ Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the \u201ccharacteristic velocity\u201d approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).", "links": [ { diff --git a/datasets/cdcb0605afa74885a66d8be0fdd2ed24_NA.json b/datasets/cdcb0605afa74885a66d8be0fdd2ed24_NA.json index 3aa4573a0c..812ebbad5c 100644 --- a/datasets/cdcb0605afa74885a66d8be0fdd2ed24_NA.json +++ b/datasets/cdcb0605afa74885a66d8be0fdd2ed24_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cdcb0605afa74885a66d8be0fdd2ed24_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/cden_artefacts_gis_1.json b/datasets/cden_artefacts_gis_1.json index 56cfa0309f..45fcf29149 100644 --- a/datasets/cden_artefacts_gis_1.json +++ b/datasets/cden_artefacts_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cden_artefacts_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected during an AAD conservation expedition to Mawson's Huts, Cape Denison, Commonwealth Bay, Antarctica in 2002 (October to December). The expedition travelled to Commonwealth Bay on board the Astrolabe (French Antarctic supply ship). An expedition report was written, a large number of photographs were taken, and a large number of artefacts were catalogued. Several GIS shapefiles were created from these data. They are point, line and poygon data showing the location of the artefacts.", "links": [ { diff --git a/datasets/cden_gis_1.json b/datasets/cden_gis_1.json index bcc55c52aa..a8bce2a0a8 100644 --- a/datasets/cden_gis_1.json +++ b/datasets/cden_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cden_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cape Denison, Commonwealth Bay, GIS dataset is a topographic database detailing huts, penguins and natural features such as moraine and lakes. The dataset includes a 5m contour interval.\n\nThese shapefiles were obtained by digitising an existing Cape Denison historical map. All information about natural features, biota, etc are sourced from the map. Note, there is more recent data, or better quality data available with other Cape Denison datasets.", "links": [ { diff --git a/datasets/cden_survey_gis_1.json b/datasets/cden_survey_gis_1.json index 92b0c4a75d..c64330cfc7 100644 --- a/datasets/cden_survey_gis_1.json +++ b/datasets/cden_survey_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cden_survey_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset was derived from a detail survey of Cape Denison, Antarctica by G. Crispo in December 1985. Features include coastline, contours, buildings and structures, lakes, areas of exposed rock and penguin colonies.\n\nSee AAD File 00/802.", "links": [ { diff --git a/datasets/ceilometer-klosters_1.0.json b/datasets/ceilometer-klosters_1.0.json index 69efdeac34..49a3b67780 100644 --- a/datasets/ceilometer-klosters_1.0.json +++ b/datasets/ceilometer-klosters_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ceilometer-klosters_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.", "links": [ { diff --git a/datasets/century_vemap_m4_820_1.json b/datasets/century_vemap_m4_820_1.json index 5a42524b5a..d5c32eb558 100644 --- a/datasets/century_vemap_m4_820_1.json +++ b/datasets/century_vemap_m4_820_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "century_vemap_m4_820_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.", "links": [ { diff --git a/datasets/cfe3102659f34d33b123b2a0043e4068_NA.json b/datasets/cfe3102659f34d33b123b2a0043e4068_NA.json index 262391a936..51eeb637ed 100644 --- a/datasets/cfe3102659f34d33b123b2a0043e4068_NA.json +++ b/datasets/cfe3102659f34d33b123b2a0043e4068_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cfe3102659f34d33b123b2a0043e4068_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Jakobshavn Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-03 and 2017-09-08. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "links": [ { diff --git a/datasets/ch2014_1.json b/datasets/ch2014_1.json index 771883bab6..10098e2c46 100644 --- a/datasets/ch2014_1.json +++ b/datasets/ch2014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ch2014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Overview The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change. # Experimental Setup Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graub\u00fcnden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m \u00d7 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999\u20132012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085). # Data Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5\u20139 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400\u2013800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.", "links": [ { diff --git a/datasets/challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0.json b/datasets/challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0.json index c5617e7419..f4d5e69649 100644 --- a/datasets/challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0.json +++ b/datasets/challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.", "links": [ { diff --git a/datasets/charter-mux-1_NA.json b/datasets/charter-mux-1_NA.json index 76e4b9d596..629c6cfa61 100644 --- a/datasets/charter-mux-1_NA.json +++ b/datasets/charter-mux-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "charter-mux-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains images from the CBERS-4/MUX over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.", "links": [ { diff --git a/datasets/charter-wfi-1_NA.json b/datasets/charter-wfi-1_NA.json index c2c4616750..2c2b6e08dd 100644 --- a/datasets/charter-wfi-1_NA.json +++ b/datasets/charter-wfi-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "charter-wfi-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains images from the WFI sensor onboard the satellites CBERS-4, CBERS-4A and AMAZONIA-1 over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.", "links": [ { diff --git a/datasets/charter-wpm-1_NA.json b/datasets/charter-wpm-1_NA.json index 917c91f23d..f2b1f4f3be 100644 --- a/datasets/charter-wpm-1_NA.json +++ b/datasets/charter-wpm-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "charter-wpm-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains images from the CBERS-4A/WPM over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.", "links": [ { diff --git a/datasets/charybdis_sat_1.json b/datasets/charybdis_sat_1.json index fb717b340d..da8076f098 100644 --- a/datasets/charybdis_sat_1.json +++ b/datasets/charybdis_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "charybdis_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Charybdis Glacier, Mac. Robertson Land, Antarctica. This map is part (c) in a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot/tracks, stations/bases, and glaciers/ice shelves. The map has only geographical co-ordinates.", "links": [ { diff --git a/datasets/chelsa-climatologies_2.1.json b/datasets/chelsa-climatologies_2.1.json index 895c7c27cb..e42b6f7686 100644 --- a/datasets/chelsa-climatologies_2.1.json +++ b/datasets/chelsa-climatologies_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chelsa-climatologies_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth\u2019s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30\u2009arc\u2009sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.   CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version. __Paper Citation:__ > _Karger DN. et al. Climatologies at high resolution for the earth\u2019s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._", "links": [ { diff --git a/datasets/chelsa_cmip5_ts_1.0.json b/datasets/chelsa_cmip5_ts_1.0.json index 971bf9a1fe..7d03a73b0d 100644 --- a/datasets/chelsa_cmip5_ts_1.0.json +++ b/datasets/chelsa_cmip5_ts_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chelsa_cmip5_ts_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Predicting future climatic conditions at high spatial resolution is essential for many applications in science. Here we present data for monthly time series of precipitation and minimum and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation sums at ~5km spatial resolution globally for the years 1850-2100. We validated the performance of the downscaling algorithm by comparing model output with observed climates for the years 1950-2069. CHELSA_cmip5_ts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.", "links": [ { diff --git a/datasets/chelsa_trace_1.0.json b/datasets/chelsa_trace_1.0.json index cca1a61aa6..ae982d36e8 100644 --- a/datasets/chelsa_trace_1.0.json +++ b/datasets/chelsa_trace_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chelsa_trace_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm. The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.", "links": [ { diff --git a/datasets/chelsacruts_1.0.json b/datasets/chelsacruts_1.0.json index 40f9d968e3..12b2fd7377 100644 --- a/datasets/chelsacruts_1.0.json +++ b/datasets/chelsacruts_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chelsacruts_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.", "links": [ { diff --git a/datasets/chem_26_1.json b/datasets/chem_26_1.json index 98e2e6e73c..14463328cd 100644 --- a/datasets/chem_26_1.json +++ b/datasets/chem_26_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chem_26_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions", "links": [ { diff --git a/datasets/chesapeake_val_2013_0.json b/datasets/chesapeake_val_2013_0.json index d7ee8278b0..681805f23d 100644 --- a/datasets/chesapeake_val_2013_0.json +++ b/datasets/chesapeake_val_2013_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chesapeake_val_2013_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2013 Chesapeake Bay measurements.", "links": [ { diff --git a/datasets/chlorophyll_65-02_1.json b/datasets/chlorophyll_65-02_1.json index 802d05dae6..0e525ca2fe 100644 --- a/datasets/chlorophyll_65-02_1.json +++ b/datasets/chlorophyll_65-02_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chlorophyll_65-02_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The variation in the phytoplankton biomass over a decadal time scale, and its relationship with the Antarctic Circumpolar Wave (ACW) and climate change, has been poorly interpreted because of the limited satellite chlorophylla (chl a) data compared with the physical parameters from satellite. We analysed a long-term chl a dataset along the Japanese Antarctic Research Expedition (JARE) cruise tracks since 1965 to investigate inter-annual variation of phytoplankton biomass. In the Southern Ocean, increasing trends of chl a and the spreading of higher chl a area to the north with 3-7 year cycles were found. Although relationships between the decadal change in chl a and climate change such as variation of sea ice extent and the El Nino are still obscure, large variation of primary production in proportion to the chl a is implied.\n\nThe chl a concentration of sea surface water has been measured routinely on board the icebreakers Fuji and Shirase during almost every cruise of the JARE.\n\nThe download file contains chlorophyll a data collected from ship tracks on JARE voyages between 1965 and 2002.\n\nThe field in this dataset are:\n\nDate (local time)\nYear\nLatitude\nLongitude\nCorrected Chlorophyll a\n\nSee the attached paper for more details.\n\nThe publications on the data collected during the 1965-1976 and 1988-1993 cruises are listed in Fukuchi [1980] and Suzuki and Fukuchi [1997], respectively. For data on the 1977-1985 and 1994-1997 cruises, see [Kanda and Fukuchi, 1979; Fukuchi and Tamura, 1982; Tanimura, 1981; Watanabe and Nakajima, 1983; Ino and Fukuchi, 1984; Sasaki, 1984; Hamada et al., 1985; Fukuda et al., 1986; Hattori and Fukuchi, 1988; Midorikawa et al., 2000]. Data post 1998-2002 cruises is in Hirawake and Fukuchi [2004]. Data from the 1986-1987 will be published in the JARE data report of digital media, including all cruise data.\n\nAuxiliary Material for paper 2004GL021394 Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002. Toru Hirawake, Tsuneo Odate and Mitsuo Fukuchi (National Institute of Polar Research, Tokyo) Geophys. Res. Lett., Vol (Num), doi:10.1029/2004GL021394 \n\nAll of the chl a data have been reported in the publications of the National Institute of Polar Research (NIPR).", "links": [ { diff --git a/datasets/chm-hp-4rtm_1.0.json b/datasets/chm-hp-4rtm_1.0.json index 1bfb029fed..f6b638b250 100644 --- a/datasets/chm-hp-4rtm_1.0.json +++ b/datasets/chm-hp-4rtm_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "chm-hp-4rtm_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankyl\u00e4, Finland. Data include: * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each) * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2) These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication: _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_ This publication must be cited when using the data. ### See also: For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy) The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572)", "links": [ { diff --git a/datasets/climate-change-scenarios-at-hourly-resolution_1.0.json b/datasets/climate-change-scenarios-at-hourly-resolution_1.0.json index b67a308ee1..dc2f3d437c 100644 --- a/datasets/climate-change-scenarios-at-hourly-resolution_1.0.json +++ b/datasets/climate-change-scenarios-at-hourly-resolution_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climate-change-scenarios-at-hourly-resolution_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In fall 2019, a new set of climate change scenarios has been released for Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are provided at daily resolution. We produced from the CH2018 dataset a new set of climate change scenarios temporally downscaled at hourly resolution. In addition, we extended this dataset integrating the meteorological stations from the Inter-Cantonal Measurement and Information System (IMIS) network, an alpine network of automatic meteorological stations operated by the WSL Institute for Snow and Avalanche Research SLF. The extension to the IMIS network is obtained using a Quantile Mapping approach in order to perform a spatial transfer of the CH2018 scenarios from the location of the MeteoSwiss stations to the location of the IMIS stations. The temporal downscaling is performed using an enhanced Delta-Change approach. This approach is based on objective criteria for assessing the quality of the determined delta and downscaled time series. In addition, this method also fixes a flaw of common quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling) related to the decrease of correlation between different variables. The idea behind the delta change approach is to take the main seasonal signal (and mean) from climate change scenarios at daily resolution and to map it to a historical time series at hourly resolution in order to modify the historical time series. The obtained time series exhibit the same seasonal signal as the original climate change time series, while it keeps the sub-daily cycle from the historical time series. The applied methods (Quantile Mapping and Delta-Change) have limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. In addition, the sub-daily cycle in the data is inherited from the historical time series, so there is no information of the climate change signal in this sub-daily cycle. A careful reading of the paper accompanying the dataset is necessary to understand the limitations and scope of application of this new dataset. This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).", "links": [ { diff --git a/datasets/climate_iceberg_1.json b/datasets/climate_iceberg_1.json index 4eaf30625b..fc7aa70640 100644 --- a/datasets/climate_iceberg_1.json +++ b/datasets/climate_iceberg_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climate_iceberg_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains iceberg observations collected routinely on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis. The observations were made each austral summer from the 1978/1979 season until the 2000/2001 season. Data included voyage number, date, time, latitude, longitude, sea ice concentration, water temperature, total icebergs, number of icebergs in each width category, the width to height ratio of selected larger tabular icebergs. It was been compiled and presented on the web by the Glaciology program of the Antarctic CRC (now ACE CRC).", "links": [ { diff --git a/datasets/climate_pressure_1.json b/datasets/climate_pressure_1.json index af44e41b8e..151435589f 100644 --- a/datasets/climate_pressure_1.json +++ b/datasets/climate_pressure_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climate_pressure_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002.", "links": [ { diff --git a/datasets/climate_sea_ice_1.json b/datasets/climate_sea_ice_1.json index 78f7957f55..1c409b3fd3 100644 --- a/datasets/climate_sea_ice_1.json +++ b/datasets/climate_sea_ice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climate_sea_ice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice. Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996. Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available.", "links": [ { diff --git a/datasets/climate_temps_1.json b/datasets/climate_temps_1.json index 3c978c0dc1..8787a42d80 100644 --- a/datasets/climate_temps_1.json +++ b/datasets/climate_temps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climate_temps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002.", "links": [ { diff --git a/datasets/climatological-snow-data-1998-2022-oshd_1.0.json b/datasets/climatological-snow-data-1998-2022-oshd_1.0.json index 1ba080bf28..8b3758818f 100644 --- a/datasets/climatological-snow-data-1998-2022-oshd_1.0.json +++ b/datasets/climatological-snow-data-1998-2022-oshd_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climatological-snow-data-1998-2022-oshd_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).", "links": [ { diff --git a/datasets/climwat.json b/datasets/climwat.json index 0e5e99f11d..38e56475c7 100644 --- a/datasets/climwat.json +++ b/datasets/climwat.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "climwat", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the ready calculation of crop water requirements, irrigation supply and irrigation scheduling for various crops for a range of climatological stations worldwide.\n \n The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries. CLIMWAT is published as Irrigation and Drainage paper No 49 in 1994 and includes a Manual with description of the use of the database with CROPWAT The data are contained in five diskettes included in the publication and can be ordered as FAO Irrigation and Drainage Paper 49 through the FAO Sales and Marketing Group.\n \n [Summary provided by the FAO.]", "links": [ { diff --git a/datasets/cmar_wh.json b/datasets/cmar_wh.json index 16703bd426..b547b7933e 100644 --- a/datasets/cmar_wh.json +++ b/datasets/cmar_wh.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmar_wh", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CSIRO Marine Data Warehouse is a repository for biological and other marine\nsurvey data collected by CSIRO Division of Marine and Atmospheric Research\n(CMAR), Australia. It contains field (observational) data from numerous\nresearch trawls and other fisheries-related surveys conducted in waters around\nAustralia by the Division since the late 1970s. At time of writing (April 2006)\nthe database is serving approximately 106,000 species-level records to OBIS.\nMultiple species records and those of taxa not identified to species level are\npresently excluded. Associated data include species counts and/or weights in\nsome but not all cases.", "links": [ { diff --git a/datasets/cmimpacts_1.json b/datasets/cmimpacts_1.json index 8c3982c4fe..ea75baa17e 100644 --- a/datasets/cmimpacts_1.json +++ b/datasets/cmimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UND Cloud Microphysics IMPACTS dataset consists of cloud particle measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The UND Cloud Microphysics IMPACTS dataset files are stored in ASCII format from January 25, 2020, through February 28, 2023.", "links": [ { diff --git a/datasets/cmx3aeri_1.json b/datasets/cmx3aeri_1.json index 256f506069..aff07bea9b 100644 --- a/datasets/cmx3aeri_1.json +++ b/datasets/cmx3aeri_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmx3aeri_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Emitted Radiance Interferometer (AERI) was used to make atmospheric temperature and moisture retrievals. AERI provides absolutely calibrated radiances which can be used for forward calculation comparisons of radiosonde and LIDAR (for CAMEX-3, the SRL) profiles and provides a reference to the airborne and ground based remote sensing instruments. Additionally, AERI radiances contain valuable temperature and water vapor information which can be used to retrieve planetary boundary layer thermodynamics. The University of Wisconsin-Madison, Space Science and Engineering Center was responsible for the AERI data collection during CAMEX-3 campaign.", "links": [ { diff --git a/datasets/cmx3andros_1.json b/datasets/cmx3andros_1.json index fd3ba605c0..8a10a3a592 100644 --- a/datasets/cmx3andros_1.json +++ b/datasets/cmx3andros_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmx3andros_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In support of CAMEX-3, numerous radiosonde and rawinsondes were launched from Andros Island, which consisted of instruments manufactured by VIS and Vaisala. Some sondes were GPS or LORAN located so that winds aloft could be determined without ground based tracking systems. Data from the sondes were used to validate several ground based instruments observing the lower troposphere.", "links": [ { diff --git a/datasets/cmx3g8_1.json b/datasets/cmx3g8_1.json index 2670460b50..30defccb07 100644 --- a/datasets/cmx3g8_1.json +++ b/datasets/cmx3g8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmx3g8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In support of the third Convection and Moisture Experiment (CAMEX-3), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3, which is known as the water vapor channel (6.75 microns).", "links": [ { diff --git a/datasets/cmx3misrep_1.json b/datasets/cmx3misrep_1.json index f747453a0f..aedc1c2fbb 100644 --- a/datasets/cmx3misrep_1.json +++ b/datasets/cmx3misrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmx3misrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Convection And Moisture EXperiment (CAMEX)-3 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.", "links": [ { diff --git a/datasets/cmx3srl_1.json b/datasets/cmx3srl_1.json index ae2d5816ae..f3b78d2a3d 100644 --- a/datasets/cmx3srl_1.json +++ b/datasets/cmx3srl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cmx3srl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 Scanning Raman LIDAR dataset collected data during the CAMEX-3 campaign on Andros Island during the period 6 August - 20 September 1998. The SRL instrument is designed to determine the composition and vertical distribution of several atmospheric constituents, specifically water vapor and aerosols.", "links": [ { diff --git a/datasets/co2_emissions_1deg_1021_1.json b/datasets/co2_emissions_1deg_1021_1.json index a764e955e2..0adadcc2e7 100644 --- a/datasets/co2_emissions_1deg_1021_1.json +++ b/datasets/co2_emissions_1deg_1021_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "co2_emissions_1deg_1021_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains decadal (1950, 1960, 1970, 1980, 1990 and 1995) estimates of gridded fossil-fuel emissions, expressed in 1,000 metric tons C per year per one degree latitude by one degree longitude. The CO2 emissions are the summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring. The years 1950 to 1990 were developed and compiled using somewhat different procedures and information than the 1995 data. The national annual estimates (Boden et al., 1996) from 1950 to 1990 were allocated to one degree grid cells based on gridded information on national boundaries and political units, and a 1984 gridded human population map (Andres et al., 1996). For the 1995 data, the population data base developed by Li (1996a) and documented by CDIAC (DB1016: Li, 1996b) was used as proxy to grid the 1995 emission estimates. There is one *.zip data file with this data set at 1.0 degree spatial resolution.", "links": [ { diff --git a/datasets/combined_ancillary_xdeg_1200_1.json b/datasets/combined_ancillary_xdeg_1200_1.json index f158a1398f..2a0b38b6cf 100644 --- a/datasets/combined_ancillary_xdeg_1200_1.json +++ b/datasets/combined_ancillary_xdeg_1200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "combined_ancillary_xdeg_1200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the ISLSCP II fixed land/water masks and percentages of land or water in each cell. There are seven zip data files: four produced from a 1-km land/water mask compiled at the Jet Propulsion Laboratory (JPL) in support of NASA's Earth Observing System; two files of a land outline overlay created from the land/water mask files created at NASA's Goddard Space Flight Center; and one file which is a latitude grid coordinate file and longitude grid coordinate file produced by the ISLSCP II staff. All of these data are provided at three spatial resolutions of .25, 0.5 and 1-degree in latitude and longitude and on a common Earth grid.", "links": [ { diff --git a/datasets/comm_alfred_1.json b/datasets/comm_alfred_1.json index 8f3f5b1b08..bd695fc679 100644 --- a/datasets/comm_alfred_1.json +++ b/datasets/comm_alfred_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "comm_alfred_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Six GPS data points collected by Alfred Wilklemayer, taken during a one year expedition at Commonwealth Bay, Antarctica.\n\nGPS Points collected at Commonwealth Bay, Antarctica, during 1997\n\nThe following GPS data points were collected opportunistically by Mr Alfred Wilklemayer, during a one year expedition in Commonwealth Bay, Antarctica.\n \nIdentification Object\tPosition\nFrozen Husky Dog 67 degrees 04'07\" S, 142 degrees 42'39\" E\nFirst Canister 67 degrees 03'69\" S, 142 degrees 42'10\" E\nSecond Canister 67 degrees 03'74\" S, 142 degrees 42'10\" E\nThird Can/Stick 67 degrees 03'28\" S, 142 degrees 42'09\" E\nFurthest Point In (during expedition) 67 degrees 05'47\" S, 142 degrees 40'02\" E\nFurthest Point West (during expedition) 67 degrees 04'06\" S, 142 degrees 06'04\" E", "links": [ { diff --git a/datasets/community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0.json b/datasets/community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0.json index 5608c8ae33..f553f4ace8 100644 --- a/datasets/community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0.json +++ b/datasets/community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background Urban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we provide the data used to investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. To do so, we installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts). The dataset contains: * The taxonomic metrics on CNBW * The taxonomic metrics on the natural enemies from CNBW * The life-history traits and performance traits", "links": [ { diff --git a/datasets/comp_runoff_monthly_xdeg_994_1.json b/datasets/comp_runoff_monthly_xdeg_994_1.json index 39797095e5..7257553d04 100644 --- a/datasets/comp_runoff_monthly_xdeg_994_1.json +++ b/datasets/comp_runoff_monthly_xdeg_994_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "comp_runoff_monthly_xdeg_994_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.", "links": [ { diff --git a/datasets/content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0.json b/datasets/content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0.json index 0fb5f27ab6..6c48e78bac 100644 --- a/datasets/content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0.json +++ b/datasets/content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Federal Office for the Environment (FOEN) is responsible for granting exemptions for forest clearances that in principle are prohibited in Switzerland. Initiators of infrastructure projects have to submit an examption approval request to the cantonal forest administration which has to inform the FOEN. The FOEN thus administers a dataset of forest clearance requests and approval decisions that can be requested there. This dataset contains information on a coding of the content of all the forest clearance requests between 2001 and 2017, that elicits whether the reason for the clearance can be attributed to \"sustainable economy\" objectives such as \"green economy\", \"bioeconomy\" and \"circular economy\".", "links": [ { diff --git a/datasets/convection-in-snow_1.0.json b/datasets/convection-in-snow_1.0.json index 5708d24704..7e8f457877 100644 --- a/datasets/convection-in-snow_1.0.json +++ b/datasets/convection-in-snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "convection-in-snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).", "links": [ { diff --git a/datasets/core_0.1.json b/datasets/core_0.1.json index 486ba6c7b6..3cee0f1032 100644 --- a/datasets/core_0.1.json +++ b/datasets/core_0.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "core_0.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "__DISCLAIMER__: CORE is still in development. Interested parties are warmly invited to join common development, to comment, discuss, find bugs, etc. __Acknowledgement:__ The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing \u200bHTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image). __License:__ The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions. ----------------------- __Summary:__ The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series). __WARNING__: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved'). With this early public sharing of the format we explicitly support the Open Science agenda, which implies __\"shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process\"__ (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world). __CORE Specifications:__ 1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers) 2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users) Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1). 3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous; Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case. 4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264) 5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer 6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size) 7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews) __Major issues to be solved:__ - Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews. - Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length. - Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds) __Example__ __Notice__: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021]. For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html). For full reproducibility, we provide the original data set and results, as well scripts for data encoding and extraction (see resources).", "links": [ { diff --git a/datasets/correct-observer-bias-only-sdms_1.0.json b/datasets/correct-observer-bias-only-sdms_1.0.json index 991feec547..bf4ee632e8 100644 --- a/datasets/correct-observer-bias-only-sdms_1.0.json +++ b/datasets/correct-observer-bias-only-sdms_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "correct-observer-bias-only-sdms_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models. Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1\u2019900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps. Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r\u22430.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.", "links": [ { diff --git a/datasets/cosmirimpacts_1.json b/datasets/cosmirimpacts_1.json index 4ae7fbc87b..8c233e8ffb 100644 --- a/datasets/cosmirimpacts_1.json +++ b/datasets/cosmirimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cosmirimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IMPACTS dataset consists of brightness temperature measurements collected by the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) flown onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. CoSMIR is a conical and cross-track scanning radiometer with frequencies\ncentered at 50.3, 52.8, 89.0, 165.5, 183.31\u00b11, 183.31\u00b13, and 183.31\u00b17 GHz. The brightness temperature data from CoSMIR are available from January 15, 2020 through February 28, 2022 in netCDF-4 format.", "links": [ { diff --git a/datasets/cosmo-wrf-documentation_1.0.json b/datasets/cosmo-wrf-documentation_1.0.json index cb1b2305bd..a345426b50 100644 --- a/datasets/cosmo-wrf-documentation_1.0.json +++ b/datasets/cosmo-wrf-documentation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cosmo-wrf-documentation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.", "links": [ { diff --git a/datasets/cossirimpacts_1.json b/datasets/cossirimpacts_1.json index 96e2ea8dd7..2e539e1543 100644 --- a/datasets/cossirimpacts_1.json +++ b/datasets/cossirimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cossirimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR) IMPACTS dataset consists of data measured onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The CoSSIR dataset consists of measured ice clouds and brightness temperatures, water vapor profiles, and snowfall rates. CoSSIR data are available from January 5, 2023, through March 2, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/cp_lidar_images_721_1.json b/datasets/cp_lidar_images_721_1.json index 3c5bc6f525..2bd667a4d0 100644 --- a/datasets/cp_lidar_images_721_1.json +++ b/datasets/cp_lidar_images_721_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cp_lidar_images_721_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote-sensing and in-situ measurements of clouds and aerosols. is Data products include: (1) cloud profiling with 30-m vertical and 200-m horizontal resolution at 1064 nm, 532 nm, and 355 nm;(2) aerosol, boundary layer, and smoke plume profiling;(3) optical depth estimates (column and by layer); and(4) extinction profiles. The CPL provides information to permit a comprehensive analysis of radiative and optical properties of optically thin clouds. Data users are asked to read and abide by the CPL data usage policy found at [http://virl.gsfc.nasa.gov/cpl/cpl_register.htm].", "links": [ { diff --git a/datasets/cplimpacts_1.json b/datasets/cplimpacts_1.json index 825c721e9f..14b6f3c3fb 100644 --- a/datasets/cplimpacts_1.json +++ b/datasets/cplimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cplimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Physics LiDAR (CPL) IMPACTS dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR (CPL) onboard the NASA ER-2 high-altitude research aircraft in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in HDF-5 format from January 15, 2020, through March 2, 2023.", "links": [ { diff --git a/datasets/crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0.json b/datasets/crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0.json index fd44f1d79c..01394156ac 100644 --- a/datasets/crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0.json +++ b/datasets/crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes material and results described in the related research article: Bergfeld, B., van Herwijnen, A., Reuter, B., Bobillier, G., Dual, J., and Schweizer, J.: Dynamic crack propagation in weak snowpack layers: Insights from high-resolution, high-speed photography, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-360, in review, 2021. # Context: In order to study crack propagation in weak snowpack layers in great detail, we recorded Propagation Saw Test (PST) experiments using a high-speed camera and applied digital image correlation (DIC) to derive displacement and strain fields in the slab, weak layer, and substrate. We demonstrated the versatility and accuracy of the DIC method by showing measurements from three PST experiments, resulting in slab fracture, crack arrest and full propagation in the related publication. # Content: - Supplementary material for related publication - Ilustrative videos showing crack propagation - High-speed recordings of the Experiments (the raw .cine files are available upon request) Processed Data containing: - displacement, velocity and acceleration fields for the three PSTs - speed and touchdown dataset", "links": [ { diff --git a/datasets/crack-propagation-speeds-in-weak-snowpack-layers_1.0.json b/datasets/crack-propagation-speeds-in-weak-snowpack-layers_1.0.json index 3147f0f7ab..90ae4bfe84 100644 --- a/datasets/crack-propagation-speeds-in-weak-snowpack-layers_1.0.json +++ b/datasets/crack-propagation-speeds-in-weak-snowpack-layers_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "crack-propagation-speeds-in-weak-snowpack-layers_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into the underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie, for scales between 5 and 25 meters, we used accelerometers placed on the snow surface, and for scales below 5 meters, we performed a Propagation Saw Test. The mean crack speeds ranged from 36 \u00b1 6 to 49 \u00b1 5 m s^{-1}, and did not exhibit scale dependence. Using the Discrete Element Method and the Material Point Method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales. This dataset includes raw data as well as crack speed estimates from the three crack propagation events. Where possible, we reproduced these field experiments with numerical models based on Discrete Element Method (DEM, Bobillier and others, 2020 and 2021) and Material Point Method (MPM. Gaume and others, 2018 and Trottet and others, 2021). The input parameters of the models were estimated from the corresponding snow profiles conducted at each test site. ## The raw data include: * Propagation Saw Test movie with mechanical fields derived from Digital image Correlation analysis of the recording * Acceleration data recorded with wireless time synchronized accelerometers placed on the snow surface during crack propagation in a whumpf. *Video of an artificially triggered avalanche with widespread crack propagation. The video was used to georeference surface cracks in order to estimate crack propagation time and distance, providing crack propagation speed estimates. * Snow profile recorded at each test site ## Experimental crack speed estimates include: * Crack speed evolution within the first meters derived from the Propagation Saw Test. * Crack speeds estimated from the time delay of the collapse, observed between different accelerometers during crack propagation of a whumpf. * Crack speed estimates from video analysis of the artificially triggered avalanche. ## Reproduced crack speeds using the DEM an MPM model: * Modelled Propagation Saw Test using MPM (2D and 3D system) and DEM. * Modelled whumpf using MPM (beam and areal configuration) * Modelled avalanche using MPM (beam and areal configuration) Beside the movies (mp4 format), all data is either provided as netCDF files or excel sheets (see readme file), depending on the amount of data. A detailed description of the three crack propagation events and how crack speed was derived, can be found in the related publication: ### References for applied models: Bobillier, G., B. Bergfeld, A. Capelli, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2020. Micromechanical modeling of snow failure. The Cryosphere, 14(1): 39-49. Bobillier, G., B. Bergfeld, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2021. Micro-mechanical insights into the dynamics of crack propagation in snow fracture experiments. Scientific Reports, 11: 11711. Gaume, J., T. Gast, J. Teran, A. van Herwijnen and C. Jiang 2018. Dynamic anticrack propagation in snow. Nature Communications, 9(1): 3047. Trottet, B., R. Simenhois, G. Bobillier, A. van Herwijnen, C. Jiang and J. Gaume 2021. From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release. EGU General Assembly 2021, Online, 19-30 Apr 2021, EGU21-8253.", "links": [ { diff --git a/datasets/cramer_leemans_637_1.json b/datasets/cramer_leemans_637_1.json index 48d6aa5f8f..f5225605a8 100644 --- a/datasets/cramer_leemans_637_1.json +++ b/datasets/cramer_leemans_637_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cramer_leemans_637_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Cramer and Leeman's (1999) global mean monthly climatology . The subset is for the area of southern Africa within the following bounds: 5 N to 35 S and 5 E to 60 E. The data are available in ASCII grid and binary image formats.", "links": [ { diff --git a/datasets/cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5.json b/datasets/cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5.json index 10a6d4c3e9..b477754d59 100644 --- a/datasets/cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5.json +++ b/datasets/cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.", "links": [ { diff --git a/datasets/cropland_612_2.json b/datasets/cropland_612_2.json index 71917ca1a4..7c10c24af9 100644 --- a/datasets/cropland_612_2.json +++ b/datasets/cropland_612_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cropland_612_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell. The magnitude and interannual variation in NPP was estimated using crop area and yield data from the U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The major harvested commodities were corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay. Total NPP estimates include both above- and below-ground components. County-level NPP in 1992 ranged from 195 to 760 gC/m2/year. The area of highest NPP, ranging from 650 to 760 gC/m2/year, was found in a band extending across Iowa, through northern Illinois, Indiana, and southwestern Ohio. Areas of moderate NPP, from 550 to 650 gC/m2/year, occurred mostly in Michigan and Wisconsin, while large areas of low NPP, from 200 to 550 gC/m2/year, occurred in North Dakota, southern Illinois, and Minnesota. The area of highest production was also the area with the largest proportion of land sown with corn and soybean. NPP for counties in Iowa varied among years (1982-1996) by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods). Revision Notes: The documentation for this data set has been modified, and the data files have been reformatted. The data files have been checked for accuracy and the contents are identical to those originally published in 2001. ", "links": [ { diff --git a/datasets/crsimpacts_1.json b/datasets/crsimpacts_1.json index 051fabbca7..f390feba9c 100644 --- a/datasets/crsimpacts_1.json +++ b/datasets/crsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "crsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud Radar System (CRS) IMPACTS dataset consists of calibrated radar reflectivity, Doppler velocity, linear depolarization ratio, and normalized radar cross-section estimates collected by the Cloud Radar System (CRS) onboard the NASA ER-2 high-altitude research aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The CRS IMPACTS dataset files are available from January 25, 2020, through February 28, 2023, in HDF-5 format.", "links": [ { diff --git a/datasets/cru_monthly_climate_xdeg_1014_1.json b/datasets/cru_monthly_climate_xdeg_1014_1.json index fba0d49d2d..8320d6411a 100644 --- a/datasets/cru_monthly_climate_xdeg_1014_1.json +++ b/datasets/cru_monthly_climate_xdeg_1014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cru_monthly_climate_xdeg_1014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly climate time series data created by the Climatic Research Unit (CRU) at the University of East Anglia, U.K.,for every year covering the period 1986 to 1995. This time series is a subset of a larger CRU monthly data set that covers the period of 1901 to 1996. The data comprise a suite of six climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapor pressure, and cloud cover. There are 13 files in this data set provided at 0.5 and 1.0 degree spatial resolutions. ", "links": [ { diff --git a/datasets/cru_monthly_mean_xdeg_1015_1.json b/datasets/cru_monthly_mean_xdeg_1015_1.json index 652d34d258..dc3e9eca9e 100644 --- a/datasets/cru_monthly_mean_xdeg_1015_1.json +++ b/datasets/cru_monthly_mean_xdeg_1015_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "cru_monthly_mean_xdeg_1015_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990, and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The data comprise a suite of climate elements: precipitation, mean, maximum, and minimum temperature, frost frequency, diurnal temperature range, radiation, wet-day frequency, vapor pressure, wind, and cloud cover. There are 23 files in this data set provided at 0.5 and 1.0 degree spatial resolutions. ", "links": [ { diff --git a/datasets/csgcpex01_1.json b/datasets/csgcpex01_1.json index 5b2c2c72d2..68eb2767e1 100644 --- a/datasets/csgcpex01_1.json +++ b/datasets/csgcpex01_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "csgcpex01_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GCPEX Snow Microphysics Case Study characterizes the 3-D microphysical evolution and distribution of snow in context of the thermodynamic environment observed during the February 24th, 2012 event of the GPM Cold-season Precipitation Experiment (GCPEx). The GPM Cold-season Precipitation Experiment occurred in Ontario, Canada during the winter season of 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of snow. This case study includes data from the Airborne Second Generation Precipitation Radar (APR-2), Dual-frequency Dual-polarized Doppler Radar (D3R), Dual Polarization Radiometer and the NCAR Cloud Microphysics Particle Probes.", "links": [ { diff --git a/datasets/csiro_australianinsect.json b/datasets/csiro_australianinsect.json index 187bf1216e..65ca550d17 100644 --- a/datasets/csiro_australianinsect.json +++ b/datasets/csiro_australianinsect.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "csiro_australianinsect", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Insect Common Names Database includes insects in the\n phylum of Arthropods, classes - Arachnida, Chilopoda, Collembola,\n Diplopoda, Insecta, Malacostraca, and Symphyla.\n \n This website database provides ready access to the correct scientific\n name of every insect or related creature for which there is a common\n (or vernacular) name in use in Australia. The site also enables the\n user to discover the common name or names used in Australia for a\n species for which the user knows only the scientific name. Species are\n also listed in family groupings. An index of commonly used\n abbreviations of authors' names has also been included. This index is\n intended to assist in the interpretation of abbreviations which may be\n encountered in entomological literature. It is recommended, however,\n that in present-day usage authors' names be quoted in full to avoid\n ambiguity.\n \n While scientific nomenclature is governed by strict rules, vernacular\n nomenclature is not. Inevitably there will be differences of opinion\n over what constitutes an appropriate common name or over whether a\n particular common name is or is not in wide use. In preparing the\n lists which follow we have endeavoured to include common names which\n are used in conversation and in the literature. We have also taken the\n opportunity to weed out a few contrived or clumsy names which have\n appeared in earlier editions of the Handbook but which seem not to be\n in use. Few Aboriginal names have been included but we believe that\n such names would enhance future versions of this website.\n \n We have included the common names of Australian butterflies listed by\n M. Braby in The Butterflies of Australia (2001), although with some\n rationalisation.\n \n The following conventions are used:\n \n 1. Where changes of scientific or common names have occurred since the\n previous edition of the Handbook, the earlier names are listed and\n cross referenced to entry's new name.\n \n 2. We have avoided hyphenation whenever possible, preferring such\n fusions as 'stemborer', leafminer', 'stumptailed', 'blackheaded',\n etc. Where a common name is taxonomically incorrect, e.g. 'whitefly'\n (Hemiptera, not Diptera) and 'whitemoth' (Trichoptera, not\n Lepidoptera) the two words comprising the name are fused. When the\n common name is taxonomically correct, the words are used separately,\n e.g. 'bed bug' and 'hawk moth'. Exceptions are made when usage over\n many years has fused two words that would be separated if this\n convention was strictly applied, e.g. blowfly, mealybug.\n \n 3. Where a common name is applied to more than one species, this is\n indicated by bracketing the name, e.g. '(canegrub)'.\n \n 4. For each species there is an indication whether the organism is an\n native, exotic or introduced as a biological control agent and that it\n has been successfully established.\n \n 5. In the distribution maps, presence of the species in a State is\n indicated by a shading of the entire State or Territory. This does not\n imply that the species necessarily occurs throughout the State or\n Territory in question. A large question mark superimposed on the map\n of Australia indicates that the distribution of the species within\n Australia is unknown to us.\n \n Information was obtained from \"http://www.ento.csiro.au/aicn/\".", "links": [ { diff --git a/datasets/d12fc40e4f254ce38303157fa460f01c_NA.json b/datasets/d12fc40e4f254ce38303157fa460f01c_NA.json index 9c3a0bd76b..8c36b85e37 100644 --- a/datasets/d12fc40e4f254ce38303157fa460f01c_NA.json +++ b/datasets/d12fc40e4f254ce38303157fa460f01c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "d12fc40e4f254ce38303157fa460f01c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly aerosol products from the AATSR instrument on the ENVISAT satellite, using the Swansea University (SU) algorithm, version 4.3. Data is available for the period 2002 - 2012.For further details about these data products please see the documentation.", "links": [ { diff --git a/datasets/d2ed0c005761475d92ca444666156c4a_NA.json b/datasets/d2ed0c005761475d92ca444666156c4a_NA.json index 795788d60e..13e1bface5 100644 --- a/datasets/d2ed0c005761475d92ca444666156c4a_NA.json +++ b/datasets/d2ed0c005761475d92ca444666156c4a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "d2ed0c005761475d92ca444666156c4a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/d34330ce3f604e368c06d76de1987ce5_NA.json b/datasets/d34330ce3f604e368c06d76de1987ce5_NA.json index c5814a7fc1..97873dce95 100644 --- a/datasets/d34330ce3f604e368c06d76de1987ce5_NA.json +++ b/datasets/d34330ce3f604e368c06d76de1987ce5_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "d34330ce3f604e368c06d76de1987ce5_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "links": [ { diff --git a/datasets/d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA.json b/datasets/d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA.json index 4b889952a7..a277832730 100644 --- a/datasets/d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA.json +++ b/datasets/d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. \\\\n\\\\nIRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products.", "links": [ { diff --git a/datasets/d545d232-ac86-49c3-a42d-67b0b9608b29_NA.json b/datasets/d545d232-ac86-49c3-a42d-67b0b9608b29_NA.json index 309daa9d17..cda1c5a6c0 100644 --- a/datasets/d545d232-ac86-49c3-a42d-67b0b9608b29_NA.json +++ b/datasets/d545d232-ac86-49c3-a42d-67b0b9608b29_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "d545d232-ac86-49c3-a42d-67b0b9608b29_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "links": [ { diff --git a/datasets/da2b8512312a4f14a928766f7f632d36_NA.json b/datasets/da2b8512312a4f14a928766f7f632d36_NA.json index 7db188e8b2..a10d154cb4 100644 --- a/datasets/da2b8512312a4f14a928766f7f632d36_NA.json +++ b/datasets/da2b8512312a4f14a928766f7f632d36_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "da2b8512312a4f14a928766f7f632d36_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation.", "links": [ { diff --git a/datasets/daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0.json b/datasets/daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0.json index 7a6609d4c4..8edc07a445 100644 --- a/datasets/daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0.json +++ b/datasets/daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "R data set containing R raster objects with 500m gridded daily modeled soil moisture and net radiation covering Switzerland for the year 2004.", "links": [ { diff --git a/datasets/daily-solute-and-isotope-of-stream-water-and-precipitation_1.0.json b/datasets/daily-solute-and-isotope-of-stream-water-and-precipitation_1.0.json index 7d6cc269e9..65f1d09603 100644 --- a/datasets/daily-solute-and-isotope-of-stream-water-and-precipitation_1.0.json +++ b/datasets/daily-solute-and-isotope-of-stream-water-and-precipitation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "daily-solute-and-isotope-of-stream-water-and-precipitation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations. An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242", "links": [ { diff --git a/datasets/daily_precip_est_793_1.json b/datasets/daily_precip_est_793_1.json index 7962813fcf..48728200ad 100644 --- a/datasets/daily_precip_est_793_1.json +++ b/datasets/daily_precip_est_793_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "daily_precip_est_793_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 401 lines, each with 341 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv.", "links": [ { diff --git a/datasets/dalmolin_thurmodeling1_1.0.json b/datasets/dalmolin_thurmodeling1_1.0.json index 2b2d0e3f5e..93181f3391 100644 --- a/datasets/dalmolin_thurmodeling1_1.0.json +++ b/datasets/dalmolin_thurmodeling1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dalmolin_thurmodeling1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre\u2013alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two\u2013stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space\u2013time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe.", "links": [ { diff --git a/datasets/danger_descriptions_avalanche_bulletin_switzerland_1.0.json b/datasets/danger_descriptions_avalanche_bulletin_switzerland_1.0.json index 3768d93574..48da1aac15 100644 --- a/datasets/danger_descriptions_avalanche_bulletin_switzerland_1.0.json +++ b/datasets/danger_descriptions_avalanche_bulletin_switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "danger_descriptions_avalanche_bulletin_switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020.", "links": [ { diff --git a/datasets/darling_sst_00.json b/datasets/darling_sst_00.json index 7a984ea729..1529d8bb46 100644 --- a/datasets/darling_sst_00.json +++ b/datasets/darling_sst_00.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "darling_sst_00", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2000 Seawater Surface Temperature Data collected off the dock at the Darling\nMarine Center, Walpole, Maine", "links": [ { diff --git a/datasets/darling_sst_01.json b/datasets/darling_sst_01.json index ebd284d977..31b80f7b9a 100644 --- a/datasets/darling_sst_01.json +++ b/datasets/darling_sst_01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "darling_sst_01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2001 Seawater Surface Temperature Data collected off the dock at the Darling\nMarine Center Walpole, Maine.", "links": [ { diff --git a/datasets/darling_sst_82-93.json b/datasets/darling_sst_82-93.json index 2651e214c7..7427211391 100644 --- a/datasets/darling_sst_82-93.json +++ b/datasets/darling_sst_82-93.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "darling_sst_82-93", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seawater Surface Temperature Data Collected between the years 1982-1989 and\n1993 off the dock at the Darling Marine Center, Walpole, Maine", "links": [ { diff --git a/datasets/data-amphibian-monitoring_1.0.json b/datasets/data-amphibian-monitoring_1.0.json index 3cb9bec0de..aa1bf433c4 100644 --- a/datasets/data-amphibian-monitoring_1.0.json +++ b/datasets/data-amphibian-monitoring_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-amphibian-monitoring_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the \"first\" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 (\"second\" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021).", "links": [ { diff --git a/datasets/data-analysis-toolkits_1.0.json b/datasets/data-analysis-toolkits_1.0.json index 1323c3eb26..f984f2374c 100644 --- a/datasets/data-analysis-toolkits_1.0.json +++ b/datasets/data-analysis-toolkits_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-analysis-toolkits_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These are condensed notes covering selected key points in data analysis and statistics. They were developed by James Kirchner for the course \"Analysis of Environmental Data\" at Berkeley in the 1990's and 2000's. They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education! License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.", "links": [ { diff --git a/datasets/data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0.json b/datasets/data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0.json index 6bc1db809d..596ed001f0 100644 --- a/datasets/data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0.json +++ b/datasets/data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## Summary * Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland * R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis ## Data The dataset included here is the \"natural\" reservoir inflow for the Luzzone reservoir. Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html). ## Codes R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels: * GEV (Generalized Extreme Value) * GPD and GPDd (Generalized Pareto Distribution including declustered version) * eGPD (extended Generalized Pareto Distribution) * MEV (Metastatistical Extreme Value)", "links": [ { diff --git a/datasets/data-broedlin-cnp_1.0.json b/datasets/data-broedlin-cnp_1.0.json index 051fc74758..4dc0a35274 100644 --- a/datasets/data-broedlin-cnp_1.0.json +++ b/datasets/data-broedlin-cnp_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-broedlin-cnp_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.", "links": [ { diff --git a/datasets/data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0.json b/datasets/data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0.json index 4397fed563..ac8e1e84fe 100644 --- a/datasets/data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0.json +++ b/datasets/data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "### Overview We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow. Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK. ### Format The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format. ### Structure In Data_Forward_Scattering.zip you will find all necessary data and model details to reproduce the results of the JGR publication \"How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels\" - \t__Model Input Data__ contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts. - __Model Code__ contains the full model Alpine3d including the radiative transfer module GROUNDEYE. - __Model Output Data__ contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018. - __Measurements Solar Testsite__ contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland. - __Postprocessing__ contains all R-Scripts used for the analysis and plotting of the corresponding data. In each of these folders you will find detailed information in the file 'About this Folder.txt'.", "links": [ { diff --git a/datasets/data-for-huelsmann_et_al_ecol_appl_2016_1.0.json b/datasets/data-for-huelsmann_et_al_ecol_appl_2016_1.0.json index 06ecbca0b3..6bf6bd0a09 100644 --- a/datasets/data-for-huelsmann_et_al_ecol_appl_2016_1.0.json +++ b/datasets/data-for-huelsmann_et_al_ecol_appl_2016_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-for-huelsmann_et_al_ecol_appl_2016_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The datasets comprise nearly 19\u2019000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics. ## Inventory data The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively. ## Mortality predictors A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory. To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests. ## Further information For further information, refer to H\u00fclsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.", "links": [ { diff --git a/datasets/data-for-numerical-investigation-of-sediment-yield_1.0.json b/datasets/data-for-numerical-investigation-of-sediment-yield_1.0.json index 1726e49ce7..21b9313d34 100644 --- a/datasets/data-for-numerical-investigation-of-sediment-yield_1.0.json +++ b/datasets/data-for-numerical-investigation-of-sediment-yield_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-for-numerical-investigation-of-sediment-yield_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the input and output files from the publication by Hirschberg et al. (2022). The input files are the climate forcing time series generated with the AWE-GEN model. The output files include the hydrological outputs, which is the same for scenarios 1-6 considered in Hirschberg et al. (2022), and the sediment-related outputs, whereas the transport-limited scenario 6 is included in the output of scenario 1. The input file includes: - time _D_ (h) - precipitation _Pr_ (mm/h) - atmospheric temperature _Ta_ (\u00b0C) - incoming shortwave radiation _Rsw_ (W/m^2) - cloudiness _N_ (-) The output files include: - hydrological outputs (accroding to time in input and normalized by basin area) - total discharge _Q_ (mm/h) - surface discharge _Qs_ (mm/h) - subsurface discharge _Qss_ (mm/h) - soil water storage _Vw_ (mm) - snow depth _snow_ (mm SWE) - snow depth change _snowacc_ (mm/h SWE) - potential evapotranspiration _PET_ (mm/h) - actual evapotranspiration _AET_ (mm/h) - sediment outputs (accroding to time in input and normalized by basin area) - hillslope landslide magnitude _ls_ (mm/h) - channel sediment storage _sc_ (mm) - hillslope sediment storage _sh_ (mm) - total sediment discharge _so_ (mm/h) - transport-limited total sediment discharge _sopot_ (mm/h) - sediment discharge by debris flows _dfs_ (mm/h) - transport-limited sediment discharge by debris flows _dfspot_", "links": [ { diff --git a/datasets/data-from-hagen-skeels-etal-pnas_1.0.json b/datasets/data-from-hagen-skeels-etal-pnas_1.0.json index f3e9315753..741c3d8234 100644 --- a/datasets/data-from-hagen-skeels-etal-pnas_1.0.json +++ b/datasets/data-from-hagen-skeels-etal-pnas_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-from-hagen-skeels-etal-pnas_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Datasets and R scripts ~~~~~Datasets Dataset_S1.csv: Distribution of species diversity in plant and vertebrate clades. Total clade level diversity and species diversity in tropical moist forests (TMF) across the Neotropics, Indomalaya and Afrotropics. Pantropical clades are found in all three TMF regions with at least one-third of the clades\u2019 total diversity spread throughout these regions. Pantropical diversity disparity (PDD) clades show lower diversity in TMF in the Afrotropics than in the Neotropics and Indomalaya. Dataset_S2.csv: Environmental and species richness data across 110 km x 110 km grid cells in Neotropical, Indomalayan and Afrotropical moist forest sites. Variables include x and y coordinates in the Behrmann equal area coordinate reference system, potential evapotranspiration (PET), mean annual temperature (MAT), mean annual precipitation (MAP), amphibian, mammal, bird and squamate reptile species richness and biogeographic region, as well as the first two principal components of a principal component analysis on PET, MAT and MAP (PC1, PC2). Dataset_S3.csv: Global reconstructed paleo-temperature estimates and spatial coordinates across 200 million years at 170,000 year intervals at 2 degree spatial resolution. Dataset_S4.csv: Gen3sis model parameters and biodiversity summary statistics. Summary statistics include the number of extant species, the number of extinct species, the total number of species, the number of species within the tropical moist forest biome boundaries in the Neotropics, the Afrotropics and Indomalaya, the pantropical index, and the pantropical disparity index, as well as the running time-step and diversity of unfinished simulations. Dataset_S5.csv: Net relatedness index (NRI) values for vertebrate clades showing an observed disparity in pantropical diversity in the Neotropical, Indomalayan and Afrotropical moist forest regions and associated P-values. Positive values indicate phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion. ~~~~~Scripts Script_1 - GLS.R. R script to replicate the linear modelling analyses. Script_2 - Gen3sis_config_template.R. R script to generate the configurations files to run the simulation experiment. Script_3 - Gen3sis_config_creator.R. R script to generate the configurations files to run the simulation experiment.", "links": [ { diff --git a/datasets/data-hagenmoos-1989-2020_1.0.json b/datasets/data-hagenmoos-1989-2020_1.0.json index d821989f73..cd14905106 100644 --- a/datasets/data-hagenmoos-1989-2020_1.0.json +++ b/datasets/data-hagenmoos-1989-2020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-hagenmoos-1989-2020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes data from three vegetation surveys in a restored raised bog (Hagenmoos) in the lowland of the canton of Z\u00fcrich (Switzerland). The bog Hagenmoos was restored by cutting shrubs and trees within the formerly peat-cutting pits and by blocking drainages. The vegetation surveys were carried out before (1989), ten years after (1999) and 30 years after restoration (2020). In each vegetation survey, all vascular plant and bryophyte species within 72 permanent plots were recorded. Of these plots, 34 are located within the formerly peat-cutting pits and 38 are located outside the peat pits. Based on presence-absence data of vascular plants and bryophytes, mean ecological indicator and strategy values based on Landolt et al. (2010) were calculated and are provided in the Excel sheet. Indicator values for light, moisture, pH, nutrients, humus, temperature and continentality and strategy values for stress, competition and ruderality were considered. Furthermore, species richness for the following groups were calculated: (1) all plant species, (2) all vascular plant species, (3) bog specialists among vascular plant species, (4) all bryophyte species, (5) bog specialists among bryophyte species. As bog specialist species, we considered all plant species listed as characteristic species of raised bogs by Feldmeyer-Christe and K\u00fcchler (2018: Moore der Schweiz. Haupt, Bern).", "links": [ { diff --git a/datasets/data-of-national-dishes-their-similarity-and-trade-flows_1.0.json b/datasets/data-of-national-dishes-their-similarity-and-trade-flows_1.0.json index 766e6a4c06..75cdb2410f 100644 --- a/datasets/data-of-national-dishes-their-similarity-and-trade-flows_1.0.json +++ b/datasets/data-of-national-dishes-their-similarity-and-trade-flows_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-of-national-dishes-their-similarity-and-trade-flows_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.", "links": [ { diff --git a/datasets/data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0.json b/datasets/data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0.json index 35ea2a5dda..3c4a515dd1 100644 --- a/datasets/data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0.json +++ b/datasets/data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw data supporting the paper \"Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients\". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are: - Alpha taxonomic community metrics: species richness and Shannon diversity - Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion. - Community weighted means of 8 functional traits - The local community contributions on the functional and taxonomic beta diversity (LCBD). The dataset also includes the following: - The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity). -The three types of protected areas, defined according to the protective measures. - The model evaluation, variable importance and partial dependece data.", "links": [ { diff --git a/datasets/data-set-of-mee-20-04-264_1.0.json b/datasets/data-set-of-mee-20-04-264_1.0.json index 1a0c5dc9be..4b6df898a0 100644 --- a/datasets/data-set-of-mee-20-04-264_1.0.json +++ b/datasets/data-set-of-mee-20-04-264_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-set-of-mee-20-04-264_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper \u201cPlant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources\u201d published in the journal 'Methods in Ecology and Evolution'.", "links": [ { diff --git a/datasets/data-snow-instability_1.0.json b/datasets/data-snow-instability_1.0.json index 4bc7d2870e..be530052fc 100644 --- a/datasets/data-snow-instability_1.0.json +++ b/datasets/data-snow-instability_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data-snow-instability_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support. Please refer to the Read-me file for further details on the data. These data are the basis of the following publication: Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.", "links": [ { diff --git a/datasets/data_ecolappl_2020_1.0.json b/datasets/data_ecolappl_2020_1.0.json index 9e83d841af..cdbf086a2f 100644 --- a/datasets/data_ecolappl_2020_1.0.json +++ b/datasets/data_ecolappl_2020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data_ecolappl_2020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data, on which the following publication below is based. **Paper Citation**: Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Sch\u00fctz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133) Please cite this paper together with the citation for the datafile. # Methods ## Study site The study area is situated within and nearby to Eigental nature reserve (47\u00b027\u201936\u201d to 47\u00b029\u201906\u201d N, 8\u00b037\u201912\u201d to 8\u00b037\u201944\u201d E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 \u00b1 136 mm and 9.14\u00b0C \u00b1 0.50\u00b0C (mean \u00b1 SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)). In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented: - **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn). - **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2). - **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997). Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altl\u00e4ufe der Glatt; 47\u00b028\u201929\u201d to 47\u00b027\u201941\u201d N, 8\u00b031\u201956\u201d to 8\u00b032\u201926\u201d E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m \u00d7 5 m) spread across the sites. Altogether, the experiment included 55 plots. ## Arthropod sampling Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m \u00d7 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera), ## Functional traits We used two sets of functional traits in this study. **Morphometric traits**: Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, \u00a9 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, \u00a9 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature. **Life-history traits** Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species\u2019 vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species\u2019 optimum habitat moisture, and moisture range, which describes the species\u2019 range of preferable moisture conditions. ### References Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensr\u00e4ume der Schweiz: \u00d6kologie - Gef\u00e4hrdung - Kennarten. 3rd ed. Ott, Bern. Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. K\u00f6hler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013. MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Z\u00fcrich. Neff, F., N. Bl\u00fcthgen, M. N. Chist\u00e9, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001\u20132015. Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands\u2014lessons from the distant and recent past. Biological Conservation 104:361\u2013376. Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754\u2013764.", "links": [ { diff --git a/datasets/data_jae_2019_1.0.json b/datasets/data_jae_2019_1.0.json index 1e671be3e7..f1f2a6e28b 100644 --- a/datasets/data_jae_2019_1.0.json +++ b/datasets/data_jae_2019_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data_jae_2019_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. __Paper Citation:__ > Resch, M.C., Sch\u00fctz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793. Please cite this paper together with the citation for the datafile. # Methods ## Study area and experimental settings The study was conducted in a nature reserve (Eigental: 47\u00b0 27\u2019 to 47\u00b0 29\u2019 N, 8\u00b0 37\u2019 E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 \u00b0C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Z\u00fcrich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation. The three restoration treatments used were: i. \u201cHarvest only\u201d: Plots are being mowed two to three times a year and the biomass is removed. ii. \u201cTopsoil\u201d: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2. iii. \u201cTopsoil+Propagules\u201d: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997. \u201cTopsoil\u201d and \u201cTopsoil+Propagules\u201d plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented. Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as \u201cInitial\u201d, and 11 control plots that represent the targeted state of donor sites for \u201cTopsoil+Propagules\u201d, further referred to as \u201cTarget\u201d. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years). ## Nematode and plant sampling Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 \u00b0C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses. We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001). The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, B\u00e4umler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010). ## References Baudirektion Kanton Z\u00fcrich (2007). 10 Jahre Naturschutz-Gesamtkonzept f\u00fcr den Kanton Z\u00fcrich 1995-2005 \u2013 Stand der Umsetzung. Z\u00fcrich: Baudirektion Kanton Z\u00fcrich. Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging. Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627 Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9 Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundz\u00fcge der Vegetationskunde (3rd ed.). Wien: Springer. Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensr\u00e4ume der Schweiz: \u00d6kologie - Gef\u00e4hrdung - Kennarten (3rd ed.). Bern: Ott. Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4 Landolt, E., B\u00e4umler, B., Erhardt, A., Hegg, O., Kl\u00f6tzli, F., L\u00e4mmler, W., \u2026 Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt. Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt. MeteoSchweiz (2018). Klimabulletin Jahr 2017, Z\u00fcrich: MeteoSchweiz. Moser, D., Gygax, A., B\u00e4umler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gef\u00e4hrteten Farn- und Bl\u00fctenpflanzen der Schweiz. Bern: BUWAL. Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press. Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004", "links": [ { diff --git a/datasets/data_wet_aval_model_1.0.json b/datasets/data_wet_aval_model_1.0.json index 60553da044..de6f90361c 100644 --- a/datasets/data_wet_aval_model_1.0.json +++ b/datasets/data_wet_aval_model_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "data_wet_aval_model_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Datasets used to implement the wet-snow avalanche activity model presented in the article: Hendrick, M., Techel, F., Volpi, M., Olevski, T., P\u00e9rez-Guill\u00e9n, C., van Herwijnen, A., Schweizer, J. (2023). Automated prediction of wet-snow avalanche activity in the Swiss Alps. Journal of Glaciology, under review Each dataset includes the input variables (weather and snowpack features) and the target variable (wet-snow avalanche day or not) used to build the model. Additionally, Dataset3_nowcast and Dataset3_forecast contain the predictions provided by the RF12 model. All input variables are described in the Appendix of the article and also in the read_me file. Further information on SNOWPACK variables is also available at https://models.slf.ch/p/snowpack/ .", "links": [ { diff --git a/datasets/database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0.json b/datasets/database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0.json index 577f6a667d..d6e1857336 100644 --- a/datasets/database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0.json +++ b/datasets/database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.", "links": [ { diff --git a/datasets/dataset-for-future-water-temperature_1.0.json b/datasets/dataset-for-future-water-temperature_1.0.json index 3a2b1f9512..4fe685a068 100644 --- a/datasets/dataset-for-future-water-temperature_1.0.json +++ b/datasets/dataset-for-future-water-temperature_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dataset-for-future-water-temperature_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands. This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).", "links": [ { diff --git a/datasets/dataset-for-ogrs-2018-publication_1.0.json b/datasets/dataset-for-ogrs-2018-publication_1.0.json index 648c685c8a..4a5dd83924 100644 --- a/datasets/dataset-for-ogrs-2018-publication_1.0.json +++ b/datasets/dataset-for-ogrs-2018-publication_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dataset-for-ogrs-2018-publication_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the road and plot data used for the geospatial analysis example showcased in \"Fostering Open Science at WSL with the EnviDat Environmental Data Portal\", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018. The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.", "links": [ { diff --git a/datasets/dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0.json b/datasets/dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0.json index 982fe10701..36986705a5 100644 --- a/datasets/dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0.json +++ b/datasets/dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository consists of the merged data from WaMos2 (2010) and WaMos2 (2020) and also includes both Corona-related surveys that have been conducted within the phase of WaMos3. WaMos3 is the third assessment of the relationship of the Swiss population to the forest after 1997 and 2010 and was conducted in 2020. As in WaMos2 in 2010, the attitude of the population to the forest as a recreation area, to wood production and to the protective and ecological functions were examined. The topic of climate change was also included. In addition, the views of adolescents between 15 and 18 years of age were taken into account for the first time. A detailed description of the provided data can be found in accompanied file \"WaMos_Metadatenbeschreibung_221027.pdf\" which also contains explanations and examples of the merging process from WaMos2 to WaMos3 as well as sampling procedures. Further, the samples itself can be processed with the help of the provided R-file \"EnviDat_WaMos_dataset.R\".", "links": [ { diff --git a/datasets/dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0.json b/datasets/dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0.json index 1cf0854f71..0b0c94cdfc 100644 --- a/datasets/dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0.json +++ b/datasets/dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01\u00b0 grid cell for the years 2016 and 2017. 100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01\u00b0 horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01\u25e6 pixels within Switzerland amounts to 48657. Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017). Please cite the following paper in connection with the dataset: __Paper Citation:__ > _Bert Kruyt, J\u00e9r\u00f4me Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_", "links": [ { diff --git a/datasets/davfair1_gis_1.json b/datasets/davfair1_gis_1.json index da3ec993fa..61936190b7 100644 --- a/datasets/davfair1_gis_1.json +++ b/datasets/davfair1_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davfair1_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 171 V5/519-6877/91 scale 1:5000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.", "links": [ { diff --git a/datasets/davfair2_gis_1.json b/datasets/davfair2_gis_1.json index fab4243d80..0f5ea4474b 100644 --- a/datasets/davfair2_gis_1.json +++ b/datasets/davfair2_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davfair2_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 120 V5/463-6877/1 scale 1:10 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.", "links": [ { diff --git a/datasets/davfair3_gis_1.json b/datasets/davfair3_gis_1.json index ea79bdeaf1..4b3ee902c8 100644 --- a/datasets/davfair3_gis_1.json +++ b/datasets/davfair3_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davfair3_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 120 SUP 1 V5/480-6877/7 scale 1:10 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.", "links": [ { diff --git a/datasets/davis_aws_1.json b/datasets/davis_aws_1.json index 77b0990d8e..234c2b862a 100644 --- a/datasets/davis_aws_1.json +++ b/datasets/davis_aws_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davis_aws_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The automatic weather stations at the Australian stations (Casey, Davis, Macquarie Island, and Mawson) were installed by the Bureau of Meteorology. They collect information on the following (in the following units):\n\ndate\n\nTimehh:mm\n\nwind speed knots\n\nwind direction degrees\n\nair temperature degrees celsius\n\nrelative humidity percent\n\nair pressurehPa\n\nTimes are in UT.\n\nMeasurements are made at 4 metres.", "links": [ { diff --git a/datasets/davis_baro_leveling_1970_1.json b/datasets/davis_baro_leveling_1970_1.json index d82d40e5ff..b21f88514b 100644 --- a/datasets/davis_baro_leveling_1970_1.json +++ b/datasets/davis_baro_leveling_1970_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davis_baro_leveling_1970_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Division carried out a traverse from Davis station in the spring of 1970, following the \"SG\" line of stakes. Two banks of aircraft altimeters, supplemented by three Surveyors Aneroids (T. Wheeler), were used for heighting bamboo trail marking stakes (installed at 1.5 mile intervals), strain grids, and any prominent topographical features. Detailed notes on the observations, method used, and corrections applied to account for pressure changes due to climatic conditions.\n\nRecords for this work have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/davis_lidar_2009_1.json b/datasets/davis_lidar_2009_1.json index 64b1ac24ed..ff24cebea0 100644 --- a/datasets/davis_lidar_2009_1.json +++ b/datasets/davis_lidar_2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davis_lidar_2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of:\n1 Lidar data captured by the Australian Antarctic Division in November 2009 in the Davis and Heidemann Valley area of the Vestfold Hills, Antarctica.\nThe files are in las format.\n2 A report about the processing of the lidar data that resulted in the las files.\nThe raw data from which the las files were derived are described by the metadata record 'High resolution digital aerial photography and LIDAR scanning of portions of the Vestfold Hills and Rauer Group' which has an Entry ID of: Davis_2009_Aerial_Photography.\nThe lidar data were captured for the purpose of creating a Digital Elevation Model of the area.", "links": [ { diff --git a/datasets/davis_strain_1971_1.json b/datasets/davis_strain_1971_1.json index 2223329b52..0bf6d437aa 100644 --- a/datasets/davis_strain_1971_1.json +++ b/datasets/davis_strain_1971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davis_strain_1971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Strain grid measurements near Davis during the traverse program for the 1971 season. Chaining out from the center pole to one of four corner stakes required two moves with an assumed 600ft \"invar\" tape. Measurement in both directions was achieved on all grids. Snow to bottom of tag measurements were recorded in centimeters plus the central two inch steel black pole.\n\nFor the theodolite readings, both left and right face azimuth angles were taken at snow level and elevation read to the bottom of the tag.\n\nPhysical records archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/davisbathy_gis_1.json b/datasets/davisbathy_gis_1.json index bdf13f0e71..f8858bacf2 100644 --- a/datasets/davisbathy_gis_1.json +++ b/datasets/davisbathy_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "davisbathy_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetric Contours and height range polygons of approaches to Davis Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings.", "links": [ { diff --git a/datasets/dc8ammr_1.json b/datasets/dc8ammr_1.json index f039394384..eab08ff5af 100644 --- a/datasets/dc8ammr_1.json +++ b/datasets/dc8ammr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8ammr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 DC-8 Airborne Multichannel Microwave Radiometer (AMMR) dataset is a browse-only dataset that consists of plotted digital count measurements collected by the Airborne Multichannel Microwave Radiometer (AMMR) during the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying the various aspects of tropical cyclones in the region. The AMMR was mounted onboard the NASA DC-8 aircraft. Daily browse files in GIF format are available for August 20, September 2, and September 17, 1998.", "links": [ { diff --git a/datasets/dc8avaps_1.json b/datasets/dc8avaps_1.json index ebf6ad4820..765900ed7d 100644 --- a/datasets/dc8avaps_1.json +++ b/datasets/dc8avaps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8avaps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 DC-8 Airborne Vertical Atmospheric Profiling System (AVAPS) dataset consists of measurements from AVAPS, which uses dropsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde's descent once each half second. These measurements were collected in support of the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying various aspects of tropical cyclones in the region. AVAPS provided vertical profiles of temperature, humidity, pressure, and winds. The dataset files are available in netCDF-3 and ASCII format with browse imagery available in GIF image format.", "links": [ { diff --git a/datasets/dc8capac_1.json b/datasets/dc8capac_1.json index 347f360a9d..5d102bf16b 100644 --- a/datasets/dc8capac_1.json +++ b/datasets/dc8capac_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8capac_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAPAC is a series of three instruments: the Forward Scattering Spectrometer Probe model 300 (FSSP-300), the Two Dimensional Optical Array Probes [Cloud and Precipitation Probes (2D-P)] and the CAPAC video. These instruments flew during CAMEX-3 upon the NASA DC-8 mounted on the left wing. Cloud and aerosol particles were exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size. The size distributions thus determined were integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation. The purpose of the CAMEX-3 mission was to study hurricanes over land and ocean in the U.S. Gulf of Mexico, Caribbean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and rain gauge sites.", "links": [ { diff --git a/datasets/dc8capacv_1.json b/datasets/dc8capacv_1.json index 5fff578b88..29062f2fb6 100644 --- a/datasets/dc8capacv_1.json +++ b/datasets/dc8capacv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8capacv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAPAC is a series of three instruments: the Forward Scattering Spectrometer Probe model 300 (FSSP-300), the Two Dimensional Optical Array Probes [Cloud and Precipitation Probes (2D-P)] and the CAPAC video. These instruments flew during CAMEX-3 upon the NASA DC-8 mounted on the left wing. Cloud and aerosol particles were exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size. The size distributions thus determined were integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation. CAPAC videos are a visual record of the particles and hydrometeors passing through the instrument housing. The purpose of the CAMEX-3 mission was to study hurricanes over land and ocean in the U.S. Gulf of Mexico, Caribbean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and rain gauge sites. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/dc8dads_1.json b/datasets/dc8dads_1.json index bc29c9b9c7..aebcb1ed95 100644 --- a/datasets/dc8dads_1.json +++ b/datasets/dc8dads_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8dads_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 DC-8 Navigation Data Acquisition and Distribution System (DADS) data files contain information recorded by navigation and data collection systems onboard the NASA DC-8 aircraft. These data files contain typical navigation data (e.g. date, time, lat/lon, altitude), and meteorological parameters (e.g. wind speed and direction, temperature, saturation vapor pressure) collected in support of the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying various aspects of tropical cyclones in the region. These data are available in ASCII file format with browse imagery available in GIF file format. Each file contains data recorded at one second intervals for each flight.", "links": [ { diff --git a/datasets/dc8jplsaw_1.json b/datasets/dc8jplsaw_1.json index aa3de63a79..d6f7132ff5 100644 --- a/datasets/dc8jplsaw_1.json +++ b/datasets/dc8jplsaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8jplsaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CAMEX-3 Jet Propulsion Laboratory (JPL) Surface Acoustic Wave (SAW) Hygrometer dataset consists of dewpoint timeline measurements acquired during each DC-8 flight in August and September of 1998.", "links": [ { diff --git a/datasets/dc8lase_1.json b/datasets/dc8lase_1.json index 8956470a71..b21ba6a5ec 100644 --- a/datasets/dc8lase_1.json +++ b/datasets/dc8lase_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8lase_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 LiDAR Atmospheric Sensing Experiment (LASE) Imagery dataset is a browse-only dataset that consists of plotted reflectivity data collected by the LiDAR Atmospheric Sensing Experiment (LASE) during the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying the various aspects of tropical cyclones in the region. The LiDAR was mounted onboard the NASA DC-8 aircraft, and the daily browse files are available from August 21 through September 5, 1998 in GIF format.", "links": [ { diff --git a/datasets/dc8laserh_1.json b/datasets/dc8laserh_1.json index aa9b18d65a..58d1d6831c 100644 --- a/datasets/dc8laserh_1.json +++ b/datasets/dc8laserh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8laserh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 Jet Propulsion Laboratory (JPL) Laser Hygrometer datasets consists of timeline measurements of water vapor content colllected during DC-8 flights flown during August and September of 1998. The JPL Laser Hygrometer acquired in situ measurments of the free airstream beyond the boundary layer within the immediate proximity of the aircraft along the flight track.", "links": [ { diff --git a/datasets/dc8macaws_1.json b/datasets/dc8macaws_1.json index a7adf953fb..12143f9f45 100644 --- a/datasets/dc8macaws_1.json +++ b/datasets/dc8macaws_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8macaws_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) was deployed during the Third Convection and Moisture Experiment (CAMEX-3). MACAWS data for the line-of-sight velocity and intensity as a function of range, backscattered intensity, turbulence approximation, and complex covariance was gathered for the period of 3 August 1998 through 22 September 1998. The objective of the CAMEX-3 mission was to study hurricanes over land and ocean in the U.S., Gulf of Mexico, Caribbean, and Western Atlantic ocean in coordination with multiple aircraft and reserach-quality radar, lightning, radiosonde, and raingauge sites.", "links": [ { diff --git a/datasets/dc8mms_1.json b/datasets/dc8mms_1.json index 653927719a..d1a8f37bf9 100644 --- a/datasets/dc8mms_1.json +++ b/datasets/dc8mms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8mms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 Meteorological Measurement System (MMS) dataset consists of atmospheric parameters measured by the MMS instruments aboard NASA DC-8 aircraft. The MMS consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the Earth, and a data acquisition system to sample, process, and record the measured quantities. The MMS dataset consits of atmospheric pressure, temperature, and wind measurements collected during the CAMEX-3 mission to study hurricanes over the land and ocean in the U.S Gulf of Mexico, Caribbean, and Western Atlantic Ocean.", "links": [ { diff --git a/datasets/dc8psr_1.json b/datasets/dc8psr_1.json index f22b294529..c30e053d53 100644 --- a/datasets/dc8psr_1.json +++ b/datasets/dc8psr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dc8psr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Polarimetric Scanning Radiometer (PSR) is a versatile airborne microwave imaging radiometer developed by the Georgia Institute of Technology and the NOAA Environmental Technology Laboratory for the purpose of obtaining polarimetric microwave emission imagery of the Earth's oceans, land, ice, clouds, and precipitation.", "links": [ { diff --git a/datasets/dd3da2570363429791b51120bdd29c02_NA.json b/datasets/dd3da2570363429791b51120bdd29c02_NA.json index 7db941d07a..6685923ccd 100644 --- a/datasets/dd3da2570363429791b51120bdd29c02_NA.json +++ b/datasets/dd3da2570363429791b51120bdd29c02_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dd3da2570363429791b51120bdd29c02_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/de883c15-85f6-435a-b5aa-3f6468ba919a_1.json b/datasets/de883c15-85f6-435a-b5aa-3f6468ba919a_1.json index d2e41e2280..3b756ca8bb 100644 --- a/datasets/de883c15-85f6-435a-b5aa-3f6468ba919a_1.json +++ b/datasets/de883c15-85f6-435a-b5aa-3f6468ba919a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "de883c15-85f6-435a-b5aa-3f6468ba919a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This one-degree latitude/longitude spatial resolution data set of Methane Emission from Animals data set was compiled at the NASA/Goddard Institute of Space Studies (GISS) from nine animal population densities.* The statistics on animal populations came from the Food and Agricultural Organization (FAO) and other sources. The animals were distributed across a one-degree latitude/longitude grid of national political boundaries, and sub-national boundaries for Australia, Brazil, Canada, China, India, USA and the former USSR. Published estimates of methane production from each type of animal were applied to the populations to yield a global distribution of annual methane emission by animals, expressed in kilograms per square kilometer of CH4 produced annually.\n \nA large spatial variability in the distribution of methane production (and the source animal populations) can clearly be seen in the global digital map. The total annual global estimate of methane emission is 75.8 teragrams (10 to the 12th power), about 55% of which is found between 25 degrees North and 55 degrees North latitude, a significant contribution to the observed north-south gradient of atmospheric methane concentration. The proper reference to this data set is \"J. Lerner, E. Matthews and I. Fung, June 1988. Methane Emission from Animals: a Global High-Resolution Data Base, Global Biogeochemical Cycles, vol. 2, no. 2, pp. 139-156.\"\n \nThe original magnetic tape containing these data came from the National Center for Atmospheric Research (NCAR-Scientific Computing Division/Data Support Section); 1850 Table Mesa Drive; Boulder, Colorado; 80307 USA. This tape contains the methane emission data file and ten animal population density data files (the nine listed below plus bovines, a combination of 'cattle' and 'dairy cows'). In addition it has three listing or program files; all of the data and non-data files are in ASCII format. While all of the 14 files have been read from tape to disk at GRID-Geneva, only the annual methane emission (kg./sq. km.) data file has been converted to a binary image format.\n \nThis data set is available as five different file types:\n \n - ASCII file of complex real (floating-point, 32-bit) numbers,\n both original file; and the IBM-compatible file;\n - 16-bit, signed integer file;\n - eight-bit unsigned integer file;\n - demonstration file (also eight-bit), useful only for visualization.\n \nType number (3) is recommended for most analytical purposes, as it contains all of the numerical information of the original file (1), but is easier to work on. Type number (4) is only recommended for those systems which cannot handle 16-bit data, and type (5) in cases where an annotated image or photoproduct only is desired.\n \nThe Methane data file is held in the Plate Carree (Simple Cylindrical) projection, has a spatial resolution of one degree latitude/longitude and consists of 180 rows (lines) by 360 columns (elements/pixels/ samples) of data. Its origin point is at 90 degrees North latitude and 180 degrees West longitude, and it extends to 90 degrees South latitude and 180 degrees East longitude. The two-byte or 16-bit per element data file comprises 130 Kb, and the one-byte file 65 kb.\n \n - Cattle, Dairy cows, Water buffalo, Sheep, Goats, Camels, Pigs, Horses and Caribou\n", "links": [ { diff --git a/datasets/deadwood-generator_1.0.json b/datasets/deadwood-generator_1.0.json index e64088fe9a..b4ea15cfed 100644 --- a/datasets/deadwood-generator_1.0.json +++ b/datasets/deadwood-generator_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "deadwood-generator_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The here presented code generates discrete three-dimensional, RAMMS::ROCKFALL readable deadwood log files (.pts-format) of windtrown forests, including the pilling effect due to slightly different throw directions.", "links": [ { diff --git a/datasets/debris-flow-prediction-based-on-rainfall_1.0.json b/datasets/debris-flow-prediction-based-on-rainfall_1.0.json index 9bf137ac15..c9a3c0e211 100644 --- a/datasets/debris-flow-prediction-based-on-rainfall_1.0.json +++ b/datasets/debris-flow-prediction-based-on-rainfall_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "debris-flow-prediction-based-on-rainfall_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021). ## How to install and run the example Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following: `$ conda env create -f environment.yml` `$ conda activate thresholds` or `$ source activate thresholds` To run an example: `$ python run_example()` It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found. ## License GNU General Public License v3.0", "links": [ { diff --git a/datasets/debris-flow-volumes-at-the-illgraben-2000-2017_1.0.json b/datasets/debris-flow-volumes-at-the-illgraben-2000-2017_1.0.json index fdb34ad7fb..f52f90a540 100644 --- a/datasets/debris-flow-volumes-at-the-illgraben-2000-2017_1.0.json +++ b/datasets/debris-flow-volumes-at-the-illgraben-2000-2017_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "debris-flow-volumes-at-the-illgraben-2000-2017_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Debris-flow bulk volumes from the WSL monitoring station. More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).", "links": [ { diff --git a/datasets/deglacial_water_isotope_composite_gicc05_1.json b/datasets/deglacial_water_isotope_composite_gicc05_1.json index 14c820ec07..8d8b8d5056 100644 --- a/datasets/deglacial_water_isotope_composite_gicc05_1.json +++ b/datasets/deglacial_water_isotope_composite_gicc05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "deglacial_water_isotope_composite_gicc05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precise information on the relative timing of north-south climate variations is a key to resolving questions concerning the mechanisms that force and couple climate changes between the hemispheres. We present a new composite record made from five well-resolved Antarctic ice core records that robustly represents the timing of regional Antarctic climate change during the last deglaciation. Using fast variations in global methane gas concentrations as time markers, the Antarctic composite is directly compared to Greenland ice core records, allowing a detailed mapping of the inter-hemispheric sequence of climate changes. Consistent with prior studies the synchronized records show that warming (and cooling) trends in Antarctica closely match cold (and warm) periods in Greenland on millennial timescales. For the first time, we also identify a sub-millennial component to the inter-hemispheric coupling. Within the Antarctic Cold Reversal the strongest Antarctic cooling occurs during the pronounced northern warmth of the Bolling. Warming then resumes in Antarctica, potentially as early as the Intra-Allerod Cold Period, but with dating uncertainty that could place it as late as the onset of the Younger Dryas stadial. There is little-to-no time lag between climate transitions in Greenland and opposing changes in Antarctica. Our results lend support to fast acting inter-hemispheric coupling mechanisms including recently proposed bipolar atmospheric teleconnections and/or rapid bipolar ocean teleconnections.\n\nThe five cores used in the Antarctic deglacial water isotope composite are: Law Dome, Byrd, EPICA Dronning Maud Land (EDML), Siple Dome and Talos Dome. The data for each core is interpolated to 20 year time steps and standardised with respect to its own mean and standard deviation over the interval 9000 to 21000 years before 1950 AD (9-21 ka BP 1950). Estimated dating uncertainty in the composite (relative to GICC05) is +/- 220 y during the interval 10-13 ka BP, +/- 200 y during the interval 13-15 ka BP and +/- 380 y during the interval 15-18 ka BP. Refer to Pedro et al., (in press) (Table 2) and original references (below) for dating uncertainties in the individual cores.\n\nThe locations and original references for the isotope data and transfers to the GICC05 timescale of the 5 Antarctic cores are as follows:\n\nLaw Dome\nLocation: 66 degrees 46'S 112 degrees 48'E Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for d18O data:\n1. Pedro et al., (in press)\n2. Morgan, V., Delmotte, M., van Ommen, T. D., Jouzel, J., Chappellaz, J., Woon, S., Masson-Delmotte,, V., and Raynaud, D.: Relative timing of deglacial climate events in Antarctica and Greenland, Science, 297, 1862-1864, 2002.\n\nByrd\nLocation: 80degrees 01'S 119 degrees 31'W Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for d18O data: Blunier, T., and Brook, E. J.: Timing of millennial-scale climate change in Antarctica and Greenland during the last glacial period, Science 291, 109-112, 2001.\n\nSiple Dome**\nLocation: 81 degrees 40'S 148 degrees 49'W Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for dD data**: Brook, E. J., White, J. W. C., Schilla, A. S. M., Bender, M. L., Barnett, B. Severinghaus, J. P., Taylor, K. C., Alley, R. B., and Steig, E. J.: Timing of millennial-scale climate change at Siple Dome, West Antarctica, during the last glacial period, Quat. Sci. Rev., 24, 1333-1343, 2005.\n**Note: As d18O values for Siple Dome during the deglaciation were not available we used appropriately scaled dD i.e. (dD-10)/8 (on advice of James White and Edward Brook, pers. comm. March 2011).\n\nEDML\nLocation: 75 degrees 00'S 00 degrees 04'E Reference for transfer to GICC05 timescale: Lemieux-Dudon, B., Blayo, E., Petit, J. -R., Waelbroeck, C., Svensson, A, Ritz, C., Barnola, J. -M., Narcisi, B. M., and Parrenin F.: Consistent dating for Antarctic and Greenland ice cores, Quat. Sci. Rev., 29, 8-20, 2010.\nReference for d18O data: EPICA community members: One-to-one coupling of glacial climate variability in Greenland and Antarctica, Nature, 444, 195-198, 2006.\n\nTalos Dome\nLocation: 72 degrees 49'S 159 degrees 11'E Reference for transfer to GICC05 timescale: Buiron, D., Chappellaz, J., Stenni, B., Frezzotti, M., Baumgartner, M.,Capron, E., Landais, A., Lemieux-Dudon, B., Masson-Delmotte, V., Montagnat, M., Parrenin, F., and Schilt, A.: TALDICE-1 age scale of the Talos Dome deep ice core, East Antarctica, Clim. Past, 7, 1--16, doi:10.5194/cp-7-1-2011,2011.\n\nReference for d18O data: Stenni, B., Buiron, D., Frezzotti, M., Albani, S., Barbante, C., Bard, E., Barnola, J. M., Baroni, M., Baumgartner, M., Bonazza, M., Capron, E., Castellano, E., Chappellaz, J., Delmonte, B., Falourd, S., Genoni, L., Iacumin, P., Jouzel, J., Kipfstuhl, S., Landais, A., Lemieux-Dudon, B., Maggi, V., Masson-Delmotte, V., Mazzola, C., Minster, B., Montagnat, M., Mulvaney, R., Narcisi, B., Oerter, H., Parrenin, F., Petit, J. R., Ritz, C., Scarchilli, C., Schilt, A., Schupbach, S., Schwander, J., Selmo, E., Severi, M., Stocker, T. F., and Udisti, R.: Expression of the bipolar see-saw in Antarctic climate records during the last deglaciation, Nature Geoscience, 4, 46-49, doi:10.1038/ngeo1026, 2011.\n\nThis work was done as part of AAS 757.", "links": [ { diff --git a/datasets/diatoms_sre3_1.json b/datasets/diatoms_sre3_1.json index a786edb9dc..61d283f5dd 100644 --- a/datasets/diatoms_sre3_1.json +++ b/datasets/diatoms_sre3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "diatoms_sre3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Full title:\n\nDiatom and associated data for a manipulative field experiment examining the effects of heavy metal and petroleum hydrocarbon contamination on benthic diatom communities in the Windmill Islands, Antarctica.\n\nA manipulative field experiment was performed to assess the effects of heavy metals and petroleum hydrocarbons on benthic diatom communities in the Windmill Islands. Three treatments were used (control, metal contaminated, and petroleum hydrocarbon contaminated), with replicates of each treatment deployed at three locations (Sparkes Bay, Brown Bay and O'Brien Bay). The datasets associated with this experiment include the concentrations of metals and hydrocarbons within samples, as well as diatom data (raw counts, and the relative abundance of benthic species).\n\nThis work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).\n\nPublic summary from project 1130:\n\nAlgal mats grow on sea floor in most shallow marine environments. They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish. We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution. We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.\n\nPublic summary from project 2201:\n\nAs a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.\n\nThe animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.\n\nThis project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.\n\nThe fields in this dataset are:\n\nSpecies\nSite\nAbundance\nTreatment Type\nAntimony\nArsenic\nCadmium\nChromium\nCopper\nIron\nLead\nManganese\nMercury\nNickel\nSilver\nTin\nZinc\nSpecial Antarctic Blend\nLube", "links": [ { diff --git a/datasets/diffuse_irradiance_791_1.json b/datasets/diffuse_irradiance_791_1.json index e9364bdd07..ef41523755 100644 --- a/datasets/diffuse_irradiance_791_1.json +++ b/datasets/diffuse_irradiance_791_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "diffuse_irradiance_791_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly mean values of diffuse irradiance fraction [f(Ediff), or ratio of diffuse-to-total irradiance] at ground level for a 30-degree solar zenith angle and in seven spectral bands (MODIS bands 1-7) as well broadband visible (400-700 nm), near-infrared (700-3000 nm) and shortwave (400-3000 nm). Values are provided for eight SAFARI 2000 core sites, including Ghanzi/Okwa River Crossing, Maun (Main and Floodplain Towers), Pandamatenga, and Tshane, Botswana; Skukuza, South Africa; Etosha National Park, Namibia; and Mongu, Zambia. The fractions were estimated with the 6S radiative transfer model, given the mean aerosol optical depth (AOT) values from AERONET sunphotometer measurements. Where sunphotometers were not deployed at a SAFARI 2000 core site, the nearest neighbor sunphotometer data were used. A rough estimate of the likely spatial extrapolation error is provided. These data can be used to estimate typical surface albedo (blue sky conditions) from the theoretical black-sky and white-sky albedo values provided in the MODIS albedo product (MOD43), as well as in other applications.Data for all eight sites are contained in one ASCII file, in csv format. The data file provides the ratio of diffuse (atmospherically-scattered) irradiance to total irradiance, both at ground level, for the eight sites in southern Africa. Mean values are provided for each of 12 months in 10 spectral bands between 0.4 and 4.0 microns, computed for a 30-degree solar zenith angle. The native resolution of the AERONET sunphotometer data varies, but is typically less than 1 hour. Information about the site location, IGBP classification, and nearest AERONET sunphotometer site is also provided.", "links": [ { diff --git a/datasets/digitizing-historical-plague_1.0.json b/datasets/digitizing-historical-plague_1.0.json index a44005e956..dbf93c616a 100644 --- a/datasets/digitizing-historical-plague_1.0.json +++ b/datasets/digitizing-historical-plague_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "digitizing-historical-plague_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.", "links": [ { diff --git a/datasets/dischmex-high-resolution-wrf-simulations-and-measurements_1.0.json b/datasets/dischmex-high-resolution-wrf-simulations-and-measurements_1.0.json index a8107df225..937f9c872f 100644 --- a/datasets/dischmex-high-resolution-wrf-simulations-and-measurements_1.0.json +++ b/datasets/dischmex-high-resolution-wrf-simulations-and-measurements_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dischmex-high-resolution-wrf-simulations-and-measurements_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented here corresponds to the publication \"Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain\" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication \"The importance of near-surface winter precipitation processes in complex alpine terrain\" (Gerber et al., 2018b). The dataset contains: * WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request). * StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016. * ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (B\u00fchler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided. For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018). __Publications:__ B\u00fchler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229\u2013243, doi:10.5194/tc-9-229-2015, 2015. Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137\u20133160, doi:10.5194/tc-12-3137-2018, 2018. Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018. Gerber, F., and Sharma, V.: Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.", "links": [ { diff --git a/datasets/dischmex-meteorological-measurements_1.0.json b/datasets/dischmex-meteorological-measurements_1.0.json index 58b8dab843..63c88b947a 100644 --- a/datasets/dischmex-meteorological-measurements_1.0.json +++ b/datasets/dischmex-meteorological-measurements_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dischmex-meteorological-measurements_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors. Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation). In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.", "links": [ { diff --git a/datasets/disdrometer-data-davos-wolfgang_1.0.json b/datasets/disdrometer-data-davos-wolfgang_1.0.json index 96ea6cc003..7c8490f295 100644 --- a/datasets/disdrometer-data-davos-wolfgang_1.0.json +++ b/datasets/disdrometer-data-davos-wolfgang_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "disdrometer-data-davos-wolfgang_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.", "links": [ { diff --git a/datasets/disdrometer-data-gotschna_1.0.json b/datasets/disdrometer-data-gotschna_1.0.json index ba6475efec..43c8b9237b 100644 --- a/datasets/disdrometer-data-gotschna_1.0.json +++ b/datasets/disdrometer-data-gotschna_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "disdrometer-data-gotschna_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A laser optical disdrometer (Parsivel\u00b2 , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.", "links": [ { diff --git a/datasets/disdrometer_laret_1.0.json b/datasets/disdrometer_laret_1.0.json index 919dee0de3..ad8bf82f02 100644 --- a/datasets/disdrometer_laret_1.0.json +++ b/datasets/disdrometer_laret_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "disdrometer_laret_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.", "links": [ { diff --git a/datasets/dispersal-prevalence-fish-traits-assemblages_1.0.json b/datasets/dispersal-prevalence-fish-traits-assemblages_1.0.json index c3691a9e5c..6c41ad7756 100644 --- a/datasets/dispersal-prevalence-fish-traits-assemblages_1.0.json +++ b/datasets/dispersal-prevalence-fish-traits-assemblages_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dispersal-prevalence-fish-traits-assemblages_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. \"A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages\" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited. The interaction of habitat dynamics with species dispersal abilities could generate gradients in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.", "links": [ { diff --git a/datasets/distributed-subcanopy-datasets_1.0.json b/datasets/distributed-subcanopy-datasets_1.0.json index ea1bb042cb..d24fc31d26 100644 --- a/datasets/distributed-subcanopy-datasets_1.0.json +++ b/datasets/distributed-subcanopy-datasets_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "distributed-subcanopy-datasets_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains datasets of sub-canopy meteorological variables acquired in coniferous forest stands in Switzerland (Davos, Engadine) and Finland (Sodankyl\u00e4) during the winters 2018 and 2019. The data are presented and used in the publication: Mazzotti, G., Essery, R., Webster, C., Malle, J., & Jonas T. (2020) Process-level evaluation of a high-resolution forest snow model using observations from mobile multi-sensor platforms Water Resources Research, under review The above publication must be cited when using this dataset, and the user is referred to the publication for additional detail. Data are grouped into 4 folders: 1) Point data includes wind speed data measured with stationary meteorological stations 2) Transect data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired with an automated calblecar system along within-stand transects 3) Grid data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired on 40x40m gridded plots using a handheld instrument, as well as snow depth data measured at the same grids. Canopy structure information derived from hemispherical images is included for each all surveyed locations as well, and an overview of the field sites is provided.", "links": [ { diff --git a/datasets/distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0.json b/datasets/distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0.json index 2c066af3d5..d1df0824c4 100644 --- a/datasets/distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0.json +++ b/datasets/distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances according to the Swiss habitat typology (TypoCH; Delarze et al. 2015) at 10x10 m resolution across Switzerland. The 20 grassland habitat types belong to the following habitat groups: fens, wet meadows, raised bogs, re-seeded and heavy fertilized grasslands, dry grasslands, nutrient-poor alpine and subalpine grasslands, nutrient-rich pastures and meadows as well as fallow grasslands. We followed a two-step approach: (1) Ensemble models provide **distribution maps of the 20 individual grassland habitat types**, using training data from various sources. Predictors were Copernicus Sentinel satellite imagery and variables describing climate, soil and topography. The performance of these maps was assessed with the True Skill Statistics and split\u2010sampling of the data. Available maps for each grassland habitat: (1) *Map of the median of predicted probability of occurrence*; (2) *Map of the standard deviation of the predicted probability of occurrence* (available upon request); (3) *Binary presence/absence map* (available upon request). For an overview, see *Overview: Maps of the individual grassland habitats*. (2) **Combined maps**: The individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert\u2010based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat\u2010type proportions with extrapolations from field surveys. Available combined maps: Map of the most likely habitat type (M1F; after regional corrections); Map of the second most likely habitat type (M2); Map of the most likely habitat type without regional corrections (available upon request); Map of the weighted median of the predicted probability of occurrence for the most/second most likely habitat type, respectively (available upon request); map of the ratio of the probabilities of occurrence of the most and second most likely habitat types (available upon request)", "links": [ { diff --git a/datasets/diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0.json b/datasets/diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0.json index f4b4e1cefa..853c7c6e73 100644 --- a/datasets/diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0.json +++ b/datasets/diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the dry Pfynwald forest a long-term experiment of WSL was initiated in 2003 with a set of irrigated and non-irrigated plots. Forest Entomologie WSL made several investigations, one of them on the effect of irrigation (or conversely of drought) on the biodiversity of epigaeic arthropods such as ground beetles and spiders. In addition, its effects were also assessed by counting galls formed by gall wasps on pubescent oak.", "links": [ { diff --git a/datasets/diversity_of_woody_species-36_1.0.json b/datasets/diversity_of_woody_species-36_1.0.json index 7fc4f29ec6..a87d4cde3d 100644 --- a/datasets/diversity_of_woody_species-36_1.0.json +++ b/datasets/diversity_of_woody_species-36_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "diversity_of_woody_species-36_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Index based on the number of tree and shrub species starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species starting at 12 cm dbh in the upper layer. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/dlhimpacts_1.json b/datasets/dlhimpacts_1.json index 631c2ff394..3ab0f7867d 100644 --- a/datasets/dlhimpacts_1.json +++ b/datasets/dlhimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dlhimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Diode Laser Hygrometer (DLH) dataset is comprised of water vapor mixing ratio measurements as well as relative humidities (both concerning liquid water and ice) which are derived from the water vapor mixing ratio and ambient static temperature and pressure provided by the TAMMS instrument suite. These measurements were made using two separate DLH instruments installed on the NASA P-3B research aircraft, and the data from these instruments were combined to provide the best combination of accuracy, dynamic range, and data coverage. The two DLH instruments are (1) the zenith-mounted system which utilizes an optical path between the zenith port and the aircraft\u2019s vertical tail, and (2) the short-path system, which utilizes an optical path between two fuselage-mounted fins. This dataset was measured during the 2023 campaign of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Earth Venture Suborbital 3 project. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The project aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The DLH data files are available for flights from January 13, 2023, through February 28, 2023, and are in the ASCII format.", "links": [ { diff --git a/datasets/doi:10.25921_sta3-3b95_Not Applicable.json b/datasets/doi:10.25921_sta3-3b95_Not Applicable.json index 39b7bf6c7b..06170fc463 100644 --- a/datasets/doi:10.25921_sta3-3b95_Not Applicable.json +++ b/datasets/doi:10.25921_sta3-3b95_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.25921/sta3-3b95_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico.", "links": [ { diff --git a/datasets/doi:10.25921_v3a2-m248_Not Applicable.json b/datasets/doi:10.25921_v3a2-m248_Not Applicable.json index 0b8808abe8..bc011f2c48 100644 --- a/datasets/doi:10.25921_v3a2-m248_Not Applicable.json +++ b/datasets/doi:10.25921_v3a2-m248_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.25921/v3a2-m248_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These water level data were digitized from a scanned marigram image associated with the tsunami event of 1945-11-27 at a tide gauge located at Karachi, Pakistan, and referenced to station datum. The Karachi marigram is one of the two instrumental records existing of the 1945 Makran tsunami and spans most of the 16 days between November 15 and December 1. The original Karachi analog record belongs to the Survey of India (SOI) and was collected and digitized by the National Institute of Oceanography (NIO) and Indian National Center for Ocean Information Services (INCOIS) for use in the publication of a few scientific papers. This digital marigram scan was reformatted into the accompanying digital, numerical time series by the Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO. Acknowledgement of SOI, NIO, and INCOIS should be included in any future scientific works using this record.", "links": [ { diff --git a/datasets/doi:10.7289_V51R6NQJ_Not Applicable.json b/datasets/doi:10.7289_V51R6NQJ_Not Applicable.json index 2c0c2de63f..c1ab611666 100644 --- a/datasets/doi:10.7289_V51R6NQJ_Not Applicable.json +++ b/datasets/doi:10.7289_V51R6NQJ_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V51R6NQJ_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V54X564T_Not Applicable.json b/datasets/doi:10.7289_V54X564T_Not Applicable.json index 28e7a920e1..7573caff6d 100644 --- a/datasets/doi:10.7289_V54X564T_Not Applicable.json +++ b/datasets/doi:10.7289_V54X564T_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V54X564T_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V55H7DGQ_Not Applicable.json b/datasets/doi:10.7289_V55H7DGQ_Not Applicable.json index 5af1633ad9..93012f941c 100644 --- a/datasets/doi:10.7289_V55H7DGQ_Not Applicable.json +++ b/datasets/doi:10.7289_V55H7DGQ_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V55H7DGQ_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V57H1GW8_Not Applicable.json b/datasets/doi:10.7289_V57H1GW8_Not Applicable.json index 8fa45c350e..c1840f65f9 100644 --- a/datasets/doi:10.7289_V57H1GW8_Not Applicable.json +++ b/datasets/doi:10.7289_V57H1GW8_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V57H1GW8_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V5862DPB_Not Applicable.json b/datasets/doi:10.7289_V5862DPB_Not Applicable.json index 20ab5552f9..4b17f341ff 100644 --- a/datasets/doi:10.7289_V5862DPB_Not Applicable.json +++ b/datasets/doi:10.7289_V5862DPB_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V5862DPB_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center.", "links": [ { diff --git a/datasets/doi:10.7289_V598856F_Not Applicable.json b/datasets/doi:10.7289_V598856F_Not Applicable.json index 755e4c474e..f010cced26 100644 --- a/datasets/doi:10.7289_V598856F_Not Applicable.json +++ b/datasets/doi:10.7289_V598856F_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V598856F_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V5C827KJ_Not Applicable.json b/datasets/doi:10.7289_V5C827KJ_Not Applicable.json index 9e66e32382..735cc9e27a 100644 --- a/datasets/doi:10.7289_V5C827KJ_Not Applicable.json +++ b/datasets/doi:10.7289_V5C827KJ_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V5C827KJ_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V5GX48VS_Not Applicable.json b/datasets/doi:10.7289_V5GX48VS_Not Applicable.json index ed8db4b7fe..9bc5619f3e 100644 --- a/datasets/doi:10.7289_V5GX48VS_Not Applicable.json +++ b/datasets/doi:10.7289_V5GX48VS_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V5GX48VS_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V5TM78D3_Not Applicable.json b/datasets/doi:10.7289_V5TM78D3_Not Applicable.json index 60cec745f3..1108f321fd 100644 --- a/datasets/doi:10.7289_V5TM78D3_Not Applicable.json +++ b/datasets/doi:10.7289_V5TM78D3_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V5TM78D3_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/doi:10.7289_V5X0657Z_Not Applicable.json b/datasets/doi:10.7289_V5X0657Z_Not Applicable.json index 04a9a1d289..c02359322d 100644 --- a/datasets/doi:10.7289_V5X0657Z_Not Applicable.json +++ b/datasets/doi:10.7289_V5X0657Z_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "doi:10.7289/V5X0657Z_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.", "links": [ { diff --git a/datasets/drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0.json b/datasets/drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0.json index fe6558c046..8c2472df24 100644 --- a/datasets/drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0.json +++ b/datasets/drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. Paper Citation: Risch Anita C., Zimmermann, Stefan, Sch\u00fctz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography Please cite this paper together with the citation for the datafile. The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.", "links": [ { diff --git a/datasets/drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0.json b/datasets/drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0.json index 4ed5dc98f1..2df8f870a5 100644 --- a/datasets/drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0.json +++ b/datasets/drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from pulse-labelling experiment with 100-year old trees of a naturally dry pine forest exposed to a 15-year-long irrigation experiment. Canopies of 10 trees were labelled for 3 hours with 13CO2 and the fate of this label was traced for one year in stem and soil respiration and in microbial biomass around these trees. Data include (1) microclimatic data and soil respiration rates of the year following pulse labelling. (2) Temporal patterns of the 13C signal and 13C excess in soil respired CO2 and microbial biomass. (3) Spatial distribution of 13C signal in the soil.", "links": [ { diff --git a/datasets/drought-and-beech-1000-beech-project_1.0.json b/datasets/drought-and-beech-1000-beech-project_1.0.json index f91e0466bb..7ba757fa82 100644 --- a/datasets/drought-and-beech-1000-beech-project_1.0.json +++ b/datasets/drought-and-beech-1000-beech-project_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "drought-and-beech-1000-beech-project_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors on mortality and drought legacy processes.", "links": [ { diff --git a/datasets/dtms0bil_247_1.json b/datasets/dtms0bil_247_1.json index 9152db87c5..77f53ea606 100644 --- a/datasets/dtms0bil_247_1.json +++ b/datasets/dtms0bil_247_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dtms0bil_247_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.", "links": [ { diff --git a/datasets/dynamics-of-insect-natural-enemies-of-bark-beetles_1.0.json b/datasets/dynamics-of-insect-natural-enemies-of-bark-beetles_1.0.json index f53c2edfc6..0587104790 100644 --- a/datasets/dynamics-of-insect-natural-enemies-of-bark-beetles_1.0.json +++ b/datasets/dynamics-of-insect-natural-enemies-of-bark-beetles_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "dynamics-of-insect-natural-enemies-of-bark-beetles_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse. Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above. The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting.", "links": [ { diff --git a/datasets/e1c0c34e0cc942898b3626efd1dcc095_NA.json b/datasets/e1c0c34e0cc942898b3626efd1dcc095_NA.json index e8355a0203..62960d60fc 100644 --- a/datasets/e1c0c34e0cc942898b3626efd1dcc095_NA.json +++ b/datasets/e1c0c34e0cc942898b3626efd1dcc095_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e1c0c34e0cc942898b3626efd1dcc095_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/e3dbdc32f7b6476e949d52d8d3990205_NA.json b/datasets/e3dbdc32f7b6476e949d52d8d3990205_NA.json index 74492c2767..1de0e083cf 100644 --- a/datasets/e3dbdc32f7b6476e949d52d8d3990205_NA.json +++ b/datasets/e3dbdc32f7b6476e949d52d8d3990205_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e3dbdc32f7b6476e949d52d8d3990205_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Zachariae glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/e43aead9947549078c2d108b2c3632b2_NA.json b/datasets/e43aead9947549078c2d108b2c3632b2_NA.json index af13079278..0e58125951 100644 --- a/datasets/e43aead9947549078c2d108b2c3632b2_NA.json +++ b/datasets/e43aead9947549078c2d108b2c3632b2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e43aead9947549078c2d108b2c3632b2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.3 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/e493802d83c846c8b76f817866fb74cc_NA.json b/datasets/e493802d83c846c8b76f817866fb74cc_NA.json index 7690c677e5..8f75df064e 100644 --- a/datasets/e493802d83c846c8b76f817866fb74cc_NA.json +++ b/datasets/e493802d83c846c8b76f817866fb74cc_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e493802d83c846c8b76f817866fb74cc_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.", "links": [ { diff --git a/datasets/e4f39152bc50466f8887bd2a343cac93_NA.json b/datasets/e4f39152bc50466f8887bd2a343cac93_NA.json index b1ee217c7f..fc051e8dfc 100644 --- a/datasets/e4f39152bc50466f8887bd2a343cac93_NA.json +++ b/datasets/e4f39152bc50466f8887bd2a343cac93_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e4f39152bc50466f8887bd2a343cac93_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ice velocities for the Greenland Northern Drainage Basin for winter 1991-1992, which have been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data has been derived from intensity-tracking of ERS-1 Ice phase (3 days repeat) data aquired between 29th December 1991 and 22nd March 1992.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevationmodel, is also provided. (Please note that in earlier versions of this product the horizontal velocities were provided as true East and North velocities). Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use this later v1.1 product.", "links": [ { diff --git a/datasets/e61704b00267405082fbd41bb710dd74_NA.json b/datasets/e61704b00267405082fbd41bb710dd74_NA.json index d8f5666f4d..efe7a7bf92 100644 --- a/datasets/e61704b00267405082fbd41bb710dd74_NA.json +++ b/datasets/e61704b00267405082fbd41bb710dd74_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e61704b00267405082fbd41bb710dd74_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.", "links": [ { diff --git a/datasets/e670dada-89ec-45ee-a24f-06026dd9794b.json b/datasets/e670dada-89ec-45ee-a24f-06026dd9794b.json index d7d725995a..3cae3df8b4 100644 --- a/datasets/e670dada-89ec-45ee-a24f-06026dd9794b.json +++ b/datasets/e670dada-89ec-45ee-a24f-06026dd9794b.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e670dada-89ec-45ee-a24f-06026dd9794b", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 100 years return period. It was generated by integration of the intensity values contained in the files \"Wind_Atlantic.AME\", \"Wind_EastPacific.AME\", \"Wind_NorthIndian.AME\", \"Wind_SudIndian.AME\", \"Wind_SudPacific.AME\" and \"Wind_WestPacific.AME\".", "links": [ { diff --git a/datasets/e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA.json b/datasets/e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA.json index 2dbc1968c0..90db80f54e 100644 --- a/datasets/e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA.json +++ b/datasets/e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps.", "links": [ { diff --git a/datasets/e7fa45e785a64481960c3b140038c948_NA.json b/datasets/e7fa45e785a64481960c3b140038c948_NA.json index 311ac7b081..45e4a0d6f4 100644 --- a/datasets/e7fa45e785a64481960c3b140038c948_NA.json +++ b/datasets/e7fa45e785a64481960c3b140038c948_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e7fa45e785a64481960c3b140038c948_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the Hagen Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-30 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "links": [ { diff --git a/datasets/e80f28ccb0504c32b403eee654a8a5b3_NA.json b/datasets/e80f28ccb0504c32b403eee654a8a5b3_NA.json index 3ef862c802..0e1b4bb95d 100644 --- a/datasets/e80f28ccb0504c32b403eee654a8a5b3_NA.json +++ b/datasets/e80f28ccb0504c32b403eee654a8a5b3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "e80f28ccb0504c32b403eee654a8a5b3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of time series of surface reflectance from the MERIS instrument on the ENVISAT satellite, produced as part of the ESA Land Cover Climate Change Initiative (CCI) project. The time series are a temporal syntheses obtained over a 7-day compositing period, and encompass 13 of the 15 MERIS spectral channels (not including bands 11 and 15). The spatial resolution is 300m for the Full Resolution (FR) data and 1000m for the Reduced Resolution (RR) data.Given the amount and size of the MERIS surface reflectance archive (10 To), the Land Cover CCI team make the data available on request, through your own disks. Please contact contact@esa-landcover-cci.org", "links": [ { diff --git a/datasets/eMASL1B_1.json b/datasets/eMASL1B_1.json index befd2903a3..0778773b38 100644 --- a/datasets/eMASL1B_1.json +++ b/datasets/eMASL1B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eMASL1B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. Prior to 1995, the MAS was deployed on the NASA's ER-2 and C-130 aircraft platforms using a 12-channel, 8-bit data system that somewhat constrained the full benefit of having a 50-channel scanning spectrometer. Beginning in January 1995, a 50-channel, 16-bit digitizer was used on the ER-2 platform, which greatly enhanced the capability of MAS to simulate MODIS data over a wide range of environmental conditions. Recently, it has undergone extensive upgrades to the optics and other components. New detectors have been installed and the spectral bands have been streamlined. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um.\r\n\r\nFor more information and for a list of MAS campaign flights visit ladsweb at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/\r\n\r\nor, visit the eMAS Homepage at:\r\n\r\nhttps://asapdata.arc.nasa.gov/emas/", "links": [ { diff --git a/datasets/eMASL2AER_1.json b/datasets/eMASL2AER_1.json index 2293be8499..18cc3346d3 100644 --- a/datasets/eMASL2AER_1.json +++ b/datasets/eMASL2AER_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eMASL2AER_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um.\r\n\r\nThe Enhanced MODIS Airborne Simulator (eMAS) L2 Aerosol Data product (eMASL2AER) consists of in-situ measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, follow plumes downwind to understand chemical transformation and air quality impacts, and assess the efficacy of satellite detections for estimating the emissions from sampled fires. These measurements were collected onboard the DC-8 aircraft during FIREX-AQ, during summer 2019. The DC-8 aircraft had a comprehensive instrument payload capable of measuring over 200 trace gases as well as aerosol microphysical, optical, and chemical properties. The eMASL2AER product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file.\r\n\r\nFor more information and for a list of MAS campaign flights visit ladsweb at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/\r\n\r\nor, visit the eMAS Homepage at:\r\n\r\nhttps://asapdata.arc.nasa.gov/emas/", "links": [ { diff --git a/datasets/eMASL2CLD_1.json b/datasets/eMASL2CLD_1.json index 3d2fcf5911..a90e2640f8 100644 --- a/datasets/eMASL2CLD_1.json +++ b/datasets/eMASL2CLD_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eMASL2CLD_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um.\r\n\r\nThe Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data product (eMASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances. Multispectral images of the reflectance and brightness temperature at 10 wavelengths between 0.66 and 13.98nm were used to derive the probability of clear sky (or cloud), cloud thermodynamic phase, and the optical thickness and effective radius of liquid water and ice clouds. The eMASL2CLD product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file.\r\n\r\n\r\n\r\nFor more information and for a list of MAS campaign flights visit ladsweb at:\r\n\r\nhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/\r\n\r\nor, visit the eMAS Homepage at:\r\n\r\nhttps://asapdata.arc.nasa.gov/emas/", "links": [ { diff --git a/datasets/eMODIS1.json b/datasets/eMODIS1.json index 3dd87b6f1a..0db8ede16f 100644 --- a/datasets/eMODIS1.json +++ b/datasets/eMODIS1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eMODIS1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "eMODIS products respond to specific land remote sensing community need for the benefits of MODIS data without the standard NASA packaging. eMODIS offers regionally based NDVI products in near-real time and historically using GeoTIFF format, non-sinusoidal map projections, and variable compositing periods. eMODIS currently (2011) produces NDVI over the continental U.S., Alaska, Central America/Caribbean, Africa and Madagascar, and Central Asia. Additional geographic and geophysical options are planned based on user requirements.", "links": [ { diff --git a/datasets/ea7a4cbe7b83450bb7a00bf3761c40d7_NA.json b/datasets/ea7a4cbe7b83450bb7a00bf3761c40d7_NA.json index d8a1c8667a..a3ba0d6bd5 100644 --- a/datasets/ea7a4cbe7b83450bb7a00bf3761c40d7_NA.json +++ b/datasets/ea7a4cbe7b83450bb7a00bf3761c40d7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ea7a4cbe7b83450bb7a00bf3761c40d7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 and Sentinel-1 SAR (Synthetic Aperture Radar) interferometry. This version of the dataset (v1.3) has been extended with grounding lines for 2017. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. The grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.Remote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone).", "links": [ { diff --git a/datasets/eacb7580e1b54afeaabb0fd2b0a53828_NA.json b/datasets/eacb7580e1b54afeaabb0fd2b0a53828_NA.json index 5c3e947d6b..aeef74eeb7 100644 --- a/datasets/eacb7580e1b54afeaabb0fd2b0a53828_NA.json +++ b/datasets/eacb7580e1b54afeaabb0fd2b0a53828_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eacb7580e1b54afeaabb0fd2b0a53828_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset contains Sea Surface Salinity (SSS) v2.31 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "links": [ { diff --git a/datasets/eaed9fba86c44e9c854dfbdec9d16b99_NA.json b/datasets/eaed9fba86c44e9c854dfbdec9d16b99_NA.json index 68a3b57489..d62fe012ef 100644 --- a/datasets/eaed9fba86c44e9c854dfbdec9d16b99_NA.json +++ b/datasets/eaed9fba86c44e9c854dfbdec9d16b99_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eaed9fba86c44e9c854dfbdec9d16b99_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2017-2018, derived from Sentinel-1 SAR data acquired from 28/12/2017 to 28/02/2018, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. In total approximately 1900 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz),derived from a digital elevation model, is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "links": [ { diff --git a/datasets/early_iceberg_obs_1.json b/datasets/early_iceberg_obs_1.json index 54358c89dc..6baecfdd7a 100644 --- a/datasets/early_iceberg_obs_1.json +++ b/datasets/early_iceberg_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "early_iceberg_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Between 1954 and 1975, iceberg observations were collected on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis as they travelled to and from Antarctica. No fixed format for data collection had been determined, and many of the observations recorded are in diary format.\n\nThe data have not been converted to electronic form, and are available only in the original logbooks held at the National Archives Office.", "links": [ { diff --git a/datasets/echidna_1045_1.json b/datasets/echidna_1045_1.json index d57b8b071c..d40c20df14 100644 --- a/datasets/echidna_1045_1.json +++ b/datasets/echidna_1045_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "echidna_1045_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains forest canopy scan data from the Echidna? Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.", "links": [ { diff --git a/datasets/ecmwf2_523_1.json b/datasets/ecmwf2_523_1.json index ee90746590..3a37ae4acb 100644 --- a/datasets/ecmwf2_523_1.json +++ b/datasets/ecmwf2_523_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecmwf2_523_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly data from the ECMWF operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface, at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.", "links": [ { diff --git a/datasets/ecmwf_met_1deg_1222_1.json b/datasets/ecmwf_met_1deg_1222_1.json index 75b79d88e0..157aadb518 100644 --- a/datasets/ecmwf_met_1deg_1222_1.json +++ b/datasets/ecmwf_met_1deg_1222_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecmwf_met_1deg_1222_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set for the ISLSCP Initiative II data collection provides meteorology data with fixed, monthly, monthly-6-hourly, 6-hourly, and 3-hourly temporal resolutions. The data were derived from the European Centre for Medium-range Weather Forecasts (ECMWF) near-surface meteorology data set, 40-year re-analysis, or ERA-40 (Simmons and Gibson, 2000), which covers the years 1957 to 2001. The data were processed onto the ISLSCP II Earth grid with a spatial resolution of 1-degree in both latitude and longitude, and span the common ISLSCP II period from 1986 to 1995.The ECMWF forecast system is called the Integrated Forecasting System (IFS) and was developed in co-operation with Meteo-France. For ERA40 it is used with 60 levels from the top of the model at 10 Pa to the lowest level at about 10 m above the surface. There are 46 compressed (.tar.gz) data files with this data set. Each uncompressed file contains space-delimited text (.asc) data files.", "links": [ { diff --git a/datasets/ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0.json b/datasets/ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0.json index d4ac853fa1..fbe5eda32e 100644 --- a/datasets/ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0.json +++ b/datasets/ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site. Data are provided on request to contact person against bilateral agreement.", "links": [ { diff --git a/datasets/ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0.json b/datasets/ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0.json index 7ad43fc6ad..c1f4383dd8 100644 --- a/datasets/ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0.json +++ b/datasets/ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. __Paper Citation:__ > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Sch\u00fctz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684. [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4). Please cite this paper together with the citation for the datafile. #Methods ##Study sites The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park\u2019s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park\u2019s foundation (14th century to 1914) (Sch\u00fctz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Sch\u00fctz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Sch\u00fctz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes. ##Experimental design To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them \u201cL/M/S/I\u201d, \u201cM/S/I\u201d, \u201cS/I\u201d, \u201cI\u201d, \u201cNone\u201d; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP. Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below). The \u201cL/M/S/I\u201d plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the \u201cM/S/I\u201d plots were unfenced, allowing access to all but the large mammals; 2) the \u201cS/I\u201d plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the \u201cI\u201d plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the \u201cNone\u201d plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots. To assess whether the design of the \u201cNone\u201d exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six \u201cmicro-climate control\u201d exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the \u201cNone\u201d exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the \u201cNone\u201d plots, but also a comparable feeding pressure by invertebrates to the \u201cI\u201d plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the \u201cNone\u201d plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the \u201cI\u201d and the \u201cNone\u201d treatments were not due to the construction of the \u201cNone\u201d exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013). ##Aboveground invertebrate sampling Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the \u201cNone\u201d plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018). We sorted the \u2248100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach. ##Sampling of plant properties The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65\u00b0C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, F\u00e4llanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 \u00b0C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 \u00b0C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study. ##Sampling of edaphic communities In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter \u00d7 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4\u00b0C. Again, we only used the 2013 data in this study. Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341]. We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 \u2013 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses. ##Sampling of soil properties We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 \u00b0C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min. Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenm\u00fchle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 \u00b0C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 \u00b5m) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 \u00b5m cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015). Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged. Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season. ##Numeral calculations and statistical analyses Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents. We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2 \uf0b4 5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the \u201cNone\u201d plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots. \tAverage ecosystem coupling was calculated as follows: Ecosystem coupling= where Xi is the absolute Coupling was calculated value of the Spearman\u2019s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80; interactions (56 in the case of the \u201cNone\u201d treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the \u201cNone\u201d treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the \u201cNone\u201d treatment).\tCoupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the \u201cNone\u201d treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the \u201cNone\u201d treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the \u201cNone\u201d treatment). To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations. We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality. We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. 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Nat Ecol Evol 2:427\u201336. https://doi.org/10.1038/s41559-017-0461-7 - Meola M, Lazzaro A, Zeyer J. 2014. Diversity, resistance and resilience of the bacterial communities at two alpine glacier forefields after a reciprocal soil transplantation. Environ Microbiol 16:1918\u201334. https://onlinelibrary.wiley.com/doi/abs/10.1111/1462-2920.12435 - Meyer ST, Koch C, Weisser WW. 2015. Towards a standardized Rapid Ecosystem Function Assessment (REFA). Trends Ecol Evol 30:390\u20137. http://www.sciencedirect.com/science/article/pii/S0169534715000968 - Norman R., Stucki JW. 1981. The determination of nitrate and nitrite in soil extracts by ultraviolet spectrophotometry. Soil Sci Soc Am J 45:347\u201353. - Ochoa-Hueso R. 2016. Non-linear disruption of ecological interactions in response to nitrogen deposition. Ecology 87:2802\u20132814. - Oostenbrink M. 1960. Estimating nematode populations by some selected methods. In: Sasser NJ, Jenkins WR, editors. Nematology. Chapel Hill, NC, USA: University of North Carolina Press. pp 85\u2013101. - R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing - Risch AC, Haynes AG, Busse MD, Filli F, Sch\u00fctz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192\u2013202. - Risch AC, Sch\u00fctz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312\u201322. - Sch\u00fctz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624\u2013633. - Sch\u00fctz M, Risch AC, Leuzinger E, Kr\u00fcsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177\u201388. - Vandegehuchte ML, van der Putten WH, Duyts H, Sch\u00fctz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speci\ufb01c. Oikos 126:212\u201323. - Vandegehuchte ML, Raschein U, Sch\u00fctz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679. - Vandegehuchte ML, Sch\u00fctz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434\u201346. - Vandegehuchte ML, Trivellone V, Sch\u00fctz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545\u201355. - Wirthner S, Frey B, Busse MD, Sch\u00fctz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296\u2013302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003 - Zumsteg A, Luster J, G\u00f6ransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552\u201364. https://doi.org/10.1007/s00248-011-9991-8", "links": [ { diff --git a/datasets/ecosystem_roots_1deg_929_1.json b/datasets/ecosystem_roots_1deg_929_1.json index 40a3349b12..415a87259b 100644 --- a/datasets/ecosystem_roots_1deg_929_1.json +++ b/datasets/ecosystem_roots_1deg_929_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecosystem_roots_1deg_929_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths.The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells. Related data sets: The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003).This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.", "links": [ { diff --git a/datasets/ecousm1.json b/datasets/ecousm1.json index 958709ac0b..d52424c876 100644 --- a/datasets/ecousm1.json +++ b/datasets/ecousm1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ecousm1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The major objectives of this project are as follows:\n\n1. To determine the composition and distribution of algal flora from a wide\nrange of habitats, which provide a conductive niche for algal population in\nAntarctica.\n2. To compare the Antarctic and tropical algal flora, in order to determine the\ndegree of species endemism based on evolutionary process.\n3. To study the important role of habitat specificity in determining the\ncomposition of diatom assemblages.\n4. To test the utility and suitability of diatom community structure as\nindicators of environmental stress.\n \nThis is done by: 1. Conducting an ecological survey of microalgal distribution\nat Australian Antarctic station sites by looking into several types of habitat.\n2. Identifying the microalgae samples collected based on morphology using light\nmicroscopy and SEM. 3. Comparing the algae community, structure and\ndistribution from the tropics. \n\nThe principal milestones of the project are as follows: 1. Information of\nmicroalgal distribution at several sites in Antarctica. 2. Collection of\nmicroalgae cultures. 3. Completion of identification of Antarctic microalgae. \nIn collaboration with the Australian Antarctic Division (AAD) we have gone on\nan expeditions to Australian Antarctic Station of Casey and Davis. Collection\nof samples was made from various sources such as water, snow and soil and we\nhave established a list of microalgae species in our collection. Comparative\nstudies on the species diversity and distribution with tropical microalgae\ncommunities are being conducted. Physiological studies are currently in\nprogress.", "links": [ { diff --git a/datasets/ect-and-rb-data-switzerland_1.0.json b/datasets/ect-and-rb-data-switzerland_1.0.json index dbe2ec720d..49de071d82 100644 --- a/datasets/ect-and-rb-data-switzerland_1.0.json +++ b/datasets/ect-and-rb-data-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ect-and-rb-data-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the data used in the publication \"On snow stability interpretation of Extended Column Test results\" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.", "links": [ { diff --git a/datasets/edc_landcover_xdeg_930_1.json b/datasets/edc_landcover_xdeg_930_1.json index e276e0cf15..b5491ef7f2 100644 --- a/datasets/edc_landcover_xdeg_930_1.json +++ b/datasets/edc_landcover_xdeg_930_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "edc_landcover_xdeg_930_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland and Belward 1997) and the 15 classes of the SiB model processed at the USGS EROS Data Center (EDC). Specifically, the resampled DISCover datasets were derived from the 1km DISCover data set compiled by the USGS. The 1km data sets for each classification scheme were aggregated to 1, 0.5 and 0.25 degree spatial resolutions for this ISLSCP II data collection. Each layer of the aggregated products corresponds to a single DISCover land cover category and the values represent the percentage of the coarse resolution cell (1 degree, etc...)occupied by that land cover category. The dominant class data show the land cover category that occupies the majority of the cell and is derived from the percentage files for each cover type. The objective of this study was to create a land cover map derived from 1 kilometer AVHRR data using a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. During this re-processing, the original EDC land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by global modelers and others. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.", "links": [ { diff --git a/datasets/edgar_atmos_emissions_1deg_1022_1.json b/datasets/edgar_atmos_emissions_1deg_1022_1.json index 6ddcfd50e8..374370728b 100644 --- a/datasets/edgar_atmos_emissions_1deg_1022_1.json +++ b/datasets/edgar_atmos_emissions_1deg_1022_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "edgar_atmos_emissions_1deg_1022_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 *.zip data files with this data set.", "links": [ { diff --git a/datasets/edna-fjord-svalbard-fish-plankton_1.0.json b/datasets/edna-fjord-svalbard-fish-plankton_1.0.json index e9e6d9eb90..8b53f74bab 100644 --- a/datasets/edna-fjord-svalbard-fish-plankton_1.0.json +++ b/datasets/edna-fjord-svalbard-fish-plankton_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "edna-fjord-svalbard-fish-plankton_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023). # Methods **Sampling** We sampled the Lillieh\u00f6\u00f6k fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lillieh\u00f6\u00f6k fjord merged with the mouth of M\u00f6ller fjord. The fjord\u2019s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-\u00b5m pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves. **Molecular** To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC). # Data content: + Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study. + Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder. # Additional informations For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.", "links": [ { diff --git a/datasets/edward_viii_sat_1.json b/datasets/edward_viii_sat_1.json index 07f8827693..e73f4d5ba1 100644 --- a/datasets/edward_viii_sat_1.json +++ b/datasets/edward_viii_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "edward_viii_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Edward VIII Gulf, Kemp Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1993. The map is at a scale of 1:100000, and was produced from a Landsat TM (WRS 139-107) scene (bands 2,3 and 4). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and penguin colonies, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/eemma_1.0.json b/datasets/eemma_1.0.json index 4fc1891cee..aa6279837a 100644 --- a/datasets/eemma_1.0.json +++ b/datasets/eemma_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eemma_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series. A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023. The user's guide is available for public use under Creative Commons CC-BY-SA. Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/", "links": [ { diff --git a/datasets/ef1627f523764eae8bbb6b81bf1f7a0a_NA.json b/datasets/ef1627f523764eae8bbb6b81bf1f7a0a_NA.json index c07f2ffe2b..71249d4af7 100644 --- a/datasets/ef1627f523764eae8bbb6b81bf1f7a0a_NA.json +++ b/datasets/ef1627f523764eae8bbb6b81bf1f7a0a_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ef1627f523764eae8bbb6b81bf1f7a0a_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\u00e2\u0080\u00a2\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\u00e2\u0080\u00a2\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\u00e2\u0080\u00a2\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \u00e2\u0080\u00a2\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \u00e2\u0080\u00a2\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.", "links": [ { diff --git a/datasets/ef5c6596cae548c6aea9dea181c7624c_NA.json b/datasets/ef5c6596cae548c6aea9dea181c7624c_NA.json index 533b47a317..5e6f31886b 100644 --- a/datasets/ef5c6596cae548c6aea9dea181c7624c_NA.json +++ b/datasets/ef5c6596cae548c6aea9dea181c7624c_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ef5c6596cae548c6aea9dea181c7624c_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "links": [ { diff --git a/datasets/ef6a9266-a210-4431-a4af-06cec4274726_NA.json b/datasets/ef6a9266-a210-4431-a4af-06cec4274726_NA.json index 77cd32d87f..379a999744 100644 --- a/datasets/ef6a9266-a210-4431-a4af-06cec4274726_NA.json +++ b/datasets/ef6a9266-a210-4431-a4af-06cec4274726_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ef6a9266-a210-4431-a4af-06cec4274726_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data.", "links": [ { diff --git a/datasets/effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0.json b/datasets/effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0.json index bfe7c619f9..5efe38af41 100644 --- a/datasets/effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0.json +++ b/datasets/effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this. The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions. We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy. This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example. Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.", "links": [ { diff --git a/datasets/effects-of-canopy-disturbance-on-swiss-forests_1.0.json b/datasets/effects-of-canopy-disturbance-on-swiss-forests_1.0.json index 5c6f32a987..f16e66c91b 100644 --- a/datasets/effects-of-canopy-disturbance-on-swiss-forests_1.0.json +++ b/datasets/effects-of-canopy-disturbance-on-swiss-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "effects-of-canopy-disturbance-on-swiss-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files refer to the data used in Scherrer et al. (2021) \"Canopy disturbances catalyse tree species shifts in Swiss forests\" in _Ecosystems_. The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory. In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication. For more detailed information about the data files please check the ReadMe.docx file.", "links": [ { diff --git a/datasets/elev_arc_250_1.json b/datasets/elev_arc_250_1.json index 8a02d819b7..9a9d438d32 100644 --- a/datasets/elev_arc_250_1.json +++ b/datasets/elev_arc_250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "elev_arc_250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Elevation contours over the NSA and SSA in ARC/Info Generate Format. Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.", "links": [ { diff --git a/datasets/elevation-profiler-first-release_1.0.json b/datasets/elevation-profiler-first-release_1.0.json index 93b4f202a4..4beaa3a309 100644 --- a/datasets/elevation-profiler-first-release_1.0.json +++ b/datasets/elevation-profiler-first-release_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "elevation-profiler-first-release_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Elevation profiler (see Krebs et al. 2015) is an open source GIS tool designed to work with ArcGIS that automatically calculates transverse or longitudinal elevation profiles of different lengths starting from a digital elevation model (e.g. high resolution Lidar DEM) and a shapefile of points (i.e. the midpoints of the profile segments). The calculated profiles are then saved in comma-separated tabular data files (.csv). GNU General Public License v2.0 only", "links": [ { diff --git a/datasets/elk-and-bison-carcasses-in-yellowstone-usa_1.0.json b/datasets/elk-and-bison-carcasses-in-yellowstone-usa_1.0.json index 26964ccfe8..cca6d4ceb6 100644 --- a/datasets/elk-and-bison-carcasses-in-yellowstone-usa_1.0.json +++ b/datasets/elk-and-bison-carcasses-in-yellowstone-usa_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "elk-and-bison-carcasses-in-yellowstone-usa_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. Paper Citation: Risch, A.C., Frossard, A., Sch\u00fctz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology doi: ... Methods Study area and study sites This study was conducted in YNP\u2019s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163\u00b0 N, 110.4169\u00b0 W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995. For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951). The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter \u201cdays since kill\u201d, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP\u2019s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter \u201ccontrol\u201d) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter \u201ccarcass\u201d). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009). Ecosystem functions and soil properties We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 \u00baC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5\u2009g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3\u2013V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively. Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, L\u00fcscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight). The other half of the soil sample was bagged in paper, dried to constant weight at 60\u00b0C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60\u00b0C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 \u2013 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s. In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18\u2019\u2019, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20\u00b0C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (\uf05bCTScontrol - CTSfinal\uf05d/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties. Statistical analyses Univariate analyses for ecosystem functions, soil biotic and abiotic properties We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (\u201cLocation\u201d), bison and elk (\u201cSpecies\u201d) and days since kill (\u201cDSK\u201d). For this purpose, we used linear mixed effect models (LMM, \u201cnlme\u201d package v 3.1 \u2013 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the \u201clsmeans\u201d package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk. We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran\u2019s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the \u201cmoran.test\u201d function in the \u201cspdep\u201d package version 1.1-3 (Bivand et al., 2019). Multivariate analyses Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray\u2013Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (\u201cvegan\u201d package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (\u201cnlme\u201d package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the \u201clsmeans\u201d package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the \u201cenvfit\u201d function in the \u201cvegan\u201d package (Oksanen et al., 2019). Indicator species analyses were performed using the multipatt function implemented in the \u201cindicspecies\u201d package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De C\u00e1ceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017). References Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., \u2026 K\u00f5ljalg, U. (2010). The UNITE database for molecular identification of fungi \u2013 recent updates and future perspectives. New Phytologist, 186(2), 281\u2013285. doi:10.1111/j.1469-8137.2009.03160.x Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403\u20131414. doi:10.1111/1755-0998.12399 Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., \u2026 Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914\u2013919. doi:10.1111/2041-210X.12073 Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., \u2026 Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3. Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159\u20133167. Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996\u20131007. doi:10.1007/s10021-009-9274-0 Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392. De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6. De C\u00e1ceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674\u20131684. doi:10.1111/j.1600-0706.2010.18334.x Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996. Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476\u20133482. doi:10.1093/bioinformatics/btv401 Frey, B., Niklaus, P. A., Kremer, J., L\u00fcscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060\u20136068. doi:10.1128/AEM.05206-11 Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018. Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., B\u00e5\u00e5th, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191\u2013199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028 Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources. Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155\u2013e155. doi:10.1093/nar/gkq545 Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145\u2013155. Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7\u20139). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK. Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10\u201312. Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources. Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Bia\u0142owie\u017ca Primeval Forest, Poland. Ecological Research, 22(5), 807\u2013813. doi:10.1007/s11284-006-0321-4 Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7 Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., \u2026 Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4. Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., \u2026 Gl\u00f6ckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590\u2013D596. doi:10.1093/nar/gks1219 Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57\u201362. R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland. R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091\u20131108. doi:10.1111/mec.13051 Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1\u20132), 191\u2013201. doi:10.1007/s11104-007-9354-x Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., \u2026 Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537\u20137541. doi:10.1128/AEM.01541-09 Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., \u2026 Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources. Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261\u20135267. doi:10.1128/AEM.00062-07 Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68\u201375. Please cite this paper together with the citation for the datafile.", "links": [ { diff --git a/datasets/em_database_1.json b/datasets/em_database_1.json index c2ee0341fb..dc893905cc 100644 --- a/datasets/em_database_1.json +++ b/datasets/em_database_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "em_database_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database contains information pertaining to the negatives taken by the laboratory since its inception. Both scanning and transmission electron micrographs are catalogued within this database.\n\nAmong other things, the database includes a large number of images of protists.\n\nThe URLs provided link to a marine specimens database, and a terrestrial and limnetic specimens database.", "links": [ { diff --git a/datasets/emergence-dynamics-of-natural-enemies_1.0.json b/datasets/emergence-dynamics-of-natural-enemies_1.0.json index d5b611e799..d0926e1656 100644 --- a/datasets/emergence-dynamics-of-natural-enemies_1.0.json +++ b/datasets/emergence-dynamics-of-natural-enemies_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "emergence-dynamics-of-natural-enemies_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.", "links": [ { diff --git a/datasets/enderby_flight_logs_1977_1.json b/datasets/enderby_flight_logs_1977_1.json index fdf3ff90d5..271d997ec1 100644 --- a/datasets/enderby_flight_logs_1977_1.json +++ b/datasets/enderby_flight_logs_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "enderby_flight_logs_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of flights over Enderby Land were carried out in January 1977 with an airborne ice radar. The flight notes on navigation, radar settings and other notes from the flight have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/enderby_flight_logs_1979_1.json b/datasets/enderby_flight_logs_1979_1.json index ec5cd4caff..03629a0f0c 100644 --- a/datasets/enderby_flight_logs_1979_1.json +++ b/datasets/enderby_flight_logs_1979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "enderby_flight_logs_1979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of flights over Enderby Land were carried out in December 1979 and January 1980 with an airborne ice radar. The flight notes on navigation, radar settings and other notes from the flight have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/enderby_land_grav_snow_1975_1.json b/datasets/enderby_land_grav_snow_1975_1.json index 012e9d33b6..fd166fb8e3 100644 --- a/datasets/enderby_land_grav_snow_1975_1.json +++ b/datasets/enderby_land_grav_snow_1975_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "enderby_land_grav_snow_1975_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Logs recording gravity and snow accumulation in Enderby Land in 1975-76.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/enderby_land_logs_1.json b/datasets/enderby_land_logs_1.json index 2e31eb82e1..c9c89a9cf5 100644 --- a/datasets/enderby_land_logs_1.json +++ b/datasets/enderby_land_logs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "enderby_land_logs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log books from field wok carried out in Enderby Land between 1972 and 1980. Information recorded includes borehole temperatures, ice movement, gravity, ice radar notes, and barometric pressure.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/enderby_reports_1978_1.json b/datasets/enderby_reports_1978_1.json index 3dbee181b4..9a08c30afa 100644 --- a/datasets/enderby_reports_1978_1.json +++ b/datasets/enderby_reports_1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "enderby_reports_1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1977/78 ANARE carried out a summer operation that was a continuation of a multi-year project in the Enderby Land region, commenced in the 1974/75 season. Programs for 1977/78 included survey, high level photography, geochronolgy, structural geology, petrology, geophysics and glaciology. The programs were air supported from a field base near Mt. King (67 degrees 04'S, 52 degrees 52'E).\n\nPlanning and daily logbooks for the program, as well as the end of season report, have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/endsplit_1.0.json b/datasets/endsplit_1.0.json index 3843a01c9b..566418d024 100644 --- a/datasets/endsplit_1.0.json +++ b/datasets/endsplit_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "endsplit_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "R scripts and demonstration data for end-member mixing and splitting: using isotopes and other tracers to determine where streamflow comes from (end-member mixing) and where precipitation goes (end-member splitting). # This package includes two R scripts: # \"EndSplit_v1.0_20200516.R\" implements end-member mixing and splitting. \"EndSplit_demo_v1.0_20200516.R\" demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set (see below). Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner. Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/ But\u2026 READ THIS CAREFULLY: ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes. These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, \"Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis\", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020. Users publishing results based on these scripts should cite that paper. Build 2020.05.16 is a minor bug fix of build 2019.10.25, which was previously released as EndSplit_v1.0_20191025.R. # The zip file \"demonstration input data.zip\" contains 8 demonstration data files (all tab-delimited text): # \"Hubbard Brook WS3 isotope data split by sampling date.txt\" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019). \"Hubbard Brook WS3 daily P and Q 1956-2014.txt\" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3. (USDA Forest Service Northern Research Station, 2016a and 2016b). \"Hubbard Brook WS3 isotope data WY2007.txt\", \"Hubbard Brook WS3 isotope data WY2008.txt\", \"Hubbard Brook WS3 isotope data WY2009.txt\", \"Hubbard Brook WS3 daily P and Q WY2007.txt\", \"Hubbard Brook WS3 daily P and Q WY2008.txt\", and \"Hubbard Brook WS3 daily P and Q WY2009.txt\" contain subsets of these data for the designated water years. As the work product of US federal employees, the data in these files are in the public domain. However, any users of these data should cite the original sources: Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019. USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a. USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b. # The zip file \"demonstration output files.zip\" contains demonstration output files # These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in \"demonstration input data.zip\"", "links": [ { diff --git a/datasets/energy-cooperatives-in-switzerland-survey-results_1.0.json b/datasets/energy-cooperatives-in-switzerland-survey-results_1.0.json index 38d7ec1b31..2481c363fa 100644 --- a/datasets/energy-cooperatives-in-switzerland-survey-results_1.0.json +++ b/datasets/energy-cooperatives-in-switzerland-survey-results_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "energy-cooperatives-in-switzerland-survey-results_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## Topic of Survey The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project \"Collective financing of renewable energy projects in Switzerland and Germany\" of the National Research Programme 71 \"Managing Energy Consumption\". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future. ## Survey Method The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software \"Sawtooth\". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by \"missing data\". # Anonymization Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives. # How to Use the Data * The data are available in CSV and SPSS (sav.) format. * A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file. # Current Embargo on Data These data are currently under embargo and will only be released when the project is completed (not before 2020). #Additional Information * The used questionnaire is provided in German and French. * Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943", "links": [ { diff --git a/datasets/ensemble-hydrograph-separation_1.4.json b/datasets/ensemble-hydrograph-separation_1.4.json index a2fdb5f72f..816198d85d 100644 --- a/datasets/ensemble-hydrograph-separation_1.4.json +++ b/datasets/ensemble-hydrograph-separation_1.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ensemble-hydrograph-separation_1.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Calculation scripts that perform ensemble hydrograph separation. Identical scripts in R and MATLAB are provided, along with demonstration input time series and the corresponding outputs. These scripts were tested on R version 3.6.2 (2019-12-12) and on MATLAB versions 2018b and 2109b. These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/. ETH Zurich, WSL, James Kirchner, and Julia Knapp make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.", "links": [ { diff --git a/datasets/envidat-lwf-12_2019-03-06.json b/datasets/envidat-lwf-12_2019-03-06.json index 3f1268d21d..f734bfb235 100644 --- a/datasets/envidat-lwf-12_2019-03-06.json +++ b/datasets/envidat-lwf-12_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-12_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of air temperature, relative humidity, wind speed and direction, global radiation, photosynthetic active radiation, UVB radiation, and precipation in an open field very close to the LWF plot as well as air temperature, relative humidity, wind speed, photosynthetic active radiation, and precipitation in the forest below the canopy. ### Purpose: ### Recording meteorological conditions ### Manual Citation: ### * Martine Rebetez, Gustav Schneiter, 1997: Meteorologie. In: Brang P. (ed.) Aufnahmeanleitung LWF. Langfristige Wald\u00f6kosystem-Forschung LWF, 4 S. * Raspe S, Beuker E, Preuhsler T, Bastrup-Birk A, 2016: Part IX: Meteorological Measurements. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Martine Rebetez, Georg von Arx, Arthur Gessler, Elisabeth Graf Pannatier, John L. Innes, Peter Jakob, Mark\u00e9ta Jetel, Marlen Kube, Magdalena N\u00f6tzli, Marcus Schaub, Maria Schmitt, Flurin Sutter, Anne Thimonier, Peter Waldner, Matthias Haeni, 2018: Meteorological data series from Swiss long-term forest ecosystem research plots since 1997. Annals of Forests Science 75: 41: 1-7. [doi: 10.1007/s13595-018-0709-7](https://doi.org/10.1007/s13595-018-0709-7) * Haeni, Matthias; von Arx, Georg; Gessler, Arthur; Graf Pannatier, Elisabeth; Innes, John L; Jakob, Peter; Jetel, Mark\u00e9ta; Kube, Marlen; N\u00f6tzli, Magdalena; Schaub, Marcus; Schmitt, Maria; Sutter, Flurin; Thimonier, Anne; Waldner, Peter; Rebetez, Martine (2016): Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF) in Switzerland, from 1996-2016. PANGAEA, [doi: 10.1594/PANGAEA.868390](https://doi.org/10.1594/PANGAEA.868390) * Gustav Schneiter, Peter Jakob, Martine Rebetez, 2004: Sieben Jahre meteorologische Datenerfassung im Schweizer Wald. Infoblatt Forschungsbereich Wald, Vol 17: 4-6 [>>>](https://www.parcs.ch/snp/pdf_public/2011_schneiteretal_datenerf_wald_wsl_2004.pdf) * Jakob P, Sutter F, Waldner P, Schneiter G (2007) Processing remote gauging-data. In: Gomez J. M., Sonnenschein M., M\u00fcller M., Welsch H., Rautenstrauch C. (ed.) Information Technologies in Environmental Engineering ITEE 2007, Third International ICSC Symposium, Springer, Berlin, Heidelberg, 211-220.", "links": [ { diff --git a/datasets/envidat-lwf-15_2019-03-06.json b/datasets/envidat-lwf-15_2019-03-06.json index e6b1627773..cc95d25896 100644 --- a/datasets/envidat-lwf-15_2019-03-06.json +++ b/datasets/envidat-lwf-15_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-15_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Throughfall (precipitation under forest canopy) is a major pathway in forest nutrient cycling, and its quantification is necessary to establish both water and nutrient budgets. Furthermore, parallel sampling of throughfall and precipitation in the open field (bulk precipitation), together with assumptions about the canopy exchange processes (uptake and leaching of nutrients), allow the atmospheric deposition of nutrients and pollutants to be quantifed. Bulk precipitation and throughfall have been sampled since 1994 or later on 15 LWF plots using 3 (in the open) and 16 (in the forest) funnel-type precipitation collectors. These collectors are replaced by 1 (open area) and 4 (forest stand) snow buckets in winter on plots with abundant precipitation in the form of snow. The length of sampling intervals is usually 14 days. ### Purpose: ### To assess a major flux of the water and nutrient budget in forests, and to quantify the atmospheric deposition of nitrogen, sulphur and other nutrients. Atmospheric deposition is one of the key factors in the causal chain between emission of air pollutants and acidifying or eutrophying effects in forest ecosystems. ### Manual Citation: ### * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosph\u00e4rische Deposition: Freiland- und Bestandesniederschl\u00e4ge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48. * Clarke N, \u017dlindra D, Ulrich E, Mosello R, Derome J, Derome K, K\u00f6nig N, L\u00f6vblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 32 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Schmitt M, Waldner P, Rihm B (2005) Atmospheric deposition on Swiss Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and Assessment, 104: 81-118. [doi: 10.1007/s10661-005-1605-9](http://doi.org/10.1007/s10661-005-1605-9) * Thimonier A, Kosonen Z, Braun S, Rihm B, Schleppi P, Schmitt M, Seitler E, Waldner P, Th\u00f6ni L (2019) Total deposition of nitrogen in Swiss forests: Comparison of assessment methods and evaluation of changes over two decades. Atmospheric Environment, 198: 335-350. [doi: 10.1016/j.atmosenv.2018.10.051](http://doi.org/10.1016/j.atmosenv.2018.10.051)", "links": [ { diff --git a/datasets/envidat-lwf-16_2019-03-06.json b/datasets/envidat-lwf-16_2019-03-06.json index cc5dace782..4692e97eb7 100644 --- a/datasets/envidat-lwf-16_2019-03-06.json +++ b/datasets/envidat-lwf-16_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-16_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stemflow (portion of precipitation running down the branches and the trunk and depositing at the base of the tree) can represent a substantial fraction of the total water and nutrient input in stands of smoothbarked species with upright branches. Stemflow was measured with silicone gutters installed on the trunk of 5 trees at three LWF plots during 1-2 years. High capacity containers were used at Novaggio. An automated tipping bucket system, allowing continuous recording of volumes and sampling of representative proportional fraction, is currentlx used at the LWF sites Laegeren, Lausanne, Othmarsingen and Sch\u00e4nis. ### Purpose: ### To quantify the contribution of stemflow to the water and nutrient budget and to the atmospheric deposition in selected forests stands. ### Manual Citation: ### * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosph\u00e4rische Deposition: Freiland- und Bestandesniederschl\u00e4ge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48. * Clarke N, \u017dlindra D, Ulrich E, Mosello R, Derome J, Derome K, K\u00f6nig N, L\u00f6vblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 32 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)", "links": [ { diff --git a/datasets/envidat-lwf-17_2019-03-06.json b/datasets/envidat-lwf-17_2019-03-06.json index 436b8e99cd..a896ee57b6 100644 --- a/datasets/envidat-lwf-17_2019-03-06.json +++ b/datasets/envidat-lwf-17_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-17_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Litterfall is a key parameter in the biogeochemical cycle of forest ecosystems, linking the tree part to the soil compartment. Litterfall has been collected on 15 LWF plots using 10 traps that are emptied every 4 to 8 weeks since 1996 or later. Both the biomass of the litter and its chemical content (including heavy metals) are measured, in order to quantify the annual return of nutrients and organic matter to the soil. Furthermore, the analysis of the temporal pattern of litterfall production gives insight into possible effects of anthropogenic and natural factors (e.g. severe drought) on the ecosystem and the vitality of the forest stand, provides information on the phenological development of the stand, and, in particular, allows mast years to be identified. At 7 broadleaved sites, litterfall was also used to estimate the leaf area index (LAI) of the forest stand. ### Purpose: ### To quantify the annual return of nutrients and organic matter to the soil. ### Manual Citation: ### * Thimonier, A., Brang, P., Ottiger, A., 1997. Kapitel C5. Streufall, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-18. * Ukonmaanaho L., Pitman R, Bastrup-Birk A, Breda N, Rautio P, 2016: Part XIII: Sampling and Analysis of Litterfall. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute for Forests Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [doi: 10.1007/s10342-009-0353-8](http://doi.org/10.1007/s10342-009-0353-8 )", "links": [ { diff --git a/datasets/envidat-lwf-18_2019-03-06.json b/datasets/envidat-lwf-18_2019-03-06.json index 1ddc2baa08..238c30044d 100644 --- a/datasets/envidat-lwf-18_2019-03-06.json +++ b/datasets/envidat-lwf-18_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-18_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Foliage has been sampled every two years since 1995/1997 on 5-6 trees of the main species on all LWF plots. Concentrations of macronutrients (N, P, K, Ca, Mg, S), carbon (C) and micronutrients are determined on leaves and current and previous year needles. The dry mass of 100 leaves or 1000 needles is determined as well. ### Purpose: ### To assess the nutrient status of the forest stands and detect possible deficiencies or imbalances, which are often indicative of processes at the ecosystem level. ### Manual Citation: ### * Brang, P., Hug, C., Thimonier, A., Zehnder, U., 1997. Kapitel D1.5 N\u00e4hrstoffversorgung von Nadeln und Bl\u00e4ttern, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-12. * Rautio P, F\u00fcrst A, Stefan K, Raitio H, Bartels U, 2016: Part XII: Sampling and Analysis of Needles and Leaves. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 19 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Graf Pannatier E, Schmitt M, Waldner P, Walthert L, Schleppi P, Dobbertin M, Kr\u00e4uchi N (2010) Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites?. European Journal of Forest Research, 129 (3): 443-461. [doi: 10.1007/s10342-009-0328-9](http://doi.org/10.1007/s10342-009-0328-9)", "links": [ { diff --git a/datasets/envidat-lwf-19_2019-03-06.json b/datasets/envidat-lwf-19_2019-03-06.json index 1aab79eb3a..030f3bf044 100644 --- a/datasets/envidat-lwf-19_2019-03-06.json +++ b/datasets/envidat-lwf-19_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-19_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relev\u00e9s were repeatedly carried out at 17 LWF plots in the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). Phytosociological relev\u00e9s were carried out in one or two concentric circular plots of 30, 200, 400 and 500 m2. All species occurring on the whole area of the LWF plot were also noted during the first vegetation survey. ### Purpose: ### To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value. ### Manual Citation: ### * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9. * Canullo R, Starlinger F, Granke O, Fischer R, Aamlid D, 2016: Part VI.1: Assessment of Ground Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, 12 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)", "links": [ { diff --git a/datasets/envidat-lwf-20_2019-03-06.json b/datasets/envidat-lwf-20_2019-03-06.json index 1af7dbbe0e..343f2b2628 100644 --- a/datasets/envidat-lwf-20_2019-03-06.json +++ b/datasets/envidat-lwf-20_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-20_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relev\u00e9s were carried out repeatedly at 17 LWF plots during in the the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). The cover of all plant species occurring in 16 1-m2 quadrats, distributed over the 43 x 43 m intensive monitoring subplot was visually assessed. Seedlings and saplings were also counted and their position within the quadrat was noted in order to assess tree regeneration. ### Purpose: ### To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value. ### Manual Citation: ### * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9. * Canullo R, Starlinger F, Granke O, Fischer R, Aamlid D, 2016: Part VI.1: Assessment of Ground Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, 12 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)", "links": [ { diff --git a/datasets/envidat-lwf-21_2019-03-06.json b/datasets/envidat-lwf-21_2019-03-06.json index b7f535bb26..8da7a8e381 100644 --- a/datasets/envidat-lwf-21_2019-03-06.json +++ b/datasets/envidat-lwf-21_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-21_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf area index (LAI), defined as the total one-sided foliage area per unit ground surface area, is one of the most important characteristics of plant canopy structure. Leaves are the active interface between the atmosphere and the ecosystem. Thus, LAI affects many ecosystem processes, including light and precipitation interception, evapotranspiration, CO2 fluxes and dry deposition. LAI was measured repeatedly in the period 1996-2013 at 18 LWF plots using 1) a LAI-2000 plant canopy analyser (Licor, Inc) and 2) hemispherical photographs of the canopy. Measurements were performed above the 16 vegetation quadrats in the 43 m x 43 m intensive monitoring subplot. In 1996-2003, LAI measurements were usually carried out on the same day as the vegetation surveys. It is also planned to characterise the potential light conditions (diffuse and direct) using the hemispherical photographs of the canopy. ### Purpose: ### 1) To estimate an important structural parameter of the forest stand, which is needed as an input variable in most ecosystem process models simulating carbon and water cycles on a stand or regional scale; and 2) to document changes in the canopy structure, and thus in light conditions, which may be responsible for changes in ground vegetation ### Manual Citation: ### * Thimonier, A., 1997. Kapitel C6. Blattfl\u00e4chenindex (LAI), in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-5. * Thimonier, A., 1997. Kapitel C7. Lichtverh\u00e4ltnisse im Wald, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-4. * Fleck S, Raspe S, Cater M, Schleppi P, Ukonmaanaho L, Greve M, Hertel C, Weis W, Rumpf, S., Thimonier, A., Chianucci, F., Becksch\u00e4fer, P., 2016: Part XVII: Leaf Area Measurements. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 34 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](https://doi.org/10.1007/s10661-010-1759-y) * Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [10.1007/s10342-009-0353-8](https://doi.org/10.1007/s10342-009-0353-8)", "links": [ { diff --git a/datasets/envidat-lwf-22_2019-03-06.json b/datasets/envidat-lwf-22_2019-03-06.json index 26ce563c82..8d09cc835b 100644 --- a/datasets/envidat-lwf-22_2019-03-06.json +++ b/datasets/envidat-lwf-22_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-22_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NH₃ concentrations were measured at 11 LWF plots (1999/2000) with Z\u00fcrcher passive samplers (Palmes-type diffusion tubes with an acidic solution as absorbent) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NH₃ concentrations were measured again at 14 plots, using two Radiello samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy. ### Purpose: ### To assess air concentrations of ammonia (NH₃) and, using deposition velocities available from the literature, to quantify the dry deposition of NH₃ (alternative method to the throughfall method). The LWF plots were part of a larger network covering the main regions of Switzerland. One objective of this larger network was to compare measured and modelled concentrations.", "links": [ { diff --git a/datasets/envidat-lwf-23_2019-03-06.json b/datasets/envidat-lwf-23_2019-03-06.json index 6e9e65b6a0..804306567a 100644 --- a/datasets/envidat-lwf-23_2019-03-06.json +++ b/datasets/envidat-lwf-23_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-23_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NO₂ concentrations were measured at 11 LWF plots (1999/2000) with passive samplers (Palmes-type diffusion tubes) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NO2 concentrations were measured again at 14 plots, using two samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy. ### Purpose: ### To assess air concentrations of nitrogen dioxide (NO2) and, using deposition velocities available from the literature, to quantify the dry deposition of NO2 (alternative method to the throughfall method).", "links": [ { diff --git a/datasets/envidat-lwf-24_2019-03-06.json b/datasets/envidat-lwf-24_2019-03-06.json index aa126e7cd8..24e9f66f3e 100644 --- a/datasets/envidat-lwf-24_2019-03-06.json +++ b/datasets/envidat-lwf-24_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-24_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phenological observations are recorded every 14 days on LWF plots where throughfall and bulk precipitation are sampled. The percentage of foliage in reference to its maximum potential development in summer, the percentage of foliage with autumnal discoloration and the percentage of fallen leaves (broadleaved stands) are estimated at the plot level. At two LWF plots (Othmarsingen, Vordemwald), phenological stages are documented on individual trees ### Purpose: ### To document the seasonal development of the canopy of trees and shrubs at the plot level", "links": [ { diff --git a/datasets/envidat-lwf-25_2019-03-06.json b/datasets/envidat-lwf-25_2019-03-06.json index ed29f2e925..360b935548 100644 --- a/datasets/envidat-lwf-25_2019-03-06.json +++ b/datasets/envidat-lwf-25_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-25_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Description of several morphological soil properties at the beginning of the monitoring campaign. The properties were described for all genetic horizons in soil pits if possible down to the parent material. In heterogeneous LWF-plots, several soil profiles were described in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert. ### Purpose: ### Morphological soil properties are important for the calculation or interpretation of chemical or physical soil properties or processes. For instance, root distribution is an important input-parameter of water balance models or soil hydromorphy strongly affects the chemical status of soil matrix and soil solution. ### Manual Citation: ### * Walthert L, L\u00fcscher P, Luster J, Peter B (2002) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470) ### Paper Citation: ### * Walthert L, Blaser P, L\u00fcscher P, Luster J, Zimmermann S (2003) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.", "links": [ { diff --git a/datasets/envidat-lwf-26_2019-03-06.json b/datasets/envidat-lwf-26_2019-03-06.json index 0f6d759308..dd1cdb9b5f 100644 --- a/datasets/envidat-lwf-26_2019-03-06.json +++ b/datasets/envidat-lwf-26_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-26_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of several chemical soil parameters at the beginning of the monitoring campaign. Most parameters were determined accordung to the manual of ICP-Integrated-Monitoring. The parameters were analysed for all genetic horizons in soil pits and additionally for fixed layers in the Intensive-Monitoring-Plots. In heterogeneous LWF-plots, several soil pits were analysed in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert. ### Purpose: ### The chemical characterisation of soil matrix down to the paraent material is realised with data from soil pits. The monitoring of the soil matrix in a frequency of roundly 15 years is effected with soil samples from Intensiv-Monitoring-Plots. For soil monitoring, pooled samples with 16 replicats are used down to a depth of 80 cm. The date of the second soil survey is not yet fixed. ### Manual Citation: ### * Walthert L, L\u00fcscher P, Luster J, Peter B (2002) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470) ### Paper Citation: ### * Walthert L, Blaser P, L\u00fcscher P, Luster J, Zimmermann S (2003) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.", "links": [ { diff --git a/datasets/envidat-lwf-27_2019-03-06.json b/datasets/envidat-lwf-27_2019-03-06.json index ed6eec9b89..55591d05da 100644 --- a/datasets/envidat-lwf-27_2019-03-06.json +++ b/datasets/envidat-lwf-27_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-27_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurement of the soil water availability to plants at 10 LWF plots every 14 days in 5 soil depths (15, 30, 50, 80, 130 cm) with 8 replicates (usually in the IM plot). The range of measurement is from water saturation until-80 kPa. ### Purpose: ### The long-term measurement of the soil water availability to plants in the root zone provides useful information about the soil moisture conditions (drought, water saturation, water easily available to plants). The measurement of the soil water suction allows to calibrate the water balance models and to validate the modelled matric potential. ### Manual Citation: ### * Peter Waldisp\u00fchl, 1997: Installation von Tensiometern auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung LWF, Birmensdorf, 2 S. * Peter Waldisp\u00fchl, Andreas Rigling, 1997: Vorgehen bei der Ablesung von Teniometern auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 2 S. * Peter Waldisp\u00fchl, 2000: Kurzanleitung f\u00fcr die TensioDB. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 12 S. + DB-Schema ### Paper Citation: ### * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)", "links": [ { diff --git a/datasets/envidat-lwf-28_2019-03-06.json b/datasets/envidat-lwf-28_2019-03-06.json index 1951a6e3bf..60454ac596 100644 --- a/datasets/envidat-lwf-28_2019-03-06.json +++ b/datasets/envidat-lwf-28_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-28_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fortnightly measurement of the soil solution chemistry in 4 soil depths with zero-tension lysimeter below the litter layer and with tension lysimeters at depths of 15, 50 and 80 cm (8 replicates) ### Purpose: ### To characterize the chemical status of the soil solution and to detect trends in soil water quality. To assess the effects of air pollution and climate chnage on soil water quality. ### Manual Citation: ### * Micha Pluess, Daniel Christen, 1999: Kurzanleitung Bodenl\u00f6sung. Langfristige Wald\u00f6kosystem-Forschung LWF, Birmensdorf, 2 S. * Nieminen TM, De Vos B, Cools N, K\u00f6nig N, Fischer R, Iost S, Meesenburg H, Nicolas M, O\u2019Dea P, Cecchini G, Ferretti M, De La Cruz A, Derome K, Lindroos AJ, Graf Pannatier E, 2016: Part XI: Soil Solution Collection and Analysis. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 20 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)", "links": [ { diff --git a/datasets/envidat-lwf-29_2019-03-06.json b/datasets/envidat-lwf-29_2019-03-06.json index 5b68128a2d..8e28bad085 100644 --- a/datasets/envidat-lwf-29_2019-03-06.json +++ b/datasets/envidat-lwf-29_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-29_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil water content at 15, 50 and 70 cm depth in Visp (3 replications) with TDR soil moisture probes (Tektronix 1502 B) from 2001 until 25.04.2013 ### Purpose: ### Improve the available data for the calibration or validation of the water balance models, i.e. the determination of the water flux needed for calculating the leaching fluxes.", "links": [ { diff --git a/datasets/envidat-lwf-30_2019-03-06.json b/datasets/envidat-lwf-30_2019-03-06.json index 47c973e7bb..b0913bb174 100644 --- a/datasets/envidat-lwf-30_2019-03-06.json +++ b/datasets/envidat-lwf-30_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-30_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil water content at 15, 50 and 80 cm depth (3 replications) with ECH2O EC-5 soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.", "links": [ { diff --git a/datasets/envidat-lwf-31_2019-03-06.json b/datasets/envidat-lwf-31_2019-03-06.json index ead09b2a23..805ac420bd 100644 --- a/datasets/envidat-lwf-31_2019-03-06.json +++ b/datasets/envidat-lwf-31_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-31_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors ### Purpose: ### Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.", "links": [ { diff --git a/datasets/envidat-lwf-32_2019-03-06.json b/datasets/envidat-lwf-32_2019-03-06.json index f1a9bf54d9..55a24b6087 100644 --- a/datasets/envidat-lwf-32_2019-03-06.json +++ b/datasets/envidat-lwf-32_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-32_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors 1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs ### Purpose: ### Explore the effect of shrubs on the water availability for pine trees in Visp.", "links": [ { diff --git a/datasets/envidat-lwf-33_2019-03-06.json b/datasets/envidat-lwf-33_2019-03-06.json index f819831846..97994ecb40 100644 --- a/datasets/envidat-lwf-33_2019-03-06.json +++ b/datasets/envidat-lwf-33_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-33_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil water content at one control and in one irrigated plot in 10, 40 and 60 cm depth (4 replications) with TDR (Tektronix 1502B cable tester, Beaverton, OR, US). ### Purpose: ### Monitoring of the soil water content ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)", "links": [ { diff --git a/datasets/envidat-lwf-34_2019-03-06.json b/datasets/envidat-lwf-34_2019-03-06.json index ed4e6011ec..5ca9287cbc 100644 --- a/datasets/envidat-lwf-34_2019-03-06.json +++ b/datasets/envidat-lwf-34_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-34_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)", "links": [ { diff --git a/datasets/envidat-lwf-36_2019-03-06.json b/datasets/envidat-lwf-36_2019-03-06.json index f0af9b8459..4f90736d11 100644 --- a/datasets/envidat-lwf-36_2019-03-06.json +++ b/datasets/envidat-lwf-36_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-36_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment: Measurements of mean ozone concentrations with passive samplers (passam ag). ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, L\u00f6vblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth \u2013 towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](http://doi.org/10.1111/1365-2745.12941) * Calatayud V, Di\u00e9guez JJ, Sicard P, Schaub M, De Marco A (2016) Testing approaches for calculating stomatal ozone fluxes from passive sampler. Science of the Total Environment. [doi:10.1016/j.scitotenv.2016.07.155](http://doi.org/10.1016/j.scitotenv.2016.07.155) * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](http://doi.org/10.1016/B978-0-08-098222-9.00019-4)", "links": [ { diff --git a/datasets/envidat-lwf-37_2019-03-06.json b/datasets/envidat-lwf-37_2019-03-06.json index 027a960764..9e65c5b2ab 100644 --- a/datasets/envidat-lwf-37_2019-03-06.json +++ b/datasets/envidat-lwf-37_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-37_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment: Continuous measurements of ozone concentrations ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, L\u00f6vblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth \u2013 towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](https://doi.org/10.1111/1365-2745.12941) * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](https://doi.org/10.1016/B978-0-08-098222-9.00019-4)", "links": [ { diff --git a/datasets/envidat-lwf-38_2019-03-06.json b/datasets/envidat-lwf-38_2019-03-06.json index e931743ed8..761a22cee1 100644 --- a/datasets/envidat-lwf-38_2019-03-06.json +++ b/datasets/envidat-lwf-38_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-38_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, L\u00f6vblad G, Krause G, Sanz MJ, 2016: Part VIII: Monitoring of Ozone Injury. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X)", "links": [ { diff --git a/datasets/envidat-lwf-45_2019-03-06.json b/datasets/envidat-lwf-45_2019-03-06.json index 31e711c854..39db4b97cb 100644 --- a/datasets/envidat-lwf-45_2019-03-06.json +++ b/datasets/envidat-lwf-45_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-45_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree circumference, height, height to crown base measurements, mortality, decay class and removal assessment on LWF plots ### Purpose: ### Assessment of tree and forest growth ### Manual Citation: ### * Matthias Dobbertin, Christian Hug, 1999: Vorl\u00e4ufige Feld-Aufnahmeanleitung f\u00fcr die BHU- und H\u00f6hen-Inventur auf LWF-Fl\u00e4chen (V1.0), Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 18 S. * Christian Hug, Matthias Dobbertin, Chris Nussbaumer, Yves Stettler, 2010: Provisorische Aufnahmeanleitung f\u00fcr die Brusth\u00f6henumfang- und H\u00f6heninventur auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 29 S. * Christian Hug, Chris Nussbaumer, Yves Stettler, 2014: Aufnahmeanleitung f\u00fcr die Brusth\u00f6henumfang und H\u00f6heninventur auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 36 S. * Dobbertin M, Neumann M, 2016: Part V: Tree Growth. In: UNECE ICP Forests, Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://dx.doi.org/10.1016/j.foreco.2013.05.040)", "links": [ { diff --git a/datasets/envidat-lwf-47_2019-03-06.json b/datasets/envidat-lwf-47_2019-03-06.json index 4a4b891137..9b6b4e3a37 100644 --- a/datasets/envidat-lwf-47_2019-03-06.json +++ b/datasets/envidat-lwf-47_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-47_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annual Crown Condition Assessment including mortality and removal and Damage Causse Assessment on the Sanasilva-Sites and LWF plots. ### Purpose: ### To assess tree and forest health and its changes and to assess occurence and extent of diseases ### Manual Citation: ### * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Fl\u00e4chen (Version 10). Sanasilva Inventur und Langfristige Wald\u00f6kosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf) * Eichhorn J, Roskams P, Potocic N, Timmermann V, Ferretti M, Mues V, Szepesi A, Durrant D, Seletkovic I, Schr\u00f6ck H-W, Nevalainen S, Bussotti F, Garcia P, Wulff S, 2016: Part IV: Visual Assessment of Crown Condition and Damaging Agents. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf ) ### Paper Citation: ### * BAFU (2017) Jahrbuch Wald und Holz 2017. Umwelt-Zustand, Bundesamt f\u00fcr Umwelt, Bern, Vol. 1718: 110 p. [>>>](http://www.bafu.admin.ch/uz-1718-d) * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [Technical Reports](http://icp-forests.net/page/icp-forests-technical-report) * Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gef\u00e4hrdung des Schweizer Waldes \u2013 eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt f\u00fcr Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)", "links": [ { diff --git a/datasets/envidat-lwf-48_2019-03-06.json b/datasets/envidat-lwf-48_2019-03-06.json index 05dffb07a0..9b28706ab2 100644 --- a/datasets/envidat-lwf-48_2019-03-06.json +++ b/datasets/envidat-lwf-48_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-48_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of damges, i.e. symptoms, extent and causes on trees. ### Purpose: ### Occurrence and extent of diseases ### Manual Citation: ### * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Fl\u00e4chen (Version 10). Sanasilva Inventur und Langfristige Wald\u00f6kosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf) * Eichhorn J, Roskams P, Potocic N, Timmermann V, Ferretti M, Mues V, Szepesi A, Durrant D, Seletkovic I, Schr\u00f6ck H-W, Nevalainen S, Bussotti F, Garcia P, Wulff S, 2016: Part IV: Visual Assessment of Crown Condition and Damaging Agents. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf ) ### Paper Citation: ### * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [>>>](http://icp-forests.net/page/icp-forests-technical-report) * K\u00f6hl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [>>>](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf) * Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gef\u00e4hrdung des Schweizer Waldes \u2013 eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt f\u00fcr Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)", "links": [ { diff --git a/datasets/envidat-lwf-49_2019-03-06.json b/datasets/envidat-lwf-49_2019-03-06.json index 508995af60..2fa7482910 100644 --- a/datasets/envidat-lwf-49_2019-03-06.json +++ b/datasets/envidat-lwf-49_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-49_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of coarse woody debris on LWF plots using the line intersect method ### Purpose: ### Assessing the amount of coarse woody debris ### Manual Citation: ### * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S. ### Paper Citation: ### * Bretz Guby, N.A., Dobbertin, M., 1996. Quantitative estimates of coarse woody debris and standing dead trees in selected Swiss forests. Glob. Ecol. Biogeogr. Lett. 5, 327-341.", "links": [ { diff --git a/datasets/envidat-lwf-50_2019-03-06.json b/datasets/envidat-lwf-50_2019-03-06.json index f3cf7c878e..6888e7486d 100644 --- a/datasets/envidat-lwf-50_2019-03-06.json +++ b/datasets/envidat-lwf-50_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-50_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of coarse woody debris on LWF plots using full count methods on defined subplots (applying ForestBiota protocoll) ### Purpose: ### Assessing the amount of coarse woody debris ### Manual Citation: ### * Franziska Heinrich, 2005: Totholz-Aufnahme mit ForestBIOTA Protokoll. Langfristige Wald\u00f6kosystem-Forschung LWF. Eidg. Forschungsanstalt WSL, Birmensdorf, 7 S. * ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org) * Fischer, R., Fischer, R., Seidling, W., Granke, O., Meyer, P., Stofer, S., Travaglini, D., 2007. ForestBIOTA \u2013 Testphase zur Erfassung der biologischen Vielfalt. AFZ/Wald 62, 1070. ### Paper Citation: ### * Seidling, W., Travaglini, D., Meyer, P., Waldner, P., Fischer, R., Granke, O., Chirici, G., Corona, P., 2014. Dead wood and stand structure - relationships for forest plots across Europe. IForest - Biogeosciences and Forestry 7, 269-281.", "links": [ { diff --git a/datasets/envidat-lwf-51_2019-03-06.json b/datasets/envidat-lwf-51_2019-03-06.json index da7698759f..61fad4edcc 100644 --- a/datasets/envidat-lwf-51_2019-03-06.json +++ b/datasets/envidat-lwf-51_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-51_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of coarse woody debris on LWF plots using the line intersect method and full count methods on subplots (repetition of the 2005 survey) ### Purpose: ### Assessing the amount of coarse woody debris ### Manual Citation: ### * ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)", "links": [ { diff --git a/datasets/envidat-lwf-52_2019-03-06.json b/datasets/envidat-lwf-52_2019-03-06.json index be37c82835..dc69bf264f 100644 --- a/datasets/envidat-lwf-52_2019-03-06.json +++ b/datasets/envidat-lwf-52_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-52_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of coarse woody debris on LWF plots using the line intersect method (repetition of the 1995 survey) ### Purpose: ### Assessing the amount of coarse woody debris ### Manual Citation: ### * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.", "links": [ { diff --git a/datasets/envidat-lwf-53_2019-03-06.json b/datasets/envidat-lwf-53_2019-03-06.json index 8688d99bd4..b819c95bcc 100644 --- a/datasets/envidat-lwf-53_2019-03-06.json +++ b/datasets/envidat-lwf-53_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-53_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessment of coarse woody debris on Sanasilva plots (16x16 km grid) using the full count methods on subplots applying BioSoil protocoll ### Purpose: ### Assessing the amount of coarse woody debris ### Manual Citation: ### * A. Bastrup-Birk, P. Nevile, G. Chirici, T. Houston, 2006: The BioSoil ForestBiodiversity Field Manual Version 1.0/1.1/1.1A for the Field Assessment 2006-07. Working Group on ForestBiodiversity, Forest Focus Demonstration Project BioSoil 2004-2005, 47 S.", "links": [ { diff --git a/datasets/envidat-lwf-54_2019-03-06.json b/datasets/envidat-lwf-54_2019-03-06.json index cbc0a43088..1531236f8b 100644 --- a/datasets/envidat-lwf-54_2019-03-06.json +++ b/datasets/envidat-lwf-54_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-54_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous sap flow measurements with Granier-needles to investigate carbon balance and water relations of trees ### Purpose: ### Assessment of water cycle processes", "links": [ { diff --git a/datasets/envidat-lwf-56_2019-03-06.json b/datasets/envidat-lwf-56_2019-03-06.json index dcac9e3271..b733832d7e 100644 --- a/datasets/envidat-lwf-56_2019-03-06.json +++ b/datasets/envidat-lwf-56_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-56_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree circumference change measurements from plastic girth bands ### Purpose: ### Assessment of annual tree stem growth ### Manual Citation: ### * Dobbertin M, Neumann M, 2016: Part V: Tree Growth. In: UNECE ICP Forests, Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://doi.org/10.1016/j.foreco.2013.05.040)", "links": [ { diff --git a/datasets/envidat-lwf-57_2019-03-06.json b/datasets/envidat-lwf-57_2019-03-06.json index dfe8592279..cca1f7916a 100644 --- a/datasets/envidat-lwf-57_2019-03-06.json +++ b/datasets/envidat-lwf-57_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-57_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Continuous stem radius measurements to investigate carbon balance and water relations of trees ### Purpose: ### Assessment of growth and water related stem changes", "links": [ { diff --git a/datasets/envidat-lwf-81_2019-03-06.json b/datasets/envidat-lwf-81_2019-03-06.json index 1b493243d4..01c6ec2224 100644 --- a/datasets/envidat-lwf-81_2019-03-06.json +++ b/datasets/envidat-lwf-81_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-81_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two stem core samples of 12 to 20 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 12 pre-dominant or dominant trees and if possible also 4 subdominant or surpressed trees. NOTE: The samplings were carried out between 1996 and 1999 for most plots and in 2003 for the plot Lantsch. The cores cover a time span depending on the age of the oldest trees on a plot. On one plot the oldest sampled tree ring grew in the year 1646. ### Purpose: ### Reconstruction of stand history and tree growth ### Manual Citation: ### * Paolo Cherubini, Matthias Dobbertin, 1997: Bestandesgeschichte (Dendrochronologie). In: Peter Brang (ed.), Aufnahmeanleitung LWF. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 3 S. * Cherubini, P.; Dobbertin, M., 1998: The Swiss long-term forest ecosystem research: methods for reconstructing forest history. In: Borghetti, M. (ed): Societ\u00e0 Italiana di Selvicoltura ed Ecologia Forestale (SISEF), Atti I: 19-22. ### Paper Citation: ### * Cherubini, P., Fontana, G., Rigling, D., Dobbertin, M., Brang, P., Innes, J.L., 2002. Tree-life history prior to death: two fungal root pathogens affect tree-ring growth differently. J. Ecol. 90, 839-850.", "links": [ { diff --git a/datasets/envidat-lwf-82_2019-03-06.json b/datasets/envidat-lwf-82_2019-03-06.json index 00980d5813..51d8b6f2e9 100644 --- a/datasets/envidat-lwf-82_2019-03-06.json +++ b/datasets/envidat-lwf-82_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-82_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There were 2 cores taken at 1.3 m height from each of 10 trees outside the LWF plot ### Purpose: ### influence of drought and nutrient availabiliy on tree growth ### Paper Citation: ### * L\u00e9vesque, M., Walthert, L., Weber, P., 2016. Soil nutrients influence growth response of temperate tree species to drought. J. Ecol. 104, 377-387.", "links": [ { diff --git a/datasets/envidat-lwf-83_2019-03-06.json b/datasets/envidat-lwf-83_2019-03-06.json index 22c2accbae..1a93174b06 100644 --- a/datasets/envidat-lwf-83_2019-03-06.json +++ b/datasets/envidat-lwf-83_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-83_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees. ### Purpose: ### N and C stable isotope signals ### Paper Citation: ### * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.", "links": [ { diff --git a/datasets/envidat-lwf-84_2019-03-06.json b/datasets/envidat-lwf-84_2019-03-06.json index 180f121162..9319d9ae99 100644 --- a/datasets/envidat-lwf-84_2019-03-06.json +++ b/datasets/envidat-lwf-84_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-84_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two stem core samples of all trees of a subplot of approximately 1 a. Basal Area, Wood volume increment per area estimation ### Purpose: ### Investigation of the relation between dendroparameters of dominance/surpression and stem growth. Abgleich Jahrringdaten mit Inventurdaten. ### Paper Citation: ### * Nehrbass-Ahles C, Babst F, Klesse S, N\u00f6tzli M, Bouriaud O, Neukom R, Dobbertin M, Frank D (2014) The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology, 20 (9): 2867\u20132885. [doi: 10.1111/gcb.12599](http://doi.org/10.1111/gcb.12599) * Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311.", "links": [ { diff --git a/datasets/envidat-lwf-86_2019-03-06.json b/datasets/envidat-lwf-86_2019-03-06.json index aeabeeb206..8a67d79c10 100644 --- a/datasets/envidat-lwf-86_2019-03-06.json +++ b/datasets/envidat-lwf-86_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-86_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling of deadwood for density and chemical analysis during summer 2009 ### Purpose: ### Determination of N and C pools of deadwood ### Paper Citation: ### * WEGGLER, K.; DOBBERTIN, M.; J\u00dcNGLING, E.; KAUFMANN, E.; TH\u00dcRIG, E., 2012. Dead wood volume to dead wood carbon: the issue of conversion factors. European Journal of Forest Research 131, 1423-1438.", "links": [ { diff --git a/datasets/envidat-lwf-87_2019-03-06.json b/datasets/envidat-lwf-87_2019-03-06.json index 4762396fc9..40ef8dda60 100644 --- a/datasets/envidat-lwf-87_2019-03-06.json +++ b/datasets/envidat-lwf-87_2019-03-06.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat-lwf-87_2019-03-06", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurement of tree ring widts in tree stem disks according to 'Br\u00e4ker O.U. (1993) Anleitung zur Entnahme von Stammscheiben auf Ertragskundefl\u00e4chen' ### Purpose: ### tree growth", "links": [ { diff --git a/datasets/envidat_232_1.0.json b/datasets/envidat_232_1.0.json index d2db6bbf4f..0f45d82dde 100644 --- a/datasets/envidat_232_1.0.json +++ b/datasets/envidat_232_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "envidat_232_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository contains data required for reproducibility of the results to be published in the associated manuscript. Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research. The datasets consist of two folders organized in zip format: 1. REPRODUCIBILITY_SIMULATION: Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript Additional files include datasets (from IMAU-FDM / RACMO, see \"credits\" below ) as well as helper python scripts to produce *.sno files which are used as initial conditions for SNOWPACK in CRYOWRF. 2. REPRODUCIBILITY_POSTPROCESSING: Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript. Each of the folders have their own readme files for more details. ### Code citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165 location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF (stable releases / institutional repo) https://github.com/vsharma-next/CRYOWRF (dev branches / developer repo) ### Publication **Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.** Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development ### Acknowledgements We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are: * Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. No\u00ebl, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. No\u00ebl, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016). The Cryosphere, 12, doi:10.5194/tc-12-1643-2018", "links": [ { diff --git a/datasets/environmental-constraints-on-tree-growth_1.0.json b/datasets/environmental-constraints-on-tree-growth_1.0.json index b31a1b8ea8..a4bb1315d7 100644 --- a/datasets/environmental-constraints-on-tree-growth_1.0.json +++ b/datasets/environmental-constraints-on-tree-growth_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "environmental-constraints-on-tree-growth_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seasonal variation in environmental constraints (vapor pressure deficit \u2013 VPD, air temperature, and soil moisture) on tree growth for the potential distribution range of seven widespread Central European tree species. We simulated environmental constraints on growth fusing 3-PG model or the species\u2019 potential distribution range within the forested area of Switzerland on a 1\u00d71 km grid for seven dominant tree species: _Larix decidua_, _Picea abies_, _Abies alba_, _Fagus sylvatica_, _Acer pseudoplatanus_, _Pinus sylvestris_, and _Quercus robur_. For this purpose, we simulated the growth of these tree species in monocultures with the average climate observed during 1961\u20131990 or 1991-2018. The stands were initialized as 2-year-old plantations with an initial density of 2,500 trees ha-1 and simulated until the age of 30 years. For each simulated month, we obtained the relative contribution of environmental constraints (VPD, temperature, and soil water) on tree growth.", "links": [ { diff --git a/datasets/environmental_layers_1.json b/datasets/environmental_layers_1.json index f1d30d43ef..0a3d7f72d7 100644 --- a/datasets/environmental_layers_1.json +++ b/datasets/environmental_layers_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "environmental_layers_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a collection of marine environmental data layers suitable for use in Southern Ocean species distribution modelling. All environmental layers have been generated at a spatial resolution of 0.1 degrees, covering the Southern Ocean extent (80 degrees S - 45 degrees S, -180 - 180 degrees). The layers include information relating to bathymetry, sea ice, ocean currents, primary production, particulate organic carbon, and other oceanographic data.\n\nAn example of reading and using these data layers in R can be found at https://australianantarcticdivision.github.io/blueant/articles/SO_SDM_data.html.\n\nThe following layers are provided:\n\n1. Layer name: depth\nDescription: Bathymetry. Downloaded from GEBCO 2014 (0.0083 degrees = 30sec arcmin resolution) and set at resolution 0.1 degrees. Then completed with the bathymetry layer manually corrected and provided in Fabri-Ruiz et al. (2017)\nValue range: -8038.722 - 0\nUnits: m\n\nSource: This study. Derived from GEBCO\nURL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/\nCitation: Fabri-Ruiz S, Saucede T, Danis B and David B (2017). Southern Ocean Echinoids database_An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, (697), 1.\n\n2. Layer name: geomorphology\nDescription: Last update on biodiversity.aq portal. Derived from O'Brien et al. (2009) seafloor geomorphic feature dataset. Mapping based on GEBCO contours, ETOPO2, seismic lines). 27 categories\nValue range: 27 categories\nUnits: categorical\nSource: This study. Derived from Australian Antarctic Data Centre\nURL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\nCitation: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10\n\n3. Layer name: sediments\nDescription: Sediment features\nValue range: 14 categories\nUnits: categorical\nSource: Griffiths 2014 (unpublished)\nURL: http://share.biodiversity.aq/GIS/antarctic/\n\n4. Layer name: slope\nDescription: Seafloor slope derived from bathymetry with the terrain function of raster R package. Computation according to Horn (1981), ie option neighbor=8. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Unit set at degrees.\nValue range: 0.000252378 - 16.94809\nUnits: degrees\nSource: This study. Derived from GEBCO\nURL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/\nCitation: Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69:14-47\n\n5. Layer name: roughness\nDescription: Seafloor roughness derived from bathymetry with the terrain function of raster R package. Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees.\nValue range: 0 - 5171.278\nUnits: unitless\nSource: This study. Derived from GEBCO\nURL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/\n\n6. Layer name: mixed layer depth\nDescription: Summer mixed layer depth climatology from ARGOS data. Regridded from 2-degree grid using nearest neighbour interpolation\nValue range: 13.79615 - 461.5424\nUnits: m\nSource: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\n\n7. Layer name: seasurface_current_speed\nDescription: Current speed near the surface (2.5m depth), derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS model)\nValue range: 1.50E-04 - 1.7\nUnits: m/s\nSource: This study. Derived from Australian Antarctic Data Centre\nURL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\nCitation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data\n\n8. Layer name: seafloor_current_speed\nDescription: Current speed near the sea floor, derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS)\nValue range: 3.40E-04 - 0.53\nUnits: m/s\nSource: This study. Derived from Australian Antarctic Data Centre\nURL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\nCitation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data\n\n9. Layer name: distance_antarctica\nDescription: Distance to the nearest part of the Antarctic continent\nValue range: 0 - 3445\nUnits: km\nSource: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\n\n10. Layer name: distance_canyon\nDescription: Distance to the axis of the nearest canyon\nValue range: 0 - 3117\nUnits: km\nSource: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\n\n11. Layer name: distance_max_ice_edge\nDescription: Distance to the mean maximum winter sea ice extent (derived from daily estimates of sea ice concentration)\nValue range: -2614.008 - 2314.433\nUnits: km\nSource: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\n\n12. Layer name: distance_shelf\nDescription: Distance to nearest area of seafloor of depth 500m or shallower\nValue range: -1296 - 1750\nUnits: km\nSource: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data\n\n13. Layer name: ice_cover_max\nDescription: Ice concentration fraction, maximum on [1957-2017] time period\nValue range: 0 - 1\nUnits: unitless\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n14. Layer name: ice_cover_mean\nDescription: Ice concentration fraction, mean on [1957-2017] time period\nValue range: 0 - 0.9708595\nUnits: unitless\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n15. Layer name: ice_cover_min\nDescription: Ice concentration fraction, minimum on [1957-2017] time period\nValue range: 0 - 0.8536261\nUnits: unitless\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n16. Layer name: ice_cover_range\nDescription: Ice concentration fraction, difference maximum-minimum on [1957-2017] time period\nValue range: 0 - 1\nUnits: unitless\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n17. Layer name: ice_thickness_max\nDescription: Ice thickness, maximum on [1957-2017] time period\nValue range: 0 - 3.471811\nUnits: m\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n18. Layer name: ice_thickness_mean\nDescription: Ice thickness, mean on [1957-2017] time period\nValue range: 0 - 1.614133\nUnits: m\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n19. Layer name: ice_thickness_min\nDescription: Ice thickness, minimum on [1957-2017] time period\nValue range: 0 - 0.7602701\nUnits: m\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n20. Layer name: ice_thickness_range\nDescription: Ice thickness, difference maximum-minimum on [1957-2017] time period\nValue range: 0 - 3.471811\nUnits: m\nSource: BioOracle accessed 24/04/2018, see Assis et al. (2018)\nURL: http://www.bio-oracle.org/\nCitation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis\n\n21. Layer name: chla_ampli_alltime_2005_2012\nDescription: Chlorophyll-a concentrations obtained from MODIS satellite data. Amplitude of pixel values (difference between maximal and minimal value encountered by each pixel during all months of the period [2005-2012])\nValue range: 0 - 77.15122\nUnits: mg/m^3\nSource: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/\nURL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php\n\n22. Layer name: chla_max_alltime_2005_2012\nDescription: Chlorophyll-a concentrations obtained from MODIS satellite data. Maximal value encountered by each pixel during all months of the period [2005-2012]\nValue range: 0 - 77.28562\nUnits: mg/m^3\nSource: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/\nURL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php\n\n23. Layer name: chla_mean_alltime_2005_2012\nDescription: Chlorophyll-a concentrations obtained from MODIS satellite data. Mean value of each pixel during all months of the period [2005-2012]\nValue range: 0 - 30.42691\nUnits: mg/m^3\nSource: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/\nURL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php\n\n24. Layer name: chla_min_alltime_2005_2012\nDescription: Chlorophyll-a concentrations obtained from MODIS satellite data. Minimal value encountered by each pixel during all months of the period [2005-2012]\nValue range: 0 - 29.02929\nUnits: mg/m^3\nSource: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/\nURL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php\n\n25. Layer name: chla_sd_alltime_2005_2012\nDescription: Chlorophyll-a concentrations obtained from MODIS satellite data. Standard deviation value of each pixel during all months of the period [2005-2012]\nValue range: 0 - 27.9877\nUnits: mg/m^3\nSource: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/\nURL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php\n\n26. Layer name: POC_2005_2012_ampli\nDescription: Particulate organic carbon, model Lutz et al. (2007). Amplitude value (difference maximal and minimal value, see previous layers) all seasonal layers [2005-2012]\nValue range: 0 - 1.31761\nUnits: g/m^2/d\nSource: This study. Following Lutz et al. (2007)\nURL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers\nCitation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).\n\n27. Layer name: POC_2005_2012_max\nDescription: Particulate organic carbon, model Lutz et al. (2007). Maximal value encountered on each pixel among all seasonal layers [2005-2012]\nValue range: 0.00332562 - 1.376601\nUnits: g/m^2/d\nSource: This study. Following Lutz et al. (2007)\nURL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers\nCitation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).\n\n28. Layer name: POC_2005_2012_mean\nDescription: Particulate organic carbon, model Lutz et al. (2007). Mean all seasonal layers [2005-2012]\nValue range: 0.003184335 - 0.5031364\nUnits: g/m^2/d\nSource: This study. Following Lutz et al. (2007)\nURL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers\nCitation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).\n\n29. Layer name: POC_2005_2012_min\nDescription: Particulate organic carbon, model Lutz et al. (2007). Minimal value encountered on each pixel among all seasonal layers [2005-2012]\nValue range: 0.003116508 - 0.1313119\nUnits: g/m^2/d\nSource: This study. Following Lutz et al. (2007)\nURL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers\nCitation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).\n\n30. Layer name: POC_2005_2012_sd\nDescription: Particulate organic carbon, model Lutz et al. (2007). Standard deviation all seasonal layers [2005-2012]\nValue range: 3.85E-08 - 0.4417001\nUnits: g/m^2/d\nSource: This study. Following Lutz et al. (2007)\nURL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers\nCitation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).\n\n31. Layer name: seafloor_oxy_1955_2012_ampli\nDescription: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor oxygen concentration over [1955-2012], modified from WOCE\nValue range: 0.001755714 - 5.285187\nUnits: mL/L\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n32. Layer name: seafloor_oxy_1955_2012_max\nDescription: Maximum value encountered for each pixel on all month layers of oxygen concentration over [1955-2012], modified from WOCE\nValue range: 3.059685 - 11.52433\nUnits: mL/L\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n33. Layer name: seafloor_oxy_1955_2012_mean\nDescription: Mean seafloor oxygen concentration over [1955-2012] (average of all monthly layers), modified from WOCE\nValue range: 2.836582 - 8.858084\nUnits: mL/L\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n34. Layer name: seafloor_oxy_1955_2012_min\nDescription: Minimum value encountered for each pixel on all month layers of seafloor oxygen concentration over [1955-2012], modified from WOCE\nValue range: 0.4315577 - 8.350794\nUnits: mL/L\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n35. Layer name: seafloor_oxy_1955_2012_sd\nDescription: Standard deviation seafloor oxygen concentration over [1955-2012] (of all monthly layers), modified from WOCE\nValue range: 0.000427063 - 1.588707\nUnits: mL/L\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n36. Layer name: seafloor_sali_2005_2012_ampli\nDescription: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE\nValue range: 0.000801086 - 4.249901\nUnits: PSU\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n37. Layer name: seafloor_sali_2005_2012_max\nDescription: Maximum value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE\nValue range: 32.90105 - 35.3997\nUnits: PSU\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n38. Layer name: seafloor_sali_2005_2012_mean\nDescription: Mean seafloor salinity over [2005-2012] (average of all monthly layers), modified from WOCE\nValue range: 32.51107 - 35.03207\nUnits: PSU\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n39. Layer name: seafloor_sali_2005_2012_min\nDescription: Minimum value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE\nValue range: 29.8904 - 34.97735\nUnits: PSU\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n40. Layer name: seafloor_sali_2005_2012_sd\nDescription: Standard deviation seafloor salinity over [2005-2012] (of all monthly layers), modified from WOCE\nValue range: 0.000251834 - 1.36245\nUnits: PSU\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n41. Layer name: seafloor_temp_2005_2012_ampli\nDescription: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE\nValue range: 0.0086 - 8.625669\nUnits: degrees C\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n42. Layer name: seafloor_temp_2005_2012_max\nDescription: Maximum value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE\nValue range: -2.021455 - 15.93171\nUnits: degrees C\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n43. Layer name: seafloor_temp_2005_2012_mean\nDescription: Mean seafloor temperature over [2005-2012] (average of all monthly layers), modified from WOCE\nValue range: -2.085796 - 13.23161\nUnits: degrees C\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n44. Layer name: seafloor_temp_2005_2012_min\nDescription: Minimum value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE\nValue range: -2.1 - 11.6431\nUnits: degrees C\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n45. Layer name: seafloor_temp_2005_2012_sd\nDescription: Standard deviation seafloor temperature over [2005-2012] (of all monthly layers), modified from WOCE\nValue range: 0.002843571 - 2.877084\nUnits: degrees C\nSource: Derived from World Ocean Circulation Experiment 2013\nURL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html\n\n46. Layer name: extreme_event_max_chl_2005_2012_ampli\nDescription: Amplitude (difference maximum-minimum) number of the number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 3\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n47. Layer name: extreme_event_max_chl_2005_2012_max\nDescription: Maximum number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 5\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n48. Layer name: extreme_event_max_chl_2005_2012_mean\nDescription: Mean of the number of extreme events calculated between 2005 and 2012\nValue range: 0 - 3.875\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n49. Layer name: extreme_event_max_chl_2005_2012_min\nDescription: Minimum number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 5\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n50. Layer name: extreme_event_min_chl_2005_2012_ampli\nDescription: Amplitude (difference maximum-minimum) number of the number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 9\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n51. Layer name: extreme_event_min_chl_2005_2012_max\nDescription: Maximum number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 11\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n52. Layer name: extreme_event_min_chl_2005_2012_mean\nDescription: Mean of the number of extreme events calculated between 2005 and 2012\nValue range: 0 - 11\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n53. Layer name: extreme_event_min_chl_2005_2012_min\nDescription: Minimum number of extreme events calculated between 2005 and 2012\nValue range: integer values 0 - 11\nUnits: unitless\nSource: derived from chlorophyll-a concentration layers\n\n54. Layer name: extreme_event_min_oxy_1955_2012_nb\nDescription: Number of extreme events (minimal seafloor oxygen concentration records) that happened between January and December of the year\nValue range: integer values 0 - 12\nUnits: per year\nSource: derived from seafloor oxygen concentration layers\n\n55. Layer name: extreme_event_max_sali_2005_2012_nb\nDescription: Number of extreme events (maximal seafloor salinity records) that happened between January and December of the year\nValue range: integer values 0 - 12\nUnits: per year\nSource: derived from seafloor salinity layers\n\n56. Layer name: extreme_event_min_sali_2005_2012_nb\nDescription: Number of extreme events (minimal seafloor salinity records) that happened between January and December of the year\nValue range: integer values 0 - 12\nUnits: per year\nSource: derived from seafloor salinity layers\n\n57. Layer name: extreme_event_max_temp_2005_2012_nb\nDescription: Number of extreme events (maximal seafloor temperature records) that happened between January and December of the year\nValue range: integer values 0 - 12\nUnits: per year\nSource: derived from seafloor temperature layers\n\n58. Layer name: extreme_event_min_temp_2005_2012_nb\nDescription: Number of extreme events (minimal seafloor temperature records) that happened between January and December of the year\nValue range: integer values 0 - 12\nUnits: per year\nSource: derived from seafloor temperature layers", "links": [ { diff --git a/datasets/er2_aerial_photos_722_1.json b/datasets/er2_aerial_photos_722_1.json index 07555cef41..ae7ccb1ce0 100644 --- a/datasets/er2_aerial_photos_722_1.json +++ b/datasets/er2_aerial_photos_722_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2_aerial_photos_722_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photography from the NASA ER-2 high-altitude aircraft was collected to provide detailed and spatially extensive documentation over parts of the SAFARI study area. The ER-2 aerial photography consists of 3,046 color-infrared (IR) transparencies collected during the SAFARI 2000 Dry Season Aircraft Campaign in August and September of 2000. ORNL DAAC has archived scanned subsets of the ER-2 aerial photography. In addition, 515 image frames have been scanned from copies of the original level-0 ER-2 aerial photography by the University of the Witwatersrand (Wits), in Pretoria, South Africa. ORNL DAAC has archived subsets of the available imagery from ARC and Wits.", "links": [ { diff --git a/datasets/er2edop_1.json b/datasets/er2edop_1.json index b3c465c452..7029c8a90d 100644 --- a/datasets/er2edop_1.json +++ b/datasets/er2edop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2edop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 ER-2 Doppler Radar (EDOP) dataset is a browse-only dataset that consists of plotted reflectivity and Doppler velocity data collected by the ER-2 Doppler Radar (EDOP) during the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying the various aspects of tropical cyclones in the region. EDOP was mounted onboard the NASA ER-2 high-altitude research aircraft from which it obtained vertical profiles of convection within tropical cyclones. The daily browse files are available from August 5 through September 27, 1998 in GIF format.", "links": [ { diff --git a/datasets/er2flog_501_1.json b/datasets/er2flog_501_1.json index bc29e8da2e..37e784d137 100644 --- a/datasets/er2flog_501_1.json +++ b/datasets/er2flog_501_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2flog_501_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 1994 and 1996, digital and analog imaging instruments mounted on the NASA ER2 aircraft collected various remotely sensed data from the atmosphere and earth's surface as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Intensive Field Campaigns (IFC).", "links": [ { diff --git a/datasets/er2lip_1.json b/datasets/er2lip_1.json index 482d13af80..0bed7d1088 100644 --- a/datasets/er2lip_1.json +++ b/datasets/er2lip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2lip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 Lightning Instrument Package (LIP) dataset contains electrical field measurements of lightning within storms studied during the Convection And Moisture EXperiment 3 (CAMEX-3). The LIP was flown aboard the NASA ER-2 aircraft, enabling vector components of the electric field (i.e, Ex, Ey, Ez) to be readily obtained, thus greatly improving knowledge of the electrical structure within storms overflown. Measurements within this dataset include field mill data, conductivity probe temperatures from two probes, and navigation data. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (i.e., tens of kV/m). Total lightning (i.e., cloud-to-ground, intracloud) is identified from the abrupt electric field changes in the data. The conductivity probe measures the air conductivity at the aircraft flight altitude. Storm electric currents can be derived using the electric field and air conductivity measurements.", "links": [ { diff --git a/datasets/er2mams_1.json b/datasets/er2mams_1.json index 4a5b093997..dab173ff42 100644 --- a/datasets/er2mams_1.json +++ b/datasets/er2mams_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2mams_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 Multispectral Atmospheric Mapping Sensor (MAMS) dataset was collected by the Multispectral Atmospheric Mapping Sensor (MAMS), which is a multispectral scanner which measures reflected radiation from the Earth's surface and clouds in eight visible/near-infrared bands, and thermal emission from the Earth' surface, clouds, and atmospheric constituents (primarily water vapor) in four infrared bands. The 5.0 mRa aperture of MAMS produces an instantaneous field-of-view (IFOV) resolution of 100 m at nadir from the nominal ER-2 altitude of 20 km. The width of the entire cross path field-of-view scanned by the sensor is 37 km, thereby providing detailed resolution of atmospheric and surface features across the swath width and along the aircraft flight track. For clouds and thunderstorm features the IFOV decreases with increasing cloud height by a factor of (Z-20)/20, where Z is the cloud height in kilometers.", "links": [ { diff --git a/datasets/er2mir_1.json b/datasets/er2mir_1.json index 604d411bcd..9a69b733f4 100644 --- a/datasets/er2mir_1.json +++ b/datasets/er2mir_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2mir_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 ER-2 Millimeter-wave Imaging Radiometer (MIR) dataset is a browse-only dataset containing plots of brightness temperature measurements collected by the Millimeter-wave Imaging Radiometer (MIR) in support of the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying various aspects of tropical cyclones in the region. During CAMEX-3, MIR operated onboard the NASA ER-2 high-altitude research aircraft, collecting brightness temperature measurements of water vapor, clouds, precipitation, and other atmospheric features. The MIR browse image files are available from August 8 through September 8, 1998 in GIF format. ", "links": [ { diff --git a/datasets/er2mts_1.json b/datasets/er2mts_1.json index 3720b1466b..947cf74f82 100644 --- a/datasets/er2mts_1.json +++ b/datasets/er2mts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2mts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 ER-2 NPOESS Aircraft Sounder Testbed - Microwave Temperature Sounder (NAST-MTS) dataset contains navigation records and microwave spectral radiance measurements taken of tropical cyclones and hurricanes during the third Convection and Moisture Experiment (CAMEX-3). The NAST-MTS contains two microwave radiometer systems covering the spectral ranges of 50 to 56 GHz and provides atmospheric temperature profiles from the flight altitude to the surface.", "links": [ { diff --git a/datasets/er2nasti_1.json b/datasets/er2nasti_1.json index 726d98aee0..3d8a2adad4 100644 --- a/datasets/er2nasti_1.json +++ b/datasets/er2nasti_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2nasti_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Atmospheric Sounding Testbed (NAST) is a suite of airborne infrared and microwave spectrometers, being developed for the Integrated Program Office (IPO), that will be flown on the NASA high altitude ER-2 aircraft as part of the risk reduction effort for NPOESS. In addition to their stand-alone scientific value, data from these airborne instruments will be used to simulate possible satellite-based radiance measurements, therefore enabling experimental validation of instrument system specifications and data processing techniques for future advanced atmospheric remote sensors (e.g., the proposed sounder component for NPOESS).", "links": [ { diff --git a/datasets/er2nav_1.json b/datasets/er2nav_1.json index 4dc73a1928..8466d10166 100644 --- a/datasets/er2nav_1.json +++ b/datasets/er2nav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2nav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The CAMEX-3 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data are available in ASCII text file format and Graphics Interchange Format, where each file contains data recorded at one second intervals for each flight.", "links": [ { diff --git a/datasets/er2navimpacts_1.json b/datasets/er2navimpacts_1.json index e3ea38083f..c77cdbcfaf 100644 --- a/datasets/er2navimpacts_1.json +++ b/datasets/er2navimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "er2navimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA ER-2 Navigation Data IMPACTS dataset contains information recorded by the onboard navigation and data collection systems of the NASA ER-2 high-altitude research aircraft. In addition to typical navigation data (e.g., date, time, latitude/longitude, and altitude) it also contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The IMPACTS navigation dataset files are available from January 15, 2020, through March 2, 2023, in ASCII-ict format.", "links": [ { diff --git a/datasets/erbe_albedo_monthly_xdeg_957_1.json b/datasets/erbe_albedo_monthly_xdeg_957_1.json index b5d965b627..4529f4a85b 100644 --- a/datasets/erbe_albedo_monthly_xdeg_957_1.json +++ b/datasets/erbe_albedo_monthly_xdeg_957_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "erbe_albedo_monthly_xdeg_957_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990, contains both the original ERBE albedo data at 2.5 degree spatial resolution, and the International Land Surface Climatology Project Initative II (ISLSCP Initiative II) albedo product re-gridded to 1 degree resolution. The goals of the ERBE were (1) to understand the radiation balance between the Sun, Earth, atmosphere, and space and (2) to establish an accurate, long-term baseline data set for detection of climate changes. Earth Radiation Budget (ERB) data are fundamental to the development of realistic climate models and to the understanding of natural and anthropogenic perturbations of the climate system. As part of ERBE, measurements of broadband shortwave radiation reflected from the Earth-atmosphere system were obtained, from which top of atmosphere albedo values were calculated. In addition, values from scenes determined to be free of clouds were analyzed separately and clear-sky albedos were derived. For this study, only the clear-sky albedos are included. The ERBE data sets for ISLSCP Initiative II contain global, top of atmosphere, clear sky albedo data from January 1986 to February 1990.", "links": [ { diff --git a/datasets/escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0.json b/datasets/escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0.json index 1dad286df9..2ed3accded 100644 --- a/datasets/escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0.json +++ b/datasets/escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.", "links": [ { diff --git a/datasets/espon-digiplan_1.0.json b/datasets/espon-digiplan_1.0.json index fc9fd54f5a..c5390a8bfa 100644 --- a/datasets/espon-digiplan_1.0.json +++ b/datasets/espon-digiplan_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "espon-digiplan_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset as a part of the international project ESPON Digiplan. The aim of this international project is to assess the extent, organisation and financing of digitisation of plan data as well as the use of these data in ESPON member countries. As a part of the in-depth case study, 7 virtual expert interviews in Switzerland and 5 virtual expert interviews in Germany were conducted with experts on the topic of digitisation of plan data. The documents contain the transcripts of the interviews. The transcripts aim to capture the content of the interviews, which is why voice raising and lowering, as well as pauses in the interview, were not specifically recorded. The interviews were conducted in German, therefore the transcripts are also in German.", "links": [ { diff --git a/datasets/eta_model_723_1.json b/datasets/eta_model_723_1.json index d01752f670..95f67f7cb8 100644 --- a/datasets/eta_model_723_1.json +++ b/datasets/eta_model_723_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eta_model_723_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "With modern computer power now capable of making mesoscale model output available in real time in the operational environment, increased attention has been given to utilizing these models in order to improve the forecasting ability of meteorologists. The National Centers for Environmental Prediction (NCEP) has developed a step-mountain eta coordinate model generally known as the ETA Model.This NCEP ETA data assimilation and prediction system (see Mesinger et al., 1988; Black, 1994) has been used by the South African Weather Bureau/Service (SAWS) to provide operational regional forecast guidance since November 1993. SAWS used this model to produce the basic meteorological data for the SAFARI project. The SAWS ETA model is a hydrostatic model with a horizontal grid spacing of approximately 48 km and 38 vertical levels, with layer depths that range from 20 m in the planetary boundary layer to 2 km at 50 mb. There have been several major ETA Model upgrades at SAWS: in March 1996, August 1998, November 1999, and August 2001.", "links": [ { diff --git a/datasets/eur11_1.0.json b/datasets/eur11_1.0.json index 58cc7f6e34..4fce23ed3e 100644 --- a/datasets/eur11_1.0.json +++ b/datasets/eur11_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "eur11_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30\u2009arc\u2009sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries. The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.", "links": [ { diff --git a/datasets/european-snow-booklet_1.0.json b/datasets/european-snow-booklet_1.0.json index 7b85235690..1f9ca9ed11 100644 --- a/datasets/european-snow-booklet_1.0.json +++ b/datasets/european-snow-booklet_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "european-snow-booklet_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 \u201cA European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)\u201d. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public. For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).", "links": [ { diff --git a/datasets/evoltree-conference-2021-birmensdorf-switzerland_1.0.json b/datasets/evoltree-conference-2021-birmensdorf-switzerland_1.0.json index 16dd2ef5da..552e37365e 100644 --- a/datasets/evoltree-conference-2021-birmensdorf-switzerland_1.0.json +++ b/datasets/evoltree-conference-2021-birmensdorf-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "evoltree-conference-2021-birmensdorf-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The first EVOLTREE Conference, taking place in hybrid format (on-site and online) at WSL Birmensdorf (Switzerland) from 14-17 September, 2021, focuses on the genomics of trees and interacting species from evolutionary, demographic, and ecological perspectives. EVOLTREE is a European network of institutions engaged in studying the evolution and functioning of forest ecosystems, in particular trees as the foundation species in forest stands. A prime topic in the face of ongoing climate change is to elucidate how trees, together with their associated organisms such as mycorrhizal fungi, respond to rapid environmental changes. The conference includes contributions that apply innovative approaches and consider the relevance of their research in the context of biodiversity conservation through natural dynamics or silvicultural interference.", "links": [ { diff --git a/datasets/ewing_0.json b/datasets/ewing_0.json index 0bb5172b1f..d003ac6fe1 100644 --- a/datasets/ewing_0.json +++ b/datasets/ewing_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ewing_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near South Africa in 2001.", "links": [ { diff --git a/datasets/example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0.json b/datasets/example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0.json index e7067a9e48..0fd25c96d9 100644 --- a/datasets/example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0.json +++ b/datasets/example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of M\u00fcnchen, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service \u201cwith funding by the European Union\u201d based on SRTM and ASTER GDEM) - Digitales Gel\u00e4ndemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung \u2013 www.geodaten.bayern.de \u2013and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.", "links": [ { diff --git a/datasets/experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0.json b/datasets/experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0.json index 2969a80187..085484573c 100644 --- a/datasets/experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0.json +++ b/datasets/experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland. The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30\u201380 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of * Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground) * Digital Elevation Model (grid resolution 2 m) obtained via UAV * Orthophoto (5 cm resolution) obtained via UAV * Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL) * StoneNode v1.0 raw data stream for equipped rocks. Further information is found in * __A. Caviezel__ et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/ * __ P. Niklaus__ et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/", "links": [ { diff --git a/datasets/experimental-rockfall-trilogy-of-surava_1.0.json b/datasets/experimental-rockfall-trilogy-of-surava_1.0.json index bffbbd3d7a..0d862e26ce 100644 --- a/datasets/experimental-rockfall-trilogy-of-surava_1.0.json +++ b/datasets/experimental-rockfall-trilogy-of-surava_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "experimental-rockfall-trilogy-of-surava_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We performed an experimental trilogy of induced rockfall experiments in a spruce stand in Surava (CH) within (i) the original forest, (ii) after a logging job, resulting in lying deadwood and (iii) the cleared, deadwwod-free state. The three experimental set-ups allow quantifying the deadwood effect on overall rockfall risk for the same forest (slope, species) in three different conditions.", "links": [ { diff --git a/datasets/explorer_0.json b/datasets/explorer_0.json index 9c4fb692d0..228af3abeb 100644 --- a/datasets/explorer_0.json +++ b/datasets/explorer_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "explorer_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made in the Caribbean Sea near the Cayman Islands between 2001 and 2003.", "links": [ { diff --git a/datasets/exrad3dimpacts_1.json b/datasets/exrad3dimpacts_1.json index 1588109fc2..4a6c87b8be 100644 --- a/datasets/exrad3dimpacts_1.json +++ b/datasets/exrad3dimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "exrad3dimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS dataset consists of horizontal wind components, uncertainties in the horizontal wind components, and radar reflectivity collected by the EXRAD instrument onboard the NASA ER-2 aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023, No deployments occurred in 2021 due to COVID-19). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD 3D Winds IMPACTS dataset files are available from January 25 through February 7, 2020 in netCDF-3 format.", "links": [ { diff --git a/datasets/exradepoch_1.json b/datasets/exradepoch_1.json index 3f79e24c8d..10020c42c8 100644 --- a/datasets/exradepoch_1.json +++ b/datasets/exradepoch_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "exradepoch_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ER-2 X-Band Doppler Radar (EXRAD) EPOCH dataset consists of radar reflectivity and Doppler velocity estimates collected by the EXRAD onboard the AV-6 Global Hawk Unmanned Aerial Vehicle research aircraft, though traditionally this instrument is flown on the NASA ER-2 aircraft. These data were gathered during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The EXRAD EPOCH dataset files are available from August 9, 2017 through August 31, 2017 in HDF-5 format.", "links": [ { diff --git a/datasets/exradimpacts_1.json b/datasets/exradimpacts_1.json index f548ae3118..6dfeaf100e 100644 --- a/datasets/exradimpacts_1.json +++ b/datasets/exradimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "exradimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ER-2 X-band Radar (EXRAD) IMPACTS dataset consists of radar reflectivity and Doppler velocity estimates collected by the EXRAD onboard the NASA ER-2 high-altitude research aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD IMPACTS dataset files are available from January 25, 2020, through March 2, 2023, in HDF-5 format.", "links": [ { diff --git a/datasets/f0580e34da524770b0a5d43c033b33dc_NA.json b/datasets/f0580e34da524770b0a5d43c033b33dc_NA.json index 8666ae78a1..3ef19b4547 100644 --- a/datasets/f0580e34da524770b0a5d43c033b33dc_NA.json +++ b/datasets/f0580e34da524770b0a5d43c033b33dc_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f0580e34da524770b0a5d43c033b33dc_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "links": [ { diff --git a/datasets/f1445bde2f1249c99bb5a59b71e9a9d7_NA.json b/datasets/f1445bde2f1249c99bb5a59b71e9a9d7_NA.json index bd24e4eb3d..344836974f 100644 --- a/datasets/f1445bde2f1249c99bb5a59b71e9a9d7_NA.json +++ b/datasets/f1445bde2f1249c99bb5a59b71e9a9d7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f1445bde2f1249c99bb5a59b71e9a9d7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u00e2\u0080\u0093 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "links": [ { diff --git a/datasets/f17f146a31b14dfd960cde0874236ee5_NA.json b/datasets/f17f146a31b14dfd960cde0874236ee5_NA.json index facd47561d..da9d72bc11 100644 --- a/datasets/f17f146a31b14dfd960cde0874236ee5_NA.json +++ b/datasets/f17f146a31b14dfd960cde0874236ee5_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f17f146a31b14dfd960cde0874236ee5_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 50 km grid spacing is also available.", "links": [ { diff --git a/datasets/f1ab07b5292f4813bd3090b51d270aa8_NA.json b/datasets/f1ab07b5292f4813bd3090b51d270aa8_NA.json index 51b6bb70f6..1ac49cf749 100644 --- a/datasets/f1ab07b5292f4813bd3090b51d270aa8_NA.json +++ b/datasets/f1ab07b5292f4813bd3090b51d270aa8_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f1ab07b5292f4813bd3090b51d270aa8_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci MODIS-Terra dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Terra) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Terra dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "links": [ { diff --git a/datasets/f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d.json b/datasets/f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d.json index f56c945722..d3dd6dbba2 100644 --- a/datasets/f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d.json +++ b/datasets/f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September.", "links": [ { diff --git a/datasets/f31e8e988c4144bebe13892b53d08e42_NA.json b/datasets/f31e8e988c4144bebe13892b53d08e42_NA.json index 7fc8cee508..4e97fb9819 100644 --- a/datasets/f31e8e988c4144bebe13892b53d08e42_NA.json +++ b/datasets/f31e8e988c4144bebe13892b53d08e42_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f31e8e988c4144bebe13892b53d08e42_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The data have been produced by S[&]T Norway", "links": [ { diff --git a/datasets/f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA.json b/datasets/f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA.json index 9fdd0fed1b..cfa785b8a0 100644 --- a/datasets/f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA.json +++ b/datasets/f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "links": [ { diff --git a/datasets/f428fffb26cf4cd5b97dfb6381cb16bb_NA.json b/datasets/f428fffb26cf4cd5b97dfb6381cb16bb_NA.json index 6a74f55eb9..59dc197144 100644 --- a/datasets/f428fffb26cf4cd5b97dfb6381cb16bb_NA.json +++ b/datasets/f428fffb26cf4cd5b97dfb6381cb16bb_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f428fffb26cf4cd5b97dfb6381cb16bb_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for OSIRIS in 2008.", "links": [ { diff --git a/datasets/f4654030223445b0bac63a23aaa60620_NA.json b/datasets/f4654030223445b0bac63a23aaa60620_NA.json index df38f39d2a..b42dbb9e50 100644 --- a/datasets/f4654030223445b0bac63a23aaa60620_NA.json +++ b/datasets/f4654030223445b0bac63a23aaa60620_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f4654030223445b0bac63a23aaa60620_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05\u00c2\u00b0 grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 \u00e2\u0080\u0093 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).", "links": [ { diff --git a/datasets/f4c34f4f0f1d4d0da06d771f6972f180_NA.json b/datasets/f4c34f4f0f1d4d0da06d771f6972f180_NA.json index 02e3d41a44..69bcd66d57 100644 --- a/datasets/f4c34f4f0f1d4d0da06d771f6972f180_NA.json +++ b/datasets/f4c34f4f0f1d4d0da06d771f6972f180_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f4c34f4f0f1d4d0da06d771f6972f180_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April.", "links": [ { diff --git a/datasets/f5ffbd016e6b44858a33ae38ed2a149e_NA.json b/datasets/f5ffbd016e6b44858a33ae38ed2a149e_NA.json index aa507ab1a7..0ff850db0b 100644 --- a/datasets/f5ffbd016e6b44858a33ae38ed2a149e_NA.json +++ b/datasets/f5ffbd016e6b44858a33ae38ed2a149e_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f5ffbd016e6b44858a33ae38ed2a149e_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "links": [ { diff --git a/datasets/f9154243fd8744bdaf2a59c39033e659_NA.json b/datasets/f9154243fd8744bdaf2a59c39033e659_NA.json index 5471af6399..0b44b616d2 100644 --- a/datasets/f9154243fd8744bdaf2a59c39033e659_NA.json +++ b/datasets/f9154243fd8744bdaf2a59c39033e659_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f9154243fd8744bdaf2a59c39033e659_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4.This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "links": [ { diff --git a/datasets/f920473c-15a2-490c-8b24-b48f9b8a0226_NA.json b/datasets/f920473c-15a2-490c-8b24-b48f9b8a0226_NA.json index 5ea54e662e..866dc5e0c8 100644 --- a/datasets/f920473c-15a2-490c-8b24-b48f9b8a0226_NA.json +++ b/datasets/f920473c-15a2-490c-8b24-b48f9b8a0226_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f920473c-15a2-490c-8b24-b48f9b8a0226_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FireBIRD mission consists of two small satellites, TET-1 and BIROS. Together, the two satellites are on an Earth observation mission that aims to detect forest fires, or high-temperature events, from space. The new infrared system provides high-quality data that is capable of measuring the spread of the fire and the amount of heat generated with great accuracy very early on - almost in real time - meaning that FireBIRD can serve as an early warning system. The data acquired from this Earth observation mission can also be used as a basis for scientific climate research. In addition to the main payload of the cameras, further experiments have been planned for developing the technology on board the small satellites. Further information can be found on the following website: http://www.dlr.de/firebird/en/ and in the FireBIRD brochure available at: http://www.dlr.de/firebird/en/Portaldata/79/Resources/dokumente/FireBIRD_Broschuere_HighRes_v3_english.pdf", "links": [ { diff --git a/datasets/f97068fa-c098-4521-87ec-357c6e3b6960_NA.json b/datasets/f97068fa-c098-4521-87ec-357c6e3b6960_NA.json index c25f31b3cc..ab813f59a0 100644 --- a/datasets/f97068fa-c098-4521-87ec-357c6e3b6960_NA.json +++ b/datasets/f97068fa-c098-4521-87ec-357c6e3b6960_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "f97068fa-c098-4521-87ec-357c6e3b6960_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-days maps.", "links": [ { diff --git a/datasets/fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA.json b/datasets/fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA.json index 05a79392f3..dbd3f2b2bd 100644 --- a/datasets/fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA.json +++ b/datasets/fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in Spotlight mode. Spotlight imaging allows for a spatial resolution of up to 2 m at a scene size of 10 km (across swath) x 10 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to:\t\t\thttps://www.dlr.de/content/de/missionen/terrasar-x.html", "links": [ { diff --git a/datasets/faamwdat_237_1.json b/datasets/faamwdat_237_1.json index fe90fa78c7..97a5ea7588 100644 --- a/datasets/faamwdat_237_1.json +++ b/datasets/faamwdat_237_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "faamwdat_237_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.", "links": [ { diff --git a/datasets/face-stillberg_1.0.json b/datasets/face-stillberg_1.0.json index 43ff9bcc58..ea4dcd2f48 100644 --- a/datasets/face-stillberg_1.0.json +++ b/datasets/face-stillberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "face-stillberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 \u00b0C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project. # Experimental design The experiment consisted of 40 hexagonal 1.1 m\u00b2 plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species. At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species. # Data description Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements.", "links": [ { diff --git a/datasets/factors-influencing-teenagers-forest-visit-frequency_1.0.json b/datasets/factors-influencing-teenagers-forest-visit-frequency_1.0.json index 30a48cdd53..bc565c4475 100644 --- a/datasets/factors-influencing-teenagers-forest-visit-frequency_1.0.json +++ b/datasets/factors-influencing-teenagers-forest-visit-frequency_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "factors-influencing-teenagers-forest-visit-frequency_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.", "links": [ { diff --git a/datasets/factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0.json b/datasets/factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0.json index 3ebc97deb8..ca2743afe5 100644 --- a/datasets/factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0.json +++ b/datasets/factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Species range limits are expected to be dramatically altered under future climate change and many species are predicted to shift their distribution upslope to track their suitable conditions (i.e. based on their niche). However, there might be large discrepancies between the speed of the upward shift of the climatic niche and the actual migration velocity of the species, especially in long-lived organisms such as trees. Here, we compared the simulations of the upslope displacement of the bioclimatic envelope of 16 tree species inhabiting temperate mountain forests under ongoing and future climate change obtained by correlative species distribution models (SDMs) to those from a dynamic forest model accounting for dispersal, competition and demography. We then partitioned the discrepancy in upslope migration velocity between the SDMs and the dynamic forest model into different components by manipulating dispersal limitation, interspecific competition and demography. This dataset contains the calibration and evaluation data used to create the bioclimatic envelope models, the predictors for the future scenarios (raster layers) and the bioclimatic input data used in the dynamic forest models used in the following publication (Scherrer et al. 2020). Paper Citation: Scherrer, D., Vitasse, Y., Guisan , A., Wohlgemuth, T., & Lischke, H. (2020). Competition and demography rather than dispersal limitation slow down upward shifts of trees\u2019 upper elevation limits in the Alps. Journal of Ecology, in press.", "links": [ { diff --git a/datasets/fasir_biophys_monthly_xdeg_970_1.json b/datasets/fasir_biophys_monthly_xdeg_970_1.json index e2c059e194..88b507e7b7 100644 --- a/datasets/fasir_biophys_monthly_xdeg_970_1.json +++ b/datasets/fasir_biophys_monthly_xdeg_970_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fasir_biophys_monthly_xdeg_970_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data set and derived biophysical parameter fields were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. This multiple resolution (1/4, 1/2 and 1 degree in latitude and longitude) biophysical parameter data set contains essential variables for the calculation of photosynthesis, and the energy and water exchange between the Earth's surface (in particular of vegetation) and the lower boundary layer of the atmosphere. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is related to the light absorption and the photosynthetic capacity of vegetation. It also serves as an intermediate variable to calculate vegetation cover fraction (Vcover), total Leaf Area Index (LAI_T), green leaf area index (LAI_G), roughness length (z0), zero plane displacement (d), and snow-free albedo. The biophysical parameters were derived assuming one canopy layer. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.", "links": [ { diff --git a/datasets/fasir_ndvi_monthly_xdeg_972_1.json b/datasets/fasir_ndvi_monthly_xdeg_972_1.json index cca66ccda2..3fd1571cf2 100644 --- a/datasets/fasir_ndvi_monthly_xdeg_972_1.json +++ b/datasets/fasir_ndvi_monthly_xdeg_972_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fasir_ndvi_monthly_xdeg_972_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. There are three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude. FASIR adjustments concentrated on reducing NDVI variations arising from atmospheric, calibration, view and illumination geometries and other effects not related to actual vegetation change.FASIR NDVI was also generated to provide inputs for computing a 17-year time series of associated biophysical parameters, provided as a separate data set in this data collection. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.", "links": [ { diff --git a/datasets/fast_ice_1997_1999_1.json b/datasets/fast_ice_1997_1999_1.json index 8cc82e3ce0..8eb028a6f1 100644 --- a/datasets/fast_ice_1997_1999_1.json +++ b/datasets/fast_ice_1997_1999_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fast_ice_1997_1999_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An image correlation technique has been applied to RADARSAT ScanSAR images from November in 1997, and November 1999, to create the first detailed maps of fast ice around East Antarctica (75E-170E). This method is based upon searching for, and distinguishing, correlated regions of the ice-covered ocean which remain stationary, in contrast to adjacent moving pack ice. Within the overlapping longitudinal range of ~86E-150.6E, the total fast-ice area is 141,450 km2 in 1997 and 152,216 km2 in 1999. Calibrated radar backscatter data are also used to determine the distribution of two fast-ice classes based on their surface roughness characteristics.\n\nThe outer boundaries of the determined fast-ice area for November in 1997 and 1999 are contained in the data files for this record.\n\nThis work has been allocated to ASAC project 3024.", "links": [ { diff --git a/datasets/fast_ice_adelie_1.json b/datasets/fast_ice_adelie_1.json index e5b512fabb..d1845786c8 100644 --- a/datasets/fast_ice_adelie_1.json +++ b/datasets/fast_ice_adelie_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fast_ice_adelie_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999.\n\nThe areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time).\n\nWhile cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/).\n\nInitial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel.\n\nA number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996).\n\nIn addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins.\n\nWe examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002).\n\nThe effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).", "links": [ { diff --git a/datasets/fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0.json b/datasets/fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0.json index 508b530495..603ee271d1 100644 --- a/datasets/fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0.json +++ b/datasets/fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**. This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one fatality. The data set commences on 01/10/1936. After the completion of a hydrological year, the new data is added. The following information is provided: * avalanche identifier * date of the accident * accuracy of the date in range of days before and after * hydrological year (always from first of october to end of september) * canton * municipality * start zone point latitude * start zone point longitude * start zone point accuracy (in meters) * start zone point elevation (in meteres above sea level) * slope aspect (main orientation of start zone) * slope inclination (in degree, steepest point within start zone) * forecasted avalanche danger level 1 (first danger) * forecasted avalanche danger level 2 (second danger) * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast) * number of dead persons * number of caught persons * number of fully buried persons * activity/location of the accident party at the time of the incident", "links": [ { diff --git a/datasets/fatal-avalanche-accidents-switzerland-1995_1.0.json b/datasets/fatal-avalanche-accidents-switzerland-1995_1.0.json index f90bb0cfcf..09dcf6c99d 100644 --- a/datasets/fatal-avalanche-accidents-switzerland-1995_1.0.json +++ b/datasets/fatal-avalanche-accidents-switzerland-1995_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fatal-avalanche-accidents-switzerland-1995_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Attention: this data is not updated after 2022 anymore. This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added. The following information is provided: * avalanche identifier * date of the accident * accuracy of the date in range of days before and after * canton * name of the locality * start zone of the avalanche * coordinates (Swiss coordinate system, approximately in middle of start zone) * accuracy of the coordinates in meters * elevation (in meteres above sea level, app. in middle of start zone) * slope aspect (main orientation of start zone) * slope inclination (in degree, steepest point within start zone) * number of dead persons * number of caught persons * number of fully buried persons * forecasted avalanche danger level * activity/location of the accident party at the time of the incident", "links": [ { diff --git a/datasets/fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA.json b/datasets/fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA.json index f9faf5b6bb..c9933ec5a6 100644 --- a/datasets/fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA.json +++ b/datasets/fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.", "links": [ { diff --git a/datasets/fb3750f5b2544403873f8788b3ed7817_NA.json b/datasets/fb3750f5b2544403873f8788b3ed7817_NA.json index bb6549da02..ec50c59acd 100644 --- a/datasets/fb3750f5b2544403873f8788b3ed7817_NA.json +++ b/datasets/fb3750f5b2544403873f8788b3ed7817_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fb3750f5b2544403873f8788b3ed7817_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00c3\u00bcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.", "links": [ { diff --git a/datasets/fbfae06e787b4fefb4b03cba2fd04bc3_NA.json b/datasets/fbfae06e787b4fefb4b03cba2fd04bc3_NA.json index 8e02c15132..16772ccd80 100644 --- a/datasets/fbfae06e787b4fefb4b03cba2fd04bc3_NA.json +++ b/datasets/fbfae06e787b4fefb4b03cba2fd04bc3_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fbfae06e787b4fefb4b03cba2fd04bc3_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "links": [ { diff --git a/datasets/fdp-grapevine-trunks-impact-xylem-phloem_1.0.json b/datasets/fdp-grapevine-trunks-impact-xylem-phloem_1.0.json index 9958e83170..ccd30f6f7e 100644 --- a/datasets/fdp-grapevine-trunks-impact-xylem-phloem_1.0.json +++ b/datasets/fdp-grapevine-trunks-impact-xylem-phloem_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fdp-grapevine-trunks-impact-xylem-phloem_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the \"Flavescence dor\u00e9e\" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R. The Flavescence dor\u00e9e phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar \u2018Chardonnay\u2019 in order to verify their response to FDp infections. In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens.", "links": [ { diff --git a/datasets/fe651dbef5d44248bef70906f4b3d12b_NA.json b/datasets/fe651dbef5d44248bef70906f4b3d12b_NA.json index 8d29b3f1d6..302a1bfd0a 100644 --- a/datasets/fe651dbef5d44248bef70906f4b3d12b_NA.json +++ b/datasets/fe651dbef5d44248bef70906f4b3d12b_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fe651dbef5d44248bef70906f4b3d12b_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR instrument. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-SMR_ODIN-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for ODIN/SMR in 2008.", "links": [ { diff --git a/datasets/feral_cat_macca_1.json b/datasets/feral_cat_macca_1.json index 2d367de635..850e570b90 100644 --- a/datasets/feral_cat_macca_1.json +++ b/datasets/feral_cat_macca_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "feral_cat_macca_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "From the referenced paper:\n\nBetween December 1976 and February 1981, 246 cats were collected. Overall sex ratio was in favour of males 1:0.8, and coat colour was tabby (74%), orange (26%) and black (2%). The breeding season extended from October to March with the peak in November-December. Mean number of embryos was 4.7 per female and evidence of females producing two litters was found. Mortality in kittens increased as they grew older, with litters of kittens greater than 1.8 kg containing two or fewer animals. Most cats lived in herbfield or tussock grassland, with very few if any in feldmark. The total population was estimated at between 169 and 252 adult cats. Observations of an adult male showed that its home range covered 41 ha, but this appeared not to be maintained during winter. It's daytime activity varied greatly, much time being spent foraging for food.\n\nDomestic cats Felis catus (L.) were feral on Macquarie Island by 1820, only 10 years after the island was discovered by sealers. Their presence was soon noted by early naturalists. Depredations by cats greatly reduced the numbers of burrow-nesting petrels and, together with the weka Gallirallus australis, cats were probably responsible for the extinction of the endemic parakeet Cyanoramphus novaezelandiae erythrotis and banded rail Rallus phillippensis before 1900. Feral cats are common on several other subantarctic islands and have been intensively studied; the only previous study on Macquarie Island was on diet. This study reports on other aspects of the biology of the feral cat on Macquarie Island.", "links": [ { diff --git a/datasets/ff4bfe39b7fe42fc993341d3cebdabb5_NA.json b/datasets/ff4bfe39b7fe42fc993341d3cebdabb5_NA.json index 5591eb8521..c08a573e63 100644 --- a/datasets/ff4bfe39b7fe42fc993341d3cebdabb5_NA.json +++ b/datasets/ff4bfe39b7fe42fc993341d3cebdabb5_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ff4bfe39b7fe42fc993341d3cebdabb5_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., S\u00c3\u00b8rensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.", "links": [ { diff --git a/datasets/ff79d140824f42dd92b204b4f1e9e7c2_NA.json b/datasets/ff79d140824f42dd92b204b4f1e9e7c2_NA.json index 76aa00837b..ebfc1b4a04 100644 --- a/datasets/ff79d140824f42dd92b204b4f1e9e7c2_NA.json +++ b/datasets/ff79d140824f42dd92b204b4f1e9e7c2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ff79d140824f42dd92b204b4f1e9e7c2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April.", "links": [ { diff --git a/datasets/ffo_Betts_1987-1989_afd_93_1.json b/datasets/ffo_Betts_1987-1989_afd_93_1.json index 5fd61d7e57..51c67adf6c 100644 --- a/datasets/ffo_Betts_1987-1989_afd_93_1.json +++ b/datasets/ffo_Betts_1987-1989_afd_93_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987-1989_afd_93_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the flux data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30-minute intervals and include the entire period 1987-1989.", "links": [ { diff --git a/datasets/ffo_Betts_1987-1989_ams_89_1.json b/datasets/ffo_Betts_1987-1989_ams_89_1.json index 4eeaed43e3..75be82db4a 100644 --- a/datasets/ffo_Betts_1987-1989_ams_89_1.json +++ b/datasets/ffo_Betts_1987-1989_ams_89_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987-1989_ams_89_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30-minute time intervals.", "links": [ { diff --git a/datasets/ffo_Betts_1987-1989_gsm_97_1.json b/datasets/ffo_Betts_1987-1989_gsm_97_1.json index 50865c910f..34c9ae4907 100644 --- a/datasets/ffo_Betts_1987-1989_gsm_97_1.json +++ b/datasets/ffo_Betts_1987-1989_gsm_97_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987-1989_gsm_97_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the gravimetric soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes 1987-1989 data.", "links": [ { diff --git a/datasets/ffo_Betts_1987-1989_nsm_101_1.json b/datasets/ffo_Betts_1987-1989_nsm_101_1.json index 3eaca73f07..83de38f4f5 100644 --- a/datasets/ffo_Betts_1987-1989_nsm_101_1.json +++ b/datasets/ffo_Betts_1987-1989_nsm_101_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987-1989_nsm_101_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes 1987-1989 data.", "links": [ { diff --git a/datasets/ffo_Betts_1987_afd_92_1.json b/datasets/ffo_Betts_1987_afd_92_1.json index 320fb1d34e..182c73349c 100644 --- a/datasets/ffo_Betts_1987_afd_92_1.json +++ b/datasets/ffo_Betts_1987_afd_92_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987_afd_92_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the flux data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30-minute intervals and include data only for 1987.", "links": [ { diff --git a/datasets/ffo_Betts_1987_ams_88_1.json b/datasets/ffo_Betts_1987_ams_88_1.json index 79d56d3701..e69b0c2792 100644 --- a/datasets/ffo_Betts_1987_ams_88_1.json +++ b/datasets/ffo_Betts_1987_ams_88_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987_ams_88_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30-minute time intervals in 1987.", "links": [ { diff --git a/datasets/ffo_Betts_1987_gsm_96_1.json b/datasets/ffo_Betts_1987_gsm_96_1.json index 59cd82a9bc..f7cf0c80df 100644 --- a/datasets/ffo_Betts_1987_gsm_96_1.json +++ b/datasets/ffo_Betts_1987_gsm_96_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987_gsm_96_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the gravimetric soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes only 1987 data.", "links": [ { diff --git a/datasets/ffo_Betts_1987_nsm_100_1.json b/datasets/ffo_Betts_1987_nsm_100_1.json index 83613e8f36..8bac4bcad3 100644 --- a/datasets/ffo_Betts_1987_nsm_100_1.json +++ b/datasets/ffo_Betts_1987_nsm_100_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1987_nsm_100_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes only 1987 data.", "links": [ { diff --git a/datasets/ffo_Betts_1988_afd_94_1.json b/datasets/ffo_Betts_1988_afd_94_1.json index 5a249ce12a..1a4879d701 100644 --- a/datasets/ffo_Betts_1988_afd_94_1.json +++ b/datasets/ffo_Betts_1988_afd_94_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1988_afd_94_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the flux data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30-minute intervals and include data only for 1988.", "links": [ { diff --git a/datasets/ffo_Betts_1988_ams_90_1.json b/datasets/ffo_Betts_1988_ams_90_1.json index 1bdb1ab2a6..76f1305f00 100644 --- a/datasets/ffo_Betts_1988_ams_90_1.json +++ b/datasets/ffo_Betts_1988_ams_90_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1988_ams_90_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30-minute time intervals in 1988.", "links": [ { diff --git a/datasets/ffo_Betts_1988_gsm_98_1.json b/datasets/ffo_Betts_1988_gsm_98_1.json index 2a68dc4bca..5ff1c3406f 100644 --- a/datasets/ffo_Betts_1988_gsm_98_1.json +++ b/datasets/ffo_Betts_1988_gsm_98_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1988_gsm_98_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the gravimetric soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes only 1988 data.", "links": [ { diff --git a/datasets/ffo_Betts_1988_nsm_102_1.json b/datasets/ffo_Betts_1988_nsm_102_1.json index b8d50efdd8..51071e6166 100644 --- a/datasets/ffo_Betts_1988_nsm_102_1.json +++ b/datasets/ffo_Betts_1988_nsm_102_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1988_nsm_102_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes only 1988 data.", "links": [ { diff --git a/datasets/ffo_Betts_1989_afd_95_1.json b/datasets/ffo_Betts_1989_afd_95_1.json index eb65c0cefd..32beddc518 100644 --- a/datasets/ffo_Betts_1989_afd_95_1.json +++ b/datasets/ffo_Betts_1989_afd_95_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1989_afd_95_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the flux data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30-minute intervals and include data only for 1989.", "links": [ { diff --git a/datasets/ffo_Betts_1989_ams_91_1.json b/datasets/ffo_Betts_1989_ams_91_1.json index faed62d07d..d863a47f05 100644 --- a/datasets/ffo_Betts_1989_ams_91_1.json +++ b/datasets/ffo_Betts_1989_ams_91_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1989_ams_91_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30-minute time intervals in 1989.", "links": [ { diff --git a/datasets/ffo_Betts_1989_gsm_99_1.json b/datasets/ffo_Betts_1989_gsm_99_1.json index 4271caff7e..4e53c6d219 100644 --- a/datasets/ffo_Betts_1989_gsm_99_1.json +++ b/datasets/ffo_Betts_1989_gsm_99_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1989_gsm_99_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the gravimetric soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Includes only 1989 data.", "links": [ { diff --git a/datasets/ffo_Betts_1989_nsm_103_1.json b/datasets/ffo_Betts_1989_nsm_103_1.json index b673b35174..43dfc003a2 100644 --- a/datasets/ffo_Betts_1989_nsm_103_1.json +++ b/datasets/ffo_Betts_1989_nsm_103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ffo_Betts_1989_nsm_103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged product of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Only 15 days available in 89.", "links": [ { diff --git a/datasets/fhstmanr_386_1.json b/datasets/fhstmanr_386_1.json index 5171180e82..50dd0905c9 100644 --- a/datasets/fhstmanr_386_1.json +++ b/datasets/fhstmanr_386_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fhstmanr_386_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raster format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.", "links": [ { diff --git a/datasets/fhstmanv_387_1.json b/datasets/fhstmanv_387_1.json index 40d532fed1..1cd5220b93 100644 --- a/datasets/fhstmanv_387_1.json +++ b/datasets/fhstmanv_387_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fhstmanv_387_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.", "links": [ { diff --git a/datasets/fiber-bundle-model-for-snow-failure_1.0.json b/datasets/fiber-bundle-model-for-snow-failure_1.0.json index 8262d77c17..f230c2112a 100644 --- a/datasets/fiber-bundle-model-for-snow-failure_1.0.json +++ b/datasets/fiber-bundle-model-for-snow-failure_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fiber-bundle-model-for-snow-failure_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation. The data underlay the figures in the publication \"Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model\" submitted for publication to \"Geophysical Research Letters\".", "links": [ { diff --git a/datasets/field-observations-of-snow-instabilities_1.0.json b/datasets/field-observations-of-snow-instabilities_1.0.json index 7f0ccfa0da..010dcb1fdf 100644 --- a/datasets/field-observations-of-snow-instabilities_1.0.json +++ b/datasets/field-observations-of-snow-instabilities_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "field-observations-of-snow-instabilities_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019. These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height. The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file. These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021. ### Acknowlegements Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas D\u00fcrr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.", "links": [ { diff --git a/datasets/fieldsunp_65_1.json b/datasets/fieldsunp_65_1.json index 627fdd480d..7804682570 100644 --- a/datasets/fieldsunp_65_1.json +++ b/datasets/fieldsunp_65_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fieldsunp_65_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field sunphotometer data collected on 8/13-15/90 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance", "links": [ { diff --git a/datasets/fieldwork_lawdome_1964_1.json b/datasets/fieldwork_lawdome_1964_1.json index 347907d923..e7b7042432 100644 --- a/datasets/fieldwork_lawdome_1964_1.json +++ b/datasets/fieldwork_lawdome_1964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fieldwork_lawdome_1964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of notes and field data collected in traverse work on Law Dome/Wilkes Land in 1964. Includes data on gravity, air pressure (barometric levelling), air temperature, wind, snow accumulation stakes, ice movement. Also includes results from S2 pit measurements.", "links": [ { diff --git a/datasets/fife_AF_dtrnd_nae_3_1.json b/datasets/fife_AF_dtrnd_nae_3_1.json index b982075ba7..d3665acf9a 100644 --- a/datasets/fife_AF_dtrnd_nae_3_1.json +++ b/datasets/fife_AF_dtrnd_nae_3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_dtrnd_nae_3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detrended boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_dtrnd_ncar_5_1.json b/datasets/fife_AF_dtrnd_ncar_5_1.json index ca9fdab111..5319ec5f60 100644 --- a/datasets/fife_AF_dtrnd_ncar_5_1.json +++ b/datasets/fife_AF_dtrnd_ncar_5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_dtrnd_ncar_5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detrended boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_dtrnd_wyo_4_1.json b/datasets/fife_AF_dtrnd_wyo_4_1.json index 1d7db902d7..62ea8d34f9 100644 --- a/datasets/fife_AF_dtrnd_wyo_4_1.json +++ b/datasets/fife_AF_dtrnd_wyo_4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_dtrnd_wyo_4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detrended boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_filtr_nae_6_1.json b/datasets/fife_AF_filtr_nae_6_1.json index a466c924dc..2ffa43a417 100644 --- a/datasets/fife_AF_filtr_nae_6_1.json +++ b/datasets/fife_AF_filtr_nae_6_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_filtr_nae_6_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Filtered boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_filtr_ncar_8_1.json b/datasets/fife_AF_filtr_ncar_8_1.json index c25bcb43e1..1df08196fd 100644 --- a/datasets/fife_AF_filtr_ncar_8_1.json +++ b/datasets/fife_AF_filtr_ncar_8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_filtr_ncar_8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Filtered boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_filtr_wyo_7_1.json b/datasets/fife_AF_filtr_wyo_7_1.json index 1b973bff4a..11a5b5b6bb 100644 --- a/datasets/fife_AF_filtr_wyo_7_1.json +++ b/datasets/fife_AF_filtr_wyo_7_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_filtr_wyo_7_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Filtered boundary layer fluxes recorded on aircraft flights over the Konza", "links": [ { diff --git a/datasets/fife_AF_raw_nae_9_1.json b/datasets/fife_AF_raw_nae_9_1.json index 65bae0cdda..7acb57214e 100644 --- a/datasets/fife_AF_raw_nae_9_1.json +++ b/datasets/fife_AF_raw_nae_9_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_raw_nae_9_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza", "links": [ { diff --git a/datasets/fife_AF_raw_ncar_11_1.json b/datasets/fife_AF_raw_ncar_11_1.json index c8c3899915..e264e9d9ce 100644 --- a/datasets/fife_AF_raw_ncar_11_1.json +++ b/datasets/fife_AF_raw_ncar_11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_raw_ncar_11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza", "links": [ { diff --git a/datasets/fife_AF_raw_wyo_10_1.json b/datasets/fife_AF_raw_wyo_10_1.json index efc65ef9d6..674c165e1e 100644 --- a/datasets/fife_AF_raw_wyo_10_1.json +++ b/datasets/fife_AF_raw_wyo_10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_AF_raw_wyo_10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza", "links": [ { diff --git a/datasets/fife_atmos_brut_drv_14_1.json b/datasets/fife_atmos_brut_drv_14_1.json index a86a2d5783..e9c6517762 100644 --- a/datasets/fife_atmos_brut_drv_14_1.json +++ b/datasets/fife_atmos_brut_drv_14_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_brut_drv_14_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data", "links": [ { diff --git a/datasets/fife_atmos_brut_son_15_1.json b/datasets/fife_atmos_brut_son_15_1.json index 4db99072f3..46c75ddf13 100644 --- a/datasets/fife_atmos_brut_son_15_1.json +++ b/datasets/fife_atmos_brut_son_15_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_brut_son_15_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Radiosonde observations from Wilf Brutsaert", "links": [ { diff --git a/datasets/fife_atmos_lidar_ht_17_1.json b/datasets/fife_atmos_lidar_ht_17_1.json index c1a7113a3f..04822c9bde 100644 --- a/datasets/fife_atmos_lidar_ht_17_1.json +++ b/datasets/fife_atmos_lidar_ht_17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_lidar_ht_17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Height of the mixed layer gas for each LIDAR shot in volume scan, then averaged", "links": [ { diff --git a/datasets/fife_atmos_ncdc_son_13_1.json b/datasets/fife_atmos_ncdc_son_13_1.json index 044167c88f..1838f92253 100644 --- a/datasets/fife_atmos_ncdc_son_13_1.json +++ b/datasets/fife_atmos_ncdc_son_13_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_ncdc_son_13_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCDC radiosonde atmospheric profile data from stations near FIFE", "links": [ { diff --git a/datasets/fife_atmos_nmc_upr_57_1.json b/datasets/fife_atmos_nmc_upr_57_1.json index b32b544e50..c59728373f 100644 --- a/datasets/fife_atmos_nmc_upr_57_1.json +++ b/datasets/fife_atmos_nmc_upr_57_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_nmc_upr_57_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NMC interpolated upper air condition data received from NESDIS", "links": [ { diff --git a/datasets/fife_atmos_noaa_son_73_1.json b/datasets/fife_atmos_noaa_son_73_1.json index 30f13a2ab0..94179c1ef5 100644 --- a/datasets/fife_atmos_noaa_son_73_1.json +++ b/datasets/fife_atmos_noaa_son_73_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_noaa_son_73_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA radiosonde data from the two stations near FIFE", "links": [ { diff --git a/datasets/fife_atmos_noaa_tov_16_1.json b/datasets/fife_atmos_noaa_tov_16_1.json index 39fc7f7fc4..1cdfa66fc6 100644 --- a/datasets/fife_atmos_noaa_tov_16_1.json +++ b/datasets/fife_atmos_noaa_tov_16_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_noaa_tov_16_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOVS data received by FIFE", "links": [ { diff --git a/datasets/fife_atmos_sodar_18_1.json b/datasets/fife_atmos_sodar_18_1.json index 2e80e375c0..211076953b 100644 --- a/datasets/fife_atmos_sodar_18_1.json +++ b/datasets/fife_atmos_sodar_18_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_sodar_18_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inversion heights as measured using a Sodar by R.L. Coulter & M.L. Wesely", "links": [ { diff --git a/datasets/fife_atmos_tempprof_124_1.json b/datasets/fife_atmos_tempprof_124_1.json index 3297ce41a6..35e6e94eb4 100644 --- a/datasets/fife_atmos_tempprof_124_1.json +++ b/datasets/fife_atmos_tempprof_124_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_tempprof_124_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature & humidity profile data derived from Brutsaert's radiosonde data", "links": [ { diff --git a/datasets/fife_atmos_wind_lid_138_1.json b/datasets/fife_atmos_wind_lid_138_1.json index 3a75df2f96..8d3519b60d 100644 --- a/datasets/fife_atmos_wind_lid_138_1.json +++ b/datasets/fife_atmos_wind_lid_138_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_wind_lid_138_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wind profile data from NOAA LIDAR measurements", "links": [ { diff --git a/datasets/fife_atmos_wind_son_139_1.json b/datasets/fife_atmos_wind_son_139_1.json index 506a847af3..40a337073b 100644 --- a/datasets/fife_atmos_wind_son_139_1.json +++ b/datasets/fife_atmos_wind_son_139_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_atmos_wind_son_139_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wind profile data derived from Brutsaert's radiosonde measurements", "links": [ { diff --git a/datasets/fife_biology_biomass_118_1.json b/datasets/fife_biology_biomass_118_1.json index f09de8079f..922db99def 100644 --- a/datasets/fife_biology_biomass_118_1.json +++ b/datasets/fife_biology_biomass_118_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_biomass_118_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biomass weight & nitrogen content for plants collected along transects & dried", "links": [ { diff --git a/datasets/fife_biology_leaf_ang_44_1.json b/datasets/fife_biology_leaf_ang_44_1.json index d1b8bb0bfe..4b53f8c8de 100644 --- a/datasets/fife_biology_leaf_ang_44_1.json +++ b/datasets/fife_biology_leaf_ang_44_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_leaf_ang_44_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Orientation of leaves of 10 different species", "links": [ { diff --git a/datasets/fife_biology_leaf_h2o_126_1.json b/datasets/fife_biology_leaf_h2o_126_1.json index 3aee3a4f92..7b5c3b6855 100644 --- a/datasets/fife_biology_leaf_h2o_126_1.json +++ b/datasets/fife_biology_leaf_h2o_126_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_leaf_h2o_126_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf water potential data collected w/ Scholander pressure chamber by University of Nebraska", "links": [ { diff --git a/datasets/fife_biology_mow_biop_55_1.json b/datasets/fife_biology_mow_biop_55_1.json index 79877ef4d6..2f69664db4 100644 --- a/datasets/fife_biology_mow_biop_55_1.json +++ b/datasets/fife_biology_mow_biop_55_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_mow_biop_55_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plant biomass data where grazing height or frequency were simulated by mowing", "links": [ { diff --git a/datasets/fife_biology_mow_exo_56_1.json b/datasets/fife_biology_mow_exo_56_1.json index 822a134989..ff70ed6515 100644 --- a/datasets/fife_biology_mow_exo_56_1.json +++ b/datasets/fife_biology_mow_exo_56_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_mow_exo_56_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground based spectral reflectance in MSS bands 1-4 from Exotech model 100-A", "links": [ { diff --git a/datasets/fife_biology_pho_box_27_1.json b/datasets/fife_biology_pho_box_27_1.json index 2e47f5341e..9c5ac23fec 100644 --- a/datasets/fife_biology_pho_box_27_1.json +++ b/datasets/fife_biology_pho_box_27_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_pho_box_27_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy photosynthesis rates and resistance chamber measurements during FIFE", "links": [ { diff --git a/datasets/fife_biology_pho_leaf_46_1.json b/datasets/fife_biology_pho_leaf_46_1.json index 5e6f7f5dcb..a2bebc3a9b 100644 --- a/datasets/fife_biology_pho_leaf_46_1.json +++ b/datasets/fife_biology_pho_leaf_46_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_pho_leaf_46_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of leaf photosynthesis rates measured with Li-Cor LI-6200", "links": [ { diff --git a/datasets/fife_biology_plantpro_69_1.json b/datasets/fife_biology_plantpro_69_1.json index d3271f99aa..c85ba85a99 100644 --- a/datasets/fife_biology_plantpro_69_1.json +++ b/datasets/fife_biology_plantpro_69_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_plantpro_69_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimates of plant standing crop, plant productions & consumption by herbivores", "links": [ { diff --git a/datasets/fife_biology_root_bio_75_1.json b/datasets/fife_biology_root_bio_75_1.json index 19bca77f2b..371a989111 100644 --- a/datasets/fife_biology_root_bio_75_1.json +++ b/datasets/fife_biology_root_bio_75_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_root_bio_75_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plant root biomass data where grazing height/frequency were simulated by mowing", "links": [ { diff --git a/datasets/fife_biology_soil_co2_105_1.json b/datasets/fife_biology_soil_co2_105_1.json index 3241f0f54b..5a1c86fb1e 100644 --- a/datasets/fife_biology_soil_co2_105_1.json +++ b/datasets/fife_biology_soil_co2_105_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_soil_co2_105_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil surface CO2 flux data collected by John Norman", "links": [ { diff --git a/datasets/fife_biology_soil_gas_106_1.json b/datasets/fife_biology_soil_gas_106_1.json index a5666e2bb2..b6109d73b4 100644 --- a/datasets/fife_biology_soil_gas_106_1.json +++ b/datasets/fife_biology_soil_gas_106_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_soil_gas_106_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nitrous oxide flux and CO2 from soil respiration measured using soil cores", "links": [ { diff --git a/datasets/fife_biology_veg_biop_135_1.json b/datasets/fife_biology_veg_biop_135_1.json index 284b731bbd..e6f46fa3f5 100644 --- a/datasets/fife_biology_veg_biop_135_1.json +++ b/datasets/fife_biology_veg_biop_135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_veg_biop_135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of leaf area index and biomass of different canopy components", "links": [ { diff --git a/datasets/fife_biology_veg_ref_137_1.json b/datasets/fife_biology_veg_ref_137_1.json index bcb1e041c1..6e2bd69464 100644 --- a/datasets/fife_biology_veg_ref_137_1.json +++ b/datasets/fife_biology_veg_ref_137_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_veg_ref_137_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LTER species names, codes, types, and other reference information", "links": [ { diff --git a/datasets/fife_biology_veg_spec_136_1.json b/datasets/fife_biology_veg_spec_136_1.json index 57f403ebb5..560d1fc1d2 100644 --- a/datasets/fife_biology_veg_spec_136_1.json +++ b/datasets/fife_biology_veg_spec_136_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_biology_veg_spec_136_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Species composition data, by site and date", "links": [ { diff --git a/datasets/fife_hydrology_strm_15m_1_1.json b/datasets/fife_hydrology_strm_15m_1_1.json index 0eca57ce14..8a6665ea63 100644 --- a/datasets/fife_hydrology_strm_15m_1_1.json +++ b/datasets/fife_hydrology_strm_15m_1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_hydrology_strm_15m_1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "USGS 15 minute stream flow data for Kings Creek on the Konza Prairie", "links": [ { diff --git a/datasets/fife_hydrology_strm_day_119_1.json b/datasets/fife_hydrology_strm_day_119_1.json index 8e1558eddf..5f0a2abdda 100644 --- a/datasets/fife_hydrology_strm_day_119_1.json +++ b/datasets/fife_hydrology_strm_day_119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_hydrology_strm_day_119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "USGS daily stream flow data for Kings Creek on the Konza Prairie", "links": [ { diff --git a/datasets/fife_hydrology_strm_st_120_1.json b/datasets/fife_hydrology_strm_st_120_1.json index bac6616cc0..9307dc9598 100644 --- a/datasets/fife_hydrology_strm_st_120_1.json +++ b/datasets/fife_hydrology_strm_st_120_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_hydrology_strm_st_120_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "USGS stream flow during storm events around Kings Creek on the Konza Prairie", "links": [ { diff --git a/datasets/fife_optical_ot_brug_62_1.json b/datasets/fife_optical_ot_brug_62_1.json index 1b02daa28a..67e5f8df53 100644 --- a/datasets/fife_optical_ot_brug_62_1.json +++ b/datasets/fife_optical_ot_brug_62_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_optical_ot_brug_62_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optical thickness data from Dr. Carol Bruegge, JPL", "links": [ { diff --git a/datasets/fife_optical_ot_c130_63_1.json b/datasets/fife_optical_ot_c130_63_1.json index 5d394c57f9..af69320fe5 100644 --- a/datasets/fife_optical_ot_c130_63_1.json +++ b/datasets/fife_optical_ot_c130_63_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_optical_ot_c130_63_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne sun photometer data taken from C-130", "links": [ { diff --git a/datasets/fife_optical_ot_calib_60_1.json b/datasets/fife_optical_ot_calib_60_1.json index e55c27a0af..4101b907ec 100644 --- a/datasets/fife_optical_ot_calib_60_1.json +++ b/datasets/fife_optical_ot_calib_60_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_optical_ot_calib_60_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cross referenced calibration optical thickness data from C-130 and KSU staff", "links": [ { diff --git a/datasets/fife_optical_ot_frasr_64_1.json b/datasets/fife_optical_ot_frasr_64_1.json index 8ce70c1c37..470978b517 100644 --- a/datasets/fife_optical_ot_frasr_64_1.json +++ b/datasets/fife_optical_ot_frasr_64_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_optical_ot_frasr_64_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerosol optical thickness data reported by Robert Fraser", "links": [ { diff --git a/datasets/fife_optical_ot_staff_66_1.json b/datasets/fife_optical_ot_staff_66_1.json index 8ca7dc7561..e2c211b106 100644 --- a/datasets/fife_optical_ot_staff_66_1.json +++ b/datasets/fife_optical_ot_staff_66_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_optical_ot_staff_66_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optical thickness data from KSU staff science", "links": [ { diff --git a/datasets/fife_sat_obs_ns001tms_59_1.json b/datasets/fife_sat_obs_ns001tms_59_1.json index 10ace7337c..ffe7038d14 100644 --- a/datasets/fife_sat_obs_ns001tms_59_1.json +++ b/datasets/fife_sat_obs_ns001tms_59_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sat_obs_ns001tms_59_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site radiance, temperature means & std. dev derived from NS001 aircraft TMS", "links": [ { diff --git a/datasets/fife_sat_obs_sat_avhr_77_1.json b/datasets/fife_sat_obs_sat_avhr_77_1.json index 6dad9af582..a12d9e60c9 100644 --- a/datasets/fife_sat_obs_sat_avhr_77_1.json +++ b/datasets/fife_sat_obs_sat_avhr_77_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sat_obs_sat_avhr_77_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site specifice radiance, exoatmospheric reflectance & surface reflectance", "links": [ { diff --git a/datasets/fife_sat_obs_sat_coef_76_1.json b/datasets/fife_sat_obs_sat_coef_76_1.json index b2e487cee1..59edabd3b2 100644 --- a/datasets/fife_sat_obs_sat_coef_76_1.json +++ b/datasets/fife_sat_obs_sat_coef_76_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sat_obs_sat_coef_76_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coefficients required to convert satellite radiances to reflectance/temperature", "links": [ { diff --git a/datasets/fife_sat_obs_sat_ltm_78_1.json b/datasets/fife_sat_obs_sat_ltm_78_1.json index 9018ac50e1..5ac16a2786 100644 --- a/datasets/fife_sat_obs_sat_ltm_78_1.json +++ b/datasets/fife_sat_obs_sat_ltm_78_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sat_obs_sat_ltm_78_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site reflectances extracted from Landsat TM imagery over FIFE study area", "links": [ { diff --git a/datasets/fife_sat_obs_sat_spot_79_1.json b/datasets/fife_sat_obs_sat_spot_79_1.json index c05ac3197a..b7e09f1e32 100644 --- a/datasets/fife_sat_obs_sat_spot_79_1.json +++ b/datasets/fife_sat_obs_sat_spot_79_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sat_obs_sat_spot_79_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site reflectances extracted from SPOT HRV imagery over FIFE study area", "links": [ { diff --git a/datasets/fife_soilmstr_peck_gam_37_1.json b/datasets/fife_soilmstr_peck_gam_37_1.json index 5483559123..c035695e89 100644 --- a/datasets/fife_soilmstr_peck_gam_37_1.json +++ b/datasets/fife_soilmstr_peck_gam_37_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_peck_gam_37_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moistures measured by airborne gamma ray, averaged in segments or transects", "links": [ { diff --git a/datasets/fife_soilmstr_peck_sm_109_1.json b/datasets/fife_soilmstr_peck_sm_109_1.json index b5daff04e3..009527ea1d 100644 --- a/datasets/fife_soilmstr_peck_sm_109_1.json +++ b/datasets/fife_soilmstr_peck_sm_109_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_peck_sm_109_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravimetric soil moisture data collected in conjunction w/ gamma ray soil study", "links": [ { diff --git a/datasets/fife_soilmstr_sm_grav_110_1.json b/datasets/fife_soilmstr_sm_grav_110_1.json index dbb883b59b..97aac2bd4d 100644 --- a/datasets/fife_soilmstr_sm_grav_110_1.json +++ b/datasets/fife_soilmstr_sm_grav_110_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_sm_grav_110_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture collected at 25mm, 75mm, and 150mm", "links": [ { diff --git a/datasets/fife_soilmstr_sm_neut_111_1.json b/datasets/fife_soilmstr_sm_neut_111_1.json index c25f8ebea9..1c294305a2 100644 --- a/datasets/fife_soilmstr_sm_neut_111_1.json +++ b/datasets/fife_soilmstr_sm_neut_111_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_sm_neut_111_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture data collected using a neutron probe 200cm in length", "links": [ { diff --git a/datasets/fife_soilmstr_sm_tran_113_1.json b/datasets/fife_soilmstr_sm_tran_113_1.json index 033b71b32e..4b5f67780a 100644 --- a/datasets/fife_soilmstr_sm_tran_113_1.json +++ b/datasets/fife_soilmstr_sm_tran_113_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_sm_tran_113_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture & bulk density measurements", "links": [ { diff --git a/datasets/fife_soilmstr_soil_imp_108_1.json b/datasets/fife_soilmstr_soil_imp_108_1.json index d7764e9cae..1ab1f03d8d 100644 --- a/datasets/fife_soilmstr_soil_imp_108_1.json +++ b/datasets/fife_soilmstr_soil_imp_108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilmstr_soil_imp_108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil impedance & temperature measured with Radio Frequency Soil Moisture Probe", "links": [ { diff --git a/datasets/fife_soilprop_soil_h2o_117_1.json b/datasets/fife_soilprop_soil_h2o_117_1.json index 0c52f39422..c050b977b4 100644 --- a/datasets/fife_soilprop_soil_h2o_117_1.json +++ b/datasets/fife_soilprop_soil_h2o_117_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soil_h2o_117_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil hydraulic properties", "links": [ { diff --git a/datasets/fife_soilprop_soil_rel_112_1.json b/datasets/fife_soilprop_soil_rel_112_1.json index f6ab045650..b29d29e29c 100644 --- a/datasets/fife_soilprop_soil_rel_112_1.json +++ b/datasets/fife_soilprop_soil_rel_112_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soil_rel_112_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture release characteristics", "links": [ { diff --git a/datasets/fife_soilprop_soildens_104_1.json b/datasets/fife_soilprop_soildens_104_1.json index c6ea4d70dc..4c3ef164e2 100644 --- a/datasets/fife_soilprop_soildens_104_1.json +++ b/datasets/fife_soilprop_soildens_104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soildens_104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil bulk density data collected on the Konza Prairie", "links": [ { diff --git a/datasets/fife_soilprop_soilhydc_107_1.json b/datasets/fife_soilprop_soilhydc_107_1.json index bf01864efb..da1e6f0f8a 100644 --- a/datasets/fife_soilprop_soilhydc_107_1.json +++ b/datasets/fife_soilprop_soilhydc_107_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soilhydc_107_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field saturated hydraulic conductivity, using constant well head permeameter", "links": [ { diff --git a/datasets/fife_soilprop_soilsurv_115_1.json b/datasets/fife_soilprop_soilsurv_115_1.json index 23ee63a1f5..884430c570 100644 --- a/datasets/fife_soilprop_soilsurv_115_1.json +++ b/datasets/fife_soilprop_soilsurv_115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soilsurv_115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil properties reference information", "links": [ { diff --git a/datasets/fife_soilprop_soilther_116_1.json b/datasets/fife_soilprop_soilther_116_1.json index f09a813cc4..33b4921a2b 100644 --- a/datasets/fife_soilprop_soilther_116_1.json +++ b/datasets/fife_soilprop_soilther_116_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_soilprop_soilther_116_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1989 FIFE staff science soil properties measurements", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brg_20_1.json b/datasets/fife_sur_flux_30_min_brg_20_1.json index 757d7b5d00..5faed131d9 100644 --- a/datasets/fife_sur_flux_30_min_brg_20_1.json +++ b/datasets/fife_sur_flux_30_min_brg_20_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brg_20_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface flux measurements by Bowen Ratio technique during FIFE", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brk_21_1.json b/datasets/fife_sur_flux_30_min_brk_21_1.json index c72200375d..02c5f5b695 100644 --- a/datasets/fife_sur_flux_30_min_brk_21_1.json +++ b/datasets/fife_sur_flux_30_min_brk_21_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brk_21_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Assessing the effects of annual burning & topography on surface energy exchanges", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brl_19_1.json b/datasets/fife_sur_flux_30_min_brl_19_1.json index 62b0ab8d89..7cda67f1bc 100644 --- a/datasets/fife_sur_flux_30_min_brl_19_1.json +++ b/datasets/fife_sur_flux_30_min_brl_19_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brl_19_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Evaluation of surface radiation and energy budget stations for FIFE", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brs_22_1.json b/datasets/fife_sur_flux_30_min_brs_22_1.json index 990886d6d6..327a723205 100644 --- a/datasets/fife_sur_flux_30_min_brs_22_1.json +++ b/datasets/fife_sur_flux_30_min_brs_22_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brs_22_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Retrieval of surface fluxes from a combination of satellite & surface platforms", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brv_23_1.json b/datasets/fife_sur_flux_30_min_brv_23_1.json index 1fb03c743e..fee6866b4f 100644 --- a/datasets/fife_sur_flux_30_min_brv_23_1.json +++ b/datasets/fife_sur_flux_30_min_brv_23_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brv_23_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Latent & sensible heat flux by Bowen Ratio & aerodynamic characterization of vegetation", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_brw_24_1.json b/datasets/fife_sur_flux_30_min_brw_24_1.json index 7167a55461..f25a3d8623 100644 --- a/datasets/fife_sur_flux_30_min_brw_24_1.json +++ b/datasets/fife_sur_flux_30_min_brw_24_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_brw_24_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface flux measurements by Bowen Ratio technique during FIFE", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_eca_30_1.json b/datasets/fife_sur_flux_30_min_eca_30_1.json index 7f2da84465..40284b0615 100644 --- a/datasets/fife_sur_flux_30_min_eca_30_1.json +++ b/datasets/fife_sur_flux_30_min_eca_30_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_eca_30_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Eddy flux & surface exchange processes in non-uniform areas", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_ecb_32_1.json b/datasets/fife_sur_flux_30_min_ecb_32_1.json index 0bd4bb7f9e..6243748453 100644 --- a/datasets/fife_sur_flux_30_min_ecb_32_1.json +++ b/datasets/fife_sur_flux_30_min_ecb_32_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_ecb_32_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Areal average evapotranspiration by measuring & modeling surface controls", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_ecg_31_1.json b/datasets/fife_sur_flux_30_min_ecg_31_1.json index 0273c968fe..1fdf453933 100644 --- a/datasets/fife_sur_flux_30_min_ecg_31_1.json +++ b/datasets/fife_sur_flux_30_min_ecg_31_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_ecg_31_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface flux measurements by eddy correlation technique during FIFE", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_ecv_33_1.json b/datasets/fife_sur_flux_30_min_ecv_33_1.json index d9bf267c32..26921dd955 100644 --- a/datasets/fife_sur_flux_30_min_ecv_33_1.json +++ b/datasets/fife_sur_flux_30_min_ecv_33_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_ecv_33_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Latent & sensible heat flux by eddy correlation & aerodynamic characterization of vegetation", "links": [ { diff --git a/datasets/fife_sur_flux_30_min_ecw_34_1.json b/datasets/fife_sur_flux_30_min_ecw_34_1.json index 8cf793c686..38d2fceed5 100644 --- a/datasets/fife_sur_flux_30_min_ecw_34_1.json +++ b/datasets/fife_sur_flux_30_min_ecw_34_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_30_min_ecw_34_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FIFE observations of surface fluxes", "links": [ { diff --git a/datasets/fife_sur_flux_basel_92_121_1.json b/datasets/fife_sur_flux_basel_92_121_1.json index b8d052fef7..7fd1314797 100644 --- a/datasets/fife_sur_flux_basel_92_121_1.json +++ b/datasets/fife_sur_flux_basel_92_121_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_flux_basel_92_121_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Baseline 92 cross-calibrated surface fluxes for 1987 data (spike checked)", "links": [ { diff --git a/datasets/fife_sur_met_ams_12_1.json b/datasets/fife_sur_met_ams_12_1.json index 0689866d63..ef8411bbaf 100644 --- a/datasets/fife_sur_met_ams_12_1.json +++ b/datasets/fife_sur_met_ams_12_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_ams_12_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "30 minute average meteorological data from NCARs Portable Automated Mesonet Station", "links": [ { diff --git a/datasets/fife_sur_met_cld_cam_28_1.json b/datasets/fife_sur_met_cld_cam_28_1.json index d1eeaebf68..8057bc3799 100644 --- a/datasets/fife_sur_met_cld_cam_28_1.json +++ b/datasets/fife_sur_met_cld_cam_28_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_cld_cam_28_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud estimates", "links": [ { diff --git a/datasets/fife_sur_met_hday_met_39_1.json b/datasets/fife_sur_met_hday_met_39_1.json index cdbb57cba0..a84c31c9d1 100644 --- a/datasets/fife_sur_met_hday_met_39_1.json +++ b/datasets/fife_sur_met_hday_met_39_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_hday_met_39_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily temperature & rainfall measured on the KSU campus", "links": [ { diff --git a/datasets/fife_sur_met_hmon_met_40_1.json b/datasets/fife_sur_met_hmon_met_40_1.json index 242a0b5768..e76253fea2 100644 --- a/datasets/fife_sur_met_hmon_met_40_1.json +++ b/datasets/fife_sur_met_hmon_met_40_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_hmon_met_40_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Manhattan, KS. average rainfall measurements for every month since January 1858", "links": [ { diff --git a/datasets/fife_sur_met_ncdc_sur_122_1.json b/datasets/fife_sur_met_ncdc_sur_122_1.json index f71a74a880..982850af23 100644 --- a/datasets/fife_sur_met_ncdc_sur_122_1.json +++ b/datasets/fife_sur_met_ncdc_sur_122_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_ncdc_sur_122_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCDC surface meteorology data for 1989", "links": [ { diff --git a/datasets/fife_sur_met_noaa_sur_58_1.json b/datasets/fife_sur_met_noaa_sur_58_1.json index d7dcc31184..d7aff70475 100644 --- a/datasets/fife_sur_met_noaa_sur_58_1.json +++ b/datasets/fife_sur_met_noaa_sur_58_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_noaa_sur_58_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hourly surface weather reports collected by NESDIS for stations near FIFE", "links": [ { diff --git a/datasets/fife_sur_met_rain_30m_2_1.json b/datasets/fife_sur_met_rain_30m_2_1.json index 645180c64f..725bd89110 100644 --- a/datasets/fife_sur_met_rain_30m_2_1.json +++ b/datasets/fife_sur_met_rain_30m_2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_rain_30m_2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "30 minute rainfall data for the Konza Prairie", "links": [ { diff --git a/datasets/fife_sur_met_rain_day_29_1.json b/datasets/fife_sur_met_rain_day_29_1.json index e10e2f89b3..94f29fbba7 100644 --- a/datasets/fife_sur_met_rain_day_29_1.json +++ b/datasets/fife_sur_met_rain_day_29_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_met_rain_day_29_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily rainfall data, by site & date", "links": [ { diff --git a/datasets/fife_sur_refl_gem_helo_38_1.json b/datasets/fife_sur_refl_gem_helo_38_1.json index ac8df9f95c..058bb17cd9 100644 --- a/datasets/fife_sur_refl_gem_helo_38_1.json +++ b/datasets/fife_sur_refl_gem_helo_38_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_gem_helo_38_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectral reflected radiances measured with Russian GEMMA spectrometer from a helicopter", "links": [ { diff --git a/datasets/fife_sur_refl_irt_grnd_72_1.json b/datasets/fife_sur_refl_irt_grnd_72_1.json index 0217bfe46c..08755ad9d6 100644 --- a/datasets/fife_sur_refl_irt_grnd_72_1.json +++ b/datasets/fife_sur_refl_irt_grnd_72_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_irt_grnd_72_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface temperatures collected w/ Everest Infrared Temperature Transducer", "links": [ { diff --git a/datasets/fife_sur_refl_irt_helo_70_1.json b/datasets/fife_sur_refl_irt_helo_70_1.json index 8adcd19a76..60a262d4a8 100644 --- a/datasets/fife_sur_refl_irt_helo_70_1.json +++ b/datasets/fife_sur_refl_irt_helo_70_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_irt_helo_70_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature data from Everest IR thermometer mounted on the helicopter", "links": [ { diff --git a/datasets/fife_sur_refl_irt_mult_71_1.json b/datasets/fife_sur_refl_irt_mult_71_1.json index ecf93d4976..1426db8109 100644 --- a/datasets/fife_sur_refl_irt_mult_71_1.json +++ b/datasets/fife_sur_refl_irt_mult_71_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_irt_mult_71_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface temp. measured w/ Everest IRT, multiple angles & Eppley IR Radiometer", "links": [ { diff --git a/datasets/fife_sur_refl_lightwnd_43_1.json b/datasets/fife_sur_refl_lightwnd_43_1.json index e1d50b9954..28f00f6739 100644 --- a/datasets/fife_sur_refl_lightwnd_43_1.json +++ b/datasets/fife_sur_refl_lightwnd_43_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_lightwnd_43_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LAI and mean tip angle from LI-COR LAI-2000 Plant Canopy Analyzer", "links": [ { diff --git a/datasets/fife_sur_refl_ltbr_ksu_41_1.json b/datasets/fife_sur_refl_ltbr_ksu_41_1.json index 3a8d82c884..e1348fc696 100644 --- a/datasets/fife_sur_refl_ltbr_ksu_41_1.json +++ b/datasets/fife_sur_refl_ltbr_ksu_41_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_ltbr_ksu_41_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LAI and PAR above & below canopy measured with light bar by KSU staff science", "links": [ { diff --git a/datasets/fife_sur_refl_ltbr_unl_42_1.json b/datasets/fife_sur_refl_ltbr_unl_42_1.json index 17c52a7662..d13740434a 100644 --- a/datasets/fife_sur_refl_ltbr_unl_42_1.json +++ b/datasets/fife_sur_refl_ltbr_unl_42_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_ltbr_unl_42_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Light bar data recorded using LICOR LI-191SA Line Quantum Sensor", "links": [ { diff --git a/datasets/fife_sur_refl_mmr_calb_51_1.json b/datasets/fife_sur_refl_mmr_calb_51_1.json index 017ebca86a..fade2418ed 100644 --- a/datasets/fife_sur_refl_mmr_calb_51_1.json +++ b/datasets/fife_sur_refl_mmr_calb_51_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_mmr_calb_51_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Barnes MMR reflected radiance from calibration panels, adjusted for sun angle", "links": [ { diff --git a/datasets/fife_sur_refl_mmr_grnd_52_1.json b/datasets/fife_sur_refl_mmr_grnd_52_1.json index 9e40a95a82..19176f0871 100644 --- a/datasets/fife_sur_refl_mmr_grnd_52_1.json +++ b/datasets/fife_sur_refl_mmr_grnd_52_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_mmr_grnd_52_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reflected radiance and reflectance values from Barnes MMR on ground", "links": [ { diff --git a/datasets/fife_sur_refl_mmr_helo_53_1.json b/datasets/fife_sur_refl_mmr_helo_53_1.json index 5555f5e624..247b7f6136 100644 --- a/datasets/fife_sur_refl_mmr_helo_53_1.json +++ b/datasets/fife_sur_refl_mmr_helo_53_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_mmr_helo_53_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Site averaged reflected radiance & reflectance from Barnes MMR from helicopter", "links": [ { diff --git a/datasets/fife_sur_refl_mmr_leaf_54_1.json b/datasets/fife_sur_refl_mmr_leaf_54_1.json index 5c6f9ffa03..49c20cc040 100644 --- a/datasets/fife_sur_refl_mmr_leaf_54_1.json +++ b/datasets/fife_sur_refl_mmr_leaf_54_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_mmr_leaf_54_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf optical properties (reflectance & transmittance) measured by Univsity of Nebraska", "links": [ { diff --git a/datasets/fife_sur_refl_parabola_68_1.json b/datasets/fife_sur_refl_parabola_68_1.json index 006d613115..c085b0b82c 100644 --- a/datasets/fife_sur_refl_parabola_68_1.json +++ b/datasets/fife_sur_refl_parabola_68_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_parabola_68_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sky & ground radiance values averaged to give equal intervals of viewing angles", "links": [ { diff --git a/datasets/fife_sur_refl_se5_gsfc_81_1.json b/datasets/fife_sur_refl_se5_gsfc_81_1.json index 93cf9376d4..beb78064e3 100644 --- a/datasets/fife_sur_refl_se5_gsfc_81_1.json +++ b/datasets/fife_sur_refl_se5_gsfc_81_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_se5_gsfc_81_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bidirectional reflectances measured with SE590 (Middleton)", "links": [ { diff --git a/datasets/fife_sur_refl_se5_helo_87_1.json b/datasets/fife_sur_refl_se5_helo_87_1.json index 3ab0235af2..18cfe5c831 100644 --- a/datasets/fife_sur_refl_se5_helo_87_1.json +++ b/datasets/fife_sur_refl_se5_helo_87_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_se5_helo_87_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SE-590 reflectance factors and radiances measured from a helicopter", "links": [ { diff --git a/datasets/fife_sur_refl_se5_leaf_85_1.json b/datasets/fife_sur_refl_se5_leaf_85_1.json index 0caea5a119..832a11b41b 100644 --- a/datasets/fife_sur_refl_se5_leaf_85_1.json +++ b/datasets/fife_sur_refl_se5_leaf_85_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_se5_leaf_85_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf optical properties (reflectance & transmittance) from SE590 & LICOR", "links": [ { diff --git a/datasets/fife_sur_refl_se5_unl_82_1.json b/datasets/fife_sur_refl_se5_unl_82_1.json index f2680928d8..efd39d6d5a 100644 --- a/datasets/fife_sur_refl_se5_unl_82_1.json +++ b/datasets/fife_sur_refl_se5_unl_82_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_se5_unl_82_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bidirectional reflectances measured with SE590", "links": [ { diff --git a/datasets/fife_sur_refl_soilrefl_114_1.json b/datasets/fife_sur_refl_soilrefl_114_1.json index 7f6789c20f..47339dd66a 100644 --- a/datasets/fife_sur_refl_soilrefl_114_1.json +++ b/datasets/fife_sur_refl_soilrefl_114_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_soilrefl_114_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectral reflectance of soils, Atlas of Soil Reflectance Properties (Stoner '80)", "links": [ { diff --git a/datasets/fife_sur_refl_unl_long_49_1.json b/datasets/fife_sur_refl_unl_long_49_1.json index ee4971ef8d..deabfae845 100644 --- a/datasets/fife_sur_refl_unl_long_49_1.json +++ b/datasets/fife_sur_refl_unl_long_49_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_unl_long_49_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average incoming longwave radiation measured by University of Nebraska", "links": [ { diff --git a/datasets/fife_sur_refl_unl_surf_123_1.json b/datasets/fife_sur_refl_unl_surf_123_1.json index f7f1ac0f13..506dabec0d 100644 --- a/datasets/fife_sur_refl_unl_surf_123_1.json +++ b/datasets/fife_sur_refl_unl_surf_123_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fife_sur_refl_unl_surf_123_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Canopy IR & air temperature, albedo, incoming and reflected shortwave, humidity", "links": [ { diff --git a/datasets/finnarp_aerosols.json b/datasets/finnarp_aerosols.json index 2e8b18f30a..4df3bda9bc 100644 --- a/datasets/finnarp_aerosols.json +++ b/datasets/finnarp_aerosols.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "finnarp_aerosols", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains:\n- neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins\n- charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins\n- tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion)\n- quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week)\n", "links": [ { diff --git a/datasets/fire-randomizer-first-release_1.0.json b/datasets/fire-randomizer-first-release_1.0.json index 3f1aa182ec..71a660faef 100644 --- a/datasets/fire-randomizer-first-release_1.0.json +++ b/datasets/fire-randomizer-first-release_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fire-randomizer-first-release_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tool\u00a0to assess fire selectivity for topographic (e.g. alitiude, slope, aspect) or land use (forest or vegetation type, distance to infrastructures) categories with Monte Carlo simulations.", "links": [ { diff --git a/datasets/fire_emissions_724_1.json b/datasets/fire_emissions_724_1.json index cac722e751..eef6b8ebb6 100644 --- a/datasets/fire_emissions_724_1.json +++ b/datasets/fire_emissions_724_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fire_emissions_724_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively. During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profiles of CO2, CO, H2O, and CH4 up to 5.5 km on 6 occasions above instrumented ground sites and below the TERRA satellite and ER-2 high-flying research aircraft as well as trace gas emissions from ten African savanna fires. These measurements are the first broad characterization of the most abundant trace gases in nascent smoke from African savanna fires (i.e., including oxygen- and nitrogen-containing species).", "links": [ { diff --git a/datasets/fire_emissions_v4_R1_1293_4.1.json b/datasets/fire_emissions_v4_R1_1293_4.1.json index c92223bcdb..be0eea5599 100644 --- a/datasets/fire_emissions_v4_R1_1293_4.1.json +++ b/datasets/fire_emissions_v4_R1_1293_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fire_emissions_v4_R1_1293_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region.", "links": [ { diff --git a/datasets/fisher_sat_1.json b/datasets/fisher_sat_1.json index 4624610e7c..7dff199bbf 100644 --- a/datasets/fisher_sat_1.json +++ b/datasets/fisher_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fisher_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Fisher Massif, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 129-110). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/flor_fna_Stillwl_1.json b/datasets/flor_fna_Stillwl_1.json index 6dce65333c..0fe38245eb 100644 --- a/datasets/flor_fna_Stillwl_1.json +++ b/datasets/flor_fna_Stillwl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "flor_fna_Stillwl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The broadscale distribution of flora (lichens, mosses, non-marine algae)and fauna (penguins, flying birds, seals)in the Stillwell Hills was mapped using GPS technology. Samples of flora were collected for taxonomic identification.\n\nData were recorded and catalogued in shapefiles.", "links": [ { diff --git a/datasets/flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0.json b/datasets/flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0.json index cda0163145..181a142b9f 100644 --- a/datasets/flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0.json +++ b/datasets/flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.", "links": [ { diff --git a/datasets/fltrepepoch_1.json b/datasets/fltrepepoch_1.json index 236fa7f955..473b0ee941 100644 --- a/datasets/fltrepepoch_1.json +++ b/datasets/fltrepepoch_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fltrepepoch_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Flight Reports EPOCH dataset consists of flight number, purpose of flight, and flight hours logged during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The mission reports are available from July 27, 2017 through August 31, 2017 in PDF format. ", "links": [ { diff --git a/datasets/flu-a-bh_1.0.json b/datasets/flu-a-bh_1.0.json index e153049b51..b8b2b8cf44 100644 --- a/datasets/flu-a-bh_1.0.json +++ b/datasets/flu-a-bh_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "flu-a-bh_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26\u00b0) North-east slope (45\u00b0). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.", "links": [ { diff --git a/datasets/fluxnet_point_1029_1.json b/datasets/fluxnet_point_1029_1.json index 35b2288a71..1cc7c1616a 100644 --- a/datasets/fluxnet_point_1029_1.json +++ b/datasets/fluxnet_point_1029_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fluxnet_point_1029_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terrestrial ecosystem and atmosphere. ", "links": [ { diff --git a/datasets/foraging_trip_duration_BI_1.json b/datasets/foraging_trip_duration_BI_1.json index 362cfe1739..ef6d17a690 100644 --- a/datasets/foraging_trip_duration_BI_1.json +++ b/datasets/foraging_trip_duration_BI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "foraging_trip_duration_BI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season.\n\nData based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick.\n\nThis work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.\n\nThe fields in this dataset are:\nYear\ntrip duration (hours)\nMean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season.", "links": [ { diff --git a/datasets/forclim_4.0.json b/datasets/forclim_4.0.json index a026550acf..d4864c1c67 100644 --- a/datasets/forclim_4.0.json +++ b/datasets/forclim_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forclim_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the \"GNU GENERAL PUBLIC LICENSE v3\" license. ![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg \"ForClim structure\")", "links": [ { diff --git a/datasets/forecast-avalanche-danger-level-european-alps-2011-2015_1.0.json b/datasets/forecast-avalanche-danger-level-european-alps-2011-2015_1.0.json index b90f138c01..a669d12add 100644 --- a/datasets/forecast-avalanche-danger-level-european-alps-2011-2015_1.0.json +++ b/datasets/forecast-avalanche-danger-level-european-alps-2011-2015_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forecast-avalanche-danger-level-european-alps-2011-2015_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci). For details on the data please refer to this publication. The dataset contains the following: - shape files for the warning regions in the Alps - highest forecast danger level for each warning region and day", "links": [ { diff --git a/datasets/forecomon-proceedings_v14.json b/datasets/forecomon-proceedings_v14.json index e04d030228..1dd37b7d67 100644 --- a/datasets/forecomon-proceedings_v14.json +++ b/datasets/forecomon-proceedings_v14.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forecomon-proceedings_v14", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7-9 June 2021, WSL, Birmensdorf, Switzerland The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed. Copyright \u00a9 2021 by WSL, Birmensdorf The authors are responsible for the content of their contribution.", "links": [ { diff --git a/datasets/forest-radiation-data_1.0.json b/datasets/forest-radiation-data_1.0.json index e056d2f73a..a8404d51b2 100644 --- a/datasets/forest-radiation-data_1.0.json +++ b/datasets/forest-radiation-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest-radiation-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper \"Shading by trees and fractional snow cover control the sub-canopy radiation budget\", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland.", "links": [ { diff --git a/datasets/forest-reserves-monitoring-in-switzerland_1.0.json b/datasets/forest-reserves-monitoring-in-switzerland_1.0.json index 7df53cd934..0065b0d3f1 100644 --- a/datasets/forest-reserves-monitoring-in-switzerland_1.0.json +++ b/datasets/forest-reserves-monitoring-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest-reserves-monitoring-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long term monitoring of natural forests provides insights into ecological processes shaping forests without human intervention. To study natural forest dynamics, the former chair of silviculture at the Swiss Federal Institute of Technology (ETH) initiated a network of forest reserves in the late 1940's. Since 2006, the monitoring is carried out in a cooperation project of the chair of Forest Ecology at ETH, the Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL) and the Federal Office for the Environment (FOEN). The project relaunch led to a streamlining of the reserve network, which now contains 33 of the original reserves and 16 new reserves. The main goal is to evaluate the effectiveness of the federal reserve policy by analysing to what extent forest reserves differ from managed forests in terms of structure, dynamics, and habitat quality.", "links": [ { diff --git a/datasets/forest-snow-model-fluela_1.0.json b/datasets/forest-snow-model-fluela_1.0.json index ba9f3f1933..3ae9091074 100644 --- a/datasets/forest-snow-model-fluela_1.0.json +++ b/datasets/forest-snow-model-fluela_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest-snow-model-fluela_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains surface datasets (in particular canopy structure fields) and meteorological input (water years 2016-2021) required to run the snow model FSM2 over the Fluela valley. Land surface datasets are available for a 1.5x2.5km model domain at 2m spatial resolution, meteorological input at hourly resolution is provided for a point and corresponds to the location of the automatic weather station / snow measurement field 5DF in Davos. Corresponding FSM2 simulations are used and analyzed in the publication 'Canopy structure, topography and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests' by Mazzotti et al. (submitted to HESSD). This publication should be cited whenever the dataset is used.", "links": [ { diff --git a/datasets/forest-snow-modelling-davos-2012-2015_1.0.json b/datasets/forest-snow-modelling-davos-2012-2015_1.0.json index f79c2f577b..08f3dc05af 100644 --- a/datasets/forest-snow-modelling-davos-2012-2015_1.0.json +++ b/datasets/forest-snow-modelling-davos-2012-2015_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest-snow-modelling-davos-2012-2015_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129. This publication must be cited when using this dataset.", "links": [ { diff --git a/datasets/forest-type-nfi_2018 (current).json b/datasets/forest-type-nfi_2018 (current).json index 40656de25b..9ecfc8179e 100644 --- a/datasets/forest-type-nfi_2018 (current).json +++ b/datasets/forest-type-nfi_2018 (current).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest-type-nfi_2018 (current)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two versions of the data are currently available: 2018 and 2016. The 2018 version presents a remote sensing-based approach for a countrywide mapping of the dominant leave type (DLT) with the two classes broadleaved and coniferous in Switzerland. The spatial resolution is 10 m with the fraction of the class broadleaf. The classification approach incorporates a random forest classifier, explanatory variables from multispectral Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the tree type map with independent NFI data revealed deviations in mixed stands. In the 2016 version (3 m spatial resolution), the classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of 3.17%).", "links": [ { diff --git a/datasets/forest_area-44_1.0.json b/datasets/forest_area-44_1.0.json index 566bd19308..535ca51248 100644 --- a/datasets/forest_area-44_1.0.json +++ b/datasets/forest_area-44_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest_area-44_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest area is the total sum of all areas classified as forest according to NFI\u2019s forest definition. The forest definition includes shrub forest. This theme is also used to assess the total area when forest and non-forest need to be distinguished. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/forest_area_by_forest_function-262_1.0.json b/datasets/forest_area_by_forest_function-262_1.0.json index 1e2ce66933..476ffc0d12 100644 --- a/datasets/forest_area_by_forest_function-262_1.0.json +++ b/datasets/forest_area_by_forest_function-262_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest_area_by_forest_function-262_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest area refers to all areas classified as forest according to NFI\u2019s forest definition. The forest definition includes shrub forest. For each forest function (including no special forest function) identified in the survey of the forestry services, the size of the associated forest area is displayed. One forest region may fulfil several different forest functions and may thus contribute to the forest area for several forest functions. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/forest_area_by_natural_hazard-260_1.0.json b/datasets/forest_area_by_natural_hazard-260_1.0.json index 813fcf1939..2c76427ca2 100644 --- a/datasets/forest_area_by_natural_hazard-260_1.0.json +++ b/datasets/forest_area_by_natural_hazard-260_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest_area_by_natural_hazard-260_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For each natural hazard process according to FOEN\u2019s SilvaProtectCH, the size of the forest area affected is given. One forest region may be affected by several different natural hazard processes and may thus contribute to the forest area affected by several different natural hazard processes. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/forest_carbon_flux_949_1.json b/datasets/forest_carbon_flux_949_1.json index 6008736678..fb5015da35 100644 --- a/datasets/forest_carbon_flux_949_1.json +++ b/datasets/forest_carbon_flux_949_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forest_carbon_flux_949_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.", "links": [ { diff --git a/datasets/forhycs-v-1-0-0-model-code_1.0.0.json b/datasets/forhycs-v-1-0-0-model-code_1.0.0.json index 5d1544050f..3481a8601b 100644 --- a/datasets/forhycs-v-1-0-0-model-code_1.0.0.json +++ b/datasets/forhycs-v-1-0-0-model-code_1.0.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "forhycs-v-1-0-0-model-code_1.0.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Model code, technical documentation and auxiliary files for the dynamic ecohydrological model FORHYCS (FORests and HYdrology under Climate change in Switzerland). FORHYCS combines two pre-existing models, the hydrological model PREVAH and the forest landscape model TreeMig. License: GPL v3", "links": [ { diff --git a/datasets/four-years-of-daily-stable-water-isotope-data_1.0.json b/datasets/four-years-of-daily-stable-water-isotope-data_1.0.json index a7fe2d5331..7be9f14cd4 100644 --- a/datasets/four-years-of-daily-stable-water-isotope-data_1.0.json +++ b/datasets/four-years-of-daily-stable-water-isotope-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "four-years-of-daily-stable-water-isotope-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.", "links": [ { diff --git a/datasets/fram25k_1.json b/datasets/fram25k_1.json index 04d9339825..b0c11fbbec 100644 --- a/datasets/fram25k_1.json +++ b/datasets/fram25k_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fram25k_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digital Photogrammetric Map Data of the Framnes Mountain Region taken from 1:45000 1996/97 aerial photography. Data Layers consist of building, contour, geology (erratics only), human, spot_height, survey, topoline, topopoly and toposurf.", "links": [ { diff --git a/datasets/framnes_contours_1.json b/datasets/framnes_contours_1.json index 84d06220a6..aa70affd1c 100644 --- a/datasets/framnes_contours_1.json +++ b/datasets/framnes_contours_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "framnes_contours_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mapping around the Framnes Mountains from Spot satellite imagery at 10 metre pixel resolution. Mapped early in 1999.\n\nSmoothed contour data edited May 2003.", "links": [ { diff --git a/datasets/framnes_route_gis_1.json b/datasets/framnes_route_gis_1.json index 88cebe6968..5f105b06bd 100644 --- a/datasets/framnes_route_gis_1.json +++ b/datasets/framnes_route_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "framnes_route_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is GIS data representing waypoints and routes in the Mawson area, Antarctica. It includes routes in the Framnes Mountains and routes west and east of Mawson along the Mawson Coast.\nThe waypoint and route data held by the Australian Antarctic Data Centre are routinely updated using feedback provided by the Australian Antarctic Division's Field Training Officers and Station Leaders with approval for changes given by the Australian Antarctic Division's Field Support Coordinator.", "links": [ { diff --git a/datasets/framnes_sat_1.json b/datasets/framnes_sat_1.json index df45c55024..f705e2320a 100644 --- a/datasets/framnes_sat_1.json +++ b/datasets/framnes_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "framnes_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Framnes Mountains, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1997. The map is at a scale of 1:100000, and was produced from SPOT XS (WRS 246-491, 246-490) and Landsat TM (WRS 137-107, 135-108) scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, refuges/depots, specially protected areas (SPA), and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/framnes_spot_10m_1.json b/datasets/framnes_spot_10m_1.json index 589cbd7d53..088b3f4247 100644 --- a/datasets/framnes_spot_10m_1.json +++ b/datasets/framnes_spot_10m_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "framnes_spot_10m_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mapping around the Framnes Mountains from Spot satellite imagery at 10 metre pixel resolution. Mapped early in 1999.", "links": [ { diff --git a/datasets/frazier_sgp_12dec2011_1.json b/datasets/frazier_sgp_12dec2011_1.json index 2ddcd89534..9127006ecc 100644 --- a/datasets/frazier_sgp_12dec2011_1.json +++ b/datasets/frazier_sgp_12dec2011_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "frazier_sgp_12dec2011_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "John van den Hoff (Australian Antarctic Division biologist) visited the Frazier Islands on 12 and 13 December 2011. He was assisted by Tim Gill (Field Training Officer), Dan Blight and Trevor Crews (Casey winterers) and Zane Hacker and Oliver Henshaw (watercraft operators). \nVisits were made to Charlton, Dewart and Nelly Islands and four cameras, tripods and supporting solar power packs were deployed on Nelly Island. The main visit occurred on 12 December and a shorter second visit to check on the cameras was made on 13 December 2011. The cameras were installed to monitor the southern giant nesting areas.\nA count was made of the number of southern giant petrels in each nesting area.\n\nA search for bird carcasses was made on Nelly Island with none found or collected.\n\nThis dataset includes the counts, camera locations and GIS polygon data representing the extents of the southern giant petrel nesting areas as observed on this visit.", "links": [ { diff --git a/datasets/frazier_sgp_14dec2005_1.json b/datasets/frazier_sgp_14dec2005_1.json index 5dafa9c79f..fa9cd4359b 100644 --- a/datasets/frazier_sgp_14dec2005_1.json +++ b/datasets/frazier_sgp_14dec2005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "frazier_sgp_14dec2005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dr Eric Woehler and Phillippa Bricher, postgraduate student at the University of Tasmania, and Marty Benavente, Field Training Officer, visited the Frazier Islands on 14 December 2005. The purpose of the visit was to conduct a census of the southern giant petrels on the islands.\nThis dataset includes the counts and GIS polygon data representing the extents of the southern giant petrel nesting areas and adelie penguin colonies observed on this visit.", "links": [ { diff --git a/datasets/fuel_evaporation_1.json b/datasets/fuel_evaporation_1.json index cb99750959..b5b7dbffe5 100644 --- a/datasets/fuel_evaporation_1.json +++ b/datasets/fuel_evaporation_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fuel_evaporation_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Evaporation Model for hydrocarbon spills. \n\nDeveloped by the Australian Antarctic Division to simulate fractionation of Special Antarctic Blend (SAB) and other diesel range fuels during evaporation.\n\nText version of notes for excel model, please read me.\n\nThis Package of files includes a pdf of the scientific paper, a readme word document and 3 excel files. The purpose of the 3 excel files are as follows:\n\nExcel File 1. Evap Model_V1_single temperature.xls. Evaporation predictions with a single sample at one temperature. This file requires input of initial composition, composition after weathering and a single temperature.\n\nExcel File 2. Evap Model_V1_five temperatures.xls. As above but with the input of 5 different temperatures.\n\nExcel File 3. Evap Model_development version, derivation of data and AAD examples.xls This is an earlier development version of the numerical model. This file is intended to allow others to see how available Antoines equation parameters were originally fitted for use with other R+UCM regions. This file includes a worksheet where the fitting parameters for Antoines eqn were calculated, a necessary task to be able to extrapolate to compounds for which explicit vapour pressure data are not available. The data input sheet has some differences to the singe temperature and five temperature model as the R+UCM regions are split up differently. Also the assumptions about how the different classes of compound (i.e. the Aliphatic and aromatic classes) behave can be altered. Some raw data from the AAD evaporation experiment is included and plotted. The Temperature comparisons worksheet summarises how ratios of interest change and allows plotting of one ratio at different temperatures (see worksheet Figs 2 and 3).\n\nRead me about background to this excel file\nBackground Notes\n\nThis excel file estimates the relative evaporation rates of different hydrocarbons from a hydrocarbon mixture (i.e. a fuel).\n\nIn this excel file, a single temperature is considered for an evaporating hydrocarbon mixture. The desired temperature and details about the hydrocarbon mixture are entered in the Main Input page.\n\nThe model was developed for use with the bulk fuels used by the Australian Antarctic Division at Casey, Davis, Mawson and Macquarie Island. This fuel, Special Antarctic Blend (SAB) starts at C9. As some of the spill sites undergoing remediation are mixed with heavier lube range hydrocarbons the options for inputs go to C36 - a typical maximum when Total Petroleum Hydrocarbon analysis is undertaken.\n\nThe model uses thermodynamic data where such data are available. Estimated thermodynamic data are used for components when specific data are unavailable.\n\nOther important information can be found in the sections listed below.\nReferences\nGC-FID data used in the model\nTemperature and Vapour pressure estimation\nOther corrections\nHow the calculations are done\nExperimental data (that supports the approach taken in this excel file)\n\nRead me about References\nNotes about the scientific publication that this model is reported in.\n\nPaper can be obtained as a pdf file from the Australian Antarctic Data Centre. The paper contains details on the evaporation experiments at +20 degrees C and -20 degrees C. The results from the experiments agree well with the calculated fractionation rates that this Excel file produces.\n\nRead me about GC-FID data for this model\nNotes about GC-FID data used in this excel model.\n\nThis model was set up to directly use GC-FID data outputs for each of the compounds of GC regions listed. Consistent units are required for the areas of each measured region (and GC bias needs to be low across the range of fuel components). Note that the summation of all the regions and compounds = total area identified in the chromatogram from C9 up to C36. This range covers the observed range of components in SAB, Arctic Blend Diesel and lubes that may have been spilled in the same area.\n\nRead me about temperature / vapour pressure estimation\nNotes about Temperature correction method\n\nRaoults law and the Antoine equation are used to calculate the composition of the evaporating portion of the fuel mixture.\n\nVapour pressure data were obtained for a range of available hydrocarbons C9 and larger in the temperature ranges that covered the site temperature ranges, approx -20 degrees C to +20 degrees C. The available data were mostly limited to n-alkanes. This situation was exacerbated because other compounds of interest are solids at these temperatures when pure. Consequently vapour pressure data are not available for these components in the liquid form at these temperatures. \n\nThe available n-alkane vapour pressure data were combined and a best fit of these data were determined as a function of effective Carbon Number (ECN). This allowed the estimation of the vapour pressure of fuel components with ECN's between the n-alkane ECN's.\n\nAs a refinement each region of the chromatogram was split into 5 classes - 3 x aliphatic fractions and 2 x aromatic fractions. The ECN used to estimate Vapour pressure of each class was slightly modified from the average retention time relative to an n-alkane. This modification was carried out to correct for specific GC-column / compound interactions - interactions that increase from acyclic to cyclic to polycyclic to hindered aromatics [i.e. multiply alkylated] to unhindered aromatics. Examination of the evaporation behaviour of n-alkanes vs. the nearest ECN regions confirms the need for the correction with the calculated evaporation profiles better matching the observed profiles.\n\nWhen an n-alkane's evaporation rate is compared to a GC region with distinctly different vapour pressures these corrections to ECN make little or no difference to the predicted selectivity during evaporation.\n\nRead me about other corrections\nNotes about other corrections used in this model.\n\nThis model does not include systems that are under diffusional control (i.e. limited by diffusion of hydrocarbons within the soil / fuel mixture). The assumptions in the model are for an evenly mixed liquid that is evaporating. The experiment was set up to avoid this problem (by rotating the flasks).\n\nSoil with evaporating fuels may well be affected by this and other problems.\n\nRead me about how calculations are done\nNotes about how the calculation is done\n\nThe fuel is divided into a number of fuel classes and specific compounds are identified. These classes are listed in the Main Input Page ready for use with the example data or other fuel data in the appropriate fuel range.\n\nR+UCM stands for Resolved + Unresolved Complex Mix but needs to be calculated excluding the specifically identified compounds like n-alkanes. Specifically identified compounds are excluded from the R+UCM so they are not double counted.\n\nMol fraction of each component is estimated. For specific compounds an exact molecular mass is known. For other R+UCM classes molecular mass is estimated from known compounds in that region. \n\nVapour pressure of the pure component or region is estimated with Antoines equation for the temperature required. This pure vapour pressure estimate is combined with liquid phase mol fraction to calculate the gas phase composition at each evaporation step.\n\nThe evaporating portion (i.e. the gas phase portion) is removed from the liquid phase portion. The Excel sheet is setup such that this subtraction accounts for approximately 1% of the initial fuel amount. The need to calculated mol fractions then back-calculated mass remaining is the reason it is not exactly 1% by mass at each step.\n\nTo avoid numerical errors, division by zero errors and rounding errors many of the calculations contain and the IF formula. When a fuel component is greater than 0 the mol fractions are calculated, otherwise a value of 0 is returned.\n\nRead me about AAD experimental data\nNotes about AAD experiments that back up the model (a full description is in the scientific publication)\n\nSmall portions of Special Antarctic Blend (SAB) fuel were placed into vials. Each vial was placed into a +20 degrees C or -20 degrees C chamber. A slow stream of nitrogen passed into the top of each vial to remove the evaporating portion of the fuel. The vials were slowly rotated to ensure even mixing of the residual fuel during rotation.\n\nPeriodically a vial was removed, weighed to calculate mass fuel evaporated, and analysed with GC-FID apparatus. After a range of vials were analysed at different levels of evaporation at the 2 temperatures a data set was obtained to validate the numerical model.", "links": [ { diff --git a/datasets/fuel_load_755_1.json b/datasets/fuel_load_755_1.json index 68b244ef01..bc371d028b 100644 --- a/datasets/fuel_load_755_1.json +++ b/datasets/fuel_load_755_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "fuel_load_755_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs. The Production Efficiency Model (PEM) was used to produce the estimated fuel loads for southern Africa for the 1999-2000 growing seasons.", "links": [ { diff --git a/datasets/full-content-of-wsl-fauna-database_1.0.json b/datasets/full-content-of-wsl-fauna-database_1.0.json index c86684a342..4d3e23d050 100644 --- a/datasets/full-content-of-wsl-fauna-database_1.0.json +++ b/datasets/full-content-of-wsl-fauna-database_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "full-content-of-wsl-fauna-database_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Complete extract of Fauna Database of WSL, containing all projects and all taxa. Meant as exchange and citation platform for sharing the data with the national data centre 'Centre Suisse de la Cartographie de la Fauna (CSCF)', and Info Fauna.", "links": [ { diff --git a/datasets/g3acld_003.json b/datasets/g3acld_003.json index 9d685c7de1..ecb2307ee9 100644 --- a/datasets/g3acld_003.json +++ b/datasets/g3acld_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3acld_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules", "links": [ { diff --git a/datasets/g3acldb_003.json b/datasets/g3acldb_003.json index b80bcbfd95..f4d452ff52 100644 --- a/datasets/g3acldb_003.json +++ b/datasets/g3acldb_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3acldb_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules", "links": [ { diff --git a/datasets/g3alsp_003.json b/datasets/g3alsp_003.json index a463dc38fa..89fd66147d 100644 --- a/datasets/g3alsp_003.json +++ b/datasets/g3alsp_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3alsp_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2 data file containing all the species products for a single lunar event", "links": [ { diff --git a/datasets/g3alspb_003.json b/datasets/g3alspb_003.json index b3a3ab87c9..e164b16d50 100644 --- a/datasets/g3alspb_003.json +++ b/datasets/g3alspb_003.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3alspb_003", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2 data file containing all the species products for a single lunar event", "links": [ { diff --git a/datasets/g3assp_004.json b/datasets/g3assp_004.json index c750547e8c..086b106b78 100644 --- a/datasets/g3assp_004.json +++ b/datasets/g3assp_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3assp_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2 data file containing all the species products for a single solar event", "links": [ { diff --git a/datasets/g3asspb_004.json b/datasets/g3asspb_004.json index 095a5d9244..45a73c3a45 100644 --- a/datasets/g3asspb_004.json +++ b/datasets/g3asspb_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3asspb_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2 data file containing all the species products for a single solar event", "links": [ { diff --git a/datasets/g3at_004.json b/datasets/g3at_004.json index 0f504074d8..b2b7122dc5 100644 --- a/datasets/g3at_004.json +++ b/datasets/g3at_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3at_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1B pixel group transmission profiles for a single solar event", "links": [ { diff --git a/datasets/g3atb_004.json b/datasets/g3atb_004.json index 7d906b0bca..fa950b715c 100644 --- a/datasets/g3atb_004.json +++ b/datasets/g3atb_004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3atb_004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 1B pixel group transmission profiles for a single solar event", "links": [ { diff --git a/datasets/g3baer_1.json b/datasets/g3baer_1.json index 93b42d05fa..a1c2e8c480 100644 --- a/datasets/g3baer_1.json +++ b/datasets/g3baer_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3baer_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\n\r\ng3baer_1 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Aerosol Product (NetCDF) V001 data product. It contains all of the aerosol data and flags for a month of solar events. \r\n\r\nLaunched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3baer_11.json b/datasets/g3baer_11.json index 9d1dc200af..fc0a840c3a 100644 --- a/datasets/g3baer_11.json +++ b/datasets/g3baer_11.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3baer_11", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\n\r\ng3baer_11 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Aerosol Product (NetCDF) V011 data product. It contains all of the aerosol data and flags for a month of solar events. \r\n\r\nLaunched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blmnc_52.json b/datasets/g3blmnc_52.json index a98669bbb5..0a277393c1 100644 --- a/datasets/g3blmnc_52.json +++ b/datasets/g3blmnc_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blmnc_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blmnc_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V052 data product. It contains all the species products for a month of lunar events (the last day of each month is omitted) . SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blmnc_53.json b/datasets/g3blmnc_53.json index 17ebde07f4..acfe31de36 100644 --- a/datasets/g3blmnc_53.json +++ b/datasets/g3blmnc_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blmnc_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V053 data product. It contains all the species products for a month of lunar events. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blsp_52.json b/datasets/g3blsp_52.json index ea8dea4061..003232dd88 100644 --- a/datasets/g3blsp_52.json +++ b/datasets/g3blsp_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blsp_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blsp_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V052 data product. It contains all the species products for a single lunar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blsp_53.json b/datasets/g3blsp_53.json index e2bf2eac3b..046634a44c 100644 --- a/datasets/g3blsp_53.json +++ b/datasets/g3blsp_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blsp_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blsp_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V053 data product. It contains all the species products for a single lunar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blspb_52.json b/datasets/g3blspb_52.json index 063492e327..3702e615e6 100644 --- a/datasets/g3blspb_52.json +++ b/datasets/g3blspb_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blspb_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blspb_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V052 data product. It contains all the species products for a single lunar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3blspb_53.json b/datasets/g3blspb_53.json index d5c6a61da0..35d48f03e2 100644 --- a/datasets/g3blspb_53.json +++ b/datasets/g3blspb_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3blspb_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3blspb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V053 data product. It contains all the species products for a single lunar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bsmnc_52.json b/datasets/g3bsmnc_52.json index d012bbbb7a..534668f471 100644 --- a/datasets/g3bsmnc_52.json +++ b/datasets/g3bsmnc_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bsmnc_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3bsmnc_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Solar Event Species Profiles (NetCDF) V052 data product. It contains all of the species products for a month of solar events (the last day of each month is omitted) . SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bsmnc_53.json b/datasets/g3bsmnc_53.json index b7746d46b5..d06afdf686 100644 --- a/datasets/g3bsmnc_53.json +++ b/datasets/g3bsmnc_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bsmnc_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\ng3bsmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Solar Event Species Profiles (NetCDF) V053 data product. It contains all of the species products for a month of solar events. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bssp_52.json b/datasets/g3bssp_52.json index d5188dcc5d..4e20dc7cdd 100644 --- a/datasets/g3bssp_52.json +++ b/datasets/g3bssp_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bssp_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3bssp_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (HDF5) V052 data product. It contains all the species products for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bssp_53.json b/datasets/g3bssp_53.json index 448f90e283..171a9e4da9 100644 --- a/datasets/g3bssp_53.json +++ b/datasets/g3bssp_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bssp_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\n\r\ng3bssp_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (HDF5) V053 data product. It contains all the species products for a single solar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bsspb_52.json b/datasets/g3bsspb_52.json index d67fbdec74..c1bc93d502 100644 --- a/datasets/g3bsspb_52.json +++ b/datasets/g3bsspb_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bsspb_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3bsspb_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (Native) V052 data product. It contains all the species products for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bsspb_53.json b/datasets/g3bsspb_53.json index 3488a22b26..b300f80964 100644 --- a/datasets/g3bsspb_53.json +++ b/datasets/g3bsspb_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bsspb_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process.\r\n\r\ng3bsspb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (Native) V053 data product. It contains all the species products for a single solar event.\r\n\r\nLaunched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bt_52.json b/datasets/g3bt_52.json index f2319ef824..fb70a34a51 100644 --- a/datasets/g3bt_52.json +++ b/datasets/g3bt_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bt_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3bt_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Lunar Event Species Profiles (HDF5) V052 data product. It contains pixel group transmission profiles for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3bt_53.json b/datasets/g3bt_53.json index c7d7b1ea43..5a9041bc12 100644 --- a/datasets/g3bt_53.json +++ b/datasets/g3bt_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3bt_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\ng3bt_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (HDF5) V053 data product. It contains pixel group transmission profiles for a single solar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3btb_52.json b/datasets/g3btb_52.json index 911d1b574a..8678955583 100644 --- a/datasets/g3btb_52.json +++ b/datasets/g3btb_52.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3btb_52", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "g3btb_52 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V052data product. It contains pixel group transmission profiles for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. Data collection for this product is ongoing. \r\rThis ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/g3btb_53.json b/datasets/g3btb_53.json index 87410c947c..ee2e87cab2 100644 --- a/datasets/g3btb_53.json +++ b/datasets/g3btb_53.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "g3btb_53", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. \r\ng3btb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V053 data product. It contains pixel group transmission profiles for a single solar event. \r\nLaunched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth\u2019s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.", "links": [ { diff --git a/datasets/gap_filled_marconi_811_1.json b/datasets/gap_filled_marconi_811_1.json index 57d621569b..440b2f8cbc 100644 --- a/datasets/gap_filled_marconi_811_1.json +++ b/datasets/gap_filled_marconi_811_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gap_filled_marconi_811_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fluxes of carbon dioxide, water vapor, and energy exchange have been measured at 38 forest, grassland, and crop sites as part of the EUROFLUX and AmeriFlux projects. A total of 97 site-years of data were compiled, primarily between 1996 and 1998 but also for 1992-1995 and 1999-2000. Half-hour flux and meteorology measurements are included plus the gap-filled half-hour estimates and aggregations to day and night, weekly, monthly, and annual periods. The FLUXNET 2000 Synthesis Workshop was held at the Marconi Conference Center, Marshall, California, June 11-14, 2000. The Marconi Flux Data Collection was compiled to aid in exploring the interactions between the terrestrial biosphere and the overlying atmosphere through carbon, water, and energy exchanges. The workshop resulted in several studies to synthesize and interpret differences and similarities in long-term measurements of carbon dioxide, water vapor, and energy exchanges between vegetation and the atmosphere for a spectrum of ecosystems. A series of synthesis papers based on these data and studies was published in a special issue of the Agriculture and Forest Meteorology, Volume 113, 2002. The papers are listed in the reference section. This data product is being archived as a record of the data used the AFM special issue. Updates and revisions to the data are available at the FLUXNET web site.The eddy covariance technique is used for long-term continuous measurements of mass and energy fluxes to capture seasonal dynamics and allow for a meaningful scaling with respect to time. The equipment and methodology were standardized among sites by using common software and instrumentation. Comparisons of ecosystem fluxes among sites are usually performed on annual or monthly sums calculated on complete data records; however, the average site data coverage during a year was only 65%. Therefore, development and application of robust and consistent data gap-filling methods was required before fluxes could be calculated. One of the outcomes of the FLUXNET project was computer applications to process the data into complete, consistent, quality assured, and documented data sets (Falge et al. 2001a,b). Gap-filled flux data from four different filling methods are reported. Selected meteorological parameters were also gap filled to support flux estimating methods and are reported along with non-filled meteorological data. Note that the measured/estimated CO2 fluxes and storage fluxes were summed into net ecosystem exchange (NEE), and ONLY NEE data are reported. ", "links": [ { diff --git a/datasets/gaz_1.json b/datasets/gaz_1.json index 3b03330fc4..7681929e23 100644 --- a/datasets/gaz_1.json +++ b/datasets/gaz_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gaz_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Gazetteer is maintained by the Australian Antarctic Data Centre and the Secretary of the Australian Antarctic Division Place Names Committee. It contains information about names in the Australian Antarctic Territory and the Territory of Heard Island and McDonald Islands. Users can search by place name, region, feature type, latitude or longitude. Displayed information includes a descriptive narrative, and where available, an image, source information and altitude.\n\nUsers can download the whole gazetteer or their search results as a KML or CSV file.", "links": [ { diff --git a/datasets/gbif-range-r_0.2.json b/datasets/gbif-range-r_0.2.json index f272e2a8c2..06967d15f4 100644 --- a/datasets/gbif-range-r_0.2.json +++ b/datasets/gbif-range-r_0.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gbif-range-r_0.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)", "links": [ { diff --git a/datasets/gcnet_1.0.json b/datasets/gcnet_1.0.json index 1d7b820f1e..5ef1139dd0 100644 --- a/datasets/gcnet_1.0.json +++ b/datasets/gcnet_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gcnet_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS). Starting October 3, 2022, the access to the latest versions of the \"ready to use\" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT) ### Background Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was \"Swiss Camp\" or the \"ETH-CU\" camp (GC-Net station #01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see \"C-file\" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. ### Overview Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland. ### The standard GC-Net station consists of: * Air temperature measurements at 2 heights above the surface * Temperature and humidity measurements at 2 heights above the surface * Wind speed and direction measured at 2 heights above the surface * Sonic distance sounder measurements for 2 snow height and distance of instruments to surface * Incoming shortwave radiation measurement * Reflected shortwave radiation measurement * Net broadband radiation (long- and short-wave) measurement * Air pressure measurement Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging. ### Dataset This dataset consists of 2 main data levels; Level 0 and Level 1. Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni\u2019s personal data archive. Level 0 data (.zip) directories contain subdirectories: * \u201cC file\u201d - contains the historical processed datafile for each station. * \u201cCampbell logger files\u201d - contains the raw csv datafiles from the stations\u2019 Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations). * \u201cPhotos\u201d - contains photographs of the station when available marked by year. Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data): [GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing \"GC-Net-level-1-data-processing\") Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead \"NEAD format\").
### Additional Details Dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.", "links": [ { diff --git a/datasets/gcos-swe-data_1.json b/datasets/gcos-swe-data_1.json index 648dedf366..f5e36ea2b9 100644 --- a/datasets/gcos-swe-data_1.json +++ b/datasets/gcos-swe-data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gcos-swe-data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station. The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes: \u2022 Raw data are archived in the snow and avalanche database at SLF. \u2022 Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation. \u2022 Historical data of these 11 stations have been digitized and all data have been quality controlled. \u2022 Detailed metadata (location of measurements) are available. \u2022 Data gaps for the two most important winter and spring dates were reconstructed based on a published SWE parameterization from co-located snow depth measurements. \u2022 Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).", "links": [ { diff --git a/datasets/gdp_xdeg_974_1.json b/datasets/gdp_xdeg_974_1.json index 921c9c5922..27686f7bf7 100644 --- a/datasets/gdp_xdeg_974_1.json +++ b/datasets/gdp_xdeg_974_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gdp_xdeg_974_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990.Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects. This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN. ", "links": [ { diff --git a/datasets/gem-bh_1.0.json b/datasets/gem-bh_1.0.json index 5000a671ad..66ff35714b 100644 --- a/datasets/gem-bh_1.0.json +++ b/datasets/gem-bh_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gem-bh_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50°) North-West slope (315°). The surface material is bedrock and borehole depth is 40 m. Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied. __Publications__ 1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579\u2013597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101. 2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157\u2013164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.", "links": [ { diff --git a/datasets/gem2_1.0.json b/datasets/gem2_1.0.json index b67c85dc61..652d8bee26 100644 --- a/datasets/gem2_1.0.json +++ b/datasets/gem2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gem2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor. Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites", "links": [ { diff --git a/datasets/generalised-stand-descriptions-within-the-swiss-nfi_1.0.json b/datasets/generalised-stand-descriptions-within-the-swiss-nfi_1.0.json index 771fae8b8a..58648fbaf8 100644 --- a/datasets/generalised-stand-descriptions-within-the-swiss-nfi_1.0.json +++ b/datasets/generalised-stand-descriptions-within-the-swiss-nfi_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "generalised-stand-descriptions-within-the-swiss-nfi_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files refer to the data and R code used in Mey et al. \"From small forest samples to generalised uni- and bimodal stand descriptions\" (2021) _Methods in Ecology and Evolution_. __Generalised stand descriptions__ are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data. The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). --------------------------------------- The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper. The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.", "links": [ { diff --git a/datasets/geocoord_556_1.json b/datasets/geocoord_556_1.json index 404c154933..05e3b51d68 100644 --- a/datasets/geocoord_556_1.json +++ b/datasets/geocoord_556_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geocoord_556_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.", "links": [ { diff --git a/datasets/geodata_0001.json b/datasets/geodata_0001.json index 21ee9416e1..e86e209282 100644 --- a/datasets/geodata_0001.json +++ b/datasets/geodata_0001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T).", "links": [ { diff --git a/datasets/geodata_0028.json b/datasets/geodata_0028.json index bb1a650281..08f45a779c 100644 --- a/datasets/geodata_0028.json +++ b/datasets/geodata_0028.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0028", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.", "links": [ { diff --git a/datasets/geodata_0032.json b/datasets/geodata_0032.json index 5cf320307c..e46ccd94d7 100644 --- a/datasets/geodata_0032.json +++ b/datasets/geodata_0032.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0032", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.", "links": [ { diff --git a/datasets/geodata_0048.json b/datasets/geodata_0048.json index 6d71df822b..36ef3fd163 100644 --- a/datasets/geodata_0048.json +++ b/datasets/geodata_0048.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0048", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.", "links": [ { diff --git a/datasets/geodata_0049.json b/datasets/geodata_0049.json index 7871862bcc..e3ef34c596 100644 --- a/datasets/geodata_0049.json +++ b/datasets/geodata_0049.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0049", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The average number of children a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality. It is expressed as children per woman.", "links": [ { diff --git a/datasets/geodata_0052.json b/datasets/geodata_0052.json index 6f3d77420d..02c780c17a 100644 --- a/datasets/geodata_0052.json +++ b/datasets/geodata_0052.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0052", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\n\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring;\nRainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine;\nVentilated improved pit latrine.", "links": [ { diff --git a/datasets/geodata_0058.json b/datasets/geodata_0058.json index 6b3356b160..af283eb868 100644 --- a/datasets/geodata_0058.json +++ b/datasets/geodata_0058.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0058", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset shows earthquake intensity zones in accordance with the 1956 version of the Modified Mercalli Scale (MM). The intensity describes exclusively the effects of an earthquake on the surface of the earth and integrates numerous parameters (such as ground acceleration, duration of an earthquake, subsoil effects). It also includes historical earthquake reports. The risk grading is based on expectations for a period of 50 years, corresponding to the mean service life of modern buildings. The probability that degrees of intensity shown on the map will be exceeded in 50 years is 20 per cent. This probability figure varies with time; i.e., it is lower for shorter periods and higher for longer periods. \n\nIn ARC/INFO, the item ZONE in the polygon attribute table (PAT) contains the following earthquake intensity values: \n\nZone Probable maximum intensity once in 50 years (MM Scale) \n\n0 V and below \n\n1 VI \n\n2 VII \n\n3 VIII \n\n4 IX and above \n\n10 indicates main waterbodies", "links": [ { diff --git a/datasets/geodata_0059.json b/datasets/geodata_0059.json index f4c8bd0b1c..49862f1c17 100644 --- a/datasets/geodata_0059.json +++ b/datasets/geodata_0059.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0059", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Holdridge Life Zones data set is from the International Institute for Applied Systems Analyses (IIASA) in Laxenburg, Austria. The data set shows the Holdridge Life Zones of the World, a combination of climate and vegetation (ecological) types, under current, so-called \"normal\" climate conditions, as well as under a presumed doubling of atmospheric CO2. The Life Zones were devised using three indicators: biotemperature (based on the growing season length and temperature); mean annual precipitation; and a potential evapotranspiration ratio, linking biotemperature with annual precipitation to define humidity provinces. The data set has a spatial resolution of one-half degree latitude/longitude, and a total of 38 life-zone classes which are listed on the accompanying legend sheet.\n\nThe Holdridge Life Zones data set includes a total of four data files. The first (HOLDNORM) is as described in the paragraph above; the second (HOLDDOUB) shows how the Life Zones would change given an assumed doubling of atmospheric CO2 (according to a General Circulation Model from the U.K. Office of Meteorology). The third and fourth data files show only those portions of the Life Zones which would undergo changes, that is for both the old classification (HOLDCHFR) before and the new classification (HOLDCHTO) after the theoretical doubling of CO2 (in effect, these areas have the appearance of 'sliver' polygons).", "links": [ { diff --git a/datasets/geodata_0060.json b/datasets/geodata_0060.json index 0ae55c8c1f..d6f7e3790a 100644 --- a/datasets/geodata_0060.json +++ b/datasets/geodata_0060.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0060", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil degradation Severity : Overall severity by which the polygon is affected by soil degradation. This item takes the degree and extent of both types into account. For the classification from 1 (low) to 4 (very high), a look-up table created by ISRIC was used. This item should be used for mapping only, not for area calculations!\n", "links": [ { diff --git a/datasets/geodata_0063.json b/datasets/geodata_0063.json index c3381478be..c42259f6b8 100644 --- a/datasets/geodata_0063.json +++ b/datasets/geodata_0063.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0063", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Humidity Index is based on a ratio of annual precipitation and potential evapotrans- piration (these data layers are described elsewhere) as P/PET, and largely follows the classification used in a 1984 UNESCO study. The Global Humidity Index surface shows mean annual potential moisture availability for the period 1951-1980, classified into four aridity zones and one humid zone, defined in this data set as follows: \n\nHyper-Arid Zone P/PET less than 0.05 \n\nArid Zone 0.05 less equal P/PET less than 0.20 \n\nSemi-Arid Zone 0.20 less equal P/PET less than 0.50 \n\nDry-Subhumid Zone 0.50 less equal P/PET less than 0.65 \n\nHumid Zone 0.65 less equal P/PET", "links": [ { diff --git a/datasets/geodata_0065.json b/datasets/geodata_0065.json index 9bd479dba3..39948331ea 100644 --- a/datasets/geodata_0065.json +++ b/datasets/geodata_0065.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0065", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice. \n", "links": [ { diff --git a/datasets/geodata_0066.json b/datasets/geodata_0066.json index c73f3c380a..3bad360b87 100644 --- a/datasets/geodata_0066.json +++ b/datasets/geodata_0066.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0066", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro-magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow-free conditions except for permanently snow-covered continental ice", "links": [ { diff --git a/datasets/geodata_0067.json b/datasets/geodata_0067.json index 7273bfb086..eecb19608b 100644 --- a/datasets/geodata_0067.json +++ b/datasets/geodata_0067.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0067", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The original data took the form of a value for each month and each box on a 0.5 degree latitude / longitude grid. The annual values are the average of their constituent months, they have been calculated by GRID-Geneva.\n \nOriginal Data Station observations were first collected by national meteorological, hydrological and related services, and were acquired through the free and unrestricted exchange of meteorological and related data. These observations were gridded by collaborators at the Climatic Research Unit (www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-reviewed scientific journal.\n\nData Source: CRU TS 2.10 Jan 2004 T. D. Mitchell, Tyndall Centre\nReference:\nMitchell T.D. and Jones P.D. 2005 An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712", "links": [ { diff --git a/datasets/geodata_0100.json b/datasets/geodata_0100.json index a544497499..1bbad63a6e 100644 --- a/datasets/geodata_0100.json +++ b/datasets/geodata_0100.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0100", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Source: International Monetary Fund, Government Finance Statistics Yearbook and data files.", "links": [ { diff --git a/datasets/geodata_0123.json b/datasets/geodata_0123.json index 8f6d0b81d9..a7292fdfe5 100644 --- a/datasets/geodata_0123.json +++ b/datasets/geodata_0123.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0123", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.\n\nNet Production Index Number (PIN) base 1999-2001\n\nPresents Net Production (Production - Feed - Seed) indices. All indices are calculated by the Laspeyres formula. Net production quantities of each commodity are weighted by 1999-2001average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 1999-2001. Indices are calculated from net production data presented on a calendar year basis.\n", "links": [ { diff --git a/datasets/geodata_0162.json b/datasets/geodata_0162.json index 4114480ba0..2c35232d55 100644 --- a/datasets/geodata_0162.json +++ b/datasets/geodata_0162.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0162", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogeographical realms were established by Udvardy on the basis of geographic and historic elements, utilizing ground-breaking work as appears on this topic in the published literature. Udvardy's paper makes reference to at least three preceding reports on this topic, and also includes an extensive bibliography of five pages. There are 8 biogeographical realms recognized by Udvardy in Paper #18: the Palearctic, the Nearctic, the Afrotropical, the Indomalayan, the Oceanian, the Australian, the Antarctic and the Neotropical. \nThe proper reference for this data set is \"Udvardy, Miklos D. F. 1975. A Classification of the Biogeographical Provinces of the World. IUCN Occasional Paper No. 18, prepared as a contribution to UNESCO's Man and the Biosphere (MAB) Program, Project No. 8. International Union for the Conservation of Nature and Natural Resources, Morges (now Gland), Switzerland, 49 pages.\" A source citation should include IUCN, as digitized by UNEP/GRID in 1986.", "links": [ { diff --git a/datasets/geodata_0165.json b/datasets/geodata_0165.json index 37cb628e0f..a96e429db0 100644 --- a/datasets/geodata_0165.json +++ b/datasets/geodata_0165.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0165", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Precipitation is \"average annual\", and is expressed in terms of millimeters (mm.) per year; Average Days of Precipitation (\"Wet Days\") is number of days per year; and Average Windspeed is expressed in terms of meters per second (note that this is not maximum windspeed, nor is there any directional content included in this data set). It is GRID's assumption that the definition of a \"wet day\" is one in which enough precipitation occurred on a given day so as to be recordable by a gauging station at a particular location.", "links": [ { diff --git a/datasets/geodata_0179.json b/datasets/geodata_0179.json index bb65259cf4..43f0ddd22d 100644 --- a/datasets/geodata_0179.json +++ b/datasets/geodata_0179.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0179", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitizing manually many paper maps.\n\nContinuing on from the tropical moist forest mapping, the next major initiative was to create the first 'World Forest Map'. This was produced in 1996 and was the first digital global forest map showing actual forest extent and protected areas with forested land. Since this achievement, significant work has been carried out to improve data sources and fill in gaps which occurred in this first attempt. This led to the production of the 'Global Overview of Forest Conservation CDROM' in 1997.\n", "links": [ { diff --git a/datasets/geodata_0180.json b/datasets/geodata_0180.json index 651056ec95..0221f83bf4 100644 --- a/datasets/geodata_0180.json +++ b/datasets/geodata_0180.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0180", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "UNEP-WCMC has been gathering and compiling spatial data on the extent and conservation status of forests since 1987. Until 1995, WCMC's work focused on tropical moist forests because of their high species diversity. GIS data were first assembled for closed moist tropical forests and used to publish the three volumes of the Conservation Atlas of Tropical Forests, covering Asia (1991), Africa (1992) and the Americas (1996). Because digital data were rare at this time, the process of assembling the forest cover data sets involved digitising manually many paper maps.", "links": [ { diff --git a/datasets/geodata_0181.json b/datasets/geodata_0181.json index 46b3ec2f34..2882b5ca52 100644 --- a/datasets/geodata_0181.json +++ b/datasets/geodata_0181.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0181", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove plants include trees, shrubs, ferns and palms. These plants are found in the tropics and sub- tropics on river banks and along coastlines, being unusually adapted to anaerobic conditions of both salt and fresh water environments. These plants have adapted to muddy, shifting, saline conditions. They produce stilt roots which project above the mud and water in order to absorb oxygen. Mangrove plants form communities which help to stabilise banks and coastlines and become home to many types of animals.", "links": [ { diff --git a/datasets/geodata_0199.json b/datasets/geodata_0199.json index 0582ce59cd..2b52521238 100644 --- a/datasets/geodata_0199.json +++ b/datasets/geodata_0199.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0199", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest plantation is a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems.\n", "links": [ { diff --git a/datasets/geodata_0200.json b/datasets/geodata_0200.json index 6fb77a25a3..cea129e880 100644 --- a/datasets/geodata_0200.json +++ b/datasets/geodata_0200.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0200", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Plantation Average Annual Change - is the annual change of a forest established by planting and/or seeding in the process of afforestation or reforestation. It consists of introduced species or, in some cases, indigenous species. Forest plantation and natural forests are included in the term forest, a term that refers to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems. ", "links": [ { diff --git a/datasets/geodata_0201.json b/datasets/geodata_0201.json index f587b774b7..713481a55c 100644 --- a/datasets/geodata_0201.json +++ b/datasets/geodata_0201.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0201", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FSC-endorsed certification of a forest site signifies that an independent evaluation by one of several FSC accredited certification bodies has shown that its management meets the internationally recognised FSC Principles and Criteria of Forest Stewardship.", "links": [ { diff --git a/datasets/geodata_0227.json b/datasets/geodata_0227.json index 4ce2195b06..09b45db9c3 100644 --- a/datasets/geodata_0227.json +++ b/datasets/geodata_0227.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0227", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. Data are in current U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files.\n", "links": [ { diff --git a/datasets/geodata_0231.json b/datasets/geodata_0231.json index 8332be5d78..6665235dfb 100644 --- a/datasets/geodata_0231.json +++ b/datasets/geodata_0231.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0231", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.) Source: Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security. Note: Data for some countries are based on partial or uncertain data or rough estimates.\n", "links": [ { diff --git a/datasets/geodata_0237.json b/datasets/geodata_0237.json index ee9e180525..7d7af26dd4 100644 --- a/datasets/geodata_0237.json +++ b/datasets/geodata_0237.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0237", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Life expectancy: The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period. It is expressed as years.\n", "links": [ { diff --git a/datasets/geodata_0261.json b/datasets/geodata_0261.json index 21f34cf88b..bff6ec5505 100644 --- a/datasets/geodata_0261.json +++ b/datasets/geodata_0261.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0261", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Groundwater produced internally: Long-term annual average groundwater recharge, generated from precipitation within the boundaries of the country. Renewable groundwater resources of the country are computed either by estimating annual infiltration rate (in arid countries) or by computing river base flow (in humid countries).", "links": [ { diff --git a/datasets/geodata_0271.json b/datasets/geodata_0271.json index 84cd77ac77..7db86a5433 100644 --- a/datasets/geodata_0271.json +++ b/datasets/geodata_0271.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0271", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTAL PRODUCTION\nThe annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. \nThe harvest from mariculture, aquaculture and other kinds of fish farming is also included.\nData include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture.\n\nTo assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise.\n\n* includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals", "links": [ { diff --git a/datasets/geodata_0278.json b/datasets/geodata_0278.json index d9d56a5b0f..0b1fd0d063 100644 --- a/datasets/geodata_0278.json +++ b/datasets/geodata_0278.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0278", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The exclusive fishing zone or fishery zone refers to an area beyond the outer limit of the territorial sea (12 nautical miles from the coast) in which the coastal State has the right to fish, subject to any concessions which may be granted to foreign fishermen. Some countries have made no claim beyond the territorial sea. Some States have claimed an exclusive fishing zone instead of the more encompassing 200 nautical mile Exclusive Economic Zone (EEZ). The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil, and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). Some States have not ratified UNCLOS and many have not yet claimed their EEZ. Given the uncertainties surrounding much of the delimitation of the fishing zones, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/\n", "links": [ { diff --git a/datasets/geodata_0279.json b/datasets/geodata_0279.json index a3210037af..0a30246395 100644 --- a/datasets/geodata_0279.json +++ b/datasets/geodata_0279.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0279", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). In cases where a country's low-water lines is within 400 nautical miles of each other the EEZ boundaries are generally established by treaty, though there are many cases where these are in dispute. Under UNCLOS, \"land-locked and geographically disadvantaged States have the right to participate on an equitable basis in exploitation of an appropriate part of the surplus of the living resources of the EEZ's of coastal States of the same region or sub-region.\" Some States have not ratified UNCLOS and many have not yet claimed their EEZ. These areas of unclaimed EEZ are the areas that a State has the right to claim under UNCLOS, but has not done so yet. Given the uncertainties surrounding much of the delimitation of the EEZ, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ ", "links": [ { diff --git a/datasets/geodata_0290.json b/datasets/geodata_0290.json index eeb20fca9c..b8968d2b9d 100644 --- a/datasets/geodata_0290.json +++ b/datasets/geodata_0290.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0290", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4.", "links": [ { diff --git a/datasets/geodata_0291.json b/datasets/geodata_0291.json index d5edb1c8f7..157cc386c5 100644 --- a/datasets/geodata_0291.json +++ b/datasets/geodata_0291.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0291", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs.\n\nConstruction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory.\n", "links": [ { diff --git a/datasets/geodata_0295.json b/datasets/geodata_0295.json index 43247d82a2..c9b4f855ea 100644 --- a/datasets/geodata_0295.json +++ b/datasets/geodata_0295.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0295", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of \"noise\" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data.\n", "links": [ { diff --git a/datasets/geodata_0331.json b/datasets/geodata_0331.json index 5e09990a2e..813f707685 100644 --- a/datasets/geodata_0331.json +++ b/datasets/geodata_0331.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0331", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files.", "links": [ { diff --git a/datasets/geodata_0335.json b/datasets/geodata_0335.json index 9ffa9f4b37..d7340b2196 100644 --- a/datasets/geodata_0335.json +++ b/datasets/geodata_0335.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0335", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files.\n", "links": [ { diff --git a/datasets/geodata_0337.json b/datasets/geodata_0337.json index 6fdabdb8ad..d2015c324c 100644 --- a/datasets/geodata_0337.json +++ b/datasets/geodata_0337.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0337", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAPTURE PRODUCTION\n\nThe annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture.\n\nTo assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise.\n\nincludes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_0344.json b/datasets/geodata_0344.json index b5fd6153a2..13cd3bb48f 100644 --- a/datasets/geodata_0344.json +++ b/datasets/geodata_0344.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0344", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total energy production is the production of primary energy, from, the total of all energy sources : hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and wastes, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural).\n", "links": [ { diff --git a/datasets/geodata_0365.json b/datasets/geodata_0365.json index dc0bc29544..45b95c7d61 100644 --- a/datasets/geodata_0365.json +++ b/datasets/geodata_0365.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0365", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was \"economically\" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: \n\n- Satellite data selection (minimal cloud cover)/acquisition; \n\n- Data pre-processing for a) geometric correction and b) cloud masking; \n\n- Data subset stratification into homogeneous spectral zones; \n\n- Data subset classification (Bayesian maximum likelihood); \n\n- Accuracy assessment (using classified Landsat MSS); \n\n- Mosaicking of classified data subsets; \n\n- Merging of final results and overlays; \n\n- Cartographic preparation\n", "links": [ { diff --git a/datasets/geodata_0368.json b/datasets/geodata_0368.json index de8de0e42a..e1a7e211be 100644 --- a/datasets/geodata_0368.json +++ b/datasets/geodata_0368.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0368", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The digital version of the Vegetation Map of the European Communities and the Council of Europe held by GRID covers all of Belgium, Denmark, France, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, the United Kingdom and the former West Germany, although the original map also covers Iceland, Norway, Sweden, Finland, Turkey and Cyprus.\n", "links": [ { diff --git a/datasets/geodata_0418.json b/datasets/geodata_0418.json index 523038263e..8c81676b36 100644 --- a/datasets/geodata_0418.json +++ b/datasets/geodata_0418.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0418", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics.\nDiseases of the Respiratory System includes: \nICD-9 BTL codes B31-B32,\nICD-9 code CH08 for some ex-USSR countries,\nICD-9 code C052 for China,\nICD-10 codes J00-J99, \nEuropean mortality indicator database (HFA-MDB), available at www.euro.who.int \nfor missing figures for some european countries:\nindicator \"3250 Deaths, Diseases of the Respiratory System\"", "links": [ { diff --git a/datasets/geodata_0436.json b/datasets/geodata_0436.json index a8387ea113..83423411e4 100644 --- a/datasets/geodata_0436.json +++ b/datasets/geodata_0436.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0436", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Affected: People requiring immediate assistance during a period of emergency, i.e. requiring basic \nsurvival needs such as food, water, shelter, sanitation and immediate medical assistance (included in \nthe field \"total affected\"); Appearance of a significant number of cases of an infectious disease \nintroduced in a region or a population that is usually free from that disease. (100 or more people \naffected).\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or \ninternational level for external assistance; An unforeseen and often sudden event that causes great \ndamage, destruction and human suffering. Though often caused by nature, disasters can have human \norigins. Wars and civil disturbances that destroy homelands and displace people are included among \nthe causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, \nearthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical \nspill), hurricane, nuclear incident, tornado, or volcano.\n\nNatural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_0438.json b/datasets/geodata_0438.json index f7c436c794..ed261914e0 100644 --- a/datasets/geodata_0438.json +++ b/datasets/geodata_0438.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0438", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. \n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_0449.json b/datasets/geodata_0449.json index fb3da0182b..830fab6579 100644 --- a/datasets/geodata_0449.json +++ b/datasets/geodata_0449.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0449", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.", "links": [ { diff --git a/datasets/geodata_0450.json b/datasets/geodata_0450.json index d20ab79ee8..36005be5fc 100644 --- a/datasets/geodata_0450.json +++ b/datasets/geodata_0450.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0450", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.", "links": [ { diff --git a/datasets/geodata_0458.json b/datasets/geodata_0458.json index b1106bd5f5..36f600f259 100644 --- a/datasets/geodata_0458.json +++ b/datasets/geodata_0458.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0458", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.", "links": [ { diff --git a/datasets/geodata_0465.json b/datasets/geodata_0465.json index 20530b4c95..499289755f 100644 --- a/datasets/geodata_0465.json +++ b/datasets/geodata_0465.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0465", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 - from Cement Production (CDIAC) is the amount of C02 created by the conversion of calcium carbonate to calcium oxide inside the kilns, and by burning large quantities of fossil fuels to heat the kilns to the 1450 C necessary for roasting limestone.\n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because:\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0469.json b/datasets/geodata_0469.json index 90defbee75..bdcd72e08f 100644 --- a/datasets/geodata_0469.json +++ b/datasets/geodata_0469.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0469", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 - from Gas Flaring (CDIAC): Annual estimations of CO2 emissions from Gas Flaring, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov. \n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because :\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0470.json b/datasets/geodata_0470.json index 16934f36e1..311a3a96f6 100644 --- a/datasets/geodata_0470.json +++ b/datasets/geodata_0470.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0470", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 - from Gas Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from gas Gas Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov.\n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because :\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0473.json b/datasets/geodata_0473.json index ca000460d5..16ea881992 100644 --- a/datasets/geodata_0473.json +++ b/datasets/geodata_0473.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0473", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 - from Liquid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Liquid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov.\n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because :\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0476.json b/datasets/geodata_0476.json index 97c1b80601..0e75d97073 100644 --- a/datasets/geodata_0476.json +++ b/datasets/geodata_0476.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0476", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 - from Solid Fuel Consumption (CDIAC): Annual estimations of CO2 emissions from Solid Fuel Consumption, primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration, Rotty (1974), and with a few national estimates provided by G. Marland. For information about the data collection methodology used by Rotty (1974) and G. Marland see http://cdiac.esd.ornl.gov.\n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because :\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0480.json b/datasets/geodata_0480.json index 1ebc0eefdd..2f20500bc4 100644 --- a/datasets/geodata_0480.json +++ b/datasets/geodata_0480.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0480", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total emissions of CO2 from fossil fuels are the sum of CO2 produced during the consumption of solid, liquid, and gaseous fuels, and from gas flaring, and cement manufacturing. The data is primarily derived from U.N. data. Thereafter supplemented with data from the U.S. Department of Energy's Energy Information Administration (Rotty) and with a few national estimates provided by G. Marland.\n\nThe sum of emissions estimates for all countries is not equal to the estimate of global total emissions because :\n\n1. The global total includes emissions from bunker fuels (i.e., fuels used by ships and aircraft during international trade) whereas these are not included in any national totals.\n\n2. The global total includes estimates for the oxidation of non-fuel hydrocarbon products (e.g., asphalt) whereas national totals do not.\n\n3. National totals include annual changes in fuel stocks whereas the global total does not.\n\n4. Due to statistical differences in the international statistics, the sum of exports from all exporters is not identical to the sum of imports by all importers.\n", "links": [ { diff --git a/datasets/geodata_0543.json b/datasets/geodata_0543.json index 62d89e864e..228dc3b67f 100644 --- a/datasets/geodata_0543.json +++ b/datasets/geodata_0543.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0543", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Flood: Significant rise of water level in a stream, lake, reservoir or coastal region. ", "links": [ { diff --git a/datasets/geodata_0588.json b/datasets/geodata_0588.json index ded22a3705..489ace7b9c 100644 --- a/datasets/geodata_0588.json +++ b/datasets/geodata_0588.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0588", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. Disaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Extreme temperature: Disaster type term comprising the two disaster subsets \"heat wave\" and \"cold wave\" (Long lasting period with extremely high or low surface temperature).\n", "links": [ { diff --git a/datasets/geodata_0613.json b/datasets/geodata_0613.json index 0ffe5a8340..193cfc9d23 100644 --- a/datasets/geodata_0613.json +++ b/datasets/geodata_0613.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0613", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Crude birth rate: number of births over a given period divided by the person-years lived by the population over that period. It is expressed as number of births per 1,000 population.", "links": [ { diff --git a/datasets/geodata_0633.json b/datasets/geodata_0633.json index 33775dd998..313c8b739e 100644 --- a/datasets/geodata_0633.json +++ b/datasets/geodata_0633.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0633", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. Data are in constant 2000 U.S. dollars. Source: World Bank national accounts data, and OECD National Accounts data files.\n", "links": [ { diff --git a/datasets/geodata_0686.json b/datasets/geodata_0686.json index d9e0c8ac38..fc777420e5 100644 --- a/datasets/geodata_0686.json +++ b/datasets/geodata_0686.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0686", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable.\n", "links": [ { diff --git a/datasets/geodata_0758.json b/datasets/geodata_0758.json index a740829952..110d80077f 100644 --- a/datasets/geodata_0758.json +++ b/datasets/geodata_0758.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0758", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database.", "links": [ { diff --git a/datasets/geodata_0771.json b/datasets/geodata_0771.json index 9a0498ca31..cdb721a304 100644 --- a/datasets/geodata_0771.json +++ b/datasets/geodata_0771.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0771", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arable Land: land under temporary crops (double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. \n\nPermanent Crops: land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee and rubber; this category includes land under flowering shrubs, fruit trees, nut trees and vines, but excludes land under trees grown for wood or timber.\n", "links": [ { diff --git a/datasets/geodata_0776.json b/datasets/geodata_0776.json index f685b2c775..6c4e0185f2 100644 --- a/datasets/geodata_0776.json +++ b/datasets/geodata_0776.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0776", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Infant mortality: probability of dying between birth and exact age 1. It is expressed as deaths per 1,000 births.", "links": [ { diff --git a/datasets/geodata_0839.json b/datasets/geodata_0839.json index 8d5a93c938..394d398cf2 100644 --- a/datasets/geodata_0839.json +++ b/datasets/geodata_0839.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0839", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cereals also includes other cereals such as mixed grains and buckwheat.\n\nThe data reported under this element represent the harvested production per unit of harvested area for crop products. In most of the cases yield data are not recorded but obtained by dividing the data stored under production element by those recorded under element: area harvested. Data are recorded in hectogramme (100 grammes) per hectare (HG/HA).", "links": [ { diff --git a/datasets/geodata_0879.json b/datasets/geodata_0879.json index b6f1f42fa6..7274efa962 100644 --- a/datasets/geodata_0879.json +++ b/datasets/geodata_0879.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0879", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead.\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Drought: Period of deficiency of moisture in the soil such that there is inadequate water required for plants, animals and human beings. \n", "links": [ { diff --git a/datasets/geodata_0885.json b/datasets/geodata_0885.json index 825f42e94a..926b337282 100644 --- a/datasets/geodata_0885.json +++ b/datasets/geodata_0885.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0885", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category.\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_0927.json b/datasets/geodata_0927.json index 482273b948..81ee1ea680 100644 --- a/datasets/geodata_0927.json +++ b/datasets/geodata_0927.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0927", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Production refers to the quantities of fuels extracted or produced, calculated after any operation for removal of inert matter or impurities (e.g. sulphur from natural gas).\n\nWaste refers to the quantities of fuels extracted or produced from Industrial waste and Municipal waste. Industrial waste consists of solid and liquid products (e.g. tyres) combusted directly, usually in specialised plants, to produce heat and/or power and that are not reported in the category solid biomass. Municipal waste consists of products that are combusted directly to produce heat and/or power and comprises wastes produced by the residential, commercial and public services sectors that are collected by local authorities for disposal in a central location. Hospital waste is included in this category. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour). \n", "links": [ { diff --git a/datasets/geodata_0930.json b/datasets/geodata_0930.json index becfb3f5f2..1419835d91 100644 --- a/datasets/geodata_0930.json +++ b/datasets/geodata_0930.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0930", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Average Annual Change \u2013 Total is the net change in forests and includes expansion of forest plantations and losses and gains in the area of natural forests.\n\nTotal Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems.\n", "links": [ { diff --git a/datasets/geodata_0932.json b/datasets/geodata_0932.json index 8bfc0428a5..b574ab0e00 100644 --- a/datasets/geodata_0932.json +++ b/datasets/geodata_0932.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0932", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AQUACULTURE PRODUCTION\n\nThe annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*.\nFarming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries.\n\nProduction of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. \n\n*includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_0938.json b/datasets/geodata_0938.json index d0aa038a4c..485bc33308 100644 --- a/datasets/geodata_0938.json +++ b/datasets/geodata_0938.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0938", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAPTURE PRODUCTION\n\nThe annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture.\n\nTo assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise.\n\n* includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_0940.json b/datasets/geodata_0940.json index 5fe13eb09c..6162b9e94d 100644 --- a/datasets/geodata_0940.json +++ b/datasets/geodata_0940.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0940", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CAPTURE PRODUCTION\n\nThe annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture.\n\nTo assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise.\n\n* includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_0960.json b/datasets/geodata_0960.json index 27454bb248..3183896506 100644 --- a/datasets/geodata_0960.json +++ b/datasets/geodata_0960.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0960", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest Fire Extent - Annual Average comprises the reported forest areas exposed to fire. Total Forest includes natural forests and forest plantations. The term is used to refer to land with a tree cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 m. Young stands that have not yet reached, but are expected to reach, a crown density of 10m percent and tree height of 5 m are included under forest, as are temporarily unstocked areas. The term includes forests used for purposes of production, protection, multiple use or conservation (i.e. forest in national parks, nature reserves and other protected areas), as well as forest stands on agricultural lands (e.g. windbreaks and shelterbelts of trees with a width of more than 20 m) and rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems.\n", "links": [ { diff --git a/datasets/geodata_0992.json b/datasets/geodata_0992.json index 962b8b508c..3aeeb593f6 100644 --- a/datasets/geodata_0992.json +++ b/datasets/geodata_0992.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_0992", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy Capacity - Nuclear is the actual capacity of the nuclear electric power industry to describe the size of generating plants. \u201cMWe\u201d is the symbol for the actual output of a generating station in megawatts of electricity.", "links": [ { diff --git a/datasets/geodata_1011.json b/datasets/geodata_1011.json index f72cc4f6ee..dc6e51547b 100644 --- a/datasets/geodata_1011.json +++ b/datasets/geodata_1011.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1011", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbon to the Atmosphere from Land-Use Change - Annual Net Flux is a numeric database with annual estimations, from 1850 through 1990, of the net flux of carbon between terrestrial ecosystems and the atmosphere. The data is the result of deliberate changes in land cover and land use, especially forest clearing for agriculture and the harvest of wood for wood products or energy. ", "links": [ { diff --git a/datasets/geodata_1018.json b/datasets/geodata_1018.json index b5f9576860..d3d7a46e19 100644 --- a/datasets/geodata_1018.json +++ b/datasets/geodata_1018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Desalinated water corresponds to the annual amount of fresh water generated by desalination of sea or brackish waters (annually estimated on the basis of the total capacity of water desalination installations).", "links": [ { diff --git a/datasets/geodata_1029.json b/datasets/geodata_1029.json index 2dc7bede0d..56bc711c35 100644 --- a/datasets/geodata_1029.json +++ b/datasets/geodata_1029.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1029", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\n\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring;\nRainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine;\nVentilated improved pit latrine.\n", "links": [ { diff --git a/datasets/geodata_1034.json b/datasets/geodata_1034.json index 5365f826ad..7066914823 100644 --- a/datasets/geodata_1034.json +++ b/datasets/geodata_1034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities.\n\nImproved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.\n", "links": [ { diff --git a/datasets/geodata_1085.json b/datasets/geodata_1085.json index 157d10cdc6..08ca853b43 100644 --- a/datasets/geodata_1085.json +++ b/datasets/geodata_1085.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1085", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "According to the UN Convention of the Law of the Sea, the Continental Shelf is the area of the seabed and subsoil which extends beyond the territorial sea to a distance of 200 nautical miles from the territorial sea baseline and beyond that distance to the outer edge of the continental margin. \n\nAreas of continental shelf that are disputed by overlaping claims by one or more nations have been excluded from this table. Areas that are of cooperative joint development between two or more nations have also been excluded. \n\nCoastal States have sovereign rights over the continental shelf (the national area of the seabed) for exploring and exploiting it; the shelf can extend at least 200 nautical miles from the shore, and more under specified circumstances. \n\nThe United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules on how the maritime jurisdictional boundaries of the different member states are set. \n\nThe UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS.\nFurther information on the Web site: http://www.maritimeboundaries.com/\n", "links": [ { diff --git a/datasets/geodata_1088.json b/datasets/geodata_1088.json index f875bb1fe6..15f49f0290 100644 --- a/datasets/geodata_1088.json +++ b/datasets/geodata_1088.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1088", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The measurement of an irregular and curving feature such as a nation's coastal length is scale-dependent and very difficult to measure. Maps of individual islands for example, frequently show great detail, whereas regional maps summarize complex coastlines into a few simple lines. In addition, coastal features are constantly changing due to erosion, etc. The only way to derive comparable statistics on coastline length is to use a single source which uses a constant scale. This is what has been attempted with the data presented in this table, however, highly complex coastlines will appear longer at higher resolutions. Estimates may differ from other published sources. \n\nBecause of the difficulty in trying to measure coastline length, these figures should be interpreted as approximations and should be used with caution. Coastline length was derived from the World Vector Shoreline database at 1:250,000 kilometers. The estimates presented here were calculated using a Geographic Information System (GIS) and an underlying database consistent for the entire world. The methodology used to estimate length is based on the following: 1) A country's coastline is made up of individual lines, and an individual line has two or more vertices and/or nodes. 2) The length between two vertices is calculated on the surface of a sphere. 3) The sum of the lengths of the pairs of vertices is aggregated for each individual line, and 4) the sum of the lengths of individual lines was aggregated for a country. In general, the coastline length of islands that are part of a country, but are not overseas territories, are included in the coastline estimate for that country (i.e., Canary Islands are included in Spain). Coastline length for overseas territories and dependencies are listed separately. Disputed areas are not included in country or regional totals.\n", "links": [ { diff --git a/datasets/geodata_1147.json b/datasets/geodata_1147.json index 8d05c1a360..2e1babd661 100644 --- a/datasets/geodata_1147.json +++ b/datasets/geodata_1147.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1147", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 from public electricity and heat production contain the sum of emissions from public electricity generation, public combined heat and power generation, and public heat plants. Public utilities are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. Emissions from own on-site use of fuel should be included. This corresponds to IPCC Source/Sink Category 1 A 1 a.\n", "links": [ { diff --git a/datasets/geodata_1150.json b/datasets/geodata_1150.json index e0c42c30b7..09c9e4e269 100644 --- a/datasets/geodata_1150.json +++ b/datasets/geodata_1150.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1150", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 from manufacturing industries and construction contain the emissions from combustion of fuels (coal, oil and gas) in industry. The IPCC Source/Sink Category 1 A 2 includes these emissions. However, in the Guidelines, the IPCC category also includes emissions from industry autoproducers that generate electricity and/or heat. The IEA data are not collected in a way that allows the energy consumption to be split by specific end-use and therefore, autoproducers are shown as a separate item (Unallocated Autoproducers). Manufacturing Industries and Construction also includes emissions from coke inputs into blast furnaces, which may be reported either in the transformation sector, the industry sector or the separate IPCC Source/Sink Category 2, Industrial Processes.", "links": [ { diff --git a/datasets/geodata_1153.json b/datasets/geodata_1153.json index daf4db0d5a..a8f0680d5d 100644 --- a/datasets/geodata_1153.json +++ b/datasets/geodata_1153.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1153", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 from transport contain emissions from the combustion of fuel (coal, oil and gas) for all transport activity, regardless of the sector, except for international marine and aviation bunkers. This corresponds to IPCC Source/Sink Category 1 A 3. In addition, the IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use.", "links": [ { diff --git a/datasets/geodata_1156.json b/datasets/geodata_1156.json index 7fb1a888e9..a84b0f58b0 100644 --- a/datasets/geodata_1156.json +++ b/datasets/geodata_1156.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1156", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).\n\nEmissions of CH4 - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from:\n\nRice cultivation (IPCC 4C);\nAnimal breeding: enteric fermentation and animal waste management (IPCC 4A and 4B,);\nSavannah burning (IPCC 4E);\nAgricultural waste burning (IPCC 4F).\nThe emissions from deforestation (IPCC 5A1) and vegetation fires (IPCC 5A2,3) are not included.\n", "links": [ { diff --git a/datasets/geodata_1162.json b/datasets/geodata_1162.json index 26ca701b8b..06d5686f26 100644 --- a/datasets/geodata_1162.json +++ b/datasets/geodata_1162.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1162", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - Total (RIVM) include \"Energy\", \"Agriculture\", \"Waste\" and \"Others\" EDGAR subdivisions.\n\n\"Energy\" comprises production, handling, transmission and combustion of fossil fuels and biofuels (IPCC category 1A and 1B); \"Agriculture\" comprises animals, animal waste, rice production, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises landfills, wastewater treatment, human wastewater disposal and waste incineration (non-energy) (IPCC category 6); \"Others\" include industrial process emissions and tropical and temperate forest fires (IPCC categories 2 and 5).\n", "links": [ { diff --git a/datasets/geodata_1165.json b/datasets/geodata_1165.json index 2d84ff6a86..a8a7c5fcd2 100644 --- a/datasets/geodata_1165.json +++ b/datasets/geodata_1165.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1165", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of CH4 - from Waste (RIVM) include emissions from:\n\nLandfills (including CH4 recovery) (IPCC 6A1,2);\nWastewater treatment (including CH4 recovery) (IPCC 6B1,2);\nHuman wastewater disposal (IPCC 6B2);\nWaste incineration (non-energy) (IPCC 6C).\n", "links": [ { diff --git a/datasets/geodata_1198.json b/datasets/geodata_1198.json index 2f08dbd5f3..44d82c4c78 100644 --- a/datasets/geodata_1198.json +++ b/datasets/geodata_1198.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1198", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of Total GHG (CO2, CH4, N2O, HFCs, PFCs and SF6) (UNFCCC), Excluding Land-Use Change and Forestry The Global Warming Potential (GWP) is an index used to translate the level of emissions of various gases into a common measure in order to compare the relative radiative forcing of different gases without directly calculating the changes in atmospheric concentrations. GWPs are calculated as the ratio of the radiative forcing that would result from the emissions of one kilogram of a greenhouse gas to that from the emission of one kilogram of carbon dioxide over a period of time (usually 100 years). Gases involved in complex atmospheric chemical processes have not been assigned GWPs.\n", "links": [ { diff --git a/datasets/geodata_1204.json b/datasets/geodata_1204.json index 00e8f9d695..1670402560 100644 --- a/datasets/geodata_1204.json +++ b/datasets/geodata_1204.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1204", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of N2O - from Agriculture (RIVM). Emissions of CH4 (Methane) from the agricultural sector include emissions from:\n\nArable Land (fertilizer use) (IPCC 4D);\nAnimal waste management (IPCC 4B);\nSavannah burning (IPCC 4E);\nAgricultural waste burning (IPCC 4F);\nCrop production (IPCC 4D);\nAnimal waste (deposited on soil - N2O) (IPCC 4B);\nAtmospheric deposition (IPCC 4D);\nLeaching and Run-off (IPCC 4D).\nThe emissions from deforestation (IPCC 5A1), vegetation fires (IPCC 5A2,3) and deforestation post burn effects (IPCC 5B1) are not included.", "links": [ { diff --git a/datasets/geodata_1210.json b/datasets/geodata_1210.json index 845a52d995..97147a663f 100644 --- a/datasets/geodata_1210.json +++ b/datasets/geodata_1210.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1210", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of N2O - Total (RIVM) include \"Energy\", \"Agriculture\", \"Waste\" and \"Others\" EDGAR subdivisions. \"Energy\" comprises combustion of fossil fuels and biofuels (IPCC category 1A and 1B); \"Agriculture\" comprises fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises human sewage discharge and waste incineration (non-energy) (IPCC category 6); \"Others\" include industrial process emissions, N2O usage and tropical and temperate forest fires (IPCC categories 2, 3 and 5).\n", "links": [ { diff --git a/datasets/geodata_1213.json b/datasets/geodata_1213.json index 51b8da30aa..10bad52d9c 100644 --- a/datasets/geodata_1213.json +++ b/datasets/geodata_1213.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1213", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO (carbon monoxide) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\".\n\n\"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A).\n", "links": [ { diff --git a/datasets/geodata_1216.json b/datasets/geodata_1216.json index 1dd4552be3..801450ed12 100644 --- a/datasets/geodata_1216.json +++ b/datasets/geodata_1216.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1216", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of NMVOC (Non-Methane Volatile Organic Compounds) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\".\n\n\"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A).", "links": [ { diff --git a/datasets/geodata_1219.json b/datasets/geodata_1219.json index 6ef60bef25..3c67700178 100644 --- a/datasets/geodata_1219.json +++ b/datasets/geodata_1219.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1219", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of NOx (Nitrogen Oxides) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\".\n\n\"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A).\n", "links": [ { diff --git a/datasets/geodata_1222.json b/datasets/geodata_1222.json index fd57d1083d..d30259ffa7 100644 --- a/datasets/geodata_1222.json +++ b/datasets/geodata_1222.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1222", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of SO2 (Sulfur dioxide) - Total (RIVM) include the following EDGAR subdivisions: \"Fuel combustion\", \u201cBiofuel combustion\u201d, \u201cFugitive\u201d, \u201cIndustry\u201d, \u201cSolvent use\u201d, \"Agriculture\", \"Waste\" and \"Others\".\n\n\"Fuel combustion\" refers to fossil fuel combustion and evaporation of NMVOC in road transport (part of IPCC category 1A); \"Biofuel combustion\" refers to traditional biofuels as well as to wood waste, paper, ethanol, etc. (part of IPCC category 1A); \"Fugitive\" comprises flaring and venting of associated gas in oil and gas production, handling / transmission losses of oil and charcoal production (IPCC category 1B); \"Industry\" refers to non-combustion industrial processes, excluding solvent use (IPCC category 2); \"Solvent use\" refers to solvent use in industry and non-industry sectors (IPCC category 3); \"Agriculture\" comprises agricultural waste burning (non-energy, on-site) and savannah burning (IPCC category 4); \"Waste\" comprises waste incineration (non-energy) (uncontrolled residential burning and controlled non-residential burning) and hazardous waste handling (IPCC category 6); \"Others\" comprises tropical forest fires and temperate forest fires (IPCC category 5A).\n", "links": [ { diff --git a/datasets/geodata_1253.json b/datasets/geodata_1253.json index 8b797746ea..4f99e0f67d 100644 --- a/datasets/geodata_1253.json +++ b/datasets/geodata_1253.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1253", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mangroves are commonly found along sheltered coastlines in the tropics and subtropics where they fulfil important socio-economic and environmental functions. These include the provision of a large variety of wood and non-wood forest products; coastal protection against the effects of wind, waves and water currents; conservation of biological diversity, including a number of endangered mammals, reptiles, amphibians and birds; protection of coral reefs, sea-grass beds and shipping lanes against siltation; and provision of habitat, spawning grounds and nutrients for a variety of fish and shellfish, including many commercial species. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves. ", "links": [ { diff --git a/datasets/geodata_1256.json b/datasets/geodata_1256.json index 0a5d7561eb..9bc68ca4fa 100644 --- a/datasets/geodata_1256.json +++ b/datasets/geodata_1256.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1256", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The World Mangrove Atlas is the first significant attempt to provide an overview of the distribution of mangroves worldwide. Mapped data showing the extent of mangroves in over 100 countries have been gathered from a wide range of sources. Mangrove trees and shrubs, including ferns and palms, are found along river banks and coastlines in tropical and subtropical countries. Their main characteristic is that they can tolerate salt and brackish water environments. Globally, there are seventy known species of mangroves.", "links": [ { diff --git a/datasets/geodata_1261.json b/datasets/geodata_1261.json index afe49dd489..e41477069a 100644 --- a/datasets/geodata_1261.json +++ b/datasets/geodata_1261.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1261", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1262.json b/datasets/geodata_1262.json index 66bb766f69..a5f83c2c89 100644 --- a/datasets/geodata_1262.json +++ b/datasets/geodata_1262.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1262", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1264.json b/datasets/geodata_1264.json index 694efffb31..490e23cede 100644 --- a/datasets/geodata_1264.json +++ b/datasets/geodata_1264.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1264", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). \n", "links": [ { diff --git a/datasets/geodata_1265.json b/datasets/geodata_1265.json index 916d88f011..771ae9987f 100644 --- a/datasets/geodata_1265.json +++ b/datasets/geodata_1265.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1265", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)\nEmissions of co2 from Power Generation (public and auto, including cogeneration)\n corresponds to IPCC category 1A1a.", "links": [ { diff --git a/datasets/geodata_1266.json b/datasets/geodata_1266.json index d093feaf74..8dc1e518f8 100644 --- a/datasets/geodata_1266.json +++ b/datasets/geodata_1266.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1266", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Power Generation (public and auto, including cogeneration) corresponds to IPCC category 1A1a. \n", "links": [ { diff --git a/datasets/geodata_1267.json b/datasets/geodata_1267.json index 1498183798..e13071c737 100644 --- a/datasets/geodata_1267.json +++ b/datasets/geodata_1267.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1267", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4.\n", "links": [ { diff --git a/datasets/geodata_1268.json b/datasets/geodata_1268.json index 1d371aac6d..6b4c9551bb 100644 --- a/datasets/geodata_1268.json +++ b/datasets/geodata_1268.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1268", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from Residential, Commercials and Other sector corresponds to IPCC category 1A4.", "links": [ { diff --git a/datasets/geodata_1269.json b/datasets/geodata_1269.json index 9a9be80880..8ec5b5860f 100644 --- a/datasets/geodata_1269.json +++ b/datasets/geodata_1269.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1269", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road corresponds to IPCC category 1A3b.", "links": [ { diff --git a/datasets/geodata_1270.json b/datasets/geodata_1270.json index eb8d5d0456..21395f02fa 100644 --- a/datasets/geodata_1270.json +++ b/datasets/geodata_1270.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1270", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of co2 from transport road; corresponds to IPCC category 1A3b.", "links": [ { diff --git a/datasets/geodata_1271.json b/datasets/geodata_1271.json index e6e0ba5237..aefd63d617 100644 --- a/datasets/geodata_1271.json +++ b/datasets/geodata_1271.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1271", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1272.json b/datasets/geodata_1272.json index 1f6957a682..2cb1397673 100644 --- a/datasets/geodata_1272.json +++ b/datasets/geodata_1272.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1272", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). \n", "links": [ { diff --git a/datasets/geodata_1273.json b/datasets/geodata_1273.json index e17444cbfb..413aa87ec6 100644 --- a/datasets/geodata_1273.json +++ b/datasets/geodata_1273.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1273", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)\nMethane production from herbivores is a by-product of enteric Emissions of CH4 from animal breeding corresponds to IPCC category 4A.\nAll Anthropogenic Sources also includes international air traffic and international shipping.", "links": [ { diff --git a/datasets/geodata_1274.json b/datasets/geodata_1274.json index f70c3b03ba..2095c9c591 100644 --- a/datasets/geodata_1274.json +++ b/datasets/geodata_1274.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1274", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Methane production from herbivores is a by-product of enteric,fermentation, a digestive process by which carbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream. Both ruminant (e.g. cattle, sheep) and non-ruminant animals (e.g. pigs, horses) produce CH 4 , although ruminants are the largest source (per unit of feed intake).", "links": [ { diff --git a/datasets/geodata_1275.json b/datasets/geodata_1275.json index b6179faaf4..8e475cc47c 100644 --- a/datasets/geodata_1275.json +++ b/datasets/geodata_1275.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1275", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1276.json b/datasets/geodata_1276.json index b7892e6f30..96678262e2 100644 --- a/datasets/geodata_1276.json +++ b/datasets/geodata_1276.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1276", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1277.json b/datasets/geodata_1277.json index 7e787bdcfc..b403b09c94 100644 --- a/datasets/geodata_1277.json +++ b/datasets/geodata_1277.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1277", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling.\n", "links": [ { diff --git a/datasets/geodata_1278.json b/datasets/geodata_1278.json index 54bd7bb292..81d4e2adc4 100644 --- a/datasets/geodata_1278.json +++ b/datasets/geodata_1278.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1278", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC) Emissions of N2O from fertilizer use in arable land: synthetic and animal waste handling.", "links": [ { diff --git a/datasets/geodata_1279.json b/datasets/geodata_1279.json index e8f9136fdc..f701f36be3 100644 --- a/datasets/geodata_1279.json +++ b/datasets/geodata_1279.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1279", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1280.json b/datasets/geodata_1280.json index d210507cec..6f6edfaad6 100644 --- a/datasets/geodata_1280.json +++ b/datasets/geodata_1280.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1280", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1281.json b/datasets/geodata_1281.json index f48dbe3b7c..54d134633e 100644 --- a/datasets/geodata_1281.json +++ b/datasets/geodata_1281.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1281", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1282.json b/datasets/geodata_1282.json index 65cc4cce51..40cda97de5 100644 --- a/datasets/geodata_1282.json +++ b/datasets/geodata_1282.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1282", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)", "links": [ { diff --git a/datasets/geodata_1283.json b/datasets/geodata_1283.json index c6d721714d..73a76b0864 100644 --- a/datasets/geodata_1283.json +++ b/datasets/geodata_1283.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1283", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). \n", "links": [ { diff --git a/datasets/geodata_1284.json b/datasets/geodata_1284.json index 69fb8459a2..328e50bfc0 100644 --- a/datasets/geodata_1284.json +++ b/datasets/geodata_1284.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1284", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC).", "links": [ { diff --git a/datasets/geodata_1285.json b/datasets/geodata_1285.json index ce79e4f8ce..aa707a565f 100644 --- a/datasets/geodata_1285.json +++ b/datasets/geodata_1285.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1285", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). \n", "links": [ { diff --git a/datasets/geodata_1286.json b/datasets/geodata_1286.json index 388c00b152..f029ababcd 100644 --- a/datasets/geodata_1286.json +++ b/datasets/geodata_1286.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1286", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global emissions source database called EDGAR has been developed jointly by TNO and RIVM to meet the urgent need of atmospheric chemistry and climate modellers and the need of policy-makers. The EDGAR database was to estimate the annual emissions of direct and indirect greenhouse gases (CO2, CH4, N2O; CO, NOx, non-methane VOC; SO2), including ozone-depleting compounds (halocarbons) for 1990 on a regional and grid basis. To meet the aim of establishing the global emissions from both anthropogenic and biogenic sources, a complete set of data would be required to estimate the total source strength of the various gases with a 1x1 degree resolution (altitude resolution of 1 km), as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC)", "links": [ { diff --git a/datasets/geodata_1315.json b/datasets/geodata_1315.json index 17fdf3b25e..7f76114863 100644 --- a/datasets/geodata_1315.json +++ b/datasets/geodata_1315.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1315", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the purpose of Desertification Atlas map production, the GRID-Nairobi data analysts required data from a fairly dense network of global climate stations. They therefore obtained both precipitation and temperature station data from UEA/CRU for two 30-year periods, 1930-59 and 1960-89. While the CRU database contained 950 precipitation station values, this number was not sufficient for interpolating two separate global surfaces to be used in a climate change study, for reasons of both temporal instability and inaccuracies of eventual area estimates. \n\nThus, GRID decided in conjunction with UEA/CRU to produce a single, high-resolution preci- pitation surface for one time period only, using the maximum number of station means available. For this surface, data from the time period 1951-1980 were selected, both in order to avoid creation of a \"timeless\" data set, and to better match the period of the GLASOD study whose data were compiled in the late 1980s.\n", "links": [ { diff --git a/datasets/geodata_1316.json b/datasets/geodata_1316.json index 17545a1a5a..f9020c6237 100644 --- a/datasets/geodata_1316.json +++ b/datasets/geodata_1316.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1316", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The original data took the form of a value for each month and each box on a 0.5 degree latitude / longitude grid. The annual values are the average of their constituent months, they have been calculated by GRID-Geneva.\n \nOriginal Data Station observations were first collected by national meteorological, hydrological and related services, and were acquired through the free and unrestricted exchange of meteorological and related data. These observations were gridded by collaborators at the Climatic Research Unit (www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-reviewed scientific journal.\n\nData Source: CRU TS 2.10 Jan 2004 T. D. Mitchell, Tyndall Centre\nReference:\nMitchell T.D. and Jones P.D. 2005 An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712.\n", "links": [ { diff --git a/datasets/geodata_1351.json b/datasets/geodata_1351.json index 2cbcad10a7..0f90e8776c 100644 --- a/datasets/geodata_1351.json +++ b/datasets/geodata_1351.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1351", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.\n", "links": [ { diff --git a/datasets/geodata_1352.json b/datasets/geodata_1352.json index 42b92c0463..9a9753f75b 100644 --- a/datasets/geodata_1352.json +++ b/datasets/geodata_1352.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1352", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.\n", "links": [ { diff --git a/datasets/geodata_1353.json b/datasets/geodata_1353.json index 87679c7227..e3ff15b19f 100644 --- a/datasets/geodata_1353.json +++ b/datasets/geodata_1353.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1353", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ther \u201cblue marble\u201d image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.", "links": [ { diff --git a/datasets/geodata_1354.json b/datasets/geodata_1354.json index 3caa9cf262..98de516d87 100644 --- a/datasets/geodata_1354.json +++ b/datasets/geodata_1354.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1354", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.\n", "links": [ { diff --git a/datasets/geodata_1355.json b/datasets/geodata_1355.json index a81c226a15..5c06f2a94f 100644 --- a/datasets/geodata_1355.json +++ b/datasets/geodata_1355.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1355", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \u201cblue marble\u201d image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.\n", "links": [ { diff --git a/datasets/geodata_1356.json b/datasets/geodata_1356.json index 567683a2be..48d8cc85b8 100644 --- a/datasets/geodata_1356.json +++ b/datasets/geodata_1356.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1356", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The \"blue marble\" image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. \n\nMuch of the information contained in this image came from a single remote-sensing device-NASA\u2019s Moderate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor\u2019s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey\u2019s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration\u2019s AVHRR sensor\u2014the Advanced Very High Resolution Radiometer.\n", "links": [ { diff --git a/datasets/geodata_1358.json b/datasets/geodata_1358.json index 55b6e29516..00ff73c496 100644 --- a/datasets/geodata_1358.json +++ b/datasets/geodata_1358.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1358", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).\n", "links": [ { diff --git a/datasets/geodata_1359.json b/datasets/geodata_1359.json index 9b24974b53..b25f6bde0a 100644 --- a/datasets/geodata_1359.json +++ b/datasets/geodata_1359.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1359", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", "links": [ { diff --git a/datasets/geodata_1360.json b/datasets/geodata_1360.json index e0b8710868..0765f540a9 100644 --- a/datasets/geodata_1360.json +++ b/datasets/geodata_1360.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1360", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", "links": [ { diff --git a/datasets/geodata_1361.json b/datasets/geodata_1361.json index 8ccb2ab0d8..f59aab5c2f 100644 --- a/datasets/geodata_1361.json +++ b/datasets/geodata_1361.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1361", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", "links": [ { diff --git a/datasets/geodata_1362.json b/datasets/geodata_1362.json index 461c2e6e4b..b3e01a16df 100644 --- a/datasets/geodata_1362.json +++ b/datasets/geodata_1362.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1362", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", "links": [ { diff --git a/datasets/geodata_1363.json b/datasets/geodata_1363.json index 5eead5a9cf..c48d7f69b4 100644 --- a/datasets/geodata_1363.json +++ b/datasets/geodata_1363.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1363", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Modified mixture analysis, geographic stratification, and other classification techniques were used to estimate forest canopy density within 1 square kilometer pixels, which formed the basis for the first two classes: the closed forest (40% - 100% canopy cover), and open or fragmented forest (10 - 40% canopy cover).", "links": [ { diff --git a/datasets/geodata_1364.json b/datasets/geodata_1364.json index 4c8985e936..ce14ed0147 100644 --- a/datasets/geodata_1364.json +++ b/datasets/geodata_1364.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1364", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. \n", "links": [ { diff --git a/datasets/geodata_1365.json b/datasets/geodata_1365.json index 45b16bc046..7cc1c8cafd 100644 --- a/datasets/geodata_1365.json +++ b/datasets/geodata_1365.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1365", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. \n", "links": [ { diff --git a/datasets/geodata_1366.json b/datasets/geodata_1366.json index 3e084af35c..4537642b78 100644 --- a/datasets/geodata_1366.json +++ b/datasets/geodata_1366.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1366", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. \n", "links": [ { diff --git a/datasets/geodata_1367.json b/datasets/geodata_1367.json index 1ee11d53e6..4f5934da58 100644 --- a/datasets/geodata_1367.json +++ b/datasets/geodata_1367.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1367", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. \n", "links": [ { diff --git a/datasets/geodata_1368.json b/datasets/geodata_1368.json index fcdc29d5a2..26f70df938 100644 --- a/datasets/geodata_1368.json +++ b/datasets/geodata_1368.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1368", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water.", "links": [ { diff --git a/datasets/geodata_1369.json b/datasets/geodata_1369.json index 80307d10ad..28394de585 100644 --- a/datasets/geodata_1369.json +++ b/datasets/geodata_1369.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1369", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The classes were delineated based on circa 1995 monthly AVHRR composite images processed using a hybrid maximum-NDVI and minimum-red compositing technique. Grid Legend: 0=Ocean, 1=Closed Forest, 2=Open or Fragmented Forest, 3=Other Wooded Land, 4=Other Land Cover, 5=Water. \n", "links": [ { diff --git a/datasets/geodata_1370.json b/datasets/geodata_1370.json index 5ce09c4d18..d1b064e093 100644 --- a/datasets/geodata_1370.json +++ b/datasets/geodata_1370.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1370", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", "links": [ { diff --git a/datasets/geodata_1371.json b/datasets/geodata_1371.json index d4c20f6e47..3b4ed1e6fb 100644 --- a/datasets/geodata_1371.json +++ b/datasets/geodata_1371.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1371", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas:\nDerived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1\nJoint Research Centre (JRC), European Commission (EC)\nhttp://www.gvm.sai.jrc.it/fire/default.htm\n\nIn collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC\n(Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) \n\nBiomass burning contributes up to 50%, 40% and 16% of the total emissions of anthropogenic origin for carbon monoxide, carbon dioxide and methane respectively. Both the scientific community and the policy makers are looking for reliable and quantitative information on the magnitude and spatial distribution of biomass burning.\n\nPlease visit the original source site at: http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/ where you can find more detailed information, downloads and the GBA2000-IMS application.\n\nThe GBA2000-IMS application informs users of the status of the project. In addition, the user can overlay burnt area maps with other sources of information such as country borders, national park boundaries and a land cover map. Moreover users can zoom in and out, change the background, the month of observation in 2000, as well as download the data and access statistics. \n\nNOTE: The GBA2000 products are currently under development. Burnt area maps are still prototype versions and might be modified/improved to take into account the comments received from the scientific community.", "links": [ { diff --git a/datasets/geodata_1372.json b/datasets/geodata_1372.json index 026d12d86e..03e7c444e4 100644 --- a/datasets/geodata_1372.json +++ b/datasets/geodata_1372.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1372", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", "links": [ { diff --git a/datasets/geodata_1373.json b/datasets/geodata_1373.json index 21b1fc8872..8dcff0c1ae 100644 --- a/datasets/geodata_1373.json +++ b/datasets/geodata_1373.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1373", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC) http://www.gvm.sai.jrc.it/fire/default.htm In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", "links": [ { diff --git a/datasets/geodata_1374.json b/datasets/geodata_1374.json index 893a4844aa..75456a4489 100644 --- a/datasets/geodata_1374.json +++ b/datasets/geodata_1374.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1374", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).\n", "links": [ { diff --git a/datasets/geodata_1375.json b/datasets/geodata_1375.json index bfef315876..a8a28a019a 100644 --- a/datasets/geodata_1375.json +++ b/datasets/geodata_1375.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1375", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal) .\n", "links": [ { diff --git a/datasets/geodata_1376.json b/datasets/geodata_1376.json index eccf0fcf82..d2dae265e4 100644 --- a/datasets/geodata_1376.json +++ b/datasets/geodata_1376.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1376", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Burnt Areas: Derived from a daily time series of coarse resolution (~ 1 km) satellite imagery: SPOT-VGT-S1 Joint Research Centre (JRC), European Commission (EC). In collaboration with CCRS (Canada), IREA-CNR (Italy), CSIRO-EOC (Australia), IFI (Russia), NRI (UK), UNEP/GRID-Geneva, UTL (Portugal).", "links": [ { diff --git a/datasets/geodata_1395.json b/datasets/geodata_1395.json index e7bc6e363b..28716970b3 100644 --- a/datasets/geodata_1395.json +++ b/datasets/geodata_1395.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1395", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The concept of the length of the available growing period (LGP) combines temperature and moisture considerations to determine the length of time crops are able to grow, hence excluding periods which are too cold or too dry or both. \n\nLGP refers to the number of days within the period of temperatures above 5\u00b0C when moisture conditions are considered adequate. Under rain-fed conditions, the begin of the LGP is linked to the start of the rainy season. The growing period for most crops continues beyond the rainy season and, to a greater or lesser extent, crops mature on moisture stored in the soil profile.\n", "links": [ { diff --git a/datasets/geodata_1398.json b/datasets/geodata_1398.json index b8a3db54c2..8caeaa4d2b 100644 --- a/datasets/geodata_1398.json +++ b/datasets/geodata_1398.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1398", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Problem soils have been defined as soils with inherent physical or chemical constraints to agricultural production. In these soils degradation hazards are more severe and adequate soil management measures are more difficult or costly to apply. Such soils, if improperly used or inadequately managed will degrade rapidly, sometimes irreversibly. As a result the land itself might go out of production. The analysis is carried out in a sequential way.\n", "links": [ { diff --git a/datasets/geodata_1399.json b/datasets/geodata_1399.json index 42d9acbfd7..c0f4f0e84f 100644 --- a/datasets/geodata_1399.json +++ b/datasets/geodata_1399.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1399", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is an indicator for the amount of stored soil moisture readily available to crops.The water retention at 2 bar suction is used to separate easily available water (EAV) from water which is more tightly held at higher suctions and difficult to abstract, especially from deeper subsoils; and in the use of a conceptual model of effective rooting depth.", "links": [ { diff --git a/datasets/geodata_1425.json b/datasets/geodata_1425.json index b8262dd90f..c4e34f867a 100644 --- a/datasets/geodata_1425.json +++ b/datasets/geodata_1425.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1425", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Net reproduction rate: The average number of daughters a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates and the mortality rates of a given period. It is expressed as number of daughters per woman.", "links": [ { diff --git a/datasets/geodata_1458.json b/datasets/geodata_1458.json index 486b0da872..7b6a14d175 100644 --- a/datasets/geodata_1458.json +++ b/datasets/geodata_1458.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1458", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: 1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, Industrial Pollution in Economic Development: Kuznets Revisited (available at www.worldbank.org/nipr). The data were updated through 2005 by the World Bank's Development Research Group using the same methodology as the initial study. ", "links": [ { diff --git a/datasets/geodata_1459.json b/datasets/geodata_1459.json index 26045a7cad..ecaac1356b 100644 --- a/datasets/geodata_1459.json +++ b/datasets/geodata_1459.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1459", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. Source: World Bank and UNIDO's industry database.", "links": [ { diff --git a/datasets/geodata_1474.json b/datasets/geodata_1474.json index 2876bb1add..cc117f5bd6 100644 --- a/datasets/geodata_1474.json +++ b/datasets/geodata_1474.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1474", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Healthy life expectancy (HALE) is based on life expectancy (LEX), but includes an adjustment for time spent in poor health. This indicator measures the equivalent number of years in full health that a newborn child can expect to live based on the current mortality rates and prevalence distribution of health states in the population.\n", "links": [ { diff --git a/datasets/geodata_1480.json b/datasets/geodata_1480.json index 8e10be6290..4772496638 100644 --- a/datasets/geodata_1480.json +++ b/datasets/geodata_1480.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1480", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The economically active population comprises all persons of either sex who furnish the supply of labour for the production of economic goods and services as defined by the United Nations systems of national accounts and balances during a specified time-reference period. According to these systems the production of economic goods and services includes all production and processing of primary products whether for the market for barter or for own consumption, the production of all other goods and services for the market and, in the case of households which produce such goods and services for the market, the corresponding production for own consumption.", "links": [ { diff --git a/datasets/geodata_1498.json b/datasets/geodata_1498.json index fe863859d1..0e17ac2144 100644 --- a/datasets/geodata_1498.json +++ b/datasets/geodata_1498.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1498", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days.", "links": [ { diff --git a/datasets/geodata_1501.json b/datasets/geodata_1501.json index 6b695fcf57..8f235654e8 100644 --- a/datasets/geodata_1501.json +++ b/datasets/geodata_1501.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1501", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The concept of drylands continues to be debated. In this data set, drylands are taken as areas with a potential hazard of desertification. The hyperarid zone is not subject to desertification and is therefore excluded. Hence drylands are defined as the arid, semi-arid and dry subhumid zones, or areas with lengths of growing periods of 1-179 days.", "links": [ { diff --git a/datasets/geodata_1525.json b/datasets/geodata_1525.json index ae3d28914d..204820b3aa 100644 --- a/datasets/geodata_1525.json +++ b/datasets/geodata_1525.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1525", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy use per GDP (Constant 2005 PPP $) is the kilogram of oil equivalent of energy use per gross domestic product converted to 2005 constant international dollars using purchasing power parity rates. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.\n\nGross Domestic Product (GDP) is the sum of gross value added by all resident producers in the economy plus any product taxes (less subsidies) not included in the valuation of output. Value added is the net output of an industry after adding up all outputs and subtracting intermediate inputs. The purchasing power parity (PPP) conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as the United States (U.S.) dollar would buy in the United States. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.", "links": [ { diff --git a/datasets/geodata_1540.json b/datasets/geodata_1540.json index d818807a81..97f82c5486 100644 --- a/datasets/geodata_1540.json +++ b/datasets/geodata_1540.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1540", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Definitions used in these data refer to the waste streams to be controlled according to the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (seeAnnex IV of the convention for complete definition and methods of treatment, movement and disposal).", "links": [ { diff --git a/datasets/geodata_1571.json b/datasets/geodata_1571.json index dca94bfb8a..4849bfc676 100644 --- a/datasets/geodata_1571.json +++ b/datasets/geodata_1571.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1571", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs. ", "links": [ { diff --git a/datasets/geodata_1624.json b/datasets/geodata_1624.json index a0caf4dd74..87721a4ab5 100644 --- a/datasets/geodata_1624.json +++ b/datasets/geodata_1624.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1624", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category.\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_1627.json b/datasets/geodata_1627.json index 26756577d9..ce31adbe29 100644 --- a/datasets/geodata_1627.json +++ b/datasets/geodata_1627.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1627", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. \n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or \ninternational level for external assistance; An unforeseen and often sudden event that causes great \ndamage, destruction and human suffering. Though often caused by nature, disasters can have human \norigins. Wars and civil disturbances that destroy homelands and displace people are included among \nthe causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, \nearthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical \nspill), hurricane, nuclear incident, tornado, or volcano.\nNatural disasters include : Droughts, Earthquakes, Extreme Temperatures, Floods, Insect infestation, Slides, Volcanic eruptions, Wave/surges, Wild fires and Wind storms.", "links": [ { diff --git a/datasets/geodata_1628.json b/datasets/geodata_1628.json index 942d35eb45..aba688fda7 100644 --- a/datasets/geodata_1628.json +++ b/datasets/geodata_1628.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1628", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\"Threatened\" includes species listed as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU).\n\nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered and it is therefore considered to be facing an extremely high risk of extinction in the wild.\n\nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered and it is therefore considered to be facing a very high risk of extinction in the wild.\n\nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable and it is therefore considered to be facing a high risk of extinction in the wild.", "links": [ { diff --git a/datasets/geodata_1644.json b/datasets/geodata_1644.json index 0393bf580f..1ab3483e1d 100644 --- a/datasets/geodata_1644.json +++ b/datasets/geodata_1644.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1644", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AQUACULTURE PRODUCTION\n\nThe annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. \n\n*includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_1646.json b/datasets/geodata_1646.json index 27667b42e9..f13d00ded7 100644 --- a/datasets/geodata_1646.json +++ b/datasets/geodata_1646.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1646", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). \n", "links": [ { diff --git a/datasets/geodata_1647.json b/datasets/geodata_1647.json index 57c496bd15..14591c07d5 100644 --- a/datasets/geodata_1647.json +++ b/datasets/geodata_1647.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1647", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format).\n", "links": [ { diff --git a/datasets/geodata_1648.json b/datasets/geodata_1648.json index 95b40dc10b..87332f169d 100644 --- a/datasets/geodata_1648.json +++ b/datasets/geodata_1648.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1648", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format)", "links": [ { diff --git a/datasets/geodata_1649.json b/datasets/geodata_1649.json index 196f575ad8..32d7896e05 100644 --- a/datasets/geodata_1649.json +++ b/datasets/geodata_1649.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1649", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). \n", "links": [ { diff --git a/datasets/geodata_1650.json b/datasets/geodata_1650.json index f3742a18f8..68bce6875c 100644 --- a/datasets/geodata_1650.json +++ b/datasets/geodata_1650.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1650", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). \n", "links": [ { diff --git a/datasets/geodata_1651.json b/datasets/geodata_1651.json index 82b8383651..85ff3c4712 100644 --- a/datasets/geodata_1651.json +++ b/datasets/geodata_1651.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1651", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format).", "links": [ { diff --git a/datasets/geodata_1652.json b/datasets/geodata_1652.json index 3e83c136ae..ef8872a7d7 100644 --- a/datasets/geodata_1652.json +++ b/datasets/geodata_1652.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1652", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). \n", "links": [ { diff --git a/datasets/geodata_1672.json b/datasets/geodata_1672.json index dbbcfe72e4..ef3ff54179 100644 --- a/datasets/geodata_1672.json +++ b/datasets/geodata_1672.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1672", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under \"forest\"); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land).", "links": [ { diff --git a/datasets/geodata_1685.json b/datasets/geodata_1685.json index 3a1b85e2c9..cb5c825eda 100644 --- a/datasets/geodata_1685.json +++ b/datasets/geodata_1685.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1685", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land area is the total area of the country excluding area under inland water bodies. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area.", "links": [ { diff --git a/datasets/geodata_1706.json b/datasets/geodata_1706.json index 8e0b7008ee..a01d0b5512 100644 --- a/datasets/geodata_1706.json +++ b/datasets/geodata_1706.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1706", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation.\nMethyl bromide (CH3Br) is used as a fumigant for high-value crops, pest control, and quarantine treatment of agricultural commodities awaiting export. Total world annual consumption is about 70,000 tonnes, most of it in the industrialized countries. It takes about 0.7 years to break down.", "links": [ { diff --git a/datasets/geodata_1708.json b/datasets/geodata_1708.json index 208d69966a..a1e9f9cf82 100644 --- a/datasets/geodata_1708.json +++ b/datasets/geodata_1708.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1708", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation.\nHydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years.", "links": [ { diff --git a/datasets/geodata_1717.json b/datasets/geodata_1717.json index 86c234b274..d89ba0e2e0 100644 --- a/datasets/geodata_1717.json +++ b/datasets/geodata_1717.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1717", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category.\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_1720.json b/datasets/geodata_1720.json index 11b509d349..d6c1bd716f 100644 --- a/datasets/geodata_1720.json +++ b/datasets/geodata_1720.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1720", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total affected: In EM-DAT, people that have been injured, affected and left homeless after a disaster are included in this category.\n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_1723.json b/datasets/geodata_1723.json index 7f0727b3ae..44a40a1c63 100644 --- a/datasets/geodata_1723.json +++ b/datasets/geodata_1723.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1723", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. \n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_1726.json b/datasets/geodata_1726.json index 7d81df6406..ff890a5070 100644 --- a/datasets/geodata_1726.json +++ b/datasets/geodata_1726.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1726", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Killed: Persons confirmed as dead and persons missing and presumed dead. \n\nDisaster: Situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance; An unforeseen and often sudden event that causes great damage, destruction and human suffering. Though often caused by nature, disasters can have human origins. Wars and civil disturbances that destroy homelands and displace people are included among the causes of disasters. Other causes can be: building collapse, blizzard, drought, epidemic, earthquake, explosion, fire, flood, hazardous material or transportation incident (such as a chemical spill), hurricane, nuclear incident, tornado, or volcano. Natural disasters include : Droughts, Extreme Temperatures, Floods, Slides, Wave/surges, Wild fires and Wind storms.\n", "links": [ { diff --git a/datasets/geodata_1730.json b/datasets/geodata_1730.json index e8fb6515fc..487bb194ad 100644 --- a/datasets/geodata_1730.json +++ b/datasets/geodata_1730.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1730", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The transport sector includes all fuels for transport except international marine bunkers [ISIC Divisions 60, 61 and 62]. It includes transport in the industry sector and covers road, railway, air, internal navigation (including small craft and coastal shipping not included under marine bunkers), fuels used for transport of materials by pipeline and non-specified transport. Fuel used for ocean, coastal and inland fishing should be included in agriculture. For many countries, the split between international civil aviation and domestic air appears to allocate fuel use for both domestic and international departures of domestically owned carriers to domestic air.", "links": [ { diff --git a/datasets/geodata_1741.json b/datasets/geodata_1741.json index f19a684b1b..bd8cfe91b3 100644 --- a/datasets/geodata_1741.json +++ b/datasets/geodata_1741.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1741", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy production comprises crude oil, natural gas liquids, refinery feedstocks, and additives as well as other hydrocarbons such as synthetic oil, mineral oils extracted from bituminous minerals (in the row production) and oils from coal and natural gas liquefaction (in the row liquefaction). Production is calculated after removal of impurities (e.g. sulphur from natural gas).\n\nA TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE.", "links": [ { diff --git a/datasets/geodata_1744.json b/datasets/geodata_1744.json index 276104e8c8..7131eb482e 100644 --- a/datasets/geodata_1744.json +++ b/datasets/geodata_1744.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1744", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gas includes natural gas (excluding natural gas liquids) and gas works gas. The latter appears as a positive figure in the \"gas works\" row but is not part of production. A TOE is defined as 41.868 gigajoules or 10 Exp 7 kilocalories. One terawatt-hour = 0.086 MTOE.", "links": [ { diff --git a/datasets/geodata_1745.json b/datasets/geodata_1745.json index 7828f42cbe..9397849098 100644 --- a/datasets/geodata_1745.json +++ b/datasets/geodata_1745.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1745", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy production Nuclear shows the primary heat equivalent of the electricity produced by a nuclear power plant with an average thermal efficiency of 33 per cent.", "links": [ { diff --git a/datasets/geodata_1761.json b/datasets/geodata_1761.json index 0578015a1a..17355d394f 100644 --- a/datasets/geodata_1761.json +++ b/datasets/geodata_1761.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1761", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Diseases of the Respiratory System: Effects are generally irritation and reduced lung function with increased incidence of respiratory disease, especially in more susceptible members of the population such as young children, the elderly and asthmatics.\nDiseases of the Respiratory System includes: \nICD-9 BTL codes B31-B32,\nICD-9 code CH08 for some ex-USSR countries,\nICD-9 code C052 for China,\nICD-10 codes J00-J99, \nEuropean mortality indicator database (HFA-MDB), available at http://www.euro.who.int, for missing figures for some european countries:\nindicator \"3250 Deaths, Diseases of the Respiratory System\"\n", "links": [ { diff --git a/datasets/geodata_1786.json b/datasets/geodata_1786.json index b6f6df70bf..1d5c652134 100644 --- a/datasets/geodata_1786.json +++ b/datasets/geodata_1786.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1786", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 3 of the Global Lakes and Wetlands Database (GLWD) comprises lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-sec resolution.", "links": [ { diff --git a/datasets/geodata_1787.json b/datasets/geodata_1787.json index f6d868c500..e6bb3240a7 100644 --- a/datasets/geodata_1787.json +++ b/datasets/geodata_1787.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1787", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created. Level 2 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of permanent open water bodies with a surface area greater equal 0.1 square km, excluding the water bodies contained in GLWD-1.\n", "links": [ { diff --git a/datasets/geodata_1788.json b/datasets/geodata_1788.json index c4dd020a0a..cc846ee6f9 100644 --- a/datasets/geodata_1788.json +++ b/datasets/geodata_1788.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1788", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Drawing upon a variety of existing maps, data and information, a new Global Lakes and Wetlands Database (GLWD) has been created.Level 1 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of the largest lakes (area greater equal 50 square km) and reservoirs (storage capacity greater equal 0.5 cubic km) worldwide, including extensive attribute data.", "links": [ { diff --git a/datasets/geodata_1824.json b/datasets/geodata_1824.json index 260dfe5936..c4b58987ce 100644 --- a/datasets/geodata_1824.json +++ b/datasets/geodata_1824.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1824", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "BOD, Biological Oxygen Demand, gives an indication of the amount of organic matter present in water bodies. A certain amount of BOD is always present in water bodies, usually around 2 mg/l O2, while higher levels of BOD could imply that the water is contaminated with bacteria and thus pose a risk to human health.", "links": [ { diff --git a/datasets/geodata_1825.json b/datasets/geodata_1825.json index 52360478ef..4664dcc25a 100644 --- a/datasets/geodata_1825.json +++ b/datasets/geodata_1825.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1825", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In water, nitrogen (N) occurs as nitrates (NO3-) and nitrites (NO2-). These are naturally occurring ions that are part of the nitrogen cycle. Nitrate is used mainly in inorganic fertilizers, and sodium nitrite is used as a food preservative, especially in cured meats. In most countries, nitrate levels in drinking-water derived from surface water do not exceed 10 mg/liter, although nitrate levels in well water often exceed 50 mg/liter; nitrite levels are normally lower, less than a few milligrams per liter (WHO 2004).", "links": [ { diff --git a/datasets/geodata_1835.json b/datasets/geodata_1835.json index 83a268ef1e..1b815c28e8 100644 --- a/datasets/geodata_1835.json +++ b/datasets/geodata_1835.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1835", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector. ", "links": [ { diff --git a/datasets/geodata_1840.json b/datasets/geodata_1840.json index 7505d60f1e..a1aea4903c 100644 --- a/datasets/geodata_1840.json +++ b/datasets/geodata_1840.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1840", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. It contributes to acid rain and can damage human health, particularly that of the young and the elderly. National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties. These inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories. We use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector.\n", "links": [ { diff --git a/datasets/geodata_1843.json b/datasets/geodata_1843.json index b86e556ff1..3f5d9804d7 100644 --- a/datasets/geodata_1843.json +++ b/datasets/geodata_1843.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1843", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties.\nThese inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories.\nWe use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector.", "links": [ { diff --git a/datasets/geodata_1846.json b/datasets/geodata_1846.json index 6d5f883ad7..3e4c66f6cc 100644 --- a/datasets/geodata_1846.json +++ b/datasets/geodata_1846.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1846", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogenous fertilizers, combustion of fuels and biomass, and aeorbic decomposition of organic matter in soils and oceans.\nNational figures only include data from UNFCCC Greenhouse Gas Inventory (GHG) Annex I Parties.\nThese inventory data are provided in the national communications under the Convention by Annex I and non-Annex I Parties, and in addition Annex I Parties submit annual national greenhouse gas inventories.\nWe use the category \"National Total\" from the GHG Database; this category does not include emissions resulting from fuel sold to ships or aircraft engaged in international transport (international bunker fuel emissions). Furthermore, the \"National Total\" does not include emissions from biomass burning or emissions or removals from the land-use change and forestry sector.\n", "links": [ { diff --git a/datasets/geodata_1848.json b/datasets/geodata_1848.json index 03c5b79923..acdab585ed 100644 --- a/datasets/geodata_1848.json +++ b/datasets/geodata_1848.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1848", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Each regional partner used the VEGA2000 dataset, providing a daily global image from the Vegetation sensor onboard the SPOT4 satellite. Each partner also used the Land Cover Classification System (LCCS) produced by FAO and UNEP (Di Gregorio and Jansen, 2000), which ensured that a standard legend was used over the globe. This hierarchical classification system allowed each partner to choose the most appropriate land cover classes which best describe their region, whilst also providing the possibility to translate regional classes to a more generalised global legend.", "links": [ { diff --git a/datasets/geodata_1890.json b/datasets/geodata_1890.json index 5e892b2ae8..bc18b491c8 100644 --- a/datasets/geodata_1890.json +++ b/datasets/geodata_1890.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1890", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Particulate matter contributes significantly to visibility reduction and, as a carrier of toxic metals and other toxic substances, exerts pressures on human health, especially fine particulates. An effort has been made to present data on particulates smaller than 2.5 microns.", "links": [ { diff --git a/datasets/geodata_1896.json b/datasets/geodata_1896.json index 2af6d9633d..ffd0737d0a 100644 --- a/datasets/geodata_1896.json +++ b/datasets/geodata_1896.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1896", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "While the HDI measures average achievement, the HPI-1 measures deprivations in the three basic dimensions of human development captured in the HDI:\n\n- A long and healthy life vulnerability to death at a relatively early age, as measured by the probability at birth of not surviving to age 40.\n\n- Knowledge exclusion from the world of reading and communications, as measured by the adult illiteracy rate.\n\n- A decent standard of living lack of access to overall economic provisioning, as measured by the unweighted average of two indicators, the percentage of the population without sustainable access to an improved water source and the percentage of children under weight for age.\n", "links": [ { diff --git a/datasets/geodata_1897.json b/datasets/geodata_1897.json index 96e2636562..6d11d77331 100644 --- a/datasets/geodata_1897.json +++ b/datasets/geodata_1897.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1897", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ecological Footprint (EF) is a measure of the consumption of renewable natural resources by a human population, be it that of a country, a region or the whole world. \n\nA population's EF is the total area of productive land or sea required to produce all the crops, meat, seafood, wood and fibre it consumes, to sustain its energy consumption and to give space for its infrastructure. \n\nThe EF can be compared with the biologically productive capacity of the land and sea available to that population.", "links": [ { diff --git a/datasets/geodata_1930.json b/datasets/geodata_1930.json index d0a91495dd..d92f969b41 100644 --- a/datasets/geodata_1930.json +++ b/datasets/geodata_1930.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1930", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Particulate matter concentrations refer to fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data for countries and aggregates for regions and income groups are urban-population weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. The state of a country\u2019s technology and pollution controls is an important determinant of particulate matter concentrations. Source: Kiren Dev Pandey, David Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton, and Katherine Bolt. \"Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS),\" World Bank, Development Research Group and Environment Department (2006).\n", "links": [ { diff --git a/datasets/geodata_1933.json b/datasets/geodata_1933.json index 5b89da7339..1153326a23 100644 --- a/datasets/geodata_1933.json +++ b/datasets/geodata_1933.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1933", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reconstruction has used monthly-mean tide gauge data from the Permanent Service for Mean Sea Level (PSMSL) database [Woodworth and Player, 2003], together with Empirical Orthogonal Functions (EOFs) from a 12-year TOPEX/Poseidon + Jason-1 satellite altimeter data set to 'reconstruct' a GMSL curve from January 1870 to December 2001.\n", "links": [ { diff --git a/datasets/geodata_1965.json b/datasets/geodata_1965.json index ade77fb335..5777d6f9b2 100644 --- a/datasets/geodata_1965.json +++ b/datasets/geodata_1965.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1965", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Growing stock\n\nVolume over bark of all living trees more than X cm in diameter at breast height. Includes the stem from ground level or stump height up to a top diameter of Y cm, and may also include branches to a minimum diameter of W cm.\n\nExplanatory notes:\n\n1. The countries must indicate the three thresholds (X, Y, W in cm) and the parts of the tree that are not included in the volume. The countries must also indicate whether the reported figures refer to volume above ground or above stump.\n\n2. The diameter is measured at 30 cm above the end of the buttresses if these are higher than 1 meter.\n\n3. Includes windfallen living trees.\n\n4. Excludes: Smaller branches, twigs, foliage, flowers, seeds, and roots. \n\nForest:\n\nLand spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use.\n\nExplanatory notes\n\n1. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n2. Includes areas with bamboo and palms provided that height and canopy cover criteria are met.\n\n3. Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest.\n\n4. Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m.\n\n5. Includes plantations primarily used for forestry or protection purposes, such as rubberwood plantations and cork oak stands.\n\n6. Excludes tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. The term is mainly related to FRA 2005 National Reporting Table T1.\n", "links": [ { diff --git a/datasets/geodata_1966.json b/datasets/geodata_1966.json index e13af741d1..b7909ec65a 100644 --- a/datasets/geodata_1966.json +++ b/datasets/geodata_1966.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1966", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest harvest rates expressed as the ratio of roundwood production and growing stock in forests. After decades of increases, harvesting of roundwood from forests appears to have levelled off in recent years. However, roundwood production is still very high and largely exceeds growth of forest stock in Asia and the Pacific. \n\nData source: GEO Data Portal, compiled from FAO, FAOStat forestry 2010, Forest Resources Assessment 2005 for 1990, 2000 and 2005, Forest Resources Assessment 2010 for 2010\n", "links": [ { diff --git a/datasets/geodata_1967.json b/datasets/geodata_1967.json index 9e1194fa8c..9abadcbfc0 100644 --- a/datasets/geodata_1967.json +++ b/datasets/geodata_1967.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1967", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Production is the production of primary energy, i.e. hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and waste, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural gas). Hydro shows the energy content of the electricity produced in hydro power plants. Hydro output excludes output from pumped storage plants.\n", "links": [ { diff --git a/datasets/geodata_1977.json b/datasets/geodata_1977.json index 1218d092aa..4cad32a7e3 100644 --- a/datasets/geodata_1977.json +++ b/datasets/geodata_1977.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1977", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of NO2, With LULUCF correspond to total emissions of NO2 and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land) ", "links": [ { diff --git a/datasets/geodata_1980.json b/datasets/geodata_1980.json index 5370102322..3df6c41fde 100644 --- a/datasets/geodata_1980.json +++ b/datasets/geodata_1980.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1980", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of NO2, Without LULUCF correspond to total emissions of NO2 without emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land)\n", "links": [ { diff --git a/datasets/geodata_1982.json b/datasets/geodata_1982.json index 7ee4571dc0..ebd26c03d1 100644 --- a/datasets/geodata_1982.json +++ b/datasets/geodata_1982.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1982", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of ghgs from waste correspond to the total emissions from solid waste disposal on land, wastewater, waste incineration and any other waste management activity. Any CO2 emissions from fossil-based products (incineration or decomposition) are not included here. CO2 from organic waste handling and decay are not included here.\n", "links": [ { diff --git a/datasets/geodata_1986.json b/datasets/geodata_1986.json index a08046b585..2c47e8e55b 100644 --- a/datasets/geodata_1986.json +++ b/datasets/geodata_1986.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1986", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of ghgs from industrial processes corresponds to emissions by-product or fugitive emissions of greenhouse gases from industrial processes. Emissions from fuel combustion in industry are included under Fuel Combustion.", "links": [ { diff --git a/datasets/geodata_1988.json b/datasets/geodata_1988.json index 77c9bcfe8d..5a9f89e83c 100644 --- a/datasets/geodata_1988.json +++ b/datasets/geodata_1988.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1988", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emission ghgs from agriculture correspond to all anthropogenic emissions from agriculture except for fuel combustion and sewage emissions.", "links": [ { diff --git a/datasets/geodata_1993.json b/datasets/geodata_1993.json index be578e3cf1..d2fc74e3bb 100644 --- a/datasets/geodata_1993.json +++ b/datasets/geodata_1993.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1993", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of ghgs from transport correspond to the emissions from the combustion and evaporation of fuel for all transport activity, regardless of the sector. Emissions from fuel sold to any air or marine vessel engaged in international transport (international bunker fuels) are not included.\n", "links": [ { diff --git a/datasets/geodata_1995.json b/datasets/geodata_1995.json index 1fdf11d0cb..6913455fff 100644 --- a/datasets/geodata_1995.json +++ b/datasets/geodata_1995.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1995", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 with LULUCF corresponds to total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).\n", "links": [ { diff --git a/datasets/geodata_1998.json b/datasets/geodata_1998.json index 4838c9bd76..7c854616fa 100644 --- a/datasets/geodata_1998.json +++ b/datasets/geodata_1998.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_1998", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CO2 without LULUCF corresponds to total emissions and removals without activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", "links": [ { diff --git a/datasets/geodata_2001.json b/datasets/geodata_2001.json index 092e7d6dcd..01dc25fd20 100644 --- a/datasets/geodata_2001.json +++ b/datasets/geodata_2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CH4 without LULUCF: Total emissions and removals without emissions from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", "links": [ { diff --git a/datasets/geodata_2004.json b/datasets/geodata_2004.json index 238c9fa374..2a61b77472 100644 --- a/datasets/geodata_2004.json +++ b/datasets/geodata_2004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Emissions of CH4 with LULUCF: Total emissions and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land).", "links": [ { diff --git a/datasets/geodata_2018.json b/datasets/geodata_2018.json index 18ec919833..cee72d075e 100644 --- a/datasets/geodata_2018.json +++ b/datasets/geodata_2018.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2018", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Metric Tonnes (T) of plant nutrients (N total nutriens).\n\nProduction P = (\u2013 M) + X + NF + C; Production = less Imports + Exports + Non fertilizer use + Consumption\nWhen the data of a country are all available then P = the country actual production; M = actual imports, X = actual exports, C =actual consumption and NF = actual non fertilizer use.\n", "links": [ { diff --git a/datasets/geodata_2021.json b/datasets/geodata_2021.json index e7488349f3..f018a23b16 100644 --- a/datasets/geodata_2021.json +++ b/datasets/geodata_2021.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2021", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fertilizer consumption refers to the application of nutrients in terms of nitrogen (N), phosphate (P2O5), and potash (K2O) consumed in agriculture by a country. All figures are given in Tonnes (T) of plant nutrients (N total nutrients).\n\nConsumption (C) = Production (P) + Imports (M) - Exports (X) - Non-Fertilizer use (NF); P + M \u2013 X \u2013 NF = C\nWhen data is not known for either fertilizer production or consumption, then the other items are used to derive the residual data. When this occurs, the data is labelled as apparent (e.g. apparent production).\nApparent Consumption AC = P + M \u2013 (NF + X); Apparent consumption = production + imports - (non-fertilizer use + exports).\n\nApparent consumption figures are developed based on the underlying assumption that supply equals consumption. However, actual apparent consumption may be underestimated due to the following: ( Non-fertilizer use assumed to be zero in the absence of data; Stocks of fertilizer assumed to be zero or stable; Country imports or exports of fertilizer data not available and assumed to be zero).", "links": [ { diff --git a/datasets/geodata_2024.json b/datasets/geodata_2024.json index 9759eadeb6..62a94dd0d8 100644 --- a/datasets/geodata_2024.json +++ b/datasets/geodata_2024.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2024", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\n", "links": [ { diff --git a/datasets/geodata_2026.json b/datasets/geodata_2026.json index 4ee2f3dab4..9d2fdaae85 100644 --- a/datasets/geodata_2026.json +++ b/datasets/geodata_2026.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2026", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\n", "links": [ { diff --git a/datasets/geodata_2027.json b/datasets/geodata_2027.json index 3f1141c35f..18a3a2f1f7 100644 --- a/datasets/geodata_2027.json +++ b/datasets/geodata_2027.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2027", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\nA part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above.\n", "links": [ { diff --git a/datasets/geodata_2028.json b/datasets/geodata_2028.json index 17a2194d87..edbac653a4 100644 --- a/datasets/geodata_2028.json +++ b/datasets/geodata_2028.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2028", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\nA part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above.\n", "links": [ { diff --git a/datasets/geodata_2029.json b/datasets/geodata_2029.json index b73936b161..1701b8bd73 100644 --- a/datasets/geodata_2029.json +++ b/datasets/geodata_2029.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2029", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\nA part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above.\n", "links": [ { diff --git a/datasets/geodata_2031.json b/datasets/geodata_2031.json index ffb4bcfe0a..80b53e7c68 100644 --- a/datasets/geodata_2031.json +++ b/datasets/geodata_2031.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2031", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Threatened species are those listed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU).\n\nCRITICALLY ENDANGERED (CR) \nA taxon is Critically Endangered when the best available evidence indicates that it meets any of the criteria A to E for Critically Endangered (see Section V), and it is therefore considered to be facing an extremely high risk of extinction in the wild. \n\nENDANGERED (EN) \nA taxon is Endangered when the best available evidence indicates that it meets any of the criteria A to E for Endangered (see Section V), and it is therefore considered to be facing a very high risk of extinction in the wild. \n\nVULNERABLE (VU) \nA taxon is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for Vulnerable (see Section V), and it is therefore considered to be facing a high risk of extinction in the wild.\nA part from the mammals, birds, amphibians and gymnosperms (i.e., those groups completely or almost completely evaluated), the figures in the last column are gross over-estimates of the percentage threatened due to biases in the assessment process towards assessing species that are thought to be threatened, species for which data are readily available, and under-reporting of Least Concern species. The true value for the percentage threatened lies somewhere in the range indicated by the two right-hand columns. In most cases this represents a very broad range. For example, the true percentage of threatened insects lies somewhere between 0.07% and 50%. Hence, although 39% of all species on the IUCN Red List are listed as threatened, this figure needs to be treated with extreme caution given the biases described above.\n", "links": [ { diff --git a/datasets/geodata_2032.json b/datasets/geodata_2032.json index 51b2534d4f..418cba5d71 100644 --- a/datasets/geodata_2032.json +++ b/datasets/geodata_2032.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2032", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The global map of Human Impact on Marine Ecosystems is an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems.", "links": [ { diff --git a/datasets/geodata_2034.json b/datasets/geodata_2034.json index e5b8ae055f..633c18ef7f 100644 --- a/datasets/geodata_2034.json +++ b/datasets/geodata_2034.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2034", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nutrient Pollution (Fertilizer) dataset represents an anthropogenic driver of ecological change for marine ecosystem.\n", "links": [ { diff --git a/datasets/geodata_2039.json b/datasets/geodata_2039.json index 9666939eea..ad9d6d0273 100644 --- a/datasets/geodata_2039.json +++ b/datasets/geodata_2039.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2039", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biodiesels includes biodiesel (a methyl-ester produced from vegetable or animal oil, of diesel quality), biodimethylether (dimethylether produced from biomass), Fischer Tropsh (Fischer Tropsh produced from biomass), cold pressed bio-oil (oil produced from oil seed through mechanical processing only) and all other liquid biofuels which are added to, blended with or used straight as transport diesel. Biodiesels includes the amounts that are blended into the diesel - it does not include the total volume of diesel into which the biodiesel is blended.\n\nA KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour).\n", "links": [ { diff --git a/datasets/geodata_2045.json b/datasets/geodata_2045.json index b5c4bd14b0..82c6db41db 100644 --- a/datasets/geodata_2045.json +++ b/datasets/geodata_2045.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2045", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogasoline includes bioethanol (ethanol produced from biomass and/or the biodegradable fraction of waste), biomethanol (methanol produced from biomass and/or the biodegradable fraction of waste), bioETBE (ethyl-tertio-butyl-ether produced on the basis of bioethanol; the percentage by volume of bioETBE that is calculated as biofuel is 47%) and bioMTBE (methyl-tertio-butyl-ether produced on the basis of biomethanol: the percentage by volume of bioMTBE that is calculated as biofuel is 36%). Biogasoline includes the amounts that are blended into the gasoline - it does not include the total volume of gasoline into which the biogasoline is blended. A KTOE is defined as 41.868 TJ (Terajoules) or 11.630 GWh (Gigawatt-Hour).\n", "links": [ { diff --git a/datasets/geodata_2048.json b/datasets/geodata_2048.json index e1e14ff153..5e61993182 100644 --- a/datasets/geodata_2048.json +++ b/datasets/geodata_2048.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2048", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Other liquid biofuels includes liquid biofuels used directly as fuel other than biogasoline or biodiesels.\n", "links": [ { diff --git a/datasets/geodata_2063.json b/datasets/geodata_2063.json index 9df944d25a..41095de276 100644 --- a/datasets/geodata_2063.json +++ b/datasets/geodata_2063.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2063", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2064.json b/datasets/geodata_2064.json index 1cb524d807..99305efa21 100644 --- a/datasets/geodata_2064.json +++ b/datasets/geodata_2064.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2064", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2065.json b/datasets/geodata_2065.json index 6c2a14f254..36a6f70cc2 100644 --- a/datasets/geodata_2065.json +++ b/datasets/geodata_2065.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2065", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2066.json b/datasets/geodata_2066.json index 442090f049..020624e922 100644 --- a/datasets/geodata_2066.json +++ b/datasets/geodata_2066.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2066", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2067.json b/datasets/geodata_2067.json index 0a1538afd9..4e067402ab 100644 --- a/datasets/geodata_2067.json +++ b/datasets/geodata_2067.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2067", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2068.json b/datasets/geodata_2068.json index 9519420ee5..945aee12c8 100644 --- a/datasets/geodata_2068.json +++ b/datasets/geodata_2068.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2068", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2069.json b/datasets/geodata_2069.json index a78d94d56f..f34f891baa 100644 --- a/datasets/geodata_2069.json +++ b/datasets/geodata_2069.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2069", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2070.json b/datasets/geodata_2070.json index 536973c5d2..64947b2faa 100644 --- a/datasets/geodata_2070.json +++ b/datasets/geodata_2070.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2070", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2071.json b/datasets/geodata_2071.json index 614790f2fd..992724ae99 100644 --- a/datasets/geodata_2071.json +++ b/datasets/geodata_2071.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2071", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2072.json b/datasets/geodata_2072.json index 0bdfb354ca..b3d3e5d666 100644 --- a/datasets/geodata_2072.json +++ b/datasets/geodata_2072.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2072", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2073.json b/datasets/geodata_2073.json index 78f3fd5fa7..6d4b40ebe3 100644 --- a/datasets/geodata_2073.json +++ b/datasets/geodata_2073.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2073", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2074.json b/datasets/geodata_2074.json index e9836c1f6b..9878b074d8 100644 --- a/datasets/geodata_2074.json +++ b/datasets/geodata_2074.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2074", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly maximum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2075.json b/datasets/geodata_2075.json index f497d88374..fbf5667467 100644 --- a/datasets/geodata_2075.json +++ b/datasets/geodata_2075.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2075", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2076.json b/datasets/geodata_2076.json index 451c29f2b3..ad037c2b27 100644 --- a/datasets/geodata_2076.json +++ b/datasets/geodata_2076.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2076", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2077.json b/datasets/geodata_2077.json index 739da464c3..212991866a 100644 --- a/datasets/geodata_2077.json +++ b/datasets/geodata_2077.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2077", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2078.json b/datasets/geodata_2078.json index df320f7c9b..31b72d087f 100644 --- a/datasets/geodata_2078.json +++ b/datasets/geodata_2078.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2078", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2079.json b/datasets/geodata_2079.json index 78fb80a5c2..a948e8e20f 100644 --- a/datasets/geodata_2079.json +++ b/datasets/geodata_2079.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2079", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978\n", "links": [ { diff --git a/datasets/geodata_2080.json b/datasets/geodata_2080.json index 57d7746e9c..ba3587930b 100644 --- a/datasets/geodata_2080.json +++ b/datasets/geodata_2080.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2080", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2081.json b/datasets/geodata_2081.json index 0a250522e2..9413eefd76 100644 --- a/datasets/geodata_2081.json +++ b/datasets/geodata_2081.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2081", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2082.json b/datasets/geodata_2082.json index fe43208955..8338ffca9e 100644 --- a/datasets/geodata_2082.json +++ b/datasets/geodata_2082.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2082", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2083.json b/datasets/geodata_2083.json index 5ff2314739..f7316d33c1 100644 --- a/datasets/geodata_2083.json +++ b/datasets/geodata_2083.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2083", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2084.json b/datasets/geodata_2084.json index 3374c3bf23..d78bd9d9c8 100644 --- a/datasets/geodata_2084.json +++ b/datasets/geodata_2084.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2084", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.\n", "links": [ { diff --git a/datasets/geodata_2085.json b/datasets/geodata_2085.json index 41eaf73eb8..ebdcdcf219 100644 --- a/datasets/geodata_2085.json +++ b/datasets/geodata_2085.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2085", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2086.json b/datasets/geodata_2086.json index c09d12bcfc..58e81087f3 100644 --- a/datasets/geodata_2086.json +++ b/datasets/geodata_2086.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2086", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "average monthly minimum temperature (\u00b0C * 10)\nThese layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.\n\nWorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978", "links": [ { diff --git a/datasets/geodata_2125.json b/datasets/geodata_2125.json index bde21c7fe3..cd193149c5 100644 --- a/datasets/geodata_2125.json +++ b/datasets/geodata_2125.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2125", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AQUACULTURE PRODUCTION\n\nThe annual series of aquaculture production begin in 1950 for the quantities and in 1984 for the values. Aquaculture is the farming of aquatic organisms including fish, mollusks, and crustaceans*. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual or corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by the public as a common property resource, with or without appropriate licenses, are the harvest of fisheries. Production of fish, crustaceans and molluscs is expressed in live weight that is the nominal weight of the aquatic organisms at the time of capture Quantities are given in tonnes. \n\n*includes all FAOSTAT group excluded aquatic animals nei, aquatic plants, aquatic mammals.\n", "links": [ { diff --git a/datasets/geodata_2126.json b/datasets/geodata_2126.json index 45423e7827..aeda58578d 100644 --- a/datasets/geodata_2126.json +++ b/datasets/geodata_2126.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2126", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TOTAL PRODUCTION\n\nThe annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture.\n\nTo assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise.\n\n* includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals\n", "links": [ { diff --git a/datasets/geodata_2127.json b/datasets/geodata_2127.json index 8084cf94e4..8baf9e4688 100644 --- a/datasets/geodata_2127.json +++ b/datasets/geodata_2127.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2127", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "World metal production", "links": [ { diff --git a/datasets/geodata_2128.json b/datasets/geodata_2128.json index 30c110d61c..a46a09fbf2 100644 --- a/datasets/geodata_2128.json +++ b/datasets/geodata_2128.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2128", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "World metal consumption", "links": [ { diff --git a/datasets/geodata_2129.json b/datasets/geodata_2129.json index a6d36f5596..60648fa975 100644 --- a/datasets/geodata_2129.json +++ b/datasets/geodata_2129.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2129", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lead production refers to World mine production (metal content).", "links": [ { diff --git a/datasets/geodata_2130.json b/datasets/geodata_2130.json index 9425f1aaf5..29bd8c5f3c 100644 --- a/datasets/geodata_2130.json +++ b/datasets/geodata_2130.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2130", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lead Consumption refers to World refined lead consumption", "links": [ { diff --git a/datasets/geodata_2131.json b/datasets/geodata_2131.json index ea2ba69274..dd91a23c4a 100644 --- a/datasets/geodata_2131.json +++ b/datasets/geodata_2131.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2131", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "World metal production (primary metal)", "links": [ { diff --git a/datasets/geodata_2134.json b/datasets/geodata_2134.json index bb973c1e7f..4cf52de684 100644 --- a/datasets/geodata_2134.json +++ b/datasets/geodata_2134.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2134", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc.\n", "links": [ { diff --git a/datasets/geodata_2135.json b/datasets/geodata_2135.json index 927f5d6d1d..35cffff274 100644 --- a/datasets/geodata_2135.json +++ b/datasets/geodata_2135.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2135", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Country area, area of the country including area under inland water bodies, but excluding offshore territorial waters. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area.", "links": [ { diff --git a/datasets/geodata_2136.json b/datasets/geodata_2136.json index bfdd36c518..8c48e22162 100644 --- a/datasets/geodata_2136.json +++ b/datasets/geodata_2136.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2136", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Forest area is the land spanning more than 0.5 hectares with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 metres (m) in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry or protective purposes, such as: rubber-wood plantations and cork, oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens.\n", "links": [ { diff --git a/datasets/geodata_2169.json b/datasets/geodata_2169.json index cc45119fdf..b2113b284c 100644 --- a/datasets/geodata_2169.json +++ b/datasets/geodata_2169.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2169", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation.\nHydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years.\n", "links": [ { diff --git a/datasets/geodata_2170.json b/datasets/geodata_2170.json index 0efe7d3019..aacd438fef 100644 --- a/datasets/geodata_2170.json +++ b/datasets/geodata_2170.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2170", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation.\nThe consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs.", "links": [ { diff --git a/datasets/geodata_2171.json b/datasets/geodata_2171.json index 2b4911c1b5..098ba7c488 100644 --- a/datasets/geodata_2171.json +++ b/datasets/geodata_2171.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2171", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs.\n", "links": [ { diff --git a/datasets/geodata_2172.json b/datasets/geodata_2172.json index 1fb1a0b762..0dfd81ab3e 100644 --- a/datasets/geodata_2172.json +++ b/datasets/geodata_2172.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2172", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Consumption of Ozone-Depleting CFCs is the sum of the consumption of the weighted tons of the individual substances in the group\u2014metric tons of the individual substance (defined in the Montreal Protocol on Substances That Deplete the Ozone Layer) multiplied by its ozone-depleting potential. Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. The consumption of CFCs is the national production plus imports, minus exports, minus destroyed quantities, minus feedstock uses of individual CFCs. National annual consumption of CFCs is the sum of the weighted tons (consumption in metric tons multiplied by the estimated ozone-depleting potential) of the individual CFCs.\n", "links": [ { diff --git a/datasets/geodata_2173.json b/datasets/geodata_2173.json index 4b0845eccf..1ceca40d21 100644 --- a/datasets/geodata_2173.json +++ b/datasets/geodata_2173.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2173", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars. Source: World Bank, International Comparison Program database.\n", "links": [ { diff --git a/datasets/geodata_2195.json b/datasets/geodata_2195.json index a4f245dfc1..cf566c1a07 100644 --- a/datasets/geodata_2195.json +++ b/datasets/geodata_2195.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2195", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LMEs are natural regions of ocean space encompassing coastal waters from river basins and estuaries to the seaward boundary of continental shelves and the outer margins of coastal currents. They are relatively large regions of 200,000 km2 or greater, the natural boundaries of which are based on four ecological criteria: bathymetry, hydrography, productivity, and trophically related populations.\n", "links": [ { diff --git a/datasets/geodata_2197.json b/datasets/geodata_2197.json index 531d8beb2c..d32bd0a320 100644 --- a/datasets/geodata_2197.json +++ b/datasets/geodata_2197.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2197", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.\n\nImproved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.\n\nImproved sanitation facilities comprise: Connection to a public sewer; Connection to a septic system; Pour-flush latrine; Simple pit latrine; Ventilated improved pit latrine.\n", "links": [ { diff --git a/datasets/geodata_2199.json b/datasets/geodata_2199.json index 18c507302a..d775467594 100644 --- a/datasets/geodata_2199.json +++ b/datasets/geodata_2199.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2199", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CARBON STOCK\n\nThe quantity of carbon in a \u201cpool\u201d, meaning a reservoir or system which has the capacity to accumulate or release carbon. Examples of carbon pools are Living biomass (including Above and below-ground biomass); Dead organic matter (including dead wood and litter); Soils (soils organic matter). The units are mass.\n", "links": [ { diff --git a/datasets/geodata_2200.json b/datasets/geodata_2200.json index 0c56094259..f1faa2c171 100644 --- a/datasets/geodata_2200.json +++ b/datasets/geodata_2200.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2200", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DESIGNATED FUNCTIONS (of Forest and Other wooded land)\nthe designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land\nowner/manager. It applies to land classified as \u201cForest\u201d and as \u201cOther wooded land\u201d.\n\nConservation of biodiversity: \nForest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas.\n\nProduction:\nForest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products.", "links": [ { diff --git a/datasets/geodata_2201.json b/datasets/geodata_2201.json index 50749e1b7c..698680e2fc 100644 --- a/datasets/geodata_2201.json +++ b/datasets/geodata_2201.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2201", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DESIGNATED FUNCTIONS (of Forest and Other wooded land) the designated function refers to the function or purpose assigned to a piece of land either by legal prescriptions or by decision of the land owner/manager. It applies to land classified as \u201cForest\u201d and as \u201cOther wooded land\u201d.\n\nConservation of biodiversity: Forest/Other wooded land designated for conservation of biological diversity. It includes, but is not limited to, Protected Areas. \n\nProduction: Forest/Other wooded land designated for production and extraction of forest goods, including both wood and non-wood forest products.", "links": [ { diff --git a/datasets/geodata_2202.json b/datasets/geodata_2202.json index 874cd79640..da00fdaace 100644 --- a/datasets/geodata_2202.json +++ b/datasets/geodata_2202.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2202", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of FRA 2010, countries were asked to provide information on the area of forest contained in protected areas systems. This is not an easy task where spatially explicit information is missing or outdated since not all protected areas are fully forested. However, most of the large, forest-rich countries did provide this information for all four reporting years. ", "links": [ { diff --git a/datasets/geodata_2203.json b/datasets/geodata_2203.json index c266420bd4..3240487eac 100644 --- a/datasets/geodata_2203.json +++ b/datasets/geodata_2203.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2203", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Public expenditure and revenue collection from forestry are measures of the financial flows between government and the forestry sector. In FRA 2010 forest revenue was defined to include all taxes, fees, charges and royalties collected specifically from the domestic production and trade of forest products, but it excluded general taxes collected from all sectors of the economy (e.g. corporation tax and sales tax).\n", "links": [ { diff --git a/datasets/geodata_2206.json b/datasets/geodata_2206.json index 11e82cd7de..e252a4137f 100644 --- a/datasets/geodata_2206.json +++ b/datasets/geodata_2206.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2206", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Food: total calories\nRefers to the total amount of food available for human consumption expressed in kilocalories (kcal). Caloric content is derived by applying the appropriate food composition factors to the quantities of the commodities and shown in million units.", "links": [ { diff --git a/datasets/geodata_2207.json b/datasets/geodata_2207.json index 91754e5439..e186032a20 100644 --- a/datasets/geodata_2207.json +++ b/datasets/geodata_2207.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2207", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed.\n", "links": [ { diff --git a/datasets/geodata_2208.json b/datasets/geodata_2208.json index 42b7f95636..472621730e 100644 --- a/datasets/geodata_2208.json +++ b/datasets/geodata_2208.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2208", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). \n\nWhen the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T).\n", "links": [ { diff --git a/datasets/geodata_2215.json b/datasets/geodata_2215.json index b051cd502b..5b1ef2be52 100644 --- a/datasets/geodata_2215.json +++ b/datasets/geodata_2215.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2215", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade.", "links": [ { diff --git a/datasets/geodata_2216.json b/datasets/geodata_2216.json index 6ebb972be2..54f7300de4 100644 --- a/datasets/geodata_2216.json +++ b/datasets/geodata_2216.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2216", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade.", "links": [ { diff --git a/datasets/geodata_2217.json b/datasets/geodata_2217.json index c86ad68aa9..fc1c5ce430 100644 --- a/datasets/geodata_2217.json +++ b/datasets/geodata_2217.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2217", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body.", "links": [ { diff --git a/datasets/geodata_2222.json b/datasets/geodata_2222.json index 828f8cc2f5..6cd9c098c7 100644 --- a/datasets/geodata_2222.json +++ b/datasets/geodata_2222.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2222", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world\u2019s population. These same stressors endanger the biodiversity of 65 percent of the world\u2019s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations.", "links": [ { diff --git a/datasets/geodata_2223.json b/datasets/geodata_2223.json index 8aa60b0235..f07760f579 100644 --- a/datasets/geodata_2223.json +++ b/datasets/geodata_2223.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2223", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2224.json b/datasets/geodata_2224.json index 0a8c33f4a1..f61304db63 100644 --- a/datasets/geodata_2224.json +++ b/datasets/geodata_2224.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2224", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. ", "links": [ { diff --git a/datasets/geodata_2225.json b/datasets/geodata_2225.json index a2a055b002..ba47ee5e06 100644 --- a/datasets/geodata_2225.json +++ b/datasets/geodata_2225.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2225", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2226.json b/datasets/geodata_2226.json index 2a282229d1..3ee5691fcc 100644 --- a/datasets/geodata_2226.json +++ b/datasets/geodata_2226.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2226", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2227.json b/datasets/geodata_2227.json index 0759b8ee6a..ebb157cbc8 100644 --- a/datasets/geodata_2227.json +++ b/datasets/geodata_2227.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2227", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2228.json b/datasets/geodata_2228.json index f13ac2d0ef..22816c01c5 100644 --- a/datasets/geodata_2228.json +++ b/datasets/geodata_2228.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2228", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2229.json b/datasets/geodata_2229.json index 653c8706ee..3a1945fba8 100644 --- a/datasets/geodata_2229.json +++ b/datasets/geodata_2229.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2229", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2230.json b/datasets/geodata_2230.json index 6df218547f..8cd4286bb9 100644 --- a/datasets/geodata_2230.json +++ b/datasets/geodata_2230.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2230", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.\n", "links": [ { diff --git a/datasets/geodata_2231.json b/datasets/geodata_2231.json index 2c1f8f5d39..c9a2c34e29 100644 --- a/datasets/geodata_2231.json +++ b/datasets/geodata_2231.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2231", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2232.json b/datasets/geodata_2232.json index 2e8683441e..dcd15e37ac 100644 --- a/datasets/geodata_2232.json +++ b/datasets/geodata_2232.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2232", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.\n", "links": [ { diff --git a/datasets/geodata_2237.json b/datasets/geodata_2237.json index 0abc0d8f01..6f482b8771 100644 --- a/datasets/geodata_2237.json +++ b/datasets/geodata_2237.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2237", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.", "links": [ { diff --git a/datasets/geodata_2240.json b/datasets/geodata_2240.json index 032fabfd9b..7579ea172f 100644 --- a/datasets/geodata_2240.json +++ b/datasets/geodata_2240.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2240", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global assessment of the probability of occurrence of excessive Arsenic concentrations", "links": [ { diff --git a/datasets/geodata_2244.json b/datasets/geodata_2244.json index a780866df8..1fa5577c51 100644 --- a/datasets/geodata_2244.json +++ b/datasets/geodata_2244.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2244", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mineral facilities and operations outside the United States compiled by the National Minerals Information Center of the USGS. This representation combines source data from five previous publications. National Minerals Information Center (NMIC) makes available a wide variety of commodity statistics and other mineral resource supply and production information both within the United States and internationally. These databases complement aggregate commodity statistics collected by the NMIC.", "links": [ { diff --git a/datasets/geodata_2245.json b/datasets/geodata_2245.json index 83b9fb4965..0986dea161 100644 --- a/datasets/geodata_2245.json +++ b/datasets/geodata_2245.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2245", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. This product is a digest in which the fields chosen are those most likely to contain valid information. This digest of the complex mineral resources database is intended for use as reference material supporting mineral resource and environmental assessments on local to regional scale worldwide.\n", "links": [ { diff --git a/datasets/geodata_2246.json b/datasets/geodata_2246.json index bcd756c978..ceb3a63d40 100644 --- a/datasets/geodata_2246.json +++ b/datasets/geodata_2246.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2246", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests.\n\nIt is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products", "links": [ { diff --git a/datasets/geodata_2247.json b/datasets/geodata_2247.json index 3c40403aeb..2be98a9f44 100644 --- a/datasets/geodata_2247.json +++ b/datasets/geodata_2247.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2247", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PEFC's Chain of Custody certification is a mechanism for tracking certified material from the forest to the final product to ensure that the wood, wood fibre or non-wood forest produce contained in the product or product line can be traced back to certified forests.\n\nIt is an essential part of the PEFC system which ensures that claims about products originating in sustainably managed forests are credible and verifiable throughout the whole supply chain. It is used to certify entities all along the value-chain of forest-based products", "links": [ { diff --git a/datasets/geodata_2251.json b/datasets/geodata_2251.json index cb30772ff0..f3f85e96a6 100644 --- a/datasets/geodata_2251.json +++ b/datasets/geodata_2251.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2251", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Green water footprint :Volume of rainwater consumed during the production process. This is particularly relevant for agricultural and forestry products (products based on crops or wood), where it refers to the total rainwater evapotranspiration (from fields and plantations) plus the water incorporated into the harvested crop or wood. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", "links": [ { diff --git a/datasets/geodata_2252.json b/datasets/geodata_2252.json index 97b0e1301e..c37f345b03 100644 --- a/datasets/geodata_2252.json +++ b/datasets/geodata_2252.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2252", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Blue water footprint ? Volume of surface and groundwater consumed as a result of the production of a good or service. Consumption refers to the volume of freshwater used and then evaporated or incorporated into a product. It also includes water abstracted from surface or groundwater in a catchment and returned to another catchment or the sea. It is the amount of water abstracted from ground- or surface water that does not return to the catchment from which it was withdrawn. Water footprint of national production ? Another term for the ?water footprint within a nation?: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", "links": [ { diff --git a/datasets/geodata_2253.json b/datasets/geodata_2253.json index 6a7d295ca3..792f8605a0 100644 --- a/datasets/geodata_2253.json +++ b/datasets/geodata_2253.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geodata_2253", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Grey water footprint \u2013 The grey water footprint of a product is an indicator of freshwater pollution that can be associated with the production of a product over its full supply chain. It is defined as the volume of freshwater that is required to assimilate the load of pollutants based on existing ambient water quality standards. It is calculated as the volume of water that is required to dilute pollutants to such an extent that the quality of the water remains above agreed water quality standards. \n\nWater footprint of national consumption \u2013 Is defined as the total amount of fresh water that is used to produce the goods and services consumed by the inhabitants of the nation. The water footprint of national consumption can be assessed in two ways. The bottom-up approach is to consider the sum of all products consumed multiplied with their respective product water footprint. In the top-down approach, the water footprint of national consumption is calculated as the total use of domestic water resources plus the gross virtual-water import minus the gross virtual-water export.\n\n\nWater footprint of national production \u2013 Another term for the \u2018water footprint within a nation\u2019: Water footprint within a nation Is defined as the total freshwater volume consumed or polluted within the territory of the nation.", "links": [ { diff --git a/datasets/geoecology_R1_656_1.json b/datasets/geoecology_R1_656_1.json index f0050fcf21..e50a1b7540 100644 --- a/datasets/geoecology_R1_656_1.json +++ b/datasets/geoecology_R1_656_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geoecology_R1_656_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.", "links": [ { diff --git a/datasets/geomorphology_australasian_seafloor_1.json b/datasets/geomorphology_australasian_seafloor_1.json index c6b22dc036..45aa263c6a 100644 --- a/datasets/geomorphology_australasian_seafloor_1.json +++ b/datasets/geomorphology_australasian_seafloor_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "geomorphology_australasian_seafloor_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A geomorphology map of the Australasian seafloor was created as a Geographic Information System layer for the study described in \nTorres, Leigh G., et al. \"From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale.\" Diversity and Distributions 19.9 (2013): 1138-1152.\nThe geomorphology map was generated using parameters derived from the General Bathymetric Chart of the World (GEBCO 2008, http://www.gebco.net/), with 30 arc-second grid resolution.\nGeomorphology features were delineated manually with a consistent spatial resolution. Each feature was assigned a primary attribute of depth zone and a secondary attribute of morphological feature. \nThe following feature classes are defined: shelf, slope, rise, plain, valley, trench, trough, basin, hills(s), mountains(s), ridges(s), plateau, seamount.\nFurther information (methods, definitions and an illustration of the geomorphology map) is provided in Appendix S2 of the paper which is available for download (see related URLs).", "links": [ { diff --git a/datasets/gfscpex_1.json b/datasets/gfscpex_1.json index 295af860e2..2655e7ab87 100644 --- a/datasets/gfscpex_1.json +++ b/datasets/gfscpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gfscpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Forecast System (GFS) CPEX dataset includes model data simulated by the Global Forecast System (GFS) model for the Convective Process Experiment (CPEX) field campaign. The NASA Convective Processes Experiment (CPEX) aircraft field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 24, 2017 through July 20, 2017 and are available in netCDF-3 format. ", "links": [ { diff --git a/datasets/gghydro_676_1.json b/datasets/gghydro_676_1.json index 17e28f77bb..749e6438bc 100644 --- a/datasets/gghydro_676_1.json +++ b/datasets/gghydro_676_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gghydro_676_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Cogley's 1998 global hydrographic data set (GGHYDRO, Release 2.2). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The subset is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, and annual water runoff for the LBA study area. The data are presented at a spatial resolution of 1-degree latitude by 1-degree longitude in ASCII GRID file format.", "links": [ { diff --git a/datasets/ghcn_631_1.json b/datasets/ghcn_631_1.json index 74f84d4f04..eb709b02fb 100644 --- a/datasets/ghcn_631_1.json +++ b/datasets/ghcn_631_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ghcn_631_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the Global Historical Climatology Network (GHCN) Version 1 database. All stations with the following bounding coordinates are included in this subset: 5W - 60E and 5N - 35S. There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1874 and the most recent from 1990.", "links": [ { diff --git a/datasets/gillock_isl_sat_1.json b/datasets/gillock_isl_sat_1.json index 67a6d3ba71..f9bfb57b9b 100644 --- a/datasets/gillock_isl_sat_1.json +++ b/datasets/gillock_isl_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gillock_isl_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Gillock Island, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1993. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-109, 128-110, 127-109, 127-110). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/gimms_ndvi_monthly_xdeg_973_1.json b/datasets/gimms_ndvi_monthly_xdeg_973_1.json index c51abcdbe6..86c0da13f9 100644 --- a/datasets/gimms_ndvi_monthly_xdeg_973_1.json +++ b/datasets/gimms_ndvi_monthly_xdeg_973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gimms_ndvi_monthly_xdeg_973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002. New features include reduced NDVI variations arising from calibration, view geometry, volcanic aerosols, and other effects not related to actual vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1994, and improved NDVI using empirical mode decomposition/reconstruction (EMD) to minimize effects of orbital drift. Global NDVI was generated to provide inputs for computing the time series of biophysical parameters contained in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II collection. NDVI is used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. ", "links": [ { diff --git a/datasets/gis103_1.json b/datasets/gis103_1.json index 4192a16a9c..a88cdb6e6a 100644 --- a/datasets/gis103_1.json +++ b/datasets/gis103_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis103_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset shows the locations of fire hydrants at Davis Station.\nThe data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.", "links": [ { diff --git a/datasets/gis104_1.json b/datasets/gis104_1.json index 3ad8853c9f..0e8281a8b0 100644 --- a/datasets/gis104_1.json +++ b/datasets/gis104_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis104_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's Davis Station GIS data were originally mapped from aerial photography (February 11, 12 1997). Refer to the metadata record 'Davis Station GIS Dataset'. \nSince then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. \nHowever, the locations of other features have been obtained from a variety of sources. \nThe data are included in the data available for download from a provided URL. \nThe data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 104.\nEach feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis108_1.json b/datasets/gis108_1.json index 2f488e47a8..eee4ad15da 100644 --- a/datasets/gis108_1.json +++ b/datasets/gis108_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis108_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Antarctic Territorial Claims GIS Dataset. This dataset is based on data displayed on the map 'Antarctica' published in 1979. The map states 'The indication of boundaries on this map and the description of the areas within them are intended to agree with national legislation of the countries concerned but are not to be treated as evidence of the official views of the Government of Australia with respect to the national legislation of other countries.'\n\nThe dataset represents Antarctic Territorial Claims from the south pole to the Antarctic coastline for each nation.", "links": [ { diff --git a/datasets/gis114_1.json b/datasets/gis114_1.json index 12c910693c..3b8874bf5f 100644 --- a/datasets/gis114_1.json +++ b/datasets/gis114_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis114_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is the product of a survey of penguin colony boundaries at Bechervaise Island by Lisa Meyer of the Australian Antarctic Division (AAD) in February 2000. \nFor each boundary point Lisa measured the distance from a surveyed metal pole to the boundary point of a penguin colony and then took a bearing with a compass back to the pole. The boundary point locations were calculated by the Australian Antarctic Data Centre from the distances and bearings using the UTM Zone 41 grid. \nThe metal poles had been surveyed during the 1999/2000 summer season as described by the metadata record 'Bechervaise Island - Survey of penguin colony markers and some infrastructure'.\nLyn Irvine of the AAD identified whether the penguin colony points were nests, colony markers or other boundary points.\nMap 13042 in the SCAR Map Catalogue displays the penguin colony boundaries.\nThe nests in penguin colonies K, L and Q were surveyed in February 2002 as described by the metadata record 'Adelie Penguin nest locations on Bechervaise Island'.", "links": [ { diff --git a/datasets/gis115_1.json b/datasets/gis115_1.json index fe0764c064..19edee85b3 100644 --- a/datasets/gis115_1.json +++ b/datasets/gis115_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis115_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the penguin colony markers and some infrastructure on Bechervaise Island. The infrastructure includes the penguin weighbridges and the telephone relay antenna. Roger Handsworth (Australian Antarctic Data Centre) conducted a detailed survey of penguin colony markers and some infrastructure during the 1999/2000 summer using a Sokkisha EDM theodolite and prism. Two additional penguin colony markers were located by Kym Newbery (ASP Engineer) in June 2000. Distances were measured from the metal pole to three other locations (other metal poles, weighbridges) using a tape measure. Accuracy 0.03m. Dates given in temporal coverage are approximate only.\n\nThe penguin colony markers were used by the Australian Antarctic Data Centre together with tape measure and compass measurements by Lisa Meyer (AAD) to determine points along the penguin colony boundaries. Some of these boundary points were nests. These data are described by the metadata record with Entry ID: gis114. \n\nThe colony markers, nest locations and infrastructure were subsequently surveyed in February 2002 by Aaron Read (surveyor) with assistance from Lyn Irvine (AAD). Refer to the metadata records with Entry IDs: gis114, bech_nest_locations.", "links": [ { diff --git a/datasets/gis119_1.json b/datasets/gis119_1.json index 74c7aa113a..4fdb7a07a1 100644 --- a/datasets/gis119_1.json +++ b/datasets/gis119_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis119_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'.\nSince then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.\n\nThe data are included in the data available for download from a Related URL below.\nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 119.\nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis11_1.json b/datasets/gis11_1.json index 881c64b166..057b933907 100644 --- a/datasets/gis11_1.json +++ b/datasets/gis11_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis11_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Casey Station dataset represents man-made facilities around Australia's Casey Station and its immediate environs. Detailed attributes are held for the data including buildings, site services, communications, fuel storage.\nThe spatial data have been compiled from low level aerial photography, ground surveys and engineering plans.\n\nDetail attribution of site services includes make, size and engineering plan number.\n\nTopographic data for Casey is part of the Windmill Islands 1:50000 Topographic Dataset (see Related URL). This data is described by the metadata record 'Windmill Islands 1:50000 Topographic GIS Dataset', Entry ID: Wind50k.\n\nChanges have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added.\nAs a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).", "links": [ { diff --git a/datasets/gis135_1.json b/datasets/gis135_1.json index 8b709e1478..7fd1aff639 100644 --- a/datasets/gis135_1.json +++ b/datasets/gis135_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis135_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This mapping completed the Larsemann Hills photogrammetric mapping project.\nThe project was commenced on 14 December 2001 and completed in April 2003.\nIt includes the integration of newly mapped data with dataset gis136. (Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000)\n\nA report on the project is available at the url given below.", "links": [ { diff --git a/datasets/gis136_1.json b/datasets/gis136_1.json index e45c4f281e..fde8449be6 100644 --- a/datasets/gis136_1.json +++ b/datasets/gis136_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis136_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The datum of this dataset is as described in GIS dataset AAT_Coastline_Landsat7.\nThe Landsat 7 image (2002-01-30 Path 126 Row 109) georeferencing was checked using Ground Control Points derived from a survey report prepared by Hydro Tasmania for the Mapping Officer of the AAD, summer 2000/2001.\nThe Ground Control Point locations were found to be within the image pixel resolution. The georeferencing of the image could not be improved on.\nTherefore no transformation, scaling or rotation was applied to the Landsat 7 image.\n\ngis136 (Larsemann Hills - mapping from Landsat 7 data captured January 2000) data were merged with the newly mapped Larsemann Hills aerial photogrammetric plotting dataset gis135.\n\nA report on the project is available at the provided URL.", "links": [ { diff --git a/datasets/gis13_1.json b/datasets/gis13_1.json index 9f75522a74..a208a71969 100644 --- a/datasets/gis13_1.json +++ b/datasets/gis13_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis13_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes features related to helicopter and fixed wing operations of the Australian Antarctic Division. The features include approach and departure paths, helicopter landing sites, exclusion zones, Wilkins runway, fixed wing skiways, areas occupied by bird colonies and vegetation.\nThe data are for local areas within Australia'a Antarctic Territory.\n\nThis dataset originally consisted of data provided by the Australian Antarctic Division's Helicopter Guidelines Discussion Group. This data was used with other data in the production of the Australian Antarctic Division's series of maps for helicopter operations. Since then additional data has been added and maps for fixed wing operations have also been produced.\n\nThis dataset does not include the helipads at the Australia's year-round stations. They are included in the datasets described by the metadata records 'Casey Station GIS Dataset', 'Davis Station GIS Dataset', 'Macquarie Island Station GIS Dataset' and 'Mawson Station GIS Dataset'.\n\nThe data are available for download from a Related URL below. Files containing the features listed above can be found in the following file groups: Casey Station, Davis Station, Gaussberg, Heard and McDonald Islands, Holme Bay and Framnes Mountains, Larsemann Hills, Macquarie Island, Macquarie Island Station, Mawson Station, Rauer Group, Scullin and Murray Monoliths, Taylor Rookery, Vestfold Hills, Windmill Islands.\n\nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 13.\nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis173_1.json b/datasets/gis173_1.json index c14ee9b462..83651e151f 100644 --- a/datasets/gis173_1.json +++ b/datasets/gis173_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis173_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Masts at Mawson Station, Antarctica.\nThis is a point dataset stored in the Geographical Information System (GIS). Attributes include mast height.", "links": [ { diff --git a/datasets/gis17_1.json b/datasets/gis17_1.json index 99df6ced14..42735b1d9c 100644 --- a/datasets/gis17_1.json +++ b/datasets/gis17_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis17_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. \nHowever, the locations of other features have been obtained from a variety of sources. \nThe data are included in the data available for download from the provided URLs. \nThe data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. \nData described by this metadata record has Dataset_id = 17. \nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis191_1.json b/datasets/gis191_1.json index 22a5a2bd46..1e0c7897b1 100644 --- a/datasets/gis191_1.json +++ b/datasets/gis191_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis191_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Point locations in shapefile format for current Australian refuges in Antarctica can be downloaded from the Australian Antarctic Data Centre's compilation of Geographic Information System (GIS) data available for downloading - see a Related URL below.\nThere are files with locations of refuges within the following file groups based on geographic area: Bunger Hills, Cape Denison (Mapping from IKONOS satellite imagery captured January 2001), Holme Bay and Framnes Mountains, Rauer Group, Scullin and Murray Monoliths, Vestfold Hills and Windmill Islands.\nThere is also a file in the Australian Antarctic Territory file group with locations of refuges not in any of the above geographic areas.", "links": [ { diff --git a/datasets/gis20_1.json b/datasets/gis20_1.json index ce81dbbc60..4728fd9205 100644 --- a/datasets/gis20_1.json +++ b/datasets/gis20_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis20_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset shows the locations of fire hydrants at Casey Station.\nThe data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.", "links": [ { diff --git a/datasets/gis250_1.json b/datasets/gis250_1.json index c8b7b2e9a2..ba123d33b9 100644 --- a/datasets/gis250_1.json +++ b/datasets/gis250_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis250_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This point GIS dataset shows the locations of the fire hydrants and fire hose reels at Mawson station, Antarctica.\nThe data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.", "links": [ { diff --git a/datasets/gis283_1.json b/datasets/gis283_1.json index 449f5957a3..9c16bbe0ea 100644 --- a/datasets/gis283_1.json +++ b/datasets/gis283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre digitises on-screen fast ice features from satellite images as required for Australian Antarctic division scientists Dr Jonny Stark and Dr Martin Riddle and will be made available, together with a report on the digitising, when their project is published. \nFeatures digitised include the extents of fast ice in the Windmill Islands and the Vestfold Hills area. \nThe data conform to the SCAR Feature Catalogue which includes data quality information. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis29_1.json b/datasets/gis29_1.json index 434dce7339..d88f3db039 100644 --- a/datasets/gis29_1.json +++ b/datasets/gis29_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis29_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is comprised of polygons representing four buildings, two storage areas and the LIDAR at Davis Station, Antarctica. The data were derived from a total station survey by Aaron Read on 11 February 2002.\n\nFurther information is provided in - 'Davis and Mawson - Antarctica, MAGIP Field Program 2001-2002'.\nSee the provided URL for a copy. An extract from the report follows:\n\nA comprehensive digital station update was unable to be carried out due to poor weather and limited time. However, using the information supplied to us by Jeremy Smith (Station Leader) the new, relocated or deleted buildings were surveyed using a Sokkia Powerset total station.", "links": [ { diff --git a/datasets/gis309_1.json b/datasets/gis309_1.json index 9f70f10672..edc26947d7 100644 --- a/datasets/gis309_1.json +++ b/datasets/gis309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre digitises on-screen topographic features from satellite images as required for various projects and applications. \n\nFeatures digitised include the ice shelves, glacier tongues and icebergs.\n\nThe resulting GIS data is available for download. See a Related URL below.\n\nThe data conform to the SCAR Feature Catalogue which includes data quality information. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis30_1.json b/datasets/gis30_1.json index 6413b909b7..e1a968b636 100644 --- a/datasets/gis30_1.json +++ b/datasets/gis30_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis30_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is comprised of polygons representing the new Hazardous Goods Store and water tank at Mawson Station, Antarctica.\nThe data were derived from a total station survey by Aaron Read on 20 February 2002.\n\nExtract from the survey report available from the provided URL:\nA comprehensive digital station database update was carried out during our stay at Mawson. The following items where surveyed as requested by Meg Dugdale (Station Leader 2001):\n1) New 600,000 L water tank\n2) New Hazardous storage building\n3) Wind turbine sites (3)", "links": [ { diff --git a/datasets/gis310_1.json b/datasets/gis310_1.json index 07977d12d8..396220c01c 100644 --- a/datasets/gis310_1.json +++ b/datasets/gis310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre digitises topographic features from satellite images as required for various applications. These features include areas of exposed rock and areas of blue ice.\nAs at August 2014 digitising had been done for the following areas:\nexposed rock - southern Prince Charles Mountains, the south-eastern part of the Amery Ice Shelf, Grove Mountains and the coastline between the Amery Ice Shelf and the Polar Times Glacier;\nblue ice - Prince Charles Mountains\n\nThe digitised exposed rock and blue ice data is available for download as shapefiles. Also available in this format are the mapping extents for the digitising. See the Related URLs below.\n\nThe data conform to the SCAR Feature Catalogue which includes data quality information. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature including details about the satellite image from which the feature was digitised.", "links": [ { diff --git a/datasets/gis31_1.json b/datasets/gis31_1.json index 56e3239d44..dc733c6f50 100644 --- a/datasets/gis31_1.json +++ b/datasets/gis31_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis31_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's Macquarie Island Station GIS Dataset was originally produced from low level aerial photography of the station and from ground surveys. Refer to the metadata record 'Macquarie Island Station GIS Dataset'.\n\nSince then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed.\nThese surveys have metadata records from which the report describing the survey can be downloaded.\nHowever, the locations of other features have been obtained from a variety of sources.\nThe data are included in the data available for download from a Related URL below.\nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.\nData described by this metadata record has Dataset_id = 31.\nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gis34_1.json b/datasets/gis34_1.json index f52888e674..1525919a6d 100644 --- a/datasets/gis34_1.json +++ b/datasets/gis34_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis34_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset consists of the locations of fire hydrants at Macquarie Island Station.\nThe data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.", "links": [ { diff --git a/datasets/gis38_1.json b/datasets/gis38_1.json index 069611433b..cea61bf854 100644 --- a/datasets/gis38_1.json +++ b/datasets/gis38_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis38_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of some features at Davis Station, Antarctica,in the Geographical Information System (GIS).\nThe features include the ionosonde aerials, an MFSA Radar aerial and mast and some management zones (Magnetic Quiet Area, Restricted Building Area).\nThe data were generated based on advice from AAD Atmospheric and Space Physics staff.", "links": [ { diff --git a/datasets/gis41_1.json b/datasets/gis41_1.json index 00ecca1c70..ac4ead568f 100644 --- a/datasets/gis41_1.json +++ b/datasets/gis41_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis41_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A GIS dataset of around Cape Denison and part of George V land created from two IKONOS satellite images.\nLayers created from digitising directly from the imagery include: mapping extent, continent, building, refuge, coastline, reef, offshore rocks, sea, snow, sheet, island, birds, rock, moraine, sea ice, lakes\n- The mapping extent layer represents the edge of the IKONOS imagery.\n- The continent layer represents the land mass shown in IKONOS imagery. It was generated using the digitised coastline and bounded by lines that represent the edge of the image.\n- The snow spatial data represents the snow cover in January 2001\n- The sheet ice spatial data represents the ice extent in January 2001\n- The penguin spatial data represents the penguin colony extents, based on guano deposits.\n- The rock spatial data represents the exposed bare rock", "links": [ { diff --git a/datasets/gis43_1.json b/datasets/gis43_1.json index 64375dd31e..96179df2c4 100644 --- a/datasets/gis43_1.json +++ b/datasets/gis43_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis43_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents the refuge, infrastructure and penguin colony markers on Bechervaise Island, Holme Bay, Antarctica.\nThe data were derived from a total station survey by Aaron Read on 22 February 2002.", "links": [ { diff --git a/datasets/gis45_1.json b/datasets/gis45_1.json index 955a207034..110ef75e6f 100644 --- a/datasets/gis45_1.json +++ b/datasets/gis45_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gis45_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's GIS data of Mawson Station was updated in 2004 using a map image provided by Dr Malcolm Arnold who wintered at the station during that year.\n\nThe data are included in the data available for download from a Related URL below. \nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. \nData described by this metadata record has Dataset_id = 45. \nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/gisdata_1.0.json b/datasets/gisdata_1.0.json index 7fdb219131..f7e9bd4c76 100644 --- a/datasets/gisdata_1.0.json +++ b/datasets/gisdata_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gisdata_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the \"Cross Blended Hypso with Shaded Relief and Water\". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com", "links": [ { diff --git a/datasets/giss_wetlands_632_1.json b/datasets/giss_wetlands_632_1.json index 11f72392d5..4efc2537cb 100644 --- a/datasets/giss_wetlands_632_1.json +++ b/datasets/giss_wetlands_632_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "giss_wetlands_632_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. The subset retains all five arrays at the 1-degree resolution but only for the area of interest. The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type.", "links": [ { diff --git a/datasets/gl_microclim_1.0.json b/datasets/gl_microclim_1.0.json index 1cb930f4d4..0986a1b2ef 100644 --- a/datasets/gl_microclim_1.0.json +++ b/datasets/gl_microclim_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gl_microclim_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## Study Aim We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns. ## Data Description Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables. ### Nuup Kangerlua (Godth\u00e5bsfjord) _Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness ### Kangerluarsunnguaq (Kobbefjord) Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height ## Methods ### Field survey in Nuup Kangerlua We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022). ### Field survey in Kangerluarsunnguaq We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022). ### Biotic variables We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height. ### Climate variables We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June \u2013 August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March \u2013 May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021). ### Topography and terrain wetness indicator variables We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how \u2018wet\u2019 an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness. ### Computer code Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim) ### Third-party data Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).", "links": [ { diff --git a/datasets/glacio_1973_barometric_levelling_1.json b/datasets/glacio_1973_barometric_levelling_1.json index 84f715c1d3..ba9f123744 100644 --- a/datasets/glacio_1973_barometric_levelling_1.json +++ b/datasets/glacio_1973_barometric_levelling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_1973_barometric_levelling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of tabulated values of atmospheric pressure along routes traveled during traverse for use in barometric leveling.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_1981_traverse_data_report_1.json b/datasets/glacio_1981_traverse_data_report_1.json index f6d921178a..b1870e985c 100644 --- a/datasets/glacio_1981_traverse_data_report_1.json +++ b/datasets/glacio_1981_traverse_data_report_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_1981_traverse_data_report_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report on the four oversnow traverses carried out in 1981 from Casey, inland to Law Dome and Wilkes Land. Includes copy of the data collected for accumulation and density measurements, barometric profiling, gravity, ice thickness and bedrock profiling, and snow temperature, surface density and oxygen isotope measurements.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_78_iagp_data_casey_1.json b/datasets/glacio_78_iagp_data_casey_1.json index 29a0502193..c930092039 100644 --- a/datasets/glacio_78_iagp_data_casey_1.json +++ b/datasets/glacio_78_iagp_data_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_78_iagp_data_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of data/observations recorded during the 1978 IAGP traverse from Casey. Included in the collection are accumulation stake height readings, barometric levelling observations, precise distance calculations between canes in the ice movement network (along with the resulting ice velocity calculation results), and an instrumentation report.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse1a_1.json b/datasets/glacio_87_traverse1a_1.json index 893d9ca317..5f51cd0b17 100644 --- a/datasets/glacio_87_traverse1a_1.json +++ b/datasets/glacio_87_traverse1a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse1a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 50 day traverse out of Casey across Law Dome/Wilkes Land at the start of 1987 carried out a large number of measurements as they travelled along their route. Meteorological observations (air pressure, temperature and relative humidity) and gravity were recorded, along with general trip observations.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse1b_1.json b/datasets/glacio_87_traverse1b_1.json index 97c7bd063d..ab631305d4 100644 --- a/datasets/glacio_87_traverse1b_1.json +++ b/datasets/glacio_87_traverse1b_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse1b_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 46 day traverse out of Casey across Law Dome/Wilkes Land carried out a large number of measurements as they traveled along their route. A number of ice core holes were drilled, and the ice temperatures down the boreholes measured. Gravity measurements and air pressure were also recorded, along with current levels on snow accumulation stakes. A copy of the traverse log is included in these notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse2_1.json b/datasets/glacio_87_traverse2_1.json index 08e3cc9d66..cab76739e2 100644 --- a/datasets/glacio_87_traverse2_1.json +++ b/datasets/glacio_87_traverse2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A copy of the travel notes of the 20 day traverse out of Casey across Law Dome/Wilkes Land. Includes a number of temperature/air pressure/wind observations.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse3a_1.json b/datasets/glacio_87_traverse3a_1.json index 0da3eea251..154bc6afcd 100644 --- a/datasets/glacio_87_traverse3a_1.json +++ b/datasets/glacio_87_traverse3a_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse3a_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 21 day traverse out of Casey across Law Dome, extending the cane grid on the dome. Has measurements of air pressure and temperature over the traverse period. Includes a copy of the travel notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse3b_1.json b/datasets/glacio_87_traverse3b_1.json index 4dfe863a19..7a964cdf8e 100644 --- a/datasets/glacio_87_traverse3b_1.json +++ b/datasets/glacio_87_traverse3b_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse3b_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A two-part (totaling 60 days including the break) traverse out of Casey across Law Dome, focussing on running an ice radar over a number of set grids at various locations. Has measurements of air pressure and temperature from the sitreps, but no radar data. Includes a copy of the travel notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse4_1.json b/datasets/glacio_87_traverse4_1.json index 628b86a23f..d0ab4bd182 100644 --- a/datasets/glacio_87_traverse4_1.json +++ b/datasets/glacio_87_traverse4_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse4_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 36 day traverse out of Casey across Law Dome, took detailed gravity and borehole temperature readings at a number of sites on the dome. Includes a copy of the travel notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_87_traverse5_1.json b/datasets/glacio_87_traverse5_1.json index 3d098294ef..76299ceeae 100644 --- a/datasets/glacio_87_traverse5_1.json +++ b/datasets/glacio_87_traverse5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_87_traverse5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A 96 day traverse out of Casey across Law Dome and Wilkes Land performed a number of measurements including air pressure, air temperature, borehole temperature, gravity and snow accumulation at snow cane markers. Includes a copy of the travel notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1978_casey_1.json b/datasets/glacio_data_report_1978_casey_1.json index b7ebf7c38e..671e7170e6 100644 --- a/datasets/glacio_data_report_1978_casey_1.json +++ b/datasets/glacio_data_report_1978_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1978_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report presenting the data collected during the 1978 ANARE Glaciology program at Casey, carried out as part of the IAGP. Measurements made include ice movement, barometric levelling, echo sounding, gravity, accumulation, trilateration resurvey, undulation study, strain grids, and surface and borehole sampling.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1979_casey_1.json b/datasets/glacio_data_report_1979_casey_1.json index 3b9883eba4..0202b045fe 100644 --- a/datasets/glacio_data_report_1979_casey_1.json +++ b/datasets/glacio_data_report_1979_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1979_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report presenting the data collected during the 1979 ANARE Glaciology program at Casey, resulting from several inland traverses. Measurements recorded include ice velocity, ice thickness, height, strain, accumulation, snow samples and coring, and a magnetic survey.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1981_casey_1.json b/datasets/glacio_data_report_1981_casey_1.json index bc13bdd357..8191374de5 100644 --- a/datasets/glacio_data_report_1981_casey_1.json +++ b/datasets/glacio_data_report_1981_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1981_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of the data from the 1981 Glaciology program at Casey, collected from several inland traverses. Measurements include accumulation and density, barometric profiling, ice movement, gravity, ice thickness and bedrock profiling, temperatures at 10m depth, surface density, and oxygen isotopes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1982_casey_1.json b/datasets/glacio_data_report_1982_casey_1.json index c6f49a7396..9fe10b4869 100644 --- a/datasets/glacio_data_report_1982_casey_1.json +++ b/datasets/glacio_data_report_1982_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1982_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report presenting the data collected during the 1982 ANARE Glaciology program at Casey, resulting from several inland traverses. Measurements recorded include ice movement, barometric levelling, bedrock profiling, accumulation and gravity.\n\nFieldwork locations were Casey, Law Dome and Wilkes Land.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1983_casey_1.json b/datasets/glacio_data_report_1983_casey_1.json index 279a33cfe5..7de5b3019c 100644 --- a/datasets/glacio_data_report_1983_casey_1.json +++ b/datasets/glacio_data_report_1983_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1983_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report presenting the data collected during the 1983 ANARE Glaciology program at Casey, resulting from five inland traverses. Measurements made include ice movement, barometric levelling, bedrock profiling, accumulation, gravity, magnetic, surface wind, 10m temperatures, stratigraphy measurements and isotope sampling, along with traverse notes.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glacio_data_report_1986_casey_1.json b/datasets/glacio_data_report_1986_casey_1.json index f98ef8bd58..d5cf68fc23 100644 --- a/datasets/glacio_data_report_1986_casey_1.json +++ b/datasets/glacio_data_report_1986_casey_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glacio_data_report_1986_casey_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report of the data collected from the 1986 Glaciology program at Casey. Includes measurements of ice movement, accumulation, snow temperature, gravity, magnetic, weather data, surface density and hardness, and a summary of all known measurements along the A, B and Undulation Lines on Law Dome.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/glide-snow-avalanche-activity-on-dorfberg-davos_1.0.json b/datasets/glide-snow-avalanche-activity-on-dorfberg-davos_1.0.json index 5aeaa54e30..fdf4d57294 100644 --- a/datasets/glide-snow-avalanche-activity-on-dorfberg-davos_1.0.json +++ b/datasets/glide-snow-avalanche-activity-on-dorfberg-davos_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glide-snow-avalanche-activity-on-dorfberg-davos_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article: Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91 We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.", "links": [ { diff --git a/datasets/glider_0.json b/datasets/glider_0.json index d3d6bc4685..2e5aad3020 100644 --- a/datasets/glider_0.json +++ b/datasets/glider_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glider_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made near Tampa along the Florida Gulf Coast to calibrate and validate glider instrumentation between 2009 and 2011.", "links": [ { diff --git a/datasets/glmcierra_1.json b/datasets/glmcierra_1.json index ef0be1e633..bc5f408a89 100644 --- a/datasets/glmcierra_1.json +++ b/datasets/glmcierra_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glmcierra_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Lightning Mapper (GLM) Cluster Integrity, Exception Resolution, and Reclustering Algorithm (CIERRA) dataset consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, series, flashes, and areas. The GLM CIERRA data addresses the artificial flash termination by the GLM ground system by recombining split flashes and filtering out more non-lightning noise. This provides researchers with a powerful tool to better investigate convective storm and lightning activity with more accurate observations as well as better incorporate spatial extent observations that can be used for aviation meteorology, lightning safety, and other studies. These data are available from January 12, 2017, through March 31, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/glmgoesL3_1.json b/datasets/glmgoesL3_1.json index ea92012474..8c3a24e568 100644 --- a/datasets/glmgoesL3_1.json +++ b/datasets/glmgoesL3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "glmgoesL3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products consist of full disk extent gridded lightning flash data collected by the Geostationary Lightning Mapper (GLM) onboard the Geostationary Operational Environmental Satellite 16 and 17 (GOES-16 and GOES-17). These satellites are a part of the GOES-R series program: a four satellite series within the National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Association (NOAA) GOES program. GLM is the first operational geostationary optical lightning detector that provides total lightning data (in-cloud, cloud-to-cloud, and cloud-to-ground flashes). While it detects each of these types of lightning, the GLM is unable to distinguish between each type. The GLM GOES L3 dataset files contain gridded lightning flash data over the Western Hemisphere in netCDF-4 format from December 31, 2017 to present as this is an ongoing dataset.", "links": [ { diff --git a/datasets/global-cryosphere-watch-data-survey_1.0.json b/datasets/global-cryosphere-watch-data-survey_1.0.json index 8414a9c77a..1a90c825fe 100644 --- a/datasets/global-cryosphere-watch-data-survey_1.0.json +++ b/datasets/global-cryosphere-watch-data-survey_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "global-cryosphere-watch-data-survey_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).", "links": [ { diff --git a/datasets/global-species-distributions-for-mammals-reptiles-and-amphibians_1.0.json b/datasets/global-species-distributions-for-mammals-reptiles-and-amphibians_1.0.json index de41e549c0..2c169684b7 100644 --- a/datasets/global-species-distributions-for-mammals-reptiles-and-amphibians_1.0.json +++ b/datasets/global-species-distributions-for-mammals-reptiles-and-amphibians_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "global-species-distributions-for-mammals-reptiles-and-amphibians_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5\u00b0 spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.", "links": [ { diff --git a/datasets/global_N_cycle_797_1.json b/datasets/global_N_cycle_797_1.json index 44d2cdaf39..831bc25e04 100644 --- a/datasets/global_N_cycle_797_1.json +++ b/datasets/global_N_cycle_797_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "global_N_cycle_797_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nitrogen is a major nutrient in terrestrial ecosystems and an important catalyst in tropospheric photochemistry. Over the last century human activities have dramatically increased inputs of reactive nitrogen (Nr, the combination of oxidized, reduced and organically bound nitrogen) to the Earth system. Nitrogen cycle perturbations have compromised air quality and human health, acidified ecosystems, and degraded and eutrophied lakes and coastal estuaries [Vitousek et al., 1997a, 1997b; Rabalais, 2002; Howarth et al., 2003; Townsend et al., 2003; Galloway et al., 2004]. To begin to quantify the changes to the global N cycle, we have assembled key flux data and N2O mixing ratios from various sources. The data assembled from different sources includes fertilizer production from 1920-2004; manure production from 1860-2004; crop N fixation estimated for three time points, 1860, 1900, 1995; tropospheric N2O mixing ratios from ice core and firn measurements, and tropospheric concentrations to cover the time period from 1756-2004. The changing N2O concentrations provide an independent index of changes to the global N cycle, in much the same way that changing carbon dioxide concentrations provide an important constraint on the global carbon cycle. The changes to the global N cycle are driven by industrialization, as indicated by fossil fuel NOx emission, and by the intensification of agriculture, as indicted by fertilizer and manure production and crop N2 fixation. The data set and the science it reflects are by nature interdisciplinary. Making the data set available through the ORNL DAAC is an attempt to make the data set available to the considerable interdisciplinary community studying the N cycle.", "links": [ { diff --git a/datasets/global_N_deposition_maps_830_1.json b/datasets/global_N_deposition_maps_830_1.json index 2066893001..1e5da0e3af 100644 --- a/datasets/global_N_deposition_maps_830_1.json +++ b/datasets/global_N_deposition_maps_830_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "global_N_deposition_maps_830_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model.", "links": [ { diff --git a/datasets/global_population_xdeg_975_1.json b/datasets/global_population_xdeg_975_1.json index 58fa13009d..57f42f635c 100644 --- a/datasets/global_population_xdeg_975_1.json +++ b/datasets/global_population_xdeg_975_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "global_population_xdeg_975_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps: * Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years. * Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years. * Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added. * The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years. * Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.", "links": [ { diff --git a/datasets/globalir_1.json b/datasets/globalir_1.json index e989222e7f..14c2b12ec0 100644 --- a/datasets/globalir_1.json +++ b/datasets/globalir_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "globalir_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Infrared Global Geostationary Composite dataset contains global composite images from the infrared channels of multiple weather satellites in geosynchronous orbit. These satellites include the Global Mobility Service (GMS) from Japan, the Geostationary Operational Environmental Satellite (GOES) from the United States, NOAA satellites, and the Meteorological Satellite (METEOSAT) from Europe spanning nearly the entire globe. The spatial resolution is 14 km before December 18, 2017, and 4 km after that with the data remapped into a Mercator projection. The data have not necessarily been cross-calibrated between sensors. The data are available in AREA McIDAS format from June 4, 1995, to January 24, 2024, and netCDF-4 format from January 25, 2024, to present. ", "links": [ { diff --git a/datasets/globalview_ch4_point_1109_1.json b/datasets/globalview_ch4_point_1109_1.json index 1581b7f6eb..4b7a8b1912 100644 --- a/datasets/globalview_ch4_point_1109_1.json +++ b/datasets/globalview_ch4_point_1109_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "globalview_ch4_point_1109_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GlobalView Methane (CH4) data product contains synchronized and smoothed time series of atmospheric CH4 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans (1995). The information needed to derive this time series is also in this data set, along with extensive documentation. The longest period of coverage is from 1984 to 1998 with some sites having shorter or longer temporal coverage. Note that the GlobalView-CH4 data products are derived from measurements but contain no actual data. To facilitate heterogeneous CH4 data use in carbon cycle modeling studies, the measurements have been processed (smoothed, interpolated, and extrapolated) resulting in extended records that are evenly incremented in time. There are 74 files with this data set which includes 71 *.zip data files. The other three files include 2 files with site information, one comma-delimited ASCII file (.csv), and one .dat file, and one .dat file which is a single reference marine boundary layer matrix file containing CH4 mixing ratios as a function of time and sine of latitude and is a by-product of the data extension procedure.", "links": [ { diff --git a/datasets/globalview_co2_point_1111_1.json b/datasets/globalview_co2_point_1111_1.json index 605290d115..fa7162a40b 100644 --- a/datasets/globalview_co2_point_1111_1.json +++ b/datasets/globalview_co2_point_1111_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "globalview_co2_point_1111_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GlobalView Carbon Dioxide (CO2) data product contains synchronized and smoothed time series of atmospheric CO2 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans, (1995). The information needed to derive this time series is also in this data set, along with extensive documentation. The longest period of coverage is from 1979 to 2001 with some sites having longer or shorter temporal coverage. Note that the GlobalView CO2 data products are derived from measurements but contain no actual data. To facilitate heterogeneous CO2 data use in carbon cycle modeling studies, the measurements have been processed (smoothed, interpolated, and extrapolated) resulting in extended records that are evenly incremented in time. There are 92 files with this data set which includes 89 *.zip data files. The other three files include 2 files with site information, one comma-delimited ASCII file (.csv), and one .dat file, and one .dat file which is a single reference marine boundary layer matrix file which contains CO2 mixing ratios as a function of time and sine of latitude and is a by-product of the data extension procedure.", "links": [ { diff --git a/datasets/globe_dem_630_1.json b/datasets/globe_dem_630_1.json index 9d7bef7522..c07500ded4 100644 --- a/datasets/globe_dem_630_1.json +++ b/datasets/globe_dem_630_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "globe_dem_630_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) data in both ASCII GRID and binary image file formats.", "links": [ { diff --git a/datasets/gls.json b/datasets/gls.json index bb0f7f553b..b48c86ef13 100644 --- a/datasets/gls.json +++ b/datasets/gls.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gls", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. NASA and the USGS have again partnered to develop the Global Land Survey 2010 (GLS2010), a new global land data set with core acquisition dates of 2008-2011. This dataset consists of both Landsat TM and ETM+ images that meet quality and cloud cover standards established by the earlier GLS collections. Data acquired in 2011 were used to fill areas of low image quality or excessive cloud cover.", "links": [ { diff --git a/datasets/gmted2010.json b/datasets/gmted2010.json index 5d72b1ffdd..d75489fe3d 100644 --- a/datasets/gmted2010.json +++ b/datasets/gmted2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gmted2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS and the NGA have collaborated on the development of a notably enhanced global elevation model named the GMTED2010 that replaces GTOPO30 as the elevation dataset of choice for global and continental scale applications.\n\nThe new model has been generated at three separate resolutions (horizontal post spacings) of 30 arc-seconds (about 1 kilometer), 15 arc-seconds (about 500 meters), and 7.5 arc-seconds (about 250 meters). This new product suite provides global coverage of all land areas from lat 84\u00b0N to 56\u00b0S for most products, and coverage from 84\u00b0N to 90\u00b0S for several products. Some areas, namely Greenland and Antarctica, do not have data available at the 15- and 7.5-arc-second resolutions because the input source data do not support that level of detail. An additional advantage of the new multi-resolution global model over GTOPO30 is that seven new raster elevation products are available at each resolution.\n", "links": [ { diff --git a/datasets/goes71_444_1.json b/datasets/goes71_444_1.json index 7480f8fe48..68604fad57 100644 --- a/datasets/goes71_444_1.json +++ b/datasets/goes71_444_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goes71_444_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days. The data include three bands with eight-bit pixel values.", "links": [ { diff --git a/datasets/goes71a_300_1.json b/datasets/goes71a_300_1.json index 29f1ff198c..db1e81c78c 100644 --- a/datasets/goes71a_300_1.json +++ b/datasets/goes71a_300_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goes71a_300_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team-14 (RSS-14) personnel at Florida State University and processed to level-1a products by BORIS personnel. The data cover the period 01-JAN-1994 through 08-JUL-1995.", "links": [ { diff --git a/datasets/goes72_554_1.json b/datasets/goes72_554_1.json index 0c0bfe7bce..23b4627969 100644 --- a/datasets/goes72_554_1.json +++ b/datasets/goes72_554_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goes72_554_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data. The data cover the time period of 05-Feb-1994 to 20-Sep-1994. The images missing from the temporal series were zero-filled to create a consistent sequence of files.", "links": [ { diff --git a/datasets/goes81_445_1.json b/datasets/goes81_445_1.json index 52f63b257b..1661e9e430 100644 --- a/datasets/goes81_445_1.json +++ b/datasets/goes81_445_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goes81_445_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS. The data cover 14-Jul-1995 to 21-Sep-1995 and 01-Jan-1996 to 03-Oct- 1996. The data start out containing three 8-bit spectral bands and end up containing five 10-bit spectral bands.", "links": [ { diff --git a/datasets/goes81a_446_1.json b/datasets/goes81a_446_1.json index d4e92374bf..a359127478 100644 --- a/datasets/goes81a_446_1.json +++ b/datasets/goes81a_446_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goes81a_446_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The level-1a GOES-8 images cover 14-Jul-1995 to 21-Sep-1995 and 12-Feb-1996 to 03-Oct-1996. The data start out as three bands with 8-bit pixel values and end up as five bands with 10-bit pixel values. The differences between the level-1 and level-1a GOES-8 data are the formatting and packaging of the data. The images missing from the temporal series of level-1 GOES-8 images were zero-filled to create files consistent in size and format.", "links": [ { diff --git a/datasets/goescpex_1.json b/datasets/goescpex_1.json index 252aa82783..0862b23f2b 100644 --- a/datasets/goescpex_1.json +++ b/datasets/goescpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goescpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES CPEX dataset contains products obtained from the Geostationary Operational Environmental Satellite 13. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 31, 2017 through July 25, 2017 and are available in netCDF-3 format.", "links": [ { diff --git a/datasets/goescpexcv_1.json b/datasets/goescpexcv_1.json index 1ef11c165b..096ec24ddc 100644 --- a/datasets/goescpexcv_1.json +++ b/datasets/goescpexcv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goescpexcv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES CPEX-CV dataset consists of single reflective band radiance products from the Advanced Baseline Imager (ABI) onboard the GOES-16 geostationary satellite. These data were gathered during the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde during August-September 2022. The campaign is a continuation of CPEX \u2013 Aerosols and Winds (CPEX-AW) and will be conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 6-20, 2022 in netCDF-4 format, with associated browse imagery in MPEG-4 format.", "links": [ { diff --git a/datasets/goesimpacts_1.json b/datasets/goesimpacts_1.json index 53bb4b7462..a21a7f5ce7 100644 --- a/datasets/goesimpacts_1.json +++ b/datasets/goesimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES IMPACTS dataset consists of single reflective band radiance products from the Advanced Baseline Imager (ABI) onboard the GOES-16 geostationary satellite. These data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The GOES IMPACTS dataset files are available in netCDF-4 format from January 1 through February 29, 2020. This dataset contains data from the GOES-16 CONUS and Mesoscale sectors, although IMPACTS uses a subset of the GOES-16 CONUS domain. The complete collection of GOES data is available from the NOAA Comprehensive Large Array-Data Stewardship System (CLASS). It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/goesrpltaviris_1.json b/datasets/goesrpltaviris_1.json index 7c6db88fbf..9aaa0f25ad 100644 --- a/datasets/goesrpltaviris_1.json +++ b/datasets/goesrpltaviris_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltaviris_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) dataset consists of radiance, reflectance, water phase, and navigation data delivered by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flown aboard the NASA ER-2 high-altitude aircraft during the GOES-R PLT field campaign. This field campaign took place from March through May 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM) satellite instruments. The GOES-R PLT AVIRIS data files are available from April 11, 2017 through May 14, 2017 in ASCII and binary formats along with browse imagery files in JPG format.", "links": [ { diff --git a/datasets/goesrpltavirisng_1.json b/datasets/goesrpltavirisng_1.json index 20a2e90708..1bdec3573d 100644 --- a/datasets/goesrpltavirisng_1.json +++ b/datasets/goesrpltavirisng_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltavirisng_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Field Campaign Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) dataset consists of radiance, reflectance, water phase, and navigation data collected by the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) for the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). AVIRIS-NG is an imaging spectrometer that measures reflected radiance at 5 nm intervals in the Visible/Short-Wave Infrared (VSWIR) spectral range from 380-2,510 nm. AVIRIS-NG flew onboard the NASA ER-2 high-altitude aircraft during the GOES-R PLT field campaign. Data files in ASCII and BINARY formats are available for March 23 and 28, 2017.", "links": [ { diff --git a/datasets/goesrpltcolma_1.json b/datasets/goesrpltcolma_1.json index c8d028bd04..3bdf6f0df3 100644 --- a/datasets/goesrpltcolma_1.json +++ b/datasets/goesrpltcolma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltcolma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Colorado Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the Colorado LMA (COLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through May 31, 2017.", "links": [ { diff --git a/datasets/goesrpltcpl_1.json b/datasets/goesrpltcpl_1.json index 49bbb6eac2..9c61e00beb 100644 --- a/datasets/goesrpltcpl_1.json +++ b/datasets/goesrpltcpl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltcpl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Cloud Physics Lidar (CPL) dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR instrument flown aboard the NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign supported post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The CPL instrument is a multi-wavelength backscatter LiDAR that provides multi-wavelength measurements of cirrus clouds and aerosols with high temporal and spatial resolution. Data files are available from April 13, 2017 through May 14, 2017 in HDF-5 format with layer information in ASCII text files. Browse imagery files in GIF format are also available.", "links": [ { diff --git a/datasets/goesrpltcrs_1.json b/datasets/goesrpltcrs_1.json index f99b08c452..9e98cba861 100644 --- a/datasets/goesrpltcrs_1.json +++ b/datasets/goesrpltcrs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltcrs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Field Campaign Cloud Radar System (CRS) dataset provides high-resolution profiles of reflectivity and Doppler velocity at aircraft nadir along the flight track. The CRS was flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March 21 to May 17, 2017 in support of post-launch product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R, now GOES-16, satellite. The CRS data files are available in netCDF-3 format with browse imagery available in PNG format.", "links": [ { diff --git a/datasets/goesrpltdclma_1.json b/datasets/goesrpltdclma_1.json index 7b59173256..13cdb9c751 100644 --- a/datasets/goesrpltdclma_1.json +++ b/datasets/goesrpltdclma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltdclma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Washington D.C. Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the Washington D.C. LMA (DCLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from April 6, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goesrpltfegs_1.json b/datasets/goesrpltfegs_1.json index b48188ff4d..051d1c873b 100644 --- a/datasets/goesrpltfegs_1.json +++ b/datasets/goesrpltfegs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltfegs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Fly\u2019s Eye GLM Simulator (FEGS) dataset consists of lightning flash, lightning pulse, and radiance data collected by the FEGS flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). These data files are available in ASCII format with browse imagery available in PNG format.", "links": [ { diff --git a/datasets/goesrpltgcas_1.json b/datasets/goesrpltgcas_1.json index ed1d1ff841..244e5aa65d 100644 --- a/datasets/goesrpltgcas_1.json +++ b/datasets/goesrpltgcas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltgcas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) dataset consist of solar backscattered radiation measured by the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March to May of 2017 in support of post-launch L1B product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM). Data files in HDF-5 format are available for March 21, 2017 through May 14, 2017.", "links": [ { diff --git a/datasets/goesrpltivanpah_1.json b/datasets/goesrpltivanpah_1.json index 15a28fbb8d..a4fd6cda1f 100644 --- a/datasets/goesrpltivanpah_1.json +++ b/datasets/goesrpltivanpah_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltivanpah_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Field Campaign Ivanpah dataset consists of surface reflectance and total optical depth data measured at Ivanpah Playa, Nevada during the GOES-R Post Launch Test (PLT) field campaign. The atmospheric measurements were made using an Automated Solar Radiometer (ASR), which tracks the sun throughout the day. Surface reflectance measurements were made using an ASD portable spectroradiometer and Spectralon reference panel. The GOES-R PLT field campaign took place from March to May of 2017 in support of post-launch L1b and L2+ product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM). The main goal of this dataset is to provide an independent validation of the AVIRIS-NG airborne instrument calibration. Data files in Excel format and browse imagery files in JPEG and PNG formats are only available for March 23 and March 28, 2017.", "links": [ { diff --git a/datasets/goesrpltksclma_1.json b/datasets/goesrpltksclma_1.json index 8dbf4fb20f..f9009253fc 100644 --- a/datasets/goesrpltksclma_1.json +++ b/datasets/goesrpltksclma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltksclma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Kennedy Space Center Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the Kennedy Space Center LMA (KSCLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goesrpltlip_1.json b/datasets/goesrpltlip_1.json index b372c2b4d8..40d1dc7868 100644 --- a/datasets/goesrpltlip_1.json +++ b/datasets/goesrpltlip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltlip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Lightning Instrument Package (LIP) dataset consists of electrical field measurements of lightning and navigation data collected by the Lightning Instrument Package (LIP) flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). These data files are available in ASCII format with browse imagery available in PDF format.", "links": [ { diff --git a/datasets/goesrpltmisrep_1.json b/datasets/goesrpltmisrep_1.json index 11ecf5fe47..f05fdc1d0b 100644 --- a/datasets/goesrpltmisrep_1.json +++ b/datasets/goesrpltmisrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltmisrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Mission Reports dataset consists of various reports filed by the scientists during the GOES-R Post Launch Test (PLT) field campaign including flight reports, weather forecasts, mission scientist reports, and plan-of-day reports. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The GOES-R PLT Mission Reports dataset contains reports from March 13 through May 17, 2017 in PDF, PNG, Microsoft Excel and Word (.xlsx and .docx) format, and KMZ format for display in Google Earth.", "links": [ { diff --git a/datasets/goesrpltnalma_1.json b/datasets/goesrpltnalma_1.json index 01d806f304..9ad5ddd18e 100644 --- a/datasets/goesrpltnalma_1.json +++ b/datasets/goesrpltnalma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltnalma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT North Alabama Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the North Alabama LMA (NALMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goesrpltnaver2_1.json b/datasets/goesrpltnaver2_1.json index 30617f2139..1fade82442 100644 --- a/datasets/goesrpltnaver2_1.json +++ b/datasets/goesrpltnaver2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltnaver2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT ER-2 Flight Navigation Data dataset consists of multiple altitude, pressure, temperature parameters, airspeed, and ground speed measurements collected by the NASA ER-2 high-altitude aircraft for flights that occurred during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). ER-2 navigation data files in ASCII-IWG1 format are available for March 21, 2017 through May 17, 2017.", "links": [ { diff --git a/datasets/goesrpltoklma_1.json b/datasets/goesrpltoklma_1.json index 8d52070d47..471a6a3fae 100644 --- a/datasets/goesrpltoklma_1.json +++ b/datasets/goesrpltoklma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltoklma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Oklahoma Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the Oklahoma LMA (OKLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goesrpltredlake_1.json b/datasets/goesrpltredlake_1.json index 1aded2094a..21f462ff93 100644 --- a/datasets/goesrpltredlake_1.json +++ b/datasets/goesrpltredlake_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltredlake_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Surface Radiance Red Lake dataset consists of surface radiation budget, ultraviolet-B (UVB) and photosynthetically active radiation (PAR) flux, meteorological (temperature, pressure, relative humidity, winds), and spectral aerosol optical thickness data collected by a mobile SURFRAD station at Red Lake, Arizona for the GOES-R Post Launch Test (PLT) field campaign. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). Data files are available in ASCII text format from March 27, 2017 through April 12, 2017. Surface reflectance measurements based on spectroradiometer data are also included in Microsoft Excel format.", "links": [ { diff --git a/datasets/goesrpltshis_1.json b/datasets/goesrpltshis_1.json index a38d368170..324be7f239 100644 --- a/datasets/goesrpltshis_1.json +++ b/datasets/goesrpltshis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltshis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Field Campaign Scanning High-Resolution Interferometer Sounder (S-HIS) dataset consists of emitted thermal radiances measured by the Scanning High-resolution Interferometer Sounder (S-HIS) flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). Data files in netCDF-3 format are available for March 21, 2017 through May 17, 2017.\n", "links": [ { diff --git a/datasets/goesrpltsolma_1.json b/datasets/goesrpltsolma_1.json index 07c9d93e40..115abd90b9 100644 --- a/datasets/goesrpltsolma_1.json +++ b/datasets/goesrpltsolma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltsolma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT Southern Ontario Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the Southern Ontario LMA (SOLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from April 1, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goesrpltwtlma_1.json b/datasets/goesrpltwtlma_1.json index 9135368b9e..e07b2f5606 100644 --- a/datasets/goesrpltwtlma_1.json +++ b/datasets/goesrpltwtlma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goesrpltwtlma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES-R PLT West Texas Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the West Texas LMA (WTXLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017.", "links": [ { diff --git a/datasets/goeswvt_1.json b/datasets/goeswvt_1.json index d56d59ab34..3e7e5a6432 100644 --- a/datasets/goeswvt_1.json +++ b/datasets/goeswvt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "goeswvt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GOES Water Vapor Transport CD contains nineteen months of geostationary satellite-derived products from the GOES-8 satellite spanning the 1987-1988 El Nino Southern Oscillation (ENSO) cycle. Water vapor transport variables was derived using the Marshall Automated Winds (MAW) tracking algorithm from GOES data are provided in daily and monthly gridded and non-gridded formats. Relative humidity was calculated using a modified version of the brightness temperature to relative humidity conversion technique. Pressure heights were assigned to each wind vector using the simple IR window technique. Data are available in binary and McIDAS format. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov", "links": [ { diff --git a/datasets/gom_bathymetry.json b/datasets/gom_bathymetry.json index ec7d74e0dc..964b013124 100644 --- a/datasets/gom_bathymetry.json +++ b/datasets/gom_bathymetry.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gom_bathymetry", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded bathymetry and topography at 15 arc second (~1/2 km grid cell size) and a 30 arc second (~1 km grid cell size) resolution were constructed for the Gulf of Maine (Longitude = 71.5 - 63 W, Latitude = 39.5 - 46 N) using available digital bathymety datasets. In addition to the grids themselves, valuable ancillary products such as corrected sounding data, digital bathymetric contour lines and shaded-relief maps were generated and are available in a variety of formats, including Arc, Matlab, GMT and ASCII. See http://pubs.usgs.gov/of/1998/of98-801/", "links": [ { diff --git a/datasets/gomc_156.json b/datasets/gomc_156.json index 908f30c28a..948116d80f 100644 --- a/datasets/gomc_156.json +++ b/datasets/gomc_156.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gomc_156", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Salem Sound Coastwatch trains volunteers to monitor tide pools through\n the Adopt-A-Tide pool program. Volunteers will help us focus special attention\n on local tide pools and catalog the diversity of both native and invasive\n species. This information will be passed on to scientists working on strategies\n to address marine invasive species.\n \n Waterbody or Watershed Names: Salem Sound\n \n ", "links": [ { diff --git a/datasets/gomc_162.json b/datasets/gomc_162.json index 768b416400..f05f23c267 100644 --- a/datasets/gomc_162.json +++ b/datasets/gomc_162.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gomc_162", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " U.S. Geological Survey studies show that the concentrations of metals\nin surface sediments of Boston Harbor are decreasing with time. This conclusion\nis supported by analysis of (1) surface sediments collected at monitoring\nstations in the outer harbor between 1977 and 1993, (2) sediment cores from\ndepositional areas of the harbor, and (3) historical data from a\ncontaminated-sediment data base, which includes information on metal and\norganic contaminants and sediment texture. During the 16 years of the\ncontinuing study, chromium, lead, mercury, silver, and zinc concentrations in\nsurface sediments have decreased by about 50 percent. Although these trends are\nindeed encouraging, concentrations of some metals in harbor sediments are still\nabove levels considered toxic to certain bottom-dwelling organisms. \n\nType: Bay\n\nWaterbody or Watershed Names: Boston Harbor\n\n", "links": [ { diff --git a/datasets/gomc_219.json b/datasets/gomc_219.json index 1c598645bb..2909ede5f2 100644 --- a/datasets/gomc_219.json +++ b/datasets/gomc_219.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gomc_219", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " The Interstate Environmental Commission is a joint agency of the\nStates of New York, New Jersey, and Connecticut. The IEC was established in\n1936 under a Compact between New York and New Jersey and approved by Congress.\nThe State of Connecticut joined the Commission in 1941.\n\nWaterbody or Watershed Names: Long Island Sound\n\n", "links": [ { diff --git a/datasets/gomc_323.json b/datasets/gomc_323.json index fe28b80d89..aa41025701 100644 --- a/datasets/gomc_323.json +++ b/datasets/gomc_323.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gomc_323", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " Parameters measured included: ammonia nitrogen, orthophosphate,\n dissolved oxygen, pH, turbidity, salinity, faecal coliform.\n \n ", "links": [ { diff --git a/datasets/gomc_40.json b/datasets/gomc_40.json index 9b30621c3f..4d39914401 100644 --- a/datasets/gomc_40.json +++ b/datasets/gomc_40.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gomc_40", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " We know that air pollution can have an effect on the health of our\nenvironment and on human health. People who have respiratory difficulties are\nparticularly sensitive to poor air quality. Children are frequently affected\nbecause of their physiology and because they tend to be more active outdoors. \nMonitoring air quality in New Brunswick helps us to better understand the\nsources, movements and effects of various substances in the air we breathe. The\ndata we collect helps us to control sources of air pollution within our\nprovince, and to negotiate with governments in other jurisdictions for controls\non air pollution that crosses borders. The more we know, the more effectively\nwe can work to protect and enhance our air quality and our environment.\n\n", "links": [ { diff --git a/datasets/gone-wild-grapevines-in-forests_1.0.json b/datasets/gone-wild-grapevines-in-forests_1.0.json index 915513eaf2..eb45df509a 100644 --- a/datasets/gone-wild-grapevines-in-forests_1.0.json +++ b/datasets/gone-wild-grapevines-in-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gone-wild-grapevines-in-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dor\u00e9e phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, Scaphoideus titanus and Orientus ishidae. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors. The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both S.titanus and O.ishidae. Eventually, the need to adapt the current FD management strategies are highlighted.", "links": [ { diff --git a/datasets/gov.noaa.ncdc:C00842_Version 1.2.json b/datasets/gov.noaa.ncdc:C00842_Version 1.2.json index b11f54b6c2..b720e7e27c 100644 --- a/datasets/gov.noaa.ncdc:C00842_Version 1.2.json +++ b/datasets/gov.noaa.ncdc:C00842_Version 1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ncdc:C00842_Version 1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Blended Global Sea Surface Winds products contain ocean surface wind vectors and wind stress on a global 0.25 degree grid, in multiple time resolutions of 6-hourly and monthly, with an 11-year (1995-2005) monthly climatology. Daily files from a direct average of the 6-hourly data were also produced but are not included in this archive. The period of record is July 9, 1987 to September 30, 2011 for product Version 1.2, released in July 2007. Wind speeds were generated by blending available and selected microwave and scatterometer observations using a Simple spatiotemporally weighted Interpolation (SI) method. The following satellite retrieval datasets from Remote Sensing Systems (RSS) were used for Version 1.2: SSMI Version 6, TMI Version 4, QSCAT Version 3a, and AMSRE Version 5 (updated using the SSMI rain rate). The wind directions are from the NCEP-DOE Reanalysis 2 (NRA-2). The model wind directions are interpolated onto the blended wind speed grids. The 6-hourly satellite-scaled global 0.25-degree grid wind stresses are computed as: taux_s = -[(w_s/w_m)**2]*taux_m tauy_s = -[(w_s/w_m)**2]*tauy_m where 's' indicates satellite-scaled values and 'm' indicates NRA-2 model values interpolated to the satellite grid. Files are in netCDF format and available to users via FTP and THREDDS. A near real-time (NRT) variant of the product is generated quasi-daily to satisfy the needs of real-time users. The publicly available NRT data were replaced by the delayed-mode research quality data on a monthly basis through the end of September 2011, at which time the Seawinds production was impacted by the loss of data from the AMSR-E instrument failure. Production of the delayed-mode research products ends with the loss of AMSR-E in Version 1.2; a future version will extend beyond September 2011. The NRT products are continued after September 2011; however, this archive only includes the delayed-mode research products as the NRT data have a lower maturity rating removing the basis for archiving those data.", "links": [ { diff --git a/datasets/gov.noaa.ncdc:C01381_Not Applicable.json b/datasets/gov.noaa.ncdc:C01381_Not Applicable.json index 7b57371585..4e5210f80f 100644 --- a/datasets/gov.noaa.ncdc:C01381_Not Applicable.json +++ b/datasets/gov.noaa.ncdc:C01381_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ncdc:C01381_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Radiation Budget Data - The Radiation Budget product suite is produced from the primary morning and afternoon Polar Orbiters. Product shows a measure of the longwave radiation emitted (W/m^2) by the earth-atmosphere system to space. The observations are displayed on a one degree equal area map for the day and night. \nThe products are: GAC long wave, HIRS long wave, longwave histogram, annual mean, monthly mean, and seasonal mean.\nThis is a NESDIS legacy product and the file naming pattern is as follows: NPR.RBSD.[SatelliteID].D[YYDDD] or NPR.RBMD.[SatelliteID].D[YYDDD]", "links": [ { diff --git a/datasets/gov.noaa.ncdc:C01560_V3.json b/datasets/gov.noaa.ncdc:C01560_V3.json index 2248a7a05a..3f773dcfe8 100644 --- a/datasets/gov.noaa.ncdc:C01560_V3.json +++ b/datasets/gov.noaa.ncdc:C01560_V3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ncdc:C01560_V3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Blended Global Biomass Burning Emissions Product version 3 (GBBEPx V3) system produces global biomass burning emissions. The product contains daily global biomass burning emissions (PM2.5, BC, CO, CO2, OC, and SO2) blended fire observations from MODIS Quick Fire Emission Dataset (QFED), VIIRS (NPP and JPSS-1) fire emissions, and Global Biomass Burning Emission Product from Geostationary satellites (GBBEP-Geo), which are in a grid cell of 0.25 \u00c3\u0097 0.3125 degree and 0.1 x 0.1 degree. It also produces hourly emissions from geostationary satellites, which is at individual fire pixels. The product output also include fire detection record in a HMS format, quality flag in biomass burning emissions, spatial pattern of PM2.5 emissions, and statistic PM2.5 information at continental scale. In Version3, daily biomass burning emissions at a FV3 C384 grid in binary format and daily biomass burning emissions at a 0.1 x 0.1 degree grid that include all the emissions species are added as new output.", "links": [ { diff --git a/datasets/gov.noaa.ncdc:C01598_Beta4.json b/datasets/gov.noaa.ncdc:C01598_Beta4.json index f05bb5d5fc..d1f2e42168 100644 --- a/datasets/gov.noaa.ncdc:C01598_Beta4.json +++ b/datasets/gov.noaa.ncdc:C01598_Beta4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ncdc:C01598_Beta4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included.", "links": [ { diff --git a/datasets/gov.noaa.ncdc:C01599_beta6.json b/datasets/gov.noaa.ncdc:C01599_beta6.json index c37f3bf993..62f517888d 100644 --- a/datasets/gov.noaa.ncdc:C01599_beta6.json +++ b/datasets/gov.noaa.ncdc:C01599_beta6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ncdc:C01599_beta6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:12_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:12_Not Applicable.json index c3f12a4037..7398afd957 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:12_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:12_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:12_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The 1906 San Francisco earthquake was the largest event (magnitude 8.3) to occur in the conterminous United States in the 20th Century. Recent estimates indicate that as many as 3,000 people lost their lives in the earthquake and ensuing fire. In terms of 1906 dollars, the total property damage amounted to about $24 million from the earthquake and $350 million from the fire. The fire destroyed 28,000 buildings in a 520-block area of San Francisco.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:16_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:16_Not Applicable.json index c3101d7732..d5be173e93 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:16_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:16_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:16_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On April 25, 1992 at 11:06 am local time (April 25 at 18:06 GMT), a magnitude 7.1 earthquake occurred in the Cape Mendocino area. Two additional earthquakes, magnitudes 6.6 and 6.7 occurred the next morning (April 26 at 00:41 and 04:18 am local time). The first earthquake was located six miles north of Petrolia, California, in a sparsely populated part of southwestern Humboldt County. Five small communities were located within a 50-mile radius of these events: Honeydew, Petrolia, Rio Dell, Scotia, and Ferndale.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:1_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:1_Not Applicable.json index 17928f6aef..59b79c2c82 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:1_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:1_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:1_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The magnitude 7.1 earthquake killed 28 people and caused $11 million property damage. Affected area: 1,554,000 sq km", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:23_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:23_Not Applicable.json index 2128b47050..283b8e2620 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:23_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:23_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:23_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On August 17, 1999, at 3:02 am local time (00:02 GMT) a magnitude (Mw) 7.4 earthquake occurred on the northern Anatolian fault. The epicenter was located very close to the south shore of the Bay of Izmit, an eastward extension of the Marmara Sea. The location of this earthquake and its proximity to a populous region on the Bay of Izmit contributed greatly to its damaging effects. The total estimated loss for port facilities in the region was around $200 million (US). Subsidence and slumping caused much of the coastal damage, but a tsunami was generated that also caused coastal damage and deaths.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:242_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:242_Not Applicable.json index a4d27e85d8..a1730d97a4 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:242_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:242_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:242_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This magnitude 6.2 earthquake caused $30 million in property damage in northern California. The epicenter of the quake was located near Mount Hamilton in the Diablo Range of the California Coast Ranges. The earthquake was felt over an area of 120,000 square kilometers in California and western Nevada.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:248_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:248_Not Applicable.json index 2bbb810b21..67cfdbcef2 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:248_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:248_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:248_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "South of Veracruz, southeastern Mexico. Damage: Severe. The earthquake caused heavy damage in the states of Morelos, Puebla, and Veracruz. Thousands were left homeless.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:251_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:251_Not Applicable.json index f5bfecbf19..e2725a7c98 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:251_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:251_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:251_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Northwestern Ecuador. Damage: Severe.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:263_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:263_Not Applicable.json index a7a218328d..06a2040121 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:263_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:263_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:263_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Barcena is on San Benedicto Island, which lies off the coast of Mexico, south of Baja California and west of Mexico City.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:264_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:264_Not Applicable.json index 714d6787c3..5787a8ee50 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:264_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:264_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:264_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This cinder cone in western Nicaragua has a name that means \"black hill.\" It has erupted more than 20 times since its birth in 1850. Explosive eruptions from the central crater are often accompanied by lava flows from the base of the cone. It is the youngest of four cinder cones scattered along a 20 km line east-southeast of Telica.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:29_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:29_Not Applicable.json index 774827b728..96aa4c63e4 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:29_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:29_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:29_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "On April 1, 1946, at 12:29 [local time] a rather strong magnitude 8.6 earthquake occurred with source to the south of Unimak Island, causing one of the most destructive tsunamis in the Pacific Ocean.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:32_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:32_Not Applicable.json index 0257efb314..c70f557a1e 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:32_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:32_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:32_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This major (magnitude 7.9) earthquake caused 77 deaths (tsunami, 46; landslide, 31). It knocked almost all wooden houses off their foundations in the Keiawa, Punaluu, and Ninole areas.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:36_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:36_Not Applicable.json index ebdad21bed..adf008b810 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:36_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:36_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:36_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Magnitude 6.3. Damage $1-$3 million. Subsidence was reported on several rural roads in the area. Liquefaction caused scores of mudpots, and oozing soil in nearby fields. One country road west of Westmorland collapsed, producing a 2-foot drop-off. In rural areas, unreinforced, concrete-lined irrigation canals were broken.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:4_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:4_Not Applicable.json index 8f2621218a..e884eefbb0 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:4_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:4_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:4_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The magnitude 6.5 earthquake killed 7 and caused 12.5 million in property damage.", "links": [ { diff --git a/datasets/gov.noaa.ngdc.mgg.photos:52_Not Applicable.json b/datasets/gov.noaa.ngdc.mgg.photos:52_Not Applicable.json index b49133964f..128b4b3dfb 100644 --- a/datasets/gov.noaa.ngdc.mgg.photos:52_Not Applicable.json +++ b/datasets/gov.noaa.ngdc.mgg.photos:52_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.ngdc.mgg.photos:52_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An earthquake measuring 8.1 struck 345 kilometers northwest of the Solomon Islands' capital Honiara at 0740 local time on 2 April. (2040 GMT 1 April). The earthquake created a tsunami causing significant damage in the Solomon Islands. Large tsunami waves (reports range from 2m to 10m) are reported to have struck the islands in the Western Province area of Solomon Islands and some parts of Papua New Guinea. Affected areas include Gizo, Simbo, Ranogga, Shortlands, Munda, Noro, Vella la Vella, Kolombangarra and parts of the southern coast of Choiseul. At least 34 were killed and several dozen missing. 5,500 people are thought to have been displaced in total. The Ministry of Health and Medical Services (MHMS) estimates that up to 50,000 people may be affected out of a total population of 100,000 in Western and Choiseul provinces.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000015_Not Applicable.json b/datasets/gov.noaa.nodc:0000015_Not Applicable.json index 9fded7d4ea..a03ee6aa32 100644 --- a/datasets/gov.noaa.nodc:0000015_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000015_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000015_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000028_Not Applicable.json b/datasets/gov.noaa.nodc:0000028_Not Applicable.json index 6afe7706dd..94b8c04f2e 100644 --- a/datasets/gov.noaa.nodc:0000028_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000028_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000028_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000029_Not Applicable.json b/datasets/gov.noaa.nodc:0000029_Not Applicable.json index 0ddfa777f0..f0024172d8 100644 --- a/datasets/gov.noaa.nodc:0000029_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000029_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000029_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000035_Not Applicable.json b/datasets/gov.noaa.nodc:0000035_Not Applicable.json index a102b6266d..17f71205d7 100644 --- a/datasets/gov.noaa.nodc:0000035_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000035_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000035_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000052_Not Applicable.json b/datasets/gov.noaa.nodc:0000052_Not Applicable.json index 2001f773f8..2ca22b5a2b 100644 --- a/datasets/gov.noaa.nodc:0000052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000064_Not Applicable.json b/datasets/gov.noaa.nodc:0000064_Not Applicable.json index fc30536209..2cf3346a75 100644 --- a/datasets/gov.noaa.nodc:0000064_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000064_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000064_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Arabesque was a multidisciplinary oceanographic research project focused on the Arabian Sea and Northwest Indian Ocean during the monsoon and intermonsoon season in 1994.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000085_Not Applicable.json b/datasets/gov.noaa.nodc:0000085_Not Applicable.json index 32348b0e6f..d9d6127b5c 100644 --- a/datasets/gov.noaa.nodc:0000085_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000103_Not Applicable.json b/datasets/gov.noaa.nodc:0000103_Not Applicable.json index aff5f6a3c9..eb0c6ca07e 100644 --- a/datasets/gov.noaa.nodc:0000103_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000103_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000103_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton and other data were collected using CalVet net in Bering sea from ALPHA HELIX. Data were collected from 01 June 1997 to 01 September 1998 by University of Alaska in Fairbanks with support from the Inner Front project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000107_Not Applicable.json b/datasets/gov.noaa.nodc:0000107_Not Applicable.json index 3b49258d25..0f57aa3828 100644 --- a/datasets/gov.noaa.nodc:0000107_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000107_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000107_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton, temperature, species identification, and other data were collected from ALPHA HELIX using net casts in the Bering Sea. Data were collected from 03 June 1997 to 07 June 1999 by University of Alaska/IMS in Fairbanks, Alaska with support from Inner Front project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000121_Not Applicable.json b/datasets/gov.noaa.nodc:0000121_Not Applicable.json index 0d1b4af722..7bf999c20d 100644 --- a/datasets/gov.noaa.nodc:0000121_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000121_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000121_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbonate data for the Weddell sea are provided from both surface samples taken along the cruise track of the US-USSR Weddell Polynya Expedition (WEPOLEX-81) and from samples taken at vertical stations. The expedition aboard the Soviet icebreaker SOMOV began on October 9, 1981, and ended on November 25, 1981.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000247_Not Applicable.json b/datasets/gov.noaa.nodc:0000247_Not Applicable.json index 35124ca53f..7046a603b6 100644 --- a/datasets/gov.noaa.nodc:0000247_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000247_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000247_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. Army Space and Missile Defense Command (SMDC) in support of the Ballistic Missile Defense Organization (BMDO) sponsored a marine biological survey at Wake Atoll, located approximately 2,100 miles west of Honolulu at 19 18' North Latitude and 166 35' East Longitude. On behalf of the SMDC, biologists from the U.S. Fish and Wildlife Service (USFWS) and the National Marine Fisheries Service (NMFS) were invited to Wake Atoll in June 1998. The purpose of the visit was to conduct baseline marine biological surveys in the vicinity of the Peacock Point outfall pipe and to examine the sites of other point-source discharges to the marine environment (i.e., power plant, desalinization plant, and stormwater outlets). The biologists were asked to (1) generally characterize the coral-reef habitats within the vicinity of the outfall, (2) document the primary species of reef fishes, corals, other macroinvertebrates, and algae that exist in those habitats, and (3) investigate whether the reef communities at the other sites appeared to have been impacted by the discharges.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000251_Not Applicable.json b/datasets/gov.noaa.nodc:0000251_Not Applicable.json index 68cebbba2a..eab5b4c778 100644 --- a/datasets/gov.noaa.nodc:0000251_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000251_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000251_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report summarizes the results of the first United States Army Kwajalein Atoll (USAKA) Activities in the Republic of the Marshall Islands (UES) inventory of endangered species and wildlife resources at USAKA, which was conducted in 1996. The 1996 inventory report is to be used as the official record of species and habitats of concern at USAKA until the results of the next inventory (1998) are reported and incorporated into the UES pursuant to the next applicable annual review. For the National Oceanographic Data Center, interest in the report focuses on the marine element. Data tables from marine surveys of sponges, corals, and mollusks are given.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000263_Not Applicable.json b/datasets/gov.noaa.nodc:0000263_Not Applicable.json index 6b3bcdb12d..702c39d21e 100644 --- a/datasets/gov.noaa.nodc:0000263_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000263_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000263_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and depth data were collected from the ALPHA HELIX from September 7, 1987 to June 11, 1988. Data were submitted by the University of Alaska - Fairbanks; Institute of Marine Science and California Department of Fish and Game. Data were collected using bottle casts in the Bering Sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000266_Not Applicable.json b/datasets/gov.noaa.nodc:0000266_Not Applicable.json index 033f030a1f..61f2885945 100644 --- a/datasets/gov.noaa.nodc:0000266_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000266_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000266_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and salinity data were collected from multiple ships from May 14, 1957 to December 18, 1999. Data were collected from the IFREMER, ORSTOM, and NEW CALEDONIA using CTD and bottle casts in a world-wide distribution. Chemical include pH, dissolved oxygen, phosphate, silicate, nitrate, nitrite, and ammonium.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000268_Not Applicable.json b/datasets/gov.noaa.nodc:0000268_Not Applicable.json index f44e058973..f68aa827a8 100644 --- a/datasets/gov.noaa.nodc:0000268_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000268_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000268_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll, temperature, depth, and irradiance data were collected using bottle from multiple vessels in a world-wide distribution from 28 February 1964 to 02 April 1994.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000298_Not Applicable.json b/datasets/gov.noaa.nodc:0000298_Not Applicable.json index 09b317f708..64353c9418 100644 --- a/datasets/gov.noaa.nodc:0000298_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000298_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000298_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and zooplankton abundance/biomass data were collected using secchi disk, zooplankton net, current meter (ADCP), bottle, and CTD casts in the Coastal Waters of California from the NEW HORIZON and DAVID STARR JORDAN. Data were collected from January 7, 2000 to July 1, 2000. Data were submitted by Scripps Institution of Oceanography as a part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000303_Not Applicable.json b/datasets/gov.noaa.nodc:0000303_Not Applicable.json index 7038e20718..40c0e2ce02 100644 --- a/datasets/gov.noaa.nodc:0000303_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000303_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000303_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, transmissivity, fluorescence, nutrients, and temperature data were collected from multiple ships from June 29, 1966 to April 22, 2000. Data were submitted by Marine Environmental Data Service. Data were collected using bottle, BT, and CTD casts in the Arctic Ocean, North Atlantic Ocean and South Atlantic Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000340_Not Applicable.json b/datasets/gov.noaa.nodc:0000340_Not Applicable.json index 2333e0610c..bb20b61568 100644 --- a/datasets/gov.noaa.nodc:0000340_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000340_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000340_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria and other data were collected from the HERMANO GINES from November 14, 1997 to November 7, 1998. Data were submitted by State University of New York - Stony Brook as part of the Carbon Retention in a Colored Ocean project. Data were collected using bottle casts in the Caribbean Sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000349_Not Applicable.json b/datasets/gov.noaa.nodc:0000349_Not Applicable.json index 80edb39da2..3ac236b0d0 100644 --- a/datasets/gov.noaa.nodc:0000349_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000349_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000349_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Depth, pressure, and water temperature data were collected at fixed platforms in the Gulf of Alaska from July 5, 1985 to October 9, 1988. These data were submitted by the University of Alaska - Fairbanks; Institute of Marine Science as part of the Inner Shelf Transfer and Recycling (ISHTAR) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000354_Not Applicable.json b/datasets/gov.noaa.nodc:0000354_Not Applicable.json index d183bae103..51b9bce071 100644 --- a/datasets/gov.noaa.nodc:0000354_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000354_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000354_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from the YAQUINA, CAYUSE, WECOMA, and THOMAS G. THOMPSON from July 8, 1974 to August 21, 1983. Data were submitted by University of Washington using bottle and CTD casts in Coastal Waters of the Washington/Oregon and Northeast Pacific Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000358_Not Applicable.json b/datasets/gov.noaa.nodc:0000358_Not Applicable.json index b240d5d2cd..cfe34da84b 100644 --- a/datasets/gov.noaa.nodc:0000358_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000358_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000358_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Barometric pressure, conductivity, temperature, and water level data were collected at fixed platforms in the North Atlantic Ocean and Coastal waters of Florida from January 1, 1977 to December 31, 1999. Data were submitted by Florida Department of Environmental Protection. These data were collected using tide gauge at the fixed locations.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000366_Not Applicable.json b/datasets/gov.noaa.nodc:0000366_Not Applicable.json index 592cb03a9e..925815a4c8 100644 --- a/datasets/gov.noaa.nodc:0000366_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000366_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000366_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air/delta/sea surface temperature, pressure, and other data were collected from the MISS GAIL in a world-wide distribution from October 21, 1957 to April 18, 1961. Data were submitted by the NOAA Oar Climate Monitoring and Diagnostics Lab.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000396_Not Applicable.json b/datasets/gov.noaa.nodc:0000396_Not Applicable.json index c7add3799d..e0ec4f4bd0 100644 --- a/datasets/gov.noaa.nodc:0000396_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000396_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000396_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll data were collected at fixed platforms in the Coastal waters of Hawaii and Northeast Pacific Ocean from September 24, 1976 to June 15, 1979. Data were submitted by the University of Hawaii, Maui. Data were collected using pump sampler.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000411_Not Applicable.json b/datasets/gov.noaa.nodc:0000411_Not Applicable.json index 825ef07fa3..edcbff209b 100644 --- a/datasets/gov.noaa.nodc:0000411_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000411_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000411_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographs were taken of the aquatic vegetation of Florida Bay, Indian River (Florida), and the Coast of Massachusetts. Photographs were scanned and geo-referenced for the purpose of mapping. Data is contained on a \"DLT\" tape and is stored \"off-site\" as a secure backup copy.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000422_Not Applicable.json b/datasets/gov.noaa.nodc:0000422_Not Applicable.json index 4b6feb2349..4c1ac85d39 100644 --- a/datasets/gov.noaa.nodc:0000422_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000422_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000422_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll data were collected from a sewage outfall site in Kaneohe Bay, Hawaii, from 1982 to 2001. The purpose of the project was to study the responses of the ecosystem to the sewage diversion from the inner bay to an offshore, deep water location and to continue monitoring the location to denote changes associated with natural environmental and anthropogenic forcing on the primary productivity. Data were submitted by the University of Hawaii at Manoa and funding was provided by the Environmental Protective Agency (EPA).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000425_Not Applicable.json b/datasets/gov.noaa.nodc:0000425_Not Applicable.json index f9587c2995..91351285a4 100644 --- a/datasets/gov.noaa.nodc:0000425_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000425_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000425_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biological, chemical, geological, and other data were collected from the R/V Kittiwait from 01 June 1998 to 01 July 1998. Data were submitted by the Washington State Department of Ecology (WADOE) as part of a 3 year, 100 site, study of toxins in the Puget Sound. Biological data include infauna surveys, amphipod bioassays, and percent urchin fertilization. Chemical data include results of tests for toxins by cytochrome and microtoxology. Geological data include determination of grain fractions.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000447_Not Applicable.json b/datasets/gov.noaa.nodc:0000447_Not Applicable.json index c7562bc5e1..e76902e3eb 100644 --- a/datasets/gov.noaa.nodc:0000447_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000447_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000447_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic samples and other data were collected from the R/V DAVIDSON and R/V BIG VALLEY from the Prince William Sound from 03 July 1990 to 25 June of 1991 . Data were collected as part of the Exxon Valdez Oil Spill Restoration Project. Data were collected by the University of Alaska - Fairbanks / Institute of Marine Science (UAK/IMS) with bottom grab sampler and include taxonomic identities and taxonomic counts of benthic animals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000501_Not Applicable.json b/datasets/gov.noaa.nodc:0000501_Not Applicable.json index 5f64a55815..a86476acf6 100644 --- a/datasets/gov.noaa.nodc:0000501_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000501_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000501_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000504_Not Applicable.json b/datasets/gov.noaa.nodc:0000504_Not Applicable.json index 4928df63ba..4f47bd09f7 100644 --- a/datasets/gov.noaa.nodc:0000504_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000504_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000504_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phytoplankton and other data were collected in the Antarctic from the NATHANIEL B. PALMER and ROGER REVELL from 17 October 1996 to 15 March 1998. Bottle data include enumeration and counts of bacteria, picoplankton, nanoplankton and nano microplankton. Bottle data also include concentrations of trace metals. CTD data include conductivity, temperature, and salinity profiles. Data were collected in support of the US Joint Global Ocean Flux Study / Antarctic Environments Southern Ocean Process Study (JGOFS/AESOPS).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000525_Not Applicable.json b/datasets/gov.noaa.nodc:0000525_Not Applicable.json index fbd9b7129e..ee437f3fce 100644 --- a/datasets/gov.noaa.nodc:0000525_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000525_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000525_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water and sediment samples were collected on annual ECOHAB Process cruises and on isolated Mote transects (10/13/99 and 10/20/99). Samples will be analyzed for brevetoxin using a competetive ELISA assay (Naar and Baden, in progress) as well as a receptor-binding assay (VanDolah et al., 1994), and have been analyzed for chlorophyll a (water only) using the Welschmeyer (1994) non-acidification technique. (To be updated when data has been analyzed.)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000599_Not Applicable.json b/datasets/gov.noaa.nodc:0000599_Not Applicable.json index ba2d17fa65..2cbe55e332 100644 --- a/datasets/gov.noaa.nodc:0000599_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000599_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000599_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains a GIS database of Aids to Navigation in the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21.\n\nThe term \"Aids to Navigation\" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, racons, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission.\n\nEach USCG District Headquarters is responsible for updating their database on an \"as needed\" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official \"light listing number\". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported.\n\nThe light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even \"real time\" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters.\n\nGeographic Information System (GIS) software is required to display the data in this NCEI accession.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000630_Not Applicable.json b/datasets/gov.noaa.nodc:0000630_Not Applicable.json index 2cb01aa95a..e98db1f46d 100644 --- a/datasets/gov.noaa.nodc:0000630_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000630_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000630_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Roi-Namur is located at the northernmost tip of Kwajalein Atoll, approximately 64 kilometers north of the U.S. Army Kwajalein Atoll (USAKA) central command post on Kwajalein Islet. Roi-Namur has a single sewage outfall, which is located at the northwestern corner of the islet. Originally, the outfall extended from shore to a point about halfway across the reef flat where the pipe ended abruptly as an upturned, uncapped elbow. Raw sewage was pumped through the pipe in pulses approximately every 15-20 minutes. Waves and shallow currents across the reef flat carried at least some of the effluent back toward shore and the lagoon, creating a potentially unhealthy situation. In order to correct this problem, USAKA implemented a plan to extend the original outfall all the way across the reef flat and into the open ocean where the predominant current flow would carry effluent-mixed waters away from the islet. Ultimately, the extended outfall was to be connected to a new sewage treatment facility that would discharge primarily treated effluent. Because of a concern that this discharge might adversely impact the coral-reef community surrounding the end of the new outfall, a baseline marine biological survey was to be conducted prior to start-up of the new sewage treatment facility. As planned, the results of this survey would provide a baseline against which the results of future surveys could be compared in order to determine whether a balanced community of indigenous species had been maintained at the site during operation of the facility. If not, conversion to secondary treatment at the facility would need to be considered. The first resurvey was planned to occur one year after start-up of the new sewage treatment facility with subsequent resurveys planned for every five years thereafter. In August 1997, biologists from the U.S. Fish and Wildlife Service (USFWS) and the National Marine Fisheries Service (NMFS) conducted the baseline marine biological survey in the vicinity of the Roi-Namur outfall. For the National Oceanographic Data Center, interest in the report focuses on the marine element. Data tables from marine surveys of reef fishes, corals, other macroinvertebrates, and algae that exist in those habitats are provided.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000670_Not Applicable.json b/datasets/gov.noaa.nodc:0000670_Not Applicable.json index 824c76d3f7..530327506b 100644 --- a/datasets/gov.noaa.nodc:0000670_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000670_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000670_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In August 2001, biologists from the U.S. Fish and Wildlife Service and the National Marine Fisheries Service were asked to conduct an assessment of the national government's capability to respond to major threats (e.g. anthropogenic and natural) to the marine habitat of the Republic of the Maldives. A marine survey was conducted at selected locations to assess impacts to the marine environment. Biologists documented reef fishes, corals, other macroinvertebrates, and algae, and provided general descriptions of the benthic community at each of four primary survey sites.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000703_Not Applicable.json b/datasets/gov.noaa.nodc:0000703_Not Applicable.json index 81034b2bb0..a8d2d1c669 100644 --- a/datasets/gov.noaa.nodc:0000703_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000703_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000703_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, current meter, and other data were collected using current meter, bottle, XBT, and CTD casts in the Gulf of Mexico from November 16, 1997 to August 8, 2000. Data were submitted by Texas A&M University as part of the Northeastern Gulf of Mexico Physical Oceanographic Program: Chemical Oceanography and Hydrography Study (NEGOM) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000732_Not Applicable.json b/datasets/gov.noaa.nodc:0000732_Not Applicable.json index 8fe8faa05c..6f9d298325 100644 --- a/datasets/gov.noaa.nodc:0000732_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000732_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000732_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria, carbon dioxide, and methane data were collected employing bottle casts from the Hermano Gines in the Cariaco Basin on the continental shelf of Venezuela. Data were collected by the State University of New York - Stony Brook (SUNY) from 03 May 2000 to 31 October 2000. Bacteria data include rates of production of bacteria and flagellates. Abundances of remineralizers (bacteria) and regenerators (protozoa) were determined using microscopic censuses. Methane data includes rates of respiration and incorporation. Data are in a comma-seperated value (.csv) fromat.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000737_Not Applicable.json b/datasets/gov.noaa.nodc:0000737_Not Applicable.json index 0a5ff41503..fae815c283 100644 --- a/datasets/gov.noaa.nodc:0000737_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000737_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000737_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria, carbon dioxide, and methane data were collected from bottle casts from the HERMANO GINES in the Cariaco Basin on the continental shelf of Venezuela. Data were collected from 30 April 2001 to 01 May 2001. Bacteria data include rates of production of bacteria and flagellates. Abundances of remineralizers (bacteria) and regenerators (protozoa) were determined using microscopic censuses. Methane data include rates of respiration and incorporation. Data was submitted by the State University of New York, Stony Brook, as a comma- seperated value (.csv) file.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000780_Not Applicable.json b/datasets/gov.noaa.nodc:0000780_Not Applicable.json index 331a22dd9c..efd76cffb5 100644 --- a/datasets/gov.noaa.nodc:0000780_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000780_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000780_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bottle, CTD, net, and other data were collected from the A.V. HUMBOLDT and the JOHAN HJORT from the Norwegian Sea. Data were collected by multiple institutions in support of the Global Ocean Ecosystems Dynamics (GLOBEC) from 02 June 1993 to 13 June 1993. Bottle data include concentration profiles of chlorophyll a,b,c. CTD data include profiles of temperature and salinity. Net data include species identities and abundance of zooplankton.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000787_Not Applicable.json b/datasets/gov.noaa.nodc:0000787_Not Applicable.json index c596d09782..0688bcf708 100644 --- a/datasets/gov.noaa.nodc:0000787_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000787_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000787_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GLOBEC (Global Ocean Ecosystem Dynamics) was initiated by SCOR and the IOC of UNESCO in 1991, to understand how global change will affect the abundance, diversity and productivity of marine populations comprising a major component of oceanic ecosystems. The aim of GLOBEC is to advance our understanding of the structure and functioning of the global ocean ecosystem, its major subsystems, and its response to physical forcing so that a capability can be developed to forecast the responses of the marine ecosystem to global change.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000794_Not Applicable.json b/datasets/gov.noaa.nodc:0000794_Not Applicable.json index 59b2191a1c..3db72157c7 100644 --- a/datasets/gov.noaa.nodc:0000794_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000794_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000794_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During 1990-1999, coral growth and fish abundance were monitored at stations located at and in the vicinity of the Waianae Ocean Outfall. Comparisons of results with fish surveys showed no significant differences in the species composition or relative abundances of fish populations at Station W-2 (the sunken ship Mahi), which is located 1.2 km south of the diffuser. Fish abundance and species richness increased at Station W- 3, which is located at the diffuser, from 1990 to 1995, decreased in 1996, and increased again in 1997 through 1999. At Station WW, an inshore station located 0.8 km from shore, fish were abundant and speciose on the armor rock covering the pipeline. The fish species seen inshore are comparable to fish species seen in similar (boulder) natural biotopes around Hawaii. There were no significant differences in total mean coral cover at selected quadrats from 1994 to 1999 at Station W-2. However, there was a significant increase (approximately 8%) in total mean coral cover at this station from 1991 to 1999. At the diffuser, corals were seen growing on the diffuser pipe and on the riser discharge ports. In 1986, when the diffuser began operation at a discharge rate of 1.5 mgd (0.07 m3/s), no corals were seen at this location. At inshore station WW, corals off the pipeline were sparsely distributed but were numerous and thriving on the armor rock over the pipeline. In 1998 the inshore transect (Alpha), off the armor rock, was covered (30%) with the alga Dictyopteris plagiogramma; however, in 1999 it disappeared. This seaweed was also abundant at this location in 1995, 1996, and 1997. The water was clear at all stations surveyed (13 to 20 m horizontal visibility), and the surrounding sediments were clean and white. No significant deleterious effect due to outfall operation and discharge were seen on the biological community at the stations surveyed. The increase in fish diversity and abundance at the diffuser since 1997 may be due to natural fluctuations in abundance or to environmental conditions suitable to the fish populations living there.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000820_Not Applicable.json b/datasets/gov.noaa.nodc:0000820_Not Applicable.json index 9ecf9a8004..2563f0fb3b 100644 --- a/datasets/gov.noaa.nodc:0000820_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000820_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000820_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria and Chlorophyll data were collected from bottle cast of the western Antarctic peninsula from the R/V Laurence M. Gould. Data were collected by the University of Nevada/Desert Research Institute (DRI) in support of the Global Ocean Ecosystems Dynamic (GLOBEC) project from 23 April 2001 to 01 September 2001. Bacteria data include profiles of bacterial abundance and biomass. Chlorophyll-a data include concentration profiles.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000829_Not Applicable.json b/datasets/gov.noaa.nodc:0000829_Not Applicable.json index c3c7c66063..5dd5542a79 100644 --- a/datasets/gov.noaa.nodc:0000829_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000829_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000829_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Broward County Florida has responsibility for the resource management of coral reefs in marine waters adjacent to Broward County. The Department of Planning and Environmental Protection is assigned the duties of monitoring the health of the coral reefs. Environmental stresses are a limiting factor in the biomass and diversity, and maintaining these populations of coral species requires an understanding of the environmental factors. One of these factors is the water temperature. Visual surveys are conducted by divers, and the staff has implemented an environmental monitoring program with water temperature as the first measured parameter. The monitoring program is on a \"not to interfere basis\" using self-recording thermographs for data acquisition. The thermographs are placed along coral reef tracks located in three separate bands near the northern most extent of the natural range for corals. The raw data are captured from the recorder by means of a laptop computer using transfer and conversion software provided by the instrument's vendor. Upon return to the office, the raw data are transferred to separate files that are then loaded into spreadsheet files. Each spreadsheet file corresponds to a single location and only one instrument. Twelve spreadsheet files are updated every sixty days for the dynamic raw data; the static geographical information is stored in a separate spreadsheet file.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000861_Not Applicable.json b/datasets/gov.noaa.nodc:0000861_Not Applicable.json index 55205abce1..4d53a2f086 100644 --- a/datasets/gov.noaa.nodc:0000861_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000861_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000861_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000879_Not Applicable.json b/datasets/gov.noaa.nodc:0000879_Not Applicable.json index f487774a09..e2500a4634 100644 --- a/datasets/gov.noaa.nodc:0000879_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000879_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000879_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand).\n\nThe role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment.\n\nThe primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000918_Not Applicable.json b/datasets/gov.noaa.nodc:0000918_Not Applicable.json index 277a372135..3bd87f754b 100644 --- a/datasets/gov.noaa.nodc:0000918_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000918_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000918_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical data were collected using bottle casts from multiple vessels in the Arctic Ocean and other Sea areas from 16 April 1948 to 17 September 2000. Data were submitted by the University of Alaska in Fairbanks, Alaska. Chemical data include alkalinity, nitrate, nitrite, oxygen, silicate, and phosphate.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000931_Not Applicable.json b/datasets/gov.noaa.nodc:0000931_Not Applicable.json index 300fb7b8cd..139a23bc31 100644 --- a/datasets/gov.noaa.nodc:0000931_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000931_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000931_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These datasets include counts of ringed seals (Phoca hispida) and other marine mammals made during aerial surveys of ringed seals on fast and pack ice of the central Alaskan Beaufort Sea during 1985-1987 and 1996-1999. The datasets includes counts of seals, by group; designation of whether seals were at holes or along cracks; ice conditions including ice deformation and ice type (fast ice or pack ice); weather conditions; time of observations, and location of observations.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0000999_Not Applicable.json b/datasets/gov.noaa.nodc:0000999_Not Applicable.json index e72d708f6f..fcd1264578 100644 --- a/datasets/gov.noaa.nodc:0000999_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0000999_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0000999_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001063_Not Applicable.json b/datasets/gov.noaa.nodc:0001063_Not Applicable.json index 2080557431..7e62cd7e35 100644 --- a/datasets/gov.noaa.nodc:0001063_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001063_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001063_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2002, quantitative photo-transect surveys documenting coral community structure off six coastal sites in Hawaii were repeated to complete longterm data sets of 12 to 30 years duration. Study sites included areas fronting resort development, active and inactive sewage outfalls, and an area where there is no anthropogenic activity, but has been subjected to a variety of storm events. At the only site within a semi-enclosed embayment erosion from surrounding pineapple fields resulted in a decrease in living coral. Such periodic sedimentation in the Bay drives a cycle of damage and recovery that results in coral community structure different than other sheltered embayments in Hawaii. At the other five sites, located in open coastal waters, coral community structure was not adversely affected by shoreline development or discharge of treated sewage effluent. Long-term studies of pristine reefs under natural stress from episodic storms indicate that recovery along the successional continuum varies with time in the different reef zones.\n\nThe results of these studies provide a framework for effective and efficient coral reef management in Hawaii. Understanding patterns of natural and maninduced stress and recovery can provide a good model for management strategies, as anthropogenic impacts are superimposed over natural stresses. Our results provide good evidence that management efforts should be concentrated in embayments and areas with restricted circulation. Because such areas comprise less than 10% of the coastal areas, it is concluded that the overall condition of coral reefs in Hawaii is good, and should remain so. While concerns of catastrophic loss from anthropogenic impact to coral reefs are valid in some areas of the world, they do not accurately depict the overall health of coral reefs in Hawaii.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001078_Not Applicable.json b/datasets/gov.noaa.nodc:0001078_Not Applicable.json index 0514d4be01..0437b35ee3 100644 --- a/datasets/gov.noaa.nodc:0001078_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001078_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001078_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria, carbon dioxide and methane measurements were collected using bottle casts in the Cariaco Basin on the continental shelf of Venezuela from 30 April 2001 to 17 January 2002. Data were submitted by Dr. Mary Scranton of State University of New York in Stony Brook with support from the CArbon Retention In A Colored Ocean (CARIACO) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001102_Not Applicable.json b/datasets/gov.noaa.nodc:0001102_Not Applicable.json index a9749ba209..83f5f742c7 100644 --- a/datasets/gov.noaa.nodc:0001102_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001102_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001102_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway meteorological data were collected during NBP01 04 to help document the surface weather conditions encountered during the cruise and to characterize the surface forcing fields in the SO GLOBEC study area during austral winter. The N.B.Palmer (NBP) arrived near the start of the large scale physical-biological survey on 27 July 2001 (YD 208) and left the area to survey the sea ice edge to the north on 26 August 2001 (YD 238). A full suite of meteorological data was collected during this 30-day period. This report provides a preliminary description of the meteorological data collected on NBP01 04 and some initial results concerning the surface forcing during winter.\n\nThe estimation of primary production has three main objectives: (1) estimation of primary productivity rates during fall and winter in the area of study as a possible source of food for krill and other zooplanktors; (2) understanding the meso-scale patterns of phytoplankton distribution with respect to physical, chemical and biological processes; (3) obtaining insight into the over-wintering dynamics of phytoplankton, including their interaction with sea ice communities. For this purpose, primary production was measured with two methods during this cruise: Photosynthesis versus Irradiance (PI) curves to estimate potential primary production and information on the dynamics of light adaptation; and finally, profiles with a Fast Repetition Rate Fluorometer (FRRF), with the aim to increase resolution in the sampling of phytoplankton activity, and the expectation of modeling primary production with this method using 14C experiments as comparison. A third approach, that of estimating daily net production with simulated in situ (SIS) experiments, was seldom performed as low irradiance levels precluded any positive carbon uptake rates. Additionally, measurements of chlorophyll and particulate carbon (POC) were taken for estimates of phytoplankton biomass, and irradiance collected from surface and profiling Photosynthetically Available Radiation (PAR) sensors.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001114_Not Applicable.json b/datasets/gov.noaa.nodc:0001114_Not Applicable.json index 98eb9e1139..e5f7a67792 100644 --- a/datasets/gov.noaa.nodc:0001114_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001114_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001114_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Pearl Harbor Biodiversity Project was funded by the Department of Defense Legacy Program, through the U. S. Navy. The project was performed in two phases. The purpose of the project was to document the history, cause, and extent of non-native species introductions in the freshwater streams and estuarine areas of Pearl Harbor.\nPhase I of the study was conducted from November 1995 through June 1997. Phase I involved investigations of the marine organisms of Pearl Harbor, with emphasis on detection of nonindigenous marine organisms that may have become established in the harbor over the past century. Fieldwork for the Phase II investigations commenced in November 1997 and ended in October 1998. Phase II studies investigated the estuarine and freshwater areas of the mouths of streams that enter the harbor's three main lochs. Data were taken\nat 16 stations.\n\nThis dataset contains observations from Phase II (as an .mdb data base and as .csv and .xsl spreadsheets and .jpg images).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001155_Not Applicable.json b/datasets/gov.noaa.nodc:0001155_Not Applicable.json index 5fc5aa9ad7..ba855dbd62 100644 --- a/datasets/gov.noaa.nodc:0001155_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001155_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001155_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biological, physical, nutrients, sediment, and other data were collected using sediment sampler-grab, bottle and CTD casts in the Arabian Sea, North/South Pacific Ocean, and North Atlantic Ocean from 08 January 1995 to 08 April 1998. Data were submitted by Woods Hole Oceanographic Institution as part of the Long Term Monitoring East-West Flower Garden Banks project. Biological data include detailed information on phytoplankton and zooplankton. Nutrients data includes nitrate, nitrite, phosphate, and silicate.\n\nThe U.S. Joint Global Ocean Flux Study (U.S. JGOFS), conceived in 1984 and organized as a major ocean research program shortly thereafter, has conducted field and modeling investigations of the global ocean carbon cycle and the processes that regulate it for a decade and a half. It has brought together biological, chemical, physical and geological oceanographers and modelers in a multidisciplinary investigation of the pools and fluxes of carbon and associated biogenic elements in the ocean.\n\nU.S. JGOFS is a component of the international Joint Global Ocean Flux Study (JGOFS), launched in 1987 under the aegis of the Scientific Committee on Oceanic Research (SCOR). Designated a core project of the International Geosphere-Biosphere Programme (IGBP) two years later, JGOFS has involved scientists from more than 30 countries in field and modeling studies. Its research program included national and international process studies conducted in many ocean basins, time-series programs and a global survey of carbon dioxide (CO2) in the ocean.\n\nThe U.S. JGOFS research program comprised four basin-scale process studies, two long-term time-series programs, participation in a global survey of (CO2) and a synthesis and modeling project. This CD-ROM contains the data acquired during the four U.S. JGOFS process studies, conducted in the North Atlantic, the equatorial Pacific, the Arabian Sea and the Southern Ocean. Data from other components of U.S. JGOFS will be published in future volumes.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001280_Not Applicable.json b/datasets/gov.noaa.nodc:0001280_Not Applicable.json index 0944d87a90..4d171b9fbb 100644 --- a/datasets/gov.noaa.nodc:0001280_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001280_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001280_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001283_Not Applicable.json b/datasets/gov.noaa.nodc:0001283_Not Applicable.json index 0decf1a635..342a30d206 100644 --- a/datasets/gov.noaa.nodc:0001283_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001283_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001283_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001284_Not Applicable.json b/datasets/gov.noaa.nodc:0001284_Not Applicable.json index f4088e308c..aeda526b36 100644 --- a/datasets/gov.noaa.nodc:0001284_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001284_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001284_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001329_Not Applicable.json b/datasets/gov.noaa.nodc:0001329_Not Applicable.json index cde73db30a..afcab7aa51 100644 --- a/datasets/gov.noaa.nodc:0001329_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001329_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001329_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) initiated a coral reef research program in 1999 to map, assess, inventory, and monitor U.S. coral reef ecosystems. These activities were implemented in response to requirements outlined in the Mapping Implementation Plan developed by the Mapping and Information Synthesis Working Group (MISWG) of the Coral Reef Task Force (CRTF). As part of the MISWG of the CRTF, NOS's Biogeography Team has been charged with the development and implementation of a plan to produce comprehensive digital coral-reef ecosystem maps for all U.S. States, Territories, and Commonwealths within five to seven years. Joint activities between Federal agencies are particularly important to map, research, monitor, manage, and restore coral reef ecosystems. In response to the Executive Order 13089, NOS is conducting research to digitally map biotic resources and coordinate a long-term monitoring program that can detect and predict change in U.S. coral reefs, and their associated habitats and biological communities.\n\nMost U.S. coral reef resources have not been digitally mapped at a scale or resolution sufficient for assessment, monitoring, and/or research to support resource management. Thus, a large portion of NOS' coral reef research activities have focused on mapping of U.S. coral reef ecosystems. The map products will provide the fundamental spatial organizing framework to implement and integrate research programs and provide the capability to effectively communicate information and results to coral reef ecosystem managers. Although the NOS coral program is relatively young, it has had tremendous success in advancing towards the goal to protect, conserve, and enhance the health of U.S. coral reef ecosystems. One objective of the program was to create benthic habitat maps to support coral reef research to enable development of products that support management needs and questions. Therefore this product was developed in collaboration with many Hawaiian partners, including Hawaii's Department of Land and Natural Resources, a leading coral reef management agency. An initial step in producing benthic habitat maps is the development of a habitat classification scheme.\n\nThis dataset focuses on the in situ data used to ground truth the mapping efforts on the main Hawaiian Islands: Hawaii, Maui, Molokai, Lanai, Oahu, Kauai, and Niihau.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001334_Not Applicable.json b/datasets/gov.noaa.nodc:0001334_Not Applicable.json index a1c4760364..1635d2fc80 100644 --- a/datasets/gov.noaa.nodc:0001334_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001334_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001334_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature profile, nutrients, and other data were collected using XCTD and CTD casts from KOFU MARU and other platforms in the North Pacific Ocean from 01 January 2002 to 31 December 2002. Data were collected and submitted by Japan Meteorological Agency (JMA).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001344_Not Applicable.json b/datasets/gov.noaa.nodc:0001344_Not Applicable.json index 68b553b5f6..54d8ae17c3 100644 --- a/datasets/gov.noaa.nodc:0001344_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001344_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001344_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001410_Not Applicable.json b/datasets/gov.noaa.nodc:0001410_Not Applicable.json index 1011f10e90..05aef49cb6 100644 --- a/datasets/gov.noaa.nodc:0001410_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001410_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001410_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A zone of deep-water reefs is thought to extend from the mid and outer shelf south of Mississippi and Alabama to at least the northwestern Florida shelf off Panama City, Florida. Reefs off Mississippi and Alabama are found in water depths of 60 to 120 m (Ludwick and Walton, 1957, Gardner et al., in press) and were the focus of a multibeam echosounder mapping survey by the U.S. Geological Survey (USGS) in 2000 (Gardner et al., 2000, in press). It is critical to determine the accurate geomorphology and type of the reefs that occur because of their importance as benthic habitats for fisheries. These data are ArcInfo GRID and XYZ ASCII format data generated from a U.S. Geological Survey multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. The data include high-resolution bathymetry and calibrated acoustic backscatter. File types include arc files .dat, .nit, and .adf. Documentation is included as metadata .txt files. Because the area is so large (i.e., the file sizes are very large), the area was subdivided into North, Central, and South regions as reflected in the data subdirectories for this accession.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001419_Not Applicable.json b/datasets/gov.noaa.nodc:0001419_Not Applicable.json index 8b6adc848b..8af9d6c213 100644 --- a/datasets/gov.noaa.nodc:0001419_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001419_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001419_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coral reefs on the islands of Kauai, Molokai, Maui, Hawaii and Oahu were surveyed for the presence and impact of marine nonindigenous and cryptogenic species (NIS) using a rapid assessment method that standardized search effort for approximately 312 m2 at each site. A total of 41 sites were surveyed by three investigators for a total of approximately 120 hours search time on the five islands. Algae, invertebrate, and fish taxa were identified on site or returned to laboratory for identity confirmation. Only 26 NIS, comprised of three species of algae, 19 invertebrates, and four fishes were recorded from a total of 486 total taxa on the entire study, and 17 of the NIS occurred at only one or two sites. The most NIS that occurred at any site was six, and 21 of the sites had less than three. If the three species of fish that were introduced in the 1950s and known to occur throughout Hawaii are excluded, over half the sites had less than two NIS.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001624_Not Applicable.json b/datasets/gov.noaa.nodc:0001624_Not Applicable.json index 407d47d12f..770280e5a1 100644 --- a/datasets/gov.noaa.nodc:0001624_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001624_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001624_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001746_Not Applicable.json b/datasets/gov.noaa.nodc:0001746_Not Applicable.json index a16afcf5f3..7eef11a101 100644 --- a/datasets/gov.noaa.nodc:0001746_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001746_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001746_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001756_Not Applicable.json b/datasets/gov.noaa.nodc:0001756_Not Applicable.json index 0971520a3a..bf13b52c72 100644 --- a/datasets/gov.noaa.nodc:0001756_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001756_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001756_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset combines the research results from a number of papers carried out under the study \"Assessment of Economic Benefits and Costs of Marine Managed Areas in Hawaii\". The studies included a paper on the fisheries benefits of MMAs (Friedlander and Cesar, 2004), a write-up of the recreational survey at the MMA sites (Van Beukering and Cesar, 2004), a background on the institutional/regulatory framework on MMAs in Hawaii (Cesar, 2004), a paper on the economic value and cost-benefit analysis of management options for MMAs (Van Beukering and Cesar, 2004) and a paper on the international experience of sustainable financing of MMAs (Cesar and van Beukering, 2004).\n\nThis dataset is basically a set of MS Word documents with mostly social-economic data embedded within tables. The habitat and fish data in this dataset are drawn from other datasets already in the NOAA archives, the NOAA Benthic Habitat Maps and the Coral Reef Assessment and Monitoring Program (CRAMP), respectively.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0001941_Not Applicable.json b/datasets/gov.noaa.nodc:0001941_Not Applicable.json index 2c1fb465c8..877ca46185 100644 --- a/datasets/gov.noaa.nodc:0001941_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0001941_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0001941_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October \"open-water\" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska.\n\nThe primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G.\n\nAll bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4.\n\nDaily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered \"on transect\", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as \"on search\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002013_Not Applicable.json b/datasets/gov.noaa.nodc:0002013_Not Applicable.json index 9af66ce076..3fa6b4e83f 100644 --- a/datasets/gov.noaa.nodc:0002013_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002013_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002013_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002170_Not Applicable.json b/datasets/gov.noaa.nodc:0002170_Not Applicable.json index 5463a4ff68..80dece8dc5 100644 --- a/datasets/gov.noaa.nodc:0002170_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002170_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002170_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002192_Not Applicable.json b/datasets/gov.noaa.nodc:0002192_Not Applicable.json index 1b98426ed6..2984aebcd9 100644 --- a/datasets/gov.noaa.nodc:0002192_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002192_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002192_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled \"The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology.\" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002193_Not Applicable.json b/datasets/gov.noaa.nodc:0002193_Not Applicable.json index 0c8b04cb82..d0e6c4c286 100644 --- a/datasets/gov.noaa.nodc:0002193_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002193_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002193_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled \"The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology.\" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002196_Not Applicable.json b/datasets/gov.noaa.nodc:0002196_Not Applicable.json index e294ed5a9c..31be1cc611 100644 --- a/datasets/gov.noaa.nodc:0002196_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002196_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002196_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled \"The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology.\" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002198_Not Applicable.json b/datasets/gov.noaa.nodc:0002198_Not Applicable.json index b9473321a4..29814d514a 100644 --- a/datasets/gov.noaa.nodc:0002198_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002198_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002198_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002199_Not Applicable.json b/datasets/gov.noaa.nodc:0002199_Not Applicable.json index 5046e64576..004f266c66 100644 --- a/datasets/gov.noaa.nodc:0002199_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002199_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002199_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Japan Meteorological Agency (JMA) has been carrying out oceanographic and marine meteorological observations on board research vessels, at the coastal water temperature observation stations and by ocean data buoys, for the purposes of the better understanding of dynamical processes of the oceanic general circulation affecting climate change, prevention and mitigation of natural disasters, and contributing to international cooperative activities.\n This Data Report contains the data obtained from the observations made by JMA in 2003 together with the explanations. The observations include the followings:\n\n1. Oceanographic and Marine Meteorological Observations on board Research Vessels Oceanographic observations are conducted in the seas adjacent to Japan and in the western North Pacific on board five vessels: Ryofu Maru, Keifu Maru, Kofu Maru, Chofu Maru and Seifu Maru.\n\n2. Coastal Water Temperature Observations\n JMA has carried out water temperature observations at the coastal stations. Historical time series of 10 day and monthly mean temperatures, daily observations and hourly observations are available in this CD-ROM.\n\n3. Ocean Data Buoy Observations\n Operational ocean data buoy observations have been made to obtain marine meteorological and oceanographic observations in the seas around Japan.\n\n\nCorrespondence relating to this Data Report may be directed to:\n\n Marine Division\n Climate and Marine Department\n Japan Meteorological Agency\n 1-3-4 Otemachi, Chiyoda-ku, Tokyo, 100-8122 JAPAN\n Facsimile: +81-3-3211-6908\n E-mail: seadata@hq.kishou.go.jp", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002270_Not Applicable.json b/datasets/gov.noaa.nodc:0002270_Not Applicable.json index 3f845359e2..ec58481b6a 100644 --- a/datasets/gov.noaa.nodc:0002270_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002270_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002270_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Collections and observations in 2002-2003 at harbor and nearby reef sites at Nawilwili and Port Allen, Kauai; Hale O Lono and Kaunakakai, Molokai; Kahului and Maalaea, Maui; and Kawaihae and Hilo, Hawaii recorded a total of 1039 taxa of marine algae, invertebrates, and fishes, 872 of which were identified to the species level. Of these 11 were new reports for Hawaii and 112 were identified as introduced or cryptogenic species (NIS), for an overall NIS component of 10.9% of the total taxa recorded. Contrasting patterns were found between the distributions of the total identified taxa and NIS, with greater numbers of total taxa occurring at reef stations and greater numbers of NIS occurring in harbors, where they composed up to 36% of the total identified taxa. Occurrence and abundance of NIS decreased systematically from maxima in highly used commercial harbors which are isolated from oceanic circulation to relatively exposed small boat harbors to fully exposed reef sites. Only a few NIS that frequently occurred at harbor sites also occurred at reef sites. These results concur with previous studies in Hawaii and the tropical Pacific that have indicated NIS to show maximum numbers in harbors and embayments with restricted oceanic circulation and few introduced or cryptogenic species to occur on coral reefs or other ocean exposed environments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002295_Not Applicable.json b/datasets/gov.noaa.nodc:0002295_Not Applicable.json index 2e1b30f3e0..4b4f086562 100644 --- a/datasets/gov.noaa.nodc:0002295_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002295_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002295_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002316_Not Applicable.json b/datasets/gov.noaa.nodc:0002316_Not Applicable.json index cabb38f2b4..cecee04f8d 100644 --- a/datasets/gov.noaa.nodc:0002316_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002316_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002316_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected in support of the CARIACO program, which is studying the relationship between surface primary production, physical forcing variables like the wind, and the settling flux of particulate carbon in the Cariaco Basin on the continental shelf of Venezuela. Data were collected from 16 January 2002 to 18 May 2004.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002352_Not Applicable.json b/datasets/gov.noaa.nodc:0002352_Not Applicable.json index cb32ff5c88..394e12c7ef 100644 --- a/datasets/gov.noaa.nodc:0002352_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002352_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002352_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The U.S. National Oceanographic Data Center (NODC) operates the Global Argo Data Repository (GADR) as the long-term archive for the International Global Argo Project (for additional information about ARGO, see http://www.argo.ucsd.edu (last accessed December 2003)). Argo data archived by the USNODC on a weekly basis starting the second quarter of FY 2003, may include real-time and/or delayed mode profiles of ocean temperature and salinity, as well as related conductivity and/or pressure measurements (if any), collected by Argo profiling floats.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002449_Not Applicable.json b/datasets/gov.noaa.nodc:0002449_Not Applicable.json index 9ac0d04c26..342bf6149f 100644 --- a/datasets/gov.noaa.nodc:0002449_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002449_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002449_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of water quality parameters were taken by Windward Community College faculty and students at eight sites in the Heeia Stream and adjacent Kaneohe Bay waters from May 2004 through March 2005. Parameters include Combined Nitrogen, Photo Oxidized Nitrate, Photo Oxidized Nitrite, Total Nitrogen, and Total Phosphate. Data provided as MS Excel spreadsheets and redundant ASCII copies were made of each with same file name except for a CSV (Comma Separated Version) extension.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002602_Not Applicable.json b/datasets/gov.noaa.nodc:0002602_Not Applicable.json index aec13b04b2..776c55b7a2 100644 --- a/datasets/gov.noaa.nodc:0002602_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002602_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002602_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Orange Keyhole Sponge, Mycale armata Thiele, was unknown in Hawaii prior to 1996. First reported in Pearl Harbor, it now occurs in virtually every commercial harbor in the main Hawaiian islands, where it can be a major component of the fouling community on harbor piers and jetties. It has been reported from a few coral reef locations near harbors, but in Kaneohe Bay it has become a major component of the benthic biota in the south bay in the last 5-10 years. A study was conducted in 2004-2005 to determine Mycale armata's distribution, abundance throughout the bay, its growth rates on permanent quadrats, and whether mechanical removal would be an effective management technique for its control. Results from 190 manta board surveys on 28 reefs and paired 25 m belt transects using photo quadrats on 19 reefs indicated that the sponge had maximal coverage in the south-central part of the bay, in the vicinity of Coconut Island.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002650_Not Applicable.json b/datasets/gov.noaa.nodc:0002650_Not Applicable.json index 4e44b16403..cb993c7b68 100644 --- a/datasets/gov.noaa.nodc:0002650_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002650_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002650_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A baseline survey of the marine biota of the island of Lanai was conducted in May 2005. This was first comprehensive study that has been made on this island for all components of its marine nearshore community. Samples and observations were taken at seven sites around the island, and all macroalgae, macroinvertebrates and fish species collected or observed were recorded. On-site observations without collections were made at two other sites. Identified species were designated as native, nonindigenous (introduced) or cryptogenic (neither demonstrably native nor introduced) according to criteria used for previous introduced species surveys in Hawaii. A total of 294 taxa were observed or identified from collected specimens, which included 16 introduced or cryptogenic species and three new reports for the Hawaiian Islands. The 16 introduced and cryptogenic species comprised 5.4% of the total identified taxa and included seven cnidarians, one polychaete, two pericards, one decapod, one bryozoan, two ascidians and three fish. By station, the introduced/cryptogenic component ranged 3 to 7 species and 3.8% to 6.8% of the total biota.\n\nThe stations included two sites at or near Kaumalapau Harbor, Lanai's principal harbor for inter-island shipping. The percent component values are similar to those that have been determined on ocean-exposed reef areas elsewhere in the Hawaiian Islands but the harbor value is well below the values in other Hawaiian harbors that are more isolated from open ocean circulation than Kaumalapau Harbor. No invasive introduced algae and only two invasive introduced invertebrates were found on the surveys. These were a single colony of the octocoral Carijoa riisei in the vicinity of Cathedrals between Manele Bay and Harbor, and a single stomatopod Gonodactylaceous falcatus at the site closest to Manele Harbor.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0002805_Not Applicable.json b/datasets/gov.noaa.nodc:0002805_Not Applicable.json index af117dc485..60b6108c48 100644 --- a/datasets/gov.noaa.nodc:0002805_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0002805_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0002805_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kaneohe Bay received increasing amounts of sewage from the 1950s through 1977. Most sewage was diverted from the bay in 1977 and early 1978. Data were collected beginning in September 1976 and continued until June 1979. The time series was re-established in June 1982 and continued to December 2005, when it was terminated. The sampling was at 1 m depth in the south sector of Kaneohe Bay, Oahu near the old outfall that ceased in 1977. Previous NODC Accessions 0000396 (1976-1979) and 0000422 (1982-1/2001) contained monthly averages of chlorophyll a, based on weekly to bi-weekly samples. This data set has the weekly to bi-weekly chlorophyll a, pheo, water temperature, secchi depth, and sample site depth. Additional data were taken from June 2004 - December 2005 and these will be available in a separate data set.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0013170_Not Applicable.json b/datasets/gov.noaa.nodc:0013170_Not Applicable.json index 0931e3c0df..02adc9e0e0 100644 --- a/datasets/gov.noaa.nodc:0013170_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0013170_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0013170_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from January 17, 2005 to January 16, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0014123_Not Applicable.json b/datasets/gov.noaa.nodc:0014123_Not Applicable.json index 77e2c5ca62..d91eae9dd7 100644 --- a/datasets/gov.noaa.nodc:0014123_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0014123_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0014123_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0014906_Not Applicable.json b/datasets/gov.noaa.nodc:0014906_Not Applicable.json index 2070c31e5f..6db7a654dd 100644 --- a/datasets/gov.noaa.nodc:0014906_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0014906_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0014906_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October \"open-water\" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska.\n\nThe primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G.\n\nAll bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4.\n\nDaily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered \"on transect\", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as \"on search\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:0033380_Not Applicable.json b/datasets/gov.noaa.nodc:0033380_Not Applicable.json index 8e3b6ecaf0..1b5c772834 100644 --- a/datasets/gov.noaa.nodc:0033380_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0033380_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0033380_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this study was to determine Mycale armata's distribution, abundance throughout the bay, its growth rates on permanent quadrats, and whether mechanical removal would be an effective management technique for its control. The study utilized both quadrat surveys and manta tow boards for data collection. Data files are in Excel, PDF, MS Word, and JPEG image formats.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0038513_Not Applicable.json b/datasets/gov.noaa.nodc:0038513_Not Applicable.json index 0b33bb336a..aeacbce445 100644 --- a/datasets/gov.noaa.nodc:0038513_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0038513_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0038513_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from May 23, 2005 to November 11, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0040205_Not Applicable.json b/datasets/gov.noaa.nodc:0040205_Not Applicable.json index c386daaa3c..5f6931dacf 100644 --- a/datasets/gov.noaa.nodc:0040205_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0040205_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0040205_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "More than 3 million measurements of surface water partial pressure of CO2 obtained over the global oceans during 1968 to 2006 are listed in the Lamont-Doherty Earth Observatory database, which includes open ocean and coastal water measurements. The data assembled include only those measured by equilibrator CO2 analyzer systems and have been quality-controlled based on the stability of the system performance, the reliability of calibrations for CO2 analysis, and the internal consistency of data. Versions up to 2007 are included in this dataset", "links": [ { diff --git a/datasets/gov.noaa.nodc:0043167_Not Applicable.json b/datasets/gov.noaa.nodc:0043167_Not Applicable.json index 8f03146da8..8422b4e6e0 100644 --- a/datasets/gov.noaa.nodc:0043167_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0043167_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0043167_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature data received at NODC on April 14, 2008 by Tim Boyer placed on the FTP server by Ann Thresher, CSIRO (COMMONWEALTH SCIENTIFIC AND INDUSTRIAL RESEARCH ORGANIZATION) for XBT/CTD comparisons", "links": [ { diff --git a/datasets/gov.noaa.nodc:0045502_Not Applicable.json b/datasets/gov.noaa.nodc:0045502_Not Applicable.json index 7f6754249e..4e1407d162 100644 --- a/datasets/gov.noaa.nodc:0045502_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0045502_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0045502_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface pCO2, sea surface temperature, sea surface salinity, and atmospheric pressure measurements collected in the North Pacific as part of the NOAA Office of Climate Observations (OCO) and U.S. Carbon Cycle Science Programs.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0045505_Not Applicable.json b/datasets/gov.noaa.nodc:0045505_Not Applicable.json index 84a63c775c..ecc8bbaadb 100644 --- a/datasets/gov.noaa.nodc:0045505_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0045505_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0045505_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AOML pCO2 underway measurements collected using in the Pacific and Atlantic from 2007 to 2008", "links": [ { diff --git a/datasets/gov.noaa.nodc:0046934_Not Applicable.json b/datasets/gov.noaa.nodc:0046934_Not Applicable.json index 635e5d28e6..8769aef8e0 100644 --- a/datasets/gov.noaa.nodc:0046934_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0046934_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0046934_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0049902_Not Applicable.json b/datasets/gov.noaa.nodc:0049902_Not Applicable.json index a0a76294ec..b9b9ff1454 100644 --- a/datasets/gov.noaa.nodc:0049902_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0049902_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0049902_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean biology data were collected in Southern Drake Passage and Scotia Sea during two research cruises supported by NSF awards. These two cruises, namely LMG0402 and NBP0606, were conducted during Februay to March 2004 and July to August 2006, respectively. Dataset includes concentration of pigments in phytoplankton, particulate organic matter concentration, macronutrients, primary productivity and microbial biomass and productivity.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0051848_Not Applicable.json b/datasets/gov.noaa.nodc:0051848_Not Applicable.json index 91a7ca5362..29a4110641 100644 --- a/datasets/gov.noaa.nodc:0051848_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0051848_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0051848_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton biomass data collected from Pacific Ocean in 1950 - 1961 years received from NMFS", "links": [ { diff --git a/datasets/gov.noaa.nodc:0053277_Not Applicable.json b/datasets/gov.noaa.nodc:0053277_Not Applicable.json index 623f260485..9671b9a310 100644 --- a/datasets/gov.noaa.nodc:0053277_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0053277_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0053277_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Zooplankton biomass data collected by Institute of Biology of the Southern Seas from the Atlantic Ocean in 1950-1989 years and received from the NMFS.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0057319_Not Applicable.json b/datasets/gov.noaa.nodc:0057319_Not Applicable.json index b313505c65..8dce189277 100644 --- a/datasets/gov.noaa.nodc:0057319_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0057319_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0057319_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A program to study freshwater circulation (sea ice + upper ocean) in the \"freshwater switchyard\" between Alert (Ellesmere Island) and the North Pole.\n\nThe project uses aircraft to take hydrographic stations on sections across the continental slope northwest of Alert.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0058268_Not Applicable.json b/datasets/gov.noaa.nodc:0058268_Not Applicable.json index 7652a3fa7b..3bc02af7ac 100644 --- a/datasets/gov.noaa.nodc:0058268_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0058268_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0058268_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The major goal of the observational program is to determine the variability of different components of the Beaufort Gyre fresh water (ocean and sea ice) system and to assess the partial concentrations of fresh water of different origin (rivers, Pacific Ocean, precipitation, ice/snow melt, etc). Using moorings, drifting buoys, shipboard, and remote sensing measurements we have been measuring time series of temperature, salinity, currents, geochemical tracers, sea ice draft, and sea level since August 2003, to determine freshwater content and freshwater fluxes in the Beaufort Gyre during a complete seasonal cycle and beyond.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0058858_Not Applicable.json b/datasets/gov.noaa.nodc:0058858_Not Applicable.json index ac9d30537f..ff8a4dd273 100644 --- a/datasets/gov.noaa.nodc:0058858_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0058858_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0058858_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0061208_Not Applicable.json b/datasets/gov.noaa.nodc:0061208_Not Applicable.json index 3f013e6b94..ed576297ff 100644 --- a/datasets/gov.noaa.nodc:0061208_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0061208_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0061208_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state.\n\nThis study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0066319_Not Applicable.json b/datasets/gov.noaa.nodc:0066319_Not Applicable.json index 2e09900cd7..343195c412 100644 --- a/datasets/gov.noaa.nodc:0066319_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0066319_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0066319_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type.\n\nSummaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland.\n\nFish data were collected by Dr. Alison Green on the same dates and transects and are available in a separate NODC accession.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068364_Not Applicable.json b/datasets/gov.noaa.nodc:0068364_Not Applicable.json index 2d571a32fd..a6c59b06d9 100644 --- a/datasets/gov.noaa.nodc:0068364_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068364_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068364_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic transects were repeated at 12 sites around Tutuila at various depths on the reef slopes and flats. Benthic coverage categories include coral species, invertebrates, and non-living substrate type. Annual surveys took place during 2005-2009. The most detailed data are from 2008. The data were provided as spreadsheets and metadata within a PDF document, focusing on the 2008 surveys.\n\nA related data set was can be found in NCEI Accession 0066319, which was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Also in 0066319 are summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068586_Not Applicable.json b/datasets/gov.noaa.nodc:0068586_Not Applicable.json index 8c76dbc798..bd5b1cee56 100644 --- a/datasets/gov.noaa.nodc:0068586_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068586_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068586_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068586)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068595_Not Applicable.json b/datasets/gov.noaa.nodc:0068595_Not Applicable.json index 53c2d2e914..e380b90a47 100644 --- a/datasets/gov.noaa.nodc:0068595_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068595_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068595_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the Gulf of Mexico from 2010-07-16 to 2010-07-22 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068595)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068596_Not Applicable.json b/datasets/gov.noaa.nodc:0068596_Not Applicable.json index 4768ba40f6..dbf8c23fbc 100644 --- a/datasets/gov.noaa.nodc:0068596_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068596_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068596_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the Gulf of Mexico from 2010-07-24 to 2010-08-02 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068596)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068597_Not Applicable.json b/datasets/gov.noaa.nodc:0068597_Not Applicable.json index 1f97f59699..d53f3ca452 100644 --- a/datasets/gov.noaa.nodc:0068597_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068597_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068597_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-08-04 to 2010-08-08 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068597)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068667_Not Applicable.json b/datasets/gov.noaa.nodc:0068667_Not Applicable.json index 1e0f0093af..d8d395cbd7 100644 --- a/datasets/gov.noaa.nodc:0068667_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068667_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068667_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are water column and sediment data largely collected in the Bering and Chukchi Seas, with some coverage for the Beaufort and East Siberian Seas, and with limited coverage for other portions of the Arctic Ocean. Data include that collected on Alpha Helix cruises 139, 165, 166, 174,189, 190, 214, and 224 between 1990 and 1999. Other data were obtained from cruises of the RV OKEAN (1993), USCGC Polar Star (1993), Mendeleev(1993), USCGC Polar Sea (2000), U.S. Canada Arctic Ocean Section(1994), and Polarstern (1993). Water column data include coverage from some to most stations on these cruises, and include bottle salinity, temperature, sigma-t, oxygen-18/16 ratios, phosphate, ammonia, silicate, nitrate, dissolved oxygen, iodine-129 concentrations, and chlorophyll a. Sediment data include plutonium and neptunium concentrations, including isotope data. Biological data include radionuclide burdens in marine mammal tissues donated by subsistence hunters on the North Slope of Alaska and in the Resolute region of Canada.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068954_Not Applicable.json b/datasets/gov.noaa.nodc:0068954_Not Applicable.json index e2951b6868..cee0e060aa 100644 --- a/datasets/gov.noaa.nodc:0068954_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068954_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068954_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship HENRY B. BIGELOW in the Gulf of Mexico from 2010-08-13 to 2010-08-22 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0068954)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0068955_Not Applicable.json b/datasets/gov.noaa.nodc:0068955_Not Applicable.json index 7df2ea7367..e16ff18fe8 100644 --- a/datasets/gov.noaa.nodc:0068955_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0068955_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0068955_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Arctic in the Gulf of Mexico from 2010-09-09 to 2010-09-14 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include temperature, dissolved oxygen, sound velocity, hydrostatic pressure, conductivity, water density, salinity and CDOM fluorescence. The instruments used to collect these data were oxygen meter, CTD and fluorometer. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068955)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069044_Not Applicable.json b/datasets/gov.noaa.nodc:0069044_Not Applicable.json index 0087c98c36..693e25a139 100644 --- a/datasets/gov.noaa.nodc:0069044_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069044_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069044_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, laboratory analysis, tows and underway oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-18 to 2010-05-22 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature, turbidity and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069044)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069045_Not Applicable.json b/datasets/gov.noaa.nodc:0069045_Not Applicable.json index 0865e9bfa5..fd80b8a599 100644 --- a/datasets/gov.noaa.nodc:0069045_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069045_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069045_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-23 to 2010-05-25 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069045)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069046_Not Applicable.json b/datasets/gov.noaa.nodc:0069046_Not Applicable.json index 19eeada532..6dc19ad537 100644 --- a/datasets/gov.noaa.nodc:0069046_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069046_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069046_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-30 to 2010-06-02 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069046)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069047_Not Applicable.json b/datasets/gov.noaa.nodc:0069047_Not Applicable.json index 66d77d527c..27345fa76c 100644 --- a/datasets/gov.noaa.nodc:0069047_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069047_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069047_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-06-04 to 2010-06-08 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069047)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069048_Not Applicable.json b/datasets/gov.noaa.nodc:0069048_Not Applicable.json index 1fb085f20e..2634c2b9d7 100644 --- a/datasets/gov.noaa.nodc:0069048_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069048_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069048_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-06-10 to 2010-06-14 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069048)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069049_Not Applicable.json b/datasets/gov.noaa.nodc:0069049_Not Applicable.json index 3c6c859be7..b83aebf052 100644 --- a/datasets/gov.noaa.nodc:0069049_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069049_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069049_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-06-16 to 2010-06-20 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069049)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069050_Not Applicable.json b/datasets/gov.noaa.nodc:0069050_Not Applicable.json index c44b60ebfd..a617fba0af 100644 --- a/datasets/gov.noaa.nodc:0069050_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069050_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069050_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-06-22 to 2010-06-26 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069050)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069051_Not Applicable.json b/datasets/gov.noaa.nodc:0069051_Not Applicable.json index 76b986e3b2..026c508bc2 100644 --- a/datasets/gov.noaa.nodc:0069051_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069051_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069051_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-07-04 to 2010-07-08 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069051)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069052_Not Applicable.json b/datasets/gov.noaa.nodc:0069052_Not Applicable.json index 9626b05b9a..11d8fc84e1 100644 --- a/datasets/gov.noaa.nodc:0069052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069052)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069053_Not Applicable.json b/datasets/gov.noaa.nodc:0069053_Not Applicable.json index ec00095757..9b61647289 100644 --- a/datasets/gov.noaa.nodc:0069053_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069053_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069053_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-07-16 to 2010-07-20 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069053)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069054_Not Applicable.json b/datasets/gov.noaa.nodc:0069054_Not Applicable.json index ae0143a8b2..e4d1f31273 100644 --- a/datasets/gov.noaa.nodc:0069054_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069054_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069054_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-07-28 to 2010-08-01 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069054)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069055_Not Applicable.json b/datasets/gov.noaa.nodc:0069055_Not Applicable.json index 952dd7e776..a6cf2ea2cc 100644 --- a/datasets/gov.noaa.nodc:0069055_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069055_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069055_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-08-03 to 2010-08-07 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069055)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069056_Not Applicable.json b/datasets/gov.noaa.nodc:0069056_Not Applicable.json index 9cc86735e4..6217fa550b 100644 --- a/datasets/gov.noaa.nodc:0069056_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069056_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069056_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-08-09 to 2010-08-12 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069056)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069057_Not Applicable.json b/datasets/gov.noaa.nodc:0069057_Not Applicable.json index d61120c0fc..05053b95b1 100644 --- a/datasets/gov.noaa.nodc:0069057_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069057_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069057_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-08-15 to 2010-08-19 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069057)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069058_Not Applicable.json b/datasets/gov.noaa.nodc:0069058_Not Applicable.json index e500ab522e..c3a501d437 100644 --- a/datasets/gov.noaa.nodc:0069058_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069058_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069058_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the CAPE HATTERAS in the Gulf of Mexico from 2010-08-21 to 2010-09-02 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069058)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069059_Not Applicable.json b/datasets/gov.noaa.nodc:0069059_Not Applicable.json index 221fbf37bb..9ed2f18cf0 100644 --- a/datasets/gov.noaa.nodc:0069059_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069059_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069059_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the CAPE HATTERAS in the Gulf of Mexico from 2010-09-04 to 2010-09-15 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069059)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069060_Not Applicable.json b/datasets/gov.noaa.nodc:0069060_Not Applicable.json index 8975927447..c377a27e17 100644 --- a/datasets/gov.noaa.nodc:0069060_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069060_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069060_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-07-15 to 2010-07-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069060)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069061_Not Applicable.json b/datasets/gov.noaa.nodc:0069061_Not Applicable.json index 91adacba78..2392ffafeb 100644 --- a/datasets/gov.noaa.nodc:0069061_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069061_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069061_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-07-25 to 2010-07-30 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069061)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069062_Not Applicable.json b/datasets/gov.noaa.nodc:0069062_Not Applicable.json index a3bc7866a1..25385b7c05 100644 --- a/datasets/gov.noaa.nodc:0069062_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069062_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069062_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-07-30 to 2010-08-03 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069062)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069063_Not Applicable.json b/datasets/gov.noaa.nodc:0069063_Not Applicable.json index 190a0f1f50..e05114a64e 100644 --- a/datasets/gov.noaa.nodc:0069063_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069063_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069063_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-08-03 to 2010-08-11 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069063)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069064_Not Applicable.json b/datasets/gov.noaa.nodc:0069064_Not Applicable.json index 9a31b0026a..fa695be758 100644 --- a/datasets/gov.noaa.nodc:0069064_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069064_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069064_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-08-13 to 2010-08-17 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069064)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069065_Not Applicable.json b/datasets/gov.noaa.nodc:0069065_Not Applicable.json index e1f7d1c67f..0b84964e5e 100644 --- a/datasets/gov.noaa.nodc:0069065_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069065_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069065_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-08-18 to 2010-08-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069065)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069066_Not Applicable.json b/datasets/gov.noaa.nodc:0069066_Not Applicable.json index 149b135132..dcbc54fb4c 100644 --- a/datasets/gov.noaa.nodc:0069066_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069066_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069066_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-07-07 to 2010-08-27 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069066)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069067_Not Applicable.json b/datasets/gov.noaa.nodc:0069067_Not Applicable.json index 997fa8e898..b4ef5bcd6f 100644 --- a/datasets/gov.noaa.nodc:0069067_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069067_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069067_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-05-27 to 2010-06-04 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD, bathythermograph - XBT, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069067)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069068_Not Applicable.json b/datasets/gov.noaa.nodc:0069068_Not Applicable.json index e92ecfad66..9331d7e14a 100644 --- a/datasets/gov.noaa.nodc:0069068_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069068_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069068_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-07-01 to 2010-07-06 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069068)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069069_Not Applicable.json b/datasets/gov.noaa.nodc:0069069_Not Applicable.json index f3b6ad7180..a605d56611 100644 --- a/datasets/gov.noaa.nodc:0069069_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069069_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069069_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the HOS Davis in the Gulf of Mexico from 2010-08-13 to 2010-08-22 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069069)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069070_Not Applicable.json b/datasets/gov.noaa.nodc:0069070_Not Applicable.json index db7a2371a3..bb0b33f8ee 100644 --- a/datasets/gov.noaa.nodc:0069070_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069070_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069070_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the HOS Davis in the Gulf of Mexico from 2010-08-26 to 2010-09-03 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069070)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069071_Not Applicable.json b/datasets/gov.noaa.nodc:0069071_Not Applicable.json index 1a4e05d131..856f3dc521 100644 --- a/datasets/gov.noaa.nodc:0069071_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069071_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069071_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the HOS Davis in the Gulf of Mexico from 2010-09-09 to 2010-09-27 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069071)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069072_Not Applicable.json b/datasets/gov.noaa.nodc:0069072_Not Applicable.json index 3e9ffdfa1a..74f20ee22b 100644 --- a/datasets/gov.noaa.nodc:0069072_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069072_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069072_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the JACK FITZ in the Gulf of Mexico from 2010-05-10 to 2010-05-13 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069072)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069073_Not Applicable.json b/datasets/gov.noaa.nodc:0069073_Not Applicable.json index 54ef155324..71a8064189 100644 --- a/datasets/gov.noaa.nodc:0069073_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069073_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069073_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the JACK FITZ in the Gulf of Mexico from 2010-05-22 to 2010-05-31 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069073)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069074_Not Applicable.json b/datasets/gov.noaa.nodc:0069074_Not Applicable.json index ae05d74513..23585d878b 100644 --- a/datasets/gov.noaa.nodc:0069074_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069074_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069074_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, laboratory analyses, physical and profile oceanographic data were collected aboard the JACK FITZ in the Gulf of Mexico from 2010-06-12 to 2010-06-20 in response to the Deepwater Horizon Oil Spill Event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. (NODC Accession 0069074)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069075_Not Applicable.json b/datasets/gov.noaa.nodc:0069075_Not Applicable.json index b3987baa83..1e1bd2f24d 100644 --- a/datasets/gov.noaa.nodc:0069075_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069075_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069075_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the JACK FITZ in the Gulf of Mexico from 2010-09-04 to 2010-09-12 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069075)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069076_Not Applicable.json b/datasets/gov.noaa.nodc:0069076_Not Applicable.json index 47eb15b2b7..4e0ef1f41b 100644 --- a/datasets/gov.noaa.nodc:0069076_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069076_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069076_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Meg L. Skansi in the Gulf of Mexico from 2010-09-04 to 2010-09-13 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069076)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069077_Not Applicable.json b/datasets/gov.noaa.nodc:0069077_Not Applicable.json index 96e7ffbe1b..78b80364c8 100644 --- a/datasets/gov.noaa.nodc:0069077_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069077_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069077_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, biological, laboratory analysis, meteorological, navigational, tows and underway oceanographic data were collected aboard NOAA Ship NANCY FOSTER in the Gulf of Mexico and North Atlantic Ocean from 2010-06-30 to 2010-07-18 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, plankton, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD, Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS), bathythermograph - XBT, bottle, fluorometer, oxygen meter and thermosalinographs along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069077)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069078_Not Applicable.json b/datasets/gov.noaa.nodc:0069078_Not Applicable.json index 6f78ae453e..b1045ad9ae 100644 --- a/datasets/gov.noaa.nodc:0069078_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069078_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069078_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Rachel Bordelon in the Gulf of Mexico from 2010-09-04 to 2010-09-13 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069078)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069079_Not Applicable.json b/datasets/gov.noaa.nodc:0069079_Not Applicable.json index c7aa4d0ef6..160f76edca 100644 --- a/datasets/gov.noaa.nodc:0069079_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069079_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069079_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-09-15 to 2010-09-22 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069079)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069080_Not Applicable.json b/datasets/gov.noaa.nodc:0069080_Not Applicable.json index a3ad572867..b7db754854 100644 --- a/datasets/gov.noaa.nodc:0069080_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069080_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069080_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-09-23 to 2010-09-28 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069080)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069081_Not Applicable.json b/datasets/gov.noaa.nodc:0069081_Not Applicable.json index 142ead331f..4fc6d30bc6 100644 --- a/datasets/gov.noaa.nodc:0069081_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069081_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069081_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Specialty Diver I in the Gulf of Mexico from 2010-09-10 to 2010-09-15 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069081)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069082_Not Applicable.json b/datasets/gov.noaa.nodc:0069082_Not Applicable.json index 4d9cfa805d..9095d7f3c8 100644 --- a/datasets/gov.noaa.nodc:0069082_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069082_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069082_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, tows and underway oceanographic data were collected aboard NOAA Ship THOMAS JEFFERSON in the Gulf of Mexico from 2010-06-03 to 2010-07-18 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069082)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069083_Not Applicable.json b/datasets/gov.noaa.nodc:0069083_Not Applicable.json index 61283873de..6c36430d4a 100644 --- a/datasets/gov.noaa.nodc:0069083_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069083_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069083_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship THOMAS JEFFERSON in the Gulf of Mexico from 2010-06-15 to 2010-06-28 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069083)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069084_Not Applicable.json b/datasets/gov.noaa.nodc:0069084_Not Applicable.json index e1d1c5063e..10dcc050c3 100644 --- a/datasets/gov.noaa.nodc:0069084_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069084_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069084_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the F. G. Walton Smith in the Gulf of Mexico from 2010-05-26 to 2010-06-02 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069084)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069085_Not Applicable.json b/datasets/gov.noaa.nodc:0069085_Not Applicable.json index 42d84d06df..8a6968d4d8 100644 --- a/datasets/gov.noaa.nodc:0069085_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Wes Bordelon in the Gulf of Mexico from 2010-09-05 to 2010-09-13 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069085)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069086_Not Applicable.json b/datasets/gov.noaa.nodc:0069086_Not Applicable.json index 1c6a634c3f..c105f2f361 100644 --- a/datasets/gov.noaa.nodc:0069086_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069086_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069086_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the Wes Bordelon in the Gulf of Mexico from 2010-08-18 to 2010-08-22 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069086)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069087_Not Applicable.json b/datasets/gov.noaa.nodc:0069087_Not Applicable.json index 5eb0c81f53..7fa552e88e 100644 --- a/datasets/gov.noaa.nodc:0069087_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069087_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069087_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the PELICAN in the Gulf of Mexico from 2010-05-10 to 2010-07-21 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069087)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069088_Not Applicable.json b/datasets/gov.noaa.nodc:0069088_Not Applicable.json index a06a411682..8b46b8d8ed 100644 --- a/datasets/gov.noaa.nodc:0069088_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069088_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069088_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the American Diver in the Gulf of Mexico on 2010-08-04 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069088)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069090_Not Applicable.json b/datasets/gov.noaa.nodc:0069090_Not Applicable.json index df8dfdfe2f..39aff46c3c 100644 --- a/datasets/gov.noaa.nodc:0069090_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069090_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069090_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-08-21 to 2010-08-25 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069090)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069091_Not Applicable.json b/datasets/gov.noaa.nodc:0069091_Not Applicable.json index ff07f37241..848308b4d5 100644 --- a/datasets/gov.noaa.nodc:0069091_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069091_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069091_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship HENRY B. BIGELOW in the Gulf of Mexico from 2010-07-28 to 2010-08-10 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069091)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069092_Not Applicable.json b/datasets/gov.noaa.nodc:0069092_Not Applicable.json index 7c68c1e297..bda6445caf 100644 --- a/datasets/gov.noaa.nodc:0069092_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069092_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069092_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-05-26 to 2010-05-30 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069092)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069093_Not Applicable.json b/datasets/gov.noaa.nodc:0069093_Not Applicable.json index 4743d9e99f..ca824ac0d1 100644 --- a/datasets/gov.noaa.nodc:0069093_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069093_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069093_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-01 to 2010-06-05 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069093)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069094_Not Applicable.json b/datasets/gov.noaa.nodc:0069094_Not Applicable.json index 4c95db5595..b298b7d463 100644 --- a/datasets/gov.noaa.nodc:0069094_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069094_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069094_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-07 to 2010-06-11 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069094)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069095_Not Applicable.json b/datasets/gov.noaa.nodc:0069095_Not Applicable.json index 2bc17f1184..36ec1504d7 100644 --- a/datasets/gov.noaa.nodc:0069095_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069095_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069095_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-13 to 2010-06-17 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069095)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069096_Not Applicable.json b/datasets/gov.noaa.nodc:0069096_Not Applicable.json index f1d61152eb..8c79eac3b0 100644 --- a/datasets/gov.noaa.nodc:0069096_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069096_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069096_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-19 to 2010-06-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069096)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069097_Not Applicable.json b/datasets/gov.noaa.nodc:0069097_Not Applicable.json index 8388fe91bb..a8220501ab 100644 --- a/datasets/gov.noaa.nodc:0069097_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069097_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069097_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-25 to 2010-06-29 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069097)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069098_Not Applicable.json b/datasets/gov.noaa.nodc:0069098_Not Applicable.json index 5bcdbc1ac3..85ea34eeda 100644 --- a/datasets/gov.noaa.nodc:0069098_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069098_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069098_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-29 to 2010-07-05 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069098)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069099_Not Applicable.json b/datasets/gov.noaa.nodc:0069099_Not Applicable.json index 943a671295..fa0a8f0a6d 100644 --- a/datasets/gov.noaa.nodc:0069099_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069099_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069099_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-07-07 to 2010-07-11 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069099)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069100_Not Applicable.json b/datasets/gov.noaa.nodc:0069100_Not Applicable.json index 1dc8191c17..c5b9c381b2 100644 --- a/datasets/gov.noaa.nodc:0069100_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069100_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069100_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-07-19 to 2010-07-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069100)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069101_Not Applicable.json b/datasets/gov.noaa.nodc:0069101_Not Applicable.json index ae2b5dce86..769a27fcbb 100644 --- a/datasets/gov.noaa.nodc:0069101_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069101_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069101_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-07-26 to 2010-07-29 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069101)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069102_Not Applicable.json b/datasets/gov.noaa.nodc:0069102_Not Applicable.json index 816b589b2a..182903f770 100644 --- a/datasets/gov.noaa.nodc:0069102_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069102_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069102_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-07-31 to 2010-08-03 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069102)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069103_Not Applicable.json b/datasets/gov.noaa.nodc:0069103_Not Applicable.json index 7fb592d1b5..cea27e0576 100644 --- a/datasets/gov.noaa.nodc:0069103_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069103_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069103_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-08-06 to 2010-08-10 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069103)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069104_Not Applicable.json b/datasets/gov.noaa.nodc:0069104_Not Applicable.json index 097b90be25..779fd42042 100644 --- a/datasets/gov.noaa.nodc:0069104_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069104_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069104_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-08-12 to 2010-08-16 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069104)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069105_Not Applicable.json b/datasets/gov.noaa.nodc:0069105_Not Applicable.json index 0a1b106500..1bc52a024f 100644 --- a/datasets/gov.noaa.nodc:0069105_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069105_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069105_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-08-18 to 2010-08-22 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069105)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069106_Not Applicable.json b/datasets/gov.noaa.nodc:0069106_Not Applicable.json index 4fb6a3dcdd..ce83ca6772 100644 --- a/datasets/gov.noaa.nodc:0069106_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069106_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069106_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-08-25 to 2010-08-29 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069106)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069107_Not Applicable.json b/datasets/gov.noaa.nodc:0069107_Not Applicable.json index b65c39d318..1e9bc1d926 100644 --- a/datasets/gov.noaa.nodc:0069107_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069107_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069107_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-08-30 to 2010-09-03 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069107)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069108_Not Applicable.json b/datasets/gov.noaa.nodc:0069108_Not Applicable.json index 012adc62fe..485a496314 100644 --- a/datasets/gov.noaa.nodc:0069108_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069108_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069108_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-09-03 to 2010-09-07 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069108)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069109_Not Applicable.json b/datasets/gov.noaa.nodc:0069109_Not Applicable.json index 54bf2bd86f..0def2b7ea4 100644 --- a/datasets/gov.noaa.nodc:0069109_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069109_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069109_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-09-07 to 2010-10-16 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069109)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069110_Not Applicable.json b/datasets/gov.noaa.nodc:0069110_Not Applicable.json index 121acb2d18..d617825a2f 100644 --- a/datasets/gov.noaa.nodc:0069110_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069110_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069110_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-09-11 to 2010-09-13 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069110)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069111_Not Applicable.json b/datasets/gov.noaa.nodc:0069111_Not Applicable.json index 5177edd168..cf38ee0bd1 100644 --- a/datasets/gov.noaa.nodc:0069111_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069111_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069111_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship PISCES in the Gulf of Mexico from 2010-07-05 to 2010-08-14 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069111)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069112_Not Applicable.json b/datasets/gov.noaa.nodc:0069112_Not Applicable.json index 2332c5ffaa..cf57b3a64e 100644 --- a/datasets/gov.noaa.nodc:0069112_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069112_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069112_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship Pisces in the Gulf of Mexico from 2010-08-18 to 2010-09-02 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069112)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069113_Not Applicable.json b/datasets/gov.noaa.nodc:0069113_Not Applicable.json index e9f747859f..97b3b04b4b 100644 --- a/datasets/gov.noaa.nodc:0069113_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069113_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069113_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship Pisces in the Gulf of Mexico from 2010-09-09 to 2010-09-17 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069113)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069114_Not Applicable.json b/datasets/gov.noaa.nodc:0069114_Not Applicable.json index 3e39e39546..c6ce622d64 100644 --- a/datasets/gov.noaa.nodc:0069114_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069114_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069114_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis, sediment analysis and underway oceanographic data were collected aboard NOAA Ship Pisces in the Gulf of Mexico from 2010-09-25 to 2010-10-03 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sediment properties, sound velocity, temperature, turbidity and water density. The instruments used to collect these data included CTD, camera, fluorometer, oxygen meter and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069114)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069115_Not Applicable.json b/datasets/gov.noaa.nodc:0069115_Not Applicable.json index 531a0db471..8ad6ef5d33 100644 --- a/datasets/gov.noaa.nodc:0069115_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069115_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069115_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the F. G. Walton Smith in the Gulf of Mexico from 2010-06-01 to 2010-06-06 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069115)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069116_Not Applicable.json b/datasets/gov.noaa.nodc:0069116_Not Applicable.json index eedf3f927b..7157e2c5ee 100644 --- a/datasets/gov.noaa.nodc:0069116_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069116_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069116_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the BUNNY BORDELON in the Gulf of Mexico from 2010-05-31 to 2010-06-02 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069116)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069117_Not Applicable.json b/datasets/gov.noaa.nodc:0069117_Not Applicable.json index c09e760e10..fe04c22ff7 100644 --- a/datasets/gov.noaa.nodc:0069117_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069117_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069117_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard the BUNNY BORDELON in the Gulf of Mexico from 2010-09-05 to 2010-09-13 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069117)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069118_Not Applicable.json b/datasets/gov.noaa.nodc:0069118_Not Applicable.json index 17950cf8c4..4c89b88fde 100644 --- a/datasets/gov.noaa.nodc:0069118_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069118_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069118_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, laboratory analyses, physical and profile oceanographic data were collected aboard the BUNNY BORDELON in the Gulf of Mexico from 2010-08-18 to 2010-08-23 in response to the Deepwater Horizon Oil Spill Event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. (NODC Accession 0069118)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069119_Not Applicable.json b/datasets/gov.noaa.nodc:0069119_Not Applicable.json index d16a9d736a..5e6e6a6422 100644 --- a/datasets/gov.noaa.nodc:0069119_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069119_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069119_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, laboratory analyses, physical and profile oceanographic data were collected aboard the JACK FITZ in the Gulf of Mexico from 2010-08-18 to 2010-08-23 in response to the Deepwater Horizon Oil Spill Event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. (NODC Accession 0069119)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069120_Not Applicable.json b/datasets/gov.noaa.nodc:0069120_Not Applicable.json index dcd0b27191..31761153da 100644 --- a/datasets/gov.noaa.nodc:0069120_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069120_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069120_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-09-04 to 2010-09-08 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, methane, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer, gas chromatograph, methane sensor and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0069120)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069126_Not Applicable.json b/datasets/gov.noaa.nodc:0069126_Not Applicable.json index 277a9eaed2..56a27550e6 100644 --- a/datasets/gov.noaa.nodc:0069126_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069126_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069126_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-09-09 to 2010-09-15 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer, gas chromatograph and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0069126)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069127_Not Applicable.json b/datasets/gov.noaa.nodc:0069127_Not Applicable.json index ea9be390a6..5487a2464b 100644 --- a/datasets/gov.noaa.nodc:0069127_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069127_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069127_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the GYRE in the Gulf of Mexico from 2010-10-07 to 2010-10-20 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sediment properties, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer, oxygen meter and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069127)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069128_Not Applicable.json b/datasets/gov.noaa.nodc:0069128_Not Applicable.json index f3f554f965..949c7040d1 100644 --- a/datasets/gov.noaa.nodc:0069128_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069128_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069128_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-06-23 to 2010-07-17 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069128)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069356_Not Applicable.json b/datasets/gov.noaa.nodc:0069356_Not Applicable.json index af39362d6d..59117a97d9 100644 --- a/datasets/gov.noaa.nodc:0069356_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069356_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069356_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-10-07 to 2010-10-17 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sediment properties, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer, oxygen meter and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069356)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069614_Not Applicable.json b/datasets/gov.noaa.nodc:0069614_Not Applicable.json index 53a76a54f4..1614fcfba2 100644 --- a/datasets/gov.noaa.nodc:0069614_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069614_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069614_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and physical oceanographic profile data were collected aboard NOAA Ship NANCY FOSTER in the Gulf of Mexico from 2010-08-13 to 2010-08-21 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0069614)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0069615_Not Applicable.json b/datasets/gov.noaa.nodc:0069615_Not Applicable.json index 8fb8bf3f05..85125b0441 100644 --- a/datasets/gov.noaa.nodc:0069615_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0069615_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0069615_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the OCEAN VERITAS in the Gulf of Mexico from 2010-09-22 to 2010-10-24 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sediment properties, sound velocity, temperature and water density. The instruments used to collect these data included CTD, bottle, fluorometer, oxygen meter and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069615)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070122_Not Applicable.json b/datasets/gov.noaa.nodc:0070122_Not Applicable.json index 2d3413225b..a110bd4643 100644 --- a/datasets/gov.noaa.nodc:0070122_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070122_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070122_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An oceanographic measurement system aboard the Alaskan ferry Tustumena operated for four years in the Alaska Coastal Current with funding from the Exxon Valdez Oil Spill Trustee Council, the North Pacific Research Board and the National Oceanic and Atmospheric Administration. Sampling water from the ships sea chest at 4 m, the underway system measured: (1) temperature and salinity basic physical variables, (2) nitrate - an essential phytoplankton nutrient, (3) chlorophyll fluorescence an indicator of phytoplankton concentration, (4) colored dissolved organic matter (CDOM) fluorescence an indicator of terrestrial runoff, and (5) optical beam transmittance an indicator of suspended sediment.\n\nInstrumentation:\nTime series instruments used on this underway system are listed by manufacturer. Data are processed using software provided by the manufacturers of the instruments along with recent calibration files when appropriate. Post processing was via Ferret data visualization and analysis software.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070330_Not Applicable.json b/datasets/gov.noaa.nodc:0070330_Not Applicable.json index d8455c60ae..aa4eab0f35 100644 --- a/datasets/gov.noaa.nodc:0070330_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070330_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070330_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-06-15 to 2010-06-25 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070330)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070331_Not Applicable.json b/datasets/gov.noaa.nodc:0070331_Not Applicable.json index 069864debc..db74bee386 100644 --- a/datasets/gov.noaa.nodc:0070331_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070331_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070331_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-07-08 to 2010-07-16 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070331)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070332_Not Applicable.json b/datasets/gov.noaa.nodc:0070332_Not Applicable.json index 10bd95d096..fd3f23d28a 100644 --- a/datasets/gov.noaa.nodc:0070332_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070332_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070332_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-07-25 to 2010-07-31 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070332)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070333_Not Applicable.json b/datasets/gov.noaa.nodc:0070333_Not Applicable.json index 2ae9f4e31e..90cc30f014 100644 --- a/datasets/gov.noaa.nodc:0070333_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070333_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070333_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, laboratory analysis and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-08-02 to 2010-08-08 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070333)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070530_Not Applicable.json b/datasets/gov.noaa.nodc:0070530_Not Applicable.json index b2854865f9..045b2451f5 100644 --- a/datasets/gov.noaa.nodc:0070530_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070530_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070530_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Transects were made at two locations on the west side of the Island of Hawaii in August 2004 to study the structure and composition of the benthic habitat. Photoquadrats were established to quantify the percent of the benthic substrate occupied by coral and algal species, through use of Coral Point Count with Excel Extensions. Rugosity along each transect was also calculated. The transects were 15 m in length with photoquadrats at each meter mark. Each set of transects at each site consists of 10 15-m long transects running ~parallel to the shore, with ends of all transects aligned, and transects spaced 10 m apart along the vertical profile of the reef from the reef flat to the sandy interface at a depth of ~25 m. Quantified data provided in Excel spreadsheets. Original JPEG images from the photoquadrats are given as well, for future users to have the opportunity to apply other methods of quantifying the benthos.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070532_Not Applicable.json b/datasets/gov.noaa.nodc:0070532_Not Applicable.json index e0c2450e9c..84017bd8a3 100644 --- a/datasets/gov.noaa.nodc:0070532_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070532_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070532_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, meteorological, navigational and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico from 2010-08-24 to 2010-09-10 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070532)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0070533_Not Applicable.json b/datasets/gov.noaa.nodc:0070533_Not Applicable.json index a16bfef885..5f4e6e1463 100644 --- a/datasets/gov.noaa.nodc:0070533_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0070533_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0070533_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, meteorological, navigational and underway oceanographic data were collected aboard NOAA Ship GORDON GUNTER in the Gulf of Mexico and North Atlantic Ocean from 2010-09-16 to 2010-09-29 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, current speed - east/west component (U), current speed - north/south component (V), dissolved oxygen, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data included ADCP, CTD, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The Acoustic Doppler Current Profiler ADCP used sonar to measure and record water current velocities and the distribution of suspended material over a range of depths. Absolute U- and V-component ocean current vectors from the ADCP collected can be used to create detailed maps of the distribution of water currents and suspended materials through the water column along the ship's path. The data from this ADCP is raw and unprocessed. Some of the datasets associated with this instrument are still incomplete and will be published as they become available. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0070533)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0072888_Not Applicable.json b/datasets/gov.noaa.nodc:0072888_Not Applicable.json index b2617f5435..5d09a6f4cb 100644 --- a/datasets/gov.noaa.nodc:0072888_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0072888_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0072888_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains a set of sea surface temperature climatologies for the Gulf of Mexico (GOM), derived from the AVHRR Pathfinder Version 5 global 4km sea surface temperature data set. These GOM climatologies were produced from 5-day cloud-screened day-night averages of Pathfinder SST data from 1982-2009, which are archived at the National Oceanographic Data Center under separate accession numbers. In addition to sea surface temperature, the climatologies also include minimum, maximum, standard deviation, and number of observations.\n\nThe climatologies are available as 32-bit Tagged Image File Format (.TIFF) data files for 1982-2009 and include seasonal and yearly time periods. The climatologies are also included as Arc Grid (.mxd) and .PNG layers with associated legends for user convenience and were assigned projection GCS_WGS_1984. An additional subdirectory contains the annual mean, season 1 (Jan-Mar) mean, season 2 (Apr -June) mean, season 3 (Jul - Sept) mean, and season 4 (Oct-Dec) mean as color-classified .PNG images with a matching shape file that we developed for use in online visualizations.\n\nA separate GOM land mask which will also display inland water bodies has been included with this accession. The land mask was developed from the Global Self-consistent, Hierarchical, High-resolution Shoreline Database v2.2.0 product (Wessel and Smith, 2011). The user should note that although quality flags were assigned consistently for all water pixels, SSTs for inland water bodies (lakes and rivers) should be used with caution, as their accuracy has not been documented and there are numerous complexities involved with determining surface temperatures in inland regions.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0073269_Not Applicable.json b/datasets/gov.noaa.nodc:0073269_Not Applicable.json index aa43076c54..1d04deab0d 100644 --- a/datasets/gov.noaa.nodc:0073269_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0073269_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0073269_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Current and Pressure recording Inverted Echo Sounder (CPIES) measurements collected during the Kuroshio Extension System Study (KESS) under the sponsorship of the National Science Foundation. The measurements were taken between April 2004 and June 2006. Data are from 46 sites. The measured quantities include bottom pressure, vertical acoustic round-trip travel time and near-bottom currents.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074372_Not Applicable.json b/datasets/gov.noaa.nodc:0074372_Not Applicable.json index 870d99d3d3..61d5e1cf57 100644 --- a/datasets/gov.noaa.nodc:0074372_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074372_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074372_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-14 to 2010-05-18 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds and suspended solids. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer and bottle along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074372)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074853_Not Applicable.json b/datasets/gov.noaa.nodc:0074853_Not Applicable.json index 2af7d9add7..689aef921c 100644 --- a/datasets/gov.noaa.nodc:0074853_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074853_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074853_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-09-07 to 2010-09-11 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH) and Volatile Organic Compounds. The instruments used to collect these data included CTD and bottle along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074853)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074854_Not Applicable.json b/datasets/gov.noaa.nodc:0074854_Not Applicable.json index 33b0bfbea7..84baaf3875 100644 --- a/datasets/gov.noaa.nodc:0074854_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074854_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074854_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, profile and laboratory analysis oceanographic data were collected aboard the Ferrel in the Gulf of Mexico from 2010-07-03 to 2010-07-07 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH) and Volatile Organic Compounds. The instruments used to collect these data included bottle along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074854)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074863_Not Applicable.json b/datasets/gov.noaa.nodc:0074863_Not Applicable.json index cf3614f5e2..1e3f9fa773 100644 --- a/datasets/gov.noaa.nodc:0074863_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074863_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074863_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and laboratory analyses oceanographic data were collected aboard the Wes Bordelon in the Gulf of Mexico from 2010-08-18 to 2010-08-22 in response to the Deepwater Horizon Oil Spill Event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH) and Volatile Organic Compounds. The instruments used to collect these data included bottle along with other physical sampling devices. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. (NODC Accession 0074863)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074904_Not Applicable.json b/datasets/gov.noaa.nodc:0074904_Not Applicable.json index d019236746..cc053b7f63 100644 --- a/datasets/gov.noaa.nodc:0074904_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074904_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074904_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the GYRE in the Gulf of Mexico from 2010-09-19 to 2010-09-28 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds and sediment properties. The instruments used to collect these data included bottle and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074904)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074905_Not Applicable.json b/datasets/gov.noaa.nodc:0074905_Not Applicable.json index 7dc834cd19..23900f2bcd 100644 --- a/datasets/gov.noaa.nodc:0074905_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074905_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074905_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the GYRE in the Gulf of Mexico from 2010-09-25 to 2010-09-28 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds and sediment properties. The instruments used to collect these data included bottle and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074905)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0074906_Not Applicable.json b/datasets/gov.noaa.nodc:0074906_Not Applicable.json index f025f78fd0..80b7782954 100644 --- a/datasets/gov.noaa.nodc:0074906_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0074906_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0074906_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, imagery, laboratory analysis and sediment analysis oceanographic data were collected aboard the GYRE in the Gulf of Mexico from 2010-10-01 to 2010-10-03 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Carbon - Total Organic, Metals, Semivolatile Organic Compounds, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds and sediment properties. The instruments used to collect these data included bottle and sediment sampler - corer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. Sediment cores were analyzed for physical characteristics, and recorded in photos and data files. The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0074906)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0077816_Not Applicable.json b/datasets/gov.noaa.nodc:0077816_Not Applicable.json index abe16556c6..7cde646c7e 100644 --- a/datasets/gov.noaa.nodc:0077816_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0077816_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0077816_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a set of monthly and yearly global day-night sea surface temperature averages, derived from the AVHRR Pathfinder Version 5 sea surface temperature cloudscreened data set in GeoTIFF format. The AVHRR Pathfinder SST data sets provide the longest, most accurate, and highest resolution consistently-reprocessed SST climate data record from the AVHRR sensor series. These data files were produced to facilitate the utilization of high resolution Pathfinder v5.0 sea surface temperature data within geographic information system (GIS) software.\n\nThese day-night combined monthly and yearly means were produced from cloud-screened day-night monthly full resolution files of Pathfinder SST data from 1985-2009. The original .HDF files are archived at the National Oceanographic Data Center under separate accession numbers. The GeoTIFF SST averages were assigned projection GCS_WGS_1984. In addition, browse images in PNG format with an associated KML file for each year are included with these data as well as detailed metadata.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0080994_Not Applicable.json b/datasets/gov.noaa.nodc:0080994_Not Applicable.json index cc867f30d3..cf31be7e37 100644 --- a/datasets/gov.noaa.nodc:0080994_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0080994_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0080994_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway measurements from OISO-1 cruise (Indian and Southern Oceans).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0081002_Not Applicable.json b/datasets/gov.noaa.nodc:0081002_Not Applicable.json index dbd85ea31e..172c3333ed 100644 --- a/datasets/gov.noaa.nodc:0081002_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0081002_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0081002_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway measurements from OISO-2 cruise (South Indian and Southern Oceans)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0081004_Not Applicable.json b/datasets/gov.noaa.nodc:0081004_Not Applicable.json index 517cc53580..36a77ac816 100644 --- a/datasets/gov.noaa.nodc:0081004_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0081004_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0081004_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Underway fCO2 measurements from OISO-4 cruise (Indian and Southern Oceans)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0081028_Not Applicable.json b/datasets/gov.noaa.nodc:0081028_Not Applicable.json index 187500d27c..06f72e51e9 100644 --- a/datasets/gov.noaa.nodc:0081028_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0081028_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0081028_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOUGH, MARION, SANAE Expeditions on board S. A. Agulhas data", "links": [ { diff --git a/datasets/gov.noaa.nodc:0083626_Not Applicable.json b/datasets/gov.noaa.nodc:0083626_Not Applicable.json index 7ffcee928a..4793802e89 100644 --- a/datasets/gov.noaa.nodc:0083626_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0083626_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0083626_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NODC Accession 0083626 includes underway chemical and physical data collected from COLUMBUS ISELIN, ENDEAVOR, GYRE, OCEANUS, and SEWARD JOHNSON in the North Atlantic Ocean from 19930511 to 19961017 and retrieved during cruise LDEO Leg No: 9. These data include CARBON DIOXIDE - MOLE FRACTION - AIR, CARBON DIOXIDE - PARTIAL PRESSURE - DIFFERENCE, CARBON DIOXIDE - PARTIAL PRESSURE - SEA, SALINITY - SURFACE WATER, and SEA SURFACE TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer, Carbon dioxide (CO2) shower head chamber equilibrator, and surface seawater intake. These data were collected by Taro Takahashi, David W. Chipman, John Goddard, and S. C. Sutherland of Lamont-Doherty Earth Observatory of Columbia University as part of OCEAN MARGINS PROGRAM.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084555_Not Applicable.json b/datasets/gov.noaa.nodc:0084555_Not Applicable.json index aa56e3b749..3aa7366855 100644 --- a/datasets/gov.noaa.nodc:0084555_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084555_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084555_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, profile, tows and underway oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-07 to 2010-05-12 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, fluorescence, suspended solids, temperature and turbidity. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer and fluorometer along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0084555)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084569_Not Applicable.json b/datasets/gov.noaa.nodc:0084569_Not Applicable.json index 2f4c91beea..49b7a1091a 100644 --- a/datasets/gov.noaa.nodc:0084569_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084569_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084569_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-06-05 to 2010-06-07 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084569)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084576_Not Applicable.json b/datasets/gov.noaa.nodc:0084576_Not Applicable.json index 9c480054d2..9ffcfe3ac8 100644 --- a/datasets/gov.noaa.nodc:0084576_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084576_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084576_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-06-07 to 2010-06-09 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084576)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084578_Not Applicable.json b/datasets/gov.noaa.nodc:0084578_Not Applicable.json index 8e19f836b8..4b4e752f89 100644 --- a/datasets/gov.noaa.nodc:0084578_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084578_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084578_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-06-09 to 2010-06-16 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The Hydrocarbon Sensor Array data are raw and provisional. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0084578)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084579_Not Applicable.json b/datasets/gov.noaa.nodc:0084579_Not Applicable.json index 43474a089e..0ae2967125 100644 --- a/datasets/gov.noaa.nodc:0084579_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084579_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084579_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-06-18 to 2010-06-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The Hydrocarbon Sensor Array data are raw and provisional. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0084579)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084580_Not Applicable.json b/datasets/gov.noaa.nodc:0084580_Not Applicable.json index 5d101f19ab..c83dd00815 100644 --- a/datasets/gov.noaa.nodc:0084580_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084580_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084580_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-06-24 to 2010-06-29 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084580)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084581_Not Applicable.json b/datasets/gov.noaa.nodc:0084581_Not Applicable.json index 7317ef533e..e8574216f0 100644 --- a/datasets/gov.noaa.nodc:0084581_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084581_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084581_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-01 to 2010-07-09 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084581)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084582_Not Applicable.json b/datasets/gov.noaa.nodc:0084582_Not Applicable.json index 8de4b36fa4..ad31cd2a58 100644 --- a/datasets/gov.noaa.nodc:0084582_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084582_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084582_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical and profile oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-11 to 2010-07-13 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included CTD, fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084582)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084583_Not Applicable.json b/datasets/gov.noaa.nodc:0084583_Not Applicable.json index fbfd0b8ed7..73689203fd 100644 --- a/datasets/gov.noaa.nodc:0084583_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084583_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084583_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-14 to 2010-07-19 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084583)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084584_Not Applicable.json b/datasets/gov.noaa.nodc:0084584_Not Applicable.json index 277a3b6c0d..7f7b7de4ac 100644 --- a/datasets/gov.noaa.nodc:0084584_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084584_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084584_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-21 to 2010-07-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084584)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084585_Not Applicable.json b/datasets/gov.noaa.nodc:0084585_Not Applicable.json index 9bf8127ae6..605bd7a490 100644 --- a/datasets/gov.noaa.nodc:0084585_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084585_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084585_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-25 to 2010-07-28 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084585)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084586_Not Applicable.json b/datasets/gov.noaa.nodc:0084586_Not Applicable.json index 962be65e45..09ac6e38bc 100644 --- a/datasets/gov.noaa.nodc:0084586_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084586_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084586_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-07-28 to 2010-08-09 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084586)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084587_Not Applicable.json b/datasets/gov.noaa.nodc:0084587_Not Applicable.json index 1d4c0224ef..2768f8137b 100644 --- a/datasets/gov.noaa.nodc:0084587_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084587_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084587_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-08-13 to 2010-08-23 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The Hydrocarbon Sensor Array data are raw and provisional. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0084587)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084588_Not Applicable.json b/datasets/gov.noaa.nodc:0084588_Not Applicable.json index 9e7f6a453e..cd5ef51bb6 100644 --- a/datasets/gov.noaa.nodc:0084588_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084588_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084588_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical oceanographic data were collected aboard the RYAN CHOUEST in the Gulf of Mexico from 2010-08-27 to 2010-09-01 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Total Petroleum Hydrocarbons (TPH) and fluorescence. The instruments used to collect these data included fluorometer and gas chromatograph along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. The Hydrocarbon Sensor Array data are raw and provisional. (NODC Accession 0084588)", "links": [ { diff --git a/datasets/gov.noaa.nodc:0084994_Not Applicable.json b/datasets/gov.noaa.nodc:0084994_Not Applicable.json index 54a5d1893a..70a660a5e8 100644 --- a/datasets/gov.noaa.nodc:0084994_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0084994_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0084994_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annual surveys are undertaken to sample bottom-sediment for biological and geochemical analyses at six stations at a depth of approximately 34 m in the vicinity of the Waianae Wastewater Treatment Plant outfall diffuser on the leeward coast of Oahu, Hawaii. All stations had sediment fractions with >90% sand. Silt fractions were no more than 4% at any station. Oxidation-reduction-potential (ORP) and total-volatile-solids measurements of these sediments indicated a nonreducing benthic environment at all stations. This data set contains examination of the sediments for species, abundance, and richness of nonmollusks, crustacean, molluscan, nematode, oligochaete, and polychaete faunas. A PDF document provides annual summaries for 2001-2010 while data files hold similar data for 2006-2009.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0087872_Not Applicable.json b/datasets/gov.noaa.nodc:0087872_Not Applicable.json index 7ce0fc1f51..ff1c78b8a9 100644 --- a/datasets/gov.noaa.nodc:0087872_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0087872_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0087872_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Deepwater Horizon Joint Analysis Group (JAG) for Surface and Sub-Surface Oceanography, Oil and Dispersant Data was a working group with membership from federal agencies, BP, and academia that was formed to analyze sub-surface oceanographic data being derived from the on-going coordinated sampling efforts by private, federal and academic scientists as part of the spill response. The goal of the JAG was to provide comprehensive characterization of the Gulf of Mexico sub-surface conditions as well as the fate and transport of dispersed petroleum as a result of the Deepwater Horizon oil spill. JAG findings were published in a series of reports for the Unified Area Command as well as the public. This accession contains Total Petroleum Hydrocarbon and Volatile Organic Analysis data from laboratory analysis, as well as in situ Chromophoric Dissolved Organic Matter and dissolved oxygen data. This dataset was compiled as part of the final JAG summary report, and referred to in Appendix 3 of that report, NOAA Technical Report NOS OR&R 27 (2012).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0094007_Not Applicable.json b/datasets/gov.noaa.nodc:0094007_Not Applicable.json index cfa389463c..078f28137c 100644 --- a/datasets/gov.noaa.nodc:0094007_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0094007_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0094007_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite-derived data for sea surface temperature, salinity, chlorophyll; euphotic depth; and modeled bottom to surface temperature differences were evaluated to assess the utility of these products as proxies for in situ measurements. The data were used to classify surface waters in three regions of the Gulf of Mexico using subcomponents and modifiers from the Coastal and Marine Ecological Classification Standard (CMECS) Water Column Component (WC) to determine if CMECS categories could be affectively used to categorize in situ data into meaningful management units. The Naval Research Laboratory at the Stennis Space Center (NRL/SSC) processed MODIS-Aqua satellite imagery covering the Gulf of Mexico from January 2005 to December 2009. Daily, level-1B image files from the NASA LAADS Web were processed through the NRL/SSC Automated Processing System (APS). Sea surface temperature and salinity were classified into CMECS WC temperature and salinity subcomponent categories, respectively.Three modifiers from the WC were also used for the pelagic classification: water column stability, productivity, and photic quality. Modeled bottom to surface temperature differences were used to assign classification for water column stability, surface chlorophyll was used to determine productivity, and euphotic depth was used to indicate the photic quality. Maps showing the CMECS Water Column Component classes for chlorophyll concentration, euphotic depth, sea surface salinity, sea surface temperature (HDF4), and bottom-to-surface temperatures (netCDF) were produced from the APS output images.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0099263_Not Applicable.json b/datasets/gov.noaa.nodc:0099263_Not Applicable.json index 446e3f4640..093765f865 100644 --- a/datasets/gov.noaa.nodc:0099263_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0099263_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0099263_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project examined the results of the field manipulative experiment that has been set up to test the ecological effects of introduced roi on reef fish associations in West Hawaii. This on-going research project, which began in September 2010, evaluates the impact of roi removal by collaborating with local fishers to remove >90% of the roi from a patch reefs in Puako, West Hawaii.\n\nIn situ observations of the introduced predatory grouper roi (Cephalopholis argus) were taken semi-annually within the coral reef ecosystem of Puako, northwest side of the Island of Hawaii October 2010 - July 2012. Visual fish transects were made at a depth range of 10-20 m. Tow board and standard visual belt transects were employed at control, reference, and treatment sites. Data include biometrics (length and weight) and biomass of roi, as well as an assemblage of other fish and feed guilds. Additional data on the movement of roi within the Puako area were collected using a fish tagging program followed by surveys for recapture and resighting.\n\nNODC Accession 0082197 contains similar data from November 2010 - June 2011. There is some overlap of data within 0082197 and the present accession.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0110496_Not Applicable.json b/datasets/gov.noaa.nodc:0110496_Not Applicable.json index d6b6a8ec91..0103d99bd3 100644 --- a/datasets/gov.noaa.nodc:0110496_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0110496_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0110496_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements of d13C in DIC were compiled mainly from WOCE and CLIVAR cruises. The dataset also contains other physical and biogeochemical variables.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0110657_Not Applicable.json b/datasets/gov.noaa.nodc:0110657_Not Applicable.json index 9efebb9bbd..8339f5f889 100644 --- a/datasets/gov.noaa.nodc:0110657_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0110657_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0110657_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains a global, 4km monthly sea surface temperature climatology derived from harmonic analysis of the AVHRR Pathfinder Version 5.0 sea surface temperature time series data for 1985-2006. The climatology is available as 12 separate files, each representing one month in a climatological year. The files are in hdf format. In addition to climatological sea surface temperature, each file contains standard deviation.\n\nIt only uses Pathfinder 5.0 data with a quality flag value of 4 or greater. It is available as 12 separate files in Hierarchical Data Format Version 4 (HDF4).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0115356_Not Applicable.json b/datasets/gov.noaa.nodc:0115356_Not Applicable.json index 94d4b5915d..2974f1fb54 100644 --- a/datasets/gov.noaa.nodc:0115356_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0115356_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0115356_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Several bureaus within the Department of Interior compiled available information from seabird observation datasets from the Atlantic Outer Continental Shelf into a single database, with the goal of conducting research and informing coastal and offshore planning activities. The cooperators were the Bureau of Ocean Energy Management's (BOEM) Environmental Studies Program (www.boem.gov/Environmental-Stewardship/Environmental-Studies/Environmental-Studies.aspx), the U.S. Fish and Wildlife Service's (USFWS) Division of Migratory Bird Management (www.fws.gov/migratorybirds/) and the U.S. Geological Survey's (USGS) Patuxent Wildlife Research Center (www.pwrc.usgs.gov). The resulting product is the Atlantic Offshore Seabird Dataset Catalog, which characterizes the survey effort and bird observations that have been collected across space and time. As of December 2013, the database contains over 70 datasets from 1906-2013 with about 300,000 records of seabird observations.\n\nThe data archived at NODC is comprised of roughly 50 datasets from 1938-2013 with about 260,000 observation records. This archive is a subset of the main database, excluding datasets from surveys where the scientific design was not specifically designed to sample marine birds (e.g. coastal portions of National Audubon Society's Christmas Bird Counts).\n\nThe full archive of scientific data contains information on individual observations as well as survey effort. Each observation record has a unique point location, date and time, species and observation count. There may also be biological information related to the sighting, such as animal age or behavior. The survey effort information (i.e. weather variables) may have been recorded for each individual observation but was more often recorded at the transect (line along which the plane or boat traveled) level. The dataset contains data primarily for seabirds, but some other observations accompanied bird data submissions and were not discarded: marine mammals, turtles, fish, and non-biological sightings such as other boats, fishing gear and trash.\n\nThe data archived at the NODC is in .csv format, with an associated file detailing the data structure in .csv format. A detailed metadata record in Federal Geographic Data Committee (FGDC) format and a final report in .pdf format is included with these data. Data use must take into account use constraints (data limitations) listed within the included metadata record, and cite the Atlantic Offshore Seabird Dataset Catalog, USGS, 2013.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0116100_Not Applicable.json b/datasets/gov.noaa.nodc:0116100_Not Applicable.json index 5e3dc63984..810e645091 100644 --- a/datasets/gov.noaa.nodc:0116100_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0116100_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0116100_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Beginning in late 1979, the Alabama Coastal Area Board (CAB) funded a series of baseline surveys on the coastal resources of Alabama, from which they could develop a monitoring program to observe any significant changes in the resources over time.\n\nEight stations within Mobile Bay, Alabama were sampled monthly from April 1980 to April 1981. Data collected included samples for benthic fauna, pelagic fauna, sediment particle size, total organic carbon, foraminifera, zooplankton, phytoplankton, chlorophyll, turbidity, river flow, and hydrographic parameters.\n\nThe subset of data presented here are for the benthic fauna, which were sampled by 0.1 m^2 Peterson grab. Fauna were enumerated and identified to the lowest taxon possible, and mainly included crustaceans, molluscs, polychaetes, and echinoderms. Data in readily accessible digital form are available from April 1980 to February 1981.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0116390_Not Applicable.json b/datasets/gov.noaa.nodc:0116390_Not Applicable.json index 7a34ae1eb9..4262c606f4 100644 --- a/datasets/gov.noaa.nodc:0116390_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0116390_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0116390_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Water Quality Act of 1987 established the National Estuary Program, which has as one of its objectives the formation of citizen groups for monitoring the quality of coastal waters. One such group that formed to monitor Mobile Bay was called Baywatch.\n\nUnder the guidance of Dr. George Crozier of the Dauphin Island Sea Lab (DISL), and in association with the Mobile Field Office of the Alabama Department of Environmental Management (ADEM), citizens were trained to collect and analyze water samples once a week between 10am and 2pm for 50 stations located in the Mobile Bay delta, Mississippi Sound, Weeks Bay, Perdido Bay, and several local streams. Parameters measured included water temperature, salinity, dissolved oxygen, turbidity, air temperature, and rainfall. Water temperature, salinity, and dissolved oxygen were measured from both surface and bottom waters where possible.\n\nThe purpose of this study was to provide a better understanding of the processes affecting the waters of coastal Alabama.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0117507_Not Applicable.json b/datasets/gov.noaa.nodc:0117507_Not Applicable.json index 7b3023c4ac..f30c335366 100644 --- a/datasets/gov.noaa.nodc:0117507_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0117507_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0117507_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quantifying the linkages between primary production and higher trophic levels is necessary to understand why particular regions can support high fisheries production. Modified dilution experiments were employed to characterize microbial communities in surface waters at four sites from within a bay to the shelf in the northern Gulf of Mexico (nGOM). Inshore surface waters were more variable than shelf surface waters due to the strong influence of river discharge. Phytoplankton (Chl a) and prokaryote biomass were both significantly higher inshore than on the shelf, with phytoplankton significantly higher than prokaryotes inshore. Virus and heterotrophic nanoflagellate abundances, however, did not differ between inshore and shelf waters. Samples were amended with nutrients (N + P) to examine the impact of nutrient limitation. Prokaryotes were nutrient limited in 14 (28%) of the experiments, while phytoplankton were nutrient limitated in 26 (52%) of the experiments. When phytoplankton were nutrient limited, prokaryote growth rates were significantly altered. A similar impact on phytoplankton growth rates occurred when prokaryotes were nutrient limited, suggesting that the two groups are in competition for resources. Grazing was detected in the majority of experiments, while viral lysis was only detected in 24% of phytoplankton and 12% of prokaryote experiments. Growth and grazing rates for both phytoplankton and prokaryotes were tightly coupled inshore and on the shelf, with significantly more phytoplankton and prokaryotes grazed inshore (average = 106% and 75%, respectively) than on the shelf (average = 55% and 57%). These findings indicate that surface waters across the estuary are highly productive, with microzooplankton grazing transferring the majority of the microbial production to higher trophic levels.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0117516_Not Applicable.json b/datasets/gov.noaa.nodc:0117516_Not Applicable.json index 32878fe2f9..deb0b6f936 100644 --- a/datasets/gov.noaa.nodc:0117516_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0117516_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0117516_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fishes and macroinvertebrates were collected using a 12.8m semi-balloon trawl with 5cm mesh in spring and fall of 2010 and 2011 in the coastal waters of Alabama and Mississippi. All species were identified to the lowest possible taxonomic level and counted. A wet weight estimate of total biomass (+ grams) was attained for each species using portable spring scales. Finfish collected in 2010 were measured for length (standard, fork, and/or total as appropriate).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0117688_Not Applicable.json b/datasets/gov.noaa.nodc:0117688_Not Applicable.json index 30f469e14f..4821e7b5e5 100644 --- a/datasets/gov.noaa.nodc:0117688_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0117688_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0117688_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a set of studies that ran from 1977-1978, and 1980-1982, around the site of Gaillard Island, Mobile Bay, Alabama, before, during, and after its construction (1979-1981). Extant data from the MESC Data Management System include sediment particle size distribution (001), identification and enumeration of benthic fauna (002), discrete hydrography and turbidity (003) during and after island construction, and discrete hydrography and turbidity before island construction (004).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0117942_Not Applicable.json b/datasets/gov.noaa.nodc:0117942_Not Applicable.json index f6f99cbff7..1757cea78c 100644 --- a/datasets/gov.noaa.nodc:0117942_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0117942_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0117942_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Phytoplankton Monitoring Network (PMN) is a part of the National Centers for Coastal Ocean Science (NCCOS). The PMN was created as an outreach program to connect volunteers and professional scientists in the monitoring of marine phytoplankton and harmful algal blooms (HABs). NOAA staff train volunteers on sampling techniques and identification methods for marine phytoplankton. There are over 50 genera, including 10 potentially toxin producing genera, of dinoflagellates and diatoms on the volunteers watch list.\n\nA qualitative collection of data that includes salinity, temperature, depth, wind speed and direction, phytoplankton counts and abundance ratios obtained from surface tows in the estuarine and marine environments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0118497_Not Applicable.json b/datasets/gov.noaa.nodc:0118497_Not Applicable.json index 67b5d50516..ba8814ac8d 100644 --- a/datasets/gov.noaa.nodc:0118497_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0118497_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0118497_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic sediment samples were collected from eleven stations in the Mobile-Tensaw River Delta on 17 June 1981. Samples were analyzed for particle size distribution characteristics. At least eight were near the wastewater discharge pipes of industrial sites, and at least two were not near potential sources of water pollution.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0118498_Not Applicable.json b/datasets/gov.noaa.nodc:0118498_Not Applicable.json index 02c681e528..d64a7560ea 100644 --- a/datasets/gov.noaa.nodc:0118498_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0118498_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0118498_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains stable isotope ratios of carbon and nitrogen in oyster shell, suspended particulate matter (SPM), and oil from the Deepwater Horizon oil spill. Shell material was sampled from oysters grown at five sites along the Mississippi-Alabama coast and in Mobile Bay before, during, and after the spill (June 2008, April-July 2010). Samples of SPM were collected from the same or equivalent sites and timeframes. Oil samples came from tar balls, mats, and semisolid oil forms collected from sediments along the shoreline from the Florida-Alabama border to Petit Bois Island in Mississippi. For all samples, the stable isotope ratios of carbon and nitrogen were determined. Oyster shell samples were also analyzed for six trace and minor elements which have been highlighted for use in detection of hydrocarbon pollution (Cd, Co, Mo, Ni, Pd, V).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0118500_Not Applicable.json b/datasets/gov.noaa.nodc:0118500_Not Applicable.json index 390ad84578..88191a37e9 100644 --- a/datasets/gov.noaa.nodc:0118500_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0118500_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0118500_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Centers for Coastal Ocean Science (NCCOS), Center for Coastal Monitoring and Assessment, Biogeography Branch (CCMA-BB) worked with partners to assess and characterize the marine environment in and around the St. Croix East End Marine Park. The Park was established in 2003. At the time of creation there were substantial data gaps hindering baseline establishments to measure performance of the management zones. NCCOS and territorial partners characterized the land and seascape conditions and the marine communities within the park zones. These characterizations revealed relevant threats to the coral reef ecosystem health and are essential for management actions. Relating data collected in the field back to habitat and bathymetric maps, CCMA-BB is then able to model and map species level and community level information.\n\nData within this set contain Acropora species, Nassau grouper, and other fauna of special concern (i.e. conch, sea urchins, lobster, and the lionfish). There is also data containing benthic habitat survey zones and analyses, land characterization (such as dirt road location, land development index, and landcover data), fish richness, and the overall impact on the Park. Data is in geospatial maps and tables with associated metadata.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0118680_Not Applicable.json b/datasets/gov.noaa.nodc:0118680_Not Applicable.json index 5cc140c310..458a3b19ae 100644 --- a/datasets/gov.noaa.nodc:0118680_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0118680_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0118680_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abundances of viruses, prokaryotes, diatoms, dinoflagellates, ciliates and heterotrophic nanoflagellates were determined over time in mesocosm experiments measuring the effects of oil, dispersant and dispersed oil on the microbial loop. Two separate experiments were carried out in June and August 2011. Abundances in the treated mesocosms were compared to a no addition control and a glucose addition control.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0118720_Not Applicable.json b/datasets/gov.noaa.nodc:0118720_Not Applicable.json index eddf788ba3..5345ddf17f 100644 --- a/datasets/gov.noaa.nodc:0118720_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0118720_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0118720_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study was based on the sediment quality triad (SQT) approach. A stratified probabilistic sampling design was utilized to characterize the Delaware Bay system in terms of chemical contamination, sediment toxicity (Microtox, amphipod bioassay; sea urchin gamete bioassay; and P450 biomarker) and benthic infaunal community structure. The purpose was to define the extent and magnitude of toxicity and other biological effects associated with contaminants in the Delaware estuary system from the fall line to the mouth of the Bay. This file contains data measured in the Delaware Bay Estuary and adjacent waters during 1997. Samples were collected for water and sediment analyses.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0124257_Not Applicable.json b/datasets/gov.noaa.nodc:0124257_Not Applicable.json index f0ee290008..b0eb35ee10 100644 --- a/datasets/gov.noaa.nodc:0124257_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0124257_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0124257_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study utilized ROV photograph transects to quantify benthic habitat and coral communities among the five habitat types (algal nodule, coralline algal reefs, deep reefs and soft bottom) identified in the Flower Garden Banks National Marine Sanctuary (FGBNMS). ROV surveys were conducted in the mid and lower mesophotic zone of the sanctuary (17-150 m) on both the East Bank and the West Bank.\nThe FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities.\nDuring the course of the sanctuary\u00c2\u0092s management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project is to provide baseline data for all benthic habitats and coral communities.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0125596_Not Applicable.json b/datasets/gov.noaa.nodc:0125596_Not Applicable.json index 0870540736..f8ce34ef48 100644 --- a/datasets/gov.noaa.nodc:0125596_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0125596_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0125596_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0125597_Not Applicable.json b/datasets/gov.noaa.nodc:0125597_Not Applicable.json index aed3e10812..d6114c4259 100644 --- a/datasets/gov.noaa.nodc:0125597_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0125597_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0125597_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:0127525_Not Applicable.json b/datasets/gov.noaa.nodc:0127525_Not Applicable.json index 503bf36b82..de1ef0f749 100644 --- a/datasets/gov.noaa.nodc:0127525_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0127525_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0127525_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous \u00e2\u0080\u009cturf\u00e2\u0080\u009d algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0128996_Not Applicable.json b/datasets/gov.noaa.nodc:0128996_Not Applicable.json index ed145e3f7f..59102907ae 100644 --- a/datasets/gov.noaa.nodc:0128996_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0128996_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0128996_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data sets show the distribution of key species and habitats, such as seabirds, bathymetry, surficial sediments, deep sea corals, and oceanographic habitats. NOAA\u00e2\u0080\u0099s Biogeography Branch worked with the New York Department of State (DOS) to interpret existing ecological information and create these new data sets. New York plans to integrate this information with other ecological and human use data compiled by others (for example, The Nature Conservancy, Northeast Fisheries Science Center) and apply ecosystem-based management and plan for ocean uses.\n\nMany academic, state and federal and non-governmental organization partners contributed to this project with data, analyses and reviews. Project partners included: the University of Alaska, Biology and Wildlife Department; University of Texas, Institute for Geophysics; The Nature Conservancy, Mid-Atlantic Marine Program; the National Marine Fisheries Service (NMFS), Northeast Fisheries Science Center, and the NMFS, Deep-Sea Coral Research and Technology Program.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0129395_Not Applicable.json b/datasets/gov.noaa.nodc:0129395_Not Applicable.json index 7a53c37395..bae3e2b185 100644 --- a/datasets/gov.noaa.nodc:0129395_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0129395_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0129395_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data represent the chlorophyll accessory pigments measured from discrete depth water samples collected in CTD-mounted 10 liter Niskin bottles as part of NOAA surveys in the central North Pacific Ocean north of Hawaii. Accessory pigments were measured post-survey at the University of Hawaii using HPLC methods.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0130065_Not Applicable.json b/datasets/gov.noaa.nodc:0130065_Not Applicable.json index e3d44094d2..c587127045 100644 --- a/datasets/gov.noaa.nodc:0130065_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0130065_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0130065_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extracted chlorophyll A, normalized to filtered volume, from suspended particulate material collected via Niskin bottle from the Gulf of California in the summers of 2004, 2005, and 2008, as well as from the Eastern Tropical North Pacific in 2008.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0130929_Not Applicable.json b/datasets/gov.noaa.nodc:0130929_Not Applicable.json index dce319c48c..87f10868a7 100644 --- a/datasets/gov.noaa.nodc:0130929_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0130929_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0130929_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This model study examines several management strategies for two marine fish species subject to isolation-by-distance (IBD): Pacific cod in the Aleutian Islands (AI) and northern rockfish in the Eastern Bering Sea (EBS) and Aleutian Islands. A one-dimensional stepping stone model was used to model isolation by distance, and was intended to mimic regions where marine species are exploited along a continental shelf. The performance of spatial assessment and management methods depended on how the range was split. Splitting anywhere within the managed area led to fewer demes falling below target and threshold biomass levels and higher yield than managing the entire area as a single unit. Equilibrium yield was maximized when each deme was assessed and managed separately and under catch cascading, in which harvest quotas within a management unit are spatially allocated based upon the distribution of survey biomass. The longer-lived rockfish declined more slowly than Pacific cod, and experienced greater depletion in biomass under disproportionate fishing effort due to lower productivity. Overall, splitting a management area of the size simulated in the model improved performance measures, and the optimal management strategy grouped management units by demes with similar relative fishing effort.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0131425_Not Applicable.json b/datasets/gov.noaa.nodc:0131425_Not Applicable.json index f6ced3c82e..32e5035dd5 100644 --- a/datasets/gov.noaa.nodc:0131425_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0131425_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0131425_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Bowhead Whale Feeding Ecology Study (BOWFEST) was initiated in May 2007 through an Interagency Agreement between the Bureau of Ocean Energy Management (BOEM) (formerly Minerals Management Service (MMS)) and the National Marine Mammal Laboratory (NMML). This was a multi-disciplinary study involving oceanography, acoustics, tagging, stomach sampling and aerial surveys and included scientists from a wide range of institutions (Woods Hole Oceanographic Institution (WHOI), University of Rhode Island (URI), University of Alaska Fairbanks (UAF), University of Washington (UW), Oregon State University (OSU), North Slope Borough (NSB), and NMML. The data described and presented here are only from the aerial survey component of this larger study. The focus of the aerial survey was to document patterns and variability in the timing and locations of bowhead whales. Using a NOAA Twin Otter, scientists from NMML conducted aerial surveys from mid-August to mid-September during this five year study between years 2007-2011. Surveys were conducted in the BOWFEST study area (continental shelf waters between 157 degree W and 152 degree W and from the coastline to 72 degree N, with most of the effort concentrated between 157 degree W and 154 degree W and between the coastline and 71 degree 44'N).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0131862_Not Applicable.json b/datasets/gov.noaa.nodc:0131862_Not Applicable.json index f9e4b1f4c9..3c5ec77919 100644 --- a/datasets/gov.noaa.nodc:0131862_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0131862_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0131862_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Visual surveys for cetaceans were conducted on the eastern Bering Sea shelf along transect lines, in association with the AFSC\u00e2\u0080\u0099s echo integration trawl surveys for walleye pollock. Surveys in 2000 and 2004 were from early June to early July, the survey in 2002 was from early June to late July, and the survey in 1999 was from early July to early August. Searches for cetaceans were conducted from the flying bridge of NOAA Ship Miller Freeman at a platform height of 12 m above the sea surface and survey speed of 18.5 22.0 km/h (10 12 kts). North south transect lines were spaced 37 km apart and defined by the historical acoustic survey for walleye pollock. Insufficient funding precluded including cetacean observers on all legs except in 2002. See Friday et al. 2012. Cetacean distribution and abundance in relation to oceanographic domains on the eastern Bering Sea shelf: 1999-2004 (http://www.sciencedirect.com/science/article/pii/S0967064512000100).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0133936_Not Applicable.json b/datasets/gov.noaa.nodc:0133936_Not Applicable.json index 8b2df6803c..d9e9c8b9ca 100644 --- a/datasets/gov.noaa.nodc:0133936_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0133936_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0133936_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Fisheries Service (NMFS) has conducted aerial counts of Cook Inlet beluga whales (Delphinapterus leucas) from 1993 to 2014 (excluding 2013). Nearly all counts were conducted during the month of June. The routine nature of these counts and the consistency in research protocol lend themselves to inter-annual trend analyses. Beginning in 2005, an aerial survey was added during the month of August to document calving groups within the upper Inlet (north of East and West Foreland). Research protocol has been based on paired observers on the shoreward side of the aircraft and a single observer and computer operator on the offshore side independently searching for marine mammals. Data on environmental conditions, time, location, species, and inclinometer angle were collected for each sighting. The counting protocol included multiple passes near each beluga group while simultaneously collecting video footage. The counting system and observer performance has been tested through paired, independent observational effort. Aerial observer counts are used to calculate median counts for each beluga group to provide a daily index for the population prior to calculating the abundance estimate. Video has been used to count the number of animals in the group to correct for missed animals in the observer counts (perception bias). One video camera had a lens set at a wide angle to view the entire beluga group while the second video camera was zoomed to approximately 10x to magnify a subsample of individual whales in the group. The zoomed video has been used to examine color ratios of white adults relative to smaller and darker juveniles and calves and correct for those individuals missed due to their size or coloration. Aerial counts and video footage of beluga whales provide the fundamental data used to calculate the abundance of and a calving index for the Cook Inlet population. The abundance estimates are applied to trends analyses to determine the status of the stock. Three datasets are included here that contain basic survey data such as latitude, longitude and sightings, as well as the counts of beluga whale groups made by the aerial observers and the results from video analysis from data collected on surveys from 1993-2012, and 2014.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0133937_Not Applicable.json b/datasets/gov.noaa.nodc:0133937_Not Applicable.json index 907ee251f1..bfb7513057 100644 --- a/datasets/gov.noaa.nodc:0133937_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0133937_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0133937_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photographic surveys for bowhead whales were conducted near Point Barrow, Alaska, from 19 April to 6 June in 2011. Approximately 4,594 photographs containing 6,801 bowhead whale images were obtained (not accounting for resightings). The 2011 field season was very successful: we flew 36 out of 49 available days and conducted 49 flights in that time; we were grounded due to weather on 13 days. The longest period of time that we were grounded due to weather (low ceilings/fog) was three days. This occurred after the migration had slowed down, during a time when few whales passed the ice perches according to the ice-based visual survey. The 2011 migration was steady with several peaks (30 April, 4-5 May, 12 May), and then the migration rate slowed down considerably after 14 May. The photographs taken in 2011 are a significant contribution to the bowhead whale photographic catalogue. They will be used to calculate a population estimate that may be used for comparison with the 2011 ice-based estimate and will provide better precision in estimates of bowhead whale life-history parameters.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0137093_Not Applicable.json b/datasets/gov.noaa.nodc:0137093_Not Applicable.json index 44d7a9f8de..e859bf5cc3 100644 --- a/datasets/gov.noaa.nodc:0137093_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0137093_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0137093_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory experiments reveal calcification rates of crustose coralline algae are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry.\n\nThe data described here result from the use of calcification accretion units, or CAUs, to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae. This effort is a partnership between CREP and Drs. Nicole Price of Bigelow Marine Laboratory and Jen Smith of Scripps Institution of Oceanography, who have extensive knowledge of marine benthic algal community ecology.\n\nCAUs are composed of two 10 x 10 centimeter (cm) flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos using stainless steel threaded rods. Calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over 2-3 year deployments. Due to the simple, low-cost design and analysis, statistically robust numbers of calcification plates can easily be deployed, recovered, and processed to provide estimates of net calcification, percent cover, and vertical accretion rates. CAUs have been deployed and replaced at existing, long-term monitoring sites during Pacific RAMP cruises, in accordance with protocols developed by Price et al. 2012. There are typically five CAU sites established at each location CREP visits with five units deployed at each site.\n\nThe study provides information about Pacific-wide spatial patterns of algal calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. In conjuction with benthic community composition data (separate dataset), the calcification rates will aid in determining the magnitude of how ocean acidification affects coral reefs in the natural environment. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive, accession 0137093.\n\nThe reef study sites are throughout the Pacific Ocean, in areas with little or no direct local anthropogenic impacts and areas of anthropogenic impact. Pacific RAMP is an ideal platform from which to collect samples over a broad range of benthic ecosystems, oceanic regimes and gradients, to observe ecological impacts of ocean acidification on natural reef systems, outside of the laboratory.\n\nAnalysis of these data will expand scientists\u00e2\u0080\u0099 capacity for assessing coral reef resilience regarding the effects of ocean acidification outside of controlled laboratory experiments. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher CO2 world and on the design of reef management strategies.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0138649_Not Applicable.json b/datasets/gov.noaa.nodc:0138649_Not Applicable.json index 556793c785..649453780b 100644 --- a/datasets/gov.noaa.nodc:0138649_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0138649_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0138649_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data correspond to that published in the analysis of the following manuscript: I.C. Enochs, Manzello, D.P., Donham, E.M., Kolodziej, G., Okano, R., et al. (in press) Shift from coral to macroalgae dominance on a volcanically acidified reef. Nature Climate Change. https://doi.org/10.1038/nclimate2758", "links": [ { diff --git a/datasets/gov.noaa.nodc:0138863_Not Applicable.json b/datasets/gov.noaa.nodc:0138863_Not Applicable.json index 037e99baef..6660dd8027 100644 --- a/datasets/gov.noaa.nodc:0138863_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0138863_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0138863_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Mammal Laboratory (NMML) has conducted passive acoustic monitoring in the Bering, Chukchi, and Western Beaufort Seas to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Species and sounds detected on sonobuoys include fin, blue, bowhead, humpback, killer, gray, minke, sperm, beluga, sei, and North Pacific right whales, walrus, ribbon and bearded seals, and seismic airguns. This short-term passive acoustic monitoring was also used to locate vocalizing species of interest for photo-identification, tagging, and behavioral studies. Recordings are available since 2007 in the Bering Sea, since 2010 in the Chukchi and Beaufort Seas, and in 2013 in the Gulf of Alaska. Both omnidirectional and DiFAR sonobuoys have been used. The vast majority of the sonobuoys were deployed opportunistically along the tracks of research cruises funded by the Bureau of Ocean Energy Management (BOEM). In one year (2009), sonobuoys were deployed opportunistically from an aerial survey plane. All sonobuoys were provided by the United States Navy (Naval Operational Logistics Support Center, Naval Surface Warfare Center, Crance Division, and the Office of the Assistant Secretary of the Navy).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0138984_Not Applicable.json b/datasets/gov.noaa.nodc:0138984_Not Applicable.json index 05e0ef6f39..d440017cb9 100644 --- a/datasets/gov.noaa.nodc:0138984_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0138984_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0138984_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "California sea lions (Zalophus californianus) and Pacific harbor seals (Phoca vitulina) use offshore oil and gas platforms as resting and foraging areas. Both species are protected by the Marine Mammal Protection Act (1972). The Bureau of Ocean Energy Management (BOEM) is required to collect information from platforms being used by California sea lions and harbor seals (or other pinniped species) with the goal of meeting environmental review and permitting requirements associated with the eventual decommissioning of offshore platforms. Decommissioning requirements are under the jurisdiction of BOEMs sister agency, the Bureau of Safety and Environmental Enforcement (BSEE). However, BOEM provides environmental studies and environmental review support for BSEE actions. To accomplish this goal, BOEM entered an inter-agency agreement with the National Marine Mammal Laboratories' California Current Ecosystem Program (CCEP; AFSC/NOAA) in 2012. Specifically, BOEM funded CCEP to conduct a study (from January 2012 to January 2015) to characterize and quantify California sea lion and Pacific harbor seal use of the platforms, including; abundance, seasonal use patterns, and age and sex class composition of animals on the platforms. Inter- (i.e. spatial) and intra- (i.e. temporal) platform comparisons were examined.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0140481_Not Applicable.json b/datasets/gov.noaa.nodc:0140481_Not Applicable.json index a3e43072d2..14ade71851 100644 --- a/datasets/gov.noaa.nodc:0140481_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0140481_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0140481_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hearing sensitivity data was collected on beluga whales in Bristol Bay with auditory evoked potential (AEP) methods for the frequencies 4, 8, 11.2, 16, 22.5, 32, 45, 54, 80, 100, 128, 150 kHz in 7 belugas in 2012 and 9 in 2014.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0143303_Not Applicable.json b/datasets/gov.noaa.nodc:0143303_Not Applicable.json index bcc2aad27a..1b6d37ceed 100644 --- a/datasets/gov.noaa.nodc:0143303_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0143303_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0143303_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Mammal Laboratory (NMML) has deployed long-term passive acoustic recorders in various locations in Alaskan waters and in the High Arctic to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Following the timing of peak calling among the various long-term recorders may provide some insight into finer-scale movements of cetaceans throughout the Bering, Chukchi, and Beaufort Seas. Changes in ambient noise levels can also be tracked. Recordings are available since 2007 in the Bering and Beaufort Seas, since 2010 in the Chukchi, and from 2008-2012 in Fram Strait. The majority of these recorders were deployed on NMML subsurface moorings, although several have been deployed on the oceanographic moorings of other researchers. Several different types of autonomous passive acoustic recorders have been deployed, most for one year. Recording parameters varied among instrument types and have evolved among projects. The majority of these recorders and deployments were funded by the Bureau of Ocean Energy Management (BOEM); however, several were funded by a grant from the Ocean Acoustics Program (NOAA/S and T).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0143928_Not Applicable.json b/datasets/gov.noaa.nodc:0143928_Not Applicable.json index 557ec10ee2..bca68b4f5b 100644 --- a/datasets/gov.noaa.nodc:0143928_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0143928_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0143928_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project was a cooperative effort between the National Ocean Service and the Florida Department of Environmental Protection's Florida Marine Research Institute (now called the Fish and Wildlife Research Institute). The goal of the effort was to produce shallow-water (from 0 to approximately 30 m water depth) benthic habitat maps of the Florida Keys and adjacent waters.\n\n The maps were generated by expert visual interpretation of 1:48,000 scale color aerial photography and subsequent photogrammetric, stereo, digital compilation of interpreted habitat polygon boundaries from aerial photography. The Minimum mapping unit = 0.4 hectare (4,047 sq m; 1 acre) for all habitat. Patch reefs may be <0.5 ha. The aerial photography was acquired using a NOAA jet from December 1991 through March 1992. The photography was acquired with 60% side and 80% forward overlap to facilitate stereo compilation. Approximately 450 aerial photographs were acquired and used for the mapping project. Ground validation of interpreted habitat polygons was performed by visual verification at actual field sites prior to compilation. Aircraft Inertial Measurement Unit data were used to correct photography positioning in photogrammetric analytical plotters. The analytical solution used in the photogrammetric plotter for positioning was applied to bundles of 30-40 adjacent, overlapping aerial photographs. The stereo positioning of the photography was < 1 m. Digital data for bundles of compiled aerial photographs from the photogrammetric stereo plotter was imported into the ESRI ArcInfo GIS. The GIS was used to merge and edit the vector and attribute features of the 15 bundles to generate a mosaic benthic habitat map of the Florida Keys and adjacent areas covered by the aerial photography. Field validation of digitized habitat features visible in the aerial photography mosaics was performed to ensure correct interpretation. An assessment of the correctness of the interpreted digital map was performed by experts familiar with the the seafloor habitat found in the Florida Keys.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0145165_Not Applicable.json b/datasets/gov.noaa.nodc:0145165_Not Applicable.json index aecd619c67..4c82c501bd 100644 --- a/datasets/gov.noaa.nodc:0145165_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0145165_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0145165_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Marine Mammal Laboratories' California Current Ecosystem Program (AFSC/NOAA) initiated and maintains census programs for California sea lions (Zalophus californianus) and northern fur seals (Callorhinus ursinus) at San Miguel and San Nicolas Islands, California. The program documents annual pup births, pup mortality, and temporal patterns in adult and juvenile presence at San Miguel Island. For both species, the database contains field data on the annual number of live pups and dead pups by location. At San Miguel Island, daily counts of adults, pups, and juveniles in a sample area are also available. The data are used to describe population trends and changes in land resource use among the species.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0146259_Not Applicable.json b/datasets/gov.noaa.nodc:0146259_Not Applicable.json index 88c5079d56..31bb5d8571 100644 --- a/datasets/gov.noaa.nodc:0146259_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0146259_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0146259_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data from the capture and recapture of over 1500 male California sea lions (Zalophus californianus) from Washington between 1989-2006. The data fields include capture data such as time, location, weight, length, and girth for each animal captured. The dataset also includes records of resights of each animal from records collected from observers from California to Vancouver Island, British Columbia, Canada. The dataset also contains information from opportunistic captures of Steller sea lions (Eumetopias jubatus) in the same region.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0146680_Not Applicable.json b/datasets/gov.noaa.nodc:0146680_Not Applicable.json index d17f50be74..2506776042 100644 --- a/datasets/gov.noaa.nodc:0146680_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0146680_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0146680_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Jurisdictional managers have expressed concerns that nutrients from the village of Vatia, Tutuila, American Samoa, are having an adverse effect on the coral reef ecosystem in Vatia Bay. Excess nutrient loads promote increases in algal growth that can have deleterious effects on corals, such as benthic algae outcompeting and overgrowing corals. Nitrogen and phosphorus can also directly impact corals by lowering fertilization success, and reducing both photosynthesis and calcification rates. Land-based contributions of nutrients come from a variety of sources; in Vatia the most likely sources are poor wastewater management from piggeries and septic systems.\nNOAA scientists conducted benthic surveys to establish a baseline against which to compare changes in the algal and coral assemblages in response to nutrient fluxes.\n\nThe data described here were collected via belt transect surveys of coral demography (adult and juvenile corals) by the NOAA Coral Reef Ecosystem Program (CREP) according to protocols established by the NOAA National Coral Reef Monitoring Program (NCRMP). In 2015 data were collected at 18 stratified randomly selected sites in Vatia Bay. These data include photoquadrat benthic images.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0146682_Not Applicable.json b/datasets/gov.noaa.nodc:0146682_Not Applicable.json index 08b6594995..905efe08ed 100644 --- a/datasets/gov.noaa.nodc:0146682_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0146682_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0146682_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data described herein are part of a NOAA Coral Reef Conservation Program (CRCP) funded project aimed at establishing baseline data for coral demographics and benthic cover and composition via Rapid Ecological Assessment (REA) surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) at Faga'alu Bay, Tutuila, American Samoa between 2012 and 2015. Photoquadrat benthic images were collected in 2012 and 2015 only, via belt transect surveys of coral demography according to protocols established by CREP in 2012 and by the NOAA National Coral Reef Monitoring Program (NCRMP) in 2015.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0147683_Not Applicable.json b/datasets/gov.noaa.nodc:0147683_Not Applicable.json index 407951b654..bf5a00ef5e 100644 --- a/datasets/gov.noaa.nodc:0147683_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0147683_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0147683_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NOAA NMFS does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in this publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause directly or indirectly the advertised product to be used or purchased because of this NMFS publication. NMFS is not responsible for any uses of these datasets beyond those for which they were intended, and NMFS makes no claims regarding the accuracy of any data provided by agencies or individuals outside NMFS. Acknowledgment of NOAA NMFS and SEAMAP would be appreciated in products derived or publications generated from this data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0148759_Not Applicable.json b/datasets/gov.noaa.nodc:0148759_Not Applicable.json index 4251667ebe..7bd8f6134c 100644 --- a/datasets/gov.noaa.nodc:0148759_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0148759_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0148759_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0148760_Not Applicable.json b/datasets/gov.noaa.nodc:0148760_Not Applicable.json index 550fb57157..a216a29ee2 100644 --- a/datasets/gov.noaa.nodc:0148760_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0148760_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0148760_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0155488_Not Applicable.json b/datasets/gov.noaa.nodc:0155488_Not Applicable.json index 846bae65c2..7d18fe6f5a 100644 --- a/datasets/gov.noaa.nodc:0155488_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0155488_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0155488_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bottom dissolved oxygen (DO) data was extracted from environmental profiles acquired during the Southeast Fisheries Science Center Mississippi Laboratories summer groundfish trawl surveys of the Western and North-central Gulf of Mexico from 1982-1998. The data were distributed to hypoxia researchers in near real time and used to generate bottom DO maps as part of the Hypoxia Watch Project (http://www.ncddc.noaa.gov/hypoxia/). The profiles were acquired with a Sea-Bird Model SB9 profiler equipped with pressure, temperature, conductivity, fluorescence, and beam transmission sensors. The data were processed with Sea-Bird software using the standard processing protocol developed by the Mississippi Laboratories. Water temperature, beam transmission, and derived salinity, DO and DO percent saturation, and density were retained in the processed files. SAS software was used to extract the bottom DO and other relevant data (e.g., date, time, position, and station number) and format the data as comma-delimited ASCII files.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0155948_Not Applicable.json b/datasets/gov.noaa.nodc:0155948_Not Applicable.json index 36fa440661..9a0c6b9917 100644 --- a/datasets/gov.noaa.nodc:0155948_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0155948_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0155948_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID: SE 11-08). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning.\n\nThe 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0155964_Not Applicable.json b/datasets/gov.noaa.nodc:0155964_Not Applicable.json index c4b14088b3..4f07162841 100644 --- a/datasets/gov.noaa.nodc:0155964_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0155964_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0155964_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 13-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning.\n\nThe 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0155998_Not Applicable.json b/datasets/gov.noaa.nodc:0155998_Not Applicable.json index a039e190de..9856e7588d 100644 --- a/datasets/gov.noaa.nodc:0155998_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0155998_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0155998_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface water samples were collected during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 12-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Samples were also collected opportunistically during some cetacean sightings.\n\nThe 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156424_Not Applicable.json b/datasets/gov.noaa.nodc:0156424_Not Applicable.json index 0122774517..8bf828cad4 100644 --- a/datasets/gov.noaa.nodc:0156424_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156424_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156424_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156425_Not Applicable.json b/datasets/gov.noaa.nodc:0156425_Not Applicable.json index 734ff28499..220250cd4c 100644 --- a/datasets/gov.noaa.nodc:0156425_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156425_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156425_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156692_Not Applicable.json b/datasets/gov.noaa.nodc:0156692_Not Applicable.json index 43a1955d6b..d8ce6cde20 100644 --- a/datasets/gov.noaa.nodc:0156692_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156692_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156692_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea. Methodologies, results, and analysis may be found in \"Enhanced macroboring and depressed calcification drive net dissolution at high-CO2 coral reef\" which is published in the Proceedings of the Royal Society, Series B", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156765_Not Applicable.json b/datasets/gov.noaa.nodc:0156765_Not Applicable.json index 6a6a4755fd..e79b6af1d0 100644 --- a/datasets/gov.noaa.nodc:0156765_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156765_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156765_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156869_Not Applicable.json b/datasets/gov.noaa.nodc:0156869_Not Applicable.json index a6c35d8ff9..fcbe522477 100644 --- a/datasets/gov.noaa.nodc:0156869_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156869_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156869_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The database contains Excel and CSV spreadsheets monitoring captive Sea Turtle rearing program. Daily feeding logs as well as water chemistry were recorded.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0156913_Not Applicable.json b/datasets/gov.noaa.nodc:0156913_Not Applicable.json index b1e5eb3ea0..b21277edad 100644 --- a/datasets/gov.noaa.nodc:0156913_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0156913_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0156913_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes census based carbonate budget data that was collected in coral reef habitats located within the SEFCRI region. Surveys (based on Perry et al 2012) were collected over the course of several weeks at four major sites: Emerald, Oakland Ridge, Barracuda, and Pillars. Within each of these sites, six transect surveys (10m each) were conducted to quantify benthic cover, macrobioerosion, and microbioerosion. Ten parrotfish surveys were also conducted to account for parrotfish erosion rates at each site. This carbonate budget data along with other sea water chemistry data collected were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA.\n\nWe measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the carbonate budget surveys that were collected to identify the sensitivity of the SEFCRI region to OA.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0157022_Not Applicable.json b/datasets/gov.noaa.nodc:0157022_Not Applicable.json index 9a90e492ea..3c92c6cd4a 100644 --- a/datasets/gov.noaa.nodc:0157022_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0157022_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0157022_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes seawater chemistry that was collected in coral reef habitats located within the SEFCRI region as well as inlets and outfalls that release nutrient rich and/or sediment laden freshwater to the coastal waters South Florida. Freshwater runoff and riverine inputs are known to be enriched in dissolved inorganic carbon, and diluted lower saline waters are known to have elevated pCO2 (e.g., Manzello et al. 2013) which is why those areas in addition to the reef sites were included in our analyses. This data along with other data collected in the field were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA.\n\nWe measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the seawater samples that were collected and analyzed to identify the carbonate chemistry in this region.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0157033_Not Applicable.json b/datasets/gov.noaa.nodc:0157033_Not Applicable.json index 717cd3829a..120ca7d228 100644 --- a/datasets/gov.noaa.nodc:0157033_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0157033_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0157033_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains information useful for red snapper stock assessment. The data set provided has count, weight, length, and location available of caught red snapper, red grouper, and other reef fishes. Catches were greatest in waters off Georgia, and declined with increasing latitude off South Carolina and North Carolina.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0157074_Not Applicable.json b/datasets/gov.noaa.nodc:0157074_Not Applicable.json index 9fab4cc7f6..078a54fb64 100644 --- a/datasets/gov.noaa.nodc:0157074_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0157074_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0157074_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0157087_Not Applicable.json b/datasets/gov.noaa.nodc:0157087_Not Applicable.json index 05e27115e0..4b6f197cac 100644 --- a/datasets/gov.noaa.nodc:0157087_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0157087_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0157087_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To better understand the functional roles of parrotfishes on coral reefs in the Caribbean this project documented the foraging behavior and diets of six species of parrotfishes (Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) at three locations (Long Reef, Cane Bay, and Buck Island) on the north shore of St. Croix, U. S. Virgin Islands. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous \u00e2\u0080\u009cturf\u00e2\u0080\u009d algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, (5) ledge, or (6) sand. In order to quantify the relative abundance of different substrates and food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the six substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, ledge, and sand) in 0.5 m x 0.5 m photoquadrats. Photographs were taken at 2.5 m intervals on 30 m transects, with a total of 10 haphazardly placed transects sampled at each site. Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0157611_Not Applicable.json b/datasets/gov.noaa.nodc:0157611_Not Applicable.json index d854195d82..e0057c7655 100644 --- a/datasets/gov.noaa.nodc:0157611_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0157611_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0157611_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A team from the Pacific Islands Fisheries Science Center (PIFSC), Coral Reef Ecosystem Program (CREP) deployed on a two-week research cruise in November 2015 to evaluate the impacts of the 2015 mass coral bleaching event in the Main Hawaiian Islands via towed-diver surveys. Areas surveyed included south Oahu, west Maui, Lana\u00e2\u0080\u0099i, and west Hawaii island. Over the course of 10 survey days, the team surveyed approximately 90 km of 15-m wide transects at depths ranging from 2 to 10 m.\n\nData provided in this dataset include benthic images that were collected during the towed-diver surveys from a camera that was mounted to the towboard. A downward-facing DSLR camera with strobes collected these photographic quadrat data by capturing an image of the benthos at 15-second intervals during the surveys.\n\nTwo additional datasets were collected during the surveys and are documented separately. Towed divers recorded visual estimates of percentage of live coral that was pale and bleached, as well as presence/absence data of condition by generic composition. Oceanographic data was collected continuously throughout each survey with a suite of sensors mounted to the towboard recording conductivity, temperature, depth, flourometry (chlorophyll-a), turbidity and dissolved oxygen.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0159386_Not Applicable.json b/datasets/gov.noaa.nodc:0159386_Not Applicable.json index 3f760f11ea..1ccd4eec23 100644 --- a/datasets/gov.noaa.nodc:0159386_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0159386_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0159386_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0159419_Not Applicable.json b/datasets/gov.noaa.nodc:0159419_Not Applicable.json index a7efb5cfa1..cd9fbf79d8 100644 --- a/datasets/gov.noaa.nodc:0159419_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0159419_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0159419_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27\u00c2\u00b058'N to 29\u00c2\u00b026'N and 87\u00c2\u00b034'W to 91\u00c2\u00b001'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0159850_Not Applicable.json b/datasets/gov.noaa.nodc:0159850_Not Applicable.json index fa0a2fac9f..3ecdbb33cb 100644 --- a/datasets/gov.noaa.nodc:0159850_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0159850_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0159850_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains hourly visual observations of burrowing behavior in penaeid shrimp.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0161311_Not Applicable.json b/datasets/gov.noaa.nodc:0161311_Not Applicable.json index 7658f2f758..fe08a63607 100644 --- a/datasets/gov.noaa.nodc:0161311_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0161311_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0161311_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0161523_Not Applicable.json b/datasets/gov.noaa.nodc:0161523_Not Applicable.json index 3d1c6dcff0..de33329bac 100644 --- a/datasets/gov.noaa.nodc:0161523_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0161523_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0161523_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0161523 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB04: PacIOOS Water Quality Buoy 04 (WQB-04): Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month.\n\nThe water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0162518_Not Applicable.json b/datasets/gov.noaa.nodc:0162518_Not Applicable.json index d9b27eb702..d9de27785e 100644 --- a/datasets/gov.noaa.nodc:0162518_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0162518_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0162518_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27\u00c2\u00b058'N to 29\u00c2\u00b026'N and 87\u00c2\u00b034'W to 91\u00c2\u00b001'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0162828_Not Applicable.json b/datasets/gov.noaa.nodc:0162828_Not Applicable.json index 1923ab81c6..6970bf08e6 100644 --- a/datasets/gov.noaa.nodc:0162828_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0162828_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0162828_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The benthic cover data described here result from benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in 2015 along transects at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) over time.\n\nBenthic habitat photographs were quantitatively analyzed using a web-based annotation tool called CoralNet (Beijbom et al. 2016). Images were analyzed to produce three functional group levels of benthic cover: Tier 1 (e.g., hard coral, soft coral, macroalgae, turf algae, etc.), Tier 2 (e.g., Hard Coral = massive, branching, foliose, encrusting, etc.; Macroalgae = upright macroalgae, encrusting macroalgae, bluegreen macroalgae, and Halimeda, etc.), and Tier 3 (e.g., Hard Coral = Astreopora sp, Favia sp, Pocillopora, etc.; Macroalgae = Caulerpa sp, Dictyosphaeria sp, Padina sp, etc.).\n\nThese benthic cover data for the Philippines provide an estimate of the benthic community composition at each climate survey site, and give context to the results from the other climate survey components (archived separately).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0162829_Not Applicable.json b/datasets/gov.noaa.nodc:0162829_Not Applicable.json index 954ccd7cef..fce3bdd4aa 100644 --- a/datasets/gov.noaa.nodc:0162829_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0162829_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0162829_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is that biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time.\n\nThe data described here were collected by CREP from ARMS moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.\n\nEach ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 \u00c2\u00b5m, and 100 \u00c2\u00b5m. The 500 \u00c2\u00b5m and 100 \u00c2\u00b5m fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. The DNA sequencing data are not included in this archival package.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0162830_Not Applicable.json b/datasets/gov.noaa.nodc:0162830_Not Applicable.json index e36f06af2b..34b811aa4b 100644 --- a/datasets/gov.noaa.nodc:0162830_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0162830_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0162830_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Photographs of the seafloor were collected during benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in 2012 and 2015 along transects at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) over time. The imagery from 2015 has been quantitatively analyzed using image analysis software to derive an estimate of percent benthic cover (archived separately).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0162831_Not Applicable.json b/datasets/gov.noaa.nodc:0162831_Not Applicable.json index 95da5a9a3e..bd7d5bb8cd 100644 --- a/datasets/gov.noaa.nodc:0162831_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0162831_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0162831_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory experiments reveal calcification rates of crustose coralline algae (CCA) are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. Calcification accretion units, or CAUs, are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae.\n\nCAUs, constructed in-house by CREP, are composed of two 10 x 10 cm flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos by SCUBA divers using stainless steel threaded rods. Deployed on the seafloor for a period of time, calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over time. By measuring the change in weight of the CAUs, the reef carbonate accretion rate can be calculated for that time period.\n\nThe calcification rate data described here were collected by CREP from CAUs moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines, in accordance with protocols developed by Price et al. (2012). Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and five CAUs were deployed at each survey site.\n\nIn conjunction with benthic community composition data (archived separately), these data serve as a baseline for detecting changes associated with changing seawater chemistry due to ocean acidification within coral reef environments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0163192_Not Applicable.json b/datasets/gov.noaa.nodc:0163192_Not Applicable.json index f788a951fb..9950d76658 100644 --- a/datasets/gov.noaa.nodc:0163192_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0163192_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0163192_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0163212_Not Applicable.json b/datasets/gov.noaa.nodc:0163212_Not Applicable.json index d1edeab48f..81dc3e9371 100644 --- a/datasets/gov.noaa.nodc:0163212_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0163212_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0163212_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying \u00e2\u0080\u00a8heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0163750_Not Applicable.json b/datasets/gov.noaa.nodc:0163750_Not Applicable.json index 1a04500172..7f7c4d414d 100644 --- a/datasets/gov.noaa.nodc:0163750_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0163750_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0163750_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0163750 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0163764_Not Applicable.json b/datasets/gov.noaa.nodc:0163764_Not Applicable.json index 15f4b9e569..2c0057db10 100644 --- a/datasets/gov.noaa.nodc:0163764_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0163764_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0163764_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0163764 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Link Port (IRL-LP) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0164194_Not Applicable.json b/datasets/gov.noaa.nodc:0164194_Not Applicable.json index ea4cf75c4e..ce1b140d07 100644 --- a/datasets/gov.noaa.nodc:0164194_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0164194_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0164194_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of this project was to examine the interrelationship between microbial activity and water column geochemistry in the world\u00e2\u0080\u0099s largest, truly marine anoxic system, the Cariaco Basin. This project focused on microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Over the 21 year program, the Stony Brook team typically staged cruises semi-annually during upwelling (Mar-May) and non- upwelling (Oct-Nov) periods. These 24-hour cruises were usually within a week of the routine monthly cruises staged by the Fundacion La Salle and University of South Florida team. Most cruises occupied only the CARIACO Ocean Time-Series station. On cruises 108 to 132, additional stations in the western basin and on the sill to the north of the Cariaco station were also sampled. Locations are given in the database. Data provided in a single MS Excel spreadsheet.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0165016_Not Applicable.json b/datasets/gov.noaa.nodc:0165016_Not Applicable.json index 820c03cb1a..3641ae3aed 100644 --- a/datasets/gov.noaa.nodc:0165016_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0165016_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0165016_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Jurisdictional managers have expressed concerns that nutrients from the village of Vatia, Tutuila, American Samoa, are having an adverse effect on the adjacent coral reef ecosystem. Excess nutrient loads promote increases in algal growth that can have deleterious effects on corals, such as benthic algae outcompeting and overgrowing corals. Nitrogen and phosphorus can also directly impact corals by lowering fertilization success, and reducing both photosynthesis and calcification rates. Land-based contributions of nutrients come from a variety of sources; in Vatia the most likely sources are poor wastewater management from piggeries and septic systems.\n\nNOAA scientists conducted benthic surveys to establish a baseline against which to compare changes in the algal and coral assemblages in response to nutrient fluxes.\n\nThe data described here were collected via belt transect surveys of coral demography (adult and juvenile corals) by the NOAA Coral Reef Ecosystem Program (CREP) according to protocols established by the NOAA National Coral Reef Monitoring Program (NCRMP). In 2015 data were collected at 18 stratified randomly selected sites in Vatia Bay. These data include:\n\n1) an assessment of coral colony density and size-class distribution for the selected monitoring sites;\n\n2) an assessment of coral recruitment at the monitoring sites; and\n\n3) an evaluation of coral colony condition, including mortality, disease, bleaching, and evidence of sediment stress.\n\nThese data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Additionally, photoquadrat benthic images were collected and analyzed for benthic cover composition (documented and archived separately).\n\nA brief report documenting the 2015 surveys conducted in Vatia and Faga'alu in Tutuila, American Samoa by the NOAA Coral Reef Ecosystem Program is in progress: Baseline Assessment of Coral Reef Community Structure and Demographics in Vatia and Faga\u00e2\u0080\u0098alu Bays, American Samoa.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0166378_Not Applicable.json b/datasets/gov.noaa.nodc:0166378_Not Applicable.json index 3569b7ee5e..2a23f45fec 100644 --- a/datasets/gov.noaa.nodc:0166378_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0166378_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0166378_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Photographs of the seafloor were collected during benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in hard bottom shallow water (< 15 m) habitats in Timor-Leste. Photographs were collected along transects at fixed climate survey sites in October 2012 and September-October 2014 (10 sites and 8 sites, respectively), and during reef fish surveys surveys at 150 sites that were selected using a stratified random sampling design in June 2013.\n\nClimate sites were established by CREP to establish ecological baselines for climate change by measuring multiple features of the coral reef environment (in addition to the data described herein) over time. The reef fish surveys were conducted to generate baseline data on the nearshore coral reef fish assemblages and associated benthic communities around Timor-Leste's north coast and Atauro Island. The photographs can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.\n\nThe imagery from 2013 and 2014 has been quantitatively analyzed using image analysis software to derive an estimate of percent benthic cover. The benthic cover data, and the associated reef fish survey data and parameters measured to establish ecological baselines for climate change are archived separately.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0167532_Not Applicable.json b/datasets/gov.noaa.nodc:0167532_Not Applicable.json index 3618d0f7ca..8c9b0b5d12 100644 --- a/datasets/gov.noaa.nodc:0167532_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0167532_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0167532_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains multibeam bathymetry, uncertainty, and backscatter GeoTiffs with 1x1 meter cell size represent water depth and acoustic intensity of the seafloor from the Phase III Long Island Sound Benthic Habitat Priority Areas of Interest in the Long Island Sound. These datasets were surveyed by NOAA Ship Nancy Foster R-352 in 2015 using 400 khz Reson 7125 multibeam sonars in coordination with the NOAA Biogeography Branch and the Integrated Ocean and Coastal Mapping Branch. The multibeam was corrected, calibrated, and integrated into a seamless 32-bit raster using CARIS and ArcGIS. Backscatter data was collected and mosaicked into a raster using Fledermaus Geocoder Toolbox, ArcGIS 10.4, and PCI Geomatica 2016 software at the Biogeography Branch by NOAA contractors.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0167946_Not Applicable.json b/datasets/gov.noaa.nodc:0167946_Not Applicable.json index c1e3f0c97d..8358acf855 100644 --- a/datasets/gov.noaa.nodc:0167946_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0167946_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0167946_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains an integrated GeoTiff with 1x1 meter cell size representing the 2014 Long Island Sound Benthic Habitat Priority Area of Interest between Brigeport, CT and Port Jefferson, NY. This integrated bathymetric raster is a mosaic of surveys from NOAA Ship Thomas Jefferson (S-222) and its two inshore launch vessels, NOAA Ship Rude (S-590), as well as surveys conducted by the Stony Brook University R/V Pritchard in coordination with the NOAA Biogeography Branch and the Office of Coastal Services between in the year 2012. Bathymetry data was collected using multibeam sonars and integrated into a seamless 32 bit raster using ArcGIS 10.1 raster calculator by the Biogeography Branch by a NOAA contractor.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0168620_Not Applicable.json b/datasets/gov.noaa.nodc:0168620_Not Applicable.json index 431aabbb48..c2daf8dfc4 100644 --- a/datasets/gov.noaa.nodc:0168620_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0168620_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0168620_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The benthic cover data described here result from benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in hard bottom shallow water (< 15 m) habitats in Timor-Leste during reef fish surveys surveys at 150 sites that were selected using a stratified random sampling design in June 2013, and along transects at fixed climate survey sites in September-October 2014 (10 sites and 8 sites, respectively).\n\nClimate sites were established by CREP to establish ecological baselines for climate change by measuring multiple features of the coral reef environment (in addition to the data described herein) over time. The reef fish surveys were conducted to generate baseline data on the nearshore coral reef fish assemblages and associated benthic communities around Timor-Leste's north coast and Atauro Island.\n\nPercent benthic cover for each site is estimated from a photo-transect (30 photographs, taken at 1-m intervals, 10+ points analyzed per photograph using Coral Point Count with Extensions). NA values represent situations where images were either not gathered or not analyzed.\n\nThese benthic cover data for Timor-Leste provide an estimate of the benthic community composition at each survey site, and give context to the results from the other survey components. The benthic images, and the associated reef fish survey data and parameters measured to establish ecological baselines for climate change are documented separately.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0168912_Not Applicable.json b/datasets/gov.noaa.nodc:0168912_Not Applicable.json index 372cdf41fc..3ac5a9a65c 100644 --- a/datasets/gov.noaa.nodc:0168912_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0168912_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0168912_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Coral belt transect surveys, focused at quantifying the relative abundance, density, and size-class distribution of the anthozoan and hydrozoan corals, as well as the condition and health state of the coral populations were conducted around the islands of Maui, Hawaii, and Oahu by the NOAA Coral Reef Ecosystem Program and the Hawaii Division of Aquatic Resources (DAR) from March 8, 2010 to November 8, 2011.\n\nThe surveys were conducted along two pre-selected transect lines. For coral observations the transect length was 12 m long and the transect width was 1 m wide (0.5 m on each side of the transect line). For coral condition and health observations, the transect length was between 12.5 and 25 m long and the transect width was 1 m, 2 m or 4 m wide (0.5 m, 1.0 m, or 2.0 m on each side of the transect line). The surveyed area was 24 m^2 per site for the coral observations, and ranged from to 25 m^2 to 200 m^2 per site for the disease observations.\n\nWithin each transect 1-m segments were surveyed, whereby in each segment all coral colonies whose center fell within 0.5 m of either side of the transect line were identified to the lowest taxonomic level possible (genus or species) and colony size visually estimated and binned by its maximum diameter in one of 7 size classes: 0-5 cm, 5-10 cm, 10-20 cm, 20-40 cm, 40-80 cm, 80-160 cm, or greater than 160 cm.\n\nWhen a coral colony exhibited signs of disease or compromised health, additional information was separately recorded. Within each of the two transects, all diseased coral colonies whose center fell within 0.5\u00e2\u0080\u00932 m on each side of each transect line were carefully examined, measured (length and width of the colony in centimeters, when survey time allowed), identified to the lowest taxonomic level possible, and assigned to one of several types of afflictions, including coral and algal diseases, bleaching, infections, infestations, discolorations, predation, pigmentation responses, skeletal growth anomalies, and tissue loss. Severity of the affliction (mild, moderate, marked, severe, acute) was also recorded for a subset of bleaching observations only. Photographic documentation was also captured (archived and documented separately).\n\nRaw survey data includes species presence, colony counts per taxon, colony size (binned sizes for coral observations, colony width and length for a subset of disease observations), affliction observed, and severity of condition (for observations of bleached corals only).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0168913_Not Applicable.json b/datasets/gov.noaa.nodc:0168913_Not Applicable.json index 3f105a6f79..c8b1395614 100644 --- a/datasets/gov.noaa.nodc:0168913_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0168913_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0168913_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data package contains coral reef community composition data gathered during Line-Point-Intercept (LPI) surveys around the islands of Maui, Hawaii, and Oahu of the main Hawaiian Islands from March 8, 2010 to November 8, 2011 as part of a joint project with the NOAA Coral Reef Ecosystem Division (CRED) and the State of Hawaii Division of Aquatic Resources (DAR). The line-point-intercept (LPI) method (Hill and Wilkinson 2004) is used to assess the percentage of cover for live corals and other benthic elements.\n\nThe surveys were conducted by a SCUBA diver swimming along two pre-selected 25-m transect lines, during which the benthic element falling directly beneath the transect line was recorded at 25- or 50-cm intervals for 100 or 50 total points/observations per transect, respectively. Benthic elements were assigned to one of ten benthic categories: live (scleractinian) corals, octocorals, dead corals, coralline algae, macroalgae, turf algae, cyanophyes, zoanthids, other sessile macro-invertebrates, and sand. Benthic organisms were identified to the lowest taxonomic level possible (corals, macroalgae, and zoanthids to genus or species). Turf algae included pavement, rock, rubble, and turf algae observations.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0169338_Not Applicable.json b/datasets/gov.noaa.nodc:0169338_Not Applicable.json index 48340b4b70..9dd7e7bb75 100644 --- a/datasets/gov.noaa.nodc:0169338_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0169338_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0169338_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data described here, including photographs, genetic sequences, and specimen information, were collected by the NOAA Coral Reef Ecosystem Program (CREP) from Autonomous Reef Monitoring Structures, or ARMS, moored for two years at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in Timor-Leste. Climate sites were established in Timor-Leste in October 2012 to establish ecological baselines for climate change by measuring multiple features of the coral reef environment (in addition to the data described herein) over time.\n\nThree ARMS units were typically deployed by SCUBA divers at each survey site. Each ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms, scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 \u00c2\u00b5m, and 100 \u00c2\u00b5m. The 500 \u00c2\u00b5m and 100 \u00c2\u00b5m fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies, photographed, and identified to the lowest taxonomic identification possible. The plate photographs, sequences generated from the DNA metabarcoding of the scrapings and the 500- and 100-\u00c2\u00b5m fractions, specimen photographs, and specimen identifications are included in the ARMS dataset. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.\n\nARMS are used by CREP to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is that biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0169370_Not Applicable.json b/datasets/gov.noaa.nodc:0169370_Not Applicable.json index 255a83a5c4..08f304c7ca 100644 --- a/datasets/gov.noaa.nodc:0169370_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0169370_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0169370_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Supported by the NOAA Coral Reef Ecosystem Program (CREP), the data provided in this data set\u00e2\u0080\u0094including fish species, lengths, counts, and benthic cover\u00e2\u0080\u0094were generated from the analysis of video footage from baited underwater video station (BRUVS) surveys conducted during NOAA Pacific Islands Fisheries Science Center (PIFSC) missions in the Main Hawaiian Islands in September 2012 and November 2013, and the Northwestern Hawaiian Islands in May and September 2014.\n\nEach BRUVS uses high-definition video cameras mounted 0.7 m apart on a base bar that is inwardly converged at 8\u00c2\u00b0, set up as a stereo-video system. The video images from the cameras are subsequently analyzed to identify fish species and length. The use of bait attracts a wide diversity of fish species into the field of view of the cameras, but CREP is also experimenting with unbaited deployments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0169725_Not Applicable.json b/datasets/gov.noaa.nodc:0169725_Not Applicable.json index d364dff9c8..e2f2fcf9bd 100644 --- a/datasets/gov.noaa.nodc:0169725_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0169725_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0169725_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The benthic cover data described herein were generated by the NOAA Coral Reef Ecosystem Program (CREP) from the quantitative analysis of photoquadrat benthic images using image analysis software, whereby random points are projected on each image and the benthic elements falling directly underneath each point are identified. The images were collected at sites in Faga'alu Bay in 2015 during belt transect surveys of coral demography. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. The benthic images and coral demography data are described and archived separately.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0169726_Not Applicable.json b/datasets/gov.noaa.nodc:0169726_Not Applicable.json index 74c585a8eb..e140fbd77b 100644 --- a/datasets/gov.noaa.nodc:0169726_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0169726_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0169726_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The benthic cover data described here were generated from the quantitative analysis of photoquadrat benthic images using image analysis software, whereby random points are projected on each image and the benthic elements falling directly underneath each point are identified. The images were collected at 18 stratified randomly selected sites in Vatia Bay in 2015 during belt transect surveys of coral demography by the NOAA Coral Reef Ecosystem Program (CREP).\n\nThe benthic cover data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. The benthic images and coral demography data are described and archived separately.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0169728_Not Applicable.json b/datasets/gov.noaa.nodc:0169728_Not Applicable.json index aaa55d2541..c2c893e9e2 100644 --- a/datasets/gov.noaa.nodc:0169728_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0169728_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0169728_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data described here were collected in Faga'alu, American Samoa in August 2012 via line-point intercept (LPI) surveys by the NOAA Coral Reef Ecosystem Program (CREP). At each survey site, a SCUBA diver quantitatively documented the benthic composition at 20-cm intervals along two 25-m transects for a total of 125 data points per transect. All living benthic elements (e.g., coral, algae, and other sessile invertebrates) were identified to the lowest taxonomic level possible. Raw survey data consist of counts of benthic elements, including but not limited to coral, carbonate pavement, sand, rubble, macroalgae, crustose coralline algae, turf algae, as well as other sessile invertebrates along the two transects. The data allows for the assessment and monitoring of community structure and composition, and provide the basis for computing quantitative estimates of benthic cover (%) at higher taxonomic levels like functional group (coral, macroalgae, turf algae) or on a finer taxonomic resolution such as genus level.\n\nThese data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Additionally, coral demographic surveys were conducted in 2013 and 2015 and coral surveys in 2012, photoquadrat benthic images were collected in 2012 and 2015, and the 2015 images were analyzed for benthic cover composition (all documented and archived separately).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0170031_Not Applicable.json b/datasets/gov.noaa.nodc:0170031_Not Applicable.json index 73ea926964..fc1c729943 100644 --- a/datasets/gov.noaa.nodc:0170031_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0170031_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0170031_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The calcification rate data described here were collected by the NOAA Coral Reef Ecosystem Program (CREP) from calcification accretion units, or CAUs, moored for two years at fixed climate survey sites and located on hard bottom shallow water (< 15 m) habitats in Timor-Leste, in accordance with protocols developed by Price et al. (2012). Five CAUs were deployed at each survey site. Climate sites were established in Timore-Leste by CREP in October 2012 to establish ecological baselines for climate change by measuring multiple features of the coral reef environment (in addition to the data described herein) over time.\n\nCAUs, constructed in-house by CREP, are composed of two 10 x 10 cm flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos by SCUBA divers using stainless steel threaded rods. Deployed on the seafloor for a period of time, calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over time. By measuring the change in weight of the CAUs, the reef carbonate accretion rate can be calculated for that time period, measured in grams per centimeter per year.\n\nLaboratory experiments reveal calcification rates of crustose coralline algae (CCA) are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. CAUs are used by CREP to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae.\n\nThese calcification rate data for Timor-Leste, along with other data collected at the climate survey sites (water temperature and chemistry, invertebrate biodiversity, and benthic cover, all archived separately), serve as a baseline for detecting changes associated with changing seawater chemistry and can be used to help scientists assess and understand how Timor-Leste's coral reefs are responding to ocean acidification.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0170454_Not Applicable.json b/datasets/gov.noaa.nodc:0170454_Not Applicable.json index 8ebf7d96c4..20c3793167 100644 --- a/datasets/gov.noaa.nodc:0170454_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0170454_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0170454_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mean bottom temperature taken off a commercial fishing vessel used during a scientific trip. Data was collected using a Tidbit v2 Temperature Data Logger from Onset Computers. The sensor was placed on the trawl doors of a demersal bottom trawl. Bottom temperature was recorded once per minute throughout the fishing tow. Tows averaged 75 minutes. Bottom temperature was then aggregated by collected the mean temperature per tow. The provided data file includes the tow number, end of tow time, location (lat and long), depth (fathoms) and average bottom temperature (C). Data are in csv format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171311_Not Applicable.json b/datasets/gov.noaa.nodc:0171311_Not Applicable.json index 92d08ee272..19ebebbbe8 100644 --- a/datasets/gov.noaa.nodc:0171311_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171311_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171311_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171311 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Indian Island in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171312_Not Applicable.json b/datasets/gov.noaa.nodc:0171312_Not Applicable.json index b62d1478c3..17be8f4050 100644 --- a/datasets/gov.noaa.nodc:0171312_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171312_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171312_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171312 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named Monterey Bay Commercial Wharf in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171313_Not Applicable.json b/datasets/gov.noaa.nodc:0171313_Not Applicable.json index 11218c6ec3..4277d9eceb 100644 --- a/datasets/gov.noaa.nodc:0171313_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171313_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171313_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171313 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Romberg Tiburon Center SFSU/CeNCOOS collected the data from their in-situ moored station named Romberg Tiburon Center Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Romberg Tiburon Center SFSU/CeNCOOS and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171314_Not Applicable.json b/datasets/gov.noaa.nodc:0171314_Not Applicable.json index f92e87d015..847642cfd9 100644 --- a/datasets/gov.noaa.nodc:0171314_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171314_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171314_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171314 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). California Polytechnic State Univeristy, San Luis Obispo, collected the data from their in-situ moored station named Morro Bay in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from California Polytechnic State Univeristy, San Luis Obispo, and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171316_Not Applicable.json b/datasets/gov.noaa.nodc:0171316_Not Applicable.json index eddfddc365..7307206bc7 100644 --- a/datasets/gov.noaa.nodc:0171316_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171316_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171316_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171316 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). California Polytechnic State Univeristy, San Luis Obispo, collected the data from their in-situ moored station named Cal Poly Pier San Luis Obispo in the North Pacific Ocean and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from California Polytechnic State Univeristy, San Luis Obispo, and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171318_Not Applicable.json b/datasets/gov.noaa.nodc:0171318_Not Applicable.json index 7723f7bf54..ee78c71a4c 100644 --- a/datasets/gov.noaa.nodc:0171318_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171318_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171318_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171318 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University/CeNCOOS collected the data from their in-situ moored station named Trinidad Head, California, in the North Pacific Ocean and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University/CeNCOOS and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171319_Not Applicable.json b/datasets/gov.noaa.nodc:0171319_Not Applicable.json index 6bca1c26bf..8ee64d6462 100644 --- a/datasets/gov.noaa.nodc:0171319_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171319_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171319_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171319 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). San Francisco State University and The California Maritime Academy collected the data from their in-situ moored station named Carquinez at the California Maritime campus in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from San Francisco State University and The California Maritime Academy and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171320_Not Applicable.json b/datasets/gov.noaa.nodc:0171320_Not Applicable.json index 0a58f93f4d..fa68aa7ac3 100644 --- a/datasets/gov.noaa.nodc:0171320_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171320_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171320_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171320 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Bodega Marine Laboratory collected the data from their in-situ moored station named Bodega Marine Laboratory seawater intake in the Greater Farallones National Marine Sanctuary and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Bodega Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171321_Not Applicable.json b/datasets/gov.noaa.nodc:0171321_Not Applicable.json index 6eda2868a0..2adee6049f 100644 --- a/datasets/gov.noaa.nodc:0171321_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171321_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171321_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171321 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Bodega Marine Laboratory collected the data from their in-situ moored station named Fort Point Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Bodega Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171322_Not Applicable.json b/datasets/gov.noaa.nodc:0171322_Not Applicable.json index 3582ddb7dd..9ad48cc9dd 100644 --- a/datasets/gov.noaa.nodc:0171322_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171322_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171322_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171322 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Bodega Marine Laboratory collected the data from their in-situ moored station named burkolator at Hog Island Oyster Company in Tomales Bay in the Greater Farallones National Marine Sanctuary and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Bodega Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171323_Not Applicable.json b/datasets/gov.noaa.nodc:0171323_Not Applicable.json index 83d88612c7..e6cd87fa77 100644 --- a/datasets/gov.noaa.nodc:0171323_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171323_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171323_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171323 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of California, Santa Cruz, collected the data from their in-situ moored station named Santa Cruz municiple wharf in the Monterey Bay National Marine Sanctuary and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from University of California, Santa Cruz, and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171324_Not Applicable.json b/datasets/gov.noaa.nodc:0171324_Not Applicable.json index 39b6e65759..c3ad1c9770 100644 --- a/datasets/gov.noaa.nodc:0171324_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171324_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171324_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171324 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named seawater input for Moss Landing Marine Laboratory in the Monterey Bay National Marine Sanctuary, North Pacific Ocean, and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171325_Not Applicable.json b/datasets/gov.noaa.nodc:0171325_Not Applicable.json index 348ec769c6..d203f167da 100644 --- a/datasets/gov.noaa.nodc:0171325_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171325_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171325_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171325 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Long Bay Observation System collected the data from their in-situ moored station named 2nd Avenue Pier in the North Atlantic Ocean. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Long Bay Observation System and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171326_Not Applicable.json b/datasets/gov.noaa.nodc:0171326_Not Applicable.json index c0d4463c1c..5a53c35cab 100644 --- a/datasets/gov.noaa.nodc:0171326_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171326_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171326_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171326 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Long Bay Observation System collected the data from their in-situ moored station named Apache Pier in the North Atlantic Ocean. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Long Bay Observation System and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171327_Not Applicable.json b/datasets/gov.noaa.nodc:0171327_Not Applicable.json index b2cd2f1745..3452f557b5 100644 --- a/datasets/gov.noaa.nodc:0171327_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171327_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171327_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171327 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Long Bay Observation System collected the data from their in-situ moored station named Cherry Grove Pier in the North Atlantic Ocean. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Long Bay Observation System and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171328_Not Applicable.json b/datasets/gov.noaa.nodc:0171328_Not Applicable.json index 8597d3d535..db7b846a99 100644 --- a/datasets/gov.noaa.nodc:0171328_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171328_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171328_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171328 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Fort Pierce (IRL-FP) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171329_Not Applicable.json b/datasets/gov.noaa.nodc:0171329_Not Applicable.json index 12a02547c1..f7f2936f6d 100644 --- a/datasets/gov.noaa.nodc:0171329_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171329_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171329_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171329 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Vero Beach (IRL-VB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171330_Not Applicable.json b/datasets/gov.noaa.nodc:0171330_Not Applicable.json index 544b394c5c..2dfda4f1fe 100644 --- a/datasets/gov.noaa.nodc:0171330_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171330_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171330_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171330 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Sebastian (IRL-SB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171331_Not Applicable.json b/datasets/gov.noaa.nodc:0171331_Not Applicable.json index 749cf123d7..7ecbdea0c7 100644 --- a/datasets/gov.noaa.nodc:0171331_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171331_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171331_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171331 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - St. Lucie Estuary (IRL-SLE) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171332_Not Applicable.json b/datasets/gov.noaa.nodc:0171332_Not Applicable.json index 5e2ced90d1..e1e0b48a05 100644 --- a/datasets/gov.noaa.nodc:0171332_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171332_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171332_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171332 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Jensen Beach (IRL-JB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171345_Not Applicable.json b/datasets/gov.noaa.nodc:0171345_Not Applicable.json index 3051476e0e..d697a8ca73 100644 --- a/datasets/gov.noaa.nodc:0171345_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171345_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171345_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171345 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Pilot's Cove, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0171346_Not Applicable.json b/datasets/gov.noaa.nodc:0171346_Not Applicable.json index a5f641d427..2dfa41203f 100644 --- a/datasets/gov.noaa.nodc:0171346_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0171346_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0171346_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0171346 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Dry Bar, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0172043_Not Applicable.json b/datasets/gov.noaa.nodc:0172043_Not Applicable.json index 9202b0957a..4f657069d2 100644 --- a/datasets/gov.noaa.nodc:0172043_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0172043_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0172043_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0172377_Not Applicable.json b/datasets/gov.noaa.nodc:0172377_Not Applicable.json index dc14015536..53e39ce353 100644 --- a/datasets/gov.noaa.nodc:0172377_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0172377_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0172377_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0172588_Not Applicable.json b/datasets/gov.noaa.nodc:0172588_Not Applicable.json index 3cae96ddd6..d94651f4e0 100644 --- a/datasets/gov.noaa.nodc:0172588_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0172588_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0172588_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0172588 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0172612_Not Applicable.json b/datasets/gov.noaa.nodc:0172612_Not Applicable.json index 4df2fe576d..51e0b24ec6 100644 --- a/datasets/gov.noaa.nodc:0172612_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0172612_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0172612_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0172612 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named Monterey Bay Commercial Wharf in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0172613_Not Applicable.json b/datasets/gov.noaa.nodc:0172613_Not Applicable.json index 872d1df99e..5a99c7d5c3 100644 --- a/datasets/gov.noaa.nodc:0172613_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0172613_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0172613_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0172613 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Indian Island in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0173246_Not Applicable.json b/datasets/gov.noaa.nodc:0173246_Not Applicable.json index 002ebca03b..5a507b81b2 100644 --- a/datasets/gov.noaa.nodc:0173246_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0173246_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0173246_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bimonthly surveys of benthic fauna were conducted at four sites in the Mobile-Tensaw River Delta from November 1981 to September 1982. Two sites were at the upper reaches of the river delta, and two were at the mouth. Fauna were enumerated and identified to lowest taxon possible. Hydrographic data were also collected, including temperature, conductivity, and dissolved oxygen.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0173316_Not Applicable.json b/datasets/gov.noaa.nodc:0173316_Not Applicable.json index c3a63d11c0..bb9077c7a0 100644 --- a/datasets/gov.noaa.nodc:0173316_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0173316_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0173316_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, total alkalinity and pH on Total scale obtained during the R/V Nathaniel B. Palmer 2015 OOISO; NBP15_11, SOCCOM cruise (EXPOCODE 320620151206) in the South Pacific Ocean from 2015-12-06 to 2016-01-02.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0175745_Not Applicable.json b/datasets/gov.noaa.nodc:0175745_Not Applicable.json index 07a52b81c4..f2e9e1db0f 100644 --- a/datasets/gov.noaa.nodc:0175745_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0175745_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0175745_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (\"SAM\") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0175783_Not Applicable.json b/datasets/gov.noaa.nodc:0175783_Not Applicable.json index acfa2ad9a6..098a008146 100644 --- a/datasets/gov.noaa.nodc:0175783_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0175783_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0175783_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15\u00c2\u00b0E and 25\u00c2\u00b0E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php", "links": [ { diff --git a/datasets/gov.noaa.nodc:0175786_Not Applicable.json b/datasets/gov.noaa.nodc:0175786_Not Applicable.json index 23a4803f5d..dcb54a5830 100644 --- a/datasets/gov.noaa.nodc:0175786_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0175786_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0175786_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0176496_Not Applicable.json b/datasets/gov.noaa.nodc:0176496_Not Applicable.json index 7c9e213442..6d3dd2bef4 100644 --- a/datasets/gov.noaa.nodc:0176496_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0176496_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0176496_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a monthly survey of Mobile Bay between April 1980 and August 1981. Extant data from the MESC Data Management System include sediment particle size distribution, discrete hydrography, identification and enumeration of benthic fauna, and identification and enumeration of water column biota.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0185741_Not Applicable.json b/datasets/gov.noaa.nodc:0185741_Not Applicable.json index 1adb1570fc..2f97e01d0a 100644 --- a/datasets/gov.noaa.nodc:0185741_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0185741_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0185741_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are from the article \u00e2\u0080\u009cSeasonal carbonate chemistry dynamics on southeast Florida coral reefs: localized acidification hotspots from navigational inlets\u00e2\u0080\u009d published in Frontiers in Marine Science. The data in this package were collected from inlets and reefs along the coast of Southeast Florida. Water was collected bi-monthly from four reefs (Oakland Ridge, Barracuda, Pillars, and Emerald) and three closely-associated inlets (Port Everglades, Bakers Haulover, and Port of Miami). Water samples were collected at these locations either at the surface (~1m depth) or immediately above the benthos measured using a rosette sampler (ECO 55, Seabird). Temperature was recorded at each depth using a CTD (SBE 19V2, Seabird). Turbidity (NTU) was measured at time of water collection. Once collected, water samples were transferred to borosilicate glass bottles, samples were fixed using 200 \u00c2\u00b5L of HgCl2 and sealed using Apiezon grease and a glass stopper. Salinity was measured using a densitometer (DMA 5000M, Anton Paar), while total alkalinity (TA) and dissolved inorganic carbon (DIC) were determined using Apollo SciTech instruments (AS-ALK2 and AS-C3, respectively). All values were measured in duplicate and corrected using certified reference materials following recommendations in Dickson et al. (2007). Aragonite saturation state (\u00ce\u00a9Arag.), Calcite saturation state (\u00ce\u00a9Ca), pH (Total scale), and the partial pressure of CO2 (pCO2) were calculated with CO2SYS (Lewis and Wallace, 1998) using the dissociation constants of Mehrbach et al. (1973) as refit by Dickson and Millero (1987) and Dickson (1990). Water samples were reserved for nutrient analyzed at time of collection to determine Total Kjeldahl Nitrogen, Total Phosphorous, and fluorescence of Chlorophyll-a. This research was supported through NOAA\u00e2\u0080\u0099s Coral Reef Conservation Program.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0185742_Not Applicable.json b/datasets/gov.noaa.nodc:0185742_Not Applicable.json index 0e3596e524..8aaf5fb058 100644 --- a/datasets/gov.noaa.nodc:0185742_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0185742_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0185742_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This package contains a set of 12 monthly mean (MM) climatologies, one for each calendar month, and the maximum monthly mean (MMM) climatology. Each climatology has global coverage at 0.05-degree (5km) spatial resolution. The climatologies were derived from NOAA Coral Reef Watch's (CRW) CoralTemp Version 1.0 product and are based on the 1985-2012 time period of the CoralTemp data. They are used in deriving CRW's Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1. MMs are used to derive the SST Anomaly product, and the MMM is used to derive CRW's Coral Bleaching HotSpot, Degree Heating Week, and Bleaching Alert Area products.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0185753_Not Applicable.json b/datasets/gov.noaa.nodc:0185753_Not Applicable.json index 1e89e94e1b..80aa27469d 100644 --- a/datasets/gov.noaa.nodc:0185753_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0185753_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0185753_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0186561_Not Applicable.json b/datasets/gov.noaa.nodc:0186561_Not Applicable.json index cf148206a4..e0a71c1992 100644 --- a/datasets/gov.noaa.nodc:0186561_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0186561_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0186561_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0191401_Not Applicable.json b/datasets/gov.noaa.nodc:0191401_Not Applicable.json index f15e7ef951..3c01eccf9f 100644 --- a/datasets/gov.noaa.nodc:0191401_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0191401_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0191401_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogeochemical and microbiological variables were measured by Stony Brook University participants (see Author List) in the CARIACO Ocean Time-Series Program in order to study the microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Samples were collected by Nikson bottles from 1995-11-13 to 2015-11-14 in the Cariaco Basin (southeastern Caribbean Sea off northeastern Venezuelan coast) aboard the B/O Hermano Gines, operated by the Fundacion La Salle, Venezuela.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0194300_Not Applicable.json b/datasets/gov.noaa.nodc:0194300_Not Applicable.json index b14f81c4af..b4a2ac696a 100644 --- a/datasets/gov.noaa.nodc:0194300_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0194300_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0194300_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0204167_Not Applicable.json b/datasets/gov.noaa.nodc:0204167_Not Applicable.json index 9a3468a680..851382712a 100644 --- a/datasets/gov.noaa.nodc:0204167_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0204167_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0204167_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes two comma separated files containing data and metadata from three cetacean observation methods from two platforms, the manned Turbo Commander aircraft and the unmanned ScanEagle. The ACEs' imagery described here was collected and analyzed in order to conduct a 3-way comparison of data and derived statistics from the following: Observers in the manned aircraft; Digital photographs from cameras mounted to the manned aircraft; Digital photographs from cameras mounted to the Unmanned Aerial Vehicle (UAV). The Arctic Aerial Calibration Experiments (ACEs) study was designed to evaluate the ability of UAS technology (i.e., airframe, payloads, sensors, and software) to detect cetaceans, identify individuals to species, estimate group size, identify calves, and estimate density in arctic waters, relative to conventional aerial surveys conducted by human observers in fixed wing aircraft and to photographic strip transect data collected from the manned aircraft.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0204646_Not Applicable.json b/datasets/gov.noaa.nodc:0204646_Not Applicable.json index cc145dd5d7..246862a74e 100644 --- a/datasets/gov.noaa.nodc:0204646_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0204646_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0204646_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site.\n\nThe benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm \"robot\" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0205786_Not Applicable.json b/datasets/gov.noaa.nodc:0205786_Not Applicable.json index fc912ee0f0..40d5d696e4 100644 --- a/datasets/gov.noaa.nodc:0205786_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0205786_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0205786_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data package presents a three-decade (1985-2017) assessment of heat stress exposure in the wider Caribbean coral reefs at the ecoregional and local scales.\n\nThe main heat stress indicator used was the Degree Heating Weeks (DHW) calculated from daily Sea Surface Temperature \"CoralTemp\" data from CRW-NOAA available from 1985 to the present and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program. Different metrics were calculated based on daily DHW and are available in this dataset:\n\na) the maximum value of DHW per pixel for the entire time series\nb) the frequency of the annual maximum values of DHW \u00e2\u0089\u00a5 4 \u00c2\u00b0C-\nweeks (a predictor of coral \"bleaching risk\") per pixel\nc) the frequency of the annual maximum values of DHW \u00e2\u0089\u00a5 8 \u00c2\u00b0C-\nweeks (a predictor of bleach-induced mortality or \"mortality risk\") per pixel\nd) the year in which the maximum of DHW occurred\ne) the trend of the annual maximum values of DHW per pixel.\n\nBased on the spatiotemporal annual maximum DHW, a new regionalization of heat stress was performed by cluster analysis with the K-means algorithm through the unsupervised classification, this new regionalization delimits the Caribbean in 8 Heat Stress Regions (HSR). We summarized spatiotemporal daily data to describe the temporal patterns at an ecoregional scale by calculating the descriptive statistics of the regional DHW on a given day. This dataset represents a new baseline and regionalization of heat stress in the wider Caribbean coral reefs that will enhance conservation and planning efforts underway.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0206155_Not Applicable.json b/datasets/gov.noaa.nodc:0206155_Not Applicable.json index 8537b4fc27..24ec12ecd7 100644 --- a/datasets/gov.noaa.nodc:0206155_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0206155_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0206155_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Along the Fisheries Oceanography in Coastal Alabama (FOCAL) Transect on the Alabama shelf, a CTD survey was conducted using Seabird SBE 25 Sealogger CTD between 06/04/2019 and 08/02/2019. Data collected measured depth (m), salinity (PSU), temperature (ITS-90, deg C), oxygen (% Saturation), oxygen (mg/L), pH (pH), specific conductance (\u00c2\u00b5S/cm), beam attenuation (1/m), beam transmission (%), density (kg/m3), conductivity (\u00c2\u00b5S/cm), PAR (\u00c2\u00b5mol m-1 s-1), fluorescence (mg/m3), and fluorescence (mg/m3). Data was collected on 2019-06-04, 2019-06-28, 2019-07-02, 2019-07-05, 2019-07-09, 2019-07-16, 2019-07-19, 2019-07-30, and 2019-08-02 during the summer of 2019.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0207181_Not Applicable.json b/datasets/gov.noaa.nodc:0207181_Not Applicable.json index b559b0f9a1..b402259239 100644 --- a/datasets/gov.noaa.nodc:0207181_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0207181_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0207181_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEI accession contains statistical model (NH3_STAT) data. Global ammonia (NH3) emissions into the atmosphere are projected to increase in the coming years with the increased use of synthetic nitrogen fertilizers and cultivation of nitrogen-fixing crops. A statistical model (NH3_STAT) is developed for characterizing atmospheric NH3 emissions from agricultural soil sources, and compared to the performance of other global and regional NH3 models (e.g., EDGAR, MASAGE, MIX and U.S. EPA). The statistical model was developed by expressing a multiple linear regression equation between NH3 emission and the physicochemical variables. The model was evaluated for 2012 NH3 emissions. The results indicate that, in comparison to other data sets, the model provides a lower global NH3 estimate by 57%, (NH3_STAT: 13.9 Tg N yr-1; EDGAR: 33.0 Tg N yr-1). We also performed a region-based analysis (U.S., India, and China) using the NH3_STAT model. For the U.S., our model produces an estimate that is 143% higher in comparison to EPA. Meanwhile, the NH3_STAT model estimate for India shows NH3 emissions between -0.8 and 1.4 times lower when compared to other data sets. A lower estimate is also seen for China, where the model estimates NH3 emissions 0.4-5 times lower than other datasets. The difference in the global estimates is attributed to the lower estimates in major agricultural countries like China and India. The statistical model captures the spatial distribution of global NH3 emissions by utilizing a simplified approach compared to other readily available datasets. Moreover, the NH3_STAT model provides an opportunity to predict future NH3 emissions in a changing world.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0208019_Not Applicable.json b/datasets/gov.noaa.nodc:0208019_Not Applicable.json index 3cabb418fb..db201d43da 100644 --- a/datasets/gov.noaa.nodc:0208019_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0208019_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0208019_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes both hydrographic (salinity, temperature, dissolved oxygen) and carbonate chemistry data collected at the Aransas Ship Channel (Port Aransas, TX) under the funding provided by the National Academy of Sciences Gulf Research Program (Grant# 2000009312) during the period of 03/08/2018-08/22/2019.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0208388_Not Applicable.json b/datasets/gov.noaa.nodc:0208388_Not Applicable.json index c270a92e46..726efd1d32 100644 --- a/datasets/gov.noaa.nodc:0208388_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0208388_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0208388_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0208388 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-04: PacIOOS Water Quality Buoy 04: Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month.\n\nThe water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209056_Not Applicable.json b/datasets/gov.noaa.nodc:0209056_Not Applicable.json index cec64267d9..07cafc6bb0 100644 --- a/datasets/gov.noaa.nodc:0209056_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209056_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209056_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains bottom temperature data collected by thermistors mounted on lobster boats in the North Atlantic and Stellwagen Bank.\nThe accession consists of one .csv file contains the following variables - the location the temperature was recorded( site), the latitude (degrees N), longitude (degrees E), depth (m) and sea water temperature (degrees C) of each record.\nThis data was collected as part of the Environmental Monitors on Lobster Traps (eMOLT) project - a non-profit collaboration of industry, science and academics devoted to the monitoring of the physical environment of the Gulf of Maine and the Southern New England Shelf.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209071_Not Applicable.json b/datasets/gov.noaa.nodc:0209071_Not Applicable.json index cbf95886f9..fb08753099 100644 --- a/datasets/gov.noaa.nodc:0209071_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209071_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209071_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209115_Not Applicable.json b/datasets/gov.noaa.nodc:0209115_Not Applicable.json index f2fe35a31d..67c047ef6b 100644 --- a/datasets/gov.noaa.nodc:0209115_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209115_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209115_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains 17 depth profiles from 20-1000 m depth on the West Florida Shelf. Parameters include aragonite saturation state, total alkalinity, DIC, temperature and salinity. The data were collected using a CTD rosette aboard a NOAA-led research expedition in August 2017 entitled \u00e2\u0080\u0098Southeast Deep Coral Initiative: Exploring Deep-Sea Corals Ecosystems of the Southeast US\u00e2\u0080\u0099. The NOAA-led survey explored deep-sea coral habitat along West Florida shelf, using the remotely operated vehicle (ROV) Odysseus aboard NOAA Ship Nancy Foster. The cruise report for the expedition is hosted online here: https://doi.org/10.7289/V5/TM-NOS-NCCOS-244 (Wagner et al 2018).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209162_Not Applicable.json b/datasets/gov.noaa.nodc:0209162_Not Applicable.json index fec1fc2f20..5a04830563 100644 --- a/datasets/gov.noaa.nodc:0209162_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209162_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209162_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0209162 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-05: PacIOOS Water Quality Buoy 05: Pelekane Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month.\n\nThe water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-05 is located in Pelekane Bay near Kawaihae Harbor on the west side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209222_Not Applicable.json b/datasets/gov.noaa.nodc:0209222_Not Applicable.json index b212f28e86..8e72ee77fd 100644 --- a/datasets/gov.noaa.nodc:0209222_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209222_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209222_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209226_Not Applicable.json b/datasets/gov.noaa.nodc:0209226_Not Applicable.json index cc9c15c313..b6533469d5 100644 --- a/datasets/gov.noaa.nodc:0209226_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209226_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209226_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability.\n\nClimate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability.\n\nThe data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209247_Not Applicable.json b/datasets/gov.noaa.nodc:0209247_Not Applicable.json index 02491ac802..df01a8be53 100644 --- a/datasets/gov.noaa.nodc:0209247_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209247_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209247_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The benthic cover and fishing-net related data described in this dataset are derived from the GIS analysis of benthic orthophotos. The source imagery was collected using a Structure from Motion (SfM) approach during in-water marine debris swim surveys conducted by snorkelers in search of derelict fishing nets. Surveys were conducted by the NOAA Fisheries, Ecosystem Sciences Division (ESD) from September 24 to October 3, 2018 at Pearl and Hermes Atoll during an ESD-led marine debris mission to the Northwestern Hawaiian Islands (NWHI) aboard NOAA Ship Oscar Elton Sette. The lagoon at Pearl and Hermes was surveyed equally across the spatial gradient, from locations where derelict fishing nets are common to locations where derelict fishing nets have never been observed.\n\nDuring the 2018 mission, only a subset of marine debris surveys resulted in a SfM survey. Fishing nets were located during swim surveys and selected for SfM if the net was interacting with coral or hard substrate, the depth of the net was within ~1\u00e2\u0080\u00934 m of the surface, and the area of the net fit within the 9 sq. meter SFM survey plot. During a SFM survey, a permanent 3 x 3 m plot was established around the center of the fishing net, and the net was photographed using a back and forth swim pattern (\u00e2\u0080\u009cbefore\u00e2\u0080\u009d photos) for later processing using a SfM approach. The net was then removed, the volume of net removed was estimated and recorded, and the same area was photographed again in the same way (\u00e2\u0080\u009cafter\u00e2\u0080\u009d photos). A nearby (>50 m distant) paired control site was also photographed using the same method (\u00e2\u0080\u009ccontrol\u00e2\u0080\u009d photos).\n\nThe photographs were processed using Agisoft Metashape software to generate orthomosaic images that were analyzed in ArcGIS for benthic cover using a random point approach. The number of points at net-impacted sites were constrained to the net coverage area and were scaled to the net area to ensure an equal point density among replicate net-impact sites. The same number of points were randomly assigned to the 3 \u00c3\u0097 3 m paired control site. Each point was classified into one of seven benthic categories: turf algae, macroalgae, sand, bare substrate, Porites compressa, sponge, or crustose coralline algae (CCA). The annotated points for each site were converted to percent cover for each benthic category. Fishing net size (sq m) and degree of fouling were also calculated from the orthophotos. Analyses were conducted to compare the benthic composition of net sites to control sites and to determine if fouling or net size contributed to these differences.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0209357_Not Applicable.json b/datasets/gov.noaa.nodc:0209357_Not Applicable.json index 851f9a466b..23c6c1de84 100644 --- a/datasets/gov.noaa.nodc:0209357_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0209357_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0209357_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0210577_Not Applicable.json b/datasets/gov.noaa.nodc:0210577_Not Applicable.json index 9828f51022..7b1a2d1c09 100644 --- a/datasets/gov.noaa.nodc:0210577_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0210577_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0210577_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0210808_Not Applicable.json b/datasets/gov.noaa.nodc:0210808_Not Applicable.json index 04407a288c..348a7f9f22 100644 --- a/datasets/gov.noaa.nodc:0210808_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0210808_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0210808_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archive package contains data on species composition, density, size, and abundance for coral reef fish as well as coral counts, benthic cover, and macroalga cover in the West Hawaii Habitat Focus Area along the Kona coast of the island of Hawaii. Data provided in this collection were gathered as part of the NOAA Habitat Blueprint initiative with support from the Coral Reef Conservation Program. Data were collected primarily by The Nature Conservancy Hawaii. Data were collected in 2015 using the Belt Transect method. This is the first year in a series of monitoring efforts which have taken place in subsequent years to evaluate the resilience of coral reefs in the Focus Area. This dataset serves as a baseline as it was collected during the 2015 coral bleaching event. The dataset accompanies the NOAA technical report Maynard et al. 2016.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0213517_Not Applicable.json b/datasets/gov.noaa.nodc:0213517_Not Applicable.json index ed39955456..c76b6fcd44 100644 --- a/datasets/gov.noaa.nodc:0213517_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0213517_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0213517_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0218215_Not Applicable.json b/datasets/gov.noaa.nodc:0218215_Not Applicable.json index fc33f49e16..15dfbddfc1 100644 --- a/datasets/gov.noaa.nodc:0218215_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0218215_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0218215_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a three-dimensional (3-D), coupled ice-ocean Finite Volume Community Ocean Model (FVCOM) hydrodynamic simulations of circulation, temperature, and water surface elevation of Lake Superior for the years 2010-2012. The model was validated with temperature observations at National Oceanic and Atmospheric Administration (NOAA) buoys and mooring data from 2010. The upwelling event observed in satellite imagery and at a mooring station was reproduced by the model, in August 2010 along the northwestern coast. FVCOM version 3.1.6 was used for these simulations including custom modifications for wind-wave mixing (Hu and Wang, 2010) and centered-difference time integration. Ice simulations used the unstructured-grid, community ice code (UG-CICE) that was included with FVCOM version 3.1.6 (Chen et al. 2011; Gao et al. 2011). North American Regional Reanalysis (NARR) 32 km data (Mesinger et al. 2006) was used as atmospheric boundary conditions which included heat flux components (i.e., \"heating_on=T\" in the namelist). To convert the NARR forcings to the FVCOM unstructured grid, the interpolation scheme built in to FVCOM (WRF2FVCOM) was used. Details for these simulations can be found in the namelist file \"narr_0913_run.nml\" included in this data archive.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0220639_Not Applicable.json b/datasets/gov.noaa.nodc:0220639_Not Applicable.json index ea8baf73dc..5854948409 100644 --- a/datasets/gov.noaa.nodc:0220639_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0220639_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0220639_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Barium isotope data from marine barites deposited throughout the world wide oceans. Samples include cold seep, hydrothermal and pelagic barites. Samples were collected from 1970 to 2006, and analyses were conducted in the NIRVANA lab at WHOI between 2016 and 2019. Data are in spreadsheet format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0221188_Not Applicable.json b/datasets/gov.noaa.nodc:0221188_Not Applicable.json index 317292e97b..98e274f087 100644 --- a/datasets/gov.noaa.nodc:0221188_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0221188_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0221188_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys.\nData are in NetCDF.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0225446_Not Applicable.json b/datasets/gov.noaa.nodc:0225446_Not Applicable.json index d1f9115120..49939d7347 100644 --- a/datasets/gov.noaa.nodc:0225446_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0225446_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0225446_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Overview\nCurrently, the LTMMP has 52 long-term monitoring sites across Saipan, Tinian, and Rota that are surveyed on a rotating biennial basis. Three main habitat types are covered: Fore reef, reef flat (lagoon), and seagrass beds (lagoon). Most sites have been selected based on their association with management concerns (runoff, sewage outfalls, urban development, etc.) and/or management actions (watershed restorations efforts, marine protected areas, etc.) and include impacted sites and relatively non-impacted reference sites. In general, monitoring surveys are conducted using standard and proven ecological field survey methods. All surveys are conducted along 3-5, 50 m transect lines laid out along the depth contour (~9m depth) on the fore reef, or along consistent habitat in the lagoon (back reef and seagrass). While benthic cover analysis provides the foundation of the CNMI monitoring program, the current protocol uses several survey types per site to provide ecological depth beyond percent cover.\n\nFore Reef\nPhotos are taken every meter along each transect line using a 0.25m2 quadrat frame, for a total of 250 photos at each site. In the office, the computer program CPCe is used to place five random points on each photo and the biota or substrate type under each point is identified. Organisms are identified to the genus level. This analysis provides benthic percent cover and community diversity. Twelve, 3 minute, 5 m radius stationary point counts (SPC) are conducted at each site to evaluate fish assemblages. Each SPC is systematically positioned throughout the length of a site (250 m). The species and size (fork length) of all food fishes within the 5 meter radius are recorded. This provides relative diversity, abundances, species compositions, size class distribution, and biomass of the fish community. Sixteen 0.25m2 quadrats are haphazardly tossed along the length of the site and every coral colony within the quadrats is identified to the species level and measured. This method provides relative diversity, abundances, species composition, and size class of the coral community. Within these same quadrats, all algae species present are identified to the species level to provide a measure of algae community composition and species richness. Finally, non-coral macro-invertebrates including sea cucumbers, urchins, crown-of-thorns starfish, giant clams, among others, are identified and counted within 1 m of each side of the transect lines (i.e. 5, 2mx50m belt transects). This provides invertebrate abundances, species composition, and diversity.\n\nSaipan Lagoon\nSaipan Lagoon habitats that are monitored include Halodule uninervis beds, staghorn Acropora thickets, and mixed coral back reefs. At lagoon sites, benthic cover is quantified using a 0.25 m2 string quadrat with six intersections, placed every meter along the transect line. The biota or substrate under each intersection is recorded to the genus level, in situ. Additionally, 10, 1 m2 quads are haphazardly placed across the length of the site (250 m) and all seagrass, algae, coral, and macro-invertebrates are identified to the species level and recorded. This method captures the relative diversity, abundance, and species compositions of lagoon communities. Finally, non-coral macro-invertebrate abundances and diversity are quantified as described above for reef slope sites.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0225545_Not Applicable.json b/datasets/gov.noaa.nodc:0225545_Not Applicable.json index c044b7eef9..f1d6c855bc 100644 --- a/datasets/gov.noaa.nodc:0225545_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0225545_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0225545_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the bulk density and pore water, sediment texture and composition data from sediment cores collected aboard R/V Weatherbird II cruises WB-0812 and WB-0813 in the northern Gulf of Mexico (nGoM) from 2012-08-14 to 2013-08-21. These data were generated for selected core sub-samples at 2mm sampling intervals for \u00e2\u0080\u009csurficial unit\u00e2\u0080\u009d and 5mm sampling resolution intervals to the base of cores. For the bulk density and pore water data, sediment cores were collected on board the R/V Weatherbird II cruise WB-0812 in the nGoM from 2012-08-14 to 2012-08-16. It reports measurement of sediment sample wet weight (g), dry weight (g) and percent pore water. Bulk density is the dry weight per sampling volume expressed as g/cm3. Whereas, sediment texture and composition data were collected aboard R/V Weatherbird II cruise WB-0813 in the nGoM from 2013-08-20 to 2013-08-21. Sediment texture values were expressed as percent gravel, sand, silt, and clay. Percent of mud can be calculated by combining percent silt and clay. Sediment composition was expressed as percent total organic matter (TOM) measured by loss on ignition (LOI), percent carbonate content measured by acid leaching, and the percent insoluble residue (IR), which was likely dominated by terrigenous clastic (land-derived) sediment sources.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0225979_Not Applicable.json b/datasets/gov.noaa.nodc:0225979_Not Applicable.json index 0d1c56558b..0bff58e8d6 100644 --- a/datasets/gov.noaa.nodc:0225979_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0225979_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0225979_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0225979 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at M\u00c4\u0081noa collected the data from their in-situ moored station named WQBAW: PacIOOS Water Quality Buoy AW (WQB-AW): Ala Wai, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at M\u00c4\u0081noa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month.\n\nThe water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-AW is located at the exit of the Ala Wai Canal, near Magic Island. Continuous sampling of this outflow area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0226059_Not Applicable.json b/datasets/gov.noaa.nodc:0226059_Not Applicable.json index 247542b6a9..3914ddb0c7 100644 --- a/datasets/gov.noaa.nodc:0226059_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0226059_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0226059_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NCEI Accession 0226059 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at M\u00c3\u0084\u00c2\u0081noa collected the data from their in-situ moored station named WQBKN: PacIOOS Water Quality Buoy KN (WQB-KN): Kilo Nalu, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at M\u00c3\u0084\u00c2\u0081noa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month.\n\nThe water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-KN is located at the Kilo Nalu Nearshore Reef Observatory, near Kakaako Waterfront Park and Kewalo Basin off of Ala Moana Boulevard in Honolulu. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0226205_Not Applicable.json b/datasets/gov.noaa.nodc:0226205_Not Applicable.json index cb09680b9d..ba8e240a4f 100644 --- a/datasets/gov.noaa.nodc:0226205_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0226205_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0226205_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0231662_Not Applicable.json b/datasets/gov.noaa.nodc:0231662_Not Applicable.json index d2c9bfbec5..a56a704991 100644 --- a/datasets/gov.noaa.nodc:0231662_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0231662_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0231662_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0232256_Not Applicable.json b/datasets/gov.noaa.nodc:0232256_Not Applicable.json index 714843a680..5bc90448da 100644 --- a/datasets/gov.noaa.nodc:0232256_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0232256_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0232256_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data described here result from coral reef assessments of reef slopes (10m depth) at permanent sites around Tutuila, American Samoa as part of the ongoing American Samoa Coral Reef Monitoring Program (ASCRMP). These surveys were conducted by members of the American Samoa Coral Reef Advisory Group between 2005 and 2017. The data was collected via SCUBA surveys and reports on coral, benthic and fish composition and derived metrics (e.g., benthic cover, coral diversity, fish diversity, fish biomass).", "links": [ { diff --git a/datasets/gov.noaa.nodc:0234331_Not Applicable.json b/datasets/gov.noaa.nodc:0234331_Not Applicable.json index c7a7e4c406..2bd24b2b2d 100644 --- a/datasets/gov.noaa.nodc:0234331_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0234331_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0234331_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains a compilation of seafloor surface benthic foraminifera assemblages, baseline stable carbon and oxygen isotope measurements from benthic foraminifera, and short-lived radioisotope measurements from sediment cores collected on multiple cruises and field sampling throughout the Gulf of Mexico and the northwestern margin of Cuba from 2010-06-13 to 2017-07-19. Stable isotope measurements were performed on Cibicidoides spp. The dataset includes the sediment core information such as location, date, and depth; benthic foraminiferal stable carbon and oxygen isotopes; and the total density and diversity calculations using Fisher\u00e2\u0080\u0099s Alpha and Shannon indices from the surface-most sub-sample from each core (typically 0-2 mm). For short-lived radioisotope measurements, samples were analyzed by gamma spectrometry with High-Purity Germanium (HPGe) gamma-ray detectors (Canberra Coaxial Planar configuration) for total 210Pb (46.5 keV), 214Pb (295 keV and 351 keV), and 214Bi (609 keV) activities. The mean activity of the 214Pb (295 keV), 214Pb (351 keV), and 214Bi (609 keV) was used as a proxy for 226Ra activity and therefore the supported 210Pb that is produced in situ. The reported excess 210Pb (210Pbxs) is the difference of the total 210Pb and the supported 210Pb.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0237816_Not Applicable.json b/datasets/gov.noaa.nodc:0237816_Not Applicable.json index 52d5200747..3095a8ccdd 100644 --- a/datasets/gov.noaa.nodc:0237816_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0237816_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0237816_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time.\n\nThe data described here were collected by CREP from ARMS units moored at fixed climate survey sites located in Kimbe Bay, Papua New Guinea. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from September 2009 to September 2012, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.\n\nEach ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 \u00c2\u00b5m, and 100 \u00c2\u00b5m. The 500 \u00c2\u00b5m and 100 \u00c2\u00b5m fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. This dataset includes information on the species counted and identified in the 2 mm fraction.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0238156_Not Applicable.json b/datasets/gov.noaa.nodc:0238156_Not Applicable.json index 61c38e213a..1f4ef7fe79 100644 --- a/datasets/gov.noaa.nodc:0238156_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0238156_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0238156_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains benthic and pelagic invertebrate stable carbon and nitrogen isotope data collected in the Chukchi Sea, U.S. Arctic during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise aboard the vessel Norseman II. The dataset contains a comma separated values (csv) files exported from Microsoft Excel. These data were generated from samples collected with trawls, grabs, and plankton nets during the research cruise. The data in the file named AMBON2015_Stable Isotopes_Database_final.csv describe the carbon and nitrogen stable isotope values of invertebrate samples with and without various chemical treatments to eliminate carbonates and lipids. Full location and taxonomic information is given for each sample.", "links": [ { diff --git a/datasets/gov.noaa.nodc:0239040_Not Applicable.json b/datasets/gov.noaa.nodc:0239040_Not Applicable.json index 7ec8643112..42f0c16901 100644 --- a/datasets/gov.noaa.nodc:0239040_Not Applicable.json +++ b/datasets/gov.noaa.nodc:0239040_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:0239040_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains benthic and pelagic invertebrate stable carbon and nitrogen isotope data collected in the Chukchi Sea, U.S. Arctic during the 6 August 2017 - 21 August 2017 Arctic Marine Biodiversity Observing Network (AMBON) research cruise aboard the vessel Norseman II. The dataset contains a comma separated values (csv) files exported from Microsoft Excel. These data were generated from samples collected with trawls, grabs, and plankton nets during the research cruise. The data in the file named AMBON2017_Stable Isotopes_Database_final.csv describe the carbon and nitrogen stable isotope values of invertebrate samples with and without various chemical treatments to eliminate carbonates and lipids. Full location and taxonomic information is given for each sample.", "links": [ { diff --git a/datasets/gov.noaa.nodc:6800230_Not Applicable.json b/datasets/gov.noaa.nodc:6800230_Not Applicable.json index 9e8fe760bf..ab75ef7c8a 100644 --- a/datasets/gov.noaa.nodc:6800230_Not Applicable.json +++ b/datasets/gov.noaa.nodc:6800230_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:6800230_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:6900225_Not Applicable.json b/datasets/gov.noaa.nodc:6900225_Not Applicable.json index c9528e69f5..30f033b1b0 100644 --- a/datasets/gov.noaa.nodc:6900225_Not Applicable.json +++ b/datasets/gov.noaa.nodc:6900225_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:6900225_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:6901098_Not Applicable.json b/datasets/gov.noaa.nodc:6901098_Not Applicable.json index 50cf14c193..830ae4fc8c 100644 --- a/datasets/gov.noaa.nodc:6901098_Not Applicable.json +++ b/datasets/gov.noaa.nodc:6901098_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:6901098_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7000052_Not Applicable.json b/datasets/gov.noaa.nodc:7000052_Not Applicable.json index 747ac21e74..5c7dc9d4b9 100644 --- a/datasets/gov.noaa.nodc:7000052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7000052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7000052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7000422_Not Applicable.json b/datasets/gov.noaa.nodc:7000422_Not Applicable.json index ca88e43121..36f4b5a717 100644 --- a/datasets/gov.noaa.nodc:7000422_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7000422_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7000422_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7000981_Not Applicable.json b/datasets/gov.noaa.nodc:7000981_Not Applicable.json index 6fef7ba5c5..999c172471 100644 --- a/datasets/gov.noaa.nodc:7000981_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7000981_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7000981_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7001081_Not Applicable.json b/datasets/gov.noaa.nodc:7001081_Not Applicable.json index 9600fcc617..a69fc72f21 100644 --- a/datasets/gov.noaa.nodc:7001081_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7001081_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7001081_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report presents data on the physical and chemical characteristics of bottom sediments in the James River estuary, Virgina. The data were generated as part of a comprehensive study of sedimentation in which the initial objective was to broadly define the distribution of sediment properties.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7100000_Not Applicable.json b/datasets/gov.noaa.nodc:7100000_Not Applicable.json index f629a04fe7..13e37f2c11 100644 --- a/datasets/gov.noaa.nodc:7100000_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7100000_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7100000_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7100048_Not Applicable.json b/datasets/gov.noaa.nodc:7100048_Not Applicable.json index bfb00b7191..df2db3bd1b 100644 --- a/datasets/gov.noaa.nodc:7100048_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7100048_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7100048_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7100165_Not Applicable.json b/datasets/gov.noaa.nodc:7100165_Not Applicable.json index 00906c5b24..a0f7f37261 100644 --- a/datasets/gov.noaa.nodc:7100165_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7100165_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7100165_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected using bottle casts in the North Pacific Ocean from January 6, 1951 to October 31, 1960. Data were submitted by Scripps Institution of Oceanography as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7100603_Not Applicable.json b/datasets/gov.noaa.nodc:7100603_Not Applicable.json index 27606c1d4e..486c6035d6 100644 --- a/datasets/gov.noaa.nodc:7100603_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7100603_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7100603_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected using bottle, BT, current meter, MBT, meteorological sensors, and secchi disk casts from January 1, 1968 to December 4, 1968. Data were submitted by Stanford University; Hopkins Marine Station as part of the California Cooperative Fisheries Investigation (CALCOFI) project. Data were processed by NODC to the NODC standard F004 water physics and chemistry format. Full F004 Format descriptions are available from the NODC homepage at www.nodc.noaa.gov/.\n\nThe F004 format contains data from measurements and analysis of physical and chemical characteristics of the water column. Chemical parameters that may be recorded are salinity, pH and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity and current velocity (east-west and north-south components). Cruise and station information may include environmental conditions of the study site at the time of observation. Data are very sparse prior to 1951.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7200096_Not Applicable.json b/datasets/gov.noaa.nodc:7200096_Not Applicable.json index 3a87c90751..6f690f7770 100644 --- a/datasets/gov.noaa.nodc:7200096_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7200096_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7200096_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7200319_Not Applicable.json b/datasets/gov.noaa.nodc:7200319_Not Applicable.json index 4166478de4..38b78040ea 100644 --- a/datasets/gov.noaa.nodc:7200319_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7200319_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7200319_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7200320_Not Applicable.json b/datasets/gov.noaa.nodc:7200320_Not Applicable.json index a838833fdb..b07cbdac72 100644 --- a/datasets/gov.noaa.nodc:7200320_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7200320_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7200320_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7200698_Not Applicable.json b/datasets/gov.noaa.nodc:7200698_Not Applicable.json index 14f651f03d..8221f4f76f 100644 --- a/datasets/gov.noaa.nodc:7200698_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7200698_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7200698_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7201127_Not Applicable.json b/datasets/gov.noaa.nodc:7201127_Not Applicable.json index 617aecd7e1..5c7b86dbb8 100644 --- a/datasets/gov.noaa.nodc:7201127_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7201127_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7201127_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7201380_Not Applicable.json b/datasets/gov.noaa.nodc:7201380_Not Applicable.json index 104023160e..22b9950fa8 100644 --- a/datasets/gov.noaa.nodc:7201380_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7201380_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7201380_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7201418_Not Applicable.json b/datasets/gov.noaa.nodc:7201418_Not Applicable.json index 5f8d5f305d..f6b25766c3 100644 --- a/datasets/gov.noaa.nodc:7201418_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7201418_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7201418_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7300167_Not Applicable.json b/datasets/gov.noaa.nodc:7300167_Not Applicable.json index 731836de1a..b2e589b079 100644 --- a/datasets/gov.noaa.nodc:7300167_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7300167_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7300167_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7300282_Not Applicable.json b/datasets/gov.noaa.nodc:7300282_Not Applicable.json index 4f5e4eb373..cb581bfd06 100644 --- a/datasets/gov.noaa.nodc:7300282_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7300282_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7300282_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7301085_Not Applicable.json b/datasets/gov.noaa.nodc:7301085_Not Applicable.json index 0593dc911c..aefb32076a 100644 --- a/datasets/gov.noaa.nodc:7301085_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7301085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7301085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7301177_Not Applicable.json b/datasets/gov.noaa.nodc:7301177_Not Applicable.json index 2e3c51404d..b6dc59d134 100644 --- a/datasets/gov.noaa.nodc:7301177_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7301177_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7301177_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400073_Not Applicable.json b/datasets/gov.noaa.nodc:7400073_Not Applicable.json index d492fb5632..98cd88e2d0 100644 --- a/datasets/gov.noaa.nodc:7400073_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400073_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400073_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400204_Not Applicable.json b/datasets/gov.noaa.nodc:7400204_Not Applicable.json index 059b693644..5d76dca48e 100644 --- a/datasets/gov.noaa.nodc:7400204_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400204_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400204_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400205_Not Applicable.json b/datasets/gov.noaa.nodc:7400205_Not Applicable.json index 8131b5a91e..2e6b1726a7 100644 --- a/datasets/gov.noaa.nodc:7400205_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400205_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400205_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400384_Not Applicable.json b/datasets/gov.noaa.nodc:7400384_Not Applicable.json index d4df8c545a..c580474e57 100644 --- a/datasets/gov.noaa.nodc:7400384_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400384_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400384_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400462_Not Applicable.json b/datasets/gov.noaa.nodc:7400462_Not Applicable.json index 24d8f4464b..080c3a7a7e 100644 --- a/datasets/gov.noaa.nodc:7400462_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400462_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400462_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400657_Not Applicable.json b/datasets/gov.noaa.nodc:7400657_Not Applicable.json index 9f199a69c6..e45259f0b6 100644 --- a/datasets/gov.noaa.nodc:7400657_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400657_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400657_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7400752_Not Applicable.json b/datasets/gov.noaa.nodc:7400752_Not Applicable.json index a0c1bc3983..f4c0bb19b3 100644 --- a/datasets/gov.noaa.nodc:7400752_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7400752_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7400752_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7500181_Not Applicable.json b/datasets/gov.noaa.nodc:7500181_Not Applicable.json index dfce84b831..191b34b2bf 100644 --- a/datasets/gov.noaa.nodc:7500181_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7500181_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7500181_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7500532_Not Applicable.json b/datasets/gov.noaa.nodc:7500532_Not Applicable.json index 1d7097a7ab..155b92ea1e 100644 --- a/datasets/gov.noaa.nodc:7500532_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7500532_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7500532_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from CTD casts from the YAQUINA from 20 June 1972 to 24 August 1973. Data were collected by the University of Washington (UW) as part of the International Decade of Ocean Exploration / Coastal Upwelling Ecosystems Analysis (IDOE/CUEA) from 08 July 1977 to 29 July 1977. Data were processed by NODC to the NODC standard F022 High-Resolution CTD/STD Output Format. Full format description is available from NODC at www.nodc.noaa.gov/General/NODC-Archive/f022.html.\n\nThe F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7500625_Not Applicable.json b/datasets/gov.noaa.nodc:7500625_Not Applicable.json index 56b92d2958..c8ca7015c9 100644 --- a/datasets/gov.noaa.nodc:7500625_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7500625_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7500625_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7600645_Not Applicable.json b/datasets/gov.noaa.nodc:7600645_Not Applicable.json index 12d4048385..8413ab4332 100644 --- a/datasets/gov.noaa.nodc:7600645_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7600645_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7600645_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7600769_Not Applicable.json b/datasets/gov.noaa.nodc:7600769_Not Applicable.json index 098b3ae4fc..7780520c6d 100644 --- a/datasets/gov.noaa.nodc:7600769_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7600769_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7600769_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601177_Not Applicable.json b/datasets/gov.noaa.nodc:7601177_Not Applicable.json index 6ae0b6e276..f6ce43be27 100644 --- a/datasets/gov.noaa.nodc:7601177_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601177_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601177_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601195_Not Applicable.json b/datasets/gov.noaa.nodc:7601195_Not Applicable.json index c2a2711327..e944173c0b 100644 --- a/datasets/gov.noaa.nodc:7601195_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601195_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601195_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601212_Not Applicable.json b/datasets/gov.noaa.nodc:7601212_Not Applicable.json index 436c0aa5f1..31108480bb 100644 --- a/datasets/gov.noaa.nodc:7601212_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601212_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601212_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601237_Not Applicable.json b/datasets/gov.noaa.nodc:7601237_Not Applicable.json index 5363a75dd8..658ae0ed66 100644 --- a/datasets/gov.noaa.nodc:7601237_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601237_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601237_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Patuxent River estuary was investigated over a 25-hour tidal cycle from October 17-18, 1972, during the Patuxent River Cooperative Study (conducted by the University of Maryland). These data were collected as part of a joint investigation by the University of Maryland's Center for Environmental and Estuarine Studies (Chesapeake Biological Lab) and the Institute for Fluid Dynamics and Applied Mathematics (College Park, Maryland). The resulting chemical, physical, and biological data were assembled into a format that could be utilized by investigators, collectively titled the Patuxent River Data Bank.\n\nThe Patuxent River Data Bank was submitted to NODC on a 9-track, 1600 BPI tape in EBCDIC and contains headers and one data file.\n\nHeat concentration (in kilocalories/liter) and instantaneous flux magnitude (in megacalories/square meter/second) were recorded over the tidal cycle. Other data associated with this study are filed under NODC Reference #'s L01574 and L01576; all data are in the Level-A directory under L01574.001.\n\nData associated with marine chemistry include: Dissolved organic carbon (milligrams/liter), Particulate carbon (milligrams/liter), salts (grams/liter), Dissolved oxygen (milligrams/liter), and total particulates (milligrams/liter). Instantaneous flux magnitudes for carbon were measured in grams/liter; for salts, in kilograms/liter; for oxygen, in milligrams/liter; and for total particulates, milligrams/liter.\n\nParameters associated with primary productivity (L505) include: Nitrate +Nitrite conc., Ammonia Nitrogen conc., Total Kjeldahl Nitrogen, Organic Phosphate conc., Total Hydrolyzable Phosphate, Active Chlorophyll-a, and Total Chlorophyll. Nutrients were measured in milligrams/liter; chlorophyll concentrations were measured in micrograms/liter. Instantaneous flux magnitudes were measured in milligrams/square meter/second.\n\nAdditional data collected during this investigation are filed under NODC Reference #'s L01575 and one tape of Patuxent River Estuary Hydro data \"OLD STUFF\"", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601613_Not Applicable.json b/datasets/gov.noaa.nodc:7601613_Not Applicable.json index 139868cf33..f0b0493822 100644 --- a/datasets/gov.noaa.nodc:7601613_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601613_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601613_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601642_Not Applicable.json b/datasets/gov.noaa.nodc:7601642_Not Applicable.json index b6c9ac4db3..7590b78130 100644 --- a/datasets/gov.noaa.nodc:7601642_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601642_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601642_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteria, taxonomic code, and other data were collected using sediment sampler and other instruments in the North Atlantic Ocean from G.W. PIERCE. Data were collected from 20 February 1976 to 23 March 1976 by Virginia Institute of Marine Science in Gloucester Point with support from the Ocean Continental Shelf - Mid Atlantic (OCS-Mid Atlantic) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7601772_Not Applicable.json b/datasets/gov.noaa.nodc:7601772_Not Applicable.json index 156807352f..7818d67750 100644 --- a/datasets/gov.noaa.nodc:7601772_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7601772_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7601772_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7617993_Not Applicable.json b/datasets/gov.noaa.nodc:7617993_Not Applicable.json index e0f4665a80..caa177e6f1 100644 --- a/datasets/gov.noaa.nodc:7617993_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7617993_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7617993_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7617994_Not Applicable.json b/datasets/gov.noaa.nodc:7617994_Not Applicable.json index fe93c3be7f..6a2e9c9dcb 100644 --- a/datasets/gov.noaa.nodc:7617994_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7617994_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7617994_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7617995_Not Applicable.json b/datasets/gov.noaa.nodc:7617995_Not Applicable.json index 2ed7e6afbc..b78ca5afd3 100644 --- a/datasets/gov.noaa.nodc:7617995_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7617995_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7617995_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700058_Not Applicable.json b/datasets/gov.noaa.nodc:7700058_Not Applicable.json index 2114f13930..1b37670c61 100644 --- a/datasets/gov.noaa.nodc:7700058_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700058_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700058_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas.\n\nData are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700179_Not Applicable.json b/datasets/gov.noaa.nodc:7700179_Not Applicable.json index 741366a5e1..fb38b8ed39 100644 --- a/datasets/gov.noaa.nodc:7700179_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700179_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700179_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700437_Not Applicable.json b/datasets/gov.noaa.nodc:7700437_Not Applicable.json index d4c786cab1..2b44d96726 100644 --- a/datasets/gov.noaa.nodc:7700437_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700437_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700437_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700455_Not Applicable.json b/datasets/gov.noaa.nodc:7700455_Not Applicable.json index 3eae9acf9d..e8b43e9dc6 100644 --- a/datasets/gov.noaa.nodc:7700455_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700455_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700455_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data was submitted by Dr. Gerald L. Engel. This study was organized to collect data on Parasite Type and Location. Parasite (both ecto- and endo-), and site of infection were looked into. SST, wave, turbidity, gear type (trawl), species, parasite (both ecto- and endo-), and site of infection (i.e. data on parasite type and location) data were collected. The documentation describes instruments employed for sampling, units, and a detailed description of the record format.\n\nThese studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS). Special codes employed by VIMS to describe parasite types and location were included as hardcopy. The original information submitted on tape has been converted into the current NODC storage format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700456_Not Applicable.json b/datasets/gov.noaa.nodc:7700456_Not Applicable.json index d6ec17f1eb..c5eca0a7b6 100644 --- a/datasets/gov.noaa.nodc:7700456_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700456_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700456_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data submitted by Dr. Gerald L. Engel. The data was collected between June 1976 and September 1976. This study was organized to collect Histopathology and Benthic data. SST, wave, turbidity, gear type (trawl v.s dredge), benthic species counts and weights were collected. These data are \"megabenthic\" species. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. The original data on tape has been converted to current NODC storage format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS).", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700466_Not Applicable.json b/datasets/gov.noaa.nodc:7700466_Not Applicable.json index 07e1d9248e..916543bdf9 100644 --- a/datasets/gov.noaa.nodc:7700466_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700466_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700466_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700472_Not Applicable.json b/datasets/gov.noaa.nodc:7700472_Not Applicable.json index e1580b5bd4..d8e9ae2c81 100644 --- a/datasets/gov.noaa.nodc:7700472_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700472_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700472_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Microbenthic data was collected by the Virginia Institute of Marine Science (VIMS) as part of the BLM/OCS Mid (NE) Atlantic program. The data were collected from the R/V W. PIERCE from February to March 1976. The data was collected to study the Benthic organisms. Benthic species were counted and benthic taxa were identified. These data were submitted by Dr. Gerald L. Engel. The measurements included station depth; surface and sediment temperature in degrees centigrade; wind speed and direction; wave/swell height and direction; instrumentation; penetration depth; gear type; sediment core dimensions (width and penetration depth in centimeters); depth of sediment corer penetration; cloud cover and turbidity. The documentation includes a detailed record format. Data were originally filed in a VIMS format tape has been converted into current NODC data storage format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700484_Not Applicable.json b/datasets/gov.noaa.nodc:7700484_Not Applicable.json index 22ef9988ce..62e7d7e57e 100644 --- a/datasets/gov.noaa.nodc:7700484_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700484_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700484_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700489_Not Applicable.json b/datasets/gov.noaa.nodc:7700489_Not Applicable.json index 4aba36ec9f..952607b988 100644 --- a/datasets/gov.noaa.nodc:7700489_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700489_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700489_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700523_Not Applicable.json b/datasets/gov.noaa.nodc:7700523_Not Applicable.json index 0b92e36905..61e79c0d3f 100644 --- a/datasets/gov.noaa.nodc:7700523_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700523_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700523_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700525_Not Applicable.json b/datasets/gov.noaa.nodc:7700525_Not Applicable.json index eca8eb2095..7ca31a9cf9 100644 --- a/datasets/gov.noaa.nodc:7700525_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700525_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700525_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from bottle and CTD casts from the KNORR and MELVILLE from 18 July 1972 to 10 June 1974. Data were collected by the Scripps Institution of Oceanography (SIO) as part of the International Decade of Ocean Exploration / Geochemical Ocean Study (IDOE/GEOSECS). Physical parameters include depth, temperature, salinity, and pressure. Chemical parameters include concentrations of oxygen, phosphate, nitrate, nitrite, and silicate. Analog data are availabe for this accession by contacting NODC user services.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700776_Not Applicable.json b/datasets/gov.noaa.nodc:7700776_Not Applicable.json index aa19a99ff4..f61b03575b 100644 --- a/datasets/gov.noaa.nodc:7700776_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700776_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700776_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700822_Not Applicable.json b/datasets/gov.noaa.nodc:7700822_Not Applicable.json index 5f95b26e88..91ff97ec78 100644 --- a/datasets/gov.noaa.nodc:7700822_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700822_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700822_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7700882_Not Applicable.json b/datasets/gov.noaa.nodc:7700882_Not Applicable.json index 5c6979ba97..86d32cde5a 100644 --- a/datasets/gov.noaa.nodc:7700882_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7700882_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7700882_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7800148_Not Applicable.json b/datasets/gov.noaa.nodc:7800148_Not Applicable.json index 020594c1d9..40d5fcb90a 100644 --- a/datasets/gov.noaa.nodc:7800148_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7800148_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7800148_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water Depth and other data collected by Woods Hole Oceanographic Institution from EASTWARD cruise from May 11-21, 1976. The high resolution Conductivity, Temperature and Depth (CTD) measurements are stored in NODC format file F022. More information regarding this file format can be obtained from the URL: http://www.nodc.noaa.gov/General/NODC-Archive/f022.html", "links": [ { diff --git a/datasets/gov.noaa.nodc:7800207_Not Applicable.json b/datasets/gov.noaa.nodc:7800207_Not Applicable.json index 9adceeb92f..a8241e76b3 100644 --- a/datasets/gov.noaa.nodc:7800207_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7800207_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7800207_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteriology data were collected using moored buoy casts and other instruments in the Delaware Bay and North Atlantic Ocean from November 5, 1976 to August 16, 1977. Data were submitted by Virginia Institute of Marine Science - Gloucester Point as part of the Ocean Continental Shelf (OCS-Mid Atlantic Ocean) project. Data has been been processed by NODC to the standard NODC F009- Bacteriology formats. Full format descriptions are available from NODC homepage at https://intra.nodc.noaa.gov/Information/Teams/apd_info/md_improvement _project2004/MULDARS_FGDCs/F009.txt\n\nThe F009 format is designed to support bacteriological studies of the water column and ocean bottom. Information on environmental conditions, physical measurements of the water and sediment, and denisty of heterotrophic, hydrocarbonclastic, and halophilic bacteria are presented. The format contains five data record types, each 80 characters in length, sorted by station ad sequence numbers. The first nine columns for all records are to be used for file name (columns 1-3) and file identifier (columns 4-9). The file identifier, to be assigned by the orginator, is an unique originator id for each data submission. After submission, the NODCreassigns to this field an unique NODC indentifier for internal use.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7800886_Not Applicable.json b/datasets/gov.noaa.nodc:7800886_Not Applicable.json index 71e05bf7dc..8a502cffdc 100644 --- a/datasets/gov.noaa.nodc:7800886_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7800886_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7800886_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms and marine toxic substances and pollutants were collected using sediment sampler and net casts in the coastal waters of the East coast of US. Data were submitted by Dr. J.C. Ayres of the Marine Fisheries Service in Highlands. Data were collected from 22 May 1974 to 27 May 1974. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms and F144- Marine Toxic Substances and Pollutants formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900006_Not Applicable.json b/datasets/gov.noaa.nodc:7900006_Not Applicable.json index c827194ab2..2e31fc28ad 100644 --- a/datasets/gov.noaa.nodc:7900006_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900006_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900006_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical data were collected using moored current meter, bottle casts, and other instruments in the Gulf of Mexico from June 18, 1978 to June 24, 1981. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Current Meter Data, and F144 Marine Toxic Substances formats. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900247_Not Applicable.json b/datasets/gov.noaa.nodc:7900247_Not Applicable.json index 4f11809f8f..775eacde5c 100644 --- a/datasets/gov.noaa.nodc:7900247_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900247_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900247_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacteriology, wind wave spectra, and benthic organism data were collected using moored buoy casts and other instruments in the Gulf of Mexico from February 1, 1978 to May 3, 1979. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the standard NODC F009- Bacteriology, F132- Benthic Organisms, and F191- Wind Wave Spectra formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F009 format is designed to support bacteriological studies of the water column and ocean bottom. Information on environmental conditions, physical measurements of the water and sediment, and denisty of heterotrophic, hydrocarbonclastic, and halophilic bacteria are presented. The format contains five data record types, each 80 characters in length, sorted by station ad sequence numbers. The first nine columns for all records are to be used for file name (columns 1-3) and file identifier (columns 4-9). The file identifier, to be assigned by the orginator, is an unique originator id for each data submission. After submission, the NODCreassigns to this field an unique NODC indentifier for internal use.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments. \"Self-documenting files containing, [datatypes].\"\n\nThe F191 format is used to report meteorological data and ocean wave spectra data from NDBO. The format contains seven data record types to: 1) Identify the buoy for position, duration, rate of sampling and heading. 2) Identify the meteorological parameters (temperature, pressure, weather, solar radiation, and surface waves). 3) Report time series frequency, density and resolution of waves. Each record is 120 characters in length, sorted by station and record type. The first nine columns for all records are to be used for file type (columns 1-3) and file identifier (column 4-9). The file identifier, to be assigned by the originator, is an unique originator id for each data submission. After submission, the NODC reassigns to this field an unique NODC identifier for internal use.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900280_Not Applicable.json b/datasets/gov.noaa.nodc:7900280_Not Applicable.json index b4dc60a049..139c9624cb 100644 --- a/datasets/gov.noaa.nodc:7900280_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900280_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900280_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical data were collected using moored current meter, bottle casts, and other instruments in the Coastal Waters of New Jersey from May 18, 1978 to October 19, 1978. Data were submitted by Nassau County Department of Health; Bureau of Water Surveillance as part of the Mesa New York Bight (MESA - NYB) project. Data has been been processed by NODC to the NODC standard F004- Current Meter Data, and F144 Marine Toxic Substances formats. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900304_Not Applicable.json b/datasets/gov.noaa.nodc:7900304_Not Applicable.json index ab54fe610b..f7ca0f96bc 100644 --- a/datasets/gov.noaa.nodc:7900304_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900304_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900304_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms and marine toxic substances and pollutants were collected using sediment sampler and net casts in the Gulf of Mexico. Data were submitted by Texas A&M University and Energy Resources Co. Inc.s. with support from the Brine Disposal project. Data were collected from the GUS III and EXCELLENCE from 24 May 1978 to 26 February 1979. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms and F144- Marine Toxic Substances and Pollutants formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900310_Not Applicable.json b/datasets/gov.noaa.nodc:7900310_Not Applicable.json index 9a3c075924..7aadf6f7d4 100644 --- a/datasets/gov.noaa.nodc:7900310_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900310_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900310_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:7900332_Not Applicable.json b/datasets/gov.noaa.nodc:7900332_Not Applicable.json index 92500df912..568288f0d2 100644 --- a/datasets/gov.noaa.nodc:7900332_Not Applicable.json +++ b/datasets/gov.noaa.nodc:7900332_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:7900332_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler and net casts in the Gulf of Mexico. Data were submitted by Texas A&M University with support from the Brine Disposal project. Data were collected from the GUS III and EXCELLENCE from 22 May 1978 to 20 April 1979.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000002_Not Applicable.json b/datasets/gov.noaa.nodc:8000002_Not Applicable.json index 7f1ff0fdcb..5a320109c4 100644 --- a/datasets/gov.noaa.nodc:8000002_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000002_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000002_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, zooplankton, and marine toxic substances data were collected using moored current meter casts and other instruments in the Gulf of Mexico from June 2, 1978 to June 2, 1979. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the standard NODC F004- Water Physics and Chemistry, F124- Zooplankton, and F144- Marine Toxic Substances formats. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000013_Not Applicable.json b/datasets/gov.noaa.nodc:8000013_Not Applicable.json index 5af8d241ed..43a901f83a 100644 --- a/datasets/gov.noaa.nodc:8000013_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000013_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000013_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms data were collected using sediment sampler and net casts from NOAA Ship DELAWARE II and other platforms in the New York Blight from 19 June 1957 to 20 July 1978. Data were submitted by the Virginia Institute of Marine Science with support from the MESA - New York Blight project.Data were processed by NODC to the NODC standard F132 Benthic Organism format. Full format description is available from NODC at www.nodc.noaa.gov/General/NODC-Archive/f132.html. An analog file for this accession is available from NODC user services.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000075_Not Applicable.json b/datasets/gov.noaa.nodc:8000075_Not Applicable.json index e0b6be7242..f66d3c39fe 100644 --- a/datasets/gov.noaa.nodc:8000075_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000075_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000075_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000132_Not Applicable.json b/datasets/gov.noaa.nodc:8000132_Not Applicable.json index 22883c4b68..9fecc9c516 100644 --- a/datasets/gov.noaa.nodc:8000132_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000132_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000132_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000236_Not Applicable.json b/datasets/gov.noaa.nodc:8000236_Not Applicable.json index f761957470..e3df3a1810 100644 --- a/datasets/gov.noaa.nodc:8000236_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000236_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000236_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000344_Not Applicable.json b/datasets/gov.noaa.nodc:8000344_Not Applicable.json index a4b245b816..60fc1ba401 100644 --- a/datasets/gov.noaa.nodc:8000344_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000344_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000344_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000417_Not Applicable.json b/datasets/gov.noaa.nodc:8000417_Not Applicable.json index 3a94a18715..b4a62249a7 100644 --- a/datasets/gov.noaa.nodc:8000417_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000417_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000417_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from bottle and CTD casts in the Indian from the MELVILLE from 04 December 1977 to 24 April 1978. Data were collected by the Scripps Institution of Oceanography (SIO) as part of the International Decade of Ocean Exploration / Geochemical Ocean Study (IDOE/GEOSECS). Physical parameters include depth, temperature, salinity, pH, and pressure. Chemical parameters include concentrations of carbon dioxide, oxygen, phosphate, nitrate, nitrite, and silicate. Analog data are available for this accession by contacting NODC user services.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000424_Not Applicable.json b/datasets/gov.noaa.nodc:8000424_Not Applicable.json index 9fbec4ff74..789a557ebe 100644 --- a/datasets/gov.noaa.nodc:8000424_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000424_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000424_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000502_Not Applicable.json b/datasets/gov.noaa.nodc:8000502_Not Applicable.json index 44fff39fcb..f693d6cbb3 100644 --- a/datasets/gov.noaa.nodc:8000502_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000502_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000502_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 26 April 1979 to 19 November 1979. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000503_Not Applicable.json b/datasets/gov.noaa.nodc:8000503_Not Applicable.json index ae18fc5ce0..92a2699f5a 100644 --- a/datasets/gov.noaa.nodc:8000503_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000503_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000503_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000523_Not Applicable.json b/datasets/gov.noaa.nodc:8000523_Not Applicable.json index d8058a2560..68aa199a71 100644 --- a/datasets/gov.noaa.nodc:8000523_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000523_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000523_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 30 July 1979 to 16 December 1979. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000543_Not Applicable.json b/datasets/gov.noaa.nodc:8000543_Not Applicable.json index da9afb51e2..0ee2a348ae 100644 --- a/datasets/gov.noaa.nodc:8000543_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000543_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000543_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000544_Not Applicable.json b/datasets/gov.noaa.nodc:8000544_Not Applicable.json index ff540cc7e1..3b8dd24803 100644 --- a/datasets/gov.noaa.nodc:8000544_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000544_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000544_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000581_Not Applicable.json b/datasets/gov.noaa.nodc:8000581_Not Applicable.json index 5f83051ed6..5344ffd064 100644 --- a/datasets/gov.noaa.nodc:8000581_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000581_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000581_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000602_Not Applicable.json b/datasets/gov.noaa.nodc:8000602_Not Applicable.json index 9ecdf79137..e72b64a37f 100644 --- a/datasets/gov.noaa.nodc:8000602_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000602_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000602_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 28 January 1980 to 28 January 1980. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8000603_Not Applicable.json b/datasets/gov.noaa.nodc:8000603_Not Applicable.json index bb97a99f2b..1663a64bf0 100644 --- a/datasets/gov.noaa.nodc:8000603_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8000603_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8000603_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 19 December 1979 to 19 December 1979. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100023_Not Applicable.json b/datasets/gov.noaa.nodc:8100023_Not Applicable.json index 90c0b27528..083cd11023 100644 --- a/datasets/gov.noaa.nodc:8100023_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100023_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100023_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100223_Not Applicable.json b/datasets/gov.noaa.nodc:8100223_Not Applicable.json index 09e10a5d17..5939b97e03 100644 --- a/datasets/gov.noaa.nodc:8100223_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100223_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100223_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 18 January 1980 to 04 November 1980. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100224_Not Applicable.json b/datasets/gov.noaa.nodc:8100224_Not Applicable.json index ed8f1b45dc..e4a96fd799 100644 --- a/datasets/gov.noaa.nodc:8100224_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100224_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100224_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the SW RESEARCHER in the Gulf of Mexico from 22 September 1977 to 16 December 1977. Data were submitted by the Science Application, INC. with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100360_Not Applicable.json b/datasets/gov.noaa.nodc:8100360_Not Applicable.json index 317ee84b21..f315eed21b 100644 --- a/datasets/gov.noaa.nodc:8100360_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100360_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100360_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, temperature and Depth probe was used to collect data from NOAA Ship DAVID STARR JORDAN. The data were collected from NE Pacific (limit-180) over one month duration from August 20, 1969 to September 17, 1969 by National Marine Fisheries Service, La Jolla, CA.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100361_Not Applicable.json b/datasets/gov.noaa.nodc:8100361_Not Applicable.json index cd679c539e..467b2ab557 100644 --- a/datasets/gov.noaa.nodc:8100361_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100361_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100361_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, temperature and Depth probe was used to collect data from NOAA Ship DAVID STARR JORDAN. The data were collected from NE Pacific (limit-180) over 12 days duration from July 1-12 1971 by National Marine Fisheries Service, La Jolla, CA. The data is stored in High-Resolution CTD Data (F022). The following URL provides more information regarding the F022 format. http://www.nodc.noaa.gov/General/NODC-Archive/f022.html", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100362_Not Applicable.json b/datasets/gov.noaa.nodc:8100362_Not Applicable.json index 346367d9a0..01b68d639f 100644 --- a/datasets/gov.noaa.nodc:8100362_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100362_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100362_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, temperature and Depth probe was used to collect data from NOAA Ship TOWNSEND CROMWELL. The data were collected from NE Pacific (limit-180) over 20 days duration from June 4-24, 1972 by National Marine Fisheries Service, La Jolla, CA. The data is stored in High-Resolution CTD Data (F022). The following URL provides more information regarding the F022 format. http://www.nodc.noaa.gov/General/NODC-Archive/f022.html", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100436_Not Applicable.json b/datasets/gov.noaa.nodc:8100436_Not Applicable.json index 594cbe3604..2375ac48cc 100644 --- a/datasets/gov.noaa.nodc:8100436_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100436_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100436_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains oceanographic data collected from bottle casts and other instruments. Data were collected from the ARGOS, Arni Fridriksson and other platforms in the Barents Sea, North Sea and other locations. Data are in the Oceanographic Station Data (SD2) format which includes meteorological, chemical and physical parameters. Data were collected from 1969-04-21 to 1979-07-12.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100438_Not Applicable.json b/datasets/gov.noaa.nodc:8100438_Not Applicable.json index 7a7cbd13c9..136ac9c68f 100644 --- a/datasets/gov.noaa.nodc:8100438_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100438_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100438_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100456_Not Applicable.json b/datasets/gov.noaa.nodc:8100456_Not Applicable.json index 3d26156a85..78df131d8e 100644 --- a/datasets/gov.noaa.nodc:8100456_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100456_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100456_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 22 May 1980 to 25 July 1980. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100458_Not Applicable.json b/datasets/gov.noaa.nodc:8100458_Not Applicable.json index 2becb5bed5..41b5fd506d 100644 --- a/datasets/gov.noaa.nodc:8100458_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100458_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100458_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100471_Not Applicable.json b/datasets/gov.noaa.nodc:8100471_Not Applicable.json index f4dd1e5dbf..b718db9722 100644 --- a/datasets/gov.noaa.nodc:8100471_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100471_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100471_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 31 March 1980 to 02 July 1980. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100491_Not Applicable.json b/datasets/gov.noaa.nodc:8100491_Not Applicable.json index 979a275fd0..08dae7830a 100644 --- a/datasets/gov.noaa.nodc:8100491_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100491_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100491_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the SW RESEARCHER in the Gulf of Mexico from 03 February 1978 to 19 October 1978. Data were submitted by the Science Application, INC. with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100494_Not Applicable.json b/datasets/gov.noaa.nodc:8100494_Not Applicable.json index a6e983ae72..ae6a304b65 100644 --- a/datasets/gov.noaa.nodc:8100494_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100494_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100494_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100495_Not Applicable.json b/datasets/gov.noaa.nodc:8100495_Not Applicable.json index 2e134cec90..37664424f9 100644 --- a/datasets/gov.noaa.nodc:8100495_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100495_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100495_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100496_Not Applicable.json b/datasets/gov.noaa.nodc:8100496_Not Applicable.json index 91294f800d..08e541d736 100644 --- a/datasets/gov.noaa.nodc:8100496_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100496_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100496_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100497_Not Applicable.json b/datasets/gov.noaa.nodc:8100497_Not Applicable.json index d954c38ae6..3000325ea4 100644 --- a/datasets/gov.noaa.nodc:8100497_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100497_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100497_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100503_Not Applicable.json b/datasets/gov.noaa.nodc:8100503_Not Applicable.json index 9473ddb897..4451b44f47 100644 --- a/datasets/gov.noaa.nodc:8100503_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100503_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100503_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100566_Not Applicable.json b/datasets/gov.noaa.nodc:8100566_Not Applicable.json index e7647ead0b..d36c09eb6d 100644 --- a/datasets/gov.noaa.nodc:8100566_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100566_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100566_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE in the Gulf of Mexico from 23 October 1980 to 18 February 1981. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100606_Not Applicable.json b/datasets/gov.noaa.nodc:8100606_Not Applicable.json index 88ef279600..129606f02c 100644 --- a/datasets/gov.noaa.nodc:8100606_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100606_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100606_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100613_Not Applicable.json b/datasets/gov.noaa.nodc:8100613_Not Applicable.json index 4fd362f39d..791a226663 100644 --- a/datasets/gov.noaa.nodc:8100613_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100613_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100613_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100667_Not Applicable.json b/datasets/gov.noaa.nodc:8100667_Not Applicable.json index d10153fbcb..b756456340 100644 --- a/datasets/gov.noaa.nodc:8100667_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100667_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100667_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100711_Not Applicable.json b/datasets/gov.noaa.nodc:8100711_Not Applicable.json index d92b9ca63b..ed128661f5 100644 --- a/datasets/gov.noaa.nodc:8100711_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100711_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100711_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from NOAA Ship TOWNSEND CROMWELL (cruise # 65) and other platforms. The data was collected over a period spanning from June 20, 1975 to July 3, 1975. Data was submitted by National Marine Fisheries Service, La Jolla, CA. The high resolution CTD data is available in the F022 format of NODC. Following URL http://www.nodc.noaa.gov/General/NODC-Archive/f022.html provides more information on the data format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8100731_Not Applicable.json b/datasets/gov.noaa.nodc:8100731_Not Applicable.json index 8a5aeb1c79..ddf53b4442 100644 --- a/datasets/gov.noaa.nodc:8100731_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8100731_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8100731_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Current direction, chemical, phytoplankton, zooplankton, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from April 17, 1980 to July 17, 1981. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F028- Phytoplankton, F069- Marine Chemistry, and F124- Zooplankton formats. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F028 format is used for data from the sampling and analysis of marine phytoplankton. Information on phytoplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: position, date, and time of sampling; bottom depth and sampling depths; volume of water filtered; and concentration of cells, carbon concentration, wet and dry weight, and counts for each species reported. Comments may be relayed in a text record.\n\nThe F069 format is used for data from chemical analyses of seawater samples. Cruise information, position, date, and time is reported for each station along with sample depth, temperature, salinity, and density (sigma-t). Chemical and biochemical parameters that may be reported include: dissolved oxygen, nitrate, nitrite, ammonia, inorganic phosphate, and silicate; dissolved organic carbon, particulate organic carbon, and particulate organic nitrogen; and apparent oxygen utilization, percent oxygen saturation, adenosine triphosphate, total phaeophytin, total chlorophyll, total suspended matter, total recoverable petroleum hydrocarbons, and total resolved light hydrocarbons.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200012_Not Applicable.json b/datasets/gov.noaa.nodc:8200012_Not Applicable.json index f0bb984dbf..ab29699c3f 100644 --- a/datasets/gov.noaa.nodc:8200012_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200012_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200012_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, marine toxic substances, benthic organisms, zooplankton, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from August 30, 1979 to September 21, 1981. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F069- Marine Chemistry, F123- Fish Shellfish Resources, F124- Zooplankton, F132- Benthic Organisms, and F144- Marine Toxic Substances formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F069 format is used for data from chemical analyses of seawater samples. Cruise information, position, date, and time is reported for each station along with sample depth, temperature, salinity, and density (sigma-t). Chemical and biochemical parameters that may be reported include: dissolved oxygen, nitrate, nitrite, ammonia, inorganic phosphate, and silicate; dissolved organic carbon, particulate organic carbon, and particulate organic nitrogen; and apparent oxygen utilization, percent oxygen saturation, adenosine triphosphate, total phaeophytin, total chlorophyll, total suspended matter, total recoverable petroleum hydrocarbons, and total resolved light hydrocarbons.\n\nThe F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comments.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments. \" Self-documenting files cotnaining [datatypes].\"", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200015_Not Applicable.json b/datasets/gov.noaa.nodc:8200015_Not Applicable.json index 7be7634389..19681b24e5 100644 --- a/datasets/gov.noaa.nodc:8200015_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200015_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200015_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200045_Not Applicable.json b/datasets/gov.noaa.nodc:8200045_Not Applicable.json index e4cd6c2d5f..f851e60816 100644 --- a/datasets/gov.noaa.nodc:8200045_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200045_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200045_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200064_Not Applicable.json b/datasets/gov.noaa.nodc:8200064_Not Applicable.json index 03ca8a1fa2..495a3b6bfa 100644 --- a/datasets/gov.noaa.nodc:8200064_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200064_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200064_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, phytoplankton, benthic organisms, zooplankton, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from February 12, 1981 to January 5, 1982. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F028- Phytoplankton, F123- Fish Shellfish Resources, F124- Zooplankton, and F132- Benthic Organisms formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F028 format is used for data from the sampling and analysis of marine phytoplankton. Information on phytoplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: position, date, and time of sampling; bottom depth and sampling depths; volume of water filtered; and concentration of cells, carbon concentration, wet and dry weight, and counts for each species reported. Comments may be relayed in a text record.\n\nThe F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comments.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200069_Not Applicable.json b/datasets/gov.noaa.nodc:8200069_Not Applicable.json index 0e032e9126..bb29648aed 100644 --- a/datasets/gov.noaa.nodc:8200069_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200069_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200069_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200079_Not Applicable.json b/datasets/gov.noaa.nodc:8200079_Not Applicable.json index 0a8eb0d073..385e5540f5 100644 --- a/datasets/gov.noaa.nodc:8200079_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200079_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200079_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200103_Not Applicable.json b/datasets/gov.noaa.nodc:8200103_Not Applicable.json index fe7a992463..684b8cce29 100644 --- a/datasets/gov.noaa.nodc:8200103_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200103_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200103_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organism and marine toxic substances and pollutants were collected using net, sediment sampler, and other instruments from NOAA Ship RESEARCHER and other platforms in the Gulf of Mexico. Data were submitted by the Energy Resources Co., INC. with support from IXTOC project. Data were collected from 23 July 1979 to 13 December 1980. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms and F144- Marine Toxic Substances and Pollutants formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.\n\nThe F144 contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200176_Not Applicable.json b/datasets/gov.noaa.nodc:8200176_Not Applicable.json index 4623fe10d0..a4f74c6343 100644 --- a/datasets/gov.noaa.nodc:8200176_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200176_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200176_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200181_Not Applicable.json b/datasets/gov.noaa.nodc:8200181_Not Applicable.json index 523015e4bb..c3f53d6335 100644 --- a/datasets/gov.noaa.nodc:8200181_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200181_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200181_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200183_Not Applicable.json b/datasets/gov.noaa.nodc:8200183_Not Applicable.json index 78aa1def9e..e1896e84a0 100644 --- a/datasets/gov.noaa.nodc:8200183_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200183_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200183_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, phytoplankton, zooplankton, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from February 17, 1980 to May 27, 1982. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F028- Phytoplankton, F069- Marine Chemistry, F123- Fish Shellfish Resource, and F124- Zooplankton formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F028 format is used for data from the sampling and analysis of marine phytoplankton. Information on phytoplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: position, date, and time of sampling; bottom depth and sampling depths; volume of water filtered; and concentration of cells, carbon concentration, wet and dry weight, and counts for each species reported. Comments may be relayed in a text record.\n\nThe F069 format is used for data from chemical analyses of seawater samples. Cruise information, position, date, and time is reported for each station along with sample depth, temperature, salinity, and density (sigma-t). Chemical and biochemical parameters that may be reported include: dissolved oxygen, nitrate, nitrite, ammonia, inorganic phosphate, and silicate; dissolved organic carbon, particulate organic carbon, and particulate organic nitrogen; and apparent oxygen utilization, percent oxygen saturation, adenosine triphosphate, total phaeophytin, total chlorophyll, total suspended matter, total recoverable petroleum hydrocarbons, and total resolved light hydrocarbons.\n\nThe F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comments.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200219_Not Applicable.json b/datasets/gov.noaa.nodc:8200219_Not Applicable.json index 96e637057d..2cec33f3c7 100644 --- a/datasets/gov.noaa.nodc:8200219_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200219_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200219_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200236_Not Applicable.json b/datasets/gov.noaa.nodc:8200236_Not Applicable.json index 5b737dd0bd..cc824eba5d 100644 --- a/datasets/gov.noaa.nodc:8200236_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200236_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200236_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8200245_Not Applicable.json b/datasets/gov.noaa.nodc:8200245_Not Applicable.json index 04632d655b..04f1d530d1 100644 --- a/datasets/gov.noaa.nodc:8200245_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8200245_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8200245_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300017_Not Applicable.json b/datasets/gov.noaa.nodc:8300017_Not Applicable.json index 614083fd80..2dae42aca6 100644 --- a/datasets/gov.noaa.nodc:8300017_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300017_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300017_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300037_Not Applicable.json b/datasets/gov.noaa.nodc:8300037_Not Applicable.json index f8bc7a34de..673ae6e3ac 100644 --- a/datasets/gov.noaa.nodc:8300037_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300037_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300037_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, phytoplankton weight, and other data were collected from the G.W. PIERCE from June 19, 1971 to September 14, 1980. Data were collected using meteorological sensors, plankton net, secchi disk, and bottle casts from the South Atlantic Ocean. Data were submitted by Skidaway Institute of Oceanography (SKIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300043_Not Applicable.json b/datasets/gov.noaa.nodc:8300043_Not Applicable.json index cca1e40aaa..7cc62fe7cf 100644 --- a/datasets/gov.noaa.nodc:8300043_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300043_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300043_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300062_Not Applicable.json b/datasets/gov.noaa.nodc:8300062_Not Applicable.json index 8a5e20fe40..20231eeaf4 100644 --- a/datasets/gov.noaa.nodc:8300062_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300062_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300062_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300075_Not Applicable.json b/datasets/gov.noaa.nodc:8300075_Not Applicable.json index 52b9aab6de..756394a000 100644 --- a/datasets/gov.noaa.nodc:8300075_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300075_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300075_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, phytoplankton, zooplankton, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from September 7, 1982 to November 11, 1982. Data were submitted by Texas A&M University as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F028- Phytoplankton, F069- Marine Chemistry, F123- Fish Shellfish Resource, and F124- Zooplankton formats. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F028 format is used for data from the sampling and analysis of marine phytoplankton. Information on phytoplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: position, date, and time of sampling; bottom depth and sampling depths; volume of water filtered; and concentration of cells, carbon concentration, wet and dry weight, and counts for each species reported. Comments may be relayed in a text record.\n\nThe F069 format is used for data from chemical analyses of seawater samples. Cruise information, position, date, and time is reported for each station along with sample depth, temperature, salinity, and density (sigma-t). Chemical and biochemical parameters that may be reported include: dissolved oxygen, nitrate, nitrite, ammonia, inorganic phosphate, and silicate; dissolved organic carbon, particulate organic carbon, and particulate organic nitrogen; and apparent oxygen utilization, percent oxygen saturation, adenosine triphosphate, total phaeophytin, total chlorophyll, total suspended matter, total recoverable petroleum hydrocarbons, and total resolved light hydrocarbons.\n\nThe F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comments.\n\nThe F124 format is used for data from sampling and analysis of marine zooplankton. Information on zooplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: cruise information, position, date, and time of sampling; bottom depth, sampling depths, temperature, and salinity; gear type, volume of water filtered, total dry and wet weight, and other data for total haul; and data for subsamples by species. Data on zooplankton catch by species may include subsample size, zooplankton concentration, life history code, and numbers of adults, juveniles, eggs, and larvae. Estimated density of holoplankton and meroplankton and data on ichthyoplankton may also be reported. A text record is available for comments. Note: there are two options for reporting subsample counts of individuals at different life history stages. If life history codes are used, only number of adults should be reported on that record. Additional separate records should then be used to report number of juveniles and so on. Alternatively, life history codes may not be used and number of adults, juveniles, and so entered in the proper fields of a single record.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300082_Not Applicable.json b/datasets/gov.noaa.nodc:8300082_Not Applicable.json index bcc85d8b5d..ac280828a3 100644 --- a/datasets/gov.noaa.nodc:8300082_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300082_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300082_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the CAPT. BRADY J and CAJUN SPECIAL in the Gulf of Mexico from 03 May 1982 to 13 October 1982. Data were submitted by the Mcneese State University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300103_Not Applicable.json b/datasets/gov.noaa.nodc:8300103_Not Applicable.json index 460fac2f5a..62107758f1 100644 --- a/datasets/gov.noaa.nodc:8300103_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300103_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300103_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature profile data were collected using XBT casts from the AFRICAN COMET and other platforms in the Pacific Ocean, Indian Ocean, Atlantic Ocean, and more locations. Data were collected from 06 June 1974 to 12 November 1980. Data were collected by the US Navy; Ships of Opportunity and Farrell Lines with support from the Gulf of Mexico NOAA/NMFS Ships of Opportunity (SOOP) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300104_Not Applicable.json b/datasets/gov.noaa.nodc:8300104_Not Applicable.json index c0c93ebce8..7c2f6f3205 100644 --- a/datasets/gov.noaa.nodc:8300104_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300104_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300104_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300131_Not Applicable.json b/datasets/gov.noaa.nodc:8300131_Not Applicable.json index d3742ccba3..e3411ec391 100644 --- a/datasets/gov.noaa.nodc:8300131_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300131_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300131_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300138_Not Applicable.json b/datasets/gov.noaa.nodc:8300138_Not Applicable.json index 7f572255dc..3bb96c799b 100644 --- a/datasets/gov.noaa.nodc:8300138_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300138_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300138_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300152_Not Applicable.json b/datasets/gov.noaa.nodc:8300152_Not Applicable.json index c36b19095a..5235fe8e50 100644 --- a/datasets/gov.noaa.nodc:8300152_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300152_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300152_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and bathythermograph data were collected using moored current meter casts and other instruments from NOAA Ship RESEARCHER and CAPT. BRADY J in the Gulf of Mexico from May 19, 1981 to April 12, 1983. Data were submitted by RAYTHEON CO. as part of the Brine Disposal project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry format and Universal Bathythermograph Output (UBT) format. Full format descriptions are available from NODC homepage at http://www.noaa.nodc.gov.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe UBT file format is used for temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Standard XBTs can obtain profiles at depths of about 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Cruise information, position, date, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Unlike the MBT data file, in which temperature values are recorded at uniform 5m intervals, the XBT Data File contains temperature values at non-uniform depths. These depths are at a minimum number of points (\"inflection points\") required to record the temperature curve to an acceptable degree of accuracy. On output, however, the user may request temperature values either at inflection points or interpolated to uniform depth increments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300168_Not Applicable.json b/datasets/gov.noaa.nodc:8300168_Not Applicable.json index cd6450de47..8b909e7611 100644 --- a/datasets/gov.noaa.nodc:8300168_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300168_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300168_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8300195_Not Applicable.json b/datasets/gov.noaa.nodc:8300195_Not Applicable.json index fe3f5fdf15..c3c1c8ab41 100644 --- a/datasets/gov.noaa.nodc:8300195_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8300195_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8300195_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll A and Phaeophytin A data collected by various ships in Monterey Bay, California. The data were collected from June 19, 1971 to June 15, 1977 as part of California Cooperative Fisheries Investigations (CALCOFI) project. The original data were recorded in ASCII and submitted on an unlabeled 9-track 1600 BPI magnetic tape. The documentation includes a record format description and title pages of technical reports associated with this investigation. Principal Investigator was Dr. Mary Silver. The study was carried out by University of California at Santa Cruz, Monterey Bay, California.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400043_Not Applicable.json b/datasets/gov.noaa.nodc:8400043_Not Applicable.json index 84559610aa..f9d1348e9a 100644 --- a/datasets/gov.noaa.nodc:8400043_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400043_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400043_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using sediment sampler casts from the EXCELLENCE and other platforms in the Gulf of Mexico from 09 December 1981 to 26 August 1985. Data were submitted by the Texas A&M University with support from the Brine Disposal project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400101_Not Applicable.json b/datasets/gov.noaa.nodc:8400101_Not Applicable.json index d3116386b3..6adfa05974 100644 --- a/datasets/gov.noaa.nodc:8400101_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400101_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400101_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400119_Not Applicable.json b/datasets/gov.noaa.nodc:8400119_Not Applicable.json index e751232ddb..6b1dae17be 100644 --- a/datasets/gov.noaa.nodc:8400119_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400119_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400119_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, benthic organisms, and other data were collected using moored current meter casts and other instruments in the Gulf of Mexico from November 11, 1983 to November 13, 1984. Data were submitted by Alaska Research Assosicates, INC. as part of the Gulf of Mexico North Continental Slope Study (MNCSS) project. Data has been been processed by NODC to the NODC standard F004- Water Physics and Chemistry, F123 Fish Shellfish Resource, and F132- Benthic Organisms formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F004 format is used for data from measurements and analyses of physical and chemical characteristics of the water column. Among chemical parameters that may be recorded are salinity, PH, and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll, and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity, and current velocity (east-west and north-south components). Cruise and station information, including environmental conditions of the study site at the time of observations, is also included.\n\nThe F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comments.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400121_Not Applicable.json b/datasets/gov.noaa.nodc:8400121_Not Applicable.json index a9fe6118dc..7b3090518e 100644 --- a/datasets/gov.noaa.nodc:8400121_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400121_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400121_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature profile and pressure data were collected using CTD from the R.V. WECOMA in the coastal waters of California from 20 April 1981 to 19 August 1982. Data were submitted by Oregon State University with support from the Coastal Ocean Dynamics Experiments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400151_Not Applicable.json b/datasets/gov.noaa.nodc:8400151_Not Applicable.json index 6b7123a8be..7d8f393359 100644 --- a/datasets/gov.noaa.nodc:8400151_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400151_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400151_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400184_Not Applicable.json b/datasets/gov.noaa.nodc:8400184_Not Applicable.json index c87122d61c..2724fdcf00 100644 --- a/datasets/gov.noaa.nodc:8400184_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400184_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400184_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ocean Station Data and Bathythermograph (XBT) data were collected from Helgoland Biological Stations using multiple German ships (ANTON DOHRN, GAUSS, FRIEDRICH HEINCKE, WALTHER HERWIG, METEOR, POSEIDON and SOLEA). The data was collected between January 1, 1973 to December 31, 1983 by Helgoland Biological Stations, Deutsches Hydrographische Institut, and Deutsches Ozeanographisches Datenzentrum.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400196_Not Applicable.json b/datasets/gov.noaa.nodc:8400196_Not Applicable.json index afe0816e5b..597653ed9c 100644 --- a/datasets/gov.noaa.nodc:8400196_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400196_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400196_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Temperature profile data were collected using XBT casts in the Northeast Atlantic Ocean from 30 May 1980 to 11 June 1980. Data were collected by the US Navy; Ships of Opportunity with support from the Gulf of Mexico NOAA/NMFS Ships of Opportunity (SOOP) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400200_Not Applicable.json b/datasets/gov.noaa.nodc:8400200_Not Applicable.json index a14f34d640..e5badc42d0 100644 --- a/datasets/gov.noaa.nodc:8400200_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400200_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400200_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms and phytoplankton were collected using sediment sampler and net casts in the Gulf of Mexico. Data were submitted by Texas A&M University with support from the Brine Disposal project. Data were collected from the CAPT. BRADY J and other platforms from 10 October 1982 to 30 November 1983. Data has been been processed by NODC to the NODC standard F028- Phytoplankton F132- Benthic Organisms formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F028 format is used for data from the sampling and analysis of marine phytoplankton. Information on phytoplankton abundance, distribution, and productivity derived from these data support studies of marine populations and ecosystems. Data reported may include: position, date, and time of sampling; bottom depth and sampling depths; volume of water filtered; and concentration of cells, carbon concentration, wet and dry weight, and counts for each species reported. Comments may be relayed in a text record.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400235_Not Applicable.json b/datasets/gov.noaa.nodc:8400235_Not Applicable.json index 0d9bf56cca..34f0345f69 100644 --- a/datasets/gov.noaa.nodc:8400235_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400235_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400235_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8400238_Not Applicable.json b/datasets/gov.noaa.nodc:8400238_Not Applicable.json index 726ccc7fb1..038da1ffba 100644 --- a/datasets/gov.noaa.nodc:8400238_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8400238_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8400238_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500003_Not Applicable.json b/datasets/gov.noaa.nodc:8500003_Not Applicable.json index a87ea74b9c..2e10406e6a 100644 --- a/datasets/gov.noaa.nodc:8500003_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500003_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500003_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500006_Not Applicable.json b/datasets/gov.noaa.nodc:8500006_Not Applicable.json index de671871c7..d3b9742b02 100644 --- a/datasets/gov.noaa.nodc:8500006_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500006_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500006_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500025_Not Applicable.json b/datasets/gov.noaa.nodc:8500025_Not Applicable.json index 2e3a7f689a..b1a769c13c 100644 --- a/datasets/gov.noaa.nodc:8500025_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500025_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500025_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500048_Not Applicable.json b/datasets/gov.noaa.nodc:8500048_Not Applicable.json index 85ceb63353..907711081c 100644 --- a/datasets/gov.noaa.nodc:8500048_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500048_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500048_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500058_Not Applicable.json b/datasets/gov.noaa.nodc:8500058_Not Applicable.json index 77da6c1b10..55883fbfa2 100644 --- a/datasets/gov.noaa.nodc:8500058_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500058_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500058_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500067_Not Applicable.json b/datasets/gov.noaa.nodc:8500067_Not Applicable.json index 608e85dcea..d8af16d4d3 100644 --- a/datasets/gov.noaa.nodc:8500067_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500067_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500067_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500069_Not Applicable.json b/datasets/gov.noaa.nodc:8500069_Not Applicable.json index a39fd77f0d..529ee80bae 100644 --- a/datasets/gov.noaa.nodc:8500069_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500069_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500069_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500070_Not Applicable.json b/datasets/gov.noaa.nodc:8500070_Not Applicable.json index 2f90274af9..99150d465f 100644 --- a/datasets/gov.noaa.nodc:8500070_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500070_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500070_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500125_Not Applicable.json b/datasets/gov.noaa.nodc:8500125_Not Applicable.json index 0551a87a9e..d0c071c5a9 100644 --- a/datasets/gov.noaa.nodc:8500125_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500125_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500125_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organism were collected using sediment sampler, BT, and bottle casts from the EASTWARD and other platforms in the Georges' Bank from 10 July 1981 to 08 June 1984. Data were submitted by the Battelle Marine Research Laboratory in New England with support from Ocean Continental Shelf - Georges' Bank project.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500126_Not Applicable.json b/datasets/gov.noaa.nodc:8500126_Not Applicable.json index 7daa272d0c..f6eea5e606 100644 --- a/datasets/gov.noaa.nodc:8500126_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500126_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500126_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500127_Not Applicable.json b/datasets/gov.noaa.nodc:8500127_Not Applicable.json index 54ee335962..fcfb08e24c 100644 --- a/datasets/gov.noaa.nodc:8500127_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500127_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500127_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500176_Not Applicable.json b/datasets/gov.noaa.nodc:8500176_Not Applicable.json index 7fcf41c5b9..a0610027fd 100644 --- a/datasets/gov.noaa.nodc:8500176_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500176_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500176_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500179_Not Applicable.json b/datasets/gov.noaa.nodc:8500179_Not Applicable.json index 6734db128d..95f0999674 100644 --- a/datasets/gov.noaa.nodc:8500179_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500179_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500179_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms data were collected using sediment sampler and net casts BELLOWS and other platforms in the Gulf of Mexico from 16 May 1974 to 20 February 1978. Data were collected and submitted by Dr. William Sackett of Texas University with support from the Outer Continental Shelf project.\n\nThe data are from field sampling or surveys of bottom dwelling marine organisms in F132 format. The data provide information on species counts and species wet weight from samples collected by point sampling (grab or core) or by tow (dredge or trawl). Three operational kinds of data include: Epifauna, Infauna, and Meiofauna benthic organisms data that roughly correspond to the different sampling methods and collecting institutions that were part of this study. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Number of individual organisms or total weight of all organisms in the sample is reported for each taxonomic category identified by the researchers (often to Genus and Species taxonomic resolution).", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500219_Not Applicable.json b/datasets/gov.noaa.nodc:8500219_Not Applicable.json index 8a3c1b1e44..2a3dfb6199 100644 --- a/datasets/gov.noaa.nodc:8500219_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500219_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500219_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500224_Not Applicable.json b/datasets/gov.noaa.nodc:8500224_Not Applicable.json index 3f29b05a48..56eb27c01a 100644 --- a/datasets/gov.noaa.nodc:8500224_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500224_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500224_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500250_Not Applicable.json b/datasets/gov.noaa.nodc:8500250_Not Applicable.json index 06d9a23bdf..b531423ebf 100644 --- a/datasets/gov.noaa.nodc:8500250_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500250_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500250_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500253_Not Applicable.json b/datasets/gov.noaa.nodc:8500253_Not Applicable.json index 63075cffab..f265f0628c 100644 --- a/datasets/gov.noaa.nodc:8500253_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500253_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500253_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500266_Not Applicable.json b/datasets/gov.noaa.nodc:8500266_Not Applicable.json index 763f665deb..75b97eb0b0 100644 --- a/datasets/gov.noaa.nodc:8500266_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500266_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500266_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8500277_Not Applicable.json b/datasets/gov.noaa.nodc:8500277_Not Applicable.json index 8dbaf114ff..70b90d61ce 100644 --- a/datasets/gov.noaa.nodc:8500277_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8500277_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8500277_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600027_Not Applicable.json b/datasets/gov.noaa.nodc:8600027_Not Applicable.json index 6b735cc03f..a476e6c3a9 100644 --- a/datasets/gov.noaa.nodc:8600027_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600027_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600027_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms were collected using net, sediment sampler casts, and other instruments from the R/V VENTURE in the Gulf of Mexico from 27 October 1980 to 29 April 1984. Data were submitted by the Woodward - Cycle Consultant with support from the S.W. Florida Shelf project. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600033_Not Applicable.json b/datasets/gov.noaa.nodc:8600033_Not Applicable.json index a03eb13bd4..73249346d2 100644 --- a/datasets/gov.noaa.nodc:8600033_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600033_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600033_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600044_Not Applicable.json b/datasets/gov.noaa.nodc:8600044_Not Applicable.json index 31605562da..22dfb848c1 100644 --- a/datasets/gov.noaa.nodc:8600044_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600044_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600044_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600047_Not Applicable.json b/datasets/gov.noaa.nodc:8600047_Not Applicable.json index 2cc21dbde9..a69d0f43b0 100644 --- a/datasets/gov.noaa.nodc:8600047_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600047_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600047_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600051_Not Applicable.json b/datasets/gov.noaa.nodc:8600051_Not Applicable.json index 4ffd01e7b9..7fab5c4776 100644 --- a/datasets/gov.noaa.nodc:8600051_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600051_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600051_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data file contains trace chemicals reported in a format similar to SD2. These data were collected by Scripps Institute of Oceanography (SIO) from July 12, 1972 to April 24, 1978 in the Atlantic, Pacific and Indian Oceans as part of the International Decade of Ocean Exploration / Geochemical Ocean Section Study (IDOE/GEOSECS) Program.\n\nThe data parameters measured were: barium, total organic carbon, deuterium, radium-226, lead-210, polonium, tritium, helium, neon, strontium-90, and cesium-137.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600114_Not Applicable.json b/datasets/gov.noaa.nodc:8600114_Not Applicable.json index 66ca5d9347..ad63f05f07 100644 --- a/datasets/gov.noaa.nodc:8600114_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600114_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600114_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High resolution CTD data was collected from NOAA Ship DAVID STARR JORDAN cruise 86 and other platforms. The data was collected by National Marine Fisheries Service, La Jolla from May 29 to June 5, 1974 from NE Pacific (limit-180). The data is available in F022 file format of NODC. More information regarding this file format can be obtained from the URL: http://www.nodc.noaa.gov/General/NODC-Archive/f022.html", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600134_Not Applicable.json b/datasets/gov.noaa.nodc:8600134_Not Applicable.json index a321c16809..cfd0c4b124 100644 --- a/datasets/gov.noaa.nodc:8600134_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600134_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600134_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600156_Not Applicable.json b/datasets/gov.noaa.nodc:8600156_Not Applicable.json index d2026bf09f..56be5ff12d 100644 --- a/datasets/gov.noaa.nodc:8600156_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600156_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600156_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600163_Not Applicable.json b/datasets/gov.noaa.nodc:8600163_Not Applicable.json index d8a5950d53..11d73754b9 100644 --- a/datasets/gov.noaa.nodc:8600163_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600163_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600163_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600200_Not Applicable.json b/datasets/gov.noaa.nodc:8600200_Not Applicable.json index 114ac6f450..8f23ed8fdf 100644 --- a/datasets/gov.noaa.nodc:8600200_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600200_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600200_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600204_Not Applicable.json b/datasets/gov.noaa.nodc:8600204_Not Applicable.json index 833d483fb6..d2c452f654 100644 --- a/datasets/gov.noaa.nodc:8600204_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600204_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600204_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600219_Not Applicable.json b/datasets/gov.noaa.nodc:8600219_Not Applicable.json index f93214b66e..2c145eaa1b 100644 --- a/datasets/gov.noaa.nodc:8600219_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600219_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600219_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600246_Not Applicable.json b/datasets/gov.noaa.nodc:8600246_Not Applicable.json index e04fb3a921..b046975145 100644 --- a/datasets/gov.noaa.nodc:8600246_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600246_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600246_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600248_Not Applicable.json b/datasets/gov.noaa.nodc:8600248_Not Applicable.json index a5dda9f7e1..c2e1fbc3a1 100644 --- a/datasets/gov.noaa.nodc:8600248_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600248_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600248_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600312_Not Applicable.json b/datasets/gov.noaa.nodc:8600312_Not Applicable.json index 7d7c43d8b0..5c1d304c61 100644 --- a/datasets/gov.noaa.nodc:8600312_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600312_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600312_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600335_Not Applicable.json b/datasets/gov.noaa.nodc:8600335_Not Applicable.json index 4c301f60ef..92c7b8bc2b 100644 --- a/datasets/gov.noaa.nodc:8600335_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600335_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600335_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8600343_Not Applicable.json b/datasets/gov.noaa.nodc:8600343_Not Applicable.json index 2bf80f49fd..badaa1fd9d 100644 --- a/datasets/gov.noaa.nodc:8600343_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8600343_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8600343_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700011_Not Applicable.json b/datasets/gov.noaa.nodc:8700011_Not Applicable.json index 86214f3b2c..8f1a092e1d 100644 --- a/datasets/gov.noaa.nodc:8700011_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700011_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700011_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700017_Not Applicable.json b/datasets/gov.noaa.nodc:8700017_Not Applicable.json index cefb7157b9..6d93b66ad9 100644 --- a/datasets/gov.noaa.nodc:8700017_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700017_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700017_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700050_Not Applicable.json b/datasets/gov.noaa.nodc:8700050_Not Applicable.json index 90ca93dfa5..8a09999d8b 100644 --- a/datasets/gov.noaa.nodc:8700050_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700050_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700050_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700052_Not Applicable.json b/datasets/gov.noaa.nodc:8700052_Not Applicable.json index 0c2ab08dca..2e9a5edd62 100644 --- a/datasets/gov.noaa.nodc:8700052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700084_Not Applicable.json b/datasets/gov.noaa.nodc:8700084_Not Applicable.json index 5b7e1f001b..59ac5fb935 100644 --- a/datasets/gov.noaa.nodc:8700084_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700084_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700084_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700115_Not Applicable.json b/datasets/gov.noaa.nodc:8700115_Not Applicable.json index ab2bfba7d4..163256e62a 100644 --- a/datasets/gov.noaa.nodc:8700115_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700115_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700115_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700145_Not Applicable.json b/datasets/gov.noaa.nodc:8700145_Not Applicable.json index b82ed33944..341df8beff 100644 --- a/datasets/gov.noaa.nodc:8700145_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700145_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700145_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700198_Not Applicable.json b/datasets/gov.noaa.nodc:8700198_Not Applicable.json index ed145872ae..c7d60c5725 100644 --- a/datasets/gov.noaa.nodc:8700198_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700198_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700198_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700199_Not Applicable.json b/datasets/gov.noaa.nodc:8700199_Not Applicable.json index cafa7d0cc3..1665b5ebee 100644 --- a/datasets/gov.noaa.nodc:8700199_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700199_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700199_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700332_Not Applicable.json b/datasets/gov.noaa.nodc:8700332_Not Applicable.json index fe756e6009..cdf7efe797 100644 --- a/datasets/gov.noaa.nodc:8700332_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700332_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700332_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data are part of the Southern California OCS Baseline Study funded by BLM and submitted by Science Applications, Inc. Coastal areas along southern California were sampled. Following is a list of purpose for which the study was conducted, the period when the data was collected and the type of data collected.\n\nSampling was done from July 1, 1975 to November 6, 1977 to obtain depth, temperature and salinity profiles. During the same time period data was collected to measure the amounts of particulate organic carbon (poc), dissolved organic carbon (doc), and ATP.\n\nAnalysis was done for intertidal hydrocarbon (hc) concentrations from July 1, 1975 to June 30, 1978. Fractions analyzed include aliphatic and aromatics, pristane and phytane, iso-n and branched hydrocarbons, odd/even preference, and the hexane, benzene and methane fractions.\n\nAnalysis was done for benthic hydrocarbon (hc) concentrations from July 1, 1975 to November 6, 1977. Fractions analyzed include aliphatic and aromatics, pristane and phytane, iso-n and branched hydrocarbons, odd/even preference, and the hexane, benzene and methane fractions.\n\nSampling was done to assess the trace metal concentrations from July 1, 1975 to November 6, 1978. Benthic fauna, sediments and the water column were analyzed for Ba, Cd, Cr, Cu, Fe, Ni, Pb, V, Zn and Al concentrations.\n\nSampling was done to assess the trace metal concentrations from July 1, 1975 to November 6, 1978. Intertidal rocky and sandy fauna, and sediments were analyzed for Ba, Cd, Cr, Cu, Fe, Ni, Pb, V, Zn and Al concentrations.\n\nBenthic coastal sediments along southern California were sampled from July 1, 1975 to November 6, 1977. The analysis includes sediment age, grain size, total organic carbon (toc), total inorganic carbon (tic), total carbon (tc), calcium carbonate content and mineral composition, as well as a description of the field conditions during sampling. Identical analysis was conducted on samples collected during July 1, 1975 to June 30, 1978.\n\nIntertidal coastal sediments along southern California were sampled from July 1, 1975 to June 30, 1978. The analysis includes sediment age, grain size, total organic carbon (toc), total inorganic carbon (tic), total carbon (tc), calcium carbonate content and mineral composition, as well as a description of the field conditions during sampling.\n\nCoastal areas along southern california were sampled from July 1, 1976 to June 30, 1978 and the composition of the benthic microfauna and benthic macrofauna was analyzed.\n\nCoastal areas along southern California were sampled from July 1, 1975 to June 30, 1978. Data includes files describing the biotic/abiotic mussel community and a species dictionary as well as a description of the field conditions.\n\nRocky coastal beaches along southern California were sampled from July 1, 1975 to June 30, 1978 and the composition of the intertidal rocky fauna was analyzed. Included in these data is a file on rocky intertidal fauna succession and a description of the field conditions.\n\nSandy coastal beaches along southern california were sampled from uly 1, 1975 to June 30, 1978 and the composition of the sandy intertidal fauna was analyzed.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8700358_Not Applicable.json b/datasets/gov.noaa.nodc:8700358_Not Applicable.json index d71f00198d..299d284a45 100644 --- a/datasets/gov.noaa.nodc:8700358_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8700358_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8700358_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The three files contain trace metal data from North, Central and South Atlantic observed in bottom sediment samples along the East Coast Continental Slope and Rise region. Samples were collected from a large number of surveys from May 1983 to July 1986. These data were collected by National Marine Fisheries Service, Woods Hole for the US East Coast Continental Rise/Slope region. The documentation includes the field variables (including region, box core number, location, and type of metal recorded) and a listing of cruises.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800093_Not Applicable.json b/datasets/gov.noaa.nodc:8800093_Not Applicable.json index dc3ee3fb1f..ee75e76e6d 100644 --- a/datasets/gov.noaa.nodc:8800093_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800093_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800093_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800096_Not Applicable.json b/datasets/gov.noaa.nodc:8800096_Not Applicable.json index 5543432cb8..a27bef47c5 100644 --- a/datasets/gov.noaa.nodc:8800096_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800096_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800096_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800101_Not Applicable.json b/datasets/gov.noaa.nodc:8800101_Not Applicable.json index 5ea8bf1d1c..e907315d0b 100644 --- a/datasets/gov.noaa.nodc:8800101_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800101_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800101_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800103_Not Applicable.json b/datasets/gov.noaa.nodc:8800103_Not Applicable.json index 33d191f850..a8bee90327 100644 --- a/datasets/gov.noaa.nodc:8800103_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800103_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800103_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800123_Not Applicable.json b/datasets/gov.noaa.nodc:8800123_Not Applicable.json index 8750626866..bbd4759099 100644 --- a/datasets/gov.noaa.nodc:8800123_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800123_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800123_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800135_Not Applicable.json b/datasets/gov.noaa.nodc:8800135_Not Applicable.json index 51a179a1ff..acae1f2f47 100644 --- a/datasets/gov.noaa.nodc:8800135_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800135_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800135_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800192_Not Applicable.json b/datasets/gov.noaa.nodc:8800192_Not Applicable.json index d192350426..78bc8a0d68 100644 --- a/datasets/gov.noaa.nodc:8800192_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800192_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800192_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms and marine toxic substances and pollutants were collected using net casts, sediment sampler, and other instruments from the GYRE and other platforms in NW Atlantic Ocean. Data were collected from 11 November 1983 to 30 July 1986. Data were submitted by Battelle Marine Research Laboratory in New England with support from the New England Shelf and Slope Program. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms and F144- Marine Toxic Substances and Pollutants formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.\n\nThe F144 format contains data on ambient concentrations of toxic substances and other pollutants in the marine environment. The data derive from laboratory analyses of samples of water, sediment, or marine organisms. Samples may have been collected near marine discharge sites or during ocean monitoring surveys of large areas. Field observations of tar deposits on beaches may also be reported. Survey information includes platform type, start and end dates, and investigator and institution. If data are collected near a discharge site, discharge location, depth, distance to shore, average volume, and other characteristics are reported. Position, date, time and environmental conditions are reported for each sample station. Environmental data may include meteorological and sea surface conditions, tide stage and height, depth of the thermocline or mixed layer surface temperature and salinity, and wave height and periods. Sample characteristics, collection methods, and laboratory techniques are reported for each sample collected and analyzed. The data record comprises concentration values (or a code to indicate trace amounts) for each chemical substance analyzed. Chemical substances are identified by codes based on the registry numbers assigned by the Chemical Abstracts Service (CAS) of the American Chemical Society. Marine organisms from which samples have been taken are identified using the 12-digit NODC Taxonomic Code. A text record is available for optional comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800194_Not Applicable.json b/datasets/gov.noaa.nodc:8800194_Not Applicable.json index 2fa01f304a..63a92cc317 100644 --- a/datasets/gov.noaa.nodc:8800194_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800194_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800194_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800210_Not Applicable.json b/datasets/gov.noaa.nodc:8800210_Not Applicable.json index 162c9bfd54..dcf867ea79 100644 --- a/datasets/gov.noaa.nodc:8800210_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800210_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800210_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800218_Not Applicable.json b/datasets/gov.noaa.nodc:8800218_Not Applicable.json index 3a63f6aba5..090e505da5 100644 --- a/datasets/gov.noaa.nodc:8800218_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800218_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800218_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800236_Not Applicable.json b/datasets/gov.noaa.nodc:8800236_Not Applicable.json index e128088788..29b1a0c2d8 100644 --- a/datasets/gov.noaa.nodc:8800236_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800236_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800236_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800279_Not Applicable.json b/datasets/gov.noaa.nodc:8800279_Not Applicable.json index 1d3133328e..a19f5408f8 100644 --- a/datasets/gov.noaa.nodc:8800279_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800279_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800279_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alaska, Institute of Marine Science is responsible for this data collected aboard the R/V Alpha Helix on cruise number HX118 between September 14, 1988 to September 29, 1988 by Dr. T.C. Royer of the Institute of Marine Science. There was a total of 77 stations in the Gulf of Alaska. The station numbers are: 1-77. Field correction for this cruise was taken from Alpha Helix cruise HX118. Field correction for the STD data was derived by comparing single bottle samples to recorded values from the STD sensors. The field correction is based on 9 samples from a total of 11 stations. The CTD data is stored in F022 format in the current NODC storage system.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8800308_Not Applicable.json b/datasets/gov.noaa.nodc:8800308_Not Applicable.json index cc59c85d00..2ff1706c85 100644 --- a/datasets/gov.noaa.nodc:8800308_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8800308_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8800308_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900002_Not Applicable.json b/datasets/gov.noaa.nodc:8900002_Not Applicable.json index ada5338725..9d52700159 100644 --- a/datasets/gov.noaa.nodc:8900002_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900002_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900002_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900036_Not Applicable.json b/datasets/gov.noaa.nodc:8900036_Not Applicable.json index 764db50194..71e02b46d8 100644 --- a/datasets/gov.noaa.nodc:8900036_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900036_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900036_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900105_Not Applicable.json b/datasets/gov.noaa.nodc:8900105_Not Applicable.json index 3e7e1d0210..a3f3c18bc9 100644 --- a/datasets/gov.noaa.nodc:8900105_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900105_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900105_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900114_Not Applicable.json b/datasets/gov.noaa.nodc:8900114_Not Applicable.json index 7e81181392..a8e1364518 100644 --- a/datasets/gov.noaa.nodc:8900114_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900114_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900114_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900116_Not Applicable.json b/datasets/gov.noaa.nodc:8900116_Not Applicable.json index c4414f18fe..e5001c3702 100644 --- a/datasets/gov.noaa.nodc:8900116_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900116_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900116_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900139_Not Applicable.json b/datasets/gov.noaa.nodc:8900139_Not Applicable.json index 7053af9f86..b58fd49495 100644 --- a/datasets/gov.noaa.nodc:8900139_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900139_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900139_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900192_Not Applicable.json b/datasets/gov.noaa.nodc:8900192_Not Applicable.json index f207a1dbd9..776a2e550f 100644 --- a/datasets/gov.noaa.nodc:8900192_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900192_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900192_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Alaska, Institute of Marine Science is responsible for this data collected aboard the R/V Alpha Helix on cruise number HX123 between May 5, 1989 to May 11, 1989 by Dr. R.T. Cooney of the Institute of Marine Science. There was a total of 51 stations in the Prince William sound area. The station numbers are: 2-52. These data were collected for the Exxon Oil Spill Monitoring Program. Funding was provided by the State of Alaska. This data was submitted to NODC earlier. This data set represents a resubmission due to fact that the earlier version had the wrong offsets applied. Field correction for the STD data was derived by comparing single bottle samples to recorded values from the STD sensors. The field correction is based on 15 samples from a total of 16 stations. The field corrections are: 3000 m CTD temperature mean (Nansen-STD) is 0.00536 and 3000 m CTD salinity mean (Nansen-STD) is -0.04131.", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900206_Not Applicable.json b/datasets/gov.noaa.nodc:8900206_Not Applicable.json index 7ed4708209..7c02f9efec 100644 --- a/datasets/gov.noaa.nodc:8900206_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900206_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900206_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900235_Not Applicable.json b/datasets/gov.noaa.nodc:8900235_Not Applicable.json index 318bafb6af..da0c6b089b 100644 --- a/datasets/gov.noaa.nodc:8900235_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900235_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900235_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900240_Not Applicable.json b/datasets/gov.noaa.nodc:8900240_Not Applicable.json index 019b580ec8..e385ed804e 100644 --- a/datasets/gov.noaa.nodc:8900240_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900240_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900240_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900269_Not Applicable.json b/datasets/gov.noaa.nodc:8900269_Not Applicable.json index 6a29a26b06..39a4dfee2d 100644 --- a/datasets/gov.noaa.nodc:8900269_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900269_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900269_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900273_Not Applicable.json b/datasets/gov.noaa.nodc:8900273_Not Applicable.json index 7b1a3fe2b2..0f42b2005a 100644 --- a/datasets/gov.noaa.nodc:8900273_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900273_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900273_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900277_Not Applicable.json b/datasets/gov.noaa.nodc:8900277_Not Applicable.json index bbbd94d441..14b63f8d9f 100644 --- a/datasets/gov.noaa.nodc:8900277_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900277_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900277_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900284_Not Applicable.json b/datasets/gov.noaa.nodc:8900284_Not Applicable.json index 490de47a2f..45a480ec97 100644 --- a/datasets/gov.noaa.nodc:8900284_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900284_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900284_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:8900299_Not Applicable.json b/datasets/gov.noaa.nodc:8900299_Not Applicable.json index d8f5ac7ee3..4b461e0fa0 100644 --- a/datasets/gov.noaa.nodc:8900299_Not Applicable.json +++ b/datasets/gov.noaa.nodc:8900299_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:8900299_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Benthic organisms data were collected using sediment sampler casts from NOAA Ship OCEANOGRAPHER in the Chukchi Sea from 06 September 1986 to 05 October 1987. Data were submitted by the University of Alaska - Fairbanks, Institute of Marine Science. Data has been been processed by NODC to the NODC standard F132- Benthic Organisms formats. Full format and format code descriptions are available at http://www.nodc.noaa.gov/General/NODC-datafmts.html.\n\nThe F132 format contains data from field sampling or surveys of bottom dwelling marine organisms. The data provide information on species abundance, distribution, and biomass; they may have been collected by point sampling (grab or core), by tow (dredge, trawl or net), by photographic surveys, or by other methods. Cruise information such as vessel, start and end dates, investigator, and institution/agency; station numbers, positions and times; and equipment and methods are reported for each survey. Environmental data reported at each sampling site may include meteorological and sea surface conditions; surface and bottom temperature, salinity and dissolved oxygen; and sediment characteristics. Number of individual organisms and total weight of organisms is reported for each species. A text record is available for comments.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000025_Not Applicable.json b/datasets/gov.noaa.nodc:9000025_Not Applicable.json index 1d24cc1d45..b276fb3c01 100644 --- a/datasets/gov.noaa.nodc:9000025_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000025_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000025_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data were collected by Texas A&M University, College Station under a program sponsored by grant MMS # 14-35-0001-30501 to Dr. Douglas C. Biggs. The physical profile data were collected in Gulf of Mexico from Ship Gyre between November 11-18, 1989. Originator's data were submitted by Dr. David Murphy in tapes that have been processed by NODC. The data is available in F022-CTD Hi resolution, C116 Bathythermograph XBT and C100 Ocean-Station Data format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000038_Not Applicable.json b/datasets/gov.noaa.nodc:9000038_Not Applicable.json index 3da1027a28..66f22f0d8f 100644 --- a/datasets/gov.noaa.nodc:9000038_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000038_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000038_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The high resolution Conductivity, Temperature and Depth (CTD) data were collected by NOAA Ship WHITING from NW Atlantic (limit-40 W). The data was collected over a three month period spanning from August 19, 1989 to November 10, 1989 by National Ocean Service (NOS) Rockville, MD.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000045_Not Applicable.json b/datasets/gov.noaa.nodc:9000045_Not Applicable.json index fd2df78087..d8433eaaf4 100644 --- a/datasets/gov.noaa.nodc:9000045_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000045_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000045_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) data with oxygen was collected off of Indian Ocean and Arabian Sea using Charles Darwin ship as part of Monsoon And Sea-Air Interaction (MASAI) project conducted between December 1986 and August 1987. The data was submitted by Dr. Donald B. Olson of Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami, FL. The originator's data has been processed by NODC and is available in C100 Ocean Station Data format and F-022 hi resolution CTD data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000080_Not Applicable.json b/datasets/gov.noaa.nodc:9000080_Not Applicable.json index 421f17c6db..96870c7365 100644 --- a/datasets/gov.noaa.nodc:9000080_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000080_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000080_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The water depth and other data available in this accession was scanned from a publication. The data was submitted by Deutsche Akademe der Wissenschaften zu Berlin. The data was collected from ship A. V. Humboldt between April 13 to June 12, 1971.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000100_Not Applicable.json b/datasets/gov.noaa.nodc:9000100_Not Applicable.json index c1aaab4969..e75bb0d74f 100644 --- a/datasets/gov.noaa.nodc:9000100_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000100_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000100_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD); and other data were collected from eleven cruises conducted using four different ships from NW Atlantic (limit-40 W). The data was collected by National Marine Fisheries Service, Woods Hole, MA over a three year period spanning from June 19, 1986 to December 15, 1989. Originator's data submitted in a tape by Dr. David Mountain has been processed by NODC and is currently available in F022 file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000114_Not Applicable.json b/datasets/gov.noaa.nodc:9000114_Not Applicable.json index 417fefc513..362760cc95 100644 --- a/datasets/gov.noaa.nodc:9000114_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000114_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000114_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data in this accession was collected using a SEACAT Conductivity and Temperature recorder to measure Conductivity, Temperature and Depth (CTD) using NOAA Ship Whiting to collect data from NW Atlantic (limit-40 W). The data was collected over two month period spanning from February 27, 1990 to April 6, 1990 by National Ocean Service, Norfolk, VA. Originator's data submitted in a diskette has been processed and is available in F022-CTD Hi resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000119_Not Applicable.json b/datasets/gov.noaa.nodc:9000119_Not Applicable.json index c7ef027388..1954910e9b 100644 --- a/datasets/gov.noaa.nodc:9000119_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000119_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000119_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected. R/V Charles Darwin was used to collect data. The data consisting of 111 casts was collected over one month period spanning from November 13, 1987 to December 16, 1987. Data was submitted by Dr. John Toole of Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000120_Not Applicable.json b/datasets/gov.noaa.nodc:9000120_Not Applicable.json index 32685d4893..55ad567d47 100644 --- a/datasets/gov.noaa.nodc:9000120_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000120_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000120_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected. R/V Oceanus was used to collect data. The data was collected from 76 stations over one month period spanning from June 15, 1983 to July 11, 1983 in NW Atlantic (limit-40 W). Data was submitted by Dr. Nelson Hogg of Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA in a tape. The originator's data has been processed by NODC and is currently available in F022-CTD Hi resolution file format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000136_Not Applicable.json b/datasets/gov.noaa.nodc:9000136_Not Applicable.json index 12523801ba..99e7eb3d95 100644 --- a/datasets/gov.noaa.nodc:9000136_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000136_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000136_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected using R/V Endeavor from NE Atlantic (limit-40 W). The data was collected over a 15 day period spanning from May 3 to May 18, 1986 by Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA. Data was submitted by Dr. Terry Joyce. The data has been processed and is available in F022-CTD Hi resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000151_Not Applicable.json b/datasets/gov.noaa.nodc:9000151_Not Applicable.json index a57dc292d4..2005ad3f40 100644 --- a/datasets/gov.noaa.nodc:9000151_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000151_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000151_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected using SEACAT recorders from North American Coast line. NOAA Ship Whiting was used to collect data. The data was collected over a three month period spanning from April 20, 1990 to June 28, 1990 by National Ocean Service, Norfolk, VA. Data was submitted in one diskette of \"SEACAT\" CTD data. The data has been processed and is currently available in F022 CTD high resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000162_Not Applicable.json b/datasets/gov.noaa.nodc:9000162_Not Applicable.json index 480a0665b7..996aa4719c 100644 --- a/datasets/gov.noaa.nodc:9000162_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000162_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000162_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected by German Vessels between 1936 and 1974 was received from the German Data Center (DOD). The data include temperature and salinity data collected by German ships in the North-East Atlantic.\n\nAssociated station and bathythermograph data has been processed by NODC. Station Data is available in C100 Ocean Station Data File format. Bathythermograph data is available in C116 XBT and C128 MBT file formats.\n\nAnton Dohrn cruise data was collected between January 8 and January 27, 1976. The data is stored in file L01196.001.\n\nMeteor cruise data was collected between August 27 and Sptember 21, 1974. The data is stored in file L01197.001.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000179_Not Applicable.json b/datasets/gov.noaa.nodc:9000179_Not Applicable.json index 9ffcd5c65a..09ce36c364 100644 --- a/datasets/gov.noaa.nodc:9000179_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000179_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000179_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected using NOAA Ship Mt. Mitchell from NW Atlantic (limit-40 W) and Gulf of Mexico. The data containing 22 casts was collected over a six month period spanning from May 3, 1989 to November 19, 1989 using the SEACAT CTD recorder by WHOI. Data was submitted by National Ocean Service (NOS), Rockville, MD in five diskettes. The data has been processed by NODC and is available in F022-CTD Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000180_Not Applicable.json b/datasets/gov.noaa.nodc:9000180_Not Applicable.json index fea04b9216..17e6b9445a 100644 --- a/datasets/gov.noaa.nodc:9000180_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000180_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000180_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Warm Core Rings project. One year data consisting of five cruises was collected from NW Atlantic (limit-40 W) using NOAA Ship Delaware II and NOAA Ship Albatross IV. The data was collected over a period spanning from September 23, 1981 to September 27, 1982. Data was submitted by National Marine Fisheries Service (NMFS), Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000225_Not Applicable.json b/datasets/gov.noaa.nodc:9000225_Not Applicable.json index af4c99386e..688a6c5d6a 100644 --- a/datasets/gov.noaa.nodc:9000225_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000225_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000225_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from Gulf of Mexico. R/V Gyre was used to collect data by Texas A&M University, College Station, TX. The data was collected over two week period between July 11-23, 1990. Data was submitted by Dr. Davis Murphy. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000233_Not Applicable.json b/datasets/gov.noaa.nodc:9000233_Not Applicable.json index ff6c1fcddc..1dd353f39c 100644 --- a/datasets/gov.noaa.nodc:9000233_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000233_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000233_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Marine Mammal Cruises. Two year data consisting of 381 casts was collected from NE Pacific (limit-180) using NOAA Ship DAVID STARR JORDAN. The data was collected over a period spanning from August 5, 1986-November 30, 1988. Data was submitted by Mr. David Behringer of Atlantic Oceanographic Meterological Laboratory, Miami, FL. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000234_Not Applicable.json b/datasets/gov.noaa.nodc:9000234_Not Applicable.json index f75998374a..f2990deabc 100644 --- a/datasets/gov.noaa.nodc:9000234_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000234_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000234_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and Ocean Station data were collected from Lamont-Doherty Earth Observatory, Palisades, NY. Data was collected from South Atlantic Ocean using Ship Discovery . The data was collected over a period spanning from April 22, 1987 to May 5, 1987. Data was submitted by Dr. Arnold L. Gordon. Data has been processed and is available in F022-CTD-Hi Resolution and C100-Ocean-Station-Data file format respectively of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000248_Not Applicable.json b/datasets/gov.noaa.nodc:9000248_Not Applicable.json index 73a0848338..2fed568b9d 100644 --- a/datasets/gov.noaa.nodc:9000248_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000248_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000248_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of EDYLOC project over five years using Ships Noroit and Thalassa. The data was collected over a period spanning from April, 1982 to April, 1987 during cruises Soldet Legs 1-7. Data was collected by Lamont-Doherty Earth Observatory, Palisades, NY and was submitted by Dr. Gilles Reverdin in a tape. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000275_Not Applicable.json b/datasets/gov.noaa.nodc:9000275_Not Applicable.json index 3349269f00..88541bb88d 100644 --- a/datasets/gov.noaa.nodc:9000275_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000275_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000275_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected over one month period from NE Atlantic (limit-40 W) using Ship Marion Dufresne during cruise 53. The data was collected over a period spanning from January 14, 1987 to February 13, 1987. Data was submitted by Dr. Gilles Reverdin of Lamont-Doherty Earth Observatory in a tape. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000276_Not Applicable.json b/datasets/gov.noaa.nodc:9000276_Not Applicable.json index ecf83394c4..77c326dbe2 100644 --- a/datasets/gov.noaa.nodc:9000276_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000276_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000276_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected over one year period from NE Atlantic (limit-40 W) using Ship Marion Dufresne during cruises 44 and 49. The data was collected over a period spanning from April 3, 1985 to May 1, 1986. Data was submitted by Dr. Gilles Reverdin of Lamont-Doherty Earth Observatory in a tape. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9000277_Not Applicable.json b/datasets/gov.noaa.nodc:9000277_Not Applicable.json index 658bf8bc9c..581cf752f5 100644 --- a/datasets/gov.noaa.nodc:9000277_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9000277_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9000277_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected over two year period from NE Atlantic (limit-40 W) using Ships Le Noroit and Le Suroit. The data was collected over a period spanning from October 20, 1981 to September 5, 1983. Data was submitted by Dr. Gilles Reverdin of Lamont-Doherty Earth Observatory in two tapes. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100003_Not Applicable.json b/datasets/gov.noaa.nodc:9100003_Not Applicable.json index 1190f7f4ef..1f127b7715 100644 --- a/datasets/gov.noaa.nodc:9100003_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100003_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100003_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from NW Atlantic (limit-40 W) by French using four different Ships JEAN CHARCOT, LE NOROIT, LE SUROIT and MARION DUFRESNE. The data were collected over a period spanning from May 25, 1979 to May 24, 1980. Data were submitted by Dr. Gilles Reverdin of Lamont-Doherty Earth Observatory, Palisades, NY in a tape. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100016_Not Applicable.json b/datasets/gov.noaa.nodc:9100016_Not Applicable.json index e803d08adf..9855b4671d 100644 --- a/datasets/gov.noaa.nodc:9100016_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100016_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100016_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other physical profile data were collected using SeaCAT sensors. The CAT in SeaCAT stands for \"Continuous Anode Technology\". Six months data were collected from Gulf of Mexico using NOAA Ship WHITING. The data were collected over a period spanning from July 27, 1990 to November 20, 1990 from 16 stations. Data were submitted by National Ocean Service, Norfolk, VA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100017_Not Applicable.json b/datasets/gov.noaa.nodc:9100017_Not Applicable.json index 3931f82ec8..e66641ad8e 100644 --- a/datasets/gov.noaa.nodc:9100017_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100017_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100017_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains Chemical Abstracts Society (CAS) parameter codes and Other Data from MULTIPLE SHIPS From Caribbean Sea and Gulf of Mexico from February 5, 1979 to May 3, 1989. Floppy disks containing all the Caribbean Oil Pollution (CARIPOL) Database were submitted by J. Price of NOS. The data were originally held and quality controlled by Dr. Atwood at NOAA/AOML. The data are divided into three groupings: Beach Tar, Floating Tar and Dissolved Tar.\n\nThe Beach Tar data (ref. no. L01174 02/05/79-05/03/89, 25 STATIONS, 26,413 RECORDS) was submitted by 21 different countries around the caribbean (different institutions and ships).\n\nThe Dissolved Tar data (ref. no. L01175 11/29/79-11/13/88, 11 STATIONS, 7,209 RECORDS) was submitted by 11 countries,\n\nThe Floating Tar data (ref. no. L01176 10/29/80-11/27/88, 9 STATIONS, 3,250 RECORDS ) by 9 countries.\n\nA description of the parameter names can be found in file structur.doc. The data are in the vax under DUA2:[LEVELA] L01174. The data was offline on May 4, 2005 when this abstract was last edited. Data should be in F144 format. NOTE: Please check the actual file format when the data is online.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100025_Not Applicable.json b/datasets/gov.noaa.nodc:9100025_Not Applicable.json index d9b49d34a0..06a3ff97e8 100644 --- a/datasets/gov.noaa.nodc:9100025_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100025_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100025_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aerial surveys of Whales data in this accession were collected from aircraft by Don Llungblad over the Chukchi Sea between September 1987 and October 1988 by Sea World Research Institute, San Diego, CA. The data were submitted by Ms. Karen M. McClune in a tape. The data have been processed and are available in F127-Marine-Animal-Sightings file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100026_Not Applicable.json b/datasets/gov.noaa.nodc:9100026_Not Applicable.json index c0115733da..0220bc4aa4 100644 --- a/datasets/gov.noaa.nodc:9100026_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100026_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100026_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The aerial surveys of Whales data in this accession were collected from aircraft by Steve Tracey over the Bering Sea between September 1987 and October 1988 by Sea World Research Institute, San Diego, CA. The data were submitted by Ms. Karen M. McClune in a tape. The data have been processed and are available in F127-Marine-Animal-Sightings file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100027_Not Applicable.json b/datasets/gov.noaa.nodc:9100027_Not Applicable.json index 0de25a62a3..0b5192edcd 100644 --- a/datasets/gov.noaa.nodc:9100027_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100027_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100027_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD), Primary production and underway data were collected as part of Processes and Resources of the Bering Sea Shelf (PROBES) Project. Ship THOMAS G. THOMPSON was used to collect data from Bering Sea. The data was collected over a period spanning from April 10, 1978 to August 15, 1980 by Pacific Marine Environmental Laboratory (PMEL) Seattle, WA. Three tapes containing data files L01200 = TAPE FILES 1 - 5; L01201 = TAPE FILE 6; and A01361 W13748 were submitted. Temperature, Chlorophyll-A, PO4-P, NO3-N, NO2-N, AND SIO3-SI were measured for primary production. The data has been copied into the current NODC data storage system.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100028_Not Applicable.json b/datasets/gov.noaa.nodc:9100028_Not Applicable.json index 0edaad2bf2..aed7272e56 100644 --- a/datasets/gov.noaa.nodc:9100028_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100028_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100028_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Ocean Station Data; the Water Depth and Temperature Data; and the Conductivity, Temperature and Depth (CTD) was collected from ship GYRE in the Gulf of Mexico by Texas A&M University, College Staion, Texas over a two week period between October 1 and October 16, 1990. The originator's data was submitted in two diskettes by Mr David J. Voegele. The data has been converted and is now available on line in C100 Ocean Station Data, C116 bathythermograph (XBT) and F022-CTD-Hi Res file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100039_Not Applicable.json b/datasets/gov.noaa.nodc:9100039_Not Applicable.json index f9ed33aebf..ec55625344 100644 --- a/datasets/gov.noaa.nodc:9100039_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100039_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100039_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) data were collected as part of Southern Coastal Plains Expedition (SCOPE) project. One month data was collected from Gulf of Maine using ship Endeavor. The data was collected over a period spanning from May 18, 1989 to June 11, 1989. Data was submitted on a floppy disk containing 199 CTD files by Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100040_Not Applicable.json b/datasets/gov.noaa.nodc:9100040_Not Applicable.json index e84e7f04ad..010cc0bd26 100644 --- a/datasets/gov.noaa.nodc:9100040_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100040_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100040_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) data were collected as part of Amazon Shelf Sediment Study (AMASSEDS) project. Data was collected from NW Atlantic (limit-40 W) using ship COLUMBUS ISELIN. The data was collected over a 10 day period between August 4 to August 13, 1989. Data was submitted on a floppy disk containing 86 CTD files by Dr. Richard Limeburner, Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100042_Not Applicable.json b/datasets/gov.noaa.nodc:9100042_Not Applicable.json index c939a7123a..6b084932a3 100644 --- a/datasets/gov.noaa.nodc:9100042_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100042_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100042_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) data were collected from Gulf of Alaska using Ship ALPHA HELIX during three cruises HX14-HX145. The data was collected over a period spanning from September 11, 1990 to December 16, 1990. Data on a tape containing 148 station ovservations was submitted by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100078_Not Applicable.json b/datasets/gov.noaa.nodc:9100078_Not Applicable.json index 7cc26a88f9..7cf7944226 100644 --- a/datasets/gov.noaa.nodc:9100078_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100078_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100078_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Seasonal Equatorial Atlantic (SEQUAL) project. Data was collected from NE Atlantic (limit-40 W) using ships CAPRICORNE and MARION DUFRESNE. The data was collected over a period spanning from July 6, 1982 to October 30 1987. Replacement for damaged tape containing French CTD data was submitted by Dr. Gilles Reverdin, Lamont-Doherty Earth Observatory, Palisades, NY. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100079_Not Applicable.json b/datasets/gov.noaa.nodc:9100079_Not Applicable.json index 5cd155d263..2920d8a0ce 100644 --- a/datasets/gov.noaa.nodc:9100079_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100079_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100079_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Office of Naval Research (ONR). CTD Data was collected from NW Atlantic (limit-40 W). Replacement data is provided for data collected using Ship CONRAD between May and June 1988. New data was collected using ship COLUMBUS ISELIN over a period spanning from November 22, 1990 to December 1, 1990. Data was submitted by Dr. Eli Katz, Lamont-Doherty Earth Observatory, Palisades, NY. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100080_Not Applicable.json b/datasets/gov.noaa.nodc:9100080_Not Applicable.json index 9110302b19..3c46a1228b 100644 --- a/datasets/gov.noaa.nodc:9100080_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100080_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100080_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected during sixteen casts using NOAA Ship MT. MITCHELL. The data was collected over a period spanning from April 17, 1990 to November 29, 1990. Data was submitted by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100081_Not Applicable.json b/datasets/gov.noaa.nodc:9100081_Not Applicable.json index 3b7a3eda9e..996388035d 100644 --- a/datasets/gov.noaa.nodc:9100081_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100081_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100081_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Antarctic Marine Ecosystem Analysis at the Ice Edge Zone (AMERIEZ) project. Data was collected from Southern Oceans (> 60 degrees South) using Ships WESTWIND and MELVILLE. The data was collected over a period spanning from November 9, 1983 to December 2, 1983. Data was submitted by Dr. Robin D. Muench, Science Applications, Inc., Bellevue, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100092_Not Applicable.json b/datasets/gov.noaa.nodc:9100092_Not Applicable.json index ac56f85847..664f697da0 100644 --- a/datasets/gov.noaa.nodc:9100092_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100092_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100092_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and Bathythermograph (XBT) data were collected as part of Texas Institutions Gulf Ecosystem Research (TIGER) program. One week data was collected from Gulf of Mexico using Ship GYRE. The data was collected over a period spanning from March 2, 1991 to March 9, 1991. Data was collected as part ofstudy supported by grant MMS # 14-35-0001-30501 to Dr. Douglas C. Biggs and was submitted by Dr. Edward Webb of Texas A&M University, College Station, TX. Data has been processed and is available in F022-CTD-Hi Resolution and C116-Bathythermograph-XBT file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100094_Not Applicable.json b/datasets/gov.noaa.nodc:9100094_Not Applicable.json index c46b1fe21e..5c310e18ee 100644 --- a/datasets/gov.noaa.nodc:9100094_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100094_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100094_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set in this accession contains 100 stations of hydrographic data collected in the northeast Atlantic, south of the Azores, aboard R/V ENDEAVOR, cruise #143. Date of the data are May 1-19, 1987. Two dissolved chlorofluorocarbons CCL3F (Freon 11) and CCL2F2 (Freon 12) were obtained at a number of stations along the cruise track.\n\nData format: the first three columns are CTD pressures (dbar), depth (meters) and CTD temperatures (Deg C) at which each water sample was collected. These columns are followed by the water sample salinity (o/oo), dissolved oxygen (ml/l), calculated variable potential temperature (Deg C), Freon 11 (pmol/kg) and Freon 12 (pmol/kg). Missing values are indicated with -9.000. The data provided by Dr. T. Joyce, Woods Hole Oceanographic Institution.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100113_Not Applicable.json b/datasets/gov.noaa.nodc:9100113_Not Applicable.json index 00a115a723..6c0418372e 100644 --- a/datasets/gov.noaa.nodc:9100113_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100113_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100113_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data was collected from TOGA Area - Pacific (30 N to 30 S) R/V MOANA WAVE. The data was collected over a period spanning from February 9, 1989 to May 10, 1989. Data was submitted by Harry Bryden of Woods Hole Oceanographic Institution, Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100116_Not Applicable.json b/datasets/gov.noaa.nodc:9100116_Not Applicable.json index 31052916fd..a2dc58520f 100644 --- a/datasets/gov.noaa.nodc:9100116_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100116_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100116_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from twenty six stations in NW Atlantic (limit-40 W) using Ship ENDEAVOR. The data was collected over a one week period spanning March 22 to March 31, 1988. Data was submitted by Dr. Mindy Hall of Woods Hole Oceanographic Institution, Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100131_Not Applicable.json b/datasets/gov.noaa.nodc:9100131_Not Applicable.json index 364bab6dcb..f4b5a19d06 100644 --- a/datasets/gov.noaa.nodc:9100131_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100131_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100131_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other time series data were collected from Resurrection Bay Area using R/V LITTLE DIPPER. The data was collected over a period spanning from March 28, 1991 to June 18, 1991. Data was submitted by Dr. Chirk Chu of Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100152_Not Applicable.json b/datasets/gov.noaa.nodc:9100152_Not Applicable.json index 1b14bb0ddc..c5d62504d8 100644 --- a/datasets/gov.noaa.nodc:9100152_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100152_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100152_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD); and bottle data were collected as part of Texas Institutions Gulf Ecosystem Research (TIGER) program in June 1991 under a grant MMS # 14-35-0001-30501 to Dr. Douglas C. Biggs. Ships GYRE and J. W. POWELL were used to collect data from Gulf of Mexico. The data was collected over a period spanning from June 6-18, 1991. Data was submitted by Mr. David J. Voegele of Texas A&M University, College Station, TX. Data has been processed and is currently available in C100-Ocean-Station-Data and F022-CTD-Hi-Resolution file formats of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100175_Not Applicable.json b/datasets/gov.noaa.nodc:9100175_Not Applicable.json index 906b9ac5f0..cb3e211e1c 100644 --- a/datasets/gov.noaa.nodc:9100175_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100175_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100175_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Woods Hole Oceanographic Institution Exchange format. The data was collected from Florida Straits using Ship ENDEAVOR. The data was collected over a period spanning from June 3, 1990 to June 16, 1990. Data was submitted by Dr. Michael C. Gregg, University of Washington, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100200_Not Applicable.json b/datasets/gov.noaa.nodc:9100200_Not Applicable.json index 90dd42af8d..2a080c8f7c 100644 --- a/datasets/gov.noaa.nodc:9100200_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100200_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100200_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains New York City Department Harbor Survey Data from years 1968 to 1990. Station data was collected as part of the NYC Department of Environmental Protection's Harbor Survey at the Hudson River along Manhatten, New York Bight, Long Island Sound. Parameters measured were salinity, dissolved oxygen, total coliform counts/ml, and fecal coliform counts/100 ml were recorded as 80-column ASCII files (SAS file format); each line in the file represents sampling data from a single site per day. Data was submitted on a diskette. A hardcopy of a README file which interprets the file format and a map of the study site is included in the documentation. Principal Investigator was Dr. Alan I. Stubin of Institute: NYC DEP (Marine Science Branch, Ward's Island).", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100203_Not Applicable.json b/datasets/gov.noaa.nodc:9100203_Not Applicable.json index ef2af33c6b..458ed092ee 100644 --- a/datasets/gov.noaa.nodc:9100203_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100203_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100203_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in TOGA Area, Atlantic as part of Trans-Atlantic Section 8 (TAS-8) study. Eighteen days data was collected from Ship OCEANUS. The data was collected over a period spanning from March 1, 1989 to March 19, 1989. Data was submitted in a tape by Kristen M. Sanborn of Scripps Institution of Oceanography, La Jolla, CA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9100241_Not Applicable.json b/datasets/gov.noaa.nodc:9100241_Not Applicable.json index 38d821b104..b0894f2266 100644 --- a/datasets/gov.noaa.nodc:9100241_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9100241_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9100241_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD), Bathythermograph (XBT) and Sound Velocity data (XSV) were collected from fifty seven stations in Greenland Sea using ships KNORR, ENDEAVOR, and OCEANUS as part of Moving Ship Tomography Project. The data was collected over a period spanning from September 11, 1988 to March 22, 1991 by The Applied Physics Laboratory. Data containing 495 files in one tape were submitted by Kate Bader, University of Washington, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution and C125-Bathythermograph-XBT-Selected Depths file format of NODC.\n\nOriginal data were submitted on a 1600 BPI magnetic tape in ASCII. Sound velocity was measured in meters/second. File L01403 contains data from 24 stations with 1,381 records collected between 09/11/88 - 10/04/88. File L01404 contains data from 33 stations with 1,919 records collected between 08/04/89 - 08/12/89.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200007_Not Applicable.json b/datasets/gov.noaa.nodc:9200007_Not Applicable.json index d6d444e47e..c20c90ea23 100644 --- a/datasets/gov.noaa.nodc:9200007_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200007_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200007_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected as part of Distribution/Abundance of Marine Mammals in Gulf of Mexico (GULFCETI) project. Data was collected from Ships KNORR and MELVILLE. The data was collected over a period spanning from November 24, 1987 and April 12, 1989. Data for South Atlantic Ventilation Experiment (SAVE) legs 1-5 and HYDROS leg 4 was submitted in five tapes by Kristen M. Sanborn of Scripps Institution of Oceanography, La Jolla, CA.\n\nThis data set contains high-resolution data collected using CTD (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200030_Not Applicable.json b/datasets/gov.noaa.nodc:9200030_Not Applicable.json index 329a35abef..01aadff935 100644 --- a/datasets/gov.noaa.nodc:9200030_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200030_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200030_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180) as part of Fisheries-Oceanography Cooperative Investigations (FOCI) project. Data was collected from NOAA Ship MILLER FREEMAN. The data was collected over a period spanning from April 2, 1991 and October 5, 1991. Tape containing 287 castes of CTD was submitted by Ms. Leslie Lawrence of Pacific Marine Environmental Laboratory (PMEL), Seattle, WA.\n\nThis data set contains high-resolution data collected using CTD (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200034_Not Applicable.json b/datasets/gov.noaa.nodc:9200034_Not Applicable.json index d3eba36bb4..05cd052e68 100644 --- a/datasets/gov.noaa.nodc:9200034_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200034_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200034_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other SEACAT data were collected from Gulf of Mexico, NE Pacific (limit-180). Data was collected from NOAA Ship DISCOVERER, NOAA Ship MT. MITCHELL, and NOAA Ship WHITING. The data containing 7 casts was collected over a period spanning from May 27, 1991 and September 5, 1991. Data was submitted in three diskettes by National Ocean Service, Rockville, MD.\n\nThis data set contains high-resolution data collected using CTD (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200039_Not Applicable.json b/datasets/gov.noaa.nodc:9200039_Not Applicable.json index 83239d2f91..4f02a64417 100644 --- a/datasets/gov.noaa.nodc:9200039_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200039_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200039_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Coastal Waters of S. Alaska. Data was collected in five cruises from Ship ALPHA HELIX. The data was collected over a period spanning from July 24, 1991 and November 14, 1991. Data was submitted in a tape from cruises L09-L011 and HX159-160 by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks, AK.\n\nThis data set contains high-resolution data collected using CTD (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200079_Not Applicable.json b/datasets/gov.noaa.nodc:9200079_Not Applicable.json index d6c131e45b..e0381b5d7f 100644 --- a/datasets/gov.noaa.nodc:9200079_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200079_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200079_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180) as part of Fieberling Seamount project. Data was collected from Ship NEW HORIZON. The data was collected over a period spanning from August 19, 1989 and September 1, 1989. Data was submitted in three diskettes by Dr. Gunnar Roden, University of Washington, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9200117_Not Applicable.json b/datasets/gov.noaa.nodc:9200117_Not Applicable.json index 9580a7f4f7..1e2d924efd 100644 --- a/datasets/gov.noaa.nodc:9200117_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9200117_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9200117_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Pressure, Temperature, and Salinity (CTD) data, plus sound velocity (cm/sec) data collected during hydrographic surveys conducted by the Pacific Environmental Laboratory (NOAA/PMEL) from NOAA Ship RAINIER between March 18, 1992 and May 14, 1992. Data were collected in the North Pacific Ocean, Gulf of Alaska with a SEACAT STD. Originator's Data was submitted as variable length ASCII files on two diskettes. Files contain header information, including cruise number, ship location and data parameters. Principal Investigator was Lt. John Griffin, Institute: NOAA/PMEL. CTD data were processed and are archived as NODC file type F022.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300097_Not Applicable.json b/datasets/gov.noaa.nodc:9300097_Not Applicable.json index 15875992e0..0ad4735d45 100644 --- a/datasets/gov.noaa.nodc:9300097_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300097_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300097_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Chukchi Sea. Data was collected from Ship ALPHA HELIX. The data was collected over a period spanning from September 21, 1992 and October 4, 1992. Data from 107 casts was submitted in one diskette by Dr. Chirk Chu of Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300107_Not Applicable.json b/datasets/gov.noaa.nodc:9300107_Not Applicable.json index 52467cb862..0dfef5eca0 100644 --- a/datasets/gov.noaa.nodc:9300107_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300107_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300107_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W). Data was collected from Ship ENDEAVOR cruise 214. The data was collected over a period spanning from June 24, 1990 to July 7, 1990. Data was submitted in a tape by Dr. Robert Pickart, Woods Hole Oceanographic Institution, Woods Hole, MA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300130_Not Applicable.json b/datasets/gov.noaa.nodc:9300130_Not Applicable.json index 6731c47981..cc1755299c 100644 --- a/datasets/gov.noaa.nodc:9300130_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300130_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300130_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and bathythermograph (XBT) data were collected in Gulf of Mexico as part of Texas Institutions Gulf Ecosystem Research (TIGER) and LATEX project funded by grant no MMS # 14-35-0001-30501. Data was collected from Ship GYRE. The data was collected over a period spanning from June 2, 1993 and June 24, 1993. Data was submitted by Mark Garner, Texas A&M University, College Station, TX. Data has been processed and is available in F022-CTD-Hi Resolution and C116 Bathythermograph file format of NODC.\n\nObjective of LATEX A was to observe currents and waves, water properties (such as temperature, salinity and nutrients), and air-sea interaction over the Texas- Louisiana shelf, with the aim of providing data adequate to describe and better understand the circulation and transport of water, nutrients and other properties over that shelf.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300144_Not Applicable.json b/datasets/gov.noaa.nodc:9300144_Not Applicable.json index fab5b02e89..efa9322996 100644 --- a/datasets/gov.noaa.nodc:9300144_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300144_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300144_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The water depth and temperature data were collected in Coastal Waters of Gulf of Mexico, NW Atlantic (limit-40 W) as part of Louisiana-Texas (LATEX part C) Gulf of Mexico Eddy Circulation Study from CAPE HENLOPEN, and DRIFTING PLATFORM between August 13, 1992 and June 5, 1993. The originator's CTD and ARGOS tracked drifting buoy data containing 2,821 records were submitted by Dr. Thomas Berger, Science Applications, Inc. Raleigh NC. The study was supported by grant no MMS 14-35-0001-30633.\n\nLATEX is a three-part, $16.2 million federal initiative funded by the U.S. Minerals Management Service (MMS) of the Department of the Interior. The study was conducted to aid MMS in reducing risks associated with oil and gas operations on the continental shelf along the Texas and Louisiana coasts from the mouth of the Mississippi River to the Rio Grande.\n\nBegun in September 1991, it was the largest physical oceanography program ever undertaken in the Gulf. The program consists of three major parts: LATEX A, B, and C, conducted by the Texas A&M University System (TAMUS), Louisiana State University (LSU), and Science Applications International Corp. (SAIC), respectively.\n\nLATEX C was carried out by researchers at SAIC and the University of Colorado. Loop Current eddies, slope eddies, and squirts and jets within the Gulf of Mexico were located and tracked by air-deployed temperature profiling instruments and drifting buoys. Using these data, scientists assessed the impact of these Gulf-wide, circulation features on shelf circulation and identified the processes that interact with the shelf. The data is currently available in F022-CTD-Hi-Resolution and F156-Drifting-Buoy file formats of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300147_Not Applicable.json b/datasets/gov.noaa.nodc:9300147_Not Applicable.json index ced5954633..39c20d71a0 100644 --- a/datasets/gov.noaa.nodc:9300147_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300147_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300147_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chlorophyll-a profiles were collected in the Atlantic Ocean and adjoining seas from March 2, 1961 to October 21, 1992. The data were collected by multiple institutions as part of the North Atlantic Chlorophyll Profile Data Set. This work was supported by funding from the European Space Agency and the Canadian Department of Fisheries and Oceans. The README.pdf file in the about/ directory contains information about the file format and data originators.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300152_Not Applicable.json b/datasets/gov.noaa.nodc:9300152_Not Applicable.json index ee256f472c..97940324c4 100644 --- a/datasets/gov.noaa.nodc:9300152_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300152_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300152_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180). Data was collected from NOAA Ship RAINIER. The data was collected over a period spanning from March 23, 1993 to July 31, 1993. Data was submitted in a diskette by Capt. Russell Arnold, Pacific Marine Environmental Laboratory, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300161_Not Applicable.json b/datasets/gov.noaa.nodc:9300161_Not Applicable.json index 85f0f2239b..6d95cb7094 100644 --- a/datasets/gov.noaa.nodc:9300161_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300161_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300161_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska, Chukchi Sea, and NW Pacific (limit-180). Data was collected from cruises HX 163, HX 165 and HX 167 of Ship ALPHA HELIX. The data was collected over a period spanning from July 24, 1992 to october 27, 1992. Data was submitted in one exabyte cassette by Dr. Thomas C. Royer, Institute of Marine Science, University of Alaska, Fairbanks, AK.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300187_Not Applicable.json b/datasets/gov.noaa.nodc:9300187_Not Applicable.json index e0abc7be7d..c675b0f5ea 100644 --- a/datasets/gov.noaa.nodc:9300187_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300187_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300187_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Mexico by SEACATs deployed in the area. Data was collected from NOAA Ship WHITING during 7 casts. The data was collected over a period spanning from April 2, 1992 to July 14, 1992. Data was submitted in one diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300196_Not Applicable.json b/datasets/gov.noaa.nodc:9300196_Not Applicable.json index cb5ac54a11..1e176b22c9 100644 --- a/datasets/gov.noaa.nodc:9300196_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300196_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300196_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9300199_Not Applicable.json b/datasets/gov.noaa.nodc:9300199_Not Applicable.json index db5b3b88ff..dbad85468f 100644 --- a/datasets/gov.noaa.nodc:9300199_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9300199_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9300199_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains Benthic and Tissue toxin data from stations in U.S. coastal waters (Coastal Waters of Western U.S. and North American Coastline-North) collected under the National Status and Trends (NS&T) program from 1984-1989. NS&T program for marine environmental quality was designed to define the geographic distribution of contaminant concentrations in tissues of marine organisms and sediments, and documenting biological responses to contamination.\n\nSamples have been collected under the original Benthic Surveillance Project (sediment and tissue samples from benthic fish) since 1984. Samples have been collected under the Mussel Watch Project (sediment and bivalves) since 1986. Both programs involved collecting samples from fixed sites on both Atlantic and Pacific coasts. Sites were selected so as not to be in close proximity to a major contamination source, as the programs objective was to quantify contamination over general areas. Chemical data from sediments collected during the benthic surveillance project, 1984-1986, is contained in a single delimited ASCII file (bssed.txt). Additional contaminated sediment data from the mussel watch program, 1986-1989, is contained in a single delimited ASCII file (mwsed.txt). These data do not include tissue analysis for contaminants. Chemicals and related parameters measured in sediments include: DDT.\n\nSince 1986, NOAA'S NS&T Program has included a component called the mussel watch project that has annually collected and chemically analyzed mussels and oysters from 177 sites at coastal and estuarine sites. Tissue samples from these mollusks have been analyzed to establish temporal trends of contaminant accumulation. Contaminants analyzed during this project include: polyaromatic hydrocarbons, polychlorinated biphenyls, chlorinated pesticides (such as ddt and its metabolites), aluminum, iron, manganese, silicon, other trace elements, and lipids. Tissue contaminant data from the mussel watch project, years 1986-1989, is contained in a single wordperfect 4.2 file, mollto90.txt. a second file, tbt_90.txt, lists the sum of concentrations of tributyl tin and its breakdown products (dibutyl tin and monobutyl tin) found in bivalve tissue samples. Tributylin (tbt) was previously used as an antifouling agent in paints, but its use on vessels under 75 feet was banned in 1988. A third file, mwsiteyr.txt, lists collection sites.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400001_Not Applicable.json b/datasets/gov.noaa.nodc:9400001_Not Applicable.json index 05b8b0aa62..2c8c306d79 100644 --- a/datasets/gov.noaa.nodc:9400001_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400001_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400001_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) SEACAT data was collected in NW Atlantic (limit-40 W). Data was collected during 17 casts from NOAA Ship WHITING. The data was collected over a period spanning from August 29, 1993 to November 21, 1993. Data was submitted in a diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400010_Not Applicable.json b/datasets/gov.noaa.nodc:9400010_Not Applicable.json index 0f77958021..7be5df5f7e 100644 --- a/datasets/gov.noaa.nodc:9400010_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400010_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400010_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W) as part of Physical Oceanography Field Program offshore North Carolina supported by grant MMS #14-35-0001-30599. Data was collected from Ship SEAWARD EXPLORER cruises SE9301, SE9303, and SE9309. The data was collected over a period spanning from February 6, 1993 and August 28, 1993. Data from 146 stations containing 7,614 records was submitted on a tape by Dr. Thomas Berger, Science Applications, Inc., Raleigh NC. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400013_Not Applicable.json b/datasets/gov.noaa.nodc:9400013_Not Applicable.json index 0037a43b3d..54b3e850b6 100644 --- a/datasets/gov.noaa.nodc:9400013_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400013_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400013_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from NOAA Ship WHITING cruises 93115-194. The data was collected over a period spanning from April 25, 1993 to July 13, 1993. Data was submitted in a diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400015_Not Applicable.json b/datasets/gov.noaa.nodc:9400015_Not Applicable.json index c07aacf22f..b9189f6c30 100644 --- a/datasets/gov.noaa.nodc:9400015_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400015_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400015_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180). Data was collected from Ship MOANA WAVE cruises MW9010 & MW9012. The data was collected over a period spanning from August 6, 1990 and December 13, 1990. Data was submitted via File transfer protocol by Dr. Pierre Flament, University of Hawaii at Manoa, Honolulu, HI. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400017_Not Applicable.json b/datasets/gov.noaa.nodc:9400017_Not Applicable.json index 7f9e586284..2b6992a0f4 100644 --- a/datasets/gov.noaa.nodc:9400017_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400017_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400017_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD); and Transmissivity and Fluorescence data were collected in Gulf of Mexico as part of Texas Institutions Gulf Ecosystem Research (TIGER) project. Data was collected from Ship GYRE cruise 93G12. The data from 15 casts containing 11,448 records was collected over a period spanning from October 28, 1993 and November 3, 1993. Data was submitted in one diskette by Mr. P.V. Pittman, Texas A&M University, College Station, TX.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400026_Not Applicable.json b/datasets/gov.noaa.nodc:9400026_Not Applicable.json index 00b6505d6e..4692a5904c 100644 --- a/datasets/gov.noaa.nodc:9400026_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400026_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400026_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea. Data was collected from Ship ALPHA HELIX cruise HX 171. The data was collected over a period spanning from June 12, 1993 and July 1, 1993. Data from 81 stations was submitted in a diskette by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400033_Not Applicable.json b/datasets/gov.noaa.nodc:9400033_Not Applicable.json index 4009a1cbd6..5c220d8cb4 100644 --- a/datasets/gov.noaa.nodc:9400033_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400033_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400033_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In April 1986 a major oil spill from a ruptured storage tank at a local refinery just east of the Caribbean entrance to the Panama Canal polluted an area of coral reefs, mangrove forests, and grassbeds along the Caribbean coast of the Republic of Panama. The area affected included a biological reserve of the Smithsonian Tropical Research Institute (STRI) where baseline biological and environmental data had beencollected for the previous 15 years. Shortly after the spill, a grant to study the effects of the spill was received from the Minerals Management Service of the United States Department of the Interior. Data was then collected from May of 1986 to October of 1991 by the Smithsonian Tropical Research Institute under Minerals Management Service contracts 14-12-0001-30355 and 14-12-0001-30393. These data are filed under NCEI Accession #9400033. Results of the study were published as a technical report of the MMS (Keller and Jackson, 1993.) The project was divided into 8 subprojects to study the chemistry of the oil and 7 different environments affected by the spill, which included seagrass, coral, and mangrove communities. The study continued in part for an additional year under another grant, but that data is not included here, nor is data collected before the spill or data collected by the STRI ESP program.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400036_Not Applicable.json b/datasets/gov.noaa.nodc:9400036_Not Applicable.json index b923a99697..4618412218 100644 --- a/datasets/gov.noaa.nodc:9400036_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400036_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400036_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea and Chukchi Sea. Data was collected from Ship ALPHA HELIX. The data was collected over a period spanning from September 9, 1993 to October 10, 1993. Data was submitted in a diskette by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400051_Not Applicable.json b/datasets/gov.noaa.nodc:9400051_Not Applicable.json index c97391ff2d..6ee6e24645 100644 --- a/datasets/gov.noaa.nodc:9400051_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400051_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400051_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea and North Pacific Ocean. Data was collected from Ship ALPHA HELIX cruise HX-175. The data was collected over a period spanning from October 16, 1993 to November 3, 1993. Data was submitted in a cassette by Dr. Chirk Chu of Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400052_Not Applicable.json b/datasets/gov.noaa.nodc:9400052_Not Applicable.json index 805f81a561..f45020e4bc 100644 --- a/datasets/gov.noaa.nodc:9400052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, zooplankton abundance, and other data were collected using fluorometer, laboratory analysis, visual analysis, and zooplankton net casts from multiple ships from the Coastal Waters of California and North Pacific Ocean from January 1, 1951 to August 28, 1993. Data were submitted by Scripps Institution of Oceanography as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400056_Not Applicable.json b/datasets/gov.noaa.nodc:9400056_Not Applicable.json index cb4affa1a5..a1a0a13bab 100644 --- a/datasets/gov.noaa.nodc:9400056_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400056_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400056_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Coastal Waters of California. Data was collected from NOAA Ship RAINIER. The data was collected over a period spanning from March 31, 1994 and April 22, 1994. Data from 3 CTD casts containing 170 records was submitted on one diskette by Capt. Russell Arnold, National Ocean Service, Seattle, WA in WHOI exchange format. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400062_Not Applicable.json b/datasets/gov.noaa.nodc:9400062_Not Applicable.json index ab89d9b5c2..66cd75bfad 100644 --- a/datasets/gov.noaa.nodc:9400062_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400062_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400062_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea, Chukchi Sea. Data was collected from Ship ALPHA HELIX cruise Aleutian Birds HX-172 funded by National Science Foundation Division of Polar Programs . The data was collected over a period spanning from July 9, 1993 and August 7, 1993. Data was submitted in a cassette by Dr. Chirk Chu of Institute of Marine Science, University of Alaska, Fairbanks, AK. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400085_Not Applicable.json b/datasets/gov.noaa.nodc:9400085_Not Applicable.json index 42fc170107..13c6e10c3e 100644 --- a/datasets/gov.noaa.nodc:9400085_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bottle data set collected on the R/V Rapuhia, a New Zealand ship which is run by New Zealand Oceanography Institute (NZOI). Data were collected from 21 February 1991 to 11 March 1991 as part of WHOI's moored current meter array experiment.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400110_Not Applicable.json b/datasets/gov.noaa.nodc:9400110_Not Applicable.json index 0ed565669f..3f43679e30 100644 --- a/datasets/gov.noaa.nodc:9400110_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400110_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400110_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska. Data was collected from Ship LITTLE DIPPER. The data was collected over a period spanning from May 21, 1992 to May 25, 1994. Oceanographic time series CTD data in 33 files was submitted in a diskette by Dr. Thomas C. Royer, Institute of Marine Science, University of Alaska, Fairbanks, AK.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400124_Not Applicable.json b/datasets/gov.noaa.nodc:9400124_Not Applicable.json index 47d1041fc8..6b5a4315a8 100644 --- a/datasets/gov.noaa.nodc:9400124_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400124_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400124_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W). Data was collected from NOAA Ship WHITING. The data was collected over a period spanning from May 20, 1994 and June 23, 1994. Data from 9 casts containing 270 records in WHOI/NODC Exchange format was submitted in one diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400134_Not Applicable.json b/datasets/gov.noaa.nodc:9400134_Not Applicable.json index b063aa33ed..3ceaac42de 100644 --- a/datasets/gov.noaa.nodc:9400134_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400134_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400134_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ammonmium, nitrite, nitrate, chlorophyll a, temperature, pressure, and other data were collected from multiple ships from March 8, 1973 to August 26, 1993. Data were collected using fluorometer, laboratory analysis, and visual analysis in the Coastal Waters of California. Data were collected by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400141_Not Applicable.json b/datasets/gov.noaa.nodc:9400141_Not Applicable.json index bcdda609de..4b68810a87 100644 --- a/datasets/gov.noaa.nodc:9400141_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400141_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400141_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W). Data was collected from NOAA Ship WHITING. The data was collected over a period spanning from May 25, 1994 to July 23, 1994. Data from 10 CTD casts containing 234 recrods was submitted in a floppy diskette by Ms. Ruby Becker of National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400142_Not Applicable.json b/datasets/gov.noaa.nodc:9400142_Not Applicable.json index 9073182060..1f12e24e1a 100644 --- a/datasets/gov.noaa.nodc:9400142_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400142_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400142_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in the Indian Ocean. Data were collected from Ship BARUNA JAYA I. The data were collected over a period spanning from July 1, 1991 and February 22, 1994. Data from 140 CTD casts containing 112,303 records were submitted on eight diskettes by two visitors from the Agency for the Assessment and Application of Technology, Indonesia to Ocean Climate Laboratory. Data was processed into NODC F022 High-Resolution CTD file format.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400150_Not Applicable.json b/datasets/gov.noaa.nodc:9400150_Not Applicable.json index 31b5ece962..d6a88a7471 100644 --- a/datasets/gov.noaa.nodc:9400150_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400150_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400150_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, Temperature and Depth (CTD) and other data were collected in Coastal Waters of California by NOAA Ship DAVID STARR JORDAN. Data were collected over a period from April 10, 1987 to May 23, 1994. CTD Data from 3,380 casts was received from Kenny Baltz of National Marine Fisheries Service, Tiburon, CA via Norm Hall by NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400159_Not Applicable.json b/datasets/gov.noaa.nodc:9400159_Not Applicable.json index a4ca10570d..6f31622fe5 100644 --- a/datasets/gov.noaa.nodc:9400159_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400159_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400159_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The depth, temperature, salinity, and oxygen data in this accession were provided by the Chinese National Oceanographic Data Center during 4th meeting of US/PRC Joint Coordination Panel for Data and Information Cooperation as part of World Ocean Circulation Experiment. Data were collected between November 1992 and December 1993 using ships XIANG YANG HONG 09, SHI YAN 3, and XIANG YANG HONG 05. The CTD data were submitted on two tapes and four diskettes.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400164_Not Applicable.json b/datasets/gov.noaa.nodc:9400164_Not Applicable.json index 054d08b7a6..cf937cba18 100644 --- a/datasets/gov.noaa.nodc:9400164_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400164_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400164_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This accession contains chemical and physical profile data containing measuremnts of depth, salinity and temperature collected between May 1986 and April 1989. Data were submitted on a diskette containing 12 files by Dr. Richard Feely of Pacific Marine Environmental Laboratory, Seattle, WA. Files were received by NODC via Ocean Climate Laboratory. Refer to publication \"Murphy, et al, NOAA Tech. Memo ERL PMEL-101\" for more details.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400167_Not Applicable.json b/datasets/gov.noaa.nodc:9400167_Not Applicable.json index 4397307fb8..205908aba8 100644 --- a/datasets/gov.noaa.nodc:9400167_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400167_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400167_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, Temperature and Depth (CTD) and other data were collected over two decades from October 29, 1972 to July 26, 1992. Data files were assembled by Mr. Russ Burgett of Woods Hole Oceanographic Institution, Woods Hole, MA. 77 files of data containing 4,757 profiles were received by NODC via File Transfer Protocol.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400203_Not Applicable.json b/datasets/gov.noaa.nodc:9400203_Not Applicable.json index 79d964aedd..3844f7c2f5 100644 --- a/datasets/gov.noaa.nodc:9400203_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400203_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400203_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Cook Inlet, Prince William Sound from NOAA Ship RAINIER. The data was collected over a period spanning from May 16, 1994 to November 4, 1994. Data from 11 CTD casts was submitted on a diskette by Capt. Russell Arnold of National Ocean Service, Seattle WA.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400205_Not Applicable.json b/datasets/gov.noaa.nodc:9400205_Not Applicable.json index 059c0cc51c..9f7a927fd1 100644 --- a/datasets/gov.noaa.nodc:9400205_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400205_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400205_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400206_Not Applicable.json b/datasets/gov.noaa.nodc:9400206_Not Applicable.json index 49e66887cc..ccb903af6f 100644 --- a/datasets/gov.noaa.nodc:9400206_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400206_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400206_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400223_Not Applicable.json b/datasets/gov.noaa.nodc:9400223_Not Applicable.json index b8c7971981..8c97f39300 100644 --- a/datasets/gov.noaa.nodc:9400223_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400223_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400223_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W). Data was collected from NOAA Ship WHITING. The data was collected over a period spanning from October 12, 1994 to November 12, 1994. One diskette of data from 14 casts was submitted by National Ocean Service, Rockville, MD.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9400225_Not Applicable.json b/datasets/gov.noaa.nodc:9400225_Not Applicable.json index 5d0ca8c8a1..19d38f49b9 100644 --- a/datasets/gov.noaa.nodc:9400225_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9400225_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9400225_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains binary raster images from landsat thematic mapper collected in Gulf of Maine between 1982 to 1985. A suite of Regional Satellite Products from Edward Bright, Martin-Marietta Energy Systems at Oak Ridge National Laboratory was submitted. Each data set is about megabyte.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500029_Not Applicable.json b/datasets/gov.noaa.nodc:9500029_Not Applicable.json index 04f5b61e5e..4e4a64fdd9 100644 --- a/datasets/gov.noaa.nodc:9500029_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500029_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500029_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea as part of Inner SHelf Transfer and recycling (ISHTAR) and \"St. Lawrence Island Polynya\" project. Data was collected from Ship ALPHA HELIX cruise HX-177. The data was collected over a period spanning from May 3, 1994 and June 8, 1994. Dr. Jackie Grebmeir, Univ. of Tenn., Knoxville was Principal Investigator funde by NSF Grant OPP-9000694. Data from 105 stations was received by NODC via Dr. Chirk Chu, University of Alaska, Institute of Marine Science, Fairbanks, AK. Data is in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500030_Not Applicable.json b/datasets/gov.noaa.nodc:9500030_Not Applicable.json index aa8c07f9b4..3ca62c50d8 100644 --- a/datasets/gov.noaa.nodc:9500030_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500030_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500030_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea and Chukchi Sea. Data was collected from Ship ALPHA HELIX. The data was collected over a period spanning from September 10, 1994 to October 10, 1994. One CTD data set from 61 stations was submitted via FTP by Dr. Thomas Weingartner, Institute of Marine Science, University of Alaska, Fairbanks. AK. Data has been replaced on May 22, 2000 by accession 000148. The new accession was submitted by Mr. S. Stillwaugh NODC NW Liaison Officer.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500031_Not Applicable.json b/datasets/gov.noaa.nodc:9500031_Not Applicable.json index b1d7861dc4..fc513a0f4e 100644 --- a/datasets/gov.noaa.nodc:9500031_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500031_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500031_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska and Bering Sea as part of Inner SHelf Transfer and recycling (ISHTAR) project. Data was collected from Ships ALPHA HELIX and LITTLE DIPPER. The data was collected over a period spanning from June 27, 1994 to January 6, 1995. 7 sets of CTD data collected from seabird from 13 stations was received by NODC from Dr. C. Peter McRoy of University of Alaska, Institute of Marine Science, Fairbanks, AK via FTP. Data is in F022-CTD-Hi Resolution file format of NODC.\n\nF022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500032_Not Applicable.json b/datasets/gov.noaa.nodc:9500032_Not Applicable.json index ce08de42a1..dec264d4f6 100644 --- a/datasets/gov.noaa.nodc:9500032_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500032_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500032_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180) as part of \"Search for direct evidence of sulpher quenching of biomass...etc.\" project funded by NSF grant OCE 9203292. Data was collected from Ships NEW HORIZON, POINT SUR and WECOMA. The data was collected over a period spanning from October 2, 1993 to April 12, 1994. CTD data from 12 stations was submitted in three diskettes by Dr. Frederick Prahl of Oregon State University, Corvallis, OR.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500048_Not Applicable.json b/datasets/gov.noaa.nodc:9500048_Not Applicable.json index 70c3e129da..e95154d1b1 100644 --- a/datasets/gov.noaa.nodc:9500048_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500048_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500048_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 28, 1992 to October 14, 1994. Data were collected using fluorometer, laboratory analysis, visual analysis, and bottle casts in the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500053_Not Applicable.json b/datasets/gov.noaa.nodc:9500053_Not Applicable.json index e8b0bef56b..2b12766438 100644 --- a/datasets/gov.noaa.nodc:9500053_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500053_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500053_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska. Data was collected from NOAA LAUNCHES. The data was collected over a period spanning from June 22, 1993 to August 25, 1993. Data was submitted by Dr. Douglas Segar of University of Alaska, Anchorage, AK.\n\nThis accession contains files with hydrocarbon, trace metal, and grain size analyses for the U.S. MMS report (OCS Study MMS 95-0009) Current Water Quality in Cook Inlet, Study. There are 9 Lotus 123 WK3 files, 1 Lotus 123 WK1 file, and 1 Lotus Freelance graphics file.\n\nFreelance File:\n\nMMSRPT.DRW Figure 12. Grain-size composition by station in Cook Inlet.\n\nLotus Files:\n\nCHPHSEDR.WK1 Table 44. Chemical and statistical results for sediment replicate samples. Table 70. Chemical and physical results for sediment replicate samples.\n\nDSSMTLS.WK3 Table 34. Concentration of total metals in suspended solids.\n\nMTLWATC1.WK3 Table 28. Cruise 1 total metal concentrations for water.\n\nMTLWATC2.WK3 Table 29. Cruise 2 total metal concentrations for water.\n\nPAHSED2.WK3 Table 43. Summary of PAH concentrations for sediment replicates.\n\nSEDSHC.WK3 Table 41. Summary of saturated hydrocarbon concentrations for sediment replicates.\n\nSEDMTLS.WK3 Table 45. Summary of total metals in sediments.\n\nSSMTLS.WK3 Table 33. Summary of total metals in suspended solids.\n\nWATSHCC1.WK3 Table 24. Cruise 1 saturated hydrocarbon concentrations for water.\n\nWATSHCC2.WK3 Table 25. Cruise 2 saturated hydrocarbon concentrations for water.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500075_Not Applicable.json b/datasets/gov.noaa.nodc:9500075_Not Applicable.json index d3d27436c9..3d150d7228 100644 --- a/datasets/gov.noaa.nodc:9500075_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500075_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500075_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea/air gas ratios data was collected in TOGA Area - Pacific (30 N to 30 S) between January 1, 1989 and December 31, 1989 during cruises conducted using ships WECOMA, KILA and MOANA WAVE as part of the Hawaii Ocean Time-Series (HOTS) project, to fulfill the requirements of the World Ocean Circulation Experiment (WOCE). Oxygen / Argon ratios; Oxygen / Nitrogen ratio and Oxygen-18 isotope / at depth vs. air were measured by University of Washington, Seattle, WA. Data was reported in Emerson, Quay, et al., \"O2, Ar, N2 and 222Rn in Surface Waters of the Subarctic Ocean: Net Biological O2 Production\", Global Biogeochemical Cycles, vol 5, pp49-69.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500100_Not Applicable.json b/datasets/gov.noaa.nodc:9500100_Not Applicable.json index 1c55dea95e..ec89e81106 100644 --- a/datasets/gov.noaa.nodc:9500100_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500100_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500100_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180) as part of Eastern Boundary Currents Accelerated Research Initiative. Data was collected from Ship WECOMA cruises # W9306A and W9308B. The data was collected over a period spanning from June 7, 1993 to September 20, 1993. Conventional CTD data from 100 casts and 165 segments (stations) of towed SEASOAR CTD data was submitted by Dr. Adrianna Huyer, Oregon State University, Corvallis OR. Four files of data and two Data Documentation Form files were received by NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500145_Not Applicable.json b/datasets/gov.noaa.nodc:9500145_Not Applicable.json index c96e107fcd..ca63f808aa 100644 --- a/datasets/gov.noaa.nodc:9500145_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500145_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500145_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains Conductivity, Temperature and Depth (CTD); Chlorophyll; and Nutrient data collected in Bering Sea as part of Inner Shelf Transfer and Recycling (ISHTAR) program collected from 1985-1995 using multiple ships. The compressed tar file ishtar.tar.Z contained ASCII files of the ISHTAR research project headed by Dr. C.P. McRoy of the Institute of Marine Science, University of Alaska Fairbanks. There are two types of files: 1. Chlorophyll (20), and 2. Nutrient (19). They are differentiated by filenames. Chlorophyll data files end in chl.dat and Nutrient data files end in nut.dat. The prefixes are cruise names. Good format information is provided with the data files.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500149_Not Applicable.json b/datasets/gov.noaa.nodc:9500149_Not Applicable.json index b50d21d5f7..660c0f1c69 100644 --- a/datasets/gov.noaa.nodc:9500149_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500149_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500149_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ALACE (Autonomous LAgrangian Circulation Explorer) is a subsurface drifter, periodically rising to the surface to relay data to ARGOS. Instrument location is then obtained from ARGOS. An ALACE profiler collects data on ascent and relays a compressed data set to ARGOS. The amount of time spent at its neutrally-buoyant depth, and then at the surface, is variable, dependent upon the deployment site and the main scientific objective of the ALACE. Profiling ALACEs generally complete a cycle every 8-10 days, spending 24 hours at the surface transmitting to ARGOS.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500152_Not Applicable.json b/datasets/gov.noaa.nodc:9500152_Not Applicable.json index de6c1d0324..94dfee6123 100644 --- a/datasets/gov.noaa.nodc:9500152_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500152_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500152_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from Ship AURORA AUSTRALIS. The data was collected over a period spanning from January 6, 1991 and March 6, 1992. Data from 343 casts containing 185,102 records was submitted via File Transfer Protocol by Ms. Edwina Tanner, Antarctic Cooperative Research Centre, University of Tasmania, Australia.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9500160_Not Applicable.json b/datasets/gov.noaa.nodc:9500160_Not Applicable.json index 651fa20ebd..b65b7d0658 100644 --- a/datasets/gov.noaa.nodc:9500160_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9500160_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9500160_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected from 73 stations in Chukchi Sea and East Siberian Sea area. The station numbers are 1-6, 8-30, 32-74, 76. Data was collected from Ship ALPHA HELIX cruise HX189. The data was collected BY Dr. J. Grebmeier of the University of Tennessee over a period spanning from August 24, 1995 to September 1, 1995. This project was funded by Office of Naval Research under grant no: NAVY N00014-94-1-1042Grebmeier. Data in NODC file format F022 was submitted by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600001_Not Applicable.json b/datasets/gov.noaa.nodc:9600001_Not Applicable.json index 2081d6cc7a..0d39ca22df 100644 --- a/datasets/gov.noaa.nodc:9600001_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600001_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600001_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Conductivity, Temperature and Depth (CTD) and other data were collected in Chukchi Sea as part of Office of Naval Research project. Data was collected from Ship ALPHA HELIX cruise HX-190. The data was collected over a period spanning from September 11, 1995 to October 8, 1995. Data was collected from 209 CTD stations by Institute of Marine Science, University of Alaska, Fairbanks, AK and was submitted by Dr Thomas Weingartner via File transfer Protocol in F022 file format of NODC.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600025_Not Applicable.json b/datasets/gov.noaa.nodc:9600025_Not Applicable.json index ea4589f298..74c94866ab 100644 --- a/datasets/gov.noaa.nodc:9600025_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600025_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600025_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600039_Not Applicable.json b/datasets/gov.noaa.nodc:9600039_Not Applicable.json index 69e196df07..cdc4e16c0d 100644 --- a/datasets/gov.noaa.nodc:9600039_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600039_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600039_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bacterial production, primary production, phytoplankton, zooplankton, biological analysis of fish, and massive fish length data were collected from the EVRIKA and other platforms in the Antarctic. Data were collected by the Atlantic Research Institute of Fishing Economy and Ocean from 23 February 1980 to 09 December 1988.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600065_Not Applicable.json b/datasets/gov.noaa.nodc:9600065_Not Applicable.json index 4c7378cee9..c5349bb118 100644 --- a/datasets/gov.noaa.nodc:9600065_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600065_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600065_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data in this accession was collected as part of Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) in TOGA Area - Pacific (30 N to 30 S) using Ship THOMAS G. THOMPSON. CTD Data were collected by University of Washington, Seattle, WA between October 13, 1992 and December 13, 1992. Five Files of CTD data were submitted by Dr. Wilford Gardner. Good documentation accompanies this data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600140_Not Applicable.json b/datasets/gov.noaa.nodc:9600140_Not Applicable.json index 4435c9c624..f510fad798 100644 --- a/datasets/gov.noaa.nodc:9600140_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600140_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600140_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrochemical, hydrophysical, and other data were collected from the ENDEAVOR and NOAA Ship ALBATROSS IV from February 11, 1995 to July 20, 1995. Data were submitted by Dr. David Mountain from the US DOC; NOAA; NATIONAL MARINE FISHERIES SERVICE - WOODS HOLE. These data were collected using meteorological sensors, secchi disks, transmissometers, bottle casts, and CTD casts in the Northwest Atlantic Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9600151_Not Applicable.json b/datasets/gov.noaa.nodc:9600151_Not Applicable.json index 692427ad6c..32dbad39e6 100644 --- a/datasets/gov.noaa.nodc:9600151_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9600151_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9600151_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700022_Not Applicable.json b/datasets/gov.noaa.nodc:9700022_Not Applicable.json index f18e8d81c6..c43af43bdc 100644 --- a/datasets/gov.noaa.nodc:9700022_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700022_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700022_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and temperature profile data were collected from CTD casts in the East China Sea, Sea of Japan, and North Pacific Ocean. Data were submitted by the Japan Meteorological Agency (JMA).", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700025_Not Applicable.json b/datasets/gov.noaa.nodc:9700025_Not Applicable.json index e0bbc28b26..f48c916804 100644 --- a/datasets/gov.noaa.nodc:9700025_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700025_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700025_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 21, 1994 to April 30, 1996. Data were collected using fluorometer, laboratory analysis, visual analysis, and bottle casts in the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700040_Not Applicable.json b/datasets/gov.noaa.nodc:9700040_Not Applicable.json index 6fda7e12bb..1d4fcedf31 100644 --- a/datasets/gov.noaa.nodc:9700040_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700040_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700040_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 4, 1995 to May 3, 1996. Data were collected using bottle casts from the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700063_Not Applicable.json b/datasets/gov.noaa.nodc:9700063_Not Applicable.json index 749341073b..b6e774756a 100644 --- a/datasets/gov.noaa.nodc:9700063_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700063_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700063_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700115_Not Applicable.json b/datasets/gov.noaa.nodc:9700115_Not Applicable.json index ea74e6d3f2..e319f848a6 100644 --- a/datasets/gov.noaa.nodc:9700115_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700115_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700115_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical and temperature profile data were collected using bottle and CTD casts from the THOMAS THOMPSON in the Pacific Ocean from March 19, 1992 to October 21, 1992. Data were collected three different universities and a institution; Oregon State University, University of Washington, Woods Hole Oceanographic Institution, and University of Maryland; Horn Point Environmental Laboratory as part of the Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700116_Not Applicable.json b/datasets/gov.noaa.nodc:9700116_Not Applicable.json index 1c88525d60..de637b67e5 100644 --- a/datasets/gov.noaa.nodc:9700116_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700116_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700116_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700205_Not Applicable.json b/datasets/gov.noaa.nodc:9700205_Not Applicable.json index d3ba40d04e..399f34d6e2 100644 --- a/datasets/gov.noaa.nodc:9700205_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700205_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700205_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700207_Not Applicable.json b/datasets/gov.noaa.nodc:9700207_Not Applicable.json index d0c55446e2..8bb3de7224 100644 --- a/datasets/gov.noaa.nodc:9700207_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700207_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700207_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700208_Not Applicable.json b/datasets/gov.noaa.nodc:9700208_Not Applicable.json index 2a0399c052..b1379be43f 100644 --- a/datasets/gov.noaa.nodc:9700208_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700208_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700208_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700210_Not Applicable.json b/datasets/gov.noaa.nodc:9700210_Not Applicable.json index 3807bee184..602154db19 100644 --- a/datasets/gov.noaa.nodc:9700210_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700210_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700210_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9700238_Not Applicable.json b/datasets/gov.noaa.nodc:9700238_Not Applicable.json index 9e5691224e..e9a303a745 100644 --- a/datasets/gov.noaa.nodc:9700238_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9700238_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9700238_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800027_Not Applicable.json b/datasets/gov.noaa.nodc:9800027_Not Applicable.json index cc71ab7c53..ebe1af4f24 100644 --- a/datasets/gov.noaa.nodc:9800027_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800027_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800027_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800037_Not Applicable.json b/datasets/gov.noaa.nodc:9800037_Not Applicable.json index ce6a83a2bd..21ecb74ac8 100644 --- a/datasets/gov.noaa.nodc:9800037_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800037_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800037_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, temperature, pressure, and salinity data were collected using bottle and CTD casts from the R/V Thomas G. Thompson in the Arabian Sea. Data were collected from July 17, 1995 to September 15, 1995. Data were collected by four different institution; Old Dominion University, Bermuda Biological Station for Research, Virginia Institute of Marine Science, and Woods Hole Oceanographic Institution as part of the Joint Global Ocean Flux Study / Arabian Sea Process Studies (JGOFS/Arabian) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800052_Not Applicable.json b/datasets/gov.noaa.nodc:9800052_Not Applicable.json index 9393baff1c..7f446cf474 100644 --- a/datasets/gov.noaa.nodc:9800052_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800052_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800052_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800085_Not Applicable.json b/datasets/gov.noaa.nodc:9800085_Not Applicable.json index 9e723714b9..d35d24517f 100644 --- a/datasets/gov.noaa.nodc:9800085_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800085_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800085_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800092_Not Applicable.json b/datasets/gov.noaa.nodc:9800092_Not Applicable.json index f5c64db95f..2cb9abe735 100644 --- a/datasets/gov.noaa.nodc:9800092_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800092_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800092_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800095_Not Applicable.json b/datasets/gov.noaa.nodc:9800095_Not Applicable.json index 90c3c1dc76..d73857c3f5 100644 --- a/datasets/gov.noaa.nodc:9800095_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800095_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800095_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800118_Not Applicable.json b/datasets/gov.noaa.nodc:9800118_Not Applicable.json index 664a858a28..51c7a5fd7b 100644 --- a/datasets/gov.noaa.nodc:9800118_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800118_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800118_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN, ROGER REVILLE, and NEW HORIZON from August 7, 1996 to April 19, 1997. Data were collected using bottle casts in the Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800119_Not Applicable.json b/datasets/gov.noaa.nodc:9800119_Not Applicable.json index 8fd265bc8a..e44dabcb13 100644 --- a/datasets/gov.noaa.nodc:9800119_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800119_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800119_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrophysical, hydrochemical, and other data were collected from CTD casts in the Gulf of Alaska from the R/V Alpha Helix from 10 October 1997 to 14 May 1998. Data were collected as part of GLOBal oceans ECosystems Dynamics Research (GLOBEC) project. Data include profiles of temperature, salinity, sigma-theta, deltas, oxygen concentration, and fluorescence.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800123_Not Applicable.json b/datasets/gov.noaa.nodc:9800123_Not Applicable.json index 36d16cd382..ec64beb7b1 100644 --- a/datasets/gov.noaa.nodc:9800123_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800123_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800123_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800129_Not Applicable.json b/datasets/gov.noaa.nodc:9800129_Not Applicable.json index d2f44777ed..bb1fc3a474 100644 --- a/datasets/gov.noaa.nodc:9800129_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800129_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800129_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical, zooplankton, and phytoplankton data were collected using bottle, CTD, fluorometer, oxygen meter, GPS, plankton trap, and sediment sampler from NOAA Ship MALCOLM BALDRIGE and NOAA Ship RESEARCHER. Data were collected from the Mississippi River and Gulf of Mexico from July 15, 1985 to May 12, 1993. Data were submitted by Dr. Nancy Rabalais from the Louisiana Universities Marine Consortium as part of the Nutrient Enhanced Coastal Ocean Productivity (NECOP) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800160_Not Applicable.json b/datasets/gov.noaa.nodc:9800160_Not Applicable.json index be93532bd4..5ec392b873 100644 --- a/datasets/gov.noaa.nodc:9800160_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800160_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800160_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical data were collected using CTD and bottle casts in the Arabian Sea from THOMAS G. THOMPSON. Data were collected from 07 March 1995 to 15 August 1995 by Lamont-Doherty Earth Observatory with support from the U.S. Joint Global Ocean Flux Study / Arabian Sea Process Studies (JOGFS/Arabian Sea) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800161_Not Applicable.json b/datasets/gov.noaa.nodc:9800161_Not Applicable.json index 5f82093c0f..c50f8263f9 100644 --- a/datasets/gov.noaa.nodc:9800161_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800161_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800161_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Chemical data were collected using CTD and bottle casts in the Arabian Sea from THOMAS G. THOMPSON. Data were collected from 08 January 1995 to 26 November 1995 by Harvard University with support from the U.S. Joint Global Ocean Flux Study / Arabian Sea Process Studies (JOGFS/Arabian Sea) project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800197_Not Applicable.json b/datasets/gov.noaa.nodc:9800197_Not Applicable.json index fb1ac96ab9..cca3502d4c 100644 --- a/datasets/gov.noaa.nodc:9800197_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800197_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800197_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The US Congress has authorized the Department of the Interior to enter into a lease agreement with the Governor of American Samoa to establish the National Park of American Samoa. This park would include a nearshore reef along the southern coast of the island of Ofu. This fringing reef on Ofu provides a natural lagoon habitat which is uncommon in American Samoa. This area supports a local subsistence fishery and provides excellent opportunities for diving and snorkeling.\n\nA survey of the nearshore reefs in the area of the proposed national park at Ofu was conducted between 7-12 September, 1992. The goals of the survey were to: 1) collect baseline data on the current status of the reefs and reef resources in the area, 2) to establish long-term monitoring stations to enable documentation of the health of the reef communities through time, and 3) to contribute information to a comprehensive coastal resource survey of Tutuila and the Manua Islands. The overall purpose of the work was to design and implement the biotic component of a reef monitoring program for the areas within and adjacent to the proposed national park site.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9800199_Not Applicable.json b/datasets/gov.noaa.nodc:9800199_Not Applicable.json index aead8c5bcb..f250dfb006 100644 --- a/datasets/gov.noaa.nodc:9800199_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9800199_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9800199_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900010_Not Applicable.json b/datasets/gov.noaa.nodc:9900010_Not Applicable.json index 88e64a857e..fe4f2e3ad8 100644 --- a/datasets/gov.noaa.nodc:9900010_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900010_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900010_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900014_Not Applicable.json b/datasets/gov.noaa.nodc:9900014_Not Applicable.json index 1787f3afd6..fa63c7ab5a 100644 --- a/datasets/gov.noaa.nodc:9900014_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900014_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900014_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900015_Not Applicable.json b/datasets/gov.noaa.nodc:9900015_Not Applicable.json index b495dc9123..e8ada4dad1 100644 --- a/datasets/gov.noaa.nodc:9900015_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900015_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900015_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900022_Not Applicable.json b/datasets/gov.noaa.nodc:9900022_Not Applicable.json index 56e4bb0fae..a893837756 100644 --- a/datasets/gov.noaa.nodc:9900022_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900022_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900022_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900054_Not Applicable.json b/datasets/gov.noaa.nodc:9900054_Not Applicable.json index 22dab24c3a..77f4875f01 100644 --- a/datasets/gov.noaa.nodc:9900054_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900054_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900054_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations.", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900094_Not Applicable.json b/datasets/gov.noaa.nodc:9900094_Not Applicable.json index 47aa105dab..90e0000e75 100644 --- a/datasets/gov.noaa.nodc:9900094_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900094_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900094_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900119_Not Applicable.json b/datasets/gov.noaa.nodc:9900119_Not Applicable.json index 4ea1b209a7..e3b34c0723 100644 --- a/datasets/gov.noaa.nodc:9900119_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900119_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900119_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900158_Not Applicable.json b/datasets/gov.noaa.nodc:9900158_Not Applicable.json index 127dd8aa22..701f1e5f2b 100644 --- a/datasets/gov.noaa.nodc:9900158_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900158_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900158_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900159_Not Applicable.json b/datasets/gov.noaa.nodc:9900159_Not Applicable.json index 59150f0c46..b61d8412f7 100644 --- a/datasets/gov.noaa.nodc:9900159_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900159_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900159_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900164_Not Applicable.json b/datasets/gov.noaa.nodc:9900164_Not Applicable.json index a7650765f9..a71677f80b 100644 --- a/datasets/gov.noaa.nodc:9900164_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900164_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900164_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900202_Not Applicable.json b/datasets/gov.noaa.nodc:9900202_Not Applicable.json index a2ff25e89c..c32158081f 100644 --- a/datasets/gov.noaa.nodc:9900202_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900202_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900202_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:9900218_Not Applicable.json b/datasets/gov.noaa.nodc:9900218_Not Applicable.json index 19c0efa9cf..19a27eadb8 100644 --- a/datasets/gov.noaa.nodc:9900218_Not Applicable.json +++ b/datasets/gov.noaa.nodc:9900218_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:9900218_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Not provided", "links": [ { diff --git a/datasets/gov.noaa.nodc:AVHRR_Pathfinder-NODC-v5.0_v5.1-climatologies_Not Applicable.json b/datasets/gov.noaa.nodc:AVHRR_Pathfinder-NODC-v5.0_v5.1-climatologies_Not Applicable.json index a3fd98b592..7ef3fb4796 100644 --- a/datasets/gov.noaa.nodc:AVHRR_Pathfinder-NODC-v5.0_v5.1-climatologies_Not Applicable.json +++ b/datasets/gov.noaa.nodc:AVHRR_Pathfinder-NODC-v5.0_v5.1-climatologies_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:AVHRR_Pathfinder-NODC-v5.0_v5.1-climatologies_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains global, 4km daily, 5-day, and monthly sea surface temperature climatologies derived from harmonic analysis of the AVHRR Pathfinder Version 5.0 and 5.1 sea surface temperature time series data for 1982-2008. The daily climatology is available as 366 separate files, each representing one day in a climatological year. The 5-day climatology is available as 73 separate files, each representing a 'pentad,' or 5-day period, in a climatological year. The monthly climatology is available as 12 separate files, each representing one month in a climatological year. The files are in netCDF-4 and fully comply with the GHRSST Data Specification 2.0 for Level 4 products. In addition to climatological sea surface temperature, each file contains standard deviation, sea ice concentration, sea ice concentration error, and land mask information.\n\nThis accession also includes 'classic', or mean, daily, 5-day, and monthly sea surface temperature climatologies derived from the same Pathfinder time series data. These climatologies were used to gap-fill the harmonic climatologies, and provided the standard deviation information.\n\nIn some ice-dominated high-latitude areas, the harmonic climatology is poorly constrained due to uneven distribution throughout the year of the valid measurements. For example, at 67.5S, 73W near the Antarctic coast, temperatures estimated in the harmonic climatology reach almost 40C. In these regions, the classic, averaged climatological values may be more reliable.", "links": [ { diff --git a/datasets/gov.noaa.nodc:BCO-DMO_Not Applicable.json b/datasets/gov.noaa.nodc:BCO-DMO_Not Applicable.json index bb5bfc14bb..e17ee03af5 100644 --- a/datasets/gov.noaa.nodc:BCO-DMO_Not Applicable.json +++ b/datasets/gov.noaa.nodc:BCO-DMO_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:BCO-DMO_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a collection of biological, chemical, physical, biogeochemical, ecological, environmental and other data collected from around the world during historical and contemporary periods of biological and chemical oceanographic exploration and research provided by the Biological and Chemical Oceanography Data Management Office (BCO-DMO). The BCO-DMO was created to serve Principal Investigators (PIs) funded by the National Science Foundation (NSF) Biological and Chemical Oceanography Sections as a location where marine biogeochemical and ecological data and information developed in the course of scientific research can easily be disseminated, protected, and stored for short and intermediate time-frames. The main objective of the BCO-DMO is to support the scientific community through improved accessibility to ocean science data. The BCO-DMO manages existing and new data sets from individual scientific investigators and collaborative groups of investigators, and makes these available via a web portal.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L2P_2.71.json b/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L2P_2.71.json index 0a38329462..b2f2a60553 100644 --- a/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L2P_2.71.json +++ b/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L2P_2.71.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-ABI_G17-STAR-L2P_2.71", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES17/STAR/nav. The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is also available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L3C_2.71.json b/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L3C_2.71.json index c3072af35b..e968e7fd85 100644 --- a/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L3C_2.71.json +++ b/datasets/gov.noaa.nodc:GHRSST-ABI_G17-STAR-L3C_2.71.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-ABI_G17-STAR-L3C_2.71", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L2P_2.70.json b/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L2P_2.70.json index 9c1bd0b395..d7553cde1f 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L2P_2.70.json +++ b/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L2P_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AHI_H08-STAR-L2P_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Himawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST. Accurate sensor calibration, image navigation and (co)registration, high spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping) offer vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P and MTSAT-2 Imagers. From altitude 35,800km, H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km at view zenith angle 67-deg, with a 10-min temporal sampling. The AHI L2P (swath) SST product is derived at the native sensor resolution using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO processes every 10-min FD data, identifies good quality ocean pixels (Petrenko et al., 2010) and derives SST using the four-band (8.4, 10.3, 11.2 and 12.3um) Non-Linear SST (NLSST) regression algorithm (Petrenko et al., 2014), trained against in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise. The collated L2P reports SSTs and brightness temperatures (BTs) in clear-sky water pixels (defined as ocean, sea, lake or river), and fill values elsewhere. All pixels with valid SSTs are recommended for use. ACSPO files also include sun-sensor geometry, l2p_flags (day/night, land, ice, twilight, and glint flags), and NCEP wind speed. The L2P is reported in NetCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume 0.6GB/day. Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/H08/STAR/nav. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). The H08 AHI SSTs and BTs are continuously validated against in situ data in SQUAM (Dash et al, 2010), and RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), 0.02-deg equal-angle gridded ACSPO L3C product is also available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L3C_2.70.json b/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L3C_2.70.json index 076d9d8f29..e648f7ec37 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L3C_2.70.json +++ b/datasets/gov.noaa.nodc:GHRSST-AHI_H08-STAR-L3C_2.70.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AHI_H08-STAR-L3C_2.70", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules available per 24hr interval, with a total data volume of 0.2GB/day. Valid SSTs are found over clear-sky oceans, sea, lakes or rivers, with fill values reported elsewhere. The following layers are reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST (Petrenko et al., 2016). The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L2P_8a.json b/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L2P_8a.json index 72dce125f5..1dcdf618de 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L2P_8a.json +++ b/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L2P_8a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AMSR2-REMSS-L2P_8a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. From about 700 km above the Earth, AMSR2 will provide us highly accurate measurements of the intensity of microwave emission and scattering. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Remote Sensing Systems (RSS, or REMSS), providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR2 instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v08\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L3U_8a.json b/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L3U_8a.json index 5f52118d35..3ddb683802 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L3U_8a.json +++ b/datasets/gov.noaa.nodc:GHRSST-AMSR2-REMSS-L3U_8a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AMSR2-REMSS-L3U_8a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. From about 700 km above the Earth, AMSR2 will provide us highly accurate measurements of the intensity of microwave emission and scattering. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Remote Sensing Systems (RSS, or REMSS), providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v8\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L2P_7.0.json b/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L2P_7.0.json index cab5c74300..7695dc913f 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L2P_7.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L2P_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AMSRE-REMSS-L2P_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L3U_7a.json b/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L3U_7a.json index f6f4ebc5e3..67be3480c1 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L3U_7a.json +++ b/datasets/gov.noaa.nodc:GHRSST-AMSRE-REMSS-L3U_7a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AMSRE-REMSS-L3U_7a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR18_G-NAVO-L2P_1.0.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR18_G-NAVO-L2P_1.0.json index dfc80702cf..25b8304840 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR18_G-NAVO-L2P_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR18_G-NAVO-L2P_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR18_G-NAVO-L2P_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR19_G-NAVO-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR19_G-NAVO-L2P_1.json index d8738c0e39..10303fba4e 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR19_G-NAVO-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR19_G-NAVO-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR19_G-NAVO-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR19_L-NAVO-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR19_L-NAVO-L2P_1.json index 0c54a24288..690192063b 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR19_L-NAVO-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR19_L-NAVO-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR19_L-NAVO-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L2P_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L2P_2.80.json index 26047383b1..586eb1bf29 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L2P_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L2P_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L2P_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. MetOp-A launched on 19 October 2006 is the first in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346). The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is also available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L3U_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L3U_2.80.json index fd28dd7aad..29bc9f146e 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L3U_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L3U_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MA-STAR-L3U_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L2P_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L2P_2.80.json index bccb48cb3c..64a21fa408 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L2P_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L2P_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L2P_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-B launched on 17 September 2012 is the second in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346). The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is also available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L3U_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L3U_2.80.json index c2385c9aa2..e849da0117 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L3U_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L3U_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MB-STAR-L3U_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite B (Metop-B) Advanced Very High Resolution Radiometer 3 (AVHRR/3) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-B AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MB-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L2P_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L2P_2.80.json index 6095302c4c..d61df6eba4 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L2P_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L2P_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L2P_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-C launched on 7 November 2018 is the third and last in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346). The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is also available.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L3U_2.80.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L3U_2.80.json index ce674cb5e1..b329518f99 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L3U_2.80.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L3U_2.80.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRF_MC-STAR-L3U_2.80", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite C (Metop-C) Advanced Very High Resolution Radiometer 3 (AVHRR/3) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-C AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MC-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTA_G-NAVO-L2P_2.0.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTA_G-NAVO-L2P_2.0.json index 1944e8390a..30d4ec0f5f 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTA_G-NAVO-L2P_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTA_G-NAVO-L2P_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRMTA_G-NAVO-L2P_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-A (MetOp-A) satellite. The SST data in this dataset are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular dataset is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTB_G-NAVO-L2P_2.0.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTB_G-NAVO-L2P_2.0.json index cb268e0017..fd25cb4571 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTB_G-NAVO-L2P_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTB_G-NAVO-L2P_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRMTB_G-NAVO-L2P_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-B (MetOp-B) satellite. The SST data in this dataset are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular dataset is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTC_G-NAVO-L2P_2.0.json b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTC_G-NAVO-L2P_2.0.json index f050dea8df..36b819db43 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRRMTC_G-NAVO-L2P_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRRMTC_G-NAVO-L2P_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRRMTC_G-NAVO-L2P_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-C (MetOp-C) satellite. The SST data in this dataset are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular dataset is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A-OSISAF-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A-OSISAF-L2P_1.json index a598e2dd10..50a251f48e 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A-OSISAF-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A-OSISAF-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A-OSISAF-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) satellite (launched 19 Oct 2006). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This product is delivered at full resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_GLB-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_GLB-OSISAF-L3C_1.json index 9c36b24f68..1924785a24 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_GLB-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_GLB-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_GLB-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is derived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_NAR-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_NAR-OSISAF-L3C_1.json index 16bddf57c6..6163791658 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_NAR-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_NAR-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_A_NAR-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B-OSISAF-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B-OSISAF-L2P_1.json index bd01541e88..42b020bf73 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B-OSISAF-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B-OSISAF-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B-OSISAF-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) satellite (launched 17 Sep 2012). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This product is delivered at full resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_GLB-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_GLB-OSISAF-L3C_1.json index 1ca1115a42..5780c896f8 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_GLB-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_GLB-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_GLB-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is derived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_NAR-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_NAR-OSISAF-L3C_1.json index 0fa26b9912..5725bc1ba5 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_NAR-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_NAR-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_METOP_B_NAR-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_NOAA19_NAR-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_NOAA19_NAR-OSISAF-L3C_1.json index 51e9db9be7..9918efc185 100644 --- a/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_NOAA19_NAR-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-AVHRR_SST_NOAA19_NAR-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-AVHRR_SST_NOAA19_NAR-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA-19 platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-CMC0.1deg-CMC-L4-GLOB_3.0.json b/datasets/gov.noaa.nodc:GHRSST-CMC0.1deg-CMC-L4-GLOB_3.0.json index c741888d09..99730275e6 100644 --- a/datasets/gov.noaa.nodc:GHRSST-CMC0.1deg-CMC-L4-GLOB_3.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-CMC0.1deg-CMC-L4-GLOB_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-CMC0.1deg-CMC-L4-GLOB_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the Advanced Very High Resolution Radiometer (AVHRR) from NOAA-18,19, the European Meteorological Operational-A (METOP-A) and Operational-B (METOP-B), and microwave data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-CMC0.2deg-CMC-L4-GLOB_2.0.json b/datasets/gov.noaa.nodc:GHRSST-CMC0.2deg-CMC-L4-GLOB_2.0.json index 880ef6d2f7..ce6bd21ab0 100644 --- a/datasets/gov.noaa.nodc:GHRSST-CMC0.2deg-CMC-L4-GLOB_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-CMC0.2deg-CMC-L4-GLOB_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-CMC0.2deg-CMC-L4-GLOB_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-DMI-L4UHfnd-NSEABALTIC-DMI_OI_1.0.json b/datasets/gov.noaa.nodc:GHRSST-DMI-L4UHfnd-NSEABALTIC-DMI_OI_1.0.json index 05bd6aa9f5..ce5e533270 100644 --- a/datasets/gov.noaa.nodc:GHRSST-DMI-L4UHfnd-NSEABALTIC-DMI_OI_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-DMI-L4UHfnd-NSEABALTIC-DMI_OI_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-DMI-L4UHfnd-NSEABALTIC-DMI_OI_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a regional 0.03 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites over the North and Baltic Seas. The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the Swedish and Finnish ice services is used to mask out areas with ice.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-DMI_OI-DMI-L4-GLOB_1.0.json b/datasets/gov.noaa.nodc:GHRSST-DMI_OI-DMI-L4-GLOB_1.0.json index 5c0cc69310..e24d2fa0a1 100644 --- a/datasets/gov.noaa.nodc:GHRSST-DMI_OI-DMI-L4-GLOB_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-DMI_OI-DMI-L4-GLOB_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-DMI_OI-DMI-L4-GLOB_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites. The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Visible Infrared Imager Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice. This dataset adheres to the version 2 GHRSST Data Processing Specification (GDS).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AMSRE_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AMSRE_1.0.json index c1b99fc9d7..2e723517da 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AMSRE_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AMSRE_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AMSRE_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset is derived from Remote Sensing Systems BMAPS (binary) format AMSR-E SST subskin data. Data were downloaded for North Atlantic region from Remote Sensing Systems every hour to capture the latest AMSRE observations. L2P data products were then produced to the GHRSST Data Processing Specification (GDS) version 1.5. Ascending (daytime) and descending (nighttime) orbits are packaged into separate netCDF files. Although the dataset designation is \"L2P\" it is actually a \"L3C\" dataset (gridded Level 3 collated) as defined by the GHRSST Data Processing Specification version 2.0. This dataset was originally produced for the GHRSST Pilot Project, the precursor to GHRSST, by the Medspiration Regional Data Assembly Center (RDAC) in Europe. As one of the first datasets produced as a prototype for GHRSST-PP it assumed an incorrect designation of \"L2P\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-ATS_NR_2P_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-ATS_NR_2P_1.0.json index 9c8100e991..c59bd9d095 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-ATS_NR_2P_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-ATS_NR_2P_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-ATS_NR_2P_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Launched in March 2002 by the European Space Agency (ESA), Envisat is the largest Earth Observation spacecraft ever built. It carries ten sophisticated optical and radar instruments to provide continuous observation and monitoring of the Earth's land, atmosphere, oceans and ice caps. The Advanced Along-Track Scanning Radiometer (AATSR) onboard the Envisat spacecraft is designed to meet the challenging task of monitoring and detecting the climate change signal of sea surface temperature (SST). It builds on the success of its predecessor instruments on the European Remote-Sensing Satellite (ERS)-1, and ERS-2 satellites, and will lead to a multi-decade record of precise and accurate global SST measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSR data are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A \"dual-view\" algorithm offering improved atmospheric correction by applying two different atmospheric path lengths is used to derive the SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies. See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. These AATSR L2P SST data are produced as part of the Group for High Resolution Sea Surface Temperature (GHRSST) Project according to the GHRSST-PP Data Processing Specification (GDS) version 1.5. This AATSR L2P dataset is the original product produced by the Medspiration Regional Data Assembly Facility (RDAC) from early 2005 to mid 2009.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_G_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_G_1.0.json index 3024670903..bed96e0dc9 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_G_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_G_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_G_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Level 2P Group for High Resolution Sea Surface Temperature (GHRSST) dataset based on multi-channel sea surface temperature (SST) retrievals from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-16 platform (launched on 21 Sep 2000). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. This particular dataset is derived from Global Area Coverage (GAC) binary AVHRR SST binary data originally produced by the US Naval Oceanographic Office (NAVO) and downloaded from the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC). GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. Finally, L2P data products are derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_L_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_L_1.0.json index b8f3d3af84..8ed963ba1d 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_L_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_L_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR16_L_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Level 2P Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Atlantic Ocean and nearby regions based on multi-channel sea surface temperature (SST) retrievals from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-16 platform (launched on 21 Sep 2000). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. This particular dataset is derived from Local Area Coverage (LAC) binary AVHRR SST binary data originally produced by the US Naval Oceanographic Office (NAVO) and downloaded from the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC). LAC are full resolution AVHRR data whose acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. Finally, L2P data products are derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_G_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_G_1.0.json index ffd7876da1..2e2e0ce0ba 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_G_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_G_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_G_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Level 2P Group for High Resolution Sea Surface Temperature (GHRSST) dataset based on multi-channel sea surface temperature (SST) retrievals from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched on 24 June 2002). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. This particular dataset is derived from Global Area Coverage (GAC) binary AVHRR SST binary data originally produced by the US Naval Oceanographic Office (NAVO) and downloaded from the JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC). GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. Finally, L2P data products are derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_L_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_L_1.0.json index 3bd5230705..c006292953 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_L_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_L_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR17_L_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Level 2P Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Atlantic Ocean and nearby regions based on multi-channel sea surface temperature (SST) retrievals from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched on 24 June 2002). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. This particular dataset is derived from Local Area Coverage (LAC) binary AVHRR SST binary data originally produced by the US Naval Oceanographic Office (NAVO) and downloaded from the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC). LAC are full resolution AVHRR data whose acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. Finally, L2P data products are derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES).", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR_METOP_A_1.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR_METOP_A_1.json index 3a5580e9a9..0d6793187b 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR_METOP_A_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR_METOP_A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-AVHRR_METOP_A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 m) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This product is delivered at full resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR16_SST_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR16_SST_1.0.json index 8876e14c62..4272d037df 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR16_SST_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR16_SST_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-NAR16_SST_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-16 platform (launched on 21 Sep 2000). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France.\n\nThe AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from direct broadcast High Resolution Picture Transmission (HRPT) AVHRR data that are re-mapped onto a stereopolar grid to produce mosaics at 2 km resolution. An operational AVHRR cloud mask is applied based on a multi-spectral thresholding algorithm (Derrien and Le Gleau 1999). Some refinements specific to the marine conditions have been introduced including the use of fine scale climatology and a fine gradient climatology to assist in the detection of clouds in areas characterised by strong thermal gradients. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES). Although the dataset designation is \"L2P\" it is actually a \"L3C\" dataset (gridded Level 3 collated) as defined by the GHRSST Data Processing Specification version 2.0. This dataset was originally produced for the GHRSST Pilot Project (GHRSST-PP), the precursor to GHRSST, by the Medspiration Regional Data Assembly Center (RDAC) in Europe. As one of the first datasets produced as a prototype for the GHRSST-PP it assumed an incorrect designation of \"L2P\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR17_SST_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR17_SST_1.0.json index cffe4b584a..5546df3b08 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR17_SST_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR17_SST_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-NAR17_SST_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched on 24 Jun 2002). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France.\n\nThe AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km.\n\nThe NAR products are SST fields derived from direct broadcast High Resolution Picture Transmission (HRPT) AVHRR data that are re-mapped onto a stereopolar grid to produce mosaics at 2 km resolution. An operational AVHRR cloud mask is applied based on a multi-spectral thresholding algorithm (Derrien and Le Gleau 1999). Some refinements specific to the marine conditions have been introduced including the use of fine scale climatology and a fine gradient climatology to assist in the detection of clouds in areas characterised by strong thermal gradients. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES). Although the dataset designation is \"L2P\" it is actually a \"L3C\" dataset (gridded Level 3 collated) as defined by the GHRSST Data Processing Specification version 2.0. This dataset was originally produced for the GHRSST Pilot Project (GHRSST-PP), the precursor to GHRSST, by the Medspiration Regional Data Assembly Center (RDAC) in Europe. As one of the first datasets produced as a prototype for the GHRSST-PP it assumed an incorrect designation of \"L2P\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR18_SST_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR18_SST_1.0.json index f199371f2b..7989b10100 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR18_SST_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-NAR18_SST_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-NAR18_SST_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched on 20 May 2005). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France.\n\nThe AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from direct broadcast High Resolution Picture Transmission (HRPT) AVHRR data that are re-mapped onto a stereopolar grid to produce mosaics at 2 km resolution. An operational AVHRR cloud mask is applied based on a multi-spectral thresholding algorithm (Derrien and Le Gleau 1999). Some refinements specific to the marine conditions have been introduced including the use of fine scale climatology and a fine gradient climatology to assist in the detection of clouds in areas characterised by strong thermal gradients. L2P data products are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5 including Single Sensor Error Statistics (SSES). Although the dataset designation is \"L2P\" it is actually a \"L3C\" dataset (gridded Level 3 collated) as defined by the GHRSST Data Processing Specification version 2.0. This dataset was originally produced for the GHRSST Pilot Project (GHRSST-PP), the precursor to GHRSST, by the Medspiration Regional Data Assembly Center (RDAC) in Europe. As one of the first datasets produced as a prototype for the GHRSST-PP it assumed an incorrect designation of \"L2P\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-SEVIRI_SST_4.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-SEVIRI_SST_4.0.json index 301d2852a6..a3279d6b26 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-SEVIRI_SST_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-SEVIRI_SST_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-SEVIRI_SST_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteosat Second Generation (MSG) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by Meteo France/ Centre de Meteorologie Spatiale (CMS), is derived from the SEVIRI instrument on the first MSG satellite (also known as Meteosat-8) that was launched on 28 August 2002. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution on a hourly basis. Remapping of original pixel size to 11.6 km resolution is made by spatial averaging, and a 3-hourly temporal resolution SST is created by averaging the hourly SSTs having the best confidence level. Data from different MSG satellites are not averaged together. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-TMI_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-TMI_1.0.json index 7d2aabda90..773baf6b77 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-TMI_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L2P-TMI_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L2P-TMI_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to SSM/I, that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in November 1997. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. \n\nThis Group for High Resolution Sea Surface Temperature (GHRSST) dataset is derived from Remote Sensing Systems BMAPS (binary) format TMI SST subskin data. Data were downloaded for the North Atlantic region from Remote Sensing Systems every hour to capture the latest TMI observations. L2P data products were then produced to the GHRSST Data Processing Specification (GDS) version 1.5. Ascending (daytime) and descending (nighttime) orbits are packaged into separate netCDF files. Although the dataset designation is \"L2P\" it is actually a \"L3C\" dataset (gridded Level 3 collated) as defined by the GHRSST Data Processing Specification version 2.0. This dataset was originally produced for the GHRSST Pilot Project, the precursor to GHRSST, by the Medspiration Regional Data Assembly Center (RDAC) in Europe. As one of the first datasets produced as a prototype for GHRSST-PP it assumed an incorrect designation of \"L2P\".", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-GLOB_AVHRR_METOP_A_1.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-GLOB_AVHRR_METOP_A_1.json index 1e3c168ee4..5fc11d3158 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-GLOB_AVHRR_METOP_A_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-GLOB_AVHRR_METOP_A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L3P-GLOB_AVHRR_METOP_A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Level 3 Group for High Resolution Sea Surface Temperature (GHRSST) dataset from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A platform (launched on 19 Oct 2006). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The SST fields are derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. The MetOp-A SST L3P data are compliant with the Group for High Resolution SST (GHRSST) Data Specification (GDS) version 1.7.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_METOP_A_1.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_METOP_A_1.json index 35674cd9d7..8cbf5e083d 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_METOP_A_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_METOP_A_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_METOP_A_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the MetOp-A platform (launched on 19 Oct 2006). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. The MetOp-A SST L3P data are compliant with the Group for High Resolution SST (GHRSST) Data Specification (GDS) version 1.7.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_NOAA_19_1.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_NOAA_19_1.json index 711d2f1053..d7b0bea8a4 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_NOAA_19_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_NOAA_19_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L3P-NAR_AVHRR_NOAA_19_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. The NOAA-19 SST L3P data are compliant with the Group for High Resolution SST (GHRSST) Data Specification (GDS) version 1.7.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L4HRfnd-GLOB-ODYSSEA_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L4HRfnd-GLOB-ODYSSEA_1.0.json index 045fec13bc..649030b60c 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L4HRfnd-GLOB-ODYSSEA_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L4HRfnd-GLOB-ODYSSEA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L4HRfnd-GLOB-ODYSSEA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at Ifremer/CERSAT (France) using optimal interpolation (OI) on a global 0.1 degree grid. It provides a daily cloud-free field of foundation sea surface temperature at approximately 10km resolution (0.1 degree) over the full globe. It is generated by merging microwave and infrared satellite sea surface temperature observations including those from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Geostationary Operational Environmental Satellite (GOES) Imager. The satellite SST observations are intercalibrated using the AATSR sensor as a reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission initially in the frame of MERSEA project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHFnd-MED_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHFnd-MED_1.0.json index 16d5d84b9a..e46524a00b 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHFnd-MED_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHFnd-MED_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L4UHFnd-MED_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily by Ifremer/CERSAT (France) using optimal interpolation (OI) on a regional 0.02 degree grid. It provides a daily cloud-free field of foundation sea surface temperature at approximately 2 km resolution (0.02 degree) for the Mediterranean Sea. It is generated by merging microwave and infrared satellite sea surface temperature observations including those from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Tropical Rainfall Measuring Mission Microwave Imager (TMI). The satellite SST observations are intercalibrated using the AATSR sensor as a reference (previously re-calibrated using all available in situ data). This dataset was the first Level 4 product produced by the GHRSST Project. It has been superseded by the ODYSSEA L4 product for this region: GHRSST Level 4 ODYSSEA Mediterranean Sea Regional Foundation Sea Surface Temperature Analysis.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-GAL-ODYSSEA_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-GAL-ODYSSEA_1.0.json index 166dd8a7a5..b2ecf15ac0 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-GAL-ODYSSEA_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-GAL-ODYSSEA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-GAL-ODYSSEA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at Ifremer/CERSAT (France) using optimal interpolation (OI) on a regional 0.02 degree grid. It provides a daily cloud-free field of foundation sea surface temperature at approximately 2 km resolution (0.02 degree) for the Galapagos Islands and the Eastern Central Pacific. It is generated by merging microwave and infrared satellite sea surface temperature observations including those from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Geostationary Operational Environmental Satellite (GOES) Imager. The satellite SST observations are intercalibrated using the AATSR sensor as a reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission initially in the frame of MERSEA project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-MED-ODYSSEA_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-MED-ODYSSEA_1.0.json index 8906f6c753..a83fbbb35a 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-MED-ODYSSEA_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-MED-ODYSSEA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-MED-ODYSSEA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at Ifremer/CERSAT (France) using optimal interpolation (OI) on a regional 0.02 degree grid. It provides a daily cloud-free field of foundation sea surface temperature at approximately 2 km resolution (0.02 degree) for the Mediterranean Sea. It is generated by merging microwave and infrared satellite sea surface temperature observations including those from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Geostationary Operational Environmental Satellite (GOES) Imager. The satellite SST observations are intercalibrated using the AATSR sensor as a reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission initially in the frame of MERSEA project. This dataset supersedes the original Level 4 product for this region: GHRSST Level 4 EUR Mediterranean Sea Regional Foundation Sea Surface Temperature Analysis.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-NWE-ODYSSEA_1.0.json b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-NWE-ODYSSEA_1.0.json index 8668eb01cd..5a2d8282df 100644 --- a/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-NWE-ODYSSEA_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-NWE-ODYSSEA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-EUR-L4UHRfnd-NWE-ODYSSEA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at Ifremer/CERSAT (France) using optimal interpolation (OI) on a regional 0.02 degree grid. It provides a daily cloud-free field of foundation sea surface temperature at approximately 2 km resolution (0.02 degree) for the North-Western European shelves. It is generated by merging microwave and infrared satellite sea surface temperature observations including those from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Geostationary Operational Environmental Satellite (GOES) Imager. The satellite SST observations are intercalibrated using the AATSR sensor as a reference (previously re-calibrated using all available in situ data). The development of the global real-time sea surface temperature at Ifremer/CERSAT is supported by European Commission initially in the frame of MERSEA project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-GAMSSA_28km-ABOM-L4-GLOB_1.0.json b/datasets/gov.noaa.nodc:GHRSST-GAMSSA_28km-ABOM-L4-GLOB_1.0.json index 7b3879200e..588a92d36a 100644 --- a/datasets/gov.noaa.nodc:GHRSST-GAMSSA_28km-ABOM-L4-GLOB_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-GAMSSA_28km-ABOM-L4-GLOB_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-GAMSSA_28km-ABOM-L4-GLOB_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-GMI-REMSS-L3U_8.2a.json b/datasets/gov.noaa.nodc:GHRSST-GMI-REMSS-L3U_8.2a.json index 8b1dba97ed..047d39ae42 100644 --- a/datasets/gov.noaa.nodc:GHRSST-GMI-REMSS-L3U_8.2a.json +++ b/datasets/gov.noaa.nodc:GHRSST-GMI-REMSS-L3U_8.2a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-GMI-REMSS-L3U_8.2a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-GOES13-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-GOES13-OSISAF-L3C_1.json index d6251786f0..0ae1a3f7f4 100644 --- a/datasets/gov.noaa.nodc:GHRSST-GOES13-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-GOES13-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-GOES13-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral algorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible in daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 30 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating 30 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-GOES16-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-GOES16-OSISAF-L3C_1.json index d34dc4fe75..af34d630bb 100644 --- a/datasets/gov.noaa.nodc:GHRSST-GOES16-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-GOES16-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-GOES16-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data is regional and part of the High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset covering the America Region (AMERICAS) based on retrievals from the Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-16 (GOES-16). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES-16 in the Eastern position. GOES-16 Imager level 1 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. The new GOES-East platform (GOES-16) enables daytime SST calculations (whereas, previously, GOES East SST was restricted to nighttime conditions). The GOES-16 SST is derived from three-bands (centered at 8.4, 10.3, and 12.3 um) algorithm. The ABI split-window configuration features three bands instead of the two found in heritage sensors. This offers additional potential but also may present a challenge if the two end bands centered at 10.3 and 12.3 um are pushed too far in the absorption lines. The 8.5-um is used in conjunction with the 10.3-um and 12.3-um bands for improved thin cirrus detection as well as for better atmospheric moisture correction in relatively dry atmospheres. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Each 30-minute observation interval is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating 30-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_A-OSISAF-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_A-OSISAF-L2P_1.json index fd841bbc43..cf74653b8e 100644 --- a/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_A-OSISAF-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_A-OSISAF-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-IASI_SST_METOP_A-OSISAF-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-A (MetOp-A) satellite (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures in the infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to 40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morning orbit (9:30 Local Solar Time equator crossing, descending node) global observations can be provided twice a day. The SST retrieval is performed and provided by the IASI L2 processor at EUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_B-OSISAF-L2P_1.json b/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_B-OSISAF-L2P_1.json index 23de9debed..0489d63e55 100644 --- a/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_B-OSISAF-L2P_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-IASI_SST_METOP_B-OSISAF-L2P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-IASI_SST_METOP_B-OSISAF-L2P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-B (MetOp-B) satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures in the infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to 40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morning orbit (9:30 Local Solar Time equator crossing, descending node) global observations can be provided twice a day. The SST retrieval is performed and provided by the IASI L2 processor at EUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Ad_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Ad_1.json index 287703b5b2..660abe3489 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Ad_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Ad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Ad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using daytime data. The algorithm is based on a weighting scheme and compositing whereby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_An_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_An_1.json index 6c2f91c27d..155dc27ff9 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_An_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_An_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_An_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE day and night. This particular dataset represents a MODIS Aqua and AMSRE composite using nighttime data. The algorithm is based on a weighting scheme and compositing whereby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Td_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Td_1.json index ee4d816aac..1300935959 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Td_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Td_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Td_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE day and night. This particular dataset represents a MODIS Terra and AMSRE composite using daytime data. The algorithm is based on a weighting scheme and compositing whereby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Tn_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Tn_1.json index 919ae1ace1..c44a7e8641 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Tn_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Tn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL-L4UHblend-NCAMERICA-RTO_SST_Tn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE day and night. This particular dataset represents a MODIS Terra and AMSRE composite using nighttime data. The algorithm is based on a weighting scheme and compositing whereby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHfnd-NCAMERICA-MUR_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHfnd-NCAMERICA-MUR_1.json index 5dd77fb4f0..2b7ca7acdb 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHfnd-NCAMERICA-MUR_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL-L4UHfnd-NCAMERICA-MUR_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL-L4UHfnd-NCAMERICA-MUR_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a regional 0.011 degree grid over the oceans off North and Central America (62N- 20S, 165W - 30W). The Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments such as: the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center.\n\nThis dataset is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio Chin from JPL.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-JPL_OUROCEAN-L4UHfnd-GLOB-G1SST_1.json b/datasets/gov.noaa.nodc:GHRSST-JPL_OUROCEAN-L4UHfnd-GLOB-G1SST_1.json index 3e3d816cf9..7de165485d 100644 --- a/datasets/gov.noaa.nodc:GHRSST-JPL_OUROCEAN-L4UHfnd-GLOB-G1SST_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-JPL_OUROCEAN-L4UHfnd-GLOB-G1SST_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-JPL_OUROCEAN-L4UHfnd-GLOB-G1SST_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the JPL OurOcean group using a multi-scale two-dimensional variational (MS-2DVAR) blending algorithm on a global 0.009 degree grid. This Global 1 km SST (G1SST) analysis uses satellite data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, the Multi-Functional Transport Satellite 1R (MTSAT-1R) radiometer, and in situ data from drifting and moored buoys.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-K10_SST-NAVO-L4-GLOB_1.0.json b/datasets/gov.noaa.nodc:GHRSST-K10_SST-NAVO-L4-GLOB_1.0.json index d15f5f75a5..bd023d4243 100644 --- a/datasets/gov.noaa.nodc:GHRSST-K10_SST-NAVO-L4-GLOB_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-K10_SST-NAVO-L4-GLOB_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-K10_SST-NAVO-L4-GLOB_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis dataset produced daily on an operational basis by the Naval Oceanographic Office (NAVO) on a global 0.1x0.1 degree grid. The K10 (NAVO 10-km gridded SST analyzed product) L4 analysis uses SST observations from the following instruments: Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The AVHRR data for this comes from the MetOp-A, MetOp-B, and NOAA-19 satellites; VIIRS data is sourced from the Suomi_NPP satellite; SEVIRI data comes from the Meteosat-8 and -11 satellites. The age (time-lag), reliability, and resolution of the data are used in the weighted average with the analysis tuned to represent SST at a reference depth of 1-meter. Input data from the AVHRR Pathfinder 9km climatology dataset (1985-1999) is used when no new satellite SST retrievals are available after 34 days. Comparing with its predecessor, this updated dataset has no major changes in Level-4 interpolated K10 algorithm, except for using different satellite instrument data, and updating metadata and file format. The major updates include: (a) updated and enhanced the granule-level metadata information, (b) converted the SST file from GHRSST Data Specification (GDS) v1.0 to v2.0, (c) added the sea_ice_fraction variable to the product, and (d) updated the filename convention to reflect compliance with GDS v2.0.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MODIS_A-JPL-L2P_2019.0.json b/datasets/gov.noaa.nodc:GHRSST-MODIS_A-JPL-L2P_2019.0.json index 39f9c3d378..b7abc7f89c 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MODIS_A-JPL-L2P_2019.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-MODIS_A-JPL-L2P_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MODIS_A-JPL-L2P_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MODIS_T-JPL-L2P_2019.0.json b/datasets/gov.noaa.nodc:GHRSST-MODIS_T-JPL-L2P_2019.0.json index 731055c6ab..467c56db95 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MODIS_T-JPL-L2P_2019.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-MODIS_T-JPL-L2P_2019.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MODIS_T-JPL-L2P_2019.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MUR-JPL-L4-GLOB_4.1.json b/datasets/gov.noaa.nodc:GHRSST-MUR-JPL-L4-GLOB_4.1.json index 8481bbbd04..253c35007c 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MUR-JPL-L4-GLOB_4.1.json +++ b/datasets/gov.noaa.nodc:GHRSST-MUR-JPL-L4-GLOB_4.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MUR-JPL-L4-GLOB_4.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.\n\nThis dataset is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata \"history:\" attribute to determine if a granule is near-realtime or retrospective.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MUR25-JPL-L4-GLOB_4.2.json b/datasets/gov.noaa.nodc:GHRSST-MUR25-JPL-L4-GLOB_4.2.json index f231eea162..af79031f84 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MUR25-JPL-L4-GLOB_4.2.json +++ b/datasets/gov.noaa.nodc:GHRSST-MUR25-JPL-L4-GLOB_4.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MUR25-JPL-L4-GLOB_4.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014).\n\nThis dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MW_IR_OI-REMSS-L4-GLOB_5.0.json b/datasets/gov.noaa.nodc:GHRSST-MW_IR_OI-REMSS-L4-GLOB_5.0.json index 7bf397a78c..634a399e02 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MW_IR_OI-REMSS-L4-GLOB_5.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-MW_IR_OI-REMSS-L4-GLOB_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MW_IR_OI-REMSS-L4-GLOB_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platform and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-MW_OI-REMSS-L4-GLOB_5.0.json b/datasets/gov.noaa.nodc:GHRSST-MW_OI-REMSS-L4-GLOB_5.0.json index 03dab67c83..8d7bd48eec 100644 --- a/datasets/gov.noaa.nodc:GHRSST-MW_OI-REMSS-L4-GLOB_5.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-MW_OI-REMSS-L4-GLOB_5.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-MW_OI-REMSS-L4-GLOB_5.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_G_1.0.json b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_G_1.0.json index a2c99e5e7c..962bc9c645 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_G_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_G_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_G_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched 24 June 2002) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_L_1.0.json b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_L_1.0.json index 97ac89e084..fe9e58946f 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_L_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_L_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR17_L_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched 24 June 2002) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR18_L_1.0.json b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR18_L_1.0.json index e35a7da1d8..86946b9096 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR18_L_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR18_L_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NAVO-L2P-AVHRR18_L_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI_1.0.json b/datasets/gov.noaa.nodc:GHRSST-NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI_1.0.json index d721fa86a4..626276cdb8 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25 degree grid at the NOAA National Climatic Data Center. This product uses optimal interpolation (OI) using data from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5 time series (when available, otherwise operational NOAA AVHRR data are used), the Advanced Microwave Scanning Radiometer-EOS (AMSR-E), and in situ ship and buoy observations. A second similar product is available back to 1981 that includes only in situ and AVHRR Pathfinder data in its analysis. The OI analysis is a daily average SST that is bias adjusted using a spatially smoothed 7-day in situ SST average and is thus tuned to about 0.3 meter. Both day and night satellite fields are independently bias adjusted. More information is available at http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily.php.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR17_L_1.json b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR17_L_1.json index 3509d1e852..f28820ed76 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR17_L_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR17_L_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR17_L_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2P swath-based Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic area from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-17 platform (launched on 24 June 2002). This particular dataset is produced by the Natural Environment Research Council (NERC) Earth Observation Data Acquisition and Analysis Service (NEODAAS) in collaboration with the National Centre for Ocean Forecasting (NCOF) in the United Kingdom. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day or more (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. NEODAAS-Dundee acquires approximately 15 AVHRR direct broadcast High Resolution Picture Transmission (HRPT) passes per day over NW Europe and the Arctic. Each pass is approximately 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into sea surface temperature (SST) products and converted to L2P specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR18_L_1.json b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR18_L_1.json index 82d64b81bb..5026b8f983 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR18_L_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR18_L_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR18_L_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2P swath-based Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic area from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched on 20 May 2005). This particular dataset is produced by the Natural Environment Research Council (NERC) Earth Observation Data Acquisition and Analysis Service (NEODAAS) in collaboration with the National Centre for Ocean Forecasting (NCOF) in the United Kingdom. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day or more (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. NEODAAS-Dundee acquires approximately 15 AVHRR direct broadcast High Resolution Picture Transmission (HRPT) passes per day over NW Europe and the Arctic. Each pass is approximately 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into sea surface temperature (SST) products and converted to L2P specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR19_L_1.json b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR19_L_1.json index ff5154b9ec..1ff15f5e01 100644 --- a/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR19_L_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR19_L_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-NEODAAS-L2P-AVHRR19_L_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level 2P swath-based Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic area from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched on 6 Feb 2009). This particular dataset is produced by the Natural Environment Research Council (NERC) Earth Observation Data Acquisition and Analysis Service (NEODAAS) in collaboration with the National Centre for Ocean Forecasting (NCOF) in the United Kingdom. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day or more (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. NEODAAS-Dundee acquires approximately 15 AVHRR direct broadcast High Resolution Picture Transmission (HRPT) passes per day over NW Europe and the Arctic. Each pass is approximately 15 minutes duration. These are immediately transferred to NEODAAS-Plymouth where they are processed into sea surface temperature (SST) products and converted to L2P specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-BLK_2.0.json b/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-BLK_2.0.json index b8a703e0d8..8b2c82ee0e 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-BLK_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-BLK_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-BLK_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-MED_2.0.json b/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-MED_2.0.json index 91ee149ff0..de9a4bc0a2 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-MED_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-MED_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OISST_HR_NRT-GOS-L4-MED_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625deg. x 0.0625deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK_2.0.json b/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK_2.0.json index 96954c6bf9..dc290c18fa 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-MED_2.0.json b/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-MED_2.0.json index 6497ac2009..fd2b4ea2fd 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-MED_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-MED_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-MED_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES11_4.0.json b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES11_4.0.json index bac89f8020..39cabbfed6 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES11_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES11_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES11_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-11 launched 3 May 2000. The radiometer aboard the satellite, The GOES I-M Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-11 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES12_4.0.json b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES12_4.0.json index 17b49dac68..835a5693d3 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES12_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES12_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OSDPD-L2P-GOES12_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-12 launched 23 July 2001. The radiometer aboard the satellite, The GOES I-M Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-12 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5. The full disk image is subsetted into granules representing distinct northern and southern regions.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MSG02_4.json b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MSG02_4.json index e32b40b959..8e95700642 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MSG02_4.json +++ b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MSG02_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OSDPD-L2P-MSG02_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Meteosat Second Generation (MSG) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 1.5.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MTSAT1R_4.json b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MTSAT1R_4.json index 400644992a..30818663d0 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MTSAT1R_4.json +++ b/datasets/gov.noaa.nodc:GHRSST-OSDPD-L2P-MTSAT1R_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OSDPD-L2P-MTSAT1R_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilised satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-1R (also known as Himawari 6) and its radiometer (MTSAT-1R Imager) was successfully launched on 26 February 2005. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-1R Imager full resolution data in satellite projection on a hourly basis. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 1.5.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-OSTIA-UKMO-L4-GLOB_2.0.json b/datasets/gov.noaa.nodc:GHRSST-OSTIA-UKMO-L4-GLOB_2.0.json index d25f2dc33d..2dc3d8c87a 100644 --- a/datasets/gov.noaa.nodc:GHRSST-OSTIA-UKMO-L4-GLOB_2.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-OSTIA-UKMO-L4-GLOB_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-OSTIA-UKMO-L4-GLOB_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.054 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferometer (IASI), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-RAMSSA_09km-ABOM-L4-AUS_1.0.json b/datasets/gov.noaa.nodc:GHRSST-RAMSSA_09km-ABOM-L4-AUS_1.0.json index 76920970b9..4b0f778be0 100644 --- a/datasets/gov.noaa.nodc:GHRSST-RAMSSA_09km-ABOM-L4-AUS_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-RAMSSA_09km-ABOM-L4-AUS_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-RAMSSA_09km-ABOM-L4-AUS_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-REMO_OI_SST_5km-UFRJ-L4-SAMERICA_1.0.json b/datasets/gov.noaa.nodc:GHRSST-REMO_OI_SST_5km-UFRJ-L4-SAMERICA_1.0.json index f3df3b1011..ec0076669c 100644 --- a/datasets/gov.noaa.nodc:GHRSST-REMO_OI_SST_5km-UFRJ-L4-SAMERICA_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-REMO_OI_SST_5km-UFRJ-L4-SAMERICA_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-REMO_OI_SST_5km-UFRJ-L4-SAMERICA_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis by the Oceanographic Modeling and Observation Network (REMO) at Applied Meteorology Laboratory/Federal University of Rio de Janeiro (LMA/UFRJ) using the Barnes sub optimal interpolation (OI) technique on a regional 0.05 degree grid. REMO uses Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) satellites series (NOAA 15, NOAA 16, NOAA 17, NOAA 18 and NOAA 19) and Microwave Imager (TMI) data from Tropical Rainfall Measuring Mission (TRMM) which is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) to generate 0.05 degree daily cloud free blended (infrared and microwave) SST products (approximately 5.5 km). The data lies between latitudes 45 S and 15 N and longitudes 70 W and 15 W region and are fully validated by in situ measurements from eleven buoys of Prediction and Research Moored Array in the Tropical Atlantic (PIRATA). AVHRR is a scanning radiometer capable of detecting energy from land, ocean and atmosphere. It operates with six spectral bands arranged in the regions of visible and infrared region. TRMM was launched in December, 1997, having an orbital inclination of 53 degree and altitude 350 km, an equatorial orbit that ranges from 40 N to 40 S and a spatial resolution of 0.25 degree (~27.75 km). Although infrared AVHRR SST data have high spatial resolution, they are contaminated by cloud cover and aerosols, while lower resolution microwave TMI data are barely influenced by these.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-AMSRE_4.0.json b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-AMSRE_4.0.json index c990d5a2af..5e7a4b8c11 100644 --- a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-AMSRE_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-AMSRE_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-AMSRE_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. Although the product designation is \"L2P_GRIDDED\" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its \"L2P_GRIDDED\" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity. In this dataset, both ascending (daytime) and descending (daytime) gridded orbital passes on packaged into the same daily file.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI_4.0.json b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI_4.0.json index 65575d4eb0..5cbe7d3bf8 100644 --- a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to SSM/I, that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in November 1997. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. In contrast to infrared SST observations, microwave retrievals can be measured through most clouds, and are also insensitive to water vapor and aerosols. Remote Sensing Systems is the producer of these gridded TMI SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. Although the product designation is \"L2P_GRIDDED\" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its \"L2P_GRIDDED\" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity. In this dataset, both ascending (daytime) and descending (daytime) gridded orbital passes on packaged into the same daily file.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-WSAT_7.0.json b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-WSAT_7.0.json index 26b30d9787..85b231d635 100644 --- a/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-WSAT_7.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-WSAT_7.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-WSAT_7.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains sea surface temperature derived from observations made by the WindSat Polarimetric Radiometer developed by the Naval Research Laboratory (NRL) and launched on 6 January 2003 aboard the Department of Defense Coriolis satellite. This radiometer is well-calibrated and contains the lower frequency channels required for SST retrievals. The radiometer operates in 5 discrete bands: 6.8, 10.7, 18.7, 23.8 and 37.0 GHz. The 10.7, 18.7 and 37.0 GHz bands are fully polarimetric whereas the 6.8 and 23.8 GHz bands have only dual polarization. The feedhorns of each frequency band trace out different arcs along the bench, therefore the Earth Incidence Angles (EIA) are different for each frequency band. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat dataset is the only one available that uses both the fore and aft look directions which results in a wider swath and more complicated swath geometry visible in the provided maps. RSS produces these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. In the data processing chain, a first stage produces a near-real-time (NRT) dataset (identified with a \"rt\" within the file name) which is made as available as soon as possible. A later second stage produces a final dataset (identified by \"v7\" within the file name) that contains more data than the NRT version. In this dataset, both ascending (evening time) and descending (morning time) gridded orbital passes are packaged into the same daily file.Although the product designation is \"L2P_GRIDDED\" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its \"L2P_GRIDDED\" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-SEVIRI_IO_SST-OSISAF-L3C_1.0.json b/datasets/gov.noaa.nodc:GHRSST-SEVIRI_IO_SST-OSISAF-L3C_1.0.json index 3f46199fe6..23ef143a62 100644 --- a/datasets/gov.noaa.nodc:GHRSST-SEVIRI_IO_SST-OSISAF-L3C_1.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-SEVIRI_IO_SST-OSISAF-L3C_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-SEVIRI_IO_SST-OSISAF-L3C_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 135W-15W. Level-3C SST, in the NetCDF format recommended by Group for High Resolution Sea Surface Temperature (GHRSST), is identical to Level-2P GHRSST products, 3 refers to gridded products and C to the fact that hourly products result from compositing 15 minutes (MSG) or 30 minutes (GOES-E) data. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), OSI SAF is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating all 15-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-SEVIRI_SST-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-SEVIRI_SST-OSISAF-L3C_1.json index 38edd9e097..5328f398b5 100644 --- a/datasets/gov.noaa.nodc:GHRSST-SEVIRI_SST-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-SEVIRI_SST-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-SEVIRI_SST-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L2P_4.0.json b/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L2P_4.0.json index ed4ce9bd91..965dead5be 100644 --- a/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L2P_4.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L2P_4.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-TMI-REMSS-L2P_4.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems has produced a Version-4 TMI ocean SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L3U_7.1a.json b/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L3U_7.1a.json index eb43ead825..162a32ad62 100644 --- a/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L3U_7.1a.json +++ b/datasets/gov.noaa.nodc:GHRSST-TMI-REMSS-L3U_7.1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-TMI-REMSS-L3U_7.1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, sea surface temperature (SST) and surface wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial processing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems (REMSS) has produced a Version-7.1a TMI SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-UPA-L2P-ATS_NR_2P_1.5.json b/datasets/gov.noaa.nodc:GHRSST-UPA-L2P-ATS_NR_2P_1.5.json index 7431732f40..4ff3c9ee41 100644 --- a/datasets/gov.noaa.nodc:GHRSST-UPA-L2P-ATS_NR_2P_1.5.json +++ b/datasets/gov.noaa.nodc:GHRSST-UPA-L2P-ATS_NR_2P_1.5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-UPA-L2P-ATS_NR_2P_1.5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Launched in March 2002 by the European Space Agency (ESA), Envisat is the largest Earth Observation spacecraft ever built. It carries ten sophisticated optical and radar instruments to provide continuous observation and monitoring of the Earth's land, atmosphere, oceans and ice caps. The Advanced Along-Track Scanning Radiometer (AATSR) onboard the Envisat spacecraft is designed to meet the challenging task of monitoring and detecting the climate change signal of sea surface temperature (SST). It builds on the success of its predecessor instruments on the European Remote-Sensing Satellite (ERS)-1, and ERS-2 satellites, and will lead to a multi-decade record of precise and accurate global SST measurements, thereby making a valuable contribution to the long-term climate record. The exceptionally high radiometric accuracy and stability of AATSR data are achieved through a number of unique features. A comprehensive pre-launch calibration programme, combined with continuous in-flight calibration, ensures that the data are continually corrected for sensor drift and degradation. A \"dual-view\" algorithm offering improved atmospheric correction by applying two different atmospheric path lengths is used to derive the SSTskin observations. The accuracies achieved with this configuration are further enhanced by using low-noise infrared detectors, cooled to their optimum operating temperature by a pair of Stirling-cycle coolers. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies. See Llewellyn-Jones et al (2001) ESA bulletin 105, Feb 2001 for a full description. These AATSR L2P SST data are produced as part of the Group for High Resolution Sea Surface Temperature (GHRSST) Project according to the GHRSST-PP Data Processing Specification (GDS) version 1.5. This particular GHRSST AATSR dataset is produced by the UK Processing and Archiving (UPA) Centre Regional Data Assembly Facility (RDAC) for ESA since mid-2008. From the perspective of data format and quality it is identical to the L2P AATSR Medspiration (EUR) RDAC dataset produced earlier in the GHRSST Project.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-JPL-L2P_2016.2.json b/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-JPL-L2P_2016.2.json index d086c84698..7928602ca2 100644 --- a/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-JPL-L2P_2016.2.json +++ b/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-JPL-L2P_2016.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-VIIRS_NPP-JPL-L2P_2016.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first. JPSS is a multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). Suomi-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data. VIIRS has 22 spectral bands ranging from 412 nm to 12 micron. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375 m), and one day-night band (DNB). VIIRS uses on-board pixel aggregation to reduce the growth in size of pixels away from nadir. Two SST products are contained in these files. The first is a skin SST produced separately for day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST products from heritage and current NASA sensors. At night, a second triple channel SST product is generated using the 3.7 , 11 and 12 micron IR channels, identified as SST_triple. Due to the sun glint in the 3.7 micron SST_triple can only be used at night. VIIRS L2P SST data have a 750 spatial resolution at nadir and are stored in ~288 five minute granules per day. Full global coverage is obtained each day. The production of VIIRS NASA L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS were responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of VIIRS ocean products. JPL acquires VIIRS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. In mid-August, 2018, the RSMAS involvement in the VIIRS SST project ceased, and the subsequent fields are not maintained. The R2016.2 supersedes the previous v2016.0 datasets.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P_3.0.json b/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P_3.0.json index 190d593fe1..e9c86b0ef2 100644 --- a/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P_3.0.json +++ b/datasets/gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P_3.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P_3.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS, AVHRR and SeaWiFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-VIIRS_SST_NPP_NAR-OSISAF-L3C_1.json b/datasets/gov.noaa.nodc:GHRSST-VIIRS_SST_NPP_NAR-OSISAF-L3C_1.json index 1ae0b42043..53b9a27076 100644 --- a/datasets/gov.noaa.nodc:GHRSST-VIIRS_SST_NPP_NAR-OSISAF-L3C_1.json +++ b/datasets/gov.noaa.nodc:GHRSST-VIIRS_SST_NPP_NAR-OSISAF-L3C_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-VIIRS_SST_NPP_NAR-OSISAF-L3C_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the North Atlantic Region (NAR) based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.", "links": [ { diff --git a/datasets/gov.noaa.nodc:GHRSST-WindSat-REMSS-L3U_7.0.1a.json b/datasets/gov.noaa.nodc:GHRSST-WindSat-REMSS-L3U_7.0.1a.json index b268336cbb..f0b9bda61b 100644 --- a/datasets/gov.noaa.nodc:GHRSST-WindSat-REMSS-L3U_7.0.1a.json +++ b/datasets/gov.noaa.nodc:GHRSST-WindSat-REMSS-L3U_7.0.1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:GHRSST-WindSat-REMSS-L3U_7.0.1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7.0.1a\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v7.0.1a\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter \"a\" refers to the file in compliance with GHRSST format.", "links": [ { diff --git a/datasets/gov.noaa.nodc:HIMB-CRAMP_Not Applicable.json b/datasets/gov.noaa.nodc:HIMB-CRAMP_Not Applicable.json index fdd18de2dd..b4d7c394d9 100644 --- a/datasets/gov.noaa.nodc:HIMB-CRAMP_Not Applicable.json +++ b/datasets/gov.noaa.nodc:HIMB-CRAMP_Not Applicable.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gov.noaa.nodc:HIMB-CRAMP_Not Applicable", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection consists of Hawaii Coral Reef Assessment and Monitoring Program (CRAMP) surveys and include quantitative estimates of substrate type, rugosity, species type, and percent coverage. Digital still images from transects are also included. The data sets consist of image files (JPEG) of digital still photographs and spreadsheets (XLS with exported redundant CSV copies).\n\nCRAMP was created during 1997-98 by leading coral reef researchers, managers and educators in Hawaii. The initial task was to develop a statewide network consisting of over 30 long-term coral reef monitoring sites and an associated database. Upon completion of the monitoring network the focus was expanded to include rapid quantitative assessments and habitat mapping on a statewide spatial scale. Today the emphasis is on using these tools to understand the ecology of Hawaiian coral reefs in relation to other geographic areas.\n\nCRAMP study sites, including all areas of concern designated by the State of Hawaii Division of Aquatic Resources (DAR), were selected from throughout the State of Hawaii based on information provided by a wide spectrum of managers, scientists, and educators. These sites represent a full range of reef habitats subjected to various degrees of anthropogenic influences ranging from severely impacted to relatively pristine sites held in conservation status. The West Hawaii Aquarium Project has augmented CRAMP with surveys of fish at CRAMP sites on the west side of the Island of Hawaii.\n\nThe purpose is to understand the ecology of Hawaiian coral reefs in relation to other geographic areas and to monitor change at each given site. The CRAMP experimental design allows detection of changes that can be attributed to various factors such as: overuse (over-fishing, anchor damage, aquarium trade collection, etc.), sedimentation, nutrient loading, catastrophic natural events (storm wave impact, lava flows), coastal construction, urbanization, global warming (bleaching), introduced species, algal invasions, and fish and invertebrate diseases.", "links": [ { diff --git a/datasets/gpcc_precip_monthly_xdeg_995_1.json b/datasets/gpcc_precip_monthly_xdeg_995_1.json index 125abad4d9..8a890d52c5 100644 --- a/datasets/gpcc_precip_monthly_xdeg_995_1.json +++ b/datasets/gpcc_precip_monthly_xdeg_995_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpcc_precip_monthly_xdeg_995_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services. ", "links": [ { diff --git a/datasets/gpcp_precip_monthly_xdeg_1003_1.json b/datasets/gpcp_precip_monthly_xdeg_1003_1.json index 0308f51498..4028922a5d 100644 --- a/datasets/gpcp_precip_monthly_xdeg_1003_1.json +++ b/datasets/gpcp_precip_monthly_xdeg_1003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpcp_precip_monthly_xdeg_1003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Climatology Project (GPCP) Version 2 data set includes global, monthly precipitation rates and associated random errors (RMSE), and a monthly precipitation climatology derived as an average from all GPCP data sets from January 1979 to December 1999. The data are derived from measured gauge data and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data sets and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.", "links": [ { diff --git a/datasets/gpcp_precip_pentad_xdeg_1002_1.json b/datasets/gpcp_precip_pentad_xdeg_1002_1.json index 037d5b1144..7f625a6a40 100644 --- a/datasets/gpcp_precip_pentad_xdeg_1002_1.json +++ b/datasets/gpcp_precip_pentad_xdeg_1002_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpcp_precip_pentad_xdeg_1002_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Climatology Project (GPCP) pentad version 1 precipitation data set includes global precipitation rates for 5-day, or pentad, periods. The data sets are derived from measured rain gauge data and merged with satellite estimates of rainfall. This is a portion of the version 1 GPCP pentad data set and covers the ISLSCP II period from 1986 to 1995. The original precipitation rates at 2.5 degrees were re-gridded to a 1 degree spatial resolution by the ISLSCP II staff. ", "links": [ { diff --git a/datasets/gpm2dc3vp_1.json b/datasets/gpm2dc3vp_1.json index 670a462ffb..354766d0ab 100644 --- a/datasets/gpm2dc3vp_1.json +++ b/datasets/gpm2dc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) C3VP dataset consists of snowfall data collected by the Two-Dimensional Video Disdrometer (2DVD) during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The 2DVD measures precipitation characteristics such as size, shape, and velocity. During C3VP, there was one 2DVD instrument deployed at the Meteorological Service of Canada (MSC) operated Centre for Atmospheric Research Experiments (CARE) facility in Ontario, Canada. The data include diameter, volume, and fall speed information for individual snowflakes. The dataset files are available in ASCII text format from December 2, 2006 through April 9, 2007.", "links": [ { diff --git a/datasets/gpm2dgcpex_1.json b/datasets/gpm2dgcpex_1.json index 854198abfa..eccf89aa1a 100644 --- a/datasets/gpm2dgcpex_1.json +++ b/datasets/gpm2dgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) GCPEX dataset was collected by the Two-Dimensional Video Disdrometer (2DVD) data, which was collected during the GPM Cold-season Precipitation Experiment (GCPEx) held in Ontario, Canada. GCPeX occurred in Ontario, Canada during the winter season of 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow.Collected from six sites, the data contains daily ascii files with information on individual snowflakes and hydrometeors, and binary files preprocessed from raw camera data. Overall data dates range from 27 October 2011 through 27 February 2012 depending on the specific site.", "links": [ { diff --git a/datasets/gpm2dhymex_1.json b/datasets/gpm2dhymex_1.json index e2ff689504..17d817bc48 100644 --- a/datasets/gpm2dhymex_1.json +++ b/datasets/gpm2dhymex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dhymex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) HyMeX data was collected during the HYdrological cycle in Mediterranean EXperiment (HyMeX), which provided data on raindrop size and precipitation drop size distribution. The 2DVD measured the size of raindrops and also recorded two side view optical images of each raindrop. Used for in situ measurements of precipitation drop size distribution, this instrument recorded orthogonal image projections of raindrops as they crossed its sensing area and provided velocity and shape of individual raindrops.The HyMeX 2DVD data were collected in France and Italy from September 12, 2012 to November 12, 2012. The data are in ASCII format.", "links": [ { diff --git a/datasets/gpm2difld_1.json b/datasets/gpm2difld_1.json index 9ded7aac94..900642b1bc 100644 --- a/datasets/gpm2difld_1.json +++ b/datasets/gpm2difld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2difld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) IFloodS dataset was collected during the GPM Ground Validation Iowa Flood Studies (IFLoodS) field campaign in central-northeastern Iowa in 2013. This campaign aimed to improve satellite precipitation measurements for flood prediction by using ground measurements to improve satellite retrieval algorithms. The Two-Dimensional Video Disdrometer (2DVD), developed by Joanneum Research (Graz, Austria), measures raindrop characteristics such as size distribution, shape, and velocity. The 2DVD IFloodS data was collected from 6 sites from April 3, 2013 to June 18, 2013. Officially, the IFloodS campaign ran from May 1 to June 15, 2013 but the 2DVD instruments were installed and calibrated prior to the start, allowing for the wider period of record. The dataset contains daily ASCII files that include measurements for various precipitation parameters.", "links": [ { diff --git a/datasets/gpm2diphx_1.json b/datasets/gpm2diphx_1.json index 1e65e75479..f8ad531e05 100644 --- a/datasets/gpm2diphx_1.json +++ b/datasets/gpm2diphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2diphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) IPHEx dataset was collected during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) held in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. Collected from five sites, the data contains daily ASCII files with information on individual hydrometeors including the number of hydrometeors, raindrop size distribution, and particle concentration. Overall data dates range from April 23, 2014 through June 17, 2014; exact dates may vary per site.", "links": [ { diff --git a/datasets/gpm2dlpvex_1.json b/datasets/gpm2dlpvex_1.json index fa7adefbee..243c75e993 100644 --- a/datasets/gpm2dlpvex_1.json +++ b/datasets/gpm2dlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) LPVEx dataset was collected during the Light Precipitation Evaluation Experiment (LPVEx), which took place in September and October 2010 in the Gulf of Finland. The experiment aimed to characterize the ability of CloudSat, the Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR), and existing/planned passive microwave (PMW) sensors, such as the GPM microwave imager (GMI), to detect light rain and evaluate their estimates of rainfall intensity in high latitude, shallow freezing level environments.The experiment leveraged in situ microphysical property measurements, coordinated remote sensing observations, and cloud resolving model simulations of high latitude precipitation systems to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms. The campaign will use these measurements to better understand the process of light rainfall formation at high latitudes and augment the currently limited database of light rainfall microphysical properties that form the critical assumptions at the root of satellite retrieval algorithm.", "links": [ { diff --git a/datasets/gpm2dmc3e_1.json b/datasets/gpm2dmc3e_1.json index 5db8b9f864..eb62007837 100644 --- a/datasets/gpm2dmc3e_1.json +++ b/datasets/gpm2dmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) MC3E dataset was collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), which provides data on raindrop size and precipitation drop size distribution. The MC3E took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leveraged the unprecedented observing infrastructure currently available in the central United States, combined with an extensive sounding array, remote sensing and in situ aircraft observations, NASA GPM ground validation remote sensors, and new ARM instrumentation purchased with American Recovery and Reinvestment Act funding. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available.", "links": [ { diff --git a/datasets/gpm2dnsstc_1.json b/datasets/gpm2dnsstc_1.json index 45c8153782..57829aeccf 100644 --- a/datasets/gpm2dnsstc_1.json +++ b/datasets/gpm2dnsstc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dnsstc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) NSSTC dataset was collected by the Two-Dimensional Video Disdrometer (2DVD), which uses two high speed line scan cameras which provide continuous measurements of size distribution, shape and fall velocities of all precipitation particles and types. This 2DVD is the third generation 2D video disdrometer designed by Joanneum Research of Graz, Austria. This dataset provides rainfall data for the Global Precipitation Measurement (GPM) Mission Ground Validation Experiment collected at the National Space Science Technology Center (NSSTC) in Hunstville, AL. There may be occasional gaps in the data when the instrument is not resident at the NSSTC and is sent to participate in field campaigns.", "links": [ { diff --git a/datasets/gpm2dvdolyx_1.json b/datasets/gpm2dvdolyx_1.json index 48ef0a6da5..9cd3f11a89 100644 --- a/datasets/gpm2dvdolyx_1.json +++ b/datasets/gpm2dvdolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dvdolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) OLYMPEX dataset contains information on individual hydrometeors including their size distribution, terminal fall speed, and total concentration collected during the Global Precipitation Measurement mission (GPM) Ground Validation (GV) Olympic Mountains Experiment (OLYMPEX). The OLYMPEX field campaign took place between November 2015 and January 2016, with additional ground sampling continuing through February 2016, on the Olympic Peninsula in the Pacific Northwest of the United States. The purpose of the campaign was to provide ground-validation data for the measurements taken by instrumentation aboard the GPM Core Observatory satellite. The Two-Dimensional Video Disdrometer (2DVD) data were collected from four sites during the campaign. The dataset files are available from October 31, 2015 through January 17, 2016 (though the exact dates may vary per site) in ASCII-tsv format. ", "links": [ { diff --git a/datasets/gpm2dwff2_2.json b/datasets/gpm2dwff2_2.json index 6354197833..ba67bcf86a 100644 --- a/datasets/gpm2dwff2_2.json +++ b/datasets/gpm2dwff2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpm2dwff2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) WFF data were collected during the Global Precipitation Mission (GPM) Ground Validation (GV) campaign at the NASA Wallops Flight Facility (WFF) in Wallops Island, Virginia. These data consist of the size, equivalent diameter, fall speed, oblateness, cross-sectional area of raindrops, particle concentration, total number of drops, total drop concentration, liquid water content, rain rate, reflectivity, and rain event characteristics. The data are in ASCII format and available from July 24, 2013 through October 5, 2015.", "links": [ { diff --git a/datasets/gpmadmirgcpex_1.json b/datasets/gpmadmirgcpex_1.json index 1794fe300c..5486f35bdf 100644 --- a/datasets/gpmadmirgcpex_1.json +++ b/datasets/gpmadmirgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmadmirgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Advanced Microwave Radiometer Rain Identification (ADMIRARI) GCPEx dataset measures brightness temperature at three frequencies (10.7, 21.0 and 36.5 GHz) and at two polarized planes (H & V). The ADMIRIRI retrieval typically provides rain/cloud liquid water path (LWP) and integrated water vapor, and for low water content cases it provides the total LWP and integrated water vapor. The ADMIRARI is a scanning radiometer like its auxiliary active instruments, which include a Micro Rain Radar (MRR) and a cloud lidar, which provide reflectivity profiles and cloud base altitude at the same scanning angle as the ADMIRIRI. This data was collected during the GPM Cold-season Precipitation Experiment (GCPEx) located in Ontario, Canada, January 14, 2012 through February 29, 2012. Reference: http://www2.meteo.uni-bonn.de/admirari.", "links": [ { diff --git a/datasets/gpmahdmetolyx_1.json b/datasets/gpmahdmetolyx_1.json index 2a18022236..875bfaf8a0 100644 --- a/datasets/gpmahdmetolyx_1.json +++ b/datasets/gpmahdmetolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmahdmetolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Albert Head (AHD) Ground Meteorological Station (MET) OLYMPEX dataset consists of precipitation rate, reflectivity, pressure, temperature, relative humidity, wind speed, and wind direction data which were measured by the MET station instruments operated by the Environment and Climate Change Canada (ECCC) and located in Albert Head, B.C., Canada. The MET station was comprised of a Vaisala FD12P Visibility Sensor, an OTT Parsivel2 Present Weather Sensor, an OTT Pluvio2 Precipitation Gauge, and a Vaisala WXT520 Weather Transmitter. The MET Station was also co-located with a CAX-1 radar to compare measurements from the MET station with the radar scans. These MET Station data files are available from November 13, 2015 through January 17, 2016 in ASCII-CSV and XML formats, with daily browse images of precipitation rate plots in PNG format.", "links": [ { diff --git a/datasets/gpmampriphx2_2.json b/datasets/gpmampriphx2_2.json index d3c6cbeff2..a72ce76db9 100644 --- a/datasets/gpmampriphx2_2.json +++ b/datasets/gpmampriphx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmampriphx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Advanced Microwave Precipitation Radiometer (AMPR) IPHEx dataset was acquired by the AMPR instrument flown aboard the high altitude ER-2 aircraft during the IPHEx field campaign in North Carolina from May 1, 2014 through June 14, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. These files include the Level 2B calibrated and georeferenced brightness temperature for the four AMPR-observed frequencies (10, 19, 37, 85 GHz). These data are archived in a netCDF-4 format that contains the calibrated brightness temperatures in addition to ER-2 aircraft navigation and instrument scene georectification variables. Corresponding browse imagery are also available in JPG format. A set of Python software has been developed for reading, plotting, and providing some additional analysis capabilities. ", "links": [ { diff --git a/datasets/gpmamprmc3e_1.json b/datasets/gpmamprmc3e_1.json index 799b183f47..237d40f6a7 100644 --- a/datasets/gpmamprmc3e_1.json +++ b/datasets/gpmamprmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmamprmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Advanced Microwave Precipitaiton Radiometer (AMPR) MC3E dataset was collected by the Advanced Microwave Precipitation Radiometer (AMPR) instrument, which played a key role in the Midlatitude Continental Convective Clouds Experiment (MC3E). The AMPR remotely sensed passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which provided multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data were collected at a combination of four microwave frequencies (10.7, 19.35, 37.1, and 85.5 GHz) with two orientations each (Vpol-to-Hpol and Hpol-to-Vpol), which were complimentary to current aircraft and satellite instrumentation. These frequencies are best suited to the study of rain systems, but were also useful to studies of other atmospheric, oceanic, and land surface processes.", "links": [ { diff --git a/datasets/gpmamprolyx_1.json b/datasets/gpmamprolyx_1.json index e95a879383..c7d61ee396 100644 --- a/datasets/gpmamprolyx_1.json +++ b/datasets/gpmamprolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmamprolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Advanced Microwave Precipitation Radiometer (AMPR) OLYMPEX dataset was collected by the AMPR instrument flown on the high altitude ER-2 research aircraft from November 9 - December 15, 2015, during the Olympic Mountains Experiment (OLYMPEX) field campaign conducted at Washington State\u2019s Olympic Peninsula. AMPR is an airborne passive microwave radiometer from which cloud, precipitation, water vapor, wind speed and wind direction can be obtained using advanced algorithms with the 10.7, 19.35, 37.1, and 85.5 GHz microwave frequency brightness temperatures measured by AMPR. The primary goal of OLYMPEX was to validate rain and snow measurements in midlatitude frontal systems moving from ocean to coast to mountains. AMPR data at the Global Hydrology Resource Center (GHRC) DAAC include netCDF format data files of brightness temperature and PNG browse files of Quality Control Flags and Brightness Temperatures.", "links": [ { diff --git a/datasets/gpmapr2gcpex_1.json b/datasets/gpmapr2gcpex_1.json index 8b2fd74c08..fdc019b7d4 100644 --- a/datasets/gpmapr2gcpex_1.json +++ b/datasets/gpmapr2gcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmapr2gcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Airborne Second Generation Precipitation Radar (APR-2) GCPEx dataset was collected during the GPM Cold-season Precipitation Experiment (GCPEx), which occurred in Ontario, Canada during the winter season of 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The Second Generation Airborne Precipitation Radar (APR-2) is a dual-frequency (13 GHz and 35 GHz), Doppler, dual-polarization radar system. It has a downward looking antenna that performs cross track scans, covering a swath that is +/- 25 degrees to each side of the aircraft path. Additional features include: simultaneous dual-frequency, matched beam operation at 13.4 and 35.6 GHz (same as GPM Dual-Frequency Precipitation Radar), simultaneous measurement of both like- and cross-polarized signals at both frequencies, Doppler operation, and real-time pulse compression (calibrated reflectivity data can be produced for large areas in the field during flight, if necessary). The APR-2 flew aboard the NASA DC-8 for the GPM Cold-season Precipitation Experiment (GCPEx) from 11 January to 25 February, 2012.", "links": [ { diff --git a/datasets/gpmapr3olyx2_2.json b/datasets/gpmapr3olyx2_2.json index ce4ae6ce95..50d1428487 100644 --- a/datasets/gpmapr3olyx2_2.json +++ b/datasets/gpmapr3olyx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmapr3olyx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Airborne Precipitation Radar 3rd Generation (APR-3) OLYMPEX V2 dataset was collected from November 12, 2015 to December 19, 2015 during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. This dataset is version -2 (V2) of the APR-3, an enhanced and upgraded instrument derived from the APR-2 used in previous field campaigns. APR-3 has the addition of W-band measurement capability, and scans cross-track from +/- 25\u00b0 to the right and left of nadir. Ku-band, Ka-band, and W-band frequency Doppler measurements are made by APR-3 from the DC-8 aircraft at 10 km altitude during OLYMPEX. The APR-3 dataset files are in HDF-5 format with JPG format browse images. This dataset contains radar reflectivity, Doppler velocity for all bands, linear depolarization ratio at Ku-band, and normalized radar cross section measurements at Ka and Ku-bands. ", "links": [ { diff --git a/datasets/gpmapuicepop_1.json b/datasets/gpmapuicepop_1.json index a6f1805317..d9c41e6024 100644 --- a/datasets/gpmapuicepop_1.json +++ b/datasets/gpmapuicepop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmapuicepop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) ICE POP dataset was collected during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE POP) field campaign in South Korea. The two major objectives of ICE POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to Global Precipitation Measurements mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format from October 31, 2015 through July 1, 2018. It should be noted that this dataset extends prior to the field campaign.", "links": [ { diff --git a/datasets/gpmapuolyx_1.json b/datasets/gpmapuolyx_1.json index e54fdd8652..22d612a53a 100644 --- a/datasets/gpmapuolyx_1.json +++ b/datasets/gpmapuolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmapuolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) OLYMPEX dataset was collected during the OLYMPEX field campaign held at Washington's Olympic Peninsula during the intense observation period of November 2015 to the end of January 2016. The dataset consists of data collected by 16 APUs. The APU is an optical laser-disdrometer based on single particle extinction that measures particle size and fall velocity. It consists of the Parsivel2 developed by OTT in Germany and supporting hardware developed by University of Alabama. This APU dataset provides precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format. ", "links": [ { diff --git a/datasets/gpmapuwff_1.json b/datasets/gpmapuwff_1.json index 5cec322a67..cdf9b5a1f7 100644 --- a/datasets/gpmapuwff_1.json +++ b/datasets/gpmapuwff_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmapuwff_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) Wallops Flight Facility (WFF) dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration obtained from six Autonomous Parsivel Units (APUs) positioned at the Wallops Flight Facility (WFF) in support of the Global Precipitation Mission (GPM). The APU is an optical laser-disdrometer based on single particle extinction that measures particle size and fall velocity. It consists of the Parsivel2 developed by OTT in Germany and supporting hardware developed by University of Alabama. Data are available in ASCII format for the period of May 6, 2013 through October 9, 2014.\n", "links": [ { diff --git a/datasets/gpmarsifld_1.json b/datasets/gpmarsifld_1.json index 2e044b3a10..f939bae0ab 100644 --- a/datasets/gpmarsifld_1.json +++ b/datasets/gpmarsifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmarsifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation United States Department of Agriculture (USDA) Agricultural Research Service (ARS) Soil Moisture IFloodS dataset was collected during the Iowa Flood Studies (IFloodS) ground measurement campaign from April 17, 2013 to June 4, 2013. The goals of the campaign were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. A total of 15 stations were deployed near the South Fork River in North Central Iowa. The soil moisture probes measure hourly instantaneous measurements of the real dielectric permittivity, soil temperature, bulk electrical conductivity, and volumetric soil moisture. This dataset also consists of precipitation amount, air temperature, relative humidity, vapor pressure, wind speed, wind direction, and solar radiation measurements. The data files are available in ASCII-csv and Excel file formats.", "links": [ { diff --git a/datasets/gpmasinaiphx_1.json b/datasets/gpmasinaiphx_1.json index 8d02d04f28..8f654f72eb 100644 --- a/datasets/gpmasinaiphx_1.json +++ b/datasets/gpmasinaiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmasinaiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Total Sky Imager IPHEx dataset was gathered during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina from May 9, 2014 through June 14, 2014. The dataset includes data from the total sky imager instrument which is part of the NASA Goddard Space Flight Center (GSFC) ACHIEVE ground-based mobile laboratory. It is an automatic, full-color sky imager system providing real-time, full color digital images of daytime sky conditions. Data files are available in the JPEG image format.", "links": [ { diff --git a/datasets/gpmasoolyx_1.json b/datasets/gpmasoolyx_1.json index 940ea4ebbb..acb6ac0eb2 100644 --- a/datasets/gpmasoolyx_1.json +++ b/datasets/gpmasoolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmasoolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Airborne Snow Observatory (ASO) OLYMPEX dataset consists of snow depth, bare earth surface, land surface classification and a Red, Green, Blue (RGB) composite image, provided at 3 m spatial resolution during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. These data were collected by a Riegl Q1560 scanning LiDAR and an ITRES CASI-1500 imaging spectrometer , both part of the NASA Airborne Snow Observatory (ASO), during two separate periods, February 8-9, 2016 and March 29-30, 2016. A previous September 2014 flight was used to obtain no-snow measurements used for deriving snow depth. The data are provided in GeoTIFF format. ", "links": [ { diff --git a/datasets/gpmavapsolyx_1.json b/datasets/gpmavapsolyx_1.json index 8278438844..053ec92281 100644 --- a/datasets/gpmavapsolyx_1.json +++ b/datasets/gpmavapsolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmavapsolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Advanced Vertical Atmospheric Profiling System (AVAPS) OLYMPEX dataset contains dropsonde vertical profiles of atmospheric pressure, air temperature, dew point temperature, relative humidity, wind direction and magnitude, and sensor location obtained during the Olympic Mountains Experiment (OLYMPEX). The AVAPS dropsondes were released during specific NASA DC-8 aircraft flights between November 12, 2015 and December 19, 2015. A total of 53 standard research dropsondes were launched in the Pacific ocean off the coast of Washington state collecting atmospheric profile observations. The AVAPS datasets are available in ASCII-eol text format.", "links": [ { diff --git a/datasets/gpmcax1cfolyx_1.json b/datasets/gpmcax1cfolyx_1.json index 316846e9e7..0bf96c8752 100644 --- a/datasets/gpmcax1cfolyx_1.json +++ b/datasets/gpmcax1cfolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcax1cfolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CAX1 Radar CFradial format OLYMPEX dataset consists of radar parameters, such as Radar reflectivity, Doppler velocity, Doppler width, Differential reflectivity, and Signal quality index, provided on a 0.4 to 1.0 km spatial resolution within the OLYMPEX field campaign study region in the state of Washington. These data were obtained for the GPM Ground Validation OLYMPEX field campaign by the SELEX Meteor 60DX10 Compact Weather (CAX1) radar. The CAX1 radar was located at the southern tip of Vancouver Island on the Canadian Forces Base (CFB) Esquimalt Albert Head (AHD) military training area. The CAX1 radar was operated by Environment and Climate Change Canada to support the OLYMPEX field campaign. These data are available in Cfradial netCDF-4 format from November 14, 2015 through April 1, 2016.", "links": [ { diff --git a/datasets/gpmcax1odolyx_1.json b/datasets/gpmcax1odolyx_1.json index 80538d4552..650eed2db2 100644 --- a/datasets/gpmcax1odolyx_1.json +++ b/datasets/gpmcax1odolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcax1odolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CAX1 Radar ODIM format OLYMPEX dataset consists of radar parameters, such as Radar reflectivity, Doppler velocity, Doppler width, Differential reflectivity, Differential phase, Differential phase shift, Correlation coefficient, and Signal Quality Index, provided on a 0.4 to 1.0 km spatial resolution within the OLYMPEX field campaign study region in state of Washington. These data were obtained for the GPM Ground Validation OLYMPEX field campaign by the SELEX Meteor 60DX10 Compact Weather (CAX1) radar. The CAX1 radar was located at the southern tip of Vancouver Island on the Canadian Forces Base (CFB) Esquimalt Albert Head (AHD) military training area. These data are available in ODIM HDF-5 format, and have corresponding browse imagery in PNG format, from November 14, 2015 through April 1, 2016.", "links": [ { diff --git a/datasets/gpmcax1rbolyx_1.json b/datasets/gpmcax1rbolyx_1.json index 43991d58cb..95e77fbff4 100644 --- a/datasets/gpmcax1rbolyx_1.json +++ b/datasets/gpmcax1rbolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcax1rbolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CAX1 Radar RB5 format OLYMPEX dataset consists of radar parameters, such as radar reflectivity, Doppler velocity, Doppler width, Differential reflectivity, and signal quality index, provided on a 0.4 to 1.0 km spatial resolution within the OLYMPEX field campaign study region in the state of Washington. These data were obtained for the GPM Ground Validation OLYMPEX field campaign by the SELEX Meteor 60DX10 Compact Weather (CAX1) radar. The CAX1 radar was located at the southern tip of Vancouver Island on the Canadian Forces Base (CFB) Esquimalt Albert Head (AHD) military training area. The CAX1 radar was operated by Environment and Climate Change Canada (ECCC) to support the OLYMPEX field campaign. These data are available in RB5 binary and PNG formats from November 13, 2015 to April 20, 2016. ", "links": [ { diff --git a/datasets/gpmceilgcpex_1.json b/datasets/gpmceilgcpex_1.json index e6214eb0a4..ad0ffed09c 100644 --- a/datasets/gpmceilgcpex_1.json +++ b/datasets/gpmceilgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmceilgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) VAISALA Ceilometer GCPEx dataset was collected during the GPM Cold-season Precipitation Experiment (GCPEx) in Huronia, Canada from January 15, 2012 through March 1, 2012. The GPM Cold-season Precipitation Experiment (GCPEx) occurred in Ontario, Canada during the winter season of 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The CT25K ceilometer uses pulsed diode laser LIDAR technology to derive backscatter profiles, cloud heights and vertical visibilities. It is also able to detect 3 cloud layers simultaneously.", "links": [ { diff --git a/datasets/gpmceiliphx_1.json b/datasets/gpmceiliphx_1.json index aa24a844e8..1e8d3b58ba 100644 --- a/datasets/gpmceiliphx_1.json +++ b/datasets/gpmceiliphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmceiliphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Vaisala Ceilometer IPHEx dataset consists of vertical profiles of attenuated backscatter and cloud base and boundary layer height data gathered during the Global Precipitation Mission (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region during an intense study period from May 1 through June 15, 2014. This dataset includes data from the Vaisala Ceilometer CL51 which is part of the NASA Goddard Space Flight Center (GSFC) Aerosol-Cloud-Humidity Interactions Exploring and Validating Enterprise (ACHIEVE) ground-based mobile laboratory. It measures vertical profiles of aerosol backscatter and performs a retrieval of cloud base detection and boundary layer structure. The data files are available from May 6 through June 16, 2014 in netCDF-3 format.", "links": [ { diff --git a/datasets/gpmchillmc3e_1.json b/datasets/gpmchillmc3e_1.json index 534d17cb2c..5d81456ee7 100644 --- a/datasets/gpmchillmc3e_1.json +++ b/datasets/gpmchillmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmchillmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CHILL Radar MC3E dataset was collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), which was held in Oklahoma were collected while the NASA ER-2 aircraft conducted a series of four legs along the 090 and 120 degree CHILL azimuths on May 24, 2011. Dual linear polarization variables as well as Doppler velocity, radial velocity, and normalized coherent power are contained in this dataset. In an effort to expand the MC3E sampling to a wider geographical area, the NASA ER-2 aircraft was directed to Northeastern Colorado while widespread rain was in progress on May 24, 2011. The aircraft flew a series of pre-defined ground tracks that coincided with radials from the CSU-CHILL radar. This aided in keeping the aircraft in the plane of a series of RHI scans done by CSU-CHILL. The single polarization CSU-Pawnee radar maintained volume coverage of the echo system while the radial flight legs were in progress. During aircraft course reversals at the ends of the radial legs, the CHILL and Pawnee radars started volume scans in synchronization to support dual Doppler wind syntheses. The Pawnee radar data are available as a seperate dataset.", "links": [ { diff --git a/datasets/gpmcilpvex_1.json b/datasets/gpmcilpvex_1.json index 813a082217..12fa6641bb 100644 --- a/datasets/gpmcilpvex_1.json +++ b/datasets/gpmcilpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcilpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Cloud Spectrometer and Impactor (CIP) LPVEx dataset provides particle size spectra for the Global Precipitation Measurement (GPM) Misson Ground Validation Experiment. Data was collected by the Cloud spectrometer and impactor (CIP) and 2D-S (2-dimensional stereo probe) aboard the University of Wyoming King Air flown in Finland during the Light Precipitation Validation Experiment (LPVEx) from August to October 2010. Lat, lon, altitude, pressure, and temperature are provided with the total concentration of particles with diameter greater than 100 microns.", "links": [ { diff --git a/datasets/gpmcitvidiphx_1.json b/datasets/gpmcitvidiphx_1.json index ea20f54b52..3a5a2a2a2c 100644 --- a/datasets/gpmcitvidiphx_1.json +++ b/datasets/gpmcitvidiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcitvidiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Citation Videos IPHEx dataset was collected during the Global Precipitation Measurement (GPM) mission Integrated Precipitation and Hydrology Experiment (IPHEx) in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. These videos show flights on June 6, 2014 and June 8, 2014. The dataset contains MP4 digital video files and videos have been sped up 12.5 times the original speed and are broken into smaller files of about 3.5 minutes each (covering 45 minutes of actual flight time).", "links": [ { diff --git a/datasets/gpmcmgcpex2_2.json b/datasets/gpmcmgcpex2_2.json index 87d351b39b..898246f56b 100644 --- a/datasets/gpmcmgcpex2_2.json +++ b/datasets/gpmcmgcpex2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmgcpex2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Cloud Microphysics GCPEx dataset includes instrument measurements of cloud microphysics, state of atmosphere parameters. bulk aerosols, three-dimensional winds, and turbulence. These measurements were taken by the University of North Dakota's (UND) Cessna Citation aircraft, an in situ platform used during the GCPEx campaign. The GPM Ground Validation UND Citation Cloud Microphysics GCPEx data are stored as a separate file for each flight, including both a primary file containing direct and derived parameters, and raw data for each cloud instrument aboard the Citation. This dataset contains measurements collected across 12 data missions from January 19, 2012 through February 24, 2012. Navigation files for this dataset were updated July 2015.", "links": [ { diff --git a/datasets/gpmcmiphx_1.json b/datasets/gpmcmiphx_1.json index b8a6846ce5..10d375aab6 100644 --- a/datasets/gpmcmiphx_1.json +++ b/datasets/gpmcmiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of North Dakota (UND) Cessna Citation aircraft, an in-situ platform for the IPHEx campaign, carried a suite of instruments for measurements of cloud microphysics, state of the atmosphere parameters, aerosols, three-dimensional winds and turbulence. The data are stored as a separate file for each flight, with a primary (*.iphex_ file containing both direct and derived parameters. Raw data files for each cloud instrument are also archived for investigators who wish to use their own processing software. Citation flight navigation data is also included in this dataset.", "links": [ { diff --git a/datasets/gpmcmmc3e_1.json b/datasets/gpmcmmc3e_1.json index 978f6776db..389647300b 100644 --- a/datasets/gpmcmmc3e_1.json +++ b/datasets/gpmcmmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Cloud Microphysics MC3E dataset was collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The University of North Dakota (UND) Cessna Citation aircraft, an in-situ platform for the MC3E campaign, carried a suite of instruments for measurements of cloud microphysics, state of the atmosphere parameters, aerosols, three-dimensional winds and turbulence. The Citation flew 15 data missions, which totaled 42.6 flight hours. The data are stored as a separate file for each flight. Raw data files for each cloud instrument are also archived to allow investigators to use their own processing software. Particle size spectra for the imaging probes were processed by NCAR and are archived and distributed as a separate dataset (Particle probes).", "links": [ { diff --git a/datasets/gpmcmolyx_1.json b/datasets/gpmcmolyx_1.json index c84fe06f8b..066cbac99f 100644 --- a/datasets/gpmcmolyx_1.json +++ b/datasets/gpmcmolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Cloud Microphysics OLYMPEX dataset includes measurements of cloud microphysics, state of the atmosphere parameters, three-dimensional winds, and turbulence. These measurements were taken during the OLYMPEX campaign by the University of North Dakota\u2019s (UND) Cessna Citation II aircraft over a series of 20 flights totaling roughly 60 flight hours. The UND Citation Cloud Microphysics data are stored as separate files for each flight, with a primary (*.olympex) file containing both direct and derived parameters. Raw data files for each instrument are also archived for investigators who wish to use their own processing software. Data are available from flights that occurred from November 12, 2015 through December 19, 2015 in ASCII, ASCII-csv, and binary formats, while browse images are available in PNG format.", "links": [ { diff --git a/datasets/gpmcmorphnifld_1.json b/datasets/gpmcmorphnifld_1.json index 5ee34e61b5..ed57e0ab15 100644 --- a/datasets/gpmcmorphnifld_1.json +++ b/datasets/gpmcmorphnifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmorphnifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA CPC Morphing Technique (CMORPH) IFloodS dataset consists of global precipitation analyses data produced by the NOAA Climate Prediction Center (CPC). The Iowa Flood Studies (IFloodS) campaign was a ground measurement campaign that took place in eastern Iowa from May 1 to June 15, 2013. The goals of the campaign were to collect detailed measurements of precipitation at the Earth's\r\nsurface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. The CPC morphing technique uses precipitation estimates from low orbiter satellite microwave observations to produce global precipitation analyses at a high temporal and spatial resolution. Data has been selected for the Iowa Flood Studies (IFloodS) field campaign which took place from April 1, 2013 to June 30, 2013. The dataset includes both the near real-time raw data and bias corrected data from NOAA in binary and netCDF format.", "links": [ { diff --git a/datasets/gpmcmorphniphx_1.json b/datasets/gpmcmorphniphx_1.json index ebea8da280..3b1f7174c7 100644 --- a/datasets/gpmcmorphniphx_1.json +++ b/datasets/gpmcmorphniphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcmorphniphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA CPC Morphing Technique (CMORPH) IPHEx dataset consists of global precipitation analyses data produced by the NOAA Climate Prediction Center (CPC) during the Global Precipitation Mission (GPM) Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The CPC morphing technique uses precipitation estimates from low orbiter satellite microwave observations to produce global precipitation analyses at a high temporal and spatial resolution. CMORPH data has been selected from May 1, 2014 through June 14, 2014, during the IPHEx field campaign. These data files are available in raw binary and netCDF-4 file format.", "links": [ { diff --git a/datasets/gpmcosmirgcpex_1.json b/datasets/gpmcosmirgcpex_1.json index 5b78c8188d..97e7f72487 100644 --- a/datasets/gpmcosmirgcpex_1.json +++ b/datasets/gpmcosmirgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcosmirgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Conical Scanning Millimeter-wave Imaging Radiometer (COSMIR) GCPEx dataset used the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR), which was utilized for the GPM Cold-season Precipitation Experiment (GCPEx) as an airborne high-frequency simulator of the GPM Microwave Imager (GMI), which was launched in 2014. The CoSMIR was modified with a new scan mode to acquire both conical and cross-track scan data simultaneously in a given flight satisfying the requirements of the Precipitation Measurement Mission (PMM) algorithm development team. The dataset provides well-calibrated radiometric data from 9 channels between 50-183 GHz with the accuracy on the order of +-1K. All channels besides the 89 and 165.5 GHz are horizontally polarized.", "links": [ { diff --git a/datasets/gpmcosmiriphx_1.json b/datasets/gpmcosmiriphx_1.json index bef7daab17..b0ab22fcd4 100644 --- a/datasets/gpmcosmiriphx_1.json +++ b/datasets/gpmcosmiriphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcosmiriphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IPHEx dataset consists of brightness temperatures from 9 channels as measured by the CoSMIR instrument onboard the NASA ER-2 aircraft during the Global Precipitation Mission (GPM) Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. CoSMIR is a conical and cross-track scanning radiometer with frequencies centered at 50.3, 52.8, 89.0, 165.5, 183.31 \u00b11, 183.31\u00b13, and 183.31\u00b17 GHz. Data files are available from May 7, 2014 through June 14, 2014 in ASCII format, with browse images available in the postscript format. ", "links": [ { diff --git a/datasets/gpmcosmirmc3e_1.json b/datasets/gpmcosmirmc3e_1.json index ff18a907db..acc66810c1 100644 --- a/datasets/gpmcosmirmc3e_1.json +++ b/datasets/gpmcosmirmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcosmirmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Conical Scanning Millimeter-wave Imaging Radiometer (COSMIR) MC3E dataset used the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR), which was utilized during the Midlatitude Continental Convective Clouds Experiment (MC3E) served as an airborne high-frequency simulator of the GPM Microwave Imager (GMI), which launched in 2014. The CoSMIR was modified with a new scan mode to acquire both conical and cross-track scan data simultaneously in a given flight satisfying the requirements of the Precipitation Measurement Mission (PMM) algorithm development team. The dataset provides well-calibrated radiometric data from 9 channels between 50-183 GHz with the accuracy on the order of +-1K. All channels besides the 89 and 165.5 GHz are horizontally polarized.", "links": [ { diff --git a/datasets/gpmcosmirolyx_1.json b/datasets/gpmcosmirolyx_1.json index f2e44a536d..f5bbc67eae 100644 --- a/datasets/gpmcosmirolyx_1.json +++ b/datasets/gpmcosmirolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcosmirolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) OLYMPEX dataset consists of brightness temperatures from 9 channels as measured by CoSMIR when flown on the NASA DC-8 aircraft during the Global Precipitation Mission (GPM) Olympic Mountains Experiment (OLYMPEX) campaign. CoSMIR is a conical and cross-track scanning radiometer with frequencies centered at 50.3, 52.8, 89.0, 165.5, 183.31+/-1, 183.31+/-3, and 183.31+/-7 GHz. Data files are available from November 5, 2015 thru December 19, 2015 in HDF-5 format, with browse imagery files in PNG format containing brightness temperature time series plots.", "links": [ { diff --git a/datasets/gpmcplolyx_1.json b/datasets/gpmcplolyx_1.json index 66e0c53cab..8740ac5ef8 100644 --- a/datasets/gpmcplolyx_1.json +++ b/datasets/gpmcplolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcplolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Cloud Physics Lidar (CPL) OLYMPEX dataset consists of extinction profiles, layer optical depth, layer lidar ratio, and aircraft parameter measurements measured by the CPL flown on the NASA ER-2 aircraft during the Global Precipitation Mission (GPM) Olympic Mountains Experiment (OLYMPEX) campaign. The CPL instrument is a multi-wavelength backscatter lidar that provides multi-wavelength measurements of cirrus and aerosols with high temporal and spatial. Data files are available from November 9, 2015 through December 15, 2015 in HDF-5 format with layer information in ASCII text files. Browse imagery files in GIF format contain optical depth and flight path images.", "links": [ { diff --git a/datasets/gpmcrsiphx_1.json b/datasets/gpmcrsiphx_1.json index a16d81106e..c07155e082 100644 --- a/datasets/gpmcrsiphx_1.json +++ b/datasets/gpmcrsiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcrsiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Cloud Radar System (CRS) IPHEx data were collected in support of the Global Precipitation Measurement (GPM) mission Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina, with an intense study period occurring from May 1, 2014 through June 15, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The ER-2 aircraft flew during the IPHEx field campaign to aid in GPM validation. The science instruments, including the CRS, onboard the aircraft acted as a proxy for GPM satellite instruments. The CRS provided high-resolution profiles of reflectivity and Doppler velocity in clouds at aircraft nadir along the flight track. The CRS data are available from May 3, 2014 through June 12, 2014 and files for this dataset are available in netCDF-3 format. ", "links": [ { diff --git a/datasets/gpmcrsolyx_1.json b/datasets/gpmcrsolyx_1.json index e3ccfcd692..413e6ad6d5 100644 --- a/datasets/gpmcrsolyx_1.json +++ b/datasets/gpmcrsolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcrsolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Cloud Radar System (CRS) OLYMPEX dataset provides radar reflectivity and Doppler velocity data collected during the Olympic Mountain Experiment (OLYMPEX). This dataset is used to estimate cloud droplet distribution for the storms monitored during the field campaign. The CRS instrument is a 94GHz W-band Doppler radar with a 3mm wavelength. CRS can be deployed as both an airborne instrument onboard NASA\u2019s high-altitude science aircraft, the Earth Resource 2 (ER-2), as well as a ground based radar system. Only the airborne mode was used during OLYMPEX. In addition to reflectivity and Doppler velocity, the data files include aircraft flight information. The CRS was flown on 10 different days between November 10, 2015 and December 10, 2015. Each data file contains one hour of flight measurements during flight in UTC time. Files for this dataset are in netCDF-3 format and readily accessible without the need of specialized software.", "links": [ { diff --git a/datasets/gpmcxsiolyx_1.json b/datasets/gpmcxsiolyx_1.json index 8c4d956603..a5dd0d557c 100644 --- a/datasets/gpmcxsiolyx_1.json +++ b/datasets/gpmcxsiolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmcxsiolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CXSI Radar Imagery OLYMPEX dataset contains radar reflectivity and precipitation rate images obtained from Environment and Climate Change Canada (ECCC)\u2019s weather radar network during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX), which was conducted to validate rain and snow measurements in mid latitude frontal systems as they move from ocean to coast to mountains and to determine how remotely sensed measurements of precipitation by GPM can be applied to a range of hydrologic, weather forecasting, and climate data. These data are available as GIF images for November 19, 2015 through December 31, 2015.", "links": [ { diff --git a/datasets/gpmd3rgcpex_1.json b/datasets/gpmd3rgcpex_1.json index bc05ee4951..0518d95261 100644 --- a/datasets/gpmd3rgcpex_1.json +++ b/datasets/gpmd3rgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmd3rgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) GCPEx and IFloodS data sets contain radar reflectivity and doppler velocity measurements. The D3R was developed by a government-industry-academic consortium with funding from NASA's Global Precipitation Measurement (GPM) Project. It operates at the ku (13.91 GHz \u00b1 25 MHz) and ku (35.56 GHz \u00b1 25 MHz) frequencies covering a fixed range from 450 m to 39.75 km. The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) GCPEx dataset is available in netCDF format. Browse images are also available in .png format.", "links": [ { diff --git a/datasets/gpmd3ricepop_1.json b/datasets/gpmd3ricepop_1.json index ffb064bee2..bbf9874166 100644 --- a/datasets/gpmd3ricepop_1.json +++ b/datasets/gpmd3ricepop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmd3ricepop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) ICE POP dataset includes reflectivity, differential reflectivity, copolar correlation coefficient, differential propagation phase, radial velocity, and spectrum width data collected by the Dual-frequency Dual-polarized Doppler Radar (D3R) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. The D3R was developed by a government-industry-academic consortium with funding from NASA's GPM Project. It operates at the ku (13.91 GHz \u00b1 25 MHz) and ka (35.56 GHz \u00b1 25 MHz) frequencies covering a fixed range from 450 m to 39.75 km. The D3R dataset files are available from November 1, 2017 through March 17, 2018 in netCDF-4 format.", "links": [ { diff --git a/datasets/gpmd3rifld_1.json b/datasets/gpmd3rifld_1.json index 49ed8272c4..d631896dea 100644 --- a/datasets/gpmd3rifld_1.json +++ b/datasets/gpmd3rifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmd3rifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) IFloodS dataset contains radar reflectivity and doppler velocity measurements from the Iowa Flood Studies (IFloodS) campaign. This campaign aimed to improve satellite precipitation measurements for flood prediction by using ground measurements to improve satellite retrieval algorithms. The D3R was developed by a government-industry-academic consortium with funding from NASA's Global Precipitation Measurement (GPM) Project. It operates at the ku (13.91 GHz \u00b1 25 MHz) and ka (35.56 GHz \u00b1 25 MHz) frequencies covering a fixed range from 450 m to 39.75 km. The D3R IFloodS dataset is available from May 9, 2013 through June 13, 2013 in netCDF-3 format with corresponding browse imagery available in PNG format.", "links": [ { diff --git a/datasets/gpmd3riphx_1.json b/datasets/gpmd3riphx_1.json index 88962748ed..d10dc43141 100644 --- a/datasets/gpmd3riphx_1.json +++ b/datasets/gpmd3riphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmd3riphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) IPHEx data set contains radar reflectivity and doppler velocity measurements. The D3R was developed by a government-industry-academic consortium with funding from NASA's Global Precipitation Measurement (GPM) Project. It operates at the ku (13.91 GHz \u00b1 25 MHz) and ku (35.56 GHz \u00b1 25 MHz) frequencies covering a fixed range from 450 m to 39.75 km. The instrument's data are available in netCDF-4 format with browse imagery available in PNG format.", "links": [ { diff --git a/datasets/gpmd3rolyx_1.json b/datasets/gpmd3rolyx_1.json index 2bdd6831b7..3b20efb4e2 100644 --- a/datasets/gpmd3rolyx_1.json +++ b/datasets/gpmd3rolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmd3rolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) OLYMPEX dataset contains radar reflectivity, velocity, differential reflectivity, differential phase, spectrum width, and co-polar correlation products collected during the Global Precipitation Measurement mission (GPM) Ground Validation (GV) Olympic Mountains Experiment (OLYMPEX). The OLYMPEX field campaign took place between November 2015 and January 2016, with additional ground sampling continuing through February 2016, on the Olympic Peninsula in the Pacific Northwest of the United States. The purpose of the campaign was to provide ground-validation data for the measurements taken by instrumentation aboard the GPM Core Observatory satellite. The Dual-frequency Dual-polarized Doppler Radar (D3R) was developed by a government-industry-academic consortium with funding from NASA\u2019s GPM mission and was used in several ground validation projects. D3R operates at the Ku-band (13.91 GHz \u00b1 25 MHz) and Ka-band (35.56 GHz \u00b1 25 MHz) frequencies, similar to the frequencies used for the GPM satellite instruments, and covers a fixed range from 450 m to 40 km. For OLYMPEX, the D3R was co-located with the NASA S-band Dual Polarimetric (NPOL) Doppler Radar at a coastal Washington state location on the Olympic Peninsula. Due to blockage caused by NPOL, the D3R measurement area is limited to a 220 degree to 120 degree sector. The GPM GV D3R OLYMPEX dataset files are available from November 8, 2015 through January 15, 2016 in netCDF-4 format along with browse imagery of reflectivity in PNG format.", "links": [ { diff --git a/datasets/gpmdowolyx2_2.json b/datasets/gpmdowolyx2_2.json index 4a298109ea..5312f714d5 100644 --- a/datasets/gpmdowolyx2_2.json +++ b/datasets/gpmdowolyx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmdowolyx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Doppler on Wheels (DOW) OLYMPEX V2 dataset was obtained by a dual-polarization and dual-frequency X-band mobile radar operated by the Center for Severe Weather Research (CSWR) during the Olympic Mountain Experiment (OLYMPEX) campaign. The DOW was deployed in the Chehalis Valley for the OLYMPEX field campaign with the goal of obtaining radar reflectivity and Doppler velocity observations in order to better understand the orographic enhancement of precipitation during frontal passages over mountain ranges. The DOW radar uses two 250 kW transmitters with a measurement range of roughly 60 km. These data are available in CFradial netCDF-4 format from November 6, 2015 through January 15, 2016.", "links": [ { diff --git a/datasets/gpmepfl_1.json b/datasets/gpmepfl_1.json index 3a22bd51b7..31fcbc5352 100644 --- a/datasets/gpmepfl_1.json +++ b/datasets/gpmepfl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmepfl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA EPFL-LTE Parsivel DSD Data Lausanne, Switzerland dataset consists of a network of 16 Parsivel disdrometers deployed on the Ecole Polytechnique Federale de Lausanne (EPFL) campus in Lausanne, Switzerland for about 16 months from March 2009 to July 2010. The distribution of the disdrometers was to cover a typical operational radar pixel (about 1x1 km2). Since all the stations were not deployed at the same time, additional data are available from November 2008 to September 2010. The dataset also consists of a list of precipitation events that occurred throughout the study period. There are two types of data, raw data and filtered volumic drop size distribution data. These data are in ASCII (.dat, .txt) format that are compressed into .gz files.", "links": [ { diff --git a/datasets/gpmer2navmc3e_1.json b/datasets/gpmer2navmc3e_1.json index d1fc5732b7..f4e81f1a43 100644 --- a/datasets/gpmer2navmc3e_1.json +++ b/datasets/gpmer2navmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmer2navmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA ER-2 Navigation Data MC3E dataset contains information recorded by an on board navigation recorder (NavRec). In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed, wind direction, and temperature. These ASCII text files were recorded every second for the length of the flight. The Flight Summaries and Flight Track Imagery dataset which includes sonde maps, radar animation, and 5-minute KICT track snapshots is distributed with this dataset.", "links": [ { diff --git a/datasets/gpmexradiphx_1.json b/datasets/gpmexradiphx_1.json index 69126cc56d..f8a2d0e478 100644 --- a/datasets/gpmexradiphx_1.json +++ b/datasets/gpmexradiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmexradiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation ER-2 X-band Radar (EXRAD) IPHEx dataset was collected in support of the Global Precipitation Measurement (GPM) mission Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in North Carolina, with an intense study period occurring from May 1, 2014 through June 15, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. EXRAD is a single-frequency X-band Doppler radar that measures reflectivity and Doppler velocity. The science instruments, including the EXRAD, onboard the NASA ER-2 aircraft acted as a proxy for GPM satellite instruments. This dataset is available in netCDF-3 file format.", "links": [ { diff --git a/datasets/gpmfltsummc3e_1.json b/datasets/gpmfltsummc3e_1.json index 93493b9057..425b6a9308 100644 --- a/datasets/gpmfltsummc3e_1.json +++ b/datasets/gpmfltsummc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmfltsummc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Flight Summaries and Flight Tracks Imagery MC3E dataset for the Midlatitude Continental Convective Clouds Experiment (MC3E) provides processed summaries from the University of North Dakota including sonde maps, a radar animation, parameter versus time charts, radar track graphs, and a summary including aircraft and instrument operational times. The MC3E took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The Flight Tracks imagery includes one animation for May 11, 2011 and the KICT 5 minute snapshots from the Real Time Mission Monitor (RTMM). This dataset is distributed with the MC3E ER-2 Navigation and the Citation Navigation datasets.", "links": [ { diff --git a/datasets/gpmgaugewff_1.json b/datasets/gpmgaugewff_1.json index a6470f3774..465cb1e547 100644 --- a/datasets/gpmgaugewff_1.json +++ b/datasets/gpmgaugewff_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgaugewff_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Met One Rain Gauge Pairs Wallops Flight Facility (WFF) dataset contains rain rate data from 4 rain gauge networks located in Virginia and Maryland near the Wallops Flight Facility (WFF): Nassawadox, Pocomoke, HalfDeg and Wallops Flight Facility (WFF) Assorted Gauges. These data were collected in support of the Global Precipitation Mission (GPM) Ground Validation (GV) campaign. The Met One Rain Gauge Pairs are tipping bucket precipitation gauges which collect precipitation amounts and calculate rain rates. The dataset contains 3 products: formatted gauge tips (GAG), interpolated one-minute rain rates for a year (GMIN), and interpolated one-minute rain rates for a month (2A56). Data are available in ASCII format for the period of April 10, 2012 through October 1, 2018.", "links": [ { diff --git a/datasets/gpmgoes13gcpexB_1.json b/datasets/gpmgoes13gcpexB_1.json index 3cc53fbbe0..9a69a5b971 100644 --- a/datasets/gpmgoes13gcpexB_1.json +++ b/datasets/gpmgoes13gcpexB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgoes13gcpexB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GOES 13 Visible and Infrared Images GCPEx dataset was produced and archived in near real time at the Global Hydrology Resource Center throughout the GPM Cold-Season Precipitation Experiment (GCPEx). These image files were created for use with the Real Time Mission Monitor (RTMM). Generally, GOES-13 images are available for all dates between January 1, 2012 and March 13, 2012 at 15 minute intervals. The GPM Ground Validation GOES-13 Visible and Infrared Images dataset files are available in PNG format and contain images over the MC3E campaign area.", "links": [ { diff --git a/datasets/gpmgoes13iphxB_1.json b/datasets/gpmgoes13iphxB_1.json index 44fb7d9fe9..9d6b97aed9 100644 --- a/datasets/gpmgoes13iphxB_1.json +++ b/datasets/gpmgoes13iphxB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgoes13iphxB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GOES 13 Visible and Infrared Images IPHEx dataset contains visible and infrared images in 3 sizes (FULL, CONUS, and EXT) from the GOES 13 Imager obtained during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign that took place in the southeast region of the United States. This collection of GOES 13 images are available at 30 minute (EXT and CONUS) and 3 hour (FULL) intervals for dates between May 1, 2014 and June 16, 2014. The GPM Ground Validation GOES 13 IPHEx data files are in PNG format.", "links": [ { diff --git a/datasets/gpmgoes13mc3eB_1.json b/datasets/gpmgoes13mc3eB_1.json index f4da3b1cef..ba1acb7d97 100644 --- a/datasets/gpmgoes13mc3eB_1.json +++ b/datasets/gpmgoes13mc3eB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgoes13mc3eB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GOES 13 Visible and Infrared Images MC3E dataset was produced and archived in near real time at the Global Hydrology Research Center throughout the GPM Midlatitude Continental Convective Clouds Experiment (MC3E). These image files were created for use with the Real Time Mission Monitor (RTMM). Generally, GOES-13 images are available for all dates between May 6, 2011 and June 30, 2011 at 15 minute intervals. The GPM Ground Validation GOES-13 Visible and Infrared Images dataset files are available in PNG format and contain images over the MC3E campaign area.", "links": [ { diff --git a/datasets/gpmgoes14iphxB_1.json b/datasets/gpmgoes14iphxB_1.json index 4367c6624a..7acc31a6b3 100644 --- a/datasets/gpmgoes14iphxB_1.json +++ b/datasets/gpmgoes14iphxB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgoes14iphxB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GOES 14 Visible and Infrared Images IPHEx dataset contains visible and infrared images from the GOES 14 Imager collected during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in the southeast region of the United States. The GPM Ground Validation GOES 14 IPHEx dataset files are available in PNG format at 1 minute intervals, for all dates between May 8, 2014 and May 24, 2014.", "links": [ { diff --git a/datasets/gpmgoes15olyxB_1.json b/datasets/gpmgoes15olyxB_1.json index 0363a1b51c..2b17c7796e 100644 --- a/datasets/gpmgoes15olyxB_1.json +++ b/datasets/gpmgoes15olyxB_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgoes15olyxB_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation GOES 15 Visible and Infrared Images OLYMPEX dataset contains visible and infrared images from the GOES 15 Imager during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. The GOES 15 images are available for all dates between November 5, 2015 and May 1, 2016 at 15 minute intervals. The GPM Ground Validation GOES 15 OLYMPEX dataset files are available in PNG format.", "links": [ { diff --git a/datasets/gpmgprof2014ifld_3.json b/datasets/gpmgprof2014ifld_3.json index 4f7a21d44b..b8dd124c15 100644 --- a/datasets/gpmgprof2014ifld_3.json +++ b/datasets/gpmgprof2014ifld_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgprof2014ifld_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Goddard Profiling Algorithm (GPROF) 2014 IFloodS dataset consists of precipitation data derived from microwave radiometers and sounders located on multiple satellites, including the Defense Meteorological Satellite Program (DMSP) F16-18, the Global Change Observation Mission \u2013 Water \"Shizuku\" (GCOM-W1), the European Space Agency's (ESA\u2019s) Meteorological Operational satellite programme (MetOp) series, and NOAA's Polar Operational Environmental Satellites (POES) series. The data have been consistently processed for the Iowa Flood Studies (IFloodS) field campaign conducted in eastern Iowa during spring 2013. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The GPROF 2014 data files are available from March 31 to July 2, 2013 in HDF-5 format.", "links": [ { diff --git a/datasets/gpmgsmapjifld_1.json b/datasets/gpmgsmapjifld_1.json index ffc19d53db..77092218d1 100644 --- a/datasets/gpmgsmapjifld_1.json +++ b/datasets/gpmgsmapjifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmgsmapjifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Global Satellite Mapping of Precipitation (GSMaP) IFloodS dataset consists of rainfall rate estimates from the GSMaP project. The GSMaP global rain rate maps are derived by a collection of algorithms that utilize microwave (MW) radiometer data and geostationary Infrared (IR) data. The GSMaP Precipitation data product is provided on a 0.1 degree spatial resolution every hour and was made available for use during the Global Precipitation Measurement (GPM) Ground Validation Iowa Flood Studies (IFloodS) field campaign. These data are available in netCDF-4 and binary formats from April 22, 2013 through June 30, 2013. The near real-time GSMaP data can be obtained from the JAXA GSMaP web page.", "links": [ { diff --git a/datasets/gpmheifld_1.json b/datasets/gpmheifld_1.json index d78a55a788..eb1249350c 100644 --- a/datasets/gpmheifld_1.json +++ b/datasets/gpmheifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmheifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Hydro-Estimator IFloodS dataset contains rainfall rate estimates derived using NOAA\u2019s Geostationary Operational Environmental Satellites (GOES) infrared (IR) brightness temperature data by researchers at the NOAA Center of Satellite Applications and Research\u2019s (STAR) using the Hydro-Estimator (H-E) algorithm. Rainfall rate estimates are produced every 15 minutes throughout the continental United States, but for this dataset, have been subset to the Iowa region for the Iowa Flood Studies (IFloodS) field campaign in support of Global Precipitation Measurement (GPM) ground validation. These data are available in netCDF-3 format and consist of rain rate values from April 25, 2013 through June 30, 2013.", "links": [ { diff --git a/datasets/gpmheiphx_1.json b/datasets/gpmheiphx_1.json index 0d578ec0c0..3d99259461 100644 --- a/datasets/gpmheiphx_1.json +++ b/datasets/gpmheiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmheiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Hydro-Estimator IPHEx dataset contains rainfall rate estimates derived using NOAA\u2019s Geostationary Operational Environmental Satellites (GOES) infrared (IR) brightness temperature data by researchers at the NOAA Center of Satellite Applications and Research\u2019s (STAR) using the Hydro-Estimator (H-E) algorithm. Rainfall rate estimates are produced every 15 minutes throughout the continental United States, but for this dataset, have been subset to the North Carolina region for the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in support of Global Precipitation Measurement (GPM) ground validation. These data are available in netCDF-4 format and consists of rain rate values from May 1, 2014 through June 16, 2014.", "links": [ { diff --git a/datasets/gpmhiwrapiphx_1.json b/datasets/gpmhiwrapiphx_1.json index 7036714ae1..fb9d3223ed 100644 --- a/datasets/gpmhiwrapiphx_1.json +++ b/datasets/gpmhiwrapiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmhiwrapiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) IPHEx dataset was collected during the Global Precipitation Measurement (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The NASA ER-2 aircraft flew during the IPHEx field campaign to aid in GPM validation. The science instruments, including the HIWRAP, onboard the aircraft acted as a proxy for GPM satellite instruments. HIWRAP is a Doppler radar that combines conical scan mode measurements at two different frequency bands (Ka- and Ku-band) and two different incidence angles (30 and 40 degrees). Twenty-one ER-2 flights occurred from May 1, 2014 through June 14, 2014. The HIWRAP dataset includes netCDF-4 files containing radar reflectivity and Doppler velocity profiles along with aircraft altitude and other navigation information.", "links": [ { diff --git a/datasets/gpmhiwrapmc3e_1.json b/datasets/gpmhiwrapmc3e_1.json index 38b89785b9..407936ce2e 100644 --- a/datasets/gpmhiwrapmc3e_1.json +++ b/datasets/gpmhiwrapmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmhiwrapmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) MC3E dataset was collected by the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), which is a dual-frequency (Ka- and Ku-band) conical scan system, configured with a nadir viewing antenna on the high-altitude (20 km) NASA ER-2 aircraft. It provides calibrated reflectivity and unfolded Doppler velocity. The GPM Ground Validation High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) MC3E dataset consists of netCDF (.nc) files and images (.gif). Measurements included within the data files are merged pulse and chirp radar reflectivity profiles at 13.9 and 33.7 GHz.", "links": [ { diff --git a/datasets/gpmhiwrapolyx_1a.json b/datasets/gpmhiwrapolyx_1a.json index 937ef4385b..881e529017 100644 --- a/datasets/gpmhiwrapolyx_1a.json +++ b/datasets/gpmhiwrapolyx_1a.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmhiwrapolyx_1a", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) OLYMPEX dataset consists of Doppler velocity and reflectivity profiles collected by the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) onboard the NASA ER-2 high-altitude research aircraft during the Global Precipitation Measurement mission (GPM) Ground Validation Olympic Mountains Experiment (OLYMPEX). The OLYMPEX field campaign took place between November 2015 and January 2016, with additional ground sampling continuing through February 2016, on the Olympic Peninsula in the Pacific Northwest of the United States. The purpose of the campaign was to provide ground-validation data for the measurements taken by instrumentation aboard the GPM Core Observatory satellite. HIWRAP is a Doppler radar that combines conical scan mode measurements at two different frequency bands (Ka- and Ku-band) and two different incidence angles (30 and 40 degrees) to obtain profiles of wind and rain. These Level 1B HIWRAP data files are available from November 10 through December 12, 2015 in netCDF-3 format.", "links": [ { diff --git a/datasets/gpmikalpvex_1.json b/datasets/gpmikalpvex_1.json index 266a9a8d53..cb6e25e55a 100644 --- a/datasets/gpmikalpvex_1.json +++ b/datasets/gpmikalpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmikalpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation C-Band Radar LPVEx datasets include radar reflectivity data from the Ikaalinen (IKA) dual-polarimetric C-Band Doppler radar in Finland during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This radar, along with four others, provided reflectivity measurements for light precipitation systems during LPVEx. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Ikaalinen C-Band Radar data files are available in RAW and UF format for October 19, 2010.", "links": [ { diff --git a/datasets/gpmjwlpvex_1.json b/datasets/gpmjwlpvex_1.json index cdbf46f713..9497b4231c 100644 --- a/datasets/gpmjwlpvex_1.json +++ b/datasets/gpmjwlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmjwlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Joss-Waldvogel Disdrometer (JW) LPVEx dataset consists of precipitation drop size distribution (DSD) data collected by the Joss-Waldvogel (JW) disdrometer during the GPM Ground Validation Light Precipitation Validation Experiment (LPVEx). This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The JW disdrometer dataset files are available in ASCII text format from September 10 through November 9, 2010.", "links": [ { diff --git a/datasets/gpmjwnsstc_1.json b/datasets/gpmjwnsstc_1.json index d18f80beda..c14a40dd27 100644 --- a/datasets/gpmjwnsstc_1.json +++ b/datasets/gpmjwnsstc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmjwnsstc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Joss-Waldvogel Disdrometer (JW) NSSTC dataset was collected by the Joss-Waldvogel (JW) disdrometer, which is an impact-type electromechanical counter designed to measure drop size distribution (DSD). This dataset provides rainfall data for the Global Precipitation Measurement (GPM) Mission Ground Validation Experiment collected at the National Space Science Technology Center (NSSTC), Huntsville, AL. There may be occasional gaps in the data when the instrument is not resident at the NSSTC and is sent to participate in field campaigns.", "links": [ { diff --git a/datasets/gpmkapxgcpex_1.json b/datasets/gpmkapxgcpex_1.json index 2e3a40c36e..0e010f074d 100644 --- a/datasets/gpmkapxgcpex_1.json +++ b/datasets/gpmkapxgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkapxgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KAPX NEXRAD GCPEx dataset was collected during January 9, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. This data set were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx datasets include data files and browse image files. These data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkarx2ifld_1.json b/datasets/gpmkarx2ifld_1.json index fc23e4b411..96bbd70531 100644 --- a/datasets/gpmkarx2ifld_1.json +++ b/datasets/gpmkarx2ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkarx2ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Next Generation Weather Radar (NEXRAD) Level II IFloodS datasets were collected from four sites (see Table 1) from March 29, 2013 to June 18, 2013 for the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign in central-northeastern Iowa. Officially, the IFloodS campaign ran from May 1 to June 15, 2013 but the NEXRAD data was collected prior to the start, allowing for the wider period of record. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and select overseas locations. The resulting data includes the base data (Level-II) and the derived products (Level-III). These Level-II datasets include three meteorological base data quantities: reflectivity, mean radial velocity, and spectrum width. The GPM Ground Validation NEXRAD Level II IFloodS data files are in a custom binary format; the visualization and decoding of the data requires specialized software. Browse imagery is available in PNG file format.", "links": [ { diff --git a/datasets/gpmkarx3ifld_1.json b/datasets/gpmkarx3ifld_1.json index 478507010c..22fa433e65 100644 --- a/datasets/gpmkarx3ifld_1.json +++ b/datasets/gpmkarx3ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkarx3ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NEXRAD Level III KARX IFloodS dataset contain precipitation products derived from selected NEXt Generation Weather RADar system (NEXRAD) radars in operation during the Iowa Flood Studies (IFloodS) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Data were gathered from four NEXRAD stations in the vicinity of the IFloodS campaign during March 29, 2013 through June 18, 2013. This dataset contains data files of digital instantaneous precipitation rate (DPR) and storm total accumulation estimates (DTA) in NIDS binary format.", "links": [ { diff --git a/datasets/gpmkatx2olyx_1.json b/datasets/gpmkatx2olyx_1.json index 2ebe898133..d79ee57c2b 100644 --- a/datasets/gpmkatx2olyx_1.json +++ b/datasets/gpmkatx2olyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkatx2olyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KATX NEXRAD OLYMPEX dataset contains data from selected NEXt Generation Weather RADar system (NEXRAD) instruments in operation during the Olympic Mountains Experiment (OLYMPEX) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Datasets gathered from three NEXRAD stations, as listed below, extend from 22 September 2015 through 01 May 2016 as part of the GPM Ground Validation OLYMPEX data. This dataset contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkboxgcpex_1.json b/datasets/gpmkboxgcpex_1.json index 5da18c4908..de2ba8f1e6 100644 --- a/datasets/gpmkboxgcpex_1.json +++ b/datasets/gpmkboxgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkboxgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KBOX NEXRAD GCPEx dataset was collected during February 6, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkbufgcpex_1.json b/datasets/gpmkbufgcpex_1.json index 46707017ad..cbc08eaedd 100644 --- a/datasets/gpmkbufgcpex_1.json +++ b/datasets/gpmkbufgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkbufgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KBUF NEXRAD GCPEx dataset was collected during February 6, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkcae2iphx_1.json b/datasets/gpmkcae2iphx_1.json index 31df9112a2..3dbd3cb144 100644 --- a/datasets/gpmkcae2iphx_1.json +++ b/datasets/gpmkcae2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkcae2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KCAE NEXRAD IPHEx datasets contain data from the KCAE NEXt Generation Weather RADar system (NEXRAD) instrument in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkcbwgcpex_1.json b/datasets/gpmkcbwgcpex_1.json index bf84fcf298..c6d32034ce 100644 --- a/datasets/gpmkcbwgcpex_1.json +++ b/datasets/gpmkcbwgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkcbwgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KCBW NEXRAD GCPEx dataset was collected during January 9, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkcradgcpex_1.json b/datasets/gpmkcradgcpex_1.json index 2f049b98cb..ceee49a003 100644 --- a/datasets/gpmkcradgcpex_1.json +++ b/datasets/gpmkcradgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkcradgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual Polarized C-Band Doppler Radar King City GCPEx dataset has special Range Height Indicator (RHI) and sector scans of several dual polarization parameters, such as temperature and reflectivity, measured by the C-Band radar during the GPM Cold-season Precipitation Experiment (GCPEx) in Ontario, Canada. Additionally, specially configured Centre for Atmospheric Research Experiments (CARE)-centric composites were also generated. Standard King City Radar (WKR) Interactive Radar Information System (IRIS) volume and Plan Position Indicator (PPI) scans, along with corresponding standard PPI imagery, are also included. Data was collected from January 15, 2012 through March 3, 2012.", "links": [ { diff --git a/datasets/gpmkcxxgcpex_1.json b/datasets/gpmkcxxgcpex_1.json index 53407f503e..b21a16437c 100644 --- a/datasets/gpmkcxxgcpex_1.json +++ b/datasets/gpmkcxxgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkcxxgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KCXX NEXRAD GCPEx dataset was collected during January 9, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkdmx2ifld_1.json b/datasets/gpmkdmx2ifld_1.json index 861f7c9a64..85160f9c7f 100644 --- a/datasets/gpmkdmx2ifld_1.json +++ b/datasets/gpmkdmx2ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkdmx2ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Next Generation Weather Radar (NEXRAD) Level II IFloodS datasets were collected from four sites (see Table 1) from March 29, 2013 to June 18, 2013 for the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign in central-northeastern Iowa. Officially, the IFloodS campaign ran from May 1 to June 15, 2013 but the NEXRAD data was collected prior to the start, allowing for the wider period of record. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and select overseas locations. The resulting data includes the base data (Level-II) and the derived products (Level-III). These Level-II datasets include three meteorological base data quantities: reflectivity, mean radial velocity, and spectrum width. The GPM Ground Validation NEXRAD Level II IFloodS data files are in a custom binary format; the visualization and decoding of the data requires specialized software. Browse imagery is available in PNG file format.", "links": [ { diff --git a/datasets/gpmkdmx3ifld_1.json b/datasets/gpmkdmx3ifld_1.json index 9502e40819..fe4bb06a81 100644 --- a/datasets/gpmkdmx3ifld_1.json +++ b/datasets/gpmkdmx3ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkdmx3ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NEXRAD Level III KDMX IFloodS dataset contain precipitation products derived from selected NEXt Generation Weather RADar system (NEXRAD) radars in operation during the Iowa Flood Studies (IFloodS) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Data were gathered from four NEXRAD stations in the vicinity of the IFloodS campaign during March 29, 2013 through June 18, 2013. This dataset contains data files of digital instantaneous precipitation rate (DPR) and storm total accumulation estimates (DTA) in NIDS binary format.", "links": [ { diff --git a/datasets/gpmkdvn2ifld_1.json b/datasets/gpmkdvn2ifld_1.json index 66e0684815..d8c399f4a9 100644 --- a/datasets/gpmkdvn2ifld_1.json +++ b/datasets/gpmkdvn2ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkdvn2ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Next Generation Weather Radar (NEXRAD) Level II IFloodS datasets were collected from four sites (see Table 1) from March 29, 2013 to June 18, 2013 for the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign in central-northeastern Iowa. Officially, the IFloodS campaign ran from May 1 to June 15, 2013 but the NEXRAD data was collected prior to the start, allowing for the wider period of record. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and select overseas locations. The resulting data includes the base data (Level-II) and the derived products (Level-III). These Level-II datasets include three meteorological base data quantities: reflectivity, mean radial velocity, and spectrum width. The GPM Ground Validation NEXRAD Level II IFloodS data files are in a custom binary format; the visualization and decoding of the data requires specialized software. Browse imagery is available in PNG file format.", "links": [ { diff --git a/datasets/gpmkdvn3ifld_1.json b/datasets/gpmkdvn3ifld_1.json index ad39c5ca9f..4fb1ec72cc 100644 --- a/datasets/gpmkdvn3ifld_1.json +++ b/datasets/gpmkdvn3ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkdvn3ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NEXRAD Level III KDVN IFloodS dataset contain precipitation products derived from selected NEXt Generation Weather RADar system (NEXRAD) radars in operation during the Iowa Flood Studies (IFloodS) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Data were gathered from four NEXRAD stations in the vicinity of the IFloodS campaign during March 29, 2013 through June 18, 2013. This dataset contains data files of digital instantaneous precipitation rate (DPR) and storm total accumulation estimates (DTA) in NIDS binary format.", "links": [ { diff --git a/datasets/gpmkerlpvex_1.json b/datasets/gpmkerlpvex_1.json index 0559fc110d..e9ea402c8b 100644 --- a/datasets/gpmkerlpvex_1.json +++ b/datasets/gpmkerlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkerlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation C-Band Radar LPVEx datasets include radar reflectivity data from the Kerava (KER) dual-polarimetric C-Band Doppler radar in Finland during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This radar, along with four others, provided reflectivity measurements for light precipitation systems during LPVEx. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Kerava C-Band Radar data files are available in RAW and UF format from September 21 through October 20, 2010.", "links": [ { diff --git a/datasets/gpmkgldmc3e_1.json b/datasets/gpmkgldmc3e_1.json index e65b864ff9..33e0eb1af6 100644 --- a/datasets/gpmkgldmc3e_1.json +++ b/datasets/gpmkgldmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkgldmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KGLD NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E) which took place in central Oklahoma; however, this dataset contains data from May 18, 2011 to June 6, 2011. MC3E was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. Radar sites include KGLD, KICT, KINX, KTLX, KTWX, KVNX. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as tarred binary files.", "links": [ { diff --git a/datasets/gpmkgsp2iphx_1.json b/datasets/gpmkgsp2iphx_1.json index 1570db31c3..e9e752b153 100644 --- a/datasets/gpmkgsp2iphx_1.json +++ b/datasets/gpmkgsp2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkgsp2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KGSP NEXRAD IPHEx dataset contain data from the KGSP NEXt Generation Weather RADar system (NEXRAD) instruments in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere.", "links": [ { diff --git a/datasets/gpmkgyxgcpex_1.json b/datasets/gpmkgyxgcpex_1.json index 0d43a82db6..e6263d4994 100644 --- a/datasets/gpmkgyxgcpex_1.json +++ b/datasets/gpmkgyxgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkgyxgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KGYX NEXRAD GCPEx dataset was collected during January 9, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkhtx2iphx_1.json b/datasets/gpmkhtx2iphx_1.json index d20c003163..57ced6d4e2 100644 --- a/datasets/gpmkhtx2iphx_1.json +++ b/datasets/gpmkhtx2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkhtx2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KHTX NEXRAD IPHEx datasets contain data from the KHTX NEXt Generation Weather RADar system (NEXRAD) instrument in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 16, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkictmc3e_1.json b/datasets/gpmkictmc3e_1.json index beb4752d20..2d550ab70c 100644 --- a/datasets/gpmkictmc3e_1.json +++ b/datasets/gpmkictmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkictmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validaiton KICT NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E) which took place in central Oklahoma. The overarching goal of MC3E was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as compressed binary files.", "links": [ { diff --git a/datasets/gpmkinxmc3e_1.json b/datasets/gpmkinxmc3e_1.json index c16c0250cc..9240262135 100644 --- a/datasets/gpmkinxmc3e_1.json +++ b/datasets/gpmkinxmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkinxmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KINX NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E) which took place in central Oklahoma. The overarching goal of MC3E was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as compressed binary files.", "links": [ { diff --git a/datasets/gpmklgx2olyx_1.json b/datasets/gpmklgx2olyx_1.json index 45ccb8b767..d5c9584c97 100644 --- a/datasets/gpmklgx2olyx_1.json +++ b/datasets/gpmklgx2olyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmklgx2olyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KLGX NEXRAD OLYMPEX dataset contains data from selected NEXt Generation Weather RADar system (NEXRAD) instruments in operation during the Olympic Mountains Experiment (OLYMPEX) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Datasets gathered from three NEXRAD stations, as listed below, extend from 22 September 2015 through 01 May 2016 as part of the GPM Ground Validation OLYMPEX data. This dataset contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkltx2iphx_1.json b/datasets/gpmkltx2iphx_1.json index 1c9a89e4fa..6934fdd86f 100644 --- a/datasets/gpmkltx2iphx_1.json +++ b/datasets/gpmkltx2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkltx2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KLTX NEXRAD IPHEx dataset contain data from the KLTX NEXt Generation Weather RADar system (NEXRAD) instrument in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkmhx2iphx_1.json b/datasets/gpmkmhx2iphx_1.json index 8b02776d44..8ff2ae770c 100644 --- a/datasets/gpmkmhx2iphx_1.json +++ b/datasets/gpmkmhx2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkmhx2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KMHX NEXRAD IPHEx dataset contain data from the KMHX NEXt Generation Weather RADar system (NEXRAD) instrument in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkmpx2ifld_1.json b/datasets/gpmkmpx2ifld_1.json index 14b46adc3f..492d34abc1 100644 --- a/datasets/gpmkmpx2ifld_1.json +++ b/datasets/gpmkmpx2ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkmpx2ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Next Generation Weather Radar (NEXRAD) Level II IFloodS datasets were collected from four sites (see Table 1) from March 29, 2013 to June 18, 2013 for the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign in central-northeastern Iowa. Officially, the IFloodS campaign ran from May 1 to June 15, 2013 but the NEXRAD data was collected prior to the start, allowing for the wider period of record. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and select overseas locations. The resulting data includes the base data (Level-II) and the derived products (Level-III). These Level-II datasets include three meteorological base data quantities: reflectivity, mean radial velocity, and spectrum width. The GPM Ground Validation NEXRAD Level II IFloodS data files are in a custom binary format; the visualization and decoding of the data requires specialized software. Browse imagery is available in PNG file format.", "links": [ { diff --git a/datasets/gpmkmpx3ifld_1.json b/datasets/gpmkmpx3ifld_1.json index 73bc0918d2..cab935b7d5 100644 --- a/datasets/gpmkmpx3ifld_1.json +++ b/datasets/gpmkmpx3ifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkmpx3ifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NEXRAD Level III KMPX IFloodS dataset contain precipitation products derived from selected NEXt Generation Weather RADar system (NEXRAD) radars in operation during the Iowa Flood Studies (IFloodS) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Data were gathered from four NEXRAD stations in the vicinity of the IFloodS campaign during March 29, 2013 through June 18, 2013. This dataset contains data files of digital instantaneous precipitation rate (DPR) and storm total accumulation estimates (DTA) in NIDS binary format.", "links": [ { diff --git a/datasets/gpmkmrx2iphx_1.json b/datasets/gpmkmrx2iphx_1.json index f97b9bf691..f32c151e87 100644 --- a/datasets/gpmkmrx2iphx_1.json +++ b/datasets/gpmkmrx2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkmrx2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KMRX NEXRAD IPHEx dataset contain data from the KMRX NEXt Generation Weather RADar system (NEXRAD) instrument in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkorlpvex_1.json b/datasets/gpmkorlpvex_1.json index 3d7a3b70db..717d3fd8d6 100644 --- a/datasets/gpmkorlpvex_1.json +++ b/datasets/gpmkorlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkorlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation C-Band Radar LPVEx datasets include radar reflectivity data from the Korpo (KOR) dual-polarimetric C-Band Doppler radar in Finland during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This radar, along with four others, provided reflectivity measurements for light precipitation systems during LPVEx. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Korpo C-Band Radar data files are available in RAW and UF format for October 19, 2010.", "links": [ { diff --git a/datasets/gpmkrax2iphx_1.json b/datasets/gpmkrax2iphx_1.json index d07bb5f193..5601ef5df7 100644 --- a/datasets/gpmkrax2iphx_1.json +++ b/datasets/gpmkrax2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkrax2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KRAX NEXRAD IPHEx dataset contain data from the KRAX NEXt Generation Weather RADar system (NEXRAD) instruments in operation during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM) and evaluate Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting in the southeast region of the United States. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. These images extend from May 1, 2014 through June 12, 2014 as part of the GPM Ground Validation IPHEx datasets. The NEXRAD datasets contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmkrtx2olyx_1.json b/datasets/gpmkrtx2olyx_1.json index 8c20887e9e..06b2b55a3c 100644 --- a/datasets/gpmkrtx2olyx_1.json +++ b/datasets/gpmkrtx2olyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkrtx2olyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KRTX NEXRAD OLYMPEX dataset contains data from selected NEXt Generation Weather RADar system (NEXRAD) instruments in operation during the Olympic Mountains Experiment (OLYMPEX) field campaign to help support the ground validation of the Global Precipitation Measurement (GPM). NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Datasets gathered from three NEXRAD stations, as listed below, extend from 03 November 2015 through 01 May 2016 as part of the GPM Ground Validation OLYMPEX data. This dataset contain browse images of base reflectivity observations in the Portable Network Graphic (PNG) format. Base radar reflectivity is the measure of transmitted power returned to the radar after intercepting a target, for example, rain droplets. This information can illustrate the amount and size distribution of water particles in a given unit volume of atmosphere. ", "links": [ { diff --git a/datasets/gpmktlxmc3e_1.json b/datasets/gpmktlxmc3e_1.json index 71c5c17ead..cf825b0b76 100644 --- a/datasets/gpmktlxmc3e_1.json +++ b/datasets/gpmktlxmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmktlxmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KTLX NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E) which took place in central Oklahoma. The overarching goal of MC3E was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as compressed binary files.", "links": [ { diff --git a/datasets/gpmktwxmc3e_1.json b/datasets/gpmktwxmc3e_1.json index 0c19437cfb..060a80bc58 100644 --- a/datasets/gpmktwxmc3e_1.json +++ b/datasets/gpmktwxmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmktwxmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KTWX NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E) which took place in central Oklahoma. The overarching goal of MC3E was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as compressed binary files.", "links": [ { diff --git a/datasets/gpmktyxgcpex_1.json b/datasets/gpmktyxgcpex_1.json index 0668c7e7f3..d15e305036 100644 --- a/datasets/gpmktyxgcpex_1.json +++ b/datasets/gpmktyxgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmktyxgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KTYX NEXRAD GCPEx dataset was collected during January 9, 2012 to March 12, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected toward achieving the overarching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD GCPEx data files are available as level 2 binary files and level 3 compressed binary files.", "links": [ { diff --git a/datasets/gpmkumlpvex_1.json b/datasets/gpmkumlpvex_1.json index 01cf39860b..9fac5be7eb 100644 --- a/datasets/gpmkumlpvex_1.json +++ b/datasets/gpmkumlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkumlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation C-Band Radar LPVEx datasets include radar reflectivity data from the Kumpula (KUM) dual-polarimetric C-Band Doppler radar in Finland during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This radar, along with four others, provided reflectivity measurements for light precipitation systems during LPVEx. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Kumpula C-band Radar data files are available in RAW and UF format, with browse imagery in PNG format from September 01, 2010 through January 31, 2011.", "links": [ { diff --git a/datasets/gpmkvnxmc3e_1.json b/datasets/gpmkvnxmc3e_1.json index 9b3089d3bc..7afb8fd990 100644 --- a/datasets/gpmkvnxmc3e_1.json +++ b/datasets/gpmkvnxmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmkvnxmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation KVNX NEXRAD MC3E dataset was collected from April 22, 2011 to June 6, 2011 for the Midlatitude Continental Convective Clouds Experiment (MC3E). The overarching goal of MC3E was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Next Generation Weather Radar system (NEXRAD) is comprised of 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) sites throughout the United States and select overseas locations. The GPM Ground Validation NEXRAD MC3E data files are available as compressed binary files.", "links": [ { diff --git a/datasets/gpmlidargcpex_1.json b/datasets/gpmlidargcpex_1.json index fdc760cd71..a3a0e107fc 100644 --- a/datasets/gpmlidargcpex_1.json +++ b/datasets/gpmlidargcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmlidargcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Aerosol and Water Vapor Lidar Quicklooks GCPEx dataset contains imagery generated from the GPM Cold-season Precipitation Experiment (GCPEx) campaign during January - March 2012 in Canada. GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The system is configured to run semi-autonomously and shuts down automatically for the duration of rain events. This dataset is comprised of measurements from two Lidar systems: the Semi Autonomous Tropospheric Aerosol Lidar and the Tropospheric Water Vapor Lidar.", "links": [ { diff --git a/datasets/gpmlipiphx_1.json b/datasets/gpmlipiphx_1.json index 2ccca2f1d5..7a91c3c308 100644 --- a/datasets/gpmlipiphx_1.json +++ b/datasets/gpmlipiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmlipiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Lightning Instrument Package (LIP) IPHEx dataset consists of electrical field measurements of lightning and navigation data collected by the Lightning Instrument Package (LIP) flown aboard a NASA ER-2 high-altitude aircraft during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) held in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. These data files are available in ASCII format and browse imagery in PNG format from May 1, 2014 through June 14, 2014.", "links": [ { diff --git a/datasets/gpmmascolyx_1.json b/datasets/gpmmascolyx_1.json index ebc27a50c9..ab7471096c 100644 --- a/datasets/gpmmascolyx_1.json +++ b/datasets/gpmmascolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmascolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Microwave Atmospheric Sounder on Cubesat (MASC) OLYMPEX dataset consists of microwave radiance measurements collected during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. These data were collected by the MASC aboard the NASA DC-8 aircraft, for dates between November 10, 2016 and December 13, 2016. The data are provided in HDF-EOS5 format. ", "links": [ { diff --git a/datasets/gpmmastmetlpvex_1.json b/datasets/gpmmastmetlpvex_1.json index cf88a4805c..09790fd724 100644 --- a/datasets/gpmmastmetlpvex_1.json +++ b/datasets/gpmmastmetlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmastmetlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Kumpula Mast Meteorological Data LPVEx dataset is comprised of temperature, radiation, and wind measurements collected by the Station for Measuring Ecosystem-Atmosphere Relations III (SMEAR III) Kumpula Mast in Helsinki, Finland. This occurred during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. These meteorological dataset files are available from September 17 through October 21, 2010 in ASCII text format.", "links": [ { diff --git a/datasets/gpmmettecgcpex_1.json b/datasets/gpmmettecgcpex_1.json index 3c5847b063..5bb93dd7c5 100644 --- a/datasets/gpmmettecgcpex_1.json +++ b/datasets/gpmmettecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmettecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Meteorological Tower Environment Canada GCPEx dataset provides temperature, relative humidity, 10 m winds, pressure and solar radiation data collected by a suite of standard meteorological instruments attached to a 10 m met tower. The GPM Cold-season Precipitation Experiment (GCPEx) addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Data was gathered over the Ontario region of Canada in 2012 from January 15th through March 1st. Browse images are available online. The observation station was assembled by Automated Transportable Meteorological Observation Station (ATMOS).", "links": [ { diff --git a/datasets/gpmmisrepgcpex_1.json b/datasets/gpmmisrepgcpex_1.json index 306b07602a..c38f8b7d23 100644 --- a/datasets/gpmmisrepgcpex_1.json +++ b/datasets/gpmmisrepgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmisrepgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Mission Reports GCPEx dataset consists of various reports filed by the scientists during the GPM Cold-season Precipitation Experiment (GCPEx) campaign which took place from January 15 - February 29, 2012 in Ontario, Canada; however, this dataset spans from September 4, 2011 to May 11, 2012. GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinating model simulations of precipitating snow. Report categories include the Mission Scientist, Mission Manager, Instrument Scientists, Weather Forecasts and Plan of Day. Many reports have additional information attached.", "links": [ { diff --git a/datasets/gpmmisrepifld_1.json b/datasets/gpmmisrepifld_1.json index 42d42dd866..0ec2a5e92e 100644 --- a/datasets/gpmmisrepifld_1.json +++ b/datasets/gpmmisrepifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmisrepifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Campaign Reports IFloodS dataset consists of various reports filed by the scientists during the GPM Ground Validation Iowa Flood Studies (IFloodS) Field Experiment, which took place from April to mid-June 2013 in Iowa. The goals of the campaign were to collect detailed measurements of precipitation at the Earth's surface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. Included in this dataset are Mission Scientist, Instrument Scientists, and Weather Forecasts. Many reports have additional information included as attachments.", "links": [ { diff --git a/datasets/gpmmisrepiphx_1.json b/datasets/gpmmisrepiphx_1.json index 5887399498..b6600bd600 100644 --- a/datasets/gpmmisrepiphx_1.json +++ b/datasets/gpmmisrepiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmisrepiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Precipitation Measurement (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) campaign was centered in the Southern Appalachians and spanned into the Piedmont and Coastal Plain regions of North Carolina. The campaign sought to characterize warm season orographic precipitation regimes, and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. The GPM Ground Validation Mission Reports IPHEx dataset contains reports from the intense campaign period which occurred during May 1, 2014 to June 13, 2014. This dataset consists of various reports filed by the scientists during the campaign. This dataset includes flight reports, weather forecasts, GPM flight forecasts, instrument reports, mission science reports, and plan-of-day reports. Many reports have additional information included as attachments.", "links": [ { diff --git a/datasets/gpmmisrepmc3e_1.json b/datasets/gpmmisrepmc3e_1.json index 73f6c3ec96..f6ed09b49a 100644 --- a/datasets/gpmmisrepmc3e_1.json +++ b/datasets/gpmmisrepmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmisrepmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Campaign Reports MC3E dataset consists of various reports filed by the scientists during the Midlatitude Continental Convective Clouds Experiment (MC3E) campaign. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. Several of the reports are from the planning, test flights, and preparation. Included in this dataset are Mission Scientist, Mission Manager, Instrument Scientists, and Weather Forecasts. Many reports have additional information included as attachments.", "links": [ { diff --git a/datasets/gpmmisrepolyx_1.json b/datasets/gpmmisrepolyx_1.json index 8e7575b47c..37145c64be 100644 --- a/datasets/gpmmisrepolyx_1.json +++ b/datasets/gpmmisrepolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmisrepolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Campaign Reports OLYMPEX dataset consists of flight reports, weather forecasts, instrument reports, scientist summaries, and plan-of-day reports collected during the Global Precipitation Measurement (GPM) Olympic Mountains Experiment (OLYMPEX) field campaign to help support the ground validation of the GPM. These campaign reports were collected during the intense operating period which occurred during November 2015 to February 2016. The various campaign reports are available in PDF, JPG, PNG, and Microsoft Powerpoint and Word formats, some of which are located within tarred data files.", "links": [ { diff --git a/datasets/gpmmrms_1.json b/datasets/gpmmrms_1.json index 001b17330e..054fb5f21a 100644 --- a/datasets/gpmmrms_1.json +++ b/datasets/gpmmrms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product dataset contains precipitation rate and type estimates, quality control products, and precipitation corrective factors products. These data products were created using the NOAA MRMS System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RAP) model analysis fields, and gauge data. It should be noted that these data products are not standard MRMS. Significant post-processing is applied to MRMS to generate products specifically adapted to satellite purposes and needs over North America. These data are available from March 2, 2014 through October 30, 2018 in ASCII format.", "links": [ { diff --git a/datasets/gpmmrrdukeiphx_1.json b/datasets/gpmmrrdukeiphx_1.json index 9dc09e59ae..34957480cc 100644 --- a/datasets/gpmmrrdukeiphx_1.json +++ b/datasets/gpmmrrdukeiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrdukeiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Duke Micro Rain Radar (MRR) IPHEx dataset was gathered during the Global Precipitation Measurement (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina from May 1, 2014 through June 15, 2014. The dataset contains measured and derived data from three MRR instruments placed in separate locations within the study region. The MRR is a Biral/Metek 24 GHz (K-band) vertically oriented Frequency Modulated Continuous Wave (FM-CW) radar that measures signal backscatter from which Doppler spectra, radar reflectivity, Doppler velocity, drop size distribution, rain rate, liquid water content, and path integrated attenuation are derived. Data files are available in ASCII data format.", "links": [ { diff --git a/datasets/gpmmrrecgcpex2_2.json b/datasets/gpmmrrecgcpex2_2.json index 067461e2bc..5ec0bd132b 100644 --- a/datasets/gpmmrrecgcpex2_2.json +++ b/datasets/gpmmrrecgcpex2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrecgcpex2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Micro Rain Radar (MRR) GCPEx V2 dataset was collected from the Micro Rain Radar (MRR) during the GPM Cold-season Precipitation Experiment (GCPEx) in Ontario, Canada during the winter season 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Operating at 24 GHz the MRR, a vertically pointing Doppler radar, retrieved quantitative rain rates, drop size distributions, radar reflectivity, and fall velocities on vertical profiles up to several kilometers above the unit. The MRR used during GCPEX is the second generation of the instrument manufactured by METEK (URL: http://metek.de/product/mrr-2/). Version 2 of this dataset became active on April 30, 2015.", "links": [ { diff --git a/datasets/gpmmrrhymex_1.json b/datasets/gpmmrrhymex_1.json index 71f10f8b4b..a458b0a6c5 100644 --- a/datasets/gpmmrrhymex_1.json +++ b/datasets/gpmmrrhymex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrhymex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA Micro Rain Radar (MRR) HyMeX is a vertically pointing Doppler radar that obtained measurements of vertical velocity, drop size distribution, rainfall rate, attenuation, liquid water content, and reflectivity factor during the HYdrological cycle in Mediterranean EXperiment (HyMeX) campaign. The HYdrological cycle in Mediterranean EXperiment (HyMeX) aimed to improve the understanding, quantification and modelling of the hydrological cycle in the Mediterranean, with emphasis on the predictability and evolution of extreme weather events, inter-annual to decadal variability of the Mediterranean coupled system, and associated trends in the context of global change. Furthermore, this campaign aimed to improve observational and modelling systems, better predict extreme events, simulate the long-term water-cycle, and provide guidelines for adaptation measures. Special Observation Period 1 (SOP1), which was from September 5 to November 6, 2012, was dedicated to heavy precipitation and flash-flooding. More information about HyMeX is available at http://www.hymex.org/.", "links": [ { diff --git a/datasets/gpmmrricepop_1.json b/datasets/gpmmrricepop_1.json index e98ea3e7e8..d440777184 100644 --- a/datasets/gpmmrricepop_1.json +++ b/datasets/gpmmrricepop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrricepop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Micro Rain Radar (MRR) ICE POP dataset was collected during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This dataset consists of precipitation data collected by two MRR instruments from November 1, 2017 to March 1, 2018. These data are available in netCDF-3 and ASCII text formats.", "links": [ { diff --git a/datasets/gpmmrrlpvex_1.json b/datasets/gpmmrrlpvex_1.json index 4f2f784640..2df101b158 100644 --- a/datasets/gpmmrrlpvex_1.json +++ b/datasets/gpmmrrlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Micro Rain Radar (MRR) LPVEx dataset was collected during the Global Precipitation Measurement (GPM) mission Ground Validation Light Precipitation Validation Experiment (LPVEx) field campaign. The LPVEx field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high altitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The MRR is a Biral/Metek 24 GHz (K-band) vertically oriented Frequency Modulated Continuous Wave (FM-CW) radar that measures signal backscatter from which Doppler spectra, radar reflectivity, Doppler velocity, drop size distribution, rain rate, liquid water content, and path integrated attenuation are derived. The dataset contains measured and derived data from MRR instruments placed at four remote sites (Jarvenpaa, Emasalo, Harmaja, and the research vessel Aranda). Data files are available in ASCII data format.", "links": [ { diff --git a/datasets/gpmmrrnaachiphx_1.json b/datasets/gpmmrrnaachiphx_1.json index 6b541d1e89..0176a684f0 100644 --- a/datasets/gpmmrrnaachiphx_1.json +++ b/datasets/gpmmrrnaachiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrnaachiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Micro Rain Radar (MRR) NASA ACHIEVE IPHEx dataset was gathered during the Global Precipitation Measurement (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina from May 1, 2014 through June 15, 2014. The dataset includes data from the MRR instrument, which is part of the NASA Goddard Space Flight Center (GSFC) ACHIEVE ground-based mobile laboratory. The MRR is a Biral/Metek 24 GHz (K-band) vertically oriented Frequency Modulated Continuous Wave (FM\u2013CW) radar that measures Doppler spectra, radar reflectivity, Doppler velocity, drop size distribution, rain rate, liquid water content, and path integrated attenuation. Data files are available in ASCII 'ave' data format. ", "links": [ { diff --git a/datasets/gpmmrrnagcpex2_2.json b/datasets/gpmmrrnagcpex2_2.json index a622e75ec2..37a6dc2740 100644 --- a/datasets/gpmmrrnagcpex2_2.json +++ b/datasets/gpmmrrnagcpex2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrnagcpex2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA Micro Rain Radar (MRR) GCPEx dataset was collected by a Micro Rain Radar (MRR), which is a vertically pointing Doppler radar which provides measurements of vertical velocity, drop size distribution, rainfall rate, attenuation, liquid water content, and reflectivity factor obtained during the GPM Cold-season Precipitation Experiment (GCPEx), which took place in Canada during Winter 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The MRR is a frequency-modulated continuous wave (FMCW) vertically pointing Doppler radar, which operates at 24.24GHz, and it is the second generation of the instrument manufactured by METEK (http://metek.de/product/mrr-2/). NASA MRR data was collected from late October 2011 through March 2013. Version 2 of the dataset became active on 13-May-2015.", "links": [ { diff --git a/datasets/gpmmrrnaifld_1.json b/datasets/gpmmrrnaifld_1.json index 6d521a931b..9b1c87cb97 100644 --- a/datasets/gpmmrrnaifld_1.json +++ b/datasets/gpmmrrnaifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrnaifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Micro Rain Radar (MRR) NASA IFloodS dataset was collected by a Micro Rain Radar (MRR), which is a vertically pointing Doppler radar which provided measurements of vertical velocity, drop size distribution, rainfall rate, attenuation, liquid water content, and reflectivity factor during the Iowa Flood Study (IFloodS), which took place in eastern Iowa during the spring of 2013. The goals of the campaign were to collect detailed measurements of precipitation at the Earth's surface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. A total of four MRRs were deployed, each adjacent to a two-dimensional video disdrometer (2DVD). Each MRR-2DVD site had one or more Autonomous Parsivel2 Unit (APU) with tipping bucket rain gauges either collocated or within 4-8 km away. The dataset covers the period of April 11, 2013 through June 16, 2013, but each MRR deployed may not contain data during the entirety of this period.", "links": [ { diff --git a/datasets/gpmmrrnaiphx_1.json b/datasets/gpmmrrnaiphx_1.json index 5ca7cb40b9..52b505051a 100644 --- a/datasets/gpmmrrnaiphx_1.json +++ b/datasets/gpmmrrnaiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrnaiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA Micro Rain Radar (MRR) is a vertically pointing Doppler radar which provided measurements of vertical velocity, drop size distribution, rainfall rate, attenuation, liquid water content, and reflectivity factor obtained during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in western North Carolina during the spring of 2014. A total of four MRRs were deployed, some co-located with other instruments and some were moved to different locations during the campaign. The dataset covers the period of April 22, 2014 through June 16, 2014, but each MRR deployed may not contain data during the entirety of this period. Two MRRs remained deployed through October 17, 2014 and data from these are also included in this dataset.", "links": [ { diff --git a/datasets/gpmmrrnamc3e_1.json b/datasets/gpmmrrnamc3e_1.json index 325ea10ff8..12067dd2e1 100644 --- a/datasets/gpmmrrnamc3e_1.json +++ b/datasets/gpmmrrnamc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrnamc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA Micro Rain Radar (MRR) MC3E dataset was collected by a Micro Rain Radar (MRR), which is a vertically pointing Doppler radar which provides measurements of vertical velocity, drop size distribution, rainfall rate, attenuation, liquid water content, and reflectivity factor obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in Oklahoma during the Spring of 2011. The MRR is a frequency-modulated continuous wave (FMCW) vertically pointing Doppler radar, which operates at 24.24GHz, and is the second generation of the instrument manufactured by METEK (URL: http://metek.de/product/mrr-2/).", "links": [ { diff --git a/datasets/gpmmrrolyx_1.json b/datasets/gpmmrrolyx_1.json index 56866f2441..17f4785a9c 100644 --- a/datasets/gpmmrrolyx_1.json +++ b/datasets/gpmmrrolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmrrolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Micro Rain Radar (MRR) OLYMPEX dataset was gathered during the Global Precipitation Measurement (GPM) Ground Validation OLYMPEX field campaign held at Washington\u2019s Olympic Peninsula from October 31, 2014 through May 22, 2016. The dataset contains measured and derived data from MRR instruments placed in four separate locations within the study region. The MRR is a Biral/Metek 24 GHz (K-band) vertically oriented Frequency Modulated Continuous Wave (FM-CW) radar that measures signal backscatter from which Doppler spectra, radar reflectivity, Doppler velocity, drop size distribution, rain rate, liquid water content, and path integrated attenuation are derived. Data files are available in ASCII data format.", "links": [ { diff --git a/datasets/gpmmwrdukeiphx_1.json b/datasets/gpmmwrdukeiphx_1.json index 35057e01b9..41070bdf1c 100644 --- a/datasets/gpmmwrdukeiphx_1.json +++ b/datasets/gpmmwrdukeiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmmwrdukeiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Duke Microwave Radiometer (MWR) IPHEx dataset consists of data collected by the MWR, which is a sensitive microwave radiometer that detects the microwave radiances at two frequencies: 23.8 and 31.4 GHz. The measurements are are used to determine the presence of vapor and liquid water molecules in the atmosphere along with other derived parameters. These data were obtained during the Integrated Precipitation and Hydrology Experiment (IPHEx) field experiment, which was held in North Carolina with the goal to characterize warm season orographic precipitation regimes and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. These data are available for May 1, 2014 through June 15, 2014 and are in netCDF-3 format.", "links": [ { diff --git a/datasets/gpmnavcitgcpex2_2.json b/datasets/gpmnavcitgcpex2_2.json index d6283e72d4..763e736c55 100644 --- a/datasets/gpmnavcitgcpex2_2.json +++ b/datasets/gpmnavcitgcpex2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavcitgcpex2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Navigation Data GCPEx V2 dataset was collected by the Cessna Citation II Research, which was aircraft, owned, and operated by the University of North Dakota (UND), participated in the GPM Cold-season Precipitation Experiment (GCPEx) by serving as an in situ microphysics sampling platform. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected to aid in the achievement Data User Guide of the overarching goal of GCPEx, which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The GCPEx navigation data set collected wind speed amongst several other parameters. It would also be beneficial to list the other parameters it collected.", "links": [ { diff --git a/datasets/gpmnavcitiphx_1.json b/datasets/gpmnavcitiphx_1.json index 575e403cbd..e1e9424342 100644 --- a/datasets/gpmnavcitiphx_1.json +++ b/datasets/gpmnavcitiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavcitiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Navigation Data IPHEx dataset supplies navigation data collected by the Cessna Citation II aircraft for flights that occurred during March 6, 2014 through June 13, 2014 for the Global Precipitation Measurement Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign in North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The Cessna Citation II Research aircraft, owned and operated by the University of North Dakota (UND), participated in the IPHEx field campaign by serving as an in situ microphysics sampling platform. This navigation dataset consists of final processed files containing records that include flight time, aircraft location (latitude, longitude, and altitude), air temperature, wind speed, and other relevant aircraft parameters in ASCII format.", "links": [ { diff --git a/datasets/gpmnavcitmc3e_1.json b/datasets/gpmnavcitmc3e_1.json index 921f153869..6f5e02b2bb 100644 --- a/datasets/gpmnavcitmc3e_1.json +++ b/datasets/gpmnavcitmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavcitmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Navigation Data MC3E dataset was collected by the Cessna Citation II Research aircraft owned and operated by the University of North Dakota (UND) participated in the Midlatitude Continental Convective Clouds Experiment (MC3E) supplying navigation data and also carrying cloud microphysics instruments. This navigation dataset consists of files (.txt) from UND containing records with flight time (UT seconds from midnight) and aircraft latitude, longitude and altitude. The dataset also contains the IWG1 data collected during the mission. Data was collected from April 1, 2011 through June 2, 2011.", "links": [ { diff --git a/datasets/gpmnavcitolyx_1.json b/datasets/gpmnavcitolyx_1.json index e72fe9d335..fef3278853 100644 --- a/datasets/gpmnavcitolyx_1.json +++ b/datasets/gpmnavcitolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavcitolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UND Citation Navigation Data OLYMPEX dataset supplies navigation data collected by the Cessna Citation II aircraft for flights that occurred during November 12, 2015 through December 19, 2015 for the Olympic Mountains Experiment (OLYMPEX) GPM Ground Validation field campaign. This navigation dataset consists of multiple altitude, pressure, temperature, airspeed, and ground speed measurements in ASCII format. ", "links": [ { diff --git a/datasets/gpmnavdc8gcpex_1.json b/datasets/gpmnavdc8gcpex_1.json index 34d0e695f2..f52d3ce523 100644 --- a/datasets/gpmnavdc8gcpex_1.json +++ b/datasets/gpmnavdc8gcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavdc8gcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation DC-8 Navigation and Housekeeping Data GCPEx dataset, which is composed of two types of files. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. National Suborbital Education and Research Center (NSERC) of the University of North Dakota (UND) provided geo-located housekeeping data containing attributes, such as altitude, pressure, air speed, and wind speed. The NASA DC-8 Navigation data in comma delimited IWG1 format were collected and utilized in-flight during the GCPEx mission and retrieved from the Real-Time Mission Monitor. Both file types are available for most of the dataset dates, however please note that there are a few dates where only the IWG1 formatted data is available.", "links": [ { diff --git a/datasets/gpmnavdc8olyx_1.json b/datasets/gpmnavdc8olyx_1.json index bdc96e52c3..3b62e63269 100644 --- a/datasets/gpmnavdc8olyx_1.json +++ b/datasets/gpmnavdc8olyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnavdc8olyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA DC-8 Navigation Data OLYMPEX dataset supplies navigation data collected by the NASA DC-8 aircraft for flights that occurred during November 5, 2015 through December 19, 2015 for the Olympic Mountains Experiment (OLYMPEX) GPM Ground Validation field campaign. This navigation dataset consists of multiple altitude, pressure, temperature, airspeed, and ground speed measurements in ASCII-IWG1 and XML data formats.", "links": [ { diff --git a/datasets/gpmnaver2iphx_1.json b/datasets/gpmnaver2iphx_1.json index 25513c94d1..693a0f7884 100644 --- a/datasets/gpmnaver2iphx_1.json +++ b/datasets/gpmnaver2iphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnaver2iphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA ER-2 Navigation Data IPHEx dataset was gathered during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina. The ER-2 Aircraft flew during the IPHEx field campaign to aid in GPM validation. The science instruments onboard the aircraft acted as a proxy for GPM satellite instruments. Twenty-one ER-2 flights occurred during May 1, 2014 through June 14, 2014. The dataset consists of navigation data, as well as meteorological parameters collected by an on-board navigation recorder every second of the flight. The data are available in ASCII and XML formats.", "links": [ { diff --git a/datasets/gpmnaver2olyx_1.json b/datasets/gpmnaver2olyx_1.json index 1ae6bbeea1..a07d9bc67f 100644 --- a/datasets/gpmnaver2olyx_1.json +++ b/datasets/gpmnaver2olyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnaver2olyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA ER-2 Navigation Data OLYMPEX dataset supplies navigation data collected by the NASA ER-2 aircraft for flights that occurred during November 9, 2015 through December 15, 2015 for one of the GPM Ground Validation field campaigns called the Olympic Mountains Experiment (OLYMPEX). This navigation dataset consists of multiple altitude, pressure, temperature parameters, airspeed, and ground speed measurements in ASCII, ASCII-IWG1, and XML data file formats.", "links": [ { diff --git a/datasets/gpmncamdc8gcpex_1.json b/datasets/gpmncamdc8gcpex_1.json index b76a0a12fb..d0b56abec6 100644 --- a/datasets/gpmncamdc8gcpex_1.json +++ b/datasets/gpmncamdc8gcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmncamdc8gcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation DC-8 Camera Nadir GCPEx dataset contains geo-located, visible-wavelength imagery of the ground obtained from the nadir camera aboard the NASA DC-8 in Canada during the Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The data is available only for February 20, 2012, a clear-air flight day. DC-8 Camera nadir data may be useful for determining snow cover and lake ice cover for emissivity studies. The dataset also includes, for convenience and reproducibility, aircraft navigation information and ground temperatures to aid in emissivity retrievals.", "links": [ { diff --git a/datasets/gpmnmqifld_1.json b/datasets/gpmnmqifld_1.json index 34e4dcb5b4..e8895cdaf8 100644 --- a/datasets/gpmnmqifld_1.json +++ b/datasets/gpmnmqifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnmqifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IFloodS dataset contains quality control products, real time rain rate estimates, hourly precipitation rate estimates, and three-dimensional reflectivity products. These data products are also referred to as Multi-Radar Multi-Sensor Precipitation Reanalysis for Satellite Validation (MRMS) product and were created using the NOAA NMQ System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RUC) model analysis fields, and Hydrometeorological Automated Data Systems (HADS) gauge data. The files provided in this dataset are from the NMQ system output obtained during the GPM Iowa Flood Studies (IFloodS) field campaign that occurred throughout Iowa. These data are available in ASCII, netCDF-4, and binary formats for the dates between April 1, 2013 through June 30, 2013.", "links": [ { diff --git a/datasets/gpmnmqiphx_1.json b/datasets/gpmnmqiphx_1.json index 3e24360dd6..d8e5a72567 100644 --- a/datasets/gpmnmqiphx_1.json +++ b/datasets/gpmnmqiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnmqiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IPHEx dataset consists of six different data products: precipitation rate, hourly rainfall accumulation, daily rainfall accumulation, hybrid scan reflectivity, three-dimensional reflectivity, and vertically integrated liquid content estimates. These data products were created using the NOAA NMQ System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RUC) model analysis fields, and Hydrometeorological Automated Data Systems (HADS) gauge data. The files provided in this dataset are from system output during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign that occurred in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. These data are available in ASCII and netCDF-4 formats for dates between April 30, 2014 through June 16, 2014.", "links": [ { diff --git a/datasets/gpmnoxpiphx_1.json b/datasets/gpmnoxpiphx_1.json index a0095618ce..27f210e28a 100644 --- a/datasets/gpmnoxpiphx_1.json +++ b/datasets/gpmnoxpiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnoxpiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA X-band dual-Polarimetric radar (NOXP) IPHEx dataset consists of differential reflectivity, differential phase shift, co-polar cross correlation, radial Doppler velocity, spectrum width, signal index, melting layer index, reflectivity, drop size distribution, and rainfall rate observations, as well as other radar parameters, collected by NOXP mobile radar during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign. The IPHEx field campaign occurred in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. The NOXP radar, operated by the NOAA National Severe Storm Laboratory (NSSL), was positioned in the Pigeon River basin of the Great Smoky Mountains of North Carolina. NOXP data are available in netCDF-3 format for dates between April 21, 2014 through June 15, 2014. The dataset includes weather condition photos taken at the NOXP radar site in JPG format.", "links": [ { diff --git a/datasets/gpmnpolifld2_2.json b/datasets/gpmnpolifld2_2.json index f96bbab257..80cd141333 100644 --- a/datasets/gpmnpolifld2_2.json +++ b/datasets/gpmnpolifld2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnpolifld2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA S-Band Dual Polarimetric (NPOL) Doppler Radar IFloodS dataset was collected from April 30 to June 16, 2013 near Traer, Iowa as part of the Global Precipitation Measurement (GPM) mission Iowa Flood Studies (IFloodS) campaign. Officially the IFloodS campaign ran from May 1 to June 15 but the NPOL Doppler radar was installed and calibrated prior to the start, allowing for the wider period of record. The NPOL radar, developed by a research team from Wallops Flight Facility, is a fully transportable and self-contained S-band (10 cm), scanning dual-polarimetric, doppler research radar that collected and operated nearly continuously during the IFloodS field campaign. It takes accurate volumetric measurements of precipitation including rainfall rate, particle size distributions, water content and precipitation type. The NPOL Doppler Radar IFloodS data is available in Universal Format (UF) with browse images available in PNG file format. ", "links": [ { diff --git a/datasets/gpmnpoliphx_1.json b/datasets/gpmnpoliphx_1.json index c5c7c8c064..1c50023569 100644 --- a/datasets/gpmnpoliphx_1.json +++ b/datasets/gpmnpoliphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnpoliphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA S-Band Dual Polarimetric (NPOL) Doppler Radar IPHEx dataset was collected during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign conducted in South Carolina from April 27, 2014 through June 16, 2014. The NPOL Doppler Radar scanned in high-resolution Plan Position Indicator (PPI), Range-Height Indicator (RHI), and PPI Sector (PPS) scan modes and provided measurements of precipitation in liquid, mixed, and ice phases. Data files are available in tarred universal format (UF) files, and browse images are available in compressed PNG files.", "links": [ { diff --git a/datasets/gpmnpolmc3e_1.json b/datasets/gpmnpolmc3e_1.json index 32ab972549..d47ed59d13 100644 --- a/datasets/gpmnpolmc3e_1.json +++ b/datasets/gpmnpolmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnpolmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA S-band Dual Polarimetric (NPOL) Doppler Radar MC3E dataset was collected by the NASA NPOL radar, which was developed by a research team from Wallops Flight Facility, is a fully transportable and self-contained S-band (10 cm), scanning dual-polarimetric, doppler research radar that collected data nearly continuously during the Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. NPOL scanned in high resolution Range Height Indicator (RHI) mode (every 40 sec) and provided measurements of precipitation in liquid, mixed and ice phase. The scanning strategy emphasized vertical structure sampling via RHI and narrow sector-volume data collections. Additional files were processed from the UF files using the Colorado State University (CSU) Hydrometeor Identification Algorithm (HID) providing classification of hydrometeors (e.g. rain, drizzle, hail, ice crystals, wet or dry snow, graupel density). Data was collected from April 11, 2011 through June 3, 2011.", "links": [ { diff --git a/datasets/gpmnpololyx2_2.json b/datasets/gpmnpololyx2_2.json index 58f2b676db..1b2db673d8 100644 --- a/datasets/gpmnpololyx2_2.json +++ b/datasets/gpmnpololyx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnpololyx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA S-Band Dual Polarimetric (NPOL) Doppler Radar OLYMPEX V2 dataset consists of rain rate, reflectivity, Doppler velocity, and other radar measurements obtained from the NPOL radar during the Global Precipitation Mission (GPM) Olympic Mountains Experiment (OLYMPEX) campaign. NPOL,developed by a research team from Wallops Flight Facility, is a fully transportable and self-contained S-band (10 cm), scanning dual-polarimetric Doppler research radar that was placed near the ocean on the Olympic Peninsula. Data files are available from November 5, 2015 thru January 15, 2016 in Universal Format (UF), with browse imagery files in PNG format containing corrected radar reflectivity, differential reflectivity, specific differential phase, differential phase, co-polar correlation, and Doppler velocity images.", "links": [ { diff --git a/datasets/gpmnpolwff_1.json b/datasets/gpmnpolwff_1.json index eacbd5e15c..45f0ed58b7 100644 --- a/datasets/gpmnpolwff_1.json +++ b/datasets/gpmnpolwff_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnpolwff_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA S-Band Dual-Polarimetric (NPOL) Doppler Radar Wallops Flight Facility (WFF) dataset consists of rain rate, reflectivity, Doppler velocity, and other radar measurements obtained from the NPOL doppler radar positioned at the Wallops Flight Facility (WFF) in support of the Global Precipitation Mission (GPM). NPOL was developed by scientists at WFF and is a fully transportable and self-contained S-band (10 cm), scanning dual-polarimetric Doppler research radar that was placed near Newark, Maryland between GPM GV missions. Data files are available from December 6, 2013 thru April 28, 2017 in Universal Format (UF), with browse files in PNG format containing images of corrected radar reflectivity, differential reflectivity, specific differential phase, co-polar correlation, and Doppler velocity images. Data are tarred into daily collections of files and zipped for storage and quick download.", "links": [ { diff --git a/datasets/gpmnrlrtifld_1.json b/datasets/gpmnrlrtifld_1.json index 186400979a..ff13486291 100644 --- a/datasets/gpmnrlrtifld_1.json +++ b/datasets/gpmnrlrtifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmnrlrtifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Naval Research Laboratory (NRL) Near-Real Time Rain Rates IFloodS data product was created for the GPM Iowa Flood Studies (IFloodS) field campaign from April 23, 2013 through June 30, 2013. The IFloodS field campaign was a ground measurement campaign that took place in eastern Iowa. The goals of the campaign were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This NRL real time rain rates data product was produced using the Probability Matching Method with rain gauge, Defense Meteorological Satellite Program (DMSP) F15 Special Sensor Microwave - Imager (SSM/I), and DMSP F16 Special Sensor Microwave - Imager/Sounder (SSMIS) data. This data product includes rain rate estimates and files are available in netCDF-4 and binary formats, as well as corresponding browse imagery in JPG format.", "links": [ { diff --git a/datasets/gpmodmlpvex_1.json b/datasets/gpmodmlpvex_1.json index dd56717f62..5f7281f456 100644 --- a/datasets/gpmodmlpvex_1.json +++ b/datasets/gpmodmlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmodmlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Optical Disdrometer (ODM) LPVEx dataset consists of precipitation particle size distribution data collected by the Eigenbrodt Optical Disdrometer (ODM) deployed onboard the RV Aranda research vessel. ODM was specifically designed to measure precipitation on ship-based platforms that experience high and variable winds. ODM\u2019s ability to maintain the optimal orientation with respect to the wind allows it to obtain more accurate precipitation measurements in this type of environment. The ODM data were collected as part of the Light Precipitation Validation Experiment (LPVEx) in September and October of 2010 around the Gulf of Finland. The overarching goals of LPVEx were to detect and understand the process of light rainfall formation at high latitudes and to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms. The ODM dataset files are available in ASCII text format from September 15 through September 26, 2010.", "links": [ { diff --git a/datasets/gpmopasscgcpex_1.json b/datasets/gpmopasscgcpex_1.json index e3486c16ac..aef0b3619b 100644 --- a/datasets/gpmopasscgcpex_1.json +++ b/datasets/gpmopasscgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmopasscgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation CARE Satellite Overpass GCPEx Images are the satellite overpass images for the GPM Cold-season Precipitation Experiment (GCPEx), which occurred in Ontario, Canada, January 15, 2012 through February 28, 2012. GCPEx addressed limitations in the GPM snowfall retrieval algorithm through the collection of microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The satellite tracks include the DMSP satellite numbers 15, 16, 17, 18, and 19. A list of starting overpass times per satellite and day is included.", "links": [ { diff --git a/datasets/gpmopassolyx_4.json b/datasets/gpmopassolyx_4.json index e1e7cacfa0..7847fe7656 100644 --- a/datasets/gpmopassolyx_4.json +++ b/datasets/gpmopassolyx_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmopassolyx_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Composite Satellite Overpasses OLYMPEX dataset provides brightness temperature, precipitation, and total column water vapor estimates from multiple satellite overpasses including DMSP F16-19, GCOM-W1, GPM, MetOp, NOAA, and NPP for the OLYMPEX field campaign. The OLYMPEX field campaign took place between November, 2015, and January, 2016, with additional ground sampling continuing through February, on the Olympic Peninsula in the Pacific Northwest of the United States. This field campaign provides ground-based validation support of the findings resulting from the Global Precipitation Measurement (GPM) Core Observatory satellite. Data files are available from November 1, 2015 thru May 1, 2016 in HDF-5 format.", "links": [ { diff --git a/datasets/gpmoumesmc3e_1.json b/datasets/gpmoumesmc3e_1.json index 547a3079aa..637c8cd7ac 100644 --- a/datasets/gpmoumesmc3e_1.json +++ b/datasets/gpmoumesmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmoumesmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Oklahoma Climatological Survey Mesonet MC3E data were collected during the Midlatitude Continental Convective Clouds Experiment (MC3E) in central Oklahoma during the April-June 2011 period. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. Collected by a network of weather stations, this dataset is composed of 15 minute and 5 minute files with one file per site per day in mts format. Data can be read as ASCII text. Multiple parameters found in this dataset include relative humidity, air temperature, wind speed and direction, precipitation and calibrated soil moisture. More information on the contents and data format can be found at http://www.mesonet.org/index.php/site/about/mdf_mts_files.", "links": [ { diff --git a/datasets/gpmpadukeiphx_1.json b/datasets/gpmpadukeiphx_1.json index be095d0908..8dcb1f8149 100644 --- a/datasets/gpmpadukeiphx_1.json +++ b/datasets/gpmpadukeiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpadukeiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Duke Parsivel IPHEx dataset were collected during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign which was held in the Southern Appalachian region, including the Piedmont and Coastal Plain regions, of North Carolina. OTT laser-based Parsivel instruments operated from May 1, 2014 through June 30, 2014. The IPHEx campaign was designed to characterize warm season orographic precipitation regimes and determine the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. The parsivel data are available in ASCII-csv format for each of the parsivel locations and contain precipitation intensity and drop parameters. ", "links": [ { diff --git a/datasets/gpmpagcpex_1.json b/datasets/gpmpagcpex_1.json index 8d87b1a590..64d53a3985 100644 --- a/datasets/gpmpagcpex_1.json +++ b/datasets/gpmpagcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpagcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) GCPEx dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The GPM Cold-season Precipitation Experiment (GCPEx) addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The APU dataset for GCPEx provides precipitation data including raindrop size, raindrop counts, precipitation drop size, precipitation rate, precipitation amount, and snowflake size, counts and distribution. The GCPEx APU data was collected from several sites in Canada during the Winter 2011-2012 period.", "links": [ { diff --git a/datasets/gpmpahymex_1.json b/datasets/gpmpahymex_1.json index e2d3093ee0..f1f3e9f734 100644 --- a/datasets/gpmpahymex_1.json +++ b/datasets/gpmpahymex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpahymex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) HyMeX dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The HYdrological cycle in Mediterranean EXperiment (HyMeX) aimed to improve the understanding, quantification and modelling of the hydrological cycle in the Mediterranean, with emphasis on the predictability and evolution of extreme weather events, inter-annual to decadal variability of the Mediterranean coupled system, and associated trends in the context of global changeThe APU dataset for HyMeX provides precipitation data including raindrop size, raindrop counts, precipitation drop size, precipitation rate, precipitation amount, and snowflake size, snowflake counts, and snowflake distribution. The HyMeX APU data were collected in Italy and France from September to November 2012.", "links": [ { diff --git a/datasets/gpmpaifld_1.json b/datasets/gpmpaifld_1.json index 13bb7965ea..001a31992c 100644 --- a/datasets/gpmpaifld_1.json +++ b/datasets/gpmpaifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpaifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) IFloodS dataset collected data from several sites in eastern Iowa during the spring of 2013. The APU dataset for the Iowa Flood Studies (IFloodS) Field Experiment provides precipitation data including precipitation drop size, counts, and distribution. The goals of the campaign were to collect detailed measurements of precipitation at the Earth's surface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. The APU is an optical disdrometer based on single particle extinction that measures particle size and fall velocity. This APU consists of the Parsivel, which was developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville.", "links": [ { diff --git a/datasets/gpmpaiphx_1.json b/datasets/gpmpaiphx_1.json index 27a41659bc..06b5d25799 100644 --- a/datasets/gpmpaiphx_1.json +++ b/datasets/gpmpaiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpaiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA Autonomous Parsivel Unit (APU) IPHEx dataset was acquired by multiple parsivel instruments during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in western North Carolina. IPHEx sought to characterize warm season orographic precipitation regimes, and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. The APU, an optical disdrometer based on single particle extinction, measures particle size, and fall velocity. ASCII encoded data files contain information on the drop size distribution and integral precipitation parameters such as precipitation rate, reflectivity, and mass-weighted mean diameter.", "links": [ { diff --git a/datasets/gpmpal_1.json b/datasets/gpmpal_1.json index 0944a1af1f..92ef3a4d7f 100644 --- a/datasets/gpmpal_1.json +++ b/datasets/gpmpal_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpal_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Passive Aquatic Listener (PAL) dataset contains underwater hydrophone data at a one-minute time step with a typical 5 km diameter footprint when deployed or drifting at 1 km depth. Areal rain rate and wind speed estimates are mutually exclusive, meaning that time periods that are unambiguously identified as rain are used to estimate rain rate, and wind speed is only estimated in the absence of rain. The data are available in netCDF-4 format and include time, interpolated geolocation data, the rain rate or wind speed estimates for each time, and location pair. PALs are deployed irregularly on Argo ocean profiling floats and moorings, typically as part of field campaigns. As such, the number of PALs collecting data is inconsistent in time and space. The entire dataset covers the period from October 18, 2010, through July 28, 2021.", "links": [ { diff --git a/datasets/gpmpalpvex_1.json b/datasets/gpmpalpvex_1.json index 27485289f9..a5f46c38ab 100644 --- a/datasets/gpmpalpvex_1.json +++ b/datasets/gpmpalpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpalpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) LPVEx dataset provides rainfall data for the Global Precipitation Measurement (GPM) Misson Ground Validation Experiment collected at four sites in Finland: Harmaja, Emasalo, Jarvenpaa, and the Gulf of Finland (Aranda) during the Light Precipitation Validation Experiment (LPVEx), which took place during September and October of 2010. The experiment leveraged in situ microphysical property measurements, coordinated remote sensing observations, and cloud resolving model simulations of high latitude precipitation systems to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms.", "links": [ { diff --git a/datasets/gpmpamc3e_1.json b/datasets/gpmpamc3e_1.json index 2f8c302ecf..e4d2b636d8 100644 --- a/datasets/gpmpamc3e_1.json +++ b/datasets/gpmpamc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpamc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) MC3E dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The APU dataset for the Midlatitude Continental Convective Clouds Experiment (MC3E) provides precipitation data including raindrop size, precipitation drop size, precipitation rate and amount. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leveraged the unprecedented observing infrastructure currently available in the central United States, combined with an extensive sounding array, remote sensing and in situ aircraft observations, NASA GPM ground validation remote sensors, and new ARM instrumentation purchased with American Recovery and Reinvestment Act funding. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms.", "links": [ { diff --git a/datasets/gpmpanoaamc3e_1.json b/datasets/gpmpanoaamc3e_1.json index 489e3f4b32..772fad0eda 100644 --- a/datasets/gpmpanoaamc3e_1.json +++ b/datasets/gpmpanoaamc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpanoaamc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA Parsivel MC3E dataset was collected in central Oklahoma during the Midlatitude Continental Convective Clouds Experiment (MC3E) from April 5, 2011 through June 6, 2011. The NOAA Parsivel dataset includes processed data consisting of either moment data (e.g., reflectivity and rain rate estimates) or raindrop number concentration estimates; the data provided a reference reflectivity to calibrate the S-band profiler during the experiment. The moment data includes 1-minute resolution estimates of rain rate, reflectivity, and other parameters related to the health of the Parsivel instrument. The raindrop number concentration data are also at 1-minute resolution and are the result of converting the observed raindrop passing the sensor into the number of raindrops expected in a unit volume per diameter interval. Both the moment data and the number concentration data were saved in daily files in ASCII format. Daily images were also generated from the Parsivel observations and contain the 1-minute reflectivity, rain rate, and number concentration N(D); browse images are saved in TIF format.", "links": [ { diff --git a/datasets/gpmpansstc_1.json b/datasets/gpmpansstc_1.json index 28d4c422a2..7ebeb1a4fa 100644 --- a/datasets/gpmpansstc_1.json +++ b/datasets/gpmpansstc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpansstc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Autonomous Parsivel Unit (APU) NSSTC dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer based on single particle extinction that measures particle size and fall velocity. This APU consists of the Parsivel, which was developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. This dataset provides rainfall data for the Global Precipitation Measurement (GPM) Mission Ground Validation Experiment collected at the National Space Science Technology Center (NSSTC) in Huntsville, Alabama. The validation effort will entail numerous GPM-specific and joint-agency/international external field campaigns, using state of the art cloud and precipitation observational infrastructure. Surface rainfall will be measured by very dense rain gauge and disdrometer networks at various field campaign sites. There may be occasional gaps in the data when the instrument is not resident at the NSSTC and is sent to participate in field campaigns.", "links": [ { diff --git a/datasets/gpmparawifld_1.json b/datasets/gpmparawifld_1.json index 415c6bb302..367ca0e508 100644 --- a/datasets/gpmparawifld_1.json +++ b/datasets/gpmparawifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmparawifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Raw Autonomous Parsivel Unit (APU) IFloodS dataset was collected by 14 Autonomous Parsivel Unit (APU) sites in eastern Iowa during the Global Precipitation Measurement (GPM) mission Iowa Flood Studies (IFloodS) field campaign. The campaign aimed to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars while simultaneously collecting data from satellites passing overhead. APU is an optical disdrometer system that measures precipitation particle size and fall velocity. This dataset consists of APU-calculated parameters and unfiltered drop spectrum data. The dataset files are available in ASCII text format from April 1 through May 24, 2013. Officially, the IFloodS campaign ran from May 1 to June 15, 2013, but the APUs were installed and had begun collecting data prior to the start of the campaign.", "links": [ { diff --git a/datasets/gpmparprbgcpex_1.json b/datasets/gpmparprbgcpex_1.json index 167170a5f0..655998c71f 100644 --- a/datasets/gpmparprbgcpex_1.json +++ b/datasets/gpmparprbgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmparprbgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NCAR Cloud Microphysics Particle Probes GCPEx data was collected during the GPM Cold-season Precipitation Experiment (GCPEx), which occurred in Ontario, Canada during the winter season of 2011 through 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The GPM Ground Validation NCAR Cloud Microphysics Particle Probes GCPEx dataset was obtained from three instruments carried aboard the University of North Dakota (UND) Cessna Citation aircraft. These probes, the 2D-C, Cloud Imaging Probe (CIP) and High Volume Precipitation Spectrometer (HVPS-3), collected particle size distributions and particle images which were processed by the National Center for Atmospheric Research (NCAR). Data were collected January 16, 2012 through February 25, 2012. A related cloud microphysics dataset, GPM Ground Validation UND Citation Cloud Microphysics GCPEx is also available.", "links": [ { diff --git a/datasets/gpmparprbiphx_1.json b/datasets/gpmparprbiphx_1.json index d2efb5dc28..796b038ef5 100644 --- a/datasets/gpmparprbiphx_1.json +++ b/datasets/gpmparprbiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmparprbiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NCAR Particle Probes IPHEx dataset consists of Ice Water Content (IWC), particle number concentration normalized by bin width, and total particle number concentration data that were collected from three particle probes onboard the University of North Dakota (UND) Citation II aircraft during the Global Precipitation Mission (GPM) Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx). These instruments include the PMS Two-Dimensional Cloud probe (2D-C), the SPEC Two-Dimensional Stereo probe (2D-S), and the SPEC High Volume Precipitation Spectrometer version 3 (HVPS-3). The IPHEx campaign took place in North Carolina with the goal of evaluating the accuracy of satellite precipitation measurements and using the collected data for hydrology models in the region. The campaign\u2019s intense study period occurred from May 1 through June 15, 2014. All instruments are two-dimensional optical array probes which record images of particles that travel through the sampling area. The data files are available from May 9 through June 12, 2014 in ASCII format using the NASA Ames format specification. Browse images of instrument array 5-sec measurements are available in PNG format.", "links": [ { diff --git a/datasets/gpmparprbmc3e_1.json b/datasets/gpmparprbmc3e_1.json index d0032b775c..201115b08c 100644 --- a/datasets/gpmparprbmc3e_1.json +++ b/datasets/gpmparprbmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmparprbmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NCAR Cloud Microphysics Particle Probes MC3E dataset was collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in central Oklahoma during the April-June 2011 period. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterization and space-based rainfall retrieval algorithms over land that had never before been available. The GPM Ground Validation NCAR Cloud Microphysics Particle Probes MC3E dataset was obtained from three instruments carried aboard the University of North Dakota (UND) Cessna Citation aircraft. These probes, the 2D-C, Cloud Imaging Probe (CIP) and High Volume Precipitation Spectrometer (HVPS-3), collected particle size distributions and particle images which were processed by the National Center for Atmospheric Research (NCAR). Data were collected April 22, 2011 through June 2, 2011.", "links": [ { diff --git a/datasets/gpmparprbolyx_1.json b/datasets/gpmparprbolyx_1.json index c3d30ab099..3aaa20057c 100644 --- a/datasets/gpmparprbolyx_1.json +++ b/datasets/gpmparprbolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmparprbolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NCAR Particle Probes OLYMPEX dataset consists of ice water content, particle concentration normalized by bin width, and total particle concentration collected from three instruments flown on the University of North Dakota (UND) Citation aircraft during selected dates in November and December 2015. The PMS Two-Dimensional Cloud probe (2D-C), the SPEC Two-dimensional Stereo probe (2D-S), and two SPEC High Volume Precipitation Spectrometer 3 (HVPS-3) instruments were used in the Global Precipitation Mission (GPM) Olympic Mountains Experiment (OLYMPEX) campaign. All instruments are two-dimensional optical array probes which record images of particles that travel through the sampling area. Data files are available in ASCII format, and browse images are available in PNG format.", "links": [ { diff --git a/datasets/gpmpawneemc3e_1.json b/datasets/gpmpawneemc3e_1.json index fbabf57f5a..11f2b95600 100644 --- a/datasets/gpmpawneemc3e_1.json +++ b/datasets/gpmpawneemc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpawneemc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Pawnee Radar MC3E dataset was collected by the Pawnee radar data for the Midlatitude Continental Convective Clouds Experiment (MC3E) held in Oklahoma were collected on May 24, 2011 to support the CHILL radar and the NASA ER-2 instrumentation data. The Pawnee is a single polarization (V polarization) Doppler radar. During the ER2 flight, the Pawnee conducted a wide azimuth opening PPI sector volume scan oriented towards the east designed to provide general 3D coverage of the ER2 flight area. When the ER2 reported starting a course reversal, the CHILL and Pawnee radars attempted to start sector scans at the same time to support dual Doppler wind analyses. In an effort to expand the MC3E sampling to a wider geographical area, the NASA ER2 aircraft was directed to Northeastern Colorado while widespread rain was in progress on May 24, 2011. The aircraft flew a series of pre-defined ground tracks that coincided with radials from the CHILL radar. This aided in keeping the aircraft in the plane of a series of RHI scans done by CHILL. The single polarization Pawnee radar maintained volume coverage of the echo system while the radial flight legs were in progress. During aircraft course reversals at the ends of the radial legs, the CHILL and Pawnee radars started volume scans in synchronization to support dual Doppler wind syntheses. CHILL and Pawnee radar data are available as separate datasets.", "links": [ { diff --git a/datasets/gpmpersucifld_1.json b/datasets/gpmpersucifld_1.json index d888ebb80b..6dd1b8e783 100644 --- a/datasets/gpmpersucifld_1.json +++ b/datasets/gpmpersucifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpersucifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS dataset is a subset from the global 30-minute PERSIANN-CCS files generated in near-real time selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign. The main goal of IFloodS were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This PERSIANN-CCS data product is available in ASCII and netCDF-4 formats from April 1, 2013 thru July 1, 2013. ", "links": [ { diff --git a/datasets/gpmpipicepop_1.json b/datasets/gpmpipicepop_1.json index 1b97a093ad..8114505bb8 100644 --- a/datasets/gpmpipicepop_1.json +++ b/datasets/gpmpipicepop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpipicepop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Precipitation Imaging Package (PIP) ICE POP dataset includes precipitation measurements and video images collected by the Precipitation Imaging Package (PIP) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to the Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. Data values obtained using PIP measurements include particle size distributions, fall velocity distributions, precipitation density estimates, and precipitation rates. The dataset files are available from June 18, 2017 through December 30, 2018 as generic data files (.dat) in ASCII-CSV format with browse imagery and video available in PNG and AVI format.", "links": [ { diff --git a/datasets/gpmplgcpex_1.json b/datasets/gpmplgcpex_1.json index 346aedc4eb..f7cb173c89 100644 --- a/datasets/gpmplgcpex_1.json +++ b/datasets/gpmplgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmplgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Pluvio Precipitation Gauge GCPEx dataset contains both one minute measurements and a cumulative record of the accumulation and intensity for liquid, solid, and mixed precipitation collected during the GPM Cold-season Precipitation Experiment (GCPEx). GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. GCPEx took place in Ontario, Canada uring the winter season of December 2011 through February 2012 where data was collected at five sites: CARE, Huronia, Steamshow, Skydive and Morton.", "links": [ { diff --git a/datasets/gpmpllpvex_1.json b/datasets/gpmpllpvex_1.json index e0e56fb4e0..9bda76a94c 100644 --- a/datasets/gpmpllpvex_1.json +++ b/datasets/gpmpllpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpllpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Pluvio Precipitation Gauge LPVEx dataset contains both one minute measurements and a cumulative record of the accumulation and intensity of liquid, solid, and mixed precipitation. This dataset was collected during the Light Precipitation Experiment (LPVEx) which was part of the Global Precipitation Measurement (GPM) Misson Ground Validation Experiment which took place in Finland from September 2010 to October 2010. The experiment leveraged in situ microphysical property measurements, coordinated remote sensing observations, and cloud resolving model simulations of high latitude precipitation systems to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms. The campaign will use these measurements to better understand the process of light rainfall formation at high latitudes and augment the currently limited database of light rainfall microphysical properties that form the critical assumptions at the root of satellite retrieval algorithm. Data were collected at three sites: Harmaja, Emasalo, and Jarvenpaa.", "links": [ { diff --git a/datasets/gpmplolyx_1.json b/datasets/gpmplolyx_1.json index 8450789ea5..2267849324 100644 --- a/datasets/gpmplolyx_1.json +++ b/datasets/gpmplolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmplolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Pluvio Precipitation Gauges OLYMPEX dataset contains one-minute precipitation rate and precipitation accumulation measurements, as well as start and end times of precipitation events, that were collected during the Olympic Mountain Experiment (OLYMPEX) field campaign on the Olympic Peninsula in the Pacific Northwest of the United States. A Pluvio 400 weighing bucket gauge created by OTT Hydromet in Kempten, Germany was used to collect data at three different sites: Neilton Point (apu04), Wynoochee Trailer (apu10), and Upper Quinault Enchanted Valley (apu30). Data were collected from October 31, 2015 through January 31, 2016, but exact dates vary by site. Data files are available in ASCII-tsv format.", "links": [ { diff --git a/datasets/gpmpmastmetlpvex_1.json b/datasets/gpmpmastmetlpvex_1.json index 48eab5eb5f..99b6ff2f96 100644 --- a/datasets/gpmpmastmetlpvex_1.json +++ b/datasets/gpmpmastmetlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpmastmetlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Physicum Building Mast Meteorological Data LPVEx dataset consists of meteorological data (temperature, pressure, wind, precipitation, and radiation) collected from the Station for Measuring Ecosystem-Atmosphere Relations III (SMEAR III) at the University of Helsinki\u2019s Physicum building rooftop weather station in Helsinki, Finland. These data were collected during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign that took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. These meteorological data files are available from September 16 through October 22, 2010 in ASCII-CSV and ASCII text formats.", "links": [ { diff --git a/datasets/gpmpossgcpex_1.json b/datasets/gpmpossgcpex_1.json index 90f43c5fa0..20598cdfb3 100644 --- a/datasets/gpmpossgcpex_1.json +++ b/datasets/gpmpossgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpossgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Precipitation Occurrence Sensor System (POSS) GCPEx dataset is comprised of data gathered during the GPM Cold-season Precipitation Experiment (GCPEx), which took place in Ontario, Canada, January 15 - March 1, 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The POSS is a bi-static X-band Doppler radar designed by Environment Canada. The POSS measures a signal whose frequency is proportional to the particle Doppler velocity and whose amplitude is proportional to the particle scattering cross-section. Its measurements can be used to provide information regarding precipitation occurrence, type, rate, and raindrop size distribution.", "links": [ { diff --git a/datasets/gpmposslpvex_1.json b/datasets/gpmposslpvex_1.json index 09252507bb..fd3e14484f 100644 --- a/datasets/gpmposslpvex_1.json +++ b/datasets/gpmposslpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmposslpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Precipitation Occurrence Sensor System (POSS) LPVEx dataset consists of precipitation and radar parameter estimates for both liquid and solid precipitation. Measurements were collected by the Precipitation Occurrence Sensor System (POSS) during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The POSS dataset files are available from September 18, 2010 through April 20, 2011 for two POSS sites: Emasalo and Jarvenpaa. The data files are in CSV format with browse imagery in PNG format.", "links": [ { diff --git a/datasets/gpmprecipmgcpex_1.json b/datasets/gpmprecipmgcpex_1.json index 1692f0fbf8..2211c4f9e8 100644 --- a/datasets/gpmprecipmgcpex_1.json +++ b/datasets/gpmprecipmgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmprecipmgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Manual Precipitation Measurements GCPEx dataset was collected during the GPM Cold-season Precipitation Experiment (GCPEx) in Ontario, Canada with data collections from January 18 - March 28, 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Precipitation amount, weight, snow water equivalent and present weather condition were recorded using a Tretyakov gauge inside a double fence intercomparison reference (DFIR) shield.", "links": [ { diff --git a/datasets/gpmprecipolyx_1.json b/datasets/gpmprecipolyx_1.json index dc86971be9..d1853f42cf 100644 --- a/datasets/gpmprecipolyx_1.json +++ b/datasets/gpmprecipolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmprecipolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Daily Precipitation Olympic Mountain Experiment (OLYMPEX) dataset consists of a single netCDF-4 data file containing estimates of daily precipitation, both rainfall and snowfall amounts, on a 1/32 degree spatial resolution grid covering the extent of the OLYMPEX field campaign region in the Olympic Mountains of the state of Washington. This data product was created for the GPM Ground Validation OLYMPEX field campaign. These VIC precipitation estimates are based on NOAA WSR-88D radar and rain gauge data incorporated in NOAA\u2019s National Severe Storms Laboratory (NSSL) local gauge bias-corrected radar quantitative precipitation estimation (QPE) model (product Q3GC) and the Mountain Mapper QPE model (product Q3MM). The VIC hydrology model was used to invert the snow water equivalent (SWE) values to derive precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. The VIC precipitation data are available from October 1, 2015 through April 30, 2016.", "links": [ { diff --git a/datasets/gpmpvigcpex_1.json b/datasets/gpmpvigcpex_1.json index 5f3da15dbf..8ee3586cab 100644 --- a/datasets/gpmpvigcpex_1.json +++ b/datasets/gpmpvigcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpvigcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Precipitation Video Imager (PVI) GCPEx dataset collected precipitation particle images and drop size distribution data from November 2011 through March 2012during the GPM Cold-season Precipitation Experiment (GCPEx). Data files in an Excel format contain the average, minimum, and logarithmic drop size distribution bin sizes and number of particles. Browse images are available online. The PVI instrument was designed by Dr. Larry Bliven at NASA Wallops Flight Facility.", "links": [ { diff --git a/datasets/gpmpvilpvex_1.json b/datasets/gpmpvilpvex_1.json index 3893f6183a..0b15d5ba5c 100644 --- a/datasets/gpmpvilpvex_1.json +++ b/datasets/gpmpvilpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmpvilpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Precipitation Video Imager (PVI) LPVEx dataset consists of precipitation particle images and drop size distribution (DSD) data collected by the Precipitation Video Imager (PVI) during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The PVI instrument was designed by Dr. Larry Bliven at NASA Wallops Flight Facility. Data files are available from September 17, 2010 through May 11, 2011 in Excel format and contain the average, minimum, and logarithmic DSD bin sizes and number of particles per unit time. Browse images are available in BMP and JPG formats.", "links": [ { diff --git a/datasets/gpmraddpgcpex_1.json b/datasets/gpmraddpgcpex_1.json index 8f942f20c9..395c2038da 100644 --- a/datasets/gpmraddpgcpex_1.json +++ b/datasets/gpmraddpgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmraddpgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Dual Polarization Radiometer GCPEx dataset includes brightness temperature measurements at frequencies 90 GHz (not polarized) and 150 GHz (HV-polarized) for the GPM Cold-season Precipitation Experiment (GCPEx) which occurred in Ontario, Canada. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. This dual polarization radiometer (DPR) is sensitive to particle orientation since it observes the brightness temperature difference between the vertical and horizontal polarization channels at 150 GHz, and it is especially important for the retrievals of particle shape and orientation with polarization observations. DPR also has a high sensitivity to the supercooled liquid water in clouds due to the high-frequency window channels. Even though the netCDF data has regular scans, the browse images are only shown at 30 and 150 degrees. Ancillary data was also captured for the internal calibration of the instrument.", "links": [ { diff --git a/datasets/gpmradioiphx_1.json b/datasets/gpmradioiphx_1.json index 08e4095673..fde46fb1ae 100644 --- a/datasets/gpmradioiphx_1.json +++ b/datasets/gpmradioiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmradioiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation UNCA Upper Air Radiosonde IPHEx dataset was collected from April 29, 2014 through June 12, 2014 during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) held in North Carolina. The goal of IPHEx was to characterize warm season orographic precipitation regimes and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. These radiosonde data files include pressure, geometric height, temperature, relative humidity, dew point temperature, wind direction, and wind speed measurements at various levels of the troposphere. The data are available in ASCII-tsv format files, and browse imagery are available as Portable Network Graphics (PNG) format files.", "links": [ { diff --git a/datasets/gpmradmecgcpex_1.json b/datasets/gpmradmecgcpex_1.json index 9e04375673..86744adcc5 100644 --- a/datasets/gpmradmecgcpex_1.json +++ b/datasets/gpmradmecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmradmecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Radiometer GCPEx dataset contains retrievals of temperature, water vapor, relative humidity, liquid water profiles and surface parameters acquired by a passive microwave radiometer during the GPM Cold-season Precipitation Experiment (GCPEx) in Ontario, Canada, February 14, 2012 through March 1, 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow.", "links": [ { diff --git a/datasets/gpmradpmgcpex_1.json b/datasets/gpmradpmgcpex_1.json index af90fd7e03..a79bf91877 100644 --- a/datasets/gpmradpmgcpex_1.json +++ b/datasets/gpmradpmgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmradpmgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Passive Microwave Radiometer and Soil Moisture-Temperature Data GCPEx dataset is consisted of data during the GPM Cold-season Precipitation Experiment (GCPEx) at the Centre for Atmospheric Research Experiments (CARE) site in Ontario, Canada during the winter season 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Data collected includes microwave brightness temperatures, snow and soil/snow-air interface (ground surface temperatures), soil surface temperatures, and soil volumetric water content. These data were acquired by multiple instruments: a passive microwave radiometer, a water content reflectometer, thermistors, soil moisture probe.", "links": [ { diff --git a/datasets/gpmradsecgcpex_1.json b/datasets/gpmradsecgcpex_1.json index 0d552245f4..492f9f877c 100644 --- a/datasets/gpmradsecgcpex_1.json +++ b/datasets/gpmradsecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmradsecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Radiosonde GCPEx dataset provides measurements of pressure, temperature, humidity, and winds collected by Vaisala RS92 Radiosondes during the GPM Cold-season Precipitation Experiment (GCPEx) at the CARE site in Ontario, Canada, January 17, 2012 through February 29, 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow.", "links": [ { diff --git a/datasets/gpmrefifld_1.json b/datasets/gpmrefifld_1.json index dc2b2c5154..e81006ba05 100644 --- a/datasets/gpmrefifld_1.json +++ b/datasets/gpmrefifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrefifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Reference Rainfall Data Product IFloodS dataset contains hourly rainfall accumulation estimates over central and northeastern Iowa for the period of 1 May to 16 June, 2013. This product is created by combining ground-based radar estimates collected for the Iowa Flood Studies (IFloodS) campaign. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The data are available in gzipped ASCII files.", "links": [ { diff --git a/datasets/gpmrefpreiphx_1.json b/datasets/gpmrefpreiphx_1.json index 12bc7cba1f..2a0b2c9a16 100644 --- a/datasets/gpmrefpreiphx_1.json +++ b/datasets/gpmrefpreiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrefpreiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Reference Precipitation IPHEx dataset consists of 10 years (December 31, 2007-December 31, 2017) of hourly rainfall intensity at 1 km2 resolution over the core region of the Integrated Precipitation and Hydrology Experiment (IPHEx), that is centered in the Pigeon River Basin in North Carolina. The goal of the IPHEx field campaign was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. Data files are available in ASCII format.", "links": [ { diff --git a/datasets/gpmrfcmpifld_1.json b/datasets/gpmrfcmpifld_1.json index 82c0f9e593..afb2689f4a 100644 --- a/datasets/gpmrfcmpifld_1.json +++ b/datasets/gpmrfcmpifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrfcmpifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Iowa Flood Center (IFC) NEXRAD Composite IFloodS dataset contains rain rate estimates derived using NEXt Generation Weather RADar system (NEXRAD) radars in operation during the Iowa Flood Studies (IFloodS) field campaign, in support of Global Precipitation Measurement (GPM) ground validation. NEXRAD is a network of 160 stationary S-Band radars dispersed throughout the United States and select locations abroad. Data were gathered in the vicinity of the IFloodS field campaign which took place in Iowa and surrounding areas during April 19, 2013 through June 30, 2013. This NEXRAD Composite data product is available in netCDF-4 or ASCII format with associated reflectivity browse imagery available in GIF format.", "links": [ { diff --git a/datasets/gpmrgachiphx_1.json b/datasets/gpmrgachiphx_1.json index ef4e41a090..d77886f448 100644 --- a/datasets/gpmrgachiphx_1.json +++ b/datasets/gpmrgachiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgachiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Rain Gauges NASA ACHIEVE IPHEx dataset was gathered during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina from May 9, 2014 through June 14, 2014. This dataset includes data from the Optical Scientific Optical Rain Gauge instrument and Novalynx Tipping Bucket Rain Gauge instrument which are both part of the NASA Goddard Space Flight Center (GSFC) ACHIEVE ground-based mobile laboratory. The optical rain gauge obtains high sensitivity optical measurements for precipitation rate and quantity, as well as measures 24-hour cumulative precipitation, precipitation rate, and temperature. The tipping bucket rain gauge is a standard tipping bucket rain gauge that measures 24-hour cumulative precipitation. Data files are available in netCDF-3 format.", "links": [ { diff --git a/datasets/gpmrgdukeiphx_1.json b/datasets/gpmrgdukeiphx_1.json index 6b2abd9c72..8510723755 100644 --- a/datasets/gpmrgdukeiphx_1.json +++ b/datasets/gpmrgdukeiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgdukeiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Duke Rain Gauge data were collected during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign which was held in the Southern Appalachian region, including the Piedmont and Coastal Plain regions of North Carolina. TB3 Model Tipping Bucket rain gauges collected precipitation data from May 1, 2014 through June 15, 2014. The IPHEx campaign was designed to characterize warm season orographic precipitation regimes and determine the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. The rain gauge data are available in ASCII-csv (comma separated) format for each of the rain gauge locations.", "links": [ { diff --git a/datasets/gpmrgifcifld_1.json b/datasets/gpmrgifcifld_1.json index 7bb29a9e8d..95d601009d 100644 --- a/datasets/gpmrgifcifld_1.json +++ b/datasets/gpmrgifcifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgifcifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Iowa Flood Center (IFC) Rain Gauges IFloodS dataset was collected during the Iowa Flood Studies (IFloodS) field campaign from April 28, 2013 through May 20, 2013 near Shueyville City, Iowa. Four observation sites (15442, 15443, 15444, and 22390), each consisting of three tipping bucket rain gauges that collected 5-minute accumulations of precipitation data. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. Specifically, this meant collecting detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars while simultaneously collecting data from satellites passing overhead. These IFC Rain Gauge data are available in ASCII format, with corresponding browse images available in PNG format.", "links": [ { diff --git a/datasets/gpmrgnaifld2_2.json b/datasets/gpmrgnaifld2_2.json index f31e2e9cfc..07a3272346 100644 --- a/datasets/gpmrgnaifld2_2.json +++ b/datasets/gpmrgnaifld2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgnaifld2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Met One Rain Gauge Pairs IFloodS V2 data measures the amount of fallen precipitation collected by a Model 380 tipping bucket rain gauge made by Met One Instruments, Inc. The gauge has a 30.5 cm diameter catchment funnel. Precipitation is collected to a resolution of 0.254 mm of liquid water for each bucket tip. These gauges measure rainfall over a 1 second interval. This data set has two types of files, the 1 second rainfall data and a 1-minute cubic-spline interpolated rain rate produced using the method described in Wang, 2008. There are two rain gauges located on each station (A or B), each with their own set of data files. Data were collected from April 2013 through December 2013 as part of the GPM Ground Validation Iowa Flood Studies (IFloodS) Field Experiment. More detailed information about the Met One Model 380 Precipitation Gauge is available at http://www.metone.com/docs/370_380_precipitation_gauge.pdf", "links": [ { diff --git a/datasets/gpmrgnaiphx2_2.json b/datasets/gpmrgnaiphx2_2.json index d9b21fc63e..0013a40659 100644 --- a/datasets/gpmrgnaiphx2_2.json +++ b/datasets/gpmrgnaiphx2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgnaiphx2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Met One Rain Gauge IPHEx V2 data were collected during the Integrated Precipitation and Hydrology Experiment (IPHEx) using Met One Model 380 tipping bucket precipitation gauges from September 11, 2013 to October 30, 2014 in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The dataset contains two ASCII files per rain gauge with two rain gauges on a station platform. The gag dataset is quality-controlled reformatted precipitation recorded in millimeters at a temporal resolution of 1 minute and the gmin dataset contains cubic spline interpolated rain rates in millimeters per hour at 1 minute resolution.", "links": [ { diff --git a/datasets/gpmrgnamc3e2_2.json b/datasets/gpmrgnamc3e2_2.json index 27082f5640..af6843d2e9 100644 --- a/datasets/gpmrgnamc3e2_2.json +++ b/datasets/gpmrgnamc3e2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgnamc3e2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Rain Gauge Pairs MC3E V2 data measures the amount of fallen precipitation collected by tipping bucket rain gauges made by Met One Instruments, Inc. and Campbell Scientific Corp. Precipitation from each is collected to a resolution of 0.254 mm of liquid water for each bucket tip. These gauges record the rainfall at a 1-minute resolution. This data set has two types of files, the recorded rainfall value (mm) and a cubic-spline interpolated rain rate (mm/hr) produced using the method described in Wang, 2008. There are two rain gauges located on each station (A or B), each with their own set of data files. Data were collected from April 2011 through June 2011 as part of the GPM Ground Validation Mid-latitude Continental Convective Cloud Experiment (MC3E). Detailed information about the Met One Model 380 Precipitation Gauge is available at http://www.metone.com/docs/370_380_precipitation_gauge.pdf. Details about the Campbell Scientific rain gauge is found at https://s.campbellsci.com/documents/us/product-brochures/b_385.pdf.", "links": [ { diff --git a/datasets/gpmrgnaolyx_1.json b/datasets/gpmrgnaolyx_1.json index 2ddaf51700..2304e1615a 100644 --- a/datasets/gpmrgnaolyx_1.json +++ b/datasets/gpmrgnaolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgnaolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Met One Rain Gauge Pairs OLYMPEX dataset contains precipitation amount and precipitation rate data collected during the Global Precipitation Measurement mission (GPM) Ground Validation (GV) Olympic Mountains Experiment (OLYMPEX). The OLYMPEX field campaign took place between November 2015 and January 2016, with additional ground sampling continuing through February 2016, on the Olympic Peninsula in the Pacific Northwest of the United States. The purpose of the campaign was to provide ground-validation data for the measurements taken by instrumentation aboard the GPM Core Observatory satellite. The Met One Rain Gauge Pairs are tipping bucket precipitation gauges which collect precipitation amounts and calculate precipitation rates. This dataset contains two ASCII-tsv files per rain gauge and two rain gauges are located on each station platform. The Met One Rain Gauge Pairs OLYMPEX dataset files are available from January 1, 2015 through June 20, 2016 in ASCII-tsv format. ", "links": [ { diff --git a/datasets/gpmrgsaiphx_1.json b/datasets/gpmrgsaiphx_1.json index 1cc1b996b6..1bbda5fda6 100644 --- a/datasets/gpmrgsaiphx_1.json +++ b/datasets/gpmrgsaiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmrgsaiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Southern Appalachian Rain Gauge IPHEx dataset was collected during the Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign consisting of 45 observation sites. The main goal of IPHEx were to characterize warm season orographic precipitation regimes and hydrologic processes in regions of complex terrain, to contribute to the development, evaluation, and improvement of remote sensing precipitation algorithms in support of the GPM mission. These data are available in ASCII-csv format from January 3, 2008 thru December 31, 2014. Data collection began in 2008 due to the entire network being funded by the NASA Precipitation Measurement Missions (PMM) to make these observations of orographic precipitation in preparation for the IPHEx field campaign.", "links": [ { diff --git a/datasets/gpmsatpaifld_1.json b/datasets/gpmsatpaifld_1.json index ef0ebb8faa..12bb435dbf 100644 --- a/datasets/gpmsatpaifld_1.json +++ b/datasets/gpmsatpaifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsatpaifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Satellite Overpasses IFloodS dataset contains plots of satellite overpass paths centered over eastern Iowa during the Global Precipitation Measurement (GPM) mission Iowa Flood Studies (IFloodS) field campaign. The campaign aimed to collect detailed measurements of precipitation at the Earth\u2019s surface while simultaneously collecting data from satellites passing overhead. This dataset consists of paths for Earth observation satellites operating during the campaign: NASA\u2019s AQUA, TERRA, and CloudSat satellites; NOAA\u2019s NOAA-15, NOAA-16, NOAA-17, NOAA-18, and Suomi NPP satellites; Europe\u2019s MetOp-A and MetOp-B satellites, and DMSP\u2019s F-15, F-16, F-17, and F-18 satellites. The satellite overpasses are provided as PNG plot images and as KML files with which the paths can be imported and viewed in Google Earth.", "links": [ { diff --git a/datasets/gpmsbdminmc3e_1.json b/datasets/gpmsbdminmc3e_1.json index a2c1efc0ea..f0953b35d1 100644 --- a/datasets/gpmsbdminmc3e_1.json +++ b/datasets/gpmsbdminmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsbdminmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA S-Band Profiler Minute Data MC3E dataset was gathered during the Midlatitude Continental Convective Clouds Experiment (MC3E) in Oklahoma from April-June 2011. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The S-band 2.8 GHz profiler measured the backscattered power from raindrops and ice particles as precipitating cloud systems pass overhead. After calibration, the instrument provided an unattenuated reflectivity estimate through the precipitation. Spectra and moment files are included in netCDF format.", "links": [ { diff --git a/datasets/gpmsbdorgmc3e_1.json b/datasets/gpmsbdorgmc3e_1.json index 3e9e81cb3a..387fa8384c 100644 --- a/datasets/gpmsbdorgmc3e_1.json +++ b/datasets/gpmsbdorgmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsbdorgmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA S-Band Profiler Original Dwell Data MC3E dataset was gathered during the Midlatitude Continental Convective Clouds Experiment (MC3E) in Oklahoma from April 16, 2011 to June 7, 2011. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The S-band profiler operated at 2.8 GHz, pointed vertically, and measured the backscattered power from raindrops and ice particles as precipitating cloud systems passed overhead. The S-band operated in two modes: precipitation mode and attenuated mode. The precipitation mode was the normal or full-power mode, and the attenuated mode was the low-power mode. The profiler alternated between modes collecting either 7 or 9 consecutive precipitation mode profiles separated by 1 attenuated mode profile. Both modes processed radar pulses collected during a 7-second dwell before calculating the Doppler velocity spectra at each radar range gate that were separated by 60-meters in the vertical. The attenuated and precipitation mode data are available in moment, pop spectra (uncalibrated raw spectra) and calibrated spectra hourly files. The S-band spectra were calibrated against the surface disdrometer to determine a radar calibration constant. Calibrated spectra were constructed for each profile and are expressed as reflectivity spectral density. After calibration, the instrument provides a reflectivity estimate through the precipitation. Data is in hourly files in the netCDF format.", "links": [ { diff --git a/datasets/gpmsbdrwncmc3e_1.json b/datasets/gpmsbdrwncmc3e_1.json index 4a27bee1e3..646a373ae1 100644 --- a/datasets/gpmsbdrwncmc3e_1.json +++ b/datasets/gpmsbdrwncmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsbdrwncmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA S-Band Profiler Raw Data NetCDF Format MC3E dataset was gathered during the Midlatitude Continental Convective Clouds Experiment (MC3E) in Oklahoma April 8, 2011 to June 7, 2011 and consists of uncalibrated Doppler velocity spectra data in units of relative power return. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The S-band 2.8 GHz profiler points vertically and measures the backscattered power from raindrops and ice particles as precipitating cloud systems pass overhead. The profiler processes radar pulses during a 7-second dwell before calculating and saving uncalibrated Doppler velocity spectra at each range gate that were separated by 60-meters vertically. Data collected during each hour are saved in two files. All precipitation mode profiles are saved in one hourly data file and all attenuated mode profiles are saved in another hourly data file. Calibrated data can be obtained from the S-band Original Dwell and Minute datasets.", "links": [ { diff --git a/datasets/gpmsbdrwspcmc3e_1.json b/datasets/gpmsbdrwspcmc3e_1.json index 9063a0f7bb..37635b11e7 100644 --- a/datasets/gpmsbdrwspcmc3e_1.json +++ b/datasets/gpmsbdrwspcmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsbdrwspcmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA S-Band Profiler Raw Data SPC Format MC3E dataset is the S-band Profiler Raw dataset was saved in Vaisala SPC format. The numeric values in both formats are exactly the same. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The S-band Profiler Raw dataset in the proprietary Vaisala SPC format was gathered during the Midlatitude Continental Convective Clouds Experiment (MC3E) in Oklahoma April 8, 2011 to June 7, 2011 and consists of uncalibrated Doppler velocity spectra data in units of relative power return. The S-band 2.8 GHz profiler points vertically and measures the backscattered power from raindrops and ice particles as precipitating cloud systems pass overhead. The profiler processes radar pulses during a 7-second dwell before calculating and saving uncalibrated Doppler velocity spectra at each range gate that were separated by 60-meters vertically. Data collected during each hour are saved in two files. All precipitation mode profiles are saved in one hourly data file and all attenuated mode profiles are saved in another hourly data file. Calibrated data can be obtained from the S-band Original Dwell and Minute datasets. Specialized read software may be purchased from Vaisala.", "links": [ { diff --git a/datasets/gpmscampriphx_1.json b/datasets/gpmscampriphx_1.json index bd160ceb36..a1bd9f9d65 100644 --- a/datasets/gpmscampriphx_1.json +++ b/datasets/gpmscampriphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmscampriphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) IPHEx dataset contains rainfall rate measurements derived using the SCaMPR algorithm to combine GOES infrared (IR) data and derived parameters as inputs. The SCaMPR algorithm is calibrated using microwave rainfall estimates from the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU). This dataset contains the values for the time period of the IPHEx campaign from April 30, 2015 to June 17, 2015. The IPHEx campaign was designed to characterize warm season orographic precipitation regimes and determine the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. These data are available in netCDF-4 format, while browse images are available in GIF format.", "links": [ { diff --git a/datasets/gpmseafluxicepop_1.json b/datasets/gpmseafluxicepop_1.json index bc6c72ff47..80eb2d2173 100644 --- a/datasets/gpmseafluxicepop_1.json +++ b/datasets/gpmseafluxicepop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmseafluxicepop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation SEA FLUX ICE POP dataset includes estimates of ocean surface latent and sensible heat fluxes, 10m wind speed, 10m air temperature, 10m air humidity, and skin sea surface temperature in support of the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to the Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This data file is available in netCDF-4 format from September 1, 2017 through April 30, 2018.", "links": [ { diff --git a/datasets/gpmsgifcifld_1.json b/datasets/gpmsgifcifld_1.json index df33d94a3f..454f4ecd95 100644 --- a/datasets/gpmsgifcifld_1.json +++ b/datasets/gpmsgifcifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsgifcifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Iowa Flood Center (IFC) Stream Flow IFloodS dataset was obtained from the IFC during the Iowa Flood Studies (IFloodS) field campaign that extended from March 31, 2013 through June 30, 2013. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. The IFC monitors stage levels using sensors attached to the side of bridges throughout Iowa. The sensor data are downloaded from the Iowa Flood Information System (IFIS) as support data for the IFloodS campaign. The IFC Stream Flow data were collected in real-time and provide measurements at 15 minute intervals. These IFC Stream Flow IFloodS data are available in XML format.", "links": [ { diff --git a/datasets/gpmsgusgsifld_1.json b/datasets/gpmsgusgsifld_1.json index cf2f85420b..3dff90b854 100644 --- a/datasets/gpmsgusgsifld_1.json +++ b/datasets/gpmsgusgsifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsgusgsifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation USGS Stream Flow IFloodS dataset was obtained from USGS during the Iowa Flood Studies (IFloodS) field campaign that extended from March 30, 2013 through June 30, 2013. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. The USGS monitors streamflow using gauges on streams and rivers throughout the U.S. For the IFloodS field campaign, streamflow data from about 200 gauges in the Iowa IFloodS study area were downloaded from the USGS web site as support data for the campaign. The USGS streamflow data were collected in real-time and provide measurements at 15-60 minute intervals. These USGS Stream Flow data are available in XML format.", "links": [ { diff --git a/datasets/gpmsimorbc3vp_1.json b/datasets/gpmsimorbc3vp_1.json index 0dcd639999..05b37bb487 100644 --- a/datasets/gpmsimorbc3vp_1.json +++ b/datasets/gpmsimorbc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsimorbc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Satellite Simulated Orbits C3VP dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.", "links": [ { diff --git a/datasets/gpmsimorblpvex_1.json b/datasets/gpmsimorblpvex_1.json index 34216c7ea4..a3b6c4fd91 100644 --- a/datasets/gpmsimorblpvex_1.json +++ b/datasets/gpmsimorblpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsimorblpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Satellite Simulated Orbits LPVEx dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.", "links": [ { diff --git a/datasets/gpmsimorbmc3e_1.json b/datasets/gpmsimorbmc3e_1.json index 4ea55b4161..0933ffd9ef 100644 --- a/datasets/gpmsimorbmc3e_1.json +++ b/datasets/gpmsimorbmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsimorbmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Satellite Simulated Orbits MC3E dataset is available in the Orbital database , which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.", "links": [ { diff --git a/datasets/gpmsimorbtwpice_1.json b/datasets/gpmsimorbtwpice_1.json index e5593ae1a0..c861d47db9 100644 --- a/datasets/gpmsimorbtwpice_1.json +++ b/datasets/gpmsimorbtwpice_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsimorbtwpice_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Satellite Simulated Orbits TWP-ICE dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.", "links": [ { diff --git a/datasets/gpmsmdukeiphx_1.json b/datasets/gpmsmdukeiphx_1.json index ae697af94e..7725dd6592 100644 --- a/datasets/gpmsmdukeiphx_1.json +++ b/datasets/gpmsmdukeiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsmdukeiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Duke Soil Moisture dataset consists of a collection of various data obtained during the Integrated Precipitation and Hydrology Experiment (IPHEx) which occurred in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina from February 27, 2014 through October 17, 2014. The various instruments used included Theta Probes, Infrared Thermometers, 200-A Soil Core Samplers, a Global Positioning System (GPS), Soil Thermometers with Scanning L-band Active Passive (SLAP) flight concurrent survey data, and CS6161 Water Reflectometers. Data are available in a variety of formats based on instrument, including shapefiles, Excel files, Word document files, and ASCII formats. Browse images of site locations and data are available in JPG format.", "links": [ { diff --git a/datasets/gpmsnowgcpex_1.json b/datasets/gpmsnowgcpex_1.json index 62c127cfd7..8e2196e8d1 100644 --- a/datasets/gpmsnowgcpex_1.json +++ b/datasets/gpmsnowgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsnowgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada Snow Surveys GCPEx dataset was manually collected during the GPM Cold-season Precipitation Experiment (GCPEx), which occurred in Ontario, Canada, January 20, 2012 through February 27, 2012 across four sites (CARE, Steamshow, Huronia Airport, and Skydive-Jump). GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Snow depth, water equivalent and density transects were surveyed weekly at each of the GCPEx sites in order to provide baseline information on the distribution of snow on the ground. Pairs of bulk density and snow water equivalent measurements were made every 25 m along the same transect using an ESC-30 (30 cm2 cross sectional area) snow corer. Snow depth measurements were made every 50 cm along a 100 m transect using a GPS equipped snow depth probe.", "links": [ { diff --git a/datasets/gpmsnowolyx_1.json b/datasets/gpmsnowolyx_1.json index e19db900be..fece0396cb 100644 --- a/datasets/gpmsnowolyx_1.json +++ b/datasets/gpmsnowolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsnowolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Snow Depth Monitoring System OLYMPEX dataset consists of snow depth, temperature, and relative humidity measurements which were collected using snow depth poles, time lapse cameras, temperature/relative humidity sensors, and manual snow surveys. This dataset was collected during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX) held on the Olympic Peninsula in the Pacific Northwest of the United States. The analyzed data files are available in netCDF-3 data format. The dataset includes the individual camera photos of snow poles taken hourly during the field campaign, provided as JPG images. There are up to 3 cameras/poles per study site location. In addition, a Microsoft Excel data file contains results of a manual snow survey taken on the specific days of the Airborne Snow Observatory OLYMPEX overflights. In total, measurements contained in this dataset extend from September 5, 2014 through August 20, 2016, but the primary field campaign data were collected during the fall 2015 to spring 2016 time period.", "links": [ { diff --git a/datasets/gpmsogcpex_1.json b/datasets/gpmsogcpex_1.json index a82cb6d607..d65bcb8b61 100644 --- a/datasets/gpmsogcpex_1.json +++ b/datasets/gpmsogcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsogcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Composite Satellite Overpasses GCPEx dataset provides satellite overpasses from the Special Sensor Microwave Imager/Sounder (SSMIS) satellites (F-16, 17, 18) during the GPM Cold-season Precipitation Experiment (GCPEx) which took place in Ontario, Canada, January 17, 2012 through February 29, 2012. The radiometric data was matched up with other datasets necessary to carry out land surface emissivity studies. These other datasets include the NEXRAD National Mosaic and Multi-Sensor QPE (NMQ) radar mosaic for knowledge of rain structure and intensity at the time of the overpass, as well as the previous accumulated precipitation prior to the satellite overpass time, the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow mapping system (to identify surface snow or ice cover), and the NASA/GMAO Modern-Era Retrospective Analysis for Research (MERRA) land and atmospheric reanalysis (for background land and atmospheric state needed for microwave radiative transfer calculations). The identified SSMIS satellite overpasses passed within 700-km of the central field site.", "links": [ { diff --git a/datasets/gpmsomc3e_1.json b/datasets/gpmsomc3e_1.json index 0f0af56b87..8c7e42d28d 100644 --- a/datasets/gpmsomc3e_1.json +++ b/datasets/gpmsomc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsomc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Composite Satellite Overpasses MC3E dataset provides satellite overpasses from the AQUA satellite during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in central Oklahoma April 22 - June 5, 2011. The radiometric data was matched up with other datasets necessary to carry out land surface emissivity studies. These other datasets include the NEXRAD National Mosaic and Multi-Sensor QPE (NMQ) radar mosaic for knowledge of rain structure and intensity at the time of the overpass, as well as the previous accumulated precipitation prior to the satellite overpass time, the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow mapping system (to identify surface snow or ice cover), and the NASA/GMAO Modern-Era Retrospective Analysis for Research (MERRA) land and atmospheric reanalysis (for background land and atmospheric state needed for microwave radiative transfer calculations). The AQUA satellite overpasses included in this dataset passed within 700-km of the central field site.", "links": [ { diff --git a/datasets/gpmsondelpvex_1.json b/datasets/gpmsondelpvex_1.json index 07e1141908..66ba3f3069 100644 --- a/datasets/gpmsondelpvex_1.json +++ b/datasets/gpmsondelpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsondelpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Radiosonde LPVEx dataset consists of sounding data collected as part of the Global Precipitation Measurement (GPM) mission Ground Validation Light Precipitation Validation Experiment (LPVEx). This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high altitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Vaisala RS92 radiosonde was used to produce vertical profiles of atmospheric temperature, pressure, humidity, and winds. The radiosondes were launched from two locations: Kumpula and Vantaa. The Upper Air Radiosonde LPVEx dataset consists of TSV data files and PNG browse image files.", "links": [ { diff --git a/datasets/gpmsondeolyx_1.json b/datasets/gpmsondeolyx_1.json index 4019f32384..b346094c08 100644 --- a/datasets/gpmsondeolyx_1.json +++ b/datasets/gpmsondeolyx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsondeolyx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Upper Air Radiosonde OLYMPEX dataset was collected from October 28, 2015 through January 16, 2016 during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX) held on the Olympic Peninsula in the Pacific Northwest of the United States. Radiosondes were released from 5 locations: 3 in US - KSLE, KUIL, and NPOL site; and 2 in Canada - ECCC instrument site and CYZT. A total of 651 radiosondes were launched and collected during OLYMPEX from these sites. In addition, Level 4 dropsonde data were reprocessed to match the Level-4 data format and content of the radiosonde files and is also provided here. The dropsondes were released from the NASA DC-8 aircraft during specific flights in December 2015 and are published as the AVAPS dataset. This Upper Air Radiosonde dataset contains Level 0 through Level 4 data containing dew point temperature, pressure, air temperature, relative humidity, horizontal wind speed, vertical wind speed, wind direction, rise or drop rate, and geopotential height measurements. The data files are available in ASCII, ASCII-EOL, and netCDF-3 formats, as well as Skew-T and time series plots in PNG format. The lower level datasets (Level 0 raw data through Level 2 data) are only available upon request from the NASA GHRC DAAC. ", "links": [ { diff --git a/datasets/gpmsslpvex_1.json b/datasets/gpmsslpvex_1.json index 95ac8135c8..0fce25a93a 100644 --- a/datasets/gpmsslpvex_1.json +++ b/datasets/gpmsslpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsslpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Special Sensor Microwave Imager/Sounder (SSMI/S) LPVEx dataset contains brightness temperature data processed from the NOAA CLASS QC temperature data records for the Light Precipitation Validation Experiment (LVPEX), part of the Global Precipitation Measurement project. The mission was to detect and characterize light rain and evaluate their estimates of rainfall intensity in high latitude, shallow freezing level environments.Only data with swaths within the area of interest are included. The temporal range of the data includes the LPVEx campaign period (September 1, 2010) and extends through the end of March 31, 2011.", "links": [ { diff --git a/datasets/gpmsurmetc3vp_1.json b/datasets/gpmsurmetc3vp_1.json index 1661a21fd6..a6ca267e69 100644 --- a/datasets/gpmsurmetc3vp_1.json +++ b/datasets/gpmsurmetc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsurmetc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Surface Meteorological Station C3VP dataset consists of meteorological data collected at the Environment Canada (EC) climate station at the Centre for Atmospheric Research Experiments (CARE) during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The GPM GV EC Surface Meteorological Station C3VP data include surface temperature and precipitation data available from November 1, 2005 through March 31, 2007 in Microsoft Excel format. ", "links": [ { diff --git a/datasets/gpmsurmetmc3e_1.json b/datasets/gpmsurmetmc3e_1.json index 9b1eff8f4f..0b213adc71 100644 --- a/datasets/gpmsurmetmc3e_1.json +++ b/datasets/gpmsurmetmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmsurmetmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA Surface Meteorological Station MC3E dataset was collected at the NOAA Southern Great Plains Facility for the Midlatitude Continental Convective Clouds Experiment (MC3E) and includes measurements of wind speed, wind direction, temperature, humidity and precipitation. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The instruments gathering this data were respectively a propeller wind monitor located 10 meters above the ground, a temperature and humidity sensor at the ground, and a tipping rain gauge at the ground. These data were collected from April 5, 2011 to June 7, 2011.", "links": [ { diff --git a/datasets/gpmtfmifld_1.json b/datasets/gpmtfmifld_1.json index 65fcff04ee..e24ca6f342 100644 --- a/datasets/gpmtfmifld_1.json +++ b/datasets/gpmtfmifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmtfmifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Global Flood Monitoring System (GFMS) Flood Maps IFloodS dataset contains global flood estimates on a 0.25 degree spatial resolution every 3 hours, from March 26, 2013 through June 30, 2013. These data are provided in support of the Iowa Flood Studies (IFloodS) experiment conducted in eastern Iowa. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The data are available in netCDF-4 and ASCII formats. Flood map and rain graph files are available in KMZ, JPG, and GIF formats.", "links": [ { diff --git a/datasets/gpmtmpaifld_7.json b/datasets/gpmtmpaifld_7.json index da3dafd6e5..99480861e8 100644 --- a/datasets/gpmtmpaifld_7.json +++ b/datasets/gpmtmpaifld_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmtmpaifld_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation TRMM Multi-satellite Precipitation Analysis (TMPA) IFloodS dataset is a subset of the TMPA 3B42RT gridded precipitation real-time product selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) held in Iowa during April 1, 2013 to June 30, 2013. The goals of IFloodS were to collect detailed measurements of precipitation at the Earth\u2019s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. TMPA is a calibration-based sequential scheme for combining microwave (MW) and infrared (IR) precipitation estimates from multiple satellites, as well as surface precipitation gauge analyses where feasible, to produce precipitation estimates at fine scales: 3-hourly, 0.25 degree maps. The TMPA IFloodS product is available in netCDF-4 and binary formats, as well as 3-hour rainfall browse images in JPG format.", "links": [ { diff --git a/datasets/gpmtmpaiphx_7.json b/datasets/gpmtmpaiphx_7.json index d39d009729..4ab8ce5d31 100644 --- a/datasets/gpmtmpaiphx_7.json +++ b/datasets/gpmtmpaiphx_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmtmpaiphx_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GPM Ground Validation TRMM Multi-satellite Precipitation Analysis (TMPA) IPHEx dataset is a subset of the TMPA 3B42RT gridded precipitation product selected for the time period of the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) held in North Carolina during May 1, 2014 to June 15, 2014. The goal of IPHEx was to characterize warm season orographic precipitation regimes and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. This dataset contains 3-hourly, 0.25 degree maps of precipitation derived using microwave (MW), infra-red (IR), surface precipitation gauge measurements, and other rain products that include the TRMM Precipitation Radar (PR) data. The IPHEx TMPA product is available in netCDF-4 and binary formats.", "links": [ { diff --git a/datasets/gpmtpshpgcpex_1.json b/datasets/gpmtpshpgcpex_1.json index fcad66c566..001f4c319a 100644 --- a/datasets/gpmtpshpgcpex_1.json +++ b/datasets/gpmtpshpgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmtpshpgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Total Precipitation Sensor (HotPlate) GCPEx dataset provides a measure of the liquid precipitation rate and accumulation for snow. Additional data includes measurements of temperature, wind speed, relative humidity, pressure, and solar and infrared radiation flux. These data were gathered during the GPM Cold-season Precipitation Experiment (GCPEx) at the CARE and SkyDive sites in Ontario, Canada during November 7, 2011 - February 21, 2012. These data sets were collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow.", "links": [ { diff --git a/datasets/gpmtrm2A25iphx_7.json b/datasets/gpmtrm2A25iphx_7.json index 455658a752..cfa6a9f201 100644 --- a/datasets/gpmtrm2A25iphx_7.json +++ b/datasets/gpmtrm2A25iphx_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmtrm2A25iphx_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation TRMM 2A25 NRT Precipitation Radar IPHEx data are estimates of instantaneous three-dimensional distribution of rain from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The TRMM 2A25 (NRT) orbital precipitation radar data from NASA GES DISC have been extracted for the southeast US region for May 1 to June 16, 2014 during the GPM Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign. This data product contains the average rainfall rate between two predefined altitudes derived from each radar beam position. Other output data include parameters of Z-R relationships (R=aZb), integrated rain rate of each beam, range bin numbers of rain layer boundaries, and many intermediate parameters. Data files are available in HDF-4 format, while corresponding browse images are also available in PNG format.", "links": [ { diff --git a/datasets/gpmuhfnoaamc3e_1.json b/datasets/gpmuhfnoaamc3e_1.json index 3dfddb2b22..119a186488 100644 --- a/datasets/gpmuhfnoaamc3e_1.json +++ b/datasets/gpmuhfnoaamc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmuhfnoaamc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NOAA UHF 449 Profiler MC3E dataset was collected during the NASA supported Midlatitude Continental Convective Clouds Experiment (MC3E). The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that had never before been available. The Ultra High Frequency 449 MHz profiler was one of three NOAA deployed instruments which also included a Parsivel and a 2.8 GHz profiler (S-Band). The 449 MHz profiler raw data files provide estimates of the vertical air motion during precipitation from near the surface to just below the freezing level. Used together with the S-band profiler, vertical profiles of raindrop size distributions can be retrieved. The raw 449MGx profiler data consists of uncalibrated Doppler velocity spectra data in units of relative power return.", "links": [ { diff --git a/datasets/gpmvanlpvex_1.json b/datasets/gpmvanlpvex_1.json index 4ddd928565..16256ddb41 100644 --- a/datasets/gpmvanlpvex_1.json +++ b/datasets/gpmvanlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmvanlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation C-Band Radar datasets include radar reflectivity data from the Vantaa (VAN) dual-polarimetric C-Band Doppler radar in Finland during the Global Precipitation Measurement (GPM) mission Light Precipitation Validation Experiment (LPVEx) field campaign. This radar, along with four others, provided reflectivity measurements for light precipitation systems during LPVEx. This field campaign took place around the Gulf of Finland, aiming to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The Vantaa C-Band Radar data files are available in RAW and UF format, with browse imagery in PNG format from September 16, 2010 through January 31, 2011.", "links": [ { diff --git a/datasets/gpmvertixgcpex_1.json b/datasets/gpmvertixgcpex_1.json index cf80198cdb..171d8f577d 100644 --- a/datasets/gpmvertixgcpex_1.json +++ b/datasets/gpmvertixgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmvertixgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation McGill Vertical Pointing X-Band (VertiX) Radar GCPEx dataset consists of radar reflectivity and Doppler velocity data collected by the Vertically Pointing X-band (VertiX) radar during the Global Precipitation Measurement (GPM) mission Cold-season Precipitation Experiment (GCPEx) field campaign in Ontario, Canada during the 2011-2012 winter season. VertiX can detect all precipitation targets and some ice clouds, as well as measure the Doppler velocity of precipitation targets. These measurements contributed to the overarching goal of GCPEx to collect various snowfall data for the improvement of GPM satellite winter precipitation estimates. These data files are available from January 15 through February 29, 2012 in netCDF-3 format with browse imagery available in GIF format. ", "links": [ { diff --git a/datasets/gpmvisecc3vp_1.json b/datasets/gpmvisecc3vp_1.json index 7c34c18484..35db88169b 100644 --- a/datasets/gpmvisecc3vp_1.json +++ b/datasets/gpmvisecc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmvisecc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation (GV) Environment Canada (EC) Visibility Sensor FD12P C3VP dataset consists of visibility and precipitation data collected at the Environment Canada Canadian Climate station at the Centre for Atmospheric Research Experiments (CARE) site during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The GPM GV Visibility Sensor FD12P C3VP data are available from October 4, 2006 through March 31, 2007 in a Microsoft Excel comma-separated variable spreadsheet. ", "links": [ { diff --git a/datasets/gpmvisecgcpex_1.json b/datasets/gpmvisecgcpex_1.json index fa64620469..4e3afa73d8 100644 --- a/datasets/gpmvisecgcpex_1.json +++ b/datasets/gpmvisecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmvisecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Visibility Sensor FD12P and Present Weather Detector GCPEx dataset contains data collected from January 15 through March 1, 2012 in Huronia, Canada for the GPM Cold-season Precipitation Experiment (GCPEx). This dataset was collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The FD12P combines the functions of a forward scatter visibility meter and a present weather detector. It also measures the intensity and the amount of both liquid and solid precipitation.", "links": [ { diff --git a/datasets/gpmvn_1.json b/datasets/gpmvn_1.json index 9bceb79866..da49610194 100644 --- a/datasets/gpmvn_1.json +++ b/datasets/gpmvn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmvn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Validation Network (VN) dataset contains reflectivity, hydrometeor identification, rain rate, correlation coefficient, and quality control variables and estimates. This data product was created using the Validation Network (VN), which performs a direct match-up of the Global Precipitation Mission (GPM)\u2019s space-based Dual-frequency Precipitation Radar (DPR) and Microwave Imager (GMI) data with ground radar data from NOAA Weather Surveillance Radar-1988 Doppler (WSR-88D) radars. These data are available from March 9, 2014 through July 31, 2021 in netCDF-3 format, though it should be noted that this dataset will be updated periodically. ", "links": [ { diff --git a/datasets/gpmwacrc3vp_1.json b/datasets/gpmwacrc3vp_1.json index dfae992bfe..d5d9f106f4 100644 --- a/datasets/gpmwacrc3vp_1.json +++ b/datasets/gpmwacrc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwacrc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation NASA W-band Airborne Cloud Radar (WACR) C3VP dataset consists of calibrated co- and cross-polarized radar reflectivity at 94 GHz during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The WACR is used for cloud sensing and microphysics. During C3VP, the WACR was deployed as a surface-based, zenith-pointing instrument in the Cloud Radar Trailer at the Centre for Atmospheric Research Experiments (CARE) facility in Ontario, Canada. The data include radar reflectivities in zenith-pointing orientation at the CARE facility. The dataset files are available in netCDF format from October 30, 2006 through March 2, 2007.", "links": [ { diff --git a/datasets/gpmwbandgcpex_1.json b/datasets/gpmwbandgcpex_1.json index f1fc40ad3b..077e42c2a3 100644 --- a/datasets/gpmwbandgcpex_1.json +++ b/datasets/gpmwbandgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwbandgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation McGill W-Band Radar GCPEx dataset was collected from February 1, 2012 to February 29, 2012 at the CARE site in Ontario, Canada as a part of the GPM Cold-season Precipitation Experiment (GCPEx). This datset was collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The W-Band radar is a single antenna, 94-GHz pulsed Doppler, vertical pointing radar system. Data products from the W-Band radar include radar reflectivity, Doppler moments, and Doppler spectra of variable lengths. The W-Band radar is primarily used to research various cloud properties. The GPM Ground Validation McGill W-Band Radar GCPEx dataset is available in netCDF format.", "links": [ { diff --git a/datasets/gpmwbandiphx_1.json b/datasets/gpmwbandiphx_1.json index 066aa5e18a..add3e85b73 100644 --- a/datasets/gpmwbandiphx_1.json +++ b/datasets/gpmwbandiphx_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwbandiphx_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation ACHIEVE W-band Cloud Radar IPHEx dataset consists of cloud and light precipitation radar observations gathered during the Global Precipitation Measurement (GPM) mission Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) Intensive Observing Period (IOP) in North Carolina from May 1 through June 15, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The dataset includes data from the ProSensing 95 GHz W-band cloud radar, which is part of the NASA Goddard Space Flight Center (GSFC) Aerosol, Cloud, Humidity, Interactions Exploring and Validating Enterprise (ACHIEVE) ground-based mobile laboratory. The W-band cloud radar is a scanning 95 GHz dual-polarization (horizontal transmission and co- and cross-polar receiving) Doppler radar used for observing liquid and ice clouds and light precipitation. The instrument measures co- and cross-polar reflectivity, radial velocity, Doppler spectrum width, and signal-to-noise ratio. Linear depolarization ratio was derived from the measured parameters. During the IPHEx campaign, the W-Band radar was used exclusively in vertical-pointing mode. The dataset files are available from May 9 through June 14, 2014 in netCDF-3 data format. ", "links": [ { diff --git a/datasets/gpmwcrlpvex_2.json b/datasets/gpmwcrlpvex_2.json index afcc4bbc97..22e4004f7c 100644 --- a/datasets/gpmwcrlpvex_2.json +++ b/datasets/gpmwcrlpvex_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwcrlpvex_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Wyoming Cloud Radar (WCR) LPVEx V2 dataset includes reflectivity and Doppler velocity measurements obtained by the Wyoming Cloud Radar (WCR) flown on board the University of Wyoming King Air (UWKA) aircraft, as well as aircraft navigation parameters. These data were collected as part of the Light Precipitation Validation Experiment (LPVEx) in September and October of 2010 around the Gulf of Finland. The dataset was collected to aid in achieving the overarching goals of LPVEx, to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms and to detect and understand the process of light rainfall formation at high latitudes. Data files are available in netCDF-3 format from September 16 through October 20, 2010 along with browse imagery in PDF and PNG format.", "links": [ { diff --git a/datasets/gpmwebecgcpex_1.json b/datasets/gpmwebecgcpex_1.json index ef07edb2d3..fc939f4317 100644 --- a/datasets/gpmwebecgcpex_1.json +++ b/datasets/gpmwebecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwebecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Web Camera Images GCPEx were taken at 5 site locations in Ontario, Canada during the GPM Cold-season Precipitation Experiment (GCPEx), which occurred January 15 through March 1, 2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Mounted as a fixed outdoor camera, the AXIS P1343-E network camera had day/night functionality with an automatically controlled IR filter, adapting to both daylight and dark lighting conditions. These images provided visual records throughout the day of the weather conditions at each site.", "links": [ { diff --git a/datasets/gpmwkacmlpvex_1.json b/datasets/gpmwkacmlpvex_1.json index d14ba595ab..accc8701f5 100644 --- a/datasets/gpmwkacmlpvex_1.json +++ b/datasets/gpmwkacmlpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwkacmlpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Wyoming King Air Cloud Microphysics LPVEx dataset includes, in addition to aircraft parameters, many scientific parameters, such as static pressure, dew point temperature, relative humidity, mixing ratio, liquid water content, and droplet concentration. These data were collected as part of the Light Precipitation Evaluation Experiment (LPVEx) from September 11, 2010 to October 20, 2010 in the Gulf of Finland. The dataset was collected to aid in achieving the overarching goals of LPVEx, to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms and to detect and understand the process of light rainfall formation at high latitudes. It should be noted that multiple instruments were carried aboard the University of Wyoming King Air (UWKA) including the Cloud Microphysics instrument and the Wyoming Cloud Radar (WCR) instrument. Data files are in netCDF-3 format. ", "links": [ { diff --git a/datasets/gpmwpecgcpex_1.json b/datasets/gpmwpecgcpex_1.json index 8911ee5265..158af758cf 100644 --- a/datasets/gpmwpecgcpex_1.json +++ b/datasets/gpmwpecgcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwpecgcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Wind Profiler GCPEx dataset provides post-processed consensus winds and daily quick look plots from the Vaisala Wind Profiler LAP 3000. The daily plots depict wind information, such as wind speed and wind direction, from the profiler and from the Global Environmental Multiscale (GEM) model data. The LAP 3000 is a pulsed Doppler radar that operates in clean air. The Wind profiler data was collected January 15, 2012 through March 1, 2012 for the GPM Cold-season Precipitation Experiment (GCPEx) at the CARE site in Ontario, Canada. This dataset was collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow.", "links": [ { diff --git a/datasets/gpmwrflpvex_1.json b/datasets/gpmwrflpvex_1.json index 481e3ae878..51709d6774 100644 --- a/datasets/gpmwrflpvex_1.json +++ b/datasets/gpmwrflpvex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwrflpvex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Weather Research and Forecasting (WRF) Images LPVEx includes model data simulated by the Weather Research and Forecasting (WRF) model for the GPM Ground Validation Light Precipitation Validation Experiment (LPVEx). This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The WRF model provided simulations of the precipitation events that were observed during the campaign. The LPVEx WRF dataset files are available from September 20 through October 20, 2010 in netCDF-3 format.", "links": [ { diff --git a/datasets/gpmwrfmc3e_1.json b/datasets/gpmwrfmc3e_1.json index b9b16285e3..05246f0af7 100644 --- a/datasets/gpmwrfmc3e_1.json +++ b/datasets/gpmwrfmc3e_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmwrfmc3e_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Weather Research and Forecasting (WRF) Images MC3E dataset consists of browse only images showing radar reflectivity, radar echo top, convective available potential energy (CAPE), temperature, geopotential height, wind speed, relative humidity, rain water, snow, cloud water, cloud ice, and graupel. These data were simulated by the Weather Research and Forecasting (WRF) for the period of the GPM Ground Validation Mid-\u00adlatitude Continental Convective Cloud Experiment (MC3E) field campaign. The overarching goal of the MC3E field campaign was to provide the most complete characterization of convective cloud systems, precipitation, and the environment ever obtained and to provide new constraints for model cumulus parameterizations and space-\u00adbased rainfall retrieval algorithms over land. Browse imagery files in PNG and GIF formats are available for April 19, 2011 through June 6, 2011.", "links": [ { diff --git a/datasets/gpmxetc3vp_1.json b/datasets/gpmxetc3vp_1.json index 9bfe290842..a72e44a68d 100644 --- a/datasets/gpmxetc3vp_1.json +++ b/datasets/gpmxetc3vp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmxetc3vp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Environment Canada (EC) Weather Station XET C3VP dataset consists of surface meteorological data collected at the Environment Canada (EC) XET station at the Centre for Atmospheric Research Experiments (CARE) during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The XET C3VP dataset file includes temperature, pressure, wind speed and direction, relative humidity, solar radiation, grass temperature, soil temperature, snow depth, sunshine, and precipitation measurements from October 4, 2006 through March 31, 2007 in ASCII-csv format.", "links": [ { diff --git a/datasets/gpmxpolifld_1.json b/datasets/gpmxpolifld_1.json index 29aa71c14d..b0e38342b1 100644 --- a/datasets/gpmxpolifld_1.json +++ b/datasets/gpmxpolifld_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gpmxpolifld_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GPM Ground Validation Iowa X-band Polarimetric Mobile Doppler Weather Radars IFloodS dataset was gathered during the IFloodS campaign from April to June 2013 throughout central and northeastern Iowa. The Iowa Flood Studies (IFloodS) was a ground measurement campaign that took place throughout Iowa from May 1 to June 15, 2013. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. Four X-band Polarimetric (XPOL) Mobile Doppler Weather Radars were used to collected high-resolution observations of precipitation. The data consists of reflectivity, Doppler velocity, spectrum width, differential reflectivity, differential phase, copolar correlation coefficient, and sound-to-noise ratios. These data are available in netCDF, and browse image files are available in .png format.", "links": [ { diff --git a/datasets/gppdi_npp_gridded_xdeg_1023_1.json b/datasets/gppdi_npp_gridded_xdeg_1023_1.json index 07a7f8b190..08d0f5224f 100644 --- a/datasets/gppdi_npp_gridded_xdeg_1023_1.json +++ b/datasets/gppdi_npp_gridded_xdeg_1023_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gppdi_npp_gridded_xdeg_1023_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Net Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. Direct measurement of NPP is not practical for large areas and so models are generally used to study the carbon cycle at a global scale. This data set contains 2 *.zip files for above ground and total NPP data. ", "links": [ { diff --git a/datasets/gppdi_npp_point_1033_1.json b/datasets/gppdi_npp_point_1033_1.json index f5f8a3af41..b7b2748b1e 100644 --- a/datasets/gppdi_npp_point_1033_1.json +++ b/datasets/gppdi_npp_point_1033_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gppdi_npp_point_1033_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Primary Production Data Initiative (GPPDI) was set up as a Focus 1 activity of the IGBP Data and Information System, a coordinated international program to improve worldwide estimates of terrestrial net primary productivity (NPP) for parameterization, calibration, and validation of NPP models at various scales.The GPPDI data collection contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The point measurements of NPP were categorized as either Class A, representing intensively studied or well-documented study sites (e.g., with site-specific climate, soils information, etc.), Class B, representing more numerous extensive sites with less documentation and site-specific information available, or Class C, representing regional collections of half-degree latitude-longitude grid cells. This data set in the ISLSCP II collection represents the GPPDI Class B NPP data. The Class B NPP data file contains biomass dynamics, climate, and site-characteristics data georeferenced to each site. There is one ASCII data file with this data set. ", "links": [ { diff --git a/datasets/gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0.json b/datasets/gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0.json index b07f1182aa..10f36d788d 100644 --- a/datasets/gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0.json +++ b/datasets/gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay. The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution. Please refer to the Read-me file for further details on the data. These data are the basis of the following publication: > Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431", "links": [ { diff --git a/datasets/grassland-use-intensity-maps-for-switzerland_1.0.json b/datasets/grassland-use-intensity-maps-for-switzerland_1.0.json index 4513d42cd4..c70dc4673d 100644 --- a/datasets/grassland-use-intensity-maps-for-switzerland_1.0.json +++ b/datasets/grassland-use-intensity-maps-for-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "grassland-use-intensity-maps-for-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018\u20132021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework. The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m \u00d7 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)). The webcam-based reference data used for verification was subsequently added on 14.02.2024.", "links": [ { diff --git a/datasets/gravity_wilkes_1964_1.json b/datasets/gravity_wilkes_1964_1.json index 6851683271..de6ee66d3b 100644 --- a/datasets/gravity_wilkes_1964_1.json +++ b/datasets/gravity_wilkes_1964_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gravity_wilkes_1964_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The results of a gravity survey done on Wilkes Ice Cap. No information in the papers on how it was done, dates, etc - just the numbers. Even year is unsure (could be 1964 or 1965 season).\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0.json b/datasets/green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0.json index 91283a1f4c..c7d909a6df 100644 --- a/datasets/green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0.json +++ b/datasets/green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the published scientific paper Gr\u0103dinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28. The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1\u201311) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12\u201336), was coded according to Table 1 of the article. As a result, we provide the folowing outputs: \u2022\tGI_Dataset_1_Items_1-12.xlsx \u2013 available on request o\tResults of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option \u2013 this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper. \u2022\tGI_Dataset_2_Items_12-36.csv \u2013 freely available o\tResults of the coding on principles of GI planning followed in strategic plans of urban regions\u2013 this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.", "links": [ { diff --git a/datasets/grinstedSBB-ECM-VIDEO.json b/datasets/grinstedSBB-ECM-VIDEO.json index 60852c7fe8..77672ab0fa 100644 --- a/datasets/grinstedSBB-ECM-VIDEO.json +++ b/datasets/grinstedSBB-ECM-VIDEO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "grinstedSBB-ECM-VIDEO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Location: Scharffenbergbotnen blue ice area, Heimefrontfjella\n \n Electrical Conductivity profile of the surface blue ice (stretching ~2.5km from near the ice fall). At the same time a video recording of the surface ice was made. Positions of the records can be tied together with DGPS.", "links": [ { diff --git a/datasets/gripapr2_1.json b/datasets/gripapr2_1.json index 8c590296d9..0e504ae482 100644 --- a/datasets/gripapr2_1.json +++ b/datasets/gripapr2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripapr2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Airborne Second Generation Precipitation Radar (APR-2) dataset was collected from the Second Generation Airborne Precipitation Radar (APR-2), which is a dual-frequency (13 GHz and 35 GHz), Doppler, dual-polarization radar system. It has a downward looking antenna that performs cross track scans. Additional features include: simultaneous dual-frequency, matched beam operation at 13.4 and 35.6 GHz (same as GPM Dual-Frequency Precipitation Radar), simultaneous measurement of both like- and cross-polarized signals at both frequencies, Doppler operation, and real-time pulse compression (calibrated reflectivity data can be produced for large areas in the field during flight, if necessary). The APR-2 flew on the NASA DC-8 for the Genesis and Rapid Intensification Processes (GRIP) experiment and collected data between Aug 17, 2010 - Sep 22, 2010 and are in HDF-4 format. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.", "links": [ { diff --git a/datasets/gripcaps_1.json b/datasets/gripcaps_1.json index 6220fd902d..0ac04ab890 100644 --- a/datasets/gripcaps_1.json +++ b/datasets/gripcaps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripcaps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Cloud Microphysics dataset was collected during the GRIP campaign from three probes: the Cloud, Aerosol, and Precipitation Spectrometer (CAPS), the Precipitation Imaging Probe (PIP), and the Cloud Droplet Probe (CDP). All are manufactured by Droplet Measurement Technologies in Boulder, CO. The CAPS is a combination of two probes, the Cloud Imaging Probe-Greyscale (CIP-G), and the Cloud and Aerosol Spectrometer (CAS). Images of particles are recorded by the CIP-G and PIP, while the CAS probe measures particle size distribution from 0.55 to 52.5 microns and the CDP measures ice amount. Some ice/liquid water content are derived from the particle size distribution. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. Data was collected 13 Aug 2010 through 25 Sep 2010.", "links": [ { diff --git a/datasets/gripdawn_1.json b/datasets/gripdawn_1.json index ca05389736..62a392279a 100644 --- a/datasets/gripdawn_1.json +++ b/datasets/gripdawn_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripdawn_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Doppler Aerosol WiNd Lidar (DAWN) Dataset was collected by the Doppler Aerosol WiNd (DAWN), a pulsed lidar, which operated aboard a NASA DC-8 aircraft during the Genesis and Rapid Intensification Processes (GRIP) field campaign. he major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. This campaign also capitalized on a number of ground networks and space-based assets, in addition to the instruments deployed on aircraft from Ft. Lauderdale, Florida ( DC-8), Houston, Texas (WB-57), and NASA Dryden Flight Research Center, California (Global Hawk). Data values include Line-of-Sight (LOS) Winds, calculated vertical profiles of horizontal wind velocity, frequency-domain signal energy and time versus latitude and longitude. Instrument details can be found in the dataset documentation. Data was gathered during August 24, 2010 thru September 22, 2010 over the Atlantic Ocean.", "links": [ { diff --git a/datasets/gripdropdc83_3.json b/datasets/gripdropdc83_3.json index 2b6c328bbf..2042a87a2d 100644 --- a/datasets/gripdropdc83_3.json +++ b/datasets/gripdropdc83_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripdropdc83_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP DC-8 Dropsonde V3 dataset consists of atmospheric pressure, dry-bulb temperature, dew point temperature, relative humidity, wind direction, wind speed, and fall rate measurements taken during 16 research flights during the Genesis and Rapid Intensification Processes (GRIP) campaign from August 17, 2010 to September 22, 2010. The GRIP campaign was conducted to better understand how tropical storms form and how these storms develop into major hurricanes. The DC-8 Airborne Vertical Atmospheric Profiling System (AVAPS) deploys integrated, highly accurate, GPS-located atmospheric profiling dropsondes to measure and record current atmospheric conditions in a vertical column below the aircraft. The dropsondes are ejected from a tube in the underside of the DC-8 aircraft. As the dropsonde descends to the surface via a parachute, it continuously measures and transmits data to the aircraft using a 400 MHz meteorological band telemetry link. Pressure, temperature and relative humidity, as well as GPS-based wind data were collected from 328 dropsondes. These Dropsonde data are in ASCII-csv file format.", "links": [ { diff --git a/datasets/gripflt_1.json b/datasets/gripflt_1.json index 385099424c..357f56d92e 100644 --- a/datasets/gripflt_1.json +++ b/datasets/gripflt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripflt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Flight Tracks and Animations dataset includes both KML files and animation files. The KML files use Google Earth to show the flight tracks on a map. The animations vary by type. Created by the Real-time Mission Monitor (RTMM) software, the .avi files show the flight track versus time superimposed over the GOES Infrared (IR) data from August 13, 2010 to September 25, 2010. The National SubOrbital Education and Research Center provided a file in two formats (.mov, .mp4) viewing hurricane Earl from the NASA DC-8 aircraft. Also a NBC newscast informs the public of the GRIP's goals during the campaign. he major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. This campaign also capitalized on a number of ground networks and space-based assets, in addition to the instruments deployed on aircraft from Ft. Lauderdale, Florida ( DC-8), Houston, Texas (WB-57), and NASA Dryden Flight Research Center, California (Global Hawk).", "links": [ { diff --git a/datasets/gripghis_1.json b/datasets/gripghis_1.json index b5cf082ce3..b95e279704 100644 --- a/datasets/gripghis_1.json +++ b/datasets/gripghis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripghis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP NOAA Global Hawk In-Flight Turbulence Sensor (GHIS) dataset was collected by the NOAA Global Hawk In-flight Turbulence Sensor (GHIS) instrument, which measures acceleration at the location of the instrument. Two accelerometers (2g and 5g full scale) are used on each of two measurement axes. The GHIS accelerometers are from the Model 1221 family manufactured by Silicon Designs, Inc. with a frequency response of 400-600Hz. The data system samples each sensor output at 1000 Hz and processes these data to produce mean, maximum, and root-mean square (RMS) values at 10 Hz. The processed data are then broadcast on the Global Hawk internet and brought to the ground via Status and User User Datagram Protocol (UDP) packets. GHIS operated on the Global Hawk for the Genesis and Rapid Intensification Processes (GRIP) experiment and collected data between Aug 15, 2010 - Sep 23, 2010. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. This campaign also capitalized on a number of ground networks and space-based assets, in addition to the instruments deployed on aircraft from Ft. Lauderdale, Florida ( DC-8), Houston, Texas (WB-57), and NASA Dryden Flight Research Center, California (Global Hawk).", "links": [ { diff --git a/datasets/gripgoes11B_1.json b/datasets/gripgoes11B_1.json index 8842df9510..129ae10b14 100644 --- a/datasets/gripgoes11B_1.json +++ b/datasets/gripgoes11B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripgoes11B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP GOES 11 Visible and Infrared Images dataset was produced and archived in near real-time at the Global Hydrology Resource Center throughout the Genesis and Rapid Intensification Processes (GRIP) campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. The GOES I-M Imager is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the Earth. These image files were created for use with the Real Time Mission Monitor (RTMM). Generally, GOES-11 images are available for all dates between August 15 and September 30, 2010 at 15 minute intervals throughout this time period.", "links": [ { diff --git a/datasets/gripgoes13B_1.json b/datasets/gripgoes13B_1.json index 56d8117ae3..fa6ddaafdb 100644 --- a/datasets/gripgoes13B_1.json +++ b/datasets/gripgoes13B_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripgoes13B_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP GOES 13 Visible and Infrared Images dataset was produced and archived in near real time at the Global Hydrology Resource Center throughout the Genesis and Rapid Intensification Processes (GRIP) campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. The GOES I-M Imager is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the Earth. These image files were created for use with the Real Time Mission Monitor (RTMM). Generally, GOES-13 images are available for all dates between August 15 and September 30, 2010 at 15 minute intervals throughout this time period.", "links": [ { diff --git a/datasets/gripgoesot_1.json b/datasets/gripgoesot_1.json index 413dcc56f5..74e19fb39a 100644 --- a/datasets/gripgoesot_1.json +++ b/datasets/gripgoesot_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripgoesot_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP GOES 13 Overshooting Top dataset was produced during the GRIP Field Experiment for use with the Real Time Mission Monitor (RTMM) tool. The major goal was to better understand how tropical storms form and develop into major hurricanes. The magnitude of each overshooting top is the difference between the 'anvil' temperature and the 'overshooting top' temperature. These magnitudess are represented as a color coded display using Google Earth, a virtual globe, map and geographical information program.", "links": [ { diff --git a/datasets/griphamsr_1.json b/datasets/griphamsr_1.json index fd08aceda7..6c8fc5dc4e 100644 --- a/datasets/griphamsr_1.json +++ b/datasets/griphamsr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griphamsr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP High-Altitude MMIC Sounding Radiometer (HAMSR) dataset was collectd by the High Altitude monolithic microwave integrated Circuit (MMIC) Sounding Radiometer (HAMSR) is a microwave atmospheric sounder developed by JPL under the NASA Instrument Incubator Program. The new HAMSR with 183GHz LNA receiver reduces noise to less than a 0.1K level improving observations of small-scale water vapor. HAMSR has 25 spectral channels which are split into 3 bands: an 8-channel band centered 53-GHz, used to infer the 3-D distribution of temperature; a 10-channel band centered at 118 GHz, used for secondary temperature sounding and assessment of scattering; and a 7-channel band centered at 183 GHz, used to measure water vapor (humidity) and cloud liquid water content in the atmosphere. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the life cycle of hurricanes.", "links": [ { diff --git a/datasets/griphirad1_1.json b/datasets/griphirad1_1.json index f0eaa8e46e..e9002c0c68 100644 --- a/datasets/griphirad1_1.json +++ b/datasets/griphirad1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griphirad1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Hurricane Imaging Radiometer (HIRAD) V1 dataset contains measurements of brightness temperature taken at 4, 5, 6 and 6.6 GHz, as well as MERRA 2 m wind speed data and JPL MUR Sea Surface Temperature data. The data is provided in netCDF format. The data were collected during the Genesis and Rapid Intensification Processes (GRIP) experiment from September 1, 2010 through September 16, 2010 for storms EARL and KARL. Rain rate and wind speed files may be obtained from the V0 HIRAD dataset. The major goal was to better understandhow tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned AirborneSystem (UAS), configuredwith a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.HIRAD is a hurricane imaging, single-polarization passive C-band radiometer with both cross-track and along-track resolution that measures strong ocean surface winds through heavy rain from an aircraft or space-based platform. Its swath width is approximately 60 degrees in either direction.", "links": [ { diff --git a/datasets/griphirad_0.json b/datasets/griphirad_0.json index 73701de106..1dce929634 100644 --- a/datasets/griphirad_0.json +++ b/datasets/griphirad_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griphirad_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Hurricane Imaging Radiometer (HIRAD) dataset was collected by the HIRAD instrument, which is a hurricane imaging, single-polarization passive C-band radiometer with both cross-track and along-track resolution that measures strong ocean surface winds through heavy rain from an aircraft or space-based platform. Its swath width is approximately 60 degrees in either direction. V0 data contains brightness temperature measurements taken at a 5 GHz frequency. Rain rate and wind speed files for Hurricane Earl have been added to the collection. HIRAD data was collected for storms Earl and Karl during the Genesis and Rapid Intensification Processes (GRIP) experiment from September 1, 2010 through September 16, 2010. The major goal was to better understandhow tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned AirborneSystem (UAS), configuredwith a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.", "links": [ { diff --git a/datasets/griphiwrap_1.json b/datasets/griphiwrap_1.json index a3c4025b3c..8dc283ee22 100644 --- a/datasets/griphiwrap_1.json +++ b/datasets/griphiwrap_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griphiwrap_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dataset was collected by the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), which is a dual-frequency (Ka- and Ku-band) conical scan system, configured with a nadir viewing antenna on the Global Hawk aircraft. The HIWRAP instrument provides calibrated reflectivity and unfolded Doppler velocity. These dual-frequency radar measurements have frequencies similar to that of the GPM. These data are from the Genesis and Rapid Intensification Processes (GRIP) experiment from September 16, 2010 through September 24, 2010. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. HIWRAP flew on the Global Hawk aircraft mainly over the Gulf of Mexico.", "links": [ { diff --git a/datasets/griplarge_1.json b/datasets/griplarge_1.json index 4013d8f165..2b608f0f77 100644 --- a/datasets/griplarge_1.json +++ b/datasets/griplarge_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griplarge_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Langley Aerosol Research Group Experiment (LARGE) dataset was collected by the Langley Aerosol Research Group Experiment (LARGE), which measures ultrafine aerosol number density, total and non-volatile aerosol number density, dry aerosol size distribution, total and submicron aerosol absorption coefficients, total and submicron aerosol scattering coefficients, and total scattering and hemispheric backscattering coefficients. Instruments used during LARGE derived aerosol size statistics (mode, number and mass mean diameters, etc.), aerosol surface area and mass loading, aerosol extinction, single scattering albedo, and angstrom coefficients. This dataset was collected during the Genesis and Rapid Intensification Processes (GRIP) experiment, which a NASA Earth science field experiment. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. The GRIP LARGE dataset collected data over the Gulf of Mexico from August 6, 2010 to September 22, 2010.", "links": [ { diff --git a/datasets/griplase_1.json b/datasets/griplase_1.json index a065ce7af3..bd6b1d122a 100644 --- a/datasets/griplase_1.json +++ b/datasets/griplase_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griplase_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Lidar Atmospheric Sensing Experiment (LASE) dataset was collected by NASA's Lidar Atmospheric Sensing Experiment (LASE) system, which is an airborne Differential Absorption Lidar (DIAL) system used to measure water vapor, aerosols, and clouds throughout the troposphere. LASE is onboard the NASA DC-8 aircraft and probes the atmosphere using lasers to transmit light in the 815-nm absorption band of water vapor. Pulses of laser light are fired vertically below the aircraft. A small fraction of the transmitted laser light is reflected from the atmosphere back to the aircraft and collected with a telescope receiver. The received light indicates the amount of water vapor along the path of the laser beam. LASE operated in the Genesis and Rapid Intensification Processes (GRIP) experiment with data spanning between August 13, 2010 through September 25, 2010. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.", "links": [ { diff --git a/datasets/griplip_1.json b/datasets/griplip_1.json index aa7081cfdb..b0fb4d574c 100644 --- a/datasets/griplip_1.json +++ b/datasets/griplip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "griplip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Lightning Instrument Package (LIP) dataset was collected by the Lightning Instrument Package (LIP), which consists of 6 rotating vane type electric field mill sensors along with a central computer to record and monitor the instruments. The field mills measure the components of the electric field over a wide dynamic range extending from fair weather electric fields, (i.e., a few to tens of V/m) to larger thunderstorm fields (i.e., tens of kV/m). During the GRIP campaign the LIP instrument package flew aboard the Global Hawk aircraft. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.", "links": [ { diff --git a/datasets/gripmisrep_1.json b/datasets/gripmisrep_1.json index 41095ef190..2e4ee2cda9 100644 --- a/datasets/gripmisrep_1.json +++ b/datasets/gripmisrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripmisrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Campaign Reports dataset consists of various reports filed by scientists during the GRIP campaign which took place 8/15/2010 - 9/30/2010; however, several of the reports are from the planning and test flights. Reports included in this dataset contain information for the Tri Agency Mission Scientists; DC-8, Global Hawk, and WB-57 Platform Scientists; DC-8, Global Hawk, and WB-57 Flight Reports and WB-57 Flight Summary; GRIP Telecons; and TropicalGRIP Forecasts. The Tri Agency Mission Scientists reports, GRIP telecons and Forecast reports were primarily filed daily, while the Platform and Flight reports exist primarily for flight days.", "links": [ { diff --git a/datasets/gripmms_1.json b/datasets/gripmms_1.json index a2629cf94f..2448c63f6c 100644 --- a/datasets/gripmms_1.json +++ b/datasets/gripmms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripmms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP DC-8 Meteorological measurement System (MMS) dataset was collected by the Meteorological Measurement System (MMS), which provides high-resolution, accurate meteorological parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector). The MMS hardware consists of 3 major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the Earth, and a data acquisition system to sample, process, and record the measured quantities. In addition to making the in flight measurements, a major and necessary step is the post mission systematic calibration and data processing. The primary data set consists of 1 Hz meteorological data (P, T, 3D winds). The secondary data set at 20 Hz includes the meteorological data and additional parameters such as Potential-Temperature; True-Air-Speed; aircraft GPS position, velocities, attitudes, acceleration and air flow data (angle-of-attack, sideslip) from August 10, 2010 through September 25, 2010. The Genesis and Rapid Intensification Processes (GRIP) experiment was a NASA Earth science field experiment. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.", "links": [ { diff --git a/datasets/gripmsg_1.json b/datasets/gripmsg_1.json index 7d88279a51..3c23e42c06 100644 --- a/datasets/gripmsg_1.json +++ b/datasets/gripmsg_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripmsg_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Meteosat Second Generation (MSG) Image Data was collected during the Genesis and Rapid Intensification Processes (GRIP) experiment from August 15, 2010 to September 30, 2010. The major goal was to better understand how tropical storms form and develop into major hurricanes. Infrared and visible radiances, and water vapor were measured. Meteosat Second Generation (MSG) consists of a series of four geostationary meteorological satellites, along with ground-based infrastructure, that will operate consecutively until 2020. The MSG system is established under cooperation between The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Space Agency (ESA) to ensure the continuity of meteorological observations from geostationary orbit. The MSG satellites carry an impressive pair of instruments, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels and provide image data which is core to operational forecasting needs, and the Geostationary Earth Radiation Budget (GERB) instrument supporting climate studies.", "links": [ { diff --git a/datasets/gripnavdc8_1.json b/datasets/gripnavdc8_1.json index 93b36baf63..e0bbdaab8a 100644 --- a/datasets/gripnavdc8_1.json +++ b/datasets/gripnavdc8_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripnavdc8_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP DC-8 Navigation and Housekeeping Data contains aircraft navigational data obtained during the GRIP campaign (15 Aug 2010 - 30 Sep 2010). The major goal was to better understand how tropical storms form and develop into major hurricanes. The NASA DC-8 is outfitted with a navigational recording system which in combination with the Research Environment for Vehicle-Embedded Analysis on Linux (REVEAL) provides detailed flight parameters such as airspeed, altitude, roll/pitch/yaw angles, ground speed, flight level wind speed, temperature and many others. The REVEAL system is a configurable embedded system for facilitating integration of instrument payloads with vehicle systems and communication links. REVEAL systems currently serve as onboard data acquisition, processing, and recording systems.", "links": [ { diff --git a/datasets/gripnavgh_1.json b/datasets/gripnavgh_1.json index 0c08dfba1b..2cf23dac15 100644 --- a/datasets/gripnavgh_1.json +++ b/datasets/gripnavgh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripnavgh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Global Hawk Navigation and Housekeeping data was collected from August 15, 2010 to September 24, 2010 during the Genesis and Rapid Intensification Processes (GRIP) field campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. The Global Hawk is an unmanned Airborne System configured with in situ and remote sensing instruments, including the Lightning Imaging Package (LIP), High Altitude Wind and Rain Profiler (HIWRAP), and High Altitude MMIC Sounding Radiometer (HAMSR). Data was collected for 7 dates and is in the IWGADTS IWG1 format. The dataset also includes XML files containing metadata documenting the parameters and their format collected during each day's flight.", "links": [ { diff --git a/datasets/gripnavwb57_1.json b/datasets/gripnavwb57_1.json index 6d25fb13d1..3d2cec35e9 100644 --- a/datasets/gripnavwb57_1.json +++ b/datasets/gripnavwb57_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripnavwb57_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP WB-57 Navigation and Housekeeping data was collected on flight days occuring between July 13 , 2010 to September 17, 2010 during the Genesis and Rapid Intensification Processes (GRIP) field campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. The NASA WB-57 is a weather research aircraft capable of operating for extended periods of time (~6.5 hours) from sea level to altitudes well over 60,000 feet (12 miles high). Both data in IWG1 format and error logs are part of this dataset.", "links": [ { diff --git a/datasets/gripradio_1.json b/datasets/gripradio_1.json index f49c766267..e011cefed5 100644 --- a/datasets/gripradio_1.json +++ b/datasets/gripradio_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripradio_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Barbados/Cape Verde radiosonde data set consists of generally two soundings per day (06Z and 12Z) launched from Barbados, and one sounding per day (12Z) launched from Cape Verde during the Genesis and Rapid Intensification Processes (GRIP) field campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. These radiosondes measure the profile of atmospheric pressure, temperature, humidity, wind speed and direction, from the ground to an altitude of up to 40 km (in general, the sondes reached at least a pressure of 100 milibars). The launch program began on August 14, 2010 and ended September 24, 2010. The sondes used were type DFM-06, built by GRAW Radiosondes, Nuremberg Germany. Most ascents were done with TOTEX 200-g latex balloons using the DMF-06 sondes. A few launches were made using TOTEX 800-g Balloons with the DFM-97 package (connected with ECC ozonesonde). On some days launch times were changed, and multiple launches were made from Barbados on September 9, 10 and 21. The data were retrieved using a GRAWMET GS-E ground station. The sample rate of the data was 4 seconds for the Barbados data and 2 seconds for the Cape Verde data.", "links": [ { diff --git a/datasets/gripstorm_1.json b/datasets/gripstorm_1.json index 917835ad97..af3e5842cd 100644 --- a/datasets/gripstorm_1.json +++ b/datasets/gripstorm_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gripstorm_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GRIP Hurricane and Tropical Storm Forecasts dataset consists of tropical cyclone model forecast tracks archived during the NASA Genesis and Rapid Intensification Processes (GRIP) field campaign. GRIP was one of three hurricane field campaigns conducted during the 2010 Atlantic/Pacific hurricane season. This tri-agency effort included NASA GRIP, the NSF Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) and the NOAA Intensity Forecasting Experiment 2010 (IFEX10). The hurricane and tropical storm forecasts data files are available from August 12 through November 14, 2010 in ASCII text format with browse files in KML format, viewable in Google Earth. The ASCII text files contain 5-day model \u201cconsensus\u201d forecasts and the KML browse files contain model forecasts ranging from 5-days to 10-days.", "links": [ { diff --git a/datasets/groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0.json b/datasets/groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0.json index 791a06055c..0ccd848f80 100644 --- a/datasets/groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0.json +++ b/datasets/groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns \u2013 Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R", "links": [ { diff --git a/datasets/gtopo30_hydro_1k.json b/datasets/gtopo30_hydro_1k.json index 9f0517440e..fb12a8d252 100644 --- a/datasets/gtopo30_hydro_1k.json +++ b/datasets/gtopo30_hydro_1k.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gtopo30_hydro_1k", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "HYDRO1k is a geographic database developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale.", "links": [ { diff --git a/datasets/gtree_1.0.json b/datasets/gtree_1.0.json index bb78e314b9..36845b6c0a 100644 --- a/datasets/gtree_1.0.json +++ b/datasets/gtree_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gtree_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded. # Experimental design The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224\u2009cm\u2009\u00d7\u200945\u2009cm) were established at each site, which were randomly assigned to the 2\u2009\u00d7\u20092 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5\u2009cm\u2009\u00d7\u200928\u2009cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018). # Data description All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013\u20132021).", "links": [ { diff --git a/datasets/gts_precip_daily_xdeg_1001_1.json b/datasets/gts_precip_daily_xdeg_1001_1.json index 1a94857fb1..4c6abb08bf 100644 --- a/datasets/gts_precip_daily_xdeg_1001_1.json +++ b/datasets/gts_precip_daily_xdeg_1001_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "gts_precip_daily_xdeg_1001_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this work was to construct a long-term data set of daily precipitation on half degree and one degree latitude/longitude grids over the global land areas. The analyses are defined by interpolating station observations from GTS (Global Telecommunications System) gauges using the algorithm of Shepard (1968). The algorithm of Shepard (1968) has been widely used to interpolate gauge observations of monthly, pentad, and daily precipitation (Rudolf 1993, Xie et al. 1996). This algorithm is used to interpolate the irregularly distributed station observations onto grid points. The weighting coefficients are inversely proportional to the gauge-grid point distance and are adjusted by a cosine function taking into account the directional isolation of each gauge relative to all other nearby gauges. There are 6 data files with this data set.", "links": [ { diff --git a/datasets/h01_shd_253_1.json b/datasets/h01_shd_253_1.json index 24538b7632..328483adf5 100644 --- a/datasets/h01_shd_253_1.json +++ b/datasets/h01_shd_253_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h01_shd_253_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the hydraulic properties of the soil at each tower flux site determined by the HYD-01 science team.", "links": [ { diff --git a/datasets/h01smpvd_255_1.json b/datasets/h01smpvd_255_1.json index a8210f69e8..0c2632aa67 100644 --- a/datasets/h01smpvd_255_1.json +++ b/datasets/h01smpvd_255_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h01smpvd_255_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the percent soil moisture by volume data that was collected by the HYD-01 group at the various tower sites.", "links": [ { diff --git a/datasets/h01uncpd_254_1.json b/datasets/h01uncpd_254_1.json index d9f06352ec..086f2c1ddb 100644 --- a/datasets/h01uncpd_254_1.json +++ b/datasets/h01uncpd_254_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h01uncpd_254_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the under canopy precipitation data that was collected in 1994, 1995, 1996 and 1997 by the HYD-01 group at the various tower sites.", "links": [ { diff --git a/datasets/h02swed_256_1.json b/datasets/h02swed_256_1.json index 4323d7003f..c43921f9ff 100644 --- a/datasets/h02swed_256_1.json +++ b/datasets/h02swed_256_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h02swed_256_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains HYD-02 snow water equivalent derived from microwave measurements from aircraft.", "links": [ { diff --git a/datasets/h03candd_258_1.json b/datasets/h03candd_258_1.json index f915b2edca..bae79e4a4d 100644 --- a/datasets/h03candd_258_1.json +++ b/datasets/h03candd_258_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03candd_258_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites. Canopy density measurements were made during the FFC-W and FFC-T 1994 in both the SSA and NSA using a forest densiometer. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems. ", "links": [ { diff --git a/datasets/h03dbhd_264_1.json b/datasets/h03dbhd_264_1.json index 17d26afc98..4ba54dd078 100644 --- a/datasets/h03dbhd_264_1.json +++ b/datasets/h03dbhd_264_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03dbhd_264_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems. ", "links": [ { diff --git a/datasets/h03scrdd_266_1.json b/datasets/h03scrdd_266_1.json index 8c2f26cbaa..64add6b011 100644 --- a/datasets/h03scrdd_266_1.json +++ b/datasets/h03scrdd_266_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03scrdd_266_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains the sub-canopy radiation data collected by HYD-3.", "links": [ { diff --git a/datasets/h03sd96d_259_1.json b/datasets/h03sd96d_259_1.json index 17ca680958..f0cb94a0cc 100644 --- a/datasets/h03sd96d_259_1.json +++ b/datasets/h03sd96d_259_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03sd96d_259_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains snow depth measurements made by HYD-3 in 1996. ", "links": [ { diff --git a/datasets/h03sntmd_261_1.json b/datasets/h03sntmd_261_1.json index 95a40026df..10cc28b92d 100644 --- a/datasets/h03sntmd_261_1.json +++ b/datasets/h03sntmd_261_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03sntmd_261_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a snow temperature table for HYD03. The snow temperature is given for various snow heights at various sites.", "links": [ { diff --git a/datasets/h03sp96d_260_1.json b/datasets/h03sp96d_260_1.json index c66df35a77..50c708c1a8 100644 --- a/datasets/h03sp96d_260_1.json +++ b/datasets/h03sp96d_260_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03sp96d_260_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains snow pit measurements made by HYD-3 in 1996.", "links": [ { diff --git a/datasets/h03stdnd_257_1.json b/datasets/h03stdnd_257_1.json index a10c99ec66..0b21c9b443 100644 --- a/datasets/h03stdnd_257_1.json +++ b/datasets/h03stdnd_257_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03stdnd_257_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites. Stem density measurements were made during the FFC-W 1996 in the SSA only using standard techniques. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems. ", "links": [ { diff --git a/datasets/h03swed_262_1.json b/datasets/h03swed_262_1.json index d9828e7bcd..ae73ae3296 100644 --- a/datasets/h03swed_262_1.json +++ b/datasets/h03swed_262_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h03swed_262_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the snow water equivalent that was calculated from measurements of snow pack density and snow depth. ", "links": [ { diff --git a/datasets/h04assd_267_1.json b/datasets/h04assd_267_1.json index 713fb9140b..c3889b3251 100644 --- a/datasets/h04assd_267_1.json +++ b/datasets/h04assd_267_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h04assd_267_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the areal snow survey data collected by HYD-04. The flight line numbers that are included correspond to the flight lines from HYD-06 measurements.", "links": [ { diff --git a/datasets/h04stsnd_268_1.json b/datasets/h04stsnd_268_1.json index 7545f91d98..519e446233 100644 --- a/datasets/h04stsnd_268_1.json +++ b/datasets/h04stsnd_268_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h04stsnd_268_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the standard snow course data collected at various sites in the NSA and SSA by HYD-04.", "links": [ { diff --git a/datasets/h06grmsd_272_1.json b/datasets/h06grmsd_272_1.json index 17168ec083..4eea07075d 100644 --- a/datasets/h06grmsd_272_1.json +++ b/datasets/h06grmsd_272_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h06grmsd_272_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains the measurements of water content of the moss/humus layer that were made at various sites on the ground by HYD-06.", "links": [ { diff --git a/datasets/h06grsmd_271_1.json b/datasets/h06grsmd_271_1.json index 2280b75472..65d5dd34a2 100644 --- a/datasets/h06grsmd_271_1.json +++ b/datasets/h06grsmd_271_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h06grsmd_271_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the measurements of soil moisture that were made at various sites on the ground by HYD-06.", "links": [ { diff --git a/datasets/h08gm94_273_1.json b/datasets/h08gm94_273_1.json index 2274b962e5..696efada78 100644 --- a/datasets/h08gm94_273_1.json +++ b/datasets/h08gm94_273_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h08gm94_273_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the gravimetric moss data collected by HYD-08 at the Black Spruce and Joey Lake sites. It contains the weights of moss turves under two different conditions.", "links": [ { diff --git a/datasets/h08gm96_274_1.json b/datasets/h08gm96_274_1.json index 405bc82167..a519519140 100644 --- a/datasets/h08gm96_274_1.json +++ b/datasets/h08gm96_274_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h08gm96_274_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the HYD-08 weights of the dried moss samples. These weights do not include the weight of the tray in which the sample was contained. Contains the HYD-08 moss measurements of water equivalent made at the moss lysimeter sites near the SSA-OBS in 19", "links": [ { diff --git a/datasets/h08gp96_275_1.json b/datasets/h08gp96_275_1.json index 04cf148c0f..f4aebaf13c 100644 --- a/datasets/h08gp96_275_1.json +++ b/datasets/h08gp96_275_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h08gp96_275_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the HYD-08 mean gross precipitation measurements made at 2 gauges at the SSA-OBS.", "links": [ { diff --git a/datasets/h09rradi_278_1.json b/datasets/h09rradi_278_1.json index f975c188e2..007a105ca8 100644 --- a/datasets/h09rradi_278_1.json +++ b/datasets/h09rradi_278_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h09rradi_278_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA. This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar)", "links": [ { diff --git a/datasets/h09stmgd_279_1.json b/datasets/h09stmgd_279_1.json index bf5425eb73..3b9b088ffb 100644 --- a/datasets/h09stmgd_279_1.json +++ b/datasets/h09stmgd_279_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h09stmgd_279_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the stream gauge data that was collected by the HYD09 group. ", "links": [ { diff --git a/datasets/h3scmet_265_1.json b/datasets/h3scmet_265_1.json index ddab777cea..9365f1e509 100644 --- a/datasets/h3scmet_265_1.json +++ b/datasets/h3scmet_265_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h3scmet_265_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains the sub-canopy meteorological data collected by HYD-3. ", "links": [ { diff --git a/datasets/h3swe96d_263_1.json b/datasets/h3swe96d_263_1.json index 202d83d2cc..d45eb9a5c2 100644 --- a/datasets/h3swe96d_263_1.json +++ b/datasets/h3swe96d_263_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h3swe96d_263_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the snow water equivalent, snow_depth, and snow density measurements made by HYD-3 with the Canadian snow sampler in 1996.", "links": [ { diff --git a/datasets/h5flxd_269_1.json b/datasets/h5flxd_269_1.json index bb921f6d41..ad9b21833f 100644 --- a/datasets/h5flxd_269_1.json +++ b/datasets/h5flxd_269_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h5flxd_269_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the HYD-05 flux, and meteorological measurements from Bear Trap Forest, Saskatchewan in the winter of 1994. Contains the HYD-05 flux, meteorological, and infrared thermometer measurements from Namekus Lake, Saskatchewan in the winters of 1994 and", "links": [ { diff --git a/datasets/h6acgsmd_270_1.json b/datasets/h6acgsmd_270_1.json index b93a3e0031..99bd18c5e0 100644 --- a/datasets/h6acgsmd_270_1.json +++ b/datasets/h6acgsmd_270_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h6acgsmd_270_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the aircraft estimates of soil moisture measured by the gamma ray instrument from HYD06.", "links": [ { diff --git a/datasets/h8thrfld_277_1.json b/datasets/h8thrfld_277_1.json index b75f64676c..ea01fc4769 100644 --- a/datasets/h8thrfld_277_1.json +++ b/datasets/h8thrfld_277_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h8thrfld_277_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the moss lysimeter measurements made by HYD-08.", "links": [ { diff --git a/datasets/h8utmdem_276_1.json b/datasets/h8utmdem_276_1.json index 1b20758736..6fdeb50c67 100644 --- a/datasets/h8utmdem_276_1.json +++ b/datasets/h8utmdem_276_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h8utmdem_276_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "DEMs produced from digitized contours at a cell resolution of 100 meters.", "links": [ { diff --git a/datasets/h9rgbl94_229_1.json b/datasets/h9rgbl94_229_1.json index 09c18a66ad..b270b5bc29 100644 --- a/datasets/h9rgbl94_229_1.json +++ b/datasets/h9rgbl94_229_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgbl94_229_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations. ", "links": [ { diff --git a/datasets/h9rgbl95_231_1.json b/datasets/h9rgbl95_231_1.json index a4d740dd6a..12889a5365 100644 --- a/datasets/h9rgbl95_231_1.json +++ b/datasets/h9rgbl95_231_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgbl95_231_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.", "links": [ { diff --git a/datasets/h9rgbl96_233_1.json b/datasets/h9rgbl96_233_1.json index f580f182ca..ec5548eea7 100644 --- a/datasets/h9rgbl96_233_1.json +++ b/datasets/h9rgbl96_233_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgbl96_233_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Belfort rain gauge data that was collected by the HYD09 group at various locations.", "links": [ { diff --git a/datasets/h9rgtb94_230_1.json b/datasets/h9rgtb94_230_1.json index 7b8ea4bde5..e116ea4e5c 100644 --- a/datasets/h9rgtb94_230_1.json +++ b/datasets/h9rgtb94_230_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgtb94_230_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.", "links": [ { diff --git a/datasets/h9rgtb95_232_1.json b/datasets/h9rgtb95_232_1.json index 9a422bb73b..195648757b 100644 --- a/datasets/h9rgtb95_232_1.json +++ b/datasets/h9rgtb95_232_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgtb95_232_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.", "links": [ { diff --git a/datasets/h9rgtb96_234_1.json b/datasets/h9rgtb96_234_1.json index 39f87bc9ce..cc7bddb892 100644 --- a/datasets/h9rgtb96_234_1.json +++ b/datasets/h9rgtb96_234_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "h9rgtb96_234_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.", "links": [ { diff --git a/datasets/habitat-map-of-switzerland_1.0.json b/datasets/habitat-map-of-switzerland_1.0.json index 79deac3336..8ec9c2d284 100644 --- a/datasets/habitat-map-of-switzerland_1.0.json +++ b/datasets/habitat-map-of-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "habitat-map-of-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse The FOEN funded project \u2018Developing a Habitat Map of Switzerland\u2019 conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification\u2019s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Habitats are mapped through a variety of approaches that can be grouped as either: 1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo\u2019s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.", "links": [ { diff --git a/datasets/hamsrcpex_1.json b/datasets/hamsrcpex_1.json index 19ab28d5d7..b97837fdb0 100644 --- a/datasets/hamsrcpex_1.json +++ b/datasets/hamsrcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hamsrcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. Data are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.", "links": [ { diff --git a/datasets/hamsrcpexaw_1.json b/datasets/hamsrcpexaw_1.json index 1b512e0979..103792d590 100644 --- a/datasets/hamsrcpexaw_1.json +++ b/datasets/hamsrcpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hamsrcpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-AW dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. HAMSR is mounted in payload zone 3 near the nose of the Global Hawk NASA aircraft. Data is available from August 17, 2021 through September 4, 2021 in netCDF-3 format, with associated browse files in PNG format.", "links": [ { diff --git a/datasets/hamsrcpexcv_1.json b/datasets/hamsrcpexcv_1.json index bac87f794c..d91184023c 100644 --- a/datasets/hamsrcpexcv_1.json +++ b/datasets/hamsrcpexcv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hamsrcpexcv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-CV dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX \u2013 Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. Data are available from September 6-30, 2022 in netCDF-4 format.", "links": [ { diff --git a/datasets/hamsrepoch_1.json b/datasets/hamsrepoch_1.json index f579b71fe2..12cd893bde 100644 --- a/datasets/hamsrepoch_1.json +++ b/datasets/hamsrepoch_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hamsrepoch_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude MMIC Sounding Radiometer (HAMSR) EPOCH dataset includes measurements gathered by the HAMSR instrument during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3 dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. HAMSR is mounted in payload zone 3 near the nose of the Global Hawk NASA aircraft. Data is available from August 9, 2017 through August 31, 2017 in netCDF-3 format.", "links": [ { diff --git a/datasets/handheld_haze_708_1.json b/datasets/handheld_haze_708_1.json index 7cef6807b2..b3f904936c 100644 --- a/datasets/handheld_haze_708_1.json +++ b/datasets/handheld_haze_708_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "handheld_haze_708_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In conjunction with the AERONET (AErosol RObotic NETwork) participation in SAFARI 2000, the USDA Forest Service deployed handheld hazemeters in western Zambia from mid-June to late September 2000. Thirty-eight hazemeters were deployed within a 900 km x 900 km region in western Zambia to verify and study the aerosol properties in MODIS data. The handheld measurements were compared with satellite measurements. The hazemeter data were used to examine the effects of inhomogeneous atmosphere on MODIS aerosol product validation and to investigate the dependency of MODIS aerosol measurements on look angle and ground vegetation.", "links": [ { diff --git a/datasets/hcmm.json b/datasets/hcmm.json index f57ba56f2b..6c2a98cece 100644 --- a/datasets/hcmm.json +++ b/datasets/hcmm.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hcmm", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The mission was the first of a series of NASA Applications Explorer Missions and is also known as AEM-A. Day/night coverage over a given area occurred at intervals ranging from 12 to 36 hours with a 16 day repeat cycle.\n\nThe satellite was operational from April 1978 to September 1980. The initial orbit of 620 km was lowered to 540 km in February of 1980. Coverage includes parts of the United States, Canada, Europe, Africa, and Australia. The source data was transmitted to seven ground stations and stored on binary magnetic tape. The source data on tape is no longer readable and the only remaining set of HCMM data is on black and white film. Since the data could be of historical value for global change research, the images have been scanned at 1000 dpi (25 micron) making the data accessible to the scientific community. The collection includes approximately 47,000 scenes with a Hotine Oblique Mercator projection.\n\nThe Heat Capacity Mapping Mission Radiometer operated with two channels. The first detected visible to near infrared (0.5 \u2013 1.1 micrometers) radiation and the second detected thermal infrared (10.5 \u2013 12.5 micrometers) radiation. HCMM nomenclature refers to the visible to near infrared channel as Vis and the thermal infrared channel as IR. The scenes are designated as Day-Vis, Day-IR or Night-IR. \n\nA HCMM scene has a width of 715 km with a resolution of 500 meters for the visible channel and 600 meters for the thermal channel.", "links": [ { diff --git a/datasets/hcmm_digital_source.json b/datasets/hcmm_digital_source.json index 158a7d0d56..a8ca59556e 100644 --- a/datasets/hcmm_digital_source.json +++ b/datasets/hcmm_digital_source.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hcmm_digital_source", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The HCMM Digital Source dataset includes approximately 2400 scenes of recovered digital data with a resolution of 100 dpi. The original scenes are 715 km wide and vary in length from 715 to 3,000 km. The file size is 3-13 MB depending on the length of the scene and is stored in a TIFF format. The source data was transmitted to seven ground stations and stored on binary magnetic tape. The source data on tape is no longer readable and the only remaining set of HCMM data is on black and white film.", "links": [ { diff --git a/datasets/heard_1998data_gis_1.json b/datasets/heard_1998data_gis_1.json index 7cd20e1202..d7fd4a4bdd 100644 --- a/datasets/heard_1998data_gis_1.json +++ b/datasets/heard_1998data_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_1998data_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This layer is stored as two datasets (point and polygon)in the Geographical Information System (GIS). Points represent landing areas, mammal, flying bird and penguin data. Polygons represent the horizontal flight limits of helicopters and areas set aside for specific management purposes.", "links": [ { diff --git a/datasets/heard_50kmap_gis_1.json b/datasets/heard_50kmap_gis_1.json index 7c2b94a5cc..ea79dbee24 100644 --- a/datasets/heard_50kmap_gis_1.json +++ b/datasets/heard_50kmap_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_50kmap_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This layer is stored as three datasets (polygon, line and point) in the Geographical Information System (GIS). Polygon data represents lakes and reefs. Line data represents reef boundaries. Point data represents spotheights, volcanic cones, refuges and a grave.\n\nAll the data in this dataset was sourced from the Heard Island 1:50 000 map, edition 3, published in 1985. The data conforms to the Australian Antarctic Spatial Data Model which includes Data Quality Indicators.", "links": [ { diff --git a/datasets/heard_bathy_gis_1.json b/datasets/heard_bathy_gis_1.json index 762627c3ed..dfbed4d134 100644 --- a/datasets/heard_bathy_gis_1.json +++ b/datasets/heard_bathy_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_bathy_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetry around Heard and McDonald Islands and the Kerguelen Plateau.\nThis layer is stored as two datasets (line and polygon) in the Geographical Information System (GIS).\nThe line data were interpolated from soundings made on ANARE voyages and soundings provided by the Royal Australian Navy Hydrographic Service.\nThe line data were polygonised to create the polygon data.\nThe data were produced for a 1:1 million scale bathymetric map (refer to link).", "links": [ { diff --git a/datasets/heard_coast_gis_1.json b/datasets/heard_coast_gis_1.json index e3504c29e4..af12c0d2ac 100644 --- a/datasets/heard_coast_gis_1.json +++ b/datasets/heard_coast_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_coast_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A coastline of Heard Island and McDonald Islands, created in the AMBIS (Australian Maritime Boundaries Information System) by Geoscience Australia (previously AUSLIG).", "links": [ { diff --git a/datasets/heard_dem_radarsat02_1.json b/datasets/heard_dem_radarsat02_1.json index 0c093f811f..a9eb020aa4 100644 --- a/datasets/heard_dem_radarsat02_1.json +++ b/datasets/heard_dem_radarsat02_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_dem_radarsat02_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a Digital Elevation Model (DEM) of Heard Island derived by interferometric processing from RADARSAT images acquired on 17 February 2002 and 13 March 2002. The DEM was created by a contractor for the Australian Antarctic Data Centre. The cell size is 10 metres.\n\nProcessing stages included:\n\n1 Detection of a coastline from a RADARSAT image of Heard Island acquired 24 January 2002 and rectified using ground control points provided by a second contractor.\n\n2 Generation of the interferometric SAR (InSAR) DEM using the RADARSAT images acquired on 17 February 2002 and 13 March 2002.\n\n3 Co-registration of the InSAR DEM with a DEM derived from stereoscopic RADARSAT images acquired in March and April of 1997 and described by the metadata record 'Heard Island RADARSAT (1997) DEM'.\n\n4 Merging of the InSAR DEM with the 1997 stereoscopic DEM and the coastline detected in stage 1.\n\n\nThe following are available for download from the Related URLs below:\n\n1 The final DEM in ArcInfo interchange or ArcInfo ascii formats.\n\n2 The rectified RADARSAT image of Heard Island acquired 24 January 2002. Rectified using ground control points and subsequently used in processing of the DEM.\n\n3 Contours generated from the DEM and the island polygon (coastline) extracted from the rectified RADARSAT image acquired 24 January 2002.\n\n4 A detailed deport describing the generation of the DEM.\n\n5 A report by\n\nDr Arko Lucieer\nCentre for Spatial Information Science\nSchool of Geography and Environmental Studies\nUniversity of Tasmania\nPrivate Bag 76\nHobart 7001\nTasmania, Australia\n\noutlining some errors and artefacts in the DEM.\nDr Lucieer produced this report while working for the Australian Antarctic Division.\n\n\nOn 3 July 2003 Henk Brolsma (Mapping Officer, Australian Antarctic Division) wrote the following email to the contractor who created the DEM. \"What I'm really interested in are the 20 metre contours for the areas with high coherency. These are the areas where most of the field work takes place and where we have a need for contours with an accuracy better than 50 metres and my reason for using INSAR in the first instance. So can you send me: 1. The 20 metre contours for the areas with high coherency? 2. The zone or line where the INSAR and Stereo Imagery were integrated? This would be very useful for the metadata.\" He did not receive a reply to that email and that was the reason why he was reluctant to make the DEM public. Since he won't now get a reply and the DEM is probably better than the 1997 DEM, he considers the 2002 DEM should now be published.", "links": [ { diff --git a/datasets/heard_dem_radarsat97_1.json b/datasets/heard_dem_radarsat97_1.json index 82a7ff8ee1..11a69a7ba3 100644 --- a/datasets/heard_dem_radarsat97_1.json +++ b/datasets/heard_dem_radarsat97_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_dem_radarsat97_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM) of Heard island, with a 50 metre grid interval, and held in UTM Zone 43(WGS-84) coordinates. Heights are referenced to mean sea level. 50 metre contours (including a coastline) were derived. Elevation range 0 - less than 2800m.", "links": [ { diff --git a/datasets/heard_dem_terrasar_1.json b/datasets/heard_dem_terrasar_1.json index 1382c7e472..b4332ae90d 100644 --- a/datasets/heard_dem_terrasar_1.json +++ b/datasets/heard_dem_terrasar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_dem_terrasar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a Digital Elevation Model (DEM) of Heard Island derived from TerraSAR_X imagery using radargrammetric methods.\nAt least one pair of ascending images and one pair of descending images were used at each location.\nThe TerraSAR_X stereo pairs were acquired between 31 October 2009 and 14 November 2009.\nThe DEM was created by a contractor for the Australian Antarctic Data Centre.\nIt is in geotiff format stored in a UTM zone 43 projection, horizontal datum WGS84.\nThe cell size is 10 metres.\n\nIncluded with the DEM are some auxiliary files and documentation.\nThis includes:\n1 an xml file with metadata;\n2 a shapefile detailing the images used for each part of the DEM;\n3 a geotiff showing the correlation between the images used at each point in the DEM;\n4 a spreadsheet with an accuracy assessment of the DEM using ground control points provided by the Australian Antarctic Data Centre.", "links": [ { diff --git a/datasets/heard_glacier_gis_1.json b/datasets/heard_glacier_gis_1.json index a08aa10f35..f616c84c41 100644 --- a/datasets/heard_glacier_gis_1.json +++ b/datasets/heard_glacier_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_glacier_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Abstract from: 'An inventory of present glaciers on Heard Island and their historical variation' by Andrew Ruddell.\n\nHeard Island is a large ice-covered volcanic cone situated in the south Indian Ocean. Its location enables unique climatic information to be obtained from a very remote and predominantly maritime region. Past studies show that while some glaciers have undergone major recession since the late 1940s, others, such as large non-calving glaciers, have shown little change in extent. The island is usually cloud covered and this has hampered aerial and ground based surveys. Using SPOT satellite imagery acquired in 1988 and supplemented by aerial photography in 1987 and a digital elevation model derived from 1997 Radarsat imagery, an inventory of glacier extent is provided and this indicates that there are a total of 29 glaciated basins (41 termini), with an area of 257 km2 and an estimated volume of 14.2 km3. The satellite imagery is used to rectify earlier estimates of glacier extent based on aerial photography in 1947 and 1980. Between 1947 and 1988 the glaciated area had decreased by 11% and volume by 12%. Approximately half of this occurred during the 1980s.\n\nA variety of historical records have been compiled and these provide evidence of glacier behaviour since the mid-1800s when they were at their greatest extent during the recorded period. The elevation range of a glacier is a good indication of glacier hypsometry and its sensitivity to mass balance and climate variations. Glaciers such as the Gotley are of large elevation range and high mass turnover. Such glaciers show little sensitivity to climate variations as they lose much of their ice through calving into the sea rather than surface melt. Glaciers of low elevation range such as those on the Laurens Peninsula are especially sensitive to climate change. Glaciers of this type indicate that while minor decadal fluctuations have occurred in the period from at least 1902 to the 1950s, the recession of many glaciers during the past 50 years has been unprecedented. The glacier variations correlate with observed temperature records.\n\nObservations of occasional volcanic eruptions since the 1880s indicate that most activity is related to lava flows from Mawson Peak and fumerole activity on its upper southwestern slope. This activity appears to have had little effect on the Gotley and Lied glaciers.", "links": [ { diff --git a/datasets/heard_ice_gis_1.json b/datasets/heard_ice_gis_1.json index 6db0d14956..641010d0b9 100644 --- a/datasets/heard_ice_gis_1.json +++ b/datasets/heard_ice_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_ice_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heard Island, ice layer. This is a polygon dataset stored in the Geographical Information System (GIS). The ice layer shows ice/snow as depicted on the Heard Island satellite image map, published in 1991. The amount of ice/snow is as captured on the SPOT image 9 Jan 1988.", "links": [ { diff --git a/datasets/heard_is_sat_1.json b/datasets/heard_is_sat_1.json index 47230194d5..887b72425f 100644 --- a/datasets/heard_is_sat_1.json +++ b/datasets/heard_is_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_is_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Heard Island and McDonald Islands, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:50000, and was produced from multispectral space imagery SPOT 1 and SPOT 2 scenes, with some areas of photography. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, refuges/depots, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/heard_island_met_obs_1985_1.json b/datasets/heard_island_met_obs_1985_1.json index b893e7d2da..5ff5ad2ae9 100644 --- a/datasets/heard_island_met_obs_1985_1.json +++ b/datasets/heard_island_met_obs_1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_island_met_obs_1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 1985 ANARE expedition to Heard Island, meteorological observations were made twice daily (at approximately GMT 0000Z and GMT 1200Z). These observations included total cloud cover, wind speed, temperature, pressure, and notes on the weather and cloud types. The observations were written in a log book, archived at the Australian Antarctic Division.\n\nLogbook(s):\n- Glaciology Heard Island Meteorological Observations 1985", "links": [ { diff --git a/datasets/heard_manage_gis_1.json b/datasets/heard_manage_gis_1.json index c55edb8d14..2804ab6178 100644 --- a/datasets/heard_manage_gis_1.json +++ b/datasets/heard_manage_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_manage_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Heard Island management zones are a polygon and line dataset stored in the Geographical Information System (GIS). These areas were developed inline with the Heard Island and McDonald Islands Marine Reserve Management Plan, published in 2005.", "links": [ { diff --git a/datasets/heard_met_1971_1.json b/datasets/heard_met_1971_1.json index 6c6f0bcc75..06c02c2118 100644 --- a/datasets/heard_met_1971_1.json +++ b/datasets/heard_met_1971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_met_1971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "As part of the expedition to Heard Island in 1971, meteorological observations were made and recorded four times a day (0000, 0600, 1200 and 1800 local time). The air pressure, pressure trend, wind speed and direction, temperature, relative humidity, cloud cover, visibility, minimum and maximum daily temperatures as well as general notes on the weather were all recorded in a log book. Notes on the instrument calibrations were also recorded.\n\nThe physical log books are archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/heard_refuge0304_gis_1.json b/datasets/heard_refuge0304_gis_1.json index 713f390fad..04c066d31f 100644 --- a/datasets/heard_refuge0304_gis_1.json +++ b/datasets/heard_refuge0304_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_refuge0304_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes the proposed field camp locations for the 2003/04 science expedition to Heard Island, the locations of the camp sites that were used during the expedition and the locations of some of the refuges on the island that were surveyed during the expedition.\nIt is a point dataset stored in the Geographical Information System (GIS).\nThe proposed field camp locations are shown in a map (refer to link below).", "links": [ { diff --git a/datasets/heard_satimage_control_1.json b/datasets/heard_satimage_control_1.json index 540293b747..80e38d2e27 100644 --- a/datasets/heard_satimage_control_1.json +++ b/datasets/heard_satimage_control_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_satimage_control_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground control points were captured in the field as described in the metadata record \"Global change, biodiversity and conservation in terrestrial and coastal ecosystems on Heard and McDonald Islands [ASAC_1181]\". The aim of this project was to identify the control points on various satellite images (AADC Image IDs 253, 267, 446 and 447) to determine image coordinates for the control points. These would then allow the satellite image to be adjusted to the ground control. There is a shapefile for each image with the features in the shapefile moved to the corresponding image location. Attributes in the shapefile include image coordinates (UTM43) and control point coordinates.", "links": [ { diff --git a/datasets/heard_species_checklist_1.json b/datasets/heard_species_checklist_1.json index 34d54b3d93..6938c6976b 100644 --- a/datasets/heard_species_checklist_1.json +++ b/datasets/heard_species_checklist_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_species_checklist_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A checklist of species that have been recorded or observed at Heard Island during various historical and ANARE scientific expeditions to the island. The data are held in the Australian Antarctic Data Centre Biodiversity Database, and updated as required.", "links": [ { diff --git a/datasets/heard_vegetation_gis_1.json b/datasets/heard_vegetation_gis_1.json index fd83eba13d..dfeb773b4a 100644 --- a/datasets/heard_vegetation_gis_1.json +++ b/datasets/heard_vegetation_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heard_vegetation_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Heard Island and McDonald Islands, vegetation layer.\nThis is a polygon dataset stored in the Geographical Information System (GIS).\nThe data represents approximately the areas of vegetation cover on these islands.", "links": [ { diff --git a/datasets/heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0.json b/datasets/heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0.json index 0b786822a3..814d8e4bff 100644 --- a/datasets/heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0.json +++ b/datasets/heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In controlled model forest ecosystems young trees were exposed to heavy metals in the soil and to acid precipitation. On spruce trees Lymantria monacha caterpillars and Cinara pilicornis aphids and on willow Pterocomma pilosum aphids were reared and monitored. Developmental time and fecundity of L. monacha were recorded and in aphids colony growth was measured.", "links": [ { diff --git a/datasets/heliemps_1.json b/datasets/heliemps_1.json index 10eacea765..5407c38b1e 100644 --- a/datasets/heliemps_1.json +++ b/datasets/heliemps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "heliemps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Creching emperor penguin (Aptenodytes forsteri) chickswere exposed to two overflights by an S-76 twin engine helicopter at 1000 m: a current operational guideline for helicopter activity in Antarctica. The flights were conducted on the same day but under different wind conditions: a morning flight with a 10 kt (18 km.hr-1) katabatic blowing perpendicular to the direction of helicopter travel and an afternoon flight with virtually no wind. Background noise levels recorded in the morning, before the helicopter flight, were significantly higher than in the afternoon, but these differences were not detectable when the helicopter was overhead. There were also no significant differences in the way chicks responded to helicopters between the morning and afternoon flight. All chicks became more vigilant when the helicopter approached and 69% either walked or ran, generally moving less than 10 m toward other chicks (i.e. not scattering). Most chicks (83%) displayed flipper-flapping, probably indicating nervous apprehension. This behaviour was seldom displayed in the absence of disturbance. Although all effects were relatively transitory, results support the introduction of more conservative guidelines for helicopter operations around breeding localities of this species.\n\nThe fields in this dataset are:\n\nTime\nAction\nDate\nLying\nStanding\nWalking\nPreening\nFlapping", "links": [ { diff --git a/datasets/helimaps_3.json b/datasets/helimaps_3.json index ba1f3c5265..f11c40c986 100644 --- a/datasets/helimaps_3.json +++ b/datasets/helimaps_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "helimaps_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A series of maps were produced for the publication \"Flight paths for helicopter operations in the Australian Antarctic Territory\", originally published in hard copy in September 2000. These superseded a series published in 1999.\n\nA new edition of the maps was produced in 2011.\n\nThe maps are digitally available from the SCAR Map Catalogue. See the Related URL below.", "links": [ { diff --git a/datasets/helipenguins_1.json b/datasets/helipenguins_1.json index 07e42bb3ae..98b5e2e34c 100644 --- a/datasets/helipenguins_1.json +++ b/datasets/helipenguins_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "helipenguins_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study aimed to quantify the effects of helicopter operations on Antarctic wildlife, with an emphasis on determining minimum safe over-flight altitudes and landing distances for a range of species. An experimental approach was adopted whereby wildlife were exposed to helicopters either over-flying or landing at specific altitudes or distances while the behaviour, and in some cases physiology, of individual animals were recorded. Two types of helicopters were used in the study: a Sikorsky S-76 (twin engine) and a Squirrel AS350 (single engine). This metadata record relates to the responses of Adelie Penguins (Pygoscelis adeliae) over a number of phases of their breeding cycle.\n\nThe fields in this dataset are:\n\nTime\nAction\nDate", "links": [ { diff --git a/datasets/herb-layer-biomass-in-swiss-forests_1.0.json b/datasets/herb-layer-biomass-in-swiss-forests_1.0.json index 1f532c22d6..033ab50e5d 100644 --- a/datasets/herb-layer-biomass-in-swiss-forests_1.0.json +++ b/datasets/herb-layer-biomass-in-swiss-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "herb-layer-biomass-in-swiss-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this project was to develop a model to estimate herb layer biomass and carbon stock based on the categorical cover estimate on each NFI sample plot. To this end, biomass and cover of the six main plant groups in the herb layer were collected from 405 1x1 m subplots on 135 study sites (15 sites in 9 strata) which were selected based on a stratified sampling approach. To ensure consistency with NFI methodology, study sites corresponded to the design of regular NFI sample plots and plant cover was estimated by trained field-crew members. Based on the dry weight of the plant biomass and the cover estimate on each subplot, a linear regression model was developed and applied to estimate herb layer biomass on each NFI sample plot.", "links": [ { diff --git a/datasets/high-resolution-static-data-for-wrf-over-switzerland_1.0.json b/datasets/high-resolution-static-data-for-wrf-over-switzerland_1.0.json index f07d6d888f..f1ec19f1cd 100644 --- a/datasets/high-resolution-static-data-for-wrf-over-switzerland_1.0.json +++ b/datasets/high-resolution-static-data-for-wrf-over-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "high-resolution-static-data-for-wrf-over-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.", "links": [ { diff --git a/datasets/highjump_scans_1.json b/datasets/highjump_scans_1.json index af7f281c85..420088950f 100644 --- a/datasets/highjump_scans_1.json +++ b/datasets/highjump_scans_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "highjump_scans_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The US Navy during Operation Highjump carried out the earliest comprehensive acquisitions in 1947-48. This operation included an intensive program of trimetrigon aerial photography acquisitions of the whole of the coastline of Antarctica and some inland areas. 50 CDs worth of images have been scanned. The Australian Antarctic Data Centre's holdings of Operation Highjump aerial \nphotography can be searched using the Data Centre's online Aerial Photograph Catalogue (see link below). On the search page the Operation Highjump aerial \nphotography can be selected as an Aerial Photography Series.", "links": [ { diff --git a/datasets/highres_10be_records_law_dome_1999_2009_2.json b/datasets/highres_10be_records_law_dome_1999_2009_2.json index b1fa6c593a..4c219be9aa 100644 --- a/datasets/highres_10be_records_law_dome_1999_2009_2.json +++ b/datasets/highres_10be_records_law_dome_1999_2009_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "highres_10be_records_law_dome_1999_2009_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file comprises five high-resolution records of 10Be concentration in snow from Law Dome, East Antarctica: DSS0102-pit, DSS0506-pit, DSS0506-core, DSS0809-core and DSS0910-core.\n\nA single composite series is constructed from three of these records (DSS0506-core, DSS0809-core and DSS0102-pit), providing a monthly-resolved time-series of 10Be concentrations at DSS over the decade spanning 1999 to 2009.\n\nThis work was done as part of AAS 2384, AAS 3064 and AAS 1172.\n\nA data update was provided by Jason Anderson on 2012-12-17.", "links": [ { diff --git a/datasets/hillshade-for-vegetation-height-model-nfi_2016 (current).json b/datasets/hillshade-for-vegetation-height-model-nfi_2016 (current).json index ac59c91eaa..d7c98005fb 100644 --- a/datasets/hillshade-for-vegetation-height-model-nfi_2016 (current).json +++ b/datasets/hillshade-for-vegetation-height-model-nfi_2016 (current).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hillshade-for-vegetation-height-model-nfi_2016 (current)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hillshade of the digital surface model (DSM), calculated from digital aerial stereo images. The image data was acquired by the Federal Office of Topography swisstopo. The resolution of the DSM is 1 m x 1 m.", "links": [ { diff --git a/datasets/historic_cropland_xdeg_966_1.json b/datasets/historic_cropland_xdeg_966_1.json index 1b4ab231f6..7233b17b14 100644 --- a/datasets/historic_cropland_xdeg_966_1.json +++ b/datasets/historic_cropland_xdeg_966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "historic_cropland_xdeg_966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models.This is a gridded data set describing the fraction of each grid cell in the globe that is occupied by cultivated land from 1700 to 1992. Data layers are provided for every 50 years from 1700 to 1850, every 10 years from 1850 to 1980, and every year from 1986 to 1992.There are two sources of global land cover/land use data. The most recent estimates are derived from satellite measurements, and are available in a spatially-explicit fashion for roughly the last 30 years. The other estimate is based on ground-based sources such as census statistics, land surveys, estimates by historical geographers, etc. These land inventory data are only available at the scale of political units, but have the advantage of being historical. Ramankutty and Foley (1998) derived a spatially-explicit data set of croplands in 1992 by synthesizing remotely-sensed land cover data with contemporary land inventory data. Furthermore, Ramankutty and Foley (1999) extended this data set into the past (back to 1700) using historical land inventory data.The data set should only be used for continental-to-global scale analysis and modeling. The data set captures the broad patterns of cropland change over history, but not necessarily the fine details at local to regional scales - please check the data quality before using it at fine spatial scales. The quality of historical data for the Russian Federation is poor. The quality of data prior to 1850 is poor -- only continental-scale historical data were used for that period. ", "links": [ { diff --git a/datasets/historic_landcover_xdeg_967_1.json b/datasets/historic_landcover_xdeg_967_1.json index e274d05d4a..2e6deb347f 100644 --- a/datasets/historic_landcover_xdeg_967_1.json +++ b/datasets/historic_landcover_xdeg_967_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "historic_landcover_xdeg_967_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).Testing against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period. ", "links": [ { diff --git a/datasets/historical_croplands_675_1.json b/datasets/historical_croplands_675_1.json index f0f6887aef..3424c49790 100644 --- a/datasets/historical_croplands_675_1.json +++ b/datasets/historical_croplands_675_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "historical_croplands_675_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of a global croplands data set (Ramankutty and Foley 1999a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format at 5-min resolution.Navin Ramankutty and Jonathan Foley, of the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, developed a global, spatially explicit data set of reconstructed historical croplands from 1700 to 1992. The method for historical reconstruction used a simple algorithm that linked contemporary satellite data and historical cropland inventory data. A spatially explicit croplands data set for 1992 was first derived by calibrating a satellite-derived land cover classification data set against cropland inventory data for 1992. This derived data set was then used within a simple land cover change model, along with historical cropland inventory data, to derive spatially explicit maps of historical croplands. The global data set was restricted to a representation of permanent croplands (i.e., excluding shifting cultivation), which follows the Food and Agriculture Organization (FAO) definition of arable lands and permanent crops. Data values represent fraction of grid cell in croplands.Data for the LBA study area are available for the years 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, and 1992. Although the global croplands data set contains data representing croplands since 1700, essentially no croplands were in the LBA study area until 1900. Data from previous years were excluded at the suggestion of the data originator.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/historical_croplands/comp/uwcrop_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/history-of-wetlands-in-switzerland-since-1850_1.0.json b/datasets/history-of-wetlands-in-switzerland-since-1850_1.0.json index db09538100..81eedfb3c4 100644 --- a/datasets/history-of-wetlands-in-switzerland-since-1850_1.0.json +++ b/datasets/history-of-wetlands-in-switzerland-since-1850_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "history-of-wetlands-in-switzerland-since-1850_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Naturally, large parts of the Swiss Plateau are characterised by wetlands and meandering rivers. That this is no longer the case today is the result of centuries of efforts to obtain dry land. But how did this process take place? What were the relevant actors and what were their motivations? And what can be said about the ecological consequences of this development? In a research project on the history of wetlands in Switzerland since 1700, we conducted (a) a historical analysis of the development of land use in wetlands and the actors involved, (b) a historical-cartographic reconstruction of wetland extent since 1850 and (c) an evaluation of ecological effects of changes in wetlands on various organisms groups. The series of GIS layers on wetland history stem from the second part of the project. The area reconstruction is based on digitized and homogenized signatures from national map series, as they have been available since about 1850. Details about the digitalization process and the homogenization procedures applied (\"Rekonstruktionen\") are included in Stuber & B\u00fcrgi 2019. __Book Citation:__ > Stuber M, B\u00fcrgi M (2019) Vom \u00aberoberten Land\u00bb zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. \"Bristol Schriftenreihe\", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.", "links": [ { diff --git a/datasets/hiwat_1.json b/datasets/hiwat_1.json index ab2651c5b6..12f04fc9b1 100644 --- a/datasets/hiwat_1.json +++ b/datasets/hiwat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hiwat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Impact Weather Assessment Toolkit (HIWAT) uses a mesoscale numerical weather prediction model and the Global Precipitation Measurement (GPM) constellation of satellites. The toolkit includes a suite of ensemble model forecasts to constrain uncertainties and provide a probabilistic forecast for improved decision-making. The toolkit provides outlooks for lightning strikes, high-impact winds, high rainfall rates, hail damage, and other weather events. The toolkit provides a 54-hour probabilistic forecast over Nepal and Bangladesh along with parts of northeast India (i.e., the Hindu Kush Himalayan region). HIWAT will also support threat assessments, such as thunderstorm intensity, using GPM and impact assessments using Landsat/MODIS land imagery to identify damage scars. The dataset files are available from April 2, 2017, through October 2, 2022, in netCDF-3 format.", "links": [ { diff --git a/datasets/hiwrapimpacts_1.json b/datasets/hiwrapimpacts_1.json index d1c2e86d1a..d14e33b9a6 100644 --- a/datasets/hiwrapimpacts_1.json +++ b/datasets/hiwrapimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hiwrapimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) IMPACTS dataset consists of Equivalent reflectivity factor, Doppler velocity, Doppler velocity spectrum width, Linear Depolarization Ratio (LDR), Ocean normalized radar cross-section, Co-polarization signal-to-noise mask estimates collected by the HIWRAP onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. These data are available from January 25, 2020, through February 28, 2023, in HDF-5 format.", "links": [ { diff --git a/datasets/holme_bay_1_10000_gibbney_1.json b/datasets/holme_bay_1_10000_gibbney_1.json index f9845b67bf..e32ea9b5e2 100644 --- a/datasets/holme_bay_1_10000_gibbney_1.json +++ b/datasets/holme_bay_1_10000_gibbney_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_1_10000_gibbney_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the metadata record for the Holme Bay 1:10000 Gibbney Island Mapping (DQI 332) mapped in March 2001 by Hydro Tasmania.", "links": [ { diff --git a/datasets/holme_bay_1_10000_rookery_1.json b/datasets/holme_bay_1_10000_rookery_1.json index d8e0862077..5f9f142491 100644 --- a/datasets/holme_bay_1_10000_rookery_1.json +++ b/datasets/holme_bay_1_10000_rookery_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_1_10000_rookery_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the metadata record for the Holme Bay 1:10000 Rookery Islands Mapping (DQI 320) mapped early in 1999 by Hydro Tasmania.", "links": [ { diff --git a/datasets/holme_bay_1_10000_wigg_1.json b/datasets/holme_bay_1_10000_wigg_1.json index c18c73326e..35160dc1d9 100644 --- a/datasets/holme_bay_1_10000_wigg_1.json +++ b/datasets/holme_bay_1_10000_wigg_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_1_10000_wigg_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the metadata record for the Holme Bay 1:10000 Wigg Island Mapping (DQI 333) mapped in March 2001 by Hydro Tasmania.", "links": [ { diff --git a/datasets/holme_bay_1_5000_B-W_Islands_1.json b/datasets/holme_bay_1_5000_B-W_Islands_1.json index 0782236acd..d8ab343caf 100644 --- a/datasets/holme_bay_1_5000_B-W_Islands_1.json +++ b/datasets/holme_bay_1_5000_B-W_Islands_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_1_5000_B-W_Islands_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the metadata record for the Holme Bay 1:5000 Bechervaise and Welch Mapping (DQI 331) mapped early in 1999 by Hydro Tasmania.", "links": [ { diff --git a/datasets/holme_bay_dem_1.json b/datasets/holme_bay_dem_1.json index 389be5d74c..17f39062ad 100644 --- a/datasets/holme_bay_dem_1.json +++ b/datasets/holme_bay_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM) of the continent coast and islands of Holme Bay, Antarctica with cell size 10 metres was interpolated from input coastline, contour and spot height (point locations with an elevation attribute) data.\n \nThe input data was sourced from the following datasets which are listed with their dataset number:\nFramnes Mountains 1:25000 Topographic GIS Dataset (dataset 55)\nHolme Bay 1:25000 GIS Dataset (dataset 57)\nHolme Bay 1:10000 Gibbney Island Mapping (dataset 90) \nHolme Bay 1:10000 Rookery Islands Mapping (dataset 91)\nHolme Bay 1:10000 Wigg Island Mapping (dataset 96)\nHolme Bay 1:5000 Bechervaise and Welch Islands Mapping (dataset 97)\nMapping around the Framnes Mountains from Spot Imagery at 10 metre pixel resolution (dataset 98)\nRobinson Group, east of Mawson, mapping from Spot satellite imagery at 20 metre pixel resolution (dataset 99)\nFramnes Mountains contours smoothed and edited (dataset 187).\n \nThe spatial coverages and estimated planimetric and vertical accuracies of datasets 57, 90, 91, 96, 97 and 99 are shown in a map linked to this metadata record. These datasets were the source data for the islands and a strip of the continent coast which was the area of interest when the DEM was created. The source data for almost all of the remaining land area, which is inland from a coastal strip, was contours from dataset 187 with an estimated planimetric accuracy of 200 metres and estimated vertical accuracy of 100 metres.\n\nThe interpolation was done using the Topo to Raster tool in ArcGIS.\nThe output DEM was clipped to the extents of the input data.\nThe dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool.\nThe DEM is stored in the UTM Zone 41S projection.\nThe horizontal datum is WGS84. The vertical datum is Mean Sea Level.\nThe DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade.\nThe DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record:\n1 A geotiff; and \n2 An ascii file in ESRI's ascii format for rasters.", "links": [ { diff --git a/datasets/holme_bay_gis_1.json b/datasets/holme_bay_gis_1.json index 78bf335d7d..795a85ec02 100644 --- a/datasets/holme_bay_gis_1.json +++ b/datasets/holme_bay_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_bay_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Holme Bay geographical infromation system (GIS) dataset includes the following features: spot heights, geology (erratics only) contours with a 5m contour interval, ice, crevasse fields, hillocks, melt lakes, moraines, rock, snow, cliffs, drift tails, flowlines, glaciers, grounding lines, ridge lines, streams, penguins, masts, lakes and refuges. The data ranges from Low Tongue to Paterson Islands along the Mawson Coast.\n\nData was captured in March 2001, photogrammetically from aerial photography.\nThe aerial photography was captured in March - April 1996 from films ANTC1024, ANTC1025, ANTC1026, ANTC1029, ANTC1031, ANTC1032, ANTC1034", "links": [ { diff --git a/datasets/holme_penguin_gis_1.json b/datasets/holme_penguin_gis_1.json index 189c36a285..368681f7e1 100644 --- a/datasets/holme_penguin_gis_1.json +++ b/datasets/holme_penguin_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "holme_penguin_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial photography (Linhof) of penguin colonies was acquired over the Holme Bay (Eric Woehler).\nThe penguin colonies were traced, then digitised (John Cox), and saved as DXF-files.\nUsing the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Data conforms to SCAR Feature Catalogue which can be searched (refer to link below).", "links": [ { diff --git a/datasets/how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0.json b/datasets/how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0.json index 002c8d4178..cc08369604 100644 --- a/datasets/how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0.json +++ b/datasets/how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions. The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations. Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards. The selected datasets and corresponding test sites are briefly introduced below: Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in St\u00f6ssel et al. (2010) and Mott et al. (2011). Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13. Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010). Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 \u00b0N, 38.8 \u00b0W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014). Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70\u00b042' S, 44\u00b020' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).", "links": [ { diff --git a/datasets/hs3avaps2_2.json b/datasets/hs3avaps2_2.json index b52217b68a..badef08326 100644 --- a/datasets/hs3avaps2_2.json +++ b/datasets/hs3avaps2_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3avaps2_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Advanced Vertical Atmospheric Profiling System (AVAPS) Dropsonde System dataset was collected by the Advanced Vertical Atmospheric Profiling System (AVAPS), built by the National Center for Atmospheric Research (NCAR), which served as the dropsonde system for the Global Hawk aircraft during the HS3 campaign. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. AVAPS dropsondes provide in-situ, high-vertical resolution measurements of atmospheric variables including pressure, temperature, humidity, geographic location, and winds, providing a vertical profile of the atmospheric conditions. The raw instrument measurement precision is as follows: pressure +-1.0 hPa, temperature +-0.2 degrees C, wind +-1 ms-1, and humidity +-7 percent. The measured information was transmitted via Iridium or Ku-Band satellite to the ground station where the Global Telecommunications System (GTS) performed additional processing for research and operational purposes.", "links": [ { diff --git a/datasets/hs3cimssbt_1.json b/datasets/hs3cimssbt_1.json index 60836f2e88..24f40f38d8 100644 --- a/datasets/hs3cimssbt_1.json +++ b/datasets/hs3cimssbt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3cimssbt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Brightness Temperature dataset contains infrared images of brightness temperature from the 15th Geostationary Operational Environmental Satellite (GOES-15) and the 10th Meteorological Satellite (METEOSAT-10) during the Hurricane and Severe Storm sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The images are available for dates between August 14, 2014 and October 3, 2014 at 15 minutes intervals in PNG format.", "links": [ { diff --git a/datasets/hs3cimsscth_1.json b/datasets/hs3cimsscth_1.json index 4658560af8..72117ad597 100644 --- a/datasets/hs3cimsscth_1.json +++ b/datasets/hs3cimsscth_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3cimsscth_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Cloud Top Height dataset contains could top height images obtained from infrared observations of the 15th Geostationary Operational Environmental Satellite (GOES-15) and the 10th Meteorological Satellite (METEOSAT-10) using the Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA) for the Hurricane and Severe Storm sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The images are available for dates between August 14, 2014 and October 3, 2014 at 15 minutes intervals in PNG format.", "links": [ { diff --git a/datasets/hs3cimsstot_1.json b/datasets/hs3cimsstot_1.json index 1b30a2335c..468db72f3f 100644 --- a/datasets/hs3cimsstot_1.json +++ b/datasets/hs3cimsstot_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3cimsstot_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Tropical Overshooting Tops dataset contains browse only data showing tropical overshooting tops derived from METEOSAT and GOES satellites for the Hurricane and Severe Storm sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The browse only data files are available for dates between August 14, 2014 and October 3, 2014 at 15 minutes intervals in KML format. ", "links": [ { diff --git a/datasets/hs3cpl_1.json b/datasets/hs3cpl_1.json index 7f60aa0e1c..15b8052d2f 100644 --- a/datasets/hs3cpl_1.json +++ b/datasets/hs3cpl_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3cpl_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Cloud Physics Lidar (CPL) dataset includes measurements gathered by the CPL instrument during the HS3 campaign which took place during the hurricane seasons of 2011 through 2014 in the Atlantic Ocean basin region. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. The CPL instrument returns information on the radiative and optical properties of cirrus clouds and aerosols at a high temporal and spatial resolution. CPL uses the 355, 532, and 1064 nm channels and has a small field of view, which eliminates multiple scattering; it offers 30 m vertical resolution and 200 m horizontal resolution. The CPL instrument measures the total (aerosol plus Rayleigh) attenuated backscatter as a function of altitude at each wavelength. Data is available in netCDF/CF format, from 2012 - 2014.", "links": [ { diff --git a/datasets/hs3fltrep_1.json b/datasets/hs3fltrep_1.json index 6cd7abab34..7498f41ec9 100644 --- a/datasets/hs3fltrep_1.json +++ b/datasets/hs3fltrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3fltrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Flight Reports provide information about flights flown by the WB-57 and Global Hawk aircrafts during the Hurricane and Severe Storms Sentinel (HS3) campaign from 2012 to 2014. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. Both aircraft are capable of extended operations ranging from 6.5 hours up to 24 hours. Together they can carry large payloads and support altitudes ranging from sea-level to altitudes in excess of 60,000 feet. The large payloads bring a new capability to the science community for measuring, monitoring and observing remote locations of Earth not feasible or practical with piloted aircraft, most other robotic or remotely operated aircraft, or space satellites. The HS3 Flight Reports include information regarding flight number, flight time (beginning and end), location of the flight (flight segments), flight purpose, and comments regarding the flight and mission. In addition, some reports include corresponding satellite imagery, maps of flight tracks and dropsonde locations, and plotted instrument retrievals.", "links": [ { diff --git a/datasets/hs3gmaodustaot_1.json b/datasets/hs3gmaodustaot_1.json index ea93621163..c5aae051e5 100644 --- a/datasets/hs3gmaodustaot_1.json +++ b/datasets/hs3gmaodustaot_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3gmaodustaot_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Global Modeling and Assimilation Office (GMAO) Dust Aerosol Optical Thickness Imagery dataset consists of browse only imagery showing dust aerosol optical thickness and wind speed/direction from the Goddard Earth Observing System Model, version 5 (GEOS-5). These data are used to see how the Saharan Air Layer (SAL) affects hurricane development during the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environmental and storm-scale internal processes, addressing the controversial role of the SAL in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The browse only data files are available for dates between August 11, 2014 and October 5, 2014 at 3-hour intervals in PNG format.", "links": [ { diff --git a/datasets/hs3hamsr_1.json b/datasets/hs3hamsr_1.json index d79092ba1f..69e358edb0 100644 --- a/datasets/hs3hamsr_1.json +++ b/datasets/hs3hamsr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3hamsr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Global Hawk High Altitude MMIC Sounding Radiometer (HAMSR) dataset includes measurements gathered by the HAMSR instrument during the HS3 campaign. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. HAMSR has 25 spectral channels which are split into 3 bands: an 8-channel band centered 53-GHz, used to infer the 3-D distribution of temperature; a 10-channel band centered at 118 GHz, used for secondary temperature sounding and assessment of scattering; and a 7-channel band centered at 183 GHz, used to measure water vapor (humidity) and cloud liquid water content in the atmosphere. This dataset also contains measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. HAMSR is mounted in payload zone 3 near the nose of the Global Hawk NASA aircraft. Data is available in netCDF/CF format.", "links": [ { diff --git a/datasets/hs3hirad_1.json b/datasets/hs3hirad_1.json index 617c171a56..2113c68cf1 100644 --- a/datasets/hs3hirad_1.json +++ b/datasets/hs3hirad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3hirad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Hurricane Imaging Radiometer (HIRAD) was collected by the Hurricane Imaging Radiometer (HIRAD), which was a multi-band passive microwave radiometer operating between 4-6.6 GHz. It used a novel interferometric aperture synthesis technique to produce high resolution wide swath observations without any mechanical scanning of the antenna. The instrument was designed to measure ocean surface wind speed in tropical storms and hurricanes. Developed in collaboration between scientists and engineers at National Aeronautics and Space Administration Marshall Space Flight Center (NASA MSFC), the University of Central Florida, and the University of Michigan, the instrument was first flown on a NASA high altitude aircraft in the Genesis and Rapid Intensification Processes (GRIP) Experiment in 2010 and was then flown for the Hurricane and Severe Storm Sentinel (HS3) in 2012-2014.", "links": [ { diff --git a/datasets/hs3hiwrap_1.json b/datasets/hs3hiwrap_1.json index ac57c94cf7..511d12678e 100644 --- a/datasets/hs3hiwrap_1.json +++ b/datasets/hs3hiwrap_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3hiwrap_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) High-Altitude Imaging Wind and Rain dataset was collected from the High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), which is a dual-frequency (Ku- and Ka-band, or approximately 14 and 35 GHz), dual-beam (30 degree and 40 degree incidence angle), conically scanning radar that has been designed for the Global Hawk aircraft during the HS3 campaign. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. HIWRAP uses solid state transmitters along with a novel pulse compression scheme that results in a system that is considerably more compact and requires less power than typical radars used for precipitation and wind measurements. By conically scanning at 10-20 rpm, its beams sweeped below the Global Hawk collecting Doppler velocity/reflectivity profiles. The unique HIWRAP sampling and phase correction strategy implemented (frequency diversity Doppler processing technique). HIWRAP's dual-wavelength operation enables it to map full tropospheric winds from cloud and precipitation volume backscatter measurements, derive information about precipitation drop-size distributions, and estimate the ocean surface winds using scatterometry techniques. Winds will be retrieved using a gridding approach similar to well-established ground-based multi-Doppler radar wind analyses. More information can be found at http://har.gsfc.nasa.gov/index.php?section", "links": [ { diff --git a/datasets/hs3navgh_1.json b/datasets/hs3navgh_1.json index 6820fa10fa..ae56720409 100644 --- a/datasets/hs3navgh_1.json +++ b/datasets/hs3navgh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3navgh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Navigation dataset consists of the real-time navigation and housekeeping data that was acquired from various instruments aboard the Global Hawk including the LN-100G IMU navigation system and the Global Hawk flight computer during the HS3 campaign. The goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes, and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. This dataset was broadcast on the Global Hawk aircraft network by the NASDAT (NASA Airborne Science Data Acquisition and Transmission unit) as 1 Hz Universal Datagram Protocol (UDP) packets. These UDP packets were generated in IWG1 format, a type of ASCII format supported by all NASA and NCAR aircraft.", "links": [ { diff --git a/datasets/hs3nrltrop_1.json b/datasets/hs3nrltrop_1.json index 34553b68fc..dff2b4ddff 100644 --- a/datasets/hs3nrltrop_1.json +++ b/datasets/hs3nrltrop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3nrltrop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Naval Research Laboratory (NRL) Tropics Satellite Data contains browse only data files, including brightness temperature, rain rate, and Red, Green, Blue (RGB) composite imagery, for the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environmental and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. These browse only data files are available for dates between April 22, 2013 and September 30, 2014 in JPG format.", "links": [ { diff --git a/datasets/hs3ships_1.json b/datasets/hs3ships_1.json index 728b467ebe..676777f7fe 100644 --- a/datasets/hs3ships_1.json +++ b/datasets/hs3ships_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3ships_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Statistical Hurricane Intensity Prediction Scheme (SHIPS) Intensity dataset was obtained from March 18, 2014 through September 30, 2014 during the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The SHIPS model provides tropical storm intensity forecasts for the Atlantic Ocean and the eastern and central North Pacific Ocean storms and invest areas. SHIPS uses GOES infrared imagery as input to the systems. These SHIPS data are available in ASCII format.", "links": [ { diff --git a/datasets/hs3shis_1.json b/datasets/hs3shis_1.json index be549a44a9..36230c1782 100644 --- a/datasets/hs3shis_1.json +++ b/datasets/hs3shis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3shis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Hurricane and Severe Storm Sentinel (HS3) Scanning High-Resolution Interferometer Sounder (S-HIS) measures emitted thermal radiances that are used to obtain temperature and water vapor profiles of the Earth's atmosphere in clear-sky conditions. Due to the S-HIS scanning capability, the instrument provides 2 km resolution (at nadir) across a 40 km wide ground swath when flown at an altitude of 20 km onboard the NASA Global Hawk unmanned aircraft. S-HIS data were collected during the 5-week HS3 field campaign study periods in the 2012 to 2014 Atlantic hurricane seasons. ", "links": [ { diff --git a/datasets/hs3wwlln_1.json b/datasets/hs3wwlln_1.json index e0e8fad286..b9479c237c 100644 --- a/datasets/hs3wwlln_1.json +++ b/datasets/hs3wwlln_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hs3wwlln_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The World Wide Lightning Location Network (WWLLN) is a global, ground-based lightning sensor network operated by the University of Washington in Seattle. This network monitors and maps global lightning activity. WWLLN has generated quality controlled global lightning data for storms studied during the 2012-2014 Hurricane and Severe Storm Sentinel (HS3) campaign to track and analyze lightning activity.", "links": [ { diff --git a/datasets/husky_sat_1.json b/datasets/husky_sat_1.json index 9c710cf3e7..a6e0a31a80 100644 --- a/datasets/husky_sat_1.json +++ b/datasets/husky_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "husky_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Husky Massif, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 131-110, 129-110, 129-111). It is projected on a Transverse Mercator projection, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0.json b/datasets/hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0.json index 3bead74de5..fc27205af1 100644 --- a/datasets/hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0.json +++ b/datasets/hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Included are three direct numerical simulations results of Stokes flow in three heterogeneous porous media obtained with OpenFoam simulations. In addition we include three data files that contain point-based extracted pores based on the post-processing as reported in the submitted paper \"Local hydraulic resistance in heterogeneous porous media\" in GRL.", "links": [ { diff --git a/datasets/hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0.json b/datasets/hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0.json index 2f26c766fd..d2b9f1130b 100644 --- a/datasets/hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0.json +++ b/datasets/hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report presents past observations and projects the future development of water temperature in Swiss lakes and rivers. Projections are made until the end of the 21st century using the CH2018 climate scenarios. Besides climate change effects on temperature, we also discuss effects on discharge for rivers, and effects on the thermal structure, and specifically the seasonal mixing regime and ice cover of lakes.", "links": [ { diff --git a/datasets/hydro-ch2018-reservoirs_1.0.json b/datasets/hydro-ch2018-reservoirs_1.0.json index d5057b40e7..fc7c3fd2b3 100644 --- a/datasets/hydro-ch2018-reservoirs_1.0.json +++ b/datasets/hydro-ch2018-reservoirs_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydro-ch2018-reservoirs_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland. Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions. Water demand was estimated for six categories: - 1) Drinking water (households and tourism), - 2) industry (second and third sector), - 3) artificial snow production, - 4) agriculture (irrigation and livestock feeding), - 5) ecology (residual flows), and - 6) hydropower. Future estimates consider changes in demand related to population growth and changes in the hydrological conditions. Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs. A detailed description of the simulation and estimation procedures can be found in * Brunner, M.I., Bj\u00f6rnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., St\u00e4hli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033\u20131047. https://doi.org/10.1016/j.scitotenv.2019.02.169. This dataset contains the following information: 1.\tShapefile with the 307 medium-sized Swiss catchments. 2. Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments. 3.\tTextfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments. 4.\tTextfiles with the storage volumes per category and catchment.", "links": [ { diff --git a/datasets/hydro-ch2018-snow_1.0.json b/datasets/hydro-ch2018-snow_1.0.json index 4aac255808..257bbebe2b 100644 --- a/datasets/hydro-ch2018-snow_1.0.json +++ b/datasets/hydro-ch2018-snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydro-ch2018-snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months. Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0\u00b0 Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels. As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.", "links": [ { diff --git a/datasets/hydro-meteorological-simulations-1981-2018_1.0.json b/datasets/hydro-meteorological-simulations-1981-2018_1.0.json index d20a0284cc..c2b1edf839 100644 --- a/datasets/hydro-meteorological-simulations-1981-2018_1.0.json +++ b/datasets/hydro-meteorological-simulations-1981-2018_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydro-meteorological-simulations-1981-2018_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.", "links": [ { diff --git a/datasets/hydro1k_elevation_xdeg_1007_1.json b/datasets/hydro1k_elevation_xdeg_1007_1.json index aaef24bb5b..e3e90f33a1 100644 --- a/datasets/hydro1k_elevation_xdeg_1007_1.json +++ b/datasets/hydro1k_elevation_xdeg_1007_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydro1k_elevation_xdeg_1007_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains coarse scale elevation and elevation-based parameters at 1.0 and 0.5-degree spatial resolutions that were developed to support a wide variety of global modeling activities through the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection. These coarse scale data have sufficient statistical information (up to fourth moment) to allow a good statistical description of the sub-cell distribution of any particular elevation parameter (i.e. elevation, slope and aspect). The database used in the development effort was the HYDRO1k product (http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/HYDRO1K) with a native spatial resolution of 1 km, the highest resolution database of global coverage of standard elevation-based derivatives (slope, aspect, elevation, compound topographic index, etc.). ", "links": [ { diff --git a/datasets/hydrographic_gghydro_636_1.json b/datasets/hydrographic_gghydro_636_1.json index b3fefe3247..595c2eb141 100644 --- a/datasets/hydrographic_gghydro_636_1.json +++ b/datasets/hydrographic_gghydro_636_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydrographic_gghydro_636_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1-degree longitude by 1-degree latitude. The data are provided in both ASCII GRID and binary image file formats.", "links": [ { diff --git a/datasets/hydropot_integral_1.0.json b/datasets/hydropot_integral_1.0.json index 2c845f13a3..163d8b480b 100644 --- a/datasets/hydropot_integral_1.0.json +++ b/datasets/hydropot_integral_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hydropot_integral_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016) ## Introduction The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (\u2264 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized. To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=\u201duse\u201d) and those that are more suitable for protection. ## Material and method The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R. A list of data sources can be found here [link to text file] A list of all parameters can be downloaded here [link to PDF and text files] ## Dataset description Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral. ## Changes in the methodology compared to the original method (Hirschi et. al 2013) * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA; individual cantons such as Zug and St. Gallen have no mapped values according to the modular concept of the federal government, Valais and Graub\u00fcnden only the main valleys, Ticino and Fribourg not completely (BAFU 2009). * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection. centrally available, therefore values in GR were equated with NA. * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values equal to NA. * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under Federal control and dams serving hydroelectricity (Dam, as of 2013). * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection, Values in GR are marked with a 5 so that the systematics in the decision tree is not affected. is affected. * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type. A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22). Therefore, an evaluation of the station data is made where critical and possible river segments with poor quality (median less than 3) exist. Only two longer and one short sections in Switzerland receive a lower median than 3 for water quality. * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number of sections, a large number of viewpoints in the layer of Swisstopo and the computation time and computability in ArcGIS, the landscape area is classified as generally viewable (equal to 1). 16 Method Additional indicators were added (see Appendix B.2): * SLS_A21 Dissection * SLS_A22 Forest areas * SLS_B03 Hiking trails * SLS_B10 Residential and vacation homes * SLS_B11 Tourist infrastructure * SLS_C01 Landfill * SLS_C03 Infrastructure * SLS_C05 Industry * SLS_C06 Agricultural land Not to be added, although present to some extent: * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is for the whole of Switzerland is limited * SLS_A15 Legally binding protection and land use planning: the individual river sections are not clearly designated, i.e. no geodata exist The following data are also not supplemented, as they are cantonal data: * SLS_A10 Cantonal nature reserves * SLS_A16 Forest reserves * SLS_A17 Cantonal inventories and contractually protected areas", "links": [ { diff --git a/datasets/hymenoptera_1.0.json b/datasets/hymenoptera_1.0.json index 47a5b2682f..d23c908b98 100644 --- a/datasets/hymenoptera_1.0.json +++ b/datasets/hymenoptera_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "hymenoptera_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types. Data are provided on request to contact person against bilateral agreement.", "links": [ { diff --git a/datasets/iagp_casey_traverse_results_1.json b/datasets/iagp_casey_traverse_results_1.json index 36c1906994..8671262b80 100644 --- a/datasets/iagp_casey_traverse_results_1.json +++ b/datasets/iagp_casey_traverse_results_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "iagp_casey_traverse_results_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Casey Traverse Program was Australia's major contribution to the International Antarctic Glaciological Project, aimed at determining the galciological regime and processes, and deducting some of the history and future of a sizeable part of the east Antarctic ice sheet approximately bounded by longitude 60 and 160E, and latitude 80S.\n\nFour traverses operated from Casey during 1981: Autumn (2 months), Winter I (2 weeks), Winter II (3 weeks), and Spring (3.5 months).\n\nData collected from the traverses included:\n\n* Ice velocity at several stations via the use of JMR Doppler Satellite surveying equipment.\n* Measurements of snow accumulation.\n* Surface profiles by barometric levelling\n* Ice thickness and bedrock profile using ice radar\n* Horizontal distances along the Undulation Line\n* 10m depth snow temperatures\n* Density of surface snow\n* Snow samples for stable oxygen isotope ration analysis\n* Regular determinations of gravity\n\nThe collected data was collated into a report that is archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/iagp_interim_survey_1973_1.json b/datasets/iagp_interim_survey_1973_1.json index f76e1403c0..d27d0657ac 100644 --- a/datasets/iagp_interim_survey_1973_1.json +++ b/datasets/iagp_interim_survey_1973_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "iagp_interim_survey_1973_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Autumn field trip of the 1973 IAGP at Casey carried out a number of surveying operations, recording the location and elevation of a series of snow canes (labelled A001-A015, along B004, B010 and INT A8A9). The report on the trip, a diagram of the snow cane layout, and the results of the survey, are archived at the Australian Antarctic Division. Copies of the raw numbers from the ice radar are also archived separately.", "links": [ { diff --git a/datasets/ibis_2_5_808_1.json b/datasets/ibis_2_5_808_1.json index caff63f504..03df5ba6a7 100644 --- a/datasets/ibis_2_5_808_1.json +++ b/datasets/ibis_2_5_808_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ibis_2_5_808_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Biosphere Simulator (or IBIS) is designed to be a comprehensive model of the terrestrial biosphere. Tthe model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). IBIS was developed by Center for Sustainability and the Global Environment (SAGE) researchers as a first step toward gaining an improved understanding of global biospheric processes and studying their potential response to human activity [Foley et al. 1996]. IBIS was constructed to explicitly link land surface and hydrological processes, terrestrial biogeochemical cycles, and vegetation dynamics within a single, physically consistent framework. Furthermore, IBIS was one of a new generation of global biosphere models, termed Dynamic Global Vegetation Models (or DGVMs), that consider transient changes in vegetation composition and structure in response to environmental change. Previous global ecosystem models have typically focused on the equilibrium state of vegetation and could not allow vegetation patterns to change over time. Version 2.5 of IBIS includes several major improvements and additions [Kucharik et al. 2000]. SAGE continues to test the performance of the model, assembling a wide range of continental- and global-scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO2 flux. Using these field data and model results for the contemporary biosphere (1965-1994), their evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO2 flux agreed reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results [Kucharik et al. 2000].", "links": [ { diff --git a/datasets/icbo2020_1.0.json b/datasets/icbo2020_1.0.json index 7b81690452..8e53bbb272 100644 --- a/datasets/icbo2020_1.0.json +++ b/datasets/icbo2020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "icbo2020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ontologies used for the case study in the publication. Creative Commons (CC) license: CC BY-NC-SA ", "links": [ { diff --git a/datasets/ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0.json b/datasets/ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0.json index 8df0504266..da91a587fc 100644 --- a/datasets/ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0.json +++ b/datasets/ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains number concentrations of ice-nucleating particles active at -15 \u00b0C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title \u201cTowards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling\u201d.", "links": [ { diff --git a/datasets/ice-radar-traverse-mirny-domec-1978_1.json b/datasets/ice-radar-traverse-mirny-domec-1978_1.json index f165d8080b..1e1a1da863 100644 --- a/datasets/ice-radar-traverse-mirny-domec-1978_1.json +++ b/datasets/ice-radar-traverse-mirny-domec-1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ice-radar-traverse-mirny-domec-1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notes from the ice radar used on the traverse from Mirny to Dome C in 1978, recording file usage for various locations, and initial observations of ice thickness from the radar.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/ice_movement_mirny-domec_1977-84_1.json b/datasets/ice_movement_mirny-domec_1977-84_1.json index 2b0828b4b8..21db3ab241 100644 --- a/datasets/ice_movement_mirny-domec_1977-84_1.json +++ b/datasets/ice_movement_mirny-domec_1977-84_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ice_movement_mirny-domec_1977-84_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Several traverses were completed from Mirny to Dome C by the Russians in the 1970s and 1980s. Precise location records of 15 stations along the traverse were completed by JMR analysis, first in the 1977/78 traverse, and repeated in the 1983/84 traverse. This allowed the calculation of ice movement, and hence ice velocity, to be made for those sites.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/ice_retreat_blooms_1.json b/datasets/ice_retreat_blooms_1.json index f83de4a5a5..35e89a2c81 100644 --- a/datasets/ice_retreat_blooms_1.json +++ b/datasets/ice_retreat_blooms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ice_retreat_blooms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set comprises animations showing the spring/summer melt of sea ice in East Antarctica and the subsequent chlorophyll blooms. SMMR-SSM/I sea ice concentration data were obtained from the National Snow and Ice Data Centre, and AMSR-E sea ice concentration data from the University of Bremen. SeaWiFS and MODIS chlorophyll-a data were obtained from the OceanColor site. SeaWiFS and SMMR-SSM/I data were used for seasons prior to 2002/03; MODIS and AMSR-E data were used for later seasons. Chl-a data were averaged over 16-day periods. The animations also show the ETOPO2 bathymetry and the fronts of the Antarctic circumpolar current (Orsi et al. 1995).", "links": [ { diff --git a/datasets/icecore_borehole_orientation_1970s_1.json b/datasets/icecore_borehole_orientation_1970s_1.json index 1a9b554492..233fe868f6 100644 --- a/datasets/icecore_borehole_orientation_1970s_1.json +++ b/datasets/icecore_borehole_orientation_1970s_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "icecore_borehole_orientation_1970s_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw orientations obtained from measurements (and re-measurements) from several ice core boreholes on Law Dome. Holes include SGP (1979), BHQ (1977,1979), SGF (1974, 1977, 1979), SGB (1979) and BHD (1977, 1979).\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/icecube_microct_snow_grainsize_1.0.json b/datasets/icecube_microct_snow_grainsize_1.0.json index 333ee907ca..9e65429611 100644 --- a/datasets/icecube_microct_snow_grainsize_1.0.json +++ b/datasets/icecube_microct_snow_grainsize_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "icecube_microct_snow_grainsize_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.", "links": [ { diff --git a/datasets/ikonos.json b/datasets/ikonos.json index 957a5d710b..dc3aea268d 100644 --- a/datasets/ikonos.json +++ b/datasets/ikonos.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ikonos", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Since its launch in September 1999, GeoEye's IKONOS satellite has provided a reliable stream of image data since January 2000, which has become the standard for commercial high-resolution satellite data products. With an altitude of 681 km and a revisit time of approximately 3 days, IKONOS produces one-meter panchromatic and four-meter multispectral imagery that can be combined to accommodate a wide range of high-resolution imagery applications.", "links": [ { diff --git a/datasets/illgraben-debris-flow-characteristics-2019-2022_1.0.json b/datasets/illgraben-debris-flow-characteristics-2019-2022_1.0.json index 62f400b6b4..ee4c84a535 100644 --- a/datasets/illgraben-debris-flow-characteristics-2019-2022_1.0.json +++ b/datasets/illgraben-debris-flow-characteristics-2019-2022_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "illgraben-debris-flow-characteristics-2019-2022_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density. This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.", "links": [ { diff --git a/datasets/imergcpex_1.json b/datasets/imergcpex_1.json index facc7c0320..efa3caa5d1 100644 --- a/datasets/imergcpex_1.json +++ b/datasets/imergcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "imergcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) CPEX dataset includes measurements gathered by IMERG during the Convective Processes Experiment (CPEX) field campaign. IMERG combines precipitation estimates from multiple passive microwave (PMW) sensors available in a 30-minute analysis time. These estimates are retrieved using the Goddard Profiling (GPROF) algorithm that converts PMW brightness temperatures to a precipitation estimate. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. IMERG combines information from the GPM satellite constellation to estimate precipitation over the majority of the Earth's surface. Data are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.", "links": [ { diff --git a/datasets/imis-measuring-network_1.0.json b/datasets/imis-measuring-network_1.0.json index e0976e75cd..ae80f33bce 100644 --- a/datasets/imis-measuring-network_1.0.json +++ b/datasets/imis-measuring-network_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "imis-measuring-network_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Intercantonal Measurement and Information System (IMIS) consists of nearly 200 automatic measuring stations. They are distributed throughout the Swiss Alps and the Jura region and, in most cases, are situated above the tree line, most frequently between 2000 and 3000 m. The stations record the conditions around the clock, in general every 30 minutes. Most IMIS stations are located in the vicinity of starting zones of potentially destructive avalanches, and provide essential information to local safety officers for public safety in settlements and on the roads. They are also used for snow-hydrological and research purposes and by the avalanche warning service of the SLF. This dataset comprises data from IMIS snow and wind stations. The snow and wind stations are usually situated close to each other and measure the key weather data required for assessing the avalanche danger. ## IMIS snow stations Snow stations are located on wind-protected flat terrain. The snowpack model SNOWPACK calculates the layers and properties of the snowpack throughout the winter for each of the IMIS snow stations. The following variables are measured or simulated in the standard programme of IMIS snow stations and are available in this dataset: - Snow depth - 24-hour new snow (SNOWPACK simulation) - Air and surface temperature - Wind speed and direction - Relative humidity - Reflected shortwave radiation - Ground temperature - Snow temperature 25 cm, 50 cm and 100 cm above the ground ## IMIS wind stations Wind stations are generally situated at higher altitudes on exposed summits or ridges. The following variables are measured in the standard programme of IMIS wind stations and are available in this dataset: - Wind speed and direction - Air temperature - Relative humidity __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__. __To download live data use our [API](https://measurement-api.slf.ch)__. __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.", "links": [ { diff --git a/datasets/impact-des-extremes-sur-les-scieries_1.0.json b/datasets/impact-des-extremes-sur-les-scieries_1.0.json index 32bfc096b7..0ea369395b 100644 --- a/datasets/impact-des-extremes-sur-les-scieries_1.0.json +++ b/datasets/impact-des-extremes-sur-les-scieries_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "impact-des-extremes-sur-les-scieries_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Extreme events impact on the Swiss forest economy: the sawmill perspective Supplementary Information This survey aimed at answering three main questions: (i) What are the Swiss sawmills challenges and actions taken after a large storm/windthrow?, (ii) How do these challenges and actions vary across sawmill size and location?, and (iii) is adaptation from the sawmills to extreme events possible, with regards to wood type, products and required infrastructure? Informations suppl\u00e9mentaires Cette enqu\u00eate visait \u00e0 r\u00e9pondre \u00e0 trois questions principales : (i) Quels sont les d\u00e9fis et les mesures prises par les scieries suisses apr\u00e8s une grosse temp\u00eate ou un coup de vent ? (ii) Comment ces d\u00e9fis et ces mesures varient-ils selon la taille et l'emplacement de la scierie ? et (iii) l'adaptation des scieries aux \u00e9v\u00e9nements extr\u00eames est-elle possible, en ce qui concerne le type de bois, les produits et l'infrastructure requise ? \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t Ziel dieser Umfrage war die Beantwortung von drei Hauptfragen: (i) Welche s sind die Herausforderungen und Massnahmen der Schweizer S\u00e4gewerke nach einem grossen Sturm/Windwurf?, (ii) Wie unterscheiden sich diese Herausforderungen und Massnahmen je nach Gr\u00f6sse und Standort des S\u00e4gewerks? und (iii) Ist eine Anpassung der S\u00e4gewerke an Extremereignisse m\u00f6glich, in Bezug auf Holzart, Produkte und erforderliche Infrastruktur?", "links": [ { diff --git a/datasets/impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0.json b/datasets/impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0.json index 13da579ca1..6b85525fef 100644 --- a/datasets/impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0.json +++ b/datasets/impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.", "links": [ { diff --git a/datasets/impulse_response_function_script_1.2.json b/datasets/impulse_response_function_script_1.2.json index c571876ec8..85ecacd278 100644 --- a/datasets/impulse_response_function_script_1.2.json +++ b/datasets/impulse_response_function_script_1.2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "impulse_response_function_script_1.2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series. Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.", "links": [ { diff --git a/datasets/in2018_v05_1.json b/datasets/in2018_v05_1.json index 999c31d1f2..34fce737a7 100644 --- a/datasets/in2018_v05_1.json +++ b/datasets/in2018_v05_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "in2018_v05_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Oceanographic measurements were collected aboard RV Investigator cruise in1805 (CSIRO voyage designation in2018_v05) from 16th October to 16th November 2018, along a number of transects across a standing meander of the Antarctic Circumpolar Current between 148o and 156oE. A total of 77 CTD vertical profile stations were taken on the cruise, most to within 12 metres of the bottom. Over 1900 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate, ammonium and nitrite), chlorophyll, POC and DOC, and for incubation experiments, using a 36 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package. Upper water column current profile data were collected by a ship mounted ADCP (75 kHz and 150 kHz). Data coverage was increased by additional transects towing a Triaxus towed CTD system. A microstructure profiler was deployed at many of the CTD stations. Meteorological and water property data were collected by the array of ship's underway sensors. An oceanographic mooring was deployed at 55o 32.544\u2019S , 150o 52.332\u2019E, and a series of floats and drifters were deployed. Bathymetry was collected by the ship\u2019s multibeam system.\n\nThe data set contains CTD 2dbar averaged data, and Niskin bottle data (core hydrochemistry of salinity, dissolved oxygen and nutrients), in text and matlab formats, and a full data report. A WOCE (CCHDO) 'exchange' format version of the data is also available on request.", "links": [ { diff --git a/datasets/inclinometer_lawdome_70s_1.json b/datasets/inclinometer_lawdome_70s_1.json index 843c60ead1..a52b7788d4 100644 --- a/datasets/inclinometer_lawdome_70s_1.json +++ b/datasets/inclinometer_lawdome_70s_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "inclinometer_lawdome_70s_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of inclinometer readings from various ice core boreholes on Law Dome in the late 1970s. Holes recorded include SGF (1974, 1977 and 1979), SBG, SGP (1979) and BHD (1977 and 1979)\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/increment-11_1.0.json b/datasets/increment-11_1.0.json index 205c64ca22..64510f1cb1 100644 --- a/datasets/increment-11_1.0.json +++ b/datasets/increment-11_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "increment-11_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increase in the volume of stemwood with bark of the trees and shrubs starting at 12 cm dbh that have survived between two inventories and of the losses (modelled for the half period), plus the volume of the gains. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/increment_star-162_1.0.json b/datasets/increment_star-162_1.0.json index 6e62d91455..6657e0a53d 100644 --- a/datasets/increment_star-162_1.0.json +++ b/datasets/increment_star-162_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "increment_star-162_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increase in the volume of stemwood with bark of the surviving trees and shrubs starting at 12 cm dbh between two inventories and the losses (modelled for the half period), plus the volume of gains. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0.json b/datasets/individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0.json index 59ce3aeb41..8def077f70 100644 --- a/datasets/individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0.json +++ b/datasets/individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "## Dataset This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern. The data was acquired on two sites (47.42\u00b0N 8.49\u00b0E and 47.504\u00b0N, 7.78\u00b0E), both of which were managed mixed temperate forest stands. Individual trees were semi-automatically segmented from the co-registered TLS point clouds. ## Background Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.", "links": [ { diff --git a/datasets/induced-rockfall-dataset-chant-sura_1.0.json b/datasets/induced-rockfall-dataset-chant-sura_1.0.json index 8223495c48..44fe384282 100644 --- a/datasets/induced-rockfall-dataset-chant-sura_1.0.json +++ b/datasets/induced-rockfall-dataset-chant-sura_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "induced-rockfall-dataset-chant-sura_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass. Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of: # Resources (individual zip-archives) __ExperimentalRuns__: Archive with all available StoneNode data streams and its respective figure (.mat files) __Input__: Archive containing folders * GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line, * UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and * VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times __EOTA__: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files. __Output__: Reconstruced trajectory information for all 82 reconstructed trajectories __Video__: available video streams for all runs ## Further information Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references: A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/ P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/ Caviezel, A., Demmel, S. E., Ringenbach, A., B\u00fchler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199\u2013210, https://doi.org/10.5194/esurf-7-199-2019, 2019", "links": [ { diff --git a/datasets/inishell-2-0-4_2.0.4.json b/datasets/inishell-2-0-4_2.0.4.json index 29c4399000..69332bc75a 100644 --- a/datasets/inishell-2-0-4_2.0.4.json +++ b/datasets/inishell-2-0-4_2.0.4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "inishell-2-0-4_2.0.4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge).", "links": [ { diff --git a/datasets/inpe_CPTEC_GLOBAl_FORECAST.json b/datasets/inpe_CPTEC_GLOBAl_FORECAST.json index 76aecf8449..6ae26e1db2 100644 --- a/datasets/inpe_CPTEC_GLOBAl_FORECAST.json +++ b/datasets/inpe_CPTEC_GLOBAl_FORECAST.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "inpe_CPTEC_GLOBAl_FORECAST", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CPTEC offers global model analysis and forecast images for twelve\n meteorological parameters. Forecast time steps range from the initial\n analysis each day out to six days. The user may choose forecasts from\n the most recent forecast run back to the previous 36 hours.\n \n Parameters Forecasted:\n \n Mean Sea Level Pressure\n Temperature at 1000 hPa\n Relative Humidity at 925 hPa, 850 hPa\n Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa\n Velocity Potential at 925 hPa, 200 hPa\n Stream Function at 925 hPa, 200 hPa\n 500/1000 hPa Thickness\n Advection of Temperature at 925 hPa, 850 hPa, 500 hPa\n Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa\n Convergence of Humidity Flux at 925 hPa, 850 hPa\n Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa\n Total Precipitation Last 24 Hours\n \n All forecast images can be obtained via World Wide Web from the CPTEC\n Home Page.\n Link to: \"http://www.cptec.inpe.br/\"", "links": [ { diff --git a/datasets/input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0.json b/datasets/input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0.json index f7c70b83fd..f86f47fa66 100644 --- a/datasets/input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0.json +++ b/datasets/input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included. ## Columns Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero", "links": [ { diff --git a/datasets/input-data-for-impact-assessment-of-homogenised-snow-series_1.0.json b/datasets/input-data-for-impact-assessment-of-homogenised-snow-series_1.0.json index af5ff76ee0..437da1344a 100644 --- a/datasets/input-data-for-impact-assessment-of-homogenised-snow-series_1.0.json +++ b/datasets/input-data-for-impact-assessment-of-homogenised-snow-series_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "input-data-for-impact-assessment-of-homogenised-snow-series_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Input data for the following research article: Impact assessment of homogenised snow depth series on trends The data consists of separate output files from the following homogenisation methods: * Climatol * HOMER * interpQM The variable is seasonal mean snow depth (HSavg) plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude)", "links": [ { diff --git a/datasets/insects_subsaharanAfrica.json b/datasets/insects_subsaharanAfrica.json index 5f021a00fa..9c1fade602 100644 --- a/datasets/insects_subsaharanAfrica.json +++ b/datasets/insects_subsaharanAfrica.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "insects_subsaharanAfrica", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "One of the most basic needs for inventorying, exploiting and\n monitoring the changes in the insect diversity of Africa is a complete\n list of species which are already know to occur in\n Africa. Surprisingly, such a basic list does not exist, despite some\n 250 years of formal scientific description of life on earth. The\n International Centre of Insect Physiology and Ecology (ICIPE), along\n with the National Museum of Natural History, is therefore sponsoring\n the production of the list, which will provide a reliable platform of\n 'standard' names for species on which many other projects depend. This\n list, or authority file, will greatly enhance communication both among\n scientists and between scientists and users of scientific data. The\n African list will also be a major contribution toward the proposed\n list of world species (e.g. the Global Biodiversity Information\n Facility (GBIF) and Species 2000 initiative of DIVERSITAS).\n \n A demonstration database is provided for the species of the orders\n Odonata (dragonflies and damselflies), Ephemeroptera (mayflies),\n Plecoptera (stoneflies), Megaloptera (alderflies),\n Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers,\n planthoppers, scales, and others), and Trichoptera (caddisflies).\n \n Invitation to collaboration: Compilation of the checklist is being\n coordinated by Nearctica (formerly Entomological Information\n Specialists), because of their experience with Nomina Insecta\n Nearctica. We are attempting to collaborate with known specialists as\n contributors and reviewers, but we welcome additional suggestions of\n collaborators. Inquires can be directed to Scott Miller\n (miller.scott@nmnh.si.edu).\n \n Information was obtained from\n \"http://entomology.si.edu/\".", "links": [ { diff --git a/datasets/instm_trawl.json b/datasets/instm_trawl.json index 8e0a3ff221..bc2c274ab2 100644 --- a/datasets/instm_trawl.json +++ b/datasets/instm_trawl.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "instm_trawl", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia \n has four laboratories. Regular trawl surveys are done by the Laboratory of \n Marine Living Resources to assess the exploitable resource stocks.\n \n This dataset consists of 7664 records of 90 families.", "links": [ { diff --git a/datasets/intercomparison-of-photogrammetric-platforms_1.0.json b/datasets/intercomparison-of-photogrammetric-platforms_1.0.json index c772298789..81f0679fbb 100644 --- a/datasets/intercomparison-of-photogrammetric-platforms_1.0.json +++ b/datasets/intercomparison-of-photogrammetric-platforms_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "intercomparison-of-photogrammetric-platforms_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper \"Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping\". __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pl\u00e9iades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.", "links": [ { diff --git a/datasets/interview-guide-and-transcripts_1.0.json b/datasets/interview-guide-and-transcripts_1.0.json index 7bb8ffded6..a277311560 100644 --- a/datasets/interview-guide-and-transcripts_1.0.json +++ b/datasets/interview-guide-and-transcripts_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "interview-guide-and-transcripts_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed.", "links": [ { diff --git a/datasets/intratrait_1.0.json b/datasets/intratrait_1.0.json index c02163a37c..1f8b6639af 100644 --- a/datasets/intratrait_1.0.json +++ b/datasets/intratrait_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "intratrait_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species\u2019 height ratio, its height relative to its nearest neighbour. The following traits and parameters were measured and are available in this data set: As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b \u03a0). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil. For more details see Rixen et al. 2022, Journal of Ecology.", "links": [ { diff --git a/datasets/inventaire-forestier-national-suisse-2009-2017_1.0.json b/datasets/inventaire-forestier-national-suisse-2009-2017_1.0.json index d320838a0c..0d29580765 100644 --- a/datasets/inventaire-forestier-national-suisse-2009-2017_1.0.json +++ b/datasets/inventaire-forestier-national-suisse-2009-2017_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "inventaire-forestier-national-suisse-2009-2017_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Swiss National Forest Inventory. Results of the fourth survey 2009\u20132017. The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings. Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland Inventaire forestier national suisse. R\u00e9sultats du quatri\u00e8me inventaire 2009-2017. Les relev\u00e9s du quatri\u00e8me inventaire forestier national suisse (IFN) ont eu lieu entre 2009 et 2017, en moyenne huit ans apr\u00e8s le troisi\u00e8me inventaire. Les r\u00e9sultats sur l\u2019\u00e9tat et l\u2019\u00e9volution de la for\u00eat suisse sont pr\u00e9sent\u00e9s et expliqu\u00e9s en d\u00e9tail. Le rapport est structur\u00e9 th\u00e9matiquement selon les crit\u00e8res et indicateurs europ\u00e9ens pour la gestion durable des for\u00eats\u2009: ressources foresti\u00e8res, sant\u00e9 et vitalit\u00e9, production de bois, diversit\u00e9 biologique, for\u00eat protectrice et socio-\u00e9conomie. L\u2019ouvrage s\u2019ach\u00e8ve par un bilan de la durabilit\u00e9 bas\u00e9 sur les r\u00e9sultats de l\u2019IFN. Mots-cl\u00e9s\u2009: surface foresti\u00e8re, volume de bois, accroissement, exploitation, structure de la for\u00eat, \u00e9tat de la for\u00eat, production de bois, biodiversit\u00e9, for\u00eat protectrice, r\u00e9cr\u00e9ation, durabilit\u00e9, r\u00e9sultats de l\u2019inventaire forestier national, Suisse Content license: All rights reserved. Copyright \u00a9 2020 by WSL, Birmensdorf.", "links": [ { diff --git a/datasets/islscp2_soils_1deg_1004_1.json b/datasets/islscp2_soils_1deg_1004_1.json index d6edf6ee51..0a88d95926 100644 --- a/datasets/islscp2_soils_1deg_1004_1.json +++ b/datasets/islscp2_soils_1deg_1004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "islscp2_soils_1deg_1004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS). The data in this data set were produced by the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) staff from data obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/). See the related data sets section below. Two-dimensional gridded maps of selected soil parameters, including soil texture, at a 1.0 by 1.0 degree spatial resolution and for two soil depths are provided. All data layers have been adjusted to match the ISLSCP II land/water mask. There are 36 data files with this data set.", "links": [ { diff --git a/datasets/isotope-lab_1.0.json b/datasets/isotope-lab_1.0.json index 8161e17f8a..dbf3bddf06 100644 --- a/datasets/isotope-lab_1.0.json +++ b/datasets/isotope-lab_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "isotope-lab_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg \"Isotope Laboratory WSL\") The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL.", "links": [ { diff --git a/datasets/isslis_v2_fin_2.json b/datasets/isslis_v2_fin_2.json index 0ddb4d45f6..150a3dbed8 100644 --- a/datasets/isslis_v2_fin_2.json +++ b/datasets/isslis_v2_fin_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "isslis_v2_fin_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth\u2019s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format.", "links": [ { diff --git a/datasets/isslisg_v2_fin_2.json b/datasets/isslisg_v2_fin_2.json index 891079199e..2944ccef6f 100644 --- a/datasets/isslisg_v2_fin_2.json +++ b/datasets/isslisg_v2_fin_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "isslisg_v2_fin_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth\u2019s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats.", "links": [ { diff --git a/datasets/iziko_Crustaceans.json b/datasets/iziko_Crustaceans.json index e18ed8f675..59c856328b 100644 --- a/datasets/iziko_Crustaceans.json +++ b/datasets/iziko_Crustaceans.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "iziko_Crustaceans", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The iziko South African Museum houses the most important crustacean (crabs,\nlobsters, shrimps, barnacles) collection in South Africa. Significant past\ncontributions were made by K.H. Barnard, J.R. Grindley and B.F. Kensley\n(Crustacea). \n\nIt currently contains 5101 records of 274 families.", "links": [ { diff --git a/datasets/iziko_molluscs.json b/datasets/iziko_molluscs.json index 01292613bf..69def47e6b 100644 --- a/datasets/iziko_molluscs.json +++ b/datasets/iziko_molluscs.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "iziko_molluscs", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The iziko South African Museum's mollusc collection of southern \n African species is the second largest mollusc collection in southern \n Africa. Significant additions were made in the past by K.H. Barnard.\n \n It currently contains 6078 records. The families were not provided.", "links": [ { diff --git a/datasets/iziko_sharks.json b/datasets/iziko_sharks.json index 2a102fb928..3b5e341ffa 100644 --- a/datasets/iziko_sharks.json +++ b/datasets/iziko_sharks.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "iziko_sharks", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This collection has global holdings. It includes numerous representatives of\n eight of the shark groups, most representatives of the Batoids and Chimaeras,\n including rare species. Significant material is being acquired from, fisheries\n research and tooth fish long-lining and fishing company by-catches.", "links": [ { diff --git a/datasets/jetty_sat_1.json b/datasets/jetty_sat_1.json index 2bbc146792..a92fc3158a 100644 --- a/datasets/jetty_sat_1.json +++ b/datasets/jetty_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jetty_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Jetty Peninsula, Mac. Robertson Land, Antarctica. This map is part (d) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot tracks, glaciers/ice shelves, and stations/bases. The map has only geographical co-ordinates.", "links": [ { diff --git a/datasets/jfetzer-phosphatase-leaching_1.0.json b/datasets/jfetzer-phosphatase-leaching_1.0.json index a4d1547731..83aaccb148 100644 --- a/datasets/jfetzer-phosphatase-leaching_1.0.json +++ b/datasets/jfetzer-phosphatase-leaching_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jfetzer-phosphatase-leaching_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate. Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest. Soil samples were taken in July 2019. For methods see publication.", "links": [ { diff --git a/datasets/jornada_albedo_667_1.json b/datasets/jornada_albedo_667_1.json index 2423c5a9e0..72f14bcaf9 100644 --- a/datasets/jornada_albedo_667_1.json +++ b/datasets/jornada_albedo_667_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jornada_albedo_667_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this study was to determine the spatial variations in field measurements of broadband albedo as related to the ground cover and under a range of solar conditions during the Prototype Validation Exercise (PROVE) at the Jornada Experimental Range in New Mexico on May 20-30, 1997.", "links": [ { diff --git a/datasets/jornada_canopy_brf_668_1.json b/datasets/jornada_canopy_brf_668_1.json index 8dbc749718..35c6e7029c 100644 --- a/datasets/jornada_canopy_brf_668_1.json +++ b/datasets/jornada_canopy_brf_668_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jornada_canopy_brf_668_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997.", "links": [ { diff --git a/datasets/jornada_landcover_lai_665_1.json b/datasets/jornada_landcover_lai_665_1.json index 6c45023065..55b1dc73e6 100644 --- a/datasets/jornada_landcover_lai_665_1.json +++ b/datasets/jornada_landcover_lai_665_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jornada_landcover_lai_665_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the PROVE exercise on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover.", "links": [ { diff --git a/datasets/jornada_mquals_666_1.json b/datasets/jornada_mquals_666_1.json index 719ad8ab65..046bfb1892 100644 --- a/datasets/jornada_mquals_666_1.json +++ b/datasets/jornada_mquals_666_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "jornada_mquals_666_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. The objective was to examine the usefulness of low-flying aircraft for Moderate Resolution Imaging Spectroradiometer (MODIS) validation of land products.", "links": [ { diff --git a/datasets/joughin_0631973.json b/datasets/joughin_0631973.json index 5be2d6b3dc..61937933b7 100644 --- a/datasets/joughin_0631973.json +++ b/datasets/joughin_0631973.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "joughin_0631973", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under \"IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles\".", "links": [ { diff --git a/datasets/kakqimpacts_1.json b/datasets/kakqimpacts_1.json index 91a58d9a6e..4db5cec2fd 100644 --- a/datasets/kakqimpacts_1.json +++ b/datasets/kakqimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kakqimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KAKQ NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kalahari_aot_h2o_vapor_719_1.json b/datasets/kalahari_aot_h2o_vapor_719_1.json index 0ef0fb27d8..d46f0ee07f 100644 --- a/datasets/kalahari_aot_h2o_vapor_719_1.json +++ b/datasets/kalahari_aot_h2o_vapor_719_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kalahari_aot_h2o_vapor_719_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000, and March 18, 2000. AOT values are provided at 340-, 440-, 675-, 870-, and 936-nm wavelengths. An estimate of the Angstrom Coefficient is also provided to allow the estimation of AOT at other wavelengths. The purpose of this data collection was primarily for documentation of the conditions at each site and to aid in the correction of remote sensing data, for validation of Earth Observation System (EOS) products such as MODIS and MISR aerosol products, and for modeling of canopy productivity.", "links": [ { diff --git a/datasets/kalahari_co2_heat_flux_765_1.json b/datasets/kalahari_co2_heat_flux_765_1.json index 661cd4b9ed..bc54e39b78 100644 --- a/datasets/kalahari_co2_heat_flux_765_1.json +++ b/datasets/kalahari_co2_heat_flux_765_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kalahari_co2_heat_flux_765_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Short-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions.", "links": [ { diff --git a/datasets/kbgmimpacts_1.json b/datasets/kbgmimpacts_1.json index 834b37bd65..cf4121cc3b 100644 --- a/datasets/kbgmimpacts_1.json +++ b/datasets/kbgmimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kbgmimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KBGM NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kboximpacts_1.json b/datasets/kboximpacts_1.json index de0af49390..3ff8fca2af 100644 --- a/datasets/kboximpacts_1.json +++ b/datasets/kboximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kboximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KBOX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kbufimpacts_1.json b/datasets/kbufimpacts_1.json index f6a89448bd..9846f24782 100644 --- a/datasets/kbufimpacts_1.json +++ b/datasets/kbufimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kbufimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KBUF NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kccximpacts_1.json b/datasets/kccximpacts_1.json index 29092e8b19..5e86817780 100644 --- a/datasets/kccximpacts_1.json +++ b/datasets/kccximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kccximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KCCX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kcleimpacts_1.json b/datasets/kcleimpacts_1.json index 936fb1f6c0..ee74d54407 100644 --- a/datasets/kcleimpacts_1.json +++ b/datasets/kcleimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kcleimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KCLE NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kcxximpacts_1.json b/datasets/kcxximpacts_1.json index e842aece6f..714378822d 100644 --- a/datasets/kcxximpacts_1.json +++ b/datasets/kcxximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kcxximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KCXX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kdiximpacts_1.json b/datasets/kdiximpacts_1.json index f84ec7e43a..a47bd178b7 100644 --- a/datasets/kdiximpacts_1.json +++ b/datasets/kdiximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kdiximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KDIX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kdoximpacts_1.json b/datasets/kdoximpacts_1.json index ff3c4e096e..9e6e051f7c 100644 --- a/datasets/kdoximpacts_1.json +++ b/datasets/kdoximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kdoximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KDOX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kdtximpacts_1.json b/datasets/kdtximpacts_1.json index 24ac268026..2b8d4a5c94 100644 --- a/datasets/kdtximpacts_1.json +++ b/datasets/kdtximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kdtximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KDTX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kdvnimpacts_1.json b/datasets/kdvnimpacts_1.json index 7625d6a8fc..6522027738 100644 --- a/datasets/kdvnimpacts_1.json +++ b/datasets/kdvnimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kdvnimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KDVN NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kenximpacts_1.json b/datasets/kenximpacts_1.json index edf7e5a891..94173bb723 100644 --- a/datasets/kenximpacts_1.json +++ b/datasets/kenximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kenximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KENX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kenya_marine.json b/datasets/kenya_marine.json index 125ae7e058..49a3129f0a 100644 --- a/datasets/kenya_marine.json +++ b/datasets/kenya_marine.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kenya_marine", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kenya Marine and Fisheries Research Institute (KMFRI) is a State Corporation in the Ministry of Fisheries Development of the Government of Kenya. It is mandated to conduct aquatic research covering all the Kenyan waters and the corresponding riparian areas including the Kenyan's EEZ in the Indian Ocean waters.\n\nThis collection was compiled from publications, and it currently consists of 3080 records of 533 families.\n", "links": [ { diff --git a/datasets/kerg_ant_bathy_1.json b/datasets/kerg_ant_bathy_1.json index 922670c250..b80e97607b 100644 --- a/datasets/kerg_ant_bathy_1.json +++ b/datasets/kerg_ant_bathy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kerg_ant_bathy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a bathymetric grid of the region 60E to 90E and 48.45S to 70S, created in a geographic coordinate system based on a WGS84 horizontal datum. The grid has a cell size of 0.005 degrees.\n\nMost of the work involved creating a bathymetric grid of the region 60E to 90E and 55S to 70S which was generated from the latest available multibeam swath bathymetry, fisheries' surveys and satellite altimetry data. A report outlining the development of this grid is available for download (see the related url below).\n\nThis grid was then merged with the bathymetric grid described by the metadata record 'Bathymetric Grid of Heard Island - Kerguelen Plateau Region (2005)', which covers the region 68E to 80E and 48S to 56S. Hence the final grid has two 'No data' areas between 48.45S to 55S: 60E to 68E and 80E to 90E.\n\nThe final grid is available for download as a geotiff and ArcInfo ascii file and contours derived from the grid are available for download as a shapefile (see the related urls below).", "links": [ { diff --git a/datasets/kfcximpacts_1.json b/datasets/kfcximpacts_1.json index 5aa8d87eae..ee4f3e7340 100644 --- a/datasets/kfcximpacts_1.json +++ b/datasets/kfcximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kfcximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KFCX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kgrbimpacts_1.json b/datasets/kgrbimpacts_1.json index 398020f366..e6f125522d 100644 --- a/datasets/kgrbimpacts_1.json +++ b/datasets/kgrbimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kgrbimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KGRB NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kgrrimpacts_1.json b/datasets/kgrrimpacts_1.json index 0fd4ed2564..153d5c7243 100644 --- a/datasets/kgrrimpacts_1.json +++ b/datasets/kgrrimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kgrrimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KGRR NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kgyximpacts_1.json b/datasets/kgyximpacts_1.json index 6a4df7f35a..2cfbcfafab 100644 --- a/datasets/kgyximpacts_1.json +++ b/datasets/kgyximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kgyximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KGYX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kilnimpacts_1.json b/datasets/kilnimpacts_1.json index 1c35350a5b..5da2f02abf 100644 --- a/datasets/kilnimpacts_1.json +++ b/datasets/kilnimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kilnimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KILN NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kilximpacts_1.json b/datasets/kilximpacts_1.json index 9dc6808d20..261f0290a7 100644 --- a/datasets/kilximpacts_1.json +++ b/datasets/kilximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kilximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KILX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kindimpacts_1.json b/datasets/kindimpacts_1.json index 1af3a387a3..97ea6831b0 100644 --- a/datasets/kindimpacts_1.json +++ b/datasets/kindimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kindimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KIND NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0.json b/datasets/kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0.json index bd0dbaa77f..9fafff5e51 100644 --- a/datasets/kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0.json +++ b/datasets/kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.", "links": [ { diff --git a/datasets/kiwximpacts_1.json b/datasets/kiwximpacts_1.json index ae3c15ce40..9dd2d9624a 100644 --- a/datasets/kiwximpacts_1.json +++ b/datasets/kiwximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kiwximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KIWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kjklimpacts_1.json b/datasets/kjklimpacts_1.json index 7621cd57c7..911252b393 100644 --- a/datasets/kjklimpacts_1.json +++ b/datasets/kjklimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kjklimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KJKL NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/klotimpacts_1.json b/datasets/klotimpacts_1.json index a5399d49a8..41249b740e 100644 --- a/datasets/klotimpacts_1.json +++ b/datasets/klotimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "klotimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KLOT NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/klwximpacts_1.json b/datasets/klwximpacts_1.json index 96a148a225..dc02f92e39 100644 --- a/datasets/klwximpacts_1.json +++ b/datasets/klwximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "klwximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KLWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kmhximpacts_1.json b/datasets/kmhximpacts_1.json index 2d3db4df89..c4a63f078c 100644 --- a/datasets/kmhximpacts_1.json +++ b/datasets/kmhximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kmhximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KMHX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kmkximpacts_1.json b/datasets/kmkximpacts_1.json index bf4d264583..581bed737b 100644 --- a/datasets/kmkximpacts_1.json +++ b/datasets/kmkximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kmkximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KMKX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/knp_fire_maps_756_1.json b/datasets/knp_fire_maps_756_1.json index 74d4a9a566..d404e62383 100644 --- a/datasets/knp_fire_maps_756_1.json +++ b/datasets/knp_fire_maps_756_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "knp_fire_maps_756_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Kruger National Park (KNP) was established in 1898 to protect wildlife on nearly 2 million hectares of the South African Lowveld. Savanna fires are common in the South African Lowveld during the dry season. The two primary sources of fire ignition, historically and currently, are human activity and lightning. This data set contains a record of all fires observed in the Kruger National Park for each of the 10 fire years from 1992 to 2001. The data were compiled from various sources, including old fire records in hardcopy and vector form, as well as satellite imagery.", "links": [ { diff --git a/datasets/knp_fire_residue_751_1.json b/datasets/knp_fire_residue_751_1.json index 94e8646675..853a705928 100644 --- a/datasets/knp_fire_residue_751_1.json +++ b/datasets/knp_fire_residue_751_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "knp_fire_residue_751_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The goal of this study was to understand the change in reflectance caused by the action of fire and the heterogeneity of fire effects (i.e., the fraction of the observation that burned and the combustion completeness of that observation). A spectral mixture model and field and satellite observations were used to compare changes in Landsat reflectance associated with fire and combustion completeness derived from field measurements at prescribed fire sites in South Africa and to substantiate and illustrate the model findings. The data are stored in a single ASCII file in comma-separate-value format (.csv).", "links": [ { diff --git a/datasets/knp_met_761_1.json b/datasets/knp_met_761_1.json index f7ab38a50c..7b6b2c47ec 100644 --- a/datasets/knp_met_761_1.json +++ b/datasets/knp_met_761_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "knp_met_761_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An eddy covariance system mounted on a tower near the Skukuza Camp in Kruger National Park, South Africa, has been operating continuously since early 2000. Meteorological measurements started in February 2000, and the first flux measurements were made in April 2000. The site is unique in that the micrometeorological instruments are positioned on a tower located between two distinct savanna types, a broad-leafed Combretum savanna and a fine-leafed Acacia savanna. Measurements on the main eddy covariance tower include net ecosystem exchange of CO2, water, and energy, and measurements of a range of meteorological variables with 30-minute averaging period.", "links": [ { diff --git a/datasets/kokximpacts_1.json b/datasets/kokximpacts_1.json index 7db9ecb119..595c13a5cc 100644 --- a/datasets/kokximpacts_1.json +++ b/datasets/kokximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kokximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KOKX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kompsat.1.coverage.of.50.european.cities_10.0.json b/datasets/kompsat.1.coverage.of.50.european.cities_10.0.json index ec425ef30c..b3430e8e63 100644 --- a/datasets/kompsat.1.coverage.of.50.european.cities_10.0.json +++ b/datasets/kompsat.1.coverage.of.50.european.cities_10.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kompsat.1.coverage.of.50.european.cities_10.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Available as a single coverage collection of data over 50 European Cities acquired by KOMPSAT-1\u2019s Electro-Optical Camera (EOC) geolocated and orthorectified. The dataset is composed by PAN imagery at 6.6 m GSD, in GeoTIFF orthorectified format.", "links": [ { diff --git a/datasets/kpbzimpacts_1.json b/datasets/kpbzimpacts_1.json index 9af413737c..5fed232dfd 100644 --- a/datasets/kpbzimpacts_1.json +++ b/datasets/kpbzimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kpbzimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KPBZ NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kraximpacts_1.json b/datasets/kraximpacts_1.json index ebdc68a9eb..e8920b5d4d 100644 --- a/datasets/kraximpacts_1.json +++ b/datasets/kraximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kraximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KRAX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/krill_risk_maps_1.json b/datasets/krill_risk_maps_1.json index 9421512a9d..ff1cc15aa6 100644 --- a/datasets/krill_risk_maps_1.json +++ b/datasets/krill_risk_maps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "krill_risk_maps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The embryonic development of Antarctic krill (Euphausia superba) is sensitive to elevated seawater CO2 levels. This data set provides the experimental data and WinBUGS code used to estimate hatch rates under experimental CO2 manipulation, as described by Kawaguchi et al. (2013).\n\nKawaguchi S, Ishida A, King R, Raymond B, Waller N, Constable A, Nicol S, Wakita M, Ishimatsu A (2013) Risk maps for Antarctic krill under projected Southern Ocean acidification. Nature Climate Change (in press)\n\nCircumpolar pCO2 projection.\nTo estimate oceanic pCO2 under the future CO2 elevated condition, we computed oceanic pCO2 using a three-dimensional ocean carbon cycle model developed for the Ocean Carbon-Cycle Model Intercomparison Project (2,3) and the projected atmospheric CO2 concentrations. The model used, referred to as the Institute for Global Change Research model in the Ocean Carbon-Cycle Model Intercomparison Project, was developed on the basis of that used in ref. 4 for the study of vertical fluxes of particulate organic matter and calcite. It is an offline carbon cycle model using physical variables such as advection and diffusion that are given by the general circulation model. The model was forced by the following four atmospheric CO2 emission scenarios and their extensions to year 2300. RCP8.5: high emission without any specific climate mitigation target; RCP6.0: medium-high emission; RCP 4.5: medium-low emission; and RCP 3.0-PD: low emission (1). Simulated perturbations in dissolved inorganic carbon relative to 1994 (the Global Ocean Data Analysis Project (GLODAP) reference year) were added to the modern dissolved inorganic carbon data in the GLODAP dataset (5). To estimate oceanic pCO2, temperature and salinity from the World Ocean Atlas data set (6) and alkalinity from the GLODAP data set were assumed to be constant.\n\nMarine ecosystems of the Southern Ocean are particularly vulnerable to ocean acidification. Antarctic krill (Euphausia superba; hereafter krill) is the key pelagic species of the region and its largest fishery resource. There is therefore concern about the combined effects of climate change, ocean acidification and an expanding fishery on krill and ultimately, their dependent predators\u2014whales, seals and penguins. However, little is known about the sensitivity of krill to ocean acidification. Juvenile and adult krill are already exposed to variable seawater carbonate chemistry because they occupy a range of habitats and migrate both vertically and horizontally on a daily and seasonal basis. Moreover, krill eggs sink from the surface to hatch at 700\u20131,000m, where the carbon dioxide partial pressure (pCO2 ) in sea water is already greater than it is in the atmosphere. Krill eggs sink passively and so cannot avoid these conditions. Here we describe the sensitivity of krill egg hatch rates to increased CO2, and present a circumpolar risk map of krill hatching success under projected pCO2 levels. We find that important krill habitats of the Weddell Sea and the Haakon VII Sea to the east are likely to become high-risk areas for krill recruitment within a century. Furthermore, unless CO2 emissions are mitigated, the Southern Ocean krill population could collapse by 2300 with dire consequences for the entire ecosystem.\n\nThe risk_maps folder contains the modelled risk maps for each of the climate change scenarios (i.e. Figure 4 in the main paper, and Figure S2 in the supplementary information). These are in ESRI gridded ASCII format, on a longitude-latitude grid with 1-degree resolution.\n\nRefs:\n1. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213-241 (2011).\n\n2. Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681-686 (2005).\n\n3. Cao, L. et al. The role of ocean transport in the uptake of anthropogenic CO2.\nBiogeosciences 6, 375-390 (2009).\n\n4. Yamanaka, Y. and Tajika, E. The role of the vertical fluxes of particulate organic matter and calcite in the oceanic carbon cycle: Studies using an ocean biogeochemical general circulation model. Glob. Biogeochem. Cycles 10,\n361-382 (1996).\n\n5. Key, R. M. et al. A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Glob. Biogeochem. Cycles 18, GB4031 (2004).\n\n6. Conkright, M. E. et al. World Ocean Atlas 2001: Objective Analyses, Data Statistics, and Figures CD-ROM Documentation (National Oceanographic Data Center, 2002).", "links": [ { diff --git a/datasets/krlximpacts_1.json b/datasets/krlximpacts_1.json index 2a15e87067..b64d123252 100644 --- a/datasets/krlximpacts_1.json +++ b/datasets/krlximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "krlximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KRLX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. ", "links": [ { diff --git a/datasets/kscmill_1.json b/datasets/kscmill_1.json index 5ffca88b65..a7f5abc49b 100644 --- a/datasets/kscmill_1.json +++ b/datasets/kscmill_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kscmill_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Advanced Ground Based Field Mill (AGBFM) network consists of 34\n(31 operational) field mills located at Kennedy Space Center (KSC),\nFlorida. The field mills measure the electrostatic vertical field.\nThis system can measure electrostatic fields in the range of 4 V/m\nto 32 kV/m at 10 Hz resolution (digitized at 50 Hz). Individual\nlightning events can be detected within approximately 50 nautical\nmiles of KSC proper.", "links": [ { diff --git a/datasets/kt_canopy_structure_768_1.json b/datasets/kt_canopy_structure_768_1.json index 038b0eac4a..0aa3f98107 100644 --- a/datasets/kt_canopy_structure_768_1.json +++ b/datasets/kt_canopy_structure_768_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_canopy_structure_768_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains leaf area index (LAI), leaf inclination angle, and canopy dimension data from study sites along the Kalahari Transect in southwest Botswana. The data were collected during the 2001 wet season field campaign of the SAFARI 2000 at a total of seven plots of 200 x 150 meter dimensions: two plots each at Tshane and Mabuasehube and three plots at Tsabong. The data set consists of measurements of leaf angle for plot dominant woody species, LAI calculated from overstory and understory photosynthetically active radiation (PAR) measurements, and canopy dimension data (i.e., crown height, crown width, and height to crown) for grass and woody vegetation for use in the parameterization of plant canopy reflectance models. The data files are stored as ASCII table files, in comma-separated-value (.csv) format, with column headers. Photographs (.jpg) are provided of each plot to provide an idea of site conditions. The photographs can be viewed on the S2K Photo Gallery pages.", "links": [ { diff --git a/datasets/kt_gps_photos_769_1.json b/datasets/kt_gps_photos_769_1.json index e1035639f4..7f2e1c592d 100644 --- a/datasets/kt_gps_photos_769_1.json +++ b/datasets/kt_gps_photos_769_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_gps_photos_769_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Global Positioning System (GPS) imprinted landscape photographs at 100-m intervals along the Large Grid Transects at Kalahari Transect sites in Botswana and at measurement sites in Kataba Forest, Mongu, Zambia, and in the vicinity of the Skukuza flux tower site in Kruger National Park, South Africa. The Kalahari sites visited were Pandamatenga, Maun, Okwa Valley, and Tshane. There are about 30 pictures per site. In a related study, vegetation cover and composition were measured at various locations along the Kalahari Transect and trends in major vegetative cover, including species types and richness, were recorded (Ringrose and Matheson, 2004). The photographs are intended to aid in the interpretation of other data sets, and can be used to suggest canopy height, gap fraction, grass, soil, and sky conditions. The photographs are provided as JPEG images.", "links": [ { diff --git a/datasets/kt_lai_770_1.json b/datasets/kt_lai_770_1.json index e05b089032..45e082bc66 100644 --- a/datasets/kt_lai_770_1.json +++ b/datasets/kt_lai_770_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_lai_770_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign from March 3rd to March 18th, 2000, to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance, leaf hemispherical transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer. LAI data are provided in this data set. LAI was intensively measured at 4 different sites in Botswana -- Pandamatenga, Maun, Okwa River, and Tshane (from north to south) -- where vegetation types range from moist closed woodlands to arid sparsely-shrub-covered grasslands.", "links": [ { diff --git a/datasets/kt_lai_trac_771_1.json b/datasets/kt_lai_trac_771_1.json index 7c372bebe2..a34d94d2e2 100644 --- a/datasets/kt_lai_trac_771_1.json +++ b/datasets/kt_lai_trac_771_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_lai_trac_771_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from the Tracing Radiation and Architecture of Canopies (TRAC) instrument were collected at five sites along the International Geosphere-Biosphere Programme (IGBP) Kalahari Transect, including Mongu in Zambia and Pandamatenga, Maun, Okwa River Crossing, and Tshane in Botswana, during the 2000 wet season field campaign (March-April) of SAFARI 2000. At the Mongu site, TRAC measurements began in August of 1999 and continued beyond the 2000 wet season field campaign, about every month for the rest of 2000. The TRAC instrument contains pyranometers that are sensitive to photosynthetically active radiation (PAR) at 400-700 nm. The TRAC measures the PAR flux transmitted through the overstory canopy continuously at 32 Hz. The parameters derived from the TRAC instrument include estimates of plant or leaf area index (PAI, LAI), overstory gap fraction, and clumping index.", "links": [ { diff --git a/datasets/kt_leaf_meas_772_1.json b/datasets/kt_leaf_meas_772_1.json index aa960dbbaf..1505bee011 100644 --- a/datasets/kt_leaf_meas_772_1.json +++ b/datasets/kt_leaf_meas_772_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_leaf_meas_772_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data presented in this data set were collected during an intensive field campaign in Botswana between February 28 and March 18, 2000 along the Kalahari Transect as part of the SAFARI 2000 wet season field campaign. The sites visited were Pandamatenga, Maun, Okwa River Crossing, and Tshane (north to south). Individual leaf blade measurements were made on replicate samples from selected dominant and subdominant tree species using an optical lens and graticule. Leaves used in the study had recently-matured new growth and were fully exposed to the sun for a significant part of the day. The data set comprises individual leaf blade dimensions along the length and width of each leaf by tree species as well as the mean of the replicate leaf length and width samples. The data are in comma-delimited ASCII format (kt_leaf_dimensions.csv).", "links": [ { diff --git a/datasets/kt_leaf_spectra_773_1.json b/datasets/kt_leaf_spectra_773_1.json index 8d0b0b9a3f..76e9e65dae 100644 --- a/datasets/kt_leaf_spectra_773_1.json +++ b/datasets/kt_leaf_spectra_773_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_leaf_spectra_773_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign between March 3 and March 18, 2000, to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance and transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer. Leaf spectral data are provided in this data set. Leaf spectral measurements were made on samples from dominant tree, shrub, and grass species at 5 different Kalahari Transect sites - Mongu in Zambia and Pandamatenga, Maun, Okwa River, and Tshane in Botswana (from north to south)- where vegetation ranges from moist closed woodlands to arid sparsely-shrub-covered grasslands.", "links": [ { diff --git a/datasets/kt_pai_estimates_774_1.json b/datasets/kt_pai_estimates_774_1.json index 23a71cb4d4..a84ad455aa 100644 --- a/datasets/kt_pai_estimates_774_1.json +++ b/datasets/kt_pai_estimates_774_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_pai_estimates_774_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was collected during February-March 2000 wet season and September 2000 dry season field campaigns of SAFARI 2000. Mongu in Zambia and Pandematenga (aka Kasane) and Tshane in Botswana were visited during the wet season campaign. Dry season data are for Mongu only. Hemispherical photographs, from which Plant Area Index (PAI) estimates are derived, were obtained at the field sites to characterize vegetation structural changes along the Kalahari Transect. The photographs are classified into sky and vegetation (trunk, green and senescent leaves, and branches) using an unsupervised classification scheme.", "links": [ { diff --git a/datasets/kt_par_794_1.json b/datasets/kt_par_794_1.json index ac46ddcf4a..d256376109 100644 --- a/datasets/kt_par_794_1.json +++ b/datasets/kt_par_794_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_par_794_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ceptometer data from a Decagon AccuPAR (Model PAR-80) were collected at four sites in Botswana during the SAFARI 2000 Kalahari Transect Wet Season Campaign (March, 2000). These sites include Maun, Pandamentanga, Ghanzi/Okwa River Crossing, and Tshane. The measurements were taken near stake flags placed at 25 m intervals along three parallel 750 m transects located 250 m apart. The ceptometer contains 80 photosynthetically active radiation (PAR) sensors fixed at 1 cm intervals along a wand and connected to a control box. The sampling protocol followed in general was to first measure above canopy incident PAR, then canopy reflected PAR, then above canopy incident PAR again, and finally, canopy transmitted PAR. The data can be used to compute fraction of photosynthetically active radiation (FPAR), intercepted PAR, leaf area index (LAI), and gap fraction. These data currently exist in raw format, but can be processed using manufacturer-provided software to estimate the derived products.The data are stored as ASCII files, in csv format, organized by site, with one file per transect. Incident, transmitted, and reflected PAR radiation values for a transect and site are in the same file. The type of measurement for each data point is known due to comments in the data files. For the Maun and Pandamatenga sites, there is an additional file containing above canopy PAR irradiance. The PAR data units are micromols m-2 s-1, and the time is in Local Time. There is also a readme file, in txt format, for each site.", "links": [ { diff --git a/datasets/kt_stem_map_775_1.json b/datasets/kt_stem_map_775_1.json index 2ecb21d88e..4856042a05 100644 --- a/datasets/kt_stem_map_775_1.json +++ b/datasets/kt_stem_map_775_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_stem_map_775_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides species distribution, basal area, height, and crown cover of woody stems at 10 sites along the Kalahari Transect where a large gradient in both the mean and variation of annual rainfall results in dramatic changes in vegetation structure. Some of the data were collected during earlier Kalahari Transect projects in 1995 and 1997 at Vastrap, South Africa; Sandveld and Sachinga, Namibia; and Maziba, Senanga, and Lukulu, Zambia. The rest of the data were collected at Mongu, Zambia; and Pandamatenga, Maun, and Tshane, Botswana during the February-March 2000 wet season field campaign of SAFARI 2000. Stem maps were generated at each site using a variable-width belt-transect approach. Tree location, species, diameter, height, and major and minor axis of crown dimensions were measured for each individual taller than 1.5 meters. For multi-stemmed individuals, the diameter of each stem was recorded separately. Canopy area was calculated to be an ellipse defined by the two major axes of measurement. Canopy height was estimated using a clinometer. Biomass was calculated following Goodman (1990) as modified by Dowty (1999).There are two ASCII data files, in comma-delimited format. The stem map file contains records of living, dead, and cut stem allometry, canopy geometry, and biomass at the SAFARI sites. The species list file provides plant family, genus, and species names, numerical codes that correspond to the stem map file, and species common names in English and AFRICAANS.", "links": [ { diff --git a/datasets/kt_veg_inventory_776_1.json b/datasets/kt_veg_inventory_776_1.json index 4a4c5e3dc1..017eff5981 100644 --- a/datasets/kt_veg_inventory_776_1.json +++ b/datasets/kt_veg_inventory_776_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_veg_inventory_776_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation cover and composition, including species types and richness assessments, were measured at four locations along the Kalahari Transect in Botswana (Pandamatenga, Maun, Okwa River, and Tshane) during the SAFARI 2000 wet season field campaign. The sites visited showed interesting degrees of variability despite the apparent homogeneity of the Kalahari sands and predominantly semi-arid savanna shrub-woodlands vegetation cover (Ringrose et al., 2003).At each site, twelve individual locations were chosen by random stratified techniques within a 30-km radius at each location, based on differences in topography, soils, and known disturbance, to help determine local variability (Huennecke et al., 2001). Data collection methods were identical at each location (Ringrose et al., 1996; 1998): (1) identification and enumeration of all species along 3 x 90-m transects, spaced 45-m apart; (2) visual estimation (tape measure and pacing) of canopy diameter along each transect; and (3) visual estimation of percent live and dead herbaceous cover, litter, and bare soil using 3 x 50 m2 quadrats spaced at 30-m intervals along each transect. In addition, vegetation components were calculated for each site comprising woody vegetation cover, green herbaceous cover in terms of grass and forbs, dead herbaceous cover, plant litter, and bare soil. Species richness was calculated as the actual number of species per three transects (270 m2) at each site (Kent and Coker, 1996).The data set consists of two data files (ASCII tables) in comma-delimited format (.csv) with descriptive header records. ", "links": [ { diff --git a/datasets/kt_woody_veg_777_1.json b/datasets/kt_woody_veg_777_1.json index 1f9efd0b2f..d97694330d 100644 --- a/datasets/kt_woody_veg_777_1.json +++ b/datasets/kt_woody_veg_777_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kt_woody_veg_777_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains species composition, basal area, height, and crown cover of all woody plants at six sites along the Kalahari Transect visited in February-March of 2000 as part of SAFARI 2000. Similar measurements on woody and herbaceous vegetation at the Skukuza Flux Tower site in Kruger National Park, South Africa, were made in June of 2000. Leaf area index was derived from measurements made using PAR sensors at each site.Sampling protocol was the same at each site, with a slight variation at Skukuza. A grid of 42 points, 6 rows of 7 columns, each 50 m apart, was laid down in an area 300 m x 350 m for the Kalahari Transect sites. At Skukuza, the grid was 7x7, or 350 m x 350 m, centered on the tower site, yielding 49 points. At each grid point, all woody plants within a circular plot of a fixed radius were identified and measured. Stem circumference was measured on all stems and basal area per stem was derived. Basal area for the circular plots, per species, was calculated and extrapolated to hectares. Tree and stem densities were determined from the number of trees and stems in subplots and extrapolated to hectares. Woody plant height and canopy cover were determined, and aboveground woody biomass and peak leaf area index were estimated. The files are in comma-delimited ASCII format, with the first line listing the data set, author, and date, followed by the data records.", "links": [ { diff --git a/datasets/ktyximpacts_1.json b/datasets/ktyximpacts_1.json index a63d3d663d..a532c81381 100644 --- a/datasets/ktyximpacts_1.json +++ b/datasets/ktyximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ktyximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KTYX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/kvwximpacts_1.json b/datasets/kvwximpacts_1.json index ec3dafdd17..f17a1f3302 100644 --- a/datasets/kvwximpacts_1.json +++ b/datasets/kvwximpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "kvwximpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The KVWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/l-band-davos-laret_1.0.json b/datasets/l-band-davos-laret_1.0.json index 190012bcb3..407ff5329f 100644 --- a/datasets/l-band-davos-laret_1.0.json +++ b/datasets/l-band-davos-laret_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "l-band-davos-laret_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset from the publication \"L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory\", under review in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. volume and issue TBD. Dataset specifics are described in the publication.", "links": [ { diff --git a/datasets/labchemistrymetamorphism_1.0.json b/datasets/labchemistrymetamorphism_1.0.json index 9f03437e0c..d55f1ffdfa 100644 --- a/datasets/labchemistrymetamorphism_1.0.json +++ b/datasets/labchemistrymetamorphism_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "labchemistrymetamorphism_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Earth\u2019s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.", "links": [ { diff --git a/datasets/labes_1.0.json b/datasets/labes_1.0.json index 9edb3cc69f..dce2986765 100644 --- a/datasets/labes_1.0.json +++ b/datasets/labes_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "labes_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception. This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES.", "links": [ { diff --git a/datasets/lai_45_1.json b/datasets/lai_45_1.json index 91d8f1fecf..29b7eb5b79 100644 --- a/datasets/lai_45_1.json +++ b/datasets/lai_45_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lai_45_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LAI estimates computed from unweighted openness by the CANOPY program from digitized canopy photographs", "links": [ { diff --git a/datasets/lake_cc_scenarios_ch2018_1.0.json b/datasets/lake_cc_scenarios_ch2018_1.0.json index 95cf34d503..4c4d37ddee 100644 --- a/datasets/lake_cc_scenarios_ch2018_1.0.json +++ b/datasets/lake_cc_scenarios_ch2018_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lake_cc_scenarios_ch2018_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset \"Lake_climate_change_scenarios_CH2018\" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled \"Lower summer lake levels in regulated perialpine lakes, caused by climate change,\" authored by Wechsler et al. in 2023. The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun). In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes.", "links": [ { diff --git a/datasets/lake_erie_aug_2014_0.json b/datasets/lake_erie_aug_2014_0.json index 67a3295d02..e9dd22e314 100644 --- a/datasets/lake_erie_aug_2014_0.json +++ b/datasets/lake_erie_aug_2014_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lake_erie_aug_2014_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "2014 Lake Erie measurements.", "links": [ { diff --git a/datasets/lambert_geology_gis_1.json b/datasets/lambert_geology_gis_1.json index 838ea4a1e0..1c4223e445 100644 --- a/datasets/lambert_geology_gis_1.json +++ b/datasets/lambert_geology_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lambert_geology_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is the GIS data used for the map 'Geology of the Lambert Glacier - Prydz Bay Region, East Antarctica' \npublished by the Australian Geological Survey Organisation in January 1998. \nThe data is in three formats: ArcInfo interchange, ArcInfo coverage and shapefile. \nA document is included with further information about the data.\nThe map is available from a URL in this metadata record.", "links": [ { diff --git a/datasets/land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0.json b/datasets/land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0.json index b3d639f832..80682c4d1c 100644 --- a/datasets/land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0.json +++ b/datasets/land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006\u20132011 and 2011\u20132016. The study also aims to understand to what extent the changes in the built environment (in terms of \u2018efficient growth\u2019 versus \u2018inefficient growth\u2019) from the 1990s to 2016 in the Austin metropolitan area corresponded with \u2018compact and efficient growth\u2019 planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 \u00d7 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.", "links": [ { diff --git a/datasets/land_cover_data-1km_627_1.json b/datasets/land_cover_data-1km_627_1.json index a552762e8a..be34f324e3 100644 --- a/datasets/land_cover_data-1km_627_1.json +++ b/datasets/land_cover_data-1km_627_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_cover_data-1km_627_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available.", "links": [ { diff --git a/datasets/land_cover_data_1deg_677_1.json b/datasets/land_cover_data_1deg_677_1.json index 369c0a06bd..2e5e0e94e5 100644 --- a/datasets/land_cover_data_1deg_677_1.json +++ b/datasets/land_cover_data_1deg_677_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_cover_data_1deg_677_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a subset of a 1-degree global land cover product (DeFries and Townshend 1994). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data are in ASCII GRID format.", "links": [ { diff --git a/datasets/land_cover_data_1km_678_1.json b/datasets/land_cover_data_1km_678_1.json index cd7663e050..7bad0948c6 100644 --- a/datasets/land_cover_data_1km_678_1.json +++ b/datasets/land_cover_data_1km_678_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_cover_data_1km_678_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Hansen et al. (1999), \"1 km Global Land Cover Data Set Derived from AVHRR,\" which was developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID file format.In recent years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, LGRSS researchers have employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 1 km. The PAL data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. The PAL data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The LGRSS researchers' aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.The 1-km global land cover product was created from 1992-1993 local area coverage (LAC) AVHRR data. The global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Forty-one metrics were developed to describe global vegetation phenology, and these data were used to make the 1-km land cover map. The final product contains 13 land cover classes.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/land_cover_data_1km/comp/glcf1km_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/land_cover_data_8km_680_1.json b/datasets/land_cover_data_8km_680_1.json index 177159bb78..3367c36c36 100644 --- a/datasets/land_cover_data_8km_680_1.json +++ b/datasets/land_cover_data_8km_680_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_cover_data_8km_680_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of an 8-km global land cover product (DeFries et al. 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID file format.To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies at the University of Maryland employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 8 km. The PAL data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, the data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project's aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.The global land cover product (Defries et al. 1998) was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Additional information and references on this data set can be found at the GLCF Web site, as well as at the LGRSS Web site (http://www.geog.umd.edu/LGRSS/intro.html). More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/comp/land_cover_data_8km/glcf8km_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/land_surf_ref_gls2000.json b/datasets/land_surf_ref_gls2000.json index 173bdb1980..f3d59d62f3 100644 --- a/datasets/land_surf_ref_gls2000.json +++ b/datasets/land_surf_ref_gls2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_surf_ref_gls2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data. Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. The result is delivered as the Landsat surface reflectance CDR. ", "links": [ { diff --git a/datasets/land_surf_ref_gls2005.json b/datasets/land_surf_ref_gls2005.json index fb82ea694d..1246b8363b 100644 --- a/datasets/land_surf_ref_gls2005.json +++ b/datasets/land_surf_ref_gls2005.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_surf_ref_gls2005", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data. Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. The result is delivered as the Landsat surface reflectance CDR.\n", "links": [ { diff --git a/datasets/land_surf_ref_gls2010.json b/datasets/land_surf_ref_gls2010.json index f15caef18f..7e1d2da01e 100644 --- a/datasets/land_surf_ref_gls2010.json +++ b/datasets/land_surf_ref_gls2010.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_surf_ref_gls2010", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data. Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. The result is delivered as the Landsat surface reflectance CDR.", "links": [ { diff --git a/datasets/land_surf_ref_l4-5.json b/datasets/land_surf_ref_l4-5.json index a24684abc6..3cc1ce342f 100644 --- a/datasets/land_surf_ref_l4-5.json +++ b/datasets/land_surf_ref_l4-5.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_surf_ref_l4-5", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data. Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. The result is delivered as the Landsat surface reflectance CDR.\n", "links": [ { diff --git a/datasets/land_surf_ref_l7_etm.json b/datasets/land_surf_ref_l7_etm.json index 329c733be1..0cca7b96f1 100644 --- a/datasets/land_surf_ref_l7_etm.json +++ b/datasets/land_surf_ref_l7_etm.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "land_surf_ref_l7_etm", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data. Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. The result is delivered as the Landsat surface reflectance CDR.", "links": [ { diff --git a/datasets/landcover_bvoc_est_764_1.json b/datasets/landcover_bvoc_est_764_1.json index 91e2fecab2..17a27c69a9 100644 --- a/datasets/landcover_bvoc_est_764_1.json +++ b/datasets/landcover_bvoc_est_764_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "landcover_bvoc_est_764_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Improved vegetation distribution and emission data for Africa south of the equator were developed for the Southern African Regional Science Initiative (SAFARI 2000) and combined with biogenic volatile organic compound (BVOC) emission measurements to estimate BVOC emissions for the southern African region. BVOC emissions were estimated for southern Africa on a monthly basis over a one-year period by combining GIS layers of vegetation, LAI, and climate with a biogenic emissions model, GLOBEIS (Guenther et al, 1993; Guenther, 1999).", "links": [ { diff --git a/datasets/landsat-2_NA.json b/datasets/landsat-2_NA.json index 1a621e75f9..fc4f253561 100644 --- a/datasets/landsat-2_NA.json +++ b/datasets/landsat-2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "landsat-2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat Collection 2 Level-2 Science Products (https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected surface reflectance (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and surface temperature (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present. This dataset represents the Brazilian archive of Level-2 data from Landsat Collection 2 (https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the Thematic Mapper (https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the Enhanced Thematic Mapper (https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and Operatational Land Imager (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and Thermal Infrared Sensor (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF (https://www.cogeo.org/) format.", "links": [ { diff --git a/datasets/landscape-technology-fit-public-evaluation_1.0.json b/datasets/landscape-technology-fit-public-evaluation_1.0.json index 21e2ed4f9b..bb042e451e 100644 --- a/datasets/landscape-technology-fit-public-evaluation_1.0.json +++ b/datasets/landscape-technology-fit-public-evaluation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "landscape-technology-fit-public-evaluation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We present stated preference data based on a national representative Swiss online panel survey for the preference of renewable energy infrastructure in landscapes. The data was collected between November 2018 to March 2019 using an online questionnaire and resulted on 1026 responses. The online questionnaire consisted of two main parts \u2013 (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure, including the \u201cfit\u201d of landscape/REI combinations and (2) online choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific renewable energy infrastructures, we also asked them to evaluate the fitting of seven different Swiss landscapes (near natural alpine areas, northern alps, touristic alpine areas, agricultural plateau, urban plateau, jura ridges, urban alpine valley) with five different REI (wind, PV ground, PV roof, power lines) combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two \u201cenergy system transformation\u201d options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes (landscape, wind energy infrastructure, photovoltaic energy infrastructure, high voltage overhead power line infrastructure) with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modelling and analysis was n=844 (12660 choice observations). An analysis of the hybrid choice model and further insights are presented in the article \u201cHow landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.\u201d", "links": [ { diff --git a/datasets/landscape_1.0.json b/datasets/landscape_1.0.json index f392394952..e7eb71111c 100644 --- a/datasets/landscape_1.0.json +++ b/datasets/landscape_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "landscape_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the published scientific paper Hersperger, A.M., B\u00fcrgi, M., Wende, W., Bac\u0103u, S. and Gr\u0103dinaru, S.R., 2020. Does landscape play a role in strategic spatial planning of European urban regions?. Landscape and Urban Planning, 194, p.103702. The goal of this research was to assess the role of landscape in contemporary strategic spatial planning. In order to assess the role of \u201clandscape\u201d in the strategic spatial plans, we focused on how plans took advantage of landscape\u2019s integrative power, how plans are based on knowledge on functioning of landscape systems, and how plans show the contribution of landscapes to human well-being. For each aspect, a number of items (detailed in Table 1 of the paper) were selected to assist the assessment. This study is based on content analysis of the strategic spatial plans of 18 European urban regions. The strategic spatial plans were retrieved from the planning authorities\u2019 websites. The cases study regions as well as the analyzed strategic spatial plans are presented in Table 2 of the paper. The authors developed a protocol containing 28 items, out of which 16 were directly derived from information presented in Table 1. As a result, we provide the following outputs: \u2022\tProtocol_items.docx \u2013 freely available - Detailed description of all the protocol items used to conduct the analysis. \u2022\tCoding results.xlsx \u2013 available on request - Results of the coding procedure. Data were used to create Figures 2, 3, 4, 5, 6 and to qualitatively present the results in the research paper.", "links": [ { diff --git a/datasets/large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0.json b/datasets/large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0.json index 8057c34e35..43a65ff5b5 100644 --- a/datasets/large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0.json +++ b/datasets/large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account. The details can be found in this publication: B\u00fchler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., K\u00fchne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.", "links": [ { diff --git a/datasets/large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0.json b/datasets/large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0.json index 795d2dc7b9..f5d2004db8 100644 --- a/datasets/large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0.json +++ b/datasets/large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Potential release files and the artificial RAMMS avalanche simulation output files as well as exposure geodataframe for the case study region of Ortner et al. 2022. Furthermore, all the necessary files to run the risk model Climada Avalnache which code is located at https://github.com/CLIMADA-project/climada_papers.", "links": [ { diff --git a/datasets/large-scale-urban-development-projects-in-european-urban-regions_1.0.json b/datasets/large-scale-urban-development-projects-in-european-urban-regions_1.0.json index b4820afdd3..ef91a3e795 100644 --- a/datasets/large-scale-urban-development-projects-in-european-urban-regions_1.0.json +++ b/datasets/large-scale-urban-development-projects-in-european-urban-regions_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "large-scale-urban-development-projects-in-european-urban-regions_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Table of Content: 1. General context of the data set \"lsUDPs\" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation 1. General context of the data set \"lsUDPs\" The data set \"lsUDPs\" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Gr\u0103dinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96\u2013105. https://doi.org/10.1016/j.cities.2019.05.032 ). 2. Background and aims of the study using the data set lsUDPs As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe. Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs). Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination. Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines. The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or \u201cconcordance\u201d, we mean that strategic projects are formulated and implemented as part of the strategic planning process (\u201chigh concordance\u201d), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (\u201cmoderate concordance\u201d). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans. We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly \u201ccause\u201d) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge). The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans? The conditions (\u201cindependent variables\u201d) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation. The outcome (\u201cdependent variable\u201d) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans 3. The data set lsUDPs 3.1 Selection of cases and data collection To generate the current data set on large-scale urban development projects in European urban regions (data set \"lsUDPs\"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability. In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s\u20132010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases \u201cbcn_airport80-90\u201d and \u201cbcn_airport00-16\u201d; realisation of Lyon Part-Dieu, cases \u201clyo_partdieu70-90\u201d and \u201clyo_partdieu00-16\u201d; MediaCityUK, cases \u201cman_salfordquays80-00\u201d and \u201cman_mediacityuk00-16\u201d). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38. 3.2 The data set lsUDPs: Data management and operationalisation Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled \u201csynthetic case descriptions\u201d (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees\u2019 accounts and to facilitate completion of the \u201ccalibration table\u201d for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases. For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called \u201ccalibration\u201d; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as \u201cfundamentally interpretative tools\u201d that \u201coperationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence\u201d (Ragin 2000:162, in \u201cFuzzy-set Social Science\u201d. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to \u201cquantify\u201d into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1). The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.", "links": [ { diff --git a/datasets/large-wood-event-database_1.0.json b/datasets/large-wood-event-database_1.0.json index 1103b96175..e8fb5983b5 100644 --- a/datasets/large-wood-event-database_1.0.json +++ b/datasets/large-wood-event-database_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "large-wood-event-database_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the context of the WoodFlow project (https://woodflow.wsl.ch), an extensive database was developed which documents recruited and transported quantities of large wood (woody debris) together with the associated catchment and flood-specific parameters. Transported large wood volumes were related to catchment area, forest cover, stream length, peak discharge, runoff volume, sediment load, and precipitation. The dataset covers flood events mostly from Switzerland, but also from other alpine catchments in Germany, Italy France and Japan.", "links": [ { diff --git a/datasets/lars_christ_sat_1.json b/datasets/lars_christ_sat_1.json index 15772c5423..7cce843fb0 100644 --- a/datasets/lars_christ_sat_1.json +++ b/datasets/lars_christ_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lars_christ_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Lars Christensen Coast, Mac. Robertson Land, Antarctica. This map is part (b) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500000, and was produced from Landsat TM and MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows glaciers/ice shelves, penguin colonies, refuges/depots. The map has only geographical co-ordinates.", "links": [ { diff --git a/datasets/lars_geology_1.json b/datasets/lars_geology_1.json index aa22a094b1..eddda0b278 100644 --- a/datasets/lars_geology_1.json +++ b/datasets/lars_geology_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lars_geology_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geology of the Larsemann Hills, Antarctica. \nGeological data from C.J.Carson, University of Melbourne and K.St&uuml;we, University of Adelaide. Additional interpretation by D.E.Thost, AGSO (now Geoscience Australia).\nThis dataset comprises only the lithology. There has been no attempt to give the ages of the lithological units.\nThis data are displayed in a draft map published in January 1997 (see link below).", "links": [ { diff --git a/datasets/lars_geology_2004_1.json b/datasets/lars_geology_2004_1.json index 7215e5a5ac..73e091465c 100644 --- a/datasets/lars_geology_2004_1.json +++ b/datasets/lars_geology_2004_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lars_geology_2004_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Larsemann Hills region is dominated by two major lithological associations, a Palaeoproterozoic felsic/mafic orthogneiss complex (Sostrene Orthogneiss) which occurs as basement to a sequence of pelitic, psammitic and felsic paragneiss (supergroup = Brattstrand Paragneiss) and felsic intrusives. The depositional age of the Brattstrand Paragneiss sequences are controversial but isotopic data suggest derivation from the basement Sostrene Orthogneiss. Current geochronology indicates that the region experienced medium to low pressure granulite-facies metamorphism during the Early Palaeozoic (~500 Ma). Although the paragneiss sequences record no evidence of earlier metamorphism, relicts of a previous metamorphic event at ~1000 Ma are preserved in the Sostrene Orthogneiss. Within the Larsemann Hills region, the Early Palaeozoic event is characterised by peak metamorphism of ~7 kbar at ~800-850 degrees C, with the post-peak evolution characterised by decompression, with some cooling, to 4 kbars at 750 degrees C, then to 2-3 kbar at 600-650 degrees C during final stages of orogenesis, with exhumation largely driven by crustal extension. Tectonic models generally argue for a continental-continental collisional scenario, with thermal input derived from a thinned mantle lithosphere.\n\nSee the document available for download at the provided URL for further information.", "links": [ { diff --git a/datasets/larsemann_envmanagement_maps_1.json b/datasets/larsemann_envmanagement_maps_1.json index 15e915b024..e329e27518 100644 --- a/datasets/larsemann_envmanagement_maps_1.json +++ b/datasets/larsemann_envmanagement_maps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "larsemann_envmanagement_maps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Australian Antarctic Data Centre's Larsemann Hills topographic GIS dataset was mapped from aerial photography. Refer to the metadata record 'Larsemann Hills - Mapping from aerial photography captured February 1998', Entry ID gis135. \nSince then GIS data with the locations and attributes of a range of features has been created from various sources, often for the purpose of environmental management. The features include station buildings, refuges, camp sites, management zones, helicopter landing areas, anchorages, beaches, a grave, monuments and Physics equipment.\nThe data are included in the GIS data available for download from a Related URL below. \nThe data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. \nData described by this metadata record has Dataset_id = 6. \nEach feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature, including the origin of the data.", "links": [ { diff --git a/datasets/larsemann_hills_dem_1.json b/datasets/larsemann_hills_dem_1.json index 7e8f3834bb..3d2ab4ff51 100644 --- a/datasets/larsemann_hills_dem_1.json +++ b/datasets/larsemann_hills_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "larsemann_hills_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM) of the Larsemann Hills with cell size 10 metres was interpolated from input coastline, contour, spot height (point locations with an elevation attribute) and lake data from the dataset described by the metadata record 'Larsemann Hills - Mapping from aerial photography captured February 1998' with Entry ID: gis135.\nThe input data for the islands and much of the continent coastal area is estimated to have horizontal accuracy of about 5 metres and vertical accuracy of about 5 metres. The input data for a mainly inland area in the south-east of the data coverage is estimated to have horizontal accuracy of about 15 metres and vertical accuracy of about 15 metres. The spatial coverage for these two categories of input data is shown in a map linked to this metadata record.\nThe interpolation was done using the Topo to Raster tool in ArcGIS.\nIn the interpolation process all cells within a lake are assigned to the minimum elevation value of all cells along the shoreline. i.e. the interpolation is flat across the lake.\nThe output DEM was clipped to the extents of the input data.\nThe dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool.\nThe DEM is stored in the UTM Zone 43S projection.\nThe horizontal datum is WGS84. The vertical datum is Mean Sea Level.\nThe DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade.\nThe DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record:\n1 A geotiff; and \n2 An ascii file in ESRI's ascii format for rasters.", "links": [ { diff --git a/datasets/larsemann_sat_1.json b/datasets/larsemann_sat_1.json index b47ba18a64..8f8cb0edd5 100644 --- a/datasets/larsemann_sat_1.json +++ b/datasets/larsemann_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "larsemann_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Larsemann Hills, Princess Elizabeth Land, Antarctica. This map (edition 2) was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. The map is at a scale of 1:25000, and was produced from a multispectral SPOT 1 - HRV 2 scene (WRS K278 J495), acquired 19 February 1988. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/larsemann_visible_disturbance_1.json b/datasets/larsemann_visible_disturbance_1.json index 22282e9ce8..0dcbe3b8e5 100644 --- a/datasets/larsemann_visible_disturbance_1.json +++ b/datasets/larsemann_visible_disturbance_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "larsemann_visible_disturbance_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Annotated large format maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica.\nThe compilation was done by Ewan McIvor of the Australian Antarctic Division and based on discussions with scientists Jim Burgess and Andy Spate.\nIncluded are locations and notes relating to:\n1 walking and vehicular routes;\n2 helicopter landing sites;\n3 a tide gauge;\n4 a fuel line;\n5 a grave site;\n6 a long term micro erosion monitoring site established in 1990 by Burgess and Spate;\n7 two ice caves; and\n8 a pliocene deposit.", "links": [ { diff --git a/datasets/larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0.json b/datasets/larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0.json index cdb4c9f138..466740280a 100644 --- a/datasets/larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0.json +++ b/datasets/larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.", "links": [ { diff --git a/datasets/latent-reserves-in-the-swiss-nfi_1.0.json b/datasets/latent-reserves-in-the-swiss-nfi_1.0.json index d9cee463d9..fabddc8dca 100644 --- a/datasets/latent-reserves-in-the-swiss-nfi_1.0.json +++ b/datasets/latent-reserves-in-the-swiss-nfi_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "latent-reserves-in-the-swiss-nfi_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files refer to the data used in Portier et al. \"\u2018Latent reserves\u2019: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered \u2018latent reserves\u2019, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Kl\u00f6tzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.", "links": [ { diff --git a/datasets/law_dome_1977_1.json b/datasets/law_dome_1977_1.json index 959545d551..db9d0b54ee 100644 --- a/datasets/law_dome_1977_1.json +++ b/datasets/law_dome_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1977 several traverses were carried out over the Law Dome area, primarily to drill new ice cores on the dome. The 1974 drill site (near Cape Folger) was redrilled to add instrumentation for inclination, while additional holes at BHQ (418m) and the dome summit (475m, 2x 30m) were also drilled.\n\nIn addition to the drilling work, two strain grids were laid out on the ice surface, and the grid laid out in 1974 was remeasured.\n\nNotes on the traverse and drilling (but few results) are contained in this record, along with the results of the strain grid surveys. Records for this work have been archived at the Australian Antarctic Division. \n\nLogbook(s): \nGlaciology Log of 1977 Field Work", "links": [ { diff --git a/datasets/law_dome_700yr_ion_chem_2.json b/datasets/law_dome_700yr_ion_chem_2.json index 31b66e8081..3fea5affff 100644 --- a/datasets/law_dome_700yr_ion_chem_2.json +++ b/datasets/law_dome_700yr_ion_chem_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_700yr_ion_chem_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main.\n\nThis work was completed as part of ASAC project 757 (ASAC_757).\n\nSpecies which have been the subject of publication and could be made available after consultation:\n\nSpecies, Period (AD), Resolution, Comments\nSO4, 1301-1995, Fine (full) \nNO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation\n(Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual\nMSA, 1841-1995, Annual\nNa, 1301-1995, Fine (full)\nNa, 1301-1995, Annual\nnon-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help.\n\nAn updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012.", "links": [ { diff --git a/datasets/law_dome_700yr_na_1.json b/datasets/law_dome_700yr_na_1.json index adb499d91b..da629da81a 100644 --- a/datasets/law_dome_700yr_na_1.json +++ b/datasets/law_dome_700yr_na_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_700yr_na_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below.", "links": [ { diff --git a/datasets/law_dome_gravity_1964_1968_1.json b/datasets/law_dome_gravity_1964_1968_1.json index c4cbb9e121..2a2ba91091 100644 --- a/datasets/law_dome_gravity_1964_1968_1.json +++ b/datasets/law_dome_gravity_1964_1968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_gravity_1964_1968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A compilation of gravity measurements taken on Law Dome from 1964-1968.\n\nThe hard copy of this document has been archived in the Australian Antarctic Division Records Store.", "links": [ { diff --git a/datasets/law_dome_gravity_1971_1.json b/datasets/law_dome_gravity_1971_1.json index 6075e4f0ea..91686e4b2a 100644 --- a/datasets/law_dome_gravity_1971_1.json +++ b/datasets/law_dome_gravity_1971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_gravity_1971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log of gravity observations made on Law Dome in 1971 and 1972.\n\nThe hard copy of this document has been archived in the Australian Antarctic Division Records Store.", "links": [ { diff --git a/datasets/law_dome_gravity_1981_1.json b/datasets/law_dome_gravity_1981_1.json index 67e6f17bd9..8bafa484d3 100644 --- a/datasets/law_dome_gravity_1981_1.json +++ b/datasets/law_dome_gravity_1981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_gravity_1981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity measurements taken on Law Dome and Wilkes Land during the spring traverse in 1981. Many readings are taken at the same location at two different times (trip out, and trip back).\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/law_dome_magnetic_1971_1.json b/datasets/law_dome_magnetic_1971_1.json index 362c92a475..adb79d8ba4 100644 --- a/datasets/law_dome_magnetic_1971_1.json +++ b/datasets/law_dome_magnetic_1971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_magnetic_1971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log of magnetic observations made on Law Dome in 1971.\n\nThe hard copy of this document has been archived in the Australian Antarctic Division Records Store.", "links": [ { diff --git a/datasets/law_dome_met_obs_1981_1.json b/datasets/law_dome_met_obs_1981_1.json index 16dbdd2cf6..bf03522331 100644 --- a/datasets/law_dome_met_obs_1981_1.json +++ b/datasets/law_dome_met_obs_1981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_met_obs_1981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Records, taken several times a day, of air pressure, air temperature, wind speed, visibility and general weather notes. Taken during the winter 1981 traverses over Law Dome and Wilkes Land.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/law_dome_wilkes_land_1984_1.json b/datasets/law_dome_wilkes_land_1984_1.json index 95d28eaf5b..5f481b0d5e 100644 --- a/datasets/law_dome_wilkes_land_1984_1.json +++ b/datasets/law_dome_wilkes_land_1984_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "law_dome_wilkes_land_1984_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw logs for snow accumulation, snow density, gravity and snow pit stratigraphy recorded during 1984 traverse season on Law Dome/Wilkes Land.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_1968_season_1.json b/datasets/lawdome_1968_season_1.json index b074d6dbcb..d1d3ff7205 100644 --- a/datasets/lawdome_1968_season_1.json +++ b/datasets/lawdome_1968_season_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_1968_season_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notes and data observations from field work out of Casey in the 1968 season. Includes data on gravity, accumulation, strain grid measurements, ice core density measurements, levelling, met obs, and echo sounding results.", "links": [ { diff --git a/datasets/lawdome_1970_1.json b/datasets/lawdome_1970_1.json index 04753ce60d..ca27dc1ae6 100644 --- a/datasets/lawdome_1970_1.json +++ b/datasets/lawdome_1970_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_1970_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log books (2) from the 1970 traverses on Law Dome, recording barometric pressure, air temperature, magnetic fields and gravity.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lawdome_1979_field_data_1.json b/datasets/lawdome_1979_field_data_1.json index 55621410f1..3e24ef5707 100644 --- a/datasets/lawdome_1979_field_data_1.json +++ b/datasets/lawdome_1979_field_data_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_1979_field_data_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of observations made during the Autumn-Spring 1979 traverses on Law Dome and Wilkes Land. Measurements include accumulation, air temperature, barometric pressure, and magnetic field strength. The data is recorded in four log books and a set of loose pages.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lawdome_1981_traverse_1.json b/datasets/lawdome_1981_traverse_1.json index 7a1baa3f50..6226f9cf5b 100644 --- a/datasets/lawdome_1981_traverse_1.json +++ b/datasets/lawdome_1981_traverse_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_1981_traverse_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log books for the traverse work carried out on Law Dome and Wilkes Land in 1981. Information recorded includes snow cane accumulation readings, barometric pressure, gravity, temperature, wind, and some oxygen isotope results.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_borehole_temp_1987_1.json b/datasets/lawdome_borehole_temp_1987_1.json index 8480403c3f..74e44e4313 100644 --- a/datasets/lawdome_borehole_temp_1987_1.json +++ b/datasets/lawdome_borehole_temp_1987_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_borehole_temp_1987_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A compilation of temperature measurements taken from ice core boreholes on Law Dome in the 1987 season. Includes detailed notes on measuring methodology, and papers on the interpretation of results from the specific equipment used to record the temperatures, as well as calibration work done.\n\nA text file of blended borehole temperature readings for the Law Dome DSS (Dome Summit South) site is available for download. A copy of the referenced publication is available to AAD staff.\n\nvan Ommen, T. D., V. I. Morgan, T. H. Jacka, S. Woon and A. Elcheikh (1999) Near-surface temperatures in the Dome Summit South (Law Dome, East Antarctica) borehole Annals of Glaciology, 29. 141-144.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lawdome_gravity_1973_74_1.json b/datasets/lawdome_gravity_1973_74_1.json index ba19f4bcd7..ca4ac834f3 100644 --- a/datasets/lawdome_gravity_1973_74_1.json +++ b/datasets/lawdome_gravity_1973_74_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1973_74_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity readings on Law Dome for the International Global Aerosol Programme (IGAP) during the 1973/1974 season.\n\nThe Casey 1973 wintering team included physicists Lyle H Supp (Arizona, USA) and Ian Lawrence McIntosh, who were possibly involved in the collection of these data. The Casey 1974 wintering team included the physicist Gregory Ross Howarth, who may also have been involved. Two geodesists from the US, DL Schneider and HL Edwards were also present, and may also have been involved.\n\nThese documents are only available in hard copy, and have been archived by the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_gravity_1975_1.json b/datasets/lawdome_gravity_1975_1.json index edc321425a..33e51e29b5 100644 --- a/datasets/lawdome_gravity_1975_1.json +++ b/datasets/lawdome_gravity_1975_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1975_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity readings taken on Law Dome during 1975.\n\nThe Casey 1975 wintering team included the geodesist from the US, Richard Joe Neff, who may have collected these data.\n\nThese documents are only available in hard copy, and have been archived by the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_gravity_1976_1.json b/datasets/lawdome_gravity_1976_1.json index 3e29b4eaf6..5e95319f7f 100644 --- a/datasets/lawdome_gravity_1976_1.json +++ b/datasets/lawdome_gravity_1976_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1976_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity measurements were taken on Law Dome during 1976 and early 1977 - this log book records those measurements.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_gravity_1978_1.json b/datasets/lawdome_gravity_1978_1.json index 91e0fc678c..74b1eb920a 100644 --- a/datasets/lawdome_gravity_1978_1.json +++ b/datasets/lawdome_gravity_1978_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1978_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Archive contains a logbook with regular gravity measurements taken several times a day at Casey from 14-Feb to 3-Mar, plus gravity measurements taken on Law Dome during the 1978 traverse season work.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_gravity_1979_1.json b/datasets/lawdome_gravity_1979_1.json index 1611a1574e..ad4bb5d29c 100644 --- a/datasets/lawdome_gravity_1979_1.json +++ b/datasets/lawdome_gravity_1979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity measurements taken during the several Law Dome/Wilkes Land traverses carried out in Autumn-Spring 1979.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lawdome_gravity_1980_1.json b/datasets/lawdome_gravity_1980_1.json index 5335945d57..5adda691c9 100644 --- a/datasets/lawdome_gravity_1980_1.json +++ b/datasets/lawdome_gravity_1980_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_gravity_1980_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity measurements taken on Law Dome during the Spring traverse in 1980.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_icecore_analysis_2013_1.json b/datasets/lawdome_icecore_analysis_2013_1.json index 7f52c0f7ab..6509b6275c 100644 --- a/datasets/lawdome_icecore_analysis_2013_1.json +++ b/datasets/lawdome_icecore_analysis_2013_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_icecore_analysis_2013_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Law Dome Summit ice cores provide a unique record of climate variability and change. These records have very high-resolution making them particularly suitable for calibration with meteorological data, and therefore improving the interpretation of ice core records more generally. A long-term data set has been obtained from previous work done on the dome. As part of an ongoing monitoring project, regular shallow cores are taken from Law Dome to ensure this record is kept up-to-date.\n\nOn the 4th of February 2013, a core was obtained from Lat -66 46'21.2\", Long 112 48' 40.8\" using a Kovac shallow ice corer. This core was analysed for:\n\n* Hydrogen Peroxide\n* Isotopic Composition\n* Trace ionic composition\n* Snow density\n\nThe results are linked to this record.", "links": [ { diff --git a/datasets/lawdome_katabatic_1984_1.json b/datasets/lawdome_katabatic_1984_1.json index 423c1e6e0c..ca6b54d510 100644 --- a/datasets/lawdome_katabatic_1984_1.json +++ b/datasets/lawdome_katabatic_1984_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_katabatic_1984_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A study was done on katabatic winds out of Casey in 1984, along the IAGP traverse route. A lot of data, including air pressure, temperature and humidity, was collected from the balloon flights used for the study.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lawdome_season_1966_1.json b/datasets/lawdome_season_1966_1.json index 9e9038a940..dd676af18c 100644 --- a/datasets/lawdome_season_1966_1.json +++ b/datasets/lawdome_season_1966_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_season_1966_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notes and data observations from field work on Law Dome in the 1966 season. Includes data on gravity, strain grid measurements, S2 studies, levelling, and angle measurements.", "links": [ { diff --git a/datasets/lawdome_season_1967_1.json b/datasets/lawdome_season_1967_1.json index 90ed09d7c6..771c286814 100644 --- a/datasets/lawdome_season_1967_1.json +++ b/datasets/lawdome_season_1967_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_season_1967_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notes and data observations from field work on Law Dome in the 1967 season. Includes data on gravity, accumulation, strain grid measurements, S2 studies, levelling, and angle measurements.", "links": [ { diff --git a/datasets/lawdome_totten_1982_1.json b/datasets/lawdome_totten_1982_1.json index 49895fed98..daac0bfacd 100644 --- a/datasets/lawdome_totten_1982_1.json +++ b/datasets/lawdome_totten_1982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_totten_1982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Logs with snow accumulation measurements, gravity, and barometric pressure during 1982 field work on Law Dome and Totten Glacier.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lawdome_traverse_logs_1986_1.json b/datasets/lawdome_traverse_logs_1986_1.json index 9fedf78872..1619aff2bc 100644 --- a/datasets/lawdome_traverse_logs_1986_1.json +++ b/datasets/lawdome_traverse_logs_1986_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lawdome_traverse_logs_1986_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of logbooks and notes from the 1986 traverses out of Casey over Law Dome and Wilkes Land. Information recorded includes accumulation, gravity, snow temperature, magnetic field, stratigraphy, barometric pressure, and meteorological observations (wind, air temperature)\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/lba_ghcn_702_1.json b/datasets/lba_ghcn_702_1.json index 250e5973ec..0b48c2ce3f 100644 --- a/datasets/lba_ghcn_702_1.json +++ b/datasets/lba_ghcn_702_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lba_ghcn_702_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1832 and the most recent from 1990. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/lba_gisswetlands_688_1.json b/datasets/lba_gisswetlands_688_1.json index f84868acca..1cfa09afb4 100644 --- a/datasets/lba_gisswetlands_688_1.json +++ b/datasets/lba_gisswetlands_688_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lba_gisswetlands_688_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of a global database compiled by Matthews and Fung (1987) on the distribution and environmental characteristics of natural wetlands. The global database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America.", "links": [ { diff --git a/datasets/lba_isric_wise_701_1.json b/datasets/lba_isric_wise_701_1.json index ec41a9d4af..f1c5654f86 100644 --- a/datasets/lba_isric_wise_701_1.json +++ b/datasets/lba_isric_wise_701_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lba_isric_wise_701_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of a subset of the ISRIC-WISE global data set of derived soil properties for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/lba_tree_cover-1km_686_1.json b/datasets/lba_tree_cover-1km_686_1.json index 1ab2f3ee43..8644021f24 100644 --- a/datasets/lba_tree_cover-1km_686_1.json +++ b/datasets/lba_tree_cover-1km_686_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lba_tree_cover-1km_686_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of the 1-km global tree cover data set (DeFries et al. 1999) developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data are in ASCII GRID format.", "links": [ { diff --git a/datasets/ldarraw_1.json b/datasets/ldarraw_1.json index 2324128175..fa05ca9761 100644 --- a/datasets/ldarraw_1.json +++ b/datasets/ldarraw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ldarraw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Lightning Detection and Ranging (LDAR) Raw data consists of level 1 lightning data collected from February 25, 1997 through June 11, 2008. The LDR system is located at the Kennedy Space Center. The center latitude and longitude of the LDAR network is 28.5387 and -80.6428. All x, y, and z values represent distance (in meters) from this location. LDAR is a volumetric lightning mapping system providing near real-time location of lightning in support of Space Shuttle operations. These data are in ASCII format.", "links": [ { diff --git a/datasets/leaf_voc_emissions_763_1.json b/datasets/leaf_voc_emissions_763_1.json index 396c233b03..8d289b50e7 100644 --- a/datasets/leaf_voc_emissions_763_1.json +++ b/datasets/leaf_voc_emissions_763_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "leaf_voc_emissions_763_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Biogenic volatile organic compounds (VOCs) comprise a significant proportion of trace gases in the atmospheric environment and play an important role in the formation of secondary air pollutants. Emissions of monoterpenes from vegetation were studied at adjacent sites in Botswana as part of the SAFARI 2000 (Southern African Regional Science Initiative). Using a LI-COR leaf cuvette, VOC emissions were measured from the dominant tree species (Colophospermum mopane) and other vegetation near Maun, Botswana.", "links": [ { diff --git a/datasets/leafchem_421_1.json b/datasets/leafchem_421_1.json index 8598e5d327..dd9f3dca3b 100644 --- a/datasets/leafchem_421_1.json +++ b/datasets/leafchem_421_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "leafchem_421_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carbon and nitrogen concentrations of fresh forest foliage were determined. Results were used to calibrate Visible/NIR reflectance for estimation of canopy carbon and nitrogen.", "links": [ { diff --git a/datasets/leafspec_424_1.json b/datasets/leafspec_424_1.json index 9a409eba82..4e5b87ad98 100644 --- a/datasets/leafspec_424_1.json +++ b/datasets/leafspec_424_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "leafspec_424_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Visible/NIR reflectance spectra data for both fresh and dry leaf samples were collected to determine the relationship of foliar chemical concentrations with reflectance.", "links": [ { diff --git a/datasets/leemans_cramer_681_1.json b/datasets/leemans_cramer_681_1.json index ed2695cdc3..b06bf55af9 100644 --- a/datasets/leemans_cramer_681_1.json +++ b/datasets/leemans_cramer_681_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "leemans_cramer_681_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Cramer and Leemans' (2001) global database of mean monthly climatology, which contains monthly averages of mean temperature, temperature range, precipitation, rain days, and sunshine hours for terrestrial areas during 1931-1960. This subset was created for the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are presented at 0.5-degree latitude/longitude resolution in ASCII GRID file format. Cramer and Leemans (2001, Version 2.1) constituted a major update of an earlier database, Leemans and Cramer (1991). The new version was generated from a larger database by means of the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983). Version 2.1 has been used widely, notably by all groups participating in the International Geosphere-Biosphere Programme's Net Primary Productivity (NPP) model intercomparison (Olsen et al. 2001).More information about the data can be found at ftp://daac.ornl.gov/data/lba/physical_climate/leemans_cramer/comp/cramer_lmns_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/legal_amazon_mask_671_1.json b/datasets/legal_amazon_mask_671_1.json index 162255e190..7732338db5 100644 --- a/datasets/legal_amazon_mask_671_1.json +++ b/datasets/legal_amazon_mask_671_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "legal_amazon_mask_671_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": " The Legal Amazon of Brazil is defined by law to include the states of Acre, Amapa, Amazonas, Para, Rondonia, Roraima, Mato Grosso, Maranhao, and Tocantins [Fundacao Instituto Brasileiro de Geografia e Estatistica (IBGE) 1991]. This is the definition used in generating the Legal Amazon mask. The 8-km Legal Amazon mask was generated by Christopher Potter at the Ecosystem Science and Technology Branch of the Earth Science Division at NASA Ames Research Center (Potter and Brooks-Genovese 1999). The mask was generated from the Digital Chart of the World available from Environmental Systems Research Institute, Inc. (ESRI). The mask is available in ASCII GRID format. The README file accompanying the mask has more information regarding data format. More information can be found at ftp://daac.ornl.gov/data/lba/human_dimensions/legal_amazon_mask/comp/legamazon_readme.pdf.", "links": [ { diff --git a/datasets/length_of_forest_edge-8_1.0.json b/datasets/length_of_forest_edge-8_1.0.json index bd6b593bfb..82de57965b 100644 --- a/datasets/length_of_forest_edge-8_1.0.json +++ b/datasets/length_of_forest_edge-8_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "length_of_forest_edge-8_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Length of the forest edge calculated on the basis of the forest boundary lines determined in the aerial photo. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/length_of_forest_roads-78_1.0.json b/datasets/length_of_forest_roads-78_1.0.json index 39c555aaa0..6f344a987a 100644 --- a/datasets/length_of_forest_roads-78_1.0.json +++ b/datasets/length_of_forest_roads-78_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "length_of_forest_roads-78_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The length of forest roads corresponds to the length of the NFI forest roads. This length was calculated according to the method of the specific NFI concerned. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/level_1_annual_co2_895_1.json b/datasets/level_1_annual_co2_895_1.json index fd0e5dae26..ab56940b6a 100644 --- a/datasets/level_1_annual_co2_895_1.json +++ b/datasets/level_1_annual_co2_895_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "level_1_annual_co2_895_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) was created to quantify and diagnose the uncertainty in inversion calculations of the global carbon budget that results from errors in simulated atmospheric transport, the choice of measured atmospheric carbon dioxide data used, and the inversion methodology employed. Under the third phase of TransCom (TransCom 3), surface-atmosphere CO2 fluxes were estimated from an intercomparison of 16 different atmospheric tracer transport models and model variants in order to assess the contribution of uncertainties in transport to the uncertainties in flux estimates for annual mean, seasonal cycle, and interannual inversions (referred to as Level 1, 2, and 3 experiments, respectively).This data set provides the model output and inversion results for the TransCom 3, Level I annual mean inversion experiments. Annual mean CO2 concentration data (GLOBALVIEW-CO2, 2000) were used to estimate CO2 sources. The annual average fluxes were estimated for the 1992-1996 period using each of the 16 transport models and a common inversion set-up (Gurney et al., 2002). Methodological choices for this control inversion were selected on the basis of knowledge gained from a wide range of sensitivity tests (Law et al., 2003). Gurney et al. (2003) present results from the control inversion for individual models as well as results from a number of sensitivity tests related to the specification of prior flux information. Additional information about the experimental protocol and results is provided in the companion files and the TransCom project web site (http://www.purdue.edu/transcom/index.php).The results of the Level 1 experiments presented here are grouped into two broad categories: forward simulation fields and response functions (model output) and estimated fluxes (inversion results).", "links": [ { diff --git a/datasets/level_2_seasonal_co2_1198_1.json b/datasets/level_2_seasonal_co2_1198_1.json index 378d7537e5..b79490e3e6 100644 --- a/datasets/level_2_seasonal_co2_1198_1.json +++ b/datasets/level_2_seasonal_co2_1198_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "level_2_seasonal_co2_1198_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides model outputs and seasonal mean CO2 fluxes from the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3), Level 2 inversion experiment. Inversion methods can be used to estimate surface CO2 fluxes from atmospheric CO2 concentration measurements, given an atmospheric transport model to relate the two. This Level 2 experiment inverted for the spatial and temporal pattern of the residual CO2 sources and sinks. There were 12 atmospheric tracer transport models utilized in this experiment. The data inverted were mean CO2 concentration data from 75 sites from the GLOBALVIEW-CO2 2000 data set for the period 1992-1996. The seasonal inversion consists of a 3 year forward simulation (365 days per year) containing 4 presubtracted tracers, 11 SF6 tracers, and 22 CO2 tracers (11 terrestrial, 11 oceanic) (Gurney et al., 2000). Carbon fluxes were estimated for each month of an average year determined as the mean of the 1990-1996 time period from an intercomparison of 12 different atmospheric tracer transport models. This data set provides input data, model output data,the cyclo inversion code, a basis function map, and estimated fluxes.", "links": [ { diff --git a/datasets/lgbt_daily_logs_1.json b/datasets/lgbt_daily_logs_1.json index f301ebb293..9b55512b9a 100644 --- a/datasets/lgbt_daily_logs_1.json +++ b/datasets/lgbt_daily_logs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lgbt_daily_logs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Glacier Basin Traverse program ran from the summer of 1989-90 to the summer of 1994-95. The aim of the program was to take multi-year measurements on the dynamics of the ice-sheet draining into the Lambert Glacier, from around the 2500m ice surface elevation contour. These measurements were then used in mass balance calculations for the whole Lambert-Amery system.\n\nDaily logs were kept for each traverse detailing activities carried out, distance travelled, and in many cases including a copy of the daily SITREP. Logs occasionally recorded other observations (weather, etc), but in most cases scientific data was recorded in other logbooks.\n\nThese logbooks have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lgbt_reports_1.json b/datasets/lgbt_reports_1.json index ccb00cefc9..bd67e0e7ca 100644 --- a/datasets/lgbt_reports_1.json +++ b/datasets/lgbt_reports_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lgbt_reports_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lambert Glacier Basin Traverse program ran from the summer of 1989-90 to the summer of 1994-95. The aim of the program was to take multi-year measurements on the dynamics of the ice-sheet draining into the Lambert Glacier, from around the 2500m ice surface elevation contour. These measurements were then used in mass balance calculations for the whole Lambert-Amery system.\n\nAnnual reports were written during this program, detailing the activities and science carried out, equipment and personnel used, travel logs, fuel consumption, and problems encountered. These reports have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/lidar-davos-wolfgang_1.0.json b/datasets/lidar-davos-wolfgang_1.0.json index 3c367a2307..c398031181 100644 --- a/datasets/lidar-davos-wolfgang_1.0.json +++ b/datasets/lidar-davos-wolfgang_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lidar-davos-wolfgang_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.", "links": [ { diff --git a/datasets/lidar-wind-profiler-data_1.0.json b/datasets/lidar-wind-profiler-data_1.0.json index 53e30e9d50..67873d5357 100644 --- a/datasets/lidar-wind-profiler-data_1.0.json +++ b/datasets/lidar-wind-profiler-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lidar-wind-profiler-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.", "links": [ { diff --git a/datasets/lidar_6.json b/datasets/lidar_6.json index a44bd489fa..edd76a3703 100644 --- a/datasets/lidar_6.json +++ b/datasets/lidar_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lidar_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The lidar profiles density, temperature, wind velocity and aerosol loading from the lower troposphere to the upper mesosphere, depending on operating mode.\n\nTwo main measurement techniques are employed. Firstly, traditional Rayleigh backscatter analysis yields temperature profiles above the top of the stratospheric aerosol layer (greater than about 27km altitude). The temperatures are obtained from lidar-derived density profiles, calibrated with in-situ radiosonde data below 40km altitude, using the standard hydrostatically-constrained perfect gas law model. When available, hydroxyl-layer temperatures obtained locally by a Czerny-Turner spectrograph are used as an upper boundary condition on the temperature retrieval algorithm. Rayleigh backscatter can be detected from altitudes as high as 100km, although useful temperatures are normally limited to below 80km. Observations of rotational-vibrational Raman backscatter from molecular oxygen or nitrogen are used to extend the temperature profiles into the lower stratosphere and upper troposphere. Profiles of aerosol-loading are derived from standard scattering-ratio analysis, allowing identification of clouds in the upper troposphere, stratosphere (Polar Stratospheric Clouds) and mesosphere (Polar Mesospheric Clouds).\n\nSecondly, spectral scans of laser backscatter are obtained with a high-resolution Fabry-Perot spectrometer. These are used to infer the line-of-sight wind speed and temperature by using the Doppler effect. Observations along 'cardinal point' lines-of-sight provide information on wind direction. In general, Doppler measurements are restricted to altitudes below about 70km based on signal detection considerations. Some information on aerosol loading is obtained from analysis of the spectral properties of the backscatter.\n\nThe lidar is capable of both day and night measurements covering a large altitude range, and in so doing will provide information for the study of climate change and a range of atmospheric phenomena on a variety of spatial and temporal scales.\n \nTaken from the 2008-2009 Progress Report:\nProgress against objectives:\nAt Davis, lidar measurements of temperature and aerosol properties were acquired for the troposphere, stratosphere and mesosphere. Additionally, ozone data were acquired for the troposphere and lower stratosphere.\n\nOngoing analyses of these data is providing new information on the composition, dynamics and climate of the polar atmosphere. During the reporting period, continued progress was achieved in international collaborative studies of Polar Stratospheric Cloud microphysics as part of the International Polar Year, and measurements of Polar Mesospheric Clouds for the Aeronomy of Ice in the Mesosphere (AIM) satellite mission. Both of these activities contribute to all 4 goals of the project. \n\nTaken from the 2009-2010 Progress Report:\nProgress against objectives:\nNew data were obtained for the study of the long-term climate in the Antarctic middle atmosphere (5-95km altitude), and atmospheric phenomena under extreme physical conditions. The highlights were: (1) Detailed measurements of ice clouds in the summer mesopause region for validation of climate models. (2) Further measurements of the properties and dynamics of Polar Stratospheric Clouds for research aimed at improving projections of the recovery of the Ozone Hole. (3) Initial measurements for a new study of the interactions between the troposphere and stratosphere which is aimed at improved knowledge of climate processes in the tropopause region.", "links": [ { diff --git a/datasets/lidar_forest_myotis-myotis_1.0.json b/datasets/lidar_forest_myotis-myotis_1.0.json index 69395cb63c..d08f45d419 100644 --- a/datasets/lidar_forest_myotis-myotis_1.0.json +++ b/datasets/lidar_forest_myotis-myotis_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lidar_forest_myotis-myotis_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.", "links": [ { diff --git a/datasets/lima.json b/datasets/lima.json index 46c81b6e63..81af6b2bf1 100644 --- a/datasets/lima.json +++ b/datasets/lima.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lima", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A team of scientists from the U.S. Geological Survey, the British Antarctic Survey, and the National Aeronautics and Space Administration, with funding from the National Science Foundation, created LIMA in support of the International Polar Year (IPY; 2007\u201308).\n", "links": [ { diff --git a/datasets/linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0.json b/datasets/linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0.json index 6d4f2623e5..c4998ce831 100644 --- a/datasets/linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0.json +++ b/datasets/linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes synchronized and independently measured water discharge, bedload transport and at-a-point bedrock erosion data in 1 minute resolution and over more than 1.5 years from the Erlenbach stream hydrological observatory, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. These measurements are of high accuracy, which have been assessed in Beer, A.R. et al. 2015. Earth Surf. Proc., 40, 530-541. doi: 10.1002/esp.3652. For the artificial bedrock (a slab of weak concrete, fixed flush with the streambed) 6 additional consecutive spatial elevation data sets of 1 mm resolution have been surveyed that allow the local continuous erosion measurements to be extended to the patch scale. This unique data set has been used to validate and calibrate bedrock erosion models for the process to intermediate scales of time (and space), whose performance then was assessed over extended time (up to bicentennial floods), based on available longer data sets of linked discharge and bedload transport (see related datasets).", "links": [ { diff --git a/datasets/lipimpacts_2.json b/datasets/lipimpacts_2.json index f1af3bae6f..d330f78923 100644 --- a/datasets/lipimpacts_2.json +++ b/datasets/lipimpacts_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lipimpacts_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lightning Instrument Package (LIP) IMPACTS dataset consists of electrical field measurements of lightning and navigation data collected by the Lightning Instrument Package (LIP) flown onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast (2020-2023). IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The V2 LIP IMPACTS data have been further filtered to remove field mill offsets that were identified in the prior V1 data. These data are available from January 15, 2020, through March 2, 2023, in ASCII format. ", "links": [ { diff --git a/datasets/lislipG_4.json b/datasets/lislipG_4.json index 713ef67401..880e5ba411 100644 --- a/datasets/lislipG_4.json +++ b/datasets/lislipG_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lislipG_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lightning Imaging Sensor (LIS) Backgrounds was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth\u2019s tropical and subtropical regions. This data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. These data are available in both HDF-4 and netCDF-4 formats.", "links": [ { diff --git a/datasets/lislip_4.json b/datasets/lislip_4.json index f47633d06a..9e6e0e9fd3 100644 --- a/datasets/lislip_4.json +++ b/datasets/lislip_4.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lislip_4", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Lightning Imaging Sensor (LIS) Science Data was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth\u2019s tropical and subtropical regions. This data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. These data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format. ", "links": [ { diff --git a/datasets/lisvhrac_1.json b/datasets/lisvhrac_1.json index 08ebc03a06..f6d2878ee9 100644 --- a/datasets/lisvhrac_1.json +++ b/datasets/lisvhrac_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lisvhrac_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Annual Climatology (VHRAC) dataset consists of gridded annual climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.", "links": [ { diff --git a/datasets/lisvhrdc_1.json b/datasets/lisvhrdc_1.json index 68db7fc621..5206845c8d 100644 --- a/datasets/lisvhrdc_1.json +++ b/datasets/lisvhrdc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lisvhrdc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Diurnal Climatology (VHRDC) dataset consists of gridded diurnal climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.", "links": [ { diff --git a/datasets/lisvhrfc_1.json b/datasets/lisvhrfc_1.json index 25bee7d1ab..34b99ff786 100644 --- a/datasets/lisvhrfc_1.json +++ b/datasets/lisvhrfc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lisvhrfc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Full Climatology (VHRFC) dataset consists of gridded full climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.", "links": [ { diff --git a/datasets/lisvhrmc_1.json b/datasets/lisvhrmc_1.json index 5211c524e9..7859446cfc 100644 --- a/datasets/lisvhrmc_1.json +++ b/datasets/lisvhrmc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lisvhrmc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Monthly Climatology (VHRMC) dataset consists of gridded monthly climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.", "links": [ { diff --git a/datasets/lisvhrsc_1.json b/datasets/lisvhrsc_1.json index 32e202c56f..a1d6408e4d 100644 --- a/datasets/lisvhrsc_1.json +++ b/datasets/lisvhrsc_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lisvhrsc_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Seasonal Climatology (VHRSC) dataset consists of gridded seasonal climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.", "links": [ { diff --git a/datasets/literature-data-of-sound-speed-in-snow_1.0.json b/datasets/literature-data-of-sound-speed-in-snow_1.0.json index 8f4632fad4..7e8d7c0bf6 100644 --- a/datasets/literature-data-of-sound-speed-in-snow_1.0.json +++ b/datasets/literature-data-of-sound-speed-in-snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "literature-data-of-sound-speed-in-snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains literature data for snow density and frequency dependency of speed of sound waves in snow. The data were either available as tabular data in the original publications or were digitized from plots contained in the original publications. The data were originally collected and used for first figure in Capelli et al. (2016) .", "links": [ { diff --git a/datasets/litter_decomp_651_1.json b/datasets/litter_decomp_651_1.json index 5870346ee9..1c25f0a2a1 100644 --- a/datasets/litter_decomp_651_1.json +++ b/datasets/litter_decomp_651_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "litter_decomp_651_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide.", "links": [ { diff --git a/datasets/lohrac_2.3.2015.json b/datasets/lohrac_2.3.2015.json index fb39a53089..e2a542f77a 100644 --- a/datasets/lohrac_2.3.2015.json +++ b/datasets/lohrac_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lohrac_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 0.5 Degree High Resolution Annual Climatology (HRAC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The HRAC dataset includes annual flash rate climatology data on a 0.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lohrfc_2.3.2015.json b/datasets/lohrfc_2.3.2015.json index 174857b070..234d2c1861 100644 --- a/datasets/lohrfc_2.3.2015.json +++ b/datasets/lohrfc_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lohrfc_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 0.5 Degree High Resolution Full Climatology (HRFC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite.The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The HRFC dataset include flash rate climatology data including raw and scaled flash on a 0.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lohrmc_2.3.2015.json b/datasets/lohrmc_2.3.2015.json index 64708ab53e..6235b0995c 100644 --- a/datasets/lohrmc_2.3.2015.json +++ b/datasets/lohrmc_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lohrmc_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 0.5 Degree High Resolution Monthly Climatology (HRMC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The HRMC dataset include monthly flash rate climatology and flash rate seasonal climatology data on a 0.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lolrac_2.3.2015.json b/datasets/lolrac_2.3.2015.json index 619c67e3d5..e18aa94d43 100644 --- a/datasets/lolrac_2.3.2015.json +++ b/datasets/lolrac_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolrac_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Annual Climatology (LRAC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRAC dataset include annual flash rate climatology data including raw and scaled flashes on a 2.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lolracts_2.3.2015.json b/datasets/lolracts_2.3.2015.json index 889b26d5d6..e8f06b9d48 100644 --- a/datasets/lolracts_2.3.2015.json +++ b/datasets/lolracts_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolracts_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Annual Climatology Time Series (LRACTS) consists of gridded climatologies of total lightning flash rates seen by the spaceborne Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS). The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRACTS dataset include annual flash rate time series data in MP4 format.", "links": [ { diff --git a/datasets/lolradc_2.3.2015.json b/datasets/lolradc_2.3.2015.json index 532af5593d..7b044532cf 100644 --- a/datasets/lolradc_2.3.2015.json +++ b/datasets/lolradc_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolradc_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Annual Dirunal Climatology (LRADC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRADC dataset include flash rate climatology data including scaled flash counts on a 2.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lolrdc_2.3.2015.json b/datasets/lolrdc_2.3.2015.json index d10a4ce043..3eb54a1ad0 100644 --- a/datasets/lolrdc_2.3.2015.json +++ b/datasets/lolrdc_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolrdc_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Diurnal Climatology (LRDC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRDC dataset include diurnal flash rate climatology data including raw and scaled flashes on a 2.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lolrfc_2.3.2015.json b/datasets/lolrfc_2.3.2015.json index cdf510541b..00105b87aa 100644 --- a/datasets/lolrfc_2.3.2015.json +++ b/datasets/lolrfc_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolrfc_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Full Climatology (LRFC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRFC dataset include flash rate climatology data including raw and scaled flashes on a 2.5 degree grid in HDF and netCDF-4 format.", "links": [ { diff --git a/datasets/lolrmts_2.3.2015.json b/datasets/lolrmts_2.3.2015.json index 8e3b190611..7aba4d3f71 100644 --- a/datasets/lolrmts_2.3.2015.json +++ b/datasets/lolrmts_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolrmts_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Monthly Climatology Time Series (LRMTS) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRMTS dataset include monthly flash rate time series data in MP4 format.", "links": [ { diff --git a/datasets/lolrts_2.3.2015.json b/datasets/lolrts_2.3.2015.json index fb419ac445..ab8781c5dc 100644 --- a/datasets/lolrts_2.3.2015.json +++ b/datasets/lolrts_2.3.2015.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lolrts_2.3.2015", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The LIS/OTD 2.5 Degree Low Resolution Time Series (LRTS) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRTS dataset include flash rate time series data in MP4 format.", "links": [ { diff --git a/datasets/long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0.json b/datasets/long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0.json index 37c7ee8cab..fba24e7454 100644 --- a/datasets/long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0.json +++ b/datasets/long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data, on which the following publication below is based. __Paper Citation:__ _Resch, M.C., Sch\u00fctz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_ __Please cite this paper together with the citation for the datafile.__ Study area and experimental design The study was conducted in and around two nature reserves, Eigental and Altl\u00e4ufe der Glatt, which were located approximately 5 km apart (47\u00b027\u00b4 to 47\u00b029\u00b4 N, 8\u00b037\u00b4 to 8\u00b032\u00b4 E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 \u00b1 0.6 \u00b0C and 990 \u00b1 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots). Collection of plants and selected soil biota data Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 \u00b0C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 \u03bcL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 \u00b0C for 3 h). Thereafter, 180 \u03bcL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 \u00b0C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 \u00b0C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 \u03bcL of genomic DNA template (ca. 1 ng/\u03bcL) in 25 \u03bcL reactions containing 5 \u03bcL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 \u03bcM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 \u03bcL BSA, and 0.25 \u03bcL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 \u00b0C for 2 min, followed by 35 cycles at 94 \u00b0C for 40 sec, 58 \u00b0C for 40 sec, 72 \u00b0C for 1 min, and a final elongation step at 72 \u00b0C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units. We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 \u00b0C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer\u00b4s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings \u2265 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level. Collection of soil physical and chemical properties We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 \u00b0C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (\u2264 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.", "links": [ { diff --git a/datasets/long_tryne_bathy_1.json b/datasets/long_tryne_bathy_1.json index cb2bc18306..8237fd7b06 100644 --- a/datasets/long_tryne_bathy_1.json +++ b/datasets/long_tryne_bathy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "long_tryne_bathy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is the result of the interpolation of bathymetry from depth measurements made in Long and Tryne Fjords in the Vestfold Hills, Antarctica (see Entry: VH_bathy_99). The Topogrid command within the ArcInfo GIS software, version 8.0.2, was used to do the interpolation. Coastline and spot height (heights above sea level) data, extracted from the Australian Antarctic Data Centre's Vestfold Hills topographic GIS dataset (see Entry: vest_hills_gis), was also used as input data to optimise the interpolation close to the coastline. See related URLs for a map showing the interpolated bathymetry.", "links": [ { diff --git a/datasets/longterm-hydrological-observatory-alptal-central-switzerland_2.0.json b/datasets/longterm-hydrological-observatory-alptal-central-switzerland_2.0.json index 5839b8445a..6840d544cc 100644 --- a/datasets/longterm-hydrological-observatory-alptal-central-switzerland_2.0.json +++ b/datasets/longterm-hydrological-observatory-alptal-central-switzerland_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "longterm-hydrological-observatory-alptal-central-switzerland_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes 54 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in sub-daily resolution can be provided on demand. Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and L\u00fcmpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal . Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited. Meteorological variables have been measured on a meadow (Erlenh\u00f6he) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and L\u00fcmpenenbach catchments). Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenh\u00f6he, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in St\u00e4hli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058. Further information on the methods and sensors can be found at https://www.wsl.ch/alptal . A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. A second update of the data set (with data from 2020 to 2022) was uploaded in March 2023.", "links": [ { diff --git a/datasets/lsa_forest_snow_1.0.json b/datasets/lsa_forest_snow_1.0.json index e522449e43..57f0ee93cd 100644 --- a/datasets/lsa_forest_snow_1.0.json +++ b/datasets/lsa_forest_snow_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lsa_forest_snow_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper \"Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements\", by Malle et al. (2021, under review). This publication must be cited when using the data. Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankyl\u00e4). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.", "links": [ { diff --git a/datasets/lsatmssd_435_1.json b/datasets/lsatmssd_435_1.json index 7d0965f84f..abeede7525 100644 --- a/datasets/lsatmssd_435_1.json +++ b/datasets/lsatmssd_435_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lsatmssd_435_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988. ", "links": [ { diff --git a/datasets/ltm_ii3a_280_1.json b/datasets/ltm_ii3a_280_1.json index 4f2e328d7f..46387229ca 100644 --- a/datasets/ltm_ii3a_280_1.json +++ b/datasets/ltm_ii3a_280_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltm_ii3a_280_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For BOREAS, the level-3A Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as fPAR and LAI. Geographically, the level-3a images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996. The images are available in binary, image-format files.", "links": [ { diff --git a/datasets/ltm_ii3b_425_1.json b/datasets/ltm_ii3b_425_1.json index 15db207c3d..c1bf1a97f0 100644 --- a/datasets/ltm_ii3b_425_1.json +++ b/datasets/ltm_ii3b_425_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltm_ii3b_425_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Geographically, the level-3b images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996.", "links": [ { diff --git a/datasets/ltm_ii3p_426_1.json b/datasets/ltm_ii3p_426_1.json index 4bdaffd7e5..5dd08cfcd2 100644 --- a/datasets/ltm_ii3p_426_1.json +++ b/datasets/ltm_ii3p_426_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltm_ii3p_426_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For BOREAS, the level-3p Landsat TM data were used to supplement the level-3s Landsat TM products. Along with the other remotely sensed images, the Landsat TM images were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Geographically, the level-3p images cover the BOREAS NSA and SSA. Temporally, the four images cover the period of 20-Aug-1988 to 07-Jun-1994. Except for the 07-Jun-1994 image which contains 7 bands, the other three only contain 3 bands.", "links": [ { diff --git a/datasets/ltm_ii3s_427_1.json b/datasets/ltm_ii3s_427_1.json index 26006e078a..6ff5f16683 100644 --- a/datasets/ltm_ii3s_427_1.json +++ b/datasets/ltm_ii3s_427_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltm_ii3s_427_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For BOREAS, the level-3s Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Geographically, the bulk of the level-3s images cover the BOREAS NSA and SSA with a few images covering the area between the NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996.", "links": [ { diff --git a/datasets/ltmmaxln_429_1.json b/datasets/ltmmaxln_429_1.json index 7d945b83d9..fa0016092a 100644 --- a/datasets/ltmmaxln_429_1.json +++ b/datasets/ltmmaxln_429_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltmmaxln_429_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification.", "links": [ { diff --git a/datasets/ltmmaxls_430_1.json b/datasets/ltmmaxls_430_1.json index 331f5443fb..313fe93eb3 100644 --- a/datasets/ltmmaxls_430_1.json +++ b/datasets/ltmmaxls_430_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltmmaxls_430_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification, the objective of which is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood classification approach was used to produce this classification.", "links": [ { diff --git a/datasets/ltmphysn_431_1.json b/datasets/ltmphysn_431_1.json index 498a7619fd..9819c6d199 100644 --- a/datasets/ltmphysn_431_1.json +++ b/datasets/ltmphysn_431_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltmphysn_431_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes.", "links": [ { diff --git a/datasets/ltmphyss_432_1.json b/datasets/ltmphyss_432_1.json index f33aab02af..4a97e14342 100644 --- a/datasets/ltmphyss_432_1.json +++ b/datasets/ltmphyss_432_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ltmphyss_432_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.", "links": [ { diff --git a/datasets/luszoning_1.0.json b/datasets/luszoning_1.0.json index b7150bd850..c84ab8c71c 100644 --- a/datasets/luszoning_1.0.json +++ b/datasets/luszoning_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "luszoning_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Table of Content: 1. General context of the data set \"LUSzoning\u201d; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning. ###1. __General context of the data set \"LUSzoning\".__ The data set \"LUSzoning\" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level. ###2.\t__Background and aims of the study using the data set \u201cLUSzoning\u201d.__ As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations. Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention. ###3.\t __The data set \u201cLUSzoning\u201d.__ The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named \u201cZaragoza_S2.tif\u201d. Furthermore, a .txt file named \u201cLegend.txt\u201d includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.", "links": [ { diff --git a/datasets/lutzow_holm_bay_bathy_1.json b/datasets/lutzow_holm_bay_bathy_1.json index b68f173611..86186211f9 100644 --- a/datasets/lutzow_holm_bay_bathy_1.json +++ b/datasets/lutzow_holm_bay_bathy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lutzow_holm_bay_bathy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The soundings were digitized from bathymetric chart:\nBathymetry of Lutzow-Holm Bukta (Lutzow-Holm Bay) by the Japanese, National Institute of Polar Research (NIPR) from Special Map Series of National Institute of Polar Research No. 4b, 2002 - map number 12852 in the SCAR map catalogue.\n\nThese data have been created by the Japanese, but as such no metadata record for the data exists in the Japanese portal of the Antarctic Master Directory. Australian users of these data should use this metadata record (providing credit to the Japanese), until a Japanese version has been created.", "links": [ { diff --git a/datasets/lwf-alptal-long-term-research-site_1.0.json b/datasets/lwf-alptal-long-term-research-site_1.0.json index 5dae02617e..ed29d9c838 100644 --- a/datasets/lwf-alptal-long-term-research-site_1.0.json +++ b/datasets/lwf-alptal-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-alptal-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg \"LWF Alptal\") LWF plot Alptal - Community: Alpthal / canton SZ - Date of installation: 31 May 1995 - Size of the plot: 0.6 ha - Altitude: 1149-1170 m - Mean slope: 23% - Geology (in German): Nordpenninikum; obere Kreide-unteres Eoz\u00e4n, W\u00e4gitaler Flysch - Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols - Woodland association after EK72: 49: Equiseto-Abietetum - Main tree species: Picea abies - Management system: high forest - Silvicultural system: selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm - Number of trees BHD >= 12 cm (2011): 321 - Maximum tree age: Picea abies 180-230 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html", "links": [ { diff --git a/datasets/lwf-beatenberg-long-term-research-site_1.0.json b/datasets/lwf-beatenberg-long-term-research-site_1.0.json index a04aca3871..c762f714db 100644 --- a/datasets/lwf-beatenberg-long-term-research-site_1.0.json +++ b/datasets/lwf-beatenberg-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-beatenberg-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/7310b935-757f-4f27-b202-9f433c9882ab/resource/555b1a19-aff3-40be-9d6d-fea9967d5691/download/lwf_beatenberg.jpg \"LWF Beatenberg\") LWF plot Beatenberg - Community: Beatenberg / canton BE - Date of installation: 25 September 1996 - Size of the plot: 2 ha - Altitude: 1490-1532 m - Mean slope: 66% - Geology (in German): Helvetikum, Terti\u00e4r, Eoz\u00e4n; Hohgantsandstein - Provisional soil type (WSL) : Gleyic Podzols - Woodland association after EK72: 57: Sphagno-Piceetum calamagrostietosum villosae - Main tree species: Picea abies - Management system: high forest - Silvicultural system: selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 46.4 cm - Number of trees BHD >= 12 cm (2011): 851 - Maximum tree age: Picea abies 190-210 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/beatenberg.html", "links": [ { diff --git a/datasets/lwf-bettlachstock-long-term-research-site_1.0.json b/datasets/lwf-bettlachstock-long-term-research-site_1.0.json index 15a6ddc4ec..485f7815f1 100644 --- a/datasets/lwf-bettlachstock-long-term-research-site_1.0.json +++ b/datasets/lwf-bettlachstock-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-bettlachstock-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/8f67193b-12cf-4871-9a9e-750b816e9d10/resource/b05db334-5bf6-42d6-a985-a5253366259d/download/lwf_bettlachstock.jpg \"LWF Bettlachstock\") LWF Plot Bettlachstock - Community: Bettlachstock / canton SO - Date of installation: 6 June 1995 - Size of the plot: 1.28 ha - Altitude: 1101-1196 m - Mean slope: 66% - Geology (in German): Kettenjura; Jura: Dogger, oberer Hauptrogenstein - Soil types (WSL) : Rendzic Leptosols; Calcaric Cambisols - Woodland association after EK72: 13 h: Cardamino-Fagetum tilietosum - Main tree species: Fagus sylvatica - Management system: high forest - Silvicultural system: forest reserve - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 49.5 cm - Number of trees BHD >= 12 cm (2011): 632 - Maximum tree age: Fagus sylvatica 170-190 yr - Picea abies 200 yr - Fraxinus excelsior 170 yr - Ulmus glabra 160 yr - Abies alba 190 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/bettlachstock.html", "links": [ { diff --git a/datasets/lwf-celerina-long-term-research-site_1.0.json b/datasets/lwf-celerina-long-term-research-site_1.0.json index dd95ed9949..0e7ba095e7 100644 --- a/datasets/lwf-celerina-long-term-research-site_1.0.json +++ b/datasets/lwf-celerina-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-celerina-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/0c244fe7-886a-4a1c-add5-5f706e995a29/resource/28e97caa-65fc-4396-8bca-860721eddfa2/download/lwf_celerina.jpg \"LWF Celerina\") LWF Plot Celerina - Community: Celerina / canton GR - Date of installation: 3 July 1996 - Size of the plot: 2 ha - Altitude (m): 1846-1896 - Mean slope: 34% - Geology (in German): Untergrund: ostalpin; pr\u00e4triadische Tiefengesteine - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie Mor\u00e4ne - Soil types (WSL): n.d. - Woodland association after EK72: 59: Larici-Pinetum cembrae - Main tree species: Pinus cembra - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 48.6 cm - Number of trees BHD >= 12 cm (2011): 469 - Maximum tree age: Pinus cembra uneven-aged - 210-250 years More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/celerina.html", "links": [ { diff --git a/datasets/lwf-chironico-long-term-research-site_1.0.json b/datasets/lwf-chironico-long-term-research-site_1.0.json index 6db2f11e56..0642c6f8b0 100644 --- a/datasets/lwf-chironico-long-term-research-site_1.0.json +++ b/datasets/lwf-chironico-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-chironico-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg \"LWF Chironico\") LWF Plot Chironico - Community: Chironico / canton TI - Date of installation: 29 August 1995 - Size of the plot: 2 ha - Altitude: 1342-1387 m - Mean slope: 35% - Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie Mor\u00e4ne, H\u00e4ngeschutt - Provisional soil type (WSL) : Distric Cambisol - Woodland association after EK72: 47: Calamagrostio villosae-Abietetum - Main tree species: Picea abies - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm - Number of trees BHD >= 12 cm (2011): 750 - Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/chironico.html", "links": [ { diff --git a/datasets/lwf-isone-long-term-research-site_1.0.json b/datasets/lwf-isone-long-term-research-site_1.0.json index cadf64d04c..d507e7a030 100644 --- a/datasets/lwf-isone-long-term-research-site_1.0.json +++ b/datasets/lwf-isone-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-isone-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg \"LWF Isone\") LWF Plot Isone - Community: Isone / canton TI - Date of installation: 5 September 1995 - Size of the plot: 2 ha - Altitude (m): 1181-1259 - Mean slope: 58% - Geology (in German): Untergrund: S\u00fcdalpin, pr\u00e4permisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfl\u00e4che: Quart\u00e4r; Mor\u00e4ne, H\u00e4ngeschutt-. - Provisional soil type (WSL) : Humic Cambisol - Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum - Main tree species: Fagus sylvatica - Management system: former coppice - Silvicultural system: unmanaged - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm - Number of trees BHD >= 12 cm (2011): 1254 - Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html", "links": [ { diff --git a/datasets/lwf-jussy-long-term-research-site_1.0.json b/datasets/lwf-jussy-long-term-research-site_1.0.json index 4334f0ba01..d9d710448a 100644 --- a/datasets/lwf-jussy-long-term-research-site_1.0.json +++ b/datasets/lwf-jussy-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-jussy-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/465d852d-af24-415f-9db2-48fe31d6dc20/resource/a39a0e35-efdf-4bef-bab1-4b0532442b34/download/lwf_jussy.jpg \"LWF Jussy\") LWF Plot Jussy - Community: Jussy / canton GE - Date of installation: 31 May 1995 - Size of the plot: 1.99 ha - Altitude: 496-506 m - Mean slope: 3% - Geology (in German): Quart\u00e4r; tonreiche w\u00fcrmeiszeitliche Grundmor\u00e4ne - Soil types (WSL) : Stagnic Luvisols - Woodland association after EK72: 35: Galio silvatici-Carpinetum - Main tree species: Quercus species - Management system: former coppices w. standards - Silvicultural system: unmanaged / group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 36.6 cm - Number of trees BHD >= 12 cm (2011): 1278 - Maximum tree age: Carpinus betulus 60 yr - Populus tremula 60 yr - Quercus petrea 90 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/jussy.html", "links": [ { diff --git a/datasets/lwf-lageren-long-term-research-site_1.0.json b/datasets/lwf-lageren-long-term-research-site_1.0.json index cdc69ce692..d37b59b47a 100644 --- a/datasets/lwf-lageren-long-term-research-site_1.0.json +++ b/datasets/lwf-lageren-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-lageren-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg \"LWF L\u00e4geren\") This research site is located on the southern slope of the L\u00e4gern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks: * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel)) * [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html)) * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/) The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)). LWF Plot L\u00e4geren - Community: Wettingen / canton AG - Date of installation: 1.05.2012 - Size of the plot: 1.34 ha - Altitude: 643 - 718 m - Mean slope: 37 % - Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt - Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina - Woodland association after Ellenberg and Kl\u00f6tzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum - Main tree species: fagus sylvatica - Management system: high forest - Silvicultural system: forest reserve - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm - Number of trees BHD >= 12 cm (2011): 503 - Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years", "links": [ { diff --git a/datasets/lwf-lantsch-long-term-research-site_1.0.json b/datasets/lwf-lantsch-long-term-research-site_1.0.json index b6e5b130b9..375a52787e 100644 --- a/datasets/lwf-lantsch-long-term-research-site_1.0.json +++ b/datasets/lwf-lantsch-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-lantsch-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/9e5d6c82-26ac-4818-9458-1ac7c8574bb0/resource/c26169dc-db58-4e69-bc74-9513dbf7bccc/download/lwf_lantsch.jpg \"LWF Lantsch\") LWF Plot Lantsch - Community: Lantsch / canton GR - Date of installation: 15 September 1997 - Size of the plot: n.d. - Altitude: 1458-1490 m - Mean slope: 16% - Geology (in German): Ostalpin. Geh\u00e4ngeschutt aus mesozoischen Schiefern, Dolomiten und Kalken - Soil types (WSL) : n.d. - Woodland association after EK72: 65: Erico-Pinetum silvestris - Main tree species: Picea abies - Management system: high forest - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 40.1 cm - Number of trees BHD >= 12 cm (2011): 709 - Maximum tree age: n.d. More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lantsch.html", "links": [ { diff --git a/datasets/lwf-lausanne-long-term-research-site_1.0.json b/datasets/lwf-lausanne-long-term-research-site_1.0.json index 0d55262c3b..e90d9191af 100644 --- a/datasets/lwf-lausanne-long-term-research-site_1.0.json +++ b/datasets/lwf-lausanne-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-lausanne-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/9f35f697-a226-4719-aefd-b8c96fe5ae7c/resource/d6a48c12-62e6-4c04-9f63-a84d5964bc55/download/lwf_lausanne.jpg \"LWF Lausanne\") LWF Plot Lausanne - Community: Lausanne / canton VD - Date of installation: 5 September 1994 - Size of the plot: 2 ha - Altitude: 800-814 m - Mean slope: 7% - Geology (in German): Untergrund: Terti\u00e4r, Mioz\u00e4n, Burdigalien, obere Meeresmolasse; Sandstein - Oberfl\u00e4che: Quart\u00e4r, W\u00fcrm; w\u00fcrmeiszeitliche Mor\u00e4ne - Soil types (WSL) : Distric Cambisols - Woodland association after EK72: 8: Milio-Fagetum - Main tree species: Fagus sylvatica - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 59.9 cm - Number of trees BHD >= 12 cm (2011): 650 - Maximum tree age: Abies alba 160-170 yr - Picea abies 160-170 yr - Fagus sylvatica 160-170 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lausanne.html", "links": [ { diff --git a/datasets/lwf-lens-long-term-research-site_1.0.json b/datasets/lwf-lens-long-term-research-site_1.0.json index 1ae6b8d6c0..bddde657dc 100644 --- a/datasets/lwf-lens-long-term-research-site_1.0.json +++ b/datasets/lwf-lens-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-lens-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/87d3ea5a-8d50-446b-9c33-cbe56057f7d3/resource/bf459948-da5a-4b05-9372-83ca5454aeca/download/lwf_lens.jpg \"LWF Lens\") LWF Plot Lens - Community: Lens / canton VS - Date of installation: 15 March 1996 - Size of the plot: 2 ha - Altitude: 1033-1093 m - Mean slope: 75% - Geology (in German): Untergrund: Penninikum, Ferret-Zone, Trias; sandiger Kalkstein - Oberfl\u00e4che: H\u00e4ngeschutt - Provisional soil type (WSL): Calcaric Cambisol - Woodland association after EK72: +- 64: Cytiso-Pinetum silvestris - Main tree species: Pinus sylvestris - Management system: high forest - Silvicultural system: unmanaged - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 31.8 cm - Number of trees BHD >= 12 cm (2011): 2304 - Maximum tree age:150-170 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lens.html", "links": [ { diff --git a/datasets/lwf-nationalpark-long-term-research-site_1.0.json b/datasets/lwf-nationalpark-long-term-research-site_1.0.json index 3c4eb1c293..abac0cd0a2 100644 --- a/datasets/lwf-nationalpark-long-term-research-site_1.0.json +++ b/datasets/lwf-nationalpark-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-nationalpark-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/4a0ff376-83d2-4ae4-bf2e-c361eb050778/resource/51e98f2d-5524-4ea6-babb-9df0b9aba8f1/download/lwf_nationalpark.jpg \"LWF Nationalpark\") LWF Plot Nationalpark - Community: Zernez / canton GR - Date of installation: 10 October 1995 - Size of the plot: 2 ha - Altitude: 1890-1907 m - Mean slope: 11% - Geology (in German): Nacheiszeitlicher Schwemmf\u00e4cher; kalkhaltige Mor\u00e4ne, Dolomite, Kalke, Tonschiefer, Rauhwacken - Provisional soil types (WSL): Rendzic Leptosol - Woodland association after EK72: 67: Erico-Pinetum montanae - Main tree species: Pinus mugo - Management system: high forest - Silvicultural system: forest reserve - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 23.7 cm - Number of trees BHD >= 12 cm (2011): 2450 - Maximum tree age: Pinus mugo 210 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/nationalpark.html", "links": [ { diff --git a/datasets/lwf-neunkirch-long-term-research-site_1.0.json b/datasets/lwf-neunkirch-long-term-research-site_1.0.json index 06daa45aac..8e465af452 100644 --- a/datasets/lwf-neunkirch-long-term-research-site_1.0.json +++ b/datasets/lwf-neunkirch-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-neunkirch-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg \"LWF Neunkirch\") LWF Plot Neunkirch - Community: Neunkirch / canton SH - Date of installation: 14 July 1995 - Size of the plot: 2 ha - Altitude (m): 554-609 - Mean slope: 58% - Geology (in German): Tafeljura, oberer Malmkalk; Malmh\u00e4ngeschutt - Soil types (WSL) : Rendzic Leptosols - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum - Main tree species: Fagus sylvatica - Management system: former coppices w. standards - Silvicultural system: reserve - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm - Number of trees BHD >= 12 cm (2011): 442 - Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html", "links": [ { diff --git a/datasets/lwf-novaggio-long-term-research-site_1.0.json b/datasets/lwf-novaggio-long-term-research-site_1.0.json index 657a7f01bc..9c3afe9920 100644 --- a/datasets/lwf-novaggio-long-term-research-site_1.0.json +++ b/datasets/lwf-novaggio-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-novaggio-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/e87358e9-4beb-487e-af68-66633e9cfc96/resource/91bd053a-61c4-4b20-b0d3-a572c983e647/download/lwf_novaggio.jpg \"LWF Novaggio\") LWF Plot Novaggio - Community: Novaggio / canton TI - Date of installation: 8.3.95 - Size of the plot: 1.5 ha - Altitude (m): 902-997 - Mean slope: 68% - Geology (in German): Untergrund: S\u00fcdalpin, pr\u00e4permisches Grundgebirge; Orthogneis, schiefriger Biotitplagioklasgneis - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie w\u00fcrmeiszeitliche Mor\u00e4ne - Provisional soil type (WSL): Kryptopodzole - Woodland association after EK72: 42: Phyteumo betonicifoliae-Quercetum castanosum - Main tree species: Quercus cerris - Management system: former coppice - Silvicultural system: unmanaged - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 27.0 cm - Number of trees BHD >= 12 cm (2011): 1130 - Maximum tree age: Castanea sativa 90 yr- Betula pendula 70 yr - Quercus cerris 70 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/novaggio.html", "links": [ { diff --git a/datasets/lwf-othmarsingen-long-term-research-site_1.0.json b/datasets/lwf-othmarsingen-long-term-research-site_1.0.json index 9eaa2c516d..7df4bd61fe 100644 --- a/datasets/lwf-othmarsingen-long-term-research-site_1.0.json +++ b/datasets/lwf-othmarsingen-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-othmarsingen-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/ebb37e73-1280-4a06-81d8-e18bc5d9c1cf/resource/64291bfd-37d4-4f88-b505-81fc85a109c2/download/lwf_othmarsingen.jpg \"LWF Othmarsingen\") LWF Plot Othmarsingen - Community: Othmarsingen / canton AG - Date of installation: 9 September 1994 - Size of the plot: 1 ha - Altitude (m): 467-500 - Mean slope: 27% - Soil types (WSL): Stagnic Luvisols, Haplic Luvisols - Woodland association after EK72: 7: Galio odorati-Fagetum typicum - Main tree species: Fagus sylvatica - Management system: former coppices w. standards - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 62.8 cm - Number of trees BHD >= 12 cm (2011): 167 - Maximum tree age: Fagus sylvatica 120-140 yr - Tilia sp. 120-140 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/othmarsingen.html", "links": [ { diff --git a/datasets/lwf-pfynwald-long-term-experimental-irrigation-site_1.0.json b/datasets/lwf-pfynwald-long-term-experimental-irrigation-site_1.0.json index c90910d9d4..e13f0aaa6d 100644 --- a/datasets/lwf-pfynwald-long-term-experimental-irrigation-site_1.0.json +++ b/datasets/lwf-pfynwald-long-term-experimental-irrigation-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-pfynwald-long-term-experimental-irrigation-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg \"LWF experimental irrigation site Pfynwald\") As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46\u00b0 18' N, 7\u00b0 36' E, 615 m ASL) offers the best conditions for such measurements. In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest. The average temperature here is 9.2\u00b0C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions.", "links": [ { diff --git a/datasets/lwf-schanis-long-term-research-site_1.0.json b/datasets/lwf-schanis-long-term-research-site_1.0.json index 4192b87dbf..f729445618 100644 --- a/datasets/lwf-schanis-long-term-research-site_1.0.json +++ b/datasets/lwf-schanis-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-schanis-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg \"LWF Sch\u00e4nis\") LWF Plot Sch\u00e4nis - Community: Sch\u00e4nis / canton SG - Date of installation: 17 September 1997 - Size of the plot: 2 ha - Altitude: 693-773 m - Mean slope: 60% - Geology (in German): Terti\u00e4r. Subalpine Molasse, Oligocaen, Chattien, Kalknagelfluh - Soil types (WSL) : n.d. - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum - Main tree species: Fagus sylvatica - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 55.8 cm - Number of trees BHD >= 12 cm (2011): 611 - Maximum tree age: Abies alba130-150 yr - Fraxinus excelsior 130-150 yr - Fagus sylvatica 130-150 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/schaenis.html", "links": [ { diff --git a/datasets/lwf-seehornwald-davos-long-term-research-site_1.0.json b/datasets/lwf-seehornwald-davos-long-term-research-site_1.0.json index 7c915f9d32..dd335b211a 100644 --- a/datasets/lwf-seehornwald-davos-long-term-research-site_1.0.json +++ b/datasets/lwf-seehornwald-davos-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-seehornwald-davos-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/801cdd7e-f5b0-4998-bb8a-bd6d2ae8baa2/resource/f2ef5505-e0ea-493d-8b86-4f27dd556da8/download/lwf_davos.jpg \"LWF Davos\") This research site is located on the Seehorn mountain near Davos within a managed subalpine coniferous forest in the Swiss Alps. Seehronwald Davos site is dedicated to forest ecosystem research with current projects focusing on topics of climate change, ecosystem carbon balance, ecophysiology, vegetation and soil sciences. The site belongs to one of the best equipped long-term forest ecology research sites of the world. Time series of climate variables, ecosystem gas exchange (eddy covariance), tree physiology records (sap flow, stem radius changes), and air pollution data cover the history of this site over more than 20 years. Records of local climate variables started in 1876. Since 2013 the site is part of [ICOS](https://www.icos-cp.eu), which awarded the infrastructure the CLASS 1 label on 21 November 2019. The site is part of the following national and international networks and encourages further synergistic collaborations with scientists from all over the world: * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel)) * ICOS Switzerland ([ICOS-CH](https://www.icos-switzerland.ch/davos)) * [TreeNet](https://treenet.info/switzerland/davos): The biological drought and growth indicator network * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/davos.html)) * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav) * Ecosystem Research ([ExpeER](http://www.expeeronline.eu/43-expeer-ta-sites/131-davos-seehornwald-switzerland.html)) * Long Term Ecological Research ([LTER](https://www.lter-europe.net)) * [ICP Forests](http://icp-forests.net): the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the Swiss Federal Institute of Technology Zurich ([ETHZ](https://www.gl.ethz.ch)), and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)) in Birmensdorf and Davos. The infrastructure is provided by the Federal Office of Environment ([FOEN](https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html)). All partners are grateful to forest owners and to the forestry service of the community of Davos for their continuous support. LWF Plot Davos - Community: Davos / canton GR - Date of installation: 15.06.2006 - Size of the plot: 0.6 ha - Altitude: : 1635-1665 - Geology (in German): Untergrund: - Oberfl\u00e4che: - Provisional soil type (WSL): - Woodland association after EK72: 58: Larici-Piceetum - Main tree species: Picea abies - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 47.0 cm - Number of trees BHD >= 12 cm (2006): 498 - Maximum tree age: Picea abies: 200 - 390 yr", "links": [ { diff --git a/datasets/lwf-tea-bag-sites_1.0.json b/datasets/lwf-tea-bag-sites_1.0.json index e70ad96948..15bfe9fd09 100644 --- a/datasets/lwf-tea-bag-sites_1.0.json +++ b/datasets/lwf-tea-bag-sites_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-tea-bag-sites_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter. The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months. The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.", "links": [ { diff --git a/datasets/lwf-visp-long-term-research-site_1.0.json b/datasets/lwf-visp-long-term-research-site_1.0.json index abfb14c3df..7df0dedd63 100644 --- a/datasets/lwf-visp-long-term-research-site_1.0.json +++ b/datasets/lwf-visp-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-visp-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg \"LWF Visp\") LWF Plot Visp - Community: Visp / canton VS - Date of installation: 13 March 1996 - Size of the plot: 2 ha - Altitude: 657-733 m - Mean slope: 80% - Geology (in German): Penninikum, Jura, B\u00fcndnerschiefer; Kalkphyllite, H\u00e4ngeschutt - Provisional soil type (WSL): Calcaric Cambisol - Woodland association after EK72: =~= 38: Arabidi turritae-Quercetum pubescentis - Main tree species: Pinus sylvestris - Management system: high forest - Silvicultural system: unmanaged - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm - Number of trees BHD >= 12 cm (2011): 650 - Maximum tree age: Pinus sylvestris uneven-aged 40-80 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/visp.html", "links": [ { diff --git a/datasets/lwf-vordemwald-long-term-research-site_1.0.json b/datasets/lwf-vordemwald-long-term-research-site_1.0.json index ad6e531e06..5d6beb0753 100644 --- a/datasets/lwf-vordemwald-long-term-research-site_1.0.json +++ b/datasets/lwf-vordemwald-long-term-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwf-vordemwald-long-term-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/bffc768d-e3b2-41b2-9b5f-3c1ecbc3ce74/resource/e129ebc8-ef61-4e19-8e89-e18d591ed8c5/download/lwf_vordemwald.jpg \"LWF Vordemwald\") LWF Plot Vordemwald - Community: Vordemwald / canton AG - Date of installation: 18 August 1995 - Size of the plot: 2 ha - Altitude: 473-487 m - Mean slope: 14% - Geology (in German): Untergrund: Oligoz\u00e4n, Aquitanien, untere S\u00fcsswassermolasse, bunte Mergel - Oberfl\u00e4che: Rissgrundmor\u00e4ne - Soil types (WSL): Distric Gleysols - Woodland association after EK72: 46: Bazzanio-Abietetum - Main tree species: Abies alba - Management system: high forest - Silvicultural system: group selection - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm - Number of trees BHD >= 12 cm (2011): 1084 - Maximum tree age: Abies alba 110 yr - Quercus sp. 190-210 yr More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/vordemwald.html", "links": [ { diff --git a/datasets/lwfmeteo-alpthal_1.0.json b/datasets/lwfmeteo-alpthal_1.0.json index 5396e61085..6c6e10a627 100644 --- a/datasets/lwfmeteo-alpthal_1.0.json +++ b/datasets/lwfmeteo-alpthal_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-alpthal_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-beatenberg_1.0.json b/datasets/lwfmeteo-beatenberg_1.0.json index 1cd92041bb..84020013fb 100644 --- a/datasets/lwfmeteo-beatenberg_1.0.json +++ b/datasets/lwfmeteo-beatenberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-beatenberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-bettlachstock_1.0.json b/datasets/lwfmeteo-bettlachstock_1.0.json index 0936d1f87b..9b41eb8d3e 100644 --- a/datasets/lwfmeteo-bettlachstock_1.0.json +++ b/datasets/lwfmeteo-bettlachstock_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-bettlachstock_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-celerina_1.0.json b/datasets/lwfmeteo-celerina_1.0.json index a6866d1e41..6ad509c193 100644 --- a/datasets/lwfmeteo-celerina_1.0.json +++ b/datasets/lwfmeteo-celerina_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-celerina_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-chironico_1.0.json b/datasets/lwfmeteo-chironico_1.0.json index 06779e3b2c..3a5fe53d19 100644 --- a/datasets/lwfmeteo-chironico_1.0.json +++ b/datasets/lwfmeteo-chironico_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-chironico_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-isone_1.0.json b/datasets/lwfmeteo-isone_1.0.json index c9e05e9311..c98bedfa22 100644 --- a/datasets/lwfmeteo-isone_1.0.json +++ b/datasets/lwfmeteo-isone_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-isone_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-jussy_1.0.json b/datasets/lwfmeteo-jussy_1.0.json index 3a28d71c17..309357ecee 100644 --- a/datasets/lwfmeteo-jussy_1.0.json +++ b/datasets/lwfmeteo-jussy_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-jussy_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-lausanne_1.0.json b/datasets/lwfmeteo-lausanne_1.0.json index b3eeb0271e..c3ffe9b274 100644 --- a/datasets/lwfmeteo-lausanne_1.0.json +++ b/datasets/lwfmeteo-lausanne_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-lausanne_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-lens_1.0.json b/datasets/lwfmeteo-lens_1.0.json index fa54383315..f1c151fb1b 100644 --- a/datasets/lwfmeteo-lens_1.0.json +++ b/datasets/lwfmeteo-lens_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-lens_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-nationalpark_1.0.json b/datasets/lwfmeteo-nationalpark_1.0.json index 4ec35ee2da..bcbf31aa5c 100644 --- a/datasets/lwfmeteo-nationalpark_1.0.json +++ b/datasets/lwfmeteo-nationalpark_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-nationalpark_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-neunkirch_1.0.json b/datasets/lwfmeteo-neunkirch_1.0.json index 4091c78d1a..69b335d2d9 100644 --- a/datasets/lwfmeteo-neunkirch_1.0.json +++ b/datasets/lwfmeteo-neunkirch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-neunkirch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-novaggio_1.0.json b/datasets/lwfmeteo-novaggio_1.0.json index cfb67fb42e..c4595fb057 100644 --- a/datasets/lwfmeteo-novaggio_1.0.json +++ b/datasets/lwfmeteo-novaggio_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-novaggio_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-othmarsingen_1.0.json b/datasets/lwfmeteo-othmarsingen_1.0.json index 1640d7678c..1af6fa16ba 100644 --- a/datasets/lwfmeteo-othmarsingen_1.0.json +++ b/datasets/lwfmeteo-othmarsingen_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-othmarsingen_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-schaenis_1.0.json b/datasets/lwfmeteo-schaenis_1.0.json index 16f0d47359..5851b95663 100644 --- a/datasets/lwfmeteo-schaenis_1.0.json +++ b/datasets/lwfmeteo-schaenis_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-schaenis_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Sch\u00e4nis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Sch\u00e4nis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-visp_1.0.json b/datasets/lwfmeteo-visp_1.0.json index 80c43bb14a..3843ed4048 100644 --- a/datasets/lwfmeteo-visp_1.0.json +++ b/datasets/lwfmeteo-visp_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-visp_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/lwfmeteo-vordemwald_1.0.json b/datasets/lwfmeteo-vordemwald_1.0.json index fe8a782958..a7661e9414 100644 --- a/datasets/lwfmeteo-vordemwald_1.0.json +++ b/datasets/lwfmeteo-vordemwald_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "lwfmeteo-vordemwald_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.", "links": [ { diff --git a/datasets/mac_isl_sat_1.json b/datasets/mac_isl_sat_1.json index 774ccab630..a0f8f80cc1 100644 --- a/datasets/mac_isl_sat_1.json +++ b/datasets/mac_isl_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mac_isl_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geo-referenced satellite images of Macquarie Island. These images were produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia. The images are at a scale of 1:200 000, and were produced from SPOT3 Pan and XS scenes. It is projected on a Transverse Mercator projection. An interim image map has been produced at a scale of 1:50 000. The details which follow relate to this interim map.", "links": [ { diff --git a/datasets/macca_SPOT3_georef_1.json b/datasets/macca_SPOT3_georef_1.json index c0cd63b027..6c72326468 100644 --- a/datasets/macca_SPOT3_georef_1.json +++ b/datasets/macca_SPOT3_georef_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_SPOT3_georef_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes a georeferenced satellite image of Macquarie Island captured on December 22, 1994 by the French SPOT satellite.\n\nThe download file contains the following files:\n\nmacquarie_island.bil\nmacquarie_island.hdr\nProcessing_information.txt\nReadme.txt\nschema.ini\n\nThe image was produced for the Australian Antarctic Division by \n\nAUSLIG Commercial (now known as Geoscience Australia), in Australia. The image was produced from SPOT3 Pan and XS scenes.\n\nIt is projected on a UTM, zone 57 projection.", "links": [ { diff --git a/datasets/macca_aeronautical_gis_2.json b/datasets/macca_aeronautical_gis_2.json index 2f65778592..54335086b7 100644 --- a/datasets/macca_aeronautical_gis_2.json +++ b/datasets/macca_aeronautical_gis_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_aeronautical_gis_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Helicopter landing sites on Macquarie Island.\nThis is a point dataset in the Geographical Information System (GIS).\n\nThis dataset is a March, 2010 revision.", "links": [ { diff --git a/datasets/macca_coast_ambis_gis_1.json b/datasets/macca_coast_ambis_gis_1.json index da99fe1ab3..9e214e635b 100644 --- a/datasets/macca_coast_ambis_gis_1.json +++ b/datasets/macca_coast_ambis_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_coast_ambis_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the coastline of Macquarie Island and adjacent islands in WGS84 lat/long coordinates.\nThe coastline was constructed from a variety of sources including SPOT satellite imagery, the Royal Australian Navy's AUS604 hydrographic chart, and photogrammetry using aerial photographs.", "links": [ { diff --git a/datasets/macca_contour_gis_1.json b/datasets/macca_contour_gis_1.json index 7441636cc9..f4c2e2dec7 100644 --- a/datasets/macca_contour_gis_1.json +++ b/datasets/macca_contour_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_contour_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contours on Macquarie Island.\nContour interval 5 metres.\nHeights are referenced to mean sea level.\nThis is a line dataset stored in the Geographical Information System (GIS).", "links": [ { diff --git a/datasets/macca_dem_gis_2.json b/datasets/macca_dem_gis_2.json index 073e5c7b88..6df812d3f1 100644 --- a/datasets/macca_dem_gis_2.json +++ b/datasets/macca_dem_gis_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_dem_gis_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A digital elevation Model (DEM) of Macquarie Island with a five metre grid interval, and held in UTM Zone 57 , WGS 84 coordinates. Heights are referenced to mean sea level. 5 metre contours to the 5 metre level were derived.\n\nThis metadata record is related to the metadata record Macquarie Island and adjacent islands 1:50000 Coastline GIS Dataset. \n\nThe DEM was improved by Dr Michael Roach, Lecturer in Geophysics, School of Earth Sciences - CODES, University of Tasmania, Hobart, Tasmania, Australia and a copy provided to the Australian Antarctic Data Centre in January 2006. This improved version is the dataset available for download.\n\nDetails about the processing done by Dr Roach are available from the link below.", "links": [ { diff --git a/datasets/macca_flyingbirds_gis_1.json b/datasets/macca_flyingbirds_gis_1.json index d62d045fa3..848ab6b9f5 100644 --- a/datasets/macca_flyingbirds_gis_1.json +++ b/datasets/macca_flyingbirds_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_flyingbirds_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Flying bird breeding colonies on Macquarie Island.\nThis is a polygon dataset stored in the Geographical Information System (GIS).\nAttributes include the species name and the time of the year during which breeding occurs.\nThe species include Black-browed Albatross, Grey-headed albatross, Southern Giant-Petrel and Wandering Albatross.", "links": [ { diff --git a/datasets/macca_geographic_data_gis_1.json b/datasets/macca_geographic_data_gis_1.json index 5418db0877..032102e6f6 100644 --- a/datasets/macca_geographic_data_gis_1.json +++ b/datasets/macca_geographic_data_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_geographic_data_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is comprised of data that contributes to the Australian Antarctic Data Centre's geographic data of Macquarie Island but is not part of a larger dataset. Data sources include GPS surveys, sketches on maps and advice from Australian Antarctic Division and Tasmanian Parks and Wildlife Service personnel. Data in this dataset has Dataset ID 81 and is included in the the Australian Antarctic Data Centre's geographic data of Macquarie Island available for download (see Related URLs below). The original data provided to the Australian Antarctic Data Centre may also be available for download (see Related URLs below).", "links": [ { diff --git a/datasets/macca_penguins_gis_1.json b/datasets/macca_penguins_gis_1.json index eea467e81b..c4420eca99 100644 --- a/datasets/macca_penguins_gis_1.json +++ b/datasets/macca_penguins_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_penguins_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a polygon GIS dataset representing penguin colonies on Macquarie Island.\nThe penguin species include Gentoo, King, Rockhopper and Royal.", "links": [ { diff --git a/datasets/macca_seals_gis_1.json b/datasets/macca_seals_gis_1.json index ba46ad1285..ed06dc45a9 100644 --- a/datasets/macca_seals_gis_1.json +++ b/datasets/macca_seals_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macca_seals_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Seal colonies on Macquarie Island.\nThis is a polygon dataset stored in the Geographical Information System (GIS).\nAttributes include the species name and whether breeding occurs within the area represented.\nThe species include Southern Elephant and Fur.", "links": [ { diff --git a/datasets/macquarie_aws_1.json b/datasets/macquarie_aws_1.json index eb871d09cb..1aee8d3770 100644 --- a/datasets/macquarie_aws_1.json +++ b/datasets/macquarie_aws_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_aws_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The automatic weather stations at the Australian stations (Casey, Davis, Macquarie Island, and Mawson) were installed by the Bureau of Meteorology. They collect information on the following (in the following units):\n\ndate\nTimehh:mm\nwind speed knots\nwind direction degrees\nair temperature degrees celsius\nrelative humidity percent\nair pressurehPa\n\nTimes are in UT.\n\nMeasurements are made at 4 metres.", "links": [ { diff --git a/datasets/macquarie_heli_zone_1.json b/datasets/macquarie_heli_zone_1.json index 41a0a0891f..cde5359cf9 100644 --- a/datasets/macquarie_heli_zone_1.json +++ b/datasets/macquarie_heli_zone_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_heli_zone_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Macquarie Island Helicopter Exclusion Zone was created in January 2005 in consultation with Peter Cusick, Parks and Wildlife Service, Tasmania. The zone was created by buffering the coastline by 1 km on the seaward side of the island, generally following the escarpment on the interior of the island and buffering the refuges by 200 m to create an approximately 400 m wide corridor to the refuges. Access corridors were also created at the station. The Australian Antarctic Data Centre's topographic data representing coastline, escarpment and refuges was used. In March 2007 the zone was modifed in consultation with Terry Reid, Parks and Wildlife Service, Tasmania. The corridors to the refuges were extended through to the escarpment. The Helicopter Exclusion Zone is shown in a map of the island (see link below).", "links": [ { diff --git a/datasets/macquarie_quickbird_mapping_1.json b/datasets/macquarie_quickbird_mapping_1.json index 32cf38d410..e04c9d2a36 100644 --- a/datasets/macquarie_quickbird_mapping_1.json +++ b/datasets/macquarie_quickbird_mapping_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_quickbird_mapping_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Features of a northwest part of Macquarie Island mapped from mosaiced pan sharpened Quickbird satellite imagery derived from Quickbird satellite imagery captured on 25 February 2003.\nThe mapped features are coastline, walking tracks and the edge of vegetation.", "links": [ { diff --git a/datasets/macquarie_sma_gis_1.json b/datasets/macquarie_sma_gis_1.json index 96d7d33ff0..d6482bad91 100644 --- a/datasets/macquarie_sma_gis_1.json +++ b/datasets/macquarie_sma_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_sma_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Macquarie Island Nature Reserve Special Management Areas were originally defined for 2003/04 and have since been updated. \nSpecial Management areas are declared from year to year to protect vulnerable species, vegetation communities or sites extremely vulnerable to human disturbance.\nRelated URLs provide:\n1 the download of a shapefile with the boundaries of the Special Management Areas; and\n2 a link to the website of Parks and Wildlife Service, Tasmania with information about the Special Management Areas.", "links": [ { diff --git a/datasets/macquarie_taspaws_grid_1.json b/datasets/macquarie_taspaws_grid_1.json index cca3b85487..00187b5332 100644 --- a/datasets/macquarie_taspaws_grid_1.json +++ b/datasets/macquarie_taspaws_grid_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_taspaws_grid_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania.\nThe grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. \nThe dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document \"The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.\"", "links": [ { diff --git a/datasets/macquarie_tracks_1.json b/datasets/macquarie_tracks_1.json index 2fc13bc7a2..9435096055 100644 --- a/datasets/macquarie_tracks_1.json +++ b/datasets/macquarie_tracks_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "macquarie_tracks_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset represents walking tracks on Macquarie Island and was compiled by the Australian Antarctic Data Centre from surveys and other sources. \nThis data is displayed in a pair of A3 1:50000 maps of Macquarie Island (see a Related URL).", "links": [ { diff --git a/datasets/madagascar_diatoms.json b/datasets/madagascar_diatoms.json index cddd2d0c94..77bee89b04 100644 --- a/datasets/madagascar_diatoms.json +++ b/datasets/madagascar_diatoms.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "madagascar_diatoms", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast.\n\nThis dataset of diatoms has been collected at three stations in Toliara Bay, and it currently consists of 2754 records of 19 families.", "links": [ { diff --git a/datasets/madagascar_dinoflagelles.json b/datasets/madagascar_dinoflagelles.json index cbce97bd87..ed2ff31cee 100644 --- a/datasets/madagascar_dinoflagelles.json +++ b/datasets/madagascar_dinoflagelles.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "madagascar_dinoflagelles", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast.\n\nThis dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families.", "links": [ { diff --git a/datasets/madagascar_fish.json b/datasets/madagascar_fish.json index 7ed1908634..4dc90c5be6 100644 --- a/datasets/madagascar_fish.json +++ b/datasets/madagascar_fish.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "madagascar_fish", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast.\n\nThis dataset has been collected in Toliara Bay, and includes bony fish, cartilagenous fish, mammals and reptiles. It currently consists of 721 records of 49 families.", "links": [ { diff --git a/datasets/madagascar_invertebrates.json b/datasets/madagascar_invertebrates.json index 2652196dbc..7da455503c 100644 --- a/datasets/madagascar_invertebrates.json +++ b/datasets/madagascar_invertebrates.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "madagascar_invertebrates", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast.\n\nThis dataset has been collected in Toliara Bay, and includes mollusks, echinoderms, crustaceans, sponges and annelids. It currently consists of 230 records of 7 phylums.", "links": [ { diff --git a/datasets/madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0.json b/datasets/madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0.json index 1208d80f5c..f2ba24c5b2 100644 --- a/datasets/madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0.json +++ b/datasets/madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species). The dataset was used for the paper Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122\u20131133. The excel file contains 5 sheets: 1) Plot information 2) Bryophyte data with species specific information, separated per substrate 3) Macrolichen data with species specific information, separated per substrate 4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly. 5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.", "links": [ { diff --git a/datasets/magnetic_domec_1977_1.json b/datasets/magnetic_domec_1977_1.json index 199e4a3728..67d1a66795 100644 --- a/datasets/magnetic_domec_1977_1.json +++ b/datasets/magnetic_domec_1977_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "magnetic_domec_1977_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Magnetic readings taken along the Russian traverse from Pioneerskaya to Dome C in 1977 and 1978.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/manual-measuring-network_1.0.json b/datasets/manual-measuring-network_1.0.json index 0d978a945e..d47c2b1684 100644 --- a/datasets/manual-measuring-network_1.0.json +++ b/datasets/manual-measuring-network_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "manual-measuring-network_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SLF avalanche warning service operates an extensive network of manual measuring sites. The sites are distributed throughout the Swiss Alps and predominantly situated in intermediate altitude zones, between 1000 and 2000 m. Some of the measurement series already span very long periods and are therefore highly valued; the data are also used for climatological and hydrological purposes. The measuring sites are in fixed locations, which are flat and wind-protected. The observers who perform the measurements are trained and paid by the SLF. Data is collected, as far as possible, from the beginning of November until the end of April and after that until half of the measuring site is snow-free. On some measuring sites event-based measurements are also collected during the summer months. If possible, measurements take place between 7 and 7.30 am local time. The following variables are measured at all measuring sites: - snow depth and 24-hour new snow at numerous sites this additional variable is measured: - water equivalent of 24-hour new snow (height of the water column in millimeters, if the new snow sample is melted, without changing the base area) __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__ __To download live data use our [API](https://measurement-api.slf.ch)__. __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.", "links": [ { diff --git a/datasets/mapss_modis_aerosol_814_1.json b/datasets/mapss_modis_aerosol_814_1.json index 35018f88b8..1b483a6ced 100644 --- a/datasets/mapss_modis_aerosol_814_1.json +++ b/datasets/mapss_modis_aerosol_814_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mapss_modis_aerosol_814_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS (Moderate Resolution Imaging Spectroradiometer) Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD04_L2 aerosol product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 26, 2000, through December 31, 2001. The process of generating the statistics involves identifying these locations on the MODIS MOD04_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following variables: cloud fraction (land); cloud fraction (ocean); particle effective radius (ocean); optical depth (land and ocean); optical depth (land, corrected); optical depth (ocean, effective average); and optical depth ratio (small ocean). ", "links": [ { diff --git a/datasets/mapss_modis_watervapor_815_1.json b/datasets/mapss_modis_watervapor_815_1.json index dcfc6c73ba..9b60c60207 100644 --- a/datasets/mapss_modis_watervapor_815_1.json +++ b/datasets/mapss_modis_watervapor_815_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mapss_modis_watervapor_815_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS (Moderate Resolution Imaging Spectroradiometer) Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD05_L2 water vapor product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 24, 2000, through March 4, 2002. The process of generating the statistics involves identifying these locations on the MODIS MOD05_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data product consists of column water-vapor amounts. During the daytime, a near-infrared algorithm is applied over clear land areas of the globe and above clouds over both land and ocean. Over clear ocean areas, water-vapor estimates are provided over the extended glint area. An infrared algorithm for deriving atmospheric profiles is also applied both day and night for Level 2. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following two variables: total column precipitable water vapor (infrared retrieved) and total column precipitable water vapor (near-infrared retrieved).", "links": [ { diff --git a/datasets/marine-fish-occurrences-of-tropical-america_1.0.json b/datasets/marine-fish-occurrences-of-tropical-america_1.0.json index 1b2f8c8084..bf5db4ef58 100644 --- a/datasets/marine-fish-occurrences-of-tropical-america_1.0.json +++ b/datasets/marine-fish-occurrences-of-tropical-america_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "marine-fish-occurrences-of-tropical-america_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "combined and cleaned occurrences of marine fish species of the Greater Caribbean and Tropical East Pacific. Data were obtain from the following sources in 2019/2020: https://gbif.org https://idigbio.org https://biogeodb.stri.si.edu/sftep/en/pages https://biogeodb.stri.si.edu/caribbean/en/pages", "links": [ { diff --git a/datasets/marine_mammal_obs_1.json b/datasets/marine_mammal_obs_1.json index ca532ae04d..094e8aa230 100644 --- a/datasets/marine_mammal_obs_1.json +++ b/datasets/marine_mammal_obs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "marine_mammal_obs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dataset of marine mammal observations made in the Southern Ocean from late 1998 to early 2000.\n\nFurther information about the data are included in a word document in the download.\n\nThe data are held in excel spreadsheets. The word document mentioned above lists the column headings for the excel spreadsheets.\n\nThe fields in this dataset are:\n\ndate\ntime\nspecies\nNumber of animals\nDistance\nBearing\nHeading\nInitial Cue\nBehaviour\nLatitude\nLongitude\nEffort status\nNotes\nWind speed\nWind direction\nActual wind speed\nActual wind direction\nSea State\nCloud cover\nVisibility\nBoat speed\nBoat course\nSpeed made good\nCourse made good\nTemperature\nWave Height\nWeather\nDepth\nSwell height\nMore notes", "links": [ { diff --git a/datasets/marsii94_407_1.json b/datasets/marsii94_407_1.json index 0178373613..5b301c28a7 100644 --- a/datasets/marsii94_407_1.json +++ b/datasets/marsii94_407_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "marsii94_407_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Meteorological Automatic Reporting System II autostations.", "links": [ { diff --git a/datasets/mas_lv2_561_1.json b/datasets/mas_lv2_561_1.json index 7f2c46cb0f..56e3d997db 100644 --- a/datasets/mas_lv2_561_1.json +++ b/datasets/mas_lv2_561_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mas_lv2_561_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.", "links": [ { diff --git a/datasets/masccpex_1.json b/datasets/masccpex_1.json index 70637c968b..f84081eba6 100644 --- a/datasets/masccpex_1.json +++ b/datasets/masccpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "masccpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Microwave Atmospheric Sounder on Cubesat (MASC) CPEX dataset contains products obtained from the MASC instrument onboard the DC-8 aircraft. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 27, 2017 through June 21, 2017 and are available in HDF-5 format.", "links": [ { diff --git a/datasets/maslv1b_560_1.json b/datasets/maslv1b_560_1.json index abb4d430a4..8021d55054 100644 --- a/datasets/maslv1b_560_1.json +++ b/datasets/maslv1b_560_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "maslv1b_560_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).", "links": [ { diff --git a/datasets/mass_of_merchantable_branches_of_live_trees-47_1.0.json b/datasets/mass_of_merchantable_branches_of_live_trees-47_1.0.json index 96453d6510..192b57da06 100644 --- a/datasets/mass_of_merchantable_branches_of_live_trees-47_1.0.json +++ b/datasets/mass_of_merchantable_branches_of_live_trees-47_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mass_of_merchantable_branches_of_live_trees-47_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of branches with a diameter of at least 7 cm from living trees and shrubs starting at 12cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/mass_of_needles_or_leaves_of_live_trees-49_1.0.json b/datasets/mass_of_needles_or_leaves_of_live_trees-49_1.0.json index d0cf435aa6..9f97509fe6 100644 --- a/datasets/mass_of_needles_or_leaves_of_live_trees-49_1.0.json +++ b/datasets/mass_of_needles_or_leaves_of_live_trees-49_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mass_of_needles_or_leaves_of_live_trees-49_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of the needles and leaves of the living trees and shrubs starting at 12 cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/massimo_1.0.json b/datasets/massimo_1.0.json index 961bd89e3f..ad84878863 100644 --- a/datasets/massimo_1.0.json +++ b/datasets/massimo_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "massimo_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development. ![alt text](https://www.envidat.ch/dataset/8fd996d1-aa7e-41b1-ae6d-1192582c62cc/resource/a12e2cfd-da45-4faf-8291-446c5763ac3c/download/massimo2__swissforlab.png)", "links": [ { diff --git a/datasets/maun_met_flux_760_1.json b/datasets/maun_met_flux_760_1.json index 924bd91bfc..e3f593a98c 100644 --- a/datasets/maun_met_flux_760_1.json +++ b/datasets/maun_met_flux_760_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "maun_met_flux_760_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To investigate potential contributions of savanna ecosystems to the Earth's carbon balance, an eddy covariance system was used to measure the seasonal variation in carbon dioxide, water vapor, and energy flux at the Maun micrometerological tower site in a broadleaf semi-arid savanna in Southern Africa, approximately 20 km east of Maun in northeastern Botswana.", "links": [ { diff --git a/datasets/mawfair1_gis_1.json b/datasets/mawfair1_gis_1.json index beddee335f..70350b1fdb 100644 --- a/datasets/mawfair1_gis_1.json +++ b/datasets/mawfair1_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawfair1_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Mawson, hand digitised from HI 99 V5/425 6762/1 (sheet A) scale 1:10 000.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID mawsonbathy_gis.", "links": [ { diff --git a/datasets/mawfair2_gis_1.json b/datasets/mawfair2_gis_1.json index a29adad937..54047acaf2 100644 --- a/datasets/mawfair2_gis_1.json +++ b/datasets/mawfair2_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawfair2_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Mawson Station. This fair sheet, HI 99 V5/425 6762/ scale 1:25 000, was hand digitised to capture soundings as point data.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID mawsonbathy_gis.", "links": [ { diff --git a/datasets/mawfair3_gis_1.json b/datasets/mawfair3_gis_1.json index d8071e96b0..77a304d339 100644 --- a/datasets/mawfair3_gis_1.json +++ b/datasets/mawfair3_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawfair3_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Royal Australian Navy soundings of approaches to Mawson, hand digitised from HI 169 V5/516 6762/3 (sheet 2) scale 1:10 000.\nThe data are not suitable for navigation.\n\nBathymetric contours derived from these and other soundings are available from the metadata record with ID mawsonbathy_gis.", "links": [ { diff --git a/datasets/mawson_aws_1.json b/datasets/mawson_aws_1.json index cf5a5ebb63..12ef52882d 100644 --- a/datasets/mawson_aws_1.json +++ b/datasets/mawson_aws_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_aws_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The automatic weather stations at the Australian stations (Casey, Davis, Macquarie Island, and Mawson) were installed by the Bureau of Meteorology. They collect information on the following (in the following units):\n\ndate\nTimehh:mm\nwind speed knots\nwind direction degrees\nair temperature degrees celsius\nrelative humidity percent\nair pressurehPa\n\nTimes are in UT.\n\nMeasurements are made at 4 metres.\n\nThe fields in this dataset are:\ndate\ntime(hh:mm)\nwind speed (knots)\nwind direction (degrees)\nair temperature (degrees celsius)\nrelative humidity (percent)\nair pressure(hPa)", "links": [ { diff --git a/datasets/mawson_coast_sat_1.json b/datasets/mawson_coast_sat_1.json index 0dbf9d6907..2517efee3b 100644 --- a/datasets/mawson_coast_sat_1.json +++ b/datasets/mawson_coast_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_coast_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Mawson Coast, Mac. Robertson Land, Antarctica. This map is part (a) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500 000, and was produced from Landsat TM, Landsat MSS and SPOT scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot tracks, glaciers/ice shelves, penguin colonies, stations/bases, and refuges/depots.\n\nThe map only has geographical co-ordinates.", "links": [ { diff --git a/datasets/mawson_glaciology_logs_1.json b/datasets/mawson_glaciology_logs_1.json index 0b2fdf5dfd..a4b73021af 100644 --- a/datasets/mawson_glaciology_logs_1.json +++ b/datasets/mawson_glaciology_logs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_glaciology_logs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Logs and journals kept of activities carried out at Mawson station. All books archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/mawson_gravity_1989_1.json b/datasets/mawson_gravity_1989_1.json index d320de4389..12598c6065 100644 --- a/datasets/mawson_gravity_1989_1.json +++ b/datasets/mawson_gravity_1989_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_gravity_1989_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gravity measurements taken at Mawson, and nearby sites around Mawson, in 1989.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/mawson_north_sat_1.json b/datasets/mawson_north_sat_1.json index d950b5c13e..3c79e76447 100644 --- a/datasets/mawson_north_sat_1.json +++ b/datasets/mawson_north_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_north_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of the northern end of the Mawson Escarpment, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1995. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 127-112). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/mawson_south_sat_1.json b/datasets/mawson_south_sat_1.json index 68ee940a8c..6d28c02bea 100644 --- a/datasets/mawson_south_sat_1.json +++ b/datasets/mawson_south_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawson_south_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Mawson Escarpment south, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (Now Geoscience Australia) Commercial, in Australia, in 1995. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 128-112, 124-112). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/mawsonbathy_gis_1.json b/datasets/mawsonbathy_gis_1.json index c1ef0aef0b..ba1cda6ed6 100644 --- a/datasets/mawsonbathy_gis_1.json +++ b/datasets/mawsonbathy_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mawsonbathy_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetric contours and height range polygons of approaches to Mawson Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings.", "links": [ { diff --git a/datasets/mbs_wilhelm_msa_hooh_1.json b/datasets/mbs_wilhelm_msa_hooh_1.json index 783e56bbea..1d63e223d6 100644 --- a/datasets/mbs_wilhelm_msa_hooh_1.json +++ b/datasets/mbs_wilhelm_msa_hooh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mbs_wilhelm_msa_hooh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy.\n\nThis work was completed as part of ASAC project 757 (ASAC_757).", "links": [ { diff --git a/datasets/mcdonald_dem_may2012_1.json b/datasets/mcdonald_dem_may2012_1.json index 36f56c1ab9..c26a5e897b 100644 --- a/datasets/mcdonald_dem_may2012_1.json +++ b/datasets/mcdonald_dem_may2012_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mcdonald_dem_may2012_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset consists of:\n1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012\n2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and\n3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM.\n4 Contours generated from the DEM. \n\nThe DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats.\nThe DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum.\n\nThe image products are geotiffs as follows.\n\nMcDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution.\n \nMcDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution.\n \nMcDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution.\n \nMcDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution.", "links": [ { diff --git a/datasets/mcm_seals.json b/datasets/mcm_seals.json index ca350b3658..d703d23d7b 100644 --- a/datasets/mcm_seals.json +++ b/datasets/mcm_seals.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mcm_seals", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Marine and Coastal Management (MCM) is one of four branches of the Department\n of Environmental Affairs and Tourism. It is the regulatory authority\n responsible for managing all marine and coastal activities. The seal data set\n is a collection of seals shot at-sea cruises, and has been collected from\n cruises around the South African Coast, and currently contains 2440 records of\n 1 family (Otariidae).", "links": [ { diff --git a/datasets/mean-insect-occupancy-1970-2020_1.0.json b/datasets/mean-insect-occupancy-1970-2020_1.0.json index 96a2ca247f..d6debfb282 100644 --- a/datasets/mean-insect-occupancy-1970-2020_1.0.json +++ b/datasets/mean-insect-occupancy-1970-2020_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mean-insect-occupancy-1970-2020_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data, on which the following publication below is based. **Paper Citation**: Neff, F., Korner-Nievergelt, F., Rey, E., Albrecht, M., Bollmann, K., Cahenzli, F., Chittaro, Y., Gossner, M. M., Mart\u00ednez-N\u00fa\u00f1ez, C., Meier, E. S., Monnerat, C., Moretti, M., Roth, T., Herzog, F., Knop, E. 2022. Different roles of concurring climate and regional land-use changes in past 40 years' insect trends. Nature Communications, DOI: [10.1038/s41467-022-35223-3](https://doi.org/10.1038/s41467-022-35223-3) Please cite this paper together with the citation for the datafile. Please also refer to this publication for details on the methods. ## Summary Mean annual occupancy estimates for 390 insect species (215 butterflies [Papilionoidea, incl. Zygaenidae moths], 103 grasshoppers [Orthoptera], 72 dragonflies [Odonata]) for nine bioclimatic zones in Switzerland. Covers the years 1970-2020 (for butterflies) and 1980-2020 (for grasshoppers and dragonflies). Mean occupancy denotes the average number of 1 km x 1 km squares in a zone occupied by the focal species. Occupancy estimates stem from occupancy-detection models run with species records data hosted and curated by [info fauna](http://www.infofauna.ch). Data on the level of single MCMC iterations of model fitting are included (4000 sampling iterations). The nine bioclimatic zones were defined based on biogeographic regions and two elevation classes (square above or below 1000 m. asl)", "links": [ { diff --git a/datasets/medical_bibliography_1.json b/datasets/medical_bibliography_1.json index 2584526f78..78a856a455 100644 --- a/datasets/medical_bibliography_1.json +++ b/datasets/medical_bibliography_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "medical_bibliography_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981.\n\nCurrently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually.\n\nThe publications are divided into the following areas:\n\nClinical medicine\nClinical medicine - case reports\nTelemedicine\nDentistry\nDiving\nEpidemiology\nPolar human research - general\nPhysiology\nImmunology\nPhotobiology\nMicrobiology\nPsychology and behavioural studies\nNutrition\nTheses\nPopular articles\nMiscellaneous\nIBEA\nPosters\n\nThe fields in this dataset are:\nAuthor\nTitle\nJournal\nYear", "links": [ { diff --git a/datasets/mega-plots_1.0.json b/datasets/mega-plots_1.0.json index f7b6a10569..00eb4b9052 100644 --- a/datasets/mega-plots_1.0.json +++ b/datasets/mega-plots_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mega-plots_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data file refers to the data used in Portier et al. \"Plot size matters: towards comparable species richness estimates across plot-based inventories\" (2022) *Ecology and Evolution*. This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/).", "links": [ { diff --git a/datasets/mendocino_mathison_peak_nff_sr.json b/datasets/mendocino_mathison_peak_nff_sr.json index 2f6d30edc9..5d4f27cdde 100644 --- a/datasets/mendocino_mathison_peak_nff_sr.json +++ b/datasets/mendocino_mathison_peak_nff_sr.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mendocino_mathison_peak_nff_sr", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This airborne laser swath mapping (ALSM) data of the San Andreas fault\nzone in northern California was acquired by TerraPoint, LLC under\ncontract to the National Aeronautics and Space Administration in\ncollaboration with the United States Geological Survey. The data\nwere acquired by means of LIght Detection And Ranging (LIDAR) using\na discrete-return, scanning laser altimeter capable of acquiring up\nto 4 returns per laser pulse. The data were acquired with a nominal\ndensity of 1 laser pulses per square meter achieved with 58% overlap\nof adjacent data swaths (all areas were mapped at least twice and\nthe data combined to produce final products). The data set consists\nof 3 parts: (1) the LIDAR point cloud providing the location and\nelevation of each laser return, along with associated acquisition\nand classification parameters, (2) a highest-surface digital\nelevation model (DEM) produced at a 6 foot grid spacing, where each\ngrid cell elevation corresponds to the highest laser return within\nthe cell (cells lacking returns are undefined, usually associated\nwith water or low reflectance surfaces such as fresh asphalt), and\n(3) a \"bald Earth\" DEM, with vegetation cover and buildings removed,\nproduced at a 6 foot grid spacing by sampling a triangular irregular\nnetwork (TIN). The TIN was constructed from those returns classified\nas being from the ground or water based on spatial filtering of the\npoint cloud. Comparison to GPS-established ground control in flat,\nvegetation-free areas indicates that the DEM vertical accuracy is 17\ncm (RMSE for 85 points). Bald Earth elevations under vegetation and\nfor water bodies are less accurate where laser returns from the\nground or water are sparse. The highest surface and bald Earth DEMs\nare distributed as georeferenced geotiff elevation and shaded relief\nimages. The grid cell values in the elevation images are orthometric\nelevations in international feet referenced to North American\nVertical Datum 1988 (NAVD-88) stored as signed floating point values\nwith undefined grid cells set to -99. The shaded relief images are\nbyte values from 0 (shaded) to 255 (illuminated) computed using ENVI\n4.0 shaded relief modeling with an illumination azimuth of 225\ndegrees, illumination elevation of 60 degrees, and a 3x3 kernel\nsize. The images are mosaics based on USGS 7.5 minute quadrangle\nboundaries. Each mosaic is an east-west strip covering the northern\nor southern half of adjacent quadrangles. File names include the\nquadrangle names, a northern (N) or southern (S) half designation, a\nbald Earth (BE) or highest-surface (FF) designation, and an\nelevation image (elev) or shaded relief image (SR) designation. FF\nrefers to full-feature indicating vegetation and buildings have not\nbeen removed.These data were developed in order to study the\ngeomorphic expression of natural hazards in support of the National\nAeronautics and Space Administration (NASA) Solid Earth and Natural\nHazards (SENH) Program, the United States Geological Survey (USGS),\nand the Geology component of the Earthscope Plate Boundary\nObservatory.\n\nSpatial Data Organization Information -\n\nDirect Spatial Reference: Raster\n Raster Object Type: Pixel\n Row Count: 1285\n Column Count: 4398\n Vertical Count: 1\n\nSpatial Reference Information -\n Horizontal Coordinate System Definition - \n Planar -\n Map Projection Name: Lambert Conformal Conic\n Standard Parallel: 38.333333\n Standard Parallel: 39.833333\n Longitude of Central Meridian: -122.000000\n Latitude of Projection Origin: 37.666667\n False Easting: 6561666.666667\n False Northing: 1640416.666667\n Planar Coordinate Encoding Method: row and column\n Coordinate Representation: \n Abscissa Resolution: 6.000000\n Ordinate Resolution: 6.000000\n Distance and Bearing Representation: \n Planar Distance Units: survey feet\n \n Geodetic Model: \n Horizontal Datum Name: North American Datum of 1983\n Ellipsoid Name: Geodetic Reference System 80\n Semi-major Axis: 6378137.000000\n Denominator of Flattening Ratio: 298.257222", "links": [ { diff --git a/datasets/met-obs-jmr-stations-1976_1.json b/datasets/met-obs-jmr-stations-1976_1.json index 87bdb61b24..977ce297a6 100644 --- a/datasets/met-obs-jmr-stations-1976_1.json +++ b/datasets/met-obs-jmr-stations-1976_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "met-obs-jmr-stations-1976_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the Mirny-Dome C traverse in 1976/77, time was spent at a number of cane sites taking JMR measurements, to determine the precise location. During this time, basic meteorological observations of air temperature and pressure were made and recorded.\n\nThese documents have been archived in the records store at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/met_profile_SA_729_1.json b/datasets/met_profile_SA_729_1.json index c9e70cd5f6..cba09383e7 100644 --- a/datasets/met_profile_SA_729_1.json +++ b/datasets/met_profile_SA_729_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "met_profile_SA_729_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km. This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa. These sonde launches were made to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaign of 2000.Vaisala RS80 sondes were launched from nine sites in South Africa between August 1, 2000 and September 30, 2000. The launch sites were Pietersburg (changed to Polokwane after 2000), Pretoria (Irene), Bethlehem, Springbok, De Aar, Durban, Cape Town, Port Elizabeth, and Gough Island. The parameters measured by the radiosonde instruments include: pressure, air temperature, relative humidity, wind speed, and wind direction.", "links": [ { diff --git a/datasets/met_profile_skukuza_728_1.json b/datasets/met_profile_skukuza_728_1.json index fa4a160ade..6520b39412 100644 --- a/datasets/met_profile_skukuza_728_1.json +++ b/datasets/met_profile_skukuza_728_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "met_profile_skukuza_728_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999, and between August 24-September 23, 2000. The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram.", "links": [ { diff --git a/datasets/meteo-at-s17-antarctica-2018-2019_1.0.json b/datasets/meteo-at-s17-antarctica-2018-2019_1.0.json index e62028546b..041cd42c85 100644 --- a/datasets/meteo-at-s17-antarctica-2018-2019_1.0.json +++ b/datasets/meteo-at-s17-antarctica-2018-2019_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "meteo-at-s17-antarctica-2018-2019_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site. A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation. At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7\u00b0 for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019. The simulations cover a 18 x 18 x 6 m\u00b3 domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.", "links": [ { diff --git a/datasets/meteo-stillberg_1.1.json b/datasets/meteo-stillberg_1.1.json index 4f6b9657bf..5cca8a408f 100644 --- a/datasets/meteo-stillberg_1.1.json +++ b/datasets/meteo-stillberg_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "meteo-stillberg_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022). # Station description The five meteorological stations were all installed at the same location (46\u00b046\u203225.015\u2033N 9\u00b052\u203201.792\u2033E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997. # Data description We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see \u2018Stillberg_meteo_metadata_stations_v1.csv\u2019). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder \u2018raw_standardized\u2019), edited (folder \u2018raw_edited\u2019) and filtered (folder \u2018filtered\u2019). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.", "links": [ { diff --git a/datasets/meteo_50_1.json b/datasets/meteo_50_1.json index de83365097..2cc67c17df 100644 --- a/datasets/meteo_50_1.json +++ b/datasets/meteo_50_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "meteo_50_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data--see also: Canopy Chemistry (OTTER)", "links": [ { diff --git a/datasets/meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0.json b/datasets/meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0.json index 1a6092a061..cf49c63c74 100644 --- a/datasets/meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0.json +++ b/datasets/meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the \"deterministic\" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.", "links": [ { diff --git a/datasets/metnavcpexaw_1.json b/datasets/metnavcpexaw_1.json index 0ff7c4ec34..36d48aabd9 100644 --- a/datasets/metnavcpexaw_1.json +++ b/datasets/metnavcpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "metnavcpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DC-8 Meteorological and Navigation Data CPEX-AW dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA DC-8 aircraft during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 17, 2021 through September 4, 2021 in ASCII format.", "links": [ { diff --git a/datasets/metnavcpexcv_1.json b/datasets/metnavcpexcv_1.json index 004336621d..04423f415b 100644 --- a/datasets/metnavcpexcv_1.json +++ b/datasets/metnavcpexcv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "metnavcpexcv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DC-8 Meteorological and Navigation Data CPEX-CV dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These data were gathered during the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign was based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX \u2013 Aerosols and Winds (CPEX-AW) and will be conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through October 3, 2022, in ASCII format.", "links": [ { diff --git a/datasets/mi_vascular_plants_census_1979_1.json b/datasets/mi_vascular_plants_census_1979_1.json index 499c4e6d02..a6217858b8 100644 --- a/datasets/mi_vascular_plants_census_1979_1.json +++ b/datasets/mi_vascular_plants_census_1979_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mi_vascular_plants_census_1979_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The atlas shows the known distribution and abundance of each vascular species on Macquarie Island immediately prior to the commencement of control measures against rabbits in 1978. It gives a baseline against which changes in the vegetation can be monitored. The effects of the introduced vertebrates on the vegetation are discussed. Additional data are given on the habitat, gregarious performance and phenology of some species.", "links": [ { diff --git a/datasets/micro_pulse_lidar_715_1.json b/datasets/micro_pulse_lidar_715_1.json index 58f0a4f7b0..f17e362b3f 100644 --- a/datasets/micro_pulse_lidar_715_1.json +++ b/datasets/micro_pulse_lidar_715_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "micro_pulse_lidar_715_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Two Micro-Pulse Lidar (MPL) systems were deployed to Africa for the SAFARI 2000 experiment. One MPL was set up in Mongu, Zambia, and the other was set up in Skukuza, South Africa. The primary focus of MPL work during SAFARI was to study the vertical distribution and optical properties of smoke from biomass burning in the region.", "links": [ { diff --git a/datasets/mirny_domec_1981_1.json b/datasets/mirny_domec_1981_1.json index 281703e688..14f01c63b4 100644 --- a/datasets/mirny_domec_1981_1.json +++ b/datasets/mirny_domec_1981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mirny_domec_1981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Log books for measurements taken during the Mirny - Dome C traverse by the Russians in 1981. Measurements include snow accumulation, magnetic readings, and barometric levelling.\n\nCopies of these documents have been archived in the records store of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/misrepcpexaw_1.json b/datasets/misrepcpexaw_1.json index 3ad66c5e1d..012c99ea39 100644 --- a/datasets/misrepcpexaw_1.json +++ b/datasets/misrepcpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "misrepcpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mission Reports CPEX-AW dataset contains daily objectives, flight times, and instrument performance during each NASA DC-8 aircraft flight during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 20, 2021 through August 27, 2021 in Microsoft Word Doc format.", "links": [ { diff --git a/datasets/misrepimpacts_1.json b/datasets/misrepimpacts_1.json index 0fe166707f..32c5c7cf0f 100644 --- a/datasets/misrepimpacts_1.json +++ b/datasets/misrepimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "misrepimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mission Reports IMPACTS dataset consists of flight plans, plans of the day, science plans, and science summaries logged by scientists during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The mission reports are available from January 8, 2020, through March 1, 2023, in PDF format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/mod13q1-6.0_NA.json b/datasets/mod13q1-6.0_NA.json index 3ed5b72e40..4f593767dd 100644 --- a/datasets/mod13q1-6.0_NA.json +++ b/datasets/mod13q1-6.0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mod13q1-6.0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.0 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.", "links": [ { diff --git a/datasets/model_36_1.json b/datasets/model_36_1.json index c22776067e..63dc2b3a9c 100644 --- a/datasets/model_36_1.json +++ b/datasets/model_36_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "model_36_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Steve Running's Forest-BGC Model (v.1991)", "links": [ { diff --git a/datasets/model_npp_xdeg_1027_1.json b/datasets/model_npp_xdeg_1027_1.json index f27b753fed..4ed1a87c2a 100644 --- a/datasets/model_npp_xdeg_1027_1.json +++ b/datasets/model_npp_xdeg_1027_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "model_npp_xdeg_1027_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains modeled annual net primary production (NPP) for the land biosphere from seventeen different global models. Annual NPP is defined as the net difference of annual carbon uptake (grams CO2/m2/yr) from the atmosphere through photosynthesis by the land vegetation and that lost back to the atmosphere through autotrophic and maintenance respiration. NPP is also related to the Net Ecosystem Exchange (NEE) of carbon accumulated by or lost from the surface by its vegetation and soils. NPP is NEE plus heterotrophic (decomposition) respiration of the vegetation and soils. Only NPP values are included in this data set as some models did not estimate NEE. Data for the mean, standard deviation and coefficient of variation of NPP for the 17 models are provided at spatial resolutions of 1.0 degree and 0.5 degrees. There are two compressed (*.zip) data files with this data set.", "links": [ { diff --git a/datasets/modeling-snow-failure-with-dem_1.0.json b/datasets/modeling-snow-failure-with-dem_1.0.json index 96b05ff081..50beb1f11c 100644 --- a/datasets/modeling-snow-failure-with-dem_1.0.json +++ b/datasets/modeling-snow-failure-with-dem_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modeling-snow-failure-with-dem_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.", "links": [ { diff --git a/datasets/modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0.json b/datasets/modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0.json index 641e67537a..303183bd27 100644 --- a/datasets/modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0.json +++ b/datasets/modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes the parallel application and the main results supporting the research article \"Modeling snow saltation: the effect of grain size and interparticle cohesion\" published at the Journal of Geophysical Research: Atmospheres. The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.", "links": [ { diff --git a/datasets/modis_20day_fast_ice_2.json b/datasets/modis_20day_fast_ice_2.json index 19c24b6238..3e4ff90aa9 100644 --- a/datasets/modis_20day_fast_ice_2.json +++ b/datasets/modis_20day_fast_ice_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_20day_fast_ice_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Maps of East Antarctic landfast sea-ice extent, generated from approx. 250,000 1 km visible/thermal infrared cloud-free MODIS composite imagery (augmented with AMSR-E 6.25-km sea-ice concentration composite imagery when required).\n\nBecause of imperfections in the MODIS composite images (typically caused by inaccurate cloud masking, persistent cloud in a given region, and/or a highly dynamic fast-ice edge), automation of the fast-ice extent retrieval process was not possible. Each image was thus classified manually. A study of errors/biases of this process revealed that most images were able to be classified with a 2-sigma accuracy of +/- ~3%. More details are provided in Fraser et al., (2010).\n\n*Version 1.2 with extra QC around the Mawson coast and Lutzow-Holm Bay\n\nThe directory named \"pngs\" contains browsable maps of fast-ice extent, in the form of Portable Network Graphics (PNG) images. Each of the 159 consecutive images (20-day intervals from Day Of Year (DOY) 61-80, 2000 to DOY 341-366, 2008) contains a map of fast-ice extent along the East Antarctic coast, generated from MODIS and AMSR-E imagery. The colour scale is as follows:\n\nDark blue: \t\tFast ice, as classified from a single 20-day MODIS composite image\nRed: \t\t\tFast ice, as classified using the previous or next 20-day MODIS composite images\nYellow: \t\tFast ice, as classified using a single 20-day AMSR-E composite image\nWhite: \t\t\tAntarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product.\nLight blue: \tSouthern ocean/pack ice/icebergs\n\n\nThese maps are also provided as unformatted binary fast ice images, in the directory named \"imgs\". These .img files are all flat binary images of dimension 4300 * 425 pixels. The data type is 8-bit byte. Within the .img files, the value for each pixel indicates its cover:\n0: Southern Ocean, pack ice or icebergs, corresponding to light blue in the PNG files.\n1: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product, corresponding to white in the PNG files.\n2: Fast ice, as classified from a single 20-day MODIS composite image, corresponding to dark blue in the PNG files\n3: Fast ice, as classified using a single 20-day AMSR-E composite image, corresponding to yellow in the PNG files\n4: Fast ice, as classified using the previous or next 20-day MODIS composite images, corresponding to red in the PNG files\n\nTo assist in georeferencing these data, files containing information on the latitude and longitude of each pixel are provided in the directory named \"geo\". These files are summarised as follows:\n\nlats.img:\t\tFile containing the latitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels.\nlons.img:\t\tFile containing the longitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels.\n\nThe .gpd Grid Point Descriptor file used to build the projection is also included. It contains parameters which you can use for matching your projection.\n\nTo refer to the time series, climatology, or maps of average persistence, please reference this paper:\nFraser, A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, East Antarctic landfast sea ice distribution and variability, 2000-08, Journal of Climate 25, 4, pp. 1137-1156, 2012\n\nIn addition, please cite the following reference when describing the process of generating these maps:\nFraser, A. D., R. A. Massom, and K. J. Michael, Generation of high-resolution East\nAntarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery, Elsevier Remote Sensing of Environment, 114 (12), 2888-2896, doi:10.1016/j.rse.2010.07.006, 2010.\n\nTo reference the techniques for generating the MODIS composite images, please use the following reference:\nFraser, A. D., R. A. Massom, and K. J. Michael, A method for compositing polar MODIS satellite images to remove cloud cover for landfast sea-ice detection, IEEE Transactions on Geoscience and Remote Sensing, 47 (9), pp. 3272-3282, doi:10.1109/TGRS.2009.2019726, 2009.\n\nPlease contact Alex Fraser (adfraser@utas.edu.au) for further information.", "links": [ { diff --git a/datasets/modis_MOD04_aerosol_813_1.json b/datasets/modis_MOD04_aerosol_813_1.json index d9d392e309..33f75d8073 100644 --- a/datasets/modis_MOD04_aerosol_813_1.json +++ b/datasets/modis_MOD04_aerosol_813_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_MOD04_aerosol_813_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The subset of the MODIS MOD04_L2 aerosol product provided in this data set represents the swaths that coincide with known times of the South African Weather Bureau/Service (SAWS) Aerocommanders JRA and JRB research aircraft missions to support aerosol research and validation activities for the SAFARI 2000 region. The MODIS aerosol product monitors the ambient aerosol optical thickness over the oceans globally and over a portion of the continents. Further, the aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. Daily Level 2 data are produced at the spatial resolution of a 10 x 10 1 km (at nadir) pixel array. The daily data files cover the period August 21, 2000, through September 26, 2000. For some data collection dates, there are two or more data files.The MOD04_L2 swath data files included in this data set are from the GSFC DAAC (V4.1.0, Collection 004). The MODIS Level 2 data files were converted from Hierarchical Data Format (HDF) to granule tables (GRANT) format. The GRANT format provides the extracted Scientific Data Set (SDS) in ASCII table form where each pixel (x,y) is represented as a row of data with georeferencing information and each SDS is provided as a separate column in the table. The ASCII tables are in comma-delimited format.", "links": [ { diff --git a/datasets/modis_MOD05_watervapor_812_1.json b/datasets/modis_MOD05_watervapor_812_1.json index 0faaacfb9a..dbd603e1a4 100644 --- a/datasets/modis_MOD05_watervapor_812_1.json +++ b/datasets/modis_MOD05_watervapor_812_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_MOD05_watervapor_812_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The MODIS precipitable water product consists of vertical column water-vapor amounts in centimeters (cm) at 1 km spatial resolution. The SAFARI 2000 product, provided in flat binary data files, is a subset of the official MODIS Level 2 MOD05 product in EOS Hierarchical Data Format (HDF) format. Specifically, the SAFARI product contains data from daytime-only MODIS granules over southern Africa for the period August 21, 2000, through September 20, 2000. A granule is the data collected over the full MODIS swath in a five-minute period. Further, the SAFARI product contains values generated by the MODIS near-infrared algorithm applied over clear land areas only (determined via the QA bit field). All values were derived from MODIS on the morning-pass Terra satellite. The product is very sensitive to boundary-layer water vapor since it is derived from attenuation of reflected solar light from the surface. This data product is essential to understanding the hydrological cycle, aerosol properties, aerosol-cloud interactions, energy budget, and climate.The MOD05 water vapor data files were converted from their original HDF format to flat binary files for this SAFARI 2000 data set. The conversion was performed using code developed in the Interactive Data Language (IDL) Version 5.5. The following Scientific Data Sets (SDS) are provided in this data set: Latitude; Longitude; Sensor_Zenith; and Water_Vapor_Near_Infrared.", "links": [ { diff --git a/datasets/modis_albedo_2002_filled_xdeg_960_1.json b/datasets/modis_albedo_2002_filled_xdeg_960_1.json index c229a1a6b2..7acda6c91a 100644 --- a/datasets/modis_albedo_2002_filled_xdeg_960_1.json +++ b/datasets/modis_albedo_2002_filled_xdeg_960_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_albedo_2002_filled_xdeg_960_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, ISLSCP II Snow-Free, Spatially Complete, 16 Day Albedo, 2002, contains 9 files for snow-free, spatially complete 16-day global black-sky albedos at local solar noon, white-sky albedos and quality information based on MODerate Resolution Imaging Spectroradiometer (MODIS) Collection 4 Albedo Products (MOD43B3). Data are provided for 7 spectral bands and 3 broad bands for a full year of MODIS data (2002). An ecosystem-dependent temporal interpolation technique was developed to fill any missing or seasonally snow-covered data in the official MOD43B3 albedo product. The resulting data set maintains the original resolution and data of the MOD43B3 product while replacing fill values to provide snow-free spatially complete maps.", "links": [ { diff --git a/datasets/modis_albedo_2002_xdeg_958_1.json b/datasets/modis_albedo_2002_xdeg_958_1.json index 360ad9efd6..df8fc8af26 100644 --- a/datasets/modis_albedo_2002_xdeg_958_1.json +++ b/datasets/modis_albedo_2002_xdeg_958_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_albedo_2002_xdeg_958_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This International Satellite Land Surface Climatology Project (ISLSCP II) MODerate resolution Image Spectroradiometer (MODIS) dataset, ISLSCP II MODIS (Collection 4) Albedo 2002, provides albedo data for the period January 2002 through December 2002.The MODIS bidirectional reflectance distribution function (BRDF) albedo product (MOD43B) provides measures of clear sky surface albedo every 16 days. Both white-sky albedo (bihemispherical reflectance) and black-sky albedo (directional hemispherical reflectance) at local solar noon are provided for 7 spectral bands and 3 broadbands. Since black-sky albedo represents the direct beam contribution while white-sky represents the completely diffuse contribution, these measures can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon.", "links": [ { diff --git a/datasets/modis_aster_fire_707_1.json b/datasets/modis_aster_fire_707_1.json index 0c5efc3ebc..645ce2de56 100644 --- a/datasets/modis_aster_fire_707_1.json +++ b/datasets/modis_aster_fire_707_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_aster_fire_707_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to determine the accuracy of the moderate resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in Southern Africa during the 2001 burning season. We utilize 18 ASTER scenes distributed throughout Southern Africa covering the time period 5 August 2001 to 6 October 2001. ", "links": [ { diff --git a/datasets/modis_burned_area_796_1.json b/datasets/modis_burned_area_796_1.json index dddfaca691..15317c63bf 100644 --- a/datasets/modis_burned_area_796_1.json +++ b/datasets/modis_burned_area_796_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_burned_area_796_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SAFARI 2000 project was selected as the first regional test for a prototype regional 500 m MODIS burned area product. The MODIS burned area product maps the 500 m location and approximate day of burning using a change detection algorithm based on a bi-directional reflectance model-based expectation method applied to the MODIS near-infrared and shortwave infrared bands (Roy et al., 2002). The algorithm was applied to recently reprocessed 500 m daily MODIS land surface reflectance data to produce burned area data sets for all of southern Africa for 2000 forward. This archived data set contains MODIS 500 m burned area products for two dry season months (July and September 2000).Burned area products are spatially explicit data sets that describe the approximate day of burning at 500 m resolution for all of southern Africa south of the Equator, including Madagascar. The burned area maps are compressed GeoTiff files. Several text files are included in the compressed files to aid ENVI (Research Systems, Inc.) users, including ENVI header files and ENVI density slice files. The data set also includes a projection parameters file.The MODIS burned area data set was validated using a methodology based upon the interpretation of multitemporal Landsat Enhanced Thematic Mapper plus (ETM+) data as described in Roy et al. (in press).", "links": [ { diff --git a/datasets/modis_h2o_heat_flux_762_1.json b/datasets/modis_h2o_heat_flux_762_1.json index f7ee723de3..c4c985fc7d 100644 --- a/datasets/modis_h2o_heat_flux_762_1.json +++ b/datasets/modis_h2o_heat_flux_762_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_h2o_heat_flux_762_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A physically-based model, Energy: Surface Towards Atmosphere (ESTA), was used to model and map the energy and water balances of a heterogeneous land surface in a savanna environment on the southern fringe of the Okavango Delta, near Maun, Botswana. ESTA is governed by remotely sensed values of surface temperature, reflection, and vegetation density. Surface reflectance data from the MODIS sensor aboard the Terra satellite were obtained for the Okavango Delta region for September of 2001.", "links": [ { diff --git a/datasets/modis_l3_albedo_840_1.json b/datasets/modis_l3_albedo_840_1.json index 86d53b4e54..9fccb625dc 100644 --- a/datasets/modis_l3_albedo_840_1.json +++ b/datasets/modis_l3_albedo_840_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_l3_albedo_840_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths (0.47mm, 0.55mm, 0.67mm, 0.86mm, 1.24mm, 1.64mm, 2.1mm, and broadband 0.3-0.7mm, 0.3-5.0mm, and 0.7-5.0mm). An exception is that no 2.1mm data for black-sky is being archived at this time. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surface albedo data, are generated at 1-minute resolution on an equal-angle grid. The maps are stored in separate HDF files for each wavelength, each 16-day period and each albedo type (white- and black-sky). Data belonging to black sky and white sky albedo have been zipped separately. This format allows the user to have flexibility to download and store only the data absolutely needed.The One-Minute Land Ecosystem Classification Product is a global (static map) data set of the International Geosphere-Biosphere Programme (IGBP) classification scheme stored on an equal-angle rectangular grid at 1-minute resolution. The dataset is generated from the official MODIS land ecosystem classification dataset, MOD12Q1 for year 2000, day 289 data (October 15, 2000). This dataset is used in generating the spatially complete albedo maps, but is also a stand-alone product designed for use by the user community. The Land Ecosystem Classification Map File product file is stored in Hierarchical Data Format (HDF).", "links": [ { diff --git a/datasets/modis_landcover_xdeg_968_1.json b/datasets/modis_landcover_xdeg_968_1.json index dc0fc46363..ac45f11df4 100644 --- a/datasets/modis_landcover_xdeg_968_1.json +++ b/datasets/modis_landcover_xdeg_968_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modis_landcover_xdeg_968_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set, ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001, contains global land cover classifications (dominant type, classification confidence and fractional cover) generated using a full year of MODerate Resolution Imaging Spectroradiometer (MODIS) data covering the period from October 2000 to October 2001. The objective of the MODIS Land Cover Product is to provide a suite of land cover types useful to global system science modelers by exploiting the information content of MODIS data in the spectral, temporal, spatial, and directional domains. These products describe the geographic distribution of the 17 land cover classification scheme proposed by the International Geosphere-Biosphere Programme (IGBP).", "links": [ { diff --git a/datasets/modiscpex_1.json b/datasets/modiscpex_1.json index acf8b6403f..a226fe97bd 100644 --- a/datasets/modiscpex_1.json +++ b/datasets/modiscpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "modiscpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) CPEX dataset includes measurements gathered by MODIS during the Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. Data are available from May 9, 2017 through July 16, 2017 in netCDF-3 format.", "links": [ { diff --git a/datasets/mogli-sdm_1.0.json b/datasets/mogli-sdm_1.0.json index ffeca011ce..3bc48daf62 100644 --- a/datasets/mogli-sdm_1.0.json +++ b/datasets/mogli-sdm_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mogli-sdm_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "**We used Swiss National Forest Inventory ([NFI](https://www.lfi.ch/index-en.php)) data to model the potential distribution of the most common woody species for the forested area of Switzerland and provide potential distribution maps that fulfill specific quality criteria with regard to predicting performance.** More details on the methods and results are described in the project summary available [here](https://www.envidat.ch/dataset/07a9c22c-9ec2-4f49-87e2-3b2d73ad81f2/resource/9bfd5308-d9be-4d01-ab78-48d33889e04e/download/mogli_summary.pdf). **The resulting maps can be viewed in a simple web-GIS application available at:** [https://www.lfi.ch/produkte/mogli/mogli-en.php](https://www.lfi.ch/produkte/mogli/mogli-en.php) **Data can be used without restrictions, but the data must be explicitly asked from the contact person of the dataset in order to obtain access.** This is a requirement to fulfill the needs of reporting towards the funding agencies.", "links": [ { diff --git a/datasets/mongu_daily_rainfall_785_1.json b/datasets/mongu_daily_rainfall_785_1.json index 4f3f9bb292..09f6e44698 100644 --- a/datasets/mongu_daily_rainfall_785_1.json +++ b/datasets/mongu_daily_rainfall_785_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_daily_rainfall_785_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains daily rainfall totals (mm) from Mongu, in the Western Province of Zambia. The data were collected with a British standard 5 inch diameter rain gauge in a yard 30 m away from the Meteorological Department building near the Mongu Airport (north of downtown and approximately 20 km from the Kataba Local Forest where the permanent 30 m Mongu tower site is located). Rainfall readings were taken by ZMD staff each morning at 06:00 GMT. These data form the official government rainfall record for Mongu.The data files consist of 3 files, one for each year (July to Jun). Each files contains monthly columns with totals for each day of the month as well as a monthly total. The data files are stored as ASCII text files in comma-separated-value (csv) format.", "links": [ { diff --git a/datasets/mongu_fpar_trac_784_1.json b/datasets/mongu_fpar_trac_784_1.json index f9e37ced0e..3d54b7af09 100644 --- a/datasets/mongu_fpar_trac_784_1.json +++ b/datasets/mongu_fpar_trac_784_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_fpar_trac_784_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from the Tracing Architecture and Radiation of Canopies (TRAC) instrument were processed to determine the fraction of intercepted photosynthetically active radiation (FPAR) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 1999 and continued into 2002, with measurements collected about every month. TRAC contains three pyranometers sensitive to PAR wavelengths, with two sensors upward looking and one downward looking. The TRAC instrument was carried along three parallel transects, each 750 m long and spaced 250 m apart, about 0.7 m off the ground on clear days near midday. The sensors measured PAR at 32 Hz, resulting in a horizontal sampling interval of about 1.7 cm (Privette et al., 2002). Each transect was divided into 25 m segments, and Fpar values, with date/time stamp, are reported for each segment. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel. PAR transmittance values were determined from the upward viewing pyranometers on the TRAC instrument. Due to the large gaps in the canopy, incident PAR was estimated from the TRAC data as 95% of the maximum PAR transmittance value for each transect. The FPAR values of all the observations were averaged to give segment-average FPAR, and segment average FPAR values were averaged to give transect-average FPAR.The data file is stored as an ASCII text file, in comma-separated-value (csv) format, with column headers.", "links": [ { diff --git a/datasets/mongu_irradiance_782_1.json b/datasets/mongu_irradiance_782_1.json index ec4f6bd335..89d663eb9d 100644 --- a/datasets/mongu_irradiance_782_1.json +++ b/datasets/mongu_irradiance_782_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_irradiance_782_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the top-of-canopy irradiance in the shortwave (0.3-2.8 micron) and photosynthetically active radiation (PAR; 0.4-0.7 micron) wavebands collected with an Eppley Precision Spectral Pyranometer (PSP) and a Skye SKE510 pyranometer, respectively. The instruments were deployed at the top of the 30-m tower in the Kataba Local Forest approximately 20 km south of Mongu in Western Province, Zambia. The data include the hourly mean and maximum values from 0500-1600 GMT (7 a.m. - 6 p.m. local time) and cover the period from September 4, 2000 to December 31, 2002. The data were obtained primarily for EOS validation and energy budget modeling.The Skye SKE510 uses a blue enhanced planar diffused silicon detector and has a fairly even response from 400 to 700 nm. The Eppley PSP is a World Meteorological Organization First Class Radiometer designed for the measurement of sun and sky radiation, totally or in defined broad wavelength bands. It comprises a circular multi-junction wire-wound thermopile. A data logger sampled the sensors at 60-second intervals and recorded the maximum and mean values every 60 minutes throughout the day.The data are contained within a single ASCII text file, in comma-separated-value format, with associated date, time, and QA information.", "links": [ { diff --git a/datasets/mongu_lai-2000_781_1.json b/datasets/mongu_lai-2000_781_1.json index 0518792757..eb6258cb5c 100644 --- a/datasets/mongu_lai-2000_781_1.json +++ b/datasets/mongu_lai-2000_781_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_lai-2000_781_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from the LAI-2000 instrument were processed to determine the leaf area index (LAI) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 2000 and continued into 2002, with measurements collected about every month throughout the growing season to examine the phenology of LAI for the site. The LAI-2000 measures the intensity of blue light in five upward-looking concentric conical rings. Measurements are made under the forest canopy and compared with open-sky measurements to determine transmittance for each of the five viewing angles. The sensor head was placed at ground level while the sensors measured light levels in conical scans. Effective leaf area was calculated from the transmittance in the different view angles based on the assumption of a random distribution of leaves (Welles and Norman, 1991).The LAI-2000 was carried along three parallel transects, each 750 m long and spaced 250 m apart. Each transect was divided into 25 m segments, and measurements were collected at the endpoints of each segment. Data from all transects were combined to provide site-average LAI for each sampling date. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel. Ground observations of LAI from this study compared with MODIS LAI products were found to be in close agreement.The data are stored in an ASCII text file, in comma-separated-value (csv) format, with column headers.", "links": [ { diff --git a/datasets/mongu_skukuza_albedo_786_1.json b/datasets/mongu_skukuza_albedo_786_1.json index a9bcd0a6f2..af66fe26a2 100644 --- a/datasets/mongu_skukuza_albedo_786_1.json +++ b/datasets/mongu_skukuza_albedo_786_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_skukuza_albedo_786_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Top-of-the-canopy broadband albedo and radiation fluxes are calculated from measurements at the Mongu and Skukuza flux tower sites in southern Africa from March 2000 through December 2002. Data were collected by instrumentation deployed at the top of the 30 m tower in the Kataba Local Forest near Mongu, Zambia, and atop the 20 m tower at the Skukuza tower site in Kruger National Park, South Africa. At the Mongu site, Kipp and Zonen albedometers housing both upward- and downward-looking pyranometers were outfitted with clear and red domes to collect broadband albedo and radiation fluxes in the shortwave (SW) and near-infrared (NIR) wavebands, respectively. The data are mean values provided at 15-minute intervals for 2000-2002. At the Skukuza tower, Kipp and Zonen albedometers (also outfitted with clear and red domes) collected broadband albedo and radiation fluxes in the SW and NIR wavebands. In addition, a pyrgeometer was used to collect longwave radiation flux in thermal infrared (TIR) wavebands. The data at Skukuza are mean values provided at 30-minute intervals for 2000-2002, except for the TIR data, which are provided for 2001 and 2002 only. For both sites, photosynthetically active radiation can be calculated from measurements.The data are provided in comma-delimited ASCII files, with column headers. The SW and NIR data for both sites are provided in one file per year per site for 2000-2002. The Skukuza longwave data are provided separately, and for years 2001 and 2002 only.", "links": [ { diff --git a/datasets/mongu_skukuza_soil_prop_789_1.json b/datasets/mongu_skukuza_soil_prop_789_1.json index aa6d3c55aa..5dad7b8dfb 100644 --- a/datasets/mongu_skukuza_soil_prop_789_1.json +++ b/datasets/mongu_skukuza_soil_prop_789_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_skukuza_soil_prop_789_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil moisture and temperature profile sensors were deployed at flux tower sites in Mongu, Zambia and Skukuza, South Africa. In addition, thermal infrared sensors were deployed to monitor surface temperature at the sites, and soil samples were collected for physical property analysis. A heat-flux plate was also installed at 10 cm depth at the Mongu site. The data cover the period variously from August, 1999 to December, 2001.At the Mongu site, three profiles of soil moisture and temperature were obtained to a maximum depth of 125 cm. These profiles were located approximately 30 meters north of the Mongu flux tower, within the Kataba Local Forest. Surface radiometric temperature was measured by thermal infrared sensors deployed on top of the 30-meter tower and on a tree. At the Skukuza site, two profiles of soil moisture and temperature were obtained to a maximum depth of 40 cm in a Combretum stand. The radiometric temperature of the tree crown and the background surface were monitored by infrared thermocouple sensors deployed on a pole at 2.5 m and 5 m heights. Soil samples were collected at different depths in the vicinity of the soil profiles at each site and were analyzed at CSIR in Pretoria to determine bulk density, texture, and particle size distribution. The data files are stored as ASCII text files, in comma-separated-value (.csv) format. Associated with each data file is a metadata (.txt ) file. Among other information, the metadata files indicate periods of missing data.", "links": [ { diff --git a/datasets/mongu_tree_rings_788_1.json b/datasets/mongu_tree_rings_788_1.json index c849b0c763..8e86a395a8 100644 --- a/datasets/mongu_tree_rings_788_1.json +++ b/datasets/mongu_tree_rings_788_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_tree_rings_788_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains tree ring data from three sites located about 25 km of the meteorological station at Mongu, Zambia. Data from about 50 individual trees are reported. In addition, chronologies (or site mean curves) that better represent common influences (e.g., in this study, the climatic signal) were developed for each site based on the individual data (Trouet, 2004; Trouet et al., 2001). The series covers a maximum of 46 years, although most series do not extend longer than 30 years. The data were collected during the SAFARI 2000 Dry Season Field Campaign of August 2000.Ten to 23 samples were taken at each site. Brachystegia bakeriana was sampled at site 1, and Brachystegia spiciformis at sites 2 and 3. The vegetation at all sites underwent primitive harvesting for subsistence earlier the same year, thus samples could be taken from freshly cut trees and no living trees were cut. At all sites, samples consisted of full stem discs. Where possible, samples were taken at breast height (1.3 m) or slightly lower. Growth ring widths were measured to the nearest 0.01 mm using LINTAB equipment and TSAP software (Rinn and Jakel, 1997). Four radii per sample disc were measured. Cross-dating and response function analyses were performed by routine dendrochronological techniques. There are two files for each site, one containing integer values representing tree ring widths (raw data), and the other containing standardized values (chronologies), for each year. The data are stored as ASCII table files in comma-separated-value (.csv) format, with column headers. ", "links": [ { diff --git a/datasets/mongu_veg_structure_795_1.json b/datasets/mongu_veg_structure_795_1.json index 3eec199fef..7e9c1099ea 100644 --- a/datasets/mongu_veg_structure_795_1.json +++ b/datasets/mongu_veg_structure_795_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mongu_veg_structure_795_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree basal area, percent tree canopy cover, and proportional contribution of main species to canopy cover were measured at 60 sampling points at 50 m intervals along six transects in the vicinity of the MODIS validation site tower in Kataba Forest, near Mongu, Zambia, in late February to early March 2000 as part of the SAFARI 2000 Wet Season Campaign. The aim of the study was to provide a broad description of the tree canopy layer around the tower.Tree and shrub species composition was recorded for each grid and measurements of canopy cover (% and rank) and frequency of occurrence (%) were made. Basal area was estimated at each grid site in a single 360 degree sweep using a basal area prism. Four estimates of canopy cover, oriented north, south, east, and west around the sample point, were taken at each grid site using a spherical densiometer and the data were averaged to give a single value for each grid. Only the canopies of trees and shrubs above 1.5 m height were measured.The data are stored in an ASCII file, in csv format. The file lists all tree and shrub species recorded and provides the proportional contribution of these species to canopy cover in each grid. Total tree basal area (m2 ha-1) and overall tree canopy cover (%) in each grid is also provided. The companion file provides additional vegetation data, graphics, long-term meteorological data, a discussion of the study results, and photographs of the study site.", "links": [ { diff --git a/datasets/monitoring-of-ash-trees_1.0.json b/datasets/monitoring-of-ash-trees_1.0.json index e71812fec7..6e3fcaec88 100644 --- a/datasets/monitoring-of-ash-trees_1.0.json +++ b/datasets/monitoring-of-ash-trees_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "monitoring-of-ash-trees_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior). We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)", "links": [ { diff --git a/datasets/monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0.json b/datasets/monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0.json index 332ee453a5..b89b59da50 100644 --- a/datasets/monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0.json +++ b/datasets/monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The population dynamics of eruptive moths were monitored with pheromone traps and the composition of the larvae's foodplants were analyzed for water content, nitrogen and total phenolics. Moth catches cover a period of 20 years, leaf analyses 10 years. For Zeiraphera griseana (= Z. diniana) only needle analyses are available. The corresponding data on the moth population dynamics are property of A. Fischlin, ETH Z\u00fcrich, and will be made available on EnviDat as well.", "links": [ { diff --git a/datasets/mortality-16_1.0.json b/datasets/mortality-16_1.0.json index 01d34f748d..c0cf3b7651 100644 --- a/datasets/mortality-16_1.0.json +++ b/datasets/mortality-16_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mortality-16_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that died or disappeared between two inventories and that were not harvested. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0.json b/datasets/mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0.json index 5b5ca4d22f..6dd6e65285 100644 --- a/datasets/mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0.json +++ b/datasets/mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "### One individual per species, vitality class (low and high) and height class (eight classes: 0\u201310, 11\u201320, 21\u201335, 36\u201360, 61\u201390, 91\u2013130, 131\u2013200 and 201\u2013500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively. ### Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015\u20132018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected. ## Variables: * species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica * species: as above * dummy: 0 - living individual, 1 - dead individual * LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g] * tree_age: in years * avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring\t * dry_mass_g: aboveground and belowground biomass * DLI: direct light index (measured only under living individuals) * BLI: diffuse light index (measured only under living individuals)\t * GLI: global light index", "links": [ { diff --git a/datasets/mortality_star-164_1.0.json b/datasets/mortality_star-164_1.0.json index e517cb310d..36465db680 100644 --- a/datasets/mortality_star-164_1.0.json +++ b/datasets/mortality_star-164_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mortality_star-164_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that died or disappeared between two inventories, but were not cut. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/mosaic-cbers4-brazil-3m-1_NA.json b/datasets/mosaic-cbers4-brazil-3m-1_NA.json index 7ef9e3aa94..796a619727 100644 --- a/datasets/mosaic-cbers4-brazil-3m-1_NA.json +++ b/datasets/mosaic-cbers4-brazil-3m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-cbers4-brazil-3m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4/WFI image mosaic of Brazil with 64m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 15, 16 and 13 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in April 2020 and ending in June 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 CBERS-4 scenes and was generated based on an existing CBERS-4/WFI image collection.", "links": [ { diff --git a/datasets/mosaic-cbers4a-paraiba-3m-1_NA.json b/datasets/mosaic-cbers4a-paraiba-3m-1_NA.json index 4ddb988a89..eac4511f25 100644 --- a/datasets/mosaic-cbers4a-paraiba-3m-1_NA.json +++ b/datasets/mosaic-cbers4a-paraiba-3m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-cbers4a-paraiba-3m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CBERS-4A/WFI image mosaic of Brazil Para\u00edba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection.", "links": [ { diff --git a/datasets/mosaic-landsat-amazon-3m-1_NA.json b/datasets/mosaic-landsat-amazon-3m-1_NA.json index 47ac763a7a..8cce712302 100644 --- a/datasets/mosaic-landsat-amazon-3m-1_NA.json +++ b/datasets/mosaic-landsat-amazon-3m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-landsat-amazon-3m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-8/OLI image mosaic of Brazilian Amazon biome with 30m of spatial resolution. The mosaic was prepared in support of TerraClass project. The true color composition is based on the OLI bands 4, 3 and 2 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2016 and ending in September of 2016, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 Landsat/OLI scenes and was generated based on an existing data cube of Landsat images.", "links": [ { diff --git a/datasets/mosaic-landsat-brazil-6m-1_NA.json b/datasets/mosaic-landsat-brazil-6m-1_NA.json index e67ea4a0b3..0222372de2 100644 --- a/datasets/mosaic-landsat-brazil-6m-1_NA.json +++ b/datasets/mosaic-landsat-brazil-6m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-landsat-brazil-6m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Landsat-8/OLI image mosaic of Brazil with 30m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the OLI bands 6, 5 and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in July 2017 and ending in June 2018, with a best pixel approach called MEDSTK, which uses the middle of the temporal composition interval to select pixels from the closest dates. More information on MEDSTK can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 8000 Landsat/OLI scenes and was generated based on an existing Landsat Image collection.", "links": [ { diff --git a/datasets/mosaic-s2-amazon-3m-1_NA.json b/datasets/mosaic-s2-amazon-3m-1_NA.json index 56de9481cd..f066f5c543 100644 --- a/datasets/mosaic-s2-amazon-3m-1_NA.json +++ b/datasets/mosaic-s2-amazon-3m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-s2-amazon-3m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-2 image mosaic of Brazilian Amazon biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in june 2022 and ending in August 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.", "links": [ { diff --git a/datasets/mosaic-s2-cerrado-2m-1_NA.json b/datasets/mosaic-s2-cerrado-2m-1_NA.json index fc03dbb0a0..676ae571da 100644 --- a/datasets/mosaic-s2-cerrado-2m-1_NA.json +++ b/datasets/mosaic-s2-cerrado-2m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-s2-cerrado-2m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 02-months of images, starting in November 2023 and ending in April 2024, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.", "links": [ { diff --git a/datasets/mosaic-s2-cerrado-4m-1_NA.json b/datasets/mosaic-s2-cerrado-4m-1_NA.json index 453416c58f..37dba7a3e6 100644 --- a/datasets/mosaic-s2-cerrado-4m-1_NA.json +++ b/datasets/mosaic-s2-cerrado-4m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-s2-cerrado-4m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 04-months of images, starting in june 2022 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.", "links": [ { diff --git a/datasets/mosaic-s2-paraiba-3m-1_NA.json b/datasets/mosaic-s2-paraiba-3m-1_NA.json index d0193c4d89..76189a64eb 100644 --- a/datasets/mosaic-s2-paraiba-3m-1_NA.json +++ b/datasets/mosaic-s2-paraiba-3m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-s2-paraiba-3m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-2 image mosaic of Brazilian Para\u00edba State with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the Federal University of Para\u00edba's Public Policy Studies Program for Early Childhood Education (NEPPS) by supporting the development of the COVID 19/PB Platform: Relations between Health, Territory and Social Protection in times of sanitary crisis, which created the Platform Covid-19/Para\u00edba: Social and Health Indicator Observatory for SUS and SUAS management. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in November 2019 and ending in January 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 800 Sentinel-2 scenes and was generated based on an existing Sentinel-2 image collection.", "links": [ { diff --git a/datasets/mosaic-s2-yanomami_territory-6m-1_NA.json b/datasets/mosaic-s2-yanomami_territory-6m-1_NA.json index 2d5f6f366c..59121e2316 100644 --- a/datasets/mosaic-s2-yanomami_territory-6m-1_NA.json +++ b/datasets/mosaic-s2-yanomami_territory-6m-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-s2-yanomami_territory-6m-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sentinel-2 image mosaic of Brazilian Yanomami Indigenous Territory with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the ICIT-FIOCRUZ Health Information Laboratory (LIS) a multi-institutional body coordinated by Fiocruz and the ministry of health, by creating a health situation database of the Yanomami Indigenous Land. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in April 2019 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.", "links": [ { diff --git a/datasets/mosaic-snow-on-sea-ice-data_1.0.json b/datasets/mosaic-snow-on-sea-ice-data_1.0.json index cc5b103acd..bf4c399ace 100644 --- a/datasets/mosaic-snow-on-sea-ice-data_1.0.json +++ b/datasets/mosaic-snow-on-sea-ice-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mosaic-snow-on-sea-ice-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data accompanying David Wagners' Dissertation. Covers model results and various input from ALPINE3D and SNOWPACK adjusted for sea ice during MOSAiC.", "links": [ { diff --git a/datasets/mountain-permafrost-hydrology_1.0.json b/datasets/mountain-permafrost-hydrology_1.0.json index d8a158f712..554cc293e0 100644 --- a/datasets/mountain-permafrost-hydrology_1.0.json +++ b/datasets/mountain-permafrost-hydrology_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mountain-permafrost-hydrology_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.", "links": [ { diff --git a/datasets/mountland-jura_1.json b/datasets/mountland-jura_1.json index 34a7c20e1e..3b806062dc 100644 --- a/datasets/mountland-jura_1.json +++ b/datasets/mountland-jura_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mountland-jura_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems. In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded \u2013 sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level. Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46\u00b054\u2019, E 6\u00b023\u2019) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46\u00b052\u2019, E 6\u00b026\u2019), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d\u2019Aubonne, N 46\u00b051\u2019, E 6\u00b037\u2019), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46\u00b047\u2019, E 6\u00b041\u2019). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario. Soil cores were assembled into rectangular PVC boxes of 60 \uf0b4 80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope S\u00e0rl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals. A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the model\u02bcs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model. The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU). #References 1. Puissant, J., C\u00e9cillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296\u2013305. 2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473\u2013474(0): 465-472. 3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia\u00a0(174)\u00a0: 1425-1435. 4. Peringer A., Siehoff S., Ch\u00e9telat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/ 5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/", "links": [ { diff --git a/datasets/mr_1.0.json b/datasets/mr_1.0.json index c22539197d..b4a1ab6903 100644 --- a/datasets/mr_1.0.json +++ b/datasets/mr_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mr_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset \"RoRCC\" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information: 1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020\u20132049, T2: 2045\u20132074, T3: 2070\u20132099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.", "links": [ { diff --git a/datasets/mrmsimpacts_1.json b/datasets/mrmsimpacts_1.json index 81c632a547..d1508ecf10 100644 --- a/datasets/mrmsimpacts_1.json +++ b/datasets/mrmsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mrmsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS dataset contains reflectivity products using the MRMS system during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data are available from January 1, 2022, through March 2, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/msutls_6.json b/datasets/msutls_6.json index e0ef1c9d0a..afcd7614f2 100644 --- a/datasets/msutls_6.json +++ b/datasets/msutls_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "msutls_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSU/MSU Lowstratosphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower stratosphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.", "links": [ { diff --git a/datasets/msutlt_6.json b/datasets/msutlt_6.json index 4508b5581a..e82a30f39c 100644 --- a/datasets/msutlt_6.json +++ b/datasets/msutlt_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "msutlt_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSU/MSU Lowtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.", "links": [ { diff --git a/datasets/msutmt_6.json b/datasets/msutmt_6.json index b4618e5d95..9d765605f5 100644 --- a/datasets/msutmt_6.json +++ b/datasets/msutmt_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "msutmt_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSU/MSU Midtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the mid-troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.", "links": [ { diff --git a/datasets/msuttp_6.json b/datasets/msuttp_6.json index 805374fdff..e84b9e7350 100644 --- a/datasets/msuttp_6.json +++ b/datasets/msuttp_6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "msuttp_6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The AMSU/MSU Tropopause Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the tropopause. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.", "links": [ { diff --git a/datasets/mt_menzies_sat_1.json b/datasets/mt_menzies_sat_1.json index dc5be6c355..2e429b6e6e 100644 --- a/datasets/mt_menzies_sat_1.json +++ b/datasets/mt_menzies_sat_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mt_menzies_sat_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite image map of Mt Menzies, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1997. The map is at a scale of 1:100000, and was produced from Landsat TM and SPOT XS scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, and gives some historical text information. The map has both geographical and UTM co-ordinates.", "links": [ { diff --git a/datasets/multifaceted-diversity-alps_1.0.json b/datasets/multifaceted-diversity-alps_1.0.json index 1c6ac4952f..c72c204feb 100644 --- a/datasets/multifaceted-diversity-alps_1.0.json +++ b/datasets/multifaceted-diversity-alps_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "multifaceted-diversity-alps_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository contains extensive data for the European Alps: - Observations of ~3,500 plant species - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal) - Flora migration rates (categorical) and ecological preferences (continuous indicator values) - Regional maps of barriers to migration and water bodies at 100-m resolution - Sampling effort, distance to roads and cities predictors at 100-m resolution - Present and future abundances over the study region at 1-km resolution (~2,000 species) - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution - Present and future conservation recommendations at 1-km resolution (26 current and future strategies) - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees) - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them", "links": [ { diff --git a/datasets/multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0.json b/datasets/multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0.json index b846e4d87c..df97d91ad6 100644 --- a/datasets/multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0.json +++ b/datasets/multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.", "links": [ { diff --git a/datasets/musondeimpacts_1.json b/datasets/musondeimpacts_1.json index a6bce8a7e7..5ee7fa87ed 100644 --- a/datasets/musondeimpacts_1.json +++ b/datasets/musondeimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "musondeimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Millersville University Upper Air Radiosondes IMPACTS dataset contains atmospheric temperature, dew point temperature, wind speed, and wind direction measurements using Vaisala\u2019s Radiosonde RS41-SGP and Sparv Embedded S1H3 Windsond during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data are available from January 16, 2022, through February 28, 2023 in ASCII format.", "links": [ { diff --git a/datasets/mwlezflx_493_1.json b/datasets/mwlezflx_493_1.json index 8b24a19259..3f8571ecbf 100644 --- a/datasets/mwlezflx_493_1.json +++ b/datasets/mwlezflx_493_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "mwlezflx_493_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data include aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration.", "links": [ { diff --git a/datasets/myd13q1-6.0_NA.json b/datasets/myd13q1-6.0_NA.json index f37af37242..2c965821dc 100644 --- a/datasets/myd13q1-6.0_NA.json +++ b/datasets/myd13q1-6.0_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "myd13q1-6.0_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.0 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.", "links": [ { diff --git a/datasets/n-availability-face-hofstetten_1.0.json b/datasets/n-availability-face-hofstetten_1.0.json index 7ffb1e763d..d6a0900f37 100644 --- a/datasets/n-availability-face-hofstetten_1.0.json +++ b/datasets/n-availability-face-hofstetten_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "n-availability-face-hofstetten_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.", "links": [ { diff --git a/datasets/n_s_dem_248_1.json b/datasets/n_s_dem_248_1.json index eca4db71f3..8f560e1644 100644 --- a/datasets/n_s_dem_248_1.json +++ b/datasets/n_s_dem_248_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "n_s_dem_248_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.", "links": [ { diff --git a/datasets/nacl_interfacial_phasechanges_1.0.json b/datasets/nacl_interfacial_phasechanges_1.0.json index b1fa173d0e..2b1f9076b4 100644 --- a/datasets/nacl_interfacial_phasechanges_1.0.json +++ b/datasets/nacl_interfacial_phasechanges_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nacl_interfacial_phasechanges_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory experiments are presented on the phase change at the surface of sodium chloride \u2013 water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution \u2013 air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.", "links": [ { diff --git a/datasets/nalma_1.json b/datasets/nalma_1.json index 2d47d861c2..b4a6615a30 100644 --- a/datasets/nalma_1.json +++ b/datasets/nalma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nalma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The North Alabama Lightning Mapping Array (NALMA) data are used to validate the Lightning Imaging Sensor (LIS) on the International Space Station (ISS), the Geostationary Lightning Mapper (GLM) instrument, and other current and future lightning measurements. These data are also used in convective storm process studies, including but not limited to validation of convection-resolving models that predict lightning. These NALMA data files are available from December 17, 2019 and are ongoing in ASCII format.", "links": [ { diff --git a/datasets/nalmaraw_1.json b/datasets/nalmaraw_1.json index 16c0aa0cc9..546f1f8251 100644 --- a/datasets/nalmaraw_1.json +++ b/datasets/nalmaraw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nalmaraw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The North Alabama Lightning Mapping Array (LMA) Raw Data are used to validate the Lightning Imaging Sensor (LIS) on the International Space Station (ISS), the Geostationary Lightning Mapper (GLM) instrument, and other current and future lightning measurements. These data are also used in convective storm process studies, including but not limited to validation of convection-resolving models that predict lightning. These NALMA data files are available from December 17, 2019 and are ongoing in ASCII format.", "links": [ { diff --git a/datasets/nam2ds_1.json b/datasets/nam2ds_1.json index a70103c899..be5cb99fe2 100644 --- a/datasets/nam2ds_1.json +++ b/datasets/nam2ds_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nam2ds_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Two-Dimensional Stereo Probe and Cloud Particle Imager dataset consists of data from two probes used to measure the size, shape, and concentration of cloud particles; the two-dimensional stereo probe (2D-S), and the cloud particle imager (CPI). Both of these probes measure particle size distributions and derives extinction, particle concentration, ice water content and particle shape. Both probes provide hi-resolution black and white images of cloud particles. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namapr2_1.json b/datasets/namapr2_1.json index fdfcf4f5df..9a34b7cecc 100644 --- a/datasets/namapr2_1.json +++ b/datasets/namapr2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namapr2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Second Generation Airborne Precipitation Radar (APR-2) dataset was collected by using the Second Generation Airborne Precipitation Radar (APR-2), which is a dual-frequency (14 GHz and 35 GHz), Doppler, dual-polarization radar system that includes digital, real-time pulse compression, extremely compact RF electronics, and a large deployable dual-frequency cylindrical parabolic antenna subsystem. This system measures radar reflectivity and doppler velocity at both the Ku- and Ka-band. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namcaps_1.json b/datasets/namcaps_1.json index 201cbf2ac7..e4d06fde3f 100644 --- a/datasets/namcaps_1.json +++ b/datasets/namcaps_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namcaps_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Cloud Microphysics (CAPS-PIP) dataset consists of particle size distributions from the Clouds, Aerosol and Preciptaition Spectrometer (CAPS) and the Precipitaiton Imaging Probe (PIP) from August 19, 2006 to September 12, 2006. These instruments yield precipitation, hydrometeor and aerosol sizes ranging from 0.55 - 100 microns. Data is in the form of images and ascii tables. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namcobalt_1.json b/datasets/namcobalt_1.json index 880102524d..ddfc9a0c27 100644 --- a/datasets/namcobalt_1.json +++ b/datasets/namcobalt_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namcobalt_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Carbon mOnoxide By Attenuated Laser Transmission (COBALT) dataset includes measurements of the carbon monoxide mixing ratio and derived carbon monoxide mixing ratio profiles in the upper troposphere/lower stratosphere using an in-situ laser absorption spectrometer. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namcvi_1.json b/datasets/namcvi_1.json index b39d30a6f9..5b011bde6d 100644 --- a/datasets/namcvi_1.json +++ b/datasets/namcvi_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namcvi_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the NAMMA CVI Cloud Condensed Water Content dataset the counterflow virtual impactor (CVI) was used to measure condensed water content (liquid water or ice in particles about 8 microns in diameter and up) and Cloud Condensation Nuclei (CCN) on the DC-8 during NASA African Monsoon Multidisciplinary Analyses (NAMMA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets. Water vapor was measured with a MayComm Tunable Diode Laser (TDL) hygrometer and non-volatile particles are examined with an optical particle counter, a condensation nuclei counter, and an impactor for subsequent chemical analyses.", "links": [ { diff --git a/datasets/namdblue_1.json b/datasets/namdblue_1.json index 25311fa1cf..d87402ebf2 100644 --- a/datasets/namdblue_1.json +++ b/datasets/namdblue_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namdblue_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA MODIS/AQUA and MODIS/TERRA Deep Blue Products dataset is a collection of images depicting the aerosol optical depth derived from the MODIS deep blue algorithm from both AQUA and TERRA satellites. Additional imagery includes the RGB and Angstrom Exponent. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namdc8nav_1.json b/datasets/namdc8nav_1.json index ceff735a5a..889b0b63e7 100644 --- a/datasets/namdc8nav_1.json +++ b/datasets/namdc8nav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namdc8nav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA DC-8 Navigation and Housekeeping (ICATS) dataset is designed to: 1) interface and process avionics and environmental paramaters from the Navigational Management System, GPS, Central Air Data Computer, Embedded GPS/INS, and analog voltage sources from aircraft and experimenters; 2) Furnish engineering unit values of selected parameters and computed functions for real-time video display, and archive ASCII data at experimenter stations; and 3) Archive engineering unit values of 'Appendix A' (to the ICATS document included with dataset documentation) on data storage for post flight retrieval. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namdlh_1.json b/datasets/namdlh_1.json index 82b705e238..6373d06608 100644 --- a/datasets/namdlh_1.json +++ b/datasets/namdlh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namdlh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Diode Laser Hygrometer (DLH) dataset uses the DLH, a near-infrared spectrometer operating from aircraft platforms, was developed by NASA's Langley and Ames Research Centers. It measures water vapor mixing ratio and derives water vapor partial pressure, relative humidity, and water vapor flux. Based upon near-infrared tunable diode technology, its spectrometer provides true in situ monitoring of water vapor concentrations with precision levels exceeding those of existing Lyman alpha and frost point hygrometers. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namdrop_1.json b/datasets/namdrop_1.json index e084cefbca..c50fbf42e1 100644 --- a/datasets/namdrop_1.json +++ b/datasets/namdrop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namdrop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA DC-8 Dropsonde dataset were collected by the DC-8 dropsonde system, which uses an integrated, highly accurate, GPS-located atmospheric profiling dropsonde measuring and recording current atmospheric conditions in a vertical column below the aircraft. hese dropsondes, also known as dropwindsondes or parachute radiosondes, are small, lightweight (less than 1 lb) cylindrical instruments that fall freely through the atmosphere, slowed somewhat by a small inflatable parachute. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namdrop_raw_1.json b/datasets/namdrop_raw_1.json index 857a4e4128..e9498a1f6c 100644 --- a/datasets/namdrop_raw_1.json +++ b/datasets/namdrop_raw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namdrop_raw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Raw DC-8 Dropsonde dataset consists of high-resolution vertical profiles of ambient pressure, temperature, relative humidity, wind speed, and wind direction obtained by the DC-8 dropsonde system during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) field campaign. The NAMMA field campaign was based in the Cape Verde Island, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets. The DC-8 dropsonde system uses an integrated, highly accurate, Global Positioning System (GPS)-located atmospheric profiling dropsonde measuring and recording current atmospheric conditions in a vertical column below the aircraft. Data files are available in ASCII format for the period of August 7, 2006 through September 12, 2006. ", "links": [ { diff --git a/datasets/namhamsr_1.json b/datasets/namhamsr_1.json index e8b025fa46..ca62e710a1 100644 --- a/datasets/namhamsr_1.json +++ b/datasets/namhamsr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namhamsr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA High Altitude MMIC Sounding Radiometer (HAMSR) dataset consists of data collected by HAMSR, which is a 25-channel microwave atmospheric sounder operating as a cross-track scanner. It operates with three bands: an 8-channel band centered around 50 GHz, used for primary temperature sounding; a 10-channel band centered around 118 GHz, used for secondary temperature sounding and assessment of scattering; and a 7-channel band centered around 183 GHz, used for water vapor (humidity) sounding. The instrument continuously self-calibrates by using internal calibration targets. Radiometric sensitivity at the composite sampling cells provided in the archive is typically 0.1 K and ranges up to 0.25 K for the stratospheric channels. Calibration accuracy is estimated at better than 1 K for temperature sounding and better than 2 K for water vapor sounding. Temperature weighting function peaks are distributed between the surface and the flight altitude. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namlarge_1.json b/datasets/namlarge_1.json index 3ccfa88989..aa3957a307 100644 --- a/datasets/namlarge_1.json +++ b/datasets/namlarge_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namlarge_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Langley Aerosol Research Group Experiment (LARGE) dataset contains data collected from the following in situ aerosol sensors: condensation nuclei counters, optical particle spectrometers, an aerodynamic particle sizer, and integrating nephelometers. These instruments measure aerosol number density, aerosol size distribution, total scattering and backscattering coefficients. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namlargen_1.json b/datasets/namlargen_1.json index c8d611fe4a..5cdbfae2a2 100644 --- a/datasets/namlargen_1.json +++ b/datasets/namlargen_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namlargen_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Langley Aerosol Research Group Experiment Navigation Data is the DC-8 NAV data (ICATS) extracted into columns with time correction. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets. This data was used with the LARGE dataset, but may also be used with other NAMMA datasets. It includes the wind speed and wind direction as well as pressure and air temperature information.", "links": [ { diff --git a/datasets/namlase_1.json b/datasets/namlase_1.json index 3827f50d7b..aec0291af3 100644 --- a/datasets/namlase_1.json +++ b/datasets/namlase_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namlase_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Lidar Atmospheric Sensing Experiment (LASE) dataset used the LASE system using the Differential Absorption Lidar (DIAL) system was operated during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign to gather water vapor mixing ratio and aerosol scattering ratio (815 nm) profiles. Other derived parameters include: relative humidity, equivalent potential temperature, virtual potential temperature, precipitable water vapor profiles, aerosol backscatter, aerosol extinction, and aerosol optical thickness profiles (815 nm). Aerosol data are reported as atmospheric scattering ratios on a logarithmic scale. Water vapor data are reported as mixing ratios (g/kg) on both a linear and logarithmic scale. LASE was operated from the NASA DC-8 aircraft during 14 NAMMA campaign flights between August 15 and September 12, 2006.", "links": [ { diff --git a/datasets/nammms_1.json b/datasets/nammms_1.json index 393a7171de..ee38a9cfdc 100644 --- a/datasets/nammms_1.json +++ b/datasets/nammms_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nammms_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA DC-8 Meteorological Measurement System (MMS) dataset used the MMS, which consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the Earth, and a data acquisition system to sample, process, and record the measured quantities. The air-motion system consists of two airflow-angle probes, three total temperature probes each with a different response time, a pitot-static pressure probe, and a dedicated static pressure system. All probes and sensors are judiciously located at specific positions of the fuselage. The aircraft-motion sensing system consists of an embedded GPS ring laser inertial navigation system, and a multiple-antenna GPS attitude reference system. Customized software was developed to control, sample, and process all sensors and hardware. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namnpol_1.json b/datasets/namnpol_1.json index db685fdafb..80d3d2e6ae 100644 --- a/datasets/namnpol_1.json +++ b/datasets/namnpol_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namnpol_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA NASA Polarimetric Doppler Weather Radar (NPOL) dataset used the NPOL, developed by a research team from Wallops Flight Facility, is a fully transportable and self-contained S-band research radar that collected and operated nearly continuously during NAMMA. Data was collected 19 August through 30 September 2006, at Kawsara, Senegal. Its continuous operation provides a full volume scan every fifteen minutes. Scans may be either 270 Km long range scans or 150 Km range for most high resolution data scans. Products available include real time PPI scans of reflectivities and velocities, and near real time displays of other radar products, including RHI's, CAPPI's, and Polarimetric products. Browse imagery is available for PPI reflectivities. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namradio_1.json b/datasets/namradio_1.json index 730b2a0560..4024ca686c 100644 --- a/datasets/namradio_1.json +++ b/datasets/namradio_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namradio_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Praia Cape Verde Radiosonde data used Sippican MarkIIa DGPS (LOS) radiosondes, which were launched in support of NASA African Monsoon Multidisciplinary Analyses (NAMMA) mission. This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets. The radiosondes released were Sippican MK-IIa units developed by Lockheed Martin. The atmospheric soundings were used to measure pressure, temperature, humidity, wind direction and speed and spatial coordinates. Data is grouped by ascending and descending flights and includes temperature, Skew-T, trajectory, wind and time series plots.", "links": [ { diff --git a/datasets/namsenegal_1.json b/datasets/namsenegal_1.json index 4673b71bb7..e2b24ea824 100644 --- a/datasets/namsenegal_1.json +++ b/datasets/namsenegal_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namsenegal_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Senegal Radiosonde and Tower Flux data includes measurements of humidity, wind speed/direction and velocity. Additionally, the flux data includes photosynthetically active radiation (PAR), air and soil temperature and heat flux data. The flux data was obtained from a tower located in Kawasara, Sengal, Africa. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namsenrg_1.json b/datasets/namsenrg_1.json index c35db481af..0d0883828e 100644 --- a/datasets/namsenrg_1.json +++ b/datasets/namsenrg_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namsenrg_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Senegal Rain Gauge Network consisted of 40 rain gauge sites (AMMA 1-40) located in various places throughout Senegal, West Africa. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets. The Rain Gauge Network consisted of the large-scale rain gauge network. The rain gauges collected one-minute accumulation data. The location and photos of each site can be found in an accompanying PDF document: NAMMA_Raingauge_network.pdf.", "links": [ { diff --git a/datasets/namsmart_1.json b/datasets/namsmart_1.json index 00157f6950..f0b43d3478 100644 --- a/datasets/namsmart_1.json +++ b/datasets/namsmart_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namsmart_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA SMART-COMMIT Mobile Laboratories dataset consists of data obtained from a suite of in situ and remote sensing instruments which measure parameters that characterize constituents of the atmosphere at a given location. The mobile system is comprised of many instruments including radiometers, lidar, particle sizers, gas monitors, meteorological sensors, tethered radiosondes, and others. Parameters measured include radiances, irradiances, back scatter profile, atmospheric state variables, aerosol scattering/absorbing, particle size distribution, trace gas concentrations, and sky images. This dataset also includes many derived products. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namtoga_1.json b/datasets/namtoga_1.json index 33962d04ce..b10c5fd57a 100644 --- a/datasets/namtoga_1.json +++ b/datasets/namtoga_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namtoga_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA TOGA Radar Data dataset consists of a collection of products derived from the NASA TOGA radar observations that were collected in the Republic of Cape Verde during the NAMMA campaign. The NASA TOGA radar is a C-band scanning radar with a beam width of 1.65 degrees. The radar was deployed on the southern tip of Sao Tiago (14.92N, 23.48W), the southern-most island in the Cape Verde islands. The radar operated nearly continuously from 15 August through 16 September, 2006, collecting measurements of horizontal radar reflectivity (ZH), radial velocity (VR) and spectral width (SW). These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namukatd_1.json b/datasets/namukatd_1.json index 7158c577de..c2d8754043 100644 --- a/datasets/namukatd_1.json +++ b/datasets/namukatd_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namukatd_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA ATD Lightning data provided by the UK Meterological Office from multiple outstations contains lightning stroke data, latitude and longitude, accuracy and weighting for fading-in flashes of lightning for the African Coast during the NAMMA experiment. Time is determined by the Arrival Time Difference (ATD) of the reporting stations. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/namzeus_1.json b/datasets/namzeus_1.json index 649b1dc73b..c8e6a8f79f 100644 --- a/datasets/namzeus_1.json +++ b/datasets/namzeus_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "namzeus_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NAMMA Lightning ZEUS data is provided by World-ZEUS Long Range Lightning Monitoring Network Data obtained from radio atmospheric signals located at thirteen ground stations spread across the European and African continents and Brazil from August 1, 2006 to October 1, 2006. Lightning activity occurring over a large part of the globe is continuously monitored at varying spatial accuracy (e.g. 10-20 km within and >50 km outside the network periphery) and high temporal (1 msec) resolution. Time is determined by the Arrival Time Difference between the time series from the pairs of the receivers. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.", "links": [ { diff --git a/datasets/nanoplastics-in-forests_1.0.json b/datasets/nanoplastics-in-forests_1.0.json index a19b4ac706..2aff0470f5 100644 --- a/datasets/nanoplastics-in-forests_1.0.json +++ b/datasets/nanoplastics-in-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nanoplastics-in-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The fate of plastic in the environment is of global concern, because its production recently has increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known about the impact of micro and nanoplastics in forest ecosystems. The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and examined if they were incorporated in different above- and belowground tissues. Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (\u0394\u03b413C<1\u2030) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not. We acknowledge that the new method developed might be not sensitive enough to unequivocally detect mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.", "links": [ { diff --git a/datasets/napf-ert-monitoring-data_1.0.json b/datasets/napf-ert-monitoring-data_1.0.json index 8648444bbf..d4c3d6a1d7 100644 --- a/datasets/napf-ert-monitoring-data_1.0.json +++ b/datasets/napf-ert-monitoring-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "napf-ert-monitoring-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process. The data is organized in two zip-files: * Napf_Raw_BIN.zip: Raw monitoring data in bin-format * Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring line The zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format The file names contain following abbreviations: * Site: Napf * Profile: Hor (horizontal profile), Ver (vertical profile) * Configuration: WS (Wenner-Schlumberger configuration) * Date: Format YYYY-MM-DD * Time: Format hhmm", "links": [ { diff --git a/datasets/napf-soil-wetness-monitoring-data_1.0.json b/datasets/napf-soil-wetness-monitoring-data_1.0.json index 5c047ac8b3..d4e86ccdf5 100644 --- a/datasets/napf-soil-wetness-monitoring-data_1.0.json +++ b/datasets/napf-soil-wetness-monitoring-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "napf-soil-wetness-monitoring-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains the soil wetness monitoring data from the publication Wicki et al. (2022). It was collected in Wasen i.E. (Napf area, Switzerland). The monitoring data is quality-controlled and aggregated to hourly values and it is provided for the study period 2019-04-05 to 2022-04-30. The following information is contained (by column): * Timestamp (UTC+1 time zone) * Site: Slope (47.02486 N, 7.81960 E), Flat (47.02302 N, 7.81760 E) * Sensor type * Measure: VWC = volumetric water content [m3 m-3], MP = matric potential [hPa], TEMP = temperature [\u00b0C], PREC = precipitation [mm] * Sensor number (per site each sensor is provided a unique identifier) * Installation depth [m] * Homogenization flag: If the data is considered homogeneous, it is given the flag 1, else the flag 0 is given * Sensor value * Normalized value: Normalization was conducted for VWC (saturation) and MP values Wicki, A., Lehmann, P., Hauck, C., and St\u00e4hli, M.: Impact of topography on in-situ soil wetness measurements for regional landslide early warning \u2013 a case study from the Swiss Alpine Foreland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-211, in review, 2022.", "links": [ { diff --git a/datasets/nascent-campaign-data-for-motos-et-al-2023_1.0.json b/datasets/nascent-campaign-data-for-motos-et-al-2023_1.0.json index fb7b4556f5..a71202b0fd 100644 --- a/datasets/nascent-campaign-data-for-motos-et-al-2023_1.0.json +++ b/datasets/nascent-campaign-data-for-motos-et-al-2023_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nascent-campaign-data-for-motos-et-al-2023_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data are described in detail in the paper \"Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds\" (https://doi.org/10.5194/egusphere-2023-530, 2023). This dataset includes particle number size distribution data from two DMPSs, chemical composition data from a Tof-ACSM, updraft velocity from an ultrasonic anemometer and a wind lidar, cloud droplet number concentration from a HOLIMO and meteorological data (wind speed and direction, temperature). Note that aerosol composition from a filter pack system, organiccarbon massfrom a high volume sampler and eBC concentration from a MAAP are available on EBAS and therefore not included here", "links": [ { diff --git a/datasets/naturalness-of-protective-forests_1.0.json b/datasets/naturalness-of-protective-forests_1.0.json index ba28cf9154..753bf58830 100644 --- a/datasets/naturalness-of-protective-forests_1.0.json +++ b/datasets/naturalness-of-protective-forests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "naturalness-of-protective-forests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data and scripts used by Scherrer et al. 2023 in the publication 'Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition'. The analysis is based on data about the tree species composition of the canopy layer in the NFI4 and information about the potential natural forest of the sites based on the NaiS classification system.", "links": [ { diff --git a/datasets/nauru99_0.json b/datasets/nauru99_0.json index 152f4ccea9..85778130fc 100644 --- a/datasets/nauru99_0.json +++ b/datasets/nauru99_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nauru99_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements made around the island of Nauru in Micronesia in 1999.", "links": [ { diff --git a/datasets/navdc8cpex_1.json b/datasets/navdc8cpex_1.json index bc54e3e9ef..926ddcb2f4 100644 --- a/datasets/navdc8cpex_1.json +++ b/datasets/navdc8cpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "navdc8cpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The DC-8 Navigation Data CPEX dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA DC-8 aircraft during the Convective Processes Experiment (CPEX) field campaign. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 25, 2017 through June 28, 2017 in ASCII format.", "links": [ { diff --git a/datasets/navghepoch_1.json b/datasets/navghepoch_1.json index 7e32ee3a85..952a1be285 100644 --- a/datasets/navghepoch_1.json +++ b/datasets/navghepoch_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "navghepoch_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Hawk Navigation EPOCH dataset consists of the real-time navigation and housekeeping data that was acquired by various instruments aboard the Global Hawk during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The data files are available from July 27, 2017 through August 31, 2017 in CSV format with associated KML browse files. ", "links": [ { diff --git a/datasets/nbi_veg_maps_787_1.json b/datasets/nbi_veg_maps_787_1.json index 59a351f50b..35ade9e143 100644 --- a/datasets/nbi_veg_maps_787_1.json +++ b/datasets/nbi_veg_maps_787_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nbi_veg_maps_787_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources include published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998); Giess (1971); Wild and Barbosa (1968); Barbosa (1970); and White (1983). Each source map delineates a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final map is made up of six independent sub-regional maps. A cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units is provided.The data set contains six GIS shapefile archives, each containing a shape file for a given region in southern Africa on a 5 x 5 degree grid. An accompanying ASCII file contains the species list associated with the map files. The regional NBI Vegetation Map (a compilation of the 6 independent sub-regional coverages) is provided as a JPEG image.", "links": [ { diff --git a/datasets/ncep_met_1deg_1226_1.json b/datasets/ncep_met_1deg_1226_1.json index 3e682ff134..1784cdf5f6 100644 --- a/datasets/ncep_met_1deg_1226_1.json +++ b/datasets/ncep_met_1deg_1226_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ncep_met_1deg_1226_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set for the ISLSCP Initiative II data collection provides near surface meteorological variables, fluxes of heat, moisture and momentum at the surface, and land surface state variables, all with a spatial resolution of 1 degree in both latitude and longitude. There are four temporal categories of data: time invariant and monthly mean annual cycle fields (together referred to as \"fixed\" fields), monthly mean fields, monthly 3-hourly diurnal, and 3-hourly fields. Two types of variables exist in this data; instantaneous fields (primarily state variables), and average fields (primarily flux fields expressed as a rate). The Center for Ocean-Land Atmosphere Studies (COLA) near-surface data set for ISLSCP II was derived from the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmospheric Model Inter-comparison Project (AMIP-II) reanalysis (http://www.cpc.ncep.noaa.gov/products/wesley/reanalysis2/), covering the years from 1979-2003. The data set for ISLSCP II covers the period from 1986 to 1995. The purpose of the reanalysis was to provide an improved version of the original NCEP/National Center for Atmospheric Research (NCAR) reanalysis for General Circulation Model (GCM) validation. To co-register the NCEP/DOE reanalysis on the ISLSCP 1-degree grid, the reanalysis data set was regridded from its native T62 Gaussian grid) resolution (192 x 94 grid boxes globally) to 1-degree ISLSCP II required resolution.There are 136 compressed (.tar.gz) data files with this data set. When extrapolated, the individual data files are in ASCII (.asc) format.", "links": [ { diff --git a/datasets/ncsusndimpacts_1.json b/datasets/ncsusndimpacts_1.json index e0d9aba909..47b5e70083 100644 --- a/datasets/ncsusndimpacts_1.json +++ b/datasets/ncsusndimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ncsusndimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NCSU Soundings IMPACTS dataset consists of atmospheric-sounding data collected by the North Carolina State University student sounding club. These data include vertical profiles of atmospheric temperature, relative humidity, pressure, wind speed, and wind direction. These rawinsondes were launched from Raleigh, NC in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The sounding data files are available in netCDF-4 format for February 20, 2020, from February 12, 2023. ", "links": [ { diff --git a/datasets/nead_0.1 (public request for comments).json b/datasets/nead_0.1 (public request for comments).json index b628ec9d5f..f23862f3ad 100644 --- a/datasets/nead_0.1 (public request for comments).json +++ b/datasets/nead_0.1 (public request for comments).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nead_0.1 (public request for comments)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "__Acknowledgement__: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format. The NEAD specifications were presented at the __\"WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century\" (Virtual Conference)__. ----------------------- __Summary:__ The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories. __License:__ The NEAD specifications are released to the public domain under a Creative Commons CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.", "links": [ { diff --git a/datasets/neophyte-risk-map-ticino_1.0.json b/datasets/neophyte-risk-map-ticino_1.0.json index c62048a5e7..655644d2d2 100644 --- a/datasets/neophyte-risk-map-ticino_1.0.json +++ b/datasets/neophyte-risk-map-ticino_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "neophyte-risk-map-ticino_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "943 disturbances in the forest of southern Switzerland have been visited and characterized with various general and specific parameters and the presence absence of woody neophyte species has also been recorded. A Generalized linear regression modelling approach with a binomial family (link function \u201clogit\u201d) was then used to analyse the effects of these parameters on the presence/absence of the six most widespread neophyte species separately (i.e Ailanthus altissima, Buddleja davidii, R. pseudoacacia, Paulownia tomentosa, Prunus laurocerasus, Trachycarpus fortunei). If needed, the models were refitted with the spmodel R-package to account for the spatial dependence. The best model for every species have been used to predict the risk of invasion on a 25 X 25m grid of 1\u2019773\u2019603 million of points covering the entire forest area under 1\u2019500 m a.s.l. Predictions over this new set of points have been computed with the predict function (v4.2.1; R core Team, 2023) and using the best select model for every neophyte species. The resulting prediction are available as a raster tiff. These presence probability risk maps for the forest area of the entire canton Ticino provide a practical tool to be used in combination with the waldmonitoring.ch data allowing to efficiently monitor the spread of woody neophyte species in new disturbances in the forest.", "links": [ { diff --git a/datasets/net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0.json b/datasets/net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0.json index 3a45344db0..9ccebc61c3 100644 --- a/datasets/net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0.json +++ b/datasets/net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Simulated net primary productivity (NPP) anomalies (percent deviation) in 1961-2018 years relative to the 1961\u20131990 reference period for _Picea abies_ and _Fagus sylvatica_. NPP was simulated for the species' potential distribution range in Switzerland on a 1 \u00d7 1 km grid using 3-PG model. We first assimilated nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017, into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from. We then estimated the NPP anomalies by first simulating the growth of _P. abies_ and _F. sylvatica_ monocultures with the average climate observed during the 1961\u20131990 period, until the age of 40 years (spin-up). The stands were simulated starting as 2-year-old plantations with an initial density of 10,000 trees/ha. Thinning was performed at age 20 and 35 to reach a final density of ca. 1,000 trees/ha at age 40. We then simulated 30 years forced by monthly resolved climatic data from either the 1961\u20131990 (reference, according to MeteoSwiss) or the 1991\u20132018 period. We neglected the first 40 years of simulations due to high variation in productivity caused by early stage stand development. To study the impact of climate extremes on NPP, we focused on the deviation in NPP (expressed in percentage difference from the reference period) during the 30 year period (age 41\u201370).", "links": [ { diff --git a/datasets/net_carbon_flux_662_1.json b/datasets/net_carbon_flux_662_1.json index 91e8404b82..587dcfe7ba 100644 --- a/datasets/net_carbon_flux_662_1.json +++ b/datasets/net_carbon_flux_662_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "net_carbon_flux_662_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting.", "links": [ { diff --git a/datasets/net_increment-80_1.0.json b/datasets/net_increment-80_1.0.json index 4f42b6c74f..78bb26f7bb 100644 --- a/datasets/net_increment-80_1.0.json +++ b/datasets/net_increment-80_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "net_increment-80_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increment including ingrowth minus the mortality. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/net_increment_star-187_1.0.json b/datasets/net_increment_star-187_1.0.json index 92af748bdf..51aa663a1b 100644 --- a/datasets/net_increment_star-187_1.0.json +++ b/datasets/net_increment_star-187_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "net_increment_star-187_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Increment with ingrowth minus the mortality. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/newcomb_bay_bathy_dem_1.json b/datasets/newcomb_bay_bathy_dem_1.json index 7ff2e48796..832f44e60c 100644 --- a/datasets/newcomb_bay_bathy_dem_1.json +++ b/datasets/newcomb_bay_bathy_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "newcomb_bay_bathy_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands \nand terrestrial and bathymetric contours derived from the DEM. \nThe data is stored in a UTM zone 49(WGS-84) projection.\nHeights are referenced to mean sea level.\nIt was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES.", "links": [ { diff --git a/datasets/nexeastimpacts_1.json b/datasets/nexeastimpacts_1.json index 03ea98f685..883fa02322 100644 --- a/datasets/nexeastimpacts_1.json +++ b/datasets/nexeastimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nexeastimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NEXRAD Mosaic East IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic East dataset is composed of Level II data from 19 NEXRAD sites in the eastern U.S.. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/nexmidwstimpacts_1.json b/datasets/nexmidwstimpacts_1.json index 604a62936d..4f7e696a3e 100644 --- a/datasets/nexmidwstimpacts_1.json +++ b/datasets/nexmidwstimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nexmidwstimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/niche-partitioning-between-wild-bees-and-honeybees_1.0.json b/datasets/niche-partitioning-between-wild-bees-and-honeybees_1.0.json index 5b21109a06..d47936f0da 100644 --- a/datasets/niche-partitioning-between-wild-bees-and-honeybees_1.0.json +++ b/datasets/niche-partitioning-between-wild-bees-and-honeybees_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "niche-partitioning-between-wild-bees-and-honeybees_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity. This dataset contains the raw and processed data supporting the findings from the paper: \"Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city\". The data contains: 1. Bee trait measurements at the species and individual-level of five functional traits. 2. The values of the feeding niche partitioning (functional dissimilarity to honeybees) 3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.", "links": [ { diff --git a/datasets/nigeria_marine.json b/datasets/nigeria_marine.json index d6fe325e0d..3a6c661bc0 100644 --- a/datasets/nigeria_marine.json +++ b/datasets/nigeria_marine.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nigeria_marine", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Nigerian Institute for Oceanography and Marine Research (NIOMR), was created from the Marine Research Division of the Federal Department of Fisheries. The Aquaculture department is mandated to research into the development of Aquaculture, including improvement of transportation devices for juveniles to reduce mortality.\n\nThis collection was compiled from publications, and it currently consists of 556 records of 106 families.", "links": [ { diff --git a/datasets/nitrogen_deposition_730_1.json b/datasets/nitrogen_deposition_730_1.json index a84c8725f8..b539ac8edd 100644 --- a/datasets/nitrogen_deposition_730_1.json +++ b/datasets/nitrogen_deposition_730_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nitrogen_deposition_730_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains data for wet and dry nitrogen-species deposition for the United States and Western Europe. Deposition data were acquired directly from monitoring programs in the United States and Europe for time periods from 1978-1994 for wet deposition and from 1989-1994 for dry deposition and evaluated using similar quality assurance criteria to ensure comparability. A standard geostatistical method (kriging) was used to interpolate data onto a 0.5 x 0.5 degree resolution map for wet and dry deposition.", "links": [ { diff --git a/datasets/nlcd_1992.json b/datasets/nlcd_1992.json index 2f781fa583..5c58d5d8ec 100644 --- a/datasets/nlcd_1992.json +++ b/datasets/nlcd_1992.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nlcd_1992", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Land Cover Dataset 1992 (NLCD1992) is a 21-class land cover classification scheme that has been applied consistently across the lower 48 United States at a spatial resolution of 30 meters. NLCD92 is based primarily on the unsupervised classification of Landsat Thematic Mapper (TM) circa 1990's satellite data. Other ancillary data sources used to generate these data included topography, census, and agricultural statistics, soil characteristics, and other types of land cover and wetland maps. NLCD1992 is the only NLCD dataset that can be downloaded by state and by user defined area from the MRLC Consortium Viewer.", "links": [ { diff --git a/datasets/nlcd_1992_2001_retrofit.json b/datasets/nlcd_1992_2001_retrofit.json index 676ec874f9..73a6b19559 100644 --- a/datasets/nlcd_1992_2001_retrofit.json +++ b/datasets/nlcd_1992_2001_retrofit.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nlcd_1992_2001_retrofit", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Developments in mapping methodology, new sources of input data, and changes in the mapping legend for the 2001 National Land Cover Database (NLCD2001) will confound any direct comparison between NLCD2001 and National Land Cover Dataset 1992 (NLCD1992). Users are cautioned that direct comparison of these two independently created land cover products is not recommended. This NLCD 1992/2001 Retrofit Land Cover Change Product was developed to offer users more accurate direct change analysis between the two products.\n\nThe NLCD 1992/2001 Retrofit Land Cover Change Product uses a specially developed methodology to provide land cover change information at the Anderson Level I classification scale (Anderson et al., 1976*), relying on decision tree classification of Landsat satellite imagery from circa 1992 and 2001. Unchanged pixels between the two dates are coded with the NLCD01 Anderson Level I class code, while changed pixels are labeled with a \"from-to\" land cover change value. Additional details about this product are available in the metadata included in the multi-zone downloadable zip file. This product is designed for regional application only and is not recommended for local scales.", "links": [ { diff --git a/datasets/nlcd_2001_ver_2.json b/datasets/nlcd_2001_ver_2.json index aa3e9c3887..6fc42d2a87 100644 --- a/datasets/nlcd_2001_ver_2.json +++ b/datasets/nlcd_2001_ver_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nlcd_2001_ver_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Land Cover Database 2001 (NLCD2001) is a 16-class (additional four classes in Alaska only) land cover classification scheme that has been applied consistently across all 50 United States and Puerto Rico at a spatial resolution of 30 meters. NLCD2001 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2001 satellite data. NLCD2001 improves on NLCD92 in that it is comprised of three different elements: land cover, percent developed impervious surface and percent tree canopy density. NLCD2001 also uses improved classification algorithms, which have resulted in data with more precise rending of spatial boundaries between the land cover classes.", "links": [ { diff --git a/datasets/nlcd_2006.json b/datasets/nlcd_2006.json index 95d7ac61d5..c21fc59f3d 100644 --- a/datasets/nlcd_2006.json +++ b/datasets/nlcd_2006.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nlcd_2006", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States.", "links": [ { diff --git a/datasets/noaa_albedo_5year-av_xdeg_959_1.json b/datasets/noaa_albedo_5year-av_xdeg_959_1.json index 06708146ef..e41dfb7322 100644 --- a/datasets/noaa_albedo_5year-av_xdeg_959_1.json +++ b/datasets/noaa_albedo_5year-av_xdeg_959_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "noaa_albedo_5year-av_xdeg_959_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this work was to produce a monthly climatology of broadband surface albedos for use in global numerical weather prediction models at the National Centers for Environmental Prediction (NCEP). Monthly means of clear-sky, surface, broadband, snow-free albedos for overhead sun illumination angle were determined using data from a five-year period from April 1985-December 1987 and January 1989-March 1991. The data set is compatible in temporal coverage and spatial resolution with a monthly climatology of green vegetation fraction (Gutman and Ignatov, 1998) delivered earlier and currently in use at NCEP. Three zip files are provided at three spatial resolutions of quarter, half and on degree, each containing 12 data files in standard ESRI ArcGIS ArcInfo Grid format, and 12 data files in ASCII format denoting defifferences between the original data set and the ISLSCP II Land Sea Mask. ", "links": [ { diff --git a/datasets/noaasndimpacts_1.json b/datasets/noaasndimpacts_1.json index c5eb723f36..ebcf4c4bc5 100644 --- a/datasets/noaasndimpacts_1.json +++ b/datasets/noaasndimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "noaasndimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NOAA Soundings IMPACTS dataset was collected from January 1, 2020, through March 1, 2023, during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The goal of IMPACTS was to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. The data are available in netCDF-4 format. ", "links": [ { diff --git a/datasets/non-native-native-plant-interactions-in-australian-grasslands_1.0.json b/datasets/non-native-native-plant-interactions-in-australian-grasslands_1.0.json index 826f11ee2b..62aa133a8b 100644 --- a/datasets/non-native-native-plant-interactions-in-australian-grasslands_1.0.json +++ b/datasets/non-native-native-plant-interactions-in-australian-grasslands_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "non-native-native-plant-interactions-in-australian-grasslands_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data, on which the following publication below is based. __Paper Citation:__ _Schlierenzauer, C., Risch, A.C., Sch\u00fctz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._ __Please cite this paper together with the citation for the datafile.__ Study area The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017). The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017). Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013). Experimental design and sampling The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36\u00b040\u2019 to 36\u00b042\u2019 S and 149\u00b038\u2019 to 149\u00b042\u2019 E) and three of them are located South (36\u00b051\u2019 to 36\u00b049\u2019 S and 149\u00b038\u2019 to 149\u00b042\u2019 E) of Candelo. Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated. References NSW - Department of Planing, Industries and Environment. 2019. \u201cLowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.\u201d https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021). NSW Government - Office of Environment and Heritage. 2017. \u201cLowland Grassy Woodland in the South East Corner Bioregion - Profile.\u201d https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021). Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf. Tozer, Mark et al. 2010. \u201cNative Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.\u201d Cunninghamia\u202f: a journal of plant ecology for eastern Australia 11(3): 359\u2013406.", "links": [ { diff --git a/datasets/npolimpacts_1.json b/datasets/npolimpacts_1.json index a1c7f2a825..98acc7cb99 100644 --- a/datasets/npolimpacts_1.json +++ b/datasets/npolimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "npolimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NASA S-Band Dual Polarimetric (NPOL) Doppler Radar IMPACTS dataset consists of rain rate, reflectivity, Doppler velocity, and other radar measurements obtained from the NPOL radar during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The goal of IMPACTS was to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The IMPACTS NPOL data are available from January 10, 2020 thru February 25, 2020. Zipped data files are in netCDF-3/CF format and contain corrected radar reflectivity, differential reflectivity, specific differential phase, differential phase, co-polar correlation, and Doppler velocity images.", "links": [ { diff --git a/datasets/ns0012bq_482_1.json b/datasets/ns0012bq_482_1.json index bb584dd6bc..50db1679d9 100644 --- a/datasets/ns0012bq_482_1.json +++ b/datasets/ns0012bq_482_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ns0012bq_482_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Collection of the NS001 images occurred over the study areas during the 1994 field campaigns. The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994.", "links": [ { diff --git a/datasets/ns001bil_440_1.json b/datasets/ns001bil_440_1.json index 2f5081af86..77f099d516 100644 --- a/datasets/ns001bil_440_1.json +++ b/datasets/ns001bil_440_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ns001bil_440_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Data collections occurred over the study areas during the 1994 field campaigns.", "links": [ { diff --git a/datasets/nsafcovr_252_1.json b/datasets/nsafcovr_252_1.json index 7e7afb7b1f..e054d34dfd 100644 --- a/datasets/nsafcovr_252_1.json +++ b/datasets/nsafcovr_252_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nsafcovr_252_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files.", "links": [ { diff --git a/datasets/nsf0232042.json b/datasets/nsf0232042.json index 219435878a..4f9b8741e7 100644 --- a/datasets/nsf0232042.json +++ b/datasets/nsf0232042.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nsf0232042", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Near complete coverage of the East Antarctic shield by ice hampers geological\n study of crustal architecture important for understanding global tectonic and\n climate history. Limited exposures in the central Transantarctic Mountains \n (CTAM), however, show that Archean and Proterozoic rocks of the shield as well\n as Neoproterozoic-lower Paleozoic sedimentary successions were involved in\n oblique convergence associated with Gondwana amalgamation. Subsequently, the\n area was overprinted by Jurassic magmatism and Cenozoic uplift. \n \n To extend the known geology of the region to ice-covered areas, we conducted an\n aeromagnetic survey flown in draped mode by helicopters over the Transantarctic\n Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew\n >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of\n 600 m, with average line spacing 2.5 km over most areas and 1.25 km over\n basement rocks exposed in the Miller and Geologists ranges. Additional lines\n flown to true north, south and west extended preliminary coverage and tied with\n existing surveys. Gravity data was collected on the ground along a central\n transect of the helicopter survey area. From December 2003 to January 2004,\n the CTAM group flew a helicopter and twin-otter aeromagnetic survey and\n collected gravity station data on the ground in profile form. These data will\n be integrated with other geologic and geophysical data in order to extend the\n known geology of the region to ice-covered areas.", "links": [ { diff --git a/datasets/number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0.json b/datasets/number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0.json index 3166d9edff..19b52f9542 100644 --- a/datasets/number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0.json +++ b/datasets/number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946. The following information is contained (by column and column title): * year * total number of hazard fatalities * number of fatalities by flood (German: Hochwasser, \u00dcberschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions. * number of fatalities by debris flow (German: Murgang). * number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure). * number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz). * number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions. * number of fatalities by lightning (German: Blitz). * number of fatalities by ice avalanche (German: Eislawine). * number of fatalities by earthquake (German: Erdbeben). * number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See). The data was collected based on newspaper research. For more information please refer to _Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._ The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche. __Restrictions: The data set is not complete.__ Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road). Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).", "links": [ { diff --git a/datasets/number_of_forest_edges-124_1.0.json b/datasets/number_of_forest_edges-124_1.0.json index 3dd1ab0b3f..bb1a8c0ed6 100644 --- a/datasets/number_of_forest_edges-124_1.0.json +++ b/datasets/number_of_forest_edges-124_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number_of_forest_edges-124_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of forest edges according to the NFI definition. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/number_of_forest_plots-125_1.0.json b/datasets/number_of_forest_plots-125_1.0.json index 185949ac7a..f543508f2d 100644 --- a/datasets/number_of_forest_plots-125_1.0.json +++ b/datasets/number_of_forest_plots-125_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number_of_forest_plots-125_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of forest sample plots (Plots). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/number_of_woody_species_from_40_cm_height-144_1.0.json b/datasets/number_of_woody_species_from_40_cm_height-144_1.0.json index 9822cd85d9..a8f75ac00a 100644 --- a/datasets/number_of_woody_species_from_40_cm_height-144_1.0.json +++ b/datasets/number_of_woody_species_from_40_cm_height-144_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number_of_woody_species_from_40_cm_height-144_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of species of living trees and shrubs starting at 40 cm plant height that occur within a 200 m2 sample plot. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/number_of_woody_species_gt_12_cm_dbh-41_1.0.json b/datasets/number_of_woody_species_gt_12_cm_dbh-41_1.0.json index dcea8a5338..b3dd2cabe2 100644 --- a/datasets/number_of_woody_species_gt_12_cm_dbh-41_1.0.json +++ b/datasets/number_of_woody_species_gt_12_cm_dbh-41_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number_of_woody_species_gt_12_cm_dbh-41_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of tree and shrub species starting at 12 cm dbh (diameter at breast height) within the 200 m2 sample plot. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/number_of_young_forest_plants_by_damage-209_1.0.json b/datasets/number_of_young_forest_plants_by_damage-209_1.0.json index 7236c9556f..bba61cb870 100644 --- a/datasets/number_of_young_forest_plants_by_damage-209_1.0.json +++ b/datasets/number_of_young_forest_plants_by_damage-209_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "number_of_young_forest_plants_by_damage-209_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of regeneration trees starting at 10 cm height up to 11.9 cm dbh with a particular type of damage or with no damage. The attribute is recorded by targeting the next regeneration tree in the centre of the subplot during NFI\u2019s regeneration survey. A regeneration tree may have more than one type of damage, which means it may contribute to the total number of regeneration trees for several different types of damage. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/nutrient-addition-stillberg_1.0.json b/datasets/nutrient-addition-stillberg_1.0.json index 27cda07156..35d863b055 100644 --- a/datasets/nutrient-addition-stillberg_1.0.json +++ b/datasets/nutrient-addition-stillberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nutrient-addition-stillberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short\u2010term fertilisation trials persisted in a longer\u2010term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation. Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha\u22121 a\u22121 was added annually to plots containing 30\u2010year\u2010old *Larix decidua* or 32\u2010year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed. # Experimental design Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha\u22121 a\u22121). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* M\u00f6hl et al (2019). # Data description The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here: ", "links": [ { diff --git a/datasets/nwrc_amphibianslowermiss.json b/datasets/nwrc_amphibianslowermiss.json index f28e0a54aa..3a76b47ee9 100644 --- a/datasets/nwrc_amphibianslowermiss.json +++ b/datasets/nwrc_amphibianslowermiss.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nwrc_amphibianslowermiss", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bottomland hardwood forests are floodplain forests distributed along\nrivers and streams throughout the central and southern United States.\nThe largest bottomland hardwood ecosystem in North America occurred\nwithin the Lower Mississippi River Alluvial Valley (LMAV). By the\n1980.s, an estimated 80% of the former 10 million ha of bottomland\nhardwood forest in the LMAV were cleared for flood control efforts,\nagriculture, and development. Forests are continuing to be cleared\ntoday at an alarming rate, and the forests that remain are highly\ndegraded and fragmented. In addition, these forests are subjected to\nextreme hydrological alterations. Over the past few decades,\nextensive efforts have begun to reforest marginal agricultural lands\nwithin the LMAV. Restoration efforts are limited by the lack of\ninformation concerning the habitat needs of bottomland wildlife\nspecies. Amphibians are one group of species for which little is\nknown about their population status or habitat requirements in the\nLMAV. Information concerning the population status of amphibians in\nthe LMAV is especially important since amphibians appear to be\ndeclining worldwide. Amphibians may also be important indicators of\nenvironmental health because of their sensitivity to land management\npractices and water quality. Understanding the habitat requirements\nof amphibians can be a step toward enhancing wildlife populations\nwithin the LMAV by providing valuable information for improving land\nmanagement practices and wetland restoration techniques.\n\nTo provide an inventory of amphibians at Tensas River and Lake Ophelia\nNational Wildlife Refuges. In addition, to determine amphibian\ndistribution patterns in the LMAV as they relate to landscape habitat\nfeatures. Research results will be used to develop reports and\nmanuscripts, and to assist land managers in management decisions to\nbenefit amphibian populations.\n\nInformation was obtained from Janene Lichtenberg for this metadata.", "links": [ { diff --git a/datasets/nymesoimpacts_1.json b/datasets/nymesoimpacts_1.json index 43c823f18a..c06bf064ad 100644 --- a/datasets/nymesoimpacts_1.json +++ b/datasets/nymesoimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "nymesoimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The New York State Mesonet IMPACTS dataset is browse-only. It consists of temperature, wind, wind direction, mean sea level pressure, precipitation, and snow depth measurements, as well as profiler Doppler LiDAR and Microwave Radiometer (MWR) measurements from the New York State Mesonet network during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The Mesonet network consists of ground weather stations, LiDAR profilers, and microwave radiometer (MWR) profilers. These browse files are available from January 3, 2020, through March 2, 2023, in PNG format.", "links": [ { diff --git a/datasets/obrienbay_bathy_dem_1.json b/datasets/obrienbay_bathy_dem_1.json index 0bbaac6ae9..26d8de7434 100644 --- a/datasets/obrienbay_bathy_dem_1.json +++ b/datasets/obrienbay_bathy_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "obrienbay_bathy_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands.", "links": [ { diff --git a/datasets/observational-data-switzerland-2016-2021_1.0.json b/datasets/observational-data-switzerland-2016-2021_1.0.json index 3ae844197d..c8559b9563 100644 --- a/datasets/observational-data-switzerland-2016-2021_1.0.json +++ b/datasets/observational-data-switzerland-2016-2021_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "observational-data-switzerland-2016-2021_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_ - danger signs - human triggered avalanches - rutschblock test results (still to be added) - extended column test results (still to be added)", "links": [ { diff --git a/datasets/observed-and-simulated-snow-profile-data-from-switzerland_1.0.json b/datasets/observed-and-simulated-snow-profile-data-from-switzerland_1.0.json index 6d90ddca43..8bf87f981b 100644 --- a/datasets/observed-and-simulated-snow-profile-data-from-switzerland_1.0.json +++ b/datasets/observed-and-simulated-snow-profile-data-from-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "observed-and-simulated-snow-profile-data-from-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes information on all observed and simulated snow profiles that were used to train and validate the random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified. The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.", "links": [ { diff --git a/datasets/observer-driven-pseudoturnover-in-vegetation-surveys_1.0.json b/datasets/observer-driven-pseudoturnover-in-vegetation-surveys_1.0.json index a2dc91a272..74bed26b14 100644 --- a/datasets/observer-driven-pseudoturnover-in-vegetation-surveys_1.0.json +++ b/datasets/observer-driven-pseudoturnover-in-vegetation-surveys_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "observer-driven-pseudoturnover-in-vegetation-surveys_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, K\u00fcchler H, K\u00fcchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science. In the framework of the project \"Monitoring the effectiveness of habitat conservation in Switzerland\", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover. The excel file contains two sheets: 1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species <0.5 m in height), S (shrub layer; woody species 0.5\u20133 m in height) and T (tree layer; woody species >3 m in height). \"cf.\" indicates uncertain identification, \"aggr.\" indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r \u2259 <0.1%, + \u2259 0.1% to <1%, 1 \u2259 1% to <5%, 2 \u2259 5% to <25%, 3 \u2259 25% to <50%, 4 \u2259 50% to <75%, 5 \u2259 75% to <100%). 2) File used for the linear mixed effects model.", "links": [ { diff --git a/datasets/oldcasey_DSM_2014_1.json b/datasets/oldcasey_DSM_2014_1.json index 1c5f6c5a9d..37f3a5da5e 100644 --- a/datasets/oldcasey_DSM_2014_1.json +++ b/datasets/oldcasey_DSM_2014_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "oldcasey_DSM_2014_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Digital Surface Model (DSM) was created by Dr Arko Lucieer of TerraLuma (http://www.terraluma.net/) and the University of Tasmania for the Terrestrial and Nearshore Ecosystems research group at the Australian Antarctic Division (TNE/AAD).\nThe resolution of the DSM is 2 cm.\nAlso included are layers derive from the DSM: hillshade, slope and 20 cm interval contours.\nAn orthophoto was also created. See the metadata record 'Orthophoto of an area at Old Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 5 February 2014' with ID 'oldcasey_ortho_2014'.\n\nThe products were requested for Australian Antarctic Science Project 4036: \nRemediation of petroleum contaminants in the Antarctic and subantarctic.\nThey cover the drainage area from a fuel spill at Old Casey that occurred in \n1982.\nThe products were created from digital photos taken on the 5th February, 2014, with a UAV piloted by Dr Zybnek Malenovsky. \nThe products were georeferenced to ground control points surveyed using differential GPS by Dr Daniel Wilkins of TNE/AAD.\nHorizontal Datum: ITRF2000.", "links": [ { diff --git a/datasets/oldcasey_buildings_gis_1.json b/datasets/oldcasey_buildings_gis_1.json index f1b6bdafdb..b3fd6f3757 100644 --- a/datasets/oldcasey_buildings_gis_1.json +++ b/datasets/oldcasey_buildings_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "oldcasey_buildings_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Work commenced on the original Casey station in 1964 and it was fully operational by February 1969. Casey was a novel concept in Antarctic stations at the time with living and sleeping quarters, and some work buildings, in a straight line and connected on the windward side by an aerodynamic corrugated iron tunnel. All were elevated on scaffolding pipe to allow the flow-through of the violent winds common in the region. The tunnel station was decommissioned, demolished and all parts returned to Australia by 1993.\n\nThe final data in this dataset is a polygon shapefile representing the buildings at the original (now called 'old') Casey station. Included also are: (i) other files used to create the final shapefile; and (ii) a Readme file with explanation about the procedure used.", "links": [ { diff --git a/datasets/olsana_1.json b/datasets/olsana_1.json index 9d8c6366a0..ea2c190851 100644 --- a/datasets/olsana_1.json +++ b/datasets/olsana_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "olsana_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLS Analog Derived Lightning dataset consists of global lightning signatures from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) that have been analyzed from filmstrip imagery. These signatures show up as horizontal streaks on the film images. The location of each of these streaks has been digitized in order to develop a preliminary database of global lightning activity. Monthly HDF data files are available for June and July 1973; Sept. - Dec. 1977; Jan. - Aug. 1978; Jan. - Dec. 1986; Jan. - Oct. 1987; Dec. 1988; Jan. - Dec. 1990; and Jan. - Dec. 1991.", "links": [ { diff --git a/datasets/olsdig10_1.json b/datasets/olsdig10_1.json index 9546f05eb6..523d338f4e 100644 --- a/datasets/olsdig10_1.json +++ b/datasets/olsdig10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "olsdig10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLS Digital Derived Lightning from DMSP F10 dataset consists of global lightning signatures from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) flown on DMSP 5D-2/F10 that have been analyzed from visible channel imagery. These signatures show up as horizontal streaks on the images. The time and location of each of these streaks have been extracted and are stored by month in HDF data files. Data are available from February 1, 1994 through May 31, 1994.", "links": [ { diff --git a/datasets/olsdig12_1.json b/datasets/olsdig12_1.json index 7bbe60dad1..6911694d1b 100644 --- a/datasets/olsdig12_1.json +++ b/datasets/olsdig12_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "olsdig12_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The OLS Digital Derived Lightning from DMSP F12 dataset consists of global lightning signatures from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) flown on DMSP 5D-2/F12 that have been analyzed from the visible channel imagery. These signatures show up as horizontal streaks on the images. The time and location of each of these streaks have been extracted and are stored by month in HDF data files. Data are available from May 1, 1995 through November 30, 1995.", "links": [ { diff --git a/datasets/olson_672_1.json b/datasets/olson_672_1.json index f5f61e4c53..777c482977 100644 --- a/datasets/olson_672_1.json +++ b/datasets/olson_672_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "olson_672_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Olson et al. (1985, 2000) \"Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation.\" This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID format.\"Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation\" is a computerized database used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups. The map is derived from patterns of preagricultural vegetation, modern areal surveys, and intensive biomass data from research sites. Work on the database was begun in 1960 and completed in 1980.Ecosystem complexes are defined for each 0.5-degree grid cell, reflecting the major climatic, topographic, and land-use patterns. Numeric codes are assigned to each vegetation type. Classifications include natural as well as human managed/modified complexes such as mainly cropped, residential, commercial, and park. The complexes are ranked by estimated organic carbon in the mass of live plants given in units of kilograms of carbon per square meter. Counting the cells of each type and adding their areas give total area estimates for the ecosystem complexes. Multiplying by carbon estimates gives corresponding estimates of carbon by ecosystem complex with in the LBA study area. The results help define the role of the terrestrial biosphere in the global carbon cycle.Information about the ecosystem classifications, as well as the procedure used to create the LBA subset can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/olson/comp/olson_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.Carbon in Live Vegetation is a computerized database, used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups.", "links": [ { diff --git a/datasets/one_deg_biomass_754_1.json b/datasets/one_deg_biomass_754_1.json index 03c00a2b0e..e2c1297662 100644 --- a/datasets/one_deg_biomass_754_1.json +++ b/datasets/one_deg_biomass_754_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "one_deg_biomass_754_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa. Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations.", "links": [ { diff --git a/datasets/open-science-support-at-wsl_1.0.json b/datasets/open-science-support-at-wsl_1.0.json index 4235d99990..ce88a4fbf4 100644 --- a/datasets/open-science-support-at-wsl_1.0.json +++ b/datasets/open-science-support-at-wsl_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "open-science-support-at-wsl_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL. Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level. EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues. EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services. For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster. Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research. _License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 \"No Rights Reserved\" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.", "links": [ { diff --git a/datasets/orbview_3.json b/datasets/orbview_3.json index c2ed13edc1..1eec3a0f26 100644 --- a/datasets/orbview_3.json +++ b/datasets/orbview_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "orbview_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "OrbView-3 satellite images were collected around the world between 2003 and 2007 by Orbital Imaging Corporation (now GeoEye) at up to one-meter resolution. The OrbView-3 data set includes 180,000 scenes of one meter resolution panchromatic, black and white, and four meter resolution multi-spectral (color and infrared) data, providing high resolution data useful for a wide range of science applications. The spacecraft ceased operation on April 23, 2007 and decayed on March 13, 2011 via a controlled reentry into the broad area Pacific Ocean.", "links": [ { diff --git a/datasets/oriental-beech-spectral-and-trait-data_1.0.json b/datasets/oriental-beech-spectral-and-trait-data_1.0.json index 19daae8c93..e44f87af10 100644 --- a/datasets/oriental-beech-spectral-and-trait-data_1.0.json +++ b/datasets/oriental-beech-spectral-and-trait-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "oriental-beech-spectral-and-trait-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset includes leaf spectroscopy, leaf traits and genetic data for oriental and european beech trees at two mature forest sites (Allenwiller in France and W\u00e4ldi in Switzerland) sampled in summer 2021 and 2022 for top and bottom of canopy leaves.", "links": [ { diff --git a/datasets/ornl_lai_point_971_1.json b/datasets/ornl_lai_point_971_1.json index f896c9da4c..f316b373df 100644 --- a/datasets/ornl_lai_point_971_1.json +++ b/datasets/ornl_lai_point_971_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ornl_lai_point_971_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf Area Index (LAI) data from the scientific literature, covering the period from 1932-2000, have been compiled at the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) to support model development and validation for products from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument. There is one data file which consists of a spreadsheet table, together with a bibliography of more than 300 original-source references. Although the majority of measurements are from natural or semi-natural ecosystems, some LAI values have been included from crops (limited to a sub-set representing different crops at different stages of development under a range of treatments). Like Net Primary Productivity (NPP), Leaf Area Index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange. Modeling and validation of coarse scale satellite measurements both require field measurements to constrain LAI values for different biomes (typical minimum, maximum values, phenology, etc.). Maximum values for point measurements are unlikely to be approached or exceeded by area-weighted LAI, which is what satellites and true spatial models are estimating.", "links": [ { diff --git a/datasets/otdlip_1.json b/datasets/otdlip_1.json index 62ef994424..4ff9cc0683 100644 --- a/datasets/otdlip_1.json +++ b/datasets/otdlip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "otdlip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Optical Transient Detector (OTD) records optical measurements of global lightning events in the daytime and nighttime. The data includes individual point (lightning) data, satellite metadata, and several derived products. The OTD was launched on 3 April 1995 aboard the Microlab-1 satellite into a near polar orbit with an inclination of 70 degrees with respect to the equator, at an altitude of 740 km.", "links": [ { diff --git a/datasets/oxygen-isotopes-plateau-1984_1.json b/datasets/oxygen-isotopes-plateau-1984_1.json index 6a1aa51405..cf6afe397d 100644 --- a/datasets/oxygen-isotopes-plateau-1984_1.json +++ b/datasets/oxygen-isotopes-plateau-1984_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "oxygen-isotopes-plateau-1984_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A total of nine stations were sampled for oxygen isotopes during the 1984 spring traverse to the Antarctic Plateau. The aim of this program was to take a number of samples from a core or a pit, at stations of known accumulation over a particular period, to see how far inland the annual cycles could accurately be traced. The samples were not taken at ice movement stations, but at canes each 2km along the line, to avoid sampling the accumulation, and thus isotope disturbance resulting from parking the vans beside the IMS poles in 1978 and 1979.\n\nThe accumulation for the cane at each sampled station was calculated for the six years since 1978, and the total multiplied by 7/6 to give the sampling depth required to cover 7 years. Seventy samples were taken at each station, i.e. approximately 10 per year. At most stations a PICO drill was used to obtain a core, and the samples cut with a stainless steel knife on the stainless sink in the living van. At the southern end of the line where the accumulation is much lower, the samples were taken from the wall of a pit, as the small length of core for each sample did not provide enough melt. The snow was sampled in the pits by sliding a flat sheet of galvanized iron into the snow at each interval starting at the top, and scraping the snow above this into a melt jar. Isotopic contamination of samples from both these methods should be negligible. All samples were melted in plastic jars, and then transferred into 5Oml plastic bottles. A total of 630 samples from 9 stations were returned to Australia for oxygen isotope analysis, carried out in Melbourne by Ted Vishart, Dick Marriot, and Gao Xiangqun.\n\nThe station/cane labels for the sample sites were:\n\nA028\nV140/4 (near GC30)\nV230/4 (near GC37)\nV270/1 (near GC38)\nV300/1 (near GC39)\nV350/1 (near GC40)\nV400/1 (near GC41)\nV450/1 (near GC42)\nV630/1 (near GC47)\n\nThe columns in the spreadsheet are:\n\nSequence Number\nCore depth (metres)\nOxygen isotope value (the number is a ratio of O18 per ml of O16, expressed as a percentage (but as parts per 1000 instead of 100))", "links": [ { diff --git a/datasets/p3metnavimpacts_1.json b/datasets/p3metnavimpacts_1.json index fab9791a44..eb2cc2740e 100644 --- a/datasets/p3metnavimpacts_1.json +++ b/datasets/p3metnavimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "p3metnavimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The P-3 Meteorological and Navigation Data IMPACTS dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA\u2019s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data are available in ASCII-ict format from January 12, 2020, through February 28, 2023.", "links": [ { diff --git a/datasets/p_pet_500m_1.0.json b/datasets/p_pet_500m_1.0.json index 6df8f3bef8..d16c83a8c8 100644 --- a/datasets/p_pet_500m_1.0.json +++ b/datasets/p_pet_500m_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "p_pet_500m_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution. Units are mm per year. Files are GeoTIFF rasters, and can be read in R using the command raster(\"pcp_ch_longterm_yr_avg.tif), after installing packages \"raster\" and \"rgdal\".", "links": [ { diff --git a/datasets/panpfcov_283_1.json b/datasets/panpfcov_283_1.json index c3d375630b..713441ad77 100644 --- a/datasets/panpfcov_283_1.json +++ b/datasets/panpfcov_283_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "panpfcov_283_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western part of the BOREAS SSA in vector form.", "links": [ { diff --git a/datasets/parprbimpacts_1.json b/datasets/parprbimpacts_1.json index 19bc3d1c90..36d0584ee2 100644 --- a/datasets/parprbimpacts_1.json +++ b/datasets/parprbimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "parprbimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The NCAR Particle Probes IMPACTS dataset consists of data collected from six instruments on the NASA P-3 aircraft, the SPEC Hawkeye Cloud Particle Imager (CPI), the Hawkeye Fast Cloud Droplet Probe (FastCDP), the Hawkeye Two-Dimensional Stereo Probe (Hawkeye2D-S), the SPEC Two-Dimensional Stereo probe (2D-S), and two SPEC High Volume Precipitation Spectrometers (HVPS3). The 2D-S and HVPS3 are two-dimensional optical array probes that record images of particles that travel through their sampling area. The recorded images are then analyzed to produce particle size distributions from 20 microns to 3 centimeters in diameter. The FastCDP is a forward scattering instrument designed to measure the size and concentration of cloud droplets between 2 and 50 microns in diameter. The CPI is a high-resolution imager with a 256-level color depth. No particle concentration estimates have been attempted with the CPI. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data files are available in netCDF-4 format, as well as browse imagery available in PNG format, from January 18, 2020, through February 26, 2020, and January 14, 2022 through February 28, 2023.", "links": [ { diff --git a/datasets/pedestrian_gentoo_1.json b/datasets/pedestrian_gentoo_1.json index 83ffdfc6a3..e53668b104 100644 --- a/datasets/pedestrian_gentoo_1.json +++ b/datasets/pedestrian_gentoo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pedestrian_gentoo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project empirically measures the effects of human activity on the behaviour and reproductive success of Gentoo penguins on Macquarie Island. This was achieved by 1) collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of guard, creche and moult, and 2) collecting reproductive success data (chicks raised to creche age per nesting pair) for gentoo penguins colonies in areas of high and low human activity. Information produced includes minimum approach guidelines.\n\nAs of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).\n\nThis work was carried out as part of ASAC project 1148 (ASAC_1148).\n\nThe fields in this dataset are:\n\nSample\nDate\nBreeding Phase\nStimulus Type\nColony\nFocal birds tape number\nWide angle tape number\nLocation within colony\nWeather\nTime\nWindspeed\nTemperature\nPrecipitation\nCloud\nPre-approach control\nApproach\nPost-approach control\nMaximum approach distance", "links": [ { diff --git a/datasets/pedestrian_king_1.json b/datasets/pedestrian_king_1.json index 26f7c833f8..d6131e16a1 100644 --- a/datasets/pedestrian_king_1.json +++ b/datasets/pedestrian_king_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pedestrian_king_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project empirically measures the effects of human activity on the behaviour of King penguins on Macquarie Island, under ASAC project 1148. This was achieved by collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation and guard. Information produced includes minimum approach guidelines.\n\nAs of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).\n\nThe fields in this dataset are:\n\nSample\nDate\nBreeding Phase\nApproach\nColony\nFocal birds tape number\nWide angle tape number\nWeather\nTime\nWindspeed\nTemperature\nPrecipitation\nCloud\nPre-approach control\nPost-approach control\nMaximum approach distance", "links": [ { diff --git a/datasets/pedestrian_royal_1.json b/datasets/pedestrian_royal_1.json index 09a97f95d0..6393cd74ad 100644 --- a/datasets/pedestrian_royal_1.json +++ b/datasets/pedestrian_royal_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pedestrian_royal_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project empirically measures the effects of human activity on the behaviour, heart rate and egg-shell surface temperature of Royal penguins on Macquarie Island, as part of ASAC project 1148. This was achieved by collecting behavioural and physiological responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation, guard, creche and moult. Both single person and group approaches were also investigated. Information produced includes minimum approach guidelines. \n\nAs of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).\n\nSome notes about some of the fields in this dataset:\n\nTemp file refers to whether or not egg shell surface temperature was also recorded for the sample, with the code below refering to the file name.\n\nNeighbour refers to the behavioural control file for each sample (neighbouring nests did not recieve an artificial egg, and provide a behavioural control for responses to human approaches without the scientific treatment).\n\nNest refers to the randomly used nest markers for each sample.\n\nHeart rate refers to whether heart rate was concurrently recorded with behaviour on the sample (both stored on Hi-8 tape).\n\nStimulus refers to whether single persons or groups of persons (5 -7, recorded within each sample) were used for the human approaches.\n\nEnvironment refers to whether approaches were conducted from colony sections abuting pebbly beach or from poa tussock environs (tussock approaches less than 50 m of the poa / pebbly beach junction).\n\nThe code system for nest simply refers to the numbered tag placed at the nest (using three colours, g=green, w=white, b=brown) which were used randomly.\n\nThe fields in this dataset are:\n\nSample\nDate\nBreeding Phase\nStimulus Type\nEnvironment\nColony\nNest\nTape\nHeart Rate\nTemp File\nNeighbour", "links": [ { diff --git a/datasets/pfynwald_2016.json b/datasets/pfynwald_2016.json index 324f5d9a6b..4ed32cb1a6 100644 --- a/datasets/pfynwald_2016.json +++ b/datasets/pfynwald_2016.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pfynwald_2016", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46\u00b0 18' N, 7\u00b0 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2\u00b0C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.", "links": [ { diff --git a/datasets/pfynwaldgasexchange_1.0.json b/datasets/pfynwaldgasexchange_1.0.json index ed105062fd..cc08951fee 100644 --- a/datasets/pfynwaldgasexchange_1.0.json +++ b/datasets/pfynwaldgasexchange_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pfynwaldgasexchange_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents.", "links": [ { diff --git a/datasets/phipsimpacts_1.json b/datasets/phipsimpacts_1.json index 7e7e1d3863..fddbb14dcd 100644 --- a/datasets/phipsimpacts_1.json +++ b/datasets/phipsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "phipsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format.", "links": [ { diff --git a/datasets/phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0.json b/datasets/phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0.json index f096b4b6ea..5ef10c2bfb 100644 --- a/datasets/phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0.json +++ b/datasets/phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites. Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019 with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019. For more details please refer to the publication.", "links": [ { diff --git a/datasets/photo_mosaic_laurens_or_1.json b/datasets/photo_mosaic_laurens_or_1.json index 688eae0b55..675c249c06 100644 --- a/datasets/photo_mosaic_laurens_or_1.json +++ b/datasets/photo_mosaic_laurens_or_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photo_mosaic_laurens_or_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The orthophoto mosaic is a rectified georeferenced image of the Heard Island, Laurens Peninsula Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric cameras (focal length unknown).", "links": [ { diff --git a/datasets/photo_mosaic_laurens_or_TopoMapping_1.json b/datasets/photo_mosaic_laurens_or_TopoMapping_1.json index fa81ca1398..9465f66be2 100644 --- a/datasets/photo_mosaic_laurens_or_TopoMapping_1.json +++ b/datasets/photo_mosaic_laurens_or_TopoMapping_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photo_mosaic_laurens_or_TopoMapping_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Heard Island, Laurens Peninsula, Topographic Data was mapped from Ortho-rectified non-metric photography. The data consists of Coastline, Crater, Volcano, Island, Lagoon, Water Storage and Watercourse datasets digitised from the photography.", "links": [ { diff --git a/datasets/photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0.json b/datasets/photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0.json index 491e494c09..9fd203d185 100644 --- a/datasets/photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0.json +++ b/datasets/photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted two drone flights with the Wingtra and DJI Phantom 4 RTK drones in Davos Wolfgang Arelen, on 25.08.2021. The data was processed with the Agisoft Metashape Professional Software.The Wingtra point cloud was further processed to derive a ground classification in individual LASTools and Terrasolid workflows.", "links": [ { diff --git a/datasets/photogrammetric-drone-data-dorfberg_1.0.json b/datasets/photogrammetric-drone-data-dorfberg_1.0.json index 0cf8a48fca..767e29a2a9 100644 --- a/datasets/photogrammetric-drone-data-dorfberg_1.0.json +++ b/datasets/photogrammetric-drone-data-dorfberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-dorfberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).", "links": [ { diff --git a/datasets/photogrammetric-drone-data-gruenboedeli_1.0.json b/datasets/photogrammetric-drone-data-gruenboedeli_1.0.json index a548241adf..6c969adf23 100644 --- a/datasets/photogrammetric-drone-data-gruenboedeli_1.0.json +++ b/datasets/photogrammetric-drone-data-gruenboedeli_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-gruenboedeli_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted various drone flights at Gr\u00fcenb\u00f6deli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software. The following products are available for download: - DSM 10cm resolution - Orthomosaic 3cm/25mm resolution - Snow Raster 10cm resolution - original RGB images", "links": [ { diff --git a/datasets/photogrammetric-drone-data-latschuelfurgga_1.0.json b/datasets/photogrammetric-drone-data-latschuelfurgga_1.0.json index 7401bcf162..e598af7ccb 100644 --- a/datasets/photogrammetric-drone-data-latschuelfurgga_1.0.json +++ b/datasets/photogrammetric-drone-data-latschuelfurgga_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-latschuelfurgga_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latsch\u00fcelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.", "links": [ { diff --git a/datasets/photogrammetric-drone-data-schuerlialp_1.0.json b/datasets/photogrammetric-drone-data-schuerlialp_1.0.json index 145e3bc20f..821469c48f 100644 --- a/datasets/photogrammetric-drone-data-schuerlialp_1.0.json +++ b/datasets/photogrammetric-drone-data-schuerlialp_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-schuerlialp_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.", "links": [ { diff --git a/datasets/photogrammetric-drone-data-wolfgang-arelen_1.0.json b/datasets/photogrammetric-drone-data-wolfgang-arelen_1.0.json index e7e28378d6..af74a00ae6 100644 --- a/datasets/photogrammetric-drone-data-wolfgang-arelen_1.0.json +++ b/datasets/photogrammetric-drone-data-wolfgang-arelen_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "photogrammetric-drone-data-wolfgang-arelen_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.", "links": [ { diff --git a/datasets/pine-insects-along-elevational-gradients_1.0.json b/datasets/pine-insects-along-elevational-gradients_1.0.json index 61cd39e757..0f3ae94a4a 100644 --- a/datasets/pine-insects-along-elevational-gradients_1.0.json +++ b/datasets/pine-insects-along-elevational-gradients_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pine-insects-along-elevational-gradients_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years. Please contact author for terms of use.", "links": [ { diff --git a/datasets/place-attachment-dataset_1.0.json b/datasets/place-attachment-dataset_1.0.json index 67077e8c67..c28906bf85 100644 --- a/datasets/place-attachment-dataset_1.0.json +++ b/datasets/place-attachment-dataset_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "place-attachment-dataset_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.", "links": [ { diff --git a/datasets/plan-statements-for-external-consistency-analysis_1.0.json b/datasets/plan-statements-for-external-consistency-analysis_1.0.json index dd757cec0a..f3b393c7b2 100644 --- a/datasets/plan-statements-for-external-consistency-analysis_1.0.json +++ b/datasets/plan-statements-for-external-consistency-analysis_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plan-statements-for-external-consistency-analysis_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the published scientific paper Bac\u0103u, S., Gr\u0103dinaru, S. R., & Hersperger, A. M. (2020). Spatial plans as relational data: Using social network analysis to assess consistency among Bucharest\u2019s planning instruments. Land Use Policy, 92. The goal of this paper was to first develop a theoretical framework for external consistency assessment in spatial plans and then to test the framework with ten spatial plans of the Bucharest (Romania) region. Specifically, the paper has the following workflow: (i) to develop a framework for consistency assessment covering four categories of external consistency; (ii) to extract relevant plan statements from all plans on the four categories; (iii) to assign one-way, symmetrical and contradictory relationships between the extracted plan statements; and (iv) to assess consistencies, inconsistencies and contradictions between plans using directed and valued network analyses. All results were then validated by applying questionnaires to local experts. The study focuses on a sample consisting of 10 spatial plans of Bucharest that: (1) are currently in force, (2) have spatial implications, (3) involve different administrative levels and (4) come from different planning sectors. The list of the reviewed planning documents can be found in Table 2 of the paper. The framework of consistency assessment contains 24 items, which can be found in Table 1 of the paper. All planning documents were read in respect to all items of the framework in order to extract plan statements used in the analysis. As a result, we provide the plan statement extracted from 10 plans on the 24 items of the framework. All data is in Romanian. The data was discussed qualitatively in the research paper.", "links": [ { diff --git a/datasets/planning-efficacy-computation-based-on-ahp_1.0.json b/datasets/planning-efficacy-computation-based-on-ahp_1.0.json index 69e7bb79e6..b33b6e00f7 100644 --- a/datasets/planning-efficacy-computation-based-on-ahp_1.0.json +++ b/datasets/planning-efficacy-computation-based-on-ahp_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-efficacy-computation-based-on-ahp_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.", "links": [ { diff --git a/datasets/planning-efficacy-questionnaire-and-interviews_1.0.json b/datasets/planning-efficacy-questionnaire-and-interviews_1.0.json index 73bb089155..8a79bfaed2 100644 --- a/datasets/planning-efficacy-questionnaire-and-interviews_1.0.json +++ b/datasets/planning-efficacy-questionnaire-and-interviews_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-efficacy-questionnaire-and-interviews_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the interviews, the survey, the report explaining survey and a xlxs table to save results.", "links": [ { diff --git a/datasets/planning-intention-in-copenhagen-urban-region_1.0.json b/datasets/planning-intention-in-copenhagen-urban-region_1.0.json index c21e872a34..2775b78b2f 100644 --- a/datasets/planning-intention-in-copenhagen-urban-region_1.0.json +++ b/datasets/planning-intention-in-copenhagen-urban-region_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-intention-in-copenhagen-urban-region_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.", "links": [ { diff --git a/datasets/planning-intention-in-hannover-urban-region_1.0.json b/datasets/planning-intention-in-hannover-urban-region_1.0.json index aad201a19a..fe48f3688a 100644 --- a/datasets/planning-intention-in-hannover-urban-region_1.0.json +++ b/datasets/planning-intention-in-hannover-urban-region_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-intention-in-hannover-urban-region_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.", "links": [ { diff --git a/datasets/planning-intentions-in-strategic-plans-of-european-urban-regions_1.0.json b/datasets/planning-intentions-in-strategic-plans-of-european-urban-regions_1.0.json index fda1647094..b25e114290 100644 --- a/datasets/planning-intentions-in-strategic-plans-of-european-urban-regions_1.0.json +++ b/datasets/planning-intentions-in-strategic-plans-of-european-urban-regions_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-intentions-in-strategic-plans-of-european-urban-regions_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions. The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete \u201cincidents\u201d or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains: \uf0d8\tDate when the information was filled in. \uf0d8\tName of the urban region and analysed strategic spatial plan . \uf0d8\tA list of all planning intentions contained in a plan, with each PI being addresses as follows: \uf02d\tName of PI as it appears in the plan \uf02d\tTranslated name of the PI (i.e. short name for easy understanding of the meaning) \uf02d\tExplanation regarding the meaning of the PI \uf02d\tWhy the PI is considered a priority for the urban region \uf02d\tSpatial information on the PI (text and cartographic representations). In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.", "links": [ { diff --git a/datasets/planning-intentions-lyon_1.0.json b/datasets/planning-intentions-lyon_1.0.json index 15e54a8bc4..12e9abbf01 100644 --- a/datasets/planning-intentions-lyon_1.0.json +++ b/datasets/planning-intentions-lyon_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "planning-intentions-lyon_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.", "links": [ { diff --git a/datasets/plant-orthoptera-trophic-networks-lif3web-projet_1.0.json b/datasets/plant-orthoptera-trophic-networks-lif3web-projet_1.0.json index 65000bc353..7207887fb7 100644 --- a/datasets/plant-orthoptera-trophic-networks-lif3web-projet_1.0.json +++ b/datasets/plant-orthoptera-trophic-networks-lif3web-projet_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plant-orthoptera-trophic-networks-lif3web-projet_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The study of ecological networks along environmental gradients has so far been limited by the difficulty of collecting large-scale dataset of comparable interactions. Here, we compiled 48 plant\u2013orthoptera interaction networks at multiple locations across the Swiss Alps (i.e. along six elevation gradient). Trophic interactions were obtained by applying next-generation sequencing methods (e.g. DNA metabarcoding) on insect feces. Together with interaction data, we also provide data of the functional trait measurement (i.e. plant leaves traits and insect mandibular strength) expected to influence the realization of the interaction. Species inventories, feces samples and functionals traits were collected during the summer 2016 and 2017. Lab work and network reconstruction were completed in 2019.", "links": [ { diff --git a/datasets/plant_soil_c_n_783_1.json b/datasets/plant_soil_c_n_783_1.json index c4169f1a6a..5609c99c94 100644 --- a/datasets/plant_soil_c_n_783_1.json +++ b/datasets/plant_soil_c_n_783_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plant_soil_c_n_783_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains measurements of the concentration and stable carbon (13C/12C) and nitrogen (15N/14N) isotope ratios of plant (leaves, roots and fungi) and soil samples from southern Africa. The study sites in Zambia, Botswana, Namibia, and South Africa are located along the Kalahari Transect precipitation gradient. Some of the sites were relatively undisturbed while others had different intensities of cultivation, domestic grazing, and fires. The data were collected to detect patterns of N cycling along precipitation and grazing gradients, including N2 fixation by legumes. Data from different multiple projects are included. The plants and soils were sampled mainly in the wet season of years 1995, 1999, and 2000, with most of the data collected during the SAFARI 2000 Kalahari Wet Season Field Campaign in February and March of 2000. Some grass samples were collected in the dry season of year 2000 (from Mongu-dambo and Sua Pan grassland sites). Soil and plant samples were analyzed in a laboratory for %C, %N, d13C, and d15N with an Optima isotope ratio mass spectrometer coupled to an elemental analyzer. The stable isotope ratios are expressed using standard delta notation in units per mil. The isotope ratios are expressed relative to the international standard PDB (Pee Dee Belemnite) for carbon and atmospheric N2 for nitrogen samples. The carbon and nitrogen contents are expressed in percentage weight of the dry sample.The data files contain numerical and character fields of varying length separated by commas (.csv format).", "links": [ { diff --git a/datasets/plotchem_420_1.json b/datasets/plotchem_420_1.json index a5abf197df..3242c6ae5f 100644 --- a/datasets/plotchem_420_1.json +++ b/datasets/plotchem_420_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plotchem_420_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Study plot canopy chemistry values were calculated from leaf chemistry and litterfall weight values. Average leaf concentrations of nitrogen and carbon were used to investigate how reflectance varies with chemistry. ", "links": [ { diff --git a/datasets/plotspec_544_1.json b/datasets/plotspec_544_1.json index f4bedd9812..44c633e12d 100644 --- a/datasets/plotspec_544_1.json +++ b/datasets/plotspec_544_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plotspec_544_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "AVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated with estimated canopy carbon and nitrogen content.", "links": [ { diff --git a/datasets/plutonium-239-240-in-southern-italy_1.0.json b/datasets/plutonium-239-240-in-southern-italy_1.0.json index 623aca9a81..9cfaae8555 100644 --- a/datasets/plutonium-239-240-in-southern-italy_1.0.json +++ b/datasets/plutonium-239-240-in-southern-italy_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "plutonium-239-240-in-southern-italy_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Quantifying the rates of soil redistribution worldwide poses a significant challenge, which has been addressed using various methods such as direct sediment measurements, models, and the use of isotopic, geochemical, and radionuclide tracers. Among these tracers, the isotope of Plutonium, specifically 239+240Pu, is a relatively recent addition to the study of soil redistribution. However, there is still a lack of direct validation for 239+240Pu as a tracer for soil redistribution. To address this gap, we conducted a study in Southern Italy using a unique sediment yield dataset that extends back to the initial fallout of 239+240Pu. Soil samples were collected from the catchment area as well as undisturbed reference sites, and 239+240Pu was extracted, measured using ICP-MS, and converted into soil redistribution rates.", "links": [ { diff --git a/datasets/pmhailclim_1.json b/datasets/pmhailclim_1.json index 026fa7e05d..973b45b620 100644 --- a/datasets/pmhailclim_1.json +++ b/datasets/pmhailclim_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pmhailclim_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Passive Microwave Hail Climatology Data Products are gridded estimates of the annual frequency of severe hailstorm occurrence, as retrieved from satellite-borne passive microwave imagery. These data products can be useful for weather and climatological research related to storms, as well as applications involving risk management and emergency management. The dataset files are available in netCDF-3 format, as well as hail climatology maps in PNG format, from January 1, 1998, through March 31, 2023.", "links": [ { diff --git a/datasets/pnet_4_and_5_817_1.json b/datasets/pnet_4_and_5_817_1.json index 208e24b519..60d6cc1a20 100644 --- a/datasets/pnet_4_and_5_817_1.json +++ b/datasets/pnet_4_and_5_817_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pnet_4_and_5_817_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PnET (Photosynthetic / EvapoTranspiration model) is a nested series of models of carbon, water, and nitrogen dynamics in forest ecosystems. The models can be used to predict transient responses in net primary production (NPP), carbon and water balances, net nitrogen (N) mineralization and nitrification and N leaching losses, resulting from changes in climate, N deposition, tropospheric ozone and land use as well as variation in species composition. The models have been developed and validated in the Northeastern U.S. at both the site and grid level (to 1-km resolution) at the Complex Systems Research Center, University of New Hampshire, by John Aber and colleagues.", "links": [ { diff --git a/datasets/pnet_m_bgc_818_1.json b/datasets/pnet_m_bgc_818_1.json index 2547b6a982..ad7f6796be 100644 --- a/datasets/pnet_m_bgc_818_1.json +++ b/datasets/pnet_m_bgc_818_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pnet_m_bgc_818_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of: Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D. Aber and G.E. Likens. 2001. The evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. Water Resources Research 37:1057-1070Gbondo-Tugbawa et al,. 2001 Excerpt from Abstract: An integrated biogeochemical model (PnET-BGC) was formulated to simulate chemical transformations of vegetation, soil, and drainage water in northern forest ecosystems. The model operates on a monthly time step and depicts the major biogeochemical processes, such as forest canopy element transformations, hydrology, soil organic matter dynamics, nitrogen cycling, geochemical weathering, and chemical equilibrium reactions involving solid and solution phases. The model was evaluated against soil and stream data at the Hubbard Brook Experimental Forest, New Hampshire. Model predictions of concentrations and fluxes of major elements generally agreed reasonably well with measured values, as estimated by normalized mean error and normalized mean absolute error. Model output of soil base saturation and stream acid neutralizing capacity were sensitive to parameter values of soil partial pressure of carbon dioxide, soil mass, soil cation exchange capacity, and soil selectivity coefficients of calcium and aluminum. PnET-BGC can be used as a tool to evaluate the response of soil and water chemistry of forest ecosystems to disturbances such as clear-cutting, climatic events, and atmospheric deposition.PnET-BGC, was used to investigate inputs and dynamics of S in a northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF) (Gbondo-Tugbawa et al., 2002). The changes in soil S pools and stream-water were simulated to assess the response 22 SO4 to both atmospheric S deposition and forest clear-cutting disturbances. Watershed studies across the northeastern United States have shown that stream losses of exceed atmospheric sulfur (S) deposition. Understanding the processes responsible for this additional source of S is critical to quantifying ecosystem response to ongoing and potential future controls on SO2 emission.", "links": [ { diff --git a/datasets/polar_star_0.json b/datasets/polar_star_0.json index d74ad33992..144a48c549 100644 --- a/datasets/polar_star_0.json +++ b/datasets/polar_star_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "polar_star_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Optical measurements taken in the Southern Ocean in 2002", "links": [ { diff --git a/datasets/pollination-experiment-insect-traits_1.0.json b/datasets/pollination-experiment-insect-traits_1.0.json index a5b5a01a36..172f0c2db7 100644 --- a/datasets/pollination-experiment-insect-traits_1.0.json +++ b/datasets/pollination-experiment-insect-traits_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pollination-experiment-insect-traits_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the interplay of local and landscape-scale drivers of plant-pollinator interactions is crucial to maintaining pollination services in urban environments. The data contains plant-pollinator interactions changed across two independent gradients of local flowering plant species richness and landscape-scale urbanisation level (proportional area of impervious surface within a 500-m radius) in 24 home gardens in the city of Zurich, Switzerland. The data also contains the trait values (tongue length, body size and activity time) of all visiting wild- and honeybees.", "links": [ { diff --git a/datasets/population_counts_BI_1.json b/datasets/population_counts_BI_1.json index 143df8455a..a7409ad257 100644 --- a/datasets/population_counts_BI_1.json +++ b/datasets/population_counts_BI_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "population_counts_BI_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Intermittent Adelie penguin population counts for Bechervaise, Verner and Petersen Islands, Mawson since 1971. Data include counts of occupied nests for the post 1990/91 data conducted on or about 2nd December. Data collected prior to this were obtained from ANARE Research Notes or field note books. These counts may not have been performed at the 'optimal' time for occupied nests counts, and when this is the case have been adjusted according to a 'correction' factor.\n\nThe post 1990/91 data were completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.\n\nThe fields in this dataset are:\n\nYear\nBechervaise Island Counts\nVerner Island Counts\nPetersen Island Counts\nDate\nSeason\nocc nests (occupied nests)", "links": [ { diff --git a/datasets/potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0.json b/datasets/potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0.json index 4cf9b8a3df..96e10c62c5 100644 --- a/datasets/potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0.json +++ b/datasets/potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., \u2018smart growth\u2019) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.", "links": [ { diff --git a/datasets/potential_veg_xdeg_961_1.json b/datasets/potential_veg_xdeg_961_1.json index 52f3d25d88..769b1649be 100644 --- a/datasets/potential_veg_xdeg_961_1.json +++ b/datasets/potential_veg_xdeg_961_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "potential_veg_xdeg_961_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999). By overlaying the two, one can determine the extent to which natural vegetation has been cleared for cultivation. This data set can be used directly within spatially-explicit climate and biogeochemical models. There are four total files in this data set. Two files contain the land cover types representing potential natural vegetation before human alteration, and two other files contain those points in the original data set submitted by the Principal Investigator that have been modified in order to match the land/water mask of the ISLSCP Initiative II.The geographic distribution of contemporary land cover types can be derived from remotely-sensed data. However, humans now dominate much of the world and there is little evidence of the pre-human-settlement natural vegetation or Potential Natural Vegetation (PNV). PNV, as defined here, does not necessarily represent the world's natural pre-human-disturbance vegetation. Rather, our definition of PNV represents the world's vegetation cover that would most likely exist now in equilibrium with present-day climate and natural disturbance, in the absence of human activities.", "links": [ { diff --git a/datasets/potential_vegetation_684_1.json b/datasets/potential_vegetation_684_1.json index 20bf536de4..186460b9e8 100644 --- a/datasets/potential_vegetation_684_1.json +++ b/datasets/potential_vegetation_684_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "potential_vegetation_684_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types.", "links": [ { diff --git a/datasets/pre_post_fire_refl_757_1.json b/datasets/pre_post_fire_refl_757_1.json index c325fb9cf9..a14b10598c 100644 --- a/datasets/pre_post_fire_refl_757_1.json +++ b/datasets/pre_post_fire_refl_757_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pre_post_fire_refl_757_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The main goal of this study was to analyze the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes of surface features by the development of relationships between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat ETM+ channels 2, 3, and 4).", "links": [ { diff --git a/datasets/predicted-cloud-droplet-numbers-davos-wolfgang_1.0.json b/datasets/predicted-cloud-droplet-numbers-davos-wolfgang_1.0.json index edc6bdd9d2..599bf88090 100644 --- a/datasets/predicted-cloud-droplet-numbers-davos-wolfgang_1.0.json +++ b/datasets/predicted-cloud-droplet-numbers-davos-wolfgang_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "predicted-cloud-droplet-numbers-davos-wolfgang_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the \u201ccharacteristic velocity\u201d approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).", "links": [ { diff --git a/datasets/pref-dep-hills_1.0.json b/datasets/pref-dep-hills_1.0.json index a63ed2dc4d..175983d3df 100644 --- a/datasets/pref-dep-hills_1.0.json +++ b/datasets/pref-dep-hills_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pref-dep-hills_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios. This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed in the original paper. For additional information, please refer to \"Preferential deposition of snow and dust over hills: governing processes and relevant scales\" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019.", "links": [ { diff --git a/datasets/preprocessing-antarctic-weather-station-aws-data-in-python_1.0.json b/datasets/preprocessing-antarctic-weather-station-aws-data-in-python_1.0.json index 6d61144c5a..28ef841fbf 100644 --- a/datasets/preprocessing-antarctic-weather-station-aws-data-in-python_1.0.json +++ b/datasets/preprocessing-antarctic-weather-station-aws-data-in-python_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "preprocessing-antarctic-weather-station-aws-data-in-python_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary. The following data sources are taken into account: * AAD: Australian Antarctic Division (https://data.aad.gov.au/aws) * ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division * AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/) * BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/) * CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en) * IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph * JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html) * NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/) * Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication * Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d\u2019Urville and Dome C, Amory, 2020) * PANGAEA: World Data Center (e.g. K\u00f6nig-Langlo, 2012) __Important notes __ * __Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.__ * Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset. * No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner. Acknowledgements -------------------------- We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted. References --------------- Amory, C. (2020). \u201cDrifting-snow statistics from multiple-year autonomous measurements in Ad\u00e9lie Land, East Antarctica\u201d. The Cryosphere, 1713\u20131725. doi: 10.5194/tc-14-1713-2020 Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted. K\u00f6nig-Langlo, G. (2012). \u201cContinuous meteorological observations at Neumayer station (2011-01)\u201d. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA. 775173", "links": [ { diff --git a/datasets/present-weather-sensor-klosters_1.0.json b/datasets/present-weather-sensor-klosters_1.0.json index bbd5ac4034..7fc4abb029 100644 --- a/datasets/present-weather-sensor-klosters_1.0.json +++ b/datasets/present-weather-sensor-klosters_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "present-weather-sensor-klosters_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).", "links": [ { diff --git a/datasets/production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0.json b/datasets/production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0.json index 5841f5b5ca..b8942b54de 100644 --- a/datasets/production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0.json +++ b/datasets/production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "L'objectif de ce livre blanc est de fournir aux d\u00e9cideurs, aux administrations et aux parties prenantes les r\u00e9sultats de recherche les plus r\u00e9cents afin de promouvoir l'utilisation optimale de la bio\u00e9nergie issue des engrais de ferme dans la transition \u00e9nerg\u00e9tique suisse. A cette fin, les r\u00e9sultats du centre de comp\u00e9tence suisse pour la recherche en bio\u00e9nergie - SCCER BIOSWEET - sont r\u00e9sum\u00e9s et pr\u00e9sent\u00e9s dans un contexte plus large. Si rien d'autre n'est mentionn\u00e9, les r\u00e9sultats se r\u00e9f\u00e8rent \u00e0 la Suisse et, dans le cas de la mati\u00e8re premi\u00e8re, aux potentiels nationaux de biomasse.", "links": [ { diff --git a/datasets/prsondecpexaw_1.json b/datasets/prsondecpexaw_1.json index de0532c53d..67bbf405f4 100644 --- a/datasets/prsondecpexaw_1.json +++ b/datasets/prsondecpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "prsondecpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Puerto Rico Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 24, 2021 through September 28, 2021 in ASCII format, with associated browse Skew-T graphs in PNG format.", "links": [ { diff --git a/datasets/pv_snow_mountain_1.0.json b/datasets/pv_snow_mountain_1.0.json index 49779f0c48..198631e30f 100644 --- a/datasets/pv_snow_mountain_1.0.json +++ b/datasets/pv_snow_mountain_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "pv_snow_mountain_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "### Overview The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication \"The bright side of PV production in snow-covered mountains\", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months. ### Format All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format. This is the case for: - Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM), - Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE), - Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster, - Electricity production from three of our validation sites (/Validation/WSL), - Measured irradiance for two validation sites (/Validation/ASRB) The \u2018Metadata\u2019 documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format. ### Structure The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the __\u2018DataProcessing\u2019__ and the __\u2018functions\u2019__ folder. Functions are called in the different Mains. __\u2018InputsFromMatlab\u2019__ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets. __\u2018Outputs\u2019__ contains the output files from the different mains (matching names, Main_CHallpixels.m \uf0e0 Prod_CHallpixels) __\u2018Publication_figures\u2019__ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures. __\u2018Validation\u2019__ contains the data sets used in the model validation: - Electricity production from three of our validation sites (/WSL), - Measured irradiance for two validation sites (/ASRB) __Electricity__ production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available. __Paper Citation:__ > _Annelen Kahl; J\u00e9r\u00f4me Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_", "links": [ { diff --git a/datasets/r-script-first-stage-sampling_1.0.json b/datasets/r-script-first-stage-sampling_1.0.json index c5b4db9d17..4e407e2cc4 100644 --- a/datasets/r-script-first-stage-sampling_1.0.json +++ b/datasets/r-script-first-stage-sampling_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r-script-first-stage-sampling_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "License: GPL-v2 The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space. The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward. Ecker, K.T., Meier, E.S. & Till\u00e9, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.", "links": [ { diff --git a/datasets/r04laifd_293_1.json b/datasets/r04laifd_293_1.json index f74c981354..9a26ed1ac9 100644 --- a/datasets/r04laifd_293_1.json +++ b/datasets/r04laifd_293_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r04laifd_293_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains Decagon Ceptometer estimates of LAI and fPAR. Contains LI-COR LAI-2000 estimates of leaf area index and mean tip angle.", "links": [ { diff --git a/datasets/r07elaid_294_1.json b/datasets/r07elaid_294_1.json index 99dadd9a08..322f4a4f0a 100644 --- a/datasets/r07elaid_294_1.json +++ b/datasets/r07elaid_294_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r07elaid_294_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains daily green fpar, LAI, needle-to-shoot area ratio, clumping index, PAI, and foliage-to-total area index for tower and auxiliary sites for IFC's 1, 2, and 3. Also contains effective LAI measurements acquired along RSS-07 transects in the BOREAS study area.", "links": [ { diff --git a/datasets/r11sunpd_297_1.json b/datasets/r11sunpd_297_1.json index 5086ae58e0..3a1a27989c 100644 --- a/datasets/r11sunpd_297_1.json +++ b/datasets/r11sunpd_297_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r11sunpd_297_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains measurements from the automatic sun photometers operated by RSS-11 (Markham).", "links": [ { diff --git a/datasets/r12sunpd_299_1.json b/datasets/r12sunpd_299_1.json index 5d85b2ad9f..c81cde79db 100644 --- a/datasets/r12sunpd_299_1.json +++ b/datasets/r12sunpd_299_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r12sunpd_299_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains measurements from the ground sun photometers operated by RSS12 (Wrigley).", "links": [ { diff --git a/datasets/r19cas94_537_1.json b/datasets/r19cas94_537_1.json index ad09560b01..5342cd94a0 100644 --- a/datasets/r19cas94_537_1.json +++ b/datasets/r19cas94_537_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r19cas94_537_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CASI images from the Chieftain Navaho aircraft taken in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance,2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo.", "links": [ { diff --git a/datasets/r19cas96_538_1.json b/datasets/r19cas96_538_1.json index 56d5a7eff6..6709cbc783 100644 --- a/datasets/r19cas96_538_1.json +++ b/datasets/r19cas96_538_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r19cas96_538_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.", "links": [ { diff --git a/datasets/r3pg-an-r-package-for-simulating-forest-growth_1.0.json b/datasets/r3pg-an-r-package-for-simulating-forest-growth_1.0.json index 7c6b7a5df9..8bb3fc1778 100644 --- a/datasets/r3pg-an-r-package-for-simulating-forest-growth_1.0.json +++ b/datasets/r3pg-an-r-package-for-simulating-forest-growth_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r3pg-an-r-package-for-simulating-forest-growth_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) .", "links": [ { diff --git a/datasets/r7laifpa_442_1.json b/datasets/r7laifpa_442_1.json index fb5a314f24..6ca7eea29c 100644 --- a/datasets/r7laifpa_442_1.json +++ b/datasets/r7laifpa_442_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "r7laifpa_442_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images. Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.", "links": [ { diff --git a/datasets/raclets-backward-trajectories_1.0.json b/datasets/raclets-backward-trajectories_1.0.json index e791c148e8..b81d770afa 100644 --- a/datasets/raclets-backward-trajectories_1.0.json +++ b/datasets/raclets-backward-trajectories_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "raclets-backward-trajectories_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.", "links": [ { diff --git a/datasets/radar-wind-profiler-davos-wolfgang_1.0.json b/datasets/radar-wind-profiler-davos-wolfgang_1.0.json index a4fd482eb0..d38d405b40 100644 --- a/datasets/radar-wind-profiler-davos-wolfgang_1.0.json +++ b/datasets/radar-wind-profiler-davos-wolfgang_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "radar-wind-profiler-davos-wolfgang_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.", "links": [ { diff --git a/datasets/radiosondes_1.0.json b/datasets/radiosondes_1.0.json index 5398cb82cf..3d5b0e73cc 100644 --- a/datasets/radiosondes_1.0.json +++ b/datasets/radiosondes_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "radiosondes_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.", "links": [ { diff --git a/datasets/ramerenwald-close-range-remote-sensing-benchmark_1.0.json b/datasets/ramerenwald-close-range-remote-sensing-benchmark_1.0.json index 3e4e50bb57..fa7ff059a2 100644 --- a/datasets/ramerenwald-close-range-remote-sensing-benchmark_1.0.json +++ b/datasets/ramerenwald-close-range-remote-sensing-benchmark_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramerenwald-close-range-remote-sensing-benchmark_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json b/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json index afef9a0d45..ca2ca8ba53 100644 --- a/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json +++ b/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Accra, Ghana_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,330 tiles and 40,786 buildings. The original dataset was sourced from the [Open Cities AI Challenge Dataset](https://doi.org/10.34911/rdnt.f94cxb) before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json index 396075f60b..1369b1eacb 100644 --- a/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Barishal, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Barishal and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,024 tiles and 41,248 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001597B000). Dataset keywords: Urban, Peri-urban, River", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Bentiu, South Sudan_1.json b/datasets/ramp Building Footprint Dataset - Bentiu, South Sudan_1.json index a8799b002c..601629d844 100644 --- a/datasets/ramp Building Footprint Dataset - Bentiu, South Sudan_1.json +++ b/datasets/ramp Building Footprint Dataset - Bentiu, South Sudan_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Bentiu, South Sudan_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Bentiu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,789 tiles and 22,396 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (104001004DAECE00). Dataset keywords: Refugee Settlement, Rural. ", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Chittagong, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Chittagong, Bangladesh_1.json index f2d8982810..c473f7b723 100644 --- a/datasets/ramp Building Footprint Dataset - Chittagong, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Chittagong, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Chittagong, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Chittagong and parts of the Kutupalong Refugee Camp and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 5,229 tiles and 38,096 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001AC98900). Dataset keywords: Agricultural, Peri-urban, Refugee Camp, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1.json index 9795553289..4c9c7ba526 100644 --- a/datasets/ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Cox's Bazaar and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 2,375 tiles and 26,875 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (10400100546E3700). Dataset keywords: Agricultural, Peri-urban, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1.json b/datasets/ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1.json index a12c0b2989..62d61f29de 100644 --- a/datasets/ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1.json +++ b/datasets/ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Dar es Salaam and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 566 tiles and 8,485 buildings. The original dataset was sourced from the [Open Cities AI Challenge Dataset](https://doi.org/10.34911/rdnt.f94cxb) before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json index 1ea3bccd68..f166dc72f8 100644 --- a/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Dhaka, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Dhaka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp fine-tune Dhaka Model and contains 11,905 tiles and 189,057 individual buildings. The satellite imagery resolution is 30 cm and sourced from Maxar ODP (BG_Dhaka_19Q3_V0_R6C3, ...R6C4, ...R3C2, ...R2C3, ...R3C4). Dataset keywords: Very Dense Urban, Rural, Agricultural, Forested. ", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json b/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json index 5194787e95..cd0bf5cbb5 100644 --- a/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json +++ b/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Hpa-an, Myanmar_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Hpa-an and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,667 tiles and 44,765 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (1040010033320500). Dataset keywords: Urban, Peri-Urban, River.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json index b8f11e323e..d08186634a 100644 --- a/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Jashore, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Jashore and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 7,310 tiles and 80,050 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (104001003BA7C900). Dataset keywords: Urban, Peri-urban, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json b/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json index b62d5c4252..95fe6005af 100644 --- a/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json +++ b/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Karnataka, India_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Karnataka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 6,288 tiles and 51,335 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (104001002CA32300). Dataset keywords: Rural, Agricultural, Peri-urban.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Les Cayes, Haiti_1.json b/datasets/ramp Building Footprint Dataset - Les Cayes, Haiti_1.json index e6d6946a66..70e125fb1d 100644 --- a/datasets/ramp Building Footprint Dataset - Les Cayes, Haiti_1.json +++ b/datasets/ramp Building Footprint Dataset - Les Cayes, Haiti_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Les Cayes, Haiti_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Les Cayes and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,430 tiles and 28,549 individual buildings. The satellite imagery resolution is 47 cm and was sourced from Maxar ODP (10300100A450A500). Dataset keywords: Urban, Peri-Urban, Rural, Coastal, Mountainous.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Lubumbashi, Democratic Republic of the Congo_1.json b/datasets/ramp Building Footprint Dataset - Lubumbashi, Democratic Republic of the Congo_1.json index 2b345b3f41..b5be2bdb2a 100644 --- a/datasets/ramp Building Footprint Dataset - Lubumbashi, Democratic Republic of the Congo_1.json +++ b/datasets/ramp Building Footprint Dataset - Lubumbashi, Democratic Republic of the Congo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Lubumbashi, Democratic Republic of the Congo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Lubumbashi and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced for the ramp project and contains 8,498 tiles and 148,459 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (1040010058041300). Dataset keywords: Urban, Peri-urban, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json b/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json index c1962d9343..39f4bb3648 100644 --- a/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json +++ b/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Manjama, Sierra Leone_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Manjama and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 4,671 tiles and 60,379 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-Urban.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1.json b/datasets/ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1.json index 118cb9b531..ce58b3c11a 100644 --- a/datasets/ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1.json +++ b/datasets/ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Mesopotamia and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,013 tiles and 33,139 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (10500100236CC900). Dataset keywords: Coastal, Urban, Peri-urban.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json b/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json index 1158343492..bf004cfcbc 100644 --- a/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json +++ b/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Muscat, Oman_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Muscat and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 2,891 tiles and 30,652 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (10500100271BF800). Dataset keywords: Urban, Peri-urban, Mountainous, Coastal, Desert.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json b/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json index c2da96cfcd..8461af9139 100644 --- a/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json +++ b/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Mzuzu, Malawi_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Mzuzu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,357 tiles and 91,391 individual buildings. The satellite imagery resolution is 45 cm and was sourced from Maxar ODP (10500100195A6700). Dataset keywords: Urban, Peri-Urban, Dense.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json b/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json index 899961da9c..f173137ddd 100644 --- a/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json +++ b/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - N'Djamena, Chad_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over N'Djamena and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced for the ramp project and contains 3,044 tiles and 124,208 individual buildings. The satellite imagery resolution is 45 cm and was sourced from Maxar ODP (10300100AA405C00). Dataset keywords: Urban, Peri-urban, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json b/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json index 5df2fc789b..d9649875f3 100644 --- a/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json +++ b/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Nairobi, Kenya_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Nairobi and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced for the ramp project and contains 1,195 tiles and 24,707 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (KE_Nairobi_19Q2_V0_R3C2). Dataset keywords: Urban, Peri-urban, Rural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Paris, France_1.json b/datasets/ramp Building Footprint Dataset - Paris, France_1.json index bbdcf518fa..cd90be8f94 100644 --- a/datasets/ramp Building Footprint Dataset - Paris, France_1.json +++ b/datasets/ramp Building Footprint Dataset - Paris, France_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Paris, France_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Paris and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,027 tiles and 3,468 buildings. The original dataset was sourced from the [SpaceNet 2 Dataset](https://mlhub.earth/data/spacenet2) before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json b/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json index 5105855ba6..5bd034ef6e 100644 --- a/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json +++ b/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Shanghai, China_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Shanghai and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,574 tiles and 7,118 buildings. The original dataset was sourced from the [SpaceNet 2 Dataset](https://mlhub.earth/data/spacenet2) before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json index 3736856e1c..f76c61c4cd 100644 --- a/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json +++ b/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Sylhet, Bangladesh_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Sylhet and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced for the ramp project and contains 16,217 tiles and 135,375 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP 2022 imagery release for a Bangladesh flood event. Dataset keywords: Peri-urban, Rural, River, Agricultural", "links": [ { diff --git a/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json b/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json index eb42a0d4b3..762348feac 100644 --- a/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json +++ b/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ramp Building Footprint Dataset - Wa, Ghana_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This chipped training dataset is over Wa and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 7,615 tiles and 68,072 individual buildings. The satellite imagery resolution is 32 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-urban", "links": [ { diff --git a/datasets/rasipanam_1.json b/datasets/rasipanam_1.json index 9b2ed6b823..b23c437978 100644 --- a/datasets/rasipanam_1.json +++ b/datasets/rasipanam_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rasipanam_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Panama, Panama dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Panama, Panama. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.", "links": [ { diff --git a/datasets/rasipapag_1.json b/datasets/rasipapag_1.json index 64b09b9ecc..e347ef9021 100644 --- a/datasets/rasipapag_1.json +++ b/datasets/rasipapag_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rasipapag_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Papagayo, Costa Rica dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Papagayo, Costa Rica. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.", "links": [ { diff --git a/datasets/rasitehuan_1.json b/datasets/rasitehuan_1.json index 5c38df69a7..1b7bdb0218 100644 --- a/datasets/rasitehuan_1.json +++ b/datasets/rasitehuan_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rasitehuan_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Tehuantepec, Mexico dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Tehuantepec, Mexico. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.", "links": [ { diff --git a/datasets/raw-data-publication-crossresistance-in-ash-new-phytologist_1.0.json b/datasets/raw-data-publication-crossresistance-in-ash-new-phytologist_1.0.json index 352a4fdcfe..1833faacf7 100644 --- a/datasets/raw-data-publication-crossresistance-in-ash-new-phytologist_1.0.json +++ b/datasets/raw-data-publication-crossresistance-in-ash-new-phytologist_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "raw-data-publication-crossresistance-in-ash-new-phytologist_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites. Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites. When and where was the data created / extracted: Summer 2020-2021", "links": [ { diff --git a/datasets/raxpolimpacts_1.json b/datasets/raxpolimpacts_1.json index 62de58faf8..bf2ae68e30 100644 --- a/datasets/raxpolimpacts_1.json +++ b/datasets/raxpolimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "raxpolimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Rapid X-band Polarimetric Radar (RaXPol) IMPACTS dataset consists of data measured from the RaXPol instrument during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The RaXPol dataset consists of various reflectivity variables. RaXPol data are available from January 29, 2022, through January 25, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0.json b/datasets/re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0.json index c74549ad79..8e51451f3a 100644 --- a/datasets/re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0.json +++ b/datasets/re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland. D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well. For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [P\u00e9rez-Guill\u00e9n et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html). For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach: 1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast. 2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast. 3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [P\u00e9rez-Guill\u00e9n et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html). All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set. In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails. - The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set. - For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.", "links": [ { diff --git a/datasets/readac_d_408_1.json b/datasets/readac_d_408_1.json index 2cbe27de85..1b9163f0c9 100644 --- a/datasets/readac_d_408_1.json +++ b/datasets/readac_d_408_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "readac_d_408_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Remote Environmental Automatic Data Acquisition Concept (READAC) autostations.", "links": [ { diff --git a/datasets/reg_aeac_284_1.json b/datasets/reg_aeac_284_1.json index bc762c885c..54888e1046 100644 --- a/datasets/reg_aeac_284_1.json +++ b/datasets/reg_aeac_284_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "reg_aeac_284_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Based on the GTOPO30 DEM produced by the USGS EDC. The BOREAS region was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.", "links": [ { diff --git a/datasets/regsoilr_285_1.json b/datasets/regsoilr_285_1.json index 76d41258a3..2854a1c0e0 100644 --- a/datasets/regsoilr_285_1.json +++ b/datasets/regsoilr_285_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "regsoilr_285_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS). Data were gridded into the Albers Equal-Area Conic (AEAC) projection.", "links": [ { diff --git a/datasets/relampagolma_1.json b/datasets/relampagolma_1.json index d3881ea48d..58f29d8b61 100644 --- a/datasets/relampagolma_1.json +++ b/datasets/relampagolma_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "relampagolma_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) Lightning Mapping Array (LMA) was an 11-station, ground-based network located in north-central Argentina from November 2018 to April 2019 in support of the RELAMPAGO field campaign. The RELAMPAGO campaign aimed to characterize the atmospheric conditions and terrain effects that facilitate the initiation and growth of intense weather systems in this region of South America. The LMA maps Very High Frequency (VHF) emissions from lightning in three dimensions. These emissions have also been grouped, temporally and spatially, into individual flashes, and the flash characteristics analyzed to produce gridded products. The dataset was produced by NASA Marshall Space Flight Center (MSFC), via an agreement with the National Oceanic and Atmospheric Administration (NOAA), in order to serve as a validation dataset for the Geostationary Lightning Mapper (GLM). These LMA data are available from November 8, 2018 through April 20, 2019 in ASCII, HDF5, and netCDF-4 format.", "links": [ { diff --git a/datasets/rema-topography-and-antarcticalc2000-for-wrf_1.0.json b/datasets/rema-topography-and-antarcticalc2000-for-wrf_1.0.json index f88b14f288..6c190e6f76 100644 --- a/datasets/rema-topography-and-antarcticalc2000-for-wrf_1.0.json +++ b/datasets/rema-topography-and-antarcticalc2000-for-wrf_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rema-topography-and-antarcticalc2000-for-wrf_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data are now available as static data input for the Weather Research and Forecasting model (WRF). Topography and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.", "links": [ { diff --git a/datasets/reproducibility-dataset-for-cryowrf-validation_1.0.json b/datasets/reproducibility-dataset-for-cryowrf-validation_1.0.json index 870c60763e..2f6f52d57b 100644 --- a/datasets/reproducibility-dataset-for-cryowrf-validation_1.0.json +++ b/datasets/reproducibility-dataset-for-cryowrf-validation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "reproducibility-dataset-for-cryowrf-validation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains data and scripts for \"CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB\" (Gerber et al., submitted). * Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021. * Static_input: Static topography input file of WRF (geo_em.d01). * WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper. * WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper. * Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper. Note: * To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022. * AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.", "links": [ { diff --git a/datasets/research-stillberg_1.0.json b/datasets/research-stillberg_1.0.json index 1149253ddc..ea7b51f8a1 100644 --- a/datasets/research-stillberg_1.0.json +++ b/datasets/research-stillberg_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "research-stillberg_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Background information The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Over almost fifty years, research at the Stillberg site combined long-term monitoring of the large-scale high-elevation afforestation with experimental manipulations simulating global change impacts. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone across different compartments and scales, from individual trees, non-tree vegetation and soils to whole ecosystems, in the context of global change resulting in more than 150 publications. # Dataset generation We compiled a comprehensive list of scientific publications covering research at the Stillberg research site by conducting searches in the literature databases Web of Science and Google Scholar, as well as in the Digital Object Repository of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (DORA). We compiled all publications about the afforestation experiment, the FACE \u00d7 warming experiment, the nutrient addition experiment, the G-TREE experiment, as well as other studies related to the Stillberg research site. # Data description The Stillberg bibliography (Stillberg_bibliography_data_v1.csv) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals. Currently the bibliography comprises literature about the main afforestation experiment, the FACE \u00d7 warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title. The bibliography is described in a metadata file (Stillberg_bibliography_metadata_v1.csv). The bibliography along with the metadata file are provided in a ZIP-folder (Stillberg_bibliography_v1.zip).", "links": [ { diff --git a/datasets/resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0.json b/datasets/resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0.json index c24814f9f0..d54309a66e 100644 --- a/datasets/resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0.json +++ b/datasets/resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset comprises a large array of ecological data for the European Alps: (1) Current soil and climate predictors at various resolutions. (2) GBIF observations of the European Alps Flora (~4,000 species). (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km) at various resolutions used in the study to generate (4); except 'expert'... (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models. (5) Ecological and altitudinal preferences of the European Alps Flora. (6) Data outputs of the related published article. (7) All scripts used for analyses. (8) Additional files used for analyses. (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km + non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.", "links": [ { diff --git a/datasets/restoring-grassland-multifunctionality_1.0.json b/datasets/restoring-grassland-multifunctionality_1.0.json index 17c89dcd3b..14202bf1cd 100644 --- a/datasets/restoring-grassland-multifunctionality_1.0.json +++ b/datasets/restoring-grassland-multifunctionality_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "restoring-grassland-multifunctionality_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Please cite this paper together with the citation for the datafile. Resch, M. C., Sch\u00fctz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration \u2013 an ecosystem multifunctionality approach. Ecological Applications 31, e02271. ### Study area The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altl\u00e4ufe der Glatt (47\u00b027\u2019 to 47\u00b029\u2019 N, 8\u00b037\u2019 to 8\u00b032\u2019 E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 \u00b1 2.0 \u00b0C (January) to 19.0 \u00b1 1.5 \u00b0C (July), and monthly precipitation range from 60 \u00b1 42 mm (January) to 118 \u00b1 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019). ### Experimental design and sampling A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017. Vegetation properties All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 \u00b0C for 48 h, and weighed (Meyer et al. 2015). ### Aboveground arthropods Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was \u201cvacuumed\" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers. ### Belowground fauna Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouch\u00e9 1977). To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups. For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 \u00b0C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019). We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods. ### Soil chemical and physical properties, soil nitrogen mineralization We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 \u00b0C until analysis, the other was dried for 48 h at 60 \u00b0C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (\u2264 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982). Ammonium (NH4+) and nitrate (NO3\u2212) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnem\u00fchle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3\u2212 concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 \u00b0C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 \u00b0C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3\u2212 at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3\u2212) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1. To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 \u00b0C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986). Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer. ### References Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101. Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison. Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht. Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19. Bouch\u00e9, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132. Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschl\u00fcssel der Regenw\u00fcrmer \u00d6sterreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131. Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison. Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensr\u00e4ume der Schweiz: \u00d6kologie \u2010 Gef\u00e4hrdung \u2010 Kennarten, Ott Verlag, Bern. Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460\u20132461. Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996\u2013998. FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome. Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018. Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison. Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2\u2014Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison. Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern. MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Z\u00fcrich. Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397. Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Sch\u00fctz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands \u2013 a functional approach. Ecological Applications 0:e02133 Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241\u2013248. Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Gl\u00f6ckner, L. Tedersoo, I. Saar, U. K\u00f5ljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259\u2013264. Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353. Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill. Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Gl\u00f6ckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596. Resch, M. C., M. Sch\u00fctz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793. Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimar\u00e3es-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. H\u00e4rdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460. Tedersoo, L., M. Bahram, S. P\u00f5lme, U. K\u00f5ljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. P\u00f5ldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. P\u00e4rtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688. Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2\u2014Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison. Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, \u03b413C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216. Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera \u2013 an outline for soil ecologists. Journal of Nematology 25:315-331.", "links": [ { diff --git a/datasets/rit1_1.0.json b/datasets/rit1_1.0.json index d4d3351ec1..738cdc8aba 100644 --- a/datasets/rit1_1.0.json +++ b/datasets/rit1_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rit1_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._ Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.", "links": [ { diff --git a/datasets/rit2_1.0.json b/datasets/rit2_1.0.json index a471155015..6110dedbeb 100644 --- a/datasets/rit2_1.0.json +++ b/datasets/rit2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rit2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.", "links": [ { diff --git a/datasets/rivdis_199_1.json b/datasets/rivdis_199_1.json index 20c320418d..6ec8c4adcb 100644 --- a/datasets/rivdis_199_1.json +++ b/datasets/rivdis_199_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rivdis_199_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Monthly River Discharge Data Set (RivDIS) contains monthly averaged discharge measurements for 1,018 stations located throughout the world from 1807-1991. The period of record varies widely from station to station with a mean of 21.5 years. The data are derived from the published UNESCO archives for river discharge, and checked against information obtained from the Global Runoff Center in Koblenz, Germany through the U.S. National Geophysical Data Center in Boulder, Colorado.", "links": [ { diff --git a/datasets/river_carbon_flux_xdeg_1028_1.json b/datasets/river_carbon_flux_xdeg_1028_1.json index a4f4a24ba8..ad2984d84a 100644 --- a/datasets/river_carbon_flux_xdeg_1028_1.json +++ b/datasets/river_carbon_flux_xdeg_1028_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "river_carbon_flux_xdeg_1028_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The River Carbon Flux data set represents estimates for the riverine export of carbon and of sediments. This data set includes the amounts of carbon and of sediments that are discharged to the oceans by rivers for each coastal grid point which receives river inputs. This data set contains three compressed (*.zip) files: the original data at 2.5 x 2.0 degrees, and global maps at spatial resolutions of 0.5 and 1.0 degree which the ISLSCP II staff has created from the original data. ", "links": [ { diff --git a/datasets/river_discharge_cpep_640_1.json b/datasets/river_discharge_cpep_640_1.json index 987f1d70c5..15678b3c7f 100644 --- a/datasets/river_discharge_cpep_640_1.json +++ b/datasets/river_discharge_cpep_640_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "river_discharge_cpep_640_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the Climate, People, and Environment Program (CPEP) Global River Discharge Data Set. The CPEP global river discharge data set is a compilation of monthly mean discharge data for over 2600 sites worldwide. The period of record is variable, from 3 years to greater than 100.", "links": [ { diff --git a/datasets/river_routing_stn_xdeg_1005_1.json b/datasets/river_routing_stn_xdeg_1005_1.json index e72b845e1b..89877f646b 100644 --- a/datasets/river_routing_stn_xdeg_1005_1.json +++ b/datasets/river_routing_stn_xdeg_1005_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "river_routing_stn_xdeg_1005_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Simulated Topological Network (STN-30p) data set provides the large-scale hydrological modeling community an accurate representation of the global river system at 0.5 degree and 1.0 degree spatial resolutions. STN-30p represents the potential connectivity of the continental land mass by assigning one of eight (E, SE, S, SW, W, NW, N, NE) possible flow directions to each continental grid cell (Jenson 1988, Band 1993). The potentiality of STN-30p reflects the fact that flow direction is assigned to every land cell regardless of the existence of actively flowing rivers. STN-30p can be viewed as a river network which would exist if sufficient surface runoff was available to form river channels everywhere. There are two data files with this data set.", "links": [ { diff --git a/datasets/rlc_admin_boundaries_699_1.json b/datasets/rlc_admin_boundaries_699_1.json index 244b9a5f1b..d2327e17c7 100644 --- a/datasets/rlc_admin_boundaries_699_1.json +++ b/datasets/rlc_admin_boundaries_699_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_admin_boundaries_699_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set of state and regional boundaries was derived from the 1:3 million scale administrative boundaries (ESRI, 1998) for the land area of the Former Soviet Union. There are 162 administrative regions distinguished in this data set. The vector map of state and regional boundaries for the FSU is in ArcView shapefile format.", "links": [ { diff --git a/datasets/rlc_fire_images_russia_694_1.json b/datasets/rlc_fire_images_russia_694_1.json index 18c90e4d88..9bbc9f228c 100644 --- a/datasets/rlc_fire_images_russia_694_1.json +++ b/datasets/rlc_fire_images_russia_694_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_fire_images_russia_694_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is made up of images of forest fires in Russia from NOAA's Operational Significant Event Imagery (OSEI) archive (http://www.osei.noaa.gov) for the 1998 and 1999 seasons. OSEI fire products include multichannel color composite imagery of wildfire and controlled burn events. Products in this event group show fire, smoke, and hotspots (FSMHS) from the fires.", "links": [ { diff --git a/datasets/rlc_fire_sumpt_695_1.json b/datasets/rlc_fire_sumpt_695_1.json index 8379b3dd3a..e29d469caf 100644 --- a/datasets/rlc_fire_sumpt_695_1.json +++ b/datasets/rlc_fire_sumpt_695_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_fire_sumpt_695_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is derived from Russian forest fire imagery from the National Forest Fire Center of Russia archive that was collected by the Center of Remote Sensing, Institute of Solar Terrestrial Physics, Irkutsk, Russia for the 1998 and 1999 fire seasons. The data are vector (point) maps of forest fire locations (1998 and 1999) in ArcView shapefile format.", "links": [ { diff --git a/datasets/rlc_forest_carbon_696_1.json b/datasets/rlc_forest_carbon_696_1.json index 03cdba5adc..8a4d96d90c 100644 --- a/datasets/rlc_forest_carbon_696_1.json +++ b/datasets/rlc_forest_carbon_696_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_forest_carbon_696_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a 1:15 million scale map of forest stand carbon for the land area of Russia (Stone et al., 2000). The objective was to create a first approximation of the forest stand carbon reserves of Russia. Data include continuous estimates of forest stand carbon in units of metric tons/ha of carbon (C) and categorized data depicting rages of forest stand carbon. The resulting maps show forest stand C by region in a spatially explicit form. It is the first map of its type for Russia of which we are aware. The mapped C represents 96% of the total of 26.1 Pg forest tree stand C described by Alexeyev and Birdsey (1994) and Alexeyev et al. (1995). Of the remaining 4%, nearly half was due to bushes, which were assumed not to be mapped in the 1973 forest cover map.The source data for the forest stand carbon map were acquired by map digitization from the Atlas of Forests for the Soviet Union (State Committee on Forests, 1973) and spatial application and arithmetic manipulation of carbon storage data from Alexeyev and Birdsey (1998).", "links": [ { diff --git a/datasets/rlc_forest_map_1973_692_1.json b/datasets/rlc_forest_map_1973_692_1.json index dd01082817..0043633139 100644 --- a/datasets/rlc_forest_map_1973_692_1.json +++ b/datasets/rlc_forest_map_1973_692_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_forest_map_1973_692_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a 1:15 million scale forest cover map for the land area of the Former Soviet Union. Twenty-two land cover classes are distinguished, of which 20 are forest cover classes. The source data were acquired by map digitization from the Atlas of Forests of the USSR (Anon. 1973) which was likely based on forestry data from the 1940s, 1950s and 1960s.", "links": [ { diff --git a/datasets/rlc_forest_map_1990_691_1.json b/datasets/rlc_forest_map_1990_691_1.json index dab32547f7..c50fcfa76e 100644 --- a/datasets/rlc_forest_map_1990_691_1.json +++ b/datasets/rlc_forest_map_1990_691_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_forest_map_1990_691_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a 1:2.5 million scale forest cover map for the land area of the Former Soviet Union that was completed in 1990 (Garsia 1990). There are forty-five classes distinguished in this data set, of which 38 are forest cover classes. The purpose of this map was to create a generalized and up-to-date map of forest cover for the USSR. This map should not be viewed as a detailed forest cover map but more like an economic forestry map. The most important tree species of a region are highlighted rather than the dominant trees species or tree cover. Very few tree species are defined. In many cases, of course, the dominant and the most important trees species are the same. ", "links": [ { diff --git a/datasets/rlc_forest_map_krasnoyarsk_693_1.json b/datasets/rlc_forest_map_krasnoyarsk_693_1.json index b3c42c6938..ad6c94f712 100644 --- a/datasets/rlc_forest_map_krasnoyarsk_693_1.json +++ b/datasets/rlc_forest_map_krasnoyarsk_693_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_forest_map_krasnoyarsk_693_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a 1:2 million scale forest cover map for the land area of the Krasnoyarsk Region, Russia. Thirty-two land cover classes are distinguished. These data were digitized from maps of the Atlas of Forests of the USSR (Anon. 1973). This map should not be strictly viewed as a map of actual forest cover, but rather as a map of dominant tree species. Very few tree species are defined, and generally, each polygon and color has only one tree species assigned to it.", "links": [ { diff --git a/datasets/rlc_land_cover_689_1.json b/datasets/rlc_land_cover_689_1.json index a91e92c9ea..562794711a 100644 --- a/datasets/rlc_land_cover_689_1.json +++ b/datasets/rlc_land_cover_689_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_land_cover_689_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a 15-kilometer resolution land cover map for the land area of the Former Soviet Union. There are sixty land cover classes distinguished in this dataset, of which 38 are forest cover classes. The data set is useful for stratification of the FSU into general sub-regions of land cover for subsequent study using higher resolution satellite data.", "links": [ { diff --git a/datasets/rlc_landcover_far_east_690_1.json b/datasets/rlc_landcover_far_east_690_1.json index 49b95bbd3e..78b132f444 100644 --- a/datasets/rlc_landcover_far_east_690_1.json +++ b/datasets/rlc_landcover_far_east_690_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_landcover_far_east_690_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a 1-kilometer resolution land cover map for the land area of the Primor'ye and Southern Khabarovsk Regions, in the Russian Far East, based on 1990 NOAA AVHRR data. Labeling of land cover classes depended upon the Russian 1990 Forest Cover Map (Garsia, 1990), the analyst's experience with AVHRR data, and Russian data sources. There are eight classes distinguished in this dataset, of which 5 are forest cover classes.The objective of this work was to create a 1-km resolution land cover map of the region of the Far Eastern Siberia based on NOAA AVHRR data which might be used by World Wildlife Fund researchers to aid in the definition of remaining habitats and range for threatened animal species (Stone and Schlesinger, 1996).", "links": [ { diff --git a/datasets/rlc_vector_data_698_1.json b/datasets/rlc_vector_data_698_1.json index 92b2da1107..6137f8d7d1 100644 --- a/datasets/rlc_vector_data_698_1.json +++ b/datasets/rlc_vector_data_698_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_vector_data_698_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of roads, drainage, railroads, utilities, and population center information in readily usable vector format for the land area of the Former Soviet Union. The purpose of this dataset was to create a completely intact vector layer which could be readily used to aid in mapping efforts for the area of the FSU. These five vector data layers were assembled from the Digital Chart of the World (DCW), 1993. Individual record attributes were stored for population centers only. Vector maps for the FSU are in ArcView shapefile format.", "links": [ { diff --git a/datasets/rlc_vegetation_1990_700_1.json b/datasets/rlc_vegetation_1990_700_1.json index 0d4aaeecd5..dac700d926 100644 --- a/datasets/rlc_vegetation_1990_700_1.json +++ b/datasets/rlc_vegetation_1990_700_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_vegetation_1990_700_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset is a 1:4 million scale vegetation map for the land area of the Former Soviet Union. Three hundred seventy-three cover classes are distinguished, of which nearly 145 are forest cover-related classes. Stone and Schlesinger (1993) digitized the map Vegetation of the Soviet Union, 1990 (Institute of Geography, 1990). ", "links": [ { diff --git a/datasets/rlc_world_forest_map_697_1.json b/datasets/rlc_world_forest_map_697_1.json index 23049caa80..c30414eb9e 100644 --- a/datasets/rlc_world_forest_map_697_1.json +++ b/datasets/rlc_world_forest_map_697_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rlc_world_forest_map_697_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.", "links": [ { diff --git a/datasets/robinson_adelie_colonies_1.json b/datasets/robinson_adelie_colonies_1.json index 561aa429bc..3baa8fc188 100644 --- a/datasets/robinson_adelie_colonies_1.json +++ b/datasets/robinson_adelie_colonies_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "robinson_adelie_colonies_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GPS surveys of Adelie Penguin colonies in the Robinson Group, Antarctica were carried out by field biologists from the Australian Antarctic Division during the 2005/06 and 2006/07 seasons. In 2005/06 point data were collected representing the presence or absence of colonies on the islands. The data were collected by Matt Low and Lisa Meyer. In 2006/07 polygon data were collected representing the colonies on the islands. The data were collected by Matt Low and Rhonda Pike. The biologists were working on a project led by Dr Colin Southwell of the Australian Antarctic Division.", "links": [ { diff --git a/datasets/rock_samples_1.json b/datasets/rock_samples_1.json index b30f362318..d84dc2aa32 100644 --- a/datasets/rock_samples_1.json +++ b/datasets/rock_samples_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rock_samples_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rocks from Australian Antarctic Division library\n\nThis collection turns out to be rather interesting with some of heritage significance. Box 1 is basically odds and ends but includes a bag of coal from the Prince Charles Mountains worthy of display.\n\nBoxes 2 and 3 probably all are Phil Law collections. Unfortunately, locality information generally is lacking, but there are some interesting rocks.\n\nBox 1.\nA.Loose samples\nTwo pale grey, rounded specimens, one with round depression. Very light weight (low density). Probably diatomite or radiolarite. Source?\n\nDark grey with some red colours. Fragment of rounded river pebble that has been broken. Very tough, either quartzite or volcanic rock. Source?\n\nScallop (Pecten meridionalis), left valve Tasmania\n\nPink and yellow chert, varnished. One part of outside looks as if it has been fossil wood. Could be recrystallised chert from fossil wood locality. Source? Could be Tasmanian.\n\nTwo small, dark, angular specimens, quite coarse grained with obvious crystal faces that flash. Specimens are of quartz and galena (PbS). Source? Could be west coast Tasmania such as Zeehan.\n\nThree elongate specimens, pale yellow/off white. They fit together to produce original specimen about 20 cm long. These are quite common around coastal Australia where rain soaks through sand, dissolves CaCO3 from surface shell material and redeposits it on the way down, perhaps along the roots of a plant. Goes by various names such as 'fossil roots' (which is wrong), travertine\n\nLarge lump of black glass. Probably furnace slag but could conceivably be volcanic glass (probably too high density for that). Vesicles (gas bubbles quite common).\n\nB. Sample bag\nA calico bag of Permian coal from the Prince Charles Mountains. Bag is labelled to Assistant Director Science but probably was given to Evlyn Barrett as there is a note inside it suggesting that it is a present. Some specimens are good and could be used for display.\n\nBox 2.\nA note in the box (from me to Knowles Kerry) notes that these rocks were collected by Phil Law. While some cards are there, they are not related to the rocks. Most would appear to be Antarctic.\n\nSample with cellotape, labelled Cape North. Fragment of vein quartz.\n\nPumice. Grey, very light weight. Floats. Product of March 1962 submarine eruption at Protector Shoal in South Sandwich Islands. Rafts of this pumice circulated around Southern Hemisphere for years, slowly disappearing as the material became dispersed, washed onto beaches (small fragments still common on Australian beaches and some on Heard Island) and as fragments rubbed together, ground small chips off and these sank. This sample has some flow structure in it from the original eruption and due to elongation of gas bubbles as it flowed and cooled. It may well be from Heard Island. &It is identical in composition to material collected by Dr Jon Stephenson in 1963 from 'flotsam north of Heard Island' collected during his period on the latter expedition (Stephenson 1964) and identified as having been derived from vast rafts of pumice released in the South Atlantic Ocean during the eruption in the South Sandwich Islands area in 1962 (Gass et al.. 1963). This is probably the same material referred to by Dr Phil Law, who commented (personal communication, 19 August 1993) that he had seen rafts of pumice near Heard Island in January 1963.& (quote from Quilty and Wheller in preparation for Heard Island symposium of 1998).\n\nFlat dark grey fragment about 1 cm thick. Otherwise triangular with sharp corners. Rock is phyllite, rather low grade metamorphic rock, originally a shale in which clay has changed to muscovite to generate the good cleavage. Source? Would like to know because I have identical material as a glacial erratic from Kerguelen Plateau.\n\n'Granite'\nTwo fragments - angular, one rounded - of grey granite. Good samples. They are not quite the same material. Angular specimen is probably strictly granodiorite (the difference is important only to geologists). It contains quartz (very pale grey, glassy), two white feldspars (plagioclase-Na-CaAlsSi3O8 - and orthoclase - KalSi3O8) which make up the bulk of the rock in roughly equal proportions and come in two grain sizes - coarse (about 1 cm) and finer (about 2 mm). Dark minerals are biotite (black mica) and hornblende (complex Fe/Mg silicate). Rounded specimen is more uniform in grain and probably has the same pale minerals but they are not so easy to identify. Dark mineral hornblende. Biotite not seen. There also is a brown mineral, sometimes rhomboid in cross section. This probably is sphene.\n\nSource of samples?\n\nRauer Island Rocks. (Probably Phil Law's own labelling) Replaced in old plastic bag and in turn in a new thin one.\n\nTwo glassy (vitreous) grey samples. Monominerallic. Vein quartz.\n\nTwo flat specimens with marked orientation of very uniform grained constituent minerals. Both high grade metamorphic rocks - amphibolite gneiss. Mineralogy - quartz, amphibole (probably hornblende), plagioclase feldspar. In one the quartz is white and in the other, more yellowish.\n\nRounded specimen with two rock types in it with clear boundary. Pale rock is quartzite and other is amphibolite, probably part of same sequence as other amphibolites.\n\nOther rock has great variation in grain size but is otherwise part of the same sequence. Darker part is amphibolite, coarser than in samples described above and with yellowish quartz and orthoclase. This rock seems to be the source of the sand grains as it is more friable than others.\n\nGarnet rich sample - Bag 1\nOne rounded sample contains a significant content of garnet in white 'matrix'. The pale material is quartz/orthoclase and there is a fine grained, high lustre black mineral that could be magnetite (Fe3 O4). Source??? Probably a Law sample.\n\nThree specimens in small bag - Bag 2\nAll are characterised by having quartz veins 1-1.5 cm thick, cutting across the sample and bounded by a layer 1-2 mm thick of a black mineral (amphibole, probably hornblende). Other constituents of the rock are yellowish quartz, traces of garnet and biotite. I couldn't identify any feldspar but would expect it. The rocks, although not labelled with a locality, are very similar to some of those described as from the Rauer Islands but there are some in the Vestfold Hills that are very similar.\n\nMetabasalt? - Bag 4 - two samples\nThese look rather like the basalt dykes that are so characteristic of the Vestfold Hills but are they? And who collected them? They probably are Phil Law collections. The dykes were intruded in a series of about 9 episodes from about 2.2 billion to 1.1 billion years. They have been altered since intrusion and while bulk composition changed little, the mineralogy did. They are now very tough rocks that break with highly angular, brittle fractures.\n\nBox 3\nJudging by the brown sample bag, I suspect these are also Phil Law collections but where from?\n\nBrown calico bag - 5 specimens\nLarge specimen is amphibolite gneiss consisting of layers that are amphibole and biotite rich. Also has traces of garnet. Locality?\n\nTwo pale specimens. Both contain prominent garnet in quartz-feldspar matrix, orthoclase dominating. Metamorphic. Locality?\n\nTwo small specimens. One is coarser than the other and has obvious garnet with hornblende, biotite, quartz and feldspar. The other is mainly hornblende/quartz but is a surface specimen, somewhat weathered.\n\nBrown paper bag (now in plastic bag - 5)\nSmall sample (two almost black specimens). These are different from anything noted above. While the black biotite is the dominant source of the colour, there is also some quartz and I suspect feldspar. There also is quite a deal of very fine acicular mineral. It could be one of several but sillimanite (one of several minerals with the formula Al2SIO3) is a possibility.\n\nLargest, dark sample. Amphibolite gneiss. Well banded. Pale bands of quartz-feldspar-muscovite (white mica). Dark bands of hornblende-biotite. Source???\n\nDominantly pale sample with dark patch. Pale part is quartz-feldspar and the dark is hornblende plus minor acicular mineral (sillimanite?).\n\nThin sample, 6 x 5 cm, 4 mm thick. Details not clear. Too fine grained but probably mainly quartz-feldspar with minor dark mineral (hornblende?).\n\nPlastic bag 6.\nLarge flat specimen and one chip off the large block. Low grade metamorphic rock, originally fine sandstone. Source?\n\nPlastic bag 7\nRock mainly of coarse K-feldspar and quartz with minor plagioclase. Rock includes layers of brown mica (phlogopite?). Metamorphic. Source?\n\nPlastic bag 8.\n8A. 3 specimens (2 are counterparts). See also 'Brown paper bag' sample above. Biotite-quartz-sillimanite.\n8B. 2 specimens. Beautiful banded gneiss. Bands are pale, dominantly quartz and dark, dominantly biotite with some hornblende.\n8C. 2 specimens. Quartz-biotite schist with trace of acicular mineral (sillimanite?) and pyrite.\n\nTwo remaining specimens.\nOne is of quartz/feldspar(?)/biotite/hornblende-sillimanite? Is feldspar correctly identified? Sieve texture.\n\nOther is subrounded boulder, greenish (chlorite?).\nPatrick G. Quilty AM\n22 November 1999", "links": [ { diff --git a/datasets/rockfall-gallery-testing-parde-2016_1.0.json b/datasets/rockfall-gallery-testing-parde-2016_1.0.json index 9dd879f094..38b978f1d1 100644 --- a/datasets/rockfall-gallery-testing-parde-2016_1.0.json +++ b/datasets/rockfall-gallery-testing-parde-2016_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rockfall-gallery-testing-parde-2016_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. \"Durchf\u00fchrung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie\", WSL-Berichte, Heft 68, 2018.", "links": [ { diff --git a/datasets/root-traits_1.0.json b/datasets/root-traits_1.0.json index c59d606ed6..17a50dfc95 100644 --- a/datasets/root-traits_1.0.json +++ b/datasets/root-traits_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root-traits_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald Data_Fig.1.xlsx Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring Data_Tab1+2_2005.xlsx Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest Data_Tab1+2_2016.xlsx Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest", "links": [ { diff --git a/datasets/root_biomass_658_1.json b/datasets/root_biomass_658_1.json index e1ed209d98..1e5936e2b8 100644 --- a/datasets/root_biomass_658_1.json +++ b/datasets/root_biomass_658_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_biomass_658_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.", "links": [ { diff --git a/datasets/root_mass_of_live_trees_zell_wutzler-210_1.0.json b/datasets/root_mass_of_live_trees_zell_wutzler-210_1.0.json index c7d279cabe..dc3fd50884 100644 --- a/datasets/root_mass_of_live_trees_zell_wutzler-210_1.0.json +++ b/datasets/root_mass_of_live_trees_zell_wutzler-210_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_mass_of_live_trees_zell_wutzler-210_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of the belowground part (roots) of living trees and shrubs starting at 12 cm dbh. The dimensions of the roots are determined according to Zell and Wutzler. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/root_nutrients_659_1.json b/datasets/root_nutrients_659_1.json index d137bf0270..66f2e34864 100644 --- a/datasets/root_nutrients_659_1.json +++ b/datasets/root_nutrients_659_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_nutrients_659_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.", "links": [ { diff --git a/datasets/root_profiles_660_1.json b/datasets/root_profiles_660_1.json index 809b0e878b..8541b25bf8 100644 --- a/datasets/root_profiles_660_1.json +++ b/datasets/root_profiles_660_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_profiles_660_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.", "links": [ { diff --git a/datasets/root_turnover_661_1.json b/datasets/root_turnover_661_1.json index 5f41bafed3..f70285c27c 100644 --- a/datasets/root_turnover_661_1.json +++ b/datasets/root_turnover_661_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_turnover_661_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature. Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.", "links": [ { diff --git a/datasets/root_water_storage_1deg_1006_1.json b/datasets/root_water_storage_1deg_1006_1.json index 22e7a77a20..eea195c394 100644 --- a/datasets/root_water_storage_1deg_1006_1.json +++ b/datasets/root_water_storage_1deg_1006_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "root_water_storage_1deg_1006_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone (\"rooting zone water storage size\") on a 1.0 degree global grid. Two inverse modeling methods were used. The first modeling approach (optimization) was based on the assumption that vegetation has adapted to the environment such that it makes optimum use of water (Kleidon and Heimann 1998). The second method (assimilation) was based on the assumption that green vegetation indicates sufficient available water for transpiration (Knorr 1997). The data set was developed to provide alternative means to describe rooting characteristics of the global vegetation cover for land surface and climate models in support of the ISLSCP Initiative II data collection. There are three files in this data set. ", "links": [ { diff --git a/datasets/ros_data_1.0.json b/datasets/ros_data_1.0.json index 68174ddefa..5757e0e5ff 100644 --- a/datasets/ros_data_1.0.json +++ b/datasets/ros_data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ros_data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of \"virtual meteorological stations\" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in W\u00fcrzer & Jonas et al. (2017), currently under review in Hydrological Processes.", "links": [ { diff --git a/datasets/rs15bmlc_483_1.json b/datasets/rs15bmlc_483_1.json index a72b2f3312..85d7a9bf60 100644 --- a/datasets/rs15bmlc_483_1.json +++ b/datasets/rs15bmlc_483_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs15bmlc_483_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.", "links": [ { diff --git a/datasets/rs16cm61_563_1.json b/datasets/rs16cm61_563_1.json index 571e0c39c3..000f3a0e1a 100644 --- a/datasets/rs16cm61_563_1.json +++ b/datasets/rs16cm61_563_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs16cm61_563_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Satellite and aircraft SAR data used in conjunction with ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).", "links": [ { diff --git a/datasets/rs17diel_301_1.json b/datasets/rs17diel_301_1.json index cbd2794cd0..00e774a319 100644 --- a/datasets/rs17diel_301_1.json +++ b/datasets/rs17diel_301_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs17diel_301_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains dielectric profile measurements taken by RSS-17 at NSA and SSA treed tower sites.", "links": [ { diff --git a/datasets/rs20c130_305_1.json b/datasets/rs20c130_305_1.json index 84651a81a3..baf88cc38a 100644 --- a/datasets/rs20c130_305_1.json +++ b/datasets/rs20c130_305_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs20c130_305_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the POLDER data collected from the C130 platform during IFCs 1 and 2 in 1994.", "links": [ { diff --git a/datasets/rs20helo_306_1.json b/datasets/rs20helo_306_1.json index 661c84977d..69a91ec9de 100644 --- a/datasets/rs20helo_306_1.json +++ b/datasets/rs20helo_306_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs20helo_306_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains POLDER data collected from the helicopter platform during IFCs 1 and 3 in 1994.", "links": [ { diff --git a/datasets/rs20prad_555_1.json b/datasets/rs20prad_555_1.json index 10c1cfcae0..e54a0e9349 100644 --- a/datasets/rs20prad_555_1.json +++ b/datasets/rs20prad_555_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs20prad_555_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A subset of images collected by the POLDER instrument mounted on the NASA/Ames C-130 aircraft over tower sites in the BOREAS study areas during the IFCs in 1994.", "links": [ { diff --git a/datasets/rs3atmos_288_1.json b/datasets/rs3atmos_288_1.json index a33eaee05f..30ea9363ae 100644 --- a/datasets/rs3atmos_288_1.json +++ b/datasets/rs3atmos_288_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs3atmos_288_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains Helicopter-based measurements of atmospheric conditions acquired during the BOReal Ecosystem-Atmosphere Study (BOREAS) processed to estimates of aerosol optical thickness and atmospheric water vapor.", "links": [ { diff --git a/datasets/rs3se590_291_1.json b/datasets/rs3se590_291_1.json index a4c75c4645..1338abaa92 100644 --- a/datasets/rs3se590_291_1.json +++ b/datasets/rs3se590_291_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs3se590_291_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains helicopter SE-590 measurements taken by RSS-03.", "links": [ { diff --git a/datasets/rs7ssatd_302_1.json b/datasets/rs7ssatd_302_1.json index c24f3acf85..9b60cdf7fe 100644 --- a/datasets/rs7ssatd_302_1.json +++ b/datasets/rs7ssatd_302_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs7ssatd_302_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the soil and stem temperature measurements collected by RSS17 at various flux sites: (1) Southern Study Area (SSA) at the Old Black Spruce (OBS), Old Jack Pine (OJP), Old Aspen (OA), and Young Jack Pine (YJP); and (2) Northern Study Area (NSA) O", "links": [ { diff --git a/datasets/rs7tmlai_441_1.json b/datasets/rs7tmlai_441_1.json index 0c9ab60f95..4431cb2e1f 100644 --- a/datasets/rs7tmlai_441_1.json +++ b/datasets/rs7tmlai_441_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rs7tmlai_441_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas. Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA. The LAI images are based on ground measurements and Landsat TM RSR images.", "links": [ { diff --git a/datasets/rss10tom_443_1.json b/datasets/rss10tom_443_1.json index db02af60c1..7e98dfbd31 100644 --- a/datasets/rss10tom_443_1.json +++ b/datasets/rss10tom_443_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss10tom_443_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations. This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution. The spatial coverage is circumpolar from latitudes of 41 to 66 degrees N latitude.", "links": [ { diff --git a/datasets/rss14srb_447_1.json b/datasets/rss14srb_447_1.json index 472b8745e7..ac4a41dd4d 100644 --- a/datasets/rss14srb_447_1.json +++ b/datasets/rss14srb_447_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss14srb_447_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W.", "links": [ { diff --git a/datasets/rss17fth_484_1.json b/datasets/rss17fth_484_1.json index 8e3fff9c85..e49f280b3d 100644 --- a/datasets/rss17fth_484_1.json +++ b/datasets/rss17fth_484_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss17fth_484_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions corresponding to soil thaw and possible canopy thaw were revealed by the data. The results demonstrated that radar provides an ability to observe thaw transitions at the beginning of the growing season, which in turn helps constrain the length of the growing season.", "links": [ { diff --git a/datasets/rss17xyf_303_1.json b/datasets/rss17xyf_303_1.json index 15daf86a16..8d282fd025 100644 --- a/datasets/rss17xyf_303_1.json +++ b/datasets/rss17xyf_303_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss17xyf_303_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains xylem flux density measurements taken by RSS-17 at SSA-OBS.", "links": [ { diff --git a/datasets/rss18opt_503_1.json b/datasets/rss18opt_503_1.json index ce9398e124..ffe091abc6 100644 --- a/datasets/rss18opt_503_1.json +++ b/datasets/rss18opt_503_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss18opt_503_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground-based sunphotometer data collected in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS)Information System (BORIS) as part of their data documentation efforts.", "links": [ { diff --git a/datasets/rss1para_286_1.json b/datasets/rss1para_286_1.json index 0af1a8510f..b188333e79 100644 --- a/datasets/rss1para_286_1.json +++ b/datasets/rss1para_286_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss1para_286_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the RSS-01 PARABOLA data sets averaged by 15 degree common view zenith and azimuth angles collected during the BOREAS field campaigns in 1994.", "links": [ { diff --git a/datasets/rss1tpwnv7r01_7R01.json b/datasets/rss1tpwnv7r01_7R01.json index b1550eb7ff..0443c66677 100644 --- a/datasets/rss1tpwnv7r01_7R01.json +++ b/datasets/rss1tpwnv7r01_7R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss1tpwnv7r01_7R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Remote Sensing Systems (RSS) Monthly 1-degree Microwave Total Precipitable Water (TPW) netCDF dataset V7R01 provides global total columnar water vapor values, or TPW, over ocean areas. This dataset contains monthly, 1-degree TPW means, a 12-month climatology made using 1988 to 2007 data, monthly anomaly maps, a trend map with associated global and tropical TPW time series and trends, and a time-latitude plot. The 1 degree TPW dataset is a merged ocean product constructed using version 7 (V7) passive microwave geophysical ocean products made publicly available by RSS (www.remss.com). TPW values for this dataset were acquired from the following satellite microwave radiometers: SSM/I F08 through F15, SSMIS F16 and F17, AMSR-E, AMSR-2, and WindSat. The radiometers used to construct this dataset were were inter-calibrated at the brightness temperature level, while the V7 ocean products were produced using a consistent processing methodology across sensors.", "links": [ { diff --git a/datasets/rss1windnv7r01_7R01.json b/datasets/rss1windnv7r01_7R01.json index 730b4b93da..92500783bd 100644 --- a/datasets/rss1windnv7r01_7R01.json +++ b/datasets/rss1windnv7r01_7R01.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss1windnv7r01_7R01", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Remote Sensing Systems (RSS) Monthly 1-degree Merged Wind Climatology netCDF dataset V7R01 provides global gridded wind speed data over ocean areas. This dataset contains a 12-month climatology using January 1, 1988 to March 31, 2016 data, monthly anomaly maps, a trend map with associated global and tropical wind speed time series, and a time-latitude plot. The wind climatology dataset is a merged ocean product constructed using the version-7 (V7) passive microwave geophysical ocean products made publicly available by Remote Sensing Systems (www.remss.com). Ocean wind measurements used to create this dataset were acquired from the following satellite microwave radiometers: SSM/I F08 through F15, SSMIS F16 and F17, AMSR-E, AMSR-2, and WindSat. The radiometers used to construct this dataset were inter-calibrated at the brightness temperature level, while the V7 ocean products were produced using a consistent processing methodology across sensors.", "links": [ { diff --git a/datasets/rss3hmmr_290_1.json b/datasets/rss3hmmr_290_1.json index a22ed0cd04..95d401a083 100644 --- a/datasets/rss3hmmr_290_1.json +++ b/datasets/rss3hmmr_290_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss3hmmr_290_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains site averaged reflected radiance and reflectance values from barnes MMR taken from helicopter.", "links": [ { diff --git a/datasets/rss4lib_292_1.json b/datasets/rss4lib_292_1.json index 402378a708..7940f99af4 100644 --- a/datasets/rss4lib_292_1.json +++ b/datasets/rss4lib_292_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss4lib_292_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains sample values and mean values of needle biochemistry data taken at jack pine sites in the SSA.", "links": [ { diff --git a/datasets/rss8brdf_505_1.json b/datasets/rss8brdf_505_1.json index d75ca5da8c..ae8468960c 100644 --- a/datasets/rss8brdf_505_1.json +++ b/datasets/rss8brdf_505_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss8brdf_505_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ground BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices. The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspend (OA) sites during IFC-3 in 1994.", "links": [ { diff --git a/datasets/rss8digi_506_1.json b/datasets/rss8digi_506_1.json index 8386938b8e..ddd1384137 100644 --- a/datasets/rss8digi_506_1.json +++ b/datasets/rss8digi_506_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss8digi_506_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA), Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994. The imagery of the canopy was taken from various perspectives. The RSS08 team also measured BRDF at the SSA-OA and -OBS sites during IFC-3.", "links": [ { diff --git a/datasets/rss8snow_428_1.json b/datasets/rss8snow_428_1.json index 4306201797..892c2a1472 100644 --- a/datasets/rss8snow_428_1.json +++ b/datasets/rss8snow_428_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rss8snow_428_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994. ", "links": [ { diff --git a/datasets/rssmif08d3d_7.json b/datasets/rssmif08d3d_7.json index ff315c8163..5bb12b3c2e 100644 --- a/datasets/rssmif08d3d_7.json +++ b/datasets/rssmif08d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif08d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Oceean Product Grids 3-Day Average from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefullyintercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 for 3-day average.", "links": [ { diff --git a/datasets/rssmif08d_7.json b/datasets/rssmif08d_7.json index d9c0da16ca..adf19d188a 100644 --- a/datasets/rssmif08d_7.json +++ b/datasets/rssmif08d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif08d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 daily.", "links": [ { diff --git a/datasets/rssmif08m_7.json b/datasets/rssmif08m_7.json index 9e8657745e..9a196a5683 100644 --- a/datasets/rssmif08m_7.json +++ b/datasets/rssmif08m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif08m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 for a monthly average.", "links": [ { diff --git a/datasets/rssmif08w_7.json b/datasets/rssmif08w_7.json index b19f9d8179..4494f44a97 100644 --- a/datasets/rssmif08w_7.json +++ b/datasets/rssmif08w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif08w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Products Grid Weekly Average from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 forweekly average.", "links": [ { diff --git a/datasets/rssmif10d3d_7.json b/datasets/rssmif10d3d_7.json index 90846ddfd0..3a9dc03f80 100644 --- a/datasets/rssmif10d3d_7.json +++ b/datasets/rssmif10d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif10d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F10 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F10 for 3-day averages.", "links": [ { diff --git a/datasets/rssmif10d_7.json b/datasets/rssmif10d_7.json index 7f789bebf6..db8340f1ed 100644 --- a/datasets/rssmif10d_7.json +++ b/datasets/rssmif10d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif10d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F10 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F10 daily.", "links": [ { diff --git a/datasets/rssmif10m_7.json b/datasets/rssmif10m_7.json index ea89852b37..889ba034cc 100644 --- a/datasets/rssmif10m_7.json +++ b/datasets/rssmif10m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif10m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F10 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F10 for monthly averages.", "links": [ { diff --git a/datasets/rssmif10w_7.json b/datasets/rssmif10w_7.json index 785d009d0a..f2c2000b2c 100644 --- a/datasets/rssmif10w_7.json +++ b/datasets/rssmif10w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif10w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F10 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F10 for weekly averages.", "links": [ { diff --git a/datasets/rssmif11d3d_7.json b/datasets/rssmif11d3d_7.json index 39ad2e4a5c..588c83d21c 100644 --- a/datasets/rssmif11d3d_7.json +++ b/datasets/rssmif11d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif11d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 for 3-day averages.", "links": [ { diff --git a/datasets/rssmif11d_7.json b/datasets/rssmif11d_7.json index 04ed409b06..7270c63864 100644 --- a/datasets/rssmif11d_7.json +++ b/datasets/rssmif11d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif11d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 daily.", "links": [ { diff --git a/datasets/rssmif11m_7.json b/datasets/rssmif11m_7.json index a9c8087516..382d49e92c 100644 --- a/datasets/rssmif11m_7.json +++ b/datasets/rssmif11m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif11m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 for monthly averages.", "links": [ { diff --git a/datasets/rssmif11w_7.json b/datasets/rssmif11w_7.json index 950ba74443..ea50df56c7 100644 --- a/datasets/rssmif11w_7.json +++ b/datasets/rssmif11w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif11w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 for weekly averages.", "links": [ { diff --git a/datasets/rssmif13d3d_7.json b/datasets/rssmif13d3d_7.json index b0afd5568b..3514bb5f18 100644 --- a/datasets/rssmif13d3d_7.json +++ b/datasets/rssmif13d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif13d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F13 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F13 for 3-day averages.", "links": [ { diff --git a/datasets/rssmif13d_7.json b/datasets/rssmif13d_7.json index 68f1f91b83..0b93c6e39b 100644 --- a/datasets/rssmif13d_7.json +++ b/datasets/rssmif13d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif13d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F13 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F13 daily.", "links": [ { diff --git a/datasets/rssmif13m_7.json b/datasets/rssmif13m_7.json index 16c720d3eb..837abc42f9 100644 --- a/datasets/rssmif13m_7.json +++ b/datasets/rssmif13m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif13m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F13 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F13 for monthly averages.", "links": [ { diff --git a/datasets/rssmif13w_7.json b/datasets/rssmif13w_7.json index 67cec5d6e9..4a75bdcf07 100644 --- a/datasets/rssmif13w_7.json +++ b/datasets/rssmif13w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif13w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F13 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F13 for weekly averages.", "links": [ { diff --git a/datasets/rssmif14d3d_7.json b/datasets/rssmif14d3d_7.json index 1146960182..5ff18e4a53 100644 --- a/datasets/rssmif14d3d_7.json +++ b/datasets/rssmif14d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif14d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F14 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F14 for a 3-day average.", "links": [ { diff --git a/datasets/rssmif14d_7.json b/datasets/rssmif14d_7.json index cf92d82d71..29416b4d82 100644 --- a/datasets/rssmif14d_7.json +++ b/datasets/rssmif14d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif14d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F14 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F14 daily.", "links": [ { diff --git a/datasets/rssmif14m_7.json b/datasets/rssmif14m_7.json index b83e026f57..84116a8fbc 100644 --- a/datasets/rssmif14m_7.json +++ b/datasets/rssmif14m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif14m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F14 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F14 for a monthly average.", "links": [ { diff --git a/datasets/rssmif14w_7.json b/datasets/rssmif14w_7.json index a0ed77c8f6..664f20309a 100644 --- a/datasets/rssmif14w_7.json +++ b/datasets/rssmif14w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif14w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F14 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F14 for a weekly average.", "links": [ { diff --git a/datasets/rssmif15d3d_7.json b/datasets/rssmif15d3d_7.json index 6ef006f93d..443919f38c 100644 --- a/datasets/rssmif15d3d_7.json +++ b/datasets/rssmif15d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif15d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F15 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F15 for a 3-day average.", "links": [ { diff --git a/datasets/rssmif15d_7.json b/datasets/rssmif15d_7.json index 202bde62f3..04e8a5eb52 100644 --- a/datasets/rssmif15d_7.json +++ b/datasets/rssmif15d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif15d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Daily from DMSP F15 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F15 daily.", "links": [ { diff --git a/datasets/rssmif15m_7.json b/datasets/rssmif15m_7.json index f9c2512ed0..6020f0a0f7 100644 --- a/datasets/rssmif15m_7.json +++ b/datasets/rssmif15m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif15m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Monthly Average from DMSP F15 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F15 for a monthly average.", "links": [ { diff --git a/datasets/rssmif15w_7.json b/datasets/rssmif15w_7.json index 79ab355c4e..85b31af80c 100644 --- a/datasets/rssmif15w_7.json +++ b/datasets/rssmif15w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif15w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F15 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F15 for a weekly average.", "links": [ { diff --git a/datasets/rssmif16d3d_7.json b/datasets/rssmif16d3d_7.json index 9f79158b3d..42a93eb715 100644 --- a/datasets/rssmif16d3d_7.json +++ b/datasets/rssmif16d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif16d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids 3-Day Average from DMSP F16 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F16 for a 3-day average.", "links": [ { diff --git a/datasets/rssmif16d_7.json b/datasets/rssmif16d_7.json index dee92faf92..d2954c9ca9 100644 --- a/datasets/rssmif16d_7.json +++ b/datasets/rssmif16d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif16d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Daily from DMSP F16 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F16 daily.", "links": [ { diff --git a/datasets/rssmif16m_7.json b/datasets/rssmif16m_7.json index 692812d4ba..c13e21815d 100644 --- a/datasets/rssmif16m_7.json +++ b/datasets/rssmif16m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif16m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Monthly Average from DMSP F16 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F16 for a monthly average.", "links": [ { diff --git a/datasets/rssmif16w_7.json b/datasets/rssmif16w_7.json index edf069a4e7..3be182b396 100644 --- a/datasets/rssmif16w_7.json +++ b/datasets/rssmif16w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif16w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Weekly Average from DMSP F16 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F16 for a weekly average.", "links": [ { diff --git a/datasets/rssmif17d3d_7.json b/datasets/rssmif17d3d_7.json index 09c0aa14e7..a90853e024 100644 --- a/datasets/rssmif17d3d_7.json +++ b/datasets/rssmif17d3d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif17d3d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids 3-Day Average from DMSP F17 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F17 for a 3-day average.", "links": [ { diff --git a/datasets/rssmif17d_7.json b/datasets/rssmif17d_7.json index a2b0b2f3ca..9899959f38 100644 --- a/datasets/rssmif17d_7.json +++ b/datasets/rssmif17d_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif17d_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Daily from DMSP F17 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F17 daily.", "links": [ { diff --git a/datasets/rssmif17m_7.json b/datasets/rssmif17m_7.json index e8d7581ddf..d5d715cf56 100644 --- a/datasets/rssmif17m_7.json +++ b/datasets/rssmif17m_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif17m_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Monthly Average from DMSP F17 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F17 for a monthly average.", "links": [ { diff --git a/datasets/rssmif17w_7.json b/datasets/rssmif17w_7.json index 55198ad82c..fcd29d439c 100644 --- a/datasets/rssmif17w_7.json +++ b/datasets/rssmif17w_7.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "rssmif17w_7", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The RSS SSMIS Ocean Product Grids Weekly Average from DMSP F17 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F17 for a weekly average.", "links": [ { diff --git a/datasets/s2k_IGBP-DIS_Soil_Surfaces_647_1.json b/datasets/s2k_IGBP-DIS_Soil_Surfaces_647_1.json index a4136bb353..7b193574f1 100644 --- a/datasets/s2k_IGBP-DIS_Soil_Surfaces_647_1.json +++ b/datasets/s2k_IGBP-DIS_Soil_Surfaces_647_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_IGBP-DIS_Soil_Surfaces_647_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SAFARI Subset - GRIDDED SURFACES of SELECTED SOIL CHARACTERISTICS (IGBP-DIS). The data-surfaces pre-generated by SoilData, at a resolution fo 5x5 arc-minutes, in ASCII GRID format for ARC INFO and for the soil depth interval 0-100 cm.", "links": [ { diff --git a/datasets/s2k_ISRIC-WISE_soil_properties_634_1.json b/datasets/s2k_ISRIC-WISE_soil_properties_634_1.json index 31f63c2124..b43c96db6c 100644 --- a/datasets/s2k_ISRIC-WISE_soil_properties_634_1.json +++ b/datasets/s2k_ISRIC-WISE_soil_properties_634_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_ISRIC-WISE_soil_properties_634_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set consists of a southern African subset of the ISRIC-WISE global data set of derived soil properties. The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995.", "links": [ { diff --git a/datasets/s2k_ISRIC_Wise_profiles_648_1.json b/datasets/s2k_ISRIC_Wise_profiles_648_1.json index e018cd8b7a..f40c4f424d 100644 --- a/datasets/s2k_ISRIC_Wise_profiles_648_1.json +++ b/datasets/s2k_ISRIC_Wise_profiles_648_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_ISRIC_Wise_profiles_648_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a southern African subset of the ISRIC-WISE International soil profile data set.", "links": [ { diff --git a/datasets/s2k_glcf1deg_626_1.json b/datasets/s2k_glcf1deg_626_1.json index 165b0a2612..b33f5b3a10 100644 --- a/datasets/s2k_glcf1deg_626_1.json +++ b/datasets/s2k_glcf1deg_626_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_glcf1deg_626_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse-resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world.", "links": [ { diff --git a/datasets/s2k_land_cover_data_8km_628_1.json b/datasets/s2k_land_cover_data_8km_628_1.json index 3aa81b6b53..830aab4a33 100644 --- a/datasets/s2k_land_cover_data_8km_628_1.json +++ b/datasets/s2k_land_cover_data_8km_628_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_land_cover_data_8km_628_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats.", "links": [ { diff --git a/datasets/s2k_olson_633_1.json b/datasets/s2k_olson_633_1.json index caccf0b131..020f2b0024 100644 --- a/datasets/s2k_olson_633_1.json +++ b/datasets/s2k_olson_633_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_olson_633_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of Olson's Major World Ecosystem Complexes for southern Africa in ASCII GRID and binary image file formats. Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation is a computerized database, used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups.", "links": [ { diff --git a/datasets/s2k_potential_vegetation_639_1.json b/datasets/s2k_potential_vegetation_639_1.json index 2807176c7c..b04ae811fd 100644 --- a/datasets/s2k_potential_vegetation_639_1.json +++ b/datasets/s2k_potential_vegetation_639_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_potential_vegetation_639_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.", "links": [ { diff --git a/datasets/s2k_soil_respiration_gridded_644_1.json b/datasets/s2k_soil_respiration_gridded_644_1.json index bcdbf0326c..a24129ce87 100644 --- a/datasets/s2k_soil_respiration_gridded_644_1.json +++ b/datasets/s2k_soil_respiration_gridded_644_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_soil_respiration_gridded_644_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data set provides estimated monthly and annual soil CO2 emissions for southern Africa (the SAFARI 2000 project region). The calculated emissions are from the respiration of both soil organisms and plant roots and are provided on a 0.5-degree grid cell basis. The data are a subset of a global data set (Raich and Potter, CDIAC 1996), reformatted and subsetted to the SAFARI region.", "links": [ { diff --git a/datasets/s2k_soller_wetlands_635_1.json b/datasets/s2k_soller_wetlands_635_1.json index 36ee2d3565..bfbed64067 100644 --- a/datasets/s2k_soller_wetlands_635_1.json +++ b/datasets/s2k_soller_wetlands_635_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_soller_wetlands_635_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern Africa subset of the Global Distribution of Freshwater Wetlands database 1-degree data and is available in ASCII GRID and binary image file formats.", "links": [ { diff --git a/datasets/s2k_zinke_soil_638_1.json b/datasets/s2k_zinke_soil_638_1.json index 279faa6209..509be1d6f8 100644 --- a/datasets/s2k_zinke_soil_638_1.json +++ b/datasets/s2k_zinke_soil_638_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "s2k_zinke_soil_638_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations.&", "links": [ { diff --git a/datasets/sabor_0.json b/datasets/sabor_0.json index 09ce154ec6..d4b1343937 100644 --- a/datasets/sabor_0.json +++ b/datasets/sabor_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sabor_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SABOR (Ship-Aircraft Bio-Optical Research) Collaborative campaign occurred from 19 Jul 2014 - 05 Aug 2014 that sampled the Gulf of Maine, near Bermuda, and shore-waters off the East coast of the United States.", "links": [ { diff --git a/datasets/sage_685_1.json b/datasets/sage_685_1.json index 4a6f21546c..cc0aa32e11 100644 --- a/datasets/sage_685_1.json +++ b/datasets/sage_685_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sage_685_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W).The global river discharge data set (Coe and Olejniczak 1999), formerly known as the \"Climate, People, and Environment Program (CPEP) Global River Discharge Database,\" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/sagehen_cycles_1.0.json b/datasets/sagehen_cycles_1.0.json index 9f36b25797..d75c8bf971 100644 --- a/datasets/sagehen_cycles_1.0.json +++ b/datasets/sagehen_cycles_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sagehen_cycles_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing. Data include 30-minute time series of - weather variables, - sap flow fluxes, - groundwater levels (in two riparian transects of shallow groundwater wells), - and stream stages (at 12 sites spanning a 500-meter elevation gradient), and daily time series of - temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations - diel cycle index values for groundwater levels and stream stages, - and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments. Google Earth Engine scripts for extracting the MODIS data are also provided.", "links": [ { diff --git a/datasets/saltation-of-cohesive-granular-materials_1.0.json b/datasets/saltation-of-cohesive-granular-materials_1.0.json index 92536ff79e..09e8104c4d 100644 --- a/datasets/saltation-of-cohesive-granular-materials_1.0.json +++ b/datasets/saltation-of-cohesive-granular-materials_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "saltation-of-cohesive-granular-materials_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics. The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity. In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.", "links": [ { diff --git a/datasets/salvage_logging-27_1.0.json b/datasets/salvage_logging-27_1.0.json index dbf75c83ba..6c2c708889 100644 --- a/datasets/salvage_logging-27_1.0.json +++ b/datasets/salvage_logging-27_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging-27_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest due to damage occurring (e.g. windthrow, avalanches, insects or rockfall), and not as the result of management planning. This feature is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester). The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/salvage_logging_due_to_insects-89_1.0.json b/datasets/salvage_logging_due_to_insects-89_1.0.json index e42a4e3c33..071dd49114 100644 --- a/datasets/salvage_logging_due_to_insects-89_1.0.json +++ b/datasets/salvage_logging_due_to_insects-89_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging_due_to_insects-89_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case insects, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester). The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/salvage_logging_due_to_insects_star-251_1.0.json b/datasets/salvage_logging_due_to_insects_star-251_1.0.json index 61b6c7b55a..5bbc9bfd33 100644 --- a/datasets/salvage_logging_due_to_insects_star-251_1.0.json +++ b/datasets/salvage_logging_due_to_insects_star-251_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging_due_to_insects_star-251_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case insects, and not because of management planning. This feature is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/salvage_logging_due_to_wind-88_1.0.json b/datasets/salvage_logging_due_to_wind-88_1.0.json index 02c7e75521..9e2c8c6855 100644 --- a/datasets/salvage_logging_due_to_wind-88_1.0.json +++ b/datasets/salvage_logging_due_to_wind-88_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging_due_to_wind-88_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case windthrow, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester). The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/salvage_logging_due_to_wind_star-250_1.0.json b/datasets/salvage_logging_due_to_wind_star-250_1.0.json index 2d42067850..c57712e5ac 100644 --- a/datasets/salvage_logging_due_to_wind_star-250_1.0.json +++ b/datasets/salvage_logging_due_to_wind_star-250_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging_due_to_wind_star-250_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case windthrow, and not because of management planning. This theme is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/salvage_logging_star-186_1.0.json b/datasets/salvage_logging_star-186_1.0.json index e1416afcda..7c74ceb77b 100644 --- a/datasets/salvage_logging_star-186_1.0.json +++ b/datasets/salvage_logging_star-186_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "salvage_logging_star-186_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were removed from the forest as a result of damage occurring (e.g. windthrow, avalanches, insects, rockfall), and not because of management planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/samsa94d_462_1.json b/datasets/samsa94d_462_1.json index 92e7dc5716..372927d5d3 100644 --- a/datasets/samsa94d_462_1.json +++ b/datasets/samsa94d_462_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsa94d_462_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1994 by the AMS suite A instrument set operated by SRC and provided to BORIS.", "links": [ { diff --git a/datasets/samsa95d_463_1.json b/datasets/samsa95d_463_1.json index 5e15669d27..99c1fdaabe 100644 --- a/datasets/samsa95d_463_1.json +++ b/datasets/samsa95d_463_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsa95d_463_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1995 by the AMS suite A instrument set operated by SRC and provided to BORIS.", "links": [ { diff --git a/datasets/samsa96d_464_1.json b/datasets/samsa96d_464_1.json index 3959dbdd55..6de2ec37d1 100644 --- a/datasets/samsa96d_464_1.json +++ b/datasets/samsa96d_464_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsa96d_464_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1996 by the AMS suite A instrument set operated by SRC and provided to BORIS. ", "links": [ { diff --git a/datasets/samsb94d_410_1.json b/datasets/samsb94d_410_1.json index 996551e39d..95415d2a0e 100644 --- a/datasets/samsb94d_410_1.json +++ b/datasets/samsb94d_410_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsb94d_410_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1994 by the AMS suite B instrument set operated by SRC and provided to BORIS.", "links": [ { diff --git a/datasets/samsb95d_411_1.json b/datasets/samsb95d_411_1.json index 4d08da82d0..552badbee7 100644 --- a/datasets/samsb95d_411_1.json +++ b/datasets/samsb95d_411_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsb95d_411_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1995 by the AMS suite B instrument set operated by SRC and provided to BORIS.", "links": [ { diff --git a/datasets/samsb96d_412_1.json b/datasets/samsb96d_412_1.json index 09bada2673..39b6b0482c 100644 --- a/datasets/samsb96d_412_1.json +++ b/datasets/samsb96d_412_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "samsb96d_412_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the data collected in 1996 by the AMS suite B instrument set operated by SRC and provided to BORIS.", "links": [ { diff --git a/datasets/sar_subsets_993_1.json b/datasets/sar_subsets_993_1.json index a1238e7d3e..e3cab708de 100644 --- a/datasets/sar_subsets_993_1.json +++ b/datasets/sar_subsets_993_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sar_subsets_993_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides Synthetic Aperture Radar (SAR) images for 42 selected sites from various terrestrial ecology and meteorological monitoring networks including FLUXNET, Ameriflux, Long Term Ecological Research (LTER), and the Greenland Climate Network (GC-Net).The data set contains at least one image for all 42 sites, and six sites have multiple images. See Table 1 for the sites and the temporal range of the available images. The scenes are in GeoTIFF format in Universal Transverse Mercator (UTM), WGS-84 projection, and 15-meter resolution.The SAR images are subset scenes of approximately 60 km x 70 km that include an established site in one of the monitoring networks. The spatial resolution of all scenes is 15 meters. These scenes are distributed as geotif files with appropriate projection information defined within the file.The acquisition mode for all data is the Fine Beam Double Polarization or FBD with the HH/HV polarization. The HH and HV channels are distributed as 3 channels to allow for an intuitive image display. The HH band is displayed in the red and blue channels and the HV band is displayed in the green channel. For some images only single polarization is available; these images are distributed as grayscale images.The source of the data is the PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor flying on the Advanced Land Observing Satellite (ALOS). The PALSAR data are in dual Polarization, HH+HV, mode. Bands HH (red and blue) and Band-HV (green) can be used to visualize land use patterns. The resulting images show vegetation in shades of green and barren land in shades of pink or purple.", "links": [ { diff --git a/datasets/saskfc1m_510_1.json b/datasets/saskfc1m_510_1.json index 3ada5e8306..d13784fde7 100644 --- a/datasets/saskfc1m_510_1.json +++ b/datasets/saskfc1m_510_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "saskfc1m_510_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU). Map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base.", "links": [ { diff --git a/datasets/saskffcc_307_1.json b/datasets/saskffcc_307_1.json index e368f0bfbb..168c0c357b 100644 --- a/datasets/saskffcc_307_1.json +++ b/datasets/saskffcc_307_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "saskffcc_307_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 1994 and 1995 hourly data from various forestry meteorology stations.", "links": [ { diff --git a/datasets/saskfire_308_1.json b/datasets/saskfire_308_1.json index 61c397003f..e74d81fde4 100644 --- a/datasets/saskfire_308_1.json +++ b/datasets/saskfire_308_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "saskfire_308_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.", "links": [ { diff --git a/datasets/satellite-avalanche-mapping-validation_1.0.json b/datasets/satellite-avalanche-mapping-validation_1.0.json index 72a1bd19a3..0bd6524aa8 100644 --- a/datasets/satellite-avalanche-mapping-validation_1.0.json +++ b/datasets/satellite-avalanche-mapping-validation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "satellite-avalanche-mapping-validation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf). Coordinate system: CH1903+_LV95 The generation of this dataset is described in detail in: Hafner, E. D., Techel, F., Leinss, S., and B\u00fchler, Y.: Mapping avalanches with satellites \u2013 evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.", "links": [ { diff --git a/datasets/sbuceilimpacts_1.json b/datasets/sbuceilimpacts_1.json index 8699762b18..dcb80c0da1 100644 --- a/datasets/sbuceilimpacts_1.json +++ b/datasets/sbuceilimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbuceilimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Ceilometers IMPACTS dataset includes ceilometer cloud height measurements collected by the Vaisala CL51, Vaisala CT25K, and Lufft Ceilometer CHM 15k ceilometers operated by the State University of New York (SUNY) Stony Brook University. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The ceilometer dataset files are available from January 1, 2020, through March 2, 2023, in netCDF-3 and netCDF-4 formats.", "links": [ { diff --git a/datasets/sbukasprimpacts_1.json b/datasets/sbukasprimpacts_1.json index 5a7e29748f..e9e641cc77 100644 --- a/datasets/sbukasprimpacts_1.json +++ b/datasets/sbukasprimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbukasprimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Ka-band Scanning Polarimetric Radar (KASPR) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) Ka-band Scanning Polarimetric Radar (KASPR) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. KASPR provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 6, 2020 through February 26, 2020 in netCDF-4 format.", "links": [ { diff --git a/datasets/sbulidarimpacts_1.json b/datasets/sbulidarimpacts_1.json index cfa231a219..01e6dc310e 100644 --- a/datasets/sbulidarimpacts_1.json +++ b/datasets/sbulidarimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbulidarimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Doppler LiDAR IMPACTS dataset consists of Doppler velocity and backscatter intensity from the Stony Brook University (SBU) Doppler LiDAR. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in netCDF-4 format from January 1 through February 26, 2020.", "links": [ { diff --git a/datasets/sbumetimpacts_1.json b/datasets/sbumetimpacts_1.json index 5e2e323db4..808033aca7 100644 --- a/datasets/sbumetimpacts_1.json +++ b/datasets/sbumetimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbumetimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Meteorological Station IMPACTS dataset consists of weather station data collected at two Stony Brook University (SBU) weather stations (1 mobile radar truck and 1 stationary site in Manhattan, New York City, New York) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The surface meteorological data variables include temperature, dew point, relative humidity, absolute humidity, mixing ratio, air pressure, windspeed, and wind direction. The dataset files are available from January 1, 2020, through January 25, 2023, in netCDF-4 and ASCII-CSV formats.", "links": [ { diff --git a/datasets/sbumrr2impacts_1.json b/datasets/sbumrr2impacts_1.json index 7baa3b0281..4ce9378131 100644 --- a/datasets/sbumrr2impacts_1.json +++ b/datasets/sbumrr2impacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbumrr2impacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Micro Rain Radar 2 (MRR-2) IMPACTS dataset consists of reflectivity, Doppler velocity, signal-to-noise ratio, spectral width, droplet size, Liquid Water Content, melting layer, drop size distribution, rain attenuation, rain rate, and radial velocity data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Both the MRR-2 and the MRR-PRO instruments were used to collect data for this dataset. The dataset files are available from January 1, 2020 through March 2, 2023 in netCDF-3 and netCDF-4/CF formats.", "links": [ { diff --git a/datasets/sbumwrimpacts_1.json b/datasets/sbumwrimpacts_1.json index c3342bc033..fd2aa2dfed 100644 --- a/datasets/sbumwrimpacts_1.json +++ b/datasets/sbumwrimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbumwrimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Microwave Radiometer (MWR) IMPACTS dataset consists of microwave radiometer data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available from January 1, 2023, through March 6, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/sbuparsimpacts_1.json b/datasets/sbuparsimpacts_1.json index 96df37b5f1..fffd7e81ef 100644 --- a/datasets/sbuparsimpacts_1.json +++ b/datasets/sbuparsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbuparsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Parsivel IMPACTS dataset consists of precipitation data collected by the Parsivel disdrometer in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Parsivel disdrometer data include particle size distribution, fall speed, radar reflectivity, and precipitation rate. The dataset files are available in netCDF-3 format from January 1, 2020, through March 2, 2023.", "links": [ { diff --git a/datasets/sbuplimpacts_1.json b/datasets/sbuplimpacts_1.json index f1c7e744d0..0a659fd279 100644 --- a/datasets/sbuplimpacts_1.json +++ b/datasets/sbuplimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbuplimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Pluvio Precipitation Gauge IMPACTS dataset consists of precipitation intensity and precipitation accumulation collected using the OTT Pluvio2 weighing rain gauge during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. NASA\u2019s Earth Venture program funded IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data files in this dataset are available in ASCII-CSV format from January 7, 2020, through March 2, 2023. ", "links": [ { diff --git a/datasets/sbuskylerimpacts_1.json b/datasets/sbuskylerimpacts_1.json index 0deb59eda4..cc2821dedf 100644 --- a/datasets/sbuskylerimpacts_1.json +++ b/datasets/sbuskylerimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbuskylerimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU X-band Phased Array Radar (SKYLER) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) X-band Phased Array Radar (SKYLER) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. SKYLER provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 17, 2022, through February 28, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/sbusndimpacts_1.json b/datasets/sbusndimpacts_1.json index c83c4e90c3..058cfea8d7 100644 --- a/datasets/sbusndimpacts_1.json +++ b/datasets/sbusndimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sbusndimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA\u2019s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile-sounding profiles were obtained about every three hours during snow events by Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020, through February 28, 2023 in netCDF-3 format.", "links": [ { diff --git a/datasets/scarmarbin_1647.json b/datasets/scarmarbin_1647.json index 9d5ce0a99b..90312a2d0c 100644 --- a/datasets/scarmarbin_1647.json +++ b/datasets/scarmarbin_1647.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1647", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1648.json b/datasets/scarmarbin_1648.json index eac90852a8..b63d58cb40 100644 --- a/datasets/scarmarbin_1648.json +++ b/datasets/scarmarbin_1648.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1648", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1649.json b/datasets/scarmarbin_1649.json index fbcabaa280..0172ed6d1b 100644 --- a/datasets/scarmarbin_1649.json +++ b/datasets/scarmarbin_1649.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1649", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1651.json b/datasets/scarmarbin_1651.json index 659674d831..e08aadf7ea 100644 --- a/datasets/scarmarbin_1651.json +++ b/datasets/scarmarbin_1651.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1651", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).", "links": [ { diff --git a/datasets/scarmarbin_1716.json b/datasets/scarmarbin_1716.json index 5bd36cf9ae..418ad28043 100644 --- a/datasets/scarmarbin_1716.json +++ b/datasets/scarmarbin_1716.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1716", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).", "links": [ { diff --git a/datasets/scarmarbin_1772.json b/datasets/scarmarbin_1772.json index d3d00bdb8f..cd4dcf4b81 100644 --- a/datasets/scarmarbin_1772.json +++ b/datasets/scarmarbin_1772.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1772", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1806.json b/datasets/scarmarbin_1806.json index c3e49053e5..73612eb995 100644 --- a/datasets/scarmarbin_1806.json +++ b/datasets/scarmarbin_1806.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1806", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1807.json b/datasets/scarmarbin_1807.json index 76cc539d71..97d34e7311 100644 --- a/datasets/scarmarbin_1807.json +++ b/datasets/scarmarbin_1807.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1807", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_1808.json b/datasets/scarmarbin_1808.json index 11e419286f..bf71bae94f 100644 --- a/datasets/scarmarbin_1808.json +++ b/datasets/scarmarbin_1808.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_1808", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic).\n", "links": [ { diff --git a/datasets/scarmarbin_987.json b/datasets/scarmarbin_987.json index 04a53362ef..872209010c 100644 --- a/datasets/scarmarbin_987.json +++ b/datasets/scarmarbin_987.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_987", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database.", "links": [ { diff --git a/datasets/scarmarbin_ABBED.json b/datasets/scarmarbin_ABBED.json index 08a93ceb54..2b786a30f0 100644 --- a/datasets/scarmarbin_ABBED.json +++ b/datasets/scarmarbin_ABBED.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scarmarbin_ABBED", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Admiralty Bay is one of the best studied sites in the maritime Antarctic. The\nfirst benthos data has been recorded in 1906 and knowledge is constantly gained\nby the research activities of permanent stations, Arctowski (Poland, since\n1977), and Ferraz (Brazil, since 1984).\n\nAdmiralty Bay is a protected area within the Antarctic Treaty System, an\n\u0093Antarctic Specially Managed Area\u0094 (ASMA). It was also a reference site under\nthe EASIZ programme, and has been or is currently investigated by several\nnations : Poland, Brazil, United States, Peru, Ecuador, Germany, The\nNetherlands, Belgium.\n\nABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish\ninitiative, which aims at compiling and linking existing information on\nAdmiralty Bay benthos biodiversity and ecology. This information will be\ndigitized into a database and linked to wider Antarctic marine biodiversity\ninitiatives, such as SCAR-MarBIN, which will disseminate the information\nthrough a web portal.\n\nBeing highly diverse in its content, formats and data providers, ABBED will\nconstitute an extremely interesting case-study for SCAR-MarBIN, allowing to\ntest strategic options which were retained for the development of the network.\nMoreover, the quality and quantity of data which will be made available to the\ncommunity will reinforce the status of Admiralty Bay as a true reference point\nfor Antarctic biodiversity research.\n\nThe project aims at developing an interactive database on the biodiversity of\nbenthic communities of Admiralty Bay, King George Island, for scientific,\nmonitoring, management and conservation purposes. It is intended to be a\nspringboard for promoting future research in this region, by centralizing the\nrelevant information for i.e. scientific programme design.", "links": [ { diff --git a/datasets/schweizerisches-landesforstinventar-2009-2017_1.0.json b/datasets/schweizerisches-landesforstinventar-2009-2017_1.0.json index 9a6fc74309..c31192881a 100644 --- a/datasets/schweizerisches-landesforstinventar-2009-2017_1.0.json +++ b/datasets/schweizerisches-landesforstinventar-2009-2017_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "schweizerisches-landesforstinventar-2009-2017_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Swiss National Forest Inventory. Results of the fourth survey 2009\u20132017. The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings. Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009\u20132017. In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt, im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate \u00fcber den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erl\u00e4utert. Der Bericht ist thematisch strukturiert nach den europ\u00e4ischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalit\u00e4t, Holzproduktion, biologische Vielfalt, Schutzwald und Sozio\u00f6konomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab. Keywords: Waldfl\u00e4che, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversit\u00e4t, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz Content license: All rights reserved. Copyright \u00a9 2020 by WSL, Birmensdorf.", "links": [ { diff --git a/datasets/scolytidae_1.0.json b/datasets/scolytidae_1.0.json index d02384ecb4..6713a05cba 100644 --- a/datasets/scolytidae_1.0.json +++ b/datasets/scolytidae_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scolytidae_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Scolytidae data from all historic up to the recent projects (29.10.2019) of WSL, collected with various methods in forests of different types. Data are provided on request to contact person against bilateral agreement.", "links": [ { diff --git a/datasets/scrxsondecpexaw_1.json b/datasets/scrxsondecpexaw_1.json index 180c7c2ec0..c896471b08 100644 --- a/datasets/scrxsondecpexaw_1.json +++ b/datasets/scrxsondecpexaw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "scrxsondecpexaw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The St. Croix Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 19, 2021 through September 14, 2021 in netCDF and ASCII formats, with associated browse imagery in PNG format.", "links": [ { diff --git a/datasets/sdm-env-layers-gdplants_1.0.json b/datasets/sdm-env-layers-gdplants_1.0.json index d55e8c2436..3e8920ff26 100644 --- a/datasets/sdm-env-layers-gdplants_1.0.json +++ b/datasets/sdm-env-layers-gdplants_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sdm-env-layers-gdplants_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).", "links": [ { diff --git a/datasets/sea_elephant_biology_1951_1.json b/datasets/sea_elephant_biology_1951_1.json index 6309d155f1..056bb7cf76 100644 --- a/datasets/sea_elephant_biology_1951_1.json +++ b/datasets/sea_elephant_biology_1951_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_elephant_biology_1951_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a copy of a scanned document which contains a report, as well as tabulated data compiled by K. Brown on Sea Elephants (Elephant Seals) at Heard Island in 1951. The data are biological in nature, and deal with:\n\nBreeding Season 1951\n Formation of the Harems\n Arrival of the Bulls\n Arrival of the Cows\n Birth of the Pups\n Lactation\n Moult\n Pup Mortality\n Fertilisation of the Cows\n Break up of the Harems\n Arrival of the Adolescents", "links": [ { diff --git a/datasets/sea_ice_extent_gis_1.json b/datasets/sea_ice_extent_gis_1.json index 6471a36bb1..132d0f8e8f 100644 --- a/datasets/sea_ice_extent_gis_1.json +++ b/datasets/sea_ice_extent_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_ice_extent_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents extents of Antarctic sea ice derived from passive microwave data. \nIt includes: maximum and minimum sea ice extent based on 1989 - 99 data; maximum sea ice extent by month for the period October - March based on 1973 - 98 data; mean sea ice extent by month based on 1973 - 1998 data; and maximum sea ice extent averaged over the period 1987 - 1998.\n\nThe data referenced by this metadata record has been sourced from another metadata record in this catalogue. For more information on the dataset see:\n\nAntarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice [climate_sea_ice].", "links": [ { diff --git a/datasets/sea_ice_extent_xdeg_981_1.json b/datasets/sea_ice_extent_xdeg_981_1.json index 686a819f77..943326b490 100644 --- a/datasets/sea_ice_extent_xdeg_981_1.json +++ b/datasets/sea_ice_extent_xdeg_981_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_ice_extent_xdeg_981_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data. These original data were re-gridded by the National Snow and Ice Data Center (NSIDC) from their original 25-km spatial resolution and EASE-Grid into equal angle Earth grids with quarter, half and one degree spatial resolutions in latitude/longitude. The ISLSCP II staff have taken the one degree resolution original data provided by the Principal Investigator and created global maps of monthly sea ice concentration on a global one degree grid using the latitude and longitude coordinates that were provided. Individual monthly files were created and written to the ASCII format. The re-gridded one degree original data were also adjusted to match the one degree ISLSCP II land/water mask. ", "links": [ { diff --git a/datasets/sea_ice_extents_1980_1988_1.json b/datasets/sea_ice_extents_1980_1988_1.json index 9a6507f957..eaf89d96e2 100644 --- a/datasets/sea_ice_extents_1980_1988_1.json +++ b/datasets/sea_ice_extents_1980_1988_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_ice_extents_1980_1988_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a scanned copy of a document detailing data on the extent of sea ice in Antarctic from 1980 to 1988. The scanned pages consist of latitude and distance of the south pole of the northern edge of Antarctic sea ice each 10 degrees of longitude. These data were originally extracted from the U.S. navy - NOAA joint ice centre weekly maps of sea ice extent, and compiled by Jo Jacka.", "links": [ { diff --git a/datasets/sea_ice_measurements_database_1.json b/datasets/sea_ice_measurements_database_1.json index 17d95a91cf..914990bb59 100644 --- a/datasets/sea_ice_measurements_database_1.json +++ b/datasets/sea_ice_measurements_database_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_ice_measurements_database_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data have been extracted from an Australian Antarctic Data Centre application, \"Sea ice measurements database\". The application has now been discontinued. The download file contains the extracted data, plus a sample data entry form. The extracted data are simply database tables that have been converted to csv format.\n\nTaken from the main page of the application:\n\nThis archive contains in-situ measurements of Antarctic sea ice and snow cover properties, collected by many national programs over the past several decades. The data include physical, biological and biogeochemical measurements on ice cores and snow pit samples, as well as ice and snow thickness measurements from drilled transects across ice floes. The data are from all regions of the Antarctic pack ice in many different months of the year.\n\nData can be submitted online using a standard proforma that can be downloaded from this site. The development of this site was a key recommendation from the International Workshop on Antarctic Sea Ice Thickness, held in Hobart, Australia in July 2006.", "links": [ { diff --git a/datasets/sea_surface_temp_1deg_980_1.json b/datasets/sea_surface_temp_1deg_980_1.json index 0ffacb017b..facdf4e46f 100644 --- a/datasets/sea_surface_temp_1deg_980_1.json +++ b/datasets/sea_surface_temp_1deg_980_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sea_surface_temp_1deg_980_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sea surface temperature (SST) is an important indicator of the state of the earth climate system as well as a key variable in the coupling between the atmosphere and the ocean. Accurate knowledge of SST is essential for climate monitoring, prediction and research. It is also a key surface boundary condition for numerical weather prediction and for other atmospheric simulations using atmospheric general circulation models and regional models. SST also is important in gas exchange between the ocean and atmosphere, including the air-sea flux of carbon. Gridded SST products have been developed to satisfy these needs. There are 3 .zip files provided with this data set.Gridded monthly and weekly sea surface temperature (SST) and long term SST monthly climatology for the period 1971-2000 are provided here. Weekly normalized error variance fields are also provided with the weekly data. The data are derived using the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) global sea surface temperature analyses that use seven days of in situ (ship and buoy) and satellite SST observations and SST values derived from sea ice concentration. These analyses are produced weekly using optimum interpolation (OI) on a 1-degree grid. The data sets included in the ISLSCP II data collection are produced using version 2 of the OI analyses, called OIv2. In this data set, the ISLSCP II staff have masked land areas based on the ISLSCP II land/water mask. A file describing the differences between the ISLSCP II mask and the original mask used is provided.", "links": [ { diff --git a/datasets/seaflux_1.json b/datasets/seaflux_1.json index 2911a98f83..9cdf15ef3c 100644 --- a/datasets/seaflux_1.json +++ b/datasets/seaflux_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seaflux_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SeaFlux Data Products dataset consists of estimates of ocean surface latent and sensible heat fluxes, 2m and 10m wind speed, 2m and 10m air temperature, 2m and 10m air humidity, and skin sea surface temperature. This data product was created by using the SeaFlux V3 model. These data are available globally from January 1, 1988 through December 31, 2018 in netCDF-4 format.", "links": [ { diff --git a/datasets/seaice_icecores_nelladan_1985_1.json b/datasets/seaice_icecores_nelladan_1985_1.json index 99c07cac7b..8db0f9493e 100644 --- a/datasets/seaice_icecores_nelladan_1985_1.json +++ b/datasets/seaice_icecores_nelladan_1985_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seaice_icecores_nelladan_1985_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During voyage 1 of 1985, sixteen ice cores were drilled from sea ice. Details from those cores include the position they were drilled, length of the core, percentage of the core that was frazil ice, and comments on the state of the core, or observations of the ice make-up.\n\nPhysical records are archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/seamap47.json b/datasets/seamap47.json index 046de48b2a..2cf2351dcd 100644 --- a/datasets/seamap47.json +++ b/datasets/seamap47.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seamap47", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara. \n PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz", "links": [ { diff --git a/datasets/seasonal-fractional-snow-covered-area-algorithm_1.0.json b/datasets/seasonal-fractional-snow-covered-area-algorithm_1.0.json index d7cf270c41..9af2d57258 100644 --- a/datasets/seasonal-fractional-snow-covered-area-algorithm_1.0.json +++ b/datasets/seasonal-fractional-snow-covered-area-algorithm_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seasonal-fractional-snow-covered-area-algorithm_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt. The current version can be found in the WSL/SLF Gitlab repository: https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area", "links": [ { diff --git a/datasets/seasonal-snow-data-wy-2016-2022_1.0.json b/datasets/seasonal-snow-data-wy-2016-2022_1.0.json index 4136664f52..9c81ad62a6 100644 --- a/datasets/seasonal-snow-data-wy-2016-2022_1.0.json +++ b/datasets/seasonal-snow-data-wy-2016-2022_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seasonal-snow-data-wy-2016-2022_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.", "links": [ { diff --git a/datasets/seawater-temp-casey-Dec03_1.json b/datasets/seawater-temp-casey-Dec03_1.json index 234cffaee1..5845b448e9 100644 --- a/datasets/seawater-temp-casey-Dec03_1.json +++ b/datasets/seawater-temp-casey-Dec03_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seawater-temp-casey-Dec03_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Water temperatures were recorded by Tidbit temperature loggers attached to experimental mesocosms suspended below the sea ice at four sites around Casey in summer 2003/04. Data are temperature in degrees Celsius automatically logged every 5 minutes between the 01/12/2003 and 31/12/2003 at Brown Bay inner (S66 16.811 E110 32.475) and McGrady Cove (S66 16.556 E110 34.392), and between 02/12/2003 and 01/01/2004 at Brown Bay outer (S66 16.811 E110 32.526) and O'Brien Bay (S66 18.730 E110 30.810). Three loggers were deployed at each site; loggers A and B - one attached to each of two mesocosms (perforated 20 litre food buckets) and another - logger I - attached to plastic tubing approximately 1 metre above the mesocosms. Only two data loggers (A and B) were deployed at Mcgrady Cove. Mesocosms were suspended two to three metres below the bottom edge of the sea ice through a 1 metre diameter hole and were periodically raised to the surface for short periods (~1 hour). This experiment was part of the short-term biomonitoring program for the Thala Valley Tip Clean-up at Casey during summer 2003/04.\n\nThese data were collected as part of ASAC project 2201 (ASAC_2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities).\n\nSee also other metadata records by Glenn Johnstone for related information.\n\nThe fields in this dataset are:\n\nDate\nTime\nTemperature\nLocation", "links": [ { diff --git a/datasets/seawifs_624_1.json b/datasets/seawifs_624_1.json index f1423e0bc7..d20bd8ad25 100644 --- a/datasets/seawifs_624_1.json +++ b/datasets/seawifs_624_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seawifs_624_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the eight core study sites of Mongu, Etosha, Kasangu, Skukuza, Mutoko, Mzola, Nampula, and Ndola. There are two main sets of local area coverage (LAC) data: Level-1 and Level-2 200-km x 200-km image subsets for seven of the sites and 400-km x 400-km image subsets for the Etosha site. The data are provided in HDF format files.", "links": [ { diff --git a/datasets/seawifs_region_625_1.json b/datasets/seawifs_region_625_1.json index 422048cb9f..a845e276cb 100644 --- a/datasets/seawifs_region_625_1.json +++ b/datasets/seawifs_region_625_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seawifs_region_625_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern African region selected from global area coverage (GAC) at 4.5-km resolution. The data are provided in HDF format files.", "links": [ { diff --git a/datasets/secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0.json b/datasets/secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0.json index 6368d2aa97..21b248e3c6 100644 --- a/datasets/secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0.json +++ b/datasets/secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository contains all WRF model outputs and observational data sets used for the paper: Georgakaki, P., Sotiropoulou, G., Vignon, \u00c9., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.", "links": [ { diff --git a/datasets/sediment-transport-observations-in-swiss-mountain-streams_1.0.json b/datasets/sediment-transport-observations-in-swiss-mountain-streams_1.0.json index 32105752c9..c9615d0b0b 100644 --- a/datasets/sediment-transport-observations-in-swiss-mountain-streams_1.0.json +++ b/datasets/sediment-transport-observations-in-swiss-mountain-streams_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sediment-transport-observations-in-swiss-mountain-streams_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan). Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avan\u00e7on de Nant near Pont de Nant (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport.", "links": [ { diff --git a/datasets/sediments_gom.json b/datasets/sediments_gom.json index 8f62831cdf..751e3a0425 100644 --- a/datasets/sediments_gom.json +++ b/datasets/sediments_gom.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sediments_gom", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The overall objective of this project is to create a database of existing data on contaminants in sediment for the Gulf of Maine region that will be useful to persons throughout the region for scientific and management purposes. This task involves identification of data sources, entry of data into the database format, validation or scientific editing of the database, some analysis and synthesis of the compiled data, and publication of the database and associated bibliographies. The tasks of locating and entering data are being shared among the principle investigators in this project because they require a thorough knowledge of the geographic regions under consideration, an understanding of the types of data identified, and familiarity with active research in these regions. This cooperative approach insures that a more thorough identification and collection of data occurs than could take place from one institution. It also insures that the compiled database will be used by all the participants and their colleagues in the future. \n\nObjectives of the work:\n\n1) Develop a comprehensive inventory (database) of available information on sediment contaminants, both inorganic and organic, for the Gulf of Maine\n\n2) Encourage the cooperation and active participation of multiple agencies and organizations in locating, incorporating, and utilizing the data.\n\n3) Place these and ancillary data in interactive, user-friendly, and readily exchangeable forms (such as desktop computer, FTP, and CD-ROM).\n\n4) Map and analyze sediment contaminant distributions in order to provide the best assemblage of information possible for use in determining contaminant baselines\n\n5) Utilize the database to address specific scientific questions about transport and fate of contaminants in the GOM system.\n\n6) Provide guidance for other agencies and organizations to further the usefulness of the data in research, resource management, and public policy decisions.\n\n7) Provide guidance on where to sample and how to analyze samples in the future to make more effective use of limited resources", "links": [ { diff --git a/datasets/seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0.json b/datasets/seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0.json index 633871dfd2..b0450d4c77 100644 --- a/datasets/seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0.json +++ b/datasets/seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. The plugin \u2018Swiss Geo Downloader\u2019, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model \u2018swissALTI3D\u2019 with the help of the \u2018Swiss Geo Downloader\u2019 and how to use it for digital planning of a cable line with the plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German! Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link to Seilaplan website: https://seilaplan.wsl.ch ********************* F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. Das Plugin Swiss Geo Downloader, welches f\u00fcr das Open Source Geoinformationssystem QGIS zur Verf\u00fcgung steht, erm\u00f6glicht frei verf\u00fcgbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgen\u00f6ssischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue H\u00f6henmodell swissALTI3D herunterladen und f\u00fcr die Seillinienplanung mit dem Plugin Seilaplan verwenden kann. Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch", "links": [ { diff --git a/datasets/seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0.json b/datasets/seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0.json index 4c9b6551e8..f8ce272fa6 100644 --- a/datasets/seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0.json +++ b/datasets/seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool \u2018Virtual Raster\u2019. This simplifies the digital planning of a cable line using the QGIS plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German! Link to Seilaplan website: https://seilaplan.wsl.ch *************************** F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenf\u00fcgen und abspeichern kann. F\u00fcr die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgew\u00e4hlt werden. Link zur Seilaplan-Website: https://seilaplan.wsl.ch", "links": [ { diff --git a/datasets/seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0.json b/datasets/seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0.json index e49a57ea1d..5979afe6e5 100644 --- a/datasets/seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0.json +++ b/datasets/seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the \u2018Swiss Geo Downloader\u2019 plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German! Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details Link to the rope map website: https://seilaplan.wsl.ch ******************** F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. Als Alternative zum Swiss Geo Downloader erkl\u00e4ren wir in diesem Tutorial Schritt f\u00fcr Schritt, wie man das n\u00f6tige H\u00f6henmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann. Link zum H\u00f6henmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details Link zur Seilaplan-Website: https://seilaplan.wsl.ch", "links": [ { diff --git a/datasets/seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0.json b/datasets/seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0.json index 0215c86dea..d01bcc6a70 100644 --- a/datasets/seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0.json +++ b/datasets/seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In order to digitally plan a cable line using the QGIS plugin \u2018Seilaplan\u2019, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German! Link for the integration of WMS data: https://wms.geo.admin.ch/ Link to the description on the Swisstopo website: https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html Link to the Seilaplan website: https://seilaplan.wsl.ch ************************** F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Daf\u00fcr m\u00fcssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. F\u00fcr die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massst\u00e4ben, das Vegetationsh\u00f6henmodell LFI oder der Waldmischungsgrad LFI. Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/ Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html Link zur Seilaplan-Website: https://seilaplan.wsl.ch", "links": [ { diff --git a/datasets/seilaplan_2.0.json b/datasets/seilaplan_2.0.json index d7f8472fca..4e08f46a83 100644 --- a/datasets/seilaplan_2.0.json +++ b/datasets/seilaplan_2.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "seilaplan_2.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM). The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority. The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports. The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport. For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012). License: GNU, General Public License, Version 2 or newer. Literature: Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.", "links": [ { diff --git a/datasets/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0.json b/datasets/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0.json index 4f2cd4f8f8..ab171723e6 100644 --- a/datasets/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0.json +++ b/datasets/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both. The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence. This dataset is the underlying dataset to: Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.", "links": [ { diff --git a/datasets/sensitivity-of-modeled-snow-instability_1.0.json b/datasets/sensitivity-of-modeled-snow-instability_1.0.json index 968db63883..9b12750bf8 100644 --- a/datasets/sensitivity-of-modeled-snow-instability_1.0.json +++ b/datasets/sensitivity-of-modeled-snow-instability_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sensitivity-of-modeled-snow-instability_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.", "links": [ { diff --git a/datasets/sentinel-1-grd-bundle-1_NA.json b/datasets/sentinel-1-grd-bundle-1_NA.json index 301091069f..1d0426136b 100644 --- a/datasets/sentinel-1-grd-bundle-1_NA.json +++ b/datasets/sentinel-1-grd-bundle-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sentinel-1-grd-bundle-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copernicus Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. This dataset contains interferometric wide swath ground range detected high resolution data available over Brazil.", "links": [ { diff --git a/datasets/sentinel-3-olci-l1-bundle-1_NA.json b/datasets/sentinel-3-olci-l1-bundle-1_NA.json index 7e5aaaea6a..e3b9f338d7 100644 --- a/datasets/sentinel-3-olci-l1-bundle-1_NA.json +++ b/datasets/sentinel-3-olci-l1-bundle-1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sentinel-3-olci-l1-bundle-1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Copernicus Sentinel-3/OLCI Level-1B product OL_1_EFR (EO processing mode for Full Resolution) over Brazil.", "links": [ { diff --git a/datasets/shadoz_ozonesonde_726_1.json b/datasets/shadoz_ozonesonde_726_1.json index 2d48310674..3aa55d446d 100644 --- a/datasets/shadoz_ozonesonde_726_1.json +++ b/datasets/shadoz_ozonesonde_726_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "shadoz_ozonesonde_726_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ozonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere. During the campaign, ozonesondes were launched daily during the height of the burning season and in a region of active biomass burning activity.", "links": [ { diff --git a/datasets/shirley_dem_1.json b/datasets/shirley_dem_1.json index d8b2b174a9..d6dc46dfaa 100644 --- a/datasets/shirley_dem_1.json +++ b/datasets/shirley_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "shirley_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes:\n(i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica;\n(ii) reliability data for the DEM;\n(iii) contours interpolated from the DEM; and \n(iv) an orthophoto created using the DEM.\n\nThe data are stored in the UTM zone 49 map projection. \nThe horizontal datum is WGS84. \n\nThe data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins.\n\nSee a related URL below for a map showing Shirley island.", "links": [ { diff --git a/datasets/simrad_SO.json b/datasets/simrad_SO.json index b2fa18c745..ac47394454 100644 --- a/datasets/simrad_SO.json +++ b/datasets/simrad_SO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "simrad_SO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Using the hull mounted Simrad EK500 Scientific Sounder System,\n acoustic returns from 38, 120, and 200 kHz transducers were recorded\n continuously along ship's track from Aug 3 - Sept 15, 2002. Of\n interest, was the acoustic returns from zooplankton patches and\n density structures, and the signel correlations with known plankton\n tows and CTD casts. The survey area included the continental margin\n to the west of the Antarctic Peninsula extending from the northern tip\n of Adelaide Island to the southern portion of Alexander Island,\n Crystal Sound, and Marguerite Bay. These data have been reduced to\n daily files and are supported by software for manipulative purposes.\n \n Ship name/cruise ID/dates of cruise\n RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002", "links": [ { diff --git a/datasets/simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0.json b/datasets/simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0.json index 627189a4c5..6b095faba2 100644 --- a/datasets/simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0.json +++ b/datasets/simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016. Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.", "links": [ { diff --git a/datasets/simulated-future-discharge-and-climatological-variables_1.0.json b/datasets/simulated-future-discharge-and-climatological-variables_1.0.json index 4acca487e1..1182893647 100644 --- a/datasets/simulated-future-discharge-and-climatological-variables_1.0.json +++ b/datasets/simulated-future-discharge-and-climatological-variables_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "simulated-future-discharge-and-climatological-variables_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km\u00b2. The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Sch\u00e4fli et al 2007) as objective functions. The calibration and validation procedures are described in detail in K\u00f6plin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, K\u00f6plin et al. 2010). The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below: - ICHEC-EC-EARTH\tDMI-HIRHAM5\t2.6\tEUR-11 - ICHEC-EC-EARTH\tDMI-HIRHAM5\t4.5\tEUR-11 - ICHEC-EC-EARTH\tDMI-HIRHAM5\t8.5\tEUR-11 - ICHEC-EC-EARTH\tSMHI-RCA4\t2.6\tEUR-11 - ICHEC-EC-EARTH\tSMHI-RCA4\t4.5\tEUR-11 - ICHEC-EC-EARTH\tSMHI-RCA4\t8.5\tEUR-11 - MOHC-HadGEM2-ES\tSMHI-RCA4\t4.5\tEUR-11 - MOHC-HadGEM2-ES\tSMHI-RCA4\t8.5\tEUR-11 - MPI-M-MPI-ESM-LR\tSMHI-RCA4\t4.5\tEUR-11 - MPI-M-MPI-ESM-LR\tSMHI-RCA4\t8.5\tEUR-11 __*References*__: -\tK\u00f6plin, N., D. Viviroli, B. Sch\u00e4dler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010. -\tNational Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich. -\tSch\u00e4fli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825. -\tViviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH. Part II - Physical model description_, Geographica Bernensia, 40, 1-89. -\tViviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001. -\tViviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.", "links": [ { diff --git a/datasets/simulating-chamois-populations_1.0.json b/datasets/simulating-chamois-populations_1.0.json index cc8c68a8ba..5c28f492ba 100644 --- a/datasets/simulating-chamois-populations_1.0.json +++ b/datasets/simulating-chamois-populations_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "simulating-chamois-populations_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# General description Genomic data, habitat suitability raster files and scripts to run gen3sis to simulate cumulative divergence over time as approximation for genetic differentiation. Scripts for basic analysis of the simulations (e.g., create distance matrix from sampling locations) are provided, too. See original publication (doi link will be provided after publication) for details. The study area are the European Alps. All data is uploaded as zipped file. Unzip them after the download and put all data in one folder. See linked publications for correct citation of the data used, use of the data without correct citation is not allowed. __Corresponding author__: Flurin Leugger, email: flurin.leugger@gmail.com # Description of the data (content of the different zip folders) ## Abiotic data ### Glaciers Folders with raster stacks with glaciated areas at 0.05\u00b0 resolution in WGS84 projection from Seguinot et al. (2018). Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., & Preusser, F. (2018). Modelling last glacial cycle ice dynamics in the Alps. _The Cryosphere, 12(10)_, 3265\u20133285. https://doi.org/10.5194/tc-12-3265-2018 ### Rivers * __river_raster_elevation_class.tif__: raster file (.tif) at 0.05\u00b0 resolution and WGS84 projection with large rivers (scenario 2 from publication). The rivers (each cell) is classified according to the elevation of the cell. Natural Earth. (2018). Rivers + lake centerlines version 4.1.0. Retrieved January 22, 2020, from https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines * __river_raster_strahler_class_5km.tif__: raster file at 0.05\u00b0 resolution and WGS84 projection with medium rivers. The rivers are classified according to their Strahler order. Food and Agriculture Organization of the United Nations. (2014). Rivers in Europe (Derived from HydroSHEDS). Retrieved January 29, 2020, from http://www.fao.org/geonetwork/srv/fr/google.kml?uuid=e0243940-e5d9-487c-8102-45180cf1a99f&layers=AQUAMAPS:37253_rivers_europe ## Fossil records * __chamois_fossil_combined_public.xlsx__: list with fossil records until 20,000 years BP from Central Europe, see linked references for citation. ## Chamois occurrences * __chamois_occurrence.csv__: Chamois presences from all sources used for the publication (see Suppl. mat. Table S1 for detailed information and correct citations of the data) aggregated at 0.05\u00b0 resolution (~5km). ## Gen3sis * __config__: folders with all configuration files used to run the simulations for the publication (different dispersal divergence parameters). * __scripts__: scripts (and helper functions) to run the gen3sis simulations including scripts for the beginning of the subsequent analysis. ## Genetic * __populations.snps.light.vcf__: vcf file of the sampled Northern chamois _(Rupicapra rupicapra)_ . The genomic data encompasses 20k SNPs (from ddRAD sequencing). * __Sequencing_final_without_slovakia.txt__: sampling locations of Northern chamois _(Rupicapra rupicapra)_ ## HSM * __habitat_suitability_hindcasting__: Aggregated habitat suitability raster files (stacks, .grd files) at 0.05\u00b0 resolution and WGS84 projection from 20,000 years BP until today in 100 year time steps. There are separate folders for each environmental variable scenario used (different terrain slope variables) an the different occurrence/pseudo-absence sampling strategy used. * __ODMAP_LeuggerEtAl__2021-10-25.csv__: ODMAP protocol", "links": [ { diff --git a/datasets/sir_c.json b/datasets/sir_c.json index 7c57b722e3..52091d658f 100644 --- a/datasets/sir_c.json +++ b/datasets/sir_c.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sir_c", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spaceborne Imaging Radar-C (SIR-C) is part of an imaging radar system that was flown on board two Space Shuttle flights (9 - 20 April, 1994 and 30 September - 11 October, 1994). The USGS distributes the C-band (5.8 cm) and L-band (23.5 cm) data. All X-band (3 cm) data is distributed by DLR.\n\nThere are several types of products that are derived from the SIR-C data:\n\n Survey Data is intended as a \"quick look\" browse for viewing the areas that were imaged by the SIR-C system. The data consists of a strip image of an entire data swath. Resolution is approximately 100 meters, processed to a 50-meter pixel spacing. Files are distributed via File Transfer Protocol (FTP) download.\n \n Precision (Standard) Data consists of a frame image of a data segment, which represents a processed subset of the data swath. It contains high-resolution multifrequency and multipolarization data. All precision data is in CEOS format.\n\nThe following types of precision data products are available:\n\n Single-Look Complex (SLC) consists of one single-look file for each scene, per frequency. Each data segment will cover 50 kilometers along the flight track, and is broken into four processing runs (two L band, two C-band). Resolution and polarization will depend on the mode in which the data was collected. Available as calibrated or uncalibrated data.\n \n Multi-Look Complex (MLC) is based on an averaging of multiple looks, and consists of one file for each scene per frequency. Each data segment will cover 100 km along the flight track, and is broken into two processing runs (one L band and one C band). Polarization will depend on the modes in which the looks were collected. The data is available in 12.5- or 25-meter pixel spacing.\n \n Reformatted Signal Data (RSD) consists of the raw radar signal data only. Each data segment will cover 100 km along the flight track, and the segment will be broken into two processing runs (L-band and C-band).\n \n Interferometry Data consists of experimental multitemporal data that covers the same area. Most data takes were collected during repeat passes within the second flight (days 7, 8, 9, and/or 10). In addition, nine data takes were collected during the second flight that were repeat passes of the first flight. Most data takes were also single polarization, although dual and quad polarization data was also collected on some passes. A Digital Elevation Model (DEM) is not included with any of the SIR-C interferometric data.\n\nThe following types of interferometry products are available:\n\n Interferometric Single-Look Complex (iSLC) consists of two or more uncalibrated SLC images that have been processed with the same Doppler centroid to allow interferometric processing. Each frame image covers 50 kilometers along the flight track. The data is available in CEOS format.\n \n Raw Interferogram product (RIn) involves the combination of two data takes over the same area to produce an interferogram for each frequency (L-band and C-band). The data is available in TAR format.\n \n Reformatted Signal Data (RSD) consists of radar signal data that has been processed from two or more data takes over the same area, but the data has not been combined. Although this is not technically an interferometric product, the RSD can then be used to generate an interferogram. Each frame will cover 100 km along the flight track. The data is available in CEOS format.", "links": [ { diff --git a/datasets/slgeo_1.json b/datasets/slgeo_1.json index 66b3182ef3..1a2e0da3b6 100644 --- a/datasets/slgeo_1.json +++ b/datasets/slgeo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "slgeo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sediment Analysis Network for Decision Support (SANDS) Landsat Geotiff dataset includes images for sediment redistribution after a hurricane on the coast of the Gulf of Mexico and then creates a product based on the analysis from September 11, 2000 to September 8, 2008. This dataset consists of the set of daytime GeoTiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis. Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida). These are seasonal data for storms.", "links": [ { diff --git a/datasets/slgsa_1.json b/datasets/slgsa_1.json index 9b7b081145..5135fa732c 100644 --- a/datasets/slgsa_1.json +++ b/datasets/slgsa_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "slgsa_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sediment Analysis Network for Decision Support (SANDS) Landsat Geological Survey of AL (GSA) Analysis dataset analyzed changes in the coastal shoreline and sedimentation using Landsat GeoTiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project. The daytime GeoTiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area (coordinates 31 to 27 North latitude and 90 to 84.25 West longitude). These are seasonal data for storms from 2001-2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images.", "links": [ { diff --git a/datasets/slow-snow-compression_1.0.json b/datasets/slow-snow-compression_1.0.json index c2ee438159..2644edfbd2 100644 --- a/datasets/slow-snow-compression_1.0.json +++ b/datasets/slow-snow-compression_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "slow-snow-compression_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.", "links": [ { diff --git a/datasets/smart_radiometers_727_1.json b/datasets/smart_radiometers_727_1.json index e5b7ec47b0..933a7da2ae 100644 --- a/datasets/smart_radiometers_727_1.json +++ b/datasets/smart_radiometers_727_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "smart_radiometers_727_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena. This allows investigators to participate in coordinated measurement campaigns, such as SAFARI 2000.The SMART instruments were deployed to the Skukuza Airport from August 15 to September 17, 2000 to take part in the SAFARI 2000 Dry Season Aircraft Campaign. The SMART-COMMIT mission is designed to pursue the following goals: Earth Observing System (EOS) validation; innovative investigations; and long-term atmospheric monitoring. The results reported in this data set are for the following instruments deployed and measurements recorded at the Skukuza Airport site within the Kruger National Park: several broadband radiometers, for global, diffuse, direct downward solar irradiance and global infrared downward irradiance; meteorological sensors, for surface air temperature, pressure, relative humidity, and wind; and a Solar Spectral Flux Radiometer (NASA Ames) for spectral solar downward irradiance.", "links": [ { diff --git a/datasets/smgeo_1.json b/datasets/smgeo_1.json index fd920ab5bb..59ba1e9093 100644 --- a/datasets/smgeo_1.json +++ b/datasets/smgeo_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "smgeo_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sediment Analysis Network for Decision Support (SANDS) MODIS GeoTIFF dataset consists of the set of GeoTIFF images provided to the Geological Survey of Alabama for their analysis. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.", "links": [ { diff --git a/datasets/smgsa_1.json b/datasets/smgsa_1.json index e0b1691379..da208f6347 100644 --- a/datasets/smgsa_1.json +++ b/datasets/smgsa_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "smgsa_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sediment Analysis Network for Decision Support (SANDS) MODIS Geological Survey of AL (GSA) Analysis dataset consists of geoTIFF images were analyzed for sediment redistribution after hurricanes on the Gulf of Mexico. These are seasonal data for storms from September 14, 2000 to September 8, 2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and/or grids, metadata, and storm temporal information for the sediment analysis images. The Geological Survey of Alabama (GSA) generated this dataset from geoTIFF MODIS images as part of the Sediment Analysis Network for Decision Support (SANDS) project.", "links": [ { diff --git a/datasets/smsub_1.json b/datasets/smsub_1.json index d460200ae9..d77d6763d5 100644 --- a/datasets/smsub_1.json +++ b/datasets/smsub_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "smsub_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sediment Analysis Network for Decision Support (SANDS) MODIS Gulf Subsetted dataset consists of daytime images for Terra and Aqua MODIS Reflectance bands 8-16, subsetted to 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida) from September 11, 2000 to September 9, 2008. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.", "links": [ { diff --git a/datasets/snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0.json b/datasets/snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0.json index 4d5934f7a9..c21d7bf367 100644 --- a/datasets/snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0.json +++ b/datasets/snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "snowBedFoam 1.0. is a snow transport solver implemented in the computational fluid dynamics software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments. To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian\u2013Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physics-based equations similar to the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018 ; Melo et al., 2022). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain. First, snowBedFoam 1.0. was applied to topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021). Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure (Hames et al., 2022). Second, snowBedFoam 1.0. was used to simulate the snow distribution around the German Antarctic research station Neumayer Station III. The effect of snow properties, fluid forcing and aerodynamic structures on the snow accumulation were assessed. snowBedFoam 1.0 was implemented in 2 different OpenFOAM versions, namely OpenFOAM-2.3.0 and OpenFOAM-5.0. The latter offers more options for turbulence models and boundary conditions. The fundamental model equations were not changed from one implementation to the other, thus both still correspond to snowBedFoam 1.0. The two branches are called snowBedFoam-v1-2.3.0 (OpenFOAM-2.3.0) and snowBedFoam-v1-5.0 (OpenFOAM-5.0). The core codes of snowBedFoam 1.0. are directly accessible on the WSL/SLF GitLab repository (more details in the Resources section).", "links": [ { diff --git a/datasets/snow-avalanche-data-davos_1.0.json b/datasets/snow-avalanche-data-davos_1.0.json index 0f317c9fa9..e33f45bd9d 100644 --- a/datasets/snow-avalanche-data-davos_1.0.json +++ b/datasets/snow-avalanche-data-davos_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-avalanche-data-davos_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total. This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related to avalanche danger level. The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records). Please refer to the Read-me file for further details on the data. These data are the basis of the following publication: Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.", "links": [ { diff --git a/datasets/snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0.json b/datasets/snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0.json index 5169632952..b4fe378cf2 100644 --- a/datasets/snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0.json +++ b/datasets/snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.", "links": [ { diff --git a/datasets/snow-deltao18-metamorphism-advection_1.0.json b/datasets/snow-deltao18-metamorphism-advection_1.0.json index 1be8c16641..4612b88249 100644 --- a/datasets/snow-deltao18-metamorphism-advection_1.0.json +++ b/datasets/snow-deltao18-metamorphism-advection_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-deltao18-metamorphism-advection_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Stable water isotopes (\u03b418O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the \u03b418O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the \u03b418O content of the snow. The type of metamorphism of the snow had a significant influence on this process. Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient \u03b418O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.", "links": [ { diff --git a/datasets/snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0.json b/datasets/snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0.json index 20a7cc0a21..8efd927172 100644 --- a/datasets/snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0.json +++ b/datasets/snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020. Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0. The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud. Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values. An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps. The work is published in:", "links": [ { diff --git a/datasets/snow-depth-mapping_1.0.json b/datasets/snow-depth-mapping_1.0.json index 5b357d771e..815d0beb5e 100644 --- a/datasets/snow-depth-mapping_1.0.json +++ b/datasets/snow-depth-mapping_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-depth-mapping_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData). For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth. The work is published in: B\u00fchler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015", "links": [ { diff --git a/datasets/snow-drift-station-3d-ultrasonic_1.0.json b/datasets/snow-drift-station-3d-ultrasonic_1.0.json index 2aa686ea1d..3f31047c7a 100644 --- a/datasets/snow-drift-station-3d-ultrasonic_1.0.json +++ b/datasets/snow-drift-station-3d-ultrasonic_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-drift-station-3d-ultrasonic_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [\u00b0C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .", "links": [ { diff --git a/datasets/snow-drift-station-flowcapt_1.0.json b/datasets/snow-drift-station-flowcapt_1.0.json index 4fce3bf19f..a2fac45a74 100644 --- a/datasets/snow-drift-station-flowcapt_1.0.json +++ b/datasets/snow-drift-station-flowcapt_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-drift-station-flowcapt_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.", "links": [ { diff --git a/datasets/snow-drift-station-micro-rain-radar_1.0.json b/datasets/snow-drift-station-micro-rain-radar_1.0.json index 79ed5a6391..66537e15a5 100644 --- a/datasets/snow-drift-station-micro-rain-radar_1.0.json +++ b/datasets/snow-drift-station-micro-rain-radar_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-drift-station-micro-rain-radar_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22\u00b0 with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).", "links": [ { diff --git a/datasets/snow-drift-station-snow-and-air-data_1.0.json b/datasets/snow-drift-station-snow-and-air-data_1.0.json index 9d7f8dbf16..86be360712 100644 --- a/datasets/snow-drift-station-snow-and-air-data_1.0.json +++ b/datasets/snow-drift-station-snow-and-air-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-drift-station-snow-and-air-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (\u00b0C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (\u00b0C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.", "links": [ { diff --git a/datasets/snow-water-equivalent-for-wagital-catchment-starting-1943_1.0.json b/datasets/snow-water-equivalent-for-wagital-catchment-starting-1943_1.0.json index 0362617aa4..f35c2b250a 100644 --- a/datasets/snow-water-equivalent-for-wagital-catchment-starting-1943_1.0.json +++ b/datasets/snow-water-equivalent-for-wagital-catchment-starting-1943_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow-water-equivalent-for-wagital-catchment-starting-1943_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Total water reserves of the snow cover [mio m3] for W\u00e4gital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2024, status 2024-04-22. Funded currently or in the past by: - Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland - Meteodat GmbH - Institute of Geography, University of Zurich - WSL Institute for Snow and Avalanche Research SLF - Institute of Geography, ETH Zurich (IAC ETH Zurich) - AG Kraftwerk W\u00e4gital (AXPO and EWZ) See also https://www.meteodat.ch/waegital.html", "links": [ { diff --git a/datasets/snow_cover_xdeg_982_1.json b/datasets/snow_cover_xdeg_982_1.json index 5edd9339be..40caa85f81 100644 --- a/datasets/snow_cover_xdeg_982_1.json +++ b/datasets/snow_cover_xdeg_982_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snow_cover_xdeg_982_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This ISLSCP data set is derived from the National Snow and Ice Data Center (NSIDC) Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent product which combines snow cover and sea ice extent at weekly intervals for October 1978 through June 2001, and snow cover alone from 1966 through June 2001. The original data set was the first representation of combined snow and sea ice measurements derived from satellite observations for the period of record. Designed to facilitate study of Northern Hemisphere seasonal fluctuations of snow cover and sea ice extent, the original NSIDC data set also includes monthly climatologies describing average extent, probability of occurrence, and variance.This data set shows the extent of snow on the land at a variety of scales (1.0 degree, 0.5 degree, 0.25 degree). The values represent the percentage of days in each month where snow was present -- 100 means 100% of the month, 80 means 80% of the month, etc. There are 4 .zip files provided. Missing data is represented by -99 for water and -88 for land. The data were originally in a yearly tabular format. The file was converted to multi-scale maps by plotting each point in the tabular data onto a map of -99 (water) and -88 (land) created from the standard ISLSCP II Land/Sea Mask. ", "links": [ { diff --git a/datasets/snowfree_albedo_1deg_956_1.json b/datasets/snowfree_albedo_1deg_956_1.json index c8763da8c3..744aac3ef5 100644 --- a/datasets/snowfree_albedo_1deg_956_1.json +++ b/datasets/snowfree_albedo_1deg_956_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snowfree_albedo_1deg_956_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains monthly average snow-free surface shortwave albedo calculated for the period 1982-1998 and estimates of background soil/litter reflectances in the visible (0.4-0.7 mm) and near-infrared (NIR) (0.7-1.0 mm) wavelengths. Biophysical Parameters derived from the FASIR-NDVI (Fourier Adjusted, Solar zenith angle correction, Interpolation, and Reconstruction of Normalized Difference Vegetation Index) data set developed for the ISLSCP Initiative II data collection for the months of January 1982 through December 1998 were used to calculate monthly mean surface albedos at 1 X 1 degree spatial resolution for vegetated land surfaces (Sellers et al, 1996b) for the wavelength interval from 0.4 to 3.0 mm. The instantaneous albedo is a function of the properties of the land surface and the solar zenith angle. The monthly mean albedo is an average weighted over time weighted by the incident radiation. NDVI data are used to generate the biophysical parameters leaf area index (LAI) and green fraction of vegetation (Greenness) used by the canopy radiative transfer model of the Simple Biosphere (SiB2) model (Sellers et al, 1996a), which computes the instantaneous albedo. This is coupled to the Colorado State University (CSU) General Circulation Model (GCM) (Randall et al, 1989) which integrates the SiB2 radiative transfer through time. The incident radiation for weighting the time-averaged albedo was provided by a previous run of the GCM using the atmospheric radiation parameterization of Harshvardhan et al (1987). The Harshvardhan parameterization models radiative transfer through the atmosphere in both the longwave and shortwave bands, including the effects of cloudiness and water vapor, carbon dioxide and ozone. The shortwave radiation distinguishes between the direct and diffuse components of the solar beam.", "links": [ { diff --git a/datasets/snowmeltlysimeter-dataset_1.0.json b/datasets/snowmeltlysimeter-dataset_1.0.json index 5cb6a8af9b..17c63f1930 100644 --- a/datasets/snowmeltlysimeter-dataset_1.0.json +++ b/datasets/snowmeltlysimeter-dataset_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snowmeltlysimeter-dataset_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland. The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.", "links": [ { diff --git a/datasets/snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0.json b/datasets/snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0.json index fb0dd517cb..bc4feb5de4 100644 --- a/datasets/snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0.json +++ b/datasets/snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.", "links": [ { diff --git a/datasets/snowmicroquakes_1.0.json b/datasets/snowmicroquakes_1.0.json index 686ada59b1..c1d9b8554e 100644 --- a/datasets/snowmicroquakes_1.0.json +++ b/datasets/snowmicroquakes_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snowmicroquakes_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature. The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.", "links": [ { diff --git a/datasets/snowmip_1.0.json b/datasets/snowmip_1.0.json index 4c6bb709ca..3d6f764ae5 100644 --- a/datasets/snowmip_1.0.json +++ b/datasets/snowmip_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "snowmip_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.", "links": [ { diff --git a/datasets/soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0.json b/datasets/soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0.json index 1ea14d4c2f..f66ff1da87 100644 --- a/datasets/soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0.json +++ b/datasets/soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). Data include: (1) properties of soils sampled in 2011 and 2019 (SOC and N concentrations and stocks, soil masses, 13C and 15N natural abundances, C/N ratios, clay content, pH, inorganic C, stoniness, bulk density); (2) litter mass loss and initial litter chemistry of dominant tree species (Quercus, Pinus, Viburnum) from a litter decomposition experiment carried out in 2014-2015; (3) soil fauna abundance sampled in 2015; (4) soil volumetric water content and soil temperature at 10 cm depth measured during the litter decomposition experiment in 2014-2015; (5) soil mesofauna (Acari and Collembola) diversity and community composition from sampling in 2017; (6) irrigation-induced changes in litterfall (2013-2014, 2016-2017), fine-root production (data 2015 from Brunner et al., 2019, Frontiers in Plant Science), annual soil respiration (estimated for 2014-2015), litter mass loss from litter decomposition experiment (May-October 2014), and SOC stocks measured in 2011 and 2019; (7) Moisture dependency of microbial soil respiration (0-10 cm depth, adapted from Joseph et al., 2020 PNAS), soil respiration measured in 2015 and abundance of Acari, Collembola and Lumbricidae sampled in 2015.", "links": [ { diff --git a/datasets/soil-moisture-measurements-davos_1.0.json b/datasets/soil-moisture-measurements-davos_1.0.json index 940cdd1a71..80698cbc51 100644 --- a/datasets/soil-moisture-measurements-davos_1.0.json +++ b/datasets/soil-moisture-measurements-davos_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil-moisture-measurements-davos_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment. There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a: * vwc_[stn].smet: containing the soil moisture measurements * station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied. * interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript. At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth. The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files. Please cite the Wever et al. (2017) reference when using this data in publications. For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.", "links": [ { diff --git a/datasets/soil-net-nitrogen-mineralisation-across-global-grasslands_1.0.json b/datasets/soil-net-nitrogen-mineralisation-across-global-grasslands_1.0.json index a34ba78fb1..3ef085ab8e 100644 --- a/datasets/soil-net-nitrogen-mineralisation-across-global-grasslands_1.0.json +++ b/datasets/soil-net-nitrogen-mineralisation-across-global-grasslands_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil-net-nitrogen-mineralisation-across-global-grasslands_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. Paper Citation: Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Sch\u00fctz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2 Please cite this paper together with the citation for the datafile. We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/", "links": [ { diff --git a/datasets/soil-respiration-exclosure-experiment_1.0.json b/datasets/soil-respiration-exclosure-experiment_1.0.json index 67b3d16304..4cc0206ae5 100644 --- a/datasets/soil-respiration-exclosure-experiment_1.0.json +++ b/datasets/soil-respiration-exclosure-experiment_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil-respiration-exclosure-experiment_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Location of data collection The Swiss National Park (SNP) is located in the southeastern part of Switzerland, and covers an area of 170 km2, 50 km2 of which is forested, 33 km2 is occupied by alpine and 3 km2 by subalpine grasslands. Elevations range from 1350 to 3170 m a.s.l., and mean annual precipitation and temperature are 871 mm and 0.6\u00b0C measured at the Park\u2019s weather station in Buffalora (1980 m a.s.l.) between 1960 and 2009 (MeteoSchweiz 2011). Founded in 1914, the SNP received minimal human disturbance for almost 100 years (no hunting, fishing, or camping, visitors are not allowed to leave the trails). Large (> 1 ha) homogeneous patches of short- and tall-grass vegetation characterize the subalpine grasslands. The average vegetation height of short-grass vegetation is 2 to 5 cm. Red fescue (Festuca rubra L.), quaking grass (Briza media L.) and common bent grass (Agrostis tenuis Sipthrob) are the predominating plant species in this vegetation type. Tussocks of evergreen sedge (Carex sempervirens Vill.) and mat grass (Nardus stricta L.) are predominant in the tall-grass vegetation, which averages 20 cm in vegetation height (Sch\u00fctz and others 2006). Short-grass vegetation developed in areas where cattle and sheep rested (high nutrient input) during agricultural land-use (from 14th century until 1914); tall-grass vegetation developed in areas where cattle and sheep used to graze, but did not rest (Sch\u00fctz and others 2003, 2006). Herbivores were shown to consume > 60% of the biomass in short-grass compared to < 20% in tall-grass vegetation (Sch\u00fctz and others 2006). The herbivore community present in the SNP can be divided into four groups based on body size/weight: large [red deer (Cervus elaphus L.) and chamois (Rupricapra rupricapra L.); 30 - 150 kg], medium [marmot (Marmota marmota L.) and snow hare (Lepus timidus L.); 3 \u2013 6 kg], and small vertebrate herbivores (small rodents: e.g. Clethrionomys spp., Microtus spp., Apodemus spp.; 30 \u2013 100 g) as well as invertebrates (e.g. grasshoppers, caterpillars, cicadas, < 5 g). Experimental design We selected 18 subalpine grassland sites (9 short-grass, 9 tall-grass vegetation). The sites were spread across the entire park on dolomite parent material at altitudes of 1975 to 2300 meters. At each site we established an exclosure network (fences) in spring 2009 (early June), immediately after snowmelt. Each exclosure network consisted of a total of five 2 \u00d7 3 m sized plots that progressively excluded the different herbivores listed above (further labeled according to the herbivore guilds that had access to the respective plots \u201cAll\u201d, \u201cMarmot/Mice/Invertebrates\u201d, \u201cMice/Invertebrates\u201d, \u201cInvertebrates\u201d, \u201cNone\u201d). The \u201cAll\u201d treatment was thus accessible to all herbivores, was not fenced and was located at least 5 m away from a 2.1 m tall and 7 \u00d7 9 m main fence that enclosed the other four treatments. This fence was constructed of 10 \u00d7 10 cm wooden posts and electrical equestrian tape (AGRARO ECO, Landi, Bern, Switzerland; 20 mm width) mounted at 0.7 m, 0.95 m, 1.2 m, 1.5 m and 2.1 m above the ground that were connected to a solar charged battery (AGRARO Sunpower S250, Landi, Bern, Switzerland). We also mounted non-electrically charged equestrian tape at 0.5 m to help exclude deer and chamois, yet allow marmots and hares to enter safely. Within each main fenced area we randomly established four 2 \u00d7 3 m plots: (1) The \u201cMarmot/Mice/Invertebrates\u201d plot remained unfenced, thus, with the exception of red deer and chamois, all herbivores were able to access the plot, (2) The \u201cMice/Invertebrates\u201d plot consisted of a 90 cm high electric sheep fence (AGRARO Weidezaunnetz ECO, Landi, Bern, Switzerland; mesh size 10 \u00d7 10 cm) connected to the solar panel and excluded all medium sized mammals (marmots, hares), but provided access for small mammals and invertebrates, (3) The \u201cInvertebrates\u201d plot provided access for invertebrates only and was surrounded by 1 m high metal mesh (Hortima AG, Hausen, Schweiz; mesh size 2 \u00d7 2 cm), (4) The \u201cNone\u201d plot was surrounded by a 1 m tall mosquito net (Sala Ferramenta AG, Biasca, Switzerland; mesh size 1.5 \u00d7 2 mm) to exclude all herbivores. This plot was covered with a roof constructed of a wooden frame lined with mosquito mesh that was mounted on the wooden corner posts. We also treated this plot with a biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) when needed to remove insects that might have entered during data collection or that hatched from the soil. !!! The here published data set only contains data for \u201cAll\u201d, and \u201cMarmot/Mice/Invertebrates\u201d (= ungulates excluded) plots !!! Data collection In-situ soil CO2 emissions were measured with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA) on two randomly selected locations on one subplot within each of the 90 plots. For each measurement the soil chamber (15 cm high; 10 cm diameter) was placed on a permanently installed PVC collar (10 cm diameter) driven five centimeters into the soil at the beginning of the study (June 2009). The measurements were conducted between 0900 and 1700 hours every two weeks from early to early September 2009, 2010, 2011 and 2013. Freshly germinated plants growing within the PVC collars were removed prior to each measurement to avoid measuring plant respiration/photosynthesis. The two measurements collected per plot every two weeks were averaged. Please acknowledge the funding of the study: funded by the Swiss National Science Foundation, SNF grant-no 31003A_122009/1 to Anita C. Risch, Martin Sch\u00fctz and Flurin Filli", "links": [ { diff --git a/datasets/soil-sealing-barcelona-milan_1.0.json b/datasets/soil-sealing-barcelona-milan_1.0.json index 6a4059d3bc..43131b1b80 100644 --- a/datasets/soil-sealing-barcelona-milan_1.0.json +++ b/datasets/soil-sealing-barcelona-milan_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil-sealing-barcelona-milan_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "__Dataset description__
This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following: * a)\tBarcelona city boundaries * b)\tBarcelona metropolitan area, \u00c0rea Metropolitana de Barcelona (AMB) * c)\tBarcelona greater city (Urban Atlas) * d)\tBarcelona functional urban area (Urban Atlas) * e)\tMilan city boundaries * f)\tMilan metropolitan area, Piano Intercomunale Milanese (PIM) * g)\tMilan greater city (Urban Atlas) * h)\tMilan functional urban area (Urban Atlas)
In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).


__Dataset composition__
The dataset is provided in .csv format and is composed of:
_IMD15_BCN_MI_Sources.csv_: Information on data sources
_IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona: * a)\tBarcelona city boundaries (label: bcn_city) * b)\tBarcelona metropolitan area, \u00c0rea metropolitana de Barcelona (AMB) (label: bcn_amb) * c)\tBarcelona greater city (Urban Atlas) (label: bcn_grc) * d)\tBarcelona functional urban area (Urban Atlas) (label: bcn_fua)
_IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan: * e)\tMilan city boundaries (label: mi_city) * f)\tMilan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim) * g)\tMilan greater city (Urban Atlas) (label: mi_grc) * h)\tMilan functional urban area (Urban Atlas) (label: mi_fua)
_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).
Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.
In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included: * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan); * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area); * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness; * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.


__Further information on the Dataset__
This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.
1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster. 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA. 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.
Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).
Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been \u2018crossed\u2019 (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following: * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The \u2018input\u2019 file is the HRL IMD raster file as described in point 1) and the \u2018output\u2019 file is each of the spatial/territorial files. The option \"Use Input Features for Clipping Geometry (optional)\u201d was selected for each of the clipping. * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the \"overwrite\" option has been selected.

Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.
The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.", "links": [ { diff --git a/datasets/soil125r_309_1.json b/datasets/soil125r_309_1.json index fcec2a5fd3..04a2126f7c 100644 --- a/datasets/soil125r_309_1.json +++ b/datasets/soil125r_309_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil125r_309_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "GIS layers that describe the soils of the BOREAS SSA. Original data were submitted as vector layers that were then gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.", "links": [ { diff --git a/datasets/soil_respiration_point_645_1.json b/datasets/soil_respiration_point_645_1.json index 8a00d4adc7..f89d7c0f44 100644 --- a/datasets/soil_respiration_point_645_1.json +++ b/datasets/soil_respiration_point_645_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soil_respiration_point_645_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992 subset for the Safari 2000 project. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes.", "links": [ { diff --git a/datasets/soilt20r_357_1.json b/datasets/soilt20r_357_1.json index 0b2d5d2d36..c9099754e8 100644 --- a/datasets/soilt20r_357_1.json +++ b/datasets/soilt20r_357_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soilt20r_357_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded from vector layers of soil maps received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.", "links": [ { diff --git a/datasets/soilt20v_533_1.json b/datasets/soilt20v_533_1.json index e77587b68e..0a0e404c01 100644 --- a/datasets/soilt20v_533_1.json +++ b/datasets/soilt20v_533_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soilt20v_533_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vector layers of soil maps. The vector layers were converted to ARC/INFO EXPORT files. These data cover 1-km diameters around each of the NSA tower sites and another layer covers the NSA-MSA.", "links": [ { diff --git a/datasets/soilte1r_312_1.json b/datasets/soilte1r_312_1.json index 295df49c72..c79e782ee6 100644 --- a/datasets/soilte1r_312_1.json +++ b/datasets/soilte1r_312_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soilte1r_312_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.", "links": [ { diff --git a/datasets/solar-biomass-additional-references_1.0.json b/datasets/solar-biomass-additional-references_1.0.json index be40eba8a6..a7155e57da 100644 --- a/datasets/solar-biomass-additional-references_1.0.json +++ b/datasets/solar-biomass-additional-references_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "solar-biomass-additional-references_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Additional references to the article: Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting? Gillianne Bowman, Thierry Huber, Vanessa Burg Energies, https://www.mdpi.com/1996-1073/16/3/1486 Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems.", "links": [ { diff --git a/datasets/soller_wetlands_674_1.json b/datasets/soller_wetlands_674_1.json index e154266d8d..cc0ff82a33 100644 --- a/datasets/soller_wetlands_674_1.json +++ b/datasets/soller_wetlands_674_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "soller_wetlands_674_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format.The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/sondecpexcv_1.json b/datasets/sondecpexcv_1.json index a33a3dd836..8203aec01b 100644 --- a/datasets/sondecpexcv_1.json +++ b/datasets/sondecpexcv_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sondecpexcv_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Radiosonde CPEX-CV dataset was collected during the Convective Processes Experiment \u2013 Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign was based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX \u2013 Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. These data files are available from September 1, 2022, through September 29, 2022 in netCDF-4 format.", "links": [ { diff --git a/datasets/sonobuoy_whale_SO.json b/datasets/sonobuoy_whale_SO.json index b1644cce5c..c78a44e952 100644 --- a/datasets/sonobuoy_whale_SO.json +++ b/datasets/sonobuoy_whale_SO.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sonobuoy_whale_SO", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Mysticete whale calls were monitored/recorded via deployment of\n directional sonobuoys during March-August 2001. This monitoring\n technique is used to study whale distribution, behavior and aid in\n estimating populations. Deployments were either random or when whales\n were observed. The observed calls are identified by species.\n Ancillary calls by seals are reported but not identified by species.\n The survey area included the continental margin to the west of the\n Antarctic Peninsula extending from the northern tip of Adelaide Island\n to the southern portion of Alexander Island, Crystal Sound, and\n Marguerite Bay.\n \n Ship names/cruise ID/cruise dates\n R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001\n RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001\n RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001\n \n Access to the original acoustic recordings should be directed to the\n Investigator identified in this description.", "links": [ { diff --git a/datasets/source-code-climate-change-scenarios-at-hourly-resolution_1.0.json b/datasets/source-code-climate-change-scenarios-at-hourly-resolution_1.0.json index b5374476b3..46b95370c8 100644 --- a/datasets/source-code-climate-change-scenarios-at-hourly-resolution_1.0.json +++ b/datasets/source-code-climate-change-scenarios-at-hourly-resolution_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "source-code-climate-change-scenarios-at-hourly-resolution_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.", "links": [ { diff --git a/datasets/sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0.json b/datasets/sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0.json index 04082c3743..c28a6bf862 100644 --- a/datasets/sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0.json +++ b/datasets/sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains all data on which the following publication below is based. Paper Citation: Guidi, C., Lehmann, M.M., Meusburger, K., Saurer, M., Vitali, V., Peter, M., Brunner, I., Hagedorn, F. (accepted). Tracing sources and turnover of soil organic matter in a long-term irrigated dry forest using a novel hydrogen isotope approach. Soil Biology and Biochemistry. Please cite this paper together with the citation for the datafile. Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). Data include: (1) Isotopic composition (stable isotope ratios of non-exchangeable hydrogen \u03b42Hn, carbon \u03b413C, and nitrogen \u03b415N) and Hn, C and N concentrations in SOM sources (fresh Pinus sylvestris needles, litter layer, fine roots), bulk SOM (organic layer, 0-2 cm, 2-5 cm, 60-80 cm), particle-size fractions (depths: 0-2 cm, 2-5 cm; cPOM: coarse POM; fPOM: fine POM; MOM: mineral-associated organic matter); (2) Mass loss, \u03b42Hn values and Hn concentrations of Pinus sylvestris fine roots and needle litter (litter decomposition experiments from Herzog et al. 2019, ISME journal, and Guidi et al. 2022, Global Change Biology); (3) Relative source contribution (foliar litter, fine roots, and mycelia) to bulk SOM and fractions estimated using Bayesian mixing models (R package MixSIAR, version 3.1.12) with irrigation and depth as fixed factors. The models were informed with \u03b413C, \u03b415N and \u03b42Hn values and C, N, and Hn concentrations of foliar litter, roots, and mycelia as input sources. Given the kinetic isotope fractionation occurring during microbial SOM decomposition, the mixing models were informed with isotope fractionation factors, representing the isotope enrichment from sources to soils; (4) Fraction of new organic Hn (Fnew) over the irrigation period, calculated using a simple end-member mixing model according to Balesdent et al. (1987) and mean residence time estimated as MRT = - t / ln (1 - Fnew), with t time in years since irrigation started and assuming single-pool model with first-order kinetics.", "links": [ { diff --git a/datasets/sowers_0739491.json b/datasets/sowers_0739491.json index 8c9c5aa2dd..eff62d0175 100644 --- a/datasets/sowers_0739491.json +++ b/datasets/sowers_0739491.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sowers_0739491", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport.", "links": [ { diff --git a/datasets/spatial-modelling-of-ecological-indicator-values_1.0.json b/datasets/spatial-modelling-of-ecological-indicator-values_1.0.json index 2c204ef094..c94cc97b90 100644 --- a/datasets/spatial-modelling-of-ecological-indicator-values_1.0.json +++ b/datasets/spatial-modelling-of-ecological-indicator-values_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spatial-modelling-of-ecological-indicator-values_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland. This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection (\"+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\"). The excel file (xlsx) provides a short description of the raster layers. Paper Citation: Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)", "links": [ { diff --git a/datasets/spatial-planning-brazil_1.0.json b/datasets/spatial-planning-brazil_1.0.json index 9adf8d8303..8b93f4cc9e 100644 --- a/datasets/spatial-planning-brazil_1.0.json +++ b/datasets/spatial-planning-brazil_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spatial-planning-brazil_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The present dataset is part of the published scientific paper entitled \u201cThe role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil\u201d (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in S\u00e3o Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the S\u00e3o Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1)\tLand use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality. 2)\tFederal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3)\tTopographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).", "links": [ { diff --git a/datasets/species-distribution-maps-gdplants_1.0.json b/datasets/species-distribution-maps-gdplants_1.0.json index 45170d36a4..003cc7582b 100644 --- a/datasets/species-distribution-maps-gdplants_1.0.json +++ b/datasets/species-distribution-maps-gdplants_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "species-distribution-maps-gdplants_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022). To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).", "links": [ { diff --git a/datasets/spectra_licor_47_1.json b/datasets/spectra_licor_47_1.json index 7b8df6e90d..23244a3f0a 100644 --- a/datasets/spectra_licor_47_1.json +++ b/datasets/spectra_licor_47_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_licor_47_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The variability of bi-directional spectral reflectance of cut conifer foliage between age classes, species and sites, measured by LICOR", "links": [ { diff --git a/datasets/spectra_perkin_48_1.json b/datasets/spectra_perkin_48_1.json index e278fc0e56..7b117c2c2a 100644 --- a/datasets/spectra_perkin_48_1.json +++ b/datasets/spectra_perkin_48_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_perkin_48_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Absolute (diffuse & specular) reflectance of leaves measured in the lab by Perkin-Elmer spectrophotometer to aid in understanding remotely sensed spectral data", "links": [ { diff --git a/datasets/spectra_se590_altitude_86_1.json b/datasets/spectra_se590_altitude_86_1.json index eeb1366fb6..bdfd00471f 100644 --- a/datasets/spectra_se590_altitude_86_1.json +++ b/datasets/spectra_se590_altitude_86_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_se590_altitude_86_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Low altitude (Ultralight) spectral reflectances of OTTER research sites measured by Spectron SE590 spectrophotometer", "links": [ { diff --git a/datasets/spectra_se590_field_80_1.json b/datasets/spectra_se590_field_80_1.json index bbf96fb049..e2a5344062 100644 --- a/datasets/spectra_se590_field_80_1.json +++ b/datasets/spectra_se590_field_80_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_se590_field_80_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectral reflectance measurements made by Spectron SE590 instruments in the context of validation of geometric-optical BRDF models", "links": [ { diff --git a/datasets/spectra_se590_lab_83_1.json b/datasets/spectra_se590_lab_83_1.json index 94bae53d0f..bce03aa6bb 100644 --- a/datasets/spectra_se590_lab_83_1.json +++ b/datasets/spectra_se590_lab_83_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_se590_lab_83_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory hemispherical reflectance spectra measurements taken to eliminate the effects of atmosphere, understory, exposed soils, mixed species and canopy architecture", "links": [ { diff --git a/datasets/spectra_se590_landscape_84_1.json b/datasets/spectra_se590_landscape_84_1.json index 81c4024620..62bb52369b 100644 --- a/datasets/spectra_se590_landscape_84_1.json +++ b/datasets/spectra_se590_landscape_84_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_se590_landscape_84_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bidirectional spectal reflectance factors of landscape elements (litter, soil, bark, scrubs & grasses, leaves) measured by Spectron SE590 spectroradiometer", "links": [ { diff --git a/datasets/spectra_target_74_1.json b/datasets/spectra_target_74_1.json index 51d1c63ed3..0c95cb7dbe 100644 --- a/datasets/spectra_target_74_1.json +++ b/datasets/spectra_target_74_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spectra_target_74_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Spectral reflectance measurements of flat field targets as reference points representative of pseudo-invariant targets as measured by Spectron SE590 spectrophotometer", "links": [ { diff --git a/datasets/spherical-model-snow-compression-3dct_1.0.json b/datasets/spherical-model-snow-compression-3dct_1.0.json index 54f8fffe1c..106c91fd92 100644 --- a/datasets/spherical-model-snow-compression-3dct_1.0.json +++ b/datasets/spherical-model-snow-compression-3dct_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spherical-model-snow-compression-3dct_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles. In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).", "links": [ { diff --git a/datasets/spot6-avalanche-outlines-16-january-2019_1.0.json b/datasets/spot6-avalanche-outlines-16-january-2019_1.0.json index 7c3205c9a1..e085011392 100644 --- a/datasets/spot6-avalanche-outlines-16-january-2019_1.0.json +++ b/datasets/spot6-avalanche-outlines-16-january-2019_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spot6-avalanche-outlines-16-january-2019_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf) The generation of the data is described in: B\u00fchler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019. The data was comprehensivly validated in a subset area in Hafner, E.D.; Techel, F.; Leinss, S.; B\u00fchler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021", "links": [ { diff --git a/datasets/spot6-avalanche-outlines-24-january-2018_1.0.json b/datasets/spot6-avalanche-outlines-24-january-2018_1.0.json index c017d4c0aa..7accdc7194 100644 --- a/datasets/spot6-avalanche-outlines-24-january-2018_1.0.json +++ b/datasets/spot6-avalanche-outlines-24-january-2018_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spot6-avalanche-outlines-24-january-2018_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf) The generation of the data is described in: B\u00fchler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019. Abstract. Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12\u2019500 km2). We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18\u2019737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period. This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters. Koordinate System: CH1903+ LV95 LN02", "links": [ { diff --git a/datasets/spot_3s_437_1.json b/datasets/spot_3s_437_1.json index 3b1eea91f5..9cc6500349 100644 --- a/datasets/spot_3s_437_1.json +++ b/datasets/spot_3s_437_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spot_3s_437_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "For BOREAS, the level-3s SPOT data, along with the other remotely sensed images,were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed landcover, and biophysical parameter maps such as FPAR and LAI. The SPOT images acquired for the BOREAS project were selected primarily to fill temporal gaps in the Landsat TM image data collection.", "links": [ { diff --git a/datasets/spot_veg_burned_790_1.json b/datasets/spot_veg_burned_790_1.json index f57930c2c8..9482e9c0b4 100644 --- a/datasets/spot_veg_burned_790_1.json +++ b/datasets/spot_veg_burned_790_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "spot_veg_burned_790_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Burned Area 2000 initiative (GBA2000) was launched by the Global Vegetation Mapping Unit of the Joint Research Centre of the European Commission, in partnership with several other institutions, to develop reliable and quantitative information on the global magnitude and spatial distribution of biomass burning. The objective of GBA2000 was to produce a map of the areas burned globally for the year 2000, using the medium resolution satellite imagery provided by the SPOT-VEGETATION (VGT) system and to derive statistics of area burned per type of vegetation cover. A subset of the global GBA20000 map was prepared for SAFARI 2000 to map the area burned in sub-Saharan Africa during 2000 on a monthly basis using VGT imagery at 1 km spatial resolution. Burned areas were identified with a classification tree, relying only on the near-infrared channel of VGT. The data used in this work are in the S1 daily synthesis format, i.e. the data are radiometrically calibrated, precisely geo-located, and corrected for atmospheric effects.The data are binary image files of area burned, BSQ format in geographic projection. There is one file for each month of 2000 and one file for all of the year 2000. There is also a comma-delimited ASCII text file that provides geographic coordinates (latitude and longitude) of the center of each pixel indicated as a burned area for all of 2000.", "links": [ { diff --git a/datasets/srb_clouds_1deg_1073_1.json b/datasets/srb_clouds_1deg_1073_1.json index 169f9c02ae..3d5c2f0ee4 100644 --- a/datasets/srb_clouds_1deg_1073_1.json +++ b/datasets/srb_clouds_1deg_1073_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "srb_clouds_1deg_1073_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains cloud and meteorology data on a 1.0 degree x 1.0 degree spatial resolution. There are eight data files (*.zip) with this data set for several cloud parameters (monthly only) and meteorological parameters including monthly surface skin temperature, monthly total column ozone, and water vapor burdens for the period 1986-1995. All monthly parameters include files with a monthly mean value, a monthly standard deviation, and monthly minimum and maximum values. ", "links": [ { diff --git a/datasets/srb_radiation_1deg_1201_1.json b/datasets/srb_radiation_1deg_1201_1.json index 0bd523a50d..cc63c00a2e 100644 --- a/datasets/srb_radiation_1deg_1201_1.json +++ b/datasets/srb_radiation_1deg_1201_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "srb_radiation_1deg_1201_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains global Surface Radiation Budget (SRB) and a few top-of-atmosphere (TOA) radiation budget parameters on a 1-degree x 1-degree spatial resolution. These parameters are provided as monthly, monthly-3 hourly (i.e. monthly average for a particular 3 hourly period) and 3-hourly averages. All monthly parameters include files with a monthly mean value, a monthly standard deviation, and monthly minimum and maximum values. The surface and TOA Shortwave (SW) radiative parameters were computed with the Pinker and Laszlo (1992) radiation model. The Longwave (LW) SRB parameters were derived with the Gupta et al. (1992) model. Meteorological inputs for all processing were taken from the Goddard Earth Observing System version 1 (GEOS-1) reanalysis data sets (Schubert et al., 1993) from the Data Assimilation Office (DAO), at NASA Goddard Space Flight Center (GSFC). Required cloud parameters were derived at NASA Langley Research Center (LaRC) from International Satellite Cloud Climatology Project (ISCCP) DX data using the algorithms developed at the NASA Goddard Institute for Space Studies (GISS) (Rossow et al., 1996). Surface albedos are derived internally in the Pinker and Laszlo SW model. There are 30 compressed data files (*.zip) with this data set. When the *.zip files are expanded, there are 114,912 3-hourly files, 42,064 diurnal files, and 6,254 monthly files. ", "links": [ { diff --git a/datasets/srfmetmd_249_1.json b/datasets/srfmetmd_249_1.json index f06034779c..2a8bc49882 100644 --- a/datasets/srfmetmd_249_1.json +++ b/datasets/srfmetmd_249_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "srfmetmd_249_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains surface meteorology data merged/interpolated from four BOREAS sites for the years 1994, 1995, and 1996.", "links": [ { diff --git a/datasets/srtm_water_body.json b/datasets/srtm_water_body.json index 458f0d73bc..d6dca93005 100644 --- a/datasets/srtm_water_body.json +++ b/datasets/srtm_water_body.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "srtm_water_body", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "\n\nThe SRTM Water Body Data files are a by-product of the data editing performed by the National Geospatial-Intelligence Agency (NGA) to produce the finished SRTM Digital Terrain Elevation Data Level 2 (DTED\u00ae 2). In accordance with the DTED\u00ae 2 specification, the terrain elevation data have been edited to portray water bodies that meet minimum capture criteria. Ocean, lake and river shorelines were identified and delineated. Lake elevations were set to a constant value. Ocean elevations were set to zero. Rivers were stepped down monotonically to maintain proper flow. After this processing was done, the shorelines from the one arc second (approx. 30-meter) DTED\u00ae 2 were saved as vectors in ESRI 3-D Shapefile format.\n\nIn most cases, two orthorectified image mosaics (one for ascending passes and one for descending passes) at a one arc second resolution were available for identifying water bodies and delineating shorelines in each 1 x1 cell. These were used as the primary source for water body editing. The guiding principle for this editing was that water must be depicted as it was in February 2000 at the time of the shuttle flight. A Landcover water layer and medium-scale maps and charts were used as supplemental data sources, generally as supporting evidence for water identified in the image mosaics. Since the Landcover water layer was derived mostly from Landsat 5 data collected a decade earlier than the Shuttle mission and the map sources had similar currency problems, there were significant seasonal and temporal differences between the depiction of water in the ancillary sources and the actual extent of water bodies in February 2000 in many instances. In rare cases, where the SRTM image mosaics were missing or unusable, Landcover was used to delineate the water in the SRTM cells. The DTED\u00ae header records for those cells are documented accordingly.\n\n", "links": [ { diff --git a/datasets/ssafcovr_251_1.json b/datasets/ssafcovr_251_1.json index 9e26d89e02..fc9ff4d00d 100644 --- a/datasets/ssafcovr_251_1.json +++ b/datasets/ssafcovr_251_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ssafcovr_251_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raster files created by processing original vector data. Data include information of forest parameters for the BOREAS SSA MSA.", "links": [ { diff --git a/datasets/ssafcovv_509_1.json b/datasets/ssafcovv_509_1.json index 08592982a4..03f1465886 100644 --- a/datasets/ssafcovv_509_1.json +++ b/datasets/ssafcovv_509_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ssafcovv_509_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set was prepared by the SERM-FBIU. The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.", "links": [ { diff --git a/datasets/stability-tests-avalanche-observations-switzerland-norway_1.0.json b/datasets/stability-tests-avalanche-observations-switzerland-norway_1.0.json index bae713fcbc..cac6678d16 100644 --- a/datasets/stability-tests-avalanche-observations-switzerland-norway_1.0.json +++ b/datasets/stability-tests-avalanche-observations-switzerland-norway_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stability-tests-avalanche-observations-switzerland-norway_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The data set consists of - Rutschblock test results (Switzerland) - Extended Column Test results (Switzerland, Norway) - Avalanche occurrence data (Switzerland, Norway). The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE). For further information regarding the data, please refer to the publication or contact the author.", "links": [ { diff --git a/datasets/stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0.json b/datasets/stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0.json index bf5615e57f..0d3d24aa6b 100644 --- a/datasets/stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0.json +++ b/datasets/stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Notice: Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this. This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022). Snow profiles and surface samples are available at different frequencies for each campaign. Please see \"Data_description.pdf\" for details. Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.", "links": [ { diff --git a/datasets/stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0.json b/datasets/stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0.json index f7bf370a27..58e5d743d3 100644 --- a/datasets/stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0.json +++ b/datasets/stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 2000, a permanent forest plot of 10 ha has been established in the core zone of the primeval beech forest of Uholka. All living and dead trees with a diameter at breast height (DBH) \u2265 60 mm were identified to species, DBH measured, stems tagged and mapped. Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories. In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka\u2010Shyrokyi Luh forest. This data set allows us to derive a high\u2010resolution digital elevation model (DEM) of the plot area. The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests.", "links": [ { diff --git a/datasets/stand_density_sdi-29_1.0.json b/datasets/stand_density_sdi-29_1.0.json index 212bbb7738..bf6e969b72 100644 --- a/datasets/stand_density_sdi-29_1.0.json +++ b/datasets/stand_density_sdi-29_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stand_density_sdi-29_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Stand Density Index (SDI) is a general measure for the density of a stocking and is based on the number of stems/ha and the average diameter of the tally trees on the sample plot. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0.json b/datasets/stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0.json index 16c92f134b..01f1a27045 100644 --- a/datasets/stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0.json +++ b/datasets/stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We present stated preference data for improved forest management measures from seven Swiss municipalities in the Cantons of Grisons and Valais. The data was collected between October 2019 and February 2020 using an online questionnaire. We invited 10289 households to participate and received 939 responses. The online questionnaire consisted of two main parts: (i) an online choice experiment and (ii) questions on the sociodemographic characteristics of the responding households. The choice experiment confronted households with twelve consecutive choice tasks. Each choice task consisted of three options with a varying degree of avalanche and rock fall risk reduction due to improved forest management. The options further differed with respect to the way the costs for the improved forest management are allocated and the way they are calculated. We additionally provided each of the options with a cost attribute, allowing for the calculation of willingness to pay measures. At the end of the choice experiment we asked five de-briefing questions and eight attitudinal questions. Additionally, we asked the responding households to state their willingness to take risks. The sociodemographic characteristics collected in the second part of the questionnaire allow for an analysis of the impact they have on the choices we observed in the first part of the questionnaire.", "links": [ { diff --git a/datasets/stations_drainage_modelling_1.json b/datasets/stations_drainage_modelling_1.json index 43803f9d92..d5e98f26dd 100644 --- a/datasets/stations_drainage_modelling_1.json +++ b/datasets/stations_drainage_modelling_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stations_drainage_modelling_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This GIS dataset is the result of modelling of surface water drainage for Australia's year-round stations in Antarctica (Casey, Davis, Mawson) and at Macquarie Island. This was done by the Australian Antarctic Data Centre in 2000 at the request of Dr Martin Riddle and Dr Ian Snape of the Australian Antarctic Division.\n\nThe modelling was done using ESRI's ArcInfo workstation. A digital elevation model (DEM) was first created from the the Australian Antarctic Data Centre's topographic data, principally surface contours, and then drainage basins and drainage paths were derived from the DEM. The drainage is predicted surface flow due to changes in elevation and doesn't take account of any other processes. Several DEMs were created for each station at different spatial extents and resolutions.\n\nThe origin of the topographic data was mapping from aerial photography. The aerial photography was flown on 4 January 1994 (Casey), 11, 12 February 1997 (Davis), 7 December 1994 (Macquarie Island) and 18 March 1996 (Mawson).\n\nThe data available for download includes for each station: \n1 the DEMs and the topographic data from which they were created; and\n2 the predicted drainage basins and drainage paths.\nThe data was originally created in ESRI's coverage (vector) and grid (raster) formats. It is provided here in ESRI's file geodatabase format.\nDocumentation is included with the data.\n\nThe modelling was done as an aid to fuel spill contingency planning and the predicted drainage paths were used in the production of a spill risk assessment map for each station to go with the Australian Antarctic Division's fuel spill contingency plan for each station. The maps are available from the SCAR Map Catalogue (see a Related URL) and have catalogue numbers 13702 to 13705.\n\nValidation of the modelling for Casey is described in M.J.Riddle, I.Snape, D.T.Smith and A.Z.Woinarski, 'Development and validation of a GIS-based dispersion model for oil spills in snow covered ground' in Proceedings of the 3rd International Conference Contaminants in Freezing Ground, Hobart 14-18 April 2002 Figures 1 and 2 in this paper are available from the SCAR Map Catalogue and have catalogue numbers 12930 and 12931.", "links": [ { diff --git a/datasets/stem-and-branchwood-data-swiss-nfi_1.0.json b/datasets/stem-and-branchwood-data-swiss-nfi_1.0.json index dd0a4aac07..a7ff25c881 100644 --- a/datasets/stem-and-branchwood-data-swiss-nfi_1.0.json +++ b/datasets/stem-and-branchwood-data-swiss-nfi_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stem-and-branchwood-data-swiss-nfi_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In the Swiss National Forest Inventory (NFI) the volume of the stem and of large (\u2265 7cm in diameter) and small branches is estimated based on allometric functions. These functions were developed based on data collected within the permanent plot network of the Experimental Forest Management (EFM) sites at WSL (David I. Forrester; Hubert Schmid; Jens Nitzsche (2021). The Experimental Forest Management network. EnviDat. doi: 10.16904/envidat.213). The data were converted to digital format in two separate steps in the mid-1970s for stemwood and the mid-1980s for branchwood. The dataset on stemwood volume contains 38\u2019864 single tree data for the mean crosswise diameter at two meter sections along the stem plus an additional measurements at 1.3 m (i.e. DBH) where the diameter is greater than or equal to 7 cm (i.e. threshold of merchantable wood) and the lengths of the stem from the base to the threshold of merchantable wood and to the tree top. The measurements were collected on 768 EFM sites in the period 1888 to 1974. The dataset on branchwood is based on a subset of the stemwood data and contains in the raw format information on 14'712 single trees. It includes aggregated data from the stemwood dataset, i.e. the DBH, the stem-diameter at 7 m from the base, and the tree height from the base to the top, as well as the measured volume of large and small branches. In 2022, the metadata of both datasets were checked, values were examined for plausibility and duplicated entries. Duplicates were removed as far as possible and the branchwood volume data were appended to the stemwood dataset to obtain a final, single file with matching single tree data. Following this evaluation the final dataset consisted of a total of 38\u2019841 trees including 14\u2019038 trees with measured branchwood data.", "links": [ { diff --git a/datasets/stem_count_of_young_forest-191_1.0.json b/datasets/stem_count_of_young_forest-191_1.0.json index d7b1183cbe..94fafa9622 100644 --- a/datasets/stem_count_of_young_forest-191_1.0.json +++ b/datasets/stem_count_of_young_forest-191_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stem_count_of_young_forest-191_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# 191# Number of regeneration trees starting at 10 cm tall up to 11.9 cm dbh recorded in NFI\u2019s regeneration survey. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/stem_number-73_1.0.json b/datasets/stem_number-73_1.0.json index fd3a4c8891..71e70cb9ea 100644 --- a/datasets/stem_number-73_1.0.json +++ b/datasets/stem_number-73_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stem_number-73_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of stems of living trees and shrubs (standing and lying) starting at 12 cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/stem_number_of_dead_wood-116_1.0.json b/datasets/stem_number_of_dead_wood-116_1.0.json index bd47ac7f43..3c38353d8e 100644 --- a/datasets/stem_number_of_dead_wood-116_1.0.json +++ b/datasets/stem_number_of_dead_wood-116_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stem_number_of_dead_wood-116_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of stems of dead trees and shrubs (standing and lying) starting at 12 cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/stem_number_of_dead_wood_nfi1-248_1.0.json b/datasets/stem_number_of_dead_wood_nfi1-248_1.0.json index 4c7bbd6ea3..4343118f09 100644 --- a/datasets/stem_number_of_dead_wood_nfi1-248_1.0.json +++ b/datasets/stem_number_of_dead_wood_nfi1-248_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stem_number_of_dead_wood_nfi1-248_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of stems of dead trees and shrubs (standing and lying) starting at 12 cm recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/stillberg-climate_1.0.json b/datasets/stillberg-climate_1.0.json index b6e371f85e..317c37ba67 100644 --- a/datasets/stillberg-climate_1.0.json +++ b/datasets/stillberg-climate_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stillberg-climate_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Important This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry \"Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l..\" The entry can be found under this link and with this DOI .", "links": [ { diff --git a/datasets/stillberg-reforestation_1.0.json b/datasets/stillberg-reforestation_1.0.json index 33131ed753..3e573d72f1 100644 --- a/datasets/stillberg-reforestation_1.0.json +++ b/datasets/stillberg-reforestation_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stillberg-reforestation_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Important This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry \"Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland.\" The entry can be found under this link and with this DOI 10.16904/envidat.397.", "links": [ { diff --git a/datasets/stillwell_geology_gis_1.json b/datasets/stillwell_geology_gis_1.json index 7b1b3ca9c0..b4f4616143 100644 --- a/datasets/stillwell_geology_gis_1.json +++ b/datasets/stillwell_geology_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stillwell_geology_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Stillwell Hills region comprises granulite-facies gneisses which record evidence for multiple episodes of deformation and metamorphism spanning more than 2500 million years. The predominant orthogneiss package (Stillwell Orthogneiss) is thought to represent the margin of an Archaean craton exposed in Enderby Land, some 150 km to the west that was reworked during the late Proterozoic. Younger additions to the crust include Palaeoproterozoic charnockitic gneiss (Scoresby Charnockite) and Meso-Neoproterozoic mafic sills and dykes (Point Noble Gneiss, Kemp Dykes) and felsic pegmatites (Cosgrove Pegmatites). Subordinate supracrustal rocks, including metaquartzite, metapelitic, metapsammitic and calc-silicate gneiss (Dovers Paragneiss, Sperring Paragneiss, Stefansson Paragneiss, Keel Paragneiss, Ives Paragneiss) are intercalated and infolded with the Archaean-Palaeoproterozoic orthogneisses. \nThis Dataset is derived from the map product 'The Geology of the Stillwell Hills, Antarctica'.\n\nThis metadata record was created using information in Geoscience Australia's metadata record at \nhttp://www.ga.gov.au/metadata-gateway/metadata/record/78535/", "links": [ { diff --git a/datasets/streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0.json b/datasets/streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0.json index 5d2439e2b6..c3e94eaddf 100644 --- a/datasets/streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0.json +++ b/datasets/streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data: - Specific discharge at the catchment outlet at 5-minute resolution (mm per hour); - Rainfall at 5-minute resolution (mm per hour); - Rainfall deuterium composition (\u2030); - Stormflow deuterium composition (\u2030); - Groundwater and baseflow deuterium compositions (\u2030). For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions (\"Deuterium_Rainfall\" and \"Deuterium_Streamwater\"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values (\"Deuterium_Snapshot\") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC). More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.", "links": [ { diff --git a/datasets/stumps-as-a-dead-wood-resource_1.0.json b/datasets/stumps-as-a-dead-wood-resource_1.0.json index efd10da5e8..c8299f8146 100644 --- a/datasets/stumps-as-a-dead-wood-resource_1.0.json +++ b/datasets/stumps-as-a-dead-wood-resource_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "stumps-as-a-dead-wood-resource_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time. The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting. The study is published in \ufeffAnnals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access). The data can be obtained from the authors upon reasonable request.", "links": [ { diff --git a/datasets/sua_pan_lai_fpar_778_1.json b/datasets/sua_pan_lai_fpar_778_1.json index 2127556707..fc94cd4814 100644 --- a/datasets/sua_pan_lai_fpar_778_1.json +++ b/datasets/sua_pan_lai_fpar_778_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sua_pan_lai_fpar_778_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to the Sua Pan salt playa in the Magkadigkadi region of Botswana during the SAFARI 2000 Dry Season Aircraft Campaign to collect various data sets for validating the MISR LAI/FPAR algorithm. Ground measurements of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) were made using the LAI-2000 plant canopy analyzer and Sunfleck PAR ceptometer, respectively, during focused periods from August 20 to August 28, 2000 at a dry grassland site adjacent to the Sua Pan. The 1 km by 1 km sampling grid was a homogeneous, relatively dense grassland, with a height of 20-100 cm and two prevalent grass types, Odyssea paucinervis and Sporobolus spicatus. Associated reflectance measurements were made with the PARABOLA and ASD instruments (Helmlinger et al., 2004a; 2004b).The data files contain measurements of LAI and PAR reflectance and transmission and a description of sky conditions during the sampling periods. With one exception, all measurements were made under clear sky conditions. PAR data were measured only on the transect scale while LAI are provided at both pixel and transect scales. PAR readings were performed at 93 transect sample points, and LAI readings were performed at 135 (93 transect and 42 subgrid) sample points. Each file also contains mean LAI and PAR values. The data files are ASCII tables, in comma-separated-value format.", "links": [ { diff --git a/datasets/sua_pan_skukuza_brdf_779_1.json b/datasets/sua_pan_skukuza_brdf_779_1.json index ef1e0d48ac..ee9d8ba136 100644 --- a/datasets/sua_pan_skukuza_brdf_779_1.json +++ b/datasets/sua_pan_skukuza_brdf_779_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sua_pan_skukuza_brdf_779_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Jet Propulsion Laboratory's (JPL) Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), version III, instrument collected radiance data covering both the upwelling and downwelling hemispheres at the Sua Pan salt playa in the Magkadigkadi region of Botswana and at the Skukuza tower site in the Kruger National Park, South Africa between August 25 and October 2, 2000 during in the SAFARI 2000 Dry Season Aircraft Campaign. PARABOLA III is a sphere-scanning radiometer that provides multi-angle measurements of sky and ground radiances on a spherical grid of 5 degrees in the zenith-to-nadir and azimuthal planes in eight spectral channels (444, 551, 581, 650, 860, 944, 1028, and 1650 nm). The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions for a vicarious calibration of the Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard the Terra satellite. Four of the PARABOLA channels (444, 551, 650, and 860 nm) are similar to those of the MISR sensor. Measurements were made on cloud-free days of Terra satellite overpasses.Each data file contains radiance counts (ASCII integer values) for 8 bands for each 3-minute data collection. Zenith and azimuth angles are implied by radiance count positions in file. Additional files contain average dark current readings, empirically determined by covering the detectors. The data can be processed to radiance using a series of second order polynomial fit coefficients, provided in the documentation file, and dark current offset. Site-specific auxiliary information is also provided, for each date of PARABOLA data collection.", "links": [ { diff --git a/datasets/sua_pan_surface_spectra_780_1.json b/datasets/sua_pan_surface_spectra_780_1.json index a0d5c38350..0cac18366e 100644 --- a/datasets/sua_pan_surface_spectra_780_1.json +++ b/datasets/sua_pan_surface_spectra_780_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sua_pan_surface_spectra_780_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to Sua Pan, a salt playa in the Magkadigkadi region of Botswana, from August 18 to September 4, 2000, during the SAFARI 2000 Dry Season Aircraft Campaign. The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions in order to provide a vicarious calibration of the MISR instrument aboard the Terra satellite. Reported here are ground-based reflectance measurements collected using an Analytical Spectral Devices (ASD) spectroradiometer at Sua Pan and adjacent grassland targets. The grasslands provided large homogeneous areas for comparison of scale between ground measurements and remote sensing results.Data files contain numeric values that represent mean reflectance over space (grassland) or wavelength range (pan), stored as ASCII files, one file per site, in comma-separated-value (.csv) format, with column headers. The Sua Pan data, collected over a 1 km x 2 km area, are presented as rows of mean reflectance (every 10 nm) for 20 points, where the mean represents the average local reflectance spectra collected within 150 m of the given latitude and longitude. The grassland data cover a 1 km2 area and are provided every nm from 1 to 2500. Each row of data contains a mean and standard deviation at a given wavelength, where the mean represents the average of 570 measurements taken over the 1 km2 area.Related data sets from Sua Pan provide ground measurements of BRDF, and LAI and FPAR (Helmlinger et al., 2004 and Buermann and Helmlinger, 2004, respectively).", "links": [ { diff --git a/datasets/summer_sea_salt_1000-2009_1.json b/datasets/summer_sea_salt_1000-2009_1.json index e3c3141369..0133ad00a6 100644 --- a/datasets/summer_sea_salt_1000-2009_1.json +++ b/datasets/summer_sea_salt_1000-2009_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "summer_sea_salt_1000-2009_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is the logged, annualised summer sea salt (December to March, DJFM) concentrations from the Law Dome ice core chemistry record, spanning 1000-2009 AD (dates apply to the year of JFM, so e.g. 1980 is an average of Dec 1979 and Jan-Mar 1980). The data are compiled from numerous ice cores drilled at the Law Dome site sequentially since 1987, and chronologically dated using volcanic horizons and annual layer counting.\n\nThe cores used are (chronologically from oldest data to newest):\n\nDSS Main\nDSS97\nDSS0102\nDSS0809\nDSS0910\n\nThe dataset has 37 'missing' summer values in instances where insufficient ice core material was available. These missing summers have been filled using linear interpolation.\n\nThis work forms part of Australian Antarctic Science (AAS) project no. 757.\n\nThe record was published as an ENSO and eastern Australian rainfall proxy record in:\n\nVance, T. R., T. D. van Ommen, M. A. J. Curran, C. T. Plummer, A. D. Moy, (2012): A millennial proxy record of ENSO and eastern Australian rainfall from the Law Dome ice core, East Antarctica. Journal of Climate, doi: 10.1175/JCLI-D-12-00003.1", "links": [ { diff --git a/datasets/sunphair_298_1.json b/datasets/sunphair_298_1.json index 48a5904b7a..95789a8227 100644 --- a/datasets/sunphair_298_1.json +++ b/datasets/sunphair_298_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sunphair_298_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).", "links": [ { diff --git a/datasets/survey-energy-transition-municipal-level-switzerland_1.0.json b/datasets/survey-energy-transition-municipal-level-switzerland_1.0.json index 771c624ac3..33bc58b4ce 100644 --- a/datasets/survey-energy-transition-municipal-level-switzerland_1.0.json +++ b/datasets/survey-energy-transition-municipal-level-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey-energy-transition-municipal-level-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains data from a survey, which was conducted in a periurban region close to Berne, Switzerland. The survey was conducted in Fall 2018 and contained opinion questions about the energy transition. Additionally, spatial data was collected using a PPGIS. While the opinion data is included in the data set, the spatial data is not. For more explanation, please consider the information sheet, the related publications or to contact the authors.", "links": [ { diff --git a/datasets/survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0.json b/datasets/survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0.json index 22847f60b1..7a3db635fc 100644 --- a/datasets/survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0.json +++ b/datasets/survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In 1989 a nation-wide survey on spruce seed and cone insects was carried out at 29 locations distributed across the 5 main geographic regions of Switzerland. The cones were incubated in a controlled environment chamber and the emerging insects were collected and identified. The cones were kept for three years to allow diapausing insects to emerge. The methods are described in more detail in the corresponding publications.", "links": [ { diff --git a/datasets/survey_1997_V3_1.json b/datasets/survey_1997_V3_1.json index f0a914db1c..b1eab43066 100644 --- a/datasets/survey_1997_V3_1.json +++ b/datasets/survey_1997_V3_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_1997_V3_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from sections of the report:\n\n1.\tIntroduction\n\nThis report details the survey work carried out on Macquarie Island during November 1997 by LANDINFO staff on behalf of the Australian Antarctic Division's Mapping Program. The main task of the survey team was to acquire aerial photography of the island to enable the production of a new topographic map of the island. Other tasks involved field checking the digital station area map (DSAM) and providing support to the tide gauge maintenance team.\n\nThe following team carried out the survey-mapping work:\nTom Gordon\t\tLANDINFO Surveyor\nRoger Handsworth\tAntarctic Division Engineer\n\nAlthough this report touches on the work carried out by Roger Handsworth and Rupert Summerson, it does not cover the specifics of their work.\n\n\n2.\tProject Brief\n\nThe survey-mapping brief lists the following tasks:\n\n1.\tAerial Photography of the Island and station area.\n2.\tAerial Photography of Judge and Clerk Island to the south and Bishop and Clerk Island to the north of Macquarie Island.\n3.\tSecond order levelling from the tide gauge bench marks to AUS 211\n4.\tUpdating the Digital Station Area map.\n\nThese tasks are listed in order of priority. A copy of the survey brief for Macquarie Island is included in Appendix A.", "links": [ { diff --git a/datasets/survey_2001_grove_1.json b/datasets/survey_2001_grove_1.json index 4cb27e55b8..a0c1cc3e5b 100644 --- a/datasets/survey_2001_grove_1.json +++ b/datasets/survey_2001_grove_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_2001_grove_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from sections of the report:\n\nANARE planned geodetic survey work in the region in the summer 2000/2001. Initially it was planned to fly into the region from Davis wintering station on four separate days, allowing enough time to complete all the intended work. However owing to the harsh flying conditions and the nature of the terrain there was only time for two visits during the 2000 / 2001 summer season.\n\nAlthough only two trips were made into the Grove Mountains a number of outcomes were achieved they included:\n-Establishment of a new geodynamic survey monument in the vicinity of Mount Harding, to strengthen the Antarctic geodetic network, and also assist with long term monitoring of crustal motion in Antarctica.\n-Several days of GPS data collected on the existing geodetic network point at Austin Nunatak - 60km to the West of Mount Harding\n-Search for two existing CHINARE geodetic control points established near Mount Harding and Zakharoff Ridge.\n\nThe work was carried out by Gary Johnston, Paul Digney and John Manning working for the Australian Surveying and Land Information Group (AUSLIG) - now Geoscience Australia (GA).", "links": [ { diff --git a/datasets/survey_2002-03_V2_V5_1.json b/datasets/survey_2002-03_V2_V5_1.json index 933a15ef68..03ebcb713a 100644 --- a/datasets/survey_2002-03_V2_V5_1.json +++ b/datasets/survey_2002-03_V2_V5_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_2002-03_V2_V5_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from sections of the Report:\n\nThe 2002-03 Mapping and Geographic Information Program (MAGIP) field season was undertaken from Davis Station. Nigel Peters from Sinclair Knight Merz undertook this season's fieldwork, the results of which are described in the following report.\n \nThe main objective for this season was to provide photo control mapping in the Rauer Group, with photo control also required at Davis Station and Marine Plains. A number of other tasks were undertaken in support of various scientific and engineering programs.\n\nThe tasks outlined in the surveyors brief are varied and numerous and have been included to provide the surveyor with a full and appropriate work program. The tasks are prioritised, usually with one or two major tasks with a number of minor tasks listed to be undertaken if the opportunity arises. This season's Survey Brief has been included in Appendix A with a summary of achievements listed in Appendix B.\n \nThe following report covers the fieldwork undertaken by myself during the 2002/2003 ANARE Summer Field Season. Data collected in support of other scientific programs has been included in this report primarily as a record of work undertaken by the mapping program. These data have been supplied to the various scientists for inclusion in their studies.\n\nSequence of Events\n\n# 4th November - 12th November 2002 - Pre-Departure Training\n - Field training for expeditioners at Bronte Park prior to the departure of V2.\n - Survey briefing at Antarctic Division by Mapping Officer, Mr Henk Brolsma\n\n# 20th November -5th December 2002 - Voyage 2\n - Final preparation and checking and replacement of damaged equipment\n - The Aurora Australis departed Hobart on the evening of 22nd November en route for Zhongshan, Davis and Mawson\n - The Aurora Australis arrived of Zhongshan on the 3rd December where Chinese personal were deployed\n - The Aurora Australis stopped approximately 1km off shore from Davis on the evening of the 4th December and arrived at Davis Station 5th December\n\n# 6th December - 10th December 2002 - Davis Station\n - Davis Resupply involving unloading and storage of food and equipment\n\n# 11th December - 31st December 2002 - Davis Station \n - Down loading Tide Gauge at Davis Station\n - GPS measurements AUS303\n - Coordination and levelling building Heights\n - Coordination of control points Rauer Group\n - Coordination of control points Davis\n\n# 1st January - 20th January 2003 - Davis Station\n - Coordination of control points Rauer Group\n - Antenna Farm levelling\n - Surveys at Brooks, Bandits and Watts huts\n\n# 21st January - 26th January 2003 - Law Base\n - Law Base Tide gauge downloading \n - GPS connections to Davis\n \n# 27th January - 9th February 2003 - Davis Station\n - Tarbuck Crag repeater survey\n - Skyline Survey Antenna Farm\n - Establish new Tide Pole at Deep Lake\n - Station duties loading equipment on to Ice Bird\n\n# 10th February - 22nd February 2003 - Voyage 5\n - Depart Davis \n - Arrive Hobart 22nd February \n\nScope of Work\n \nThe Antarctic Mapping Officer Mr Henk Brolsma provided the scope of works within the Surveyors Brief for the 2002- 2003 field survey program (Appendix A). The following is a summation of the survey requirements for this season.\n\n# Rauer Group \n - Photo control are required throughout the Rauer Group at specified locations\n\n# Davis \n - Down Load Tide gauge\n - Timed water level measurements\n - Levelling between tide gauge benchmarks, including GPS observation\n - Update station map and determine levels for all building floors, roof levels and the ground at the corner of every building\n - Photo control for orthophoto at Davis and at Heideman Bay\n\n# Zhongshan\n - Download tide gauge\n - Timed water level measurements\n - Height connection from Law Base to tide gauge bench mark\n - Level between tide gauge benchmarks\n - Check existing marks established for tide gauge location\n\n# Vestfold Hills\n - Deep Lake depth pole\n - Take pole readings\n - Repair depth pole\n - Lake levelling\n - Location of bench marks", "links": [ { diff --git a/datasets/survey_2004-2005_geodesy_1.json b/datasets/survey_2004-2005_geodesy_1.json index 7f1d165cb3..34d5123587 100644 --- a/datasets/survey_2004-2005_geodesy_1.json +++ b/datasets/survey_2004-2005_geodesy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_2004-2005_geodesy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from sections of the report:\n\nProject officers Samim Naebkhil and Michael Moore from Geodetic operations within Geoscience Australia Earth Monitoring Group took part in the 2004/2005 Summer Antarctic Geodesy Program. Samim Naebkhil left Hobart aboard Aurora Australis on Voyage 1 on the 5th of October, 2004 to Casey. Michael Moore left aboard V2 in November 2004.\n\nThe 2004/2005 season had a number of aims and objectives to be meet.\nThese included:\n- Reference Mark Survey of Australian Regional GPS network in Antarctica Casey AUS100, Davis AUS099 and Mawson AUS064.\n- Levelling from the GPS marks to the Tide Gauge Bench Marks\n- Upgrade of Australian Regional GPS network in Antarctica Casey AUS100, Davis AUS099, and Mawson AUS064.\n- Reference Mark Survey and Upgrade of Continuous GPS Sites (CGPS) AUS351 Grove Mountains, Wilson Bluff and Mt. Creswell.\n- Perform an ICESAT calibration at Mt. Creswell\n- GPS observation at Larseman Hills over Geodynamic mark AUS334 and Tide gauge Bench Mark NMVS278.\n- GPS observation over the Tide Gauge Bench Marks at Casey, Davis and Mawson.\n- Gravity observations at Casey, Davis and Mawson.\n- GPS observations over including the Chinese and Russian survey Marks to strength the Australian Geodetic Network and an establishment of a unified datum.\n- GPS observations over various Bench Marks in the Vestfolds hills to strengthen the geometric geoid.\n- Assist other science programs with Geodesy related support.\nDespite all efforts made to accomplish all the aimed tasks for this season, a number of these tasks could not be done and were beyond our control due to late arrival and associated problems with the CASA planes.\nThe following tasks were not able to be done this season:\n- Reference Mark Survey of Mawson ARGN AUS064.\n- Orthometric Leveling from Mawson ARGN AUS064 to Tide Gauge Bench Marks\n- Reference Mark Survey and upgrades of CGPS sites Wilson Bluff and Mt. Creswell\n- Perform an ICESAT calibration at Mt. Creswell\n- Gravity connection Mawson.\n\nThis work contributed towards AAS (ASAC) project 1159.", "links": [ { diff --git a/datasets/survey_2006-2007_geodesy_1.json b/datasets/survey_2006-2007_geodesy_1.json index 5906a1fe6f..9d41655d55 100644 --- a/datasets/survey_2006-2007_geodesy_1.json +++ b/datasets/survey_2006-2007_geodesy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_2006-2007_geodesy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Taken from sections of the report:\n\nIn recent years, Geoscience Australia (GA) has increased its capability on the Antarctic continent with the installation of Continuous Global Positioning System (CGPS) sites in the Prince Charles Mountains and Grove Mountains. Over the course of the 2006/07 Antarctic summer, Alex Woods and Nick Brown from Geoscience Australia (GA) collaborated with Dan Zwartz of the Australian National University (ANU) to install new CGPS sites at the Bunger Hills and Richardson Lake and perform maintenance of the CGPS sites at the Grove Mountains, Wilson Bluff, Daltons Corner and Beaver Lake.\n\nThe primary aim of the CGPS sites is to provide a reference frame for Antarctica, which is used to determine the long-term movement of the Antarctic plate. Data from Casey, Mawson and Davis is supplied to the International GPS Service (IGS) and in turn used in the derivation of the International Terrestrial Reference Frame (ITRF). The sites also open up opportunities for research into post-glacial rebound and plate tectonics.\n\nIn many respects CGPS sites in Antarctica are still in their infancy. Since the mid 1990's Geoscience Australia and the Australian National University have been testing new technology and various methods to determine the most effective way of running a CGPS site in Antarctica.\n\nA more detailed review of Australia's involvement in Antarctic GPS work can be found in (Corvino, 2004)\n\nIn addition, a reconnaissance survey was undertaken at Syowa Station to determine whether a local tie survey could be performed on the Syowa VLBI antenna in the future. Upgrades were made to the Davis and Mawson CGPS stations and geodetic survey tasks such as reference mark surveys, tide gauge benchmark levelling and GPS surveys were performed at both Davis and Mawson stations. In addition, work requested by Geoscience Australia's Nuclear Monitoring Project, the Australian Government Antarctic Division (AGAD) and the University of Tasmania (UTAS) were completed.\n\nThe 2006/07 Geoscience Australia Antarctic expedition proved to be one of the most successful Antarctic seasons by geodetic surveyors from Geoscience Australia. All intended field locations were visited and all work tasks were completed.\n\nBackground\nThe primary aim of the CGPS sites is to provide a reference frame for Antarctica, which is used to determine the long-term movement of the Antarctic plate. Data from Casey, Mawson and Davis is supplied to the International GPS Service (IGS) and in turn used in the derivation of the International Terrestrial Reference Frame (ITRF). The sites also open up opportunities for research into post-glacial rebound and plate tectonics.\n\nIn many respects CGPS sites in Antarctica are still in their infancy. Since the mid 1990's Geoscience Australia and the Australian National University have been testing new technology and various methods to determine the most effective way of running a CGPS site in Antarctica.\n\nDr John Gibson from The University of Tasmania requested that Alex Woods and Nick Brown collect moss samples from any locations visited during the Antarctic summer field season. While working in the field only a few moss specimens were found. No moss or lichen specimens were observed at locations such as Wilson Bluff, Dalton Corner, Beaver Lake or the Grove Mountains. Moss samples were collected at Richardson Lake and Mawson Station and these samples were frozen after collection and returned to Australia.\n\nThis work contributed towards AAS (ASAC) project 1159.", "links": [ { diff --git a/datasets/survey_v6_2003_1.json b/datasets/survey_v6_2003_1.json index 092e985160..0b0dd6883d 100644 --- a/datasets/survey_v6_2003_1.json +++ b/datasets/survey_v6_2003_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "survey_v6_2003_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Voyage 6 of the Australian Antarctic Program for 2002/03 resupplied the station at Macquarie Island. Five days were spent at the island: March 24 - 28. During this time some surveys were carried out by Paul Boland (DPIWE, Tasmania) and David Smith (Australian Antarctic Data Centre). \n\nTasks carried out by Paul included the following: \n(i) Detailed surveys of the huts and other infrastructure (eg generator platforms, walkways) at Green Gorge and Bauer Bay, a nearby stream at Green Gorge, nearby walking tracks at Bauer Bay. This work was done for Henk Brolsma (AAD Mapping Officer). Such features can potentially be used to rectify aerial photographs or satellite images. The Bauer Bay data have been used to rectify a Digital Globe satellite image of north-western Macquarie Island. Refer to the metadata record with ID macquarie_quickbird_mosimage. A survey mark was also established near the Bauer Bay hut. \n(ii) A vegetation survey at Handspike Corner for Dr Dana Bergstrom (AAD RISCC program). \n(iii) A survey of the old tip site, the Power House and the bunds at the station for Dr Ian Snape and Dr John Rayner (AAD Human Impacts Program). \n\nThe data resulting from the surveys are available for downloading from three related URLs below. \n1. The dxf file, spreadsheet with levelling data and annotated photos provided by Paul; 2. Shapefiles created from the point data in the dxf file and stored in the horizontal datum ITRF2000@2000 used by Paul (note: the dxf file needs to be referred to for descriptions of the points); 3. Shapefiles representing the features surveyed at the station for the Human Impacts Program, created from the point data in the dxf file and stored in the horizontal datum WGS84. \n\nThe transformation from ITRF2000@2000 to WGS84 for this data was carried out by applying \"The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N.\" given on page 3 of the survey report \"Macquarie Island OSG Survey Campaign, Voyage 8 Round Trip, March 2002\" by John VanderNiet and Nick Bowden. For more information about this survey work please contact Henk Brolsma (AAD Mapping Officer). \n\nA GPS base station was also set up for much of the resupply period with the antenna mounted on the roof of the Biology building. Paul surveyed the antenna position. Trimble .ssf, RINEX and .dat files were collected.\nThis base station data and data collected by the Geoscience Australia permanent base station, MAC1, during the resupply period are available for download from a related URL below. \n\nDavid used a Trimble Geoexplorer GPS to survey points at 5 metre intervals along two 50 metre transects laid out by Lee Belbin (Australian Antarctic Data Centre) near the Biology building at the station. At each point Pat Lewis (PhD student, IASOS, University of Tasmania) collected invertebrates using a pooter for a fixed period of time while Perpetua Turner (AAD RISCC program) made notes about the vegetation and environment. This work was done for Dr Penny Greenslade (ANU) and the samples and data were given to her back at the AAD. The transect sample points were differentially corrected using the base station data and are available for download from a link below. David also collected the locations of the two navigation guides on The Isthmus and Tractor Rock which is the southern extent of Station Limits on the east coast. These locations were also differentially corrected. The locations of the two navigation guides are available for download from the link below. The location of Tractor Rock is in the unofficial Australian Antarctic Gazetteer (see link below) as this name has not been approved by the Nomenclature Board of Tasmania.", "links": [ { diff --git a/datasets/swe-measurements-gnss-along-a-steep-elevation-gradient_1.0.json b/datasets/swe-measurements-gnss-along-a-steep-elevation-gradient_1.0.json index 3873ebc430..cbef846b6c 100644 --- a/datasets/swe-measurements-gnss-along-a-steep-elevation-gradient_1.0.json +++ b/datasets/swe-measurements-gnss-along-a-steep-elevation-gradient_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swe-measurements-gnss-along-a-steep-elevation-gradient_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46\u00b049\u201947\u2019\u2019 N, 9\u00b048\u201934\u2019\u2019E), Laret (1515 m asl, . 46\u00b050\u20192\u2019\u2019N, 9\u00b052\u201917\u2019\u2019E), Klosters (1200 m asl, 46\u00b051\u201949\u2019\u2019N, 9\u00b053\u201917\u2019\u2019E), and K\u00fcblis (815 m asl, 46\u00b054\u201948\u2019\u2019N, 9\u00b046\u201954\u2019\u2019E).", "links": [ { diff --git a/datasets/swe2hs-calibration-and-validation-data_1.0.json b/datasets/swe2hs-calibration-and-validation-data_1.0.json index f60a31dcee..59553d8e53 100644 --- a/datasets/swe2hs-calibration-and-validation-data_1.0.json +++ b/datasets/swe2hs-calibration-and-validation-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swe2hs-calibration-and-validation-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data in this repository was used for the calibration and validation of the SWE2HS model in the following publication: Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258 Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.", "links": [ { diff --git a/datasets/swiss-biomass-potentials_1.0.json b/datasets/swiss-biomass-potentials_1.0.json index d909cdd27a..1232ea28ba 100644 --- a/datasets/swiss-biomass-potentials_1.0.json +++ b/datasets/swiss-biomass-potentials_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss-biomass-potentials_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Switzerland has a reliable and cost efficient energy system. Due to phase out of nuclear energy it is necessary to find new options to maintain this powerful energy system. The Swiss energy strategy 2050 aims to reduce CO2-emissions, increase efficiency and promote renewable energies. The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) examined relevant woody and non-woody biomass quantities (cubic meters, fresh-, dry weight) and their energy potentials (in Petajoules: primary energy and biomethane) with a similar methodological approach. The work was done within the frame of the Swiss Competence Centers for Energy Research (SCCER) especially in line with the SCCER Biomass for Swiss energy future (Biosweet). With a uniform and consistent approach for the current potentials ten biomass categories were estimated and aggregated for the whole of Switzerland. In this context solutions for the technical, social and political challenges are promoted. First, considering the different biomass resources characteristics and available data, appropriate methods at the finest scale possible were elaborated to estimate the annual quantities which could theoretically be collected (theoretical potential). Then, explicit and rational restrictions for sustainable bio-energy production were defined according to the current state of the art and subtracted from the theoretical potential to obtain the sustainable potential. The main restrictions are competing material utilizations, environmental factors and supply costs. Finally, the additional sustainable potential was estimated considering the current bioenergy production. Our main purpose was to provide potentials for developing conversion technologies as well as a detailed and comprehensive basis of the Swiss biomass potentials for energy use for economic and political decision makers. The complete report is available under https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A13277/datastream/PDF/view", "links": [ { diff --git a/datasets/swiss-canopy-crane-ii-research-site_1.0.json b/datasets/swiss-canopy-crane-ii-research-site_1.0.json index e34f7a209b..3ed024d98a 100644 --- a/datasets/swiss-canopy-crane-ii-research-site_1.0.json +++ b/datasets/swiss-canopy-crane-ii-research-site_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss-canopy-crane-ii-research-site_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg \"Swiss Canopy Crane II\") This research site is located near H\u00f6lstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.", "links": [ { diff --git a/datasets/swiss-fluxnet-davos_1.0.json b/datasets/swiss-fluxnet-davos_1.0.json index 8ceaa4cfc9..9e9e1cdb8a 100644 --- a/datasets/swiss-fluxnet-davos_1.0.json +++ b/datasets/swiss-fluxnet-davos_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss-fluxnet-davos_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN). Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method. In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station. __Measurements__ - Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site. - Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4). - Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O. - Auxiliary micrometeorology and soil climate measurements. __Data availability__ Near real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset. __Data policy__ ICOS data license: [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license) __Detailed site info__: [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)", "links": [ { diff --git a/datasets/swiss-fluxnet-lageren_1.0.json b/datasets/swiss-fluxnet-lageren_1.0.json index 800073338e..575f578d87 100644 --- a/datasets/swiss-fluxnet-lageren_1.0.json +++ b/datasets/swiss-fluxnet-lageren_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss-fluxnet-lageren_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swiss FluxNet Site L\u00e4geren is a managed mixed deciduous mountain forest located on the steep L\u00e4geren mountain (NW of Zurich, Swiss Plateau). The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and the National Air Pollution Monitoring Network (NABEL). In addition to Swiss FluxNet, the site is part of the Long-term Forest Ecosystem Research (LWF) of WSL and the biological drought and growth indicator network (TreeNet) of WSL. __Measurements__ - Ecosystem flux measurements of CO2, H2O vapour are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site. - Sub-canopy eddy fluxes (CO2, H2O), soil respiration campaigns - Continuous CO2 profile measurements. - Auxiliary micrometeorology and soil climate measurements. __Data availability__ All data are available from the European Fluxes Database Cluster, but are also part of Fluxnet2015 dataset. __Data policy__ ICOS data license: [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license) __Detailed site info__: [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae//](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)", "links": [ { diff --git a/datasets/swiss-municipalities-survey-on-spatial-planning-instruments_1.0.json b/datasets/swiss-municipalities-survey-on-spatial-planning-instruments_1.0.json index 9094414ec9..605c565049 100644 --- a/datasets/swiss-municipalities-survey-on-spatial-planning-instruments_1.0.json +++ b/datasets/swiss-municipalities-survey-on-spatial-planning-instruments_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss-municipalities-survey-on-spatial-planning-instruments_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.", "links": [ { diff --git a/datasets/swiss_landscape_services_change_1.0.json b/datasets/swiss_landscape_services_change_1.0.json index 4c4ad40a4c..40e3e51c81 100644 --- a/datasets/swiss_landscape_services_change_1.0.json +++ b/datasets/swiss_landscape_services_change_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss_landscape_services_change_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data and scripts of publication: Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Lo\u00efc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications. Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services. Data Folder: Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored Scripts Folder: Contains scripts organized into subfolders depending on their purpose Note: Some abbreviations within the scripts and data are derived from German words and not English.", "links": [ { diff --git a/datasets/swiss_lulc_forecast_21th_century_1.0.json b/datasets/swiss_lulc_forecast_21th_century_1.0.json index dad17a1992..83e5c6bc08 100644 --- a/datasets/swiss_lulc_forecast_21th_century_1.0.json +++ b/datasets/swiss_lulc_forecast_21th_century_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swiss_lulc_forecast_21th_century_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using >200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.", "links": [ { diff --git a/datasets/swissfungi-distribution-of-fungi-in-switzerland_1.0.json b/datasets/swissfungi-distribution-of-fungi-in-switzerland_1.0.json index bc61c8f634..99f1b52a43 100644 --- a/datasets/swissfungi-distribution-of-fungi-in-switzerland_1.0.json +++ b/datasets/swissfungi-distribution-of-fungi-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swissfungi-distribution-of-fungi-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland. The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature. The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information. License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.", "links": [ { diff --git a/datasets/swisslichens_1.0.json b/datasets/swisslichens_1.0.json index 8eb52a6421..9434d7dff7 100644 --- a/datasets/swisslichens_1.0.json +++ b/datasets/swisslichens_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "swisslichens_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland. The SwissLichens database currently contains more than 120\u2019000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature. Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available. They are free of charge for research projects and can be requested from InfoSpecies using a form. Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.", "links": [ { diff --git a/datasets/synchrony_spongymoth_budburst_1.0.json b/datasets/synchrony_spongymoth_budburst_1.0.json index 8de303436c..66ddc0ac36 100644 --- a/datasets/synchrony_spongymoth_budburst_1.0.json +++ b/datasets/synchrony_spongymoth_budburst_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "synchrony_spongymoth_budburst_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The files correspond to the data and R-script used for the analyses of the following paper \"Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate\" published in Ecology and Evolution by Vitasse et al. 2023. There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses. Input data: 'Synchrony_Cuttings_Pheno.txt':", "links": [ { diff --git a/datasets/sys_etm.json b/datasets/sys_etm.json index 140e4a6703..72331316f6 100644 --- a/datasets/sys_etm.json +++ b/datasets/sys_etm.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "sys_etm", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge. \n\nThe Landsat archive provides a rich collection of information about the Earth's land surface. Major characteristics of changes to the surface of the planet can be detected, measured, and analyzed using Landsat data. The information obtainable from the historical and current Landsat data play a key role in studying surface changes through time.\n", "links": [ { diff --git a/datasets/ta0704_1.json b/datasets/ta0704_1.json index c43a88e31e..5450b9dabb 100644 --- a/datasets/ta0704_1.json +++ b/datasets/ta0704_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ta0704_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the CTD and Niskin bottle data set from the RV Tangaroa cruise tan0704, 7th Mar 2007 to 29th Mar 2007, along the Macquarie Ridge. This was the deployment cruise for the Macquarie Ridge mooring array. Dissolved oxygen data have been removed from this data set (oxygen bottle data never analysed). There were a total of 75 CTD casts on this cruise.", "links": [ { diff --git a/datasets/ta0803_1.json b/datasets/ta0803_1.json index 1831b06d3d..92944e9ee1 100644 --- a/datasets/ta0803_1.json +++ b/datasets/ta0803_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ta0803_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is the CTD data set from RV Tangaroa cruise tan0803, 26th March to 26th April 2008, along the Macquarie Ridge. This was the recovery cruise for the Macquarie Ridge mooring array.\n\nThe primary aims of the oceanographic program were:\n\n1. recovery of a New Zealand/Australia collaborative mooring array spanning two gaps in the Macquarie Ridge north of Macquarie Island, and \n2. occupation of a CTD transect running south from New Zealand to 60o S along the Macquarie Ridge.\n\nEight of the nine moorings were successfully recovered. The mooring at site number 3 (NIWA gear) was unrecoverable, with acoustic release communication indicating only the bottom portion of the mooring remaining and lying flat on the ocean floor. Complete details of the mooring work are included in a separate mooring recovery report. Mooring instruments were downloaded on the ship, with a very high percentage of successful data recording. Ship maneouvering and deck operations all went well throughout the recoveries. Shiptime at the mooring locations was well spent, with daylight hours dedicated to mooring recovery, and night time used for nearby CTD, swath mapping, coring and sea mount activities, and for unspooling of mooring line. The additional container space created on the top deck portside (above the trawldeck) proved extremely valuable for stowage of mooring gear. \n\n58 CTD's were completed during the cruise, including 54 along the main transect, and 4 at coring locations (part of the geology program). Main transect CTD's included 2 across the northern mooring group, and 3 across the southern mooring group. Most casts were to within 25 metres of the bottom. Instrument problems resulted in incomplete casts at the following locations: CTD 9, CTD 11 and CTD 27. CTD 46 was skipped due to bad weather, while further instrument problems prevented a cast at the southernmost site (CTD 50). Niskin bottles were sampled at each station for dissolved oxygen and salinity, with a subset of stations selected for 18O sampling. Some stations were additionally sampled for DIC, alkalinity, 13C, silicate, and U/Th, as part of the geology program. \n\nNote that dissolved oxygen data have been removed from this data set, as oxygen bottle samples were never analysed.", "links": [ { diff --git a/datasets/tammsimpacts_1.json b/datasets/tammsimpacts_1.json index 6f017a73d6..b2c578829c 100644 --- a/datasets/tammsimpacts_1.json +++ b/datasets/tammsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tammsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Turbulent Air Motion Measurement System (TAMMS) IMPACTS dataset consists of wind speed, wind direction, and cross-wind speed measurements from the TAMMS instrument onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The files are available from January 18, 2020, through February 28, 2023, in ASCII-ict format. ", "links": [ { diff --git a/datasets/tc4ampr_1.json b/datasets/tc4ampr_1.json index c11e44af86..a07f399a1d 100644 --- a/datasets/tc4ampr_1.json +++ b/datasets/tc4ampr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tc4ampr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TC4 AMPR Brightness Temperature (TB) dataset consists of brightness temperature data from July 19, 2007 through August 8, 2007. The Tropical Composition, Cloud and Climate Coupling (TC4) mission field experiment was completed during July and August 2007 was based out of San Jose, Costa Rica. The Advanced Microwave Precipitation Radiometer (AMPR) instrument played a key role in the experiment. The AMPR remotely senses passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation. These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes.", "links": [ { diff --git a/datasets/tcihirad_2.1.json b/datasets/tcihirad_2.1.json index 0de2cad335..d067bdb11f 100644 --- a/datasets/tcihirad_2.1.json +++ b/datasets/tcihirad_2.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcihirad_2.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Tropical Cyclone Intensity (TCI) Hurricane Imaging Radiometer (HIRAD) dataset was created for the TCI field campaign from August 30, 2015 through October 23, 2015. The goal of the TCI field campaign was to improve the prediction of tropical cyclone (TC) intensity and structure change. The specific focus was to have an improved understanding of TC upper-level outflow layer processes and dynamics. These Hurricane Imaging Radiometer (HIRAD) data were obtained from the instrument onboard the NASA WB-57 aircraft flow on specific dates during the campaign. The data files include brightness temperature, rain rate, wind speed, and sea surface temperature estimates in netCDF-3 format, with corresponding browse imagery in PNG format.", "links": [ { diff --git a/datasets/tcspaero_1.json b/datasets/tcspaero_1.json index 6c34d9a75f..80dac2db87 100644 --- a/datasets/tcspaero_1.json +++ b/datasets/tcspaero_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspaero_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP Aerosonde dataset consists of measurements of air temperature, pressure, and relative humidity were made on each flight using two Vaisalla RS902 sondes located under the wings of the aerosonde aircraft. A Heiltronics KT11.k6 infrared pyrometer was used to measure sea surface temperatures (SST). The TCSP Field Experiment was held during the month of July, 2005, in Costa Rica. The mission was to study the processes associated with tropical waves passing over Central America to the Pacific ocean, where they would eventually form tropical cyclones.", "links": [ { diff --git a/datasets/tcspampr_2.json b/datasets/tcspampr_2.json index 4efee30c5c..8a62831eff 100644 --- a/datasets/tcspampr_2.json +++ b/datasets/tcspampr_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspampr_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP AMPR Brightness Temperature (TB) dataset consists of brightness temperature measurements from July 5, 2005 to July 27, 2005. The Advanced Microwave Precipitation Radiometer (AMPR) remotely senses passive microwave signatures of geophysical parameters from an airborne platform. The instrument is a low noise system which can provide multi-frequency microwave imagery with high spatial and temporal resolution. AMPR data are collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) unique to current NASA aircraft instrumentation. These frequencies are well suited to the study of rain cloud systems, but are also useful to studies of various ocean and land surface processes. AMPR data were collected at four microwave frequencies (10.7, 19.35, 37.1 and 85.5 GHz).", "links": [ { diff --git a/datasets/tcspcrs_1.json b/datasets/tcspcrs_1.json index ec8b084e33..0edd880eec 100644 --- a/datasets/tcspcrs_1.json +++ b/datasets/tcspcrs_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspcrs_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP Cloud Radar System (CRS) datasets consists of vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track. The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It provided high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The Tropical Cloud Systems and Processes (TCSP) mission used the ER-2 mode. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcspecmwf_1.json b/datasets/tcspecmwf_1.json index f3074cc911..c6dc4cea0f 100644 --- a/datasets/tcspecmwf_1.json +++ b/datasets/tcspecmwf_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspecmwf_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP European Centre for Medium-Range Weather Forecasts (ECMWF) dataset consists of three-hour forecast/analysis data for the Tropical Cloud Systems and Processes (TCSP) field campaign, supplied by ECMWF. The TCSP field campaign was conducted from July 1 through July 27, 2005 out of the Juan Santamaria Airfield in San Jose, Costa Rica. TCSP collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind, and air pressure that creates ideal birthing conditions for tropical storms, hurricanes, and related phenomena. The goal of this mission was to help better understand how hurricanes and other tropical storms are formed and intensify. The ECMWF three-hour forecast/analysis data are in a gridded binary (GRIB) format and tarred into daily files.", "links": [ { diff --git a/datasets/tcspedop_1.json b/datasets/tcspedop_1.json index 818c352730..eedf791e40 100644 --- a/datasets/tcspedop_1.json +++ b/datasets/tcspedop_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspedop_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP ER-2 DOPPLER RADAR (EDOP) dataset was collected by the ER-2 Doppler radar (EDOP), which is an X-band (9.6 GHz) Doppler radar mounted in the nose of the ER-2 aircraft that provides vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track. The instrument has two fixed antennas, one pointing at nadir and the second pointing approximately 33 degrees ahead of nadir. The beam width of the antenna is 3 degree in the vertical and horizontal directions which, for a 20 km altitude, yields a nadir footprint at the surface of 1 km. Each Antenna measures the doppler velocity, doppler spectral width, and reflectivity factor. Doppler velocities provide a measure of the pulse volume-weighted hydrometer motion (hydrometer fallspeed + air motion). Vertical air motion can be calculated from the nadir beam by removing the fallspeed contribution with an approximation. The linear depolarization ratio (the ratio of the cross-polar to the co-polar reflectivites) can be measured along the forward beam. EDOP provides measurements from a forward pointing beam that is used in combination with the nadir beam for estimating the along-track winds. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcsper2nav_1.json b/datasets/tcsper2nav_1.json index 4c6a3a4425..ac9706d94b 100644 --- a/datasets/tcsper2nav_1.json +++ b/datasets/tcsper2nav_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcsper2nav_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP ER-2 Navigation Data contains information recorded by the on-board navigation and data collection systems of the NASA ER-2 high-altitude research aircraft. In addition to typical navigation data (e.g., date, time, latitude/longitude, and altitude) it contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data were collected during the Tropical Cloud Systems and Processes (TCSP) field campaign in July 2005, with flights based out of Juan Santamaria Airport in San Jose, Costa Rica. The main goal of the campaign was to gain further insight into the structure and lifecycle of tropical weather systems. These navigation dataset files are available from July 2 through July 27, 2005 in ASCII and PDF formats.", "links": [ { diff --git a/datasets/tcspgoes_1.json b/datasets/tcspgoes_1.json index 77a6f7439c..09ad1f9f17 100644 --- a/datasets/tcspgoes_1.json +++ b/datasets/tcspgoes_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspgoes_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP GOES Visible and Infrared Images dataset was collected in support of the Tropical Cloud Systems and Processes (TCSP) mission, visible and infrared imagery from the Geostationary Operational Environmental Satellite 11 and 12 (GOES11, GOES 12) was collected and archived. Two channels were archived: channel 1-- visible (0.65 microns), and channel 2-- infrared (11 microns). Data files in McIDAS format as well as browse images were created. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcspgrsw_1.json b/datasets/tcspgrsw_1.json index 6ed4bfd0e6..a72f3cb232 100644 --- a/datasets/tcspgrsw_1.json +++ b/datasets/tcspgrsw_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspgrsw_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP GOES 11 Rapid Scan Winds dataset was generated from image triplets with 30 or 60 minute intervals, and occasionally 15 minute intervals. During Geostationary Operational Environmental Satellites (GOES) special rapid-scan operations, co-located images are available at intervals of 7.5, 5, 3, and even 1 minute. The area covered is reduced as the interval decreases. In this experiment, images at five minute intervals were used for the 0.65 micrometer visible, 3.9 micrometer infrared (darkness only), and 10.7 micrometer IR channels. GOES-11 was brought out of storage and image products were centered on the TCSP study region. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify. Regular image processing was available beginning on 12 July. The scan schedule was maintained through the end of July.", "links": [ { diff --git a/datasets/tcsphamsr_1.json b/datasets/tcsphamsr_1.json index 50ff31c989..57f8c978fb 100644 --- a/datasets/tcsphamsr_1.json +++ b/datasets/tcsphamsr_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcsphamsr_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The High Altitude MMIC Sounding Radiometer (HAMSR) is a 25-channel microwave atmospheric sounder operating as a cross-track scanner. There are three bands: an 8-channel band near 50-GHz, used for primary temperature sounding; a 10-channel band near 118 GHz, used for secondary temperature sounding and assessment of scattering; a 7-channel band near 183 GHz, used for water vapor sounding. The instrument is continuously self-calibrating using internal calibration targets. Radiometric sensitivity at the composite sampling cells provided in the archive is typically 0.1 and ranges up to 0.25 K for the stratospheric channels. Calibration accuracy is estimated at better than 1 K for temperature sounding and better than 2 K for water vapor sounding. Temperature weighting function peaks are distributed between the surface and the flight altitude. HAMSR was mounted in a wing pod of a NASA ER-2 research aircraft. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify. Regular image processing was available beginning on 12 July. The scan schedule was maintained through the end of July.", "links": [ { diff --git a/datasets/tcsplip_1.json b/datasets/tcsplip_1.json index 61dd568ce1..8a24fa28ec 100644 --- a/datasets/tcsplip_1.json +++ b/datasets/tcsplip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcsplip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP ER-2 Lightning Instrument Package (LIP) dataset consists of electrical field measurements of lightning from seven field mills, air conductivity data from a two channel conductivity probe, and navigation data, for the period of July 2 to July 27, 2005. These data were collected by the Lightning Instrument Package (LIP) flown aboard the NASA ER-2 high-altitude aircraft during the Tropical Cloud Systems and Processes (TCSP) field campaign in July 2005. The main goal of the campaign was to gain further insight into the structure and lifecycle of tropical weather systems. The TCSP ER-2 LIP data are provided in ASCII text files with PNG browse image files. ", "links": [ { diff --git a/datasets/tcspmas_1.json b/datasets/tcspmas_1.json index c9859fa641..2ea9dd8981 100644 --- a/datasets/tcspmas_1.json +++ b/datasets/tcspmas_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspmas_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP ER-2 MODIS Airborne Simulator (MAS) dataset was collected by a MODIS Airborne Simulator (MAS), which is a multi-spectral line-scanner system that acquires image data in 50 spectral bands over wavelengths ranging from 0.46 to 14.3 microns. Flown on the ER-2 aircraft at an operating altitude of 19.8 km (65,000 ft.), it produces nominal pixel sizes of 50 meters. MAS includes nine spectral bands in the visible/near infrared, 16 bands in the shortwave infrared, 16 bands in the mid-wave infrared, and nine bands in the thermal infrared regions of the spectrum. The instrument field-of-view is 86 degrees, with an IFOV of 2.5 mrad. The MAS collected calibrated multi-spectral imagery from the ER-2 aircraft during the TCSP experiment. The MAS was developed by NASA primarily to validate L1B and L2 science products from the EOS satellite program. MAS data enables (1) the mapping of sub-pixel variation within the co-incident footprints of many orbital instruments (e.g. MODIS, AIRS, HIRS, AVHRR, GOES) in the visible and thermal infrared spectral regions and (2) the estimation of surface, aerosol, and cloud properties at 50 meter spatial resolution. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcspmisrep_1.json b/datasets/tcspmisrep_1.json index d133b689b4..6bac916d68 100644 --- a/datasets/tcspmisrep_1.json +++ b/datasets/tcspmisrep_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspmisrep_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective and notes. The Tropical Cloud Systems and Processes (TCSP) mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcspmtp_1.json b/datasets/tcspmtp_1.json index 0419e8681a..bf0afbde2b 100644 --- a/datasets/tcspmtp_1.json +++ b/datasets/tcspmtp_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcspmtp_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP ER-2 Microwave Temperature Profiler (MTP) dataset was collected by the ER-2 Microwave Temperature Profiler (MTP), which is a passive microwave radiometer which measures the thermal emission from oxygen molecules in the atmosphere for a selection of elevation angles (normally 10 between +60 and -58 degrees). The current observing frequencies are 55.5, 56.6 and 58.8 GHz. Measured 'brightness temperature' versus elevation angle is converted to air temperature versus altitude using a modified statistical retrieval procedure with a Bayesian component. An altitude temperature profile (ATP) is produced in this manner every 13 seconds or approximately 3 km along the flight path. The ATP can be used to produce a color-coded temperature curtain (CTC) of the temperature field which the ER2 has flown through, and to identify the tropopause location. ATPs can also be used to locate altitudes where the air is cold enough to condense nitric acid or water vapor to form polar stratospheric clouds (PSCs). The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/tcsptico_1.json b/datasets/tcsptico_1.json index 856c447642..1714c33a9e 100644 --- a/datasets/tcsptico_1.json +++ b/datasets/tcsptico_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tcsptico_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TCSP TICOSONDE-AURA 2005 dataset consists of 4 soundings per day (00, 06, 12, and 18 UT) launched from Juan Santamaria International Airport, WMO station 78762, latitude 10 degrees N and 84.2 degrees W. The launch program began at 00 UT on 16 June 2005 and ended 00 UT 24 August 2005. With a very few exceptions, the sondes were Vaiasala model RS92-SGP and the ground station was a DigiCORA MW11 equipped for GPS wind-finding and upgraded for RS92 telemetry. A small number of ascents were made with RS90-AG and RS80-15G sondes. Most ascents were done with 500-g latex balloons filled with hydrogen. Exceptions included 24 ascents at 06 and 18UT in July that were piggybacked on a larger payload consisting of the University of Colorado Cryogenic Frostpoint Hygrometer (CFH) and an ECC ozonesonde. Median termination altitude for all ascents was approximately 26 km. Data were recorded at the maximum MW11 sample rate of one every two seconds. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.", "links": [ { diff --git a/datasets/te01ssld_530_1.json b/datasets/te01ssld_530_1.json index 03cf753c08..863feb4268 100644 --- a/datasets/te01ssld_530_1.json +++ b/datasets/te01ssld_530_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te01ssld_530_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides a set of soil properties for the SSA. The soil samples were collected at sets of soil pits. Major soil properties include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.", "links": [ { diff --git a/datasets/te04bbag_319_1.json b/datasets/te04bbag_319_1.json index 3b4e0a694b..c68a539d3d 100644 --- a/datasets/te04bbag_319_1.json +++ b/datasets/te04bbag_319_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te04bbag_319_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 1996 TE-04 data of branch bag studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the open MPH-1000 system.", "links": [ { diff --git a/datasets/te04gxda_320_1.json b/datasets/te04gxda_320_1.json index 3e8f4dd3ac..a9c044a00e 100644 --- a/datasets/te04gxda_320_1.json +++ b/datasets/te04gxda_320_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te04gxda_320_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TE-04 data on gas exchange studies of photosynthesis, respiration and stomatal conductance of boreal forest species using the MPH-1000 system. ", "links": [ { diff --git a/datasets/te07dend_333_1.json b/datasets/te07dend_333_1.json index fc854d19b0..5336b1467f 100644 --- a/datasets/te07dend_333_1.json +++ b/datasets/te07dend_333_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te07dend_333_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains tree summary information for the TE-07 dendrology data.", "links": [ { diff --git a/datasets/te07sapf_334_1.json b/datasets/te07sapf_334_1.json index ec8443058b..e552e91aef 100644 --- a/datasets/te07sapf_334_1.json +++ b/datasets/te07sapf_334_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te07sapf_334_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains sap flow data collected by TE-07.", "links": [ { diff --git a/datasets/te08bchm_335_1.json b/datasets/te08bchm_335_1.json index b138227740..d48c830df7 100644 --- a/datasets/te08bchm_335_1.json +++ b/datasets/te08bchm_335_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te08bchm_335_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains bark biochemical data collected by TE-08.", "links": [ { diff --git a/datasets/te08bopt_336_1.json b/datasets/te08bopt_336_1.json index 81c64a1b63..c7f3c537b0 100644 --- a/datasets/te08bopt_336_1.json +++ b/datasets/te08bopt_336_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te08bopt_336_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains bark opical data collected by the TE-08 team during the BOREAS field campaign.", "links": [ { diff --git a/datasets/te09cd_341_1.json b/datasets/te09cd_341_1.json index e4ce681f40..da84af6632 100644 --- a/datasets/te09cd_341_1.json +++ b/datasets/te09cd_341_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te09cd_341_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains foliar chlorophyll measurements collected by the TE-09 team. ", "links": [ { diff --git a/datasets/te09gxda_337_1.json b/datasets/te09gxda_337_1.json index c7039a6de6..ad0e7a5c6d 100644 --- a/datasets/te09gxda_337_1.json +++ b/datasets/te09gxda_337_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te09gxda_337_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains in situ diurnal gas exchange and water potential data for forest stand in the Northern Study Area collected by TE-09. ", "links": [ { diff --git a/datasets/te09npd_343_1.json b/datasets/te09npd_343_1.json index 61bede1aeb..0842d551aa 100644 --- a/datasets/te09npd_343_1.json +++ b/datasets/te09npd_343_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te09npd_343_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TE-09 data on the response of photosynthetic capacity to foliage nitrogen concentration and photosynthetic capacity in the canopies of Norther Study Area sites.", "links": [ { diff --git a/datasets/te09pnd_342_1.json b/datasets/te09pnd_342_1.json index 7d3bf368f9..0a62540cca 100644 --- a/datasets/te09pnd_342_1.json +++ b/datasets/te09pnd_342_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te09pnd_342_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data table contains data collected by TE09 on the relationship between PAR and foliage nitrogen in the canopies of NSA-OBS, NSA-UBS, NSA-YJP, NSA-OJP and NSA-OASP.", "links": [ { diff --git a/datasets/te09prd_344_1.json b/datasets/te09prd_344_1.json index 9d8cb9b9c1..7e3049bc53 100644 --- a/datasets/te09prd_344_1.json +++ b/datasets/te09prd_344_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te09prd_344_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains photosynthetic response of boreal tree species to varied parameters. Collected by TE-09 BOREAS group in the Northern Study Area.", "links": [ { diff --git a/datasets/te10lfch_345_1.json b/datasets/te10lfch_345_1.json index ed79cb4dfe..170f7b0ec9 100644 --- a/datasets/te10lfch_345_1.json +++ b/datasets/te10lfch_345_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te10lfch_345_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Leaf chemistry data collected by TE-10. Contains 3 granules: 1) biochemical data; 2) biochemical data on a per dry weight basis; and 3) biochemical carbon, hydrogen, and nitrogen data given on a per dry weight basis and on a per area basis.", "links": [ { diff --git a/datasets/te10lgxd_346_1.json b/datasets/te10lgxd_346_1.json index 17da514ab5..f259b327f4 100644 --- a/datasets/te10lgxd_346_1.json +++ b/datasets/te10lgxd_346_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te10lgxd_346_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains gas exchange data collected by TE-10 during the BOREAS project.", "links": [ { diff --git a/datasets/te10lopt_531_1.json b/datasets/te10lopt_531_1.json index 8927f4528e..07d6a9d6a9 100644 --- a/datasets/te10lopt_531_1.json +++ b/datasets/te10lopt_531_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te10lopt_531_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected in support of efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites. The data were collected during the growing seasons of 1994 and 1996.", "links": [ { diff --git a/datasets/te10prd_347_1.json b/datasets/te10prd_347_1.json index fb9b371b9f..fe8070cded 100644 --- a/datasets/te10prd_347_1.json +++ b/datasets/te10prd_347_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te10prd_347_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the 1994 oxygen evolution and leaf characteristics data collected by TE-10.", "links": [ { diff --git a/datasets/te11lgxd_348_1.json b/datasets/te11lgxd_348_1.json index e31f921196..52c15b6679 100644 --- a/datasets/te11lgxd_348_1.json +++ b/datasets/te11lgxd_348_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te11lgxd_348_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data collected by BOREAS TE-11 team on CO2 assimilation and transpiration in the SSA.", "links": [ { diff --git a/datasets/te11sapf_349_1.json b/datasets/te11sapf_349_1.json index 4cc3997724..79799c86ac 100644 --- a/datasets/te11sapf_349_1.json +++ b/datasets/te11sapf_349_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te11sapf_349_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains sap flow data collected by TE-11.", "links": [ { diff --git a/datasets/te11smet_350_1.json b/datasets/te11smet_350_1.json index 3028ee2cfa..c13f129713 100644 --- a/datasets/te11smet_350_1.json +++ b/datasets/te11smet_350_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te11smet_350_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected in support of efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis.", "links": [ { diff --git a/datasets/te12h2op_354_1.json b/datasets/te12h2op_354_1.json index 60548c4985..f267d683e9 100644 --- a/datasets/te12h2op_354_1.json +++ b/datasets/te12h2op_354_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te12h2op_354_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data on water potential for the Southern Study Area, Fen, Young Jack Pine, Young Aspen, and Old Black Spruce sites.", "links": [ { diff --git a/datasets/te12lgex_351_1.json b/datasets/te12lgex_351_1.json index 6a2f520625..28dbdc505d 100644 --- a/datasets/te12lgex_351_1.json +++ b/datasets/te12lgex_351_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te12lgex_351_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data collected by TE-12 of single leaf gas exchange properties of dominant vascular plant species in the SSA in 1994 and 1995.", "links": [ { diff --git a/datasets/te12lod_352_1.json b/datasets/te12lod_352_1.json index 05cc66f5d2..ceca6feb8c 100644 --- a/datasets/te12lod_352_1.json +++ b/datasets/te12lod_352_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te12lod_352_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains leaf optical properties data collected by the BOREAS TE-12 team.", "links": [ { diff --git a/datasets/te12parc_485_1.json b/datasets/te12parc_485_1.json index 870c381065..e6c73e4eca 100644 --- a/datasets/te12parc_485_1.json +++ b/datasets/te12parc_485_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te12parc_485_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains measurements of average incoming PAR through the forest canopy by TE-12.", "links": [ { diff --git a/datasets/te12sgd_353_1.json b/datasets/te12sgd_353_1.json index aee2646e9f..c372e6d9a2 100644 --- a/datasets/te12sgd_353_1.json +++ b/datasets/te12sgd_353_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te12sgd_353_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the shoot geometry data for both coniferous and deciduous samples taken by BOREAS TE-12.", "links": [ { diff --git a/datasets/te13biom_355_2.json b/datasets/te13biom_355_2.json index 4baeae023d..0262e577c7 100644 --- a/datasets/te13biom_355_2.json +++ b/datasets/te13biom_355_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te13biom_355_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Boreal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology (TE)-13 team collected data on site characteristics, soil profiles, woody debris, overstory vegetation, and understory vegetation from approximately 100 sites in the Southern Study Area (SSA), Northern Study Area (NSA), and Transect Areas in the boreal forest. This data sets provides 3 reports published by the Canadian Forest Service for the BOREAS project in pdf file format.", "links": [ { diff --git a/datasets/te17pem_486_1.json b/datasets/te17pem_486_1.json index cd6218bfa3..99ee1f8dea 100644 --- a/datasets/te17pem_486_1.json +++ b/datasets/te17pem_486_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te17pem_486_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A BOREAS version of the Global Production Efficiency Model(www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region.", "links": [ { diff --git a/datasets/te18geos_532_1.json b/datasets/te18geos_532_1.json index 6dfaa07bcc..3e669dac6d 100644 --- a/datasets/te18geos_532_1.json +++ b/datasets/te18geos_532_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te18geos_532_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The GEOSAIL model was created by combining the SAIL (Scattering from Arbitrarily Inclined Leaves) model with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. Tree shapes are described by cylinders or cones distributed over a plane. Spectral reflectance and transmittance of trees are calculated from the SAIL model to determine the reflectance of the three components used in the geometric model: illuminated canopy, illuminated background, shadowed canopy, and shadowed background.", "links": [ { diff --git a/datasets/te18ls30_557_1.json b/datasets/te18ls30_557_1.json index a23b96cd34..1996405424 100644 --- a/datasets/te18ls30_557_1.json +++ b/datasets/te18ls30_557_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te18ls30_557_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS TE-18 team used a radiometric rectification process to produce standardized DN values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities. Each of the reference scenes had coincident atmospheric optical thickness measurements made by RSS-11.", "links": [ { diff --git a/datasets/te18ls60_564_1.json b/datasets/te18ls60_564_1.json index 06d7bed954..af789efdac 100644 --- a/datasets/te18ls60_564_1.json +++ b/datasets/te18ls60_564_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te18ls60_564_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS TE-18 team used a radiometric rectification process to produce standardized DN values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities.", "links": [ { diff --git a/datasets/te19modl_487_1.json b/datasets/te19modl_487_1.json index c8fccf4f36..c77523f8cf 100644 --- a/datasets/te19modl_487_1.json +++ b/datasets/te19modl_487_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te19modl_487_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Spruce and Moss Model (SPAM) was designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration. The model can be used to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil.", "links": [ { diff --git a/datasets/te1ch4fx_310_1.json b/datasets/te1ch4fx_310_1.json index 2f6707c09d..d6e07d8206 100644 --- a/datasets/te1ch4fx_310_1.json +++ b/datasets/te1ch4fx_310_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te1ch4fx_310_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TE-01 CH4 flux data for the southern study area old aspen site.", "links": [ { diff --git a/datasets/te1fennt_313_1.json b/datasets/te1fennt_313_1.json index a4e2976737..34c0a5fdd9 100644 --- a/datasets/te1fennt_313_1.json +++ b/datasets/te1fennt_313_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te1fennt_313_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TE-01 methane flux and inorganic concentrations at the southern study area fen site.", "links": [ { diff --git a/datasets/te1fxobs_311_1.json b/datasets/te1fxobs_311_1.json index 7663107c76..f4a13f4a44 100644 --- a/datasets/te1fxobs_311_1.json +++ b/datasets/te1fxobs_311_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te1fxobs_311_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TE-01 CH4 and CO2 flux data for the southern study area OBS site.", "links": [ { diff --git a/datasets/te20site_488_1.json b/datasets/te20site_488_1.json index 3f5a14d9c0..13a19638dd 100644 --- a/datasets/te20site_488_1.json +++ b/datasets/te20site_488_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te20site_488_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.", "links": [ { diff --git a/datasets/te20sldn_356_1.json b/datasets/te20sldn_356_1.json index 52ab4fbd2d..65cec1fbdf 100644 --- a/datasets/te20sldn_356_1.json +++ b/datasets/te20sldn_356_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te20sldn_356_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the soils lab data that were collected by TE-20 in 1994.", "links": [ { diff --git a/datasets/te20supp_489_1.json b/datasets/te20supp_489_1.json index d16e734525..f391195268 100644 --- a/datasets/te20supp_489_1.json +++ b/datasets/te20supp_489_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te20supp_489_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data for use in developing and testing models of forest ecosystem dynamics. The data set describes the soils and landscape characteristics of the NSA-MSA and tower sites.", "links": [ { diff --git a/datasets/te21smet_358_1.json b/datasets/te21smet_358_1.json index 4c0c690dcc..7f7b15701a 100644 --- a/datasets/te21smet_358_1.json +++ b/datasets/te21smet_358_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te21smet_358_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains meteorological measurement data collected by TE-21.", "links": [ { diff --git a/datasets/te22allm_490_1.json b/datasets/te22allm_490_1.json index c1e30518ef..9616eb6702 100644 --- a/datasets/te22allm_490_1.json +++ b/datasets/te22allm_490_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te22allm_490_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.", "links": [ { diff --git a/datasets/te22ring_491_1.json b/datasets/te22ring_491_1.json index be1ccf0383..7d2f53bba7 100644 --- a/datasets/te22ring_491_1.json +++ b/datasets/te22ring_491_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te22ring_491_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree core data from several sites in the SSA and NSA collected in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas. A sample of the file types resulting from the analysis of the tree cores is provided. ", "links": [ { diff --git a/datasets/te23arch_492_1.json b/datasets/te23arch_492_1.json index c772c7e63b..32fa9f372b 100644 --- a/datasets/te23arch_492_1.json +++ b/datasets/te23arch_492_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te23arch_492_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Hemispherical photographs collected in support of the effort to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of LAI, Leaf angle, Gap fraction, and Clumping index. ", "links": [ { diff --git a/datasets/te23mapp_359_1.json b/datasets/te23mapp_359_1.json index c90cefebee..b496efb42a 100644 --- a/datasets/te23mapp_359_1.json +++ b/datasets/te23mapp_359_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te23mapp_359_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Describes the mapped plot data and the mapped plot site data taken by TE-23.", "links": [ { diff --git a/datasets/te2flrsp_315_1.json b/datasets/te2flrsp_315_1.json index 813c0ef3c7..71bcda3797 100644 --- a/datasets/te2flrsp_315_1.json +++ b/datasets/te2flrsp_315_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te2flrsp_315_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains foliage respiration data collected by TE-02.", "links": [ { diff --git a/datasets/te2rtrsp_316_1.json b/datasets/te2rtrsp_316_1.json index 824bc2ba12..9bb61c4d2e 100644 --- a/datasets/te2rtrsp_316_1.json +++ b/datasets/te2rtrsp_316_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te2rtrsp_316_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains fine root respiration data collected by TE-02.", "links": [ { diff --git a/datasets/te2stsap_317_1.json b/datasets/te2stsap_317_1.json index 4fdb51cbb3..d2e8b87e4f 100644 --- a/datasets/te2stsap_317_1.json +++ b/datasets/te2stsap_317_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te2stsap_317_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains stem growth and sapwood data collected by TE-02.", "links": [ { diff --git a/datasets/te2wdrs2_314_1.json b/datasets/te2wdrs2_314_1.json index 2d42f7aef6..c43f255a09 100644 --- a/datasets/te2wdrs2_314_1.json +++ b/datasets/te2wdrs2_314_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te2wdrs2_314_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains wood respiration continuous measurement data collected by TE-02.", "links": [ { diff --git a/datasets/te2wdrsp_318_1.json b/datasets/te2wdrsp_318_1.json index dc319bec6f..d641dd7e36 100644 --- a/datasets/te2wdrsp_318_1.json +++ b/datasets/te2wdrsp_318_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te2wdrsp_318_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains wood respiration data collected by TE-02.", "links": [ { diff --git a/datasets/te5airs_321_1.json b/datasets/te5airs_321_1.json index a72c72a58e..4314d5b79d 100644 --- a/datasets/te5airs_321_1.json +++ b/datasets/te5airs_321_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5airs_321_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This table contains data collected by TE05 in the NSA and SSA on air stable istope measurements.", "links": [ { diff --git a/datasets/te5co2pd_322_1.json b/datasets/te5co2pd_322_1.json index 1d1ec74c17..0dbb6c843a 100644 --- a/datasets/te5co2pd_322_1.json +++ b/datasets/te5co2pd_322_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5co2pd_322_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the CO2 profile concentration measurements made by the TE-05 BOREAS team in the NSA and SSA.", "links": [ { diff --git a/datasets/te5lciso_323_1.json b/datasets/te5lciso_323_1.json index 0a30acc418..39bb118299 100644 --- a/datasets/te5lciso_323_1.json +++ b/datasets/te5lciso_323_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5lciso_323_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains leaf carbon isotope data collected by TE-05 during 1994.", "links": [ { diff --git a/datasets/te5lgxd_324_1.json b/datasets/te5lgxd_324_1.json index c89b7a8b69..1cb04b5b68 100644 --- a/datasets/te5lgxd_324_1.json +++ b/datasets/te5lgxd_324_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5lgxd_324_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the leaf gas exchange data collected by TE-05 in the NSA and SSA.", "links": [ { diff --git a/datasets/te5metd_326_1.json b/datasets/te5metd_326_1.json index 066049ed47..a623e6d39f 100644 --- a/datasets/te5metd_326_1.json +++ b/datasets/te5metd_326_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5metd_326_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains meteorological data collected during field campaigns by TE-05.", "links": [ { diff --git a/datasets/te5soilr_325_1.json b/datasets/te5soilr_325_1.json index 76f02865cd..8cf1b384c9 100644 --- a/datasets/te5soilr_325_1.json +++ b/datasets/te5soilr_325_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5soilr_325_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data collected by TE-05 on soil gas exchange data in the NSA and SSA in 1994.", "links": [ { diff --git a/datasets/te5treer_327_1.json b/datasets/te5treer_327_1.json index 70e91f38f5..448fc59573 100644 --- a/datasets/te5treer_327_1.json +++ b/datasets/te5treer_327_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te5treer_327_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains tree ring width and C13 isotope cellulose ratio data collected by TE-05.", "links": [ { diff --git a/datasets/te6allom_329_1.json b/datasets/te6allom_329_1.json index 57e6e0e667..864e6e6549 100644 --- a/datasets/te6allom_329_1.json +++ b/datasets/te6allom_329_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6allom_329_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains allometry data collected by TE-06.", "links": [ { diff --git a/datasets/te6bmflg_330_1.json b/datasets/te6bmflg_330_1.json index ab4ead34f8..d78bb72871 100644 --- a/datasets/te6bmflg_330_1.json +++ b/datasets/te6bmflg_330_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6bmflg_330_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains biomass data collected by TE-06.", "links": [ { diff --git a/datasets/te6h2opd_332_1.json b/datasets/te6h2opd_332_1.json index 366a2465cf..baa085baa2 100644 --- a/datasets/te6h2opd_332_1.json +++ b/datasets/te6h2opd_332_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6h2opd_332_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains data collected by TE-06 on pre-dawn leaf water potential and foliage moisture content.", "links": [ { diff --git a/datasets/te6mltvg_331_1.json b/datasets/te6mltvg_331_1.json index 8740460884..6d253b7c0e 100644 --- a/datasets/te6mltvg_331_1.json +++ b/datasets/te6mltvg_331_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6mltvg_331_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Describes the average data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.Describes the single point data values derived from the multi-vegetation imager used by TE-06 during the BOREAS project.", "links": [ { diff --git a/datasets/te6npp_200_1.json b/datasets/te6npp_200_1.json index 5bad897be1..4a56df389c 100644 --- a/datasets/te6npp_200_1.json +++ b/datasets/te6npp_200_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6npp_200_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains Net primary production data gathered by the TE-06 group in the NSA and SSA.", "links": [ { diff --git a/datasets/te6satns_328_1.json b/datasets/te6satns_328_1.json index 115193ae15..1ad44c0787 100644 --- a/datasets/te6satns_328_1.json +++ b/datasets/te6satns_328_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te6satns_328_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains soil temperature data collected by TE-06 during the BOREAS project.", "links": [ { diff --git a/datasets/te9bioav_339_1.json b/datasets/te9bioav_339_1.json index b9a2185bfb..1e1208c30d 100644 --- a/datasets/te9bioav_339_1.json +++ b/datasets/te9bioav_339_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te9bioav_339_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains values of canopy biochemistry derived by the TE-09 team. ", "links": [ { diff --git a/datasets/te9biopd_340_1.json b/datasets/te9biopd_340_1.json index c79c5e0068..ae21d912e5 100644 --- a/datasets/te9biopd_340_1.json +++ b/datasets/te9biopd_340_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te9biopd_340_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains sample measurements of the canopy biochemistry measured by the TE-09 team. ", "links": [ { diff --git a/datasets/te9spref_338_1.json b/datasets/te9spref_338_1.json index 1081918f78..fd204efc5a 100644 --- a/datasets/te9spref_338_1.json +++ b/datasets/te9spref_338_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "te9spref_338_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA. ", "links": [ { diff --git a/datasets/temperature-dependent-life-history-ips-typographus_1.0.json b/datasets/temperature-dependent-life-history-ips-typographus_1.0.json index 9332dcc38a..ee45fcf6af 100644 --- a/datasets/temperature-dependent-life-history-ips-typographus_1.0.json +++ b/datasets/temperature-dependent-life-history-ips-typographus_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "temperature-dependent-life-history-ips-typographus_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ips typographus was reared in climate chambers at constant temperatures of 12, 15, 20, 25, 30 and 33\u00b0C. Developmental times from egg to teneral beetle stages and daily oviposition of females from preoviposition phase to their death were recorded. From these data life tables were computed and the data were used for modelling.", "links": [ { diff --git a/datasets/terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0.json b/datasets/terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0.json index 03115349a0..c50237e040 100644 --- a/datasets/terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0.json +++ b/datasets/terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions. Notes: * All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system). * From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors. * The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy. * By walking along fixed corridors, disturbance of the snow was limited.", "links": [ { diff --git a/datasets/tf01soil_511_1.json b/datasets/tf01soil_511_1.json index db09804836..17d10c25cc 100644 --- a/datasets/tf01soil_511_1.json +++ b/datasets/tf01soil_511_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf01soil_511_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected in support of the effort to characterize and interpret soil information at the SSA-OA tower site in 1994. Data collected include soil respiration, temperature, moisture, and gravimetric data.", "links": [ { diff --git a/datasets/tf01tflx_512_1.json b/datasets/tf01tflx_512_1.json index 5032230709..513b924deb 100644 --- a/datasets/tf01tflx_512_1.json +++ b/datasets/tf01tflx_512_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf01tflx_512_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy, cargon dioxide, and momentum flux data collected above the canopy along with meteorological and soils data at the BOREAS SSA-OA site", "links": [ { diff --git a/datasets/tf01uflx_513_1.json b/datasets/tf01uflx_513_1.json index 3f4f4b360a..07814b30e1 100644 --- a/datasets/tf01uflx_513_1.json +++ b/datasets/tf01uflx_513_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf01uflx_513_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy, carbon dioxide, and momentum flux data collected under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.", "links": [ { diff --git a/datasets/tf02tflx_515_1.json b/datasets/tf02tflx_515_1.json index 6b61268d04..04b8982451 100644 --- a/datasets/tf02tflx_515_1.json +++ b/datasets/tf02tflx_515_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf02tflx_515_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy, carbon dioxide, water vaport, and momentum flux data collected above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Daily precipitation data from several gauges were also collected.", "links": [ { diff --git a/datasets/tf04flux_451_1.json b/datasets/tf04flux_451_1.json index 5ac0a76c74..00d7f96be4 100644 --- a/datasets/tf04flux_451_1.json +++ b/datasets/tf04flux_451_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf04flux_451_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the SSA-YJP towerflux site by the TF-04 group.", "links": [ { diff --git a/datasets/tf07flux_452_1.json b/datasets/tf07flux_452_1.json index 322545f151..07513c1668 100644 --- a/datasets/tf07flux_452_1.json +++ b/datasets/tf07flux_452_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf07flux_452_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the SSA-OBS towerflux site by the TF-07 group.", "links": [ { diff --git a/datasets/tf08ceil_453_1.json b/datasets/tf08ceil_453_1.json index 0ceb9dceb2..031fa21d5e 100644 --- a/datasets/tf08ceil_453_1.json +++ b/datasets/tf08ceil_453_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf08ceil_453_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ceilometer measurements of cloud characteristics made by the TF-08 team at the NSA-OJP and SSA-OBS sites.", "links": [ { diff --git a/datasets/tf08tflx_516_1.json b/datasets/tf08tflx_516_1.json index 25312fe8f0..f7b05a24ed 100644 --- a/datasets/tf08tflx_516_1.json +++ b/datasets/tf08tflx_516_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf08tflx_516_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Energy, carbon dioxide, and water vapor flux data collected by the BOREAS TF-08 team.", "links": [ { diff --git a/datasets/tf10flux_454_1.json b/datasets/tf10flux_454_1.json index 523c1c2884..5e1b75b339 100644 --- a/datasets/tf10flux_454_1.json +++ b/datasets/tf10flux_454_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf10flux_454_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the NSA-YJP tower flux site by the TF-10 group. Measurements of stomatal conductance collected at the NSA-YJP site by the TF-10 team.", "links": [ { diff --git a/datasets/tf10fxmt_368_1.json b/datasets/tf10fxmt_368_1.json index 2a27ecca53..5203c24506 100644 --- a/datasets/tf10fxmt_368_1.json +++ b/datasets/tf10fxmt_368_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf10fxmt_368_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the NSA-Fen tower flux site by the TF-10 group.", "links": [ { diff --git a/datasets/tf11biom_369_1.json b/datasets/tf11biom_369_1.json index 808b1e8896..34c92f7030 100644 --- a/datasets/tf11biom_369_1.json +++ b/datasets/tf11biom_369_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11biom_369_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains plant coverage, plant biomass, and estimated net primary productivity collected by TF-11.", "links": [ { diff --git a/datasets/tf11conc_370_1.json b/datasets/tf11conc_370_1.json index 629f099ed3..a10031d0d3 100644 --- a/datasets/tf11conc_370_1.json +++ b/datasets/tf11conc_370_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11conc_370_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains temperature, pH, and CH4 and CO2 concentration profiles in the surface 50 cm of peat.", "links": [ { diff --git a/datasets/tf11dcom_372_1.json b/datasets/tf11dcom_372_1.json index 78a47c38a2..713499a9a3 100644 --- a/datasets/tf11dcom_372_1.json +++ b/datasets/tf11dcom_372_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11dcom_372_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains decomposition rates of a standard substrate (wheat straw).", "links": [ { diff --git a/datasets/tf11flux_371_1.json b/datasets/tf11flux_371_1.json index 0bc059f6a2..1d973139db 100644 --- a/datasets/tf11flux_371_1.json +++ b/datasets/tf11flux_371_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11flux_371_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains CH4 and CO2 static chamber fluxes at the SSA-FEN.", "links": [ { diff --git a/datasets/tf11lai_458_1.json b/datasets/tf11lai_458_1.json index 3dd39b7192..8755e12ecb 100644 --- a/datasets/tf11lai_458_1.json +++ b/datasets/tf11lai_458_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11lai_458_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TF-11 Leaf Area Index (LAI) data collected at the SSA Fen site.", "links": [ { diff --git a/datasets/tf11leaf_456_1.json b/datasets/tf11leaf_456_1.json index 6bda9d2e5f..6ad63b00f1 100644 --- a/datasets/tf11leaf_456_1.json +++ b/datasets/tf11leaf_456_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11leaf_456_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TF-11 leaf gas exchange data made with the LI-6200 and LI-6400 systems.", "links": [ { diff --git a/datasets/tf11sflm_455_1.json b/datasets/tf11sflm_455_1.json index e6e159e941..21e67b5dd9 100644 --- a/datasets/tf11sflm_455_1.json +++ b/datasets/tf11sflm_455_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11sflm_455_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TF-11 CO2 chamber flux measurements made with the LI-6200 under water surface film conditions and the TF-11 CO2 chamber concentration measurements made using the Gas Chromatograph-Flame Ionization Detector.", "links": [ { diff --git a/datasets/tf11soil_457_1.json b/datasets/tf11soil_457_1.json index 2bdd7e7f7c..847977cbea 100644 --- a/datasets/tf11soil_457_1.json +++ b/datasets/tf11soil_457_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11soil_457_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TF-11 soil surface CO2 flux data that were measured using a portable gas exchange system.", "links": [ { diff --git a/datasets/tf11tfx_373_1.json b/datasets/tf11tfx_373_1.json index 93c96b18b1..36188fd1a5 100644 --- a/datasets/tf11tfx_373_1.json +++ b/datasets/tf11tfx_373_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf11tfx_373_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the SSA-Fen tower flux site by the TF-11 group.", "links": [ { diff --git a/datasets/tf1ch4_514_1.json b/datasets/tf1ch4_514_1.json index f7b9391a3b..5140bc1f99 100644 --- a/datasets/tf1ch4_514_1.json +++ b/datasets/tf1ch4_514_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf1ch4_514_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains methane (CH4) and nitrous oxide (N2O)fluxes that were measured at the BOREAS SSA-OA site", "links": [ { diff --git a/datasets/tf2met_504_1.json b/datasets/tf2met_504_1.json index a7f767f15a..d5c47498e6 100644 --- a/datasets/tf2met_504_1.json +++ b/datasets/tf2met_504_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf2met_504_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The BOREAS TF-02 team collected various trace gas and energy flux data along with meteorological parameters at the SSA-OA site. This data set contains meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m.", "links": [ { diff --git a/datasets/tf3acco2_360_1.json b/datasets/tf3acco2_360_1.json index 86c000faa2..ae9609a67b 100644 --- a/datasets/tf3acco2_360_1.json +++ b/datasets/tf3acco2_360_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf3acco2_360_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Automated measurements of CO2 exchange at the moss surface of NSA-OBS.", "links": [ { diff --git a/datasets/tf3tflxd_361_1.json b/datasets/tf3tflxd_361_1.json index 7c37055a2f..5707d1cdff 100644 --- a/datasets/tf3tflxd_361_1.json +++ b/datasets/tf3tflxd_361_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf3tflxd_361_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the NSA-OBS tower flux site by the TF-03 group.", "links": [ { diff --git a/datasets/tf4ssafx_362_1.json b/datasets/tf4ssafx_362_1.json index caea973cc5..735ff73d78 100644 --- a/datasets/tf4ssafx_362_1.json +++ b/datasets/tf4ssafx_362_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf4ssafx_362_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains fluxes of carbon dioxide and methane across the soil-air interface in four ages of jack pine forest at the Southern Study Area. Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine stands.", "links": [ { diff --git a/datasets/tf4ssasp_363_1.json b/datasets/tf4ssasp_363_1.json index eb1531fd4f..51200a98c2 100644 --- a/datasets/tf4ssasp_363_1.json +++ b/datasets/tf4ssasp_363_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf4ssasp_363_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TF-04 Soil profile CH4 concentration data.", "links": [ { diff --git a/datasets/tf5tflxd_364_1.json b/datasets/tf5tflxd_364_1.json index 5ccee57603..1b1eaacba1 100644 --- a/datasets/tf5tflxd_364_1.json +++ b/datasets/tf5tflxd_364_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf5tflxd_364_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the SSA-OJP tower flux site by the TF-05 group.", "links": [ { diff --git a/datasets/tf6fxmet_365_1.json b/datasets/tf6fxmet_365_1.json index 60040f3c59..63f07a7229 100644 --- a/datasets/tf6fxmet_365_1.json +++ b/datasets/tf6fxmet_365_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf6fxmet_365_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains meteorology data collected at the SSA-YA tower flux site by the TF6 group. These data were reported at 10 minute intervals. The flux and ancillary data collected at the SSA-YA tower flux site by the TF6 group.", "links": [ { diff --git a/datasets/tf9brflx_366_1.json b/datasets/tf9brflx_366_1.json index 04bb3350c7..54fb7e6e10 100644 --- a/datasets/tf9brflx_366_1.json +++ b/datasets/tf9brflx_366_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf9brflx_366_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CO2 and H2O vapor exchange and ancillary data collected from enclosures around the black spruce branches. Data collected by the TF-09 group at the SSA-OBS site.", "links": [ { diff --git a/datasets/tf9tflxd_367_1.json b/datasets/tf9tflxd_367_1.json index 7c93f7ea39..115341f3d2 100644 --- a/datasets/tf9tflxd_367_1.json +++ b/datasets/tf9tflxd_367_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tf9tflxd_367_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Soil temperature and heat flux data from the SSA-OBS site, collected by the TF-09 group; and the flux and ancillary data collected at the SSA-OBS tower flux site by the TF-09 group.", "links": [ { diff --git a/datasets/tgb03doc_459_1.json b/datasets/tgb03doc_459_1.json index 7f76662019..fda53acb8b 100644 --- a/datasets/tgb03doc_459_1.json +++ b/datasets/tgb03doc_459_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb03doc_459_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-03 dissolved organic carbon concentration data for northern study area.", "links": [ { diff --git a/datasets/tgb10ocd_396_1.json b/datasets/tgb10ocd_396_1.json index d01321f500..4c4c40a7ed 100644 --- a/datasets/tgb10ocd_396_1.json +++ b/datasets/tgb10ocd_396_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb10ocd_396_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains oxidant (O3, H2O2, ROOH) concentration data collected by TGB-10 for the Southern Study Area.", "links": [ { diff --git a/datasets/tgb10ofd_397_1.json b/datasets/tgb10ofd_397_1.json index b3b65767fe..54db223fbc 100644 --- a/datasets/tgb10ofd_397_1.json +++ b/datasets/tgb10ofd_397_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb10ofd_397_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains oxidant flux data collected by TGB-10 for sites in the Southern Study Area.", "links": [ { diff --git a/datasets/tgb10voc_398_1.json b/datasets/tgb10voc_398_1.json index ee287335a3..c1c709a627 100644 --- a/datasets/tgb10voc_398_1.json +++ b/datasets/tgb10voc_398_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb10voc_398_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains biogenic VOC data collected by TGB-10 during the summer of 1994.", "links": [ { diff --git a/datasets/tgb12cfd_517_1.json b/datasets/tgb12cfd_517_1.json index 555be41bf5..1a6b5e96f0 100644 --- a/datasets/tgb12cfd_517_1.json +++ b/datasets/tgb12cfd_517_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12cfd_517_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated.", "links": [ { diff --git a/datasets/tgb12ci_399_1.json b/datasets/tgb12ci_399_1.json index 15f57d91f4..bbb95036a7 100644 --- a/datasets/tgb12ci_399_1.json +++ b/datasets/tgb12ci_399_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12ci_399_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-12 soil CO2 flux data for sites in the northern study area.", "links": [ { diff --git a/datasets/tgb12rad_400_1.json b/datasets/tgb12rad_400_1.json index b2f4eec484..7ef9d05ba9 100644 --- a/datasets/tgb12rad_400_1.json +++ b/datasets/tgb12rad_400_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12rad_400_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains RADON-222 activity in soil gas collected by TGB-12 at 5 BOREAS auxiliary sites in the northern study area.", "links": [ { diff --git a/datasets/tgb12rfd_401_1.json b/datasets/tgb12rfd_401_1.json index c4b1c59d7b..e908cffa86 100644 --- a/datasets/tgb12rfd_401_1.json +++ b/datasets/tgb12rfd_401_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12rfd_401_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains RADON-222 flux data data collected by TGB-12 in the northern study area.", "links": [ { diff --git a/datasets/tgb12scd_402_1.json b/datasets/tgb12scd_402_1.json index d0f7513215..44ec737628 100644 --- a/datasets/tgb12scd_402_1.json +++ b/datasets/tgb12scd_402_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12scd_402_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the soil carbon data collected by TGB-12 in the northern study area.", "links": [ { diff --git a/datasets/tgb12sci_558_1.json b/datasets/tgb12sci_558_1.json index d9d883421a..09bdba89af 100644 --- a/datasets/tgb12sci_558_1.json +++ b/datasets/tgb12sci_558_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb12sci_558_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data collected to support analysis of soil carbon content in the NSA. Other ancillary information was stored and provided in two sets of soil pit description and surface vegetation transect files. In addition, a site description file provides more information on the positioning of the sampling sites.", "links": [ { diff --git a/datasets/tgb1ccfd_374_1.json b/datasets/tgb1ccfd_374_1.json index f138f36b7b..4519c35824 100644 --- a/datasets/tgb1ccfd_374_1.json +++ b/datasets/tgb1ccfd_374_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb1ccfd_374_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-01 carbon chamber flux data for Northern Study Area. ", "links": [ { diff --git a/datasets/tgb1ccsd_377_1.json b/datasets/tgb1ccsd_377_1.json index 412d497dd7..71a588acb4 100644 --- a/datasets/tgb1ccsd_377_1.json +++ b/datasets/tgb1ccsd_377_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb1ccsd_377_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-01 carbon soil profile data for Northern Study Area.", "links": [ { diff --git a/datasets/tgb1cfd_375_1.json b/datasets/tgb1cfd_375_1.json index 1d02cb8ab5..95674b7144 100644 --- a/datasets/tgb1cfd_375_1.json +++ b/datasets/tgb1cfd_375_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb1cfd_375_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains CH4 tower flux data collected by BOREAS science group TGB01 in the Northern Study Area.", "links": [ { diff --git a/datasets/tgb1sfd_376_1.json b/datasets/tgb1sfd_376_1.json index a2f1ce9939..e9c272544c 100644 --- a/datasets/tgb1sfd_376_1.json +++ b/datasets/tgb1sfd_376_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb1sfd_376_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TGB-01 SF6 flux data for the Northern Study Area OJP and YJP sites.", "links": [ { diff --git a/datasets/tgb3cofd_381_1.json b/datasets/tgb3cofd_381_1.json index e35cfaa680..848ebb464c 100644 --- a/datasets/tgb3cofd_381_1.json +++ b/datasets/tgb3cofd_381_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb3cofd_381_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-03 methane flux and CO2 flux data for uplands in the northern study area.", "links": [ { diff --git a/datasets/tgb3plsp_382_1.json b/datasets/tgb3plsp_382_1.json index 948264f006..f62875cdeb 100644 --- a/datasets/tgb3plsp_382_1.json +++ b/datasets/tgb3plsp_382_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb3plsp_382_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Composition of plant species that were within the collars used to measure net ecosystem exchange (NEE).", "links": [ { diff --git a/datasets/tgb3wd_380_1.json b/datasets/tgb3wd_380_1.json index 186a64dc1b..33600aac21 100644 --- a/datasets/tgb3wd_380_1.json +++ b/datasets/tgb3wd_380_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb3wd_380_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains water table and peat temperature data collected by the TGB-03 team for sites in the northern study area.", "links": [ { diff --git a/datasets/tgb4flux_460_1.json b/datasets/tgb4flux_460_1.json index 7cac72195b..f1a66560a6 100644 --- a/datasets/tgb4flux_460_1.json +++ b/datasets/tgb4flux_460_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb4flux_460_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The flux and ancillary data collected at the NSA-BP tower flux site by the TGB-04 group.", "links": [ { diff --git a/datasets/tgb4wsed_461_1.json b/datasets/tgb4wsed_461_1.json index 0b4b047828..c8c75b6a3f 100644 --- a/datasets/tgb4wsed_461_1.json +++ b/datasets/tgb4wsed_461_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb4wsed_461_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-04 water and sediment temperature data for northern study area (tower beaver pond site).", "links": [ { diff --git a/datasets/tgb5cflx_384_1.json b/datasets/tgb5cflx_384_1.json index 7a21cfdc13..1975121f6b 100644 --- a/datasets/tgb5cflx_384_1.json +++ b/datasets/tgb5cflx_384_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb5cflx_384_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TGB-05 CO, CO2, and CH4 flux data for sites in the Boreas Northern Study Area.", "links": [ { diff --git a/datasets/tgb5docd_385_1.json b/datasets/tgb5docd_385_1.json index cca9067c53..b18c113201 100644 --- a/datasets/tgb5docd_385_1.json +++ b/datasets/tgb5docd_385_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb5docd_385_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TGB-05 dissolved organic carbon data for the Northern Study Area beaver pond site.", "links": [ { diff --git a/datasets/tgb5nnfd_383_1.json b/datasets/tgb5nnfd_383_1.json index 36093f7e6b..6ed8ec6294 100644 --- a/datasets/tgb5nnfd_383_1.json +++ b/datasets/tgb5nnfd_383_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb5nnfd_383_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the TGB-05 NO and N2O flux data for sites in the Northern Study Area. ", "links": [ { diff --git a/datasets/tgb6chrc_388_1.json b/datasets/tgb6chrc_388_1.json index d8426892b5..f459006b5b 100644 --- a/datasets/tgb6chrc_388_1.json +++ b/datasets/tgb6chrc_388_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb6chrc_388_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 1993, 1994, and 1996 methane concentrations from TGB-06 in the NSA and SSA.", "links": [ { diff --git a/datasets/tgb7aaho_389_1.json b/datasets/tgb7aaho_389_1.json index 646804eb9c..8e3f919849 100644 --- a/datasets/tgb7aaho_389_1.json +++ b/datasets/tgb7aaho_389_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb7aaho_389_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains herbicide and organic chlorine concentrations in ambient air samples for TGB-07 in the SSA.", "links": [ { diff --git a/datasets/tgb7ddho_390_1.json b/datasets/tgb7ddho_390_1.json index cdcd87497f..d46252e926 100644 --- a/datasets/tgb7ddho_390_1.json +++ b/datasets/tgb7ddho_390_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb7ddho_390_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains herbicide and organic chlorine fluxes in dry deposition samples for TGB-07 in the SSA.", "links": [ { diff --git a/datasets/tgb7rwho_391_1.json b/datasets/tgb7rwho_391_1.json index dd336da249..adb7a6a9ff 100644 --- a/datasets/tgb7rwho_391_1.json +++ b/datasets/tgb7rwho_391_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb7rwho_391_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains 1993 and 1994 herbicide concentrations in rain samples for TGB-07 in the SSA.", "links": [ { diff --git a/datasets/tgb8mono_392_1.json b/datasets/tgb8mono_392_1.json index 230fd8b260..1826a91f50 100644 --- a/datasets/tgb8mono_392_1.json +++ b/datasets/tgb8mono_392_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb8mono_392_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains monoterpene emission and concentration data collected by the TGB-08 BOREAS science group.", "links": [ { diff --git a/datasets/tgb8prds_393_1.json b/datasets/tgb8prds_393_1.json index b2969824a0..d131364fb0 100644 --- a/datasets/tgb8prds_393_1.json +++ b/datasets/tgb8prds_393_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb8prds_393_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-08 Photosynthesis data measured at 30C by month.", "links": [ { diff --git a/datasets/tgb8scds_394_1.json b/datasets/tgb8scds_394_1.json index 0b771a4197..6e2c7239fc 100644 --- a/datasets/tgb8scds_394_1.json +++ b/datasets/tgb8scds_394_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb8scds_394_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains starch concentration data collected by TGB-08 from the SSA-OBS and SSA-OJP BOREAS sites in 1994.", "links": [ { diff --git a/datasets/tgb9nmhc_395_1.json b/datasets/tgb9nmhc_395_1.json index b1dbe0ba7d..99acd05adc 100644 --- a/datasets/tgb9nmhc_395_1.json +++ b/datasets/tgb9nmhc_395_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgb9nmhc_395_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains the mixing ratio and concentration of Non-Methane HydroCarbons (NMHC) taken by the TGB-09 group.", "links": [ { diff --git a/datasets/tgbfenfx_378_1.json b/datasets/tgbfenfx_378_1.json index aacb40dd38..0990bb7ee5 100644 --- a/datasets/tgbfenfx_378_1.json +++ b/datasets/tgbfenfx_378_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgbfenfx_378_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-03 methane flux data for fens in the Northern Study Area.", "links": [ { diff --git a/datasets/tgbfenne_379_1.json b/datasets/tgbfenne_379_1.json index 616d883c70..6419479d54 100644 --- a/datasets/tgbfenne_379_1.json +++ b/datasets/tgbfenne_379_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tgbfenne_379_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.", "links": [ { diff --git a/datasets/the-experimental-forest-management-network_1.0.json b/datasets/the-experimental-forest-management-network_1.0.json index c783e5932a..a550ca4ada 100644 --- a/datasets/the-experimental-forest-management-network_1.0.json +++ b/datasets/the-experimental-forest-management-network_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "the-experimental-forest-management-network_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The EFM network is one of the longest running scientific projects in Switzerland and has been collecting growth and yield data since the late 1880\u2019s. As of 2021, 28 plots had been monitored for at least 100 years and 81 for at least 75 years. The network is used to examine silvicultural treatments across a range of species, climate and edaphic conditions. There are currently 465 plots covering a total area of 148 hectares. Over the > 130-year history of the project, at least another 1000 plots were monitored and then deactivated after they reached their experimental goal (e.g. end of the rotation). The data from all 1480 plots are available for analyses.", "links": [ { diff --git a/datasets/the-origin_1.0.json b/datasets/the-origin_1.0.json index 31ecac8427..bd6e3da1f9 100644 --- a/datasets/the-origin_1.0.json +++ b/datasets/the-origin_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "the-origin_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland. Dataset available upon request (An agreement between the data provider and the data recipient is necessary).", "links": [ { diff --git a/datasets/the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0.json b/datasets/the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0.json index e319073def..897be9d6c8 100644 --- a/datasets/the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0.json +++ b/datasets/the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol. The present dataset is part of the published scientific paper entitled \u201cLandscape ecological concepts in planning: review of recent developments\u201d (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning? We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015\u20132019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the \u201cpdfsearch\u201d package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:   1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.   2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.   3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1). This data was used to produce the Figure 3a at the original publication.   4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication. To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material \u2013 4 Integration of landscape ecological concepts into planning), and we provided two outputs:   5. Planning steps. This data provides the number of times landscape ecological concepts were addressed in each planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the original publication.   6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning steps described above?", "links": [ { diff --git a/datasets/three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6.json b/datasets/three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6.json index 3a0e9b4b67..9210b9535e 100644 --- a/datasets/three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6.json +++ b/datasets/three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Here the updated versions of debrisInterMixing are provided for download. The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., H\u00fcrlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017. DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter. For more information, please contact albrecht.vonboetticher@wasserbau.ch. For a recent summary on applications see the DFHM8 contribution at https://www.e3s-conferences.org/articles/e3sconf/abs/2023/52/e3sconf_dfhm82023_02024/e3sconf_dfhm82023_02024.html - DOI: https://doi.org/10.1051/e3sconf/202341502024 UPDATE: DebrisInterMixing for OpenFOAM-7 is available, please contact albrecht.vonboetticher@wasserbau.ch. DebrisInterMixing with OpenFOAM-10 is ready but not yet validated.", "links": [ { diff --git a/datasets/timber_125_1.json b/datasets/timber_125_1.json index b98128db2c..18e280ff07 100644 --- a/datasets/timber_125_1.json +++ b/datasets/timber_125_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "timber_125_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Height, crown width, DBH, and height-to-crown distance collected using variable-radius plot sampling with steel tape and hand-held compass to locate points along transect", "links": [ { diff --git a/datasets/time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0.json b/datasets/time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0.json index af396e3ca2..a705ab5e92 100644 --- a/datasets/time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0.json +++ b/datasets/time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set includes material and results described in the related research article: Bergfeld, B., van Herwijnen A., Bobillier, G., Rosendahl P., Wei\u00dfgraeber P., Adam V., Dual, J., and Schweizer, J.: Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and sustained propagation, Natural Hazards and Earth System Sciences, 23, 293-315, https://doi.org/10.5194/nhess-23-293-2023, 2023. We performed a series of propagation saw test experiments, up to ten meters long, over a period of 10 weeks and analyzed these using digital image correlation techniques. We derived the elastic modulus of the slab, the elastic modulus of the weak layer and the specific fracture energy of the weak layer with a homogeneous and a layered slab model. During crack propagation, we measured crack speed, touchdown distance and the energy dissipation due to compaction and dynamic fracture. Our data set provides unique insight and valuable data to validate models.", "links": [ { diff --git a/datasets/tims0bil_282_1.json b/datasets/tims0bil_282_1.json index 5722006b58..63f6242c5c 100644 --- a/datasets/tims0bil_282_1.json +++ b/datasets/tims0bil_282_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tims0bil_282_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover, biophysical parameter maps such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI), and surface thermal properties.", "links": [ { diff --git a/datasets/tims1bsq_436_1.json b/datasets/tims1bsq_436_1.json index bca3a5e713..25906ba18e 100644 --- a/datasets/tims1bsq_436_1.json +++ b/datasets/tims1bsq_436_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tims1bsq_436_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas. The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994. ", "links": [ { diff --git a/datasets/tmiwop_3.json b/datasets/tmiwop_3.json index 819e98f107..b663cdb7e3 100644 --- a/datasets/tmiwop_3.json +++ b/datasets/tmiwop_3.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tmiwop_3", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM Microwave Imager (TMI) Wentz Ocean Products dataset used the TRMM Microwave Imager (TMI), which is a 5-channel, dual-polarized, passive microwave radiometer. The TMI is used to measure several important meteorological parameters over sea surfaces, such as precipitation rate, wind speed, wapter vapor, and sea surface temperature. The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz. It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.", "links": [ { diff --git a/datasets/topoclim-v-1-0-code_1.0.json b/datasets/topoclim-v-1-0-code_1.0.json index e7ed94faf4..4cc099ad8f 100644 --- a/datasets/topoclim-v-1-0-code_1.0.json +++ b/datasets/topoclim-v-1-0-code_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "topoclim-v-1-0-code_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md. License GPL v.3", "links": [ { diff --git a/datasets/topoclim-v1-1-code_1.1.json b/datasets/topoclim-v1-1-code_1.1.json index 8c2e3d2d5f..20a49287f7 100644 --- a/datasets/topoclim-v1-1-code_1.1.json +++ b/datasets/topoclim-v1-1-code_1.1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "topoclim-v1-1-code_1.1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.", "links": [ { diff --git a/datasets/torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0.json b/datasets/torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0.json index 8fbdbb9692..e27704516a 100644 --- a/datasets/torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0.json +++ b/datasets/torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects. The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area. The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term. These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions", "links": [ { diff --git a/datasets/total_basal_area-2_1.0.json b/datasets/total_basal_area-2_1.0.json index 5f7f926f1a..cc92ab0e5b 100644 --- a/datasets/total_basal_area-2_1.0.json +++ b/datasets/total_basal_area-2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_basal_area-2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sum of the stem cross-section areas of all living and dead trees and shrubs starting at 12 cm dbh at a height of 1.3 m (dbh measurement height). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_basal_area_nfi1-238_1.0.json b/datasets/total_basal_area_nfi1-238_1.0.json index 414e22da5d..0eab61afb1 100644 --- a/datasets/total_basal_area_nfi1-238_1.0.json +++ b/datasets/total_basal_area_nfi1-238_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_basal_area_nfi1-238_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Sum of stem cross-section areas at a height of 1.3 m (dbh measurement height) of all living and dead trees and shrubs starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_stem_number-3_1.0.json b/datasets/total_stem_number-3_1.0.json index 46b9bda43c..a2157e88e1 100644 --- a/datasets/total_stem_number-3_1.0.json +++ b/datasets/total_stem_number-3_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_stem_number-3_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of stems of all living and dead trees and shrubs starting at 12 cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_stem_number_by_cause_of_damage-218_1.0.json b/datasets/total_stem_number_by_cause_of_damage-218_1.0.json index 783e992d25..4526dcfb4c 100644 --- a/datasets/total_stem_number_by_cause_of_damage-218_1.0.json +++ b/datasets/total_stem_number_by_cause_of_damage-218_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_stem_number_by_cause_of_damage-218_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of all living and dead trees and shrubs starting at 12 cm dbh where a particular cause of damage (including no damage, dead or lying) was determined. One tree may have damage with more than one type of origin, which means it may contribute to the total number of stems with damage with several different types of origin. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_stem_number_by_type_of_damage-208_1.0.json b/datasets/total_stem_number_by_type_of_damage-208_1.0.json index 7fd77d537c..9d7a06f510 100644 --- a/datasets/total_stem_number_by_type_of_damage-208_1.0.json +++ b/datasets/total_stem_number_by_type_of_damage-208_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_stem_number_by_type_of_damage-208_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of all living and dead trees and shrubs starting at 12 cm dbh where a particular type of damage (including no damage, dead or lying) was observed. One tree may have more than one type of damage, which means it may contribute to the total number of stems for several different types of damage. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_stem_number_nfi1-243_1.0.json b/datasets/total_stem_number_nfi1-243_1.0.json index a7869891d5..017e164650 100644 --- a/datasets/total_stem_number_nfi1-243_1.0.json +++ b/datasets/total_stem_number_nfi1-243_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_stem_number_nfi1-243_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of stems of all living and dead trees and shrubs starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_timber_volume-23_1.0.json b/datasets/total_timber_volume-23_1.0.json index 8dfb98e051..4d849bd115 100644 --- a/datasets/total_timber_volume-23_1.0.json +++ b/datasets/total_timber_volume-23_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_timber_volume-23_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all living and dead trees and shrubs (standing and lying) starting at 12 cm dbh. This corresponds to the sum of the volumes of growing stock and deadwood. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/total_timber_volume_nfi1-242_1.0.json b/datasets/total_timber_volume_nfi1-242_1.0.json index d13af9e94a..bbf0be674b 100644 --- a/datasets/total_timber_volume_nfi1-242_1.0.json +++ b/datasets/total_timber_volume_nfi1-242_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "total_timber_volume_nfi1-242_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all living and dead trees and shrubs starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/tpwcpex_1.json b/datasets/tpwcpex_1.json index dd249dfb96..25ee1e5b09 100644 --- a/datasets/tpwcpex_1.json +++ b/datasets/tpwcpex_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tpwcpex_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Total Precipitable Water (TPW) CPEX dataset contains products obtained from the MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected supporting the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign occurred in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 24, 2017, through September 20, 2017, and in netCDF-4 format.", "links": [ { diff --git a/datasets/trace_metals_1.json b/datasets/trace_metals_1.json index d0799bc9eb..9f88d62fd4 100644 --- a/datasets/trace_metals_1.json +++ b/datasets/trace_metals_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trace_metals_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Iron, manganese and aluminium concentrations have been determined in modern and ancient East Antarctic snow from sea-ice and continental sites. Modern snow samples were collected from sites in Prydz Bay, Dumont d'Urville sea, Ross Sea and Princess Elizabeth Land. Trace metal concentrations in ancient snow were determined form ice cores drilled from Law Dome, Wilkes Land. The ice cores analysed included DSS, DEO8-1 and BHC1.\n\nThis work was completed as part of ASAC project 827 (ASAC_827).", "links": [ { diff --git a/datasets/trajectory_images_792_1.json b/datasets/trajectory_images_792_1.json index 118aedacbf..d012a3e11d 100644 --- a/datasets/trajectory_images_792_1.json +++ b/datasets/trajectory_images_792_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trajectory_images_792_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa for the period August 14, 2000 to September 23, 2000. These sites are Durban, Middleburg, Pietersburg, and Springbok, South Africa; Maun, Botswana; Mongu, Zambia; and Windhoek, Namibia. The twice daily three-dimensional wind field (at 0000 and 1200 UTC) was used as input to the trajectory model. By integrating the vertical motion of the air parcels over a period of time, the trajectory model was able to forecast the net vertical displacement of air parcels during 12-hour periods. The resulting trajectory plots represent the three-dimensional transport of air in time and can be used to examine what is happening in the low-to-mid troposphere during flight and ground-based observations. These levels are most significant in terms of the thermodynamic structure of the troposphere, especially the stable layers and accumulation of material between and below them, as well containing the major levels of subsidence over the subcontinent. The trajectory model output and thermodynamic profiles of the troposphere were used to position aircraft for sampling trace gases, aerosols and other species during the SAFARI 2000 field campaign and to predict regions of high aerosol and trace gas concentrations downwind.The model output data are daily forward and backward trajectory plots at 850 hPa, 700 hPa, and 500 hPa pressure levels for each location. The plots are provided as JPEG images with coordinate, date, and time stamps.", "links": [ { diff --git a/datasets/tree-ring-data-earlybrowning-2018_1.0.json b/datasets/tree-ring-data-earlybrowning-2018_1.0.json index aa7d85f6c7..5e6f452d77 100644 --- a/datasets/tree-ring-data-earlybrowning-2018_1.0.json +++ b/datasets/tree-ring-data-earlybrowning-2018_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tree-ring-data-earlybrowning-2018_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Tree-ring data and tree location from 470 European beech trees (Fagus sylvatica L.) located in the northern part of Switzerland. 278 trees showed drought-induced premature leaf discoloration and shedding in summer 2018 and 192 showed normal leaf fall. The trees were selected from the \"1000-Beech-Project\" published by Frei et al. 2022 and the data was analyzed in Neycken et al 2023 (in preparation). The corresponding crown data are archived in the EnviDat data portal https://doi.org/10.16904/envidat.422 (Frei et al. 2023). All other data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Publications related to original data set and crown data: Wohlgemuth, T., Kistler, M., Aymon, C., Hagedorn, F., Gessler, A., Gossner, M.M., Queloz, V., V\u00f6gtli, I., Wasem, U., Vitasse, Y., Rigling, A., 2020. Fr\u00fcher Laubfall der Buche w\u00e4hrend der Sommertrockenheit 2018: Resistenz oder Schw\u00e4chesymptom? Schweizerische Zeitschrift fur Forstwesen 171, 257\u2013269. https://doi.org/10.3188/szf.2020.0257 Frei, E.R., Gossner, M.M., Vitasse, Y., Queloz, V., Dubach, V., Gessler, A., Ginzler, C., Hagedorn, F., Meusburger, K., Moor, M., Sambl\u00e1s Vives, E., Rigling, A., Uitentuis, I., von Arx, G., Wohlgemuth, T., 2022. European beech dieback after premature leaf senescence during the 2018 drought in northern Switzerland. Plant Biol J 24, 1132\u20131145. https://doi.org/10.1111/plb.13467 Publication related to tree-ring data and growth analysis: Neycken et al 2023 (in preparation)", "links": [ { diff --git a/datasets/tree-ring-laser-ablation-data_1.0.json b/datasets/tree-ring-laser-ablation-data_1.0.json index cde1f64d42..2d9ef2c20c 100644 --- a/datasets/tree-ring-laser-ablation-data_1.0.json +++ b/datasets/tree-ring-laser-ablation-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tree-ring-laser-ablation-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal. In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which derived from air pollution and may be used as indicators over the period 1965\u20132020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources. Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.", "links": [ { diff --git a/datasets/tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0.json b/datasets/tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0.json index 1f60cd3865..8a50d63798 100644 --- a/datasets/tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0.json +++ b/datasets/tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Raw tree ring data and climate used in the following paper: Vitasse Y, Bottero A, Cailleret M et al. (2019) Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob Chang Biol, 25, 3781-3792.", "links": [ { diff --git a/datasets/tree_cover-1km_641_1.json b/datasets/tree_cover-1km_641_1.json index be9e36d5ab..bbafed3066 100644 --- a/datasets/tree_cover-1km_641_1.json +++ b/datasets/tree_cover-1km_641_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tree_cover-1km_641_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set consists of a southern African subset of the 1-km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.", "links": [ { diff --git a/datasets/trichonesia_0.json b/datasets/trichonesia_0.json index f596c4ad50..7ae8f5b715 100644 --- a/datasets/trichonesia_0.json +++ b/datasets/trichonesia_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trichonesia_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from Indonesia made during 1998.", "links": [ { diff --git a/datasets/trichopria_drosophilae_nuclear_microsats_1.0.json b/datasets/trichopria_drosophilae_nuclear_microsats_1.0.json index ae5ced955e..d0012fbdc8 100644 --- a/datasets/trichopria_drosophilae_nuclear_microsats_1.0.json +++ b/datasets/trichopria_drosophilae_nuclear_microsats_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trichopria_drosophilae_nuclear_microsats_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_ This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population). Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.", "links": [ { diff --git a/datasets/trichototo_0.json b/datasets/trichototo_0.json index 283bfdbc87..e13255656e 100644 --- a/datasets/trichototo_0.json +++ b/datasets/trichototo_0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trichototo_0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Measurements from the North Australian Coast in the Timur and Arafura Seas in 1999.", "links": [ { diff --git a/datasets/trmlbalip_1.json b/datasets/trmlbalip_1.json index 2adaf4159d..006491b9da 100644 --- a/datasets/trmlbalip_1.json +++ b/datasets/trmlbalip_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trmlbalip_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM-LBA Lightning Instrument Package (LIP) dataset consists of electrical field measurements of lightning from eight field mills, conductivity probe temperatures from two probes, and navigation data, for the period of January 22 through February 24, 1999. These data were collected by the LIP instrument flown aboard the NASA ER-2 high-altitude aircraft over the Amazon River basin in Brazil during the Tropical Rainfall Measuring Mission-Large-Scale Biosphere-Atmosphere Experiment in Amazonia (TRMM-LBA) field campaign. The LIP instrument was used to validate measurements by the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS). These data are provided in HDF-4 format with browse imagery available in GIF format. ", "links": [ { diff --git a/datasets/trmmtcpfl1_1.json b/datasets/trmmtcpfl1_1.json index 5ec837ce97..463fc608d7 100644 --- a/datasets/trmmtcpfl1_1.json +++ b/datasets/trmmtcpfl1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "trmmtcpfl1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The TRMM Cyclone Precipitation Feature (TCPF) Database - Level 1 provides Tropical Rainfall Measuring Mission (TRMM)-based tropical cyclone data in a common framework for hurricane science research. This dataset aggregated observations from each of the TRMM instruments for each satellite orbit that was coincident with a tropical cyclone in any of the six TC-prone ocean basins. These swath data were co-located and subsetted to a 20-degree longitude by 20-degree latitude bounding box centered on the tropical storm, which is typically large enough to observe the various sizes of TCs and their immediate environments. The TCPF Level 1 dataset was created by researchers at Florida International University (FIU) and the University of Utah (UU) from the UU TRMM Precipitation Feature database. The TCPF database was built by extracting those precipitation features that are identified as tropical cyclones (TC) using the TC best-track data provided by National Hurricane Center or the US Navy's Joint Typhoon Warning Center.", "links": [ { diff --git a/datasets/tschamut2014_1.0.json b/datasets/tschamut2014_1.0.json index 14df4dcabc..a828cb3d98 100644 --- a/datasets/tschamut2014_1.0.json +++ b/datasets/tschamut2014_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "tschamut2014_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories. An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time. The dataset contains all data that were gathered through above field campaign.", "links": [ { diff --git a/datasets/turbulence-patchy-snow-cover_1.0.json b/datasets/turbulence-patchy-snow-cover_1.0.json index a8fb1ddc88..6d4361be13 100644 --- a/datasets/turbulence-patchy-snow-cover_1.0.json +++ b/datasets/turbulence-patchy-snow-cover_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "turbulence-patchy-snow-cover_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the raw data that is analyzed in the publication entitled \"Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow\". Please find information on the individual data files in the description of the files. The data was recorded during a comprehensive field campaign in May and June 2021 at D\u00fcrrboden at the end of Dischma valley close to Davos (Graub\u00fcnden, CH).", "links": [ { diff --git a/datasets/twig_mass_of_live_trees-48_1.0.json b/datasets/twig_mass_of_live_trees-48_1.0.json index 87f1cba095..86073627d8 100644 --- a/datasets/twig_mass_of_live_trees-48_1.0.json +++ b/datasets/twig_mass_of_live_trees-48_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "twig_mass_of_live_trees-48_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Dry weight (mass) of branches with a diameter under 7 cm from living trees and shrubs starting at 12cm dbh. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/twin_pups_1.json b/datasets/twin_pups_1.json index 0be221db61..544c0d5a69 100644 --- a/datasets/twin_pups_1.json +++ b/datasets/twin_pups_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "twin_pups_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phocid seals give birth annually, generally to a single pup. Twins have been reported occasionally, either from observations made in utero or from observations of live pups in the field. Examples of the former are reports of two embryos in a Weddell seal, Leptonychotes weddellii and of twin foetuses of a southern elephant seal, Mirounga leonina. Observations of two pups suckling one adult female have been reported for weddell seals. For southern elephant seals, an adult female that expelled two placentae and gave birth to a pup while another newborn pup was nuzzling the female, has also been reported. Here we use the expression 'apparent twins' to refer to reports of twin weddell seal pups that are based solely on field observations of two pups with the same adult female on several occasions.\n\nThe data arising from this study has been recorded in the form of 3 observations. These observations can be found in the referenced paper. A copy of this paper is available for download as a pdf document from the provided URL.", "links": [ { diff --git a/datasets/ualbmrr2impacts_1.json b/datasets/ualbmrr2impacts_1.json index 27c2f08dcb..801de80cfe 100644 --- a/datasets/ualbmrr2impacts_1.json +++ b/datasets/ualbmrr2impacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ualbmrr2impacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UAlbany Micro Rain Radar 2 (MRR-2) IMPACTS dataset consists of reflectivity, Doppler velocity, signal-to-noise ratio, spectral width, droplet size, Liquid Water Content, melting layer, drop size distribution, rain attenuation, rain rate, and radial velocity data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The MRR-2 instrument was used to collect data for this dataset. The dataset files are available from January 30, 2020, through February 28, 2023, in netCDF-3 and netCDF-4 format.", "links": [ { diff --git a/datasets/ualbparsimpacts_1.json b/datasets/ualbparsimpacts_1.json index 781b158be6..9ce600dbe5 100644 --- a/datasets/ualbparsimpacts_1.json +++ b/datasets/ualbparsimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ualbparsimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UAlbany Parsivel IMPACTS dataset consists of precipitation data collected by a Parsivel2 disdrometer in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Parsivel disdrometer data include particle size distribution, fall speed, radar reflectivity, and precipitation rate. The dataset files are available in netCDF-4 format from 30 January 2020 through 28 February 2023.", "links": [ { diff --git a/datasets/ualbsndimpacts_1.json b/datasets/ualbsndimpacts_1.json index d9fef37c12..e0f56e76f8 100644 --- a/datasets/ualbsndimpacts_1.json +++ b/datasets/ualbsndimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "ualbsndimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The UAlbany Soundings IMPACTS dataset consists of data measured with the iMet-3050A sounding system using 200-g meteorological balloons during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The UAlbany Soundings IMPACTS dataset consists of atmospheric pressure, relative humidity, mixing ratio, wind speed, and wind direction measurements. These data are available from January 5, 2023, through March 1, 2023, in ASCII format.", "links": [ { diff --git a/datasets/uas-based-snow-depth-maps-bramabuel-davos-ch_1.0.json b/datasets/uas-based-snow-depth-maps-bramabuel-davos-ch_1.0.json index d2d4c1e6ee..66806b0424 100644 --- a/datasets/uas-based-snow-depth-maps-bramabuel-davos-ch_1.0.json +++ b/datasets/uas-based-snow-depth-maps-bramabuel-davos-ch_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "uas-based-snow-depth-maps-bramabuel-davos-ch_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Br\u00e4mab\u00fcel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03. A detailed description is given here: B\u00fchler, Y., Adams, M. S., B\u00f6sch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016. Abstract: Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain. Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Fl\u00fcela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.", "links": [ { diff --git a/datasets/uav-datasets-for-three-alpine-glaciers_1.0.json b/datasets/uav-datasets-for-three-alpine-glaciers_1.0.json index 1da402646c..fab4c54dbe 100644 --- a/datasets/uav-datasets-for-three-alpine-glaciers_1.0.json +++ b/datasets/uav-datasets-for-three-alpine-glaciers_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "uav-datasets-for-three-alpine-glaciers_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "### UAV-derived DSMs and orthoimages Unmanned Aerial Vehicle (UAV) surveys were conducted between 2015 and 2016 on the __Sankt Annafirn__, __Findelen-__ and __Griesgletscher__, situated in the __Swiss Alps__. Three surveys at the Sankt Annafirn allowed for a full glacier coverage, four surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a __high resolution orthoimage__ and a __Digital Surface Model (DSM)__ was created. ### UAV surveys: Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 . The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area. A folder for each dataset is available (see folder name in table), which contains: - An orthoimage __*glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*__ - A Digital Surface Model __*glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*__ - The Agisoft Photoscan automatic processing report __*glacier_date_photoscan_report.pdf*__ where: - __*glacier*__ is the name of the surveyed glacier - __*date*__ is the date of the UAV image acquisition - __*photoscan*__ is the name of the photogrammetric software - __*oi*__ or __*dsm*__ the type of dataset - __*CH1903+_LV95*__ is the coordinate system and datum of the dataset - __*0.1m*__ is the resolution of the dataset in meter - __*.tif*__ is the extention of the dataset   Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below. __Paper Citation:__ > _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles\u2019Imagery on Glaciers, Remote Sensing, 9, 186, 1-15, [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._ The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.", "links": [ { diff --git a/datasets/uiucsndimpacts_1.json b/datasets/uiucsndimpacts_1.json index 058086c5a3..58db859067 100644 --- a/datasets/uiucsndimpacts_1.json +++ b/datasets/uiucsndimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "uiucsndimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Mobile UIUC Soundings IMPACTS dataset consists of atmospheric sounding data collected by rawinsondes launched during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. These data include vertical profiles of atmospheric temperature, relative humidity, pressure, wind speed, and wind direction. Specifically, these rawinsondes were provided by the University of Illinois at Urbana-Champaign (UIUC). IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The sounding data files are available in netCDF-4 format from January 18 through February 25, 2022, though it should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.", "links": [ { diff --git a/datasets/uk_met_c-130_720_1.json b/datasets/uk_met_c-130_720_1.json index e40e294204..062aeb8a42 100644 --- a/datasets/uk_met_c-130_720_1.json +++ b/datasets/uk_met_c-130_720_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "uk_met_c-130_720_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Met Office C-130 research aircraft was based at Windhoek, Namibia, between September 5-16, 2000, where it conducted a series of flights over Namibia as part of the SAFARI 2000 Dry Season Aircraft Campaign. The aims of the Met Office's research were as follows: (1) In-situ measurements of the physical, chemical and optical properties of the aerosol. The data set includes aerosol samples ranging from near source regions to aged plumes several hundreds of kilometres from source, some of which have been cloud processed. (2) Investigation of the direct radiative impact of aerosol over sea, land and low-level cloud. (3) In-situ measurements of aerosol properties in conjunction with ground-based sites to validate the ground-based retrievals of, for example, aerosol size distributions. (4) In-situ measurements of aerosol properties in conjunction with TERRA overpasses, in order to validate the satellite-based retrievals of aerosol properties. (5) In-situ measurements of stratus/stratocumulus cloud of Namibia/Angola in conjunction with TERRA overpasses, in order to validate satellite-based retrievals of cloud properties.", "links": [ { diff --git a/datasets/umd_landcover_xdeg_969_1.json b/datasets/umd_landcover_xdeg_969_1.json index 48c5298190..6131aa715b 100644 --- a/datasets/umd_landcover_xdeg_969_1.json +++ b/datasets/umd_landcover_xdeg_969_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "umd_landcover_xdeg_969_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of the International Satellite Land Surface Climatology Project (ISLSCP II) study that produced this data set, ISLSCP II University of Maryland Global Land Cover Classifications 1992-1993, was to create a land cover map derived from 1 kilometer Advanced Very High Resolution Radiometer (AVHRR) data using all available bands, derived Normalized Difference Vegetation Index (NDVI), and a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids. During this re-processing, the original University of Maryland (UMD) land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by modelers of global biogeochemical cycles and others in need of an internally consistent, global depiction of land cover. This 1km map was also one of the Moderate resolution Imaging Spectroradiometer (MODIS) at-launch land cover maps. This product describes the geographic distributions of 13 classes of vegetation cover (plus water and unclassified classes) based on a modified International Geosphere-Biosphere Programme (IGBP) legend. The data set also provides the fraction of each of the 15 classes within the coarser resolution cells, at three spatial resolutions of 0.25, 0.5 and 1.0 degrees in latitude and longitude. ", "links": [ { diff --git a/datasets/und_refl_304_1.json b/datasets/und_refl_304_1.json index e60189d59b..9d7a2f210e 100644 --- a/datasets/und_refl_304_1.json +++ b/datasets/und_refl_304_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "und_refl_304_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.", "links": [ { diff --git a/datasets/unep_marineturtle.json b/datasets/unep_marineturtle.json index 7f392a98a6..f8bc1d2d84 100644 --- a/datasets/unep_marineturtle.json +++ b/datasets/unep_marineturtle.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "unep_marineturtle", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Distribution of marine turtles in the Indian Ocean. Information was\nobtained from published and unpublished literature, and through\nliaison with turtle fieldworkers. It was intended that the database\nwould be of use to a wide audience, including biologists, coastal\nplanners and those concerned with emergency response to oil spills.\n\nAssessing the level of demand for these data, and the feasibility of\nmaintaining data to reflect best available information.", "links": [ { diff --git a/datasets/urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0.json b/datasets/urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0.json index 964c0ac0d5..3c9b9fdc36 100644 --- a/datasets/urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0.json +++ b/datasets/urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "1. Stand characteristics of treeline ecotone along 18 elevational gradients of the Ural mountains. 2. Extrapolated climate data at treeline using nearby meteo station (1976-2006). 3. Air and soil temperatures measured in situ at treeline in the South and Polar Urals. Soil temperature sensors were placed at 10 cm depth in open areas in between tree clusters but not under tree canopy. 4. Further plot specific information is available upon request.", "links": [ { diff --git a/datasets/urn:eop:VITO:CGS_S1_GRD_L1_V001.json b/datasets/urn:eop:VITO:CGS_S1_GRD_L1_V001.json index 0696ec6a89..0332e5ec9a 100644 --- a/datasets/urn:eop:VITO:CGS_S1_GRD_L1_V001.json +++ b/datasets/urn:eop:VITO:CGS_S1_GRD_L1_V001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:CGS_S1_GRD_L1_V001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model such as WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution. The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. For the IW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarisation.", "links": [ { diff --git a/datasets/urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001.json b/datasets/urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001.json index 92db37ab6e..49d6dfee56 100644 --- a/datasets/urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001.json +++ b/datasets/urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sigma0 product describes how much of the radar signal that was sent out by Sentinel-1 is reflected back to the sensor, and depends on the characteristics of the surface. This product is derived from the L1-GRD product. Typical SAR data processing, which produces level 1 images such as L1-GRD product, does not include radiometric corrections and significant radiometric bias remains. Therefore, it is necessary to apply the radiometric correction to SAR images so that the pixel values of the SAR images truly represent the radar backscatter of the reflecting surface. The radiometric correction is also necessary for the comparison of SAR images acquired with different sensors, or acquired from the same sensor but at different times, in different modes, or processed by different processors. For this Sigma0 product, radiometric calibration was performed using a specific Look Up Table (LUT) that is provided with each original GRD product. This LUT applies a range-dependent gain including the absolute calibration constant, in addition to a constant offset. Next to calibration, also orbit correction, border noise removal, thermal noise removal, and range doppler terrain correction steps were applied during production of Sigma0. The terrain correction step is intended to compensate for distortions due to topographical variations of the scene and the tilt of the satellite sensor, so that the geometric representation of the image will be as close as possible to the real world.", "links": [ { diff --git a/datasets/urn:eop:VITO:CGS_S1_SLC_L1_V001.json b/datasets/urn:eop:VITO:CGS_S1_SLC_L1_V001.json index ca728a143b..51fd4dc9a2 100644 --- a/datasets/urn:eop:VITO:CGS_S1_SLC_L1_V001.json +++ b/datasets/urn:eop:VITO:CGS_S1_SLC_L1_V001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:CGS_S1_SLC_L1_V001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track. The products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution (single look). IW mode captures three sub-swaths using Terrain Observation with Progressive Scans SAR (TOPSAR). IW SLC products contain one image per sub-swath and one per polarisation channel, for a total of three (single polarisation) or six (dual polarisation) images in an IW product. Each sub-swath image consists of a series of bursts, where each burst has been processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image with black-fill demarcation in between. There is sufficient overlap between adjacent bursts and between sub-swaths to ensure continuous coverage of the ground as provided in GRD products. The images for all bursts in all sub-swaths are resampled to a common pixel spacing grid in range and azimuth while preserving the phase information.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_ACTIVECROPLAND_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_ACTIVECROPLAND_V1_V1.json index 5176a4eda1..0a86fe7a59 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_ACTIVECROPLAND_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_ACTIVECROPLAND_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_ACTIVECROPLAND_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal active cropland products provide binary maps for all growing seasons as defined by the WorldCereal global crop calendars, showing where active cropland has been detected. Seasonal active cropland is defined by the WorldCereal system as actively cultivated cropland during a specific growing season. In order for a pixel to be labeled as active during a particular growing season, a full crop growth cycle (sowing, growing, senescence and harvesting) needs to take place within the designated time period. Note that this active marker is not crop-type specific and only consider specific seasonality. This also means in practice that any crop grown (slightly) outside the predefined growing seasons will not be flagged as active cropland in any of the seasons covered by these products. The WorldCereal active cropland products were generated within the respective annual temporary crops mask.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_IRRIGATION_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_IRRIGATION_V1_V1.json index 8ed856ab63..dbe04a0c05 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_IRRIGATION_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_IRRIGATION_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_IRRIGATION_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal active irrigation products provide binary maps for all growing seasons as defined by the WorldCereal global crop calendars, showing where active irrigation has been detected. Seasonally actively irrigated cropland is defined by the WorldCereal system as a piece of land that is extensively irrigated during a specific growing season where, without irrigation applied at regular intervals, crop growth would be significantly reduced or impossible. Incidental irrigation, such as irrigation that has been applied only during the sowing period of a crop, is not translated to actively irrigated cropland. The WorldCereal active irrigation products were generated within the respective annual temporary crops mask.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_MAIZE_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_MAIZE_V1_V1.json index 7aec4fff66..1e8dc8cbb7 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_MAIZE_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_MAIZE_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_MAIZE_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal maize products provide binary maps for the maize growing seasons as defined by the WorldCereal global crop calendars, showing where maize is grown. The WorldCereal maize products were generated within the respective annual temporary crops mask.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_SPRINGCEREALS_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_SPRINGCEREALS_V1_V1.json index cb0b27e67d..f38d6d4318 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_SPRINGCEREALS_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_SPRINGCEREALS_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_SPRINGCEREALS_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal spring cereals products provide binary maps for the spring cereals growing season as defined by the WorldCereal global crop calendars, showing where spring cereals are grown. This season describes the spring cereals season in northern latitudes. Spring cereals include wheat, barley and rye, which belong to the Triticeae tribe. These crops were grouped together because their spectral signatures and growing seasons were too similar to reliably distinguish them at a global scale. The WorldCereal spring cereals products were generated within the respective annual temporary crops mask.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_TEMPORARYCROPS_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_TEMPORARYCROPS_V1_V1.json index b08e35cdb7..8a99cafcbd 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_TEMPORARYCROPS_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_TEMPORARYCROPS_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_TEMPORARYCROPS_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal annual temporary crops products identify land used for crops with a less-than-one-year growing cycle which must be newly sown or planted for further production after the harvest. Sugar cane, asparagus and cassava are also considered as temporary crops, despite the fact that they remain in the field for more than one year. The WorldCereal temporary crops products exclude perennial crops as well as (temporary) pastures. These products are generated once a year, the period being defined in a region by the end of the last growing season that is considered by the WorldCereal system.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_WINTERCEREALS_V1_V1.json b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_WINTERCEREALS_V1_V1.json index 48d607ef5f..d648bb7af5 100644 --- a/datasets/urn:eop:VITO:ESA_WORLDCEREAL_WINTERCEREALS_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WORLDCEREAL_WINTERCEREALS_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WORLDCEREAL_WINTERCEREALS_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WorldCereal winter cereals products provide binary maps for the winter cereals growing season as defined by the WorldCereal global crop calendars, showing where cereals are grown. This season describes the main cereals season in a region. Cereals include wheat, barley and rye, which belong to the Triticeae tribe. These crops were grouped together because their spectral signatures and growing seasons were too similar to reliably distinguish them at a global scale. The WorldCereal winter cereals products were generated within the respective annual temporary crops mask.", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1_V1.json index ab6c6e1664..de3666e855 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover product is a global land cover map with 11 land cover classes. WorldCover provides worldwide coverage at 10 meters and is provided per 3 x 3 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover product has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 3 x 3 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_10m_2020_v100_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_V1_V1.json index 5e9537ac00..191ac4f04a 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2020_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_10m_2020_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover product is a global land cover map with 11 land cover classes. WorldCover provides worldwide coverage at 10 meters and is provided per 3 x 3 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover product has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 3 x 3 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_10m_2020_v100_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2_V2.json b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2_V2.json index 59ccec3147..8b1f53bd07 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2_V2.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover product is a global land cover map with 11 land cover classes. WorldCover provides worldwide coverage at 10 meters and is provided per 3 x 3 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover product has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 3 x 3 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_10m_2021_v200_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_V2_V2.json b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_V2_V2.json index 9848f00b06..49b9e4803a 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_V2_V2.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_10m_2021_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_10m_2021_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover product is a global land cover map with 11 land cover classes. WorldCover provides worldwide coverage at 10 meters and is provided per 3 x 3 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover product has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 3 x 3 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_10m_2021_v200_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2020_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2020_V1_V1.json index edae2bd25a..c9be6d4924 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2020_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2020_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_NDVI_10m_2020_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 yearly NDVI percentiles color composite is a color image made from images in blue (NDVI p10), green (NDVI p50), red (NDVI p90). It is generated by taking the NDVI timeseries for 2020, after removing clouds and cloud shadows. From the time series the 10th, 50th and 90th percentiles are computed. Evergreen vegetation will appear bright white, unvegetated surfaces will be dark. Surface that shows partial vegetation during the year will appear in color from yellow to red. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-2 yearly NDVI percentiles has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_NDVI_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 1 x 1 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_NDVI_10m_2020_v100_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2021_V2_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2021_V2_V1.json index 8bd35970d8..753c029f01 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2021_V2_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_NDVI_10m_2021_V2_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_NDVI_10m_2021_V2_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 yearly NDVI percentiles color composite is a color image made from images in blue (NDVI p10), green (NDVI p50), red (NDVI p90). It is generated by taking the NDVI timeseries for 2021, after removing clouds and cloud shadows. From the time series the 10th, 50th and 90th percentiles are computed. Evergreen vegetation will appear bright white, unvegetated surfaces will be dark. Surface that shows partial vegetation during the year will appear in color from yellow to red. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-2 yearly NDVI percentiles has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_NDVI_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 1 x 1 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_NDVI_10m_2021_v200_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2020_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2020_V1_V1.json index 9ab4f4d6ac..fa9cadf87e 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2020_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2020_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2020_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-1 color image is a color image made from images in VV, VH and VV/VH. It is generated by taking the median composite for 2020. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-1 color image has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2021_V2_V2.json b/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2021_V2_V2.json index 25f0dc72ac..6352b18b92 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2021_V2_V2.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2021_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_S1VVVHratio_10m_2021_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-1 color image is a color image made from images in VV, VH and VV/VH. It is generated by taking the median composite for 2021. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-1 color image has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2020_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2020_V1_V1.json index b4ae719b64..610cb1fc50 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2020_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2020_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2020_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 color image is a color image made from images in Blue (B3), Green (B4), Red (B5) and Infrared (B8). It is generated by taking the median composite for 2020. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-1 color image has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2021_V2_V2.json b/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2021_V2_V2.json index 42cedec821..b52bd6f8a7 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2021_V2_V2.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2021_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_S2RGBNIR_10m_2021_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 color image is a color image made from images in Blue (B3), Green (B4), Red (B5) and Infrared (B8). It is generated by taking the median composite for 2021. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-2 color image has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2020_V1_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2020_V1_V1.json index 751e6df2f3..066049cfd7 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2020_V1_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2020_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_SWIR_10m_2020_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 yearly SWIR median composite is a color image made from images in blue (SWIR B11), green (SWIR B12), red (SWIR B11). It is generated by taking the SWIR timeseries median for 2020, after removing clouds and cloud shadows. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-2 yearly SWIR percentiles has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands).\t\t\t\tNaming convention: ESA_WorldCover_SWIR_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 1 x 1 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_SWIR_10m_2020_v100_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2021_V2_V1.json b/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2021_V2_V1.json index e243361cac..b6b0ded7bc 100644 --- a/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2021_V2_V1.json +++ b/datasets/urn:eop:VITO:ESA_WorldCover_SWIR_10m_2021_V2_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:ESA_WorldCover_SWIR_10m_2021_V2_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ESA WorldCover Sentinel-2 yearly SWIR median composite is a color image made from images in blue (SWIR B11), green (SWIR B12), red (SWIR B11). It is generated by taking the SWIR timeseries median for 2021, after removing clouds and cloud shadows. Worldwide coverage at 10 meters is provided per 1 x 1 degree tile. Note that ocean areas and inner ice sheets do not have tiles. Data is provided as Cloud Optimized GeoTIFFs. The ESA WorldCover Sentinel-2 yearly SWIR percentiles has been produced by a consortium lead by VITO (Belgium) together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria) and Wageningen University (The Netherlands). Naming convention: ESA_WorldCover_SWIR_10m_[YEAR]_[VERSION]_[TILE]_[LAYER].tif/ \t\t\t\t* [YEAR] indicates the reference year (observation period) in four digits.\t\t\t\t* [VERSION] shows the product version. The version denoted as vMmr (e.g. v201), with \u00e2\u0080\u0098M\u00e2\u0080\u0099 representing the major version (e.g. v2), \u00e2\u0080\u0098m\u00e2\u0080\u0099 the minor version (starting from 0) and \u00e2\u0080\u0098r\u00e2\u0080\u0099 the production run number (starting from 1) \t\t\t\t* [TILE] the designation of the 1 x 1 degree tile, composed of the 2-digit latitude and 3-digit longitude of the lower-left corner. Example: N19W100 for the tile covering the area from 100W to 99W and 19N to 20N.\t\t\t\tFor example: ESA_WorldCover_SWIR_10m_2021_v200_N19W100_Map.tif/", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L1C_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_L1C_HDF_V2_1.json index ca4125c389..2479df19e5 100644 --- a/datasets/urn:eop:VITO:PROBAV_L1C_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_L1C_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L1C_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "PROBA-V Level1C Collection 2 data. Radiometrically corrected Level 1B data (i.e. unprojected TOA reflectance), given per strip/camera. Pixel digital numbers are converted to radiance values. Image remains in raw sensor geometry (unprojected). The spatial resolution varies between 100 and 300 m for VNIR and between 200 and 600 m for SWIR.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_COG_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_COG_V1_NA.json index 165415af1e..cdb8d49a35 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_COG_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_COG_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_100M_ANTAR_COG_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 100 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_HDF_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_HDF_V1_NA.json index 30b82e10d2..702316afb8 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_HDF_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_100M_ANTAR_HDF_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_100M_ANTAR_HDF_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 100 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_100M_COG_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_100M_COG_V2_NA.json index e5e051c58e..38b439ee1a 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_100M_COG_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_100M_COG_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_100M_COG_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 100 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_100M_HDF_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_100M_HDF_V2_NA.json index 2bc2c49ea7..78194df750 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_100M_HDF_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_100M_HDF_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_100M_HDF_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 100 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_COG_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_COG_V1_NA.json index 2c24ceb1f4..7c6b660c1b 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_COG_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_COG_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_COG_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see also https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C (P\u00e2\u0080\u0093product) data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 1 km.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_HDF_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_HDF_V1_NA.json index 7664b27a5b..1f141d5363 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_HDF_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_HDF_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_1KM_ANTAR_HDF_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level2A product contains the projected Level1C (P\u00e2\u0080\u0093product) data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 1 km.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_COG_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_COG_V2_NA.json index d9189de925..68eec7f2f3 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_COG_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_COG_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_1KM_COG_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO, see also https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C (P\u00e2\u0080\u0093product) data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 1 km.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_HDF_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_HDF_V2_NA.json index 0cd8745390..37b5b0c338 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_1KM_HDF_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_1KM_HDF_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_1KM_HDF_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Level2A product contains the projected Level1C (P\u00e2\u0080\u0093product) data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 1 km.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_COG_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_COG_V1_NA.json index 0b1ae1cc4f..7ee813539e 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_COG_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_COG_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_333M_ANTAR_COG_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 333 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_HDF_V1_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_HDF_V1_NA.json index 45725a5d58..cc3de9c706 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_HDF_V1_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_333M_ANTAR_HDF_V1_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_333M_ANTAR_HDF_V1_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 333 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_333M_COG_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_333M_COG_V2_NA.json index f776d3285c..231741f366 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_333M_COG_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_333M_COG_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_333M_COG_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 333 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_L2A_333M_HDF_V2_NA.json b/datasets/urn:eop:VITO:PROBAV_L2A_333M_HDF_V2_NA.json index ffc7c01b5a..b321758836 100644 --- a/datasets/urn:eop:VITO:PROBAV_L2A_333M_HDF_V2_NA.json +++ b/datasets/urn:eop:VITO:PROBAV_L2A_333M_HDF_V2_NA.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_L2A_333M_HDF_V2_NA", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The product original source is the PROBA-V mission and has been produced by VITO see https://doi.org/10.5270/PRV-2vvxhtt.A Level2A product contains the projected Level1C data, i.e. radiometrically and geometrically corrected TOA reflectance values projected on a uniform grid, given per band/camera. The applied coordinate reference system is the \u00e2\u0080\u009cGeographic Lat/Lon\u00e2\u0080\u009d (EPSG:4326) projection with a spatial resolution of 333 m.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_COG_V2_1.json index f71bc021e3..ccf73d5fa5 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_1KM_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_HDF_V2_1.json index 97eb4c92b2..b5013c6ec2 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_1KM_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_1KM_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_COG_V2_1.json index aeac296f9f..619dacee0d 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_333M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_HDF_V2_1.json index a7e40f20ad..72f70eb399 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_333M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_333M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_COG_V2_1.json index 99f8625104..70a425dcbf 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 NDVI data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_HDF_V2_1.json index 426740477d..b7c4a7bbe3 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_NDVI_1KM_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 NDVI data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_COG_V2_1.json index ee3a14a521..59852f465a 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 NDVI data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_HDF_V2_1.json index 4fa73a5511..2244132e57 100644 --- a/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S10_TOC_NDVI_333M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 NDVI data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_COG_V2_1.json index 76b95d58b2..3ab6c3cd42 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_HDF_V2_1.json index 535435cb67..efde046daa 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_COG_V2_1.json index 8621f8433a..9d6d860174 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_1KM_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_HDF_V2_1.json index f8feb4d648..977f101921 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_1KM_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_1KM_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_COG_V2_1.json index 51b55799d8..45451a949d 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_333M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_HDF_V2_1.json index 50d3990d05..dc926a72c0 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOA_333M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOA_333M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_COG_V2_1.json index 5583d8ea32..89e4f82e35 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_HDF_V2_1.json index 5ce7397d26..0540f5a16c 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_COG_V2_1.json index 160af5174b..8a8dbc8edb 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_1KM_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_HDF_V2_1.json index 5e70787721..c60c6b1cf0 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_1KM_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_1KM_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_COG_V2_1.json index 0fe7bc6ee6..4fe52661ce 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_333M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_HDF_V2_1.json index 7278239069..422c151596 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_333M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_333M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_COG_V2_1.json index 49390e488c..40aa52e416 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_HDF_V2_1.json index 7318162f19..675fd65d7f 100644 --- a/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S1_TOC_NDVI_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_COG_V2_1.json index e45e8cd3ff..0fc22d0e67 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOA_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_HDF_V2_1.json index 4d6ea62cce..748ff96d8b 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOA_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOA_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_COG_V2_1.json index 1a97120d5f..16a266f05a 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOC_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_HDF_V2_1.json index 87d67ea60e..db3f4f1c37 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOC_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOC_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_COG_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_COG_V2_1.json index 5456043959..5da85cad80 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_COG_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_COG_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_COG_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_HDF_V2_1.json b/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_HDF_V2_1.json index 2e9bb20b11..9cd9195043 100644 --- a/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_HDF_V2_1.json +++ b/datasets/urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_HDF_V2_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:PROBAV_S5_TOC_NDVI_100M_HDF_V2_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Level 3 data products are variables mapped on uniform space-time grid scales and are the result of combining multiple scenes (e.g. S1/S5/S10) to cover the user\u00e2\u0080\u0099s region of interest.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S1_SLC_COHERENCE_V1_V001.json b/datasets/urn:eop:VITO:TERRASCOPE_S1_SLC_COHERENCE_V1_V001.json index 8d39f44160..bf4e898800 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S1_SLC_COHERENCE_V1_V001.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S1_SLC_COHERENCE_V1_V001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S1_SLC_COHERENCE_V1_V001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Interferometric Coherence product is the amplitude of the complex correlation coefficient between two images. In simple way, coherence describes similarity between two images in a range between zero to one. Zero means pixels were totally different where one means pixels were same exactly.The product algorithm starts from two ESA Level-1 SLC products which are from the same area, the same relative orbit number and preferably from within a short time interval (6 days is the shortest possible with S1A and S1B pairs). The original paired images go through a workflow of orbit correction, co-registration through back-geocoding, coherence calculation and terrain correction by making use of standard SNAP tools as part of the Sentinel-1 toolbox (S1TBX).The terrain correction step is intended to compensate for distortions due to topographical variations of the scene and the tilt of the satellite sensor, so that the geometric representation of the image will be as close as possible to the real world.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_CCC_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_CCC_V2_V2.json index 09c692846d..59ac55b06a 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_CCC_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_CCC_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_CCC_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CCC corresponds to the Chlorophyll Canopy Content Index.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_CHL_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_CHL_V1_V1.json index 9a2f109166..151a4a1ada 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_CHL_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_CHL_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_CHL_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CHL corresponds to the Chlorophyll-a water quality products, units are expressed in (mg m-3).", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_CWC_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_CWC_V2_V2.json index 4949df3cb3..a0c81bc145 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_CWC_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_CWC_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_CWC_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "CWC corresponds to the Chlorophyll Water Content.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_FAPAR_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_FAPAR_V2_V2.json index 4c113f9e62..6b9d84a756 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_FAPAR_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_FAPAR_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_FAPAR_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "FAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy.The FAPAR value results directly from the radiative transfer model in the canopy which is computed instantaneously. It depends on canopy structure, vegetation element optical properties and illumination conditions. FAPAR is very useful as input to a number of primary productivity models which run at the daily time step. Consequently, the product definition should correspond to the daily integrated FAPAR value that can be approached by computation of the clear sky daily integrated FAPAR values as well as the FAPAR value computed for diffuse conditions. The SENTINEL 2 FAPAR product corresponds to the instantaneous black-sky around 10:15 which is a close approximation of the daily integrated black-sky FAPAR value. The FAPAR refers only to the green parts of the canopy.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_FCOVER_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_FCOVER_V2_V2.json index b6229fd69d..d1b3dfc7f7 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_FCOVER_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_FCOVER_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_FCOVER_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Fraction of vegetation Cover (FCOVER) corresponds to the gap fraction for nadir direction. It is used to separate vegetation and soil in energy balance processes, including temperature and evapotranspiration. It is computed from the leaf area index and other canopy structural variables and does not depend on variables such as the geometry of illumination as compared to FAPAR. For this reason, it is a very good candidate for the replacement of classical vegetation indices for the monitoring of green vegetation. Because of the linear relationship with radiometric signal, FCOVER will be only marginally scale dependent. Note that similarly to LAI and FAPAR, only the green elements will be considered, either belonging both to the overstorey and understorey.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_L1C_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_L1C_V2.json index 91a10a2fb1..4e3fbaf5ad 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_L1C_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_L1C_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_L1C_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Level-1C product is composed of 100x100 km2 tiles (ortho-images in UTM/WGS84 projection). The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. In Level-1C products, pixel coordinates refer to the upper left corner of the pixel.Level-1C products will additionally include Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure).", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_LAI_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_LAI_V2_V2.json index e6f3fe6cb6..3ca0285fc6 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_LAI_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_LAI_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_LAI_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "LAI was defined by Committee of the Earth Observation System (CEOS) as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The resulting SENTNEL LAI products are relatively consistent with the actual LAI for low LAI values and \u00e2\u0080\u0098non-forest\u00e2\u0080\u0099 surfaces; while for forests, particularly for needle leaf types, significant departures with the true LAI are expected.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_NDVI_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_NDVI_V2_V2.json index 39b45a2e36..64b0280e62 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_NDVI_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_NDVI_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_NDVI_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SENTINEL-2 Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band , and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependant.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_RHOW_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_RHOW_V1_V1.json index fb09bbe2cc..5db3c7f875 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_RHOW_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_RHOW_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_RHOW_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "L2A atmospheric corrected Water Leaving Reflectance (RHOW) products, generated using the iCOR processing tool.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_SPM_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_SPM_V1_V1.json index ba3d3fecce..22d3ee1067 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_SPM_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_SPM_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_SPM_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The SENTINEL-2 SPM corresponds to the Suspended Particulate Matter water quality products, units are expressed in (mg L-1).", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_TOC_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_TOC_V2_V2.json index 4a15ff0de8..8d192680d5 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_TOC_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_TOC_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_TOC_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "L2A atmospheric corrected Top-Of-Canopy (TOC) products, generated using the Sen2COR processing tool.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S2_TUR_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S2_TUR_V1_V1.json index 0eb92d5ce6..a92f12815e 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S2_TUR_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S2_TUR_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S2_TUR_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "TUR corresponds to the Turbidity water quality products, units are expressed in Formazin Nephelometric Unit (FNU).", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S10_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S10_V1_V1.json index 1c7c12579f..604bb8315a 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S10_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S10_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S3_LST_3_S10_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sentinel-3, Level 3 Land Surface Temperature S10 products (S3_LST_3_S10) contain 1 km 10-daily LST composites. TMeasurements are provided on a regular latitude-longitude grid, with an equatorial sampling distance of approximately 1 km (1\u00c2\u00b0 / 112). The LST and LST_unc are provided. LST is derived as the mean value of unflagged S3_LST_3_S1_V1 inputs. LST_unc is propagated from S3_LST_3_S1_V1 inputs\", \"title\": \"Sentinel-3 Level 2 Synergy 1 km VEGETATION-Like 10-daily synthesis TOC reflectance and NDVI - V1.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S1_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S1_V1_V1.json index 757c743fb0..efe7e28d6d 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S1_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S3_LST_3_S1_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S3_LST_3_S1_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sentinel-3, Level 3 Land Surface Temperature S1 products (S3_LST_3_S1) contain 1 km daily LST composites. TMeasurements are provided on a regular latitude-longitude grid, with an equatorial sampling distance of approximately 1 km (1\u00c2\u00b0 / 112). The LST and LST_unc are provided, derived from unflagged S3_SL_2_LST inputs at minimum viewing zenith angle.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_V10_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_V10_V1_V1.json index 0b817675bb..97b11a3ba2 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_V10_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_V10_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S3_SY_2_V10_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sentinel-3, Level 2 Synergy V10 products (SY_2_V10) contain 1 km VEGETATION-like TOC reflectances and NDVI. The '1 km VEGETATION-like product' label means that measurements are provided on a regular latitude-longitude grid, with an equatorial sampling distance of approximately 1 km (1\u00c2\u00b0 / 112). The NDVI is provided, derived from surface reflectances in B2 and B3.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_VG1_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_VG1_V1_V1.json index fef9212b7d..6674192c8b 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_VG1_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S3_SY_2_VG1_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S3_SY_2_VG1_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Sentinel-3, Level 2 Synergy VG1 products (SY_2_VG1) contain 1 km VEGETATION-like TOC reflectances and NDVI. The '1 km VEGETATION-like product' label means that measurements are provided on a regular latitude-longitude grid, with an equatorial sampling distance of approximately 1 km (1\u00c2\u00b0 / 112). The NDVI is provided, derived from surface reflectances in B2 and B3.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V1_V1.json index 4ad62b3f50..d691b8bd76 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CH4 retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V2_V2.json index 1e900f7256..ab3c368f17 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CH4 retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V1_V1.json index d669a7e150..14dfae5946 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 CH4 values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V2_V2.json index 247e48b1cc..55dc56a0ac 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 CH4 values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V1_V1.json index ae1b11392e..3b53146e95 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CH4 retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 CH4 data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V2_V2.json index c6e3602bdc..c4cccbee0f 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CH4_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CH4 retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 CH4 data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V1_V1.json index bb9a9e1cc7..ad6e61e6d5 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CO retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V2_V2.json index fd2c2b50a7..a494e23655 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CO retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V1_V1.json index ceb8a0c0cc..dbf23dc71c 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 CO values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V2_V2.json index 1cabf89d7a..4a5e08741d 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 CO values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V1_V1.json index 979081898d..b5574750f8 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CO retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 CO data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V2_V2.json index e07984f908..3d84785746 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_CO_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI CO retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 CO data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V1_V1.json index f2230a931a..fd0e67dc3b 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI HCHO retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V2_V2.json index a4ab4d323b..f3d362c1b0 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI HCHO retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V1_V1.json index cf4be5bfeb..b662a9355a 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 HCHO values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V2_V2.json index 9b1d2b5d49..b7900f0744 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 HCHO values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V1_V1.json index facb665f50..5f2a7c3fb1 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI HCHO retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 HCHO data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V2_V2.json index 8530ff8355..28d5d4f505 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_HCHO_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI HCHO retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 HCHO data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TD_V2_V2.json index 82989bfad7..0dbe174c67 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals using CAMS. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TM_V2_V2.json index ae3743cd41..8cec4fe04c 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals using CAMS. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TY_V2_V2.json index ad8e4dd908..5e69f37f46 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_CAMS_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals using CAMS. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TD_V2_V2.json index 442f203d59..03f438bec2 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 Surface retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TM_V2_V2.json index 1e390842ed..cd9b673c7f 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 Surface retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TY_V2_V2.json index 5bce0b9248..50ceb0efef 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_SURFACE_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 Surface retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1_V1.json index effad48376..5e0a476b2b 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V2_V2.json index 1b4add02b0..a4eae48dc5 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V1_V1.json index 36869eb66d..514f4d0870 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 NO2 values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V2_V2.json index 89650b412f..a8c488f61b 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The L3 binning algorithm calcualtes a weighted monthly average density based on the daily Level-3 NO2 values.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V1_V1.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V1_V1.json index e8f84a9e75..a08b3b6170 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V1_V1.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V1_V1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V1_V1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 NO2 data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V2_V2.json index f1d05d6e79..a4d6c87f92 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 TROPOMI NO2 retrievals. The L3 binning algorithm calculates a weighted yearly average density based on daily Level-3 NO2 data.", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2.json index c8714ae522..3bb2edfc03 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 Sulfur Dioxide (SO2) vertical column products using COvariance-Based Retrieval Algorithm (COBRA) retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2.json index 8b65531731..83570ce554 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 Sulfur Dioxide (SO2) vertical column products using COvariance-Based Retrieval Algorithm (COBRA) retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2.json b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2.json index cb0fca4bf8..6a1153be9b 100644 --- a/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2.json +++ b/datasets/urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Contains binned Level-2 Sulfur Dioxide (SO2) vertical column products using COvariance-Based Retrieval Algorithm (COBRA) retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html?", "links": [ { diff --git a/datasets/urn:ogc:def:EOP:VITO:VGT_P_1.json b/datasets/urn:ogc:def:EOP:VITO:VGT_P_1.json index 1606cd333f..17ca6544bf 100644 --- a/datasets/urn:ogc:def:EOP:VITO:VGT_P_1.json +++ b/datasets/urn:ogc:def:EOP:VITO:VGT_P_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:ogc:def:EOP:VITO:VGT_P_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VGT-P (P= physical) products are adapted for scientific applications requiring highly accurate physical measurements. The data is corrected for system errors (error registration of the different channels, calibration of all the detectors along the line-array detectors for each spectral band) and resampled to predefined geographic projections chosen by the user. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere (TOA). Auxiliary data supplied with the products allow users to process the original reflectance values using their own algorithms. The image products cover all or a part of a VEGETATION segment (data strip over land). The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level2A/Level2A", "links": [ { diff --git a/datasets/urn:ogc:def:EOP:VITO:VGT_S10_1.json b/datasets/urn:ogc:def:EOP:VITO:VGT_S10_1.json index dfefc63015..b6f90874ff 100644 --- a/datasets/urn:ogc:def:EOP:VITO:VGT_S10_1.json +++ b/datasets/urn:ogc:def:EOP:VITO:VGT_S10_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:ogc:def:EOP:VITO:VGT_S10_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3", "links": [ { diff --git a/datasets/urn:ogc:def:EOP:VITO:VGT_S1_1.json b/datasets/urn:ogc:def:EOP:VITO:VGT_S1_1.json index 85bdd0aaaa..c572a04845 100644 --- a/datasets/urn:ogc:def:EOP:VITO:VGT_S1_1.json +++ b/datasets/urn:ogc:def:EOP:VITO:VGT_S1_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "urn:ogc:def:EOP:VITO:VGT_S1_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VGT-S1 products (daily synthesis) are composed of the 'Best available' ground reflectance measurements of all segments received during one day for the entire surface of the Earth. This is done for each of the images covering the same geographical area. The areas distant from the equator have more overlapping parts so the choice for the best pixel will be out of more data. These products provide data from all spectral bands, the NDVI and auxiliary data on image acquisition parameters. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3", "links": [ { diff --git a/datasets/usgs_brd_pwrc_bioeco.json b/datasets/usgs_brd_pwrc_bioeco.json index 503dfcc72c..4e3ffda0d2 100644 --- a/datasets/usgs_brd_pwrc_bioeco.json +++ b/datasets/usgs_brd_pwrc_bioeco.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_brd_pwrc_bioeco", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Biomonitoring of Environmental Status and Trends (BEST) program is\ndesigned to assess and monitor the effects of environmental contaminants on\nbiological resources, particularly those under the stewardship of the\nDepartment of the Interior. BEST examines contaminant issues at national,\nregional, and local scales, and uses field monitoring techniques and\ninformation assessment tools tailored to each scale. As part of this program,\nthe threat of contaminants and other anthropogenic activities to terrestrial\nvertebrates residing in or near to Atlantic coast estuarine ecosystems is being\nevaluated by data synthesis and field activities. One of the objectives of this\nproject is to evaluate the relative sensitivity and suitability of various\nwildlife species for regional contaminant monitoring of estuaries and\necological risk assessment.\n\nThe purpose of the data is to assess and monitor the effects of environmental\ncontaminants on biological resources, particularly those under the stewardship\nof the Department of the Interior. BEST examines contaminant issues at\nnational, regional, and local scales, and uses field monitoring techniques and\ninformation assessment tools tailored to each scale. As part of this program,\nthe threat of contaminants and other anthropogenic activities to terrestrial\nvertebrates residing in or near to Atlantic coast estuarine ecosystems is being\nevaluated by data synthesis and field activities. One of the objectives of this\nproject is to evaluate the relative sensitivity and suitability of various\nwildlife species for regional contaminant monitoring of estuaries and\necological risk assessment.\n\nInformation was obtained from http://www.pwrc.usgs.gov/contaminants-online/ and\nfrom Dr. Barnett Rattner of the U.S. Geological Survey, Patuxent Wildlife\nResearch Center.", "links": [ { diff --git a/datasets/usgs_brd_pwrc_ceetv.json b/datasets/usgs_brd_pwrc_ceetv.json index 1336dd9220..a1a7d68dc9 100644 --- a/datasets/usgs_brd_pwrc_ceetv.json +++ b/datasets/usgs_brd_pwrc_ceetv.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_brd_pwrc_ceetv", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Biomonitoring of Environmental Status and Trends (BEST) program of the\nDepartment of the resources under their stewardship. In accordance with the\ndesire of many to continuously monitor the environmental health of our\nestuaries, much can be learned by summarizing existing temporal, geographic,\nand phylogenetic contaminant information. To this end, retrospective\ncontamiant exposure and effects data for amphibians, reptiles, birds and\nmammals residing within 30 km. of the Atlantic, Gulf, Pacific, Alaskan, and\nHawaiian coastal estuaries are being assembled through searches of published\nliterature (e.g., Fish and Wildlife Review; BIOSIS) and databases (e.g., US EPA\nEcological Incident Information System; USGS Diagnostic and Epizootic\nDatabases), and compilation of summary data from unpublished reports of\ngovernment natural resource agencies, private conservation groups, and\nuniversities. These contaminant vertebrates (CEE-TV) are being summarized\nusing ACCESS in a 120 field format including species, collection time and site\ncoordinates, sample matrix, contaminant concentration, biomarker and\nbioindicator responses, and source of information. This CEE-TV database\n(>11,000 records) has been imported into the ARC/INFO geographic information\nsystem (GIS), for purposes of examining geographic coverage and trends, and to\nidentify critical data gaps. A preliminary risk assessment has been conducted\nto identify and characterize contaminants and other stressors potentially\naffecting terrestrial vertebrates that reside, migrate through or reproduce in\nthese estuaries.\n\nThe purpose of the Contaminant Exposure and Effects--Terrestrial Vertebrates\n(CEE-TV) Database is to provide a summary of known contaminant exposure and\neffects in terrestrial vertebrates in coastal and estuarine habitat.\n\nData Set Credit goes to Jennifer Pearson, Nancy Golden, Lynda Garrett, Jonathan\nCohen, Karen Eisenreich, Elise Larsen, Rebecca Kershnar, Roger Hothem.", "links": [ { diff --git a/datasets/usgs_fresc_cpfs_centrmojavegmap.json b/datasets/usgs_fresc_cpfs_centrmojavegmap.json index 096ef95a66..42e87b93e8 100644 --- a/datasets/usgs_fresc_cpfs_centrmojavegmap.json +++ b/datasets/usgs_fresc_cpfs_centrmojavegmap.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_fresc_cpfs_centrmojavegmap", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Central Mojave Vegetation Map (mojveg.e00) displays vegetation and other\nland cover types in the eastern Mojave of California. Map labels represent\nalliances and groups of alliances as described by the National Vegetation\nClassification. The nominal minimum mapping unit is 5 hectares. Each map unit\nis labeled by a primary land cover type and a secondary type where applicable.\nIn addition, the source of data for labeling each map unit is also identified\nin the attribute table for each map unit. Data were developed using field\nvisits, 1:32,000 aerial photography, SPOT satellite imagery, and predictive\nmodeling.\n\nThese data were developed as part of the Department of Defense Legacy funded\nMojave Desert Ecosystem Program. Decision tree models used in the development\nof this data set are described in detail in the Central Mojave Vegetation\nMapping Project Final Report.", "links": [ { diff --git a/datasets/usgs_global_fiducials.json b/datasets/usgs_global_fiducials.json index fafbf76130..e824592c74 100644 --- a/datasets/usgs_global_fiducials.json +++ b/datasets/usgs_global_fiducials.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_global_fiducials", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Global Fiducials Library (GFL) is a long-term archive of images from U.S. National Imagery Systems which represents a long-term periodic record for selected scientifically important sites. The GFL was created to be the collection, archive and data management component of the Global Fiducials Program.\n\nThe Global Fiducials Program is a collaborative effort between Federal Civil Agencies, Academia, and the Intelligence Community. The principal goal of the Global Fiducials Program was to build and maintain a long-term record of data to support scientists and policy makers involved in that collaborative effort. At the inception of the Program, it was hoped that at some point - perhaps as much as twenty-five years into the future - the acquired data could be openly released to support future scientists and policy makers as well. Since the 1990s, the Global Fiducials Program has been periodically collecting images of environmentally significant sites around the world.\n\nThe GFL, which is the archive that maintains this long-term imagery record, is managed by the U.S. Geological Survey under the National Civil Applications Program, in partnership with the Civil Applications Committee\n", "links": [ { diff --git a/datasets/usgs_nawqa_acf_streamflow.json b/datasets/usgs_nawqa_acf_streamflow.json index a21e7870f6..ba1c70f4dd 100644 --- a/datasets/usgs_nawqa_acf_streamflow.json +++ b/datasets/usgs_nawqa_acf_streamflow.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nawqa_acf_streamflow", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface- and ground-water quality data were collected in the\nApalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to\nSeptember 1995 as part of the USGS National Water Quality Assessment\n(NAWQA) program described below. The ACF River basin drains about\n19,800 square miles in western Georgia, eastern Alabama, and the\nFlorida panhandle into the Apalachicola Bay, which discharges into the\nGulf of Mexico. Data collected as part of this study focused on five\nmajor land uses: poultry production in the headwaters of the\nChattahoochee River, urban and suburban areas of Metropolitan Atlanta\nand Columbus, silviculture in the piedmont and fall line hills, and\nrow crop agriculture in the upper coastal plain (clastic hydrogeologic\nsetting) and the lower coastal plain (karst hydrogeologic setting).\n\nThis description is for the streamflow data. Continuous daily\nstreamflow data is available for the nine surface-water sites, where\nthe most water-quality data collection was performed. These sites are\ngaged as continuous streamflow sites and include three mainstem\nintegrator sites and six landuse indicator sites for the water years\n1992-1995. Streamflow data can be viewed on the screen or downloaded\nas an RDB file. The user first selects streamflow from the main\noptions menu. The user is asked to complete a form that provides site\nselection and year of interest. The user then chooses to view or\ndownload the table.\n\nThese data and associated locator maps are accessible on the World\nWide Web at the ACF NAWQA home page. Data are presented in manageable\ntables that are grouped based on land use, site type, and project\ncomponent. The user can view maps and data tables on the computer\nscreen, or downloaded data tables as tab delimited (RDB) files.\n\nData collected as part of the ACF River basin study are presented by\nproject component: surface-water, ground-water, special studies,\nstreamflow, ancillary, and quality assurance data. The water-quality\ndata are presented by major headings, including water-column,\nbed-sediment and tissue, and biological. The data are further\nsubdivided into data sets consisting of related constituents. Data\ntables can be viewed on the users computer screen or retrieved to a\nusers computer as a tab delimited Relational Data Base (RDB) file. To\nreduce the size of the pesticide, volatile organic compound, bed\nsediment and tissue, and trace element tables, only those compounds\nfound equal to, or above the minimum reporting limit (MRL) for one or\nmore sites within a group, are shown. The remaining compounds were not\ndetected. A complete list of constituent names and MRL's are\navailable.\n\nThe National Water-Quality Assessment (NAWQA) Program of the\nU.S. Geological Survey (USGS) is designed to describe the status and\ntrends in the quality of the Nation's ground- and surface-water\nresources and to provide a sound understanding of the natural and\nhuman factors that affect the quality of these resources (Leahy and\nothers, 1990). Because much of the public concern over water quality\nstems from a desire to protect both human health and aquatic life, the\nNAWQA Program will, in addition to measuring physical and chemical\nindicators of water-quality, assess the status of aquatic life through\nsurveys of fish, invertebrates, and benthic algae, and habitat\nconditions (National Research Council, 1990). As an integrated\nassessment of water quality incorporating physical, chemical, and\nbiological components, the NAWQA Program is ecological in approach.", "links": [ { diff --git a/datasets/usgs_nawqa_acf_surfacewater.json b/datasets/usgs_nawqa_acf_surfacewater.json index 964e7691e1..a94d39b8bb 100644 --- a/datasets/usgs_nawqa_acf_surfacewater.json +++ b/datasets/usgs_nawqa_acf_surfacewater.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nawqa_acf_surfacewater", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface- and ground-water quality data were collected in the\nApalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to\nSeptember 1995 as part of the USGS National Water Quality Assessment\n(NAWQA) program described below. The ACF River basin drains about\n19,800 square miles in western Georgia, eastern Alabama, and the\nFlorida panhandle into the Apalachicola Bay, which discharges into the\nGulf of Mexico. Data collected as part of this study focused on five\nmajor land uses: poultry production in the headwaters of the\nChattahoochee River, urban and suburban areas of Metropolitan Atlanta\nand Columbus, silviculture in the piedmont and fall line hills, and\nrow crop agriculture in the upper coastal plain (clastic hydrogeologic\nsetting) and the lower coastal plain (karst hydrogeologic setting).\n\nThis description is for the surface-water sites which are grouped\nbased on six landuse classifications: poultry, suburban, urban,\nsilviculture, agriculture (clastic geology) and agriculure (karst\ngeology), and by site type: main stem and tributary. The data are\ngrouped into three catogories including water column, bed sediment and\ntissue, and Biological. The data are further subdivided into sets of\nrelated constituents. A complete list of constituent names and MRL's\nis available.\n\nThe user can view and retrieve these surface-water data sets:\n\nWater Column: Field Measurements, Nutrients, Major Ions, Suspended\nSediment, Organic Carbon, Turbidity, Pesticides .\n\nBed-Sediment and Tissue: Semivolitile Organic Compounds in Sediment,\nOrganochlorine Compounds in Sediment, Major and Trace Elements in\nSediment, Organochlorine Compounds in Tissue, Trace Elements in Tissue.\n\nBiological: Algae, Fish, Invertebrates.\n\nPhysical, chemical, and biological data were collected at 132 stream\nsites and at 15 locations within 6 reservoirs. The monitoring network\nis a nested design with a core of fixed monitoring sites (integrator\nand indicator sites), a group of land-use comparison sites, and a\ngroup of mixed land use sites including large tributaries and main\nstem rivers that provide spatial distribution. Water samples were\ncollected at frequencies varying from hourly to annually, depending on\nthe intended purpose, and were analyzed for nutrients, carbon,\npesticides, major ions, and field parameters.\n\nThese data and associated locator maps are accessible on the World\nWide Web at the ACF NAWQA home page. Data are presented in manageable\ntables that are grouped based on land use, site type, and project\ncomponent. The user can view maps and data tables on the computer\nscreen, or downloaded data tables as tab delimited (RDB) files.\n\nData collected as part of the ACF River basin study are presented by\nproject component: surface-water, ground-water, special studies,\nstreamflow, ancillary, and quality assurance data. The water-quality\ndata are presented by major headings, including water-column,\nbed-sediment and tissue, and biological. The data are further\nsubdivided into data sets consisting of related constituents. Data\ntables can be viewed on the users computer screen or retrieved to a\nusers computer as a tab delimited Relational Data Base (RDB) file. To\nreduce the size of the pesticide, volatile organic compound, bed\nsediment and tissue, and trace element tables, only those compounds\nfound equal to, or above the minimum reporting limit (MRL) for one or\nmore sites within a group, are shown. The remaining compounds were not\ndetected. A complete list of constituent names and MRL's are\navailable.\n\nThe National Water-Quality Assessment (NAWQA) Program of the\nU.S. Geological Survey (USGS) is designed to describe the status and\ntrends in the quality of the Nation's ground- and surface-water\nresources and to provide a sound understanding of the natural and\nhuman factors that affect the quality of these resources (Leahy and\nothers, 1990). Because much of the public concern over water quality\nstems from a desire to protect both human health and aquatic life, the\nNAWQA Program will, in addition to measuring physical and chemical\nindicators of water-quality, assess the status of aquatic life through\nsurveys of fish, invertebrates, and benthic algae, and habitat\nconditions (National Research Council, 1990). As an integrated\nassessment of water quality incorporating physical, chemical, and\nbiological components, the NAWQA Program is ecological in approach.", "links": [ { diff --git a/datasets/usgs_nawqa_acfriver_groundwater.json b/datasets/usgs_nawqa_acfriver_groundwater.json index f59157bc57..7134aa2dc8 100644 --- a/datasets/usgs_nawqa_acfriver_groundwater.json +++ b/datasets/usgs_nawqa_acfriver_groundwater.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nawqa_acfriver_groundwater", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Surface- and ground-water quality data were collected in the\nApalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to\nSeptember 1995 as part of the USGS National Water Quality Assessment\n(NAWQA) program described below. The ACF River basin drains about\n19,800 square miles in western Georgia, eastern Alabama, and the\nFlorida panhandle into the Apalachicola Bay, which discharges into the\nGulf of Mexico. Data collected as part of this study focused on five\nmajor land uses: poultry production in the headwaters of the\nChattahoochee River, urban and suburban areas of Metropolitan Atlanta\nand Columbus, silviculture in the piedmont and fall line hills, and\nrow crop agriculture in the upper coastal plain (clastic hydrogeologic\nsetting) and the lower coastal plain (karst hydrogeologic setting).\n\nThis description is for the ground-water data. Data for the\nground-water component of the ACF River basin study were collected as\npart of three studies: Study Unit Survey, Land Use Studies (Urban and\nAgriculture) and Agricultural flow system study. The data are grouped\nby study component and site type (wells, springs, drains, and pore\nwater) and are subdivided into sets of data consisting of related\nconstituents. A complete list of constituent names and MRL's are\navailable.\n\nThe user can view and retrieve these ground-water data sets:\nField measurements, Nutrients, Organic carbon, Turbidity, Major Ions,\nPesticides, Trace elements (collected as part of the Study Unit Survey\nand Urban Landuse only), Volatile organic compounds, Radionuclides and\nStable isotopes.\n\nGround-water quality data were collected at 161 sites within the ACF\nRiver basin. These sites included a combination of monitoring and\ndomestic wells, springs and seeps, and subsurface drains. The data are\nconcentrated in the Metropolitan Atlanta (urban land use) area and in\nthe coastal plain (agricultural land use).\n\nThese data and associated locator maps are accessible on the World\nWide Web at the ACF NAWQA home page. Data are presented in manageable\ntables that are grouped based on land use, site type, and project\ncomponent. The user can view maps and data tables on the computer\nscreen, or downloaded data tables as tab delimited (RDB) files.\n\nData collected as part of the ACF River basin study are presented by\nproject component: surface-water, ground-water, special studies,\nstreamflow, ancillary, and quality assurance data. The water-quality\ndata are presented by major headings, including water-column,\nbed-sediment and tissue, and biological. The data are further\nsubdivided into data sets consisting of related constituents. Data\ntables can be viewed on the users computer screen or retrieved to a\nusers computer as a tab delimited Relational Data Base (RDB) file. To\nreduce the size of the pesticide, volatile organic compound, bed\nsediment and tissue, and trace element tables, only those compounds\nfound equal to, or above the minimum reporting limit (MRL) for one or\nmore sites within a group, are shown. The remaining compounds were not\ndetected. A complete list of constituent names and MRL's are\navailable.\n\nThe National Water-Quality Assessment (NAWQA) Program of the\nU.S. Geological Survey (USGS) is designed to describe the status and\ntrends in the quality of the Nation's ground- and surface-water\nresources and to provide a sound understanding of the natural and\nhuman factors that affect the quality of these resources (Leahy and\nothers, 1990). Because much of the public concern over water quality\nstems from a desire to protect both human health and aquatic life, the\nNAWQA Program will, in addition to measuring physical and chemical\nindicators of water-quality, assess the status of aquatic life through\nsurveys of fish, invertebrates, and benthic algae, and habitat\nconditions (National Research Council, 1990). As an integrated\nassessment of water quality incorporating physical, chemical, and\nbiological components, the NAWQA Program is ecological in approach.", "links": [ { diff --git a/datasets/usgs_nps_agatefossilbeds.json b/datasets/usgs_nps_agatefossilbeds.json index ccf5877620..caff7466cd 100644 --- a/datasets/usgs_nps_agatefossilbeds.json +++ b/datasets/usgs_nps_agatefossilbeds.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_agatefossilbeds", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Agate Fossil Beds NM were visited, described, and\ndocumented in a digital database. The database consists of 2 parts - (1)\nPhysical Descriptive Data, and (2) Species Listings.\n\nThe purpose of the field plots was to provide National Parks with the necessary\ntools to effectively manage their natural resources. Plot data is collected and\nanalyzed to develop a classification (using the Standardized National\nVegetation Classification System) and description of vegetation types in\npreparation for photointerpretation and mapping of the monument's vegetation\ntypes. \n\nThe field plotting took place in the Agate Fossil Beds National Monument and a\n400 meter buffer.\n\nField sampling was done using releve plots. The descriptive plot data were\ncollected for 39 sites whose vegetation represents a full spectrum of alliance\ntypes present within Agate Fossil Beds National Monument and its immediate\nsurroundings. Physical description - Attributes collected for each site\ninclude: a plot number, a unique plot identification code, community name,\nfield name, state, park name, quad name, map projection, datum, GPS file name,\nraw UTM coordinates, differentially corrected UTM coordinates, plot survey\ndate, name(s) of surveyors, length, width, photo type, elevation, slope,\naspect, topographic position, landform, surface geology, Cowardin System\ncategory, hydrology, surface material description, soil texture, soil drainage,\nleaf phenology, leaf type, and physiognomy. Species - Individual species\ndescribed at each of 39 plots is listed, one line per species, with the\nfollowing information: Plot Identification Code, Numeric Species Code, Species\nName, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%,\n6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy,\nS1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana,\nE=epiphyte).\n\nInformation for this metadata was taken from\n\"http://biology.usgs.gov/npsveg/agfo/metaagfofield.html\".", "links": [ { diff --git a/datasets/usgs_nps_agatefossilbedsspatial.json b/datasets/usgs_nps_agatefossilbedsspatial.json index ba23cfd068..922cdd3b5a 100644 --- a/datasets/usgs_nps_agatefossilbedsspatial.json +++ b/datasets/usgs_nps_agatefossilbedsspatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_agatefossilbedsspatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\nDivision (BRD) of the U.S. Geological Survey (USGS), has implemented a program\nto \"develop a uniform hierarchical vegetation methodology\" at a national level.\nThe program will also create a geographic information system (GIS) database for\nthe parks under its management. The purpose of the data is to document the\nstate of vegetation within the NPS service area during the 1990's, thereby\nproviding a baseline study for further analysis at the Regional or Service-wide\nlevel. The vegetation units of this map were determined through stereoscopic\ninterpretation of aerial photographs supported by field sampling and ecological\nanalysis. The vegetation boundaries were identified on the photographs by means\nof the photographic signatures and collateral information on slope, hydrology,\ngeography, and vegetation in accordance with the Standardized National\nVegetation Classification System (October 1995). The mapped vegetation reflects\nconditions that existed during the specific year and season that the aerial\nphotographs were taken (July, 1995). There is an inherent margin of error in\nthe use of aerial photography for vegetation delineation and classification.\n\nThe purpose of this spatial data is to provide the National Park Service the\nnecessary tools to manage the natural resources within this park system.\nSeveral parks, representing different regions, environmental conditions, and\nvegetation types, were chosen by BRD to be part of the prototype phase of the\nprogram. The initial goal of the prototype phase is to \"develop, test, refine,\nand finalize the standards and protocols\" to be used during the production\nphase of the project. This includes the development of a standardized\nvegetation classification system for each park and the establishment of\nphotointerpretation, field, and accuracy assessment procedures. Agate Fossil\nBeds National Monument was designated as one of the prototype parks. The\nmonument is located in the high Great Plains. It contains prairie, hill, and\nriverine environments, with vegetation types that include prairie grassland,\nriverine woodland, and wetlands. The vegetation units were photointerpreted\nfrom stereo-paired, natural color photography.\n\nAgate Fossil Beds National Monument was created by the National Park Service on\nJune 5, 1965. The park occupies 4.5 square miles of land straddling the\nNiobrara River in the middle of the Nebraska Panhandle. The park is noted for\nits history, prehistoric fossils, and scenic quality. Historically, the park\nwas a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park\nhas a collection of ranching and Native American artifacts and memorabilia as a\nresult of its donation from the Ranch. Paleontologically, the park contains a\nnumber of Miocene fossil quarries that were excavated through the late 19th\ncentury and early 20th century. From a scenic aspect, the park has views of\nrolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an\nactive part of the landscape. The park is located in the grassy rolling hills\nof Western Nebraska. The park landscape consists of the east-west trending\ncap-rocked northern and southern hills, with the treeless Niobrara River\nfloodplain running down the middle of the valley. The city of Harrison is\nlocated 23 miles to the north, Mitchell is 34 miles to the south. State Highway\n29 runs north-south through the western part of the park.\n\n The Vegetation mapping was conducted in Agate Fossil Beds National Moument,\nNebraska with a 400 meter buffer.\n\nA total of 39 plots were obtained from July 10 through August 15, 1995. These\nplots were used by TNC to describe the vegetation associations found within the\npark. These descriptions are in the companion report by TNC. Map Validation A\nfield trip was conducted in August of 1997 to assess the initial mapping effort\nand to refine map class.\n\nInformation for this metadata was taken from\n\"http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html\" and converted to the\nNASA Directory Interchange Format.\n\nAnother site to obtain the data is located at \nOnline_Resource: \"ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe\".", "links": [ { diff --git a/datasets/usgs_nps_congareeswamp.json b/datasets/usgs_nps_congareeswamp.json index 44a15022a3..ccb396f579 100644 --- a/datasets/usgs_nps_congareeswamp.json +++ b/datasets/usgs_nps_congareeswamp.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_congareeswamp", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Congaree Swamp National Monument were visited,\ndescribed, and documented in a digital database. The database consists of 2\nparts - (1) Physical Descriptive Data, and (2) Species Listings.\n\nThe vegetation plots were used to describe the vegetation in and around\nCongaree Swamp National Monument and to assist in developing a final mapping\nclassification system.\n\nOn June 30, 1983, Congaree Swamp National Monument became an International\nBiosphere Reserve. Congaree is noted for containing one of the last significant\nstands of old growth bottomland hardwood forest, over 11,000 acres in all. The\nMonument contains over 90 species of trees, 16 of which hold state records for\nsize. Included in this list of records is a national record sweet gum with a\nbasal circumference of nearly 20 feet.\n\nCongaree Swamp National Monument is located approximately 15 miles southeast of\nColumbia, the state capitol of South Carolina. Old Bluff Highway (old Highway\n48) lies just north of the Monument boundary. The eastern boundary is located\njust northwest of the confluence of the Congaree and Wateree Rivers. The\nMonument extends west to where Cedar Creek and Myers Creek join.\n\nThe methods used for the sampling and analysis of vegetation data and the\ndevelopment of the classification generally followed the standards Doutline in\nthe Field Methods for Vegetation Mapping document\n\"http://biology.usgs.gov/npsveg/fieldmethods/index.html\" produced for the\nUSGS-NPS Vegetation Mapping project. This process began with the development of\na provisional list of twenty-five vegetation types from teh International\nClassification of Ecological Communities (ICEC) that were thought to have a\nhigh likelihood of being in the park based on an initial field visit on 13-14\nJune, 1996.\n\nOne hundred twenty-eight plots were sampled by two two-person field teams in\nJuly, August, and September of 1996. In a devation from the methodology\noutlined in the Field Methods document, initial sample points were selected in\norder to have plots in each of the aerial photograph signature types. The\ngradsect approach was rejected because there appeared to be no potential for\nstratifying sampling of the park based on slope, aspect, elevation, soil or\nother natural features due to a lack of available information. Furthermore,\nbecause of isolation from roads and trails of many portions of the park, it was\ndeemed not feasible to use a transect to establish plot locations. After\nsampling, plots were tentatively assigned to the ICEC at the alliance level and\nour goal was to have at least five plots in each of the twenty-five provisional\nvegetation types. TIme limitations precluded the ability of the field teams to\ninstall ten plots in each of the expected vegetation types as recommended in\nthe Field Methods document.\n\nThe information for the metadata came from\n\"http://biology.usgs.gov/npsveg/cosw/metacoswfield.html\"", "links": [ { diff --git a/datasets/usgs_nps_congareeswampspatial.json b/datasets/usgs_nps_congareeswampspatial.json index 8bbcd4a5c9..6b06daa7eb 100644 --- a/datasets/usgs_nps_congareeswampspatial.json +++ b/datasets/usgs_nps_congareeswampspatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_congareeswampspatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\n Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program\n to \"develop a uniform hierarchical vegetation methodology\" at a national level.\n The program will also create a geographic information system (GIS) database for\n the parks under its management. The purpose of the data is to document the\n state of vegetation within the NPS service area during the 1990's, thereby\n providing a baseline study for further analysis at the Regional or Service-wide\n level. The vegetation units of this map were determined through stereoscopic\n interpretation of aerial photographs supported by field sampling and ecological\n analysis. The vegetation boundaries were identified on the photographs by means\n of the photographic signatures and collateral information on slope, hydrology,\n geography, and vegetation in accordance with the Standardized National\n Vegetation Classification System (October 1995). The mapped vegetation reflects\n conditions that existed during the specific year and season that the aerial\n photographs were taken (April, 1996). There is an inherent margin of error in\n the use of aerial photography for vegetation delineation and classification.\n \n The purpose of this spatial data is to provide the National Park Service the\n necessary tools to manage the natural resources within this park system.\n Several parks, representing different regions, environmental conditions, and\n vegetation types, were chosen by BRD to be part of the prototype phase of the\n program. The initial goal of the prototype phase is to \"develop, test, refine,\n and finalize the standards and protocols\" to be used during the production\n phase of the project. This includes the development of a standardized\n vegetation classification system for each park and the establishment of\n photointerpretation, field, and accuracy assessment procedures. Congaree Swamp\n National Monument was designated as one of the prototype parks. Congaree Swamp\n National Monument, established in 1976, was designated as one of the prototypes\n within the National Park System. The park contains approximately 22,200 acres\n (34 square miles). Congaree Swamp National Monument is located approximately 15\n miles southeast of Columbia, the state capitol of South Carolina. The Congaree\n River, draining over 8,000 square miles of Piedmont land to the northwest,\n forms the southern border.\n \n On June 30, 1983, Congaree Swamp National Monument became an International\n Biosphere Reserve. Congaree is noted for containing one of the last significant\n stands of old growth bottomland hardwood forest, over 11,000 acres in all. The\n Monument contains over 90 species of trees, 16 of which hold state records for\n size. Included in this list of records is a national record sweet gum with a\n basal circumference of nearly 20 feet.\n \n Congaree Swamp National Monument is located approximately 15 miles southeast of\n Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway\n 48) lies just north of the Monument boundary. The eastern boundary is located\n just northwest of the confluence of the Congaree and Wateree Rivers. The\n Monument extends west to where Cedar Creek and Myers Creek join.\n \n The normal process in vegetation mapping is to conduct an initial field\n reconnaissance, map the vegetation units through photointerpretation, and then\n conduct a field verification. The field reconnaissance visit serves two major\n functions. First, the photointerpreter keys the signature on the aerial photos\n to the vegetation on the ground at each signature site. Second, the\n photointerpreter becomes familiar with the flora, vegetation communities and\n local ecology that occur in the study area. Park and/or TNC field biologists\n that are familiar with the local vegetation and ecology of the park are present\n to help the photointerpreter understand these elements and their relationship\n with the geography of the park. Upon completion of the field reconnaissance,\n photo interpreters delineate vegetation units on mylar that overlay the 9x9\n aerial photos. This effort is conducted in accordance with the TNC vegetation\n classification and criteria for defining each community or alliance. The\n initial mapping is then followed by a field verification session, whose purpose\n is to verify that the vegetation units were mapped correctly. Any PI related\n questions are also addressed during the visit. The vegetation mapping at\n Congaree Swamp National Monument in general followed the normal mapping\n procedure as described in the above paragraph with two major exceptions: 1)\n Preliminary delineations for most of the park, including a set of Focused\n Transect overlays that were labeled with an initial PI signature commenced\n prior to the field reconnaissance visit. 2) A TNC classification did not exist\n at the time the initial delineations began. TNC ecologist and AIS photo\n interpreters worked together to develop an interim signature key which\n addressed what was known at the time. At that time, no comprehensive study\n containing plot data was available to create an interim classification.\n \n From the onset of the Vegetation Inventory and Mapping Program, a standardized\n program-wide mapping criteria has been used. The mapping criteria contains a\n set of documented working decision rules used to facilitate the maintenance of\n accuracy and consistency of the photointerpretation. This criteria assists the\n user in understanding the characteristics, definition and context for each\n vegetation community. The mapping criteria for Congaree Swamp National Monument\n was composed of four parts: The standardized program-wide general mapping\n criteria A park specific mapping criteria A working photo signature key The TNC\n classification, key and descriptions The following sections detail the mapping\n criteria used during the photointerpretation of Congaree Swamp. General Mapping\n Criteria The mapping criteria at Congaree Swamp are a modified version from\n previously mapped parks. The criteria differs primarily in that the height and\n density variables were not mapped at Congaree Swamp. Instead, two additional\n variables were addressed: pre-hurricane Hugo community types and areas of pine\n that have been logged since the time of the 1976 aerial photography. These two\n categories will be addressed in the Park Specific Mapping Criteria section of\n this report. Since forest densities within the Monument are nearly always\n greater than 60%, it served little or no purpose in addressing this element as\n a separate attribute in the database. In addition it was also determined that\n height categories are extremely difficult to map in the Monument due to\n variability of the tree emergent layer, and lack of any significant reference\n points that help in determining canopy heights. Alliance / Community\n Associations The assignment of alliance and community association to the\n vegetation is based on criteria formulated by the field effort and\n classification development. In the case of Congaree Swamp National Monument,\n TNC provided AIS with a tentative community classification in April 1998. A\n final vegetation classification, key, and descriptions of each alliance and\n community, was provided in October 1998. In addition, TNC provided AIS with\n detailed plot data showing how the communities were developed in the Monument.\n \n The information for the metadata came from\n \"http://biology.usgs.gov/npsveg/cosw/metacoswspatial.html\" and was converted to\n the NASA Directory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_d_microbialcontam.json b/datasets/usgs_nps_d_microbialcontam.json index b8a7f28919..4a6d72ed8b 100644 --- a/datasets/usgs_nps_d_microbialcontam.json +++ b/datasets/usgs_nps_d_microbialcontam.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_d_microbialcontam", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The study area is the watershed for the Chattahoochee River from Buford Dam to\njust downstream of the mouth of Peachtree Creek. This study area includes the\nentire Chattahoochee River National Recreation Area, much of Metropolitan\nAtlanta, and extends downstream of two major wastewater treatment plant\noutfalls for the City of Atlanta and Cobb County.\n\nThe 2-year study is for fiscal years 1999 and 2000. There are six months of\nmicrobial sampling in each fiscal year spanning from April 1, 1999 through\nMarch 30, 2000.\n\nThis study measures fecal-indicator bacteria (fecal coliform, E. coli, and\nenterococci) every five days from April 1, 1999 to September 30, 1999 and every\n8 days from October 1, 1999 to March 30, 2000 at three main stem Chattahoochee\nRiver sites. The five-day and eight-day sampling intervals will ensure mid week\nand weekend flow conditions are sampled. Indicator bacteria samples will also\nbe collected during one 26-hour period to look at diel fluctuations. Another\nindicator bacteria (Clostridium perfringens), F-specific coliphages, somatic\ncoliphages, and chemical sewage tracers will be measured as part of several\nsynoptic surveys at 3 fixed sites and 9 synoptic sites.\n\nThe 2-year project investigates the existence, severity, and extent of\nmicrobial contamination in the Chattahoochee River and 8 major tributaries\nwithin the Chattahoochee River National Recreation Area (CRNRA). High levels of\nfecal-indicator bacteria are the principal basis for impairment of streams in\nthe CRNRA. Three data-collection activities include:\n\n1.Fixed interval: Sample fecal-indicator bacteria and predictor variables\n(stream stage, stream flow, turbidity, and field water-quality parameters)\nevery 5 days from April 1 to September 30, 1999 and every 8 days from October\n1, 1999 to March 30, 2000 at 3 Chattahoochee River sites. (view map) 2.Synoptic\nsurveys: Sample fecal-indicator bacteria, Clostridium perfringens, viruses,\npredictor variables, and chemical sewage tracers at 4 Chattahoochee River sites\nand 8 tributary sites during critical seasons and hydrologic conditions. 3.Diel\nsamples: Sample fecal-indicator bacteria and predictor variables every 2 hours\nfor one 26-hour period (August 4-5, 1999) at the Chattahoochee River at\nAtlanta, which is downstream of the CRNRA.\n\nFour proposed main stem sampling sites in downstream order on the Chattahoochee\nRiver include:\n\n1.Chattahoochee River at Settles Bridge Road near Suwanee\n2.Chattahoochee River at Johnsons Ferry Road near Atlanta\n3.Chattahoochee River at Atlanta (Paces Ferry Road; downstream from Palisades\nUnit)\n4.Chattahoochee River at State Highway 280, near Atlanta (Synoptic site only;\ndownstream from all of the CRNRA, much of Metropolitan Atlanta, and 2 major\nwastewater treatment outfalls for the City of Atlanta and Cobb County; will\nprovide microbial data for a Chattahoochee River site directly affected by\npoint sources of wastewater effluent)\n\nEight proposed tributary sampling sites within the CRNRA watershed in\ndownstream order include:\n\n1.James Creek near Cumming (James Burgess Road)\n2.Suwanee Creek near Suwanee (at US Route 23, Buford Hwy)\n3.Johns Creek near Warsaw (Buice Road)\n4.Crooked Creek near Norcross (Spalding Road)\n5.Big Creek near Roswell (below Water Works intake)\n6.Willeo Creek near Roswell (State Route 120)\n7.Sope Creek near Marietta (Lower Roswell Road)\n8.Rottenwood Creek near Smyrna (Interstate Parkway North)\n\nIn general, fecal-indicator bacteria are used to assess the public-health\nacceptability of water. The concentration of indicator bacteria is a measure of\nwater safety for body-contact recreation or for consumption (Myers and\nSylvester, 1997). Indicator bacteria do not typically cause diseases\n(pathogenic), but they indicate the possible presence of pathogenic organisms.\nEscherichia coli (E. coli) and enterococci are currently the preferred fecal\nindicators for recreational freshwaters because they are superior to fecal\ncoliforms and fecal streptococci as predictors of swimming-associated\ngastroenteritis (Cabelli, 1977; Dufour, 1984); however fecal coliforms are\nstill used by many states including Georgia to monitor recreational waters.\nMost historical indicator bacteria data for surface water within the CRNRA are\nfecal coliform counts collected once a month on a mid-weekday during normal\nworking hours. This study proposes to measure fecal coliform using the membrane\nfilter technique (preferred over the broth technique used by Georgia EPD),E.\ncoli, and enterococci every five days during the recreation season at three\nmain stem sites. The five-day cycle will ensure mid week and weekend flow\nconditions are sampled. All samples will be collected using USGS protocols for\nbacteria and equal width interval (EWI) sampling.\n\nClostridium perfringens (C. perfringens) is another indicator bacteria that is\npresent in large numbers in human and animal wastes, and its spores are more\nresistant to disinfection and environmental stresses than are most other\nbacteria. It is also a sensitive indicator of microorganisms that enter streams\nfrom point sources (Sorenson and others, 1989). It must be analyzed under\nanaerobic conditions in a laboratory and is best attempted by a biologist or\nhighly trained technician. This study proposes to measure C. perfringens at 4\nmain stem and 8 tributary sites as part of synoptic surveys during critical\nseasons and hydrologic conditions.\n\nBecause monitoring of enteric viruses is recognized as being difficult,time\nconsuming, and expensive, some researchers advocate the use of coliphage for\nroutine viral monitoring. Coliphages are bacteriophages that infect and\nreplicate in coliform bacteria. Although somatic and Fecal-Specific coliphages\nare not consistently found in feces, they are found in high numbers in sewage\nand are thought to be reliable indicators of the sewage contamination of waters\n(International Association on Water Pollution Research and Control, 1991).\nColiphage is also recognized to be representative of the survival transport of\nviruses in the environment. However, to date, they have not been found to\ncorrelate with the presence of pathogenic viruses. This study proposes to\nmeasure enteric viruses at 4 main stem and 8 tributary sites as part of\nsynoptic surveys during critical seasons and hydrologic conditions.", "links": [ { diff --git a/datasets/usgs_nps_devilstower.json b/datasets/usgs_nps_devilstower.json index a8b82a5c13..1bd543ce7f 100644 --- a/datasets/usgs_nps_devilstower.json +++ b/datasets/usgs_nps_devilstower.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_devilstower", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Devils Tower NM were visited, described, and\ndocumented in a digital database. The database consists of 2 parts - (1)\nPhysical Descriptive Data, and (2) Species Listings.\n\nThe purpose of the data is to provide National Parks with the necessary tools\nto effectively manage their natural resources. Plot data is collected and\nanalyzed to develop a classification (using the Standardized National\nVegetation Classification System) and description of vegetation types in\npreparation for photointerpretation and mapping of the monument's vegetation\ntypes.\n\nThe geographic extent of the data set is Devils Tower National Monument and\nabout a 2 mile environs around Monument Boundaries - Black Hills, Wyoming, USA\n\nField sampling was conducted using releve plots.\n\nInformation was obtained from\n\"http://biology.usgs.gov/npsveg/deto/metadetofield.html\"", "links": [ { diff --git a/datasets/usgs_nps_devilstowerspatial.json b/datasets/usgs_nps_devilstowerspatial.json index 61a14d7c20..8db57738f7 100644 --- a/datasets/usgs_nps_devilstowerspatial.json +++ b/datasets/usgs_nps_devilstowerspatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_devilstowerspatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\nDivision BRD) of the U.S. Geological Survey (USGS), has implemented a program\nto \"develop a uniform hierarchical vegetation methodology\" at a national level.\nThe program will also create a geographic information system (GIS) database for\nthe parks under its management. The purpose of the data is to document the\nstate of vegetation within the NPS service area during the 1990's, thereby\nproviding a baseline study for further analysis at the Regional or Service-wide\nlevel. The vegetation at Devils Tower National Monument was mapped using\n1:16,000 scale U.S. Forest Service Color Aerial Photography acquired July 29,\n1993. The mapping classification used two separate classification systems. All\nnatural vegetation used the National Vegetation Classification System (NVCS) as\na base. The vegetation classification was created after extensive on site\nsampling and numerical analysis. The vegetation map units were derived from the\nvegetation classification. Other non-natural or cultural mapping units used the\nAnderson Level II classification system. The mapped area includes a buffer\naround the Monument boundary.\n\nThis mapping effort originates from a long-term vegetation monitoring program\nthat is part of a larger Inventory and Monitoring (I&M) program started by the\nNational Park Service (NPS). I&M goals are, among others, to map the\nvegetation of all national parks and monuments and provide a baseline inventory\nof vegetation. The I&M program currently works in close cooperation with the\nBiological Resources Division (BRD) of the United States Geological Survey\n(USGS). The USGS/BRD continues overall management and oversight of all ongoing\nmapping efforts in close cooperation with the NPS.\n\nThe purposes of the mapping effort are varied and include the following:\nProvides support for NPS Resources Management. Promotes vegetation-related\nresearch for both NPS and USGS/BRD. Provides support for NPS Planning and\nCompliance. Adds to the information base for NPS Interpretation. Assists in NPS\nOperations.\n\nThe geographic extent of the data set is Devils Tower National Monument and\nabout a 2 mile environs around Monument Boundaries - Black Hills, Wyoming, USA.\n\nInformation was obtained from\n\"http://biology.usgs.gov/npsveg/deto/metadetospatial.html\" and converted to\nNASA Directory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_fortlaramie.json b/datasets/usgs_nps_fortlaramie.json index b74a9ec798..ae6cb79ca3 100644 --- a/datasets/usgs_nps_fortlaramie.json +++ b/datasets/usgs_nps_fortlaramie.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_fortlaramie", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Fort Laramie NHS were visited, described, and\ndocumented in a digital database. The database consists of two parts - (1)\nPhysical Descriptive and Stratum Data, and (2) Species Listings.\n\nThe purpose of the field plots is to provide National Parks with the necessary\ntools to effectively manage their natural resources. Plot data is collected and\nanalyzed to develop a classification (using the Standardized National\nVegetation Classification System) and description of vegetation types in\npreparation for photointerpretation and mapping of the monument's vegetation\ntypes.\n\nThe dataset is of the Fort Laramie National Historic Site and surroundings.\nFort Laramie is located in Goshen County, Wyoming.\n\nField sampling using releve plots.\n\nInformation for this metadata was obtained from\n\"http://biology.usgs.gov/npsveg/fola/metafolafield.html\" and put into NASA\nDirectory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_fortlaramiespatial.json b/datasets/usgs_nps_fortlaramiespatial.json index 36efd00221..71ce8a7b08 100644 --- a/datasets/usgs_nps_fortlaramiespatial.json +++ b/datasets/usgs_nps_fortlaramiespatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_fortlaramiespatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\nDivision (BRD) of the U.S. Geological Survey (USGS), has implemented a program\nto \"develop a uniform hierarchical vegetation methodology\" at a national level.\nThe program will also create a geographic information system (GIS) database for\nthe parks under its management. The purpose of the data is to document the\nstate of vegetation within the NPS service area during the 1990's, thereby\nproviding a baseline study for further analysis at the Regional or Service-wide\nlevel. The vegetation units of this map were determined through stereoscopic\ninterpretation of aerial photographs supported by field sampling and ecological\nanalysis. The vegetation boundaries were identified on the photographs by means\nof the photographic signatures and collateral information on slope, hydrology,\ngeography, and vegetation in accordance with the Standardized National\nVegetation Classification System (October 1995). The mapped vegetation reflects\nconditions that existed during the specific year and season that the aerial\nphotographs were taken (July, 1995). There is an inherent margin of error in\nthe use of aerial photography for vegetation delineation and classification.\n\nThe purpose of this spatial data is to provide the National Park Service the\nnecessary tools to manage the natural resources within this park system.\nSeveral parks, representing different regions, environmental conditions, and\nvegetation types, were chosen by BRD to be part of the prototype phase of the\nprogram. The initial goal of the prototype phase is to \"develop, test, refine,\nand finalize the standards and protocols\" to be used during the production\nphase of the project. This includes the development of a standardized\nvegetation classification system for each park and the establishment of\nphotointerpretation, field, and accuracy assessment procedures. Fort Laramie\nNational Historic Site was designated as one of the prototype parks. The\nmonument is located in the high Great Plains. It contains prairie, hill, and\nriverine environments, with vegetation types that include upland woodland,\nprairie grassland, riverine woodland, and wetlands.\n\nFort Laramie National Historic Site was created by the National Park Service on\nJuly 16, 1938. The park occupies 833 acres of land on the Laramie River, west\nof its confluence with the North Platte River in western Wyoming. Bureau of\nLand Management land south of the park (referred to as Plot 3) and northwest of\nthe park (referred to as Plots 1 and 5) are also within the mapping study area.\nThe park is primarily preserved as an historic site. The fort site was occupied\nfirst as a fur trading center, then subsequently as a military outpost. It\nfurther served as a way station for trappers, traders, and emigrants on the\nOregon Trail. The old fort site, located in the western end of the park,\ncontains a complex of restored buildings and ruins, dating from mid and late\n19th century, surrounding a lawn quadrangle. The remainder of the park contains\ndisturbed prairie and floodplains. The park itself lies mainly on the\nfloodplain terrace of the Laramie River, with a portion on the North Platte\nRiver floodplain terrace just west of their confluence. A small portion of the\nnorthwest corner of the park lies above the terrace. Plot 3 lies directly south\nof the park, across the Fort Laramie Canal. It is an area of rolling hills.\nPlots 1 and 5 lie 1/4 mile northwest of the park, also in rolling hills. The\npark is surrounded by rolling hills that are used for grazing and some\nagricultural cultivation. The city of Fort Laramie is located 3 miles to the\nnortheast of the park.\n\nThe sampling approach used in this mapping effort was typical of small park\nsampling, where all polygons within the park boundary are sampled. Two levels\nof field data gathering were conducted in this park; plots and observations.\nPlots represented the most intensive sampling of the landscape and used TNC's\n'Plot Form'. Observations consisted of brief descriptions and were designed to\nobtain a quick overview of the landscape without spending a large amount of\ntime at each sample site. Observation points used the 'Observation Form' data\nsheet. Examples of both 'Plot' and 'Observation' forms are included in the\ncompanion report by TNC. Initially, plots were used to describe the vegetation\nof the park. A total of 49 plots were obtained from July through August 1996.\nThese plots were used by TNC to describe the vegetation associations found\nwithin the park. These descriptions are in the companion report by TNC. Map\nValidation A field trip was conducted in July of 1997 to assess the initial\nmapping effort and to refine map class.\n\nThe data can also be obtained from\n\"ftp://ftp.cbi.usgs.gov/pub/vegmapping/fola/fola.exe\".\n\nInformation for this metadata was obtained from\n\"http://biology.usgs.gov/npsveg/fola/metafolaspatial.html\" and put into NASA\nDirectory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_isleroyale.json b/datasets/usgs_nps_isleroyale.json index 026425c86b..d66b82eda7 100644 --- a/datasets/usgs_nps_isleroyale.json +++ b/datasets/usgs_nps_isleroyale.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_isleroyale", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Isle Royale National Park were visited, described,\nand documented in a digital database. The database consists of 2 parts - (1)\nPhysical Descriptive Data and (2) Species Listings.\n\nThe vegetation plots were used to describe the vegetation in and around Wind\nCave National Park and to assist in developing a final mapping classification\nsystem.\n\nThe purpose of the vegetation plots was to provide National Parks with the\nnecessary tools to effectively manage their natural resources. Plot data are\ncollected and analyzed to develop a classification (using the Standardized\nNational Vegetation Classification System) and description of vegetation types\nin preparation for photointerpretation and mapping of the Park's vegetation\ntypes.\n\nIsle Royale National Park was authorized on March 3, 1931; it was formally\nestablished in 1940, and officially dedicated in 1946. Most of the park's land\narea (98%) was designated as a Wilderness area in October 1976, and later\nadditions increased the total Wilderness to 99% of the park. The park was\ndesignated an International Biosphere Reserve in 1980.\n\nField sampling was performed using releve plots.\n\nInformation for this metadata was obtained from the site\nhttp://biology.usgs.gov/npsveg/isro/metaisrofield.html and converted to NASA\nDirectory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_isleroyalespatial.json b/datasets/usgs_nps_isleroyalespatial.json index 6993131cb5..cc77369832 100644 --- a/datasets/usgs_nps_isleroyalespatial.json +++ b/datasets/usgs_nps_isleroyalespatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_isleroyalespatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\nDivision (BRD) of the U.S. Geological Survey (USGS), has implemented a program\nto \"develop a uniform hierarchical vegetation methodology\" at a national level.\nThe program will also create a geographic information system (GIS) database for\nthe parks under its management. The purpose of the data is to document the\nstate of vegetation within the NPS service area during the 1990's, thereby\nproviding a baseline study for further analysis at the Regional or Service-wide\nlevel. The vegetation units of this map were determined through stereoscopic\ninterpretation of aerial photographs supported by field sampling and ecological\nanalysis. The vegetation boundaries were identified on the photographs by means\nof the photographic signatures and collateral information on slope, hydrology,\ngeography, and vegetation in accordance with the Standardized National\nVegetation Classification System (October 1995). The mapped vegetation reflects\nconditions that existed during the specific year and season that the aerial\nphotographs were taken (spring - 1996 and fall - 1994). There is an inherent\nmargin of error in the use of aerial photography for vegetation delineation and\nclassification.\n\nThe purpose of this spatial data is to provide the National Park Service the\nnecessary tools to wisely manage the natural resources within this park system.\nSeveral parks, representing different regions, environmental conditions, and\nvegetation types, were chosen by BRD to be part of the prototype phase of the\nprogram. The initial goal of the prototype phase is to \"develop, test, refine,\nand finalize the standards and protocols\" to be used during the production\nphase of the project. This includes the development of a standardized\nvegetation classification system for each park and the establishment of\nphotointerpretation, field, and accuracy assessment procedures. Isle Royale\nNational Park was initially identified as one of the prototypes within the\nNational Park System for the USGS-NPS Vegetation Mapping Program. Isle Royale\nNational Park was established March 3, 1931 and was also designated as an\nInternational Biosphere Reserve in 1980. The park contains approximately\n571,790 acres of land and water (893 square miles) of which 133,782 acres is\nland and the rest is open water of Lake Superior as well as inland lakes and\nponds. Isle Royale National Park is an archipelago of islands located in the\nnorthwestern region of Lake Superior close to the United States-Canada border.\nThe main island, Isle Royale, consists of a series of ridges and valleys\nrunning the length of the island and is surrounded by approximately 200 smaller\nislands. The primary methods of transportation on the island are hiking and\nboating. Isle Royale National Park was authorized on March 3, 1931; it was\nformally established in 1940, and officially dedicated in 1946. Most of the\npark's land area (98%) was designated as a Wilderness area in October 1976, and\nlater additions increased the total Wilderness to 99% of the park. The park was\ndesignated an International Biosphere Reserve in 1980.\n\nIsle Royale National Park is an archipelago of islands located in the\nnorthwestern region of Lake Superior close to the United States-Canada border.\nThe park is located about 60 miles northwest of Michigan.s Keweenaw Peninsula,\nabout 22 miles east of Grand Portage, Minnesota, and about 35 miles southeast\nof Thunder Bay, Ontario.\n\nInformation for this metadata was obtained from the site\n\"http://biology.usgs.gov/npsveg/isro/metaisrospatial.html\" and converted to\nNASA Directory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_jewelcave.json b/datasets/usgs_nps_jewelcave.json index 272321bfb7..6170dd3d07 100644 --- a/datasets/usgs_nps_jewelcave.json +++ b/datasets/usgs_nps_jewelcave.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_jewelcave", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Jewel Cave NM were visited, described, and\ndocumented in a digital database. The database consists of 2 parts - (1)\nPhysical Descriptive Data, and (2) Species Listing.\n\nThe purpose is to provide National Parks with the necessary tools to\neffectively manage their natural resources. Plot data is collected and analyzed\nto develop a classification (using the Standardized National Vegetation\nClassification System) and description of vegetation types in preparation for\nphotointerpretation and mapping of the monument's vegetation types.\n\nField sampling was conducted using releve plots.\n\nInformation for this metadata was obtained from the site\n\"http://biology.usgs.gov/npsveg/jeca/metajecafield.html\" and put into NASA\nDirectory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_jewelcavespatial.json b/datasets/usgs_nps_jewelcavespatial.json index 11049f7906..e1addb87ab 100644 --- a/datasets/usgs_nps_jewelcavespatial.json +++ b/datasets/usgs_nps_jewelcavespatial.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_jewelcavespatial", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Park Service (NPS), in conjunction with the Biological Resources\nDivision (BRD) of the U.S. Geological Survey (USGS), has implemented a program\nto \"develop a uniform hierarchical vegetation methodology\" at a national level.\nThe program will also create a geographic information system (GIS) database for\nthe parks under its management. The purpose of the data is to document the\nstate of vegetation within the NPS service area during the 1990's, thereby\nproviding a baseline study for further analysis at the Regional or Service-wide\nlevel. The vegetation at Jewel Cave National Monument was mapped using 1:16,000\nscale U.S. Forest Service Color Aerial Photography acquired August 22, 1993. \nThe mapping classification used two separate classification systems. All\nnatural vegetation used the National Vegetation Classification System (NVCS) as\na base. The vegetation classification was created after extensive on site\nsampling and numerical analysis. The vegetation map units were derived from the\nvegetation classification. Other non-natural or cultural mapping units used the\nAnderson Level II classification system. The mapped area includes a buffer\naround the Monument boundary.\n\nThis mapping effort originates from a long-term vegetation monitoring program\nthat is part of a larger Inventory and Monitoring (I&M) program started by the\nNational Park Service (NPS). I&M goals are, among others, to map the\nvegetation of all national parks and monuments and provide a baseline inventory\nof vegetation. The I&M program currently works in close cooperation with the\nBiological Resources Division (BRD) of the United States Geological Survey\n(USGS). The USGS/BRD continues overall management and oversight of all ongoing\nmapping efforts in close cooperation with the NPS.\n\nThe purposes of the mapping effort are varied and include the following:\nProvides support for NPS Resources Management. Promotes vegetation-related\nresearch for both NPS and USGS/BRD. Provides support for NPS Planning and\nCompliance. Adds to the information base for NPS Interpretation. Assists in NPS\nOperations.\n\nThe location of the mapping is Jewel Cave National Monument and about a 2 mile\nenvirons around Monument Boundaries - Black Hills, South Dakota.\n\nJewel Cave National Monument was responsible for obtaining permission from\nadjacent land owners for property access for sampling purposes. Most of the\nprivate lands were under some form of grazing or farming. Consequently,\nsampling on these lands was not necessary. The remainder of the lands within\nthe mapping area are U.S. Forest Service Lands so permission was not necessary.\nTo reduce duplicating previous work and to help in our effort we collected\nexisting vegetation reports and maps from the staff at Jewel Cave National\nMonument. These materials were referenced during the mapping process and the\ninformation contained in them was incorporated where it was deemed useful.\nBecause soils also affect the distribution of vegetation, soil maps and soil\ndescriptions were also obtained as reference. These were not converted to a\ndigital file. Digital elevation models (DEM) were obtained to create slope and\naspect maps that helped in determining vegetation community distribution. The\nsampling approach used in this mapping effort was typical of small park\nsampling, where all polygons within the park boundary are sampled. Two levels\nof field data gathering were conducted in this park; plots and observations.\nPlots represented the most intensive sampling of the landscape and used TNC's\n'Plot Form'. Observations consisted of brief descriptions and were designed to\nobtain a quick overview of the landscape without spending a large amount of\ntime at each sample site. Observation points used the 'Observation Form' data\nsheet. Examples of both 'Plot' and 'Observation' forms are included in the\ncompanion report by TNC. Initially, plots were used to describe the vegetation\nof the park. A total of 28 plots were obtained from July 29 through August 1,\n1996. These plots were used by TNC to describe the vegetation associations\nfound within the park. These descriptions are in the companion report by TNC.\nMap Validation A field trip was conducted in May of 1997 to assess the initial\nmapping effort and to refine map classes.\n\nInformation for this metadata was obtained from the site\n\"http://biology.usgs.gov/npsveg/jeca/metajecaspatial.html\" and put into NASA\nDirectory Interchange Format.", "links": [ { diff --git a/datasets/usgs_nps_mountrushmore.json b/datasets/usgs_nps_mountrushmore.json index 111dbb301e..fce6ae7a60 100644 --- a/datasets/usgs_nps_mountrushmore.json +++ b/datasets/usgs_nps_mountrushmore.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_nps_mountrushmore", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Vegetation field plots at Mount Rushmore NM were visited, described, and\ndocumented in a digital database. The database consists of 2 parts - (1)\nPhysical Descriptive Data, and (2) Species Listings.\n\nThe purpose of the data plots were to provide National Parks with the necessary\ntools to effectively manage their natural resources. Plot data is collected and\nanalyzed to develop a classification (using the Standardized National\nVegetation Classification System) and description of vegetation types in\npreparation for photointerpretation and mapping of the monument's vegetation\ntypes.\n\nField sampling was conducted using releve plots.\n\nInformation for this metadata was obtained from the site\n\"http://biology.usgs.gov/npsveg/moru/metamorufield.html\" and put into\nNASA Directory Interchange Format.", "links": [ { diff --git a/datasets/usgs_npwrc_acutetoxicity_Version 06JUL2000.json b/datasets/usgs_npwrc_acutetoxicity_Version 06JUL2000.json index db8d9162d5..c5f7dc8445 100644 --- a/datasets/usgs_npwrc_acutetoxicity_Version 06JUL2000.json +++ b/datasets/usgs_npwrc_acutetoxicity_Version 06JUL2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_acutetoxicity_Version 06JUL2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory studies were conducted to determine the acute toxicity of three\nammonia-based fire retardants (Fire-Trol LCA-F, Fire-Trol LCM-R, and Phos-Chek\n259F), five surfactant-based fire-suppressant foams (FireFoam 103B, FireFoam\n104, Fire Quench, ForExpan S, and Pyrocap B-136), three nitrogenous chemicals\n(ammonia, nitrate, and nitrite) and two anionic surfactants (linear\nalkylbenzene sulfonate [LAS] and sodium dodecyl sulfate [SDS]) to juvenile\nrainbow trout Oncorhynchus mykiss in soft water. The descending rank order of\ntoxicity (96-h concentration lethal to 50% of test organisms [96-h LC50]) for\nthe fire retardants was as follows: Phos-Chek 259F (168 mg/L) > Fire-Trol LCA-F\n(942 mg/L) = Fire-Trol LCM-R (1,141 mg/L). The descending rank order of\ntoxicity for the foams was as follows: FireFoam 103B (12.2 mg/L) = FireFoam 104\n(13.0 mg/L) > ForExpan S (21.8 mg/L) > Fire Quench (39.0 mg/L) > Pyrocap B-136\n(156 mg/L). Except for Pyrocap B-136, the foams were more toxic than the fire\nretardants. Un-ionized ammonia (NH3; 0.125 mg/L as N) was about six times more\ntoxic than nitrite (0.79 mg/L NO2-N) and about 13,300 times more toxic than\nnitrate (1,658 mg/L NO3-N). Linear alkylbenzene sulfonate (5.0 mg/L) was about\nfive times more toxic than SDS (24.9 mg/L). Estimated total ammonia and NH3\nconcentrations at the 96-h LC50s of the fire retardants indicated that ammonia\nwas the primary toxic component in these formulations. Based on estimated\nanionic surfactant concentrations at the 96-h LC50s of the foams and reference\nsurfactants, LAS was intermediate in toxicity and SDS was less toxic to rainbow\ntrout when compared with the foams. Comparisons of recommended application\nconcentrations to the test results indicate that accidental inputs of these\nchemicals into streams require substantial dilutions (100-1,750-fold) to reach\nconcentrations nonlethal to rainbow trout.", "links": [ { diff --git a/datasets/usgs_npwrc_alpha_Version 16MAY2000.json b/datasets/usgs_npwrc_alpha_Version 16MAY2000.json index 63b402050a..4cd74278e0 100644 --- a/datasets/usgs_npwrc_alpha_Version 16MAY2000.json +++ b/datasets/usgs_npwrc_alpha_Version 16MAY2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_alpha_Version 16MAY2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The prevailing view of a wolf (Canis lupus) pack is that of a group of\nindividuals ever vying for dominance but held in check by the \"alpha\" pair, the\nalpha male and the alpha female. Most research on the social dynamics of wolf\npacks, however, has been conducted on non-natural assortments of captive\nwolves. Here I describe the wolf-pack social order as it occurs in nature,\ndiscuss the alpha concept and social dominance and submission, and present data\non the precise relationships among members in free-living packs based on a\nliterature review and 13 summers of observations of wolves on Ellesmere Island,\nNorthwest Territories, Canada. I conclude that the typical wolf pack is a\nfamily, with the adult parents guiding the activities of the group in a\ndivision-of-labor system in which the female predominates primarily in such\nactivities as pup care and defense and the male primarily during foraging and\nfood-provisioning and the travels associated with them.", "links": [ { diff --git a/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json b/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json index ab7214d197..c41a88d5e2 100644 --- a/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json +++ b/datasets/usgs_npwrc_canvasbacks_Version 13NOV2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_canvasbacks_Version 13NOV2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Age, productivity, and other factors affecting breeding performance of\ncanvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested\nwhether reproductive performance of female canvasbacks varied with age and\nselected environmental factors in southwestern Manitoba from 1974 to 1980.\nNeither clutch size, nest parasitism, nest success, nor the number of\nducklings/brood varied with age. Return rates, nest initiation dates,\nrenesting, and hen success were age-related. Return rates averaged 21% for\nsecond-year (SY) and 69% for after-second-year (ASY) females (58% for\nthird-year and 79% for after-third-year females). Additionally, water\nconditions and spring temperatures influenced chronology of arrival, timing of\nnesting, and reproductive success. Nest initiation by birds of all ages was\naffected by minimum April temperatures. Clutch size was higher in nests\ninitiated earlier. Interspecific nest parasitism did not affect clutch size,\nnest success, hen success, or hatching success. Nest success was lower in dry\nyears (17%) than in moderately wet (54%) or wet (60%) years. Nests per female\nwere highest during wet years. No nests of SY females were found in dry years.\nIn years of moderate to good wetland conditions, females of all ages nested. \nPredation was the primary factor influencing nest success. Hen success averaged\n58% over all years. The number of ducklings surviving 20 days averaged\n4.7/brood. Because SY females have lower return rates and hen success than ASY\nfemales, especially during drier years, management to increase canvasback\npopulations might best be directed to increasing first year recruitment (no. of\nfemales returning to breed) and to increasing overall breeding success by\nreducing predation and enhancing local habitat conditions during nesting.", "links": [ { diff --git a/datasets/usgs_npwrc_ducks_Version 07JAN98.json b/datasets/usgs_npwrc_ducks_Version 07JAN98.json index 769b4bcc60..d4069b897a 100644 --- a/datasets/usgs_npwrc_ducks_Version 07JAN98.json +++ b/datasets/usgs_npwrc_ducks_Version 07JAN98.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_ducks_Version 07JAN98", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Waterfowl inventories taken during the breeding season are recognized\nas a basic technique in assessing the number of ducks per unit\narea. That waterfowl censusing is still an inexact technology leading\nto divergent interpretations of results is also recognized. The\ninexactness stems from a wide spectrum of factors that include\nweather, breeding phenology, asynchronous nesting periods, vegetative\ngrowth, species present and their daily activity, previous field\nexperience of personnel, plus others (Stewart et al., 1958; Diem and\nLu, 1960; Crissey, 1963a). In spite of the possible errors, accurate\nestimates are necessary to our understanding of production rates of\nall North American breeding waterfowl. Statistically adequate censuses\nof breeding pairs and accurate predictions of young produced per pair\nstill remain as two of the primary statistics in determining yearly\nrecruitment rate of species breeding in particular units of pond\nhabitats. Without precise breeding pair and production data, the\nproblems involved in describing the reproductive potential of any\nspecies and its environmental or density-dependent limiting factors\ncannot be adequately resolved.\n\nThe purposes of this paper are to (1) describe methods used to\nestimate yearly breeding pair abundance on two study areas, one in\nManitoba and the other in Saskatchewan; (2) assess the relative\nconsistency, precision, and accuracy of pair counts as related to the\nbreeding biology of duck species; and (3) recommend census methods\nthat can more closely approximate absolute populations breeding in\nparkland and grassland habitats.", "links": [ { diff --git a/datasets/usgs_npwrc_graywolves_Version 30APR2001.json b/datasets/usgs_npwrc_graywolves_Version 30APR2001.json index 5aebfe4717..67972af9cd 100644 --- a/datasets/usgs_npwrc_graywolves_Version 30APR2001.json +++ b/datasets/usgs_npwrc_graywolves_Version 30APR2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_graywolves_Version 30APR2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We evaluated the accuracy and precision of tooth wear for aging gray wolves\n(Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or\nknown-minimum-age skulls. Estimates of age using tooth wear and a commercial\ncementum annuli-aging service were useful for wolves up to 14 years old. The\nprecision of estimates from cementum annuli was greater than estimates from\ntooth wear, but tooth wear estimates are more applicable in the field. We\ntended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The\ncommercial service aged young wolves with cementum annuli to within year of\nactual age, but under estimated ages of wolves 9 years old by 1-3 years. No\ndifferences were detected in tooth wear patterns for wild wolves from Alaska,\nMinnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not\nappropriate for aging wolves with an underbite that prevented normal wear or\nseverely broken and missing teeth.", "links": [ { diff --git a/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json b/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json index cdfbe2b4ff..966447ae3d 100644 --- a/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json +++ b/datasets/usgs_npwrc_incidentalmarinecatc_Version 11APR2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_incidentalmarinecatc_Version 11APR2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The incidental take of marine birds was estimated for the following North\nPacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean\nsquid, and Taiwanese squid and large-mesh combined. The take was estimated by\nassuming that the data represented a random sample from an unstratified\npopulation of all driftnet fisheries in the North Pacific. Estimates for 13\nspecies or species groups are presented, along with some discussion of\ninadequacies of the design. About 416,000 marine birds were estimated to be\ntaken incidentally during the 1990 season; 80 % of these were in the Japanese\nsquid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan\nAlbatrosses were the most common species in the bycatch.\n\nRegression models were also developed to explore the relations between bycatch\nrate of three groups Black-footed Albatross, Laysan Albatross, and \"dark\"\nshearwatersand various explanatory variables, such as latitude, longitude,\nmonth, vessel, sea surface temperature, and net soak time (length of time nets\nwere in the water). This was done for only the Japanese squid fishery, for\nwhich the most complete information was available. For modeling purposes,\nfishing operations for each vessel were grouped into 5-degree blocks of\nlatitude and longitude.\n\nResults of model building indicated that vessel had a significant influence on\nbycatch rates of all three groups. This finding emphasizes the importance of\nthe sample of vessels being representative of the entire fleet. In addition,\nbycatch rates of all three groups varied spatially and temporally. Bycatch\nrates for Laysan Albatrosses tended to decline during the fishing season,\nwhereas those for Black-footed Albatrosses and dark shearwaters tended to\nincrease as the season progressed. Bycatch rates were positively related to net\nsoak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark\nshearwaters were lower for higher sea surface temperatures.", "links": [ { diff --git a/datasets/usgs_npwrc_manitobaspiders_Version 16JUL97.json b/datasets/usgs_npwrc_manitobaspiders_Version 16JUL97.json index 6c41344990..9853226f7b 100644 --- a/datasets/usgs_npwrc_manitobaspiders_Version 16JUL97.json +++ b/datasets/usgs_npwrc_manitobaspiders_Version 16JUL97.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_manitobaspiders_Version 16JUL97", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An annotated list of spider species is compiled from museum collections and\n several personal collections. This list includes 483 species in 20 families;\n 139 species are new provincial records. The spider fauna of Manitoba is\n compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's\n spider fauna is composed of northern elements (arctic or subarctic species),\n boreal elements (holarctic or nearctic), and eastern elements (mainly species\n of the eastern deciduous forest), and a few that are regarded as introductions\n from abroad. Forty-three species reach the limits of their ranges here. This\n relatively small province (6.5% of the total land mass of Canada) contains 59%\n of the Canadian spider families and 37% of the Canadian species.", "links": [ { diff --git a/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json b/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json index 55f53a570b..8dc81fc78b 100644 --- a/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json +++ b/datasets/usgs_npwrc_muskoxen_Version 31MAY2000.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_muskoxen_Version 31MAY2000", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A lack of young muskoxen (Ovibos moschatus) and arctic hares (Lepus arcticus)\n in the Eureka area of Ellesmere Island, Northwest Territories (now Nunavut),\n Canada, was observed during summer 1998, in contrast to most other years since\n 1986. Evidence of malnourished muskoxen was also found. Early winter weather\n and a consequent 50% reduction of the 1997 summer replenishment period appeared\n to be the most likely cause, giving rise to a new hypothesis about conditions\n that might cause adverse demographic effects in arctic herbivores.\n \n The study area included a 150 km2 region of the Fosheim Peninsula in a 180o arc\n north of Eureka, Ellesmere Island, Nunavut, Canada (all within about 9 km of\n 80oN, 86oW). The area, extending from Eureka Sound to Remus Creek and from\n Slidre Fiord to Eastwind Lake, included shoreline, hills, lowlands, creek\n bottoms, and the west side of Blacktop Ridge. An associate, Layne Adams, and I\n spent 1-11 July 1998 in this area on all-terrain vehicles, following a pair of\n wolves Canis lupus (Mech, 1994). Adams and I also surveyed the surrounding area\n with binoculars for prey animals, in much the same manner that my assistants\n and I have practiced for one to six weeks each summer in the same area since\n 1986 (Mech, 1995, 1997). Because both muskoxen and arctic hares were common\n residents of the area during most years and were not the focus of our studies,\n no standardized counts were made. However, general field notes were sufficient\n to document that during most summers both species and their young were present.", "links": [ { diff --git a/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json b/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json index af0f495c5b..90455230f2 100644 --- a/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json +++ b/datasets/usgs_npwrc_nestingsuccess_Version 26MAR2001.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_nestingsuccess_Version 26MAR2001", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "We followed 3094 upland nests of several species of ducks. Clutches in most\nnests were lost to predation. We related daily nest predation rates to indices\nof activity of eight egg-eating predators, precipitation during the nesting\nseason, and measures of wetland conditions. Activity indices of red fox (Vulpes\nvulpes), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor)\nactivity were positively correlated, as were activity indices of coyote (Canis\nlatrans), Franklin's ground squirrel (Spermophilus franklinii), and\nblack-billed magpie (Pica pica). Indices of fox and coyote activity were\nstrongly negatively correlated (r = early-season nests were lower in areas and\nyears in which larger fractions of seasonal wetlands contained water. For\nlate-season nests, a similar relationship held involving semipermanent\nwetlands. We suspect that the wetland measures, which reflect precipitation\nduring some previous period, also indicate vegetation growth and the abundance\nof buffer prey, factors that may influence nest predation rates.", "links": [ { diff --git a/datasets/usgs_npwrc_purpleloostrife_Version 04JUN99.json b/datasets/usgs_npwrc_purpleloostrife_Version 04JUN99.json index 63ff2238fc..7f79f7ba2e 100644 --- a/datasets/usgs_npwrc_purpleloostrife_Version 04JUN99.json +++ b/datasets/usgs_npwrc_purpleloostrife_Version 04JUN99.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_purpleloostrife_Version 04JUN99", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Purple loosestrife (Lythrum salicaria), a native of Eurasia, is an introduced\nperennial plant in North American wetlands that displaces other wetland plants.\nAlthough not well studied, purple loosestrife is widely believed to have little\nvalue as habitat for birds. To examine the value of purple loosestrife as\navian breeding habitat, we conducted early, mid-, and late season bird surveys\nduring two years (1994 and 1995) at 258 18-m (0.1 ha) fixed-radius plots in\ncoastal wetlands of Saginaw Bay, Lake Huron. We found that\nloosestrife-dominated habitats had higher avian densities, but lower avian\ndiversities than other vegetation types. The six most commonly observed bird\nspecies in all habitats combined were Sedge Wren (Cistothorus platensis), Marsh\nWren (C. palustris), Yellow Warbler (Dendroica petechia), Common Yellowthroat\n(Geothylpis trichas), Swamp Sparrow (Melospiza georgiana), and Red-winged\nBlackbird (Agelaius phoeniceus). Swamp Sparrow densities were highest and Marsh\nWren densities were lowest in loosestrife dominated habitats. We observed ten\nbreeding species in loosestrife dominated habitats. We conclude that avian use\nof loosestrife warrants further quantitative investigation because avian use\nmay be higher than is commonly believed. Received 27 May 1998, accepted 26 Aug.\n1998.", "links": [ { diff --git a/datasets/usgs_npwrc_saltmam.json b/datasets/usgs_npwrc_saltmam.json index adec7f1918..01e95d2199 100644 --- a/datasets/usgs_npwrc_saltmam.json +++ b/datasets/usgs_npwrc_saltmam.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgs_npwrc_saltmam", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wildlife species in this brochure have been grouped into four categories:\nBirds, Mammals, Reptiles and Amphibians, and Fish. \n\nAll mammals listed are considered resident species with the exception of the\nbats which migrate on a seasonal basis like many of the birds. Families follow\nthat of A Field Guide to the Mammals by Burt and Grossenheider.", "links": [ { diff --git a/datasets/usgsbrdasc00000004.json b/datasets/usgsbrdasc00000004.json index 83cb3efb55..375886bdb4 100644 --- a/datasets/usgsbrdasc00000004.json +++ b/datasets/usgsbrdasc00000004.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdasc00000004", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Ambient air quality monitoring is important in Denali, to document baseline\nconditions and to track long term trends.\n\n Denali National Park and Preserve is the only National Park in Alaska\ndesignated as class I under the Clean Air Act.\n\n Geographic Description:\n Specific coordinates in the Denali National Park and Preserve, Alaska.\nDenali National Park and Preserve is located in the central Alaska Range,\napproximately 210 km southwest of Fairbanks, Alaska.\n\n Methodology:\n Denali currently participates in three nationwide air quality monitoring\nnetworks: National Atmospheric Deposition Program (NADP), Interagency\nMonitoring of Protected Visual Environments (IMPROVE), National Park Service\nGaseous Pollutant Monitoring Network (ozone monitoring). Air quality\nmonitoring protocols have been written for each network, and approved by the\nrespective network steering committees. Since there is no local control over\nmethodology, the network manuals are the park's guiding documents. This is a\ncompilation of network protocols.", "links": [ { diff --git a/datasets/usgsbrdfcsc_d_seagrass.json b/datasets/usgsbrdfcsc_d_seagrass.json index f2a51a67b2..e17246f0c5 100644 --- a/datasets/usgsbrdfcsc_d_seagrass.json +++ b/datasets/usgsbrdfcsc_d_seagrass.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdfcsc_d_seagrass", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The population of manatees in Puerto Rico is the only group of Antillean\nmanatees (Trichechus manatus manatus) managed and protected by the United\nStates. The Manatee Recovery Plan for the Puerto Rico Population of West\nIndian Manatees includes requirements to identify and manage habitats and\ndevelop criteria and biological information important to its recovery. To this\nend, the Sirenia Project initiated telemetry studies of manatees in Puerto Rico\nat the U.S. Naval Station Roosevelt Roads (RRNS) in 1992. Concurrently, the\nProject began gathering information on habitats critical to manatee in eastern\nPuerto Rico. Computer aided mapping based on the interpretation of aerial\nphotographs and field groundtruthing was used in the current project to define\nthese habitats and map their distribution in the area of high manatee use. \nBenthic habitats along approximately 32 miles (52 kilometers of RRNS shoreline\nwere mapped. Field assessment and characterization of important seagrass\nhabitats was conducted as a means of identifying seagrass and macroalgae\ncommunities, especially in areas with known manatee feeding sites.\n\nThe purpose of this dataset is to identify and manage manatee habitats and to\ndevelop biological information important to the manatees' recovery.\n\nData was obtained during ground truthing in October, 1994 and June, 1995. One\nhundred and twenty-five sites, many representing questions raised during\npreliminary habitat delineations were visited, along with sites with\ncharacteristic signatures useful for broader interpretations. Transects were\nmade over several areas with rapidly changing benthic communities and confusing\nsignatures. Data recorded at each site included depth (range 0.5-7.1 m),\nclassification, dominant community, subdominant community, and pertinent\ncomments. the locations of all groundtruth sites were plotted onto one Arc Cad\nlayer of mapping information.\n\nGroundtruthing was used to field verify and correct the initial delineations\nmade. Improvements were made to the draft classification scheme based on field\nobservations. Sites of questionable draft delineations were located on the\nwater and confirmed or corrected. Known manatee use of the area for resting or\nfeeding was noted. These sites were accurately located on the overlay for\ninclusion on the maps.\n\nSite location (latitude and longitude) was determined with a Garmin 45 GPS and\nwater depth (tape), temperature (hand-held mercury thermometer), and salinity\n(hand-held temperature compensated refractometer) recorded. In addition,\nsalinity measurements were made at select nearshore locations to assess the\ninfluence of drainage creeks and ditches on nearshore water salinity. \nUnderwater video photography and 35 mm photography were used to document\nobservations.\n\nA review of vertical images of waters of RRNS was taken on December 17, 1993,\nfor the United States Navy, along with other collateral information, was used\nto develop a benthic habitat classification system useful for mapping benthic\ncommunities in the area. The system developed for this project was similar to\nthat developed for Geographic Information System (GIS) mapping of benthic\ncommunities in the Florida Keys National Marine Sanctuary. Clear acetate\noverlays were placed over the 9\" x 9\" aerial prints and the polygon method of\ndelineation used to outline habitats on the overlays.\n\nComputer aided design methods (PC Arc Cad) were used to create a shoreline base\nmap from navigational charts for this region of Puerto Rico. Habitat polygons\nextending as far from shore as allowed by the resolution of the images were\ndigitized onto the base map. A minimum mapping unit of 0.5 acres was applied\nbased on the scale and quality of the images. Once finalized, maps were\nprinted in both color and black-line.\n\nThe information for this metadata was partially taken from the document Mapping\nand Characterizing Seagrass Areas Important to Manatees in Puerto Rico -\nBenthic Communities Mapping and Assessment. Prepared for the U.S. Department of\nInterior, National Biological Service, Sirenia Project. Prepared by Curtis\nKruer, Senior Biologist, Caribbean Fisheries Consultants, Inc.", "links": [ { diff --git a/datasets/usgsbrdfcsc_d_vieques.json b/datasets/usgsbrdfcsc_d_vieques.json index 3e2c9cac19..428ac7f992 100644 --- a/datasets/usgsbrdfcsc_d_vieques.json +++ b/datasets/usgsbrdfcsc_d_vieques.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdfcsc_d_vieques", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vieques Island Mapping Project was initiated in September 1995 as a\ncooperative effort between NSRR and the Sirenia Project (Military\nInterdepartmental Purchase Request no. NOO38995MP00012). Caribbean Fisheries\nConsultants, Inc. was contracted by the Sirenia Project to help produce the\ndesired information in conjunction with Sirenia Project biologists. Products\ninclude maps delineating Vieques' benthic habitat and coastal wetlands, an\nelectronic georeferenced habitat map (UTM coordinate system) in a format\ncompatible with ARC/INFO (Environmental Systems Research Institute, Inc.) and\na report describing methods used, the classification scheme, and the\nrelationship of these habitats to manatee use of Vieques Island. These map\nproducts complement the Navy's Vieques Land Use Management Plan by identifying\nmarine resources targeted for protection in the plan.\n\nObjectives include producing maps of the coastal seagrass beds and other bottom\nhabitat (including coral reefs) surrounding the island of Vieques and\ncharacterizing the species composition and density of seagrasses in areas\nfrequented by manatees near Vieques.\n\nGround truthing by boat around Vieques Island was conducted from May 14 to May\n19 1996 and from October 4 through October 9 1996. The ground truthing was\nconducted to verify the interpretation of benthic habitat visible in the\nimages, verify accuracy of the shoreline limits, and refine the habitat\nclassification scheme used for the Vieques maps. Three hundred and thirty-two\nground truth stations were established around Vieques Island, located on the\naerial image overlays, and digitized. These sites are plotted as a layer on\nthe habitat map. The listing of ground truth sites includes site identifier,\nlatitude and longitude, community classification, depteh, dominant community\nelements, less dominant elements, and other pertinent information. Latitude\nand longitude were obtained for each station in the field using a Garmin 45 GPS\nunit. Water depth for each station was determined from a Hummingbird LCR - 400\nVideo Fathometer with transom mounted transducer. Underwater Hi-8 video and 35\nmm photography were used to document observations at selected sites.\n\nThe habitat classification scheme used is similar to that used by Kruer and\nothers in southern Forida seagrass beds and other benthic habitats in the\nFlorida Keys National Marine Sanctuary and Biscayne National Park. This scheme,\nalso used for benthic habitat mapping at NSRR in 1994/1995 (Kruer 1995), was\nrefined for the Vieques Island mapping project by adding the category \"sand\nbottom with rock\". Also, mangroves were mapped in interior areas. \n\nThe information for this metadata was partially taken from the report\n- Mapping and characterization of Nearshore Benthic Habitats around Vieques\nIsland, Puerto Rico.", "links": [ { diff --git a/datasets/usgsbrdnpwrc_d_birds_checklists_Version 12MAY03.json b/datasets/usgsbrdnpwrc_d_birds_checklists_Version 12MAY03.json index 690c705c38..2b95a85b74 100644 --- a/datasets/usgsbrdnpwrc_d_birds_checklists_Version 12MAY03.json +++ b/datasets/usgsbrdnpwrc_d_birds_checklists_Version 12MAY03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrc_d_birds_checklists_Version 12MAY03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This resource is known as Bird Checklists of the United States. Bird\nChecklists of the United States. For years, people and groups have developed\nlistings or checklists of birds that occur in a particular region. Information\non the distribution or seasonal occurrence of birds in an area, however, can\nchange over time. Bird checklists often are outdated in only a few years after\nprinting, but budget and time constraints prohibit regular updates. The\nInternet provides new opportunities for the compilation and dissemination of\ncurrent information on bird distribution. Here we offer bird checklists\ndeveloped by others that indicate the seasonal occurrence of birds in state,\nfederal, and private management areas, nature preserves, and other areas of\nspecial interest in the United States.\n\nBird checklists exist for Great Plains States: Colorado, Iowa, Kansas,\nMinnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma,\nSouth Dakota, Texas, Wyoming; East of Great Plains states: Alabama, Arkansas,\nConnecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky,\nLouisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, New\nHampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode\nIsland, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin;\nand West of Great Plains: Arizona, California, Idaho, Nevada, Oregon, Utah,\nWashington.\n\nIt is hoped that these checklists will serve several purposes. First, we hope\nthe checklists will help bird enthusiasts decide where to visit. A visit to\nthese unique areas can be a rewarding experience for both the amateur and\nexpert birdwatcher. Second, we hope that these checklists will provide\npotential visitors with a guide to birds that might occur in a region during a\nparticular season. The checklists were kept simple to facilitate printing so\nthey can be easily carried into the field. And third, we hope that these\nchecklists will stimulate and encourage visitors to these areas to help improve\nthe accuracy and completeness of the checklists. The information in some\nchecklists already has been updated; these checklists contain more current\ninformation than the printed versions.\n\nSightings of birds and other wildlife are an important part of monitoring\nwildlife use. Visitors are encouraged to share their observations of rare,\naberrant, or occasional birds with the staff at these areas. With each\nchecklist, we have included an address for visitors to send information on rare\nbirds so that checklists can be updated. To assist in establishing standards in\nobservation and reporting, we also provide a Record Documentation Form to\ndocument supporting details of rare bird observations.\n\nThe efforts and dedication of the many birders, birding groups, biologists, and\nresource managers who developed these checklists are acknowledged.\n\nThe information for this metadata was partially taken from the\nNorthern Prairie Wildlife Research Center website at\nhttp://www.npwrc.usgs.gov/resource/birds/chekbird/index.htm", "links": [ { diff --git a/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json b/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json index 697b1e02f5..1c3caadd97 100644 --- a/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json +++ b/datasets/usgsbrdnpwrc_d_ndfleas_Version 16JUL97.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrc_d_ndfleas_Version 16JUL97", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains distribution maps for the following species of fleas:\nAetheca wagneri, Amaradix euphorbi, Amphipsylla sibirica pollionis,\nCallistopsyllus terinus campestris, Cediopsylla inaequalis inaequalis,\nCeratophyllus (Ceratophyllus) idius, Corrodopsylla curvata curvata,\nChaetopsylla lotoris, Ctenocephalides canis, Epitedia faceta, Epitedia\nwenmanni, Euhoplopsyllus glacialis affinis, Eumolpianus eumolpi eumolpi,\nFoxella ignota albertensis, Hystrichopsylla dippiei dippiei, Megabothris\n(Megabothris) asio megacolpus, Megabothris (Amegabothris) lucifer, Meringis\njamesoni, Myodopsylla insignis, Nearctopsylla genalis hygini, Neopsylla\ninopina, Nosopsyllus fasciatus, Oropsylla (Oropsylla) arctomys, Opisodasys\n(Opisodasys) pseudarctomys, Orchopeas caedens, Orchopeas howardi,\nPeromyscopsylla hamifer, Pleochaetis exilis, Pules irritans, Rhadinopsylla\n(Actenophthalmus) fraterna, Thrassis bacchi bacchi.\n\nThe information for this metadata was partially taken from the Northern Prairie\nWildlife Research Center website at\nhttp://www.npwrc.usgs.gov/resource/insects/ndfleas/", "links": [ { diff --git a/datasets/usgsbrdnpwrcb00000013_Version 30SEP2002.json b/datasets/usgsbrdnpwrcb00000013_Version 30SEP2002.json index bb711716c4..5d22f5ed5d 100644 --- a/datasets/usgsbrdnpwrcb00000013_Version 30SEP2002.json +++ b/datasets/usgsbrdnpwrcb00000013_Version 30SEP2002.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcb00000013_Version 30SEP2002", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This bibliography lists as many fisheries biology and related references as\npossible from North Dakota and South Dakota waters for use by fishery\nbiologists. Selected references from contiguous states sharing river basins\nwith the Dakotas are included. Studies in the Missouri River downstream from\nGavins Point Dam are also included. In addition to published fishery and\nrelated aquatic studies, attempts were made to list all dissertations and\nMasters theses in these fields.", "links": [ { diff --git a/datasets/usgsbrdnpwrcb00000016_Version 16JUL97.json b/datasets/usgsbrdnpwrcb00000016_Version 16JUL97.json index 770adabd21..6eaa28093e 100644 --- a/datasets/usgsbrdnpwrcb00000016_Version 16JUL97.json +++ b/datasets/usgsbrdnpwrcb00000016_Version 16JUL97.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcb00000016_Version 16JUL97", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The success of vegetation management programs for waterfowl is dependent on\nknowing the physical and physiological requirements of the target species.\nLakes and riverine impoundments that contain an abundance of the American\nwildcelery plant (Vallisneria americana) have traditionally been favored by\ncanvasback ducks (Aythya valisineria) and other waterfowl species as feeding\nareas during migration. Information on the ecology of V. americana is\nsummarized to serve as a guide for potential wetland restoration projects.\nBecause of the geographic diversity and wetland conditions in which V.\namericana is found, we have avoided making hard-and-fast conclusions about the\nrequirements of the plant. Rather, we present as much general information as\npossible and provide the sources of more specific information. Vallisneria\namericana is a submersed aquatic plant that has management potential.\nTechniques are described for transplanting winter buds from one location to\nanother. Management programs that employ these techniques should define\nobjectives clearly and evaluate the water regime carefully before initiating a\nmajor effort.", "links": [ { diff --git a/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json b/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json index 184c0e2bd8..aca9ae4bab 100644 --- a/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json +++ b/datasets/usgsbrdnpwrcd00000002_Version 02MAR98.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcd00000002_Version 02MAR98", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Laboratory studies with algae, aquatic invertebrates, and fish. Short-term\ntoxicity tests showed that both fire-retardant and foam-suppressant chemicals\nwere very toxic to aquatic organisms including algae, aquatic invertebrates,\nand fish. Foam-suppressant are more toxic than fire-retardant chemicals. The\nprimary toxicant in fire-retardants is the ammonia component, whereas the\nnitrite and nitrate components do not seem to contribute much to the toxicity\nof the formulations. In foam suppressants the primary toxicant is the\nsurfactant component. The most sensitive life-stage for fish is the swim-up\nstage. Accidental spills of fire-fighting chemicals in streams could cause\nsubstantial fish kills depending on the stream size and flow rate. For example,\nthe retardant Fire-Trol GTS-R is prepared for field use by mixing 1.66 pounds\nper gallon of water to produce 1.1 gallons of slurry, which is equivalent to\n198,930 mg/liter. Comparing the concentration of FT GTS-R field mixture to the\nacute toxicity values for the most sensitive life stage for rainbow trout gives\na ratio of 853 in soft water and 1474 in hard water. Applying a safety factor\nof 100 to this ratio suggests a dilution of 85, 300 in soft water and 147,400\nin hard water is needed to lower the chemical concentration in a receiving\nwater to limit adverse effects, i.e., fish kill, in a stream. For rainbow\ntrout, other dilution factors would be 52,100 for Fire-Trol LCG-R, 85,600 for\nPhos-Chek D75-F, 71,400 for Phos-Chek WD-881, and 50,000 for Silv-ex.\nFire-fighting chemicals are very toxic in aquatic environments and fire control\nmanagers need to consider protection of aquatic resources, especially if\nendangered species are present.", "links": [ { diff --git a/datasets/usgsbrdnpwrcd00000012_Version 31JUL97.json b/datasets/usgsbrdnpwrcd00000012_Version 31JUL97.json index 508e0ee8cb..f4c45069a1 100644 --- a/datasets/usgsbrdnpwrcd00000012_Version 31JUL97.json +++ b/datasets/usgsbrdnpwrcd00000012_Version 31JUL97.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcd00000012_Version 31JUL97", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Breeding bird populations in North Dakota were compared using surveys conducted\nin 1967 and 1992-93. In decreasing order, the five most frequently occurring\nspecies were Horned Lark (Eremophia alpestris), Brown-headed Cowbird (Molothrus\nater), Western Meadowlark (Sturnella neglecta), Red-winged Blackbird (Agelaius\nphoeniceus), and Eastern Kingbird (Tyrannus tyrannus). The five most abundant\nspecies - Horned Lark, Chestnut-collared Longspur (Calcarius ornatus),\nRed-winged Blackbird, Western Meadowlark, and Brown-headed Cowbird - accounted\nfor 31-41% of the estimated statewide breeding bird population in the three\nyears. Although species composition remained relatively similar among years,\nbetween-year patterns in abundance and frequency varied considerably among\nspecies. Data from this survey and the North American Breeding Bird Survey\nindicated that species exhibiting significant declines were primarily\ngrassland- and wetland-breeding birds, whereas species exhibiting significant\nincreases primarily were those associated with human structures and woody\nvegetation. Population declines and increases for species with similar habitat\nassociations paralleled breeding habitat changes, providing evidence that\nfactors on the breeding grounds are having a detectable effect on breeding\nbirds in the northern Great Plains.", "links": [ { diff --git a/datasets/usgsbrdnpwrcd0000001_Version 15DEC98.json b/datasets/usgsbrdnpwrcd0000001_Version 15DEC98.json index 1252c06ae5..b27818dfda 100644 --- a/datasets/usgsbrdnpwrcd0000001_Version 15DEC98.json +++ b/datasets/usgsbrdnpwrcd0000001_Version 15DEC98.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcd0000001_Version 15DEC98", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The invasion of exotic plants is becoming a problem in many ecosystems\nincluding some areas in Rocky Mountain National Park (RMNP) (Rocky Mountain\nNational Park Resource Management Reports #1 and #13). Some exotic species,\nsuch as leafy spurge and spotted knapweed, are capable of rapidly colonizing\nareas, altering community composition, and even displacing native species\n(Belcher and Wilson 1989, Tyser and Key 1988). In many cases, the processes of\ninvasion are poorly documented, and little information is available on an\narea's past history. However, there is a large amount of information available\nin the literature which relates to the life history traits of exotic species\nand the distribution of exotic species. This information can be used to help\npredict the potential distribution and threat of exotic species to ecosystems. \n Exotic plants can be thought of as those plants which did not originally occur\nin the ecosystem, and have since been introduced to the area. The National Park\nService (NPS) defines an exotic species as, \"those that occur in a given place\nas a result of direct or indirect, deliberate, or accidental actions by\nhumans.\" This somewhat conservative definition of exotic species is necessary\nto insure that natural resources in national parks are preserved. NPS policy\ngenerally prohibits the introduction of exotic species into natural areas of\nnational parks. Exotic species which threaten park resources or public health\nare to be managed or eliminated if possible. In addition, the NPS recently\nsigned a memorandum of understanding with 10 other federal and state agencies\nin the state of Colorado. This agreement states that all paid management\nagencies will work with private and county entities to manage exotic plants\nand, in particular, \"noxious weeds.\" RMNP is currently working with Estes Park\nin exotic plant control as part of this agreement.", "links": [ { diff --git a/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json b/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json index 51d026b251..1ad9117000 100644 --- a/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json +++ b/datasets/usgsbrdnpwrcd0000003_Version 16JUL97.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcd0000003_Version 16JUL97", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The expansion of outdoor recreational activities has increased greatly the\ninteraction between the public and waterfowl and waterfowl habitat. The effects\nof these interactions on waterfowl habitats are more visible and obvious,\nwhereas the effects of interactions which disrupt the normal behavior of\nwaterfowl are more subtle and often overlooked, but perhaps no less of a\nproblem than destruction of habitat. This bibliography contains excerpts or\nannotations from 211 articles that contained information about effects of human\ndisturbances on waterfowl. Indices are provided for subject/keywords,\ngeographic locations, species of waterfowl, and authors used in this\nbibliography.", "links": [ { diff --git a/datasets/usgsbrdnpwrcs0000004_Version 12MAY03.json b/datasets/usgsbrdnpwrcs0000004_Version 12MAY03.json index a553a33889..a3cf7934f9 100644 --- a/datasets/usgsbrdnpwrcs0000004_Version 12MAY03.json +++ b/datasets/usgsbrdnpwrcs0000004_Version 12MAY03.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "usgsbrdnpwrcs0000004_Version 12MAY03", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Northern Prairie has a long history of studying nest success of upland nesting\nducks. Over the years, we have developed standardized procedures for collecting\nand analyzing these types of data. Data forms and instruction manuals developed\nby the Center are used widely by biologists throughout the northern Great\nPlains and elsewhere. Extensive use of standardized procedures led to a\ncooperative effort among Federal, State, Private, and other Non-Government\nOrganizations that has allowed us to compile the Nest File, a data base of more\nthan 75,000 duck nests spanning 30+ years in the northern Great Plains.", "links": [ { diff --git a/datasets/validation-of-the-critical-crack-length-in-snowpack_1.0.json b/datasets/validation-of-the-critical-crack-length-in-snowpack_1.0.json index 1fd98c8504..054410e6f9 100644 --- a/datasets/validation-of-the-critical-crack-length-in-snowpack_1.0.json +++ b/datasets/validation-of-the-critical-crack-length-in-snowpack_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "validation-of-the-critical-crack-length-in-snowpack_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included. These data were analyzed and results were published in Richter et al. (2019). Please refer to the Readme file for further details on the data. These data are the basis of the following publication: Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353\u20133366, https://doi.org/10.5194/tc-13-3353-2019, 2019.", "links": [ { diff --git a/datasets/vanderford_data_1983_85_1.json b/datasets/vanderford_data_1983_85_1.json index b545e56252..2604b63d59 100644 --- a/datasets/vanderford_data_1983_85_1.json +++ b/datasets/vanderford_data_1983_85_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vanderford_data_1983_85_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results.\n\nThese documents have been archived at the Australian Antarctic Division.", "links": [ { diff --git a/datasets/vanderford_gravity_1980_1.json b/datasets/vanderford_gravity_1980_1.json index 342c6ea007..6e4faa41fd 100644 --- a/datasets/vanderford_gravity_1980_1.json +++ b/datasets/vanderford_gravity_1980_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vanderford_gravity_1980_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A collection of gravity readings, taken on the Vanderford Glacier in February 1980. Also includes barometric pressure readings, taken at the same time, for determining the height of the location where the reading was taken.\n\nPhysical copies of these documents have been stored in the Australian Antarctic Division records store.", "links": [ { diff --git a/datasets/vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0.json b/datasets/vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0.json index 97474aab25..ed7b0bcc1a 100644 --- a/datasets/vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0.json +++ b/datasets/vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Summary This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25\u00b0 x 0.25\u00b0 (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information. # Main model characteristics The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation. ### Model Sublimation Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel. ### Model Air Parcel Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation. # References Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6 Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705", "links": [ { diff --git a/datasets/veg_continuous_fields_xdeg_931_1.json b/datasets/veg_continuous_fields_xdeg_931_1.json index 677b95f095..e41bbe92d1 100644 --- a/datasets/veg_continuous_fields_xdeg_931_1.json +++ b/datasets/veg_continuous_fields_xdeg_931_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "veg_continuous_fields_xdeg_931_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.", "links": [ { diff --git a/datasets/vegdri.json b/datasets/vegdri.json index 39ab38e7bd..30a81de727 100644 --- a/datasets/vegdri.json +++ b/datasets/vegdri.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vegdri", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The National Drought Mitigation Center produces VegDRI in collaboration with the US Geological Survey's (USGS) Center for Earth Resources Observation and Science (EROS), and the High Plains Regional Climate Center (HPRCC), with sponsorship from the US Department of Agriculture's (USDA) Risk Management Agency (RMA). Main researchers working on VegDRI are Dr. Brian Wardlow and Dr. Tsegaye Tadesse at the NDMC, and Jesslyn Brown with the USGS, and Dr. Yingxin Gu with ASRC Research and Technology Solutions, contractor for the USGS at EROS.\n\nVegDRI maps are produced every two weeks and provide regional to sub-county scale information about drought's effects on vegetation. In 2006, VegDRI covered seven states in the Northern Great Plains (CO, KS, MT, NE, ND, SD, and WY). It expanded across eight more states in 2007 to cover the rest of the Great Plains (NM, OK, MO, and TX) and parts of the Upper Midwest (IA, IL, MN, and WI). VegDRI expanded to include the western U.S. in 2008 (WA, ID, OR, UT, CA, AZ, NV). In May 2009 VegDRI began depicting the eastern states as well, covering the entire conterminous 48-state area.", "links": [ { diff --git a/datasets/vegetation-height-model-nfi_2019 (current).json b/datasets/vegetation-height-model-nfi_2019 (current).json index a6d4a0f7ef..a07d375139 100644 --- a/datasets/vegetation-height-model-nfi_2019 (current).json +++ b/datasets/vegetation-height-model-nfi_2019 (current).json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vegetation-height-model-nfi_2019 (current)", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 \u00d7 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual vegetation heights using a digital terrain model (DTM) based on laser data with the buildings masked out, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we use summer aerial images from six years. Latest version is from 2019.", "links": [ { diff --git a/datasets/vegsoils_wilhend_642_1.json b/datasets/vegsoils_wilhend_642_1.json index f72fb36bd6..6bf4d3eb66 100644 --- a/datasets/vegsoils_wilhend_642_1.json +++ b/datasets/vegsoils_wilhend_642_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vegsoils_wilhend_642_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation and Soils 1-degree data. The data are available in both ASCII GRID and binary image file formats.", "links": [ { diff --git a/datasets/vemap-1_VEMAP1_CDROM_566_1.json b/datasets/vemap-1_VEMAP1_CDROM_566_1.json index fe84ddd362..69c5f8c93c 100644 --- a/datasets/vemap-1_VEMAP1_CDROM_566_1.json +++ b/datasets/vemap-1_VEMAP1_CDROM_566_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_VEMAP1_CDROM_566_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The VEMAP 1: Model Input Database CD-ROM ISO image contains long-term data that were used as input in comparing models during Phase 1 of the Vegetation Ecosystem Modeling and Analysis Project. Compiled and model-generated data sets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States. Dates of the data sets range between 1895 and 1996. The data are gridded at 0.5 degree latitude by 0.5 degree longitude.", "links": [ { diff --git a/datasets/vemap-1_VEMAP_Alaska_1344_1.json b/datasets/vemap-1_VEMAP_Alaska_1344_1.json index 6717798fb1..db58493190 100644 --- a/datasets/vemap-1_VEMAP_Alaska_1344_1.json +++ b/datasets/vemap-1_VEMAP_Alaska_1344_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_VEMAP_Alaska_1344_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set provides the results of the development of The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 transient climate change scenarios for the state of Alaska, USA. The data include gridded monthly historical and future estimates of maximum and minimum temperature, solar radiation, vapor pressure, irradiance, relative humidity and potential evapotranspiration at 0.5-degree spatial resolution. Historical data are for the period 1922-1996; future estimates cover the period 1997-2100.", "links": [ { diff --git a/datasets/vemap-1_climate_224_1.json b/datasets/vemap-1_climate_224_1.json index e5ea0512f1..1801f71e40 100644 --- a/datasets/vemap-1_climate_224_1.json +++ b/datasets/vemap-1_climate_224_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_climate_224_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Climate", "links": [ { diff --git a/datasets/vemap-1_geog_222_1.json b/datasets/vemap-1_geog_222_1.json index c7be4c5dfb..e517103061 100644 --- a/datasets/vemap-1_geog_222_1.json +++ b/datasets/vemap-1_geog_222_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_geog_222_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Georeferencing", "links": [ { diff --git a/datasets/vemap-1_results_731_1.json b/datasets/vemap-1_results_731_1.json index f94451abbe..e3a854cccc 100644 --- a/datasets/vemap-1_results_731_1.json +++ b/datasets/vemap-1_results_731_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_results_731_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.Selected variable output results from the VEMAP Phase I modeling exercise are now available for several combinations of biogeochemistry and biogeography models and climate change scenarios through the ORNL DAAC. For a description of the models and climate scenarios employed in the VEMAP 1 project and a discussion of the results please refer to the following publication: VEMAP Members. 1995. Vegetation/Ecosystem Modeling and Analysis Project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochem. Cycles 9:407-437.", "links": [ { diff --git a/datasets/vemap-1_scenario_223_1.json b/datasets/vemap-1_scenario_223_1.json index 4f8ef2e9d5..b716091891 100644 --- a/datasets/vemap-1_scenario_223_1.json +++ b/datasets/vemap-1_scenario_223_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_scenario_223_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Climate Scenarios", "links": [ { diff --git a/datasets/vemap-1_siteFile_226_1.json b/datasets/vemap-1_siteFile_226_1.json index 2bd4f45ff5..69cfb9f64f 100644 --- a/datasets/vemap-1_siteFile_226_1.json +++ b/datasets/vemap-1_siteFile_226_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_siteFile_226_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Site Files", "links": [ { diff --git a/datasets/vemap-1_soil_227_1.json b/datasets/vemap-1_soil_227_1.json index c6cdf99809..b0499cd6cf 100644 --- a/datasets/vemap-1_soil_227_1.json +++ b/datasets/vemap-1_soil_227_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_soil_227_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Soil", "links": [ { diff --git a/datasets/vemap-1_veg_225_1.json b/datasets/vemap-1_veg_225_1.json index d7a31dd398..0f87f957b9 100644 --- a/datasets/vemap-1_veg_225_1.json +++ b/datasets/vemap-1_veg_225_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-1_veg_225_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States: Vegetation", "links": [ { diff --git a/datasets/vemap-2_TCLIMATE_annual_571_1.json b/datasets/vemap-2_TCLIMATE_annual_571_1.json index 1b4fe0f71a..21c122f906 100644 --- a/datasets/vemap-2_TCLIMATE_annual_571_1.json +++ b/datasets/vemap-2_TCLIMATE_annual_571_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TCLIMATE_annual_571_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States. The data set is a ~100 year fully gridded annual time series of climate that includes realistic interannual variability.", "links": [ { diff --git a/datasets/vemap-2_TCLIMATE_daily_620_1.json b/datasets/vemap-2_TCLIMATE_daily_620_1.json index be04935908..c02d99b1ed 100644 --- a/datasets/vemap-2_TCLIMATE_daily_620_1.json +++ b/datasets/vemap-2_TCLIMATE_daily_620_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TCLIMATE_daily_620_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VEMAP Phase 2 has developed a data set of ~100-year gridded monthly and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical time series and projected scenarios of climate, atmospheric CO2, and N-Deposition, and N-Deposition", "links": [ { diff --git a/datasets/vemap-2_TCLIMATE_monthly_568_1.json b/datasets/vemap-2_TCLIMATE_monthly_568_1.json index a7de1ca574..ac9dd54f37 100644 --- a/datasets/vemap-2_TCLIMATE_monthly_568_1.json +++ b/datasets/vemap-2_TCLIMATE_monthly_568_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TCLIMATE_monthly_568_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "An integrated input data set for ecosystem and vegetation modeling for the conterminous United States. The data set is a ~100 year gridded monthly time series of climate that includes realistic interannual variability.", "links": [ { diff --git a/datasets/vemap-2_TSCENARIO_annual_570_1.json b/datasets/vemap-2_TSCENARIO_annual_570_1.json index f4db6358e4..4403b2a160 100644 --- a/datasets/vemap-2_TSCENARIO_annual_570_1.json +++ b/datasets/vemap-2_TSCENARIO_annual_570_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TSCENARIO_annual_570_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data sets of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments with transient greenhouse gas and sulfate aerosol forcing.", "links": [ { diff --git a/datasets/vemap-2_TSCENARIO_daily_618_1.json b/datasets/vemap-2_TSCENARIO_daily_618_1.json index c8e5b59628..6ea7e9e712 100644 --- a/datasets/vemap-2_TSCENARIO_daily_618_1.json +++ b/datasets/vemap-2_TSCENARIO_daily_618_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TSCENARIO_daily_618_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "VEMAP Phase 2 has developed a number of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments. The purpose of these scenarios is to reflect time-dependent changes in surface climate from AOGCMs in terms of both (1) long-term trends and (2) changes in multiyear (3-5 yr) to decadal variability, such as El Nino/Southern Oscillation (ENSO).", "links": [ { diff --git a/datasets/vemap-2_TSCENARIO_monthly_567_1.json b/datasets/vemap-2_TSCENARIO_monthly_567_1.json index 9c13b38a9f..d9251c3553 100644 --- a/datasets/vemap-2_TSCENARIO_monthly_567_1.json +++ b/datasets/vemap-2_TSCENARIO_monthly_567_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_TSCENARIO_monthly_567_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Data sets of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments with transient greenhouse gas and sulfate aerosol forcing.", "links": [ { diff --git a/datasets/vemap-2_results_annual_766_1.json b/datasets/vemap-2_results_annual_766_1.json index 109b3a8fa3..dcd31829af 100644 --- a/datasets/vemap-2_results_annual_766_1.json +++ b/datasets/vemap-2_results_annual_766_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_results_annual_766_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phase 2 of the VEMAP Project developed historical (1895-1993) gridded data sets of climate (temperature, precipitation, solar radiation, humidity, and wind speed) and projected (1994-2100) gridded annual and monthly climate data sets using output from two climate system models [CCCma (Canadian Centre for Climate Modeling and Analysis) and Hadley Centre models]. Two Phase 2 model experiments were run. First, a set of selected biogeochemical models and coupled biogeochemical-biogeographical models were run from 1895 to 1993 to compare model responses to the historical time series and current ecosystem biogeochemistry. Second, these same models were run on the projected 1994 to 2100 data to compare their ecological responses to transient scenarios of climate and atmospheric CO2 change. Model runs were performed for daily, monthly, and annual gridded data sets. The output of the annual model runs in VEMAP grid format are contained in this data set. The models investigated included five biogeochemical cycling models, which simulate plant production and nutrient cycles.", "links": [ { diff --git a/datasets/vemap-2_results_monthly_767_1.json b/datasets/vemap-2_results_monthly_767_1.json index 5152073a0c..e090ff0879 100644 --- a/datasets/vemap-2_results_monthly_767_1.json +++ b/datasets/vemap-2_results_monthly_767_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vemap-2_results_monthly_767_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Phase 2 developed historical (1895-1993) gridded data sets of climate (temperature, precipitation, solar radiation, humidity, and wind speed) and projected (1994-2100) gridded annual and monthly climate data sets using output from two climate system models [CCCma (Canadian Centre for Climate Modeling and Analysis) and Hadley Centre models]. Two Phase 2 model experiments were run. First, a set of selected biogeochemical models and coupled biogeochemical-biogeographical models were run from 1895 to 1993 to compare model responses to the historical time series and current ecosystem biogeochemistry. Second, these same models were run on the projected 1994 to 2100 data to compare their ecological responses to transient scenarios of climate and atmospheric CO2 change. Model runs were performed for daily, monthly, and annual gridded data sets. The output of the monthly model runs in VEMAP grid format are contained in this data set.", "links": [ { diff --git a/datasets/vest_lake_samples_gis_1.json b/datasets/vest_lake_samples_gis_1.json index b44c36500a..e34637460c 100644 --- a/datasets/vest_lake_samples_gis_1.json +++ b/datasets/vest_lake_samples_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vest_lake_samples_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains locations of sample sites for Ellis Fjord (1989), Organic Lake (1985) and Deep Lake (1975, 1975) in the Vestfold Hills. \n \nUnfortunately little is known as to what samples were collected. It is believed that water samples were taken at all locations, and that bottom sediment samples were taken at least at Deep Lake.\n\nWhen questioned in 2009, the investigating scientist was unable to remember exactly what work was done. The original maps may provide some clues.", "links": [ { diff --git a/datasets/vestfold_hills_dem_1.json b/datasets/vestfold_hills_dem_1.json index d15f94c037..cf1daa73ba 100644 --- a/datasets/vestfold_hills_dem_1.json +++ b/datasets/vestfold_hills_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vestfold_hills_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A Digital Elevation Model (DEM) of the Vestfold Hills with cell size 10 metres was interpolated from input coastline, contour, spot height (point locations with an elevation attribute) and lake data from the dataset described by the metadata record 'Vestfold Hills 1:25000 Topographic GIS Dataset' with Entry ID: vest_hills_gis.\nThe contour data is estimated to have horizontal accuracy of about 12 metres and vertical accuracy of about 5 metres. The spot heights are estimated to have horizontal accuracy of about 2 metres and vertical accuracy of about 1 metre.\nThe interpolation was done using the Topo to Raster tool in ArcGIS.\nIn the interpolation process all cells within a lake are assigned to the minimum elevation value of all cells along the shoreline. i.e. the interpolation is flat across the lake.\nThe output DEM was clipped to the extents of the input data.\nThe dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool.\nThe DEM is stored in the UTM Zone 44S projection.\nThe horizontal datum is WGS84. The vertical datum is Mean Sea Level.\nThe DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade.\nThe DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record:\n1 A tiff, georeferenced with a world file; and \n2 An ascii file in ESRI's ascii format for rasters.", "links": [ { diff --git a/datasets/vestfold_seals_gis_1.json b/datasets/vestfold_seals_gis_1.json index a7255ad99d..04a2975e65 100644 --- a/datasets/vestfold_seals_gis_1.json +++ b/datasets/vestfold_seals_gis_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vestfold_seals_gis_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset represents Weddell Seal haulout and pupping sites in the Vestfold Hills, Antarctica.\nThe data were sourced from a dataset compiled by Samantha Lake and described by the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'. She used a reporting grid described by the metadata record 'Weddell seal reporting grid of the Vestfold Hills, Antarctica' to show observations made over 24 years (pupping areas) and 28 years (non-breeding areas). The map Samantha produced of pupping areas is linked to the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'.\n\nPolygons were generated by copying relevant grid rectangles from a digital version of the reporting grid, referring to the maps produced by Samantha; the grid rectangles used were those in which there had been greater than 20 observations (pupping), 17 observations (non-breeding).\nThe data was used in an A3 map of the Vestfold Hills published by the Australian Antarctic Data Centre in October 2001 and which is available from a Related URL below.\n\nThe data are included in the data available for download from a Related URL below.\nThe data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. \nData described by this metadata record has Dataset_id = 155. \nEach feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.", "links": [ { diff --git a/datasets/vineyard-plots-in-southern-switzerland_1.0.json b/datasets/vineyard-plots-in-southern-switzerland_1.0.json index 7714589757..f9a0574bc8 100644 --- a/datasets/vineyard-plots-in-southern-switzerland_1.0.json +++ b/datasets/vineyard-plots-in-southern-switzerland_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "vineyard-plots-in-southern-switzerland_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Geospatial vector data (shapefile) representing the cadastral plots in the Canton Ticino and the Moesa region (southern Switzerland) having a part of the surface occupied by vineyards in the years 1989 and/or 2020 according to the corresponding edition of the Swiss national topographic maps in the scale 1:25,000 and to the topographic landscape model of Switzerland swissTLM3D (Federal office of topography Swisstopo). In the attribute table there is many variables which describe the topography of the site, the characteristics of the plots and the evolution of the wine growing area inside the plot between 1989 and 2020. Coordinate system: EPSG:2056 - Swiss CH1903+ / LV95.", "links": [ { diff --git a/datasets/volume-21_1.0.json b/datasets/volume-21_1.0.json index b1802fdbc2..e4cc689965 100644 --- a/datasets/volume-21_1.0.json +++ b/datasets/volume-21_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "volume-21_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of living trees and shrubs (standing and lying) starting at 12 cm dbh. This corresponds internationally to the \"growing stock\". The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/volume_of_bole_wood_hg_2000-167_1.0.json b/datasets/volume_of_bole_wood_hg_2000-167_1.0.json index 94982aa126..d0fb593eb8 100644 --- a/datasets/volume_of_bole_wood_hg_2000-167_1.0.json +++ b/datasets/volume_of_bole_wood_hg_2000-167_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "volume_of_bole_wood_hg_2000-167_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wood volume of the stem without bark or stump at least 7 cm in diameter (limit of coarse wood) of all trees and shrubs starting at 12 cm dbh, based on the stem-form functions according to Kaufmann (2001). The definition of the assortment is based on the 2000 edition of the Trading Practices (Handelsgebr\u00e4uchen Ausgabe 2000\u00a0). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/volume_of_bole_wood_hg_2010-211_1.0.json b/datasets/volume_of_bole_wood_hg_2010-211_1.0.json index c0ee69eda0..ccaad67d21 100644 --- a/datasets/volume_of_bole_wood_hg_2010-211_1.0.json +++ b/datasets/volume_of_bole_wood_hg_2010-211_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "volume_of_bole_wood_hg_2010-211_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wood volume of the trunk without bark or branches at least 7 cm in diameter (limit for coarse wool) of all trees and shrubs starting at 12 cm dbh, based on the stem-form function according to Kaufmann (2001). The definition of the assortment is based on the 2010 edition of the Trading Practices (Handelsgebr\u00e4uchen Ausgabe 2010). __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/volume_of_dead_wood-24_1.0.json b/datasets/volume_of_dead_wood-24_1.0.json index 226d0bb5eb..5d81fb2041 100644 --- a/datasets/volume_of_dead_wood-24_1.0.json +++ b/datasets/volume_of_dead_wood-24_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "volume_of_dead_wood-24_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all dead trees and shrubs (standing and lying) starting at 12 cm dbh. Unlike this theme\u00a0, the \"Amount of deadwood according to the method of NFI3\" includes all lying deadwood starting at 7 cm in diameter. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/volume_of_dead_wood_nfi1-249_1.0.json b/datasets/volume_of_dead_wood_nfi1-249_1.0.json index 8526986a8d..9a0feaa7b3 100644 --- a/datasets/volume_of_dead_wood_nfi1-249_1.0.json +++ b/datasets/volume_of_dead_wood_nfi1-249_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "volume_of_dead_wood_nfi1-249_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all dead trees and shrubs (standing and lying) starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/voyages_2.json b/datasets/voyages_2.json index b975b251f4..71d16ff74a 100644 --- a/datasets/voyages_2.json +++ b/datasets/voyages_2.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "voyages_2", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This document contains detailed descriptions of Antarctic and subantarctic voyages undertaken by Australians or in which Australians participated in between 1947 and 1989. It also includes lists of wintering personnel at Heard Island, Macquarie Island, Mawson, Casey, Davis, Wilkes and various field parties. Some information about summer personnel has also been recorded.\n\nThe voyages are presented in chronological order, and contain information such as the name of the ship, dates of the voyage, destination, ship's master, and personnel details.\n\nThe document also contains some details of Antarctic and subantarctic flights undertaken in support of the voyages (e.g. by the RAAF - Royal Australian Air Force).\n\nA second file (a spreadsheet) provides the number of personnel wintering at ANARE (Australian National Antarctic Research Expeditions) stations between 1948 and 1982. These stations include Heard Island, Macquarie Island, Davis, Wilkes, Repstat (Replacement Station at Wilkes), Casey and the Amery Ice Shelf.", "links": [ { diff --git a/datasets/waddington_0352584.json b/datasets/waddington_0352584.json index cf3bba8d2f..ad2713baae 100644 --- a/datasets/waddington_0352584.json +++ b/datasets/waddington_0352584.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "waddington_0352584", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This is a collaborative proposal by Principal Investigators at the University of Washington and the Desert Research Institute. They will make detailed measurements of the temporal and spatial variations of firn compaction to advance knowledge and understanding of ice deformation and across different fields, including remote sensing, snow morphology, and paleoclimatology. They will make detailed measurements through two winter and three summer seasons at Summit Greenland using the concept of Borehole Optical Stratigraphy, which will use a borehole camera to record details of the wall. These details can be tracked over time to determine vertical motion and strain, which in the shallow depth is dominated by firn compaction. Quantitative understanding of firn compaction is important for remote-sensing mass-balance studies, which seek to measure and interpret the changing height of the ice sheet; the surface can rise due to snow accumulation, and fall due to ice flow and increased densification rates. Quantitative knowledge of all three processes is essential. Evidence suggests that the rate of densification undergoes a seasonal cycle, related to the seasonal cycle of temperature. When interpreting ice core trapped-gas data for paleoclimate, it is important to know at what point the gas was actually trapped in the ice. The pores do not close off until deep in the firn, leading to a difference between the age of the ice and the age of the trapped gas. If summer high temperatures have more impact on compaction than mean annual temperatures, the gas-age/ice-age offset might be incorrectly calculated. Greater understanding of firn densification physics will help the interpretation of these records. \n\nThis data covers accumulation rates occurring between 1980-2008, and the data were collected between 2004-2008.", "links": [ { diff --git a/datasets/waldinventursihlwald_1.0.json b/datasets/waldinventursihlwald_1.0.json index 95196f1d6d..dd201f1bcd 100644 --- a/datasets/waldinventursihlwald_1.0.json +++ b/datasets/waldinventursihlwald_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "waldinventursihlwald_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "# Supplementary Data Sample Plot Inventory Sihlwald The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label \u2018Nature discovery park\u2019 (\u2018Naturerlebnispark\u2019). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003. This dataset contains supplementary tables for the publication by Br\u00e4ndli et al. (2020). The metadata file describes the structure of the tables.", "links": [ { diff --git a/datasets/water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0.json b/datasets/water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0.json index 1c251fe72c..843d013663 100644 --- a/datasets/water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0.json +++ b/datasets/water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Swiss forests' water availability during the 2015 and 2018 droughts was modelled by implementing the mechanistic Soil-Vegetation-Atmosphere-Transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information and measured soil matric potential and eddy covariance data. Data include 1) csv of soil matrix potential and eddy covariance data, 2) csv of posterior model parameters, 3) geotiffs of plant-available water storage capacity until 1m soil depth and the potential rooting depth, 4) geotiffs of yearly average (2014-2019) of precipitation (P), actual evapotranspiration (ETa), evaporation as the sum of soil, snow and interception evaporation (E), actual transpiration (Ta), runoff (F) and total soil water storage (SWAT), 5) csv of simulated root water uptake aggregated for different soil depths per deciduous and coniferous trees across Switzerland at daily resolution and cumulative root fraction per soil depth for coniferous and deciduous sites, 6) geotiffs of the ratio of actual to potential transpiration (-) as mean of non-drought years 2014, 2016, 2017, 2019 and 2015 and 2018 for the month June, July, August, September and October, 7) geotiff of mean soil matric potential in the rooting zone in August 2018, 8) geotiffs of gravitational water capacity (mm) until 1 m soil depth and the maximum rooting depth (mrd), 9) geotiffs of uncertainties of the available water storage capacity (AWC) until 1m soil depth and the mean maximum rooting depth (mrd), 10) csv of average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest.", "links": [ { diff --git a/datasets/water-isotopes-plynlimon_1.0.json b/datasets/water-isotopes-plynlimon_1.0.json index 6b153d6393..116e937dbb 100644 --- a/datasets/water-isotopes-plynlimon_1.0.json +++ b/datasets/water-isotopes-plynlimon_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "water-isotopes-plynlimon_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.", "links": [ { diff --git a/datasets/wbandimpacts_1.json b/datasets/wbandimpacts_1.json index bfa3f7798e..1b390866aa 100644 --- a/datasets/wbandimpacts_1.json +++ b/datasets/wbandimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wbandimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format.", "links": [ { diff --git a/datasets/weather-snowpack-danger_ratings-data_1.0.json b/datasets/weather-snowpack-danger_ratings-data_1.0.json index 7ff58e7345..554116deb2 100644 --- a/datasets/weather-snowpack-danger_ratings-data_1.0.json +++ b/datasets/weather-snowpack-danger_ratings-data_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "weather-snowpack-danger_ratings-data_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings. These data are the basis of the following publication: P\u00e9rez-Guill\u00e9n, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., P\u00e9rez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031\u20132056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.", "links": [ { diff --git a/datasets/weather-station-wolfgangpass_1.0.json b/datasets/weather-station-wolfgangpass_1.0.json index 33700cc76b..8bef9511bd 100644 --- a/datasets/weather-station-wolfgangpass_1.0.json +++ b/datasets/weather-station-wolfgangpass_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "weather-station-wolfgangpass_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).", "links": [ { diff --git a/datasets/weather_station_klosters_1.0.json b/datasets/weather_station_klosters_1.0.json index 449290433a..bedf832b4c 100644 --- a/datasets/weather_station_klosters_1.0.json +++ b/datasets/weather_station_klosters_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "weather_station_klosters_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).", "links": [ { diff --git a/datasets/wed_sat_99_1.json b/datasets/wed_sat_99_1.json index c861cb0a5e..16222637a4 100644 --- a/datasets/wed_sat_99_1.json +++ b/datasets/wed_sat_99_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wed_sat_99_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set contains the results from a study of the behaviour of Weddell seals (Leptonychotes weddelli) at the Vestfold Hills, Prydz Bay, Antarctica. Three satellite transmitters were deployed on tagged female Weddell seals at the Vestfold Hills mid-winter (June) 1999. The transmitters were recovered in December, late in the pupping season. In total, the three transmitters were deployed and active 170 days, 175 days and 180 days. I used the first two classes of data to get fixes with a standard deviation less than 1 km. Most seal holes were more that 1 km apart (see Entry: wed_survey) so at this resolution we can distinguish between haul-out sites. We examine the number and range of locations used by the individual seals. We use all data collectively to look at diurnal and seasonal changes in haul-out bouts. None of the seals were located at sites outside the area of fast ice at the Vestfold Hills, although one seal was sighted on new fast-ice (20 - 40 cm thick). Considering the long bouts in the water, and that we only tracked haul-out locations, the results do not eliminate the possibility that the seals made long trips at sea.\n\nThe original data are stored by the Australian Antarctic Division in the ARGOS system on the mainframe Alpha. The transmitter numbers are 23453, 7074 and 7075.", "links": [ { diff --git a/datasets/weird_1.0.json b/datasets/weird_1.0.json index dea71d59d2..e8ffce1826 100644 --- a/datasets/weird_1.0.json +++ b/datasets/weird_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "weird_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high resolution thermal infrared camera pointing at synthetic screens, a 30Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen\u2019s surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind speed reflect multiple layers of different static stability within 2m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind speed profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes. Here you find the data and the documented code used to create the plots in the publication.", "links": [ { diff --git a/datasets/wetlands-of-zurich_1.0.json b/datasets/wetlands-of-zurich_1.0.json index 4d331607b4..43eb216343 100644 --- a/datasets/wetlands-of-zurich_1.0.json +++ b/datasets/wetlands-of-zurich_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wetlands-of-zurich_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes data on species richness of vascular plants and bryophytes in 55 wetlands of the canton of Z\u00fcrich (Switzerland) as well as recent and historic data on the area and connectivity of these 55 wetlands and was used for the paper Jamin A., Peintinger M., Gimmi U., Holderegger R., Bergamini A. (2020) Evidence for a possible extinction debt in Swiss wetland specialist plants. Ecology and Evolution. Species richness data are available for vascular plants and bryophytes. The field survey was carried out between June 5 and August 10, 2012. The survey covered all wetland (fen) types in the canton of Z\u00fcrich. For data collection, at least half a day per wetland was spent searching for species. Within each wetland all different vegetation types were covered until no new species were found to get as complete species lists as possible. In the Excel file information on species richness of the following groups is provided: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix 1a of the wetland inventory of Switzerland (BUWAL, 1990). Based on the data of Gimmi et al. (2011), the area of all wetlands in 1850, 1900, 1950 and 2000 were determined as well as the wetland area within buffers 2km in radius with the center of the wetland as starting point. These data are also provided in the Excel sheet. Moreover, for each wetland mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants were calculated and are provided in Excel sheet. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.", "links": [ { diff --git a/datasets/wfj-cal_1.0.json b/datasets/wfj-cal_1.0.json index df89595038..57a6671dcc 100644 --- a/datasets/wfj-cal_1.0.json +++ b/datasets/wfj-cal_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wfj-cal_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the development of DAISY, the snowpack model we realised that we did not have enough accurate calibration measurements. We needed more reliable measurements of snow temperatures and settlements within the snow cover. Therefore, from winter 1990/91 the thermal development of the season snow cover in the test field with self-developed temperature harps was measured. These temperature harps can move freely with the snow cover, in contrast to the usually fixed temperature profiles. With these harps, it became possible to monitor the temperatures and settlements of the individual layers throughout the winter. Additionally, the surface temperature, snow level and the usual meteorological parameters such as air temperature, humidity, wind speed and the radiation in various wavelength ranges were measured. Furthermore, conventional snow profiles were recorded with measurements of densities, hardness, grain sizes and grain shapes. During three winters, this facility was intensively used for monitoring purposes. The support and monitoring of these measurements and the accompanying, very time-intensive manual measurements were carried out by Peter Weilenmann and Franz Herzog. The results of these measurements in winter 1990/91, 1991/92 and 1992/93 are given in the internal report No. 723. The use of these measurements for the validation of DAISY and MiniDAISY are gathered internally in report No. 724..726.", "links": [ { diff --git a/datasets/wfj2_1.0.json b/datasets/wfj2_1.0.json index 6ab2f8c3e6..f3ffaee74f 100644 --- a/datasets/wfj2_1.0.json +++ b/datasets/wfj2_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wfj2_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset provides HS, TSS and TS50, TS100, TS150 at the station WFJ2 situated on the Weisfluhjoch research site (2536 m asl). It has been created from merging ENET and IMIS datsets to form a continuous timeseries from 1992- present. ENET is at 1 h resolution whereas IMIS is 30 min. This is a level 2 dataset as defined [here](http://models.slf.ch/p/dataset-processing/).", "links": [ { diff --git a/datasets/wfj_ice_layers_1.0.json b/datasets/wfj_ice_layers_1.0.json index 8f1bcf3b91..3ec0504195 100644 --- a/datasets/wfj_ice_layers_1.0.json +++ b/datasets/wfj_ice_layers_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wfj_ice_layers_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.", "links": [ { diff --git a/datasets/wfj_rhossa_1.0.json b/datasets/wfj_rhossa_1.0.json index da0b718a61..7c73890395 100644 --- a/datasets/wfj_rhossa_1.0.json +++ b/datasets/wfj_rhossa_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wfj_rhossa_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The WFJ_RHOSSA dataset contains multi-instrument, multi-resolution snow stratigraphy measurements for the seasonal evolution of the snowpack from the Weissfluhjoch research site, Davos, Switzerland. The measurements were initiated during the RHOSSA field campaign conducted in the winter season 2015\u20132016 with a focus on density (RHO) and specific surface area (SSA) measurements. The Instruments and methods used in the campaign at different spatial and temporal resolution are: SnowMicroPen, Density Cutter, IceCube, Traditional profiles, Stability tests and X-ray tomography. The measurements are complemented by simulation data from the model SNOWPACK.", "links": [ { diff --git a/datasets/white_model_parameters_652_1.json b/datasets/white_model_parameters_652_1.json index 4d02a544f8..75747ccd38 100644 --- a/datasets/white_model_parameters_652_1.json +++ b/datasets/white_model_parameters_652_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "white_model_parameters_652_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Various aspects of primary production of a variety of plant species found in natural temperate biomes were compiled from literature and presented for use with process-based ecosystem simulation models or ecosystem studies. Information was selected according to the input parameter needs of the BIOME-BGC process-based simulation model.", "links": [ { diff --git a/datasets/whitney_dem_1.json b/datasets/whitney_dem_1.json index 0e01035ff8..861178ddcf 100644 --- a/datasets/whitney_dem_1.json +++ b/datasets/whitney_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "whitney_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset includes a 10 metre resolution digital elevation model (DEM) of the Whitney Point area of the Windmill Islands, Antarctica and an orthophoto created using the DEM.\n\nThe data are stored in the UTM zone 49 map projection. \nThe horizontal datum is WGS84. \n\nThe data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins.\n\nSee a related URL below for a map showing Whitney Point.", "links": [ { diff --git a/datasets/wilhend_687_1.json b/datasets/wilhend_687_1.json index a507a014b4..f818745674 100644 --- a/datasets/wilhend_687_1.json +++ b/datasets/wilhend_687_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wilhend_687_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of a global vegetation and soils data set by Wilson and Henderson-Sellers (1985a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format.The original global data set (Wilson and Henderson-Sellers 1985a) is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from maps depicting natural vegetation, forestry, agriculture, land use, and soil, and they were archived at a resolution of 1 latitude by 1 longitude. The data set indicates soil type, soil data reliability, primary vegetation, secondary vegetation, and land cover data reliability. Approximately 50 land cover classifications are used, including categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful in areas with cover types that may have a fragmented distribution, such as in areas of urban development. The soil type data are classified according to climatically important properties for GCMs, and they indicate color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data, forming a coherent and consistent data set. The reliability of the land cover data is ranked on a scale of 1 to 5 (high to low). The reliability of the soil data is ranked as high, good, moderate, fair, or poor.Recommendations for the use of these data, as well as more detailed information can be found in Wilson and Henderson-Sellers (1985b).Further data set information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/wilhend/comp/wilhend_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.", "links": [ { diff --git a/datasets/willmott_673_1.json b/datasets/willmott_673_1.json index 3f08d88a1d..ef8c1280fe 100644 --- a/datasets/willmott_673_1.json +++ b/datasets/willmott_673_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "willmott_673_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10 N to 25 S, 30 to 85 W. The data are in ASCII GRID format. The data consist of the following: Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at \"Willmott, Matsuura, and Collaborators' Global Climate Resource Pages\" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for \"Available Climate Data,\" register or sign in, and follow the link for \"South American Climate Data.\" Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf. ", "links": [ { diff --git a/datasets/wind-topo_model_0.1.0.json b/datasets/wind-topo_model_0.1.0.json index 1b7ae96b68..c2de7e83d8 100644 --- a/datasets/wind-topo_model_0.1.0.json +++ b/datasets/wind-topo_model_0.1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wind-topo_model_0.1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain. It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input. This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM. Accompanies the publication \"Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning\" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 Please cite this publication if you use Wind-Topo or derive new models from it. The code can be used under the GNU Affero General Public License (AGPL).", "links": [ { diff --git a/datasets/wind_dem_1.json b/datasets/wind_dem_1.json index 28ad3f2331..9b85e06a7d 100644 --- a/datasets/wind_dem_1.json +++ b/datasets/wind_dem_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wind_dem_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This DEM includes all the inshore and offshore islands, all the peninsulas and the lower slopes of the icecap leading up to Law Dome. The DEM has a cell size of 10 m.", "links": [ { diff --git a/datasets/windmill_bathy_surveys_1.json b/datasets/windmill_bathy_surveys_1.json index 5972db367c..1ac2f20e92 100644 --- a/datasets/windmill_bathy_surveys_1.json +++ b/datasets/windmill_bathy_surveys_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "windmill_bathy_surveys_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands.\n\nThis dataset resulted from bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands, carried out in February and March 1997 as part of ASAC Project 2201. The surveys were carried out by Jonny Stark and Tim Ryan in the workboat the 'Southern Comfort'.", "links": [ { diff --git a/datasets/winston_bathy_1.json b/datasets/winston_bathy_1.json index 87a1e87177..b1b84b7794 100644 --- a/datasets/winston_bathy_1.json +++ b/datasets/winston_bathy_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "winston_bathy_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted.\n\nWinston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m.", "links": [ { diff --git a/datasets/wisperimpacts_1.json b/datasets/wisperimpacts_1.json index 3c51dfd554..fb47f07097 100644 --- a/datasets/wisperimpacts_1.json +++ b/datasets/wisperimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wisperimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS dataset consists of condensed water contents, water vapor measurements, and isotope ratios in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in ASCII format from January 18, 2020, through February 28, 2023.", "links": [ { diff --git a/datasets/wml_bilderstudie_1.0.json b/datasets/wml_bilderstudie_1.0.json index f31c1b73b3..f0ec2d972c 100644 --- a/datasets/wml_bilderstudie_1.0.json +++ b/datasets/wml_bilderstudie_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wml_bilderstudie_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the \"WaMos meets LFI\" (WML) project.", "links": [ { diff --git a/datasets/wmlganzeschweiz_1.0.json b/datasets/wmlganzeschweiz_1.0.json index a13a3fbda1..440dd895ee 100644 --- a/datasets/wmlganzeschweiz_1.0.json +++ b/datasets/wmlganzeschweiz_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wmlganzeschweiz_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted.", "links": [ { diff --git a/datasets/wood-mobilization-survey_1.0.json b/datasets/wood-mobilization-survey_1.0.json index e147f9ee06..145c6d9e92 100644 --- a/datasets/wood-mobilization-survey_1.0.json +++ b/datasets/wood-mobilization-survey_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wood-mobilization-survey_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Understanding the market behavior of forest owners and managers is important to identify effective and efficient policy instruments that enhance wood provisioning. We conducted a choice experiment (CE) at two study sites in south-eastern Germany (Upper Bavaria and Lower Franconia) and two in north-eastern Switzerland (Grisons and Aargau) to elicit foresters\u2019 preferences for different supply channels, contract lengths, wood prices and duration of business relations. CE belong to the stated preference methods to analyze individual decision making. Respondents had to choose among three options based on different attribute levels in 12 consecutive choice sets. Our study site comparison identified regional differences and particularities, which should be taken into account when promoting wood mobilization. The success of policy instruments, such as the promotion of bundling organizations and long-term contracts, can vary depending on the specific structural and institutional conditions, like existing marketing channels, as well as on behavioral aspects of the particular public and private decision makers.", "links": [ { diff --git a/datasets/woody_biomass_657_1.json b/datasets/woody_biomass_657_1.json index cab8623619..ef7a6d92c5 100644 --- a/datasets/woody_biomass_657_1.json +++ b/datasets/woody_biomass_657_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "woody_biomass_657_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Estimates of the woody biomass density and pools were derived at the county scale of resolution of all forests of the eastern United States using new approaches for converting inventoried wood volume to estimates of above and belowground biomass.", "links": [ { diff --git a/datasets/wrfimpacts_1.json b/datasets/wrfimpacts_1.json index ae9b9e319d..a11893ade5 100644 --- a/datasets/wrfimpacts_1.json +++ b/datasets/wrfimpacts_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wrfimpacts_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.", "links": [ { diff --git a/datasets/wsl-drought-initiative-2018_1.0.json b/datasets/wsl-drought-initiative-2018_1.0.json index 13f64f91aa..0b94c11f34 100644 --- a/datasets/wsl-drought-initiative-2018_1.0.json +++ b/datasets/wsl-drought-initiative-2018_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wsl-drought-initiative-2018_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018). Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen). __Datafile:__ _LWF_beech_plots_litterfall_pollen.xlsx_ 1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion. 2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models.", "links": [ { diff --git a/datasets/wslintern-article-envidat-supports-open-science_1.0.json b/datasets/wslintern-article-envidat-supports-open-science_1.0.json index a330c2a439..3ca71e3516 100644 --- a/datasets/wslintern-article-envidat-supports-open-science_1.0.json +++ b/datasets/wslintern-article-envidat-supports-open-science_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wslintern-article-envidat-supports-open-science_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The article \"EnviDat Supports Open Science\" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat\u2019s support. License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.", "links": [ { diff --git a/datasets/wwllnmth_1.json b/datasets/wwllnmth_1.json index b94ea11f9f..54e6b6ce4d 100644 --- a/datasets/wwllnmth_1.json +++ b/datasets/wwllnmth_1.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wwllnmth_1", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004. Since 2013, the number of global WWLLN sensors has remained largely consistent. This WWLLN Monthly Thunder Hour dataset is calculated from lightning detections from 1 January 2013 onward and is an ongoing dataset. A thunder hour is an hour during which thunder can be heard at a given location. Thunder hours represent a historical measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to geographic variations in the detection capabilities of a lightning location system. Thunder hours are the number of hours in a given month during which at least two WWLLN strokes were observed within 15 km of each grid point. Each file includes the monthly accumulated thunder hours for one year. The data are provided at 0.05\u00b0 latitude and longitude resolution.", "links": [ { diff --git a/datasets/wygisc_wolphoyo.json b/datasets/wygisc_wolphoyo.json index b0f61ea4c3..87a7666f02 100644 --- a/datasets/wygisc_wolphoyo.json +++ b/datasets/wygisc_wolphoyo.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "wygisc_wolphoyo", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "The purpose of this data was to provide a base layer of aerial photos\n at the watershed scale for two areas used as part of a the Wyoming\n Open Land pilot area.\n \n Digital and registered aerial photos of Crazy Woman and Clear Creek\n Watersheds, Wyoming. Each photo represents approximatley one-quarter\n of a U.S.G.S. Topographic map (north-east, north-west, south-each and\n south-west quarters). TIFF image format.", "links": [ { diff --git a/datasets/yield-15_1.0.json b/datasets/yield-15_1.0.json index 3f9dd8d50a..cb65ef7e56 100644 --- a/datasets/yield-15_1.0.json +++ b/datasets/yield-15_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield-15_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_and_mortality-13_1.0.json b/datasets/yield_and_mortality-13_1.0.json index fe03938d0c..e2527a7aff 100644 --- a/datasets/yield_and_mortality-13_1.0.json +++ b/datasets/yield_and_mortality-13_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_and_mortality-13_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled, died or disappeared between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_and_mortality_star-163_1.0.json b/datasets/yield_and_mortality_star-163_1.0.json index 4c0cbf2f59..452c9237b4 100644 --- a/datasets/yield_and_mortality_star-163_1.0.json +++ b/datasets/yield_and_mortality_star-163_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_and_mortality_star-163_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were used, died or disappeared between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_of_live_bole_wood-87_1.0.json b/datasets/yield_of_live_bole_wood-87_1.0.json index 905bdb70de..aa65a42e47 100644 --- a/datasets/yield_of_live_bole_wood-87_1.0.json +++ b/datasets/yield_of_live_bole_wood-87_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_of_live_bole_wood-87_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood at least 7 cm in diameter (limit for coarse wood) without the bark and stump that were living trees or shrubs starting at 12 cm dbh in the pre-inventory and were cut between two inventories. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_of_merchantable_branches-112_1.0.json b/datasets/yield_of_merchantable_branches-112_1.0.json index d83a69ddc5..11a01741a1 100644 --- a/datasets/yield_of_merchantable_branches-112_1.0.json +++ b/datasets/yield_of_merchantable_branches-112_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_of_merchantable_branches-112_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wood volume of branches with bark at least 7 cm in diameter (limit for coarse wood) of all living trees and shrubs starting at 12 cm dbh that were present in the pre-inventory and cut meanwhile. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_of_merchantable_timber-114_1.0.json b/datasets/yield_of_merchantable_timber-114_1.0.json index e02416552c..9115c7083a 100644 --- a/datasets/yield_of_merchantable_timber-114_1.0.json +++ b/datasets/yield_of_merchantable_timber-114_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_of_merchantable_timber-114_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Wood volume of the stem (without bark and stump) and the branches (with bark) at least 7 cm in diameter (limit for coarse wood) from trees and shrubs starting at 12 cm dbh that were living in the pre-inventory and were cut between the two inventories. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/yield_star-161_1.0.json b/datasets/yield_star-161_1.0.json index 39df7e06d2..ae42753593 100644 --- a/datasets/yield_star-161_1.0.json +++ b/datasets/yield_star-161_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "yield_star-161_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh cut between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/datasets/young_forest_with_browsing_damage-193_1.0.json b/datasets/young_forest_with_browsing_damage-193_1.0.json index fb04767f96..5101b79a3e 100644 --- a/datasets/young_forest_with_browsing_damage-193_1.0.json +++ b/datasets/young_forest_with_browsing_damage-193_1.0.json @@ -1,7 +1,7 @@ { "type": "Collection", "id": "young_forest_with_browsing_damage-193_1.0", - "stac_version": "1.0.0", + "stac_version": "1.1.0", "description": "Number of regeneration trees where browsing of the shoots from the previous year was recorded in NFI\u2019s regeneration survey. __Citation:__ > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_", "links": [ { diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json index f0ccdd9351..0a993dadb3 100644 --- a/nasa_cmr_catalog.json +++ b/nasa_cmr_catalog.json @@ -140799,7 +140799,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2484086031-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2484086031-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MCD19A1_061", - "description": "The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. ", + "description": "The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. ", "license": "proprietary" }, { @@ -140890,7 +140890,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2484086411-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2484086411-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MCD19A3D_061", - "description": "The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC\u2019s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer. Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.", + "description": "The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC\u2019s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer. Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.", "license": "proprietary" }, { @@ -147650,7 +147650,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MOD11_L2_061", - "description": "The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", + "description": "The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).", "license": "proprietary" }, { @@ -147923,7 +147923,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MOD17A2HGF_061", - "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", + "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", "license": "proprietary" }, { @@ -147975,7 +147975,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545303093-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545303093-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MOD21A1N_061", - "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", + "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "license": "proprietary" }, { @@ -152512,7 +152512,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794824-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794824-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MYD17A2HGF_061", - "description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", + "description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ", "license": "proprietary" }, { @@ -152629,7 +152629,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565805776-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565805776-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MYD21_061", - "description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", + "description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ", "license": "proprietary" }, { @@ -214646,13 +214646,13 @@ "id": "VJ103MODLL_021", "title": "VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V021", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2018-01-05", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314612-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314612-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ103MODLL_021", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VJ103MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.002) swath product for accurate geolocation information.", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VJ103MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.001) swath product for accurate geolocation information.", "license": "proprietary" }, { @@ -214737,13 +214737,13 @@ "id": "VJ109GA_002", "title": "VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2018-01-05", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841524-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841524-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ109GA_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", "license": "proprietary" }, { @@ -214932,13 +214932,13 @@ "id": "VJ114A1_002", "title": "VIIRS/JPSS1 Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310874-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310874-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ114A1_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", "license": "proprietary" }, { @@ -214971,13 +214971,13 @@ "id": "VJ114IMG_002", "title": "VIIRS/JPSS1 Active Fires 6-Min L2 Swath 375m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2018-01-05", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2734197957-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2734197957-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ114IMG_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as thermal anomalies. The VJ114IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products. Use of the VJ103MODLL data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS). ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.002) data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS). ", "license": "proprietary" }, { @@ -214997,13 +214997,13 @@ "id": "VJ114_002", "title": "VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2018-01-05", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310869-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310869-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ114_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.021) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS). ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (Vj114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vJ103modll.001) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS). ", "license": "proprietary" }, { @@ -215036,26 +215036,26 @@ "id": "VJ121A1D_002", "title": "VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310887-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310887-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ121A1D_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule. ", "license": "proprietary" }, { "id": "VJ121A1N_002", "title": "VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310892-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310892-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ121A1N_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule. ", "license": "proprietary" }, { @@ -215127,13 +215127,13 @@ "id": "VJ121_002", "title": "VIIRS/JPSS1 Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310883-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310883-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ121_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters. The VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). Provided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters. The VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). Provided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule. ", "license": "proprietary" }, { @@ -215335,13 +215335,13 @@ "id": "VJ143IA1_002", "title": "VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143IA1_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VJ143IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. ", "license": "proprietary" }, { @@ -215361,13 +215361,13 @@ "id": "VJ143IA2_002", "title": "VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143IA2_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VJ143_ATBD_V2.pdf). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "license": "proprietary" }, { @@ -215387,13 +215387,13 @@ "id": "VJ143IA3_002", "title": "VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143IA3_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", "license": "proprietary" }, { @@ -215413,13 +215413,13 @@ "id": "VJ143IA4_002", "title": "VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143IA4_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", "license": "proprietary" }, { @@ -215439,13 +215439,13 @@ "id": "VJ143MA1_002", "title": "VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143MA1_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. ", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "license": "proprietary" }, { @@ -215465,13 +215465,13 @@ "id": "VJ143MA2_002", "title": "VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143MA2_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name.", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) (https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3) (https://doi.org/10.5067/VIIRS/VJ143MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "license": "proprietary" }, { @@ -215491,13 +215491,13 @@ "id": "VJ143MA3_002", "title": "VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143MA3_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format.", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) (https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format.", "license": "proprietary" }, { @@ -215517,13 +215517,13 @@ "id": "VJ143MA4_002", "title": "VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2018-01-01", + "state_date": "2017-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VJ143MA4_002", - "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.", + "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "license": "proprietary" }, { @@ -216037,13 +216037,13 @@ "id": "VNP03MODLL_002", "title": "VIIRS/NPP Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310947-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545310947-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP03MODLL_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth's geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the VNP14 swath product for accurate geolocation information.", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth\u2019s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60\u00b0 North to 60\u00b0 South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VNP14) (https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information.", "license": "proprietary" }, { @@ -216154,13 +216154,13 @@ "id": "VNP09GA_002", "title": "VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841556-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2631841556-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP09GA_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. ", "license": "proprietary" }, { @@ -216492,13 +216492,13 @@ "id": "VNP14A1_002", "title": "VIIRS/NPP Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314541-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314541-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP14A1_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", + "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. ", "license": "proprietary" }, { @@ -216518,13 +216518,13 @@ "id": "VNP14IMG_002", "title": "VIIRS/NPP Active Fires 6-Min L2 Swath 375m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2734202914-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2734202914-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP14IMG_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This Level 2 product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events. Due to its higher spatial resolution, the VNP14IMG active fire product provides greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters in comparison to the VNP14 fire data product. The VNP14IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS). ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the Suomi National Polar Orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well asermal anomalies. identifying th The VNP14IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS). ", "license": "proprietary" }, { @@ -216557,13 +216557,13 @@ "id": "VNP14_002", "title": "VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314536-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314536-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP14_002", - "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). ", + "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). ", "license": "proprietary" }, { @@ -216622,13 +216622,13 @@ "id": "VNP21A1D_002", "title": "VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314555-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314555-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP21A1D_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. ", "license": "proprietary" }, { @@ -216648,13 +216648,13 @@ "id": "VNP21A1N_002", "title": "VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314559-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314559-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP21A1N_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. ", "license": "proprietary" }, { @@ -216752,13 +216752,13 @@ "id": "VNP21_002", "title": "VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314550-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314550-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP21_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. ", "license": "proprietary" }, { @@ -218312,13 +218312,13 @@ "id": "VNP43IA1_002", "title": "VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43IA1_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. ", "license": "proprietary" }, { @@ -218351,13 +218351,13 @@ "id": "VNP43IA2_002", "title": "VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43IA2_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. VNP43IA2 provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "license": "proprietary" }, { @@ -218390,13 +218390,13 @@ "id": "VNP43IA3_002", "title": "VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43IA3_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.", "license": "proprietary" }, { @@ -218429,13 +218429,13 @@ "id": "VNP43IA4_002", "title": "VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43IA4_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.", "license": "proprietary" }, { @@ -218468,13 +218468,13 @@ "id": "VNP43MA1_002", "title": "VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43MA1_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format.", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "license": "proprietary" }, { @@ -218507,13 +218507,13 @@ "id": "VNP43MA2_002", "title": "VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43MA2_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name.", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. ", "license": "proprietary" }, { @@ -218546,13 +218546,13 @@ "id": "VNP43MA3_002", "title": "VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43MA3_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format.", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format.", "license": "proprietary" }, { @@ -218585,13 +218585,13 @@ "id": "VNP43MA4_002", "title": "VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002", "catalog": "LPCLOUD STAC Catalog", - "state_date": "2012-01-17", + "state_date": "2012-01-19", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VNP43MA4_002", - "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. ", + "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.", "license": "proprietary" }, { diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 6bb74e2a8f..1bb292080e 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -10832,14 +10832,14 @@ MCD18C2_062 MODIS/Terra+Aqua Photosynthetically Active Radiation Daily/3-Hour L3 MCD19A1CMGL_061 MODIS/Terra+Aqua Surface Reflectance (Bands 1-7) from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807727-LPCLOUD.umm_json The MCD19A1CMGL Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 1-7) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGL product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45° and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm. The MCD19A1CMGL MAIAC Surface Reflectance product for land bands includes 25 Science Dataset (SDS) layers: BRF for bands 1-7, BRF normalized to a fixed geometry for bands 1-7, NBAR for bands 1-7, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included. proprietary MCD19A1CMGO_061 MODIS/Terra+Aqua Surface Reflectance (Bands 8-12) from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807729-LPCLOUD.umm_json The MCD19A1CMGO Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 8-12) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGO product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45° and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm. The MCD19A1CMGO MAIAC Surface Reflectance product for ocean bands includes 19 Science Dataset (SDS) layers: BRF for bands 8-12, BRF normalized to a fixed geometry for bands 8-12, NBAR for bands 8-12, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included. proprietary MCD19A1N_6.1NRT MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km SIN Grid NRT LANCEMODIS STAC Catalog 2022-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2407808348-LANCEMODIS.umm_json The MODIS Near Real Time (NRT) Combined Terra and Aqua Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product (MCD19A1N) produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1N product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary -MCD19A1_061 MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2484086031-LPCLOUD.umm_json The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary +MCD19A1_061 MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2484086031-LPCLOUD.umm_json The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary MCD19A2CMG_061 MODIS/Terra+Aqua AOD and Water Vapor from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807733-LPCLOUD.umm_json The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products (MCD19A1CMGL (https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and MCD19A1CMGO (https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)). The MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. proprietary MCD19A2N_6.1NRT MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2022-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2407807500-LANCEMODIS.umm_json The Moderate Resolution Imaging Spectroradiometer (MODIS) Near Real Time (NRT) Combined Terra and Aqua Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth gridded Level 2 product (MCD19A2N) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A2N product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1N product. The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary MCD19A2_006 MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006 LPCLOUD STAC Catalog 2000-02-26 2023-02-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763289461-LPCLOUD.umm_json The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD19A2 Version 6.1 data product (https://doi.org/10.5067/MODIS/MCD19A2.061). The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) variables: blue band AOD at 0.47 micron, green band AOD at 0.55 micron, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1 km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5 km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 micron created using a composite of all available orbits. Each SDS variable within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS variable. Improvements/Changes from Previous Versions * New product for MODIS Version 6. proprietary MCD19A2_061 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2324689816-LPCLOUD.umm_json The MCD19A2 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the AOD SDS layers. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary MCD19A3CMG_061 MODIS/Terra+Aqua Vegetation Index from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807736-LPCLOUD.umm_json The MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption. The MCD19A3CMG Vegetation Index data product contains the following Science Dataset (SDS) layers: NDVI, NDVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view, gap-filled NDVI, EVI, and EVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view. A low-resolution browse image is also included showing NDVI created using a composite of all available orbits. proprietary MCD19A3DN_6.1NRT MODIS/Terra+Aqua BRDF Model Parameters Daily NRT L3 Global 1km SIN Grid LANCEMODIS STAC Catalog 2023-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2407808961-LANCEMODIS.umm_json The MODIS Near Real Time (NRT) Combined Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product (MCD19A3DN) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A3DN product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary -MCD19A3D_061 MODIS/Terra+Aqua BRDF Model Parameters Daily L3 Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2484086411-LPCLOUD.umm_json The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC’s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer. Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary +MCD19A3D_061 MODIS/Terra+Aqua BRDF Model Parameters Daily L3 Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2484086411-LPCLOUD.umm_json The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC’s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer. Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary MCD43A1N_6.1NRT MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid LANCEMODIS STAC Catalog 2021-09-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2128952410-LANCEMODIS.umm_json The MODIS Near Real Time (NRT) MCD43A1N, MODIS Combined Aqua and Terra Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters is produced daily using 16 days of Terra and Aqua MODIS data. This global gridded tiled product provides model parameters/coefficients (isotropic, volume and surface) for characterizing the BRDF of each pixel at 500m resolution in the sinusoidal map projection. BRDF at each pixel for the current day is derived by inverting all available good quality corrected surface reflectance observations acquired by Terra and Aqua MODIS from the 16-day period ending with the current data day. The daily observation are weighed as a function of quality, observation coverage and temporal distance from the current data date. Model parameters are stored as 3D datasets for each of the 7 land bands, visible, near-infrared and shortwave bands along with corresponding mandatory QA flags. There is a significant change in the science algorithm of the Collection 61 (C61) NRT BRDF/Albedo products and, therefore significant differences/discontinuities between the C6 and C61 products. C61 algorithm changes are intended to minimize the differences between the NRT and Standard BRDF. The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day. Additional information at MODIS Land Science Team website at https://modis-land.gsfc.nasa.gov/brdf.html proprietary MCD43A1_061 MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061 LPCLOUD STAC Catalog 2000-02-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2343116130-LPCLOUD.umm_json The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43A3)(https://doi.org/10.5067/MODIS/MCD43A3.061) and BRDF (MCD43A4) (https://doi.org/10.5067/MODIS/MCD43A4.061) products. The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the quality layers for each of the 10 bands. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary MCD43A2N_6.1NRT MODIS/Terra+Aqua BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid LANCEMODIS STAC Catalog 2017-02-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2129016354-LANCEMODIS.umm_json The MODIS Near Real Time (NRT) Combined Aqua and Terra Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality, MCD43A2N is a L3 daily 16-day composite global gridded tiled product that provides full set of quality control flags for use in determining the quality of the retrievals at pixel level in the daily L3 BRDF/Albedo suite of products: BRDF/Albedo Model Parameters (MCD43A1N), Albedo (MCD43A3N) and the NBAR (MCD43A4N). There is a significant change in the science algorithm of the Collection 61 (C61) NRT BRDF/Albedo products and, therefore significant differences/discontinuities between the C6 and C61 products. C61 algorithm changes are intended to minimize the differences between the NRT and Standard BRDF. The C61 NRT BRDF code has been modified to allow for an extra round of magnitude inversion, following a full inversion using the full set of inputs. This extra magnitude inversion will only use the set of 9 days that are overlapping between standard and NRT, with the highest weight being assigned to the last day. Additional information from MODIS Land Science Team at https://modis-land.gsfc.nasa.gov/brdf.html proprietary @@ -11359,7 +11359,7 @@ MOD11C2_061 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 0.05 MOD11C3_061 MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565788897-LPCLOUD.umm_json The MOD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of MOD11C1 (https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary MOD11CM1D_005 MODIS/Terra Monthly mean Day-Time Land Surface Temperature at 1x1 degree V005 (MOD11CM1D) at GES DISC GES_DISC STAC Catalog 2000-03-01 2015-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239897978-GES_DISC.umm_json The dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30. proprietary MOD11CM1N_005 MODIS/Terra Monthly mean Night-Time Land Surface Temperature at 1x1 degree V005 (MOD11CM1N) at GES DISC GES_DISC STAC Catalog 2000-03-01 2015-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239898011-GES_DISC.umm_json The dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30. proprietary -MOD11_L2_061 MODIS/Terra Land Surface Temperature/Emissivity 5-Min L2 Swath 1km V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.umm_json The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary +MOD11_L2_061 MODIS/Terra Land Surface Temperature/Emissivity 5-Min L2 Swath 1km V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.umm_json The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary MOD11_L2_6.1NRT MODIS/Terra Land Surface Temperature/Emissivity 5-Min L2 Swath 1km NRT LANCEMODIS STAC Catalog 2021-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2007658455-LANCEMODIS.umm_json The MODIS/Terra level-2 Land Surface Temperature and Emissivity (LST/E) Near Real Time (NRT) with Shortname MOD11_L2, incorporate 1 km pixels, which are produced daily at 5-minute increments using the generalized split-window algorithm. This algorithm is optimally used to separate ranges of atmospheric column water vapor and lower boundary air surface temperatures into tractable sub-ranges. The surface emissivities in bands 31 and 32 are estimated from land cover types. The data inputs include the MODIS L1B calibrated and geolocated radiances, geolocation, cloud mask, atmospheric profiles, land and snow cover. The MOD11_L2 data set comprises swath data obtained in 5-minute sensor collection periods, and includes the following Science Data Set (SDS) layers: - LST- Quality control assessment- Error estimates- Bands 31 and 32 emissivities- Zenith angle of the pixel view- Observation time- Geographic coordinates for every five scan lines and samples. Produced daily, MOD11_L2 is an unprojected level-2 product, which provides the input for the level-3 products. proprietary MOD13A1_061 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565788901-LPCLOUD.umm_json The MOD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary MOD13A2_061 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565788905-LPCLOUD.umm_json The MOD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary @@ -11380,11 +11380,11 @@ MOD15A2PHN_6 MODIS/Terra LAI-FPAR Phenology annual L4 Global 1km SIN Grid LAADS MOD16A2GF_061 MODIS/Terra Net Evapotranspiration Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791021-LPCLOUD.umm_json The MOD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MOD16A2GF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A2GF is the improved MOD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MOD16A2_061 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2343113232-LPCLOUD.umm_json The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MOD16A3GF_061 MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791024-LPCLOUD.umm_json The MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MOD16A3GF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A3GF is the improved MOD16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary -MOD17A2HGF_061 MODIS/Terra Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.umm_json The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary +MOD17A2HGF_061 MODIS/Terra Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.umm_json The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MOD17A2H_061 MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791027-LPCLOUD.umm_json The MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MOD17A3HGF_061 MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2001-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791034-LPCLOUD.umm_json The MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products (MOD17A2H)(https://doi.org/10.5067/MODIS/MOD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR). The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MOD21A1D_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545303088-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary -MOD21A1N_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545303093-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary +MOD21A1N_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545303093-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MOD21A2_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791040-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MOD21C1_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791044-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21C1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MOD21C2_061 MODIS/Terra Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565791047-LPCLOUD.umm_json A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary @@ -11733,7 +11733,7 @@ MYD15A2H_061 MODIS/Aqua Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 MYD16A2GF_061 MODIS/Aqua Net Evapotranspiration Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794067-LPCLOUD.umm_json The MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MYD16A2_061 MODIS/Aqua Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794064-LPCLOUD.umm_json The MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MYD16A3GF_061 MODIS/Aqua Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794069-LPCLOUD.umm_json The MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products. Improvements/Changes from Previous Changes * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary -MYD17A2HGF_061 MODIS/Aqua Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794824-LPCLOUD.umm_json The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary +MYD17A2HGF_061 MODIS/Aqua Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794824-LPCLOUD.umm_json The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MYD17A2H_061 MODIS/Aqua Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794796-LPCLOUD.umm_json The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MYD17A3HGF_061 MODIS/Aqua Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565794850-LPCLOUD.umm_json The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H)(https://doi.org/10.5067/MODIS/MYD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR). The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. proprietary MYD21A1D_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805783-LPCLOUD.umm_json A suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary @@ -11742,7 +11742,7 @@ MYD21A2_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity 8-Day L3 Globa MYD21C1_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805805-LPCLOUD.umm_json A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MYD21C2_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805807-LPCLOUD.umm_json A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (https://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MYD21C3_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity Monthly L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2002-07-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805812-LPCLOUD.umm_json A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C3 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary -MYD21_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity 5-Min L2 1km V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805776-LPCLOUD.umm_json The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary +MYD21_061 MODIS/Aqua Land Surface Temperature/3-Band Emissivity 5-Min L2 1km V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805776-LPCLOUD.umm_json The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). proprietary MYD21_6.1NRT MODIS/Aqua Land Surface Temperature/3-Band Emissivity 5-Min L2 1km NRT LANCEMODIS STAC Catalog 2021-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2073479587-LANCEMODIS.umm_json The MODIS/Aqua Land Surface Temperature/3-Band Emissivity (LST&E) 5-Min L2 1km data product, short-name MYD21 is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/107/MOD21_ATBD.pdf)). The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and more. proprietary MYD28C2_061 MODIS/Aqua Water Reservoir 8-Day L3 Global V061 LPCLOUD STAC Catalog 2002-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805818-LPCLOUD.umm_json The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves (https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Aqua satellite (MYD09Q1). The MYD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. proprietary MYD28C3_061 MODIS/Aqua Water Reservoir Monthly L3 Global V061 LPCLOUD STAC Catalog 2002-07-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565805823-LPCLOUD.umm_json The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MYD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) (https://www.sciencedirect.com/science/article/pii/S0034425720304776?via%3Dihub) via MODIS Land Surface Temperature (LST) (MYD21) and meteorological data from Global Land Data Assimilation System (GLDAS) (https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MYD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume. proprietary @@ -16519,14 +16519,14 @@ VJ103DNB_NRT_2.1 VIIRS/JPSS1 Day/Night Band Resolution Terrain Corrected Geoloca VJ103IMG_2.1 VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation L1 6-Min Swath 375 m LAADS STAC Catalog 2017-12-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105086226-LAADS.umm_json The VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m product, short-name VJ103IMG, contains the derived line- of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. proprietary VJ103IMG_NRT_2 VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m NRT LANCEMODIS STAC Catalog 2019-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1604644159-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m (VJ103IMG_NRT) product includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG_NRT Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team. proprietary VJ103IMG_NRT_2.1 VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6 Min L1 Swath 375m NRT LANCEMODIS STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208793489-LANCEMODIS.umm_json The VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m Near Real Time (NRT) product, short-name VJ103IMG_NRT includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG_NRT Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team. proprietary -VJ103MODLL_021 VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V021 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314612-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VJ103MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.002) swath product for accurate geolocation information. proprietary +VJ103MODLL_021 VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V021 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314612-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VJ103MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VJ114) (https://doi.org/10.5067/viirs/vj114.001) swath product for accurate geolocation information. proprietary VJ103MOD_2.1 VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation L1 6-Min Swath 750m LAADS STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105084593-LAADS.umm_json The VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VJ103MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. proprietary VJ103MOD_NRT_2 VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m NRT LANCEMODIS STAC Catalog 2019-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1604697279-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m (VJ103MOD_NRT) product includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team. proprietary VJ103MOD_NRT_2.1 VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6 Min L1 Swath 750m NRT LANCEMODIS STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208781576-LANCEMODIS.umm_json The VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Near Real Time (NRT) product, short-name VJ103MOD_NRT includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team. proprietary VJ109A1_002 VIIRS/JPSS1 Surface Reflectance 8-Day L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2501959919-LPCLOUD.umm_json The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) surface reflectance (VJ109A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. proprietary VJ109CMG_002 VIIRS/JPSS1 Surface Reflectance Daily L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519121257-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance Climate Modeling Grid (VJ109CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. proprietary VJ109CMG_NRT_2 VIIRS/JPSS1 Surface Reflectance Daily L3 Global 0.05 Deg CMG NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2780814625-LANCEMODIS.umm_json The VJ109CMG_NRT is a Near Real Time (NRT) daily surface reflectance Climate Modeling Grid Version 2 product which provides an estimate of land surface reflectance from the NOAA-20 (previously called JPSS1) Visible Infrared Imager Radiometer Suite (VIIRS) sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product, aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration). All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products. For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf or visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html proprietary -VJ109GA_002 VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631841524-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. proprietary +VJ109GA_002 VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631841524-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. proprietary VJ109GA_NRT_2 VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2781246545-LANCEMODIS.umm_json The VJ109GA_NRT is a Near Real Time (NRT) VIIRS/JPSS1 Surface Reflectance Daily L2G Global 1km and 500m SIN Grid product. The VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. VJ109GA is a Level-2G surface reflectance product produced on a 10km x 10km grid. The VNP09GA surface reflectance product is composed of all available surface reflectance observations for a given day over a set of tiles with global coverage. The tile numbering scheme and boundaries are the same as for MODIS. The first set of observations for each data set and grid cell are projected onto a two-dimensional grid and stored as 10km square tiles at 500m and 1 km resolution. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product , aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration). All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products. For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf or visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html proprietary VJ109H1_002 VIIRS/JPSS1 Surface Reflectance 8-Day L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519120226-LPCLOUD.umm_json The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VJ109H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. proprietary VJ109_2 VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m LAADS STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2849305562-LAADS.umm_json The VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VJ109, are estimates of surface reflectance in each of the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VJ109 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. proprietary @@ -16541,22 +16541,22 @@ VJ113A2_002 VIIRS/JPSS1 Vegetation Indices 16-Day L3 Global 1km SIN Grid V002 LP VJ113A3_002 VIIRS/JPSS1 Vegetation Indices Monthly L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519123479-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VJ113A3 product: EVI and NDVI. proprietary VJ113C1_002 VIIRS/JPSS1 Vegetation Indices 16-Day L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310861-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C1 product: EVI and NDVI. proprietary VJ113C2_002 VIIRS/JPSS1 Vegetation Indices Monthly L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310866-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C2 product: EVI and NDVI. proprietary -VJ114A1_002 VIIRS/JPSS1 Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310874-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. proprietary +VJ114A1_002 VIIRS/JPSS1 Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310874-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. proprietary VJ114IMGTDL_NRT_2 VIIRS (NOAA-20/JPSS-1) I Band 375 m Active Fire Product NRT (Vector data) LANCEMODIS STAC Catalog 2024-10-01 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C3264430167-LANCEMODIS.umm_json Near real-time (NRT) NOAA-20 (formally JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on the instrument's 375 m nominal resolution data. Compared to other coarser resolution (≥1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization. VJ114IMGTDL_NRT are available in the following formats: TXT, SHP, KML, and WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. proprietary VJ114IMGT_NRT_2 VIIRS NOAA-20 (JPSS-1) 375m, I-Band Active Fire Product NRT (Vector Data) LANCEMODIS STAC Catalog 2016-01-01 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1355615368-LANCEMODIS.umm_json Near real-time (NRT) NOAA-20 (formally JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on the instrument's 375 m nominal resolution data. Compared to other coarser resolution (≥1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization. VJ114IMGTDL_NRT are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. For the HDF version see: VJ114IMG_NRT Recommended reading: VIIRS 375m Active Fire Algorithm User Guide (https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf) (updated December 2015). proprietary -VJ114IMG_002 VIIRS/JPSS1 Active Fires 6-Min L2 Swath 375m V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2734197957-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as thermal anomalies. The VJ114IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products. Use of the VJ103MODLL data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS). proprietary +VJ114IMG_002 VIIRS/JPSS1 Active Fires 6-Min L2 Swath 375m V002 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2734197957-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.002) data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS). proprietary VJ114IMG_NRT_2 VIIRS/JPSS1 Active Fires 6-Min L2 Swath 375m NRT LANCEMODIS STAC Catalog 2020-07-15 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1907902788-LANCEMODIS.umm_json The VJ114IMG_NRT is a Near Real Time (NRT) NOAA-20 (formally JPSS-1) /VIIRS 375 m active fire detection data product. Compared to other coarser resolution (≥1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization. The algorithm uses all five 375 m VIIRS channels to detect fires and separate land, water, and cloud pixels in the image. Additional 750 m channels complement the available VIIRS multispectral data. Those channels are used as input to the baseline active fire detection product, which provides continuity to the EOS/MODIS 1 km Fire and Thermal Anomalies product. The VIIRS 375 m fire detection data is a Level 2 product based on the input Science Data Record (SDR) Level 1 swath format. The NRT product is currently available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). The data are formatted as NetCDF4 files. Complementary ASCII files containing the short list of fire pixels detected are also available through LANCE FIRMS processing systems. For more information read VIIRS 375 m Active Fire Algorithm User Guide at https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf and Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. doi:10.1016/j.rse.2013.12.008 PDF from UMD or visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/ proprietary -VJ114_002 VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310869-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.021) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS). proprietary +VJ114_002 VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310869-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (Vj114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products. Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vJ103modll.001) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS). proprietary VJ114_NRT_2 VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT - V2 LANCEMODIS STAC Catalog 2024-03-05 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2888590350-LANCEMODIS.umm_json The VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product, short-name VJ114_NRT is based on the MODIS Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics. The VJ114_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation. The product provides day and nighttime active fire detection over land and water (from gas flares). The VJ114 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. For more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/ proprietary VJ115A2H_002 VIIRS/JPSS1 Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310879-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VJ115A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NOAA-20 satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VJ115A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VJ115A2H granule: LAI and FPAR. proprietary -VJ121A1D_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310887-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule. proprietary -VJ121A1N_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310892-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule. proprietary +VJ121A1D_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310887-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule. proprietary +VJ121A1N_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310892-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule. proprietary VJ121A2_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310897-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VJ121A2) combines the daily (VJ121A1D) (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and (VJ121A1N) (http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VJ121A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VJ121A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121A2 granule. proprietary VJ121C1_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310901-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VJ121C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VJ121) (https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). proprietary VJ121C2_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity 8-Day L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310905-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ121C2) combines the daily (VJ121A1D) (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and (VJ121A1N) (http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf. proprietary VJ121C3_002 VIIRS/JPSS1 Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310909-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VJ121C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VJ121C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VJ121A1D (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and VJ121A1N (http://doi.org/10.5067/VIIRS/VJ121A1N.002) daily acquisitions from the monthly period. Unlike the VJ121A1 data sets where the daytime and nighttime acquisitions are separate products, the VJ121C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). proprietary VJ121IMG_NRT_2 VIIRS/JPSS1 Land Surface Temperature and Emissivity 6-Min L2 Swath 375m NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2788958149-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 375m product with short-name VJ121IMG_NRT, is the same product but at 375m spatial resolution. The VJ121 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf and user guide at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf proprietary -VJ121_002 VIIRS/JPSS1 Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310883-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). Provided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule. proprietary +VJ121_002 VIIRS/JPSS1 Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310883-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VJ121 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). Provided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule. proprietary VJ121_NRT_2 VIIRS/JPSS1 Land Surface Temperature and Emissivity 6-Min L2 Swath 750m LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781382411-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 750m product (VJ121_NRT) uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for the three VIIRS thermal infrared bands M14 (8.55 micrometer), M15 (10.76 micrometer), and M16 (12 micrometer) at a spatial resolution of 750 m at nadir. The VJ!21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf and user guide at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf proprietary VJ128C2_002 VIIRS/JPSS1 Water Reservoir Area 8-day L3 Global V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2730257708-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VJ128C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C2 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the VIIRS satellite (VJ109H1). The VJ128C2 data product consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, and water storage capacity. proprietary VJ128C3_002 VIIRS/JPSS1 Water Reservoir Monthly L3 Global V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2696224576-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Monthly Level 3 (L3) Global (VJ128C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C3 data product is a composite of the 8-day area classifications from VJ128C2 which is converted to provide monthly elevation and water storage. The Lake Temperature and Evaporation Model (LTEM) with input from VIIRS Land Surface Temperature and Emissivity (VJ121A2) and meteorological data from Global Land Data Assimilation System (GLDAS) are used to produce monthly evaporation rates and volume losses. The VJ128C3 data product provides a monthly time series that consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, water storage capacity, evaporation rate, and evaporation volume. proprietary @@ -16572,21 +16572,21 @@ VJ143DNBA2_002 VIIRS/JPSS1 DNB BRDF/Albedo Quality Daily L3 Global 1km SIN Grid VJ143DNBA3_002 VIIRS/JPSS1 DNB BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2837651949-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. proprietary VJ143DNBA4_002 VIIRS/JPSS1 DNB BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2837653430-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. proprietary VJ143IA1N_2 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108740-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VJ143IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. proprietary -VJ143IA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VJ143IA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VJ143IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VJ143IA2N_2 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108845-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary -VJ143IA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary +VJ143IA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VJ143_ATBD_V2.pdf). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VJ143IA3N_2 VIIRS/JPSS1 Albedo Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808098739-LANCEMODIS.umm_json The VIIRS/JPSS1 Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. proprietary -VJ143IA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary +VJ143IA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4) (https://doi.org/10.5067/VIIRS/VJ143IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VJ143IA4N_2 VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108867-LANCEMODIS.umm_json The VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA4N product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product. The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. proprietary -VJ143IA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VJ143IA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) (https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3) (https://doi.org/10.5067/VIIRS/VJ143IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VJ143MA1N_2 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131488-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VJ143MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. proprietary -VJ143MA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. proprietary +VJ143MA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary VJ143MA2N_2 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131412-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary -VJ143MA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. proprietary +VJ143MA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) (https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3) (https://doi.org/10.5067/VIIRS/VJ143MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VJ143MA3N_2 VIIRS/JPSS1 Albedo Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131352-LANCEMODIS.umm_json The VIIRS/JPSS1 Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS). proprietary -VJ143MA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. proprietary +VJ143MA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) (https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4) (https://doi.org/10.5067/VIIRS/VJ143MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VJ143MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VJ143MA4N_2 VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131137-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. proprietary -VJ143MA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. proprietary +VJ143MA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary VJ146A1G_NRT_2 VIIRS/JPSS1 Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781431577-LANCEMODIS.umm_json The Near Real Time (NRT) NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VJ146A1G_NRT, is same as VJ146A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VJ146A1_NRT_2 VIIRS/JPSS1 Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781438623-LANCEMODIS.umm_json The first of two Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) based Near Real Time (NRT) datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/JPSS1 Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT. Known by its short-name, VJ146A1_NRT, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VJ201_NRT_2 VIIRS/JPSS2 Raw Radiances in Counts 6-Min L1A Swath NRT LANCEMODIS STAC Catalog 2024-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2837614569-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS/JPSS2 Raw Radiances in Counts 6-Min L1A Swath, short-name VJ201_NRT data product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted. For more information download VIIRS Level 1 Product User's Guide at: https://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf proprietary @@ -16626,7 +16626,7 @@ VNP03DNB_NRT_2 VIIRS/NPP Day/Night Band Moderate Resolution Terrain-Corrected Ge VNP03IMG_2 VIIRS/NPP Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375 m LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105092163-LAADS.umm_json The VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m, short-name VNP03IMG, product contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. proprietary VNP03IMG_NRT_2 VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m NRT LANCEMODIS STAC Catalog 2021-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2185522599-LANCEMODIS.umm_json The VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m Near Real Time (NRT) product, short-name VNP03IMG includes the geolocation fields that are calculated for each VIIRS imagery resolution band (I-band) Line of sight (LOS) for all orbits at the nominal resolution of 375 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 32 detectors in an ideal I-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03IMG Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 375m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by a large number of subsequent VIIRS Imagery Resolution products, particularly those produced by the Land team. proprietary VNP03MODLL_001 VIIRS/NPP Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-06-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1410800182-LPDAAC_ECS.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 1 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VNP14) (https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information. proprietary -VNP03MODLL_002 VIIRS/NPP Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310947-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth's geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the VNP14 swath product for accurate geolocation information. proprietary +VNP03MODLL_002 VIIRS/NPP Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m Light V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310947-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the (VNP14) (https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information. proprietary VNP03MOD_2 VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation L1 6-Min Swath 750 m LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105092427-LAADS.umm_json The VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750 m product, short-name VNP03MOD, contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. proprietary VNP03MOD_NRT_2 VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m NRT LANCEMODIS STAC Catalog 2021-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2185511251-LANCEMODIS.umm_json The VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation L1 6-Min Swath 750m Near Real Time (NRT) product, short-name VNP03MOD_NRT includes the geolocation fields that are calculated for each VIIRS moderate resolution band (M-band) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in an ideal M-band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit ephemeris data, the instrument telemetry and the digital elevation model. The geolocation fields contained within the VNP03MOD Geolocation files include geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/water mask for each 750m sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors for any of the VIIRS bands. This product is used as input by subsequent VIIRS Moderate Resolution products, particularly those produced by the Land team. proprietary VNP09A1_001 VIIRS/NPP Surface Reflectance 8-Day L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 2024-06-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1373412073-LPDAAC_ECS.umm_json The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. proprietary @@ -16635,7 +16635,7 @@ VNP09CMG_001 VIIRS/NPP Surface Reflectance Daily L3 Global 0.05 Deg CMG V001 LPD VNP09CMG_002 VIIRS/NPP Surface Reflectance Daily L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519126793-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. proprietary VNP09CMG_NRT_2 VIIRS/NPP Surface Reflectance Daily L3 Global 0.05 Deg CMG NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2780134650-LANCEMODIS.umm_json TheVNP09CMG_NRT is a Near Real Time (NRT) daily surface reflectance Climate Modeling Grid Version 2 product which provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration). All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products. For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdf or visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/Products/NASA/ReflectanceESDR.html proprietary VNP09GA_001 VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-06-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1373412034-LPDAAC_ECS.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 1 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. proprietary -VNP09GA_002 VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631841556-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. proprietary +VNP09GA_002 VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631841556-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. proprietary VNP09GA_NRT_2 NPP/VIIRS Surface Reflectance Daily L2G Global 1km and 500m SIN Grid LANCEMODIS STAC Catalog 2023-10-10 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2780105555-LANCEMODIS.umm_json The VNP09GA_NRT is a Near Real Time (NRT) S-NPP/VIIRS 500m and 1km Daily Level 2G Surface Reflectance product. The NPP/ VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. VNP09GA is a Level-2G surface reflectance product produced on a 10km x 10km grid. The VNP09GA surface reflectance product is composed of all available surface reflectance observations for a given day over a set of tiles with global coverage. The tile numbering scheme and boundaries are the same as for MODIS. The first set of observations for each data set and grid cell are projected onto a two-dimensional grid and stored as 10km square tiles at 500m and 1 km resolution. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration). All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products. For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v1.1.pdf or visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html proprietary VNP09H1_001 VIIRS/NPP Surface Reflectance 8-Day L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 2024-06-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1373412048-LPDAAC_ECS.umm_json The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VNP09H1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. proprietary VNP09H1_002 VIIRS/NPP Surface Reflectance 8-Day L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519125808-LPCLOUD.umm_json The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VNP09H1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. proprietary @@ -16661,19 +16661,19 @@ VNP13C1_002 VIIRS/NPP Vegetation Indices 16-Day L3 Global 0.05Deg CMG V002 LPCLO VNP13C2_001 VIIRS/NPP Vegetation Indices Monthly L3 Global 0.05Deg CMG V001 LPDAAC_ECS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1392010618-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI. proprietary VNP13C2_002 VIIRS/NPP Vegetation Indices Monthly L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631828311-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI. proprietary VNP14A1_001 VIIRS/NPP Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-06-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1523387372-LPDAAC_ECS.umm_json The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. proprietary -VNP14A1_002 VIIRS/NPP Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314541-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies and Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. proprietary +VNP14A1_002 VIIRS/NPP Thermal Anomalies and Fire Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314541-LPCLOUD.umm_json The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies/Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. proprietary VNP14IMGTDL_NRT_2 VIIRS (S-NPP) I Band 375 m Active Fire Product NRT (Vector data) LANCEMODIS STAC Catalog 2016-01-01 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1942970257-LANCEMODIS.umm_json Near real-time (NRT) Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on that instrument's 375 m nominal resolution data. Compared to other coarser resolution (≥1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization. VNP14IMGTDL_NRT are available through NASA FIRMS in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. proprietary -VNP14IMG_002 VIIRS/NPP Active Fires 6-Min L2 Swath 375m V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2734202914-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This Level 2 product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events. Due to its higher spatial resolution, the VNP14IMG active fire product provides greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters in comparison to the VNP14 fire data product. The VNP14IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS). proprietary +VNP14IMG_002 VIIRS/NPP Active Fires 6-Min L2 Swath 375m V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2734202914-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor located on the Suomi National Polar Orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well asermal anomalies. identifying th The VNP14IMG product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS). proprietary VNP14IMG_NRT_2 VIIRS/NPP Active Fires 6-Min L2 Swath 375m - NRT LANCEMODIS STAC Catalog 2020-02-14 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1886251885-LANCEMODIS.umm_json The VNP14IMG_NRT is a Near Real Time (NRT) S-NPP/VIIRS 375 m active fire detection data product (Schroeder 2014). The product is built on the EOS/MODIS fire product heritage [Kaufman et al., 1998; Giglio et al., 2003], using a multi-spectral contextual algorithm to identify sub-pixel fire activity and other thermal anomalies in the Level 1 (swath) input data. The algorithm uses all five 375 m VIIRS channels to detect fires and separate land, water, and cloud pixels in the image. Additional 750 m channels complement the available VIIRS multispectral data. Those channels are used as input to the baseline active fire detection product, which provides continuity to the EOS/MODIS 1 km Fire and Thermal Anomalies product. The VIIRS 375 m fire detection data is a Level 2 product based on the input Science Data Record (SDR) Level 1 swath format. The NRT product is currently available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). The data are formatted as NetCDF4 files. Complementary ASCII files containing the short list of fire pixels detected are also available through LANCE FIRMS processing systems. For more information read VIIRS 375 m Active Fire Algorithm User Guide at https://earthdata.nasa.gov/files/VIIRS_375m_Users_guide_Dec15_v2.pdf and Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. doi:10.1016/j.rse.2013.12.008 PDF from UMD or visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/ proprietary VNP14_001 VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-06-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1392010612-LPDAAC_ECS.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 1 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). proprietary -VNP14_002 VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314536-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). proprietary +VNP14_002 VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314536-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) (https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). proprietary VNP14_NRT_2 VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT - V2 LANCEMODIS STAC Catalog 2024-03-05 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2888489803-LANCEMODIS.umm_json The VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product, short-name VNP14_NRT is based on the MODIS Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics. The VNP14_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation. The product provides day and nighttime active fire detection over land and water (from gas flares). The VNP14 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. For more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/ proprietary VNP15A2H_001 VIIRS/NPP Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 2024-06-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099490-LPDAAC_ECS.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 1 data product provides information about the vegetative canopy layer at 500 meter resolution (VNP15A2H). The VIIRS sensor is located aboard the NOAA/NASA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR. proprietary VNP15A2H_002 VIIRS/NPP Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314545-LPCLOUD.umm_json The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VNP15A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR. proprietary VNP21A1D_001 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1442270800-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. proprietary -VNP21A1D_002 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314555-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. proprietary +VNP21A1D_002 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314555-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. proprietary VNP21A1N_001 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1442270801-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 1 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. proprietary -VNP21A1N_002 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314559-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. proprietary +VNP21A1N_002 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Night V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314559-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. proprietary VNP21A2_001 VIIRS/NPP Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-25 2024-04-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1553237573-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.001) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.001) products over an 8-day compositing period into a single product. The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule. proprietary VNP21A2_002 VIIRS/NPP Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314562-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule. proprietary VNP21C1_002 VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314566-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VNP21C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). proprietary @@ -16681,7 +16681,7 @@ VNP21C2_002 VIIRS/NPP Land Surface Temperature/Emissivity 8-Day L3 Global 0.05De VNP21C3_002 VIIRS/NPP Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314573-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VNP21C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VNP21C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VNP21A1D (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and VNP21A1N (http://doi.org/10.5067/VIIRS/VNP21A1N.002) daily acquisitions from the monthly period. Unlike the VNP21A1 data sets where the daytime and nighttime acquisitions are separate products, the VNP21C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). proprietary VNP21IMG_NRT_2 VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 375m NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2788950354-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS Land Surface Temperature and Emissivity 6-Min L2 Swath 375m product with short-name VNP21IMG_NRT, is the same product but at 375m spatial resolution. The VNP21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf and user guide at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf proprietary VNP21_001 VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V001 LPDAAC_ECS STAC Catalog 2012-01-19 2024-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099493-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 1 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. proprietary -VNP21_002 VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314550-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. proprietary +VNP21_002 VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314550-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp21_atbd.pdf). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. proprietary VNP21_NRT_2 VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750 m LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780626371-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRSLand Surface Temperature and Emissivity 6-Min L2 Swath 750m product (VNP21_NRT) uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for the three VIIRS thermal infrared bands M14 (8.55 micrometer), M15 (10.76 micrometer), and M16 (12 micrometer) at a spatial resolution of 750 m at nadir. The VNP21 algorithm is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Emissivity Separation (TES) algorithm. This algorithm uses full radiative transfer simulations for the atmospheric correction, and an emissivity model based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity at native pixel resolution. Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_ATBD_v2.1.pdf and user guide at: https://viirsland.gsfc.nasa.gov/PDF/VNP21_LSTE_user_guide.pdf proprietary VNP22C2_001 VIIRS/NPP Land Cover Dynamics Yearly L3 Global 0.05 Deg CMG V001 LPDAAC_ECS STAC Catalog 2013-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1712040022-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. proprietary VNP22C2_002 VIIRS/NPP Land Surface Phenology Yearly L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2847915522-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. proprietary @@ -16801,28 +16801,28 @@ VNP43DNBA4_001 VIIRS/NPP DNB BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global VNP43DNBA4_002 VIIRS/NPP DNB BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2847929303-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. proprietary VNP43IA1N_2 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807589174-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VNP43IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. proprietary VNP43IA1_001 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099489-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 1 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary -VNP43IA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VNP43IA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VNP43IA2N_2 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807586151-LANCEMODIS.umm_json The VIIRS/JPSS1 Level 3 16-Day BRDF/Albedo - 500m Near Real Time (NRT), short-name VNP43IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary VNP43IA2_001 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449611-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary -VNP43IA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary +VNP43IA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. VNP43IA2 provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VNP43IA3N_2 VIIRS/NPP Albedo Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807588708-LANCEMODIS.umm_json The VIIRS/NPP Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. proprietary VNP43IA3_001 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449608-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary -VNP43IA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary +VNP43IA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VNP43IA4N_2 VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807591231-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA4N product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. proprietary VNP43IA4_001 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099497-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary -VNP43IA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VNP43IA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VNP43MA1N_2 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807604939-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VNP43MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. proprietary VNP43MA1_001 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449609-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary -VNP43MA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. proprietary +VNP43MA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary VNP43MA2N_2 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807623096-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary VNP43MA2_001 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449612-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary -VNP43MA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. proprietary +VNP43MA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VNP43MA3N_2 VIIRS/NPP Albedo Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807625522-LANCEMODIS.umm_json The VIIRS/NPP Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS). proprietary VNP43MA3_001 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099488-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary -VNP43MA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. proprietary +VNP43MA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.002), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V2.pdf). The VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VNP43MA4N_2 VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807627777-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. proprietary VNP43MA4_001 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449610-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary -VNP43MA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. proprietary +VNP43MA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary VNP46A1G_NRT_2 VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat. Lon. Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780764136-LANCEMODIS.umm_json The Near Real Time (NRT) Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1G_NRT, is same as VNP46A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VNP46A1_1 VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1897815356-LAADS.umm_json The first of two VIIRS DNB-based datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night. Known by its short-name, VNP46A1, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VNP46A1_2 VIIRS/NPP Daily Gridded Day Night Band 500 m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.umm_json The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf proprietary